{"id": "http://arxiv.org/abs/2108.02442v1", "title": "House Price Determinants and Market Segmentation in Boulder, Colorado: A Hedonic Price Approach", "abstract": "In this research we perform hedonic regression model to examine the residential property price determinants in the city of Boulder in the state of Colorado, USA. The urban housing markets are too compounded to be considered as homogeneous markets. The heterogeneity of an urban property market requires creation of market segmentation. To test whether residential properties in the real estate market in the city of Boulder are analyzed and predicted in the disaggregate level or at an aggregate level we stratify the housing market based on both property types and location and estimate separate hedonic price models for each submarket. The results indicate that the implicit values of the property characteristics are not identical across property types and locations in the city of Boulder and market segmentation exists.", "published": "2021-08-05T08:19:48Z", "authors": ["Mahdieh Yazdani"], "categories": ["econ.GN"], "doi": "", "_query": "hedonic house price", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/1804.05040v2", "title": "Stable Outcomes in Modified Fractional Hedonic Games", "abstract": "In coalition formation games self-organized coalitions are created as a result of the strategic interactions of independent agents. For each couple of agents $(i,j)$, weight $w_{i,j}=w_{j,i}$ reflects how much agents $i$ and $j$ benefit from belonging to the same coalition. We consider the modified fractional hedonic game, that is a coalition formation game in which agents' utilities are such that the total benefit of agent $i$ belonging to a coalition (given by the sum of $w_{i,j}$ over all other agents $j$ belonging to the same coalition) is averaged over all the other members of that coalition, i.e., excluding herself. Modified fractional hedonic games constitute a class of succinctly representable hedonic games. We are interested in the scenario in which agents, individually or jointly, choose to form a new coalition or to join an existing one, until a stable outcome is reached. To this aim, we consider common stability notions, leading to strong Nash stable outcomes, Nash stable outcomes or core stable outcomes: we study their existence, complexity and performance, both in the case of general weights and in the case of 0-1 weights. In particular, we completely characterize the existence of the considered stable outcomes and show many tight or asymptotically tight results on the performance of these natural stable outcomes for modified fractional hedonic games, also highlighting the differences with respect to the model of fractional hedonic games, in which the total benefit of an agent in a coalition is averaged over all members of that coalition, i.e., including herself.", "published": "2018-04-13T17:12:37Z", "authors": ["Gianpiero Monaco", "Luca Moscardelli", "Yllka Velaj"], "categories": ["cs.GT"], "doi": "", "_query": "hedonic house price", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2110.07151v1", "title": "Machine Learning, Deep Learning, and Hedonic Methods for Real Estate Price Prediction", "abstract": "In recent years several complaints about racial discrimination in appraising home values have been accumulating. For several decades, to estimate the sale price of the residential properties, appraisers have been walking through the properties, observing the property, collecting data, and making use of the hedonic pricing models. However, this method bears some costs and by nature is subjective and biased. To minimize human involvement and the biases in the real estate appraisals and boost the accuracy of the real estate market price prediction models, in this research we design data-efficient learning machines capable of learning and extracting the relation or patterns between the inputs (features for the house) and output (value of the houses). We compare the performance of some machine learning and deep learning algorithms, specifically artificial neural networks, random forest, and k nearest neighbor approaches to that of hedonic method on house price prediction in the city of Boulder, Colorado. Even though this study has been done over the houses in the city of Boulder it can be generalized to the housing market in any cities. The results indicate non-linear association between the dwelling features and dwelling prices. In light of these findings, this study demonstrates that random forest and artificial neural networks algorithms can be better alternatives over the hedonic regression analysis for prediction of the house prices in the city of Boulder, Colorado.", "published": "2021-10-14T04:43:04Z", "authors": ["Mahdieh Yazdani"], "categories": ["econ.EM"], "doi": "", "_query": "hedonic house price", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/1306.6137v1", "title": "Effects Of Zoning On Housing Option Value", "abstract": "The research explores the subject of zoning effect on price value of a house in a certain designated zone. Zoning is defined as a land under planned use. This results in price value of the land. As the area of the land decreases an increase in price occurs. This research does not specify the magnitude of a certain unit by which a housing price will increase or decrease but it estimates the housing option value. This research helps the policy makers to improve long term policy decisions. This research will showcase zoning effects on housing option value for the specific zones in the city of Normal. The observations are taken for different parcels of total neighborhoods in the city of Normal. This research will use hedonic model for conducting a regression analysis on the subject. The hypothesis taken into consideration before starting the research is to prove that more 50 percent variation in the housing price is affected by the zoning characteristics. This research will try to prove the hypothesis by comparing the assessed values with the predicted values from the model.", "published": "2013-06-26T05:43:46Z", "authors": ["Prathamesh Muzumdar"], "categories": ["stat.AP"], "doi": "", "_query": "hedonic house price", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2305.00044v4", "title": "Hedonic Prices and Quality Adjusted Price Indices Powered by AI", "abstract": "We develop empirical models that efficiently process large amounts of unstructured product data (text, images, prices, quantities) to produce accurate hedonic price estimates and derived indices. To achieve this, we generate abstract product attributes (or ``features'') from descriptions and images using deep neural networks. These attributes are then used to estimate the hedonic price function. To demonstrate the effectiveness of this approach, we apply the models to Amazon's data for first-party apparel sales, and estimate hedonic prices. The resulting models have a very high out-of-sample predictive accuracy, with $R^2$ ranging from $80\\%$ to $90\\%$. Finally, we construct the AI-based hedonic Fisher price index, chained at the year-over-year frequency, and contrast it with the CPI and other electronic indices.", "published": "2023-04-28T18:37:59Z", "authors": ["Patrick Bajari", "Zhihao Cen", "Victor Chernozhukov", "Manoj Manukonda", "Suhas Vijaykumar", "Jin Wang", "Ramon Huerta", "Junbo Li", "Ling Leng", "George Monokroussos", "Shan Wang"], "categories": ["econ.GN", "cs.LG"], "doi": "10.1016/j.jeconom.2025.106052", "_query": "hedonic house price", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2102.08162v1", "title": "Integrating Floor Plans into Hedonic Models for Rent Price Appraisal", "abstract": "Online real estate platforms have become significant marketplaces facilitating users' search for an apartment or a house. Yet it remains challenging to accurately appraise a property's value. Prior works have primarily studied real estate valuation based on hedonic price models that take structured data into account while accompanying unstructured data is typically ignored. In this study, we investigate to what extent an automated visual analysis of apartment floor plans on online real estate platforms can enhance hedonic rent price appraisal. We propose a tailored two-staged deep learning approach to learn price-relevant designs of floor plans from historical price data. Subsequently, we integrate the floor plan predictions into hedonic rent price models that account for both structural and locational characteristics of an apartment. Our empirical analysis based on a unique dataset of 9174 real estate listings suggests that current hedonic models underutilize the available data. We find that (1) the visual design of floor plans has significant explanatory power regarding rent prices - even after controlling for structural and locational apartment characteristics, and (2) harnessing floor plans results in an up to 10.56% lower out-of-sample prediction error. We further find that floor plans yield a particularly high gain in prediction performance for older and smaller apartments. Altogether, our empirical findings contribute to the existing research body by establishing the link between the visual design of floor plans and real estate prices. Moreover, our approach has important implications for online real estate platforms, which can use our findings to enhance user experience in their real estate listings.", "published": "2021-02-16T14:05:33Z", "authors": ["Kirill Solovev", "Nicolas Pröllochs"], "categories": ["cs.LG", "econ.GN", "stat.AP"], "doi": "10.1145/3442381.3449967", "_query": "hedonic house price", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2509.21460v2", "title": "Forecasting House Prices", "abstract": "This article identifies the factors that drove house prices in 13 advanced countries over the past 35 years. It does so based on Breiman s (2001) random forest model. Shapley values indicate that annual house price growth across countries is explained first and foremost by price momentum, initial valuations (proxied by price to rent ratios) and household credit growth. Partial effects of explanatory variables are also elicited and suggest important non-linearities, for instance as to what concerns the effects of CPI inflation on house price growth. The out-of-sample forecast test reveals that the random forest model delivers 44% lower house price variation root square mean errors (RMSEs) and 45% lower mean absolute errors (MAEs) when compared to an OLS model that uses the same set of 10 pre-determined explanatory variables. Notably, the same model works well for all countries, as the random forest attributes minimal values to country fixed effects.", "published": "2025-09-25T19:27:37Z", "authors": ["Emanuel Kohlscheen"], "categories": ["econ.GN"], "doi": "", "_query": "hedonic house price", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/1009.5557v1", "title": "A Mobile Application for Smart House Remote Control System", "abstract": "At the start of the second decade of 21th century, the time has come to make the Smart Houses a reality for regular use. The different parts of a Smart House are researched but there are still distances from an applicable system, using the modern technology. In this paper we present an overview of the Smart House subsystems necessary for controlling the house using a mobile application efficiently and securely. The sequence diagram of the mobile application connecting to the server application and also the use-cases possible are presented. The challenges faced in designing the mobile application and illustrating the updated house top plane view in that application, are discussed and solutions are adapted for it. Finally the designed mobile application was implemented and the important sections of it were described, such as the interactive house top view map which indicates the status of the devices using predefined icons. The facilities to manage the scheduled tasks and defined rules are also implemented in this mobile application that was developed for use in Windows Mobile platform. This application has the capability of connecting to the main server using GPRS mobile internet and SMS. This system is expected to be an important step towards a unified system structure that can be used efficiently in near future regular houses.", "published": "2010-09-27T18:13:37Z", "authors": ["Amir Rajabzadeh", "Ali Reza Manashty", "Zahra Forootan Jahromi"], "categories": ["cs.OH"], "doi": "", "_query": "hedonic house price", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/1809.03834v1", "title": "House Price Modeling with Digital Census", "abstract": "Urban house prices are strongly associated with local socioeconomic factors. In literature, house price modeling is based on socioeconomic variables from traditional census, which is not real-time, dynamic and comprehensive. Inspired by the emerging concept of \"digital census\" - using large-scale digital records of human activities to measure urban population dynamics and socioeconomic conditions, we introduce three typical datasets, namely 311 complaints, crime complaints and taxi trips, into house price modeling. Based on the individual housing sales data in New York City, we provide comprehensive evidence that these digital census datasets can substantially improve the modeling performances on both house price levels and changes, regardless whether traditional census is included or not. Hence, digital census can serve as both effective alternatives and complements to traditional census for house price modeling.", "published": "2018-08-29T16:13:58Z", "authors": ["Enwei Zhu", "Stanislav Sobolevsky"], "categories": ["econ.EM", "physics.soc-ph"], "doi": "", "_query": "hedonic house price", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2310.08133v1", "title": "Multi Level Dense Layer Neural Network Model for Housing Price Prediction", "abstract": "Predicting the price of a house remains a challenging issue that needs to be addressed. Research has attempted to establish a model with different methods and algorithms to predict the housing price, from the traditional hedonic model to a neural network algorithm. However, many existing algorithms in the literature are proposed without any finetuning and customization in the model. In this paper, the author attempted to propose a novel neural network-based model to improve the performance of housing price prediction. Inspired by the modular neural network, the proposed model consists of a three-level neural network that is capable to process information in parallel. The author compared several state-of-the-art algorithms available in the literature on the Boston housing dataset to evaluate the effectiveness of the proposed model. The results show that the proposed model provides better accuracy and outperforms existing algorithms in different evaluation metrics. The code for the implementation is available https://github.com/wijayarobert/MultiLevelDenseLayerNN", "published": "2023-10-12T08:46:26Z", "authors": ["Robert Wijaya"], "categories": ["cs.CY"], "doi": "", "_query": "hedonic house price", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2402.04082v1", "title": "An Optimal House Price Prediction Algorithm: XGBoost", "abstract": "An accurate prediction of house prices is a fundamental requirement for various sectors including real estate and mortgage lending. It is widely recognized that a property value is not solely determined by its physical attributes but is significantly influenced by its surrounding neighbourhood. Meeting the diverse housing needs of individuals while balancing budget constraints is a primary concern for real estate developers. To this end, we addressed the house price prediction problem as a regression task and thus employed various machine learning techniques capable of expressing the significance of independent variables. We made use of the housing dataset of Ames City in Iowa, USA to compare support vector regressor, random forest regressor, XGBoost, multilayer perceptron and multiple linear regression algorithms for house price prediction. Afterwards, we identified the key factors that influence housing costs. Our results show that XGBoost is the best performing model for house price prediction.", "published": "2024-02-06T15:36:06Z", "authors": ["Hemlata Sharma", "Hitesh Harsora", "Bayode Ogunleye"], "categories": ["cs.LG", "cs.AI", "stat.AP", "stat.ME"], "doi": "10.3390/analytics3010003", "_query": "hedonic house price", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2405.18434v1", "title": "Modeling the Feedback of AI Price Estimations on Actual Market Values", "abstract": "Public availability of Artificial Intelligence generated information can change the markets forever, and its factoring into economical dynamics may take economists by surprise, out-dating models and schools of thought. Real estate hyper-inflation is not a new phenomenon but its consistent and almost monotonous persistence over 12 years, coinciding with prominence of public estimation information from Zillow, a successful Mass Real Estate Estimator (MREE), could not escape unobserved. What we model is a repetitive theoretical game between the MREE and the home owners, where each player has secret information and expertise. If the intention is to keep housing affordable and maintain old American lifestyle with broad home-ownership, new challenges are defined. Simulations show that a simple restriction of MREE-style price estimation availability to opt-in properties may help partially reduce feedback loop by acting on its likely causes, as suggested by experimental simulation models. The conjecture that the MREE pressure on real estate inflation rate is correlated with the absolute MREE estimation errors, which is logically explainable, is then validated in simulations.", "published": "2024-03-13T03:44:13Z", "authors": ["Viorel Silaghi", "Zobaida Alssadi", "Ben Mathew", "Majed Alotaibi", "Ali Alqarni", "Marius Silaghi"], "categories": ["econ.GN", "cs.AI", "cs.CE", "cs.CY", "cs.GT"], "doi": "", "_query": "hedonic house price", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2407.12267v1", "title": "Generating 3D House Wireframes with Semantics", "abstract": "We present a new approach for generating 3D house wireframes with semantic enrichment using an autoregressive model. Unlike conventional generative models that independently process vertices, edges, and faces, our approach employs a unified wire-based representation for improved coherence in learning 3D wireframe structures. By re-ordering wire sequences based on semantic meanings, we facilitate seamless semantic integration during sequence generation. Our two-phase technique merges a graph-based autoencoder with a transformer-based decoder to learn latent geometric tokens and generate semantic-aware wireframes. Through iterative prediction and decoding during inference, our model produces detailed wireframes that can be easily segmented into distinct components, such as walls, roofs, and rooms, reflecting the semantic essence of the shape. Empirical results on a comprehensive house dataset validate the superior accuracy, novelty, and semantic fidelity of our model compared to existing generative models. More results and details can be found on https://vcc.tech/research/2024/3DWire.", "published": "2024-07-17T02:33:34Z", "authors": ["Xueqi Ma", "Yilin Liu", "Wenjun Zhou", "Ruowei Wang", "Hui Huang"], "categories": ["cs.CV", "cs.GR"], "doi": "", "_query": "hedonic house price", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/1104.2719v1", "title": "An autoregressive approach to house price modeling", "abstract": "A statistical model for predicting individual house prices and constructing a house price index is proposed utilizing information regarding sale price, time of sale and location (ZIP code). This model is composed of a fixed time effect and a random ZIP (postal) code effect combined with an autoregressive component. The former two components are applied to all home sales, while the latter is applied only to homes sold repeatedly. The time effect can be converted into a house price index. To evaluate the proposed model and the resulting index, single-family home sales for twenty US metropolitan areas from July 1985 through September 2004 are analyzed. The model is shown to have better predictive abilities than the benchmark S&P/Case--Shiller model, which is a repeat sales model, and a conventional mixed effects model. Finally, Los Angeles, CA, is used to illustrate a historical housing market downturn.", "published": "2011-04-14T10:54:29Z", "authors": ["Chaitra H. Nagaraja", "Lawrence D. Brown", "Linda H. Zhao"], "categories": ["stat.AP"], "doi": "10.1214/10-AOAS380", "_query": "hedonic house price", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2604.18605v1", "title": "Exploring Drivers of Extreme Housing Prices in Australia", "abstract": "In recent years Australia has observed a growing, unexplained resilience of increasing house price trends. Here, we seek to understand what is driving Australia's indestructible asset using insights from market experts. We construct a differential equation model of house price to develop intuition for its historical behaviour and responsiveness to changes in mortgage rates. Using this model, we identify a point of 'decoupling' between house price and mortgage rate in the system with supply limitations found to be the main driver for this change. From there, modern extreme value techniques are implemented on real-world data to investigate how the effectiveness of mortgage rate in moderating extreme house price has changed before and after this historical decoupling. We find that without an increase in the housing supply chain, through either deregulation or reduced competition with government building, an 11\\% increase in mortgage rate will be needed to slow extreme housing costs.", "published": "2026-04-10T01:43:04Z", "authors": ["Grace Burtenshaw", "Ashley Burtenshaw", "Meagan Carney"], "categories": ["q-fin.GN", "math.PR", "stat.AP"], "doi": "", "_query": "hedonic house price", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/1103.5971v1", "title": "Housing risk and return: Evidence from a housing asset-pricing model", "abstract": "This paper investigates the risk-return relationship in determination of housing asset pricing. In so doing, the paper evaluates behavioral hypotheses advanced by Case and Shiller (1988, 2002, 2009) in studies of boom and post-boom housing markets. The paper specifies and tests a multi-factor housing asset pricing model. In that model, we evaluate whether the market factor as well as other measures of risk, including idiosyncratic risk, momentum, and MSA size effects, have explanatory power for metropolitan-specific housing returns. Further, we test the robustness of the asset pricing results to inclusion of controls for socioeconomic variables commonly represented in the house price literature, including changes in employment, affordability, and foreclosure incidence. We find a sizable and statistically significant influence of the market factor on MSA house price returns. Moreover we show that market betas have varied substantially over time. Also, results are largely robust to the inclusion of other explanatory variables, including standard measures of risk and other housing market fundamentals. Additional tests of model validity using the Fama-MacBeth framework offer further strong support of a positive risk and return relationship in housing. Our findings are supportive of the application of a housing investment risk-return framework in explanation of variation in metro-area cross-section and time-series US house price returns. Further, results strongly corroborate Case-Shiller survey research indicating the importance of speculative forces in the determination of U.S. housing returns.", "published": "2011-03-30T15:55:56Z", "authors": ["Karl Case", "John Cotter", "Stuart Gabriel"], "categories": ["q-fin.PM", "q-fin.PR", "q-fin.ST"], "doi": "", "_query": "hedonic house price", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/1008.1376v1", "title": "House Price Distributions of Taiwan: A Preliminary Study", "abstract": "The house price distributions of Taiwan are analyzed. The tail of the cumulative distribution function (CDF) follows an approximate power law with an exponent equals to -2.4 while the distribution of the house price per unit area displays a lognormal distribution. Implications of the results are also discussed.", "published": "2010-08-08T01:23:28Z", "authors": ["Chung-I Chou", "S. P. Li"], "categories": ["physics.soc-ph", "physics.data-an"], "doi": "", "_query": "hedonic house price", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/1709.08432v1", "title": "House Price Prediction Using LSTM", "abstract": "In this paper, we use the house price data ranging from January 2004 to October 2016 to predict the average house price of November and December in 2016 for each district in Beijing, Shanghai, Guangzhou and Shenzhen. We apply Autoregressive Integrated Moving Average model to generate the baseline while LSTM networks to build prediction model. These algorithms are compared in terms of Mean Squared Error. The result shows that the LSTM model has excellent properties with respect to predict time series. Also, stateful LSTM networks and stack LSTM networks are employed to further study the improvement of accuracy of the house prediction model.", "published": "2017-09-25T11:31:50Z", "authors": ["Xiaochen Chen", "Lai Wei", "Jiaxin Xu"], "categories": ["cs.LG", "stat.ML"], "doi": "", "_query": "hedonic house price", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2409.05335v1", "title": "A Multi-Modal Deep Learning Based Approach for House Price Prediction", "abstract": "Accurate prediction of house price, a vital aspect of the residential real estate sector, is of substantial interest for a wide range of stakeholders. However, predicting house prices is a complex task due to the significant variability influenced by factors such as house features, location, neighborhood, and many others. Despite numerous attempts utilizing a wide array of algorithms, including recent deep learning techniques, to predict house prices accurately, existing approaches have fallen short of considering a wide range of factors such as textual and visual features. This paper addresses this gap by comprehensively incorporating attributes, such as features, textual descriptions, geo-spatial neighborhood, and house images, typically showcased in real estate listings in a house price prediction system. Specifically, we propose a multi-modal deep learning approach that leverages different types of data to learn more accurate representation of the house. In particular, we learn a joint embedding of raw house attributes, geo-spatial neighborhood, and most importantly from textual description and images representing the house; and finally use a downstream regression model to predict the house price from this jointly learned embedding vector. Our experimental results with a real-world dataset show that the text embedding of the house advertisement description and image embedding of the house pictures in addition to raw attributes and geo-spatial embedding, can significantly improve the house price prediction accuracy. The relevant source code and dataset are publicly accessible at the following URL: https://github.com/4P0N/mhpp", "published": "2024-09-09T05:26:33Z", "authors": ["Md Hasebul Hasan", "Md Abid Jahan", "Mohammed Eunus Ali", "Yuan-Fang Li", "Timos Sellis"], "categories": ["cs.LG", "cs.AI", "cs.CV"], "doi": "", "_query": "hedonic house price", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2204.09050v1", "title": "House Price Prediction Based On Deep Learning", "abstract": "Since ancient times, what Chinese people have been pursuing is very simple, which is nothing more than \"to live and work happily, to eat and dress comfortable\". Today, more than 40 years after the reform and opening, people have basically solved the problem of food and clothing, and the urgent problem is housing. Nowadays, due to the storm of long-term rental apartment intermediary platforms such as eggshell, increasing the sense of insecurity of renters, as well as the urbanization in recent years and the scramble for people in major cities, this will make the future real estate market competition more intense. In order to better grasp the real estate price, let consumers buy a house reasonably, and provide a reference for the government to formulate policies, this paper summarizes the existing methods of house price prediction and proposes a house price prediction method based on mixed depth vision and text features.", "published": "2022-04-19T20:12:16Z", "authors": ["Yuying Wu", "Youshan Zhang"], "categories": ["cs.LG"], "doi": "", "_query": "hedonic house price", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/1609.08399v1", "title": "House price estimation from visual and textual features", "abstract": "Most existing automatic house price estimation systems rely only on some textual data like its neighborhood area and the number of rooms. The final price is estimated by a human agent who visits the house and assesses it visually. In this paper, we propose extracting visual features from house photographs and combining them with the house's textual information. The combined features are fed to a fully connected multilayer Neural Network (NN) that estimates the house price as its single output. To train and evaluate our network, we have collected the first houses dataset (to our knowledge) that combines both images and textual attributes. The dataset is composed of 535 sample houses from the state of California, USA. Our experiments showed that adding the visual features increased the R-value by a factor of 3 and decreased the Mean Square Error (MSE) by one order of magnitude compared with textual-only features. Additionally, when trained on the benchmark textual-only features housing dataset, our proposed NN still outperformed the existing model published results.", "published": "2016-09-27T13:15:31Z", "authors": ["Eman Ahmed", "Mohamed Moustafa"], "categories": ["cs.CV"], "doi": "", "_query": "hedonic house price", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2009.00254v1", "title": "Boosting House Price Predictions using Geo-Spatial Network Embedding", "abstract": "Real estate contributes significantly to all major economies around the world. In particular, house prices have a direct impact on stakeholders, ranging from house buyers to financing companies. Thus, a plethora of techniques have been developed for real estate price prediction. Most of the existing techniques rely on different house features to build a variety of prediction models to predict house prices. Perceiving the effect of spatial dependence on house prices, some later works focused on introducing spatial regression models for improving prediction performance. However, they fail to take into account the geo-spatial context of the neighborhood amenities such as how close a house is to a train station, or a highly-ranked school, or a shopping center. Such contextual information may play a vital role in users' interests in a house and thereby has a direct influence on its price. In this paper, we propose to leverage the concept of graph neural networks to capture the geo-spatial context of the neighborhood of a house. In particular, we present a novel method, the Geo-Spatial Network Embedding (GSNE), that learns the embeddings of houses and various types of Points of Interest (POIs) in the form of multipartite networks, where the houses and the POIs are represented as attributed nodes and the relationships between them as edges. Extensive experiments with a large number of regression techniques show that the embeddings produced by our proposed GSNE technique consistently and significantly improve the performance of the house price prediction task regardless of the downstream regression model.", "published": "2020-09-01T06:17:21Z", "authors": ["Sarkar Snigdha Sarathi Das", "Mohammed Eunus Ali", "Yuan-Fang Li", "Yong-Bin Kang", "Timos Sellis"], "categories": ["cs.LG", "stat.ML"], "doi": "", "_query": "hedonic house price", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2312.14325v1", "title": "Exploring Distributions of House Prices and House Price Indices", "abstract": "We use house prices (HP) and house price indices (HPI) as a proxy to income distribution. Specifically, we analyze sale prices in the 1970-2010 window of over 116,000 single-family homes in Hamilton County, Ohio, including Cincinnati metro area of about 2.2 million people. We also analyze HPI, published by Federal Housing Finance Agency (FHFA), for nearly 18,000 US ZIP codes that cover a period of over 40 years starting in 1980's. If HP can be viewed as a first derivative of income, HPI can be viewed as its second derivative. We use generalized beta (GB) family of functions to fit distributions of HP and HPI since GB naturally arises from the models of economic exchange described by stochastic differential equations. Our main finding is that HP and multi-year HPI exhibit a negative Dragon King (nDK) behavior, wherein power-law distribution tail gives way to an abrupt decay to a finite upper limit value, which is similar to our recent findings for realized volatility of S\\&P500 index in the US stock market. This type of tail behavior is best fitted by a modified GB (mGB) distribution. Tails of single-year HPI appear to show more consistency with power-law behavior, which is better described by a GB Prime (GB2) distribution. We supplement full distribution fits by mGB and GB2 with direct linear fits (LF) of the tails. Our numerical procedure relies on evaluation of confidence intervals (CI) of the fits, as well as of p-values that give the likelihood that data come from the fitted distributions.", "published": "2023-12-21T22:38:24Z", "authors": ["Jiong Liu", "Hamed Farahani", "R. A. Serota"], "categories": ["econ.EM", "econ.TH"], "doi": "10.3390/economies12020047", "_query": "hedonic house price", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2305.08887v1", "title": "Covariate-distance Weighted Regression (CWR): A Case Study for Estimation of House Prices", "abstract": "Geographically weighted regression (GWR) is a popular tool for modeling spatial heterogeneity in a regression model. However, the current weighting function used in GWR only considers the geographical distance, while the attribute similarity is totally ignored. In this study, we proposed a covariate weighting function that combines the geographical distance and attribute distance. The covariate-distance weighted regression (CWR) is the extension of GWR including geographical distance and attribute distance. House prices are affected by numerous factors, such as house age, floor area, and land use. Prediction model is used to help understand the characteristics of regional house prices. The CWR was used to understand the relationship between the house price and controlling factors. The CWR can consider the geological and attribute distances, and produce accurate estimates of house price that preserve the weight matrix for geological and attribute distance functions. Results show that the house attributes/conditions and the characteristics of the house, such as floor area and house age, might affect the house price. After factor selection, in which only house age and floor area of a building are considered, the RMSE of the CWR model can be improved by 2.9%-26.3% for skyscrapers when compared to the GWR. CWR can effectively reduce estimation errors from traditional spatial regression models and provide novel and feasible models for spatial estimation.", "published": "2023-05-15T03:05:57Z", "authors": ["Hone-Jay Chu", "Po-Hung Chen", "Sheng-Mao Chang", "Muhammad Zeeshan Ali", "Sumriti Ranjan Patra"], "categories": ["cs.LG"], "doi": "", "_query": "hedonic house price", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2504.13459v1", "title": "The impact of institutional quality on the relation between FDI and house prices in ASEAN emerging countries", "abstract": "This study investigates the relationship between house prices and capital inflows in ASEAN emerging economies, and the impact of institutional quality on that relation. Using a unique balanced panel data set of six emerging countries in ASEAN from 2009 to 2019, we employ various econometric techniques to examine the impact of foreign direct investment (FDI) on the house price index. Our findings indicate a long-run relationship and Granger causality from FDI to the house price index in these markets, and we also find evidence of co-movement between the stock price index and the house price index. Additionally, our results suggest that better institutions reduce the impact of FDI on host country housing markets in the context of ASEAN emerging economies. This is one of the first studies to shed light on the role of institutional quality in the effect of FDIs on housing prices in this region.", "published": "2025-04-18T04:33:07Z", "authors": ["Hoang-Anh Le"], "categories": ["econ.GN"], "doi": "", "_query": "hedonic house price", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/1110.2790v1", "title": "Regularity properties of optimal transportation problems arising in hedonic pricing models", "abstract": "We study a form of optimal transportation surplus functions which arise in hedonic pricing models. We derive a formula for the Ma-Trudinger-Wang curvature of these functions, yielding necessary and sufficient conditions for them to satisfy \\textbf{(A3w)}. We use this to give explicit new examples of surplus functions satisfying \\textbf{(A3w)}, of the form $b(x,y)=H(x+y)$ where $H$ is a convex function on $\\mathbb{R}^n$. We also show that the space of equilibrium contracts in the hedonic pricing model has the maximal possible dimension, a result of potential economic interest.", "published": "2011-10-12T20:02:13Z", "authors": ["Brendan Pass"], "categories": ["math.AP"], "doi": "", "_query": "hedonic house price", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/physics/0606064v1", "title": "A Roof over your Head; House Price Peaks in the UK and Ireland", "abstract": "We analyse, following recent work of Roehner, changes in house prices for both the UK and Ireland. We conclude that prices in London have reached a tipping point and prices relative to inflation are set to fall over the next few years. If inflation does not rise then a hard landing seems likely. House prices in the Irish Republic are shown to have broken away from the moderate rise still to be found in Northern Ireland and Dublin has emerged as another global 'hot spot'. An evolution of Dublin house prices similar to that in London can be anticipated. Keywords: Econophysics, house prices, real estate, prediction PACS: 89.65.Gh, 89.90.+n", "published": "2006-06-07T14:07:28Z", "authors": ["Peter Richmond"], "categories": ["physics.soc-ph", "physics.pop-ph"], "doi": "10.1016/j.physa.2006.09.003", "_query": "hedonic house price", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2602.18592v1", "title": "A Roof Over Risk: A House Price-at-Risk Framework for Hungary", "abstract": "This paper develops a House Price-at-Risk framework to examine how housing subsidies, credit conditions, and supply factors influence the distribution of house price growth in Hungary. Using quantile regression with adaptive LASSO variable selection, we identify variables driving downside versus upside risks across multiple horizons. Financial stress dominates the lower tail at short horizons, while unemployment and affordability constraints become the primary drivers of downside risk at longer horizons. Housing subsidies exhibit pro-cyclical characteristics, concentrating significant positive effects on the upper quantiles while leaving the lower tail largely unaffected. Supply-side variables display horizon-dependent sign reversals, with construction permits exerting upward pressure on prices in the short run but moderating them as supply materialises. Uncertainty decomposition reveals persistent left-tail dominance across all horizons. These findings suggest that macroprudential frameworks should account for the distributional effects of housing subsidies, particularly their pro-cyclical influence on house price growth.", "published": "2026-02-20T19:57:51Z", "authors": ["Tibor Szendrei", "Nikolett Vágó", "Katalin Varga"], "categories": ["econ.EM"], "doi": "", "_query": "hedonic house price", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/1612.08486v1", "title": "Understanding the Impacts of Dark Pools on Price Discovery", "abstract": "This paper investigates the impact of dark pools on price discovery (the efficiency of prices on stock exchanges to aggregate information). Assets are traded in either an exchange or a dark pool, with the dark pool offering better prices but lower execution rates. Informed traders receive noisy and heterogeneous signals about an asset's fundamental. We find that informed traders use dark pools to mitigate their information risk and there is a sorting effect: in equilibrium, traders with strong signals trade in exchanges, traders with moderate signals trade in dark pools, and traders with weak signals do not trade. As a result, dark pools have an amplification effect on price discovery. That is, when information precision is high (information risk is low), the majority of informed traders trade in the exchange hence adding a dark pool enhances price discovery, whereas when information precision is low (information risk is high), the majority of the informed traders trade in the dark pool hence adding a dark pool impairs price discovery. The paper reconciles the conflicting empirical evidence and produces novel empirical predictions. The paper also provides regulatory suggestions with dark pools on current equity markets and in emerging markets.", "published": "2016-12-27T03:26:29Z", "authors": ["Linlin Ye"], "categories": ["q-fin.GN", "q-fin.TR"], "doi": "", "_query": "hedonic house price", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/1803.00919v1", "title": "House Price Modeling over Heterogeneous Regions with Hierarchical Spatial Functional Analysis", "abstract": "Online real-estate information systems such as Zillow and Trulia have gained increasing popularity in recent years. One important feature offered by these systems is the online home price estimate through automated data-intensive computation based on housing information and comparative market value analysis. State-of-the-art approaches model house prices as a combination of a latent land desirability surface and a regression from house features. However, by using uniformly damping kernels, they are unable to handle irregularly shaped regions or capture land value discontinuities within the same region due to the existence of implicit sub-communities, which are common in real-world scenarios. In this paper, we explore the novel application of recent advances in spatial functional analysis to house price modeling and propose the Hierarchical Spatial Functional Model (HSFM), which decomposes house values into land desirability at both the global scale and hidden local scales as well as the feature regression component. We propose statistical learning algorithms based on finite-element spatial functional analysis and spatial constrained clustering to train our model. Extensive evaluations based on housing data in a major Canadian city show that our proposed approach can reduce the mean relative house price estimation error down to 6.60%.", "published": "2018-03-01T03:41:55Z", "authors": ["Bang Liu", "Borislav Mavrin", "Di Niu", "Linglong Kong"], "categories": ["cs.CE"], "doi": "10.1109/ICDM.2016.0134", "_query": "hedonic house price", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2105.06060v1", "title": "House Price Prediction using Satellite Imagery", "abstract": "In this paper we show how using satellite images can improve the accuracy of housing price estimation models. Using Los Angeles County's property assessment dataset, by transferring learning from an Inception-v3 model pretrained on ImageNet, we could achieve an improvement of ~10% in R-squared score compared to two baseline models that only use non-image features of the house.", "published": "2021-05-13T03:25:32Z", "authors": ["Sina Jandaghi Semnani", "Hoormazd Rezaei"], "categories": ["cs.LG", "stat.ML"], "doi": "", "_query": "hedonic house price", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2112.06192v1", "title": "Housing Price Prediction Model Selection Based on Lorenz and Concentration Curves: Empirical Evidence from Tehran Housing Market", "abstract": "This study contributes a house price prediction model selection in Tehran City based on the area between Lorenz curve (LC) and concentration curve (CC) of the predicted price by using 206,556 observed transaction data over the period from March 21, 2018, to February 19, 2021. Several different methods such as generalized linear models (GLM) and recursive partitioning and regression trees (RPART), random forests (RF) regression models, and neural network (NN) models were examined house price prediction. We used 90% of all data samples which were chosen randomly to estimate the parameters of pricing models and 10% of remaining datasets to test the accuracy of prediction. Results showed that the area between the LC and CC curves (which are known as ABC criterion) of real and predicted prices in the test data sample of the random forest regression model was less than by other models under study. The comparison of the calculated ABC criteria leads us to conclude that the nonlinear regression models such as RF regression models give an accurate prediction of house prices in Tehran City.", "published": "2021-12-12T09:44:28Z", "authors": ["Mohammad Mirbagherijam"], "categories": ["econ.EM"], "doi": "", "_query": "hedonic house price", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2102.07491v1", "title": "Entropy methods for identifying hedonic models", "abstract": "This paper contributes to the literature on hedonic models in two ways. First, it makes use of Queyranne's reformulation of a hedonic model in the discrete case as a network flow problem in order to provide a proof of existence and integrality of a hedonic equilibrium and efficient computation of hedonic prices. Second, elaborating on entropic methods developed in Galichon and Salanié (2014), this paper proposes a new identification strategy for hedonic models in a single market. This methodology allows one to introduce heterogeneities in both consumers' and producers' attributes and to recover producers' profits and consumers' utilities based on the observation of production and consumption patterns and the set of hedonic prices.", "published": "2021-02-15T11:49:21Z", "authors": ["Arnaud Dupuy", "Alfred Galichon", "Marc Henry"], "categories": ["econ.EM"], "doi": "10.1007/s11579-014-0125-1", "_query": "hedonic house price", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2112.07273v1", "title": "Urban Housing Prices and Migration's Fertility Intentions: Based on the 2018 China Migrants' Dynamic Survey", "abstract": "While the size of China's mobile population continues to expand, the fertility rate is significantly lower than the stable generation replacement level of the population, and the structural imbalance of human resource supply has attracted widespread attention. This paper uses LPM and Probit models to estimate the impact of house prices on the fertility intentions of the mobile population based on data from the 2018 National Mobile Population Dynamics Monitoring Survey. The lagged land sales price is used as an instrumental variable of house price to mitigate the potential endogeneity problem. The results show that for every 100\\% increase in the ratio of house price to household income of mobile population, the fertility intention of the female mobile population of working age at the inflow location will decrease by 4.42\\%, and the marginal effect of relative house price on labor force fertility intention is EXP(-0.222); the sensitivity of mobile population fertility intention to house price is affected by the moderating effect of infrastructure construction at the inflow location. The willingness to have children in the inflow area is higher for female migrants of working age with lower age, smaller family size and higher education. Based on the above findings, the study attempts to provide a new practical perspective for the mainline institutional change and balanced economic development in China's economic transition phase.", "published": "2021-12-14T10:09:08Z", "authors": ["Jingwen Tan", "Shixi Kang"], "categories": ["econ.GN"], "doi": "", "_query": "hedonic house price", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/1306.3531v1", "title": "The convergence of regional house prices in the USA in the context of the stress testing of financial institutions", "abstract": "I studied the convergence of regional house prices to national prices in USA by analyzing time-series of house price indices of 9 Census Divisions. I found the evidence of the convergence in some parts of the country using asymmetric unit root tests. The fact that the evidence of the convergence is not present in large parts of the country raises an issue of execution and interpretation of results of Federal Reserve Bank's annual stress testing of the US banking system.", "published": "2013-06-14T23:57:54Z", "authors": ["Argyn Kuketayev"], "categories": ["q-fin.RM", "stat.AP"], "doi": "", "_query": "hedonic house price", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2405.07456v1", "title": "Boosting House Price Estimations with Multi-Head Gated Attention", "abstract": "Evaluating house prices is crucial for various stakeholders, including homeowners, investors, and policymakers. However, traditional spatial interpolation methods have limitations in capturing the complex spatial relationships that affect property values. To address these challenges, we have developed a new method called Multi-Head Gated Attention for spatial interpolation. Our approach builds upon attention-based interpolation models and incorporates multiple attention heads and gating mechanisms to capture spatial dependencies and contextual information better. Importantly, our model produces embeddings that reduce the dimensionality of the data, enabling simpler models like linear regression to outperform complex ensembling models. We conducted extensive experiments to compare our model with baseline methods and the original attention-based interpolation model. The results show a significant improvement in the accuracy of house price predictions, validating the effectiveness of our approach. This research advances the field of spatial interpolation and provides a robust tool for more precise house price evaluation. Our GitHub repository.contains the data and code for all datasets, which are available for researchers and practitioners interested in replicating or building upon our work.", "published": "2024-05-13T04:12:03Z", "authors": ["Zakaria Abdellah Sellam", "Cosimo Distante", "Abdelmalik Taleb-Ahmed", "Pier Luigi Mazzeo"], "categories": ["cs.LG"], "doi": "", "_query": "hedonic house price", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2404.05178v1", "title": "Estimating granular house price distributions in the Australian market using Gaussian mixtures", "abstract": "A new methodology is proposed to approximate the time-dependent house price distribution at a fine regional scale using Gaussian mixtures. The means, variances and weights of the mixture components are related to time, location and dwelling type through a non linear function trained by a deep functional approximator. Price indices are derived as means, medians, quantiles or other functions of the estimated distributions. Price densities for larger regions, such as a city, are calculated via a weighted sum of the component density functions. The method is applied to a data set covering all of Australia at a fine spatial and temporal resolution. In addition to enabling a detailed exploration of the data, the proposed index yields lower prediction errors in the practical task of individual dwelling price projection from previous sales values within the three major Australian cities. The estimated quantiles are also found to be well calibrated empirically, capturing the complexity of house price distributions.", "published": "2024-04-08T04:01:43Z", "authors": ["Willem P Sijp", "Anastasios Panagiotelis"], "categories": ["econ.EM"], "doi": "", "_query": "hedonic house price", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/1807.07155v2", "title": "Take a Look Around: Using Street View and Satellite Images to Estimate House Prices", "abstract": "When an individual purchases a home, they simultaneously purchase its structural features, its accessibility to work, and the neighborhood amenities. Some amenities, such as air quality, are measurable while others, such as the prestige or the visual impression of a neighborhood, are difficult to quantify. Despite the well-known impacts intangible housing features have on house prices, limited attention has been given to systematically quantifying these difficult to measure amenities. Two issues have led to this neglect. Not only do few quantitative methods exist that can measure the urban environment, but that the collection of such data is both costly and subjective. We show that street image and satellite image data can capture these urban qualities and improve the estimation of house prices. We propose a pipeline that uses a deep neural network model to automatically extract visual features from images to estimate house prices in London, UK. We make use of traditional housing features such as age, size, and accessibility as well as visual features from Google Street View images and Bing aerial images in estimating the house price model. We find encouraging results where learning to characterize the urban quality of a neighborhood improves house price prediction, even when generalizing to previously unseen London boroughs. We explore the use of non-linear vs. linear methods to fuse these cues with conventional models of house pricing, and show how the interpretability of linear models allows us to directly extract proxy variables for visual desirability of neighborhoods that are both of interest in their own right, and could be used as inputs to other econometric methods. This is particularly valuable as once the network has been trained with the training data, it can be applied elsewhere, allowing us to generate vivid dense maps of the visual appeal of London streets.", "published": "2018-07-18T21:10:08Z", "authors": ["Stephen Law", "Brooks Paige", "Chris Russell"], "categories": ["econ.EM", "cs.CV"], "doi": "10.1145/3342240", "_query": "hedonic house price", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2503.18332v2", "title": "Regional House Price Dynamics in Australia: Insights into Lifestyle and Mining Dynamics through PCA", "abstract": "This report applies Principal Component Analysis (PCA) to regional house price indexes to uncover dominant trends in Australia's housing market. Regions are assigned PCA-derived scores that reveal which underlying market forces are most influential in each area, enabling broad classification of local housing markets. The approach highlights where price movements tend to align across regions, even those geographically distant. The three most dominant trends are described in detail and, together with the regional scores, provide objective tools for policymakers, researchers, and real estate professionals.", "published": "2025-03-24T04:31:17Z", "authors": ["Willem Sijp"], "categories": ["econ.GN"], "doi": "", "_query": "hedonic house price", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2207.04455v1", "title": "The Price of Stability for First Price Auction", "abstract": "This paper establishes the Price of Stability (PoS) for First Price Auctions, for all equilibrium concepts that have been studied in the literature: Bayes Nash Equilibrium $\\subsetneq$ Bayes Correlated Equilibrium $\\subsetneq$ Bayes Coarse Correlated Equilibrium} $\\bullet$ Bayes Nash Equilibrium: For independent valuations, the tight PoS is $1 - 1/ e^{2} \\approx 0.8647$, matching the counterpart Price of Anarchy (PoA) bound \\cite{JL22}. For correlated valuations, the tight $\\PoS$ is $1 - 1 / e \\approx 0.6321$, matching the counterpart PoA bound \\cite{ST13,S14}. This result indicates that, in the worst cases, efficiency degradation depends not on different selections among Bayes Nash Equilibria. $\\bullet$ Bayesian Coarse Correlated Equilibrium: For independent or correlated valuations, the tight PoS is always $1 = 100\\%$, i.e., no efficiency degradation, different from the counterpart PoA bound $1 - 1 / e \\approx 0.6321$ \\cite{ST13,S14}. This result indicates that First Price Auctions can be fully efficient when we allow the more general equilibrium concepts.", "published": "2022-07-10T12:49:36Z", "authors": ["Yaonan Jin", "Pinyan Lu"], "categories": ["cs.GT"], "doi": "", "_query": "hedonic house price", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/1012.3039v2", "title": "London house prices are power-law distributed", "abstract": "We explore the house price distributions for the English cities of London, Manchester, Bristol, Newcastle, Birmingham and Leeds. We find Pareto (power law) behaviour in their upper tails, which is clearly distinct from lognormal and gamma distributions in the cases of London, Manchester and Newcastle. For London, the city with the lowest power, this is a striking match with that found in the wealth distribution of the super-rich. We propose an index of Housing Wealth Inequality based on the Pareto exponent and analogous to the Gini coefficient, and comment on its possible uses.", "published": "2010-12-14T14:24:45Z", "authors": ["Niall MacKay"], "categories": ["physics.soc-ph"], "doi": "", "_query": "hedonic house price", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2505.01591v2", "title": "Machine Learning Fairness in House Price Prediction: A Case Study of America's Expanding Metropolises", "abstract": "As a basic human need, housing plays a key role in enhancing health, well-being, and educational outcome in society, and the housing market is a major factor for promoting quality of life and ensuring social equity. To improve the housing conditions, there has been extensive research on building Machine Learning (ML)-driven house price prediction solutions to accurately forecast the future conditions, and help inform actions and policies in the field. In spite of their success in developing high-accuracy models, there is a gap in our understanding of the extent to which various ML-driven house price prediction approaches show ethnic and/or racial bias, which in turn is essential for the responsible use of ML, and ensuring that the ML-driven solutions do not exacerbate inequity. To fill this gap, this paper develops several ML models from a combination of structural and neighborhood-level attributes, and conducts comprehensive assessments on the fairness of ML models under various definitions of privileged groups. As a result, it finds that the ML-driven house price prediction models show various levels of bias towards protected attributes (i.e., race and ethnicity in this study). Then, it investigates the performance of different bias mitigation solutions, and the experimental results show their various levels of effectiveness on different ML-driven methods. However, in general, the in-processing bias mitigation approach tends to be more effective than the pre-processing one in this problem domain. Our code is available at https://github.com/wahab1412/housing_fairness.", "published": "2025-05-02T21:26:21Z", "authors": ["Abdalwahab Almajed", "Maryam Tabar", "Peyman Najafirad"], "categories": ["cs.LG"], "doi": "", "_query": "hedonic house price", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/1503.05550v1", "title": "Club Convergence of House Prices: Evidence from China's Ten Key Cities", "abstract": "The latest global financial tsunami and its follow-up global economic recession has uncovered the crucial impact of housing markets on financial and economic systems. The Chinese stock market experienced a markedly fall during the global financial tsunami and China's economy has also slowed down by about 2\\%-3\\% when measured in GDP. Nevertheless, the housing markets in diverse Chinese cities seemed to continue the almost nonstop mania for more than ten years. However, the structure and dynamics of the Chinese housing market are less studied. Here we perform an extensive study of the Chinese housing market by analyzing ten representative key cities based on both linear and nonlinear econophysical and econometric methods. We identify a common collective driving force which accounts for 96.5\\% of the house price growth, indicating very high systemic risk in the Chinese housing market. The ten key cities can be categorized into clubs and the house prices of the cities in the same club exhibit an evident convergence. These findings from different methods are basically consistent with each other. The identified city clubs are also consistent with the conventional classification of city tiers. The house prices of the first-tier cities grow the fastest, and those of the third- and fourth-tier cities rise the slowest, which illustrates the possible presence of a ripple effect in the diffusion of house prices in different cities.", "published": "2015-03-18T03:46:56Z", "authors": ["Hao Meng", "Wen-Jie Xie", "Wei-Xing Zhou"], "categories": ["q-fin.ST", "physics.soc-ph"], "doi": "10.1142/S0217979215501817", "_query": "hedonic house price", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2102.02392v1", "title": "Lapsing Quickly into Fatalism: Bell on Backward Causation", "abstract": "This is a dialogue between Huw Price and Travis Norsen, loosely inspired by a letter that Price received from J. S. Bell in 1988. The main topic of discussion is Bell's views about retrocausal approaches to quantum theory, and their relevance to contemporary issues.", "published": "2021-02-04T03:22:50Z", "authors": ["Travis Norsen", "Huw Price"], "categories": ["quant-ph", "physics.hist-ph"], "doi": "", "_query": "hedonic house price", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/1707.04868v1", "title": "Forecasting the U.S. Real House Price Index", "abstract": "The 2006 sudden and immense downturn in U.S. House Prices sparked the 2007 global financial crisis and revived the interest about forecasting such imminent threats for economic stability. In this paper we propose a novel hybrid forecasting methodology that combines the Ensemble Empirical Mode Decomposition (EEMD) from the field of signal processing with the Support Vector Regression (SVR) methodology that originates from machine learning. We test the forecasting ability of the proposed model against a Random Walk (RW) model, a Bayesian Autoregressive and a Bayesian Vector Autoregressive model. The proposed methodology outperforms all the competing models with half the error of the RW model with and without drift in out-of-sample forecasting. Finally, we argue that this new methodology can be used as an early warning system for forecasting sudden house prices drops with direct policy implications.", "published": "2017-07-16T11:36:16Z", "authors": ["Vasilios Plakandaras", "Rangan Gupta", "Periklis Gogas", "Theophilos Papadimitriou"], "categories": ["q-fin.CP"], "doi": "", "_query": "hedonic house price", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2101.02033v1", "title": "Boarding House Renting Price Prediction Using Deep Neural Network Regression on Mobile Apps", "abstract": "Boarding house is the most important requirement, especially for college students who live far away from the city, place of his origin or house. However, the problem we see now is the uneven distribution of study places in Indonesia which 75% of the best top educational institutions come from the island of Java. So, students who are looking for boarding houses rent requires more effort in comparing the various aspects desired. They need to survey one by one to the boarding house they want, even though they can survey online, it still requires more effort to pay attention to the desired facilities one by one. Therefore, we then created an Mobile Application that can predict prices based on student needs by comparing several variables, namely city, area, type of boarding house, and facilities. So, students can easily estimate the ideal price. The results of this study prove that we have succeeded in predicting prices for boarding houses rent well based on the variables we have determined, and modeling that variables using Deep Neural Network Regression.", "published": "2020-12-31T15:12:10Z", "authors": ["Malik Abdul Aziz", "Fahmi Nurrahim", "Prastyo Eko Susanto", "Yurio Windiatmoko"], "categories": ["cs.CY", "cs.LG"], "doi": "", "_query": "hedonic house price", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2312.13953v1", "title": "Subtle Sound Design: Designing for experience blend in a historic house museum", "abstract": "In this article, we present and discuss a user-study prototype, developed for Bakkehuset historic house museum in Copenhagen. We examine how the prototype - a digital sound installation - can expand visitors' experiences of the house and offer encounters with immaterial cultural heritage. Historic house museums often hold back on utilizing digital communication tools inside the houses, since a central purpose of this type of museum is to preserve an original environment. Digital communication tools however hold great potential for facilitating rich encounters with cultural heritage and in particular with the immaterial aspects of museum collections and their histories. In this article we present our design steps and choices, aiming at subtly and seamlessly adding a digital dimension to a historic house. Based on qualitative interviews, we evaluate how the sound installation at Bakkehuset is sensed, interpreted, and used by visitors as part of their museum experience. In turn, we shed light on the historic house museum as a distinct design context for designing hybrid visitor experiences and point to the potentials of digital communication tools in this context.", "published": "2023-12-21T15:40:39Z", "authors": ["Mia F. Yates", "Anders Sundnes Løvlie"], "categories": ["cs.HC"], "doi": "10.1145/3633476", "_query": "hedonic house price", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2202.04358v1", "title": "House Price Valuation Model Based on Geographically Neural Network Weighted Regression: The Case Study of Shenzhen, China", "abstract": "Confronted with the spatial heterogeneity of real estate market, some traditional research utilized Geographically Weighted Regression (GWR) to estimate the house price. However, its kernel function is non-linear, elusive, and complex to opt bandwidth, the predictive power could also be improved. Consequently, a novel technique, Geographical Neural Network Weighted Regression (GNNWR), has been applied to improve the accuracy of real estate appraisal with the help of neural networks. Based on Shenzhen house price dataset, this work conspicuously captures the weight distribution of different variants at Shenzhen real estate market, which GWR is difficult to materialize. Moreover, we focus on the performance of GNNWR, verify its robustness and superiority, refine the experiment process with 10-fold cross-validation, extend its application area from natural to socioeconomic geospatial data. It's a practical and trenchant way to assess house price, and we demonstrate the effectiveness of GNNWR on a complex socioeconomic dataset.", "published": "2022-02-09T09:40:54Z", "authors": ["Zimo Wang", "Yicheng Wang", "Sensen Wu"], "categories": ["stat.AP", "cs.LG"], "doi": "", "_query": "hedonic house price", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2210.14266v1", "title": "Hedonic Models of Real Estate Prices: GAM and Environmental Factors", "abstract": "We consider the use of P-spline generalized additive hedonic models for real estate prices in large U.S. cities, contrasting their predictive efficiency against linear and polynomial based generalized linear models. Using intrinsic and extrinsic factors available from Redfin, we show that GAM models are capable of describing 84% to 92% of the variance in the expected ln(sales price), based upon 2021 data. As climate change is becoming increasingly important, we utilized the GAM model to examine the significance of environmental factors in two urban centers on the northwest coast. The results indicate city dependent differences in the significance of environmental factors. We find that inclusion of the environmental factors increases the adjusted R-squared of the GAM model by less than one percent.", "published": "2022-10-25T18:38:52Z", "authors": ["Jason R. Bailey", "Davide Lauria", "W. Brent Lindquist", "Stefan Mittnik", "Svetlozar T. Rachev"], "categories": ["q-fin.CP"], "doi": "", "_query": "hedonic house price", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2412.01666v2", "title": "Quantifying Core Stability Relaxations in Hedonic Games", "abstract": "We study relationships between different relaxed notions of core stability in hedonic games, which are a class of coalition formation games. Our unified approach applies to a newly introduced family of hedonic games, called $α$-hedonic games, which contains previously studied variants such as fractional and additively separable hedonic games. In particular, we derive an upper bound on the maximum factor with which a blocking coalition of a certain size can improve upon an outcome in which no deviating coalition of size at most $q$ exists. Counterintuitively, we show that larger blocking coalitions might sometimes have lower improvement factors. We discuss the tightness conditions of our bound, as well as its implications on the price of anarchy of core relaxations. Our general result has direct implications for several well-studied classes of hedonic games, allowing us to prove two open conjectures by Fanelli et al. (2021) for fractional hedonic games.", "published": "2024-12-02T16:16:02Z", "authors": ["Tom Demeulemeester", "Jannik Peters"], "categories": ["cs.GT", "cs.DM", "math.CO"], "doi": "", "_query": "hedonic house price", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2412.14624v2", "title": "The Diffusive Nature of Housing Prices", "abstract": "We analyze the French housing market prices in the period 1970-2022, with high-resolution data from 2018 to 2022. The spatial correlation of the observed price field exhibits logarithmic decay characteristic of the two-dimensional random diffusion equation -- local interactions may create long-range correlations. We introduce a stylized model, used in the past to model spatial regularities in voting patterns, that accounts for both spatial and temporal correlations with reasonable values of parameters. Our analysis reveals that price shocks are persistent in time and their amplitude is strongly heterogeneous in space. Our study confirms and quantifies the diffusive nature of housing prices that was anticipated long ago (Clapp et al. 1994, Pollakowski et al. 1997), albeit on much restricted, local data sets.", "published": "2024-12-19T08:21:51Z", "authors": ["Antoine-Cyrus Becharat", "Michael Benzaquen", "Jean-Philippe Bouchaud"], "categories": ["cond-mat.stat-mech", "econ.GN", "physics.soc-ph"], "doi": "", "_query": "hedonic house price", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/1701.04431v1", "title": "Interpolating between matching and hedonic pricing models", "abstract": "We consider the theoretical properties of a model which encompasses bi-partite matching under transferable utility on the one hand, and hedonic pricing on the other. This framework is intimately connected to tripartite matching problems (known as multi-marginal optimal transport problems in the mathematical literature). We exploit this relationship in two ways; first, we show that a known structural result from multi-marginal optimal transport can be used to establish an upper bound on the dimension of the support of stable matchings. Next, assuming the distribution of agents on one side of the market is continuous, we identify a condition on their preferences that ensures purity and uniqueness of the stable matching; this condition is a variant of a known condition in the mathematical literature, which guarantees analogous properties in the multi-marginal optimal transport problem. We exhibit several examples of surplus functions for which our condition is satisfied, as well as some for which it fails.", "published": "2017-01-16T19:36:10Z", "authors": ["Brendan Pass"], "categories": ["econ.GN", "math.AP", "math.OC"], "doi": "", "_query": "hedonic house price", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2210.06261v1", "title": "Predicting housing prices and analyzing real estate market in the Chicago suburbs using Machine Learning", "abstract": "The pricing of housing properties is determined by a variety of factors. However, post-pandemic markets have experienced volatility in the Chicago suburb area, which have affected house prices greatly. In this study, analysis was done on the Naperville/Bolingbrook real estate market to predict property prices based on these housing attributes through machine learning models, and to evaluate the effectiveness of such models in a volatile market space. Gathering data from Redfin, a real estate website, sales data from 2018 up until the summer season of 2022 were collected for research. By analyzing these sales in this range of time, we can also look at the state of the housing market and identify trends in price. For modeling the data, the models used were linear regression, support vector regression, decision tree regression, random forest regression, and XGBoost regression. To analyze results, comparison was made on the MAE, RMSE, and R-squared values for each model. It was found that the XGBoost model performs the best in predicting house prices despite the additional volatility sponsored by post-pandemic conditions. After modeling, Shapley Values (SHAP) were used to evaluate the weights of the variables in constructing models.", "published": "2022-10-12T14:41:53Z", "authors": ["Kevin Xu", "Hieu Nguyen"], "categories": ["cs.LG"], "doi": "", "_query": "hedonic house price", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/1812.09452v1", "title": "The Price of BitCoin: GARCH Evidence from High Frequency Data", "abstract": "This is the first paper that estimates the price determinants of BitCoin in a Generalised Autoregressive Conditional Heteroscedasticity framework using high frequency data. Derived from a theoretical model, we estimate BitCoin transaction demand and speculative demand equations in a GARCH framework using hourly data for the period 2013-2018. In line with the theoretical model, our empirical results confirm that both the BitCoin transaction demand and speculative demand have a statistically significant impact on the BitCoin price formation. The BitCoin price responds negatively to the BitCoin velocity, whereas positive shocks to the BitCoin stock, interest rate and the size of the BitCoin economy exercise an upward pressure on the BitCoin price.", "published": "2018-12-22T05:25:15Z", "authors": ["Pavel Ciaian", "d'Artis Kancs", "Miroslava Rajcaniova"], "categories": ["q-fin.ST", "econ.GN"], "doi": "", "_query": "hedonic house price", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/1404.0879v1", "title": "Utility indifference pricing of derivatives written on industrial loss indexes", "abstract": "We consider the problem of pricing derivatives written on some industrial loss index via utility indifference pricing. The industrial loss index is modelled by a compound Poisson process and the insurer can adjust her portfolio by choosing the risk loading, which in turn determines the demand. We compute the price of a CAT(spread) option written on that index using utility indifference pricing.", "published": "2014-04-03T12:34:01Z", "authors": ["Gunther Leobacher", "Philip Ngare"], "categories": ["q-fin.PR", "q-fin.PM", "q-fin.RM"], "doi": "", "_query": "hedonic house price", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/1806.00037v1", "title": "MULTIGRAIN: Simulating mixtures of multiple dust grains and gas with SPH", "abstract": "We present MULTIGRAIN, an algorithm for simulating multiple phases of small dust grains embedded in a gas, building on our earlier work in simulating two-phase mixtures of gas and dust in SPH (Laibe & Price 2012a,b; Price & Laibe 2015). The MULTIGRAIN method (Hutchison, Price & Laibe 2018) is more accurate than single-phase simulations because the gas experiences a backreaction from each dust phase and communicates this change to the other phases, thereby indirectly coupling the dust phases together. The MULTIGRAIN method is fast, explicit and low storage, requiring only an array of dust fractions and their derivatives defined for each resolution element. We demonstrate the MULTIGRAIN algorithm on test problems related to the settling of dust in the discs of gas around young stars, where solar systems are born. Finally I will discuss possible extensions of the method to incorporate both large and small grains, together with recent improvements in our numerical techniques for gas and dust mixtures. In particular, I will show how the 'overdamping' problem identified by Laibe & Price (2012a) can be solved.", "published": "2018-05-31T18:22:21Z", "authors": ["Daniel J. Price", "Mark Hutchison", "Guillaume Laibe"], "categories": ["astro-ph.IM"], "doi": "", "_query": "hedonic house price", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2202.11416v1", "title": "Price formation in financial markets: a game-theoretic perspective", "abstract": "We propose two novel frameworks to study the price formation of an asset negotiated in an order book. Specifically, we develop a game-theoretic model in many-person games and mean-field games, considering costs stemming from limited liquidity. We derive analytical formulas for the formed price in terms of the realized order flow. We also identify appropriate conditions that ensure the convergence of the price we find in the finite population game to that of its mean-field counterpart. We numerically assess our results with a large experiment using high-frequency data from ten stocks listed in the NASDAQ, a stock listed in B3 in Brazil, and a cryptocurrency listed in Binance.", "published": "2022-02-23T10:59:35Z", "authors": ["David Evangelista", "Yuri Saporito", "Yuri Thamsten"], "categories": ["q-fin.TR"], "doi": "", "_query": "hedonic house price", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/1706.08501v2", "title": "A Simulator for Hedonic Games", "abstract": "Hedonic games are meant to model how coalitions of people form and break apart in the real world. However, it is difficult to run simulations when everything must be done by hand on paper. We present an online software that allows fast and visual simulation of several types of hedonic games. http://lukemiles.org/hedonic-games/", "published": "2017-06-26T17:47:32Z", "authors": ["Luke Harold Miles"], "categories": ["cs.MA", "cs.AI"], "doi": "", "_query": "hedonic house price", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2407.15905v1", "title": "A divide-and-conquer approach for spatio-temporal analysis of large house price data from Greater London", "abstract": "Statistical research in real estate markets, particularly in understanding the spatio-temporal dynamics of house prices, has garnered significant attention in recent times. Although Bayesian methods are common in spatio-temporal modeling, standard Markov chain Monte Carlo (MCMC) techniques are usually slow for large datasets such as house price data. To tackle this problem, we propose a divide-and-conquer spatio-temporal modeling approach. This method involves partitioning the data into multiple subsets and applying an appropriate Gaussian process model to each subset in parallel. The results from each subset are then combined using the Wasserstein barycenter technique to obtain the global parameters for the original problem. The proposed methodology allows for multiple observations per spatial and time unit, thereby offering added benefits for practitioners. As a real-life application, we analyze house price data of more than 0.6 million transactions from 983 middle layer super output areas in London over a period of eight years. The methodology provides insightful findings about the effects of various amenities, trend patterns, and the relationship between prices and carbon emissions. Furthermore, as demonstrated through a cross-validation study, it shows good predictive accuracy while balancing computational efficiency.", "published": "2024-07-22T14:29:13Z", "authors": ["Kapil Gupta", "Soudeep Deb"], "categories": ["stat.AP"], "doi": "", "_query": "hedonic house price", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2102.07655v2", "title": "Dense for the Price of Sparse: Improved Performance of Sparsely Initialized Networks via a Subspace Offset", "abstract": "That neural networks may be pruned to high sparsities and retain high accuracy is well established. Recent research efforts focus on pruning immediately after initialization so as to allow the computational savings afforded by sparsity to extend to the training process. In this work, we introduce a new `DCT plus Sparse' layer architecture, which maintains information propagation and trainability even with as little as 0.01% trainable kernel parameters remaining. We show that standard training of networks built with these layers, and pruned at initialization, achieves state-of-the-art accuracy for extreme sparsities on a variety of benchmark network architectures and datasets. Moreover, these results are achieved using only simple heuristics to determine the locations of the trainable parameters in the network, and thus without having to initially store or compute with the full, unpruned network, as is required by competing prune-at-initialization algorithms. Switching from standard sparse layers to DCT plus Sparse layers does not increase the storage footprint of a network and incurs only a small additional computational overhead.", "published": "2021-02-12T00:05:02Z", "authors": ["Ilan Price", "Jared Tanner"], "categories": ["cs.LG"], "doi": "", "_query": "hedonic house price", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/1903.00817v1", "title": "Cultural evolution in Vietnam's early 20th century: a Bayesian networks analysis of Franco-Chinese house designs", "abstract": "The study of cultural evolution has taken on an increasingly interdisciplinary and diverse approach in explicating phenomena of cultural transmission and adoptions. Inspired by this computational movement, this study uses Bayesian networks analysis, combining both the frequentist and the Hamiltonian Markov chain Monte Carlo (MCMC) approach, to investigate the highly representative elements in the cultural evolution of a Vietnamese city's architecture in the early 20th century. With a focus on the façade design of 68 old houses in Hanoi's Old Quarter (based on 78 data lines extracted from 248 photos), the study argues that it is plausible to look at the aesthetics, architecture and designs of the house façade to find traces of cultural evolution in Vietnam, which went through more than six decades of French colonization and centuries of sociocultural influence from China. The in-depth technical analysis, though refuting the presumed model on the probabilistic dependency among the variables, yields several results, the most notable of which is the strong influence of Buddhism over the decorations of the house façade. Particularly, in the top 5 networks with the best Bayesian Information criterion (BIC) scores and p<0.05, the variable for decorations (DC) always has a direct probabilistic dependency on the variable B for Buddhism. The paper then checks the robustness of these models using Hamiltonian MCMC method and find the posterior distributions of the models' coefficients all satisfy the technical requirement. Finally, this study suggests integrating Bayesian statistics in social sciences in general and for study of cultural evolution and architectural transformation in particular.", "published": "2019-03-03T03:47:11Z", "authors": ["Quan-Hoang Vuong", "Quang-Khiem Bui", "Viet-Phuong La", "Thu-Trang Vuong", "Manh-Toan Ho", "Hong-Kong T. Nguyen", "Hong-Ngoc Nguyen", "Kien-Cuong P. Nghiem", "Manh-Tung Ho"], "categories": ["stat.AP"], "doi": "10.1016/j.ssaho.2019.100001", "_query": "hedonic house price", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/1112.4632v4", "title": "The Price of Matching Selfish Vertices", "abstract": "We analyze the setting of minimum-cost perfect matchings with selfish vertices through the price of anarchy (PoA) and price of stability (PoS) lens. The underlying solution concept used for this analysis is the Gale-Shapley stable matching notion, where the preferences are determined so that each player (vertex) wishes to minimize the cost of her own matching edge.", "published": "2011-12-20T09:57:00Z", "authors": ["Yuval Emek", "Tobias Langner", "Roger Wattenhofer"], "categories": ["cs.CG"], "doi": "", "_query": "hedonic house price", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2409.08912v1", "title": "Joint spatial modeling of mean and non-homogeneous variance combining semiparametric SAR and GAMLSS models for hedonic prices", "abstract": "In the context of spatial econometrics, it is very useful to have methodologies that allow modeling the spatial dependence of the observed variables and obtaining more precise predictions of both the mean and the variability of the response variable, something very useful in territorial planning and public policies. This paper proposes a new methodology that jointly models the mean and the variance. Also, it allows to model the spatial dependence of the dependent variable as a function of covariates and to model the semiparametric effects in both models. The algorithms developed are based on generalized additive models that allow the inclusion of non-parametric terms in both the mean and the variance, maintaining the traditional theoretical framework of spatial regression. The theoretical developments of the estimation of this model are carried out, obtaining desirable statistical properties in the estimators. A simulation study is developed to verify that the proposed method has a remarkable predictive capacity in terms of the mean square error and shows a notable improvement in the estimation of the spatial autoregressive parameter, compared to other traditional methods and some recent developments. The model is also tested on data from the construction of a hedonic price model for the city of Bogota, highlighting as the main result the ability to model the variability of housing prices, and the wealth in the analysis obtained.", "published": "2024-09-13T15:28:05Z", "authors": ["J. D. Toloza-Delgado", "O. O. Melo", "N. A. Cruz"], "categories": ["stat.ME", "stat.CO"], "doi": "10.1016/j.spasta.2024.100864", "_query": "hedonic house price", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2209.03936v2", "title": "Shift in house price estimates during COVID-19 reveals effect of crisis on collective speculation", "abstract": "We exploit a city-level panel comprised of individual house price estimates to estimate the impact of COVID-19 on both small and big real-estate markets in California USA. Descriptive analysis of spot house price estimates, including contemporaneous price uncertainty and 30-day price change for individual properties listed on the online real-estate platform Zillow.com, together facilitate quantifying both the excess valuation and valuation confidence attributable to this global socio-economic shock. Our quasi-experimental pre-/post-COVID-19 design spans several years around 2020 and leverages contemporaneous price estimates of rental properties - i.e., real estate entering the habitation market, just not for purchase (off-market) and hence free of speculation - as an appropriate counterfactual to properties listed for sale, which are subject to on-market speculation. Combining unit-level matching and multivariate difference-in-difference regression approaches, we obtain consistent estimates regarding the sign and magnitude of excess price growth observed after the pandemic onset. Specifically, our results indicate that properties listed for sale appreciated an additional 1% per month above what would be expected in the absence of the pandemic. This corresponds to an excess annual price growth of roughly 12.7 percentage points, which accounts for more than half of the actual annual price growth in 2021 observed across the studied regions. Simultaneously, uncertainty in price estimates decreased, signaling the irrational confidence characteristic of prior asset bubbles. We explore how these two trends are related to market size, local market supply and borrowing costs, which altogether lend support for the counterintuitive roles of uncertainty and interruptions in decision-making.", "published": "2022-07-21T17:47:20Z", "authors": ["Alexander M. Petersen"], "categories": ["q-fin.GN", "physics.soc-ph"], "doi": "10.1140/epjds/s13688-024-00488-9", "_query": "hedonic house price", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/1804.03315v1", "title": "Individual and Group Stability in Neutral Restrictions of Hedonic Games", "abstract": "We consider a class of coalition formation games called hedonic games, i.e., games in which the utility of a player is completely determined by the coalition that the player belongs to. We first define the class of subset-additive hedonic games and show that they have the same representation power as the class of hedonic games. We then define a restriction of subset-additive hedonic games that we call subset-neutral hedonic games and generalize a result by Bogomolnaia and Jackson (2002) by showing the existence of a Nash stable partition and an individually stable partition in such games. We also consider neutrally anonymous hedonic games and show that they form a subclass of the subset-additive hedonic games. Finally, we show the existence of a core stable partition that is also individually stable in neutrally anonymous hedonic games by exhibiting an algorithm to compute such a partition.", "published": "2018-04-10T02:37:23Z", "authors": ["Warut Suksompong"], "categories": ["cs.GT"], "doi": "10.1016/j.mathsocsci.2015.07.004", "_query": "hedonic house price", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/1411.5497v1", "title": "Time-warped growth processes, with applications to the modeling of boom-bust cycles in house prices", "abstract": "House price increases have been steady over much of the last 40 years, but there have been occasional declines, most notably in the recent housing bust that started around 2007, on the heels of the preceding housing bubble. We introduce a novel growth model that is motivated by time-warping models in functional data analysis and includes a nonmonotone time-warping component that allows the inclusion and description of boom-bust cycles and facilitates insights into the dynamics of asset bubbles. The underlying idea is to model longitudinal growth trajectories for house prices and other phenomena, where temporal setbacks and deflation may be encountered, by decomposing such trajectories into two components. A first component corresponds to underlying steady growth driven by inflation that anchors the observed trajectories on a simple first order linear differential equation, while a second boom-bust component is implemented as time warping. Time warping is a commonly encountered phenomenon and reflects random variation along the time axis. Our approach to time warping is more general than previous approaches by admitting the inclusion of nonmonotone warping functions. The anchoring of the trajectories on an underlying linear dynamic system also makes the time-warping component identifiable and enables straightforward estimation procedures for all model components. The application to the dynamics of housing prices as observed for 19 metropolitan areas in the U.S. from December 1998 to July 2013 reveals that the time setbacks corresponding to nonmonotone time warping vary substantially across markets and we find indications that they are related to market-specific growth rates.", "published": "2014-11-20T10:42:29Z", "authors": ["Jie Peng", "Debashis Paul", "Hans-Georg Müller"], "categories": ["stat.AP"], "doi": "10.1214/14-AOAS740", "_query": "hedonic house price", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/1507.03474v1", "title": "Simple Causes of Complexity in Hedonic Games", "abstract": "Hedonic games provide a natural model of coalition formation among self-interested agents. The associated problem of finding stable outcomes in such games has been extensively studied. In this paper, we identify simple conditions on expressivity of hedonic games that are sufficient for the problem of checking whether a given game admits a stable outcome to be computationally hard. Somewhat surprisingly, these conditions are very mild and intuitive. Our results apply to a wide range of stability concepts (core stability, individual stability, Nash stability, etc.) and to many known formalisms for hedonic games (additively separable games, games with W-preferences, fractional hedonic games, etc.), and unify and extend known results for these formalisms. They also have broader applicability: for several classes of hedonic games whose computational complexity has not been explored in prior work, we show that our framework immediately implies a number of hardness results for them.", "published": "2015-07-13T14:36:17Z", "authors": ["Dominik Peters", "Edith Elkind"], "categories": ["cs.GT"], "doi": "", "_query": "hedonic house price", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2405.14503v1", "title": "Radial analysis and scaling of housing prices in French urban areas", "abstract": "Urban scaling laws summarize how urban attributes evolve with city size. Recent criticism questions notably the aggregate view of this approach, which leads to neglecting the internal structure of cities. This is all the more relevant for housing prices due to their important variations across space. Based on a dataset compiling millions of real estate transactions over the period 2017-2021, we investigate the regularities of the radial (center-periphery) profiles of housing prices across cities, with respect to their size. Results are threefold. First, they corroborate prior findings in the urban scaling literature stating that largest cities agglomerate higher housing prices. Second, we find that housing price radial profiles scale in three dimensions with the power 1/5 of city population. After rescaling, great regularities between radial profiles can be observed, although some locational amenities have a significant impact on prices. Third, it appears that our rescaled profiles approach fails to explain housing price variations in the city center across cities. In fact, prices near the city center rise much faster with city size than those in the periphery. This has strong implications for low-income households seeking homeownership, because prohibitive prices in the center may contribute to pushing them out into peripheral locations.", "published": "2024-05-23T12:38:20Z", "authors": ["Gaëtan Laziou", "Rémi Lemoy", "Marion Le Texier"], "categories": ["physics.soc-ph"], "doi": "", "_query": "hedonic house price", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2501.06241v1", "title": "Predicting House Rental Prices in Ghana Using Machine Learning", "abstract": "This study investigates the efficacy of machine learning models for predicting house rental prices in Ghana, addressing the need for accurate and accessible housing market information. Utilising a comprehensive dataset of rental listings, we trained and evaluated various models, including CatBoost, XGBoost, and Random Forest. CatBoost emerged as the best-performing model, achieving an $R^2$ of 0.876, demonstrating its ability to effectively capture complex relationships within the housing market. Feature importance analysis revealed that location-based features, number of bedrooms, bathrooms, and furnishing status are key drivers of rental prices. Our findings provide valuable insights for stakeholders, including real estate professionals, investors, and policymakers, while also highlighting opportunities for future research, such as incorporating temporal data and exploring regional variations.", "published": "2025-01-08T15:40:46Z", "authors": ["Philip Adzanoukpe"], "categories": ["cs.LG", "stat.AP"], "doi": "10.20944/preprints202412.1927.v1", "_query": "hedonic house price", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/1706.09007v1", "title": "Strategyproof Mechanisms for Additively Separable Hedonic Games and Fractional Hedonic Games", "abstract": "Additively separable hedonic games and fractional hedonic games have received considerable attention. They are coalition forming games of selfish agents based on their mutual preferences. Most of the work in the literature characterizes the existence and structure of stable outcomes (i.e., partitions in coalitions), assuming that preferences are given. However, there is little discussion on this assumption. In fact, agents receive different utilities if they belong to different partitions, and thus it is natural for them to declare their preferences strategically in order to maximize their benefit. In this paper we consider strategyproof mechanisms for additively separable hedonic games and fractional hedonic games, that is, partitioning methods without payments such that utility maximizing agents have no incentive to lie about their true preferences. We focus on social welfare maximization and provide several lower and upper bounds on the performance achievable by strategyproof mechanisms for general and specific additive functions. In most of the cases we provide tight or asymptotically tight results. All our mechanisms are simple and can be computed in polynomial time. Moreover, all the lower bounds are unconditional, that is, they do not rely on any computational or complexity assumptions.", "published": "2017-06-27T18:47:49Z", "authors": ["Michele Flammini", "Gianpiero Monaco", "Qiang Zhang"], "categories": ["cs.AI", "cs.GT"], "doi": "", "_query": "hedonic house price", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2507.08641v1", "title": "Pricing and hedging the prepayment option of mortgages under stochastic housing market activity", "abstract": "Prepayment risk embedded in fixed-rate mortgages forms a significant fraction of a financial institution's exposure. The embedded prepayment option bears the same interest rate risk as an exotic interest rate swap with a suitable stochastic notional. Focusing on penalty-free prepayment because of the contract owner's relocation to a new house, we model the prepayment option value as an European-type interest rate receiver swaption with stochastic maturity matching the stochastic time of relocation. This is a convenient representation since it allows us to compute the prepayment option value in terms of well-known pricing formulas for European-type swaptions. We investigate the effect of a stochastic housing market activity as the explanatory variable for the distribution of the relocation time, as opposed to the conventional assumption of a deterministic housing market activity. We prove that the housing market covariance drives the prepayment option price difference between the stochastic setting and its deterministic counterpart. The prepayment option exposure is hedged using market instruments based on Delta-Gamma replication. Furthermore, since the housing market activity is a non-tradable risk factor, we perform non-standard actuarial hedging focusing on controlling the prepayment option exposure yield by risky housing market scenarios.", "published": "2025-07-11T14:43:02Z", "authors": ["Leonardo Perotti", "Lech A. Grzelak", "Cornelis W. Oosterlee"], "categories": ["q-fin.PR"], "doi": "", "_query": "hedonic house price", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/1002.0650v2", "title": "Magnetic fields and radiative feedback in the star formation process", "abstract": "Star formation is a complex process involving the interplay of many physical effects, including gravity, turbulent gas dynamics, magnetic fields and radiation. Our understanding of the process has improved substantially in recent years, primarily as a result of our increased ability to incorporate the relevant physics in numerical calculations of the star formation process. In this contribution we present an overview of our recent studies of star cluster formation in turbulent, magnetised clouds using self-gravitating radiation-magnetohydrodynamics calculations (Price and Bate 2008, 2009). Our incorporation of magnetic fields and radiative transfer into the Smoothed Particle Hydrodynamics method are discussed. We highlight how magnetic fields and radiative heating of the gas around newborn stars can solve several of the key puzzles in star formation, including an explanation for why star formation is such a slow and inefficient process. However, the presence of magnetic fields at observed strengths in collapsing protostellar cores also leads to problems on smaller scales, including a difficulty in forming protostellar discs and binary stars (Price and Bate 2007, Hennebelle and Teyssier 2008), which suggests that our understanding of the role of magnetic fields in star formation is not yet complete.", "published": "2010-02-03T05:55:54Z", "authors": ["Daniel J. Price", "Matthew R. Bate"], "categories": ["astro-ph.GA", "astro-ph.SR"], "doi": "10.1063/1.3460126", "_query": "hedonic house price", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/1310.4260v1", "title": "A Switch for Artificial Resistivity and Other Dissipation Terms", "abstract": "We describe a new switch to reduce dissipation from artificial resistivity in Smoothed Particle Magnetohydrodynamics simulations. The switch utilises the gradient of the magnetic field to detect shocks, setting alpha_B = h |gradB| / |B|. This measures the relative degree of discontinuity, and the switch is not dependent on the absolute field strength. We present results comparing the new resistivity switch to the switch of Price & Monaghan (2005), showing that it is more robust in capturing shocks (especially in weak fields), while leading to less overall dissipation. The design of this switch is generalised to create similar switches for artificial viscosity and thermal conduction, with proof of concept tests conducted on a Sod shock tube and Kelvin-Helmholtz instabilities.", "published": "2013-10-16T04:07:02Z", "authors": ["Terrence Tricco", "Daniel Price"], "categories": ["astro-ph.IM", "physics.comp-ph"], "doi": "", "_query": "hedonic house price", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2509.03834v1", "title": "From Leiden to Pleasure Island: The Constant Potts Model for Community Detection as a Hedonic Game", "abstract": "Community detection is one of the fundamental problems in data science which consists of partitioning nodes into disjoint communities. We present a game-theoretic perspective on the Constant Potts Model (CPM) for partitioning networks into disjoint communities, emphasizing its efficiency, robustness, and accuracy. Efficiency: We reinterpret CPM as a potential hedonic game by decomposing its global Hamiltonian into local utility functions, where the local utility gain of each agent matches the corresponding increase in global utility. Leveraging this equivalence, we prove that local optimization of the CPM objective via better-response dynamics converges in pseudo-polynomial time to an equilibrium partition. Robustness: We introduce and relate two stability criteria: a strict criterion based on a novel notion of robustness, requiring nodes to simultaneously maximize neighbors and minimize non-neighbors within communities, and a relaxed utility function based on a weighted sum of these objectives, controlled by a resolution parameter. Accuracy: In community tracking scenarios, where initial partitions are used to bootstrap the Leiden algorithm with partial ground-truth information, our experiments reveal that robust partitions yield higher accuracy in recovering ground-truth communities.", "published": "2025-09-04T02:50:54Z", "authors": ["Lucas Lopes Felipe", "Konstantin Avrachenkov", "Daniel Sadoc Menasche"], "categories": ["cs.LG", "cs.AI", "cs.GT"], "doi": "10.1016/j.physa.2025.130989", "_query": "hedonic house price", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2602.05888v2", "title": "Metric Hedonic Games on the Line", "abstract": "Hedonic games are fundamental models for investigating the formation of coalitions among a set of strategic agents, where every agent has a certain utility for every possible coalition of agents it can be part of. To avoid the intractability of defining exponentially many utilities for all possible coalitions, many variants with succinct representations of the agents' utility functions have been devised and analyzed, e.g., modified fractional hedonic games by Monaco et al. [JAAMAS 2020]. We extend this by studying a novel succinct variant that is related to modified fractional hedonic games. In our model, each agent has a fixed type-value and an agent's cost for some given coalition is based on the differences between its value and those of the other members of its coalition. This allows to model natural situations like athletes forming training groups with similar performance levels or voters that partition themselves along a political spectrum. In particular, we investigate natural variants where an agent's cost is defined by distance thresholds, or by the maximum or average value difference to the other agents in its coalition. For these settings, we study the existence of stable coalition structures, their properties, and their quality in terms of the price of anarchy and the price of stability. Further, we investigate the impact of limiting the maximum number of coalitions. Despite the simple setting with metric distances on a line, we uncover a rich landscape of models, partially with counter-intuitive behavior. Also, our focus on both swap stability and jump stability allows us to study the influence of fixing the number and the size of the coalitions. Overall, we find that stable coalition structures always exist but that their properties and quality can vary widely.", "published": "2026-02-05T17:05:08Z", "authors": ["Merlin de la Haye", "Pascal Lenzner", "Farehe Soheil", "Marcus Wunderlich"], "categories": ["cs.GT", "cs.AI"], "doi": "", "_query": "hedonic house price", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2601.08957v2", "title": "The Connection Between Monetary Policy and Housing Prices: Public Perception and Expert Communication", "abstract": "We study how the general public perceives the link between monetary policy and housing markets. Using a large-scale, cross-country survey experiment in Austria, Germany, Italy, Sweden, and the United Kingdom, we examine households' understanding of monetary policy, their beliefs about its impact on house prices, and how these beliefs respond to expert information. We find that while most respondents grasp the basic mechanisms of conventional monetary policy and recognize the connection between interest rates and house prices, literacy regarding unconventional monetary policy is very low. Beliefs about the monetary policy-housing nexus are malleable and respond to information, particularly when it is provided by academic economists rather than central bankers. Monetary policy literacy is strongly related to education, gender, age, and experience in housing and mortgage markets. Our results highlight the central role of housing in how households interpret monetary policy and point to the importance of credible and inclusive communication strategies for effective policy transmission.", "published": "2026-01-13T19:51:06Z", "authors": ["Philipp Poyntner", "Sofie R. Waltl"], "categories": ["econ.GN"], "doi": "", "_query": "hedonic house price", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2005.06562v1", "title": "A solution to the overdamping problem when simulating dust-gas mixtures with smoothed particle hydrodynamics", "abstract": "We present a fix to the overdamping problem found by Laibe & Price (2012) when simulating strongly coupled dust-gas mixtures using two different sets of particles using smoothed particle hydrodynamics. Our solution is to compute the drag at the barycentre between gas and dust particle pairs when computing the drag force by reconstructing the velocity field, similar to the procedure in Godunov-type solvers. This fixes the overdamping problem at negligible computational cost, but with additional memory required to store velocity derivatives. We employ slope limiters to avoid spurious oscillations at shocks, finding the van Leer Monotonized Central limiter most effective.", "published": "2020-05-13T19:56:04Z", "authors": ["Daniel J. Price", "Guillaume Laibe"], "categories": ["astro-ph.IM", "astro-ph.EP", "physics.comp-ph"], "doi": "10.1093/mnras/staa1366", "_query": "hedonic house price", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/astro-ph/0310790v1", "title": "Smoothed Particle Magnetohydrodynamics II. Variational principles and variable smoothing length terms", "abstract": "In this paper we show how a Lagrangian variational principle can be used to derive the SPMHD (smoothed particle magnetohydrodynamics) equations for ideal MHD. We also consider the effect of a variable smoothing length in the SPH kernels after which we demonstrate by numerical tests that the consistent treatment of terms relating to the gradient of the smoothing length in the SPMHD equations significantly improves the accuracy of the algorithm. Our results complement those obtained in a companion paper (Price and Monaghan 2003a, paper I) for non ideal MHD where artificial dissipative terms were included to handle shocks.", "published": "2003-10-28T12:31:58Z", "authors": ["D. J. Price", "J. J. Monaghan"], "categories": ["astro-ph"], "doi": "10.1111/j.1365-2966.2004.07346.x", "_query": "hedonic house price", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2204.06967v1", "title": "JUE Insight: The (Non-)Effect of Opportunity Zones on Housing Prices", "abstract": "Will the Opportunity Zones (OZ) program, America's largest new place-based policy in decades, generate neighborhood change? We compare single-family housing price growth in OZs with price growth in areas that were eligible but not included in the program. We also compare OZs to their nearest geographic neighbors. Our most credible estimates rule out price impacts greater than 0.5 percentage points with 95% confidence, suggesting that, so far, home buyers don't believe that this subsidy will generate major neighborhood change. OZ status reduces prices in areas with little employment, perhaps because buyers think that subsidizing new investment will increase housing supply. Mixed evidence suggests that OZs may have increased residential permitting.", "published": "2022-04-14T13:48:40Z", "authors": ["Jiafeng Chen", "Edward Glaeser", "David Wessel"], "categories": ["econ.GN"], "doi": "10.1016/j.jue.2022.103451", "_query": "hedonic house price", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2508.03156v1", "title": "Unveiling Location-Specific Price Drivers: A Two-Stage Cluster Analysis for Interpretable House Price Predictions", "abstract": "House price valuation remains challenging due to localized market variations. Existing approaches often rely on black-box machine learning models, which lack interpretability, or simplistic methods like linear regression (LR), which fail to capture market heterogeneity. To address this, we propose a machine learning approach that applies two-stage clustering, first grouping properties based on minimal location-based features before incorporating additional features. Each cluster is then modeled using either LR or a generalized additive model (GAM), balancing predictive performance with interpretability. Constructing and evaluating our models on 43,309 German house property listings from 2023, we achieve a 36% improvement for the GAM and 58% for LR in mean absolute error compared to models without clustering. Additionally, graphical analyses unveil pattern shifts between clusters. These findings emphasize the importance of cluster-specific insights, enhancing interpretability and offering practical value for buyers, sellers, and real estate analysts seeking more reliable property valuations.", "published": "2025-08-05T07:00:42Z", "authors": ["Paul Gümmer", "Julian Rosenberger", "Mathias Kraus", "Patrick Zschech", "Nico Hambauer"], "categories": ["cs.LG"], "doi": "", "_query": "hedonic house price", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/quant-ph/9602020v1", "title": "Locality, Independence and the Pro-Liberty Bell", "abstract": "Construed as an argument against hidden variable theories, Bell's Theorem assumes that hidden variables would be independent of future measurement settings. This Independence Assumption (IA) is rarely questioned. Bell considered relaxing it to avoid non-locality, but thought that the resulting view left no room for free will. However, Bell seems to have failed to distinguish two different strategies for giving up IA. One strategy takes for granted the Principle of the Common Cause, which requires that a correlation between hidden variables and measurement settings be explained by a joint correlation with some unknown factor in their common past. The other strategy rejects the Principle of the Common Cause, and argues that the required correlation might be due to the known interaction between the object system and the measuring device in their common future. Bell and most others who have discussed these issues have focussed on the former strategy, but because the two approaches have not been properly distinguished, it has not been well appreciated that there is a quite different way to relax IA. This paper distinguishes the two strategies, and argues that the latter is considerably more appealing than the former.", "published": "1996-02-28T00:54:30Z", "authors": ["Huw Price"], "categories": ["quant-ph"], "doi": "", "_query": "hedonic house price", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2102.12112v2", "title": "Modeling Price Clustering in High-Frequency Prices", "abstract": "The price clustering phenomenon manifesting itself as an increased occurrence of specific prices is widely observed and well-documented for various financial instruments and markets. In the literature, however, it is rarely incorporated into price models. We consider that there are several types of agents trading only in specific multiples of the tick size resulting in an increased occurrence of these multiples in prices. For example, stocks on the NYSE and NASDAQ exchanges are traded with precision to one cent but multiples of five cents and ten cents occur much more often in prices. To capture this behavior, we propose a discrete price model based on a mixture of double Poisson distributions with dynamic volatility and dynamic proportions of agent types. The model is estimated by the maximum likelihood method. In an empirical study of DJIA stocks, we find that higher instantaneous volatility leads to weaker price clustering at the ultra-high frequency. This is in sharp contrast with results at low frequencies which show that daily realized volatility has a positive impact on price clustering.", "published": "2021-02-24T08:06:36Z", "authors": ["Vladimír Holý", "Petra Tomanová"], "categories": ["q-fin.ST"], "doi": "10.1080/14697688.2022.2050285", "_query": "hedonic house price", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/1705.10116v1", "title": "Fractional Hedonic Games", "abstract": "The work we present in this paper initiated the formal study of fractional hedonic games, coalition formation games in which the utility of a player is the average value he ascribes to the members of his coalition. Among other settings, this covers situations in which players only distinguish between friends and non-friends and desire to be in a coalition in which the fraction of friends is maximal. Fractional hedonic games thus not only constitute a natural class of succinctly representable coalition formation games, but also provide an interesting framework for network clustering. We propose a number of conditions under which the core of fractional hedonic games is non-empty and provide algorithms for computing a core stable outcome. By contrast, we show that the core may be empty in other cases, and that it is computationally hard in general to decide non-emptiness of the core.", "published": "2017-05-29T11:03:56Z", "authors": ["Haris Aziz", "Florian Brandl", "Felix Brandt", "Paul Harrenstein", "Martin Olsen", "Dominik Peters"], "categories": ["cs.GT"], "doi": "", "_query": "hedonic house price", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2405.17505v1", "title": "Predicting Rental Price of Lane Houses in Shanghai with Machine Learning Methods and Large Language Models", "abstract": "Housing has emerged as a crucial concern among young individuals residing in major cities, including Shanghai. Given the unprecedented surge in property prices in this metropolis, young people have increasingly resorted to the rental market to address their housing needs. This study utilizes five traditional machine learning methods: multiple linear regression (MLR), ridge regression (RR), lasso regression (LR), decision tree (DT), and random forest (RF), along with a Large Language Model (LLM) approach using ChatGPT, for predicting the rental prices of lane houses in Shanghai. It applies these methods to examine a public data sample of about 2,609 lane house rental transactions in 2021 in Shanghai, and then compares the results of these methods. In terms of predictive power, RF has achieved the best performance among the traditional methods. However, the LLM approach, particularly in the 10-shot scenario, shows promising results that surpass traditional methods in terms of R-Squared value. The three performance metrics: mean squared error (MSE), mean absolute error (MAE), and R-Squared, are used to evaluate the models. Our conclusion is that while traditional machine learning models offer robust techniques for rental price prediction, the integration of LLM such as ChatGPT holds significant potential for enhancing predictive accuracy.", "published": "2024-05-26T07:01:33Z", "authors": ["Tingting Chen", "Shijing Si"], "categories": ["cs.LG", "cs.CL"], "doi": "", "_query": "hedonic house price", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/quant-ph/9610036v1", "title": "Time Symmetry in Microphysics", "abstract": "Physics takes for granted that interacting physical systems with no common history are independent, before their interaction. This principle is time-asymmetric, for no such restriction applies to systems with no common future, after an interaction. The time-asymmetry is normally attributed to boundary conditions. I argue that there are two distinct independence principles of this kind at work in contemporary physics, one of which cannot be attributed to boundary conditions, and therefore conflicts with the assumed T (or CPT) symmetry of microphysics. I note that this may have interesting ramifications in quantum mechanics.", "published": "1996-10-22T23:16:19Z", "authors": ["Huw Price"], "categories": ["quant-ph"], "doi": "", "_query": "hedonic house price", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/1509.07062v1", "title": "Boolean Hedonic Games", "abstract": "We study hedonic games with dichotomous preferences. Hedonic games are cooperative games in which players desire to form coalitions, but only care about the makeup of the coalitions of which they are members; they are indifferent about the makeup of other coalitions. The assumption of dichotomous preferences means that, additionally, each player's preference relation partitions the set of coalitions of which that player is a member into just two equivalence classes: satisfactory and unsatisfactory. A player is indifferent between satisfactory coalitions, and is indifferent between unsatisfactory coalitions, but strictly prefers any satisfactory coalition over any unsatisfactory coalition. We develop a succinct representation for such games, in which each player's preference relation is represented by a propositional formula. We show how solution concepts for hedonic games with dichotomous preferences are characterised by propositional formulas.", "published": "2015-09-23T17:07:54Z", "authors": ["Haris Aziz", "Paul Harrenstein", "Jérôme Lang", "Michael Wooldridge"], "categories": ["cs.GT", "cs.AI"], "doi": "", "_query": "hedonic house price", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2603.02456v6", "title": "When Does Static Willingness to Pay Mislead? A Framework for Dynamic Hedonic Valuation", "abstract": "Many policy counterfactuals depend on how consumers value product attributes such as sugar, caffeine, alcohol, or emissions. Standard hedonic and differentiated-products models typically impose time-separable preferences. But when attributes are habit forming, current consumption can shift future marginal valuations, so static willingness-to-pay may be insufficient for policy counterfactuals. I develop a nonparametric revealed-preference framework for dynamic hedonic valuation, deriving necessary and sufficient conditions for rationalising observed prices and choices. Using cereal scanner data, I show that the hedonic representation restricts prices, while habits improve behavioural coherence conditional on that representation. The framework diagnoses when static willingness-to-pay is defensible for policy.", "published": "2026-03-02T22:57:30Z", "authors": ["Josephine Auer"], "categories": ["econ.TH", "econ.EM", "econ.GN"], "doi": "", "_query": "hedonic house price", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2212.06986v4", "title": "Why entanglement?", "abstract": "In this piece, written for a general audience, we propose a mechanism for quantum entanglement. The key ingredient is collider bias. In the language of causal models, a collider is a variable causally influenced by two or more other variables. Conditioning on a collider typically produces non-causal correlations between its contributing causes. This phenomenon can produce associations analogous to Bell correlations, in suitable post-selected ensembles. Such collider artefacts may become real connections, resembling causality, if a collider is 'constrained' (e.g., by a future boundary condition). We consider the time-reversed analogues of these points in the context of retrocausal models of QM. Retrocausality yields a collider at the source of an EPR-Bell particle pair, and in this case constraint of the collider is possible by normal methods of experimental preparation. It follows that connections resembling causality may emerge across such colliders, from one branch of the experiment to the other. Our hypothesis is that this constrained retrocausal collider bias is the origin of entanglement. This piece is based on a suggestion first made in arXiv:2101.05370v4 [quant-ph], and is an ancestor of an essay now published online in Aeon magazine [Price & Wharton 2023a]. In an updated version of the argument in arXiv:2309.10969 [quant-ph] we (i) demonstrate its application in a real Bell experiment; and (ii) show that we can do without an explicit postulate of retrocausality", "published": "2022-12-14T02:46:22Z", "authors": ["Huw Price", "Ken Wharton"], "categories": ["quant-ph", "physics.hist-ph", "physics.pop-ph"], "doi": "", "_query": "hedonic house price", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/1510.07129v1", "title": "Bayesian Inference for High Dimensional Changing Linear Regression with Application to Minnesota House Price Index Data", "abstract": "In many applications, the dataset under investigation exhibits heterogeneous regimes that are more appropriately modeled using piece-wise linear models for each of the data segments separated by change-points. Although there have been much work on change point linear regression for the low dimensional case, high-dimensional change point regression is severely underdeveloped. Motivated by the analysis of Minnesota House Price Index data, we propose a fully Bayesian framework for fitting changing linear regression models in high-dimensional settings. Using segment-specific shrinkage and diffusion priors, we deliver full posterior inference for the change points and simultaneously obtain posterior probabilities of variable selection in each segment via an efficient Gibbs sampler. Additionally, our method can detect an unknown number of change points and accommodate different variable selection constraints like grouping or partial selection. We substantiate the accuracy of our method using simulation experiments for a wide range of scenarios. We apply our approach for a macro-economic analysis of Minnesota house price index data. The results strongly favor the change point model over a homogeneous (no change point) high-dimensional regression model.", "published": "2015-10-24T10:28:14Z", "authors": ["Abhirup Datta", "Hui Zou", "Sudipto Banerjee"], "categories": ["math.ST", "stat.AP"], "doi": "", "_query": "hedonic house price", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2502.12116v3", "title": "Floods do not sink prices, historical memory does: How flood risk impacts the Italian housing market", "abstract": "Do home prices incorporate flood risk in the immediate aftermath of specific flood events, or is it the repeated exposure over the years that plays a more significant role? We address this question through the first systematic study of the Italian housing market, which is an ideal case study because it is highly exposed to floods, though unevenly distributed across the national territory. Using a novel dataset containing about 550,000 mortgage-financed transactions between 2016 and 2024, as well as hedonic regressions and a difference-in-difference design, we find that: (i) specific floods do not decrease home prices in areas at risk; (ii) the repeated exposure to floods in flood-prone areas leads to a price decline, up to 4\\% in the most frequently flooded regions; (iii) responses are heterogeneous by buyers' income and age. Young buyers (with limited exposure to prior floods) do not obtain any price reduction for settling in risky areas, while experienced buyers do. At the same time, buyers who settle in risky areas have lower incomes than buyers in safe areas in the most affected regions. Our results emphasize the importance of cultural and institutional factors in understanding how flood risk affects the housing market and socioeconomic outcomes.", "published": "2025-02-17T18:41:23Z", "authors": ["Anna Bellaver", "Lorenzo Costantini", "Ariadna Fosch", "Anna Monticelli", "David Scala", "Marco Pangallo"], "categories": ["econ.GN"], "doi": "", "_query": "hedonic house price", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/astro-ph/0310789v1", "title": "Smoothed Particle Magnetohydrodynamics I. Algorithms and tests in one dimension", "abstract": "In this paper we show how the Smoothed Particle Hydrodynamics (SPH) equations for ideal magnetohydrodynamics (MHD) can be written in conservation form with the positivity of the dissipation guaranteed. We call the resulting algorithm Smoothed Particle Magnetohydrodynamics (SPMHD). The equations appear to be accurate, robust and easy to apply and do not suffer from the instabilities known to exist previously in formulations of the SPMHD equations. In addition we formulate our MHD equations such that errors associated with non-zero divergence of the magnetic field are naturally propagated by the flow and should therefore remain small. In this and a companion paper (Price and Monaghan 2003b) we present a wide range of numerical tests in one dimension to show that the algorithm gives very good results for one dimensional flows in both adiabatic and isothermal MHD. For the one dimensional tests the field structure is either two or three dimensional. The algorithm has many astrophysical applications and is particularly suited to star formation problems.", "published": "2003-10-28T12:27:40Z", "authors": ["D. J. Price", "J. J. Monaghan"], "categories": ["astro-ph"], "doi": "10.1111/j.1365-2966.2004.07345.x", "_query": "hedonic house price", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/1602.05342v2", "title": "Hedonic Games with Graph-restricted Communication", "abstract": "We study hedonic coalition formation games in which cooperation among the players is restricted by a graph structure: a subset of players can form a coalition if and only if they are connected in the given graph. We investigate the complexity of finding stable outcomes in such games, for several notions of stability. In particular, we provide an efficient algorithm that finds an individually stable partition for an arbitrary hedonic game on an acyclic graph. We also introduce a new stability concept -in-neighbor stability- which is tailored for our setting. We show that the problem of finding an in-neighbor stable outcome admits a polynomial-time algorithm if the underlying graph is a path, but is NP-hard for arbitrary trees even for additively separable hedonic games; for symmetric additively separable games we obtain a PLS-hardness result.", "published": "2016-02-17T09:18:30Z", "authors": ["Ayumi Igarashi", "Edith Elkind"], "categories": ["cs.GT"], "doi": "", "_query": "hedonic house price", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2307.13232v1", "title": "Changes in Risk Appreciation, and Short Memory of House Buyers When the Market is Hot, a Case Study of Christchurch, New Zealand", "abstract": "In this paper house prices in Christchurch are analyzed over three distinct periods of time: post-2011 earthquake, pre-COVID-19 lockdown, and post-COVID-19 lockdown using the well-established hedonic price model. Results show that buyers, in periods that are temporally distant from the 2011 Christchurch earthquake, value the risk of potential earthquake damage to a property differently from buyers soon after the earthquake. We find that there are observable shifts in hedonic prices across the different time periods, specifically for section size pre and post COVID-19 lockdown.", "published": "2023-07-25T03:46:38Z", "authors": ["Emil Mendoza", "Fabian Dunker", "Marco Reale"], "categories": ["q-fin.GN", "stat.AP"], "doi": "", "_query": "hedonic house price", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2602.18506v1", "title": "Control in Hedonic Games", "abstract": "We initiate the study of control in hedonic games, where an external actor influences coalition formation by adding or deleting agents. We consider three basic control goals (1) enforcing that an agent is not alone (NA); (2) enforcing that a pair of agents is in the same coalition (PA); (3) enforcing that all agents are in the same grand coalition (GR), combined with two control actions: adding agents (AddAg) or deleting agents (DelAg). We analyze these problems for friend-oriented and additive preferences under individual rationality, individual stability, Nash stability, and core stability. We provide a complete computational complexity classification for control in hedonic games.", "published": "2026-02-18T11:52:04Z", "authors": ["Jiehua Chen", "Jakob Guttmann", "Merisa Mustajbašić", "Sofia Simola"], "categories": ["cs.GT", "cs.MA"], "doi": "", "_query": "hedonic house price", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/1807.04516v1", "title": "A Bayesian Nonparametric Approach to Geographic Regression Discontinuity Designs: Do School Districts Affect NYC House Prices?", "abstract": "Most research on regression discontinuity designs (RDDs) has focused on univariate cases, where only those units with a \"forcing\" variable on one side of a threshold value receive a treatment. Geographical regression discontinuity designs (GeoRDDs) extend the RDD to multivariate settings with spatial forcing variables. We propose a framework for analysing GeoRDDs, which we implement using Gaussian process regression. This yields a Bayesian posterior distribution of the treatment effect at every point along the border. We address nuances of having a functional estimand defind on a border with potentially intricate topology, particularly when defining and estimating causal estimands of the local average treatment effect (LATE). The Bayesian estimate of the LATE can also be used as a test statistic in a hypothesis test with good frequentist properties, which we validate using simulations and placebo tests. We demonstrate our methodology with a dataset of property sales in New York City, to assess whether there is a discontinuity in housing prices at the border between two school district. We find a statistically significant difference in price across the border between the districts with $p$=0.002, and estimate a 20% higher price on average for a house on the more desirable side.", "published": "2018-07-11T09:57:29Z", "authors": ["Maxime Rischard", "Zach Branson", "Luke Miratrix", "Luke Bornn"], "categories": ["stat.AP"], "doi": "", "_query": "hedonic house price", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/1912.04661v1", "title": "Adaptive Dynamic Model Averaging with an Application to House Price Forecasting", "abstract": "Dynamic model averaging (DMA) combines the forecasts of a large number of dynamic linear models (DLMs) to predict the future value of a time series. The performance of DMA critically depends on the appropriate choice of two forgetting factors. The first of these controls the speed of adaptation of the coefficient vector of each DLM, while the second enables time variation in the model averaging stage. In this paper we develop a novel, adaptive dynamic model averaging (ADMA) methodology. The proposed methodology employs a stochastic optimisation algorithm that sequentially updates the forgetting factor of each DLM, and uses a state-of-the-art non-parametric model combination algorithm from the prediction with expert advice literature, which offers finite-time performance guarantees. An empirical application to quarterly UK house price data suggests that ADMA produces more accurate forecasts than the benchmark autoregressive model, as well as competing DMA specifications.", "published": "2019-12-10T12:03:58Z", "authors": ["Alisa Yusupova", "Nicos G. Pavlidis", "Efthymios G. Pavlidis"], "categories": ["econ.EM", "stat.ME"], "doi": "", "_query": "hedonic house price", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/astro-ph/0509083v1", "title": "Smoothed Particle Magnetohydrodynamics III. Multidimensional tests and the div B = 0 constraint", "abstract": "In two previous papers (Price & Monaghan 2004a,b) (papers I,II) we have described an algorithm for solving the equations of Magnetohydrodynamics (MHD) using the Smoothed Particle Hydrodynamics (SPH) method. The algorithm uses dissipative terms in order to capture shocks and has been tested on a wide range of one dimensional problems in both adiabatic and isothermal MHD. In this paper we investigate multidimensional aspects of the algorithm, refining many of the aspects considered in papers I and II and paying particular attention to the code's ability to maintain the div B = 0 constraint associated with the magnetic field. In particular we implement a hyperbolic divergence cleaning method recently proposed by Dedner et al. (2002) in combination with the consistent formulation of the MHD equations in the presence of non-zero magnetic divergence derived in papers I and II. Various projection methods for maintaining the divergence-free condition are also examined. Finally the algorithm is tested against a wide range of multidimensional problems used to test recent grid-based MHD codes. A particular finding of these tests is that in SPMHD the magnitude of the divergence error is dependent on the number of neighbours used to calculate a particle's properties and only weakly dependent on the total number of particles. Whilst many improvements could still be made to the algorithm, our results suggest that the method is ripe for application to problems of current theoretical interest, such as that of star formation.", "published": "2005-09-05T11:45:39Z", "authors": ["D. J. Price", "J. J. Monaghan"], "categories": ["astro-ph"], "doi": "10.1111/j.1365-2966.2005.09576.x", "_query": "hedonic house price", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/1110.4119v1", "title": "Integration and Contagion in US Housing Markets", "abstract": "This paper explores integration and contagion among US metropolitan housing markets. The analysis applies Federal Housing Finance Agency (FHFA) house price repeat sales indexes from 384 metropolitan areas to estimate a multi-factor model of U.S. housing market integration. It then identifies statistical jumps in metropolitan house price returns as well as MSA contemporaneous and lagged jump correlations. Finally, the paper evaluates contagion in housing markets via parametric assessment of MSA house price spatial dynamics. A R-squared measure reveals an upward trend in MSA housing market integration over the 2000s to approximately .83 in 2010. Among California MSAs, the trend was especially pronounced, as average integration increased from about .55 in 1997 to close to .95 in 2008! The 2000s bubble period similarly was characterized by elevated incidence of statistical jumps in housing returns. Again, jump incidence and MSA jump correlations were especially high in California. Analysis of contagion among California markets indicates that house price returns in San Francisco often led those of surrounding communities; in contrast, southern California MSA house price returns appeared to move largely in lock step. The high levels of housing market integration evidenced in the analysis suggest limited investor opportunity to diversify away MSA-specific housing risk. Further, results suggest that macro and policy shocks propagate through a large number of MSA housing markets. Research findings are relevant to all market participants, including institutional investors in MBS as well as those who regulate housing, the housing GSEs, mortgage lenders, and related financial institutions.", "published": "2011-10-18T20:42:59Z", "authors": ["John Cotter", "Stuart Gabriel", "Richard Roll"], "categories": ["q-fin.GN"], "doi": "", "_query": "hedonic house price", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2310.05139v1", "title": "Maximizing Utilitarian and Egalitarian Welfare of Fractional Hedonic Games on Tree-like Graphs", "abstract": "Fractional hedonic games are coalition formation games where a player's utility is determined by the average value they assign to the members of their coalition. These games are a variation of graph hedonic games, which are a class of coalition formation games that can be succinctly represented. Due to their applicability in network clustering and their relationship to graph hedonic games, fractional hedonic games have been extensively studied from various perspectives. However, finding welfare-maximizing partitions in fractional hedonic games is a challenging task due to the nonlinearity of utilities. In fact, it has been proven to be NP-hard and can be solved in polynomial time only for a limited number of graph classes, such as trees. This paper presents (pseudo)polynomial-time algorithms to compute welfare-maximizing partitions in fractional hedonic games on tree-like graphs. We consider two types of social welfare measures: utilitarian and egalitarian. Tree-like graphs refer to graphs with bounded treewidth and block graphs. A hardness result is provided, demonstrating that the pseudopolynomial-time solvability is the best possible under the assumption P$\\neq$NP.", "published": "2023-10-08T12:18:08Z", "authors": ["Tesshu Hanaka", "Airi Ikeyama", "Hirotaka Ono"], "categories": ["cs.GT", "cs.AI"], "doi": "", "_query": "hedonic house price", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2502.18614v1", "title": "Towards Using Voice for Hedonic Shopping Motivations", "abstract": "Besides the utilitarian aspects of online shopping, hedonic motivations play a significant role in shaping the shopping behavior of online users. With the increased popularity of voice-enabled devices, online shopping platforms have attempted to drive online shopping on voice. However, we explain why voice might be more suitable for the hedonic aspects of shopping. We introduce a prototype that enables such focus in a voice experience and share our findings from a qualitative study.", "published": "2025-02-25T19:59:55Z", "authors": ["Morteza Behrooz", "Preetham Kolari", "Fred Zaw", "Lindsay Kenzig", "Arnav Jhala"], "categories": ["cs.HC"], "doi": "", "_query": "hedonic house price", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2007.12127v1", "title": "Generating Empirical Core Size Distributions of Hedonic Games using a Monte Carlo Method", "abstract": "Data analytics allows an analyst to gain insight into underlying populations through the use of various computational approaches, including Monte Carlo methods. This paper discusses an approach to apply Monte Carlo methods to hedonic games. Hedonic games have gain popularity over the last two decades leading to several research articles that are concerned with the necessary, sufficient, or both conditions of the existence of a core partition. Researchers have used analytical methods for this work. We propose that using a numerical approach will give insights that might not be available through current analytical methods. In this paper, we describe an approach to representing hedonic games, with strict preferences, in a matrix form that can easily be generated; that is, a hedonic game with randomly generated preferences for each player. Using this generative approach, we were able to create and solve, i.e., find any core partitions, of millions of hedonic games. Our Monte Carlo experiment generated games with up to thirteen players. The results discuss the distribution form of the core size of the games of a given number of players. We also discuss computational considerations. Our numerical study of hedonic games gives insight into the underlying properties of hedonic games.", "published": "2020-07-23T16:54:41Z", "authors": ["Andrew J. Collins", "Sheida Etemadidavan", "Wael Khallouli"], "categories": ["econ.TH"], "doi": "", "_query": "hedonic house price", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/1309.5437v1", "title": "A switch to reduce resistivity in smoothed particle magnetohydrodynamics", "abstract": "Artificial resistivity is included in Smoothed Particle Magnetohydrodynamics simulations to capture shocks and discontinuities in the magnetic field. Here we present a new method for adapting the strength of the applied resistivity so that shocks are captured but the dissipation of the magnetic field away from shocks is minimised. Our scheme utilises the gradient of the magnetic field as a shock indicator, setting α_B = h|gradB|/|B|, such that resistivity is switched on only where strong discontinuities are present. The advantage to this approach is that the resistivity parameter does not depend on the absolute field strength. The new switch is benchmarked on a series of shock tube tests demonstrating its ability to capture shocks correctly. It is compared against a previous switch proposed by Price & Monaghan (2005), showing that it leads to lower dissipation of the field, and in particular, that it succeeds at capturing shocks in the regime where the Alfvén speed is much less than the sound speed (i.e., when the magnetic field is very weak). It is also simpler. We also demonstrate that our recent constrained divergence cleaning algorithm has no difficulty with shock tube tests, in contrast to other implementations.", "published": "2013-09-21T05:12:52Z", "authors": ["Terrence S. Tricco", "Daniel J. Price"], "categories": ["astro-ph.IM", "astro-ph.CO", "astro-ph.GA", "astro-ph.HE", "astro-ph.SR"], "doi": "10.1093/mnras/stt1776", "_query": "hedonic house price", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/1505.01164v1", "title": "Achieving a Hyperlocal Housing Price Index: Overcoming Data Sparsity by Bayesian Dynamical Modeling of Multiple Data Streams", "abstract": "Understanding how housing values evolve over time is important to policy makers, consumers and real estate professionals. Existing methods for constructing housing indices are computed at a coarse spatial granularity, such as metropolitan regions, which can mask or distort price dynamics apparent in local markets, such as neighborhoods and census tracts. A challenge in moving to estimates at, for example, the census tract level is the sparsity of spatiotemporally localized house sales observations. Our work aims at addressing this challenge by leveraging observations from multiple census tracts discovered to have correlated valuation dynamics. Our proposed Bayesian nonparametric approach builds on the framework of latent factor models to enable a flexible, data-driven method for inferring the clustering of correlated census tracts. We explore methods for scalability and parallelizability of computations, yielding a housing valuation index at the level of census tract rather than zip code, and on a monthly basis rather than quarterly. Our analysis is provided on a large Seattle metropolitan housing dataset.", "published": "2015-05-05T20:01:08Z", "authors": ["You Ren", "Emily B. Fox", "Andrew Bruce"], "categories": ["stat.AP", "stat.ME", "stat.ML"], "doi": "", "_query": "hedonic house price", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2511.11531v1", "title": "Deviation Dynamics in Cardinal Hedonic Games", "abstract": "Computing stable partitions in hedonic games is a challenging task because there exist games in which stable outcomes do not exist. Even more, these No-instances can often be leveraged to prove computational hardness results. We make this impression rigorous in a dynamic model of cardinal hedonic games by providing meta theorems. These imply hardness of deciding about the possible or necessary convergence of deviation dynamics based on the mere existence of No-instances. Our results hold for additively separable, fractional, and modified fractional hedonic games (ASHGs, FHGs, and MFHGs). Moreover, they encompass essentially all reasonable stability notions based on single-agent deviations. In addition, we propose dynamics as a method to find individually rational and contractually individual stable (CIS) partitions in ASHGs. In particular, we find that CIS dynamics from the singleton partition possibly converge after a linear number of deviations but may require an exponential number of deviations in the worst case.", "published": "2025-11-14T18:06:28Z", "authors": ["Valentin Zech", "Martin Bullinger"], "categories": ["cs.GT", "cs.DS"], "doi": "", "_query": "hedonic house price", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2502.15905v1", "title": "Improving ex ante accuracy assessment in predicting house price dispersion: evidence from the USA", "abstract": "The study focuses on improving the ex ante prediction accuracy assessment in the case of forecasting various house price dispersion measures in the USA. It addresses a critical gap in real estate market forecasting by proposing a novel method for assessing ex ante prediction accuracy under unanticipated shocks. The proposal is based on a parametric bootstrap approach under a misspecified model, allowing for the simulation of future values and estimation of prediction errors in case of unexpected price changes. The study highlights the limitations of the traditional approach that fails to account for unforeseen market events and provides a more in-depth understanding of how prediction accuracy changes under unexpected scenarios. The proposed methods offers valuable insights for real estate market management by enabling more robust risk assessment and decision-making in the face of unexpected market fluctuations. Real data application is based on longitudinal U.S. data on real estate transactions.", "published": "2025-02-21T19:45:29Z", "authors": ["Adam Chwila", "Monika Hadaś-Dyduch", "Małgorzata Krzciuk", "Tomasz Stachurski", "Alicja Wolny-Dominiak", "Tomasz Żądło"], "categories": ["stat.ME", "stat.AP"], "doi": "", "_query": "hedonic house price", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/1201.4754v1", "title": "Existence of Stability in Hedonic Coalition Formation Games", "abstract": "In this paper, we examine \\emph{hedonic coalition formation games} in which each player's preferences over partitions of players depend only on the members of his coalition. We present three main results in which restrictions on the preferences of the players guarantee the existence of stable partitions for various notions of stability. The preference restrictions pertain to \\emph{top responsiveness} and \\emph{bottom responsiveness} which model optimistic and pessimistic behavior of players respectively. The existence results apply to natural subclasses of \\emph{additive separable hedonic games} and \\emph{hedonic games with \\B-preferences}. It is also shown that our existence results cannot be strengthened to the case of stronger known stability concepts.", "published": "2012-01-23T16:15:22Z", "authors": ["Haris Aziz", "Florian Brandl"], "categories": ["cs.GT"], "doi": "", "_query": "hedonic house price", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2411.05713v1", "title": "Settling the Complexity of Popularity in Additively Separable and Fractional Hedonic Games", "abstract": "We study coalition formation in the framework of hedonic games. There, a set of agents needs to be partitioned into disjoint coalitions, where agents have a preference order over coalitions. A partition is called popular if it does not lose a majority vote among the agents against any other partition. Unfortunately, hedonic games need not admit popular partitions and prior work suggests significant computational hardness. We confirm this impression by proving that deciding about the existence of popular partitions in additively separable and fractional hedonic games is $Σ_2^p$-complete. This settles the complexity of these problems and is the first work that proves completeness of popularity for the second level of the polynomial hierarchy.", "published": "2024-11-08T17:15:43Z", "authors": ["Martin Bullinger", "Matan Gilboa"], "categories": ["cs.GT"], "doi": "", "_query": "hedonic house price", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/1206.6159v2", "title": "Constrained Hyperbolic Divergence Cleaning for Smoothed Particle Magnetohydrodynamics", "abstract": "We present a constrained formulation of Dedner et al's hyperbolic/parabolic divergence cleaning scheme for enforcing the \\nabla\\dot B = 0 constraint in Smoothed Particle Magnetohydrodynamics (SPMHD) simulations. The constraint we impose is that energy removed must either be conserved or dissipated, such that the scheme is guaranteed to decrease the overall magnetic energy. This is shown to require use of conjugate numerical operators for evaluating \\nabla\\dot B and \\nablaψ in the SPMHD cleaning equations. The resulting scheme is shown to be stable at density jumps and free boundaries, in contrast to an earlier implementation by Price & Monaghan (2005). Optimal values of the damping parameter are found to be σ = 0.2-0.3 in 2D and σ = 0.8-1.2 in 3D. With these parameters, our constrained Hamiltonian formulation is found to provide an effective means of enforcing the divergence constraint in SPMHD, typically maintaining average values of h |\\nabla\\dot B| / |B| to 0.1-1%, up to an order of magnitude better than artificial resistivity without the associated dissipation in the physical field. Furthermore, when applied to realistic, 3D simulations we find an improvement of up to two orders of magnitude in momentum conservation with a corresponding improvement in numerical stability at essentially zero additional computational expense.", "published": "2012-06-27T02:56:22Z", "authors": ["Terrence S. Tricco", "Daniel J. Price"], "categories": ["astro-ph.IM", "astro-ph.GA", "astro-ph.SR", "physics.comp-ph"], "doi": "10.1016/j.jcp.2012.06.039", "_query": "hedonic house price", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2502.11521v2", "title": "Detecting Various DeFi Price Manipulations with LLM Reasoning", "abstract": "DeFi (Decentralized Finance) is one of the most important applications of today's cryptocurrencies and smart contracts. It manages hundreds of billions in Total Value Locked (TVL) on-chain, yet it remains susceptible to common DeFi price manipulation attacks. Despite state-of-the-art (SOTA) systems like DeFiRanger and DeFort, we found that they are less effective to non-standard price models in custom DeFi protocols, which account for 44.2% of the 95 DeFi price manipulation attacks reported over the past three years. In this paper, we introduce the first LLM-based approach, DeFiScope, for detecting DeFi price manipulation attacks in both standard and custom price models. Our insight is that large language models (LLMs) have certain intelligence to abstract price calculation from smart contract source code and infer the trend of token price changes based on the extracted price models. To further strengthen LLMs in this aspect, we leverage Foundry to synthesize on-chain data and use it to fine-tune a DeFi price-specific LLM. Together with the high-level DeFi operations recovered from low-level transaction data, DeFiScope detects various DeFi price manipulations according to systematically mined patterns. Experimental results show that DeFiScope achieves a high recall of 80% on real-world attacks, a precision of 96% on suspicious transactions, and zero false alarms on benign transactions, significantly outperforming SOTA approaches. Moreover, we evaluate DeFiScope's cost-effectiveness and demonstrate its practicality by helping our industry partner confirm 147 real-world price manipulation attacks, including discovering 81 previously unknown historical incidents.", "published": "2025-02-17T07:45:03Z", "authors": ["Juantao Zhong", "Daoyuan Wu", "Ye Liu", "Maoyi Xie", "Yang Liu", "Yi Li", "Ning Liu"], "categories": ["cs.CR", "cs.AI"], "doi": "", "_query": "hedonic house price", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/1903.11530v2", "title": "Market Dynamics: On Directional Information Derived From (Time, Execution Price, Shares Traded) Transaction Sequences", "abstract": "A new approach to obtaining market--directional information, based on a non-stationary solution to the dynamic equation \"future price tends to the value that maximizes the number of shares traded per unit time\" [1] is presented. In our previous work[2], we established that it is the share execution flow ($I=dV/dt$) and not the share trading volume ($V$) that is the driving force of the market, and that asset prices are much more sensitive to the execution flow $I$ (the dynamic impact) than to the traded volume $V$ (the regular impact). In this paper, an important advancement is achieved: we define the \"scalp-price\" ${\\cal P}$ as the sum of only those price moves that are relevant to market dynamics; the criterion of relevance is a high $I$. Thus, only \"follow the market\" (and not \"little bounce\") events are included in ${\\cal P}$. Changes in the scalp-price defined this way indicate a market trend change - not a bear market rally or a bull market sell-off; the approach can be further extended to non-local price change. The software calculating the scalp--price given market observations triples (time, execution price, shares traded) is available from the authors.", "published": "2019-03-27T16:29:04Z", "authors": ["Vladislav Gennadievich Malyshkin"], "categories": ["q-fin.TR", "q-fin.CP"], "doi": "", "_query": "hedonic house price", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2510.22817v1", "title": "Wildfire and house prices: A synthetic control case study of Altadena (Jan 2025)", "abstract": "This study uses the Synthetic Control Method (SCM) to estimate the causal impact of a January 2025 wildfire on housing prices in Altadena, California. We construct a 'synthetic' Altadena from a weighted average of peer cities to serve as a counterfactual; this approach assumes no spillover effects on the donor pool. The results reveal a substantial negative price effect that intensifies over time. Over the six months following the event, we estimate an average monthly loss of $32,125. The statistical evidence for this effect is nuanced. Based on the robust post-to-pre-treatment RMSPE ratio, the result is statistically significant at the 10% level (p = 0.0508). In contrast, the effect is not statistically significant when measured by the average post-treatment gap (p = 0.3220). This analysis highlights the significant financial risks faced by communities in fire-prone regions and demonstrates SCM's effectiveness in evaluating disaster-related economic damages.", "published": "2025-10-26T20:16:28Z", "authors": ["Yibo Sun"], "categories": ["econ.GN"], "doi": "", "_query": "hedonic house price", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/1903.00303v1", "title": "Hedonic Diversity Games", "abstract": "We consider a coalition formation setting where each agent belongs to one of the two types, and agents' preferences over coalitions are determined by the fraction of the agents of their own type in each coalition. This setting differs from the well-studied Schelling's model in that some agents may prefer homogeneous coalitions, while others may prefer to be members of a diverse group, or a group that mostly consists of agents of the other type. We model this setting as a hedonic game and investigate the existence of stable outcomes using hedonic games solution concepts. We show that a core stable outcome may fail to exist and checking the existence of core stable outcomes is computationally hard. On the other hand, we propose an efficient algorithm to find an individually stable outcome under the natural assumption that agents' preferences over fractions of the agents of their own type are single-peaked.", "published": "2019-03-01T14:06:47Z", "authors": ["Robert Bredereck", "Edith Elkind", "Ayumi Igarashi"], "categories": ["cs.GT"], "doi": "", "_query": "hedonic house price", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2404.05543v1", "title": "Optimal Allocation of Tasks and Price of Anarchy of Distributed Optimization in Networked Computing Facilities", "abstract": "The allocation of computing tasks for networked distributed services poses a question to service providers on whether centralized allocation management be worth its cost. Existing analytical models were conceived for users accessing computing resources with practically indistinguishable (hence irrelevant for the allocation decision) delays, which is typical of services located in the same distant data center. However, with the rise of the edge-cloud continuum, a simple analysis of the sojourn time that computing tasks observe at the server misses the impact of diverse latency values imposed by server locations. We therefore study the optimization of computing task allocation with a new model that considers both distance of servers and sojourn time in servers. We derive exact algorithms to optimize the system and we show, through numerical analysis and real experiments, that differences in server location in the edge-cloud continuum cannot be neglected. By means of algorithmic game theory, we study the price of anarchy of a distributed implementation of the computing task allocation problem and unveil important practical properties such as the fact that the price of anarchy tends to be small -- except when the system is overloaded -- and its maximum can be computed with low complexity.", "published": "2024-04-08T14:10:14Z", "authors": ["Vincenzo Mancuso", "Paolo Castagno", "Leonardo Badia", "Matteo Sereno", "Marco Ajmone Marsan"], "categories": ["cs.GT", "cs.DC", "cs.PF"], "doi": "", "_query": "hedonic house price", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/1511.01207v2", "title": "Trajectory based models. Evaluation of minmax pricing bounds", "abstract": "The paper studies sub and super-replication price bounds for contingent claims defined on general trajectory based market models. No prior probabilistic or topological assumptions are placed on the trajectory space, trading is assumed to take place at a finite number of occasions but not bounded in number nor necessarily equally spaced in time. For a given option, there exists an interval bounding the set of possible fair prices; such interval exists under more general conditions than the usual no-arbitrage requirement. The paper develops a backward recursive method to evaluate the option bounds; the global minmax optimization, defining the price interval, is reduced to a local minmax optimization via dynamic programming. Trajectory sets are introduced for which existing non-probabilistic markets models are nested as a particular case. Several examples are presented, the effect of the presence of arbitrage on the price bounds is illustrated.", "published": "2015-11-04T04:47:01Z", "authors": ["Ivan Degano", "Sebastian Ferrando", "Alfredo Gonzalez"], "categories": ["q-fin.MF"], "doi": "", "_query": "hedonic house price", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2112.04906v3", "title": "Enhancing Column Generation by a Machine-Learning-Based Pricing Heuristic for Graph Coloring", "abstract": "Column Generation (CG) is an effective method for solving large-scale optimization problems. CG starts by solving a sub-problem with a subset of columns (i.e., variables) and gradually includes new columns that can improve the solution of the current subproblem. The new columns are generated as needed by repeatedly solving a pricing problem, which is often NP-hard and is a bottleneck of the CG approach. To tackle this, we propose a Machine-Learning-based Pricing Heuristic (MLPH)that can generate many high-quality columns efficiently. In each iteration of CG, our MLPH leverages an ML model to predict the optimal solution of the pricing problem, which is then used to guide a sampling method to efficiently generate multiple high-quality columns. Using the graph coloring problem, we empirically show that MLPH significantly enhancesCG as compared to six state-of-the-art methods, and the improvement in CG can lead to substantially better performance of the branch-and-price exact method.", "published": "2021-12-08T03:58:25Z", "authors": ["Yunzhuang Shen", "Yuan Sun", "Xiaodong Li", "Andrew Eberhard", "Andreas Ernst"], "categories": ["math.OC", "cs.AI", "cs.LG"], "doi": "", "_query": "hedonic house price", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2101.09673v2", "title": "Incentive Mechanism Design for Federated Learning: Hedonic Game Approach", "abstract": "Incentive mechanism design is crucial for enabling federated learning. We deal with clustering problem of agents contributing to federated learning setting. Assuming agents behave selfishly, we model their interaction as a stable coalition partition problem using hedonic games where agents and clusters are the players and coalitions, respectively. We address the following question: is there a family of hedonic games ensuring a Nash-stable coalition partition? We propose the Nash-stable set which determines the family of hedonic games possessing at least one Nash-stable partition, and analyze the conditions of non-emptiness of the Nash-stable set. Besides, we deal with the decentralized clustering. We formulate the problem as a non-cooperative game and prove the existence of a potential game.", "published": "2021-01-24T08:35:02Z", "authors": ["Cengis Hasan"], "categories": ["cs.GT"], "doi": "", "_query": "hedonic house price", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2503.15046v3", "title": "Refined enumeration of planar Eulerian orientations", "abstract": "We address the enumeration of Eulerian orientations of 4-valent planar maps according to three parameters: the number of vertices, the number of alternating vertices (having in/out/in/out incident edges), and the number of clockwise oriented faces. This is a refinement of the six vertex model studied by Kostov, then Zinn-Justin and Elvey Price, where one only considers the first two parameters. Via a bijection of Ambjorn and Budd, our problem is equivalent to the enumeration of Eulerian partial orientations of general planar maps, counted by the number of edges, the number of undirected edges, and the number of vertices. We first derive from combinatorial arguments a system of functional equations characterising the associated trivariate series $Q(t,ω,v)$. We then derive from this system a compact characterisation of this series. We use it to determine $Q(t,ω,v)$ in three two-parameter cases. The first two cases correspond to setting the variable $ω$ counting alternating vertices (or undirected edges after the AB bijection) to $0$ or $1$: when $ω=0$ we count Eulerian orientations of general planar maps by edges and vertices, and when $ω=1$ we count Eulerian orientations of quartic maps by vertices and clockwise faces. The final forms of these two series, namely $Q(t,0, v)$ and $Q(t,1,v)$, refine those obtained by the authors in an earlier paper for $v=1$. The third case that we solve, namely $v=1$ (but $ω$ arbitrary), is the standard six-vertex model, for which we provide a new proof of the formula of Elvey Price and Zinn-Justin involving Jacobi theta functions. This new derivation remains purely in the world of formal power series, not relying on complex analysis. Our results also use a more direct approach to solving the functional equations, in contrast to the guess and check approaches used in previous work.", "published": "2025-03-19T09:33:45Z", "authors": ["Mireille Bousquet-Mélou", "Andrew Elvey Price"], "categories": ["math.CO"], "doi": "", "_query": "hedonic house price", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2407.10176v1", "title": "On shock capturing in smoothed particle hydrodynamics", "abstract": "For the past 20 years, our approach to shock capturing in smoothed particle hydrodynamics (SPH) has been to use artificial viscosity and conductivity terms supplemented by switches to control excess dissipation away from shocks (Monaghan 1997; Morris & Monaghan 1997). This approach has been demonstrated to be superior to approximate Riemann solvers in a recent comparison (Puri & Ramachandran 2014). The Cullen & Dehnen (2010) switch is regarded as the state of the art. But are we missing something? I will present a novel approach to shock capturing in SPH that utilises the philosophy of approximate Riemann solvers but provides a direct improvement on the ability to reduce excess dissipation away from shocks while preserving the fidelity of the shock itself.", "published": "2024-07-14T12:15:41Z", "authors": ["Daniel J. Price"], "categories": ["astro-ph.IM"], "doi": "", "_query": "hedonic house price", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2011.01778v1", "title": "Hedonic Expertise Games", "abstract": "We consider a team formation setting where agents have varying levels of expertise in a global set of required skills, and teams are ranked with respect to how well the expertise of teammates complement each other. We model this setting as a hedonic game, and we show that this class of games possesses many desirable properties, some of which are as follows: A partition that is Nash stable, core stable and Pareto optimal is always guaranteed to exist. A contractually individually stable partition (and a Nash stable partition in a restricted setting) can be found in polynomial-time. A core stable partition can be approximated within a factor of $1 - \\frac{1}{e}$, and this bound is tight unless $\\sf P = NP$. We also introduce a larger and relatively general class of games, which we refer to as monotone submodular hedonic games with common ranking property. We show that the above multi-concept existence guarantee also holds for this larger class of games.", "published": "2020-11-03T15:19:38Z", "authors": ["Bugra Caskurlu", "Fatih Erdem Kizilkaya", "Berkehan Ozen"], "categories": ["cs.GT"], "doi": "", "_query": "hedonic house price", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/1204.1515v1", "title": "Natural selection. IV. The Price equation", "abstract": "The Price equation partitions total evolutionary change into two components. The first component provides an abstract expression of natural selection. The second component subsumes all other evolutionary processes, including changes during transmission. The natural selection component is often used in applications. Those applications attract widespread interest for their simplicity of expression and ease of interpretation. Those same applications attract widespread criticism by dropping the second component of evolutionary change and by leaving unspecified the detailed assumptions needed for a complete study of dynamics. Controversies over approximation and dynamics have nothing to do with the Price equation itself, which is simply a mathematical equivalence relation for total evolutionary change expressed in an alternative form. Disagreements about approach have to do with the tension between the relative valuation of abstract versus concrete analyses. The Price equation's greatest value has been on the abstract side, particularly the invariance relations that illuminate the understanding of natural selection. Those abstract insights lay the foundation for applications in terms of kin selection, information theory interpretations of natural selection, and partitions of causes by path analysis. I discuss recent critiques of the Price equation by Nowak and van Veelen.", "published": "2012-04-06T17:02:46Z", "authors": ["Steven A. Frank"], "categories": ["q-bio.PE"], "doi": "10.1111/j.1420-9101.2012.02498.x", "_query": "hedonic house price", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2505.16287v1", "title": "Machine learning approach to stock price crash risk", "abstract": "In this study, we propose a novel machine-learning-based measure for stock price crash risk, utilizing the minimum covariance determinant methodology. Employing this newly introduced dependent variable, we predict stock price crash risk through cross-sectional regression analysis. The findings confirm that the proposed method effectively captures stock price crash risk, with the model demonstrating strong performance in terms of both statistical significance and economic relevance. Furthermore, leveraging a newly developed firm-specific investor sentiment index, the analysis identifies a positive correlation between stock price crash risk and firm-specific investor sentiment. Specifically, higher levels of sentiment are associated with an increased likelihood of stock price crash risk. This relationship remains robust across different firm sizes and when using the detoned version of the firm-specific investor sentiment index, further validating the reliability of the proposed approach.", "published": "2025-05-22T06:33:35Z", "authors": ["Abdullah Karasan", "Ozge Sezgin Alp", "Gerhard-Wilhelm Weber"], "categories": ["q-fin.CP"], "doi": "10.1007/s10479-025-06596-7", "_query": "hedonic house price", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2205.11939v1", "title": "On Hedonic Games with Common Ranking Property", "abstract": "Hedonic games are a prominent model of coalition formation, in which each agent's utility only depends on the coalition she resides. The subclass of hedonic games that models the formation of general partnerships, where output is shared equally among affiliates, is referred to as hedonic games with common ranking property (HGCRP). Aside from their economic motivation, HGCRP came into prominence since they are guaranteed to have core stable solutions that can be found efficiently. We improve upon existing results by proving that every instance of HGCRP has a solution that is Pareto optimal, core stable and individually stable. The economic significance of this result is that efficiency is not to be totally sacrificed for the sake of stability in HGCRP. We establish that finding such a solution is {\\bf NP-hard} even if the sizes of the coalitions are bounded above by $3$; however, it is polynomial time solvable if the sizes of the coalitions are bounded above by $2$. We show that the gap between the total utility of a core stable solution and that of the socially-optimal solution (OPT) is bounded above by $n$, where $n$ is the number of agents, and that this bound is tight. Our investigations reveal that computing OPT is inapproximable within better than $O(n^{1-ε})$ for any fixed $ε> 0$, and that this inapproximability lower bound is polynomially tight. However, OPT can be computed in polynomial time if the sizes of the coalitions are bounded above by $2$.", "published": "2022-05-24T10:10:40Z", "authors": ["Bugra Caskurlu", "Fatih Erdem Kizilkaya"], "categories": ["cs.GT", "econ.TH"], "doi": "", "_query": "hedonic house price", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2511.13200v1", "title": "A Price to Enter: Anticipatory Housing Market Sorting and Access Inequality under New York's Congestion Pricing", "abstract": "This study examines how congestion pricing shapes housing market outcomes and spatial equity in New York City. Using high-frequency sales and rental data and a combination of propensity score matching difference-in-differences, geographic regression discontinuity, and event study designs, the analysis identifies distinct short-run adjustment patterns triggered by the policy announcement. Housing prices inside the toll zone fell by about 3.3% and rents by 3%, with the sharpest declines occurring immediately after the announcement. These effects weakened over time, and price resilience emerged among premium properties, indicating early market sorting and growing segmentation. The Geo-RDD results show a clear boundary penalty, with properties just inside the cordon experiencing more pronounced declines than otherwise similar properties just outside. Renters and lower-value segments were more exposed to early adjustment pressures, while implementation-stage effects were limited. The findings suggest that congestion pricing can reshape urban space not only by altering mobility incentives but also by redistributing access and opportunity. Equity-oriented design that includes early-stage support for boundary neighborhoods and renters, along with reinvestment of revenues into untolled transit access, is important for ensuring that the benefits of congestion pricing are shared rather than concentrated.", "published": "2025-11-17T10:09:37Z", "authors": ["Mingzhi Xiao", "Yuki Takayama"], "categories": ["econ.GN"], "doi": "", "_query": "hedonic house price", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/1201.6429v2", "title": "Bounding the inefficiency of outcomes in generalized second price auctions", "abstract": "The Generalized Second Price (GSP) auction is the primary auction used for monetizing the use of the Internet. It is well-known that truthtelling is not a dominant strategy in this auction and that inefficient equilibria can arise. In this paper we study the space of equilibria in GSP, and quantify the efficiency loss that can arise in equilibria under a wide range of sources of uncertainty, as well as in the full information setting. The traditional Bayesian game models uncertainty in the valuations (types) of the participants. The Generalized Second Price (GSP) auction gives rise to a further form of uncertainty: the selection of quality factors resulting in uncertainty about the behavior of the underlying ad allocation algorithm. The bounds we obtain apply to both forms of uncertainty, and are robust in the sense that they apply under various perturbations of the solution concept, extending to models with information asymmetries and bounded rationality in the form of learning strategies. We present a constant bound (2.927) on the factor of the efficiency loss (\\emph{price of anarchy}) of the corresponding game for the Bayesian model of partial information about other participants and about ad quality factors. For the full information setting, we prove a surprisingly low upper bound of 1.282 on the price of anarchy over pure Nash equilibria, nearly matching a lower bound of 1.259 for the case of three advertisers. Further, we do not require that the system reaches equilibrium, and give similarly low bounds also on the quality degradation for any no-regret learning outcome. Our conclusion is that the number of advertisers in the auction has almost no impact on the price of anarchy, and that the efficiency of GSP is very robust with respect to the belief and rationality assumptions imposed on the participants.", "published": "2012-01-31T03:36:26Z", "authors": ["Ioannis Caragiannis", "Christos Kaklamanis", "Panagiotis Kanellopoulos", "Maria Kyropoulou", "Brendan Lucier", "Renato Paes Leme", "Éva Tardos"], "categories": ["cs.GT"], "doi": "", "_query": "hedonic house price", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2511.22370v1", "title": "Solving Four Open Problems about Core Stability in Altruistic Hedonic Games", "abstract": "Hedonic games -- at the interface of cooperative game theory and computational social choice -- are coalition formation games in which the players have preferences over the coalitions they can join. Kerkmann et al. [13] introduced altruistic hedonic games where the players' utilities depend not only on their own but also on their friends' valuations of coalitions. The complexity of the verification problem for core stability has remained open in four variants of altruistic hedonic games: namely, for the variants with average- and minimum-based \"equal-treatment\" and \"altruistic-treatment\" preferences. We solve these four open questions by proving the corresponding problems coNP-complete; our reductions rely on rather intricate gadgets in the related networks of friends.", "published": "2025-11-27T12:12:01Z", "authors": ["Jörg Rothe", "Ildikó Schlotter"], "categories": ["cs.GT", "cs.CC"], "doi": "10.4204/EPTCS.437.3", "_query": "hedonic house price", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/1803.08265v2", "title": "The generating function of planar Eulerian orientations", "abstract": "The enumeration of planar maps equipped with an Eulerian orientation has attracted attention in both combinatorics and theoretical physics since at least 2000. The case of 4-valent maps is particularly interesting: these orientations are in bijection with properly 3-coloured quadrangulations, while in physics they correspond to configurations of the ice model. We solve both problems -- namely the enumeration of planar Eulerian orientations and of 4-valent planar Eulerian orientations -- by expressing the associated generating functions as the inverses (for the composition of series) of simple hypergeometric series. Using these expressions, we derive the asymptotic behaviour of the number of planar Eulerian orientations, thus proving earlier predictions of Kostov, Zinn-Justin, Elvey Price and Guttmann. This behaviour, $μ^n /(n \\log n)^2$, prevents the associated generating functions from being D-finite. Still, these generating functions are differentially algebraic, as they satisfy non-linear differential equations of order $2$. Differential algebraicity has recently been proved for other map problems, in particular for maps equipped with a Potts model. Our solutions mix recursive and bijective ingredients. In particular, a preliminary bijection transforms our oriented maps into maps carrying a height function on their vertices. In the 4-valent case, we also observe an unexpected connection with the enumeration of maps equipped with a spanning tree that is internally inactive in the sense of Tutte. This connection remains to be explained combinatorially.", "published": "2018-03-22T08:39:15Z", "authors": ["Mireille Bousquet-Mélou", "Andrew Elvey Price"], "categories": ["math.CO"], "doi": "10.1016/j.jcta.2019.105183", "_query": "hedonic house price", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/1509.02333v2", "title": "Precise Complexity of the Core in Dichotomous and Additive Hedonic Games", "abstract": "Hedonic games provide a general model of coalition formation, in which a set of agents is partitioned into coalitions, with each agent having preferences over which other players are in her coalition. We prove that with additively separable preferences, it is $Σ_2^p$-complete to decide whether a core- or strict-core-stable partition exists, extending a result of Woeginger (2013). Our result holds even if valuations are symmetric and non-zero only for a constant number of other agents. We also establish $Σ_2^p$-completeness of deciding non-emptiness of the strict core for hedonic games with dichotomous preferences. Such results establish that the core is much less tractable than solution concepts such as individual stability.", "published": "2015-09-08T12:04:15Z", "authors": ["Dominik Peters"], "categories": ["cs.GT"], "doi": "", "_query": "hedonic house price", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2607.27277v1", "title": "Complexity of Strong Popularity in Additively Separable Hedonic Games", "abstract": "In a hedonic game, agents need to be partitioned into coalitions, and have a preference order over partitions. A partition is called strongly popular if it beats any other partition in a majority vote among the agents. We focus on the fundamental class of additively separable hedonic games (ASHGs), where agents have additive valuations that induce their preferences. We prove that determining the existence of strongly popular partitions in ASHGs is complete for PCW, a recently introduced complexity class which lies in between $P^{NP}$ and $S_2^P$ (Gilboa et al., 2025). This settles an open problem by Brandt and Bullinger (2022) and Bullinger and Gilboa (2025).", "published": "2026-07-29T12:59:05Z", "authors": ["Matan Gilboa"], "categories": ["cs.GT", "cs.CC"], "doi": "", "_query": "hedonic house price", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/1706.07705v4", "title": "Spatially filtered unconditional quantile regression: Application to a hedonic analysis", "abstract": "Unconditional quantile regression (UQR) attracts attention in various fields to investigate the impacts of explanatory variables on quantiles of the marginal distribution of an explained variable. This study attempts to introduce spatial dependence into the UQR within the framework of random effects eigenvector spatial filtering, resulting in the model that we term the spatially filtered UQR (SF-UQR). We then develop a computationally efficient approach for SF-UQR estimation. Finally, the performance of the SF-UQR is tested with a hedonic land price model for the Tokyo metropolitan area. SF-UQR is implemented in an R package, \"spmoran.\"", "published": "2017-06-23T13:51:52Z", "authors": ["Daisuke Murakami", "Hajime Seya"], "categories": ["stat.ME"], "doi": "", "_query": "hedonic house price", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2606.09564v4", "title": "Option prices from operational-time reaction-boundary lattices", "abstract": "We consider the role of a continuum operational time $u$, its mapping to calendar time $t$, and their relation to event time in option-pricing problems. We derive option-pricing equations from an operational-time Markov lattice rather than from a calendar-time diffusion. The primitive model is a homogeneous nearest-neighbour log-price lattice; state-dependent local variance is represented through its general local-kernel extension. Its Chapman--Kolmogorov decomposition yields discrete forward and backward equations. In price variables, the backward equation gives a generalized European pricing PDE and reduces to Black--Scholes--Merton under the risk-neutral drift restriction and constant volatility. Interpreted as a reaction boundary, the lattice gives a structural route from boundary variance to projected local volatility. The key point is the separation of the operational kernel, the calendar-time clock projection, and the pricing-measure choice. Within the deterministic one-factor local-volatility class, an option surface identifies the projected clock--variance combination rather than its operational variance and clock components separately. The resulting hierarchy also distinguishes full calendar-generator equivalence from weaker terminal or continuum matching, and clarifies why incompleteness concerns unspanned clock, jump, or renewal risks rather than random time alone.", "published": "2026-06-08T14:38:58Z", "authors": ["Chris Angstmann", "Tim Gebbie"], "categories": ["q-fin.PR", "q-fin.MF", "q-fin.TR"], "doi": "", "_query": "hedonic house price", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2303.18161v2", "title": "Nash equilibria for relative investors with (non)linear price impact", "abstract": "We consider the strategic interaction of $n$ investors who are able to influence a stock price process and at the same time measure their utilities relative to the other investors. Our main aim is to find Nash equilibrium investment strategies in this setting in a financial market driven by a Brownian motion and investigate the influence the price impact has on the equilibrium. We consider both CRRA and CARA utility functions. Our findings show that the problem is well-posed as long as the price impact is at most linear. Moreover, numerical results reveal that the investors behave very aggressively when the price impact is beyond a critical parameter.", "published": "2023-03-31T15:54:36Z", "authors": ["Nicole Bäuerle", "Tamara Göll"], "categories": ["math.OC", "q-fin.MF"], "doi": "10.1007/s11579-024-00356-0", "_query": "hedonic house price", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2409.16579v1", "title": "Friend- and Enemy-oriented Hedonic Games With Strangers Full Version", "abstract": "We introduce friend- and enemy-oriented hedonic games with strangers (FOHGS and EOHGS respectively), two classes of hedonic games wherein agents are classified as friends, enemies, or strangers under the assumption that strangers will become either friends or enemies ex post facto. For several notions of stability in FOHGS and EOHGS, we characterize the hardness of verification for possible and necessary stability. We characterize the hardness of deciding whether possibly and necessarily X stable partitions exist for a given stability notion X. We prove that necessarily internally stable partitions always exist and provide sufficient conditions for necessary contractual individual stability.", "published": "2024-09-25T03:17:46Z", "authors": ["TJ Schlueter", "Makoto Yokoo"], "categories": ["cs.GT"], "doi": "", "_query": "hedonic house price", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2202.09210v2", "title": "Hedonic Diversity Games: A Complexity Picture with More than Two Colors", "abstract": "Hedonic diversity games are a variant of the classical Hedonic games designed to better model a variety of questions concerning diversity and fairness. Previous works mainly targeted the case with two diversity classes (represented as colors in the model) and provided some initial complexity-theoretic and existential results concerning Nash and individually stable outcomes. Here, we design new algorithms accompanied with lower bounds which provide a complete parameterized-complexity picture for computing Nash and individually stable outcomes with respect to the most natural parameterizations of the problem. Crucially, our results hold for general Hedonic diversity games where the number of colors is not necessarily restricted to two, and show that -- apart from two trivial cases -- a necessary condition for tractability in this setting is that the number of colors is bounded by the parameter. Moreover, for the special case of two colors we resolve an open question asked in previous work (Boehmer and Elkind, AAAI 2020).", "published": "2022-02-18T14:16:33Z", "authors": ["Robert Ganian", "Thekla Hamm", "Dušan Knop", "Šimon Schierreich", "Ondřej Suchý"], "categories": ["cs.GT"], "doi": "10.1016/j.artint.2023.104017", "_query": "hedonic house price", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2406.01373v1", "title": "Stability in Random Hedonic Games", "abstract": "Partitioning a large group of employees into teams can prove difficult because unsatisfied employees may want to transfer to other teams. In this case, the team (coalition) formation is unstable and incentivizes deviation from the proposed structure. Such a coalition formation scenario can be modeled in the framework of hedonic games and a significant amount of research has been devoted to the study of stability in such games. Unfortunately, stable coalition structures are not guaranteed to exist in general and their practicality is further hindered by computational hardness barriers. We offer a new perspective on this matter by studying a random model of hedonic games. For three prominent stability concepts based on single-agent deviations, we provide a high probability analysis of stability in the large agent limit. Our first main result is an efficient algorithm that outputs an individually and contractually Nash-stable partition with high probability. Our second main result is that the probability that a random game admits a Nash-stable partition tends to zero. Our approach resolves the two major downsides associated with individual stability and contractual Nash stability and reveals agents acting single-handedly are usually to blame for instabilities.", "published": "2024-06-03T14:39:41Z", "authors": ["Martin Bullinger", "Sonja Kraiczy"], "categories": ["cs.GT"], "doi": "", "_query": "hedonic house price", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/1509.05322v1", "title": "Computing stable outcomes in symmetric additively-separable hedonic games", "abstract": "We study the computational complexity of finding stable outcomes in hedonic games, which are a class of coalition formation games. We restrict our attention to symmetric additively-separable hedonic games, which are a nontrivial subclass of such games that are guaranteed to possess stable outcomes. These games are specified by an undirected edge- weighted graph: nodes are players, an outcome of the game is a partition of the nodes into coalitions, and the utility of a node is the sum of incident edge weights in the same coalition. We consider several stability requirements defined in the literature. These are based on restricting feasible player deviations, for example, by giving existing coalition members veto power. We extend these restrictions by considering more general forms of preference aggregation for coalition members. In particular, we consider voting schemes to decide if coalition members will allow a player to enter or leave their coalition. For all of the stability requirements we consider, the existence of a stable outcome is guaranteed by a potential function argument, and local improvements will converge to a stable outcome. We provide an almost complete characterization of these games in terms of the tractability of computing such stable outcomes. Our findings comprise positive results in the form of polynomial-time algorithms, and negative (PLS-completeness) results. The negative results extend to more general hedonic games.", "published": "2015-09-17T16:33:23Z", "authors": ["Martin Gairing", "Rahul Savani"], "categories": ["cs.GT"], "doi": "", "_query": "hedonic house price", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2603.03260v2", "title": "Does Entry of Food-and-Drink Establishments Raise Local House Prices? Event-Study Evidence from London", "abstract": "Restaurants, cafes, pubs, and takeaways are among the most visible markers of neighborhood change, yet whether their arrival is capitalised into nearby housing values remains empirically unsettled. We assemble a London-wide panel linking Land Registry prices, non-domestic EPC lodgement timings for food-and-drink establishments, and neighborhood amenity measures at the LSOA level. Our preferred annual event-study design defines treatment as the first clean-onset year in which an LSOA records at least two eligible EPC lodgements for food-and-drink establishments, after a two-year lookback with no prior entries. In this specification, pre-trend tests are not rejected in either the stacked or Sun-Abraham estimators, and log house prices rise gradually from about 0.5% in the event year to roughly 3.4--3.7% by years four and five. The results are consistent with local amenity capitalization following commercial entry, while remaining appropriately cautious about endogenous siting and concurrent redevelopment.", "published": "2026-03-03T18:51:20Z", "authors": ["Wanqi Liu", "Rong Zhao"], "categories": ["physics.soc-ph", "econ.GN"], "doi": "", "_query": "hedonic house price", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/1903.05534v2", "title": "Robustness against Agent Failure in Hedonic Games", "abstract": "We study how stability can be maintained even after any set of at most k players leave their groups, in the context of hedonic games. While stability properties ensure an outcome to be robust against players' deviations, it has not been considered how an unexpected change caused by a sudden deletion of players affects stable outcomes. In this paper, we propose a novel criterion that reshapes stability form robustness aspect. We observe that some stability properties can be no longer preserved even when a single agent is removed. However, we obtain positive results by focusing on symmetric friend-oriented hedonic games. We prove that we can efficiently decide the existence of robust outcomes with respect to Nash stability under deletion of any number of players or contractual individual stability under deletion of a single player. We also show that symmetric additively separable games always admit an individual stable outcome that is robust with respect to individual rationality.", "published": "2019-03-13T15:07:32Z", "authors": ["Ayumi Igarashi", "Kazunori Ota", "Yuko Sakurai", "Makoto Yokoo"], "categories": ["cs.GT"], "doi": "", "_query": "hedonic house price", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2505.13642v2", "title": "Non-Obvious Manipulability in Additively Separable and Fractional Hedonic Games", "abstract": "Hedonic Games are a well-established model for describing the formation of coalitions. In this work, we considered the design of Non-Obviously Manipulable (NOM) mechanisms, that are mechanisms that bounded rational agents may fail to recognize as manipulable, for two relevant classes of succinctly representable Hedonic Games, namely Additively Separable and Fractional Hedonic Games. In these classes, agents have cardinal scores towards other agents, and their preferences towards different coalitions are determined by aggregating these scores. Moreover, the quality of an outcome can also be easily evaluated through these scores by means of the utilitarian social welfare. We first prove that, when scores can be arbitrary, every welfare-maximizing mechanism is NOM, and, when scores are limited in a continuous interval, then there exist tie-breaking rules making welfare-maximizing mechanisms NOM. Next, we focus on efficient NOM mechanisms, since there is no known polynomial-time algorithm to compute welfare-maximizing outcomes in the considered classes of hedonic games. To this aim, we first prove a characterization of NOM mechanisms that simplifies the class of mechanisms of interest. Then, we design a NOM mechanism returning approximations that essentially match the best-known approximation achievable in polynomial time. Finally, we turn our attention to discrete scores, and specifically, the case that scores are $\\{-x, 0, 1\\}$ for $x > 0$. We prove that the ability to design welfare-maximizing NOM mechanisms depends on the magnitude of the scores. In particular, for $x > 1$, we prove that a welfare-maximizing NOM mechanism exists only when $x$ is very large. For $x \\leq 1$, instead, we observe that a welfare-maximizing NOM mechanism always exists except when $x$ lies in the interval $[a, b]$ where $a \\approx 2/n^2$ and $b \\approx 1/n$.", "published": "2025-05-19T18:29:09Z", "authors": ["Diodato Ferraioli", "Maria Fomenko", "Giovanna Varricchio"], "categories": ["cs.GT", "cs.MA"], "doi": "", "_query": "hedonic house price", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/1105.1824v2", "title": "Individual-based stability in hedonic games depending on the best or worst players", "abstract": "We consider coalition formation games in which each player has preferences over the other players and his preferences over coalitions are based on the best player ($\\mathcal{B}$-/B-hedonic games) or the worst player ($\\mathcal{W}$/W-hedonic games) in the coalition. We show that for $\\mathcal{B}$-hedonic games, an individually stable partition is guaranteed to exist and can be computed efficiently. Similarly, there exists a polynomial-time algorithm which returns a Nash stable partition (if one exists) for $\\mathcal{B}$-hedonic games with strict preferences. Both $\\mathcal{W}$- and W-hedonic games are equivalent if individual rationality is assumed. It is also shown that for B- or $\\mathcal{W}$-hedonic games, checking whether a Nash stable partition or an individually stable partition exists is NP-complete even in some cases for strict preferences. We identify a key source of intractability in compact coalition formation games in which preferences over players are extended to preferences over coalitions.", "published": "2011-05-09T23:51:47Z", "authors": ["Haris Aziz", "Paul Harrenstein", "Evangelia Pyrga"], "categories": ["cs.GT"], "doi": "", "_query": "hedonic house price", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2003.07197v1", "title": "A Hedonic Metric Approach to Estimating the Demand for Differentiated Products: An Application to Retail Milk Demand", "abstract": "This article introduces the Hedonic Metric (HM) approach as an original method to model the demand for differentiated products. Using this approach, initially, we create an n-dimensional hedonic space based on the characteristic information available to consumers. Next, we allocate products into this space and estimate the elasticities using distances. Our model makes it possible to estimate a large number of differentiated products in a single demand system. We applied our model to estimate the retail demand for fluid milk products.", "published": "2020-03-12T12:06:19Z", "authors": ["Osman Gulseven", "Michael Wohlgenant"], "categories": ["econ.GN"], "doi": "", "_query": "hedonic house price", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/1110.3250v5", "title": "On a stochastic differential equation arising in a price impact model", "abstract": "We provide sufficient conditions for the existence and uniqueness of solutions to a stochastic differential equation which arises in a price impact model. These conditions are stated as smoothness and boundedness requirements on utility functions or Malliavin differentiability of payoffs and endowments.", "published": "2011-10-14T15:33:51Z", "authors": ["Peter Bank", "Dmitry Kramkov"], "categories": ["q-fin.TR", "math.PR"], "doi": "10.1016/j.spa.2012.10.011", "_query": "hedonic house price", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/1904.05328v1", "title": "PropTech for Proactive Pricing of Houses in Classified Advertisements in the Indian Real Estate Market", "abstract": "Property Technology (PropTech) is the next big thing that is going to disrupt the real estate market. Nowadays, we see applications of Machine Learning (ML) and Artificial Intelligence (AI) in almost all the domains but for a long time the real estate industry was quite slow in adopting data science and machine learning for problem solving and improving their processes. However, things are changing quite fast as we see a lot of adoption of AI and ML in the US and European real estate markets. But the Indian real estate market has to catch-up a lot. This paper proposes a machine learning approach for solving the house price prediction problem in the classified advertisements. This study focuses on the Indian real estate market. We apply advanced machine learning algorithms such as Random forest, Gradient boosting and Artificial neural networks on a real world dataset and compare the performance of these methods. We find that the Random forest method is the best performer in terms of prediction accuracy.", "published": "2019-03-28T03:38:05Z", "authors": ["Sayan Putatunda"], "categories": ["cs.SI"], "doi": "", "_query": "hedonic house price", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/1904.07444v2", "title": "Equilibria in a large production economy with an infinite dimensional commodity space and price dependent preferences", "abstract": "We prove the existence of a competitive equilibrium in a production economy with infinitely many commodities and a measure space of agents whose preferences are price dependent. We employ a saturated measure space for the set of agents and apply recent results for an infinite dimensional separable Banach space such as Lyapunov's convexity theorem and an exact Fatou's lemma to obtain the result.", "published": "2019-04-16T03:57:58Z", "authors": ["Hyo Seok Jang", "Sangjik Lee"], "categories": ["econ.TH", "math.OC"], "doi": "", "_query": "hedonic house price", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/1709.09570v6", "title": "Identification of hedonic equilibrium and nonseparable simultaneous equations", "abstract": "This paper derives conditions under which preferences and technology are nonparametrically identified in hedonic equilibrium models, where products are differentiated along more than one dimension and agents are characterized by several dimensions of unobserved heterogeneity. With products differentiated along a quality index and agents characterized by scalar unobserved heterogeneity, single crossing conditions on preferences and technology provide identifying restrictions in Ekeland, Heckman and Nesheim (2004) and Heckman, Matzkin and Nesheim (2010). We develop similar shape restrictions in the multi-attribute case. These shape restrictions, which are based on optimal transport theory and generalized convexity, allow us to identify preferences for goods differentiated along multiple dimensions, from the observation of a single market. We thereby derive nonparametric identification results for nonseparable simultaneous equations and multi-attribute hedonic equilibrium models with (possibly) multiple dimensions of unobserved heterogeneity. One of our results is a proof of absolute continuity of the distribution of endogenously traded qualities, which is of independent interest.", "published": "2017-09-27T15:07:36Z", "authors": ["Victor Chernozhukov", "Alfred Galichon", "Marc Henry", "Brendan Pass"], "categories": ["econ.EM", "econ.TH"], "doi": "", "_query": "hedonic house price", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2411.19206v2", "title": "A general framework for pricing and hedging under local viability", "abstract": "In this paper, a new approach for solving the problems of pricing and hedging derivatives is introduced in a general frictionless market setting. The method is applicable even in cases where an equivalent local martingale measure fails to exist. Our main results include a new superhedging duality for American options when wealth processes can be negative and trading strategies are subject to a cone constraint. This answers one of the questions raised by Fernholz, Karatzas and Kardaras.", "published": "2024-11-28T15:25:50Z", "authors": ["Huy N. Chau", "Miklos Rasonyi"], "categories": ["q-fin.PR"], "doi": "", "_query": "hedonic house price", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2506.09539v1", "title": "Disentangling Spatial and Structural Drivers of Housing Prices through Bayesian Networks: A Case Study of Madrid, Barcelona, and Valencia", "abstract": "Understanding how housing prices respond to spatial accessibility, structural attributes, and typological distinctions is central to contemporary urban research and policy. In cities marked by affordability stress and market segmentation, models that offer both predictive capability and interpretive clarity are increasingly needed. This study applies discrete Bayesian networks to model residential price formation across Madrid, Barcelona, and Valencia using over 180,000 geo-referenced housing listings. The resulting probabilistic structures reveal distinct city-specific logics. Madrid exhibits amenity-driven stratification, Barcelona emphasizes typology and classification, while Valencia is shaped by spatial and structural fundamentals. By enabling joint inference, scenario simulation, and sensitivity analysis within a transparent framework, the approach advances housing analytics toward models that are not only accurate but actionable, interpretable, and aligned with the demands of equitable urban governance.", "published": "2025-06-11T09:15:02Z", "authors": ["Alvaro Garcia Murga", "Manuele Leonelli"], "categories": ["stat.AP"], "doi": "", "_query": "hedonic house price", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2302.09438v1", "title": "Does Machine Learning Amplify Pricing Errors in the Housing Market? -- The Economics of Machine Learning Feedback Loops", "abstract": "Machine learning algorithms are increasingly employed to price or value homes for sale, properties for rent, rides for hire, and various other goods and services. Machine learning-based prices are typically generated by complex algorithms trained on historical sales data. However, displaying these prices to consumers anchors the realized sales prices, which will in turn become training samples for future iterations of the algorithms. The economic implications of this machine learning \"feedback loop\" - an indirect human-algorithm interaction - remain relatively unexplored. In this work, we develop an analytical model of machine learning feedback loops in the context of the housing market. We show that feedback loops lead machine learning algorithms to become overconfident in their own accuracy (by underestimating its error), and leads home sellers to over-rely on possibly erroneous algorithmic prices. As a consequence at the feedback loop equilibrium, sale prices can become entirely erratic (relative to true consumer preferences in absence of ML price interference). We then identify conditions (choice of ML models, seller characteristics and market characteristics) where the economic payoffs for home sellers at the feedback loop equilibrium is worse off than no machine learning. We also empirically validate primitive building blocks of our analytical model using housing market data from Zillow. We conclude by prescribing algorithmic corrective strategies to mitigate the effects of machine learning feedback loops, discuss the incentives for platforms to adopt these strategies, and discuss the role of policymakers in regulating the same.", "published": "2023-02-18T23:20:57Z", "authors": ["Nikhil Malik", "Emaad Manzoor"], "categories": ["econ.TH", "cs.CY"], "doi": "", "_query": "hedonic house price", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2603.21826v1", "title": "Individual Rationality in Constrained Hedonic Games: Additively Separable and Fractional Preferences", "abstract": "Hedonic games are an archetypal problem in coalition formation, where a set of selfish agents want to partition themselves into stable coalitions. In this work, we focus on two natural constraints on the possible outcomes. First, we require that exactly k coalitions are created. Then, loosely following the model of Bilò et al. (AAAI 2022), we assume that each of the k coalitions is additionally associated with a lower and upper bound on its size. The notion of stability that we study is that of individual rationality (IR), which requires that no agent strictly prefers to be alone compared to being in his or her coalition. Although IR is trivially satisfiable even in the most general models of hedonic games, the complexity picture of deciding whether an IR allocation exists, considering the above constraints, is unexpectedly rich. We reveal that tractable fragments of this computational problem require surprisingly nontrivial arguments, even if we restrict ourselves to additively separable and fractional hedonic games. Our tractability results, achieved by exploiting the structure of the underlying preference graph, are also complemented by their intractability counterparts, painting a fairly complete picture of the tractability landscape of this problem.", "published": "2026-03-23T11:07:30Z", "authors": ["Foivos Fioravantes", "Harmender Gahlawat", "Nikolaos Melissinos", "Šimon Schierreich"], "categories": ["cs.GT"], "doi": "", "_query": "hedonic house price", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/0807.3960v1", "title": "Existence, uniqueness and efficiency of equilibrium in hedonic markets with multidimenstional types", "abstract": "We study equilibrium in hedonic markets, when consumers and suppliers have reservation utilities, and the utility functions are separable with respect to price. There is one indivisible good, which comes in different qualities; each consumer buys 0 or 1 unit, and each supplier sells 0 or 1 unit. Consumer types, supplier types and qualities can be either discrete of continuous, in which case they are allowed to be multidimensional. Prices play a double role: they keep some agents out of the market, and they match the remaining ones pairwise. We define equilibrium prices and equilibrium distributions, and we prove that equilibria exist, we investigate to what extend equilibrium prices and distributions are unique, and we prove that equilibria are efficient. In the particular case when there is a continuum of types, and a generalized Spence-Mirrlees condition is satisfied, we prove the existence of a pure equilibrium, where demand distributions are in fact demand functions, and we show to what extent it is unique. The proofs rely on convex analysis, and care has been given to illustrate the theory with examples.", "published": "2008-07-24T20:02:49Z", "authors": ["Ivar Ekeland"], "categories": ["q-fin.TR", "math.OC"], "doi": "", "_query": "hedonic house price", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/1802.05870v1", "title": "The dynamic impact of monetary policy on regional housing prices in the US: Evidence based on factor-augmented vector autoregressions", "abstract": "In this study interest centers on regional differences in the response of housing prices to monetary policy shocks in the US. We address this issue by analyzing monthly home price data for metropolitan regions using a factor-augmented vector autoregression (FAVAR) model. Bayesian model estimation is based on Gibbs sampling with Normal-Gamma shrinkage priors for the autoregressive coefficients and factor loadings, while monetary policy shocks are identified using high-frequency surprises around policy announcements as external instruments. The empirical results indicate that monetary policy actions typically have sizeable and significant positive effects on regional housing prices, revealing differences in magnitude and duration. The largest effects are observed in regions located in states on both the East and West Coasts, notably California, Arizona and Florida.", "published": "2018-02-16T09:08:34Z", "authors": ["Manfred M. Fischer", "Florian Huber", "Michael Pfarrhofer", "Petra Staufer-Steinnocher"], "categories": ["econ.EM"], "doi": "", "_query": "hedonic house price", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2303.16289v2", "title": "Long-term experimental study of price responsive predictive control in a real occupied single-family house with heat pump", "abstract": "The continuous introduction of renewable electricity and increased consumption through electrification of the transport and heating sector challenges grid stability. This study investigates load shifting through demand side management as a solution. We present a four-month experimental study of a low-complexity, hierarchical Model Predictive Control approach for demand side management in a near-zero emission occupied single-family house in Denmark. The control algorithm uses a price signal, weather forecast, a single-zone building model, and a non-linear heat pump efficiency model to generate a space-heating schedule. The weather-compensated, commercial heat pump is made to act smart grid-ready through outdoor temperature input override to enable heat boosting and forced stops to accommodate the heating schedule. The cost reduction from the controller ranged from 2-33% depending on the chosen comfort level. The experiment demonstrates that load shifting is feasible and cost-effective, even without energy storage, and that the current price scheme provides an incentive for Danish end-consumers to shift heating loads. However, issues related to controlling the heat pump through input-manipulation were identified, and the authors propose a more promising path forward involving coordination with manufacturers and regulators to make commercial heat pumps truly smart grid-ready.", "published": "2023-03-28T20:08:20Z", "authors": ["Simon Thorsteinsson", "Alex Arash Sand Kalaee", "Pierre Vogler-Finck", "Henrik Lund Stærmose", "Ivan Katic", "Jan Dimon Bendtsen"], "categories": ["eess.SY"], "doi": "10.1016/j.apenergy.2023.121398", "_query": "hedonic house price", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2007.15769v2", "title": "Instrument variable detection with graph learning : an application to high dimensional GIS-census data for house pricing", "abstract": "Endogeneity bias and instrument variable validation have always been important topics in statistics and econometrics. In the era of big data, such issues typically combine with dimensionality issues and, hence, require even more attention. In this paper, we merge two well-known tools from machine learning and biostatistics---variable selection algorithms and probablistic graphs---to estimate house prices and the corresponding causal structure using 2010 data on Sydney. The estimation uses a 200-gigabyte ultrahigh dimensional database consisting of local school data, GIS information, census data, house characteristics and other socio-economic records. Using \"big data\", we show that it is possible to perform a data-driven instrument selection efficiently and purge out the invalid instruments. Our approach improves the sparsity of variable selection, stability and robustness in the presence of high dimensionality, complicated causal structures and the consequent multicollinearity, and recovers a sparse and intuitive causal structure. The approach also reveals an efficiency and effectiveness in endogeneity detection, instrument validation, weak instrument pruning and the selection of valid instruments. From the perspective of machine learning, the estimation results both align with and confirms the facts of Sydney house market, the classical economic theories and the previous findings of simultaneous equations modeling. Moreover, the estimation results are consistent with and supported by classical econometric tools such as two-stage least square regression and different instrument tests. All the code may be found at \\url{https://github.com/isaac2math/solar_graph_learning}.", "published": "2020-07-30T23:11:54Z", "authors": ["Ning Xu", "Timothy C. G. Fisher", "Jian Hong"], "categories": ["stat.ML", "cs.LG", "stat.AP"], "doi": "", "_query": "hedonic house price", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2309.01418v1", "title": "Social Factors in P2P Energy Trading Using Hedonic Games", "abstract": "Lately, the energy communities have gained a lot of attention as they have the potential to significantly contribute to the resilience and flexibility of the energy system, facilitating widespread integration of intermittent renewable energy sources. Within these communities the prosumers can engage in peer-to-peer trading, fostering local collaborations and increasing awareness about energy usage and flexible consumption. However, even under these favorable conditions, prosumer engagement levels remain low, requiring trading mechanisms that are aligned with their social values and expectations. In this paper, we introduce an innovative hedonic game coordination and cooperation model for P2P energy trading among prosumers which considers the social relationships within an energy community to create energy coalitions and facilitate energy transactions among them. We defined a heuristic that optimizes the prosumers coalitions, considering their social and energy price preferences and balancing the energy demand and supply within the community. We integrated the proposed hedonic game model into a state-of-the-art blockchain-based P2P energy flexibility market and evaluated its performance within an energy community of prosumers. The evaluation results on a blockchain-based P2P energy flexibility market show the effectiveness in considering social factors when creating coalitions, increasing the total amount of energy transacted in a market session by 5% compared with other game theory-based solutions. Finally, it shows the importance of the social dimensions of P2P energy transactions, the positive social dynamics in the energy community increasing the amount of energy transacted by more than 10% while contributing to a more balanced energy demand and supply within the community.", "published": "2023-09-04T08:02:49Z", "authors": ["Dan Mitrea", "Viorica Chifu", "Tudor Cioara", "Ionut Anghel", "Cristina Pop"], "categories": ["cs.DC", "cs.AI", "cs.GT"], "doi": "", "_query": "hedonic house price", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/1812.09249v2", "title": "Testing Stability Properties in Graphical Hedonic Games", "abstract": "In hedonic games, players form coalitions based on individual preferences over the group of players they could belong to. Several concepts to describe the stability of coalition structures in a game have been proposed and analysed in the literature. However, prior research focuses on algorithms with time complexity that is at least linear in the input size. In the light of very large games that arise from, e.g., social networks and advertising, we initiate the study of sublinear time property testing algorithms for existence and verification problems under several notions of coalition stability in a model of hedonic games represented by graphs with bounded degree. In graph property testing, one shall decide whether a given input has a property (e.g., a game admits a stable coalition structure) or is far from it, i.e., one has to modify at least an $ε$-fraction of the input (e.g., the game's preferences) to make it have the property. In particular, we consider verification of perfection, individual rationality, Nash stability, (contractual) individual stability, and core stability. While there is always a Nash-stable coalition structure (which also implies individually stable coalitions), we show that the existence of a perfect coalition structure is not tautological but can be tested. All our testers have one-sided error and time complexity that is independent of the input size.", "published": "2018-12-21T16:44:46Z", "authors": ["Hendrik Fichtenberger", "Anja Rey"], "categories": ["cs.GT"], "doi": "10.1007/s10458-021-09505-x", "_query": "hedonic house price", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2505.09620v1", "title": "The impact of economic policies on housing prices. Approximations and predictions in the UK, the US, France, and Switzerland from the 1980s to today", "abstract": "I show that house prices can be modeled using machine learning (kNN and tree-bagging) and a small dataset composed of macro-economic factors (MEF), including an inflation metric (CPI), US treasury rates (10-yr), Gross Domestic Product (GDP), and portfolio size of central banks (ECB, FED). This set of parameters covers all the parties involved in a transaction (buyer, seller, and financing facility) while ignoring the intrinsic properties of each asset and encompassing local (inflation) and liquidity issues that may impede each transaction composing a market. The model here takes the point of view of a real estate trader who is interested in both the financing and the price of the transaction. Machine Learning allows for the discrimination of two periods within the dataset. Unconventional policies of central banks may have allowed some institutional investors to arbitrage between real estate returns and other bond markets (sovereign and corporate). Finally, to assess the models' relative performances, I performed various sensitivity tests, which tend to constrain the possibilities of each approach for each need. I also show that some models can predict the evolution of prices over the next 4 quarters with uncertainties that outperform existing index uncertainties.", "published": "2025-04-10T12:46:54Z", "authors": ["Nicolas Houlié"], "categories": ["q-fin.ST"], "doi": "", "_query": "hedonic house price", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/1405.3360v2", "title": "On the Nash Stability in the Hedonic Coalition Formation Games", "abstract": "This paper studies the Nash stability in hedonic coalition formation games. We address the following issue: for a general problem formulation, is there any utility allocation method ensuring a Nash-stable partition? We propose the definition of the Nash-stable core. We study the conditions for having a non-empty Nash-stable core. More precisely, we show how relaxed efficiency in utility sharing method allows to ensure a non-empty Nash-stable core.", "published": "2014-05-14T04:54:08Z", "authors": ["Cengis Hasan", "Jean-Marie Gorce", "Eitan Altman"], "categories": ["cs.GT"], "doi": "", "_query": "hedonic house price", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/1404.5203v1", "title": "Towards a Monotonicity-Compliant Price Index for the Art Market", "abstract": "Notwithstanding almost forty years of efforts, the market for paintings still lacks a widely accepted price index. In this paper, we introduce a simple and intuitive metric to construct such index. Our metric is based on the price of a painting divided by its area. This formulation rests on a solid mathematical foundation as it corresponds to a particular type of hedonic model. However, unlike indexes based on the time-dummy coefficients of conventional hedonic models, this index satisfies the monotonicity condition. We demonstrate with a simple example the advantages of our metric. We also show the dangers of relying on the time-dummy coefficients of conventional hedonic models to estimate returns and generate price indexes.", "published": "2014-04-21T14:11:41Z", "authors": ["Ventura Charlin", "Arturo Cifuentes"], "categories": ["q-fin.GN"], "doi": "", "_query": "hedonic house price", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/1401.8142v1", "title": "The Integrated Size and Price Optimization Problem", "abstract": "We present the Integrated Size and Price Optimization Problem (ISPO) for a fashion discounter with many branches. Based on a two-stage stochastic programming model with recourse, we develop an exact algorithm and a production-compliant heuristic that produces small optimality gaps. In a field study we show that a distribution of supply over branches and sizes based on ISPO solutions is significantly better than a one-stage optimization of the distribution ignoring the possibility of optimal pricing.", "published": "2014-01-31T11:50:44Z", "authors": ["Miriam Kießling", "Sascha Kurz", "Jörg Rambau"], "categories": ["math.OC", "q-fin.GN"], "doi": "10.3934/naco.2012.2.669", "_query": "hedonic house price", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/1210.0986v1", "title": "The First Observations with the GRT at Parkes", "abstract": "In this contribution Marcus Price gives a first hand account of some of the very first scientific observations undertaken with the \"Giant Radio Telescope\" at Parkes. It was clearly a very exciting time of discovery, enabled by superb engineering.", "published": "2012-10-03T05:10:39Z", "authors": ["R. M. Price"], "categories": ["astro-ph.IM"], "doi": "", "_query": "hedonic house price", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2609.29691v1", "title": "Online House Allocation with Subsidy", "abstract": "House allocation is a fundamental problem in which each agent is assigned exactly one house. While the classical model assumes that all houses are available before the allocation is computed, many practical settings require decisions to be made as houses become available over time. We introduce the online house allocation problem, where houses arrive sequentially and the algorithm must maintain an allocation without knowledge of future arrivals. Unlike online fair division, the one-house-per-agent constraint makes recourse an inherent part of the problem, as accepting a newly arrived house may require reassigning previously allocated houses. We study online house allocation under subsidy-based fairness, where monetary subsidies eliminate envy among agents. We show that envy-freeability can always be maintained online using bounded recourse and that reassignment chains of length linear in the number of agents are unavoidable in the worst case. In contrast, minimizing the total subsidy is fundamentally harder: no deterministic online algorithm against an adaptive adversary, and no randomized online algorithm against a non-adaptive adversary, admits a bounded competitive ratio, even for two agents and four houses. We complement these impossibilities by showing that exact online subsidy minimization is possible whenever there is at most one extra house beyond the number of agents, and that this guarantee is best possible with respect to the number of extra houses. Finally, we develop learning-augmented algorithms that recover the offline optimum under accurate predictions while providing explicit robustness guarantees when predictions are inaccurate.", "published": "2026-08-31T01:14:42Z", "authors": ["Nicholas Teh", "Saar Cohen", "Karen Frilya Celine", "Benjamin Yu", "Michael J. Wooldridge"], "categories": ["cs.GT"], "doi": "", "_query": "hedonic house price", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2503.01884v2", "title": "Contextual Quantum Neural Networks for Stock Price Prediction", "abstract": "In this paper, we apply quantum machine learning (QML) to predict the stock prices of multiple assets using a contextual quantum neural network. Our approach captures recent trends to predict future stock price distributions, moving beyond traditional models that focus on entire historical data, enhancing adaptability and precision. Utilizing the principles of quantum superposition, we introduce a new training technique called the quantum batch gradient update (QBGU), which accelerates the standard stochastic gradient descent (SGD) in quantum applications and improves convergence. Consequently, we propose a quantum multi-task learning (QMTL) architecture, specifically, the share-and-specify ansatz, that integrates task-specific operators controlled by quantum labels, enabling the simultaneous and efficient training of multiple assets on the same quantum circuit as well as enabling efficient portfolio representation with logarithmic overhead in the number of qubits. This architecture represents the first of its kind in quantum finance, offering superior predictive power and computational efficiency for multi-asset stock price forecasting. Through extensive experimentation on S\\&P 500 data for Apple, Google, Microsoft, and Amazon stocks, we demonstrate that our approach not only outperforms quantum single-task learning (QSTL) models but also effectively captures inter-asset correlations, leading to enhanced prediction accuracy. Our findings highlight the transformative potential of QML in financial applications, paving the way for more advanced, resource-efficient quantum algorithms in stock price prediction and other complex financial modeling tasks.", "published": "2025-02-26T22:39:23Z", "authors": ["Sharan Mourya", "Hannes Leipold", "Bibhas Adhikari"], "categories": ["cs.LG", "cs.AI"], "doi": "10.1038/s41598-025-34413-5", "_query": "hedonic house price", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2108.11915v1", "title": "Cooling Measures and Housing Wealth: Evidence from Singapore", "abstract": "Excessive house price growth was at the heart of the financial crisis in 2007/08. Since then, many countries have added cooling measures to their regulatory frameworks. It has been found that these measures can indeed control price growth, but no one has examined whether this has adverse consequences for the housing wealth distribution. We examine this for Singapore, which started in 2009 to target price growth over ten rounds in total. We find that welfare from housing wealth in the last round might not be higher than before 2009. This depends on the deflator used to convert nominal into real prices. Irrespective of the deflator, we can reject that welfare increased monotonically over the different rounds.", "published": "2021-08-26T17:11:35Z", "authors": ["Wolfgang Karl Härdle", "Rainer Schulz", "Taojun Sie"], "categories": ["q-fin.GN"], "doi": "", "_query": "hedonic house price", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2302.13034v1", "title": "Does Noise Affect Housing Prices? A Case Study in the Urban Area of Thessaloniki", "abstract": "Real estate markets depend on various methods to predict housing prices, including models that have been trained on datasets of residential or commercial properties. Most studies endeavor to create more accurate machine learning models by utilizing data such as basic property characteristics as well as urban features like distances from amenities and road accessibility. Even though environmental factors like noise pollution can potentially affect prices, the research around this topic is limited. One of the reasons is the lack of data. In this paper, we reconstruct and make publicly available a general purpose noise pollution dataset based on published studies conducted by the Hellenic Ministry of Environment and Energy for the city of Thessaloniki, Greece. Then, we train ensemble machine learning models, like XGBoost, on property data for different areas of Thessaloniki to investigate the way noise influences prices through interpretability evaluation techniques. Our study provides a new noise pollution dataset that not only demonstrates the impact noise has on housing prices, but also indicates that the influence of noise on prices significantly varies among different areas of the same city.", "published": "2023-02-25T09:11:57Z", "authors": ["Georgios Kamtziridis", "Dimitris Vrakas", "Grigorios Tsoumakas"], "categories": ["cs.LG"], "doi": "", "_query": "hedonic house price", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2604.12659v1", "title": "Do VLMs Truly \"Read\" Candlesticks? A Multi-Scale Benchmark for Visual Stock Price Forecasting", "abstract": "Vision-language models(VLMs) are increasingly applied to visual stock price forecasting, yet existing benchmarks inadequately evaluate their understanding of stock price in candlestick charts. First, prior studies fail to isolate VLMs' comprehension of visual inputs genuinely improves predictive performance and whether VLMs truly comprehend candlestick patterns. Further, most existing datasets and evaluation setups are designed around single-period or tabular inputs. However, human analysts strongly rely on multi-scale candlestick charts, where longer-term horizons capture trend direction and shorter-term horizons provide cues for inflection points, making it difficult to systematically assess VLMs' ability to integrate short-term and long-term visual market dynamics. To bridge this gap, we construct a multi-scale candlestick charts dataset and a standardized evaluation framework to assess VLMs' ability to utilize multi-scale visual market signals. Evaluation combines confusion-matrix-based diagnostics with information coefficient(IC) time series metrics and includes XGBoost as a feature-based temporal baseline. Using this dataset, we benchmark representative VLMs and analyze their ability to leverage multi-scale stock price data. Experimental results show that most VLMs perform well only under persistent uptrend or downtrend conditions, while exhibiting weak predictive capability in more common market scenarios. We also identify significant prediction biases and limited sensitivity to explicitly specified forecast horizons in prompts, indicating inherent limitations in precise temporal reasoning.", "published": "2026-04-14T12:26:34Z", "authors": ["Kaiqi Hu", "Linda Xiao", "Shiyue Xu", "Ziyi Tang", "Mingwen Liu"], "categories": ["cs.LG", "cs.CL"], "doi": "", "_query": "hedonic house price", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2510.16898v2", "title": "Adaptive Online Learning with LSTM Networks for Energy Price Prediction", "abstract": "Accurate prediction of electricity prices is crucial for stakeholders in the energy market, particularly for grid operators, energy producers, and consumers. This study focuses on developing a predictive model leveraging Long Short-Term Memory (LSTM) networks to forecast day-ahead electricity prices in the California energy market. The model incorporates a variety of features, including historical price data, weather conditions, and the energy generation mix. A novel custom loss function that integrates Mean Absolute Error (MAE), Jensen-Shannon Divergence (JSD), and a smoothness penalty is introduced to enhance the prediction accuracy and interpretability. Additionally, an adaptive online learning framework is implemented to allow the model to adapt to new data incrementally, ensuring continuous relevance and accuracy. The results demonstrate that the custom loss function can improve the model's performance, aligning predicted prices more closely with actual values, particularly during peak intervals. The adaptive online learning framework achieves the best overall performance, reducing MSE, MAE, and RMSE by approximately 23.0%, 3.4%, and 12.2%, respectively, compared with the next-best model, namely the static model, which is trained once and kept fixed during testing. The inclusion of the energy generation mix further enhances the model's predictive capabilities, highlighting the importance of comprehensive feature integration. This research provides a robust framework for electricity price forecasting, which offers valuable insights and tools for better decision-making in dynamic electricity markets.", "published": "2025-10-19T15:48:38Z", "authors": ["Salih Salihoglu", "Ibrahim Ahmed", "Afshin Asadi"], "categories": ["cs.LG", "cs.AI"], "doi": "10.1007/s41060-026-01251-1", "_query": "hedonic house price", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/1901.01774v1", "title": "Location-Centered House Price Prediction: A Multi-Task Learning Approach", "abstract": "Accurate house prediction is of great significance to various real estate stakeholders such as house owners, buyers, investors, and agents. We propose a location-centered prediction framework that differs from existing work in terms of data profiling and prediction model. Regarding data profiling, we define and capture a fine-grained location profile powered by a diverse range of location data sources, such as transportation profile (e.g., distance to nearest train station), education profile (e.g., school zones and ranking), suburb profile based on census data, facility profile (e.g., nearby hospitals, supermarkets). Regarding the choice of prediction model, we observe that a variety of approaches either consider the entire house data for modeling, or split the entire data and model each partition independently. However, such modeling ignores the relatedness between partitions, and for all prediction scenarios, there may not be sufficient training samples per partition for the latter approach. We address this problem by conducting a careful study of exploiting the Multi-Task Learning (MTL) model. Specifically, we map the strategies for splitting the entire house data to the ways the tasks are defined in MTL, and each partition obtained is aligned with a task. Furthermore, we select specific MTL-based methods with different regularization terms to capture and exploit the relatedness between tasks. Based on real-world house transaction data collected in Melbourne, Australia. We design extensive experimental evaluations, and the results indicate a significant superiority of MTL-based methods over state-of-the-art approaches. Meanwhile, we conduct an in-depth analysis on the impact of task definitions and method selections in MTL on the prediction performance, and demonstrate that the impact of task definitions on prediction performance far exceeds that of method selections.", "published": "2019-01-07T12:36:57Z", "authors": ["Guangliang Gao", "Zhifeng Bao", "Jie Cao", "A. K. Qin", "Timos Sellis", "Zhiang Wu"], "categories": ["cs.LG", "stat.ML"], "doi": "", "_query": "hedonic house price", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2406.09744v2", "title": "The Cost and Complexity of Minimizing Envy in House Allocation", "abstract": "We study almost-envy-freeness in house allocation, where $m$ houses are to be allocated among $n$ agents so that every agent receives exactly one house. An envy-free allocation need not exist, and therefore we may have to settle for relaxations of envy-freeness. But typical relaxations such as envy-free up to one good do not make sense for house allocation, as every agent is required to receive exactly one house. Hence we turn to different aggregate measures of envy as markers of fairness. In particular, we define the amount of envy experienced by an agent $a$ w.r.t. an allocation to be the number of agents that agent $a$ envies under that allocation. We quantify the envy generated by an allocation using three different metrics: 1) the number of agents who are envious; 2) the maximum amount of envy experienced by any agent; and 3) the total amount of envy experienced by all agents, and look for allocations that minimize one of the three metrics. We thus study three computational problems corresponding to each of the three metrics and prove a host of algorithmic and hardness results. We also suggest practical approaches for these problems via integer linear program (ILP) formulations and report the findings of our experimental evaluation of ILPs. Finally, we study the price of fairness (PoF), which quantifies the loss of welfare we must suffer due to the fairness requirements, and we prove a number of results on PoF, including tight bounds as well as algorithms that simultaneously optimize both welfare and fairness.", "published": "2024-06-14T06:23:59Z", "authors": ["Jayakrishnan Madathil", "Neeldhara Misra", "Aditi Sethia"], "categories": ["cs.GT"], "doi": "", "_query": "hedonic house price", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2603.23070v2", "title": "Knowledge management in House of Graphs", "abstract": "The House of Graphs is an online database of graphs which can be accessed at https://houseofgraphs.org/. It serves as a central repository for complete lists of graphs for various graph classes. However, its main feature is a searchable database of so-called \"interesting\" graphs. The development of the original House of Graphs started in 2010 and it was completely rebuilt in 2021-2022. Each graph in the database is accompanied by a significant amount of meta-data such as a name, drawings, precomputed graph invariants, and comments. Given this amount of information and the importance of reliability in the scientific world, robust data management is essential to ensure accuracy and consistency across the database. In this article, we therefore focus on knowledge management in the House of Graphs and describe the inner workings of the House of Graphs and how we ensure that its data is coherent, qualitative and stable.", "published": "2026-03-24T11:07:05Z", "authors": ["Gauvain Devillez", "Sven D'hondt", "Jan Goedgebeur"], "categories": ["math.CO", "cs.DB", "cs.DM"], "doi": "", "_query": "hedonic house price", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/1802.09959v1", "title": "Valuation, Liquidity Price, and Stability of Cryptocurrencies", "abstract": "Cryptocurrencies are examined through the asset flow equations and experimental asset markets. Since tangible value of a typical cryptocurrency is non-existent, the theory suggests that price will gravitate toward liquidity value, i.e., the total amount of cash available for purchase of the asset divided by the number of units. Thus it is unlikely that cryptocurrencies in their current form will be stable in the absence of a mechanism of a link to value.", "published": "2018-02-27T15:17:46Z", "authors": ["Carey Caginalp", "Gunduz Caginalp"], "categories": ["q-fin.MF", "cs.CR"], "doi": "10.1073/pnas.1722031115", "_query": "hedonic house price", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2507.09208v1", "title": "The calibration house in JUNO", "abstract": "As an auxiliary system within the calibration system of the Jiangmen Underground Neutrino Observatory, a calibration house is designed to provide interfaces for connecting the central detector and accommodating various calibration sub-systems. Onsite installation has demonstrated that the calibration house interfaces are capable of effectively connecting to the central detector and supporting the installation of complex and sophisticated calibration sub-systems. Additionally, controlling the levels of radon and oxygen within the calibration house is critical. Radon can increase the experimental background, while oxygen can degrade the quality of the liquid scintillator. The oxygen concentration can be maintained at levels below 10 parts per million, and the radon concentration can be kept below 15 mBq/m$^{3}$. This paper will provide detailed information on the calibration house and its methods for radon and oxygen concentration control.", "published": "2025-07-12T09:02:44Z", "authors": ["J. Hui", "R. Li", "Y. Wu", "T. Zhang", "Z. Chen", "A. Freegard", "J. Huang", "H. Lai", "Y. Liao", "J. Liu", "Y. Meng", "A. Takenaka", "Z. Xiang", "P. Zhang", "Y. Zhang"], "categories": ["physics.ins-det", "hep-ex"], "doi": "", "_query": "hedonic house price", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/1212.2236v1", "title": "Core stability in hedonic coalition formation", "abstract": "In many economic, social and political situations individuals carry out activities in groups (coalitions) rather than alone and on their own. Examples range from households and sport clubs to research networks, political parties and trade unions. The underlying game theoretic framework is known as coalition formation. This survey discusses the notion of core stability in hedonic coalition formation (where each player's happiness only depends on the other members of his coalition but not on how the remaining players outside his coalition are grouped). We present the central concepts and algorithmic approaches in the area, provide many examples, and pose a number of open problems.", "published": "2012-12-10T22:23:19Z", "authors": ["Gerhard J. Woeginger"], "categories": ["cs.GT", "cs.DM", "cs.DS", "math.CO"], "doi": "", "_query": "hedonic house price", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/1908.11554v2", "title": "Computational Complexity of Hedonic Games on Sparse Graphs", "abstract": "The additively separable hedonic game (ASHG) is a model of coalition formation games on graphs. In this paper, we intensively and extensively investigate the computational complexity of finding several desirable solutions, such as a Nash stable solution, a maximum utilitarian solution, and a maximum egalitarian solution in ASHGs on sparse graphs including bounded-degree graphs, bounded-treewidth graphs, and near-planar graphs. For example, we show that finding a maximum egalitarian solution is weakly NP-hard even on graphs of treewidth 2, whereas it can be solvable in polynomial time on trees. Moreover, we give a pseudo fixed parameter algorithm when parameterized by treewidth.", "published": "2019-08-30T06:26:11Z", "authors": ["Tesshu Hanaka", "Hironori Kiya", "Yasuhide Maei", "Hirotaka Ono"], "categories": ["cs.CC"], "doi": "", "_query": "hedonic house price", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/1506.07312v4", "title": "Universal expressions of population change by the Price equation: natural selection, information, and maximum entropy production", "abstract": "The Price equation shows the unity between the fundamental expressions of change in biology, in information and entropy descriptions of populations, and in aspects of thermodynamics. The Price equation partitions the change in the average value of a metric between two populations. A population may be composed of organisms or particles or any members of a set to which we can assign probabilities. A metric may be biological fitness or physical energy or the output of an arbitrarily complicated function that assigns quantitative values to members of the population. The first part of the Price equation describes how directly applied forces change the probabilities assigned to members of the population when holding constant the metrical values of the members---a fixed metrical frame of reference. The second part describes how the metrical values change, altering the metrical frame of reference. In canonical examples, the direct forces balance the changing metrical frame of reference, leaving the average or total metrical values unchanged. In biology, relative reproductive success (fitness) remains invariant as a simple consequence of the conservation of total probability. In physics, systems often conserve total energy. Nonconservative metrics can be described by starting with conserved metrics, and then studying how coordinate transformations between conserved and nonconserved metrics alter the geometry of the dynamics and the aggregate values of populations. From this abstract perspective, key results from different subjects appear more simply as universal geometric principles for the dynamics of populations subject to the constraints of particular conserved quantities", "published": "2015-06-24T10:41:22Z", "authors": ["Steven A. Frank"], "categories": ["cond-mat.stat-mech", "q-bio.PE"], "doi": "10.1002/ece3.2922", "_query": "hedonic house price", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/1904.05270v1", "title": "Google Street View image of a house predicts car accident risk of its resident", "abstract": "Road traffic injuries are a leading cause of death worldwide. Proper estimation of car accident risk is critical for appropriate allocation of resources in healthcare, insurance, civil engineering, and other industries. We show how images of houses are predictive of car accidents. We analyze 20,000 addresses of insurance company clients, collect a corresponding house image using Google Street View, and annotate house features such as age, type, and condition. We find that this information substantially improves car accident risk prediction compared to the state-of-the-art risk model of the insurance company and could be used for price discrimination. From this perspective, public availability of house images raises legal and social concerns, as they can be a proxy of ethnicity, religion and other sensitive data.", "published": "2019-04-10T16:16:31Z", "authors": ["Kinga Kita", "Łukasz Kidziński"], "categories": ["stat.AP"], "doi": "", "_query": "hedonic house price", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2004.07571v1", "title": "Explaining herding and volatility in the cyclical price dynamics of urban housing markets using a large scale agent-based model", "abstract": "Urban housing markets, along with markets of other assets, universally exhibit periods of strong price increases followed by sharp corrections. The mechanisms generating such non-linearities are not yet well understood. We develop an agent-based model populated by a large number of heterogeneous households. The agents' behavior is compatible with economic rationality, with the trend-following behavior found to be essential in replicating market dynamics. The model is calibrated using several large and distributed datasets of the Greater Sydney region (demographic, economic and financial) across three specific and diverse periods since 2006. The model is not only capable of explaining price dynamics during these periods, but also reproduces the novel behavior actually observed immediately prior to the market peak in 2017, namely a sharp increase in the variability of prices. This novel behavior is related to a combination of trend-following aptitude of the household agents (rational herding) and their propensity to borrow.", "published": "2020-04-16T10:17:50Z", "authors": ["Kirill S. Glavatskiy", "Mikhail Prokopenko", "Adrian Carro", "Paul Ormerod", "Michael Harre"], "categories": ["q-fin.CP", "nlin.AO", "physics.soc-ph"], "doi": "", "_query": "hedonic house price", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2311.02067v2", "title": "Solving Woeginger's Hiking Problem: Wonderful Partitions in Anonymous Hedonic Games", "abstract": "A decade ago, Gerhard Woeginger posed an open problem that became well-known as \"Woeginger's Hiking Problem\": Consider a group of $n$ people that want to go hiking; everyone expresses preferences over the size of their hiking group in the form of an interval between $1$ and $n$. Is it possible to efficiently assign the $n$ people to a set of hiking subgroups so that every person approves the size of their assigned subgroup? The problem is also known as efficiently deciding if an instance of an anonymous Hedonic Game with interval approval preferences admits a wonderful partition. We resolve the open problem in the affirmative by presenting an $O(n^5)$ time algorithm for Woeginger's Hiking Problem. Our solution is based on employing a dynamic programming approach for a specific rectangle stabbing problem from computational geometry. Moreover, we propose natural, more demanding extensions of the problem, e.g., maximizing the number of satisfied participants and variants with single-peaked preferences, and show that they are also efficiently solvable. Last but not least, we employ our solution to efficiently compute a partition that maximizes the egalitarian welfare for anonymous single-peaked Hedonic Games.", "published": "2023-11-03T17:52:09Z", "authors": ["Andrei Constantinescu", "Pascal Lenzner", "Rebecca Reiffenhäuser", "Daniel Schmand", "Giovanna Varricchio"], "categories": ["cs.GT", "cs.DS"], "doi": "", "_query": "hedonic house price", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2006.10092v1", "title": "Housing Market Prediction Problem using Different Machine Learning Algorithms: A Case Study", "abstract": "Developing an accurate prediction model for housing prices is always needed for socio-economic development and well-being of citizens. In this paper, a diverse set of machine learning algorithms such as XGBoost, CatBoost, Random Forest, Lasso, Voting Regressor, and others, are being employed to predict the housing prices using public available datasets. The housing datasets of 62,723 records from January 2015 to November 2019 are obtained from Florida Volusia County Property Appraiser website. The records are publicly available and include the real estate or economic database, maps, and other associated information. The database is usually updated weekly according to the State of Florida regulations. Then, the housing price prediction models using machine learning techniques are developed and their regression model performances are compared. Finally, an improved housing price prediction model for assisting the housing market is proposed. Particularly, a house seller or buyer, or a real estate broker can get insight in making better-informed decisions considering the housing price prediction. The empirical results illustrate that based on prediction model performance, Coefficient of Determination (R2), Mean Square Error (MSE), Mean Absolute Error (MAE), and computational time, the XGBoost algorithm performs superior to the other models to predict the housing price.", "published": "2020-06-17T18:16:24Z", "authors": ["Shashi Bhushan Jha", "Radu F. Babiceanu", "Vijay Pandey", "Rajesh Kumar Jha"], "categories": ["cs.LG", "stat.ML"], "doi": "", "_query": "hedonic house price", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2004.01664v2", "title": "A sharp version of Price's law for wave decay on asymptotically flat spacetimes", "abstract": "We prove Price's law with an explicit leading order term for solutions $φ(t,x)$ of the scalar wave equation on a class of stationary asymptotically flat $(3+1)$-dimensional spacetimes including subextremal Kerr black holes. Our precise asymptotics in the full forward causal cone imply in particular that $φ(t,x)=c t^{-3}+\\mathcal O(t^{-4+})$ for bounded $|x|$, where $c\\in\\mathbb C$ is an explicit constant. This decay also holds along the event horizon on Kerr spacetimes and thus renders a result by Luk-Sbierski on the linear scalar instability of the Cauchy horizon unconditional. We moreover prove inverse quadratic decay of the radiation field, with explicit leading order term. We establish analogous results for scattering by stationary potentials with inverse cubic spatial decay. On the Schwarzschild spacetime, we prove pointwise $t^{-2 l-3}$ decay for waves with angular frequency at least $l$, and $t^{-2 l-4}$ decay for waves which are in addition initially static. This definitively settles Price's law for linear scalar waves in full generality. The heart of the proof is the analysis of the resolvent at low energies. Rather than constructing its Schwartz kernel explicitly, we proceed more directly using the geometric microlocal approach to the limiting absorption principle pioneered by Melrose and recently extended to the zero energy limit by Vasy.", "published": "2020-04-03T16:37:34Z", "authors": ["Peter Hintz"], "categories": ["math.AP", "gr-qc"], "doi": "10.1007/s00220-021-04276-8", "_query": "hedonic house price", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/hep-th/0505177v2", "title": "Effective action of (massive) IIA on manifolds with SU(3) structure", "abstract": "In this paper we consider compactifications of massive type IIA supergravity on manifolds with SU(3) structure. We derive the gravitino mass matrix of the effective four-dimensional N = 2 theory and show that vacuum expectation values of the scalar fields naturally induce spontaneous partial supersymmetry breaking. We go on to derive the superpotential and the Kaehler potential for the resulting N = 1 theories. As an example we consider the SU(3) structure manifold SU(3)/U(1)xU(1) and explicitly find N = 1 supersymmetric minima where all the moduli are stabilised at non-trivial values without the use of non-perturbative effects.", "published": "2005-05-19T16:01:15Z", "authors": ["Thomas House", "Eran Palti"], "categories": ["hep-th"], "doi": "10.1103/PhysRevD.72.026004", "_query": "hedonic house price", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2106.06925v1", "title": "On the Complexity of Fair House Allocation", "abstract": "We study fairness in house allocation, where $m$ houses are to be allocated among $n$ agents so that every agent receives one house. We show that maximizing the number of envy-free agents is hard to approximate to within a factor of $n^{1-γ}$ for any constant $γ>0$, and that the exact version is NP-hard even for binary utilities. Moreover, we prove that deciding whether a proportional allocation exists is computationally hard, whereas the corresponding problem for equitability can be solved efficiently.", "published": "2021-06-13T05:45:21Z", "authors": ["Naoyuki Kamiyama", "Pasin Manurangsi", "Warut Suksompong"], "categories": ["cs.GT", "cs.CC"], "doi": "10.1016/j.orl.2021.06.006", "_query": "hedonic house price", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2002.10898v2", "title": "Hedonic Seat Arrangement Problems", "abstract": "In this paper, we study a variant of hedonic games, called \\textsc{Seat Arrangement}. The model is defined by a bijection from agents with preferences for each other to vertices in a graph $G$. The utility of an agent depends on the neighbors assigned in the graph. More precisely, it is the sum over all neighbors of the preferences that the agent has towards the agent assigned to the neighbor. We first consider the price of stability and fairness for different classes of preferences. In particular, we show that there is an instance such that the price of fairness ({\\sf PoF}) is unbounded in general. Moreover, we show an upper bound $\\tilde{d}(G)$ and an almost tight lower bound $\\tilde{d}(G)-1/4$ of {\\sf PoF}, where $\\tilde{d}(G)$ is the average degree of an input graph. Then we investigate the computational complexity of problems to find certain ``good'' seat arrangements, say \\textsc{Utilitarian Arrangement}, \\textsc{Egalitarian Arrangement}, \\textsc{Stable Arrangement}, and \\textsc{Envy-free Arrangement}. We give dichotomies of computational complexity of four \\textsc{Seat Arrangement} problems from the perspective of the maximum order of connected components in an input graph. For the parameterized complexity, \\textsc{Utilitarian Arrangement} can be solved in time $n^{O(γ)}$, while it cannot be solved in time $f(γ)n^{o(γ)}$ under ETH, where $n$ is the number of agents and $γ$ is the vertex cover number of an input graph. Moreover, we show that \\textsc{Egalitarian Arrangement} and \\textsc{Envy-free Arrangement} are weakly NP-hard even on graphs of bounded vertex cover number. Finally, we prove that determining whether a stable arrangement can be obtained from a given arrangement by $k$ swaps is W[1]-hard when parameterized by $k+γ$, whereas it can be solved in time $n^{O(k)}$.", "published": "2020-02-25T14:38:14Z", "authors": ["Hans L. Bodlaender", "Tesshu Hanaka", "Lars Jaffke", "Hirotaka Ono", "Yota Otachi", "Tom C. van der Zanden"], "categories": ["cs.GT", "cs.CC", "cs.DS"], "doi": "", "_query": "hedonic house price", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2512.15720v1", "title": "Hidden Order in Trades Predicts the Size of Price Moves", "abstract": "Financial markets exhibit an apparent paradox: while directional price movements remain largely unpredictable--consistent with weak-form efficiency--the magnitude of price changes displays systematic structure. Here we demonstrate that real-time order-flow entropy, computed from a 15-state Markov transition matrix at second resolution, predicts the magnitude of intraday returns without providing directional information. Analysis of 38.5 million SPY trades over 36 trading days reveals that conditioning on entropy below the 5th percentile increases subsequent 5-minute absolute returns by a factor of 2.89 (t = 12.41, p < 0.0001), while directional accuracy remains at 45.0%--statistically indistinguishable from chance (p = 0.12). This decoupling arises from a fundamental symmetry: entropy is invariant under sign permutation, detecting the presence of informed trading without revealing its direction. Walk-forward validation across five non-overlapping test periods confirms out-of-sample predictability, and label-permutation placebo tests yield z = 14.4 against the null. These findings suggest that information-theoretic measures may serve as volatility state variables in market microstructure, though the limited sample (36 days, single instrument) requires extended validation.", "published": "2025-12-02T23:20:46Z", "authors": ["Mainak Singha"], "categories": ["q-fin.TR", "q-fin.ST", "stat.AP", "stat.ME"], "doi": "", "_query": "hedonic house price", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2509.23684v1", "title": "Hedonic Neurons: A Mechanistic Mapping of Latent Coalitions in Transformer MLPs", "abstract": "Fine-tuned Large Language Models (LLMs) encode rich task-specific features, but the form of these representations, especially within MLP layers, remains unclear. Empirical inspection of LoRA updates shows that new features concentrate in mid-layer MLPs, yet the scale of these layers obscures meaningful structure. Prior probing suggests that statistical priors may strengthen, split, or vanish across depth, motivating the need to study how neurons work together rather than in isolation. We introduce a mechanistic interpretability framework based on coalitional game theory, where neurons mimic agents in a hedonic game whose preferences capture their synergistic contributions to layer-local computations. Using top-responsive utilities and the PAC-Top-Cover algorithm, we extract stable coalitions of neurons: groups whose joint ablation has non-additive effects. We then track their transitions across layers as persistence, splitting, merging, or disappearance. Applied to LLaMA, Mistral, and Pythia rerankers fine-tuned on scalar IR tasks, our method finds coalitions with consistently higher synergy than clustering baselines. By revealing how neurons cooperate to encode features, hedonic coalitions uncover higher-order structure beyond disentanglement and yield computational units that are functionally important, interpretable, and predictive across domains.", "published": "2025-09-28T06:55:24Z", "authors": ["Tanya Chowdhury", "Atharva Nijasure", "Yair Zick", "James Allan"], "categories": ["cs.LG"], "doi": "", "_query": "hedonic house price", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/1905.00468v2", "title": "Envy-Freeness in House Allocation Problems", "abstract": "We consider the house allocation problem, where $m$ houses are to be assigned to $n$ agents so that each agent gets exactly one house. We present a polynomial-time algorithm that determines whether an envy-free assignment exists, and if so, computes one such assignment. We also show that an envy-free assignment exists with high probability if the number of houses exceeds the number of agents by a logarithmic factor.", "published": "2019-05-01T19:45:40Z", "authors": ["Jiarui Gan", "Warut Suksompong", "Alexandros A. Voudouris"], "categories": ["cs.GT"], "doi": "10.1016/j.mathsocsci.2019.07.005", "_query": "hedonic house price", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/1910.04666v1", "title": "Set systems related to a house allocation problem", "abstract": "We are given a set $A$ of buyers, a set $B$ of houses, and for each buyer a preference list, i.e., an ordering of the houses. A house allocation is an injective mapping $τ$ from $A$ to $B$, and $τ$ is strictly better than another house allocation $τ'\\neq τ$ if for every buyer $i$, $τ'(i)$ does not come before $τ(i)$ in the preference list of $i$. A house allocation is Pareto optimal if there is no strictly better house allocation. Let $s(τ)$ be the image of $τ$ (i.e., the set of houses sold in the house allocation $τ$). We are interested in the largest possible cardinality $f(m)$ of the family of sets $s(τ)$ for Pareto optimal mappings $τ$ taken over all sets of preference lists of $m$ buyers. We improve the earlier upper bound on $f(m)$ given by Asinowski, Keszegh and Miltzow by making a connection between this problem and some problems in extremal set theory.", "published": "2019-10-10T16:05:08Z", "authors": ["Dániel Gerbner", "Balázs Keszegh", "Abhishek Methuku", "Dániel T. Nagy", "Balázs Patkós", "Casey Tompkins", "Chuanqi Xiao"], "categories": ["math.CO"], "doi": "10.1112/jlms.12484", "_query": "hedonic house price", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2502.03012v1", "title": "A Framework to Monitor the Effects of External Shocks on Housing Markets", "abstract": "We develop a framework to holistically test for and monitor the impact of different types of events affecting a country's housing market, yet originating from housing-external sources. We classify events along three dimensions leading to testable hypotheses: prices versus quantities, supply versus demand, and immediate versus gradually evolving. These dimensions translate into guidance about which data type, statistical measure and testing strategy should be used. To perform such test suitable statistical models are needed which we implement as a hierarchical hedonic price model and a complementary count model. These models are amended by regime and contextual variables as suggested by our classification strategy. We apply this framework to the Austrian real estate market together with three disruptive events triggered by the COVID-19 pandemic, a policy tightening mortgage lending standards, as well as the cost-of-living crisis that came along with increased financing costs. The tests yield the expected results and, by that, some housing market puzzles are resolved. Deviating from the prior classification exercise means that some developments would have been undetected. Further, adopting our framework consistently when performing empirical research on residential real estate would lead to better comparable research results and, by that, would allow researchers to draw meta-conclusions from the bulk of studies available across time and space.", "published": "2025-02-05T09:13:33Z", "authors": ["Anja Hahn", "Sanela Omerovic", "Sofie Waltl"], "categories": ["econ.GN"], "doi": "", "_query": "hedonic house price", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/1806.06424v1", "title": "The House of a Reciprocal Algebraic Integer", "abstract": "Let $α$ be an algebraic integer of degree $d$, which is reciprocal. The house of $α$ is the largest modulus of its conjugates. We compute the minimum of the houses of all reciprocal algebraic integers of degree $d$ which are not roots of unity, say $\\mathrm{mr}(d)$, for $d$ at most 34. We prove several lemmata and use them to avoid unnecessary calculations. The computations suggest several conjectures. The direct consequence of the last one is the conjecture of Schinzel and Zassenhaus. We demonstrate the utility of $d$-th power of the house of $α$.", "published": "2018-06-17T18:26:07Z", "authors": ["Dragan Stankov"], "categories": ["math.NT"], "doi": "", "_query": "hedonic house price", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/0906.0307v1", "title": "Conformal House", "abstract": "We investigate the gauge dynamics of nonsupersymmetric SU(N) gauge theories featuring the simultaneous presence of fermionic matter transforming according to two distinct representations of the underlying gauge group. We bound the regions of flavors and colors which can yield a physical infrared fixed point. As a consistency check we recover the previously investigated conformal windows bounds when restricting to a single matter representation. The earlier conformal windows can be imagined to be part now of the new conformal house. We predict the nonperturbative anomalous dimensions at the infrared fixed points. We further investigate the effects of adding mass terms to the condensates on the conformal house chiral dynamics and construct the simplest instanton induced effective Lagrangian terms.", "published": "2009-06-01T15:46:51Z", "authors": ["Thomas A Ryttov", "Francesco Sannino"], "categories": ["hep-ph", "hep-lat", "hep-th"], "doi": "10.1142/S0217751X10050391", "_query": "hedonic house price", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2210.17253v1", "title": "House of Graphs 2.0: a database of interesting graphs and more", "abstract": "In 2012 we announced the House of Graphs (https://houseofgraphs.org) [Discrete Appl. Math. 161 (2013), 311-314], which was a new database of graphs. The House of Graphs hosts complete lists of graphs of various graph classes, but its main feature is a searchable database of so called \"interesting\" graphs, which includes graphs that already occurred as extremal graphs or as counterexamples to conjectures. An important aspect of this database is that it can be extended by users of the website. Over the years, several new features and graph invariants were added to the House of Graphs and users uploaded many interesting graphs to the website. But as the development of the original House of Graphs website started in 2010, the underlying frameworks and technologies of the website became outdated. This is why we completely rebuilt the House of Graphs using modern frameworks to build a maintainable and expandable web application that is future-proof. On top of this, several new functionalities were added to improve the application and the user experience. This article describes the changes and new features of the new House of Graphs website.", "published": "2022-10-31T12:18:26Z", "authors": ["Kris Coolsaet", "Sven D'hondt", "Jan Goedgebeur"], "categories": ["math.CO", "cs.DM"], "doi": "10.1016/j.dam.2022.10.013", "_query": "hedonic house price", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2209.07440v2", "title": "Envy-freeness in 3D Hedonic Games", "abstract": "We study the problem of partitioning a set of agents into coalitions based on the agents' additively separable preferences, which can also be viewed as a hedonic game. We apply three successively weaker solution concepts, namely envy-freeness, weakly justified envy-freeness, and justified envy-freeness. In a model in which coalitions may have any size, trivial solutions exist for these concepts, which provides a strong motivation for placing restrictions on coalition size. In this paper, we require feasible coalitions to have size three. We study the existence of partitions that are envy-free, weakly justified envy-free, and justified envy-free, and the computational complexity of finding such partitions, if they exist. We present a comprehensive complexity classification, in terms of the restrictions placed on the agents' preferences. From this, we identify a general trend that for the three successively weaker solution concepts, existence and polynomial-time solvability hold under successively weaker restrictions.", "published": "2022-09-15T16:42:07Z", "authors": ["Michael McKay", "Ágnes Cseh", "David Manlove"], "categories": ["cs.GT", "cs.DS"], "doi": "", "_query": "hedonic house price", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2403.01162v1", "title": "Envy-Free House Allocation with Minimum Subsidy", "abstract": "House allocation refers to the problem where $m$ houses are to be allocated to $n$ agents so that each agent receives one house. Since an envy-free house allocation does not always exist, we consider finding such an allocation in the presence of subsidy. We show that computing an envy-free allocation with minimum subsidy is NP-hard in general, but can be done efficiently if $m$ differs from $n$ by an additive constant or if the agents have identical utilities.", "published": "2024-03-02T10:31:27Z", "authors": ["Davin Choo", "Yan Hao Ling", "Warut Suksompong", "Nicholas Teh", "Jian Zhang"], "categories": ["cs.GT", "cs.CC"], "doi": "10.1016/j.orl.2024.107103", "_query": "hedonic house price", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/1910.03800v1", "title": "Art Pricing with Computer Graphic Techniques", "abstract": "This paper makes the first attempt to introduce the tools from computer graphics into the art pricing research. We argue that the creation of a painting calls for a combination of conceptual effort and painting effort from the artist. However, as the important price determinants, both efforts are long missing in the traditional hedonic model because they are hard to measure. This paper draws on the digital pictures of auctioned paintings from various renowned artists, and applies the image recognition techniques to measure the variances of lines and colors of these paintings. We then use them as the proxies for the artist's painting effort, and include them in the hedonic regression to test their significance. Our results show that the variances of lines and colors of a painting can significantly positively explain the sales price in a general context. Our suggested measurements can better capture the content heterogeneity of paintings hence improving on the traditional art pricing methodology. Our approach also provides a quantitative perspective for both valuation and authentication of paintings.", "published": "2019-10-09T05:43:20Z", "authors": ["Lan Ju", "Zhiyong Tu", "Changyong Xue"], "categories": ["q-fin.GN"], "doi": "", "_query": "hedonic house price", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/1303.2915v2", "title": "Modeling US housing prices by spatial dynamic structural equation models", "abstract": "This article proposes a spatial dynamic structural equation model for the analysis of housing prices at the State level in the USA. The study contributes to the existing literature by extending the use of dynamic factor models to the econometric analysis of multivariate lattice data. One of the main advantages of our model formulation is that by modeling the spatial variation via spatially structured factor loadings, we entertain the possibility of identifying similarity \"regions\" that share common time series components. The factor loadings are modeled as conditionally independent multivariate Gaussian Markov Random Fields, while the common components are modeled by latent dynamic factors. The general model is proposed in a state-space formulation where both stationary and nonstationary autoregressive distributed-lag processes for the latent factors are considered. For the latent factors which exhibit a common trend, and hence are cointegrated, an error correction specification of the (vector) autoregressive distributed-lag process is proposed. Full probabilistic inference for the model parameters is facilitated by adapting standard Markov chain Monte Carlo (MCMC) algorithms for dynamic linear models to our model formulation. The fit of the model is discussed for a data set of 48 States for which we model the relationship between housing prices and the macroeconomy, using State level unemployment and per capita personal income.", "published": "2013-03-12T15:23:05Z", "authors": ["Pasquale Valentini", "Luigi Ippoliti", "Lara Fontanella"], "categories": ["stat.AP"], "doi": "10.1214/12-AOAS613", "_query": "hedonic house price", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/1406.6100v1", "title": "Probabilistic flows of inhabitants in urban areas and self-organization in housing markets", "abstract": "We propose a simple probabilistic model to explain the spatial structure of the rent distribution of housing market in city of Sapporo. Here we modify the mathematical model proposed by Gauvin et. al. Especially, we consider the competition between two distances, namely, the distance between house and center, and the distance between house and office. Computer simulations are carried out to reveal the self-organized spatial structure appearing in the rent distribution. We also compare the resulting distribution with empirical rent distribution in Sapporo as an example of cities designated by ordinance. We find that the lowest ranking agents (from the viewpoint of the lowest `willing to pay') are swept away from relatively attractive regions and make several their own `communities' at low offering price locations in the city.", "published": "2014-06-23T22:11:26Z", "authors": ["Takao Hishikawa", "Jun-ichi Inoue"], "categories": ["physics.soc-ph", "q-fin.GN"], "doi": "", "_query": "hedonic house price", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/1105.2902v1", "title": "A Multi-Purpose Scenario-based Simulator for Smart House Environments", "abstract": "Developing smart house systems has been a great challenge for researchers and engineers in this area because of the high cost of implementation and evaluation process of these systems, while being very time consuming. Testing a designed smart house before actually building it is considered as an obstacle towards an efficient smart house project. This is because of the variety of sensors, home appliances and devices available for a real smart environment. In this paper, we present the design and implementation of a multi-purpose smart house simulation system for designing and simulating all aspects of a smart house environment. This simulator provides the ability to design the house plan and different virtual sensors and appliances in a two dimensional model of the virtual house environment. This simulator can connect to any external smart house remote controlling system, providing evaluation capabilities to their system much easier than before. It also supports detailed adding of new emerging sensors and devices to help maintain its compatibility with future simulation needs. Scenarios can also be defined for testing various possible combinations of device states; so different criteria and variables can be simply evaluated without the need of experimenting on a real environment.", "published": "2011-05-14T15:11:00Z", "authors": ["Zahra Forootan Jahromi", "Amir Rajabzadeh", "Ali Reza Manashty"], "categories": ["cs.AI"], "doi": "", "_query": "hedonic house price", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2202.07919v3", "title": "HousE: Knowledge Graph Embedding with Householder Parameterization", "abstract": "The effectiveness of knowledge graph embedding (KGE) largely depends on the ability to model intrinsic relation patterns and mapping properties. However, existing approaches can only capture some of them with insufficient modeling capacity. In this work, we propose a more powerful KGE framework named HousE, which involves a novel parameterization based on two kinds of Householder transformations: (1) Householder rotations to achieve superior capacity of modeling relation patterns; (2) Householder projections to handle sophisticated relation mapping properties. Theoretically, HousE is capable of modeling crucial relation patterns and mapping properties simultaneously. Besides, HousE is a generalization of existing rotation-based models while extending the rotations to high-dimensional spaces. Empirically, HousE achieves new state-of-the-art performance on five benchmark datasets. Our code is available at https://github.com/anrep/HousE.", "published": "2022-02-16T08:13:23Z", "authors": ["Rui Li", "Jianan Zhao", "Chaozhuo Li", "Di He", "Yiqi Wang", "Yuming Liu", "Hao Sun", "Senzhang Wang", "Weiwei Deng", "Yanming Shen", "Xing Xie", "Qi Zhang"], "categories": ["cs.AI"], "doi": "", "_query": "hedonic house price", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2406.06524v2", "title": "Gas Fees on the Ethereum Blockchain: From Foundations to Derivatives Valuations", "abstract": "The gas fee, paid for inclusion in the blockchain, is analyzed in two parts. First, we consider how effort in terms of resources required to process and store a transaction turns into a gas limit, which, through a fee, comprised of the base and priority fee in the current version of Ethereum, is converted into the cost paid by the user. We adhere closely to the Ethereum protocol to simplify the analysis and to constrain the design choices when considering multidimensional gas. Second, we assume that the gas price is given deus ex machina by a fractional Ornstein-Uhlenbeck process and evaluate various derivatives. These contracts can, for example, mitigate gas cost volatility. The ability to price and trade forwards besides the existing spot inclusion into the blockchain could enable users to hedge against future cost fluctuations. Overall, this paper offers a comprehensive analysis of gas fee dynamics on the Ethereum blockchain, integrating supply-side constraints with demand-side modelling to enhance the predictability and stability of transaction costs.", "published": "2024-06-10T17:59:46Z", "authors": ["Bernhard K Meister", "Henry CW Price"], "categories": ["q-fin.PM", "q-fin.GN"], "doi": "", "_query": "hedonic house price", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/1008.0074v3", "title": "Stable partitions in additively separable hedonic games", "abstract": "An important aspect in systems of multiple autonomous agents is the exploitation of synergies via coalition formation. In this paper, we solve various open problems concerning the computational complexity of stable partitions in additively separable hedonic games. First, we propose a polynomial-time algorithm to compute a contractually individually stable partition. This contrasts with previous results such as the NP-hardness of computing individually stable or Nash stable partitions. Secondly, we prove that checking whether the core or the strict core exists is NP-hard in the strong sense even if the preferences of the players are symmetric. Finally, it is shown that verifying whether a partition consisting of the grand coalition is contractually strict core stable or Pareto optimal is coNP-complete.", "published": "2010-07-31T08:54:34Z", "authors": ["Haris Aziz", "Felix Brandt", "Hans Georg Seedig"], "categories": ["cs.GT", "cs.CC"], "doi": "10.1016/j.artint.2012.09.006", "_query": "hedonic house price", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/1308.3324v1", "title": "History Based Coalition Formation in Hedonic Context Using Trust", "abstract": "In this paper we address the problem of coalition formation in hedonic context. Our modelling tries to be as realistic as possible. In previous models, once an agent joins a coalition it would not be able to leave the coalition and join the new one; in this research we made it possible to leave a coalition but put some restrictions to control the behavior of agents. Leaving or staying of an agent in a coalition will affect on the trust of the other agents included in this coalition. Agents will use the trust values in computing the expected utility of coalitions. Three different risk behaviors are introduced for agents that want to initiate a coalition. Using these risk behaviors, some simulations are made and results are analyzed.", "published": "2013-08-15T07:33:28Z", "authors": ["Ahmadreza Ghaffarizadeh", "Vicki H. Allan"], "categories": ["cs.MA", "cs.AI"], "doi": "10.5121/ijaia.2013.4401", "_query": "hedonic house price", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2503.06017v1", "title": "Welfare Approximation in Additively Separable Hedonic Games", "abstract": "Partitioning a set of $n$ items or agents while maximizing the value of the partition is a fundamental algorithmic task. We study this problem in the specific setting of maximizing social welfare in additively separable hedonic games. Unfortunately, this task faces strong computational boundaries: Extending previous results, we show that approximating welfare by a factor of $n^{1-ε}$ is NP-hard, even for severely restricted weights. However, we can obtain a randomized $\\log n$-approximation on instances for which the sum of input valuations is nonnegative. Finally, we study two stochastic models of aversion-to-enemies games, where the weights are derived from Erdős-Rényi or multipartite graphs. We obtain constant-factor and logarithmic-factor approximations with high probability.", "published": "2025-03-08T02:37:27Z", "authors": ["Martin Bullinger", "Vaggos Chatziafratis", "Parnian Shahkar"], "categories": ["cs.GT", "cs.DS"], "doi": "", "_query": "hedonic house price", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/1606.00424v3", "title": "Residential income segregation: A behavioral model of the housing market", "abstract": "We represent the functioning of the housing market and study the relation between income segregation, income inequality and house prices by introducing a spatial Agent-Based Model (ABM). Differently from traditional models in urban economics, we explicitly specify the behavior of buyers and sellers and the price formation mechanism. Buyers who differ by income select among heterogeneous neighborhoods using a probabilistic model of residential choice; sellers employ an aspiration level heuristic to set their reservation offer price; prices are determined through a continuous double auction. We first provide an approximate analytical solution of the ABM, shedding light on the structure of the model and on the effect of the parameters. We then simulate the ABM and find that: (i) a more unequal income distribution lowers the prices globally, but implies stronger segregation; (ii) a spike of the demand in one part of the city increases the prices all over the city; (iii) subsidies are more efficient than taxes in fostering social mixing.", "published": "2016-06-01T19:53:26Z", "authors": ["Marco Pangallo", "Jean Pierre Nadal", "Annick Vignes"], "categories": ["econ.GN", "nlin.AO"], "doi": "", "_query": "hedonic house price", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2301.13756v1", "title": "PAC learning and stabilizing Hedonic Games: towards a unifying approach", "abstract": "We study PAC learnability and PAC stabilizability of Hedonic Games (HGs), i.e., efficiently inferring preferences or core-stable partitions from samples. We first expand the known learnability/stabilizability landscape for some of the most prominent HGs classes, providing results for Friends and Enemies Games, Bottom Responsive, and Anonymous HGs. Then, having a broader view in mind, we attempt to shed light on the structural properties leading to learnability/stabilizability, or lack thereof, for specific HGs classes. Along this path, we focus on the fully expressive Hedonic Coalition Nets representation of HGs. We identify two sets of conditions that lead to efficient learnability, and which encompass all of the known positive learnability results. On the side of stability, we reveal that, while the freedom of choosing an ad hoc adversarial distribution is the most obvious hurdle to achieving PAC stability, it is not the only one. First, we show a distribution independent necessary condition for PAC stability. Then, we focus on $\\W$-games, where players have individual preferences over other players and evaluate coalitions based on the least preferred member. We prove that these games are PAC stabilizable under the class of bounded distributions, which assign positive probability mass to all coalitions. Finally, we discuss why such a result is not easily extendable to other HGs classes even in this promising scenario. Namely, we establish a purely computational property necessary for achieving PAC stability.", "published": "2023-01-31T16:45:36Z", "authors": ["Simone Fioravanti", "Michele Flammini", "Bojana Kodric", "Giovanna Varricchio"], "categories": ["cs.GT", "cs.AI"], "doi": "", "_query": "hedonic house price", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/1610.07292v1", "title": "Population growth, interest rate, and housing tax in the transitional China", "abstract": "This paper combines and develops the models in Lastrapes (2002) and Mankiw & Weil (1989), which enables us to analyze the effects of interest rate and population growth shocks on housing price in one integrated framework. Based on this model, we carry out policy simulations to examine whether the housing (stock or flow) tax reduces the housing price fluctuations caused by interest rate or population growth shocks. Simulation results imply that the choice of housing tax tools depends on the kind of shock that housing market faces. In the situation where the housing price volatility is caused by the population growth shock, the flow tax can reduce the volatility of housing price while the stock tax makes no difference to it. If the shock is resulting from the interest rate, the policy maker should not impose any kind of the housing taxes. Furthermore, the effect of one kind of the housing tax can be strengthened by that of the other type of housing tax.", "published": "2016-10-24T06:22:26Z", "authors": ["Ling-Yun He", "Xing-Chun Wen"], "categories": ["q-fin.GN", "econ.GN"], "doi": "10.1016/j.physa.2016.11.057", "_query": "hedonic house price", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/0809.0979v1", "title": "A housing-demographic multi-layered nonlinear model to test regulation strategies", "abstract": "We propose a novel multi-layered nonlinear model that is able to capture and predict the housing-demographic dynamics of the real-state market by simulating the transitions of owners among price-based house layers. This model allows us to determine which parameters are most effective to smoothen the severity of a potential market crisis. The International Monetary Fund (IMF) has issued severe warnings about the current real-state bubble in the United States, the United Kingdom, Ireland, the Netherlands, Australia and Spain in the last years. Madrid (Spain), in particular, is an extreme case of this bubble. It is, therefore, an excellent test case to analyze housing dynamics in the context of the empirical data provided by the Spanish National Institute of Statistics and other sources of data. The model is able to predict the mean house occupancy, and shows that i) the house market conditions in Madrid are unstable but not critical; and ii) the regulation of the construction rate is more effective than interest rate changes. Our results indicate that to accommodate the construction rate to the total population of first-time buyers is the most effective way to maintain the system near equilibrium conditions. In addition, we show that to raise interest rates will heavily affect the poorest housing bands of the population while the middle class layers remain nearly unaffected.", "published": "2008-09-05T08:51:28Z", "authors": ["Ramon Huerta", "Fernando Corbacho", "Luis F. Lago-Fernandez"], "categories": ["nlin.CG", "q-fin.GN"], "doi": "", "_query": "hedonic house price", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/1306.2831v1", "title": "Systemic risk and spatiotemporal dynamics of the US housing market", "abstract": "Housing markets play a crucial role in economies and the collapse of a real-estate bubble usually destabilizes the financial system and causes economic recessions. We investigate the systemic risk and spatiotemporal dynamics of the US housing market (1975-2011) at the state level based on the Random Matrix Theory (RMT). We identify rich economic information in the largest eigenvalues deviating from RMT predictions and unveil that the component signs of the eigenvectors contain either geographical information or the extent of differences in house price growth rates or both. Our results show that the US housing market experienced six different regimes, which is consistent with the evolution of state clusters identified by the box clustering algorithm and the consensus clustering algorithm on the partial correlation matrices. Our analysis uncovers that dramatic increases in the systemic risk are usually accompanied with regime shifts, which provides a means of early detection of housing bubbles.", "published": "2013-06-12T14:05:13Z", "authors": ["Hao Meng", "Wen-Jie Xie", "Zhi-Qiang Jiang", "Boris Podobnik", "Wei-Xing Zhou", "H. Eugene Stanley"], "categories": ["q-fin.ST"], "doi": "10.1038/srep03655", "_query": "hedonic house price", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2505.00296v3", "title": "Minimizing Envy and Maximizing Happiness in Graphical House Allocation", "abstract": "We study the house allocation problem in a setting where agents are connected by a graph representing friendships. In this model, two agents can only envy each other if they are neighbors (i.e., friends) in the graph. Each agent has a set of preferred (liked) houses and dislikes the rest. An agent $a$ is said to envy a friend $b$ if $a$ is not assigned any house she likes, while $b$ is allocated a house that $a$ likes. This framework is known as graphical house allocation. Within this framework, we investigate two central problems. The first problem is to compute a house allocation that minimizes the number of envious agents. Multiple such allocations may exist that achieve the same minimum level of envy. Among all allocations that minimize envy, the second problem aims to find one that maximizes the number of agents who receive one of their preferred houses. We present a detailed complexity-theoretic analysis of these problems. In particular, we show that both problems can be solved in polynomial time when each agent prefers at most one house. However, both become NP-hard even when agents are allowed to prefer at most two houses, thereby highlighting the tight boundary between tractability and intractability. Additionally, we design exact algorithms for both problems under certain structural conditions on the agent graph, such as when the graph is sparse, has a small balanced separator, or admits a small vertex cover. These algorithms are significantly faster than the naive brute-force approach.", "published": "2025-05-01T04:38:09Z", "authors": ["Anubhav Dhar", "Ashlesha Hota", "Palash Dey", "Sudeshna Kolay"], "categories": ["cs.DS", "cs.CC"], "doi": "", "_query": "hedonic house price", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2109.07738v2", "title": "Noise Robust Core-Stable Coalitions of Hedonic Games", "abstract": "We consider the coalition formation games with an additional component, `noisy preferences'. Moreover, such noisy preferences are available only for a sample of coalitions. We propose a multiplicative noise model and obtain the prediction probability, defined as the probability that the estimated PAC core-stable partition of the noisy game is also PAC core-stable for the unknown noise-free game. This prediction probability depends on the probability of a combinatorial construct called an `agreement event'. We explicitly obtain the agreement probability for $n$ agent noisy game with l\\geq 2 support noise distribution. For a user-given satisfaction value on this probability, we identify the noise regimes for which an estimated partition is noise robust; that is, it is PAC core-stable in both noisy and noise-free games. We obtain similar robustness results when the estimated partition is not PAC core-stable. These noise regimes correspond to the level sets of the agreement probability function and are non-convex sets. Moreover, an important fact is that the prediction probability can be high even if high noise values occur with a high probability. Further, for a class of top-responsive hedonic games, we obtain the bounds on the extra noisy samples required to get noise robustness with a user-given satisfaction value. We completely solve the noise robustness problem of a $2$ agent hedonic game. In particular, we obtain the prediction probability function for l=2 and l=3 noise support cases. For l=2, the prediction probability is convex in noise probability, but the noise robust regime is non-convex. Its minimum value, called the safety value, is 0.62; so, below 0.62, the noise robust regime is the entire probability simplex. However, for l \\geq 3, the prediction probability is non-convex; so, the safety value is the global minima of a non-convex function and is computationally hard.", "published": "2021-09-16T06:13:48Z", "authors": ["Prashant Trivedi", "Nandyala Hemachandra"], "categories": ["cs.GT"], "doi": "", "_query": "hedonic house price", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/1005.4540v3", "title": "Optimal Partitions in Additively Separable Hedonic Games", "abstract": "We conduct a computational analysis of fair and optimal partitions in additively separable hedonic games. We show that, for strict preferences, a Pareto optimal partition can be found in polynomial time while verifying whether a given partition is Pareto optimal is coNP-complete, even when preferences are symmetric and strict. Moreover, computing a partition with maximum egalitarian or utilitarian social welfare or one which is both Pareto optimal and individually rational is NP-hard. We also prove that checking whether there exists a partition which is both Pareto optimal and envy-free is $Σ_{2}^{p}$-complete. Even though an envy-free partition and a Nash stable partition are both guaranteed to exist for symmetric preferences, checking whether there exists a partition which is both envy-free and Nash stable is NP-complete.", "published": "2010-05-25T11:38:41Z", "authors": ["Haris Aziz", "Felix Brandt", "Hans Georg Seedig"], "categories": ["cs.GT"], "doi": "10.1016/j.artint.2012.09.006", "_query": "hedonic house price", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/1801.07357v2", "title": "CHALET: Cornell House Agent Learning Environment", "abstract": "We present CHALET, a 3D house simulator with support for navigation and manipulation. CHALET includes 58 rooms and 10 house configuration, and allows to easily create new house and room layouts. CHALET supports a range of common household activities, including moving objects, toggling appliances, and placing objects inside closeable containers. The environment and actions available are designed to create a challenging domain to train and evaluate autonomous agents, including for tasks that combine language, vision, and planning in a dynamic environment.", "published": "2018-01-23T00:22:25Z", "authors": ["Claudia Yan", "Dipendra Misra", "Andrew Bennnett", "Aaron Walsman", "Yonatan Bisk", "Yoav Artzi"], "categories": ["cs.AI"], "doi": "", "_query": "hedonic house price", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2208.14207v1", "title": "Understanding intra-day price formation process by agent-based financial market simulation: calibrating the extended chiarella model", "abstract": "This article presents XGB-Chiarella, a powerful new approach for deploying agent-based models to generate realistic intra-day artificial financial price data. This approach is based on agent-based models, calibrated by XGBoost machine learning surrogate. Following the Extended Chiarella model, three types of trading agents are introduced in this agent-based model: fundamental traders, momentum traders, and noise traders. In particular, XGB-Chiarella focuses on configuring the simulation to accurately reflect real market behaviours. Instead of using the original Expectation-Maximisation algorithm for parameter estimation, the agent-based Extended Chiarella model is calibrated using XGBoost machine learning surrogate. It is shown that the machine learning surrogate learned in the proposed method is an accurate proxy of the true agent-based market simulation. The proposed calibration method is superior to the original Expectation-Maximisation parameter estimation in terms of the distance between historical and simulated stylised facts. With the same underlying model, the proposed methodology is capable of generating realistic price time series in various stocks listed at three different exchanges, which indicates the universality of intra-day price formation process. For the time scale (minutes) chosen in this paper, one agent per category is shown to be sufficient to capture the intra-day price formation process. The proposed XGB-Chiarella approach provides insights that the price formation process is comprised of the interactions between momentum traders, fundamental traders, and noise traders. It can also be used to enhance risk management by practitioners.", "published": "2022-08-29T15:35:39Z", "authors": ["Kang Gao", "Perukrishnen Vytelingum", "Stephen Weston", "Wayne Luk", "Ce Guo"], "categories": ["q-fin.CP", "q-fin.GN"], "doi": "10.1002/wilm.11014", "_query": "hedonic house price", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/physics/0602033v3", "title": "Community Structure in the United States House of Representatives", "abstract": "We investigate the networks of committee and subcommittee assignments in the United States House of Representatives from the 101st--108th Congresses, with the committees connected by ``interlocks'' or common membership. We examine the community structure in these networks using several methods, revealing strong links between certain committees as well as an intrinsic hierarchical structure in the House as a whole. We identify structural changes, including additional hierarchical levels and higher modularity, resulting from the 1994 election, in which the Republican party earned majority status in the House for the first time in more than forty years. We also combine our network approach with analysis of roll call votes using singular value decomposition to uncover correlations between the political and organizational structure of House committees.", "published": "2006-02-04T22:41:34Z", "authors": ["Mason A. Porter", "Peter J. Mucha", "M. E. J. Newman", "A. J. Friend"], "categories": ["physics.soc-ph", "cond-mat.stat-mech", "cs.MA", "nlin.AO", "physics.data-an"], "doi": "10.1063/1.2390556", "_query": "hedonic house price", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/1610.04805v1", "title": "Beyond Spatial Auto-Regressive Models: Predicting Housing Prices with Satellite Imagery", "abstract": "When modeling geo-spatial data, it is critical to capture spatial correlations for achieving high accuracy. Spatial Auto-Regression (SAR) is a common tool used to model such data, where the spatial contiguity matrix (W) encodes the spatial correlations. However, the efficacy of SAR is limited by two factors. First, it depends on the choice of contiguity matrix, which is typically not learnt from data, but instead, is assumed to be known apriori. Second, it assumes that the observations can be explained by linear models. In this paper, we propose a Convolutional Neural Network (CNN) framework to model geo-spatial data (specifi- cally housing prices), to learn the spatial correlations automatically. We show that neighborhood information embedded in satellite imagery can be leveraged to achieve the desired spatial smoothing. An additional upside of our framework is the relaxation of linear assumption on the data. Specific challenges we tackle while implementing our framework include, (i) how much of the neighborhood is relevant while estimating housing prices? (ii) what is the right approach to capture multiple resolutions of satellite imagery? and (iii) what other data-sources can help improve the estimation of spatial correlations? We demonstrate a marked improvement of 57% on top of the SAR baseline through the use of features from deep neural networks for the cities of London, Birmingham and Liverpool.", "published": "2016-10-16T01:17:19Z", "authors": ["Archith J. Bency", "Swati Rallapalli", "Raghu K. Ganti", "Mudhakar Srivatsa", "B. S. Manjunath"], "categories": ["cs.CV"], "doi": "", "_query": "hedonic house price", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2110.06875v3", "title": "The core of housing markets from an agent's perspective: Is it worth sprucing up your home?", "abstract": "We study housing markets as introduced by Shapley and Scarf (1974). We investigate the computational complexity of various questions regarding the situation of an agent $a$ in a housing market $H$: we show that it is $\\mathsf{NP}$-hard to find an allocation in the core of $H$ where (i) $a$ receives a certain house, (ii) $a$ does not receive a certain house, or (iii) $a$ receives a house other than her own. We prove that the core of housing markets respects improvement in the following sense: given an allocation in the core of $H$ where agent $a$ receives a house $h$, if the value of the house owned by $a$ increases, then the resulting housing market admits an allocation in its core in which $a$ receives either $h$, or a house that $a$ prefers to $h$; moreover, such an allocation can be found efficiently. We further show an analogous result in the Stable Roommates setting by proving that stable matchings in a one-sided market also respect improvement.", "published": "2021-10-13T17:11:06Z", "authors": ["Ildikó Schlotter", "Péter Biró", "Tamás Fleiner"], "categories": ["cs.GT", "cs.DS"], "doi": "", "_query": "hedonic house price", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2603.07890v1", "title": "Visualizing Coalition Formation: From Hedonic Games to Image Segmentation", "abstract": "We propose image segmentation as a visual diagnostic testbed for coalition formation in hedonic games. Modeling pixels as agents on a graph, we study how a granularization parameter shapes equilibrium fragmentation and boundary structure. On the Weizmann single-object benchmark, we relate multi-coalition equilibria to binary protocols by measuring whether the converged coalitions overlap with a foreground ground-truth. We observe transitions from cohesive to fragmented yet recoverable equilibria, and finally to intrinsic failure under excessive fragmentation. Our core contribution links multi-agent systems with image segmentation by quantifying the impact of mechanism design parameters on equilibrium structures.", "published": "2026-03-09T02:08:17Z", "authors": ["Pedro Henrique de Paula França", "Lucas Lopes Felipe", "Daniel Sadoc Menasché"], "categories": ["cs.AI", "cs.CV"], "doi": "", "_query": "hedonic house price", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2402.18973v1", "title": "Privacy Management and Interface Design for a Smart House", "abstract": "In today's life, more and more people tend to opt for a smart house. In this way, the idea of including technology has become popular worldwide. Despite this concept's many benefits, managing security remains an essential problem due to the shared activities. The Internet of Things system behind a smart house is based on several sensors to measure temperature, humidity, air quality, and movement. Because of being supervised every day through sensors and controlling their house only with a simple click, many people can be afraid of this new approach in terms of their privacy, and this fact can constrain them from following their habits. The security aspects should be constantly analyzed to keep the data's confidentiality and make people feel safe in their own houses. In this context, the current paper puts light on an alternative design of a platform in which the safety of homeowners is the primary purpose, and they maintain complete control over the data generated by smart devices. The current research highlights the role of security and interface design in controlling a smart house. The study underscores the importance of providing an interface that can be used easily by any person to manage data and live activities in a modern residence in an era dominated by continuously developing technology.", "published": "2024-02-29T09:26:41Z", "authors": ["Ana-Maria Comeaga", "Iuliana Marin"], "categories": ["cs.CR", "cs.SE"], "doi": "", "_query": "hedonic house price", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2609.06308v1", "title": "Housing Price Appreciation, Housing Affordability, and the Spatial Restructuring of Toronto Commuting", "abstract": "This study investigates the spatial relationships among housing-market appreciation, housing affordability, and automobile commuting to Toronto across 23 census metropolitan areas and municipalities in southern Ontario from 2016 to 2021. Utilizing Statistics Canada Census data, the analysis employed linear and quadratic ordinary least-squares regression, Global and Local Moran's I, and residual spatial diagnostics to assess geographic patterns in average dwelling values, owner shelter-cost burden, and lone-driver commuting. The study found that average dwelling values increased significantly throughout the region, maintaining a strong nonlinear relationship with distance from Toronto, which indicates a persistent metropolitan accessibility gradient. Commuter volumes also demonstrated pronounced nonlinear distance gradients; however, housing appreciation was not significantly correlated with percentage changes in commuting ($R^2$ = .045, p = .331), suggesting that the expansion of the housing market did not result in proportional growth in Toronto-bound automobile travel. The shelter-cost burden exhibited weaker distance relationships and greater residual spatial dependence, indicating localized affordability influences not fully explained by distance or dwelling values. Overall, the findings suggest an increasingly extensive regional housing market with spatially heterogeneous affordability and commuting outcomes, highlighting the importance of integrating housing, transportation, and regional planning.", "published": "2026-09-05T23:36:50Z", "authors": ["Shawn Berry"], "categories": ["econ.GN", "stat.OT"], "doi": "", "_query": "hedonic house price", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2110.12394v1", "title": "Housing property rights and social integration of migrant population: based on the 2017 china migrants' dynamic survey", "abstract": "Push-pull theory, one of the most important macro theories in demography, argues that population migration is driven by a combination of push (repulsive) forces at the place of emigration and pull (attractive) forces at the place of emigration. Based on the push-pull theory, this paper shows another practical perspective of the theory by measuring the reverse push and pull forces from the perspective of housing property rights. We use OLS and sequential Probit models to analyze the impact of urban and rural property rights factors on the social integration of the migrant population-based, on \"China Migrants' Dynamic Survey\". We found that after controlling for personal and urban characteristics, there is a significant negative effect of rural property rights (homestead) ownership of the mobile population on their socio-economic integration, and cultural and psychological integration in the inflow area. The effect of urban house price on social integration of the migrant population is consistent with the \"inverted U-shaped\" nonlinear assumption: when the house price to income ratio of the migrant population in the inflow area increases beyond the inflection point, its social integration level decreases. That is, there is an inverse push force and pull force mechanism of housing property rights on population mobility.", "published": "2021-10-24T09:07:35Z", "authors": ["Jingwen Tan", "Shixi Kang"], "categories": ["econ.GN"], "doi": "", "_query": "hedonic house price", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2010.00413v2", "title": "Resilient Urban Housing Markets: Shocks vs. Fundamentals", "abstract": "In the face of a pandemic, urban protests, and an affordability crisis, is the desirability of dense urban settings at a turning point? Assessing cities' long term trends remains challenging. The first part of this chapter describes the short-run dynamics of the housing market in 2020. Evidence from prices and price-to-rent ratios suggests expectations of resilience. Zip-level evidence suggests a short-run trend towards suburbanization, and some impacts of urban protests on house prices. The second part of the chapter analyzes the long-run dynamics of urban growth between 1970 and 2010. It analyzes what, in such urban growth, is explained by short-run shocks as opposed to fundamentals such as education, industrial specialization, industrial diversification, urban segregation, and housing supply elasticity. This chapter's original results as well as a large established body of literature suggest that fundamentals are the key drivers of growth. The chapter illustrates this finding with two case studies: the New York City housing market after September 11, 2001; and the San Francisco Bay Area in the aftermath of the 1989 Loma Prieta earthquake. Both areas rebounded strongly after these shocks, suggesting the resilience of the urban metropolis.", "published": "2020-10-01T13:59:53Z", "authors": ["Amine Ouazad"], "categories": ["econ.GN", "physics.soc-ph"], "doi": "", "_query": "hedonic house price", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2608.22216v1", "title": "The Complexity of Minimizing Subsidies in Envy-Free House Allocation", "abstract": "The house allocation problem is a classical one-sided matching problem that concerns the assignment of a set of $m$ houses to $n$ agents according to their preferences, where each agent is assigned exactly one house. Among the various objectives studied in this setting, envy-freeness is one of the most widely adopted fairness criteria. As envy-free house allocations do not always exist, we address this challenge by introducing subsidies and aim to compute allocations that achieve envy-freeness with minimum total subsidy. For binary instances, we show that a total subsidy of at most $(n-1)$ suffices to guarantee envy-freeness in house allocation, and this bound is tight. Building on the known NP-hardness for general utilities, we further show that computing an allocation that minimizes the total subsidy is NP-hard, even under binary utilities. However, when there are only a bounded number of types of agents with binary utilities, the problem can be solved in polynomial time. Finally, we present a polynomial time algorithm that computes the minimum subsidy required to achieve envy-freeness for two types of agents with general utilities.", "published": "2026-08-23T04:40:34Z", "authors": ["Sijia Dai", "Minming Li", "Xiaowei Wu", "Yong Zhang"], "categories": ["cs.GT", "cs.CC"], "doi": "", "_query": "hedonic house price", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/1906.10250v3", "title": "Swap Dynamics in Single-Peaked Housing Markets", "abstract": "This paper focuses on the problem of fairly and efficiently allocating resources to agents. We consider a specific setting, usually referred to as a housing market, where each agent must receive exactly one resource (and initially owns one). In this framework, in the domain of linear preferences, the Top Trading Cycle (TTC) algorithm is the only procedure satisfying Pareto-optimality, individual rationality and strategy-proofness. Under the restriction of single-peaked preferences, Crawler enjoys the same properties. These two centralized procedures might however involve long trading cycles. In this paper we focus instead on procedures involving the shortest cycles: bilateral swap-deals. In such swap dynamics, the agents perform pairwise mutually improving deals until reaching a swap-stable allocation (no improving swap-deal is possible). We prove that in the single-peaked domain every swap-stable allocation is Pareto-optimal, showing the efficiency of the swap dynamics. In fact, this domain turns out to be maximal when it comes to guaranteeing this property. Besides, both the outcome of TTC and Crawler can always be reached by sequences of swaps. However, some Pareto-optimal allocations are not reachable through improving swap-deals. We further analyze the outcome of swap dynamics through social welfare notions, in our context the average or minimum rank of the resources obtained by agents in the final allocation. We start by providing a worst-case analysis of these procedures. Finally, we present an extensive experimental study in which different versions of swap dynamics are compared to other existing allocation procedures. We show that they exhibit good results on average in this domain, under different cultures for generating synthetic data.", "published": "2019-06-24T22:12:01Z", "authors": ["Aurélie Beynier", "Nicolas Maudet", "Simon Rey", "Parham Shams"], "categories": ["cs.AI", "cs.MA"], "doi": "", "_query": "hedonic house price", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2307.12482v2", "title": "Tight Approximations for Graphical House Allocation", "abstract": "The Graphical House Allocation problem asks: how can $n$ houses (each with a fixed non-negative value) be assigned to the vertices of an undirected graph $G$, so as to minimize the \"aggregate local envy\", i.e., the sum of absolute differences along the edges of $G$? This problem generalizes the classical Minimum Linear Arrangement problem, as well as the well-known House Allocation Problem from Economics, the latter of which has notable practical applications in organ exchanges. Recent work has studied the computational aspects of Graphical House Allocation and observed that the problem is NP-hard and inapproximable even on particularly simple classes of graphs, such as vertex disjoint unions of paths. However, the dependence of any approximations on the structural properties of the underlying graph had not been studied. In this work, we give a complete characterization of the approximability of the Graphical House Allocation problem. We present algorithms to approximate the optimal envy on general graphs, trees, planar graphs, bounded-degree graphs, bounded-degree planar graphs, and bounded-degree trees. For each of these graph classes, we then prove matching lower bounds, showing that in each case, no significant improvement can be attained unless P = NP. We also present general approximation ratios as a function of structural parameters of the underlying graph, such as treewidth; these match the aforementioned tight upper bounds in general, and are significantly better approximations for many natural subclasses of graphs. Finally, we present constant factor approximation schemes for the special classes of complete binary trees and random graphs.", "published": "2023-07-24T02:30:34Z", "authors": ["Hadi Hosseini", "Andrew McGregor", "Rik Sengupta", "Rohit Vaish", "Vignesh Viswanathan"], "categories": ["cs.DS", "cs.DM", "cs.GT", "math.CO"], "doi": "", "_query": "hedonic house price", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2411.17724v1", "title": "Incentives to Build Houses, Trade Houses, or Trade House Building Skills in Simulated Worlds under Various Governing Systems or Institutions: Comparing Multi-agent Reinforcement Learning to Generative Agent-based Model", "abstract": "It has been shown that social institutions impact human motivations to produce different behaviours, such as amount of working or specialisation in labor. With advancement in artificial intelligence (AI), specifically large language models (LLMs), now it is possible to perform in-silico simulations to test various hypotheses around this topic. Here, I simulate two somewhat similar worlds using multi-agent reinforcement learning (MARL) framework of the AI-Economist and generative agent-based model (GABM) framework of the Concordia. In the extended versions of the AI-Economist and Concordia, the agents are able to build houses, trade houses, and trade house building skill. Moreover, along the individualistic-collectivists axis, there are a set of three governing systems: Full-Libertarian, Semi-Libertarian/Utilitarian, and Full-Utilitarian. Additionally, in the extended AI-Economist, the Semi-Libertarian/Utilitarian system is further divided to a set of three governing institutions along the discriminative axis: Inclusive, Arbitrary, and Extractive. Building on these, I am able to show that among governing systems and institutions of the extended AI-Economist, under the Semi-Libertarian/Utilitarian and Inclusive government, the ratios of building houses to trading houses and trading house building skill are higher than the rest. Furthermore, I am able to show that in the extended Concordia when the central government care about equality in the society, the Full-Utilitarian system generates agents building more houses and trading more house building skill. In contrast, these economic activities are higher under the Full-Libertarian system when the central government cares about productivity in the society. Overall, the focus of this paper is to compare and contrast two advanced techniques of AI, MARL and GABM, to simulate a similar social phenomena with limitations.", "published": "2024-11-21T08:52:42Z", "authors": ["Aslan S. Dizaji"], "categories": ["cs.MA"], "doi": "", "_query": "hedonic house price", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2511.07022v1", "title": "Fair Societies: Algorithms for House Allocations", "abstract": "House Allocations concern with matchings involving one-sided preferences, where houses serve as a proxy encoding valuable indivisible resources (e.g. organs, course seats, subsidized public housing units) to be allocated among the agents. Every agent must receive exactly one resource. We study algorithmic approaches towards ensuring fairness in such settings. Minimizing the number of envious agents is known to be NP-complete (Kamiyama et al. 2021). We present two tractable approaches to deal with the computational hardness. When the agents are presented with an initial allocation of houses, we aim to refine this allocation by reallocating a bounded number of houses to reduce the number of envious agents. We show an efficient algorithm when the agents express preference for a bounded number of houses. Next, we consider single peaked preference domain and present a polynomial time algorithm for finding an allocation that minimize the number of envious agents. We further extend it to satisfy Pareto efficiency. Our former algorithm works for other measures of envy such as total envy, or maximum envy, with suitable modifications. Finally, we present an empirical analysis recording the fairness-welfare trade-off of our algorithms.", "published": "2025-11-10T12:09:27Z", "authors": ["Hadi Hosseini", "Sanjukta Roy", "Aditi Sethia"], "categories": ["cs.GT"], "doi": "", "_query": "hedonic house price", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2306.15039v1", "title": "An Integral-based Approach for the Vector Potential in Smoothed Particle Magnetohydrodynamics", "abstract": "A new implementation for the time evolution of the magnetic vector potential is obtained for smoothed particle magnetohydrodynamics by considering the induction equation in integral form. Galilean invariance is achieved through proper gauge choice. This new discretisation is tested using the Orszag-Tang MHD vortex in a 3D configuration. The corresponding conservative equations of motion are derived, but are not found to solve the MHD equations in the continuum limit. Tests are performed using a hybrid approach instead, whereby the equations of motion based on the magnetic field instead of vector potential are used. Test results experience the same numerical instability as with the Price (2010) formulation. We conclude that this new formulation is non-viable.", "published": "2023-06-26T19:57:49Z", "authors": ["Terrence S. Tricco", "Daniel J. Price"], "categories": ["astro-ph.IM", "physics.plasm-ph"], "doi": "", "_query": "hedonic house price", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2402.10815v1", "title": "Core Stability in Additively Separable Hedonic Games of Low Treewidth", "abstract": "Additively Separable Hedonic Game (ASHG) are coalition-formation games where we are given a graph whose vertices represent $n$ selfish agents and the weight of each edge $uv$ denotes how much agent $u$ gains (or loses) when she is placed in the same coalition as agent $v$. We revisit the computational complexity of the well-known notion of core stability of ASHGs, where the goal is to construct a partition of the agents into coalitions such that no group of agents would prefer to diverge from the given partition and form a new (blocking) coalition. Since both finding a core stable partition and verifying that a given partition is core stable are intractable problems ($Σ_2^p$-complete and coNP-complete respectively) we study their complexity from the point of view of structural parameterized complexity, using standard graph-theoretic parameters, such as treewidth.", "published": "2024-02-16T16:39:14Z", "authors": ["Tesshu Hanaka", "Noleen Köhler", "Michael Lampis"], "categories": ["cs.DS", "cs.CC", "cs.GT"], "doi": "", "_query": "hedonic house price", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2608.29843v1", "title": "The Price of Intelligence: A Quality-Adjusted Price Index for AI Services", "abstract": "Posted prices for AI inference have fallen steadily since 2024, yet the measured speed of that fall depends almost entirely on the method of measurement. This paper constructs quality-adjusted price indices for the AI inference market from public data. The panel assembles 21,024 posted-price observations across 3,208 models and 86 providers and joins them to 4,605 benchmark scores through a latent quality index estimated from benchmark response patterns, so the quality ladder of the hedonic tradition is built here from evaluations in place of product characteristics. Measured by the matched-model methods that statistical agencies apply to software, inference prices fell at 0.10 log points a year. The quality-adjusted index fell at 0.73, so 87% of the decline is invisible to current methods, with direct consequences for measured competition, concentration and productivity in this market. Counted per completed task, moreover, the buyer's price stopped falling. Reasoning models raised token consumption faster than token prices fell, and the seller's and buyer's prices accordingly diverged. A pre-registered validity audit disciplines the quality measure and yields the sharpest result. Excluding contamination-flagged benchmarks leaves model rankings intact at 0.998 yet moves the index by 0.49 log points a year, so the leaderboard-stability arguments standard in AI evaluation offer no defence of economic statistics built on benchmarks. Prices, quality and the audit are fully reproducible from public sources at zero cost.", "published": "2026-08-30T15:17:14Z", "authors": ["Louis Yiven Zhu"], "categories": ["econ.GN", "cs.LG"], "doi": "", "_query": "hedonic house price", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2201.13158v1", "title": "FEN-Hedonic Games with Distance-Based Preferences", "abstract": "Hedonic games formalize coalition formation scenarios where players evaluate an outcome based on the coalition they are contained in. Due to a large number of possible coalitions, compact representations of these games are crucial. We complement known compact representation models by a distance-based approach: Players' preferences are encoded in a bipolar manner by ordinal preferences over a small set of known neighbouring players, coalitions are represented by adequate preference orders from a player's perspective, and preferences over coalitions are extended based on a directed form of Hausdorff-Kendall-tau distance between individual preferences and coalitions. We show that this model satisfies desirable axiomatic properties and has reasonable computational complexity in terms of selected individual-based stability notions.", "published": "2022-01-31T12:07:16Z", "authors": ["Anja Rey", "Lisa Rey"], "categories": ["cs.GT"], "doi": "", "_query": "hedonic house price", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2501.15916v2", "title": "Online Housing Market", "abstract": "This paper studies an online variant of the celebrated housing market problem, where each agent has a single house and seeks to exchange it for another based on her preferences. In this online setting, agents may arrive and depart at any time, meaning that not all agents are present on the housing market simultaneously. I extend the well known serial dictatorship and Gale s top trading cycle mechanisms to this online scenario, aiming to retain their desirable properties such as Pareto efficiency, individual rationality, and strategy proofness. These extensions also seek to prevent agents from strategically delaying their arrival or advancing their departure. I demonstrate that achieving all of these properties simultaneously is impossible in the online context, and I present several variants that achieve different subsets of these properties.", "published": "2025-01-27T10:05:49Z", "authors": ["Julien Lesca"], "categories": ["cs.GT", "cs.AI"], "doi": "", "_query": "hedonic house price", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/1905.03111v1", "title": "Parallel and Distributed Algorithms for the housing allocation Problem", "abstract": "We give parallel and distributed algorithms for the housing allocation problem. In this problem, there is a set of agents and a set of houses. Each agent has a strict preference list for a subset of houses. We need to find a matching such that some criterion is optimized. One such criterion is Pareto Optimality. A matching is Pareto optimal if no coalition of agents can be strictly better off by exchanging houses among themselves. We also study the housing market problem, a variant of the housing allocation problem, where each agent initially owns a house. In addition to Pareto optimality, we are also interested in finding the core of a housing market. A matching is in the core if there is no coalition of agents that can be better off by breaking away from other agents and switching houses only among themselves. In the first part of this work, we show that computing a Pareto optimal matching of a house allocation is in {\\bf CC} and computing the core of a housing market is {\\bf CC}-hard. Given a matching, we also show that verifying whether it is in the core can be done in {\\bf NC}. We then give an algorithm to show that computing a maximum Pareto optimal matching for the housing allocation problem is in {\\bf RNC}^2 and quasi-{\\bf NC}^2. In the second part of this work, we present a distributed version of the top trading cycle algorithm for finding the core of a housing market. To that end, we first present two algorithms for finding all the disjoint cycles in a functional graph: a Las Vegas algorithm which terminates in $O(\\log l)$ rounds with high probability, where $l$ is the length of the longest cycle, and a deterministic algorithm which terminates in $O(\\log^* n \\log l)$ rounds, where $n$ is the number of nodes in the graph. Both algorithms work in the synchronous distributed model and use messages of size $O(\\log n)$.", "published": "2019-05-08T14:46:32Z", "authors": ["Xiong Zheng", "Vijay Garg"], "categories": ["cs.DC"], "doi": "", "_query": "hedonic house price", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2001.10772v1", "title": "On Maximizing Egalitarian Value in K-coalitional Hedonic Games", "abstract": "This paper considers the problem of dividing agents among coalitions. We concentrate on Additively Separable Hedonic Games (ASHG's), in which each agent has a non-negative value for every other agent and her utility is the sum of the values she assigns to the members of her coalition. Unlike previous work, we analyze a model where exactly $k$ coalitions must be formed, and the goal is to maximize the utility of the agent which is worst off, i.e., the egalitarian social welfare. We show that this problem is hard, even when the number of agents should be equally divided among the coalitions. We thus propose a heuristic that maximizes the egalitarian social welfare and maximizes the average utility of each agent as a secondary goal. Using extensive simulations, both on synthetic and real data, we demonstrate the effectiveness of our approach. Specifically, our heuristic provides solutions that are much fairer than the solutions that maximize the average social welfare, while still providing a relatively high average social welfare.", "published": "2020-01-29T12:11:31Z", "authors": ["Naftali Waxman", "Sarit Kraus", "Noam Hazon"], "categories": ["cs.GT"], "doi": "", "_query": "hedonic house price", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/nlin/0505043v1", "title": "A network analysis of committees in the United States House of Representatives", "abstract": "Network theory provides a powerful tool for the representation and analysis of complex systems of interacting agents. Here we investigate the United States House of Representatives network of committees and subcommittees, with committees connected according to ``interlocks'' or common membership. Analysis of this network reveals clearly the strong links between different committees, as well as the intrinsic hierarchical structure within the House as a whole. We show that network theory, combined with the analysis of roll call votes using singular value decomposition, successfully uncovers political and organizational correlations between committees in the House without the need to incorporate other political information.", "published": "2005-05-17T15:30:38Z", "authors": ["Mason A. Porter", "Peter J. Mucha", "M. E. J. Newman", "Casey M. Warmbrand"], "categories": ["nlin.AO", "cs.MA", "math.ST", "physics.data-an", "physics.soc-ph"], "doi": "10.1073/pnas.0500191102", "_query": "hedonic house price", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/1802.08238v1", "title": "What are the most important factors that influence the changes in London Real Estate Prices? How to quantify them?", "abstract": "In recent years, real estate industry has captured government and public attention around the world. The factors influencing the prices of real estate are diversified and complex. However, due to the limitations and one-sidedness of their respective views, they did not provide enough theoretical basis for the fluctuation of house price and its influential factors. The purpose of this paper is to build a housing price model to make the scientific and objective analysis of London's real estate market trends from the year 1996 to 2016 and proposes some countermeasures to reasonably control house prices. Specifically, the paper analyzes eight factors which affect the house prices from two aspects: housing supply and demand and find out the factor which is of vital importance to the increase of housing price per square meter. The problem of a high level of multicollinearity between them is solved by using principal components analysis.", "published": "2018-02-22T18:50:51Z", "authors": ["Yiyang Gu"], "categories": ["stat.AP", "q-fin.GN"], "doi": "", "_query": "hedonic house price", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2303.08225v1", "title": "Graph Transformer GANs for Graph-Constrained House Generation", "abstract": "We present a novel graph Transformer generative adversarial network (GTGAN) to learn effective graph node relations in an end-to-end fashion for the challenging graph-constrained house generation task. The proposed graph-Transformer-based generator includes a novel graph Transformer encoder that combines graph convolutions and self-attentions in a Transformer to model both local and global interactions across connected and non-connected graph nodes. Specifically, the proposed connected node attention (CNA) and non-connected node attention (NNA) aim to capture the global relations across connected nodes and non-connected nodes in the input graph, respectively. The proposed graph modeling block (GMB) aims to exploit local vertex interactions based on a house layout topology. Moreover, we propose a new node classification-based discriminator to preserve the high-level semantic and discriminative node features for different house components. Finally, we propose a novel graph-based cycle-consistency loss that aims at maintaining the relative spatial relationships between ground truth and predicted graphs. Experiments on two challenging graph-constrained house generation tasks (i.e., house layout and roof generation) with two public datasets demonstrate the effectiveness of GTGAN in terms of objective quantitative scores and subjective visual realism. New state-of-the-art results are established by large margins on both tasks.", "published": "2023-03-14T20:35:45Z", "authors": ["Hao Tang", "Zhenyu Zhang", "Humphrey Shi", "Bo Li", "Ling Shao", "Nicu Sebe", "Radu Timofte", "Luc Van Gool"], "categories": ["cs.CV", "cs.AI"], "doi": "", "_query": "hedonic house price", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2607.17633v1", "title": "Social Integration and Housing Behaviours of Immigrants: Evidence from Singapore's Public Housing Market", "abstract": "This study investigates the impact of social integration on immigrants' housing behaviours from a temporal perspective, using Singapore's differential public housing policies on immigrants as a quasi-natural experiment. With the support of a local town council, we conducted a survey on social integration among 1,128 immigrant and local households living in public housing estates. In the public open rental housing market - primarily accommodating yet-to-integrate immigrants - we find immigrant renters live up to 3.04% farther from their workplace and pay lower rents up to 0.67% per additional year of residency. Such impacts are more substantial among minority ethnic groups. The results remain robust when using alternative subjective or objective measures of social integration. However, in the public resale housing market - primarily accommodating native and well-integrated naturalised citizens - we find that naturalised citizens face no price premiums relative to native homebuyers, implying no further effect of integration on housing prices after well-integration. This study extends the literature of spatial assimilation focusing on ethnic residential segregations and is generalizable to cities with few ethnic enclaves.", "published": "2026-07-20T07:40:03Z", "authors": ["Yi Fan", "Ho Pin Teo", "Yong Tu", "Wayne Xinwei Wan"], "categories": ["econ.GN"], "doi": "10.1016/j.cities.2026.107220", "_query": "hedonic house price", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2402.08484v2", "title": "The Computational Complexity of the Housing Market", "abstract": "We prove that the classic problem of finding a competitive equilibrium in an exchange economy with indivisible goods, money, and unit-demand agents is PPAD-complete. In this \"housing market\", agents have preferences over the house and amount of money they end up with, but can experience income effects. Our results contrast with the existence of polynomial-time algorithms for related problems: Top Trading Cycles for the \"housing exchange\" problem in which there are no transfers and the Hungarian algorithm for the \"housing assignment\" problem in which agents' utilities are linear in money. Along the way, we prove that the Rainbow-KKM problem, a total search problem based on a generalization by Gale of the Knaster-Kuratowski-Mazurkiewicz lemma, is PPAD-complete. Our reductions also imply bounds on the query complexity of finding competitive equilibrium.", "published": "2024-02-13T14:20:41Z", "authors": ["Edwin Lock", "Zephyr Qiu", "Alexander Teytelboym"], "categories": ["cs.GT", "cs.CC"], "doi": "", "_query": "hedonic house price", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/1811.06418v1", "title": "Adversarial Examples from Cryptographic Pseudo-Random Generators", "abstract": "In our recent work (Bubeck, Price, Razenshteyn, arXiv:1805.10204) we argued that adversarial examples in machine learning might be due to an inherent computational hardness of the problem. More precisely, we constructed a binary classification task for which (i) a robust classifier exists; yet no non-trivial accuracy can be obtained with an efficient algorithm in (ii) the statistical query model. In the present paper we significantly strengthen both (i) and (ii): we now construct a task which admits (i') a maximally robust classifier (that is it can tolerate perturbations of size comparable to the size of the examples themselves); and moreover we prove computational hardness of learning this task under (ii') a standard cryptographic assumption.", "published": "2018-11-15T15:08:12Z", "authors": ["Sébastien Bubeck", "Yin Tat Lee", "Eric Price", "Ilya Razenshteyn"], "categories": ["cs.LG", "cs.CC", "cs.CR", "stat.ML"], "doi": "", "_query": "hedonic house price", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/1811.01295v4", "title": "The reciprocal algebraic integers having small houses", "abstract": "Let $α$ be an algebraic integer of degree $d$, which is reciprocal. The house of $α$ is the largest modulus of its conjugates. We proved that $d$-th power of the house of reciprocal $α$ has a limit point. We presented a property of antireciprocal hexanomials. We compute the minimum of the houses of all reciprocal algebraic integers of degree $d$ having the minimal polynomial which is a factor of a $D$-th degree reciprocal or antireciprocal polynomial with at most eight monomials, say $\\mathrm{mr}(d)$, for $d$ at most 180, $D\\le 1.5d$ and $D\\le 210$. We show that it is not necessary to take into account unprimitive polynomials. The computations suggest several conjectures.", "published": "2018-11-03T22:49:11Z", "authors": ["Dragan Stankov"], "categories": ["math.NT"], "doi": "10.1080/10586458.2021.1982425", "_query": "hedonic house price", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/1209.2540v1", "title": "Modelling of c-C2H4O formation on grain surfaces", "abstract": "Despite its potential reactivity due to ring strain, ethylene oxide (c-C2H4O) is a complex molecule that seems to be stable under the physical conditions of an interstellar dense core; indeed it has been detected towards several high-mass star forming regions with a column density of the order of 10e13cm-2 (Ikeda et al. 2001). To date, its observational abundances cannot be reproduced by chemical models and this may be due to the significant contribution played by its chemistry on grain surfaces. Recently, Ward and Price (2011) have performed experiments in order to investigate the surface formation of ethylene oxide starting with oxygen atoms and ethylene ice as reactants. We present a chemical model which includes the most recent experimental results from Ward and Price (2011) on the formation of c-C2H4O. We study the influence of the physical parameters of dense cores on the abundances of c-C2H4O. We verify that ethylene oxide can indeed be formed during the cold phase (when the ISM dense cores are formed), via addition of an oxygen atom across the C=C double bond of the ethylene molecule, and released by thermal desorption during the hot core phase. A qualitative comparison between our theoretical results and those from the observations shows that we are able to reproduce the abundances of ethylene oxide towards high-mass star-forming regions.", "published": "2012-09-12T09:53:52Z", "authors": ["A. Occhiogrosso", "S. Viti", "M. D. Ward", "S. D. Price"], "categories": ["astro-ph.GA", "astro-ph.SR"], "doi": "10.1111/j.1365-2966.2012.22112.x", "_query": "hedonic house price", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/1905.13578v1", "title": "Acoustics of Margravial Opera House Bayreuth", "abstract": "The Margravial Opera House Bayreuth, built between 1745 and 1750, is a well preserved Baroque court theatre designed by Giuseppe Galli Bibiena [1]. It provides an opportunity to experience not only the visual but also the acoustic design of opera theatres in the 18th century, as the bell-shaped auditorium along with the decoratively painted canvas remains intact. Using balloons and hand-claps as sound sources, we characterize the impulse response of this opera house after its recent renovation. The reverberation time (RT), early decay time (EDT) and clarity factor are characterized and discussed in comparison to historical Italian theatres of a similar age.", "published": "2019-05-31T12:34:08Z", "authors": ["Sebastian Krauss", "Simeon Völkel", "Christoph Dobner", "Alexandra Völkel", "Kai Huang"], "categories": ["physics.pop-ph"], "doi": "", "_query": "hedonic house price", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/1509.03915v3", "title": "An Impossibility Result for Housing Markets with Fractional Endowments", "abstract": "The housing market setting constitutes a fundamental model of exchange economies of goods. Most of the work concerning housing markets does not cater for randomized assignments or allocation of time-shares. House allocation with fractional endowments of houses was considered by Athanassoglou and Sethuraman (2011) who posed the open problem whether individual rationality, weak strategyproofness, and efficiency are compatible for the setting. We show that the three axioms are incompatible.", "published": "2015-09-14T00:22:54Z", "authors": ["Haris Aziz"], "categories": ["cs.GT", "cs.DS"], "doi": "", "_query": "hedonic house price", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2408.12523v1", "title": "Weighted Envy-Freeness in House Allocation", "abstract": "The classic house allocation problem involves assigning $m$ houses to $n$ agents based on their utility functions, ensuring each agent receives exactly one house. A key criterion in these problems is satisfying fairness constraints such as envy-freeness. We extend this problem by considering agents with arbitrary weights, focusing on the concept of weighted envy-freeness, which has been extensively studied in fair division. We present a polynomial-time algorithm to determine whether weighted envy-free allocations exist and, if so, to compute one. Since weighted envy-free allocations do not always exist, we also investigate the potential of achieving such allocations through the use of subsidies. We provide several characterizations for weighted envy-freeable allocations (allocations that can be turned weighted envy-free by introducing subsidies) and show that they do not always exist, which is different from the unweighted setting. Furthermore, we explore the existence of weighted envy-freeable allocations in specific scenarios and outline the conditions under which they exist.", "published": "2024-08-22T16:26:47Z", "authors": ["Sijia Dai", "Yankai Chen", "Xiaowei Wu", "Yicheng Xu", "Yong Zhang"], "categories": ["cs.GT"], "doi": "", "_query": "hedonic house price", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2003.06988v1", "title": "House-GAN: Relational Generative Adversarial Networks for Graph-constrained House Layout Generation", "abstract": "This paper proposes a novel graph-constrained generative adversarial network, whose generator and discriminator are built upon relational architecture. The main idea is to encode the constraint into the graph structure of its relational networks. We have demonstrated the proposed architecture for a new house layout generation problem, whose task is to take an architectural constraint as a graph (i.e., the number and types of rooms with their spatial adjacency) and produce a set of axis-aligned bounding boxes of rooms. We measure the quality of generated house layouts with the three metrics: the realism, the diversity, and the compatibility with the input graph constraint. Our qualitative and quantitative evaluations over 117,000 real floorplan images demonstrate that the proposed approach outperforms existing methods and baselines. We will publicly share all our code and data.", "published": "2020-03-16T03:16:12Z", "authors": ["Nelson Nauata", "Kai-Hung Chang", "Chin-Yi Cheng", "Greg Mori", "Yasutaka Furukawa"], "categories": ["cs.CV"], "doi": "", "_query": "hedonic house price", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/1111.3089v1", "title": "Dusty gas with SPH - II. Implicit timestepping and astrophysical drag regimes", "abstract": "In a companion paper (Laibe & Price 2011b), we have presented an algorithm for simulating two-fluid gas and dust mixtures in Smoothed Particle Hydrodynamics (SPH). In this paper, we develop an implicit timestepping method that preserves the exact conservation of the both linear and angular momentum in the underlying SPH algorithm, but unlike previous schemes, allows the iterations to converge to arbitrary accuracy and is suited to the treatment of non- linear drag regimes. The algorithm presented in Paper I is also extended to deal with realistic astrophysical drag regimes, including both linear and non-linear Epstein and Stokes drag. The scheme is benchmarked against the test suite presented in Paper I, including i) the analytic solutions of the dustybox problem and ii) solutions of the dustywave, dustyshock, dustysedov and dustydisc obtained with explicit timestepping. We find that the implicit method is 1- 10 times faster than the explicit temporal integration when the ratio r between the the timestep and the drag stopping time is 1 < r < 1000.", "published": "2011-11-14T02:28:49Z", "authors": ["Guillaume Laibe", "Daniel J. Price"], "categories": ["astro-ph.IM"], "doi": "10.1111/j.1365-2966.2011.20201.x", "_query": "hedonic house price", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/1911.08669v1", "title": "Individual-Based Stability in Hedonic Diversity Games", "abstract": "In hedonic diversity games (HDGs), recently introduced by Bredereck, Elkind and Igarashi (2019), each agent belongs to one of two classes (men and women, vegetarians and meat-eaters, junior and senior researchers), and agents' preferences over coalitions are determined by the fraction of agents from their class in each coalition. Bredereck et al. show that while an HDG may fail to have a Nash stable (NS) or a core stable (CS) outcome, every HDG in which all agents have single-peaked preferences admits an individually stable (IS) outcome, which can be computed in polynomial time. In this work, we extend and strengthen these results in several ways. First, we establish that the problem of deciding if an HDG has an NS outcome is NP-complete, but admits an XP algorithm with respect to the size of the smaller class. Second, we show that, in fact, all HDGs admit IS outcomes that can be computed in polynomial time; our algorithm for finding such outcomes is considerably simpler than that of Bredereck et al. We also consider two ways of generalizing the model of Bredereck et al. to k > 2 classes. We complement our theoretical results by empirical analysis, comparing the IS outcomes found by our algorithm, the algorithm of Bredereck et al. and a natural better-response dynamics.", "published": "2019-11-20T02:29:07Z", "authors": ["Niclas Boehmer", "Edith Elkind"], "categories": ["cs.GT"], "doi": "", "_query": "hedonic house price", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2208.01969v7", "title": "Regulation and Frontier Housing Supply", "abstract": "Regulation is a major driver of housing supply, yet often difficult to observe directly. We show that frontier cost, the non-land cost of producing housing absent regulation, is identified from prices and quantities alone, without instruments even when quantity is endogenous in a mean regression. Identification requires regulation to enter as a nonnegative wedge with zero in its conditional support. The difference between price and frontier cost yields the regulatory tax. We apply the approach to new multi-floor, multi-family residential construction in Israel. Accounting for random housing quality, we estimate economies of scale at low heights (minimum efficient scale about five floors), nearly constant marginal cost at middle heights, and an elasticity of substitution between land and non-land inputs of about 0.15 to 0.2 at the greatest heights. The estimated mean regulatory tax is 47% of housing prices, with substantial variation across locations, and is positively correlated with centrality, density, and prices. We also construct a lower bound allowing quality to differ systematically over location and time, assuming weak complementarity between quality and demand. In 2017, when prices were highest in our sample and the bound is most informative, we bound the mean regulatory tax between 38% (using a 2km radius) and 53%.", "published": "2022-08-03T10:45:41Z", "authors": ["Dan Ben-Moshe", "David Genesove"], "categories": ["econ.GN"], "doi": "", "_query": "hedonic house price", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2207.11690v2", "title": "HouseX: A Fine-grained House Music Dataset and its Potential in the Music Industry", "abstract": "Machine sound classification has been one of the fundamental tasks of music technology. A major branch of sound classification is the classification of music genres. However, though covering most genres of music, existing music genre datasets often do not contain fine-grained labels that indicate the detailed sub-genres of music. In consideration of the consistency of genres of songs in a mixtape or in a DJ (live) set, we have collected and annotated a dataset of house music that provide 4 sub-genre labels, namely future house, bass house, progressive house and melodic house. Experiments show that our annotations well exhibit the characteristics of different categories. Also, we have built baseline models that classify the sub-genre based on the mel-spectrograms of a track, achieving strongly competitive results. Besides, we have put forward a few application scenarios of our dataset and baseline model, with a simulated sci-fi tunnel as a short demo built and rendered in a 3D modeling software, with the colors of the lights automated by the output of our model.", "published": "2022-07-24T08:19:19Z", "authors": ["Xinyu Li"], "categories": ["cs.SD", "cs.LG", "eess.AS"], "doi": "", "_query": "hedonic house price", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2604.14124v1", "title": "Icy Volatile Enhancements in Evolving Protoplanetary Disks", "abstract": "Protoplanetary disk ice lines shape a multitude of planet formation processes, setting the environmental composition through evolution. Ice line locations depend on molecular sublimation and deposition properties, but in dynamic disks where temperature and density structures change, so do the expected compositions of planets and planetesimals. In turbulent viscous disks with particle drift, thermal evolution, and desorption/adsorption, Price et al. 2021 demonstrated that the CO/H$_2$O ice ratio beyond the CO ice line can become enhanced by $\\sim10\\times$. We expand on their work by incorporating additional carbon, nitrogen, and oxygen species, more particle sizes, and a broader disk parameter exploration. We find that before $\\sim0.5$Myr, volatile ices are enhanced relative to H$_2$O as the outer disk is desiccated by drift, while at later disk times outward advection and volatile deposition further increase relative volatile icy enhancements beyond the evolving critical disk radius. The outcome of these combined relative icy enhancement to H$_2$O mechanisms is solid C/O $\\sim$ N/O $\\sim1$ beyond the hypervolatile ice lines, much higher than expected in static disks. Hypervolatiles (N$_2$, CO, and CH$_4$) robustly increase to $\\sim100\\times$ across the explored parameter space, while mid-volatiles (CO$_2$ and NH$_3$) are sensitive to model choices, with enhancements ranging from $\\sim2-50\\times$. Together these results demonstrate that coupling disk dynamics with simple sublimation and deposition chemistry is fundamental to predicting grain, planetesimal, and planetary compositions, particularly the role of advection in redistributing volatiles across disk radii.", "published": "2026-04-15T17:49:26Z", "authors": ["Elizabeth Yunerman", "Ellen Price", "Karin Öberg"], "categories": ["astro-ph.EP"], "doi": "10.3847/1538-4357/ae60fe", "_query": "hedonic house price", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2606.02795v1", "title": "Recovering Direct Price Effects of Environmental Amenities in Housing Markets: Regression and Causal Machine Learning Model Assessment with Empirical Monte Carlo Simulation", "abstract": "Hedonic price models are widely used to assess how environmental amenities affect property values, yet methodological guidance for estimating direct price effects remains sparse. We conduct an empirical Monte Carlo simulation to evaluate the performance of traditional and causal machine learning approaches for estimating the direct unmediated price effect of spatially delineated amenities on treated properties (DUET), a conservative lower-bound approximation for welfare changes with direct applications to benefit-cost analysis. Where previous simulations rely on parametric assumptions, we retain the actual data-generating process underlying over 1 million property transactions from upstate New York (1990--2024). By randomly assigning \"treatment locations\" across iterations we establish a \"ground truth\" that allows us to precisely measure estimation error. Our results demonstrate that generalized difference-in-differences (DID) regression consistently outperforms baseline DID and two-way fixed effects models across all scenarios. Causal Machine Learning (CML) methods, particularly causal forest DID, achieve comparable performance to generalized DID in most scenarios. In larger samples (above 3,000 treated) increasingly common in contemporary hedonic studies, CML approaches offer substantial advantages when properly specified. Based on empirical simulation results, we provide a set of method-specific best practice recommendations for both traditional regression and causal machine learning approaches.", "published": "2026-06-01T18:59:44Z", "authors": ["Zhenshan Chen", "Klaus Moeltner", "Matthew Mair"], "categories": ["econ.EM", "stat.ML"], "doi": "", "_query": "hedonic house price", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/cs/0101035v2", "title": "The Guidebook, the Friend, and the Room: Visitor Experience in a Historic House", "abstract": "In this paper, we describe an electronic guidebook prototype and report on a study of its use in a historic house. Supported by mechanisms in the guidebook, visitors constructed experiences that had a high degree of interaction with three entities: the guidebook, their companions, and the house and its contents. For example, we found that most visitors played audio descriptions played through speakers (rather than using headphones or reading textual descriptions) to facilitate communication with their companions.", "published": "2001-01-28T07:14:46Z", "authors": ["Allison Woodruff", "Paul M. Aoki", "Amy Hurst", "Margaret H. Szymanski"], "categories": ["cs.HC"], "doi": "10.1145/634067.634229", "_query": "hedonic house price", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2601.20079v1", "title": "Techno-economic optimization of a heat-pipe microreactor, part II: multi-objective optimization analysis", "abstract": "Heat-pipe microreactors (HPMRs) are compact and transportable nuclear power systems exhibiting inherent safety, well-suited for deployment in remote regions where access is limited and reliance on costly fossil fuels is prevalent. In prior work, we developed a design optimization framework that incorporates techno-economic considerations through surrogate modeling and reinforcement learning (RL)-based optimization, focusing solely on minimizing the levelized cost of electricity (LCOE) by using a bottom-up cost estimation approach. In this study, we extend that framework to a multi-objective optimization that uses the Pareto Envelope Augmented with Reinforcement Learning (PEARL) algorithm. The objectives include minimizing both the rod-integrated peaking factor ($F_{Δh}$) and LCOE -- subject to safety and operational constraints. We evaluate three cost scenarios: (1) a high-cost axial and drum reflectors, (2) a low-cost axial reflector, and (3) low-cost axial and drum reflectors. Our findings indicate that reducing the solid moderator radius, pin pitch, and drum coating angle -- all while increasing the fuel height -- effectively lowers $F_{Δh}$. Across all three scenarios, four key strategies consistently emerged for optimizing LCOE: (1) minimizing the axial reflector contribution when costly, (2) reducing control drum reliance, (3) substituting expensive tri-structural isotropic (TRISO) fuel with axial reflector material priced at the level of graphite, and (4) maximizing fuel burnup. While PEARL demonstrates promise in navigating trade-offs across diverse design scenarios, discrepancies between surrogate model predictions and full-order simulations remain. Further improvements are anticipated through constraint relaxation and surrogate development, constituting an ongoing area of investigation.", "published": "2026-01-27T21:54:25Z", "authors": ["Paul Seurin", "Dean Price"], "categories": ["cs.LG", "physics.comp-ph"], "doi": "", "_query": "hedonic house price", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2303.10832v1", "title": "A Strategy-proof Mechanism For Networked Housing Markets", "abstract": "This paper studies a house allocation problem in a networked housing market, where agents can invite others to join the system in order to enrich their options. Top Trading Cycle is a well-known matching mechanism that achieves a set of desirable properties in a market without invitations. However, under a tree-structured networked market, existing agents have to strategically propagate the barter market as their invitees may compete in the same house with them. Our impossibility result shows that TTC cannot work properly in a networked housing market. Hence, we characterize the possible competitions between inviters and invitees, which lead agents to fail to refer others truthfully (strategy-proof). We then present a novel mechanism based on TTC, avoiding the aforementioned competition to ensure all agents report preference and propagate the barter market truthfully. Unlike the existing mechanisms, the agents' preferences are less restricted under our mechanism. Furthermore, we show by simulations that our mechanism outperforms the existing matching mechanisms in terms of the number of swaps and agents' satisfaction.", "published": "2023-03-20T02:35:41Z", "authors": ["Youjia Zhang", "Pingzhong Tang"], "categories": ["cs.GT"], "doi": "", "_query": "hedonic house price", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/1702.08251v1", "title": "Hessian corrections to Hybrid Monte Carlo", "abstract": "A method for the introduction of second-order derivatives of the log likelihood into HMC algorithms is introduced, which does not require the Hessian to be evaluated at each leapfrog step but only at the start and end of trajectories.", "published": "2017-02-27T12:12:46Z", "authors": ["Thomas House"], "categories": ["stat.CO"], "doi": "", "_query": "hedonic house price", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/1811.12466v1", "title": "Consistency of Forecasts for the U.S. House of Representatives", "abstract": "We consider the performance of the foremost academic House of Representatives forecasting models in the 2018 elections. In creating open-source implementations of these models, we outline key underlying assumptions. We find that although the results were unsurprising, they indicate a weakening of many traditional forecasting indicators.", "published": "2018-11-29T20:20:38Z", "authors": ["Henry Bendekgey"], "categories": ["stat.AP"], "doi": "", "_query": "hedonic house price", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2207.11332v2", "title": "Comparing baseball players across eras via novel Full House Modeling", "abstract": "A new methodological framework suitable for era-adjusting baseball statistics is developed in this article. Within this methodological framework specific models are motivated. We call these models Full House Models. Full House Models work by balancing the achievements of Major League Baseball (MLB) players within a given season and the size of the MLB talent pool from which a player came. We demonstrate the utility of Full House Models in an application of comparing baseball players' performance statistics across eras. Our results reveal a new ranking of baseball's greatest players which include several modern players among the top all-time players. Modern players are elevated by Full House Modeling because they come from a larger talent pool. Sensitivity and multiverse analyses which investigate the how results change with changes to modeling inputs including the estimate of the talent pool are presented.", "published": "2022-07-22T20:53:52Z", "authors": ["Shen Yan", "Adrian Burgos", "Christopher Kinson", "Daniel J. Eck"], "categories": ["stat.AP", "stat.ME"], "doi": "", "_query": "hedonic house price", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2109.01992v1", "title": "Balanced House Allocation", "abstract": "We introduce balancedness a fairness axiom in house allocation problems. It requires a mechanism to assign the top choice, the second top choice, and so on, on the same number of profiles for each agent. This axiom guarantees equal treatment of all agents at the stage in which the mechanism is announced when all preference profiles are equally likely. We show that, with an interesting exception for the three-agent case, Top Trading Cycles from individual endowments is the only mechanism that is balanced, efficient, and group strategy-proof.", "published": "2021-09-05T04:53:31Z", "authors": ["Xinghua Long", "Rodrigo A. Velez"], "categories": ["econ.TH", "cs.GT"], "doi": "", "_query": "hedonic house price", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2001.06457v3", "title": "Neglecting Uncertainties Biases House-Elevation Decisions to Manage Riverine Flood Risks", "abstract": "Homeowners around the world elevate houses to manage flood risks. Deciding how high to elevate a house poses a nontrivial decision problem. The U.S. Federal Emergency Management Agency (FEMA) recommends elevating existing houses to the Base Flood Elevation (the elevation of the 100-yr flood) plus a freeboard. This recommendation neglects many uncertainties. Here we analyze a case-study of riverine flood risk management using a multi-objective robust decision-making framework in the face of deep uncertainties. While the quantitative results are location-specific, the approach and overall insights are generalizable. We find strong interactions between the economic, engineering, and Earth science uncertainties, illustrating the need for expanding on previous integrated analyses to further understand the nature and strength of these connections. Considering deep uncertainties surrounding flood hazards, the discount rate, the house lifetime, and the fragility can increase the economically optimal house elevation to values well above FEMA recommendation.", "published": "2020-01-17T18:00:11Z", "authors": ["Mahkameh Zarekarizi", "Vivek Srikrishnan", "Klaus Keller"], "categories": ["stat.AP", "physics.data-an", "q-fin.ST"], "doi": "10.1038/s41467-020-19188-9", "_query": "hedonic house price", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2108.01368v2", "title": "Robust Compressed Sensing MRI with Deep Generative Priors", "abstract": "The CSGM framework (Bora-Jalal-Price-Dimakis'17) has shown that deep generative priors can be powerful tools for solving inverse problems. However, to date this framework has been empirically successful only on certain datasets (for example, human faces and MNIST digits), and it is known to perform poorly on out-of-distribution samples. In this paper, we present the first successful application of the CSGM framework on clinical MRI data. We train a generative prior on brain scans from the fastMRI dataset, and show that posterior sampling via Langevin dynamics achieves high quality reconstructions. Furthermore, our experiments and theory show that posterior sampling is robust to changes in the ground-truth distribution and measurement process. Our code and models are available at: \\url{https://github.com/utcsilab/csgm-mri-langevin}.", "published": "2021-08-03T08:52:06Z", "authors": ["Ajil Jalal", "Marius Arvinte", "Giannis Daras", "Eric Price", "Alexandros G. Dimakis", "Jonathan I. Tamir"], "categories": ["cs.LG", "cs.CV", "cs.IT", "stat.ML"], "doi": "", "_query": "hedonic house price", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/1407.3569v1", "title": "Dust and gas mixtures with multiple grain species - a one-fluid approach", "abstract": "We derive the single-fluid evolution equations describing a mixture made of a gas phase and an arbitrary number of dust phases, generalising the approach developed in Laibe & Price (2014a). A generalisation for continuous dust distributions as well as analytic approximations for strong drag regimes are also provided. This formalism lays the foundation for numeri- cal simulations of dust populations in a wide range of astrophysical systems while avoiding limitations associated with a multiple-fluid treatment. The usefulness of the formalism is illustrated on a series of analytical problems, namely the dustybox, dustyshock and dustywave problems as well as the radial drift of grains and the streaming instability in protoplanetary discs. We find physical effects specific to the presence of several dust phases and multiple drag timescales, including non-monotonic evolution of the differential velocity between phases and increased efficiency of the linear growth of the streaming instability. Interestingly, it is found that under certain conditions, large grains can migrate outwards in protoplanetary discs. This may explain the presence of small pebbles at several hundreds of astronomical units from their central star.", "published": "2014-07-14T08:51:39Z", "authors": ["Guillaume Laibe", "Daniel J. Price"], "categories": ["astro-ph.EP"], "doi": "10.1093/mnras/stu1367", "_query": "hedonic house price", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/1402.3646v1", "title": "Statistical analysis of the price and subjective quality ratings on Australian wines", "abstract": "Consumers have a wide choice regarding the purchase of wine. Wines vary across varieties, regions, years of vintage, alcohol content, price and expert ratings. In this study we explored the relationship between those variables and whether a combination of explanatory variables can predict outcome variables using data on selected 2710 Australian wines rates by the latest book of James Halliday1. We analysed two dependent (outcome) variables, namely the rating and the price, and their (non-linear) relationship between explanatory variables and each other. We observe that the rating scale is not linear, as consumers may believe, and that a narrow range of rating scores is used for the ascertained wines. Across all wines, approximately 20% of variation in log(Price) can be explained by the explanatory factors state, variety, vintage and alcohol percentage. If the residuals from the model for rating is also taken as an explanatory variable for price, then a total of 53% of variation in log(Price) is explained by the model. For those wines ascertained to be in Halliday1, there is more variability between white wines than between red wines that is explained by explanatory variables such as State and variety. In comparison with hedonic pricing analyses in previous studies on French and Australian wines, a smaller proportion of variation in price and rating was explained in Australian wines that were in the 2014 edition of Halliday.", "published": "2014-02-15T05:07:56Z", "authors": ["Peter M. Visscher"], "categories": ["stat.AP"], "doi": "", "_query": "hedonic house price", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/1707.05489v3", "title": "Vision-based Real Estate Price Estimation", "abstract": "Since the advent of online real estate database companies like Zillow, Trulia and Redfin, the problem of automatic estimation of market values for houses has received considerable attention. Several real estate websites provide such estimates using a proprietary formula. Although these estimates are often close to the actual sale prices, in some cases they are highly inaccurate. One of the key factors that affects the value of a house is its interior and exterior appearance, which is not considered in calculating automatic value estimates. In this paper, we evaluate the impact of visual characteristics of a house on its market value. Using deep convolutional neural networks on a large dataset of photos of home interiors and exteriors, we develop a method for estimating the luxury level of real estate photos. We also develop a novel framework for automated value assessment using the above photos in addition to home characteristics including size, offered price and number of bedrooms. Finally, by applying our proposed method for price estimation to a new dataset of real estate photos and metadata, we show that it outperforms Zillow's estimates.", "published": "2017-07-18T06:29:02Z", "authors": ["Omid Poursaeed", "Tomas Matera", "Serge Belongie"], "categories": ["cs.CV", "cs.LG"], "doi": "10.1007/s00138-018-0922-2", "_query": "hedonic house price", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2601.15864v2", "title": "Minimum Envy Graphical House Allocation Beyond Identical Valuations", "abstract": "House allocation is an extremely well-studied problem in the field of fair allocation, where the goal is to assign $n$ houses to $n$ agents while satisfying certain fairness criterion, e.g., envy-freeness. To model social interactions, the Graphical House Allocation framework introduces a social graph $G$, in which each vertex corresponds to an agent, and an edge $(u, v)$ corresponds to the potential of agent $u$ to envy the agent $v$, based on their allocations and valuations. In undirected social graphs, the potential for envy is in both the directions. In the Minimum Envy Graphical House Allocation (ME-GHA) problem, given a set of $n$ agents, $n$ houses, a social graph, and agent's valuation functions, the goal is to find an allocation that minimizes the total envy summed up over all the edges of $G$. Recent work, [Hosseini et al., AAMAS 2023, AAMAS 2024] studied ME-GHA in the regime of polynomial-time algorithms, and designed exact and approximation algorithms, for certain graph classes under identical agent valuations. We initiate the study of \\gha with non-identical valuations, a setting that has so far remained unexplored. We investigate the multivariate (parameterized) complexity of \\gha by identifying structural restrictions on the social graph and valuation functions that yield tractability. We also design moderately exponential-time algorithms for several graph classes, and a polynomial-time algorithm for {binary valuations that returns an allocation with envy at most one when the social graph has maximum degree at most one.", "published": "2026-01-22T11:12:09Z", "authors": ["Tanmay Inamdar", "Pallavi Jain", "Pranjal Pandey"], "categories": ["cs.GT", "cs.DS"], "doi": "", "_query": "hedonic house price", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2510.12641v1", "title": "Single-Deviation Stability in Additively Separable Hedonic Games with Constrained Coalition Sizes", "abstract": "We study stability in additively separable hedonic games when coalition sizes have to respect fixed size bounds. We consider four classic notions of stability based on single-agent deviations, namely, Nash stability, individual stability, contractual Nash stability, and contractual individual stability. For each stability notion, we consider two variants: in one, the coalition left behind by a deviator must still be of a valid size, and in the other there is no such constraint. We provide a full picture of the existence of stable outcomes with respect to given size parameters. Additionally, when there are only upper bounds, we fully characterize the computational complexity of the associated existence problem. In particular, we obtain polynomial-time algorithms for contractual individual stability and contractual Nash stability, where the latter requires an upper bound of 2. We obtain further results for Nash stability and contractual individual stability, when the lower bound is at least 2.", "published": "2025-10-14T15:33:59Z", "authors": ["Martin Bullinger", "Adam Dunajski", "Edith Elkind", "Matan Gilboa"], "categories": ["cs.GT", "cs.DS"], "doi": "", "_query": "hedonic house price", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2003.00397v1", "title": "Intelligent Home 3D: Automatic 3D-House Design from Linguistic Descriptions Only", "abstract": "Home design is a complex task that normally requires architects to finish with their professional skills and tools. It will be fascinating that if one can produce a house plan intuitively without knowing much knowledge about home design and experience of using complex designing tools, for example, via natural language. In this paper, we formulate it as a language conditioned visual content generation problem that is further divided into a floor plan generation and an interior texture (such as floor and wall) synthesis task. The only control signal of the generation process is the linguistic expression given by users that describe the house details. To this end, we propose a House Plan Generative Model (HPGM) that first translates the language input to a structural graph representation and then predicts the layout of rooms with a Graph Conditioned Layout Prediction Network (GC LPN) and generates the interior texture with a Language Conditioned Texture GAN (LCT-GAN). With some post-processing, the final product of this task is a 3D house model. To train and evaluate our model, we build the first Text-to-3D House Model dataset.", "published": "2020-03-01T04:28:48Z", "authors": ["Qi Chen", "Qi Wu", "Rui Tang", "Yuhan Wang", "Shuai Wang", "Mingkui Tan"], "categories": ["cs.CV"], "doi": "", "_query": "hedonic house price", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2202.06925v1", "title": "Hedonic Games and Treewidth Revisited", "abstract": "We revisit the complexity of the well-studied notion of Additively Separable Hedonic Games (ASHGs). Such games model a basic clustering or coalition formation scenario in which selfish agents are represented by the vertices of an edge-weighted digraph $G=(V,E)$, and the weight of an arc $uv$ denotes the utility $u$ gains by being in the same coalition as $v$. We focus on (arguably) the most basic stability question about such a game: given a graph, does a Nash stable solution exist and can we find it efficiently? We study the (parameterized) complexity of ASHG stability when the underlying graph has treewidth $t$ and maximum degree $Δ$. The current best FPT algorithm for this case was claimed by Peters [AAAI 2016], with time complexity roughly $2^{O(Δ^5t)}$. We present an algorithm with parameter dependence $(Δt)^{O(Δt)}$, significantly improving upon the parameter dependence on $Δ$ given by Peters, albeit with a slightly worse dependence on $t$. Our main result is that this slight performance deterioration with respect to $t$ is actually completely justified: we observe that the previously claimed algorithm is incorrect, and that in fact no algorithm can achieve dependence $t^{o(t)}$ for bounded-degree graphs, unless the ETH fails. This, together with corresponding bounds we provide on the dependence on $Δ$ and the joint parameter establishes that our algorithm is essentially optimal for both parameters, under the ETH. We then revisit the parameterization by treewidth alone and resolve a question also posed by Peters by showing that Nash Stability remains strongly NP-hard on stars under additive preferences. Nevertheless, we also discover an island of mild tractability: we show that Connected Nash Stability is solvable in pseudo-polynomial time for constant $t$, though with an XP dependence on $t$ which, as we establish, cannot be avoided.", "published": "2022-02-14T18:37:00Z", "authors": ["Tesshu Hanaka", "Michael Lampis"], "categories": ["cs.GT", "cs.CC", "cs.DS"], "doi": "", "_query": "hedonic house price", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2101.00913v1", "title": "Credit Crunch: The Role of Household Lending Capacity in the Dutch Housing Boom and Bust 1995-2018", "abstract": "What causes house prices to rise and fall? Economists identify household access to credit as a crucial factor. \"Loan-to-Value\" and \"Debt-to-GDP\" ratios are the standard measures for credit access. However, these measures fail to explain the depth of the Dutch housing bust after the 2009 Financial Crisis. This work is the first to model household lending capacity based on the formulas that Dutch banks use in the mortgage application process. We compare the ability of regression models to forecast housing prices when different measures of credit access are utilised. We show that our measure of household lending capacity is a forward-looking, highly predictive variable that outperforms `Loan-to-Value' and debt ratios in forecasting the Dutch crisis. Sharp declines in lending capacity foreshadow the market deceleration.", "published": "2021-01-04T12:10:07Z", "authors": ["Menno Schellekens", "Taha Yasseri"], "categories": ["stat.AP", "q-fin.GN"], "doi": "", "_query": "hedonic house price", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2601.21573v2", "title": "Characteristics Design: A Hedonic Approach to Optimal Product Differentiation", "abstract": "Building on the generalized hedonic-linear model of Pellegrino (2025), this paper studies optimal product differentiation when a representative consumer has preferences over product characteristics. Under multiproduct monopoly, the monopolist's choice of product characteristics is always aligned with the social planner's optimum, despite underproduction. By contrast, under oligopoly, multiple equilibria can arise that differ qualitatively in their patterns of characteristics design. We show that, while oligopoly equilibria exhibiting product differentiation yield higher welfare than those with product concentration, the degree of product differentiation under oligopoly remains below the socially optimal level. As a result, social welfare under oligopoly is typically lower than under monopoly, highlighting a key advantage of coordination in characteristics design. We extend the analysis to settings with overlapping ownership structures and show that common ownership can improve welfare by inducing firms to soften competition through increased product differentiation rather than output reduction.", "published": "2026-01-29T11:34:58Z", "authors": ["Masaki Miyashita"], "categories": ["econ.TH"], "doi": "", "_query": "hedonic house price", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2203.09655v2", "title": "Hedonic Games With Friends, Enemies, and Neutrals: Resolving Open Questions and Fine-Grained Complexity", "abstract": "We investigate verification and existence problems for prominent stability concepts in hedonic games with friends, enemies, and optionally with neutrals [8, 16]. We resolve several (long-standing) open questions [4, 16, 20, 23] and show that for friend-oriented preferences, under the friends and enemies model, it is coNP-complete to verify whether a given agent partition is (strictly) core stable, while under the friends, enemies, and neutrals model, it is NP-complete to determine whether an individual stable partition exists. We further look into natural restricted cases from the literature, such as when the friends and enemies relationships are symmetric, when the initial coalitions have bounded size, when the vertex degree in the friendship graph (resp. the union of friendship and enemy graph) is bounded, or when such graph is acyclic or close to being acyclic. We obtain a complete (parameterized) complexity picture regarding these cases.", "published": "2022-03-17T23:31:48Z", "authors": ["Jiehua Chen", "Gergely Csáji", "Sanjukta Roy", "Sofia Simola"], "categories": ["cs.GT", "cs.MA"], "doi": "", "_query": "hedonic house price", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2510.21044v1", "title": "House Thermal Model Estimation: Robustness Across Seasons and Setpoints", "abstract": "Achieving the flexibility from house heating, cooling, and ventilation systems (HVAC) has the potential to enable large-scale demand response by aggregating HVAC load adjustments across many homes. This demand response strategy helps distribution grid to flexibly ramp-up or ramp-down local load demand so that it can optimally match the bulk power system generation profile. However, achieving this capability requires house thermal models that are both computationally efficient and robust to operating conditions. In this work, parameters of the Resistance-Capacitance (RC) network thermal model for houses are estimated using three optimization algorithms: Nonlinear Least Squares (NLS), Batch Estimation (BE), and Maximum Likelihood Estimation (MLE). The resulting models are evaluated through a Forward-Simulation across four different seasons and three setpoints. The results illustrate a principled way of selecting reduced order models and estimation methods with respect to the robustness offered to seasonal and setpoint variations in training-testing datasets", "published": "2025-10-23T22:58:14Z", "authors": ["Kunal Shankar", "Ninad Gaikwad", "Anamika Dubey"], "categories": ["eess.SY"], "doi": "", "_query": "hedonic house price", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2311.11101v2", "title": "$\\varepsilon$-fractional Core Stability in Hedonic Games", "abstract": "Hedonic Games (HGs) are a classical framework modeling coalition formation of strategic agents guided by their individual preferences. According to these preferences, it is desirable that a coalition structure (i.e. a partition of agents into coalitions) satisfies some form of stability. The most well-known and natural of such notions is arguably core-stability. Informally, a partition is core-stable if no subset of agents would like to deviate by regrouping in a so-called core-blocking coalition. Unfortunately, core-stable partitions seldom exist and even when they do, it is often computationally intractable to find one. To circumvent these problems, we propose the notion of $\\varepsilon$-fractional core-stability, where at most an $\\varepsilon$-fraction of all possible coalitions is allowed to core-block. It turns out that such a relaxation may guarantee both existence and polynomial-time computation. Specifically, we design efficient algorithms returning an $\\varepsilon$-fractional core-stable partition, with $\\varepsilon$ exponentially decreasing in the number of agents, for two fundamental classes of HGs: Simple Fractional and Anonymous. From a probabilistic point of view, being the definition of $\\varepsilon$-fractional core equivalent to requiring that uniformly sampled coalitions core-block with probability lower than $\\varepsilon$, we further extend the definition to handle more complex sampling distributions. Along this line, when valuations have to be learned from samples in a PAC-learning fashion, we give positive and negative results on which distributions allow the efficient computation of outcomes that are $\\varepsilon$-fractional core-stable with arbitrarily high confidence.", "published": "2023-11-18T15:30:29Z", "authors": ["Simone Fioravanti", "Michele Flammini", "Bojana Kodric", "Giovanna Varricchio"], "categories": ["cs.GT", "cs.AI"], "doi": "", "_query": "hedonic house price", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/1004.1957v1", "title": "Modelling Shear Flows with SPH and Grid Based Methods", "abstract": "Given the importance of shear flows for astrophysical gas dynamics, we study the evolution of the Kelvin-Helmholtz instability (KHI) analytically and numerically. We derive the dispersion relation for the two-dimensional KHI including viscous dissipation. The resulting expression for the growth rate is then used to estimate the intrinsic viscosity of four numerical schemes depending on code-specific as well as on physical parameters. Our set of numerical schemes includes the Tree-SPH code VINE, an alternative SPH formulation developed by Price (2008), and the finite-volume grid codes FLASH and PLUTO. In the first part, we explicitly demonstrate the effect of dissipation-inhibiting mechanisms such as the Balsara viscosity on the evolution of the KHI. With VINE, increasing density contrasts lead to a continuously increasing suppression of the KHI (with complete suppression from a contrast of 6:1 or higher). The alternative SPH formulation including an artificial thermal conductivity reproduces the analytically expected growth rates up to a density contrast of 10:1. The second part addresses the shear flow evolution with FLASH and PLUTO. Both codes result in a consistent non-viscous evolution (in the equal as well as in the different density case) in agreement with the analytical prediction. The viscous evolution studied with FLASH shows minor deviations from the analytical prediction.", "published": "2010-04-12T13:55:19Z", "authors": ["Veronika Junk", "Stefanie Walch", "Fabian Heitsch", "Andreas Burkert", "Markus Wetzstein", "Marc Schartmann", "Daniel Price"], "categories": ["astro-ph.GA"], "doi": "10.1111/j.1365-2966.2010.17039.x", "_query": "hedonic house price", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2201.04003v1", "title": "Housing Market Forecasting using Home Showing Events", "abstract": "Both buyers and sellers face uncertainty in real estate transactions in about when to time a transaction and at what cost. Both buyers and sellers make decisions without knowing the present and future state of the large and dynamic real estate market. Current approaches rely on analysis of historic transactions to price a property. However, as we show in this paper, the transaction data alone cannot be used to forecast demand. We develop a housing demand index based on microscopic home showings events data that can provide decision-making support for buyers and sellers on a very granular time and spatial scale. We use statistical modeling to develop a housing market demand forecast up to twenty weeks using high-volume, high-velocity data on home showings, listing events, and historic sales data. We demonstrate our analysis using data from seven million individual records sourced from a unique, proprietary dataset that has not previously been explored in application to the real estate market. We then employ a series of predictive models to estimate current and forecast future housing demand. A housing demand index provides insight into the level of demand for a home on the market and to what extent current demand represents future expectation. As a result, these indices provide decision-making support into important questions about when to sell or buy, or the elasticity present in the housing demand market, which impact price negotiations, price-taking and price-setting expectations. This forecast is especially valuable because it helps buyers and sellers to know on a granular and timely basis if they should engage in a home transaction or adjust their home price both in current and future states based on our forecasted housing demand index.", "published": "2022-01-06T21:51:54Z", "authors": ["Yuanyuan Zha", "Susan T. Parker", "James J. Foster", "Vadim Sokolov"], "categories": ["stat.AP"], "doi": "", "_query": "hedonic house price", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2505.06163v2", "title": "The Power of Matching for Online Fractional Hedonic Games", "abstract": "We study coalition formation in the framework of fractional hedonic games (FHGs). The objective is to maximize social welfare in an online model where agents arrive one by one and must be assigned to coalitions immediately and irrevocably. A recurrent theme in online coalition formation is that online matching algorithms, where coalitions are restricted to size at most $2$, yield good competitive ratios. For example, computing maximal matchings achieves the optimal competitive ratio for general online FHGs. However, this ratio is bounded only if agents' valuations are themselves bounded. We identify optimal algorithms with constant competitive ratios in two related settings, independent of the range of agent valuations. First, under random agent arrival, we present an asymptotically optimal $(\\frac{1}{3}-\\frac 1n)$-competitive algorithm, where $n$ is the number of agents. This result builds on our identification of an optimal matching algorithm in a general model of online matching with edge weights and an unknown number of agents. In this setting, we also achieve an asymptotically optimal competitive ratio of $\\frac{1}{3}-\\frac 1n$. Second, when agents arrive in an arbitrary order but algorithms are allowed to irrevocably and entirely dissolve coalitions, we show that another matching-based algorithm achieves an optimal competitive ratio of $\\frac{1}{6 + 4\\sqrt{2}}$.", "published": "2025-05-09T16:13:35Z", "authors": ["Martin Bullinger", "René Romen", "Alexander Schlenga"], "categories": ["cs.GT", "cs.DS"], "doi": "", "_query": "hedonic house price", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2207.02896v1", "title": "Mortgage-Rate-Adjusted Home Prices", "abstract": "In this paper, we investigate the impact of mortgage rates on home prices, and how the impact may be used to help property purchase discussions at individual buyer level and to adjust home price indices across time. A mortgage-rate-adjusted \"effective price\" is derived to measure near term property price in the presence of (expected) mortgage rate changes. A price-mortgage rate neutrality line is then constructed based on the \"effective price\" to help differentiate various market scenarios in the near term, which can be used by prospective buyers in their \"to-buy or not-to-buy\" deliberations. At the market level, effective home prices allow for neutralization of mortgage rates on the movement of the housing market. An application of the neutralization strategy to the Case-Shiller Home Price Index (HPI) indicates that the U.S. housing market has been considerably affected by the dynamics of mortgage rates in a long run. But mortgage rates do no appear a primary driver of the extraordinary home price increase during the COVID-19 pandemic.", "published": "2022-07-06T18:16:44Z", "authors": ["Honggao Cao"], "categories": ["q-fin.GN"], "doi": "", "_query": "hedonic house price", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/1312.3552v1", "title": "Counting Popular Matchings in House Allocation Problems", "abstract": "We study the problem of counting the number of popular matchings in a given instance. A popular matching instance consists of agents A and houses H, where each agent ranks a subset of houses according to their preferences. A matching is an assignment of agents to houses. A matching M is more popular than matching M' if the number of agents that prefer M to M' is more than the number of people that prefer M' to M. A matching M is called popular if there exists no matching more popular than M. McDermid and Irving gave a poly-time algorithm for counting the number of popular matchings when the preference lists are strictly ordered. We first consider the case of ties in preference lists. Nasre proved that the problem of counting the number of popular matching is #P-hard when there are ties. We give an FPRAS for this problem. We then consider the popular matching problem where preference lists are strictly ordered but each house has a capacity associated with it. We give a switching graph characterization of popular matchings in this case. Such characterizations were studied earlier for the case of strictly ordered preference lists (McDermid and Irving) and for preference lists with ties (Nasre). We use our characterization to prove that counting popular matchings in capacitated case is #P-hard.", "published": "2013-12-12T17:21:20Z", "authors": ["Rupam Acharyya", "Sourav Chakraborty", "Nitesh Jha"], "categories": ["cs.DS"], "doi": "", "_query": "hedonic house price", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2608.02559v2", "title": "House-monotone multi-level apportionment has logarithmic quota discrepancy", "abstract": "Multi-level apportionment allocates integer seats through a hierarchy of groups. Schmidt-Kraepelin, Suksompong, and Wijaya proved that, at every fixed house size, lower and upper quota can be satisfied simultaneously; they also constructed house-monotone rules satisfying either quota separately. They left open whether one rule can satisfy lower quota, upper quota, and house monotonicity together, even when quota is required only relative to the root. We give a negative answer. For a full binary comb with $D$ equally entitled leaves, every house-monotone allocation sequence induces a sequence of seat recipients. Quota for the nested comb groups would force every grid-aligned prefix discrepancy to be below one. A midpoint embedding then bounds the full interval discrepancy by this quantity plus $1/2$, contradicting Schmidt's logarithmic lower bound. Conversely, a binary van der Corput seat schedule has comb-prefix error at most $\\log D/(3\\log 2)+1$. Thus the optimal worst-case error on the comb is $Θ(\\log D)$, and for sufficiently large finite $D$ no house-monotone quota rule exists. The proof isolates a static--dynamic gap: each house size admits a quota-feasible allocation, but the feasible allocations cannot be embedded into one monotone path. In quantization language, the result characterizes the order of the embedded-quantization penalty for progressive one-hot rounding on the comb.", "published": "2026-08-03T17:42:18Z", "authors": ["Lav R. Varshney"], "categories": ["cs.GT", "cs.IT", "eess.SP"], "doi": "", "_query": "hedonic house price", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2008.05880v1", "title": "Lifelong Property Price Prediction: A Case Study for the Toronto Real Estate Market", "abstract": "We present Luce, the first life-long predictive model for automated property valuation. Luce addresses two critical issues of property valuation: the lack of recent sold prices and the sparsity of house data. It is designed to operate on a limited volume of recent house transaction data. As a departure from prior work, Luce organizes the house data in a heterogeneous information network (HIN) where graph nodes are house entities and attributes that are important for house price valuation. We employ a Graph Convolutional Network (GCN) to extract the spatial information from the HIN for house-related data like geographical locations, and then use a Long Short Term Memory (LSTM) network to model the temporal dependencies for house transaction data over time. Unlike prior work, Luce can make effective use of the limited house transactions data in the past few months to update valuation information for all house entities within the HIN. By providing a complete and up-to-date house valuation dataset, Luce thus massively simplifies the downstream valuation task for the targeting properties. We demonstrate the benefit of Luce by applying it to large, real-life datasets obtained from the Toronto real estate market. Extensive experimental results show that Luce not only significantly outperforms prior property valuation methods but also often reaches and sometimes exceeds the valuation accuracy given by independent experts when using the actual realization price as the ground truth.", "published": "2020-08-12T07:32:16Z", "authors": ["Hao Peng", "Jianxin Li", "Zheng Wang", "Renyu Yang", "Mingzhe Liu", "Mingming Zhang", "Philip S. Yu", "Lifang He"], "categories": ["cs.LG"], "doi": "", "_query": "hedonic house price", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/1111.5533v1", "title": "Lie algebra solution of population models based on time-inhomogeneous Markov chains", "abstract": "Many natural populations are well modelled through time-inhomogeneous stochastic processes. Such processes have been analysed in the physical sciences using a method based on Lie algebras, but this methodology is not widely used for models with ecological, medical and social applications. This paper presents the Lie algebraic method, and applies it to three biologically well motivated examples. The result of this is a solution form that is often highly computationally advantageous.", "published": "2011-11-23T16:01:54Z", "authors": ["Thomas House"], "categories": ["math.PR", "q-bio.PE"], "doi": "", "_query": "hedonic house price", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/1405.4024v1", "title": "Algebraic moment closure for population dynamics on discrete structures", "abstract": "Moment closure on general discrete structures often requires one of the following: (i) an absence of short closed loops (zero clustering); (ii) existence of a spatial scale; (iii) ad hoc assumptions. Algebraic methods are presented to avoid the use of such assumptions for populations based on clumps, and are applied to both SIR and macroparasite disease dynamics. One approach involves a series of approximations that can be derived systematically, and another is exact and based on Lie algebraic methods.", "published": "2014-05-15T21:59:04Z", "authors": ["Thomas House"], "categories": ["q-bio.PE", "math.OA"], "doi": "", "_query": "hedonic house price", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2508.07116v1", "title": "Reflexive Modules, the Infinite Root Algebra and the Generating Hypothesis", "abstract": "This thesis concerns the algebraic consequences of Freyd's Generating Hypothesis, and explores the question of whether there exists a self-injective ring R that can be constructed purely algebraically that exhibits some of the known properties of the stable homotopy ring, including some conjectured properties that follow from Freyd's Generating Hypothesis. As an example, we investigate the infinite root algebra of Hahn series P, firstly by establishing results for the related Hahn ring A. In particular, we prove that the Theta-reflexive A-modules and the multibasic A-modules are the same.", "published": "2025-08-09T22:53:56Z", "authors": ["Oliver House"], "categories": ["math.AT", "math.KT"], "doi": "", "_query": "hedonic house price", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2501.00976v1", "title": "Non-obvious Manipulability in Hedonic Games with Friends Appreciation Preferences", "abstract": "In this paper, we study non-obvious manipulability (NOM), a relaxed form of strategyproofness, in the context of Hedonic Games (HGs) with Friends Appreciation (FA) preferences. In HGs, the aim is to partition agents into coalitions according to their preferences which solely depend on the coalition they are assigned to. Under FA preferences, agents consider any other agent either a friend or an enemy, preferring coalitions with more friends and, in case of ties, the ones with fewer enemies. Our goal is to design mechanisms that prevent manipulations while optimizing social welfare. Prior research established that computing a welfare maximizing (optimum) partition for FA preferences is not strategyproof, and the best-known approximation to the optimum subject to strategyproofness is linear in the number of agents. In this work, we explore NOM to improve approximation results. We first prove the existence of a NOM mechanism that always outputs the optimum; however, we also demonstrate that the computation of an optimal partition is NP-hard. To address this complexity, we focus on approximation mechanisms and propose a NOM mechanism guaranteeing a $(4+o(1))$-approximation in polynomial time. Finally, we briefly discuss NOM in the case of Enemies Aversion (EA) preferences, the counterpart of FA, where agents give priority to coalitions with fewer enemies and show that no mechanism computing the optimum can be NOM.", "published": "2025-01-01T22:59:58Z", "authors": ["Michele Flammini", "Maria Fomenko", "Giovanna Varricchio"], "categories": ["cs.GT"], "doi": "", "_query": "hedonic house price", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2312.11496v1", "title": "A Hedonic Index for Collectables Arising from Modelling Diamond Prices", "abstract": "This article describes a case study concerned with modelling the price of wholesale diamonds, as part of a project to develop an online diamond auction platform. The work was extended to exploring how to develop an index that could be used to track market trends of wholesale diamond prices. The approach we used is readily generalised to defining market indices for so-called Collectables, and can provide the basis for construction of derivatives. With the burgeoning interest in new markets of collectables such as those generated by the concept of a Non-Fungible Token, it is reasonable to suppose that there will be concomitant increasing interest in developing derivatives for these markets.", "published": "2023-12-05T06:05:37Z", "authors": ["Nicholas I Fisher", "Alan J Lee"], "categories": ["q-fin.PR"], "doi": "", "_query": "hedonic house price", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2410.05327v3", "title": "Investigating the Trade-off between Infections and Social Interactions Using a Compact Model of Endemic Infections on Networks", "abstract": "In many epidemiological and ecological contexts, there is a trade-off between infections and interactions. This arises because the links between individuals capable of spreading infections are also often associated with beneficial activities. Here, we consider how the presence of explicit network structure changes the optimal solution of a class of infection-interaction trade-offs. In order to do this, we develop and analyse a low-dimensional dynamical system approximating the network SIS epidemic. We find that network structure in the form of heterogeneous numbers of contacts can have a significant impact on the optimal number of contacts that comes out of a trade-off model.", "published": "2024-10-05T18:14:30Z", "authors": ["Bunlang Thatchai", "Christopher E. Overton", "Thomas House"], "categories": ["physics.soc-ph", "q-bio.PE"], "doi": "10.1080/29937574.2025.2468221", "_query": "hedonic house price", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/1706.07721v1", "title": "Investigating prescriptions for artificial resistivity in smoothed particle magnetohydrodynamics", "abstract": "In numerical simulations, artificial terms are applied to the evolution equations for stability. To prove their validity, these terms are thoroughly tested in test problems where the results are well known. However, they are seldom tested in production-quality simulations at high resolution where they interact with a plethora of physical and numerical algorithms. We test three artificial resistivities in both the Orszag-Tang vortex and in a star formation simulation. From the Orszag-Tang vortex, the Price et. al. (2017) artificial resistivity is the least dissipative thus captures the density and magnetic features; in the star formation algorithm, each artificial resistivity algorithm interacts differently with the sink particle to produce various results, including gas bubbles, dense discs, and migrating sink particles. The star formation simulations suggest that it is important to rely upon physical resistivity rather than artificial resistivity for convergence.", "published": "2017-06-23T14:26:59Z", "authors": ["James Wurster", "Matthew R. Bate", "Daniel J. Price", "Terrence S. Tricco"], "categories": ["astro-ph.IM"], "doi": "", "_query": "hedonic house price", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2106.06867v1", "title": "A Spatially Dependent Probabilistic Model for House Hunting in Ant Colonies", "abstract": "Ant species such as Temnothorax albipennis select a new nest site in a distributed fashion that, if modeled correctly, can serve as useful information for site selection algorithms for robotic swarms and other applications. Studying and replicating the ants' house hunting behavior will also illuminate useful distributed strategies that have evolved in nature. Many of the existing models of househunting behaviour for T. albipennis make the assumption that all candidate nest sites are equally distant from the ants' home nest, or that an ant has an equal probability of finding each candidate nest site. However, realistically this is not the case, as nests that are further away from the home nest and nests that are difficult to access are less likely to be found, even if they are of higher quality. We extend previous house-hunting models to account for a pairwise distance metric between nests, compare our results to those of real colonies, and use our results to examine the effects of house hunting in nests of different spatial orientations. Our incorporation of distances in the ant model appear to match empirical data in situations where a distance-quality tradeoff between nests is relevant. Furthermore, the model continues to be on par with previous house-hunting models in experiments where all candidate nests are equidistant from the home nest, as is typically assumed.", "published": "2021-06-12T21:42:51Z", "authors": ["Grace Cai", "Wendy Wu", "Wayne Zhao", "Jiajia Zhao", "Nancy Lynch"], "categories": ["cs.MA"], "doi": "", "_query": "hedonic house price", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/1807.11645v2", "title": "On algebraic integers of bounded house and preperiodicity in polynomial semigroup dynamics", "abstract": "We consider semigroup dynamical systems defined by several polynomials over a number field $\\mathbb{K}$, and the orbit (tree) they generate at a given point. We obtain finiteness results for the set of preperiodic points of such systems that fall in the cyclotomic closure of $\\mathbb{K}$. More generally, we consider the finiteness of initial points in the cyclotomic closure for which the orbit contains an algebraic integer of bounded house. This work extends previous results for classical obits generated by one polynomial over $\\mathbb{K}$ obtained initially by Dvornicich and Zannier (for preperiodic points), and then by Chen and Ostafe (for roots of unity and elements of bounded house in orbits).", "published": "2018-07-31T03:01:47Z", "authors": ["Alina Ostafe", "Marley Young"], "categories": ["math.NT"], "doi": "", "_query": "hedonic house price", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2608.14461v2", "title": "Individual Rationality in Constrained Hedonic Games: Friends, Enemies, and Neutrals", "abstract": "We study constrained coalition formation in games induced by friends, enemies, and neutrals, under the two standard refinements of additively separable preferences: friend-oriented and enemy-oriented. We ask for partitions that are individually rational (IR), while additionally requiring exactly $k$ non-empty coalitions, each satisfying a prescribed lower and upper bound on its size. Although IR alone is trivial to satisfy for any hedonic game, the size constraints make it computationally intractable to decide whether a feasible partition exists. The two models tell strikingly different stories. Under enemy-oriented preferences, the problem collapses to size-constrained graph coloring, and its complexity follows accordingly. Under friend-oriented preferences, however, the picture is far more intricate, and is governed by the enmity structure rather than the friendships. The complexity is further shaped by two factors: how strict the imposed size requirements are, and whether relationships are symmetric or asymmetric, with several cases turning out tractable in the symmetric setting but intractable once asymmetry is allowed. Charting this boundary in terms of both classical and parameterized complexity, we provide a complete understanding of which properties of the friend/enemy structure are responsible for hardness.", "published": "2026-08-14T16:42:52Z", "authors": ["Šimon Schierreich", "Ildikó Schlotter"], "categories": ["cs.GT"], "doi": "", "_query": "hedonic house price", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2202.11061v2", "title": "In This Apportionment Lottery, the House Always Wins", "abstract": "Apportionment is the problem of distributing $h$ indivisible seats across states in proportion to the states' populations. In the context of the US House of Representatives, this problem has a rich history and is a prime example of interactions between mathematical analysis and political practice. Grimmett (2004) suggested to apportion seats in a randomized way such that each state receives exactly their proportional share $q_i$ of seats in expectation (ex ante proportionality) and receives either $\\lfloor q_i \\rfloor$ or $\\lceil q_i \\rceil$ many seats ex post (quota). However, there is a vast space of randomized apportionment methods satisfying these two axioms, and so we additionally consider prominent axioms from the apportionment literature. Our main result is a randomized method satisfying quota, ex ante proportionality and house monotonicity - a property that prevents paradoxes when the number of seats changes and which we require to hold ex post. This result is based on a generalization of dependent rounding on bipartite graphs, which we call cumulative rounding and which might be of independent interest, as we demonstrate via applications beyond apportionment.", "published": "2022-02-22T17:46:11Z", "authors": ["Paul Gölz", "Dominik Peters", "Ariel D. Procaccia"], "categories": ["cs.GT"], "doi": "10.1145/3490486.3538299", "_query": "hedonic house price", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/1204.3549v2", "title": "House of Graphs: a database of interesting graphs", "abstract": "In this note we present House of Graphs (http://hog.grinvin.org) which is a new database of graphs. The key principle is to have a searchable database and offer -- next to complete lists of some graph classes -- also a list of special graphs that already turned out to be interesting and relevant in the study of graph theoretic problems or as counterexamples to conjectures. This list can be extended by users of the database.", "published": "2012-04-16T16:06:47Z", "authors": ["Gunnar Brinkmann", "Kris Coolsaet", "Jan Goedgebeur", "Hadrien Melot"], "categories": ["math.CO", "cs.DM"], "doi": "10.1016/j.dam.2012.07.018", "_query": "hedonic house price", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2605.19030v1", "title": "Nash Welfare in Additively Separable Hedonic Games", "abstract": "Additively separable hedonic games (ASHGs) are a prominent model of coalition formation where agents' preferences are derived from their individual valuations of peers. While social welfare maximization in ASHGs has traditionally focused mostly on utilitarian welfare, Nash welfare -- a well-established metric in economics which balances fairness with efficiency and offers scale invariance -- has been entirely overlooked. In this paper, we initiate the study of Nash welfare in ASHGs. We point out desirable properties fulfilled by partitions with high Nash welfare. This includes guaranteed contractual Nash stability in symmetric games, even for any approximation of Nash welfare. This is particularly appealing since, as for other welfare notions, Nash welfare turns out to be NP-hard to maximize, even for the ASHG subclass of symmetric aversion to enemies games (AEGs). A main focus of our study is on approximation algorithms for the Nash welfare objective. We present packing-based algorithms with approximation ratios for well-established subclasses of ASHGs: $n-1$ for AEGs and $2n$ for appreciation of friends games. This is complemented by a strict inapproximability result showing it is NP-hard to approximate Nash welfare within a factor of $1.0000759$ in general ASHGs. Further, we investigate the restricted settings with an upper bound on the coalition size or number of coalitions, and draw the boundary between the cases admitting efficient algorithms and those yielding NP-hardness: bounding the allowed size or number of coalitions by $2$ admits polynomial-time solvability, whereas bounds of $3$ or more yield NP-hardness or unbounded inapproximability.", "published": "2026-05-18T18:55:07Z", "authors": ["Marta Pagano", "Alexander Schlenga"], "categories": ["cs.GT", "cs.MA", "econ.TH"], "doi": "", "_query": "hedonic house price", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2502.13703v1", "title": "Parameterized Complexity of Hedonic Games with Enemy-Oriented Preferences", "abstract": "Hedonic games model settings in which a set of agents have to be partitioned into groups which we call coalitions. In the enemy aversion model, each agent has friends and enemies, and an agent prefers to be in a coalition with as few enemies as possible and, subject to that, as many friends as possible. A partition should be stable, i.e., no subset of agents prefer to be together rather than being in their assigned coalition under the partition. We look at two stability concepts: core stability and strict core stability. This yields several algorithmic problems: determining whether a (strictly) core stable partition exists, finding such a partition, and checking whether a given partition is (strictly) core stable. Several of these problems have been shown to be NP-complete, or even beyond NP. This motivates the study of parameterized complexity. We conduct a thorough computational study using several parameters: treewidth, number of friends, number of enemies, partition size, and coalition size. We give polynomial algorithms for restricted graph classes as well as FPT algorithms with respect to the number of friends an agent may have and the treewidth of the graph representing the friendship or enemy relations. We show W[1]-hardness or para-NP-hardness with respect to the other parameters. We conclude this paper with results in the setting in which agents can have neutral relations with each other, including hardness-results for very restricted cases.", "published": "2025-02-19T13:19:56Z", "authors": ["Martin Durand", "Laurin Erlacher", "Johanne Müller Vistisen", "Sofia Simola"], "categories": ["cs.GT", "cs.MA"], "doi": "", "_query": "hedonic house price", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2606.22760v1", "title": "Pricing the Unpriced Asset: A Standards-Based Method for Valuing Enterprise Data under IAS 38 and IAS 2", "abstract": "The recognition and measurement of data assets under current accounting standards presents significant challenges. While International Accounting Standard 38 (IAS 38) provides a framework for intangible asset recognition, data assets frequently fail to meet capitalisation criteria due to difficulties in demonstrating separability, establishing reliable cost measurement, and proving probable future economic benefits. The widespread failure to easily and reliably value data causes mispricing and allocative distortions across data and artificial intelligence markets. This paper introduces a two-layer valuation progression for authenticated data assets, that is, datasets that have met IAS 38 recognition criteria through established legal provenance and contractual boundaries. The first layer, D-Val, is the auditable cost-basis valuation consistent with IAS 38. D-Val is defined as D-Val = Cp * Avt, where Cp is the reliably measurable production cost and Avt is the appreciation or depreciation factor applied over time. Under prevailing interpretations of IAS 38, Av is constrained to values less than or equal to one absent an active market revaluation, rendering D-Val a strictly cost-less-amortisation figure. The second layer, A-Val, is a theoretically grounded commercial valuation that incorporates scarcity, rivalry, completeness, accuracy, and explicit premia for provenance authentication and independent audit. A-Val is not auditable as fair value under current practice but serves as a defensible commercial valuation during the period before active markets for authenticated data assets mature. As authenticated data markets mature parameter assumptions improve providing a foundation for iterative refinement of the model.", "published": "2026-06-22T01:59:04Z", "authors": ["Natasha E. Blycha", "James Myint", "Dean Arden", "Michael Small", "Conor Blycha", "Schellie-Jayne Price", "Steve Bailey", "Ryan Feng", "Rachael Johnson", "Adam Myer"], "categories": ["cs.CE"], "doi": "", "_query": "hedonic house price", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2003.11565v2", "title": "The Millennial Boom, the Baby Bust, and the Housing Market", "abstract": "As baby boomers have begun to downsize and retire, their preferences now overlap with millennials' predilection for urban amenities and smaller living spaces. This confluence in tastes between the two largest age segments of the U.S. population has meaningfully changed the evolution of home prices in the United States. Utilizing a Bartik shift-share instrument for demography-driven demand shocks, we show that from 2000 to 2018 (i) the price growth of four- and five-bedroom houses has lagged the prices of one- and two-bedroom homes, (ii) within local labor markets, the relative home prices in baby boomer-rich zip codes have declined compared with millennial-rich neighborhoods, and (iii) the zip codes with the largest relative share of smaller homes have grown fastest. These patterns have become more pronounced during the latest economic cycle. We show that the effects are concentrated in areas where housing supply is most inelastic. If this pattern in the housing market persists or expands, the approximately 16.5 trillion in real estate wealth held by households headed by those aged 55 or older will be significantly affected. We find little evidence that these upcoming changes have been incorporated into current prices.", "published": "2020-03-25T18:03:45Z", "authors": ["Marijn A. Bolhuis", "Judd N. L. Cramer"], "categories": ["econ.GN"], "doi": "", "_query": "hedonic house price", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2607.25105v1", "title": "Simulating Single Transferable Voting for the Colorado House of Representatives", "abstract": "Social choice theory research demonstrates that single transferable voting (STV) results in more proportionally representative legislative bodies. We aim to understand how using multi-member districts and ranked ballots with STV would affect the representation of political parties in the Colorado House of Representatives. We investigated this objective by producing 10,000 multi-member districting plans of Colorado, generating ranked ballots for each of these plans using returns from the 2022 Colorado attorney general race, and simulating STV using these ballots. Our simulated STV elections for the Colorado House of Representatives gave more proportional representation for Democrats and Republicans than the current first-past-the-post system. Future research should explore how the implementation of STV would influence the representation of racial and language minority groups in the Colorado General Assembly to provide guidance on electoral reform in the state.", "published": "2026-07-27T22:08:39Z", "authors": ["Nora Nelson Laird", "Beth Malmskog", "Iris Pixler", "Katherine Rodbell"], "categories": ["cs.CY"], "doi": "", "_query": "hedonic house price", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/1507.06336v1", "title": "Hessian corrections to the Metropolis Adjusted Langevin Algorithm", "abstract": "A natural method for the introduction of second-order derivatives of the log likelihood into MCMC algorithms is introduced, based on Taylor expansion of the Langevin equation followed by exact solution of the truncated system.", "published": "2015-07-22T20:40:55Z", "authors": ["Thomas House"], "categories": ["stat.CO", "stat.ME"], "doi": "", "_query": "hedonic house price", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2301.01323v2", "title": "Graphical House Allocation", "abstract": "The classical house allocation problem involves assigning $n$ houses (or items) to $n$ agents according to their preferences. A key criterion in such problems is satisfying some fairness constraints such as envy-freeness. We consider a generalization of this problem wherein the agents are placed along the vertices of a graph (corresponding to a social network), and each agent can only experience envy towards its neighbors. Our goal is to minimize the aggregate envy among the agents as a natural fairness objective, i.e., the sum of all pairwise envy values over all edges in a social graph. When agents have identical and evenly-spaced valuations, our problem reduces to the well-studied problem of linear arrangements. For identical valuations with possibly uneven spacing, we show a number of deep and surprising ways in which our setting is a departure from this classical problem. More broadly, we contribute several structural and computational results for various classes of graphs, including NP-hardness results for disjoint unions of paths, cycles, stars, or cliques, and fixed-parameter tractable (and, in some cases, polynomial-time) algorithms for paths, cycles, stars, cliques, and their disjoint unions. Additionally, a conceptual contribution of our work is the formulation of a structural property for disconnected graphs that we call separability which results in efficient parameterized algorithms for finding optimal allocations.", "published": "2023-01-03T19:26:15Z", "authors": ["Hadi Hosseini", "Justin Payan", "Rik Sengupta", "Rohit Vaish", "Vignesh Viswanathan"], "categories": ["cs.GT", "cs.AI"], "doi": "", "_query": "hedonic house price", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2503.08074v1", "title": "Hedonic Adaptation in the Age of AI: A Perspective on Diminishing Satisfaction Returns in Technology Adoption", "abstract": "The fast paced progress of artificial intelligence (AI) through scaling laws connecting rising computational power with improving performance has created tremendous technological breakthroughs. These breakthroughs do not translate to corresponding user satisfaction improvements, resulting in a general mismatch. This research suggests that hedonic adaptation the psychological process by which people revert to a baseline state of happiness after drastic change provides a suitable model for understanding this phenomenon. We argue that user satisfaction with AI follows a logarithmic path, thus creating a longterm \"satisfaction gap\" as people rapidly get used to new capabilities as expectations. This process occurs through discrete stages: initial excitement, declining returns, stabilization, and sporadic resurgence, depending on adaptation rate and capability introduction. These processes have far reaching implications for AI research, user experience design, marketing, and ethics, suggesting a paradigm shift from sole technical scaling to methods that sustain perceived value in the midst of human adaptation. This perspective reframes AI development, necessitating practices that align technological progress with people's subjective experience.", "published": "2025-03-11T06:08:36Z", "authors": ["Venkat Ram Reddy Ganuthula", "Krishna Kumar Balaraman", "Nimish Vohra"], "categories": ["econ.GN"], "doi": "", "_query": "hedonic house price", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/1607.01999v1", "title": "Inferring the contiguity matrix for spatial autoregressive analysis with applications to house price prediction", "abstract": "Inference methods in traditional statistics, machine learning and data mining assume that data is generated from an independent and identically distributed (iid) process. Spatial data exhibits behavior for which the iid assumption must be relaxed. For example, the standard approach in spatial regression is to assume the existence of a contiguity matrix which captures the spatial autoregressive properties of the data. However all spatial methods, till now, have assumed that the contiguity matrix is given apriori or can be estimated by using a spatial similarity function. In this paper we propose a convex optimization formulation to solve the spatial autoregressive regression (SAR) model in which both the contiguity matrix and the non-spatial regression parameters are unknown and inferred from the data. We solve the problem using the alternating direction method of multipliers (ADMM) which provides a solution which is both robust and efficient. While our approach is general we use data from housing markets of Boston and Sydney to both guide the analysis and validate our results. A novel side effect of our approach is the automatic discovery of spatial clusters which translate to submarkets in the housing data sets.", "published": "2016-07-07T13:14:43Z", "authors": ["Somwrita Sarkar", "Sanjay Chawla"], "categories": ["econ.GN"], "doi": "", "_query": "hedonic house price", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/1006.4531v1", "title": "Generalised network clustering and its dynamical implications", "abstract": "A parameterisation of generalised network clustering, in the form of four-motif prevalences, is presented. This involves three real parameters that are conditional on one- two- and three-motif prevalences. Interpretations of these real parameters are presented that motivate a set of rewiring schemes to create appropriately clustered networks. Finally, the dynamical implications of higher order structure, as parameterised, for a contact process are considered.", "published": "2010-06-23T14:37:33Z", "authors": ["Thomas House"], "categories": ["physics.soc-ph", "q-bio.PE"], "doi": "", "_query": "hedonic house price", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2511.10808v1", "title": "Seasonality in the U.S. Housing Market: Post-Pandemic Shifts and Regional Dynamics", "abstract": "Seasonality has traditionally shaped the U.S. housing market, with activity peaking in spring-summer and declining in autumn-winter. However, recent disruptions, particularly post-COVID-19, raise questions about shift in these patterns. This study analyzes housing market date (1991-2024) to examine evolving seasonality and regional heterogeneity. Using Housing Price Index (HPI), inventory and sales data from the Federal Housing Finance Agency and U.S. Census Bureau, seasonal components are extracted via the X-13-ARIMA procedure, and statistical tests assess variations across regions. The results confirm seasonal fluctuations in prices and volumes, with recent shifts toward earlier annual peak (March-April) and amplified seasonal effects. Regional variations align with differences in climate and market structure, while prices and sales volumes exhibit in-phase movement, suggesting thick-market momentum behaviour. These findings highlight key implications for policymakers, realtors and investors navigating post-pandemic market dynamics, offering insights into the timing and interpretation of housing market activities.", "published": "2025-11-13T21:20:54Z", "authors": ["Yihan Hu", "Yifei Huang", "Weizhao Wang"], "categories": ["econ.GN", "stat.AP"], "doi": "", "_query": "hedonic house price", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2303.11365v5", "title": "Housing Bubbles with Phase Transitions", "abstract": "We analyze how equilibrium housing prices are determined in the process of economic development within an overlapping generations model with perfect housing and rental markets. We characterize the rent growth rate in all equilibria. The economy exhibits a two-stage phase transition: as incomes of home buyers rise, the equilibrium regime changes from fundamental to bubble possibility, where fundamental and bubbly equilibria coexist. With even higher incomes, fundamental equilibria disappear and housing bubbles become a necessity. We also discuss extensions and refinements such as equilibrium uniqueness, multiple savings vehicles, welfare implications, credit- and expectation-driven bubbles, and testable implications of our theory.", "published": "2023-03-20T18:07:22Z", "authors": ["Tomohiro Hirano", "Alexis Akira Toda"], "categories": ["econ.TH"], "doi": "", "_query": "hedonic house price", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/1006.3483v2", "title": "Exact epidemic dynamics for generally clustered, complex networks", "abstract": "The last few years have seen remarkably fast progress in the understanding of statistics and epidemic dynamics of various clustered networks. This paper considers a class of networks based around a concept (the locale) that allows asymptotically exact results to be derived for epidemic dynamics. While there is no restriction on the motifs that can be found in such graphs, each node must be uniquely assigned to a generally clustered subgraph to obtain analytic traction.", "published": "2010-06-17T14:41:26Z", "authors": ["Thomas House"], "categories": ["q-bio.PE"], "doi": "", "_query": "hedonic house price", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/1111.4875v2", "title": "Modelling Epidemics on Networks", "abstract": "Infectious disease remains, despite centuries of work to control and mitigate its effects, a major problem facing humanity. This paper reviews the mathematical modelling of infectious disease epidemics on networks, starting from the simplest Erdos-Renyi random graphs, and building up structure in the form of correlations, heterogeneity and preference, paying particular attention to the links between random graph theory, percolation and dynamical systems representing transmission. Finally, the problems posed by networks with a large number of short closed looks are discussed.", "published": "2011-11-21T14:10:52Z", "authors": ["Thomas House"], "categories": ["physics.pop-ph", "q-bio.PE"], "doi": "10.1080/00107514.2011.644443", "_query": "hedonic house price", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/0907.1853v1", "title": "Housing Market Microstructure", "abstract": "In this article, we develop a model for the evolution of real estate prices. A wide range of inputs, including stochastic interest rates and changing demands for the asset, are considered. Maximizing their expected utility, home owners make optimal sale decisions given these changing market conditions. Using these optimal sale decisions, we simulate the implied evolution of housing prices providing insights into the recent subprime lending crisis.", "published": "2009-07-10T15:54:17Z", "authors": ["Hazer Inaltekin", "Robert Jarrow", "Mehmet Saglam", "Yildiray Yildirim"], "categories": ["q-fin.TR"], "doi": "", "_query": "hedonic house price", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/1403.2878v1", "title": "Non-Markovian stochastic epidemics in extremely heterogeneous populations", "abstract": "A feature often observed in epidemiological networks is significant heterogeneity in degree. A popular modelling approach to this has been to consider large populations with highly heterogeneous discrete contact rates. This paper defines an individual-level non-Markovian stochastic process that converges on standard ODE models of such populations in the appropriate asymptotic limit. A generalised Sellke construction is derived for this model, and this is then used to consider final outcomes in the case where heterogeneity follows a truncated Zipf distribution.", "published": "2014-03-12T10:36:29Z", "authors": ["Thomas House"], "categories": ["q-bio.PE", "math.PR"], "doi": "", "_query": "hedonic house price", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2208.06997v1", "title": "Combining deep learning and crowdsourcing geo-images to predict housing quality in rural China", "abstract": "Housing quality is an essential proxy for regional wealth, security and health. Understanding the distribution of housing quality is crucial for unveiling rural development status and providing political proposals. However,present rural house quality data highly depends on a top-down, time-consuming survey at the national or provincial level but fails to unpack the housing quality at the village level. To fill the gap between accurately depicting rural housing quality conditions and deficient data,we collect massive rural images and invite users to assess their housing quality at scale. Furthermore, a deep learning framework is proposed to automatically and efficiently predict housing quality based on crowd-sourcing rural images.", "published": "2022-08-15T03:58:03Z", "authors": ["Weipan Xu", "Yu Gu", "Yifan Chen", "Yongtian Wang", "Weihuan Deng", "Xun Li"], "categories": ["cs.LG"], "doi": "", "_query": "hedonic house price", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/1310.3128v1", "title": "Endemic infections are always possible on regular networks", "abstract": "We study the dependence of the largest component in regular networks on the clustering coefficient, showing that its size changes smoothly without undergoing a phase transition. We explain this behaviour via an analytical approach based on the network structure, and provide an exact equation describing the numerical results. Our work indicates that intrinsic structural properties always allow the spread of epidemics on regular networks.", "published": "2013-10-11T14:02:56Z", "authors": ["Charo I. Del Genio", "Thomas House"], "categories": ["physics.soc-ph", "cs.SI", "q-bio.PE"], "doi": "10.1103/PhysRevE.88.040801", "_query": "hedonic house price", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2408.02339v2", "title": "Modeling the impact of Climate transition on real estate prices", "abstract": "In this work, we propose a model to quantify the impact of the climate transition on a property in housing market. We begin by noting that property is an asset in an economy. That economy is organized in sectors, driven by its productivity which is a multidimensional Ornstein-Uhlenbeck process, while the climate transition is declined thanks to the carbon price, a continuous deterministic process. We then extend the sales comparison approach and the income approach to valuate an energy inefficient real estate asset. We obtain its value as the difference between the price of an equivalent efficient building following an exponential Ornstein-Uhlenbeck as well as the actualized renovation costs and the actualized sum of the future additional energy costs (before and after the renovation date). These costs are due to the inefficiency of the building, before an optimal renovation date which depends on the carbon price process. Moreover, since the renovation increases the efficiency of the building, which is random, the future additional energy costs become smaller and even zero if the optimal energy efficiency is reached. Finally, we carry out simulations based on the French economy and the house price index of France. The findings support the conclusion that the order of magnitude of the depreciation obtained by our model is the same as the empirical observations.", "published": "2024-08-05T09:34:47Z", "authors": ["Lionel Sopgoui"], "categories": ["q-fin.RM", "econ.GN", "q-fin.GN"], "doi": "", "_query": "hedonic house price", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2107.10455v1", "title": "Time Varying Risk in U.S. Housing Sector and Real Estate Investment Trusts Equity Return", "abstract": "This study examines how housing sector volatilities affect real estate investment trust (REIT) equity return in the United States. I argue that unexpected changes in housing variables can be a source of aggregate housing risk, and the first principal component extracted from the volatilities of U.S. housing variables can predict the expected REIT equity returns. I propose and construct a factor-based housing risk index as an additional factor in asset price models that uses the time-varying conditional volatility of housing variables within the U.S. housing sector. The findings show that the proposed housing risk index is economically and theoretically consistent with the risk-return relationship of the conditional Intertemporal Capital Asset Pricing Model (ICAPM) of Merton (1973), which predicts an average maximum of 5.6 percent of risk premium in REIT equity return. In subsample analyses, the positive relationship is not affected by sample periods' choice but shows higher housing risk beta values for the 2009-18 sample period. The relationship remains significant after controlling for VIX, Fama-French three factors, and a broad set of macroeconomic and financial variables. Moreover, the proposed housing beta also accurately forecasts U.S. macroeconomic and financial conditions.", "published": "2021-07-22T04:57:39Z", "authors": ["Masud Alam"], "categories": ["econ.GN"], "doi": "", "_query": "hedonic house price", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2602.04638v1", "title": "Inference for Within- and Between-Partnership Transmission Rates for HIV Infection", "abstract": "HIV transmission within serodiscordant couples remains a significant public health challenge, particularly in sub-Saharan Africa. Estimating the rate of such infection, alongside the rates of introduction of infection from outside the partnership, is a special case of the more general epidemiological challenge of inferring intensities of within- and between-group intensities of transmission. This study presents a stochastic susceptible-infected (SI) pair model for estimating key epidemiological parameters governing HIV transmission within and between couples, which we further extend to account for gender-specific differences in infection dynamics. Using a likelihood-based inference approach, we estimate transmission parameters and associated uncertainty from observed data. These values can be used to inform infection prevention strategies for HIV, and the methodology proposed can be generalised to other epidemiological settings.", "published": "2026-02-04T15:07:29Z", "authors": ["Irene García Muñoz", "Ian Hall", "Thomas House"], "categories": ["stat.AP"], "doi": "", "_query": "hedonic house price", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/1404.5324v1", "title": "For principled model fitting in mathematical biology", "abstract": "The mathematical models used to capture features of complex, biological systems are typically non-linear, meaning that there are no generally valid simple relationships between their outputs and the data that might be used to validate them. This invalidates the assumptions behind standard statistical methods such as linear regression, and often the methods used to parameterise biological models from data are ad hoc. In this perspective, I will argue for an approach to model fitting in mathematical biology that incorporates modern statistical methodology without losing the insights gained through non-linear dynamic models, and will call such an approach principled model fitting. Principled model fitting therefore involves defining likelihoods of observing real data on the basis of models that capture key biological mechanisms.", "published": "2014-04-21T20:32:28Z", "authors": ["Thomas House"], "categories": ["q-bio.QM"], "doi": "", "_query": "hedonic house price", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2301.01222v1", "title": "A Multi-Source Information Learning Framework for Airbnb Price Prediction", "abstract": "With the development of technology and sharing economy, Airbnb as a famous short-term rental platform, has become the first choice for many young people to select. The issue of Airbnb's pricing has always been a problem worth studying. While the previous studies achieve promising results, there are exists deficiencies to solve. Such as, (1) the feature attributes of rental are not rich enough; (2) the research on rental text information is not deep enough; (3) there are few studies on predicting the rental price combined with the point of interest(POI) around the house. To address the above challenges, we proposes a multi-source information embedding(MSIE) model to predict the rental price of Airbnb. Specifically, we first selects the statistical feature to embed the original rental data. Secondly, we generates the word feature vector and emotional score combination of three different text information to form the text feature embedding. Thirdly, we uses the points of interest(POI) around the rental house information generates a variety of spatial network graphs, and learns the embedding of the network to obtain the spatial feature embedding. Finally, this paper combines the three modules into multi source rental representations, and uses the constructed fully connected neural network to predict the price. The analysis of the experimental results shows the effectiveness of our proposed model.", "published": "2023-01-01T11:07:55Z", "authors": ["Lu Jiang", "Yuanhan Li", "Na Luo", "Jianan Wang", "Qiao Ning"], "categories": ["cs.LG", "cs.IR"], "doi": "", "_query": "hedonic house price", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/hep-th/0409114v1", "title": "G_2 Domain Walls in M-theory", "abstract": "M-theory is considered in its low-energy limit on a G_2 manifold with non-vanishing flux. Using the Killing spinor equations for linear flux, an explicit set of first-order bosonic equations for supersymmetric solutions is found. These solutions describe a warped product of a domain wall in four-dimensional space-time and a deformed G_2 manifold. It is shown how these domain walls arise from the perspective of the associated four-dimensional N=1 effective supergravity theories. We also discuss the inclusion of membrane and M5-brane sources.", "published": "2004-09-10T16:26:42Z", "authors": ["Thomas House", "Andre Lukas"], "categories": ["hep-th"], "doi": "10.1103/PhysRevD.71.046006", "_query": "hedonic house price", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/1601.07415v2", "title": "Heterogeneous network epidemics: real-time growth, variance and extinction of infection", "abstract": "Recent years have seen a large amount of interest in epidemics on networks as a way of representing the complex structure of contacts capable of spreading infections through the modern human population. The configuration model is a popular choice in theoretical studies since it combines the ability to specify the distribution of the number of contacts (degree) with analytical tractability. Here we consider the early real-time behaviour of the Markovian SIR epidemic model on a configuration model network using a multi-type branching process. We find closed-form analytic expressions for the mean and variance of the number of infectious individuals as a function of time and the degree of the initially infected individual(s), and write down a system of differential equations for the probability of extinction that are numerically fast compared to Monte Carlo simulation. We show that these quantities are all sensitive to the degree distribution - in particular we confirm that the mean prevalence of infection depends on the first two moments of the degree distribution and the variance in prevalence depends on the first three moments of the degree distribution. In contrast to most existing analytic approaches, the accuracy of these results does not depend on having a large number of infectious individuals, meaning that in the large population limit they would be asymptotically exact even for one initial infectious individual.", "published": "2016-01-27T15:41:23Z", "authors": ["Frank Ball", "Thomas House"], "categories": ["q-bio.PE", "math.PR"], "doi": "10.1007/s00285-016-1092-3", "_query": "hedonic house price", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2504.17113v1", "title": "Cybernetic Governance in a Coliving House", "abstract": "We report an 18-month field experiment in distributed digital institutions: a nine-bedroom Los Angeles coliving house that runs without managers, while sustaining 98% occupancy and below-market rents. Drawing on Elinor Ostrom's commons theory, we outline design principles and three digital mechanisms that form the institutional core: 1) A continuous-auction chore scheduler turns regenerative labor into a time-indexed points market; residents meet a 100-point monthly obligation by claiming tasks whose value rises linearly with neglect. 2) A pairwise-preference layer lets participants asynchronously reprioritize tasks, translating meta-governance into low-cognition spot inputs. 3) A symbolic \"hearts\" ledger tracks norm compliance through automated enforcement, lightweight challenges, and peer-awarded karma. Together, these mechanisms operationalize cybernetic principles--human sensing, machine bookkeeping, real-time feedback--while minimizing dependence on privileged roles. Our exploratory data (567 chore claims, 255 heart events, and 551 group purchases) show that such tooling can sustain reliable commons governance without continuous leadership, offering a transferable design palette for online communities, coliving houses, and other digitally mediated collectives.", "published": "2025-04-23T21:45:59Z", "authors": ["Daniel Kronovet", "Seth Frey", "Joseph DeSimone"], "categories": ["cs.CY", "cs.HC", "econ.GN"], "doi": "", "_query": "hedonic house price", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/1304.4763v1", "title": "Dynamics of Stochastic Epidemics on Heterogeneous Networks", "abstract": "Epidemic models currently play a central role in our attempts to understand and control infectious diseases. Here, we derive a model for the diffusion limit of stochastic susceptible-infectious-removed (SIR) epidemic dynamics on a heterogeneous network. Using this, we consider analytically the early asymptotic exponential growth phase of such epidemics, showing how the higher order moments of the network degree distribution enter into the stochastic behaviour of the epidemic. We find that the first three moments of the network degree distribution are needed to specify the variance in disease prevalence fully, meaning that the skewness of the degree distribution affects the variance of the prevalence of infection. We compare these asymptotic results to simulation and find a close agreement for city-sized populations.", "published": "2013-04-17T10:47:02Z", "authors": ["Matthew Graham", "Thomas House"], "categories": ["q-bio.PE", "math.PR"], "doi": "10.1007/s00285-013-0679-1", "_query": "hedonic house price", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2308.09501v2", "title": "Towards More Realistic Models for Refugee Integration: Anonymous Refugee Housing with Upper-Bounds", "abstract": "[Knop and Schierreich; AAMAS~'23] recently introduced a novel model for refugee housing, where we are given a city's topology represented as a graph, a set of inhabitants who occupy some vertices of the topology, and a set of refugees that we need to house in unoccupied vertices. Moreover, each inhabitant approves specific numbers of refugees in his neighbourhood, and our goal is to find housing such that every inhabitant approves the number of refugees housed in his neighbourhood. However, such a model suffers from several problems: (i) it is computationally hard to find desirable housing, and (ii) the inhabitants' preferences are not necessarily continuous, which hardly occurs in practice. To avoid these objections, we introduce a restricted variant of the problem that is more closely aligned with practical scenarios of refugee housing and study its complexity.", "published": "2023-08-18T12:16:36Z", "authors": ["Šimon Schierreich"], "categories": ["cs.GT"], "doi": "", "_query": "hedonic house price", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/hep-th/0412006v2", "title": "M-theory Compactifications on Manifolds with G2 Structure", "abstract": "In this paper we study M-theory compactifications on manifolds of G2 structure. By computing the gravitino mass term in four dimensions we derive the general form for the superpotential which appears in such compactifications and show that beside the normal flux term there is a term which appears only for non-minimal G2 structure. We further apply these results to compactifications on manifolds with weak G2 holonomy and make a couple of statements regarding the deformation space of such manifolds. Finally we show that the superpotential derived from fermionic terms leads to the potential that can be derived from the explicit compactification, thus strengthening the conjectures we make about the space of deformations of manifolds with weak G2 holonomy.", "published": "2004-12-01T20:27:54Z", "authors": ["Thomas House", "Andrei Micu"], "categories": ["hep-th"], "doi": "10.1088/0264-9381/22/9/016", "_query": "hedonic house price", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/1303.4201v1", "title": "The rate of convergence to early asymptotic behaviour in age-structured epidemic models", "abstract": "Age structure is incorporated in many types of epidemic model. Often it is convenient to assume that such models converge to early asymptotic behaviour quickly, before the susceptible population has been appreciably depleted. We make use of dynamical systems theory to show that for some reasonable parameter values, this convergence can be slow. Such a possibility should therefore be considered when parameterising age-structured epidemic models.", "published": "2013-03-18T10:40:53Z", "authors": ["Christopher A. Rhodes", "Thomas House"], "categories": ["q-bio.PE"], "doi": "10.1016/j.tpb.2013.02.003", "_query": "hedonic house price", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2011.09129v1", "title": "Pricing the Information Quantity in Artworks", "abstract": "In the traditional art pricing models, the variables that capture the painting's content are often missing. Recent research starts to apply the computer graphic techniques to extract the information from the painting content. Most of the research concentrates on the reading of the color information from the painting images and analyzes how different color compositions can affect the sales prices of paintings. This paper takes a different approach, and tries to abstract away from the interpretation of the content information, while only focus on measuring the quantity of information contained. We extend the concept of Shannon entropy in information theory to the painting's scenario, and suggest using the variances of a painting's composing elements, i.e., line, color, value, shape/form and space, to measure the amount of information in the painting. These measures are calculated at the pixel level based on a picture's digital image. We include them into the traditional hedonic regression model to test their significance based on the auction samples from two famous artists (Picasso and Renoir). We find that all the variance measurements can significantly explain the sales price either at 1% or 5% level. The adjusted R square is also increased by more than ten percent. Our method greatly improves the traditional pricing models, and may also find applications in other areas such as art valuation and authentication.", "published": "2020-11-18T10:55:29Z", "authors": ["Lan Ju", "Zhiyong Tu", "Changyong Xue"], "categories": ["q-fin.GN"], "doi": "", "_query": "hedonic house price", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/1012.1974v1", "title": "Epidemic prediction and control in clustered populations", "abstract": "There has been much recent interest in modelling epidemics on networks, particularly in the presence of substantial clustering. Here, we develop pairwise methods to answer questions that are often addressed using epidemic models, in particular: on the basis of potential observations early in an outbreak, what can be predicted about the epidemic outcomes and the levels of intervention necessary to control the epidemic? We find that while some results are independent of the level of clustering (early growth predicts the level of `leaky' vaccine needed for control and peak time, while the basic reproductive ratio predicts the random vaccination threshold) the relationship between other quantities is very sensitive to clustering.", "published": "2010-12-09T10:59:24Z", "authors": ["Thomas House", "Matt J Keeling"], "categories": ["q-bio.PE"], "doi": "", "_query": "hedonic house price", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/1603.03073v2", "title": "Mechanisms for House Allocation with Existing Tenants under Dichotomous Preferences", "abstract": "We consider house allocation with existing tenants in which each agent has dichotomous preferences. We present strategyproof, polynomial-time, and (strongly) individually rational algorithms that satisfy the maximum number of agents. For the endowment only model, one of the algorithms also returns a core-stable allocation.", "published": "2016-03-09T21:54:10Z", "authors": ["Haris Aziz"], "categories": ["cs.GT"], "doi": "", "_query": "hedonic house price", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2203.07924v2", "title": "Fast, slow convergence, and concentration in the house of cards replicator-mutator model", "abstract": "We propose a fine analysis of the various possible long time behaviours of the solutions of the replicator-mutator equation with so-called Kingman's house of cards mutations. In particular, we give what is to our knowledge the first concentration result for this model.", "published": "2022-03-15T14:05:35Z", "authors": ["Bertrand Cloez", "Pierre Gabriel"], "categories": ["math.AP", "math.PR"], "doi": "10.57262/die037-0708-547", "_query": "hedonic house price", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2407.04664v1", "title": "The Degree of Fairness in Efficient House Allocation", "abstract": "The classic house allocation problem is primarily concerned with finding a matching between a set of agents and a set of houses that guarantees some notion of economic efficiency (e.g. utilitarian welfare). While recent works have shifted focus on achieving fairness (e.g. minimizing the number of envious agents), they often come with notable costs on efficiency notions such as utilitarian or egalitarian welfare. We investigate the trade-offs between these welfare measures and several natural fairness measures that rely on the number of envious agents, the total (aggregate) envy of all agents, and maximum total envy of an agent. In particular, by focusing on envy-free allocations, we first show that, should one exist, finding an envy-free allocation with maximum utilitarian or egalitarian welfare is computationally tractable. We highlight a rather stark contrast between utilitarian and egalitarian welfare by showing that finding utilitarian welfare maximizing allocations that minimize the aforementioned fairness measures can be done in polynomial time while their egalitarian counterparts remain intractable (for the most part) even under binary valuations. We complement our theoretical findings by giving insights into the relationship between the different fairness measures and conducting empirical analysis.", "published": "2024-07-05T17:18:56Z", "authors": ["Hadi Hosseini", "Medha Kumar", "Sanjukta Roy"], "categories": ["cs.GT", "cs.DS"], "doi": "", "_query": "hedonic house price", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2402.05152v2", "title": "Is the price right? Reconceptualizing price and income elasticity to anticipate price perception issues", "abstract": "Price perception by consumers represents a challenge to the ability of a business to correctly and profitably price and sell their products or services in a given market and any new target market. Complicating the perception of prices is the dynamics of price and income elasticity, both of which are key for estimating demand. This article proposes a novel and elegant identity that conceptualizes elasticity as a means of quantifying the potential for price perception problems in a market. Elasticity studies from 1990 to 2023 (n=30) were sampled to evaluate the relationship between price and income elasticity for various consumer commodities using the identity. The results suggest that, given known price and income elasticity values, a business can anticipate pricing perception problems in advance and address the potential for damaging distortion of their value proposition. Further, the business can use this insight to correctly choose a strategic pricing approach.", "published": "2024-02-07T13:42:23Z", "authors": ["Shawn Berry"], "categories": ["econ.GN"], "doi": "", "_query": "hedonic house price", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2004.02706v1", "title": "What do online listings tell us about the housing market?", "abstract": "Traditional data sources for the analysis of housing markets show several limitations, that recently started to be overcome using data coming from housing sales advertisements (ads) websites. In this paper, using a large dataset of ads in Italy, we provide the first comprehensive analysis of the problems and potential of these data. The main problem is that multiple ads (\"duplicates\") can correspond to the same housing unit. We show that this issue is mainly caused by sellers' attempt to increase visibility of their listings. Duplicates lead to misrepresentation of the volume and composition of housing supply, but this bias can be corrected by identifying duplicates with machine learning tools. We then focus on the potential of these data. We show that the timeliness, granularity, and online nature of these data allow monitoring of housing demand, supply and liquidity, and that the (asking) prices posted on the website can be more informative than transaction prices.", "published": "2020-04-06T14:40:09Z", "authors": ["Michele Loberto", "Andrea Luciani", "Marco Pangallo"], "categories": ["econ.EM", "econ.GN"], "doi": "", "_query": "hedonic house price", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2610.15796v1", "title": "A Robust Numerical Framework for Multi-Strain Epidemics", "abstract": "Epidemiological models often take the form of systems of differential equations of varying levels of complexity. In multi-strain models, complexity arises from the interaction of diverse pathogen types that may exhibit different patterns of cross-immunity due to the host's immune system. Where previous work has tended to focus on theoretical analysis of this complexity, or model simplification, less effort has been given to efficient numerical exploration of full multi-strain disease dynamics. Here, we propose a computational framework for representing multi-strain disease transmission models, through a mathematically transparent hard-coded mapping between sets and integers. By mapping infection histories into Boolean matrices, the framework eliminates explicit iteration over strain combinations while preserving key biological processes, including sequential infection and partial cross-immunity. Benchmark comparisons of numerical solutions of our matrix formulation and a set-indexed implementation demonstrate substantial performance gains. Across models with increasing numbers of strains, the matrix formulation consistently reduces runtime, with a speedup exceeding two orders of magnitude for systems with 11 strains. We present an application of this approach to the case of COVID-19 in Indonesia, focusing on the replacement of the Delta variant by Omicron. We hope that our approach represents a robust computational approach to automatically generate diverse multi-strain epidemic models that can be solved in a computationally efficient manner for a variety of scientific and public-health questions.", "published": "2026-10-11T17:35:52Z", "authors": ["Fadilah Ilahi", "Ian Hall", "Thomas House"], "categories": ["q-bio.PE"], "doi": "", "_query": "hedonic house price", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2107.06545v1", "title": "Total Effect Analysis of Vaccination on Household Transmission in the Office for National Statistics COVID-19 Infection Survey", "abstract": "We investigate the distribution of numbers of secondary cases in households in the Office for National Statistics COVID-19 Infection Survey (ONS CIS), stratified by timing of vaccination and infection in the households. This shows a total effect of a statistically significant approximate halving of the secondary attack rate in households following vaccination.", "published": "2021-07-14T08:28:58Z", "authors": ["Thomas House", "Lorenzo Pellis", "Emma Pritchard", "Angela R. McLean", "A. Sarah Walker"], "categories": ["q-bio.PE", "stat.AP"], "doi": "", "_query": "hedonic house price", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/1810.06066v1", "title": "Consistent Approximation of Epidemic Dynamics on Degree-heterogeneous Clustered Networks", "abstract": "Realistic human contact networks capable of spreading infectious disease, for example studied in social contact surveys, exhibit both significant degree heterogeneity and clustering, both of which greatly affect epidemic dynamics. To understand the joint effects of these two network properties on epidemic dynamics, the effective degree model of Lindquist et al. is reformulated with a new moment closure to apply to highly clustered networks. A simulation study comparing alternative ODE models and stochastic simulations is performed for SIR (Susceptible-Infected-Removed) epidemic dynamics, including a test for the conjectured error behaviour in Pellis et al., providing evidence that this novel model can be a more accurate approximation to epidemic dynamics on complex networks than existing approaches.", "published": "2018-10-14T17:13:51Z", "authors": ["A. Bishop", "I. Z. Kiss", "T. House"], "categories": ["q-bio.PE", "physics.soc-ph"], "doi": "", "_query": "hedonic house price", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2210.03342v1", "title": "Impact of COVID-19 Lockdowns on House Sparrows: Comparative Study from an Indian Context", "abstract": "Due to the outbreak of COVID-19, the last couple of years have been drastic in terms of human behavioral patterns. People were forced to stay at home for a very long duration because of the strict lockdown measures imposed by governments all over the globe. India was no exception, wherein the Indian government imposed several very strict lockdowns all across the country, which restricted human activities and their social behaviors. However, such restrictions were seen to have a positive impact on environment and ecology. In this paper, we aim to study the changes in House Sparrow sightings, as a result of the lockdowns. Are the house sparrows back? is the question we try to answer, using appropriate exploratory analysis and statistical modelling.", "published": "2022-10-07T06:13:46Z", "authors": ["Sudeep R. Bapat", "Sreeranjini T. M."], "categories": ["stat.AP", "stat.CO"], "doi": "", "_query": "hedonic house price", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2107.09027v2", "title": "Northcott numbers for the house and the Weil height", "abstract": "For an algebraic number $α$ and $γ\\in \\mathbb{R}$, $h(α)$ be the (logarithmic) Weil height, and $h_γ(α)=(\\mathrm{deg}α)^γh(α)$ be the $γ$-weighted (logarithmic) Weil height of $α$. Let $f:\\overline{\\mathbb{Q}}\\to [0,\\infty)$ be a function on the algebraic numbers $\\overline{\\mathbb{Q}}$, and let $S\\subset \\overline{\\mathbb{Q}}$. The Northcott number $\\mathcal{N}_f(S)$ of $S$, with respect to $f$, is the infimum of all $X\\geq 0$ such that $\\{α\\in S; f(α)< X\\}$ is infinite. This paper studies the set of Northcott numbers $\\mathcal{N}_f(\\mathcal{O})$ for subrings of $\\overline{\\mathbb{Q}}$ for the house, the Weil height, and the $γ$-weighted Weil height. We show: (1) Every $t\\geq 1$ is the Northcott number of a ring of integers of a field w.r.t. the house. (2) For each $t\\geq 0$ there exists a field with Northcott number in $ [t,2t]$ w.r.t. the Weil height $h(\\cdot)$. (3) For all $0\\leq γ\\leq 1$ and $γ'<γ$ there exists a field $K$ with $\\mathcal{N}_{h_{γ'}}(K)=0$ and $\\mathcal{N}_{h_γ}(K)=\\infty$. For $(1)$ we provide examples that satisfy an analogue of Julia Robinon's property (JR), examples that satisfy an analogue of Vidaux and Videla's isolation property, and examples that satisfy neither of those. Item $(2)$ concerns a question raised by Vidaux and Videla due to its direct link with decidability theory via the Julia Robinson number. Item (3) is a strong generalisation of the known fact that there are fields that satisfy the Lehmer conjecture but which are not Bogomolov in the sense of Bombieri and Zannier.", "published": "2021-07-19T17:17:28Z", "authors": ["Fabien Pazuki", "Niclas Technau", "Martin Widmer"], "categories": ["math.NT"], "doi": "", "_query": "hedonic house price", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/1609.08450v2", "title": "Stochastic epidemic dynamics on extremely heterogeneous networks", "abstract": "Networks of contacts capable of spreading infectious diseases are often observed to be highly heterogeneous, with the majority of individuals having fewer contacts than the mean, and a significant minority having relatively very many contacts. We derive a two-dimensional diffusion model for the full temporal behavior of the stochastic susceptible-infectious-recovered (SIR) model on such a network, by making use of a time-scale separation in the deterministic limit of the dynamics. This low-dimensional process is an accurate approximation to the full model in the limit of large populations, even for cases when the time-scale separation is not too pronounced, provided the maximum degree is not of the order of the population size.", "published": "2016-09-27T13:56:43Z", "authors": ["César Parra-Rojas", "Thomas House", "Alan J. McKane"], "categories": ["physics.soc-ph", "physics.bio-ph", "q-bio.PE"], "doi": "10.1103/PhysRevE.94.062408", "_query": "hedonic house price", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2509.18212v1", "title": "Personalised Pricing: The Demise of the Fixed Price?", "abstract": "An online seller or platform is technically able to offer every consumer a different price for the same product, based on information it has about the customers. Such online price discrimination exacerbates concerns regarding the fairness and morality of price discrimination, and the possible need for regulation. In this chapter, we discuss the underlying basis of price discrimination in economic theory, and its popular perception. Our surveys show that consumers are critical and suspicious of online price discrimination. A majority consider it unacceptable and unfair, and are in favour of a ban. When stores apply online price discrimination, most consumers think they should be informed about it. We argue that the General Data Protection Regulation (GDPR) applies to the most controversial forms of online price discrimination, and not only requires companies to disclose their use of price discrimination, but also requires companies to ask customers for their prior consent. Industry practice, however, does not show any adoption of these two principles.", "published": "2025-09-21T11:39:08Z", "authors": ["Joost Poort", "Frederik Zuiderveen Borgesius"], "categories": ["cs.CY"], "doi": "", "_query": "hedonic house price", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/1805.12101v1", "title": "Unravelling Airbnb Predicting Price for New Listing", "abstract": "This paper analyzes Airbnb listings in the city of San Francisco to better understand how different attributes such as bedrooms, location, house type amongst others can be used to accurately predict the price of a new listing that optimal in terms of the host's profitability yet affordable to their guests. This model is intended to be helpful to the internal pricing tools that Airbnb provides to its hosts. Furthermore, additional analysis is performed to ascertain the likelihood of a listings availability for potential guests to consider while making a booking. The analysis begins with exploring and examining the data to make necessary transformations that can be conducive for a better understanding of the problem at large while helping us make hypothesis. Moving further, machine learning models are built that are intuitive to use to validate the hypothesis on pricing and availability and run experiments in that context to arrive at a viable solution. The paper then concludes with a discussion on the business implications, associated risks and future scope.", "published": "2018-05-25T14:07:17Z", "authors": ["Paridhi Choudhary", "Aniket Jain", "Rahul Baijal"], "categories": ["q-fin.GN"], "doi": "", "_query": "hedonic house price", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2508.15248v1", "title": "Estimating profitable price bounds for prescriptive price optimization", "abstract": "Pricing of products and services, which has a significant impact on consumer demand, is one of the most important factors in maximizing business profits. Prescriptive price optimization is a prominent data-driven pricing methodology consisting of two phases: demand forecasting and price optimization. In the practice of prescriptive price optimization, the price of each item is typically set within a predetermined range defined by lower and upper bounds. Narrow price ranges can lead to missed opportunities, while wide price ranges run the risk of proposing unrealistic prices; therefore, determining profitable price bounds while maintaining the reliability of the suggested prices is a critical challenge that directly affects the effectiveness of prescriptive price optimization. We propose two methods for estimating price bounds in prescriptive price optimization so that future total revenue derived from the optimized prices will be maximized. Our first method for price bounds estimation uses the bootstrap procedure to estimate confidence intervals for optimal prices. Our second method uses the Nelder--Mead simplex method for black-box price bounds optimization that maximizes total revenue estimated through $K$-fold cross-validation. Experimental results with synthetic price--demand datasets demonstrate that our methods successfully narrowed down the price range while maintaining high revenues, particularly when the number of items was small or the demand noise level was low. Moreover, as more data accumulated, the comparative advantage of our methods further increased.", "published": "2025-08-21T05:19:26Z", "authors": ["Masato Inokuma", "Shunnosuke Ikeda", "Yuichi Takano"], "categories": ["math.OC", "cs.CE"], "doi": "", "_query": "hedonic house price", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2403.15392v1", "title": "Radon mitigation by soil depressurisation case study: radon concentration and pressure field extension monitoring in a pilot house in Spain", "abstract": "A one-year monitoring study was conducted in a pilot house with high radon levels to investigate the ability and efficiency of radon mitigation by soil depressurisation (SD) both active and passive. The study included monitoring of radon concentration, pressure field extension (pfe) under the slab and some atmospheric parameters for different testing phases. Periods in which the house remained closed to foster radon accumulation were alternated with phases of active and passive soil depressurisation under different conditions. The behaviour of the radon concentration in the pilot house was analysed along with the influence of atmospheric variables, significant correlations were found for the radon concentration with atmospheric pressure, outdoor temperature and wind. From the pfe analysis it was proven that the pressure drop with distance from the suction point of the SD system is proportional to the depressurisation generated. It was found also that the permeability characterisation of the pilot house agrees with the literature about granular fill materials characterisation for radon SD systems across Europe. Radon reductions in excess of 85% were achieved for the different testing phases in all cases. Finally, from the results it was stated that a fan power of 23 W is sufficient to ensure radon reductions over 85% for dwellings with similar aggregate layer and soil permeability.", "published": "2024-02-08T12:22:50Z", "authors": ["Marta Fuente", "Jamie Goggins", "Daniel Rabago", "Ismael Fuente", "Carlos Sainz", "Mark Foley"], "categories": ["cs.CE"], "doi": "10.1016/j.scitotenv.2019.133746", "_query": "hedonic house price", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2104.10281v1", "title": "Nonlinear Pricing with Misspecified and Arbitrary Perception of the Marginal Price", "abstract": "In the context of nonlinear prices, the empirical evidence suggests that the consumers have cognitive biases represented in a limited understanding of nonlinear price structures, and they respond to some alternative perceptions of the marginal prices. In particular, consumers usually make choices based more on the average than the marginal prices, which can result in a suboptimal behavior. Taking the misspecification in the marginal price as exogenous, this document analyzes how is the optimal quadratic price scheme for a monopolist and the optimal quadratic price scheme that maximizes welfare when there is a continuum of representative consumers with an arbitrary perception of the marginal price. Under simple preferences and costs functional forms and very straightforward hypotheses, the results suggest that the bias in the marginal price doesn't affect the maximum welfare attainable with quadratic price schemes, and it has a negligible effect over the efficiency cost caused by the monopolist, so the misspecification in the marginal price is not relevant for welfare increasing policies. However, almost always the misspecification in the marginal price is beneficial for the monopolist. An interesting result is that under these functional forms, more misspecification in the marginal price is beneficial for both the consumers and the monopolist if the level of bias is low, so not always is socially optima to have educated consumers about the real marginal price. Finally, the document shows that the bias in the marginal price has a negative effect on reduction of aggregate consumption using two-tier increasing tariffs, which are commonly used for reduction of aggregate consumption.", "published": "2021-04-20T23:22:42Z", "authors": ["Diego Alejandro Murillo Taborda"], "categories": ["econ.GN"], "doi": "", "_query": "hedonic house price", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/1312.7449v4", "title": "Extinction times in the subcritical stochastic SIS logistic epidemic", "abstract": "Many real epidemics of an infectious disease are not straightforwardly super- or sub-critical, and the understanding of epidemic models that exhibit such complexity has been identified as a priority for theoretical work. We provide insights into the near-critical regime by considering the stochastic SIS logistic epidemic, a well-known birth-and-death chain used to model the spread of an epidemic within a population of a given size $N$. We study the behaviour of the process as the population size $N$ tends to infinity. Our results cover the entire subcritical regime, including the \"barely subcritical\" regime, where the recovery rate exceeds the infection rate by an amount that tends to 0 as $N \\to \\infty$ but more slowly than $N^{-1/2}$. We derive precise asymptotics for the distribution of the extinction time and the total number of cases throughout the subcritical regime, give a detailed description of the course of the epidemic, and compare to numerical results for a range of parameter values. We hypothesise that features of the course of the epidemic will be seen in a wide class of other epidemic models, and we use real data to provide some tentative and preliminary support for this theory.", "published": "2013-12-28T16:49:27Z", "authors": ["Graham Brightwell", "Thomas House", "Malwina Luczak"], "categories": ["math.PR"], "doi": "10.1007/s00285-018-1210-5", "_query": "hedonic house price", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2407.14101v1", "title": "Justified Fairness in House Allocation Problems: two Characterizations of Strategy-proof Mechanisms", "abstract": "We consider the house allocation problems with strict preferences, where monetary transfers are not allowed. We propose two properties in the spirit of justified fairness. Interestingly, together with other well-studied properties (strategy-proofness and non-bossiness), our two new properties identify serial dictatorships and sequential dictatorships, respectively.", "published": "2024-07-19T08:12:54Z", "authors": ["Di Feng", "Jacob Coreno"], "categories": ["econ.TH"], "doi": "", "_query": "hedonic house price", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/1911.12115v1", "title": "Generalised Linear Models for Dependent Binary Outcomes with Applications to Household Stratified Pandemic Influenza Data", "abstract": "Much traditional statistical modelling assumes that the outcome variables of interest are independent of each other when conditioned on the explanatory variables. This assumption is strongly violated in the case of infectious diseases, particularly in close-contact settings such as households, where each individual's probability of infection is strongly influenced by whether other household members experience infection. On the other hand, general multi-type transmission models of household epidemics quickly become unidentifiable from data as the number of types increases. This has led to a situation where it is has not been possible to draw consistent conclusions from household studies of infectious diseases, for example in the event of an influenza pandemic. Here, we present a generalised linear modelling framework for binary outcomes in sub-units that can (i) capture the effects of non-independence arising from a transmission process and (ii) adjust estimates of disease risk and severity for differences in study population characteristics. This model allows for computationally fast estimation, uncertainty quantification, covariate choice and model selection. In application to real pandemic influenza household data, we show that it is formally favoured over existing modelling approaches.", "published": "2019-11-27T12:48:19Z", "authors": ["Timothy Kinyanjui", "Thomas House"], "categories": ["q-bio.PE"], "doi": "", "_query": "hedonic house price", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2603.25235v2", "title": "Bayesian Inference for Epidemic Final Size Datasets with Hidden Underlying Household Structure", "abstract": "Households represent a key unit of interest in infectious disease epidemiology, in both empirical studies and mathematical modelling. The within-household transmission potential of a disease is often summarised by a secondary attack ratio (SAR). Despite its widespread use, the SAR depends on the household size distribution (HHSD) seen during the study period, making it difficult to generalise to new contexts. Extending estimates of transmission potential to new populations instead requires estimates of person-to-person transmission rates which can be convoluted with data on population structure to parametrise mechanistic transmission models. In this study we present a new Bayesian inference method which uses an MCMC algorithm to infer the transmission intensity by imputing the unreported household structure underlying the epidemic. This method can be run on household epidemiological data reported at varying levels of resolution. For synthetic data from a realistic underlying HHSD, we were able to achieve over 90% coverage in our estimates of transmission rate consistently. We were also able to consistently achieve over 90% coverage for data generated with a pathological underlying HHSD, given strong information about the HHSD. Using an existing dataset which recorded micro-scale household epidemiological outcomes during the COVID-19 pandemic, we show that stratifying observed SARs by household size substantially reduces the uncertainty in estimates. Our findings suggest that researchers conducting household epidemiological studies can improve the utility of results for infectious disease modellers by reporting household-stratified estimates. These results aim to encourage the reporting of higher resolution outputs in epidemiological field work as, in the absence of strong priors, transmission parameters were not easily identifiable from low resolution datasets, which are often reported.", "published": "2026-03-26T09:37:01Z", "authors": ["Joseph Brooks", "Thomas House", "Lorenzo Pellis", "Joe Hilton"], "categories": ["stat.AP"], "doi": "", "_query": "hedonic house price", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2404.07132v1", "title": "Hedonic Models Incorporating ESG Factors for Time Series of Average Annual Home Prices", "abstract": "Using data from 2000 through 2022, we analyze the predictive capability of the annual numbers of new home constructions and four available environmental, social, and governance factors on the average annual price of homes sold in eight major U.S. cities. We contrast the predictive capability of a P-spline generalized additive model (GAM) against a strictly linear version of the commonly used generalized linear model (GLM). As the data for the annual price and predictor variables constitute non-stationary time series, to avoid spurious correlations in the analysis we transform each time series appropriately to produce stationary series for use in the GAM and GLM models. While arithmetic returns or first differences are adequate transformations for the predictor variables, for the average price response variable we utilize the series of innovations obtained from AR(q)-ARCH(1) fits. Based on the GAM results, we find that the influence of ESG factors varies markedly by city, reflecting geographic diversity. Notably, the presence of air conditioning emerges as a strong factor. Despite limitations on the length of available time series, this study represents a pivotal step toward integrating ESG considerations into predictive real estate models.", "published": "2024-04-10T16:09:47Z", "authors": ["Jason R. Bailey", "W. Brent Lindquist", "Svetlozar T. Rachev"], "categories": ["q-fin.CP"], "doi": "", "_query": "hedonic house price", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2302.05808v1", "title": "Long-term option pricing with a lower reflecting barrier", "abstract": "This paper considers the pricing of long-term options on assets such as housing, where either government intervention or the economic nature of the asset is assumed to limit large falls in prices. The observed asset price is modelled by a geometric Brownian motion (the 'notional price') reflected at a lower barrier. The resulting observed price has standard dynamics but with localised intervention at the barrier, which allows arbitrage with interim losses; this is funded by the government's unlimited powers of intervention, and its exploitation is subject to credit constraints. Despite the lack of an equivalent martingale measure for the observed price, options on this price can be expressed as compound options on the arbitrage-free notional price, to which standard risk-neutral arguments can be applied. Because option deltas tend to zero when the observed price approaches the barrier, hedging with the observed price gives the same results as hedging with the notional price, and so exactly replicates option payoffs. Hedging schemes are not unique, with the cheapest scheme for any derivative being the one which best exploits the interventions at the barrier. The price of a put is clear: direct replication has a lower initial cost than synthetic replication, and the replication portfolio always has positive value. The price of a call is ambiguous: synthetic replication has a lower initial cost than direct replication, but the replication portfolio may give interim losses, and so the preferred replication strategy (and hence price) of a call may depend on what margin payments need to be made on these losses.", "published": "2023-02-11T22:59:34Z", "authors": ["R. Guy Thomas"], "categories": ["q-fin.PR", "q-fin.MF", "q-fin.RM"], "doi": "10.1017/S1748499522000227", "_query": "hedonic house price", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2306.17309v1", "title": "Retail Pricing Format and Rigidity of Regular Prices", "abstract": "We study the price rigidity of regular and sale prices, and how it is affected by pricing formats (pricing strategies). We use data from three large Canadian stores with different pricing formats (Every-Day-Low-Price, Hi-Lo, and Hybrid) that are located within a 1 km radius of each other. Our data contains both the actual transaction prices and actual regular prices as displayed on the store shelves. We combine these data with two generated regular price series (filtered prices and reference prices) and study their rigidity. Regular price rigidity varies with store formats because different format stores treat sale prices differently, and consequently define regular prices differently. Correspondingly, the meanings of price cuts and sale prices vary across store formats. To interpret the findings, we consider the store pricing format distribution across the US.", "published": "2023-06-29T21:35:32Z", "authors": ["Sourav Ray", "Avichai Snir", "Daniel Levy"], "categories": ["econ.GN"], "doi": "", "_query": "hedonic house price", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2606.31086v1", "title": "CasaMaestro: Multi-View Panoramas for House-Scale 3D Reconstruction", "abstract": "The rise of home-deployed embodied AI systems is driving a growing need for fast, metric 3D reconstruction of residential spaces to support navigation, interaction, and long-horizon task execution. However, the commonly used pinhole-camera 3D reconstruction pipelines struggle to model large indoor residences efficiently due to their limited field of view, to which achieving full coverage across multiple rooms often requires thousands of images and incurs drift from long chains of incremental alignment. In this work, we present CasaMaestro (Spanish words meaning ``house'' and ``master''), a feedforward model that can take only twenty to fifty sparse multi-view indoor panoramas as input and directly predicts metric depth along with camera poses, allowing fast point-cloud reconstruction of the entire house with full coverage. CasaMaestro is the first model that supports house-scale reconstruction with multi-view panoramas. Experiments show that CasaMaestro can robustly provide high quality results in both real-world and synthetic scenes, which can serve as a strong foundation for acquiring house-scale 3D indoor assets to be applied in close-loop simulation.", "published": "2026-06-30T03:21:57Z", "authors": ["Yuzhou Ji", "Xiaotian Yang", "Zhipeng Zhang"], "categories": ["cs.CV"], "doi": "", "_query": "hedonic house price", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/0910.3219v1", "title": "Turbulent flow over a house in a simulated hurricane boundary layer", "abstract": "Every year hurricanes and other extreme wind storms cause billions of dollars in damage worldwide. For residential construction, such failures are usually associated with roofs, which see the largest aerodynamic loading. However, determining aerodynamic loads on different portions of North American houses is complicated by the lack of clear load paths and non-linear load sharing in wood frame roofs. This problem of fluid-structure interaction requires both wind tunnel testing and full-scale structural testing. A series of wind tunnel tests have been performed on a house in a simulated atmospheric boundary layer (ABL), with the resulting wind-induced pressures applied to the full-scale structure. The ABL was simulated for flow over open country terrain where both velocity and turbulence intensity profiles, as well as spectra, were matched with available full scale measurements for this type of terrain. The first set of measurements was 600 simultaneous surface pressure measurements over the entire house. A key feature of the surface pressure field is the occurrence of large, highly non-Gaussian, peak uplift (suctions) on the roof. In order to better understand which flow features cause this, PIV experiments were performed on the wind tunnel model. These experiments were performed with time-resolved PIV (sampling rate of 500 Hz) for a duration of 30 seconds. From the fluid dynamics videos (low- and high-resolution) generated from the PIV data it is clear that strong circulation is generated at the windward edge of the roof. These vortices are eventually shed and convect along the roof. It is the presence of this concentrated circulation which is responsible for the peak loading observed.", "published": "2009-10-16T20:12:28Z", "authors": ["Zachary Taylor", "Murray Morrison", "Roi Gurka", "Gregory Kopp"], "categories": ["physics.flu-dyn"], "doi": "", "_query": "hedonic house price", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2107.07148v1", "title": "What Image Features Boost Housing Market Predictions?", "abstract": "The attractiveness of a property is one of the most interesting, yet challenging, categories to model. Image characteristics are used to describe certain attributes, and to examine the influence of visual factors on the price or timeframe of the listing. In this paper, we propose a set of techniques for the extraction of visual features for efficient numerical inclusion in modern-day predictive algorithms. We discuss techniques such as Shannon's entropy, calculating the center of gravity, employing image segmentation, and using Convolutional Neural Networks. After comparing these techniques as applied to a set of property-related images (indoor, outdoor, and satellite), we conclude the following: (i) the entropy is the most efficient single-digit visual measure for housing price prediction; (ii) image segmentation is the most important visual feature for the prediction of housing lifespan; and (iii) deep image features can be used to quantify interior characteristics and contribute to captivation modeling. The set of 40 image features selected here carries a significant amount of predictive power and outperforms some of the strongest metadata predictors. Without any need to replace a human expert in a real-estate appraisal process, we conclude that the techniques presented in this paper can efficiently describe visible characteristics, thus introducing perceived attractiveness as a quantitative measure into the predictive modeling of housing.", "published": "2021-07-15T06:32:10Z", "authors": ["Zona Kostic", "Aleksandar Jevremovic"], "categories": ["cs.CV", "cs.LG"], "doi": "10.1109/TMM.2020.2966890", "_query": "hedonic house price", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/1412.6274v1", "title": "Y Chromosome of Aisin Gioro, the Imperial House of Qing Dynasty", "abstract": "House of Aisin Gioro is the imperial family of the last dynasty in Chinese history - Qing Dynasty (1644 - 1911). Aisin Gioro family originated from Jurchen tribes and developed the Manchu people before they conquered China. By investigating the Y chromosomal short tandem repeats (STRs) of 7 modern male individuals who claim belonging to Aisin Gioro family (in which 3 have full records of pedigree), we found that 3 of them (in which 2 keep full pedigree, whose most recent common ancestor is Nurgaci) shows very close relationship (1 - 2 steps of difference in 17 STR) and the haplotype is rare. We therefore conclude that this haplotype is the Y chromosome of the House of Aisin Gioro. Further tests of single nucleotide polymorphisms (SNPs) indicates that they belong to Haplogroup C3b2b1*-M401(xF5483), although their Y-STR results are distant to the \"star cluster\", which also belongs to the same haplogroup. This study forms the base for the pedigree research of the imperial family of Qing Dynasty by means of genetics.", "published": "2014-12-19T10:27:22Z", "authors": ["Shi Yan", "Harumasa Tachibana", "Lan-Hai Wei", "Ge Yu", "Shao-Qing Wen", "Chuan-Chao Wang"], "categories": ["q-bio.PE"], "doi": "10.1038/jhg.2015.28", "_query": "hedonic house price", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2607.26788v1", "title": "Stable and Budget-Feasible Coalition Formation for Clustered Federated Learning: A Hedonic Potential-Game Approach", "abstract": "Clustered federated learning benefits from organizing heterogeneous participants into coalitions that train coalition-specific models, but such clustering is sustainable only if participants prefer their assigned coalition and the required transfers are affordable. We develop a transferable-surplus model separating learning benefit, system cost, participant cost, and monetary transfers; an allocation rule converts coalition surplus into hedonic preferences, and weak budget feasibility guarantees nonnegative retained coordinator surplus. For symmetric pairwise allocations the induced game is an exact potential game: a Nash-stable partition exists, every strict better-response process converges, and with destination consent accepted better responses reach an individually stable partition. We characterize feasibility of bounded pair incentives and verify the exponentially many budget constraints in polynomial oracle time when retained slack is submodular. Decomposing welfare into participant potential and retained slack yields additive and multiplicative price-of-stability guarantees, the latter asymptotically tight; exact balance gives welfare-optimal stability only on the pairwise-representable class, and budget feasibility alone permits unbounded welfare loss. Global potential maximization equals weighted maximum-agreement correlation clustering, and approximation followed by stabilization satisfies an end-to-end welfare bound governed by retained slack and negative-edge mass, attained by an explicit construction. In a preregistered five-seed CIFAR-10 study the mechanism reaches the certified estimated-table welfare optimum on every primary instance, equal-surplus sharing has no Nash-stable outcome on three, and pairwise validation gain gives far more reliable pair signs than gradient alignment.", "published": "2026-07-29T11:29:45Z", "authors": ["Cengis Hasan"], "categories": ["cs.GT", "cs.LG"], "doi": "", "_query": "hedonic house price", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2604.15580v1", "title": "Housing Decisions under Mobility Risk: A Stochastic Threshold Approach", "abstract": "We develop a stochastic free-boundary model of housing tenure decisions in markets with high mobility risk, such as areas near military installations. Housing prices and rents follow correlated diffusion processes, and households face an uncertain relocation horizon. We derive a closed-form characterization of the optimal buy-versus-rent boundary in terms of the price-to-rent ratio. The model highlights how mobility risk reduces the value of ownership by shortening the effective holding period and increasing uncertainty. As a result, identical price-to-rent ratios can imply different optimal decisions across locations. Numerical illustrations show how variations in volatility and relocation intensity shift the threshold, providing a structural interpretation of observed cross-market heterogeneity. The framework offers a tractable tool for analyzing housing decisions under uncertainty and for interpreting price-to-rent ratios in high-mobility environments.", "published": "2026-04-16T23:25:14Z", "authors": ["Hui Wu"], "categories": ["math.OC"], "doi": "", "_query": "hedonic house price", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/1912.05164v3", "title": "Third-Degree Price Discrimination Versus Uniform Pricing", "abstract": "We compare the profit of the optimal third-degree price discrimination policy against a uniform pricing policy. A uniform pricing policy offers the same price to all segments of the market. Our main result establishes that for a broad class of third-degree price discrimination problems with concave profit functions (in the price space) and common support, a uniform price is guaranteed to achieve one half of the optimal monopoly profits. This profit bound holds for any number of segments and prices that the seller might use under third-degree price discrimination. We establish that these conditions are tight and that weakening either common support or concavity can lead to arbitrarily poor profit comparisons even for regular or monotone hazard rate distributions.", "published": "2019-12-11T08:15:49Z", "authors": ["Dirk Bergemann", "Francisco Castro", "Gabriel Weintraub"], "categories": ["econ.GN", "cs.GT", "econ.TH"], "doi": "", "_query": "hedonic house price", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/1503.05139v1", "title": "Pricing of Warrants with Stock Price Dependent Threshold Conditions", "abstract": "Warrants with stock price dependent threshold conditions give the right to buy specially issued stocks, if the performance of the stock price satisfies some requirements. Existence of these derivatives changes the price process of the underlying. We show that in the presence of such warrants one cannot assume that the stock market is arbitrage free and that the stock is tradeable at every time moment with the same price for buying and selling. This means that the usual methods for deriving fair prices for such warrants cannot be used. We start from a simple model for the firm's value process and discuss some ways to specify a related model for the stock price process in the presence of warrants with threshold conditions. We also discuss how indifference pricing approach can be used for pricing such warrants.", "published": "2015-03-17T17:53:01Z", "authors": ["Ander Olvik", "Raul Kangro"], "categories": ["q-fin.PR"], "doi": "", "_query": "hedonic house price", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2105.01913v4", "title": "Identifying hidden coalitions in the US House of Representatives by optimally partitioning signed networks based on generalized balance", "abstract": "In network science, identifying optimal partitions of a signed network into internally cohesive and mutually divisive clusters based on generalized balance theory is computationally challenging. We reformulate and generalize two binary linear programming models that tackle this challenge, demonstrating their practicality by applying them them to partition networks of collaboration in the US House of Representatives. These models guarantee a globally optimal network partition and can be practically applied to signed networks containing up to 30,000 edges. In the US House context, we find that a three-cluster partition is better than a conventional two-cluster partition, where the otherwise hidden third coalition is composed of highly effective legislators who are ideologically aligned with the majority party.", "published": "2021-05-05T07:57:41Z", "authors": ["Samin Aref", "Zachary P. Neal"], "categories": ["cs.SI", "cs.CY", "math.OC", "physics.soc-ph"], "doi": "10.1038/s41598-021-98139-w", "_query": "hedonic house price", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2207.09827v2", "title": "Comparing directed networks via denoising graphlet distributions", "abstract": "Network comparison is a widely-used tool for analyzing complex systems, with applications in varied domains including comparison of protein interactions or highlighting changes in structure of trade networks. In recent years, a number of network comparison methodologies based on the distribution of graphlets (small connected network subgraphs) have been introduced. In particular, NetEmd has recently achieved state of the art performance in undirected networks. In this work, we propose an extension of NetEmd to directed networks and deal with the significant increase in complexity of graphlet structure in the directed case by denoising through linear projections. Simulation results show that our framework is able to improve on the performance of a simple translation of the undirected NetEmd algorithm to the directed case, especially when networks differ in size and density.", "published": "2022-07-20T11:26:44Z", "authors": ["Miguel E. P. Silva", "Robert E. Gaunt", "Luis Ospina-Forero", "Caroline Jay", "Thomas House"], "categories": ["cs.SI", "stat.AP"], "doi": "10.1093/comnet/cnad006", "_query": "hedonic house price", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2405.14909v2", "title": "Interpretable Price Bounds Estimation with Shape Constraints in Price Optimization", "abstract": "This study addresses the interpretable estimation of price bounds in the context of price optimization. In recent years, price-optimization methods have become indispensable for maximizing revenue and profits. However, effective application of these methods to real-world pricing operations remains a significant challenge. It is crucial for operators responsible for setting prices to utilize reasonable price bounds that are not only interpretable but also acceptable. Despite this necessity, most studies assume that price bounds are given constant values, and few have explored reasonable determinations of these bounds. Therefore, we propose a comprehensive framework for determining price bounds that includes both the estimation and adjustment of these bounds. Specifically, we first estimate price bounds using three distinct approaches based on historical pricing data. Then, we adjust the estimated price bounds by solving an optimization problem that incorporates shape constraints. This method allows the implementation of price optimization under practical and reasonable price bounds suitable for real-world applications. We report the effectiveness of our proposed method through numerical experiments using historical pricing data from actual services.", "published": "2024-05-23T10:30:16Z", "authors": ["Shunnosuke Ikeda", "Naoki Nishimura", "Shunji Umetani"], "categories": ["cs.GT", "cs.AI", "math.OC"], "doi": "", "_query": "hedonic house price", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/1802.04774v2", "title": "Asset Price Volatility and Price Extrema", "abstract": "The relationship between price volatilty and a market extremum is examined using a fundamental economics model of supply and demand. By examining randomness through a microeconomic setting, we obtain the implications of randomness in the supply and demand, rather than assuming that price has randomness on an empirical basis. Within a very general setting the volatility has an extremum that precedes the extremum of the price. A key issue is that randomness arises from the supply and demand, and the variance in the stochastic differential equation govening the logarithm of price must reflect this. Analogous results are obtained by further assuming that the supply and demand are dependent on the deviation from fundamental value of the asset.", "published": "2018-02-13T18:07:43Z", "authors": ["Carey Caginalp", "Gunduz Caginalp"], "categories": ["q-fin.MF", "math.PR", "math.ST"], "doi": "", "_query": "hedonic house price", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/1010.4499v1", "title": "Hedonic Coalition Formation for Distributed Task Allocation among Wireless Agents", "abstract": "Autonomous wireless agents such as unmanned aerial vehicles or mobile base stations present a great potential for deployment in next-generation wireless networks. While current literature has been mainly focused on the use of agents within robotics or software applications, we propose a novel usage model for self-organizing agents suited to wireless networks. In the proposed model, a number of agents are required to collect data from several arbitrarily located tasks. Each task represents a queue of packets that require collection and subsequent wireless transmission by the agents to a central receiver. The problem is modeled as a hedonic coalition formation game between the agents and the tasks that interact in order to form disjoint coalitions. Each formed coalition is modeled as a polling system consisting of a number of agents which move between the different tasks present in the coalition, collect and transmit the packets. Within each coalition, some agents can also take the role of a relay for improving the packet success rate of the transmission. The proposed algorithm allows the tasks and the agents to take distributed decisions to join or leave a coalition, based on the achieved benefit in terms of effective throughput, and the cost in terms of delay. As a result of these decisions, the agents and tasks structure themselves into independent disjoint coalitions which constitute a Nash-stable network partition. Moreover, the proposed algorithm allows the agents and tasks to adapt the topology to environmental changes such as the arrival/removal of tasks or the mobility of the tasks. Simulation results show how the proposed algorithm improves the performance, in terms of average player (agent or task) payoff, of at least 30.26% (for a network of 5 agents with up to 25 tasks) relatively to a scheme that allocates nearby tasks equally among agents.", "published": "2010-10-21T15:32:03Z", "authors": ["Walid Saad", "Zhu Han", "Tamer Basar", "Merouane Debbah", "Are Hjørungnes"], "categories": ["cs.IT", "cs.GT"], "doi": "10.1109/TMC.2010.242", "_query": "hedonic house price", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2102.02935v1", "title": "Does Collateral Value Affect Asset Prices? Evidence from a Natural Experiment in Texas", "abstract": "Does the ability to pledge an asset as collateral, after purchase, affect its price? This paper identifies the impact of collateral service flows on house prices, exploiting a plausibly exogenous constitutional amendment in Texas which legalized home equity loans in 1998. The law change increased Texas house prices 4%; this is price-based evidence that households are credit-constrained and value home equity loans to facilitate consumption smoothing. Prices rose more in locations with inelastic supply, higher pre-law house prices, higher income, and lower unemployment. These estimates reveal that richer households value the option to pledge their home as collateral more strongly.", "published": "2021-02-05T00:37:49Z", "authors": ["Albert Alex Zevelev"], "categories": ["econ.GN"], "doi": "10.1093/rfs/hhaa117", "_query": "hedonic house price", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/1501.04824v2", "title": "Real-time growth rate for general stochastic SIR epidemics on unclustered networks", "abstract": "Networks have become an important tool for infectious disease epidemiology. Most previous theoretical studies of transmission network models have either considered simple Markovian dynamics at the individual level, or have focused on the invasion threshold and final outcome of the epidemic. Here, we provide a general theory for early real-time behaviour of epidemics on large configuration model networks (i.e. static and locally unclustered), in particular focusing on the computation of the Malthusian parameter that describes the early exponential epidemic growth. Analytical, numerical and Monte-Carlo methods under a wide variety of Markovian and non-Markovian assumptions about the infectivity profile are presented. Numerous examples provide explicit quantification of the impact of the network structure on the temporal dynamics of the spread of infection and provide a benchmark for validating results of large scale simulations.", "published": "2015-01-20T14:39:05Z", "authors": ["Lorenzo Pellis", "Simon E. F. Spencer", "Thomas House"], "categories": ["q-bio.PE", "math.PR"], "doi": "", "_query": "hedonic house price", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2306.09529v1", "title": "House-Swapping with Objective Indifferences", "abstract": "We study the classic house-swapping problem of Shapley and Scarf (1974) in a setting where agents may have \"objective\" indifferences, i.e., indifferences that are shared by all agents. In other words, if any one agent is indifferent between two houses, then all agents are indifferent between those two houses. The most direct interpretation is the presence of multiple copies of the same object. Our setting is a special case of the house-swapping problem with general indifferences. We derive a simple, easily interpretable algorithm that produces the unique strict core allocation of the house-swapping market, if it exists. Our algorithm runs in square polynomial time, a substantial improvement over the cubed time methods for the more general problem.", "published": "2023-06-15T22:16:22Z", "authors": ["Will Sandholtz", "Andrew Tai"], "categories": ["econ.TH", "cs.DM", "cs.GT"], "doi": "", "_query": "hedonic house price", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2512.03284v2", "title": "Think, Then Look: Active Spatial Reasoning for House-Scale 3D Scene Understanding", "abstract": "Spatial reasoning in large-scale 3D environments remains challenging for current vision--language models, which are typically constrained to room-scale scenarios. We formalize Active House-Scale Spatial Reasoning (AHSR), a new paradigm in which a model reasons over a pre-built house-scale 3D map via virtual spatial tool invocations to answer spatial questions, without exhaustive scene-wide processing. To support AHSR research, we introduce H$^2$U3D (Holistic House Understanding in 3D), the first benchmark targeting house-scale 3D scene understanding, featuring environments with an average aggregate floor area of 250.8 m$^2$ and up to three floors, together with hierarchical coarse-to-fine visual representations. Building on H$^2$U3D, we propose SpatialReasoner, an AHSR framework trained via supervised fine-tuning with self-correction, followed by reinforcement learning with a task-aware adaptive exploration reward. SpatialReasoner achieves state-of-the-art performance on H$^2$U3D with 64.9% overall accuracy, outperforming strong baselines including GPT-5.4 and Gemini-3.5-Flash, and generalizes effectively to MT-HM3D and HM-EQA. These results demonstrate the clear advantage of active map-directed exploration over passive scene-wide processing in house-scale 3D understanding.", "published": "2025-12-02T22:49:01Z", "authors": ["Hongpei Zheng", "Shijie Li", "Lin Qian", "Zhenghao Li", "Qijun Yang", "Yanran Li", "Hujun Yin"], "categories": ["cs.CV"], "doi": "", "_query": "hedonic house price", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/1311.5652v2", "title": "Interspecific Introgressive Origin of Genomic Diversity in the House Mouse", "abstract": "We report on a genome-wide scan for introgression in the house mouse (Mus musculus domesticus) involving the Algerian mouse (Mus spretus), using samples from the ranges of sympatry and allopatry in Africa and Europe. Our analysis reveals wide variability in introgression signatures along the genomes, as well as across the samples. We find that fewer than half of the autosomes in each genome harbor all detectable introgression, while the X chromosome has none. Further, European mice carry more M. spretus alleles than the sympatric African ones. Using the length distribution and sharing patterns of introgressed genomic tracts across the samples, we infer, first, that at least three distinct hybridization events involving M. spretus have occurred, one of which is ancient, and the other two are recent (one presumably due to warfarin rodenticide selection). Second, several of the inferred introgressed tracts contain genes that are likely to confer adaptive advantage. Third, introgressed tracts might contain driver genes that determine the evolutionary fate of those tracts. Further, functional analysis revealed introgressed genes that are essential to fitness, including the Vkorc1 gene, which is implicated in rodenticide resistance, and olfactory receptor genes. Our findings highlight the extent and role of introgression in nature, and call for careful analysis and interpretation of house mouse data in evolutionary and genetic studies.", "published": "2013-11-22T04:35:47Z", "authors": ["Kevin J. Liu", "Ethan Steinberg", "Alexander Yozzo", "Ying Song", "Michael H. Kohn", "Luay Nakhleh"], "categories": ["q-bio.PE"], "doi": "", "_query": "hedonic house price", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2102.13157v1", "title": "Does Bankruptcy Protection Affect Asset Prices? Evidence from changes in Homestead Exemptions", "abstract": "Does the ability to protect an asset from unsecured creditors affect its price? This paper identifies the impact of bankruptcy protection on house prices using 139 changes in homestead exemptions. Large increases in the homestead exemption raised house prices 3% before 2005. Smaller exemption increases, to adjust for inflation, did not affect house prices. The effect disappeared after BAPCPA, a 2005 federal law designed to prevent bankruptcy abuse. The effect was bigger in inelastic locations.", "published": "2021-02-25T20:32:45Z", "authors": ["Yildiray Yildirim", "Albert Alex Zevelev"], "categories": ["econ.GN"], "doi": "", "_query": "hedonic house price", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/1204.5171v1", "title": "ConocoPhillips' share price model revisited", "abstract": "Three years ago we found a statistically reliable link between ConocoPhillips' (NYSE: COP) stock price and the difference between the core and headline CPI in the United States. In this article, the original relationship is revisited with new data available since 2009. The agreement between the observed monthly closing price (adjusted for dividends and splits) and that predicted from the CPI difference is confirmed. The original quantitative link is validated. In order to improve the accuracy of the COP price prediction a series of advanced models is developed. The original set of two major CPIs is extended by smaller components of the headline CPIs (e.g. the CPIs of motor fuel and housing energy) and several PPIs (e.g. the PPIs of crude oil and coal) which may be inherently related to ConocoPhillips and other energy companies. These advanced models have demonstrated much lower modeling errors with better statistical properties. The earlier reported quasi-linear trend in the CPI difference is also revisited. This trend allows for an accurate prediction of the COP prices at a five to ten year horizon.", "published": "2012-04-21T07:45:54Z", "authors": ["Ivan Kitov"], "categories": ["q-fin.GN"], "doi": "", "_query": "hedonic house price", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2512.02564v1", "title": "Retail Price Ripples", "abstract": "Much like small ripples in a stream, which get lost in the larger waves, small changes in retail prices often fly under the radar of public perceptions, while large price changes appear as marketing moves associated with demand and competition. Unnoticed, these could increase consumers out of pocket expenses. Indeed, retailers could boost their profits by making numerous small price increases or by obfuscating large price increases with numerous small price decreases, thereby bypassing the consumers full attention and consideration, and triggering consumer fairness concerns. Yet only a handful of papers study small price changes. Extant results are often based on a single retailer, limited products, short time span, and legacy datasets dating back to the 1980s and 1990s, leaving their current practical relevance questionable. Researchers have also questioned whether the reported observations of small price changes are artifacts of measurement errors driven by data aggregation. In a series of analyses of a large dataset containing almost 79 billion weekly price observations from 2006 to 2015, covering 527 products, and about 35,000 stores across 161 retailers, we find robust evidence of asymmetric pricing in the small, where small price increases outnumber small price decreases, but no such asymmetry is present in the large. We also document the reverse phenomenon, where small price decreases outnumber small price increases. Our results are robust to several possible measurement issues. Importantly, our findings indicate a greater current relevance and generalizability of such asymmetric pricing practices than the existing literature recognizes.", "published": "2025-12-02T09:36:00Z", "authors": ["Xiao Ling", "Sourav Ray", "Daniel Levy"], "categories": ["econ.GN"], "doi": "10.13140/RG.2.2.24328.07686", "_query": "hedonic house price", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2009.00947v4", "title": "Cyclotomic preperiodic points for morphisms in affine spaces and preperiodic points with bounded house and height", "abstract": "Under special conditions, we prove that the set of preperiodic points for semigroups of self-morphisms of affine spaces falling on cyclotomic closures is not dense. generalising results of Ostafe and Young (2020). We also extend previous results about boundness of house and height on certain preperiodicity sets of higher dimension in semigroup dynamics.", "published": "2020-09-02T11:03:56Z", "authors": ["Jorge Mello"], "categories": ["math.NT"], "doi": "", "_query": "hedonic house price", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/1909.09799v1", "title": "On the Price of Anarchy for High-Price Links", "abstract": "We study Nash equilibria and the price of anarchy in the classic model of Network Creation Games introduced by Fabrikant, Luthra, Maneva, Papadimitriou and Shenker in 2003. This is a selfish network creation model where players correspond to nodes in a network and each of them can create links to the other $n-1$ players at a prefixed price $α> 0$. The player's goal is to minimise the sum of her cost buying edges and her cost for using the resulting network. One of the main conjectures for this model states that the price of anarchy, i.e. the relative cost of the lack of coordination, is constant for all $α$. This conjecture has been confirmed for $α= O(n^{1-δ})$ with $δ\\geq 1/\\log n$ and for $α> 4n-13$. The best known upper bound on the price of anarchy for the remaining range is $2^{O(\\sqrt{\\log n})}$. We give new insights into the structure of the Nash equilibria for $α> n$ and we enlarge the range of the parameter $α$ for which the price of anarchy is constant. Specifically, we prove that for any small $ε>0$, the price of anarchy is constant for $α> n(1+ε)$ by showing that any biconnected component of any non-trivial Nash equilibrium, if it exists, has at most a constant number of nodes.", "published": "2019-09-21T09:10:16Z", "authors": ["Carme Àlvarez", "Arnau Messegué"], "categories": ["cs.GT"], "doi": "", "_query": "hedonic house price", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/1404.5245v6", "title": "Size versus truthfulness in the House Allocation problem", "abstract": "We study the House Allocation problem (also known as the Assignment problem), i.e., the problem of allocating a set of objects among a set of agents, where each agent has ordinal preferences (possibly involving ties) over a subset of the objects. We focus on truthful mechanisms without monetary transfers for finding large Pareto optimal matchings. It is straightforward to show that no deterministic truthful mechanism can approximate a maximum cardinality Pareto optimal matching with ratio better than 2. We thus consider randomised mechanisms. We give a natural and explicit extension of the classical Random Serial Dictatorship Mechanism (RSDM) specifically for the House Allocation problem where preference lists can include ties. We thus obtain a universally truthful randomised mechanism for finding a Pareto optimal matching and show that it achieves an approximation ratio of $\\frac{e}{e-1}$. The same bound holds even when agents have priorities (weights) and our goal is to find a maximum weight (as opposed to maximum cardinality) Pareto optimal matching. On the other hand we give a lower bound of $\\frac{18}{13}$ on the approximation ratio of any universally truthful Pareto optimal mechanism in settings with strict preferences. In the case that the mechanism must additionally be non-bossy with an additional technical assumption, we show by utilising a result of Bade that an improved lower bound of $\\frac{e}{e-1}$ holds. This lower bound is tight since RSDM for strict preference lists is non-bossy. We moreover interpret our problem in terms of the classical secretary problem and prove that our mechanism provides the best randomised strategy of the administrator who interviews the applicants.", "published": "2014-04-21T17:08:42Z", "authors": ["Piotr Krysta", "David Manlove", "Baharak Rastegari", "Jinshan Zhang"], "categories": ["cs.GT"], "doi": "10.1007/s00453-019-00584-7", "_query": "hedonic house price", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/1207.6205v3", "title": "Option prices with call prices", "abstract": "There exist several methods how more general options can be priced with call prices. In this article, we extend these results to cover a wider class of options and market models. In particular, we introduce a new pricing formula which can be used to price more general options if prices for call options and digital options are known for every strike price. Moreover, we derive similar results for barrier type options. As a consequence, we obtain a static hedging for general options in the general class of models. Our result can be utilised in several significant applications. As a simple example, we derive an upper bound for the value of a general American option with convex payoff and characterise conditions under which the value of this option equals to the value of the corresponding European option.", "published": "2012-07-26T08:53:08Z", "authors": ["Lauri Viitasaari"], "categories": ["q-fin.PR", "math.PR"], "doi": "", "_query": "hedonic house price", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/1505.03354v1", "title": "Exact and approximate moment closures for non-Markovian network epidemics", "abstract": "Moment-closure techniques are commonly used to generate low-dimensional deterministic models to approximate the average dynamics of stochastic systems on networks. The quality of such closures is usually difficult to asses and the relationship between model assumptions and closure accuracy are often difficult, if not impossible, to quantify. Here we carefully examine some commonly used moment closures, in particular a new one based on the concept of maximum entropy, for approximating the spread of epidemics on networks by reconstructing the probability distributions over triplets based on those over pairs. We consider various models (SI, SIR, SEIR and Reed-Frost-type) under Markovian and non-Markovian assumption characterising the latent and infectious periods. We initially study two special networks, namely the open triplet and closed triangle, for which we can obtain analytical results. We then explore numerically the exactness of moment closures for a wide range of larger motifs, thus gaining understanding of the factors that introduce errors in the approximations, in particular the presence of a random duration of the infectious period and the presence of overlapping triangles in a network. We also derive a simpler and more intuitive proof than previously available concerning the known result that pair-based moment closure is exact for the Markovian SIR model on tree-like networks under pure initial conditions. We also extend such a result to all infectious models, Markovian and non-Markovian, in which susceptibles escape infection independently from each infected neighbour and for which infectives cannot regain susceptible status, provided the network is tree-like and initial conditions are pure. This works represent a valuable step in deepening understanding of the assumptions behind moment closure approximations and for putting them on a more rigorous mathematical footing.", "published": "2015-05-13T12:29:35Z", "authors": ["Lorenzo Pellis", "Thomas House", "Matt J. Keeling"], "categories": ["q-bio.PE", "math.PR"], "doi": "", "_query": "hedonic house price", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2209.15553v2", "title": "Modelling and classifying joint trajectories of self-reported mood and pain in a large cohort study", "abstract": "It is well-known that mood and pain interact with each other, however individual-level variability in this relationship has been less well quantified than overall associations between low mood and pain. Here, we leverage the possibilities presented by mobile health data, in particular the \"Cloudy with a Chance of Pain\" study, which collected longitudinal data from the residents of the UK with chronic pain conditions. Participants used an App to record self-reported measures of factors including mood, pain and sleep quality. The richness of these data allows us to perform model-based clustering of the data as a mixture of Markov processes. Through this analysis we discover four endotypes with distinct patterns of co-evolution of mood and pain over time. The differences between endotypes are sufficiently large to play a role in clinical hypothesis generation for personalised treatments of comorbid pain and low mood.", "published": "2022-09-30T16:08:26Z", "authors": ["Rajenki Das", "Mark Muldoon", "Mark Lunt", "John McBeth", "Belay Birlie Yimer", "Thomas House"], "categories": ["stat.AP"], "doi": "10.1371/journal.pdig.0000204", "_query": "hedonic house price", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2107.04638v1", "title": "Robust Clearing Price Mechanisms for Reserve Price Optimization", "abstract": "Setting an effective reserve price for strategic bidders in repeated auctions is a central question in online advertising. In this paper, we investigate how to set an anonymous reserve price in repeated auctions based on historical bids in a way that balances revenue and incentives to misreport. We propose two simple and computationally efficient methods to set reserve prices based on the notion of a clearing price and make them robust to bidder misreports. The first approach adds random noise to the reserve price, drawing on techniques from differential privacy. The second method applies a smoothing technique by adding noise to the training bids used to compute the reserve price. We provide theoretical guarantees on the trade-offs between the revenue performance and bid-shading incentives of these two mechanisms. Finally, we empirically evaluate our mechanisms on synthetic data to validate our theoretical findings.", "published": "2021-07-09T19:20:48Z", "authors": ["Zhe Feng", "Sébastien Lahaie"], "categories": ["cs.GT"], "doi": "", "_query": "hedonic house price", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2305.09479v1", "title": "Dissertation on Applied Microeconomics of Freemium Pricing Strategies in Mobile App Market", "abstract": "In my dissertation, I will analyze how the product market position of a mobile app affects its pricing strategies, which in turn impacts an app's monetization process. Using natural language processing and k-mean clustering on apps' text descriptions, I created a new variable that measures the distinctiveness of an app as compared to its peers. I created four pricing variables, price, cumulative installs, and indicators of whether an app contains in-app ads and purchases. I found that the effect differs for successful apps and less successful apps. I measure the success here using cumulative installs and the firms that developed the apps. Based on third-party rankings and the shape of the distribution of installs, I set two thresholds and divided apps into market-leading and market-follower apps. The market-leading sub-sample consists of apps with high cumulative installs or developed by prestigious firms, and the market-follower sub-sample consists of the rest of the apps. I found that the impact of being niche is smaller in the market-leading apps because of their relatively higher heterogeneity. In addition, being niche also impact utility apps differently from hedonic apps or apps with two-sided market characteristics. For the special gaming category, being niche has some effect but is smaller than in the market follower sub-sample. My research provides novel empirical evidence of digital products to various strands of theoretical research, including the optimal distinctiveness theory, product differentiation, price discrimination in two or multi-sided markets, and consumer psychology.", "published": "2023-04-22T04:19:06Z", "authors": ["Naixin Zhu"], "categories": ["econ.GN"], "doi": "", "_query": "hedonic house price", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2204.07506v2", "title": "Price and Payoff Autocorrelations in a Multi-Period Consumption-Based Asset Pricing Model", "abstract": "This paper highlights the hidden dependence of the basic pricing equation of a multi-period consumption-based asset pricing model on price and payoff autocorrelations. We obtain the approximations of the basic pricing equation that describe the mean price \"to-day,\" mean payoff \"next-day,\" price and payoff volatilities, and price and payoff autocorrelations. The deep conjunction of the consumption-based model with other versions of asset pricing, such as ICAPM, APM, etc. (Cochrane, 2001), emphasizes that our results are valid for other pricing models.", "published": "2022-03-05T19:39:10Z", "authors": ["Victor Olkhov"], "categories": ["econ.GN", "q-fin.PR"], "doi": "", "_query": "hedonic house price", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2104.04605v3", "title": "Inferring Risks of Coronavirus Transmission from Community Household Data", "abstract": "The response of many governments to the COVID-19 pandemic has involved measures to control within- and between-household transmission, providing motivation to improve understanding of the absolute and relative risks in these contexts. Here, we perform exploratory, residual-based, and transmission-dynamic household analysis of the Office for National Statistics (ONS) COVID-19 Infection Survey (CIS) data from 26 April 2020 to 15 July 2021 in England. This provides evidence for: (i) temporally varying rates of introduction of infection into households broadly following the trajectory of the overall epidemic and vaccination programme; (ii) Susceptible-Infectious Transmission Probabilities (SITPs) of within-household transmission in the 15-35% range; (iii) the emergence of the Alpha and Delta variants, with the former being around 50% more infectious than wildtype and 35% less infectious than Delta within households; (iv) significantly (in the range 25-300%) more risk of bringing infection into the household for workers in patient-facing roles pre-vaccine; (v) increased risk for secondary school-age children of bringing the infection into the household when schools are open; (vi) increased risk for primary school-age children of bringing the infection into the household when schools were open since the emergence of new variants.", "published": "2021-04-09T21:12:20Z", "authors": ["Thomas House", "Heather Riley", "Lorenzo Pellis", "Koen B. Pouwels", "Sebastian Bacon", "Arturas Eidukas", "Kaveh Jahanshahi", "Rosalind M. Eggo", "A. Sarah Walker"], "categories": ["stat.AP", "q-bio.PE"], "doi": "", "_query": "hedonic house price", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2301.07058v2", "title": "Calculation of Epidemic First Passage and Peak Time Probability Distributions", "abstract": "Understanding the timing of the peak of a disease outbreak forms an important part of epidemic forecasting. In many cases, such information is essential for planning increased hospital bed demand and for designing of public health interventions. The time taken for an outbreak to become large is inherently stochastic, and therefore uncertain, but after a sufficient number of infections has been reached the subsequent dynamics can be modelled accurately using ordinary differential equations. Here, we present analytical and numerical methods for approximating the time at which a stochastic model of a disease outbreak reaches a large number of cases and for quantifying the uncertainty arising from demographic stochasticity around that time. We then project this uncertainty forwards in time using an ordinary differential equation model in order to obtain a distribution for the peak timing of the epidemic that agrees closely with large simulations but that, for error tolerances relevant to most realistic applications, requires a fraction of the computational cost of full Monte Carlo approaches.", "published": "2023-01-17T18:18:52Z", "authors": ["Jacob Curran-Sebastian", "Lorenzo Pellis", "Ian Hall", "Thomas House"], "categories": ["q-bio.PE", "math.PR"], "doi": "", "_query": "hedonic house price", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2610.20149v1", "title": "Elementary Proofs of Commonality of the House Graph and $K_4$ with One Edge Subdivided", "abstract": "A graph $H$ is called common if the number of monochromatic copies of $H$ in a $2$-edge-coloring of a large complete graph is asymptotically minimized by the random coloring with an equal proportion of each color. The house graph and $K_4$ with one edge subdivided once are known to be common by the flag algebra method. Grzesik, Lee, Lidický, and Volec asked for ``human-friendly'' proofs of the commonality of these two graphs. In this short note, we give flag-algebra-free proofs that both graphs are common.", "published": "2026-10-14T13:08:56Z", "authors": ["Hao Chen"], "categories": ["math.CO"], "doi": "", "_query": "hedonic house price", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/1501.05798v2", "title": "Near-critical SIR epidemic on a random graph with given degrees", "abstract": "Emergence of new diseases and elimination of existing diseases is a key public health issue. In mathematical models of epidemics, such phenomena involve the process of infections and recoveries passing through a critical threshold where the basic reproductive ratio is 1. In this paper, we study near-critical behaviour in the context of a susceptible-infective-recovered (SIR) epidemic on a random (multi)graph on $n$ vertices with a given degree sequence. We concentrate on the regime just above the threshold for the emergence of a large epidemic, where the basic reproductive ratio is $1 + ω(n) n^{-1/3}$, with $ω(n)$ tending to infinity slowly as the population size, $n$, tends to infinity. We determine the probability that a large epidemic occurs, and the size of a large epidemic. Our results require basic regularity conditions on the degree sequences, and the assumption that the third moment of the degree of a random susceptible vertex stays uniformly bounded as $n \\to \\infty$. As a corollary, we determine the probability and size of a large near-critical epidemic on a standard binomial random graph in the `sparse' regime, where the average degree is constant. As a further consequence of our method, we obtain an improved result on the size of the giant component in a random graph with given degrees just above the critical window, proving a conjecture by Janson and Luczak.", "published": "2015-01-23T13:35:06Z", "authors": ["Svante Janson", "Malwina Luczak", "Peter Windridge", "Thomas House"], "categories": ["math.PR"], "doi": "", "_query": "hedonic house price", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2009.10532v1", "title": "A rapidly updating stratified mix-adjusted median property price index model", "abstract": "Homeowners, first-time buyers, banks, governments and construction companies are highly interested in following the state of the property market. Currently, property price indexes are published several months out of date and hence do not offer the up-to-date information which housing market stakeholders need in order to make informed decisions. In this article, we present an updated version of a central-price tendency based property price index which uses geospatial property data and stratification in order to compare similar houses. The expansion of the algorithm to include additional parameters owing to a new data structure implementation and a richer dataset allows for the construction of a far smoother and more robust index than the original algorithm produced.", "published": "2020-09-22T13:23:03Z", "authors": ["Robert Miller", "Phil Maguire"], "categories": ["stat.AP", "stat.CO"], "doi": "", "_query": "hedonic house price", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2604.17576v1", "title": "Strategic Pricing and Consumer Welfare under One-Sided Price Regulation", "abstract": "Motivated by Germany's April 2026 fuel price regulation, in this note I study a two-period pricing problem with demand uncertainty and a rule that prohibits more than one price increase during the day. Under flexible pricing, the firm chooses the static monopoly price in each period. Under the regulation, by contrast, it may price strategically high in period 1 to preserve flexibility in period 2. I show that the regulation weakly raises expected average prices. The increase is strict when future high demand is sufficiently likely and the gap between high and low demand is large; otherwise, expected average prices are unchanged. Consumer surplus rises when expected prices do not, and decreases otherwise.", "published": "2026-04-19T18:44:05Z", "authors": ["Philipp Denter"], "categories": ["econ.TH"], "doi": "", "_query": "hedonic house price", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2209.04764v3", "title": "A Mathematical Analysis of the 2022 Alaska Special Election for US House", "abstract": "The August 2022 Alaska Special Election for US House contained many interesting features from the perspective of social choice theory. This election used instant runoff voting (often referred to as ranked choice voting) to elect a winner, and many of the weaknesses of this voting method were on display in this election. For example, the Condorcet winner is different from the instant runoff winner, and the election demonstrated a monotonicity paradox. The election also demonstrated a no show paradox; as far as we are aware, this election represents the first document American ranked choice election to demonstrate this paradox.", "published": "2022-09-11T01:09:15Z", "authors": ["Adam Graham-Squire", "David McCune"], "categories": ["econ.GN"], "doi": "", "_query": "hedonic house price", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2307.02260v1", "title": "Does Transport Inequality Perpetuate Housing Insecurity?", "abstract": "With trends of urbanisation on the rise, providing adequate housing to individuals remains a complex issue to be addressed. Often, the slow output of relevant housing policies, coupled with quickly increasing housing costs, leaves individuals with the burden of finding housing that is affordable and safe. In this paper, we unveil how urban planning, not just housing policies, can prevent individuals from accessing better housing conditions. We begin by proposing a clustering approach to characterising levels of housing insecurity in a city, by considering multiple dimensions of housing. Then we define levels of transit efficiency in 20 US cities by comparing public transit journeys to car-based journeys. Finally, we use geospatial autocorrelation to highlight how commuting to areas associated with better housing conditions results in transit commute times of over 30 minutes in most cities, and commute times of over an hour in some cases. Ultimately, we show the role that public transportation plays in locking vulnerable demographics into a cycle of poverty, thus motivating a more holistic approach to addressing housing insecurity that extends beyond changing housing policies.", "published": "2023-07-05T13:00:17Z", "authors": ["Nandini Iyer", "Ronaldo Menezes", "Hugo Barbosa"], "categories": ["physics.soc-ph"], "doi": "", "_query": "hedonic house price", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/1702.06810v3", "title": "Pricing average price advertising options when underlying spot market prices are discontinuous", "abstract": "Advertising options have been recently studied as a special type of guaranteed contracts in online advertising, which are an alternative sales mechanism to real-time auctions. An advertising option is a contract which gives its buyer a right but not obligation to enter into transactions to purchase page views or link clicks at one or multiple pre-specified prices in a specific future period. Different from typical guaranteed contracts, the option buyer pays a lower upfront fee but can have greater flexibility and more control of advertising. Many studies on advertising options so far have been restricted to the situations where the option payoff is determined by the underlying spot market price at a specific time point and the price evolution over time is assumed to be continuous. The former leads to a biased calculation of option payoff and the latter is invalid empirically for many online advertising slots. This paper addresses these two limitations by proposing a new advertising option pricing framework. First, the option payoff is calculated based on an average price over a specific future period. Therefore, the option becomes path-dependent. The average price is measured by the power mean, which contains several existing option payoff functions as its special cases. Second, jump-diffusion stochastic models are used to describe the movement of the underlying spot market price, which incorporate several important statistical properties including jumps and spikes, non-normality, and absence of autocorrelations. A general option pricing algorithm is obtained based on Monte Carlo simulation. In addition, an explicit pricing formula is derived for the case when the option payoff is based on the geometric mean. This pricing formula is also a generalized version of several other option pricing models discussed in related studies.", "published": "2017-02-22T14:26:31Z", "authors": ["Bowei Chen", "Mohan Kankanhalli"], "categories": ["cs.GT"], "doi": "10.1109/TKDE.2018.2867027", "_query": "hedonic house price", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/1310.5047v1", "title": "Higher-order structure and epidemic dynamics in clustered networks", "abstract": "Clustering is typically measured by the ratio of triangles to all triples, open or closed. Generating clustered networks, and how clustering affects dynamics on networks, is reasonably well understood for certain classes of networks \\cite{vmclust, karrerclust2010}, e.g., networks composed of lines and non-overlapping triangles. In this paper we show that it is possible to generate networks which, despite having the same degree distribution and equal clustering, exhibit different higher-order structure, specifically, overlapping triangles and other order-four (a closed network motif composed of four nodes) structures. To distinguish and quantify these additional structural features, we develop a new network metric capable of measuring order-four structure which, when used alongside traditional network metrics, allows us to more accurately describe a network's topology. Three network generation algorithms are considered: a modified configuration model and two rewiring algorithms. By generating homogeneous networks with equal clustering we study and quantify their structural differences, and using SIS (Susceptible-Infected-Susceptible) and SIR (Susceptible-Infected-Recovered) dynamics we investigate computationally how differences in higher-order structure impact on epidemic threshold, final epidemic or prevalence levels and time evolution of epidemics. Our results suggest that characterising and measuring higher-order network structure is needed to advance our understanding of the impact of network topology on dynamics unfolding on the networks.", "published": "2013-10-18T14:58:47Z", "authors": ["Martin Ritchie", "Luc Berthouze", "Thomas House", "Istvan Z. Kiss"], "categories": ["physics.soc-ph", "cs.SI", "q-bio.PE"], "doi": "10.1016/j.jtbi.2014.01.025", "_query": "hedonic house price", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2312.11286v1", "title": "Envy-free House Allocation under Uncertain Preferences", "abstract": "We study the envy-free house allocation problem when agents have uncertain preferences over items and consider several well-studied preference uncertainty models. The central problem that we focus on is computing an allocation that has the highest probability of being envy-free. We show that each model leads to a distinct set of algorithmic and complexity results, including detailed results on (in-)approximability. En route, we consider two related problems of checking whether there exists an allocation that is possibly or necessarily envy-free. We give a complete picture of the computational complexity of these two problems for all the uncertainty models we consider.", "published": "2023-12-18T15:26:49Z", "authors": ["Haris Aziz", "Isaiah Iliffe", "Bo Li", "Angus Ritossa", "Ankang Sun", "Mashbat Suzuki"], "categories": ["cs.GT"], "doi": "", "_query": "hedonic house price", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2605.10617v1", "title": "Logarithmic scaling of selective sweep curves: from tents to houses", "abstract": "One of the classical results of mathematical population genetics states that the frequency of a beneficial mutant's offspring, on its way to fixation in a large population, looks like a logistic curve. A logarithmic scaling (both in height and time) of these selective sweep curves leads (in the case of strong selection) to a tent-like shape in the large population limit: First the logarithmic frequency of the mutant increases linearly from 0 to 1, then that of the former resident decreases from 1 to 0. For moderate selection the logarithmic frequencies develop (in the large population limit) a jump at the beginning/the end of the sweep, which takes the shape of the tent into that of a house. Our main result (proved for the Moran model) assesses the regularity of this convergence in the large population limit: It is uniform in the house's roof (phases of linear growth and decline) and \"Skorokhod $M_1$\" in the house's walls (closely around the jumps). Apart from interest in its own right, we anticipate that this result and the proof techniques will be instrumental for extending the description of clonal interference by Poissonian interacting trajectories (as it was done in Hermann et al. (2024) for strong selection) also to moderate selection.", "published": "2026-05-11T14:12:37Z", "authors": ["Florin Boenkost", "Felix Hermann", "András Tóbiás", "Anton Wakolbinger"], "categories": ["math.PR", "q-bio.PE"], "doi": "", "_query": "hedonic house price", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2605.21358v1", "title": "From Summer to Spring: A Shift in US Housing Market Seasonality", "abstract": "The US housing market exhibits pronounced seasonal cycles: prices and sales rise through spring, peak in summer, and decline through autumn and winter. Since 2021, this pattern has shifted earlier in the calendar year, with spring strengthening at the expense of the traditional summer peak. A leading explanation for housing market seasonality is the search-and-matching model of Ngai and Tenreyro (2014), which links these cycles to household mobility through a thick-market mechanism. In this framework, periods with higher mobility generate thicker markets and higher prices and transaction volumes. Viewed through this lens, a shift in the seasonal cycle of prices and sales raises the question of whether the timing of household moves has changed. Did residential mobility shift earlier in the calendar year after 2021? We find that it did. Using SIPP data, and corroborating evidence from Google Trends indicators, we document a post-2021 shift in mobility toward spring. We extend the model to a monthly frequency, prove the existence and uniqueness of the equilibrium, and calibrate it to the observed mobility patterns. The calibrated model reproduces the spring shift in both prices and transaction volumes, consistent with the view that a change in the timing of household mobility alone can account for the recent shift in housing market seasonality.", "published": "2026-05-20T16:23:25Z", "authors": ["Yihan Hu", "Cemil Selcuk"], "categories": ["econ.GN"], "doi": "", "_query": "hedonic house price", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2003.03636v2", "title": "NuZZ: numerical Zig-Zag sampling for general models", "abstract": "Markov chain Monte Carlo (MCMC) is a key algorithm in computational statistics, and as datasets grow larger and models grow more complex, many popular MCMC algorithms become too computationally expensive to be practical. Recent progress has been made on this problem through development of MCMC algorithms based on Piecewise Deterministic Markov Processes (PDMPs), irreversible processes that can be engineered to converge at a rate which is independent of the size of data. While there has understandably been a surge of theoretical studies following these results, PDMPs have so far only been implemented for models where certain gradients can be bounded, which is not possible in many statistical contexts. Focusing on the Zig-Zag process, we present the Numerical Zig-Zag (NuZZ) algorithm, which is applicable to general statistical models without the need for bounds on the gradient of the log posterior. This allows us to perform numerical experiments on: (i) how the Zig-Zag dynamics behaves on some test problems with common challenging features; and (ii) how the error between the target and sampled distributions evolves as a function of computational effort for different MCMC algorithms including NuZZ. Moreover, due to the specifics of the NuZZ algorithms, we are able to give an explicit bound on the Wasserstein distance between the exact posterior and its numerically perturbed counterpart in terms of the user-specified numerical tolerances of NuZZ.", "published": "2020-03-07T18:46:13Z", "authors": ["Filippo Pagani", "Augustin Chevallier", "Sam Power", "Thomas House", "Simon Cotter"], "categories": ["stat.ME", "math.NA", "stat.CO"], "doi": "", "_query": "hedonic house price", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/1906.11099v1", "title": "A comparison of apartment rent price prediction using a large dataset: Kriging versus DNN", "abstract": "The hedonic approach based on a regression model has been widely adopted for the prediction of real estate property price and rent. In particular, a spatial regression technique called Kriging, a method of interpolation that was advanced in the field of spatial statistics, are known to enable high accuracy prediction in light of the spatial dependence of real estate property data. Meanwhile, there has been a rapid increase in machine learning-based prediction using a large (big) dataset and its effectiveness has been demonstrated in previous studies. However, no studies have ever shown the extent to which predictive accuracy differs for Kriging and machine learning techniques using big data. Thus, this study compares the predictive accuracy of apartment rent price in Japan between the nearest neighbor Gaussian processes (NNGP) model, which enables application of Kriging to big data, and the deep neural network (DNN), a representative machine learning technique, with a particular focus on the data sample size (n = 10^4, 10^5, 10^6) and differences in predictive performance. Our analysis showed that, with an increase in sample size, the out-of-sample predictive accuracy of DNN approached that of NNGP and they were nearly equal on the order of n = 10^6. Furthermore, it is suggested that, for both higher and lower end properties whose rent price deviates from the median, DNN may have a higher predictive accuracy than that of NNGP.", "published": "2019-06-25T14:15:31Z", "authors": ["Hajime Seya", "Daiki Shiroi"], "categories": ["stat.AP", "cs.LG", "stat.ML"], "doi": "", "_query": "hedonic house price", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2002.04212v1", "title": "Quantum coupled-wave theory of price formation in financial markets: price measurement, dynamics and ergodicity", "abstract": "We explore nature of price formation in financial markets and develop a theory of bid and ask price dynamics in which the two prices form due to quantum-chaotic interaction between buy and sell orders. In this model bid and ask prices are represented by eigenvalues of a 2x2 price operator corresponding to 'bid' and 'ask' eigenstates, while randomness of price operator results in price fluctuations that destroy oscillatory effects. We show that this theory adequately captures behavior of bid-ask spread and allows to model bid and ask price dynamics in a coordinated way. We also discuss ergodicity properties of price formation and show how directional price movement occurs due to ergodicity violation in a quantum process instead of the commonly believed forces acting on price. This theory has wide range of applications such as trade execution modeling, large order pricing and risk valuation for illiquid securities.", "published": "2020-02-11T05:37:31Z", "authors": ["Jack Sarkissian"], "categories": ["q-fin.TR"], "doi": "10.1016/j.physa.2020.124300", "_query": "hedonic house price", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2310.11771v2", "title": "Perpetual Futures Pricing", "abstract": "Perpetual futures are contracts without expiration date in which the anchoring of the futures price to the spot price is ensured by periodic funding payments from long to short. We derive explicit expressions for the no-arbitrage price of various perpetual contracts, including linear, inverse, and quantos futures in both discrete and continuous-time. In particular, we show that the futures price is given by the risk-neutral expectation of the spot sampled at a random time that reflects the intensity of the price anchoring. Furthermore, we identify funding specifications that guarantee the coincidence of futures and spot prices, and show that for such specifications perpetual futures contracts can be replicated by dynamic trading in primitive securities.", "published": "2023-10-18T07:57:37Z", "authors": ["Damien Ackerer", "Julien Hugonnier", "Urban Jermann"], "categories": ["q-fin.PR", "q-fin.MF"], "doi": "", "_query": "hedonic house price", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/1309.0567v2", "title": "Detection and measurement of spin-dependent dynamics in random telegraph signals", "abstract": "A quantum point contact was used to observe single-electron fluctuations of a quantum dot in a GaAs heterostructure. The resulting random telegraph signals (RTS) contain statistical information about the electron spin state if the tunneling dynamics are spin-dependent. We develop a statistical method to extract information about spin-dependent dynamics from RTS and use it to demonstrate that these dynamics can be studied in the thermal energy regime. The tunneling rates of each spin state are independently measured in a finite external magnetic field. We confirm previous findings of a decrease in overall tunneling rates for the spin excited state compared to the ground state as an external magnetic field is increased.", "published": "2013-09-03T01:01:45Z", "authors": ["M. G. House", "Ming Xiao", "GuoPing Guo", "HaiOu Li", "Gang Cao", "M. M. Rosenthal", "HongWen Jiang"], "categories": ["cond-mat.mes-hall"], "doi": "10.1103/PhysRevLett.111.126803", "_query": "hedonic house price", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2308.13014v1", "title": "Tracking the Structure and Sentiment of Vaccination Discussions on Mumsnet", "abstract": "Vaccination is one of the most impactful healthcare interventions in terms of lives saved at a given cost, leading the anti-vaccination movement to be identified as one of the top 10 threats to global health in 2019 by the World Health Organization. This issue increased in importance during the COVID-19 pandemic where, despite good overall adherence to vaccination, specific communities still showed high rates of refusal. Online social media has been identified as a breeding ground for anti-vaccination discussions. In this work, we study how vaccination discussions are conducted in the discussion forum of Mumsnet, a United Kingdom based website aimed at parents. By representing vaccination discussions as networks of social interactions, we can apply techniques from network analysis to characterize these discussions, namely network comparison, a task aimed at quantifying similarities and differences between networks. Using network comparison based on graphlets -- small connected network subgraphs -- we show how the topological structure vaccination discussions on Mumsnet differs over time, in particular before and after COVID-19. We also perform sentiment analysis on the content of the discussions and show how the sentiment towards vaccinations changes over time. Our results highlight an association between differences in network structure and changes to sentiment, demonstrating how network comparison can be used as a tool to guide and enhance the conclusions from sentiment analysis.", "published": "2023-08-24T18:28:35Z", "authors": ["Miguel E. P. Silva", "Rigina Skeva", "Thomas House", "Caroline Jay"], "categories": ["cs.SI", "cs.CY", "cs.HC", "physics.soc-ph"], "doi": "", "_query": "hedonic house price", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/1212.0662v1", "title": "A stochastic model of social interaction in wild house mice", "abstract": "We investigate to what extent the interaction dynamics of a population of wild house mouse (Mus musculus domesticus) in their environment can be explained by a simple stochastic model. We use a Markov chain model to describe the transitions of mice in a discrete space of nestboxes, and implement a multi-agent simulation of the model. We find that some important features of our behavioural dataset can be reproduced using this simplified stochastic representation, and discuss the improvements that could be made to our model in order to increase the accuracy of its predictions. Our findings have implications for the understanding of the complexity underlying social behaviour in the animal kingdom and the cognitive requirements of such behaviour.", "published": "2012-12-04T10:07:56Z", "authors": ["Nicolas Perony", "Barbara König", "Frank Schweitzer"], "categories": ["q-bio.QM"], "doi": "", "_query": "hedonic house price", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2202.09323v2", "title": "Market-Based Price Autocorrelation", "abstract": "This paper assumes that the randomness of market trade values and volumes determines the properties of stochastic market prices. We derive the direct dependence of the first two price statistical moments and price volatility on statistical moments, volatilities, and correlations of market trade values and volumes. That helps describe the dependence of market-based price autocorrelation between times t and t-τ on statistical moments and correlations between trade values and volumes. That highlights the impact of the randomness of the size of market deals on price statistical moments and autocorrelation. Statistical moments and correlations of market trade values and volumes are assessed by conventional frequency-based probabilities. The distinctions between market-based price autocorrelation and autocorrelation that are assessed by the frequency-based probability analysis of price time series reveal the different approaches to the definitions of price probabilities. To forecast market-based price autocorrelation, one should predict the statistical moments and correlations of trade values and volumes.", "published": "2022-02-15T19:40:10Z", "authors": ["Victor Olkhov"], "categories": ["econ.GN", "q-fin.GN", "q-fin.PR", "q-fin.ST"], "doi": "", "_query": "hedonic house price", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2504.02488v2", "title": "A Behaviour and Disease Model of Testing and Isolation", "abstract": "There has been interest in the interactions between infectious disease dynamics and behaviour for most of the history of mathematical epidemiology. This has included consideration of which mathematical models best capture each phenomenon, as well as their interaction, but typically in a manner that is agnostic to the exact behaviour in question. Here, we investigate interacting behaviour and disease dynamics specifically related to decisions around testing and isolation. To carry out our investigation we extend an existing \"behaviour and disease\" (BaD) model by incorporating the dynamics of symptomatic testing and isolation, including the influence of positive tests on perception of infection risk. We provide a dynamical systems analysis of the ordinary differential equations that define this model, providing theoretical results on its behaviour early in a new outbreak (particularly its basic reproduction number) and endemicity of the system (its steady states and associated stability criteria). We then supplement these findings with a numerical analysis to inform how temporal and cumulative outbreak metrics depend on the model parameter values for epidemic and endemic regimes. We observe novel model outputs such as epidemics that have more observed cases detected through increased testing, but are less objectively severe in terms of total number of infections.", "published": "2025-04-03T11:10:08Z", "authors": ["Matthew Ryan", "Roslyn I. Hickson", "Edward M. Hill", "Thomas House", "Valerie Isham", "Dongni Zhang", "Mick G. Roberts"], "categories": ["q-bio.PE", "physics.soc-ph"], "doi": "", "_query": "hedonic house price", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2204.13102v1", "title": "The Price and Cost of Bitcoin", "abstract": "Explaining changes in bitcoin's price and predicting its future have been the foci of many research studies. In contrast, far less attention has been paid to the relationship between bitcoin's mining costs and its price. One popular notion is the cost of bitcoin creation provides a support level below which this cryptocurrency's price should never fall because if it did, mining would become unprofitable and threaten the maintenance of bitcoin's public ledger. Other research has used mining costs to explain or forecast bitcoin's price movements. Competing econometric analyses have debunked this idea, showing that changes in mining costs follow changes in bitcoin's price rather than preceding them, but the reason for this behavior remains unexplained in these analyses. This research aims to employ economic theory to explain why econometric studies have failed to predict bitcoin prices and why mining costs follow movements in bitcoin prices rather than precede them. We do so by explaining the chain of causality connecting a bitcoin's price to its mining costs.", "published": "2022-04-27T17:57:55Z", "authors": ["John E. Marthinsen", "Steven R. Gordon"], "categories": ["econ.GN"], "doi": "10.1016/j.qref.2022.04.003", "_query": "hedonic house price", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/1809.08960v1", "title": "On a gap between rational annuitization price for producer and price for customer", "abstract": "The paper studies pricing of insurance products focusing on the pricing of annuities under uncertainty. This pricing problem is crucial for financial decision making and was studied intensively, however, many open questions still remain. In particular, there is a so-called \"annuity puzzle\" related to certain inconsistency of existing financial theory with the empirical observations for the annuities market. The paper suggests a pricing method based on the risk minimization such that both producer and customer seek to minimize the mean square hedging error accepted as a measure of risk. This leads to two different versions of the pricing problem: the selection of the annuity price given the rate of regular payments, and the selection of the rate of payments given the annuity price. It appears that solutions of these two problems are different. This can contribute to explanation for the \"annuity puzzle\".", "published": "2018-09-24T14:16:48Z", "authors": ["Nikolai Dokuchaev"], "categories": ["econ.GN", "q-fin.MF"], "doi": "10.1057/s41272-018-00163-5", "_query": "hedonic house price", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2105.13903v4", "title": "Three Remarks On Asset Pricing", "abstract": "We consider the consumption-based asset pricing model, derive a new modified basic pricing equation, and present its successive approximations using the Taylor series expansions of the investor's utility during the averaging time interval. For linear and quadratic Taylor approximations, we derive new expressions for the mean price, mean payoff, volatility, skewness, and the asset's amount that define the maximum of the investor's utility. We discuss the market-based origin of price probability. We use volume weighted average price (VWAP) as a market-based average price and introduce market-based price volatility. The use of VWAP results in zero correlations between the price p and trade volume U. We derive a correlation between price p and squares of trade volume {U^2} and between squares of price {p^2} and volume {U^2}. To predict market-based price volatility, one should forecast the 2-d statistical moments of the market trade values and volumes at the same horizon T.", "published": "2021-05-24T17:22:24Z", "authors": ["Victor Olkhov"], "categories": ["econ.GN", "q-fin.GN", "q-fin.PR"], "doi": "", "_query": "hedonic house price", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2609.28451v1", "title": "Inter-Temporal Price Constraints in Dynamic Pricing: Performance Guarantees Under Price Monotonicity and Promotion Fatigue", "abstract": "We study dynamic pricing problems under inter-temporal price constraints. We have resources with limited capacities. At each time period, we decide which products to make available and what prices to charge for the available products. The sale probability for a product depends on its price. If we make a sale for a product, then we collect a revenue reflecting the price and consume the capacities of a combination of resources. We work with two types of inter-temporal constraints. In price monotonicity, the prices charged for a product at different time periods have to be monotone. In promotion fatigue, we can discount a product at most once over each time interval of a fixed length. Computing the optimal policy is intractable. We use fluid approximations to construct policies. Traditionally, policies from fluid approximations make randomized decisions at each time period by following an optimal solution to the fluid approximation, but such randomized decisions easily violate price monotonicity or promotion fatigue constraints. We develop policies that sample price paths according to an optimal solution to the fluid approximation, while satisfying the inter-temporal constraints. Letting $c_{\\min}$ be the smallest initial capacity of a resource and $L$ be the maximum number of resources used by a product, our policies have a performance guarantee of $\\max\\Big\\{ \\frac{1}{8L}, \\, \\frac{1}{2} - \\sqrt{\\frac{\\log c_{\\min}}{2 \\,c_{\\min}}} - \\frac{L}{c_{\\min}}\\Big\\}$. Thus, under large resource capacities, our policies are guaranteed to obtain at least half of the optimal total expected revenue. The latter performance guarantee is tight in the sense that no policy can, in general, obtain more than half of the optimal objective value of the fluid approximation even under large resource capacities. We unify our approach to open the path for extensions to other inter-temporal price constraints.", "published": "2026-09-23T17:47:41Z", "authors": ["Weiyuan Li", "Paat Rusmevichientong", "Huseyin Topaloglu"], "categories": ["math.OC", "cs.DM"], "doi": "", "_query": "hedonic house price", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/1402.0410v1", "title": "Minimum Price in Search Model", "abstract": "This paper investigates the effects of a low bound price. To do so, a popular and empirically proven model (Stahl (89') \\cite{Stahl89}) is used. The model is extended to include an exogenously given bound on prices sellers can offer, excluding prices below such bound. The finding are rather surprising - when the bound is set sufficiently high expected price offered (EPO) by sellers drops significantly. The result seem to be robust in the parameters of the model, and driven by the information provided to consumers by such legislation step: when the limitation is set at sufficiently high levels all consumers anticipate the bound price, and searchers reject any price above it. As a result sellers offer the bound price as a pure strategy.", "published": "2014-02-03T16:04:19Z", "authors": ["Sergey Kuniavsky"], "categories": ["cs.GT"], "doi": "", "_query": "hedonic house price", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2609.21478v1", "title": "Bricks or Cash? Externalities of Housing Upgrading in High-density Cities", "abstract": "We estimate housing externalities in a high-density city, exploiting the staggered rollout of Singapore's nationwide Main Upgrading Programme for public housing. Controlling for nonrandom neighborhood exposure, we find that upgrading raises treated buildings' prices by 11.5% upon completion and neighboring buildings' resale prices by about 2% within 500 meters, decaying to zero beyond. A model with distance-decaying externalities shows that in dense settings spillovers justify the distortions of in-kind provision; this advantage diminishes and reverses at lower densities. Administrative data on over 2 million residents show that upgrading disproportionately retains older incumbents, suggesting age-specific amenities as an underexplored externality channel.", "published": "2026-09-18T08:26:44Z", "authors": ["Sumit Agarwal", "Ying Deng", "Yi Fan", "Qi Gao", "Jing Li", "Lin Ma"], "categories": ["econ.GN"], "doi": "", "_query": "hedonic house price", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2508.17301v2", "title": "Price Regulation and Network Spillovers", "abstract": "We study price regulation for a monopolist operating in networked markets with demand spillovers. Achieving efficiency requires price reductions proportional to consumers' Katz-Bonacich centralities, which generally cannot be implemented by commonly used price regulations. Moreover, these regulations become asymptotically welfare neutral as spillovers grow. Nevertheless, some price regulations may still benefit consumers. In particular, average-price regulation robustly increases consumer surplus. By contrast, banning price discrimination increases consumer surplus only when more central consumers have higher intrinsic willingness to pay.", "published": "2025-08-24T11:08:18Z", "authors": ["Chengqing Li", "Junjie Zhou"], "categories": ["econ.TH"], "doi": "", "_query": "hedonic house price", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/1806.00898v1", "title": "Competitive pricing despite search costs if lower price signals quality", "abstract": "I show that firms price almost competitively and consumers can infer product quality from prices in markets where firms differ in quality and production cost, and learning prices is costly. Bankruptcy risk or regulation links higher quality to lower cost. If high-quality firms have lower cost, then they can signal quality by cutting prices. Then the low-quality firms must cut prices to retain customers. This price-cutting race to the bottom ends in a separating equilibrium in which the low-quality firms charge their competitive price and the high-quality firms charge slightly less.", "published": "2018-06-03T23:52:22Z", "authors": ["Sander Heinsalu"], "categories": ["econ.GN", "econ.TH"], "doi": "", "_query": "hedonic house price", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2411.18085v1", "title": "MONOPOLY: Learning to Price Public Facilities for Revaluing Private Properties with Large-Scale Urban Data", "abstract": "The value assessment of private properties is an attractive but challenging task which is widely concerned by a majority of people around the world. A prolonged topic among us is ``\\textit{how much is my house worth?}''. To answer this question, most experienced agencies would like to price a property given the factors of its attributes as well as the demographics and the public facilities around it. However, no one knows the exact prices of these factors, especially the values of public facilities which may help assess private properties. In this paper, we introduce our newly launched project ``Monopoly'' (named after a classic board game) in which we propose a distributed approach for revaluing private properties by learning to price public facilities (such as hospitals etc.) with the large-scale urban data we have accumulated via Baidu Maps. To be specific, our method organizes many points of interest (POIs) into an undirected weighted graph and formulates multiple factors including the virtual prices of surrounding public facilities as adaptive variables to parallelly estimate the housing prices we know. Then the prices of both public facilities and private properties can be iteratively updated according to the loss of prediction until convergence. We have conducted extensive experiments with the large-scale urban data of several metropolises in China. Results show that our approach outperforms several mainstream methods with significant margins. Further insights from more in-depth discussions demonstrate that the ``Monopoly'' is an innovative application in the interdisciplinary field of business intelligence and urban computing, and it will be beneficial to tens of millions of our users for investments and to the governments for urban planning as well as taxation.", "published": "2024-11-27T06:44:41Z", "authors": ["Miao Fan", "Jizhou Huang", "An Zhuo", "Ying Li", "Ping Li", "Haifeng Wang"], "categories": ["cs.AI", "cs.SI"], "doi": "", "_query": "hedonic house price", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/0803.4416v1", "title": "Consistent price systems and face-lifting pricing under transaction costs", "abstract": "In markets with transaction costs, consistent price systems play the same role as martingale measures in frictionless markets. We prove that if a continuous price process has conditional full support, then it admits consistent price systems for arbitrarily small transaction costs. This result applies to a large class of Markovian and non-Markovian models, including geometric fractional Brownian motion. Using the constructed price systems, we show, under very general assumptions, the following ``face-lifting'' result: the asymptotic superreplication price of a European contingent claim $g(S_T)$ equals $\\hat{g}(S_0)$, where $\\hat{g}$ is the concave envelope of $g$ and $S_t$ is the price of the asset at time $t$. This theorem generalizes similar results obtained for diffusion processes to processes with conditional full support.", "published": "2008-03-31T09:55:40Z", "authors": ["Paolo Guasoni", "Miklós Rásonyi", "Walter Schachermayer"], "categories": ["q-fin.PR", "math.PR"], "doi": "10.1214/07-AAP461", "_query": "hedonic house price", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2203.08902v1", "title": "Price Tracing: Linking Nodal Prices in Optimized Power Systems", "abstract": "Optimizing the total cost of power systems is a common tool for network operation and planning. Besides valuable information about how to run and possibly expand a power system, the optimization provides an optimal Locational Marginal Price per node and time step. This price can be seen as the price of electricity paid by consumers and purchased by suppliers, while maximizing social welfare. Naturally, it is a direct result of the optimization problem, and therefore does not give any information about its internal composition. This paper shows that by applying Flow Tracing, an algorithm for tracking flows in complex networks, it is possible to interlink Locational Marginal Prices in a coherent mathematical way. This does not only lead to important insights into the price structure, but also provides an intuitive decomposition and allocation of all system costs. Then individual consumers see how much they have to pay to individual generators and transmission lines in the power system. This method, introduced as Price Tracing, outperforms similar approaches provided by the literature, since the resulting cost allocations are transparent, plausible, and consistent with the Locational Marginal Prices from the optimization. The Price Tracing method is applied and discussed on behalf of a power system model of Germany with a high share of renewable power. The presented analysis and its implications can help in finding a more efficient market design to promote renewable power supply.", "published": "2022-03-16T19:25:48Z", "authors": ["Fabian Hofmann", "Markus Schlott"], "categories": ["physics.soc-ph"], "doi": "", "_query": "hedonic house price", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/1811.08803v1", "title": "Distinguishing correlation from causation using genome-wide association studies", "abstract": "Genome-wide association studies (GWAS) have emerged as a rich source of genetic clues into disease biology, and they have revealed strong genetic correlations among many diseases and traits. Some of these genetic correlations may reflect causal relationships. We developed a method to quantify causal relationships between genetically correlated traits using GWAS summary association statistics. In particular, our method quantifies what part of the genetic component of trait 1 is also causal for trait 2 using mixed fourth moments $E(α_1^2α_1α_2)$ and $E(α_2^2α_1α_2)$ of the bivariate effect size distribution. If trait 1 is causal for trait 2, then SNPs affecting trait 1 (large $α_1^2$) will have correlated effects on trait 2 (large $α_1α_2$), but not vice versa. We validated this approach in extensive simulations. Across 52 traits (average $N=331$k), we identified 30 putative genetically causal relationships, many novel, including an effect of LDL cholesterol on decreased bone mineral density. More broadly, we demonstrate that it is possible to distinguish between genetic correlation and causation using genetic association data.", "published": "2018-11-21T16:01:51Z", "authors": ["Luke J. O'Connor", "Alkes L. Price"], "categories": ["stat.ME", "cs.LG", "stat.ML"], "doi": "", "_query": "hedonic house price", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/1403.5320v1", "title": "Single-charge detection by an atomic precision tunnel junction", "abstract": "We demonstrate sensitive detection of single charges using a planar tunnel junction 8.5nm wide and 17.2nm long defined by an atomically precise phosphorus doping profile in silicon. The conductance of the junction responds to a nearby gate potential and also to changes in the charge state of a quantum dot patterned 52nm away. The response of this detector is monotonic across the entire working voltage range of the device, which will make it particularly useful for studying systems of multiple quantum dots. The charge sensitivity is maximized when the junction is most conductive, suggesting that more sensitive detection can be achieved by shortening the length of the junction to increase its conductance.", "published": "2014-03-20T22:46:57Z", "authors": ["M. G. House", "E. Peretz", "J. G. Keizer", "S. J. Hile", "M. Y. Simmons"], "categories": ["cond-mat.mes-hall"], "doi": "10.1063/1.4869032", "_query": "hedonic house price", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/1609.09412v2", "title": "Experimental Measurement of the Berry Curvature from Anomalous Transport", "abstract": "Geometrical properties of energy bands underlie fascinating phenomena in a wide-range of systems, including solid-state materials, ultracold gases and photonics. Most famously, local geometrical characteristics like the Berry curvature can be related to global topological invariants such as those classifying quantum Hall states or topological insulators. Regardless of the band topology, however, any non-zero Berry curvature can have important consequences, such as in the semi-classical evolution of a wave packet. Here, we experimentally demonstrate for the first time that wave packet dynamics can be used to directly map out the Berry curvature. To this end, we use optical pulses in two coupled fibre loops to study the discrete time-evolution of a wave packet in a 1D geometrical \"charge\" pump, where the Berry curvature leads to an anomalous displacement of the wave packet under pumping. This is both the first direct observation of Berry curvature effects in an optical system, and, more generally, the proof-of-principle demonstration that semi-classical dynamics can serve as a high-resolution tool for mapping out geometrical properties.", "published": "2016-09-29T16:24:13Z", "authors": ["Martin Wimmer", "Hannah M. Price", "Iacopo Carusotto", "Ulf Peschel"], "categories": ["physics.optics", "cond-mat.quant-gas"], "doi": "10.1038/nphys4050", "_query": "hedonic house price", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2203.02089v1", "title": "A Robust Statistical Analysis of the Role of Hydropower on the System Electricity Price and Price Volatility", "abstract": "Hydroelectric power (hydropower) is unique in that it can function as both a conventional source of electricity and as backup storage (pumped hydroelectric storage) for providing energy in times of high demand on the grid. This study examines the impact of hydropower on system electricity price and price volatility in the region served by the New England Independent System Operator (ISONE) from 2014 - 2020. We perform a robust holistic analysis of the mean and quantile effects, as well as the marginal contributing effects of hydropower in the presence of solar and wind resources. First, the price data is adjusted for deterministic temporal trends, correcting for seasonal, weekend, and diurnal effects that may obscure actual representative trends in the data. Using multiple linear regression and quantile regression, we observe that hydropower contributes to a reduction in the system electricity price and price volatility. While hydropower has a weak impact on decreasing price and volatility at the mean, it has greater impact at extreme quantiles (> 70th percentile). At these higher percentiles, we find that hydropower provides a stabilizing effect on price volatility in the presence of volatile resources such as wind. We conclude with a discussion of the observed relationship between hydropower and system electricity price and volatility.", "published": "2022-03-04T01:58:13Z", "authors": ["Olukunle O. Owolabi", "Kathryn Lawson", "Sanhita Sengupta", "Yingsi Huang", "Lan Wang", "Chaopeng Shen", "Mila Getmansky Sherman", "Deborah A. Sunter"], "categories": ["q-fin.GN"], "doi": "", "_query": "hedonic house price", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2506.14708v1", "title": "Discrete time shadow price revisited", "abstract": "In the paper discrete time shadow price is constructed for the market with several assets with given bid and ask prices. Shadow price is the price such that the problem of optimal utility from terminal wealth on the market without transaction costs gives the same value function as in the case of bid and ask prices. In the paper we solve first static problem for two assets and then we construct the shadow price for dynamic model. Finally we present construction of the shadow price for several assets.", "published": "2025-06-17T16:47:57Z", "authors": ["Tomasz Rogala", "Łukasz Stettner"], "categories": ["math.OC"], "doi": "", "_query": "hedonic house price", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/0903.4833v2", "title": "Recovering a time-homogeneous stock price process from perpetual option prices", "abstract": "It is well known how to determine the price of perpetual American options if the underlying stock price is a time-homogeneous diffusion. In the present paper we consider the inverse problem, that is, given prices of perpetual American options for different strikes, we show how to construct a time-homogeneous stock price model which reproduces the given option prices.", "published": "2009-03-27T15:53:31Z", "authors": ["Erik Ekström", "David Hobson"], "categories": ["math.PR", "q-fin.PR"], "doi": "10.1214/10-AAP720", "_query": "hedonic house price", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2506.13967v1", "title": "High-Dimensional Spatial-Plus-Vertical Price Relationships and Price Transmission: A Machine Learning Approach", "abstract": "Price transmission has been studied extensively in agricultural economics through the lens of spatial and vertical price relationships. Classical time series econometric techniques suffer from the \"curse of dimensionality\" and are applied almost exclusively to small sets of price series, either prices of one commodity in a few regions or prices of a few commodities in one region. However, an agrifood supply chain usually contains several commodities (e.g., cattle and beef) and spans numerous regions. Failing to jointly examine multi-region, multi-commodity price relationships limits researchers' ability to derive insights from increasingly high-dimensional price datasets of agrifood supply chains. We apply a machine-learning method - specifically, regularized regression - to augment the classical vector error correction model (VECM) and study large spatial-plus-vertical price systems. Leveraging weekly provincial-level data on the piglet-hog-pork supply chain in China, we uncover economically interesting changes in price relationships in the system before and after the outbreak of a major hog disease. To quantify price transmission in the large system, we rely on the spatial-plus-vertical price relationships identified by the regularized VECM to visualize comprehensive spatial and vertical price transmission of hypothetical shocks through joint impulse response functions. Price transmission shows considerable heterogeneity across regions and commodities as the VECM outcomes imply and display different dynamics over time.", "published": "2025-06-16T20:16:56Z", "authors": ["Mindy L. Mallory", "Rundong Peng", "Meilin Ma", "H. Holly Wang"], "categories": ["econ.EM"], "doi": "", "_query": "hedonic house price", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/1310.2775v1", "title": "On price of symmetrisation", "abstract": "We introduce the price of symmetrisation, a concept that aims to compare fundamental differences (gap and quotient) between values of a given graph invariant for digraphs and the values of the same invariant of the symmetric versions of these digraphs. Basically, given some invariant our goal is to characterise digraphs that maximise price of symmetrisation. In particular, we show that for some invariants, as diameter or domination number, the problem is easy. The main contribution of this paper is about (partial) results on the price of symmetrisation of the average distance. It appears to be much more intricate than the simple cases mentioned above. First, we state a conjecture about digraphs that maximise this price of symmetrisation. Then, we prove that this conjecture is true for some particular class of digraphs (called bags) but it remains open for general digraphs. Moreover, we study several graph transformations in order to remove some configurations that do not appear in the conjectured extremal digraphs.", "published": "2013-10-10T11:22:45Z", "authors": ["Absil Romain", "Hadrien Mélot"], "categories": ["cs.DM", "math.CO"], "doi": "", "_query": "hedonic house price", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2101.06710v1", "title": "House of algebraic integers symmetric about the unit circle", "abstract": "We give a Schinzel-Zassenhaus-type lower bound for the maximum modulus of roots of a monic integer polynomial with all roots symmetric with respect to the unit circle. Our results extend a recent work of Dimitrov, who proved the general Schinzel-Zassenhaus conjecture by using the Pólya rationality theorem for a power series with integer coefficients, and some estimates for logarithmic capacity (transfinite diameter) of sets. We use an enhancement of Pólya's result obtained by Robinson, which involves Laurent-type rational functions with small supremum norms, thereby replacing the logarithmic capacity with a smaller quantity. This smaller quantity is expressed via a weighted Chebyshev constant for the set associated with Dimitrov's function used in Robinson's rationality theorem. Our lower bound for the house confirms a conjecture of Boyd.", "published": "2021-01-17T16:52:08Z", "authors": ["Igor E. Pritsker"], "categories": ["math.NT", "math.CV"], "doi": "", "_query": "hedonic house price", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2511.23455v2", "title": "The Price of Progress: Price Performance and the Future of AI", "abstract": "Language models have seen enormous progress on advanced benchmarks in recent years, but much of this progress has only been possible by using more costly models. Benchmarks may therefore present a warped picture of progress in practical capabilities *per dollar*. To remedy this, we use data from Artificial Analysis and Epoch AI to form the largest dataset of current and historical prices to run benchmarks to date. We find that the price for a given level of benchmark performance has decreased remarkably fast, around $5\\times$ to $10\\times$ per year, for frontier models on knowledge, reasoning, math, and software engineering benchmarks. These reductions in the cost of AI inference are due to economic forces, hardware efficiency improvements, and algorithmic efficiency improvements. Isolating out open models to control for competition effects and dividing by hardware price declines, we estimate that algorithmic efficiency progress is around $3\\times$ per year. However, at the same time, the price of running frontier models is rising between $3\\times$ to $18\\times$ per year due to bigger models and larger reasoning demands. Finally, we recommend that evaluators both publicize and take into account the price of benchmarking as an essential part of measuring the real-world impact of AI.", "published": "2025-11-28T18:47:33Z", "authors": ["Hans Gundlach", "Jayson Lynch", "Matthias Mertens", "Neil Thompson"], "categories": ["cs.LG", "cs.AI", "cs.CY"], "doi": "", "_query": "hedonic house price", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2008.11806v2", "title": "The Time Function of Stock Price", "abstract": "This paper tends to define the quantitative relationship between the stock price and time as a time function. Based on the empirical evidence that the log-return of a stock is the series of white noise, a mathematical model of the integral white noise is established to describe the phenomenon of stock price movement. A deductive approach is used to derive the auto-correlation function, displacement formula and power spectral density of the stock price movement, which reveals not only the characteristics and rules of the movement but also the predictability of the stock price. The deductive fundamental is provided for the price analysis, prediction and risk management of portfolio investment.", "published": "2020-08-11T03:02:14Z", "authors": ["Shengfeng Mei", "Hong Gao"], "categories": ["q-fin.ST"], "doi": "", "_query": "hedonic house price", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2506.19953v1", "title": "An experiment in price perception error", "abstract": "The process of consumer decision-making is multidimensional, and price perception is a very important but still not well-understood dimension for both marketers and consumers. Although heuristics or mental shortcuts are seen as biased and can cause decision errors, consumers tend to use price knowledge heuristics for purchase decisions, sometimes relying on old information. This study examined the effects of individual attitudinal and demographic factors on the ability of 351 respondents to correctly guess the prices of 13 products and services using ANOVA and the development of a seven-factor price perception model using latent variable analysis. While most respondents preferred to either research prices first or compare prices to a similar product, they either systematically underestimated or overestimated the prices of the products. In the ANOVA, brand loyalty, importance of substitute products and knockoff products, how financially well-off the respondent household was growing up, product quality importance, haggling tendency, and level of income were statistically significant predictors of price perception error. Latent variable analysis revealed that demographics, decision making, and price sensitivity factors had the greatest influence on price perception error. Level of education and income, frequency of regret, coupon importance, and how respondents chose to save or spend money were significant latent variables.", "published": "2025-06-24T19:02:49Z", "authors": ["Shawn Berry"], "categories": ["econ.GN"], "doi": "", "_query": "hedonic house price", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2510.08246v1", "title": "Does everyone have a price? Understanding people's attitude towards online and offline price discrimination", "abstract": "Online stores can present a different price to each customer. Such algorithmic personalised pricing can lead to advanced forms of price discrimination based on the characteristics and behaviour of individual consumers. We conducted two consumer surveys among a representative sample of the Dutch population (N=1233 and N=1202), to analyse consumer attitudes towards a list of examples of price discrimination and dynamic pricing. A vast majority finds online price discrimination unfair and unacceptable, and thinks it should be banned. However, some pricing strategies that have been used by companies for decades are almost equally unpopular. We analyse the results to better understand why people dislike many types of price discrimination.", "published": "2025-10-09T14:05:04Z", "authors": ["Joost Poort", "Frederik J. Zuiderveen Borgesius"], "categories": ["cs.CY"], "doi": "10.14763/2019.1.1383", "_query": "hedonic house price", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2604.27594v3", "title": "Structural description of (bull, house)-free graphs", "abstract": "The bull is a graph consisting of a triangle and two pendant edges. The P_5 is the chordless path on five vertices. The house is the complement of a P_5. A graph is k-critical if it is k-chromatic but each of its proper induced subgraphs is (k-1)-colorable. It is known that the number of k-critical P_5-free graphs and bull-free graphs are infinite for large enough k. We give a structural description of (bull, house)-free graphs and also (bull, P_5)-free graphs. Using these structural properties we prove that for any fixed k, the number of k-critical (bull, P_5)-free graphs is finite. This improves on a result of Huang, Li and Xia (Critical (P_5, bull)-free graphs, Discrete Applied Mathematics 334 (2023) 15-25). A graph G is perfectly divisible if for each induced subgraph H of G with at least one edge, V(H) can be partitioned into two sets V_1, V_2 such that every largest clique of H contains a vertex in V_i for i = 1,2. Chudnovsky and Sivaraman proved that (P_5, bull)-free graphs are perfectly divisible (Perfect divisibility and 2-divisibility, Journal of Graph Theory 90 (2019) 54-60). Our structural result allows us to give a short proof of this theorem.", "published": "2026-04-30T08:43:57Z", "authors": ["Manoj Belavadi", "Chinh T. Hoang"], "categories": ["math.CO"], "doi": "", "_query": "hedonic house price", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/1204.3968v1", "title": "Convolutional Neural Networks Applied to House Numbers Digit Classification", "abstract": "We classify digits of real-world house numbers using convolutional neural networks (ConvNets). ConvNets are hierarchical feature learning neural networks whose structure is biologically inspired. Unlike many popular vision approaches that are hand-designed, ConvNets can automatically learn a unique set of features optimized for a given task. We augmented the traditional ConvNet architecture by learning multi-stage features and by using Lp pooling and establish a new state-of-the-art of 94.85% accuracy on the SVHN dataset (45.2% error improvement). Furthermore, we analyze the benefits of different pooling methods and multi-stage features in ConvNets. The source code and a tutorial are available at eblearn.sf.net.", "published": "2012-04-18T03:48:38Z", "authors": ["Pierre Sermanet", "Soumith Chintala", "Yann LeCun"], "categories": ["cs.CV", "cs.LG", "cs.NE"], "doi": "", "_query": "hedonic house price", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2508.08345v2", "title": "Do AI Companies Make Good on Voluntary Commitments to the White House?", "abstract": "Voluntary commitments are central to international AI governance, as demonstrated by recent voluntary guidelines from the White House to the G7, from Bletchley Park to Seoul. How do major AI companies make good on their commitments? We score companies based on their publicly disclosed behavior by developing a detailed rubric based on their eight voluntary commitments to the White House in 2023. We find significant heterogeneity: while the highest-scoring company (OpenAI) scores a 83% overall on our rubric, the average score across all companies is just 53%. The companies demonstrate systemically poor performance for their commitment to model weight security with an average score of 17%: 11 of the 16 companies receive 0% for this commitment. Our analysis highlights a clear structural shortcoming that future AI governance initiatives should correct: when companies make public commitments, they should proactively disclose how they meet their commitments to provide accountability, and these disclosures should be verifiable. To advance policymaking on corporate AI governance, we provide three directed recommendations that address underspecified commitments, the role of complex AI supply chains, and public transparency that could be applied towards AI governance initiatives worldwide.", "published": "2025-08-11T11:23:28Z", "authors": ["Jennifer Wang", "Kayla Huang", "Kevin Klyman", "Rishi Bommasani"], "categories": ["cs.CY", "cs.AI"], "doi": "", "_query": "hedonic house price", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/1104.2037v1", "title": "Disc Heating: Comparing the Milky Way with Cosmological Simulations", "abstract": "We present the analysis of a suite of simulations run with different particle-and grid-based cosmological hydrodynamical codes and compare them with observational data of the Milky Way. This is the first study to make comparisons of properties of galaxies simulated with particle and grid-based codes. Our analysis indicates that there is broad agreement between these different modelling techniques. We study the velocity dispersion - age relation for disc stars at z=0 and find that four of the simulations are more consistent with observations by Holmberg et al. (2008) in which the stellar disc appears to undergo continual/secular heating. Two other simulations are in better agreement with the Quillen & Garnett (2001) observations that suggest a \"saturation\" in the heating profile for young stars in the disc. None of the simulations have thin discs as old as that of the Milky Way. We also analyse the kinematics of disc stars at the time of their birth for different epochs in the galaxies' evolution and find that in some simulations old stars are born cold within the disc and are subsequently heated, while other simulations possess old stellar populations which are born relatively hot. The models which are in better agreement with observations of the Milky Way's stellar disc undergo significantly lower minor-merger/assembly activity after the last major merger - i.e. once the disc has formed. All of the simulations are significantly \"hotter\" than the Milky Way disc; on top of the effects of mergers, we find a \"floor\" in the dispersion that is related to the underlying treatment of the heating and cooling of the interstellar medium, and the low density threshold which such codes use for star formation. This finding has important implications for all studies of disc heating that use hydrodynamical codes.", "published": "2011-04-11T20:00:01Z", "authors": ["Elisa House", "Chris B. Brook", "Brad K. Gibson", "Patricia Sanchez-Blazquez", "Stephanie Courty", "Christopher G. Few", "Fabio Governato", "Daisuke Kawata", "Rok Roskar", "Matthias Steinmetz", "Gregory S. Stinson", "Romain Teyssier"], "categories": ["astro-ph.CO", "astro-ph.GA"], "doi": "10.1111/j.1365-2966.2011.18891.x", "_query": "hedonic house price", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2501.12583v1", "title": "Chasing price drains liquidity", "abstract": "Assuming that the price in a Uniswap v3 style Automated Market Maker (AMM) follows a Geometric Brownian Motion (GBM), we prove that the strategy that adjusts the position of liquidity to track the current price leads to a deterministic and exponentially fast decay of liquidity. Next, assuming that there is a Centralized Exchange (CEX), in which the price follows a GBM and the AMM price mean reverts to the CEX price, we show numerically that the same strategy still leads to decay. Last, we propose a strategy that increases the liquidity even without compounding fees earned through liquidity provision.", "published": "2025-01-22T02:13:55Z", "authors": ["Yizhou Cao", "Yepeng Ding", "Ruichao Jiang", "Long Wen"], "categories": ["cs.CE"], "doi": "", "_query": "hedonic house price", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/1712.01385v5", "title": "Quantum Bounds for Option Prices", "abstract": "Option pricing is the most elemental challenge of mathematical finance. Knowledge of the prices of options at every strike is equivalent to knowing the entire pricing distribution for a security, as derivatives contingent on the security can be replicated using options. The available data may be insufficient to determine this distribution precisely, however, and the question arises: What are the bounds for the option price at a specified strike, given the market-implied constraints? Positivity of the price map imposed by the principle of no-arbitrage is here utilised, via the Gelfand-Naimark-Segal construction, to transform the problem into the domain of operator algebras. Optimisation in this larger context is essentially geometric, and the outcome is simultaneously super-optimal for all commutative subalgebras. This generates an upper bound for the price of a basket option. With innovative decomposition of the assets in the basket, the result is used to create converging families of price bounds for vanilla options, interpolate the volatility smile, price options on cross FX rates, and analyse the relationships between swaption and caplet prices.", "published": "2017-12-04T21:44:31Z", "authors": ["Paul McCloud"], "categories": ["q-fin.MF", "math.OA"], "doi": "", "_query": "hedonic house price", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2205.04181v1", "title": "Price DOES Matter! Modeling Price and Interest Preferences in Session-based Recommendation", "abstract": "Session-based recommendation aims to predict items that an anonymous user would like to purchase based on her short behavior sequence. The current approaches towards session-based recommendation only focus on modeling users' interest preferences, while they all ignore a key attribute of an item, i.e., the price. Many marketing studies have shown that the price factor significantly influences users' behaviors and the purchase decisions of users are determined by both price and interest preferences simultaneously. However, it is nontrivial to incorporate price preferences for session-based recommendation. Firstly, it is hard to handle heterogeneous information from various features of items to capture users' price preferences. Secondly, it is difficult to model the complex relations between price and interest preferences in determining user choices. To address the above challenges, we propose a novel method Co-guided Heterogeneous Hypergraph Network (CoHHN) for session-based recommendation. Towards the first challenge, we devise a heterogeneous hypergraph to represent heterogeneous information and rich relations among them. A dual-channel aggregating mechanism is then designed to aggregate various information in the heterogeneous hypergraph. After that, we extract users' price preferences and interest preferences via attention layers. As to the second challenge, a co-guided learning scheme is designed to model the relations between price and interest preferences and enhance the learning of each other. Finally, we predict user actions based on item features and users' price and interest preferences. Extensive experiments on three real-world datasets demonstrate the effectiveness of the proposed CoHHN. Further analysis reveals the significance of price for session-based recommendation.", "published": "2022-05-09T10:47:15Z", "authors": ["Xiaokun Zhang", "Bo Xu", "Liang Yang", "Chenliang Li", "Fenglong Ma", "Haifeng Liu", "Hongfei Lin"], "categories": ["cs.IR", "cs.AI"], "doi": "10.1145/3477495.3532043", "_query": "hedonic house price", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2208.03135v2", "title": "Modeling Price Elasticity for Occupancy Prediction in Hotel Dynamic Pricing", "abstract": "Demand estimation plays an important role in dynamic pricing where the optimal price can be obtained via maximizing the revenue based on the demand curve. In online hotel booking platform, the demand or occupancy of rooms varies across room-types and changes over time, and thus it is challenging to get an accurate occupancy estimate. In this paper, we propose a novel hotel demand function that explicitly models the price elasticity of demand for occupancy prediction, and design a price elasticity prediction model to learn the dynamic price elasticity coefficient from a variety of affecting factors. Our model is composed of carefully designed elasticity learning modules to alleviate the endogeneity problem, and trained in a multi-task framework to tackle the data sparseness. We conduct comprehensive experiments on real-world datasets and validate the superiority of our method over the state-of-the-art baselines for both occupancy prediction and dynamic pricing.", "published": "2022-08-04T13:58:04Z", "authors": ["Fanwei Zhu", "Wendong Xiao", "Yao Yu", "Ziyi Wang", "Zulong Chen", "Quan Lu", "Zemin Liu", "Minghui Wu", "Shenghua Ni"], "categories": ["econ.GN", "cs.IR", "cs.LG"], "doi": "", "_query": "hedonic house price", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2212.07102v2", "title": "Artificial intelligence-driven digital twin of a modern house demonstrated in virtual reality", "abstract": "A digital twin is a powerful tool that can help monitor and optimize physical assets in real-time. Simply put, it is a virtual representation of a physical asset, enabled through data and simulators, that can be used for a variety of purposes such as prediction, monitoring, and decision-making. However, the concept of digital twin can be vague and difficult to understand, which is why a new concept called \"capability level\" has been introduced. This concept categorizes digital twins based on their capability and defines a scale from zero to five, with each level indicating an increasing level of functionality. These levels are standalone, descriptive, diagnostic, predictive, prescriptive, and autonomous. By understanding the capability level of a digital twin, we can better understand its potential and limitations. To demonstrate the concepts, we use a modern house as an example. The house is equipped with a range of sensors that collect data about its internal state, which can then be used to create digital twins of different capability levels. These digital twins can be visualized in virtual reality, allowing users to interact with and manipulate the virtual environment. The current work not only presents a blueprint for developing digital twins but also suggests future research directions to enhance this technology. Digital twins have the potential to transform the way we monitor and optimize physical assets, and by understanding their capabilities, we can unlock their full potential.", "published": "2022-12-14T08:48:37Z", "authors": ["Elias Mohammed Elfarri", "Adil Rasheed", "Omer San"], "categories": ["cs.CV", "cs.GR", "cs.HC"], "doi": "", "_query": "hedonic house price", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/1312.2371v3", "title": "Tight Bounds for the Price of Anarchy of Simultaneous First Price Auctions", "abstract": "We study the Price of Anarchy of simultaneous first-price auctions for buyers with submodular and subadditive valuations. The current best upper bounds for the Bayesian Price of Anarchy of these auctions are e/(e-1) [Syrgkanis and Tardos 2013] and 2 [Feldman et al. 2013], respectively. We provide matching lower bounds for both cases even for the case of full information and for mixed Nash equilibria via an explicit construction. We present an alternative proof of the upper bound of e/(e-1) for first-price auctions with fractionally subadditive valuations which reveals the worst-case price distribution, that is used as a building block for the matching lower bound construction. We generalize our results to a general class of item bidding auctions that we call bid-dependent auctions (including first-price auctions and all-pay auctions) where the winner is always the highest bidder and each bidder's payment depends only on his own bid. Finally, we apply our techniques to discriminatory price multi-unit auctions. We complement the results of [de Keijzer et al. 2013] for the case of subadditive valuations, by providing a matching lower bound of 2. For the case of submodular valuations, we provide a lower bound of 1.109. For the same class of valuations, we were able to reproduce the upper bound of e/(e-1) using our non-smooth approach.", "published": "2013-12-09T10:30:46Z", "authors": ["George Christodoulou", "Annamária Kovács", "Alkmini Sgouritsa", "Bo Tang"], "categories": ["cs.GT"], "doi": "", "_query": "hedonic house price", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2502.08613v8", "title": "The Relative Entropy of Expectation and Price", "abstract": "As operators acting on the undetermined final settlement of a derivative security, expectation is linear but price is non-linear. When the market of underlying securities is incomplete, non-linearity emerges from the bid-offer around the mid price that accounts for the residual risks of the optimal funding and hedging strategy. At the extremes, non-linearity also arises from the embedded options on capital that are exercised upon default. In this essay, these convexities are quantified in an entropic risk metric that evaluates the strategic risks, which is realised as a cost with the introduction of bilateral margin. Price is then adjusted for market incompleteness and the risk of default caused by the exhaustion of capital. In the complete market theory, price is derived from a martingale condition. In the incomplete market theory presented here, price is instead derived from a log-martingale condition: \\begin{equation} p=-\\frac{1}α\\log\\mathbb{E}\\exp[-αP] \\notag \\end{equation} for the price $p$ and payoff $P$ of a funded and hedged derivative security, where the price measure $\\mathbb{E}$ has minimum entropy relative to economic expectations, and the parameter $α$ matches the risk aversion of the investor. This price principle is easily applied to standard models for market evolution, with applications considered here in model risk analysis, deep hedging and quantum information.", "published": "2025-02-12T18:03:07Z", "authors": ["Paul McCloud"], "categories": ["q-fin.MF"], "doi": "", "_query": "hedonic house price", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2406.19554v3", "title": "A Network-Based Measure of Cosponsorship Influence on Bill Passing in the United States House of Representatives", "abstract": "Each year, the United States Congress considers thousands of legislative proposals to select bills to present to the US President to sign into law. Naturally, the decision processes of members of Congress are subject to peer influence. In this paper, we examine the effect on bill passage of accrued influence between US Congress members in the US House of Representatives. We explore how the influence of a bill's cosponsors affects the bill's outcome (specifically, whether or not it passes in the House). We define a notion of influence by analyzing the structure of a network that we construct using cosponsorship dynamics. We award `influence' between a pair of Congress members when they cosponsor a bill that achieves some amount of legislative success. We find that properties of the bill cosponsorship network can be a useful signal to examine influence in Congress; they help explain why some bills pass and others fail. We compare our measure of influence to off-the-shelf centrality measures and conclude that our influence measure is more indicative of bill passage.", "published": "2024-06-27T22:10:56Z", "authors": ["Sarah Sotoudeh", "Mason A. Porter", "Sanjukta Krishnagopal"], "categories": ["cs.SI", "physics.soc-ph"], "doi": "", "_query": "hedonic house price", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/math/0612075v1", "title": "Option Pricing without Price Dynamics: A Probabilistic Approach", "abstract": "Employing probabilistic techniques we compute best possible upper and lower bounds on the price of an option on one or two assets with continuous piecewise linear payoff function based on prices of simple call options of possibly distinct maturities and the no-arbitrage condition, but without any assumption on the price dynamics of underlying assets. We show that the problem reduces to solving linear optimization problems that we explicitly characterize. We report numerical results that illustrate the effectiveness of the algorithms we develop.", "published": "2006-12-03T23:59:09Z", "authors": ["Dimitris Bertsimas", "Natasha Bushueva"], "categories": ["math.PR", "q-fin.PR"], "doi": "", "_query": "hedonic house price", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2011.10113v2", "title": "Price Impact on Term Structure", "abstract": "We introduce a first theory of price impact in presence of an interest-rates term structure. We explain how one can formulate instantaneous and transient price impact on bonds with different maturities, including a cross price impact that is endogenous to the term structure. We connect the introduced impact to classic no-arbitrage theory for interest rate markets, showing that impact can be embedded in the pricing measure and that no-arbitrage can be preserved. We present pricing examples in presence of price impact and numerical examples of how impact changes the shape of the term structure. Finally, to show that our approach is applicable we solve an optimal execution problem in interest rate markets with the type of price impact we developed in the paper.", "published": "2020-11-19T21:42:35Z", "authors": ["Damiano Brigo", "Federico Graceffa", "Eyal Neuman"], "categories": ["q-fin.TR", "q-fin.PM"], "doi": "", "_query": "hedonic house price", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2008.05195v2", "title": "Competitive Demand Learning: A Non-cooperative Pricing Algorithm with Coordinated Price Experimentation", "abstract": "We consider a periodical equilibrium pricing problem for multiple firms over a planning horizon of T periods. At each period, firms set their selling prices and receive stochastic demand from consumers. Firms do not know their underlying demand curve, but they wish to determine the selling prices to maximize total revenue under competition. Hence, they have to do some price experiments such that the observed demand data are informative to make price decisions. However, uncoordinated price updating can render the demand information gathered by price experimentation less informative or inaccurate. We design a nonparametric learning algorithm to facilitate coordinated dynamic pricing, in which competitive firms estimate their demand functions based on observations and adjust their pricing strategies in a prescribed manner. We show that the pricing decisions, determined by estimated demand functions, converge to underlying equilibrium as time progresses. We obtain a bound of the revenue difference that has an order of O(F^2 T^3/4) and a regret bound that has an order of O(F T^1/2) with respect to the number of the competitive firms F and T . We also develop a modified algorithm to handle the situation where some firms may have the knowledge of the demand curve.", "published": "2020-08-12T09:26:24Z", "authors": ["Yongge Yang", "Yu-Ching Lee", "Po-An Chen"], "categories": ["cs.GT"], "doi": "", "_query": "hedonic house price", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/1708.09020v1", "title": "Learning to Price with Reference Effects", "abstract": "As a firm varies the price of a product, consumers exhibit reference effects, making purchase decisions based not only on the prevailing price but also the product's price history. We consider the problem of learning such behavioral patterns as a monopolist releases, markets, and prices products. This context calls for pricing decisions that intelligently trade off between maximizing revenue generated by a current product and probing to gain information for future benefit. Due to dependence on price history, realized demand can reflect delayed consequences of earlier pricing decisions. As such, inference entails attribution of outcomes to prior decisions and effective exploration requires planning price sequences that yield informative future outcomes. Despite the considerable complexity of this problem, we offer a tractable systematic approach. In particular, we frame the problem as one of reinforcement learning and leverage Thompson sampling. We also establish a regret bound that provides graceful guarantees on how performance improves as data is gathered and how this depends on the complexity of the demand model. We illustrate merits of the approach through simulations.", "published": "2017-08-29T20:40:10Z", "authors": ["Abbas Kazerouni", "Benjamin Van Roy"], "categories": ["cs.GT", "cs.AI"], "doi": "", "_query": "hedonic house price", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2111.05728v4", "title": "Diversity of symptom phenotypes in SARS-CoV-2 community infections observed in multiple large datasets", "abstract": "Through the use of cutting-edge unsupervised classification techniques from statistics and machine learning, we characterise symptom phenotypes among symptomatic SARS-CoV-2 PCR-positive community cases. We first analyse each dataset in isolation and across age bands, before using methods that allow us to compare multiple datasets. While we observe separation due to the total number of symptoms experienced by cases, we also see a separation of symptoms into gastrointestinal, respiratory and other types, and different symptom co-occurrence patterns at the extremes of age. In this way, we are able to demonstrate the deep structure of symptoms of COVID-19 without usual biases due to study design. This is expected to have implications for the identification and management of community SARS-CoV-2 cases and could be further applied to symptom-based management of other diseases and syndromes.", "published": "2021-11-10T15:12:29Z", "authors": ["Martyn Fyles", "Karina-Doris Vihta", "Carole H Sudre", "Harry Long", "Rajenki Das", "Caroline Jay", "Tom Wingfield", "Fergus Cumming", "William Green", "Pantelis Hadjipantelis", "Joni Kirk", "Claire J Steves", "Sebastien Ourselin", "Graham F Medley", "Elizabeth Fearon", "Thomas House"], "categories": ["stat.AP", "q-bio.PE"], "doi": "", "_query": "hedonic house price", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/1805.02574v1", "title": "Optimal Pricing in Repeated Posted-Price Auctions", "abstract": "We study revenue optimization pricing algorithms for repeated posted-price auctions where a seller interacts with a single strategic buyer that holds a fixed private valuation. We show that, in the case when both the seller and the buyer have the same discounting in their cumulative utilities (revenue and surplus), there exist two optimal algorithms. The first one constantly offers the Myerson price, while the second pricing proposes a \"big deal\": pay for all goods in advance (at the first round) or get nothing. However, when there is an imbalance between the seller and the buyer in the patience to wait for utility, we find that the constant pricing, surprisingly, is no longer optimal. First, it is outperformed by the pricing algorithm \"big deal\", when the seller's discount rate is lower than the one of the buyer. Second, in the inverse case of a less patient buyer, we reduce the problem of finding an optimal algorithm to a multidimensional optimization problem (a multivariate analogue of the functional used to determine Myerson's price) that does not admit a closed form solution in general, but can be solved by numerical optimization techniques (e.g., gradient ones). We provide extensive analysis of numerically found optimal algorithms to demonstrate that they are non-trivial, may be non-consistent, and generate larger expected revenue than the constant pricing with the Myerson price.", "published": "2018-05-07T15:30:54Z", "authors": ["Arsenii Vanunts", "Alexey Drutsa"], "categories": ["cs.GT"], "doi": "", "_query": "hedonic house price", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2601.18544v5", "title": "Inflation and the Distortion of Relative Prices", "abstract": "This paper presents a production network model of inflationary dynamics in which inflation can have near-zero correlation with the size of price change yet generate significant distortions in relative prices. New money enters unevenly across firms and percolates through buyer--seller links, thereby displacing relative prices even when every price is free to adjust and every market clears. Under state-dependent price stickiness, firms absorb higher inflation through more frequent rather than larger price changes, leaving the distortion intact. The distortion depends critically on the spectral gap of the production network, which sets the rate at which the economy converges to equilibrium. We quantify the mechanism on a reconstructed production network with the universe of firms in the United States. At a moderate rate of inflation, the typical firm's relative-price distortion is of the same magnitude as one quarter's inflation. The size of price change remains essentially uncorrelated with the magnitude of relative-price distortion.", "published": "2026-01-26T14:49:11Z", "authors": ["Vipin P Veetil"], "categories": ["econ.TH", "cs.SI"], "doi": "", "_query": "hedonic house price", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2210.00488v1", "title": "Zero-Ending Prices, Cognitive Convenience, and Price Rigidity", "abstract": "We assess the role of cognitive convenience in the popularity and rigidity of 0 ending prices in convenience settings. Studies show that 0 ending prices are common at convenience stores because of the transaction convenience that 0 ending prices offer. Using a large store level retail CPI data, we find that 0 ending prices are popular and rigid at convenience stores even when they offer little transaction convenience. We corroborate these findings with two large retail scanner price datasets from Dominicks and Nielsen. In the Dominicks data, we find that there are more 0 endings in the prices of the items in the front end candies category than in any other category, even though these prices have no effect on the convenience of the consumers check out transaction. In addition, in both Dominicks and Nielsens datasets, we find that 0 ending prices have a positive effect on demand. Ruling out consumer antagonism and retailers use of heuristics in pricing, we conclude that 0 ending prices are popular and rigid, and that they increase demand at convenience settings, not only for their transaction convenience, but also for the cognitive convenience they offer.", "published": "2022-10-02T11:14:07Z", "authors": ["Avichai Snir", " Haipeng", " Chen", "Daniel Levy"], "categories": ["econ.GN"], "doi": "", "_query": "hedonic house price", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/cond-mat/0311457v2", "title": "Order book approach to price impact", "abstract": "Buying and selling stocks causes price changes, which are described by the price impact function. To explain the shape of this function, we study the Island ECN orderbook. In addition to transaction data, the orderbook contains information about potential supply and demand for a stock. The virtual price impact calculated from this information is four times stronger than the actual one and explains it only partially. However, we find a strong anticorrelation between price changes and order flow, which strongly reduces the virtual price impact and provides for a quantitative explanation of the empirical price impact function.", "published": "2003-11-19T18:06:29Z", "authors": ["Philipp Weber", "Bernd Rosenow"], "categories": ["cond-mat"], "doi": "", "_query": "hedonic house price", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2403.07617v1", "title": "Price Gouging or Market Forces? Fairness Perceptions of Price Hikes in the Pandemic", "abstract": "We report the results of surveys we conducted in the US and Israel in 2020, a time when many prices increased following the spread of the pandemic. To assess respondents perceptions of price increases, we focus on goods whose prices have increased during the pandemic, including some essential goods. Consistent with the principle of dual entitlement, we find that respondents perceive price increases as more acceptable if they are due to cost shocks than if they are due to demand shocks. However, we also find large differences across the two populations, as well as across goods.", "published": "2024-03-12T12:59:23Z", "authors": ["Avichai Snir", "Daniel Levy", "Dudi Levy", "Haipeng Allan Chen"], "categories": ["econ.GN"], "doi": "10.13140/RG.2.2.26574.06727", "_query": "hedonic house price", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2407.21209v3", "title": "Algorithm-Assisted Decision Making and Racial Disparities in Housing: A Study of the Allegheny Housing Assessment Tool", "abstract": "The demand for housing assistance across the United States far exceeds the supply, leaving housing providers the task of prioritizing clients for receipt of this limited resource. To be eligible for federal funding, local homelessness systems are required to implement assessment tools as part of their prioritization processes. The Vulnerability Index Service Prioritization Decision Assistance Tool (VI-SPDAT) is the most commonly used assessment tool nationwide. Recent studies have criticized the VI-SPDAT as exhibiting racial bias, which may lead to unwarranted racial disparities in housing provision. In response to these criticisms, some jurisdictions have developed alternative tools, such as the Allegheny Housing Assessment (AHA), which uses algorithms to assess clients' risk levels. Drawing on data from its deployment, we conduct descriptive and quantitative analyses to evaluate whether replacing the VI-SPDAT with the AHA affects racial disparities in housing allocation. We find that the VI-SPDAT tended to assign higher risk scores to white clients and lower risk scores to Black clients, and that white clients were served at a higher rates pre-AHA deployment. While post-deployment service decisions became better aligned with the AHA score, and the distribution of AHA scores is similar across racial groups, we do not find evidence of a corresponding decrease in disparities in service rates. We attribute the persistent disparity to the use of Alt-AHA, a survey-based tool that is used in cases of low data quality, as well as group differences in eligibility-related factors, such as chronic homelessness and veteran status. We discuss the implications for housing service systems seeking to reduce racial disparities in their service delivery.", "published": "2024-07-30T21:44:48Z", "authors": ["Lingwei Cheng", "Cameron Drayton", "Alexandra Chouldechova", "Rhema Vaithianathan"], "categories": ["cs.HC", "econ.GN"], "doi": "", "_query": "hedonic house price", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2006.16805v4", "title": "Pricing Fresh Data", "abstract": "We introduce the concept of {\\it fresh data trading}, in which a destination user requests, and pays for, fresh data updates from a source provider, and data freshness is captured by the {\\it age of information} (AoI) metric. Keeping data fresh relies on frequent data updates by the source, which motivates the source to {\\it price fresh data}. In this work, the destination incurs an age-related cost, modeled as a general increasing function of the AoI. The source designs a pricing mechanism to maximize its profit; the destination chooses a data update schedule to trade off its payments to the source and its age-related cost. Depending on different real-time applications and scenarios, we study both a predictable-deadline and an unpredictable-deadline models. The key challenge of designing the optimal pricing scheme lies in the destination's time-interdependent valuations, due to the nature of AoI and the infinite-dimensional and dynamic optimization. To this end, we consider three pricing schemes that exploit and understand the profitability of three different dimensions in designing pricing: a {\\it time-dependent} pricing scheme, in which the price for each update depends on when it is requested; a {\\it quantity-based} pricing scheme, in which the price of each update depends on how many updates have been previously requested; a {\\it subscription-based} pricing scheme, in which the price for each update is flat-rate but the source charges an additional subscription fee. Our analysis reveals that the optimal subscription-based pricing maximizes the source's profit among all possible pricing schemes under both predictable deadline and unpredictable deadline models; the optimal quantity-based pricing scheme is only optimal with a predictable deadline; the time-dependent pricing scheme, under the unpredictable deadline, is asymptotically optimal under significant time discounting.", "published": "2020-06-28T23:51:59Z", "authors": ["Meng Zhang", "Ahmed Arafa", "Jianwei Huang", "H. Vincent Poor"], "categories": ["cs.NI", "cs.IT"], "doi": "", "_query": "hedonic house price", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2203.01384v1", "title": "Descending Price Auctions with Bounded Number of Price Levels and Batched Prophet Inequality", "abstract": "We consider descending price auctions for selling $m$ units of a good to unit demand i.i.d. buyers where there is an exogenous bound of $k$ on the number of price levels the auction clock can take. The auctioneer's problem is to choose price levels $p_1 > p_2 > \\cdots > p_{k}$ for the auction clock such that auction expected revenue is maximized. The prices levels are announced prior to the auction. We reduce this problem to a new variant of prophet inequality, which we call \\emph{batched prophet inequality}, where a decision-maker chooses $k$ (decreasing) thresholds and then sequentially collects rewards (up to $m$) that are above the thresholds with ties broken uniformly at random. For the special case of $m=1$ (i.e., selling a single item), we show that the resulting descending auction with $k$ price levels achieves $1- 1/e^k$ of the unrestricted (without the bound of $k$) optimal revenue. That means a descending auction with just 4 price levels can achieve more than 98\\% of the optimal revenue. We then extend our results for $m>1$ and provide a closed-form bound on the competitive ratio of our auction as a function of the number of units $m$ and the number of price levels $k$.", "published": "2022-03-02T19:59:15Z", "authors": ["Saeed Alaei", "Ali Makhdoumi", "Azarakhsh Malekian", "Rad Niazadeh"], "categories": ["cs.GT", "cs.DM", "cs.DS", "econ.TH"], "doi": "", "_query": "hedonic house price", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2507.09734v1", "title": "Boltzmann Price: Toward Understanding the Fair Price in High-Frequency Markets", "abstract": "In this paper, we introduce a parametrized family of prices derived from the Maximum Entropy Principle. The price is obtained from the distribution that minimizes bias, given the bid and ask volume imbalance at the top of the order book. Under specific parameter choices, it closely approximates the mid-price or the weighted mid-price. Using probabilities of bid and ask states, we propose a model of price dynamics in which both drift and volatility are driven by volume imbalance. Compared to standard models like Bachelier or Geometric Brownian Motion with constant volatility, our model can generate higher kurtosis and heavy-tailed distributions. Additionally, the drift term naturally emerges as a consequence of the order book imbalance. We validate the model through simulation and demonstrate its fit to historical equity data. The model provides a theoretical framework, integrating price, volume imbalance, and spread.", "published": "2025-07-13T18:17:48Z", "authors": ["Przemysław Rola"], "categories": ["q-fin.TR"], "doi": "", "_query": "hedonic house price", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/0802.2413v1", "title": "On the Scarf-Hirota model in the price-scaled price adjustment process", "abstract": "Hirota's results given in (Hirota.M.,1981) on the asymptotically stability are generalized to the price-scaled price adjustment process.", "published": "2008-02-18T04:10:12Z", "authors": ["Tatsuro Yamamoto", "Masanari Asano", "Yoshio Togawa", "Masanori Ohya"], "categories": ["math.DS"], "doi": "", "_query": "hedonic house price", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/1608.04146v2", "title": "Avoiding algebraic integers of bounded house in orbits of rational functions over cyclotomic closures", "abstract": "Let $k$ be a number field with cyclotomic closure $k^{\\mathrm{cyc}}$, and let $h \\in k^{\\mathrm{cyc}}(x)$. For $A \\ge 1$ a real number, we show that \\[ \\{ α\\in k^{\\mathrm{cyc}} : h(α) \\in \\overline{\\mathbb Z} \\text{ has house at most } A \\} \\] is finite for many $h$. We also show that for many such $h$ the same result holds if $h(α)$ is replaced by orbits $h(h(\\cdots h(α)))$. This generalizes a result proved by Ostafe that concerns avoiding roots of unity, which is the case $A=1$.", "published": "2016-08-14T22:18:56Z", "authors": ["Evan Chen"], "categories": ["math.NT"], "doi": "10.1090/proc/14115", "_query": "hedonic house price", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/1901.04770v1", "title": "Empirical forward price distribution from Bitcoin option prices", "abstract": "Report presents analysis of empirical distribution of future returns of bitcoin (BTC) from BTUSD inverse option prices. Logistic pdf is chosen as underlying distribution to fit option prices. The result is satisfactory and suggests that these prices can be described with just three or even one parameter. Fitted Logistic pdf matches forward price movements upto a scaling factor. Nevertheless, this observation stands alone and does not allow stochastic description of underlying prices with logistic pdf in similar fashion as it is done within Black-Scholes modelling framework. Put-call parity relationship is derived connecting prices of vanilla inverse options and futures.", "published": "2019-01-15T11:21:42Z", "authors": ["Nikolai Zaitsev"], "categories": ["q-fin.ST"], "doi": "", "_query": "hedonic house price", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/0903.2428v1", "title": "Price Impact", "abstract": "We define what \"Price Impact\" means, and how it is measured and modelled in the recent literature. Although this notion seems to convey the idea of a forceful and intuitive mechanism, we discuss why things might not be that simple. Empirical studies show that while the correlation between signed order flow and price changes is strong, the impact of trades on prices is neither linear in volume nor permanent. Impact allows private information to be reflected in prices, but by the same token, random fluctuations in order flow must also contribute to the volatility of markets.", "published": "2009-03-13T16:42:00Z", "authors": ["J. P. Bouchaud"], "categories": ["q-fin.TR", "q-fin.GN"], "doi": "", "_query": "hedonic house price", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2107.14113v1", "title": "Neural network approximation for superhedging prices", "abstract": "This article examines neural network-based approximations for the superhedging price process of a contingent claim in a discrete time market model. First we prove that the $α$-quantile hedging price converges to the superhedging price at time $0$ for $α$ tending to $1$, and show that the $α$-quantile hedging price can be approximated by a neural network-based price. This provides a neural network-based approximation for the superhedging price at time $0$ and also the superhedging strategy up to maturity. To obtain the superhedging price process for $t>0$, by using the Doob decomposition it is sufficient to determine the process of consumption. We show that it can be approximated by the essential supremum over a set of neural networks. Finally, we present numerical results.", "published": "2021-07-29T15:37:08Z", "authors": ["Francesca Biagini", "Lukas Gonon", "Thomas Reitsam"], "categories": ["q-fin.MF"], "doi": "", "_query": "hedonic house price", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2008.07356v1", "title": "Estimating action plans for smart poultry houses", "abstract": "In poultry farming, the systematic choice, update, and implementation of periodic (t) action plans define the feed conversion rate (FCR[t]), which is an acceptable measure for successful production. Appropriate action plans provide tailored resources for broilers, allowing them to grow within the so-called thermal comfort zone, without wast or lack of resources. Although the implementation of an action plan is automatic, its configuration depends on the knowledge of the specialist, tending to be inefficient and error-prone, besides to result in different FCR[t] for each poultry house. In this article, we claim that the specialist's perception can be reproduced, to some extent, by computational intelligence. By combining deep learning and genetic algorithm techniques, we show how action plans can adapt their performance over the time, based on previous well succeeded plans. We also implement a distributed network infrastructure that allows to replicate our method over distributed poultry houses, for their smart, interconnected, and adaptive control. A supervision system is provided as interface to users. Experiments conducted over real data show that our method improves 5% on the performance of the most productive specialist, staying very close to the optimal FCR[t].", "published": "2020-08-17T14:28:48Z", "authors": ["Darlan Felipe Klotz", "Richardson Ribeiro", "Fabrício Enembreck", "Gustavo Denardin", "Marco Barbosa", "Dalcimar Casanova", "Marcelo Teixeira"], "categories": ["cs.AI", "eess.SY"], "doi": "", "_query": "hedonic house price", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2406.09419v1", "title": "Sentient House: Designing for Discourse", "abstract": "The Sentient House project is an investigation into approaches that the artistdesigner can take to better involve the public in developing a critical perspective on pervasive technology in the home and the surrounding environment. Using Interaction Design approaches including workshops, surveys, rapidprototyping and critical thinking, this thesis suggests a framework for developing a more participatory atmosphere for Critical Design. As the world becomes more connected, and smarter, citizens concerns are being sidelined in favour of rapid progress and solutionism. Many of these initiatives are backed by government and commercial concerns who may not have the publics best interest at heart. The designs and approaches generated from this public participation seek to provide an outlet for a more agonistic debate and to develop tools and approaches to engage the public in questioning and addressing how technology affects them in the future. The outcomes of this research suggest that the public is receptive to a more active involvement in designing their digital future, and that the designer can be a critical component in revealing hidden consequences and alternative pathways for a more transparent and desirable future.", "published": "2024-02-14T18:45:29Z", "authors": ["Robert Collins"], "categories": ["cs.HC", "cs.CY"], "doi": "", "_query": "hedonic house price", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/1105.5717v1", "title": "Is there a bubble in LinkedIn's stock price?", "abstract": "Recent academic work has developed a method to determine, in real time, if a given stock is exhibiting a price bubble. Currently there is speculation in the financial press concerning the existence of a price bubble in the aftermath of the recent IPO of LinkedIn. We analyze stock price tick data from the short lifetime of this stock through May 24, 2011, and we find that LinkedIn has a price bubble.", "published": "2011-05-28T13:35:11Z", "authors": ["Robert Jarrow", "Younes Kchia", "Philip Protter"], "categories": ["q-fin.RM", "q-fin.ST"], "doi": "", "_query": "hedonic house price", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2203.01477v1", "title": "A Matching Mechanism for Provision of Housing to the Marginalized", "abstract": "During this pandemic, there have been unprecedented community and local government efforts to slow down the spread of the coronavirus, and also to protect our local economies. One such effort is California's project Roomkey that provided emergency housing to over 2000 vulnerable persons but fell short of the set goal of 15,000. It is projected that the homelessness problem will only get worse after the pandemic. With that in mind, we borrow from efforts like project Roomkey and suggest a solution that looks to improve upon these efforts to efficiently assign housing to the unhoused in our communities. The pandemic, together with the project Roomkey, shed light on the underlying supply demand mismatch that presents an opportunity for a matching mechanism solution to assigning housing options to the unhoused in a way that maximizes social welfare and minimizes susceptibility to strategic manipulation. Additionally, we argue that this automated solution would cut down on the amount of funding and personnel required for the assignment of housing to unhoused persons. Our solution is not intended to replace current solutions to homeless housing assignments but rather improve upon them. We can not postpone a proper solution to homelessness anymore, the time is now as the need for an efficient solution is most dire.", "published": "2022-03-03T01:34:36Z", "authors": ["J Ceasar Aguma"], "categories": ["cs.GT", "cs.CY", "econ.GN"], "doi": "", "_query": "hedonic house price", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2305.13227v1", "title": "Trustless Price Feeds of Cryptocurrencies: Pathfinder", "abstract": "Price feeds of securities is a critical component for many financial services, allowing for collateral liquidation, margin trading, derivative pricing and more. With the advent of blockchain technology, value in reporting accurate prices without a third party has become apparent. There have been many attempts at trying to calculate prices without a third party, in which each of these attempts have resulted in being exploited by an exploiter artificially inflating the price. The industry has then shifted to a more centralized design, fetching price data from multiple centralized sources and then applying statistical methods to reach a consensus price. Even though this strategy is secure compared to reading from a single source, enough number of sources need to report to be able to apply statistical methods. As more sources participate in reporting the price, the feed gets more secure with the slowest feed becoming the bottleneck for query response time, introducing a tradeoff between security and speed. This paper provides the design and implementation details of a novel method to algorithmically compute security prices in a way that artificially inflating targeted pools has no effect on the reported price of the queried asset. We hypothesize that the proposed algorithm can report accurate prices given a set of possibly dishonest sources.", "published": "2023-05-22T17:00:23Z", "authors": ["Orhan Koc"], "categories": ["q-fin.CP", "q-fin.TR"], "doi": "", "_query": "hedonic house price", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/1907.09144v1", "title": "Consistent upper price bounds for exotic options given a finite number of call prices and their convergence", "abstract": "We consider the problem of finding a consistent upper price bound for exotic options whose payoff depends on the stock price at two different predetermined time points (e.g. Asian option), given a finite number of observed call prices for these maturities. A model-free approach is used, only taking into account that the (discounted) stock price process is a martingale under the no-arbitrage condition. In case the payoff is directionally convex we obtain the worst case marginal pricing measures. The speed of convergence of the upper price bound is determined when the number of observed stock prices increases. We illustrate our findings with some numerical computations.", "published": "2019-07-22T06:09:45Z", "authors": ["Nicole Bäuerle", "Daniel Schmithals"], "categories": ["q-fin.MF", "math.OC"], "doi": "10.1142/S0219024921500114", "_query": "hedonic house price", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2007.05056v4", "title": "Multimodal price prediction", "abstract": "Price prediction is one of the examples related to forecasting tasks and is a project based on data science. Price prediction analyzes data and predicts the cost of new products. The goal of this research is to achieve an arrangement to predict the price of a cellphone based on its specifications. So, five deep learning models are proposed to predict the price range of a cellphone, one unimodal and four multimodal approaches. The multimodal methods predict the prices based on the graphical and non-graphical features of cellphones that have an important effect on their valorizations. Also, to evaluate the efficiency of the proposed methods, a cellphone dataset has been gathered from GSMArena. The experimental results show 88.3% F1-score, which confirms that multimodal learning leads to more accurate predictions than state-of-the-art techniques.", "published": "2020-07-09T20:46:13Z", "authors": ["Aidin Zehtab-Salmasi", "Ali-Reza Feizi-Derakhshi", "Narjes Nikzad-Khasmakhi", "Meysam Asgari-Chenaghlu", "Saeideh Nabipour"], "categories": ["cs.CV", "cs.LG"], "doi": "10.1007/s40745-021-00326-z", "_query": "hedonic house price", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2202.07086v1", "title": "Price Cycles in Ridesharing Platforms", "abstract": "In ridesharing platforms such as Uber and Lyft, it is observed that drivers sometimes collaboratively go offline when the price is low, and then return after the price has risen due to the perceived lack of supply. This collective strategy leads to cyclic fluctuations in prices and available drivers, resulting in poor reliability and social welfare. We study a continuous time, non-atomic model and prove that such online/offline strategies may form a Nash equilibrium among drivers, but lead to a lower total driver payoff if the market is sufficiently dense. Further, we show how to set price floors that effectively mitigate the emergence and impact of price cycles.", "published": "2022-02-14T23:15:19Z", "authors": ["Chenkai Yu", "Hongyao Ma", "Adam Wierman"], "categories": ["cs.GT", "cs.MA", "math.OC"], "doi": "", "_query": "hedonic house price", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2302.13997v3", "title": "Host Community Respecting Refugee Housing", "abstract": "We propose a novel model for refugee housing respecting the preferences of the accepting community and refugees themselves. In particular, we are given a topology representing the local community, a set of inhabitants occupying some vertices of the topology, and a set of refugees that should be housed on the empty vertices of the graph. Both the inhabitants and the refugees have preferences over the structure of their neighborhood. We are specifically interested in the problem of finding housing such that the preferences of every individual are met; using game-theoretical words, we are looking for housing that is stable with respect to some well-defined notion of stability. We investigate conditions under which the existence of equilibria is guaranteed and study the computational complexity of finding such a stable outcome. As the problem is NP-hard even in very simple settings, we employ the parameterized complexity framework to give a finer-grained view of the problem's complexity with respect to natural parameters and structural restrictions of the given topology.", "published": "2023-02-27T17:42:03Z", "authors": ["Dušan Knop", "Šimon Schierreich"], "categories": ["cs.GT", "cs.DS", "econ.TH"], "doi": "", "_query": "hedonic house price", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2006.16703v1", "title": "Expectation and Price in Incomplete Markets", "abstract": "Risk-neutral pricing dictates that the discounted derivative price is a martingale in a measure equivalent to the economic measure. The residual ambiguity for incomplete markets is here resolved by minimising the entropy of the price measure from the economic measure, subject to mark-to-market constraints, following arguments based on the optimisation of portfolio risk. The approach accounts for market and funding convexities and incorporates available price information, interpolating between methodologies based on expectation and replication.", "published": "2020-06-30T11:52:34Z", "authors": ["Paul McCloud"], "categories": ["q-fin.MF", "q-fin.PR"], "doi": "", "_query": "hedonic house price", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/1809.08027v1", "title": "On the Constant Price of Anarchy Conjecture", "abstract": "We study Nash equilibria and the price of anarchy in the classic model of Network Creation Games introduced by Fabrikant et al. In this model every agent (node) buys links at a prefixed price $α> 0$ in order to get connected to the network formed by all the $n$ agents. In this setting, the reformulated tree conjecture states that for $α> n$, every Nash equilibrium network is a tree. Moreover, Demaine et al. conjectured that the price of anarchy for this model is constant. Since it was shown that the price of anarchy for trees is constant, if the tree conjecture were true, then the price of anarchy would be constant for $α> n$. Up to now it has been proved that the \\PoA is constant $(i)$ in the \\emph{lower range}, for $α= O(n^{1-δ})$ with $δ\\geq \\frac{1}{\\log n}$ and $(ii)$ in the \\emph{upper range}, for $α> 4n-13$. In contrast, the best upper bound known for the price of anarchy for the remaining range is $2^{O(\\sqrt{\\log n})}$. In this paper we give new insights into the structure of the Nash equilibria for $α> n$ and we enlarge the range of the parameter $α$ for which the price of anarchy is constant. Specifically, we prove that the price of anarchy is constant for $α> n(1+ε)$ by showing that every equilibrium of diameter greater than some prefixed constant is a tree.", "published": "2018-09-21T10:37:51Z", "authors": ["C. Àlvarez", "A. Messegué"], "categories": ["cs.GT"], "doi": "", "_query": "hedonic house price", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2306.09659v2", "title": "Randomized Robust Price Optimization", "abstract": "The robust multi-product pricing problem is to determine the prices of a collection of products so as to maximize the worst-case revenue, where the worst case is taken over an uncertainty set of demand models that the firm expects could be realized in practice. A tacit assumption in this approach is that the pricing decision is a deterministic decision: the prices of the products are fixed and do not vary. In this paper, we consider a randomized approach to robust pricing, where a decision maker specifies a distribution over potential price vectors so as to maximize its worst-case revenue over an uncertainty set of demand models. We formally define this problem - the randomized robust price optimization problem - and analyze when a randomized price scheme performs as well as a deterministic scheme versus when it yields a benefit. We also propose solution methods for obtaining an optimal randomization scheme over a discrete set of candidate price vectors and show how these methods are applicable for common demand models, such as the linear, semi-log and log-log demand models. We numerically compare the randomized and deterministic approaches on a variety of synthetic and real problem instances; on instances derived from a real grocery retail scanner dataset, we show that the improvement in worst-case revenue can be as high as 92%. Using the same grocery retail scanner dataset, we also show that the randomized approach can produce price prescriptions that achieve higher out-of-sample revenue than the nominal and deterministic robust approaches.", "published": "2023-06-16T07:28:38Z", "authors": ["Xinyi Guan", "Velibor V. Mišić"], "categories": ["math.OC"], "doi": "", "_query": "hedonic house price", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/1308.6256v3", "title": "G-consistent price system and bid-ask pricing for European contingent claims under Knightian uncertainty", "abstract": "The target of this paper is to consider model the risky asset price on the financial market under the Knightian uncertainty, and pricing the ask and bid prices of the uncertain risk. We use the nonlinear analysis tool, i.e., G-frame work [26], to construct the model of the risky asset price and bid-ask pricing for the European contingent claims under Knightian uncertain financial market. Firstly, we consider the basic risky asset price model on the uncertain financial market, which we construct here is the model with drift uncertain and volatility uncertain. We describe such model by using generalized G-Brownian motion and call it as G-asset price system. We present the uncertain risk premium which is uncertain and distributed with maximum distribution. We derive the closed form of bid-ask price of the European contingent claim against the underlying risky asset with G-asset price system as the discounted conditional G-expecation of the claim, and the bid and ask prices are the viscosity solutions to the nonlinear HJB equations.Furthermore, we consider the main part of this paper, i.e., consider the risky asset on the Knightian uncertain financial market with the price fluctuation shows as continuous trajectories. We propose the G-conditional full support condition by using uncertain capacity, and the risky asset price path satisfying the G-conditional full support condition could be approximated by its G-consistent asset price systems. We derive that the bid and ask prices of the European contingent claim against such risky asset under uncertain can be expressed by discounted of some conditional G-expectation of the claim. We give examples, such as G-Markovian processes and the geometric fractional G-Brownian motion [9], satisfying the G-conditional full support condition.", "published": "2013-08-28T19:20:00Z", "authors": ["Wei Chen"], "categories": ["q-fin.PR", "q-fin.RM"], "doi": "", "_query": "hedonic house price", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2102.13289v3", "title": "Optimal Pricing of Information", "abstract": "A decision maker is choosing between an active action (e.g., purchase a house, invest certain stock) and a passive action. The payoff of the active action depends on the buyer's private type and also an unknown state of nature. An information seller can design experiments to reveal information about the realized state to the decision maker, and would like to maximize profit from selling such information. We characterize, in closed-form, the revenue-optimal information selling mechanism for the seller. After eliciting the buyer's type, the optimal mechanism charges the buyer an upfront payment and then simply reveals whether the realized state exceeds a certain threshold or not. The optimal mechanism features both price discrimination and information discrimination. The special buyer type who is a-priori indifferent between the active and passive action benefits the most from participating in the mechanism.", "published": "2021-02-26T03:15:19Z", "authors": ["Shuze Liu", "Weiran Shen", "Haifeng Xu"], "categories": ["cs.GT"], "doi": "10.1145/3465456.3467551", "_query": "hedonic house price", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/1012.2606v2", "title": "Modeling urban housing market dynamics: can the socio-spatial segregation preserve some social diversity?", "abstract": "Addressing issues of social diversity, we introduce a model of housing transactions between agents who are heterogeneous in their willingness to pay. A key assumption is that agents' preferences for a location depend on both an intrinsic attractiveness and on the social characteristics of the neighborhood. The stationary space distribution of income is analytically and numerically characterized. The main results are that socio-spatial segregation occurs if -- and only if -- the social influence is strong enough, but even so, some social diversity is preserved at most locations. Comparison with data on the Paris housing market shows that the results reproduce general trends of price distribution and spatial income segregation.", "published": "2010-12-13T00:34:34Z", "authors": ["Laetitia Gauvin", "Annick Vignes", "Jean-Pierre Nadal"], "categories": ["physics.soc-ph", "cs.SI"], "doi": "", "_query": "hedonic house price", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/cond-mat/0403723v1", "title": "Market depth and price dynamics: A note", "abstract": "This note explores the consequences of nonlinear price impact functions on price dynamics within the chartist-fundamentalist framework. Price impact functions may be nonlinear with respect to trading volume. As indicated by recent empirical studies, a given transaction may cause a large (small) price change if market depth is low (high). Simulations reveal that such a relationship may create endogenous complex price fluctuations even if the trading behavior of chartists and fundamentalists is linear.", "published": "2004-03-30T07:00:22Z", "authors": ["Frank Westerhoff"], "categories": ["cond-mat.other", "q-fin.TR"], "doi": "10.1142/S0129183104006455", "_query": "hedonic house price", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2410.14904v2", "title": "Switchback Price Experiments with Forward-Looking Demand", "abstract": "Switchback experiments are widely used in pricing applications. We consider a retailer running such an experiment for the price of a single product, with infinite supply. In each period, the seller chooses a price from a set of predefined prices consisting of an original price and a few discounted price levels. The goal is to estimate the demand gradient at the original price to adjust the original price and improve revenue after the experiment. In our model, in each period, a unit mass of buyers arrives on the market, with values distributed based on a time-varying process. Crucially, buyers are forward-looking with a discounted utility and will choose not to purchase now if they expect to face a discounted price in the near future. We show that such forward-looking behavior introduces bias in naive estimators of the demand gradient, due to intertemporal interference. Furthermore, although experiments with only two price points are common in practice, we prove that no estimator using data from such experiments can recover the correct demand gradient, even in the limit of an infinitely long experiment with an infinitesimal price discount. Moreover, we characterize the form of the bias of naive estimators. Finally, we show that with a simple three-price-level experiment, the seller can remove the bias due to strategic forward-looking behavior and construct an estimator for the demand gradient that asymptotically recovers the truth. Our results provide practical guidance for platforms to obtain unbiased demand gradients from price experiments in the presence of forward-looking demand.", "published": "2024-10-18T23:11:31Z", "authors": ["Yifan Wu", "Ramesh Johari", "Vasilis Syrgkanis", "Gabriel Y. Weintraub"], "categories": ["cs.GT", "econ.EM", "econ.TH"], "doi": "", "_query": "hedonic house price", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/0909.0892v2", "title": "Price of Anarchy for Greedy Auctions", "abstract": "We consider auctions in which greedy algorithms, paired with first-price or critical-price payment rules, are used to resolve multi-parameter combinatorial allocation problems. We study the price of anarchy for social welfare in such auctions. We show for a variety of equilibrium concepts, including Bayes-Nash equilibrium and correlated equilibrium, the resulting price of anarchy bound is close to the approximation factor of the underlying greedy algorithm.", "published": "2009-09-04T13:39:25Z", "authors": ["Brendan Lucier", "Allan Borodin"], "categories": ["cs.GT"], "doi": "", "_query": "hedonic house price", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/nlin/0005018v2", "title": "Spatial competition and price formation", "abstract": "We look at price formation in a retail setting, that is, companies set prices, and consumers either accept prices or go someplace else. In contrast to most other models in this context, we use a two-dimensional spatial structure for information transmission, that is, consumers can only learn from nearest neighbors. Many aspects of this can be understood in terms of generalized evolutionary dynamics. In consequence, we first look at spatial competition and cluster formation without price. This leads to establishement size distributions, which we compare to reality. After some theoretical considerations, which at least heuristically explain our simulation results, we finally return to price formation, where we demonstrate that our simple model with nearly no organized planning or rationality on the part of any of the agents indeed leads to an economically plausible price.", "published": "2000-05-09T13:01:27Z", "authors": ["Kai Nagel", "Martin Shubik", "Maya Paczuski", "Per Bak"], "categories": ["nlin.AO"], "doi": "10.1016/S0378-4371(00)00392-7", "_query": "hedonic house price", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/1911.12598v1", "title": "Online Pricing with Reserve Price Constraint for Personal Data Markets", "abstract": "The society's insatiable appetites for personal data are driving the emergency of data markets, allowing data consumers to launch customized queries over the datasets collected by a data broker from data owners. In this paper, we study how the data broker can maximize her cumulative revenue by posting reasonable prices for sequential queries. We thus propose a contextual dynamic pricing mechanism with the reserve price constraint, which features the properties of ellipsoid for efficient online optimization, and can support linear and non-linear market value models with uncertainty. In particular, under low uncertainty, our pricing mechanism provides a worst-case regret logarithmic in the number of queries. We further extend to other similar application scenarios, including hospitality service, online advertising, and loan application, and extensively evaluate three pricing instances of noisy linear query, accommodation rental, and impression over MovieLens 20M dataset, Airbnb listings in U.S. major cities, and Avazu mobile ad click dataset, respectively. The analysis and evaluation results reveal that our proposed pricing mechanism incurs low practical regret, online latency, and memory overhead, and also demonstrate that the existence of reserve price can mitigate the cold-start problem in a posted price mechanism, and thus can reduce the cumulative regret.", "published": "2019-11-28T08:55:47Z", "authors": ["Chaoyue Niu", "Zhenzhe Zheng", "Fan Wu", "Shaojie Tang", "Guihai Chen"], "categories": ["cs.CE", "cs.GT", "cs.LG"], "doi": "", "_query": "hedonic house price", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2310.07444v2", "title": "Measuring the impact of green retrofits on CO$_2$ emissions: London Housing Case Study", "abstract": "The residential sector is responsible for approximately 20 percent of the world's energy-related greenhouse gas emissions. In order to improve this reality, it is essential to help and support the leading actor in this scene, the homeowner. In general, buildings are complex objects as each home may have very particular parameters: different materials (windows, walls, types of roofs, etc.), uses, and habits. No two cases are identical. In this paper, we addressed this problem by proposing a method to obtain estimates of energy and carbon savings resulting from a class of retrofit projects and housing profiles. We applied our formulas to conduct a case study of the London residential sector, supported by the publicly available Energy Performance Certificates (EPC) dataset. As a result of a multi-project renovation of a house, including loft insulation, double-glazed windows, and energy-efficient lighting (LED), we found that the average family in the Greater London area could save approximately 9 MWh in energy consumption, 1,700 kgCO$_2$ in carbon emissions and more than GBP 800 on the energy bills, over a year, in contrast to a cost of around GBP 9,000 (or a return on investment of 11 years). In another example, applying the method to calculate what is needed to install heat pumps in each house in London, a total of GBP 16.9 billion would be spent, leading to savings of GBP 450 million per year (return on investment of 37 years). Finally, an overall upgrade to loft insulation would cost London households GBP 1.4 billion, generating GBP 290 million in annual savings (a 5-year return on investment).", "published": "2023-10-11T12:48:26Z", "authors": ["F. C. Correia", "M. Fazion"], "categories": ["stat.AP", "physics.soc-ph"], "doi": "", "_query": "hedonic house price", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2508.09280v2", "title": "Carbon Pricing in Traffic Networks", "abstract": "We study how externality pricing can steer traffic toward Wardrop equilibria respecting prescribed budgets. Our multi-commodity model encompasses carbon pricing, cordon pricing, and tradable credit schemes. We characterize the flows attainable when one common price may be assigned to each externality. We show that for continuous travel times and flow-independent externalities, every feasible externality budget is implementable. For flow-dependent externalities, smoothness bounds quantify the necessary budget relaxation; this links budget implementability to the price of anarchy. If travel times and externalities admit convex potentials, we give conditions for constructing prices implementing a given budget through convex optimization and specify explicit prices ensuring that relaxed budgets are respected by every equilibrium. As, for a single separable constant externality, total externality is nonincreasing in its price, this enables us to compute the whole price--equilibrium curve in finite time for separable, continuous, strictly increasing, piecewise affine travel times. Although this curve may have exponentially many pieces, the minimum price implementing a feasible budget is computable in polynomial time. The same machinery computes the range of market-clearing prices for tradable credit schemes. A computational study on several realistic traffic networks illustrates emission reductions from carbon pricing and the effects of cordon pricing on emissions within the cordon.", "published": "2025-08-12T18:41:20Z", "authors": ["Svenja M. Griesbach", "Tobias Harks", "Max Klimm", "Michael Markl", "Philipp Warode"], "categories": ["cs.GT", "math.OC"], "doi": "", "_query": "hedonic house price", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2605.27658v1", "title": "Historical Developments in Probability Measures for Asset Pricing: From State Prices to Modern Pricing Kernels", "abstract": "This review summarizes the historical development of probability measures in asset pricing, from early mathematical finance and state price theory to risk-neutral valuation, martingale measures, forward measures, stochastic discount factors, incomplete-market measure selection, benchmark pricing, robust and nonlinear pricing, and modern data-driven probability transformations. The central theme is that asset pricing is not merely an exercise in estimating physical probabilities. Instead, pricing theory constructs, transforms, or selects probability measures so that market prices can be represented as expectations after discounting, numeraire normalization, marginal utility weighting, entropy penalization, calibration, or information conditioning. The paper emphasizes landmark contributions including Bachelier's probabilistic model of speculation, Arrow-Debreu state-contingent claims, Black-Scholes-Merton option pricing, Harrison-Kreps and Harrison-Pliska's martingale formalization, Delbaen and Schachermayer's fundamental theorem, Breeden-Litzenberger implied state price densities, change of numeraire methods, Hansen-Jagannathan stochastic discount factor restrictions, Cochrane's SDF synthesis, and recent empirical and machine learning work on learned pricing kernels. Text-, attention-, and sentiment-based probability transformations are treated as recent information-adjusted forecasting extensions that complement, rather than replace, martingale, numeraire, SDF, and incomplete-market frameworks. The paper also collects key formulas for state prices, stochastic discount factors, Radon-Nikodym densities, Girsanov changes of measure, risk-neutral valuation, forward measures, implied densities, coherent risk measures, benchmark pricing, learned SDFs, and information-adjusted forecasting.", "published": "2026-05-26T20:22:20Z", "authors": ["Zhang Chen", "Chen Kay"], "categories": ["q-fin.MF"], "doi": "", "_query": "hedonic house price", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2506.16264v1", "title": "Pricing under the Benchmark Approach", "abstract": "The paper summarizes key results of the benchmark approach with a focus on the concept of benchmark-neutral pricing. It applies these results to the pricing of an extreme-maturity European put option on a well-diversified stock index. The growth optimal portfolio of the stocks is approximated by a well-diversified stock portfolio and modeled by a drifted time-transformed squared Bessel process of dimension four. It is shown that the benchmark-neutral price of a European put option is theoretically the minimal possible price and the respective risk-neutral put price turns out to be significantly more expensive.", "published": "2025-06-19T12:26:40Z", "authors": ["Eckhard Platen"], "categories": ["q-fin.MF", "q-fin.GN"], "doi": "", "_query": "hedonic house price", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2006.06519v2", "title": "Reserve Price Optimization for First Price Auctions", "abstract": "The display advertising industry has recently transitioned from second- to first-price auctions as its primary mechanism for ad allocation and pricing. In light of this, publishers need to re-evaluate and optimize their auction parameters, notably reserve prices. In this paper, we propose a gradient-based algorithm to adaptively update and optimize reserve prices based on estimates of bidders' responsiveness to experimental shocks in reserves. Our key innovation is to draw on the inherent structure of the revenue objective in order to reduce the variance of gradient estimates and improve convergence rates in both theory and practice. We show that revenue in a first-price auction can be usefully decomposed into a \\emph{demand} component and a \\emph{bidding} component, and introduce techniques to reduce the variance of each component. We characterize the bias-variance trade-offs of these techniques and validate the performance of our proposed algorithm through experiments on synthetic data and real display ad auctions data from Google ad exchange.", "published": "2020-06-11T15:35:19Z", "authors": ["Zhe Feng", "Sébastien Lahaie", "Jon Schneider", "Jinchao Ye"], "categories": ["cs.GT", "cs.LG", "econ.EM", "stat.ML"], "doi": "", "_query": "hedonic house price", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/1011.1065v4", "title": "Price Differentiation for Communication Networks", "abstract": "We study the optimal usage-based pricing problem in a resource-constrained network with one profit-maximizing service provider and multiple groups of surplus-maximizing users. With the assumption that the service provider knows the utility function of each user (thus complete information), we find that the complete price differentiation scheme can achieve a large revenue gain (e.g., 50%) compared to no price differentiation, when the total network resource is comparably limited and the high willingness to pay users are minorities. However, the complete price differentiation scheme may lead to a high implementational complexity. To trade off the revenue against the implementational complexity, we further study the partial price differentiation scheme, and design a polynomial-time algorithm that can compute the optimal partial differentiation prices. We also consider the incomplete information case where the service provider does not know which group each user belongs to. We show that it is still possible to realize price differentiation under this scenario, and provide the sufficient and necessary condition under which an incentive compatible differentiation scheme can achieve the same revenue as under complete information.", "published": "2010-11-04T06:25:34Z", "authors": ["Shuqin Li", "Jianwei Huang"], "categories": ["cs.NI"], "doi": "", "_query": "hedonic house price", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2007.02076v2", "title": "Note on simulation pricing of $π$-options", "abstract": "In this work, we adapt a Monte Carlo algorithm introduced by Broadie and Glasserman (1997) to price a $π$-option. This method is based on the simulated price tree that comes from discretization and replication of possible trajectories of the underlying asset's price. As a result this algorithm produces the lower and the upper bounds that converge to the true price with the increasing depth of the tree. Under specific parametrization, this $π$-option is related to relative maximum drawdown and can be used in the real-market environment to protect a portfolio against volatile and unexpected price drops. We also provide some numerical analysis.", "published": "2020-07-04T11:38:56Z", "authors": ["Zbigniew Palmowski", "Tomasz Serafin"], "categories": ["q-fin.CP"], "doi": "", "_query": "hedonic house price", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2210.10146v1", "title": "Housing Forecasts via Stock Market Indicators", "abstract": "Through the reinterpretation of housing data as candlesticks, we extend Nature Scientific Reports' article by Liang and Unwin [LU22] on stock market indicators for COVID-19 data, and utilize some of the most prominent technical indicators from the stock market to estimate future changes in the housing market, comparing the findings to those one would obtain from studying real estate ETF's. By providing an analysis of MACD, RSI, and Candlestick indicators (Bullish Engulfing, Bearish Engulfing, Hanging Man, and Hammer), we exhibit their statistical significance in making predictions for USA data sets (using Zillow Housing data) and also consider their applications within three different scenarios: a stable housing market, a volatile housing market, and a saturated market. In particular, we show that bearish indicators have a much higher statistical significance then bullish indicators, and we further illustrate how in less stable or more populated countries, bearish trends are only slightly more statistically present compared to bullish trends.", "published": "2022-10-18T20:23:43Z", "authors": ["Varun Mittal", "Laura P. Schaposnik"], "categories": ["econ.GN"], "doi": "10.1016/j.heliyon.2023.e16286", "_query": "hedonic house price", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2104.09597v1", "title": "Price Optimization with Practical Constraints", "abstract": "In this paper, we study a retailer price optimization problem which includes the practical constraints: maximum number of price changes and minimum amount of price change (if a change is recommended). We provide a closed-form formula for the Euclidean projection onto the feasible set defined by these two constraints, based on which a simple gradient projection algorithm is proposed to solve the price optimization problem. We study the convergence and solution quality of the proposed algorithm. We extend the base model to include upper/lower bounds on the individual product prices and solve it with some adjustments to the gradient projection algorithm. Numerical results are reported to demonstrate the performance of the proposed algorithm.", "published": "2021-04-19T20:10:32Z", "authors": ["Xiaojie Wang", "Hsin-Chan Huang", "Lanshan Han", "Alvin Lim"], "categories": ["math.OC"], "doi": "", "_query": "hedonic house price", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2310.12341v1", "title": "Price dispersion across online platforms: Evidence from hotel room prices in London (UK)", "abstract": "This paper studies the widespread price dispersion of homogeneous products across different online platforms, even when consumers can easily access price information from comparison websites. We collect data for the 200 most popular hotels in London (UK) and document that prices vary widely across booking sites while making reservations for a hotel room. Additionally, we find that prices listed across different platforms tend to converge as the booking date gets closer to the date of stay. However, the price dispersion persists until the date of stay, implying that the \"law of one price\" does not hold. We present a simple theoretical model to explain this and show that in the presence of aggregate demand uncertainty and capacity constraints, price dispersion could exist even when products are homogeneous, consumers are homogeneous, all agents have perfect information about the market structure, and consumers face no search costs to acquire information about the products. Our theoretical intuition and robust empirical evidence provide additional insights into price dispersion across online platforms in different institutional settings. Our study complements the existing literature that relies on consumer search costs to explain the price dispersion phenomenon.", "published": "2023-10-18T21:40:08Z", "authors": ["Debashrita Mohapatra", "Debi Prasad Mohapatra", "Ram Sewak Dubey"], "categories": ["econ.GN"], "doi": "", "_query": "hedonic house price", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2409.08359v1", "title": "Participatory Science and Machine Learning Applied to Millions of Sources in the Hobby-Eberly Telescope Dark Energy Experiment", "abstract": "We are merging a large participatory science effort with machine learning to enhance the Hobby-Eberly Telescope Dark Energy Experiment (HETDEX). Our overall goal is to remove false positives, allowing us to use lower signal-to-noise data and sources with low goodness-of-fit. With six million classifications through Dark Energy Explorers, we can confidently determine if a source is not real at over 94% confidence level when classified by at least ten individuals; this confidence level increases for higher signal-to-noise sources. To date, we have only been able to apply this direct analysis to 190,000 sources. The full sample of HETDEX will contain around 2-3M sources, including nearby galaxies ([O II] emitters), distant galaxies (Lyman-alpha emitters or LAEs), false positives, and contamination from instrument issues. We can accommodate this tenfold increase by using machine learning with visually-vetted samples from Dark Energy Explorers. We have already increased by over ten-fold in number of sources that have been visually vetted from our previous pilot study where we only had 14,000 visually vetted LAE candidates. This paper expands on the previous work increasing the visually-vetted sample from 14,000 to 190,000. In addition, using our currently visually-vetted sample, we generate a real or false positive classification for the full candidate sample of 1.2 million LAEs. We currently have approximately 17,000 volunteers from 159 countries around the world. Thus, we are applying participatory or citizen scientist analysis to our full HETDEX dataset, creating a free educational opportunity that requires no prior technical knowledge.", "published": "2024-09-12T18:51:17Z", "authors": ["Lindsay R. House", "Karl Gebhardt", "Keely Finkelstein", "Erin Mentuch Cooper", "Dustin Davis", "Daniel J. Farrow", "Donald P. Schneider"], "categories": ["astro-ph.IM", "astro-ph.CO", "astro-ph.GA", "physics.ed-ph"], "doi": "10.3847/1538-4357/ad782c", "_query": "hedonic house price", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/1610.08665v3", "title": "Direct semi-parametric estimation of the state price density implied in option prices", "abstract": "We present a model for direct semi-parametric estimation of the State Price Density (SPD) implied in quoted option prices. We treat the observed prices as expected values of possible pay-offs at maturity, weighted by the unknown probability density function. We model the logarithm of the latter as a smooth function while matching the expected values of the potential pay-offs with the observed prices. This leads to a special case of the penalized composite link model. Our estimates do not rely on any parametric assumption on the underlying asset price dynamics and are consistent with no-arbitrage conditions. The model shows excellent performance in simulations and in application to real data.", "published": "2016-10-27T09:03:58Z", "authors": ["Gianluca Frasso", "Paul H. C. Eilers"], "categories": ["stat.AP"], "doi": "", "_query": "hedonic house price", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/0905.2480v1", "title": "A statistical analysis of product prices in online markets", "abstract": "We empirically investigate fluctuations in product prices in online markets by using a tick-by-tick price data collected from a Japanese price comparison site, and find some similarities and differences between product and asset prices. The average price of a product across e-retailers behaves almost like a random walk, although the probability of price increase/decrease is higher conditional on the multiple events of price increase/decrease. This is quite similar to the property reported by previous studies about asset prices. However, we fail to find a long memory property in the volatility of product price changes. Also, we find that the price change distribution for product prices is close to an exponential distribution, rather than a power law distribution. These two findings are in a sharp contrast with the previous results regarding asset prices. We propose an interpretation that these differences may stem from the absence of speculative activities in product markets; namely, e-retailers seldom repeat buy and sell of a product, unlike traders in asset markets.", "published": "2009-05-15T06:48:07Z", "authors": ["Takayuki Mizuno", "Tsutomu Watanabe"], "categories": ["physics.soc-ph", "physics.data-an"], "doi": "10.1140/epjb/e2009-00439-1", "_query": "hedonic house price", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2207.08495v1", "title": "The role of regular asymptomatic testing in reducing the impact of a COVID-19 wave", "abstract": "Testing for infection with SARS-CoV-2 is an important intervention in reducing onwards transmission of COVID-19, particularly when combined with the isolation and contact-tracing of positive cases. Many countries with the capacity to do so have made use of lab-processed Polymerase Chain Reaction (PCR) testing targeted at individuals with symptoms and the contacts of confirmed cases. Alternatively, Lateral Flow Tests (LFTs) are able to deliver a result quickly, without lab-processing and at a relatively low cost. Their adoption can support regular mass asymptomatic testing, allowing earlier detection of infection and isolation of infectious individuals. In this paper we extend and apply the agent-based epidemic modelling framework Covasim to explore the impact of regular asymptomatic testing on the peak and total number of infections in an emerging COVID-19 wave. We explore testing with LFTs at different frequency levels within a population with high levels of immunity and with background symptomatic PCR testing, case isolation and contact tracing for testing. The effectiveness of regular asymptomatic testing was compared with `lockdown' interventions seeking to reduce the number of non-household contacts across the whole population through measures such as mandating working from home and restrictions on gatherings. Since regular asymptomatic testing requires only those with a positive result to reduce contact, while lockdown measures require the whole population to reduce contact, any policy decision that seeks to trade off harms from infection against other harms will not automatically favour one over the other. Our results demonstrate that, where such a trade off is being made, at moderate rates of early exponential growth regular asymptomatic testing has the potential to achieve significant infection control without the wider harms associated with additional lockdown measures.", "published": "2022-07-18T10:22:55Z", "authors": ["Miguel E. P. Silva", "Martyn Fyles", "Li Pi", "Jasmina Panovska-Griffiths", "Caroline Jay", "Thomas House", "Elizabeth Fearon"], "categories": ["q-bio.PE"], "doi": "", "_query": "hedonic house price", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/1101.5617v1", "title": "Optimal Pricing in Networks with Externalities", "abstract": "We study the optimal pricing strategies of a monopolist selling a divisible good (service) to consumers that are embedded in a social network. A key feature of our model is that consumers experience a (positive) local network effect. In particular, each consumer's usage level depends directly on the usage of her neighbors in the social network structure. Thus, the monopolist's optimal pricing strategy may involve offering discounts to certain agents, who have a central position in the underlying network. First, we consider a setting where the monopolist can offer individualized prices and derive an explicit characterization of the optimal price for each consumer as a function of her network position. In particular, we show that it is optimal for the monopolist to charge each agent a price that is proportional to her Bonacich centrality in the social network. In the second part of the paper, we discuss the optimal strategy of a monopolist that can only choose a single uniform price for the good and derive an algorithm polynomial in the number of agents to compute such a price. Thirdly, we assume that the monopolist can offer the good in two prices, full and discounted, and study the problem of determining which set of consumers should be given the discount. We show that the problem is NP-hard, however we provide an explicit characterization of the set of agents that should be offered the discounted price. Next, we describe an approximation algorithm for finding the optimal set of agents. We show that if the profit is nonnegative under any feasible price allocation, the algorithm guarantees at least 88% of the optimal profit. Finally, we highlight the value of network information by comparing the profits of a monopolist that does not take into account the network effects when choosing her pricing policy to those of a monopolist that uses this information optimally.", "published": "2011-01-28T20:31:42Z", "authors": ["Ozan Candogan", "Kostas Bimpikis", "Asuman Ozdaglar"], "categories": ["cs.GT", "cs.NI"], "doi": "", "_query": "hedonic house price", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/1903.01744v2", "title": "Scaling Features of Price-Volume Cross-Correlation", "abstract": "Price without transaction makes no sense. Trading volume authenticates its corresponding price, so there exist mutual information and correlation between price and trading volume. We are curious about fractal features of this correlation and need to know how structures in different scales translate information. To explore the influence of investment size (trading volume), price-wise (gain/loss), and time-scale effects, we analyzed the price and trading volume and their coupling by applying the MF-DXA method. Our results imply that price, trading volume, and price-volume coupling exhibit a power law and are also multifractal. Meanwhile, considering developed markets, the price-volume couplings are significantly negatively correlated. However, in emerging markets, the price has less of a contribution to price-volume coupling. In emerging markets in comparison with the developed markets, trading volume and price are more independent.", "published": "2019-03-05T09:33:40Z", "authors": ["Jamshid Ardalankia", "Mohammad Osoolian", "Emmanuel Haven", "G. Reza Jafari"], "categories": ["q-fin.CP", "q-fin.ST"], "doi": "10.1016/j.physa.2019.124111", "_query": "hedonic house price", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2105.13320v1", "title": "Neural Options Pricing", "abstract": "This research investigates pricing financial options based on the traditional martingale theory of arbitrage pricing applied to neural SDEs. We treat neural SDEs as universal Itô process approximators. In this way we can lift all assumptions on the form of the underlying price process, and compute theoretical option prices numerically. We propose a variation of the SDE-GAN approach by implementing the Wasserstein distance metric as a loss function for training. Furthermore, it is conjectured that the error of the option price implied by the learnt model can be bounded by the very Wasserstein distance metric that was used to fit the empirical data.", "published": "2021-05-27T17:22:30Z", "authors": ["Timothy DeLise"], "categories": ["q-fin.MF", "cs.CE", "cs.LG", "q-fin.CP"], "doi": "", "_query": "hedonic house price", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/1810.12840v1", "title": "Asset Price Distributions and Efficient Markets", "abstract": "We explore a decomposition in which returns on a large class of portfolios relative to the market depend on a smooth non-negative drift and changes in the asset price distribution. This decomposition is obtained using general continuous semimartingale price representations, and is thus consistent with virtually any asset pricing model. Fluctuations in portfolio relative returns depend on stochastic time-varying dispersion in asset prices. Thus, our framework uncovers an asset pricing factor whose existence emerges from an accounting identity universal across different economic and financial environments, a fact that has deep implications for market efficiency. In particular, in a closed, dividend-free market in which asset price dispersion is relatively constant, a large class of portfolios must necessarily outperform the market portfolio over time. We show that price dispersion in commodity futures markets has increased only slightly, and confirm the existence of substantial excess returns that co-vary with changes in price dispersion as predicted by our theory.", "published": "2018-10-30T16:27:49Z", "authors": ["Ricardo T. Fernholz", "Caleb Stroup"], "categories": ["q-fin.PM", "q-fin.GN"], "doi": "", "_query": "hedonic house price", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2304.09743v1", "title": "The Price of Explainability for Clustering", "abstract": "Given a set of points in $d$-dimensional space, an explainable clustering is one where the clusters are specified by a tree of axis-aligned threshold cuts. Dasgupta et al. (ICML 2020) posed the question of the price of explainability: the worst-case ratio between the cost of the best explainable clusterings to that of the best clusterings. We show that the price of explainability for $k$-medians is at most $1+H_{k-1}$; in fact, we show that the popular Random Thresholds algorithm has exactly this price of explanability, matching the known lower bound constructions. We complement our tight analysis of this particular algorithm by constructing instances where the price of explanability (using any algorithm) is at least $(1-o(1)) \\ln k$, showing that our result is best possible, up to lower-order terms. We also improve the price of explanability for the $k$-means problem to $O(k \\ln \\ln k)$ from the previous $O(k \\ln k)$, considerably closing the gap to the lower bounds of $Ω(k)$. Finally, we study the algorithmic question of finding the best explainable clustering: We show that explainable $k$-medians and $k$-means cannot be approximated better than $O(\\ln k)$, under standard complexity-theoretic conjectures. This essentially settles the approximability of explainable $k$-medians and leaves open the intriguing possibility to get significantly better approximation algorithms for $k$-means than its price of explainability.", "published": "2023-04-19T15:29:12Z", "authors": ["Anupam Gupta", "Madhusudhan Reddy Pittu", "Ola Svensson", "Rachel Yuan"], "categories": ["cs.DS"], "doi": "", "_query": "hedonic house price", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/1011.6402v3", "title": "The Price Impact of Order Book Events", "abstract": "We study the price impact of order book events - limit orders, market orders and cancelations - using the NYSE TAQ data for 50 U.S. stocks. We show that, over short time intervals, price changes are mainly driven by the order flow imbalance, defined as the imbalance between supply and demand at the best bid and ask prices. Our study reveals a linear relation between order flow imbalance and price changes, with a slope inversely proportional to the market depth. These results are shown to be robust to seasonality effects, and stable across time scales and across stocks. We argue that this linear price impact model, together with a scaling argument, implies the empirically observed \"square-root\" relation between price changes and trading volume. However, the relation between price changes and trade volume is found to be noisy and less robust than the one based on order flow imbalance.", "published": "2010-11-29T22:05:46Z", "authors": ["Rama Cont", "Arseniy Kukanov", "Sasha Stoikov"], "categories": ["q-fin.TR", "q-fin.ST"], "doi": "10.1093/jjfinec/nbt003", "_query": "hedonic house price", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2205.04661v2", "title": "Pricing with algorithms", "abstract": "This paper studies Markov perfect equilibria in a repeated duopoly model where sellers choose algorithms. An algorithm is a mapping from the competitor's price to own price. Once set, algorithms respond quickly. Customers arrive randomly and so do opportunities to revise the algorithm. In the simple game with two possible prices, monopoly outcome is the unique equilibrium for standard functional forms of the profit function. More generally, with multiple prices, exercise of market power is the rule -- in all equilibria, the expected payoff of both sellers is above the competitive outcome, and that of at least one seller is close to or above the monopoly outcome. Sustenance of such collusion seems outside the scope of standard antitrust laws for it does not involve any direct communication.", "published": "2022-05-10T04:15:21Z", "authors": ["Rohit Lamba", "Sergey Zhuk"], "categories": ["econ.TH"], "doi": "", "_query": "hedonic house price", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2203.12042v2", "title": "Price Manipulability in First-Price Auctions", "abstract": "First-price auctions have many desirable properties, including uniquely possessing some, like credibility. However, first-price auctions are also inherently non-truthful, and non-truthfulness may result in instability and inefficiencies. Given these pros and cons, we seek to quantify the extent to which first-price auctions are susceptible to manipulation. In this work we adopt a metric that was introduced in the context of bitcoin fee design markets: the percentage change in payment that can be achieved by being strategic. We study the behavior of this metric for single-unit and $k$-unit auction environments with $n$ i.i.d. buyers, and seek conditions under which the percentage change tends to zero as $n$ grows large. To the best of our knowledge, ours is the first rigorous study of the extent to which large multi-unit first price auctions are susceptible to manipulation. We provide an almost complete picture of the conditions under which they are truthful in the large, and exhibit some surprising boundaries.", "published": "2022-03-22T21:05:33Z", "authors": ["Johannes Brustle", "Paul Dütting", "Balasubramanian Sivan"], "categories": ["cs.GT"], "doi": "", "_query": "hedonic house price", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/math/0503550v1", "title": "On the super replication price of unbounded claims", "abstract": "In an incomplete market the price of a claim f in general cannot be uniquely identified by no arbitrage arguments. However, the ``classical'' super replication price is a sensible indicator of the (maximum selling) value of the claim. When f satisfies certain pointwise conditions (e.g., f is bounded from below), the super replication price is equal to sup_QE_Q[f], where Q varies on the whole set of pricing measures. Unfortunately, this price is often too high: a typical situation is here discussed in the examples. We thus define the less expensive weak super replication price and we relax the requirements on f by asking just for ``enough'' integrability conditions. By building up a proper duality theory, we show its economic meaning and its relation with the investor's preferences. Indeed, it turns out that the weak super replication price of f coincides with sup_{Q\\in M_Φ}E_Q[f], where M_Φ is the class of pricing measures with finite generalized entropy (i.e., E[Φ(\\frac{dQ}{dP})]<\\infty) and where Φis the convex conjugate of the utility function of the investor.", "published": "2005-03-24T15:11:24Z", "authors": ["Sara Biagini", "Marco Frittelli"], "categories": ["math.PR", "q-fin.PR"], "doi": "10.1214/105051604000000459", "_query": "hedonic house price", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/1710.03205v1", "title": "Behavioral Finance Option Pricing Formulas Consistent with Rational Dynamic Asset Pricing", "abstract": "We derive behavioral finance option pricing formulas consistent with the rational dynamic asset pricing theory. In the existing behavioral finance option pricing formulas, the price process of the representative agent is not a semimartingale, which leads to arbitrage opportunities for the option seller. In the literature on behavioral finance option pricing it is allowed the option buyer and seller to have different views on the instantaneous mean return of the underlying price process, which leads to arbitrage opportunities according to Black (1972). We adjust the behavioral finance option pricing formulas to be consistent with the rational dynamic asset pricing theory, by introducing transaction costs on the velocity of trades which offset the gains from the arbitrage trades.", "published": "2017-10-09T17:33:20Z", "authors": ["Svetlozar Rachev", "Stoyan Stoyanov", "Frank J. Fabozzi"], "categories": ["q-fin.PR"], "doi": "", "_query": "hedonic house price", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2605.17031v1", "title": "A Joint Synthetic Housing-Household Inventory", "abstract": "Accurately understanding the interactions between humans and the built environment requires integrated representations of both the buildings and the populations that occupy them. However, high-fidelity datasets that jointly capture detailed housing structures and demographic characteristics at the household level do not currently exist. This paper presents a framework for constructing a joint housing-household inventory that explicitly links individuals and households to compatible housing units from the National Structure Inventory (NSI), while preserving realistic population densities and demographic distributions. The framework integrates three components: (i) synthetic population generation from American Community Survey (ACS) Public Use Microdata Sample (PUMS) records that preserve complex intra-household relationships; (ii) a deep contrastive learning model that quantifies housing-household compatibility; and (iii) a hierarchical optimization-based allocation procedure that enforces building-level capacity and block-group-level demographic constraints. The generated synthetic population attains high statistical realism relative to the census microdata, and the contrastive learning model identifies compatible housing-household pairs with high predictive accuracy. Applied to coastal North Carolina, evaluations at building, neighborhood, and regional scales show that the joint inventory matches block-group-level demographic distributions, reproduces observed spatial population patterns without systematic bias, and maintains consistent allocation quality across urban, suburban, and rural contexts. By enabling coupled household- and building-level analyses, the resulting inventory supports a broad range of applications, including disaster resilience planning, housing and affordability analysis, energy-use assessment, and public health research.", "published": "2026-05-16T14:58:44Z", "authors": ["Xiao Qian", "Shangjia Dong", "Rachel Davidson"], "categories": ["cs.CY"], "doi": "", "_query": "hedonic house price", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2211.04954v2", "title": "Macroeconomic performance of oil price shocks in Russia", "abstract": "Oil price fluctuations severely impact the economies of both oil-exporting and importing countries. High oil prices can benefit oil exporters by increasing foreign currency inflow; however, an economy can suffer from a weakening of the manufacturing sectors and experience a significant downtrend in the country's price competitiveness as the domestic currency appreciates. We investigate the oil price fluctuations from Q1, 2004 to Q3, 2021 and their impact on the Russian macroeconomic indicators, particularly industrial production, exchange rate, inflation and interest rates. We assess whether and how much the Russian macroeconomic variables have been responsive to the oil price fluctuations in recent years. The outcomes from VAR model confirm that the monetary channel is more responsive to oil price shocks than the fiscal one. Regarding fiscal channel of the oil price impact, industrial production is strongly pro-cyclical to oil price shocks. As for the monetary channel, higher oil price volatility is pressuring the Russian ruble, inflation and interest rates are substantially counter-cyclical to oil price shocks.", "published": "2022-11-08T09:44:42Z", "authors": ["Ayaz Zeynalov", "Kristina Tiron"], "categories": ["econ.GN"], "doi": "", "_query": "hedonic house price", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2212.05650v2", "title": "Price & Choose", "abstract": "We describe a two-stage mechanism that fully implements the set of efficient outcomes in two-agent environments with quasi-linear utilities. The mechanism asks one agent to set prices for each outcome, and the other agent to make a choice, paying the corresponding price: Price \\& Choose. We extend our implementation result in three main directions: an arbitrary number of players, non-quasi linear utilities, and robustness to max-min behavior. Finally, we discuss how to reduce the payoff inequality between players while still achieving efficiency.", "published": "2022-12-12T01:28:58Z", "authors": ["Federico Echenique", "Matías Núñez"], "categories": ["econ.TH"], "doi": "", "_query": "hedonic house price", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2506.07993v3", "title": "Stochastic portfolio theory with price impact", "abstract": "We develop a framework for stochastic portfolio theory (SPT), which incorporates modern nonlinear price impact and impact decay models. Our main result is the derivation of the celebrated master formula for additive functional generation of trading strategies in a general high-dimensional market model with price impact. We also derive formulas for an investor's relative wealth with respect to the market portfolio, conditions that guarantee positive observed market prices and a stochastic differential equation governing the dynamics of the observed price, the investor's holdings and the price impact state processes. As an application of these results, we develop conditions for relative arbitrage in the price impact setting analogous to previously obtained results for the frictionless setting. We then apply our framework to backtest the quadratic and entropy generating functions on historical US equity data, illustrating how price impact can negatively affect portfolio performance.", "published": "2025-06-09T17:57:46Z", "authors": ["David Itkin"], "categories": ["q-fin.MF", "math.PR"], "doi": "", "_query": "hedonic house price", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2111.00451v2", "title": "Utility Indifference Pricing with High Risk Aversion and Small Linear Price Impact", "abstract": "We consider the Bachelier model with linear price impact. Exponential utility indifference prices are studied for vanilla European options and we compute their non-trivial scaling limit for a vanishing price impact which is inversely proportional to the risk aversion. Moreover, we find explicitly a family of portfolios which are asymptotically optimal.", "published": "2021-10-31T10:10:49Z", "authors": ["Yan Dolinsky", "Shir Moshe"], "categories": ["q-fin.MF", "q-fin.PM"], "doi": "", "_query": "hedonic house price", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2405.08398v2", "title": "Exploring the spatial segmentation of housing markets from online listings", "abstract": "The real estate market shows an inherent connection to space. Real estate agencies unevenly operate and specialize across space, price and type of properties, thereby segmenting the market into submarkets. We introduce here a methodology based on multipartite networks to detect the spatial segmentation emerging from data on housing online listings. Considering the spatial information of the listings, we build a bipartite network that connects agencies and spatial units. This bipartite network is projected into a network of spatial units, whose connections account for similarities in the agency ecosystem. We then apply clustering methods to this network to segment markets into spatially-coherent regions, which are found to be robust across different clustering detection algorithms, discretization of space and spatial scales, and across countries with case studies in France and Spain. This methodology addresses the long-standing issue of housing market segmentation, relevant in disciplines such as urban studies and spatial economics, and with implications for policymaking.", "published": "2024-05-14T07:52:42Z", "authors": ["David Abella", "Johann H. Martínez", "Mattia Mazzoli", "Thibault Le Corre", "Julien Migozzi", "Eduard Alonso-Paulí", "Rafel Crespí-Cladera", "Thomas Louail", "José J. Ramasco"], "categories": ["physics.soc-ph"], "doi": "", "_query": "hedonic house price", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2506.17239v1", "title": "Price equilibria with positive margins in loyal-strategic markets with discrete prices", "abstract": "In competitive supply chains (SCs), pricing decisions are crucial, as they directly impact market share and profitability. Traditional SC models often assume continuous pricing for mathematical convenience, overlooking the practical reality of discrete price increments driven by currency constraints. Additionally, customer behavior, influenced by loyalty and strategic considerations, plays a significant role in purchasing decisions. To address these gaps, this study examines a SC model involving one supplier and two manufacturers, incorporating realistic factors such as customer demand segmentation and discrete price setting. Our analysis shows that the Nash equilibria (NE) among manufacturers are not unique, we then discuss the focal equilibrium. Our analysis also reveals that low denomination factors can lead to instability as the corresponding game does not have NE. Numerical simulations demonstrate that even small changes in price increments significantly affect the competitive dynamics and market share distribution.", "published": "2025-06-05T05:50:11Z", "authors": ["Gurkirat Wadhwa", "Akansh Verma", "Veeraruna Kavitha", "Priyank Sinha"], "categories": ["q-fin.GN"], "doi": "", "_query": "hedonic house price", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/1901.05024v2", "title": "Econophysics of Asset Price, Return and Multiple Expectations", "abstract": "This paper describes asset price and return disturbances as result of relations between transactions and multiple kinds of expectations. We show that disturbances of expectations can cause fluctuations of trade volume, price and return. We model price disturbances for transactions made under all types of expectations as weighted sum of partial price and trade volume disturbances for transactions made under separate kinds of expectations. Relations on price allow present return as weighted sum of partial return and trade volume \"return\" for transactions made under separate expectations. Dependence of price disturbances on trade volume disturbances as well as dependence of return on trade volume \"return\" cause dependence of volatility and statistical distributions of price and return on statistical properties of trade volume disturbances and trade volume \"return\" respectively.", "published": "2019-01-15T19:06:42Z", "authors": ["Victor Olkhov"], "categories": ["econ.GN"], "doi": "10.21314/JNTF.2019.055", "_query": "hedonic house price", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/1709.06367v1", "title": "Selfish Jobs with Favorite Machines: Price of Anarchy vs Strong Price of Anarchy", "abstract": "We consider the well-studied game-theoretic version of machine scheduling in which jobs correspond to self-interested users and machines correspond to resources. Here each user chooses a machine trying to minimize her own cost, and such selfish behavior typically results in some equilibrium which is not globally optimal: An equilibrium is an allocation where no user can reduce her own cost by moving to another machine, which in general need not minimize the makespan, i.e., the maximum load over the machines. We provide tight bounds on two well-studied notions in algorithmic game theory, namely, the price of anarchy and the strong price of anarchy on machine scheduling setting which lies in between the related and the unrelated machine case. Both notions study the social cost (makespan) of the worst equilibrium compared to the optimum, with the strong price of anarchy restricting to a stronger form of equilibria. Our results extend a prior study comparing the price of anarchy to the strong price of anarchy for two related machines (Epstein, Acta Informatica 2010), thus providing further insights on the relation between these concepts. Our exact bounds give a qualitative and quantitative comparison between the two models. The bounds also show that the setting is indeed easier than the two unrelated machines: In the latter, the strong price of anarchy is $2$, while in ours it is strictly smaller.", "published": "2017-09-19T12:10:28Z", "authors": ["Cong Chen", "Paolo Penna", "Yinfeng Xu"], "categories": ["cs.GT"], "doi": "10.1007/978-3-319-71147-8_16", "_query": "hedonic house price", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2512.01139v3", "title": "Market Sensitivities and Growth Differentials Across Australian Housing Markets", "abstract": "Australian house prices have risen strongly since the mid-1990s, but growth has been highly uneven across regions. Raw growth figures obscure whether these differences reflect persistent structural trends or cyclical fluctuations. We address this by estimating a three-factor model in levels for regional repeat-sales log price indexes over 1995-2024. The model decomposes each regional index into a national Market factor, two stationary spreads (Mining and Lifestyle) that capture mean-reverting geographic cycles, and a city-specific residual. The Mining spread, proxied by a Perth-Sydney index differential, reflects resource-driven oscillations in relative performance; the Lifestyle spread captures amenity-driven coastal and regional cycles. The Market loading isolates each region's fundamental sensitivity, beta, to national growth, so that a city's growth under an assumed national change is calculated from its beta once mean-reverting spreads are netted out. Comparing realised paths to these factor-implied trajectories indicates when a city is historically elevated or depressed, and attributes the gap to Mining or Lifestyle spreads. Expanding-window ARIMAX estimation reveals that Market betas are stable across major shocks (the mining boom, the Global Financial Crisis, and COVID-19), while Mining and Lifestyle behave as stationary spreads that widen forecast funnels without overturning the cross-sectional ranking implied by beta. Melbourne amplifies national growth, Sydney tracks the national trend closely, and regional areas dampen it. The framework thus provides a simple, factor-based tool for interpreting regional growth differentials and their persistence.", "published": "2025-11-30T23:29:03Z", "authors": ["Willem P Sijp"], "categories": ["econ.EM"], "doi": "", "_query": "hedonic house price", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2201.03784v4", "title": "Price Heterogeneity as a Source of Heterogeneous Demand", "abstract": "We explore heterogeneous prices as a source of heterogeneous or stochastic demand. Heterogeneous prices could arise either because there is actual price variation among consumers or because consumers (mis)perceive prices differently. Our main result says the following: if heterogeneous prices have a distribution among consumers that is (in a sense) stable across observations, then a model where consumers have a common utility function but face heterogeneous prices has precisely the same implications as a heterogeneous preference/random utility model (with no price heterogeneity).", "published": "2022-01-11T05:07:05Z", "authors": ["John K. -H. Quah", "Gerelt Tserenjigmid"], "categories": ["econ.TH"], "doi": "", "_query": "hedonic house price", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2101.01388v1", "title": "A Model of Market Making and Price Impact", "abstract": "Traders constantly consider the price impact associated with changing their positions. This paper seeks to understand how price impact emerges from the quoting strategies of market makers. To this end, market making is modeled as a dynamic auction using the mathematical framework of Stochastic Differential Games. In Nash Equilibrium, the market makers' quoting strategies generate a price impact function that is of the same form as the celebrated Almgren-Chriss model. The key insight is that price impact is the mechanism through which market makers earn profits while matching their books. As such, price impact is an essential feature of markets where flow is intermediated by market makers.", "published": "2021-01-05T07:33:44Z", "authors": ["Angad Singh"], "categories": ["q-fin.MF", "math.OC"], "doi": "", "_query": "hedonic house price", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2409.04047v2", "title": "Uniform price auction with quantity constraints", "abstract": "We study the equilibria of uniform price auctions where many asymmetric bidders have flat demands up to their respective quantity constraints. We present an iterative procedure that systematically finds an equilibrium outcome as well as an ascending auction that has this outcome as a dominant strategy equilibrium outcome. Demand reduction and low price equilibrium may occur since it is advantageous for a bidder to give up some of his/her demand and get the remaining demand at a low price rather than to get his/her entire demand at a higher price. We show that a low price equilibrium is the only possible equilibrium when no bidder's quantity constraint is large enough to cover the supply.", "published": "2024-09-06T06:35:10Z", "authors": ["Kiho Yoon"], "categories": ["econ.TH"], "doi": "10.1515/bejte-2025-0074", "_query": "hedonic house price", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/1509.01216v1", "title": "Dynamic Model of the Price Dispersion of Homogeneous Goods", "abstract": "Presented is an analytic microeconomic model of the temporal price dispersion of homogeneous goods in polypoly markets. This new approach is based on the idea that the price dispersion has its origin in the dynamics of the purchase process. The price dispersion is determined by the chance that demanded and supplied product units meet in a given price interval. It can be characterized by a fat-tailed Laplace distribution for short and by a lognormal distribution for long time horizons. Taking random temporal variations of demanded and supplied units into account both the mean price and also the standard deviation of the price dispersion are governed by a lognormal distribution. A comparison with empirical investigations confirms the model statements.", "published": "2015-05-27T11:11:47Z", "authors": ["Joachim Kaldasch"], "categories": ["econ.GN"], "doi": "10.9734/BJEMT/2015/17849", "_query": "hedonic house price", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2510.04464v2", "title": "Identification in Auctions with Truncated Transaction Prices", "abstract": "I establish nonparametric identification results in first- and second-price auctions when transaction prices are truncated by a binding reserve price under a range of information structures. When the number of potential bidders is fixed and known across all auctions, if only the transaction price is observed, the bidders' private-value distribution is identified in second-price auctions but not in first-price auctions. Identification in first-price auctions can be achieved if either the number of active bidders or the number of auctions with no sales is observed. When the number of potential bidders varies across auctions and is unknown, the bidders' private-value distribution is identified in first-price auctions but not in second-price auctions, provided that both the transaction price and the number of active bidders are observed. I derive analogous results to auctions with entry costs, which face a similar truncation issue when data on potential bidders who do not enter are missing.", "published": "2025-10-06T03:38:53Z", "authors": ["Tonghui Qi"], "categories": ["econ.EM"], "doi": "", "_query": "hedonic house price", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2406.06023v2", "title": "The Limits of Interval-Regulated Price Discrimination", "abstract": "In this paper, we study third-degree price discrimination in a model first presented by Bergemann, Brooks, and Morris [2015]. Since such price discrimination might create market segments with vastly different posted prices, we consider regulating these prices, specifically, by restricting them to lie within an interval. Given a price interval, we consider segmentations of the market where a seller, who is oblivious to the existence of such regulation, still posts prices within the price interval. We show the following surprising result: For any market and price interval where such segmentation is feasible, there is always a different segmentation that optimally transfers all excess surplus to the consumers. In addition, we characterize the entire space of buyer and seller surplus that is achievable by such segmentation, including maximizing seller surplus, and simultaneously minimizing buyer and seller surplus. A key technical challenge is that the classical segmentation method of Bergemann, Brooks, and Morris [2015] fails under price constraints. To address this, we develop three intuitive but fundamentally distinct segmentation constructions, each tailored to a different surplus objective. These constructions maintain different invariants, reflect different economic intuitions, and collectively form the core of our regulated surplus characterization.", "published": "2024-06-10T05:04:33Z", "authors": ["Kamesh Munagala", "Yiheng Shen", "Renzhe Xu"], "categories": ["econ.TH", "cs.DS", "cs.GT"], "doi": "", "_query": "hedonic house price", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2002.07561v3", "title": "The Market Price of Risk for Delivery Periods: Pricing Swaps and Options in Electricity Markets", "abstract": "In electricity markets, futures contracts typically function as a swap since they deliver the underlying over a period of time. In this paper, we introduce a market price for the delivery periods of electricity swaps, thereby opening an arbitrage-free pricing framework for derivatives based on these contracts. Furthermore, we use a weighted geometric averaging of an artificial geometric futures price over the corresponding delivery period. Without any need for approximations, this averaging results in geometric swap price dynamics. Our framework allows for including typical features as the Samuelson effect, seasonalities, and stochastic volatility. In particular, we investigate the pricing procedures for electricity swaps and options in line with Arismendi et al. (2016), Schneider and Tavin (2018), and Fanelli and Schmeck (2019). A numerical study highlights the differences between these models depending on the delivery period.", "published": "2020-02-18T13:53:15Z", "authors": ["Annika Kemper", "Maren D. Schmeck", "Anna Kh. Balci"], "categories": ["q-fin.PR", "q-fin.MF"], "doi": "", "_query": "hedonic house price", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2209.12542v2", "title": "A Hamiltonian Approach to Floating Barrier Option Pricing", "abstract": "Hamiltonian approach in quantum mechanics provides a new thinking for barrier option pricing. For proportional floating barrier step options, the option price changing process is similar to the one dimensional trapezoid potential barrier scattering problem in quantum mechanics; for floating double-barrier step options, the option price changing process is analogous to a particle moving in a finite symmetric square potential well. Using Hamiltonian methodology, the analytical expressions of pricing kernel and option price could be derived. Numerical results of option price as a function of underlying price, floating rate, interest rate and exercise price are shown, which are consistent with the results given by mathematical calculations.", "published": "2022-09-26T09:58:45Z", "authors": ["Qi Chen", "Hong-tao Wang", "Chao Guo"], "categories": ["q-fin.PR"], "doi": "10.1007/s10955-023-03209-0", "_query": "hedonic house price", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/1905.04910v2", "title": "The Price of Fairness for Indivisible Goods", "abstract": "We investigate the efficiency of fair allocations of indivisible goods using the well-studied price of fairness concept. Previous work has focused on classical fairness notions such as envy-freeness, proportionality, and equitability. However, these notions cannot always be satisfied for indivisible goods, leading to certain instances being ignored in the analysis. In this paper, we focus instead on notions with guaranteed existence, including envy-freeness up to one good (EF1), balancedness, maximum Nash welfare (MNW), and leximin. We also introduce the concept of strong price of fairness, which captures the efficiency loss in the worst fair allocation as opposed to that in the best fair allocation as in the price of fairness. We mostly provide tight or asymptotically tight bounds on the worst-case efficiency loss for allocations satisfying these notions, for both the price of fairness and the strong price of fairness.", "published": "2019-05-13T08:43:26Z", "authors": ["Xiaohui Bei", "Xinhang Lu", "Pasin Manurangsi", "Warut Suksompong"], "categories": ["cs.GT", "cs.MA"], "doi": "10.1007/s00224-021-10039-8", "_query": "hedonic house price", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/1206.4283v1", "title": "Pricing of insurance policies against cloud storage price rises", "abstract": "When a company migrates to cloud storage, the way back is neither easy nor cheap. The company is then locked up in the storage contract and exposed to upward market prices, which reduce the company's profit and may even bring it below zero. We propose a protection means based on an insurance contract, by which the cloud purchaser is indemnified when the current storage price exceeds a pre-defined threshold. By applying the financial options theory, we provide a formula for the insurance price (the premium). By using historical data on market prices for disks, we apply the formula in realistic scenarios. We show that the premium grows nearly quadratically with the length of the coverage period as long as this is below one year, but grows more slowly, though faster than linearly, over longer coverage periods.", "published": "2012-06-15T18:39:12Z", "authors": ["Loretta Mastroeni", "Maurizio Naldi"], "categories": ["cs.OH"], "doi": "", "_query": "hedonic house price", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2502.04824v1", "title": "Estimating the duration of RT-PCR positivity for SARS-CoV-2 from doubly interval censored data with undetected infections", "abstract": "Monitoring the incidence of new infections during a pandemic is critical for an effective public health response. General population prevalence surveys for SARS-CoV-2 can provide high-quality data to estimate incidence. However, estimation relies on understanding the distribution of the duration that infections remain detectable. This study addresses this need using data from the Coronavirus Infection Survey (CIS), a long-term, longitudinal, general population survey conducted in the UK. Analyzing these data presents unique challenges, such as doubly interval censoring, undetected infections, and false negatives. We propose a Bayesian nonparametric survival analysis approach, estimating a discrete-time distribution of durations and integrating prior information derived from a complementary study. Our methodology is validated through a simulation study, including its resilience to model misspecification, and then applied to the CIS dataset. This results in the first estimate of the full duration distribution in a general population, as well as methodology that could be transferred to new contexts.", "published": "2025-02-07T10:56:02Z", "authors": ["Joshua Blake", "Paul Birrell", "A. Sarah Walker", "Koen B. Pouwels", "Thomas House", "Brian D. M. Tom", "Theodore Kypraios", "Daniela De Angelis"], "categories": ["stat.ME", "stat.AP"], "doi": "", "_query": "hedonic house price", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/1904.05656v4", "title": "Pricing under Fairness Concerns", "abstract": "This paper proposes a theory of pricing premised upon the assumptions that customers dislike unfair prices---those marked up steeply over cost---and that firms take these concerns into account when setting prices. Since they do not observe firms' costs, customers must extract costs from prices. The theory assumes that customers infer less than rationally: when a price rises due to a cost increase, customers partially misattribute the higher price to a higher markup---which they find unfair. Firms anticipate this response and trim their price increases, which drives the passthrough of costs into prices below one: prices are somewhat rigid. Embedded in a New Keynesian model as a replacement for the usual pricing frictions, our theory produces monetary nonneutrality: when monetary policy loosens and inflation rises, customers misperceive markups as higher and feel unfairly treated; firms mitigate this perceived unfairness by reducing their markups; in general equilibrium, employment rises. The theory also features a hybrid short-run Phillips curve, realistic impulse responses of output and employment to monetary and technology shocks, and an upward-sloping long-run Phillips curve.", "published": "2019-04-11T12:13:54Z", "authors": ["Erik Eyster", "Kristof Madarasz", "Pascal Michaillat"], "categories": ["econ.TH", "econ.GN"], "doi": "10.1093/jeea/jvaa041", "_query": "hedonic house price", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/1910.10005v1", "title": "The CMMV Pricing Model in Practice", "abstract": "Mainstream financial econometrics methods are based on models well tuned to replicate price dynamics, but with little to no economic justification. In particular, the randomness in these models is assumed to result from a combination of exogenous factors. In this paper, we present a model originating from game theory, whose corresponding price dynamics are a direct consequence of the information asymmetry between private and institutional investors. This model, namely the CMMV pricing model, is therefore rooted in market microstructure. The pricing methods derived from it also appear to fit very well historical price data. Indeed, as evidenced in the last section of the paper, the CMMV model does a very good job predicting option prices from readily available data. It also enables to recover the dynamic of the volatility surface.", "published": "2019-10-22T14:26:15Z", "authors": ["Bernard De Meyer", "Moussa Dabo"], "categories": ["q-fin.PR"], "doi": "", "_query": "hedonic house price", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/cond-mat/0012419v1", "title": "The price dynamics of common trading strategies", "abstract": "A deterministic trading strategy can be regarded as a signal processing element that uses external information and past prices as inputs and incorporates them into future prices. This paper uses a market maker based method of price formation to study the price dynamics induced by several commonly used financial trading strategies, showing how they amplify noise, induce structure in prices, and cause phenomena such as excess and clustered volatility.", "published": "2000-12-21T21:00:30Z", "authors": ["J. Doyne Farmer", "Shareen Joshi"], "categories": ["cond-mat.stat-mech", "q-fin.TR"], "doi": "", "_query": "hedonic house price", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/1904.08884v1", "title": "Negative Prices in Network Pricing Games", "abstract": "In a Stackelberg network pricing game, a leader sets prices for a given subset of edges so as to maximize profit, after which one or multiple followers choose a shortest path from their source to sink. We study the counter-intuitive phenomenon that the use of negative prices by the leader may in fact increase his profit. In doing so, we answer the following two questions. First, how much more profit can the leader earn by setting negative prices? Second, for which network topologies can the profit be increased by using negative prices? Our main result shows that the profit with negative prices can be a factor $Θ(\\log (m\\cdot\\bar k))$ larger than the maximum profit with positive prices, where $m$ is the number of priceable edges in the graph and $\\bar k \\leq 2^m$ the number of followers. In particular, this factor cannot be bounded for the single follower case, and can even grow linearly in $m$ if the number of followers is large. Our second result shows that series-parallel graphs with a single follower and Stackelberg games with matroid followers are immune to the negative price paradox.", "published": "2019-04-18T16:50:09Z", "authors": ["Andrés Cristi", "Marc Schröder"], "categories": ["cs.GT"], "doi": "", "_query": "hedonic house price", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2310.17165v5", "title": "Price Experimentation and Interference", "abstract": "In this paper, we examine the biases that arise when firms run A/B tests on continuous parameters to estimate global treatment effects on performance metrics of interest; we particularly focus on price experiments to measure the price impact on quantity demanded, and on profit. In canonical A/B experimental estimators, biases emerge due to interference between market participants. We employ structural modeling and differential calculus to derive intuitive characterizations of these biases. We then specialize our general model to the standard revenue-management pricing problem. This setting highlights a fundamental risk innate to A/B pricing experiments: that the canonical estimator for the expected change in profits, counterintuitively, can have the wrong sign in expectation. In other words, following the guidance of canonical estimators may lead firms to move prices (or fees) in the wrong direction, inadvertently decreasing profits. We introduce a novel debiasing technique for these canonical experiments, requiring only that firms equally split units between treatment and control. We apply these results to a two-sided market model, and demonstrate how the \"change of sign\" regime depends on market factors such as the supply/demand imbalance, and the price markup. We conclude by calibrating our revenue-management pricing model to published empirical estimates from Airbnb marketplaces, demonstrating that estimators with the wrong sign are not a knife-edge issue, and that they may be prevalent enough to be of concern to practitioners.", "published": "2023-10-26T05:32:41Z", "authors": ["Ramesh Johari", "Orrie B. Page", "Gabriel Y. Weintraub"], "categories": ["stat.ME"], "doi": "", "_query": "hedonic house price", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2506.12318v4", "title": "A House Monotone, Coherent, and Droop Proportional Ranked Candidate Voting Method", "abstract": "A proportional ranked candidate voting method is described that satisfies ranked candidate analogs of the voting criteria satisfied by Phragmen's approval voting procedure. The method complies with Droop proportionality, house monotonicity, and coherence. The last two properties are the ranked-candidate analogs of the divisor method properties of always avoiding the Alabama and New State paradoxes. The method is free rider resistant and adding a candidate to each ballot that is universally preferred to all other candidates does not alter the relative winning order of the other candidates. The highest ranked candidate in the list is the Instant Runoff winner, which is shown to be in at least one Droop proportional set of N winners for all N, giving a quantitative meaning to the notion that the Instant Runoff winner has both core and broad voter support.", "published": "2025-06-14T02:45:01Z", "authors": ["Ross Hyman"], "categories": ["econ.TH"], "doi": "", "_query": "hedonic house price", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/1904.07583v2", "title": "Clearing price distributions in call auctions", "abstract": "We propose a model for price formation in financial markets based on clearing of a standard call auction with random orders, and verify its validity for prediction of the daily closing price distribution statistically. The model considers random buy and sell orders, placed following demand- and supply-side valuation distributions; an equilibrium equation then leads to a distribution for clearing price and transacted volume. Bid and ask volumes are left as free parameters, permitting possibly heavy-tailed or very skewed order flow conditions. In highly liquid auctions, the clearing price distribution converges to an asymptotically normal central limit, with mean and variance in terms of supply/demand-valuation distributions and order flow imbalance. By means of simulations, we illustrate the influence of variations in order flow and valuation distributions on price/volume, noting a distinction between high- and low-volume auction price variance. To verify the validity of the model statistically, we predict a year's worth of daily closing price distributions for 5 constituents of the Eurostoxx 50 index; Kolmogorov-Smirnov statistics and QQ-plots demonstrate with ample statistical significance that the model predicts closing price distributions accurately, and compares favourably with alternative methods of prediction.", "published": "2019-04-16T10:27:31Z", "authors": ["M. Derksen", "B. Kleijn", "R. de Vilder"], "categories": ["q-fin.TR"], "doi": "", "_query": "hedonic house price", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/1901.06467v1", "title": "Option Pricing in Illiquid Markets with Jumps", "abstract": "The classical linear Black--Scholes model for pricing derivative securities is a popular model in financial industry. It relies on several restrictive assumptions such as completeness, and frictionless of the market as well as the assumption on the underlying asset price dynamics following a geometric Brownian motion. The main purpose of this paper is to generalize the classical Black--Scholes model for pricing derivative securities by taking into account feedback effects due to an influence of a large trader on the underlying asset price dynamics exhibiting random jumps. The assumption that an investor can trade large amounts of assets without affecting the underlying asset price itself is usually not satisfied, especially in illiquid markets. We generalize the Frey--Stremme nonlinear option pricing model for the case the underlying asset follows a Levy stochastic process with jumps. We derive and analyze a fully nonlinear parabolic partial-integro differential equation for the price of the option contract. We propose a semi-implicit numerical discretization scheme and perform various numerical experiments showing influence of a large trader and intensity of jumps on the option price.", "published": "2019-01-19T04:56:22Z", "authors": ["Jose Cruz", "Daniel Sevcovic"], "categories": ["q-fin.MF"], "doi": "", "_query": "hedonic house price", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2608.20819v1", "title": "PRICE: Pricing-based Resource Incentives for Quality-of-Result-aware Computing at the Edge", "abstract": "Edge nodes are capacity-constrained by design, yet many edge workloads can trade result quality for resource efficiency at runtime. Existing edge pricing mechanisms largely treat requests as fixed-configuration submissions and rarely exploit per-request quality flexibility under overload. We present PRICE, an incentive mechanism that couples a utilization-dependent price signal to per-request quality selection. As utilization increases, rising acceptance prices make resource-intensive variants less likely to be selected, shifting accepted requests toward lighter execution and allowing the node to serve significantly more requests while operating near capacity. Evaluation on real hardware under sustained overload shows that PRICE outperforms both fixed-allocation and dynamic-pricing baselines in accepted throughput and CPU utilization. The results are robust across pricing function families, task-duration distributions, and client populations. Result-quality flexibility is a powerful but underused control dimension for overload management at the edge, and pricing is an effective mechanism to exploit it.", "published": "2026-08-21T07:40:31Z", "authors": ["Uwe Gropengießer", "Sebastian Frenz", "Max Mühlhäuser"], "categories": ["cs.DC"], "doi": "", "_query": "hedonic house price", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2506.10640v2", "title": "Asymmetric price adjustment over the business cycle", "abstract": "Studies of micro-level price datasets find more frequent small price increases than decreases, which can be explained by consumer inattention because time-constrained shoppers might ignore small price changes. Recent empirical studies of the link between shopping behavior and price attention over the business cycle find that consumers are more attentive to prices during economic downturns, and less attentive during economic booms. These two sets of findings have a testable implication. The asymmetry in small price changes should vary over the business cycle. It should diminish during recessions and strengthen during expansions. We test this prediction using a large US store-level dataset with more than 98 million weekly price observations for the years 1989-1997, which includes an 8-month recession period, as defined by the NBER. We compare price adjustments between periods of recession - high unemployment, and expansion - low unemployment. Focusing on small price changes, we find, consistent with our hypothesis, that there is a greater asymmetry in small price changes during periods of low unemployment compared to the periods of high unemployment, implying that firms price-setting behavior varies over the business cycle.", "published": "2025-06-12T12:25:47Z", "authors": ["Daniel Levy", " Haipeng", " Chen", "Sourav Ray", "Elliot Charette", "Xiao Ling", "Weihong Zhao", "Mark Bergen", "Avichai Snir"], "categories": ["econ.GN"], "doi": "", "_query": "hedonic house price", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2601.17964v2", "title": "Pass-through with Price Dispersion", "abstract": "How do cost shocks pass through to prices in markets with price dispersion? We decompose the problem into two layers. In the competition layer, consumers' consideration sets determine equilibrium distributions of normalized margins. In the curvature layer, demand elasticity maps these margins into prices and pass-through rates. We prove the pricing game is strategically equivalent to a game over normalized margins, so equilibrium margin distributions are invariant to demand and costs. This separation yields closed-form pass-through formulas at each quantile of the price distribution, robust bounds across demand specifications, and sharp comparative statics linking market structure to incidence.", "published": "2026-01-25T19:44:17Z", "authors": ["Brian C. Albrecht", "Mark Whitmeyer"], "categories": ["econ.TH"], "doi": "", "_query": "hedonic house price", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2603.18021v2", "title": "Anomaly prediction in XRP price with topological features", "abstract": "The aim of this research is to study XRP cryptoasset price dynamics, with a particular focus on forecasting atypical price movements. Recent studies suggest that topological properties of transaction graphs are highly informative for understanding cryptocurrency price behavior. In this work, we show that specific topological properties of the XRP transaction graphs provide important information about extreme XRP price surges, and can be used for more competitive prediction of anomalous price dynamics.", "published": "2026-03-05T06:31:01Z", "authors": ["Illia Donhauzer", "Pierluigi Cesana", "Tomoyuki Shirai", "Yuichi Ikeda"], "categories": ["q-fin.ST"], "doi": "", "_query": "hedonic house price", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2310.04367v2", "title": "A Marketplace Price Anomaly Detection System at Scale", "abstract": "Online marketplaces execute large volume of price updates that are initiated by individual marketplace sellers each day on the platform. This price democratization comes with increasing challenges with data quality. Lack of centralized guardrails that are available for a traditional online retailer causes a higher likelihood for inaccurate prices to get published on the website, leading to poor customer experience and potential for revenue loss. We present MoatPlus (Masked Optimal Anchors using Trees, Proximity-based Labeling and Unsupervised Statistical-features), a scalable price anomaly detection framework for a growing marketplace platform. The goal is to leverage proximity and historical price trends from unsupervised statistical features to generate an upper price bound. We build an ensemble of models to detect irregularities in price-based features, exclude irregular features and use optimized weighting scheme to build a reliable price bound in real-time pricing pipeline. We observed that our approach improves precise anchor coverage by up to 46.6% in high-vulnerability item subsets", "published": "2023-10-06T16:41:51Z", "authors": ["Akshit Sarpal", "Qiwen Kang", "Fangping Huang", "Yang Song", "Lijie Wan"], "categories": ["stat.ML", "cs.LG"], "doi": "", "_query": "hedonic house price", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2310.00231v2", "title": "The Distributional Impact of Inflation in Pakistan: A Case Study of a New Price Focused Microsimulation Framework, PRICES", "abstract": "This paper develops a microsimulation model to simulate the distributional impact of price changes using Household Budget Survey data, income survey data and an Input Output Model. The primary purpose is to describe the model components. The secondary purpose is to demonstrate one component of the model by assessing the distributional and welfare impact of recent price changes in Pakistan. Over the period of November 2020 to November 2022, headline inflation 41.5 percent, with food and transportation prices increasing most. The analysis shows that despite large increases in energy prices, the importance of energy prices for the welfare losses due to inflation is limited because energy budget shares are small and inflation is relatively low. The overall distributional impact of recent price changes is mildly progressive, but household welfare is impacted significantly irrespective of households position along the income distribution. The biggest driver of welfare losses at the bottom of the income distribution was food price inflation, while inflation in other goods and services was the biggest driver at the top. To compensate households for increased living costs, transfers would need to be on average 40 percent of pre-inflation expenditure, assuming constant incomes. Behavioural responses to price changes have a negligible impact on the overall welfare cost to households.", "published": "2023-09-30T02:30:28Z", "authors": ["Cathal ODonoghue", "Beenish Amjad", "Jules Linden", "Nora Lustig", "Denisa Sologon", "Yang Wang"], "categories": ["econ.GN"], "doi": "", "_query": "hedonic house price", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/1604.07463v1", "title": "Dynamic Pricing with Demand Covariates", "abstract": "We consider a firm that sells products over $T$ periods without knowing the demand function. The firm sequentially sets prices to earn revenue and to learn the underlying demand function simultaneously. A natural heuristic for this problem, commonly used in practice, is greedy iterative least squares (GILS). At each time period, GILS estimates the demand as a linear function of the price by applying least squares to the set of prior prices and realized demands. Then a price that maximizes the revenue, given the estimated demand function, is used for the next time period. The performance is measured by the regret, which is the expected revenue loss from the optimal (oracle) pricing policy when the demand function is known. Recently, den Boer and Zwart (2014) and Keskin and Zeevi (2014) demonstrated that GILS is sub-optimal. They introduced algorithms which integrate forced price dispersion with GILS and achieve asymptotically optimal performance. In this paper, we consider this dynamic pricing problem in a data-rich environment. In particular, we assume that the firm knows the expected demand under a particular price from historical data, and in each period, before setting the price, the firm has access to extra information (demand covariates) which may be predictive of the demand. We prove that in this setting GILS achieves asymptotically optimal regret of order $\\log(T)$. We also show the following surprising result: in the original dynamic pricing problem of den Boer and Zwart (2014) and Keskin and Zeevi (2014), inclusion of any set of covariates in GILS as potential demand covariates (even though they could carry no information) would make GILS asymptotically optimal. We validate our results via extensive numerical simulations on synthetic and real data sets.", "published": "2016-04-25T22:22:11Z", "authors": ["Sheng Qiang", "Mohsen Bayati"], "categories": ["stat.ML"], "doi": "", "_query": "hedonic house price", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/math/0605049v1", "title": "Pricing with coherent risk", "abstract": "This paper deals with applications of coherent risk measures to pricing in incomplete markets. Namely, we study the No Good Deals pricing technique based on coherent risk. Two forms of this technique are presented: one defines a good deal as a trade with negative risk; the other one defines a good deal as a trade with unusually high RAROC. For each technique, the fundamental theorem of asset pricing and the form of the fair price interval are presented. The model considered includes static as well as dynamic models, models with an infinite number of assets, models with transaction costs, and models with portfolio constraints. In particular, we prove that in a model with proportional transaction costs the fair price interval converges to the fair price interval in a frictionless model as the coefficient of transaction costs tends to zero. Moreover, we study some problems in the ``pure'' theory of risk measures: we present a simple geometric solution of the capital allocation problem and apply it to define the coherent risk contribution. The mathematical tools employed are probability theory, functional analysis, and finite-dimensional convex analysis.", "published": "2006-05-02T12:15:51Z", "authors": ["Alexander S. Cherny"], "categories": ["math.PR", "q-fin.RM"], "doi": "", "_query": "hedonic house price", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/1812.10619v1", "title": "Predicting the Stock Price of Frontier Markets Using Modified Black-Scholes Option Pricing Model and Machine Learning", "abstract": "The Black-Scholes Option pricing model (BSOPM) has long been in use for valuation of equity options to find the prices of stocks. In this work, using BSOPM, we have come up with a comparative analytical approach and numerical technique to find the price of call option and put option and considered these two prices as buying price and selling price of stocks of frontier markets so that we can predict the stock price (close price). Changes have been made to the model to find the parameters strike price and the time of expiration for calculating stock price of frontier markets. To verify the result obtained using modified BSOPM we have used machine learning approach using the software Rapidminer, where we have adopted different algorithms like the decision tree, ensemble learning method and neural network. It has been observed that, the prediction of close price using machine learning is very similar to the one obtained using BSOPM. Machine learning approach stands out to be a better predictor over BSOPM, because Black-Scholes-Merton equation includes risk and dividend parameter, which changes continuously. We have also numerically calculated volatility. As the prices of the stocks goes high due to overpricing, volatility increases at a tremendous rate and when volatility becomes very high market tends to fall, which can be observed and determined using our modified BSOPM. The proposed modified BSOPM has also been explained based on the analogy of Schrodinger equation (and heat equation) of quantum physics.", "published": "2018-12-27T04:34:18Z", "authors": ["Reaz Chowdhury", "M. R. C. Mahdy", "Tanisha Nourin Alam", "Golam Dastegir Al Quaderi"], "categories": ["q-fin.ST", "q-fin.MF"], "doi": "", "_query": "hedonic house price", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/1910.12130v4", "title": "Price mediated contagion through capital ratio requirements with VWAP liquidation prices", "abstract": "We develop a framework for price-mediated contagion in financial systems where banks are forced to liquidate assets to satisfy a risk-weight based capital adequacy requirement. In constructing this modeling framework, we introduce a two-tier pricing structure: the volume weighted average price that is obtained by any bank liquidating assets and the terminal mark-to-market price used to account for all assets held at the end of the clearing process. We consider the case of multiple illiquid assets and develop conditions for the existence and uniqueness of clearing prices. We provide a closed-form representation for the sensitivity of these clearing prices to the system parameters, and use this result to quantify: (1) the cost of regulation, in stress scenarios, faced by the system as a whole and the individual banks, and (2) the value of providing bailouts to consider when such notions are financially advisable. Numerical case studies are provided to study the application of this model to data.", "published": "2019-10-26T20:25:34Z", "authors": ["Tathagata Banerjee", "Zachary Feinstein"], "categories": ["q-fin.MF"], "doi": "", "_query": "hedonic house price", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2403.07166v1", "title": "Small Price Changes, Sales Volume, and Menu Cost", "abstract": "The finding of small price changes in many retail price datasets is often viewed as a puzzle. We show that a possible explanation for the presence of small price changes is related to sales volume, an observation that has been overlooked in the existing literature. Analyzing a large retail scanner price dataset that contains information on both prices and sales volume, we find that small price changes are more frequent when products sales volume is high. This finding holds across product categories, within product categories, and for individual products. It is also robust to various sensitivity analyses such as measurement errors, the definition of small price changes, the inclusion of measures of price synchronization, the size of producers, the time horizon used to compute the average sales volume, the revenues, the competition, shoppers characteristics, etc.", "published": "2024-03-11T21:09:30Z", "authors": ["Doron Sayag", "Avichai Snir", "Daniel Levy"], "categories": ["econ.GN"], "doi": "", "_query": "hedonic house price", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/cond-mat/0312703v2", "title": "What really causes large price changes?", "abstract": "We study the cause of large fluctuations in prices in the London Stock Exchange. This is done at the microscopic level of individual events, where an event is the placement or cancellation of an order to buy or sell. We show that price fluctuations caused by individual market orders are essentially independent of the volume of orders. Instead, large price fluctuations are driven by liquidity fluctuations, variations in the market's ability to absorb new orders. Even for the most liquid stocks there can be substantial gaps in the order book, corresponding to a block of adjacent price levels containing no quotes. When such a gap exists next to the best price, a new order can remove the best quote, triggering a large midpoint price change. Thus, the distribution of large price changes merely reflects the distribution of gaps in the limit order book. This is a finite size effect, caused by the granularity of order flow: In a market where participants placed many small orders uniformly across prices, such large price fluctuations would not happen. We show that this explains price fluctuations on longer timescales. In addition, we present results suggesting that the risk profile varies from stock to stock, and is not universal: lightly traded stocks tend to have more extreme risks.", "published": "2003-12-30T17:40:54Z", "authors": ["J. Doyne Farmer", "Laszlo Gillemot", "Fabrizio Lillo", "Szabolcs Mike", "Anindya Sen"], "categories": ["cond-mat.other", "q-fin.TR"], "doi": "", "_query": "hedonic house price", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/1705.00330v1", "title": "Random evolutionary dynamics driven by fitness and house-of-cards mutations. Sampling formulae", "abstract": "We first revisit the multi-allelic mutation-fitness balance problem, especially when mutations obey a house of cards condition, where the discrete-time deterministic evolutionary dynamics of the allelic frequencies derives from a Shahshahani potential. We then consider multi-allelic Wright-Fisher stochastic models whose deviation to neutrality is from the Shahsha-hani mutation/selection potential. We next focus on the weak selection, weak mutation cases and, making use of a Gamma calculus, we compute the normalizing partition functions of the invariant probability densities appearing in their Wright-Fisher diffusive approximations. Using these results, Generalized Ewens sampling formulae (ESF) from the equilibrium distributions are derived. We start treating the ESF in the mixed mutation/selection potential case and then we restrict ourselves to the ESF in the simpler house-of-cards mutations only situation. We also address some issues concerning sampling problems from infinitely-many alleles weak limits.", "published": "2017-04-30T15:42:36Z", "authors": ["Thierry Huillet"], "categories": ["q-bio.PE", "cond-mat.stat-mech"], "doi": "10.1007/s10955-017-1802-2", "_query": "hedonic house price", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/1301.5877v1", "title": "Pricing Using a Homogeneously Saturated Equation", "abstract": "A homogeneously saturated equation for the time development of the price of a financial asset is presented and investigated for the pricing of European call options using noise that is distributed as a Student's t-distribution. In the limit that the saturation parameter of the equation equals zero, the standard model of geometric motion for the price of an asset is obtained. The homogeneously saturated equation for the price of an asset is similar to a simple equation for the output of a homogeneously broadened laser. The homogeneously saturated equation tends to limit the range of returns and thus seems to be realistic. Fits to linear returns obtained from the adjusted closing values for the S&P 500 index were used to obtain best-fit parameters for Student's t-distributions and for normal distributions, and these fits were used to price options, and to compare approaches to modelling prices. This work has value in understanding the pricing of assets and of European call options.", "published": "2013-01-24T19:49:58Z", "authors": ["Daniel T. Cassidy"], "categories": ["q-fin.PR", "q-fin.GN"], "doi": "", "_query": "hedonic house price", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2506.00765v2", "title": "HouseTS: A Large-Scale, Multimodal Spatiotemporal U.S. Housing Dataset and Benchmark", "abstract": "Accurate long-horizon house-price forecasting requires benchmarks that capture temporal dynamics together with time-varying local context. However, existing public resources remain fragmented: many datasets have limited spatial coverage, temporal depth, or multimodal alignment; the robustness of modern deep forecasters and time-series foundation models on housing data is not well characterized; and aerial imagery is rarely leveraged in a time-aware and interpretable manner at scale. To bridge these gaps, we present HouseTS (House Time Series), a multimodal spatiotemporal dataset for ZIP-code-level housing-market analysis, covering monthly signals from March 2012 to December 2023 across over 6,000 ZIP codes in 30 major U.S. metropolitan areas. HouseTS aligns monthly housing-market indicators, monthly POI dynamics, and annual census-based socioeconomic variables under a unified schema, and includes time-stamped annual aerial imagery. Building on HouseTS, we define standardized long-horizon forecasting tasks for univariate and multivariate prediction and benchmark 16 model families spanning statistical methods, classical machine learning, deep neural networks, and time-series foundation models in both zero-shot and fine-tuned modes. We also provide image-derived textual change annotations from multi-year aerial image sequences via a vision--language pipeline with LLM-as-judge and human verification to support scalable interpretability analyses. HouseTS is available on Kaggle, with code and documentation on GitHub.", "published": "2025-06-01T00:52:41Z", "authors": ["Shengkun Wang", "Yanshen Sun", "Fanglan Chen", "Linhan Wang", "Naren Ramakrishnan", "Chang-Tien Lu", "Yinlin Chen"], "categories": ["cs.AI"], "doi": "", "_query": "hedonic house price", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2609.05235v1", "title": "PRICE: A Systematic Study of LLM Adaptation Choices for Bitcoin Price Forecasting", "abstract": "Cryptocurrency markets exhibit extreme volatility and non-stationary dynamics that challenge conventional forecasting methods. Although Large Language Models (LLMs) have shown promise for time series forecasting, the combined effects of adaptation choices remain largely unexplored in financial settings. This study introduces PRICE, a structured approach for adapting LLMs to short-term Bitcoin price forecasting. Built on a 4-bit quantized LLaMA-3 8B model, PRICE investigates how fine-tuning, numerical representation, prompting, inference, and decoding jointly influence forecasting performance. PRICE integrates Parameter-efficient fine-tuning with Low-Rank Adaptation (LoRA), Recursive multi-step inference, Integer-rounded numerical representation, Context-Task-Format (CTF) prompting, and Exact zero-temperature decoding. Ablation studies show that each component contributes to forecasting accuracy and reliability. LoRA enables efficient training on limited hardware, recursive inference improves accuracy, integer-rounded values reduce errors, CTF prompting outperforms Chain-of-Thought, Implicit Chain-of-Thought (iCoT), and few-shot prompting, and zero-temperature decoding improves stability during recursive forecasting. Comparative evaluation against eight transformer-based and time-series foundation models shows that PRICE achieves the lowest forecasting errors on both validation and test sets while maintaining robust performance across evaluation periods. Despite being based on a model primarily pretrained on text rather than time-series data, PRICE achieves competitive or superior performance relative to specialized foundation models. These findings demonstrate that adaptation choices critically determine the accuracy and robustness of LLMs for numerical time-series forecasting.", "published": "2026-09-04T15:01:38Z", "authors": ["Maryam Fakhari", "Mehran Safayani"], "categories": ["cs.LG", "cs.AI", "math.NA"], "doi": "", "_query": "hedonic house price", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2208.10650v1", "title": "Efficiency of the First-Price Auction in the Autobidding World", "abstract": "We study the price of anarchy of the first-price auction in the autobidding world, where bidders can be either utility maximizers (i.e., traditional bidders) or value maximizers (i.e., autobidders). We show that with autobidders only, the price of anarchy of the first-price auction is $1/2$, and with both kinds of bidders, the price of anarchy degrades to about $0.457$ (the precise number is given by an optimization). These results complement the recent result by Jin and Lu [2022] showing that the price of anarchy of the first-price auction with traditional bidders only is $1 - 1/e^2$. We further investigate a setting where the seller can utilize machine-learned advice to improve the efficiency of the auctions. There, we show that as the accuracy of the advice increases, the price of anarchy improves smoothly from about $0.457$ to $1$.", "published": "2022-08-22T23:49:46Z", "authors": ["Yuan Deng", "Jieming Mao", "Vahab Mirrokni", "Hanrui Zhang", "Song Zuo"], "categories": ["cs.GT", "cs.DS"], "doi": "", "_query": "hedonic house price", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/1412.5520v2", "title": "Indifference prices and implied volatilities", "abstract": "We consider a general local-stochastic volatility model and an investor with exponential utility. For a European-style contingent claim, whose payoff may depend on either a traded or non-traded asset, we derive an explicit approximation for both the buyer's and seller's indifference price. For European calls on a traded asset, we translate indifference prices into an explicit approximation of the buyer's and seller's implied volatility surface. For European claims on a non-traded asset, we establish rigorous error bounds for the indifference price approximation. Finally, we implement our indifference price and implied volatility approximations in two examples.", "published": "2014-12-17T19:00:47Z", "authors": ["Matthew Lorig"], "categories": ["q-fin.MF"], "doi": "", "_query": "hedonic house price", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/1307.2537v1", "title": "Strong Price of Anarchy and Coalitional Dynamics", "abstract": "We introduce a framework for studying the effect of cooperation on the quality of outcomes in utility games. Our framework is a coalitional analog of the smoothness framework of non-cooperative games. Coalitional smoothness implies bounds on the strong price of anarchy, the loss of quality of coalitionally stable outcomes, as well as bounds on coalitional versions of coarse correlated equilibria and sink equilibria, which we define as out-of-equilibrium myopic behavior as determined by a natural coalitional version of best-response dynamics. Our coalitional smoothness framework captures existing results bounding the strong price of anarchy of network design games. We show that in any monotone utility-maximization game, if each player's utility is at least his marginal contribution to the welfare, then the strong price of anarchy is at most 2. This captures a broad class of games, including games with a very high price of anarchy. Additionally, we show that in potential games the strong price of anarchy is close to the price of stability, the quality of the best Nash equilibrium.", "published": "2013-07-09T18:29:52Z", "authors": ["Yoram Bachrach", "Vasilis Syrgkanis", "Eva Tardos", "Milan Vojnovic"], "categories": ["cs.GT"], "doi": "", "_query": "hedonic house price", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/cond-mat/0012014v3", "title": "Option pricing and perfect hedging on correlated stocks", "abstract": "We develop a theory for option pricing with perfect hedging in an inefficient market model where the underlying price variations are autocorrelated over a time tau. This is accomplished by assuming that the underlying noise in the system is derived by an Ornstein-Uhlenbeck, rather than from a Wiener process. With a modified portfolio consisting in calls, secondary calls and bonds we achieve a riskless strategy which results in a closed expression for the European call price which is always lower than Black-Scholes price. We also obtain a partial differential equation for the option price and study the sensitivity to several parameters and the risk of the dynamics of the call price.", "published": "2000-12-01T17:10:10Z", "authors": ["Josep Perello", "Jaume Masoliver"], "categories": ["cond-mat", "physics.soc-ph"], "doi": "10.1016/S0378-4371(03)00619-8", "_query": "hedonic house price", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2607.11590v1", "title": "Copositive Characterizations of Convex Hull Pricing", "abstract": "Due to the nonconvex binary constraints of unit commitment (UC), no uniform linear pricing scheme supports the optimal dispatch. Convex hull pricing (CHP) and copositive duality pricing (CDP) both address this problem. CHP derives the price from the subgradient of the value function of the convex hull relaxation of UC. CDP refers to several different pricing mechanisms that can be constructed from the dual multipliers of the completely positive programming reformulation. In this work, we define a centralized convex hull price over the joint feasible set of UC and prove that, under non-degeneracy, it coincides with the marginal copositive duality price. Numerical experiments on the Scarf example validate this equivalence and quantify the pricing gap introduced by the semidefinite restriction.", "published": "2026-07-13T14:11:37Z", "authors": ["Madhusudan Ghosh", "Antoine Lesage-Landry", "Joshua Adam Taylor"], "categories": ["math.OC", "eess.SY"], "doi": "", "_query": "hedonic house price", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/1502.03359v2", "title": "Asymptotic indifference pricing in exponential Lévy models", "abstract": "Financial markets based on Lévy processes are typically incomplete and option prices depend on risk attitudes of individual agents. In this context, the notion of utility indifference price has gained popularity in the academic circles. Although theoretically very appealing, this pricing method remains difficult to apply in practice, due to the high computational cost of solving the nonlinear partial integro-differential equation associated to the indifference price. In this work, we develop closed form approximations to exponential utility indifference prices in exponential Lévy models. To this end, we first establish a new non-asymptotic approximation of the indifference price which extends earlier results on small risk aversion asymptotics of this quantity. Next, we use this formula to derive a closed-form approximation of the indifference price by treating the Lévy model as a perturbation of the Black-Scholes model. This extends the methodology introduced in a recent paper for smooth linear functionals of Lévy processes (A. Černý, S. Denkl and J. Kallsen, arXiv:1309.7833) to nonlinear and non-smooth functionals. Our closed formula represents the indifference price as the linear combination of the Black-Scholes price and correction terms which depend on the variance, skewness and kurtosis of the underlying Lévy process, and the derivatives of the Black-Scholes price. As a by-product, we obtain a simple explicit formula for the spread between the buyer's and the seller's indifference price. This formula allows to quantify, in a model-independent fashion, how sensitive a given product is to jump risk in the limit of small jump size.", "published": "2015-02-11T16:19:44Z", "authors": ["Clément Ménassé", "Peter Tankov"], "categories": ["q-fin.PR", "q-fin.CP"], "doi": "", "_query": "hedonic house price", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2004.00117v1", "title": "Challenges in control of Covid-19: short doubling time and long delay to effect of interventions", "abstract": "Early assessments of the spreading rate of COVID-19 were subject to significant uncertainty, as expected with limited data and difficulties in case ascertainment, but more reliable inferences can now be made. Here, we estimate from European data that COVID-19 cases are expected to double initially every three days, until social distancing interventions slow this growth, and that the impact of such measures is typically only seen nine days - i.e. three doubling times - after their implementation. We argue that such temporal patterns are more critical than precise estimates of the basic reproduction number for initiating interventions. This observation has particular implications for the low- and middle-income countries currently in the early stages of their local epidemics.", "published": "2020-03-31T21:16:09Z", "authors": ["Lorenzo Pellis", "Francesca Scarabel", "Helena B. Stage", "Christopher E. Overton", "Lauren H. K. Chappell", "Katrina A. Lythgoe", "Elizabeth Fearon", "Emma Bennett", "Jacob Curran-Sebastian", "Rajenki Das", "Martyn Fyles", "Hugo Lewkowicz", "Xiaoxi Pang", "Bindu Vekaria", "Luke Webb", "Thomas House", "Ian Hall"], "categories": ["q-bio.PE"], "doi": "", "_query": "hedonic house price", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2609.12537v1", "title": "The House with a Million Windows: Interactive Fiction for Narrative Restorying", "abstract": "AI-assisted writing can flatten meaning in human storytelling, enabling the production of homogeneous outputs without the intentional effort and sense-making writing entails. To address this challenge, we present The House with a Million Windows (HWAMW), an LLM-based interactive fiction system designed to help users explore both the breadth and depth of potential meanings within their personal stories -- drawing on a psychological paradigm called the restorying intervention. In HWAMW, users play through a text-based narrative in which they tell a story, then encounter a set of LLM-generated \"windows\" reframing it according to different literary styles. Empirical evidence shows that HWAMW increases users' sense of narrative identity, while an expert review explores how this effect is achieved. Our findings suggest that HWAMW facilitates restorying and offers a valuable paradigm for AI-assisted writing, wherein LLMs do not tell our stories but rather help us see greater potential in the stories we tell.", "published": "2026-09-11T07:47:43Z", "authors": ["Cody Kommers", "Sarah G Immel", "Drew Hemment", "Mina Lee"], "categories": ["cs.CL", "cs.HC"], "doi": "", "_query": "hedonic house price", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2110.02735v3", "title": "Optimal pricing for electricity retailers based on data-driven consumers' price-response", "abstract": "In the present work we tackle the problem of finding the optimal price tariff to be set by a risk-averse electric retailer participating in the pool and whose customers are price-sensitive. We assume that the retailer has access to a sufficiently large smart-meter dataset from which it can statistically characterize the relationship between the tariff price and the demand load of its clients. Three different models are analyzed to predict the aggregated load as a function of the electricity prices and other parameters, as humidity or temperature. More specifically, we train linear regression (predictive) models to forecast the resulting demand load as a function of the retail price. Then we will insert this model in a quadratic optimization problem which evaluates the optimal price to be offered. This optimization problem accounts for different sources of uncertainty including consumer's response, pool prices and renewable source availability, and relies on a stochastic and risk-averse formulation. In particular, one important contribution of this work is to base the scenario generation and reduction procedure on the statistical properties of the resulting predictive model. This allows us to properly quantify (data-driven) not only the expected value but the level of uncertainty associated with the main problem parameters. Moreover, we consider both standard forward based contracts and the recently introduced power purchase agreement contracts as risk-hedging tools for the retailer. The results are promising as profits are found for the retailer with highly competitive prices and some possible improvements are shown if richer datasets could be available in the future. A realistic case study and multiple sensitivity analyses have been performed to characterize the risk-aversion behavior of the retailer considering price-sensitive consumers.", "published": "2021-10-04T10:06:52Z", "authors": ["Román Pérez-Santalla", "Miguel Carrión", "Carlos Ruiz"], "categories": ["math.OC", "econ.GN", "stat.AP"], "doi": "10.1007/s11750-022-00622-8", "_query": "hedonic house price", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/1405.4498v1", "title": "The Economics of BitCoin Price Formation", "abstract": "This paper analyses the relationship between BitCoin price and supply-demand fundamentals of BitCoin, global macro-financial indicators and BitCoin attractiveness for investors. Using daily data for the period 2009-2014 and applying time-series analytical mechanisms, we find that BitCoin market fundamentals and BitCoin attractiveness for investors have a significant impact on BitCoin price. Our estimates do not support previous findings that the macro-financial developments are driving BitCoin price.", "published": "2014-05-18T12:59:05Z", "authors": ["Pavel Ciaian", "Miroslava Rajcaniova", "d'Artis Kancs"], "categories": ["econ.GN", "q-fin.GN"], "doi": "", "_query": "hedonic house price", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2204.12723v6", "title": "On the limitations of data-based price discrimination", "abstract": "The classic third degree price discrimination (3PD) model requires the knowledge of the distribution of buyer valuations and the covariate to set the price conditioned on the covariate. In terms of generating revenue, the classic result shows that 3PD is at least as good as uniform pricing. What if the seller has to set a price based only on a sample of observations from the underlying distribution? Is it still obvious that the seller should engage in 3PD? This paper sheds light on these fundamental questions. In particular, the comparison of the revenue performance between 3PD and uniform pricing is ambiguous overall when prices are set based on samples. This finding is in the nature of statistical learning under uncertainty: a curse of dimensionality, but also other small sample complications.", "published": "2022-04-27T06:33:37Z", "authors": ["Haitian Xie", "Ying Zhu", "Denis Shishkin"], "categories": ["cs.GT", "cs.IT", "cs.LG", "econ.TH"], "doi": "", "_query": "hedonic house price", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2607.16891v2", "title": "Certified-Gap Dual-Price Policies for Real-Time Truckload Bid Acceptance with Relocating, Clock-Constrained Resources", "abstract": "A truckload carrier must accept or reject each load tender within seconds. The decision depends on fleet state, hours-of-service (HOS) clocks, and appointment windows. We model this as a weakly coupled dynamic program in which the resources relocate and carry clocks: serving a request moves the truck to a new market and depletes its clocks, and whether a truck can serve a request depends on its state. Occupancy-based reusable-resource models do not cover this setting. We build a real-time dual-price policy from the same Lagrangian relaxation that gives the problem's upper bound. Policy and bound come from one object, so every run reports a certified optimality gap. We prove three things. First, the certificate is valid for any duals, any discretization, and any surrogate quality. Second, the policy's same-time spatial-gradient rule is exactly fluid complementary slackness, and the policy is asymptotically optimal in the subcritical fluid regime; the fitted prices are also portable across sample paths, by linear-programming basis stability. Third, certificates have limits: per-resource Lagrangian slack can stay bounded away from zero at every fleet size. We exhibit a three-truck kernel with an exact rational certificate and a replication lemma. On a public closed-loop benchmark with thirty paired seeds, the policy -- which needs no rollout labels, only one offline dual solve -- beats a rollout-trained surrogate on two of three scenarios (tight: +2.0 pp, 95% CI [+0.5, +3.6], Wilcoxon p = 0.023; mild: +3.5 pp, CI [+2.4, +4.5]) and ties the third. It decides in 0.04-0.09 ms, three orders of magnitude faster than the Monte Carlo rollout teacher. Its certificates are stable across ten bounded instances per scenario, at 57-64% of optimal, within 3-6 points of what the 1000x-slower teacher certifies.", "published": "2026-07-18T17:16:42Z", "authors": ["Aswin Chandrasekaran"], "categories": ["cs.LG", "cs.AI", "math.OC"], "doi": "", "_query": "hedonic house price", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2101.11137v2", "title": "The Breakthrough Listen Search for Intelligent Life: Searching for Technosignatures in Observations of TESS Targets of Interest", "abstract": "Exoplanetary systems are prime targets for the Search for Extraterrestrial Intelligence (SETI). With the recent uptick in the identification of candidate and confirmed exoplanets through the work of missions like the Transiting Exoplanet Survey Satellite (TESS), we are beginning to understand that Earth-like planets are common. In this work, we extend the Breakthrough Listen (BL) search for extraterrestrial intelligence to include targeted searches of stars identified by TESS as potential exoplanet hosts. We report on 113 30-min cadence observations collected for 28 targets selected from the TESS Input Catalog (TIC) from among those identified as containing signatures of transiting planets. The targets were searched for narrowband signals from 1-11 GHz using the turboSETI pipeline architecture modified for compatibility with the Google Cloud environment. Data were searched for drift rates of +/-4 Hz/s above a minimum signal-to-noise threshold of 10, following the parameters of previous searches conducted by Price et al. (2020) and Enriquez et al. (2017). The observations presented in this work establish some of the deepest limits to date over such a wide band (1-11 GHz) for life beyond Earth. We determine that fewer than 12.72% of the observed targets possess transmitters operating at these frequencies with an Equivalent Isotropic Radiated Power (EIRP) greater than our derived threshold of 4.9*10^(14) W.", "published": "2021-01-26T23:59:16Z", "authors": ["Raffy Traas", "Steve Croft", "Vishal Gajjar", "Howard Isaacson", "Matt Lebofsky", "David H. E. MacMahon", "Karen Perez", "Danny C. Price", "Sofia Sheikh", "Andrew P. V. Siemion", "Shane Smith", "Jamie Drew", "S. Pete Worden"], "categories": ["astro-ph.EP", "astro-ph.IM"], "doi": "10.3847/1538-3881/abf649", "_query": "hedonic house price", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/cond-mat/0311306v1", "title": "Does the price multiplier effect also hold for stocks?", "abstract": "The price multiplier effect provides precious insight into the behavior of investors during episodes of speculative trading. It tells us that the higher the price of an asset is (within a set of similar assets) the more its price is likely to increase during the upgoing phase of a speculative price peak. In short, instead of being risk averse, as is often assumed, investors rather seem to be ``risk prone''. While this effect is known to hold for several sorts of assets, it has not yet been possible to test it for stocks because the price of one share has no intrinsic significance which means that one cannot say that a stock $ A $ is more expensive than a stock $ B $ on the basis of its price. In this paper we show that the price-dividend ratio gives a good basis for assessing the price of stocks in an intrinsic way. When this alternative measure is used instead, it turns out that the price multiplier effect also holds for stocks, at least if one concentrates on samples of companies which are sufficiently homogeneous.", "published": "2003-11-13T11:46:08Z", "authors": ["Sergei Maslov", "Bertrand M. Roehner"], "categories": ["cond-mat"], "doi": "10.1142/S0129183103005510", "_query": "hedonic house price", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/1110.4669v1", "title": "Bridge Copula Model for Option Pricing", "abstract": "In this paper we present a new multi-asset pricing model, which is built upon newly developed families of solvable multi-parameter single-asset diffusions with a nonlinear smile-shaped volatility and an affine drift. Our multi-asset pricing model arises by employing copula methods. In particular, all discounted single-asset price processes are modeled as martingale diffusions under a risk-neutral measure. The price processes are so-called UOU diffusions and they are each generated by combining a variable (Ito) transformation with a measure change performed on an underlying Ornstein-Uhlenbeck (Gaussian) process. Consequently, we exploit the use of a normal bridge copula for coupling the single-asset dynamics while reducing the distribution of the multi-asset price process to a multivariate normal distribution. Such an approach allows us to simulate multidimensional price paths in a precise and fast manner and hence to price path-dependent financial derivatives such as Asian-style and Bermudan options using the Monte Carlo method. We also demonstrate how to successfully calibrate our multi-asset pricing model by fitting respective equity option and asset market prices to the single-asset models and their return correlations (i.e. the copula function) using the least-square and maximum-likelihood estimation methods.", "published": "2011-10-21T00:36:26Z", "authors": ["Giuseppe Campolieti", "Roman N. Makarov", "Andrey Vasiliev"], "categories": ["q-fin.PR", "q-fin.CP"], "doi": "", "_query": "hedonic house price", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2404.03581v1", "title": "Consumer Behavior under Benevolent Price Discrimination", "abstract": "Extensive research shows that consumers are generally averse to price discrimination. However, instruments of differential pricing can benefit consumer surplus and alleviate inequity through targeted price discounts. This paper examines how these outcome considerations influence consumer reactions to price discrimination. Six studies with 3951 participants show that a large share of consumers is willing to costly switch away from a store that introduces a discount for low-income consumers. This happens irrespective of whether income differences are due to luck or merit. While the price-discriminating store does attract some new high-income consumers, it cannot compensate the loss of existing consumers. Allowing for altruistic preferences by simulating a market mechanism increases costly support for price discounts, but does not alleviate consumer aversions. Finally, we provide evidence that warm glow drives costly support for price discounts.", "published": "2024-04-04T16:43:40Z", "authors": ["Alexander Erlei", "Mattheus Brenig", "Nils Engelbrecht"], "categories": ["econ.GN"], "doi": "", "_query": "hedonic house price", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2208.09372v3", "title": "Non-Stationary Dynamic Pricing Via Actor-Critic Information-Directed Pricing", "abstract": "This paper presents a novel non-stationary dynamic pricing algorithm design, where pricing agents face incomplete demand information and market environment shifts. The agents run price experiments to learn about each product's demand curve and the profit-maximizing price, while being aware of market environment shifts to avoid high opportunity costs from offering sub-optimal prices. The proposed ACIDP extends information-directed sampling (IDS) algorithms from statistical machine learning to include microeconomic choice theory, with a novel pricing strategy auditing procedure to escape sub-optimal pricing after market environment shift. The proposed ACIDP outperforms competing bandit algorithms including Upper Confidence Bound (UCB) and Thompson sampling (TS) in a series of market environment shifts.", "published": "2022-08-19T14:37:37Z", "authors": ["Po-Yi Liu", "Chi-Hua Wang", "Henghsiu Tsai"], "categories": ["stat.ML", "cs.LG", "econ.GN"], "doi": "", "_query": "hedonic house price", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2605.05535v2", "title": "Housing Potential Common Data Model and City Digital Twin", "abstract": "The evaluation of housing potential requires consideration of a location from multiple perspectives, ranging from zoning and land use to population characteristics and access to services. This research introduces the Housing Potential Common Data Model (HPCDM) to overcome existing data silos, serving as a standard to support integration and interoperability across the diverse range of datasets that are required for housing potential analysis. This report details the evaluation of the model along with the creation of a City Digital Twin for housing and a pilot dashboard application to demonstrate a practical implementation. Beyond the technical framework, this work identifies critical barriers to adoption and provides actionable mitigation strategies for urban planners and stakeholders.", "published": "2026-05-07T00:27:47Z", "authors": ["Megan Katsumi", "Mark Fox", "Anderson Wong", "Divnoor Chatha"], "categories": ["cs.AI"], "doi": "", "_query": "hedonic house price", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2608.20865v1", "title": "Parameters Overshadowed by Price Lags: Load, Climate, and Calendar Effects in ERCOT Day-Ahead Price Formation", "abstract": "Accurate electricity price forecasting is critical for smart grid stability, yet the heavy reliance on historical price lags in modern predictive models often obscures the fundamental physical drivers of market volatility. This paper proposes a regime-sensitive, explainable artificial intelligence (XAI) framework to unmask the hidden roles of load, climate, and calendar variables in the ERCOT Day-Ahead Market (2014-2024). Utilizing a Histogram-based Gradient Boosting Regressor (HGBR), we introduce a Suppression Ratio to quantify how price lags overshadow physical parameters. Our analysis reveals that while lags provide short-term memory during normal conditions, they fail to capture extreme dynamics; notably, their inclusion increased forecasting error (MAE/RMSE) during price spikes. By isolating these effects, the study revealed that price spikes are not merely the result of continuously rising temperatures, but rather emerge from the system reacting sharply within a specific temperature range. These findings underscore the importance of moving beyond lag-based models to uncover the true drivers of extreme price events.", "published": "2026-08-21T08:33:11Z", "authors": ["Yunus Bicen", "Eman Hammad"], "categories": ["eess.SY"], "doi": "", "_query": "hedonic house price", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2105.08804v3", "title": "Efficient approximations for utility-based pricing", "abstract": "In a context of illiquidity, the reservation price is a well-accepted alternative to the usual martingale approach which does not apply. However, this price is not available in closed form and requires numerical methods such as Monte Carlo or polynomial approximations to evaluate it. We show that these methods can be inaccurate and propose a deterministic decomposition of the reservation price using the Lambert function. This decomposition allows us to perform an improved Monte Carlo method, which we name Lambert Monte Carlo (LMC) and to give deterministic approximations of the reservation price and of the optimal strategies based on the Lambert function. We also give an answer to the problem of selecting a hedging asset that minimizes the reservation price and also the cash invested. Our theoretical results are illustrated by numerical simulations.", "published": "2021-05-18T19:48:06Z", "authors": ["Laurence Carassus", "Massinissa Ferhoune"], "categories": ["q-fin.CP", "q-fin.PR"], "doi": "", "_query": "hedonic house price", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/0903.3346v1", "title": "The Transfer Pricing Problem with Non-Linearities", "abstract": "A number of approaches to solving the well-known transfer pricing problem are known. However, few models satisfactorily resolve the core problem of allowing both the source and receiving divisions to earn a profit on transfers during a period in such a way that sub-optimal output levels are avoided. In 1969, Samuel proposed to use a transfer price schedule instead of just a single transfer price. An essential improvement of Samuels' model was given by Tomkins (1990) in his pragmatic-analytical transfer pricing approach, which is a combination of a single cost-plus transfer price and the pragmatic process of negotiation. This fundamental approach was developed under the assumption that the net average revenue curve for the final product is linear. In this paper, Tomkins' pragmatic-analytical model is further developed for non-linear net average revenue curves. In particular, typical quadratic functions are considered and corresponding transfer price schedules are determined. A similar technique can be used for the transfer pricing problem with any net average revenue curve.", "published": "2009-03-19T15:16:02Z", "authors": ["S. Zverovich"], "categories": ["q-fin.GN"], "doi": "", "_query": "hedonic house price", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/1005.0051v1", "title": "Crude oil and motor fuel: Fair price revisited", "abstract": "In April 2009, we introduced a model representing the evolution of motor fuel price (a subcategory of the consumer price index of transportation) relative to the overall CPI as a linear function of time. Under our framework, all price deviations from the linear trend are transient and the price must promptly return to the trend. Specifically, the model predicted that \"the price for motor fuel in the US will also grow by 50% by the end of 2009. Oil price is expected to rise by ~50% as well, from its current value of ~$50 per barrel\". The behavior of actual price has shown that this prediction is accurate in both amplitude and trajectory shape. Hence, one can conclude that the concept of price decomposition into a short-term (oscillating) and long-term (linear trend) components is valid. According to the model, the price of motor fuel and crude oil will be falling to the level of $30 per barrel during the next 5 to 8 years.", "published": "2010-05-01T07:11:32Z", "authors": ["Ivan O. Kitov", "Oleg I. Kitov"], "categories": ["q-fin.GN", "q-fin.ST"], "doi": "", "_query": "hedonic house price", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/1609.06844v1", "title": "Posted Pricing sans Discrimination", "abstract": "In the quest for market mechanisms that are easy to implement, yet close to optimal, few seem as viable as posted pricing. Despite the growing body of impressive results, the performance of most posted price mechanisms however, rely crucially on price discrimination when multiple copies of a good are available. For the more general case with non-linear production costs on each good, hardly anything is known for general multi-good markets. With this in mind, we study a Bayesian setting where the seller can produce any number of copies of a good but faces convex production costs for the same, and buyers arrive sequentially. Our main contribution is a framework for non-discriminatory pricing in the presence of production costs: the framework yields posted price mechanisms with O(1)-approximation factors for fractionally subadditive (XoS) buyers, logarithmic approximations for subadditive buyers, and also extends to settings where the seller is oblivious to buyer valuations. Our work presents the first known results for Bayesian settings with production costs and is among the few posted price mechanisms that do not charge buyers differently for the same good.", "published": "2016-09-22T07:37:15Z", "authors": ["Shreyas Sekar"], "categories": ["cs.GT"], "doi": "", "_query": "hedonic house price", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/1904.06899v2", "title": "How to Price Fresh Data", "abstract": "We introduce the concept of a fresh data market, in which a destination user requests, and pays for, fresh data updates from a source provider. Data freshness is captured by the {\\it age of information} (AoI) metric, defined as the time elapsed since the latest update has reached the destination. The source incurs an operational cost, modeled as an increasing convex function of the number of updates. The destination incurs an age-related cost, modeled as an increasing convex function of the AoI. The source charges the destination for each update and designs a pricing mechanism to maximize its profit; the destination on the other hand chooses a data update schedule to minimize the summation of its payments to the source and its age-related cost. The interaction among the source and destination is hence game-theoretic. Motivated by the existing pricing literature, we first study a time-dependent pricing scheme, in which the price for each update depends on when it is requested. We show in this case that the game equilibrium leads to only one data update, which does not yield the maximum profit to the source. This motivates us to consider a quantity-based pricing scheme, in which the price of each update depends on how many updates have been previously requested. We show that among all pricing schemes in which the price of an update may vary according to both time and quantity, the quantity-based pricing scheme performs best: it maximizes the source's profit and minimizes the social cost of the system, defined as the aggregate source's operational cost and the destination's age-related cost. Numerical results show that the optimal quantity-based pricing can be 27% more profitable for the source and incurs 54% less social cost, compared with the optimal time-dependent pricing.", "published": "2019-04-15T08:05:58Z", "authors": ["Meng Zhang", "Ahmed Arafa", "Jianwei Huang", "H. Vincent Poor"], "categories": ["cs.NI", "cs.IT"], "doi": "", "_query": "hedonic house price", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/1810.03494v3", "title": "k-price auctions and Combination auctions", "abstract": "We provide an exact analytical solution of the Nash equilibrium for $k$- price auctions. We also introduce a new type of auction and demonstrate that it has fair solutions other than the second price auctions, therefore paving the way for replacing second price auctions.", "published": "2018-09-26T15:33:48Z", "authors": ["Martin Mihelich", "Yan Shu"], "categories": ["q-fin.MF", "cs.GT", "econ.GN"], "doi": "", "_query": "hedonic house price", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/1502.02819v1", "title": "The pricing of lookback options and binomial approximation", "abstract": "Refining a discrete model of Cheuk and Vorst we obtain a closed formula for the price of a European lookback option at any time between emission and maturity. We derive an asymptotic expansion of the price as the number of periods tends to infinity, thereby solving a problem posed by Lin and Palmer. We prove, in particular, that the price in the discrete model tends to the price in the continuous Black-Scholes model. Our results are based on an asymptotic expansion of the binomial cumulative distribution function that improves several recent results in the literature.", "published": "2015-02-10T09:14:20Z", "authors": ["Karl Grosse-Erdmann", "Fabien Heuwelyckx"], "categories": ["q-fin.MF"], "doi": "", "_query": "hedonic house price", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/1712.03031v1", "title": "An Empirical Study on Price Differentiation Based on System Fingerprints", "abstract": "Price differentiation describes a marketing strategy to determine the price of goods on the basis of a potential customer's attributes like location, financial status, possessions, or behavior. Several cases of online price differentiation have been revealed in recent years. For example, different pricing based on a user's location was discovered for online office supply chain stores and there were indications that offers for hotel rooms are priced higher for Apple users compared to Windows users at certain online booking websites. One potential source for relevant distinctive features are \\emph{system fingerprints}, i.\\,e., a technique to recognize users' systems by identifying unique attributes such as the source IP address or system configuration. In this paper, we shed light on the ecosystem of pricing at online platforms and aim to detect if and how such platform providers make use of price differentiation based on digital system fingerprints. We designed and implemented an automated price scanner capable of disguising itself as an arbitrary system, leveraging real-world system fingerprints, and searched for price differences related to different features (e.\\,g., user location, language setting, or operating system). This system allows us to explore price differentiation cases and expose those characteristic features of a system that may influence a product's price.", "published": "2017-12-08T11:24:10Z", "authors": ["Thomas Hupperich", "Dennis Tatang", "Nicolai Wilkop", "Thorsten Holz"], "categories": ["cs.CR"], "doi": "", "_query": "hedonic house price", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/1802.04644v2", "title": "Price of Anarchy for Mean Field Games", "abstract": "The price of anarchy, originally introduced to quantify the inefficiency of selfish behavior in routing games, is extended to mean field games. The price of anarchy is defined as the ratio of a worst case social cost computed for a mean field game equilibrium to the optimal social cost as computed by a central planner. We illustrate properties of such a price of anarchy on linear quadratic extended mean field games, for which explicit computations are possible. Various asymptotic behaviors of the price of anarchy are proved for limiting behaviors of the coefficients in the model and numerics are presented.", "published": "2018-02-08T18:38:45Z", "authors": ["Rene Carmona", "Christy V. Graves", "Zongjun Tan"], "categories": ["math.OC"], "doi": "", "_query": "hedonic house price", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/1304.6846v2", "title": "Time-independent pricing of options in range bound markets", "abstract": "Assuming that price of the underlying stock is moving in range bound, the Black-Scholes formula for options pricing supports a separation of variables. The resulting time-independent equation is solved employing different behavior of the option price function and three significant results are deduced. The first is the probability of stock price penetration through support or resistance level, called transmission coefficient. The second is the distance that price will go through once stock price penetrates out of the range bound. The last one is a predicted short time dramatic fall in the stock volatility right ahead of price tunneling. All three results are useful tools that give market practitioners valuable insights in choosing the right time to get involved in an option contract, about how far the price will go in case of a breakout, and how to correctly interpret volatility downfalls.", "published": "2013-04-25T09:43:54Z", "authors": ["Ovidiu Racorean"], "categories": ["q-fin.PR"], "doi": "", "_query": "hedonic house price", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/1904.05315v1", "title": "Bitcoin Price Prediction: An ARIMA Approach", "abstract": "Bitcoin is considered the most valuable currency in the world. Besides being highly valuable, its value has also experienced a steep increase, from around 1 dollar in 2010 to around 18000 in 2017. Then, in recent years, it has attracted considerable attention in a diverse set of fields, including economics and computer science. The former mainly focuses on studying how it affects the market, determining reasons behinds its price fluctuations, and predicting its future prices. The latter mainly focuses on its vulnerabilities, scalability, and other techno-crypto-economic issues. Here, we aim at revealing the usefulness of traditional autoregressive integrative moving average (ARIMA) model in predicting the future value of bitcoin by analyzing the price time series in a 3-years-long time period. On the one hand, our empirical studies reveal that this simple scheme is efficient in sub-periods in which the behavior of the time-series is almost unchanged, especially when it is used for short-term prediction, e.g. 1-day. On the other hand, when we try to train the ARIMA model to a 3-years-long period, during which the bitcoin price has experienced different behaviors, or when we try to use it for a long-term prediction, we observe that it introduces large prediction errors. Especially, the ARIMA model is unable to capture the sharp fluctuations in the price, e.g. the volatility at the end of 2017. Then, it calls for more features to be extracted and used along with the price for a more accurate prediction of the price. We have further investigated the bitcoin price prediction using an ARIMA model, trained over a large dataset, and a limited test window of the bitcoin price, with length $w$, as inputs. Our study sheds lights on the interaction of the prediction accuracy, choice of ($p,q,d$), and window size $w$.", "published": "2019-04-04T12:18:42Z", "authors": ["Amin Azari"], "categories": ["cs.SI", "q-fin.ST"], "doi": "", "_query": "hedonic house price", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2009.14278v2", "title": "Price, Volatility and the Second-Order Economic Theory", "abstract": "We introduce the price probability measure η(p;t) that defines the mean price p(1;t), mean square price p(2;t), price volatility σp2(t)and all price n-th statistical moments p(n;t) as ratio of sums of n-th degree values C(n;t) and volumes U(n;t) of market trades aggregated during certain time interval Δ. The definition of the mean price p(1;t) coincides with definition of the volume weighted average price (VWAP) introduced at least 30 years ago. We show that price volatility σp2(t) forecasting requires modeling evolution of the sums of second-degree values C(2;t) and volumes U(2;t). We call this model as second-order economic theory. We use numerical continuous risk ratings as ground for risk assessment of economic agents and distribute agents by risk ratings as coordinates. We introduce continuous economic media approximation of squares of values and volumes of agents trades and their flows aggregated during time interval Δ. We take into account expectations that govern agents trades and introduce aggregated expectations alike to aggregated trades. We derive equations for continuous economic media approximation on the second-degree trades. In the linear approximation we derive mean square price p(2;t) and volatility σp2(t) disturbances as functions of the first and second-degree trades disturbances. Description of each next n-th price statistical moment p(n;t) with respect to the unit price measure η(p;t) depends on sums of n-th degree values C(n;t) and volumes U(n;t) of market trades and hence requires development of the corresponding n-th order economic theory.", "published": "2020-09-06T11:20:34Z", "authors": ["Victor Olkhov"], "categories": ["q-fin.GN", "econ.GN"], "doi": "", "_query": "hedonic house price", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/1705.02474v2", "title": "Price sensitivities for a general stochastic volatility model", "abstract": "We deal with the calculation of price sensitivities for stochastic volatility models. General forms for the dynamics of the underlying asset price and its volatility are considered. We make use of the chaotic (or Malliavin) calculus to compute the price sensitivities. The obtained results are applied to several recent stochastic volatility models as well as the existing ones that are commonly used by practitioners. Each price sensitivity is a source of financial risk. The suggested formulas are expected to improve on the hedging of the underlying risk.", "published": "2017-05-06T11:08:02Z", "authors": ["Youssef El-Khatib", "Abdulnasser Hatemi-J"], "categories": ["math.PR"], "doi": "", "_query": "hedonic house price", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/1104.4596v1", "title": "Price dynamics in a Markovian limit order market", "abstract": "We propose and study a simple stochastic model for the dynamics of a limit order book, in which arrivals of market order, limit orders and order cancellations are described in terms of a Markovian queueing system. Through its analytical tractability, the model allows to obtain analytical expressions for various quantities of interest such as the distribution of the duration between price changes, the distribution and autocorrelation of price changes, and the probability of an upward move in the price, {\\it conditional} on the state of the order book. We study the diffusion limit of the price process and express the volatility of price changes in terms of parameters describing the arrival rates of buy and sell orders and cancelations. These analytical results provide some insight into the relation between order flow and price dynamics in order-driven markets.", "published": "2011-04-24T00:16:56Z", "authors": ["Rama Cont", "Adrien De Larrard"], "categories": ["q-fin.TR", "q-fin.ST"], "doi": "10.1137/110856605", "_query": "hedonic house price", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/1505.03799v1", "title": "Distributed House-Hunting in Ant Colonies", "abstract": "We introduce the study of the ant colony house-hunting problem from a distributed computing perspective. When an ant colony's nest becomes unsuitable due to size constraints or damage, the colony must relocate to a new nest. The task of identifying and evaluating the quality of potential new nests is distributed among all ants. The ants must additionally reach consensus on a final nest choice and the full colony must be transported to this single new nest. Our goal is to use tools and techniques from distributed computing theory in order to gain insight into the house-hunting process. We develop a formal model for the house-hunting problem inspired by the behavior of the Temnothorax genus of ants. We then show a Ω(log n) lower bound on the time for all n ants to agree on one of k candidate nests. We also present two algorithms that solve the house-hunting problem in our model. The first algorithm solves the problem in optimal O(log n) time but exhibits some features not characteristic of natural ant behavior. The second algorithm runs in O(k log n) time and uses an extremely simple and natural rule for each ant to decide on the new nest.", "published": "2015-05-14T17:09:54Z", "authors": ["Mohsen Ghaffari", "Cameron Musco", "Tsvetomira Radeva", "Nancy Lynch"], "categories": ["cs.DC"], "doi": "", "_query": "hedonic house price", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2605.16064v2", "title": "Misspecified Estimate-then-Optimize Leads to Supra-Competitive Prices", "abstract": "We study whether simple algorithmic pricing systems can systematically produce collusive-like prices in multi-firm markets. We consider firms that price using a myopic estimate-then-optimize rule: each repeatedly fits a demand model to its own price and sales history and sets the price that maximizes estimated profit. This demand model is misspecified, omitting competitors' prices. We analyze the dynamics of this rule when it is initialized by an exploration phase of independent random prices. We characterize when this pipeline converges to supra-competitive prices above the Nash equilibrium, via a fluid-limit ordinary differential equation analysis. We show that supra-competitive prices arise when firms initially explore within similar price ranges on the same side of the Nash price. Moreover, prices can be substantially above the Nash price; we show that prices can reach monopoly levels under symmetric exploration. Simulations calibrated to a real multifamily rental market confirm that supra-competitive outcomes arise robustly beyond our theoretical assumptions, including under finite horizons, heterogeneous products, and nonlinear logit demand.", "published": "2026-05-15T15:29:23Z", "authors": ["Jackie Baek", "Vivek F. Farias", "Farrell Wu"], "categories": ["cs.GT", "cs.AI", "econ.TH"], "doi": "", "_query": "hedonic house price", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2609.33799v1", "title": "Oracle-Parametrized Constant Function Market Makers: From Price Feeds to Pricing Rules", "abstract": "This paper introduces oracle-parametrized automated market makers (OP-AMMs), i.e., automated market makers whose quoted price depends jointly on the pool reserves and an external oracle price. In doing so, we extend the information-agnostic AMM framework to settings, such as tokenized securities, for which price discovery occurs off-chain. Under a strict oracle-contraction condition, we show that the quoted price of any OP-AMM interpolates between the oracle price and an implicit autarkic price determined by the pool reserves. We then derive a general loss-versus-rebalancing (LVR) decomposition that separates the residual exposure to market lags from the losses induced by oracle errors. This analysis is further extended to stale, discrete-update oracles and to sandwich attacks around oracle updates. Using this framework, we find conditions under which OP-AMMs simultaneously increase local capital efficiency and reduce normalized LVR relative to information-agnostic AMMs. However, sufficiently noisy or stale oracles can reverse these gains. A counterfactual backtest using one-second SPY NBBO data is provided to demonstrate these trade-offs. In particular, we map the Pareto-efficient frontier of oracle-parametrized constant function market maker (OP-CFMM) designs across stylized oracle regimes.", "published": "2026-09-27T17:55:49Z", "authors": ["Hamed Amini", "Zachary Feinstein"], "categories": ["q-fin.TR", "q-fin.MF"], "doi": "", "_query": "hedonic house price", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/1304.5156v1", "title": "Option pricing, Bayes risks and Applications", "abstract": "A statistical decision problem is hidden in the core of option pricing. A simple form for the price C of a European call option is obtained via the minimum Bayes risk, R_B, of a 2-parameter estimation problem, thus justifying calling C Bayes (B-)price. The result provides new insight in option pricing, among others obtaining C for some stock-price models using the underlying probability instead of the risk neutral probability and giving R_B an economic interpretation. When logarithmic stock prices follow Brownian motion, discrete normal mixture and hyperbolic Levy motion the obtained B-prices are \"fair\" prices. A new expression for the price of American call option is also obtained and statistical modeling of R_B can be used when pricing European and American call options.", "published": "2013-04-18T15:02:11Z", "authors": ["Yannis G. Yatracos"], "categories": ["q-fin.PR", "stat.AP"], "doi": "", "_query": "hedonic house price", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2005.09447v1", "title": "Graphs with no induced house nor induced hole have the de Bruijn-Erdős property", "abstract": "A set of n points in the plane which are not all collinear defines at least n distinct lines. Chen and Chvátal conjectured in 2008 that a similar result can be achieved in the broader context of finite metric spaces. This conjecture remains open even for graph metrics. In this article we prove that graphs with no induced house nor induced cycle of length at least~5 verify the desired property. We focus on lines generated by vertices at distance at most 2, define a new notion of ``good pairs'' that might have application in larger families, and finally use a discharging technique to count lines in irreducible graphs.", "published": "2020-05-19T13:50:46Z", "authors": ["Pierre Aboulker", "Laurent Beaudou", "Martín Matamala", "José Zamora"], "categories": ["cs.DM", "math.CO"], "doi": "", "_query": "hedonic house price", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/1702.03603v1", "title": "Mean Field Voter Model of Election to the House of Representatives in Japan", "abstract": "In this study, we propose a mechanical model of a plurality election based on a mean field voter model. We assume that there are three candidates in each electoral district, i.e., one from the ruling party, one from the main opposition party, and one from other political parties. The voters are classified as fixed supporters and herding (floating) voters with ratios of $1-p$ and $p$, respectively. Fixed supporters make decisions based on their information and herding voters make the same choice as another randomly selected voter. The equilibrium vote-share probability density of herding voters follows a Dirichlet distribution. We estimate the composition of fixed supporters in each electoral district and $p$ using data from elections to the House of Representatives in Japan (43rd to 47th). The spatial inhomogeneity of fixed supporters explains the long-range spatial and temporal correlations. The estimated values of $p$ are close to the estimates obtained from a survey.", "published": "2017-02-13T01:18:12Z", "authors": ["Fumiaki Sano", "Masato Hisakado", "Shintaro Mori"], "categories": ["physics.soc-ph", "physics.data-an"], "doi": "10.7566/JPSCP.16.011016", "_query": "hedonic house price", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2205.01255v1", "title": "Incorporating Financial Hardship in Measuring the Mental Health Impact of Housing Stress", "abstract": "Housing expenditure tends to be sticky and costly to adjust, and makes up a large proportion of household expenditure. Additionally, the loss of housing can have catastrophic consequences. These specific features of housing expenditure imply that housing stress could cause negative mental health impacts. This research investigates the effects of housing stress on mental health, contributing to the literature by nesting housing stress within a measure of financial hardship, thus improving robustness to omitted variables and creating a natural comparison group for matching. Fixed effects (FE) regressions and a difference-in-differences (DID) methodology are estimated utilising data from the Household Income and Labour Dynamics in Australia (HILDA) Survey. The results show that renters who are in housing stress have a significant decline in self-reported mental health, with those in prior financial hardship being more severely affected. In contrast, there is little to no evidence of housing stress impacting on owners with a mortgage. The results also suggest that the mental health impact of housing stress is more important than some, but not all, aspects of financial hardship.", "published": "2022-05-03T00:38:56Z", "authors": ["Timothy Ludlow", "Jonas Fooken", "Christiern Rose", "Kam Tang"], "categories": ["econ.GN"], "doi": "", "_query": "hedonic house price", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/1011.5950v1", "title": "Networks and the Epidemiology of Infectious Disease", "abstract": "The science of networks has revolutionised research into the dynamics of interacting elements. It could be argued that epidemiology in particular has embraced the potential of network theory more than any other discipline. Here we review the growing body of research concerning the spread of infectious diseases on networks, focusing on the interplay between network theory and epidemiology. The review is split into four main sections, which examine: the types of network relevant to epidemiology; the multitude of ways these networks can be characterised; the statistical methods that can be applied to infer the epidemiological parameters on a realised network; and finally simulation and analytical methods to determine epidemic dynamics on a given network. Given the breadth of areas covered and the ever-expanding number of publications, a comprehensive review of all work is impossible. Instead, we provide a personalised overview into the areas of network epidemiology that have seen the greatest progress in recent years or have the greatest potential to provide novel insights. As such, considerable importance is placed on analytical approaches and statistical methods which are both rapidly expanding fields. Throughout this review we restrict our attention to epidemiological issues.", "published": "2010-11-27T07:38:19Z", "authors": ["Leon Danon", "Ashley P. Ford", "Thomas House", "Chris P. Jewell", "Matt J. Keeling", "Gareth O. Roberts", "Joshua V. Ross", "Matthew C. Vernon"], "categories": ["physics.soc-ph", "cs.SI", "q-bio.PE"], "doi": "10.1155/2011/284909", "_query": "hedonic house price", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/math/0310223v1", "title": "Consistent Estimation of Pricing Kernels from Noisy Price Data", "abstract": "If pricing kernels are assumed non-negative then the inverse problem of finding the pricing kernel is well-posed. The constrained least squares method provides a consistent estimate of the pricing kernel. When the data are limited, a new method is suggested: relaxed maximization of the relative entropy. This estimator is also consistent. Keywords: $ε$-entropy, non-parametric estimation, pricing kernel, inverse problems.", "published": "2003-10-15T14:44:55Z", "authors": ["Vladislav Kargin"], "categories": ["math.ST", "math.NA", "q-fin.ST"], "doi": "", "_query": "hedonic house price", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2409.02777v3", "title": "Oh the Prices You'll See: Designing a Fair Exchange System to Mitigate Personalized Pricing", "abstract": "Many online marketplaces personalize prices based on consumer attributes. Since these prices are private, consumers may be unaware that they have spent more on a good than the lowest possible price, and cannot easily take action to pay less. In this paper, we introduce a fairness-centered exchange system that takes advantage of personalized pricing, while still allowing consumers to individually benefit. Our system produces a matching of consumers to promote trading; the lower-paying consumer buys the good for the higher-paying consumer for some fee. We explore various modeling choices and fairness targets to determine which schema will leave consumers best off, while also earning revenue for the system itself. We show that when consumers individually negotiate the transaction price, and our fairness objective is to minimize mean net cost, we are able to achieve the most fair outcomes. Conversely, when transaction prices are centrally set, consumers are often unwilling to transact. When price dispersion (or range) is high, the system can reduce the mean net cost to each individual by $66\\%$, or the mean net cost to a group by $69\\%$. We find that a high dispersion of original prices is necessary for our system to be viable. Higher dispersion can actually lead to decreased net price paid by consumers, and act as a check against extreme personalization, increasing seller accountability. Our results provide theoretical evidence that such a system could improve fairness for consumers while sustaining itself financially.", "published": "2024-09-04T14:55:59Z", "authors": ["Aditya Karan", "Naina Balepur", "Hari Sundaram"], "categories": ["cs.GT"], "doi": "10.1145/3715275.3732076", "_query": "hedonic house price", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2501.07827v1", "title": "Prediction Interval Construction Method for Electricity Prices", "abstract": "Accurate prediction of electricity prices plays an essential role in the electricity market. To reflect the uncertainty of electricity prices, price intervals are predicted. This paper proposes a novel prediction interval construction method. A conditional generative adversarial network is first presented to generate electricity price scenarios, with which the prediction intervals can be constructed. Then, different generated scenarios are stacked to obtain the probability densities, which can be applied to accurately reflect the uncertainty of electricity prices. Furthermore, a reinforced prediction mechanism based on the volatility level of weather factors is introduced to address the spikes or volatile prices. A case study is conducted to verify the effectiveness of the proposed novel prediction interval construction method. The method can also provide the probability density of each price scenario within the prediction interval and has the superiority to address the volatile prices and price spikes with a reinforced prediction mechanism.", "published": "2025-01-14T04:02:08Z", "authors": ["Xin Lu"], "categories": ["cs.LG", "eess.SY"], "doi": "", "_query": "hedonic house price", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2003.01945v1", "title": "A mean-field game price model with noise", "abstract": "In this paper, we propose a mean-field game model for the price formation of a commodity whose production is subjected to random fluctuations. The model generalizes existing deterministic price formation models. Agents seek to minimize their average cost by choosing their trading rates with a price that is characterized by a balance between supply and demand. The supply and the price processes are assumed to follow stochastic differential equations. Here, we show that, for linear dynamics and quadratic costs, the optimal trading rates are determined in feedback form. Hence, the price arises as the solution to a stochastic differential equation, whose coefficients depend on the solution of a system of ordinary differential equations.", "published": "2020-03-04T08:34:41Z", "authors": ["Diogo Gomes", "Julian Gutierrez", "Ricardo Ribeiro"], "categories": ["math.AP"], "doi": "", "_query": "hedonic house price", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2505.10514v2", "title": "Pricing in Queues with Abandonments: Optimal Policies and Practical Heuristics", "abstract": "We investigate the optimal pricing strategy in a service-providing framework, where customers can leave the system prior to service completion. In this setting, a price is quoted to an incoming customer based on the current number of customers in the system. When the quoted price is lower than the price the incoming customer is willing to pay (which follows a fixed probability distribution), then the customer joins the system and a reward equal to the quoted price is earned. A cost is incurred upon abandonment and a holding cost is incurred for customers waiting to be served. Our goal is to determine the pricing policy that maximizes the long-run average profit. Unlike traditional queueing systems without abandonments, we show that the optimal quoted prices do not always increase with the queue length in this setting. We fully characterize the possible structure of the optimal dynamic pricing policy and provide conditions guaranteeing that the optimal policy is increasing in the number of customers in the system. Moreover, we introduce two heuristics that simplify the optimal dynamic pricing policy. Both heuristics admit customers until the number of customers in the system reaches a certain threshold. The cutoff-static policy charges all admitted customers a fixed price while the two-price policy charges one price when the arriving customer can enter service immediately and another price if the customer needs to wait. By selecting the price(s) and threshold that maximize the long-run average profit, both heuristics achieve near optimality in general and the two-price policy provides more robustness compared to the cutoff-static policy.", "published": "2025-05-15T17:20:28Z", "authors": ["Jieqi Di", "Sigrún Andradóttir", "Hayriye Ayhan"], "categories": ["math.OC"], "doi": "", "_query": "hedonic house price", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/1808.04701v2", "title": "Measuring Market Performance with Stochastic Demand: Price of Anarchy and Price of Uncertainty", "abstract": "Globally operating suppliers face the rising challenge of wholesale pricing under scarce data about retail demand, in contrast to better informed, locally operating retailers. At the same time, as local businesses proliferate, markets congest and retail competition increases. To capture these strategic considerations, we employ the classic Cournot model and extend it to a two-stage supply chain with an upstream supplier who operates under demand uncertainty and multiple downstream retailers who compete over quantity. The supplier's belief about retail demand is modeled via a continuous probability distribution function F. If F has the decreasing generalized mean residual life property, then the supplier's optimal pricing policy exists and is the unique fixed point of the mean residual life function. We evaluate the realized Price of Uncertainty and show that there exist demand levels for which market performs better when the supplier prices under demand uncertainty. In general, performance worsens for lower values of realized demand. We examine the effects of increasing competition on supply chain efficiency via the realized Price of Anarchy and complement our findings with numerical results.", "published": "2018-08-11T07:00:55Z", "authors": ["Costis Melolidakis", "Stefanos Leonardos", "Constandina Koki"], "categories": ["cs.GT"], "doi": "10.1007/978-3-319-99383-6_21", "_query": "hedonic house price", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/0906.4838v1", "title": "Forecasting Model for Crude Oil Price Using Artificial Neural Networks and Commodity Futures Prices", "abstract": "This paper presents a model based on multilayer feedforward neural network to forecast crude oil spot price direction in the short-term, up to three days ahead. A great deal of attention was paid on finding the optimal ANN model structure. In addition, several methods of data pre-processing were tested. Our approach is to create a benchmark based on lagged value of pre-processed spot price, then add pre-processed futures prices for 1, 2, 3,and four months to maturity, one by one and also altogether. The results on the benchmark suggest that a dynamic model of 13 lags is the optimal to forecast spot price direction for the short-term. Further, the forecast accuracy of the direction of the market was 78%, 66%, and 53% for one, two, and three days in future conclusively. For all the experiments, that include futures data as an input, the results show that on the short-term, futures prices do hold new information on the spot price direction. The results obtained will generate comprehensive understanding of the crude oil dynamic which help investors and individuals for risk managements.", "published": "2009-06-26T04:25:12Z", "authors": ["Siddhivinayak Kulkarni", "Imad Haidar"], "categories": ["cs.NE", "q-fin.PM"], "doi": "", "_query": "hedonic house price", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2508.20539v6", "title": "Quality at Stale Prices", "abstract": "We study a continuous-time game in which a firm privately chooses quality and a public-information pricing desk revises its posted price at Poisson opportunities. Customers learn from purchases and silence. For an open set of primitives, we construct an equilibrium in which a previously optimal price sustains quality even though the desk would lower it upon revision. Quality costs exceed every immediate sales return, making the continuation reward necessary. We characterise both the resulting quality episodes and their lifetime incidence. A purchase ends an episode by raising reputation, a revision by changing the price, and silence by lowering reputation. Near the zero-quality threshold, the quality probability has a limiting parabolic profile and most episodes end through silence. If $δ$ is the cost shortfall below that threshold, the expected lifetime number of high-quality decisions and the probability of any such decision are both of order $δ^{3/2}$ from a specified upstream state. The persistent type is eventually learned, strategic quality ceases, and prices settle at type-specific levels. The construction is local at fixed finite discounting.", "published": "2025-08-28T08:28:30Z", "authors": ["Georgy Lukyanov", "Konstantin Shamruk", "Ekaterina Logina"], "categories": ["econ.TH"], "doi": "", "_query": "hedonic house price", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2009.11007v1", "title": "Pricing Cryptocurrency Options", "abstract": "Cryptocurrencies, especially Bitcoin (BTC), which comprise a new digital asset class, have drawn extraordinary worldwide attention. The characteristics of the cryptocurrency/BTC include a high level of speculation, extreme volatility and price discontinuity. We propose a pricing mechanism based on a stochastic volatility with a correlated jump (SVCJ) model and compare it to a flexible co-jump model by Bandi and Renò (2016). The estimation results of both models confirm the impact of jumps and co-jumps on options obtained via simulation and an analysis of the implied volatility curve. We show that a sizeable proportion of price jumps are significantly and contemporaneously anti-correlated with jumps in volatility. Our study comprises pioneering research on pricing BTC options. We show how the proposed pricing mechanism underlines the importance of jumps in cryptocurrency markets.", "published": "2020-09-23T09:04:17Z", "authors": ["Ai Jun Hou", "Weining Wang", "Cathy Y. H. Chen", "Wolfgang Karl Härdle"], "categories": ["q-fin.ST"], "doi": "10.1093/jjfinec/nbaa006", "_query": "hedonic house price", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2205.07256v5", "title": "Market-Based Asset Price Probability", "abstract": "The random values and volumes of consecutive trades made at the exchange with shares of security determine its mean, variance, and higher statistical moments. The volume weighted average price (VWAP) is the simplest example of such a dependence. We derive the dependence of the market-based variance and 3rd statistical moment of prices on the means, variances, covariances, and 3rd moments of the values and volumes of market trades. The usual frequency-based assessments of statistical moments of prices are the limited case of market-based statistical moments if we assume that all volumes of consecutive trades with security are constant during the averaging interval. To forecast market-based variance of price, one should predict the first two statistical moments and the correlation of values and volumes of consecutive trades at the same horizon. We explain how that limits the number of predicted statistical moments of prices by the first two and the accuracy of the forecasts of the price probability by the Gaussian distribution. This limitation also reduces the reliability of Value-at-Risk by Gaussian approximation. The accounting for the randomness of trade volumes and the use of VWAP results in zero price-volume correlations. To study the price-volume empirical statistical dependence, one should calculate correlations of prices and squares of trade volumes or correlations of squares of prices and volumes. To improve the accuracy and reliability of large macroeconomic and market models like those developed by BlackRock's Aladdin, JP Morgan, and the U.S. Fed., the developers should explicitly account for the impact of random trade volumes and use market-based statistical moments of asset prices.", "published": "2022-05-15T10:50:06Z", "authors": ["Victor Olkhov"], "categories": ["econ.GN", "q-fin.GN", "q-fin.PR"], "doi": "", "_query": "hedonic house price", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2206.01468v7", "title": "Multi-Asset Bubbles Equilibrium Price Dynamics", "abstract": "The price-bubble and crash process formation is theoretically investigated in a two-asset equilibrium model. Sufficient and necessary conditions are derived for the existence of average equilibrium price dynamics of different agent-based models, where agents are distinguished in terms of factor and investment trading strategies. In line with experimental results, we show that assets with a positive average dividend, i.e., with a strictly declining fundamental value, display at the equilibrium price the typical hump-shaped bubble observed in experimental asset markets. Moreover, a misvaluation effect is observed in the asset with a constant fundamental value, triggered by the other asset that displays the price bubble shape when a sharp price decline is exhibited at the end of the market.", "published": "2022-06-03T09:33:56Z", "authors": ["Francesco Cordoni"], "categories": ["q-fin.TR", "econ.GN"], "doi": "", "_query": "hedonic house price", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/1709.00468v1", "title": "An Option Pricing Model with Memory", "abstract": "We obtain option pricing formulas for stock price models in which the drift and volatility terms are functionals of a continuous history of the stock prices. That is, the stock dynamics follows a nonlinear stochastic functional differential equation. A model with full memory is obtained via approximation through a stock price model in which the continuous path dependence does not go up to the present: there is a memory gap. A strong solution is obtained by closing the gap. Fair option prices are obtained through an equivalent (local) martingale measure via Girsanov's Theorem and therefore are given in terms of a conditional expectation. The models maintain the completeness of the market and have no arbitrage opportunities.", "published": "2017-09-01T20:21:55Z", "authors": ["Flavia Sancier", "Salah Mohammed"], "categories": ["q-fin.PR"], "doi": "10.1002/ijop.12622", "_query": "hedonic house price", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/1408.6065v2", "title": "Shadow prices for continuous processes", "abstract": "In a financial market with a continuous price process and proportional transaction costs we investigate the problem of utility maximization of terminal wealth. We give sufficient conditions for the existence of a shadow price process, i.e.~a least favorable frictionless market leading to the same optimal strategy and utility as in the original market under transaction costs. The crucial ingredients are the continuity of the price process and the hypothesis of \"no unbounded profit with bounded risk\". A counter-example reveals that these hypotheses cannot be relaxed.", "published": "2014-08-26T10:26:31Z", "authors": ["Christoph Czichowsky", "Walter Schachermayer", "Junjian Yang"], "categories": ["q-fin.PM", "math.OC"], "doi": "", "_query": "hedonic house price", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/cond-mat/0103273v1", "title": "Energy price risk management", "abstract": "The price of electricity is far more volatile than that of other commodities normally noted for extreme volatility. Demand and supply are balanced on a knife-edge because electric power cannot be economically stored, end user demand is largely weather dependent, and the reliability of the grid is paramount. The possibility of extreme price movements increases the risk of trading in electricity markets. However, a number of standard financial tools cannot be readily applied to pricing and hedging electricity derivatives. In this paper we present arguments why this is the case.", "published": "2001-03-13T14:11:03Z", "authors": ["Rafal Weron"], "categories": ["cond-mat"], "doi": "10.1016/S0378-4371(00)00276-4", "_query": "hedonic house price", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2404.07684v6", "title": "Merger Analysis with Unobserved Prices", "abstract": "Standard empirical tools for merger analysis assume price data, which are often unavailable. I characterize sufficient conditions for identifying the unilateral effects of mergers without price data using the first-order approach and merger simulation. Data on merging firms' revenues, margins, and revenue diversion ratios are sufficient to identify their gross upward pricing pressure indices and compensating marginal cost reductions. Standard discrete-continuous demand assumptions facilitate the identification of revenue diversion ratios as well as the feasibility of merger simulation in terms of percentage change in price. I apply the framework to the Albertsons/Safeway (2015) and Staples/Office Depot (2016) mergers.", "published": "2024-04-11T12:23:57Z", "authors": ["Paul S. Koh"], "categories": ["econ.EM"], "doi": "", "_query": "hedonic house price", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2412.02148v1", "title": "Mining Tweets to Predict Future Bitcoin Price", "abstract": "Bitcoin has increased investment interests in people during the last decade. We have seen an increase in the number of posts on social media platforms about cryptocurrency, especially Bitcoin. This project focuses on analyzing user tweet data in combination with Bitcoin price data to see the relevance between price fluctuations and the conversation between millions of people on Twitter. This study also exploits this relationship between user tweets and bitcoin prices to predict the future bitcoin price. We are utilizing novel techniques and methods to analyze the data and make price predictions.", "published": "2024-12-03T04:09:19Z", "authors": ["Ashutosh Hathidara", "Gaurav Atavale", "Suyash Chaudhary"], "categories": ["cs.AI"], "doi": "", "_query": "hedonic house price", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/1403.2990v1", "title": "A Framework for Pricing Schemes for Networks under Complete Information", "abstract": "The revenue maximization problem of service provider is considered and different pricing schemes to solve the above problem are implemented. The service provider can choose an apt pricing scheme subjected to limited resources, if he knows the utility function and identity of the user. The complete price differentiation can achieve a large revenue gain but has high implementation complexity. The partial price differentiation scheme to overcome the high implementational complexity of complete price differentiation scheme is also studied. A polynomial- time algorithm is designed for partial price differentiation scheme that can compute the optimal partial differentiation prices. The willingness of the users to pay is also considered while designing price differentiation schemes.", "published": "2014-03-12T15:53:41Z", "authors": ["Aditya Goyal", "Akanksha Tyagi", "Manisha Bhende", "Swapnil Kawade"], "categories": ["cs.NI"], "doi": "10.14445/22315381/IJETT-V9P214", "_query": "hedonic house price", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/1901.01927v1", "title": "Price-Coupling Games and the Generation Expansion Planning Problem", "abstract": "In this paper, we introduce and study a class of games called price-coupling games that arise in many scenarios, especially in the electricity industry. In a price-coupling game, there is a part of the objective function of a player which has an identical form for all players and there is coupling in the cost functions of players through a price which is determined uniformly for all players by an independent entity called the \\textit{price-determining player} (e.g. independent system operator (ISO) in an electricity market). This price appears in the objective function only in the part which is identical for all players. We study the existence of equilibria in such games under two broad categories, namely \\textit{price-anticipative} and \\textit{price-taking} formulations. In the price-anticipative formulation, the players anticipate the price and make their decisions while in the price-taking formulation, the players make their decisions considering the price as a given parameter. We model the price-anticipative case as a leader-follower formulation where the players (leaders) conjecture the price (follower's decision) and make their decision. The price-taking formulation is modelled as an $N+1$ player game with the additional player as the price-determining player. The existence of an equilibrium in such games are not easy mainly because of the coupled-constraint structure of the game and the non-convexity of the action set. We give conditions for the existence of equilibria in both formulations. We apply our results to analyze the existence of an equilibrium in the generation expansion planning problem using the above results.", "published": "2019-01-07T17:15:46Z", "authors": ["Mathew P. Abraham", "Ankur A. Kulkarni"], "categories": ["math.OC", "cs.GT"], "doi": "", "_query": "hedonic house price", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2308.13642v1", "title": "The Potential of Quantum Techniques for Stock Price Prediction", "abstract": "We explored the potential applications of various Quantum Algorithms for stock price prediction by conducting a series of experimental simulations using both Classical as well as Quantum Hardware. Firstly, we extracted various stock price indicators, such as Moving Averages (MA), Average True Range (ATR), and Aroon, to gain insights into market trends and stock price movements. Next, we employed Quantum Annealing (QA) for feature selection and Principal Component Analysis (PCA) for dimensionality reduction. Further, we transformed the stock price prediction task essentially into a classification problem. We trained the Quantum Support Vector Machine (QSVM) to predict price movements (whether up or down) contrasted their performance with classical models and analyzed their accuracy on a dataset formulated using Quantum Annealing and PCA individually. We focused on the stock price prediction and binary classification of stock prices for four different companies, namely Apple, Visa, Johnson and Jonson, and Honeywell. We primarily used the real-time stock data of the raw stock prices of these companies. We compared various Quantum Computing techniques with their classical counterparts in terms of accuracy and F-score of the prediction model. Through these experimental simulations, we shed light on the potential advantages and limitations of Quantum Algorithms in stock price prediction and contribute to the growing body of knowledge at the intersection of Quantum Computing and Finance.", "published": "2023-08-25T19:26:41Z", "authors": ["Naman S", "Gaurang B", "Neel S", "Aswath Babu H"], "categories": ["q-fin.CP"], "doi": "", "_query": "hedonic house price", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/1407.7315v1", "title": "Effective and simple VWAP option pricing model", "abstract": "Volume weighted average price (VWAP) options are a popular security type in many countries, but despite their popularity very few pricing models have been developed so far for VWAP options. This can be explained by the fact that the VWAP pricing problem is set in an incomplete market since there is no underlying with which to hedge the volume risk, and hence there is no uniquely defined price. Any price, which is obtained will include a market price of volume risk which must be determined from the corresponding volume statistics. Our analysis strongly supports the hypothesis that the empirical volume statistics of ASX equities can be described reasonably well by fitted gamma distributions. Based on this observation we suggest a simple gamma process-based model that allows for the exact analytic pricing of VWAP options in a rather straightforward way.", "published": "2014-07-28T04:00:08Z", "authors": ["Alexander Buryak", "Ivan Guo"], "categories": ["q-fin.PR"], "doi": "", "_query": "hedonic house price", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/1812.01270v1", "title": "An Optimal Extraction Problem with Price Impact", "abstract": "A price-maker company extracts an exhaustible commodity from a reservoir, and sells it instantaneously in the spot market. In absence of any actions of the company, the commodity's spot price evolves either as a drifted Brownian motion or as an Ornstein-Uhlenbeck process. While extracting, the company affects the market price of the commodity, and its actions have an impact on the dynamics of the commodity's spot price. The company aims at maximizing the total expected profits from selling the commodity, net of the total expected proportional costs of extraction. We model this problem as a two-dimensional degenerate singular stochastic control problem with finite fuel. To determine its solution, we construct an explicit solution to the associated Hamilton-Jacobi-Bellman equation, and then verify its actual optimality through a verification theorem. On the one hand, when the (uncontrolled) price is a drifted Brownian motion, it is optimal to extract whenever the current price level is larger or equal than an endogenously determined constant threshold. On the other hand, when the (uncontrolled) price evolves as an Ornstein-Uhlenbeck process, we show that the optimal extraction rule is triggered by a curve depending on the current level of the reservoir. Such a curve is a strictly decreasing $C^{\\infty}$-function for which we are able to provide an explicit expression. Finally, our study is complemented by a theoretical and numerical analysis of the dependency of the optimal extraction strategy and value function on the model's parameters.", "published": "2018-12-04T08:22:14Z", "authors": ["Giorgio Ferrari", "Torben Koch"], "categories": ["math.OC", "q-fin.MF"], "doi": "", "_query": "hedonic house price", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2111.03158v2", "title": "Pricing Query Complexity of Revenue Maximization", "abstract": "The common way to optimize auction and pricing systems is to set aside a small fraction of the traffic to run experiments. This leads to the question: how can we learn the most with the smallest amount of data? For truthful auctions, this is the \\emph{sample complexity} problem. For posted price auctions, we no longer have access to samples. Instead, the algorithm is allowed to choose a price $p_t$; then for a fresh sample $v_t \\sim \\mathcal{D}$ we learn the sign $s_t = sign(p_t - v_t) \\in \\{-1,+1\\}$. How many pricing queries are needed to estimate a given parameter of the underlying distribution? We give tight upper and lower bounds on the number of pricing queries required to find an approximately optimal reserve price for general, regular and MHR distributions. Interestingly, for regular distributions, the pricing query and sample complexities match. But for general and MHR distributions, we show a strict separation between them. All known results on sample complexity for revenue optimization follow from a variant of using the optimal reserve price of the empirical distribution. In the pricing query complexity setting, we show that learning the entire distribution within an error of $ε$ in Levy distance requires strictly more pricing queries than to estimate the reserve. Instead, our algorithm uses a new property we identify called \\emph{relative flatness} to quickly zoom into the right region of the distribution to get the optimal pricing query complexity.", "published": "2021-11-04T20:57:28Z", "authors": ["Renato Paes Leme", "Balasubramanian Sivan", "Yifeng Teng", "Pratik Worah"], "categories": ["cs.GT"], "doi": "", "_query": "hedonic house price", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2103.05611v2", "title": "Optimal Pricing with a Single Point", "abstract": "We study the following fundamental data-driven pricing problem. How can/should a decision-maker price its product based on data at a single historical price? How valuable is such data? We consider a decision-maker who optimizes over (potentially randomized) pricing policies to maximize the worst-case ratio of the revenue she can garner compared to an oracle with full knowledge of the distribution of values, when the latter is only assumed to belong to a broad non-parametric set. In particular, our framework applies to the widely used regular and monotone non-decreasing hazard rate (mhr) classes of distributions. For settings where the seller knows the exact probability of sale associated with one historical price or only a confidence interval for it, we fully characterize optimal performance and near-optimal pricing algorithms that adjust to the information at hand. The framework we develop is general and allows to characterize optimal performance for deterministic or more general randomized mechanisms, and leads to fundamental novel insights on the value of data for pricing. As examples, against mhr distributions, we show that it is possible to guarantee $85\\%$ of oracle performance if one knows that half of the customers have bought at the historical price, and if only $1\\%$ of the customers bought, it still possible to guarantee $51\\%$ of oracle performance.", "published": "2021-03-09T18:34:06Z", "authors": ["Amine Allouah", "Achraf Bahamou", "Omar Besbes"], "categories": ["cs.GT", "cs.IT"], "doi": "", "_query": "hedonic house price", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/1606.09376v1", "title": "The Design of Arbitrage-Free Data Pricing Schemes", "abstract": "Motivated by a growing market that involves buying and selling data over the web, we study pricing schemes that assign value to queries issued over a database. Previous work studied pricing mechanisms that compute the price of a query by extending a data seller's explicit prices on certain queries, or investigated the properties that a pricing function should exhibit without detailing a generic construction. In this work, we present a formal framework for pricing queries over data that allows the construction of general families of pricing functions, with the main goal of avoiding arbitrage. We consider two types of pricing schemes: instance-independent schemes, where the price depends only on the structure of the query, and answer-dependent schemes, where the price also depends on the query output. Our main result is a complete characterization of the structure of pricing functions in both settings, by relating it to properties of a function over a lattice. We use our characterization, together with information-theoretic methods, to construct a variety of arbitrage-free pricing functions. Finally, we discuss various tradeoffs in the design space and present techniques for efficient computation of the proposed pricing functions.", "published": "2016-06-30T07:44:18Z", "authors": ["Shaleen Deep", "Paraschos Koutris"], "categories": ["cs.DB", "cs.GT"], "doi": "", "_query": "hedonic house price", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2605.13320v1", "title": "The fine structure of electricity price volatility", "abstract": "We conduct the first rigorous study of electricity price volatility for the full panel of electricity prices across three European generation zones. By interpreting the observed day-ahead prices as local averages of a latent price process governed by a stochastic partial differential equation, we develop estimators of the weekly integrated variance. The inherently infinite dimensional setting introduce several complications that are not relevant in the conventional finite dimensional semimartingale setting, and we spend considerable effort in dealing with these. In particular, we must account for both mean-reversion in prices and semigroup-smoothing in the estimated variance. We provide a detailed decomposition and interpretation of the empirical estimates across three vastly different European generation zones, namely Germany, Norway, and Spain. Our findings indicate that each zone has very different drivers of volatility, and that the impact of generation variables differs considerably. We document that leverage effects appear to be present at first sight, but disappear once we condition on suitable state variables, thereby showing that electricity price volatility does not generally exhibit asymmetric responses to price shocks.", "published": "2026-05-13T10:34:08Z", "authors": ["Thomas K. Kloster", "Fred Espen Benth"], "categories": ["q-fin.GN", "econ.EM"], "doi": "", "_query": "hedonic house price", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/1308.6759v3", "title": "Prospect Agents and the Feedback Effect on Price Fluctuations", "abstract": "A microeconomic approach is proposed to derive the fluctuations of risky asset price, where the market participants are modeled as prospect trading agents. As asset price is generated by the temporary equilibrium between demand and supply, the agents' trading behaviors can affect the price process in turn, which is called the feedback effect. The prospect agents make actions based on their reactions to gains and losses, and as a consequence of the feedback effect, a relationship between the agents' trading behavior and the price fluctuations is constructed, which explains the implied volatility skew and smile observed in actual market.", "published": "2013-08-30T14:42:28Z", "authors": ["Yipeng Yang", "Allanus Tsoi"], "categories": ["q-fin.PR"], "doi": "", "_query": "hedonic house price", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2112.06430v1", "title": "Predicting Airbnb Rental Prices Using Multiple Feature Modalities", "abstract": "Figuring out the price of a listed Airbnb rental is an important and difficult task for both the host and the customer. For the former, it can enable them to set a reasonable price without compromising on their profits. For the customer, it helps understand the key drivers for price and also provides them with similarly priced places. This price prediction regression task can also have multiple downstream uses, such as in recommendation of similar rentals based on price. We propose to use geolocation, temporal, visual and natural language features to create a reliable and accurate price prediction algorithm.", "published": "2021-12-13T05:57:05Z", "authors": ["Aditya Ahuja", "Aditya Lahiri", "Aniruddha Das"], "categories": ["cs.LG"], "doi": "", "_query": "hedonic house price", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/1707.01284v1", "title": "The Bitcoin price formation: Beyond the fundamental sources", "abstract": "Much significant research has been done to investigate various facets of the link between Bitcoin price and its fundamental sources. This study goes beyond by looking into least to most influential factors-across the fundamental, macroeconomic, financial, speculative and technical determinants as well as the 2016 events-which drove the value of Bitcoin in times of economic and geopolitical chaos. We use a Bayesian quantile regression to inspect how the structure of dependence of Bitcoin price and its determinants varies across the entire conditional distribution of Bitcoin price movements. In doing so, three groups of determinants were derived. The use of Bitcoin in trade and the uncertainty surrounding China's deepening slowdown, Brexit and India's demonetization were found to be the most potential contributors of Bitcoin price when the market is improving. The intense anxiety over Donald Trump being the president of United States was shown to be a positive determinant pushing up the price of Bitcoin when the market is functioning around the normal mode. The velocity of bitcoins in circulation, the gold price, the Venezuelan currency demonetization and the hash rate were found to be the fundamentals influencing the Bitcoin price when the market is heading into decline.", "published": "2017-07-05T09:39:04Z", "authors": ["Jamal Bouoiyour", "Refk Selmi"], "categories": ["q-fin.CP"], "doi": "", "_query": "hedonic house price", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/1304.2141v1", "title": "Robust price bounds for the forward starting straddle", "abstract": "In this article we consider the problem of giving a robust, model-independent, lower bound on the price of a forward starting straddle with payoff $|F_{T_1} - F_{T_0}|$ where $0<T_0<T_1$. Rather than assuming a model for the underlying forward price $(F_t)_{t \\geq 0}$, we assume that call prices for maturities $T_0<T_1$ are given and hence that the marginal laws of the underlying are known. The primal problem is to find the model which is consistent with the observed call prices, and for which the price of the forward starting straddle is minimised. The dual problem is to find the cheapest semi-static subhedge. Under an assumption on the supports of the marginal laws, but no assumption that the laws are atom-free or in any other way regular, we derive explicit expressions for the coupling which minimises the price of the option, and the form of the semi-static subhedge.", "published": "2013-04-08T09:23:27Z", "authors": ["David Hobson", "Martin Klimmek"], "categories": ["q-fin.PR", "math.OC", "math.PR"], "doi": "", "_query": "hedonic house price", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/1909.12227v1", "title": "Stock Prices Prediction using Deep Learning Models", "abstract": "Financial markets have a vital role in the development of modern society. They allow the deployment of economic resources. Changes in stock prices reflect changes in the market. In this study, we focus on predicting stock prices by deep learning model. This is a challenge task, because there is much noise and uncertainty in information that is related to stock prices. So this work uses sparse autoencoders with one-dimension (1-D) residual convolutional networks which is a deep learning model, to de-noise the data. Long-short term memory (LSTM) is then used to predict the stock price. The prices, indices and macroeconomic variables in past are the features used to predict the next day's price. Experiment results show that 1-D residual convolutional networks can de-noise data and extract deep features better than a model that combines wavelet transforms (WT) and stacked autoencoders (SAEs). In addition, we compare the performances of model with two different forecast targets of stock price: absolute stock price and price rate of change. The results show that predicting stock price through price rate of change is better than predicting absolute prices directly.", "published": "2019-09-25T11:38:25Z", "authors": ["Jialin Liu", "Fei Chao", "Yu-Chen Lin", "Chih-Min Lin"], "categories": ["cs.LG"], "doi": "", "_query": "hedonic house price", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/cond-mat/0101175v1", "title": "Coarse-graining and Self-similarity of Price Fluctuations", "abstract": "We propose a new approach for analyzing price fluctuations in their strongly correlated regime ranging from minutes to months. This is done by employing a self-similarity assumption for the magnitude of coarse-grained price fluctuation or volatility. The existence of a Cramer function, the characteristic function for self-similarity, is confirmed by analyzing real price data from a stock market. We also discuss the close interrelation among our approach, the scaling-of-moments method and the multifractal approach for price fluctuations.", "published": "2001-01-12T07:58:52Z", "authors": ["Yoshi Fujiwara", "Hirokazu Fujisaka"], "categories": ["cond-mat.stat-mech", "q-fin.ST"], "doi": "10.1016/S0378-4371(01)00135-2", "_query": "hedonic house price", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2303.02576v3", "title": "Interpreting and Countering Collusion in Deep-Learning Pricing Algorithms", "abstract": "Algorithmic pricing raises a question of interpretation as well as intervention: when autonomous deep-learning pricing systems sustain supracompetitive prices, what strategic pattern have they learned, and how might market institutions alter it? This paper develops an interpretable framework for studying learned collusion in repeated pricing environments. The framework embeds strategic deep learning networks in a differentiated-products Bertrand market and compresses recent price histories into finite states that record price levels, rival price movements, and movement persistence. This state representation preserves the dynamic information relevant for reward and punishment while making learned behavior economically interpretable. In the baseline environment, agents learn supracompetitive prices and exhibit a coherent collusive asymmetry: they punish rival price cuts and accommodate rival price increases. The paper then uses this framework to study an order-book mechanism that assembles temporary buyer commitments and allocates them to sellers willing to make sufficiently deep undercuts, partially insulating those undercutters from retaliatory punishment. The mechanism lowers realized prices in the main symmetric-cost design and remains effective in the main robustness exercises. Further analysis shows that this price reduction operates through the intended channel: qualifying undercuts become less exposed to subsequent punishment, reducing the continuation loss that sustains high-price states. The results show how interpretable learning frameworks can connect algorithmic pricing outcomes to economic mechanisms, and how market design can target the enforcement channel behind learned collusion.", "published": "2023-03-05T04:11:28Z", "authors": ["Soumen Banerjee"], "categories": ["econ.TH"], "doi": "", "_query": "hedonic house price", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/1704.04354v1", "title": "An empirical behavioural order-driven model with price limit rules", "abstract": "We develop an empirical behavioural order-driven (EBOD) model, which consists of an order placement process and an order cancellation process. Price limit rules are introduced in the definition of relative price. The order placement process is determined by several empirical regularities: the long memory in order directions, the long memory in relative prices, the asymmetric distribution of relative prices, and the nonlinear dependence of the average order size and its standard deviation on the relative price. Order cancellation follows a Poisson process with the arrival rate determined from real data and the cancelled order is determined according to the empirical distributions of relative price level and relative position at the same price level. All these ingredients of the model are derived based on the empirical microscopic regularities in the order flows of stocks on the Shenzhen Stock Exchange. The model is able to produce the main stylized facts in real markets. Computational experiments uncover that asymmetric setting of price limits will cause the stock price diverging exponentially when the up price limit is higher than the down price limit and vanishing vice versus. We also find that asymmetric price limits have influences on stylized facts. Our EBOD model provides a suitable computational experiment platform for academics, market participants and policy makers.", "published": "2017-04-14T05:53:35Z", "authors": ["Gao-Feng Gu", "Xiong Xiong", "Hai-Chuan Xu", "Wei Zhang", "Yong-Jie Zhang", "Wei Chen", "Wei-Xing Zhou"], "categories": ["q-fin.CP", "q-fin.TR"], "doi": "10.1186/s40854-021-00288-4", "_query": "hedonic house price", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/hep-ph/9808275v1", "title": "Haggling over the fine-tuning price", "abstract": "We amplify previous discussions of the fine-tuning price to be paid by supersymmetric models in the light of LEP data, especially the lower bound on the Higgs boson mass, studying in particular its power of discrimination between different parameter regions and different theoretical assumptions. The analysis is performed using the full one-loop effective potential. The whole range of $\\tanβ$ is discussed, including large values. In the minimal supergravity model with universal gaugino and scalar masses, a small fine-tuning price is possible only for intermediate values of $\\tanβ$. However, the fine-tuning price in this region is significantly higher if we require $b-τ$ Yukawa-coupling unification. On the other hand, price reductions are obtained if some theoretical relation between MSSM parameters is assumed, in particular between $μ_0$, $M_{1/2}$ and $A_0$. Significant price reductions are obtained for large $\\tanβ$ if non-universal soft Higgs mass parameters are allowed. Nevertheless, in all these cases, the requirement of small fine tuning remains an important constraint on the superpartner spectrum. We also study input relations between MSSM parameters suggested in some interpretations of string theory: the price may depend significantly on these inputs, potentially providing guidance for building string models. However, in the available models the fine-tuning price may not be reduced significantly.", "published": "1998-08-10T14:49:02Z", "authors": ["P. H. Chankowski", "J. Ellis", "M. Olechowski", "S. Pokorski"], "categories": ["hep-ph"], "doi": "10.1016/S0550-3213(99)00025-5", "_query": "hedonic house price", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2205.12726v8", "title": "The Quantum-House Effect: Filling the Gap Between Classicality and Quantum Discord", "abstract": "We introduce the quantum-house effect, a non-local phenomenon which apparently does not require quantum discord to be present. It suffices for the effect if neither subsystem of a bipartite system is in a pure state. This way, the quantum-house effect completely fills the gap between trivial correlations and quantum discord. However, we discuss why the situation is more subtle than that, by showing that in a concrete cryptographic setting called \"the quantum-house game\", the ability to produce quantum discord is in fact necessary for the quantum-house effect to work. Then, we suggest a principle called \"quantum detachment\" to characterize where quantumness in general departs from classicality, based on the information a physical system contains about itself. The quantum-house effect is demonstrated on SpinQ Gemini, a 2-qubit liquid-state NMR desktop quantum computer.", "published": "2022-05-24T08:55:31Z", "authors": ["Tamás Varga"], "categories": ["quant-ph"], "doi": "", "_query": "hedonic house price", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/1809.09466v1", "title": "Derivatives pricing using signature payoffs", "abstract": "We introduce signature payoffs, a family of path-dependent derivatives that are given in terms of the signature of the price path of the underlying asset. We show that these derivatives are dense in the space of continuous payoffs, a result that is exploited to quickly price arbitrary continuous payoffs. This approach to pricing derivatives is then tested with European options, American options, Asian options, lookback options and variance swaps. As we show, signature payoffs can be used to price these derivatives with very high accuracy.", "published": "2018-09-25T13:30:12Z", "authors": ["Imanol Perez Arribas"], "categories": ["q-fin.CP", "q-fin.MF", "q-fin.PR"], "doi": "", "_query": "hedonic house price", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/1605.05422v2", "title": "Optimization Beyond Prediction: Prescriptive Price Optimization", "abstract": "This paper addresses a novel data science problem, prescriptive price optimization, which derives the optimal price strategy to maximize future profit/revenue on the basis of massive predictive formulas produced by machine learning. The prescriptive price optimization first builds sales forecast formulas of multiple products, on the basis of historical data, which reveal complex relationships between sales and prices, such as price elasticity of demand and cannibalization. Then, it constructs a mathematical optimization problem on the basis of those predictive formulas. We present that the optimization problem can be formulated as an instance of binary quadratic programming (BQP). Although BQP problems are NP-hard in general and computationally intractable, we propose a fast approximation algorithm using a semi-definite programming (SDP) relaxation, which is closely related to the Goemans-Williamson's Max-Cut approximation. Our experiments on simulation and real retail datasets show that our prescriptive price optimization simultaneously derives the optimal prices of tens/hundreds products with practical computational time, that potentially improve 8.2% of gross profit of those products.", "published": "2016-05-18T02:46:14Z", "authors": ["Shinji Ito", "Ryohei Fujimaki"], "categories": ["math.OC", "cs.LG", "stat.ML"], "doi": "", "_query": "hedonic house price", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2601.13896v1", "title": "From geometry to sustainability: Optimal shapes of hip roof houses", "abstract": "In this paper, we develop a rigorous mathematical framework for the optimization of hip roof house geometry, with the primary goal of minimizing the external surface of the building envelope for a given set of design constraints. Five optimization scenarios are systematically analyzed: fixed volume, fixed footprint ratio, fixed slenderness ratio, fixed floor area, and constrained height. For each case, explicit formulas for the optimal dimensions are derived, offering architects and engineers practical guidelines for improving material efficiency, reducing construction costs, and enhancing energy performance. To illustrate the practical relevance of the theoretical results, case studies of real-world hip roof houses are presented, revealing both inefficiencies in common practice and near-optimal examples. Furthermore, a freely available software application has been developed to support designers in applying the optimization methods directly to architectural projects. The findings confirm that square-based footprints combined with balanced slenderness ratios yield the most efficient forms, while deviations toward elongated or flattened proportions significantly increase energy and material demands. This work demonstrates how mathematical modeling and architectural design can be integrated to support sustainable architecture, providing both theoretical insight and practical tools for shaping energy-efficient, cost-effective, and aesthetically coherent residential buildings.", "published": "2026-01-20T12:26:35Z", "authors": ["Ewa Rokita-Magdziarz", "Barbara Gronostajska", "Marcin Magdziarz"], "categories": ["math.OC"], "doi": "", "_query": "hedonic house price", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/1607.00454v2", "title": "Limit order trading with a mean reverting reference price", "abstract": "Optimal control models for limit order trading often assume that the underlying asset price is a Brownian motion since they deal with relatively short time scales. The resulting optimal bid and ask limit order prices tend to track the underlying price as one might expect. This is indeed the case with the model of Avellaneda and Stoikov (2008), which has been studied extensively. We consider here this model under the condition when the underlying price is mean reverting. Our main result is that when time is far from the terminal, the optimal price for bid and ask limit orders is constant, which means that it does not track the underlying price. Numerical simulations confirm this behavior. When the underlying price is mean reverting, then for times sufficiently far from terminal, it is more advantageous to focus on the mean price and ignore fluctuations around it. Mean reversion suggests that limit orders will be executed with some regularity, and this is why they are optimal. We also explore intermediate time regimes where limit order prices are influenced by the inventory of outstanding orders. The duration of this intermediate regime depends on the liquidity of the market as measured by specific parameters in the model.", "published": "2016-07-02T02:44:37Z", "authors": ["Saran Ahuja", "George Papanicolaou", "Weiluo Ren", "Tzu-Wei Yang"], "categories": ["q-fin.TR"], "doi": "", "_query": "hedonic house price", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2107.03801v1", "title": "Pareto optimal and popular house allocation with lower and upper quotas", "abstract": "In the house allocation problem with lower and upper quotas, we are given a set of applicants and a set of projects. Each applicant has a strictly ordered preference list over the projects, while the projects are equipped with a lower and an upper quota. A feasible matching assigns the applicants to the projects in such a way that a project is either matched to no applicant or to a number of applicants between its lower and upper quota. In this model we study two classic optimality concepts: Pareto optimality and popularity. We show that finding a popular matching is hard even if the maximum lower quota is 2 and that finding a perfect Pareto optimal matching, verifying Pareto optimality, and verifying popularity are all NP-complete even if the maximum lower quota is 3. We complement the last three negative results by showing that the problems become polynomial-time solvable when the maximum lower quota is 2, thereby answering two open questions of Cechlárová and Fleiner. Finally, we also study the parameterized complexity of all four mentioned problems.", "published": "2021-07-08T12:17:49Z", "authors": ["Ágnes Cseh", "Tobias Friedrich", "Jannik Peters"], "categories": ["cs.GT"], "doi": "", "_query": "hedonic house price", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/0911.4801v2", "title": "Existence of Shadow Prices in Finite Probability Spaces", "abstract": "A shadow price is a process lying within the bid/ask prices of a market with proportional transaction costs, such that maximizing expected utility from consumption in the frictionless market with this price process leads to the same maximal utility as in the original market with transaction costs. For finite probability spaces, this note provides an elementary proof for the existence of such a shadow price.", "published": "2009-11-25T10:39:48Z", "authors": ["Jan Kallsen", "Johannes Muhle-Karbe"], "categories": ["q-fin.PM"], "doi": "", "_query": "hedonic house price", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2201.05486v2", "title": "A computational framework for modelling infectious disease policy based on age and household structure with applications to the COVID-19 pandemic", "abstract": "The widespread, and in many countries unprecedented, use of non-pharmaceutical interventions (NPIs) during the COVID-19 pandemic has highlighted the need for mathematical models which can estimate the impact of these measures while accounting for the highly heterogeneous risk profile of COVID-19. Models accounting either for age structure or the household structure necessary to explicitly model many NPIs are commonly used in infectious disease modelling, but models incorporating both levels of structure present substantial computational and mathematical challenges due to their high dimensionality. Here we present a modelling framework for the spread of an epidemic that includes explicit representation of age structure and household structure. Our model is formulated in terms of tractable systems of ordinary differential equations for which we provide an open-source Python implementation. Such tractability leads to significant benefits for model calibration, exhaustive evaluation of possible parameter values, and interpretability of results. We demonstrate the flexibility of our model through four policy case studies, where we quantify the likely benefits of the following measures which were either considered or implemented in the UK during the current COVID-19 pandemic: control of within- and between-household mixing through NPIs; formation of support bubbles during lockdown periods; out-of-household isolation (OOHI); and temporary relaxation of NPIs during holiday periods. Our ordinary differential equation formulation and associated analysis demonstrate that multiple dimensions of risk stratification and social structure can be incorporated into infectious disease models without sacrificing mathematical tractability. This model and its software implementation expand the range of tools available to infectious disease policy analysts.", "published": "2022-01-14T14:47:49Z", "authors": ["Joe Hilton", "Heather Riley", "Lorenzo Pellis", "Rabia Aziza", "Samuel P. C. Brand", "Ivy K. Kombe", "John Ojal", "Andrea Parisi", "Matt J. Keeling", "D. James Nokes", "Robert Manson-Sawko", "Thomas House"], "categories": ["q-bio.PE", "stat.OT"], "doi": "10.1371/journal.pcbi.1010390", "_query": "hedonic house price", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/physics/0702029v1", "title": "Correlation of coming limit price with order book in stock markets", "abstract": "We examine the correlation of the limit price with the order book, when a limit order comes. We analyzed the Rebuild Order Book of Stock Exchange Electronic Trading Service, which is the centralized order book market of London Stock Exchange. As a result, the limit price is broadly distributed around the best price according to a power-law, and it isn't randomly drawn from the distribution, but has a strong correlation with the size of cumulative unexecuted limit orders on the price. It was also found that the limit price, on the coarse-grained price scale, tends to gather around the price which has a large size of cumulative unexecuted limit orders.", "published": "2007-02-04T05:58:21Z", "authors": ["Jun-ichi Maskawa"], "categories": ["physics.data-an", "physics.soc-ph", "q-fin.ST", "q-fin.TR"], "doi": "10.1016/j.physa.2007.04.091", "_query": "hedonic house price", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/1211.1860v4", "title": "On the Inefficiency of the Uniform Price Auction", "abstract": "We present our results on Uniform Price Auctions, one of the standard sealed-bid multi-unit auction formats, for selling multiple identical units of a single good to multi-demand bidders. Contrary to the truthful and economically efficient multi-unit Vickrey auction, the Uniform Price Auction encourages strategic bidding and is socially inefficient in general. The uniform pricing rule is, however, widely popular by its appeal to the natural anticipation, that identical items should be identically priced. In this work we study equilibria of the Uniform Price Auction for bidders with (symmetric) submodular valuation functions, over the number of units that they win. We investigate pure Nash equilibria of the auction in undominated strategies; we produce a characterization of these equilibria that allows us to prove that a fraction 1-1/e of the optimum social welfare is always recovered in undominated pure Nash equilibrium -- and this bound is essentially tight. Subsequently, we study the auction under the incomplete information setting and prove a bound of 4-2/k on the economic inefficiency of (mixed) Bayes Nash equilibria that are supported by undominated strategies.", "published": "2012-11-08T14:18:01Z", "authors": ["Evangelos Markakis", "Orestis Telelis"], "categories": ["cs.GT"], "doi": "", "_query": "hedonic house price", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/1611.02442v3", "title": "Price Doubling and Item Halving: Robust Revenue Guarantees for Item Pricing", "abstract": "We study approximation algorithms for revenue maximization based on static item pricing, where a seller chooses prices for various goods in the market, and then the buyers purchase utility-maximizing bundles at these given prices. We formulate two somewhat general techniques for designing good pricing algorithms for this setting: Price Doubling and Item Halving. Using these techniques, we unify many of the existing results in the item pricing literature under a common framework, as well as provide several new item pricing algorithms for approximating both revenue and social welfare. More specifically, for a variety of settings with item pricing, we show that it is possible to deterministically obtain a log-approximation for revenue and a constant-approximation for social welfare simultaneously: thus one need not sacrifice revenue if the goal is to still have decent welfare guarantees. %In addition, we provide a new black-box reduction from revenue to welfare based on item pricing, which immediately gives us new revenue-approximation algorithms (e.g., for gross substitutes valuations). The main technical contribution of this paper is a $O((\\log m + \\log k)^2)$-approximation algorithm for revenue maximization based on the Item Halving technique, for settings where buyers have XoS valuations, where $m$ is the number of goods and $k$ is the average supply. Surprisingly, ours is the first known item pricing algorithm with polylogarithmic approximations for such general classes of valuations, and partially resolves an important open question from the algorithmic pricing literature about the existence of item pricing algorithms with logarithmic factors for general valuations. We also use the Item Halving framework to form envy-free item pricing mechanisms for the popular setting of multi-unit markets, providing a log-approximation to revenue in this case.", "published": "2016-11-08T09:25:41Z", "authors": ["Elliot Anshelevich", "Shreyas Sekar"], "categories": ["cs.GT"], "doi": "", "_query": "hedonic house price", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/1910.01778v2", "title": "Utility-based pricing and hedging of contingent claims in Almgren-Chriss model with temporary price impact", "abstract": "In this paper, we construct the utility-based optimal hedging strategy for a European-type option in the Almgren-Chriss model with temporary price impact. The main mathematical challenge of this work stems from the degeneracy of the second order terms and the quadratic growth of the first order terms in the associated HJB equation, which makes it difficult to establish sufficient regularity of the value function needed to construct the optimal strategy in a feedback form. By combining the analytic and probabilistic tools for describing the value function and the optimal strategy, we establish the feedback representation of the latter. We use this representation to derive an explicit asymptotic expansion of the utility indifference price of the option, which allows us to quantify the price impact in options' market via the price impact coefficient in the underlying market.", "published": "2019-10-04T01:51:39Z", "authors": ["Ibrahim Ekren", "Sergey Nadtochiy"], "categories": ["q-fin.PR", "math.OC", "math.PR"], "doi": "", "_query": "hedonic house price", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/1312.4622v1", "title": "Coupled mode theory of stock price formation", "abstract": "We develop a theory of bid and ask price dynamics where the two prices form due to interaction of buy and sell orders. In this model the two prices are represented by eigenvalues of a 2x2 price operator corresponding to \"bid\" and \"ask\" eigenstates. Matrix elements of price operator fluctuate in time which results in phase jitter for eigenstates. We show that the theory reflects very important characteristics of bid and ask dynamics and order density in the order book. Calibration examples are provided for stocks at various time scales. Lastly, this model allows to quantify and measure risk associated with spread and its fluctuations.", "published": "2013-12-17T03:20:23Z", "authors": ["Jack Sarkissian"], "categories": ["q-fin.TR"], "doi": "", "_query": "hedonic house price", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2208.12067v2", "title": "Pricing Stocks with Trading Volumes", "abstract": "The present paper proposes a new framework for describing the stock price dynamics. In the traditional geometric Brownian motion model and its variants, volatility plays a vital role. The modern studies of asset pricing expand around volatility, trying to improve the understanding of it and remove the gap between the theory and market data. Unlike this, we propose to replace volatility with trading volume in stock pricing models. This pricing strategy is based on two hypotheses: a price-volume relation with an idea borrowed from fluid flows and a white-noise hypothesis for the price rate of change (ROC) that is verified via statistic testing on actual market data. The new framework can be easily adopted to local volume and stochastic volume models for the option pricing problem, which will point out a new possible direction for this central problem in quantitative finance.", "published": "2022-08-25T12:57:47Z", "authors": ["Ben Duan", "Yutian Li", "Dawei Lu", "Yang Lu", "Ran Zhang"], "categories": ["q-fin.MF"], "doi": "", "_query": "hedonic house price", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2608.12566v1", "title": "Domus: An Open-Data Web Platform for House History Research", "abstract": "House history -- the record of who lived in a building, who owned it, and how its address changed over time -- is a central concern of genealogical and local history research. Wikidata and OpenHistoricalMap together provide an open-data infrastructure, CC0-licensed and capable of representing this data: Wikidata through its linked-data property model with temporal qualifiers, OpenHistoricalMap through historical building footprints with start and end dates. What has been missing is a domain-specific tool that makes contributing and discovering building history accessible to genealogists and local historians. Domus fills this role: a map-based web platform that guides users through structured, crowdsourced editing of building records in Wikidata and displays corresponding OpenHistoricalMap footprints. The application requires no custom backend -- authentication, data reading, and data writing all operate through public APIs in the browser, and all data are stored under CC0 in public collaborative databases. This paper describes the genealogical data model, the architecture, and key technical design decisions.", "published": "2026-08-12T20:15:48Z", "authors": ["David M. Straub"], "categories": ["cs.DL"], "doi": "", "_query": "hedonic house price", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/1711.00405v1", "title": "The Price of Information in Combinatorial Optimization", "abstract": "Consider a network design application where we wish to lay down a minimum-cost spanning tree in a given graph; however, we only have stochastic information about the edge costs. To learn the precise cost of any edge, we have to conduct a study that incurs a price. Our goal is to find a spanning tree while minimizing the disutility, which is the sum of the tree cost and the total price that we spend on the studies. In a different application, each edge gives a stochastic reward value. Our goal is to find a spanning tree while maximizing the utility, which is the tree reward minus the prices that we pay. Situations such as the above two often arise in practice where we wish to find a good solution to an optimization problem, but we start with only some partial knowledge about the parameters of the problem. The missing information can be found only after paying a probing price, which we call the price of information. What strategy should we adopt to optimize our expected utility/disutility? A classical example of the above setting is Weitzman's \"Pandora's box\" problem where we are given probability distributions on values of $n$ independent random variables. The goal is to choose a single variable with a large value, but we can find the actual outcomes only after paying a price. Our work is a generalization of this model to other combinatorial optimization problems such as matching, set cover, facility location, and prize-collecting Steiner tree. We give a technique that reduces such problems to their non-price counterparts, and use it to design exact/approximation algorithms to optimize our utility/disutility. Our techniques extend to situations where there are additional constraints on what parameters can be probed or when we can simultaneously probe a subset of the parameters.", "published": "2017-11-01T15:46:48Z", "authors": ["Sahil Singla"], "categories": ["cs.DS"], "doi": "", "_query": "hedonic house price", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/1909.07837v2", "title": "The value of knowing the market price of risk", "abstract": "This paper presents an optimal allocation problem in a financial market with one risk-free and one risky asset, when the market is driven by a stochastic market price of risk. We solve the problem in continuous time, for an investor with a Constant Relative Risk Aversion (CRRA) utility, under two scenarios: when the market price of risk is observable (the {\\em full information case}), and when it is not (the {\\em partial information case}). The corresponding market models are complete in the partial information case and incomplete in the other case, hence the two scenarios exhibit rather different features. We study how the access to more accurate information on the market price of risk affects the optimal strategies and we determine the maximal price that the investor would be willing to pay to get such information. In particular, we examine two cases of additional information, when an exact observation of the market price of risk is available either at time $0$ only (the {\\em initial information case}), or during the whole investment period (the {\\em dynamic information case}).", "published": "2019-09-17T14:20:36Z", "authors": ["Katia Colaneri", "Stefano Herzel", "Marco Nicolosi"], "categories": ["q-fin.PM"], "doi": "", "_query": "hedonic house price", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/physics/0509194v2", "title": "An empirical behavioral model of price formation", "abstract": "Although behavioral economics has demonstrated that there are many situations where rational choice is a poor empirical model, it has so far failed to provide quantitative models of economic problems such as price formation. We make a step in this direction by developing empirical models that capture behavioral regularities in trading order placement and cancellation using data from the London Stock Exchange. For order placement we show that the probability of placing an order at a given price is well approximated by a Student distribution with less than two degrees of freedom, centered on the best quoted price. This result is surprising because it implies that trading order placement is symmetric, independent of the bid-ask spread, and the same for buying and selling. We also develop a crude but simple cancellation model that depends on the position of an order relative to the best price and the imbalance between buying and selling orders in the limit order book. These results are combined to construct a stochastic representative agent model, in which the orders and cancellations are described in terms of conditional probability distributions. This model is used to simulate price formation and the results are compared to real data from the London Stock Exchange. Without adjusting any parameters based on price data, the model produces good predictions for the magnitude and functional form of the distribution of returns and the bid-ask spread.", "published": "2005-09-22T22:04:26Z", "authors": ["Szabolcs Mike", "J. Doyne Farmer"], "categories": ["physics.soc-ph", "q-fin.TR"], "doi": "", "_query": "hedonic house price", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/1408.6292v1", "title": "Finish Them!: Pricing Algorithms for Human Computation", "abstract": "Given a batch of human computation tasks, a commonly ignored aspect is how the price (i.e., the reward paid to human workers) of these tasks must be set or varied in order to meet latency or cost constraints. Often, the price is set up-front and not modified, leading to either a much higher monetary cost than needed (if the price is set too high), or to a much larger latency than expected (if the price is set too low). Leveraging a pricing model from prior work, we develop algorithms to optimally set and then vary price over time in order to meet a (a) user-specified deadline while minimizing total monetary cost (b) user-specified monetary budget constraint while minimizing total elapsed time. We leverage techniques from decision theory (specifically, Markov Decision Processes) for both these problems, and demonstrate that our techniques lead to upto 30\\% reduction in cost over schemes proposed in prior work. Furthermore, we develop techniques to speed-up the computation, enabling users to leverage the price setting algorithms on-the-fly.", "published": "2014-08-27T02:01:48Z", "authors": ["Yihan Gao", "Aditya Parameswaran"], "categories": ["cs.GT"], "doi": "", "_query": "hedonic house price", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/1410.7317v2", "title": "Continuous time analysis of fleeting discrete price moves", "abstract": "This paper proposes a novel model of financial prices where: (i) prices are discrete; (ii) prices change in continuous time; (iii) a high proportion of price changes are reversed in a fraction of a second. Our model is analytically tractable and directly formulated in terms of the calendar time and price impact curve. The resulting càdlàg price process is a piecewise constant semimartingale with finite activity, finite variation and no Brownian motion component. We use moment-based estimations to fit four high frequency futures data sets and demonstrate the descriptive power of our proposed model. This model is able to describe the observed dynamics of price changes over three different orders of magnitude of time intervals.", "published": "2014-10-27T17:13:58Z", "authors": ["Neil Shephard", "Justin J. Yang"], "categories": ["q-fin.TR", "math.PR"], "doi": "10.1080/01621459.2016.1192544", "_query": "hedonic house price", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/1709.09465v2", "title": "Convergence of utility indifference prices to the superreplication price in a multiple-priors framework", "abstract": "This paper formulates an utility indifference pricing model for investors trading in a discrete time financial market under non-dominated model uncertainty. The investors preferences are described by strictly increasing concave random functions defined on the positive axis. We prove that under suitable conditions the multiple-priors utility indifference prices of a contingent claim converge to its multiple-priors superreplication price. We also revisit the notion of certainty equivalent for random utility functions and establish its relation with the absolute risk aversion.", "published": "2017-09-27T12:06:23Z", "authors": ["Romain Blanchard", "Laurence Carassus"], "categories": ["q-fin.MF"], "doi": "", "_query": "hedonic house price", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2501.15338v2", "title": "Fairness-aware Contextual Dynamic Pricing with Strategic Buyers", "abstract": "Contextual pricing strategies are prevalent in online retailing, where the seller adjusts prices based on products' attributes and buyers' characteristics. Although such strategies can enhance seller's profits, they raise concerns about fairness when significant price disparities emerge among specific groups, such as gender or race. These disparities can lead to adverse perceptions of fairness among buyers and may even violate the law and regulation. In contrast, price differences can incentivize disadvantaged buyers to strategically manipulate their group identity to obtain a lower price. In this paper, we investigate contextual dynamic pricing with fairness constraints, taking into account buyers' strategic behaviors when their group status is private and unobservable from the seller. We propose a dynamic pricing policy that simultaneously achieves price fairness and discourages strategic behaviors. Our policy achieves an upper bound of $O(\\sqrt{T}+H(T))$ regret over $T$ time horizons, where the term $H(T)$ arises from buyers' assessment of the fairness of the pricing policy based on their learned price difference. When buyers are able to learn the fairness of the price policy, this upper bound reduces to $O(\\sqrt{T})$. We also prove an $Ω(\\sqrt{T})$ regret lower bound of any pricing policy under our problem setting. We support our findings with extensive experimental evidence, showcasing our policy's effectiveness. In our real data analysis, we observe the existence of price discrimination against race in the loan application even after accounting for other contextual information. Our proposed pricing policy demonstrates a significant improvement, achieving 35.06% reduction in regret compared to the benchmark policy.", "published": "2025-01-25T22:30:37Z", "authors": ["Pangpang Liu", "Will Wei Sun"], "categories": ["cs.GT", "cs.LG", "stat.ML"], "doi": "", "_query": "hedonic house price", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/1204.1381v1", "title": "Price Jump Prediction in Limit Order Book", "abstract": "A limit order book provides information on available limit order prices and their volumes. Based on these quantities, we give an empirical result on the relationship between the bid-ask liquidity balance and trade sign and we show that liquidity balance on best bid/best ask is quite informative for predicting the future market order's direction. Moreover, we define price jump as a sell (buy) market order arrival which is executed at a price which is smaller (larger) than the best bid (best ask) price at the moment just after the precedent market order arrival. Features are then extracted related to limit order volumes, limit order price gaps, market order information and limit order event information. Logistic regression is applied to predict the price jump from the limit order book's feature. LASSO logistic regression is introduced to help us make variable selection from which we are capable to highlight the importance of different features in predicting the future price jump. In order to get rid of the intraday data seasonality, the analysis is based on two separated datasets: morning dataset and afternoon dataset. Based on an analysis on forty largest French stocks of CAC40, we find that trade sign and market order size as well as the liquidity on the best bid (best ask) are consistently informative for predicting the incoming price jump.", "published": "2012-04-06T00:07:09Z", "authors": ["Ban Zheng", "Eric Moulines", "Frédéric Abergel"], "categories": ["q-fin.TR"], "doi": "", "_query": "hedonic house price", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/1902.07856v1", "title": "The Markovian Price of Information", "abstract": "Suppose there are $n$ Markov chains and we need to pay a per-step \\emph{price} to advance them. The \"destination\" states of the Markov chains contain rewards; however, we can only get rewards for a subset of them that satisfy a combinatorial constraint, e.g., at most $k$ of them, or they are acyclic in an underlying graph. What strategy should we choose to advance the Markov chains if our goal is to maximize the total reward \\emph{minus} the total price that we pay? In this paper we introduce a Markovian price of information model to capture settings such as the above, where the input parameters of a combinatorial optimization problem are given via Markov chains. We design optimal/approximation algorithms that jointly optimize the value of the combinatorial problem and the total paid price. We also study \\emph{robustness} of our algorithms to the distribution parameters and how to handle the \\emph{commitment} constraint. Our work brings together two classical lines of investigation: getting optimal strategies for Markovian multi-armed bandits, and getting exact and approximation algorithms for discrete optimization problems using combinatorial as well as linear-programming relaxation ideas.", "published": "2019-02-21T03:40:09Z", "authors": ["Anupam Gupta", "Haotian Jiang", "Ziv Scully", "Sahil Singla"], "categories": ["cs.DS"], "doi": "", "_query": "hedonic house price", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2304.07348v1", "title": "Using Dark Energy Explorers and Machine Learning to Enhance the Hobby-Eberly Telescope Dark Energy Experiment", "abstract": "We present analysis using a citizen science campaign to improve the cosmological measures from the Hobby-Eberly Telescope Dark Energy Experiment (HETDEX). The goal of HETDEX is to measure the Hubble expansion rate, $H(z)$, and angular diameter distance, $D_A(z)$, at $z =$ 2.4, each to percent-level accuracy. This accuracy is determined primarily from the total number of detected Lyman-$α$ emitters (LAEs), the false positive rate due to noise, and the contamination due to [O II] emitting galaxies. This paper presents the citizen science project, Dark Energy Explorers, with the goal of increasing the number of LAEs, decreasing the number of false positives due to noise and the [O II] galaxies. Initial analysis shows that citizen science is an efficient and effective tool for classification most accurately done by the human eye, especially in combination with unsupervised machine learning. Three aspects from the citizen science campaign that have the most impact are 1) identifying individual problems with detections, 2) providing a clean sample with 100% visual identification above a signal-to-noise cut, and 3) providing labels for machine learning efforts. Since the end of 2022, Dark Energy Explorers has collected over three and a half million classifications by 11,000 volunteers in over 85 different countries around the world. By incorporating the results of the Dark Energy Explorers we expect to improve the accuracy on the $D_A(z)$ and $H(z)$ parameters at $z =$ 2.4 by 10 - 30%. While the primary goal is to improve on HETDEX, Dark Energy Explorers has already proven to be a uniquely powerful tool for science advancement and increasing accessibility to science worldwide.", "published": "2023-04-14T18:45:54Z", "authors": ["Lindsay R. House", "Karl Gebhardt", "Keely Finkelstein", "Erin Mentuch Cooper", "Dustin Davis", "Robin Ciardullo", "Daniel J Farrow", "Steven L. Finkelstein", "Caryl Gronwall", "Donghui Jeong", "L. Clifton Johnson", "Chenxu Liu", "Benjamin P. Thomas", "Gregory Zeimann"], "categories": ["astro-ph.IM", "astro-ph.CO", "astro-ph.GA", "physics.space-ph"], "doi": "10.3847/1538-4357/accdd0", "_query": "hedonic house price", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/0704.1976v1", "title": "Information-Based Asset Pricing", "abstract": "A new framework for asset price dynamics is introduced in which the concept of noisy information about future cash flows is used to derive the price processes. In this framework an asset is defined by its cash-flow structure. Each cash flow is modelled by a random variable that can be expressed as a function of a collection of independent random variables called market factors. With each such \"X-factor\" we associate a market information process, the values of which are accessible to market agents. Each information process is a sum of two terms; one contains true information about the value of the market factor; the other represents \"noise\". The noise term is modelled by an independent Brownian bridge. The market filtration is assumed to be that generated by the aggregate of the independent information processes. The price of an asset is given by the expectation of the discounted cash flows in the risk-neutral measure, conditional on the information provided by the market filtration. When the cash flows are the dividend payments associated with equities, an explicit model is obtained for the share-price, and the prices of options on dividend-paying assets are derived. Remarkably, the resulting formula for the price of a European call option is of the Black-Scholes-Merton type. The information-based framework also generates a natural explanation for the origin of stochastic volatility.", "published": "2007-04-16T10:50:48Z", "authors": ["Dorje C. Brody", "Lane P. Hughston", "Andrea Macrina"], "categories": ["q-fin.PR", "math.PR", "math.ST"], "doi": "", "_query": "hedonic house price", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/0910.1430v1", "title": "State price density estimation via nonparametric mixtures", "abstract": "We consider nonparametric estimation of the state price density encapsulated in option prices. Unlike usual density estimation problems, we only observe option prices and their corresponding strike prices rather than samples from the state price density. We propose to model the state price density directly with a nonparametric mixture and estimate it using least squares. We show that although the minimization is taken over an infinitely dimensional function space, the minimizer always admits a finite dimensional representation and can be computed efficiently. We also prove that the proposed estimate of the state price density function converges to the truth at a ``nearly parametric'' rate.", "published": "2009-10-08T07:42:06Z", "authors": ["Ming Yuan"], "categories": ["q-fin.CP", "q-fin.PR", "stat.AP"], "doi": "10.1214/09-AOAS246", "_query": "hedonic house price", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2003.03975v1", "title": "Price-aware Recommendation with Graph Convolutional Networks", "abstract": "In recent years, much research effort on recommendation has been devoted to mining user behaviors, i.e., collaborative filtering, along with the general information which describes users or items, e.g., textual attributes, categorical demographics, product images, and so on. Price, an important factor in marketing --- which determines whether a user will make the final purchase decision on an item --- surprisingly, has received relatively little scrutiny. In this work, we aim at developing an effective method to predict user purchase intention with the focus on the price factor in recommender systems. The main difficulties are two-fold: 1) the preference and sensitivity of a user on item price are unknown, which are only implicitly reflected in the items that the user has purchased, and 2) how the item price affects a user's intention depends largely on the product category, that is, the perception and affordability of a user on item price could vary significantly across categories. Towards the first difficulty, we propose to model the transitive relationship between user-to-item and item-to-price, taking the inspiration from the recently developed Graph Convolution Networks (GCN). The key idea is to propagate the influence of price on users with items as the bridge, so as to make the learned user representations be price-aware. For the second difficulty, we further integrate item categories into the propagation progress and model the possible pairwise interactions for predicting user-item interactions. We conduct extensive experiments on two real-world datasets, demonstrating the effectiveness of our GCN-based method in learning the price-aware preference of users. Further analysis reveals that modeling the price awareness is particularly useful for predicting user preference on items of unexplored categories.", "published": "2020-03-09T08:57:29Z", "authors": ["Yu Zheng", "Chen Gao", "Xiangnan He", "Yong Li", "Depeng Jin"], "categories": ["cs.IR"], "doi": "", "_query": "hedonic house price", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2402.12562v2", "title": "Dynamic Pricing and Learning with Long-term Reference Effects", "abstract": "We consider a dynamic pricing problem where customer response to the current price is impacted by the customer price expectation, aka reference price. We study a simple and novel reference price mechanism where reference price is the average of the past prices offered by the seller. As opposed to the more commonly studied exponential smoothing mechanism, in our reference price mechanism the prices offered by seller have a longer term effect on the future customer expectations. We show that under this mechanism, a markdown policy is near-optimal irrespective of the parameters of the model. This matches the common intuition that a seller may be better off by starting with a higher price and then decreasing it, as the customers feel like they are getting bargains on items that are ordinarily more expensive. For linear demand models, we also provide a detailed characterization of the near-optimal markdown policy along with an efficient way of computing it. We then consider a more challenging dynamic pricing and learning problem, where the demand model parameters are apriori unknown, and the seller needs to learn them online from the customers' responses to the offered prices while simultaneously optimizing revenue. The objective is to minimize regret, i.e., the $T$-round revenue loss compared to a clairvoyant optimal policy. This task essentially amounts to learning a non-stationary optimal policy in a time-variant Markov Decision Process (MDP). For linear demand models, we provide an efficient learning algorithm with an optimal $\\tilde{O}(\\sqrt{T})$ regret upper bound.", "published": "2024-02-19T21:36:54Z", "authors": ["Shipra Agrawal", "Wei Tang"], "categories": ["cs.LG", "cs.GT"], "doi": "", "_query": "hedonic house price", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2411.02661v1", "title": "Pricing and Competition for Generative AI", "abstract": "Compared to classical machine learning (ML) models, generative models offer a new usage paradigm where (i) a single model can be used for many different tasks out-of-the-box; (ii) users interact with this model over a series of natural language prompts; and (iii) the model is ideally evaluated on binary user satisfaction with respect to model outputs. Given these characteristics, we explore the problem of how developers of new generative AI software can release and price their technology. We first develop a comparison of two different models for a specific task with respect to user cost-effectiveness. We then model the pricing problem of generative AI software as a game between two different companies who sequentially release their models before users choose their preferred model for each task. Here, the price optimization problem becomes piecewise continuous where the companies must choose a subset of the tasks on which to be cost-effective and forgo revenue for the remaining tasks. In particular, we reveal the value of market information by showing that a company who deploys later after knowing their competitor's price can always secure cost-effectiveness on at least one task, whereas the company who is the first-to-market must price their model in a way that incentivizes higher prices from the latecomer in order to gain revenue. Most importantly, we find that if the different tasks are sufficiently similar, the first-to-market model may become cost-ineffective on all tasks regardless of how this technology is priced.", "published": "2024-11-04T22:52:45Z", "authors": ["Rafid Mahmood"], "categories": ["cs.GT", "cs.LG"], "doi": "", "_query": "hedonic house price", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2310.03105v1", "title": "Efficiency of the Generalized Second-Price Auction for Value Maximizers", "abstract": "We study the price of anarchy of the generalized second-price auction where bidders are value maximizers (i.e., autobidders). We show that in general the price of anarchy can be as bad as $0$. For comparison, the price of anarchy of running VCG is $1/2$ in the autobidding world. We further show a fined-grained price of anarchy with respect to the discount factors (i.e., the ratios of click probabilities between lower slots and the highest slot in each auction) in the generalized second-price auction, which highlights the qualitative relation between the smoothness of the discount factors and the efficiency of the generalized second-price auction.", "published": "2023-10-04T18:49:54Z", "authors": ["Yuan Deng", "Mohammad Mahdian", "Jieming Mao", "Vahab Mirrokni", "Hanrui Zhang", "Song Zuo"], "categories": ["cs.GT"], "doi": "", "_query": "hedonic house price", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2609.35111v1", "title": "The missing price feedback: Why studies overstate local peaks from synchronized home batteries under dynamic pricing", "abstract": "Existing studies consistently find that home batteries and electric vehicles optimized against real-time electricity prices create new load peaks in local distribution grids, because all assets respond to the same price signal in sync. However, all but one of the 18 studies reviewed here treat wholesale prices as exogenous. This paper argues that treating prices as non-responsive biases the result: in reality, charging in low-price hours raises the wholesale price, which dampens the incentive to charge. I test this by simulating households with rooftop solar and home batteries under a spot-based retail tariff, calibrated to German data for 2025 with a model of equilibrium wholesale prices. With exogenous prices, my results confirm the finding from the previous literature: synchronous charging pushes the coincident import peak of the local grid to 83% above its no-battery level. With endogenous prices, the same fleet leaves the peak 6% below the no-battery benchmark. In other words, treating prices as endogenous reduces the coincident peak at high battery penetration nearly by half. This finding is robust across alternative price functions and exogenous price paths, different measures of the coincident peak, 25 scenario variations and three historical years. I conclude that new local peaks remain possible, particularly in grids where battery deployment runs ahead of the national average, but the risk and magnitude are considerably smaller than the existing literature suggests.", "published": "2026-09-28T13:15:13Z", "authors": ["Lion Hirth"], "categories": ["econ.GN"], "doi": "", "_query": "hedonic house price", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/1404.5943v5", "title": "Price of Anarchy for Auction Revenue", "abstract": "This paper develops tools for welfare and revenue analyses of Bayes-Nash equilibria in asymmetric auctions with single-dimensional agents. We employ these tools to derive price of anarchy results for social welfare and revenue. Our approach separates the standard smoothness framework into two distinct parts, isolating the analysis common to any auction from the analysis specific to a given auction. The first part relates a bidder's contribution to welfare in equilibrium to their contribution to welfare in the optimal auction using the price the bidder faces for additional allocation. Intuitively, either an agent's utility and hence contribution to welfare is high, or the price she has to pay for additional allocation is high relative to her value. We call this condition value covering; it holds in every Bayes-Nash equilibrium of any auction. The second part, revenue covering, relates the prices bidders face for additional allocation to the revenue of the auction, using an auction's rules and feasibility constraints. Combining the two parts gives approximation results to the optimal welfare, and, under the right conditions, the optimal revenue. In mechanisms with reserve prices, our welfare results show approximation with respect to the optimal mechanism with the same reserves. As a center-piece result, we analyze the single-item first-price auction with individual monopoly reserves. When each distribution satisfies a regularity condition the auction's revenue is at least a $2e/(e-1) \\approx 3.16$ approximation to the revenue of the optimal auction. We also give bounds for matroid auctions with first-price or all-pay semantics, and the generalized first-price position auction. Finally, we give an extension theorem for simultaneous composition, i.e., when multiple auctions are run simultaneously, with single-valued, unit-demand agents.", "published": "2014-04-23T19:57:35Z", "authors": ["Jason Hartline", "Darrell Hoy", "Sam Taggart"], "categories": ["cs.GT"], "doi": "", "_query": "hedonic house price", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/1112.3680v2", "title": "The Robust Price of Anarchy of Altruistic Games", "abstract": "We study the inefficiency of equilibria for various classes of games when players are (partially) altruistic. We model altruistic behavior by assuming that player i's perceived cost is a convex combination of 1-α_i times his direct cost and α_i times the social cost. Tuning the parameters α_i allows smooth interpolation between purely selfish and purely altruistic behavior. Within this framework, we study altruistic extensions of linear congestion games, fair cost-sharing games and valid utility games. We derive (tight) bounds on the price of anarchy of these games for several solution concepts. Thereto, we suitably adapt the smoothness notion introduced by Roughgarden and show that it captures the essential properties to determine the robust price of anarchy of these games. Our bounds show that for congestion games and cost-sharing games, the worst-case robust price of anarchy increases with increasing altruism, while for valid utility games, it remains constant and is not affected by altruism. However, the increase in the price of anarchy is not a universal phenomenon: for symmetric singleton linear congestion games, we derive a bound on the pure price of anarchy that decreases as the level of altruism increases. Since the bound is also strictly lower than the robust price of anarchy, it exhibits a natural example in which Nash equilibria are more efficient than more permissive notions of equilibrium.", "published": "2011-12-15T21:51:12Z", "authors": ["Po-An Chen", "Bart de Keijzer", "David Kempe", "Guido Schaefer"], "categories": ["cs.GT"], "doi": "", "_query": "hedonic house price", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/nlin/0403056v1", "title": "Majority Orienting Model for the Oscillation of Market Price", "abstract": "The present paper introduces a majority orienting model in which the dealers' behavior changes based on the influence of the price to show the oscillation of stock price in the stock market. We show the oscillation of the price for the model by applying the van der Pol equation which is a deterministic approximation of our model.", "published": "2004-03-31T08:03:59Z", "authors": ["Hisanao Takahashi", "Yoshiaki Itoh"], "categories": ["nlin.SI", "q-fin.TR"], "doi": "10.1140/epjb/e2004-00054-8", "_query": "hedonic house price", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/1303.2478v2", "title": "The Price of Connectivity for Vertex Cover", "abstract": "The vertex cover number of a graph is the minimum number of vertices that are needed to cover all edges. When those vertices are further required to induce a connected subgraph, the corresponding number is called the connected vertex cover number, and is always greater or equal to the vertex cover number. Connected vertex covers are found in many applications, and the relationship between those two graph invariants is therefore a natural question to investigate. For that purpose, we introduce the {\\em Price of Connectivity}, defined as the ratio between the two vertex cover numbers. We prove that the price of connectivity is at most 2 for arbitrary graphs. We further consider graph classes in which the price of connectivity of every induced subgraph is bounded by some real number $t$. We obtain forbidden induced subgraph characterizations for every real value $t \\leq 3/2$. We also investigate critical graphs for this property, namely, graphs whose price of connectivity is strictly greater than that of any proper induced subgraph. Those are the only graphs that can appear in a forbidden subgraph characterization for the hereditary property of having a price of connectivity at most $t$. In particular, we completely characterize the critical graphs that are also chordal. Finally, we also consider the question of computing the price of connectivity of a given graph. Unsurprisingly, the decision version of this question is NP-hard. In fact, we show that it is even complete for the class $Θ_2^P = P^{NP[\\log]}$, the class of decision problems that can be solved in polynomial time, provided we can make $O(\\log n)$ queries to an NP-oracle. This paves the way for a thorough investigation of the complexity of problems involving ratios of graph invariants.", "published": "2013-03-11T10:35:51Z", "authors": ["Eglantine Camby", "Jean Cardinal", "Samuel Fiorini", "Oliver Schaudt"], "categories": ["cs.DM", "math.CO"], "doi": "", "_query": "hedonic house price", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2405.08089v1", "title": "Comparative Study of Bitcoin Price Prediction", "abstract": "Prediction of stock prices has been a crucial and challenging task, especially in the case of highly volatile digital currencies such as Bitcoin. This research examineS the potential of using neural network models, namely LSTMs and GRUs, to forecast Bitcoin's price movements. We employ five-fold cross-validation to enhance generalization and utilize L2 regularization to reduce overfitting and noise. Our study demonstrates that the GRUs models offer better accuracy than LSTMs model for predicting Bitcoin's price. Specifically, the GRU model has an MSE of 4.67, while the LSTM model has an MSE of 6.25 when compared to the actual prices in the test set data. This finding indicates that GRU models are better equipped to process sequential data with long-term dependencies, a characteristic of financial time series data such as Bitcoin prices. In summary, our results provide valuable insights into the potential of neural network models for accurate Bitcoin price prediction and emphasize the importance of employing appropriate regularization techniques to enhance model performance.", "published": "2024-05-13T18:10:34Z", "authors": ["Ali Mohammadjafari"], "categories": ["q-fin.ST", "cs.LG"], "doi": "", "_query": "hedonic house price", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2106.08274v2", "title": "Elasticity Based Demand Forecasting and Price Optimization for Online Retail", "abstract": "We study a problem of an online retailer who observes the unit sales of a product, and dynamically changes the retail price, in order to maximize the expected revenue. Assuming the demand of the product is price sensitive, we are interested in the optimal pricing policy when future demand is uncertain. We build a system to investigate the relationship between retail price and demand and estimate the demand function. The system predicts demand and revenue at a given retail price. We formulate a revenue maximization problem over a discrete finite time horizon with discrete retail price. The optimal pricing policy is solved based on the predicted demand and revenue values. With computational experiments, we investigate the effect of optimal pricing policy to inventory management.", "published": "2021-06-03T22:23:32Z", "authors": ["Chengcheng Liu", "Mátyás A. Sustik"], "categories": ["eess.SY", "cs.CE", "math.OC"], "doi": "", "_query": "hedonic house price", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2603.29763v1", "title": "Option Pricing on Automated Market Maker Tokens", "abstract": "We derive the stochastic price process for tokens whose sole price discovery mechanism is a constant-product automated market maker (AMM). When the net flow into the pool follows a diffusion, the token price follows a constant elasticity of variance (CEV) process, nesting Black-Scholes as the limiting case of infinite liquidity. We obtain closed-form European option prices and introduce liquidity-adjusted Greeks. The CEV structure generates a leverage effect -- volatility rises as price falls -- whose normalized implied volatility skew depends only on the pool's weighting parameter, not on pool depth: Black-Scholes underprices 20%-out-of-the-money puts by roughly 6% in implied volatility terms at every pool depth, while the absolute pricing discrepancy vanishes as pools deepen. Empirically, after controlling for pool depth and flow volatility, realized return variance across 90 Bittensor subnets exhibits a strongly negative price elasticity, decisively rejecting geometric Brownian motion and consistent with the CEV prediction. A complementary delta-hedged backtest across 82 subnets confirms near-identical hedging errors at the money, consistent with the prediction that pricing differences are concentrated in the wings.", "published": "2026-03-31T14:03:00Z", "authors": ["Philip Z. Maymin"], "categories": ["q-fin.PR", "q-fin.ST", "q-fin.TR"], "doi": "", "_query": "hedonic house price", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2304.01709v1", "title": "With great power (prices) comes great tail pipe emissions? \\\\ A natural experiment of electricity prices and electric car adoption", "abstract": "A fundemantal and unanswered question for the widely shared goal of electrifying passenger vehicles is how the price of electricity, which can vary greatly across countries and regions, affects buying behavior. I make use of a natural experiment in Norway in the period 2021-2022 when large price differences between north and south emerged to estimate the effect of electricity prices on the decision to purchase a pure battery-electric vehicle. Simple difference estimates along the border of the price zones as well as a difference-in-difference regression model suggest a significant but economically modest effect of a 2-4\\% reduction in the probability of purchasing an electric vehicle in the high price zone. A counterfactual simulation suggests that there would have been about 3000 to 6000 fewer electric vehicles sold in the high-price south compared to a scenario where the south had equally low prices as in the north.", "published": "2023-04-04T11:14:26Z", "authors": ["Johannes Mauritzen"], "categories": ["econ.GN", "stat.AP"], "doi": "", "_query": "hedonic house price", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/1102.3009v1", "title": "Non - Randomness Stock Market Price Model", "abstract": "A new model for the stock market price analysis is proposed. It is suggested to look at price as an everywhere discontinuous function of time of bounded variation.", "published": "2011-02-15T09:28:47Z", "authors": ["Aleksey Kharevsky"], "categories": ["q-fin.GN"], "doi": "", "_query": "hedonic house price", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2609.38554v1", "title": "Auctions with Price Predictions", "abstract": "We design auctions for the sale of a single item with unlimited supply given a single prediction of the revenue-maximizing uniform price. This departs from prior work on auctions with predictions which typically assumes predictions of every bidder's value. Our main result is a characterization of the Pareto frontier for consistency and robustness attainable by any universally truthful auction. We show that a mechanism that randomizes between posting the predicted price and conducting an optimal prior-free fallback auction is Pareto-optimal. We then extend our results to achieve graceful degradation of revenue as a function of the prediction accuracy. Finally, we study how such a prediction can be obtained from historical market data through the lens of learning theory. Together, our results give an end-to-end account of how a price prediction can be learned and used robustly to maximize auction revenue.", "published": "2026-09-29T21:14:49Z", "authors": ["Muthu Sundar", "Alec Sun", "Siddharth Prasad", "Dravyansh Sharma"], "categories": ["cs.GT"], "doi": "", "_query": "hedonic house price", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/cond-mat/0403761v3", "title": "Long memory stochastic volatility in option pricing", "abstract": "The aim of this paper is to present a simple stochastic model that accounts for the effects of a long-memory in volatility on option pricing. The starting point is the stochastic Black-Scholes equation involving volatility with long-range dependence. We consider the option price as a sum of classical Black-Scholes price and random deviation describing the risk from the random volatility. By using the fact the option price and random volatility change on different time scales, we find the asymptotic equation for the derivation involving fractional Brownian motion. The solution to this equation allows us to find the pricing bands for options.", "published": "2004-03-31T14:28:37Z", "authors": ["Sergei Fedotov", "Abby Tan"], "categories": ["cond-mat.other", "q-fin.PR"], "doi": "", "_query": "hedonic house price", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/1901.01030v3", "title": "Multi-Product Dynamic Pricing in High-Dimensions with Heterogeneous Price Sensitivity", "abstract": "We consider the problem of multi-product dynamic pricing, in a contextual setting, for a seller of differentiated products. In this environment, the customers arrive over time and products are described by high-dimensional feature vectors. Each customer chooses a product according to the widely used Multinomial Logit (MNL) choice model and her utility depends on the product features as well as the prices offered. The seller a-priori does not know the parameters of the choice model but can learn them through interactions with customers. The seller's goal is to design a pricing policy that maximizes her cumulative revenue. This model is motivated by online marketplaces such as Airbnb platform and online advertising. We measure the performance of a pricing policy in terms of regret, which is the expected revenue loss with respect to a clairvoyant policy that knows the parameters of the choice model in advance and always sets the revenue-maximizing prices. We propose a pricing policy, named M3P, that achieves a $T$-period regret of $O(\\log(Td) ( \\sqrt{T}+ d\\log(T)))$ under heterogeneous price sensitivity for products with features of dimension $d$. We also use tools from information theory to prove that no policy can achieve worst-case $T$-regret better than $Ω(\\sqrt{T})$.", "published": "2019-01-04T09:33:41Z", "authors": ["Adel Javanmard", "Hamid Nazerzadeh", "Simeng Shao"], "categories": ["stat.ML", "cs.GT", "cs.LG"], "doi": "", "_query": "hedonic house price", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2410.07893v2", "title": "Ormer: A Manipulation-resistant and Gas-efficient Blockchain Pricing Oracle for DeFi", "abstract": "Price feeds of cryptocurrencies are essential for Decentralized Finance (DeFi) applications to realize fundamental trading and exchanging functionalities, which are retrieved from external price data sources such as exchanges and input to on-chain smart contracts in real-time. Currently, arithmetic mean based time-weighted average price (TWAP) oracles are widely used to process price feeds by averaging asset price with short time frame to achieve reliable and gas-efficient pricing. However, recent research indicates that TWAP is vulnerable to price manipulation attacks, resulting in abnormal price fluctuations and severe financial loss. Even worse, TWAP oracles usually set a relatively long time frame setting to prevent such attack. However, it would further introduce long delays and high price deviation errors from the market asset price. To address this issue, we propose a novel on-chain gas-efficient pricing algorithm (Ormer) that heuristically estimates the median of asset price within an observation window based on a piecewise-parabolic formula, while the time delay is suppressed by fusing estimations with different window sizes. Our evaluation based on multiple pairs of token swapping price feed across different chains show that Ormer reduces the mean absolute price error by 15.3% and the time delay by 49.3% compared to TWAP. For gas efficiency, regardless of the number of price observations, an encoding mechanism with constant storage requirement is employed without saving all the historical data for median estimation. Surprisingly, the lowest gas consumption of Ormer is even 15.2% less than TWAP, and the oracle querying fee would be saved up to ~20K USD per day for DeFi participants.", "published": "2024-10-10T13:18:00Z", "authors": ["Dongbin Bai", "Jiannong Cao", "Yinfeng Cao", "Long Wen", "Milos Stojmenovic"], "categories": ["cs.CR"], "doi": "", "_query": "hedonic house price", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/1110.3133v1", "title": "Price impact asymmetry of institutional trading in Chinese stock market", "abstract": "The asymmetric price impact between the institutional purchases and sales of 32 liquid stocks in Chinese stock markets in year 2003 is carefully studied. We analyze the price impact in both drawup and drawdown trends with consecutive positive and negative daily price changes, and test the dependence of the price impact asymmetry on the market condition. For most of the stocks institutional sales have a larger price impact than institutional purchases, and larger impact of institutional purchases only exists in few stocks with primarily increasing tendencies. We further study the mean return of trades surrounding institutional transactions, and find the asymmetric behavior also exists before and after institutional transactions. A new variable is proposed to investigate the order book structure, and it can partially explain the price impact of institutional transactions. A linear regression for the price impact of institutional transactions further confirms our finding that institutional sales primarily have a larger price impact than institutional purchases in the bearish year 2003.", "published": "2011-10-14T07:44:20Z", "authors": ["Fei Ren", "Li-Xin Zhong"], "categories": ["q-fin.TR"], "doi": "10.1016/j.physa.2011.12.049", "_query": "hedonic house price", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2403.06303v3", "title": "A Unifying Approach for the Pricing of Debt Securities", "abstract": "We propose a unifying framework for the pricing of debt securities under general time-inhomogeneous short-rate diffusion processes. The pricing of bonds, bond options, callable/putable bonds, and convertible bonds (CBs) is covered. Using continuous-time Markov chain (CTMC) approximations, we obtain closed-form matrix expressions to approximate the price of bonds and bond options under general one-dimensional short-rate processes. A simple and efficient algorithm is also developed to price callable/putable debt. The availability of a closed-form expression for the price of zero-coupon bonds allows for the perfect fit of the approximated model to the current market term structure of interest rates, regardless of the complexity of the underlying diffusion process selected. We further consider the pricing of CBs under general bi-dimensional time-inhomogeneous diffusion processes to model equity and short-rate dynamics. Credit risk is also incorporated into the model using the approach of Tsiveriotis and Fernandes (1998). Based on a two-layer CTMC method, an efficient algorithm is developed to approximate the price of convertible bonds. When conversion is only allowed at maturity, a closed-form matrix expression is obtained. Numerical experiments show the accuracy and efficiency of the method across a wide range of model parameters and short-rate models.", "published": "2024-03-10T20:39:01Z", "authors": ["Marie-Claude Vachon", "Anne Mackay"], "categories": ["q-fin.PR", "q-fin.CP", "q-fin.MF"], "doi": "", "_query": "hedonic house price", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2512.02247v1", "title": "An exact pricing algorithm for revenue maximization under the logit demand function", "abstract": "Determining the optimal selling price is a challenge in revenue management, especially in markets characterized by nonlinear and price-sensitive demand. While traditional models, such as linear, power, and exponential demand functions, offer analytical convenience, they often fail to capture realistic purchase dynamics, leading to suboptimal pricing. The logit demand function addresses these limitations through its bounded, S-shaped curve, offering a more realistic representation of consumer behavior. Despite its advantages, most existing literature relies on heuristic approaches, such as pricing at the inflection point, which prioritizes maximum price sensitivity but does not guarantee maximum revenue. This study proposes a novel, exact pricing algorithm that analytically derives the revenue-maximizing price under the logit demand function using the Lambert W function. By providing a closed-form solution, the approach eliminates reliance on heuristic iterative methods and corrects the common practice of considering the inflection point price as market price. In fact, we demonstrate that the optimal price is consistently lower than the inflection-point price under reasonable assumptions, leading to lower prices for consumers and higher revenue for sellers. Numerical experiments illustrate the proposed algorithm and examine the changes in the optimality gap as demand function parameters vary. Results indicate that the optimal price is consistently lower than the inflection-point price, with an average 20% price reduction accompanied by a 15% increase in revenue.", "published": "2025-12-01T22:33:41Z", "authors": ["Moddassir Khan Nayeem", "Omar Abbaas", "Suzan Alaswad", "Sinan Salman"], "categories": ["math.OC"], "doi": "10.1016/j.eswa.2026.132223", "_query": "hedonic house price", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2303.12527v2", "title": "The Market Price of Jump Risk for Delivery Periods: Pricing of Electricity Swaps with Geometric Averaging", "abstract": "In this paper, we extend the market price of risk for delivery periods (MPDP) of electricity swap contracts by introducing a dimension for jump risk. As introduced by Kemper et al. (2022), the MPDP arises through the use of geometric averaging while pricing electricity swaps in a geometric framework. We adjust the work by Kemper et al. (2022) in two directions: First, we examine a Merton type model taking jumps into account. Second, we transfer the model to the physical measure by implementing mean-reverting behavior. We compare swap prices resulting from the classical arithmetic (approximated) average to the geometric weighted average. Under the physical measure, we discover a decomposition of the swap's market price of risk into the classical one and the MPDP.", "published": "2023-03-22T13:02:30Z", "authors": ["Annika Kemper", "Maren Diane Schmeck"], "categories": ["q-fin.PR", "q-fin.MF"], "doi": "", "_query": "hedonic house price", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2010.09285v1", "title": "Equilibrium price in intraday electricity markets", "abstract": "We formulate an equilibrium model of intraday trading in electricity markets. Agents face balancing constraints between their customers consumption plus intraday sales and their production plus intraday purchases. They have continuously updated forecast of their customers consumption at maturity with decreasing volatility error. Forecasts are prone to idiosyncratic noise as well as common noise (weather). Agents production capacities are subject to independent random outages, which are each modelled by a Markov chain. The equilibrium price is defined as the price that minimises trading cost plus imbalance cost of each agent and satisfies the usual market clearing condition. Existence and uniqueness of the equilibrium are proved, and we show that the equilibrium price and the optimal trading strategies are martingales. The main economic insights are the following. (i) When there is no uncertainty on generation, it is shown that the market price is a convex combination of forecasted marginal cost of each agent, with deterministic weights. Furthermore, the equilibrium market price follows Almgren and Chriss's model and we identify the fundamental part as well as the permanent market impact. It turns out that heterogeneity across agents is a necessary condition for the Samuelson's effect to hold. (ii) When there is production uncertainty, the price volatility becomes stochastic but converges to the case without production uncertainty when the number of agents increases to infinity. Further, on a two-agent case, we show that the potential outages of a low marginal cost producer reduces her sales position.", "published": "2020-10-19T07:56:50Z", "authors": ["René Aid", "Andrea Cosso", "Huyên Pham"], "categories": ["q-fin.CP"], "doi": "", "_query": "hedonic house price", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2402.07032v1", "title": "Field demonstration of predictive heating control for an all-electric house in a cold climate", "abstract": "Efficient electric heat pumps that replace fossil-fueled heating systems could significantly reduce greenhouse gas emissions. However, electric heat pumps can sharply increase electricity demand, causing high utility bills and stressing the power grid. Residential neighborhoods could see particularly high electricity demand during cold weather, when heat demand rises and heat pump efficiencies fall. This paper presents the development and field demonstration of a predictive control system for an air-to-air heat pump with backup electric resistance heat. The control system adjusts indoor temperature set-points based on weather forecasts, occupancy conditions, and data-driven models of the building and heating equipment. Field tests from January to March of 2023 in an occupied, all-electric, 208 m^2 detached single-family house in Indiana, USA, included outdoor temperatures as low as -15 C. On average over these tests, the control system reduced daily heating energy use by 19% (95% confidence interval: 13--24%), energy used for backup heat by 38%, and the frequency of using the highest stage (19 kW) of backup heat by 83%. Concurrent surveys of residents showed that the control system maintained satisfactory thermal comfort. The control system could reduce the house's total annual heating costs by about $300 (95% confidence interval: 23--34%). These real-world results could strengthen the case for deploying predictive home heating control, bringing the technology one step closer to reducing emissions, utility bills, and power grid impacts at scale.", "published": "2024-02-10T19:45:54Z", "authors": ["Elias N. Pergantis", " Priyadarshan", "Nadah Al Theeb", "Parveen Dhillon", "Jonathan P. Ore", "Davide Ziviani", "Eckhard A. Groll", "Kevin J. Kircher"], "categories": ["eess.SY"], "doi": "", "_query": "hedonic house price", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/1501.07473v2", "title": "Information in stock prices and some consequences: A model-free approach", "abstract": "The price of a stock will rarely follow the assumed model and a curious investor or a Regulatory Authority may wish to obtain a probability model the prices support. A risk neutral probability ${\\cal P}^*$ for the stock's price at time $T$ is determined in closed form from the prices before $T$ without assuming a price model. The findings indicate that ${\\cal P}^*$ may be a mixture. Under mild conditions on the prices the necessary and sufficient condition to obtain ${\\cal P}^*$ is the coincidence at $T$ of the stock price ranges assumed by the stock's trader and buyer. This result clarifies the relation between market's informational efficiency and the arbitrage-free option pricing methodology. It also shows that in an incomplete market there are risk neutral probabilities not supported by each stock and their use can be limited. ${\\cal P}^*$-price $C$ for the stock's European call option expiring at $T$ is obtained. Among other results it is shown for \"calm\" prices, like the log-normal, that i) $C$ is the Black-Scholes-Merton price thus confirming its validity for various stock prices, ii) the buyer's price carries an exponentially increasing volatility premium and its difference with $C$ provides a measure of the market risk premium.", "published": "2015-01-24T19:27:57Z", "authors": ["Yannis G. Yatracos"], "categories": ["q-fin.GN", "q-fin.MF"], "doi": "", "_query": "hedonic house price", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/1703.00062v1", "title": "Optimal Investment and Pricing in the Presence of Defaults", "abstract": "We consider the optimal investment problem when the traded asset may default, causing a jump in its price. For an investor with constant absolute risk aversion, we compute indifference prices for defaultable bonds, as well as a price for dynamic protection against default. For the latter problem, our work complements Sircar & Zariphopoulou (2007), where it is implicitly assumed the investor is protected against default. We consider a factor model where the asset's instantaneous return, variance, correlation and default intensity are driven by a time-homogenous diffusion X taking values in an arbitrary region E. We identify the certainty equivalent with a semi-linear degenerate elliptic partial differential equation with quadratic growth in both function and gradient. Under a minimal integrability assumption on the market price of risk, we show the certainty equivalent is a classical solution. In particular, our results cover when X is a one-dimensional affine diffusion and when returns, variances and default intensities are also affine. Numerical examples highlight the relationship between the factor process and both the indifference price and default insurance. Lastly, we show the insurance protection price is not the default intensity under the dual optimal measure.", "published": "2017-02-28T21:23:28Z", "authors": ["Tetsuya Ishikawa", "Scott Robertson"], "categories": ["q-fin.MF", "q-fin.RM"], "doi": "", "_query": "hedonic house price", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2205.01875v2", "title": "Machine Learning based Framework for Robust Price-Sensitivity Estimation with Application to Airline Pricing", "abstract": "We consider the problem of dynamic pricing of a product in the presence of feature-dependent price sensitivity. Developing practical algorithms that can estimate price elasticities robustly, especially when information about no purchases (losses) is not available, to drive such automated pricing systems is a challenge faced by many industries. Based on the Poisson semi-parametric approach, we construct a flexible yet interpretable demand model where the price related part is parametric while the remaining (nuisance) part of the model is non-parametric and can be modeled via sophisticated machine learning (ML) techniques. The estimation of price-sensitivity parameters of this model via direct one-stage regression techniques may lead to biased estimates due to regularization. To address this concern, we propose a two-stage estimation methodology which makes the estimation of the price-sensitivity parameters robust to biases in the estimators of the nuisance parameters of the model. In the first-stage we construct estimators of observed purchases and prices given the feature vector using sophisticated ML estimators such as deep neural networks. Utilizing the estimators from the first-stage, in the second-stage we leverage a Bayesian dynamic generalized linear model to estimate the price-sensitivity parameters. We test the performance of the proposed estimation schemes on simulated and real sales transaction data from the Airline industry. Our numerical studies demonstrate that our proposed two-stage approach reduces the estimation error in price-sensitivity parameters from 25\\% to 4\\% in realistic simulation settings. The two-stage estimation techniques proposed in this work allows practitioners to leverage modern ML techniques to robustly estimate price-sensitivities while still maintaining interpretability and allowing ease of validation of its various constituent parts.", "published": "2022-05-04T03:35:12Z", "authors": ["Ravi Kumar", "Shahin Boluki", "Karl Isler", "Jonas Rauch", "Darius Walczak"], "categories": ["stat.ML", "cs.LG", "econ.EM", "stat.AP"], "doi": "", "_query": "hedonic house price", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/1102.2015v4", "title": "Real estate appraisal of land lots using GAMLSS models", "abstract": "The valuation of real estates (e.g., house, land, among others) is of extreme importance for decision making. Their singular characteristics make valuation through hedonic pricing methods dificult since the theory does not specify the correct regression functional form nor which explanatory variables should be included in the hedonic equation. In this article we perform real estate appraisal using a class of regression models proposed by Rigby & Stasinopoulos (2005): generalized additive models for location, scale and shape (GAMLSS). Our empirical analysis shows that these models seem to be more appropriate for estimation of the hedonic prices function than the regression models currently used to that end.", "published": "2011-02-10T00:24:21Z", "authors": ["Lutemberg Florencio", "Francisco Cribari-Neto", "Raydonal Ospina"], "categories": ["stat.AP"], "doi": "", "_query": "hedonic house price", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/math/0702828v1", "title": "Price systems for markets with transaction costs and control problems for some finance problems", "abstract": "In a market with transaction costs, the price of a derivative can be expressed in terms of (preconsistent) price systems (after Kusuoka (1995)). In this paper, we consider a market with binomial model for stock price and discuss how to generate the price systems. From this, the price formula of a derivative can be reformulated as a stochastic control problem. Then the dynamic programming approach can be used to calculate the price. We also discuss optimization of expected utility using price systems.", "published": "2007-02-27T12:59:43Z", "authors": ["Tzuu-Shuh Chiang", "Shang-Yuan Shiu", "Shuenn-Jyi Sheu"], "categories": ["math.PR", "q-fin.CP"], "doi": "10.1214/074921706000001094", "_query": "hedonic house price", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2007.05216v2", "title": "Price Optimization in Fashion E-commerce", "abstract": "With the rapid growth in the fashion e-commerce industry, it is becoming extremely challenging for the E-tailers to set an optimal price point for all the products on the platform. By establishing an optimal price point, they can maximize overall revenue and profit for the platform. In this paper, we propose a novel machine learning and optimization technique to find the optimal price point at an individual product level. It comprises three major components. Firstly, we use a demand prediction model to predict the next day demand for each product at a certain discount percentage. Next step, we use the concept of price elasticity of demand to get the multiple demand values by varying the discount percentage. Thus we obtain multiple price demand pairs for each product and we have to choose one of them for the live platform. Typically fashion e-commerce has millions of products, so there can be many permutations. Each permutation will assign a unique price point for all the products, which will sum up to a unique revenue number. To choose the best permutation which gives maximum revenue, a linear programming optimization technique is used. We have deployed the above methods in the live production environment and conducted several AB tests. According to the AB test result, our model is improving the revenue by 1 percent and gross margin by 0.81 percent.", "published": "2020-07-10T07:40:28Z", "authors": ["Sajan Kedia", "Samyak Jain", "Abhishek Sharma"], "categories": ["cs.LG", "stat.ML"], "doi": "", "_query": "hedonic house price", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/cond-mat/0308012v1", "title": "Multifractal Properties of Price Fluctuations of Stocks and Commodities", "abstract": "We analyze daily prices of 29 commodities and 2449 stocks, each over a period of $\\approx 15$ years. We find that the price fluctuations for commodities have a significantly broader multifractal spectrum than for stocks. We also propose that multifractal properties of both stocks and commodities can be attributed mainly to the broad probability distribution of price fluctuations and secondarily to their temporal organization. Furthermore, we propose that, for commodities, stronger higher order correlations in price fluctuations result in broader multifractal spectra.", "published": "2003-08-01T13:53:32Z", "authors": ["Kaushik Matia", "Yosef Ashkenazy", "H. Eugene Stanley"], "categories": ["cond-mat.stat-mech", "q-fin.ST"], "doi": "10.1209/epl/i2003-00194-y", "_query": "hedonic house price", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2012.11066v2", "title": "Fairness, Welfare, and Equity in Personalized Pricing", "abstract": "We study the interplay of fairness, welfare, and equity considerations in personalized pricing based on customer features. Sellers are increasingly able to conduct price personalization based on predictive modeling of demand conditional on covariates: setting customized interest rates, targeted discounts of consumer goods, and personalized subsidies of scarce resources with positive externalities like vaccines and bed nets. These different application areas may lead to different concerns around fairness, welfare, and equity on different objectives: price burdens on consumers, price envy, firm revenue, access to a good, equal access, and distributional consequences when the good in question further impacts downstream outcomes of interest. We conduct a comprehensive literature review in order to disentangle these different normative considerations and propose a taxonomy of different objectives with mathematical definitions. We focus on observational metrics that do not assume access to an underlying valuation distribution which is either unobserved due to binary feedback or ill-defined due to overriding behavioral concerns regarding interpreting revealed preferences. In the setting of personalized pricing for the provision of goods with positive benefits, we discuss how price optimization may provide unambiguous benefit by achieving a \"triple bottom line\": personalized pricing enables expanding access, which in turn may lead to gains in welfare due to heterogeneous utility, and improve revenue or budget utilization. We empirically demonstrate the potential benefits of personalized pricing in two settings: pricing subsidies for an elective vaccine, and the effects of personalized interest rates on downstream outcomes in microcredit.", "published": "2020-12-21T01:01:56Z", "authors": ["Nathan Kallus", "Angela Zhou"], "categories": ["cs.LG", "cs.CY", "stat.ML"], "doi": "", "_query": "hedonic house price", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2602.08429v1", "title": "On- and off-chain demand and supply drivers of Bitcoin price", "abstract": "Around three quarters of Bitcoin transactions take place off-chain. Despite their significance, the vast majority of the empirical literature on cryptocurrencies focuses on on-chain transactions. This paper presents one of the first analysis of both on- and off-chain demand- and supply-side factors. Two hypotheses relating on-chain and off-chain demand and supply drivers to the Bitcoin price are tested in an ARDL model with daily data from 2019 to 2024. Our estimates document the differential contributions of on-chain and off-chain drivers on the Bitcoin price. Off-chain demand pressures have a significant impact on the Bitcoin price in the long-run. In the short-run, both demand and supply drivers significantly affect the Bitcoin price. Regarding transactions on the blockchain, only on-chain demand pressures are statistically significant - both in the long- and short-run. These findings confirm the dual nature of the Bitcoin price dynamics, where also market fundamentals affect the Bitcoin price in addition to speculative drivers. Bitcoin whale trading has less significant impact on price in the long-run, while is more pronounced contemporaneously and one-period lag.", "published": "2026-02-09T09:38:45Z", "authors": ["Pavel Ciaian", "d'Artis Kancs", "Miroslava Rajcaniova"], "categories": ["econ.GN"], "doi": "", "_query": "hedonic house price", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2402.11864v2", "title": "The Price of Information", "abstract": "When an investor is faced with the option to purchase additional information regarding an asset price, how much should she pay? To address this question, we solve for the indifference price of information in a setting where a trader maximizes her expected utility of terminal wealth over a finite time horizon. If she does not purchase the information, then she solves a partial information stochastic control problem, while, if she does purchase the information, then she pays a cost and receives partial information about the asset's trajectory. We further demonstrate that when the investor can purchase the information at any stopping time prior to the end of the trading horizon, she chooses to do so at a deterministic time.", "published": "2024-02-19T06:12:07Z", "authors": ["Sebastian Jaimungal", "Xiaofei Shi"], "categories": ["q-fin.MF"], "doi": "", "_query": "hedonic house price", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2609.29674v2", "title": "The Sequential Price of Continual Learning", "abstract": "Sequential task updates are fundamental to continual learning, but their recency bias can impose a lasting performance cost. We study this cost in an overparameterized linear-regression model with i.i.d. task sampling. We prove that distribution-level forgetting and population loss converge to the same stationary limit. We quantify the additional loss incurred by sequential exact fitting, or the sequential price. In more homogeneous task geometries, it equals the intrinsic loss asymptotically attained by joint training, making the total loss twice as large. We further analyze fixed-strength elastic weight consolidation (EWC) under general task curvatures and characterize its stationary sequential price at every regularization strength. Under strong regularization, the price decays inversely with EWC strength while the mean-square coupling horizon grows proportionally. Experiments on Jester and Rotated MNIST support the predicted sequential price and its reduction by EWC, with quantitative agreement on real-world tasks satisfying the theory's assumptions and qualitative agreement under nonlinear finite-step training.", "published": "2026-08-30T14:27:18Z", "authors": ["Zonghuan Xu", "Xingjun Ma"], "categories": ["cs.LG", "cs.AI"], "doi": "", "_query": "hedonic house price", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/1010.3084v1", "title": "The Hungry Fly: Hydrodynamics of feeding in the common house fly", "abstract": "A large number of insect species feed primarily on a fluid diet. To do so, they must overcome the numerous challenges that arise in the design of high-efficiency, miniature pumps. Although the morphology of insect feeding structures has been described for decades, their dynamics remain largely unknown even in the most well studied species (e.g. fruit fly). Here, in the fluid dynamics video, we demonstrate in-vivo imaging and microsurgery to elucidate the design principles of feeding structures of the common house fly. Using high-resolution X-ray absorption microscopy, we record in-vivo flow of sucrose solutions through the body over many hours during fly feeding. Borrowing from microsurgery techniques common in neurophysiology, we are able to perturb the pump to a stall position and thus evaluate function under load conditions. Furthermore, fluid viscosity-dependent feedback is observed for optimal pump performance. As the gut of the fly starts to fill up, feedback from the stretch receptors in the cuticle dictates the effective flow rate. Finally, via comparative analysis between the housefly, blow fly, fruit fly and bumble bees, we highlight the common design principles and the role of interfacial phenomena in feeding.", "published": "2010-10-15T07:28:11Z", "authors": ["Manu Prakash", "Miles Steele"], "categories": ["physics.flu-dyn", "physics.bio-ph"], "doi": "10.1063/1.3640023", "_query": "hedonic house price", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2411.13427v1", "title": "Price Setting Rules, Rounding Tax, and Inattention Penalty", "abstract": "We study the price rounding regulation in Israel, which outlawed non-0-ending prices, forcing retailers to round 9 ending prices, which in many stores, comprised more than 60 percent of all prices. The goal of the regulation was to eliminate the rounding tax, the extra amount consumers paid because of price rounding, which was necessitated by the abolition of low denomination coins, and the inattention tax, the extra amount consumers paid the retailers because of their inattention to the prices rightmost digits. Using 4 different datasets, we assess the success of the government in achieving these goals, focusing on fast moving consumer goods, a category of products strongly affected by the price rounding regulation. We focus on the response of the retailers to the price rounding regulation and find that although the government succeeded in eliminating the rounding tax, the bottom line is that shoppers end up paying more, not less, because of the regulation, underscoring, once again, the warning of Milton Friedman that policies should be judged by their results, not by their intentions.", "published": "2024-11-20T16:09:57Z", "authors": ["Doron Sayag", "Avichai Snir", "Daniel Levy"], "categories": ["econ.GN"], "doi": "10.13140/RG.2.2.14308.80002", "_query": "hedonic house price", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2102.03038v2", "title": "Bounds and Heuristics for Multi-Product Personalized Pricing", "abstract": "We present tight bounds and heuristics for personalized, multi-product pricing problems. Under mild conditions we show that the best price in the direction of a positive vector results in profits that are guaranteed to be at least as large as a fraction of the profits from optimal personalized pricing. For unconstrained problems, the fraction depends on the factor and on optimal price vectors for the different customer types. For constrained problems the factor depends on the factor and a ratio of the constraints. Using a factor vector with equal components results in uniform pricing and has exceedingly mild sufficient conditions for the bound to hold. A robust factor is presented that achieves the best possible performance guarantee. As an application, our model yields a tight lower-bound on the performance of linear pricing relative to optimal personalized non-linear pricing, and suggests effective non-linear price heuristics relative to personalized solutions. Additionally, our model provides guarantees for simple strategies such as bundle-size pricing and component-pricing with respect to optimal personalized mixed bundle pricing. Heuristics to cluster customer types are also developed with the goal of improving performance by allowing each cluster to price along its own factor. Numerical results are presented for a variety of demand models that illustrate the tradeoffs between using the economic factor and the robust factor for each cluster, as well as the tradeoffs between using a clustering heuristic with a worst case performance of two and a machine learning clustering algorithm. In our experiments economically motivated factors coupled with machine learning clustering heuristics performed best.", "published": "2021-02-05T07:52:56Z", "authors": ["Guillermo Gallego", "Gerardo Berbeglia"], "categories": ["econ.TH", "math.OC"], "doi": "", "_query": "hedonic house price", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/0712.1093v1", "title": "The derivatives of Asian call option prices", "abstract": "The distribution of a time integral of geometric Brownian motion is not well understood. To price an Asian option and to obtain measures of its dependence on the parameters of time, strike price, and underlying market price, it is essential to have the distribution of time integral of geometric Brownian motion and it is also required to have a way to manipulate its distribution. We present integral forms for key quantities in the price of Asian option and its derivatives ({\\it{delta, gamma,theta, and vega}}). For example for any $a>0$ $\\mathbb{E} [ (A_t -a)^+] = t -a + a^{2} \\mathbb{E} [ (a+A_t)^{-1} \\exp (\\frac{2M_t}{a+ A_t} - \\frac{2}{a}) ]$, where $A_t = \\int^t_0 \\exp (B_s -s/2) ds$ and $M_t =\\exp (B_t -t/2).$", "published": "2007-12-07T08:40:48Z", "authors": ["Jungmin Choi", "Kyounghee Kim"], "categories": ["q-fin.PR", "math.PR"], "doi": "", "_query": "hedonic house price", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/1611.07636v1", "title": "Mechanism Design for Multi-Type Housing Markets", "abstract": "We study multi-type housing markets, where there are $p\\ge 2$ types of items, each agent is initially endowed one item of each type, and the goal is to design mechanisms without monetary transfer to (re)allocate items to the agents based on their preferences over bundles of items, such that each agent gets one item of each type. In sharp contrast to classical housing markets, previous studies in multi-type housing markets have been hindered by the lack of natural solution concepts, because the strict core might be empty. We break the barrier in the literature by leveraging AI techniques and making natural assumptions on agents' preferences. We show that when agents' preferences are lexicographic, even with different importance orders, the classical top-trading-cycles mechanism can be extended while preserving most of its nice properties. We also investigate computational complexity of checking whether an allocation is in the strict core and checking whether the strict core is empty. Our results convey an encouragingly positive message: it is possible to design good mechanisms for multi-type housing markets under natural assumptions on preferences.", "published": "2016-11-23T04:30:42Z", "authors": ["Sibel Adali", "Sujoy Sikdar", "Lirong Xia"], "categories": ["cs.GT"], "doi": "", "_query": "hedonic house price", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2304.14262v2", "title": "A flow-based ascending auction to compute buyer-optimal Walrasian prices", "abstract": "We consider a market where a set of objects is sold to a set of buyers, each equipped with a valuation function for the objects. The goal of the auctioneer is to determine reasonable prices together with a stable allocation. One definition of \"reasonable\" and \"stable\" is a Walrasian equilibrium, which is a tuple consisting of a price vector together with an allocation satisfying the following desirable properties: (i) the allocation is market-clearing in the sense that as much as possible is sold, and (ii) the allocation is stable in the sense that every buyer ends up with an optimal set with respect to the given prices. Moreover, \"buyer-optimal\" means that the prices are smallest possible among all Walrasian prices. In this paper, we present a combinatorial network flow algorithm to compute buyer-optimal Walrasian prices in a multi-unit matching market with additive valuation functions and buyer demands. The algorithm can be seen as a generalization of the classical housing market auction and mimics the very natural procedure of an ascending auction. We use our structural insights to prove monotonicity of the buyer-optimal Walrasian prices with respect to changes in supply or demand.", "published": "2023-04-27T15:19:33Z", "authors": ["Katharina Eickhoff", "S. Thomas McCormick", "Britta Peis", "Niklas Rieken", "Laura Vargas Koch"], "categories": ["cs.GT"], "doi": "10.1002/net.22218", "_query": "hedonic house price", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2402.11754v1", "title": "Attraction Via Prices and Information", "abstract": "We study the ramifications of increased commitment power for information provision in an oligopolistic market with search frictions. Although prices are posted and, therefore, guide search, if firms cannot commit to information provision policies, there is no active search at equilibrium so consumers visit (and purchase from) at most one firm. If firms can guide search by both their prices and information policies, there exists a unique symmetric equilibrium exhibiting price dispersion and active search. Nevertheless, when the market is thin, consumers prefer the former case, which features intense price competition. Firms always prefer the latter.", "published": "2024-02-19T00:53:18Z", "authors": ["Pak Hung Au", "Mark Whitmeyer"], "categories": ["econ.TH"], "doi": "", "_query": "hedonic house price", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/1906.01713v3", "title": "Optimal auction duration: A price formation viewpoint", "abstract": "We consider an auction market in which market makers fill the order book during a given time period while some other investors send market orders. We define the clearing price of the auction as the price maximizing the exchanged volume at the clearing time according to the supply and demand of each market participants. Then we derive in a semi-explicit form the error made between this clearing price and the efficient price as a function of the auction duration. We study the impact of the behavior of market takers on this error. To do so we consider the case of naive market takers and that of rational market takers playing a Nash equilibrium to minimize their transaction costs. We compute the optimal duration of the auctions for 77 stocks traded on Euronext and compare the quality of price formation process under this optimal value to the case of a continuous limit order book. Continuous limit order books are found to be usually sub-optimal. However, in term of our metric, they only moderately impair the quality of price formation process. Order of magnitude of optimal auction durations is from 2 to 10 minutes.", "published": "2019-06-04T20:16:00Z", "authors": ["Paul Jusselin", "Thibaut Mastrolia", "Mathieu Rosenbaum"], "categories": ["q-fin.TR", "math.OC", "math.PR"], "doi": "", "_query": "hedonic house price", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2308.13822v3", "title": "Dynamic Pricing for Reusable Resources: The Power of Two Prices", "abstract": "Motivated by real-world applications such as rental and cloud computing services, we investigate pricing for reusable resources. We consider a system where a single resource with a fixed number of identical copies serves customers with heterogeneous willingness-to-pay (WTP), and the usage duration distribution is general. Optimal dynamic policies are computationally intractable when usage durations are not memoryless, so existing literature has focused on static pricing, which incurs a steady-state performance loss of ${O}(\\sqrt{c})$ compared to optimality when supply and demand scale with $c$. We propose a class of dynamic \"stock-dependent\" policies that 1) are computationally tractable and 2) can attain a steady-state performance loss of $o(\\sqrt{c})$. We give parametric bounds based on the local shape of the reward function at the optimal fluid admission probability and show that the performance loss of stock-dependent policies can be as low as ${O}((\\log{c})^2)$. We characterize the tight performance loss for stock-dependent policies and show that they can in fact be achieved by a simple two-price policy that sets a higher price when the stock is below some threshold and a lower price otherwise. We extend our results to settings with multiple resources and multiple customer classes. Finally, we demonstrate this \"minimally dynamic\" class of two-price policies performs well numerically, even in non-asymptotic settings, suggesting that a little dynamicity can go a long way.", "published": "2023-08-26T09:19:26Z", "authors": ["Santiago R. Balseiro", "Will Ma", "Wenxin Zhang"], "categories": ["math.OC", "cs.DS"], "doi": "", "_query": "hedonic house price", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2007.03762v4", "title": "Transfer Learning for Electricity Price Forecasting", "abstract": "Electricity price forecasting is an essential task in all the deregulated markets of the world. The accurate prediction of the day-ahead electricity prices is an active research field and available data from various markets can be used as an input for forecasting. A collection of models have been proposed for this task, but the fundamental question on how to use the available big data is often neglected. In this paper, we propose to use transfer learning as a tool for utilizing information from other electricity price markets for forecasting. We pre-train a neural network model on source markets and finally do a fine-tuning for the target market. Moreover, we test different ways to use the rich input data from various electricity price markets. Our experiments on four different day-ahead markets indicate that transfer learning improves the electricity price forecasting performance in a statistically significant manner. Furthermore, we compare our results with stateof-the-art methods in a rolling window scheme to demonstrate the performance of the transfer learning approach.", "published": "2020-07-05T17:24:36Z", "authors": ["Salih Gunduz", "Umut Ugurlu", "Ilkay Oksuz"], "categories": ["eess.SP", "cs.LG", "stat.ML"], "doi": "", "_query": "hedonic house price", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/cs/9809004v1", "title": "Performance / Price Sort", "abstract": "NTsort is an external sort on WindowsNT 5.0. It has minimal functionality but excellent price performance. In particular, running on mail-order hardware it can sort 1.5 GB for a penny. For commercially available sorts, Postman Sort from Robert Ramey Software Development has elapsed time performance comparable to NTsort, while using less processor time. It can sort 1.27 GB for a penny (12.7 million records.) These sorts set new price-performance records. This paper documents this and proposes that the PennySort benchmark be revised to Performance/Price sort: a simple GB/$ sort metric based on a two-pass external sort.", "published": "1998-09-02T00:49:25Z", "authors": ["Jim Gray", "Joshua Coates", "Chris Nyberg"], "categories": ["cs.DB", "cs.PF"], "doi": "", "_query": "hedonic house price", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2206.13916v1", "title": "Grid Tariffs for Peak Demand Reduction: Is there a Price Signal Conflict with Electricity Spot Prices?", "abstract": "The electricity grid is expected to require vast investments due to the decarbonization-by-electrification trend, calling for a change in grid tariff design which provides proper incentives for reducing peak loads. However, price signals from grid tariffs could be distorted from electricity spot prices which also represents a significant of the total consumer electricity bill. This paper attempts to identify whether there is a price signal conflict between grid tariffs and spot prices. Four different grid tariff designs are compared, using a generic demand response model as part of a cost-minimizing linear program to simulate the reduction in peak load. The method is applied to metered electricity demand from 3608 consumers in Oslo, Norway. Results show that new grid tariff designs reduce peak loads by 1-4%, and that reduction in peak load is smaller when consumers are subject to electricity spot prices.", "published": "2022-06-28T11:49:55Z", "authors": ["Sigurd Bjarghov", "Matthias Hofmann"], "categories": ["math.OC", "eess.SY"], "doi": "", "_query": "hedonic house price", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2005.04937v1", "title": "Using statistics and mathematical modelling to understand infectious disease outbreaks: COVID-19 as an example", "abstract": "During an infectious disease outbreak, biases in the data and complexities of the underlying dynamics pose significant challenges in mathematically modelling the outbreak and designing policy. Motivated by the ongoing response to COVID-19, we provide a toolkit of statistical and mathematical models beyond the simple SIR-type differential equation models for analysing the early stages of an outbreak and assessing interventions. In particular, we focus on parameter estimation in the presence of known biases in the data, and the effect of non-pharmaceutical interventions in enclosed subpopulations, such as households and care homes. We illustrate these methods by applying them to the COVID-19 pandemic.", "published": "2020-05-11T09:06:43Z", "authors": ["Christopher E. Overton", "Helena B. Stage", "Shazaad Ahmad", "Jacob Curran-Sebastian", "Paul Dark", "Rajenki Das", "Elizabeth Fearon", "Timothy Felton", "Martyn Fyles", "Nick Gent", "Ian Hall", "Thomas House", "Hugo Lewkowicz", "Xiaoxi Pang", "Lorenzo Pellis", "Robert Sawko", "Andrew Ustianowski", "Bindu Vekaria", "Luke Webb"], "categories": ["q-bio.PE", "physics.soc-ph"], "doi": "10.1016/j.idm.2020.06.008", "_query": "hedonic house price", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/cs/0106028v1", "title": "Pricing Virtual Paths with Quality-of-Service Guarantees as Bundle Derivatives", "abstract": "We describe a model of a communication network that allows us to price complex network services as financial derivative contracts based on the spot price of the capacity in individual routers. We prove a theorem of a Girsanov transform that is useful for pricing linear derivatives on underlying assets, which can be used to price many complex network services, and it is used to price an option that gives access to one of several virtual channels between two network nodes, during a specified future time interval. We give the continuous time hedging strategy, for which the option price is independent of the service providers attitude towards risk. The option price contains the density function of a sum of lognormal variables, which has to be evaluated numerically.", "published": "2001-06-12T12:06:47Z", "authors": ["Lars Rasmusson"], "categories": ["cs.NI", "cs.CE"], "doi": "", "_query": "hedonic house price", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/1403.7830v1", "title": "Pseudo Linear Pricing Rule for Utility Indifference Valuation", "abstract": "This paper considers exponential utility indifference pricing for a multidimensional non-traded assets model, and provides two linear approximations for the utility indifference price. The key tool is a probabilistic representation for the utility indifference price by the solution of a functional differential equation, which is termed \\emph{pseudo linear pricing rule}. We also provide an alternative derivation of the quadratic BSDE representation for the utility indifference price.", "published": "2014-03-30T23:18:17Z", "authors": ["Vicky Henderson", "Gechun Liang"], "categories": ["q-fin.PM", "math.PR"], "doi": "", "_query": "hedonic house price", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2108.05436v1", "title": "Friddy multiagent price stabilization model", "abstract": "In a multiagent network model consisting of nodes, each network node has an agent and priced Friddy coins, and the agent can buy or sell Friddy coins in the marketplace. Though every node may not effectively have an equal price during the transaction time, the prices have to reach equilibrium by iterating buy and sell transactions on a macro level.", "published": "2021-08-11T20:33:42Z", "authors": ["Abdelrahman Elsharawy"], "categories": ["cs.AI"], "doi": "", "_query": "hedonic house price", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2109.01478v2", "title": "A random-supply Mean Field Game price model", "abstract": "We consider a market where a finite number of players trade an asset whose supply is a stochastic process. The price formation problem consists of finding a price process that ensures that when agents act optimally to minimize their trading costs, the market clears, and supply meets demand. This problem arises in market economies, including electricity generation from renewable sources in smart grids. Our model includes noise on the supply side, which is counterbalanced on the consumption side by storing energy or reducing the demand according to a dynamic price process. By solving a constrained minimization problem, we prove that the Lagrange multiplier corresponding to the market-clearing condition defines the solution of the price formation problem. For the linear-quadratic structure, we characterize the price process of a continuum population using optimal control techniques. We include numerical schemes for the price computation in the finite and infinite games, and we illustrate the model using real data.", "published": "2021-09-03T12:41:43Z", "authors": ["Diogo Gomes", "Julian Gutierrez", "Ricardo Ribeiro"], "categories": ["math.AP"], "doi": "", "_query": "hedonic house price", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/1405.4809v1", "title": "Optimal pricing for optimal transport", "abstract": "Suppose that $c(x,y)$ is the cost of transporting a unit of mass from $x\\in X$ to $y\\in Y$ and suppose that a mass distribution $μ$ on $X$ is transported optimally (so that the total cost of transportation is minimal) to the mass distribution $ν$ on $Y$. Then, roughly speaking, the Kantorovich duality theorem asserts that there is a price $f(x)$ for a unit of mass sold (say by the producer to the distributor) at $x$ and a price $g(y)$ for a unit of mass sold (say by the distributor to the end consumer) at $y$ such that for any $x\\in X$ and $y\\in Y$, the price difference $g(y)-f(x)$ is not greater than the cost of transportation $c(x,y)$ and such that there is equality $g(y)-f(x)=c(x,y)$ if indeed a nonzero mass was transported (via the optimal transportation plan) from $x$ to $y$. We consider the following optimal pricing problem: suppose that a new pricing policy is to be determined while keeping a part of the optimal transportation plan fixed and, in addition, some prices at the sources of this part are also kept fixed. From the producers' side, what would then be the highest compatible pricing policy possible? From the consumers' side, what would then be the lowest compatible pricing policy possible? In the framework of $c$-convexity theory, we have recently introduced and studied optimal $c$-convex $c$-antiderivatives and explicit constructions of these optimizers were presented. In the present paper we employ optimal $c$-convex $c$-antiderivatives and conclude that these are natural solutions to the optimal pricing problems mentioned above. This type of problems drew attention in the past and existence results were previously established in the case where $X=Y=R^n$ under various specifications. We solve the above problem for general spaces $X,Y$ and real-valued, lower semicontinuous cost functions $c$.", "published": "2014-05-19T17:05:08Z", "authors": ["Sedi Bartz", "Simeon Reich"], "categories": ["math.FA", "math.OC"], "doi": "", "_query": "hedonic house price", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2001.02734v1", "title": "Gasoline Pricing Policies for Transportation Safety", "abstract": "Economic factors can have substantial effects on transportation crash trends. This study makes a comprehensive examination of the relationship between the retail gasoline price (including state and federal fuel taxes) and transportation fatal crashes from 2007 to 2016 in the US. Data on motor vehicle, bicycle and pedestrian fatal crashes come from Fatality Analysis Reporting System (FARS) provided by the National Highway Safety Administration (NHTSA) and the gasoline price data is from U.S. Energy Information Administration (EIA). Random effect negative binomial regression models are used to estimate the impact of inflation-adjusted gasoline prices on trends of transportation fatal crashes. Initial results combined with results of previous studies showed that gender and transportation mean type (motorcycle, non-motorcycle, bicycle and pedestrian) play prominent roles in interpreting the final model, so by using random effect negative binomial regression, seven models are developed to evaluate the effects of gasoline price changes on total population, male, female, motorcyclists, non-motorcyclists, bicyclists and pedestrians separately. Our findings suggest that increasing the gasoline prices will not significantly alter the number of total fatal crashes. However, by looking at different vehicle types, it is estimated that one dollar increase in adjusted gasoline price is associated with 24.2% increase in the number of motorcycle fatal crashes, 1.9% decrease in the number of non-motorcycle fatal crashes, and 0.7% decrease in the number of pedestrian fatal crashes. Also, there is no noticeable difference between male and female in response to the gasoline price changes.", "published": "2020-01-08T20:48:45Z", "authors": ["Nima Safaei", "Chao Zhou"], "categories": ["stat.AP"], "doi": "", "_query": "hedonic house price", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2012.09577v3", "title": "Pricing of European options in incomplete jump diffusion markets", "abstract": "We study option prices in financial markets where the risky asset prices are modelled by jump diffusions. It was proposed by Schweizer (1996) in a general semimartingale setting, following earlier works by Föllmer and Sondermann (1986) and Bouleau and Lamberton (1989), that the right price of such an option is the initial wealth needed to make it possible to generate by a self-financing portfolio a terminal wealth which is as close as possible to the payoff F in the sense of variance. Schweizer calls this price the approximation price and he investigates interesting general properties of this price and its corresponding optimal portfolio. -However, neither of these authors compute explicitly this price in concrete cases. This is the motivation for the current paper: We apply stochastic control methods to compute this price in the setting of markets with assets described by jump diffusions. Our method involves Stackelberg games and a suitably modified stochastic maximum principle. We show that such an optimal initial wealth, denoted by z^, with corresponding optimal portfolio π^ exist and are unique. - If the coefficients of the risky asset prices are deterministic, we show that z^=E_{Q*}[F], for a specific equivalent martingale measure (EMM) Q*. This shows in particular that the minimal variance price is free from arbitrage. -Then for the general case we apply a suitable maximum principle of optimal stochastic control to relate the minimal variance price z^=p_{mv}(F) to the Hamiltonian and its adjoint processes, and we show that, under some conditions, z^=E_{Q_0}[F] for any Q_0 in a family M_0 of EMMs, described by the set of solutions of a system of linear equations. -Finally, we illustrate our results by looking at specific examples.", "published": "2020-12-17T13:36:01Z", "authors": ["Nacira Agram", "Bernt Øksendal"], "categories": ["math.OC"], "doi": "", "_query": "hedonic house price", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2407.03521v3", "title": "Algorithmic Collusion And The Minimum Price Markov Game", "abstract": "This paper introduces the Minimum Price Markov Game (MPMG), a theoretical model that reasonably approximates real-world first-price markets following the minimum price rule, such as public auctions. The goal is to provide researchers and practitioners with a framework to study market fairness and regulation in both digitized and non-digitized public procurement processes, amid growing concerns about algorithmic collusion in online markets. Using multi-agent reinforcement learning-driven artificial agents, we demonstrate that (i) the MPMG is a reliable model for first-price market dynamics, (ii) the minimum price rule is generally resilient to non-engineered tacit coordination among rational actors, and (iii) when tacit coordination occurs, it relies heavily on self-reinforcing trends. These findings contribute to the ongoing debate about algorithmic pricing and its implications.", "published": "2024-07-03T21:40:34Z", "authors": ["Igor Sadoune", "Marcelin Joanis", "Andrea Lodi"], "categories": ["cs.GT", "econ.GN"], "doi": "", "_query": "hedonic house price", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/1009.2329v2", "title": "Tick size and price diffusion", "abstract": "A tick size is the smallest increment of a security price. It is clear that at the shortest time scale on which individual orders are placed the tick size has a major role which affects where limit orders can be placed, the bid-ask spread, etc. This is the realm of market microstructure and there is a vast literature on the role of tick size on market microstructure. However, tick size can also affect price properties at longer time scales, and relatively less is known about the effect of tick size on the statistical properties of prices. The present paper is divided in two parts. In the first we review the effect of tick size change on the market microstructure and the diffusion properties of prices. The second part presents original results obtained by investigating the tick size changes occurring at the New York Stock Exchange (NYSE). We show that tick size change has three effects on price diffusion. First, as already shown in the literature, tick size affects price return distribution at an aggregate time scale. Second, reducing the tick size typically leads to an increase of volatility clustering. We give a possible mechanistic explanation for this effect, but clearly more investigation is needed to understand the origin of this relation. Third, we explicitly show that the ability of the subordination hypothesis in explaining fat tails of returns and volatility clustering is strongly dependent on tick size. While for large tick sizes the subordination hypothesis has significant explanatory power, for small tick sizes we show that subordination is not the main driver of these two important stylized facts of financial market.", "published": "2010-09-13T09:11:45Z", "authors": ["Gabriele La Spada", "J. Doyne Farmer", "Fabrizio Lillo"], "categories": ["q-fin.ST"], "doi": "", "_query": "hedonic house price", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/1707.08681v2", "title": "Gerrymandering and the net number of US House seats won due to vote-distribution asymmetries", "abstract": "Using the recently introduced declination function, we estimate the net number of seats won in the US House of Representatives due to asymmetries in vote distributions. Such asymmetries can arise from combinations of partisan gerrymandering and inherent geographic advantage. Our estimates show significant biases in favor of the Democrats prior to the mid 1990s and significant biases in favor of Republicans since then. We find net differences of 28, 20 and 25 seats in favor of the Republicans in the years 2012, 2014 and 2016, respectively. The validity of our results is supported by the technique of simulated packing and cracking. We also use this technique to show that the presidential-vote logistic regression model is insensitive to the packing and cracking by which partisan gerrymanders are achieved.", "published": "2017-07-27T01:46:28Z", "authors": ["Jeffrey S. Buzas", "Gregory S. Warrington"], "categories": ["stat.AP"], "doi": "", "_query": "hedonic house price", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/1912.10640v1", "title": "Pricing of the Geometric Asian Options Under a Multifactor Stochastic Volatility Model", "abstract": "This paper focuses on the pricing of continuous geometric Asian options (GAOs) under a multifactor stochastic volatility model. The model considers fast and slow mean reverting factors of volatility, where slow volatility factor is approximated by a quadratic arc. The asymptotic expansion of the price function is assumed, and the first order price approximation is derived using the perturbation techniques for both floating and fixed strike GAOs. Much simplified pricing formulae for the GAOs are obtained in this multifactor stochastic volatility framework. The zeroth order term in the price approximation is the modified Black-Scholes price for the GAOs. This modified price is expressed in terms of the Black-Scholes price for the GAOs. The accuracy of the approximate option pricing formulae is established, and the model parameter is also estimated by capturing the volatility smiles.", "published": "2019-12-23T06:23:37Z", "authors": ["Gifty Malhotra", "R. Srivastava", "H. C. Taneja"], "categories": ["q-fin.PR", "q-fin.MF"], "doi": "", "_query": "hedonic house price", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/1305.5404v1", "title": "Pure Price of Anarchy for Generalized Second Price Auction", "abstract": "The Generalized Second Price auction (GSP) has been widely used by search engines to sell ad slots. Previous studies have shown that the pure Price Of Anarchy (POA) of GSP is 1.25 when there are two ad slots and 1.259 when three ad slots. For the cases with more than three ad slots, however, only some untight upper bounds of the pure POA were obtained. In this work, we improve previous results in two aspects: (1) We prove that the pure POA for GSP is 1.259 when there are four ad slots, and (2) We show that the pure POA for GSP with more than four ad slots is also 1.259 given the bidders are ranked according to a particular permutation.", "published": "2013-05-23T12:57:26Z", "authors": ["Wenkui Ding", "Tao Wu", "Tao Qin", "Tie-Yan Liu"], "categories": ["cs.GT"], "doi": "", "_query": "hedonic house price", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/1511.01564v1", "title": "Pricing Parisian down-and-in options", "abstract": "In this paper, we price American-style Parisian down-and-in call options under the Black-Scholes framework. Usually, pricing an American-style option is much more difficult than pricing its European-style counterpart because of the appearance of the optimal exercise boundary in the former. Fortunately, the optimal exercise boundary associated with an American-style Parisian knock-in option only appears implicitly in its pricing partial differential equation (PDE) systems, instead of explicitly as in the case of an American-style Parisian knock-out option. We also recognize that the \"moving window\" technique developed for pricing European-style Parisian up-and-out options can be adopted to price American-style Parisian knock-in options as well. In particular, we obtain a simple analytical solution for American-style Parisian down-and-in call options and our new formula is written in terms of four double integrals, which can be easily computed numerically.", "published": "2015-11-05T00:49:08Z", "authors": ["Song-Ping Zhu", "Nhat-Tan Le", "Wen-Ting Chen", "Xiaoping Lu"], "categories": ["q-fin.PR"], "doi": "10.1016/j.aml.2014.10.019", "_query": "hedonic house price", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2509.13840v1", "title": "Characterizing Human Limb Movements Using An In-House Multi-Channel Non-Invasive Surface-EMG System", "abstract": "Electromyography (EMG) signals are obtained from muscle cell activity. The recording and analysis of EMG signals has several applications. The EMG is of diagnostic importance for treating patients suffering from neurological and neuromuscular disorders. Conventional methods involve placement of invasive electrodes within the muscles to record EMG signals. The goal is to showcase the usage of surface based EMG signals to characterize all possible human limb movements. An in-house non-invasive EMG signal acquisition system that offers characterization of human limb actions is a suitable candidate for motor impairment studies and easily extendable to design bionics control specifically for neuromuscular disorder patients. An in-house 8-channel surface-EMG signal acquisition system was designed, fabricated, and employed for characterizing specific movements of upper and lower limb. The non-invasive acquisition system captures the compound electromuscular activity generated from the group of muscles. The EMG acquisition system was designed as a modular structure where the front end analog circuit designs were replicated for all 8 channels, and were designed to function independently. Support vector machine (SVM) as classifier models were developed offline to successfully characterize different human limb actions. The in house built 8 channel acquisition system with ML classifier models were utilized to successfully characterize movements at various joints of the upper and lower limb including fingers, wrist, elbow, shoulder, knee, and ankle individually.", "published": "2025-09-17T09:12:06Z", "authors": ["Vinay C K", "Vikas Vazhayil", "Madhav rao"], "categories": ["eess.SY"], "doi": "", "_query": "hedonic house price", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2006.02596v3", "title": "Option Pricing in Markets with Informed Traders", "abstract": "The objective of this paper is to introduce the theory of option pricing for markets with informed traders within the framework of dynamic asset pricing theory. We introduce new models for option pricing for informed traders in complete markets where we consider traders with information on the stock price direction and stock return mean. The Black-Scholes-Merton option pricing theory is extended for markets with informed traders, where price processes are following continuous-diffusions. By doing so, the discontinuity puzzle in option pricing is resolved. Using market option data, we estimate the implied surface of the probability for a stock upturn, the implied mean stock return surface, and implied trader information intensity surface.", "published": "2020-06-04T00:17:39Z", "authors": ["Yuan Hu", "Abootaleb Shirvani", "Stoyan Stoyanov", "Young Shin Kim", "Frank J. Fabozzi", "Svetlozar T. Rachev"], "categories": ["q-fin.MF"], "doi": "10.1142/S0219024920500375", "_query": "hedonic house price", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/0705.1680v1", "title": "Option Pricing Using Bayesian Neural Networks", "abstract": "Options have provided a field of much study because of the complexity involved in pricing them. The Black-Scholes equations were developed to price options but they are only valid for European styled options. There is added complexity when trying to price American styled options and this is why the use of neural networks has been proposed. Neural Networks are able to predict outcomes based on past data. The inputs to the networks here are stock volatility, strike price and time to maturity with the output of the network being the call option price. There are two techniques for Bayesian neural networks used. One is Automatic Relevance Determination (for Gaussian Approximation) and one is a Hybrid Monte Carlo method, both used with Multi-Layer Perceptrons.", "published": "2007-05-11T15:55:31Z", "authors": ["Michael Maio Pires", "Tshilidzi Marwala"], "categories": ["cs.CE", "cs.NE"], "doi": "", "_query": "hedonic house price", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/1903.01655v1", "title": "Cross-shareholding networks and stock price synchronicity: Evidence from China", "abstract": "This paper investigates the effect of cross-shareholding on stock price synchronicity, as a measure of price informativeness, of the listed firms in the Chinese stock market. We gauge firms' levels of cross-shareholdings in terms of centrality in the cross-shareholding network. It is confirmed that it is through a noise-reducing process that cross-shareholding promotes price synchronicity and reduces price delay. More importantly, this effect on price informativeness is pronounced for large firms and in the periods of market downturns. Overall, our analyses provide insights into the relation between the ownership structure and price informativeness.", "published": "2019-03-05T04:05:21Z", "authors": ["Fenghua Wen", "Yujie Yuan", "Wei-Xing Zhou"], "categories": ["q-fin.ST"], "doi": "10.1002/ijfe.1828", "_query": "hedonic house price", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2501.15834v2", "title": "The Strong Core of Housing Markets with Partial Order Preferences", "abstract": "We study the strong core of housing markets when agents' preferences over houses are expressed as partial orders. We provide a structural characterization of the strong core, and propose an efficient algorithm that finds an allocation in the strong core or decides that it is empty, even in the presence of forced and forbidden arcs. The algorithm satisfies the property of group-strategyproofness. Additionally, we show that certain results known for the strong core in the case when agents' preferences are weak orders can be extended to the setting with partial order preferences; among others, we show that the strong core in such housing markets satisfies the property of respecting improvements.", "published": "2025-01-27T07:42:37Z", "authors": ["Ildikó Schlotter", "Lydia Mirabel Mendoza-Cadena"], "categories": ["cs.GT"], "doi": "", "_query": "hedonic house price", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/1301.3114v2", "title": "Estimating the efficient price from the order flow: a Brownian Cox process approach", "abstract": "At the ultra high frequency level, the notion of price of an asset is very ambiguous. Indeed, many different prices can be defined (last traded price, best bid price, mid price,...). Thus, in practice, market participants face the problem of choosing a price when implementing their strategies. In this work, we propose a notion of efficient price which seems relevant in practice. Furthermore, we provide a statistical methodology enabling to estimate this price form the order flow.", "published": "2013-01-14T20:25:50Z", "authors": ["Sylvain Delattre", "Christian Y. Robert", "Mathieu Rosenbaum"], "categories": ["q-fin.TR", "q-fin.ST"], "doi": "", "_query": "hedonic house price", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2607.12205v1", "title": "A Unified Credit Expansion Theory on Housing Cycle: Causal Evidence for Within- and Cross-Metro Patterns in the Prior, Boom, Bust, and Recovery Periods", "abstract": "During the 1999-2019 U.S. housing cycle, three empirical facts present a puzzle: in the boom period, the correlation between income growth and mortgage growth is (1) negative across ZIP codes within a metropolitan area, but (2) positive across metropolitan areas, and (3) the metropolitan areas that experience the worst bust also show the strongest recovery. I develop a unified credit expansion theory that explains both within- and cross-metro patterns in the prior, boom, bust, and recovery periods (including the three facts above) and generates new testable implications of ``double differences\" (cross ZIP codes and cross metros) for the four periods. Following the idea of ``Economic Base Theory\", I construct local economic exposure to net export growth as the driving force of local economy and credit expansion. For the identification strategy, I use a new instrumental variable approach from the International trade literature for the following empirical results. First, I show that high-net-export-growth metros experience a stronger boom-bust-recovery housing cycle due to credit expansion in private-label mortgages (PLMs), rather than in government-sponsored enterprise mortgages (GSEMs), because only the former can legally respond to local economic conditions. Second, for the ``double differences\", I define a low-minus-high (LMH) factor as the private-label mortgage (and house price) growth in low-income ZIP codes minus that in high-income ZIP codes within the same metropolitan area. I show that this low-minus-high factor (as a measure of credit expansion) in the high-net-export-growth metros is more positive during the boom period, more negative during the bust period, and slightly more positive in the recovery period than in the low-net-export-growth metros. Lastly, I employ five tests to demonstrate that ``speculation\" is unlikely to play a dominant role in this housing cycle.", "published": "2026-07-13T23:15:50Z", "authors": ["Bo Li"], "categories": ["q-fin.GN"], "doi": "", "_query": "hedonic house price", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2204.00320v2", "title": "Branch and Price for Submodular Bin Packing", "abstract": "The Submodular Bin Packing (SMBP) problem asks for packing unsplittable items into a minimal number of bins for which the capacity utilization function is submodular. SMBP is equivalent to chance-constrained and robust bin packing problems under various conditions. SMBP is a hard binary nonlinear programming optimization problem. In this paper, we propose a branch-and-price algorithm to solve this problem. The resulting price subproblems are submodular knapsack problems, and we propose a tailored exact branch-and-cut algorithm based on a piece-wise linear relaxation to solve them. To speed up column generation, we develop a hybrid pricing strategy to replace the exact pricing algorithm with a fast pricing heuristic. We test our algorithms on instances generated as suggested in the literature. The computational results show the efficiency of our branch-and-price algorithm and the proposed pricing techniques.", "published": "2022-04-01T09:58:56Z", "authors": ["Liding Xu", "Claudia D'Ambrosio", "Sonia Haddad Vanier", "Emiliano Traversi"], "categories": ["math.OC"], "doi": "", "_query": "hedonic house price", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2012.06008v1", "title": "Price Suggestion for Online Second-hand Items with Texts and Images", "abstract": "This paper presents an intelligent price suggestion system for online second-hand listings based on their uploaded images and text descriptions. The goal of price prediction is to help sellers set effective and reasonable prices for their second-hand items with the images and text descriptions uploaded to the online platforms. Specifically, we design a multi-modal price suggestion system which takes as input the extracted visual and textual features along with some statistical item features collected from the second-hand item shopping platform to determine whether the image and text of an uploaded second-hand item are qualified for reasonable price suggestion with a binary classification model, and provide price suggestions for second-hand items with qualified images and text descriptions with a regression model. To satisfy different demands, two different constraints are added into the joint training of the classification model and the regression model. Moreover, a customized loss function is designed for optimizing the regression model to provide price suggestions for second-hand items, which can not only maximize the gain of the sellers but also facilitate the online transaction. We also derive a set of metrics to better evaluate the proposed price suggestion system. Extensive experiments on a large real-world dataset demonstrate the effectiveness of the proposed multi-modal price suggestion system.", "published": "2020-12-10T22:50:42Z", "authors": ["Liang Han", "Zhaozheng Yin", "Zhurong Xia", "Mingqian Tang", "Rong Jin"], "categories": ["cs.AI"], "doi": "", "_query": "hedonic house price", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2510.05459v1", "title": "Metric criteria for fixed price of countable groups", "abstract": "We establish general criteria for a countable group $Γ$ to have fixed price 1 depending on a choice of left-invariant proper metric on $Γ$. We apply this criterion to show that if $Γ_1,Γ_2$ are two countable groups satisfying a certain growth condition then $Γ_1\\times Γ_2$ has fixed price 1. For example, $Γ\\times Γ$ has fixed price 1 for any countable group $Γ$.", "published": "2025-10-06T23:45:20Z", "authors": ["Erin Bevilacqua", "Lewis Bowen"], "categories": ["math.GR", "math.DS"], "doi": "", "_query": "hedonic house price", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/1408.1352v1", "title": "A simple model of local prices and associated risk evaluation", "abstract": "A simple spin system is constructed to simulate dynamics of asset prices and studied numerically. The outcome for the distribution of prices is shown to depend both on the dimension of the system and the introduction of price into the link measure. For dimensions below 2, the associated risk is high and the price distribution is bimodal. For higher dimensions, the price distribution is Gaussian and the associated risk is much lower. It is suggested that the results are relevant to rare assets or situations where few players are involved in the deal making process.", "published": "2014-08-01T22:04:59Z", "authors": ["Krzysztof Urbanowicz", "Peter Richmond", "Janusz A. Hołyst"], "categories": ["q-fin.GN", "physics.data-an"], "doi": "", "_query": "hedonic house price", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2604.00556v1", "title": "HabitatAgent: An End-to-End Multi-Agent System for Housing Consultation", "abstract": "Housing selection is a high-stakes and largely irreversible decision problem. We study housing consultation as a decision-support interface for housing selection. Existing housing platforms and many LLM-based assistants often reduce this process to ranking or recommendation, resulting in opaque reasoning, brittle multi-constraint handling, and limited guarantees on factuality. We present HabitatAgent, the first LLM-powered multi-agent architecture for end-to-end housing consultation. HabitatAgent comprises four specialized agent roles: Memory, Retrieval, Generation, and Validation. The Memory Agent maintains multi-layer user memory through internal stages for constraint extraction, memory fusion, and verification-gated updates; the Retrieval Agent performs hybrid vector--graph retrieval (GraphRAG); the Generation Agent produces evidence-referenced recommendations and explanations; and the Validation Agent applies multi-tier verification and targeted remediation. Together, these agents provide an auditable and reliable workflow for end-to-end housing consultation. We evaluate HabitatAgent on 100 real user consultation scenarios (300 multi-turn question--answer pairs) under an end-to-end correctness protocol. A strong single-stage baseline (Dense+Rerank) achieves 75% accuracy, while HabitatAgent reaches 95%.", "published": "2026-04-01T06:59:50Z", "authors": ["Hongyang Yang", "Yanxin Zhang", "Yang She", "Yue Xiao", "Hao Wu", "Yiyang Zhang", "Jiapeng Hou", "Rongshan Zhang"], "categories": ["cs.LG", "cs.AI", "cs.ET", "q-fin.CP", "q-fin.RM"], "doi": "", "_query": "hedonic house price", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/1009.3810v1", "title": "Asset pricing with random information flow", "abstract": "In the information-based approach to asset pricing the market filtration is modelled explicitly as a superposition of signals concerning relevant market factors and independent noise. The rate at which the signal is revealed to the market then determines the overall magnitude of asset volatility. By letting this information flow rate random, we obtain an elementary stochastic volatility model within the information-based approach. Such an extension is economically justified on account of the fact that in real markets information flow rates are rarely measurable. Effects of having a random information flow rate is investigated in detail in the context of a simple model setup. Specifically, the price process of the asset is derived, and its characteristic behaviours are revealed via simulation studies. The price of a European-style option is worked out, showing that the model has a sufficient flexibility to fit volatility surface. As an extension of the random information flow model, price manipulation is considered. A simple model is used to show how the skewness of the manipulated and unmanipulated price processes take opposite signature.", "published": "2010-09-20T13:21:37Z", "authors": ["Dorje C. Brody", "Yan Tai Law"], "categories": ["q-fin.PR"], "doi": "", "_query": "hedonic house price", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/1802.03050v1", "title": "Thompson Sampling for Dynamic Pricing", "abstract": "In this paper we apply active learning algorithms for dynamic pricing in a prominent e-commerce website. Dynamic pricing involves changing the price of items on a regular basis, and uses the feedback from the pricing decisions to update prices of the items. Most popular approaches to dynamic pricing use a passive learning approach, where the algorithm uses historical data to learn various parameters of the pricing problem, and uses the updated parameters to generate a new set of prices. We show that one can use active learning algorithms such as Thompson sampling to more efficiently learn the underlying parameters in a pricing problem. We apply our algorithms to a real e-commerce system and show that the algorithms indeed improve revenue compared to pricing algorithms that use passive learning.", "published": "2018-02-08T21:17:28Z", "authors": ["Ravi Ganti", "Matyas Sustik", "Quoc Tran", "Brian Seaman"], "categories": ["stat.ML", "cs.LG"], "doi": "", "_query": "hedonic house price", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/1510.03797v1", "title": "Complex Politics: A Quantitative Semantic and Topological Analysis of UK House of Commons Debates", "abstract": "This study is a first, exploratory attempt to use quantitative semantics techniques and topological analysis to analyze systemic patterns arising in a complex political system. In particular, we use a rich data set covering all speeches and debates in the UK House of Commons between 1975 and 2014. By the use of dynamic topic modeling (DTM) and topological data analysis (TDA) we show that both members and parties feature specific roles within the system, consistent over time, and extract global patterns indicating levels of political cohesion. Our results provide a wide array of novel hypotheses about the complex dynamics of political systems, with valuable policy applications.", "published": "2015-10-13T17:49:09Z", "authors": ["Stefano Gurciullo", "Michael Smallegan", "María Pereda", "Federico Battiston", "Alice Patania", "Sebastian Poledna", "Daniel Hedblom", "Bahattin Tolga Oztan", "Alexander Herzog", "Peter John", "Slava Mikhaylov"], "categories": ["physics.soc-ph", "cs.CL", "cs.SI"], "doi": "", "_query": "hedonic house price", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2603.18920v1", "title": "The Optimal Reset-Hour of a Once-Daily Petrol Price Increase Limit", "abstract": "A German ministry recently proposed a limit of at most one price increase per day for petrol stations. At what time should the price reset be allowed in order to lower price levels the most throughout the day? To answer this question, I infer the share of price-sensitive consumers for every hour of the day from German petrol station price data, based on a simple spatial-competition model. I focus on weekdays, which are the relevant target because commuter demand is less flexible than weekend demand. Hourly petrol station prices peak at 07:00 and bottom out at 19:00. Given the inferred composition of price-sensitivity throughout the day and hourly passenger-car traffic frequencies as a proxy for quantity, I evaluate every possible reset-hour of the new policy. The lowest traffic-weighted average price is achieved by an 11:00 reset. With this reset-hour, the resulting equilibrium price throughout the day is constant. This would lead to lower prices in the morning but higher prices in the evening, harming price-sensitive consumers but benefiting morning commuters and firms.", "published": "2026-03-19T13:56:32Z", "authors": ["Christoph Siemroth"], "categories": ["econ.GN"], "doi": "", "_query": "hedonic house price", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2109.12980v1", "title": "On the nature of monetary and price inflation and hyperinflation", "abstract": "Monetary inflation is a sustained increase in the money supply than can result in price inflation, which is a rise in the general level of prices of goods and services. The objectives of this paper were to develop economic models to (1) predict the annual rate of growth in the US consumer price index (CPI), based on the annual growth in the US broad money supply (BMS), the annual growth in US real GDP, and the annual growth in US savings, over the time period 2001 to 2019; (2) investigate the means by which monetary and price inflation can develop into monetary and price hyperinflation. The hypothesis that the annual rate of growth in the US CPI is a function of the annual growth in the US BMS minus the annual growth in US real GDP minus the annual growth in US savings, over the time period investigated, has been shown to be the case. However, an exact relationship required the use of a non-zero residual term. A mathematical statistical formulation of a hyperinflationary process has been provided and used to quantify the period of hyperinflation in the Weimar Republic, from July 1922 until the end of November 1923.", "published": "2021-08-18T09:39:31Z", "authors": ["Laurence Francis Lacey"], "categories": ["q-fin.GN", "stat.ME"], "doi": "", "_query": "hedonic house price", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/0902.4159v2", "title": "Liquidity Crisis, Granularity of the Order Book and Price Fluctuations", "abstract": "We introduce a microscopic model for the dynamics of the order book to study how the lack of liquidity influences price fluctuations. We use the average density of the stored orders (granularity $g$) as a proxy for liquidity. This leads to a Price Impact Surface which depends on both volume $ω$ and $g$. The dependence on the volume (averaged over the granularity) of the Price Impact Surface is found to be a concave power law function $<φ(ω,g)>_g\\simω^δ$ with $δ\\approx 0.59$. Instead the dependence on the granularity is $φ(ω,g|ω)\\sim g^α$ with $α\\approx-1$, showing a divergence of price fluctuations in the limit $g\\to 0$. Moreover, even in intermediate situations of finite liquidity, this effect can be very large and it is a natural candidate for understanding the origin of large price fluctuations.", "published": "2009-02-24T14:40:03Z", "authors": ["M. Cristelli", "V. Alfi", "L. Pietronero", "A. Zaccaria"], "categories": ["q-fin.TR"], "doi": "10.1140/epjb/e2009-00353-6", "_query": "hedonic house price", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2608.21334v4", "title": "Across-Design Uncertainty in Short Pricing Panels: Inference and Identification", "abstract": "Short observational pricing panels often contain many data points but very few actual price changes. This paper shows that this sparsity creates a hidden source of error that standard statistical methods miss. When estimating price effects, most of the uncertainty does not come from sample size within a panel, but from the specific history of price movements observed. Standard confidence intervals fail because they only measure variation within the panel, ignoring this broader design-level error. Using simulations, we find that this cross-design variation accounts for most of the estimation error, causing standard methods to significantly understate uncertainty. First, we show that cross-design error decreases predictably as the total volume of price variation increases. Second, adding more data from regions that share the same price trends does not fix the issue; true precision improves only when combining data across units with independent price trajectories. Third, applying a simple variance-component adjustment across independently priced units restores accurate statistical coverage. We confirm these findings in real-world store scanner data, showing that products and pricing zones behave as if they have far fewer independent price movements than their raw counts suggest. Ultimately, reliable inference in passive pricing data requires genuine, independent variation, which can be achieved through controlled regional price testing.", "published": "2026-08-21T17:40:31Z", "authors": ["Pedro Cadahia Delgado"], "categories": ["cs.LG", "econ.EM"], "doi": "", "_query": "hedonic house price", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/1304.4690v1", "title": "On option pricing in illiquid markets with jumps", "abstract": "One of the shortcomings of the Black and Scholes model on option pricing is the assumption that trading of the underlying asset does not affect the price of that asset. This assumption can be fulfilled only in perfectly liquid markets. Since most markets are illquid, this assumption might be too restrictive. Thus, taking into account the price impact in option pricing is an important issue. This issue has been dealt with, to some extent, for illiquid markets by assuming a continuous process, mainly based on the Brownian motion. However, the recent financial crisis and its effects on the global stock markets have propagated the urgent need for more realistic models where the stochastic process describing the price trajectories involves random jumps. Nonetheless, works related to markets with jumps are scant compared to the continuous ones. In addition, these previous studies do not deal with illiquid markets. The contribution of this paper is to tackle the pricing problem for options in illiquid markets with jumps as well as the hedging strategy within this context, which is the first of its kind to the best knowledge.", "published": "2013-04-17T04:58:06Z", "authors": ["Youssef El-Khatib", "Abdulnasser Hatemi-J"], "categories": ["q-fin.PR"], "doi": "", "_query": "hedonic house price", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/1310.4962v6", "title": "A New Dynamic Pricing Model based on Convex Hull Pricing", "abstract": "This paper presents a new dynamic pricing model (a.k.a. real-time pricing) that reflects startup costs of generators. Dynamic pricing, which is a method to control demand by pricing electricity at hourly (or more often) intervals, has been studied by many researchers. They assume that the cost functions of suppliers are convex, although they may be nonconvex because of the startup costs of generators in practice. We provide a dynamic pricing model that takes into account such cost functions within the settings of unit commitment problems (UCPs). Our model gives convex hull price (CHP), which has not been used in the context of dynamic pricing, though it is known that the CHP minimizes the uplift payment which is disadvantageous to suppliers for a given demand. In addition, we apply an iterative algorithm based on the subgradient method to solve our model. Numerical experiments show the efficiency of our model on reducing uplift payments. The prices determined by our algorithm give sufficiently small uplift payments in a realistic computational time.", "published": "2013-10-18T09:30:15Z", "authors": ["Naoki Ito", "Akiko Takeda", "Toru Namerikawa"], "categories": ["math.OC"], "doi": "", "_query": "hedonic house price", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2302.00166v1", "title": "Market-Based Coordination of Price-Responsive Demand Using Dantzig-Wolfe Decomposition Method", "abstract": "With the increased share of Distributed Generation (DG) and Demand Responsive (DR) loads in the power systems, new approaches based on the game theory framework have been proposed to tackle the problem of coordination of Price Responsive Devices (PRD). The PRDs are modeled as self-benefiting players who try to optimize their consumption based on the price. In this paper, for the first time, a new algorithm based on the Dantzig-Wolfe (DW) Decomposition method to solve the coordination problem of self-benefiting PRDs in a distributed fashion has been proposed. By utilizing the distributed nature of Dantzig-Wolfe, the PRD's self-benefiting algorithms are modeled as sub-problems of the DW, and the coordinator (or the grid operator) who collects energy consumption of PRDs (their energy bid), solves the master problem of the DW and calculate the price signal accordingly. The proposed algorithm is fast since the subproblem in DW (which could be millions of PRDs) can be solved simultaneously. Furthermore, based on the DW theory, if the PRDs subproblems are convex, reaching the optimal point (Equal to Nash Equilibrium) in limited iterations is guaranteed. A simulation with 8 participant households has been conducted to evaluate the model. Each house is equipped with two types of loads: an Electric Vehicle (EV) as a sample of interruptible loads and an Electric Water Heater (EWH) as a sample of Thermostatically Control Loads (TCA). The results show that when the algorithm reaches the optimal point, the generation cost and the user payment (based on the marginal cost of generation) decrease. Furthermore, the aggregate load's Peak to Average (PAR) reduces significantly.", "published": "2023-02-01T01:08:55Z", "authors": ["Foad Najafi", "Matthias Fripp"], "categories": ["eess.SY", "math.OC"], "doi": "", "_query": "hedonic house price", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/0812.4010v1", "title": "Discrete Time vs Continuous Time Stock-price Dynamics and implications for Option Pricing", "abstract": "In the present paper we construct stock price processes with the same marginal log-normal law as that of a geometric Brownian motion and also with the same transition density (and returns' distributions) between any two instants in a given discrete-time grid. We then illustrate how option prices based on such processes differ from Black and Scholes', in that option prices can be either arbitrarily close to the option intrinsic value or arbitrarily close to the underlying stock price. We also explain that this is due to the particular way one models the stock-price process in between the grid time instants which are relevant for trading. The theoretical result concerning scalar stochastic differential equations with prescribed diffusion coefficient whose densities evolve in a prescribed exponential family, on which part of the paper is based, is presented in detail.", "published": "2008-12-21T03:01:28Z", "authors": ["Damiano Brigo", "Fabio Mercurio"], "categories": ["q-fin.PR"], "doi": "", "_query": "hedonic house price", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2506.08718v1", "title": "Price Discovery in Cryptocurrency Markets", "abstract": "This document analyzes price discovery in cryptocurrency markets by comparing centralized and decentralized exchanges, as well as spot and futures markets. The study focuses first on Ethereum (ETH) and then applies a similar approach to Bitcoin (BTC). Chapter 1 outlines the theoretical framework, emphasizing the structural differences between centralized exchanges and decentralized finance mechanisms, especially Automated Market Makers (AMMs). It also explains how to construct an order book from a liquidity pool in a decentralized setting for comparison with centralized exchanges. Chapter 2 describes the methodological tools used: Hasbrouck's Information Share, Gonzalo and Granger's Permanent-Transitory decomposition, and the Hayashi-Yoshida estimator. These are applied to explore lead-lag dynamics, cointegration, and price discovery across market types. Chapter 3 presents the empirical analysis. For ETH, it compares price dynamics on Binance and Uniswap v2 over a one-year period, focusing on five key events in 2024. For BTC, it analyzes the relationship between spot and futures prices on the CME. The study estimates lead-lag effects and cointegration in both cases. Results show that centralized markets typically lead in ETH price discovery. In futures markets, while they tend to lead overall, high-volatility periods produce mixed outcomes. The findings have key implications for traders and institutions regarding liquidity, arbitrage, and market efficiency. Various metrics are used to benchmark the performance of modified AMMs and to understand the interaction between decentralized and centralized structures.", "published": "2025-06-10T12:07:11Z", "authors": ["Juan Plazuelo Pascual", "Carlos Tardon Rubio", "Juan Toro Cebada", "Angel Hernando Veciana"], "categories": ["q-fin.TR"], "doi": "", "_query": "hedonic house price", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2302.11643v3", "title": "An Empirical Analysis of Optimal Nonlinear Pricing in Business-to-Business Markets", "abstract": "In continuous-choice settings, consumers decide not only on whether to purchase a product, but also on how much to purchase. Thus, firms optimize a full price schedule rather than a single price point. This paper provides a methodology to empirically estimate the optimal schedule under multi-dimensional consumer heterogeneity with a focus on B2B applications. We apply our method to novel data from an educational-services firm that contains purchase-size information not only for deals that materialized, but also for potential deals that eventually failed. We show that this data, combined with identifying assumptions, helps infer how price sensitivity varies with \"customer size\". Using our estimated model, we show that the optimal second-degree price discrimination (i.e., optimal nonlinear tariff) improves the firm's profit upon linear pricing by at least 8.2%. That said, this second-degree price discrimination scheme only recovers 7.1% of the gap between the profitability of linear pricing and that of infeasible first degree price discrimination. We also conduct several further simulation analyses (i) empirically quantifying the magnitude by which incentive-compatibility constraints impact the optimal pricing and profits, (ii) comparing the role of demand- v.s. cost-side factors in shaping the optimal price schedule, and (iii) studying the implications of fixed fees for the optimal contract and profitability.", "published": "2023-02-22T20:39:55Z", "authors": ["Soheil Ghili", "Russ Yoon"], "categories": ["econ.GN"], "doi": "", "_query": "hedonic house price", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2003.04606v3", "title": "Pricing Interest Rate Derivatives under Volatility Uncertainty", "abstract": "In this paper, we study the pricing of contracts in fixed income markets under volatility uncertainty in the sense of Knightian uncertainty or model uncertainty. The starting point is an arbitrage-free bond market under volatility uncertainty. The uncertainty about the volatility is modeled by a G-Brownian motion, which drives the forward rate dynamics. The absence of arbitrage is ensured by a drift condition. Such a setting leads to a sublinear pricing measure for additional contracts, which yields either a single price or a range of prices. Similar to the forward measure approach, we define the forward sublinear expectation to simplify the pricing of cashflows. Under the forward sublinear expectation, we obtain a robust version of the expectations hypothesis, and we show how to price options on forward prices. In addition, we develop pricing methods for contracts consisting of a stream of cashflows, since the nonlinearity of the pricing measure implies that we cannot price a stream of cashflows by pricing each cashflow separately. With these tools, we derive robust pricing formulas for all major interest rate derivatives. The pricing formulas provide a link to the pricing formulas of traditional models without volatility uncertainty and show that volatility uncertainty naturally leads to unspanned stochastic volatility.", "published": "2020-03-10T09:45:47Z", "authors": ["Julian Hölzermann"], "categories": ["q-fin.PR", "q-fin.MF"], "doi": "", "_query": "hedonic house price", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/cond-mat/0401132v1", "title": "Large stock price changes: volume or liquidity?", "abstract": "We analyze large stock price changes of more than five standard deviations for i) TAQ data for the year 1997 and ii) order book data from the Island ECN for the year 2002. We argue that large price changes are not due to large trading volumes. Instead, we find that extreme price fluctuations are mainly caused by a low density of limit orders stored in the order book, i.e. a small liquidity.", "published": "2004-01-08T20:57:02Z", "authors": ["Philipp Weber", "Bernd Rosenow"], "categories": ["cond-mat"], "doi": "", "_query": "hedonic house price", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/1902.10497v1", "title": "Pricing of claims in discrete time with partial information", "abstract": "We consider the pricing problem of a seller with delayed price information. By using Lagrange duality, a dual problem is derived, and it is proved that there is no duality gap. This gives a characterization of the seller's price of a contingent claim. Finally, we analyze the dual problem, and compare the prices offered by two sellers with delayed and full information respectively.", "published": "2019-02-27T12:58:32Z", "authors": ["Kristina Rognlien Dahl"], "categories": ["math.OC"], "doi": "10.1007/s00245-013-9200-x", "_query": "hedonic house price", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2201.12275v1", "title": "Efficiency of Ad Auctions with Price Displaying", "abstract": "Most of the economic reports forecast that almost half of the worldwide market value unlocked by AI over the next decade (up to 6 trillion USD per year) will be in marketing&sales. In particular, AI will enable the optimization of more and more intricate economic settings, in which multiple different activities need to be jointly automated. This is the case of, e.g., Google Hotel Ads and Tripadvisor, where auctions are used to display ads of similar products or services together with their prices. As in classical ad auctions, the ads are ranked depending on the advertisers' bids, whereas, differently from classical settings, ads are displayed together with their prices, so as to provide a direct comparison among them. This dramatically affects users' behavior, as well as the properties of ad auctions. We show that, in such settings, social welfare maximization can be achieved by means of a direct-revelation mechanism that jointly optimizes, in polynomial time, the ads allocation and the advertisers' prices to be displayed with them. However, in practice it is unlikely that advertisers allow the mechanism to choose prices on their behalf. Indeed, in commonly-adopted mechanisms, ads allocation and price optimization are decoupled, so that the advertisers optimize prices and bids, while the mechanism does so for the allocation, once prices and bids are given. We investigate how this decoupling affects the efficiency of mechanisms. In particular, we study the Price of Anarchy (PoA) and the Price of Stability (PoS) of indirect-revelation mechanisms with both VCG and GSP payments, showing that the PoS for the revenue may be unbounded even with two slots, and the PoA for the social welfare may be as large as the number of slots. Nevertheless, we show that, under some assumptions, simple modifications to the indirect-revelation mechanism with VCG payments achieve a PoS of 1 for the revenue.", "published": "2022-01-28T17:49:32Z", "authors": ["Matteo Castiglioni", "Diodato Ferraioli", "Nicola Gatti", "Alberto Marchesi", "Giulia Romano"], "categories": ["cs.GT"], "doi": "", "_query": "hedonic house price", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2209.11837v1", "title": "Doubly Fair Dynamic Pricing", "abstract": "We study the problem of online dynamic pricing with two types of fairness constraints: a \"procedural fairness\" which requires the proposed prices to be equal in expectation among different groups, and a \"substantive fairness\" which requires the accepted prices to be equal in expectation among different groups. A policy that is simultaneously procedural and substantive fair is referred to as \"doubly fair\". We show that a doubly fair policy must be random to have higher revenue than the best trivial policy that assigns the same price to different groups. In a two-group setting, we propose an online learning algorithm for the 2-group pricing problems that achieves $\\tilde{O}(\\sqrt{T})$ regret, zero procedural unfairness and $\\tilde{O}(\\sqrt{T})$ substantive unfairness over $T$ rounds of learning. We also prove two lower bounds showing that these results on regret and unfairness are both information-theoretically optimal up to iterated logarithmic factors. To the best of our knowledge, this is the first dynamic pricing algorithm that learns to price while satisfying two fairness constraints at the same time.", "published": "2022-09-23T20:02:09Z", "authors": ["Jianyu Xu", "Dan Qiao", "Yu-Xiang Wang"], "categories": ["cs.LG", "econ.EM", "stat.ML"], "doi": "", "_query": "hedonic house price", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/0905.4657v1", "title": "Indifference price with general semimartingales", "abstract": "For utility functions $u$ finite valued on $\\mathbb{R}$, we prove a duality formula for utility maximization with random endowment in general semimartingale incomplete markets. The main novelty of the paper is that possibly non locally bounded semimartingale price processes are allowed. Following Biagini and Frittelli \\cite{BiaFri06}, the analysis is based on the duality between the Orlicz spaces $(L^{\\widehat{u}}, (L^{\\widehat{u}})^*)$ naturally associated to the utility function. This formulation enables several key properties of the indifference price $π(B)$ of a claim $B$ satisfying conditions weaker than those assumed in literature. In particular, the indifference price functional $π$ turns out to be, apart from a sign, a convex risk measure on the Orlicz space $L^{\\widehat{u}}$.", "published": "2009-05-28T14:00:42Z", "authors": ["Sara Biagini", "Marco Frittelli", "Matheus R. Grasselli"], "categories": ["q-fin.PR", "q-fin.CP"], "doi": "", "_query": "hedonic house price", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2010.15105v1", "title": "Price response functions and spread impact in correlated financial markets", "abstract": "Recent research on the response of stock prices to trading activity revealed long lasting effects, even across stocks of different companies. These results imply non-Markovian effects in price formation and when trading many stocks at the same time, in particular trading costs and price correlations. How the price response is measured depends on data set and research focus. However, it is important to clarify, how the details of the price response definition modify the results. Here, we evaluate different price response implementations for the Trades and Quotes (TAQ) data set from the NASDAQ stock market and find that the results are qualitatively the same for two different definitions of time scale, but the response can vary by up to a factor of two. Further, we show the key importance of the order between trade signs and returns, displaying the changes in the signal strength. Moreover, we confirm the dominating contribution of immediate price response directly after a trade, as we find that delayed responses are suppressed. Finally, we test the impact of the spread in the price response, detecting that large spreads have stronger impact.", "published": "2020-10-28T17:45:32Z", "authors": ["Juan C. Henao-Londono", "Sebastian M. Krause", "Thomas Guhr"], "categories": ["q-fin.ST", "physics.data-an"], "doi": "10.1140/epjb/s10051-021-00077-z", "_query": "hedonic house price", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/1503.02200v3", "title": "Sequential Posted Price Mechanisms with Correlated Valuations", "abstract": "We study the revenue performance of sequential posted price mechanisms and some natural extensions, for a general setting where the valuations of the buyers are drawn from a correlated distribution. Sequential posted price mechanisms are conceptually simple mechanisms that work by proposing a take-it-or-leave-it offer to each buyer. We apply sequential posted price mechanisms to single-parameter multi-unit settings in which each buyer demands only one item and the mechanism can assign the service to at most k of the buyers. For standard sequential posted price mechanisms, we prove that with the valuation distribution having finite support, no sequential posted price mechanism can extract a constant fraction of the optimal expected revenue, even with unlimited supply. We extend this result to the the case of a continuous valuation distribution when various standard assumptions hold simultaneously. In fact, it turns out that the best fraction of the optimal revenue that is extractable by a sequential posted price mechanism is proportional to ratio of the highest and lowest possible valuation. We prove that for two simple generalizations of these mechanisms, a better revenue performance can be achieved: if the sequential posted price mechanism has for each buyer the option of either proposing an offer or asking the buyer for its valuation, then a Omega(1/max{1,d}) fraction of the optimal revenue can be extracted, where d denotes the degree of dependence of the valuations, ranging from complete independence (d=0) to arbitrary dependence (d=n-1). Moreover, when we generalize the sequential posted price mechanisms further, such that the mechanism has the ability to make a take-it-or-leave-it offer to the i-th buyer that depends on the valuations of all buyers except i's, we prove that a constant fraction (2-sqrt{e})/4~0.088 of the optimal revenue can be always be extracted.", "published": "2015-03-07T19:02:16Z", "authors": ["Marek Adamczyk", "Allan Borodin", "Diodato Ferraioli", "Bart de Keijzer", "Stefano Leonardi"], "categories": ["cs.GT"], "doi": "", "_query": "hedonic house price", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2004.01238v1", "title": "Greater search cost reduces prices", "abstract": "The optimal price of each firm falls in the search cost of consumers, in the limit to the monopoly price, despite the exit of lower-value consumers in response to costlier search. Exit means that fewer inframarginal consumers remain. The decrease in marginal buyers is smaller, because part of demand is composed of customers coming from rival firms. These buyers can be held up and are not marginal. Higher search cost reduces the fraction of incoming switchers among buyers, which decreases the hold-up motive, thus the price.", "published": "2020-04-02T19:38:09Z", "authors": ["Sander Heinsalu"], "categories": ["econ.TH", "cs.GT"], "doi": "", "_query": "hedonic house price", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/1110.4175v1", "title": "The Price of Anarchy (POA) of network coding and routing based on average pricing mechanism", "abstract": "The congestion pricing is an efficient allocation approach to mediate demand and supply of network resources. Different from the previous pricing using Affine Marginal Cost (AMC), we focus on studying the game between network coding and routing flows sharing a single link when users are price anticipating based on an Average Cost Sharing (ACS) pricing mechanism. We characterize the worst-case efficiency bounds of the game compared with the optimal, i.e., the price-of anarchy (POA), which can be low bound 50% with routing only. When both network coding and routing are applied, the POA can be as low as 4/9. Therefore, network coding cannot improve the POA significantly under the ACS. Moreover, for more efficient use of limited resources, it indicates the sharing users have a higher tendency to choose network coding.", "published": "2011-10-19T03:51:06Z", "authors": ["Wang Gang", "Dai Xia"], "categories": ["cs.NI", "cs.GT", "cs.IT"], "doi": "", "_query": "hedonic house price", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/1811.07220v2", "title": "Determination of the Lévy Exponent in Asset Pricing Models", "abstract": "We consider the problem of determining the Lévy exponent in a Lévy model for asset prices given the price data of derivatives. The model, formulated under the real-world measure $\\mathbb P$, consists of a pricing kernel $\\{π_t\\}_{t\\geq0}$ together with one or more non-dividend-paying risky assets driven by the same Lévy process. If $\\{S_t\\}_{t\\geq0}$ denotes the price process of such an asset then $\\{π_t S_t\\}_{t\\geq0}$ is a $\\mathbb P$-martingale. The Lévy process $\\{ ξ_t \\}_{t\\geq0}$ is assumed to have exponential moments, implying the existence of a Lévy exponent $ψ(α) = t^{-1}\\log \\mathbb E(\\rm e^{αξ_t})$ for $α$ in an interval $A \\subset \\mathbb R$ containing the origin as a proper subset. We show that if the initial prices of power-payoff derivatives, for which the payoff is $H_T = (ζ_T)^q$ for some time $T>0$, are given for a range of values of $q$, where $\\{ζ_t\\}_{t\\geq0}$ is the so-called benchmark portfolio defined by $ζ_t = 1/π_t$, then the Lévy exponent is determined up to an irrelevant linear term. In such a setting, derivative prices embody complete information about price jumps: in particular, the spectrum of the price jumps can be worked out from current market prices of derivatives. More generally, if $H_T = (S_T)^q$ for a general non-dividend-paying risky asset driven by a Lévy process, and if we know that the pricing kernel is driven by the same Lévy process, up to a factor of proportionality, then from the current prices of power-payoff derivatives we can infer the structure of the Lévy exponent up to a transformation $ψ(α) \\rightarrow ψ(α+ μ) - ψ(μ) + c α$, where $c$ and $μ$ are constants.", "published": "2018-11-17T21:01:10Z", "authors": ["George Bouzianis", "Lane Hughston"], "categories": ["q-fin.MF"], "doi": "10.1142/S0219024919500080", "_query": "hedonic house price", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/1303.7445v1", "title": "Agent-based modeling of a price information trading business", "abstract": "We describe an agent-based simulation of a fictional (but feasible) information trading business. The Gas Price Information Trader (GPIT) buys information about real-time gas prices in a metropolitan area from drivers and resells the information to drivers who need to refuel their vehicles. Our simulation uses real world geographic data, lifestyle-dependent driving patterns and vehicle models to create an agent-based model of the drivers. We use real world statistics of gas price fluctuation to create scenarios of temporal and spatial distribution of gas prices. The price of the information is determined on a case-by-case basis through a simple negotiation model. The trader and the customers are adapting their negotiation strategies based on their historical profits. We are interested in the general properties of the emerging information market: the amount of realizable profit and its distribution between the trader and customers, the business strategies necessary to keep the market operational (such as promotional deals), the price elasticity of demand and the impact of pricing strategies on the profit.", "published": "2013-03-29T17:40:04Z", "authors": ["Saad Ahmad Khan", "Ladislau Boloni"], "categories": ["cs.AI", "q-fin.GN"], "doi": "", "_query": "hedonic house price", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/1612.09152v2", "title": "A Risk-Neutral Equilibrium Leading to Uncertain Volatility Pricing", "abstract": "We study the formation of derivative prices in equilibrium between risk-neutral agents with heterogeneous beliefs about the dynamics of the underlying. Under the condition that the derivative cannot be shorted, we prove the existence of a unique equilibrium price and show that it incorporates the speculative value of possibly reselling the derivative. This value typically leads to a bubble; that is, the price exceeds the autonomous valuation of any given agent. Mathematically, the equilibrium price operator is of the same nonlinear form that is obtained in single-agent settings with strong aversion against model uncertainty. Thus, our equilibrium leads to a novel interpretation of this price.", "published": "2016-12-29T14:12:31Z", "authors": ["Johannes Muhle-Karbe", "Marcel Nutz"], "categories": ["q-fin.MF", "math.OC"], "doi": "", "_query": "hedonic house price", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/1807.05917v2", "title": "Hedging with physical or cash settlement under transient multiplicative price impact", "abstract": "We solve the superhedging problem for European options in an illiquid extension of the Black-Scholes model, in which transactions have transient price impact and the costs and the strategies for hedging are affected by physical or cash settlement requirements at maturity. Our analysis is based on a convenient choice of reduced effective coordinates of magnitudes at liquidation for geometric dynamic programming. The price impact is transient over time and multiplicative, ensuring non-negativity of underlying asset prices while maintaining an arbitrage-free model. The basic (log-)linear example is a Black-Scholes model with relative price impact being proportional to the volume of shares traded, where the transience for impact on log-prices is being modelled like in Obizhaeva-Wang \\cite{ObizhaevaWang13} for nominal prices. More generally, we allow for non-linear price impact and resilience functions. The viscosity solutions describing the minimal superhedging price are governed by the transient character of the price impact and by the physical or cash settlement specifications. Pricing equations under illiquidity extend no-arbitrage pricing a la Black-Scholes for complete markets in a non-paradoxical way (cf.\\ {Ç}etin, Soner and Touzi \\cite{CetinSonerTouzi10}) even without additional frictions, and can recover it in base cases.", "published": "2018-07-16T15:21:25Z", "authors": ["Dirk Becherer", "Todor Bilarev"], "categories": ["q-fin.PR", "math.PR", "q-fin.TR"], "doi": "", "_query": "hedonic house price", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/1106.4957v4", "title": "Maximum entropy distribution of stock price fluctuations", "abstract": "The principle of absence of arbitrage opportunities allows obtaining the distribution of stock price fluctuations by maximizing its information entropy. This leads to a physical description of the underlying dynamics as a random walk characterized by a stochastic diffusion coefficient and constrained to a given value of the expected volatility, taking in this way into account the information provided by the existence of an option market. This model is validated by a comprehensive comparison with observed distributions of both price return and diffusion coefficient. Expected volatility is the only parameter in the model and can be obtained by analysing option prices. We give an analytic formulation of the probability density function for price returns which can be used to extract expected volatility from stock option data. This distribution is of high practical interest since it should be preferred to a Gaussian when dealing with the problem of pricing derivative financial contracts.", "published": "2011-06-24T12:55:42Z", "authors": ["Rosario Bartiromo"], "categories": ["q-fin.ST", "physics.data-an"], "doi": "", "_query": "hedonic house price", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2209.05471v2", "title": "PATE: Property, Amenities, Traffic and Emotions Coming Together for Real Estate Price Prediction", "abstract": "Real estate prices have a significant impact on individuals, families, businesses, and governments. The general objective of real estate price prediction is to identify and exploit socioeconomic patterns arising from real estate transactions over multiple aspects, ranging from the property itself to other contributing factors. However, price prediction is a challenging multidimensional problem that involves estimating many characteristics beyond the property itself. In this paper, we use multiple sources of data to evaluate the economic contribution of different socioeconomic characteristics such as surrounding amenities, traffic conditions and social emotions. Our experiments were conducted on 28,550 houses in Beijing, China and we rank each characteristic by its importance. Since the use of multi-source information improves the accuracy of predictions, the aforementioned characteristics can be an invaluable resource to assess the economic and social value of real estate. Code and data are available at: https://github.com/IndigoPurple/PATE", "published": "2022-08-29T12:31:10Z", "authors": ["Yaping Zhao", "Ramgopal Ravi", "Shuhui Shi", "Zhongrui Wang", "Edmund Y. Lam", "Jichang Zhao"], "categories": ["cs.CY"], "doi": "", "_query": "hedonic house price", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/0707.0336v2", "title": "Pricing Options on Defaultable Stocks", "abstract": "In this note, we develop stock option price approximations for a model which takes both the risk o default and the stochastic volatility into account. We also let the intensity of defaults be influenced by the volatility. We show that it might be possible to infer the risk neutral default intensity from the stock option prices. Our option price approximation has a rich implied volatility surface structure and fits the data implied volatility well. Our calibration exercise shows that an effective hazard rate from bonds issued by a company can be used to explain the implied volatility skew of the implied volatility of the option prices issued by the same company.", "published": "2007-07-03T03:28:35Z", "authors": ["Erhan Bayraktar"], "categories": ["cs.CE"], "doi": "", "_query": "hedonic house price", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/cond-mat/9906048v1", "title": "Scale-invariance and contingent claim pricing", "abstract": "Prices of tradables can only be expressed relative to each other at any instant of time. This fundamental fact should therefore also hold for contigent claims, i.e. tradable instruments, whose prices depend on the prices of other tradables. We show that this property induces local scale-invariance in the problem of pricing contingent claims. Due to this symmetry we do not require any martingale techniques to arrive at the price of a claim. If the tradables are driven by Brownian motion, we find, in a natural way, that this price satisfies a PDE. Both posses a manifest gauge-invariance. A unique solution can only be given when we impose restrictions on the drifts of volatilities of the tradables, i.e. the underlying market structure. We give some examples of the application of this PDE to the pricing of claims. In the Black-Scholes world we show the equivalence of our formulation with the standard approach. It is stressed that the formulation in terms of tradables leads to a significant conceptual simplification of the pricing-problem.", "published": "1999-06-03T16:12:11Z", "authors": ["Jiri Hoogland", "Dimitri Neumann"], "categories": ["cond-mat", "math.AP"], "doi": "", "_query": "hedonic house price", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/1912.08791v1", "title": "Forecasting significant stock price changes using neural networks", "abstract": "Stock price prediction is a rich research topic that has attracted interest from various areas of science. The recent success of machine learning in speech and image recognition has prompted researchers to apply these methods to asset price prediction. The majority of literature has been devoted to predicting either the actual asset price or the direction of price movement. In this paper, we study a hitherto little explored question of predicting significant changes in stock price based on previous changes using machine learning algorithms. We are particularly interested in the performance of neural network classifiers in the given context. To this end, we construct and test three neural network models including multi-layer perceptron, convolutional net, and long short term memory net. As benchmark models we use random forest and relative strength index methods. The models are tested using 10-year daily stock price data of four major US public companies. Test results show that predicting significant changes in stock price can be accomplished with a high degree of accuracy. In particular, we obtain substantially better results than similar studies that forecast the direction of price change.", "published": "2019-11-21T11:48:48Z", "authors": ["Firuz Kamalov"], "categories": ["q-fin.TR", "cs.LG", "stat.ML"], "doi": "10.1007/s00521-020-04942-3", "_query": "hedonic house price", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/1905.00711v1", "title": "Nonparametric pricing and hedging of exotic derivatives", "abstract": "In the spirit of Arrow-Debreu, we introduce a family of financial derivatives that act as primitive securities in that exotic derivatives can be approximated by their linear combinations. We call these financial derivatives signature payoffs. We show that signature payoffs can be used to nonparametrically price and hedge exotic derivatives in the scenario where one has access to price data for other exotic payoffs. The methodology leads to a computationally tractable and accurate algorithm for pricing and hedging using market prices of a basket of exotic derivatives that has been tested on real and simulated market prices, obtaining good results.", "published": "2019-05-02T13:03:43Z", "authors": ["Terry Lyons", "Sina Nejad", "Imanol Perez Arribas"], "categories": ["q-fin.MF"], "doi": "", "_query": "hedonic house price", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/1310.7857v3", "title": "Sticky continuous processes have consistent price systems", "abstract": "Under proportional transaction costs, a price process is said to have a consistent price system, if there is a semimartingale with an equivalent martingale measure that evolves within the bid-ask spread. We show that a continuous, multi-asset price process has a consistent price system, under arbitrarily small proportional transaction costs, if it satisfies a natural multi-dimensional generalization of the stickiness condition introduced by Guasoni [Math. Finance 16(3), 569-582 (2006)].", "published": "2013-10-29T16:09:35Z", "authors": ["Christian Bender", "Mikko S. Pakkanen", "Hasanjan Sayit"], "categories": ["q-fin.PR", "math.PR"], "doi": "10.1239/jap/1437658617", "_query": "hedonic house price", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2307.06726v1", "title": "The Price of Equity with Binary Valuations and Few Agent Types", "abstract": "In fair division problems, the notion of price of fairness measures the loss in welfare due to a fairness constraint. Prior work on the price of fairness has focused primarily on envy-freeness up to one good (EF1) as the fairness constraint, and on the utilitarian and egalitarian welfare measures. Our work instead focuses on the price of equitability up to one good (EQ1) (which we term price of equity) and considers the broad class of generalized $p$-mean welfare measures (which includes utilitarian, egalitarian, and Nash welfare as special cases). We derive fine-grained bounds on the price of equity in terms of the number of agent types (i.e., the maximum number of agents with distinct valuations), which allows us to identify scenarios where the existing bounds in terms of the number of agents are overly pessimistic. Our work focuses on the setting with binary additive valuations, and obtains upper and lower bounds on the price of equity for $p$-mean welfare for all $p \\leqslant 1$. For any fixed $p$, our bounds are tight up to constant factors. A useful insight of our work is to identify the structure of allocations that underlie the upper (respectively, the lower) bounds simultaneously for all $p$-mean welfare measures, thus providing a unified structural understanding of price of fairness in this setting. This structural understanding, in fact, extends to the more general class of binary submodular (or matroid rank) valuations. We also show that, unlike binary additive valuations, for binary submodular valuations the number of agent types does not provide bounds on the price of equity.", "published": "2023-07-13T12:37:25Z", "authors": ["Umang Bhaskar", "Neeldhara Misra", "Aditi Sethia", "Rohit Vaish"], "categories": ["cs.GT"], "doi": "", "_query": "hedonic house price", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/1301.1135v1", "title": "Hawkes model for price and trades high-frequency dynamics", "abstract": "We introduce a multivariate Hawkes process that accounts for the dynamics of market prices through the impact of market order arrivals at microstructural level. Our model is a point process mainly characterized by 4 kernels associated with respectively the trade arrival self-excitation, the price changes mean reversion the impact of trade arrivals on price variations and the feedback of price changes on trading activity. It allows one to account for both stylized facts of market prices microstructure (including random time arrival of price moves, discrete price grid, high frequency mean reversion, correlation functions behavior at various time scales) and the stylized facts of market impact (mainly the concave-square-root-like/relaxation characteristic shape of the market impact of a meta-order). Moreover, it allows one to estimate the entire market impact profile from anonymous market data. We show that these kernels can be estimated from the empirical conditional mean intensities. We provide numerical examples, application to real data and comparisons to former approaches.", "published": "2013-01-07T09:27:26Z", "authors": ["E. Bacry", "J. F Muzy"], "categories": ["q-fin.TR", "physics.data-an"], "doi": "", "_query": "hedonic house price", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2502.05898v1", "title": "Rising Marginal Costs, Rising Prices?", "abstract": "We present empirical evidence on the relationship between demand shocks and price changes, conditional on returns to scale. We find that in industries with decreasing returns to scale, demand increases (which raise costs) correspond to price increases. Whereas, in industries with increasing returns to scale, demand increases (which lower costs) correspond to stable prices. We interpret the results with a theory of imperfect competition and returns to scale. For prices to remain stable following a cost decrease, markups necessarily rise. For prices to increase as cost increases, it is not necessary for markups to change but does not preclude their role. From a macroeconomic perspective, our results imply that inflation dynamics and the effectiveness of monetary policy depend on market structures.", "published": "2025-02-09T13:28:47Z", "authors": ["Joel Kariel", "Anthony Savagar"], "categories": ["econ.GN"], "doi": "", "_query": "hedonic house price", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2411.09937v2", "title": "Refined and Segmented Price Sentiment Indices from Survey Comments", "abstract": "We aim to enhance a price sentiment index and to more precisely understand price trends from the perspective of not only consumers but also businesses. We extract comments related to prices from the Economy Watchers Survey conducted by the Cabinet Office of Japan and classify price trends using a large language model (LLM). We classify whether the survey sample reflects the perspective of consumers or businesses, and whether the comments pertain to goods or services by utilizing information on the fields of comments and the industries of respondents included in the Economy Watchers Survey. From these classified price-related comments, we construct price sentiment indices not only for a general purpose but also for more specific objectives by combining perspectives on consumers and prices, as well as goods and services. It becomes possible to achieve a more accurate classification of price directions by employing a LLM for classification. Furthermore, integrating the outputs of multiple LLMs suggests the potential for the better performance of the classification. The use of more accurately classified comments allows for the construction of an index with a higher correlation to existing indices than previous studies. We demonstrate that the correlation of the price index for consumers, which has a larger sample size, is further enhanced by selecting comments for aggregation based on the industry of the survey respondents.", "published": "2024-11-15T04:22:21Z", "authors": ["Masahiro Suzuki", "Hiroki Sakaji"], "categories": ["cs.CL", "q-fin.CP"], "doi": "", "_query": "hedonic house price", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/1205.6439v1", "title": "A Mathematical Reformulation of the Reference Price", "abstract": "Reference prices have long been studied in applied economics and business research. One of the classic formulations of the reference price is in terms of an iterative function of past prices. There are a number of limitations of such a formulation, however. Such limitations include burdensome computational time to estimate parameters, an inability to truly account for customer heterogeneity, and an estimation procedure that implies a misspecified model. Managerial recommendations based on inferences from such a model can be quite misleading. We mathematically reformulate the reference price by developing a closed-form expansion that addresses the aforementioned issues, enabling one to elicit truly meaningful managerial advice from the model. We estimate our model on a real world data set to illustrate the efficacy of our approach. Our work is not only useful from a modeling perspective, but also has important behavioral and managerial implications, which modelers and non-modelers alike would find useful.", "published": "2012-05-29T18:05:14Z", "authors": ["Kevin D. Dayaratna", "P. K. Kannan"], "categories": ["stat.AP", "stat.CO"], "doi": "", "_query": "hedonic house price", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2503.16912v1", "title": "Construction and sample path properties of diffusion house-moving between two curves", "abstract": "The purpose of this paper is to introduce the construction of a stochastic process called ``diffusion house-moving'' and to explore its properties. We study the weak convergence of diffusion bridges conditioned to stay between two curves, and we refer to this limit as diffusion house-moving. Applying this weak convergence result, we give the sample path properties of diffusion house-moving.", "published": "2025-03-21T07:23:20Z", "authors": ["Kensuke Ishitani", "Soma Nishino"], "categories": ["math.PR"], "doi": "", "_query": "hedonic house price", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/1705.02473v2", "title": "Computation of second order price sensitivities in depressed markets", "abstract": "Risk management in financial derivative markets requires inevitably the calculation of the different price sensitivities. The literature contains an abundant amount of research works that have studied the computation of these important values. Most of these works consider the well-known Black and Scholes model where the volatility is assumed to be constant. Moreover, to our best knowledge, they compute only the first order price sensitivities. Some works that attempt to extend to markets affected by financial crisis appeared recently. However, none of these papers deal with the calculation of the price sensitivities of second order. Providing second derivatives for the underlying price sensitivities is an important issue in financial risk management because the investor can determine whether or not each source of risk is increasing at an increasing rate. In this paper, we work on the computation of second order prices sensitivities for a market under crisis. The underlying second order price sensitivities are derived explicitly. The obtained formulas are expected to improve on the accuracy of the hedging strategies during a financial crunch.", "published": "2017-05-06T10:49:45Z", "authors": ["Youssef El-Khatib", "Abdulnasser Hatemi-J"], "categories": ["q-fin.PR"], "doi": "", "_query": "hedonic house price", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2002.11680v1", "title": "Large Fluctuations in Locational Marginal Prices", "abstract": "This paper investigates large fluctuations of Locational Marginal Prices (LMPs) in wholesale energy markets caused by volatile renewable generation profiles. Specifically, we study events of the form $\\mathbb{P} \\Big ( \\mathbf{LMP} \\notin \\prod_{i=1}^n [α_i^-, α_i^+] \\Big),$ where $\\mathbf{LMP}$ is the vector of LMPs at the $n$ power grid nodes, and $\\boldsymbolα^-,\\boldsymbolα^+\\in\\mathbb{R}^n$ are vectors of price thresholds specifying undesirable price occurrences. By exploiting the structure of the supply-demand matching mechanism in power grids, we look at LMPs as deterministic piecewise affine, possibly discontinuous functions of the stochastic input process, modeling uncontrollable renewable generation. We utilize techniques from large deviations theory to identify the most likely ways for extreme price spikes to happen, and to rank the nodes of the power grid in terms of their likelihood of experiencing a price spike. Our results are derived in the case of Gaussian fluctuations and are validated numerically on the IEEE 14-bus test case.", "published": "2020-02-26T18:18:21Z", "authors": ["Tommaso Nesti", "John Moriarty", "Alessandro Zocca", "Bert Zwart"], "categories": ["math.OC", "eess.SY", "math.PR", "physics.soc-ph"], "doi": "10.1098/rsta.2019.0438", "_query": "hedonic house price", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/1007.3316v1", "title": "Pricing in an equilibrium based model for a large investor", "abstract": "We study a financial model with a non-trivial price impact effect. In this model we consider the interaction of a large investor trading in an illiquid security, and a market maker who is quoting prices for this security. We assume that the market maker quotes the prices such that by taking the other side of the investor's demand, the market maker will arrive at maturity with the maximal expected utility of the terminal wealth. Within this model we provide an explicit recursive pricing formula for an exponential utility function, as well as an asymptotic expansion for the price for a \"small\" simple demand.", "published": "2010-07-20T00:45:59Z", "authors": ["David German"], "categories": ["q-fin.PR"], "doi": "", "_query": "hedonic house price", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/physics/0603012v1", "title": "The Process of price formation and the skewness of asset returns", "abstract": "Distributions of assets returns exhibit a slight skewness. In this note we show that our model of endogenous price formation \\cite{Reimann2006} creates an asymmetric return distribution if the price dynamics are a process in which consecutive trading periods are dependent from each other in the sense that opening prices equal closing prices of the former trading period. The corresponding parameter $α$ is estimated from daily prices from 01/01/1999 - 12/31/2004 for 9 large indices. For the S&P 500, the skewness distribution of all its constituting assets is also calculated. The skewness distribution due to our model is compared with the distribution of the empirical skewness values of the ingle assets.", "published": "2006-03-02T10:07:02Z", "authors": ["Stefan Reimann"], "categories": ["physics.soc-ph", "q-fin.ST"], "doi": "", "_query": "hedonic house price", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/1706.01131v2", "title": "Strategic Dynamic Pricing with Network Effects", "abstract": "We study the optimal pricing strategy of a monopolist selling homogeneous goods to customers over multiple periods. The customers choose their time of purchase to maximize their payoff that depends on their valuation of the product, the purchase price, and the utility they derive from past purchases of others, termed the network effect. We first show that the optimal price sequence is non-decreasing. Therefore, by postponing purchase to future rounds, customers trade-off a higher utility from the network effects with a higher price. We then show that a customer's equilibrium strategy can be characterized by a threshold rule in which at each round a customer purchases the product if and only if her valuation exceeds a certain threshold. This implies that customers face an inference problem regarding the valuations of others, i.e., observing that a customer has not yet purchased the product, signals that her valuation is below a threshold. We consider a block model of network interactions, where there are blocks of buyers subject to the same network effect. A natural benchmark, this model allows us to provide an explicit characterization of the optimal price sequence asymptotically as the number of agents goes to infinity, which notably is linearly increasing in time with a slope that depends on the network effect through a scalar given by the sum of entries of the inverse of the network weight matrix. Our characterization shows that increasing the \"imbalance\" in the network defined as the difference between the in and out degree of the nodes increases the revenue of the monopolist. We further study the effects of price discrimination and show that in earlier periods monopolist offers lower prices to blocks with higher Bonacich centrality to encourage them to purchase, which in turn further incentivizes other customers to buy in subsequent periods.", "published": "2017-06-04T19:09:13Z", "authors": ["Ali Makhdoumi", "Azarakhsh Malekian", "Asuman Ozdaglar"], "categories": ["cs.GT"], "doi": "", "_query": "hedonic house price", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/1309.6164v2", "title": "Pricing and Hedging Derivative Securities with Unknown Local Volatilities", "abstract": "A common assumption in financial engineering is that the market price for any derivative coincides with an objectively defined risk-neutral price - a plausible assumption only if traders collectively possess objective knowledge about the price dynamics of the underlying security over short time scales. Here we assume that traders have an objective knowledge about the underlying security's price trajectories only for large time scales. We show that avoidance of arbitrage that is still feasible uniquely determines the prices of options with large expiration times, and we derive limit theorems useful for estimation of model parameters and present-value analysis of derivative portfolios.", "published": "2013-09-24T14:09:56Z", "authors": ["Kerry W. Fendick"], "categories": ["q-fin.PR", "math.PR"], "doi": "", "_query": "hedonic house price", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2607.06355v1", "title": "Entropic Dynamics of Jump-Diffusion Option Pricing", "abstract": "Standard models of stock price dynamics and option valuation usually begin by postulating stochastic processes. This paper develops an entropic inference framework that derives these processes from information constraints. The key symmetry is that markets reward returns rather than price levels, which selects log price as the dynamical variable. Price changes are represented by two channels. The continuous channel carries constraints of continuity and directionality. The jump channel carries the arrival rate and the first two moments of jump size. Since these constraints apply to disjoint parts of the microstate, the channels factorize. The resulting dynamics is the Merton jump diffusion, with Geometric Brownian Motion as the no jump limit. The log price density satisfies the Kolmogorov Feller equation, whose no jump limit is the Fokker Planck equation. The same inferential principle, with no arbitrage imposed through the mean log return, selects the Esscher transform from the many martingale measures available in an incomplete market. The option price then satisfies Mertons partial integro differential equation, and the risk neutral mixture of lognormal distributions generates the implied volatility smile. The Black Scholes results are recovered when jumps vanish. What changes from one model to another is not the inference, but the information supplied to it.", "published": "2026-07-07T14:53:50Z", "authors": ["Mohammad Abedi"], "categories": ["q-fin.PR", "q-fin.MF", "q-fin.ST"], "doi": "", "_query": "hedonic house price", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2205.08913v2", "title": "Price Interpretability of Prediction Markets: A Convergence Analysis", "abstract": "Prediction markets are long known for prediction accuracy. This study systematically explores the fundamental properties of prediction markets, addressing questions about their information aggregation process and the factors contributing to their remarkable efficacy. We propose a novel multivariate utility (MU) based mechanism that unifies several existing automated market-making schemes. Using this mechanism, we establish the convergence results for markets comprised of risk-averse traders who have heterogeneous beliefs and repeatedly interact with the market maker. We demonstrate that the resulting limiting wealth distribution aligns with the Pareto efficient frontier defined by the utilities of all market participants. With the help of this result, we establish analytical and numerical results for the limiting price in different market models. Specifically, we show that the limiting price converges to the geometric mean of agent beliefs in exponential utility-based markets. In risk-measure-based markets, we construct a family of risk measures that satisfy the convergence criteria and prove that the price can converge to a unique level represented by the weighted power mean of agent beliefs. In broader markets with Constant Relative Risk Aversion (CRRA) utilities, we reveal that the limiting price can be characterized by systems of equations that encapsulate agent beliefs, risk parameters, and wealth. Despite the potential impact of traders' trading sequences on the limiting price, we establish a price invariance result for markets with a large trader population. Using this result, we propose an efficient approximation scheme for the limiting price.", "published": "2022-05-18T13:15:11Z", "authors": ["Dian Yu", "Jianjun Gao", "Weiping Wu", "Zizhuo Wang"], "categories": ["q-fin.TR", "cs.LG"], "doi": "", "_query": "hedonic house price", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2607.20273v1", "title": "A Product-Neighbourhood Criterion for Fixed Price One", "abstract": "We prove a flexible criterion for fixed price one, applying in particular to higher-rank lattices over local fields and automorphism groups of affine buildings, including lattices in exotic buildings. It also recovers fixed price one for amenable groups. We then establish a locally compact version, and deduce that every product of two noncompact, compactly generated, unimodular locally compact groups, as well as every lattice in such a product, has fixed price one.", "published": "2026-07-22T15:24:53Z", "authors": ["Raz Slutsky"], "categories": ["math.GR", "math.DS"], "doi": "", "_query": "hedonic house price", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/1211.0856v2", "title": "Heat Kernel Framework for Asset Pricing in Finite Time", "abstract": "A heat kernel approach is proposed for the development of a general, flexible, and mathematically tractable asset pricing framework in finite time. The pricing kernel, giving rise to the price system in an incomplete market, is modelled by weighted heat kernels which are driven by multivariate Markov processes and which provide enough degrees of freedom in order to calibrate to relevant data, e.g. to the term structure of bond prices. It is shown how, for a class of models, the prices of bonds, caplets, and swaptions can be computed in closed form. The dynamical equations for the price processes are derived, and explicit formulae are obtained for the short rate of interest, the risk premium, and for the stochastic volatility of prices. Several of the closed-form asset price models presented in this paper are driven by combinations of Markovian jump processes with different probability laws. Such models provide a rich basis for consistent applications in several sectors of a financial market including equity, fixed-income, commodities, and insurance. The flexible, multidimensional and multivariate structure, on which the asset price models are constructed, lends itself well to the transparent modelling of dependence across asset classes. As an illustration, the impact on prices by spiralling debt, a typical feature of a financial crisis, is modelled explicitly, and contagion effects are readily observed in the dynamics of asset returns.", "published": "2012-11-05T13:06:26Z", "authors": ["Andrea Macrina"], "categories": ["q-fin.PR", "math.PR"], "doi": "", "_query": "hedonic house price", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2503.16437v1", "title": "Haunted House: A text-based game for comparing the flexibility of mental models in humans and LLMs", "abstract": "This study introduces \"Haunted House\" a novel text-based game designed to compare the performance of humans and large language models (LLMs) in model-based reasoning. Players must escape from a house containing nine rooms in a 3x3 grid layout while avoiding the ghost. They are guided by verbal clues that they get each time they move. In Study 1, the results from 98 human participants revealed a success rate of 31.6%, significantly outperforming seven state-of-the-art LLMs tested. Out of 140 attempts across seven LLMs, only one attempt resulted in a pass by Claude 3 Opus. Preliminary results suggested that GPT o3-mini-high performance might be higher, but not at the human level. Further analysis of 29 human participants' moves in Study 2 indicated that LLMs frequently struggled with random and illogical moves, while humans exhibited such errors less frequently. Our findings suggest that current LLMs encounter difficulties in tasks that demand active model-based reasoning, offering inspiration for future benchmarks.", "published": "2025-02-12T16:19:40Z", "authors": ["Brett Puppart", "Paul-Henry Paltmann", "Jaan Aru"], "categories": ["cs.HC", "cs.AI", "q-bio.NC"], "doi": "", "_query": "hedonic house price", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2003.06184v2", "title": "Coronavirus and oil price crash", "abstract": "Coronavirus (COVID-19) creates fear and uncertainty, hitting the global economy and amplifying the financial markets volatility. The oil price reaction to COVID-19 was gradually accommodated until March 09, 2020, when, 49 days after the release of the first coronavirus monitoring report by the World Health Organization (WHO), Saudi Arabia floods the market with oil. As a result, international prices drop with more than 20% in one single day. Against this background, the purpose of this paper is to investigate the impact of COVID-19 numbers on crude oil prices, while controlling for the impact of financial volatility and the United States (US) economic policy uncertainty. Our ARDL estimation shows that the COVID-19 daily reported cases of new infections have a marginal negative impact on the crude oil prices in the long run. Nevertheless, by amplifying the financial markets volatility, COVID-19 also has an indirect effect on the recent dynamics of crude oil prices.", "published": "2020-03-13T10:15:04Z", "authors": ["Claudiu Albulescu"], "categories": ["q-fin.ST", "q-fin.CP"], "doi": "", "_query": "hedonic house price", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2001.03099v1", "title": "Using Networks and Partial Differential Equations to Predict Bitcoin Price", "abstract": "Over the past decade, the blockchain technology and its Bitcoin cryptocurrency have received considerable attention. Bitcoin has experienced significant price swings in daily and long-term valuations. In this paper, we propose a partial differential equation (PDE) model on the bitcoin transaction network for predicting bitcoin price. Through analysis of bitcoin subgraphs or chainlets, the PDE model captures the influence of transaction patterns on bitcoin price over time and combines the effect of all chainlet clusters. In addition, Google Trends Index is incorporated to the PDE model to reflect the effect of bitcoin market sentiment. The experiment shows that the average accuracy of daily bitcoin price prediction is 0.82 for 362 consecutive days in 2017. The results demonstrate the PDE model is capable of predicting bitcoin price. The paper is the first attempt to apply a PDE model to the bitcoin transaction network for predicting bitcoin price.", "published": "2020-01-08T17:31:37Z", "authors": ["Yufang Wang", "Haiyan Wang"], "categories": ["q-fin.PR"], "doi": "10.1063/5.0002759", "_query": "hedonic house price", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/1204.3156v1", "title": "Price and Quantity Trajectories: Second-order Dynamics", "abstract": "In two previous papers the author developed a second-order price adjustment (tâtonnement) process. This paper extends the approach to include both quantity and price adjustments. We demonstrate three results: a analogue to physical energy, called \"activity\" arises naturally in the model, and is not conserved in general; price and quantity trajectories must either end at a local minimum of a scalar potential or circulate endlessly; and disturbances into a subspace of substitutable commodities decay over time. From this we argue, although we do not prove, that the model features global stability, combined with local instability, a characteristic of many real markets. Following these observations and a brief survey of empirical results for price-setting and consumption behavior in markets for \"real\" goods (as opposed to financial markets), we conjecture that Stigler and Becker's well-known theory of consumer preference opens the possibility of substantial degeneracy in commodity space, and therefore that price and quantity trajectories could lie on a relatively low-dimensional subspace within the full commodity space.", "published": "2012-04-14T09:33:51Z", "authors": ["Eric Kemp-Benedict"], "categories": ["q-fin.GN"], "doi": "", "_query": "hedonic house price", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2009.14097v2", "title": "When Local Governments' Stay-at-Home Orders Meet the White House's \"Opening Up America Again\"", "abstract": "On April 16th, The White House launched \"Opening up America Again\" (OuAA) campaign while many U.S. counties had stay-at-home orders in place. We created a panel data set of 1,563 U.S. counties to study the impact of U.S. counties' stay-at-home orders on community mobility before and after The White House's campaign to reopen the country. Our results suggest that before the OuAA campaign stay-at-home orders brought down time spent in retail and recreation businesses by about 27% for typical conservative and liberal counties. However, after the launch of OuAA campaign, the time spent at retail and recreational businesses in a typical conservative county increased significantly more than in liberal counties (15% increase in a typical conservative county Vs. 9% increase in a typical liberal county). We also found that in conservative counties with stay-at-home orders in place, time spent at retail and recreational businesses increased less than that of conservative counties without stay-at-home orders. These findings illuminate to what extent residents' political ideology could determine to what extent they follow local orders and to what extent the White House's OuAA campaign polarized the obedience between liberal and conservative counties. The silver lining in our study is that even when the federal government was reopening the country, the local authorities that enforced stay-at-home restrictions were to some extent effective.", "published": "2020-09-29T15:27:25Z", "authors": ["Reza Mousavi", "Bin Gu"], "categories": ["econ.GN"], "doi": "", "_query": "hedonic house price", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/1607.02598v1", "title": "Security Pricing as an Enabler of Cyber-Insurance: A First Look at Differentiated Pricing Markets", "abstract": "Despite the promising potential of network risk management services (e.g., cyber-insurance) to improve information security, their deployment is relatively scarce, primarily due to such service companies being unable to guarantee profitability. As a novel approach to making cyber-insurance services more viable, we explore a symbiotic relationship between security vendors (e.g., Symantec) capable of price differentiating their clients, and cyber-insurance agencies having possession of information related to the security investments of their clients. The goal of this relationship is to (i) allow security vendors to price differentiate their clients based on security investment information from insurance agencies, (ii) allow the vendors to make more profit than in homogeneous pricing settings, and (iii) subsequently transfer some of the extra profit to cyber-insurance agencies to make insurance services more viable. \\noindent In this paper, we perform a theoretical study of a market for differentiated security product pricing, primarily with a view to ensuring that security vendors (SVs) make more profit in the differentiated pricing case as compared to the case of non-differentiated pricing. In order to practically realize such pricing markets, we propose novel and \\emph{computationally efficient} consumer differentiated pricing mechanisms for SVs based on (i) the market structure, (ii) the communication network structure of SV consumers captured via a consumer's \\emph{Bonacich centrality} in the network, and (iii) security investment amounts made by SV consumers.", "published": "2016-07-09T11:07:07Z", "authors": ["Ranjan Pal", "Leana Golubchik", "Konstantinos Psounis", "Pan Hui"], "categories": ["cs.GT"], "doi": "", "_query": "hedonic house price", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/1201.5448v1", "title": "Determinants of immediate price impacts at the trade level in an emerging order-driven market", "abstract": "The common wisdom argues that, in general, large trades cause large price changes, while small trades cause small price changes. However, for extremely large price changes, the trade size and news play a minor role, while the liquidity (especially price gaps on the limit order book) is a more influencing factor. Hence, there might be other influencing factors of immediate price impacts of trades. In this paper, through mechanical analysis of price variations before and after a trade of arbitrary size, we identify that the trade size, the bid-ask spread, the price gaps and the outstanding volumes at the bid and ask sides of the limit order book have impacts on the changes of prices. We propose two regression models to investigate the influences of these microscopic factors on the price impact of buyer-initiated partially filled trades, seller-initiated partially filled trades, buyer-initiated filled trades, and seller-initiated filled trades. We find that they have quantitatively similar explanation powers and these factors can account for up to 44% of the price impacts. Large trade sizes, wide bid-ask spreads, high liquidity at the same side and low liquidity at the opposite side will cause a large price impact. We also find that the liquidity at the opposite side has a more influencing impact than the liquidity at the same side. Our results shed new lights on the determinants of immediate price impacts.", "published": "2012-01-26T08:09:17Z", "authors": ["Wei-Xing Zhou"], "categories": ["q-fin.ST", "q-fin.TR"], "doi": "10.1088/1367-2630/14/2/023055", "_query": "hedonic house price", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/1001.0880v1", "title": "Does Security Transaction Volume-Price Behavior Resemble a Probability Wave?", "abstract": "Motivated by how transaction amount constrain trading volume and price volatility in stock market, we, in this paper, study the relation between volume and price if amount of transaction is given. We find that accumulative trading volume gradually emerges a kurtosis near the price mean value over a trading price range when it takes a longer trading time, regardless of actual price fluctuation path, time series, or total transaction volume in the time interval. To explain the volume-price behavior, we, in terms of physics, propose a transaction energy hypothesis, derive a time-independent transaction volume-price probability wave equation, and get two sets of analytical volume distribution eigenfunctions over a trading price range. By empiric test, we show the existence of coherence in stock market and demonstrate the model validation at this early stage. The volume-price behaves like a probability wave.", "published": "2010-01-06T12:33:20Z", "authors": ["Leilei Shi"], "categories": ["q-fin.TR", "q-fin.ST"], "doi": "10.1016/j.physa.2005.10.016", "_query": "hedonic house price", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/1207.3412v2", "title": "A quantum mechanical model for the relationship between stock price and stock ownership", "abstract": "The trade of a fixed stock can be regarded as the basic process that measures its momentary price. The stock price is exactly known only at the time of sale when the stock is between traders, that is, only in the case when the owner is unknown. We show that the stock price can be better described by a function indicating at any moment of time the probabilities for the possible values of price if a transaction takes place. This more general description contains partial information on the stock price, but it also contains partial information on the stock owner. By following the analogy with quantum mechanics, we assume that the time evolution of the function describing the stock price can be described by a Schrodinger type equation.", "published": "2012-07-14T11:19:48Z", "authors": ["Liviu-Adrian Cotfas"], "categories": ["q-fin.GN", "math-ph"], "doi": "10.1063/1.4766764", "_query": "hedonic house price", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2509.14116v1", "title": "Minimum pricing or volumetric taxation? Quantity, quality and competition effects of price regulations in alcohol markets", "abstract": "Reforming alcohol price regulations in wine-producing countries is challenging, as current price regulations reflect the alignment of cultural preferences with economic interests rather than public health concerns. We evaluate and compare the impact of counterfactual alcohol pricing policies on consumer behaviors, firms, and markets in France. We develop a micro-founded partial equilibrium model that accounts for consumer preferences over purchase volumes across alcohol categories and over product quality within categories, and for firms' strategic price-setting. After calibration on household scanner data, we compare the impacts of replacing current taxes by ethanol-based volumetric taxes with a minimum unit price (MUP) policy of 0.50 Euro per standard drink. The results show that the MUP in addition to the current tax outperforms a tax reform in reducing ethanol purchases (-15% vs. -10% for progressive taxation), especially among heavy drinking households (-17%). The MUP increases the profits of small and medium wine firms (+39%) while decreasing the profits of large manufacturers and retailers (-39%) and maintaining tax revenues stable. The results support the MUP as a targeted strategy to reduce harmful consumption while benefiting small and medium wine producers. This study provides ex-ante evidence that is crucial for alcohol pricing policies in wine-producing countries.", "published": "2025-09-17T15:57:37Z", "authors": ["Celine Bonnet", "Fabrice Etile", "Sebastien Lecocq"], "categories": ["econ.GN"], "doi": "", "_query": "hedonic house price", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2003.03479v2", "title": "Step on the Gas? A Better Approach for Recommending the Ethereum Gas Price", "abstract": "In the Ethereum network, miners are incentivized to include transactions in a block depending on the gas price specified by the sender. The sender of a transaction therefore faces a trade-off between timely inclusion and cost of his transaction. Existing recommendation mechanisms aggregate recent gas price data on a per-block basis to suggest a gas price. We perform an empirical analysis of historic block data to motivate the use of a predictive model for gas price recommendation. Subsequently, we propose a novel mechanism that combines a deep-learning based price forecasting model as well as an algorithm parameterized by a user-specific urgency value to recommend gas prices. In a comprehensive evaluation on real-world data, we show that our approach results on average in costs savings of more than 50% while only incurring an inclusion delay of 1.3 blocks, when compared to the gas price recommendation mechanism of the most widely used Ethereum client.", "published": "2020-03-07T01:08:44Z", "authors": ["Sam M. Werner", "Paul J. Pritz", "Daniel Perez"], "categories": ["cs.DC"], "doi": "", "_query": "hedonic house price", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/1406.5722v1", "title": "The price of fairness for a small number of indivisible items", "abstract": "Incorporating fairness criteria in optimization problems comes at a certain cost, which is measured by the so-called price of fairness. Here we consider the allocation of indivisible goods. For envy-freeness as fairness criterion it is known from literature that the price of fairness can increase linearly in terms of the number of agents. For the constructive lower bound a quadratic number of items was used. In practice this might be inadequately large. So we introduce the price of fairness in terms of both the number of agents and items, i.e., key parameters which generally may be considered as common and available knowledge. It turned out that the price of fairness increases sublinear if the number of items is not too much larger than the number of agents. For the special case of coincide of both counts exact asymptotics could be determined. Additionally an efficient integer programming formulation is given.", "published": "2014-06-22T13:01:32Z", "authors": ["Sascha Kurz"], "categories": ["cs.GT"], "doi": "", "_query": "hedonic house price", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/1406.6615v1", "title": "The Critical Price Of The American Put Near Maturity In The Jump Diffusion Model", "abstract": "We study the behavior of the critical price of an American put option near maturity in the Jump diffusion model when the underlying stock pays dividends at a continuous rate and the limit of the critical price is smaller than the stock price. In particular, we prove that, unlike the case where the limit is equal to the strike price, jumps can influence the convergence rate.", "published": "2014-06-25T15:43:20Z", "authors": ["Aych Bouselmi", "Damien Lamberton"], "categories": ["math.PR"], "doi": "", "_query": "hedonic house price", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2505.05353v2", "title": "Weighted Envy-Freeness Revisited: Indivisible Resource and House Allocations", "abstract": "Envy-Freeness is one of the most fundamental and important concepts in fair allocation. Some recent studies have focused on the concept of weighted envy-freeness. Under this concept, each agent is assigned a weight, and their valuations are divided by their weights when assessing fairness. This concept can promote more fairness in some scenarios. But on the other hand, experimental research has shown that this weighted envy-freeness significantly reduces the likelihood of fair allocations. When we must allocate the resources, we may propose fairness concepts with lower requirements that are potentially more feasible to implement. In this paper, we revisit weighted envy-freeness and propose a new concept called SumAvg-envy-freeness, which substantially increases the existence of fair allocations. This new concept can be seen as a complement of the normal weighted envy-fairness. Furthermore, we systematically study the computational complexity of finding fair allocations under the old and new weighted fairness concepts in two types of classic problems: Indivisible Resource Allocation and House Allocation. Our study provides a comprehensive characterization of various properties of weighted envy-freeness.", "published": "2025-05-08T15:42:01Z", "authors": ["Yuxi Liu", "Mingyu Xiao"], "categories": ["cs.GT"], "doi": "", "_query": "hedonic house price", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/cs/0109036v2", "title": "Competition and Price Dispersion in International Long Distance Calling", "abstract": "This paper examines the relationship between changes in telecommunications provider concentration on international long distance routes and changes in prices on those routes. Overall, decreased concentration is associated with significantly lower prices to consumers of long distance services. However, the relationship between concentration and price varies according to the type of long distance plan considered. For the international flagship plans frequently selected by more price-conscious consumers of international long distance, increased competition on a route is associated with lower prices. In contrast, for the basic international plans that are the default selection for consumers, increased competition on a route is actually associated with higher prices. Thus, somewhat surprisingly, price dispersion appears to increase as competition increases.", "published": "2001-09-19T18:42:27Z", "authors": ["Sean F. Ennis"], "categories": ["cs.CY"], "doi": "", "_query": "hedonic house price", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2405.19887v2", "title": "Modulational Instability of the time-fractional Ivancevic option pricing model and the Coupled Nonlinear volatility and option price model", "abstract": "We study the time-fractional Ivancevic option pricing model and the coupled nonlinear volatility and option price model via both modulational instability (MI) analysis and direct simulations. For the coupled volatility and option pricing model the coupling term for both the volatility and the option price equation is the same, the MI results are dependent on it, and the stability of the volatility exists for the same condition as that of the price. The numerical simulations are done to confirm the conditions of MI. For the time-fractional model the analysis shows that for some values of the Hurst exponent MI exists for negative values of the adaptive market heat potential. Also, the sign of the volatility does not affect the MI, even though for some values of the volatility the MI can be suppressed. Direct numerical simulation shows the existance of solitons for negative values of the adaptive market potential where instabilty exists due to the value of the Hurst exponent.", "published": "2024-05-30T09:46:30Z", "authors": ["C. Gaafele", "Edmond B. Madimabe", "K. Ndebele", "P. Otlaadisa", "B. Mozola", "T. Matabana", "K. Seamolo", "P. Pilane"], "categories": ["nlin.PS"], "doi": "", "_query": "hedonic house price", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/1907.00558v1", "title": "Improved Forecasting of Cryptocurrency Price using Social Signals", "abstract": "Social media signals have been successfully used to develop large-scale predictive and anticipatory analytics. For example, forecasting stock market prices and influenza outbreaks. Recently, social data has been explored to forecast price fluctuations of cryptocurrencies, which are a novel disruptive technology with significant political and economic implications. In this paper we leverage and contrast the predictive power of social signals, specifically user behavior and communication patterns, from multiple social platforms GitHub and Reddit to forecast prices for three cyptocurrencies with high developer and community interest - Bitcoin, Ethereum, and Monero. We evaluate the performance of neural network models that rely on long short-term memory units (LSTMs) trained on historical price data and social data against price only LSTMs and baseline autoregressive integrated moving average (ARIMA) models, commonly used to predict stock prices. Our results not only demonstrate that social signals reduce error when forecasting daily coin price, but also show that the language used in comments within the official communities on Reddit (r/Bitcoin, r/Ethereum, and r/Monero) are the best predictors overall. We observe that models are more accurate in forecasting price one day ahead for Bitcoin (4% root mean squared percent error) compared to Ethereum (7%) and Monero (8%).", "published": "2019-07-01T05:51:05Z", "authors": ["Maria Glenski", "Tim Weninger", "Svitlana Volkova"], "categories": ["q-fin.ST", "cs.LG", "cs.SI", "stat.ML"], "doi": "", "_query": "hedonic house price", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/1104.2308v1", "title": "Non - Randomness Stock Market Price Model (Amended)", "abstract": "A new model for the stock market price analysis is proposed. It is suggested to look at price as an everywhere discontinuous function of time of bounded variation.", "published": "2011-04-12T19:14:29Z", "authors": ["Aleksey Kharevsky"], "categories": ["q-fin.GN"], "doi": "", "_query": "hedonic house price", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/cond-mat/0312560v2", "title": "Power law for the calm-time interval of price changes", "abstract": "In this paper, we describe a newly discovered statistical property of time series data for daily price changes. We conducted quantitative investigation of the {\\it calm-time intervals} of price changes for 800 companies listed in the Tokyo Stock Exchange, and for the Nikkei 225 index over a 27-year period from January 4, 1975 to December 28, 2001. A calm-time interval is defined as the interval between two successive price changes above a fixed threshold. We found that the calm-time interval distribution of price changes obeys a power law decay. Furthermore, we show that the power-law exponent decreases monotonically with respect to the threshold. Keyword: econophysics, stock price changes, calm time interval, power-laws; PACS: 89.90.+n; 05.40.Df;", "published": "2003-12-21T17:49:59Z", "authors": ["Taisei Kaizoji", "Michiyo Kaizoji"], "categories": ["cond-mat.stat-mech", "physics.soc-ph", "q-fin.ST"], "doi": "10.1016/j.physa.2003.12.054", "_query": "hedonic house price", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/1909.07896v2", "title": "No-Arbitrage Commodity Option Pricing with Market Manipulation", "abstract": "We design three continuous--time models in finite horizon of a commodity price, whose dynamics can be affected by the actions of a representative risk--neutral producer and a representative risk--neutral trader. Depending on the model, the producer can control the drift and/or the volatility of the price whereas the trader can at most affect the volatility. The producer can affect the volatility in two ways: either by randomizing her production rate or, as the trader, using other means such as spreading false information. Moreover, the producer contracts at time zero a fixed position in a European convex derivative with the trader. The trader can be price-taker, as in the first two models, or she can also affect the volatility of the commodity price, as in the third model. We solve all three models semi--explicitly and give closed--form expressions of the derivative price over a small time horizon, preventing arbitrage opportunities to arise. We find that when the trader is price-taker, the producer can always compensate the loss in expected production profit generated by an increase of volatility by a gain in the derivative position by driving the price at maturity to a suitable level. Finally, in case the trader is active, the model takes the form of a nonzero-sum linear-quadratic stochastic differential game and we find that when the production rate is already at its optimal stationary level, there is an amount of derivative position that makes both players better off when entering the game.", "published": "2019-09-17T15:38:07Z", "authors": ["René Aïd", "Giorgia Callegaro", "Luciano Campi"], "categories": ["q-fin.MF"], "doi": "", "_query": "hedonic house price", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2401.01516v1", "title": "A Complete Landscape for the Price of Envy-Freeness", "abstract": "We study the efficiency of fair allocations using the well-studied price of fairness concept, which quantitatively measures the worst-case efficiency loss when imposing fairness constraints. Previous works provided partial results on the price of fairness with well-known fairness notions such as envy-freeness up to one good (EF1) and envy-freeness up to any good (EFX). In this paper, we give a complete characterization for the price of envy-freeness in various settings. In particular, we first consider the two-agent case under the indivisible-goods setting and present tight ratios for the price of EF1 (for scaled utility) and EFX (for unscaled utility), which resolve questions left open in the literature. Next, we consider the mixed goods setting which concerns a mixture of both divisible and indivisible goods. We focus on envy-freeness for mixed goods (EFM), which generalizes both envy-freeness and EF1, as well as its strengthening called envy-freeness up to any good for mixed goods (EFXM), which generalizes envy-freeness and EFX. To this end, we settle the price of EFM and EFXM by providing a complete picture of tight bounds for two agents and asymptotically tight bounds for $n$ agents, for both scaled and unscaled utilities.", "published": "2024-01-03T02:55:34Z", "authors": ["Zihao Li", "Shengxin Liu", "Xinhang Lu", "Biaoshuai Tao", "Yichen Tao"], "categories": ["cs.GT"], "doi": "", "_query": "hedonic house price", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/1903.03987v2", "title": "Price competition with uncertain quality and cost", "abstract": "Consumers in many markets are uncertain about firms' qualities and costs, so buy based on both the price and the quality inferred from it. Optimal pricing depends on consumer heterogeneity only when firms with higher quality have higher costs, regardless of whether costs and qualities are private or public. If better quality firms have lower costs, then good quality is sold cheaper than bad under private costs and qualities, but not under public. However, if higher quality is costlier, then price weakly increases in quality under both informational environments.", "published": "2019-03-10T13:20:23Z", "authors": ["Sander Heinsalu"], "categories": ["econ.TH"], "doi": "", "_query": "hedonic house price", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2305.00337v1", "title": "A Practical and Economical Bayesian Approach to Gas Price Prediction", "abstract": "On the Ethereum network, it is challenging to determine a gas price that ensures a transaction will be included in a block within a user's required timeline without overpaying. One way of addressing this problem is through the use of gas price oracles that utilize historical block data to recommend gas prices. However, when transaction volumes increase rapidly, these oracles often underestimate or overestimate the price. In this paper, we demonstrate how Gaussian process models can predict the distribution of the minimum price in an upcoming block when transaction volumes are increasing. This is effective because these processes account for time correlations between blocks. We performed an empirical analysis using the Gaussian process model on historical block data and compared the performance with GasStation-Express and Geth gas price oracles. The results suggest that when transactions volumes fluctuate greatly, the Gaussian process model offers a better estimation. Further, we demonstrated that GasStation-Express and Geth can be improved upon by using a smaller training sample size which is properly pre-processed. Based on the results of empirical analysis, we recommended a gas price oracle made up of a hybrid model consisting of both the Gaussian process and GasStation-Express. This oracle provides efficiency, accuracy, and better cost.", "published": "2023-04-29T20:24:49Z", "authors": ["ChihYun Chuang", "TingFang Lee"], "categories": ["stat.AP", "cs.CE"], "doi": "10.1007/978-3-030-84337-3_13", "_query": "hedonic house price", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2101.00223v1", "title": "Pricing spread option with liquidity adjustments", "abstract": "We study the pricing and hedging of European spread options on correlated assets when, in contrast to the standard framework and consistent with imperfect liquidity markets, the trading in the stock market has a direct impact on stocks prices. We consider a partial-impact and a full-impact model in which the price impact is caused by every trading strategy in the market. The generalized Black-Scholes pricing partial differential equations (PDEs) are obtained and analysed. We perform a numerical analysis to exhibit the illiquidity effect on the replication strategy of the European spread option. Compared to the Black-Scholes model or a partial impact model, the trader in the full impact model buys more stock to replicate the option, and this leads to a higher option price.", "published": "2021-01-01T12:30:02Z", "authors": ["Kevin Shuai Zhang", "Traian Pirvu"], "categories": ["q-fin.CP"], "doi": "", "_query": "hedonic house price", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2605.06060v1", "title": "Arbitrage and the Stability of AMM Price Tracking", "abstract": "Automated market makers (AMMs) quote prices from pool state rather than from a limit order book. AMM pools often stay close to a reference price because arbitrageurs correct profitable mispricing. A large part of decentralized finance therefore relies on a simple economic premise: once the AMM price drifts away from the reference price, arbitrage incentives push it back. This paper studies when that premise is strong enough to guarantee block-scale stability. We model the gap between the reference price and the AMM price as a stochastic tracking error, treat arbitrage as the corrective input, and place blockchain execution inside the loop through fees, discrete blocks, transaction ordering, delays, and transaction failure. The detailed execution layer is reduced to the total successful correction confirmed in each block. Under a block-level correction condition, we prove geometric ergodicity of the tracking error and obtain explicit one-step bounds that connect tracking quality to liquidity and execution quality. We also show in a constant-product example how fees, fixed execution costs, and local liquidity map into the no-trade band and the optimal corrective trade. Finally, we build empirical proxies for the theorem quantities from realized block data and use them to organize reduced and mechanism-focused simulations whose comparative statics are consistent with the theory. The contribution is to turn a basic economic intuition behind decentralized finance into a quantitative stability statement together with a tractable calibration interface.", "published": "2026-05-07T11:44:09Z", "authors": ["Peihao Li", "Nadia Dahmani", "Wenqi Cai"], "categories": ["cs.CE", "eess.SY"], "doi": "", "_query": "hedonic house price", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/cond-mat/0103621v1", "title": "Measuring long-range dependence in electricity prices", "abstract": "The price of electricity is far more volatile than that of other commodities normally noted for extreme volatility. The possibility of extreme price movements increases the risk of trading in electricity markets. However, underlying the process of price returns is a strong mean-reverting mechanism. We study this feature of electricity returns by means of Hurst R/S analysis, Detrended Fluctuation Analysis and periodogram regression.", "published": "2001-03-30T08:46:03Z", "authors": ["Rafal Weron"], "categories": ["cond-mat.stat-mech", "q-fin.ST"], "doi": "", "_query": "hedonic house price", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/1403.7681v3", "title": "Uncertain Price Competition in a Duopoly with Heterogeneous Availability", "abstract": "We study the price competition in a duopoly with an arbitrary number of buyers. Each seller can offer multiple units of a commodity depending on the availability of the commodity which is random and may be different for different sellers. Sellers seek to select a price that will be attractive to the buyers and also fetch adequate profits. The selection will in general depend on the number of units available with the seller and also that of its competitor - the seller may only know the statistics of the latter. The setting captures a secondary spectrum access network, a non-neutral Internet, or a microgrid network in which unused spectrum bands, resources of ISPs, and excess power units constitute the respective commodities of sale. We analyze this price competition as a game, and identify a set of necessary and sufficient properties for the Nash Equilibrium (NE). The properties reveal that sellers randomize their price using probability distributions whose support sets are mutually disjoint and in decreasing order of the number of availability. We prove the uniqueness of a symmetric NE in a symmetric market, and explicitly compute the price distribution in the symmetric NE.", "published": "2014-03-29T23:51:05Z", "authors": ["Mohammad Hassan Lotfi", "Saswati Sarkar"], "categories": ["cs.GT"], "doi": "10.1109/TAC.2015.2450091", "_query": "hedonic house price", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/1410.8224v1", "title": "Efficient price dynamics in a limit order market: an utility indifference approach", "abstract": "We construct an utility-based dynamic asset pricing model for a limit order market. The price is nonlinear in volume and subject to market impact. We solve an optimal hedging problem under the market impact and derive the dynamics of the efficient price, that is, the asset price when a representative liquidity demander follows an optimal strategy. We show that a Pareto efficient allocation is achieved under a completeness condi- tion. We give an explicit representation of the efficient price for several examples. In particular, we observe that the volatility of the asset depends on the convexity of an initial endowment. Further, we observe that an asset price crash is invoked by an endowment shock. We establish a dynamic programming principle under an incomplete framework.", "published": "2014-10-30T01:12:00Z", "authors": ["Masaaki Fukasawa"], "categories": ["q-fin.PR", "math.PR"], "doi": "", "_query": "hedonic house price", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2110.05891v1", "title": "Group network effects in price competition", "abstract": "The partition of society into groups, polarization, and social networks are part of most conversations today. How do they influence price competition? We discuss Bertrand duopoly equilibria with demand subject to network effects. Contrary to models where network effects depend on one aggregate variable (demand for each choice), partitioning the dependence into groups creates a wealth of pure price equilibria with profit for both price setters, even if positive network effects are the dominant element of the game. If there is some asymmetry in how groups interact, two groups are sufficient. If network effects are based on undirected and unweighted graphs, at least five groups are required but, without other differentiation, outcomes are symmetric.", "published": "2021-10-12T11:05:29Z", "authors": ["Renato Soeiro", "Alberto Pinto"], "categories": ["econ.TH"], "doi": "", "_query": "hedonic house price", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2212.11787v1", "title": "Macro carbon price prediction with support vector regression and Paris accord targets", "abstract": "Carbon neutralization is an urgent task in society because of the global warming threat. And carbon trading is an essential market mechanics to solve carbon reduction targets. Macro carbon price prediction is vital in the useful management and decision-making of the carbon market. We focus on the EU carbon market and we choose oil price, coal price, gas price, and DAX index to be the four market factors in predicting carbon price, and also we select carbon emission targets from Paris Accord as the political factor in the carbon market in terms of the macro view of the carbon price prediction. Thus we use these five factors as inputs to predict the future carbon yearly price in 2030 with the support vector regression models. We use grid search and cross validation to guarantee the prediction performance of our models. We believe this model will have great applications in the macro carbon price prediction.", "published": "2022-11-30T02:42:13Z", "authors": ["Jinhui Li"], "categories": ["q-fin.GN", "econ.GN", "stat.AP"], "doi": "", "_query": "hedonic house price", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2310.13254v2", "title": "Socially Optimal Energy Usage via Adaptive Pricing", "abstract": "A central challenge in using price signals to coordinate the electricity consumption of a group of users is the operator's lack of knowledge of the users due to privacy concerns. In this paper, we develop a two-time-scale incentive mechanism that alternately updates between the users and a system operator. As long as the users can optimize their own consumption subject to a given price, the operator does not need to know or attempt to learn any private information of the users for price design. Users adjust their consumption following the price and the system redesigns the price based on the users' consumption. We show that under mild assumptions, this iterative process converges to the social welfare solution. In particular, the cost of the users need not always be convex and its consumption can be the output of a machine learning-based load control algorithm.", "published": "2023-10-20T03:21:58Z", "authors": ["Jiayi Li", "Matthew Motoki", "Baosen Zhang"], "categories": ["cs.GT", "eess.SY"], "doi": "", "_query": "hedonic house price", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2405.03496v3", "title": "Price-Aware Automated Market Makers: Models Beyond Brownian Prices and Static Liquidity", "abstract": "In this paper, we introduce a suite of models for price-aware automated market making platforms willing to optimize their quotes. These models incorporate advanced price dynamics, including stochastic volatility, jumps, and microstructural price models based on Hawkes processes. Additionally, we address the variability in demand from liquidity takers through models that employ either Hawkes or Markov-modulated Poisson processes. Each model is analyzed with particular emphasis placed on the complexity of the numerical methods required to compute optimal quotes.", "published": "2024-05-06T14:07:02Z", "authors": ["Philippe Bergault", "Louis Bertucci", "David Bouba", "Olivier Guéant", "Julien Guilbert"], "categories": ["q-fin.TR"], "doi": "", "_query": "hedonic house price", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/1908.01103v2", "title": "Derivation of non-classical stochastic price dynamics equations", "abstract": "We analyze the relative price change of assets starting from basic supply/demand considerations subject to arbitrary motivations. The resulting stochastic differential equation has coefficients that are functions of supply and demand. We derive these rigorously. The variance in the relative price change is then also dependent on the supply and demand, and is closely connected to the expected return. An important consequence for risk assessment and options pricing is the implication that variance is highest when the magnitude of price change is greatest, and lowest near market extrema. This occurs even if supply and demand are not dependent on price trend. The stochastic equation differs from the standard equation in mathematical finance in which the expected return and variance are decoupled. The methodology has implications for the basic framework for risk assessment, suggesting that volatility should be measured in the context of regimes of price change. The model we propose shows how investors are often misled by the apparent calm of markets near a market peak. Risk assessment methods utilizing volatility can be improved using this formulation.", "published": "2019-08-03T01:28:09Z", "authors": ["Carey Caginalp", "Gunduz Caginalp"], "categories": ["econ.TH", "math.PR"], "doi": "", "_query": "hedonic house price", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2309.10220v1", "title": "Comparing effects of price limit and circuit breaker in stock exchanges by an agent-based model", "abstract": "The prevention of rapidly and steeply falling market prices is vital to avoid financial crisis. To this end, some stock exchanges implement a price limit or a circuit breaker, and there has been intensive investigation into which regulation best prevents rapid and large variations in price. In this study, we examine this question using an artificial market model that is an agent-based model for a financial market. Our findings show that the price limit and the circuit breaker basically have the same effect when the parameters, limit price range and limit time range, are the same. However, the price limit is less effective when limit the time range is smaller than the cancel time range. With the price limit, many sell orders are accumulated around the lower limit price, and when the lower limit price is changed before the accumulated sell orders are cancelled, it leads to the accumulation of sell orders of various prices. These accumulated sell orders essentially act as a wall against buy orders, thereby preventing price from rising. Caution should be taken in the sense that these results pertain to a limited situation. Specifically, our finding that the circuit breaker is better than the price limit should be adapted only in cases where the reason for falling prices is erroneous orders and when individual stocks are regulated.", "published": "2023-09-19T00:23:25Z", "authors": ["Takanobu Mizuta", "Isao Yagi"], "categories": ["q-fin.CP", "q-fin.TR"], "doi": "", "_query": "hedonic house price", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2610.10172v1", "title": "Price Discrimination and Revealed Consumer Welfare", "abstract": "We take a revealed-preference approach to study the welfare effects of price discrimination. An analyst observes price and quantity data before and after a monopolist segments the market. The analyst knows neither the aggregate demand curve nor the information available to the monopolist. We characterize the set of observable price-quantity pairs that are rationalizable by some market segmentation. We then obtain sharp bounds on the change in consumer surplus arising from some segmentation. Finally, we provide conditions under which the data identifies an unambiguous increase (decrease) in consumer surplus.", "published": "2026-10-07T14:46:34Z", "authors": ["João Thereze", "Udayan Vaidya"], "categories": ["econ.TH"], "doi": "", "_query": "hedonic house price", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2602.15981v1", "title": "A Theoretical Approach to Stablecoin Design via Price Windows", "abstract": "In this paper, we explore the short- and long-term stability of backed stablecoins offering constant mint and redeem prices to all agents. We refer to such designs as price window-based, since the mint and redeem prices constrain the stablecoin's market equilibrium. We show that, without secondary stabilization mechanisms, price window designs cannot achieve both short- and long-term stability unless they are backed by already-stable reserves. In particular, the mechanism faces a tradeoff: either risk eventual reserve depletion through persistent arbitrage by a speculator, or widen the distance between mint and redeem prices enough to disincentivize arbitrage. In the latter case, however, the market price of the stablecoin inherits the volatility of its backing asset, with fluctuations that can be proportional to the backing asset's own volatility.", "published": "2026-02-17T20:17:51Z", "authors": ["Katherine Molinet", "Aris Filos-Ratsikas"], "categories": ["cs.GT", "cs.CR"], "doi": "", "_query": "hedonic house price", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2601.19095v1", "title": "Dual Analysis of Continuous-time Economic Dispatch and Its Price Implications", "abstract": "As load varies continuously over time, it is essential to provide continuous-time price signals that accurately reflect supply-demand balance. However, conventional discrete-time economic dispatch fails to capture the intra-temporal variations in load and generation. Its dual solution--the marginal price--may distort economic signals, leading to inefficient market incentives. To analyze these issues, this paper develops a continuous-time dispatch model and derives its dual formulation for price analysis. The continuous-time dispatch produces dual variables that can be interpreted as price signals. Piecewise time-indexed generation and price trajectories are then constructed through a parametric programming approach. The resulting price, represented by the Lagrange multiplier of the system-wide power balance constraint, evolves piecewise along the continuous-time load profile. Each segment corresponds to a critical region characterized by a set of active constraints. Furthermore, we discuss the impact of unit-specific ramping constraints on price implications. Results indicate that continuous-time generation and price trajectories provide deeper insights into the price distortions and inefficient incentives inherent in discrete-time formulations. The proposed methodology is validated on an illustrative 5-bus system and a modified IEEE RTS-2019.", "published": "2026-01-27T01:58:42Z", "authors": ["Menghan Zhang", "Caisheng Wang"], "categories": ["eess.SY"], "doi": "", "_query": "hedonic house price", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/1709.08621v1", "title": "A sentiment-based model for the BitCoin: theory, estimation and option pricing", "abstract": "In recent literature it is claimed that BitCoin price behaves more likely to a volatile stock asset than a currency and that changes in its price are influenced by sentiment about the BitCoin system itself; in Kristoufek [10] the author analyses transaction based as well as popularity based potential drivers of the BitCoin price finding positive evidence. Here, we endorse this finding and consider a bivariate model in continuous time to describe the price dynamics of one BitCoin as well as a second factor, affecting the price itself, which represents a sentiment indicator. We prove that the suggested model is arbitrage-free under a mild condition and, based on risk-neutral evaluation, we obtain a closed formula to approximate the price of European style derivatives on the BitCoin. By applying the same approximation technique to the joint likelihood of a discrete sample of the bivariate process, we are also able to fit the model to market data. This is done by using both the Volume and the number of Google searches as possible proxies for the sentiment factor. Further, the performance of the pricing formula is assessed on a sample of market option prices obtained by the website deribit.com.", "published": "2017-09-23T21:42:40Z", "authors": ["Alessandra Cretarola", "Gianna Figà-Talamanca", "Marco Patacca"], "categories": ["q-fin.MF"], "doi": "10.1007/s10203-019-00262-x", "_query": "hedonic house price", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2309.02447v2", "title": "Economic Complexity Limits Accuracy of Price Probability Predictions by Gaussian Distributions", "abstract": "We discuss the economic reasons why the predictions of price and return statistical moments in the coming decades, in the best case, will be limited by their averages and volatilities. That limits the accuracy of the forecasts of price and return probabilities by Gaussian distributions. The economic origin of these restrictions lies in the fact that the predictions of the market-based n-th statistical moments of price and return for n=1,2,.., require the description of the economic variables of the n-th order that are determined by sums of the n-th degrees of values or volumes of market trades. The lack of existing models that describe the evolution of the economic variables determined by the sums of the 2nd degrees of market trades results in the fact that even predictions of the volatilities of price and return are very uncertain. One can ignore existing economic barriers that we highlight but cannot overcome or resolve them. The accuracy of predictions of price and return probabilities substantially determines the reliability of asset pricing models and portfolio theories. The restrictions on the accuracy of predictions of price and return statistical moments reduce the reliability and veracity of modern asset pricing and portfolio theories.", "published": "2023-08-24T09:49:20Z", "authors": ["Victor Olkhov"], "categories": ["q-fin.GN", "econ.GN", "q-fin.PR", "q-fin.RM"], "doi": "", "_query": "hedonic house price", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/1811.11379v4", "title": "Option Pricing in a Regime Switching Jump Diffusion Model", "abstract": "This paper presents the solution to a European option pricing problem by considering a regime-switching jump diffusion model of the underlying financial asset price dynamics. The regimes are assumed to be the results of an observed pure jump process, driving the values of interest rate and volatility coefficient. The pure jump process is assumed to be a semi-Markov process on finite state space. This consideration helps to incorporate a specific type of memory influence in the asset price. Under this model assumption, the locally risk minimizing price of the European type path-independent options is found. The Föllmer-Schweizer decomposition is adopted to show that the option price satisfies an evolution problem, as a function of time, stock price, market regime, and the stagnancy period. To be more precise, the evolution problem involves a linear, parabolic, degenerate and non-local system of integro-partial differential equations. We have established existence and uniqueness of classical solution to the evolution problem in an appropriate class.", "published": "2018-11-28T04:30:38Z", "authors": ["Anindya Goswami", "Omkar Manjarekar", "Anjana R"], "categories": ["q-fin.PR", "math.PR", "q-fin.MF"], "doi": "", "_query": "hedonic house price", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2008.01618v2", "title": "Distributionally Robust Pricing in Independent Private Value Auctions", "abstract": "A seller chooses a reserve price in a second-price auction to maximize worst-case expected revenue when she knows only the mean of value distribution and an upper bound on either values themselves or variance. Values are private and iid. Using an indirect technique, we prove that it is always optimal to set the reserve price to the seller's own valuation. However, the maxmin reserve price may not be unique. If the number of bidders is sufficiently high, all prices below the seller's valuation, including zero, are also optimal. A second-price auction with the reserve equal to seller's value (or zero) is an asymptotically optimal mechanism (among all ex post individually rational mechanisms) as the number of bidders grows without bound.", "published": "2020-08-04T14:59:45Z", "authors": ["Alex Suzdaltsev"], "categories": ["econ.TH", "cs.GT"], "doi": "", "_query": "hedonic house price", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/1506.03621v2", "title": "Convergence of Estimated Option Price in a Regime switching Market", "abstract": "In an observed generalized semi-Markov regime, estimation of transition rate of regime switching leads towards calculation of locally risk minimizing option price. Despite the uniform convergence of estimated step function of transition rate, to meet the existence of classical solution of the modified price equation, the estimator is approximated in the class of smooth functions and furthermore, the convergence is established. Later, the existence of the solution of the modified price equation is verified and the point-wise convergence of such approximation of option price is proved to answer the tractability of its application in Finance. To demonstrate the consistency in result a numerical experiment has been reported.", "published": "2015-06-11T10:59:55Z", "authors": ["Anindya Goswami", "Sanket Nandan"], "categories": ["q-fin.PR", "math.PR", "math.ST"], "doi": "10.1007/s13226-016-0182-7", "_query": "hedonic house price", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2205.01417v1", "title": "The Price of Hierarchical Clustering", "abstract": "Hierarchical Clustering is a popular tool for understanding the hereditary properties of a data set. Such a clustering is actually a sequence of clusterings that starts with the trivial clustering in which every data point forms its own cluster and then successively merges two existing clusters until all points are in the same cluster. A hierarchical clustering achieves an approximation factor of $α$ if the costs of each $k$-clustering in the hierarchy are at most $α$ times the costs of an optimal $k$-clustering. We study as cost functions the maximum (discrete) radius of any cluster ($k$-center problem) and the maximum diameter of any cluster ($k$-diameter problem). In general, the optimal clusterings do not form a hierarchy and hence an approximation factor of $1$ cannot be achieved. We call the smallest approximation factor that can be achieved for any instance the price of hierarchy. For the $k$-diameter problem we improve the upper bound on the price of hierarchy to $3+2\\sqrt{2}\\approx 5.83$. Moreover we significantly improve the lower bounds for $k$-center and $k$-diameter, proving a price of hierarchy of exactly $4$ and $3+2\\sqrt{2}$, respectively.", "published": "2022-05-03T11:11:55Z", "authors": ["Anna Arutyunova", "Heiko Röglin"], "categories": ["cs.DS"], "doi": "", "_query": "hedonic house price", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2505.18061v1", "title": "Posted Pricing and Competition in Large Markets", "abstract": "Posted price mechanisms are prevalent in allocating goods within online marketplaces due to their simplicity and practical efficiency. We explore a fundamental scenario where buyers' valuations are independent and identically distributed, focusing specifically on the allocation of a single unit. Inspired by the rapid growth and scalability of modern online marketplaces, we investigate optimal performance guarantees under the assumption of a significantly large market. We show a large market benefit when using fixed prices, improving the known guarantee of $1-1/e\\approx 0.632$ to $0.712$. We then study the case of selling $k$ identical units, and we prove that the optimal fixed price guarantee approaches $1-1/\\sqrt{2k π}$, which implies that the large market advantage vanishes as $k$ grows. We use real-world auction data to test our fixed price policies in the large market regime. Next, under the large market assumption, we show that the competition complexity for the optimal posted price mechanism is constant, and we identify precise scaling factors for the number of bidders that enable it to match benchmark performance. Remarkably, our findings break previously established worst-case impossibility results, underscoring the practical robustness and efficiency of posted pricing in large-scale marketplaces.", "published": "2025-05-23T16:07:56Z", "authors": ["José Correa", "Vasilis Livanos", "Dana Pizarro", "Victor Verdugo"], "categories": ["cs.GT"], "doi": "", "_query": "hedonic house price", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2012.14860v1", "title": "Self-sustained price bubbles driven by Bitcoin innovations and adaptive behavior", "abstract": "We show that infinite divisibility of a trading commodity leads to a self-sustained price bubble when traders use adaptive investment strategies. The adaptive strategy can be viewed as a psychological response of a trader to the situation when the trader's estimation of future prices does not match the actual, realized price. We use a multi-agent model to illustrate the price bubble formation and to quantify its main statistical properties such as the return, the volatility, and the systematic risk of the price bubble to crash. We discuss the plausibility for bubbles to drive prices of digital currencies.", "published": "2020-12-29T17:19:51Z", "authors": ["Misha Perepelitsa", "Ilya Timofeyev"], "categories": ["q-fin.TR"], "doi": "10.13140/RG.2.2.10733.00480", "_query": "hedonic house price", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2312.12916v3", "title": "A generalized Price equation for fuzzy set-mappings", "abstract": "The Price equation provides a formal account of selection building on a right-total mapping between two classes of individuals, that is usually interpreted as a parent-offspring relation. This paper presents a new formulation of the Price equation in terms of fuzzy set-mappings to account for structures where the targets of selection may vary in the degree to which they belong to the classes of 'parents' and 'offspring,' and in the degree to which these two classes of individuals are related. The fuzzy set formulation widens the scope of the Price equation such that it equally applies to natural selection, cultural selection, operant selection, and even selection in physical systems.", "published": "2023-12-20T10:52:54Z", "authors": ["Matthias Borgstede"], "categories": ["q-bio.PE"], "doi": "", "_query": "hedonic house price", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2509.10092v1", "title": "Price Formation in a Highly-Renewable, Sector-Coupled Energy System", "abstract": "As variable renewable energy increases and more demand is electrified, we expect price formation in wholesale electricity markets to transition from being dominated by fossil fuel generators to being dominated by the opportunity costs of storage and demand management. In order to analyse this transition, we introduce a new method to investigate price formation based on a mapping from the dual variables of the energy system optimisation problem to the bids and asks of electricity suppliers and consumers. This allows us to build the full supply and demand curves in each hour. We use this method to analyse price formation in a sector-coupled, climate-neutral energy system model for Germany, PyPSA-DE, with high temporal resolution and myopic foresight in 5-year steps from 2020 until full decarbonisation in 2045. We find a clear transition from distinct price levels, corresponding to fossil fuels, to a smoother price curve set by variable renewable energy sources, batteries and electrolysis. Despite higher price volatility, the fully decarbonised system clears with non-zero prices in 75% of all hours. Our results suggest that flexibility and cross-sectoral demand bidding play a vital role in stabilising electricity prices in a climate-neutral future. These findings are highly relevant for guiding investment decisions and informing policy, particularly in support of dynamic pricing, the expansion of energy storage across multiple timescales, and the coordinated development of renewable and flexibility technologies.", "published": "2025-09-12T09:36:05Z", "authors": ["Julian Geis", "Fabian Neumann", "Michael Lindner", "Philipp Härtel", "Tom Brown"], "categories": ["econ.GN"], "doi": "10.1016/j.eneco.2026.109213", "_query": "hedonic house price", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/cond-mat/0501699v1", "title": "Volatility conditional on price trends", "abstract": "The influence of the past price behaviour on the realized volatility is investigated in the present article. The results show that trending (drifting) prices lead to increased (decreased) realized volatility. This ``volatility induced by trend'' constitutes a new stylized fact. The past price behaviour is measured by a product of 2 non overlapping returns, of the form r L[r] where L is the lag operator. The effect is studied empirically using USD/CHF foreign exchange data, in a large range of time horizons. A set of ARCH based processes are modified in order to include the trend effect, and their forecasting performances are compared. For a better forecast, it is shown that the main factor is the shape of the memory kernel (i.e. power law), and the following factor is the inclusion of the trend effect.", "published": "2005-01-28T10:06:23Z", "authors": ["Gilles Zumbach"], "categories": ["cond-mat.other", "q-fin.ST"], "doi": "", "_query": "hedonic house price", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/1905.13733v2", "title": "Data assimilation in price formation", "abstract": "We consider the problem of estimating the density of buyers and vendors in a nonlinear parabolic price formation model using measurements of the price and the transaction rate. Our approach is based on a work by Puel et al., see \\cite{Puel2002}, and results in a optimal control problem. We analyse this problems and provide stability estimates for the controls as well as the unknown density in the presence of measurement errors. Our analytic findings are supported with numerical experiments.", "published": "2019-05-31T17:35:27Z", "authors": ["Martin Burger", "Jan-Frederik Pietschmann", "Marie-Therese Wolfram"], "categories": ["math.OC", "math.AP", "math.NA"], "doi": "10.1088/1361-6420/ab6d5a", "_query": "hedonic house price", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2208.02949v1", "title": "A data-driven approach for modeling the behavior of stock prices", "abstract": "In this paper, we describe two approaches to model the behavior of stock prices. The first approach considers the underlying probability distribution of day-to-day price differences. The second approach models the movement of the price as a stochastic birth-death process. We demonstrated the two approaches using historical opening prices of Apple inc. and compared the simulated prices from the two approaches to the actual ones using information theory metrics.", "published": "2022-08-05T01:13:15Z", "authors": ["Khalid Aram"], "categories": ["stat.AP"], "doi": "", "_query": "hedonic house price", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/1406.6862v1", "title": "Coping with area price risk in electricity markets: Forecasting Contracts for Difference in the Nordic power market", "abstract": "Contracts for Difference (CfDs) are forwards on the spread between an area price and the system price. Together with the system price forwards, these products are used to hedge the area price risk in the Nordic electricity market. The CfDs are typically available for the next two months, three quarters and three years. This is fine, except that CfDs are not traded at NASDAQ OMX Commodities for every Nord Pool Spot price area. We therefore ask the hypothetical question: What would the CfD market price have been, say in the price area NO2, if it had been traded? We build regression models for each observable price area, and use Bayesian elicitation techniques to obtain prior information on how similar the different price areas are to forecast the price in an area where CfDs are not traded.", "published": "2014-06-26T12:21:51Z", "authors": ["Egil Ferkingstad", "Anders Løland"], "categories": ["stat.AP", "q-fin.ST"], "doi": "", "_query": "hedonic house price", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2603.20636v1", "title": "A Modular LLM Framework for Explainable Price Outlier Detection", "abstract": "Detecting product price outliers is important for retail and e-commerce stores as erroneous or unexpectedly high prices adversely affect competitiveness, revenue, and consumer trust. Classical techniques offer simple thresholds while ignoring the rich semantic relationships among product attributes. We propose an agentic Large Language Model (LLM) framework that treats outlier price flagging as a reasoning task grounded in related product detection and comparison. The system processes the prices of target products in three stages: (i) relevance classification selects price-relevant similar products using product descriptions and attributes; (ii) relative utility assessment evaluates the target product against each similar product along price influencing dimensions (e.g., brand, size, features); (iii) reasoning-based decision aggregates these justifications into an explainable price outlier judgment. The framework attains over 75% agreement with human auditors on a test dataset, and outperforms zero-shot and retrieval based LLM techniques. Ablation studies show the sensitivity of the method to key hyper-parameters and testify on its flexibility to be applied to cases with different accuracy requirement and auditor agreements.", "published": "2026-03-21T04:11:22Z", "authors": ["Shadi Sartipi", "John Wu", "Sina Ghotbi", "Nikhita Vedula", "Shervin Malmasi"], "categories": ["cs.CL", "cs.CE"], "doi": "", "_query": "hedonic house price", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2110.06193v1", "title": "EpiBeds: Data informed modelling of the COVID-19 hospital burden in England", "abstract": "The first year of the COVID-19 pandemic put considerable strain on the national healthcare system in England. In order to predict the effect of the local epidemic on hospital capacity in England, we used a variety of data streams to inform the construction and parameterisation of a hospital progression model, which was coupled to a model of the generalised epidemic. We named this model EpiBeds. Data from a partially complete patient-pathway line-list was used to provide initial estimates of the mean duration that individuals spend in the different hospital compartments. We then fitted EpiBeds using complete data on hospital occupancy and hospital deaths, enabling estimation of the proportion of individuals that follow different clinical pathways, and the reproduction number of the generalised epidemic. The construction of EpiBeds makes it straightforward to adapt to different patient pathways and settings beyond England. As part of the UK response to the pandemic, EpiBeds has provided weekly forecasts to the NHS for hospital bed occupancy and admissions in England, Wales, Scotland, and Northern Ireland.", "published": "2021-10-12T17:46:51Z", "authors": ["Christopher E. Overton", "Lorenzo Pellis", "Helena B. Stage", "Francesca Scarabel", "Joshua Burton", "Christophe Fraser", "Ian Hall", "Thomas A. House", "Chris Jewell", "Anel Nurtay", "Filippo Pagani", "Katrina A. Lythgoe"], "categories": ["q-bio.PE", "stat.AP"], "doi": "10.1371/journal.pcbi.1010406", "_query": "hedonic house price", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/1301.6415v1", "title": "A primer on reflexivity and price dynamics under systemic risk", "abstract": "A simple quantitative example of a reflexive feedback process and the resulting price dynamics after an exogenous price shock to a financial network is presented. Furthermore, an outline of a theory that connects financial reflexivity, which stems from cross-ownership and delayed or incomplete information, and no-arbitrage pricing theory under systemic risk is provided.", "published": "2013-01-27T23:28:56Z", "authors": ["Tom Fischer"], "categories": ["q-fin.GN"], "doi": "", "_query": "hedonic house price", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/1910.08641v1", "title": "Nonhedgeable risk and Credit Risk Pricing", "abstract": "We introduce a new model for pricing corporate bonds, which is a modification of the classical model of Merton. In this new model, we drop the liquidity assumption of the firm's asset value process, and assume that there is a liquidly traded asset in the market whose value is correlated with the firm's asset value, and all portfolios can be constructed using solely this asset and the money market account. We formulate the market price of the corporate bond as the product of the price of an optimal replicating portfolio and exp(- kappa x replication error), where kappa is a positive constant. The interpretation is that the representative investor accepts the price of the optimal replicating portfolio as a benchmark, however, requests compensation for the non-hedgeable risk. We show that if the replication error is measured relative to the firm's value, the resulting formula is arbitrage free with mild restrictions on the parameters.", "published": "2019-10-18T22:06:39Z", "authors": ["Juan Dong", "Lyudmila Korobenko", "Deniz Sezer"], "categories": ["q-fin.PR", "math.PR"], "doi": "", "_query": "hedonic house price", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2112.09534v3", "title": "Path Integral Method for Proportional Step and Proportional Double-Barrier Step Option Pricing", "abstract": "Path integral method in quantum mechanics provides a new thinking for barrier option pricing. For proportional step options, the option price changing process is similar to the one dimensional trapezoid potential barrier scattering problem in quantum mechanics; for double-barrier step options, the option price changing process is analogous to a particle moving in a finite symmetric square potential well. Using path integral method, the analytical expressions of pricing kernel and option price could be derived. Numerical results of option price as a function of underlying price, potential and exercise price are shown, which are consistent with the results given by mathematical method.", "published": "2021-12-16T09:30:01Z", "authors": ["Qi Chen", "Chao Guo"], "categories": ["q-fin.PR"], "doi": "", "_query": "hedonic house price", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/1202.4007v3", "title": "Pricing for Large Positions in Contingent Claims", "abstract": "Approximations to utility indifference prices are provided for a contingent claim in the large position size limit. Results are valid for general utility functions on the real line and semi-martingale models. It is shown that as the position size approaches infinity, the utility function's decay rate for large negative wealths is the primary driver of prices. For utilities with exponential decay, one may price like an exponential investor. For utilities with a power decay, one may price like a power investor after a suitable adjustment to the rate at which the position size becomes large. In a sizable class of diffusion models, limiting indifference prices are explicitly computed for an exponential investor. Furthermore, the large claim limit is seen to endogenously arise as the hedging error for the claim vanishes.", "published": "2012-02-17T20:28:23Z", "authors": ["Scott Robertson"], "categories": ["q-fin.PR", "math.PR"], "doi": "", "_query": "hedonic house price", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2312.10633v1", "title": "Some Ideas for Improving Stock Price Prediction Based on Machine Learning", "abstract": "Stock price prediction is a complicated and interesting task. Noisy trends make stock pricing sensitive and complicated while the economical motivation behind, keeps it interesting for researchers and investors. In this paper we are to outline two novel ideas for stock pricing. We also test each of our suggested algorithms for predicting the price of 6 stocks from different sectors. To show the efficiency of our proposed algorithm, we compare the predicted prices with real values and also perform a backtest to verify that the annual returns based on real data and predicted price are almost the same.", "published": "2023-12-17T07:32:16Z", "authors": ["Negin Bagherpour"], "categories": ["math.OC", "cs.CE"], "doi": "", "_query": "hedonic house price", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2305.09168v5", "title": "Static Pricing Guarantees for Queueing Systems", "abstract": "We consider a general queueing system with price-sensitive customers in which the service provider seeks to balance two objectives, maximizing the average revenue rate and minimizing the average queue length. Customers arrive according to a Poisson process, observe an offered price, and decide to join the queue if their valuation exceeds the price. The queue is operated first-in first-out, can have multiple servers, and the service times are exponential. Our model represents applications in areas like make-to-order manufacturing, cloud computing, and food delivery. The optimal solution for our model is dynamic; the price changes as the state of the system changes. However, such dynamic pricing policies may be undesirable for a variety of reasons. In this work, we provide non-asymptotic performance guarantees for a simple and natural class of static pricing policies which charge a fixed price up to a certain occupancy threshold and then allow no more customers into the system. Despite the mixed-sign objective, we are able to show our policy can guarantee a constant fraction of the optimal dynamic pricing policy in the worst-case. We also show that our policy yields a family of bi-criteria approximations that simultaneously guarantee a constant fraction of the optimal revenue with at most a constant factor increase in expected queue length. For instance, our policy for the M/M/1 setting can be set so that its worst-case guarantees is at least 50, 66, 75, or 80% of the optimal revenue and at most a 0, 16, 54, or 100% increase in the optimal queue length, respectively. We also provide guarantees for settings with multiple servers as well as the expected sojourn time objective. In a large simulation, we show that our class of policies is at most 4% sub-optimal on average.", "published": "2023-05-16T04:57:18Z", "authors": ["Jacob Bergquist", "Adam N. Elmachtoub"], "categories": ["cs.DS", "cs.GT"], "doi": "", "_query": "hedonic house price", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/1912.01923v1", "title": "A Low Computational Approach for Price Tag Recognition", "abstract": "In this work we discuss the task of search, localization and recognition of price zone within a photograph of the price tag. The task is being addressed for the case when image is acquired by small-scale digital camera and calculation device has significant resource constraints. The proposed approach is based on Niblack binarization algorithm, analysis and clasterization of connected components in conditions of known price tag geometrical model. The algorithm was tested on a private dataset and has shown high quality.", "published": "2019-12-04T12:17:26Z", "authors": ["M. A. Aliev", "D. A. Bocharov", "I. A. Kunina", "D. P. Nikolaev"], "categories": ["cs.CV"], "doi": "", "_query": "hedonic house price", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/1410.8160v3", "title": "Pricing and Hedging Long-Term Options", "abstract": "In this article, we investigate the behavior of long-term options. In many cases, option prices follow an exponential decay (or growth) rate for further maturity dates. We determine under what conditions option prices are characterized by this property. To see this, we use the martingale extraction method through which a pricing operator is transformed into a semigroup operator, which is easier to address. We also explore notions of hedging long-term options. Hedging is an attempt to reduce market risks, and we investigate the price sensitivities (Greeks) with respect to such risks, which are typically repre- sented by variations in the underlying process of an option. We combine the Malliavin calculus with the martingale extraction method to analyze Greeks. We see that the ratios between Greeks and the option price are expressed in a simple form in the long term.", "published": "2014-10-29T20:52:36Z", "authors": ["Hyungbin Park"], "categories": ["q-fin.MF", "q-fin.PR"], "doi": "", "_query": "hedonic house price", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/1408.2464v1", "title": "The existence and uniqueness of a power price equilibrium", "abstract": "We propose a term structure power price model that, in contrast to widely accepted no-arbitrage based approaches, accounts for the non-storable nature of power. It belongs to a class of equilibrium game theoretic models with players divided into producers and consumers. The consumers' goal is to maximize a mean-variance utility function subject to satisfying an inelastic demand of their own clients (e.g households, businesses etc.) to whom they sell the power. The producers, who own a portfolio of power plants each defined by a running fuel (e.g. gas, coal, oil...) and physical characteristics (e.g. efficiency, capacity, ramp up/down times...), similarly, seek to maximize a mean-variance utility function consisting of power, fuel, and emission prices subject to production constraints. Our goal is to determine the term structure of the power price at which production matches consumption. In this paper we show that in such a setting the equilibrium price exists and also discuss the conditions for its uniqueness.", "published": "2014-08-11T16:40:31Z", "authors": ["Miha Troha", "Raphael Hauser"], "categories": ["math.OC"], "doi": "", "_query": "hedonic house price", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/cond-mat/0011286v1", "title": "A Dynamical Thermostat Approach To Financial Asset Price Dynamics", "abstract": "A dynamical price formation model for financial assets is presented. It aims to capture the essence of speculative trading where mispricings of assets are used to make profits. It is shown that together with the incorporation of the concept of risk aversion of agents the model is able to reproduce several key characteristics of financial price series. The approach is contrasted to the conventional view of price formation in financial economics.", "published": "2000-11-16T17:36:29Z", "authors": ["Stefan Thurner"], "categories": ["cond-mat"], "doi": "10.1063/1.1386819", "_query": "hedonic house price", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/1412.8414v2", "title": "Accounting for Earnings Announcements in the Pricing of Equity Options", "abstract": "We study an option pricing framework that accounts for the price impact of an earnings announcement (EA), and analyze the behavior of the implied volatility surface prior to the event. On the announcement date, we incorporate a random jump to the stock price to represent the shock due to earnings. We consider different distributions of the scheduled earnings jump as well as different underlying stock price dynamics before and after the EA date. Our main contributions include analytical option pricing formulas when the underlying stock price follows the Kou model along with a double-exponential or Gaussian EA jump on the announcement date. Furthermore, we derive analytic bounds and asymptotics for the pre-EA implied volatility under various models. The calibration results demonstrate adequate fit of the entire implied volatility surface prior to an announcement. We also compare the risk-neutral distribution of the EA jump to its historical distribution. Finally, we discuss the valuation and exercise strategy of pre-EA American options, and illustrate an analytical approximation and numerical results.", "published": "2014-12-29T18:30:01Z", "authors": ["Tim Leung", "Marco Santoli"], "categories": ["q-fin.PR", "q-fin.CP"], "doi": "", "_query": "hedonic house price", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2305.03224v1", "title": "Carbon Price Forecasting with Quantile Regression and Feature Selection", "abstract": "Carbon futures has recently emerged as a novel financial asset in the trading markets such as the European Union and China. Monitoring the trend of the carbon price has become critical for both national policy-making as well as industrial manufacturing planning. However, various geopolitical, social, and economic factors can impose substantial influence on the carbon price. Due to its volatility and non-linearity, predicting accurate carbon prices is generally a difficult task. In this study, we propose to improve carbon price forecasting with several novel practices. First, we collect various influencing factors, including commodity prices, export volumes such as oil and natural gas, and prosperity indices. Then we select the most significant factors and disclose their optimal grouping for explainability. Finally, we use the Sparse Quantile Group Lasso and Adaptive Sparse Quantile Group Lasso for robust price predictions. We demonstrate through extensive experimental studies that our proposed methods outperform existing ones. Also, our quantile predictions provide a complete profile of future prices at different levels, which better describes the distributions of the carbon market.", "published": "2023-05-05T01:02:08Z", "authors": ["Tianqi Pang", "Kehui Tan", "Chenyou Fan"], "categories": ["cs.LG", "q-fin.ST"], "doi": "", "_query": "hedonic house price", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/1611.08006v1", "title": "Pricing Energy in the Presence of Renewables", "abstract": "At present, electricity markets largely ignore the fact that renewable power producers impose significant externalities on non-renewable energy producers. This is because consumers are generally guaranteed electricity within certain load parameters. The intermittent nature of production by renewable energy producers implies that they rely on non-renewable producers so that the aggregate power delivered meets the promised quality of service. This implicit insurance provided by the non-renewable power sector to consumers is not currently priced and leads to an often ignored, hidden monetary transfer from non-renewable producers to renewable producers. As the fraction of energy supplied by renewable resources increases, these externalities also increase. In this paper, we quantify these externalities by developing the market clearing price of energy in the presence of renewable energy. We consider a day-ahead electricity market where renewable and non-renewable generators bid by proposing their asking price per unit of energy to an independent system operator (ISO). The ISO's problem is a multi-stage stochastic optimization problem to dispatch energy from each generator to minimize the cost of purchased energy on behalf of the consumers. We incorporate the notion of load variance using the Conditional Value-at-Risk (CVAR) measure in the day-ahead electricity market to ensure that the generators are able to meet the load within a desired confidence level. We analytically derive the market clearing price of energy as a function of CVAR. It is shown that a higher penetration level of the renewable energies may increase the market clearing price of energy.", "published": "2016-11-23T21:26:17Z", "authors": ["Ashkan Zeinalzadeh", "Indraneel Chakraborty", "Vijay Gupta"], "categories": ["math.OC"], "doi": "", "_query": "hedonic house price", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2510.12447v1", "title": "Bayesian Optimization for Dynamic Pricing and Learning", "abstract": "Dynamic pricing is the practice of adjusting the selling price of a product to maximize a firm's revenue by responding to market demand. The literature typically distinguishes between two settings: infinite inventory, where the firm has unlimited stock and time to sell, and finite inventory, where both inventory and selling horizon are limited. In both cases, the central challenge lies in the fact that the demand function -- how sales respond to price -- is unknown and must be learned from data. Traditional approaches often assume a specific parametric form for the demand function, enabling the use of reinforcement learning (RL) to identify near-optimal pricing strategies. However, such assumptions may not hold in real-world scenarios, limiting the applicability of these methods. In this work, we propose a Gaussian Process (GP) based nonparametric approach to dynamic pricing that avoids restrictive modeling assumptions. We treat the demand function as a black-box function of the price and develop pricing algorithms based on Bayesian Optimization (BO) -- a sample-efficient method for optimizing unknown functions. We present BO-based algorithms tailored for both infinite and finite inventory settings and provide regret guarantees for both regimes, thereby quantifying the learning efficiency of our methods. Through extensive experiments, we demonstrate that our BO-based methods outperform several state-of-the-art RL algorithms in terms of revenue, while requiring fewer assumptions and offering greater robustness. This highlights Bayesian Optimization as a powerful and practical tool for dynamic pricing in complex, uncertain environments.", "published": "2025-10-14T12:28:06Z", "authors": ["Anush Anand", "Pranav Agrawal", "Tejas Bodas"], "categories": ["cs.LG"], "doi": "", "_query": "hedonic house price", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2011.03987v1", "title": "Unifying the theory of storage and the risk premium by an unobservable intrinsic electricity price", "abstract": "In this paper we introduce a new concept for modelling electricity prices through the introduction of an unobservable intrinsic electricity price $p(τ)$. We use it to connect the classical theory of storage with the concept of a risk premium. We derive prices for all common contracts such as the intraday spot price, the day-ahead spot price, and futures prices. Finally, we propose an explicit model from the class of structural models and conduct an empirical analysis, where we find an overall negative risk premium.", "published": "2020-11-08T14:15:36Z", "authors": ["Wieger Hinderks", "Ralf Korn", "Andreas Wagner"], "categories": ["q-fin.MF", "math.PR"], "doi": "", "_query": "hedonic house price", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2003.00880v1", "title": "Introducing Fuzzy Layers for Deep Learning", "abstract": "Many state-of-the-art technologies developed in recent years have been influenced by machine learning to some extent. Most popular at the time of this writing are artificial intelligence methodologies that fall under the umbrella of deep learning. Deep learning has been shown across many applications to be extremely powerful and capable of handling problems that possess great complexity and difficulty. In this work, we introduce a new layer to deep learning: the fuzzy layer. Traditionally, the network architecture of neural networks is composed of an input layer, some combination of hidden layers, and an output layer. We propose the introduction of fuzzy layers into the deep learning architecture to exploit the powerful aggregation properties expressed through fuzzy methodologies, such as the Choquet and Sugueno fuzzy integrals. To date, fuzzy approaches taken to deep learning have been through the application of various fusion strategies at the decision level to aggregate outputs from state-of-the-art pre-trained models, e.g., AlexNet, VGG16, GoogLeNet, Inception-v3, ResNet-18, etc. While these strategies have been shown to improve accuracy performance for image classification tasks, none have explored the use of fuzzified intermediate, or hidden, layers. Herein, we present a new deep learning strategy that incorporates fuzzy strategies into the deep learning architecture focused on the application of semantic segmentation using per-pixel classification. Experiments are conducted on a benchmark data set as well as a data set collected via an unmanned aerial system at a U.S. Army test site for the task of automatic road segmentation, and preliminary results are promising.", "published": "2020-02-21T19:33:30Z", "authors": ["Stanton R. Price", "Steven R. Price", "Derek T. Anderson"], "categories": ["cs.CV", "cs.LG", "stat.ML"], "doi": "10.1109/FUZZ-IEEE.2019.8858790", "_query": "hedonic house price", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2310.15643v1", "title": "Navigating ICT In-House Procurement in Finland: Evaluating Legal Frameworks and Practical Challenges", "abstract": "In-house procurement is a controversial issue in the field of public procurement. Simply put, such procurement allows overlooking certain aspects of fair and equal treatment of vendors. This paper presents qualitative research on in-house ICT procurement within Finnish municipalities. Semi-structured interviews were conducted to gather insights from municipal stakeholders. Using grounded theory approach, data analysis shows intricate dynamics between Finnish municipalities and in-house entities associated with them. Still, it is clear that the legal framework governing in-house procurement remains intricate and debated.", "published": "2023-10-24T09:04:29Z", "authors": ["Reetta Ghezzi", "Minnamaria Korhonen", "Hannu Vilpponen", "Tommi Mikkonen"], "categories": ["cs.SE"], "doi": "", "_query": "hedonic house price", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/1807.10114v3", "title": "The Evolution of Security Prices Is Not Stochastic but Governed by a Physicomathematical Law", "abstract": "Since Bachelier's thesis in 1900 (laying the foundation of the stochastic process, or Brownian motion, as a model of stock price changes), attempts at understanding the nature of prices and at predicting them have failed. Statistical methods have only found minor regularities/anomalies, and other mathematical and physical approaches do not work. This leads researchers to consider that the evolution of security prices is basically random, and, thus, inherently not predictable. We show that the evolution of security prices is not a stochastic process but largely deterministic and governed by a physical law. The law takes the form of a physicomathematical theory centered around a purely mathematical function, unrelated to models and statistical methods. It can be described as an \"isodense\" network of moving regression curves of an order greater than or equal to 1. The salient aspect of the function is that, when inputting a time series of any security into the function, new mathematical objects emerge spontaneously, and these objects exhibit the unique property of attracting and repelling the quantity. The graphical representation of the function is called a \"topological network\" due to the preeminence of shapes over metrics, and the emergent objects are called \"characteristic figures\" (mainly \"cords\"). The attraction and repulsion of the price by the cords results in the price bouncing from cord to cord. Thus, the price has to be considered as driven by the cords in a semi-deterministic manner (leaning towards deterministic). With a function that describes the evolution of the price, we now understand the reason behind each price movement and can also predict prices both qualitatively and quantitatively. The function is universal, does not rely on any fitting, and, due to its extreme sensitivity, reveals the hidden order in financial time series data that existing research never uncovered.", "published": "2018-07-01T20:22:44Z", "authors": ["Wally Tzara"], "categories": ["q-fin.GN", "nlin.AO"], "doi": "", "_query": "hedonic house price", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/1912.11216v6", "title": "Evolutionary Dynamics of Investors Expectations and Market Price Movement", "abstract": "The paper presents a step forward into the development of the theory of meaning. Stock and financial markets are examined from communication-theoretical perspective on the dynamics of information and meaning. This study focuses on the link between the dynamics of investors' expectations and market price movement. The model for market asset price dynamiscs, based on non-linear evolutionary equation linking investors' expectations and market asset price movement, is provided. Model predictions are tested on various FX, energy, food, and indices markets along different time frames. The results suggest that model predicted time series is co-integrated with asset time series which implies that the prop[osed model can be used to forecast future price movement.", "published": "2019-12-24T05:52:18Z", "authors": ["Inga Ivanova"], "categories": ["q-fin.ST"], "doi": "", "_query": "hedonic house price", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2403.00458v2", "title": "Electric vehicle pricing and battery costs: A misaligned assumption", "abstract": "Although electric vehicles (EVs) are a climate friendly alternative to internal combustion engine vehicles (ICEVs), EV adoption is challenged by higher up-front procurement prices. Existing discourse attributes this price differential to high battery costs and reasons that lowering these costs will reduce EV upfront price differentials. However, other factors beyond battery price may influence prices. Leveraging data for over 400 EV models and trims sold in the United Sates between 2011-2023, we scrutinize these factors. We find that contrary to existing discourse, EV MSRP has increased over time despite declining EV battery costs. We attribute this increase to the growing accommodation of attributes that strongly influence EV prices but have long been underappreciated in mainstream discourse. Furthermore, and relevant to decarbonization efforts, we observe that continued reductions in pack-level battery costs are unlikely to deliver price parity between EVs and ICEVs. Were pack level battery costs reduced to zero, EV MSRP would decrease by $4,025, estimates that are insufficient to offset observed price differences between EVs and ICEVs. These findings warrant attention as decarbonization efforts increasingly emphasize EVs as a pathway for complying with domestic and international climate agreements.", "published": "2024-03-01T11:30:25Z", "authors": ["Lucas Woodley", "Chung Yi See", "Vasco Rato Santos", "Megan Yeo", "Daniel Palmer", "Sebastian Nosenzo", "Ashley Nunes"], "categories": ["econ.EM"], "doi": "", "_query": "hedonic house price", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2109.15198v1", "title": "Nonlinear Prices, Homogeneous Goods, Search", "abstract": "We analyze competition on nonlinear prices in homogeneous goods markets with consumer search. In equilibrium firms offer two-part tariffs consisting of a linear price and lump-sum fee. The equilibrium production is socially efficient as the linear price of equilibrium two-part tariffs equals to the production marginal cost. Firms thus compete in lump-sum fees, which are dispersed in equilibrium. We show that sellers enjoy higher profit, whereas consumers are worse-off with two-part tariffs than with linear prices. The competition softens because with two-part tariffs firms can make effective per-consumer demand less elastic than the actual demand.", "published": "2021-09-30T15:16:05Z", "authors": ["Atabek Atayev"], "categories": ["econ.TH"], "doi": "", "_query": "hedonic house price", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/0912.5448v1", "title": "Universal Behavior of Extreme Price Movements in Stock Markets", "abstract": "Many studies assume stock prices follow a random process known as geometric Brownian motion. Although approximately correct, this model fails to explain the frequent occurrence of extreme price movements, such as stock market crashes. Using a large collection of data from three different stock markets, we present evidence that a modification to the random model -- adding a slow, but significant, fluctuation to the standard deviation of the process -- accurately explains the probability of different-sized price changes, including the relative high frequency of extreme movements. Furthermore, we show that this process is similar across stocks so that their price fluctuations can be characterized by a single curve. Because the behavior of price fluctuations is rooted in the characteristics of volatility, we expect our results to bring increased interest to stochastic volatility models, and especially to those that can produce the properties of volatility reported here.", "published": "2009-12-30T11:18:18Z", "authors": ["Miguel A. Fuentes", "Austin Gerig", "Javier Vicente"], "categories": ["q-fin.ST"], "doi": "10.1371/journal.pone.0008243", "_query": "hedonic house price", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2512.18296v1", "title": "Privacy Data Pricing: A Stackelberg Game Approach", "abstract": "Data markets are emerging as key mechanisms for trading personal and organizational data. Traditional data pricing studies -- such as query-based or arbitrage-free pricing models -- mainly emphasize price consistency and profit maximization but often neglect privacy constraints and strategic interactions. The widespread adoption of differential privacy (DP) introduces a fundamental privacy-utility trade-off: noise protects individuals' privacy but reduces data accuracy and market value. This paper develops a Stackelberg game framework for pricing DP data, where the market maker (leader) sets the price function and the data buyer (follower) selects the optimal query precision under DP constraints. We derive the equilibrium strategies for both parties under a balanced pricing function where the pricing decision variable enters linearly into the original pricing model. We obtain closed-form solutions for the optimal variance and pricing level, and determine the boundary conditions for market participation. Furthermore, we extend the analysis to Stackelberg games involving nonlinear power pricing functions. The model bridges DP and economic mechanism design, offering a unified foundation for incentive-compatible and privacy-conscious data pricing in data markets.", "published": "2025-12-20T09:59:39Z", "authors": ["Lijun Bo", "Weiqiang Chang"], "categories": ["cs.GT", "stat.AP"], "doi": "", "_query": "hedonic house price", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/1911.08858v2", "title": "Non-separating immersions of spheres and Bing houses", "abstract": "We construct Bing houses in all dimensions $n \\geq 3$, obtaining non-separating PL immersions of $\\mathbb{S}^n \\rightarrow\\mathbb{R}^{n+1}$.", "published": "2019-11-20T12:32:17Z", "authors": ["Michael Freedman", "T. Tam Nguyen Phan"], "categories": ["math.GT", "math.DG"], "doi": "", "_query": "hedonic house price", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/1905.13622v2", "title": "Characterizing Shadow Price via Lagrangian Multiplier for Nonsmooth Problem", "abstract": "In this paper, a relation between shadow price and the Lagrangian multiplier for nonsmooth problem is explored. It is shown that the Lagrangian Multiplier is the upper bound of shadow price for convex optimization and a class of Lipschtzian optimizations. This work can be used in shadow pricing for nonsmooth situation. The several nonsmooth functions involved in this class of Lipschtzian optimizations is listed. Finally, an application to electricity pricing is discussed.", "published": "2019-05-31T13:57:14Z", "authors": ["Yan Gao"], "categories": ["econ.TH"], "doi": "", "_query": "hedonic house price", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2403.09265v6", "title": "Zonal vs. Nodal Pricing: An Analysis of Different Pricing Rules in the German Day-Ahead Market", "abstract": "The European electricity market is based on large pricing zones with a uniform day-ahead price. The energy transition leads to changes in supply and demand and increasing redispatch costs. In an attempt to ensure efficient market clearing and congestion management, the EU Commission has mandated the Bidding Zone Review (BZR) to reevaluate the configuration of European bidding zones. Based on a unique data set published in the context of the BZR for the target year 2025, we analyze the short-run effects of various pricing rules for the German-Luxembourgish bidding zone. We compare market clearing and pricing for different zonal models, including their generation and redispatch costs. In numerical experiments with this dataset, the differences in the average prices in different zones are low. The total costs across different configurations are similar and the reduction of standard deviations in prices is also small. This might be different with other load and generation scenarios, but the BZR data is important as it was created to make a decision about splits of the existing bidding zones. We can replicate several results from the BZR study, except the large cost savings when moving from one to two price zones in Germany and Luxembourg. In addition to the four zonal configurations analyzed in the BZR study, we compare these against a nodal pricing system. While the total cost savings after introducing zonal splits were less than 1%, nodal pricing led to savings of 5-6%. We also evaluate differences of nodal pricing rules with respect to the necessary uplift payments, which is relevant in the context of the discussion on non-uniform pricing in the EU. While the study focuses on Germany, the analysis is relevant beyond and feeds into the broader discussion about pricing rules in non-convex markets.", "published": "2024-03-14T10:41:31Z", "authors": ["Johannes Knörr", "Martin Bichler", "Teodora Dobos"], "categories": ["econ.GN"], "doi": "", "_query": "hedonic house price", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/1909.05761v1", "title": "Pricing Reliability Options under different electricity prices' regimes", "abstract": "Reliability Options are capacity remuneration mechanisms aimed at enhancing security of supply in electricity systems. They can be framed as call options on electricity sold by power producers to System Operators. This paper provides a comprehensive mathematical treatment of Reliability Options. Their value is first derived by means of closed-form pricing formulae, which are obtained under several assumptions about the dynamics of electricity prices and strike prices. Then, the value of the Reliability Option is simulated under a real-market calibration, using data of the Italian power market. We finally perform sensitivity analyses to highlight the impact of the level and volatility of both power and strike price, of the mean reversion speeds and of the correlation coefficient on the Reliability Options' value.", "published": "2019-09-12T15:44:19Z", "authors": ["Luisa Andreis", "Maria Flora", "Fulvio Fontini", "Tiziano Vargiolu"], "categories": ["q-fin.PR"], "doi": "", "_query": "hedonic house price", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/1502.03945v2", "title": "Efficiency and complexity of price competition among single-product vendors", "abstract": "Motivated by recent progress on pricing in the AI literature, we study marketplaces that contain multiple vendors offering identical or similar products and unit-demand buyers with different valuations on these vendors. The objective of each vendor is to set the price of its product to a fixed value so that its profit is maximized. The profit depends on the vendor's price itself and the total volume of buyers that find the particular price more attractive than the price of the vendor's competitors. We model the behaviour of buyers and vendors as a two-stage full-information game and study a series of questions related to the existence, efficiency (price of anarchy) and computational complexity of equilibria in this game. To overcome situations where equilibria do not exist or exist but are highly inefficient, we consider the scenario where some of the vendors are subsidized in order to keep prices low and buyers highly satisfied.", "published": "2015-02-13T11:18:00Z", "authors": ["Ioannis Caragiannis", "Xenophon Chatzigeorgiou", "Panagiotis Kanellopoulos", "George A. Krimpas", "Nikos Protopapas", "Alexandros A. Voudouris"], "categories": ["cs.GT", "cs.AI", "cs.CC"], "doi": "", "_query": "hedonic house price", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2604.03961v1", "title": "Financial Relativity: An Information-Geometric Interpretation of Asset Pricing", "abstract": "Classical asset pricing relies on the risk-neutral measure $Q$ for valuation, yet its economic interpretation is typically anchored in a physical measure $P$. This creates an inherent asymmetry: pricing is governed by $Q$, while meaning resides in $P$, making it difficult to provide a unified account of asset pricing within a single conceptual framework. This paper proposes an alternative perspective based on information geometry, termed Financial Relativity. Its central principle is the relativity of probabilistic reference frames: $P$ and $Q$ have no intrinsic hierarchy, but instead represent geometric structures induced by different informational constraints. Terminal structural information shapes probability geometry, which in turn governs how information is expressed in prices. Within this framework, the risk-neutral measure is reinterpreted as a posterior probability geometry. Asset prices are characterized as geometric projections of terminal payoffs onto information subspaces, and their dynamics reflect the progressive manifestation of structural information under evolving geometry. We develop both discrete and continuous financial field equations to describe the formation of probability geometry and derive geodesic price dynamics in which volatility arises endogenously from posterior uncertainty. The framework provides a unified explanation for price fluctuations, event-driven behavior, and risk premia, and yields testable implications, including structural links between volatility and posterior variance and measures of price informational efficiency. By integrating structural information, probability measures, and price dynamics within a single geometric framework, the paper offers a coherent, extensible, and empirically tractable reinterpretation of asset pricing.", "published": "2026-04-05T04:30:56Z", "authors": ["Li Lin"], "categories": ["q-fin.MF"], "doi": "", "_query": "hedonic house price", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2111.09111v1", "title": "Forecasting Crude Oil Price Using Event Extraction", "abstract": "Research on crude oil price forecasting has attracted tremendous attention from scholars and policymakers due to its significant effect on the global economy. Besides supply and demand, crude oil prices are largely influenced by various factors, such as economic development, financial markets, conflicts, wars, and political events. Most previous research treats crude oil price forecasting as a time series or econometric variable prediction problem. Although recently there have been researches considering the effects of real-time news events, most of these works mainly use raw news headlines or topic models to extract text features without profoundly exploring the event information. In this study, a novel crude oil price forecasting framework, AGESL, is proposed to deal with this problem. In our approach, an open domain event extraction algorithm is utilized to extract underlying related events, and a text sentiment analysis algorithm is used to extract sentiment from massive news. Then a deep neural network integrating the news event features, sentimental features, and historical price features is built to predict future crude oil prices. Empirical experiments are performed on West Texas Intermediate (WTI) crude oil price data, and the results show that our approach obtains superior performance compared with several benchmark methods.", "published": "2021-11-14T08:48:43Z", "authors": ["Jiangwei Liu", "Xiaohong Huang"], "categories": ["cs.LG", "cs.AI", "cs.CL", "econ.GN", "stat.AP"], "doi": "10.1109/ACCESS.2021.3124802", "_query": "hedonic house price", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/1903.11205v3", "title": "Airbnb's disruption of the housing structure in London", "abstract": "This paper explores Airbnb, a peer-to-peer platform for short-term rental of housing accommodation, examining the geographical pattern of those establishments using data from London. Our purpose is to analyse whether or not the diversity of dwelling types correlate with the distribution of listings. We use a measure of spread based on entropy to indicate the diversity of dwelling types and look at its relationship with the distribution of Airbnb establishments, as well as the type of home ownership using correlation analysis. It is important to note that our study only considers domestic building types, and excludes any information on the diversity of land uses. Two important findings emerge from our analysis. Firstly, the spatial location of Airbnb rentals is negatively correlated with the diversity of dwelling types, and positively correlated with a single dwelling type, which corresponds in general to purpose built flats, conversions and flats in commercial buildings. Secondly, Airbnb is associated with areas that have a high proportion of privately rented properties, detracting more than 1.4% of the housing supply into short-term rentals. Such a phenomenon can reach up to 20% in some neighbourhoods, further exacerbating the process of gentrification. Finally, we discuss the implications of these findings as instruments to inform policies associated with the 'sharing' economy in relation to the disruption of the housing structure.", "published": "2019-03-27T00:49:15Z", "authors": ["Zahratu Shabrina", "Elsa Arcaute", "Michael Batty"], "categories": ["cs.CY", "physics.soc-ph"], "doi": "", "_query": "hedonic house price", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2009.04786v2", "title": "Price formation and optimal trading in intraday electricity markets", "abstract": "We develop a tractable equilibrium model for price formation in intraday electricity markets in the presence of intermittent renewable generation. Using stochastic control theory, we identify the optimal strategies of agents with market impact and exhibit the Nash equilibrium in closed form for a finite number of agents as well as in the asymptotic framework of mean field games. Our model reproduces the empirical features of intraday market prices, such as increasing price volatility at the approach of the delivery date and the correlation between price and renewable infeed forecasts, and relates these features with market characteristics like liquidity, number of agents, and imbalance penalty.", "published": "2020-09-10T11:40:38Z", "authors": ["Olivier Féron", "Peter Tankov", "Laura Tinsi"], "categories": ["q-fin.PR", "q-fin.TR"], "doi": "", "_query": "hedonic house price", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2001.01860v2", "title": "A simple microstructural explanation of the concavity of price impact", "abstract": "This article provides a simple explanation of the asymptotic concavity of the price impact of a meta-order via the microstructural properties of the market. This explanation is made more precise by a model in which the local relationship between the order flow and the fundamental price (i.e. the local price impact) is linear, with a constant slope, which makes the model dynamically consistent. Nevertheless, the expected impact on midprice from a large sequence of co-directional trades is nonlinear and asymptotically concave. The main practical conclusion of the proposed explanation is that, throughout a meta-order, the volumes at the best bid and ask prices change (on average) in favor of the executor. This conclusion, in turn, relies on two more concrete predictions, one of which can be tested, at least for large-tick stocks, using publicly available market data.", "published": "2020-01-07T02:38:02Z", "authors": ["Sergey Nadtochiy"], "categories": ["q-fin.TR", "q-fin.MF"], "doi": "", "_query": "hedonic house price", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/1511.09208v1", "title": "Algorithms as Mechanisms: The Price of Anarchy of Relax-and-Round", "abstract": "Many algorithms that are originally designed without explicitly considering incentive properties are later combined with simple pricing rules and used as mechanisms. The resulting mechanisms are often natural and simple to understand. But how good are these algorithms as mechanisms? Truthful reporting of valuations is typically not a dominant strategy (certainly not with a pay-your-bid, first-price rule, but it is likely not a good strategy even with a critical value, or second-price style rule either). Our goal is to show that a wide class of approximation algorithms yields this way mechanisms with low Price of Anarchy. The seminal result of Lucier and Borodin [SODA 2010] shows that combining a greedy algorithm that is an $α$-approximation algorithm with a pay-your-bid payment rule yields a mechanism whose Price of Anarchy is $O(α)$. In this paper we significantly extend the class of algorithms for which such a result is available by showing that this close connection between approximation ratio on the one hand and Price of Anarchy on the other also holds for the design principle of relaxation and rounding provided that the relaxation is smooth and the rounding is oblivious. We demonstrate the far-reaching consequences of our result by showing its implications for sparse packing integer programs, such as multi-unit auctions and generalized matching, for the maximum traveling salesman problem, for combinatorial auctions, and for single source unsplittable flow problems. In all these problems our approach leads to novel simple, near-optimal mechanisms whose Price of Anarchy either matches or beats the performance guarantees of known mechanisms.", "published": "2015-11-30T09:08:30Z", "authors": ["Paul Dütting", "Thomas Kesselheim", "Éva Tardos"], "categories": ["cs.GT", "cs.DS"], "doi": "", "_query": "hedonic house price", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2308.06935v1", "title": "Insurance pricing on price comparison websites via reinforcement learning", "abstract": "The emergence of price comparison websites (PCWs) has presented insurers with unique challenges in formulating effective pricing strategies. Operating on PCWs requires insurers to strike a delicate balance between competitive premiums and profitability, amidst obstacles such as low historical conversion rates, limited visibility of competitors' actions, and a dynamic market environment. In addition to this, the capital intensive nature of the business means pricing below the risk levels of customers can result in solvency issues for the insurer. To address these challenges, this paper introduces reinforcement learning (RL) framework that learns the optimal pricing policy by integrating model-based and model-free methods. The model-based component is used to train agents in an offline setting, avoiding cold-start issues, while model-free algorithms are then employed in a contextual bandit (CB) manner to dynamically update the pricing policy to maximise the expected revenue. This facilitates quick adaptation to evolving market dynamics and enhances algorithm efficiency and decision interpretability. The paper also highlights the importance of evaluating pricing policies using an offline dataset in a consistent fashion and demonstrates the superiority of the proposed methodology over existing off-the-shelf RL/CB approaches. We validate our methodology using synthetic data, generated to reflect private commercially available data within real-world insurers, and compare against 6 other benchmark approaches. Our hybrid agent outperforms these benchmarks in terms of sample efficiency and cumulative reward with the exception of an agent that has access to perfect market information which would not be available in a real-world set-up.", "published": "2023-08-14T04:44:56Z", "authors": ["Tanut Treetanthiploet", "Yufei Zhang", "Lukasz Szpruch", "Isaac Bowers-Barnard", "Henrietta Ridley", "James Hickey", "Chris Pearce"], "categories": ["q-fin.PR", "cs.LG", "q-fin.ST"], "doi": "", "_query": "hedonic house price", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/1912.04565v1", "title": "Market Price of Trading Liquidity Risk and Market Depth", "abstract": "Price impact of a trade is an important element in pre-trade and post-trade analyses. We introduce a framework to analyze the market price of liquidity risk, which allows us to derive an inhomogeneous Bernoulli ordinary differential equation. We obtain two closed form solutions, one of which reproduces the linear function of the order flow in Kyle (1985) for informed traders. However, when traders are not as asymmetrically informed, an S-shape function of the order flow is obtained. We perform an empirical intra-day analysis on Nikkei futures to quantify the price impact of order flow and compare our results with industry's heuristic price impact functions. Our model of order flow yields a rich framework for not only to estimate the liquidity risk parameters, but also to provide a plausible cause of why volatility and correlation are stochastic in nature. Finally, we find that the market depth encapsulates the market price of liquidity risk.", "published": "2019-12-10T08:20:26Z", "authors": ["Masaaki Kijima", "Christopher Ting"], "categories": ["q-fin.TR", "econ.EM", "q-fin.CP", "q-fin.MF", "stat.CO"], "doi": "10.1142/S0219024919500456", "_query": "hedonic house price", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2507.17939v3", "title": "Dynamic access pricing control for fair and stable resource sharing", "abstract": "We consider the co-existence of users with different price sensitivities under dynamic (or surge) pricing that regulates how they access a shared resource. To balance supply and demand in such settings, some dynamic pricing systems adopt a monotonically increasing price scheme which is well known to be socially regressive, driving out price-sensitive users in favor of price-insensitive ones. Here, we propose a new dynamic pricing scheme based on a non-monotonic price function that allows all user classes to coexist more fairly when price-insensitive users are present, but automatically reverts to conventional dynamic pricing when they leave the system, even though the exact price-sensitivity composition is unobservable. The resulting system consists of nonlinear ODEs, which we further investigate to characterize its equilibria and their domains of attraction, first for a single price-sensitive class competing with a price-insensitive one and then for the general $n$-class case. Numerical simulations demonstrate the effectiveness of the proposed design.", "published": "2025-07-23T21:13:18Z", "authors": ["Christopher King", "Fabian R. Wirth", "Homayoun Hamedmoghadam", "Christos G. Cassandras", "Robert N. Shorten"], "categories": ["math.OC"], "doi": "", "_query": "hedonic house price", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2406.01027v1", "title": "PRICE: A Pretrained Model for Cross-Database Cardinality Estimation", "abstract": "Cardinality estimation (CardEst) is essential for optimizing query execution plans. Recent ML-based CardEst methods achieve high accuracy but face deployment challenges due to high preparation costs and lack of transferability across databases. In this paper, we propose PRICE, a PRetrained multI-table CardEst model, which addresses these limitations. PRICE takes low-level but transferable features w.r.t. data distributions and query information and elegantly applies self-attention models to learn meta-knowledge to compute cardinality in any database. It is generally applicable to any unseen new database to attain high estimation accuracy, while its preparation cost is as little as the basic one-dimensional histogram-based CardEst methods. Moreover, PRICE can be finetuned to further enhance its performance on any specific database. We pretrained PRICE using 30 diverse datasets, completing the process in about 5 hours with a resulting model size of only about 40MB. Evaluations show that PRICE consistently outperforms existing methods, achieving the highest estimation accuracy on several unseen databases and generating faster execution plans with lower overhead. After finetuning with a small volume of databasespecific queries, PRICE could even find plans very close to the optimal ones. Meanwhile, PRICE is generally applicable to different settings such as data updates, data scaling, and query workload shifts. We have made all of our data and codes publicly available at https://github.com/StCarmen/PRICE.", "published": "2024-06-03T06:21:53Z", "authors": ["Tianjing Zeng", "Junwei Lan", "Jiahong Ma", "Wenqing Wei", "Rong Zhu", "Pengfei Li", "Bolin Ding", "Defu Lian", "Zhewei Wei", "Jingren Zhou"], "categories": ["cs.DB", "cs.LG"], "doi": "10.14778/3712221.3712231", "_query": "hedonic house price", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/1702.00144v1", "title": "Zipf's law for share price and company fundamentals", "abstract": "We statistically investigate the distribution of share price and the distributions of three common financial indicators using data from approximately 8,000 companies publicly listed worldwide for the period 2004-2013. We find that the distribution of share price follows Zipf's law; that is, it can be approximated by a power law distribution with exponent equal to 1. An examination of the distributions of dividends per share, cash flow per share, and book value per share - three financial indicators that can be assumed to influence corporate value (i.e. share price) - shows that these distributions can also be approximated by a power law distribution with power-law exponent equal to 1. We estimate a panel regression model in which share price is the dependent variable and the three financial indicators are explanatory variables. The two-way fixed effects model that was selected as the best model has quite high power for explaining the actual data. From these results, we can surmise that the reason why share price follows Zipf's law is that corporate value, i.e. company fundamentals, follows Zipf's law.", "published": "2017-02-01T06:22:09Z", "authors": ["Taisei Kaizoji", "Michiko Miyano"], "categories": ["econ.GN", "q-fin.ST"], "doi": "", "_query": "hedonic house price", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/0903.0680v3", "title": "Quantum Neural Computation for Option Price Modelling", "abstract": "We propose a new cognitive framework for option price modelling, using quantum neural computation formalism. Briefly, when we apply a classical nonlinear neural-network learning to a linear quantum Schrödinger equation, as a result we get a nonlinear Schrödinger equation (NLS), performing as a quantum stochastic filter. In this paper, we present a bidirectional quantum associative memory model for the Black--Scholes--like option price evolution, consisting of a pair of coupled NLS equations, one governing the stochastic volatility and the other governing the option price, both self-organizing in an adaptive `market heat potential', trained by continuous Hebbian learning. This stiff pair of NLS equations is numerically solved using the method of lines with adaptive step-size integrator. Keywords: Option price modelling, Quantum neural computation, nonlinear Schrödinger equations, leverage effect, bidirectional associative memory", "published": "2009-03-04T02:59:17Z", "authors": ["Vladimir G. Ivancevic"], "categories": ["q-fin.CP", "nlin.AO", "nlin.PS", "q-fin.PR"], "doi": "", "_query": "hedonic house price", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/1004.3939v1", "title": "Price Trackers Inspired by Immune Memory", "abstract": "In this paper we outline initial concepts for an immune inspired algorithm to evaluate price time series data. The proposed solution evolves a short term pool of trackers dynamically through a process of proliferation and mutation, with each member attempting to map to trends in price movements. Successful trackers feed into a long term memory pool that can generalise across repeating trend patterns. Tests are performed to examine the algorithm's ability to successfully identify trends in a small data set. The influence of the long term memory pool is then examined. We find the algorithm is able to identify price trends presented successfully and efficiently.", "published": "2010-04-22T15:01:02Z", "authors": ["William Wilson", "Phil Birkin", "Uwe Aickelin"], "categories": ["cs.AI", "cs.NE", "physics.data-an", "q-fin.PM"], "doi": "", "_query": "hedonic house price", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/cond-mat/0006133v1", "title": "Asians and cash dividends: Exploiting symmetries in pricing theory", "abstract": "In this article we present new results for the pricing of arithmetic Asian options within a Black-Scholes context. To derive these results we make extensive use of the local scale invariance that exists in the theory of contingent claim pricing. This allows us to derive, in a natural way, a simple PDE for the price of arithmetic Asians options. In the case of European average strike options, a proper choice of numeraire reduces the dimension of this PDE to one, leading to a PDE similar to the one derived by Rogers and Shi. We solve this PDE, finding a Laplace-transform representation for the price of average strike options, both seasoned and unseasoned. This extends the results of Geman and Yor, who discussed the case of average price options. Next we use symmetry arguments to show that prices of average strike and average price options can be expressed in terms of each other. Finally we show, again using symmetries, that plain vanilla options on stocks paying known cash dividends are closely related to arithmetic Asians, so that all the new techniques can be directly applied to this case.", "published": "2000-06-08T13:28:31Z", "authors": ["Jiri Hoogland", "Dimitri Neumann"], "categories": ["cond-mat.stat-mech", "q-fin.PR"], "doi": "", "_query": "hedonic house price", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2303.09397v1", "title": "Cryptocurrency Price Prediction using Twitter Sentiment Analysis", "abstract": "The cryptocurrency ecosystem has been the centre of discussion on many social media platforms, following its noted volatility and varied opinions. Twitter is rapidly being utilised as a news source and a medium for bitcoin discussion. Our algorithm seeks to use historical prices and sentiment of tweets to forecast the price of Bitcoin. In this study, we develop an end-to-end model that can forecast the sentiment of a set of tweets (using a Bidirectional Encoder Representations from Transformers - based Neural Network Model) and forecast the price of Bitcoin (using Gated Recurrent Unit) using the predicted sentiment and other metrics like historical cryptocurrency price data, tweet volume, a user's following, and whether or not a user is verified. The sentiment prediction gave a Mean Absolute Percentage Error of 9.45%, an average of real-time data, and test data. The mean absolute percent error for the price prediction was 3.6%.", "published": "2023-03-03T18:42:01Z", "authors": ["Haritha GB", "Sahana N. B"], "categories": ["q-fin.ST", "cs.AI", "cs.CL", "cs.LG"], "doi": "10.5121/csit.2023.130302", "_query": "hedonic house price", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2206.04013v3", "title": "The influence of color on prices of abstract paintings", "abstract": "Determination of price of an artwork is a fundamental problem in cultural economics. In this work we investigate what impact visual characteristics of a painting have on its price. We construct a number of visual features measuring complexity of the painting, its points of interest, segmentation-based features, local color features, and features based on Itten and Kandinsky theories, and utilize mixed-effects model to study impact of these features on the painting price. We analyze the influence of the color on the example of the most complex art style - abstractionism, by created Kandinsky, for which the color is the primary basis. We use Itten's theory - the most recognized color theory in art history, from which the largest number of subtheories was born. For this day it is taken as the base for teaching artists. We utilize novel dataset of 3885 paintings collected from Christie's and Sotheby's and find that color harmony has some explanatory power, color complexity metrics are insignificant and color diversity explains price well.", "published": "2022-06-08T17:05:23Z", "authors": ["Maksim Borisov", "Valeria Kolycheva", "Alexander Semenov", "Dmitry Grigoriev"], "categories": ["econ.GN"], "doi": "", "_query": "hedonic house price", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/1911.10149v4", "title": "Asset Price Bubbles in market models with proportional transaction costs", "abstract": "We study asset price bubbles in market models with proportional transaction costs $λ\\in (0,1)$ and finite time horizon $T$ in the setting of [49]. By following [28], we define the fundamental value $F$ of a risky asset $S$ as the price of a super-replicating portfolio for a position terminating in one unit of the asset and zero cash. We then obtain a dual representation for the fundamental value by using the super-replication theorem of [50]. We say that an asset price has a bubble if its fundamental value differs from the ask-price $(1+λ)S$. We investigate the impact of transaction costs on asset price bubbles and show that our model intrinsically includes the birth of a bubble.", "published": "2019-11-22T17:17:41Z", "authors": ["Francesca Biagini", "Thomas Reitsam"], "categories": ["q-fin.MF"], "doi": "", "_query": "hedonic house price", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2210.01726v3", "title": "Detecting asset price bubbles using deep learning", "abstract": "In this paper we employ deep learning techniques to detect financial asset bubbles by using observed call option prices. The proposed algorithm is widely applicable and model-independent. We test the accuracy of our methodology in numerical experiments within a wide range of models and apply it to market data of tech stocks in order to assess if asset price bubbles are present. Under a given condition on the pricing of call options under asset price bubbles, we are able to provide a theoretical foundation of our approach for positive and continuous stochastic asset price processes. When such a condition is not satisfied, we focus on local volatility models. To this purpose, we give a new necessary and sufficient condition for a process with time-dependent local volatility function to be a strict local martingale.", "published": "2022-10-04T16:30:49Z", "authors": ["Francesca Biagini", "Lukas Gonon", "Andrea Mazzon", "Thilo Meyer-Brandis"], "categories": ["q-fin.MF"], "doi": "", "_query": "hedonic house price", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/1706.10009v2", "title": "Pricing Social Goods", "abstract": "Social goods are goods that grant value not only to their owners but also to the owners' surroundings, be it their families, friends or office mates. The benefit a non-owner derives from the good is affected by many factors, including the type of the good, its availability, and the social status of the non-owner. Depending on the magnitude of the benefit and on the price of the good, a potential buyer might stay away from purchasing the good, hoping to free ride on others' purchases. A revenue-maximizing seller who sells social goods must take these considerations into account when setting prices for the good. The literature on optimal pricing has advanced considerably over the last decade, but little is known about optimal pricing schemes for selling social goods. In this paper, we conduct a systematic study of revenue-maximizing pricing schemes for social goods: we introduce a Bayesian model for this scenario, and devise nearly-optimal pricing schemes for various types of externalities, both for simultaneous sales and for sequential sales.", "published": "2017-06-30T03:31:00Z", "authors": ["Alon Eden", "Tomer Ezra", "Michal Feldman"], "categories": ["cs.GT"], "doi": "", "_query": "hedonic house price", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/1003.5514v3", "title": "Asymptotic and Exact Pricing of Options on Variance", "abstract": "We consider the pricing of derivatives written on the discretely sampled realized variance of an underlying security. In the literature, the realized variance is usually approximated by its continuous-time limit, the quadratic variation of the underlying log-price. Here, we characterize the small-time limits of options on both objects. We find that the difference between them strongly depends on whether or not the stock price process has jumps. Subsequently, we propose two new methods to evaluate the price of options on the discretely sampled realized variance. One of the methods is approximative; it is based on correcting prices of options on quadratic variation by our asymptotic results. The other method is exact; it uses a novel randomization approach and applies Fourier-Laplace techniques. We compare the methods and illustrate our results by some numerical examples.", "published": "2010-03-29T12:02:07Z", "authors": ["Martin Keller-Ressel", "Johannes Muhle-Karbe"], "categories": ["q-fin.PR", "math.PR"], "doi": "", "_query": "hedonic house price", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2608.29818v1", "title": "Decarbonising price formation: unit-level evidence on battery storage and the imbalance price in the GB Balancing Mechanism", "abstract": "Renewables now dominate Great Britain's generation mix but rarely occupy the marginal price-setting position, which raises the question of which flexible technologies translate a renewable-rich system into real-time price formation. This study reconstructs the price-ranked edge of the eligible bid or offer stack in the GB Balancing Mechanism across 50,684 half-hourly Settlement Periods from 2023 to 2025 and attributes it to individual Balancing Mechanism Units, separating long-system bid-active from short-system offer-active conditions and retaining co-marginal ties. Batteries rose from 0.8% to 36.2% of bid-active and from 3.2% to 26.9% of offer-active marginality, displacing combined-cycle gas on the bid side and pumped storage on the offer side. Capacity-normalised marginal capture rose on both sides, and unit-quarter fixed effects show that an additional 100 MW of Balancing Mechanism active capacity was associated with a 0.55 and 0.48 percentage-point increase in quarterly bid- and offer-side marginal share. By 2025, battery actions were around £9 to 10/MWh more favourable than volume-matched non-battery alternatives drawn from the same ladder, yet batteries were under-represented by 12.9 percentage points in the highest-priced 5% of short-system periods. Individual batteries remain consistent with price-taking, while the fleet has become endogenous to routine balancing price formation.", "published": "2026-08-30T14:41:06Z", "authors": ["Robert Dalton", "Aidan O'Sullivan"], "categories": ["stat.AP", "econ.GN"], "doi": "", "_query": "hedonic house price", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/1210.6372v6", "title": "Optimal execution and block trade pricing: a general framework", "abstract": "In this article, we develop a general framework to study optimal execution and to price block trades. We prove existence of optimal liquidation strategies and we provide regularity results for optimal strategies under very general hypotheses. We exhibit a Hamiltonian characterization for the optimal strategy that can be used for numerical approximation. We also focus on the important topic of block trade pricing and we propose a methodology to give a price to financial (il)liquidity. In particular, we provide a closed-form formula for the price of a block trade when there is no time constraint to liquidate.", "published": "2012-10-23T20:37:56Z", "authors": ["Olivier Guéant"], "categories": ["q-fin.TR"], "doi": "", "_query": "hedonic house price", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/1608.00280v1", "title": "Pricing Weakly Model Dependent Barrier Products", "abstract": "We discuss the pricing methodology for Bonus Certificates and Barrier Reverse-Convertible Structured Products. Pricing for a European barrier condition is straightforward for products of both types and depends on an efficient interpolation of observed market option pricing. Pricing products We discuss the pricing methodology for Bonus Certificates and Barrier Reverse-Convertible Structured Products. Pricing for a European barrier condition is straightforward for products of both types and depends on an efficient interpolation of observed market option pricing. Pricing products with an American barrier condition requires stochastic modelling. We show that for typical market parameters, this stochastic pricing problem can be systematically reduced to evaluating only one fairly simple stochastic parameter being the asymmetry of hitting the barrier. Eventually, pricing Bonus Certificates and Barrier Reverse Convertibles with an American barrier condition, shows to be dependent on stochastic modelling only within a range of $\\pm\\frac{2}{3}$ of accuracy - e.g. within this accuracy limitation we can price these products without stochastic modelling. We show that the remaining price component is weakly dependent on the stochastic models. Combining these together, we prove to have established an almost model independent pricing procedure for Bonus Certificates and Barrier Reverse-Convertible Structured Products with American barrier conditions.", "published": "2016-07-31T22:54:13Z", "authors": ["Jan Kuklinski", "Panagiotis Papaioannou", "Kevin Tyloo"], "categories": ["q-fin.PR", "q-fin.MF"], "doi": "", "_query": "hedonic house price", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2512.12871v1", "title": "CapOptix: An Options-Framework for Capacity Market Pricing", "abstract": "Electricity markets are under increasing pressure to maintain reliability amidst rising renewable penetration, demand variability, and occasional price shocks. Traditional capacity market designs often fall short in addressing this by relying on expected-value metrics of energy unserved, which overlook risk exposure in such systems. In this work, we present CapOptix, a capacity pricing framework that interprets capacity commitments as reliability options, i.e., financial derivatives of wholesale electricity prices. CapOptix characterizes the capacity premia charged by accounting for structural price shifts modeled by the Markov Regime Switching Process. We apply the framework to historical price data from multiple electricity markets and compare the resulting premium ranges with existing capacity remuneration mechanisms.", "published": "2025-12-14T23:08:38Z", "authors": ["Millend Roy", "Agostino Capponi", "Vladimir Pyltsov", "Yinbo Hu", "Vijay Modi"], "categories": ["eess.SY", "q-fin.CP", "q-fin.PR", "stat.AP"], "doi": "", "_query": "hedonic house price", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/1804.09151v2", "title": "Optimal Investment, Demand and Arbitrage under Price Impact", "abstract": "This paper studies the optimal investment problem with random endowment in an inventory-based price impact model with competitive market makers. Our goal is to analyze how price impact affects optimal policies, as well as both pricing rules and demand schedules for contingent claims. For exponential market makers preferences, we establish two effects due to price impact: constrained trading, and non-linear hedging costs. To the former, wealth processes in the impact model are identified with those in a model without impact, but with constrained trading, where the (random) constraint set is generically neither closed nor convex. Regarding hedging, non-linear hedging costs motivate the study of arbitrage free prices for the claim. We provide three such notions, which coincide in the frictionless case, but which dramatically differ in the presence of price impact. Additionally, we show arbitrage opportunities, should they arise from claim prices, can be exploited only for limited position sizes, and may be ignored if outweighed by hedging considerations. We also show that arbitrage inducing prices may arise endogenously in equilibrium, and that equilibrium positions are inversely proportional to the market makers' representative risk aversion. Therefore, large positions endogenously arise in the limit of either market maker risk neutrality, or a large number of market makers.", "published": "2018-04-24T17:20:30Z", "authors": ["Michail Anthropelos", "Scott Robertson", "Konstantinos Spiliopoulos"], "categories": ["q-fin.MF", "math.OC"], "doi": "", "_query": "hedonic house price", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/1202.2585v1", "title": "Minimax Option Pricing Meets Black-Scholes in the Limit", "abstract": "Option contracts are a type of financial derivative that allow investors to hedge risk and speculate on the variation of an asset's future market price. In short, an option has a particular payout that is based on the market price for an asset on a given date in the future. In 1973, Black and Scholes proposed a valuation model for options that essentially estimates the tail risk of the asset price under the assumption that the price will fluctuate according to geometric Brownian motion. More recently, DeMarzo et al., among others, have proposed more robust valuation schemes, where we can even assume an adversary chooses the price fluctuations. This framework can be considered as a sequential two-player zero-sum game between the investor and Nature. We analyze the value of this game in the limit, where the investor can trade at smaller and smaller time intervals. Under weak assumptions on the actions of Nature (an adversary), we show that the minimax option price asymptotically approaches exactly the Black-Scholes valuation. The key piece of our analysis is showing that Nature's minimax optimal dual strategy converges to geometric Brownian motion in the limit.", "published": "2012-02-12T23:41:12Z", "authors": ["Jacob Abernethy", "Rafael M. Frongillo", "Andre Wibisono"], "categories": ["q-fin.CP", "cs.GT", "q-fin.PR"], "doi": "", "_query": "hedonic house price", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/1803.05075v1", "title": "Stock Price Prediction using Principle Components", "abstract": "The literature provides strong evidence that stock prices can be predicted from past price data. Principal component analysis (PCA) is a widely used mathematical technique for dimensionality reduction and analysis of data by identifying a small number of principal components to explain the variation found in a data set. In this paper, we describe a general method for stock price prediction using covariance information, in terms of a dimension reduction operation based on principle component analysis. Projecting the noisy observation onto a principle subspace leads to a well-conditioned problem. We illustrate our method on daily stock price values for five companies in different industries. We investigate the results based on mean squared error and directional change statistic of prediction, as measures of performance, and volatility of prediction as a measure of risk.", "published": "2018-03-13T23:34:26Z", "authors": ["Mahsa Ghorbani", "Edwin K. P. Chong"], "categories": ["q-fin.MF"], "doi": "", "_query": "hedonic house price", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2410.12495v1", "title": "Price impact and long-term profitability of energy storage", "abstract": "We study the price impact of storage facilities in electricity markets and analyze the long-term profitability of these facilities in prospective scenarios of energy transition. To this end, we begin by characterizing the optimal operating strategy for a stylized storage system, assuming an arbitrary exogenous price process. Following this, we determine the equilibrium price in a market comprising storage systems (acting as price takers), renewable energy producers, and conventional producers with a defined supply function, all driven by an exogenous demand process. The price process is characterized as a solution to a fully coupled system of forward-backward stochastic differential equations, for which we establish existence and uniqueness under appropriate assumptions. We finally illustrate the impact of storage on intraday electricity prices through numerical examples and show how the revenues of storage agents may evolve in prospective energy transition scenarios from RTE, the French energy electricity network operator, taking into account both the increasing penetration of renewable energies and the self-cannibalization effect of growing storage capacity. We find that both the average revenues and the interquantile ranges increase in all scenarios, highlighting higher expected profits and higher risk for storage assets.", "published": "2024-10-16T12:16:22Z", "authors": ["Roxana Dumitrescu", "Redouane Silvente", "Peter Tankov"], "categories": ["q-fin.MF"], "doi": "", "_query": "hedonic house price", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/1404.4014v3", "title": "Option Pricing Accuracy for Estimated Heston Models", "abstract": "We consider assets for which price $X_t$ and squared volatility $Y_t$ are jointly driven by Heston joint stochastic differential equations (SDEs). When the parameters of these SDEs are estimated from $N$ sub-sampled data $(X_{nT}, Y_{nT})$, estimation errors do impact the classical option pricing PDEs. We estimate these option pricing errors by combining numerical evaluation of estimation errors for Heston SDEs parameters with the computation of option price partial derivatives with respect to these SDEs parameters. This is achieved by solving six parabolic PDEs with adequate boundary conditions. To implement this approach, we also develop an estimator $\\hat λ$ for the market price of volatility risk, and we study the sensitivity of option pricing to estimation errors affecting $\\hat λ$. We illustrate this approach by fitting Heston SDEs to 252 daily joint observations of the S\\&P 500 index and of its approximate volatility VIX, and by numerical applications to European options written on the S\\&P 500 index.", "published": "2014-04-15T18:34:40Z", "authors": ["Robert Azencott", "Yutheeka Gadhyan", "Roland Glowinski"], "categories": ["q-fin.MF"], "doi": "", "_query": "hedonic house price", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2011.08620v1", "title": "Static Hedging of Weather and Price Risks in Electricity Markets", "abstract": "We present the closed-form solution to the problem of hedging price and quantity risks for energy retailers (ER), using financial instruments based on electricity price and weather indexes. Our model considers an ER who is intermediary in a regulated electricity market. ERs buy a fixed quantity of electricity at a variable cost and must serve a variable demand at a fixed cost. Thus ERs are subject to both price and quantity risks. To hedge such risks, an ER could construct a portfolio of financial instruments based on price and weather indexes. We construct the closed form solution for the optimal portfolio for the mean-Var model in the discrete setting. Our model does not make any distributional assumption.", "published": "2020-11-17T13:24:01Z", "authors": ["Javier Pantoja Robayo", "Juan C. Vera"], "categories": ["q-fin.MF"], "doi": "", "_query": "hedonic house price", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2007.09201v1", "title": "Bond indifference prices and indifference yield curves", "abstract": "In a market with stochastic interest rates, we consider an investor who can either (i) invest all if his money in a savings account or (ii) purchase zero-coupon bonds and invest the remainder of his wealth in a savings account. The indifference price of the bond is the price for which the investor could achieve the same expected utility under both scenarios. In an affine term structure setting, under the assumption that an investor has a utility function in either exponential or power form, we show that the indifference price of a zero-coupon bond is the root of an integral expression. As an example, we compute bond indifference prices and the corresponding indifference yield curves in the Vasicek setting and interpret the results.", "published": "2020-07-17T19:32:48Z", "authors": ["Matthew Lorig"], "categories": ["q-fin.CP"], "doi": "", "_query": "hedonic house price", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/1210.4915v1", "title": "Self-Confirming Price Prediction Strategies for Simultaneous One-Shot Auctions", "abstract": "Bidding in simultaneous auctions is challenging because an agent's value for a good in one auction may depend on the uncertain outcome of other auctions: the so-called exposure problem. Given the gap in understanding of general simultaneous auction games, previous works have tackled this problem with heuristic strategies that employ probabilistic price predictions. We define a concept of self-confirming prices, and show that within an independent private value model, Bayes-Nash equilibrium can be fully characterized as a profile of optimal price prediction strategies with self-confirming predictions. We exhibit practical procedures to compute approximately optimal bids given a probabilistic price prediction, and near self-confirming price predictions given a price-prediction strategy. An extensive empirical game-theoretic analysis demonstrates that self-confirming price prediction strategies are effective in simultaneous auction games with both complementary and substitutable preference structures.", "published": "2012-10-16T17:56:19Z", "authors": ["Michael P. Wellman", "Eric Sodomka", "Amy Greenwald"], "categories": ["cs.GT"], "doi": "", "_query": "hedonic house price", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/0808.3360v1", "title": "Criticality Characteristics of Current Oil Price Dynamics", "abstract": "Methodology that recently lead us to predict to an amazing accuracy the date (July 11, 2008) of reverse of the oil price up trend is briefly summarized and some further aspects of the related oil price dynamics elaborated. This methodology is based on the concept of discrete scale invariance whose finance-prediction-oriented variant involves such elements as log-periodic self-similarity, the universal preferred scaling factor lambda=2, and allows a phenomenon of the \"super-bubble\". From this perspective the present (as of August 22, 2008) violent - but still log-periodically decelerating - decrease of the oil prices is associated with the decay of such a \"super- bubble\" that has started developing about one year ago on top of the longer-term oil price increasing phase (normal bubble) whose ultimate termination is evaluated to occur in around mid 2010.", "published": "2008-08-25T13:57:13Z", "authors": ["Stanislaw Drozdz", "Jaroslaw Kwapien", "Pawel Oswiecimka"], "categories": ["q-fin.ST", "physics.soc-ph"], "doi": "", "_query": "hedonic house price", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2508.08325v2", "title": "Algorithmic Collusion of Pricing and Advertising on E-commerce Platforms", "abstract": "When online sellers use AI learning algorithms to automatically compete on e-commerce platforms, there is concern that they will learn to coordinate on higher than competitive prices. However, this concern was primarily raised in single-dimension price competition. We investigate whether this prediction holds when sellers make pricing and advertising decisions together, i.e., two-dimensional decisions. We analyze competition in multi-agent reinforcement learning, and use a large-scale dataset from Amazon.com to provide empirical evidence. We show that when consumers have high search costs, learning algorithms can coordinate on prices lower than competitive prices, facilitating a win-win-win for consumers, sellers, and platforms. This occurs because algorithms learn to coordinate on lower advertising bids, which lower advertising costs, leading to lower prices and enlarging demand on the platform. We also show that our results generalize to any learning algorithm that uses exploration of price and advertising bids. Consistent with our predictions, an empirical analysis shows that price levels exhibit a negative interaction between estimated consumer search costs and algorithm usage index. We analyze the platform's strategic response and find that reserve price adjustments will not increase platform profits, but commission adjustments will, while maintaining the beneficial outcomes for both sellers and consumers.", "published": "2025-08-09T18:44:17Z", "authors": ["Hangcheng Zhao", "Ron Berman"], "categories": ["econ.GN", "cs.AI", "cs.CE", "cs.GT", "cs.LG"], "doi": "", "_query": "hedonic house price", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/1905.03721v2", "title": "Show, Price and Negotiate: A Negotiator with Online Value Look-Ahead", "abstract": "Negotiation, as an essential and complicated aspect of online shopping, is still challenging for an intelligent agent. To that end, we propose the Price Negotiator, a modular deep neural network that addresses the unsolved problems in recent studies by (1) considering images of the items as a crucial, though neglected, source of information in a negotiation, (2) heuristically finding the most similar items from an external online source to predict the potential value and an acceptable agreement price, (3) predicting a general price-based action at each turn which is fed into the language generator to output the supporting natural language, and (4) adjusting the prices based on the predicted actions. Empirically, we show that our model, that is trained in both supervised and reinforcement learning setting, significantly improves negotiation on the CraigslistBargain dataset, in terms of the agreement price, price consistency, and dialogue quality.", "published": "2019-05-07T06:20:59Z", "authors": ["Amin Parvaneh", "Ehsan Abbasnejad", "Qi Wu", "Javen Qinfeng Shi", "Anton van den Hengel"], "categories": ["cs.CV"], "doi": "10.1109/TMM.2021.3065169", "_query": "hedonic house price", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2005.02351v1", "title": "Defining an intrinsic stickiness parameter of stock price returns", "abstract": "We introduce a non linear pricing model of individual stock returns that defines a stickiness parameter of the returns. The pricing model resembles the capital asset pricing model used in finance but has a non linear component inspired from models of earth quake tectonic plate movements. The link to tectonic plate movements happens, since price movements of a given stock index is seen adding stress to its components of individual stock returns, in order to follow the index. How closely individual stocks follow the indexs price movements, can then be used to define their stickiness", "published": "2020-05-05T17:36:49Z", "authors": ["Naji Massad", "Jørgen Vitting Andersen"], "categories": ["q-fin.ST", "q-fin.GN"], "doi": "10.1016/j.physa.2020.124464", "_query": "hedonic house price", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2110.00771v2", "title": "Non-average price impact in order-driven markets", "abstract": "We present a measurement of price impact in order-driven markets that does not require averages across executions or scenarios. Given the order book data associated with one single execution of a sell metaorder, we measure its contribution to price decrease during the trade. We do so by modelling the limit order book using state-dependent Hawkes processes, and by defining the price impact profile of the execution as a function of the compensator of a stochastic process in our model. We apply our measurement to a data set from NASDAQ, and we conclude that the clustering of sell child orders has a bigger impact on price than their sizes.", "published": "2021-10-02T10:10:09Z", "authors": ["Claudio Bellani", "Damiano Brigo", "Mikko Pakkanen", "Leandro Sanchez-Betancourt"], "categories": ["q-fin.TR", "q-fin.ST"], "doi": "", "_query": "hedonic house price", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2212.00197v1", "title": "GAN-MC: a Variance Reduction Tool for Derivatives Pricing", "abstract": "We propose a parameter-free model for estimating the price or valuation of financial derivatives like options, forwards and futures using non-supervised learning networks and Monte Carlo. Although some arbitrage-based pricing formula performs greatly on derivatives pricing like Black-Scholes on option pricing, generative model-based Monte Carlo estimation(GAN-MC) will be more accurate and holds more generalizability when lack of training samples on derivatives, underlying asset's price dynamics are unknown or the no-arbitrage conditions can not be solved analytically. We analyze the variance reduction feature of our model and to validate the potential value of the pricing model, we collect real world market derivatives data and show that our model outperforms other arbitrage-based pricing models and non-parametric machine learning models. For comparison, we estimate the price of derivatives using Black-Scholes model, ordinary least squares, radial basis function networks, multilayer perception regression, projection pursuit regression and Monte Carlo only models.", "published": "2022-12-01T00:53:25Z", "authors": ["Weishi Wang"], "categories": ["stat.AP", "stat.ME"], "doi": "", "_query": "hedonic house price", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2105.10205v1", "title": "Rational Dynamic Price Model for Demand Response Programs in Modern Distribution Systems", "abstract": "Demand response (DR) refers to change in electricity consumption pattern of customers during on-peak hours in lieu of financial gains to reduce stress on distribution systems. Existing dynamic price models have not provided adequate success to price-based demand response (PBDR) programs. It happened as these models have raised typical socio-economic problems pertaining to cross-subsidy, free-riders, social inequity, assured profit of utilities, financial gains and comfort of customers, etc. This paper presents a new dynamic price model for PBDR in distribution systems which aims to overcome some of the above mentioned problems of the existing price models. The main aim of the developed price model is to overcome the problems of cross-subsidy and free-riders of the existing price models for widespread acceptance, deployment and efficient utilization of PBDR programs in contemporary distribution systems. Proposed price model generates demand-linked price signal that imposes different price signals to different customers during on-peak hours and remains static otherwise. This makes proposed model a class apart from other existing models. The novelty of the proposed model lies in the fact that the financial benefits and penalties pertaining to DR are self-adjusted among customers while preserving social equity and profit of the utility. Such an ideology has not been yet addressed in the literature. Detailed investigation of application results on a standard test bench reveals that the proposed model equally cares regarding the interests of both customers and utility. For economic assessment, a comparison of the proposed price model with the existing pricing models is also performed.", "published": "2021-05-21T08:41:19Z", "authors": ["Rayees A. Thokar", "Nikhil Gupta", "K. R. Niazi", "Anil Swarnkar", "Nand K. Meena"], "categories": ["eess.SY"], "doi": "", "_query": "hedonic house price", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2312.13448v5", "title": "Implied CO$_{\\textbf{2}}$-Price and Interest Rate of Carbon", "abstract": "By its nature, the so-called social cost of carbon (SCC(t)) will likely not cover the cost induced by climate change (damage cost and abatement cost) if it is used as a CO$_2$-price. It is a marginal price only. We define an implied CO$_2$-price that covers the climate change-induced costs. The price can be interpreted as a \\textit{polluter pays principle}. A numerical analysis using a classical DICE model reveals that the cost-implied CO$_2$ price is around 500 \\$/tCO$_2$, while the corresponding price associated with the SCC is about 50 \\$/tCO$_2$. In addition, we define the internal rate of return of carbon abatement and calculate it for the classical DICE model. This rate is much higher than the model's discount rate, which may suggest the advantage of financing abatement by loans.", "published": "2023-12-20T21:55:26Z", "authors": ["Christian P. Fries"], "categories": ["q-fin.MF"], "doi": "", "_query": "hedonic house price", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/1905.00853v1", "title": "The Declining Price Anomaly is not Universal in Multi-Buyer Sequential Auctions (but almost is)", "abstract": "The declining price anomaly states that the price weakly decreases when multiple copies of an item are sold sequentially over time. The anomaly has been observed in a plethora of practical applications. On the theoretical side, Gale and Stegeman proved that the anomaly is guaranteed to hold in full information sequential auctions with exactly two buyers. We prove that the declining price anomaly is not guaranteed in full information sequential auctions with three or more buyers. This result applies to both first-price and second-price sequential auctions. Moreover, it applies regardless of the tie-breaking rule used to generate equilibria in these sequential auctions. To prove this result we provide a refined treatment of subgame perfect equilibria that survive the iterative deletion of weakly dominated strategies and use this framework to experimentally generate a very large number of random sequential auction instances. In particular, our experiments produce an instance with three bidders and eight items that, for a specific tie-breaking rule, induces a non-monotonic price trajectory. Theoretic analyses are then applied to show that this instance can be used to prove that for every possible tie-breaking rule there is a sequential auction on which it induces a non-monotonic price trajectory. On the other hand, our experiments show that non-monotonic price trajectories are extremely rare. In over six million experiments only a 0.000183 proportion of the instances violated the declining price anomaly.", "published": "2019-05-02T17:01:58Z", "authors": ["Vishnu V. Narayan", "Enguerrand Prebet", "Adrian Vetta"], "categories": ["cs.GT", "econ.TH"], "doi": "", "_query": "hedonic house price", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/1409.5801v1", "title": "Pricing and hedging of energy spread options and volatility modulated Volterra processes", "abstract": "We derive the price of a spread option based on two assets which follow a bivariate volatility modulated Volterra process dynamics. Such a price dynamics is particularly relevant in energy markets, modelling for example the spot price of power and gas. Volatility modulated Volterra processes are in general not semimartingales, but contain several special cases of interest in energy markets like for example continuous-time autoregressive moving average processes. Based on a change of measure, we obtain a pricing expression based on a univariate Fourier transform of the payoff function and the characteristic function of the price dynamics. Moreover, the spread option price can be expressed in terms of the forward prices on the underlying dynamics assets. We compute a linear system of equations for the quadratic hedge for the spread option in terms of a portfolio of underlying forward contracts.", "published": "2014-09-19T20:01:09Z", "authors": ["Fred Espen Benth", "Hanna Zdanowicz"], "categories": ["q-fin.PR", "math.PR"], "doi": "", "_query": "hedonic house price", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2212.00674v2", "title": "Quantity restrictions and price discounts on Russian oil", "abstract": "Following Russia's invasion of Ukraine, Western countries have looked for ways to limit Russia's oil income. This paper considers, theoretically and quantitatively, two such options: 1) an export-quantity restriction and 2) a forced discount on Russian oil. We build a quantifiable model of the global oil market and analyze how each of these policies affect: which Russian oil fields fall out of production; the global oil supply; and the global oil price. By these statics we derive the effects of the policies on Russian oil profits and oil-importers' economic surplus. The effects on Russian oil profits are substantial. In the short run (within the first year), a quantity restriction of 20% yields Russian losses of 62 million USD per day, equivalent to 1.2% of GDP and 32% of military spending. In the long run (beyond a year) new investments become unprofitable. Losses rise to 100 million USD per day, 2% of GDP and 56% of military spending. A price discount of 20% is even more harmful to Russia, yielding losses of 152 million USD per day, equivalent to 3.1% of GDP and 85% of military spending in the short run and long run. A price discount puts generally more burden on Russia and less on importers compared to a quantity restriction. In fact, a price discount implies net gains for oil importers as it essentially redistributes oil rents from Russia to importers. If the restrictions are expected to last for long, the burden on oil importers decreases. Overall, both policies at all levels imply larger relative losses for Russia than for oil importers (in shares of their GDP). The case for a price discount on Russian oil is thus strong. However, Russia may choose not to export at the discounted price, in which case the price-discount sanction becomes a de facto supply restriction.", "published": "2022-12-01T17:30:28Z", "authors": ["Henrik Wachtmeister", "Johan Gars", "Daniel Spiro"], "categories": ["econ.GN"], "doi": "", "_query": "hedonic house price", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2412.06837v1", "title": "Innovative Sentiment Analysis and Prediction of Stock Price Using FinBERT, GPT-4 and Logistic Regression: A Data-Driven Approach", "abstract": "This study explores the comparative performance of cutting-edge AI models, i.e., Finaance Bidirectional Encoder representations from Transsformers (FinBERT), Generatice Pre-trained Transformer GPT-4, and Logistic Regression, for sentiment analysis and stock index prediction using financial news and the NGX All-Share Index data label. By leveraging advanced natural language processing models like GPT-4 and FinBERT, alongside a traditional machine learning model, Logistic Regression, we aim to classify market sentiment, generate sentiment scores, and predict market price movements. This research highlights global AI advancements in stock markets, showcasing how state-of-the-art language models can contribute to understanding complex financial data. The models were assessed using metrics such as accuracy, precision, recall, F1 score, and ROC AUC. Results indicate that Logistic Regression outperformed the more computationally intensive FinBERT and predefined approach of versatile GPT-4, with an accuracy of 81.83% and a ROC AUC of 89.76%. The GPT-4 predefined approach exhibited a lower accuracy of 54.19% but demonstrated strong potential in handling complex data. FinBERT, while offering more sophisticated analysis, was resource-demanding and yielded a moderate performance. Hyperparameter optimization using Optuna and cross-validation techniques ensured the robustness of the models. This study highlights the strengths and limitations of the practical applications of AI approaches in stock market prediction and presents Logistic Regression as the most efficient model for this task, with FinBERT and GPT-4 representing emerging tools with potential for future exploration and innovation in AI-driven financial analytics", "published": "2024-12-07T05:20:31Z", "authors": ["Olamilekan Shobayo", "Sidikat Adeyemi-Longe", "Olusogo Popoola", "Bayode Ogunleye"], "categories": ["cs.LG", "cs.AI", "q-fin.ST", "stat.AP", "stat.CO"], "doi": "10.3390/bdcc8110143", "_query": "hedonic house price", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2601.18117v1", "title": "Decentralized Multi-product Pricing: Diagonal Dominance, Nash Equilibrium, and Price of Anarchy", "abstract": "Decentralized decision making in multi--product firms can lead to efficiency losses when autonomous decision makers fail to internalize cross--product demand interactions. This paper quantifies the magnitude of such losses by analyzing the Price of Anarchy in a pricing game in which each decision maker independently sets prices to maximize its own product--level revenue. We model demand using a linear system that captures both substitution and complementarity effects across products. We first establish existence and uniqueness of a pure--strategy Nash equilibrium under economically standard diagonal dominance conditions. Our main contribution is the derivation of a tight worst--case lower bound on the ratio between decentralized revenue and the optimal centralized revenue. We show that this efficiency loss is governed by a single scalar parameter, denoted by $μ$, which measures the aggregate strength of cross--price effects relative to own--price sensitivities. In particular, we prove that the revenue ratio is bounded below by $4(1-μ)/(2-μ)^2$, and we demonstrate the tightness of this bound by constructing a symmetric market topology in which the bound is exactly attained. We further refine the analysis by providing an instance--exact characterization of efficiency loss based on the spectral properties of the demand interaction matrix. Together, these results offer a quantitative framework for assessing the trade--off between centralized pricing and decentralized autonomy in multi--product firms.", "published": "2026-01-26T04:02:27Z", "authors": ["Boxiao Chen", "Jiashuo Jiang", "Stefanus Jasin"], "categories": ["cs.GT", "math.OC"], "doi": "", "_query": "hedonic house price", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2308.12291v1", "title": "The Quantum-House Effect and Its Demonstration on SpinQ Gemini", "abstract": "We introduce the quantum-house effect, a non-local quantum phenomenon which goes against classical intuition. We show how the effect can be achieved with any bipartite quantum state where neither subsystem is in a pure state. Besides its theoretical description, the quantum-house effect is also demonstrated on SpinQ Gemini, a 2-qubit liquid-state NMR desktop quantum computer.", "published": "2023-07-22T18:48:50Z", "authors": ["Tamás Varga"], "categories": ["physics.gen-ph"], "doi": "10.1007/s12045-023-1544-1", "_query": "hedonic house price", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/cond-mat/0312547v1", "title": "Traders' strategy with price feedbacks in financial market", "abstract": "We introduce an autoregressive-type model of prices in financial market taking into account the self-modulation effect. We find that traders are mainly using strategies with weighted feedbacks of past prices. These feedbacks are responsible for the slow diffusion in short times, apparent trends and power law distribution of price changes.", "published": "2003-12-20T10:19:02Z", "authors": ["Takayuki Mizuno", "Tohur Nakano", "Misako Takayasu", "Hideki Takayasu"], "categories": ["cond-mat.stat-mech", "q-fin.TR"], "doi": "10.1016/j.physa.2004.06.145", "_query": "hedonic house price", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2305.07006v1", "title": "Fair Price Discrimination", "abstract": "A seller is pricing identical copies of a good to a stream of unit-demand buyers. Each buyer has a value on the good as his private information. The seller only knows the empirical value distribution of the buyer population and chooses the revenue-optimal price. We consider a widely studied third-degree price discrimination model where an information intermediary with perfect knowledge of the arriving buyer's value sends a signal to the seller, hence changing the seller's posterior and inducing the seller to set a personalized posted price. Prior work of Bergemann, Brooks, and Morris (American Economic Review, 2015) has shown the existence of a signaling scheme that preserves seller revenue, while always selling the item, hence maximizing consumer surplus. In a departure from prior work, we ask whether the consumer surplus generated is fairly distributed among buyers with different values. To this end, we aim to maximize welfare functions that reward more balanced surplus allocations. Our main result is the surprising existence of a novel signaling scheme that simultaneously $8$-approximates all welfare functions that are non-negative, monotonically increasing, symmetric, and concave, compared with any other signaling scheme. Classical examples of such welfare functions include the utilitarian social welfare, the Nash welfare, and the max-min welfare. Such a guarantee cannot be given by any consumer-surplus-maximizing scheme -- which are the ones typically studied in the literature. In addition, our scheme is socially efficient, and has the fairness property that buyers with higher values enjoy higher expected surplus, which is not always the case for existing schemes.", "published": "2023-05-11T17:45:06Z", "authors": ["Siddhartha Banerjee", "Kamesh Munagala", "Yiheng Shen", "Kangning Wang"], "categories": ["cs.GT", "cs.DS", "econ.TH"], "doi": "", "_query": "hedonic house price", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/1808.09382v2", "title": "Emergence of Turbulent Epochs in Oil Prices", "abstract": "Oil price data have a complicated multi-scale structure that may vary with time. We use time-frequency analysis to identify the main features of these variations and, in particular, the regime shifts. The analysis is based on a wavelet-based decomposition and analysis of the associated scale spectrum. The joint estimation of the local Hurst exponent and volatility is the key to detect and identify regime shifting and switching of the oil price. The framework involves in particular modeling in terms of a process of `multi-fractional' type so that both the roughness and the volatility of the price process may vary with time. Special epochs then emerge as a result of these degrees of freedom, moreover, as a result of the special type of spectral estimator used. These special epochs are discussed and related to historical events. Some of them are not detected by standard analysis based on maximum likelihood estimation. The paper presents a novel algorithm for robust detection of such special epochs and multi-fractional behavior in financial or other types of data. In the financial context insight about such behavior of the asset price is important to evaluate financial contracts involving the asset.", "published": "2018-08-28T16:12:02Z", "authors": ["Josselin Garnier", "Knut Solna"], "categories": ["q-fin.ST"], "doi": "10.1016/j.chaos.2019.03.016", "_query": "hedonic house price", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2103.10015v1", "title": "Robust Price Optimization of Multiple Products under Interval Uncertainties", "abstract": "In this paper, we solve the multiple product price optimization problem under interval uncertainties of the price sensitivity parameters in the demand function. The objective of the price optimization problem is to maximize the overall revenue of the firm where the decision variables are the prices of the products supplied by the firm. We propose an approach that yields optimal solutions under different variations of the estimated price sensitivity parameters. We adopt a robust optimization approach by building a data-driven uncertainty set for the parameters, and then construct a deterministic counterpart for the robust optimization model. The numerical results show that two objectives are fulfilled: the method reflects the uncertainty embedded in parameter estimations, and also an interval is obtained for optimal prices. We also conducted a simulation study to which we compared the results of our approach. The comparisons show that although robust optimization is deemed to be conservative, the results of the proposed approach show little loss compared to those from the simulation.", "published": "2021-03-18T04:30:11Z", "authors": ["Mahdi Hamzeei", "Alvin Lim", "Jiefeng Xu"], "categories": ["math.OC"], "doi": "10.1057/s41272-021-00351-w", "_query": "hedonic house price", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/1711.05567v2", "title": "Fully-dynamic risk-indifference pricing and no-good-deal bounds", "abstract": "The seller's risk-indifference price evaluation is studied. We propose a dynamic risk-indifference pricing criteria derived from a fully-dynamic family of risk measures on the $L_p$-spaces for $p\\in [1,\\infty]$. The concept of fully-dynamic risk measures extends the one of dynamic risk measures by adding the actual possibility of changing the risk perspectives over time. The family is then characterised by a double time index. Our framework fits well the study of both short and long term investments. In this dynamic framework we analyse whether the risk-indifference pricing criterion actually provides a proper convex price system, for which time-consistency is guaranteed. It turns out that the analysis is quite delicate and necessitates an adequate setting. This entails the use of capacities and an extension of the whole price system to the Banach spaces derived by the capacity seminorms. Furthermore, we consider the relationship of the fully-dynamic risk-indifference price with no-good-deal bounds. Recall that no-good-deal pricing guarantees that not only arbitrage opportunities are excluded, but also all deals that are \"too good to be true\". We shall provide necessary and sufficient conditions on the fully-dynamic risk measures so that the corresponding risk-indifference prices satisfy the no-good-deal bounds. The use of no-good-deal bounds also provides a method to select the risk measures and then construct a proper fully-dynamic risk-indifference price system in the $L_2$-spaces.", "published": "2017-11-15T13:36:41Z", "authors": ["Jocelyne Bion-Nadal", "Giulia Di Nunno"], "categories": ["math.PR"], "doi": "", "_query": "hedonic house price", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/1003.2138v1", "title": "Need-based Communication for Smart Grid: When to Inquire Power Price?", "abstract": "In smart grid, a home appliance can adjust its power consumption level according to the realtime power price obtained from communication channels. Most studies on smart grid do not consider the cost of communications which cannot be ignored in many situations. Therefore, the total cost in smart grid should be jointly optimized with the communication cost. In this paper, a probabilistic mechanism of locational margin price (LMP) is applied and a model for the stochastic evolution of the underlying load which determines the power price is proposed. Based on this framework of power price, the problem of determining when to inquire the power price is formulated as a Markov decision process and the corresponding elements, namely the action space, system state and reward function, are defined. Dynamic programming is then applied to obtain the optimal strategy. A simpler myopic approach is proposed by comparing the cost of communications and the penalty incurred by using the old value of power price. Numerical results show the significant performance gain of the optimal strategy of price inquiry, as well as the near-optimality of the myopic approach.", "published": "2010-03-10T16:04:59Z", "authors": ["Husheng Li", "Robert C. Qiu"], "categories": ["cs.IT"], "doi": "", "_query": "hedonic house price", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/cond-mat/0210710v1", "title": "More statistical properties of order books and price impact", "abstract": "We investigate present some new statistical properties of order books. We analyse data from the Nasdaq and investigate (a) the statistics of incoming limit order prices, (b) the shape of the average order book, and (c) the typical life time of a limit order as a function of the distance from the best price. We also determine the `price impact' function using French and British stocks, and find a logarithmic, rather than a power-law, dependence of the price response on the volume. The weak time dependence of the response function shows that the impact is, surprisingly, quasi-permanent, and suggests that trading itself is interpreted by the market as new information.", "published": "2002-10-31T16:13:39Z", "authors": ["Marc Potters", "Jean-Philippe Bouchaud"], "categories": ["cond-mat"], "doi": "10.1016/S0378-4371(02)01896-4", "_query": "hedonic house price", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2108.04505v2", "title": "Optimal Revenue Guarantees for Pricing in Large Markets", "abstract": "Posted price mechanisms (PPM) constitute one of the predominant practices to price goods in online marketplaces and their revenue guarantees have been a central object of study in the last decade. We consider a basic setting where the buyers' valuations are independent and identically distributed and there is a single unit on sale. It is well-known that this setting is equivalent to the so-called i.i.d. prophet inequality, for which optimal guarantees are known and evaluate to 0.745 in general (equivalent to a PPM with dynamic prices) and $1 - 1/e \\approx 0.632$ in the fixed threshold case (equivalent to a fixed price PPM). In this paper we consider an additional assumption, namely, that the underlying market is very large. This is modeled by first fixing a valuation distribution F and then making the number of buyers grow large, rather than considering the worst distribution for each possible market size. In this setting Kennedy and Kertz [Ann. Probab. 1991] breaks the 0.745 fraction achievable in general with a dynamic threshold policy. We prove that this large market benefit continue to hold when using fixed price PPMs, and show that the guarantee of 0.632 actually improves to 0.712. We then move to study the case of selling k identical units and we prove that the revenue gap of the fixed price PPM approaches $1-1/\\sqrt{ 2kπ}$. As this bound is achievable without the large market assumption, we obtain the somewhat surprising result that the large market advantage vanishes as $k$ grows.", "published": "2021-08-10T08:18:20Z", "authors": ["José Correa", "Dana Pizarro", "Victor Verdugo"], "categories": ["math.OC"], "doi": "", "_query": "hedonic house price", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2208.07217v1", "title": "Comparison of Forecasting Methods of House Electricity Consumption for Honda Smart Home", "abstract": "The electricity consumption of buildings composes a major part of the city's energy consumption. Electricity consumption forecasting enables the development of home energy management systems resulting in the future design of more sustainable houses and a decrease in total energy consumption. Energy performance in buildings is influenced by many factors like ambient temperature, humidity, and a variety of electrical devices. Therefore, multivariate prediction methods are preferred rather than univariate. The Honda Smart Home US data set was selected to compare three methods for minimizing forecasting errors, MAE and RMSE: Artificial Neural Networks, Support Vector Regression, and Fuzzy Rule-Based Systems for Regression by constructing many models for each method on a multivariate data set in different time terms. The comparison shows that SVR is a superior method over the alternatives.", "published": "2022-08-11T19:04:41Z", "authors": ["Farshad Ahmadi Asl", "Mehmet Bodur"], "categories": ["cs.LG", "cs.AI"], "doi": "10.5121/csit.2022.121311", "_query": "hedonic house price", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2512.14662v3", "title": "Fixed-Income Pricing and the Replication of Liabilities", "abstract": "This paper develops a model-free framework for static fixed-income pricing and the replication of liability cash flows. The absence of static arbitrage across a universe of fixed-income instruments is equivalent to the existence of a strictly positive discount curve reproducing all observed prices. Linear programming duality then identifies the least-cost super-replication price with the largest value that any admissible discount curve assigns to the liability, so that the resulting bounds are attained and cannot be improved. Complementary slackness confines over-replication to dates that the optimal discount vector prices at zero, and a least-cost portfolio matches the liability exactly at no fewer dates than the rank of the cash-flow matrix. We also obtain generic uniqueness of that portfolio, an interpolation between quadratic hedging and super-replication, and a static treatment of swap--repo strategies. On US Treasury cross-sections the observed prices violate the law of one price, so that a discount curve must be estimated rather than bootstrapped; the least-cost portfolio then matches an annuity liability at almost every cash-flow date.", "published": "2025-12-16T18:23:16Z", "authors": ["Damir Filipović"], "categories": ["q-fin.MF"], "doi": "", "_query": "hedonic house price", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2511.00039v1", "title": "Graph-Attentive MAPPO for Dynamic Retail Pricing", "abstract": "Dynamic pricing in retail requires policies that adapt to shifting demand while coordinating decisions across related products. We present a systematic empirical study of multi-agent reinforcement learning for retail price optimization, comparing a strong MAPPO baseline with a graph-attention-augmented variant (MAPPO+GAT) that leverages learned interactions among products. Using a simulated pricing environment derived from real transaction data, we evaluate profit, stability across random seeds, fairness across products, and training efficiency under a standardized evaluation protocol. The results indicate that MAPPO provides a robust and reproducible foundation for portfolio-level price control, and that MAPPO+GAT further enhances performance by sharing information over the product graph without inducing excessive price volatility. These results indicate that graph-integrated MARL provides a more scalable and stable solution than independent learners for dynamic retail pricing, offering practical advantages in multi-product decision-making.", "published": "2025-10-28T00:15:59Z", "authors": ["Krishna Kumar Neelakanta Pillai Santha Kumari Amma"], "categories": ["cs.AI", "cs.LG"], "doi": "", "_query": "hedonic house price", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/1810.04697v4", "title": "Prices, Profits, Proxies, and Production", "abstract": "This paper studies nonparametric identification and counterfactual bounds for heterogeneous firms that can be ranked in terms of productivity. Our approach works when quantities and prices are latent, rendering standard approaches inapplicable. Instead, we require observation of profits or other optimizing-values such as costs or revenues, and either prices or price proxies of flexibly chosen variables. We extend classical duality results for price-taking firms to a setup with discrete heterogeneity, endogeneity, and limited variation in possibly latent prices. Finally, we show that convergence results for nonparametric estimators may be directly converted to convergence results for production sets.", "published": "2018-10-10T18:15:29Z", "authors": ["Victor H. Aguiar", "Nail Kashaev", "Roy Allen"], "categories": ["econ.EM"], "doi": "", "_query": "hedonic house price", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/1703.10806v2", "title": "Probabilistic Mid- and Long-Term Electricity Price Forecasting", "abstract": "The liberalization of electricity markets and the development of renewable energy sources has led to new challenges for decision makers. These challenges are accompanied by an increasing uncertainty about future electricity price movements. The increasing amount of papers, which aim to model and predict electricity prices for a short period of time provided new opportunities for market participants. However, the electricity price literature seem to be very scarce on the issue of medium- to long-term price forecasting, which is mandatory for investment and political decisions. Our paper closes this gap by introducing a new approach to simulate electricity prices with hourly resolution for several months up to three years. Considering the uncertainty of future events we are able to provide probabilistic forecasts which are able to detect probabilities for price spikes even in the long-run. As market we decided to use the EPEX day-ahead electricity market for Germany and Austria. Our model extends the X-Model which mainly utilizes the sale and purchase curve for electricity day-ahead auctions. By applying our procedure we are able to give probabilities for the due to the EEG practical relevant event of six consecutive hours of negative prices. We find that using the supply and demand curve based model in the long-run yields realistic patterns for the time series of electricity prices and leads to promising results considering common error measures.", "published": "2017-03-31T09:13:08Z", "authors": ["Florian Ziel", "Rick Steinert"], "categories": ["stat.AP", "q-fin.ST"], "doi": "10.1016/j.rser.2018.05.038", "_query": "hedonic house price", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2602.10483v1", "title": "Pricing Query Complexity of Multiplicative Revenue Approximation", "abstract": "We study the pricing query complexity of revenue maximization for a single buyer whose private valuation is drawn from an unknown distribution. In this setting, the seller must learn the optimal monopoly price by posting prices and observing only binary purchase decisions, rather than the realized valuations. Prior work has established tight query complexity bounds for learning a near-optimal price with additive error $\\varepsilon$ when the valuation distribution is supported on $[0,1]$. However, our understanding of how to learn a near-optimal price that achieves at least a $(1-\\varepsilon)$ fraction of the optimal revenue remains limited. In this paper, we study the pricing query complexity of the single-buyer revenue maximization problem under such multiplicative error guarantees in several settings. Observe that when pricing queries are the only source of information about the buyer's distribution, no algorithm can achieve a non-trivial approximation, since the scale of the distribution cannot be learned from pricing queries alone. Motivated by this fundamental impossibility, we consider two natural and well-motivated models that provide \"scale hints\": (i) a one-sample hint, in which the algorithm observes a single realized valuation before making pricing queries; and (ii) a value-range hint, in which the valuation support is known to lie within $[1, H]$. For each type of hint, we establish pricing query complexity guarantees that are tight up to polylogarithmic factors for several classes of distributions, including monotone hazard rate (MHR) distributions, regular distributions, and general distributions.", "published": "2026-02-11T03:42:42Z", "authors": ["Wei Tang", "Yifan Wang", "Mengxiao Zhang"], "categories": ["cs.GT", "cs.LG"], "doi": "", "_query": "hedonic house price", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2201.02729v1", "title": "Bitcoin Price Predictive Modeling Using Expert Correction", "abstract": "The paper studies the linear model for Bitcoin price which includes regression features based on Bitcoin currency statistics, mining processes, Google search trends, Wikipedia pages visits. The pattern of deviation of regression model prediction from real prices is simpler comparing to price time series. It is assumed that this pattern can be predicted by an experienced expert. In such a way, using the combination of the regression model and expert correction, one can receive better results than with either regression model or expert opinion only. It is shown that Bayesian approach makes it possible to utilize the probabilistic approach using distributions with fat tails and take into account the outliers in Bitcoin price time series.", "published": "2022-01-06T15:11:51Z", "authors": ["Bohdan M. Pavlyshenko"], "categories": ["q-fin.ST", "cs.LG"], "doi": "", "_query": "hedonic house price", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/1808.05996v1", "title": "$k$th price auctions and Catalan numbers", "abstract": "This paper establishes an interesting link between $k$th price auctions and Catalan numbers by showing that for distributions that have linear density, the bid function at any symmetric, increasing equilibrium of a $k$th price auction with $k\\geq 3$ can be represented as a finite series of $k-2$ terms whose $\\ell$th term involves the $\\ell$th Catalan number. Using an integral representation of Catalan numbers, together with some classical combinatorial identities, we derive the closed form of the unique symmetric, increasing equilibrium of a $k$th price auction for a non-uniform distribution.", "published": "2018-08-17T20:59:41Z", "authors": ["Abdel-Hameed Nawar", "Debapriya Sen"], "categories": ["econ.TH"], "doi": "10.1016/j.econlet.2018.08.018", "_query": "hedonic house price", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/1807.10537v1", "title": "Investigating Wheat Price with a Multi-Agent Model", "abstract": "In this paper, we build a computational model for the analysis of international wheat spot price formation, its dynamics and the dynamics of internationally exchanged quantities. The model has been calibrated using FAOSTAT data to evaluate its in-sample predictive power. The model is able to generate wheat prices in twelve international markets and wheat used quantities in twenty-four world regions. The time span considered goes from 1992 to 2013. In our study, a particular attention was paid to the impact of Russian Federation's 2010 grain export ban on wheat price and internationally traded quantities. Among other results, we find that wheat average weighted world price in 2013 would have been 3.55\\% lower than the observed one if the Russian Federation would not have imposed the export ban in 2010.", "published": "2018-07-27T11:18:19Z", "authors": ["Gianfranco Giulioni", "Edmondo Di Giuseppe", "Massimiliano Pasqui", "Piero Toscano", "Francesco Miglietta"], "categories": ["econ.TH"], "doi": "", "_query": "hedonic house price", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2301.05677v2", "title": "Price impact in equity auctions: zero, then linear", "abstract": "Using high-quality data, we report several statistical regularities of equity auctions in the Paris stock exchange. First, the average order book density is linear around the auction price at the time of auction clearing and has a large peak at the auction price. While the peak is due to slow traders, the order density shape is the result of subtle dynamics. The impact of a new market order or cancellation at the auction time can be decomposed into three parts as a function of the size of the additional order: (1) zero impact, caused by the discrete nature of prices, sometimes up to a surprisingly large additional volume relative to the auction volume (2) linear impact for additional orders up to a large fraction of the auction volume (3) for even larger orders price impact is non-linear, frequently super-linear.", "published": "2023-01-13T17:56:12Z", "authors": ["Mohammed Salek", "Damien Challet", "Ioane Muni Toke"], "categories": ["q-fin.ST", "q-fin.TR"], "doi": "", "_query": "hedonic house price", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/cond-mat/0106657v1", "title": "Quantifying Stock Price Response to Demand Fluctuations", "abstract": "We address the question of how stock prices respond to changes in demand. We quantify the relations between price change $G$ over a time interval $Δt$ and two different measures of demand fluctuations: (a) $Φ$, defined as the difference between the number of buyer-initiated and seller-initiated trades, and (b) $Ω$, defined as the difference in number of shares traded in buyer and seller initiated trades. We find that the conditional expectations $<G >_Ω$ and $<G >_Φ$ of price change for a given $Ω$ or $Φ$ are both concave. We find that large price fluctuations occur when demand is very small --- a fact which is reminiscent of large fluctuations that occur at critical points in spin systems, where the divergent nature of the response function leads to large fluctuations.", "published": "2001-06-29T22:51:37Z", "authors": ["Vasiliki Plerou", "Parameswaran Gopikrishnan", "Xavier Gabaix", "H. Eugene Stanley"], "categories": ["cond-mat.stat-mech", "cond-mat.dis-nn", "q-fin.TR"], "doi": "10.1103/PhysRevE.66.027104", "_query": "hedonic house price", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2101.01024v2", "title": "Model-free price bounds under dynamic option trading", "abstract": "In this paper we extend discrete time semi-static trading strategies by also allowing for dynamic trading in a finite amount of options, and we study the consequences for the model-independent super-replication prices of exotic derivatives. These include duality results as well as a precise characterization of pricing rules for the dynamically tradable options triggering an improvement of the price bounds for exotic derivatives in comparison with the conventional price bounds obtained through the martingale optimal transport approach.", "published": "2021-01-04T15:24:15Z", "authors": ["Ariel Neufeld", "Julian Sester"], "categories": ["q-fin.MF", "math.PR", "q-fin.CP"], "doi": "", "_query": "hedonic house price", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/cond-mat/0109280v1", "title": "Market application of the percolation model: Relative price distribution", "abstract": "We study a variant of the Cont-Bouchaud model which utilizes the perco lation approach of multi-agent simulations of the stock market fluctuations. Here, instead of considering the relative price change as the difference of the total demand and total supply, we consider the relative price change to be proportiona l to the ``relative'' difference of demand and supply (the ratio of the difference in total demand and total supply to the sum of the total demand and total supply). We then study the probability distribution of the price changes.", "published": "2001-09-15T17:52:05Z", "authors": ["Anirban Chakraborti"], "categories": ["cond-mat.stat-mech"], "doi": "10.1142/S0129183102002900", "_query": "hedonic house price", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2004.00473v1", "title": "Dimensional Pricing Based on Dynamism of Load", "abstract": "Electricity supply is not simply a matter of quantity, but a time lasting service that matches with a wave-like load curve. It logically deserves a pricing based on the curve per se rather than simply integral of load. This paper introduces to price electricity consumption based on dynamism of load, which is an equivalent characterization of load. Orthonormal basis with definable and distinguishable periodicity that can linearly express load curves constitutes a space to capture the dynamism, where coefficients of the basis quantify the dynamism in multiple dimensions. A price function is proposed to map the coefficients to a numerical charge. A pricing model on the space specialized by the Fourier series is derived for the simplest one-source-one-subscriber system and generalized to a single-bus system with multiple sources and multiple subscribers. Examples will demonstrate the use of the proposed pricing and its effectiveness in reflecting the cost of generation to cope with load dynamism and guaranteeing fairness.", "published": "2020-04-01T14:42:40Z", "authors": ["Jinghuan Ma", "Jie Gu", "Zhijian Jin"], "categories": ["eess.SY"], "doi": "", "_query": "hedonic house price", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2312.15999v1", "title": "Pricing with Contextual Elasticity and Heteroscedastic Valuation", "abstract": "We study an online contextual dynamic pricing problem, where customers decide whether to purchase a product based on its features and price. We introduce a novel approach to modeling a customer's expected demand by incorporating feature-based price elasticity, which can be equivalently represented as a valuation with heteroscedastic noise. To solve the problem, we propose a computationally efficient algorithm called \"Pricing with Perturbation (PwP)\", which enjoys an $O(\\sqrt{dT\\log T})$ regret while allowing arbitrary adversarial input context sequences. We also prove a matching lower bound at $Ω(\\sqrt{dT})$ to show the optimality regarding $d$ and $T$ (up to $\\log T$ factors). Our results shed light on the relationship between contextual elasticity and heteroscedastic valuation, providing insights for effective and practical pricing strategies.", "published": "2023-12-26T11:07:37Z", "authors": ["Jianyu Xu", "Yu-Xiang Wang"], "categories": ["cs.LG", "econ.EM", "stat.ML"], "doi": "", "_query": "hedonic house price", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2302.12319v1", "title": "Age and market capitalization drive large price variations of cryptocurrencies", "abstract": "Cryptocurrencies are considered the latest innovation in finance with considerable impact across social, technological, and economic dimensions. This new class of financial assets has also motivated a myriad of scientific investigations focused on understanding their statistical properties, such as the distribution of price returns. However, research so far has only considered Bitcoin or at most a few cryptocurrencies, whilst ignoring that price returns might depend on cryptocurrency age or be influenced by market capitalization. Here, we therefore present a comprehensive investigation of large price variations for more than seven thousand digital currencies and explore whether price returns change with the coming-of-age and growth of the cryptocurrency market. We find that tail distributions of price returns follow power-law functions over the entire history of the considered cryptocurrency portfolio, with typical exponents implying the absence of characteristic scales for price variations in about half of them. Moreover, these tail distributions are asymmetric as positive returns more often display smaller exponents, indicating that large positive price variations are more likely than negative ones. Our results further reveal that changes in the tail exponents are very often simultaneously related to cryptocurrency age and market capitalization or only to age, with only a minority of cryptoassets being affected just by market capitalization or neither of the two quantities. Lastly, we find that the trends in power-law exponents usually point to mixed directions, and that large price variations are likely to become less frequent only in about 28\\% of the cryptocurrencies as they age and grow in market capitalization.", "published": "2023-02-23T20:31:11Z", "authors": ["Arthur A. B. Pessa", "Matjaz Perc", "Haroldo V. Ribeiro"], "categories": ["physics.soc-ph", "q-fin.ST"], "doi": "10.1038/s41598-023-30431-3", "_query": "hedonic house price", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2201.07903v1", "title": "Identification of Direct Socio-Geographical Price Discrimination: An Empirical Study on iPhones", "abstract": "Price discrimination is a practice where firms utilize varying sensitivities to prices among consumers to increase profits. The welfare effects of price discrimination are not agreed on among economists, but identification of such actions may contribute to our standing of firms' pricing behaviors. In this letter, I use econometric tools to analyze whether Apple Inc, one of the largest companies in the globe, is practicing price discrimination on the basis of socio-economical and geographical factors. My results indicate that iPhones are significantly (p $<$ 0.01) more expensive in markets where competitions are weak or where Apple has a strong market presence. Furthermore, iPhone prices are likely to increase (p $<$ 0.01) in developing countries/regions or markets with high income inequality.", "published": "2022-01-19T23:01:17Z", "authors": ["Davidson Cheng"], "categories": ["econ.EM"], "doi": "", "_query": "hedonic house price", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2607.09956v1", "title": "Optimizing ARDL Models for Retail Sales Forecasting and Fair Pricing", "abstract": "Pricing food products to balance profitability with consumer welfare is a central challenge for retailers. Dynamic pricing is widely used to maximize revenue, yet most pricing models optimize business objectives while overlooking consumer fairness. This paper studies the risk of consumer exploitation under dynamic food pricing in Canada and proposes a methodology that embeds fairness constraints directly into retail sales forecasting. We model total retail trade sales with a log--log Autoregressive Distributed Lag (ARDL) specification, in which the coefficient on a product price is a sales elasticity, and pose the pricing problem as maximizing forecast sales subject to price bounds anchored to the Consumer Price Index (CPI). We solve this problem with both Linear Programming (LP) and Simulated Annealing (SA), under single-product and multi-product configurations. A key finding is that the fitted nominal elasticities are positive. As a result, an unconstrained sales-maximizer would push every price to its upper bound, and the CPI ceiling is the safeguard that prevents this. Simulated Annealing instead settles on conservative, interior prices that lower consumer cost while still meeting the sales target. We benchmark forecast accuracy against naive, seasonal-naive, ARIMA, and SARIMA baselines, and a CPI-deflated re-specification shows that the positive nominal elasticities are largely an inflation-driven artifact. The result is a transparent, fairness-aware pricing framework.", "published": "2026-07-10T20:19:38Z", "authors": ["Sujay Uday Rittikar"], "categories": ["cs.LG", "cs.CE"], "doi": "", "_query": "hedonic house price", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2409.14157v1", "title": "Price predictability in limit order book with deep learning model", "abstract": "This study explores the prediction of high-frequency price changes using deep learning models. Although state-of-the-art methods perform well, their complexity impedes the understanding of successful predictions. We found that an inadequately defined target price process may render predictions meaningless by incorporating past information. The commonly used three-class problem in asset price prediction can generally be divided into volatility and directional prediction. When relying solely on the price process, directional prediction performance is not substantial. However, volume imbalance improves directional prediction performance.", "published": "2024-09-21T14:40:13Z", "authors": ["Kyungsub Lee"], "categories": ["q-fin.ST", "q-fin.TR", "stat.ML"], "doi": "", "_query": "hedonic house price", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2011.03653v2", "title": "No-regret Learning in Price Competitions under Consumer Reference Effects", "abstract": "We study long-run market stability for repeated price competitions between two firms, where consumer demand depends on firms' posted prices and consumers' price expectations called reference prices. Consumers' reference prices vary over time according to a memory-based dynamic, which is a weighted average of all historical prices. We focus on the setting where firms are not aware of demand functions and how reference prices are formed but have access to an oracle that provides a measure of consumers' responsiveness to the current posted prices. We show that if the firms run no-regret algorithms, in particular, online mirror descent(OMD), with decreasing step sizes, the market stabilizes in the sense that firms' prices and reference prices converge to a stable Nash Equilibrium (SNE). Interestingly, we also show that there exist constant step sizesunder which the market stabilizes. We further characterize the rate of convergence to the SNE for both decreasing and constant OMD step sizes.", "published": "2020-11-07T01:38:20Z", "authors": ["Negin Golrezaei", "Patrick Jaillet", "Jason Cheuk Nam Liang"], "categories": ["cs.GT"], "doi": "", "_query": "hedonic house price", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2610.09985v1", "title": "Marrying Pricing and Advertising with LLMs", "abstract": "We study a sequential pricing problem in which a seller jointly posts a price and an advertisement generated by a large language model (LLM). The seller aims to maximize revenue under an unknown product demand that depends on both decisions, while observing only whether each offer leads to a purchase. We propose an online actor-critic algorithm that combines low-rank adaptation (LoRA) of a pretrained LLM with a demand model fitted to available data. At each round, the actor generates an advertisement, and the critic estimates purchase probabilities to guide price selection. Then, the resulting feedback is used to update both the actor and the critic, with the critic's revenue estimates providing a baseline for policy gradient updates of the actor. To evaluate our approach, we develop an evaluation framework with three synthetic demand models and a demand simulator built from real-world marketplace data. Finally, we compare our algorithm with benchmarks that do not jointly optimize price selection and advertisement generation, achieving expected revenue gains over the reference policy of 5.69%, 5.18% and 55.96% under the three synthetic demand models and 5.81% under the marketplace simulator.", "published": "2026-10-07T12:50:08Z", "authors": ["Alessandro Barro", "Francesco Bacchiocchi", "Francesco Emanuele Stradi", "Alberto Marchesi"], "categories": ["cs.GT", "cs.LG"], "doi": "", "_query": "hedonic house price", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2109.13905v1", "title": "Intra-Day Price Simulation with Generative Adversarial Modelling of the Order Flow", "abstract": "Intra-day price variations in financial markets are driven by the sequence of orders, called the order flow, that is submitted at high frequency by traders. This paper introduces a novel application of the Sequence Generative Adversarial Networks framework to model the order flow, such that random sequences of the order flow can then be generated to simulate the intra-day variation of prices. As a benchmark, a well-known parametric model from the quantitative finance literature is selected. The models are fitted, and then multiple random paths of the order flow sequences are sampled from each model. Model performances are then evaluated by using the generated sequences to simulate price variations, and we compare the empirical regularities between the price variations produced by the generated and real sequences. The empirical regularities considered include the distribution of the price log-returns, the price volatility, and the heavy-tail of the log-returns distributions. The results show that the order sequences from the generative model are better able to reproduce the statistical behaviour of real price variations than the sequences from the benchmark.", "published": "2021-09-28T17:55:09Z", "authors": ["Ye-Sheen Lim", "Denise Gorse"], "categories": ["q-fin.ST", "cs.LG", "q-fin.TR"], "doi": "", "_query": "hedonic house price", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2002.09549v3", "title": "Optimal Signal-Adaptive Trading with Temporary and Transient Price Impact", "abstract": "We study optimal liquidation in the presence of linear temporary and transient price impact along with taking into account a general price predicting finite-variation signal. We formulate this problem as minimization of a cost-risk functional over a class of absolutely continuous and signal-adaptive strategies. The stochastic control problem is solved by following a probabilistic and convex analytic approach. We show that the optimal trading strategy is given by a system of four coupled forward-backward SDEs, which can be solved explicitly. Our results reveal how the induced transient price distortion provides together with the predictive signal an additional predictor about future price changes. As a consequence, the optimal signal-adaptive trading rate trades off exploiting the predictive signal against incurring the transient displacement of the execution price from its unaffected level. This answers an open question from Lehalle and Neuman [29] as we show how to derive the unique optimal signal-adaptive liquidation strategy when price impact is not only temporary but also transient.", "published": "2020-02-21T20:56:08Z", "authors": ["Eyal Neuman", "Moritz Voß"], "categories": ["q-fin.TR", "math.PR"], "doi": "", "_query": "hedonic house price", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2308.08549v1", "title": "Effects of Daily News Sentiment on Stock Price Forecasting", "abstract": "Predicting future prices of a stock is an arduous task to perform. However, incorporating additional elements can significantly improve our predictions, rather than relying solely on a stock's historical price data to forecast its future price. Studies have demonstrated that investor sentiment, which is impacted by daily news about the company, can have a significant impact on stock price swings. There are numerous sources from which we can get this information, but they are cluttered with a lot of noise, making it difficult to accurately extract the sentiments from them. Hence the focus of our research is to design an efficient system to capture the sentiments from the news about the NITY50 stocks and investigate how much the financial news sentiment of these stocks are affecting their prices over a period of time. This paper presents a robust data collection and preprocessing framework to create a news database for a timeline of around 3.7 years, consisting of almost half a million news articles. We also capture the stock price information for this timeline and create multiple time series data, that include the sentiment scores from various sections of the article, calculated using different sentiment libraries. Based on this, we fit several LSTM models to forecast the stock prices, with and without using the sentiment scores as features and compare their performances.", "published": "2023-08-02T06:42:39Z", "authors": ["S. Srinivas", "R. Gadela", "R. Sabu", "A. Das", "G. Nath", "V. Datla"], "categories": ["q-fin.ST", "cs.LG"], "doi": "", "_query": "hedonic house price", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/1505.02416v2", "title": "Portfolio optimisation beyond semimartingales: shadow prices and fractional Brownian motion", "abstract": "While absence of arbitrage in frictionless financial markets requires price processes to be semimartingales, non-semimartingales can be used to model prices in an arbitrage-free way, if proportional transaction costs are taken into account. In this paper, we show, for a class of price processes which are not necessarily semimartingales, the existence of an optimal trading strategy for utility maximisation under transaction costs by establishing the existence of a so-called shadow price. This is a semimartingale price process, taking values in the bid ask spread, such that frictionless trading for that price process leads to the same optimal strategy and utility as the original problem under transaction costs. Our results combine arguments from convex duality with the stickiness condition introduced by P. Guasoni. They apply in particular to exponential utility and geometric fractional Brownian motion. In this case, the shadow price is an Ito process. As a consequence we obtain a rather surprising result on the pathwise behaviour of fractional Brownian motion: the trajectories may touch an Ito process in a one-sided manner without reflection.", "published": "2015-05-10T18:04:48Z", "authors": ["Christoph Czichowsky", "Walter Schachermayer"], "categories": ["q-fin.MF", "q-fin.PM"], "doi": "", "_query": "hedonic house price", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/1106.5706v2", "title": "Theory of Information Pricing", "abstract": "In financial markets valuable information is rarely circulated homogeneously, because of time required for information to spread. However, advances in communication technology means that the 'lifetime' of important information is typically short. Hence, viewed as a tradable asset, information shares the characteristics of a perishable commodity: while it can be stored and transmitted freely, its worth diminishes rapidly in time. In view of recent developments where internet search engines and other information providers are offering information to financial institutions, the problem of pricing information is becoming increasingly important. With this in mind, a new formulation of utility-indifference argument is introduced and used as a basis for pricing information. Specifically, we regard information as a quantity that converts a prior distribution into a posterior distribution. The amount of information can then be quantified by relative entropy. The key to our utility indifference argument is to equate the maximised a posterior utility, after paying certain cost for the information, with the a posterior expectation of the utility based on the a priori optimal strategy. This formulation leads to one price for a given quantity of upside information; and another price for a given quantity of downside information. The ideas are illustrated by means of simple examples.", "published": "2011-06-28T15:20:27Z", "authors": ["Dorje C. Brody", "Yan Tai Law"], "categories": ["q-fin.PR"], "doi": "", "_query": "hedonic house price", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2311.17011v4", "title": "Pricing and hedging for a sticky diffusion", "abstract": "We introduce a financial market model featuring a risky asset whose price follows a sticky geometric Brownian motion and a riskless asset that grows with a constant interest rate $r\\in \\mathbb R $. We prove that this model satisfies No Arbitrage (NA) and No Free Lunch with Vanishing Risk (NFLVR) only when $r=0 $. Under this condition, we derive the corresponding arbitrage-free pricing equation, assess replicability and representation of the replication strategy. We then show that all locally bounded replicable payoffs for the standard Black--Scholes model are also replicable for the sticky model. Last, we evaluate via numerical experiments the impact of hedging in discrete time and of misrepresenting price stickiness.", "published": "2023-11-28T18:06:25Z", "authors": ["Alexis Anagnostakis"], "categories": ["q-fin.MF", "math.PR"], "doi": "10.1017/jpr.2025.2", "_query": "hedonic house price", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2608.29366v1", "title": "Strike Price Optimization for ISO New England's Day-Ahead Ancillary Services", "abstract": "ISO New England's (ISO-NE) Day-Ahead Ancillary Services Initiative settles reserve products as financial call options on real-time energy prices. The system-wide strike price creates an efficiency-reliability tradeoff: increasing it lowers competitive reserve offers, but weakens resources' incentives to incur preparation costs and remain available for real-time performance. The existing strike price rule does not explicitly account for heterogeneous resource incentives and reserve requirements. We develop an optimization framework that selects the highest strike price while ensuring that enough resources retain an incentive to prepare and collectively satisfy the reserve requirements. Because a resource's preparation decision may affect the resulting real-time price distribution, its incentive depends on an unobservable counterfactual. To address this, we derive a tight lower-bound certificate using only the available conditional price distribution and a bound on the resource's price impact. We show that each resource enters the optimization through a single incentive threshold and that any finite optimal strike price occurs at one of these thresholds. This yields a tractable exact solution method based on threshold calculations and a small number of linear feasibility checks. Using reconstructed ISO-NE conditional price distributions and representative gas-fired resources, we find that higher heat-rate combustion turbines are more likely than combined-cycle resources to constrain the strike price choice.", "published": "2026-08-29T16:58:03Z", "authors": ["Karl Zhu", "Parviz Alivand", "Jinye Zhao", "Tongxin Zheng", "Dimitris Bertsimas"], "categories": ["math.OC"], "doi": "", "_query": "hedonic house price", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2208.04985v2", "title": "Pricing Novel Goods", "abstract": "We study a bilateral trade problem where a principal has private information that is revealed with delay, such as a seller who does not yet know her production cost. Postponing the contracting process incurs a costly delay, while early contracting with limited information can create incentive issues, as the principal might misrepresent private information that will be revealed later. We show that the optimal mechanism can effectively address these challenges by leveraging the sequential nature of the problem. The optimal mechanism is a menu of two-part tariffs, where the variable part is determined by the principal's incentives and the fixed part by the agent's incentives. As two-part tariffs might be impractical in some applications, we also study price mechanisms. We show that the optimal price mechanism often entails trade at both the ex-ante and ex-post stages. Dynamic price mechanisms can lower the cost of delay by transacting with high-type agents early and relax the incentive constraints by postponing contracts with lower-type agents. We also generalize our analysis to costly learning and study ex-post efficiency in our context.", "published": "2022-08-09T18:26:14Z", "authors": ["Francesco Giovannoni", "Toomas Hinnosaar"], "categories": ["econ.TH"], "doi": "", "_query": "hedonic house price", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2107.07725v2", "title": "Learning to Price against a Budget and ROI Constrained Buyer", "abstract": "Internet advertisers (buyers) repeatedly procure ad impressions from ad platforms (sellers) with the aim to maximize total conversion (i.e. ad value) while respecting both budget and return-on-investment (ROI) constraints for efficient utilization of limited monetary resources. Facing such a constrained buyer who aims to learn her optimal strategy to acquire impressions, we study from a seller's perspective how to learn and price ad impressions through repeated posted price mechanisms to maximize revenue. For this two-sided learning setup, we propose a learning algorithm for the seller that utilizes an episodic binary-search procedure to identify a revenue-optimal selling price. We show that such a simple learning algorithm enjoys low seller regret when within each episode, the budget and ROI constrained buyer approximately best responds to the posted price. We present simple yet natural buyer's bidding algorithms under which the buyer approximately best responds while satisfying budget and ROI constraints, leading to a low regret for our proposed seller pricing algorithm. The design of our seller algorithm is motivated by the fact that the seller's revenue function admits a bell-shaped structure when the buyer best responds to prices under budget and ROI constraints, enabling our seller algorithm to identify revenue-optimal selling prices efficiently.", "published": "2021-07-16T06:50:52Z", "authors": ["Negin Golrezaei", "Patrick Jaillet", "Jason Cheuk Nam Liang", "Vahab Mirrokni"], "categories": ["cs.GT"], "doi": "", "_query": "hedonic house price", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/1007.1631v1", "title": "Price dynamics in financial markets: a kinetic approach", "abstract": "The use of kinetic modelling based on partial differential equations for the dynamics of stock price formation in financial markets is briefly reviewed. The importance of behavioral aspects in market booms and crashes and the role of agents' heterogeneity in emerging power laws for price distributions is emphasized and discussed.", "published": "2010-07-09T17:29:50Z", "authors": ["Dario Maldarella", "Lorenzo Pareschi"], "categories": ["q-fin.TR", "cond-mat.stat-mech", "q-fin.ST"], "doi": "", "_query": "hedonic house price", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2007.07990v4", "title": "Static pricing for multi-unit prophet inequalities", "abstract": "We study a pricing problem where a seller has $k$ identical copies of a product, buyers arrive sequentially, and the seller prices the items aiming to maximize social welfare. When $k=1$, this is the so called \"prophet inequality\" problem for which there is a simple pricing scheme achieving a competitive ratio of $1/2$. On the other end of the spectrum, as $k$ goes to infinity, the asymptotic performance of both static and adaptive pricing is well understood. We provide a static pricing scheme for the small-supply regime: where $k$ is small but larger than $1$. Prior to our work, the best competitive ratio known for this setting was the $1/2$ that follows from the single-unit prophet inequality. Our pricing scheme is easy to describe as well as practical -- it is anonymous, non-adaptive, and order-oblivious. We pick a single price that equalizes the expected fraction of items sold and the probability that the supply does not sell out before all customers are served; this price is then offered to each customer while supply lasts. This extends an approach introduced by Samuel-Cahn for the case of $k=1$. This pricing scheme achieves a competitive ratio that increases gradually with the supply. Subsequent work by Jiang, Ma, and Zhang shows that our pricing scheme is the optimal static pricing for every value of $k$.", "published": "2020-07-15T20:57:29Z", "authors": ["Shuchi Chawla", "Nikhil Devanur", "Thodoris Lykouris"], "categories": ["cs.GT", "cs.DS"], "doi": "", "_query": "hedonic house price", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/1603.07610v1", "title": "Going Out of Business: Auction House Behavior in the Massively Multi-Player Online Game", "abstract": "The in-game economies of massively multi-player online games (MMOGs) are complex systems that have to be carefully designed and managed. This paper presents the results of an analysis of auction house data from the MMOG Glitch, across a 14 month time period, the entire lifetime of the game. The data comprise almost 3 million data points, over 20,000 unique players and more than 650 products. Furthermore, an interactive visualization, based on Sankey flow diagrams, is presented which shows the proportion of the different clusters across each time bin, as well as the flow of players between clusters. The diagram allows evaluation of migration of players between clusters as a function of time, as well as churn analysis. The presented work provides a template analysis and visualization model for progression-based or temporal-based analysis of player behavior broadly applicable to games.", "published": "2016-03-24T15:00:35Z", "authors": ["Anders Drachen", "Joseph Riley", "Shawna Baskin", "Diego Klabjan"], "categories": ["cs.CY", "cs.HC", "stat.ML"], "doi": "", "_query": "hedonic house price", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/0708.4178v1", "title": "Relationship between degree of efficiency and prediction in stock price changes", "abstract": "This study investigates empirically whether the degree of stock market efficiency is related to the prediction power of future price change using the indices of twenty seven stock markets. Efficiency refers to weak-form efficient market hypothesis (EMH) in terms of the information of past price changes. The prediction power corresponds to the hit-rate, which is the rate of the consistency between the direction of actual price change and that of predicted one, calculated by the nearest neighbor prediction method (NN method) using the out-of-sample. In this manuscript, the Hurst exponent and the approximate entropy (ApEn) are used as the quantitative measurements of the degree of efficiency. The relationship between the Hurst exponent, reflecting the various time correlation property, and the ApEn value, reflecting the randomness in the time series, shows negative correlation. However, the average prediction power on the direction of future price change has the strongly positive correlation with the Hurst exponent, and the negative correlation with the ApEn. Therefore, the market index with less market efficiency has higher prediction power for future price change than one with higher market efficiency when we analyze the market using the past price change pattern. Furthermore, we show that the Hurst exponent, a measurement of the long-term memory property, provides more significant information in terms of prediction of future price changes than the ApEn and the NN method.", "published": "2007-08-30T15:31:16Z", "authors": ["Cheoljun Eom", "Gabjin Oh", "Woo-Sung Jung"], "categories": ["q-fin.ST", "physics.soc-ph"], "doi": "10.1016/j.physa.2008.05.059", "_query": "hedonic house price", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2011.09717v1", "title": "Topological Price of Anarchy Bounds for Clustering Games on Networks", "abstract": "We consider clustering games in which the players are embedded in a network and want to coordinate (or anti-coordinate) their strategy with their neighbors. The goal of a player is to choose a strategy that maximizes her utility given the strategies of her neighbors. Recent studies show that even very basic variants of these games exhibit a large Price of Anarchy: A large inefficiency between the total utility generated in centralized outcomes and equilibrium outcomes in which players selfishly try to maximize their utility. Our main goal is to understand how structural properties of the network topology impact the inefficiency of these games. We derive topological bounds on the Price of Anarchy for different classes of clustering games. These topological bounds provide a more informative assessment of the inefficiency of these games than the corresponding (worst-case) Price of Anarchy bounds. As one of our main results, we derive (tight) bounds on the Price of Anarchy for clustering games on Erdős-Rényi random graphs (where every possible edge in the network is present with a fixed probability), which, depending on the graph density, stand in stark contrast to the known Price of Anarchy bounds.", "published": "2020-11-19T08:43:06Z", "authors": ["Pieter Kleer", "Guido Schäfer"], "categories": ["cs.GT"], "doi": "10.1007/978-3-030-35389-6_18", "_query": "hedonic house price", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2401.01526v1", "title": "An arbitrage driven price dynamics of Automated Market Makers in the presence of fees", "abstract": "We present a model for price dynamics in the Automated Market Makers (AMM) setting. Within this framework, we propose a reference market price following a geometric Brownian motion. The AMM price is constrained by upper and lower bounds, determined by constant multiplications of the reference price. Through the utilization of local times and excursion-theoretic approaches, we derive several analytical results, including its time-changed representation and limiting behavior.", "published": "2024-01-03T03:40:51Z", "authors": ["Joseph Najnudel", "Shen-Ning Tung", "Kazutoshi Yamazaki", "Ju-Yi Yen"], "categories": ["q-fin.MF"], "doi": "", "_query": "hedonic house price", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2102.02593v1", "title": "The housing problem and revealed preference theory: duality and an application", "abstract": "This paper exhibits a duality between the theory of Revealed Preference of Afriat and the housing allocation problem of Shapley and Scarf. In particular, it is shown that Afriat's theorem can be interpreted as a second welfare theorem in the housing problem. Using this duality, the revealed preference problem is connected to an optimal assignment problem, and a geometrical characterization of the rationalizability of experiment data is given. This allows in turn to give new indices of rationalizability of the data, and to define weaker notions of rationalizability, in the spirit of Afriat's efficiency index.", "published": "2021-02-04T13:09:50Z", "authors": ["Ivar Ekeland", "Alfred Galichon"], "categories": ["econ.GN"], "doi": "10.1007/s00199-012-0719-x", "_query": "hedonic house price", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2202.13874v1", "title": "Time Series Analysis of Blockchain-Based Cryptocurrency Price Changes", "abstract": "In this paper we apply neural networks and Artificial Intelligence (AI) to historical records of high-risk cryptocurrency coins to train a prediction model that guesses their price. This paper's code contains Jupyter notebooks, one of which outputs a timeseries graph of any cryptocurrency price once a CSV file of the historical data is inputted into the program. Another Jupyter notebook trains an LSTM, or a long short-term memory model, to predict a cryptocurrency's closing price. The LSTM is fed the close price, which is the price that the currency has at the end of the day, so it can learn from those values. The notebook creates two sets: a training set and a test set to assess the accuracy of the results. The data is then normalized using manual min-max scaling so that the model does not experience any bias; this also enhances the performance of the model. Then, the model is trained using three layers -- an LSTM, dropout, and dense layer-minimizing the loss through 50 epochs of training; from this training, a recurrent neural network (RNN) is produced and fitted to the training set. Additionally, a graph of the loss over each epoch is produced, with the loss minimizing over time. Finally, the notebook plots a line graph of the actual currency price in red and the predicted price in blue. The process is then repeated for several more cryptocurrencies to compare prediction models. The parameters for the LSTM, such as number of epochs and batch size, are tweaked to try and minimize the root mean square error.", "published": "2022-02-19T04:28:07Z", "authors": ["Jacques Fleischer", "Gregor von Laszewski", "Carlos Theran", "Yohn Jairo Parra Bautista"], "categories": ["cs.LG", "cs.DC"], "doi": "", "_query": "hedonic house price", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/1001.3570v2", "title": "Security Pricing with Information-Sensitive Discounting", "abstract": "In this paper incomplete-information models are developed for the pricing of securities in a stochastic interest rate setting. In particular we consider credit-risky assets that may include random recovery upon default. The market filtration is generated by a collection of information processes associated with economic factors, on which interest rates depend, and information processes associated with market factors used to model the cash flows of the securities. We use information-sensitive pricing kernels to give rise to stochastic interest rates. Semi-analytical expressions for the price of credit-risky bonds are derived, and a number of recovery models are constructed which take into account the perceived state of the economy at the time of default. The price of European-style call bond options is deduced, and it is shown how examples of hybrid securities, like inflation-linked credit-risky bonds, can be valued. Finally, a cumulative information process is employed to develop pricing kernels that respond to the amount of aggregate debt of an economy.", "published": "2010-01-20T12:46:59Z", "authors": ["Andrea Macrina", "Priyanka A. Parbhoo"], "categories": ["q-fin.PR"], "doi": "", "_query": "hedonic house price", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2105.11376v2", "title": "Can we imitate the principal investor's behavior to learn option price?", "abstract": "This paper presents a framework of imitating the principal investor's behavior for optimal pricing and hedging options. We construct a non-deterministic Markov decision process for modeling stock price change driven by the principal investor's decision making. However, low signal-to-noise ratio and instability that are inherent in equity markets pose challenges to determine the state transition (stock price change) after executing an action (the principal investor's decision) as well as decide an action based on current state (spot price). In order to conquer these challenges, we resort to a Bayesian deep neural network for computing the predictive distribution of the state transition led by an action. Additionally, instead of exploring a state-action relationship to formulate a policy, we seek for an episode based visible-hidden state-action relationship to probabilistically imitate the principal investor's successive decision making. Unlike conventional option pricing that employs analytical stochastic processes or utilizes time series analysis to model and sample underlying stock price movements, our algorithm simulates stock price paths by imitating the principal investor's behavior which requires no preset probability distribution and fewer predetermined parameters. Eventually the optimal option price is learned by reinforcement learning to maximize the cumulative risk-adjusted return of a dynamically hedged portfolio over simulated price paths.", "published": "2021-05-24T16:08:58Z", "authors": ["Xin Jin"], "categories": ["q-fin.PR", "cs.LG"], "doi": "", "_query": "hedonic house price", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/1910.01860v1", "title": "Online repeated posted price auctions with a demand side platform", "abstract": "We consider an online ad network problem in which an ad exchange auctions ad slots and intermediaries called demand side platforms (DSPs) buy these ad slots for their clients (advertisers). An intermediary represents multiple advertisers. Different types of ad slots are auctioned by the ad exchange, e.g., video ad, banner ad etc. We study repeated posted price auctions for homogeneous and heterogeneous items when there is an intermediary. In a posted price auction, the auctioneer sets a fixed reserve price. The buyer can accept the price and win the ad slot or reject the price. We analyze the system from the auctioneer's perspective and show that the optimal reserve price is dynamic for heterogeneous items. We also investigate system from intermediary's perspective and devise algorithms for scheduling advertisers. Often the advertisers have budget constraints and impression constraints. We formulate a revenue optimization problem at the intermediary and also consider the problem of scheduling advertisers with budget and impression constraints. Finally, we present a numerical study for the single seller and advertiser model which considers various valuation distributions such as uniform, exponential and lognormal.", "published": "2019-10-04T10:33:55Z", "authors": ["Rahul Meshram", "Kesav Kaza"], "categories": ["cs.GT", "eess.SY"], "doi": "", "_query": "hedonic house price", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/1703.00608v1", "title": "Impact of Optimal Storage Allocation on Price Volatility in Electricity Markets", "abstract": "Recent studies show that the fast growing expansion of wind power generation may lead to extremely high levels of price volatility in wholesale electricity markets. Storage technologies, regardless of their specific forms e.g. pump-storage hydro, large-scale or distributed batteries, are capable of alleviating the extreme price volatility levels due to their energy usage time shifting, fast-ramping and price arbitrage capabilities. In this paper, we propose a stochastic bi-level optimization model to find the optimal nodal storage capacities required to achieve a certain price volatility level in a highly volatile electricity market. The decision on storage capacities is made in the upper level problem and the operation of strategic/regulated generation, storage and transmission players is modeled at the lower level problem using an extended Cournot-based stochastic game. The South Australia (SA) electricity market, which has recently experienced high levels of price volatility, is considered as the case study for the proposed storage allocation framework. Our numerical results indicate that 80% price volatility reduction in SA electricity market can be achieved by installing either 340 MWh regulated storage or 420 MWh strategic storage. In other words, regulated storage firms are more efficient in reducing the price volatility than strategic storage firms.", "published": "2017-03-02T04:03:45Z", "authors": ["Amin Masoumzadeh", "Ehsan Nekouei", "Tansu Alpcan", "Deb Chattopadhyay"], "categories": ["math.OC"], "doi": "10.1109/TPWRS.2017.2727075", "_query": "hedonic house price", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2204.03300v1", "title": "Approximating Nash Equilibrium for Production Control with Sticky Price", "abstract": "We study a mean field game problem arising from the production control for multiple firms with price stickiness in the commodity market. The price dynamics for each firm is described as a (controlled) jump-diffusion process with mean-field interaction. Each firm aims to maximize her expectation of cumulative net profit coupled with each other through price processes. By solving the limiting control problem and a fixed-point problem, we construct an explicit approximating Nash equilibrium when the number of firms grows large.", "published": "2022-04-07T09:03:39Z", "authors": ["Chunmei Jiang", "Tongqing Li", "Jie Yu"], "categories": ["math.OC"], "doi": "", "_query": "hedonic house price", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/1012.2160v1", "title": "Insider Trading in the Market with Rational Expected Price", "abstract": "Kyle (1985) builds a pioneering and influential model, in which an insider with long-lived private information submits an optimal order in each period given the market maker's pricing rule. An inconsistency exists to some extent in the sense that the ``constant pricing rule \" actually assumes an adaptive expected price with pricing rule given before insider making the decision, and the ``market efficiency\" condition, however, assumes a rational expected price and implies that the pricing rule can be influenced by insider's strategy. We loosen the ``constant pricing rule \" assumption by taking into account sufficiently the insider's strategy has on pricing rule. According to the characteristic of the conditional expectation of the informed profits, three different models vary with insider's attitudes regarding to risk are presented. Compared to Kyle (1985), the risk-averse insider in Model 1 can obtain larger guaranteed profits, the risk-neutral insider in Model 2 can obtain a larger ex ante expectation of total profits across all periods and the risk-seeking insider in Model 3 can obtain larger risky profits. Moreover, the limit behaviors of the three models when trading frequency approaches infinity are given, showing that Model 1 acquires a strong-form efficiency, Model 2 acquires the Kyle's (1985) continuous equilibrium, and Model 3 acquires an equilibrium with information released at an increasing speed.", "published": "2010-12-10T02:29:09Z", "authors": ["Fuzhou Gong", "Deqing Zhou"], "categories": ["q-fin.TR"], "doi": "", "_query": "hedonic house price", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/1804.03178v4", "title": "Power of Bonus in Pricing for Crowdsourcing", "abstract": "We consider a simple form of pricing for a crowdsourcing system, where pricing policy is published a priori, and workers then decide their task acceptance. Such a pricing form is widely adopted in practice for its simplicity, e.g., Amazon Mechanical Turk, although additional sophistication to pricing rule can enhance budget efficiency. With the goal of designing efficient and simple pricing rules, we study the impact of the following two design features in pricing policies: (i) personalization tailoring policy worker-by-worker and (ii) bonus payment to qualified task completion. In the Bayesian setting, where the only prior distribution of workers' profiles is available, we first study the Price of Agnosticism (PoA) that quantifies the utility gap between personalized and common pricing policies. We show that PoA is bounded within a constant factor under some mild conditions, and the impact of bonus is essential in common pricing. These analytic results imply that complex personalized pricing can be replaced by simple common pricing once it is equipped with a proper bonus payment. To provide insights on efficient common pricing, we then study the efficient mechanisms of bonus payment for several profile distribution regimes which may exist in practice. We provide primitive experiments on Amazon Mechanical Turk, which support our analytical findings.", "published": "2018-04-09T18:41:33Z", "authors": ["Suho Shin", "Hoyong Choi", "Yung Yi", "Jungseul Ok"], "categories": ["cs.GT"], "doi": "", "_query": "hedonic house price", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2101.10151v1", "title": "Pricing Energy Storage in Real-time Market", "abstract": "The problem of pricing utility-scale energy storage resources (ESRs) in the real-time electricity market is considered. Under a rolling-window dispatch model where the operator centrally dispatches generation and consumption under forecasting uncertainty, it is shown that almost all uniform pricing schemes, including the standard locational marginal pricing (LMP), result in lost opportunity costs that require out-of-the-market settlements. It is also shown that such settlements give rise to disincentives for generating firms and storage participants to bid truthfully, even when these market participants are rational price-takers in a competitive market. Temporal locational marginal pricing (TLMP) is proposed for ESRs as a generalization of LMP to an in-market discriminative form. TLMP is a sum of the system-wide energy price, LMP, and the individual state-of-charge price. It is shown that, under arbitrary forecasting errors, the rolling-window implementation of TLMP eliminates the lost opportunity costs and provides incentives to price-taking firms to bid truthfully with their marginal costs. Numerical examples show insights into the effects of uniform and non-uniform pricing mechanisms on dispatch following and truthful bidding incentives.", "published": "2021-01-25T15:02:15Z", "authors": ["Cong Chen", "Lang Tong", "Ye Guo"], "categories": ["eess.SY", "econ.GN"], "doi": "", "_query": "hedonic house price", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2411.10791v1", "title": "Optimal Fixed-Price Mechanism with Signaling", "abstract": "Consider a trade market with one seller and multiple buyers. The seller aims to sell an indivisible item and maximize their revenue. This paper focuses on a simple and popular mechanism--the fixed-price mechanism. Unlike the standard setting, we assume there is information asymmetry between buyers and the seller. Specifically, we allow the seller to design information before setting the fixed price, which implies that we study the mechanism design problem in a broader space. We call this mechanism space the fixed-price signaling mechanism. We assume that buyers' valuation of the item depends on the quality of the item. The seller can privately observe the item's quality, whereas buyers only see its distribution. In this case, the seller can influence buyers' valuations by strategically disclosing information about the item's quality, thereby adjusting the fixed price. We consider two types of buyers with different levels of rationality: ex-post individual rational (IR) and ex-interim individual rational. We show that when the market has only one buyer, the optimal revenue generated by the fixed-price signaling mechanism is identical to that of the fixed-price mechanism, regardless of the level of rationality. Furthermore, when there are multiple buyers in the market and all of them are ex-post IR, we show that there is no fixed-price mechanism that is obedient for all buyers. However, if all buyers are ex-interim IR, we show that the optimal fixed-price signaling mechanism will generate more revenue for the seller than the fixed-price mechanism.", "published": "2024-11-16T12:42:16Z", "authors": ["Zhikang Fan", "Weiran Shen"], "categories": ["cs.GT"], "doi": "", "_query": "hedonic house price", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/cond-mat/0401055v2", "title": "Large price changes on small scales", "abstract": "In this study we examine the evolution of price, volume, and the bid-ask spread after extreme 15 minute intraday price changes on the NYSE and the NASDAQ. We find that due to strong behavioral trading there is an overreaction. Furthermore we find that volatility which increases sharply at the event decays according to a power law with an exponent of approximately 0.4, i.e., much faster than the autocorrelation function of volatility.", "published": "2004-01-06T09:33:45Z", "authors": ["A. G. Zawadowski", "J. Kertesz", "G. Andor"], "categories": ["cond-mat.stat-mech", "q-fin.ST"], "doi": "10.1016/j.physa.2004.06.121", "_query": "hedonic house price", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2102.05751v6", "title": "Price Discrimination in International Airline Markets", "abstract": "We develop a model of inter-temporal and intra-temporal price discrimination by monopoly airlines to study the ability of different discriminatory pricing mechanisms to increase efficiency and the associated distributional implications. To estimate the model, we use unique data from international airline markets with flight-level variation in prices across time, cabins, and markets and information on passengers' reasons for travel and time of purchase. The current pricing practice yields approximately 77% of the first-best welfare. The source of this inefficiency arises primarily from private information about passenger valuations, not dynamic uncertainty about demand. We also find that if airlines could discriminate between business and leisure passengers, total welfare would improve at the expense of business passenger surplus. Also, replacing the current pricing that involves screening passengers across cabin classes with offering a single cabin class has minimal effect on total welfare.", "published": "2021-02-10T21:49:55Z", "authors": ["Gaurab Aryal", "Charles Murry", "Jonathan W. Williams"], "categories": ["econ.GN"], "doi": "", "_query": "hedonic house price", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2310.15687v1", "title": "Reducing residential emissions: carbon pricing vs. subsidizing retrofits", "abstract": "In this paper, we compare different mitigation policies when housing investments are irreversible. We use a general equilibrium model with non-homothetic preferences and an elaborate setup of the residential housing and energy production sector. In the first-best transition, the energy demand plays only a secondary role. However, this changes when optimal carbon taxes are not available. While providing subsidies for retrofits results in the lowest direct costs for households, it ultimately leads to the highest aggregate costs and proves to be an ineffective way to decarbonize the economy. In the second-best context, a phased-in carbon price outperforms the subsidy-based transition.", "published": "2023-10-24T09:59:53Z", "authors": ["Alkis Blanz", "Beatriz Gaitan"], "categories": ["econ.GN"], "doi": "", "_query": "hedonic house price", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/math/0404447v1", "title": "Indifference pricing and hedging in stochastic volatility models", "abstract": "We apply the concepts of utility based pricing and hedging of derivatives in stochastic volatility markets and introduce a new class of \"reciprocal affine\" models for which the indifference price and optimal hedge portfolio for pure volatility claims are efficiently computable. We obtain a general formula for the market price of volatility risk in these models and calculate it explicitly for the case of an exponential utility.", "published": "2004-04-24T19:01:28Z", "authors": ["M. R. Grasselli", "T. R. Hurd"], "categories": ["math.PR", "math.OC", "q-fin.PR"], "doi": "", "_query": "hedonic house price", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2307.12695v3", "title": "Propagation of a carbon price in a credit portfolio through macroeconomic factors", "abstract": "We study how the climate transition through a low-carbon economy, implemented by carbon pricing, propagates in a credit portfolio and precisely describe how carbon price dynamics affects credit risk measures such as probability of default, expected and unexpected losses. We adapt a stochastic multisectoral model to take into account the greenhouse gases (GHG) emissions costs of both sectoral firms' production and consumption, as well as sectoral household's consumption. GHG emissions costs are the product of carbon prices, provided by the NGFS transition scenarios, and of GHG emissions. For each sector, our model yields the sensitivity of firms' production and households' consumption to carbon price and the relationships between sectors. It allows us to analyze the short-term effects of the carbon price as opposed to standard IAM (such as REMIND), which are deterministic and only capture long-term trends. Finally, we use a DCF methodology to compute firms' values which we then combine with a structural credit risk model to describe how the carbon price impacts credit risk measures. We obtain that the carbon price distorts the distribution of the firm's value, increases banking fees charged to clients (materialized by the bank provisions), and reduces banks' profitability (translated by the economic capital). In addition, the randomness we introduce provides extra flexibility to take into account uncertainties on the productivity and on the different transition scenarios. We also compute the sensitivities of the credit risk measures with respect to changes in the carbon price, yielding further criteria for a more accurate assessment of climate transition risk in a credit portfolio. This work provides a preliminary methodology to calculate the evolution of credit risk measures of a credit portfolio, starting from a given climate transition scenario described by a carbon price.", "published": "2023-07-24T11:21:06Z", "authors": ["Géraldine Bouveret", "Jean-François Chassagneux", "Smail Ibbou", "Antoine Jacquier", "Lionel Sopgoui"], "categories": ["q-fin.RM", "econ.GN", "q-fin.MF"], "doi": "", "_query": "hedonic house price", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/1508.05347v3", "title": "Pricing Queries Approximately Optimally", "abstract": "Data as a commodity has always been purchased and sold. Recently, web services that are data marketplaces have emerged that match data buyers with data sellers. So far there are no guidelines how to price queries against a database. We consider the recently proposed query-based pricing framework of Koutris et al and ask the question of computing optimal input prices in this framework by formulating a buyer utility model. We establish the interesting and deep equivalence between arbitrage-freeness in the query-pricing framework and envy-freeness in pricing theory for appropriately chosen buyer valuations. Given the approximation hardness results from envy-free pricing we then develop logarithmic approximation pricing algorithms exploiting the max flow interpretation of the arbitrage-free pricing for the restricted query language proposed by Koutris et al. We propose a novel polynomial-time logarithmic approximation pricing scheme and show that our new scheme performs better than the existing envy-free pricing algorithms instance-by-instance. We also present a faster pricing algorithm that is always greater than the existing solutions, but worse than our previous scheme. We experimentally show how our pricing algorithms perform with respect to the existing envy-free pricing algorithms and to the optimal exponentially computable solution, and our experiments show that our approximation algorithms consistently arrive at about 99% of the optimal.", "published": "2015-08-21T18:19:28Z", "authors": ["Vasilis Syrgkanis", "Johannes Gehrke"], "categories": ["cs.GT", "cs.DB"], "doi": "", "_query": "hedonic house price", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/cond-mat/9807397v2", "title": "Option Pricing Model for Incomplete Market", "abstract": "The problem of determining the European-style option price in the incomplete market has been examined within the framework of stochastic optimization. An analytic method based on the discrete dynamic programming equation (Bellman equation) has been developed that gives the general formalism for determining the option price and the optimal trading strategy (optimal control policy) that reduces total risk inherent in writing the option. The basic purpose of paper is to present an effective algorithm that can be used in practice. Keywords: option pricing, incomplete market, transaction costs, stochastic optimization, Bellman equation.", "published": "1998-07-30T10:00:14Z", "authors": ["Sergei Fedotov", "Sergei Mikhailov"], "categories": ["cond-mat.stat-mech", "q-fin.PR"], "doi": "", "_query": "hedonic house price", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/1003.4307v1", "title": "Bottleneck Routing Games with Low Price of Anarchy", "abstract": "We study {\\em bottleneck routing games} where the social cost is determined by the worst congestion on any edge in the network. In the literature, bottleneck games assume player utility costs determined by the worst congested edge in their paths. However, the Nash equilibria of such games are inefficient since the price of anarchy can be very high and proportional to the size of the network. In order to obtain smaller price of anarchy we introduce {\\em exponential bottleneck games} where the utility costs of the players are exponential functions of their congestions. We find that exponential bottleneck games are very efficient and give a poly-log bound on the price of anarchy: $O(\\log L \\cdot \\log |E|)$, where $L$ is the largest path length in the players' strategy sets and $E$ is the set of edges in the graph. By adjusting the exponential utility costs with a logarithm we obtain games whose player costs are almost identical to those in regular bottleneck games, and at the same time have the good price of anarchy of exponential games.", "published": "2010-03-22T23:31:55Z", "authors": ["Rajgopal Kannan", "Costas Busch"], "categories": ["cs.GT"], "doi": "10.1007/978-3-642-16170-4_20", "_query": "hedonic house price", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2001.11275v1", "title": "The Impact of Oil and Gold Prices Shock on Tehran Stock Exchange: A Copula Approach", "abstract": "There are several researches that deal with the behavior of SEs and their relationships with different economical factors. These range from papers dealing with this subject through econometrical procedures to statistical methods known as copula. This article considers the impact of oil and gold price on Tehran Stock Exchange market (TSE). Oil and gold are two factors that are essential for the economy of Iran and their price are determined in the global market. The model used in this study is ARIMA-Copula. We used data from January 1998 to January 2011 as training data to find the appropriate model. The cross validation of model is measured by data from January 2011 to June 2011. We conclude that: (i) there is no significant direct relationship between gold price and the TSE index, but the TSE is indirectly influenced by gold price through other factors such as oil; and (ii) the TSE is not independent of the volatility in oil price and Clayton copula can describe such dependence structure between TSE and the oil price. Based on the property of Clayton copula, which has lower tail dependency, as the oil price drops, stock index falls. This means that decrease in oil price has an adverse effect on Iranian economy.", "published": "2020-01-30T11:59:50Z", "authors": ["Amir T. Payandeh Najafabadi", "Marjan Qazvini", "Reza Ofoghi"], "categories": ["q-fin.ST", "stat.AP"], "doi": "10.22099/ijes.2014.1560", "_query": "hedonic house price", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2203.12657v1", "title": "A Case Study on Nutek India Limited, Regarding Deep Fall in Share Price", "abstract": "Manipulating the security price is an act of artificially inflating or deflating the price of a security. Generally, manipulation is defined as a series of transactions designed to raise or lower a price of a security or to give the appearance of trading for the purpose of inducing others to buy or sell. In essence, a manipulation is intentional interference with the free forces of supply and demand. In this paper we have tried to study the reasons behind drastic fall in share price of Nutek India Limited.", "published": "2022-02-06T10:45:34Z", "authors": ["Gurjeet Singh", "Pankaj Nagar"], "categories": ["q-fin.GN"], "doi": "", "_query": "hedonic house price", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/1003.2692v1", "title": "Modeling share prices of banks and bankrupts", "abstract": "Share prices of financial companies from the S&P 500 list have been modeled by a linear function of consumer price indices in the USA. The Johansen and Engle-Granger tests for cointegration both demonstrated the presence of an equilibrium long-term relation between observed and predicted time series. Econometrically, the pricing concept is valid. For several companies, share prices are defined only by CPI readings in the past. Therefore, our empirical pricing model is a deterministic one. For a few companies, including Lehman Brothers, AIG, Freddie Mac and Fannie Mae, negative share prices could be foreseen in May-September 2008. One might interpret the negative share prices as a sign of approaching bankruptcies.", "published": "2010-03-13T08:52:47Z", "authors": ["Ivan O. Kitov"], "categories": ["q-fin.ST"], "doi": "", "_query": "hedonic house price", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/physics/0210025v1", "title": "Pricing European Options in Realistic Markets", "abstract": "We investigate the relation between the fair price for European-style vanilla options and the distribution of short-term returns on the underlying asset ignoring transaction and other costs. We compute the risk-neutral probability density conditional on the total variance of the asset's returns when the option expires. If the asset's future price has finite expectation, the option's fair value satisfies a parabolic partial differential equation of the Black-Scholes type in which the variance of the asset's returns rather than a trading time is the evolution parameter. By immunizing the portfolio against large-scale price fluctuations of the asset, the valuation of options is extended to the realistic case\\cite{St99} of assets whose short-term returns have finite variance but very large, or even infinite, higher moments. A dynamic Delta-hedged portfolio that is statically insured against exceptionally large fluctuations includes at least two different options on the asset. The fair value of an option in this case is determined by a universal drift function that is common to all options on the asset. This drift is interpreted as the premium for an investment exposed to risk due to exceptionally large variations of the asset's price. It affects the option valuation like an effective cost-of-carry for the underlying in the Black-Scholes world would. The derived pricing formula for options in realistic markets is arbitrage free by construction. A simple model with constant drift qualitatively reproduces the often observed volatility -skew and -term structure.", "published": "2002-10-06T13:29:19Z", "authors": ["Martin Schaden"], "categories": ["physics.soc-ph", "physics.data-an", "q-fin.PR"], "doi": "", "_query": "hedonic house price", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/1907.05926v1", "title": "On the Price of Anarchy of Cost-Sharing in Real-Time Scheduling Systems", "abstract": "We study cost-sharing games in real-time scheduling systems where the activation cost of the server at any given time is a function of its load. We focus on monomial cost functions and consider both the case when the degree is less than one (inducing positive externalities for the jobs) and when it is greater than one (inducing negative externalities for the jobs). For the former case, we provide tight price of anarchy bounds which show that the price of anarchy grows to infinity as a polynomial of the number of jobs in the game. For the latter, we observe that existing results provide constant and tight (asymptotically in the degree of the monomial) bounds on the price of anarchy. We then switch our attention to improving the price of anarchy by means of a simple coordination mechanism that has no knowledge of the instance. We show that our mechanism reduces the price of anarchy of games with $n$ jobs and unit activation costs from $Θ(\\sqrt{n})$ to $2$. We also show that for a restricted class of instances a similar improvement is achieved for monomial activation costs. This is not the case, however, for unrestricted instances of monomial costs for which we prove that the price of anarchy remains super-constant for our mechanism.", "published": "2019-07-12T19:29:42Z", "authors": ["Eirini Georgoulaki", "Kostas Kollias"], "categories": ["cs.GT"], "doi": "", "_query": "hedonic house price", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/1110.5197v1", "title": "Memory effects in stock price dynamics: evidences of technical trading", "abstract": "Technical trading represents a class of investment strategies for Financial Markets based on the analysis of trends and recurrent patterns of price time series. According standard economical theories these strategies should not be used because they cannot be profitable. On the contrary it is well-known that technical traders exist and operate on different time scales. In this paper we investigate if technical trading produces detectable signals in price time series and if some kind of memory effect is introduced in the price dynamics. In particular we focus on a specific figure called supports and resistances. We first develop a criterion to detect the potential values of supports and resistances. As a second step, we show that memory effects in the price dynamics are associated to these selected values. In fact we show that prices more likely re-bounce than cross these values. Such an effect is a quantitative evidence of the so-called self-fulfilling prophecy that is the self-reinforcement of agents' belief and sentiment about future stock prices' behavior.", "published": "2011-10-24T11:02:17Z", "authors": ["Federico Garzarelli", "Matthieu Cristelli", "Andrea Zaccaria", "Luciano Pietronero"], "categories": ["q-fin.ST", "q-fin.TR"], "doi": "", "_query": "hedonic house price", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2211.14806v2", "title": "Efficient Demand Response Location Targeting for Price Spike Mitigation by Exploiting Price-demand Relationship", "abstract": "Demand response (DR) leverages demand-side flexibility, offering a promising approach to enhance market conditions like mitigating wholesale price spikes. However, poorly chosen DR locations can inadvertently increase electricity prices. For that, we introduce a method to rigorously select DR locations and corresponding demand reductions. We formulate a bilevel program where the upper level determines the DR locations and demand reductions while ensuring the average nodal prices meet a predetermined target. The lower level tackles an economic dispatch (ED) problem and feeds the resulting nodal prices back to the upper level based on post-DR demands. This bilevel formulation presents challenges due to the lower-level non-convexity affecting the upper-level constraints on average nodal prices. To address this, we propose to replace the lower level with a piecewise linear function representing the price-demand relationship, solving iteratively for each linear segment. This results in a tractable mixed-integer linear program. An acceleration strategy is proposed to further reduce the computation time. Numerical studies demonstrate the ability of the proposed approach to reduce prices to a desired level. Besides, we empirically show that the proposed approach is robust against inaccurate system parameters and can reduce computation time by over 50%.", "published": "2022-11-27T12:10:37Z", "authors": ["Yufan Zhang", "Honglin Wen", "Tao Feng", "Yize Chen"], "categories": ["eess.SY"], "doi": "", "_query": "hedonic house price", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/1609.09066v1", "title": "Refugee Resettlement Housing Scout", "abstract": "According to the United States High Commission for Refugees (UNHCr), there are 65.3 million forcibly displaced people in the world today, 21.5 million of them being refugees. This has led to an unprecedented refugee crisis which has led countries to accept refugee families and to resettle them. Diverse agencies are helping refugees coming to US to resettle and start their new life in the country. One of the first and most challenging steps of this process is to find affordable housing that also meets a suite of additional constraints and priorities. These include being within a mile of public transportation and near schools, faith centers and international grocery stores. We detail an interactive data-driven web-based tool, which incorporates in one consolidated platform most of the needed information. The tool searches, filters and demonstrates a list of possible housing locations, and allows for the dynamic prioritization based on user-specified importance weights on the diverse criteria. The platform was created in a partnership with New American Pathways, a nonprofit that supports refugee resettlement in the metro Atlanta, but exemplifies a methodology that can help many other organizations with similar goals.", "published": "2016-09-28T04:48:20Z", "authors": ["Unaiza Ahsan", "Oleksandra Sopova", "Wes Stayton", "Bistra Dilkina"], "categories": ["cs.CY"], "doi": "", "_query": "hedonic house price", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2209.08778v1", "title": "Price Formation in Field Prediction Markets: the Wisdom in the Crowd", "abstract": "Prediction markets are a popular, prominent, and successful structure for a collective intelligence platform. However the exact mechanism by which information known to the participating traders is incorporated into the market price is unknown. Kyle (1985) detailed a model for price formation in continuous auctions with information distributed heterogeneously amongst market participants. This paper demonstrates a novel method derived from the Kyle model applied to data from a field experiment prediction market. The method is able to identify traders whose trades have price impact that adds a significant amount of information to the market price. Traders who are not identified as informed in aggregate have price impact consistent with noise trading. Results are reproduced on other prediction market datasets. Ultimately the results provide strong evidence in favor of the Kyle model in a field market setting, and highlight an under-discussed advantage of prediction markets over alternative group forecasting mechanisms: that the operator of the market does not need to have information on the distribution of information amongst participating traders.", "published": "2022-09-19T06:04:49Z", "authors": ["Frederik Bossaerts", "Nitin Yadav", "Peter Bossaerts", "Chad Nash", "Torquil Todd", "Torsten Rudolf", "Rowena Hutchins", "Anne-Louise Ponsonby", "Karl Mattingly"], "categories": ["q-fin.PR", "q-fin.TR"], "doi": "", "_query": "hedonic house price", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2108.08229v1", "title": "Tilted Platforms: Rental Housing Technology and the Rise of Urban Big Data Oligopolies", "abstract": "This article interprets emerging scholarship on rental housing platforms -- particularly the most well-known and used short- and long-term rental housing platforms - and considers how the technological processes connecting both short-term and long-term rentals to the platform economy are transforming cities. It discusses potential policy approaches to more equitably distribute benefits and mitigate harms. We argue that information technology is not value-neutral. While rental housing platforms may empower data analysts and certain market participants, the same cannot be said for all users or society at large. First, user-generated online data frequently reproduce the systematic biases found in traditional sources of housing information. Evidence is growing that the information broadcasting potential of rental housing platforms may increase rather than mitigate sociospatial inequality. Second, technology platforms curate and shape information according to their creators' own financial and political interests. The question of which data -- and people -- are hidden or marginalized on these platforms is just as important as the question of which data are available. Finally, important differences in benefits and drawbacks exist between short-term and long-term rental housing platforms, but are underexplored in the literature: this article unpacks these differences and proposes policy recommendations.", "published": "2021-08-18T16:36:51Z", "authors": ["Geoff Boeing", "Max Besbris", "David Wachsmuth", "Jake Wegmann"], "categories": ["econ.GN", "cs.CY"], "doi": "10.1186/s42854-021-00024-2", "_query": "hedonic house price", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2303.17014v1", "title": "Option pricing using a skew random walk pricing tree", "abstract": "Motivated by the Corns-Satchell, continuous time, option pricing model, we develop a binary tree pricing model with underlying asset price dynamics following Itô-Mckean skew Brownian motion. While the Corns-Satchell market model is incomplete, our discrete time market model is defined in the natural world; extended to the risk neutral world under the no-arbitrage condition where derivatives are priced under uniquely determined risk-neutral probabilities; and is complete. The skewness introduced in the natural world is preserved in the risk neutral world. Furthermore, we show that the model preserves skewness under the continuous-time limit. We provide numerical applications of our model to the valuation of European put and call options on exchange-traded funds tracking the S&P Global 1200 index.", "published": "2023-03-29T20:41:11Z", "authors": ["Yuan Hu", "W. Brent Lindquist", "Svetlozar T. Rachev", "Frank J. Fabozzi"], "categories": ["q-fin.MF"], "doi": "", "_query": "hedonic house price", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/1211.1696v1", "title": "On the Economic Value and Price-Responsiveness of Ramp-Constrained Storage", "abstract": "The primary concerns of this paper are twofold: to understand the economic value of storage in the presence of ramp constraints and exogenous electricity prices, and to understand the implications of the associated optimal storage management policy on qualitative and quantitative characteristics of storage response to real-time prices. We present an analytic characterization of the optimal policy, along with the associated finite-horizon time-averaged value of storage. We also derive an analytical upperbound on the infinite-horizon time-averaged value of storage. This bound is valid for any achievable realization of prices when the support of the distribution is fixed, and highlights the dependence of the value of storage on ramp constraints and storage capacity. While the value of storage is a non-decreasing function of price volatility, due to the finite ramp rate, the value of storage saturates quickly as the capacity increases, regardless of volatility. To study the implications of the optimal policy, we first present computational experiments that suggest that optimal utilization of storage can, in expectation, induce a considerable amount of price elasticity near the average price, but little or no elasticity far from it. We then present a computational framework for understanding the behavior of storage as a function of price and the amount of stored energy, and for characterization of the buy/sell phase transition region in the price-state plane. Finally, we study the impact of market-based operation of storage on the required reserves, and show that the reserves may need to be expanded to accommodate market-based storage.", "published": "2012-11-07T21:34:59Z", "authors": ["Ali Faghih", "Mardavij Roozbehani", "Munther A. Dahleh"], "categories": ["math.OC", "math.DS"], "doi": "", "_query": "hedonic house price", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/1503.00019v2", "title": "Error analysis in Fourier methods for option pricing", "abstract": "We provide a bound for the error committed when using a Fourier method to price European options when the underlying follows an exponential \\levy dynamic. The price of the option is described by a partial integro-differential equation (PIDE). Applying a Fourier transformation to the PIDE yields an ordinary differential equation that can be solved analytically in terms of the characteristic exponent of the \\levy process. Then, a numerical inverse Fourier transform allows us to obtain the option price. We present a novel bound for the error and use this bound to set the parameters for the numerical method. We analyse the properties of the bound for a dissipative and pure-jump example. The bound presented is independent of the asymptotic behaviour of option prices at extreme asset prices. The error bound can be decomposed into a product of terms resulting from the dynamics and the option payoff, respectively. The analysis is supplemented by numerical examples that demonstrate results comparable to and superior to the existing literature.", "published": "2015-02-27T21:44:31Z", "authors": ["Fabián Crocce", "Juho Häppölä", "Jonas Kiessling", "Raúl Tempone"], "categories": ["q-fin.PR"], "doi": "", "_query": "hedonic house price", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/1906.10888v1", "title": "European Option Pricing of electricity under exponential functional of Lévy processes with Price-Cap principle", "abstract": "We propose a new model for electricity pricing based on the price cap principle. The particularity of the model is that the asset price is an exponential functional of a jump Lévy process. This model can capture both mean reversion and jumps which are observed in electricity market. It is shown that the value of an European option of this asset is the unique viscosity solution of a partial integro-differential equation (PIDE). A numerical approximation of this solution by the finite differences method is provided. The consistency, stability and convergence results of the scheme are given. Numerical simulations are performed under a smooth initial condition.", "published": "2019-06-26T07:45:28Z", "authors": ["Martin Kegnenlezom", "Patrice Takam Soh", "Antoine-Marie Bogso", "Yves Emvudu Wono"], "categories": ["q-fin.PR", "math.PR"], "doi": "", "_query": "hedonic house price", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/1912.04774v2", "title": "Voluntary Disclosure and Personalized Pricing", "abstract": "Central to privacy concerns is that firms may use consumer data to price discriminate. A common policy response is that consumers should be given control over which firms access their data and how. Since firms learn about a consumer's preferences based on the data seen and the consumer's disclosure choices, the equilibrium implications of consumer control are unclear. We study whether such measures improve consumer welfare in monopolistic and competitive markets. We find that consumer control can improve consumer welfare relative to both perfect price discrimination and no personalized pricing. First, consumers can use disclosure to amplify competitive forces. Second, consumers can disclose information to induce even a monopolist to lower prices. Whether consumer control improves welfare depends on the disclosure technology and market competitiveness. Simple disclosure technologies suffice in competitive markets. When facing a monopolist, a consumer needs partial disclosure possibilities to obtain any welfare gains.", "published": "2019-12-10T15:41:10Z", "authors": ["S. Nageeb Ali", "Greg Lewis", "Shoshana Vasserman"], "categories": ["econ.TH"], "doi": "", "_query": "hedonic house price", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/1204.4611v1", "title": "Applications of the Likelihood Theory in Finance: Modelling and Pricing", "abstract": "This paper discusses the connection between mathematical finance and statistical modelling which turns out to be more than a formal mathematical correspondence. We like to figure out how common results and notions in statistics and their meaning can be translated to the world of mathematical finance and vice versa. A lot of similarities can be expressed in terms of LeCam's theory for statistical experiments which is the theory of the behaviour of likelihood processes. For positive prices the arbitrage free financial assets fit into filtered experiments. It is shown that they are given by filtered likelihood ratio processes. From the statistical point of view, martingale measures, completeness and pricing formulas are revisited. The pricing formulas for various options are connected with the power functions of tests. For instance the Black-Scholes price of a European option has an interpretation as Bayes risk of a Neyman Pearson test. Under contiguity the convergence of financial experiments and option prices are obtained. In particular, the approximation of Ito type price processes by discrete models and the convergence of associated option prices is studied. The result relies on the central limit theorem for statistical experiments, which is well known in statistics in connection with local asymptotic normal (LAN) families. As application certain continuous time option prices can be approximated by related discrete time pricing formulas.", "published": "2012-04-20T13:03:37Z", "authors": ["Arnold Janssen", "Martin Tietje"], "categories": ["math.ST"], "doi": "", "_query": "hedonic house price", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2601.09872v1", "title": "A continuous-time Kyle model with price-responsive traders", "abstract": "Classical Kyle-type models of informed trading typically treat noise trader demand as purely exogenous. In reality, many market participants react to price movements and news, generating feedback effects that can significantly alter market dynamics. This paper develops a continuous-time Kyle framework in which two types of price-responsive traders (momentum and contrarian traders) adjust their demand in response to price signals. This extension yields a finite-dimensional Kalman filter for price discovery and leads to a forward-backward Riccati system characterizing equilibrium. We show that when feedback is weak, equilibrium exists and is unique as a smooth perturbation of the classical Kyle solution, allowing us to derive explicit comparative statics for insider profits and price informativeness. For stronger feedback, the model generates rich dynamics, including potential multiplicity of equilibria and amplification effects. Our framework thus bridges the gap between purely exogenous noise and more realistic, behaviorally motivated trading.", "published": "2026-01-14T21:05:37Z", "authors": ["Eunjung Noh"], "categories": ["q-fin.MF"], "doi": "", "_query": "hedonic house price", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/1611.07098v3", "title": "Risk-Sensitive Learning and Pricing for Demand Response", "abstract": "We consider the setting in which an electric power utility seeks to curtail its peak electricity demand by offering a fixed group of customers a uniform price for reductions in consumption relative to their predetermined baselines. The underlying demand curve, which describes the aggregate reduction in consumption in response to the offered price, is assumed to be affine and subject to unobservable random shocks. Assuming that both the parameters of the demand curve and the distribution of the random shocks are initially unknown to the utility, we investigate the extent to which the utility might dynamically adjust its offered prices to maximize its cumulative risk-sensitive payoff over a finite number of $T$ days. In order to do so effectively, the utility must design its pricing policy to balance the tradeoff between the need to learn the unknown demand model (exploration) and maximize its payoff (exploitation) over time. In this paper, we propose such a pricing policy, which is shown to exhibit an expected payoff loss over $T$ days that is at most $O(\\sqrt{T}\\log(T))$, relative to an oracle pricing policy that knows the underlying demand model. Moreover, the proposed pricing policy is shown to yield a sequence of prices that converge to the oracle optimal prices in the mean square sense.", "published": "2016-11-21T23:19:24Z", "authors": ["Kia Khezeli", "Eilyan Bitar"], "categories": ["cs.LG", "math.OC"], "doi": "", "_query": "hedonic house price", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2112.08215v1", "title": "Two-Price Equilibrium", "abstract": "Walrasian equilibrium is a prominent market equilibrium notion, but rarely exists in markets with indivisible items. We introduce a new market equilibrium notion, called two-price equilibrium (2PE). A 2PE is a relaxation of Walrasian equilibrium, where instead of a single price per item, every item has two prices: one for the item's owner and a (possibly) higher one for all other buyers. Thus, a 2PE is given by a tuple $(\\textbf{S},\\mathbf{\\hat{p}},\\mathbf{\\check{p}})$ of an allocation $\\textbf{S}$ and two price vectors $\\mathbf{\\hat{p}},\\mathbf{\\check{p}}$, where every buyer $i$ is maximally happy with her bundle $S_i$, given prices $\\mathbf{\\check{p}}$ for items in $S_i$ and prices $\\mathbf{\\hat{p}}$ for all other items. 2PE generalizes previous market equilibrium notions, such as conditional equilibrium, and is related to relaxed equilibrium notions like endowment equilibrium. We define the {\\em discrepancy} of a 2PE -- a measure of distance from Walrasian equilibrium -- as the sum of differences $\\hat{p}_j-\\check{p}_j$ over all items (normalized by social welfare). We show that the social welfare degrades gracefully with the discrepancy; namely, the social welfare of a 2PE with discrepancy $d$ is at least a fraction $\\frac{1}{d+1}$ of the optimal welfare. We use this to establish welfare guarantees for markets with subadditive valuations over identical items. In particular, we show that every such market admits a 2PE with at least $1/7$ of the optimal welfare. This is in contrast to Walrasian equilibrium or conditional equilibrium which may not even exist. Our techniques provide new insights regarding valuation functions over identical items, which we also use to characterize instances that admit a WE.", "published": "2021-12-15T15:45:56Z", "authors": ["Michal Feldman", "Galia Shabtai", "Aner Wolfenfeld"], "categories": ["cs.GT"], "doi": "", "_query": "hedonic house price", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/math/0503235v1", "title": "A brief note on the soundness of Bermudan option pricing via cubature", "abstract": "The subject of this study is an iterative Bermudan option pricing algorithm based on (high-dimensional) cubature. We show that the sequence of Bermudan prices (as functions of the underlying assets' logarithmic start prices) resulting from the iteration is bounded and increases monotonely to the approximate perpetual Bermudan option price; the convergence is linear in the supremum norm with the discount factor being the convergence factor. Furthermore, we prove a characterisation of this approximated perpetual Bermudan price as the smallest fixed point of the iteration procedure.", "published": "2005-03-12T22:57:19Z", "authors": ["Frederik S. Herzberg"], "categories": ["math.PR", "math.NA"], "doi": "", "_query": "hedonic house price", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2406.16400v2", "title": "Stochastic Path-Dependent Volatility Models for Price-Storage Dynamics in Natural Gas Markets and Discrete-Time Swing Option Pricing", "abstract": "This paper is devoted to the price-storage dynamics in natural gas markets. A novel stochastic path-dependent volatility model is introduced with path-dependence in both price volatility and storage increments. Model calibrations are conducted for both the price and storage dynamics. Further, we discuss the pricing problem of discrete-time swing options using the dynamic programming principle, and a deep learning-based method is proposed for numerical approximations. A numerical algorithm is provided, followed by a convergence analysis result for the deep-learning approach.", "published": "2024-06-24T08:03:20Z", "authors": ["Jinniao Qiu", "Antony Ware", "Yang Yang"], "categories": ["q-fin.MF"], "doi": "", "_query": "hedonic house price", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2506.02284v1", "title": "Learning Optimal Posted Prices for a Unit-Demand Buyer", "abstract": "We study the problem of learning the optimal item pricing for a unit-demand buyer with independent item values, and the learner has query access to the buyer's value distributions. We consider two common query models in the literature: the sample access model where the learner can obtain a sample of each item value, and the pricing query model where the learner can set a price for an item and obtain a binary signal on whether the sampled value of the item is greater than our proposed price. In this work, we give nearly tight sample complexity and pricing query complexity of the unit-demand pricing problem.", "published": "2025-06-02T21:48:12Z", "authors": ["Yifeng Teng", "Yifan Wang"], "categories": ["cs.GT", "cs.DS", "cs.LG"], "doi": "", "_query": "hedonic house price", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/1710.07867v1", "title": "The Price of Anarchy for Transportation Networks with Mixed Autonomy", "abstract": "We study routing behavior in transportation networks with mixed autonomy, that is, networks in which a fraction of the vehicles on each road are equipped with autonomous capabilities such as adaptive cruise control that enable reduced headways and increased road capacity. Motivated by capacity models developed for such roads with mixed autonomy, we consider transportation networks in which the delay on each road or link is an affine function of two quantities: the number of vehicles with autonomous capabilities on the link and the number of regular vehicles on the link. We particularly study the price of anarchy for such networks, that is, the ratio of the total delay experienced by selfish routing to the socially optimal routing policy. Unlike the case when all vehicles are of the same type, for which the price of anarchy is known to be bounded, we first show that the price of anarchy can be arbitrarily large for such mixed autonomous networks. Next, we define a notion of asymmetry equal to the maximum possible travel time improvement due to the presence of autonomous vehicles. We show that when the degree of asymmetry of all links in the network is bounded by a factor less than 4, the price of anarchy is bounded. We also bound the bicriteria, which is a bound on the cost of selfishly routing traffic compared to the cost of optimally routing additional traffic. These bounds depend on the degree of asymmetry and recover classical bounds on the price of anarchy and bicriteria in the case when no asymmetry exists. Further, we show with examples that these bound are tight in particular cases.", "published": "2017-10-22T00:15:29Z", "authors": ["Daniel A. Lazar", "Samuel Coogan", "Ramtin Pedarsani"], "categories": ["math.OC"], "doi": "", "_query": "hedonic house price", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/1905.05683v2", "title": "Capacity and Price Competition in Markets with Congestion Effects", "abstract": "We study oligopolistic competition in service markets where firms offer a service to customers. The service quality of a firm - from the perspective of a customer - depends on the congestion and the charged price. A firm can set a price for the service offered and additionally decides on the service capacity in order to mitigate congestion. The total profit of a firm is derived from the gained revenue minus the capacity investment cost. Firms simultaneously set capacities and prices in order to maximize their profit and customers subsequently choose the services with lowest combined cost (congestion and price). For this basic model, Johari et al. (2010) derived the first existence and uniqueness results of pure Nash equilibria (PNE) assuming mild conditions on congestion functions. Their existence proof relies on Kakutani's fixed-point theorem and a key assumption for the theorem to work is that demand for service is elastic (modeled by a smooth and strictly decreasing inverse demand function). In this paper, we consider the case of perfectly inelastic demand, i.e. there is a fixed volume of customers requesting service. This scenario applies to realistic cases where customers are not willing to drop out of the market, e.g. if prices are regulated by reasonable price caps. We investigate existence, uniqueness and quality of PNE for models with inelastic demand and price caps. We show that for linear congestion cost functions, there exists a PNE. This result requires a completely new proof approach compared to previous approaches, since the best response correspondences of firms may be empty, thus standard fixed-point arguments are not directly applicable. We show that the game is C-secure (see McLennan et al. (2011)), which leads to the existence of PNE. We furthermore show that the PNE is unique, and that the efficiency compared to a social optimum is unbounded in general.", "published": "2019-05-14T15:55:41Z", "authors": ["Tobias Harks", "Anja Schedel"], "categories": ["cs.GT", "math.OC"], "doi": "", "_query": "hedonic house price", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2308.14989v3", "title": "Efficiency in Multiple-Type Housing Markets", "abstract": "We consider multiple-type housing markets (Moulin, 1995), which extend Shapley-Scarf housing markets (Shapley and Scarf, 1974) from one dimension to higher dimensions. In this model, Pareto efficiency is incompatible with individual rationality and strategy-proofness (Konishi et al., 2001). Therefore, we consider two weaker efficiency properties: coordinatewise efficiency and pairwise efficiency. We show that these two properties both (i) are compatible with individual rationality and strategy-proofness, and (ii) help us to identify two specific mechanisms. To be more precise, on various domains of preference profiles, together with other well-studied properties (individual rationality, strategy-proofness, and non-bossiness), coordinatewise efficiency and pairwise efficiency respectively characterize two extensions of the top-trading-cycles mechanism (TTC): the coordinatewise top-trading-cycles mechanism (cTTC) and the bundle top-trading-cycles mechanism (bTTC). Moreover, we propose several variations of our efficiency properties, and we find that each of them is either satisfied by cTTC or bTTC, or leads to an impossibility result (together with individual rationality and strategy-proofness). Therefore, our characterizations can be primarily interpreted as a compatibility test: any reasonable efficiency property that is not satisfied by cTTC or bTTC could be considered incompatible with individual rationality and strategy-proofness. The external validity of our results in the context of general environments is also discussed. For multiple-type housing markets with strict preferences, our characterization of bTTC constitutes the first characterization of an extension of the prominent TTC mechanism", "published": "2023-08-29T02:38:23Z", "authors": ["Di Feng"], "categories": ["econ.TH"], "doi": "", "_query": "hedonic house price", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/cond-mat/9808206v1", "title": "Back to basics: historical option pricing revisited", "abstract": "We reconsider the problem of option pricing using historical probability distributions. We first discuss how the risk-minimisation scheme proposed recently is an adequate starting point under the realistic assumption that price increments are uncorrelated (but not necessarily independent) and of arbitrary probability density. We discuss in particular how, in the Gaussian limit, the Black-Scholes results are recovered, including the fact that the average return of the underlying stock disappears from the price (and the hedging strategy). We compare this theory to real option prices and find these reflect in a surprisingly accurate way the subtle statistical features of the underlying asset fluctuations.", "published": "1998-08-19T09:18:05Z", "authors": ["Jean-Philippe Bouchaud", "Marc Potters"], "categories": ["cond-mat"], "doi": "10.1098/rsta.1999.0414", "_query": "hedonic house price", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2508.06071v2", "title": "A Game-Theoretic Foundation for Bitcoin's Price: A Security-Utility Equilibrium", "abstract": "This paper introduces a structural game-theoretic model to value decentralized digital assets like Bitcoin. Instead of relying on speculative beliefs, it frames the asset's price within a Rational-Expectations Security-Utility Nash Equilibrium (RESUNE). This equilibrium is a fixed point where the market-clearing price dictates the hash rate through a free-entry mining model, which in turn endogenously sets the network's security. The security, defined as one minus the probability of a 51% attack, is determined via a global games model of attacker coordination, providing a unique and continuous security function. We prove the existence of a RESUNE and offer conditions for its uniqueness and stability. The model predicts that the stabilizing direct effect of price on demand must outweigh the potentially destabilizing feedback from price to security. The framework generates testable predictions, such as a protocol halving causing a contraction in both hash rate and price. A structural Vector Autoregression (VAR) model is proposed to test this mechanism. The model decomposes Bitcoin's value into transactional utility, security, and speculative components and explains the observed unidirectional causality from price to hash rate.", "published": "2025-08-08T07:09:21Z", "authors": ["Liang Chen"], "categories": ["cs.CR"], "doi": "", "_query": "hedonic house price", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2512.22271v1", "title": "Choice Modeling and Pricing for Scheduled Services", "abstract": "We describe a novel framework for discrete choice modeling and price optimization for settings where scheduled service options (often hierarchical) are offered to customers, which is applicable across many businesses including some within Amazon. In such business settings, the customers would see multiple options, often substitutable, with their features and their prices. These options typically vary in the start and/or end time of the service requested, such as the date of service or a service time window. The costs and demand can vary widely across these different options, resulting in the need for different prices. We propose a system which allows for segmenting the marketplace (as defined by the particular business) using decision trees, while using parametric discrete choice models within each market segment to accurately estimate conversion behavior. Using parametric discrete choice models allows us to capture important behavioral aspects like reference price effects which naturally occur in scheduled service applications. In addition, we provide natural and fast heuristics to do price optimization. For one such Amazon business where we conducted a live A/B experiment, this new framework outperformed the existing pricing system in every key metric, increasing our target performance metric by 19%, while providing a robust platform to support future new services of the business. The model framework has now been in full production for this business since Q4 2023.", "published": "2025-12-24T23:18:22Z", "authors": ["Adam N. Elmachtoub", "Kumar Goutam", "Roger Lederman"], "categories": ["econ.GN"], "doi": "", "_query": "hedonic house price", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/1608.06204v1", "title": "Towards the Interactive Effects of Demand Response Participation on Electricity Spot Market Price", "abstract": "The electricity market is threatened by supply scarcity, which may lead to very sharp price spikes in the spot market. On the other hand, demand-side's activities could effectively mitigate the supply scarcity and absorb most of these shocks and therefore smooth out the price volatility. In this paper, the positive effects of employing demand response programs on the spot market price are investigated. A demand-price elasticity based model is used to simulate the customer reaction function in the presence of a real time pricing. The demand achieve by DR program is used to adjust the spot market price by using a price regression model. SAS software is used to run the multiple linear regression model and MATLAB is used to simulate the demand response model. The approach is applied on one week data in summer 2014 of Connecticut in New England ISO. It could be concluded from the results of this study that applying DR program smooths out most of the price spikes in the electricity spot market and considerably reduces the customers' electricity cost.", "published": "2016-08-22T16:01:40Z", "authors": ["Saeed Mohajeryami", "Milad Doostan", "Seyedmahdi Moghadasi", "Peter Schwarz"], "categories": ["eess.SY"], "doi": "", "_query": "hedonic house price", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2401.15108v2", "title": "Tacit algorithmic collusion in deep reinforcement learning guided price competition: A study using EV charge pricing game", "abstract": "Players in pricing games with complex structures are increasingly adopting artificial intelligence (AI) aided learning algorithms to make pricing decisions for maximizing profits. This is raising concern for the antitrust agencies as the practice of using AI may promote tacit algorithmic collusion among otherwise independent players. Recent studies of games in canonical forms have shown contrasting claims ranging from none to a high level of tacit collusion among AI-guided players. In this paper, we examine the concern for tacit collusion by considering a practical game where EV charging hubs compete by dynamically varying their prices. Such a game is likely to be commonplace in the near future as EV adoption grows in all sectors of transportation. The hubs source power from the day-ahead (DA) and real-time (RT) electricity markets as well as from in-house battery storage systems. Their goal is to maximize profits via pricing and efficiently managing the cost of power usage. To aid our examination, we develop a two-step data-driven methodology. The first step obtains the DA commitment by solving a stochastic model. The second step generates the pricing strategies by solving a competitive Markov decision process model using a multi-agent deep reinforcement learning (MADRL) framework. We evaluate the resulting pricing strategies using an index for the level of tacit algorithmic collusion. An index value of zero indicates no collusion (perfect competition) and one indicates full collusion (monopolistic behavior). Results from our numerical case study yield collusion index values between 0.14 and 0.45, suggesting a low to moderate level of collusion.", "published": "2024-01-25T16:51:52Z", "authors": ["Diwas Paudel", "Tapas K. Das"], "categories": ["cs.LG", "cs.AI", "econ.GN", "eess.SY"], "doi": "", "_query": "hedonic house price", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/0809.4781v3", "title": "On contingent claims pricing in incomplete markets: A risk sharing approach", "abstract": "In an incomplete market setting, we consider two financial agents, who wish to price and trade a non-replicable contingent claim. Assuming that the agents are utility maximizers, we propose a transaction price which is a result of the minimization of a convex combination of their utility differences. We call this price the risk sharing price, we prove its existence for a large family of utility functions and we state some of its properties. As an example, we analyze extensively the case where both agents report exponential utility.", "published": "2008-09-27T15:53:24Z", "authors": ["Michail Anthropelos", "Nikolaos E. Frangos", "Stylianos Z. Xanthopoulos", "Athanasios N. Yannacopoulos"], "categories": ["q-fin.CP", "math.OC", "math.PR", "q-fin.TR"], "doi": "", "_query": "hedonic house price", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/1407.1769v3", "title": "Discrete, Non Probabilistic Market Models. Arbitrage and Pricing Intervals", "abstract": "The paper develops general, discrete, non-probabilistic market models and minmax price bounds leading to price intervals for European options. The approach provides the trajectory based analogue of martingale-like properties as well as a generalization that allows a limited notion of arbitrage in the market while still providing coherent option prices. Several properties of the price bounds are obtained, in particular a connection with risk neutral pricing is established for trajectory markets associated to a continuous-time martingale model.", "published": "2014-07-07T16:55:13Z", "authors": ["Sebastian E. Ferrando", "Alfredo L. Gonzalez", "Ivan L. Degano", "Massoome Rahsepar"], "categories": ["q-fin.MF", "q-fin.PR"], "doi": "", "_query": "hedonic house price", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2004.13708v2", "title": "Classical Option Pricing and Some Steps Further", "abstract": "This paper considers the asset price p as relations C=pV between the value C and the volume V of the executed transactions and studies the consequences of this definition for the option pricing equations. We show that the classical BSM model implicitly assumes that value C and volume V of transactions follow identical Brownian processes. Violation of this identity leads to 2-dimensional BSM-like equation with two constant volatilities. We show that agents expectations those approve execution of transactions can further increase the dimension of the BSM model. We study the case when agents expectations may depend on the option price data and show that such assumption can lead to the nonlinear BSM-like equations. We reconsider the Heston stochastic volatility model for the price determined by the value and the volume and derive 3-dimensional BSM-like model with stochastic value volatility and constant volume volatility. Variety of the BSM-like equations states the problem of reasonable balance between the accuracy and the complexity of the option pricing equations.", "published": "2020-04-27T19:16:45Z", "authors": ["Victor Olkhov"], "categories": ["q-fin.PR", "q-fin.ST"], "doi": "", "_query": "hedonic house price", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/1805.05431v1", "title": "Comparison of Classical and Nonlinear Models for Short-Term Electricity Price Prediction", "abstract": "Electricity is bought and sold in wholesale markets at prices that fluctuate significantly. Short-term forecasting of electricity prices is an important endeavor because it helps electric utilities control risk and because it influences competitive strategy for generators. As the \"smart grid\" grows, short-term price forecasts are becoming an important input to bidding and control algorithms for battery operators and demand response aggregators. While the statistics and machine learning literature offers many proposed methods for electricity price prediction, there is no consensus supporting a single best approach. We test two contrasting machine learning approaches for predicting electricity prices, regression decision trees and recurrent neural networks (RNNs), and compare them to a more traditional ARIMA implementation. We conduct the analysis on a challenging dataset of electricity prices from ERCOT, in Texas, where price fluctuation is especially high. We find that regression decision trees in particular achieves high performance compared to the other methods, suggesting that regression trees should be more carefully considered for electricity price forecasting.", "published": "2018-05-02T20:15:36Z", "authors": ["Elaheh Fata", "Igor Kadota", "Ian Schneider"], "categories": ["cs.CY", "cs.LG", "stat.ML"], "doi": "", "_query": "hedonic house price", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2508.09869v1", "title": "The Price of EF1 for Few Agents with Additive Ternary Valuations", "abstract": "We consider a resource allocation problem with agents that have additive ternary valuations for a set of indivisible items, and bound the price of envy-free up to one item (EF1) allocations. For a large number $n$ of agents, we show a lower bound of $Ω(\\sqrt{n})$, implying that the price of EF1 is no better than when the agents have general subadditive valuations. We then focus on instances with few agents and show that the price of EF1 is $12/11$ for $n=2$, and between $1.2$ and $1.256$ for $n=3$.", "published": "2025-08-13T15:05:52Z", "authors": ["Maria Kyropoulou", "Alexandros A. Voudouris"], "categories": ["cs.GT"], "doi": "10.1016/j.orl.2025.107351", "_query": "hedonic house price", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2012.06009v1", "title": "Vision-based Price Suggestion for Online Second-hand Items", "abstract": "Different from shopping in physical stores, where people have the opportunity to closely check a product (e.g., touching the surface of a T-shirt or smelling the scent of perfume) before making a purchase decision, online shoppers rely greatly on the uploaded product images to make any purchase decision. The decision-making is challenging when selling or purchasing second-hand items online since estimating the items' prices is not trivial. In this work, we present a vision-based price suggestion system for the online second-hand item shopping platform. The goal of vision-based price suggestion is to help sellers set effective prices for their second-hand listings with the images uploaded to the online platforms. First, we propose to better extract representative visual features from the images with the aid of some other image-based item information (e.g., category, brand). Then, we design a vision-based price suggestion module which takes the extracted visual features along with some statistical item features from the shopping platform as the inputs to determine whether an uploaded item image is qualified for price suggestion by a binary classification model, and provide price suggestions for items with qualified images by a regression model. According to two demands from the platform, two different objective functions are proposed to jointly optimize the classification model and the regression model. For better model training, we also propose a warm-up training strategy for the joint optimization. Extensive experiments on a large real-world dataset demonstrate the effectiveness of our vision-based price prediction system.", "published": "2020-12-10T22:56:29Z", "authors": ["Liang Han", "Zhaozheng Yin", "Zhurong Xia", "Li Guo", "Mingqian Tang", "Rong Jin"], "categories": ["cs.CV", "cs.AI"], "doi": "", "_query": "hedonic house price", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/1612.05021v1", "title": "Dynamic Modeling of Price Responsive Demand in Real-time Electricity Market: Empirical Analysis", "abstract": "In this paper, we study the price responsiveness of electricity consumption from empirical commercial and industrial load data obtained from Texas. Employing a dynamical system perspective, we show that price responsive demand can be modeled as a hybrid of a Hammerstein model with delay following a price surge, and a linear ARX model under moderate price changes. It is observed that electricity consumption therefore has unique characteristics including (1) qualitatively distinct response between moderate and extremely high prices; and (2) a time delay associated with the response to high prices. It is shown that these observed features may render traditional approaches to demand response and retail pricing based on classical economic theories ineffective. In particular, ultimate real-time retail pricing may be limitedly beneficial than as considered in classical economic theories.", "published": "2016-12-15T11:23:52Z", "authors": ["Jaeyong An", "P. R. Kumar", "Le Xie"], "categories": ["stat.AP", "q-fin.GN"], "doi": "", "_query": "hedonic house price", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/1501.04548v1", "title": "Effect of Volatility Clustering on Indifference Pricing of Options by Convex Risk Measures", "abstract": "In this article, we look at the effect of volatility clustering on the risk indifference price of options described by Sircar and Sturm in their paper (Sircar, R., & Sturm, S. (2012). From smile asymptotics to market risk measures. Mathematical Finance. Advance online publication. doi:10.1111/mafi.12015). The indifference price in their article is obtained by using dynamic convex risk measures given by backward stochastic differential equations. Volatility clustering is modelled by a fast mean-reverting volatility in a stochastic volatility model for stock price. Asymptotics of the indifference price of options and their corresponding implied volatility are obtained in this article, as the mean-reversion time approaches zero. Correction terms to the asymptotic option price and implied volatility are also obtained.", "published": "2015-01-19T16:45:32Z", "authors": ["Rohini Kumar"], "categories": ["q-fin.MF"], "doi": "10.1080/1350486X.2014.949805", "_query": "hedonic house price", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2304.02356v3", "title": "Unifying Market Microstructure and Dynamic Asset Pricing", "abstract": "We introduce a discrete binary tree for pricing contingent claims with the underlying security prices exhibiting history dependence characteristic of that induced by market microstructure phenomena. Example dependencies considered include moving average or autoregressive behavior. Our model is market-complete, arbitrage-free, and preserves all of the parameters governing the historical (natural world) price dynamics when passing to an equivalent martingale (risk-neutral) measure. Specifically, this includes the instantaneous mean and variance of the asset return and the instantaneous probabilities for the direction of asset price movement. We believe this is the first paper to demonstrate the ability to include market microstructure effects in dynamic asset/option pricing in a market-complete, no-arbitrage, format.", "published": "2023-04-05T10:45:25Z", "authors": ["Davide Lauria", "W. Brent Lindquist", "Svetlozar T. Rachev", "Yuan Hu"], "categories": ["q-fin.MF", "q-fin.PR"], "doi": "", "_query": "hedonic house price", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/1811.01077v5", "title": "Dynamic Pricing (and Assortment) under a Static Calendar", "abstract": "This work is motivated by our collaboration with a large consumer packaged goods (CPG) company. We have found that while the company appreciates the advantages of dynamic pricing, they deem it operationally much easier to plan out a static price calendar in advance. We investigate the efficacy of static control policies for revenue management problems whose optimal solution is inherently dynamic. In these problems, a firm has limited inventory to sell over a finite time horizon, over which heterogeneous customers stochastically arrive. We consider both pricing and assortment controls, and derive simple static policies in the form of a price calendar or a planned sequence of assortments, respectively. In the assortment planning problem, we also differentiate between the static vs. dynamic substitution models of customer demand. We show that our policies are within 1-1/e (approximately 0.63) of the optimum under stationary (IID) demand, and 1/2 of the optimum under non-stationary demand, with both guarantees approaching 1 if the starting inventories are large. We adapt the technique of prophet inequalities from optimal stopping theory to pricing and assortment problems, and our results are relative to the linear programming relaxation. Under the special case of IID single-item pricing, our results improve the understanding of irregular and discrete demand curves, by showing that a static calendar can be (1-1/e)-approximate if the prices are sorted high-to-low. Finally, we demonstrate on both data from the CPG company and synthetic data from the literature that our simple price and assortment calendars are effective.", "published": "2018-11-02T20:27:01Z", "authors": ["Will Ma", "David Simchi-Levi", "Jinglong Zhao"], "categories": ["cs.DS"], "doi": "", "_query": "hedonic house price", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2601.07452v1", "title": "A Note on 'The Limits of Price Discrimination' by Bergemann, Brooks, and Morris", "abstract": "This note revisits the analysis of third-degree price discrimination developed by Bergemann et al. (2015), which characterizes the set of consumer-producer surplus pairs that can be achieved through market segmentation. This was proved by means of market segmentation with random prices, but it was claimed that any segmentation with possibly random pricing has a corresponding direct segmentation, where a deterministic price is charged in each market segment. However, the latter claim is not correct under the definition of market segmentation given in the paper, and we provide counterexamples. We then propose an alternative definition to resolve this issue and examine the implications of the difference between the two definitions in terms of the main result of their paper.", "published": "2026-01-12T11:48:48Z", "authors": ["Keita Kuwahara"], "categories": ["econ.TH"], "doi": "", "_query": "hedonic house price", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/1207.6011v1", "title": "Analysis of cloud storage prices", "abstract": "Cloud storage is fast securing its role as a major repository for both consumers and business customers. Many companies now offer storage solutions, sometimes for free for limited amounts of capacity. We have surveyed the pricing plans of a selection of major cloud providers and compared them using the unit price as the means of comparison. All the providers, excepting Amazon, adopt a bundling pricing scheme; Amazon follows instead a block-declining pricing policy. We compare the pricing plans through a double approach: a pointwise comparison for each value of capacity, and an overall comparison using a two-part tariff approximation and a Pareto-dominance criterion. Under both approaches, most providers appear to offer pricing plans that are more expensive and can be excluded from a procurement selection in favour of a limited number of dominant providers.", "published": "2012-07-25T14:45:55Z", "authors": ["Loretta Mastroeni", "Maurizio Naldi"], "categories": ["cs.DC"], "doi": "", "_query": "hedonic house price", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/1411.4916v1", "title": "Combinatorial Auctions via Posted Prices", "abstract": "We study anonymous posted price mechanisms for combinatorial auctions in a Bayesian framework. In a posted price mechanism, item prices are posted, then the consumers approach the seller sequentially in an arbitrary order, each purchasing her favorite bundle from among the unsold items at the posted prices. These mechanisms are simple, transparent and trivially dominant strategy incentive compatible (DSIC). We show that when agent preferences are fractionally subadditive (which includes all submodular functions), there always exist prices that, in expectation, obtain at least half of the optimal welfare. Our result is constructive: given black-box access to a combinatorial auction algorithm A, sample access to the prior distribution, and appropriate query access to the sampled valuations, one can compute, in polytime, prices that guarantee at least half of the expected welfare of A. As a corollary, we obtain the first polytime (in n and m) constant-factor DSIC mechanism for Bayesian submodular combinatorial auctions, given access to demand query oracles. Our results also extend to valuations with complements, where the approximation factor degrades linearly with the level of complementarity.", "published": "2014-11-18T17:17:57Z", "authors": ["Michal Feldman", "Nick Gravin", "Brendan Lucier"], "categories": ["cs.GT"], "doi": "", "_query": "hedonic house price", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2104.09309v2", "title": "Foreign exchange markets: price response and spread impact", "abstract": "We carry out a detailed large-scale data analysis of price response functions in the spot foreign exchange market for different years and different time scales. Such response functions provide quantitative information on the deviation from Markovian behavior. The price response functions show an increase to a maximum followed by a slow decrease as the time lag grows, in trade time scale and in physical time scale, for all analyzed years. Furthermore, we use a price increment point (pip) bid-ask spread definition to group different foreign exchange pairs and analyze the impact of the spread in the price response functions. We find that large pip spreads have a stronger impact on the response. This is similar to what has been found in stock markets.", "published": "2021-04-15T19:37:41Z", "authors": ["Juan Camilo Henao Londono", "Thomas Guhr"], "categories": ["q-fin.ST"], "doi": "10.1016/j.physa.2021.126587", "_query": "hedonic house price", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/1505.07210v2", "title": "Intertemporal Substitutability, Risk Aversion and Asset Prices", "abstract": "Is the elasticity of intertemporal substitution (EIS) more or less than one? This question can be answered by confronting theoretical results of asset pricing models with investor behaviour during episodes of stock market panic. If we consider these episodes as periods of high risk aversion, then lower asset prices are in fact associated with higher risk aversion. However, according to theoretical models, risky asset price is an increasing function of the coefficient of risk aversion only if the EIS exceeds unity. It may therefore be concluded that the EIS must be more than one to reconcile theory with the observed stock price decline during periods of panic.", "published": "2015-05-27T07:15:26Z", "authors": ["Dominique Pepin"], "categories": ["q-fin.CP", "q-fin.GN"], "doi": "", "_query": "hedonic house price", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2209.07706v3", "title": "Understanding NFT Price Moves through Tweets Keywords Analysis", "abstract": "Non-Fungible Token (NFT) is evolving with the rise of the cryptocurrency market and the development of blockchain techniques, which leads to an emerging NFT market that has become prosperous rapidly then followed by a cooldown. Nevertheless, the overall rise procedure of the NFT market has not been well understood. To this end, we consider that social media communities evolving alongside the market growth, are worth exploring and reasoning about, as the mineable information might unveil the market behaviors. We explore the procedure from the perspective of NFT Twitter communities and its impact on the NFT price moves with two experiments. We perform a Granger causality test on the number of tweets and the NFT price time series and find that the number of tweets has a positive impact on (Granger-causes) the price or reversely for larger part of the 19 top authentic projects but seldom copycat projects. Besides, to investigate the price moves predictability, we experiment on predicting Markov normalized NFT price (representing the direction and magnitude of price moves) given tweets-extracted word features and interpret the feature importance to find insights. Our results show that social media words as the predictors result in all 19 top projects having a testing accuracy evidently above the baseline. Based on the feature importance analysis, we find that both general market-related words and NFT event-related words have a markedly positive contribution in predicting price moves. We summarize the characteristics including categorization and sentiment for the words with the top and least feature importance.", "published": "2022-09-16T04:05:47Z", "authors": ["Junliang Luo", "Yongzheng Jia", "Xue Liu"], "categories": ["cs.SI"], "doi": "10.1145/3582515.3609562", "_query": "hedonic house price", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2209.07706v3", "title": "Understanding NFT Price Moves through Tweets Keywords Analysis", "abstract": "Non-Fungible Token (NFT) is evolving with the rise of the cryptocurrency market and the development of blockchain techniques, which leads to an emerging NFT market that has become prosperous rapidly then followed by a cooldown. Nevertheless, the overall rise procedure of the NFT market has not been well understood. To this end, we consider that social media communities evolving alongside the market growth, are worth exploring and reasoning about, as the mineable information might unveil the market behaviors. We explore the procedure from the perspective of NFT Twitter communities and its impact on the NFT price moves with two experiments. We perform a Granger causality test on the number of tweets and the NFT price time series and find that the number of tweets has a positive impact on (Granger-causes) the price or reversely for larger part of the 19 top authentic projects but seldom copycat projects. Besides, to investigate the price moves predictability, we experiment on predicting Markov normalized NFT price (representing the direction and magnitude of price moves) given tweets-extracted word features and interpret the feature importance to find insights. Our results show that social media words as the predictors result in all 19 top projects having a testing accuracy evidently above the baseline. Based on the feature importance analysis, we find that both general market-related words and NFT event-related words have a markedly positive contribution in predicting price moves. We summarize the characteristics including categorization and sentiment for the words with the top and least feature importance.", "published": "2022-09-16T04:05:47Z", "authors": ["Junliang Luo", "Yongzheng Jia", "Xue Liu"], "categories": ["cs.SI"], "doi": "10.1145/3582515.3609562", "_query": "hedonic house price", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2305.04884v1", "title": "Predicting the Price Movement of Cryptocurrencies Using Linear Law-based Transformation", "abstract": "The aim of this paper is to investigate the effect of a novel method called linear law-based feature space transformation (LLT) on the accuracy of intraday price movement prediction of cryptocurrencies. To do this, the 1-minute interval price data of Bitcoin, Ethereum, Binance Coin, and Ripple between 1 January 2019 and 22 October 2022 were collected from the Binance cryptocurrency exchange. Then, 14-hour nonoverlapping time windows were applied to sample the price data. The classification was based on the first 12 hours, and the two classes were determined based on whether the closing price rose or fell after the next 2 hours. These price data were first transformed with the LLT, then they were classified by traditional machine learning algorithms with 10-fold cross-validation. Based on the results, LLT greatly increased the accuracy for all cryptocurrencies, which emphasizes the potential of the LLT algorithm in predicting price movements.", "published": "2023-04-27T15:04:08Z", "authors": ["Marcell T. Kurbucz", "Péter Pósfay", "Antal Jakovác"], "categories": ["q-fin.ST", "cs.AI", "cs.CV", "cs.LG", "q-fin.CP"], "doi": "", "_query": "hedonic house price", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2205.13826v4", "title": "Multivariate Probabilistic Forecasting of Intraday Electricity Prices using Normalizing Flows", "abstract": "Electricity is traded on various markets with different time horizons and regulations. Short-term intraday trading becomes increasingly important due to the higher penetration of renewables. In Germany, the intraday electricity price typically fluctuates around the day-ahead price of the European Power EXchange (EPEX) spot markets in a distinct hourly pattern. This work proposes a probabilistic modeling approach that models the intraday price difference to the day-ahead contracts. The model captures the emerging hourly pattern by considering the four 15 min intervals in each day-ahead price interval as a four-dimensional joint probability distribution. The resulting nontrivial, multivariate price difference distribution is learned using a normalizing flow, i.e., a deep generative model that combines conditional multivariate density estimation and probabilistic regression. Furthermore, this work discusses the influence of different external impact factors based on literature insights and impact analysis using explainable artificial intelligence (XAI). The normalizing flow is compared to an informed selection of historical data and probabilistic forecasts using a Gaussian copula and a Gaussian regression model. Among the different models, the normalizing flow identifies the trends with the highest accuracy and has the narrowest prediction intervals. Both the XAI analysis and the empirical experiments highlight that the immediate history of the price difference realization and the increments of the day-ahead price have the most substantial impact on the price difference.", "published": "2022-05-27T08:38:20Z", "authors": ["Eike Cramer", "Dirk Witthaut", "Alexander Mitsos", "Manuel Dahmen"], "categories": ["cs.LG"], "doi": "", "_query": "hedonic house price", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2610.17450v1", "title": "Unit-Demand Pricing for Min-Buying Customers", "abstract": "We study the unit-demand pricing problem under \\textit{min-buying} behavior, where customers purchase the lowest-priced item that yields non-negative utility. In contrast to the quasi-linear model, we show that uniform pricing becomes substantially more effective under min-buying. First, we study symmetric environments. We show that for min-buying customers with independent and identically distributed valuations, a uniform price across items is optimal. We then extend this optimality result to all valuations drawn from exchangeable and affiliated distributions. However, uniform pricing is not optimal in all symmetric environments: for min-buying customers we construct an exchangeable and non-affiliated valuation distribution for which the gap between uniform pricing and optimal item pricing is logarithmic in the number of items. This gap is tight and matches the worst-case gap in the quasi-linear setting. Next, we consider asymmetric environments in which valuations are independent but not identically distributed. When these valuations are regular and customers are min-buying, we show that a uniform price guarantees approximately 69.86\\% of the optimal item-pricing revenue and this is tight. Overall, our results highlight that the value of non-uniform pricing substantially diminishes, and often disappears entirely, under more conservative demand models in which customers engage in satisficing behaviors such as min-buying.", "published": "2026-10-12T22:08:37Z", "authors": ["Titing Cui", "Michael Hamilton", "Jourdain Lamperski", "Kasra Tari"], "categories": ["cs.GT", "econ.TH"], "doi": "", "_query": "hedonic house price", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2401.01487v1", "title": "Natural Language Processing and Multimodal Stock Price Prediction", "abstract": "In the realm of financial decision-making, predicting stock prices is pivotal. Artificial intelligence techniques such as long short-term memory networks (LSTMs), support-vector machines (SVMs), and natural language processing (NLP) models are commonly employed to predict said prices. This paper utilizes stock percentage change as training data, in contrast to the traditional use of raw currency values, with a focus on analyzing publicly released news articles. The choice of percentage change aims to provide models with context regarding the significance of price fluctuations and overall price change impact on a given stock. The study employs specialized BERT natural language processing models to predict stock price trends, with a particular emphasis on various data modalities. The results showcase the capabilities of such strategies with a small natural language processing model to accurately predict overall stock trends, and highlight the effectiveness of certain data features and sector-specific data.", "published": "2024-01-03T01:21:30Z", "authors": ["Kevin Taylor", "Jerry Ng"], "categories": ["cs.LG", "cs.CL"], "doi": "", "_query": "hedonic house price", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2605.24241v2", "title": "The petrol station game: the regional average price. A mean field analysis", "abstract": "In January 2023, the obligation for petrol stations to display the average fuel price calculated on a regional basis was introduced by the Italian Government. A mean field game model is here proposed to describe the evolution of the fuel price. In particular, each petrol station decides its own price, for the next day, taking account of an estimate of the average price, to be communicated by the Local Authority. The existence and the evolution of a mean field equilibrium are analytically investigated. Some simulations are compared with real data from the Autonomous Province of Trento, Italy.", "published": "2026-05-22T21:33:44Z", "authors": ["Fabio Bagagiolo", "Federico Pontiroli", "Ivan Romanò"], "categories": ["math.OC"], "doi": "", "_query": "hedonic house price", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2603.17764v2", "title": "Robust Dynamic Pricing and Admission Control with Fairness Guarantees", "abstract": "Dynamic pricing is commonly used to regulate congestion in shared service systems. This paper is motivated by the fact that in the presence of users with varying price sensitivity (responsiveness), conventional monotonic pricing can lead to unfair outcomes by disproportionately excluding price-elastic users, particularly under high or uncertain demand. We therefore develop a fairness-oriented mechanism under demand uncertainty. The paper's contributions are twofold. First, we show that when fairness is imposed as a hard state constraint, the optimal (revenue maximizing) pricing policy is generally non-monotonic in demand. This structural result departs fundamentally from standard surge pricing rules and reveals that price reduction under heavy load may be necessary to maintain equitable access. Second, we address the problem that price elasticity among heterogeneous users is unobservable. To solve it, we develop a robust dynamic pricing and admission control framework that enforces capacity and fairness constraints for all user type distributions consistent with aggregate measurements. By integrating integral High Order Control Barrier Functions (iHOCBFs) with a robust optimization framework under uncertain user-type distribution, we obtain a controller that guarantees forward invariance of safety and fairness constraints while optimizing revenue. Numerical experiments demonstrate improved fairness and revenue performance relative to monotonic surge pricing policies.", "published": "2026-03-18T14:23:36Z", "authors": ["Yingqing Chen", "Anni Li", "Christos G. Cassandras", "Homayoun Hamedmoghadam", "Fabian Wirth", "Robert Shorten"], "categories": ["eess.SY"], "doi": "", "_query": "hedonic house price", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/1608.00756v2", "title": "A continuous and efficient fundamental price on the discrete order book grid", "abstract": "This paper develops a model of liquidity provision in financial markets by adapting the Madhavan, Richardson, and Roomans (1997) price formation model to realistic order books with quote discretization and liquidity rebates. We postulate that liquidity providers observe a fundamental price which is continuous, efficient, and can assume values outside the interval spanned by the best quotes. We confirm the predictions of our price formation model with extensive empirical tests on large high-frequency datasets of 100 liquid Nasdaq stocks. Finally we use the model to propose an estimator of the fundamental price based on the rebate adjusted volume imbalance at the best quotes and we empirically show that it outperforms other simpler estimators.", "published": "2016-08-02T10:13:43Z", "authors": ["Julius Bonart", "Fabrizio Lillo"], "categories": ["q-fin.TR", "q-fin.ST"], "doi": "", "_query": "hedonic house price", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/1408.5510v1", "title": "Consistent Price Systems under Model Uncertainty", "abstract": "We develop a version of the fundamental theorem of asset pricing for discrete-time markets with proportional transaction costs and model uncertainty. A robust notion of no-arbitrage of the second kind is defined and shown to be equivalent to the existence of a collection of strictly consistent price systems.", "published": "2014-08-23T16:13:29Z", "authors": ["Bruno Bouchard", "Marcel Nutz"], "categories": ["q-fin.MF", "math.PR"], "doi": "", "_query": "hedonic house price", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/cond-mat/0102042v1", "title": "To sell or not to sell? Behavior of shareholders during price collapses", "abstract": "It is a common belief that the behavior of shareholders depends upon the direction of price fluctuations: if prices increase they buy, if prices decrease they sell. That belief, however, is more based on ``common sense'' than on facts. In this paper we present evidence for a specific class of shareholders which shows that the actual behavior of shareholders can be markedly different.", "published": "2001-02-02T17:28:46Z", "authors": ["Bertrand M. Roehner"], "categories": ["cond-mat.stat-mech", "q-fin.ST"], "doi": "10.1142/S0129183101001390", "_query": "hedonic house price", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/1907.10101v3", "title": "The Price of Anarchy in Routing Games as a Function of the Demand", "abstract": "The price of anarchy has become a standard measure of the efficiency of equilibria in games. Most of the literature in this area has focused on establishing worst-case bounds for specific classes of games, such as routing games or more general congestion games. Recently, the price of anarchy in routing games has been studied as a function of the traffic demand, providing asymptotic results in light and heavy traffic. The aim of this paper is to study the price of anarchy in nonatomic routing games in the intermediate region of the demand. To achieve this goal, we begin by establishing some smoothness properties of Wardrop equilibria and social optima for general smooth costs. In the case of affine costs we show that the equilibrium is piecewise linear, with break points at the demand levels at which the set of active paths changes. We prove that the number of such break points is finite, although it can be exponential in the size of the network. Exploiting a scaling law between the equilibrium and the social optimum, we derive a similar behavior for the optimal flows. We then prove that in any interval between break points the price of anarchy is smooth and it is either monotone (decreasing or increasing) over the full interval, or it decreases up to a certain minimum point in the interior of the interval and increases afterwards. We deduce that for affine costs the maximum of the price of anarchy can only occur at the break points. For general costs we provide counterexamples showing that the set of break points is not always finite.", "published": "2019-07-23T18:59:31Z", "authors": ["Roberto Cominetti", "Valerio Dose", "Marco Scarsini"], "categories": ["cs.GT", "math.OC"], "doi": "10.1007/s10107-021-01701-7", "_query": "hedonic house price", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/1807.00573v1", "title": "Strategic behaviour and indicative price diffusion in Paris Stock Exchange auctions", "abstract": "We report statistical regularities of the opening and closing auctions of French equities, focusing on the diffusive properties of the indicative auction price. Two mechanisms are at play as the auction end time nears: the typical price change magnitude decreases, favoring underdiffusion, while the rate of these events increases, potentially leading to overdiffusion. A third mechanism, caused by the strategic behavior of traders, is needed to produce nearly diffusive prices: waiting to submit buy orders until sell orders have decreased the indicative price and vice-versa.", "published": "2018-07-02T10:00:03Z", "authors": ["Damien Challet"], "categories": ["q-fin.ST"], "doi": "", "_query": "hedonic house price", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2404.02458v2", "title": "Network-Aware and Welfare-Maximizing Dynamic Pricing for Energy Sharing", "abstract": "The proliferation of behind-the-meter (BTM) distributed energy resources (DER) within the electrical distribution network presents significant supply and demand flexibilities, but also introduces operational challenges such as voltage spikes and reverse power flows. In response, this paper proposes a network-aware dynamic pricing framework tailored for energy-sharing coalitions that aggregate small, but ubiquitous, BTM DER downstream of a distribution system operator's (DSO) revenue meter that adopts a generic net energy metering (NEM) tariff. By formulating a Stackelberg game between the energy-sharing market leader and its prosumers, we show that the dynamic pricing policy induces the prosumers toward a network-safe operation and decentrally maximizes the energy-sharing social welfare. The dynamic pricing mechanism involves a combination of a locational ex-ante dynamic price and an ex-post allocation, both of which are functions of the energy sharing's BTM DER. The ex-post allocation is proportionate to the price differential between the DSO NEM price and the energy-sharing locational price. Simulation results using real DER data and the IEEE 13-bus test systems illustrate the dynamic nature of network-aware pricing at each bus, and its impact on voltage.", "published": "2024-04-03T05:02:10Z", "authors": ["Ahmed S. Alahmed", "Guido Cavraro", "Andrey Bernstein", "Lang Tong"], "categories": ["eess.SY", "math.OC"], "doi": "", "_query": "hedonic house price", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/0907.3273v2", "title": "New procedures for testing whether stock price processes are martingales", "abstract": "We propose procedures for testing whether stock price processes are martingales based on limit order type betting strategies. We first show that the null hypothesis of martingale property of a stock price process can be tested based on the capital process of a betting strategy. In particular with high frequency Markov type strategies we find that martingale null hypotheses are rejected for many stock price processes.", "published": "2009-07-19T08:48:03Z", "authors": ["Kei Takeuchi", "Akimichi Takemura", "Masayuki Kumon"], "categories": ["q-fin.ST"], "doi": "", "_query": "hedonic house price", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2602.12066v2", "title": "Chaos and Misallocation under Price Controls", "abstract": "Price controls kill the incentive for arbitrage. We prove a Chaos Theorem: under a binding price ceiling, suppliers are indifferent across destinations, so arbitrarily small cost differences can determine the entire allocation. The economy tips to corner outcomes in which some markets are fully served while others are starved; small parameter changes flip the identity of the corners, generating discontinuous welfare jumps. These corner allocations create a distinct source of cross-market misallocation, separate from the aggregate quantity loss (the Harberger triangle) and from within-market misallocation emphasized in prior work. They also create an identification problem: welfare depends on demand far from the observed equilibrium. We derive sharp bounds on misallocation that require no parametric assumptions. In an efficient allocation, shadow prices are equalized across markets; combined with the adding-up constraint, this collapses the infinite-dimensional welfare problem to a one-dimensional search over a common shadow price, with extremal losses achieved by piecewise-linear demand schedules. Calibrating the bounds to station-level AAA survey data from the 1973--74 U.S. gasoline crisis, misallocation losses range from roughly 1 to 9 times the Harberger triangle.", "published": "2026-02-12T15:24:35Z", "authors": ["Brian C. Albrecht", "Alex Tabarrok", "Mark Whitmeyer"], "categories": ["econ.GN"], "doi": "", "_query": "hedonic house price", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2207.14225v1", "title": "Electricity Price Forecasting Model based on Gated Recurrent Units", "abstract": "The participation of consumers and producers in demand response programs has increased in smart grids, which reduces investment and operation costs of power systems. Also, with the advent of renewable energy sources, the electricity market is becoming more complex and unpredictable. To effectively implement demand response programs, forecasting the future price of electricity is very crucial for producers in the electricity market. Electricity prices are very volatile and change under the influence of various factors such as temperature, wind speed, rainfall, intensity of commercial and daily activities, etc. Therefore, considering the influencing factors as dependent variables can increase the accuracy of the forecast. In this paper, a model for electricity price forecasting is presented based on Gated Recurrent Units. The electrical load consumption is considered as an input variable in this model. Noise in electricity price seriously reduces the efficiency and effectiveness of analysis. Therefore, an adaptive noise reducer is integrated into the model for noise reduction. The SAEs are then used to extract features from the de-noised electricity price. Finally, the de-noised features are fed into the GRU to train predictor. Results on real dataset shows that the proposed methodology can perform effectively in prediction of electricity price.", "published": "2022-07-28T16:49:03Z", "authors": ["Nafise Rezaei", "Roozbeh Rajabi", "Abouzar Estebsari"], "categories": ["cs.LG", "cs.CV"], "doi": "", "_query": "hedonic house price", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2407.21759v1", "title": "Optimal price signal generation for demand-side energy management", "abstract": "Renewable Energy Sources play a key role in smart energy systems. To achieve 100% renewable energy, utilizing the flexibility potential on the demand side becomes the cost-efficient option to balance the grid. However, it is not trivial to exploit these available capacities and flexibility options profitably. The amount of available flexibility is a complex and time-varying function of the price signal and weather forecasts. In this work, we use a Flexibility Function to represent the relationship between the price signal and the demand and investigate optimization problems for the price signal computation. Consequently, this study considers the higher and lower levels in the hierarchy from the markets to appliances, households, and districts. This paper investigates optimal price generation via the Flexibility Function and studies its employment in controller design for demand-side management, its capability to provide ancillary services for balancing throughout the Smart Energy Operating System, and its effect on the physical level performance. Sequential and simultaneous approaches for computing the price signal, along with various cost functions are analyzed and compared. Simulation results demonstrate the generated price/penalty signal and its employment in a model predictive controller.", "published": "2024-07-31T17:28:31Z", "authors": ["Seyed Shahabaldin Tohidi", "Henrik Madsen", "Davide Calì", "Tobias K. S. Ritschel"], "categories": ["eess.SY"], "doi": "", "_query": "hedonic house price", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2112.13254v3", "title": "On Dynamic Pricing with Covariates", "abstract": "We consider dynamic pricing with covariates under a generalized linear demand model: a seller can dynamically adjust the price of a product over a horizon of $T$ time periods, and at each time period $t$, the demand of the product is jointly determined by the price and an observable covariate vector $x_t\\in\\mathbb{R}^d$ through a generalized linear model with unknown co-efficients. Most of the existing literature assumes the covariate vectors $x_t$'s are independently and identically distributed (i.i.d.); the few papers that relax this assumption either sacrifice model generality or yield sub-optimal regret bounds. In this paper, we show that UCB and Thompson sampling-based pricing algorithms can achieve an $O(d\\sqrt{T}\\log T)$ regret upper bound without assuming any statistical structure on the covariates $x_t$. Our upper bound on the regret matches the lower bound up to logarithmic factors. We thus show that (i) the i.i.d. assumption is not necessary for obtaining low regret, and (ii) the regret bound can be independent of the (inverse) minimum eigenvalue of the covariance matrix of the $x_t$'s, a quantity present in previous bounds. Moreover, we consider a constrained setting of the dynamic pricing problem where there is a limited and unreplenishable inventory and we develop theoretical results that relate the best achievable algorithm performance to a variation measure with respect to the temporal distribution shift of the covariates. We also discuss conditions under which a better regret is achievable and demonstrate the proposed algorithms' performance with numerical experiments.", "published": "2021-12-25T16:30:13Z", "authors": ["Hanzhao Wang", "Kalyan Talluri", "Xiaocheng Li"], "categories": ["cs.LG", "stat.ML"], "doi": "", "_query": "hedonic house price", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2307.07103v3", "title": "A Hamiltonian Approach to Barrier Option Pricing Under Vasicek Model", "abstract": "In this paper, we study option pricing under Vasicek Model by a Hamiltonian approach. Since the interest rate changes with time, we split the time to maturity into infinite steps, and the matrix element during each step could be calculated by quantum mechanics methods. Using completeness condition, the pricing kernel and the integral expression of option price could also be derived. Numerical results of option prices as functions of underlying asset price, floating rate and regression rate are also shown.", "published": "2023-07-14T00:25:52Z", "authors": ["Chao Guo", "Ning Yao"], "categories": ["q-fin.PR", "quant-ph"], "doi": "", "_query": "hedonic house price", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2412.00920v2", "title": "Neural Network Approach to Demand Estimation and Dynamic Pricing in Retail", "abstract": "This paper contributes to the literature on parametric demand estimation by using deep learning to model consumer preferences. Traditional econometric methods often struggle with limited within-product price variation, a challenge addressed by the proposed neural network approach. The proposed method estimates the functional form of the demand and demonstrates higher performance in both simulations and empirical applications. Notably, under low price variation, the machine learning model outperforms econometric approaches, reducing the mean squared error of initial price parameter estimates by nearly threefold. In empirical setting, the ML model consistently predicts a negative relationship between demand and price in 100% of cases, whereas the econometric approach fails to do so in 20% of cases. The suggested model incorporates a wide range of product characteristics, as well as prices of other products and competitors.", "published": "2024-12-01T18:09:32Z", "authors": ["Kirill Safonov"], "categories": ["econ.GN"], "doi": "", "_query": "hedonic house price", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2607.24973v1", "title": "Discrete dividends after maturity adjust the stock and strike prices", "abstract": "The standard method to price European calls on a discrete dividend-paying stock is to subtract the present value of the dividends from the initial stock price in the Black-Scholes formula. However, when there are dividends after maturity, this is inconsistent with the model. Within the escrowed dividend model, we highlight an extension of the Black-Scholes formula in which those dividends after maturity adjust both the stock price and strike price, allowing for calls over all maturities to be priced in a model-consistent way. As a related application to American calls with a single dividend before maturity, we establish a neglected case where it is always optimal to early exercise and derive an extension of the Roll-Geske-Whaley formula when there are dividends after maturity, including fully characterising the optimal exercise policy.", "published": "2026-07-27T18:27:04Z", "authors": ["Kevin W. Lu"], "categories": ["q-fin.MF"], "doi": "", "_query": "hedonic house price", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2610.18383v1", "title": "Computation of Price Signals for Controlling Flexible Prosumers in Distribution Grids Incorporating Stochasticity and Fairness", "abstract": "This paper proposes a method for distribution system operators (DSOs) to design spatially fair dynamic price signals for flexible prosumers, accounting for both distribution grid constraints and the stochastic behavior of prosumers. Since locational pricing can introduce price unfairness when grid constraints are active, the proposed approach includes mechanisms to enforce price fairness across the network. The method involves the DSO solving a scenario-based optimal power flow (OPF) problem to derive, similarly to locational marginal prices in transmission systems, the Lagrange multipliers associated with grid constraints and to compute individualized price signals for each prosumer under various PV generation and demand scenarios. Subsequently, two methods are introduced to aggregate these individualized prices into a single fair price across prosumers. Their performance is evaluated through simulations on a model distribution grid, comparing prosumer revenues, grid constraint violations, and analyzing the impact of fairness on reducing cost variance among prosumers.", "published": "2026-10-13T13:35:21Z", "authors": ["Plouton Grammatikos", "Chloé Voirol", "Pierre Roduit", "Fabrizio Sossan"], "categories": ["eess.SY"], "doi": "", "_query": "hedonic house price", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2307.00412v1", "title": "Adam Smith's Theory of Value: A Reappraisal of Classical Price Discovery", "abstract": "The relevance of Adam Smith for understanding human morality and sociality is recognized in the growing interest in his work on moral sentiments among scholars of various academic backgrounds. But, paradoxically, Adam Smith's theory of economic value enjoys a less prominent stature today among economists, who, while they view him as the 'father of modern economics', considered him more as having had the right intuitions about a market economy than as having developed the right concepts and the technical tools for studying it. Yet the neoclassical tradition, which replaced the classical school around 1870, failed to provide a satisfactory theory of market price formation. Adam Smith's sketch of market price formation (Ch. VII, Book I, Wealth of Nations), and more generally the classical view of competition as a collective higgling and bargaining process, as this paper argues, offers a helpful foundation on which to build a modern theory of market price formation, despite any shortcomings of the original classical formulation (notably its insistence on long-run, natural value). Also, with hindsight, the experimental market findings established the remarkable stability, efficiency, and robustness of the old view of competition, suggesting a rehabilitation of classical price discovery. This paper reappraises classical price theory as Adam Smith articulated it; we explicate key propositions from his price theory and derive them from a simple model, which is an elementary sketch of the authors' more general theory of competitive market price formation.", "published": "2023-07-01T19:15:01Z", "authors": ["Sabiou Inoua", "Vernon Smith"], "categories": ["econ.GN"], "doi": "", "_query": "hedonic house price", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/1610.09875v1", "title": "Loading Pricing of Catastrophe Bonds and Other Long-Dated, Insurance-Type Contracts", "abstract": "Catastrophe risk is a major threat faced by individuals, companies, and entire economies. Catastrophe (CAT) bonds have emerged as a method to offset this risk and a corresponding literature has developed that attempts to provide a market-consistent pricing methodology for these and other long-dated, insurance-type contracts. This paper aims to unify and generalize several of the widely-used pricing approaches for long-dated contracts with a focus on stylized CAT bonds and market-consistent valuation. It proposes a loading pricing concept that combines the theoretically possible minimal price of a contract with its formally obtained risk neutral price, without creating economically meaningful arbitrage. A loading degree controls how much influence the formally obtained risk neutral price has on the market price. A key finding is that this loading degree has to be constant for a minimally fluctuating contract, and is an important, measurable characteristic for prices of long-dated contracts. Loading pricing allows long-dated, insurance-type contracts to be priced less expensively and with higher return on investment than under classical pricing approaches. Loading pricing enables insurance companies to accumulate systematically reserves needed to manage its risk of ruin in a market consistent manner.", "published": "2016-10-31T11:38:52Z", "authors": ["Eckhard Platen", "David Taylor"], "categories": ["q-fin.MF", "q-fin.PR"], "doi": "", "_query": "hedonic house price", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2405.00537v2", "title": "Quantifying Price Improvement in Order Flow Auctions", "abstract": "This work introduces a framework for evaluating onchain order flow auctions (OFAs), emphasizing the metric of price improvement. Utilizing a set of open-source tools, our methodology systematically attributes price improvements to specific modifiable inputs of the system such as routing efficiency, gas optimization, and priority fee settings. When applied to leading Ethereum-based trading interfaces such as 1Inch and Uniswap, the results reveal that auction-enhanced interfaces can provide statistically significant improvements in trading outcomes, averaging 4-5 basis points in our sample. We further identify the sources of such price improvements to be added liquidity for large swaps. This research lays a foundation for future innovations in blockchain based trading platforms.", "published": "2024-05-01T14:19:30Z", "authors": ["Brad Bachu", "Xin Wan", "Ciamac C. Moallemi"], "categories": ["q-fin.TR"], "doi": "", "_query": "hedonic house price", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/1804.08218v1", "title": "Econometric Modeling of Regional Electricity Spot Prices in the Australian Market", "abstract": "Wholesale electricity markets are increasingly integrated via high voltage interconnectors, and inter-regional trade in electricity is growing. To model this, we consider a spatial equilibrium model of price formation, where constraints on inter-regional flows result in three distinct equilibria in prices. We use this to motivate an econometric model for the distribution of observed electricity spot prices that captures many of their unique empirical characteristics. The econometric model features supply and inter-regional trade cost functions, which are estimated using Bayesian monotonic regression smoothing methodology. A copula multivariate time series model is employed to capture additional dependence -- both cross-sectional and serial-- in regional prices. The marginal distributions are nonparametric, with means given by the regression means. The model has the advantage of preserving the heavy right-hand tail in the predictive densities of price. We fit the model to half-hourly spot price data in the five interconnected regions of the Australian national electricity market. The fitted model is then used to measure how both supply and price shocks in one region are transmitted to the distribution of prices in all regions in subsequent periods. Finally, to validate our econometric model, we show that prices forecast using the proposed model compare favorably with those from some benchmark alternatives.", "published": "2018-04-23T01:52:35Z", "authors": ["Michael Stanley Smith", "Thomas S. Shively"], "categories": ["econ.EM", "stat.AP"], "doi": "", "_query": "hedonic house price", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2012.15705v2", "title": "A Bayesian viewpoint on the price formation process", "abstract": "We introduce a simple framework in which market participants update their prior about an efficient price with a model-based learning process. We show that exponential intensities for the arrival of aggressive orders arise naturally in this setting. Our approach allows us to fully describe market dynamics in the case with Brownian efficient price and informed market takers. We are also able to revisit the emergence of market impact due to meta-order splitting, making several connections with existing literature.", "published": "2020-12-31T16:46:34Z", "authors": ["Joffrey Derchu"], "categories": ["q-fin.TR"], "doi": "", "_query": "hedonic house price", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2606.04890v1", "title": "Welfare Maximization in Bilateral Trade: Improved Approximation Guarantees Beyond the Fixed Price Barrier", "abstract": "We study the setting of welfare maximization in bilateral trade, where the values of both the buyer and the seller are drawn from independent distributions. Our goal is to maximize social welfare. In this setting, fixed price mechanisms have been extensively studied. In a fixed price mechanism, there is a price $p$ that depends only on the distributions of the buyer and the seller. Trade occurs if and only if the buyer's value is at least $p$ and the seller's value is at most $p$. A long line of work has culminated in determining almost exactly the approximation ratios achievable by fixed price mechanisms: there exists a fixed price mechanism that obtains at least a $0.72$ fraction of the social welfare, but no fixed price mechanism can guarantee more than a $0.7381$ fraction of it [Cai and Wu, STOC'23; Liu, Ren, and Wang, STOC'23]. No other incentive-compatible mechanism is known to beat the performance of fixed-price mechanisms in this setting. This paper shows how to achieve a larger fraction of the optimal welfare with other classes of mechanisms. Specifically, we study the buyer-offering mechanism with a reserve price. In this mechanism, the buyer observes its value and makes a take-it-or-leave-it offer to the seller, where the offer is at least the reserve price. Beyond its simplicity, this natural mechanism is attractive because the seller always has a dominant strategy: accept the offer if its value is at most the offer, and otherwise reject it. We show that there always exists a reserve price that guarantees a $0.746$ fraction of the social welfare. This not only improves upon the best previously known approximation guarantee for the problem, but also demonstrates that fixed-price mechanisms are not optimal in this setting.", "published": "2026-06-03T13:51:49Z", "authors": ["Shahar Dobzinski", "Ariel Shaulker"], "categories": ["cs.GT"], "doi": "", "_query": "hedonic house price", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2601.08962v2", "title": "Warp speed price moves: Jumps after earnings announcements", "abstract": "Corporate earnings announcements unpack large bundles of public information that should, in efficient markets, trigger jumps in stock prices. Testing this implication is difficult in practice, as it requires noisy high-frequency data from after-hours markets, where most earnings announcements are released. Using a unique dataset and a new microstructure noise-robust jump test, we show that earnings announcements almost always induce jumps in the stock price of announcing firms. They also significantly raise the probability of price co-jumps in non-announcing firms and the market. We find that returns from a post-announcement trading strategy are consistent with efficient price formation after 2016.", "published": "2026-01-13T20:06:47Z", "authors": ["Kim Christensen", "Allan Timmermann", "Bezirgen Veliyev"], "categories": ["econ.EM"], "doi": "10.1016/j.jfineco.2025.104010", "_query": "hedonic house price", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/1302.5650v1", "title": "On a Boltzmann type price formation model", "abstract": "In this paper we present a Boltzmann type price formation model, which is motivated by a parabolic free boundary model for the evolution of the prize presented by Lasry and Lions in 2007. We discuss the mathematical analysis of the Boltzmann type model and show that its solutions converge to solutions of the model by Lasry and Lions as the transaction rate tends to infinity. Furthermore we analyse the behaviour of the initial layer on the fast time scale and illustrate the price dynamics with various numerical experiments.", "published": "2013-02-22T17:18:23Z", "authors": ["Martin Burger", "Luis Caffarelli", "Peter Markowich", "Marie-Therese Wolfram"], "categories": ["math.AP"], "doi": "10.1098/rspa.2013.0126", "_query": "hedonic house price", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/1710.10293v2", "title": "Polynomial processes for power prices", "abstract": "Polynomial processes have the property that expectations of polynomial functions (of degree $n$, say) of the future state of the process conditional on the current state are given by polynomials (of degree $\\leq n$) of the current state. Here we explore the application of polynomial processes in the context of structural models for energy prices. We focus on the example of Alberta power prices, derive one- and two-factor models for spot prices. We examine their performance in numerical experiments, and demonstrate that the richness of the dynamics they are able to generate makes them well suited for modelling even extreme examples of energy price behaviour.", "published": "2017-10-27T18:33:04Z", "authors": ["Damir Filipovic", "Martin Larsson", "Tony Ware"], "categories": ["q-fin.CP"], "doi": "", "_query": "hedonic house price", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2010.04287v2", "title": "Jump Models with delay -- option pricing and logarithmic Euler-Maruyama scheme", "abstract": "In this paper, we obtain the existence, uniqueness and positivity of the solution to delayed stochastic differential equations with jumps. This equation is then applied to model the price movement of the risky asset in a financial market and the Black-Scholes formula for the price of European options is obtained together with the hedging portfolios. The option price is evaluated analytically at the last delayed period by using the Fourier transformation technique. But in general, there is no analytical expression for the option price. To evaluate the price numerically we then use the Monte-Carlo method. To this end, we need to simulate the delayed stochastic differential equations with jumps. We propose a logarithmic Euler-Maruyama scheme to approximate the equation and prove that all the approximations remain positive and the rate of convergence of the scheme is proved to be $0.5$.", "published": "2020-10-08T22:43:39Z", "authors": ["Nishant Agrawal", "Yaozhong Hu"], "categories": ["q-fin.MF"], "doi": "", "_query": "hedonic house price", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/1307.6727v1", "title": "Quantum Tunneling of Stock Price in Range Bound Market Conditions", "abstract": "Applications of Quantum Tunneling effect have long gone beyond the traditional physical meaning. Initially created by Gamow to explain α-decay of nuclear particles, along the time, quantum tunneling found fertile domain of research in chemistry and recently in biology, where the new discipline of Quantum Biology emerges. The present paper extends the applicability of quantum tunneling to financial markets. In a recent paper [1] a time-independent equation for pricing the options having the underlying stock in a range bound markets is found. The equation is identical with a time-independent Schrodinger equation but incorporates elements of finance. The financial time-independent equation for option pricing is solved to explain a particular explosive violent movement of stock price in range bound markets. The aforementioned particular stock price movement is assimilated with a quantum tunneling effect. The probability of stock price to quantum tunneling out of the bounded region, known as transmission coefficient, is deduced. Quantum aspects of tunneling effect in financial markets are discussed. Recent evidences of price quantum tunneling in stock market are also shown.", "published": "2013-07-25T12:56:28Z", "authors": ["Ovidiu Racorean"], "categories": ["q-fin.PR"], "doi": "", "_query": "hedonic house price", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/1609.07574v4", "title": "Dynamic Pricing in High-dimensions", "abstract": "We study the pricing problem faced by a firm that sells a large number of products, described via a wide range of features, to customers that arrive over time. Customers independently make purchasing decisions according to a general choice model that includes products features and customers' characteristics, encoded as $d$-dimensional numerical vectors, as well as the price offered. The parameters of the choice model are a priori unknown to the firm, but can be learned as the (binary-valued) sales data accrues over time. The firm's objective is to minimize the regret, i.e., the expected revenue loss against a clairvoyant policy that knows the parameters of the choice model in advance, and always offers the revenue-maximizing price. This setting is motivated in part by the prevalence of online marketplaces that allow for real-time pricing. We assume a structured choice model, parameters of which depend on $s_0$ out of the $d$ product features. We propose a dynamic policy, called Regularized Maximum Likelihood Pricing (RMLP) that leverages the (sparsity) structure of the high-dimensional model and obtains a logarithmic regret in $T$. More specifically, the regret of our algorithm is of $O(s_0 \\log d \\cdot \\log T)$. Furthermore, we show that no policy can obtain regret better than $O(s_0 (\\log d + \\log T))$.", "published": "2016-09-24T06:02:24Z", "authors": ["Adel Javanmard", "Hamid Nazerzadeh"], "categories": ["stat.ML", "cs.LG"], "doi": "", "_query": "hedonic house price", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2110.13303v1", "title": "Negotiating Networks in Oligopoly Markets for Price-Sensitive Products", "abstract": "We present a novel framework to learn functions that estimate decisions of sellers and buyers simultaneously in an oligopoly market for a price-sensitive product. In this setting, the aim of the seller network is to come up with a price for a given context such that the expected revenue is maximized by considering the buyer's satisfaction as well. On the other hand, the aim of the buyer network is to assign probability of purchase to the offered price to mimic the real world buyers' responses while also showing price sensitivity through its action. In other words, rejecting the unnecessarily high priced products. Similar to generative adversarial networks, this framework corresponds to a minimax two-player game. In our experiments with simulated and real-world transaction data, we compared our framework with the baseline model and demonstrated its potential through proposed evaluation metrics.", "published": "2021-10-25T22:29:48Z", "authors": ["Naman Shukla", "Kartik Yellepeddi"], "categories": ["cs.LG", "cs.AI", "cs.CE", "econ.EM"], "doi": "", "_query": "hedonic house price", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2507.21950v1", "title": "Regional Price Dynamics and Market Integration in the U.S. Beef Industry: An Econometric Analysis", "abstract": "The United States, a leading global producer and consumer of beef, continues to face substantial challenges in achieving price harmonization across its regional markets. This paper evaluates the validity of the Law of One Price (LOP) in the U.S. beef industry and investigates causal relationships among regional price dynamics. Through a series of econometric tests, we establish that regional price series are integrated of order one, displaying non-stationarity in levels and stationarity in first differences. The analysis reveals partial LOP compliance in the Northeast and West, while full convergence remains elusive at the national level. Although no region demonstrates persistent price leadership, Southern prices appear particularly sensitive to exogenous shocks. These findings reflect asymmetrical integration across U.S. beef markets and suggest the presence of structural frictions that hinder complete market unification.", "published": "2025-07-29T16:02:21Z", "authors": ["Leonardo Manríquez-Méndez"], "categories": ["econ.EM"], "doi": "", "_query": "hedonic house price", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2507.07053v1", "title": "Portfolio optimization in incomplete markets and price constraints determined by maximum entropy in the mean", "abstract": "A solution to a portfolio optimization problem is always conditioned by constraints on the initial capital and the price of the available market assets. If a risk neutral measure is known, then the price of each asset is the discounted expected value of the asset's price under this measure. But if the market is incomplete, the risk neutral measure is not unique, and there is a range of possible prices for each asset, which can be identified with bid-ask ranges. We present in this paper an effective method to determine the current prices of a collection of assets in incomplete markets, and such that these prices comply with the cost constraints for a portfolio optimization problem. Our workhorse is the method of maximum entropy in the mean to adjust a distortion function from bid-ask market data. This distortion function plays the role of a risk neutral measure, which is used to price the assets, and the distorted probability that it determines reproduces bid-ask market values. We carry out numerical examples to study the effect on portfolio returns of the computation of prices of the assets conforming the portfolio with the proposed methodology.", "published": "2025-07-03T17:04:28Z", "authors": ["Argimiro Arratia", "Henryk Gzyl"], "categories": ["math.OC", "q-fin.CP", "q-fin.PM", "q-fin.RM"], "doi": "10.1007/s10614-019-09954-3", "_query": "hedonic house price", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/1405.6573v1", "title": "Allocating Indivisible Resources under Price Rigidities in Polynomial Time", "abstract": "In many realistic problems of allocating resources, economy efficiency must be taken into consideration together with social equality, and price rigidities are often made according to some economic and social needs. We study the computational issues of dynamic mechanisms for selling multiple indivisible items under price rigidities. We propose a polynomial algorithm that can be used to find over-demanded sets of items, and then introduce a dynamic mechanism with rationing to discover constrained Walrasian equilibria under price rigidities in polynomial time. We also address the computation of sellers' expected profits and items' expected prices, and discuss strategical issues in the sense of expected profits.", "published": "2014-05-26T13:48:07Z", "authors": ["Wei Huang", "Jian Lou", "Zhonghua Wen"], "categories": ["cs.GT", "cs.MA"], "doi": "", "_query": "hedonic house price", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/physics/0508203v1", "title": "Impact of Stock Market Structure on Intertrade Time and Price Dynamics", "abstract": "The NYSE and NASDAQ stock markets have very different structures and there is continuing controversy over whether differences in stock price behaviour are due to market structure or company characteristics. As the influence of market structure on stock prices may be obscured by exogenous factors such as demand and supply, we hypothesize that modulation of the flow of transactions due to market operations may carry a stronger imprint of the internal market mechanism. We analyse times between consecutive transactions (ITT) for NYSE and NASDAQ stocks, and we relate the dynamical properties of the ITT with those of the corresponding price fluctuations. We find a robust scale-invariant temporal organisation in the ITT of stocks which is independent of individual company characteristics and industry sector, but which depends on market structure. We find that stocks registered on the NASDAQ exhibit stronger correlations in their transaction timing within a trading day, compared with NYSE stocks. Further, we find that companies that transfer from the NASDAQ to the NYSE show a reduction in the correlation strength of transaction timing within a trading day, after the move, suggesting influences of market structure. Surprisingly, we also observe that stronger power-law correlations in the ITT are coupled with stronger power-law correlations in absolute price returns and higher price volatility, suggesting a strong link between the dynamical properties of ITT and the corresponding price fluctuations over a broad range of time scales. Comparing the NYSE and NASDAQ, we demonstrate that the higher correlations we find in ITT for NASDAQ stocks are matched by higher correlations in absolute price returns and by higher volatility, suggesting that market structure may affect price behaviour through information contained in transaction timing.", "published": "2005-08-28T21:06:58Z", "authors": ["Ainslie Yuen", "Plamen Ch. Ivanov"], "categories": ["physics.soc-ph", "q-fin.ST"], "doi": "", "_query": "hedonic house price", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/1112.4385v3", "title": "Shadow price in the power utility case", "abstract": "We consider the problem of maximizing expected power utility from consumption over an infinite horizon in the Black-Scholes model with proportional transaction costs, as studied in Shreve and Soner [Ann. Appl. Probab. 4 (1994) 609-692]. Similar to Kallsen and Muhle-Karbe [Ann. Appl. Probab. 20 (2010) 1341-1358], we derive a shadow price, that is, a frictionless price process with values in the bid-ask spread which leads to the same optimal policy.", "published": "2011-12-19T16:06:25Z", "authors": ["Attila Herczegh", "Vilmos Prokaj"], "categories": ["q-fin.PM", "math.PR"], "doi": "10.1214/14-AAP1058", "_query": "hedonic house price", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2409.04106v1", "title": "CryptoAnalytics: Cryptocoins Price Forecasting with Machine Learning Techniques", "abstract": "This paper introduces CryptoAnalytics, a software toolkit for cryptocoins price forecasting with machine learning (ML) techniques. Cryptocoins are tradable digital assets exchanged for specific trading prices. While history has shown the extreme volatility of such trading prices, the ability to efficiently model and forecast the time series resulting from the exchange price volatility remains an open research challenge. Good results can been achieved with state-of-the-art ML techniques, including Gradient-Boosting Machines (GBMs) and Recurrent Neural Networks (RNNs). CryptoAnalytics is a software toolkit to easily train these models and make inference on up-to-date cryptocoin trading price data, with facilities to fetch datasets from one of the main leading aggregator websites, i.e., CoinMarketCap, train models and infer the future trends. This software is implemented in Python. It relies on PyTorch for the implementation of RNNs (LSTM and GRU), while for GBMs, it leverages on XgBoost, LightGBM and CatBoost.", "published": "2024-09-06T08:20:02Z", "authors": ["Pasquale De Rosa", "Pascal Felber", "Valerio Schiavoni"], "categories": ["cs.CE"], "doi": "10.1016/j.softx.2024.101663", "_query": "hedonic house price", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2003.13560v3", "title": "Discriminatory Price Mechanism for Smart Grid", "abstract": "We consider a scenario where a retailer can set different prices for different consumers in a smart grid. The retailer's objective is to maximize the revenue, minimize the operating cost, and maximize the consumer's welfare. The retailer wants to optimize a convex combination of the above objectives using price signals specific to each consumer. However, variability in unit prices across consumers is bounded by a parameter $η$, hence limiting the discrimination. We formulate the pricing problem as a Stackelberg game where the retailer is the leader and consumers are followers. Since the retailer's optimization problem turns out to be non-convex, we convexify it via relaxations. We provide performance guarantees for the relaxations in the asymptotic sense (when number of consumers tends to $\\infty$). Further, we show that despite the variability in pricing, the pricing scheme proposed by our model is fair as higher prices are charged to consumers who have higher willingness for demand. We extend our analysis to the scenario where consumers can feed energy back to the grid via net-metering. We show that our pricing policy promotes fairness even in this scenario as prosumers who contribute more to the grid, are given large cuts on buying rates. The policy is also found to incentivize more prosumers to invest in renewable energy, thus encouraging sustainability.", "published": "2020-03-30T15:34:54Z", "authors": ["Diptangshu Sen", "Kushaagra Goyal", "Varun Ramamohan", "Arnob Ghosh"], "categories": ["math.OC", "cs.GT", "eess.SY"], "doi": "", "_query": "hedonic house price", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2111.03112v1", "title": "My House, My Rules: Learning Tidying Preferences with Graph Neural Networks", "abstract": "Robots that arrange household objects should do so according to the user's preferences, which are inherently subjective and difficult to model. We present NeatNet: a novel Variational Autoencoder architecture using Graph Neural Network layers, which can extract a low-dimensional latent preference vector from a user by observing how they arrange scenes. Given any set of objects, this vector can then be used to generate an arrangement which is tailored to that user's spatial preferences, with word embeddings used for generalisation to new objects. We develop a tidying simulator to gather rearrangement examples from 75 users, and demonstrate empirically that our method consistently produces neat and personalised arrangements across a variety of rearrangement scenarios.", "published": "2021-11-04T19:17:19Z", "authors": ["Ivan Kapelyukh", "Edward Johns"], "categories": ["cs.RO", "cs.LG"], "doi": "", "_query": "hedonic house price", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/cond-mat/0202143v1", "title": "A Path Integral Way to Option Pricing", "abstract": "An efficient computational algorithm to price financial derivatives is presented. It is based on a path integral formulation of the pricing problem. It is shown how the path integral approach can be worked out in order to obtain fast and accurate predictions for the value of a large class of options, including those with path-dependent and early exercise features. As examples, the application of the method to European and American options in the Black-Scholes model is illustrated. A particularly simple and fast semi-analytical approximation for the price of American options is derived. The results of the algorithm are compared with those obtained with the standard procedures known in the literature and found to be in good agreement.", "published": "2002-02-08T14:06:30Z", "authors": ["G. Montagna", "O. Nicrosini", "N. Moreni"], "categories": ["cond-mat.stat-mech", "q-fin.PR"], "doi": "10.1016/S0378-4371(02)00796-3", "_query": "hedonic house price", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2102.07742v3", "title": "Dynamic Pricing with Limited Commitment", "abstract": "A monopolist wants to sell one item per period to a consumer with evolving and persistent private information. The seller sets a price each period depending on the history so far, but cannot commit to future prices. We show that, regardless of the degree of persistence, any equilibrium under a D1-style refinement gives the seller revenue no higher than what she would get from posting all prices in advance.", "published": "2021-02-15T18:37:54Z", "authors": ["Martino Banchio", "Frank Yang"], "categories": ["econ.TH"], "doi": "", "_query": "hedonic house price", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2203.06077v1", "title": "Econometric Modeling of Intraday Electricity Market Price with Inadequate Historical Data", "abstract": "The intraday (ID) electricity market has received an increasing attention in the recent EU electricity-market discussions. This is partly because the uncertainty in the underlying power system is growing and the ID market provides an adjustment platform to deal with such uncertainties. Hence, market participants need a proper ID market price model to optimally adjust their positions by trading in the market. Inadequate historical data for ID market price makes it more challenging to model. This paper proposes long short-term memory, deep convolutional generative adversarial networks, and No-U-Turn sampler algorithms to model ID market prices. Our proposed econometric ID market price models are applied to the Nordic ID price data and their promising performance are illustrated.", "published": "2022-03-11T16:55:31Z", "authors": ["Saeed Mohammadi", "Mohammad Reza Hesamzadeh"], "categories": ["eess.SY", "cs.LG", "stat.AP"], "doi": "", "_query": "hedonic house price", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2511.12629v2", "title": "Bandit Learning in Housing Markets", "abstract": "The housing market, also known as one-sided matching market, is a classic exchange economy model where each agent on the demand side initially owns an indivisible good (a house) and has a personal preference over all goods. The goal is to find a core-stable allocation that exhausts all mutually beneficial exchanges among subgroups of agents. While this model has been extensively studied in economics and computer science due to its broad applications, little attention has been paid to settings where preferences are unknown and must be learned through repeated interactions. In this paper, we propose a statistical learning model within the multi-player multi-armed bandit framework, where players (agents) learn their preferences over arms (goods) from stochastic rewards. We introduce the notion of \\emph{core regret} for each player as the market objective. We study both centralized and decentralized approaches, proving $O(\\log T / Δ^2)$ upper bounds on regret, where $T$ is the time horizon and $Δ$ is the minimum preference gap among players. For the decentralized setting, we also establish a matching lower bound, demonstrating that our algorithm is order-optimal.", "published": "2025-11-16T14:50:44Z", "authors": ["Shiyun Lin"], "categories": ["cs.GT"], "doi": "", "_query": "hedonic house price", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/1910.09149v3", "title": "Operational Valuation for Energy Storage under Multi-stage Price Uncertainties", "abstract": "This paper presents an analytical method for calculating the operational value of an energy storage device under multi-stage price uncertainties. Our solution calculates the storage value function from price distribution functions directly instead of sampling discrete scenarios, offering improved modeling accuracy over tail distribution events such as price spikes and negative prices. The analytical algorithm offers very high computational efficiency in solving multi-stage stochastic programming for energy storage and can easily be implemented within any software and hardware platform, while numerical simulation results show the proposed method is up to 100,000 times faster than a benchmark stochastic-dual dynamic programming solver even in small test cases. Case studies are included to demonstrate the impact of price variability on the valuation results, and a battery charging example using historical prices for New York City.", "published": "2019-10-21T04:58:16Z", "authors": ["Bolun Xu", "Audun Botterud", "Magnus Korpas"], "categories": ["math.OC"], "doi": "", "_query": "hedonic house price", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2409.06011v2", "title": "Investigating the effects of housing instability on depression, anxiety, and mental health treatment in childhood and adolescence", "abstract": "Housing instability is a widespread phenomenon in the United States. In combination with other social determinants of health, housing instability affects children's overall health and development. Drawing on data from the 2022 National Survey of Children's Health, we employed multiple logistic regression models to understand how sociodemographic factors, especially housing instability, affect mental health outcomes and treatment access for youth aged 6-17 years. Our results show that youth facing housing instability have a higher likelihood of experiencing anxiety (OR: 1.42, p<0.001) and depression (OR: 1.57, p<0.001). Furthermore, youth experiencing both mental health conditions and housing instability are significantly less likely to receive mental health services in the past year, indicating the substantial barriers they face in accessing mental health care. Based on our findings, we highlight opportunities for digital mental health interventions to provide children experiencing housing instability with more accessible and consistent mental health services.", "published": "2024-09-09T19:10:42Z", "authors": ["Rachael Zehrung", "Di Hu", "Yawen Guo", "Kai Zheng", "Yunan Chen"], "categories": ["cs.HC"], "doi": "", "_query": "hedonic house price", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2210.15035v2", "title": "Pricing and Electric Vehicle Charging Equilibria", "abstract": "We study equilibria in an Electric Vehicle (EV) charging game, a cost minimization game inherent to decentralized charging control strategy for EV power demand management. In our model, each user optimizes its total cost which is sum of direct power cost and the indirect dissatisfaction cost. We show that, taking player specific price independent dissatisfaction cost in to account, contrary to popular belief, herding only happens at lower EV uptake. Moreover, this is true for both linear and logistic dissatisfaction functions. We study the question of existence of price profiles to induce a desired equilibrium. We define two types of equilibria, distributed and non-distributed equilibria, and show that under logistic dissatisfaction, only non-distributed equilibria are possible by feasibly setting prices. In linear case, both type of equilibria are possible but price discrimination is necessary to induce distributed equilibria. Finally, we show that in the case of symmetric EV users, mediation cannot improve upon Nash equilibria.", "published": "2022-10-26T21:07:05Z", "authors": ["Trivikram Dokka", "Jorge Bruno", "Sonali SenGupta", "Chowdhury Mohammad Sakib Anwar"], "categories": ["econ.TH"], "doi": "", "_query": "hedonic house price", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2512.22599v1", "title": "Cryptocurrency Price Prediction Using Parallel Gated Recurrent Units", "abstract": "According to the advent of cryptocurrencies and Bitcoin, many investments and businesses are now conducted online through cryptocurrencies. Among them, Bitcoin uses blockchain technology to make transactions secure, transparent, traceable, and immutable. It also exhibits significant price fluctuations and performance, which has attracted substantial attention, especially in financial sectors. Consequently, a wide range of investors and individuals have turned to investing in the cryptocurrency market. One of the most important challenges in economics is price forecasting for future trades. Cryptocurrencies are no exception, and investors are looking for methods to predict prices; various theories and methods have been proposed in this field. This paper presents a new deep model, called \\emph{Parallel Gated Recurrent Units} (PGRU), for cryptocurrency price prediction. In this model, recurrent neural networks forecast prices in a parallel and independent way. The parallel networks utilize different inputs, each representing distinct price-related features. Finally, the outputs of the parallel networks are combined by a neural network to forecast the future price of cryptocurrencies. The experimental results indicate that the proposed model achieves mean absolute percentage errors (MAPE) of 3.243% and 2.641% for window lengths 20 and 15, respectively. Our method therefore attains higher accuracy and efficiency with fewer input data and lower computational cost compared to existing methods.", "published": "2025-12-27T14:04:21Z", "authors": ["Milad Asadpour", "Alireza Rezaee", "Farshid Hajati"], "categories": ["cs.LG"], "doi": "", "_query": "hedonic house price", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/1803.11227v2", "title": "AI Blue Book: Vehicle Price Prediction using Visual Features", "abstract": "In this work, we build a series of machine learning models to predict the price of a product given its image, and visualize the features that result in higher or lower price predictions. We collect two novel datasets of product images and their MSRP prices for this purpose: a bicycle dataset and a car dataset. We set baselines for price regression using linear regression on histogram of oriented gradients (HOG) and convolutional neural network (CNN) features, and a baseline for price segment classification using a multiclass SVM. For our main models, we train several deep CNNs using both transfer learning and our own architectures, for both regression and classification. We achieve strong results on both datasets, with deep CNNs significantly outperforming other models in a variety of metrics. Finally, we use several recently-developed methods to visualize the image features that result in higher or lower prices.", "published": "2018-03-29T19:22:23Z", "authors": ["Richard R. Yang", "Steven Chen", "Edward Chou"], "categories": ["cs.CV"], "doi": "", "_query": "hedonic house price", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/1705.08563v3", "title": "Simple Pricing Schemes for the Cloud", "abstract": "The problem of pricing the cloud has attracted much recent attention due to the widespread use of cloud computing and cloud services. From a theoretical perspective, several mechanisms that provide strong efficiency or fairness guarantees and desirable incentive properties have been designed. However, these mechanisms often rely on a rigid model, with several parameters needing to be precisely known in order for the guarantees to hold. In this paper, we consider a stochastic model and show that it is possible to obtain good welfare and revenue guarantees with simple mechanisms that do not make use of the information on some of these parameters. In particular, we prove that a mechanism that sets the same price per time step for jobs of any length achieves at least 50% of the welfare and revenue obtained by a mechanism that can set different prices for jobs of different lengths, and the ratio can be improved if we have more specific knowledge of some parameters. Similarly, a mechanism that sets the same price for all servers even though the servers may receive different kinds of jobs can provide a reasonable welfare and revenue approximation compared to a mechanism that is allowed to set different prices for different servers.", "published": "2017-05-23T23:49:55Z", "authors": ["Ian A. Kash", "Peter Key", "Warut Suksompong"], "categories": ["cs.GT"], "doi": "10.1145/3327973", "_query": "hedonic house price", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2402.11716v1", "title": "The Price of Abandoning Dark Matter Is Nonlocality", "abstract": "We consider the classic question posed by Pardo and Spergel about the price of abandoning dark matter in the context of an invariant, metric-based theory of gravity. Our answer is that the price is nonlocality. This has been known for some time in the context of the quasi-static regime. We show that it also applies for cosmology and we exhibit a model which reproduces standard CDM successes such as perturbations in the cosmic microwave background, baryon acoustic oscillations and structure formation.", "published": "2024-02-18T21:48:25Z", "authors": ["C. Deffayet", "R. P. Woodard"], "categories": ["gr-qc", "astro-ph.CO", "hep-th"], "doi": "", "_query": "hedonic house price", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2609.04712v1", "title": "Convex Modeling of Price Cross-Impact over Time", "abstract": "Transaction costs can make or break a trading strategy, particularly in relative-value trading of commodity and macro markets, where edges are a few basis points. Price impact is a central component of transaction cost. Price impact models usually include self-impact (a trade in a contract moves that contract's price) but omit two well-documented effects: cross-impact (a trade in one contract also moves the prices of related contracts) and transient impact (price impact decays over time, so an unwind recovers part of the entry cost). A model without these effects overprices the impact of relative-value trades, whose correlated legs are built and unwound over days, and so forgoes potentially profitable trades. This paper models both effects with a convex quadratic cost. In each period, a positive semidefinite matrix built from volatility, volume, and correlation forecasts couples trades across contracts. A power-law decay kernel then couples trades across periods. The resulting cost admits no price manipulation even when liquidity varies over the planning horizon. The model is demonstrated empirically on calendar spread trading of crude oil futures around the commodity index roll.", "published": "2026-09-04T04:32:55Z", "authors": ["Vincent Yinjun-Wang", "Madeleine Udell"], "categories": ["math.OC", "q-fin.CP", "q-fin.PM", "q-fin.RM"], "doi": "", "_query": "hedonic house price", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2412.07075v1", "title": "Conformal Uncertainty Quantification of Electricity Price Predictions for Risk-Averse Storage Arbitrage", "abstract": "This paper proposes a risk-averse approach to energy storage price arbitrage, leveraging conformal uncertainty quantification for electricity price predictions. The method addresses the significant challenges posed by the inherent volatility and uncertainty of real-time electricity prices, which create substantial risks of financial losses for energy storage participants relying on future price forecasts to plan their operations. The framework comprises a two-layer prediction model to quantify real-time price uncertainty confidence intervals with high coverage. The framework is distribution-free and can work with any underlying point prediction model. We evaluate the quantification effectiveness through storage price arbitrage application by managing the risk of participating in the real-time market. We design a risk-averse policy for profit-maximization of energy storage arbitrage to find the safest storage schedule with very minimal losses. Using historical data from New York State and synthetic price predictions, our evaluations demonstrate that this framework can achieve good profit margins with less than $35\\%$ purchases.", "published": "2024-12-10T00:31:15Z", "authors": ["Saud Alghumayjan", "Ming Yi", "Bolun Xu"], "categories": ["math.OC", "stat.ML"], "doi": "", "_query": "hedonic house price", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2606.05363v3", "title": "Oblivious Learning and Collusive Pricing", "abstract": "On a platform with many sellers, should a pricing algorithm explicitly model competitors' prices when learning demand? Classical arguments suggest that ignoring competitors induces model misspecification and inefficiency, yet findings from algorithmic collusion suggest that ignoring competitor prices may, surprisingly, facilitate collusive outcomes and improve profits. We study this problem in a competitive market with unknown noisy demand, in which sellers repeatedly set prices, either incorporating competitor prices in learning their demand models (informed), or ignoring them (oblivious). We show that, relative to a monopolist, an oblivious seller in a competitive market must conduct more aggressive price exploration to compensate for the loss of dynamic competitor information. When all sellers are oblivious, prices converge to the competitive outcome under persistent exploration, while a continuum of pseudo-equilibria arises when exploration is \"insufficient.\" In markets with a mix of oblivious and informed sellers, the informed strictly out-earn the oblivious. In game-theoretic terms, the unique Nash equilibrium is the all-informed market, in which prices converge to the competitive outcome efficiently, and oblivious modeling does not robustly lead to collusive patterns.", "published": "2026-06-03T19:10:55Z", "authors": ["Yuhang Wu", "Assaf Zeevi"], "categories": ["cs.GT", "cs.LG", "econ.TH", "math.OC"], "doi": "", "_query": "hedonic house price", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2501.10196v1", "title": "Pricing Mechanisms versus Non-Pricing Mechanisms for Demand Side Management in Microgrids", "abstract": "In this paper, we compare pricing and non-pricing mechanisms for implementing demand-side management (DSM) mechanisms in a neighborhood in Helsinki, Finland. We compare load steering based on peak load-reduction using the profile steering method, and load steering based on market price signals, in terms of peak loads, losses, and device profiles. We found that there are significant differences between the two methods; the peak-load reduction control strategies contribute to reducing peak power and improving power flow stability, while strategies primarily based on prices result in higher peaks and increased grid losses. Our results highlight the need to potentially move away from market-price-based DSM to DSM incentivization and control strategies that are based on peak load reductions and other system requirements.", "published": "2025-01-17T13:39:54Z", "authors": ["Cassia Nunes Almeida", "Arun Narayanan", "Hafiz Majid Hussain", "Pedro H. J. Nardelli"], "categories": ["eess.SY"], "doi": "", "_query": "hedonic house price", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/1705.00672v5", "title": "Portfolio Choice with Small Temporary and Transient Price Impact", "abstract": "We study portfolio selection in a model with both temporary and transient price impact introduced by Garleanu and Pedersen (2016). In the large-liquidity limit where both frictions are small, we derive explicit formulas for the asymptotically optimal trading rate and the corresponding minimal leading-order performance loss. We find that the losses are governed by the volatility of the frictionless target strategy, like in models with only temporary price impact. In contrast, the corresponding optimal portfolio not only tracks the frictionless optimizer, but also exploits the displacement of the market price from its unaffected level.", "published": "2017-05-01T19:17:41Z", "authors": ["Ibrahim Ekren", "Johannes Muhle-Karbe"], "categories": ["q-fin.PM", "math.OC"], "doi": "", "_query": "hedonic house price", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/1606.08515v1", "title": "Directly Constraining Marginal Prices", "abstract": "The marginal price of electricity traditionally depends on the dual variables associated with relevant optimization goals. Particularly, in the optimal power flow realm, prices represent the cost of supplying an additional unit of power at each bus; for the economic dispatch case, dual variables represent the cost of supplying an additional unit of power to the whole system. Dual variables are useful for many additional tasks, including the analysis of system congestion and the determination of the cost of load adjustments. In this letter, departing from conventional optimal power flow analysis, it is shown how constraints on relevant dual variables affect the prices of electricity, allowing for market settings and demand response programs that accept bids and caps on individual prices.", "published": "2016-06-27T23:52:01Z", "authors": ["Kyri Baker"], "categories": ["math.OC"], "doi": "", "_query": "hedonic house price", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2103.16388v1", "title": "Text Mining of Stocktwits Data for Predicting Stock Prices", "abstract": "Stock price prediction can be made more efficient by considering the price fluctuations and understanding the sentiments of people. A limited number of models understand financial jargon or have labelled datasets concerning stock price change. To overcome this challenge, we introduced FinALBERT, an ALBERT based model trained to handle financial domain text classification tasks by labelling Stocktwits text data based on stock price change. We collected Stocktwits data for over ten years for 25 different companies, including the major five FAANG (Facebook, Amazon, Apple, Netflix, Google). These datasets were labelled with three labelling techniques based on stock price changes. Our proposed model FinALBERT is fine-tuned with these labels to achieve optimal results. We experimented with the labelled dataset by training it on traditional machine learning, BERT, and FinBERT models, which helped us understand how these labels behaved with different model architectures. Our labelling method competitive advantage is that it can help analyse the historical data effectively, and the mathematical function can be easily customised to predict stock movement.", "published": "2021-03-13T03:29:14Z", "authors": ["Mukul Jaggi", "Priyanka Mandal", "Shreya Narang", "Usman Naseem", "Matloob Khushi"], "categories": ["q-fin.ST", "cs.LG"], "doi": "10.3390/asi4010013", "_query": "hedonic house price", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/1907.06118v2", "title": "Online Rental Housing Market Representation and the Digital Reproduction of Urban Inequality", "abstract": "As the rental housing market moves online, the Internet offers divergent possible futures: either the promise of more-equal access to information for previously marginalized homeseekers, or a reproduction of longstanding information inequalities. Biases in online listings' representativeness could impact different communities' access to housing search information, reinforcing traditional information segregation patterns through a digital divide. They could also circumscribe housing practitioners' and researchers' ability to draw broad market insights from listings to understand rental supply and affordability. This study examines millions of Craigslist rental listings across the US and finds that they spatially concentrate and over-represent whiter, wealthier, and better-educated communities. Other significant demographic differences exist in age, language, college enrollment, rent, poverty rate, and household size. Most cities' online housing markets are digitally segregated by race and class, and we discuss various implications for residential mobility, community legibility, gentrification, housing voucher utilization, and automated monitoring and analytics in the smart cities paradigm. While Craigslist contains valuable crowdsourced data to better understand affordability and available rental supply in real-time, it does not evenly represent all market segments. The Internet promises information democratization, and online listings can reduce housing search costs and increase choice sets. However, technology access/preferences and information channel segregation can concentrate such information-broadcasting benefits in already-advantaged communities, reproducing traditional inequalities and reinforcing residential sorting and segregation dynamics. Technology platforms like Craigslist construct new institutions with the power to shape spatial economies.", "published": "2019-07-13T18:56:57Z", "authors": ["Geoff Boeing"], "categories": ["econ.GN", "stat.AP"], "doi": "10.1177/0308518X19869678", "_query": "hedonic house price", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2501.11164v1", "title": "A statistical technique for cleaning option price data", "abstract": "Recorded option pricing datasets are not always freely available. Additionally, these datasets often contain numerous prices which are either higher or lower than can reasonably be expected. Various reasons for these unexpected observations are possible, including human error in the recording of the details associated with the option in question. In order for the analyses performed on these datasets to be reliable, it is necessary to identify and remove these options from the dataset. In this paper, we list three distinct problems often found in recorded option price datasets alongside means of addressing these. The methods used are justified using sound statistical reasoning and remove option prices violating the standard assumption of no arbitrage. An attractive aspect of the proposed technique is that no option pricing model-based assumptions are used. Although the discussion is restricted to European options, the procedure is easily modified for use with exotic options as well. As a final contribution, the paper contains a link to six option pricing datasets which have already been cleaned using the proposed methods and can be freely used by researchers.", "published": "2025-01-19T20:39:55Z", "authors": ["Jaco Visagie"], "categories": ["q-fin.CP"], "doi": "", "_query": "hedonic house price", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2309.04216v3", "title": "Liquidity Dynamics in RFQ Markets and Impact on Pricing", "abstract": "To assign a value to a portfolio, it is common to use Mark-to-Market prices. However, how should one proceed when the securities are illiquid? When transaction prices are scarce, how can one use all the available real-time information? In this article, we address these questions for over-the-counter (OTC) markets based on requests for quotes (RFQs). We extend the concept of micro-price, which was recently introduced for assets exchanged through limit order books in the market microstructure literature, and incorporate ideas from the recent literature on OTC market making. To account for liquidity imbalances in RFQ markets, we use an approach based on bidimensional Markov-modulated Poisson processes. Beyond extending the concept of micro-price to RFQ markets, we introduce the new concept of Fair Transfer Price. Our concepts of price can be used to value securities fairly, even when the market is relatively illiquid and/or tends to be one-sided.", "published": "2023-09-08T09:01:51Z", "authors": ["Philippe Bergault", "Olivier Guéant"], "categories": ["q-fin.TR", "q-fin.ST"], "doi": "", "_query": "hedonic house price", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/1407.4376v3", "title": "Common price and volatility jumps in noisy high-frequency data", "abstract": "We introduce a statistical test for simultaneous jumps in the price of a financial asset and its volatility process. The proposed test is based on high-frequency data and is robust to market microstructure frictions. For the test, local estimators of volatility jumps at price jump arrival times are designed using a nonparametric spectral estimator of the spot volatility process. A simulation study and an empirical example with NASDAQ order book data demonstrate the practicability of the proposed methods and highlight the important role played by price volatility co-jumps.", "published": "2014-07-16T16:28:41Z", "authors": ["Markus Bibinger", "Lars Winkelmann"], "categories": ["math.ST"], "doi": "", "_query": "hedonic house price", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2009.01317v1", "title": "Towards Earnings Call and Stock Price Movement", "abstract": "Earnings calls are hosted by management of public companies to discuss the company's financial performance with analysts and investors. Information disclosed during an earnings call is an essential source of data for analysts and investors to make investment decisions. Thus, we leverage earnings call transcripts to predict future stock price dynamics. We propose to model the language in transcripts using a deep learning framework, where an attention mechanism is applied to encode the text data into vectors for the discriminative network classifier to predict stock price movements. Our empirical experiments show that the proposed model is superior to the traditional machine learning baselines and earnings call information can boost the stock price prediction performance.", "published": "2020-08-23T20:38:14Z", "authors": ["Zhiqiang Ma", "Grace Bang", "Chong Wang", "Xiaomo Liu"], "categories": ["q-fin.ST", "cs.CE", "cs.CL", "cs.LG"], "doi": "", "_query": "hedonic house price", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2301.01555v2", "title": "Optimal Liquidation with High Risk Aversion and Small Linear Price Impact", "abstract": "We consider the Bachelier model with linear price impact. Exponential utility indifference prices are studied for vanilla European options in the case where the investor is required to liquidate her position. Our main result is establishing a non-trivial scaling limit for a vanishing price impact which is inversely proportional to the risk aversion. We compute the limit of the corresponding utility indifference prices and find explicitly a family of portfolios which are asymptotically optimal.", "published": "2023-01-04T11:53:49Z", "authors": ["Leonid Dolinskyi", "Yan Dolinsky"], "categories": ["q-fin.MF"], "doi": "", "_query": "hedonic house price", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2007.06242v3", "title": "Optimal Bounds on the Price of Fairness for Indivisible Goods", "abstract": "In the allocation of resources to a set of agents, how do fairness guarantees impact the social welfare? A quantitative measure of this impact is the price of fairness, which measures the worst-case loss of social welfare due to fairness constraints. While initially studied for divisible goods, recent work on the price of fairness also studies the setting of indivisible goods. In this paper, we resolve the price of two well-studied fairness notions for the allocation of indivisible goods: envy-freeness up to one good (EF1), and approximate maximin share (MMS). For both EF1 and 1/2-MMS guarantees, we show, via different techniques, that the price of fairness is $O(\\sqrt{n})$, where $n$ is the number of agents. From previous work, it follows that our bounds are tight. Our bounds are obtained via efficient algorithms. For 1/2-MMS, our bound holds for additive valuations, whereas for EF1, our bound holds for the more general class of subadditive valuations. This resolves an open problem posed by Bei et al. (2019).", "published": "2020-07-13T08:49:53Z", "authors": ["Siddharth Barman", "Umang Bhaskar", "Nisarg Shah"], "categories": ["cs.GT", "cs.DS"], "doi": "", "_query": "hedonic house price", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/1602.04950v3", "title": "Deviations in expected price impact for small transaction volumes under fee restructuring", "abstract": "We report on the occurrence of an anomaly in the price impacts of small transaction volumes following a change in the fee structure of an electronic market. We first review evidence for the existence of a master curve for price impact on the Johannesburg Stock Exchange (JSE). On attempting to re-estimate a master curve after fee reductions, it is found that the price impact corresponding to smaller volume trades is greater than expected relative to prior estimates for a range of listed stocks. We show that a master curve for price impact can be found following rescaling by an appropriate liquidity proxy, providing a means for practitioners to approximate price impact curves without onerous processing of tick data.", "published": "2016-02-16T09:04:50Z", "authors": ["Michael Harvey", "Dieter Hendricks", "Tim Gebbie", "Diane Wilcox"], "categories": ["q-fin.TR"], "doi": "10.1016/j.physa.2016.11.042", "_query": "hedonic house price", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2306.00599v1", "title": "The Cost of Misspecifying Price Impact", "abstract": "Portfolio managers' orders trade off return and trading cost predictions. Return predictions rely on alpha models, whereas price impact models quantify trading costs. This paper studies what happens when trades are based on an incorrect price impact model, so that the portfolio either over- or under-trades its alpha signal. We derive tractable formulas for these misspecification costs and illustrate them on proprietary trading data. The misspecification costs are naturally asymmetric: underestimating impact concavity or impact decay shrinks profits, but overestimating concavity or impact decay can even turn profits into losses.", "published": "2023-06-01T12:14:18Z", "authors": ["Natascha Hey", "Jean-Philippe Bouchaud", "Iacopo Mastromatteo", "Johannes Muhle-Karbe", "Kevin Webster"], "categories": ["q-fin.TR"], "doi": "", "_query": "hedonic house price", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2312.06114v2", "title": "The virial theorem and the Price equation", "abstract": "We observe that the time averaged continuous Price equation is identical to the positive momentum virial theorem, and we discuss the applications and implications of this connection.", "published": "2023-12-11T04:37:11Z", "authors": ["Catherine Felce", "Steinunn Liorsdóttir", "Lior Pachter"], "categories": ["physics.bio-ph", "q-bio.QM"], "doi": "", "_query": "hedonic house price", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/cond-mat/9609144v1", "title": "Price Variations in a Stock Market With Many Agents", "abstract": "Large variations in stock prices happen with sufficient frequency to raise doubts about existing models, which all fail to account for non-Gaussian statistics. We construct simple models of a stock market, and argue that the large variations may be due to a crowd effect, where agents imitate each other's behavior. The variations over different time scales can be related to each other in a systematic way, similar to the Levy stable distribution proposed by Mandelbrot to describe real market indices. In the simplest, least realistic case, exact results for the statistics of the variations are derived by mapping onto a model of diffusing and annihilating particles, which has been solved by quantum field theory methods. When the agents imitate each other and respond to recent market volatility, different scaling behavior is obtained. In this case the statistics of price variations is consistent with empirical observations. The interplay between ``rational'' traders whose behavior is derived from fundamental analysis of the stock, including dividends, and ``noise traders'', whose behavior is governed solely by studying the market dynamics, is investigated. When the relative number of rational traders is small, ``bubbles'' often occur, where the market price moves outside the range justified by fundamental market analysis. When the number of rational traders is larger, the market price is generally locked within the price range they define.", "published": "1996-09-16T18:43:15Z", "authors": ["P. Bak", "M. Paczuski", "M. Shubik"], "categories": ["cond-mat"], "doi": "10.1016/S0378-4371(97)00401-9", "_query": "hedonic house price", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2005.02953v2", "title": "The Pricing of Quanto Options: An empirical copula approach", "abstract": "The quanto option is a cross-currency derivative in which the pay-off is given in foreign currency and then converted to domestic currency, through a constant exchange rate, used for the conversion and determined at contract inception. Hence, the dependence relation between the option underlying asset price and the exchange rate plays an important role in quanto option pricing. In this work, we suggest to use empirical copulas to price quanto options. Numerical illustrations show that the flexibility provided by this approach, concerning the dependence relation of the two underlying stochastic processes, results in non-negligible pricing differences when contrasted to other models.", "published": "2020-05-06T16:57:58Z", "authors": ["Rafael Felipe Carmargo Prudencio", "Christian D. Jäkel"], "categories": ["q-fin.MF"], "doi": "", "_query": "hedonic house price", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2309.09176v3", "title": "Odd period cycles and ergodic properties in price dynamics for an exchange economy", "abstract": "In the first part of this paper (Sections 1-4), we study a standard exchange economy model with Cobb-Douglas type consumers and give a necessary and sufficient condition for the existence of an odd period cycle in the Walras-Samuelson (tatonnement) price adjustment process. We also give a sufficient condition for a price to be eventually attracted to a chaotic region. In the second part (Sections 5 and 6), we investigate ergodic properties of the price dynamics showing that the existence of chaos is not necessarily bad. (The future is still predictable on average.) Moreover, supported by a celebrated work of Avila et al. (Invent. Math., 2003), we conduct a sensitivity analysis to investigate a relationship between the ergodic sum (of prices) and the speed of price adjustment. We believe that our methods in this paper can be used to analyse many other chaotic economic models.", "published": "2023-09-17T06:55:36Z", "authors": ["Tomohiro Uchiyama"], "categories": ["econ.GN"], "doi": "", "_query": "hedonic house price", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/cs/0407029v1", "title": "Static versus Dynamic Arbitrage Bounds on Multivariate Option Prices", "abstract": "We compare static arbitrage price bounds on basket calls, i.e. bounds that only involve buy-and-hold trading strategies, with the price range obtained within a multi-variate generalization of the Black-Scholes model. While there is no gap between these two sets of prices in the univariate case, we observe here that contrary to our intuition about model risk for at-the-money calls, there is a somewhat large gap between model prices and static arbitrage prices, hence a similarly large set of prices on which a multivariate Black-Scholes model cannot be calibrated but where no conclusion can be drawn on the presence or not of a static arbitrage opportunity.", "published": "2004-07-10T16:17:26Z", "authors": ["Alexandre d'Aspremont"], "categories": ["cs.CE"], "doi": "", "_query": "hedonic house price", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/physics/0506224v1", "title": "Impact of Investor's Varying Risk Aversion on the Dynamics of Asset Price Fluctuations", "abstract": "While the investors' responses to price changes and their price forecasts are well accepted major factors contributing to large price fluctuations in financial markets, our study shows that investors' heterogeneous and dynamic risk aversion (DRA) preferences may play a more critical role in the dynamics of asset price fluctuations. We propose and study a model of an artificial stock market consisting of heterogeneous agents with DRA, and we find that DRA is the main driving force for excess price fluctuations and the associated volatility clustering. We employ a popular power utility function, $U(c,γ)=\\frac{c^{1-γ}-1}{1-γ}$ with agent specific and time-dependent risk aversion index, $γ_i(t)$, and we derive an approximate formula for the demand function and aggregate price setting equation. The dynamics of each agent's risk aversion index, $γ_i(t)$ (i=1,2,...,N), is modeled by a bounded random walk with a constant variance $δ^2$. We show numerically that our model reproduces most of the ``stylized'' facts observed in the real data, suggesting that dynamic risk aversion is a key mechanism for the emergence of these stylized facts.", "published": "2005-06-30T08:29:21Z", "authors": ["Baosheng Yuan", "Kan Chen"], "categories": ["physics.soc-ph", "physics.comp-ph", "q-fin.ST"], "doi": "", "_query": "hedonic house price", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/1309.7119v3", "title": "Stock price direction prediction by directly using prices data: an empirical study on the KOSPI and HSI", "abstract": "The prediction of a stock market direction may serve as an early recommendation system for short-term investors and as an early financial distress warning system for long-term shareholders. Many stock prediction studies focus on using macroeconomic indicators, such as CPI and GDP, to train the prediction model. However, daily data of the macroeconomic indicators are almost impossible to obtain. Thus, those methods are difficult to be employed in practice. In this paper, we propose a method that directly uses prices data to predict market index direction and stock price direction. An extensive empirical study of the proposed method is presented on the Korean Composite Stock Price Index (KOSPI) and Hang Seng Index (HSI), as well as the individual constituents included in the indices. The experimental results show notably high hit ratios in predicting the movements of the individual constituents in the KOSPI and HIS.", "published": "2013-09-27T05:35:50Z", "authors": ["Yanshan Wang"], "categories": ["cs.CE", "cs.LG", "q-fin.ST"], "doi": "10.1504/IJBIDM.2014.065091", "_query": "hedonic house price", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2512.20190v1", "title": "Pricing of wrapped Bitcoin and Ethereum on-chain options", "abstract": "This paper measures price differences between Hegic option quotes on Arbitrum and a model-based benchmark built on Black--Scholes model with regime-sensitive volatility estimated via a two-regime MS-AR-(GJR)-GARCH model. Using option-level feasible GLS, we find benchmark prices exceed Hegic quotes on average, especially for call options. The price spread rises with order size, strike, maturity, and estimated volatility, and falls with trading volume. By underlying, wrapped Bitcoin options show larger and more persistent spreads, while Ethereum options are closer to the benchmark. The framework offers a data-driven analysis for monitoring and calibrating on-chain option pricing logic.", "published": "2025-12-23T09:29:57Z", "authors": ["Anastasiia Zbandut"], "categories": ["q-fin.PR", "q-fin.RM"], "doi": "", "_query": "hedonic house price", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2103.16918v2", "title": "A survey of electricity spot and futures price models for risk management applications", "abstract": "This review presents the set of electricity price models proposed in the literature since the opening of power markets. We focus on price models applied to financial pricing and risk management. We classify these models according to their ability to represent the random behavior of prices and some of their characteristics. In particular, this classification helps users to choose among the most suitable models for their risk management problems.", "published": "2021-03-31T09:17:28Z", "authors": ["Thomas Deschatre", "Olivier Féron", "Pierre Gruet"], "categories": ["q-fin.MF", "q-fin.RM"], "doi": "", "_query": "hedonic house price", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2312.15282v3", "title": "Causal Forecasting for Pricing", "abstract": "This paper proposes a novel method for demand forecasting in a pricing context. Here, modeling the causal relationship between price as an input variable to demand is crucial because retailers aim to set prices in a (profit) optimal manner in a downstream decision making problem. Our methods bring together the Double Machine Learning methodology for causal inference and state-of-the-art transformer-based forecasting models. In extensive empirical experiments, we show on the one hand that our method estimates the causal effect better in a fully controlled setting via synthetic, yet realistic data. On the other hand, we demonstrate on real-world data that our method outperforms forecasting methods in off-policy settings (i.e., when there's a change in the pricing policy) while only slightly trailing in the on-policy setting.", "published": "2023-12-23T15:38:22Z", "authors": ["Douglas Schultz", "Johannes Stephan", "Julian Sieber", "Trudie Yeh", "Manuel Kunz", "Patrick Doupe", "Tim Januschowski"], "categories": ["stat.ML", "cs.LG"], "doi": "", "_query": "hedonic house price", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/1812.10930v1", "title": "On the use and abuse of Price equation concepts in ecology", "abstract": "In biodiversity and ecosystem functioning (BEF) research, the Loreau-Hector (LH) statistical scheme is widely-used to partition the effect of biodiversity on ecosystem properties into a \"complementarity effect\" and a \"selection effect\". This selection effect was originally considered analogous to the selection term in the Price equation from evolutionary biology. However, a key paper published over thirteen years ago challenged this interpretation by devising a new tripartite partitioning scheme that purportedly quantified the role of selection in biodiversity experiments more accurately. This tripartite method, as well as its recent spatiotemporal extension, were both developed as an attempt to apply the Price equation in a BEF context. Here, we demonstrate that the derivation of this tripartite method, as well as its spatiotemporal extension, involve a set of incoherent and nonsensical mathematical arguments driven largely by naïve visual analogies with the original Price equation, that result in neither partitioning scheme quantifying any real property in the natural world. Furthermore, we show that Loreau and Hector's original selection effect always represented a true analog of the original Price selection term, making the tripartite partitioning scheme a nonsensical solution to a non-existent problem [...]", "published": "2018-12-28T09:11:20Z", "authors": ["Pradeep Pillai", "Tarik C. Gouhier"], "categories": ["q-bio.PE"], "doi": "", "_query": "hedonic house price", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/1102.5501v1", "title": "Extension theorems for linear operators on $L_\\infty$ and application to price systems", "abstract": "In an $L_\\infty$-framework, we present a few extension theorems for linear operators. We focus the attention on majorant preserving and sandwich preserving types of extensions. These results are then applied to the study of price systems derived by a reasonable restriction of the class of equivalent martingale measures applicable. First we consider equivalent martingale measures with bounds on densities and the corresponding prices bounded by linear minorant and majorant. Then we consider prices bounded by bid-ask dynamics. Finally we study price systems consistent with no-good-deal pricing measures for given bounds on the Sharpe ratio. Within this study we introduce the definition of dynamic no-good-deal pricing measure.", "published": "2011-02-27T13:23:55Z", "authors": ["Jocelyne Bion-Nadal", "Giulia Di Nunno"], "categories": ["math.PR", "q-fin.PR"], "doi": "", "_query": "hedonic house price", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2303.13956v2", "title": "Pitman's Theorem, Black-Scholes Equation, and Derivative Pricing for Fundraisers", "abstract": "We propose a financial market model that comprises a savings account and a stock. The stock price process is modeled as a one-dimensional diffusion, in which two types of agents exist: an ordinary investor and a fundraiser who buys or sells stocks as funding activities. Although the investor information is the natural filtration of the diffusion, the fundraiser possesses additional information regarding the funding, as well as additional cash flows as a result of the funding. This concept is modeled using Pitman's theorem for the three-dimensional Bessel process. Two contributions are presented: First, the prices of European options for the fundraiser are derived. Second, a numerical scheme for call option prices in a market with a bubble is proposed, where multiple solutions exist for the Black--Scholes equation and the derivative prices are characterized as the smallest nonnegative supersolution. More precisely, the call option price in such a market is approximated from below by the prices for the fundraiser. This scheme overcomes the difficulty that stems from the discrepancy that the payoff shows linear growth, whereas the price function shows strictly sublinear growth.", "published": "2023-03-24T12:29:17Z", "authors": ["Yukihiro Tsuzuki"], "categories": ["q-fin.MF"], "doi": "", "_query": "hedonic house price", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2509.01499v1", "title": "When Do Consumers Lose from Variable Electricity Pricing?", "abstract": "Time-varying electricity pricing better reflects the varying cost of electricity compared to flat-rate pricing. Variations between peak and off-peak costs are increasing due to weather variation, renewable intermittency, and increasing electrification of demand. Empirical and theoretical studies suggest that variable pricing can lower electricity supply costs and reduce grid stress. However, the distributional impacts, particularly on low-income consumers, remain understudied. This paper develops a theoretical framework to analyze how consume heterogeneity affects welfare outcomes when electricity markets transition from flat-rate to time-varying pricing, considering realistic assumptions about heterogeneous consumer demand, supply costs, and utility losses from unmet consumption. We derive sufficient conditions for identifying when consumers lose utility from pricing reforms and compare welfare effects across consumer types. Our findings reveal that consumer vulnerability depends on the interaction of consumption timing, demand flexibility capabilities, and price sensitivity levels. Consumers with high peak-period consumption and inflexible demand, characteristics often associated with low-income households, are most vulnerable to welfare losses. Critically, we demonstrate that demand flexibility provides welfare protection only when coincident with large price changes. Our equilibrium analysis reveals that aggregate flexibility patterns generate spillover effects through pricing mechanisms, with peak periods experiencing greater price changes when they have less aggregate flexibility, potentially concentrating larger price increases among vulnerable populations that have a limited ability to respond. These findings suggest that variable pricing policies should be accompanied by targeted policies ensuring equitable access to demand response capabilities and pricing benefits.", "published": "2025-09-01T14:20:27Z", "authors": ["Nathan Engelman Lado", "Richard Chen", "Saurabh Amin"], "categories": ["econ.GN"], "doi": "", "_query": "hedonic house price", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2110.11581v1", "title": "A Two-stage Pricing Strategy Considering Learning Effects and Word-of-Mouth", "abstract": "This paper proposes a two-stage pricing strategy for nondurable (such as typical electronics) products, where retail price is cut down at certain time points of the product lifecycle. We consider learning effect of electronic products that, with the accumulation of production, average production cost decreases over time as manufacturers get familiar with the production process. Moreover, word-of-mouth (WOM) of existing customers is used to analyze future demand, which is sensitive to the difference between the actual reliability and the perceived reliability of products. We theoretically prove the existence and uniqueness of the optimal switch time between the two stages and the optimal price in each stage. In addition, warranty as another important factor of electronic products is also considered, whose interaction with word-of-mouth as well as the corresponding influences on total profit are analyzed. Interestingly, our findings indicate that (1) the main reason for manufacturers to cut down prices for electronic products pertains to the learning effects; (2) even through both internal factors (e.g., the learning effects of manufacturers) and external factors (e.g., the price elasticity of customers) have impacts on product price, their influence on manufacturer's profit is widely divergent; (3) generally warranty weakens the influence of external advertising on the reliability estimate, because warranty price only partially reflects the actual reliability information of products; (4) and the optimal warranty price can increase the profits for the manufacturer by approximately 10%.", "published": "2021-10-22T04:18:53Z", "authors": ["Yanrong Li", "Lai Wei", "Wei Jiang"], "categories": ["econ.GN"], "doi": "", "_query": "hedonic house price", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/1710.08394v1", "title": "Fixed Price Approximability of the Optimal Gain From Trade", "abstract": "Bilateral trade is a fundamental economic scenario comprising a strategically acting buyer and seller, each holding valuations for the item, drawn from publicly known distributions. A mechanism is supposed to facilitate trade between these agents, if such trade is beneficial. It was recently shown that the only mechanisms that are simultaneously DSIC, SBB, and ex-post IR, are fixed price mechanisms, i.e., mechanisms that are parametrised by a price p, and trade occurs if and only if the valuation of the buyer is at least p and the valuation of the seller is at most p. The gain from trade is the increase in welfare that results from applying a mechanism; here we study the gain from trade achievable by fixed price mechanisms. We explore this question for both the bilateral trade setting, and a double auction setting where there are multiple buyers and sellers. We first identify a fixed price mechanism that achieves a gain from trade of at least 2/r times the optimum, where r is the probability that the seller's valuation does not exceed the buyer's valuation. This extends a previous result by McAfee. Subsequently, we improve this approximation factor in an asymptotic sense, by showing that a more sophisticated rule for setting the fixed price results in an expected gain from trade within a factor O(log(1/r)) of the optimal gain from trade. This is asymptotically the best approximation factor possible. Lastly, we extend our study of fixed price mechanisms to the double auction setting defined by a set of multiple i.i.d. unit demand buyers, and i.i.d. unit supply sellers. We present a fixed price mechanism that achieves a gain from trade that achieves for all epsilon > 0 a gain from trade of at least (1-epsilon) times the expected optimal gain from trade with probability 1 - 2/e^{#T epsilon^2 /2}, where #T is the expected number of trades resulting from the double auction.", "published": "2017-10-23T17:17:37Z", "authors": ["Riccardo Colini-Baldeschi", "Paul Goldberg", "Bart de Keijzer", "Stefano Leonardi", "Stefano Turchetta"], "categories": ["cs.GT"], "doi": "", "_query": "hedonic house price", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/1805.04728v1", "title": "Effects of a Price limit Change on Market Stability at the Intraday Horizon in the Korean Stock Market", "abstract": "This paper investigates the effects of a price limit change on the volatility of the Korean stock market's (KRX) intraday stock price process. Based on the most recent transaction data from the KRX, which experienced a change in the price limit on June 15, 2015, we examine the change in realized variance after the price limit change to investigate the overall effects of the change on the intraday market volatility. We then analyze the effects in more detail by applying the discrete Fourier transform (DFT) to the data set. We find evidence that the market becomes more volatile in the intraday horizon because of the increase in the amplitudes of the low-frequency components of the price processes after the price limit change. Therefore, liquidity providers are in a worse situation than they were prior to the change.", "published": "2018-05-12T14:56:03Z", "authors": ["Wonse Kim", "Sungjae Jun"], "categories": ["q-fin.TR"], "doi": "", "_query": "hedonic house price", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/1404.0648v2", "title": "Dynamic optimal execution in a mixed-market-impact Hawkes price model", "abstract": "We study a linear price impact model including other liquidity takers, whose flow of orders either follows a Poisson or a Hawkes process. The optimal execution problem is solved explicitly in this context, and the closed-formula optimal strategy describes in particular how one should react to the orders of other traders. This result enables us to discuss the viability of the market. It is shown that Poissonian arrivals of orders lead to quite robust Price Manipulation Strategies in the sense of Huberman and Stanzl. Instead, a particular set of conditions on the Hawkes model balances the self-excitation of the order flow with the resilience of the price, excludes Price Manipulation Strategies and gives some market stability.", "published": "2014-04-02T18:13:34Z", "authors": ["Aurélien Alfonsi", "Pierre Blanc"], "categories": ["q-fin.TR"], "doi": "", "_query": "hedonic house price", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2111.08221v2", "title": "Fairness-aware Online Price Discrimination with Nonparametric Demand Models", "abstract": "Price discrimination, which refers to the strategy of setting different prices for different customer groups, has been widely used in online retailing. Although it helps boost the collected revenue for online retailers, it might create serious concerns about fairness, which even violates the regulation and laws. This paper studies the problem of dynamic discriminatory pricing under fairness constraints. In particular, we consider a finite selling horizon of length $T$ for a single product with two groups of customers. Each group of customers has its unknown demand function that needs to be learned. For each selling period, the seller determines the price for each group and observes their purchase behavior. While existing literature mainly focuses on maximizing revenue, ensuring fairness among different customers has not been fully explored in the dynamic pricing literature. This work adopts the fairness notion from Cohen et al. (2022). For price fairness, we propose an optimal dynamic pricing policy regarding regret, which enforces the strict price fairness constraint. In contrast to the standard $\\sqrt{T}$-type regret in online learning, we show that the optimal regret in our case is $\\tilde{O}(T^{4/5})$. We further extend our algorithm to a more general notion of fairness, which includes demand fairness as a special case. To handle this general class, we propose a soft fairness constraint and develop a dynamic pricing policy that achieves $\\tilde{O}(T^{4/5})$ regret. We also demonstrate that our algorithmic techniques can be adapted to more general scenarios such as fairness among multiple groups of customers.", "published": "2021-11-16T04:31:02Z", "authors": ["Xi Chen", "Jiameng Lyu", "Xuan Zhang", "Yuan Zhou"], "categories": ["cs.LG", "stat.ML"], "doi": "", "_query": "hedonic house price", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/1501.00818v2", "title": "Forecasting day ahead electricity spot prices: The impact of the EXAA to other European electricity markets", "abstract": "In our paper we analyze the relationship between the day-ahead electricity price of the Energy Exchange Austria (EXAA) and other day-ahead electricity prices in Europe. We focus on markets, which settle their prices after the EXAA, which enables traders to include the EXAA price into their calculations. For each market we employ econometric models to incorporate the EXAA price and compare them with their counterparts without the price of the Austrian exchange. By employing a forecasting study, we find that electricity price models can be improved when EXAA prices are considered.", "published": "2015-01-05T11:07:11Z", "authors": ["Florian Ziel", "Rick Steinert", "Sven Husmann"], "categories": ["q-fin.TR", "econ.EM", "q-fin.ST", "stat.AP", "stat.OT"], "doi": "10.1016/j.eneco.2015.08.005", "_query": "hedonic house price", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/1505.08117v1", "title": "Predictability of price movements in deregulated electricity markets", "abstract": "In this paper we investigate predictability of electricity prices in the Canadian provinces of Alberta and Ontario, as well as in the US Mid-C market. Using scale-dependent detrended fluctuation analysis, spectral analysis, and the probability distribution analysis we show that the studied markets exhibit strongly anti-persistent properties suggesting that their dynamics can be predicted based on historic price records across the range of time scales from one hour to one month. For both Canadian markets, the price movements reveal three types of correlated behavior which can be used for forecasting. The discovered scenarios remain the same on different time scales up to one month as well as for on- and off- peak electricity data. These scenarios represent sharp increases of prices and are not present in the Mid-C market due to its lower volatility. We argue that extreme price movements in this market should follow the same tendency as the more volatile Canadian markets. The estimated values of the Pareto indices suggest that the prediction of these events can be statistically stable. The results obtained provide new relevant information for managing financial risks associated with the dynamics of electricity derivatives over time frame exceeding one day.", "published": "2015-01-23T00:52:10Z", "authors": ["Olga Y. Uritskaya", "Vadim M. Uritsky"], "categories": ["q-fin.ST", "physics.data-an", "physics.soc-ph"], "doi": "", "_query": "hedonic house price", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2002.08207v1", "title": "Inventory effects on the price dynamics of VSTOXX futures quantified via machine learning", "abstract": "The VSTOXX index tracks the expected 30-day volatility of the EURO STOXX 50 equity index. Futures on the VSTOXX index can, therefore, be used to hedge against economic uncertainty. We investigate the effect of trader inventory on the price of VSTOXX futures through a combination of stochastic processes and machine learning methods. We formulate a simple and efficient pricing methodology for VSTOXX futures, which assumes a Heston-type stochastic process for the underlying EURO STOXX 50 market. Under these dynamics, approximate analytical formulas for the implied volatility smile and the VSTOXX index have recently been derived. We use the EURO STOXX 50 option implied volatilities and the VSTOXX index value to estimate the parameters of this Heston model. Following the calibration, we calculate theoretical VSTOXX future prices and compare them to the actual market prices. While theoretical and market prices are usually in line, we also observe time periods, during which the market price does not agree with our Heston model. We collect a variety of market features that could potentially explain the price deviations and calibrate two machine learning models to the price difference: a regularized linear model and a random forest. We find that both models indicate a strong influence of accumulated trader positions on the VSTOXX futures price.", "published": "2020-02-19T14:30:31Z", "authors": ["Daniel Guterding"], "categories": ["q-fin.TR", "q-fin.PR"], "doi": "10.1016/j.jfds.2021.06.001", "_query": "hedonic house price", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/1803.06917v1", "title": "Universal features of price formation in financial markets: perspectives from Deep Learning", "abstract": "Using a large-scale Deep Learning approach applied to a high-frequency database containing billions of electronic market quotes and transactions for US equities, we uncover nonparametric evidence for the existence of a universal and stationary price formation mechanism relating the dynamics of supply and demand for a stock, as revealed through the order book, to subsequent variations in its market price. We assess the model by testing its out-of-sample predictions for the direction of price moves given the history of price and order flow, across a wide range of stocks and time periods. The universal price formation model is shown to exhibit a remarkably stable out-of-sample prediction accuracy across time, for a wide range of stocks from different sectors. Interestingly, these results also hold for stocks which are not part of the training sample, showing that the relations captured by the model are universal and not asset-specific. The universal model --- trained on data from all stocks --- outperforms, in terms of out-of-sample prediction accuracy, asset-specific linear and nonlinear models trained on time series of any given stock, showing that the universal nature of price formation weighs in favour of pooling together financial data from various stocks, rather than designing asset- or sector-specific models as commonly done. Standard data normalizations based on volatility, price level or average spread, or partitioning the training data into sectors or categories such as large/small tick stocks, do not improve training results. On the other hand, inclusion of price and order flow history over many past observations is shown to improve forecasting performance, showing evidence of path-dependence in price dynamics.", "published": "2018-03-19T13:46:37Z", "authors": ["Justin Sirignano", "Rama Cont"], "categories": ["q-fin.ST", "q-fin.TR", "stat.ML"], "doi": "", "_query": "hedonic house price", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2301.02797v1", "title": "Option pricing under the normal SABR model with Gaussian quadratures", "abstract": "The stochastic-alpha-beta-rho (SABR) model has been widely adopted in options trading. In particular, the normal ($β=0$) SABR model is a popular model choice for interest rates because it allows negative asset values. The option price and delta under the SABR model are typically obtained via asymptotic implied volatility approximation, but these are often inaccurate and arbitrageable. Using a recently discovered price transition law, we propose a Gaussian quadrature integration scheme for price options under the normal SABR model. The compound Gaussian quadrature sum over only 49 points can calculate a very accurate price and delta that are arbitrage-free.", "published": "2023-01-07T07:47:09Z", "authors": ["Jaehyuk Choi", "Byoung Ki Seo"], "categories": ["q-fin.PR", "q-fin.CP", "q-fin.MF"], "doi": "", "_query": "hedonic house price", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/1708.02411v1", "title": "Nonlinear price impact from linear models", "abstract": "The impact of trades on asset prices is a crucial aspect of market dynamics for academics, regulators and practitioners alike. Recently, universal and highly nonlinear master curves were observed for price impacts aggregated on all intra-day scales [1]. Here we investigate how well these curves, their scaling, and the underlying return dynamics are captured by linear \"propagator\" models. We find that the classification of trades as price-changing versus non-price-changing can explain the price impact nonlinearities and short-term return dynamics to a very high degree. The explanatory power provided by the change indicator in addition to the order sign history increases with increasing tick size. To obtain these results, several long-standing technical issues for model calibration and -testing are addressed. We present new spectral estimators for two- and three-point cross-correlations, removing the need for previously used approximations. We also show when calibration is unbiased and how to accurately reveal previously overlooked biases. Therefore, our results contribute significantly to understanding both recent empirical results and the properties of a popular class of impact models.", "published": "2017-08-08T09:03:32Z", "authors": ["Felix Patzelt", "Jean-Philippe Bouchaud"], "categories": ["q-fin.TR"], "doi": "10.1088/1742-5468/aa9335", "_query": "hedonic house price", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2307.00492v2", "title": "Stochastic Approach for Price Optimization Problems with Decision-dependent Uncertainty", "abstract": "Price determination is a central research topic of revenue management in marketing. The important aspect in pricing is controlling the stochastic behavior of demand, and the previous studies have tackled price optimization problems with uncertainties. However, many of those studies assumed that uncertainties are independent of decision variables (i.e., prices) and did not consider situations where demand uncertainty depends on price. Although some price optimization studies have dealt with decision-dependent uncertainty, they make application-specific assumptions in order to obtain an optimal solution or an approximation solution. To handle a wider range of applications with decision-dependent uncertainty, we propose a general non-convex stochastic optimization formulation. This approach aims to maximize the expectation of a revenue function with respect to a random variable representing demand under a decision-dependent distribution. We derived an unbiased stochastic gradient estimator by using a well-tuned variance reduction parameter and used it for a projected stochastic gradient descent method to find a stationary point of our problem. We conducted synthetic experiments and simulation experiments with real data on a retail service application. The results show that the proposed method outputs solutions with higher total revenues than baselines.", "published": "2023-07-02T06:46:03Z", "authors": ["Yuya Hikima", "Akiko Takeda"], "categories": ["math.OC"], "doi": "", "_query": "hedonic house price", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/1110.0403v2", "title": "Pricing and Semimartingale Representations of Vulnerable Contingent Claims in Regime-Switching Markets", "abstract": "Using a suitable change of probability measure, we obtain a novel Poisson series representation for the arbitrage- free price process of vulnerable contingent claims in a regime-switching market driven by an underlying continuous- time Markov process. As a result of this representation, along with a short-time asymptotic expansion of the claim's price process, we develop an efficient method for pricing claims whose payoffs may depend on the full path of the underlying Markov chain. The proposed approach is applied to price not only simple European claims such as defaultable bonds, but also a new type of path-dependent claims that we term self-decomposable, as well as the important class of vulnerable call and put options on a stock. We provide a detailed error analysis and illustrate the accuracy and computational complexity of our method on several market traded instruments, such as defaultable bond prices, barrier options, and vulnerable call options. Using again our Poisson series representation, we show differentiability in time of the pre-default price function of European vulnerable claims, which enables us to rigorously deduce Feynman-Kac representations for the pre-default pricing function and new semimartingale representations for the price process of the vulnerable claim under both risk-neutral and objective probability measures.", "published": "2011-10-03T16:22:29Z", "authors": ["Agostino Capponi", "Jose Figueroa-Lopez", "Jeffrey Nisen"], "categories": ["q-fin.CP", "q-fin.PM", "q-fin.PR", "q-fin.RM"], "doi": "", "_query": "hedonic house price", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2308.02276v1", "title": "Optimal Liquidation with Conditions on Minimum Price", "abstract": "The classical optimal trading problem is the closure of a position in an asset over a time interval; the trader maximizes an expected utility under the constraint that the position be fully closed by terminal time. Since the asset price is stochastic, the liquidation constraint may be too restrictive; the trader may want to relax it or slow down/stop trading depending on price behavior. We consider two additional parameters that serve these purposes within the Almgren-Chriss framework: a binary valued process $I$ that prescribes when trading takes place and a measurable set $S$ that prescribes when full liquidation is required. We give four examples for $S$ and $I$ which are defined in terms of a lower bound for the price process. The terminal cost of the control problem is $\\infty$ over $S$ representing the liquidation constraint. The permanent price impact parameter enters the problem as the negative part of the terminal cost over $S^c$. $I$ modifies the running cost. A terminal cost that can take negative values implies 1) the backward stochastic differential equation (BSDE) associated with the value function of the control problem can explode to $-\\infty$ backward in time and 2) existence results on minimal supersolutions of BSDE with singular terminal values and monotone drivers are not directly applicable. A key part of the solution is an assumption that balances market volume and the permanent price impact parameter and a lower bound on the BSDE based on this assumption. When liquidation costs are quadratic, the problem is convex and, under a general filtration, the minimal supersolution of the BSDE gives the value function and the optimal control. For the non-quadratic case, we assume a stochastic volatility model and focus on choices of $I$ and $S$ that are Markovian or can be broken into Markovian pieces. These give PDE/PDE-system representations for the value functions.", "published": "2023-08-04T12:00:53Z", "authors": ["Mervan Aksu", "Alexandre Popier", "Ali Devin Sezer"], "categories": ["math.PR"], "doi": "", "_query": "hedonic house price", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2405.00235v1", "title": "Blockchain Price vs. Quantity Controls", "abstract": "This paper studies the optimal transaction fee mechanisms for blockchains, focusing on the distinction between price-based ($\\mathcal{P}$) and quantity-based ($\\mathcal{Q}$) controls. By analyzing factors such as demand uncertainty, validator costs, cryptocurrency price fluctuations, price elasticity of demand, and levels of decentralization, we establish criteria that determine the selection of transaction fee mechanisms. We present a model framed around a Nash bargaining game, exploring how blockchain designers and validators negotiate fee structures to balance network welfare with profitability. Our findings suggest that the choice between $\\mathcal{P}$ and $\\mathcal{Q}$ mechanisms depends critically on the blockchain's specific technical and economic features. The study concludes that no single mechanism suits all contexts and highlights the potential for hybrid approaches that adaptively combine features of both $\\mathcal{P}$ and $\\mathcal{Q}$ to meet varying demands and market conditions.", "published": "2024-04-30T23:03:00Z", "authors": ["Abdoulaye Ndiaye"], "categories": ["econ.GN"], "doi": "10.1007/978-3-031-78676-1_17", "_query": "hedonic house price", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/1905.02666v5", "title": "Option Pricing using Quantum Computers", "abstract": "We present a methodology to price options and portfolios of options on a gate-based quantum computer using amplitude estimation, an algorithm which provides a quadratic speedup compared to classical Monte Carlo methods. The options that we cover include vanilla options, multi-asset options and path-dependent options such as barrier options. We put an emphasis on the implementation of the quantum circuits required to build the input states and operators needed by amplitude estimation to price the different option types. Additionally, we show simulation results to highlight how the circuits that we implement price the different option contracts. Finally, we examine the performance of option pricing circuits on quantum hardware using the IBM Q Tokyo quantum device. We employ a simple, yet effective, error mitigation scheme that allows us to significantly reduce the errors arising from noisy two-qubit gates.", "published": "2019-05-07T16:14:09Z", "authors": ["Nikitas Stamatopoulos", "Daniel J. Egger", "Yue Sun", "Christa Zoufal", "Raban Iten", "Ning Shen", "Stefan Woerner"], "categories": ["quant-ph"], "doi": "10.22331/q-2020-07-06-291", "_query": "hedonic house price", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2007.02553v4", "title": "On robust fundamental theorems of asset pricing in discrete time", "abstract": "This paper is devoted to a study of robust fundamental theorems of asset pricing in discrete time and finite horizon settings. Uncertainty is modelled by a (possibly uncountable) family of price processes on the same probability space. Our technical assumption is the continuity of the price processes with respect to uncertain parameters. In this setting, we introduce a new topological framework which allows us to use the classical arguments in arbitrage pricing theory involving $L^p$ spaces, the Hahn-Banach separation theorem and other tools from functional analysis. The first result is the equivalence of a ``no robust arbitrage\" condition and the existence of a new ``robust pricing system\". The second result shows superhedging dualities and the existence of superhedging strategies without restrictive conditions on payoff functions, unlike other related studies. The third result discusses completeness in the present robust setting. When other options are available for static trading, we could reduce the set of robust pricing systems and hence the superhedging prices.", "published": "2020-07-06T06:47:10Z", "authors": ["Huy N. Chau"], "categories": ["q-fin.MF"], "doi": "", "_query": "hedonic house price", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2505.17203v2", "title": "Transfer Faster, Price Smarter: Minimax Dynamic Pricing under Cross-Market Preference Shift", "abstract": "We study contextual dynamic pricing when a target market can leverage K auxiliary markets -- offline logs or concurrent streams -- whose mean utilities differ by a structured preference shift. We propose Cross-Market Transfer Dynamic Pricing (CM-TDP), the first algorithm that provably handles such model-shift transfer and delivers minimax-optimal regret for both linear and non-parametric utility models. For linear utilities of dimension d, where the difference between source- and target-task coefficients is $s_{0}$-sparse, CM-TDP attains regret $\\tilde{O}((d*K^{-1}+s_{0})\\log T)$. For nonlinear demand residing in a reproducing kernel Hilbert space with effective dimension $α$, complexity $β$ and task-similarity parameter $H$, the regret becomes $\\tilde{O}\\!(K^{-2αβ/(2αβ+1)}T^{1/(2αβ+1)} + H^{2/(2α+1)}T^{1/(2α+1)})$, matching information-theoretic lower bounds up to logarithmic factors. The RKHS bound is the first of its kind for transfer pricing and is of independent interest. Extensive simulations show up to 50% lower cumulative regret and 5 times faster learning relative to single-market pricing baselines. By bridging transfer learning, robust aggregation, and revenue optimization, CM-TDP moves toward pricing systems that transfer faster, price smarter.", "published": "2025-05-22T18:18:17Z", "authors": ["Yi Zhang", "Elynn Chen", "Yujun Yan"], "categories": ["stat.ME", "cs.LG", "stat.AP"], "doi": "", "_query": "hedonic house price", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/1511.01132v1", "title": "Liquid Price of Anarchy", "abstract": "Incorporating budget constraints into the analysis of auctions has become increasingly important, as they model practical settings more accurately. The social welfare function, which is the standard measure of efficiency in auctions, is inadequate for settings with budgets, since there may be a large disconnect between the value a bidder derives from obtaining an item and what can be liquidated from her. The Liquid Welfare objective function has been suggested as a natural alternative for settings with budgets. Simple auctions, like simultaneous item auctions, are evaluated by their performance at equilibrium using the Price of Anarchy (PoA) measure -- the ratio of the objective function value of the optimal outcome to the worst equilibrium. Accordingly, we evaluate the performance of simultaneous item auctions in budgeted settings by the Liquid Price of Anarchy (LPoA) measure -- the ratio of the optimal Liquid Welfare to the Liquid Welfare obtained in the worst equilibrium. Our main result is that the LPoA for mixed Nash equilibria is bounded by a constant when bidders are additive and items can be divided into sufficiently many discrete parts. Our proofs are robust, and can be extended to achieve similar bounds for simultaneous second price auctions as well as Bayesian Nash equilibria. For pure Nash equilibria, we establish tight bounds on the LPoA for the larger class of fractionally-subadditive valuations. To derive our results, we develop a new technique in which some bidders deviate (surprisingly) toward a non-optimal solution. In particular, this technique does not fit into the smoothness framework.", "published": "2015-11-03T21:58:05Z", "authors": ["Yossi Azar", "Michal Feldman", "Nick Gravin", "Alan Roytman"], "categories": ["cs.GT"], "doi": "", "_query": "hedonic house price", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2507.01970v1", "title": "News Sentiment Embeddings for Stock Price Forecasting", "abstract": "This paper will discuss how headline data can be used to predict stock prices. The stock price in question is the SPDR S&P 500 ETF Trust, also known as SPY that tracks the performance of the largest 500 publicly traded corporations in the United States. A key focus is to use news headlines from the Wall Street Journal (WSJ) to predict the movement of stock prices on a daily timescale with OpenAI-based text embedding models used to create vector encodings of each headline with principal component analysis (PCA) to exact the key features. The challenge of this work is to capture the time-dependent and time-independent, nuanced impacts of news on stock prices while handling potential lag effects and market noise. Financial and economic data were collected to improve model performance; such sources include the U.S. Dollar Index (DXY) and Treasury Interest Yields. Over 390 machine-learning inference models were trained. The preliminary results show that headline data embeddings greatly benefit stock price prediction by at least 40% compared to training and optimizing a machine learning system without headline data embeddings.", "published": "2025-06-19T17:30:07Z", "authors": ["Ayaan Qayyum"], "categories": ["q-fin.ST", "cs.LG"], "doi": "", "_query": "hedonic house price", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2007.08726v1", "title": "Tight Bounds for the Price of Anarchy and Stability in Sequential Transportation Games", "abstract": "In this paper, we analyze a transportation game first introduced by Fotakis, Gourvès, and Monnot in 2017, where players want to be transported to a common destination as quickly as possible and, in order to achieve this goal, they have to choose one of the available buses. We introduce a sequential version of this game and provide bounds for the Sequential Price of Stability and the Sequential Price of Anarchy in both metric and non-metric instances, considering three social cost functions: the total traveled distance by all buses, the maximum distance traveled by a bus, and the sum of the distances traveled by all players (a new social cost function that we introduce). Finally, we analyze the Price of Stability and the Price of Anarchy for this new function in simultaneous transportation games.", "published": "2020-07-17T02:40:36Z", "authors": ["Francisco J. M. da Silva", "Flávio K. Miyazawa", "Ieremies V. F. Romero", "Rafael C. S. Schouery"], "categories": ["cs.GT"], "doi": "", "_query": "hedonic house price", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/1803.02486v1", "title": "Pricing index options by static hedging under finite liquidity", "abstract": "We develop a model for indifference pricing in derivatives markets where price quotes have bid-ask spreads and finite quantities. The model quantifies the dependence of the prices and hedging portfolios on an investor's beliefs, risk preferences and financial position as well as on the price quotes. Computational techniques of convex optimisation allow for fast computation of the hedging portfolios and prices as well as sensitivities with respect to various model parameters. We illustrate the techniques by pricing and hedging of exotic derivatives on S&P index using call and put options, forward contracts and cash as the hedging instruments. The optimized static hedges provide good approximations of the options payouts and the spreads between indifference selling and buying prices are quite narrow as compared with the spread between super- and subhedging prices.", "published": "2018-03-07T00:25:02Z", "authors": ["John Armstrong", "Teemu Pennanen", "Udomsak Rakwongwan"], "categories": ["q-fin.PR"], "doi": "", "_query": "hedonic house price", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/1910.04879v1", "title": "Predicting Auction Price of Vehicle License Plate with Deep Residual Learning", "abstract": "Due to superstition, license plates with desirable combinations of characters are highly sought after in China, fetching prices that can reach into the millions in government-held auctions. Despite the high stakes involved, there has been essentially no attempt to provide price estimates for license plates. We present an end-to-end neural network model that simultaneously predict the auction price, gives the distribution of prices and produces latent feature vectors. While both types of neural network architectures we consider outperform simpler machine learning methods, convolutional networks outperform recurrent networks for comparable training time or model complexity. The resulting model powers our online price estimator and search engine.", "published": "2019-10-08T16:38:06Z", "authors": ["Vinci Chow"], "categories": ["cs.CV", "cs.LG", "econ.GN"], "doi": "10.1007/978-3-030-26142-9_16", "_query": "hedonic house price", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2204.01435v1", "title": "The Potential Method For Price-Formation Models", "abstract": "We consider the mean-field game price formation model introduced by Gomes and Saúde. In this MFG model, agents trade a commodity whose supply can be deterministic or stochastic. Agents maximize profit, taking into account current and future prices. The balance between supply and demand determines the price. We introduce a potential function that converts the MFG into a convex variational problem. This variational formulation is particularly suitable for machine learning approaches. Here, we use a recurrent neural network to solve this problem. In the last section of the paper, we compare our results with known analytical solutions.", "published": "2022-04-04T12:37:45Z", "authors": ["Yuri Ashrafyan", "Tigran Bakaryan", "Diogo Gomes", "Julian Gutierrez"], "categories": ["math.NA"], "doi": "", "_query": "hedonic house price", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2207.06442v1", "title": "Kinematics and Mass Distributions for Non-Spherical Deprojected Sérsic Density Profiles and Applications to Multi-Component Galactic Systems", "abstract": "Using kinematics to decompose galaxies' mass profiles, including the dark matter contribution, often requires parameterization of the baryonic mass distribution based on ancillary information. One such model choice is a deprojected Sérsic profile with an assumed intrinsic geometry. The case of flattened, deprojected Sérsic models has previously been applied to flattened bulges in local star-forming galaxies (SFGs), but can also be used to describe the thick, turbulent disks in distant SFGs. Here we extend this previous work that derived density ($ρ$) and circular velocity ($v_{\\rm circ}$) curves by additionally calculating the spherically-enclosed 3D mass profiles ($M_{\\rm sph}$). Using these profiles, we compare the projected and 3D mass distributions, quantify the differences between the projected and 3D half-mass radii ($R_{\\rm e}; r_{\\rm 1/2,mass,3D}$), and present virial coefficients relating $v_{\\rm circ}(R)$ and $M_{\\rm sph}(<r=R)$ or $M_{\\rm tot}$. We then quantify differences between mass fraction estimators for multi-component systems, particularly for dark matter fractions, and consider the compound effects of measuring dark matter fractions at the projected versus 3D half-mass radii. While the fraction estimators produce only minor differences, using different aperture radius definitions can strongly impact the inferred dark matter fraction. As pressure support is important in analysis of gas kinematics (particularly at high redshifts), we also calculate the self-consistent pressure support correction profiles, which generally predict less pressure support than for the self-gravitating disk case. These results have implications for comparisons between simulation and observational measurements, and for the interpretation of SFG kinematics at high redshifts. A set of precomputed tables and the code to calculate the profiles are made publicly available. [Abridged]", "published": "2022-07-13T18:00:13Z", "authors": ["S. H. Price", "H. Übler", "N. M. Förster Schreiber", "P. T. de Zeeuw", "A. Burkert", "R. Genzel", "L. J. Tacconi", "R. I. Davies", "C. P. Price"], "categories": ["astro-ph.GA"], "doi": "10.1051/0004-6361/202244143", "_query": "hedonic house price", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/1506.07504v1", "title": "Objective Variables for Probabilistic Revenue Maximization in Second-Price Auctions with Reserve", "abstract": "Many online companies sell advertisement space in second-price auctions with reserve. In this paper, we develop a probabilistic method to learn a profitable strategy to set the reserve price. We use historical auction data with features to fit a predictor of the best reserve price. This problem is delicate - the structure of the auction is such that a reserve price set too high is much worse than a reserve price set too low. To address this we develop objective variables, a new framework for combining probabilistic modeling with optimal decision-making. Objective variables are \"hallucinated observations\" that transform the revenue maximization task into a regularized maximum likelihood estimation problem, which we solve with an EM algorithm. This framework enables a variety of prediction mechanisms to set the reserve price. As examples, we study objective variable methods with regression, kernelized regression, and neural networks on simulated and real data. Our methods outperform previous approaches both in terms of scalability and profit.", "published": "2015-06-24T19:20:18Z", "authors": ["Maja R. Rudolph", "Joseph G. Ellis", "David M. Blei"], "categories": ["stat.ML", "cs.AI", "cs.GT", "cs.LG", "stat.AP"], "doi": "", "_query": "hedonic house price", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2205.00974v1", "title": "Cross Cryptocurrency Relationship Mining for Bitcoin Price Prediction", "abstract": "Blockchain finance has become a part of the world financial system, most typically manifested in the attention to the price of Bitcoin. However, a great deal of work is still limited to using technical indicators to capture Bitcoin price fluctuation, with little consideration of historical relationships and interactions between related cryptocurrencies. In this work, we propose a generic Cross-Cryptocurrency Relationship Mining module, named C2RM, which can effectively capture the synchronous and asynchronous impact factors between Bitcoin and related Altcoins. Specifically, we utilize the Dynamic Time Warping algorithm to extract the lead-lag relationship, yielding Lead-lag Variance Kernel, which will be used for aggregating the information of Altcoins to form relational impact factors. Comprehensive experimental results demonstrate that our C2RM can help existing price prediction methods achieve significant performance improvement, suggesting the effectiveness of Cross-Cryptocurrency interactions on benefitting Bitcoin price prediction.", "published": "2022-04-28T02:39:42Z", "authors": ["Panpan Li", "Shengbo Gong", "Shaocong Xu", "Jiajun Zhou", "Yu Shanqing", "Qi Xuan"], "categories": ["q-fin.ST", "cs.AI", "cs.LG"], "doi": "", "_query": "hedonic house price", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2409.05990v1", "title": "FairHome: A Fair Housing and Fair Lending Dataset", "abstract": "We present a Fair Housing and Fair Lending dataset (FairHome): A dataset with around 75,000 examples across 9 protected categories. To the best of our knowledge, FairHome is the first publicly available dataset labeled with binary labels for compliance risk in the housing domain. We demonstrate the usefulness and effectiveness of such a dataset by training a classifier and using it to detect potential violations when using a large language model (LLM) in the context of real-estate transactions. We benchmark the trained classifier against state-of-the-art LLMs including GPT-3.5, GPT-4, LLaMA-3, and Mistral Large in both zero-shot and few-shot contexts. Our classifier outperformed with an F1-score of 0.91, underscoring the effectiveness of our dataset.", "published": "2024-09-09T18:34:26Z", "authors": ["Anusha Bagalkotkar", "Aveek Karmakar", "Gabriel Arnson", "Ondrej Linda"], "categories": ["cs.LG", "cs.CY"], "doi": "", "_query": "hedonic house price", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/1907.11984v1", "title": "Investigating the effect of competitiveness power in estimating the average weighted price in electricity market", "abstract": "This paper evaluates the impact of the power extent on price in the electricity market. The competitiveness extent of the electricity market during specific times in a day is considered to achieve this. Then, the effect of competitiveness extent on the forecasting precision of the daily power price is assessed. A price forecasting model based on multi-layer perception via back propagation with the Levenberg-Marquardt mechanism is used. The Residual Supply Index (RSI) and other variables that affect prices are used as inputs to the model to evaluate the market competitiveness. The results show that using market power indices as inputs helps to increase forecasting accuracy. Thus, the competitiveness extent of the market power in different daily time periods is a notable variable in price formation. Moreover, market players cannot ignore the explanatory power of market power in price forecasting. In this research, the real data of the electricity market from 2013 is used and the main source of data is the Grid Management Company in Iran.", "published": "2019-07-27T22:13:38Z", "authors": ["Naser Rostamni", "Tarik A. Rashid"], "categories": ["q-fin.ST", "cs.LG"], "doi": "10.1016/j.tej.2019.106628", "_query": "hedonic house price", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/1507.08042v1", "title": "Randomization beats Second Price as a Prior-Independent Auction", "abstract": "Designing revenue optimal auctions for selling an item to $n$ symmetric bidders is a fundamental problem in mechanism design. Myerson (1981) shows that the second price auction with an appropriate reserve price is optimal when bidders' values are drawn i.i.d. from a known regular distribution. A cornerstone in the prior-independent revenue maximization literature is a result by Bulow and Klemperer (1996) showing that the second price auction without a reserve achieves $(n-1)/n$ of the optimal revenue in the worst case. We construct a randomized mechanism that strictly outperforms the second price auction in this setting. Our mechanism inflates the second highest bid with a probability that varies with $n$. For two bidders we improve the performance guarantee from $0.5$ to $0.512$ of the optimal revenue. We also resolve a question in the design of revenue optimal mechanisms that have access to a single sample from an unknown distribution. We show that a randomized mechanism strictly outperforms all deterministic mechanisms in terms of worst case guarantee.", "published": "2015-07-29T07:31:56Z", "authors": ["Hu Fu", "Nicole Immolica", "Brendan Lucier", "Philipp Strack"], "categories": ["cs.GT"], "doi": "", "_query": "hedonic house price", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/1801.02702v3", "title": "Revealed Price Preference: Theory and Empirical Analysis", "abstract": "To determine the welfare implications of price changes in demand data, we introduce a revealed preference relation over prices. We show that the absence of cycles in this relation characterizes a consumer who trades off the utility of consumption against the disutility of expenditure. Our model can be applied whenever a consumer's demand over a strict subset of all available goods is being analyzed; it can also be extended to settings with discrete goods and nonlinear prices. To illustrate its use, we apply our model to a single-agent data set and to a data set with repeated cross-sections. We develop a novel test of linear hypotheses on partially identified parameters to estimate the proportion of the population who are revealed better off due to a price change in the latter application. This new technique can be used for nonparametric counterfactual analysis more broadly.", "published": "2018-01-08T22:22:30Z", "authors": ["Rahul Deb", "Yuichi Kitamura", "John K. -H. Quah", "Jörg Stoye"], "categories": ["econ.EM", "econ.TH"], "doi": "10.1093/restud/rdac041", "_query": "hedonic house price", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/cond-mat/0011506v1", "title": "Option Pricing and Hedging with Temporal Correlations", "abstract": "We consider the problem of option pricing and hedging when stock returns are correlated in time. Within a quadratic-risk minimisation scheme, we obtain a general formula, valid for weakly correlated non-Gaussian processes. We show that for Gaussian price increments, the correlations are irrelevant, and the Black-Scholes formula holds with the volatility of the price increments on the scale of the re-hedging. For non-Gaussian processes, further non trivial corrections to the `smile' are brought about by the correlations, even when the hedge is the Black-Scholes Delta-hedge. We introduce a compact notation which eases the computations and could be of use to deal with more complicated models.", "published": "2000-11-29T17:52:22Z", "authors": ["Lorenzo Cornalba", "Jean-Philippe Bouchaud", "Marc Potters"], "categories": ["cond-mat"], "doi": "", "_query": "hedonic house price", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2310.18646v1", "title": "Predicting Agricultural Commodities Prices with Machine Learning: A Review of Current Research", "abstract": "Agricultural price prediction is crucial for farmers, policymakers, and other stakeholders in the agricultural sector. However, it is a challenging task due to the complex and dynamic nature of agricultural markets. Machine learning algorithms have the potential to revolutionize agricultural price prediction by improving accuracy, real-time prediction, customization, and integration. This paper reviews recent research on machine learning algorithms for agricultural price prediction. We discuss the importance of agriculture in developing countries and the problems associated with crop price falls. We then identify the challenges of predicting agricultural prices and highlight how machine learning algorithms can support better prediction. Next, we present a comprehensive analysis of recent research, discussing the strengths and weaknesses of various machine learning techniques. We conclude that machine learning has the potential to revolutionize agricultural price prediction, but further research is essential to address the limitations and challenges associated with this approach.", "published": "2023-10-28T09:09:29Z", "authors": ["Nhat-Quang Tran", "Anna Felipe", "Thanh Nguyen Ngoc", "Tom Huynh", "Quang Tran", "Arthur Tang", "Thuy Nguyen"], "categories": ["cs.AI", "cs.LG"], "doi": "", "_query": "hedonic house price", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2408.08738v1", "title": "The Blessing of Strategic Customers in Personalized Pricing", "abstract": "We consider a feature-based personalized pricing problem in which the buyer is strategic: given the seller's pricing policy, the buyer can augment the features that they reveal to the seller to obtain a low price for the product. We model the seller's pricing problem as a stochastic program over an infinite-dimensional space of pricing policies where the radii by which the buyer can perturb the features are strictly positive. We establish that the sample average approximation of this problem is asymptotically consistent; that is, we prove that the objective value of the sample average approximation converges almost surely to the objective value of the stochastic problem as the number of samples tends to infinity under mild technical assumptions. This consistency guarantee thus shows that incorporating strategic consumer behavior into a data-driven pricing problem can, in addition to making the pricing problem more realistic, also help prevent overfitting.", "published": "2024-08-16T13:36:21Z", "authors": ["Zhi Chen", "Bradley Sturt", "Weijun Xie"], "categories": ["math.OC"], "doi": "", "_query": "hedonic house price", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/1808.07621v1", "title": "Latent Dirichlet Allocation for Internet Price War", "abstract": "Internet market makers are always facing intense competitive environment, where personalized price reductions or discounted coupons are provided for attracting more customers. Participants in such a price war scenario have to invest a lot to catch up with other competitors. However, such a huge cost of money may not always lead to an improvement of market share. This is mainly due to a lack of information about others' strategies or customers' willingness when participants develop their strategies. In order to obtain this hidden information through observable data, we study the relationship between companies and customers in the Internet price war. Theoretically, we provide a formalization of the problem as a stochastic game with imperfect and incomplete information. Then we develop a variant of Latent Dirichlet Allocation (LDA) to infer latent variables under the current market environment, which represents the preferences of customers and strategies of competitors. To our best knowledge, it is the first time that LDA is applied to game scenario. We conduct simulated experiments where our LDA model exhibits a significant improvement on finding strategies in the Internet price war by including all available market information of the market maker's competitors. And the model is applied to an open dataset for real business. Through comparisons on the likelihood of prediction for users' behavior and distribution distance between inferred opponent's strategy and the real one, our model is shown to be able to provide a better understanding for the market environment. Our work marks a successful learning method to infer latent information in the environment of price war by the LDA modeling, and sets an example for related competitive applications to follow.", "published": "2018-08-23T03:39:52Z", "authors": ["Chenchen Li", "Xiang Yan", "Xiaotie Deng", "Yuan Qi", "Wei Chu", "Le Song", "Junlong Qiao", "Jianshan He", "Junwu Xiong"], "categories": ["cs.AI"], "doi": "", "_query": "hedonic house price", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2501.03114v1", "title": "Environmental Policy in General Equilibrium under Market Power and Price Discrimination", "abstract": "This study constructs a novel analytical general equilibrium model to compare environmental policies in a setting where oligopolistic energy firms engage in third-degree price discrimination across residential consumers and industrial firms. Closed-form solutions demonstrate the impact on prices and quantities. The resulting welfare change is decomposed across three distortions: output, price discrimination, and externality. This study finds that the output distortion and price discrimination welfare effects generally move in opposite directions under policies such as an emission tax or a two-part instrument. Numerical analysis compares policies and finds scenarios where the output distortion and price discrimination welfare changes fully offset and thus leaves the net welfare gain of the externality correction. In this way, environmental policy can be designed to mitigate output distortion welfare concerns when firms have market power.", "published": "2025-01-06T16:21:19Z", "authors": ["Tengjiao Chen", "Daniel H. Karney"], "categories": ["econ.GN"], "doi": "", "_query": "hedonic house price", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/1310.4142v2", "title": "Quantum harmonic oscillator in option pricing", "abstract": "The Black-Scholes model anticipates rather well the observed prices for options in the case of a strike price that is not too far from the current price of the underlying asset. Some useful extensions can be obtained by an adequate modification of the coefficients in the Black-Scholes equation. We investigate from a mathematical point of view an extension directly related to the quantum harmonic oscillator. In the considered case, the solution is the sum of a series involving the Hermite-Gauss functions. A finite-dimensional version is obtained by using a finite oscillator and the Harper functions. This simplified model keeps the essential characteristics of the continuous one and uses finite sums instead of series and integrals.", "published": "2013-10-15T18:44:04Z", "authors": ["Liviu-Adrian Cotfas", "Nicolae Cotfas"], "categories": ["q-fin.CP", "math-ph", "quant-ph"], "doi": "", "_query": "hedonic house price", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2209.15037v2", "title": "Quantitative Fundamental Theorem of Asset Pricing", "abstract": "In this paper we provide a quantitative analysis to the concept of arbitrage, that allows to deal with model uncertainty without imposing the no-arbitrage condition. In markets that admit ``small arbitrage\", we can still make sense of the problems of pricing and hedging. The pricing measures here will be such that asset price processes are close to being martingales, and the hedging strategies will need to cover some additional cost. We show a quantitative version of the Fundamental Theorem of Asset Pricing and of the Super-Replication Theorem. Finally, we study robustness of the amount of arbitrage and existence of respective pricing measures, showing stability of these concepts with respect to a strong adapted Wasserstein distance.", "published": "2022-09-29T18:22:47Z", "authors": ["Beatrice Acciaio", "Julio Backhoff", "Gudmund Pammer"], "categories": ["q-fin.MF"], "doi": "", "_query": "hedonic house price", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/1602.07628v1", "title": "The Invisible Hand of Laplace: the Role of Market Structure in Price Convergence and Oscillation", "abstract": "A fundamental question about a market is under what conditions, and then how rapidly, does price signaling cause price equilibration. Qualitatively, this ought to depend on how well-connected the market is. We address this question quantitatively for a certain class of Arrow-Debreu markets with continuous-time proportional tâtonnement dynamics. We show that the algebraic connectivity of the market determines the effectiveness of price signaling equilibration. This also lets us study the rate of external noise that a market can tolerate and still maintain near-equilibrium prices.", "published": "2016-02-24T18:21:04Z", "authors": ["Yuval Rabani", "Leonard J. Schulman"], "categories": ["econ.GN"], "doi": "10.1016/j.jmateco.2021.102475", "_query": "hedonic house price", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/1103.2579v1", "title": "Prices of Anarchy, Information, and Cooperation in Differential Games", "abstract": "The price of anarchy (PoA) has been widely used in static games to quantify the loss of efficiency due to noncooperation. Here, we extend this concept to a general differential games framework. In addition, we introduce the price of information (PoI) to characterize comparative game performances under different information structures, as well as the price of cooperation to capture the extent of benefit or loss a player accrues as a result of altruistic behavior. We further characterize PoA and PoI for a class of scalar linear quadratic differential games under open-loop and closed-loop feedback information structures. We also obtain some explicit bounds on these indices in a large population regime.", "published": "2011-03-14T03:27:44Z", "authors": ["Tamer Basar", "Quanyan Zhu"], "categories": ["eess.SY", "cs.GT", "math.OC"], "doi": "", "_query": "hedonic house price", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2405.10640v3", "title": "COMET: NFT Price Prediction with Wallet Profiling", "abstract": "As the non-fungible token (NFT) market flourishes, price prediction emerges as a pivotal direction for investors gaining valuable insight to maximize returns. However, existing works suffer from a lack of practical definitions and standardized evaluations, limiting their practical application. Moreover, the influence of users' multi-behaviour transactions that are publicly accessible on NFT price is still not explored and exhibits challenges. In this paper, we address these gaps by presenting a practical and hierarchical problem definition. This approach unifies both collection-level and token-level task and evaluation methods, which cater to varied practical requirements of investors. To further understand the impact of user behaviours on the variation of NFT price, we propose a general wallet profiling framework and develop a COmmunity enhanced Multi-bEhavior Transaction graph model, named COMET. COMET profiles wallets with a comprehensive view and considers the impact of diverse relations and interactions within the NFT ecosystem on NFT price variations, thereby improving prediction performance. Extensive experiments conducted in our deployed system demonstrate the superiority of COMET, underscoring its potential in the insight toolkit for NFT investors.", "published": "2024-05-17T09:06:09Z", "authors": ["Tianfu Wang", "Liwei Deng", "Chao Wang", "Jianxun Lian", "Yue Yan", "Nicholas Jing Yuan", "Qi Zhang", "Hui Xiong"], "categories": ["cs.SI"], "doi": "", "_query": "hedonic house price", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/cond-mat/0309416v2", "title": "On the origin of power law tails in price fluctuations", "abstract": "In a recent Nature paper, Gabaix et al. \\cite{Gabaix03} presented a theory to explain the power law tail of price fluctuations. The main points of their theory are that volume fluctuations, which have a power law tail with exponent roughly -1.5, are modulated by the average market impact function, which describes the response of prices to transactions. They argue that the average market impact function follows a square root law, which gives power law tails for prices with exponent roughly -3. We demonstrate that the long-memory nature of order flow invalidates their statistical analysis of market impact, and present a more careful analysis that properly takes this into account. This makes it clear that the functional form of the average market impact function varies from market to market, and in some cases from stock to stock. In fact, for both the London Stock Exchange and the New York Stock Exchange the average market impact function grows much slower than a square root law; this implies that the exponent for price fluctuations predicted by modulations of volume fluctuations is much too big. We find that for LSE stocks the distribution of transaction volumes does not even have a power law tail. This makes it clear that volume fluctuations do not determine the power law tail of price returns.", "published": "2003-09-17T23:20:45Z", "authors": ["J. Doyne Farmer", "Fabrizio Lillo"], "categories": ["cond-mat.stat-mech", "q-fin.ST"], "doi": "", "_query": "hedonic house price", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/cond-mat/0112422v6", "title": "A quantitative model of trading and price formation in financial markets", "abstract": "We use standard physics techniques to model trading and price formation in a market under the assumption that order arrival and cancellations are Poisson random processes. This model makes testable predictions for the most basic properties of a market, such as the diffusion rate of prices, which is the standard measure of financial risk, and the spread and price impact functions, which are the main determinants of transaction cost. Guided by dimensional analysis, simulation, and mean field theory, we find scaling relations in terms of order flow rates. We show that even under completely random order flow the need to store supply and demand to facilitate trading induces anomalous diffusion and temporal structure in prices.", "published": "2001-12-23T23:57:11Z", "authors": ["Marcus G. Daniels", "J. Doyne Farmer", "Laszlo Gillemot", "Giulia Iori", "Eric Smith"], "categories": ["cond-mat.stat-mech", "q-fin.TR"], "doi": "10.1103/PhysRevLett.90.108102", "_query": "hedonic house price", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2403.05441v3", "title": "Bayesian Hierarchical Probabilistic Forecasting of Intraday Electricity Prices", "abstract": "We address the need for forecasting methodologies that handle large uncertainties in electricity prices for continuous intraday markets by incorporating parameter uncertainty and using a broad set of covariables. This study presents the first Bayesian forecasting of electricity prices traded on the German intraday market. Endogenous and exogenous covariables are handled via Orthogonal Matching Pursuit (OMP) and regularising priors. The target variable is the IDFull price index, with forecasts given as posterior predictive distributions. Validation uses the highly volatile 2022 electricity prices, which have seldom been studied. As a benchmark, we use all intraday transactions at the time of forecast to compute a live IDFull value. According to market efficiency, it should not be possible to improve on this last-price benchmark. However, we observe significant improvements in point measures and probability scores, including an average reduction of $5.9\\,\\%$ in absolute errors and an average increase of $1.7\\,\\%$ in accuracy when forecasting whether the IDFull exceeds the day-ahead price. Finally, we challenge the use of LASSO in electricity price forecasting, showing that OMP results in superior performance, specifically an average reduction of $22.7\\,\\%$ in absolute error and $20.2\\,\\%$ in the continuous ranked probability score.", "published": "2024-03-08T16:51:27Z", "authors": ["Daniel Nickelsen", "Gernot Müller"], "categories": ["stat.AP", "cs.LG"], "doi": "10.1016/j.apenergy.2024.124975", "_query": "hedonic house price", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/1607.04710v2", "title": "A Descending Price Auction for Matching Markets", "abstract": "This work presents a descending-price-auction algorithm to obtain the maximum market-clearing price vector (MCP) in unit-demand matching markets with m items by exploiting the combinatorial structure. With a shrewd choice of goods for which the prices are reduced in each step, the algorithm only uses the combinatorial structure, which avoids solving LPs and enjoys a strongly polynomial runtime of $O(m^4)$. Critical to the algorithm is determining the set of under-demanded goods for which we reduce the prices simultaneously in each step of the algorithm. This we accomplish by choosing the subset of goods that maximize a skewness function, which makes the bipartite graph series converges to the combinatorial structure at the maximum MCP in $O(m^2)$ steps. A graph coloring algorithm is proposed to find the set of goods with the maximal skewness value that yields $O(m^4)$ complexity.", "published": "2016-07-16T06:46:34Z", "authors": ["Shih-Tang Su", "Jacob D. Abernethy", "Grant Schoenebeck", "Vijay G. Subramanian"], "categories": ["cs.GT"], "doi": "", "_query": "hedonic house price", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2010.14979v1", "title": "Monetary-fiscal interactions under price level targeting", "abstract": "The adoption of a \"makeup\" strategy is one of the proposals in the ongoing review of the Fed's monetary policy framework. Another suggestion, to avoid the zero lower bound, is a more active role for fiscal policy. We put together these ideas to study monetary-fiscal interactions under price level targeting. Under price level targeting and a fiscally-led regime, we find that following a deflationary demand shock: (i) the central bank increases (rather than decreases) the policy rate; (ii) the central bank, thus, avoids the zero lower bound; (iii) price level targeting is generally welfare improving if compared to inflation targeting.", "published": "2020-10-28T13:41:24Z", "authors": ["Guido Ascari", "Anna Florio", "Alessandro Gobbi"], "categories": ["econ.GN"], "doi": "", "_query": "hedonic house price", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/1401.6735v1", "title": "Option Pricing of Twin Assets", "abstract": "How to price and hedge claims on nontraded assets are becoming increasingly important matters in option pricing theory today. The most common practice to deal with these issues is to use another similar or \"closely related\" asset or index which is traded, for hedging purposes. Implicitly, traders assume here that the higher the correlation between the traded and nontraded assets, the better the hedge is expected to perform. This raises the question as to how \\textquoteleft{}closely related\\textquoteright{} the assets really are. In this paper, the concept of twin assets is introduced, focusing the discussion precisely in what does it mean for two assets to be similar. Our findings point to the fact that, in order to have very similar assets, for example identical twins, high correlation measures are not enough. Specifically, two basic criteria of similarity are pointed out: i) the coefficient of variation of the assets and ii) the correlation between assets. From here, a method to measure the level of similarity between assets is proposed, and secondly, an option pricing model of twin assets is developed. The proposed model allows us to price an option of one nontraded asset using its twin asset, but this time knowing explicitly what levels of errors we are facing. Finally, some numerical illustrations show how twin assets behave depending upon their levels of similarities, and how their potential differences will traduce in MAPE (mean absolute percentage error) for the proposed option pricing model.", "published": "2014-01-27T04:28:41Z", "authors": ["Marcelo J. Villena", "Axel A. Araneda"], "categories": ["q-fin.PR"], "doi": "", "_query": "hedonic house price", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2109.03638v1", "title": "Evaluating Presidential Support in the Brazilian House of Representatives Through a Network-Based Approach", "abstract": "Conflicts between the executive and legislative powers are a common, and even expected, characteristic of presidential systems, with some governments being more successful in the activity of obtaining support from the Congress than others. In the case of Brazil, specifically, this factor is considered crucial in the so called \"coalition governments\", with direct positive or negative consequences for the president, in terms of government performance during his (her) term. In this work, we investigate this problem by testing and comparing two different methods for evaluating the government support in the Brazilian House of Representatives. The first method is a more traditional one, being based on roll-call voting data, and measures the presidential support at the legislators level. The second method uses a network-based approach, and performs the same type of analysis but at the parties level. The obtained results, when applying both methods on legislative data comprising the period from 1998 until 2019, indicate that both methods are valid, with common features being found not only between the results provided by the two of them, but also when comparing their results with those obtained by previous and relevant studies in this field, by using the same type of data but different methodologies.", "published": "2021-09-08T13:31:36Z", "authors": ["Tiago Colliri"], "categories": ["cs.SI", "physics.soc-ph"], "doi": "", "_query": "hedonic house price", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2504.17313v1", "title": "Tokenizing Stock Prices for Enhanced Multi-Step Forecast and Prediction", "abstract": "Effective stock price forecasting (estimating future prices) and prediction (estimating future price changes) are pivotal for investors, regulatory agencies, and policymakers. These tasks enable informed decision-making, risk management, strategic planning, and superior portfolio returns. Despite their importance, forecasting and prediction are challenging due to the dynamic nature of stock price data, which exhibit significant temporal variations in distribution and statistical properties. Additionally, while both forecasting and prediction targets are derived from the same dataset, their statistical characteristics differ significantly. Forecasting targets typically follow a log-normal distribution, characterized by significant shifts in mean and variance over time, whereas prediction targets adhere to a normal distribution. Furthermore, although multi-step forecasting and prediction offer a broader perspective and richer information compared to single-step approaches, it is much more challenging due to factors such as cumulative errors and long-term temporal variance. As a result, many previous works have tackled either single-step stock price forecasting or prediction instead. To address these issues, we introduce a novel model, termed Patched Channel Integration Encoder (PCIE), to tackle both stock price forecasting and prediction. In this model, we utilize multiple stock channels that cover both historical prices and price changes, and design a novel tokenization method to effectively embed these channels in a cross-channel and temporally efficient manner. Specifically, the tokenization process involves univariate patching and temporal learning with a channel-mixing encoder to reduce cumulative errors. Comprehensive experiments validate that PCIE outperforms current state-of-the-art models in forecast and prediction tasks.", "published": "2025-04-24T07:15:05Z", "authors": ["Zhuohang Zhu", "Haodong Chen", "Qiang Qu", "Xiaoming Chen", "Vera Chung"], "categories": ["cs.CE", "q-fin.CP"], "doi": "", "_query": "hedonic house price", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/1010.4812v1", "title": "Polynomial Bottleneck Congestion Games with Optimal Price of Anarchy", "abstract": "We study {\\em bottleneck congestion games} where the social cost is determined by the worst congestion of any resource. These games directly relate to network routing problems and also job-shop scheduling problems. In typical bottleneck congestion games, the utility costs of the players are determined by the worst congested resources that they use. However, the resulting Nash equilibria are inefficient, since the price of anarchy is proportional on the number of resources which can be high. Here we show that we can get smaller price of anarchy with the bottleneck social cost metric. We introduce the {\\em polynomial bottleneck games} where the utility costs of the players are polynomial functions of the congestion of the resources that they use. In particular, the delay function for any resource $r$ is $C_{r}^\\M$, where $C_r$ is the congestion measured as the number of players that use $r$, and $\\M \\geq 1$ is an integer constant that defines the degree of the polynomial. The utility cost of a player is the sum of the individual delays of the resources that it uses. The social cost of the game remains the same, namely, it is the worst bottleneck resource congestion: $\\max_{r} C_r$. We show that polynomial bottleneck games are very efficient and give price of anarchy $O(|R|^{1/(\\M+1)})$, where $R$ is the set of resources. This price of anarchy is tight, since we demonstrate a game with price of anarchy $Ω(|R|^{1/(\\M+1)})$, for any $\\M \\geq 1$. We obtain our tight bounds by using two proof techniques: {\\em transformation}, which we use to convert arbitrary games to simpler games, and {\\em expansion}, which we use to bound the price of anarchy in a simpler game.", "published": "2010-10-22T20:44:09Z", "authors": ["Rajgopal Kannan", "Costas Busch", "Athanasios Vasilakos"], "categories": ["cs.GT"], "doi": "", "_query": "hedonic house price", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2408.08483v1", "title": "Enhancement of price trend trading strategies via image-induced importance weights", "abstract": "We open up the \"black-box\" to identify the predictive general price patterns in price chart images via the deep learning image analysis techniques. Our identified price patterns lead to the construction of image-induced importance (triple-I) weights, which are applied to weighted moving average the existing price trend trading signals according to their level of importance in predicting price movements. From an extensive empirical analysis on the Chinese stock market, we show that the triple-I weighting scheme can significantly enhance the price trend trading signals for proposing portfolios, with a thoughtful robustness study in terms of network specifications, image structures, and stock sizes. Moreover, we demonstrate that the triple-I weighting scheme is able to propose long-term portfolios from a time-scale transfer learning, enhance the news-based trading strategies through a non-technical transfer learning, and increase the overall strength of numerous trading rules for portfolio selection.", "published": "2024-08-16T02:07:15Z", "authors": ["Zhoufan Zhu", "Ke Zhu"], "categories": ["q-fin.PM", "stat.ML"], "doi": "", "_query": "hedonic house price", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/1611.02026v1", "title": "Pricing Derivatives in a Regime Switching Market with Time Inhomogeneous Volatility", "abstract": "This paper studies pricing derivatives in an age-dependent semi-Markov modulated market. We consider a financial market where the asset price dynamics follow a regime switching geometric Brownian motion model in which the coefficients depend on finitely many age-dependent semi-Markov processes. We further allow the volatility coefficient to depend on time explicitly. Under these market assumptions, we study locally risk minimizing pricing of a class of European options. It is shown that the price function can be obtained by solving a non-local B-S-M type PDE. We establish existence and uniqueness of a classical solution of the Cauchy problem. We also find another characterization of price function via a system of Volterra integral equation of second kind. This alternative representation leads to computationally efficient methods for finding price and hedging. Finally, we analyze the PDE to establish continuous dependence of the solution on the instantaneous transition rates of semi-Markov processes. An explicit expression of quadratic residual risk is also obtained.", "published": "2016-11-07T12:50:57Z", "authors": ["Milan Kumar Das", "Anindya Goswami", "Tanmay S. Patankar"], "categories": ["q-fin.PR", "math.PR", "q-fin.MF"], "doi": "10.1080/07362994.2018.1448996", "_query": "hedonic house price", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2002.08286v1", "title": "Price impact equilibrium with transaction costs and TWAP trading", "abstract": "We prove the existence of an equilibrium in a model with transaction costs and price impact where two agents are incentivized to trade towards a target. The two types of frictions -- price impact and transaction costs -- lead the agents to two distinct changes in their optimal investment approach: price impact causes agents to continuously trade in smaller amounts, while transaction costs cause the agents to cease trading before the end of the trading period. As the agents lose wealth because of transaction costs, the exchange makes a profit. We prove the existence of a strictly positive optimal transaction cost from the exchange's perspective.", "published": "2020-02-19T16:59:34Z", "authors": ["Eunjung Noh", "Kim Weston"], "categories": ["q-fin.MF", "q-fin.TR"], "doi": "", "_query": "hedonic house price", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/astro-ph/0411165v1", "title": "Space Climate Manifestation in Earth Prices - from Medieval England Up to Modern Usa", "abstract": "In this study we continue to search for possible manifestations of space weather influence on prices of agricultural products and consumables. We note that the connection between solar activity and prices is based on the causal chain that includes several nonlinear transition elements. These non-linear elements are characterized by threshold sensitivity to external parameters and lead to very inhomogeneous local sensitivity of the price to space weather conditions. It is noted that \"soft type\" models are the most adequate for description of this class of connections. Two main observational effects suitable for testing causal connections of this type of sensitivity are considered: burst-like price reactions on changes in solar activity and price asymmetry for selected phases of the sunspot cycle. The connection, discovered earlier for wheat prices of Medieval England, is examined in this work on the basis of another 700-year data set of consumable prices in England. Using the same technique as in the previous part of our work (Pistilnik and Yom Din 2004) we show that statistical parameters of the interval distributions for price bursts of consumables basket and for sunspot minimum states are similar one to another, like it was reported earlier for wheat price bursts. Possible sources of these consistencies between three different multiyear samples are discussed. For search of possible manifestations of the \"space weather - wheat market\" connection in modern time, we analyze dynamics of wheat prices in the USA in the twentieth century. We show that the wheat prices revealed a maximum/minimum price asymmetry consistent with the phases of the sunspot cycle. We discuss possible explanations of this observed asymmetry, unexpected under conditions of globalization of the modern wheat market.", "published": "2004-11-06T18:32:19Z", "authors": ["L. A. Pustilnik", "G. Yom Din"], "categories": ["astro-ph"], "doi": "10.1007/s11207-005-5198-9", "_query": "hedonic house price", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/1305.3351v2", "title": "Quality Sensitive Price Competition in Spectrum Oligopoly", "abstract": "We investigate a spectrum oligopoly where primary users allow secondary access in lieu of financial remuneration. Transmission qualities of the licensed bands fluctuate randomly. Each primary needs to select the price of its channel with the knowledge of its own channel state but not that of its competitors. Secondaries choose among the channels available on sale based on their states and prices. We formulate the price selection as a non-cooperative game and prove that a symmetric Nash equilibrium (NE) strategy profile exists uniquely. We explicitly compute this strategy profile and analytically and numerically evaluate its efficiency. Our structural results provide certain key insights about the unique symmetric NE.", "published": "2013-05-15T03:42:45Z", "authors": ["Arnob Ghosh", "Saswati Sarkar"], "categories": ["cs.GT"], "doi": "10.1109/ISIT.2013.6620730", "_query": "hedonic house price", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/1106.1401v1", "title": "Volatility of Power Grids under Real-Time Pricing", "abstract": "The paper proposes a framework for modeling and analysis of the dynamics of supply, demand, and clearing prices in power system with real-time retail pricing and information asymmetry. Real-time retail pricing is characterized by passing on the real-time wholesale electricity prices to the end consumers, and is shown to create a closed-loop feedback system between the physical layer and the market layer of the power system. In the absence of a carefully designed control law, such direct feedback between the two layers could increase volatility and lower the system's robustness to uncertainty in demand and generation. A new notion of generalized price-elasticity is introduced, and it is shown that price volatility can be characterized in terms of the system's maximal relative price elasticity, defined as the maximal ratio of the generalized price-elasticity of consumers to that of the producers. As this ratio increases, the system becomes more volatile, and eventually, unstable. As new demand response technologies and distributed storage increase the price-elasticity of demand, the architecture under examination is likely to lead to increased volatility and possibly instability. This highlights the need for assessing architecture systematically and in advance, in order to optimally strike the trade-offs between volatility, economic efficiency, and system reliability.", "published": "2011-06-07T17:53:12Z", "authors": ["Mardavij Roozbehani", "Munther A Dahleh", "Sanjoy K Mitter"], "categories": ["eess.SY", "math.DS", "math.OC", "q-fin.PR"], "doi": "", "_query": "hedonic house price", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/1904.04225v3", "title": "A Forward Electricity Contract Price Projection: A Market Equilibrium Approach", "abstract": "This work presents a methodology for forward electricity contract price projection based on market equilibrium and social welfare optimization. In the methodology supply and demand for forward contracts are produced in such a way that each agent (generator/load/trader) optimizes a risk adjusted expected value of its revenue/cost. When uncertainties are represented by a discrete number of scenarios, a key result in the paper is that contract price corresponds to the dual variable of the equilibrium constraints in the linear programming problem associated to the optimization of total agents' welfare. Besides computing an equilibrium contract price for a given year, the methodology can also be used to compute the evolution of the probability distribution associated to a contract price with a future delivery period; this an import issue in quantifying forward contract risks. Examples of the methodology application are presented and discussed", "published": "2019-04-08T17:51:44Z", "authors": ["Mateus A. Cavaliere", "Sergio Granville", "Gerson C. Oliveira", "Mario V. F. Pereira"], "categories": ["math.OC", "q-fin.PR"], "doi": "", "_query": "hedonic house price", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/1804.04170v1", "title": "Optimal liquidation under stochastic price impact", "abstract": "We assume a continuous-time price impact model similar to Almgren-Chriss but with the added assumption that the price impact parameters are stochastic processes modeled as correlated scalar Markov diffusions. In this setting, we develop trading strategies for a trader who desires to liquidate his inventory but faces price impact as a result of his trading. For a fixed trading horizon, we perform coefficient expansion on the Hamilton-Jacobi-Bellman equation associated with the trader's value function. The coefficient expansion yields a sequence of partial differential equations that we solve to give closed-form approximations to the value function and optimal liquidation strategy. We examine some special cases of the optimal liquidation problem and give financial interpretations of the approximate liquidation strategies in these cases. Finally, we provide numerical examples to demonstrate the effectiveness of the approximations.", "published": "2018-04-11T19:03:47Z", "authors": ["Weston Barger", "Matthew Lorig"], "categories": ["q-fin.TR"], "doi": "", "_query": "hedonic house price", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2510.01196v1", "title": "Location Matters: Leveraging Multi-Resolution Geo-Embeddings for Housing Search", "abstract": "QuintoAndar Group is Latin America's largest housing platform, revolutionizing property rentals and sales. Headquartered in Brazil, it simplifies the housing process by eliminating paperwork and enhancing accessibility for tenants, buyers, and landlords. With thousands of houses available for each city, users struggle to find the ideal home. In this context, location plays a pivotal role, as it significantly influences property value, access to amenities, and life quality. A great location can make even a modest home highly desirable. Therefore, incorporating location into recommendations is essential for their effectiveness. We propose a geo-aware embedding framework to address sparsity and spatial nuances in housing recommendations on digital rental platforms. Our approach integrates an hierarchical H3 grid at multiple levels into a two-tower neural architecture. We compare our method with a traditional matrix factorization baseline and a single-resolution variant using interaction data from our platform. Embedding specific evaluation reveals richer and more balanced embedding representations, while offline ranking simulations demonstrate a substantial uplift in recommendation quality.", "published": "2025-09-01T18:49:03Z", "authors": ["Ivo Silva", "Pedro Nogueira", "Guilherme Bonaldo"], "categories": ["cs.IR", "cs.LG"], "doi": "10.1145/3705328.3748136", "_query": "hedonic house price", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/1112.2059v1", "title": "Randomised Mixture Models for Pricing Kernels", "abstract": "Numerous kinds of uncertainties may affect an economy, e.g. economic, political, and environmental ones. We model the aggregate impact by the uncertainties on an economy and its associated financial market by randomised mixtures of Lévy processes. We assume that market participants observe the randomised mixtures only through best estimates based on noisy market information. The concept of incomplete information introduces an element of stochastic filtering theory in constructing what we term \"filtered Esscher martingales\". We make use of this family of martingales to develop pricing kernel models. Examples of bond price models are examined, and we show that the choice of the random mixture has a significant effect on the model dynamics and the types of movements observed in the associated yield curves. Parameter sensitivity is analysed and option price processes are derived. We extend the class of pricing kernel models by considering a weighted heat kernel approach, and develop models driven by mixtures of Markov processes.", "published": "2011-12-09T10:08:39Z", "authors": ["Andrea Macrina", "Priyanka A. Parbhoo"], "categories": ["q-fin.GN", "math.PR"], "doi": "", "_query": "hedonic house price", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2610.00755v2", "title": "Learning to Price Electricity for Optimal Demand Response", "abstract": "There is considerable interest in using time-varying electricity prices to shape consumer demand response, and better align energy demand with renewable production. However, optimal prices generally vary over time in response to complex signals such as weather forecasts, sunrise/sunset times, and day-of-week patterns; and existing methods are not able to make efficient use of such rich contextual information. Here, we propose a neural-network-based algorithm for contextual energy pricing, modeling pricing as a Stackelberg game and leveraging a mean-field solution representation from Mehrabi et al.(2024). The approach learns constrained mappings from contextual features to feasible price signals. We validate our approach by simulating the energy grid in several US cities, and show that incorporating contextual information can considerably increase the value of the demand response programs.", "published": "2026-09-30T21:50:02Z", "authors": ["Jing Shang", "Mohammad Mehrabi", "Xinyang Zhou", "Mahmoud Saleh", "Andrey Bernstein", "Stefan Wager"], "categories": ["stat.ML", "cs.LG", "stat.AP"], "doi": "", "_query": "hedonic house price", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2304.09761v1", "title": "An innovative Deep Learning Based Approach for Accurate Agricultural Crop Price Prediction", "abstract": "Accurate prediction of agricultural crop prices is a crucial input for decision-making by various stakeholders in agriculture: farmers, consumers, retailers, wholesalers, and the Government. These decisions have significant implications including, most importantly, the economic well-being of the farmers. In this paper, our objective is to accurately predict crop prices using historical price information, climate conditions, soil type, location, and other key determinants of crop prices. This is a technically challenging problem, which has been attempted before. In this paper, we propose an innovative deep learning based approach to achieve increased accuracy in price prediction. The proposed approach uses graph neural networks (GNNs) in conjunction with a standard convolutional neural network (CNN) model to exploit geospatial dependencies in prices. Our approach works well with noisy legacy data and produces a performance that is at least 20% better than the results available in the literature. We are able to predict prices up to 30 days ahead. We choose two vegetables, potato (stable price behavior) and tomato (volatile price behavior) and work with noisy public data available from Indian agricultural markets.", "published": "2023-04-15T10:54:57Z", "authors": ["Mayank Ratan Bhardwaj", "Jaydeep Pawar", "Abhijnya Bhat", " Deepanshu", "Inavamsi Enaganti", "Kartik Sagar", "Y. Narahari"], "categories": ["cs.LG", "cs.AI", "q-fin.ST"], "doi": "", "_query": "hedonic house price", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2608.14018v1", "title": "Equilibrium Pricing in Oligopolistic Data Markets", "abstract": "We study equilibrium pricing in oligopolistic data markets with budget-constrained buyers (e.g., machine learning companies purchasing data to improve model accuracy) and strategic data sellers. Sellers compete by setting prices for their datasets, giving rise to a pricing game whose pure Nash equilibria correspond to equilibrium prices. While equilibrium prices are guaranteed for rivalrous goods via competitive equilibrium, we show that the non-rivalry of data fundamentally alters this picture: an exact Nash equilibrium (NE) need not exist, and in fact, 1.363-approximate NE may also not exist under uniform pricing. We therefore investigate relaxed equilibrium notions. Allowing sellers to use beyond-uniform pricing---specifically, piecewise-linear convex pricing functions---guarantees approximate stability within a constant factor: there exists a pricing profile in which no seller can improve revenue by a factor of two by deviating to any uniform price (a 2-approximate NE against uniform deviations). Finally, our simulations demonstrate fast convergence and empirical approximation guarantees that outperform the worst-case bound of 2.", "published": "2026-08-14T07:03:45Z", "authors": ["Bhaskar Ray Chaudhury", "Jugal Garg", "Eklavya Sharma", "Jiaxin Song"], "categories": ["cs.GT"], "doi": "", "_query": "hedonic house price", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/cond-mat/0101001v2", "title": "A simple model of price formation", "abstract": "A simple Ising spin model which can describe the mechanism of price formation in financial markets is proposed. In contrast to other agent-based models, the influence does not flow inward from the surrounding neighbors to the center site, but spreads outward from the center to the neighbors. The model thus describes the spread of opinions among traders. It is shown via standard Monte Carlo simulations that very simple rules lead to dynamics that duplicate those of asset prices.", "published": "2000-12-30T13:04:49Z", "authors": ["K. Sznajd-Weron", "R. Weron"], "categories": ["cond-mat.stat-mech", "q-fin.ST"], "doi": "10.1142/S0129183102003000", "_query": "hedonic house price", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2608.01406v1", "title": "Algorithmic collusion under asynchronous price updating", "abstract": "This paper investigates the effect of asynchrony in agents' updates in the emergence of algorithmic collusion. We present a continuous-time model for algorithmic collusion in which two firms use $Q$-learning algorithms to set prices asynchronously in a Bertrand duopoly. The firms update their prices at times dictated by a Poisson clock. By controlling the extent of agents' asynchrony, we run extensive numerical experiments with three specifications of the algorithm to investigate the emergence of algorithmic collusion. The strength of collusion is measured by a standard collusion index, as well as by automatically detecting the reward-punishment schemes. This is done by recording a large number of algorithms' reactions to unilateral price cuts and comparing them with the reactions of untrained algorithms. Our findings indicate that asynchrony hampers collusion, especially when the algorithms are stateless. When they condition on their competitor's previous prices, the sensitivity of algorithmic collusion to asynchrony varies depending on the type of information they have access to. The implications of these results for the regulation of algorithmic pricing are discussed.", "published": "2026-08-02T17:39:44Z", "authors": ["Ivan Conjeaud", "Gaspard Abel", "Argyris Kalogeratos"], "categories": ["econ.TH", "cs.GT"], "doi": "", "_query": "hedonic house price", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/1803.09444v1", "title": "Cliquet option pricing with Meixner processes", "abstract": "We investigate the pricing of cliquet options in a geometric Meixner model. The considered option is of monthly sum cap style while the underlying stock price model is driven by a pure-jump Meixner--Lévy process yielding Meixner distributed log-returns. In this setting, we infer semi-analytic expressions for the cliquet option price by using the probability distribution function of the driving Meixner--Lévy process and by an application of Fourier transform techniques. In an introductory section, we compile various facts on the Meixner distribution and the related class of Meixner--Lévy processes. We also propose a customized measure change preserving the Meixner distribution of any Meixner process.", "published": "2018-03-26T07:26:06Z", "authors": ["Markus Hess"], "categories": ["q-fin.PR", "math.PR"], "doi": "10.15559/18-VMSTA96", "_query": "hedonic house price", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/1807.05396v1", "title": "On the optimal choice of strike conventions in exchange option pricing", "abstract": "An important but rarely-addressed option pricing question is how to choose appropriate strikes for implied volatility inputs when pricing more exotic multi-asset derivatives. By means of Malliavin Calculus we construct an optimal log-linear strikevconvention for exchange options under stochastic volatility models. This novel approach allows us to minimize the difference between the corresponding Margrabe computed price and the true option price. We show that this optimal convention does not depend on the specific stochastic volatility model chosen. Numerical examples are given which provide strong support to the new methodology.", "published": "2018-07-14T13:21:38Z", "authors": ["Elisa Alòs", "Michael Coulon"], "categories": ["q-fin.MF", "q-fin.PR"], "doi": "", "_query": "hedonic house price", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2510.03633v1", "title": "Predicting Stock Price Movement with LLM-Enhanced Tweet Emotion Analysis", "abstract": "Accurately predicting short-term stock price movement remains a challenging task due to the market's inherent volatility and sensitivity to investor sentiment. This paper discusses a deep learning framework that integrates emotion features extracted from tweet data with historical stock price information to forecast significant price changes on the following day. We utilize Meta's Llama 3.1-8B-Instruct model to preprocess tweet data, thereby enhancing the quality of emotion features derived from three emotion analysis approaches: a transformer-based DistilRoBERTa classifier from the Hugging Face library and two lexicon-based methods using National Research Council Canada (NRC) resources. These features are combined with previous-day stock price data to train a Long Short-Term Memory (LSTM) model. Experimental results on TSLA, AAPL, and AMZN stocks show that all three emotion analysis methods improve the average accuracy for predicting significant price movements, compared to the baseline model using only historical stock prices, which yields an accuracy of 13.5%. The DistilRoBERTa-based stock prediction model achieves the best performance, with accuracy rising from 23.6% to 38.5% when using LLaMA-enhanced emotion analysis. These results demonstrate that using large language models to preprocess tweet content enhances the effectiveness of emotion analysis which in turn improves the accuracy of predicting significant stock price movements.", "published": "2025-10-04T02:31:44Z", "authors": ["An Vuong", "Susan Gauch"], "categories": ["cs.LG", "cs.AI"], "doi": "", "_query": "hedonic house price", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2610.21442v1", "title": "Fundamental Theorem of Discrete Asset Pricing", "abstract": "Motivated by discrete price quotes in continuous time, we seek a fundamental theorem based on no arbitrage rather than a condition on limiting trading opportunities. We consider a vector of discounted prices taking values in a locally finite subset of $R^d$ in continuous time. For a locally bounded càdlàg price, we prove that no arbitrage, no free lunch with vanishing risk, no free lunch, and the existence of an equivalent local martingale measure are equivalent. An elementary finite-dimensional reduction of portfolios yields a common pathwise bound on admissible wealth. This bound reduces closedness to convex averaging in a weighted Hilbert space, after which the Kreps-Yan theorem applies. Trading gains are finite Stieltjes sums, and the proof uses no Itô integration.", "published": "2026-10-15T07:49:21Z", "authors": ["Masaaki Fukasawa"], "categories": ["q-fin.MF"], "doi": "", "_query": "hedonic house price", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/0912.3771v1", "title": "Tremor price dynamics in the world's network of stock exchanges", "abstract": "We use insight from a model of earth tectonic plate movement to obtain a new understanding of the build up and release of stress in the price dynamics of the worlds stock exchanges. Nonlinearity enters the model due to a behavioral attribute of humans reacting disproportionately to big changes. This nonlinear response allows us to classify price movements of a given stock index as either being generated due to specific economic news for the country in question, or by the ensemble of the worlds stock exchanges reacting together like a complex system. Similar in structure to the Capital Asset Pricing Model in Finance, the model predicts how an individual stock exchange should be priced in terms of the performance of the global market of exchanges, but with human behavioral characteristics included in the pricing. A number of the models assumptions are validated against empirical data for 24 of the worlds leading stock exchanges. We show how treshold effects can lead to synchronization in the global network of stock exchanges.", "published": "2009-12-18T19:05:22Z", "authors": ["Jorgen Vitting Andersen", "Andrzej Nowak", "Giulia Rotundo", "Lael Parrott"], "categories": ["q-fin.GN", "physics.soc-ph", "q-fin.ST"], "doi": "", "_query": "hedonic house price", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2205.11439v1", "title": "Probabilistic forecasting of German electricity imbalance prices", "abstract": "The exponential growth of renewable energy capacity has brought much uncertainty to electricity prices and to electricity generation. To address this challenge, the energy exchanges have been developing further trading possibilities, especially the intraday and balancing markets. For an energy trader participating in both markets, the forecasting of imbalance prices is of particular interest. Therefore, in this manuscript we conduct a very short-term probabilistic forecasting of imbalance prices, contributing to the scarce literature in this novel subject. The forecasting is performed 30 minutes before the delivery, so that the trader might still choose the trading place. The distribution of the imbalance prices is modelled and forecasted using methods well-known in the electricity price forecasting literature: lasso with bootstrap, gamlss, and probabilistic neural networks. The methods are compared with a naive benchmark in a meaningful rolling window study. The results provide evidence of the efficiency between the intraday and balancing markets as the sophisticated methods do not substantially overperform the intraday continuous price index. On the other hand, they significantly improve the empirical coverage. The analysis was conducted on the German market, however it could be easily applied to any other market of similar structure.", "published": "2022-05-23T16:32:20Z", "authors": ["Michał Narajewski"], "categories": ["q-fin.ST", "econ.EM", "stat.ML"], "doi": "", "_query": "hedonic house price", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/1810.01730v3", "title": "Submodular Stochastic Probing with Prices", "abstract": "We introduce Stochastic Probing with Prices (SPP), a variant of the Stochastic Probing (SP) model in which we must pay a price to probe an element. A SPP problem involves two set systems $(N,\\mathcal{I}_{in})$ and $(N,\\mathcal{I}_{out})$ where each $e\\in N$ is active with probability $p_e$. To discover whether $e$ is active, it must be probed by paying the price $Δ_e$. If it is probed and active, then it is irrevocably added to the solution. Moreover, at all times, the set of probed elements must lie in $\\mathcal{I}_{out}$, and the solution (the set of probed and active elements) must lie in $\\mathcal{I}_{in}$. The goal is to maximize a set function $f$ minus the cost of the probes. We give a bi-criteria approximation algorithm to the online version of this problem, in which the elements are shown to the algorithm in a possibly adversarial order. Our results translate to state-of-the-art approximations for the traditional (online) stochastic probing problem.", "published": "2018-10-03T13:31:07Z", "authors": ["Ben Chugg", "Takanori Maehara"], "categories": ["cs.DS"], "doi": "", "_query": "hedonic house price", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/1701.08711v5", "title": "Predicting Auction Price of Vehicle License Plate with Deep Recurrent Neural Network", "abstract": "In Chinese societies, superstition is of paramount importance, and vehicle license plates with desirable numbers can fetch very high prices in auctions. Unlike other valuable items, license plates are not allocated an estimated price before auction. I propose that the task of predicting plate prices can be viewed as a natural language processing (NLP) task, as the value depends on the meaning of each individual character on the plate and its semantics. I construct a deep recurrent neural network (RNN) to predict the prices of vehicle license plates in Hong Kong, based on the characters on a plate. I demonstrate the importance of having a deep network and of retraining. Evaluated on 13 years of historical auction prices, the deep RNN's predictions can explain over 80 percent of price variations, outperforming previous models by a significant margin. I also demonstrate how the model can be extended to become a search engine for plates and to provide estimates of the expected price distribution.", "published": "2017-01-30T17:14:25Z", "authors": ["Vinci Chow"], "categories": ["cs.CL", "cs.LG", "econ.GN", "stat.ML"], "doi": "10.1016/j.eswa.2019.113008", "_query": "hedonic house price", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/1704.06027v1", "title": "Structural price model for electricity coupled markets", "abstract": "We propose a new structural model that can compute the electricity spot and forward prices in two coupled markets with limited interconnection and multiple fuels. We choose a structural approach in order to represent some key characteristics of electricity spot prices such as their link to fuel prices, consumption level and production fleet. With this model, explicit formulas are also available for forward prices and other derivatives. We give some illustrative results of the behaviour of spot and forward prices, and of the values of transmission rights.", "published": "2017-04-20T06:56:43Z", "authors": ["Clemence Alasseur", "Olivier Feron"], "categories": ["q-fin.MF"], "doi": "", "_query": "hedonic house price", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/1209.4718v1", "title": "Stock Price Dynamics and Option Valuations under Volatility Feedback Effect", "abstract": "According to the volatility feedback effect, an unexpected increase in squared volatility leads to an immediate decline in the price-dividend ratio. In this paper, we consider the properties of stock price dynamics and option valuations under the volatility feedback effect by modeling the joint dynamics of stock price, dividends, and volatility in continuous time. Most importantly, our model predicts the negative effect of an increase in squared return volatility on the value of deep-in-the-money call options and, furthermore, attempts to explain the volatility puzzle. We theoretically demonstrate a mechanism by which the market price of diffusion return risk, or an equity risk-premium, affects option prices and empirically illustrate how to identify that mechanism using forward-looking information on option contracts. Our theoretical and empirical results support the relevance of the volatility feedback effect. Overall, the results indicate that the prevailing practice of ignoring the time-varying dividend yield in option pricing can lead to oversimplification of the stock market dynamics.", "published": "2012-09-21T06:26:38Z", "authors": ["Juho Kanniainen", "Robert Piché"], "categories": ["q-fin.PR", "q-fin.ST"], "doi": "10.1016/j.physa.2012.10.004", "_query": "hedonic house price", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2403.15490v2", "title": "Increasing retrofit device adoption in social housing: evidence from two field experiments in Belgium", "abstract": "Energy efficient technologies are particularly important for social housing settings: they offer the potential to improve tenants' wellbeing through monetary savings and comfort, while reducing emissions of entire communities. Slow uptake of innovative energy technology in social housing has been associated with a lack of trust and the perceived risks of adoption. To counteract both, we designed a communication campaign for a retrofit technology for heating including social norms for technology adoption and concretely experienced benefits. We report two randomized controlled trials (RCT) in two different social housing communities in Belgium. In the first study, randomization was on housing block level: the communication led to significant higher uptake rates compared to the control group, (b = 1.7, p = .024). In the second study randomization occurred on apartment level, again yielding a significant increase (b = 1.62, p = 0.02), when an interaction with housing blocks was considered. We discuss challenges of conducting randomized controlled trials in social housing communities.", "published": "2024-03-21T14:49:05Z", "authors": ["Mona Bielig", "Celina Kacperski", "Florian Kutzner"], "categories": ["cs.CY", "cs.HC"], "doi": "10.1016/j.jenvp.2024.102284", "_query": "hedonic house price", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/0803.1769v1", "title": "Stock price jumps: news and volume play a minor role", "abstract": "In order to understand the origin of stock price jumps, we cross-correlate high-frequency time series of stock returns with different news feeds. We find that neither idiosyncratic news nor market wide news can explain the frequency and amplitude of price jumps. We find that the volatility patterns around jumps and around news are quite different: jumps are followed by increased volatility, whereas news tend on average to be followed by lower volatility levels. The shape of the volatility relaxation is also markedly different in the two cases. Finally, we provide direct evidence that large transaction volumes are_not_ responsible for large price jumps. We conjecture that most price jumps are induced by order flow fluctuations close to the point of vanishing liquidity.", "published": "2008-03-12T13:32:42Z", "authors": ["Armand Joulin", "Augustin Lefevre", "Daniel Grunberg", "Jean-Philippe Bouchaud"], "categories": ["q-fin.ST", "physics.data-an", "physics.soc-ph"], "doi": "", "_query": "hedonic house price", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2411.04011v2", "title": "Predicting and Publishing Accurate Imbalance Prices Using Monte Carlo Tree Search", "abstract": "The growing reliance on renewable energy sources, particularly solar and wind, has introduced challenges due to their uncontrollable production. This complicates maintaining the electrical grid balance, prompting some transmission system operators in Western Europe to implement imbalance tariffs that penalize unsustainable power deviations. These tariffs create an implicit demand response framework to mitigate grid instability. Yet, several challenges limit active participation. In Belgium, for example, imbalance prices are only calculated at the end of each 15-minute settlement period, creating high risk due to price uncertainty. This risk is further amplified by the inherent volatility of imbalance prices, discouraging participation. Although transmission system operators provide minute-based price predictions, the system imbalance volatility makes accurate price predictions challenging to obtain and requires sophisticated techniques. Moreover, publishing price estimates can prompt participants to adjust their schedules, potentially affecting the system balance and the final price, adding further complexity. To address these challenges, we propose a Monte Carlo Tree Search method that publishes accurate imbalance prices while accounting for potential response actions. Our approach models the system dynamics using a neural network forecaster and a cluster of virtual batteries controlled by reinforcement learning agents. Compared to Belgium's current publication method, our technique improves price accuracy by 20.4% under ideal conditions and by 12.8% in more realistic scenarios. This research addresses an unexplored, yet crucial problem, positioning this paper as a pioneering work in analyzing the potential of more advanced imbalance price publishing techniques.", "published": "2024-11-06T15:49:28Z", "authors": ["Fabio Pavirani", "Jonas Van Gompel", "Seyed Soroush Karimi Madahi", "Bert Claessens", "Chris Develder"], "categories": ["eess.SY", "cs.AI", "cs.LG"], "doi": "", "_query": "hedonic house price", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/1701.02646v1", "title": "Economic information from Smart Meter: Nexus Between Demand Profile and Electricity Retail Price Between Demand Profile and Electricity Retail Price", "abstract": "In this paper, we demonstrate that a consumer's marginal system impact is only determined by their demand profile rather than their demand level. Demand profile clustering is identical to cluster consumers according to their marginal impacts on system costs. A profile-based uniform-rate price is economically efficient as real-time pricing. We develop a criteria system to evaluate the economic efficiency of an implemented retail price scheme in a distribution system by comparing profile clustering and daily-average clustering. Our criteria system can examine the extent of a retail price scheme's inefficiency even without information about the distribution system's daily cost structure. We analyze data from a real distribution system in China. In this system, targeting each consumer's high-impact days is more efficient than target high-impact consumers.", "published": "2016-09-16T21:14:59Z", "authors": ["Yang Yu", "Guangyi Liu", "Wendong Zhu", "Fei Wang", "Bin Shu", "Kai Zhang", "Ram Rajagopal", "Nicolas Astier"], "categories": ["econ.GN"], "doi": "", "_query": "hedonic house price", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/1510.04690v1", "title": "On Capturing the Spreading Dynamics over Trading Prices in the Market", "abstract": "While market is a social field where information flows over the interacting agents, there have been not so many methods to observe the spreading information in the prices comprising the market. By incorporating the entropy transfer in information theory in its relation to the Granger causality, the paper proposes a tree of weighted directed graph of market to detect the changes of price might affect other price changes. We compare the proposed analysis with the similar tree representation built from the correlation coefficients of stock prices in order to have insight of possibility in seeing the collective behavior of the market in general.", "published": "2015-10-15T19:58:00Z", "authors": ["Hokky Situngkir"], "categories": ["q-fin.ST"], "doi": "", "_query": "hedonic house price", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/1812.07415v2", "title": "Change of Measure in Midcurve Pricing", "abstract": "We derive measure change formulae required to price midcurve swaptions in the forward swap annuity measure with stochastic annuities' ratios. We construct the corresponding linear and exponential terminal swap rate pricing models and show how they capture the midcurve swaption correlation skew.", "published": "2018-12-10T09:36:39Z", "authors": ["K. E. Feldman"], "categories": ["q-fin.PR", "q-fin.MF"], "doi": "", "_query": "hedonic house price", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2609.18149v1", "title": "Research on the Price Prediction Algorithms of Major Cryptocurrencies and a Basic Transaction Framework", "abstract": "Through long-term observation and time series analysis of Bitcoin and Ethereum, we found the similarity in long-term consistent price trends, especially in the weekly K-line. We denoised and smoothed the historical data of their prices, and further derived the periodicity of their price change trends and the timing of buying and selling. We have for the first time proposed that the full application of central bank digital currencies (CBDC) is a key signal for investors to reduce their holdings of cryptocurrencies or even exit the market. In addition, we proposed momentum opening/closing prices to replace the traditional nominal opening/closing prices to accurately describe the price trends of the 24/7 financial trading market. We found that the cryptocurrency market can be regarded as a relatively independent financial market, thereby designing a safer and more efficient arbitrage strategy.", "published": "2026-09-16T05:30:19Z", "authors": ["Shengjian Chen"], "categories": ["cs.CE", "cs.GT"], "doi": "", "_query": "hedonic house price", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2312.01020v3", "title": "ResNLS: An Improved Model for Stock Price Forecasting", "abstract": "Stock prices forecasting has always been a challenging task. Although many research projects try to address the problem, few of them pay attention to the varying degrees of dependencies between stock prices. In this paper, we introduce a hybrid model that improves the prediction of stock prices by emphasizing the dependencies between adjacent stock prices. The proposed model, ResNLS, is mainly composed of two neural architectures, ResNet and LSTM. ResNet serves as a feature extractor to identify dependencies between stock prices, while LSTM analyzes the initial time series data with the combination of dependencies, which are considered as residuals. Our experiment reveals that when the closing price data for the previous 5 consecutive trading days is used as input, the performance of the model (ResNLS-5) is optimal compared to those with other inputs. Furthermore, ResNLS-5 demonstrates at least a 20% improvement over current state-of-the-art baselines. To verify whether ResNLS-5 can help clients effectively avoid risks and earn profits in the stock market, we construct a quantitative trading framework for back testing. The result shows that the trading strategy based on ResNLS-5 predictions can successfully mitigate losses during declining stock prices and generate profits in periods of rising stock prices.", "published": "2023-12-02T03:55:37Z", "authors": ["Yuanzhe Jia", "Ali Anaissi", "Basem Suleiman"], "categories": ["cs.LG", "cs.AI", "cs.CE"], "doi": "", "_query": "hedonic house price", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/1206.5224v4", "title": "Stock prices assessment: proposal of a new index based on volume weighted historical prices through the use of computer modeling", "abstract": "The importance of considering the volumes to analyze stock prices movements can be considered as a well-accepted practice in the financial area. However, when we look at the scientific production in this field, we still cannot find a unified model that includes volume and price variations for stock assessment purposes. In this paper we present a computer model that could fulfill this gap, proposing a new index to evaluate stock prices based on their historical prices and volumes traded. Besides the model can be considered mathematically very simple, it was able to improve significantly the performance of agents operating with real financial data. Based on the results obtained, and also on the very intuitive logic of our model, we believe that the index proposed here can be very useful to help investors on the activity of determining ideal price ranges for buying and selling stocks in the financial market.", "published": "2012-06-22T18:30:05Z", "authors": ["Tiago Colliri", "Fernando F. Ferreira"], "categories": ["q-fin.ST", "cs.LG"], "doi": "10.1109/BWSS.2012.23", "_query": "hedonic house price", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2504.18982v1", "title": "On Bitcoin Price Prediction", "abstract": "In recent years, cryptocurrencies have attracted growing attention from both private investors and institutions. Among them, Bitcoin stands out for its impressive volatility and widespread influence. This paper explores the predictability of Bitcoin's price movements, drawing a parallel with traditional financial markets. We examine whether the cryptocurrency market operates under the efficient market hypothesis (EMH) or if inefficiencies still allow opportunities for arbitrage. Our methodology combines theoretical reviews, empirical analyses, machine learning approaches, and time series modeling to assess the extent to which Bitcoin's price can be predicted. We find that while, in general, the Bitcoin market tends toward efficiency, specific conditions, including information asymmetries and behavioral anomalies, occasionally create exploitable inefficiencies. However, these opportunities remain difficult to systematically identify and leverage. Our findings have implications for both investors and policymakers, particularly regarding the regulation of cryptocurrency brokers and derivatives markets.", "published": "2025-04-26T17:48:11Z", "authors": ["Grégory Bournassenko"], "categories": ["q-fin.ST", "stat.OT"], "doi": "", "_query": "hedonic house price", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2111.04427v1", "title": "What Is the Price of Data? A Measurement Study of Commercial Data Marketplaces", "abstract": "A large number of Data Marketplaces (DMs) have appeared in the last few years to help owners monetise their data, and data buyers fuel their marketing process, train their ML models, and perform other data-driven decision processes. In this paper, we present a first of its kind measurement study of the growing DM ecosystem and shed light on several totally unknown facts about it. For example, we show that the median price of live data products sold under a subscription model is around US\\$1,400 per month. For one-off purchases of static data, the median price is around US\\$2,200. We analyse the prices of different categories of data and show that products about telecommunications, manufacturing, automotive, and gaming command the highest prices. We also develop classifiers for comparing prices across different DMs as well as a regression analysis for revealing features that correlate with data product prices.", "published": "2021-10-25T10:39:47Z", "authors": ["Santiago Andrés Azcoitia", "Costas Iordanou", "Nikolaos Laoutaris"], "categories": ["cs.CY"], "doi": "", "_query": "hedonic house price", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/1404.2514v4", "title": "Quality Sensitive Price Competition in Spectrum Oligopoly:Part 1", "abstract": "We investigate a spectrum oligopoly market where primaries lease their channels to secondaries in lieu of financial remuneration. Transmission quality of a channel evolves randomly. Each primary has to select the price it would quote without knowing the transmission qualities of its competitors' channels. Each secondary buys a channel depending on the price and the transmission quality a channel offers. We formulate the price selection problem as a non co-operative game with primaries as players. In the one-shot game, we show that there exists a unique symmetric Nash Equilibrium(NE) strategy profile and explicitly compute it. Our analysis reveals that under the NE strategy profile a primary prices its channel to render high quality channel more preferable to the secondary; this negates the popular belief that prices ought to be selected to render channels equally preferable to the secondary regardless of their qualities. We show the loss of revenue in the asymptotic limit due to the non co-operation of primaries. In the repeated version of the game, we characterize a subgame perfect NE where a primary can attain a payoff arbitrarily close to the payoff it would obtain when primaries co-operate.", "published": "2014-04-06T04:14:03Z", "authors": ["Arnob Ghosh", "Saswati Sarkar"], "categories": ["cs.GT"], "doi": "", "_query": "hedonic house price", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/1804.09468v1", "title": "Price of Anarchy for Mechanisms with Risk-Averse Agents", "abstract": "We study the price of anarchy of mechanisms in the presence of risk-averse agents. Previous work has focused on agents with quasilinear utilities, possibly with a budget. Our model subsumes this as a special case but also captures that agents might be less sensitive to payments than in the risk-neutral model. We show that many positive price-of-anarchy results proved in the smoothness framework continue to hold in the more general risk-averse setting. A sufficient condition is that agents can never end up with negative quasilinear utility after playing an undominated strategy. This is true, e.g., for first-price and second-price auctions. For all-pay auctions, similar results do not hold: We show that there are Bayes-Nash equilibria with arbitrarily bad social welfare compared to the optimum.", "published": "2018-04-25T10:29:53Z", "authors": ["Thomas Kesselheim", "Bojana Kodric"], "categories": ["cs.GT"], "doi": "", "_query": "hedonic house price", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2312.10306v1", "title": "Mapping Housing Stock Characteristics from Drone Images for Climate Resilience in the Caribbean", "abstract": "Comprehensive information on housing stock is crucial for climate adaptation initiatives aiming to reduce the adverse impacts of climate-extreme hazards in high-risk regions like the Caribbean. In this study, we propose a workflow for rapidly generating critical baseline housing stock data using very high-resolution drone images and deep learning techniques. Specifically, our work leverages the Segment Anything Model and convolutional neural networks for the automated generation of building footprints and roof classification maps. By strengthening local capacity within government agencies to leverage AI and Earth Observation-based solutions, this work seeks to improve the climate resilience of the housing sector in small island developing states in the Caribbean.", "published": "2023-12-16T03:49:40Z", "authors": ["Isabelle Tingzon", "Nuala Margaret Cowan", "Pierre Chrzanowski"], "categories": ["cs.CV"], "doi": "10.1017/eds.2024.46", "_query": "hedonic house price", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/1501.01892v3", "title": "Optimal Asset Liquidation with Multiplicative Transient Price Impact", "abstract": "We study a multiplicative transient price impact model for an illiquid financial market, where trading causes price impact which is multiplicative in relation to the current price, transient over time with finite rate of resilience, and non-linear in the order size. We construct explicit solutions for the optimal control and the value function of singular optimal control problems to maximize expected discounted proceeds from liquidating a given asset position. A free boundary problem, describing the optimal control, is solved for two variants of the problem where admissible controls are monotone or of bounded variation.", "published": "2015-01-08T16:08:05Z", "authors": ["Dirk Becherer", "Todor Bilarev", "Peter Frentrup"], "categories": ["math.OC", "math.PR", "q-fin.TR"], "doi": "10.1007/s00245-017-9418-0", "_query": "hedonic house price", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2602.21274v1", "title": "Optimal extraction with an impact on diffusion-jump pricing", "abstract": "We study an optimal extraction problem where the agent's actions in the spot market exert an additive proportional negative impact on the commodity price. The commodity price dynamics, prior to any activity by the agent, are evolved by a drifted Brownian motion with jumps. The agent's primary aim is to identify an optimal extraction strategy that maximizes their expected net profits.", "published": "2026-02-24T16:13:16Z", "authors": ["Johanna Garzón", "Jhonatan S. Mora Rodríguez", "Harold A. Moreno-Franco"], "categories": ["math.OC"], "doi": "", "_query": "hedonic house price", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/1601.01128v1", "title": "Option pricing in the model with stochastic volatility driven by Ornstein--Uhlenbeck process. Simulation", "abstract": "We consider a discrete-time approximation of paths of an Ornstein--Uhlenbeck process as a mean for estimation of a price of European call option in the model of financial market with stochastic volatility. The Euler--Maruyama approximation scheme is implemented. We determine the estimates for the option price for predetermined sets of parameters. The rate of convergence of the price and an average volatility when discretization intervals tighten are determined. Discretization precision is analyzed for the case where the exact value of the price can be derived.", "published": "2016-01-06T10:29:43Z", "authors": ["Sergii Kuchuk-Iatsenko", "Yuliya Mishura"], "categories": ["q-fin.CP", "math.PR", "q-fin.PR"], "doi": "10.15559/15-VMSTA43", "_query": "hedonic house price", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/1605.06564v1", "title": "Double-Sided Energy Auction Equilibrium Under Price Anticipation", "abstract": "This paper investigates the problem of proportionally fair double sided energy auction involving buying and selling agents. The grid is assumed to be operating under islanded mode. A distributed auction algorithm that can be implemented by an aggregator, as well as a possible approach by which the agents may approximate price anticipation is considered. Equilibrium conditions arising due to price anticipation is analyzed. A modified auction to mitigate the resulting loss in efficiency due to such behavior is suggested. This modified auction allows the aggregate social welfare of the agents to be arbitrarily close to that attainable with price taking agents. Next, equilibrium conditions when the aggregator collects a surcharge price per unit of energy traded is examined. A biobjective optimization problem is identified that takes into account both the agents social welfare as well as the aggregator revenue from the surcharge. Results of extensive simulations, which corroborate the theoretical analysis, are reported.", "published": "2016-05-21T00:25:40Z", "authors": ["M. Nazif Faqiry", "Sanjoy Das"], "categories": ["cs.GT"], "doi": "", "_query": "hedonic house price", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/1605.04948v2", "title": "Quantum theory of securities price formation in financial markets", "abstract": "We develop a theory of securities price formation and dynamics based on quantum approach and without presuming any similarities with quantum mechanics. Disorder introduced by trading environment leads to probability distribution of returns that is not a smooth curve, but a speckle-pattern fluctuating in both price coordinate and time. This means that any given return can at times acquire a substantial probability of occurring while remaining low on average in time. Still, due to local character of order interaction during price formation the distribution width grows smoothly, has a minimum value at small time scale and a square root behavior at large time scale. Examples of calibration to market data, both intraday and daily, are provided.", "published": "2016-04-12T19:45:07Z", "authors": ["Jack Sarkissian"], "categories": ["q-fin.TR"], "doi": "", "_query": "hedonic house price", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/1806.05401v1", "title": "The Theoretical Price of a Share-Based Payment with Performance Conditions and Implications for the Current Accounting Standards", "abstract": "Although the growth of share-based payments with performance conditions (hereafter, SPPC) is prominent today, the theoretical price of SPPC has not been sufficiently studied. Reflecting such a situation, the current accounting standards for share-based payments issued in 2004 have had many problems. This paper develops a theoretical SPPC price model with a framework for a marginal utility-based price, which previous studies proposed is the price of contingent claims in an incomplete market. This paper's contribution is fivefold. First, we restricted the stochastic process to a certain class to demonstrate how to consistently change all variables' probability distributions, which affect the SPPC payoff. Second, we explicitly indicated not only the stochastic processes of the stock price process and performance variables under the changed probability, but also how the changes in the performance variables' drift coefficients related to stock betas. Third, we proposed a convenient model in application that uses only a few parameters. Fourth, we provided a method to estimate the parameters and improve the estimation of both the price and parameters. Fifth, we illustrated the problems in current accounting standards and indicated how the theoretical price model can significantly improve them.", "published": "2018-06-14T07:41:26Z", "authors": ["Masahiro Fujimoto"], "categories": ["q-fin.MF"], "doi": "", "_query": "hedonic house price", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2002.03319v1", "title": "Crowded trades, market clustering, and price instability", "abstract": "Crowded trades by similarly trading peers influence the dynamics of asset prices, possibly creating systemic risk. We propose a market clustering measure using granular trading data. For each stock the clustering measure captures the degree of trading overlap among any two investors in that stock. We investigate the effect of crowded trades on stock price stability and show that market clustering has a causal effect on the properties of the tails of the stock return distribution, particularly the positive tail, even after controlling for commonly considered risk drivers. Reduced investor pool diversity could thus negatively affect stock price stability.", "published": "2020-02-09T08:53:43Z", "authors": ["Marc van Kralingen", "Diego Garlaschelli", "Karolina Scholtus", "Iman van Lelyveld"], "categories": ["q-fin.ST", "q-fin.RM", "q-fin.TR"], "doi": "10.3390/e23030336", "_query": "hedonic house price", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/0905.3191v1", "title": "Dynamic and Non-Uniform Pricing Strategies for Revenue Maximization", "abstract": "We consider the Item Pricing problem for revenue maximization in the limited supply setting, where a single seller with $n$ items caters to $m$ buyers with unknown subadditive valuation functions who arrive in a sequence. The seller sets the prices on individual items. Each buyer buys a subset of yet unsold items that maximizes her utility. Our goal is to design pricing strategies that guarantee an expected revenue that is within a small factor $α$ of the maximum possible social welfare -- an upper bound on the maximum revenue that can be generated. Most earlier work has focused on the unlimited supply setting, where selling items to some buyer does not affect their availability to the future buyers. Balcan et. al. (EC 2008) studied the limited supply setting, giving a randomized strategy that assigns a single price to all items (uniform strategy), and never changes it (static strategy), that gives an $2^{O(\\sqrt{\\log n \\log \\log n})}$-approximation, and moreover, no static uniform pricing strategy can give better than $2^{Ω(\\log^{1/4} n)}$- approximation. We improve this lower bound to $2^{Ω(sqrt{\\log n})}$. We consider dynamic uniform strategies, which can change the price upon the arrival of each buyer but the price on all unsold items is the same at all times, and static non-uniform strategies, which can assign different prices to different items but can never change it after setting it initially. We design such pricing strategies that give a poly-logarithmic approximation to maximum revenue. Thus in the limited supply setting, our results highlight a strong separation between the power of dynamic and non-uniform pricing versus static uniform pricing. To our knowledge, this is the first non-trivial analysis of dynamic and non-uniform pricing schemes for revenue maximization.", "published": "2009-05-19T22:15:12Z", "authors": ["Tanmoy Chakraborty", "Zhiyi Huang", "Sanjeev Khanna"], "categories": ["cs.GT"], "doi": "", "_query": "hedonic house price", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/cond-mat/0408277v1", "title": "Multifractality in the stock market: price increments versus waiting times", "abstract": "By applying the multifractal detrended fluctuation analysis to the high-frequency tick-by-tick data from Deutsche Börse both in the price and in the time domains, we investigate multifractal properties of the time series of logarithmic price increments and inter-trade intervals of time. We show that both quantities reveal multiscaling and that this result holds across different stocks. The origin of the multifractal character of the corresponding dynamics is, among others, the long-range correlations in price increments and in inter-trade time intervals as well as the non-Gaussian distributions of the fluctuations. Since the transaction-to-transaction price increments do not strongly depend on or are almost independent of the inter-trade waiting times, both can be sources of the observed multifractal behaviour of the fixed-delay returns and volatility. The results presented also allow one to evaluate the applicability of the Multifractal Model of Asset Returns in the case of tick-by-tick data.", "published": "2004-08-12T12:21:35Z", "authors": ["P. Oswiecimka", "J. Kwapien", "S. Drozdz"], "categories": ["cond-mat.other", "q-fin.ST"], "doi": "10.1016/j.physa.2004.08.025", "_query": "hedonic house price", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/1410.0673v2", "title": "Fair bilateral prices in Bergman's model", "abstract": "Bielecki and Rutkowski (2014) introduced and studied a generic nonlinear market model, which includes several risky assets, multiple funding accounts and margin accounts. In this paper, we examine the pricing and hedging of contract both from the perspective of the hedger and the counterparty with arbitrary initial endowments. We derive inequalities for unilateral prices and we give the range for either fair bilateral prices or bilaterally profitable prices. We also study the monotonicity of a unilateral price with respect to the initial endowment. Our study hinges on results for BSDE driven by continuous martingales obtained in Nie and Rutkowski (2014), but we also derive the pricing PDEs for path-independent contingent claims of European style in a Markovian framework.", "published": "2014-10-02T04:12:12Z", "authors": ["Tianyang Nie", "Marek Rutkowski"], "categories": ["q-fin.MF", "q-fin.PR"], "doi": "", "_query": "hedonic house price", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/1512.06582v1", "title": "Asymptotic pricing in large financial markets", "abstract": "The problem of hedging and pricing sequences of contingent claims in large financial markets is studied. Connection between asymptotic arbitrage and behavior of the $α$~-~quantile price is shown. The large Black-Scholes model is carefully examined.", "published": "2015-12-21T11:14:07Z", "authors": ["Michał Barski"], "categories": ["q-fin.MF", "math.PR"], "doi": "", "_query": "hedonic house price", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/0904.1480v1", "title": "Risk-averse asymptotics for reservation prices", "abstract": "An investor's risk aversion is assumed to tend to infinity. In a fairly general setting, we present conditions ensuring that the respective utility indifference prices of a given contingent claim converge to its super replication price.", "published": "2009-04-09T08:56:23Z", "authors": ["Laurence Carassus", "Miklos Rasonyi"], "categories": ["math.PR"], "doi": "", "_query": "hedonic house price", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/math/0703077v1", "title": "Ratio of price to expectation and complete Bernstein functions", "abstract": "For a game with positive expectation and some negative profit, a unique price exists, at which the optimal proportion of investment reaches its maximum. For a game with parallel translated profit, the ratio of this price to its expectation tends to converge toward less than or equal to 1/2 if its expectation converges to 0. In this paper, we will investigate such properties by using the integral representations of a complete Bernstein function and establish several Abelian and Tauberian theorems.", "published": "2007-03-03T02:59:04Z", "authors": ["Yukio Hirashita"], "categories": ["math.OC"], "doi": "", "_query": "hedonic house price", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/1508.03651v3", "title": "A conjecture about the efficiency of first price mechanisms", "abstract": "We present different versions of a conjecture which would express that first price mechanisms never work very badly in a very general class of problems. The definitions include most of the problems where there is a principal (seller) who has the right to exclude others from the game. The exact definitions are motivated by the \"first price mechanism\" in E Cs: \"Efficient Teamwork\", but the conjecture is relevant for most auction problems, e.g. for combinatorial auctions.", "published": "2015-04-18T13:30:47Z", "authors": ["Endre Csóka"], "categories": ["econ.GN", "cs.GT"], "doi": "", "_query": "hedonic house price", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/1603.03874v1", "title": "Analysis of the nonlinear option pricing model under variable transaction costs", "abstract": "In this paper we analyze a nonlinear Black--Scholes model for option pricing under variable transaction costs. The diffusion coefficient of the nonlinear parabolic equation for the price $V$ is assumed to be a function of the underlying asset price and the Gamma of the option. We show that the generalizations of the classical Black--Scholes model can be analyzed by means of transformation of the fully nonlinear parabolic equation into a quasilinear parabolic equation for the second derivative of the option price. We show existence of a classical smooth solution and prove useful bounds on the option prices. Furthermore, we construct an effective numerical scheme for approximation of the solution. The solutions are obtained by means of the efficient numerical discretization scheme of the Gamma equation. Several computational examples are presented.", "published": "2016-03-12T08:58:51Z", "authors": ["Daniel Sevcovic", "Magdalena Zitnanska"], "categories": ["q-fin.PR"], "doi": "", "_query": "hedonic house price", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2507.12733v3", "title": "The Query Complexity of Uniform Pricing", "abstract": "Real-world pricing mechanisms are typically optimized using training data, a setting corresponding to the \\textit{pricing query complexity} problem in Mechanism Design. The previous work [LSTW23] studies the \\textit{single-distribution} case, with tight bounds of $\\widetildeΘ(\\varepsilon^{-3})$ for a \\textit{general} distribution and $\\widetildeΘ(\\varepsilon^{-2})$ for either a \\textit{regular} or \\textit{monotone-hazard-rate (MHR)} distribution, where $\\varepsilon \\in (0, 1)$ denotes the (additive) revenue loss of a learned uniform price relative to the Bayesian-optimal uniform price. This can be directly interpreted as ``the query complexity of the {\\em \\textsf{Uniform Pricing}} mechanism, in the \\textit{single-distribution} case''. Yet in the \\textit{multi-distribution} case, can the regularity and MHR conditions still lead to improvements over the tight bound $\\widetildeΘ(\\varepsilon^{-3})$ for general distributions? We answer this question in the negative, by establishing a (near-)matching lower bound $Ω(\\varepsilon^{-3})$ for either \\textit{two regular distributions} or \\textit{three MHR distributions}. We also address the \\textit{regret minimization} problem and, in comparison with the folklore upper bound $\\widetilde{O}(T^{2 / 3})$ for general distributions (see, e.g., [SW24]), establish a (near-)matching lower bound $Ω(T^{2 / 3})$ for either \\textit{two regular distributions} or \\textit{three MHR distributions}, via a black-box reduction. Again, this is in stark contrast to the tight bound $\\widetildeΘ(T^{1 / 2})$ for a single regular or MHR distribution.", "published": "2025-07-17T02:31:01Z", "authors": ["Houshuang Chen", "Yaonan Jin", "Pinyan Lu", "Chihao Zhang"], "categories": ["cs.GT"], "doi": "", "_query": "hedonic house price", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2305.05134v1", "title": "To AI or not to AI, to Buy Local or not to Buy Local: A Mathematical Theory of Real Price", "abstract": "In the past several decades, the world's economy has become increasingly globalized. On the other hand, there are also ideas advocating the practice of ``buy local'', by which people buy locally produced goods and services rather than those produced farther away. In this paper, we establish a mathematical theory of real price that determines the optimal global versus local spending of an agent which achieves the agent's optimal tradeoff between spending and obtained utility. Our theory of real price depends on the asymptotic analysis of a Markov chain transition probability matrix related to the network of producers and consumers. We show that the real price of a product or service can be determined from the involved Markov chain matrix, and can be dramatically different from the product's label price. In particular, we show that the label prices of products and services are often not ``real'' or directly ``useful'': given two products offering the same myopic utility, the one with lower label price may not necessarily offer better asymptotic utility. This theory shows that the globality or locality of the products and services does have different impacts on the spending-utility tradeoff of a customer. The established mathematical theory of real price can be used to determine whether to adopt or not to adopt certain artificial intelligence (AI) technologies from an economic perspective.", "published": "2023-05-09T02:43:47Z", "authors": ["Huan Cai", "Catherine Xu", "Weiyu Xu"], "categories": ["econ.TH", "cs.AI", "math.OC"], "doi": "", "_query": "hedonic house price", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2608.02759v2", "title": "Quota and population monotonicity across house sizes are incompatible for apportionment to four states", "abstract": "Apportionment converts fractional entitlements into integer seat allocations. Quota requires each state to receive the floor or ceiling of its standard quota, whereas population monotonicity prohibits a state whose population weakly increases from losing a seat to a state whose population weakly decreases. Gölz, Peters, and Procaccia recently removed the order-preservation assumptions used in classical incompatibility results by giving a five-state construction; together with the three-state Webster possibility result, this left four states as the unresolved boundary. We prove that no deterministic four-state apportionment solution satisfies both axioms under their definition, which permits the compared house sizes to differ. The proof is a finite logical gadget. Conditional on one quota choice at a central profile, twelve auxiliary profiles encode three bits and force a frustrated cycle. The argument uses only transfers between states whose populations are unchanged and assumes neither anonymity, neutrality, order preservation, homogeneity, nor other regularity conditions. It also applies to relative population monotonicity. Geometrically, the result is a global compatibility obstruction for quota-constrained lattice rounding, rather than an average-distortion bound.", "published": "2026-08-03T18:04:55Z", "authors": ["Lav R. Varshney"], "categories": ["cs.GT", "eess.SP"], "doi": "", "_query": "hedonic house price", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/1304.3875v3", "title": "Game-theoretic Understanding of Price Dynamics in Mobile Communication Services", "abstract": "In the mobile communication services, users wish to subscribe to high quality service with a low price level, which leads to competition between mobile network operators (MNOs). The MNOs compete with each other by service prices after deciding the extent of investment to improve quality of service (QoS). Unfortunately, the theoretic backgrounds of price dynamics are not known to us, and as a result, effective network planning and regulative actions are hard to make in the competitive market. To explain this competition more detail, we formulate and solve an optimization problem applying the two-stage Cournot and Bertrand competition model. Consequently, we derive a price dynamics that the MNOs increase and decrease their service prices periodically, which completely explains the subsidy dynamics in the real world. Moving forward, to avoid this instability and inefficiency, we suggest a simple regulation rule which leads to a Pareto-optimal equilibrium point. Moreover, we suggest regulator's optimal actions corresponding to user welfare and the regulator's revenue.", "published": "2013-04-14T03:36:34Z", "authors": ["Seung Min Yu", "Seong-Lyun Kim"], "categories": ["cs.GT"], "doi": "", "_query": "hedonic house price", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2502.02543v1", "title": "Posted Price Mechanisms for Online Allocation with Diseconomies of Scale", "abstract": "This paper addresses the online $k$-selection problem with diseconomies of scale (OSDoS), where a seller seeks to maximize social welfare by optimally pricing items for sequentially arriving buyers, accounting for increasing marginal production costs. Previous studies have investigated deterministic dynamic pricing mechanisms for such settings. However, significant challenges remain, particularly in achieving optimality with small or finite inventories and developing effective randomized posted price mechanisms. To bridge this gap, we propose a novel randomized dynamic pricing mechanism for OSDoS, providing a tighter lower bound on the competitive ratio compared to prior work. Our approach ensures optimal performance in small inventory settings (i.e., when $k$ is small) and surpasses existing online mechanisms in large inventory settings (i.e., when $k$ is large), leading to the best-known posted price mechanism for optimizing online selection and allocation with diseconomies of scale across varying inventory sizes.", "published": "2025-02-04T18:13:50Z", "authors": ["Hossein Nekouyan Jazi", "Bo Sun", "Raouf Boutaba", "Xiaoqi Tan"], "categories": ["cs.GT", "cs.DS"], "doi": "", "_query": "hedonic house price", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2403.03812v1", "title": "ProbSAINT: Probabilistic Tabular Regression for Used Car Pricing", "abstract": "Used car pricing is a critical aspect of the automotive industry, influenced by many economic factors and market dynamics. With the recent surge in online marketplaces and increased demand for used cars, accurate pricing would benefit both buyers and sellers by ensuring fair transactions. However, the transition towards automated pricing algorithms using machine learning necessitates the comprehension of model uncertainties, specifically the ability to flag predictions that the model is unsure about. Although recent literature proposes the use of boosting algorithms or nearest neighbor-based approaches for swift and precise price predictions, encapsulating model uncertainties with such algorithms presents a complex challenge. We introduce ProbSAINT, a model that offers a principled approach for uncertainty quantification of its price predictions, along with accurate point predictions that are comparable to state-of-the-art boosting techniques. Furthermore, acknowledging that the business prefers pricing used cars based on the number of days the vehicle was listed for sale, we show how ProbSAINT can be used as a dynamic forecasting model for predicting price probabilities for different expected offer duration. Our experiments further indicate that ProbSAINT is especially accurate on instances where it is highly certain. This proves the applicability of its probabilistic predictions in real-world scenarios where trustworthiness is crucial.", "published": "2024-03-06T16:00:50Z", "authors": ["Kiran Madhusudhanan", "Gunnar Behrens", "Maximilian Stubbemann", "Lars Schmidt-Thieme"], "categories": ["cs.LG", "cs.AI"], "doi": "", "_query": "hedonic house price", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/1202.4332v1", "title": "A parsimonious model for intraday European option pricing", "abstract": "A stochastic model for pure-jump diffusion (the compound renewal process) can be used as a zero-order approximation and as a phenomenological description of tick-by-tick price fluctuations. This leads to an exact and explicit general formula for the martingale price of a European call option. A complete derivation of this result is presented by means of elementary probabilistic tools.", "published": "2012-02-20T14:10:09Z", "authors": ["Enrico Scalas", "Mauro Politi"], "categories": ["q-fin.PR", "math.PR"], "doi": "", "_query": "hedonic house price", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2208.14231v1", "title": "Prediction-based One-shot Dynamic Parking Pricing", "abstract": "Many U.S. metropolitan cities are notorious for their severe shortage of parking spots. To this end, we present a proactive prediction-driven optimization framework to dynamically adjust parking prices. We use state-of-the-art deep learning technologies such as neural ordinary differential equations (NODEs) to design our future parking occupancy rate prediction model given historical occupancy rates and price information. Owing to the continuous and bijective characteristics of NODEs, in addition, we design a one-shot price optimization method given a pre-trained prediction model, which requires only one iteration to find the optimal solution. In other words, we optimize the price input to the pre-trained prediction model to achieve targeted occupancy rates in the parking blocks. We conduct experiments with the data collected in San Francisco and Seattle for years. Our prediction model shows the best accuracy in comparison with various temporal or spatio-temporal forecasting models. Our one-shot optimization method greatly outperforms other black-box and white-box search methods in terms of the search time and always returns the optimal price solution.", "published": "2022-08-30T12:59:58Z", "authors": ["Seoyoung Hong", "Heejoo Shin", "Jeongwhan Choi", "Noseong Park"], "categories": ["cs.LG", "cs.AI", "math.OC"], "doi": "10.1145/3511808.3557421", "_query": "hedonic house price", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2111.11315v2", "title": "Investing in crypto: speculative bubbles and cyclic stochastic price pumps", "abstract": "The problem of investing into a cryptocurrency market requires good understanding of the processes that regulate the price of the currency. In this paper we offer a view of a cryptocurrency market as an environment for realization of a self-organized speculative scheme that results in a formation of a characteristic price bubble as a transient phenomenon. We use microscale, agent-based models to simulate the system behavior and derive macroscale ODE models to estimate such parameters as the return rate and the market value of investments. We provide the formula for the total risk of the system as a sum of two independent components, one being characteristic of the price bubble and the other of the investor behavior.", "published": "2021-11-22T16:16:16Z", "authors": ["Misha Perepelitsa"], "categories": ["q-fin.TR"], "doi": "", "_query": "hedonic house price", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2609.35402v1", "title": "Persistent Partners Raise Prices Among Learning Agents", "abstract": "When pricing agents meet repeatedly on a platform, the platform decides who faces whom. We ask whether that choice moves the prices the agents learn, and whether a rise comes with learned punishment. In a pre-registered randomised experiment in the Bertrand duopoly of Calvano et al., each agent's price is set by a tabular Q-learning module, not by the small language model attached to it, and we randomise whether each agent keeps its partner, sees its rival's prices and can send messages. Keeping the same partner raises the level of profits, averaged over training, by 0.27 of the gap between competitive and monopoly profit (95% CI 0.20 to 0.35, all twenty paired runs positive), our registered primary result, and the resting price by 0.17 of the Nash-to-monopoly range (post hoc). A plain tabular learner reproduces the effect in all 25 further blocks, and there one permanent partner raises the level more than about three do (+0.23 against +0.05, exploratory). Where rival prices are hidden, the price-setting module cannot see a cut, so cannot punish it, yet the resting price rises as much and the rise lasts to the end of training, while with visible rivals it shrinks with longer training (post hoc). Where the rival is visible, a static best responder accounts for a third to a half of what a forced-deviation probe reads as punishment, on the starts where the rival can see the cut, and net of it the registered test of learned punishment is inconclusive. A test that looks only for punishment would thus miss the rise where the rival is hidden, while a check for profitable deviations flags most of those prices (post hoc). In an exploratory extension, untrained Qwen2.5 7B and 14B models under one prompt show the effect when the rival's price is left out of the prompt and inconsistently when it is shown, the 7B result replicating on fresh blocks, while two other model families show none.", "published": "2026-09-28T15:24:23Z", "authors": ["Paul-Peter Arslan", "Yubin Kim", "Xiao Xiao"], "categories": ["cs.LG", "cs.MA"], "doi": "", "_query": "hedonic house price", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/1805.11317v1", "title": "Neural networks for stock price prediction", "abstract": "Due to the extremely volatile nature of financial markets, it is commonly accepted that stock price prediction is a task full of challenge. However in order to make profits or understand the essence of equity market, numerous market participants or researchers try to forecast stock price using various statistical, econometric or even neural network models. In this work, we survey and compare the predictive power of five neural network models, namely, back propagation (BP) neural network, radial basis function (RBF) neural network, general regression neural network (GRNN), support vector machine regression (SVMR), least squares support vector machine regresssion (LS-SVMR). We apply the five models to make price prediction of three individual stocks, namely, Bank of China, Vanke A and Kweichou Moutai. Adopting mean square error and average absolute percentage error as criteria, we find BP neural network consistently and robustly outperforms the other four models.", "published": "2018-05-29T09:14:49Z", "authors": ["Yue-Gang Song", "Yu-Long Zhou", "Ren-Jie Han"], "categories": ["q-fin.ST", "cs.LG", "stat.ML"], "doi": "", "_query": "hedonic house price", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/1202.6632v1", "title": "Coherent Price Systems and Uncertainty-Neutral Valuation", "abstract": "We consider fundamental questions of arbitrage pricing arising when the uncertainty model is given by a set of possible mutually singular probability measures. With a single probability model, essential equivalence between the absence of arbitrage and the existence of an equivalent martingale measure is a folk theorem, see Harrison and Kreps (1979). We establish a microeconomic foundation of sublinear price systems and present an extension result. In this context we introduce a prior dependent notion of marketed spaces and viable price systems. We associate this extension with a canonically altered concept of equivalent symmetric martingale measure sets, in a dynamic trading framework under absence of prior depending arbitrage. We prove the existence of such sets when volatility uncertainty is modeled by a stochastic differential equation, driven by Peng's G-Brownian motions.", "published": "2012-02-29T18:23:51Z", "authors": ["Patrick Beißner"], "categories": ["q-fin.GN"], "doi": "", "_query": "hedonic house price", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/1907.12665v1", "title": "Airbnb Price Prediction Using Machine Learning and Sentiment Analysis", "abstract": "Pricing a rental property on Airbnb is a challenging task for the owner as it determines the number of customers for the place. On the other hand, customers have to evaluate an offered price with minimal knowledge of an optimal value for the property. This paper aims to develop a reliable price prediction model using machine learning, deep learning, and natural language processing techniques to aid both the property owners and the customers with price evaluation given minimal available information about the property. Features of the rentals, owner characteristics, and the customer reviews will comprise the predictors, and a range of methods from linear regression to tree-based models, support-vector regression (SVR), K-means Clustering (KMC), and neural networks (NNs) will be used for creating the prediction model.", "published": "2019-07-29T21:45:42Z", "authors": ["Pouya Rezazadeh Kalehbasti", "Liubov Nikolenko", "Hoormazd Rezaei"], "categories": ["cs.LG", "stat.ML"], "doi": "10.1007/978-3-030-84060-0_11", "_query": "hedonic house price", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2603.25696v1", "title": "Input-Output Price Parity and Farm Profitability: A Strategic Perspective for Karnataka", "abstract": "Agricultural pricing policies are crucial for farm profitability and food security in India. This study analysed how input and output prices significantly influence the profitability of cereals in Karnataka, with the strategic support prices playing a crucial role in maintaining the price parity. The average annual TFP growth was 1.041 per cent. Rising input costs, particularly for human labour, led to reduced profitability for Jowar (6.12 per cent) and Ragi (4.89 per cent). The net effect was adverse for Jowar (-1.50 per cent) and Ragi (-0.86 per cent) due to rising input costs outpacing output prices. The study recommended increasing the MSP for Jowar (60 per cent) and Ragi (46.24 per cent) above the existing levels. A strategic price adjusted for changing input costs can stabilise farm incomes and promote sustainable production, enabling efficient pricing policies.", "published": "2026-03-26T17:44:41Z", "authors": [" Vaishnavi", " Lokesha", " H.", " Vedamurthy", "K. B.", "Manojkumar Patil"], "categories": ["econ.GN"], "doi": "10.35716/ijed-25143", "_query": "hedonic house price", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/1712.01570v1", "title": "Robust toll pricing: A novel approach", "abstract": "We study a robust toll pricing problem where toll setters and users have different level of information when taking their decisions. Toll setters do not have full information on the costs of the network and rely on historical information when determining toll rates, whereas users decide on the path to use from origin to destination knowing toll rates and having, in addition, more accurate traffic data. Toll setters often also face constraints on price experimentation which means less opportunity for price revision. Motivated by this we propose a novel robust pricing methodology for fixing prices where we take non-adversarial view of nature different from the existing robust approaches. We show that our non-adversarial robustness results in less conservative pricing decisions compared to traditional adversarial nature setting. We start by first considering a single origin-destination parallel network in this new robust setting and formulate the robust toll pricing problem as a distributionally robust optimization problem, for which we develop an exact algorithm based on a mixed-integer programming formulation and a heuristic based on two-point support distribution. We further extend our formulations to more general networks and show how our algorithms can be adapted for the general networks. Finally, we illustrate the usefulness of our approach by means of numerical experiments both on randomly generated networks and on the data recorded on the road network of the city of Chicago.", "published": "2017-12-05T11:08:00Z", "authors": ["Trivikram Dokka", "Alain B. Zemkoho", "Sonali Sen Gupta", "Fabrice T. Nobibon"], "categories": ["math.OC", "cs.GT"], "doi": "", "_query": "hedonic house price", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/0804.2589v2", "title": "Option pricing under stochastic volatility: the exponential Ornstein-Uhlenbeck model", "abstract": "We study the pricing problem for a European call option when the volatility of the underlying asset is random and follows the exponential Ornstein-Uhlenbeck model. The random diffusion model proposed is a two-dimensional market process that takes a log-Brownian motion to describe price dynamics and an Ornstein-Uhlenbeck subordinated process describing the randomness of the log-volatility. We derive an approximate option price that is valid when (i) the fluctuations of the volatility are larger than its normal level, (ii) the volatility presents a slow driving force toward its normal level and, finally, (iii) the market price of risk is a linear function of the log-volatility. We study the resulting European call price and its implied volatility for a range of parameters consistent with daily Dow Jones Index data.", "published": "2008-04-16T13:26:50Z", "authors": ["Josep Perello", "Ronnie Sircar", "Jaume Masoliver"], "categories": ["q-fin.PR", "physics.comp-ph", "physics.soc-ph"], "doi": "10.1088/1742-5468/2008/06/P06010", "_query": "hedonic house price", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/1702.00215v1", "title": "A confidence-based model for asset and derivative prices in the BitCoin market", "abstract": "We endorse the idea, suggested in recent literature, that BitCoin prices are influenced by sentiment and confidence about the underlying technology; as a consequence, an excitement about the BitCoin system may propagate to BitCoin prices causing a Bubble effect, the presence of which is documented in several papers about the cryptocurrency. In this paper we develop a bivariate model in continuous time to describe the price dynamics of one BitCoin as well as the behavior of a second factor affecting the price itself, which we name confidence indicator. The two dynamics are possibly correlated and we also take into account a delay between the confidence indicator and its delivered effect on the BitCoin price. Statistical properties of the suggested model are investigated and its arbitrage-free property is shown. Further, based on risk-neutral evaluation, a quasi-closed formula is derived for European style derivatives on the BitCoin. A short numerical application is finally provided.", "published": "2017-02-01T11:51:34Z", "authors": ["Alessandra Cretarola", "Gianna Figà-Talamanca"], "categories": ["q-fin.MF"], "doi": "10.1007/s10203-019-00262-x", "_query": "hedonic house price", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2510.09347v2", "title": "LLP: LLM-Based Product Pricing in E-commerce", "abstract": "Unlike Business-to-Consumer e-commerce platforms (e.g., Amazon), inexperienced individual sellers on Consumer-to-Consumer platforms (e.g., eBay) often face significant challenges in setting prices for their second-hand products efficiently. Therefore, numerous studies have been proposed for automating price prediction. However, most of them are based on static regression models, which suffer from poor generalization performance and fail to capture market dynamics (e.g., the price of a used iPhone decreases over time). Inspired by recent breakthroughs in Large Language Models (LLMs), we introduce LLP, the first LLM-based generative framework for second-hand product pricing. LLP first retrieves similar products to better align with the dynamic market change. Afterwards, it leverages the LLMs' nuanced understanding of key pricing information in free-form text to generate accurate price suggestions. To strengthen the LLMs' domain reasoning over retrieved products, we apply a two-stage optimization, supervised fine-tuning (SFT) followed by group relative policy optimization (GRPO), on a dataset built via bidirectional reasoning. Moreover, LLP employs a confidence-based filtering mechanism to reject unreliable price suggestions. Extensive experiments demonstrate that LLP substantially surpasses existing methods while generalizing well to unseen categories. We have successfully deployed LLP on Xianyu\\footnote\\{Xianyu is China's largest second-hand e-commerce platform.\\}, significantly outperforming the previous pricing method. Under the same 30\\% product coverage, it raises the static adoption rate (SAR) from 40\\% to 72\\%, and maintains a strong SAR of 47\\% even at 90\\% recall.", "published": "2025-10-10T13:00:57Z", "authors": ["Hairu Wang", "Sheng You", "Qiheng Zhang", "Xike Xie", "Shuguang Han", "Yuchen Wu", "Fei Huang", "Jufeng Chen"], "categories": ["cs.CL"], "doi": "", "_query": "hedonic house price", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/0911.1610v2", "title": "Pricing Fixed-Income Securities in an Information-Based Framework", "abstract": "In this paper we introduce a class of information-based models for the pricing of fixed-income securities. We consider a set of continuous- time information processes that describe the flow of information about market factors in a monetary economy. The nominal pricing kernel is at any given time assumed to be given by a function of the values of information processes at that time. By use of a change-of-measure technique we derive explicit expressions for the price processes of nominal discount bonds, and deduce the associated dynamics of the short rate of interest and the market price of risk. The interest rate positivity condition is expressed as a differential inequality. We proceed to the modelling of the price-level, which at any given time is also taken to be a function of the values of the information processes at that time. A simple model for a stochastic monetary economy is introduced in which the prices of nominal discount bonds and inflation-linked notes can be expressed in terms of aggregate consumption and the liquidity benefit generated by the money supply.", "published": "2009-11-09T09:22:41Z", "authors": ["Lane P. Hughston", "Andrea Macrina"], "categories": ["q-fin.PR"], "doi": "", "_query": "hedonic house price", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/physics/0701065v1", "title": "Gearboxes: indirect identification of dynamic forces transmitted to housing through bearings", "abstract": "In order to determine indirectly the generalized forces applied on the gearbox, two approaches are tested, one is a numerical method, the other is experimental. For the numerical method, the vibration response of the gearbox can be evaluated with use of a finite element analysis and a spectral and iterative method. It's confirmed that applying the generalized forces transmitted through the bearings on the empty housing allows to rebuild the housing response (reciprocity principle). The simulation on the time- and space-averaged mean square vibration velocity shows the vibration response of the housing is induced principally by the axial exciting forces, and secondarily by the moments Mx and My. transmitted on the free side (the 6 mm thick face). Realistic estimation of these excitation forces needs a modelling of the whole gearbox taking into account the mechanical properties of the housing. For the experimental approach, an inverse method based upon the FRF measurement and characterisation of gearbox vibration response is developed. Beginning from the equivalent forces obtained by this inverse method, a resulting force and a moment are derived for representing the distributed force acting on each bearing. The comparison between the results obtained by these two methods shows the effectiveness of this indirect force determination methodology.", "published": "2007-01-05T15:04:55Z", "authors": ["Gu Xiang Zhang", "Emmanuel Rigaud", "Jean-Claude Pascal", "Jean Sabot"], "categories": ["physics.class-ph"], "doi": "", "_query": "hedonic house price", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/1307.5440v3", "title": "Tick Size Reduction and Price Clustering in a FX Order Book", "abstract": "We investigate the statistical properties of the EBS order book for the EUR/USD and USD/JPY currency pairs and the impact of a ten-fold tick size reduction on its dynamics. A large fraction of limit orders are still placed right at or halfway between the old allowed prices. This generates price barriers where the best quotes lie for much of the time, which causes the emergence of distinct peaks in the average shape of the book at round distances. Furthermore, we argue that this clustering is mainly due to manual traders who remained set to the old price resolution. Automatic traders easily take price priority by submitting limit orders one tick ahead of clusters, as shown by the prominence of buy (sell) limit orders posted with rightmost digit one (nine).", "published": "2013-07-20T17:09:06Z", "authors": ["Mehdi Lallouache", "Frédéric Abergel"], "categories": ["q-fin.TR", "q-fin.ST"], "doi": "10.1016/j.physa.2014.09.016", "_query": "hedonic house price", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2211.14570v1", "title": "The Role of In-House Procurement According to Finnish Municipalities' Purchase Invoice Data", "abstract": "Public sector is a large consumer of ICT systems and services, used for various public services. Tendering for such systems is governed by laws aimed at eliminating unfair advantages and offering all possible parties equal opportunities to participate in the tendering process. In this article, we study in-house rpocurement, where the acquiring organization is an owner of the subcontractor that delivers the system or the service. Municipalities' purchase invoice data is used to determine how much municipalities in Finland depend on in-house procurement. In conclusion, the understanding if included municipalities have ICT service and development units within the organizations needs closer examination, as in-house companies may offer municipalities with limited resources divided costs in the public procurement process.", "published": "2022-11-26T13:50:27Z", "authors": ["Reetta-Kaisa Ghezzi", "Minnamaria Korhonen", "Hannu Vilpponen", "Tommi Mikkonen"], "categories": ["cs.CY"], "doi": "", "_query": "hedonic house price", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2506.15103v2", "title": "How does online shopping affect offline price sensitivity?", "abstract": "The rapid rise of e-commerce has transformed consumer behavior, prompting questions about how online adoption influences offline shopping. We examine whether consumers who adopt a retailer's online shopping channels become more price-sensitive in their subsequent offline purchases with that retailer. Using transaction-level data from a large Brazilian pet supplies retailer operating both online and offline, we compare \"adopters\" -- customers who began shopping online after a period of offline-only purchasing -- with \"non-adopters\" who remained offline-only. We estimate a discrete choice logit model with individual-level heterogeneity, based on an algorithm that can handle both high-dimensional fixed effects and price endogeneity. We then apply a staggered difference-in-differences approach to the estimated price elasticities and obtain the Average Treatment Effect on the Treated (ATT). We find that offline price sensitivity increases significantly after online adoption in three out of four product categories, particularly in items with low switching costs, such as pet hygiene. These results underscore the importance of recognizing cross-channel effects in consumer behavior and contribute to the literature on pricing and multichannel retailing by identifying online adoption as a key driver of offline price sensitivity.", "published": "2025-06-18T03:20:02Z", "authors": ["Shirsho Biswas", "Hema Yoganarasimhan", "Haonan Zhang"], "categories": ["econ.GN"], "doi": "", "_query": "hedonic house price", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2607.00050v1", "title": "Revising price coordination in the classical and neoclassical economics based on elementary cellular automata", "abstract": "The coordination of prices in economics is one of the most complex phenomena. In particular, the classical and neoclassical approaches related to the economic theory provide some insights into such a complex coordination based on different formulations. However, these formulations have not been successful for explaining simple mechanisms to understand and predict a set of prices that theoretically clears all markets. Consequently, elementary cellular automata can contribute to clarify such a coordination problem by using simple computational rules to describe the theoretical bases of the classical and neoclassical economics. Therefore, we propose to use this type of cellular automata for explaining different escenarios of price coordination in which simple rules of price interactions generate stable and unstable patterns of coordination. We used an explorative data analysis based on the Shannon entropy for computing the uncertainty related to such generated patterns of coordination, and a Monte Carlo simulation approximation based on a Spearman correlation for evaluating the statistical significance of such price coordination. Findings suggested that the classical economics provides a consistent approach for understanding the coordination of prices because it emphasizes human interactions based on logical choices related to an objective data. On the other hand, the neoclassical approach does not propose any type of mechanism for describing the price coordination. The neoclassical individual is just a spectator and receiver of the unpredictable and supposed event of price coordination. As a result, by modeling the economic theory based on computational concepts, we reveal facts and believes behind the classical and neoclassical economics.", "published": "2026-06-29T23:55:16Z", "authors": ["Igor Lugo", "Martha G. Alatriste-Contreras"], "categories": ["nlin.CG", "econ.TH"], "doi": "", "_query": "hedonic house price", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2606.01874v2", "title": "The Price of Decentralization in Block Building", "abstract": "Decentralized block building mechanisms replace the monopoly of a single proposer with multiple builders. However, their censorship-resistance and fair-access benefits depend not only on the number of builders, but also on where builders are geographically positioned to provide transaction coverage. We study this tension between builder location choice, user transaction coverage, and utility concentration by modeling decentralized block building as a stochastic coverage game. Builders choose regions, information sources emit transactions over a block construction round, and latency determines whether a transaction is received before the deadline. We show that the game is an exact potential game and admits a pure Nash equilibrium. We prove an asymptotically tight factor-2 bound on the Price of Anarchy, which we interpret as the price of decentralization from uncoordinated builder placement. We also study builder utility concentration, showing that the lowest-utility builder earns at least half of the highest-utility builder's payoff, and the utility-share HHI is at most 12.5% above the egalitarian benchmark. We complement the theory with simulations under richer latency and source environments and show that slot times, builder participation, and reward-sharing rules are important protocol design choices that can shape the price of decentralization.", "published": "2026-06-01T08:20:18Z", "authors": ["Burak Öz", "Fei Wu", "Luis Correia", "Sen Yang", "Bruno Mazorra", "Stefanos Leonardos"], "categories": ["cs.GT"], "doi": "", "_query": "hedonic house price", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2512.17758v3", "title": "Day-Ahead Electricity Price Forecasting Using Merit-Order Curves Time Series", "abstract": "We introduce a general, simple, and computationally efficient functional data analysis framework for forecasting day-ahead supply and demand merit-order curves, and the resulting electricity price. We conduct a rigorous empirical comparison on data from the Italian (GME), German (EPEX-DE-LU), and French (EPEX-FR) day-ahead markets over the 2023-2024 period, analyzing curve forecasting performance, price forecasting performance, and the relationship between the two. We find that strong curve forecasting performance does not necessarily translate into strong price forecasting performance, with important implications for curve model evaluation and selection when price forecasting is among the objectives. We also show that this functional data representation approach consistently outperforms the original discretization-based approach of Ziel and Steinert (2016) on price forecasting across all three markets. Finally, the proposed curve-based approach is competitive with state-of-the-art price-based models for two out of three markets (GME and EPEX-FR), and substantially improves accuracy during midday hours (when prices frequently drop due to high renewable generation) with MAE reductions of up to 27% in those windows. For EPEX-DE-LU, however, price-based models retain a clear and significant advantage.", "published": "2025-12-19T16:32:30Z", "authors": ["Guillaume Koechlin", "Filippo Bovera", "Piercesare Secchi"], "categories": ["stat.AP"], "doi": "", "_query": "hedonic house price", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2010.04993v1", "title": "Fair Pricing In Heterogeneous Internet of Things Wireless Access Networks Using Crowdsourcing", "abstract": "Price and the quality of service are two key factors taken into account by wireless network users when they choose their network provider. The recent advances in wireless technology and massive infrastructure deployments has led to better coverage, and currently at each given wirelessly covered area there are a few network providers and each have different pricing strategies. These providers can potentially set unfair expensive prices for their services. In this paper, we propose a novel crowdsourcing-based approach for fair wireless service pricing in Internet of Things (IoT). In our considered oligopoly, the regulatory sets a dynamic maximum allowed price of service to prevent anti-trust behaviour and unfair service pricing. We propose a three-tire pricing model where the regulator, wireless network providers and clients are the players of our game. Our method takes client preferences into account in pricing and discovers the fair service pricing just above the marginal costs of each network provider. Our results show that our model is not prone to collusion and will converges only if one network announces the fair price", "published": "2020-10-10T13:27:27Z", "authors": ["Vahid Haghighatdoost", "Siavash Khorsandi", "Hamed Ahmadi"], "categories": ["cs.NI"], "doi": "", "_query": "hedonic house price", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/1412.0845v1", "title": "On the Robustness of the Approximate Price of Anarchy in Generalized Congestion Games", "abstract": "One of the main results shown through Roughgarden's notions of smooth games and robust price of anarchy is that, for any sum-bounded utilitarian social function, the worst-case price of anarchy of coarse correlated equilibria coincides with that of pure Nash equilibria in the class of weighted congestion games with non-negative and non-decreasing latency functions and that such a value can always be derived through the, so called, smoothness argument. We significantly extend this result by proving that, for a variety of (even non-sum-bounded) utilitarian and egalitarian social functions and for a broad generalization of the class of weighted congestion games with non-negative (and possibly decreasing) latency functions, the worst-case price of anarchy of $ε$-approximate coarse correlated equilibria still coincides with that of $ε$-approximate pure Nash equilibria, for any $ε\\geq 0$. As a byproduct of our proof, it also follows that such a value can always be determined by making use of the primal-dual method we introduced in a previous work. It is important to note that our scenario of investigation is beyond the scope of application of the robust price of anarchy (for as it is currently defined), so that our result seems unlikely to be alternatively proved via the smoothness framework.", "published": "2014-12-02T10:30:27Z", "authors": ["Vittorio Bilò"], "categories": ["cs.GT"], "doi": "", "_query": "hedonic house price", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2607.12156v3", "title": "(Early) AI Compute Asset Pricing", "abstract": "Compute (computing power) is a scarce, capital-intensive input at the center of the AI economy. Compute capital expenditure and service flow already exceed 1% of U.S. GDP and are growing rapidly. The price of compute reflects uncertainty over AI adoption. The announced launch of compute futures turns this uncertainty into a tradable risk, raising questions on the pricing of a new asset class. We provide an early asset-pricing framework for compute. We begin by discussing the underlying compute rental market and its indexation. We then turn to pricing: 1) direct no-arbitrage links between futures prices and current spot prices fail due to the non-storable nature of compute, 2) synthetic futures prices from existing term rental contracts are likely upper bounds on true futures prices and, 3) upon financialization, futures prices will be investors' expectations of spot prices at expiration net of a risk premium. Using synthetic futures as stand-ins before the compute futures market launches, we construct the first compute futures return panel sorted by GPU generation and maturity. Our preliminary evidence is consistent with a positive compute risk premium, suggesting hedging pressure on the part of compute providers.", "published": "2026-07-13T21:04:25Z", "authors": ["Federico M. Bandi", "Yinan Su"], "categories": ["q-fin.PR"], "doi": "", "_query": "hedonic house price", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/1606.08269v1", "title": "An agent behavior based model for diffusion price processes with application to phase transition and oscillations", "abstract": "We present an agent behavior based microscopic model for diffusion price processes. As such we provide a model not only containing a convenient framework for describing socio-economic behavior, but also a sophisticated link to price dynamics. We furthermore establish the circumstances under which the dynamics converge to diffusion processes in the large market limit. To demonstrate the applicability of a separation of behavior and price process, we show how herding behavior of market participants can lead to equilibria transition and oscillations in diffusion price processes.", "published": "2016-06-27T13:33:00Z", "authors": ["Christof Henkel"], "categories": ["math.PR", "econ.GN"], "doi": "", "_query": "hedonic house price", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/1601.02319v2", "title": "Dynamic Pricing and Distributed Energy Management for Demand Response", "abstract": "The problem of dynamic pricing of electricity in a retail market is considered. A Stackelberg game is used to model interactions between a retailer and its customers; the retailer sets the day-ahead hourly price of electricity and consumers adjust real-time consumptions to maximize individual consumer surplus. For thermostatic demands, the optimal aggregated demand is shown to be an affine function of the day-ahead hourly price. A complete characterization of the trade-offs between consumer surplus and retail profit is obtained. The Pareto front of achievable trade-offs is shown to be concave, and each point on the Pareto front is achieved by an optimal day-ahead hourly price. Effects of integrating renewables and local storage are analyzed. It is shown that benefits of renewable integration all go to the retailer when the capacity of renewable is relatively small. As the capacity increases beyond a certain threshold, the benefit from renewable that goes to consumers increases.", "published": "2016-01-11T04:36:19Z", "authors": ["Liyan Jia", "Lang Tong"], "categories": ["math.OC"], "doi": "", "_query": "hedonic house price", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2211.01798v1", "title": "Phase Transitions in Learning and Earning under Price Protection Guarantee", "abstract": "Motivated by the prevalence of ``price protection guarantee\", which allows a customer who purchased a product in the past to receive a refund from the seller during the so-called price protection period (typically defined as a certain time window after the purchase date) in case the seller decides to lower the price, we study the impact of such policy on the design of online learning algorithm for data-driven dynamic pricing with initially unknown customer demand. We consider a setting where a firm sells a product over a horizon of $T$ time steps. For this setting, we characterize how the value of $M$, the length of price protection period, can affect the optimal regret of the learning process. We show that the optimal regret is $\\tildeΘ(\\sqrt{T}+\\min\\{M,\\,T^{2/3}\\})$ by first establishing a fundamental impossible regime with novel regret lower bound instances. Then, we propose LEAP, a phased exploration type algorithm for \\underline{L}earning and \\underline{EA}rning under \\underline{P}rice Protection to match this lower bound up to logarithmic factors or even doubly logarithmic factors (when there are only two prices available to the seller). Our results reveal the surprising phase transitions of the optimal regret with respect to $M$. Specifically, when $M$ is not too large, the optimal regret has no major difference when compared to that of the classic setting with no price protection guarantee. We also show that there exists an upper limit on how much the optimal regret can deteriorate when $M$ grows large. Finally, we conduct extensive numerical experiments to show the benefit of LEAP over other heuristic methods for this problem.", "published": "2022-11-03T13:36:00Z", "authors": ["Qing Feng", "Ruihao Zhu", "Stefanus Jasin"], "categories": ["stat.ML", "cs.LG"], "doi": "", "_query": "hedonic house price", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2206.09452v1", "title": "Demand Analysis with a Thin Price Sample", "abstract": "For about 125 items of food, the Consumer Expenditure Survey (CES) schedule of the Indian National Sample Survey asks the interviewer to obtain both quantity and value of household consumption during the reference period from the respondent. This would appear to put a great burden on the respondent. But it is likely that the price usually paid is almost the same within each first stage unit (fsu). The present work proposes a new sampling scheme to estimate demand elasticities of essential food items. While the conventional sampling method used in practice (e.g. in NSS consumer expenditure survey) involves seeking price information from many households sampled from a fsu, the proposed procedure involves only one household chosen randomly from every fsu for price data collection and thus requires much less interview burden. Using unit records for vegetable items in the NSS's 2011-12 CES, our results show that in spite of requiring much less data, the new scheme captures the household food consumption behavior as precisely as before.", "published": "2022-06-19T17:08:58Z", "authors": ["Monitirtha Dey", "Arpan Kumar", "Diganta Mukherjee"], "categories": ["stat.AP", "stat.ME"], "doi": "", "_query": "hedonic house price", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/1109.6909v1", "title": "Pricing stocks with yardsticks and sentiments", "abstract": "Human decision making by professionals trading daily in the stock market can be a daunting task. It includes decisions on whether to keep on investing or to exit a market subject to huge price swings, and how to price in news or rumors attributed to a specific stock. The question then arises how professional traders, who specialize in daily buying and selling large amounts of a given stock, know how to properly price a given stock on a given day? Here we introduce the idea that people use heuristics, or \"rules of thumb\", in terms of \"yard sticks\" from the performance of the other stocks in a stock index. The under- /over-performance with respect to such a yard stick then signifies a general negative/positive sentiment of the market participants towards a given stock. Using empirical data of the Dow Jones Industrial Average, stocks are shown to have daily performances with a clear tendency to cluster around the measures introduced by the yard sticks. We illustrate how sentiments, most likely due to insider information, can influence the performance of a given stock over period of months, and in one case years.", "published": "2011-09-30T17:41:19Z", "authors": ["Sebastıan Martınez Bustos", "Jorgen Vitting Andersen", "Michel Miniconi", "Andrzej Nowak", "Magdalena Roszczynska-Kurasinska", "David Bree"], "categories": ["q-fin.GN", "physics.soc-ph"], "doi": "", "_query": "hedonic house price", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2402.06711v1", "title": "Dodging the electricity price hike: Can demand-side flexibility compensate for spot price increases for households in Germany?", "abstract": "In 2022, energy prices skyrocketed across Europe, with average day-ahead spot market prices in Germany 2.43 times higher than the previous year, hinting at future trends. At the same time, electricity infrastructure is expected to be overutilized in some regions in the future due to the uptake of electric vehicles, heat pumps, PV systems, and other appliances in the residential sector. Dynamic electricity pricing for households is proposed as a solution to alleviate infrastructure strain and reduce costs. The question arises as to what impact dynamic electricity prices as recently seen on the day-ahead spot market in Germany would have on the use of flexibility in households and whether it can compensate for cost increases for households. We analyze the cost-effectiveness of utilizing flexibility through a home energy management system (HEMS) and smart meters against increased self-consumption using a HEMS and static tariffs. We show that with higher electricity prices and price spreads, a higher share of households can offset initial investments in HEMS and metering operation costs by utilizing flexibility from their electric vehicles, heat pumps, and battery storage systems. While increasing self-consumption remains beneficial for households with heat pumps and PV systems, dynamic electricity tariffs are financially more advantageous for those with electric vehicles. The study also determines the maximum additional annual costs for HEMS and metering point operation that still allow cost savings for 75% of households. these costs range from 126 to 145 Euro in the self-consumption scenario depending and from 50 to 111 Euro in the dynamic electricity tariff scenario, highlighting the financial viability of flexibility utilization in response to current pricing trends.", "published": "2024-02-09T12:54:53Z", "authors": ["Judith Stute", "Sabine Pelka", "Matthias Kühnbach", "Marian Klobasa"], "categories": ["physics.soc-ph"], "doi": "10.1016/j.adapen.2024.100174", "_query": "hedonic house price", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/1703.00927v2", "title": "When is selfish routing bad? The price of anarchy in light and heavy traffic", "abstract": "This paper examines the behavior of the price of anarchy as a function of the traffic inflow in nonatomic congestion games with multiple origin-destination (O/D) pairs. Empirical studies in real-world networks show that the price of anarchy is close to 1 in both light and heavy traffic, thus raising the question: can these observations be justified theoretically? We first show that this is not always the case: the price of anarchy may remain a positive distance away from 1 for all values of the traffic inflow, even in simple three-link networks with a single O/D pair and smooth, convex costs. On the other hand, for a large class of cost functions (including all polynomials), the price of anarchy does converge to 1 in both heavy and light traffic, irrespective of the network topology and the number of O/D pairs in the network. We also examine the rate of convergence of the price of anarchy, and we show that it follows a power law whose degree can be computed explicitly when the network's cost functions are polynomials.", "published": "2017-03-02T19:35:48Z", "authors": ["Riccardo Colini-Baldeschi", "Roberto Cominetti", "Panayotis Mertikopoulos", "Marco Scarsini"], "categories": ["cs.GT", "math.OC"], "doi": "", "_query": "hedonic house price", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/1509.00372v2", "title": "Electricity Price Forecasting using Sale and Purchase Curves: The X-Model", "abstract": "Our paper aims to model and forecast the electricity price by taking a completely new perspective on the data. It will be the first approach which is able to combine the insights of market structure models with extensive and modern econometric analysis. Instead of directly modeling the electricity price as it is usually done in time series or data mining approaches, we model and utilize its true source: the sale and purchase curves of the electricity exchange. We will refer to this new model as X-Model, as almost every deregulated electricity price is simply the result of the intersection of the electricity supply and demand curve at a certain auction. Therefore we show an approach to deal with a tremendous amount of auction data, using a subtle data processing technique as well as dimension reduction and lasso based estimation methods. We incorporate not only several known features, such as seasonal behavior or the impact of other processes like renewable energy, but also completely new elaborated stylized facts of the bidding structure. Our model is able to capture the non-linear behavior of the electricity price, which is especially useful for predicting huge price spikes. Using simulation methods we show how to derive prediction intervals for probabilistic forecasting. We describe and show the proposed methods for the day-ahead EPEX spot price of Germany and Austria.", "published": "2015-09-01T16:04:42Z", "authors": ["Florian Ziel", "Rick Steinert"], "categories": ["q-fin.TR", "q-fin.ST"], "doi": "10.1016/j.eneco.2016.08.008", "_query": "hedonic house price", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/1208.0371v1", "title": "Can Metropolitan Housing Risk be Diversified? A Cautionary Tale from the Recent Boom and Bust", "abstract": "Geographic diversification is fundamental to risk mitigation among investors and insurers of housing, mortgages, and mortgage-related derivatives. To characterize diversification potential, we provide estimates of integration, spatial correlation, and contagion among US metropolitan housing markets. Results reveal a high and increasing level of integration among US markets over the decade of the 2000s, especially in California. We apply integration results to assess the risk of alternative housing investment portfolios. Portfolio simulation indicates reduced diversification potential and increased risk in the wake of estimated increases in metropolitan housing market integration. Research findings provide new insights regarding the synchronous non-performance of geographically-disparate MBS investments during the late 2000s.", "published": "2012-08-01T21:42:56Z", "authors": ["John Cotter", "Stuart Gabriel", "Richard Roll"], "categories": ["q-fin.PM"], "doi": "", "_query": "hedonic house price", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/cond-mat/0104313v1", "title": "Derivation of ARCH(1) process from market price changes based on deterministic microscopic multi-agent", "abstract": "A model of fluctuations in the market price including many deterministic dealers, who predict their buying and selling prices from the latest price change, is developed. We show that price changes of the model is approximated by ARCH(1) process. We conclude that predictions of dealers affected by the past price changes cause the fat tails of probability density function. We believe that this study bridges stochastic processes in econometrics with multi-agent simulation approaches.", "published": "2001-04-17T23:29:42Z", "authors": ["Aki-Hiro Sato", "Hideki Takayasu"], "categories": ["cond-mat.stat-mech", "q-fin.ST"], "doi": "", "_query": "hedonic house price", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2001.00737v1", "title": "Option Pricing in an Investment Risk-Return Setting", "abstract": "In this paper, we combine modern portfolio theory and option pricing theory so that a trader who takes a position in a European option contract and the underlying assets can construct an optimal portfolio such that at the moment of the contract's maturity the contract is perfectly hedged. We derive both the optimal holdings in the underlying assets for the trader's optimal mean-variance portfolio and the amount of unhedged risk prior to maturity. Solutions assuming the cases where the price dynamics in the underlying assets follow discrete binomial price dynamics, continuous diffusions, stochastic volatility, volatility-of-volatility, and Merton-jump diffusion are derived.", "published": "2020-01-03T06:16:14Z", "authors": ["Abootaleb Shirvani", "Frank J. Fabozzi", "Stoyan V. Stoyanov"], "categories": ["q-fin.MF"], "doi": "", "_query": "hedonic house price", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2411.08126v3", "title": "A Tale of Two Cities: Pessimism and Opportunism in Offline Dynamic Pricing", "abstract": "We study offline dynamic pricing when historical data provide incomplete coverage of the price space such that some candidate prices, including the optimal one, may be entirely unobserved. This setting is common in practice and is especially difficult in dynamic environments. Existing offline reinforcement learning methods typically rely on full or partial coverage and can therefore perform poorly in such settings. We develop a nonparametric partial identification framework for offline dynamic pricing that exploits the monotonicity of demand in price to bound the value of unobserved prices. Within this framework, we formulate two dynamic decision rules: a pessimistic policy that maximizes worst-case revenue and an opportunistic policy that minimizes worst-case regret. These rules are tailored to a sequential no-coverage environment and are not direct extensions of existing pessimistic offline RL or static opportunistic approaches. We establish finite-sample regret bounds for both policies, recovering the standard rate when the optimal price is covered and quantifying the additional cost when it is not. We also develop efficient algorithms and show, through simulations and an airline ticket application, that our methods outperform standard offline RL baselines in no-coverage settings. Managerially, the framework provides a practical mapping from a firm's risk posture to its pricing policy: firms seeking revenue stability and downside protection should prefer the pessimistic policy, whereas firms willing to bear measured risk for potential gains from underexplored prices should prefer the opportunistic policy.", "published": "2024-11-12T19:09:41Z", "authors": ["Zeyu Bian", "Lan Wang", "Zhengling Qi"], "categories": ["stat.ML", "cs.LG"], "doi": "", "_query": "hedonic house price", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2508.13409v1", "title": "Pricing insurance policies with offsetting relationship", "abstract": "This paper investigates the benefits of incorporating diversification effects into the pricing process of insurance policies from two different business lines. The paper shows that, for the same risk reduction, insurers pricing policies jointly can have a competitive advantage over those pricing them separately. However, the choice of competitiveness constrains the underwriting flexibility of joint pricers. The paper goes a step further by modeling explicitly the relationship between premiums and the number of customers in each line. Using the total collected premiums as a criterion to compare the competing strategies, the paper provides conditions for the optimal pricing decision based on policyholders' sensitivity to price discounts. The results are illustrated for a portfolio of annuities and assurances. Further, using non-life data from the Brazilian insurance market, an empirical exploration shows that most pairs satisfy the condition for being priced jointly, even when pairwise correlations are high.", "published": "2025-08-19T00:08:10Z", "authors": ["Hamza Hanbali"], "categories": ["econ.TH"], "doi": "10.1017/S1748499521000208", "_query": "hedonic house price", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/1611.00885v2", "title": "Pricing Perpetual Put Options by the Black-Scholes Equation with a Nonlinear Volatility Function", "abstract": "We investigate qualitative and quantitative behavior of a solution of the mathematical model for pricing American style of perpetual put options. We assume the option price is a solution to the stationary generalized Black-Scholes equation in which the volatility function may depend on the second derivative of the option price itself. We prove existence and uniqueness of a solution to the free boundary problem. We derive a single implicit equation for the free boundary position and the closed form formula for the option price. It is a generalization of the well-known explicit closed form solution derived by Merton for the case of a constant volatility. We also present results of numerical computations of the free boundary position, option price and their dependence on model parameters.", "published": "2016-11-03T05:30:13Z", "authors": ["Maria do Rosario Grossinho", "Yaser Kord Faghan", "Daniel Sevcovic"], "categories": ["q-fin.MF"], "doi": "", "_query": "hedonic house price", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2112.08071v2", "title": "Stock prices and Macroeconomic indicators: Investigating a correlation in Indian context", "abstract": "The objective of this paper is to find the existence of a relationship between stock market prices and the fundamental macroeconomic indicators. We build a Vector Auto Regression (VAR) model comprising of nine major macroeconomic indicators (interest rate, inflation, exchange rate, money supply, gdp, fdi, trade-gdp ratio, oil prices, gold prices) and then try to forecast them for next 5 years. Finally we calculate cross-correlation of these forecasted values with the BSE Sensex closing price for each of those years. We find very high correlation of the closing price with exchange rate and money supply in the Indian economy.", "published": "2021-12-15T12:23:11Z", "authors": ["Dhruv Rawat", "Sujay Patni", "Ram Mehta"], "categories": ["econ.GN"], "doi": "", "_query": "hedonic house price", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2502.08124v2", "title": "Price and Assortment Optimization under the Multinomial Logit Model with Opaque Products", "abstract": "An opaque product is a product for which only partial information is disclosed to the buyer at the time of purchase. Opaque products are common in sectors such as travel and online retail, where the brand or product color is hidden in the opaque product. Opaque products enable sellers to target customers who prefer a price discount in exchange for being flexible about the product they receive. In this paper, we integrate opaque products and traditional products together into the multinomial logit (MNL) choice model and study the associated price and assortment optimization problems. For the price optimization problem, we show a surprising result that uniform pricing is optimal which implies it has the same optimal pricing solution and value as the traditional MNL model. Although adding an opaque product may enhance revenue given arbitrary traditional product prices, our result shows that this advantage disappears when all prices are optimized jointly. For the assortment problem, we first study the setting where the seller chooses both the offered assortment and the support of the opaque product, and prove that there exists an optimal assortment that is nested by revenue. We then study the case where the opaque support is required to coincide with the offered assortment which is common in practice. Here, we show that the optimal assortment is nested-by-valuation for uniformly priced products. For arbitrary prices, we propose a nested-by-revenue-and-valuation heuristic with a theoretical approximation guarantee that performs extremely well in our numerical study.", "published": "2025-02-12T05:06:02Z", "authors": ["Omar El Housni", "Adam N. Elmachtoub", "Harsh Sheth", "Jiaqi Shi"], "categories": ["math.OC"], "doi": "", "_query": "hedonic house price", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/1112.2406v2", "title": "On the game interpretation of a shadow price process in utility maximization problems under transaction costs", "abstract": "To any utility maximization problem under transaction costs one can assign a frictionless model with a price process $S^*$, lying in the bid/ask price interval $[\\underline S, \\bar{S}]$. Such process $S^*$ is called a \\emph{shadow price} if it provides the same optimal utility value as in the original model with bid-ask spread. We call $S^*$ a \\emph{generalized shadow price} if the above property is true for the \\emph{relaxed} utility function in the frictionless model. This relaxation is defined as the lower semicontinuous envelope of the original utility, considered as a function on the set $[\\underline S, \\bar{S}]$, equipped with some natural weak topology. We prove the existence of a generalized shadow price under rather weak assumptions and mark its relation to a saddle point of the trader/market zero-sum game, determined by the relaxed utility function. The relation of the notion of a shadow price to its generalization is illustrated by several examples. Also, we briefly discuss the interpretation of shadow prices via Lagrange duality.", "published": "2011-12-11T21:38:29Z", "authors": ["Dmitry B. Rokhlin"], "categories": ["q-fin.PM"], "doi": "", "_query": "hedonic house price", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2606.14050v1", "title": "Battery Bidding under Price Uncertainty in Wholesale Electricity Markets", "abstract": "Grid-scale batteries increasingly influence outcomes in wholesale electricity markets, but their observed bid patterns remain difficult to interpret. In particular, bids that appear to reflect strategic withholding may instead arise from rational operations under price uncertainty and risk management. We develop an asset-level model of a price-taking battery that submits stepwise buy and sell bid curves in the day-ahead market under a finite set of price scenarios. The battery chooses quantity--price pairs to maximize a mean--CVaR objective subject to physical and market constraints. A direct formulation is a mixed-integer linear program, but we show that its integer decisions can be removed, yielding an exact linear programming reformulation suitable for empirical analysis. Our empirical results deliver three insights. First, withholding behavior can arise even without market power, because scarce stored energy and uncertain future prices increase the value of holding energy. Second, the effect of uncertainty depends on the state of charge: when stored energy is scarce, greater uncertainty raises sell bid prices, whereas when stored energy is abundant it can lower them. Third, risk management reshapes bid curves into layered structures that secure profitable execution across a broad set of scenarios while preserving some exposure to rare but valuable price spikes.", "published": "2026-06-12T02:51:55Z", "authors": ["Vincent Yinjun-Wang", "Madeleine Udell"], "categories": ["math.OC", "econ.GN", "eess.SY", "q-fin.PM", "q-fin.RM"], "doi": "", "_query": "hedonic house price", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/1210.4000v1", "title": "Price-Setting of Market Makers: A Filtering Problem with an Endogenous Filtration", "abstract": "We study the price-setting problem of market makers under risk neutrality and perfect competition in continuous time. Thereby we follow the classic Glosten-Milgrom model that defines bid and ask prices as expectations of a true value of the asset given the market makers' partial information that includes the customers trading decisions. The true value is modeled as a Markov process that can be observed by the customers with some noise at Poisson times. We analyze the price-setting problem in a mathematically rigorous way by solving a filtering problem with an endogenous filtration that depends on the bid and ask price process quoted by the market maker. Under some conditions we show existence and uniqueness of the price processes.", "published": "2012-10-15T12:36:15Z", "authors": ["Christoph Kühn", "Matthias Riedel"], "categories": ["q-fin.TR"], "doi": "", "_query": "hedonic house price", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2512.21621v1", "title": "Mean-Field Price Formation on Trees with a Network of Relative Performance Concerns", "abstract": "Financial firms and institutional investors are routinely evaluated based on their performance relative to their peers. These relative performance concerns significantly influence risk-taking behavior and market dynamics. While the literature studying Nash equilibrium under such relative performance competitions is extensive, its effect on asset price formation remains largely unexplored. This paper investigates mean-field equilibrium price formation of a single risky stock in a discrete-time market where agents exhibit exponential utility and relative performance concerns. Unlike existing literature that typically treats asset prices as exogenous, we impose a market-clearing condition to determine the price dynamics endogenously within a relative performance equilibrium. Using a binomial tree framework, we establish the existence and uniqueness of the market-clearing mean-field equilibrium in both single- and multi-population settings. Finally, we provide illustrative numerical examples demonstrating the equilibrium price distributions and agents' optimal position sizes.", "published": "2025-12-25T10:50:09Z", "authors": ["Masaaki Fujii"], "categories": ["q-fin.MF", "econ.GN", "q-fin.GN", "q-fin.PM"], "doi": "", "_query": "hedonic house price", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/1503.03548v1", "title": "Statistical Properties and Pre-hit Dynamics of Price Limit Hits in the Chinese Stock Markets", "abstract": "Price limit trading rules are adopted in some stock markets (especially emerging markets) trying to cool off traders' short-term trading mania on individual stocks and increase market efficiency. Under such a microstructure, stocks may hit their up-limits and down-limits from time to time. However, the behaviors of price limit hits are not well studied partially due to the fact that main stock markets such as the US markets and most European markets do not set price limits. Here, we perform detailed analyses of the high-frequency data of all A-share common stocks traded on the Shanghai Stock Exchange and the Shenzhen Stock Exchange from 2000 to 2011 to investigate the statistical properties of price limit hits and the dynamical evolution of several important financial variables before stock price hits its limits. We compare the properties of up-limit hits and down-limit hits. We also divide the whole period into three bullish periods and three bearish periods to unveil possible differences during bullish and bearish market states. To uncover the impacts of stock capitalization on price limit hits, we partition all stocks into six portfolios according to their capitalizations on different trading days. We find that the price limit trading rule has a cooling-off effect (object to the magnet effect), indicating that the rule takes effect in the Chinese stock markets. We find that price continuation is much more likely to occur than price reversal on the next trading day after a limit-hitting day, especially for down-limit hits, which has potential practical values for market practitioners.", "published": "2015-03-12T01:23:01Z", "authors": ["Yu-Lei Wan", "Wen-Jie Xie", "Gao-Feng Gu", "Zhi-Qiang Jiang", "Wei Chen", "Xiong Xiong", "Wei Zhang", "Wei-Xing Zhou"], "categories": ["q-fin.ST"], "doi": "10.1371/journal.pone.0120312", "_query": "hedonic house price", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/1906.02907v1", "title": "Optimal Resource Procurement and the Price of Causality", "abstract": "This paper studies the problem of procuring diverse resources in a forward market to cover a set $\\bf{E}$ of uncertain demand signals $\\bf{e}$. We consider two scenarios: (a) $\\bf{e}$ is revealed all at once by an oracle (b) $\\bf{e}$ reveals itself causally. Each scenario induces an optimal procurement cost. The ratio between these two costs is defined as the {\\em price of causality}. It captures the additional cost of not knowing the future values of the uncertain demand signal. We consider two application contexts: procuring energy reserves from a forward capacity market, and purchasing virtual machine instances from a cloud service. An upper bound on the price of causality is obtained, and the exact price of causality is computed for some special cases. The algorithmic basis for all these computations is set containment linear programming. A mechanism is proposed to allocate the procurement cost to consumers who in aggregate produce the demand signal. We show that the proposed cost allocation is fair, budget-balanced, and respects the cost-causation principle. The results are validated through numerical simulations.", "published": "2019-06-07T05:43:20Z", "authors": ["Sen Li", "Akhil Shetty", "Kameshwar Poolla", "Pravin Varaiya"], "categories": ["math.OC"], "doi": "", "_query": "hedonic house price", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2101.03788v1", "title": "Second Hand Price Prediction for Tesla Vehicles", "abstract": "The Tesla vehicles became very popular in the car industry as it was affordable in the consumer market and it left no carbon footprint. Due to the large decline in the stock prices of Tesla Inc. at the beginning of 2019, Tesla owners started selling their vehicles in the used car market. These used car prices depended on attributes such as the model of the vehicle, year of production, miles driven, and the battery used for the vehicle. Prices were different for a specific vehicle in different months. In this paper, it is discussed how a machine learning technique is being implemented in order to develop a second-hand Teslavehicle price prediction system. To reach this goal, different machine learning techniques such as decision trees, support vector machine (SVM), random forest, and deep learning were investigated and finally was implemented with boosted decision tree regression. I the future, it is intended to use a more sophisticated algorithm for better accuracy.", "published": "2021-01-11T09:54:13Z", "authors": ["Sayed Erfan Arefin"], "categories": ["cs.LG"], "doi": "", "_query": "hedonic house price", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/1506.06648v1", "title": "Strategy-proof Pricing Approach for Cloud Market", "abstract": "In this paper, we design and develop a pricing model applicable to strategy proof pricing. To provide an economic stability towards its consumers. The economic model we use is Vickrey-Clarke-Groves (VCG). By this each service provider has to provide a true cost of its services in the cloud market. For the selection of suitable service for the consumer we adopt a dynamic programing based algorithm and VCG is used to calculate the payment. Strategy proof pricing offers a unique cloud pricing service that takes the complexity out of traditional pricing and enables cloud providers to price accurately, consistently and competitively", "published": "2015-06-22T15:23:54Z", "authors": ["Chetan Chawla", "Inderveer Chana"], "categories": ["cs.DC"], "doi": "", "_query": "hedonic house price", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2507.11353v1", "title": "Propagation of carbon price shocks through the value chain: the mean-field game of defaults", "abstract": "We introduce a new mean-field game framework to analyze the impact of carbon pricing in a multi-sector economy with defaultable firms. Each sector produces a homogeneous good, with its price endogenously determined through market clearing. Firms act as price takers and maximize profits by choosing an optimal allocation of inputs-including labor, emissions, and intermediate goods from other sectors-while interacting through the endogenous sectoral price. Firms also choose their default timing to maximize shareholder value. Formally, we model the economy as an optimal stopping mean-field game within each sector. The resulting system of coupled mean-field games admits a linear programming formulation that characterizes Nash equilibria in terms of population measure flows. We prove the existence of a linear programming Nash equilibrium and establish uniqueness of the associated price system. Numerical illustrations are presented for firms with constant elasticity of substitution (CES) production functions. In a stylized single-sector economy, carbon price shocks induce substitution between emissions and labor. In a three-sector economy, the manufacturing sector faces consumer demand and requires inputs from a brown sector, which can be increasingly replaced by green-sector goods as carbon prices rise. These experiments reveal that carbon price shocks can generate substantial spillover effects along the value chain, underscoring the importance of sectoral interdependencies in shaping effective decarbonization pathways.", "published": "2025-07-15T14:24:31Z", "authors": ["Zorana Grbac", "Simone Pavarana", "Thorsten Schmidt", "Peter Tankov"], "categories": ["econ.GN"], "doi": "", "_query": "hedonic house price", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/1610.00157v1", "title": "A Performance-Based Scheme for Pricing Resources in the Cloud", "abstract": "With the rapid growth of the cloud computing marketplace, the issue of pricing resources in the cloud has been the subject of much study in recent years. In this paper, we identify and study a new issue: how to price resources in the cloud so that the customer's risk is minimized, while at the same time ensuring that the provider accrues his fair share. We do this by correlating the revenue stream of the customer to the prices charged by the provider. We show that our mechanism is incentive compatible in that it is in the best interest of the customer to provide his true revenue as a function of the resources rented. We next add another restriction to the price function, i.e., that it be linear. This removes the distortion that creeps in when the customer has to pay more money for less resources. Our algorithms for both the schemes mentioned above are efficient.", "published": "2016-10-01T16:38:03Z", "authors": ["Kamal Jain", "Tung Mai", "Vijay V. Vazirani"], "categories": ["cs.GT"], "doi": "", "_query": "hedonic house price", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2008.09454v1", "title": "Detecting and repairing arbitrage in traded option prices", "abstract": "Option price data are used as inputs for model calibration, risk-neutral density estimation and many other financial applications. The presence of arbitrage in option price data can lead to poor performance or even failure of these tasks, making pre-processing of the data to eliminate arbitrage necessary. Most attention in the relevant literature has been devoted to arbitrage-free smoothing and filtering (i.e. removing) of data. In contrast to smoothing, which typically changes nearly all data, or filtering, which truncates data, we propose to repair data by only necessary and minimal changes. We formulate the data repair as a linear programming (LP) problem, where the no-arbitrage relations are constraints, and the objective is to minimise prices' changes within their bid and ask price bounds. Through empirical studies, we show that the proposed arbitrage repair method gives sparse perturbations on data, and is fast when applied to real world large-scale problems due to the LP formulation. In addition, we show that removing arbitrage from prices data by our repair method can improve model calibration with enhanced robustness and reduced calibration error.", "published": "2020-08-21T12:49:33Z", "authors": ["Samuel N. Cohen", "Christoph Reisinger", "Sheng Wang"], "categories": ["q-fin.PR", "q-fin.CP"], "doi": "10.1080/1350486X.2020.1846573", "_query": "hedonic house price", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2506.23130v1", "title": "The Florence Price Art Song Dataset and Piano Accompaniment Generator", "abstract": "Florence B. Price was a composer in the early 20th century whose music reflects her upbringing in the American South, her African heritage, and her Western classical training. She is noted as the first African-American woman to have a symphony performed by a major orchestra. Her music has recently received renewed attention from both the public and the research community, decades after her death. In addition to other genres, Price was a prolific composer for solo voice and piano. Music historians have documented the existence of 134 art songs and piano/voice arrangements for spirituals and folk songs written by Price. We release a digital catalog of 112 of these works in MuseScore, MusicXML, MIDI, and PDF format. We also use this dataset to fine-tune a symbolic music generation model to generate accompaniments to melodies, and we conduct a blind listening experiment that shows that accompaniments generated by our model are perceived as being reflective of Florence Price's style more frequently than accompaniments generated by a baseline model. We release our model as the Florence Price Piano Accompaniment Generator alongside our dataset.", "published": "2025-06-29T07:45:26Z", "authors": ["Tao-Tao He", "Martin E. Malandro", "Douglas Shadle"], "categories": ["cs.SD", "eess.AS"], "doi": "", "_query": "hedonic house price", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2001.03967v1", "title": "Pricing Exchange Options under Stochastic Correlation", "abstract": "In this paper we study the pricing of exchange options when underlying assets have stochastic volatility and stochastic correlation. An approximation using a closed-form approximation based on a Taylor expansion of the conditional price is proposed. Numerical results are illustrated for exchanges between WTI and Brent type oil prices.", "published": "2020-01-12T17:56:25Z", "authors": ["Enrique Villamor", "Pablo Olivares"], "categories": ["q-fin.PR", "q-fin.CP"], "doi": "", "_query": "hedonic house price", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/1208.2775v5", "title": "Physical approach to price momentum and its application to momentum strategy", "abstract": "We introduce various quantitative and mathematical definitions for price momentum of financial instruments. The price momentum is quantified with velocity and mass concepts originated from the momentum in physics. By using the physical momentum of price as a selection criterion, the weekly contrarian strategies are implemented in South Korea KOSPI 200 and US S&P 500 universes. The alternative strategies constructed by the physical momentum achieve the better expected returns and reward-risk measures than those of the traditional contrarian strategy in weekly scale. The portfolio performance is not understood by the Fama-French three-factor model.", "published": "2012-08-14T03:19:28Z", "authors": ["Jaehyung Choi"], "categories": ["q-fin.GN", "physics.soc-ph", "q-fin.PM"], "doi": "10.1016/j.physa.2014.07.075", "_query": "hedonic house price", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2202.10944v2", "title": "Convex Surrogate Loss Functions for Contextual Pricing with Transaction Data", "abstract": "We study an off-policy contextual pricing problem where the seller has access to samples of prices that customers were previously offered, whether they purchased at that price, and auxiliary features describing the customer and/or item being sold. This is in contrast to the well-studied setting in which samples of the customer's valuation (willingness to pay) are observed. In our setting, the observed data is influenced by the previous pricing policy, and we do not know how customers would have responded to alternative prices. We introduce suitable loss functions for this setting that can be directly optimized to find an effective pricing policy with expected revenue guarantees, without the need for estimation of an intermediate demand function. We focus on convex loss functions. This is particularly relevant when linear pricing policies are desired for interpretability reasons, resulting in a tractable convex revenue optimization problem. We propose generalized hinge and quantile pricing loss functions that price at a multiplicative factor of the conditional expected valuation or a particular quantile of the prices that sold, despite the valuation data not being observed. We prove expected revenue bounds for these pricing policies respectively when the valuation distribution is log-concave, and we provide generalization bounds for the finite sample case. Finally, we conduct simulations on both synthetic and real-world data to demonstrate that this approach is competitive with, and in some settings outperforms, state-of-the-art methods in contextual pricing.", "published": "2022-02-16T22:35:39Z", "authors": ["Max Biggs"], "categories": ["cs.LG", "cs.IR", "stat.ML"], "doi": "", "_query": "hedonic house price", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2001.01127v1", "title": "Forecasting Bitcoin closing price series using linear regression and neural networks models", "abstract": "This paper studies how to forecast daily closing price series of Bitcoin, using data on prices and volumes of prior days. Bitcoin price behaviour is still largely unexplored, presenting new opportunities. We compared our results with two modern works on Bitcoin prices forecasting and with a well-known recent paper that uses Intel, National Bank shares and Microsoft daily NASDAQ closing prices spanning a 3-year interval. We followed different approaches in parallel, implementing both statistical techniques and machine learning algorithms. The SLR model for univariate series forecast uses only closing prices, whereas the MLR model for multivariate series uses both price and volume data. We applied the ADF -Test to these series, which resulted to be indistinguishable from a random walk. We also used two artificial neural networks: MLP and LSTM. We then partitioned the dataset into shorter sequences, representing different price regimes, obtaining best result using more than one previous price, thus confirming our regime hypothesis. All the models were evaluated in terms of MAPE and relativeRMSE. They performed well, and were overall better than those obtained in the benchmarks. Based on the results, it was possible to demonstrate the efficacy of the proposed methodology and its contribution to the state-of-the-art.", "published": "2020-01-04T21:04:05Z", "authors": ["Nicola Uras", "Lodovica Marchesi", "Michele Marchesi", "Roberto Tonelli"], "categories": ["q-fin.ST", "cs.LG", "stat.ML"], "doi": "", "_query": "hedonic house price", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/physics/0605133v1", "title": "Real estate price peaks: a comparative overview", "abstract": "First, we emphasize that the real estate price peaks which are currently under way in many industrialized countries (one important exception is Japan) share many of the characteristics of previous historical price peaks. In particular, we show that: (i) In the present episode real price increases are, at least for now, of the same order of magnitude as in previous episodes, typically of the order of 80 percent to 100 percent. (ii) Historically, price peaks turned out to be symmetrical with respect to the peak; soft landing, i.e. an upgoing phase followed by a plateau, has rarely (if ever) been observed. (iii) The inflated demand is mainly boosted by investors and high-income buyers. (iv) In the present as well as in previous episodes, the main engines in the upgoing phase have been the hot markets which developed in major cities such as London, Los Angeles, New York, Paris, San Francisco or Sydney. In our conclusion, we propose a prediction for real estate prices in the West of the United States over the period 2005-2011. We also point out that investment funds, which already play a key role in stock markets, have in recent times began to heavily invest in real estate. In the future, one can expect them to become major players in property markets worldwide. The outcome of the present episode will tell us how quickly this transformation evolves. Thus, if the height of the present peak substantially surpasses the magnitude of previous ones, one may infer that investment funds have been able to establish strong communication channels between real estate assets on the one hand and financial assets (e.g. bonds, stocks, options) on the other hand.", "published": "2006-05-16T11:10:42Z", "authors": ["Bertrand M. Roehner"], "categories": ["physics.soc-ph"], "doi": "", "_query": "hedonic house price", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2002.11660v1", "title": "An Optimal Procedure to Check Pareto-Optimality in House Markets with Single-Peaked Preferences", "abstract": "Recently, the problem of allocating one resource per agent with initial endowments (house markets) has seen a renewed interest: indeed, while in the domain of strict preferences the Top Trading Cycle algorithm is known to be the only procedure guaranteeing Pareto-optimality, individual rationality, and strategy proofness. However, the situation differs in the single-peaked domain. Indeed, Bade presented the Crawler, an alternative procedure enjoying the same properties, with the additional advantage of being implementable in obviously dominant strategies. In this paper we further investigate the Crawler and propose the Diver, a variant which checks optimally whether an allocation is Pareto-optimal for single-peaked preferences, thus improving over known techniques used for checking Pareto-optimality in more general domains. We also prove that the Diver is asymptotically optimal in terms of communication complexity.", "published": "2020-02-14T17:24:55Z", "authors": ["Aurélie Beynier", "Nicolas Maudet", "Simon Rey", "Parham Shams"], "categories": ["cs.GT", "cs.AI"], "doi": "", "_query": "hedonic house price", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/1807.07088v1", "title": "A mean-field game approach to price formation in electricity markets", "abstract": "Here, we introduce a price-formation model where a large number of small players can store and trade electricity. Our model is a constrained mean-field game (MFG) where the price is a Lagrange multiplier for the supply vs. demand balance condition. We establish the existence of a unique solution using a fixed-point argument. In particular, we show that the price is well-defined and it is a Lipschitz function of time. Then, we study linear-quadratic models that can be solved explicitly and compare our model with real data.", "published": "2018-07-18T18:06:38Z", "authors": ["Diogo Gomes", "João Saúde"], "categories": ["math.AP"], "doi": "", "_query": "hedonic house price", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/1805.09616v3", "title": "Optimal pricing for peer-to-peer sharing with network externalities", "abstract": "In this paper, we analyse how a peer-to-peer sharing platform should price its service (when imagined as an excludable public good) to maximize profit, when each user's participation adds value to the platform service by creating a positive externality to other participants. To characterize network externalities as a function of the number of participants, we consider different bounded and unbounded user utility models. The bounded utility model fits many infrastructure sharing applications with bounded network value, in which complete coverage has a finite user valuation (e.g., WiFi or hotspot). The unbounded utility model fits the large scale data sharing and explosion in social media, where it is expected that the network value follows Metcalfe's or Zipf's law. For both models, we analyze the optimal pricing schemes to select heterogeneous users in the platform under complete and incomplete information of users' service valuations. We propose the concept of price of information (PoI) to characterize the profit loss due to lack of information, and present provable PoI bounds for different utility models. We show that the PoI=2 for the bounded utility model, meaning that just half of profit is lost, whereas the PoI>=2 for the unbounded utility model and increases as for a less concave utility function. We also show that the complicated differentiated pricing scheme which is optimal under incomplete user information, can be replaced by a single uniform price scheme that is asymptotic optimal. Finally, we extend our pricing schemes to a two-sided market by including a new group of `pure' service users contributing no externalities, and show that the platform may charge zero price to the original group of users in order to attract the pure user group.", "published": "2018-05-24T11:45:57Z", "authors": ["Yunpeng Li", "Costas Courcoubetis", "Lingjie Duan", "Richard Weber"], "categories": ["cs.GT"], "doi": "", "_query": "hedonic house price", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/cond-mat/0204574v1", "title": "Price Drops, Fluctuations, and Correlation in a Multi-Agent Model of Stock Markets", "abstract": "In this paper we compare market price fluctuations with the response to fundamental price drops within the Lux-Marchesi model which is able to reproduce the most important stylized facts of real market data. Major differences can be observed between the decay of spontaneous fluctuations and of changes due to external perturbations reflecting the absence of detailed balance, i.e., of the validity of the fluctuation-dissipation theorem. We found that fundamental price drops are followed by an overshoot with a rather robust characteristic time.", "published": "2002-04-26T12:25:31Z", "authors": ["A. G. Zawadowski", "R. Karadi", "J. Kertesz"], "categories": ["cond-mat.stat-mech", "q-fin.TR"], "doi": "10.1016/S0378-4371(02)01213-X", "_query": "hedonic house price", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/0710.2758v1", "title": "The fundamental theorem of asset pricing under proportional transaction costs", "abstract": "We extend the fundamental theorem of asset pricing to a model where the risky stock is subject to proportional transaction costs in the form of bid-ask spreads and the bank account has different interest rates for borrowing and lending. We show that such a model is free of arbitrage if and only if one can embed in it a friction-free model that is itself free of arbitrage, in the sense that there exists an artificial friction-free price for the stock between its bid and ask prices and an artificial interest rate between the borrowing and lending interest rates such that, if one discounts this stock price by this interest rate, then the resulting process is a martingale under some non-degenerate probability measure. Restricting ourselves to the simple case of a finite number of time steps and a finite number of possible outcomes for the stock price, the proof follows by combining classical arguments based on finite-dimensional separation theorems with duality results from linear optimisation.", "published": "2007-10-15T09:37:08Z", "authors": ["Alet Roux"], "categories": ["q-fin.PR", "math.PR"], "doi": "", "_query": "hedonic house price", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2405.14893v2", "title": "Revisiting Day-ahead Electricity Price: Simple Model Save Millions", "abstract": "Accurate day-ahead electricity price forecasting is essential for residential welfare, yet current methods often fall short in forecast accuracy. We observe that commonly used time series models struggle to utilize the prior correlation between price and demand-supply, which, we found, can contribute a lot to a reliable electricity price forecaster. Leveraging this prior, we propose a simple piecewise linear model that significantly enhances forecast accuracy by directly deriving prices from readily forecastable demand-supply values. Experiments in the day-ahead electricity markets of Shanxi province and ISO New England reveal that such forecasts could potentially save residents millions of dollars a year compared to existing methods. Our findings underscore the value of suitably integrating time series modeling with economic prior for enhanced electricity price forecasting accuracy.", "published": "2024-05-20T08:27:14Z", "authors": ["Linian Wang", "Jianghong Liu", "Huibin Zhang", "Leye Wang"], "categories": ["cs.LG", "econ.EM"], "doi": "", "_query": "hedonic house price", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2103.10185v4", "title": "About subordinated generalizations of 3 classical models of option pricing", "abstract": "In this paper, we investigate the relation between Bachelier and Black-Scholes models driven by the infinitely divisible inverse subordinators. Such models, in contrast to their classical equivalents, can be used in markets where periods of stagnation are observed. We introduce the subordinated Cox-Ross-Rubinstein model and prove that the price of the underlying in that model converges in distribution and in Skorokhod space to the price of underlying in the subordinated Black-Scholes model defined in [31]. Motivated by this fact we price the selected option contracts using the binomial trees. The results are compared to other numerical methods.", "published": "2021-03-18T11:49:07Z", "authors": ["Michał Balcerek", "Grzegorz Krzyżanowski", "Marcin Magdziarz"], "categories": ["math.NA"], "doi": "", "_query": "hedonic house price", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2306.14186v1", "title": "Statistical electricity price forecasting: A structural approach", "abstract": "The availability of historical data related to electricity day-ahead prices and to the underlying price formation process is limited. In addition, the electricity market in Europe is facing a rapid transformation, which limits the representativeness of older observations for predictive purposes. On the other hand, machine learning methods that gained traction also in the domain of electricity price forecasting typically require large amounts of data. This study analyses the effectiveness of encoding well-established domain knowledge to mitigate the need for large training datasets. The domain knowledge is incorporated by imposing a structure on the price forecasting problem; the resulting accuracy gains are quantified in an experiment. Compared to an \"unstructured\" purely statistical model, it is shown that introducing intermediate quantity forecasts of load, renewable infeed, and cross-border exchange, paired with the estimation of supply curves, can result in a NRMSE reduction by 0.1 during daytime hours. The statistically most significant improvements are achieved in the first day of the forecasting horizon when a purely statistical model is combined with structured models. Finally, results are evaluated and interpreted with regard to the dynamic market conditions observed in Europe during the experiment period (from the 1st October 2022 to the 30th April 2023), highlighting the adaptive nature of models that are trained on shorter timescales.", "published": "2023-06-25T09:54:28Z", "authors": ["Raffaele Sgarlato"], "categories": ["stat.AP", "econ.GN"], "doi": "", "_query": "hedonic house price", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/1203.3653v1", "title": "Optimal pricing using online auction experiments: A Pólya tree approach", "abstract": "We show how a retailer can estimate the optimal price of a new product using observed transaction prices from online second-price auction experiments. For this purpose we propose a Bayesian Pólya tree approach which, given the limited nature of the data, requires a specially tailored implementation. Avoiding the need for a priori parametric assumptions, the Pólya tree approach allows for flexible inference of the valuation distribution, leading to more robust estimation of optimal price than competing parametric approaches. In collaboration with an online jewelry retailer, we illustrate how our methodology can be combined with managerial prior knowledge to estimate the profit maximizing price of a new jewelry product.", "published": "2012-03-16T10:02:58Z", "authors": ["Edward I. George", "Sam K. Hui"], "categories": ["stat.AP"], "doi": "10.1214/11-AOAS503", "_query": "hedonic house price", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/1709.10388v1", "title": "Optimal Reserve Price for Online Ads Trading Based on Inventory Identification", "abstract": "The online ads trading platform plays a crucial role in connecting publishers and advertisers and generates tremendous value in facilitating the convenience of our lives. It has been evolving into a more and more complicated structure. In this paper, we consider the problem of maximizing the revenue for the seller side via utilizing proper reserve price for the auctions in a dynamical way. Predicting the optimal reserve price for each auction in the repeated auction marketplaces is a non-trivial problem. However, we were able to come up with an efficient method of improving the seller revenue by mainly focusing on adjusting the reserve price for those high-value inventories. Previously, no dedicated work has been performed from this perspective. Inspired by Paul and Michael, our model first identifies the value of the inventory by predicting the top bid price bucket using a cascade of classifiers. The cascade is essential in significantly reducing the false positive rate of a single classifier. Based on the output of the first step, we build another cluster of classifiers to predict the price separations between the top two bids. We showed that although the high-value auctions are only a small portion of all the traffic, successfully identifying them and setting correct reserve price would result in a significant revenue lift. Moreover, our optimization is compatible with all other reserve price models in the system and does not impact their performance. In other words, when combined with other models, the enhancement on exchange revenue will be aggregated. Simulations on randomly sampled Yahoo ads exchange (YAXR) data showed stable and expected lift after applying our model.", "published": "2017-08-23T02:22:24Z", "authors": ["Zhihui Xie", "Kuang-Chih Lee", "Liang Wang"], "categories": ["cs.GT", "cs.IR"], "doi": "10.1145/3124749.3124760", "_query": "hedonic house price", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/1504.01093v1", "title": "Pricing Online Decisions: Beyond Auctions", "abstract": "We consider dynamic pricing schemes in online settings where selfish agents generate online events. Previous work on online mechanisms has dealt almost entirely with the goal of maximizing social welfare or revenue in an auction settings. This paper deals with quite general settings and minimizing social costs. We show that appropriately computed posted prices allow one to achieve essentially the same performance as the best online algorithm. This holds in a wide variety of settings. Unlike online algorithms that learn about the event, and then make enforceable decisions, prices are posted without knowing the future events or even the current event, and are thus inherently dominant strategy incentive compatible. In particular we show that one can give efficient posted price mechanisms for metrical task systems, some instances of the $k$-server problem, and metrical matching problems. We give both deterministic and randomized algorithms. Such posted price mechanisms decrease the social cost dramatically over selfish behavior where no decision incurs a charge. One alluring application of this is reducing the social cost of free parking exponentially.", "published": "2015-04-05T07:47:58Z", "authors": ["Ilan Reuven Cohen", "Alon Eden", "Amos Fiat", "Łukasz Jeż"], "categories": ["cs.GT", "cs.DS"], "doi": "", "_query": "hedonic house price", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/1807.03435v4", "title": "Improved Revenue Bounds for Posted-Price and Second-Price Mechanisms", "abstract": "We study revenue maximization through sequential posted-price (SPP) mechanisms in single-dimensional settings with $n$ buyers and independent but not necessarily identical value distributions. We construct the SPP mechanisms by considering the best of two simple pricing rules: one that imitates the revenue optimal mchanism, namely the Myersonian mechanism, via the taxation principle and the other that posts a uniform price. Our pricing rules are rather generalizable and yield the first improvement over long-established approximation factors in several settings. We design factor-revealing mathematical programs that crisply capture the approximation factor of our SPP mechanism. In the single-unit setting, our SPP mechanism yields a better approximation factor than the state of the art prior to our work (Azar, Chiplunkar & Kaplan, 2018). In the multi-unit setting, our SPP mechanism yields the first improved approximation factor over the state of the art after over nine years (Yan, 2011 and Chakraborty et al., 2010). Our results on SPP mechanisms immediately imply improved performance guarantees for the equivalent free-order prophet inequality problem. In the position auction setting, our SPP mechanism yields the first higher-than $1-1/e$ approximation factor. In eager second-price (ESP) auctions, our two simple pricing rules lead to the first improved approximation factor that is strictly greater than what is obtained by the SPP mechanism in the single-unit setting.", "published": "2018-07-10T01:20:30Z", "authors": ["Hedyeh Beyhaghi", "Negin Golrezaei", "Renato Paes Leme", "Martin Pal", "Balasubramanian Sivan"], "categories": ["cs.GT"], "doi": "", "_query": "hedonic house price", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2502.17906v4", "title": "Why do financial prices exhibit Brownian motion despite predictable order flow?", "abstract": "In financial market microstructure, there are two enigmatic empirical laws: (i) the market-order flow has predictable persistence due to metaorder splitters by institutional investors, well formulated as the Lillo-Mike-Farmer model. However, this phenomenon seems paradoxical given the diffusive and unpredictable price dynamics; (ii) the price impact $I(Q)$ of a large metaorder $Q$ follows the square-root law, $I(Q)\\propto \\sqrt{Q}$. Here we theoretically reveal why price dynamics follows Brownian motion despite predictable order flow by unifying these enigmas. We generalize the Lillo-Mike-Farmer model to nonlinear price-impact dynamics, which is mapped to an exactly solvable Lévy-walk model. Our exact solution shows that the price dynamics remains diffusive under the square-root law, even under persistent order flow. This work illustrates the crucial role of the square-root law in mitigating large price movements by large metaorders, thereby leading to the Brownian price dynamics, consistently with the efficient market hypothesis over long timescales.", "published": "2025-02-25T07:12:03Z", "authors": ["Yuki Sato", "Kiyoshi Kanazawa"], "categories": ["q-fin.TR", "cond-mat.stat-mech", "econ.GN", "q-fin.MF", "q-fin.PR"], "doi": "", "_query": "hedonic house price", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2204.03968v2", "title": "Machine Learning architectures for price formation models", "abstract": "Here, we study machine learning (ML) architectures to solve a mean-field games (MFGs) system arising in price formation models. We formulate a training process that relies on a min-max characterization of the optimal control and price variables. Our main theoretical contribution is the development of a posteriori estimates as a tool to evaluate the convergence of the training process. We illustrate our results with numerical experiments for linear dynamics and both quadratic and non-quadratic models.", "published": "2022-04-08T09:55:48Z", "authors": ["Diogo Gomes", "Julián Gutiérrez", "Mathieu Laurière"], "categories": ["math.OC"], "doi": "", "_query": "hedonic house price", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/1602.06213v1", "title": "Modeling Stock Price Dynamics with Fuzzy Opinion Networks", "abstract": "We propose a mathematical model for the word-of-mouth communications among stock investors through social networks and explore how the changes of the investors' social networks influence the stock price dynamics and vice versa. An investor is modeled as a Gaussian fuzzy set (a fuzzy opinion) with the center and standard deviation as inputs and the fuzzy set itself as output. Investors are connected in the following fashion: the center input of an investor is taken as the average of the neighbors' outputs, where two investors are neighbors if their fuzzy opinions are close enough to each other, and the standard deviation (uncertainty) input is taken with local, global or external reference schemes to model different scenarios of how investors define uncertainties. The centers and standard deviations of the fuzzy opinions are the expected prices and their uncertainties, respectively, that are used as inputs to the price dynamic equation. We prove that with the local reference scheme the investors converge to different groups in finite time, while with the global or external reference schemes all investors converge to a consensus within finite time and the consensus may change with time in the external reference case. We show how to model trend followers, contrarians and manipulators within this mathematical framework and prove that the biggest enemy of a manipulator is the other manipulators. We perform Monte Carlo simulations to show how the model parameters influence the price dynamics, and we apply a modified version of the model to the daily closing prices of fifteen top banking and real estate stocks in Hong Kong for the recent two years from Dec. 5, 2013 to Dec. 4, 2015 and discover that a sharp increase of the combined uncertainty is a reliable signal to predict the reversal of the current price trend.", "published": "2016-02-19T16:35:05Z", "authors": ["Li-Xin Wang"], "categories": ["q-fin.TR", "cs.SI", "eess.SY", "q-fin.CP"], "doi": "", "_query": "hedonic house price", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/1610.08210v1", "title": "Price of Transparency in Strategic Machine Learning", "abstract": "Based on the observation that the transparency of an algorithm comes with a cost for the algorithm designer when the users (data providers) are strategic, this paper studies the impact of strategic intent of the users on the design and performance of transparent ML algorithms. We quantitatively study the {\\bf price of transparency} in the context of strategic classification algorithms, by modeling the problem as a nonzero-sum game between the users and the algorithm designer. The cost of having a transparent algorithm is measured by a quantity, named here as price of transparency which is the ratio of the designer cost at the Stackelberg equilibrium, when the algorithm is transparent (which allows users to be strategic) to that of the setting where the algorithm is not transparent.", "published": "2016-10-26T07:17:27Z", "authors": ["Emrah Akyol", "Cedric Langbort", "Tamer Basar"], "categories": ["cs.GT"], "doi": "", "_query": "hedonic house price", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/math/0302243v2", "title": "Static Arbitrage Bounds on Basket Option Prices", "abstract": "We consider the problem of computing upper and lower bounds on the price of a European basket call option, given prices on other similar baskets. Although this problem is very hard to solve exactly in the general case, we show that in some instances the upper and lower bounds can be computed via simple closed-form expressions, or linear programs. We also introduce an efficient linear programming relaxation of the general problem based on an integral transform interpretation of the call price function. We show that this relaxation is tight in some of the special cases examined before.", "published": "2003-02-19T22:13:08Z", "authors": ["Alexandre d'Aspremont", "Laurent El Ghaoui"], "categories": ["math.OC", "math.PR", "q-fin.ST"], "doi": "", "_query": "hedonic house price", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/1903.04106v1", "title": "Pricing Formulae of Power Binary and Normal Distribution Standard Options and Applications", "abstract": "In this paper the Buchen's pricing formulae of (higher order) asset and bond binary options are incorporated into the pricing formula of power binary options and a pricing formula of \"the normal distribution standard options\" with the maturity payoff related to a power function and the density function of normal distribution is derived. And as their applications, pricing formulae of savings plans that provide a choice of indexing and discrete geometric average Asian options are derived and the fact that the price of discrete geometric average Asian option converges to the price of continuous geometric average Asian option when the largest distance between neighboring monitoring times goes to zero is proved.", "published": "2019-03-11T02:43:57Z", "authors": ["Hyong-Chol O", "Dae-Sung Choe"], "categories": ["q-fin.PR"], "doi": "", "_query": "hedonic house price", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2605.12559v1", "title": "Coordination Failures and Stackelberg Leadership in Housing Development with Network Effects", "abstract": "I study coordination failures in housing development markets with network effects, where the value of building depends on aggregate supply. When network effects are sufficiently strong and convex, multiple equilibria arise: a low-supply coordination failure and a high-supply outcome. Without a coordination mechanism, equilibrium is indeterminate. I introduce a large developer who moves first in a Stackelberg game, committing to housing supply before atomistic developers make entry decisions. The main result is that the large developer always commits at least to the high-supply equilibrium, eliminating the coordination failure by pushing past the unstable threshold that separates the low and high outcomes. The result is unconditional; it holds for general demand functions and cost distributions, and does not depend on which stable continuation equilibrium materializes. The leader's commitment inverts standard monopoly intuition: first-mover commitment can improve welfare by resolving a coordination problem that atomistic markets cannot solve on their own. I also characterize when the developer builds beyond the high equilibrium into a monopoly region, and show that the market underprovides housing relative to the social optimum.", "published": "2026-05-11T21:03:38Z", "authors": ["Vaibhav Rangan"], "categories": ["econ.TH"], "doi": "", "_query": "hedonic house price", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/1502.01125v1", "title": "Equilibrium Pricing in an Order Book Environment: Case Study for a Spin Model", "abstract": "When modelling stock market dynamics, the price formation is often based on an equilbrium mechanism. In real stock exchanges, however, the price formation is goverend by the order book. It is thus interesting to check if the resulting stylized facts of a model with equilibrium pricing change, remain the same or, more generally, are compatible with the order book environment. We tackle this issue in the framework of a case study by embedding the Bornholdt-Kaizoji-Fujiwara spin model into the order book dynamics. To this end, we use a recently developed agent based model that realistically incorporates the order book. We find realistic stylized facts. We conclude for the studied case that equilibrium pricing is not needed and that the corresponding assumption of a \"fundamental\" price may be abandoned.", "published": "2015-02-04T08:52:49Z", "authors": ["Frederik Meudt", "Thilo A. Schmitt", "Rudi Schäfer", "Thomas Guhr"], "categories": ["q-fin.CP", "q-fin.TR"], "doi": "10.1016/j.physa.2016.01.073", "_query": "hedonic house price", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2303.16660v2", "title": "Price Optimization Combining Conjoint Data and Purchase History: A Causal Modeling Approach", "abstract": "Pricing decisions of companies require an understanding of the causal effect of a price change on the demand. When real-life pricing experiments are infeasible, data-driven decision-making must be based on alternative data sources such as purchase history (sales data) and conjoint studies where a group of customers is asked to make imaginary purchases in an artificial setup. We present an approach for price optimization that combines population statistics, purchase history and conjoint data in a systematic way. We build on the recent advances in causal inference to identify and quantify the effect of price on the purchase probability at the customer level. The identification task is a transportability problem whose solution requires a parametric assumption on the differences between the conjoint study and real purchases. The causal effect is estimated using Bayesian methods that take into account the uncertainty of the data sources. The pricing decision is made by comparing the estimated posterior distributions of gross profit for different prices. The approach is demonstrated with simulated data resembling the features of real-world data.", "published": "2023-03-29T13:10:56Z", "authors": ["Lauri Valkonen", "Santtu Tikka", "Jouni Helske", "Juha Karvanen"], "categories": ["stat.AP"], "doi": "10.1353/obs.2024.a929116", "_query": "hedonic house price", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/1510.05875v7", "title": "An elementary approach to the option pricing problem", "abstract": "Our goal here is to discuss the pricing problem of European and American options in discrete time using elementary calculus so as to be an easy reference for first year undergraduate students. Using the binomial model we compute the fair price of European and American options. We explain the notion of Arbitrage and the notion of the fair price of an option using common sense. We give a criterion that the holder can use to decide when it is appropriate to exercise the option. We prove the put-call parity formulas for both European and American options and we discuss the relation between American and European options. We give also the bounds for European and American options. We also discuss the portfolio's optimization problem and the fair value in the case where the holder can not produce the opposite portfolio.", "published": "2015-10-20T13:12:26Z", "authors": ["Nikolaos Halidias"], "categories": ["q-fin.MF", "math.NA"], "doi": "", "_query": "hedonic house price", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2410.07378v1", "title": "Static Pricing for Online Selection Problem and its Variants", "abstract": "This paper studies an online selection problem, where a seller seeks to sequentially sell multiple copies of an item to arriving buyers. We consider an adversarial setting, making no modeling assumptions about buyers' valuations for the items except acknowledging a finite support. In this paper, we focus on a class of static pricing algorithms that sample a price from a pre-determined distribution and sell items to buyers whose valuations exceed the sampled price. Such algorithms are of practical interests due to their advantageous properties, such as ease of implementation and non-discrimination over prices. Our work shows that the simple static pricing strategy can achieve strong guarantees comparable to the best known dynamic pricing algorithms. Particularly, we design the optimal static pricing algorithms for the adversarial online selection problem and its two important variants: the online assignment problem and the online selection with convex cost. The static pricing algorithms can even attain the optimal competitive ratios among all online algorithms for the online selection problem and the online assignment problem. To achieve these results, we propose an economics-based approach in the competitive analysis of static pricing algorithms, and develop a novel representative function-based approach to derive the lower bounds. We expect these approaches will be useful in related problems such as online matching.", "published": "2024-10-09T18:53:17Z", "authors": ["Bo Sun", "Hossein Nekouyan Jazi", "Xiaoqi Tan", "Raouf Boutaba"], "categories": ["cs.GT", "cs.DS"], "doi": "", "_query": "hedonic house price", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/1407.4452v2", "title": "New Pricing Framework: Options and Bonds", "abstract": "A unified analytical pricing framework with involvement of the shot noise random process has been introduced and elaborated. Two exactly solvable new models have been developed. The first model has been designed to value options. It is assumed that asset price stochastic dynamics follows a Geometric Shot Noise motion. A new arbitrage-free integro-differential option pricing equation has been found and solved. The put-call parity has been proved and the Greeks have been calculated. Three additional new Greeks associated with market model parameters have been introduced and evaluated. It has been shown that in diffusion approximation the developed option pricing model incorporates the well-known Black-Scholes equation and its solution. The stochastic dynamic origin of the Black-Scholes volatility has been uncovered. The new option pricing model has been generalized based on asset price dynamics modeled by the superposition of Geometric Brownian motion and Geometric Shot Noise. To model stochastic dynamics of a short term interest rate, the second model has been introduced and developed based on Langevin type equation with shot noise. A new bond pricing formula has been obtained. It has been shown that in diffusion approximation the developed bond pricing formula goes into the well-known Vasicek solution. The stochastic dynamic origin of the long-term mean and instantaneous volatility of the Vasicek model has been uncovered. A generalized bond pricing model has been introduced and developed based on short term interest rate stochastic dynamics modeled by superposition of a standard Wiener process and shot noise. Despite the non-Gaussianity of probability distributions involved, all newly elaborated models have the same degree of analytical tractability as the Black-Scholes model and the Vasicek model.", "published": "2014-07-16T14:10:08Z", "authors": ["Nick Laskin"], "categories": ["q-fin.PR", "q-fin.MF"], "doi": "", "_query": "hedonic house price", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2005.05310v1", "title": "Rational Finance Approach to Behavioral Option Pricing", "abstract": "When pricing options, there may be different views on the instantaneous mean return of the underlying price process. According to Black (1972), where there exist heterogeneous views on the instantaneous mean return, this will result in arbitrage opportunities. Behavioral finance proponents argue that such heterogenous views are likely to occur and this will not impact option pricing models proposed by rational dynamic asset pricing theory and will not give rise to volatility smiles. To rectify this, a leading advocate of behavioral finance has proposed a behavioral option pricing model. As there may be unexplored links between the behavioral and rational approaches to option pricing, in this paper we revisit Shefrin (2008) option pricing model as an example and suggest one approach to modify this behavioral finance option pricing formula to be consistent with rational dynamic asset pricing theory by introducing arbitrage transaction costs which offset the gains from arbitrage trades.", "published": "2020-05-10T07:44:19Z", "authors": ["Jiexin Dai", "Abootaleb Shirvani", "Frank J. Fabozzi"], "categories": ["q-fin.CP", "q-fin.PR"], "doi": "", "_query": "hedonic house price", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/1601.06672v1", "title": "Pricing Vehicle Sharing with Proximity Information", "abstract": "For vehicle sharing schemes, where drop-off positions are not fixed, we propose a pricing scheme, where the price depends in part on the distance between where a vehicle is being dropped off and where the closest shared vehicle is parked. Under certain restrictive assumptions, we show that this pricing leads to a socially optimal spread of the vehicles within a region.", "published": "2016-01-25T16:55:30Z", "authors": ["Jakub Marecek", "Robert Shorten", "Jia Yuan Yu"], "categories": ["math.OC", "cs.AI", "cs.MA"], "doi": "10.1109/ICBDSC.2016.7460378", "_query": "hedonic house price", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2305.05850v1", "title": "New Formulations and Pricing Mechanisms for Stochastic Electricity Market Clearing Problem", "abstract": "We present new formulations of the stochastic electricity market clearing problem based on the principles of stochastic programming. Previous analyses have established that the canonical stochastic programming model effectively captures the relationship between the day-ahead and real-time dispatch and prices. The resulting quantities exhibit desirable guarantees of revenue adequacy, cost recovery, and price distortion in expectation. Motivated by these results, we develop alternative stochastic programming model formulations that differ in how we model the nonanticipativity requirement. We design new pricing mechanisms by developing suitable dual optimization problems for these models. We analyze these pricing mechanisms to assess their ability to provide revenue adequacy for the system operators, cost recovery for generators, and price distortion. Unlike the previous mechanisms that yield the desired properties in expectation, one of our pricing mechanisms extends the benefits of cost recovery and bounded price distortion under every scenario while retaining the long-run revenue adequacy for the system operator. We demonstrate these benefits through numerical experiments conducted on well-known test systems.", "published": "2023-05-10T02:47:04Z", "authors": ["Sakitha Ariyarathne", "Harsha Gangammanavar"], "categories": ["eess.SY", "math.OC"], "doi": "", "_query": "hedonic house price", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2204.04774v1", "title": "Revenue Management Under the Markov Chain Choice Model with Joint Price and Assortment Decisions", "abstract": "Finding the optimal product prices and product assortment are two fundamental problems in revenue management. Usually, a seller needs to jointly determine the prices and assortment while managing a network of resources with limited capacity. However, there is not yet a tractable method to efficiently solve such a problem. Existing papers studying static joint optimization of price and assortment cannot incorporate resource constraints. Then we study the revenue management problem with resource constraints and price bounds, where the prices and the product assortments need to be jointly determined over time. We showed that under the Markov chain (MC) choice model (which subsumes the multinomial logit (MNL) model), we could reformulate the choice-based joint optimization problem as a tractable convex conic optimization problem. We also proved that an optimal solution with a constant price vector exists even with constraints on resources. In addition, a solution with both constant assortment and price vector can be optimal when there is no resource constraint.", "published": "2022-04-10T21:30:51Z", "authors": ["Anton J. Kleywegt", "Hongzhang Shao"], "categories": ["math.OC", "econ.EM"], "doi": "", "_query": "hedonic house price", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2302.11902v1", "title": "On price-induced minmax matchings", "abstract": "We study a natural combinatorial pricing problem for sequentially arriving buyers with equal budgets. Each buyer is interested in exactly one pair of items and purchases this pair if and only if, upon arrival, both items are still available and the sum of the item prices does not exceed the budget. The goal of the seller is to set prices to the items such that the number of transactions is maximized when buyers arrive in adversarial order. Formally, we are given an undirected graph where vertices represent items and edges represent buyers. Once prices are set to the vertices, edges with a total price exceeding the buyers' budgets are evicted. Any arrival order of the buyers leads to a set of transactions that forms a maximal matching in this subgraph, and an adversarial arrival order results in a minimum maximal matching. In order to measure the performance of a pricing strategy, we compare the size of such a matching to the size of a maximum matching in the original graph. It was shown by Correa et al. [IPCO 2022] that the best ratio any pricing strategy can guarantee lies within $[1/2, 2/3]$. Our contribution to the problem is two-fold: First, we provide several characterizations of subgraphs that may result from pricing schemes. Second, building upon these, we show an improved upper bound of $3/5$ and a lower bound of $1/2 + 2/n$, where $n$ is the number of items.", "published": "2023-02-23T10:18:28Z", "authors": ["Christoph Dürr", "Mathieu Mari", "Ulrike Schmidt-Kraepelin"], "categories": ["cs.GT", "cs.CE", "cs.DS"], "doi": "", "_query": "hedonic house price", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2304.06060v3", "title": "European Option Pricing Under Generalized Tempered Stable Process: Empirical Analysis", "abstract": "The paper investigates the performance of the European option price when the log asset price follows a rich class of Generalized Tempered Stable (GTS) distribution. The GTS distribution is an alternative to Normal distribution and $α$-stable distribution for modeling asset return and many physical and economic systems. The data used in the option pricing computation comes from fitting the GTS distribution to the underlying S\\&P 500 Index return distribution. The Esscher transform method shows that the GTS distribution preserves its structure. The extended Black-Scholes formula and the Generalized Black-Scholes Formula are applied in the study. The 12-point rule Composite Newton-Cotes Quadrature and the Fractional Fast Fourier (FRFT) algorithms were implemented, and they yield the same European option price at two decimal places. Compared to the option price under the GTS distribution, the Black-Scholes (BS) model is underpriced for the Near-The-Money (NTM) and the in-the-money (ITM) options. However, the BS model and GTS European options yield the same option price for the deep out-of-the-money (OTM) and the deep-in-the-money (ITM) options.", "published": "2023-04-12T16:15:24Z", "authors": ["A. H. Nzokem"], "categories": ["q-fin.PR", "math.PR"], "doi": "", "_query": "hedonic house price", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2402.10196v1", "title": "A Trembling House of Cards? Mapping Adversarial Attacks against Language Agents", "abstract": "Language agents powered by large language models (LLMs) have seen exploding development. Their capability of using language as a vehicle for thought and communication lends an incredible level of flexibility and versatility. People have quickly capitalized on this capability to connect LLMs to a wide range of external components and environments: databases, tools, the Internet, robotic embodiment, etc. Many believe an unprecedentedly powerful automation technology is emerging. However, new automation technologies come with new safety risks, especially for intricate systems like language agents. There is a surprisingly large gap between the speed and scale of their development and deployment and our understanding of their safety risks. Are we building a house of cards? In this position paper, we present the first systematic effort in mapping adversarial attacks against language agents. We first present a unified conceptual framework for agents with three major components: Perception, Brain, and Action. Under this framework, we present a comprehensive discussion and propose 12 potential attack scenarios against different components of an agent, covering different attack strategies (e.g., input manipulation, adversarial demonstrations, jailbreaking, backdoors). We also draw connections to successful attack strategies previously applied to LLMs. We emphasize the urgency to gain a thorough understanding of language agent risks before their widespread deployment.", "published": "2024-02-15T18:51:32Z", "authors": ["Lingbo Mo", "Zeyi Liao", "Boyuan Zheng", "Yu Su", "Chaowei Xiao", "Huan Sun"], "categories": ["cs.CL", "cs.AI"], "doi": "", "_query": "hedonic house price", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2206.07132v1", "title": "Dynamics of a Binary Option Market with Exogenous Information and Price Sensitivity", "abstract": "In this paper, we derive and analyze a continuous of a binary option market with exogenous information. The resulting non-linear system has a discontinuous right hand side, which can be analyzed using zero-dimensional Filippov surfaces. Under general assumptions on purchasing rules, we show that when exogenous information is constant in the binary asset market, the price always converges. We then investigate market prices in the case of changing information, showing empirically that price sensitivity has a strong effect on price lag vs. information. We conclude with open questions on general $n$-ary option markets. As a by-product of the analysis, we show that these markets are equivalent to a simple recurrent neural network, helping to explain some of the predictive power associated with prediction markets, which are usually designed as $n$-ary option markets.", "published": "2022-05-18T14:39:16Z", "authors": ["Hannah Gampe", "Christopher Griffin"], "categories": ["q-fin.TR", "econ.EM", "physics.soc-ph"], "doi": "10.1016/j.cnsns.2022.106994", "_query": "hedonic house price", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/1307.4531v1", "title": "Crowd-assisted Search for Price Discrimination in E-Commerce: First results", "abstract": "After years of speculation, price discrimination in e-commerce driven by the personal information that users leave (involuntarily) online, has started attracting the attention of privacy researchers, regulators, and the press. In our previous work we demonstrated instances of products whose prices varied online depending on the location and the characteristics of perspective online buyers. In an effort to scale up our study we have turned to crowd-sourcing. Using a browser extension we have collected the prices obtained by an initial set of 340 test users as they surf the web for products of their interest. This initial dataset has permitted us to identify a set of online stores where price variation is more pronounced. We have focused on this subset, and performed a systematic crawl of their products and logged the prices obtained from different vantage points and browser configurations. By analyzing this dataset we see that there exist several retailers that return prices for the same product that vary by 10%-30% whereas there also exist isolated cases that may vary up to a multiplicative factor, e.g., x2. To the best of our efforts we could not attribute the observed price gaps to currency, shipping, or taxation differences.", "published": "2013-07-17T08:24:40Z", "authors": ["Jakub Mikians", "László Gyarmati", "Vijay Erramilli", "Nikolaos Laoutaris"], "categories": ["cs.CY"], "doi": "", "_query": "hedonic house price", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2302.08911v1", "title": "DSE Stock Price Prediction using Hidden Markov Model", "abstract": "Stock market forecasting is a classic problem that has been thoroughly investigated using machine learning and artificial neural network based tools and techniques. Interesting aspects of this problem include its time reliance as well as its volatility and other complex relationships. To combine them, hidden markov models (HMMs) have been utilized to anticipate the price of stocks. We demonstrated the Maximum A Posteriori (MAP) HMM method for predicting stock prices for the next day based on previous data. An HMM is trained by analyzing the fractional change in the stock price as well as the intraday high and low values. It is then utilized to produce a MAP estimate across all possible stock prices for the next day. The approach demonstrated in our work is quite generalized and can be used to predict the stock price for any company, given that the HMM is trained on the dataset of that company's stocks dataset. We evaluated the accuracy of our models using some extensively used accuracy metrics for regression problems and came up with a satisfactory outcome.", "published": "2023-01-26T13:28:34Z", "authors": ["Raihan Tanvir", "Md Tanvir Rouf Shawon", "Md. Golam Rabiul Alam"], "categories": ["q-fin.ST", "cs.CE"], "doi": "", "_query": "hedonic house price", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2609.27134v1", "title": "Rare States and Long-Run Pricing", "abstract": "Macroeconomic crises are rare, yet they can have large effects on asset prices. Because the probability of entering a crisis from normal times is small relative to the probability of recovery, the dynamics are nearly reducible: setting the crisis-entry probability to zero makes crisis states unreachable from normal states. We develop a graph-theoretic and spectral analysis of long-run pricing under reducible and nearly reducible dynamics. We show that, at the reducible limit, physical and pricing dynamics induce the same classes of economic states and accessibility relations, but rank their long-run importance differently: by recurrence and by class-specific pricing rates. Moreover, a claim's price depends only on its pricing corridor---the classes of states lying on directed paths from the initial state to states where the payoff is positive---and the highest class-specific pricing rate within this corridor determines the claim's long-run pricing rate. When the reducible-limit corridor excludes the globally pricing-dominant class, we characterize how restoring rare transitions introduces a contribution from that class with a weight that vanishes as the transitions become rarer. This contribution becomes dominant only beyond a crossover maturity, which increases with the rarity of the connecting paths and decreases with the dominant class's pricing-rate advantage. Finally, a consumption-based disaster-recovery application shows how preferences, consumption risk, cash-flow exposure, and recovery timing jointly shape the long-run pricing dominance of disaster states and crossover maturities.", "published": "2026-09-22T22:50:25Z", "authors": ["Ye Lu", "John Stachurski"], "categories": ["econ.TH"], "doi": "", "_query": "hedonic house price", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2607.17119v1", "title": "Dynamic Pricing for a Two-Sided Data Market Platform", "abstract": "We study a continuous-time dynamic pricing problem for a data platform that purchases raw data from privacy-sensitive providers and sells data products to consumers. The platform controls both the acquisition price offered to providers and the selling price charged to consumers. Provider and consumer arrivals are modeled by point processes whose intensities depend on the platform's current data stock, capturing feedback between data accumulation and market participation. We formulate the platform's problem as an infinite-horizon stochastic control problem with a jump-diffusion state process and derive the associated nonlinear integro-differential HJB equation. We prove that the value function is the unique viscosity solution, establish classical regularity under suitable conditions, and verify the optimal feedback pricing policy. Finally, we conduct numerical analyses to examine the influences of model parameters on the optimal pricing policies.", "published": "2026-07-19T08:11:18Z", "authors": ["Lijun Bo", "Dongfang Yang", "Yijie Huang"], "categories": ["math.OC"], "doi": "", "_query": "hedonic house price", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2104.11684v1", "title": "Pricing Asian Options with Correlators", "abstract": "We derive a series expansion by Hermite polynomials for the price of an arithmetic Asian option. This series requires the computation of moments and correlators of the underlying price process, but for a polynomial jump-diffusion, these are given in closed form, hence no numerical simulation is required to evaluate the series. This allows, for example, for the explicit computation of Greeks. The weight function defining the Hermite polynomials is a Gaussian density with scale $b$. We find that the rate of convergence for the series depends on $b$, for which we prove a lower bound to guarantee convergence. Numerical examples show that the series expansion is accurate but unstable for initial values of the underlying process far from zero, mainly due to rounding errors.", "published": "2021-04-23T16:22:06Z", "authors": ["Silvia Lavagnini"], "categories": ["q-fin.PR", "q-fin.CP"], "doi": "", "_query": "hedonic house price", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2002.06975v1", "title": "Cross-sectional Stock Price Prediction using Deep Learning for Actual Investment Management", "abstract": "Stock price prediction has been an important research theme both academically and practically. Various methods to predict stock prices have been studied until now. The feature that explains the stock price by a cross-section analysis is called a \"factor\" in the field of finance. Many empirical studies in finance have identified which stocks having features in the cross-section relatively increase and which decrease in terms of price. Recently, stock price prediction methods using machine learning, especially deep learning, have been proposed since the relationship between these factors and stock prices is complex and non-linear. However, there are no practical examples for actual investment management. In this paper, therefore, we present a cross-sectional daily stock price prediction framework using deep learning for actual investment management. For example, we build a portfolio with information available at the time of market closing and invest at the time of market opening the next day. We perform empirical analysis in the Japanese stock market and confirm the profitability of our framework.", "published": "2020-02-17T14:28:42Z", "authors": ["Masaya Abe", "Kei Nakagawa"], "categories": ["q-fin.PM"], "doi": "10.1145/3399871.3399889", "_query": "hedonic house price", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2309.14994v1", "title": "Measurement Models For Sailboats Price vs. Features And Regional Areas", "abstract": "In this study, we investigated the relationship between sailboat technical specifications and their prices, as well as regional pricing influences. Utilizing a dataset encompassing characteristics like length, beam, draft, displacement, sail area, and waterline, we applied multiple machine learning models to predict sailboat prices. The gradient descent model demonstrated superior performance, producing the lowest MSE and MAE. Our analysis revealed that monohulled boats are generally more affordable than catamarans, and that certain specifications such as length, beam, displacement, and sail area directly correlate with higher prices. Interestingly, lower draft was associated with higher listing prices. We also explored regional price determinants and found that the United States tops the list in average sailboat prices, followed by Europe, Hong Kong, and the Caribbean. Contrary to our initial hypothesis, a country's GDP showed no direct correlation with sailboat prices. Utilizing a 50% cross-validation method, our models yielded consistent results across test groups. Our research offers a machine learning-enhanced perspective on sailboat pricing, aiding prospective buyers in making informed decisions.", "published": "2023-09-26T15:03:05Z", "authors": ["Jiaqi Weng", "Chunlin Feng", "Yihan Shao"], "categories": ["cs.LG", "cs.AI"], "doi": "", "_query": "hedonic house price", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/1905.05813v1", "title": "The connection between multiple prices of an Option at a given time with single prices defined at different times: The concept of weak-value in quantum finance", "abstract": "We introduce a new tool for predicting the evolution of an option for the cases where at some specific time, there is a high-degree of uncertainty for identifying its price. We work over the special case where we can predict the evolution of the system by joining a single price for the Option, defined at some specific time with a pair of prices defined at another instant. This is achieved by describing the evolution of the system through a financial Hamiltonian. The extension to the case of multiple prices at a given instant is straightforward. We also explain how to apply these results in real situations.", "published": "2019-05-14T19:50:25Z", "authors": ["Ivan Arraut", "Alan Au", "Alan Ching-biu Tse", "Carlos Segovia"], "categories": ["q-fin.PR"], "doi": "10.1016/j.physa.2019.04.264", "_query": "hedonic house price", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/1001.3176v1", "title": "Analyzing the prices of the most expensive sheet iron all over the world: Modeling, prediction and regime change", "abstract": "The private car license plates issued in Shanghai are bestowed the title of \"the most expensive sheet iron all over the world\", more expensive than gold. A citizen has to bid in an monthly auction to obtain a license plate for his new private car. We perform statistical analysis to investigate the influence of the minimal price $P_{\\min}$ of the bidding winners, the quota $N_{\\rm{quota}}$ of private car license plates, the number $N_{\\rm{bidder}}$ of bidders, as well as two external shocks including the legality debate of the auction in 2004 and the auction regime reform in January 2008 on the average price $P_{\\rm{mean}}$ of all bidding winners. It is found that the legality debate of the auction had marginal transient impact on the average price in a short time period. In contrast, the change of the auction rules has significant permanent influence on the average price, which reduces the price by about 3020 yuan Renminbi. It means that the average price exhibits nonlinear behaviors with a regime change. The evolution of the average price is independent of the number $N_{\\rm{bidder}}$ of bidders in both regimes. In the early regime before January 2008, the average price $P_{\\rm{mean}}$ was influenced only by the minimal price $P_{\\min}$ in the preceding month with a positive correlation. In the current regime since January 2008, the average price is positively correlated with the minimal price and the quota in the preceding month and negatively correlated with the quota in the same month. We test the predictive power of the two models using 2-year and 3-year moving windows and find that the latter outperforms the former. It seems that the auction market becomes more efficient after the auction reform since the prediction error increases.", "published": "2010-01-19T00:55:55Z", "authors": ["Fu-Tie Song", "Wei-Xing Zhou"], "categories": ["q-fin.ST"], "doi": "10.1016/j.physa.2010.04.010", "_query": "hedonic house price", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/1511.07907v2", "title": "Competitive Charging Station Pricing for Plug-in Electric Vehicles", "abstract": "This paper considers the problem of charging station pricing and plug-in electric vehicles (PEVs) station selection. When a PEV needs to be charged, it selects a charging station by considering the charging prices, waiting times, and travel distances. Each charging station optimizes its charging price based on the prediction of the PEVs' charging station selection decisions and the other station's pricing decision, in order to maximize its profit. To obtain insights of such a highly coupled system, we consider a one-dimensional system with two competing charging stations and Poisson arriving PEVs. We propose a multi-leader-multi-follower Stackelberg game model, in which the charging stations (leaders) announce their charging prices in Stage I, and the PEVs (followers) make their charging station selections in Stage II. We show that there always exists a unique charging station selection equilibrium in Stage II, and such equilibrium depends on the charging stations' service capacities and the price difference between them. We then characterize the sufficient conditions for the existence and uniqueness of the pricing equilibrium in Stage I. We also develop a low complexity algorithm that efficiently computes the pricing equilibrium and the subgame perfect equilibrium of the two-stage Stackelberg game.", "published": "2015-11-24T22:45:32Z", "authors": ["Wei Yuan", "Jianwei Huang", "Ying Jun Zhang"], "categories": ["cs.GT", "eess.SY", "math.OC"], "doi": "", "_query": "hedonic house price", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/1405.1247v1", "title": "Stylized facts of price gaps in limit order books: Evidence from Chinese stocks", "abstract": "Price gap, defined as the logarithmic price difference between the first two occupied price levels on the same side of a limit order book (LOB), is a key determinant of market depth, which is one of the dimensions of liquidity. However, the properties of price gaps have not been thoroughly studied due to the less availability of ultrahigh frequency data. In the paper, we rebuild the LOB dynamics based on the order flow data of 26 A-share stocks traded on the Shenzhen Stock Exchange in 2003. Three key empirical statistical properties of price gaps are investigated. We find that the distribution of price gaps has a power-law tail for all stocks with an average tail exponent close to 3.2. Applying modern statistical methods, we confirm that the gap time series are long-range correlated and possess multifractal nature. These three features vary from stock to stock and are not universal. Furthermore, we also unveil buy-sell asymmetry phenomena in the properties of price gaps on the buy and sell sides of the LOBs for individual stocks. These findings deepen our understanding of the dynamics of liquidity of common stocks and can be used to calibrate agent-based computational financial models.", "published": "2014-05-06T12:38:35Z", "authors": ["Gao-Feng Gu", "Xiong Xiong", "Yong-Jie Zhang", "Wei Chen", "Wei Zhang", "Wei-Xing Zhou"], "categories": ["q-fin.TR", "q-fin.ST"], "doi": "10.1016/j.chaos.2015.10.031", "_query": "hedonic house price", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/1907.12025v1", "title": "Marked Hawkes process modeling of price dynamics and volatility estimation", "abstract": "A simple Hawkes model have been developed for the price tick structure dynamics incorporating market microstructure noise and trade clustering. In this paper, the model is extended with random mark to deal with more realistic price tick structures of equities. We examine the impact of jump in price dynamics to the future movements and dependency between the jump sizes and ground intensities. We also derive the volatility formula based on stochastic and statistical methods and compare with realized volatility in simulation and empirical studies. The marked Hawkes model is useful to estimate the intraday volatility similarly in the case of simple Hawkes model.", "published": "2019-07-28T06:33:18Z", "authors": ["Kyungsub Lee", "Byoung Ki Seo"], "categories": ["q-fin.ST", "q-fin.TR"], "doi": "10.1016/j.jempfin.2016.08.004", "_query": "hedonic house price", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/1909.09057v1", "title": "The Effect of Oil Price on United Arab Emirates Goods Trade Deficit with the United States", "abstract": "We seek to investigate the effect of oil price on UAE goods trade deficit with the U.S. The current increase in the price of oil and the absence of significant studies in the UAE economy are the main motives behind the current study. Our paper focuses on a small portion of UAE trade, which is 11% of the UAE foreign trade, however, it is a significant part since the U.S. is a major trade partner with the UAE. The current paper concludes that oil price has a significant positive influence on real imports. At the same time, oil price does not have a significant effect on real exports. As a result, any increase in the price of oil increases goods trade deficit of the UAE economy. The policy implication of the current paper is that the revenue of oil sales is not used to encourage UAE real exports.", "published": "2019-09-19T15:55:40Z", "authors": ["Osama D. Sweidan", "Bashar H. Malkawi"], "categories": ["econ.GN"], "doi": "", "_query": "hedonic house price", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/1407.2991v3", "title": "Price of Anarchy with Heterogeneous Latency Functions", "abstract": "In this paper we consider the price of anarchy (PoA) in multi-commodity flows where the latency or delay function on an edge has a heterogeneous dependency on the flow commodities, i.e. when the delay on each link is dependent on the flow of individual commodities, rather than on the aggregate flow. An application of this study is the performance analysis of a network with differentiated traffic that may arise when traffic is prioritized according to some type classification. This study has implications in the debate on net-neutrality. We provide price of anarchy bounds for networks with $k$ (types of) commodities where each link is associated with heterogeneous polynomial delays, i.e. commodity $i$ on edge $e$ faces delay specified by $g_{i1}(e)f^θ_1(e) + g_{i2}(e)f^θ_2(e) + \\ldots + g_{ik}(e)f^θ_k(e) + c_i(e), $ where $f_i(e)$ is the flow of the $i$th commodity through edge $e$, $θ\\in {\\cal N}$, $g_{i1}(e), g_{i2}(e), \\ldots, g_{ik}(e)$ and $c_i(e)$ are nonnegative constants. We consider both atomic and non-atomic flows. For networks with decomposable delay functions where the delay induced by a particular commodity is the same, i.e. delays on edge $e$ are defined by $a_1(e)f_1^θ(e) + a_2(e)f_2^θ(e) + \\ldots + c(e)$ where $\\forall j , \\forall e: g_{1j}(e) = g_{2j}(e) = \\ldots = a_j(e)$, we show an improved bound on the price of anarchy. Further, we show bounds on the price of anarchy for uniform latency functions where each edge of the network has the same delay function.", "published": "2014-07-11T00:28:34Z", "authors": ["Sanjiv Kapoor", "Junghwan Shin"], "categories": ["cs.GT"], "doi": "", "_query": "hedonic house price", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2305.12309v1", "title": "Uniform Pricing vs Pay as Bid in 100%-Renewables Electricity Markets: A Game-theoretical Analysis", "abstract": "This paper evaluates market equilibrium under different pricing mechanisms in a two-settlement 100%-renewables electricity market. Given general probability distributions of renewable energy, we establish game-theoretical models to analyze equilibrium bidding strategies, market prices, and profits under uniform pricing (UP) and pay-as-bid pricing (PAB). We prove that UP can incentivize suppliers to withhold bidding quantities and lead to price spikes. PAB can reduce the market price, but it may lead to a mixed-strategy price equilibrium. Then, we present a regulated uniform pricing scheme (RUP) based on suppliers' marginal costs that include penalty costs for real-time deviations. We show that RUP can achieve lower yet positive prices and profits compared with PAB in a duopoly market, which approximates the least-cost system outcome. Simulations with synthetic and real data find that under PAB and RUP, higher uncertainty of renewables and real-time shortage penalty prices can increase the market price by encouraging lower bidding quantities, thereby increasing suppliers' profits.", "published": "2023-05-21T00:50:39Z", "authors": ["Dongwei Zhao", "Audun Botterud", "Marija Ilic"], "categories": ["eess.SY", "cs.GT", "econ.TH"], "doi": "", "_query": "hedonic house price", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2106.00923v3", "title": "Posted Price versus Hybrid Mechanisms in Freight Transportation Marketplaces", "abstract": "We consider a freight platform that serves as an intermediary between shippers and carriers in a truckload transportation network. The platform's objective is to design a policy that determines prices for shippers and payments to carriers, as well as how carriers are matched to loads to be transported, to maximize its long-run average profit. We propose a two-stage decision framework to model carriers' load choice behavior, where carriers choose a lane according to the multinomial logit (MNL) model based on the platform's posted price in the first stage and book a load in the second stage. We analyze two types of carrier-side mechanisms commonly used by freight platforms: a posted price mechanism and a hybrid mechanism where carriers can either book loads at posted price or submit their bids in an auction. The proposed mechanisms are constructed using a fluid approximation model to incorporate carrier interactions in the freight network. We show that the hybrid mechanism has higher profits than the posted price mechanism. We prove tight bounds between these mechanisms for varying market sizes. The findings are validated through a numerical simulation using industry data from the U.S. freight market.", "published": "2021-06-02T03:48:16Z", "authors": ["Ruoran Chen", "Sungwoo Kim", "He Wang", "Xuan Wang"], "categories": ["math.OC"], "doi": "", "_query": "hedonic house price", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2401.00970v2", "title": "Almost Perfect Shadow Prices", "abstract": "Shadow prices simplify the derivation of optimal trading strategies in markets with transaction costs by transferring optimization into a more tractable, frictionless market. This paper establishes that a naïve shadow price Ansatz for maximizing long term returns given average volatility yields a strategy that is, for small bid-ask-spreads, asymptotically optimal at third order. Considering the second-order impact of transaction costs, such a strategy is essentially optimal. However, for risk aversion different from one, we devise alternative strategies that outperform the shadow market at fourth order. Finally, it is shown that the risk-neutral objective rules out the existence of shadow prices.", "published": "2024-01-01T22:45:55Z", "authors": ["Eberhard Mayerhofer"], "categories": ["q-fin.PM", "math.OC", "math.PR"], "doi": "", "_query": "hedonic house price", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2303.13192v1", "title": "Mechanism Design for Ad Auctions with Display Prices", "abstract": "In many applications, ads are displayed together with the prices, so as to provide a direct comparison among similar products or services. The price-displaying feature not only influences the consumers' decisions, but also affects the advertisers' bidding behaviors. In this paper, we study ad auctions with display prices from the perspective of mechanism design, in which advertisers are asked to submit both the costs and prices of their products. We provide a characterization for all incentive compatible auctions with display prices, and use it to design auctions under two scenarios. In the former scenario, the display prices are assumed to be exogenously determined. For this setting, we derive the welfare-maximizing and revenue-maximizing auctions for any realization of the price profile. In the latter, advertisers are allowed to strategize display prices in their own interests. We investigate two families of allocation policies within the scenario and identify the equilibrium prices accordingly. Our results reveal that the display prices do affect the design of ad auctions and the platform can leverage such information to optimize the performance of ad delivery.", "published": "2023-03-23T11:46:48Z", "authors": ["Bin Li", "Yahui Lei"], "categories": ["cs.AI", "cs.GT", "cs.MA"], "doi": "", "_query": "hedonic house price", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/1204.2736v1", "title": "Optimal execution and price manipulations in time-varying limit order books", "abstract": "This paper focuses on an extension of the Limit Order Book (LOB) model with general shape introduced by Alfonsi, Fruth and Schied. Here, the additional feature allows a time-varying LOB depth. We solve the optimal execution problem in this framework for both discrete and continuous time strategies. This gives in particular sufficient conditions to exclude Price Manipulations in the sense of Huberman and Stanzl or Transaction-Triggered Price Manipulations (see Alfonsi, Schied and Slynko). These conditions give interesting qualitative insights on how market makers may create or not price manipulations.", "published": "2012-04-12T14:27:07Z", "authors": ["Aurélien Alfonsi", "José Infante Acevedo"], "categories": ["q-fin.TR", "math.OC"], "doi": "", "_query": "hedonic house price", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/1407.4355v1", "title": "Pricing for local and global WiFi markets", "abstract": "This paper analyzes two pricing schemes commonly used in WiFi markets: the flat-rate and the usage-based pricing. The flat-rate pricing encourages the maximum usage, while the usage-based pricing can flexibly attract more users especially those with low valuations in mobile Internet access. First, we use theoretical analysis to compare the two schemes and show that for a single provider in a market, as long as the WiFi capacity is abundant, the flat-rate pricing leads to more revenue. Second, we study how a global provider (e.g., Skype) collaborates with this monopolist in each local market to provide a global WiFi service. We formulate {the interactions between the global and local providers as a dynamic game. In Stage I, the global provider bargains with the local provider in each market to determine the global WiFi service price and revenue sharing agreement. In Stage II, local users and travelers choose local or global WiFi services. We analytically show that the global provider prefers to use the usage-based pricing to avoid a severe competition with the local provider. At the equilibrium, the global provider always shares the majority of his revenue with the local provider to incentivize the cooperation. Finally, we analytically study how the interaction changes if the local market has more than one local provider. In this case, the global provider can integrate the coverages of multiple local providers and provide a better service. Compared to the local monopoly case, local market competition enables the global provider to share less revenue with each of the local providers. However, we numerically show that the global provider's revenue could decrease, as he shares his revenue with more providers and can only charge a lower price.", "published": "2014-07-16T15:57:21Z", "authors": ["Lingjie Duan", "Jianwei Huang", "Biying Shou"], "categories": ["cs.GT", "cs.NI"], "doi": "", "_query": "hedonic house price", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2007.10131v1", "title": "The Price of Anarchy of Two-Buyer Sequential Multiunit Auctions", "abstract": "We study the efficiency of sequential multiunit auctions with two-buyers and complete information. For general valuation functions, we show that the price of anarchy is exactly $1/T$ for auctions with $T$ items for sale. For concave valuation functions, we show that the price of anarchy is bounded below by $1-1/e\\simeq 0.632$. This bound is asymptotically tight as the number of items sold tends to infinity.", "published": "2020-07-20T14:11:54Z", "authors": ["Mete Şeref Ahunbay", "Adrian Vetta"], "categories": ["cs.GT"], "doi": "", "_query": "hedonic house price", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/1312.1304v1", "title": "On the asymptotic behavior of a Boltzmann-type price formation model", "abstract": "In this paper we study the asymptotic behavior of a Boltzmann type price formation model, which describes the trading dynamics in a financial market. In many of these markets trading happens at high frequencies and low transactions costs. This observation motivates the study of the limit as the number of transactions $k$ tends to infinity, the transaction cost $a$ to zero and $ka=const$. Furthermore we illustrate the price dynamics with numerical simulations.", "published": "2013-12-04T19:57:25Z", "authors": ["Martin Burger", "Luis Caffarelli", "Peter A. Markowich", "Marie-Therese Wolfram"], "categories": ["math.AP"], "doi": "", "_query": "hedonic house price", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2002.02876v2", "title": "Equal Risk Pricing and Hedging of Financial Derivatives with Convex Risk Measures", "abstract": "In this paper, we consider the problem of equal risk pricing and hedging in which the fair price of an option is the price that exposes both sides of the contract to the same level of risk. Focusing for the first time on the context where risk is measured according to convex risk measures, we establish that the problem reduces to solving independently the writer and the buyer's hedging problem with zero initial capital. By further imposing that the risk measures decompose in a way that satisfies a Markovian property, we provide dynamic programming equations that can be used to solve the hedging problems for both the case of European and American options. All of our results are general enough to accommodate situations where the risk is measured according to a worst-case risk measure as is typically done in robust optimization. Our numerical study illustrates the advantages of equal risk pricing over schemes that only account for a single party, pricing based on quadratic hedging (i.e. $ε$-arbitrage pricing), or pricing based on a fixed equivalent martingale measure (i.e. Black-Scholes pricing). In particular, the numerical results confirm that when employing an equal risk price both the writer and the buyer end up being exposed to risks that are more similar and on average smaller than what they would experience with the other approaches.", "published": "2020-02-07T16:20:59Z", "authors": ["Saeed Marzban", "Erick Delage", "Jonathan Yumeng Li"], "categories": ["math.OC", "q-fin.PR"], "doi": "", "_query": "hedonic house price", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/1510.08335v1", "title": "A Stochastic Electricity Market Clearing Formulation with Consistent Pricing Properties", "abstract": "We argue that deterministic market clearing formulations introduce arbitrary distortions between day-ahead and expected real-time prices that bias economic incentives and block diversification. We extend and analyze the stochastic clearing formulation proposed by Pritchard et al. (2010) in which the social surplus function induces penalties between day-ahead and real-time quantities. We prove that the formulation yields price distortions that are bounded by the bid prices, and we show that adding a similar penalty term to transmission flows and phase angles ensures boundedness throughout the network. We prove that when the price distortions are zero, day-ahead quantities converge to the quantile of real-time counterparts. The undesired effects of price distortions suggest that stochastic settings provide significant benefits over deterministic ones that go beyond social surplus improvements. We propose additional metrics to evaluate these benefits.", "published": "2015-10-28T15:02:44Z", "authors": ["Victor M. Zavala", "Kibaek Kim", "Mihai Anitescu", "John Birge"], "categories": ["econ.GN", "math.OC"], "doi": "", "_query": "hedonic house price", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2609.28270v1", "title": "Predicting Quantization Price for Selecting PTQ Configurations Before Deployment", "abstract": "Weight-space post-training quantization (PTQ) must choose finite formats, granularities, quantizer families, transformations, and bits before the completed quantized model reveals its output-distribution drift. Existing PTQ methods predict important pieces of this degradation, including reconstruction error, Hessian sensitivity, transformation effects, and downstream loss, but these pieces are usually scored after fixing the quantization geometry or inside separate configuration families. We formulate weight-space PTQ as pre-deployment configuration selection using priced layer-output error. Each admissible layer configuration is treated as an error generator with a deployment cost, which induces a layer-output error covariance $\\boldsymbolΣ_l(α_l)$, and the full-precision model prices that covariance by downstream curvature, $\\widehatρ_l(α_l)=\\frac{1}{2}\\operatorname{Tr}\\left(\\widehat{\\mathbf{H}}_l\\,\\widehat{\\boldsymbolΣ}_l(α_l)\\right)$. The price follows from full-precision-to-quantized forward KL, whose first-order term cancels at the reference model. It turns reconstruction and diagonal scores into reduced proxies that drop price factors, while finite formats, codebooks, granularities, and equivalent transformations become comparable candidates through the covariances they induce and the costs they pay. A trace reduction then yields a calibration-time price table and a budgeted price-guided selector, making fixed-geometry bit allocation a special case rather than the organizing problem.", "published": "2026-09-23T15:25:44Z", "authors": ["Junbin Qiu", "Jian Mu", "Weitong Zhang", "Yao Shu"], "categories": ["cs.CL", "cs.LG"], "doi": "", "_query": "hedonic house price", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/cond-mat/9910212v1", "title": "Growth Optimal Investment and Pricing of Derivatives", "abstract": "We introduce a criterion how to price derivatives in incomplete markets, based on the theory of growth optimal strategy in repeated multiplicative games. We present reasons why these growth-optimal strategies should be particularly relevant to the problem of pricing derivatives. We compare our result with other alternative pricing procedures in the literature, and discuss the limits of validity of the lognormal approximation. We also generalize the pricing method to a market with correlated stocks. The expected estimation error of the optimal investment fraction is derived in a closed form, and its validity is checked with a small-scale empirical test.", "published": "1999-10-14T13:34:15Z", "authors": ["Erik Aurell", "Roberto Baviera", "Ola Hammarlid", "Maurizio Serva", "Angelo Vulpiani"], "categories": ["cond-mat.stat-mech", "q-fin.PR"], "doi": "10.1016/S0378-4371(00)00005-4", "_query": "hedonic house price", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2210.15613v3", "title": "The law of one price in quadratic hedging and mean-variance portfolio selection", "abstract": "The law of one price (LOP) broadly asserts that identical financial flows should command the same price. We show that, when properly formulated, LOP is the minimal condition for a well-defined mean-variance portfolio selection framework without degeneracy. Crucially, the paper identifies a new mechanism through which LOP can fail in a continuous-time $L^2$ setting without frictions, namely 'trading from just before a predictable stopping time', which surprisingly identifies LOP violations even for continuous price processes. Closing this loophole allows to give a version of the \"Fundamental Theorem of Asset Pricing\" appropriate in the quadratic context, establishing the equivalence of the economic concept of LOP with the probabilistic property of the existence of a local $\\scr{E}$-martingale state price density. The latter provides unique prices for all square-integrable claims in an extended market and subsequently plays an important role in quadratic hedging and mean-variance portfolio selection. Mathematically, we formulate a novel variant of the uniform boundedness principle for conditionally linear functionals on the $L^0$ module of conditionally square-integrable random variables. We then study the representation of time-consistent families of such functionals in terms of stochastic exponentials of a fixed local martingale.", "published": "2022-10-27T16:58:43Z", "authors": ["Aleš Černý", "Christoph Czichowsky"], "categories": ["math.OC", "q-fin.PM"], "doi": "10.1007/s00780-025-00563-7", "_query": "hedonic house price", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/1109.0738v2", "title": "Pricing Derivatives on Multiscale Diffusions: an Eigenfunction Expansion Approach", "abstract": "Using tools from spectral analysis, singular and regular perturbation theory, we develop a systematic method for analytically computing the approximate price of a derivative-asset. The payoff of the derivative-asset may be path-dependent. Additionally, the process underlying the derivative may exhibit killing (i.e. jump to default) as well as combined local/nonlocal stochastic volatility. The nonlocal component of volatility is multiscale, in the sense that it is driven by one fast-varying and one slow-varying factor. The flexibility of our modeling framework is contrasted by the simplicity of our method. We reduce the derivative pricing problem to that of solving a single eigenvalue equation. Once the eigenvalue equation is solved, the approximate price of a derivative can be calculated formulaically. To illustrate our method, we calculate the approximate price of three derivative-assets: a vanilla option on a defaultable stock, a path-dependent option on a non-defaultable stock, and a bond in a short-rate model.", "published": "2011-09-04T18:33:35Z", "authors": ["Matthew Lorig"], "categories": ["q-fin.CP", "math.SP", "q-fin.PR"], "doi": "", "_query": "hedonic house price", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2507.23505v2", "title": "The effect of a new power interconnector on energy prices volatility: the case of Sicily", "abstract": "Integrating energy islands into the European electricity market is a key challenge for the energy transition. This study investigates the impact of the Sorgente-Rizziconi interconnector on electricity price volatility in Sicily. Before its commissioning on 28 May 2016, the Sicilian electricity market zone was poorly interconnected with the Italian mainland. Using daily data from 2015 to 2018, the analysis applies a semi-parametric GARCH model with a logistic intervention function to estimate changes in conditional price variance. A fully non-parametric additive model is employed as a robustness check, allowing the data to shape volatility dynamics without imposing a predefined structure. The results reveal that the new interconnector significantly increased price volatility in Sicily, without reducing average price levels. No significant effects were observed in other Italian market zones. These findings highlight the context-dependent nature of infrastructure impacts and suggest that physical integration alone does not guarantee price stability. The results have important implications for energy policy, investment planning, and risk management in electricity markets.", "published": "2025-07-31T12:42:51Z", "authors": ["Francesco Lisi", "Pierdomenico Duttilo", "Marina Bertolini"], "categories": ["stat.AP"], "doi": "10.1002/asmb.70102", "_query": "hedonic house price", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/math/0701650v2", "title": "Pricing Options in Incomplete Equity Markets via the Instantaneous Sharpe Ratio", "abstract": "We use a continuous version of the standard deviation premium principle for pricing in incomplete equity markets by assuming that the investor issuing an unhedgeable derivative security requires compensation for this risk in the form of a pre-specified instantaneous Sharpe ratio. First, we apply our method to price options on non-traded assets for which there is a traded asset that is correlated to the non-traded asset. Our main contribution to this particular problem is to show that our seller/buyer prices are the upper/lower good deal bounds of Cochrane and Saá-Requejo (2000) and of Björk and Slinko (2006) and to determine the analytical properties of these prices. Second, we apply our method to price options in the presence of stochastic volatility. Our main contribution to this problem is to show that the instantaneous Sharpe ratio, an integral ingredient in our methodology, is the negative of the market price of volatility risk, as defined in Fouque, Papanicolaou, and Sircar (2000).", "published": "2007-01-23T18:13:54Z", "authors": ["Erhan Bayraktar", "Virginia R. Young"], "categories": ["math.OC", "q-fin.PR"], "doi": "", "_query": "hedonic house price", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/1801.06966v2", "title": "Accurate Evaluation of Asset Pricing Under Uncertainty and Ambiguity of Information", "abstract": "Since exchange economy considerably varies in the market assets, asset prices have become an attractive research area for investigating and modeling ambiguous and uncertain information in today markets. This paper proposes a new generative uncertainty mechanism based on the Bayesian Inference and Correntropy (BIC) technique for accurately evaluating asset pricing in markets. This technique examines the potential processes of risk, ambiguity, and variations of market information in a controllable manner. We apply the new BIC technique to a consumption asset-pricing model in which the consumption variations are modeled using the Bayesian network model with observing the dynamics of asset pricing phenomena in the data. These dynamics include the procyclical deviations of price, the countercyclical deviations of equity premia and equity volatility, the leverage impact and the mean reversion of excess returns. The key findings reveal that the precise modeling of asset information can estimate price changes in the market effectively.", "published": "2018-01-22T06:38:32Z", "authors": ["Farouq Abdulaziz Masoudy"], "categories": ["q-fin.GN", "econ.EM"], "doi": "", "_query": "hedonic house price", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2501.01660v1", "title": "The (Exact) Price of Cardinality for Indivisible Goods: A Parametric Perspective", "abstract": "We adopt a parametric approach to analyze the worst-case degradation in social welfare when the allocation of indivisible goods is constrained to be fair. Specifically, we are concerned with cardinality-constrained allocations, which require that each agent has at most $k$ items in their allocated bundle. We propose the notion of the price of cardinality, which captures the worst-case multiplicative loss of utilitarian or egalitarian social welfare resulting from imposing the cardinality constraint. We then characterize tight or almost-tight bounds on the price of cardinality as exact functions of the instance parameters, demonstrating how the social welfare improves as $k$ is increased. In particular, one of our main results refines and generalizes the existing asymptotic bound on the price of balancedness, as studied by Bei et al. [BLMS21]. We also further extend our analysis to the problem where the items are partitioned into disjoint categories, and each category has its own cardinality constraint. Through a parametric study of the price of cardinality, we provide a framework which aids decision makers in choosing an ideal level of cardinality-based fairness, using their knowledge of the potential loss of utilitarian and egalitarian social welfare.", "published": "2025-01-03T06:29:32Z", "authors": ["Alexander Lam", "Bo Li", "Ankang Sun"], "categories": ["cs.GT"], "doi": "", "_query": "hedonic house price", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2210.10938v2", "title": "Never Say Never: Optimal Exclusion and Reserve Prices with Expectations-Based Loss-Averse Buyers", "abstract": "We study reserve prices in auctions with independent private values when bidders are expectations-based loss averse. We find that the optimal public reserve price excludes fewer bidder types than under risk neutrality. Moreover, we show that public reserve prices are not optimal as the seller can earn a higher revenue with mechanisms that better leverage the ``attachment effect''. We discuss two such mechanisms: i) an auction with a secrete and random reserve price, and ii) a two-stage mechanism where an auction with a public reserve price is followed by a negotiation if the reserve price is not met. Both of these mechanisms expose more bidders to the attachment effect, thereby increasing bids and ultimately revenue.", "published": "2022-10-20T00:32:23Z", "authors": ["Benjamin Balzer", "Antonio Rosato"], "categories": ["econ.TH"], "doi": "", "_query": "hedonic house price", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/1605.03081v1", "title": "On the Price of Anarchy of Highly Congested Nonatomic Network Games", "abstract": "We consider nonatomic network games with one source and one destination. We examine the asymptotic behavior of the price of anarchy as the inflow increases. In accordance with some empirical observations, we show that, under suitable conditions, the price of anarchy is asymptotic to one. We show with some counterexamples that this is not always the case. The counterexamples occur in very simple parallel graphs.", "published": "2016-05-10T16:19:08Z", "authors": ["Riccardo Colini-Baldeschi", "Roberto Cominetti", "Marco Scarsini"], "categories": ["cs.GT", "math.OC"], "doi": "10.1007/978-3-662-53354-3_10", "_query": "hedonic house price", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/1907.07673v1", "title": "Network Based Pricing for 3D Printing Services in Two-Sided Manufacturing-as-a-Service Marketplace", "abstract": "This paper presents approaches to determine a network based pricing for 3D printing services in the context of a two-sided manufacturing-as-a-service marketplace. The intent is to provide cost analytics to enable service bureaus to better compete in the market by moving away from setting ad-hoc and subjective prices. A data mining approach with machine learning methods is used to estimate a price range based on the profile characteristics of 3D printing service suppliers. The model considers factors such as supplier experience, supplier capabilities, customer reviews and ratings from past orders, and scale of operations among others to estimate a price range for suppliers' services. Data was gathered from existing marketplace websites, which was then used to train and test the model. The model demonstrates an accuracy of 65% for US based suppliers and 59% for Europe based suppliers to classify a supplier's 3D Printer listing in one of the seven price categories. The improvement over baseline accuracy of 25% demonstrates that machine learning based methods are promising for network based pricing in manufacturing marketplaces. Conventional methodologies for pricing services through activity based costing are inefficient in strategically pricing 3D printing service offering in a connected marketplace. As opposed to arbitrarily determining prices, this work proposes an approach to determine prices through data mining methods to estimate competitive prices. Such tools can be built into online marketplaces to help independent service bureaus to determine service price rates.", "published": "2019-07-17T19:14:55Z", "authors": ["Deepak Pahwa", "Binil Starly"], "categories": ["cs.LG", "stat.ML"], "doi": "10.1108/RPJ-01-2019-0018", "_query": "hedonic house price", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2507.03477v1", "title": "REAL: Benchmarking Abilities of Large Language Models for Housing Transactions and Services", "abstract": "The development of large language models (LLMs) has greatly promoted the progress of chatbot in multiple fields. There is an urgent need to evaluate whether LLMs can play the role of agent in housing transactions and services as well as humans. We present Real Estate Agent Large Language Model Evaluation (REAL), the first evaluation suite designed to assess the abilities of LLMs in the field of housing transactions and services. REAL comprises 5,316 high-quality evaluation entries across 4 topics: memory, comprehension, reasoning and hallucination. All these entries are organized as 14 categories to assess whether LLMs have the knowledge and ability in housing transactions and services scenario. Additionally, the REAL is used to evaluate the performance of most advanced LLMs. The experiment results indicate that LLMs still have significant room for improvement to be applied in the real estate field.", "published": "2025-07-04T11:05:44Z", "authors": ["Kexin Zhu", "Yang Han"], "categories": ["cs.AI"], "doi": "", "_query": "hedonic house price", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2602.12888v2", "title": "Conjectural Variations in Competitive Dynamic Pricing: A Learning Foundation via Experimentation Design and Feedback Structure", "abstract": "We study competitive dynamic pricing among multiple sellers, motivated by the rise of large-scale experimentation and algorithmic pricing in retail and online marketplaces. Sellers repeatedly set prices using simple learning rules and observe their own realized demand, while possibly observing only a subset of rivals' prices, even though demand depends on all sellers' prices and is subject to random shocks. Each seller runs local price experiments, such as switchback-style designs, and updates a focal price using a linear demand estimate fitted to its own demand data and the competitor prices it observes. Under certain conditions on demand, the resulting dynamics converge to a Conjectural Variations (CV) equilibrium, a classic static equilibrium notion in which each seller best responds under a conjecture that rivals' prices co-move systematically to changes in its own price. Unlike standard CV models that treat conjectures as behavioral primitives, we show that these conjectures arise endogenously from the interaction between the feedback structure and the correlation structure of experimentation. When a seller does not observe some rivals' prices, correlated experimentation induces an omitted-variable bias in demand estimation. We show that this bias determines the conjectures that govern the long-run equilibrium. Notably, when this learning bias vanishes, for example under full price feedback or independent experimentation of unobserved rivals, the learning dynamics converge to the standard Nash equilibrium. We provide simple sufficient conditions on demand for convergence in standard models and establish a finite-sample guarantee, showing that the mean squared price error decays at a rate of $\\widetilde O (T^{-1/2})$.", "published": "2026-02-13T12:45:09Z", "authors": ["Bar Light", "Wenyu Wang"], "categories": ["cs.GT"], "doi": "", "_query": "hedonic house price", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2506.22055v1", "title": "crypto price prediction using lstm+xgboost", "abstract": "The volatility and complex dynamics of cryptocurrency markets present unique challenges for accurate price forecasting. This research proposes a hybrid deep learning and machine learning model that integrates Long Short-Term Memory (LSTM) networks and Extreme Gradient Boosting (XGBoost) for cryptocurrency price prediction. The LSTM component captures temporal dependencies in historical price data, while XGBoost enhances prediction by modeling nonlinear relationships with auxiliary features such as sentiment scores and macroeconomic indicators. The model is evaluated on historical datasets of Bitcoin, Ethereum, Dogecoin, and Litecoin, incorporating both global and localized exchange data. Comparative analysis using Mean Absolute Percentage Error (MAPE) and Min-Max Normalized Root Mean Square Error (MinMax RMSE) demonstrates that the LSTM+XGBoost hybrid consistently outperforms standalone models and traditional forecasting methods. This study underscores the potential of hybrid architectures in financial forecasting and provides insights into model adaptability across different cryptocurrencies and market contexts.", "published": "2025-06-27T09:49:25Z", "authors": ["Mehul Gautam"], "categories": ["cs.LG"], "doi": "", "_query": "hedonic house price", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/cond-mat/0310351v1", "title": "Modeling of waiting times and price changes in currency exchange data", "abstract": "A theory which describes the share price evolution at financial markets as a continuous-time random walk has been generalized in order to take into account the dependence of waiting times t on price returns x. A joint probability density function (pdf) which uses the concept of a Lévy stable distribution is worked out. The theory is fitted to high-frequency US$/Japanese Yen exchange rate and low-frequency 19th century Irish stock data. The theory has been fitted both to price return and to waiting time data and the adherence to data, in terms of the chi-squared test statistic, has been improved when compared to the old theory.", "published": "2003-10-15T15:03:01Z", "authors": ["Przemyslaw Repetowicz", "Peter Richmond"], "categories": ["cond-mat.stat-mech", "q-fin.TR"], "doi": "10.1016/j.physa.2004.06.162", "_query": "hedonic house price", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/1610.00327v1", "title": "Improving Comparison Shopping Agents' Competence through Selective Price Disclosure", "abstract": "The plethora of comparison shopping agents (CSAs) in today's markets enables buyers to query more than a single CSA when shopping, and an inter-CSAs competition naturally arises. We suggest a new approach, termed \"selective price disclosure\", which improves the attractiveness of a CSA by removing some of the prices in the outputted list. The underlying idea behind this approach is to affect the buyer's beliefs regarding the chance of obtaining more attractive prices. The paper presents two methods, which are suitable for fully-rational buyers, for deciding which prices among those known to the CSA should be disclosed. The effectiveness and efficiency of the methods are evaluated using real data collected from five CSAs. The methods are also evaluated with human subjects, showing that selective price disclosure can be highly effective in this case as well, however, the disclosed subset of prices should be extracted in a different (simplistic) manner.", "published": "2016-10-02T18:15:10Z", "authors": ["Chen Hajaj", "Noam Hazon", "David Sarne"], "categories": ["cs.GT"], "doi": "10.1016/j.elerap.2015.08.006", "_query": "hedonic house price", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/1307.5540v2", "title": "On the Pricing of Storable Commodities", "abstract": "This paper introduces an information-based model for the pricing of storable commodities such as crude oil and natural gas. The model uses the concept of market information about future supply and demand as a basis for valuation. Physical ownership of a commodity is taken to provide a stream of convenience dividends equivalent to a continuous cash flow. The market filtration is assumed to be generated jointly by (i) current and past levels of the dividend rate, and (ii) partial information concerning the future of the dividend flow. The price of a commodity is the expectation under a suitable pricing measure of the totality of the discounted risk-adjusted future convenience dividend, conditional on the information provided by the market filtration. In the situation where the dividend rate is modelled by an Ornstein-Uhlenbeck process, the prices of options on commodities can be derived in closed form. The approach that we present can be applied to other assets that yield potentially negative effective cash flows, such as real estate, factories, refineries, mines, and power generating plants.", "published": "2013-07-21T16:27:25Z", "authors": ["Dorje C. Brody", "Lane P. Hughston", "Xun Yang"], "categories": ["q-fin.PR", "math.PR"], "doi": "", "_query": "hedonic house price", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2208.07919v2", "title": "Dynamic Pricing for Non-fungible Resources: Designing Multidimensional Blockchain Fee Markets", "abstract": "Public blockchains implement a fee mechanism to allocate scarce computational resources across competing transactions. Most existing fee market designs utilize a joint, fungible unit of account (e.g., gas in Ethereum) to price otherwise non-fungible resources such as bandwidth, computation, and storage, by hardcoding their relative prices. Fixing the relative price of each resource in this way inhibits granular price discovery, limiting scalability and opening up the possibility of denial-of-service attacks. As a result, many prominent networks such as Ethereum and Solana have proposed multi-dimensional fee markets. In this paper, we provide a principled way to design fee markets that efficiently price multiple non-fungible resources. Starting from a loss function specified by the network designer, we show how to compute dynamic prices that align the network's incentives (to minimize the loss) with those of the users and miners (to maximize their welfare), even as demand for these resources changes. Our pricing mechanism follows from a natural decomposition of the network designer's problem into two parts that are related to each other via the resource prices. These results can be used to efficiently set fees in order to improve network performance.", "published": "2022-08-16T19:32:56Z", "authors": ["Theo Diamandis", "Alex Evans", "Tarun Chitra", "Guillermo Angeris"], "categories": ["math.OC", "cs.CR", "cs.GT"], "doi": "", "_query": "hedonic house price", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2504.20429v3", "title": "Estimating the housing production function with unobserved land heterogeneity", "abstract": "Housing supply in dense cities depends on the ability of builders to substitute capital for scarce land. This margin is difficult to estimate because builders choose capital after observing microgeographic conditions that are only imperfectly observed by researchers. This paper develops a method for estimating revenue-based housing production functions in this setting. Because observed capital variation reflects both technological substitution and endogenous responses to latent local conditions, existing estimators can transmit unobserved heterogeneity into the estimated production function. The method treats the unobserved local conditions that affect capital choice as a scalar Markov state and combines the capital share equation with Markov moments implemented using repeated cross-sectional construction data. Monte Carlo simulations show that the estimator recovers capital and land elasticities under flexible production technologies when capital choices respond to latent local conditions. An application to newly constructed housing in Tokyo's 23 special wards illustrates how the method can be implemented in a dense single-city setting. The results show that explicitly modeling latent local heterogeneity matters for estimated capital-land elasticities and implies returns to scale close to one.", "published": "2025-04-29T04:51:35Z", "authors": ["Yusuke Adachi"], "categories": ["econ.GN"], "doi": "", "_query": "hedonic house price", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/1109.4859v1", "title": "The Food Crises: A quantitative model of food prices including speculators and ethanol conversion", "abstract": "Recent increases in basic food prices are severely impacting vulnerable populations worldwide. Proposed causes such as shortages of grain due to adverse weather, increasing meat consumption in China and India, conversion of corn to ethanol in the US, and investor speculation on commodity markets lead to widely differing implications for policy. A lack of clarity about which factors are responsible reinforces policy inaction. Here, for the first time, we construct a dynamic model that quantitatively agrees with food prices. The results show that the dominant causes of price increases are investor speculation and ethanol conversion. Models that just treat supply and demand are not consistent with the actual price dynamics. The two sharp peaks in 2007/2008 and 2010/2011 are specifically due to investor speculation, while an underlying upward trend is due to increasing demand from ethanol conversion. The model includes investor trend following as well as shifting between commodities, equities and bonds to take advantage of increased expected returns. Claims that speculators cannot influence grain prices are shown to be invalid by direct analysis of price setting practices of granaries. Both causes of price increase, speculative investment and ethanol conversion, are promoted by recent regulatory changes---deregulation of the commodity markets, and policies promoting the conversion of corn to ethanol. Rapid action is needed to reduce the impacts of the price increases on global hunger.", "published": "2011-09-21T19:28:04Z", "authors": ["Marco Lagi", "Yavni Bar-Yam", "Karla Z. Bertrand", "Yaneer Bar-Yam"], "categories": ["q-fin.GN", "physics.soc-ph", "q-fin.ST"], "doi": "", "_query": "hedonic house price", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/1904.06757v1", "title": "Price Setting on a Network", "abstract": "Most products are produced and sold by supply chain networks, where an interconnected network of producers and intermediaries set prices to maximize their profits. I show that there exists a unique equilibrium in a price-setting game on a network. The key distortion reducing both total profits and social welfare is multiple-marginalization, which is magnified by strategic interactions. Individual profits are proportional to influentiality, which is a new measure of network centrality defined by the equilibrium characterization. The results emphasize the importance of the network structure when considering policy questions such as mergers or trade tariffs.", "published": "2019-04-14T20:49:43Z", "authors": ["Toomas Hinnosaar"], "categories": ["cs.GT", "econ.GN", "econ.TH"], "doi": "", "_query": "hedonic house price", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/cond-mat/0409375v2", "title": "A Theory of Fluctuations in Stock Prices", "abstract": "The distribution of price returns for a class of uncorrelated diffusive dynamics is considered. The basic assumptions are (1) that there is a \"consensus\" value associated with a stock, and (2) that the rate of diffusion depends on the deviation of the stock price from the consensus value. We find an analytical expression for the distribution of returns in terms of the diffusion rate, when the consensus value is assumed to be fixed in time. The analytical solution is shown to match computed histograms in two simple cases. Differences that result when the consensus value is allowed to change with time are presented qualitative explanations.", "published": "2004-09-14T23:34:40Z", "authors": ["A. L. Alejandro-Quinones", "K. E. Bassler", "M. Field", "J. L. McCauley", "M. Nicol", "I. Timofeyef", "A. Torok", "G. H. Gunaratne"], "categories": ["cond-mat.other", "q-fin.ST"], "doi": "", "_query": "hedonic house price", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2610.04348v1", "title": "A Price-Based Framework for Stochastic Portfolio Theory", "abstract": "We develop a price-based framework for stochastic portfolio theory in which trading strategies are generated from nominal price weights and evaluated relative to a price-weighted benchmark. Stock splits and reverse splits induce jumps in the generating weights without changing the value of existing investments. In a semimartingale market with predictable adjustment events, we incorporate the corresponding share adjustments into the self-financing condition and construct additively and multiplicatively generated strategies with explicit jump-corrected wealth decompositions. For additive generation, an entropy-based example exhibits relative wealth tending to $-\\infty$ almost surely under repeated splits, demonstrating why the usual argument for long-horizon relative arbitrage does not extend directly. We also show that advance adjustment information alone provides no model-free guarantee of improved performance for functionally generated portfolios. For multiplicative generation, wealth-preserving restarts retain the classical portfolio allocation evaluated at the current price weights. We also establish a correspondence with the capitalization-based framework that preserves absolute wealth and yields a strategy-independent benchmark conversion. We illustrate the framework using daily NYSE data, comparing price- and capitalization-weighted benchmarks and the corresponding diversity-weighted portfolios across price- and capitalization-selected universes.", "published": "2026-10-03T07:18:09Z", "authors": ["Jongbong An", "Donghan Kim"], "categories": ["q-fin.MF"], "doi": "", "_query": "hedonic house price", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2207.10823v2", "title": "A Sealed-bid Auction with Fund Binding: Preventing Maximum Bidding Price Leakage", "abstract": "In an open-bid auction, a bidder can know the budgets of other bidders. Thus, a sealed-bid auction that hides bidding prices is desirable. However, in previous sealed-bid auction protocols, it has been difficult to provide a ``fund binding'' property, which would guarantee that a bidder has funds more than or equal to the bidding price and that the funds are forcibly withdrawn when the bidder wins. Thus, such protocols are vulnerable to false bidding. As a solution, many protocols employ a simple deposit method in which each bidder sends a deposit to a smart contract, which is greater than or equal to the bidding price, before the bidding phase. However, this deposit reveals the maximum bidding price, and it is preferable to hide this information. In this paper, we propose a sealed-bid auction protocol that provides a fund binding property. Our protocol not only hides the bidding price and a maximum bidding price, but also provides fund binding, simultaneously. For hiding the maximum bidding price, we pay attention to the fact that usual Ethereum transactions and transactions for sending funds to a one-time address have the same transaction structure, and it seems that they are indistinguishable. We discuss how much bidding transactions are hidden. We also employ DECO (Zhang et al,. CCS 2020) that proves the validity of the data to a verifier in which the data are taken from a source without showing the data itself. Finally, we give our implementation which shows transaction fees required and compare it to a sealed-bid auction protocol employing the simple deposit method.", "published": "2022-07-22T00:15:02Z", "authors": ["Kota Chin", "Keita Emura", "Kazumasa Omote", "Shingo Sato"], "categories": ["cs.CR", "cs.GT"], "doi": "", "_query": "hedonic house price", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/1607.07392v1", "title": "Option pricing with fractional stochastic volatility and discontinuous payoff function of polynomial growth", "abstract": "We consider the pricing problem related to payoffs that can have discontinuities of polynomial growth. The asset price dynamic is modeled within the Black and Scholes framework characterized by a stochastic volatility term driven by a fractional Ornstein-Uhlenbeck process. In order to solve the aforementioned problem, we consider three approaches. The first one consists in a suitable transformation of the initial value of the asset price, in order to eliminate possible discontinuities. Then we discretize both the Wiener process and the fractional Brownian motion and estimate the rate of convergence of the related discretized price to its real value, the latter one being impossible to be evaluated analytically. The second approach consists in considering the conditional expectation with respect to the entire trajectory of the fractional Brownian motion (fBm). Then we derive a closed formula which involves only integral functional depending on the fBm trajectory, to evaluate the price; finally we discretize the fBm and estimate the rate of convergence of the associated numerical scheme to the option price. In both cases the rate of convergence is the same and equals $n^{-rH}$, where $n$ is a number of the points of discretization, $H$ is the Hurst index of fBm, and $r$ is the Hölder exponent of volatility function. The third method consists in calculating the density of the integral functional depending on the trajectory of the fBm via Malliavin calculus also providing the option price in terms of the associated probability density.", "published": "2016-07-25T18:12:35Z", "authors": ["Viktor Bezborodov", "Luca Di Persio", "Yuliya Mishura"], "categories": ["math.PR"], "doi": "", "_query": "hedonic house price", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2108.12042v2", "title": "Price modelling under generalized fractional Brownian motion", "abstract": "The Generalized fractional Brownian motion (gfBm) is a stochastic process that acts as a generalization for both fractional, sub-fractional, and standard Brownian motion. Here we study its use as the main driver for price fluctuations, replacing the standard Brownian Brownian motion in the well-known Black-Scholes model. By the derivation of the generalized fractional Ito's lemma and the related effective Fokker-Planck equation, we discuss its application to both the option pricing problem valuing European options, and the computation of Value-at-Risk and Expected Shortfall. Moreover, the option prices are computed for a CEV-type model driven by gfBm.", "published": "2021-08-26T21:39:49Z", "authors": ["Axel A. Araneda"], "categories": ["q-fin.MF"], "doi": "", "_query": "hedonic house price", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/0910.0443v1", "title": "Stackelberg Pricing is Hard to Approximate within $2-ε$", "abstract": "Stackelberg Pricing Games is a two-level combinatorial pricing problem studied in the Economics, Operation Research, and Computer Science communities. In this paper, we consider the decade-old shortest path version of this problem which is the first and most studied problem in this family. The game is played on a graph (representing a network) consisting of {\\em fixed cost} edges and {\\em pricable} or {\\em variable cost} edges. The fixed cost edges already have some fixed price (representing the competitor's prices). Our task is to choose prices for the variable cost edges. After that, a client will buy the cheapest path from a node $s$ to a node $t$, using any combination of fixed cost and variable cost edges. The goal is to maximize the revenue on variable cost edges. In this paper, we show that the problem is hard to approximate within $2-ε$, improving the previous \\APX-hardness result by Joret [to appear in {\\em Networks}]. Our technique combines the existing ideas with a new insight into the price structure and its relation to the hardness of the instances.", "published": "2009-10-02T19:54:54Z", "authors": ["Parinya Chalermsook", "Bundit Laekhanukit", "Danupon Nanongkai"], "categories": ["cs.GT", "cs.CC", "cs.DS"], "doi": "", "_query": "hedonic house price", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/1503.05475v1", "title": "Almost-sure hedging with permanent price impact", "abstract": "We consider a financial model with permanent price impact. Continuous time trading dynamics are derived as the limit of discrete rebalancing policies. We then study the problem of super-hedging a European option. Our main result is the derivation of a quasi-linear pricing equation. It holds in the sense of viscosity solutions. When it admits a smooth solution, it provides a perfect hedging strategy.", "published": "2015-03-18T16:39:37Z", "authors": ["B. Bouchard", "G. Loeper", "Y. Zou"], "categories": ["q-fin.PR", "math.PR", "q-fin.TR"], "doi": "", "_query": "hedonic house price", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2509.04452v1", "title": "Directional Price Forecasting in the Continuous Intraday Market under Consideration of Neighboring Products and Limit Order Books", "abstract": "The increasing penetration of variable renewable energy and flexible demand technologies, such as electric vehicles and heat pumps, introduces significant uncertainty in power systems, resulting in greater imbalance; defined as the deviation between scheduled and actual supply or demand. Short-term power markets, such as the European continuous intraday market, play a critical role in mitigating these imbalances by enabling traders to adjust forecasts close to real time. Due to the high volatility of the continuous intraday market, traders increasingly rely on electricity price forecasting to guide trading decisions and mitigate price risk. However most electricity price forecasting approaches in the literature simplify the forecasting task. They focus on single benchmark prices, neglecting intra-product price dynamics and price signals from the limit order book. They also underuse high-frequency and cross-product price data. In turn, we propose a novel directional electricity price forecasting method for hourly products in the European continuous intraday market. Our method incorporates short-term features from both hourly and quarter-hourly products and is evaluated using German European Power Exchange data from 2024-2025. The results indicate that features derived from the limit order book are the most influential exogenous variables. In addition, features from neighboring products; especially those with delivery start times that overlap with the trading period of the target product; improve forecast accuracy. Finally, our evaluation of the value captured by our electricity price forecasting suggests that the proposed electricity price forecasting method has the potential to generate profit when applied in trading strategies.", "published": "2025-08-20T09:52:14Z", "authors": ["Timothée Hornek", "Sergio Potenciano Menci", "Ivan Pavić"], "categories": ["q-fin.ST"], "doi": "", "_query": "hedonic house price", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2005.08929v2", "title": "Disaster Resilience and Asset Prices", "abstract": "This paper investigates whether security markets price the effect of social distancing on firms' operations. We document that firms that are more resilient to social distancing significantly outperformed those with lower resilience during the COVID-19 outbreak, even after controlling for the standard risk factors. Similar cross-sectional return differentials already emerged before the COVID-19 crisis: the 2014-19 cumulative return differential between more and less resilient firms is of similar size as during the outbreak, suggesting growing awareness of pandemic risk well in advance of its materialization. Finally, we use stock option prices to infer the market's return expectations after the onset of the pandemic: even at a two-year horizon, stocks of more pandemic-resilient firms are expected to yield significantly lower returns than less resilient ones, reflecting their lower exposure to disaster risk. Hence, going forward, markets appear to price exposure to a new risk factor, namely, pandemic risk.", "published": "2020-05-18T17:56:02Z", "authors": ["Marco Pagano", "Christian Wagner", "Josef Zechner"], "categories": ["q-fin.GN", "econ.GN", "q-fin.RM"], "doi": "", "_query": "hedonic house price", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2503.08696v1", "title": "Multimodal Stock Price Prediction: A Case Study of the Russian Securities Market", "abstract": "Classical asset price forecasting methods primarily rely on numerical data, such as price time series, trading volumes, limit order book data, and technical analysis indicators. However, the news flow plays a significant role in price formation, making the development of multimodal approaches that combine textual and numerical data for improved prediction accuracy highly relevant. This paper addresses the problem of forecasting financial asset prices using the multimodal approach that combines candlestick time series and textual news flow data. A unique dataset was collected for the study, which includes time series for 176 Russian stocks traded on the Moscow Exchange and 79,555 financial news articles in Russian. For processing textual data, pre-trained models RuBERT and Vikhr-Qwen2.5-0.5b-Instruct (a large language model) were used, while time series and vectorized text data were processed using an LSTM recurrent neural network. The experiments compared models based on a single modality (time series only) and two modalities, as well as various methods for aggregating text vector representations. Prediction quality was estimated using two key metrics: Accuracy (direction of price movement prediction: up or down) and Mean Absolute Percentage Error (MAPE), which measures the deviation of the predicted price from the true price. The experiments showed that incorporating textual modality reduced the MAPE value by 55%. The resulting multimodal dataset holds value for the further adaptation of language models in the financial sector. Future research directions include optimizing textual modality parameters, such as the time window, sentiment, and chronological order of news messages.", "published": "2025-03-05T21:20:32Z", "authors": ["Kasymkhan Khubiev", "Mikhail Semenov"], "categories": ["q-fin.ST", "cs.LG", "q-fin.CP"], "doi": "10.25209/2079-3316-2025-16-1-83-130", "_query": "hedonic house price", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/1207.2848v1", "title": "Pricing of Fluctuations in Electricity Markets", "abstract": "In an electric power system, demand fluctuations may result in significant ancillary cost to suppliers. Furthermore, in the near future, deep penetration of volatile renewable electricity generation is expected to exacerbate the variability of demand on conventional thermal generating units. We address this issue by explicitly modeling the ancillary cost associated with demand variability. We argue that a time-varying price equal to the suppliers' instantaneous marginal cost may not achieve social optimality, and that consumer demand fluctuations should be properly priced. We propose a dynamic pricing mechanism that explicitly encourages consumers to adapt their consumption so as to offset the variability of demand on conventional units. Through a dynamic game-theoretic formulation, we show that (under suitable convexity assumptions) the proposed pricing mechanism achieves social optimality asymptotically, as the number of consumers increases to infinity. Numerical results demonstrate that compared with marginal cost pricing, the proposed mechanism creates a stronger incentive for consumers to shift their peak load, and therefore has the potential to reduce the need for long-term investment in peaking plants.", "published": "2012-07-12T05:29:15Z", "authors": ["John N. Tsitsiklis", "Yunjian Xu"], "categories": ["math.OC", "math.DS"], "doi": "", "_query": "hedonic house price", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2510.06287v2", "title": "Quantum-Theoretical Re-interpretation of Pricing Theory", "abstract": "Motivated by Heisenberg's observable-only stance, we replace latent \"information\" (filtrations, hidden diffusions, state variables) with observable transitions between price states. On a discrete price lattice with a Hilbert-space representation, shift operators and the spectral calculus of the price define observable frequency operators and a translation-invariant convolution generator. Combined with jump operators that encode transition intensities, this yields a completely positive, translation-covariant Lindblad semigroup. Under the risk-neutral condition the framework leads to a nonlocal pricing equation that is diagonal in Fourier space; in the small-mesh diffusive limit its generator converges to the classical Black-Scholes-Merton operator. We do not propose another parametric model. We propose a foundation for model construction that is observable, first-principles, and mathematically natural. Noncommutativity emerges from the observable shift algebra rather than being postulated. The jump-intensity ledger determines tail behavior and short-maturity smiles and implies testable links between extreme-event probabilities and implied-volatility wings. Future directions: (i) multi-asset systems on higher-dimensional lattices with vector shifts and block kernels; (ii) state- or flow-dependent kernels as \"financial interactions\" leading to nonlinear master equations while preserving linear risk-neutral pricing; (iii) empirical tests of the predicted scaling relations between jump intensities and market extremes.", "published": "2025-10-07T02:36:12Z", "authors": ["Tian Xin"], "categories": ["q-fin.PR", "quant-ph"], "doi": "", "_query": "hedonic house price", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/1711.05865v2", "title": "Pricing Football Players using Neural Networks", "abstract": "We designed a multilayer perceptron neural network to predict the price of a football (soccer) player using data on more than 15,000 players from the football simulation video game FIFA 2017. The network was optimized by experimenting with different activation functions, number of neurons and layers, learning rate and its decay, Nesterov momentum based stochastic gradient descent, L2 regularization, and early stopping. Simultaneous exploration of various aspects of neural network training is performed and their trade-offs are investigated. Our final model achieves a top-5 accuracy of 87.2% among 119 pricing categories, and places any footballer within 6.32% of his actual price on average.", "published": "2017-11-16T00:14:49Z", "authors": ["Sourya Dey"], "categories": ["cs.LG"], "doi": "", "_query": "hedonic house price", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2605.30672v1", "title": "Residual Supply and the Price of Risk Absorption", "abstract": "When redeeming open-end funds sell and natural buyers do not step in at once, some limited-capital investor must take the other side and carry the inventory until prices recover. This paper asks what return that investor requires. A continuous-time market-clearing model delivers an expected-return restriction in which the price of residual supply depends on inventory risk, trading costs, funding frictions, and the scarcity of balance sheet available to absorb it. Mapping U.S. mutual fund flows through predetermined holdings over 2003--2024, we measure one observable component of this residual supply. Forced-sale pressure predicts actual fund selling, contemporaneous price declines, and positive returns over the following one to six months. The premium roughly doubles when market-wide absorption capacity is tight, and it concentrates in stocks with thin investor bases and limited trading capacity -- precisely the cross section in which clearing the imbalance should be most costly, and a pattern that mechanical return reversal does not generate.", "published": "2026-05-29T00:05:09Z", "authors": ["Ziyao Wang"], "categories": ["q-fin.GN", "econ.GN"], "doi": "", "_query": "hedonic house price", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2403.20033v1", "title": "A novel decision fusion approach for sale price prediction using Elastic Net and MOPSO", "abstract": "Price prediction algorithms propose prices for every product or service according to market trends, projected demand, and other characteristics, including government rules, international transactions, and speculation and expectation. As the dependent variable in price prediction, it is affected by several independent and correlated variables which may challenge the price prediction. To overcome this challenge, machine learning algorithms allow more accurate price prediction without explicitly modeling the relatedness between variables. However, as inputs increase, it challenges the existing machine learning approaches regarding computing efficiency and prediction effectiveness. Hence, this study introduces a novel decision level fusion approach to select informative variables in price prediction. The suggested metaheuristic algorithm balances two competitive objective functions, which are defined to improve the prediction utilized variables and reduce the error rate simultaneously. To generate Pareto optimal solutions, an Elastic net approach is employed to eliminate unrelated and redundant variables to increase the accuracy. Afterward, we propose a novel method for combining solutions and ensuring that a subset of features is optimal. Two various real datasets evaluate the proposed price prediction method. The results support the suggested superiority of the model concerning its relative root mean square error and adjusted correlation coefficient.", "published": "2024-03-29T07:59:33Z", "authors": ["Amir Eshaghi Chaleshtori"], "categories": ["stat.ML", "cs.LG", "cs.NE"], "doi": "", "_query": "hedonic house price", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2605.01878v1", "title": "Random trade timing and power-law tails in realized prices", "abstract": "This paper studies stochastic mechanisms under which light-tailed latent price dynamics yield realized prices with power-law tails. The realized price is modeled as $P_T=e^{X_T}$, where $X$ is a Markov-modulated Lévy process and $T$ is the random time of the next trade. We consider two trade-timing environments. In the intertrade-incidence model, trades occur on a discrete grid with type-dependent probabilities. In the intertrade-time model, the waiting time to the next trade is generalized Erlang, allowing for heterogeneous arrival rates and transaction-completion delays. We show that random trade timing can generate Pareto-type tails, possibly with a logarithmic correction, in realized prices even when the latent price process is light-tailed. In both models, the tail exponent is determined by the least frequent trading type, while the proportions of faster-trading types affect only the scale constant. We also provide sufficient conditions under which these Pareto-type tails sharpen to exact Paretian tails. These results identify random trade timing and heterogeneity in trading behavior as a general mechanism for generating power-law tails in realized prices.", "published": "2026-05-03T13:46:16Z", "authors": ["Won-Ki Seo"], "categories": ["math.PR"], "doi": "", "_query": "hedonic house price", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2610.21711v1", "title": "How language model agents reshape price competition", "abstract": "Repeated Bertrand competition is a stringent benchmark for algorithmic pricing: in homogeneous-goods markets, standard learning procedures typically select the stage-game Nash outcome with prices near marginal cost. We show that LLM pricing agents can escape this benchmark, depending on deployment decisions. LLMs outperform standard online learners through faster adaptation, but this advantage largely disappears against full-information learners. An extracted surrogate algorithm illustrates how LLMs in specific deployments profit from regularly undercutting slower learners. Agents that post one price every period from raw market histories that include rival prices can sustain supracompetitive regimes in self-play. Agents that periodically emit probability distributions from summarized histories of their own profits instead move toward the competitive region in self-play. Markets mixing these interfaces can generate dynamic regimes in which LLMs establish a high-price anchor and regularly undercut it to capture demand. These regimes can keep both competitors' prices above marginal cost. Deployment choices shape these outcomes, shifting the focus from stationary equilibria to dynamic pricing regimes.", "published": "2026-10-15T11:32:32Z", "authors": ["Martin Bichler", "Philip Hierhager", "Kassian Köck", "Fabian Pieroth"], "categories": ["cs.GT", "cs.LG", "cs.MA", "econ.GN"], "doi": "", "_query": "hedonic house price", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/1911.11623v1", "title": "A Vietnamese information retrieval system for product-price", "abstract": "A price information retrieval (IR) system allows users to search and view differences among prices of specific products. Building product-price driven IR system is a challenging and active research area. Approaches entirely depending products information provided by shops via interface environment encounter limitations of database. While automatic systems specifically require product names and commercial websites for their input. For both paradigms, approaches of building product-price IR system for Vietnamese are still very limited. In this paper, we introduce an automatic Vietnamese IR system for product-price by identifying and storing Xpath patterns to extract prices of products from commercial websites. Experiments of our system show promising results.", "published": "2019-11-26T15:14:40Z", "authors": ["Tien-Thanh Vu", "Dat Quoc Nguyen"], "categories": ["cs.IR"], "doi": "10.1109/GRC.2011.6122681", "_query": "hedonic house price", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/1408.0258v1", "title": "The Pricing War Continues: On Competitive Multi-Item Pricing", "abstract": "We study a game with \\emph{strategic} vendors who own multiple items and a single buyer with a submodular valuation function. The goal of the vendors is to maximize their revenue via pricing of the items, given that the buyer will buy the set of items that maximizes his net payoff. We show this game may not always have a pure Nash equilibrium, in contrast to previous results for the special case where each vendor owns a single item. We do so by relating our game to an intermediate, discrete game in which the vendors only choose the available items, and their prices are set exogenously afterwards. We further make use of the intermediate game to provide tight bounds on the price of anarchy for the subset games that have pure Nash equilibria; we find that the optimal PoA reached in the previous special cases does not hold, but only a logarithmic one. Finally, we show that for a special case of submodular functions, efficient pure Nash equilibria always exist.", "published": "2014-08-01T18:30:00Z", "authors": ["Omer Lev", "Joel Oren", "Craig Boutilier", "Jeffery S. Rosenschein"], "categories": ["cs.GT", "cs.MA"], "doi": "", "_query": "hedonic house price", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2208.09642v2", "title": "Exploring Price Accuracy on Uniswap V3 in Times of Distress", "abstract": "Financial markets have evolved over centuries, and exchanges have converged to rely on the order book mechanism for market making. Latency on the blockchain, however, has prevented decentralized exchanges (DEXes) from utilizing the order book mechanism and instead gave rise to the development of market designs that are better suited to a blockchain. Although the first widely popularized DEX, Uniswap V2, stood out through its astonishing simplicity, a recent design overhaul introduced with Uniswap V3 has introduced increasing levels of complexity aiming to increase capital efficiency. In this work, we empirically study the ability of Unsiwap V3 to handle unexpected price shocks. Our analysis finds that the prices on Uniswap V3 were inaccurate during the recent abrupt price drops of two stablecoins: UST and USDT. We identify the lack of agility required of Unsiwap V3 liquidity providers as the root cause of these worrying price inaccuracies. Additionally, we outline that there are too few incentives for liquidity providers to enter liquidity pools, given the elevated volatility in such market conditions.", "published": "2022-08-20T09:12:30Z", "authors": ["Lioba Heimbach", "Eric Schertenleib", "Roger Wattenhofer"], "categories": ["q-fin.PR", "cs.CE"], "doi": "", "_query": "hedonic house price", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2402.16145v1", "title": "Egalitarian Price of Fairness for Indivisible Goods", "abstract": "In the context of fair division, the concept of price of fairness has been introduced to quantify the loss of welfare when we have to satisfy some fairness condition. In other words, it is the price we have to pay to guarantee fairness. Various settings of fair division have been considered previously; we extend to the setting of indivisible goods by using egalitarian welfare as the welfare measure, instead of the commonly used utilitarian welfare. We provide lower and upper bounds for various fairness and efficiency conditions such as envy-freeness up to one good (EF1) and maximum Nash welfare (MNW).", "published": "2024-02-25T16:54:51Z", "authors": ["Karen Frilya Celine", "Muhammad Ayaz Dzulfikar", "Ivan Adrian Koswara"], "categories": ["cs.GT", "cs.MA"], "doi": "10.1007/978-981-99-7019-3_3", "_query": "hedonic house price", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2402.05574v3", "title": "Quantum Amplitude Loading for Rainbow Options Pricing", "abstract": "This work introduces a novel approach to price rainbow options, a type of path-independent multi-asset derivatives, with quantum computers. Leveraging the Iterative Quantum Amplitude Estimation method, we present an end-to-end quantum circuit implementation, emphasizing efficiency by delaying the transition to price space. Moreover, we analyze two different amplitude loading techniques for handling exponential functions. Experiments on the IBM QASM simulator validate our quantum pricing model, contributing to the evolving field of quantum finance.", "published": "2024-02-08T11:19:32Z", "authors": ["Francesca Cibrario", "Or Samimi Golan", "Giacomo Ranieri", "Emanuele Dri", "Mattia Ippoliti", "Ron Cohen", "Christian Mattia", "Bartolomeo Montrucchio", "Amir Naveh", "Davide Corbelletto"], "categories": ["quant-ph", "q-fin.PR"], "doi": "", "_query": "hedonic house price", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/1612.08767v1", "title": "Pricing of Asian-type and Basket Options via Upper and Lower Bounds", "abstract": "This paper sets out to provide a general framework for the pricing of average-type options via lower and upper bounds. This class of options includes Asian, basket and options on the volume-weighted average price. We demonstrate that in cases under discussion lower bounds allow for the dimensionality of the problem to be reduced and that these methods provide reasonable approximations to the price of the option. Keywords: Asian options, Basket options, Lower and Upper bounds, Volume-weighted average prices (VWAP), Levy processes.", "published": "2016-12-27T22:36:47Z", "authors": ["Alexander Novikov", "Scott Alexander", "Nino Kordzakhia", "Timothy Ling"], "categories": ["q-fin.MF"], "doi": "", "_query": "hedonic house price", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/1305.0085v1", "title": "Pricing Public Goods for Private Sale", "abstract": "We consider the pricing problem faced by a seller who assigns a price to a good that confers its benefits not only to its buyers, but also to other individuals around them. For example, a snow-blower is potentially useful not only to the household that buys it, but also to others on the same street. Given that the seller is constrained to selling such a (locally) public good via individual private sales, how should he set his prices given the distribution of values held by the agents? We study this problem as a two-stage game. In the first stage, the seller chooses and announces a price for the product. In the second stage, the agents (each having a private value for the good) decide simultaneously whether or not they will buy the product. In the resulting game, which can exhibit a multiplicity of equilibria, agents must strategize about whether they will themselves purchase the good to receive its benefits. In the case of a fully public good (where all agents benefit whenever any agent purchases), we describe a pricing mechanism that is approximately revenue-optimal (up to a constant factor) when values are drawn from a regular distribution. We then study settings in which the good is only \"locally\" public: agents are arranged in a network and share benefits only with their neighbors. We describe a pricing method that approximately maximizes revenue, in the worst case over equilibria of agent behavior, for any $d$-regular network. Finally, we show that approximately optimal prices can be found for general networks in the special case that private values are drawn from a uniform distribution. We also discuss some barriers to extending these results to general networks and regular distributions.", "published": "2013-05-01T05:14:41Z", "authors": ["Michal Feldman", "David Kempe", "Brendan Lucier", "Renato Paes Leme"], "categories": ["cs.GT"], "doi": "", "_query": "hedonic house price", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/cond-mat/0405390v1", "title": "Zipf's Law Distributions for Korean Stock Prices", "abstract": "This paper investigates the rank distribution, cumulative probability, and probability density of price returns for the stocks traded in the KSE and the KOSDAQ market. This research demonstrates that the rank distribution is consistent approximately with the Zipf's law with exponent $α= -1.00$ (KSE) and -1.31 (KOSDAQ), similar that of stock prices traded on the TSE. In addition, the cumulative probability distribution follows a power law with scaling exponent $β= -1.23$ (KSE) and -1.45 (KOSDAQ). In particular, the evidence displays that the probability density of normalized price returns for two kinds of assets almost has the form of an exponential function, similar to the result in the TSE and the NYSE.", "published": "2004-05-18T08:38:22Z", "authors": ["Kyungsik Kim", "S. -M. Yoon", "C. Christopher Lee", "K. H. Chang"], "categories": ["cond-mat.stat-mech", "q-fin.ST"], "doi": "", "_query": "hedonic house price", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2310.01815v1", "title": "What Determines the Price of NFTs?", "abstract": "In the evolving landscape of digital art, Non-Fungible Tokens (NFTs) have emerged as a groundbreaking platform, bridging the realms of art and technology. NFTs serve as the foundational framework that has revolutionized the market for digital art, enabling artists to showcase and monetize their creations in unprecedented ways. NFTs combine metadata stored on the blockchain with off-chain data, such as images, to create a novel form of digital ownership. It is not fully understood how these factors come together to determine NFT prices. In this study, we analyze both on-chain and off-chain data of NFT collections trading on OpenSea to understand what influences NFT pricing. Our results show that while text and image data of the NFTs can be used to explain price variations within collections, the extracted features do not generalize to new, unseen collections. Furthermore, we find that an NFT collection's trading volume often relates to its online presence, like social media followers and website traffic.", "published": "2023-10-03T06:09:59Z", "authors": ["Vivian Ziemke", "Benjamin Estermann", "Roger Wattenhofer", "Ye Wang"], "categories": ["cs.CE", "cs.LG"], "doi": "", "_query": "hedonic house price", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/1203.0050v1", "title": "LP-based Covering Games with Low Price of Anarchy", "abstract": "We present a new class of vertex cover and set cover games. The price of anarchy bounds match the best known constant factor approximation guarantees for the centralized optimization problems for linear and also for submodular costs -- in contrast to all previously studied covering games, where the price of anarchy cannot be bounded by a constant (e.g. [6, 7, 11, 5, 2]). In particular, we describe a vertex cover game with a price of anarchy of 2. The rules of the games capture the structure of the linear programming relaxations of the underlying optimization problems, and our bounds are established by analyzing these relaxations. Furthermore, for linear costs we exhibit linear time best response dynamics that converge to these almost optimal Nash equilibria. These dynamics mimic the classical greedy approximation algorithm of Bar-Yehuda and Even [3].", "published": "2012-02-29T23:44:38Z", "authors": ["Georgios Piliouras", "Tomas Valla", "Laszlo A. Vegh"], "categories": ["cs.GT", "cs.DM"], "doi": "", "_query": "hedonic house price", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2608.10711v1", "title": "Optimal Pricing and Hedging of SOFR Derivatives", "abstract": "Thousands of SOFR derivatives are available in exchanges and OTC, but the market remains illiquid and incomplete. Such a market is beyond the scope of classic risk-neutral approaches that imply linear pricing rules and, at best, approximate hedging strategies whose hedging error may be difficult to quantify. This paper develops an indifference pricing model which is consistent with observed derivative quotes, the agent's financial position and views about the uncertain future as well as risk preferences as described by a convex risk measure. In addition to prices and hedging strategies, the model gives an explicit description of the hedging error and the associated risk. The approach is illustrated numerically using hundreds of CME-listed derivatives to price and hedge unreplicable OTC SOFR derivatives. The indifference prices are computed in less than a minute on a regular PC. We find that the optimal hedging portfolios tend to be sparse but still provide good approximations of the derivative payouts.", "published": "2026-08-11T09:31:19Z", "authors": ["Teemu Pennanen", "Waleed Taoum"], "categories": ["q-fin.PR"], "doi": "", "_query": "hedonic house price", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2508.20677v2", "title": "Pricing American options time-capped by a drawdown event in a Lévy market", "abstract": "This paper presents a derivation of the explicit price for the perpetual American put option time-capped by the first drawdown epoch beyond a predefined level. We consider the market in which an asset price is described by geometric Lévy process with downward exponential jumps. We show that the optimal stopping rule is the first time when the asset price gets below a special value. The proof relies on martingale arguments and the fluctuation theory of Lévy processes. We also provide a numerical analysis.", "published": "2025-08-28T11:37:28Z", "authors": ["Zbigniew Palmowski", "Paweł Stȩpniak"], "categories": ["math.PR", "q-fin.MF"], "doi": "", "_query": "hedonic house price", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/1709.09618v7", "title": "Monopoly Pricing in Vertical Markets with Demand Uncertainty", "abstract": "Pricing decisions are often made when market information is still poor. In turn, existing theoretical models often reason about the response of optimal prices to changing market characteristics without exploiting all available information about the demand distribution. Our aim is to develop a theory for the optimization and systematic comparison of prices between different instances of the same market under various forms of knowledge about the corresponding demand distributions. We revisit the classic problem of monopoly pricing under demand uncertainty in a vertical market with an upstream supplier and multiple forms of downstream competition between arbitrary symmetric retailers. In all cases, demand uncertainty falls to the supplier who acts first and sets a uniform price before the retailers observe the realized demand and place their orders. Our main methodological contribution is that we express the price elasticity of expected demand in terms of the mean residual demand (MRD) function of the demand distribution. This leads to a closed form characterization of the points of unitary elasticity that maximize the supplier's profits and the derivation of a mild unimodality condition for the supplier's objective function that generalizes the widely used increasing generalized failure rate (IGFR) condition. A direct implication is that optimal prices between different markets can be ordered if the markets can be stochastically ordered according to their MRD functions or equivalently, their elasticities. Using the above, we develop a systematic framework to compare optimal prices between different market instances via the rich theory of stochastic orders. This leads to comparative statics that challenge previously established economic insights about the effects of market size, demand transformations and demand variability on monopolistic prices.", "published": "2017-09-27T16:49:35Z", "authors": ["Stefanos Leonardos", "Costis Melolidakis", "Constandina Koki"], "categories": ["math.OC", "cs.GT", "math.PR"], "doi": "10.1007/s10479-021-04067-3", "_query": "hedonic house price", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2409.11339v4", "title": "A Derivative Pricing Perspective on Liquidity Tokens in Constant Product Market Makers", "abstract": "In decentralized finance, any individual can pool their assets into an automated market maker (AMM) -- herein we focus on the constant product market maker (CPMM) -- in exchange for a claim on a fraction of future pool assets and fees earned from the market making operations. This position is represented by a liquidity token, whose prevailing on-chain price is effectively the initial deposited assets. Though this price is well-defined, we treat the liquidity token as a derivative position in the prices of the underlying assets for the CPMM in order to deduce risk-neutral pricing and hedging formulas, not dissimilar to the Black-Scholes result. Adopting this perspective, in a frictionless environment, hedging the CPMM liquidity token under fair valuation should produce a riskless process, which therefore grows at the risk-free rate, something that is not seen in empirical case studies under the prevailing price. With our novel pricing formula, we construct a method to calibrate a volatility to data which provides an updated (non-market) valuation which is consistent with the (near-continuous) replication strategy out-of-sample. We conclude with a discussion of novel AMM design considerations motivated by this derivative-pricing perspective.", "published": "2024-09-17T16:41:58Z", "authors": ["Maxim Bichuch", "Zachary Feinstein"], "categories": ["q-fin.MF", "q-fin.PR", "q-fin.RM"], "doi": "", "_query": "hedonic house price", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2009.12920v2", "title": "Privacy-Preserving Dynamic Personalized Pricing with Demand Learning", "abstract": "The prevalence of e-commerce has made detailed customers' personal information readily accessible to retailers, and this information has been widely used in pricing decisions. When involving personalized information, how to protect the privacy of such information becomes a critical issue in practice. In this paper, we consider a dynamic pricing problem over $T$ time periods with an \\emph{unknown} demand function of posted price and personalized information. At each time $t$, the retailer observes an arriving customer's personal information and offers a price. The customer then makes the purchase decision, which will be utilized by the retailer to learn the underlying demand function. There is potentially a serious privacy concern during this process: a third party agent might infer the personalized information and purchase decisions from price changes from the pricing system. Using the fundamental framework of differential privacy from computer science, we develop a privacy-preserving dynamic pricing policy, which tries to maximize the retailer revenue while avoiding information leakage of individual customer's information and purchasing decisions. To this end, we first introduce a notion of \\emph{anticipating} $(\\varepsilon, δ)$-differential privacy that is tailored to dynamic pricing problem. Our policy achieves both the privacy guarantee and the performance guarantee in terms of regret. Roughly speaking, for $d$-dimensional personalized information, our algorithm achieves the expected regret at the order of $\\tilde{O}(\\varepsilon^{-1} \\sqrt{d^3 T})$, when the customers' information is adversarially chosen. For stochastic personalized information, the regret bound can be further improved to $\\tilde{O}(\\sqrt{d^2T} + \\varepsilon^{-2} d^2)$", "published": "2020-09-27T18:32:34Z", "authors": ["Xi Chen", "David Simchi-Levi", "Yining Wang"], "categories": ["cs.CR", "cs.GT", "cs.LG", "stat.ML"], "doi": "", "_query": "hedonic house price", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2112.13681v1", "title": "Wholesale Electricity Price Forecasting using Integrated Long-term Recurrent Convolutional Network Model", "abstract": "Electricity price is a key factor affecting the decision-making for all market participants. Accurate forecasting of electricity prices is very important and is also very challenging since electricity price is highly volatile due to various factors. This paper proposes an integrated long-term recurrent convolutional network (ILRCN) model to predict electricity prices considering the majority contributing attributes to the market price as input. The proposed ILRCN model combines the functionalities of convolutional neural network and long short-term memory (LSTM) algorithm along with the proposed novel conditional error correction term. The combined ILRCN model can identify the linear and non-linear behavior within the input data. We have used ERCOT wholesale market price data along with load profile, temperature, and other factors for the Houston region to illustrate the proposed model. The performance of the proposed ILRCN electricity price forecasting model is verified using performance/evaluation metrics like mean absolute error and accuracy. Case studies reveal that the proposed ILRCN model is accurate and efficient in electricity price forecasting as compared to the support vector machine (SVM) model, fully-connected neural network model, LSTM model and the LRCN model without the conditional error correction stage.", "published": "2021-12-23T06:45:12Z", "authors": ["Vasudharini Sridharan", "Mingjian Tuo", "Xingpeng Li"], "categories": ["cs.LG", "eess.SY"], "doi": "", "_query": "hedonic house price", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/1811.02535v1", "title": "The Role of Demand-Side Flexibility in Hedging Electricity Price Volatility in Distribution Grids", "abstract": "Locational Marginal Price (LMP) is a dual variable associated with supply-demand matching and represents the cost of delivering power to a particular location if the load at that location increases. In recent times it become more volatile due to increased integration of renewables that are intermittent. The issue of price volatility is further heightened during periods of grid congestion. Motivated by these problems, we propose a market design where, by constraining dual variables, we determine the amount of demand-side flexibility required to limit the rise of LMP. Through our proposed approach a price requesting load can specify its maximum willingness to pay for electricity and through demand-side flexibility hedge against price volatility. For achieving this, an organizational structure for flexibility management is proposed that exhibits the coordination required between the Distribution System Operator (DSO), an aggregator and the price requesting load. To demonstrate the viability of our proposed formulation, we run an illustrative simulation under infinite and finite line capacities.", "published": "2018-11-06T18:06:20Z", "authors": ["Shantanu Chakraborty", "Remco Verzijlbergh", "Milos Cvetkovic", "Kyri Baker", "Zofia Lukszo"], "categories": ["math.OC"], "doi": "", "_query": "hedonic house price", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2504.12771v1", "title": "Classification-Based Analysis of Price Pattern Differences Between Cryptocurrencies and Stocks", "abstract": "Cryptocurrencies are digital tokens built on blockchain technology, with thousands actively traded on centralized exchanges (CEXs). Unlike stocks, which are backed by real businesses, cryptocurrencies are recognized as a distinct class of assets by researchers. How do investors treat this new category of asset in trading? Are they similar to stocks as an investment tool for investors? We answer these questions by investigating cryptocurrencies' and stocks' price time series which can reflect investors' attitudes towards the targeted assets. Concretely, we use different machine learning models to classify cryptocurrencies' and stocks' price time series in the same period and get an extremely high accuracy rate, which reflects that cryptocurrency investors behave differently in trading from stock investors. We then extract features from these price time series to explain the price pattern difference, including mean, variance, maximum, minimum, kurtosis, skewness, and first to third-order autocorrelation, etc., and then use machine learning methods including logistic regression (LR), random forest (RF), support vector machine (SVM), etc. for classification. The classification results show that these extracted features can help to explain the price time series pattern difference between cryptocurrencies and stocks.", "published": "2025-04-17T09:12:27Z", "authors": ["Yu Zhang", "Zelin Wu", "Claudio Tessone"], "categories": ["q-fin.ST"], "doi": "", "_query": "hedonic house price", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2309.13668v1", "title": "Quantum Privacy-Preserving Price E-Negotiation", "abstract": "Privacy-preserving price e-negotiation (3PEN) is an important topic of secure multi-party computation (SMC) in the electronic commerce field, and the key point of its security is to guarantee the privacy of seller's and buyer's prices. In this study, a novel and efficient quantum solution to the 3PEN problem is proposed, where the oracle operation and the qubit comparator are utilized to obtain the comparative results of buyer's and seller's prices, and then quantum counting is executed to summarize the total number of products which meets the trading conditions. Analysis shows that our solution not only guarantees the correctness and the privacy of 3PEN, but also has lower communication complexity than those classical ones.", "published": "2023-09-24T15:31:55Z", "authors": ["Wen-Jie Liu", "Chun-Tang Li", "Yu Zheng", "Yong Xu", "Yin-Song Xu"], "categories": ["quant-ph", "cs.CR", "cs.ET"], "doi": "10.1007/s10773-019-04201-9", "_query": "hedonic house price", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2306.12969v1", "title": "Stock Price Prediction using Dynamic Neural Networks", "abstract": "This paper will analyze and implement a time series dynamic neural network to predict daily closing stock prices. Neural networks possess unsurpassed abilities in identifying underlying patterns in chaotic, non-linear, and seemingly random data, thus providing a mechanism to predict stock price movements much more precisely than many current techniques. Contemporary methods for stock analysis, including fundamental, technical, and regression techniques, are conversed and paralleled with the performance of neural networks. Also, the Efficient Market Hypothesis (EMH) is presented and contrasted with Chaos theory using neural networks. This paper will refute the EMH and support Chaos theory. Finally, recommendations for using neural networks in stock price prediction will be presented.", "published": "2023-06-18T20:06:44Z", "authors": ["David Noel"], "categories": ["q-fin.ST", "cs.CE", "cs.LG"], "doi": "", "_query": "hedonic house price", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/1811.06650v4", "title": "Asymptotics for Small Nonlinear Price Impact: a PDE Approach to the Multidimensional Case", "abstract": "We provide an asymptotic expansion of the value function of a multidimensional utility maximization problem from consumption with small non-linear price impact. In our model cross-impacts between assets are allowed. In the limit for small price impact, we determine the asymptotic expansion of the value function around its frictionless version. The leading order correction is characterized by a nonlinear second order PDE related to an ergodic control problem and a linear parabolic PDE. We illustrate our result on a multivariate geometric Brownian motion price model.", "published": "2018-11-16T01:55:12Z", "authors": ["Erhan Bayraktar", "Thomas Caye", "Ibrahim Ekren"], "categories": ["math.AP", "math.OC", "q-fin.TR"], "doi": "", "_query": "hedonic house price", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2002.08492v2", "title": "Equal Risk Pricing of Derivatives with Deep Hedging", "abstract": "This article presents a deep reinforcement learning approach to price and hedge financial derivatives. This approach extends the work of Guo and Zhu (2017) who recently introduced the equal risk pricing framework, where the price of a contingent claim is determined by equating the optimally hedged residual risk exposure associated respectively with the long and short positions in the derivative. Modifications to the latter scheme are considered to circumvent theoretical pitfalls associated with the original approach. Derivative prices obtained through this modified approach are shown to be arbitrage-free. The current paper also presents a general and tractable implementation for the equal risk pricing framework inspired by the deep hedging algorithm of Buehler et al. (2019). An $ε$-completeness measure allowing for the quantification of the residual hedging risk associated with a derivative is also proposed. The latter measure generalizes the one presented in Bertsimas et al. (2001) based on the quadratic penalty. Monte Carlo simulations are performed under a large variety of market dynamics to demonstrate the practicability of our approach, to perform benchmarking with respect to traditional methods and to conduct sensitivity analyses.", "published": "2020-02-19T22:59:59Z", "authors": ["Alexandre Carbonneau", "Frédéric Godin"], "categories": ["q-fin.CP", "q-fin.MF", "q-fin.PR", "q-fin.RM"], "doi": "", "_query": "hedonic house price", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2204.11735v1", "title": "Forecasting Electricity Prices", "abstract": "Forecasting electricity prices is a challenging task and an active area of research since the 1990s and the deregulation of the traditionally monopolistic and government-controlled power sectors. Although it aims at predicting both spot and forward prices, the vast majority of research is focused on short-term horizons which exhibit dynamics unlike in any other market. The reason is that power system stability calls for a constant balance between production and consumption, while being weather (both demand and supply) and business activity (demand only) dependent. The recent market innovations do not help in this respect. The rapid expansion of intermittent renewable energy sources is not offset by the costly increase of electricity storage capacities and modernization of the grid infrastructure. On the methodological side, this leads to three visible trends in electricity price forecasting research as of 2022. Firstly, there is a slow, but more noticeable with every year, tendency to consider not only point but also probabilistic (interval, density) or even path (also called ensemble) forecasts. Secondly, there is a clear shift from the relatively parsimonious econometric (or statistical) models towards more complex and harder to comprehend, but more versatile and eventually more accurate statistical/machine learning approaches. Thirdly, statistical error measures are nowadays regarded as only the first evaluation step. Since they may not necessarily reflect the economic value of reducing prediction errors, more and more often, they are complemented by case studies comparing profits from scheduling or trading strategies based on price forecasts obtained from different models.", "published": "2022-04-25T15:46:26Z", "authors": ["Katarzyna Maciejowska", "Bartosz Uniejewski", "Rafał Weron"], "categories": ["q-fin.ST", "eess.SP", "stat.AP", "stat.ML"], "doi": "10.1093/acrefore/9780190625979.013.667", "_query": "hedonic house price", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/1208.5258v2", "title": "A Theory of Pricing Private Data", "abstract": "Personal data has value to both its owner and to institutions who would like to analyze it. Privacy mechanisms protect the owner's data while releasing to analysts noisy versions of aggregate query results. But such strict protections of individual's data have not yet found wide use in practice. Instead, Internet companies, for example, commonly provide free services in return for valuable sensitive information from users, which they exploit and sometimes sell to third parties. As the awareness of the value of the personal data increases, so has the drive to compensate the end user for her private information. The idea of monetizing private data can improve over the narrower view of hiding private data, since it empowers individuals to control their data through financial means. In this paper we propose a theoretical framework for assigning prices to noisy query answers, as a function of their accuracy, and for dividing the price amongst data owners who deserve compensation for their loss of privacy. Our framework adopts and extends key principles from both differential privacy and query pricing in data markets. We identify essential properties of the price function and micro-payments, and characterize valid solutions.", "published": "2012-08-26T22:08:11Z", "authors": ["Chao Li", "Daniel Yang Li", "Gerome Miklau", "Dan Suciu"], "categories": ["cs.CR", "cs.DB"], "doi": "10.1145/2448496.2448502", "_query": "hedonic house price", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/physics/0606011v4", "title": "Martingale Option Pricing", "abstract": "We show that our generalization of the Black-Scholes partial differential equation (pde) for nontrivial diffusion coefficients is equivalent to a Martingale in the risk neutral discounted stock price. Previously, this was proven for the case of the Gaussian logarithmic returns model by Harrison and Kreps, but we prove it for much a much larger class of returns models where the diffusion coefficient depends on both returns x and time t. That option prices blow up if fat tails in logarithmic returns x are included in the market dynamics is also explained.", "published": "2006-06-01T18:53:49Z", "authors": ["J. L. McCauley", "G. H. Gunaratne", "K. E. Bassler"], "categories": ["physics.soc-ph", "q-fin.PR"], "doi": "10.1016/j.physa.2007.02.038", "_query": "hedonic house price", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/1901.00345v2", "title": "Equilibrium price and optimal insider trading strategy under stochastic liquidity with long memory", "abstract": "In this paper, the Kyle model of insider trading is extended by characterizing the trading volume with long memory and allowing the noise trading volatility to follow a general stochastic process. Under this newly revised model, the equilibrium conditions are determined, with which the optimal insider trading strategy, price impact and price volatility are obtained explicitly. The volatility of the price volatility appears excessive, which is a result of the fact that a more aggressive trading strategy is chosen by the insider when uninformed volume is higher. The optimal trading strategy turns out to possess the property of long memory, and the price impact is also affected by the fractional noise.", "published": "2019-01-02T12:47:45Z", "authors": ["Ben-zhang Yang", "Xinjiang He", "Nan-jing Huang"], "categories": ["q-fin.MF"], "doi": "", "_query": "hedonic house price", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2603.03152v4", "title": "Political Shocks and Price Discovery in Prediction Markets: Evidence from the 2024 U.S. Presidential Election", "abstract": "What do trading and prices each reveal when political news hits a prediction market? We answer using Polymarket's on-chain ledger around three shocks in the 2024 U.S. presidential election: the Biden-Trump debate, the assassination attempt on Trump, and Biden's withdrawal. Trading rises after every shock, mainly among incumbents with greater prior activity and with larger realized gains on pre-event portfolios. Price adjustment differs across shocks. The debate's price increase largely reverses, the assassination-attempt repricing persists, and Biden's withdrawal generates the heaviest trading with little change in the Trump price. The price response tracks what the news reveals about linked candidates and how much was already anticipated, not the amount of trading. Trading volume measures participation, while belief revision must be read from linked outcome prices and the surprise in the news.", "published": "2026-03-03T16:47:58Z", "authors": ["Kwok Ping Tsang", "Zichao Yang"], "categories": ["econ.GN"], "doi": "", "_query": "hedonic house price", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2403.19486v3", "title": "Distributionally robust monopoly pricing: Switching from low to high prices in volatile markets", "abstract": "Problem definition: Traditional monopoly pricing assumes sellers have full information about consumer valuations. We consider monopoly pricing under limited information, where a seller only knows the mean, variance and support of the valuation distribution. The objective is to maximize expected revenue by selecting the optimal fixed price. Methodology/results: We adopt a distributionally robust framework, where the seller considers all valuation distributions that comply with the limited information. We formulate a maximin problem which seeks to maximize expected revenue for the worst-case valuation distribution. The minimization problem that identifies the worst-case valuation distribution is solved using primal-dual methods, and in turn leads to an explicitly solvable maximization problem. This yields a closed-form optimal pricing policy and a new fundamental principle prescribing when to use low and high robust prices. Managerial implications: We show that the optimal policy switches from low to high prices when variance becomes sufficiently large, yielding significant performance gains compared with existing robust prices that generally decay with market uncertainty. This presents guidelines for when the seller should switch from targeting mass markets to niche markets. Similar guidelines are obtained for delay-prone services with rational utility-maximizing customers, underlining the universality and wide applicability of the novel pricing policy.", "published": "2024-03-28T15:16:11Z", "authors": ["Tim S. G. van Eck", "Pieter Kleer", "Johan S. H. van Leeuwaarden"], "categories": ["math.OC", "math.PR"], "doi": "10.1287/msom.2024.0952", "_query": "hedonic house price", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/cond-mat/9902046v1", "title": "Derivative pricing with virtual arbitrage", "abstract": "In this paper we derive an effective equation for derivative pricing which accounts for the presence of virtual arbitrage opportunities and their elimination by the market. We model the arbitrage return by a stochastic process and find an equation for the average derivative price. This is an integro-differential equation which, in the absence of the virtual arbitrage or for an infinitely fast market reaction, reduces to the Black-Scholes equation. Explicit formulas are obtained for European call and put vanilla options.", "published": "1999-02-03T12:28:40Z", "authors": ["Kirill Ilinski", "Alexander Stepanenko"], "categories": ["cond-mat.stat-mech", "q-fin.PR"], "doi": "", "_query": "hedonic house price", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2511.10912v1", "title": "Evaluating Large Language Models on Rare Disease Diagnosis: A Case Study using House M.D", "abstract": "Large language models (LLMs) have demonstrated capabilities across diverse domains, yet their performance on rare disease diagnosis from narrative medical cases remains underexplored. We introduce a novel dataset of 176 symptom-diagnosis pairs extracted from House M.D., a medical television series validated for teaching rare disease recognition in medical education. We evaluate four state-of-the-art LLMs such as GPT 4o mini, GPT 5 mini, Gemini 2.5 Flash, and Gemini 2.5 Pro on narrative-based diagnostic reasoning tasks. Results show significant variation in performance, ranging from 16.48% to 38.64% accuracy, with newer model generations demonstrating a 2.3 times improvement. While all models face substantial challenges with rare disease diagnosis, the observed improvement across architectures suggests promising directions for future development. Our educationally validated benchmark establishes baseline performance metrics for narrative medical reasoning and provides a publicly accessible evaluation framework for advancing AI-assisted diagnosis research.", "published": "2025-11-14T02:54:58Z", "authors": ["Arsh Gupta", "Ajay Narayanan Sridhar", "Bonam Mingole", "Amulya Yadav"], "categories": ["cs.CL", "cs.AI"], "doi": "", "_query": "hedonic house price", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/1512.03485v1", "title": "Price discrimination for energy trading in smart grid: A game theoretic approach", "abstract": "Pricing schemes are an important smart grid feature to affect typical energy usage behavior of energy users (EUs). However, most existing schemes use the assumption that a buyer pays the same price per unit of energy to all suppliers at any particular time when energy is bought. By contrast, here a discriminate pricing technique using game theory is studied. A cake cutting game is investigated, in which participating EUs in a smart community decide on the price per unit of energy to charge a shared facility controller (SFC) in order to sell surplus energy. The focus is to study fairness criteria to maximize sum benefits to EUs and ensure an envy-free energy trading market. A benefit function is designed that leverages generation of discriminate pricing by each EU, according to the amount of surplus energy that an EU trades with the SFC and the EU's sensitivity to price. It is shown that the game possesses a socially optimal, and hence also Pareto optimal, solution. Further, an algorithm that can be implemented by each EU in a distributed manner to reach the optimal solution is proposed. Numerical case studies are given that demonstrate beneficial properties of the scheme.", "published": "2015-12-10T23:40:14Z", "authors": ["Wayes Tushar", "Chau Yuen", "David Smith", "H. Vincent Poor"], "categories": ["eess.SY", "cs.GT"], "doi": "", "_query": "hedonic house price", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2010.01180v2", "title": "Reinforcement Learning of Sequential Price Mechanisms", "abstract": "We introduce the use of reinforcement learning for indirect mechanisms, working with the existing class of sequential price mechanisms, which generalizes both serial dictatorship and posted price mechanisms and essentially characterizes all strongly obviously strategyproof mechanisms. Learning an optimal mechanism within this class forms a partially-observable Markov decision process. We provide rigorous conditions for when this class of mechanisms is more powerful than simpler static mechanisms, for sufficiency or insufficiency of observation statistics for learning, and for the necessity of complex (deep) policies. We show that our approach can learn optimal or near-optimal mechanisms in several experimental settings.", "published": "2020-10-02T19:57:25Z", "authors": ["Gianluca Brero", "Alon Eden", "Matthias Gerstgrasser", "David C. Parkes", "Duncan Rheingans-Yoo"], "categories": ["cs.GT", "cs.AI", "cs.LG"], "doi": "", "_query": "hedonic house price", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2308.01436v2", "title": "Price-Aware Deep Learning for Electricity Markets", "abstract": "While deep learning gradually penetrates operational planning, its inherent prediction errors may significantly affect electricity prices. This letter examines how prediction errors propagate into electricity prices, revealing notable pricing errors and their spatial disparity in congested power systems. To improve fairness, we propose to embed electricity market-clearing optimization as a deep learning layer. Differentiating through this layer allows for balancing between prediction and pricing errors, as oppose to minimizing prediction errors alone. This layer implicitly optimizes fairness and controls the spatial distribution of price errors across the system. We showcase the price-aware deep learning in the nexus of wind power forecasting and short-term electricity market clearing.", "published": "2023-08-02T21:16:05Z", "authors": ["Vladimir Dvorkin", "Ferdinando Fioretto"], "categories": ["cs.LG", "eess.SY", "math.OC"], "doi": "", "_query": "hedonic house price", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/1909.01268v1", "title": "Are Bitcoins price predictable? Evidence from machine learning techniques using technical indicators", "abstract": "The uncertainties in future Bitcoin price make it difficult to accurately predict the price of Bitcoin. Accurately predicting the price for Bitcoin is therefore important for decision-making process of investors and market players in the cryptocurrency market. Using historical data from 01/01/2012 to 16/08/2019, machine learning techniques (Generalized linear model via penalized maximum likelihood, random forest, support vector regression with linear kernel, and stacking ensemble) were used to forecast the price of Bitcoin. The prediction models employed key and high dimensional technical indicators as the predictors. The performance of these techniques were evaluated using mean absolute percentage error (MAPE), root mean square error (RMSE), mean absolute error (MAE), and coefficient of determination (R-squared). The performance metrics revealed that the stacking ensemble model with two base learner (random forest and generalized linear model via penalized maximum likelihood) and support vector regression with linear kernel as meta-learner was the optimal model for forecasting Bitcoin price. The MAPE, RMSE, MAE, and R-squared values for the stacking ensemble model were 0.0191%, 15.5331 USD, 124.5508 USD, and 0.9967 respectively. These values show a high degree of reliability in predicting the price of Bitcoin using the stacking ensemble model. Accurately predicting the future price of Bitcoin will yield significant returns for investors and market players in the cryptocurrency market.", "published": "2019-09-03T16:03:13Z", "authors": ["Samuel Asante Gyamerah"], "categories": ["q-fin.ST", "econ.EM", "stat.AP", "stat.ML"], "doi": "", "_query": "hedonic house price", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2411.08631v1", "title": "Deep Generative Demand Learning for Newsvendor and Pricing", "abstract": "We consider data-driven inventory and pricing decisions in the feature-based newsvendor problem, where demand is influenced by both price and contextual features and is modeled without any structural assumptions. The unknown demand distribution results in a challenging conditional stochastic optimization problem, further complicated by decision-dependent uncertainty and the integration of features. Inspired by recent advances in deep generative learning, we propose a novel approach leveraging conditional deep generative models (cDGMs) to address these challenges. cDGMs learn the demand distribution and generate probabilistic demand forecasts conditioned on price and features. This generative approach enables accurate profit estimation and supports the design of algorithms for two key objectives: (1) optimizing inventory for arbitrary prices, and (2) jointly determining optimal pricing and inventory levels. We provide theoretical guarantees for our approach, including the consistency of profit estimation and convergence of our decisions to the optimal solution. Extensive simulations-ranging from simple to complex scenarios, including one involving textual features-and a real-world case study demonstrate the effectiveness of our approach. Our method opens a new paradigm in management science and operations research, is adaptable to extensions of the newsvendor and pricing problems, and holds potential for solving other conditional stochastic optimization problems.", "published": "2024-11-13T14:17:26Z", "authors": ["Shijin Gong", "Huihang Liu", "Xinyu Zhang"], "categories": ["stat.ML", "cs.LG", "math.OC"], "doi": "", "_query": "hedonic house price", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2412.05070v1", "title": "Enhancing Fourier pricing with machine learning", "abstract": "Fourier pricing methods such as the Carr-Madan formula or the COS method are classic tools for pricing European options for advanced models such as the Heston model. These methods require tuning parameters such as a damping factor, a truncation range, a number of terms, etc. Estimating these tuning parameters is difficult or computationally expensive. Recently, machine learning techniques have been proposed for fast pricing: they are able to learn the functional relationship between the parameters of the Heston model and the option price. However, machine learning techniques suffer from error control and require retraining for different error tolerances. In this research, we propose to learn the tuning parameters of the Fourier methods (instead of the prices) using machine learning techniques. As a result, we obtain very fast algorithms with full error control: Our approach works with any error tolerance without retraining, as demonstrated in numerical experiments using the Heston model.", "published": "2024-12-06T14:27:59Z", "authors": ["Gero Junike", "Hauke Stier"], "categories": ["q-fin.MF"], "doi": "", "_query": "hedonic house price", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2302.07631v1", "title": "Path Integral Method for Pricing Proportional Step Double-Barrier Option with Time Dependent Parameters", "abstract": "Path integral method in quantum mechanics provides a new thinking for barrier option pricing. For proportional double-barrier step (PDBS) options, the option price changing process is analogous to a particle moving in a finite symmetric square potential well. We have derived the pricing kernel of PDBS options with time dependent interest rate and volatility. Numerical results of option price as a function of underlying asset price are shown as well. Path integral method can be easily generalized to the pricing of PDBS options with curved boundaries.", "published": "2023-02-15T12:53:34Z", "authors": ["Qi Chen", "Chao Guo"], "categories": ["q-fin.PR", "quant-ph"], "doi": "", "_query": "hedonic house price", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2404.06489v4", "title": "Challenges in Finding Stable Price Zones in European Electricity Markets: Aiming to Square the Circle?", "abstract": "The European day-ahead electricity market is split into multiple bidding zones with a uniform price. The increase in renewables leads to a growing number of interventions in the generation of energy sources and increasing redispatch costs. To ensure efficient congestion management, the EU Commission mandated a Bidding Zone Review (BZR) to reevaluate the configuration of European bidding zones. An integral part of this process was a locational marginal pricing study. Based on these prices, alternative bidding zone configurations were proposed. These bidding zones shall be stable and robust over time. For Germany, four configurations were suggested. We analyzed the proposed configurations considering different clustering algorithms and periods based on the publicly released data set in the context of the BZR, and found that the configurations do not reduce the price standard deviations within zones much, and the average prices across zones are similar. Other configurations identified based on clustering the prices lead to lower price variance but they are not geographically coherent. Independent of the clustering features and algorithms used, the resulting clusters are not stable over time. Interestingly, the effect of a split on prices would be low based on an analysis of the BZR data set.", "published": "2024-04-09T17:43:28Z", "authors": ["Teodora Dobos", "Martin Bichler", "Johannes Knörr"], "categories": ["econ.GN"], "doi": "", "_query": "hedonic house price", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/1603.07225v4", "title": "Numerical stability of a hybrid method for pricing options", "abstract": "We develop and study stability properties of a hybrid approximation of functionals of the Bates jump model with stochastic interest rate that uses a tree method in the direction of the volatility and the interest rate and a finite-difference approach in order to handle the underlying asset price process. We also propose hybrid simulations for the model, following a binomial tree in the direction of both the volatility and the interest rate, and a space-continuous approximation for the underlying asset price process coming from a Euler-Maruyama type scheme. We show that our methods allow to obtain efficient and accurate European and American option prices. Numerical experiments are provided, and show the reliability and the efficiency of the algorithms.", "published": "2016-03-23T15:10:58Z", "authors": ["Maya Briani", "Lucia Caramellino", "Giulia Terenzi", "Antonino Zanette"], "categories": ["q-fin.CP"], "doi": "10.1142/S0219024919500365", "_query": "hedonic house price", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2006.02726v8", "title": "Construction and sample path properties of Brownian house-moving between two curves", "abstract": "This study aims to construct a stochastic process called \"Brownian house-moving,\" which is a Brownian bridge conditioned to stay between two curves. To construct this process, statements are prepared on the weak convergence of conditioned Brownian motions, conditioned Brownian bridges, and conditioned three-dimensional Bessel bridges. Moreover, the sample path properties of Brownian house-moving are studied as well.", "published": "2020-06-04T09:31:01Z", "authors": ["Kensuke Ishitani", "Daisuke Hatakenaka", "Keisuke Suzuki"], "categories": ["math.PR"], "doi": "", "_query": "hedonic house price", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2102.10756v3", "title": "Equilibrium Price Formation with a Major Player and its Mean Field Limit", "abstract": "In this article, we consider the problem of equilibrium price formation in an incomplete securities market consisting of one major financial firm and a large number of minor firms. They carry out continuous trading via the securities exchange to minimize their cost while facing idiosyncratic and common noises as well as stochastic order flows from their individual clients. The equilibrium price process that balances demand and supply of the securities, including the functional form of the price impact for the major firm, is derived endogenously both in the market of finite population size and in the corresponding mean field limit.", "published": "2021-02-22T03:43:34Z", "authors": ["Masaaki Fujii", "Akihiko Takahashi"], "categories": ["q-fin.MF", "econ.GN", "q-fin.TR"], "doi": "", "_query": "hedonic house price", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2112.03513v1", "title": "Change of persistence in European electricity spot prices", "abstract": "The European Power Exchange has introduced day-ahead auctions and continuous trading spot markets to facilitate the insertion of renewable electricity. These markets are designed to balance excess or lack of power in short time periods, which leads to a large stochastic variability of the electricity prices. Furthermore, the different markets show different stochastic memory in their electricity price time series, which seem to be the cause for the large volatility. In particular, we show the antithetical temporal correlation in the intraday 15 minutes spot markets in comparison to the day-ahead hourly market. We contrast the results from Detrended Fluctuation Analysis (DFA) to a new method based on the Kramers--Moyal equation in scale. For very short term ($<12$ hours), all price time series show positive temporal correlations (Hurst exponent $H>0.5$) except for the intraday 15 minute market, which shows strong negative correlations ($H<0.5$). For longer term periods covering up to two days, all price time series are anti-correlated ($H<0.5$).", "published": "2021-12-07T05:48:45Z", "authors": ["Leonardo Rydin Gorjão", "Dirk Witthaut", "Pedro G. Lind", "Wided Medjroubi"], "categories": ["q-fin.ST", "physics.soc-ph"], "doi": "", "_query": "hedonic house price", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2405.00701v8", "title": "Learning parameter dependence for Fourier-based option pricing with tensor trains", "abstract": "A long-standing issue in mathematical finance is the speed-up of option pricing, especially for multi-asset options. A recent study has proposed to use tensor train learning algorithms to speed up Fourier transform (FT)-based option pricing, utilizing the ability of tensor trains to compress high-dimensional tensors. Another usage of the tensor train is to compress functions, including their parameter dependence. Here, we propose a pricing method, where, by a tensor train learning algorithm, we build tensor trains that approximate functions appearing in FT-based option pricing with their parameter dependence and efficiently calculate the option price for the varying input parameters. As a benchmark test, we run the proposed method to price a multi-asset option for the various values of volatilities and present asset prices. We show that, in the tested cases involving up to 11 assets, the proposed method outperforms Monte Carlo-based option pricing with $10^6$ paths in terms of computational complexity while keeping better accuracy.", "published": "2024-04-17T01:57:19Z", "authors": ["Rihito Sakurai", "Haruto Takahashi", "Koichi Miyamoto"], "categories": ["q-fin.CP", "quant-ph"], "doi": "10.3390/math13111828", "_query": "hedonic house price", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2602.12782v1", "title": "Empirical Validation of a Dual-Defense Mechanism Reshaping Wholesale Electricity Price Dynamics in Singapore", "abstract": "While ex-ante screening and static price caps are global standards for mitigating price volatility, Singapore's electricity market employs a unique dual-defense mechanism integrating vesting contracts (VC) with a temporary price cap (TPC). Using high-frequency data from 2021 to 2024, this paper evaluates this mechanism and yields three primary findings. First, a structural trade-off exists within the VC framework: while VC quantity (VCQ) suppresses average prices, it paradoxically exacerbates instability via liquidity squeezes. Conversely, VC price (VCP) functions as a tail-risk anchor, dominating at extreme quantiles where VCQ efficacy wanes. Second, a structural break around the 2023 reform reveals a fundamental re-mapping of price dynamics; the previously positive pass-through from offer ratios to clearing prices was largely neutralized post-reform. Furthermore, diagnostics near the TPC threshold show no systematic evidence of strategic bid shading, confirming the TPC's operational integrity. Third, the dual-defense mechanism exhibits a critical synergy that resolves the volatility trade-off. The TPC reverses the volatility penalty of high VCQ, shifting the elasticity of conditional volatility from a destabilizing 0.636 to a stabilizing -0.213. This synergy enables the framework to enhance tail-risk control while eliminating liquidity-related stability costs. We conclude that this dual-defense mechanism successfully decouples price suppression from liquidity risks, thereby maximizing market stability.", "published": "2026-02-13T10:07:49Z", "authors": ["Huang Zhenyu", "Yuan Zhao"], "categories": ["eess.SY", "econ.EM"], "doi": "", "_query": "hedonic house price", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2307.15785v1", "title": "Safe Pricing Mechanisms for Distributed Resource Allocation with Bandit Feedback", "abstract": "In societal-scale infrastructures, such as electric grids or transportation networks, pricing mechanisms are often used as a way to shape users' demand in order to lower operating costs and improve reliability. Existing approaches to pricing design for safety-critical networks often require that users are queried beforehand to negotiate prices, which has proven to be challenging to implement in the real-world. To offer a more practical alternative, we develop learning-based pricing mechanisms that require no input from the users. These pricing mechanisms aim to maximize the utility of the users' consumption by gradually estimating the users' price response over a span of $T$ time steps (e.g., days) while ensuring that the infrastructure network's safety constraints that limit the users' demand are satisfied at all time steps. We propose two different algorithms for the two different scenarios when, 1) the utility function is chosen by the central coordinator to achieve a social objective and 2) the utility function is defined by the price response under the assumption that the users are self-interested agents. We prove that both algorithms enjoy $\\tilde{O} (T^{2/3})$ regret with high probability. We then apply these algorithms to demand response pricing for the smart grid and numerically demonstrate their effectiveness.", "published": "2023-07-28T19:55:27Z", "authors": ["Spencer Hutchinson", "Berkay Turan", "Mahnoosh Alizadeh"], "categories": ["eess.SY"], "doi": "", "_query": "hedonic house price", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/1602.02860v1", "title": "Impact of integrity attacks on real-time pricing in smart grids", "abstract": "Modern information and communication technologies used by smart grids are subject to cybersecurity threats. This paper studies the impact of integrity attacks on real-time pricing (RTP), a key feature of smart grids that uses such technologies to improve system efficiency. Recent studies have shown that RTP creates a closed loop formed by the mutually dependent real-time price signals and price-taking demand. Such a closed loop can be exploited by an adversary whose objective is to destabilize the pricing system. Specifically, small malicious modifications to the price signals can be iteratively amplified by the closed loop, causing inefficiency and even severe failures such as blackouts. This paper adopts a control-theoretic approach to deriving the fundamental conditions of RTP stability under two broad classes of integrity attacks, namely, the scaling and delay attacks. We show that the RTP system is at risk of being destabilized only if the adversary can compromise the price signals advertised to smart meters by reducing their values in the scaling attack, or by providing old prices to over half of all consumers in the delay attack. The results provide useful guidelines for system operators to analyze the impact of various attack parameters on system stability, so that they may take adequate measures to secure RTP systems.", "published": "2016-02-09T04:57:11Z", "authors": ["Rui Tan", "Varun Badrinath Krishna", "David K. Y. Yau", "Zbigniew Kalbarczyk"], "categories": ["cs.CR"], "doi": "10.1145/2508859.2516705", "_query": "hedonic house price", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/1906.06483v1", "title": "Option Pricing via Multi-path Autoregressive Monte Carlo Approach", "abstract": "The pricing of financial derivatives, which requires massive calculations and close-to-real-time operations under many trading and arbitrage scenarios, were largely infeasible in the past. However, with the advancement of modern computing, the efficiency has substantially improved. In this work, we propose and design a multi-path option pricing approach via autoregression (AR) process and Monte Carlo Simulations (MCS). Our approach learns and incorporates the price characteristics into AR process, and re-generates the price paths for options. We apply our approach to price weekly options underlying Taiwan Stock Exchange Capitalization Weighted Stock Index (TAIEX) and compare the results with prior practiced models, e.g., Black-Scholes-Merton and Binomial Tree. The results show that our approach is comparable with prior practiced models.", "published": "2019-06-15T07:06:54Z", "authors": ["Wei-Cheng Chen", "Wei-Ho Chung"], "categories": ["q-fin.PR"], "doi": "", "_query": "hedonic house price", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2005.07080v3", "title": "Optimal long-term investment in illiquid markets when prices have negative memory", "abstract": "In a discrete-time financial market model with instantaneous price impact, we find an asymptotically optimal strategy for an investor maximizing her expected wealth. The asset price is assumed to follow a process with negative memory. We determine how the optimal growth rate depends on the impact parameter and on the covariance decay rate of the price.", "published": "2020-05-14T15:50:11Z", "authors": ["Miklós Rásonyi", "Lóránt Nagy"], "categories": ["math.PR"], "doi": "", "_query": "hedonic house price", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/math/0509232v2", "title": "Properties of option prices in models with jumps", "abstract": "We study convexity and monotonicity properties of option prices in a model with jumps using the fact that these prices satisfy certain parabolic integro-differential equations. Conditions are provided under which preservation of convexity holds, i.e. under which the value, calculated under a chosen martingale measure, of an option with a convex contract function is convex as a function of the underlying stock price. The preservation of convexity is then used to derive monotonicity properties of the option value with respect to the different parameters of the model, such as the volatility, the jump size and the jump intensity.", "published": "2005-09-10T10:30:14Z", "authors": ["Erik Ekström", "Johan Tysk"], "categories": ["math.AP", "q-fin.ST"], "doi": "", "_query": "hedonic house price", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2507.13026v2", "title": "Disjoint Tours and the Price of Diversity", "abstract": "We study a variant of the Traveling Salesman Problem, where instead of finding a single tour, we want to find a pair of two edge-disjoint tours whose longer tour is as short as possible. We investigate the Price of Diversity (PoD) for this problem, which is the ratio of the cost of the longer of the two tours and the cost of a single optimal tour, in the worst case over all possible instances. We prove (almost) tight bounds on this quantity for a special 1-dimensional scenario and for general metric spaces. We believe that the Price-of-Diversity framework that we introduce is interesting in its own right, and may lead to follow-up work on other problems as well.", "published": "2025-07-17T11:55:42Z", "authors": ["Mark de Berg", "Andrés López Martínez", "Frits Spieksma"], "categories": ["cs.DS"], "doi": "", "_query": "hedonic house price", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/1802.04778v1", "title": "The Quotient of Normal Random Variables And Application to Asset Price Fat Tails", "abstract": "The quotient of random variables with normal distributions is examined and proven to have have power law decay, with density $f\\left( x\\right) \\simeq f_{0}x^{-2}$, with the coefficient depending on the means and variances of the numerator and denominator and their correlation. We also obtain the conditional probability densities for each of the four quadrants given by the signs of the numerator and denominator for arbitrary correlation $ρ\\in\\lbrack-1,1).$ For $ρ=-1$ we obtain a particularly simple closed form solution for all $x\\in$ $\\mathbb{R}$. The results are applied to a basic issue in economics and finance, namely the density of relative price changes. Classical finance stipulates a normal distribution of relative price changes, though empirical studies suggest a power law at the tail end. By considering the supply and demand in a basic price change model, we prove that the relative price change has density that decays with an $x^{-2}$ power law. Various parameter limits are established.", "published": "2018-02-13T18:14:27Z", "authors": ["Carey Caginalp", "Gunduz Caginalp"], "categories": ["q-fin.MF", "math.PR", "math.ST"], "doi": "10.1016/j.physa.2018.02.077", "_query": "hedonic house price", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2106.02149v2", "title": "Optimal Pricing Schemes for an Impatient Buyer", "abstract": "A patient seller aims to sell a good to an impatient buyer (i.e., one who discounts utility over time). The buyer will remain in the market for a period of time $T$, and her private value is drawn from a publicly known distribution. What is the revenue-optimal pricing-curve (sequence of (price, time) pairs) for the seller? Is randomization of help here? Is the revenue-optimal pricing curve computable in polynomial time? We answer these questions in this paper. We give an efficient algorithm for computing the revenue-optimal pricing curve. We show that pricing curves, that post a price at each point of time and let the buyer pick her utility maximizing time to buy, are revenue-optimal among a much broader class of sequential lottery mechanisms. I.e., mechanisms that allow the seller to post a menu of lotteries at each point of time cannot get any higher revenue than pricing curves. We also show that the even broader class of mechanisms that allow the menu of lotteries to be adaptively set, can earn strictly higher revenue than that of pricing curves, and the revenue gap can be as big as the support size of the buyer's value distribution.", "published": "2021-06-03T21:53:37Z", "authors": ["Yuan Deng", "Jieming Mao", "Balasubramanian Sivan", "Kangning Wang"], "categories": ["cs.GT", "cs.DS", "econ.TH"], "doi": "10.1137/1.9781611977554.ch16", "_query": "hedonic house price", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/1309.1420v4", "title": "Fundamental Theorem of Asset Pricing under Transaction costs and Model uncertainty", "abstract": "We prove the Fundamental Theorem of Asset Pricing for a discrete time financial market where trading is subject to proportional transaction cost and the asset price dynamic is modeled by a family of probability measures, possibly non-dominated. Using a backward-forward scheme, we show that when the market consists of a money market account and a single stock, no-arbitrage in a quasi-sure sense is equivalent to the existence of a suitable family of consistent price systems. We also show that when the market consists of multiple dynamically traded assets and satisfies \\emph{efficient friction}, strict no-arbitrage in a quasi-sure sense is equivalent to the existence of a suitable family of strictly consistent price systems.", "published": "2013-09-05T17:57:53Z", "authors": ["Erhan Bayraktar", "Yuchong Zhang"], "categories": ["math.PR", "q-fin.PR"], "doi": "", "_query": "hedonic house price", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2603.15176v1", "title": "The Price of Universal Temporal Reachability", "abstract": "Dynamic networks are graphs in which edges are available only at specific time instants, modeling connections that change over time. The dynamic network creation game studies this setting as a strategic interaction where each vertex represents a player. Players can add or remove time-labeled edges in order to minimize their personal cost. This cost has two components: a construction cost, calculated as the number of time instants during which a player maintains edges multiplied by a constant $α$, and a communication cost, defined as the average distance to all other vertices in the network. Communication occurs through temporal paths, which are sequences of adjacent edges with strictly increasing time labels and no repeated vertices. We show for the shortest distance (minimizing the number of edges) that the price of anarchy can be proportional to the number of vertices, contrasting the constant price conjectured for static networks.", "published": "2026-03-16T12:10:55Z", "authors": ["Binh-Minh Bui-Xuan", "Nhat-Minh Nguyen", "Sébastien Tixeuil", "Yukiko Yamauchi"], "categories": ["cs.DS"], "doi": "", "_query": "hedonic house price", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2605.23953v1", "title": "Game-Theoretic Modeling of Heterogeneous Investor Interactions for Stock Price Forecasting", "abstract": "Accurate stock price forecasting has consistently remained a pivotal yet challenging FinTech task that underpins quantitative trading and investment decision making. Recent efforts have been dedicated to modeling various complex relationships among stocks in the stock market toward more reliable stock price forecasting.These methods depend heavily on strong static prior assumptions by modeling either temporal dependencies within individual stocks or spatial dependencies across different stocks based on predefined structures, while the complex market dynamics that drive stock price movements remain unexplored. To alleviate this issue, we propose a novel game-theoretic modeling method that captures heterogeneous investor interactions for stock price forecasting. The core idea is to embed game-theoretic mechanisms into the heterogeneous graph structure to finely model the dynamic strategic interactions among heterogeneous investors with respect to target stocks. Additionally, temporal positional encoding is adopted to reflect the differentiated influences of each game event at different time steps within the time window on future stock price movements. Leveraging heterogeneous graph networks, we proxy the intricate dynamics of the stock market through investor games and enable real-time information propagation and node updates among all nodes. Extensive experiments conducted on two real-world benchmark dataset demonstrate that our method effectively outperforms state-of-the-art stock price forecasting methods.", "published": "2026-05-11T03:24:07Z", "authors": ["Yong Zhang", "Xinxiao Wu", "Yunde Jia", "Che Sun"], "categories": ["q-fin.TR", "cs.GT", "cs.LG"], "doi": "", "_query": "hedonic house price", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/1303.1296v1", "title": "The Pricing of A Moving Barrier Option", "abstract": "We provided an analytical representation of the price of a barrier option with one type of special moving barrier. We consider the case that risk free rate, dividend rate and stock volatility are time dependent. We get a pricing formula and put call parity for barrier option when the moving barrier has a special relation with risk free rate, dividend rate and stock volatility.", "published": "2013-03-06T10:47:11Z", "authors": ["Hyong-chol O"], "categories": ["q-fin.PR", "q-fin.CP", "q-fin.RM"], "doi": "10.2139/ssrn.742924", "_query": "hedonic house price", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/1309.1420v4", "title": "Fundamental Theorem of Asset Pricing under Transaction costs and Model uncertainty", "abstract": "We prove the Fundamental Theorem of Asset Pricing for a discrete time financial market where trading is subject to proportional transaction cost and the asset price dynamic is modeled by a family of probability measures, possibly non-dominated. Using a backward-forward scheme, we show that when the market consists of a money market account and a single stock, no-arbitrage in a quasi-sure sense is equivalent to the existence of a suitable family of consistent price systems. We also show that when the market consists of multiple dynamically traded assets and satisfies \\emph{efficient friction}, strict no-arbitrage in a quasi-sure sense is equivalent to the existence of a suitable family of strictly consistent price systems.", "published": "2013-09-05T17:57:53Z", "authors": ["Erhan Bayraktar", "Yuchong Zhang"], "categories": ["math.PR", "q-fin.PR"], "doi": "", "_query": "hedonic house price", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2211.13714v1", "title": "The Wade Formula and the oil price fluctuation: An Optimal Control Theory approach", "abstract": "By considering the Wade Formula, we propose a model to study the evolution of the oil price per barrel. Our model shows that the policy of diversification of the energy is to be supported. This model is proposed to see how it is possible to control parameters so that the oil price should decrease.", "published": "2022-11-24T17:04:44Z", "authors": ["Pierre Mendy", "Babacar M. Ndiaye", "Diaraf Seck", "Idrissa Ly"], "categories": ["math.OC"], "doi": "", "_query": "hedonic house price", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/1002.2084v1", "title": "A Sublogarithmic Approximation for Highway and Tollbooth Pricing", "abstract": "An instance of the tollbooth problem consists of an undirected network and a collection of single-minded customers, each of which is interested in purchasing a fixed path subject to an individual budget constraint. The objective is to assign a per-unit price to each edge in a way that maximizes the collective revenue obtained from all customers. The revenue generated by any customer is equal to the overall price of the edges in her desired path, when this cost falls within her budget; otherwise, that customer will not purchase any edge. Our main result is a deterministic algorithm for the tollbooth problem on trees whose approximation ratio is O(log m / log log m), where m denotes the number of edges in the underlying graph. This finding improves on the currently best performance guarantees for trees, due to Elbassioni et al. (SAGT '09), as well as for paths (commonly known as the highway problem), due to Balcan and Blum (EC '06). An additional interesting consequence is a computational separation between tollbooth pricing on trees and the original prototype problem of single-minded unlimited supply pricing, under a plausible hardness hypothesis due to Demaine et al. (SODA '06).", "published": "2010-02-10T12:21:26Z", "authors": ["Iftah Gamzu", "Danny Segev"], "categories": ["cs.DS", "cs.GT"], "doi": "", "_query": "hedonic house price", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2211.07797v2", "title": "Energy Storage Price Arbitrage via Opportunity Value Function Prediction", "abstract": "This paper proposes a novel energy storage price arbitrage algorithm combining supervised learning with dynamic programming. The proposed approach uses a neural network to directly predicts the opportunity cost at different energy storage state-of-charge levels, and then input the predicted opportunity cost into a model-based arbitrage control algorithm for optimal decisions. We generate the historical optimal opportunity value function using price data and a dynamic programming algorithm, then use it as the ground truth and historical price as predictors to train the opportunity value function prediction model. Our method achieves 65% to 90% profit compared to perfect foresight in case studies using different energy storage models and price data from New York State, which significantly outperforms existing model-based and learning-based methods. While guaranteeing high profitability, the algorithm is also light-weighted and can be trained and implemented with minimal computational cost. Our results also show that the learned prediction model has excellent transferability. The prediction model trained using price data from one region also provides good arbitrage results when tested over other regions.", "published": "2022-11-14T23:31:11Z", "authors": ["Ningkun Zheng", "Xiaoxiang Liu", "Bolun Xu", "Yuanyuan Shi"], "categories": ["eess.SY", "cs.LG"], "doi": "", "_query": "hedonic house price", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2512.17791v1", "title": "Near-Maturity Asymptotics of Critical Prices of American Put Options under Exponential Lévy Models", "abstract": "In the present paper, we study the near-maturity ($t\\rightarrow T^{-}$) convergence rate of the optimal early-exercise price $b(t)$ of an American put under an exponential Lévy model with a {\\it nonzero} Brownian component. Two important settings, not previous covered in the literature, are considered. In the case that the optimal exercise price converges to the strike price ($b(T^{-})=K$), we contemplate models with negative jumps of unbounded variation (i.e., processes that exhibit high activity of negative jumps or sudden falls in asset prices). In the second case, when the optimal exercise price tend to a value lower than $K$, we consider infinite activity jumps (though still of bounded variations), extending existing results for models with finite jump activity (finitely many jumps in any finite interval). In both cases, we show that $b(T^{-})-b(t)$ is of order $\\sqrt{T-t}$ with explicit constants proportionality. Furthermore, we also derive the second-order near-maturity expansion of the American put price around the critical price along a certain parabolic branch.", "published": "2025-12-19T16:59:26Z", "authors": ["José E. Figueroa-López", "Ruoting Gong"], "categories": ["q-fin.MF", "math.PR"], "doi": "", "_query": "hedonic house price", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2002.01578v1", "title": "Rental Housing Spot Markets: How Online Information Exchanges Can Supplement Transacted-Rents Data", "abstract": "Traditional US rental housing data sources such as the American Community Survey and the American Housing Survey report on the transacted market - what existing renters pay each month. They do not explicitly tell us about the spot market - i.e., the asking rents that current homeseekers must pay to acquire housing - though they are routinely used as a proxy. This study compares governmental data to millions of contemporaneous rental listings and finds that asking rents diverge substantially from these most recent estimates. Conventional housing data understate current market conditions and affordability challenges, especially in cities with tight and expensive rental markets.", "published": "2020-02-04T23:23:35Z", "authors": ["Geoff Boeing", "Jake Wegmann", "Junfeng Jiao"], "categories": ["econ.GN", "cs.CY", "stat.AP"], "doi": "10.1177/0739456X20904435", "_query": "hedonic house price", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/1409.4890v1", "title": "Can Market Risk Perception Drive Inefficient Prices? Theory and Evidence", "abstract": "This work presents an asset pricing model that under rational expectation equilibrium perspective shows how, depending on risk aversion and noise volatility, a risky-asset has one equilibrium price that differs in term of efficiency: an informational efficient one (similar to Campbell and Kyle (1993)), and another one where price diverges from its informational efficient level. The former Pareto dominates (is dominated by) the latter in presence of low (high) market risk perception. The estimates of the model using S&P 500 Index support the theoretical findings, and the estimated inefficient equilibrium price captures the higher risk premium and higher volatility observed during the Dot.com bubble 1995--2000.", "published": "2014-09-17T07:51:59Z", "authors": ["Matteo Formenti"], "categories": ["q-fin.GN", "q-fin.TR"], "doi": "", "_query": "hedonic house price", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2510.07759v1", "title": "Accelerated Price Adjustment for Fisher Markets with Exact Recovery of Competitive Equilibrium", "abstract": "The canonical price-adjustment process, tâtonnement, typically fails to converge to the exact competitive equilibrium (CE) and requires a high iteration complexity of $\\tilde{\\mathcal{O}}(1/ε)$ to compute $ε$-CE prices in widely studied linear and quasi-linear Fisher markets. This paper proposes refined price-adjustment processes to overcome these limitations. By formulating the task of finding CE of a (quasi-)linear Fisher market as a strongly convex nonsmooth minimization problem, we develop a novel accelerated price-adjustment method (APM) that finds an $ε$-CE price in $\\tilde{\\mathcal{O}}(1/\\sqrtε)$ lightweight iterations, which significantly improves upon the iteration complexities of tâtonnement methods. Furthermore, through our new formulation, we construct a recovery oracle that maps approximate CE prices to exact CE prices at a low computational cost. By coupling this recovery oracle with APM, we obtain an adaptive price-adjustment method whose iterates converge to CE prices in finite steps. To the best of our knowledge, this is the first convergence guarantee to exact CE for price-adjustment methods in linear and quasi-linear Fisher markets. Our developments pave the way for efficient lightweight computation of CE prices. We also present numerical results to demonstrate the fast convergence of the proposed methods and the efficient recovery of CE prices.", "published": "2025-10-09T03:59:03Z", "authors": ["He Chen", "Chonghe Jiang", "Anthony Man-Cho So"], "categories": ["math.OC"], "doi": "", "_query": "hedonic house price", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/1004.1804v2", "title": "Interacting Many-Investor Models, Opinion Formation and Price Formation with Non-extensive Statistics", "abstract": "We seek to utilize the nonextensive statistics to the microscopic modeling of the interacting many-investor dynamics that drive the price changes in a market. The statistics of price changes are known to be fit well by the Students-T and power-law distributions of the nonextensive statistics. We therefore derive models of interacting investors that are based on the nonextensive statistics and which describe the excess demand and formation of price.", "published": "2010-04-11T12:45:00Z", "authors": ["Fredrick Michael"], "categories": ["q-fin.TR", "q-fin.ST"], "doi": "", "_query": "hedonic house price", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2207.01277v1", "title": "Pricing multi-asset derivatives by variational quantum algorithms", "abstract": "Pricing a multi-asset derivative is an important problem in financial engineering, both theoretically and practically. Although it is suitable to numerically solve partial differential equations to calculate the prices of certain types of derivatives, the computational complexity increases exponentially as the number of underlying assets increases in some classical methods, such as the finite difference method. Therefore, there are efforts to reduce the computational complexity by using quantum computation. However, when solving with naive quantum algorithms, the target derivative price is embedded in the amplitude of one basis of the quantum state, and so an exponential complexity is required to obtain the solution. To avoid the bottleneck, the previous study~[Miyamoto and Kubo, IEEE Transactions on Quantum Engineering, \\textbf{3}, 1--25 (2022)] utilizes the fact that the present price of a derivative can be obtained by its discounted expected value at any future point in time and shows that the quantum algorithm can reduce the complexity. In this paper, to make the algorithm feasible to run on a small quantum computer, we use variational quantum simulation to solve the Black-Scholes equation and compute the derivative price from the inner product between the solution and a probability distribution. This avoids the measurement bottleneck of the naive approach and would provide quantum speedup even in noisy quantum computers. We also conduct numerical experiments to validate our method. Our method will be an important breakthrough in derivative pricing using small-scale quantum computers.", "published": "2022-07-04T09:11:15Z", "authors": ["Kenji Kubo", "Koichi Miyamoto", "Kosuke Mitarai", "Keisuke Fujii"], "categories": ["quant-ph", "q-fin.CP", "q-fin.PR"], "doi": "", "_query": "hedonic house price", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/1106.0519v2", "title": "Extreme-Value Theorems for Optimal Multidimensional Pricing", "abstract": "We provide a near-optimal, computationally efficient algorithm for the unit-demand pricing problem, where a seller wants to price n items to optimize revenue against a unit-demand buyer whose values for the items are independently drawn from known distributions. For any chosen accuracy eps>0 and item values bounded in [0,1], our algorithm achieves revenue that is optimal up to an additive error of at most eps, in polynomial time. For values sampled from Monotone Hazard Rate (MHR) distributions, we achieve a (1-eps)-fraction of the optimal revenue in polynomial time, while for values sampled from regular distributions the same revenue guarantees are achieved in quasi-polynomial time. Our algorithm for bounded distributions applies probabilistic techniques to understand the statistical properties of revenue distributions, obtaining a reduction in the search space of the algorithm via dynamic programming. Adapting this approach to MHR and regular distributions requires the proof of novel extreme value theorems for such distributions. As a byproduct, our techniques establish structural properties of approximately-optimal and near-optimal solutions. We show that, for values independently distributed according to MHR distributions, pricing all items at the same price achieves a constant fraction of the optimal revenue. Moreover, for all eps >0, g(1/eps) distinct prices suffice to obtain a (1-eps)-fraction of the optimal revenue, where g(1/eps) is quadratic in 1/eps and independent of n. Similarly, for all eps>0 and n>0, at most g(1/(eps log n)) distinct prices suffice if the values are independently distributed according to regular distributions, where g() is a polynomial function. Finally, when the values are i.i.d. from some MHR distribution, we show that, if n is a sufficiently large function of 1/eps, a single price suffices to achieve a (1-eps)-fraction of the optimal revenue.", "published": "2011-06-02T21:37:16Z", "authors": ["Yang Cai", "Constantinos Daskalakis"], "categories": ["cs.GT", "cs.DS"], "doi": "", "_query": "hedonic house price", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/1211.2090v2", "title": "Improving the H_k-Bound on the Price of Stability in Undirected Shapley Network Design Games", "abstract": "In this paper we show that the price of stability of Shapley network design games on undirected graphs with k players is at most (k^3(k+1)/2-k^2) / (1+k^3(k+1)/2-k^2) H_k = (1 - Θ(1/k^4)) H_k, where H_k denotes the k-th harmonic number. This improves on the known upper bound of H_k, which is also valid for directed graphs but for these, in contrast, is tight. Hence, we give the first non-trivial upper bound on the price of stability for undirected Shapley network design games that is valid for an arbitrary number of players. Our bound is proved by analyzing the price of stability restricted to Nash equilibria that minimize the potential function of the game. We also present a game with k=3 players in which such a restricted price of stability is 1.634. This shows that the analysis of Bilò and Bove (Journal of Interconnection Networks, Volume 12, 2011) is tight. In addition, we give an example for three players that improves the lower bound on the (unrestricted) price of stability to 1.571.", "published": "2012-11-09T10:05:53Z", "authors": ["Yann Disser", "Andreas Emil Feldmann", "Max Klimm", "Matúš Mihalák"], "categories": ["cs.GT"], "doi": "", "_query": "hedonic house price", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2211.12376v4", "title": "An Intraday GARCH Model for Discrete Price Changes and Irregularly Spaced Observations", "abstract": "We develop a novel observation-driven model for high-frequency prices. We account for irregularly spaced observations, simultaneous transactions, discreteness of prices, and market microstructure noise. The relation between trade durations and price volatility, as well as intraday patterns of trade durations and price volatility, is captured using smoothing splines. The dynamic model is based on the zero-inflated Skellam distribution with time-varying volatility in a score-driven framework. Market microstructure noise is filtered by including a moving average component. The model is estimated by the maximum likelihood method. In an empirical study of the IBM stock, we demonstrate that the model provides a good fit to the data. Besides modeling intraday volatility, it can also be used to measure daily realized volatility.", "published": "2022-11-22T16:17:03Z", "authors": ["Vladimír Holý"], "categories": ["q-fin.ST"], "doi": "", "_query": "hedonic house price", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/0905.3870v1", "title": "On the short-term influence of oil price changes on stock markets in GCC countries: linear and nonlinear analyses", "abstract": "This paper examines the short-run relationships between oil prices and GCC stock markets. Since GCC countries are major world energy market players, their stock markets may be susceptible to oil price shocks. To account for the fact that stock markets may respond nonlinearly to oil price shocks, we have examined both linear and nonlinear relationships. Our findings show that there are significant links between the two variables in Qatar, Oman, and UAE. Thus, stock markets in these countries react positively to oil price increases. For Bahrain, Kuwait, and Saudi Arabia we found that oil price changes do not affect stock market returns.", "published": "2009-05-24T05:04:38Z", "authors": ["Mohamed El Hedi Arouri", "Julien Fouquau"], "categories": ["q-fin.ST"], "doi": "", "_query": "hedonic house price", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/math/0005238v1", "title": "Modelling of stock price changes: a real analysis approach", "abstract": "The paper discusses a path-wise approach to stock price modelling.", "published": "2000-05-23T22:26:52Z", "authors": ["Rimas Norvaisa"], "categories": ["math.PR", "math.CA"], "doi": "", "_query": "hedonic house price", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2012.03739v1", "title": "Exploring the Usage of Online Food Delivery Data for Intra-Urban Job and Housing Mobility Detection and Characterization", "abstract": "Human mobility plays a critical role in urban planning and policy-making. However, at certain spatial and temporal resolutions, it is very challenging to track, for example, job and housing mobility. In this study, we explore the usage of a new modality of dataset, online food delivery data, to detect job and housing mobility. By leveraging millions of meal orders from a popular online food ordering and delivery service in Beijing, China, we are able to detect job and housing moves at much higher spatial and temporal resolutions than using traditional data sources. Popular moving seasons and origins/destinations can be well identified. More importantly, we match the detected moves to both macro- and micro-level factors so as to characterize job and housing dynamics. Our findings suggest that commuting distance is a major factor for job and housing mobility. We also observe that: (1) For home movers, there is a trade-off between lower housing cost and shorter commuting distance given the urban spatial structure; (2) For job hoppers, those who frequently work overtime are more likely to reduce their working hours by switching jobs. While this new modality of dataset has its limitations, we believe that ensemble approaches would be promising, where a mash-up of multiple datasets with different characteristic limitations can provide a more comprehensive picture of job and housing dynamics. Our work demonstrates the effectiveness of utilizing food delivery data to detect and analyze job and housing mobility, and contributes to realizing the full potential of ensemble-based approaches.", "published": "2020-12-04T02:48:53Z", "authors": ["Yawen Zhang", "Seth Spielman", "Qi Liu", "Si Shen", "Jason Shuo Zhang", "Qin Lv"], "categories": ["cs.CY"], "doi": "", "_query": "hedonic house price", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2512.07154v2", "title": "Asian option valuation under price impact", "abstract": "We develop a tractable framework for valuing Asian options when trading the underlying generates market impact and execution costs. Starting from a discrete-time, quote-level model, we construct a reference midpoint suitable for Asian payoffs and separate market impact into a transient component and a permanent drift distortion driven by signed trading. This specification admits continuous-time limits where the midpoint and impact state converge to a coupled system in which the midpoint drift depends on the transient impact state and in the endogenous regime on the hedger's trading rate, with correlated price and order-flow shocks. We study valuation in two complementary regimes. In an exogenous benchmark, the impact state evolves independently of the hedger. When the order-flow volatility is deterministic, we obtain a closed-form expression for the geometric Asian call. In an endogenous regime, trading volumes feed back into prices and costs, leading to a stochastic control problem and Hamilton-Jacobi-Bellman equations. We define reservation bid and ask prices via cost-based indifference which produces an impact-driven bid-ask spread. For computations, we propose a CRR-style tree-based Bellman algorithm. Numerical experiments show that exogenous impact effects are modest relative to frictionless benchmarks, while endogenous indifference prices generate nontrivial bid-ask spreads that grow super-linearly in impact parameters, widen when execution costs are lower, and shrink with faster mean reversion, highlighting the interaction between averaging in Asian options, price impact effects, and strategic trading.", "published": "2025-12-08T04:38:02Z", "authors": ["Priyanshu Tiwari", "Sourav Majumdar"], "categories": ["q-fin.MF"], "doi": "", "_query": "hedonic house price", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/1910.00258v1", "title": "Isogeometric analysis in option pricing", "abstract": "Isogeometric analysis is a recently developed computational approach that integrates finite element analysis directly into design described by non-uniform rational B-splines (NURBS). In this paper we show that price surfaces that occur in option pricing can be easily described by NURBS surfaces. For a class of stochastic volatility models, we develop a methodology for solving corresponding pricing partial integro-differential equations numerically by isogeometric analysis tools and show that a very small number of space discretization steps can be used to obtain sufficiently accurate results. Presented solution by finite element method is especially useful for practitioners dealing with derivatives where closed-form solution is not available.", "published": "2019-10-01T08:43:54Z", "authors": ["Jan Pospíšil", "Vladimír Švígler"], "categories": ["q-fin.CP", "math.AP", "math.NA"], "doi": "10.1080/00207160.2018.1494826", "_query": "hedonic house price", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/math/0612691v1", "title": "Option pricing with log-stable Lévy processes", "abstract": "We model the logarithm of the price (log-price) of a financial asset as a random variable obtained by projecting an operator stable random vector with a scaling index matrix $\\underline{\\underline{E}}$ onto a non-random vector. The scaling index $\\underline{\\underline{E}}$ models prices of the individual financial assets (stocks, mutual funds, etc.). We find the functional form of the characteristic function of real powers of the price returns and we compute the expectation value of these real powers and we speculate on the utility of these results for statistical inference. Finally we consider a portfolio composed of an asset and an option on that asset. We derive the characteristic function of the deviation of the portfolio, \\mbox{${\\mathfrak D}_t^{({\\mathfrak t})}$}, defined as a temporal change of the portfolio diminished by the the compound interest earned. We derive pseudo-differential equations for the option as a function of the log-stock-price and time and we find exact closed-form solutions to that equation. These results were not known before. Finally we discuss how our solutions correspond to other approximate results known from literature,in particular to the well known Black & Scholes equation.", "published": "2006-12-22T10:07:23Z", "authors": ["Przemysław Repetowicz", "Peter Richmond"], "categories": ["math.PR", "math.ST", "q-fin.PR"], "doi": "10.1007/978-88-470-0665-2_6", "_query": "hedonic house price", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/1503.06851v2", "title": "Price and capacity competition in balancing markets with energy storage", "abstract": "Energy storage can absorb variability from the rising number of wind and solar power producers. Storage is different from the conventional generators that have traditionally balanced supply and demand on fast time scales due to its hard energy capacity constraints, dynamic coupling, and low marginal costs. These differences are leading system operators to propose new mechanisms for enabling storage to participate in reserve and real-time energy markets. The persistence of market power and gaming in electricity markets suggests that these changes will expose new vulnerabilities. We develop a new model of strategic behavior among storages in energy balancing markets. Our model is a two-stage game that generalizes a classic model of capacity followed by Bertrand-Edgeworth price competition by explicitly modeling storage dynamics and uncertainty in the pricing stage. By applying the model to balancing markets with storage, we are able to compare capacity and energy-based pricing schemes, and to analyze the dynamic effects of the market horizon and energy losses due to leakage. Our first key finding is that capacity pricing leads to higher prices and higher capacity commitments, and that energy pricing leads to lower, randomized prices and lower capacity commitments. Second, we find that a longer market horizon and higher physical efficiencies lead to lower prices by inducing the storage to compete to have their states of charge cycled more frequently.", "published": "2015-03-23T21:54:17Z", "authors": ["Josh A. Taylor", "Johanna L. Mathieu", "Duncan S. Callaway", "Kameshwar Poolla"], "categories": ["cs.GT"], "doi": "", "_query": "hedonic house price", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2007.01467v1", "title": "Quantum Pricing with a Smile: Implementation of Local Volatility Model on Quantum Computer", "abstract": "Applications of the quantum algorithm for Monte Carlo simulation to pricing of financial derivatives have been discussed in previous papers. However, up to now, the pricing model discussed in such papers is Black-Scholes model, which is important but simple. Therefore, it is motivating to consider how to implement more complex models used in practice in financial institutions. In this paper, we then consider the local volatility (LV) model, in which the volatility of the underlying asset price depends on the price and time. We present two types of implementation. One is the register-per-RN way, which is adopted in most of previous papers. In this way, each of random numbers (RNs) required to generate a path of the asset price is generated on a separated register, so the required qubit number increases in proportion to the number of RNs. The other is the PRN-on-a-register way, which is proposed in the author's previous work. In this way, a sequence of pseudo-random numbers (PRNs) generated on a register is used to generate paths of the asset price, so the required qubit number is reduced with a trade-off against circuit depth. We present circuit diagrams for these two implementations in detail and estimate required resources: qubit number and T-count.", "published": "2020-07-03T02:54:25Z", "authors": ["Kazuya Kaneko", "Koichi Miyamoto", "Naoyuki Takeda", "Kazuyoshi Yoshino"], "categories": ["quant-ph", "q-fin.PR"], "doi": "", "_query": "hedonic house price", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/1512.06050v1", "title": "Pricing the Ramping Reserve and Capacity Reserve in Real Time Markets", "abstract": "The increasing penetration of renewable energy in recent years has led to more uncertainties in power systems. In order to maintain system reliability and security, electricity market operators need to keep certain reserves in the Security-Constrained Economic Dispatch (SCED) problems. A new concept, deliverable generation ramping reserve, is proposed in this paper. The prices of generation ramping reserves and generation capacity reserves are derived in the Affine Adjustable Robust Optimization framework. With the help of these prices, the valuable reserves can be identified among the available reserves. These prices provide crucial information on the values of reserve resources, which are critical for the long-term flexibility investment. The market equilibrium based on these prices is analyzed. Simulations on a 3-bus system and the IEEE 118-bus system are performed to illustrate the concept of ramping reserve price and capacity reserve price. The impacts of the reserve credit on market participants are discussed.", "published": "2015-12-18T17:32:54Z", "authors": ["Hongxing Ye", "Zuyi Li"], "categories": ["math.OC", "eess.SY"], "doi": "", "_query": "hedonic house price", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2601.16488v1", "title": "Anonymous Pricing in Large Markets", "abstract": "We study revenue maximization when a seller offers $k$ identical units to ex ante heterogeneous, unit-demand buyers. While anonymous pricing can be $Θ(\\log k)$ worse than optimal in general multi-unit environments, we show that this pessimism disappears in large markets, where no single buyer accounts for a non-negligible share of optimal revenue. Under (quasi-)regularity, anonymous pricing achieves a $2+O(1/\\sqrt{k})$ approximation to the optimal mechanism; the worst-case ratio is maximized at about $2.47$ when $k=1$ and converges to $2$ as $k$ grows. This indicates that the gains from third-degree price discrimination are mild in large markets.", "published": "2026-01-23T06:28:58Z", "authors": ["Yaonan Jin", "Yingkai Li"], "categories": ["cs.GT", "econ.TH"], "doi": "", "_query": "hedonic house price", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2601.01279v3", "title": "Supracompetitive Pricing Under AI Monoculture", "abstract": "When competing sellers delegate pricing to a shared AI model, such as a large language model, correlated recommendations combined with performance-driven updates aggregating seller feedback raise a key question: can standard AI deployment practices inadvertently produce supracompetitive pricing? We develop a stylized duopoly model in which two sellers receive pricing recommendations from a shared AI characterized by two parameters: a propensity parameter capturing the model's tendency to set high prices and an output-fidelity parameter measuring alignment between this tendency and actual outputs, with propensity updated via periodic retraining on observed outcomes. We find that configuring AI models for robustness and reproducibility can lead to supracompetitive pricing via a phase transition. Below a critical output-fidelity threshold, competitive pricing is the unique stable outcome. Above it, the model exhibits bistability: both competitive and supracompetitive pricing are locally stable, with the realized outcome determined by the model's initial propensity. Supracompetitive pricing raises average prices, but occasional low-price recommendations complicate detection. With perfect output fidelity, full price coordination emerges from any interior initial propensity. For finite training batches of size $b$, when the initial propensity lies in the supracompetitive basin, the probability of supracompetitive pricing approaches 1 as $b$ increases, with the region of indeterminate outcomes shrinking at rate $O(1/\\sqrt{b})$. Any factor reducing alignment between the model's propensity and sellers' actual pricing, whether through diversifying AI providers, introducing recommendation noise, or reducing seller adherence, pushes the market toward competitive outcomes.", "published": "2026-01-03T20:38:21Z", "authors": ["Shengyu Cao", "Ming Hu"], "categories": ["econ.TH", "cs.AI", "cs.CE", "cs.CL", "cs.GT"], "doi": "", "_query": "hedonic house price", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2409.12376v1", "title": "Prediction of Brent crude oil price based on LSTM model under the background of low-carbon transition", "abstract": "In the field of global energy and environment, crude oil is an important strategic resource, and its price fluctuation has a far-reaching impact on the global economy, financial market and the process of low-carbon development. In recent years, with the gradual promotion of green energy transformation and low-carbon development in various countries, the dynamics of crude oil market have become more complicated and changeable. The price of crude oil is not only influenced by traditional factors such as supply and demand, geopolitical conflict and production technology, but also faces the challenges of energy policy transformation, carbon emission control and new energy technology development. This diversified driving factor makes the prediction of crude oil price not only very important in economic decision-making and energy planning, but also a key issue in financial markets.In this paper, the spot price data of European Brent crude oil provided by us energy information administration are selected, and a deep learning model with three layers of LSTM units is constructed to predict the crude oil price in the next few days. The results show that the LSTM model performs well in capturing the overall price trend, although there is some deviation during the period of sharp price fluctuation. The research in this paper not only verifies the applicability of LSTM model in energy market forecasting, but also provides data support for policy makers and investors when facing the uncertainty of crude oil price.", "published": "2024-09-19T00:25:22Z", "authors": ["Yuwen Zhao", "Baojun Hu", "Sizhe Wang"], "categories": ["cs.LG", "cs.DC"], "doi": "", "_query": "hedonic house price", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/1803.09967v1", "title": "Reinforcement Learning for Fair Dynamic Pricing", "abstract": "Unfair pricing policies have been shown to be one of the most negative perceptions customers can have concerning pricing, and may result in long-term losses for a company. Despite the fact that dynamic pricing models help companies maximize revenue, fairness and equality should be taken into account in order to avoid unfair price differences between groups of customers. This paper shows how to solve dynamic pricing by using Reinforcement Learning (RL) techniques so that prices are maximized while keeping a balance between revenue and fairness. We demonstrate that RL provides two main features to support fairness in dynamic pricing: on the one hand, RL is able to learn from recent experience, adapting the pricing policy to complex market environments; on the other hand, it provides a trade-off between short and long-term objectives, hence integrating fairness into the model's core. Considering these two features, we propose the application of RL for revenue optimization, with the additional integration of fairness as part of the learning procedure by using Jain's index as a metric. Results in a simulated environment show a significant improvement in fairness while at the same time maintaining optimisation of revenue.", "published": "2018-03-27T09:00:48Z", "authors": ["Roberto Maestre", "Juan Duque", "Alberto Rubio", "Juan Arévalo"], "categories": ["cs.LG", "cs.AI", "stat.ML"], "doi": "", "_query": "hedonic house price", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/1806.00793v2", "title": "Transfer Topic Labeling with Domain-Specific Knowledge Base: An Analysis of UK House of Commons Speeches 1935-2014", "abstract": "Topic models are widely used in natural language processing, allowing researchers to estimate the underlying themes in a collection of documents. Most topic models use unsupervised methods and hence require the additional step of attaching meaningful labels to estimated topics. This process of manual labeling is not scalable and suffers from human bias. We present a semi-automatic transfer topic labeling method that seeks to remedy these problems. Domain-specific codebooks form the knowledge-base for automated topic labeling. We demonstrate our approach with a dynamic topic model analysis of the complete corpus of UK House of Commons speeches 1935-2014, using the coding instructions of the Comparative Agendas Project to label topics. We show that our method works well for a majority of the topics we estimate; but we also find that institution-specific topics, in particular on subnational governance, require manual input. We validate our results using human expert coding.", "published": "2018-06-03T13:22:10Z", "authors": ["Alexander Herzog", "Peter John", "Slava Jankin Mikhaylov"], "categories": ["cs.CL", "cs.CY"], "doi": "", "_query": "hedonic house price", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/1906.00564v1", "title": "C2P2: A Collective Cryptocurrency Up/Down Price Prediction Engine", "abstract": "We study the problem of predicting whether the price of the 21 most popular cryptocurrencies (according to coinmarketcap.com) will go up or down on day d, using data up to day d-1. Our C2P2 algorithm is the first algorithm to consider the fact that the price of a cryptocurrency c might depend not only on historical prices, sentiments, global stock indices, but also on the prices and predicted prices of other cryptocurrencies. C2P2 therefore does not predict cryptocurrency prices one coin at a time --- rather it uses similarity metrics in conjunction with collective classification to compare multiple cryptocurrency features to jointly predict the cryptocurrency prices for all 21 coins considered. We show that our C2P2 algorithm beats out a recent competing 2017 paper by margins varying from 5.1-83% and another Bitcoin-specific prediction paper from 2018 by 16%. In both cases, C2P2 is the winner on all cryptocurrencies considered. Moreover, we experimentally show that the use of similarity metrics within our C2P2 algorithm leads to a direct improvement for 20 out of 21 cryptocurrencies ranging from 0.4% to 17.8%. Without the similarity component, C2P2 still beats competitors on 20 out of 21 cryptocurrencies considered. We show that all these results are statistically significant via a Student's t-test with p<1e-5. Check our demo at https://www.cs.dartmouth.edu/dsail/demos/c2p2", "published": "2019-06-03T04:20:46Z", "authors": ["Chongyang Bai", "Tommy White", "Linda Xiao", "V. S. Subrahmanian", "Ziheng Zhou"], "categories": ["cs.LG", "stat.ML"], "doi": "", "_query": "hedonic house price", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/1404.3160v1", "title": "Pricing of Basket Options Using Polynomial Approximations", "abstract": "In this paper we use Bernstein and Chebyshev polynomials to approximate the price of some basket options under a bivariate Black-Scholes model. The method consists in expanding the price of a univariate related contract after conditioning on the remaining underlying assets and calculating the mixed exponential-power moments of a Gaussian distribution that arise as a consequence of such approximation. Our numerical implementation on spread contracts shows the method is as accurate as a standard Monte Carlo approach at considerable lesser computational effort.", "published": "2014-04-11T17:26:08Z", "authors": ["Pablo Olivares"], "categories": ["q-fin.PR"], "doi": "", "_query": "hedonic house price", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/1211.5372v2", "title": "Drift in Transaction-Level Asset Price Models", "abstract": "We study the effect of drift in pure-jump transaction-level models for asset prices in continuous time, driven by point processes. The drift is as-sumed to arise from a nonzero mean in the efficient shock series. It follows that the drift is proportional to the driving point process itself, i.e. the cumulative number of transactions. This link reveals a mechanism by which properties of intertrade durations (such as heavy tails and long memory) can have a strong impact on properties of average returns, thereby poten-tially making it extremely difficult to determine long-term growth rates or to reliably detect an equity premium. We focus on a basic univariate model for log price, coupled with general assumptions on the point process that are satisfied by several existing flexible models, allowing for both long mem-ory and heavy tails in durations. Under our pure-jump model, we obtain the limiting distribution for the suitably normalized log price. This limiting distribution need not be Gaussian, and may have either finite variance or infinite variance. We show that the drift can affect not only the limiting dis-tribution for the normalized log price, but also the rate in the corresponding normalization. Therefore, the drift (or equivalently, the properties of dura-tions) affects the rate of convergence of estimators of the growth rate, and can invalidate standard hypothesis tests for that growth rate. As a rem-edy to these problems, we propose a new ratio statistic which behaves more", "published": "2012-11-22T20:49:34Z", "authors": ["Wen Cao", "Clifford Hurvich", "Philippe Soulier"], "categories": ["math.ST"], "doi": "", "_query": "hedonic house price", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/1705.00284v2", "title": "Periodic strategies in optimal execution with multiplicative price impact", "abstract": "In this work we study the optimal execution problem with multiplicative price impact in algorithm trading, when an agent holds an initial position of shares of a financial asset. The inter-selling-decision times are modelled by the arrival times of a Poisson process. The criterion to be optimised consists in maximising the expected net present value of gains of the agent, and it is proved that an optimal strategy has a barrier form, depending only on the number of shares left and the level of asset price.", "published": "2017-04-30T08:06:47Z", "authors": ["Daniel Hernández-Hernández", "Harold A. Moreno-Franco", "José Luis Pérez"], "categories": ["q-fin.MF", "math.OC"], "doi": "", "_query": "hedonic house price", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2509.10553v1", "title": "A Stochastic Model for Illiquid Stock Prices and its Conclusion about Correlation Measurement", "abstract": "This study explores the behavioral dynamics of illiquid stock prices in a listed stock market. Illiquidity, characterized by wide bid and ask spreads affects price formation by decoupling prices from standard risk and return relationships and increasing sensitivity to market sentiment. We model the prices at the Uganda Securities Exchange (USE) which is illiquid in that the prices remain constant much of the time thus complicating price modelling. We circumvent this challenge by combining the Markov model (MM) with two models; the exponential Ornstein Uhlenbeck model (XOU) and geometric Brownian motion (gBm). In the combined models, the MM was used to capture the constant prices in the stock prices while the XOU and gBm captured the stochastic price dynamics. We modelled stock prices using the combined models, as well as XOU and gBm alone. We found that USE stocks appeared to have low correlation with one another. Using theoretical analysis, simulation study and empirical analysis, we conclude that this apparent low correlation is due to illiquidity. In particular data simulated from combined MM-gBm, in which the gBm portion were highly correlated resulted in a low measured correlation when the Markov chain had a higher transition from zero state to zero state.", "published": "2025-09-09T12:40:23Z", "authors": ["Erina Nanyonga", "Juma Kasozi", "Fred Mayambala", "Hassan W. Kayondo", "Matt Davison"], "categories": ["q-fin.ST"], "doi": "10.1080/27684830.2026.2680821", "_query": "hedonic house price", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/0906.5489v1", "title": "Improved and Developed Upper Bound of Price of Anarchy in Two Echelon Case", "abstract": "Price of anarchy, the performance ratio, which could characterize the loss of efficiency of the distributed supply chain management compared with the integrated supply chain management is discussed by utilizing newsvendor problem in single period which is well-known. In particular, some of remarkable distributed policies are handled, the performance ratios in each case which have been investigated in the previous works are analyzed theoretically and the tighter upper bound of price of anarchy and the lower bound are presented. Furthermore our approach is developed based on a generalized framework and a geometric interpretation of price of anarchy is appeared via the literature of convex optimization.", "published": "2009-06-30T11:50:07Z", "authors": ["T. Shinzato", "I. Kaku"], "categories": ["q-fin.GN"], "doi": "", "_query": "hedonic house price", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2305.00362v1", "title": "Electricity Price Prediction for Energy Storage System Arbitrage: A Decision-focused Approach", "abstract": "Electricity price prediction plays a vital role in energy storage system (ESS) management. Current prediction models focus on reducing prediction errors but overlook their impact on downstream decision-making. So this paper proposes a decision-focused electricity price prediction approach for ESS arbitrage to bridge the gap from the downstream optimization model to the prediction model. The decision-focused approach aims at utilizing the downstream arbitrage model for training prediction models. It measures the difference between actual decisions under the predicted price and oracle decisions under the true price, i.e., decision error, by regret, transforms it into the tractable surrogate regret, and then derives the gradients to predicted price for training prediction models. Based on the prediction and decision errors, this paper proposes the hybrid loss and corresponding stochastic gradient descent learning method to learn prediction models for prediction and decision accuracy. The case study verifies that the proposed approach can efficiently bring more economic benefits and reduce decision errors by flattening the time distribution of prediction errors, compared to prediction models for only minimizing prediction errors.", "published": "2023-04-30T00:43:26Z", "authors": ["Linwei Sang", "Yinliang Xu", "Huan Long", "Qinran Hu", "Hongbin Sun"], "categories": ["cs.LG", "eess.SY"], "doi": "", "_query": "hedonic house price", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/1705.01142v1", "title": "Machine Learning for Better Models for Predicting Bond Prices", "abstract": "Bond prices are a reflection of extremely complex market interactions and policies, making prediction of future prices difficult. This task becomes even more challenging due to the dearth of relevant information, and accuracy is not the only consideration--in trading situations, time is of the essence. Thus, machine learning in the context of bond price predictions should be both fast and accurate. In this course project, we use a dataset describing the previous 10 trades of a large number of bonds among other relevant descriptive metrics to predict future bond prices. Each of 762,678 bonds in the dataset is described by a total of 61 attributes, including a ground truth trade price. We evaluate the performance of various supervised learning algorithms for regression followed by ensemble methods, with feature and model selection considerations being treated in detail. We further evaluate all methods on both accuracy and speed. Finally, we propose a novel hybrid time-series aided machine learning method that could be applied to such datasets in future work.", "published": "2017-03-31T15:12:49Z", "authors": ["Swetava Ganguli", "Jared Dunnmon"], "categories": ["q-fin.ST", "cs.CE"], "doi": "", "_query": "hedonic house price", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2109.12477v1", "title": "Reputation dependent pricing strategy: analysis based on a Chinese C2C marketplace", "abstract": "Most online markets establish reputation systems to assist building trust between sellers and buyers. Sellers' reputations not only provide guidelines for buyers but may also inform sellers their optimal pricing strategy. In this research, we assumed two types of buyer: informed buyers and uninformed buyers. Informed buyers know more about the reputation about the seller but may incur a search cost. Then we developed a benchmark model and a competition model. We found that high reputation sellers and low reputation sellers adapt different pricing strategy depending on the informativeness of buyers and the competition among sellers. With a large proportion of informed buyers, high reputation sellers may charge lower price than low reputation sellers, which exists a negative price premium effect, in contrast to conclusions of some previous studies. Empirical findings were in consistence with our theoretical models. We collected data of five categories of products, televisions, laptops, cosmetics, shoes, and beverages, from Taobao, a leading C2C Chinese online market. Negative price premium effect was observed for TVs, laptops, and cosmetics; price premium effect was observed for beverages; no significant trend was observed for shoes. We infer product value and market complexity are the main factors of buyer informativeness.", "published": "2021-09-26T02:46:57Z", "authors": ["Zehao Chen", "Yanchen Zhu", "Tianyang Shen", "Yufan Ye"], "categories": ["econ.GN"], "doi": "", "_query": "hedonic house price", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2509.18099v1", "title": "Path-dependent, ESG-valued, option pricing in the Bachelier-Black-Scholes-Merton model", "abstract": "We extend the application of the Cherny-Shiryaev-Yor invariance principle to a unified Bachelier-Black-Scholes-Merton (BBSM) dynamic pricing model. This extension incorporates the influence of the history of the dynamics (i.e., the path dynamics) of a market index on stock price changes. We add an ESG rating component to the price of the risky asset (stock), in such a manner that the impact of the ESG rating on the stock valuation can be explored through variation in the value of a single parameter. We develop discrete, binary tree, option pricing under this extended model. Using an empirical data set of 10 stocks chosen from the Nasdaq-100, we fit the model to stock price changes and compare model-based and published European call option prices.", "published": "2025-08-26T01:58:47Z", "authors": ["Bhathiya Divelgama", "Nancy Asare Nyarko", "W. Brent Lindquist", "Svetlozar T. Rachev", "Blessing Omotade"], "categories": ["q-fin.PR"], "doi": "", "_query": "hedonic house price", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/1809.08092v2", "title": "Power Market Price Forecasting via Deep Learning", "abstract": "A study on power market price forecasting by deep learning is presented. As one of the most successful deep learning frameworks, the LSTM (Long short-term memory) neural network is utilized. The hourly prices data from the New England and PJM day-ahead markets are used in this study. First, a LSTM network is formulated and trained. Then the raw input and output data are preprocessed by unit scaling, and the trained network is tested on the real price data under different input lengths, forecasting horizons and data sizes. Its performance is also compared with other existing methods. The forecasted results demonstrate that, the LSTM deep neural network can outperform the others under different application settings in this problem.", "published": "2018-09-18T19:00:18Z", "authors": ["Yongli Zhu", "Songtao Lu", "Renchang Dai", "Guangyi Liu", "Zhiwei Wang"], "categories": ["cs.LG", "stat.ML"], "doi": "", "_query": "hedonic house price", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2506.13271v1", "title": "One-dimensional vs. Multi-dimensional Pricing in Blockchain Protocols", "abstract": "Blockchain transactions consume diverse resources, foremost among them storage, but also computation, communication, and others. Efficiently charging for these resources is crucial for effective system resource allocation and long-term economic viability. The prevailing approach, one-dimensional pricing, sets a single price for a linear combination of resources. However, this often leads to under-utilization when resource capacities are limited. Multi-dimensional pricing, which independently prices each resource, offers an alternative but presents challenges in price discovery. This work focuses on the welfare achieved by these two schemes. We prove that multi-dimensional pricing is superior under stable blockchain conditions. Conversely, we show that one-dimensional pricing outperforms its multi-dimensional counterpart in transient states, exhibiting faster convergence and greater computational tractability. These results highlight a critical trade-off: while multi-dimensional pricing offers efficiency gains at equilibrium, its implementation incurs costs associated with system transitions. Our findings underscore the necessity for a deeper understanding of these transient effects before widespread adoption. Finally, we propose mechanisms that aim to mitigate some of these issues, paving the way for future research.", "published": "2025-06-16T09:12:09Z", "authors": ["Aggelos Kiayias", "Elias Koutsoupias", "Giorgos Panagiotakos", "Kyriaki Zioga"], "categories": ["cs.GT"], "doi": "", "_query": "hedonic house price", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2601.05202v3", "title": "Stock Market Price Prediction using Neural Prophet with Deep Neural Network", "abstract": "Stock market price prediction is a significant interdisciplinary research domain that depends at the intersection of finance, statistics, and economics. Forecasting Accurately predicting stock prices has always been a focal point for various researchers. However, existing statistical approaches for time-series prediction often fail to effectively forecast the probability range of future stock prices. Hence, to solve this problem, the Neural Prophet with a Deep Neural Network (NP-DNN) is proposed to predict stock market prices. The preprocessing technique used in this research is Z-score normalization, which normalizes stock price data by removing scale differences, making patterns easier to detect. Missing value imputation fills gaps in historical data, enhancing the models use of complete information for more accurate predictions. The Multi-Layer Perceptron (MLP) learns complex nonlinear relationships among stock market prices and extracts hidden patterns from the input data, thereby creating meaningful feature representations for better prediction accuracy. The proposed NP-DNN model achieved an accuracy of 99.21% compared with other approaches using the Fused Large Language Model. Keywords: deep neural network, forecasting stock prices, multi-layer perceptron, neural prophet, stock market price prediction.", "published": "2026-01-08T18:24:22Z", "authors": ["Navin Chhibber", "Sunil Khemka", "Navneet Kumar Tyagi", "Rohit Tewari", "Bireswar Banerjee", "Piyush Ranjan"], "categories": ["cs.AI", "cs.LG"], "doi": "", "_query": "hedonic house price", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/1703.05709v4", "title": "Incorporating statistical model error into the calculation of acceptability prices of contingent claims", "abstract": "The determination of acceptability prices of contingent claims requires the choice of a stochastic model for the underlying asset price dynamics. Given this model, optimal bid and ask prices can be found by stochastic optimization. However, the model for the underlying asset price process is typically based on data and found by a statistical estimation procedure. We define a confidence set of possible estimated models by a nonparametric neighborhood of a baseline model. This neighborhood serves as ambiguity set for a multi-stage stochastic optimization problem under model uncertainty. We obtain distributionally robust solutions of the acceptability pricing problem and derive the dual problem formulation. Moreover, we prove a general large deviations result for the nested distance, which allows to relate the bid and ask prices under model ambiguity to the quality of the observed data.", "published": "2017-03-16T16:34:18Z", "authors": ["Martin Glanzer", "Georg Ch. Pflug", "Alois Pichler"], "categories": ["q-fin.PR"], "doi": "10.1007/s10107-018-1352-7", "_query": "hedonic house price", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2507.11480v3", "title": "Pricing energy spread options with variance gamma-driven Ornstein-Uhlenbeck dynamics", "abstract": "We consider the pricing of energy spread options for spot prices following an exponential Ornstein-Uhlenbeck process driven by a sum of independent multivariate variance gamma processes, which gives rise to mean-reverting, infinite activity price dynamics. Within this class of driving processes, the Esscher transform is used to obtain an equivalent martingale measure with a focus on the weak variance alpha-gamma process. By deriving an analytical formula for the cumulant generating function of the innovation term, we obtain a pricing formula for forwards and apply the FFT method of Hurd and Zhou to price spread options. Lastly, we demonstrate how the model should be both estimated on energy prices under the real world measure and calibrated on forward or call prices, and provide numerical results for the pricing of spread options.", "published": "2025-07-15T16:48:24Z", "authors": ["Tim Leung", "Kevin W. Lu"], "categories": ["q-fin.MF"], "doi": "", "_query": "hedonic house price", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/0709.2721v2", "title": "Pricing, Competition, and Routing for Selfish and Strategic Nodes in Multi-hop Relay Networks", "abstract": "We study a pricing game in multi-hop relay networks where nodes price their services and route their traffic selfishly and strategically. In this game, each node (1) announces pricing functions which specify the payments it demands from its respective customers depending on the amount of traffic they route to it and (2) allocates the total traffic it receives to its service providers. The profit of a node is the difference between the revenue earned from servicing others and the cost of using others' services. We show that the socially optimal routing of such a game can always be induced by an equilibrium where no node can increase its profit by unilaterally changing its pricing functions or routing decision. On the other hand, there may also exist inefficient equilibria. We characterize the loss of efficiency by deriving the price of anarchy at inefficient equilibria. We show that the price of anarchy is finite for oligopolies with concave marginal cost functions, while it is infinite for general topologies and cost functions.", "published": "2007-09-17T20:52:29Z", "authors": ["Yufang Xi", "Edmund M. Yeh"], "categories": ["cs.GT"], "doi": "", "_query": "hedonic house price", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/1404.7356v1", "title": "Analysis of a decision model in the context of equilibrium pricing and order book pricing", "abstract": "An agent-based model for financial markets has to incorporate two aspects: decision making and price formation. We introduce a simple decision model and consider its implications in two different pricing schemes. First, we study its parameter dependence within a supply-demand balance setting. We find realistic behavior in a wide parameter range. Second, we embed our decision model in an order book setting. Here we observe interesting features which are not present in the equilibrium pricing scheme. In particular, we find a nontrivial behavior of the order book volumes which reminds of a trend switching phenomenon. Thus, the decision making model alone does not realistically represent the trading and the stylized facts. The order book mechanism is crucial.", "published": "2014-04-29T13:35:33Z", "authors": ["Daniel C. Wagner", "Thilo A. Schmitt", "Rudi Schäfer", "Thomas Guhr", "Dietrich E. Wolf"], "categories": ["q-fin.TR"], "doi": "10.1016/j.physa.2014.08.013", "_query": "hedonic house price", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/1403.0851v1", "title": "Asset Prices and Risk Aversion", "abstract": "The standard asset pricing models (the CCAPM and the Epstein-Zin non-expected utility model) counterintuitively predict that equilibrium asset prices can rise if the representative agent's risk aversion increases. If the income effect, which implies enhanced saving as a result of an increase in risk aversion, dominates the substitution effect, which causes the representative agent to reallocate his portfolio in favour of riskless assets, the demand for securities increases. Thus, asset prices are forced to rise when the representative agent is more risk adverse. By disentangling risk aversion and intertemporal substituability, we demonstrate that the risky asset price is an increasing function of the coefficient of risk aversion only if the elasticity of intertemporal substitution (EIS) exceeds unity. This result, which was first proved par Epstein (1988) in a stationary economy setting with a constant risk aversion, is shown to hold true for non-stationary economies with a variable or constant risk aversion coefficient. The conclusion is that the EIS probably exceeds unity.", "published": "2014-03-04T16:45:52Z", "authors": ["Dominique Pepin"], "categories": ["q-fin.CP", "q-fin.GN", "q-fin.PR"], "doi": "", "_query": "hedonic house price", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2003.06928v2", "title": "Low-dimensional approximations of high-dimensional asset price models", "abstract": "We consider high-dimensional asset price models that are reduced in their dimension in order to reduce the complexity of the problem or the effect of the curse of dimensionality in the context of option pricing. We apply model order reduction (MOR) to obtain a reduced system. MOR has been previously studied for asymptotically stable controlled stochastic systems with zero initial conditions. However, stochastic differential equations modeling price processes are uncontrolled, have non-zero initial states and are often unstable. Therefore, we extend MOR schemes and combine ideas of techniques known for deterministic systems. This leads to a method providing a good pathwise approximation. After explaining the reduction procedure, the error of the approximation is analyzed and the performance of the algorithm is shown conducting several numerical experiments. Within the numerics section, the benefit of the algorithm in the context of option pricing is pointed out.", "published": "2020-03-15T21:43:39Z", "authors": ["Martin Redmann", "Christian Bayer", "Pawan Goyal"], "categories": ["math.PR", "math.NA"], "doi": "", "_query": "hedonic house price", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/math/0605064v1", "title": "Pricing and hedging in incomplete markets with coherent risk", "abstract": "We propose a pricing technique based on coherent risk measures, which enables one to get finer price intervals than in the No Good Deals pricing. The main idea consists in splitting a liability into several parts and selling these parts to different agents. The technique is closely connected with the convolution of coherent risk measures and equilibrium considerations. Furthermore, we propose a way to apply the above technique to the coherent estimation of the Greeks.", "published": "2006-05-02T14:59:24Z", "authors": ["Alexander S. Cherny", "Dilip B. Madan"], "categories": ["math.PR", "q-fin.RM"], "doi": "", "_query": "hedonic house price", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2306.03052v1", "title": "Forecasting Crude Oil Prices Using Reservoir Computing Models", "abstract": "Accurate crude oil price prediction is crucial for financial decision-making. We propose a novel reservoir computing model for forecasting crude oil prices. It outperforms popular deep learning methods in most scenarios, as demonstrated through rigorous evaluation using daily closing price data from major stock market indices. Our model's competitive advantage is further validated by comparing it with recent deep-learning approaches. This study introduces innovative reservoir computing models for predicting crude oil prices, with practical implications for financial practitioners. By leveraging advanced techniques, market participants can enhance decision-making and gain valuable insights into crude oil market dynamics.", "published": "2023-06-05T17:23:26Z", "authors": ["Kaushal Kumar"], "categories": ["cs.LG"], "doi": "", "_query": "hedonic house price", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2609.36257v1", "title": "When Hedging Changes the Payoff: Option Replication with Price Impact and Execution Costs", "abstract": "Hedging a derivative by trading the underlying asset changes the payoff that the hedging intended to replicate. We study this phenomenon when trading generates price impact and execution costs. In a binomial model, we characterize replication through a fixed-point equation. In continuous time, we derive a nonlinear pricing PDE whose implicit terminal condition captures the nature of the moving target problem of the hedger. For monotone convex Lipschitz payoffs (such as calls and puts) we establish exact replication under midpoint execution costs. Numerical experiments illustrate: (i) how price impact shifts the effective strike, (ii) the non-linear dependence of the option price on the number of contracts, (iii) how execution costs smooth terminal holdings, (iv) the extent to which the hedger's own trading can bring an otherwise worthless option into the money, and (v) we explain the spread and the shape of the limit order book in the options market based on the price impact and the shape of the limit order book of the underlying.", "published": "2026-09-28T20:47:12Z", "authors": ["David Itkin", "Leandro Sánchez-Betancourt"], "categories": ["q-fin.MF", "math.PR", "q-fin.TR"], "doi": "", "_query": "hedonic house price", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2112.02368v1", "title": "A stochastic control approach to bid-ask price modelling", "abstract": "This paper develops a model for the bid and ask prices of a European type asset by formulating a stochastic control problem. The state process is governed by a modified geometric Brownian motion whose drift and diffusion coefficients depend on a Markov chain. A Girsanov theorem for Markov chains is implemented for the change of coefficients, including the diffusion coefficient which cannot be changed by the usual Girsanov theorem for Brownian motion. The price of a European type asset is then determined using an Esscher transform and a system of partial differential equations. A dynamic programming principle and a maximum/minimum principle associated with the stochastic control problem are then derived to model bid and ask prices. These prices are not quotes of traders or market makers but represent estimates in our model on which reasonable quantities could be traded.", "published": "2021-12-04T16:05:52Z", "authors": ["Engel John C. Dela Vega", "Robert J. Elliott"], "categories": ["q-fin.MF"], "doi": "", "_query": "hedonic house price", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/1410.0384v3", "title": "Indifference pricing for Contingent Claims: Large Deviations Effects", "abstract": "We study utility indifference prices and optimal purchasing quantities for a non-traded contingent claim in an incomplete semi-martingale market with vanishing hedging errors. We make connections with the theory of large deviations. We concentrate on sequences of semi-complete markets where in the $n^{th}$ market, the claim $B_n$ admits the decomposition $B_n = D_n+Y_n$. Here, $D_n$ is replicable by trading in the underlying assets $S_n$, but $Y_n$ is independent of $S_n$. Under broad conditions, we may assume that $Y_n$ vanishes in accordance with a large deviations principle as $n$ grows. In this setting, for an exponential investor, we identify the limit of the average indifference price $p_n(q_n)$, for $q_n$ units of $B_n$, as $n\\rightarrow \\infty$. We show that if $|q_n|\\rightarrow\\infty$, the limiting price typically differs from the price obtained by assuming bounded positions $\\sup_n|q_n|<\\infty$, and the difference is explicitly identifiable using large deviations theory. Furthermore, we show that optimal purchase quantities occur at the large deviations scaling, and hence large positions arise endogenously in this setting.", "published": "2014-10-01T20:58:14Z", "authors": ["Scott Robertson", "Konstantinos Spiliopoulos"], "categories": ["math.PR", "q-fin.MF"], "doi": "", "_query": "hedonic house price", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/cond-mat/9808168v1", "title": "Pricing defaultable debt: some exact results", "abstract": "In this letter, I consider the issue of pricing risky debt by following Merton's approach. I generalize Merton's results to the case where the interest rate is modeled by the CIR term structure. Exact closed forms are provided for the risky debt's price.", "published": "1998-08-16T18:22:23Z", "authors": ["D. F. Wang"], "categories": ["cond-mat.stat-mech", "q-fin.PR"], "doi": "", "_query": "hedonic house price", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/1012.2124v1", "title": "Discrete Price Updates Yield Fast Convergence in Ongoing Markets with Finite Warehouses", "abstract": "This paper shows that in suitable markets, even with out-of-equilibrium trade allowed, a simple price update rule leads to rapid convergence toward the equilibrium. In particular, this paper considers a Fisher market repeated over an unbounded number of time steps, with the addition of finite sized warehouses to enable non-equilibrium trade. The main result is that suitable tatonnement style price updates lead to convergence in a significant subset of markets satisfying the Weak Gross Substitutes property. Throughout this process the warehouse are always able to store or meet demand imbalances (the needed capacity depends on the initial imbalances). Finally, our price update rule is robust in a variety of regards: 1. The updates for each good depend only on information about that good (its current price, its excess demand since its last update) and occur asynchronously from updates to other prices. 2. The process is resilient to error in the excess demand data. 3. Likewise, the process is resilient to discreteness, i.e. a limit to divisibility, both of goods and money.", "published": "2010-12-09T21:10:53Z", "authors": ["Richard Cole", "Lisa Fleischer", "Ashish Rastogi"], "categories": ["cs.GT"], "doi": "", "_query": "hedonic house price", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2602.00188v1", "title": "Learning to Price: Interpretable Attribute-Level Models for Dynamic Markets", "abstract": "Dynamic pricing in high-dimensional markets poses fundamental challenges of scalability, uncertainty, and interpretability. Existing low-rank bandit formulations learn efficiently but rely on latent features that obscure how individual product attributes influence price. We address this by introducing an interpretable \\emph{Additive Feature Decomposition-based Low-Dimensional Demand (\\textbf{AFDLD}) model}, where product prices are expressed as the sum of attribute-level contributions and substitution effects are explicitly modeled. Building on this structure, we propose \\textbf{ADEPT} (Additive DEcomposition for Pricing with cross-elasticity and Time-adaptive learning)-a projection-free, gradient-free online learning algorithm that operates directly in attribute space and achieves a sublinear regret of $\\tilde{\\mathcal{O}}(\\sqrt{d}T^{3/4})$. Through controlled synthetic studies and real-world datasets, we show that ADEPT (i) learns near-optimal prices under dynamic market conditions, (ii) adapts rapidly to shocks and drifts, and (iii) yields transparent, attribute-level price explanations. The results demonstrate that interpretability and efficiency in autonomous pricing agents can be achieved jointly through structured, attribute-driven representations.", "published": "2026-01-30T07:52:07Z", "authors": ["Srividhya Sethuraman", "Chandrashekar Lakshminarayanan"], "categories": ["cs.AI", "cs.LG"], "doi": "10.65109/FJUA8337", "_query": "hedonic house price", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2511.05512v1", "title": "Estimating the Impact of the Bitcoin Halving on Its Price Using Synthetic Control", "abstract": "The third Bitcoin halving that took place in May 2020 cut down the mining reward from 12.5 to 6.25 BTC per block and thus slowed down the rate of issuance of new Bitcoins, making it more scarce. The fourth and most recent halving happened in April 2024, cutting the block reward further to 3.125 BTC. If the demand did not decrease simultaneously after these halvings, then the neoclassical economic theory posits that the price of Bitcoin should have increased due to the halving. But did it, in fact, increase for that reason, or is this a post hoc fallacy? This paper uses synthetic control to construct a weighted Bitcoin that is different from its counterpart in one aspect - it did not undergo halving. Comparing the price trajectory of the actual and the simulated Bitcoins, I find evidence of a positive effect of the 2024 Bitcoin halving on its price three months later. The magnitude of this effect is one fifth of the total percentage change in the price of Bitcoin during the study period - from April 2, 2023, to July 21, 2024 (17 months). The second part of the study fails to obtain a statistically significant and robust causal estimate of the effect of the 2020 Bitcoin halving on Bitcoin's price. This is the first paper analyzing the effect of halving causally, building on the existing body of correlational research.", "published": "2025-10-25T05:00:23Z", "authors": ["Vladislav Virtonen"], "categories": ["econ.GN", "econ.EM", "stat.AP"], "doi": "", "_query": "hedonic house price", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2012.02966v1", "title": "Assessing the effects of seasonal tariff-rate quotas on vegetable prices in Switzerland", "abstract": "Causal estimation of the short-term effects of tariff-rate quotas (TRQs) on vegetable producer prices is hampered by the large variety and different growing seasons of vegetables and is therefore rarely performed. We quantify the effects of Swiss seasonal TRQs on domestic producer prices of a variety of vegetables based on a difference-in-differences estimation using a novel dataset of weekly producer prices for Switzerland and neighbouring countries. We find that TRQs increase prices of most vegetables by more than 20% above the prices in neighbouring countries during the main harvest time for most vegetables and even more than 50% for some vegetables. The effects are stronger for more perishable vegetables and for conventionally produced ones compared with organic vegetables. However, we do not find clear-cut effects of TRQs on the week-to-week price volatility of vegetables although the overall lower price volatility in Switzerland compared with neighbouring countries might be a result of the TRQ system in place.", "published": "2020-12-05T07:24:52Z", "authors": ["Daria Loginova", "Marco Portmann", "Martin Huber"], "categories": ["econ.GN", "stat.AP"], "doi": "", "_query": "hedonic house price", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/1109.5418v1", "title": "Fixed Price of Groups and Percolation", "abstract": "We prove that for every finitely generated group $Γ$, at least one of the following holds: (1) $Γ$ has fixed price; (2) each of its Cayley graphs $G$ has infinitely many infinite clusters for some Bernoulli percolation on $G$.", "published": "2011-09-25T23:55:18Z", "authors": ["Russell Lyons"], "categories": ["math.DS", "math.PR"], "doi": "", "_query": "hedonic house price", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/1912.00840v3", "title": "Distributional Locational Marginal Pricing Based Optimization for Electric Vehicle Charging Management", "abstract": "Electric power generation, transmission, and distribution systems are attracting a large amount of interest from researchers with the development of the smart grid technologies. A smart grid aims at effective control and conditioning of the distribution of electricity. Pricing signal based distribution system are seen as one of the novel ways to achieve control in a smart grid. In our work, we propose to use a pricing signal modeled after the locational marginal price in the transmission system to locally provide price data to the users. The formulation and implementation of the distributional locational marginal price (DLMP) are achieved to develop a fair pricing model. The work is further practically implemented to a grid with Electric Vehicles in addition to the conventional load. The increasing popularity of EVs because of their ability to reduce greenhouse gas (GHG) emissions will pose a greater challenge in terms of demand on the power grid. We propose to use the DLMP modeling to alleviate congestion in the power grid and develop an optimal charging schedule for EVs.", "published": "2019-12-02T15:01:17Z", "authors": ["Abhishek Tyagi", "Ram Bhagat"], "categories": ["eess.SY"], "doi": "", "_query": "hedonic house price", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2512.11252v1", "title": "Personalized Pricing in Social Networks with Individual and Group Fairness Considerations", "abstract": "Personalized pricing assigns different prices to customers for the same product based on customer-specific features to improve retailer revenue. However, this practice often raises concerns about fairness at both the individual and group levels. At the individual level, a customer may perceive unfair treatment if he/she notices being charged a higher price than others. At the group level, pricing disparities can result in discrimination against certain protected groups, such as those defined by gender or race. Existing studies on fair pricing typically address individual and group fairness separately. This paper bridges the gap by introducing a new formulation of the personalized pricing problem that incorporates both dimensions of fairness in social network settings. To solve the problem, we propose FairPricing, a novel framework based on graph neural networks (GNNs) that learns a personalized pricing policy using customer features and network topology. In FairPricing, individual perceived unfairness is captured through a penalty on customer demand, and thus the profit objective, while group-level discrimination is mitigated using adversarial debiasing and a price regularization term. Unlike existing optimization-based personalized pricing, which requires re-optimization whenever the network updates, the pricing policy learned by FairPricing assigns personalized prices to all customers in an updated network based on their features and the new network structure, thereby generalizing to network changes. Extensive experimental results show that FairPricing achieves high profitability while improving individual fairness perceptions and satisfying group fairness requirements.", "published": "2025-12-12T03:20:29Z", "authors": ["Zeyu Chen", "Bintong Chen", "Wei Qian", "Jing Huang"], "categories": ["cs.CY", "cs.SI"], "doi": "", "_query": "hedonic house price", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/1607.03802v1", "title": "Continuous-time Marginal Pricing of Power Trajectories in Power Systems", "abstract": "In this paper we formulate of the Economic Dispatch (ED) problem in Power Systems in continuous time and include in it ramping constraints to derive an expression of the price that reflects some important inter-temporal constraints of the power units. The motivation for looking at this problem is the scarcity of ramping resources and their increasing importance motivated by the variability of power resources, particularly due to the addition of solar power, which exacerbates the need of fast ramping units in the early morning and early evening hours. We show that the solution for the marginal price can be found through Euler-Lagrange equations and we argue that this price signal better reflects the market value of power in the presence of significant ramps in net-load.", "published": "2016-07-11T21:59:50Z", "authors": ["Anna Scaglione"], "categories": ["math.OC"], "doi": "", "_query": "hedonic house price", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2206.12647v1", "title": "Feedback Dynamics of the Low-Income Rental Housing Market: Exploring Policy Responses to COVID-19", "abstract": "The COVID-19 recession threatens mass housing insecurity that undermines economic recovery. Unprecedented federal policy responses halt court-ordered evictions, but questions remain whether policies adequately account for dynamics that drive landlord-tenant interactions including accumulations of rental and mortgage arrears, rental unit availability, and low-income housing options. A system dynamics model probes complex feedback dynamics driving tenant and landlord decision-making in the low-income rental housing market pre- and post-pandemic protections. Feedback loops highlight tradeoffs considered by low-income tenants and landlords in the context of scarcity and uncertainty. Simulations suggest the eviction moratorium reduced evictions by 51% before its expiration in late 2021, but rental arrears, overcrowding, and homelessness remained high. Federal emergency rental assistance further contributed to keeping evictions low, but showed only modest effects on housing insecurity and homelessness. More rapid allocation of funds would substantially increase impact. Failure to address underlying financial hardship and limited affordable housing undermine COVID recovery.", "published": "2022-06-25T13:09:37Z", "authors": ["Katherine Marcal", "Patrick J. Fowler", "Peter S. Hovmand"], "categories": ["cs.CY"], "doi": "", "_query": "hedonic house price", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2105.05669v1", "title": "Carbon Leakage in a European Power System with Inhomogeneous Carbon Prices", "abstract": "Global warming is one of the main threats to the future of humanity and extensive emissions of greenhouse gases are found to be the main cause of global temperature rise as well as climate change. During the last decades international attention has focused on this issue, as well as on searching for viable solutions to mitigate global warming. In this context, the pricing of greenhouse gas emissions turned out to be the most prominent mechanism: First, to lower the emissions, and second, to capture their external costs. By now, various carbon dioxide taxes have been adopted by several countries in Europe and around the world; moreover, the list of these countries is growing. However, there is no standardized approach and the price for carbon varies significantly from one country to another. Regionally diversified carbon prices in turn lead to carbon leakage, which will offset the climate protection goals. In this paper, a simplified European power system with flexible carbon prices regarding the Gross Domestic Product (GDP) is investigated. A distribution parameter that quantifies carbon leakage is defined and varied together with the base carbon price, where the combination of both parameters describes the spatially resolved price distribution, i.e. the effective carbon pricing among the European regions. It is shown that inhomogeneous carbon prices will indeed lead to significant carbon leakage across the continent, and that coal-fired electricity generation will remain a cheap and therefore major source of power in Eastern and South-Eastern Europe - representing a potential risk for the long term decarbonization targets within the European Union.", "published": "2021-05-12T14:00:30Z", "authors": ["Markus Schlott", "Omar El Sayed", "Mariia Bilousova", "Fabian Hofmann", "Alexander Kies", "Horst Stöcker"], "categories": ["econ.GN", "physics.soc-ph"], "doi": "", "_query": "hedonic house price", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2605.12099v1", "title": "Bayesian Dynamic Modeling of Realized Volatility in Financial Asset Price Forecasting", "abstract": "We present a new class of Bayesian dynamic models for bivariate price-realized volatility time series in financial forecasting. A novel dynamic gamma process model adopted for realized volatility is integrated with traditional Bayesian dynamic linear models (DLMs) for asset price series. This represents reduced-form volatility leverage and feedback effects through use of realized volatility proxies in conditional DLMs for prices or returns, coupled with the synthesis of higher frequency data to track and anticipate volatility fluctuations. Analysis is computationally straightforward, extending conjugate-form Bayesian analyses for sequential filtering and model monitoring with simple and direct simulation for forecasting. A main applied setting is equity return forecasting with daily prices and realized volatility from high-frequency, intraday data. Detailed empirical studies of multiple S&P sector ETFs highlight the improvements achievable in asset price forecasting relative to standard models and deliver contextual insights on the nature and practical relevance of volatility leverage and feedback effects. The analytic structure and negligible extra computational cost will enable scaling to higher dimensions for multivariate price series forecasting for decouple/recouple portfolio construction and risk management applications.", "published": "2026-05-12T13:15:09Z", "authors": ["Patrick Woitschig", "Mike West"], "categories": ["stat.ME", "q-fin.ST"], "doi": "", "_query": "hedonic house price", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2503.24324v2", "title": "Mitigating Financial Risk from Climate-Induced Agricultural Price Volatility", "abstract": "Agricultural price volatility, driven by market dynamics and meteorological factors such as temperature and precipitation, poses challenges for sustainable finance, planning, and policy. This study analyzes the impact of climate on crop price volatility for soybean in Madhya Pradesh (India) and Illinois (US), rice in Assam (India), wheat in North Dakota (US), cotton in Gujarat (India), and corn in Iowa (US). Using CMIP6 climate projections from the Copernicus Climate Change Service, we examine historical climate patterns and evaluate two future scenarios: SSP2-4.5 (moderate) and SSP5-8.5 (severe). We estimate conditional price volatility using the Exponential Generalized Autoregressive Conditional Heteroskedasticity (EGARCH) model, and forecast this volatility with a Seasonal Autoregressive Integrated Moving Average with Exogenous Regressors (SARIMAX) model that incorporates meteorological variables. Finally, we apply the Black-Scholes framework to evaluate the cost of put-option-based insurance, which provides protection to farmers against adverse price drops linked to climate change. Our results highlight the role of meteorological data in improving agricultural risk modelling, enabling better design of insurance mechanisms, price stabilization tools, and sustainable policy interventions under climate uncertainty.", "published": "2025-03-31T17:11:00Z", "authors": ["Sourish Das", "Sudeep Shukla", "Abbinav Sankar Kailasam", "Anish Rai", "Sejal Garg", "Anirban Chakraborti"], "categories": ["stat.AP", "econ.GN", "physics.soc-ph", "q-fin.RM"], "doi": "", "_query": "hedonic house price", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2301.13575v2", "title": "Indifference pricing of pure endowments in a regime-switching market model", "abstract": "In this paper, we study the exponential utility indifference pricing of pure endowment policies within a stochastic-factor model for an insurer who also invests in a financial market. Our framework incorporates a hazard rate modeled as an observable diffusion process, while the risky asset price follows a jump-diffusion process driven by a continuous-time finite-state Markov chain, effectively capturing different economic regimes. Using the classical stochastic control approach based on the Hamilton-Jacobi-Bellman equation, we derive optimal investment strategies with and without the insurance derivative and characterize the indifference price as a classical solution to a linear partial differential equation (PDE). Additionally, we provide a probabilistic representation of the indifference price via an extension of the Feynman-Kac formula and show that it satisfies a suitable backward PDE. Finally, some numerical experiments are conducted to perform sensitivity analyses, highlighting the impact of key model parameters.", "published": "2023-01-31T12:00:27Z", "authors": ["Alessandra Cretarola", "Benedetta Salterini"], "categories": ["q-fin.PM"], "doi": "", "_query": "hedonic house price", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2106.01920v1", "title": "Convolutional Neural Network(CNN/ConvNet) in Stock Price Movement Prediction", "abstract": "With technological advancements and the exponential growth of data, we have been unfolding different capabilities of neural networks in different sectors. In this paper, I have tried to use a specific type of Neural Network known as Convolutional Neural Network(CNN/ConvNet) in the stock market. In other words, I have tried to construct and train a convolutional neural network on past stock prices data and then tried to predict the movement of stock price i.e. whether the stock price would rise or fall, in the coming time.", "published": "2021-06-03T15:14:46Z", "authors": ["Kunal Bhardwaj"], "categories": ["cs.NE", "cs.AI", "cs.CV", "cs.LG"], "doi": "", "_query": "hedonic house price", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2405.00051v1", "title": "Arbitrage impact on the relationship between XRP price and correlation tensor spectra of transaction networks", "abstract": "The increasing use of cryptoassets for international remittances has proven to be faster and more cost-effective, particularly for migrants without access to traditional banking. However, the inherent volatility of cryptoasset prices, independent of blockchain-based remittance mechanisms, introduces potential risks during periods of high volatility. This study investigates the intricate dynamics between XRP price fluctuations across diverse crypto exchanges and the correlation of the largest singular values of the correlation tensor of XRP transaction networks. Particularly, we show the impact of arbitrage opportunities across different crypto exchanges on the relationship between XRP price and correlation tensor spectra of transaction networks. Distinct periods, non-bubble and bubble, showcase different characteristics in XRP price fluctuations. Establishing a connection between XRP price and transaction networks, we compute correlation tensors and singular values, emphasizing the significance of the largest singular value. Comparisons with reshuffled and Gaussian random correlation tensors validate the uniqueness of the empirical tensor. A set of simulated weekly XRP prices, resembling arbitrage opportunities across various crypto exchanges, further confirms the robustness of our findings. It reveals a pronounced anti-correlation during bubble periods and a non-significant correlation during non-bubble periods with the largest singular value, irrespective of price fluctuations across different crypto exchanges.", "published": "2024-04-15T08:34:28Z", "authors": ["Abhijit Chakraborty", "Yuichi Ikeda"], "categories": ["physics.soc-ph", "q-fin.GN", "q-fin.ST"], "doi": "", "_query": "hedonic house price", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2606.13419v1", "title": "Realtime price impact detection", "abstract": "An important question for an algo trader working an order is to understand if their actions are moving the market against them -- i.e., causing market impact. The conventional answer usually is one of two: (i) monitor price slippage in real-time, potentially reducing adverse activity with increased slippage, or (ii) do away with dynamic trading adjustments and rely on semi-static rules based on ex-post estimates of slippage over a large sample of events. Realtime monitoring fails because reliably estimating slippage is statistically expensive -- it requires hundreds of fills before it can be told apart from background volatility. More fundamentally however, it does not establish causality. Observed adverse price moves may be caused by the trader's own actions, or by an unrelated participant competing for the same liquidity and capturing the same alpha. The optimal response (say, slow down vs.\\ speed up) is opposite in the two cases. We propose a method that detects price impact, on a per-action basis, by measuring the timing synchronicity between a trader's actions and subsequent adverse market events. The method at heart is a test for statistical \\emph{surprise} in the timing of adverse events post trader action. We must be clear in that we do make a leap of faith here and assume that surprisingly fast adverse market events are evidence of causation and that the action triggered them -- a direct signature of impact and information leakage. Validating it requires real execution data; we set out the empirical tests that would do so.", "published": "2026-06-11T14:49:42Z", "authors": ["Ilija I Zovko"], "categories": ["q-fin.TR"], "doi": "", "_query": "hedonic house price", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/1610.01450v9", "title": "Asset Pricing with Random Volatility", "abstract": "This paper proposes to model asset price dynamics with a mixture of diffusion processes where the instantaneous volatility of the underlying diffusion process contains a random vector. The marginal probability distributions of the proposed process can match exactly the risk-neutral distributions implied by both spot vanilla options and forward start options. We can also derive the explicit pricing formula for derivatives that have a closed-form solution under Generalized Geometric Brownian Motion.", "published": "2016-10-05T14:39:02Z", "authors": ["Xin Liu"], "categories": ["q-fin.MF", "q-fin.PR"], "doi": "", "_query": "hedonic house price", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/1905.01526v5", "title": "LP-based Approximation for Personalized Reserve Prices", "abstract": "We study the problem of computing data-driven personalized reserve prices in eager second price auctions without having any assumption on valuation distributions. Here, the input is a data-set that contains the submitted bids of $n$ buyers in a set of auctions and the problem is to return personalized reserve prices $\\textbf r$ that maximize the revenue earned on these auctions by running eager second price auctions with reserve $\\textbf r$. For this problem, which is known to be APX-hard, we present a novel LP formulation and a rounding procedure which achieves a $(1+2(\\sqrt{2}-1)e^{\\sqrt{2}-2})^{-1} \\approx 0.684$-approximation. This improves over the $\\frac{1}{2}$-approximation algorithm due to Roughgarden and Wang. We show that our analysis is tight for this rounding procedure. We also bound the integrality gap of the LP, which shows that it is impossible to design an algorithm that yields an approximation factor larger than $0.828$ with respect to this LP.", "published": "2019-05-04T17:14:38Z", "authors": ["Mahsa Derakhshan", "Negin Golrezaei", "Renato Paes Leme"], "categories": ["cs.GT"], "doi": "", "_query": "hedonic house price", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2411.02709v1", "title": "Carbon price fluctuation prediction using blockchain information A new hybrid machine learning approach", "abstract": "In this study, the novel hybrid machine learning approach is proposed in carbon price fluctuation prediction. Specifically, a research framework integrating DILATED Convolutional Neural Networks (CNN) and Long Short-Term Memory (LSTM) neural network algorithm is proposed. The advantage of the combined framework is that it can make feature extraction more efficient. Then, based on the DILATED CNN-LSTM framework, the L1 and L2 parameter norm penalty as regularization method is adopted to predict. Referring to the characteristics of high correlation between energy indicator price and blockchain information in previous literature, and we primarily includes indicators related to blockchain information through regularization process. Based on the above methods, this paper uses a dataset containing an amount of data to carry out the carbon price prediction. The experimental results show that the DILATED CNN-LSTM framework is superior to the traditional CNN-LSTM architecture. Blockchain information can effectively predict the price. Since parameter norm penalty as regularization, Ridge Regression (RR) as L2 regularization is better than Smoothly Clipped Absolute Deviation Penalty (SCAD) as L1 regularization in price forecasting. Thus, the proposed RR-DILATED CNN-LSTM approach can effectively and accurately predict the fluctuation trend of the carbon price. Therefore, the new forecasting methods and theoretical ecology proposed in this study provide a new basis for trend prediction and evaluating digital assets policy represented by the carbon price for both the academia and practitioners.", "published": "2024-11-05T01:12:17Z", "authors": ["H. Wang", "Y. Pang", "D. Shang"], "categories": ["cs.LG", "stat.ML"], "doi": "", "_query": "hedonic house price", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/1907.05381v1", "title": "Adaptive Pricing in Insurance: Generalized Linear Models and Gaussian Process Regression Approaches", "abstract": "We study the application of dynamic pricing to insurance. We view this as an online revenue management problem where the insurance company looks to set prices to optimize the long-run revenue from selling a new insurance product. We develop two pricing models: an adaptive Generalized Linear Model (GLM) and an adaptive Gaussian Process (GP) regression model. Both balance between exploration, where we choose prices in order to learn the distribution of demands & claims for the insurance product, and exploitation, where we myopically choose the best price from the information gathered so far. The performance of the pricing policies is measured in terms of regret: the expected revenue loss caused by not using the optimal price. As is commonplace in insurance, we model demand and claims by GLMs. In our adaptive GLM design, we use the maximum quasi-likelihood estimation (MQLE) to estimate the unknown parameters. We show that, if prices are chosen with suitably decreasing variability, the MQLE parameters eventually exist and converge to the correct values, which in turn implies that the sequence of chosen prices will also converge to the optimal price. In the adaptive GP regression model, we sample demand and claims from Gaussian Processes and then choose selling prices by the upper confidence bound rule. We also analyze these GLM and GP pricing algorithms with delayed claims. Although similar results exist in other domains, this is among the first works to consider dynamic pricing problems in the field of insurance. We also believe this is the first work to consider Gaussian Process regression in the context of insurance pricing. These initial findings suggest that online machine learning algorithms could be a fruitful area of future investigation and application in insurance.", "published": "2019-07-02T08:18:54Z", "authors": ["Yuqing Zhang", "Neil Walton"], "categories": ["econ.EM", "math.OC", "q-fin.MF", "q-fin.RM", "stat.ML"], "doi": "", "_query": "hedonic house price", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/1303.1134v1", "title": "Utility maximisation and utility indifference price for exponential semi-martingale models with random factor", "abstract": "We consider utility maximization problem for semi-martingale models depending on a random factor $ξ$. We reduce initial maximization problem to the conditional one, given $ξ=u$, which we solve using dual approach. For HARA utilities we consider information quantities like Kullback-Leibler information and Hellinger integrals, and corresponding information processes. As a particular case we study exponential Levy models depending on random factor. In that case the information processes are deterministic and this fact simplify very much indifference price calculus. Then we give the equations for indifference prices. We show that indifference price for seller and minus indifference price for buyer are risk measures. Finally, we apply the results to Geometric Brownian motion case. Using identity in law technique we give the explicit expression for information quantities. Then, the previous formulas for indifference price can be applied.", "published": "2013-03-05T18:38:39Z", "authors": ["Anastasia Ellanskaya", "Lioudmila Vostrikova"], "categories": ["q-fin.PR", "math.PR"], "doi": "", "_query": "hedonic house price", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2604.14257v2", "title": "Mapping the causal structure of price formation in Texas's transitioning electricity market", "abstract": "Renewable deployment and rising demand from electrification and large digital loads are transforming electricity markets. However, how these developments reshape electricity price dynamics remains poorly understood, leaving system planners, capacity investors, and market participants reliant on assumptions from a thermal-dominated era that may no longer hold. We use causal discovery to study the evolution of wholesale electricity prices in Texas, which is undergoing rapid transformation. Our findings overturn the view of Texas as a gas-price-driven market, demonstrating that wind generation has become the dominant causal driver of day-ahead prices, with effects more than three times greater than those of natural gas. Yet wind's price-suppressing effect is weakening during peak periods, and wind growth redistributes congestion costs to distant load centres. Furthermore, rising load in South and West Texas alters system prices and regional differentials. Uncovering the evolving spatiotemporal nature of causal drivers, our analysis reveals that the pace, geographic siting, and relative scale of new generation and large loads will be decisive for future electricity price risks, infrastructure needs, and investments.", "published": "2026-04-15T15:05:19Z", "authors": ["Shiva Madadkhani", "Nils Sturma", "Mathias Drton", "Svetlana Ikonnikova"], "categories": ["econ.GN", "stat.AP"], "doi": "", "_query": "hedonic house price", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2408.14169v1", "title": "Dynamic Pricing for Electric Vehicle Charging", "abstract": "Dynamic pricing is a promising strategy to address the challenges of smart charging, as traditional time-of-use (ToU) rates and stationary pricing (SP) do not dynamically react to changes in operating conditions, reducing revenue for charging station (CS) vendors and affecting grid stability. Previous studies evaluated single objectives or linear combinations of objectives for EV CS pricing solutions, simplifying trade-offs and preferences among objectives. We develop a novel formulation for the dynamic pricing problem by addressing multiple conflicting objectives efficiently instead of solely focusing on one objective or metric, as in earlier works. We find optimal trade-offs or Pareto solutions efficiently using Non-dominated Sorting Genetic Algorithms (NSGA) II and NSGA III. A dynamic pricing model quantifies the relationship between demand and price while simultaneously solving multiple conflicting objectives, such as revenue, quality of service (QoS), and peak-to-average ratios (PAR). A single method can only address some of the above aspects of dynamic pricing comprehensively. We present a three-part dynamic pricing approach using a Bayesian model, multi-objective optimization, and multi-criteria decision-making (MCDM) using pseudo-weight vectors. To address the research gap in CS pricing, our method selects solutions using revenue, QoS, and PAR metrics simultaneously. Two California charging sites' real-world data validates our approach.", "published": "2024-08-26T10:32:21Z", "authors": ["Arun Kumar Kalakanti", "Shrisha Rao"], "categories": ["cs.DC", "cs.AI"], "doi": "", "_query": "hedonic house price", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2209.00858v1", "title": "A Discussion of Discrimination and Fairness in Insurance Pricing", "abstract": "Indirect discrimination is an issue of major concern in algorithmic models. This is particularly the case in insurance pricing where protected policyholder characteristics are not allowed to be used for insurance pricing. Simply disregarding protected policyholder information is not an appropriate solution because this still allows for the possibility of inferring the protected characteristics from the non-protected ones. This leads to so-called proxy or indirect discrimination. Though proxy discrimination is qualitatively different from the group fairness concepts in machine learning, these group fairness concepts are proposed to 'smooth out' the impact of protected characteristics in the calculation of insurance prices. The purpose of this note is to share some thoughts about group fairness concepts in the light of insurance pricing and to discuss their implications. We present a statistical model that is free of proxy discrimination, thus, unproblematic from an insurance pricing point of view. However, we find that the canonical price in this statistical model does not satisfy any of the three most popular group fairness axioms. This seems puzzling and we welcome feedback on our example and on the usefulness of these group fairness axioms for non-discriminatory insurance pricing.", "published": "2022-09-02T07:31:37Z", "authors": ["Mathias Lindholm", "Ronald Richman", "Andreas Tsanakas", "Mario V. Wüthrich"], "categories": ["cs.LG", "q-fin.ST", "stat.ML"], "doi": "", "_query": "hedonic house price", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2008.11788v1", "title": "Share Price Prediction of Aerospace Relevant Companies with Recurrent Neural Networks based on PCA", "abstract": "The capital market plays a vital role in marketing operations for aerospace industry. However, due to the uncertainty and complexity of the stock market and many cyclical factors, the stock prices of listed aerospace companies fluctuate significantly. This makes the share price prediction challengeable. To improve the prediction of share price for aerospace industry sector and well understand the impact of various indicators on stock prices, we provided a hybrid prediction model by the combination of Principal Component Analysis (PCA) and Recurrent Neural Networks. We investigated two types of aerospace industries (manufacturer and operator). The experimental results show that PCA could improve both accuracy and efficiency of prediction. Various factors could influence the performance of prediction models, such as finance data, extracted features, optimisation algorithms, and parameters of the prediction model. The selection of features may depend on the stability of historical data: technical features could be the first option when the share price is stable, whereas fundamental features could be better when the share price has high fluctuation. The delays of RNN also depend on the stability of historical data for different types of companies. It would be more accurate through using short-term historical data for aerospace manufacturers, whereas using long-term historical data for aerospace operating airlines. The developed model could be an intelligent agent in an automatic stock prediction system, with which, the financial industry could make a prompt decision for their economic strategies and business activities in terms of predicted future share price, thus improving the return on investment. Currently, COVID-19 severely influences aerospace industries. The developed approach can be used to predict the share price of aerospace industries at post COVID-19 time.", "published": "2020-08-26T20:16:33Z", "authors": ["Linyu Zheng", "Hongmei He"], "categories": ["q-fin.ST", "cs.LG"], "doi": "", "_query": "hedonic house price", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/1312.4227v3", "title": "Matching distributions: Asset pricing with density shape correction", "abstract": "We investigate a statistical-static hedging technique for pricing assets considered as single-step stochastic cash flows. The valuation is based on constructing in a canonical way a European style derivative on a benchmark security such that the physical payoff distribution coincides with the (corrected) physical asset price distribution. It turns out that this pricing technique is economically viable under some natural cases. The fundamental properties of the pricing rule arising in this way are investigated here. This gives rise to a novel way of estimating state price density. Our approach has some tangible benefits: its principle is transparent, and it is easy to implement numerically while avoiding many issues typically involved in such an estimation. As an application, it is shown how this method can be used in performing kurtosis corrections to the standard Black-Scholes-Merton model by a mixture of several types of distributions. In fact, the technique is non-parametric in nature, and it can handle in principle any physical distribution, e.g., a multimodal one. Some other interesting applications are discussed as well.", "published": "2013-12-16T02:09:28Z", "authors": ["Jarno Talponen"], "categories": ["q-fin.PR", "math.PR"], "doi": "", "_query": "hedonic house price", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/1905.00731v3", "title": "Static Pricing: Universal Guarantees for Reusable Resources", "abstract": "We consider a fundamental pricing model in which a fixed number of units of a reusable resource are used to serve customers. Customers arrive to the system according to a stochastic process and upon arrival decide whether or not to purchase the service, depending on their willingness-to-pay and the current price. The service time during which the resource is used by the customer is stochastic and the firm may incur a service cost. This model represents various markets for reusable resources such as cloud computing, shared vehicles, rotable parts, and hotel rooms. In the present paper, we analyze this pricing problem when the firm attempts to maximize a weighted combination of three central metrics: profit, market share, and service level. Under Poisson arrivals, exponential service times, and standard assumptions on the willingness-to-pay distribution, we establish a series of results that characterize the performance of static pricing in such environments. In particular, while an optimal policy is fully dynamic in such a context, we prove that a static pricing policy simultaneously guarantees 78.9% of the profit, market share, and service level from the optimal policy. Notably, this result holds for any service rate and number of units the firm operates. Our proof technique relies on a judicious construction of a static price that is derived directly from the optimal dynamic pricing policy. In the special case where there are two units and the induced demand is linear, we also prove that the static policy guarantees 95.5% of the profit from the optimal policy. Our numerical findings on a large testbed of instances suggest that the latter result is quite indicative of the profit obtained by the static pricing policy across all parameters.", "published": "2019-05-02T13:38:54Z", "authors": ["Omar Besbes", "Adam N. Elmachtoub", "Yunjie Sun"], "categories": ["cs.DS", "cs.GT", "math.OC"], "doi": "", "_query": "hedonic house price", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/1610.05583v3", "title": "Price Dynamics Via Expectations, and the Role of Money Therein", "abstract": "Beyond its obvious macro-economic relevance, fiat money has important micro-economic implications. They matter for addressing No. 8 in Smale's \"Mathematical Problems for the Next Century\": extend the mathematical model of general equilibrium theory to include price adjustments. In the canonical Arrow-Debreu framework, equilibrium prices are set by a fictitious auctioneer. Removing that fiction raises the question of how prices are set and adjusted by decentralised actors with incomplete information. We investigate this question through a very basic model where a unique factor of production, labour, produces a single consumption good, called jelly for brevity. The point of the model is to study a price dynamics based on the firm's expectations about jelly demand and labour supply. The system tends towards economic equilibrium, however, depending on the initial conditions it might not get there. In different model versions, different kinds of money are introduced. Compared to the case of no money, the introduction of money as a store of value facilitates the system reaching economic equilibrium. If money is introduced as a third commodity, i.e. there is also a demand for money, the system dynamics in general becomes more complex.", "published": "2016-10-18T12:50:52Z", "authors": ["Gesine A. Steudle", "Saini Yang", "Carlo C. Jaeger"], "categories": ["econ.GN"], "doi": "", "_query": "hedonic house price", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2205.06677v1", "title": "Collective behavior of stock prices in the time of crisis as a response to the external stimulus", "abstract": "We analyze the interaction between stock prices of big companies in the USA and Germany using Granger Causality. We claim that the increase in pair-wise Granger causality interaction between prices in the times of crisis is the consequence of simultaneous response of the markets to the outside events or external stimulus that is considered as a common driver to all the stocks, not a result of real causal predictability between the prices themselves. An alternative approach through recurrence analysis in single stock price series supports this claim. The observed patterns in the price of stocks are modelled by adding a multiplicative exogenous term as the representative for external factors to the geometric Brownian motion model for stock prices. Altogether, we can detect and model the effects of the Great Recession as a consequence of the mortgage crisis in 2007/2008 as well as the impacts of the Covid out-break in early 2020", "published": "2022-05-09T13:58:14Z", "authors": ["Maryam Zamani", "Sander Paekivi", "Philipp Meyer", "Holger Kantz"], "categories": ["q-fin.ST"], "doi": "", "_query": "hedonic house price", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2511.22185v2", "title": "Textual semantics and machine learning methods for data product pricing", "abstract": "Reasonable pricing of data products enables data trading platforms to maximize revenue and foster the growth of the data trading market. The textual semantics of data products are vital for pricing and contain significant value that remains largely underexplored. Therefore, to investigate how textual features influence data product pricing, we employ five prevalent text representation techniques to encode the descriptive text of data products. And then, we employ six machine learning methods to predict data product prices, including linear regression, neural networks, decision trees, support vector machines, random forests, and XGBoost. Our empirical design consists of two tasks: a regression task that predicts the continuous price of data products, and a classification task that discretizes price into ordered categories. Furthermore, we conduct feature importance analysis by the mRMR feature selection method and SHAP-based interpretability techniques. Based on empirical data from the AWA Data Exchange, we find that for predicting continuous prices, Word2Vec text representations capturing semantic similarity yield superior performance. In contrast, for price-tier classification tasks, simpler representations that do not rely on semantic similarity, such as Bag-of-Words and TF-IDF, perform better. SHAP analysis reveals that semantic features related to healthcare and demographics tend to increase prices, whereas those associated with weather and environmental topics are linked to lower prices. This analytical framework significantly enhances the interpretability of pricing models.", "published": "2025-11-27T07:51:31Z", "authors": ["Ruize Gao", "Feng Xiao", "Jinpu Li", "Shaoze Cui"], "categories": ["cs.CE"], "doi": "10.4310/RME.260221154937", "_query": "hedonic house price", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2301.10840v1", "title": "Improved Bitcoin Price Prediction based on COVID-19 data", "abstract": "Social turbulence can affect people financial decisions, causing changes in spending and saving. During a global turbulence as significant as the COVID-19 pandemic, such changes are inevitable. Here we examine how the effects of COVID-19 on various jurisdictions influenced the global price of Bitcoin. We hypothesize that lock downs and expectations of economic recession erode people trust in fiat (government-issued) currencies, thus elevating cryptocurrencies. Hence, we expect to identify a causal relation between the turbulence caused by the pandemic, demand for Bitcoin, and ultimately its price. To test the hypothesis, we merged datasets of Bitcoin prices and COVID-19 cases and deaths. We also engineered extra features and applied statistical and machine learning (ML) models. We applied a Random Forest model (RF) to identify and rank the feature importance, and ran a Long Short-Term Memory (LSTM) model on Bitcoin prices data set twice: with and without accounting for COVID-19 related features. We find that adding COVID-19 data into the LSTM model improved prediction of Bitcoin prices.", "published": "2023-01-25T21:37:37Z", "authors": ["Palina Niamkova", "Rafael Moreira"], "categories": ["cs.LG", "cs.AI"], "doi": "", "_query": "hedonic house price", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2603.25874v1", "title": "A Market Design Proposal for Decoupling Carbon and Electricity Prices", "abstract": "In European day-ahead electricity markets, carbon allowance costs passed through by marginal fossil plants raise consumer expenditure and generate inframarginal rents for non-emitting generators. We propose a settlement modification: when the zonal day-ahead price exceeds a threshold, non-emitting generation is remunerated at the clearing price minus a fixed CO2 proxy deduction, while all other units continue to receive the uniform price. The mechanism thus reallocates a part of the inframarginal rents to consumers. Using hourly data we estimate static average expenditure reductions of about 8.5% in Austria and 4.7% in Germany in 2025. We discuss bidding incentives around the threshold, interactions with Contracts for Difference, implementation in coupled bidding zones, and a gas-cost variant for the 2022 energy crisis.", "published": "2026-03-26T20:00:29Z", "authors": ["Simon Finster", "Bernhard Kasberger", "Simon Rütten"], "categories": ["econ.GN"], "doi": "", "_query": "hedonic house price", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/1606.09194v1", "title": "A multilayer approach for price dynamics in financial markets", "abstract": "We introduce a new Self-Organized Criticality (SOC) model for simulating price evolution in an artificial financial market, based on a multilayer network of traders. The model also implements, in a quite realistic way with respect to previous studies, the order book dy- namics, by considering two assets with variable fundamental prices. Fat tails in the probability distributions of normalized returns are observed, together with other features of real financial markets.", "published": "2016-06-29T17:32:24Z", "authors": ["Alessio Emanuele Biondo", "Alessandro Pluchino", "Andrea Rapisarda"], "categories": ["q-fin.TR", "physics.soc-ph"], "doi": "", "_query": "hedonic house price", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2504.10459v3", "title": "The Price of Competitive Information Disclosure", "abstract": "In many decision-making scenarios, individuals strategically choose what information to disclose to optimize their own outcomes. It is unclear whether such strategic information disclosure can lead to good societal outcomes. To address this question, we consider a competitive Bayesian persuasion model in which multiple agents selectively disclose information about their qualities to a principal, who aims to choose the candidates with the highest qualities. Using the price-of-anarchy framework, we quantify the inefficiency of such strategic disclosure. We show that the price of anarchy is at most a constant when the agents have independent quality distributions, even if their utility functions are heterogeneous. This result provides the first theoretical guarantee on the limits of inefficiency in Bayesian persuasion with competitive information disclosure.", "published": "2025-04-14T17:46:13Z", "authors": ["Siddhartha Banerjee", "Kamesh Munagala", "Yiheng Shen", "Kangning Wang"], "categories": ["cs.GT"], "doi": "", "_query": "hedonic house price", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2607.21502v1", "title": "A Needs Assessment for Measuring Geographic - Legislative Associations in the U.S. House of Representatives", "abstract": "Political legislation affects the well-being and livelihoods of constituents. In the U.S. Congress a representative's voting record on bills and legislation is public. These bills have themes associated with them, such as veterans' affairs, coastal monitoring, agricultural appropriations, etc. A bill on veterans' affairs may affect a constituency differently if they have a high percentage of veterans. In this work, we demonstrate how congressional vote outcomes can be merged with typical geographic information systems (GIS) data to help compare a legislator's votes with the geographies of their constituencies to measure the association between district features and legislators' decisions. We retrieved and tagged bills from the 118th U.S. House of Representatives (Jan. 2023 - Jan. 2025) by manually assigning each bill a set of themes. We then retrieved spatial data at the congressional district level related to each theme. We built a backend database to connect bill information and spatial data, and an interactive map prototype displayed the spatial data related to each theme associated with each bill. Our work helps identify challenges to creating a more accessible and complete system that would facilitate the visualization and analysis of the nature of contemporary Congressional representation. This technical report is primarily written for members of the data science community, and specifically those interested in legislative and geographic data.", "published": "2026-07-23T16:50:58Z", "authors": ["Ashu Gupta", "Cameron Owens", "Benjamin Wilson-Langman", "Max Hill", "Jason Cox", "Joshua Clinton", "Clio Andris"], "categories": ["cs.HC"], "doi": "", "_query": "hedonic house price", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/1803.08831v3", "title": "A structural Heath-Jarrow-Morton framework for consistent intraday, spot, and futures electricity prices", "abstract": "In this paper we introduce a flexible HJM-type framework that allows for consistent modelling of intraday, spot, futures, and option prices. This framework is based on stochastic processes with economic interpretations and consistent with the initial term structure given in the form of a price forward curve. Furthermore, the framework allows for existing day-ahead spot price models to be used in an HJM setting. We include several explicit examples of classical spot price models but also show how structural models and factor models can be formulated within the framework.", "published": "2018-03-23T15:19:00Z", "authors": ["Wieger Hinderks", "Andreas Wagner", "Ralf Korn"], "categories": ["q-fin.MF", "math.PR"], "doi": "", "_query": "hedonic house price", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/cond-mat/9910376v1", "title": "Speculative trading: the price multiplier effect", "abstract": "During a speculative episode the price of an item jumps from an initial level p_1 to a peak level p_2 before more or less returning to level p_1. The ratio p_2/p_1 is referred to as the amplitude A of the peak. This paper shows that for a given market the peak amplitude is a linear function of the logarithm of the price at the beginning of the speculative episode; with p_1 expressed in 1999 euros the relationship takes the form: $ A=a\\ln p_1 +b $; the values of the parameter a turn out to be relatively independent of the market considered: $ a \\simeq 0.5 $, the values of the parameter b are more market-dependent, but are stable in the course of time for a given market. This relationship suggests that the higher the stakes the more \"bullish\" the market becomes. Possible mechanisms of this \"risk affinity\" effect are discussed.", "published": "1999-10-23T18:11:41Z", "authors": ["B. M. Roehner"], "categories": ["cond-mat.stat-mech", "q-fin.TR"], "doi": "10.1007/s100510050144", "_query": "hedonic house price", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/1705.01965v1", "title": "Makespan Minimization via Posted Prices", "abstract": "We consider job scheduling settings, with multiple machines, where jobs arrive online and choose a machine selfishly so as to minimize their cost. Our objective is the classic makespan minimization objective, which corresponds to the completion time of the last job to complete. The incentives of the selfish jobs may lead to poor performance. To reconcile the differing objectives, we introduce posted machine prices. The selfish job seeks to minimize the sum of its completion time on the machine and the posted price for the machine. Prices may be static (i.e., set once and for all before any arrival) or dynamic (i.e., change over time), but they are determined only by the past, assuming nothing about upcoming events. Obviously, such schemes are inherently truthful. We consider the competitive ratio: the ratio between the makespan achievable by the pricing scheme and that of the optimal algorithm. We give tight bounds on the competitive ratio for both dynamic and static pricing schemes for identical, restricted, related, and unrelated machine settings. Our main result is a dynamic pricing scheme for related machines that gives a constant competitive ratio, essentially matching the competitive ratio of online algorithms for this setting. In contrast, dynamic pricing gives poor performance for unrelated machines. This lower bound also exhibits a gap between what can be achieved by pricing versus what can be achieved by online algorithms.", "published": "2017-05-04T18:21:56Z", "authors": ["Michal Feldman", "Amos Fiat", "Alan Roytman"], "categories": ["cs.GT"], "doi": "", "_query": "hedonic house price", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2509.24720v1", "title": "Dynamic Pricing of an Expiring Item under Strategic Buyers with Stochastic Arrival", "abstract": "We study the optimal dynamic pricing of an expiring ticket or voucher, sold by a time-sensitive seller to strategic buyers who arrive stochastically with private values. The expiring nature creates a conflict: the seller's urgency to sell before expiration drives price reductions, which in turn incentivize buyers to wait. We seek the seller's optimal pricing policy that resolves this tension. The main analytical challenge is that buyer type is two-dimensional (valuation and arrival time), which makes equilibrium intractable under general strategies. To address this, we introduce the Value-Based Threshold (VBT) strategy, a tractable framework that decouples these two dimensions. Using this framework, we prove equilibrium existence via an ordinary differential equation and provide a constructive procedure for its characterization. We then derive near-optimal pricing policies for two stylized regimes: a constant price in thin markets and a linear discount in thick markets. Numerical frontier analysis confirms these benchmarks and shows how optimal policy adapts as the seller's time sensitivity changes. Our findings clarify the conflict between quick sales and strategic waiting. Sellers facing thick markets or high time sensitivity benefit from linear discounts, while in thin markets a constant price neutralizes buyers' incentive to wait. We also show this simple policy remains robust across broad conditions. For patient sellers, a quasi-auction schedule that maintains a high price until a sharp final drop is most effective in aggregating demand.", "published": "2025-09-29T12:48:03Z", "authors": ["Suyeon Choi", "Changhyun Kwon", "Seungki Min"], "categories": ["cs.GT"], "doi": "", "_query": "hedonic house price", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2112.04245v1", "title": "Do fundamentals shape the price response? A critical assessment of linear impact models", "abstract": "We compare the predictions of the stationary Kyle model, a microfounded multi-step linear price impact model in which market prices forecast fundamentals through information encoded in the order flow, with those of the propagator model, a purely data-driven model in which trades mechanically impact prices with a time-decaying kernel. We find that, remarkably, both models predict the exact same price dynamics at high frequency, due to the emergence of universality at small time scales. On the other hand, we find those models to disagree on the overall strength of the impact function by a quantity that we are able to relate to the amount of excess-volatility in the market. We reveal a crossover between a high-frequency regime in which the market reacts sub-linearly to the signed order flow, to a low-frequency regime in which prices respond linearly to order flow imbalances. Overall, we reconcile results from the literature on market microstructure (sub-linearity in the price response to traded volumes) with those relating to macroeconomically relevant timescales (in which a linear relation is typically assumed).", "published": "2021-12-08T11:44:41Z", "authors": ["Michele Vodret", "Iacopo Mastromatteo", "Bence Tóth", "Michael Benzaquen"], "categories": ["q-fin.TR", "cond-mat.stat-mech"], "doi": "", "_query": "hedonic house price", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/1609.06193v3", "title": "Stability Analysis of Wholesale Electricity Markets under Dynamic Consumption Models and Real-Time Pricing", "abstract": "This paper analyzes stability conditions for wholesale electricity markets under real-time retail pricing and realistic consumption models with memory, which explicitly take into account previous electricity prices and consumption levels. By passing on the current retail price of electricity from supplier to consumer and feeding the observed consumption back to the supplier, a closed-loop dynamical system for electricity prices and consumption arises whose stability is to be investigated. Under mild assumptions on the generation cost of electricity and consumers' backlog disutility functions, we show that, for consumer models with price memory only, market stability is achieved if the ratio between the consumers' marginal backlog disutility and the suppliers' marginal cost of supply remains below a fixed threshold. Further, consumer models with price and consumption memory can result in greater stability regions and faster convergence to the equilibrium compared to models with price memory alone, if consumption deviations from nominal demand are adequately penalized.", "published": "2016-09-20T14:20:09Z", "authors": ["Datong P. Zhou", "Mardavij Roozbehani", "Munther A. Dahleh", "Claire J. Tomlin"], "categories": ["eess.SY"], "doi": "", "_query": "hedonic house price", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2609.17395v1", "title": "The price of anarchy in the max-distance network creation game is not constant", "abstract": "At edge price $α=1$, we construct an infinite family of pure Nash equilibria of the unilateral max-distance network creation game with $\\PoA\\ge2^{\\sqrt{\\log_2 n}-O(\\log\\log n)}$. Together with the known upper bound, this gives $2^{Θ(\\sqrt{\\log n})}$ along the constructed sequence of population sizes. We subdivide every edge of the bipartite double cover of a distance-uniform graph with large diameter constructed by Lavrov, Loh and Messegué, and let each subdivision vertex buy its two incident edges. A distance calculation rules out every profitable unilateral deviation. The equilibria are not strict. We also give a short proof that the price of anarchy is constant for every polynomially vanishing edge price.", "published": "2026-09-15T16:31:32Z", "authors": ["Christoph Schlegel"], "categories": ["cs.GT", "cs.DM"], "doi": "", "_query": "hedonic house price", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/1707.00807v1", "title": "General Price Bounds for Guaranteed Annuity Options", "abstract": "In this paper, we are concerned with the valuation of Guaranteed Annuity Options (GAOs) under the most generalised modelling framework where both interest and mortality rates are stochastic and correlated. Pricing these type of options in the correlated environment is a challenging task and no closed form solution exists in the literature. We employ the use of doubly stochastic stopping times to incorporate the randomness about the time of death and employ a suitable change of measure to facilitate the valuation of survival benefit, there by adapting the payoff of the GAO in terms of the payoff of a basket call option. We derive general price bounds for GAOs by utilizing a conditioning approach for the lower bound and arithmetic-geometric mean inequality for the upper bound. The theory is then applied to affine models to present some very interesting formulae for the bounds under the affine set up. Numerical examples are furnished and benchmarked against Monte Carlo simulations to estimate the price of a GAO for a variety of affine processes governing the evolution of mortality and the interest rate.", "published": "2017-07-04T03:05:07Z", "authors": ["Raj Kumari Bahl", "Sotirios Sabanis"], "categories": ["q-fin.PR", "math.PR", "stat.CO"], "doi": "", "_query": "hedonic house price", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/1903.06288v1", "title": "Optimal Price of Anarchy in Cost-Sharing Games", "abstract": "The design of distributed algorithms is central to the study of multiagent systems control. In this paper, we consider a class of combinatorial cost-minimization problems and propose a framework for designing distributed algorithms with a priori performance guarantees that are near-optimal. We approach this problem from a game-theoretic perspective, assigning agents cost functions such that the equilibrium efficiency (price of anarchy) is optimized. Once agents' cost functions have been specified, any algorithm capable of computing a Nash equilibrium of the system inherits a performance guarantee matching the price of anarchy. Towards this goal, we formulate the problem of computing the price of anarchy as a tractable linear program. We then present a framework for designing agents' local cost functions in order to optimize for the worst-case equilibrium efficiency. Finally, we investigate the implications of our findings when this framework is applied to systems with convex, nondecreasing costs.", "published": "2019-03-14T22:44:55Z", "authors": ["Rahul Chandan", "Dario Paccagnan", "Jason R. Marden"], "categories": ["eess.SY", "cs.GT", "cs.MA"], "doi": "", "_query": "hedonic house price", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2003.07736v1", "title": "Anomalous supply shortages from dynamic pricing in on-demand mobility", "abstract": "Dynamic pricing schemes are increasingly employed across industries to maintain a self-organized balance of demand and supply. However, throughout complex dynamical systems, unintended collective states exist that may compromise their function. Here we reveal how dynamic pricing may induce demand-supply imbalances instead of preventing them. Combining game theory and time series analysis of dynamic pricing data from on-demand ride-hailing services, we explain this apparent contradiction. We derive a phase diagram demonstrating how and under which conditions dynamic pricing incentivizes collective action of ride-hailing drivers to induce anomalous supply shortages. By disentangling different timescales in price time series of ride-hailing services at 137 locations across the globe, we identify characteristic patterns in the price dynamics reflecting these anomalous supply shortages. Our results provide systemic insights for the regulation of dynamic pricing, in particular in publicly accessible mobility systems, by unraveling under which conditions dynamic pricing schemes promote anomalous supply shortages.", "published": "2020-03-16T17:45:21Z", "authors": ["Malte Schröder", "David-Maximilian Storch", "Philip Marszal", "Marc Timme"], "categories": ["physics.soc-ph", "econ.EM"], "doi": "10.1038/s41467-020-18370-3", "_query": "hedonic house price", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2002.05970v1", "title": "Intertemporal Price Discrimination with Time-Varying Valuations", "abstract": "A firm that sells a non perishable product considers intertemporal price discrimination in the objective of maximizing its long-run average revenue. We consider a general model of patient customers with changing valuations. Arriving customers wait for a random but bounded length of time and purchase the product when its price falls below their valuation, which varies following a stochastic process. We show the optimality of a class of cyclic strategies and obtain an algorithm that yields them. When the pace of intertemporal pricing is constrained to be comparable to the upper bound on customers patience levels, we have a good control on the cycle length and on the structure of the optimizing cyclic policies. The results also hold for forward looking strategic customers as well as under an alternative objective of maximizing infinite horizon discounted revenue. We cast our results in a general framework of optimizing the long-run average revenue for a class of revenue functions that we denote {\\it weakly coupled}, in which the revenue per period depends on a finite number of neighboring prices. We analyze in detail the case of Markovian valuations where we can obtain closed form formulations of the revenue function and some refinements of our main results. We also extend our results to the case of patience levels that are bounded only in expectation.", "published": "2020-02-14T11:23:49Z", "authors": ["Araman Victor", "Fayad Bassam"], "categories": ["math.OC", "math.PR"], "doi": "", "_query": "hedonic house price", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/cs/0102011v1", "title": "A Price Dynamics in Bandwidth Markets for Point-to-point Connections", "abstract": "We simulate a network of N routers and M network users making concurrent point-to-point connections by buying and selling router capacity from each other. The resources need to be acquired in complete sets, but there is only one spot market for each router. In order to describe the internal dynamics of the market, we model the observed prices by N-dimensional Ito-processes. Modeling using stochastic processes is novel in this context of describing interactions between end-users in a system with shared resources, and allows a standard set of mathematical tools to be applied. The derived models can also be used to price contingent claims on network capacity and thus to price complex network services such as quality of service levels, multicast, etc.", "published": "2001-02-15T10:44:06Z", "authors": ["Lars Rasmusson", "Erik Aurell"], "categories": ["cs.NI", "cond-mat.soft", "cs.MA"], "doi": "", "_query": "hedonic house price", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2304.14385v4", "title": "Dynamic Pricing and Advertising with Demand Learning", "abstract": "We consider a novel pricing and advertising framework in which a seller not only sets the product price but also designs flexible advertising schemes to influence customers' valuations of the product. We impose no structural restriction on the seller's feasible advertising strategies and allow her to advertise the product by disclosing or concealing any information. Following the information design literature, we model this fully flexible advertising as the seller choosing an arbitrary information policy that signals the product quality to customers. Customers observe the advertising signal and form a Bayesian posterior belief over the product quality. We investigate two questions in this work: (1) What is the value of advertising? To what extent can advertising enhance a seller's revenue? (2) Without any a priori knowledge of the customers' demand function, how can a seller adaptively learn and optimize both pricing and advertising strategies using past purchase responses? To study the first question, we quantify the value of advertising by comparing the optimal revenue from jointly designing advertising and a single posted price with the optimal revenue from pricing alone. We show that advertising can increase revenue by at most a factor of two, and this bound is tight. For the second question, we study the seller's dynamic pricing and advertising problem under demand uncertainty. Our main result for this question is a computationally efficient online algorithm that achieves the optimal $O(T^{2/3} (m\\log T)^{1/3})$ regret rate when the valuation function is linear in the product quality. Here, $m$ is the cardinality of the discrete product quality domain and $T$ is the time horizon.", "published": "2023-04-27T17:52:06Z", "authors": ["Shipra Agrawal", "Yiding Feng", "Wei Tang"], "categories": ["cs.GT", "cs.LG"], "doi": "", "_query": "hedonic house price", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/1911.05949v1", "title": "Online Second Price Auction with Semi-bandit Feedback Under the Non-Stationary Setting", "abstract": "In this paper, we study the non-stationary online second price auction problem. We assume that the seller is selling the same type of items in $T$ rounds by the second price auction, and she can set the reserve price in each round. In each round, the bidders draw their private values from a joint distribution unknown to the seller. Then, the seller announced the reserve price in this round. Next, bidders with private values higher than the announced reserve price in that round will report their values to the seller as their bids. The bidder with the highest bid larger than the reserved price would win the item and she will pay to the seller the price equal to the second-highest bid or the reserve price, whichever is larger. The seller wants to maximize her total revenue during the time horizon $T$ while learning the distribution of private values over time. The problem is more challenging than the standard online learning scenario since the private value distribution is non-stationary, meaning that the distribution of bidders' private values may change over time, and we need to use the \\emph{non-stationary regret} to measure the performance of our algorithm. To our knowledge, this paper is the first to study the repeated auction in the non-stationary setting theoretically. Our algorithm achieves the non-stationary regret upper bound $\\tilde{\\mathcal{O}}(\\min\\{\\sqrt{\\mathcal S T}, \\bar{\\mathcal{V}}^{\\frac{1}{3}}T^{\\frac{2}{3}}\\})$, where $\\mathcal S$ is the number of switches in the distribution, and $\\bar{\\mathcal{V}}$ is the sum of total variation, and $\\mathcal S$ and $\\bar{\\mathcal{V}}$ are not needed to be known by the algorithm. We also prove regret lower bounds $Ω(\\sqrt{\\mathcal S T})$ in the switching case and $Ω(\\bar{\\mathcal{V}}^{\\frac{1}{3}}T^{\\frac{2}{3}})$ in the dynamic case, showing that our algorithm has nearly optimal \\emph{non-stationary regret}.", "published": "2019-11-14T05:46:42Z", "authors": ["Haoyu Zhao", "Wei Chen"], "categories": ["cs.LG", "cs.DS", "cs.GT", "stat.ML"], "doi": "", "_query": "hedonic house price", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/1304.3824v13", "title": "Pricing and Valuation under the Real-World Measure", "abstract": "In general it is not clear which kind of information is supposed to be used for calculating the fair value of a contingent claim. Even if the information is specified, it is not guaranteed that the fair value is uniquely determined by the given information. A further problem is that asset prices are typically expressed in terms of a risk-neutral measure. This makes it difficult to transfer the fundamental results of financial mathematics to econometrics. I show that the aforementioned problems evaporate if the financial market is complete and sensitive. In this case, after an appropriate choice of the numeraire, the discounted price processes turn out to be uniformly integrable martingales under the real-world measure. This leads to a Law of One Price and a simple real-world valuation formula in a model-independent framework where the number of assets as well as the lifetime of the market can be finite or infinite.", "published": "2013-04-13T17:15:13Z", "authors": ["Gabriel Frahm"], "categories": ["q-fin.GN"], "doi": "", "_query": "hedonic house price", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2102.09827v1", "title": "Minimal entropy and uniqueness of price equilibria in a pure exchange economy", "abstract": "We introduce uncertainty into a pure exchange economy and establish a connection between Shannon's differential entropy and uniqueness of price equilibria. The following conjecture is proposed under the assumption of a uniform probability distribution: entropy is minimal if and only if the price is unique for every economy. We show the validity of this conjecture for an arbitrary number of goods and two consumers and, under certain conditions, for an arbitrary number of consumers and two goods.", "published": "2021-02-19T09:38:59Z", "authors": ["Andrea Loi", "Stefano Matta"], "categories": ["econ.TH"], "doi": "", "_query": "hedonic house price", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2609.29700v1", "title": "Storage Options and Endogenous Commodity Prices in Continuous Time", "abstract": "We study the price formation of a storable commodity when the decision to sell or keep the commodity is treated as an embedded storage option. The price process is not imposed exogenously. Instead, a candidate price function determines the demand dynamics, while the optimal stopping value generated by those dynamics produces a new price function. We call a fixed point of this feedback mechanism an endogenous storage-option equilibrium (ESOE), using a separate acronym to distinguish the continuous-time construction studied here from the stationary rational expectations equilibrium terminology of the classical competitive-storage literature. A global existence result is established on a compact admissible class under a transparent drift--discount condition. The argument avoids the interval-by-interval extension of local fixed points and does not claim uniqueness from Schauder's theorem. We then characterize the equilibrium as a nonlinear obstacle problem and develop a monotone finite-difference/policy-iteration method with an asymptotically consistent far-field boundary condition. The numerical analysis separates fixed-point convergence from spatial discretization error and is supplemented by Monte Carlo validation, scaling diagnostics, comparative statics, and the distribution of optimal selling times. The results clarify how demand elasticity, discounting, depreciation, and volatility jointly determine the storage premium and the selling threshold.", "published": "2026-08-31T07:56:49Z", "authors": ["Nader Karimi", "Erfan Salavati", "Hojatollah Adibi"], "categories": ["q-fin.RM"], "doi": "", "_query": "hedonic house price", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2402.15828v4", "title": "Pricing of geometric Asian options in the Volterra-Heston model", "abstract": "Geometric Asian options are a type of options where the payoff depends on the geometric mean of the underlying asset over a certain period of time. This paper is concerned with the pricing of such options for the class of Volterra-Heston models, covering the rough Heston model. We are able to derive semi-closed formulas for the prices of geometric Asian options with fixed and floating strikes for this class of stochastic volatility models. These formulas require the explicit calculation of the conditional joint Fourier transform of the logarithm of the stock price and the logarithm of the geometric mean of the stock price over time. Linking our problem to the theory of affine Volterra processes, we find a representation of this Fourier transform as a suitably constructed stochastic exponential, which depends on the solution of a Riccati-Volterra equation. Finally we provide a numerical study for our results in the rough Heston model.", "published": "2024-02-24T14:41:56Z", "authors": ["Florian Aichinger", "Sascha Desmettre"], "categories": ["q-fin.PR", "math.PR"], "doi": "", "_query": "hedonic house price", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2306.04065v1", "title": "Sustainability criterion implied externality pricing for resource extraction", "abstract": "A dynamic model is constructed that generalises the Hartwick and Van Long (2020) endogenous discounting setup by introducing externalities and asks what implications this has for optimal natural resource extraction with constant consumption. It is shown that a modified form of the Hotelling and Hartwick rule holds in which the externality component of price is a specific function of the instantaneous user costs and cross price elasticities. It is demonstrated that the externality adjusted marginal user cost of remaining natural reserves is equal to the marginal user cost of extracted resources invested in human-made reproducible capital. This lends itself to a discrete form with a readily intuitive economic interpretation that illuminates the stepwise impact of externality pricing on optimal extraction schedules.", "published": "2023-06-06T23:48:59Z", "authors": ["Daniel Grainger"], "categories": ["econ.GN"], "doi": "10.1016/j.econlet.2023.111448", "_query": "hedonic house price", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/1912.07700v1", "title": "A Robust Predictive Model for Stock Price Prediction Using Deep Learning and Natural Language Processing", "abstract": "Prediction of future movement of stock prices has been a subject matter of many research work. There is a gamut of literature of technical analysis of stock prices where the objective is to identify patterns in stock price movements and derive profit from it. Improving the prediction accuracy remains the single most challenge in this area of research. We propose a hybrid approach for stock price movement prediction using machine learning, deep learning, and natural language processing. We select the NIFTY 50 index values of the National Stock Exchange of India, and collect its daily price movement over a period of three years (2015 to 2017). Based on the data of 2015 to 2017, we build various predictive models using machine learning, and then use those models to predict the closing value of NIFTY 50 for the period January 2018 till June 2019 with a prediction horizon of one week. For predicting the price movement patterns, we use a number of classification techniques, while for predicting the actual closing price of the stock, various regression models have been used. We also build a Long and Short-Term Memory - based deep learning network for predicting the closing price of the stocks and compare the prediction accuracies of the machine learning models with the LSTM model. We further augment the predictive model by integrating a sentiment analysis module on twitter data to correlate the public sentiment of stock prices with the market sentiment. This has been done using twitter sentiment and previous week closing values to predict stock price movement for the next week. We tested our proposed scheme using a cross validation method based on Self Organizing Fuzzy Neural Networks and found extremely interesting results.", "published": "2019-12-09T20:50:07Z", "authors": ["Sidra Mehtab", "Jaydip Sen"], "categories": ["q-fin.ST", "cs.LG"], "doi": "10.36227/techrxiv.15023361.v1", "_query": "hedonic house price", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2307.00410v1", "title": "A Classical Model of Speculative Asset Price Dynamics", "abstract": "In retrospect, the experimental findings on competitive market behavior called for a revival of the old, classical, view of competition as a collective higgling and bargaining process (as opposed to price-taking behaviors) founded on reservation prices (in place of the utility function). In this paper, we specialize the classical methodology to deal with speculation, an important impediment to price stability. The model involves typical features of a field or lab asset market setup and lends itself to an experimental test of its specific predictions; here we use the model to explain three general stylized facts, well established both empirically and experimentally: the excess, fat-tailed, and clustered volatility of speculative asset prices. The fat tails emerge in the model from the amplifying nature of speculation, leading to a random-coefficient autoregressive return process (and power-law tails); the volatility clustering is due to the traders' long memory of news; bubbles are a persistent phenomenon in the model, and, assuming the standard lab present value pattern, the bubble size increases with the proportion of speculators and decreases with the trading horizon.", "published": "2023-07-01T19:02:17Z", "authors": ["Sabiou Inoua", "Vernon Smith"], "categories": ["q-fin.GN", "econ.TH"], "doi": "", "_query": "hedonic house price", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2502.04811v1", "title": "On the Price of Anarchy in Packet Routing Games with FIFO", "abstract": "We investigate packet routing games in which network users selfishly route themselves through a network over discrete time, aiming to reach the destination as quickly as possible. Conflicts due to limited capacities are resolved by the first-in, first-out (FIFO) principle. Building upon the line of research on packet routing games initiated by Werth et al., we derive the first non-trivial bounds for packet routing games with FIFO. Specifically, we show that the price of anarchy is at most 2 for the important and well-motivated class of uniformly fastest route equilibria introduced by Scarsini et al. on any linear multigraph. We complement our results with a series of instances on linear multigraphs, where the price of stability converges to at least $\\frac{e}{e-1}$. Furthermore, our instances provide a lower bound for the price of anarchy of continuous Nash flows over time on linear multigraphs which establishes the first lower bound of $\\frac{e}{e-1}$ on a graph class where the monotonicity conjecture is proven by Correa et al.", "published": "2025-02-07T10:33:03Z", "authors": ["Daniel Schmand", "Torben Schürenberg", "Martin Strehler"], "categories": ["cs.GT"], "doi": "", "_query": "hedonic house price", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2506.23504v1", "title": "Hybrid Approach for Electricity Price Forecasting using AlexNet and LSTM", "abstract": "The recent development of advanced machine learning methods for hybrid models has greatly addressed the need for the correct prediction of electrical prices. This method combines AlexNet and LSTM algorithms, which are used to introduce a new model with higher accuracy in price forecasting. Despite RNN and ANN being effective, they often fail to deal with forex time sequence data. The traditional methods do not accurately forecast the prices. These traditional methods only focus on demand and price which leads to insufficient analysis of data. To address this issue, using the hybrid approach, which focuses on external variables that also effect the predicted prices. Nevertheless, due to AlexNet's excellent feature extraction and LSTM's learning sequential patterns, the prediction accuracy is vastly increased. The model is built on the past data, which has been supplied with the most significant elements like demand, temperature, sunlight, and rain. For example, the model applies methods, such as minimum-maximum scaling and a time window, to predict the electricity prices of the future. The results show that this hybrid model is good than the standalone ones in terms of accuracy. Although we got our accuracy rating of 97.08, it shows higher accompaniments than remaining models RNN and ANN with accuracies of 96.64 and 96.63 respectively.", "published": "2025-06-30T04:06:24Z", "authors": ["Bosubabu Sambana", "Kotamsetty Geethika Devi", "Bandi Rajeswara Reddy", "Galeti Mohammad Hussain", "Gownivalla Siddartha"], "categories": ["cs.AI"], "doi": "10.1109/IDCIOT64235.2025.10915181", "_query": "hedonic house price", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/1706.04163v2", "title": "Universal scaling and nonlinearity of aggregate price impact in financial markets", "abstract": "How and why stock prices move is a centuries-old question still not answered conclusively. More recently, attention shifted to higher frequencies, where trades are processed piecewise across different timescales. Here we reveal that price impact has a universal non-linear shape for trades aggregated on any intra-day scale. Its shape varies little across instruments, but drastically different master curves are obtained for order-volume and -sign impact. The scaling is largely determined by the relevant Hurst exponents. We further show that extreme order flow imbalance is not associated with large returns. To the contrary, it is observed when the price is \"pinned\" to a particular level. Prices move only when there is sufficient balance in the local order flow. In fact, the probability that a trade changes the mid-price falls to zero with increasing (absolute) order-sign bias along an arc-shaped curve for all intra-day scales. Our findings challenge the widespread assumption of linear aggregate impact. They imply that market dynamics on all intra-day timescales are shaped by correlations and bilateral adaptation in the flows of liquidity provision and taking.", "published": "2017-06-13T17:03:10Z", "authors": ["Felix Patzelt", "Jean-Philippe Bouchaud"], "categories": ["q-fin.TR"], "doi": "10.1103/PhysRevE.97.012304", "_query": "hedonic house price", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2512.10076v1", "title": "Microfoundations and the Causal Interpretation of Price-Exposure Designs", "abstract": "This paper studies regional exposure designs that use commodity prices as instruments to study local effects of aggregate shocks. Unlike standard shift share designs that leverage differential exposure to many shocks, the price exposure relies on exogenous variation from a single shock, leading to challenges for both identification and inference. We motivate the design using a multi sector labor model. Under the model and a potential outcomes framework, we characterize the 2SLS and TWFE estimands as weighted averages of region and sector specific effects plus contamination terms driven by the covariance structure of prices and by general-equilibrium output responses. We derive conditions under which these estimands have a clear causal interpretation and provide simple sensitivity analysis procedures for violations. Finally, we show that standard inference procedures suffer from an overrejection problem in price-exposure designs. We derive a new standard error estimator and show its desirable finite-sample properties through Monte Carlo simulations. In an application to gold mining and homicides in the Amazon, the price exposure standard errors are roughly twice as large as conventional clustered standard errors, making the main effect statistically insignificant.", "published": "2025-12-10T20:57:08Z", "authors": ["Luca Moreno-Louzada", "Guilherme Figueira", "Pedro Picchetti"], "categories": ["econ.EM"], "doi": "", "_query": "hedonic house price", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2105.09581v1", "title": "Valuation of European Options under an Uncertain Market Price of Volatility Risk", "abstract": "We propose a model to quantify the effect of parameter uncertainty on the option price in the Heston model. More precisely, we present a Hamilton-Jacobi-Bellman framework which allows us to evaluate best and worst case scenarios under an uncertain market price of volatility risk. For the numerical approximation the Hamilton--Jacobi--Bellman equation is reformulated to enable the solution with a finite element method. A case study with butterfly options exhibits how the dependence of Delta on the magnitude of the uncertainty is nonlinear and highly varied across the parameter regime. Keywords: Uncertain market price, Volatility risk, Hamilton-Jacobi-Bellman equation, Finite element method, Uncertainty quantification", "published": "2021-05-20T08:14:39Z", "authors": ["Bartosz Jaroszkowski", "Max Jensen"], "categories": ["q-fin.PR", "q-fin.CP"], "doi": "", "_query": "hedonic house price", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2105.07822v1", "title": "Acquisitive Crimes, Time of Day, and Multiunit Housing in the City of Milwaukee", "abstract": "According to \"Social Disorganization\" theory, criminal activity increases if the societal institutions that might be responsible for maintaining order are weakened. Do large apartment buildings, which often have fairly transient populations and low levels of community involvement, have disproportionately high rates of crime? Do these rates differ during the daytime or nighttime, depending when residents are present, or away from their property? This study examines four types of \"acquisitive\" crime in Milwaukee during 2014. Overall, nighttime crimes are shown to be more dispersed than daytime crimes. A spatial regression estimation finds that the density of multiunit housing is positively related to all types of crime except burglaries, but not for all times of day. Daytime robberies, in particular, increase as the density of multiunit housing increases.", "published": "2021-05-13T00:51:06Z", "authors": ["Scott W. Hegerty"], "categories": ["econ.GN"], "doi": "", "_query": "hedonic house price", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2012.03706v1", "title": "Pricing Security in Proof-of-Work Systems", "abstract": "A key component of security in decentralized blockchains is proof of opportunity cost among block producers. In the case of proof-of-work (PoW), currently used by the most prominent systems, the cost is due to spent computation. In this paper, we characterize the security investment of miners in terms of its cost in fiat money. This enables comparison of security allocations across PoW blockchains that generally use different PoW algorithms and reward miners in different cryptocurrency units. We prove that there exists a unique allocation equilibrium, depending on market prices only, that is achieved by both strategic miners (who contemplate the actions of others) and by miners seeking only short-term profit. In fact, the latter will unknowingly compensate for any attempt to deliberately shift security allocation away from equilibrium. Our conclusions are supported analytically through the development of a Markov decision process, game theoretical analysis, and derivation of no arbitrage conditions. We corroborate those results with empirical evidence from more than two years of blockchain and price data. Overall agreement is strong. We show that between January 1, 2018 and August 1, 2020, market prices predicted security allocation between Bitcoin and Bitcoin Cash with error less than 0.6%. And from the beginning of October 2019, until August 1, 2020, market prices predicted security allocation between Bitcoin and Litecoin with error of 0.45%. These results are further corroborated by our establishment of Granger-causality between change in market prices and change in security allocation. To demonstrate the practicality of our results, we describe a trustless oracle that leverages the equilibrium to estimate the price ratios of PoW cryptocurrencies from on-chain information only.", "published": "2020-12-07T14:05:11Z", "authors": ["George Bissias", "Rainer Böhme", "David Thibodeau", "Brian N. Levine"], "categories": ["cs.CR"], "doi": "", "_query": "hedonic house price", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/physics/0602015v1", "title": "Market efficiency and the long-memory of supply and demand: Is price impact variable and permanent or fixed and temporary?", "abstract": "In this comment we discuss the problem of reconciling the linear efficiency of price returns with the long-memory of supply and demand. We present new evidence that shows that efficiency is maintained by a liquidity imbalance that co-moves with the imbalance of buyer vs. seller initiated transactions. For example, during a period where there is an excess of buyer initiated transactions, there is also more liquidity for buy orders than sell orders, so that buy orders generate smaller and less frequent price responses than sell orders. At the moment a buy order is placed the transaction sign imbalance tends to dominate, generating a price impact. However, the liquidity imbalance rapidly increases with time, so that after a small number of time steps it cancels all the inefficiency caused by the transaction sign imbalance, bounding the price impact. While the view presented by Bouchaud et al. of a fixed and temporary bare price impact is self-consistent and formally correct, we argue that viewing this in terms of a variable but permanent price impact provides a simpler and more natural view. This is in the spirit of the original conjecture of Lillo and Farmer, but generalized to allow for finite time lags in the build up of the liquidity imbalance after a transaction. We discuss the possible strategic motivations that give rise to the liquidity imbalance and offer an alternative hypothesis. We also present some results that call into question the statistical significance of large swings in expected price impact at long times.", "published": "2006-02-02T12:02:40Z", "authors": ["J. Doyne Farmer", "Austin Gerig", "Fabrizio Lillo", "Szabolcs Mike"], "categories": ["physics.soc-ph", "q-fin.TR"], "doi": "", "_query": "hedonic house price", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/1701.01515v1", "title": "Analytic properties of American option prices under a modified Black-Scholes equation with spatial fractional derivatives", "abstract": "This paper investigates analytic properties of American option prices under the finite moment log-stable (FMLS) model. Under this model the price of American options is characterised by the free boundary problem of a fractional partial differential equation (FPDE) system. Using the technique of approximation we prove that the American put price under the FMLS model is convex with respect the underlying price, and specify the impact of the tail index on option prices.", "published": "2017-01-06T00:38:51Z", "authors": ["Wenting Chen", "Kai Du", "Xinzi Qiu"], "categories": ["q-fin.CP"], "doi": "10.1016/j.physa.2017.08.068", "_query": "hedonic house price", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2202.12844v1", "title": "Individual Fairness in Feature-Based Pricing for Monopoly Markets", "abstract": "We study fairness in the context of feature-based price discrimination in monopoly markets. We propose a new notion of individual fairness, namely, α-fairness, which guarantees that individuals with similar features face similar prices. First, we study discrete valuation space and give an analytical solution for optimal fair feature-based pricing. We show that the cost of fair pricing is defined as the ratio of expected revenue in an optimal feature-based pricing to the expected revenue in an optimal fair feature-based pricing (CoF) can be arbitrarily large in general. When the revenue function is continuous and concave with respect to the prices, we show that one can achieve CoF strictly less than 2, irrespective of the model parameters. Finally, we provide an algorithm to compute fair feature-based pricing strategy that achieves this CoF.", "published": "2022-02-25T17:35:24Z", "authors": ["Shantanu Das", "Swapnil Dhamal", "Ganesh Ghalme", "Shweta Jain", "Sujit Gujar"], "categories": ["cs.GT"], "doi": "", "_query": "hedonic house price", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/cond-mat/0211260v1", "title": "Pricing Derivatives by Path Integral and Neural Networks", "abstract": "Recent progress in the development of efficient computational algorithms to price financial derivatives is summarized. A first algorithm is based on a path integral approach to option pricing, while a second algorithm makes use of a neural network parameterization of option prices. The accuracy of the two methods is established from comparisons with the results of the standard procedures used in quantitative finance.", "published": "2002-11-13T16:53:46Z", "authors": ["G. Montagna", "M. Morelli", "O. Nicrosini", "P. Amato", "M. Farina"], "categories": ["cond-mat.stat-mech", "cond-mat.dis-nn", "q-fin.PR"], "doi": "10.1016/S0378-4371(02)01907-6", "_query": "hedonic house price", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/1410.7263v2", "title": "Pricing in Social Networks with Negative Externalities", "abstract": "We study the problems of pricing an indivisible product to consumers who are embedded in a given social network. The goal is to maximize the revenue of the seller. We assume impatient consumers who buy the product as soon as the seller posts a price not greater than their values of the product. The product's value for a consumer is determined by two factors: a fixed consumer-specified intrinsic value and a variable externality that is exerted from the consumer's neighbors in a linear way. We study the scenario of negative externalities, which captures many interesting situations, but is much less understood in comparison with its positive externality counterpart. We assume complete information about the network, consumers' intrinsic values, and the negative externalities. The maximum revenue is in general achieved by iterative pricing, which offers impatient consumers a sequence of prices over time. We prove that it is NP-hard to find an optimal iterative pricing, even for unweighted tree networks with uniform intrinsic values. Complementary to the hardness result, we design a 2-approximation algorithm for finding iterative pricing in general weighted networks with (possibly) nonuniform intrinsic values. We show that, as an approximation to optimal iterative pricing, single pricing can work rather well for many interesting cases, but theoretically it can behave arbitrarily bad.", "published": "2014-10-27T14:56:27Z", "authors": ["Zhigang Cao", "Xujin Chen", "Xiaodong Hu", "Changjun Wang"], "categories": ["cs.GT", "cs.DM", "cs.SI"], "doi": "", "_query": "hedonic house price", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2405.14403v2", "title": "A practical scenario generation method for electricity prices on day-ahead and intraday spot markets", "abstract": "The increasing interest in demand-side management (DSM) as part of the energy cost optimization calls for effective methods to determine representative electricity prices for energy optimization and scheduling investigations. We propose a practical method to construct price profiles of day-ahead (DA) and intraday (ID) electricity spot markets. We construct single-day and single-week price profiles based on historical market time series to provide ready-to-use price data sets. Our method accounts for dominant mechanisms in price variation to preserve critical statistical features (e.g., mean and standard deviation) and transient patterns in the constructed profiles. Unlike common scenario generation approaches, the method is deterministic, with few degrees of freedom and minimal application effort. Our method ensures consistency between ID and DA price profiles when both are considered and introduces profile scaling to enable multiple scenario generation. Finally, we compare the constructed profiles to clustering techniques in a DSM case study, noting similar cost results.", "published": "2024-05-23T10:21:18Z", "authors": ["Chrysanthi Papadimitriou", "Jan C. Schulze", "Alexander Mitsos"], "categories": ["stat.AP", "cs.CE", "physics.soc-ph"], "doi": "10.1016/j.compchemeng.2025.109118", "_query": "hedonic house price", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/1905.06315v1", "title": "Higher order approximation of call option prices under stochastic volatility models", "abstract": "In the present paper, a decomposition formula for the call price due to Alòs is transformed into a Taylor type formula containing an infinite series with stochastic terms. The new decomposition may be considered as an alternative to the decomposition of the call price found in a recent paper of Alòs, Gatheral and Radoičić. We use the new decomposition to obtain various approximations to the call price in the Heston model with sharper estimates of the error term than in the previously known approximations. One of the formulas obtained in the present paper has five significant terms and an error estimate of the form $O(ν^{3}(\\left|ρ\\right|+ν))$, where $ν$ is the vol-vol parameter, and $ρ$ is the correlation coefficient between the price and the volatility in the Heston model. Another approximation formula contains seven more terms and the error estimate is of the form $O(ν^4(1+|ρ|)$. For the uncorrelated Hestom model ($ρ=0$), we obtain a formula with four significant terms and an error estimate $O(ν^6)$. Numerical experiments show that the new approximations to the call price perform especially well in the high volatility mode.", "published": "2019-05-15T17:47:25Z", "authors": ["Archil Gulisashvili", "Raúl Merino", "Marc Lagunas", "Josep Vives"], "categories": ["q-fin.CP"], "doi": "", "_query": "hedonic house price", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2509.23851v1", "title": "Price discrimination, algorithmic decision-making, and European non-discrimination law", "abstract": "Our society can benefit immensely from algorithmic decision-making and similar types of artificial intelligence. But algorithmic decision-making can also have discriminatory effects. This paper examines that problem, using online price differentiation as an example of algorithmic decision-making. With online price differentiation, a company charges different people different prices for identical products, based on information the company has about those people. The main question in this paper is: to what extent can non-discrimination law protect people against online price differentiation? The paper shows that online price differentiation and algorithmic decision-making could lead to indirect discrimination, for instance harming people with a certain ethnicity. Indirect discrimination occurs when a practice is neutral at first glance, but ends up discriminating against people with a protected characteristic, such as ethnicity. In principle, non-discrimination law prohibits indirect discrimination. The paper also shows, however, that non-discrimination law has flaws when applied to algorithmic decision-making. For instance, algorithmic discrimination can remain hidden: people may not realise that they are being discriminated against. And many types of unfair - some might say discriminatory - algorithmic decisions are outside the scope of current non-discrimination law.", "published": "2025-09-28T12:41:48Z", "authors": ["Frederik Zuiderveen Borgesius"], "categories": ["cs.CY"], "doi": "10.54648/eulr2020017", "_query": "hedonic house price", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2503.01040v1", "title": "Pricing time-capped American options using Least Squares Monte Carlo method", "abstract": "In this paper, we adopt the least squares Monte Carlo (LSMC) method to price time-capped American options. The aforementioned cap can be an independent random variable or dependent on asset price at random time. We allow various time caps. In particular, we give an algorithm for pricing the American options capped by the first drawdown epoch. We focus on the geometric Lévy market. We prove that our estimator converges to the true price as one takes the discretisation step tending to zero and the number of trajectories going to infinity.", "published": "2025-03-02T22:07:54Z", "authors": ["Paweł Stȩpniak", "Zbigniew Palmowski"], "categories": ["q-fin.MF"], "doi": "", "_query": "hedonic house price", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/math/0205120v1", "title": "Pricing rule based on non-arbitrage arguments for random volatility and volatility smile", "abstract": "We consider a generic market model with a single stock and with random volatility. We assume that there is a number of tradable options for that stock with different strike prices. The paper states the problem of finding a pricing rule that gives Black-Scholes price for at-money options and such that the market is arbitrage free for any number of tradable options, even if there are two Brownian motions only: one drives the stock price, the other drives the volatility process. This problem is reduced to solving a parabolic equation.", "published": "2002-05-10T22:47:17Z", "authors": ["Nikolai Dokuchaev"], "categories": ["math.PR", "math.OC", "q-fin.PR"], "doi": "", "_query": "hedonic house price", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2101.03086v1", "title": "Market Making with Stochastic Liquidity Demand: Simultaneous Order Arrival and Price Change Forecasts", "abstract": "We provide an explicit characterization of the optimal market making strategy in a discrete-time Limit Order Book (LOB). In our model, the number of filled orders during each period depends linearly on the distance between the fundamental price and the market maker's limit order quotes, with random slope and intercept coefficients. The high-frequency market maker (HFM) incurs an end-of-the-day liquidation cost resulting from linear price impact. The optimal placement strategy incorporates in a novel and parsimonious way forecasts about future changes in the asset's fundamental price. We show that the randomness in the demand slope reduces the inventory management motive, and that a positive correlation between demand slope and investors' reservation prices leads to wider spreads. Our analysis reveals that the simultaneous arrival of buy and sell market orders (i) reduces the shadow cost of inventory, (ii) leads the HFM to reduce price pressures to execute larger flows, and (iii) introduces patterns of nonlinearity in the intraday dynamics of bid and ask spreads. Our empirical study shows that the market making strategy outperforms those which ignores randomness in demand, simultaneous arrival of buy and sell market orders, and local drift in the fundamental price.", "published": "2021-01-08T16:24:23Z", "authors": ["Agostino Capponi", "José E. Figueroa-López", "Chuyi Yu"], "categories": ["q-fin.TR"], "doi": "", "_query": "hedonic house price", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/1906.07862v1", "title": "An Integral Formulation and Convex Hull Pricing for Unit Commitment", "abstract": "Reducing uplift payments has been a challenging problem for most wholesale markets in US. The main difficulty comes from the unit commitment discrete decision makings. Recently convex hull pricing has shown promises to reduce the uplift payments. However, it has been intractable to obtain the optimal convex hull price. In this paper, we describe an innovative approach to decide the optimal convex hull price by simply solving a linear program. We also provide an example to illustrate the calculation process. The final computational experiments on a revised IEEE-118 bus system verify the cost effectiveness by utilizing our proposed approach.", "published": "2019-06-19T00:54:11Z", "authors": ["Yanan Yu", "Yongpei Guan", "Yonghong Chen"], "categories": ["eess.SY"], "doi": "", "_query": "hedonic house price", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2003.08976v1", "title": "The generalized Price equation: forces that change population statistics", "abstract": "The Price equation partitions the change in the expected value of a population measure. The first component describes the partial change caused by altered frequencies. The second component describes the partial change caused by altered measurements. In biology, frequency changes often associate with the direct effect of natural selection. Measure changes reflect processes during transmission that alter trait values. More broadly, the two components describe the direct forces that change population composition and the altered frame of reference that changes measured values. The classic Price equation is limited to population statistics that can expressed as the expected value of a measure. Many statistics cannot be expressed as expected values, such as the harmonic mean and the family of rescaled diversity measures. We generalize the Price equation to any population statistic that can be expressed as a function of frequencies and measurements. We obtain the generalized partition between the direct forces that cause frequency change and the altered frame of reference that changes measurements.", "published": "2020-03-19T18:43:59Z", "authors": ["Steven A. Frank", "William Godsoe"], "categories": ["q-bio.PE"], "doi": "", "_query": "hedonic house price", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2008.02087v1", "title": "PriceAggregator: An Intelligent System for Hotel Price Fetching", "abstract": "This paper describes the hotel price aggregation system - PriceAggregator, deployed at Agoda, a global online travel agency for hotels, vacation rentals, flights and airport transfer. Agoda aggregates non-direct suppliers' hotel rooms to ensure that Agoda's customers always have the widest selection of hotels, room types and packages. As of today, Agoda aggregates millions of hotels. The major challenge is that each supplier only allows Agoda to fetch for the hotel price with a limited amount of Queries Per Second (QPS). Due to the sheer volume of Agoda's user search traffic, this limited amount of QPS is never enough to cover all user searches. Inevitably, many user searches have to be ignored. Hence, booking lost. To overcome the challenge, we built PriceAggregator. PriceAggregator intelligently determines when, how and what to send to the suppliers to fetch for price. In this paper, we not only prove PriceAggregator is optimal theoretically but also demonstrate that PriceAggregator performs well in practice. PriceAggregator has been deployed in Agoda. Extensive online A/B experimentation have shown that PriceAggregator increases Agoda's bookings significantly.", "published": "2020-06-30T01:28:25Z", "authors": ["Jiangwei Zhang", "Li Zhang", "Vigneshwaran Raveendran", "Ziv Ben-Zuk", "Leonard Lu"], "categories": ["cs.DC", "cs.LG", "stat.ML"], "doi": "", "_query": "hedonic house price", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2012.13331v2", "title": "Computation of Convex Hull Prices in Electricity Markets with Non-Convexities using Dantzig-Wolfe Decomposition", "abstract": "The presence of non-convexities in electricity markets has been an active research area for about two decades. The -- inevitable under current marginal cost pricing -- problem of guaranteeing that no market participant incurs losses in the day-ahead market is addressed in current practice through make-whole payments a.k.a. uplift. Alternative pricing rules have been studied to deal with this problem. Among them, Convex Hull (CH) prices associated with minimum uplift have attracted significant attention. Several US Independent System Operators (ISOs) have considered CH prices but resorted to approximations, mainly because determining exact CH prices is computationally challenging, while providing little intuition about the price formation rationale. In this paper, we describe the CH price estimation problem by relying on Dantzig-Wolfe decomposition and Column Generation, as a tractable, highly paralellizable, and exact method -- i.e., yielding exact, not approximate, CH prices -- with guaranteed finite convergence. Moreover, the approach provides intuition on the underlying price formation rationale. A test bed of stylized examples provide an exposition of the intuition in the CH price formation. In addition, a realistic ISO dataset is used to support scalability and validate the proof-of-concept.", "published": "2020-12-24T17:06:26Z", "authors": ["Panagiotis Andrianesis", "Dimitris Bertsimas", "Michael C. Caramanis", "William W. Hogan"], "categories": ["math.OC", "econ.GN", "eess.SY"], "doi": "", "_query": "hedonic house price", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/1009.2928v1", "title": "The endogenous dynamics of markets: price impact and feedback loops", "abstract": "We review the evidence that the erratic dynamics of markets is to a large extent of endogenous origin, i.e. determined by the trading activity itself and not due to the rational processing of exogenous news. In order to understand why and how prices move, the joint fluctuations of order flow and liquidity - and the way these impact prices - become the key ingredients. Impact is necessary for private information to be reflected in prices, but by the same token, random fluctuations in order flow necessarily contribute to the volatility of markets. Our thesis is that the latter contribution is in fact dominant, resulting in a decoupling between prices and fundamental values, at least on short to medium time scales. We argue that markets operate in a regime of vanishing revealed liquidity, but large latent liquidity, which would explain their hyper-sensitivity to fluctuations. More precisely, we identify a dangerous feedback loop between bid-ask spread and volatility that may lead to micro-liquidity crises and price jumps. We discuss several other unstable feedback loops that should be relevant to account for market crises: imitation, unwarranted quantitative models, pro-cyclical regulation, etc.", "published": "2010-09-15T13:54:42Z", "authors": ["Jean-Philippe Bouchaud"], "categories": ["q-fin.ST", "cond-mat.stat-mech"], "doi": "", "_query": "hedonic house price", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2307.00571v2", "title": "The fundamental theorem of asset pricing with and without transaction costs", "abstract": "We prove a version of the fundamental theorem of asset pricing (FTAP) in continuous time that is based on the strict no-arbitrage condition and that is applicable to both frictionless markets and markets with proportional transaction costs. We consider a market with a single risky asset whose ask price process is higher than or equal to its bid price process. Neither the concatenation property of the set of wealth processes, that is used in the proof of the frictionless FTAP, nor some boundedness property of the trading volume of admissible strategies usually argued with in models with a nonvanishing bid-ask spread need to be satisfied in our model.", "published": "2023-07-02T13:44:12Z", "authors": ["Christoph Kühn"], "categories": ["q-fin.MF", "math.PR"], "doi": "10.1111/mafi.12453", "_query": "hedonic house price", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/1510.03205v2", "title": "Price response in correlated financial markets: empirical results", "abstract": "Previous studies of the stock price response to individual trades focused on single stocks. We empirically investigate the price response of one stock to the trades of other stocks. How large is the impact of one stock on others and vice versa? -- This impact of trades on the price change across stocks appears to be transient instead of permanent. Performing different averages, we distinguish active and passive responses. The two average responses show different characteristic dependences on the time lag. The passive response exhibits a shorter response period with sizeable volatilities, and the active response a longer period. We also study the response for a given stock with respect to different sectors and to the whole market. Furthermore, we compare the self-response with the various cross-responses. The correlation of the trade signs is a short-memory process for a pair of stocks, but it turns into a long-memory process when averaged over different pairs of stocks.", "published": "2015-10-12T09:51:49Z", "authors": ["Shanshan Wang", "Rudi Schäfer", "Thomas Guhr"], "categories": ["q-fin.ST", "q-fin.TR"], "doi": "", "_query": "hedonic house price", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2511.22291v1", "title": "Online Dynamic Pricing of Complementary Products", "abstract": "Traditional pricing paradigms, once dominated by static models and rule-based heuristics, are increasingly being replaced by dynamic, data-driven approaches powered by machine learning algorithms. Despite their growing sophistication, most dynamic pricing algorithms focus on optimizing the price of each product independently, disregarding potential interactions among items. By neglecting these interdependencies in consumer demand across related goods, sellers may fail to capture the full potential of coordinated pricing strategies. In this paper, we address this problem by exploring dynamic pricing mechanisms designed explicitly for complementary products, aiming to exploit their joint demand structure to maximize overall revenue. We present an online learning algorithm considering both positive and negative interactions between products' demands. The algorithm utilizes transaction data to identify advantageous complementary relationships through an integer programming problem between different items, and then optimizes pricing strategies using data-driven and computationally efficient multi-armed bandit solutions based on heteroscedastic Gaussian processes. We validate our solution in a simulated environment, and we demonstrate that our solution improves the revenue w.r.t. a comparable learning algorithm ignoring such interactions.", "published": "2025-11-27T10:12:51Z", "authors": ["Marco Mussi", "Marcello Restelli"], "categories": ["cs.LG"], "doi": "", "_query": "hedonic house price", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2605.02040v3", "title": "Analytic approximation for Bachelier option prices and applications", "abstract": "It is well-known that, in the Bachelier model, when asset prices and volatilities are uncorrelated, the implied volatility coincides with the fair value of the volatility swap. In this paper, via classical Itô calculus and Taylor expansions, we write the price for out-of-the-money (OTM) and in-the-money (ITM) options as an expansion with respect to the moneyness, where the coefficients are related to the negative (non-integer) powers of the future mean volatility. As an a application, we use it as a control variate to reduce the variance of Monte Carlo option prices in the correlated case.", "published": "2026-05-03T20:09:41Z", "authors": ["Elisa Alòs", "Òscar Burés"], "categories": ["q-fin.CP"], "doi": "", "_query": "hedonic house price", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/cond-mat/0204331v7", "title": "Option Pricing Formulas based on a non-Gaussian Stock Price Model", "abstract": "Options are financial instruments that depend on the underlying stock. We explain their non-Gaussian fluctuations using the nonextensive thermodynamics parameter $q$. A generalized form of the Black-Scholes (B-S) partial differential equation, and some closed-form solutions are obtained. The standard B-S equation ($q=1$) which is used by economists to calculate option prices requires multiple values of the stock volatility (known as the volatility smile). Using $q=1.5$ which well models the empirical distribution of returns, we get a good description of option prices using a single volatility.", "published": "2002-04-15T21:39:53Z", "authors": ["Lisa Borland"], "categories": ["cond-mat.stat-mech", "q-fin.PR"], "doi": "10.1103/PhysRevLett.89.098701", "_query": "hedonic house price", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2505.04042v1", "title": "Ethical Appetite: Consumer Preferences and Price Premiums for Animal Welfare-Friendly Food Products", "abstract": "This study examines how consumer attitudes toward animal welfare influence food selection and pricing using real-world market data from a Swiss supermarket. Our findings indicate that higher animal welfare standards are consistently associated with higher prices, suggesting that ethical considerations play a significant role in generating price premiums based on consumer preferences. On average, a one-point increase in the animal welfare score (ranging from 1 to 5, with 5 being the highest) corresponds to a 16.4% price increase, with the effect being most pronounced in Dairy & Eggs (25.3%), compared to Meat & Fish (14.3%). These results highlight the psychological and behavioral factors underlying consumer preferences for ethically produced foods. Additionally, we find limited evidence of a price premium for climate-friendly food products, observed only in Yogurts & Desserts, a subcategory within Dairy & Eggs. Our findings contribute to the understanding of how ethical food attributes influence consumer decision-making and pricing in retail settings.", "published": "2025-05-07T00:45:45Z", "authors": ["Voraprapa Nakavachara", "Chanon Thongtai", "Thanarat Chalidabhongse", "Chanathip Pharino"], "categories": ["econ.GN"], "doi": "", "_query": "hedonic house price", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2111.09933v2", "title": "Loss Functions for Discrete Contextual Pricing with Observational Data", "abstract": "We study a pricing setting where each customer is offered a contextualized price based on customer and/or product features. Often only historical sales data are available, so we observe whether a customer purchased a product at the price prescribed rather than the customer's true valuation. Such observational data are influenced by historical pricing policies, which introduce difficulties in evaluating the effectiveness of future policies. The goal of this paper is to formulate loss functions that can be used for evaluating pricing policies directly from observational data, rather than going through an intermediate demand estimation stage, which may suffer from bias. To achieve this, we adapt ideas from machine learning with corrupted labels, where we consider each observed purchase decision as a known probabilistic transformation of the customer's valuation. From this transformation, we derive a class of unbiased loss functions. Within this class, we identify minimum variance estimators and estimators robust to poor demand estimation. Furthermore, we show that for contextual pricing, estimators popular in the off-policy evaluation literature fall within this class of loss functions. We offer managerial insights into scenarios under which these estimators are effective.", "published": "2021-11-18T20:12:57Z", "authors": ["Max Biggs", "Ruijiang Gao", "Wei Sun"], "categories": ["cs.LG", "stat.ML"], "doi": "", "_query": "hedonic house price", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2304.12487v1", "title": "Queue Routing Strategies to Improve Equitable Housing Coordination in New York City", "abstract": "Runaway and homeless youth (RHY) are a group of youth and young adults who are at high risk of being exploited through human trafficking. Although access to housing and support services is an effective way to decrease their vulnerability to being exploited, research reveals that coordination of these services provided to RHY by non-profit and government organizations is neither standardized, nor efficient. This situation often causes decreased, delayed, and inequitable access to these scarce housing resources. In this study, we aim to increase the housing system efficiency and reduce the barriers that are contributing to inequitable access to housing through simulation modeling and analyses. Specifically, we simulate a set of crisis and emergency shelters in New York City, funded by a single governmental organization, considering a queuing network with pools of multiple parallel servers, servers with demographic eligibility criteria, stochastic RHY arrival, impatient youth behaviour (possibility of abandonment), and a decision-maker (coordinator) that determines which server pool RHY is routed to. This simulation allows us to evaluate the impact of different queue routing strategies. Our simulation results show that by changing the way RHY is routed to shelters, we can reduce the average wait time by approximately a day and decrease the proportion of RHY abandoning the shelters by 13%.", "published": "2023-04-24T22:58:51Z", "authors": ["Yaren Bilge Kaya", "Kayse Lee Maass"], "categories": ["math.NA", "math.PR"], "doi": "", "_query": "hedonic house price", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2002.07595v1", "title": "Market Power in Convex Hull Pricing", "abstract": "The start up costs in many kinds of generators lead to complex cost structures, which in turn yield severe market loopholes in the locational marginal price (LMP) scheme. Convex hull pricing (a.k.a. extended LMP) is proposed to improve the market efficiency by providing the minimal uplift payment to the generators. In this letter, we consider a stylized model where all generators share the same generation capacity. We analyze the generators' possible strategic behaviors in such a setting, and then propose an index for market power quantification in the convex hull pricing schemes.", "published": "2020-02-15T09:10:06Z", "authors": ["Jian Sun", "Chenye Wu"], "categories": ["math.OC", "cs.CE", "econ.GN"], "doi": "", "_query": "hedonic house price", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/1109.2631v1", "title": "Optimal trade execution and price manipulation in order books with time-varying liquidity", "abstract": "In financial markets, liquidity is not constant over time but exhibits strong seasonal patterns. In this article we consider a limit order book model that allows for time-dependent, deterministic depth and resilience of the book and determine optimal portfolio liquidation strategies. In a first model variant, we propose a trading dependent spread that increases when market orders are matched against the order book. In this model no price manipulation occurs and the optimal strategy is of the wait region - buy region type often encountered in singular control problems. In a second model, we assume that there is no spread in the order book. Under this assumption we find that price manipulation can occur, depending on the model parameters. Even in the absence of classical price manipulation there may be transaction triggered price manipulation. In specific cases, we can state the optimal strategy in closed form.", "published": "2011-09-12T21:31:42Z", "authors": ["Antje Fruth", "Torsten Schoeneborn", "Mikhail Urusov"], "categories": ["q-fin.TR", "math.OC"], "doi": "", "_query": "hedonic house price", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/1604.08735v1", "title": "Pricing Bermudan options under local Lévy models with default", "abstract": "We consider a defaultable asset whose risk-neutral pricing dynamics are described by an exponential Lévy-type martingale. This class of models allows for a local volatility, local default intensity and a locally dependent Lévy measure. We present a pricing method for Bermudan options based on an analytical approximation of the characteristic function combined with the COS method. Due to a special form of the obtained characteristic function the price can be computed using a Fast Fourier Transform-based algorithm resulting in a fast and accurate calculation. The Greeks can be computed at almost no additional computational cost. Error bounds for the approximation of the characteristic function as well as for the total option price are given.", "published": "2016-04-29T08:45:03Z", "authors": ["Anastasia Borovykh", "Cornelis W. Oosterlee", "Andrea Pascucci"], "categories": ["q-fin.PR"], "doi": "", "_query": "hedonic house price", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2602.01376v1", "title": "Keeping Up with the Correlations: Stochastic Spot/Volatility Correlation and Exotic Pricing", "abstract": "We consider a novel use case for the Double Heston model (Christoffersen et al,, 2009), where the two Heston sub-variances have different spot/volatility correlations but the same volatility of volatility and mean reversion speed. This parameterization generalizes the traditional Heston stochastic volatility model (Heston, 1993) to include stochastic spot/volatility correlation. It is an affine model, allowing European options to be priced efficiently by numerically integrating over a closed-form characteristic function. This model incorporates a key dynamic relevant for pricing barrier derivatives in the foreign exchange markets: a positive correlation between moves in implied volatility skew and moves in the spot price. We analyze that correlation and its impact on both barrier option pricing and volatility swap pricing. Those price impacts are comparable to or larger than the bid/ask spreads for these products. Adding stochastic spot/volatility correlation increases the prices of out-of-the-money knockout options and one touch options, assuming that the model is calibrated to market vanilla option prices. It also increases the fair strike of volatility swaps compared to the Heston model.", "published": "2026-02-01T18:23:39Z", "authors": ["Mark Higgins"], "categories": ["q-fin.PR"], "doi": "", "_query": "hedonic house price", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2605.15411v1", "title": "Harnessing Unimodality in Semiparametric Contextual Pricing via Oracle Price Map Learning", "abstract": "We study contextual dynamic pricing in a semiparametric scalar-index valuation model where the latent value is $v_t=μ_\\ast(\\mathsf c_t)+ξ_t$, with an unknown utility map $μ_\\ast$ and an unknown additive noise distribution. The key decision object is the one-dimensional oracle price map $u\\mapsto p^\\ast(u)$ induced by the scalar index $u=μ_\\ast(\\mathsf c)$ and the noise tail. Under the $β$-Hölder smoothness of the tail function for $β\\geq 2$ and a revenue-geometry condition that gives a unique, stable, interior maximizer, this oracle map is itself $(β-1)$-smooth. We exploit such structure through $\\mathsf{ORBIT}$, a modular coarse-to-fine policy that takes a scalar pilot index as input, localizes a benchmark price in each active bin, and learns a local polynomial approximation of the oracle map inside a trust region via bandit convex optimization. For the baseline linear utility model $μ_\\ast(\\mathsf c)=\\mathsf c^\\topθ_\\ast$, an adaptive elliptical exploration scheme constructs the required scalar pilot online without distributional assumptions on the contexts. The resulting policy achieves regret $\\widetilde{O}\\big(T^{\\frac{2β-1}{4β-3}}+\\sqrt{dT}\\big)$. For fixed $d$, we establish a matching lower bound in the horizon dependence, unveiling that the nonparametric oracle-map learning term is minimax sharp. The same scalar-pilot interface also yields extensions to sparse high-dimensional linear utility and nonparametric Hölder utility.", "published": "2026-05-14T20:53:23Z", "authors": ["Yingying Fan", "Yuxuan Han", "Jinchi Lv", "Xiaocong Xu", "Zhengyuan Zhou"], "categories": ["stat.ML", "cs.LG", "math.OC"], "doi": "", "_query": "hedonic house price", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/1604.00105v3", "title": "Option pricing under fast-varying long-memory stochastic volatility", "abstract": "Recent empirical studies suggest that the volatility of an underlying price process may have correlations that decay slowly under certain market conditions. In this paper, the volatility is modeled as a stationary process with long-range correlation properties in order to capture such a situation, and we consider European option pricing. This means that the volatility process is neither a Markov process nor a martingale. However, by exploiting the fact that the price process is still a semimartingale and accordingly using the martingale method, we can obtain an analytical expression for the option price in the regime where the volatility process is fast mean-reverting. The volatility process is modeled as a smooth and bounded function of a fractional Ornstein-Uhlenbeck process. We give the expression for the implied volatility, which has a fractional term structure.", "published": "2016-04-01T02:20:20Z", "authors": ["Josselin Garnier", "Knut Solna"], "categories": ["q-fin.PR", "math.PR"], "doi": "", "_query": "hedonic house price", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2011.08011v2", "title": "Robust Analysis of Stock Price Time Series Using CNN and LSTM-Based Deep Learning Models", "abstract": "Prediction of stock price and stock price movement patterns has always been a critical area of research. While the well-known efficient market hypothesis rules out any possibility of accurate prediction of stock prices, there are formal propositions in the literature demonstrating accurate modeling of the predictive systems that can enable us to predict stock prices with a very high level of accuracy. In this paper, we present a suite of deep learning-based regression models that yields a very high level of accuracy in stock price prediction. To build our predictive models, we use the historical stock price data of a well-known company listed in the National Stock Exchange (NSE) of India during the period December 31, 2012 to January 9, 2015. The stock prices are recorded at five minutes intervals of time during each working day in a week. Using these extremely granular stock price data, we build four convolutional neural network (CNN) and five long- and short-term memory (LSTM)-based deep learning models for accurate forecasting of the future stock prices. We provide detailed results on the forecasting accuracies of all our proposed models based on their execution time and their root mean square error (RMSE) values.", "published": "2020-11-07T16:07:10Z", "authors": ["Sidra Mehtab", "Jaydip Sen", "Subhasis Dasgupta"], "categories": ["q-fin.ST", "cs.LG"], "doi": "10.1109/ICECA49313.2020.9297652", "_query": "hedonic house price", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2110.08784v1", "title": "On ruin probabilities with investments in a risky asset with a switching regime price", "abstract": "We investigate the asymptotic of ruin probabilities when the company invests its reserve in a risky asset with a switching regime price. We assume that the asset price is a conditional geometric Brownian motion with parameters modulated by a Markov process with a finite number of states. Using the technique of the implicit renewal theory we obtain the rate of convergence to zero of the ruin probabilities as the initial capital tends to infinity.", "published": "2021-10-17T10:32:03Z", "authors": ["Yuri Kabanov", "Serguei Pergamenshchikov"], "categories": ["math.PR"], "doi": "", "_query": "hedonic house price", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2307.04055v2", "title": "Contextual Dynamic Pricing with Strategic Buyers", "abstract": "Personalized pricing, which involves tailoring prices based on individual characteristics, is commonly used by firms to implement a consumer-specific pricing policy. In this process, buyers can also strategically manipulate their feature data to obtain a lower price, incurring certain manipulation costs. Such strategic behavior can hinder firms from maximizing their profits. In this paper, we study the contextual dynamic pricing problem with strategic buyers. The seller does not observe the buyer's true feature, but a manipulated feature according to buyers' strategic behavior. In addition, the seller does not observe the buyers' valuation of the product, but only a binary response indicating whether a sale happens or not. Recognizing these challenges, we propose a strategic dynamic pricing policy that incorporates the buyers' strategic behavior into the online learning to maximize the seller's cumulative revenue. We first prove that existing non-strategic pricing policies that neglect the buyers' strategic behavior result in a linear $Ω(T)$ regret with $T$ the total time horizon, indicating that these policies are not better than a random pricing policy. We then establish that our proposed policy achieves a sublinear regret upper bound of $O(\\sqrt{T})$. Importantly, our policy is not a mere amalgamation of existing dynamic pricing policies and strategic behavior handling algorithms. Our policy can also accommodate the scenario when the marginal cost of manipulation is unknown in advance. To account for it, we simultaneously estimate the valuation parameter and the cost parameter in the online pricing policy, which is shown to also achieve an $O(\\sqrt{T})$ regret bound. Extensive experiments support our theoretical developments and demonstrate the superior performance of our policy compared to other pricing policies that are unaware of the strategic behaviors.", "published": "2023-07-08T23:06:42Z", "authors": ["Pangpang Liu", "Zhuoran Yang", "Zhaoran Wang", "Will Wei Sun"], "categories": ["stat.ML", "cs.AI", "cs.GT", "cs.LG"], "doi": "", "_query": "hedonic house price", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/1905.12859v1", "title": "Heterogeneity in demand and optimal price conditioning for local rail transport", "abstract": "This paper describes the results of research project on optimal pricing for LLC \"Perm Local Rail Company\". In this study we propose a regression tree based approach for estimation of demand function for local rail tickets considering high degree of demand heterogeneity by various trip directions and the goals of travel. Employing detailed data on ticket sales for 5 years we estimate the parameters of demand function and reveal the significant variation in price elasticity of demand. While in average the demand is elastic by price, near a quarter of trips is characterized by weakly elastic demand. Lower elasticity of demand is correlated with lower degree of competition with other transport and inflexible frequency of travel.", "published": "2019-05-30T05:32:20Z", "authors": ["Evgeniy M. Ozhegov", "Alina Ozhegova"], "categories": ["econ.EM", "cs.LG"], "doi": "", "_query": "hedonic house price", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/1805.00429v3", "title": "Trisections of surface complements and the Price twist", "abstract": "Given an $S\\cong \\mathbb{R}P^2$ smoothly embedded in a 4-manifold $X^4$ with Euler number 2 or -2, the Price twist is a surgery operation on $ν(S)$ yielding (up to) three different 4-manifolds: $X^4,τ_S(X^4),Σ_S(X^4)$. This is of particular interest when $X^4=S^4$, as then $Σ_S(X^4)$ is a homotopy 4-sphere which is not obviously diffeomorphic to $S^4$. In this paper, we show how to produce a trisection description of each Price twist on $S\\subset X^4$ by producing a relative trisection of $X^4\\setminusν(S)$. Moreover, we show how to produce a trisection description of general surface complements in 4-manifolds.", "published": "2018-05-01T16:53:06Z", "authors": ["Seungwon Kim", "Maggie Miller"], "categories": ["math.GT"], "doi": "10.2140/agt.2020.20.343", "_query": "hedonic house price", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/1209.4175v1", "title": "Hierarchical structure of stock price fluctuations in financial markets", "abstract": "The financial market and turbulence have been broadly compared on account of the same quantitative methods and several common stylized facts they shared. In this paper, the She-Leveque (SL) hierarchy, proposed to explain the anomalous scaling exponents deviated from Kolmogorov monofractal scaling of the velocity fluctuation in fluid turbulence, is applied to study and quantify the hierarchical structure of stock price fluctuations in financial markets. We therefore observed certain interesting results: (i) The hierarchical structure related to multifractal scaling generally presents in all the stock price fluctuations we investigated. (ii) The quantitatively statistical parameters that describes SL hierarchy are different between developed financial markets and emerging ones, distinctively. (iii) For the high-frequency stock price fluctuation, the hierarchical structure varies with different time period. All these results provide a novelty analogy in turbulence and financial market dynamics and a insight to deeply understand the multifractality in financial markets.", "published": "2012-09-19T08:04:01Z", "authors": ["Ya-Chun Gao", "Shi-Min Cai", "Bing-Hong Wang"], "categories": ["q-fin.ST"], "doi": "10.1088/1742-5468/2012/12/P12016", "_query": "hedonic house price", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/1906.06613v1", "title": "The Price of Local Fairness in Multistage Selection", "abstract": "The rise of algorithmic decision making led to active researches on how to define and guarantee fairness, mostly focusing on one-shot decision making. In several important applications such as hiring, however, decisions are made in multiple stage with additional information at each stage. In such cases, fairness issues remain poorly understood. In this paper we study fairness in $k$-stage selection problems where additional features are observed at every stage. We first introduce two fairness notions, local (per stage) and global (final stage) fairness, that extend the classical fairness notions to the $k$-stage setting. We propose a simple model based on a probabilistic formulation and show that the locally and globally fair selections that maximize precision can be computed via a linear program. We then define the price of local fairness to measure the loss of precision induced by local constraints; and investigate theoretically and empirically this quantity. In particular, our experiments show that the price of local fairness is generally smaller when the sensitive attribute is observed at the first stage; but globally fair selections are more locally fair when the sensitive attribute is observed at the second stage---hence in both cases it is often possible to have a selection that has a small price of local fairness and is close to locally fair.", "published": "2019-06-15T21:00:53Z", "authors": ["Vitalii Emelianov", "George Arvanitakis", "Nicolas Gast", "Krishna Gummadi", "Patrick Loiseau"], "categories": ["stat.ML", "cs.LG"], "doi": "10.24963/ijcai.2019/809", "_query": "hedonic house price", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2304.05005v2", "title": "Bayes correlated equilibria, no-regret dynamics in Bayesian games, and the price of anarchy", "abstract": "This paper investigates equilibrium computation and the price of anarchy for Bayesian games, which are the fundamental models of games with incomplete information. In normal-form games with complete information, it is known that efficiently computable no-regret dynamics converge to correlated equilibria, and the price of anarchy for correlated equilibria can be bounded for a broad class of games called smooth games. However, in Bayesian games, as surveyed by Forges (1993), several non-equivalent extensions of correlated equilibria exist, and it remains unclear whether they can be efficiently computed or whether their price of anarchy can be bounded. In this paper, we identify a natural extension of correlated equilibria that can be computed efficiently and is guaranteed to have bounds on the price of anarchy in various games. First, we propose a variant of regret called untruthful swap regret. If each player minimizes it in repeated play of Bayesian games, the empirical distribution of these dynamics is guaranteed to converge to communication equilibria, which is one of the extensions of correlated equilibria proposed by Myerson (1982). We present an efficient algorithm for minimizing untruthful swap regret with a sublinear upper bound, which we prove to be tight in terms of the number of types. As a result, by simulating the dynamics with our algorithm, we can approximately compute a communication equilibrium in polynomial time. Furthermore, we extend existing lower bounds on the price of anarchy based on the smoothness arguments from Bayes--Nash equilibria to equilibria obtained by the proposed dynamics.", "published": "2023-04-11T06:22:51Z", "authors": ["Kaito Fujii"], "categories": ["cs.GT", "cs.LG"], "doi": "", "_query": "hedonic house price", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/1510.07199v1", "title": "Coherent CVA and FVA with Liability Side Pricing of Derivatives", "abstract": "This article presents FVA and CVA of a bilateral derivative in a coherent manner, based on recent developments in fair value accounting and ISDA standards. We argue that a derivative liability, after primary risk factors being hedged, resembles in economics an issued variable funding note, and should be priced at the market rate of the issuer's debt. For the purpose of determining the fair value, the party on the liability side is economically neutral to make a deposit to the other party, which earns his current debt rate and effectively provides funding and hedging for the party holding the derivative asset. The newly derived partial differential equation for an option discounts the derivative's receivable part with counterparty's curve and payable part with own financing curve. The price difference from the counterparty risk free price, or total counterparty risk adjustment, is precisely defined by discounting the product of the risk free price and the credit spread at the local liability curve. Subsequently the adjustment can be broken into a default risk component -- CVA and a funding component -- FVA, consistent with a simple note's fair value treatment and in accordance with the usual understanding of a bond's credit spread consisting of a CDS spread and a basis. As for FVA, we define a cost -- credit funding adjustment (CFA) and a benefit -- debit funding adjustment (DFA), in parallel to CVA and DVA and attributed to counterparty's and own funding basis. This resolves a number of outstanding FVA debate issues, such as double counting, violation of the law of one price, misuse of cash flow discounting, and controversial hedging of own default risk. It also allows an integrated implementation strategy and reuse of existing CVA infrastructure.", "published": "2015-10-25T01:45:34Z", "authors": ["Wujiang Lou"], "categories": ["q-fin.PR", "q-fin.MF"], "doi": "", "_query": "hedonic house price", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2604.12139v1", "title": "Estimating Price Elasticity Matrices", "abstract": "The relationship between demand and prices of a set of products can be modeled as a linear mapping from logarithmic price changes to logarithmic changes in demand. We consider the problem of estimating the coefficient matrix of this mapping, the elasticity matrix, based on observed data consisting of real-valued prices and integer-valued demands. We regularize the estimation problem by imposing a factor model structure, i.e., that the elasticity matrix is diagonal plus low-rank, similar to factor models used for financial returns. Maximizing the likelihood of observations of this model is a bi-convex problem, meaning that there is a partition of the variables in which it is convex in each set when the other is fixed. We propose and compare three methods for finding a locally optimal estimate. The first is based on alternating maximization, and involves solving a sequence of convex problems. The second method exploits efficient gradient computations in a gradient ascent method. The final method is to use a general purpose nonlinear programming method. While all methods give the same result on numerical examples, the gradient ascent method is substantially faster, due to its efficient gradient evaluations. We report the likelihood with different hyper-parameters for synthetic and real-world data, with similar results. For synthetic data, we also report the realized profit when using the elasticity estimate for optimal pricing, which is maximized for the same set of hyper-parameters that also maximizes the likelihood. This paper is accompanied by easy to use open source Python code for fitting elasticity matrices to observed data, using our three numerical methods.", "published": "2026-04-13T23:40:43Z", "authors": ["Maximilian Schaller", "Stephen Boyd"], "categories": ["math.OC"], "doi": "", "_query": "hedonic house price", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2408.05628v1", "title": "Forecasting Day-Ahead Electricity Prices in the Integrated Single Electricity Market: Addressing Volatility with Comparative Machine Learning Methods", "abstract": "This paper undertakes a comprehensive investigation of electricity price forecasting methods, focused on the Irish Integrated Single Electricity Market, particularly on changes during recent periods of high volatility. The primary objective of this research is to evaluate and compare the performance of various forecasting models, ranging from traditional machine learning models to more complex neural networks, as well as the impact of different lengths of training periods. The performance metrics, mean absolute error, root mean square error, and relative mean absolute error, are utilized to assess and compare the accuracy of each model. A comprehensive set of input features was investigated and selected from data recorded between October 2018 and September 2022. The paper demonstrates that the daily EU Natural Gas price is a more useful feature for electricity price forecasting in Ireland than the daily Henry Hub Natural Gas price. This study also shows that the correlation of features to the day-ahead market price has changed in recent years. The price of natural gas on the day and the amount of wind energy on the grid that hour are significantly more important than any other features. More specifically speaking, the input fuel for electricity has become a more important driver of the price of it, than the total generation or demand. In addition, it can be seen that System Non-Synchronous Penetration (SNSP) is highly correlated with the day-ahead market price, and that renewables are pushing down the price of electricity.", "published": "2024-08-10T20:43:21Z", "authors": ["Ben Harkin", "Xueqin Liu"], "categories": ["cs.LG", "cs.AI", "eess.SY"], "doi": "", "_query": "hedonic house price", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/1906.09880v3", "title": "Online Revenue Maximization for Server Pricing", "abstract": "Efficient and truthful mechanisms to price resources on remote servers/machines has been the subject of much work in recent years due to the importance of the cloud market. This paper considers revenue maximization in the online stochastic setting with non-preemptive jobs and a unit capacity server. One agent/job arrives at every time step, with parameters drawn from an underlying unknown distribution. We design a posted-price mechanism which can be efficiently computed, and is revenue-optimal in expectation and in retrospect, up to additive error. The prices are posted prior to learning the agent's type, and the computed pricing scheme is deterministic, depending only on the length of the allotted time interval and on the earliest time the server is available. If the distribution of agent's type is only learned from observing the jobs that are executed, we prove that a polynomial number of samples is sufficient to obtain a near-optimal truthful pricing strategy.", "published": "2019-06-24T12:26:13Z", "authors": ["Shant Boodaghians", "Federico Fusco", "Stefano Leonardi", "Yishay Mansour", "Ruta Mehta"], "categories": ["cs.GT"], "doi": "10.1007/s10458-022-09544-y", "_query": "hedonic house price", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2003.05114v2", "title": "A price on warming with a supply chain directed market", "abstract": "Existing emissions trading system (ETS) designs inhibit emissions but do not constrain warming to any fxed level, preventing certainty of the global path of warming. Instead, they have the indirect objective of reducing emissions. They provide poor future price information. And they have high transaction costs for implementation, requiring treaties and laws. To address these shortcomings, this paper proposes a novel double-sided auction mechanism of emissions permits and sequestration contracts tied to temperature. This mechanism constrains warming for many (e.g., 150) years into the future and every auction would provide price information for this time range. In addition, this paper proposes a set of market rules and a bottom-up implementation path. A coalition of businesses begin implementation with jurisdictions joining as they are ready. The combination of the selected market rules and the proposed implementation path appear to incentivize participation. This design appears to be closer to \"first best\" with a lower cost of mitigation than any in the literature, while increasing the certainty of avoiding catastrophic warming. This design should also have a faster pathway to implementation. A numerical simulation shows surprising results, e.g., that static prices are wrong, prices should evolve over time in a way that contradicts other recent proposals, and \"global warming potential\" as used in existing ETSs are generally erroneous.", "published": "2020-03-11T05:04:39Z", "authors": ["John F. Raffensperger"], "categories": ["econ.GN"], "doi": "10.1007/s43621-021-00011-4", "_query": "hedonic house price", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2312.07882v3", "title": "A semi-parametric approach for estimating consumer valuation distributions using second price auctions", "abstract": "We focus on online second price auctions, where bids are made sequentially, and the winning bidder pays the maximum of the second-highest bid and a seller specified starting price. For many such auctions, the seller does not see all the bids or the total number of bidders accessing the auction, and only observes the current selling prices throughout the course of the auction. We develop a novel semi-parametric approach to estimate the underlying consumer valuation distribution based on this data. Previous semi-parametric or non-parametric approaches in the literature only use the final selling price and assume knowledge of the total number of bidders. The resulting estimate, in particular, can be used by the seller to compute the optimal profit-maximizing price for the product. Our approach is free of tuning parameters, and we demonstrate its computational and statistical efficiency in a variety of simulation settings, and also on an Xbox 7-day auction dataset on eBay.", "published": "2023-12-13T03:58:02Z", "authors": ["Sourav Mukherjee", "Ziqian Yang", "Rohit K Patra", "Kshitij Khare"], "categories": ["stat.ME", "cs.GT", "stat.AP"], "doi": "", "_query": "hedonic house price", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/1406.3597v1", "title": "An $H_{n/2}$ Upper Bound on the Price of Stability of Undirected Network Design Games", "abstract": "In the network design game with $n$ players, every player chooses a path in an edge-weighted graph to connect her pair of terminals, sharing costs of the edges on her path with all other players fairly. We study the price of stability of the game, i.e., the ratio of the social costs of a best Nash equilibrium (with respect to the social cost) and of an optimal play. It has been shown that the price of stability of any network design game is at most $H_n$, the $n$-th harmonic number. This bound is tight for directed graphs. For undirected graphs, the situation is dramatically different, and tight bounds are not known. It has only recently been shown that the price of stability is at most $H_n \\left(1-\\frac{1}{Θ(n^4)} \\right)$, while the worst-case known example has price of stability around 2.25. In this paper we improve the upper bound considerably by showing that the price of stability is at most $H_{n/2} + ε$ for any $ε$ starting from some suitable $n \\geq n(ε)$.", "published": "2014-06-13T18:00:18Z", "authors": ["Akaki Mamageishvili", "Matúš Mihalák", "Simone Montemezzani"], "categories": ["cs.GT"], "doi": "", "_query": "hedonic house price", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2312.03422v2", "title": "Adaptive flexibility function in smart energy systems: A linearized price-demand mapping approach", "abstract": "This paper proposes an adaptive mechanism for price signal generation using a piecewise linear approximation of a flexibility function with unknown parameters. In this adaptive approach, the price signal is parameterized and the parameters are changed adaptively such that the output of the flexibility function follows the reference demand signal provided by the involved aggregator. This is guaranteed using the Lyapunov stability theorem. The proposed method does not require an estimation algorithm for unknown parameters, that eliminates the need for persistency of excitation of signals, and consequently, simplifies offering the flexibility services. Furthermore, boundedness of the price signal is ensured using a projection algorithm in the adaptive system. We present simulation results that demonstrate the price generation results using the proposed approaches.", "published": "2023-12-06T11:12:06Z", "authors": ["Seyed Shahabaldin Tohidi", "Henrik Madsen", "Georgios Tsaousoglou", "Tobias K. S. Ritschel"], "categories": ["eess.SY"], "doi": "", "_query": "hedonic house price", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/1310.1882v12", "title": "Implicit transaction costs and the fundamental theorems of asset pricing", "abstract": "This paper studies arbitrage pricing theory in financial markets with implicit transaction costs. We extend the existing theory to include the more realistic possibility that the price at which the investors trade is dependent on the traded volume. The investors in the market always buy at the ask and sell at the bid price. Implicit transaction costs are composed of two terms, one is able to capture the bid-ask spread, and the second the price impact. Moreover, a new definition of a self-financing portfolio is obtained. The self-financing condition suggests that continuous trading is possible, but is restricted to predictable trading strategies having cádlág (right-continuous with left limits) and cáglád (left-continuous with right limits) paths of bounded quadratic variation and of finitely many jumps. That is, cádlág and cáglád predictable trading strategies of infinite variation, with finitely many jumps and of finite quadratic variation are allowed in our setting. Restricting ourselves to cáglád predictable trading strategies, we show that the existence of an equivalent probability measure is equivalent to the absence of arbitrage opportunities, so that the first fundamental theorem of asset pricing (FFTAP) holds. It is also shown that the use of continuous and bounded variation trading strategies can improve the efficiency of hedging in a market with implicit transaction costs. To better understand how to apply the theory proposed we provide an example of an implicit transaction cost economy that is linear and non-linear in the order size.", "published": "2013-10-07T18:37:22Z", "authors": ["Erindi Allaj"], "categories": ["q-fin.PR", "q-fin.GN"], "doi": "", "_query": "hedonic house price", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/1806.02661v2", "title": "New mechanism for repeated posted price auction with a strategic buyer without discounting", "abstract": "On ad exchange platforms the place for advertisement is sold through different kinds of auctions. However, it is not uncommon the situation where the seller repeatedly encounters only one buyer, thus the posted price auction degenerates into a monopoly-monopsony game with asymmetric information and nearly an infinite number of rounds; on each round the seller proposes the price and the buyer accepts or rejects it. I learned this problem from a discussion with members of Yandex research team and my main motivation was to find an incentive-compatible seller's strategy. In this short paper such a strategy is proposed and a corresponding distortion at the top type lower bound (Spence-Mirrlees property, actually) for the surplus of the buyer is established; this shows that the proposed strategy is the best possible. The key ingredients are the following. The main leash that the buyer has is the frequency of accepted deals. Once this frequency (as a function on the buyer's type) is fixed, the strategy randomly chooses between the {\\it rewarding} price which incentivises the buyer to reveal his type (the higher the type, the more average surplus the buyer has), the {\\it adaptation} price which allows the buyer to communicate that his type is higher then the current guess of the cook, and the {\\it type confirmation} price which disincentivises the buyer to pretend that his type is higher than it is.", "published": "2018-05-29T20:53:23Z", "authors": ["Nikita Kalinin"], "categories": ["cs.GT", "math.CO"], "doi": "", "_query": "hedonic house price", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2604.09931v1", "title": "Online Electricity Pricing from Frequency Measurements", "abstract": "Frequency dynamics in power systems reflect active power imbalance in real time, thereby providing an instantaneous signal to inform electricity pricing. However, existing real-time markets operate on much slower timescales and fail to exploit this signal. In this letter, we develop integrated market--frequency dynamics that enable online pricing directly from frequency measurements. Representing the real-time market as a dynamic price-discovery process, and integrating this process with the grid frequency dynamics, we derive an explicit price formation mechanism from frequency measurements. This mechanism manifests as a distributed PID-like controller for each generator, where frequency response is driven and remunerated by electricity prices derived solely from local frequency measurements.", "published": "2026-04-10T22:22:10Z", "authors": ["Xinwei Liu", "Vladimir Dvorkin"], "categories": ["eess.SY", "cs.CE", "math.OC"], "doi": "", "_query": "hedonic house price", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2505.09332v2", "title": "The non-simply connected Price twist for the 4-sphere", "abstract": "A cutting and pasting operation on a $P^2$-knot $S$ in a $4$-manifold is called the Price twist. The Price twist for the $4$-sphere $S^4$ yields at most three $4$-manifolds up to diffeomorphism, namely, the $4$-sphere $S^4$, the other homotopy $4$-sphere $Σ_{S}(S^4)$ and a non-simply connected $4$-manifold $τ_{S}(S^4)$. In this paper, we study some properties and diffeomorphism types of $τ_{S}(S^4)$ for $P^2$-knots $S$ of Kinoshita type.", "published": "2025-05-14T12:27:44Z", "authors": ["Tsukasa Isoshima", "Tatsumasa Suzuki"], "categories": ["math.GT"], "doi": "", "_query": "hedonic house price", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2608.04332v1", "title": "Informational Content of Auction Prices", "abstract": "We study auctions of k identical objects to n bidders, each of whom wants at most one. The objects have a common but unknown value and the bidders receive private signals about this value. The discriminatory price auction and the uniform-price auction are compared in terms of how informative the resulting auction prices (not bids) are in conveying the true value to an outside observer/investor. Since both auctions have symmetric, monotone equilibria, the problem reduces to comparing the informativeness of the highest order statistic of a sample to the (k+1)st highest. We find sufficient conditions under which the highest order statistic is superior---in the sense of Lehmann---in this regard. The sufficient conditions involve the informativeness of high versus low signals and the ratio k/n of objects to bidders. These conditions are also qualitatively necessary.", "published": "2026-08-05T01:20:40Z", "authors": ["Yu Awaya", "Vijay Krishna", "Eduard Osipov"], "categories": ["econ.TH"], "doi": "", "_query": "hedonic house price", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2401.08077v1", "title": "Transformer-based approach for Ethereum Price Prediction Using Crosscurrency correlation and Sentiment Analysis", "abstract": "The research delves into the capabilities of a transformer-based neural network for Ethereum cryptocurrency price forecasting. The experiment runs around the hypothesis that cryptocurrency prices are strongly correlated with other cryptocurrencies and the sentiments around the cryptocurrency. The model employs a transformer architecture for several setups from single-feature scenarios to complex configurations incorporating volume, sentiment, and correlated cryptocurrency prices. Despite a smaller dataset and less complex architecture, the transformer model surpasses ANN and MLP counterparts on some parameters. The conclusion presents a hypothesis on the illusion of causality in cryptocurrency price movements driven by sentiments.", "published": "2024-01-16T03:03:39Z", "authors": ["Shubham Singh", "Mayur Bhat"], "categories": ["cs.LG", "cs.AI", "q-fin.PR"], "doi": "", "_query": "hedonic house price", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2410.02074v1", "title": "Price-guided user attention in large-scale E-commerce group recommendation", "abstract": "Existing group recommender systems utilize attention mechanisms to identify critical users who influence group decisions the most. We analyzed user attention scores from a widely-used group recommendation model on a real-world E-commerce dataset and found that item price and user interaction history significantly influence the selection of critical users. When item prices are low, users with extensive interaction histories are more influential in group decision-making. Conversely, their influence diminishes with higher item prices. Based on these observations, we propose a novel group recommendation approach that incorporates item price as a guiding factor for user aggregation. Our model employs an adaptive sigmoid function to adjust output logits based on item prices, enhancing the accuracy of user aggregation. Our model can be plugged into any attention-based group recommender system if the price information is available. We evaluate our model's performance on a public benchmark and a real-world dataset. We compare it with other state-of-the-art group recommendation methods. Our results demonstrate that our price-guided user attention approach outperforms the state-of-the-art methods in terms of hit ratio and mean square error.", "published": "2024-10-02T22:46:51Z", "authors": ["Yang Shi", "Young-joo Chung"], "categories": ["cs.IR", "cs.LG"], "doi": "", "_query": "hedonic house price", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/1605.00634v1", "title": "Why have asset price properties changed so little in 200 years", "abstract": "We first review empirical evidence that asset prices have had episodes of large fluctuations and been inefficient for at least 200 years. We briefly review recent theoretical results as well as the neurological basis of trend following and finally argue that these asset price properties can be attributed to two fundamental mechanisms that have not changed for many centuries: an innate preference for trend following and the collective tendency to exploit as much as possible detectable price arbitrage, which leads to destabilizing feedback loops.", "published": "2016-05-02T19:39:27Z", "authors": ["Jean-Philippe Bouchaud", "Damien Challet"], "categories": ["q-fin.GN", "q-fin.TR"], "doi": "", "_query": "hedonic house price", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/1105.4789v1", "title": "Stochastic Price Dynamics Implied By the Limit Order Book", "abstract": "In this paper we present a novel approach to the determination of fat tails in financial data by studying the information contained in the limit order book. In an order-driven market buyers and sellers may submit limit orders, which are executed when the price touches a pre-specified lower, respectively higher, limit-price. We show that, in equilibrium, the collection of all such orders - the limit order book - implies a volatility smile, similar to observations from option pricing in the Black-Scholes model. We also show how a jump-diffusion process can be explicitly inferred to account for the volatility smile.", "published": "2011-05-24T14:50:39Z", "authors": ["Alex Langnau", "Yanko Punchev"], "categories": ["q-fin.TR", "q-fin.ST"], "doi": "", "_query": "hedonic house price", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/physics/0510257v4", "title": "News and price returns from threshold behaviour and vice-versa: exact solution of a simple agent-based market model", "abstract": "Starting from an exact relationship between news, threshold and price return distributions in the stationary state, I discuss the ability of the Ghoulmie-Cont-Nadal model of traders to produce fat-tailed price returns. Under normal conditions, this model is not able to transform Gaussian news into fat-tailed price returns. When the variance of the news so small that only the players with zero threshold can possibly react to news, this model produces Levy-distributed price returns with a -1 exponent. In the special case of super-linear price impact functions, fat-tailed returns are obtained from well-behaved news.", "published": "2005-10-28T16:37:57Z", "authors": ["Damien Challet"], "categories": ["physics.soc-ph", "cond-mat.stat-mech", "q-fin.TR"], "doi": "10.1088/0305-4470/39/48/001", "_query": "hedonic house price", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2604.06068v1", "title": "Beyond Black-Scholes: A Computational Framework for Option Pricing Using Heston, GARCH, and Jump Diffusion Models", "abstract": "This research addresses accurate option pricing by employing models beyond the traditional Black-Scholes framework. While Black-Scholes provides a closed-form solution, it is limited by assumptions of constant volatility, no dividends, and continuous price movements. To overcome these limitations, we use Monte Carlo simulation alongside the GARCH model, Heston stochastic volatility model, and Merton jump-diffusion model. The Black-Scholes-Monte Carlo method simulates diverse stock price paths using geometric Brownian motion. The GARCH model forecasts time-varying volatility from historical data. The Heston model incorporates stochastic volatility to capture volatility clustering and skew. The Merton jump-diffusion model adds sudden price jumps via a Poisson process. Results show the Heston model consistently produces estimates closer to market prices, while the Merton model performs well for volatile assets with sudden price movements. The GARCH model provides improved volatility forecasts for future option price prediction. All experiments used live market data from November 2024.", "published": "2026-04-07T16:48:26Z", "authors": ["Karmanpartap Singh Sidhu", "Pranshi Saxena"], "categories": ["q-fin.CP"], "doi": "", "_query": "hedonic house price", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/1806.05579v1", "title": "A new approach for American option pricing: The Dynamic Chebyshev method", "abstract": "We introduce a new method to price American options based on Chebyshev interpolation. In each step of a dynamic programming time-stepping we approximate the value function with Chebyshev polynomials. The key advantage of this approach is that it allows to shift the model-dependent computations into an offline phase prior to the time-stepping. In the offline part a family of generalised (conditional) moments is computed by an appropriate numerical technique such as a Monte Carlo, PDE or Fourier transform based method. Thanks to this methodological flexibility the approach applies to a large variety of models. Online, the backward induction is solved on a discrete Chebyshev grid, and no (conditional) expectations need to be computed. For each time step the method delivers a closed form approximation of the price function along with the options' delta and gamma. Moreover, the same family of (conditional) moments yield multiple outputs including the option prices for different strikes, maturities and different payoff profiles. We provide a theoretical error analysis and find conditions that imply explicit error bounds for a variety of stock price models. Numerical experiments confirm the fast convergence of prices and sensitivities. An empirical investigation of accuracy and runtime also shows an efficiency gain compared with the least-square Monte-Carlo method introduced by Longstaff and Schwartz (2001).", "published": "2018-06-14T14:37:34Z", "authors": ["Kathrin Glau", "Mirco Mahlstedt", "Christian Pötz"], "categories": ["q-fin.CP"], "doi": "", "_query": "hedonic house price", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/1605.05397v4", "title": "New Insights into Rental Housing Markets across the United States: Web Scraping and Analyzing Craigslist Rental Listings", "abstract": "Current sources of data on rental housing - such as the census or commercial databases that focus on large apartment complexes - do not reflect recent market activity or the full scope of the U.S. rental market. To address this gap, we collected, cleaned, analyzed, mapped, and visualized 11 million Craigslist rental housing listings. The data reveal fine-grained spatial and temporal patterns within and across metropolitan housing markets in the U.S. We find some metropolitan areas have only single-digit percentages of listings below fair market rent. Nontraditional sources of volunteered geographic information offer planners real-time, local-scale estimates of rent and housing characteristics currently lacking in alternative sources, such as census data.", "published": "2016-05-17T23:29:58Z", "authors": ["Geoff Boeing", "Paul Waddell"], "categories": ["stat.AP", "stat.CO", "stat.ME"], "doi": "10.1177/0739456X16664789", "_query": "hedonic house price", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/1805.07610v1", "title": "Bitcoin price and its marginal cost of production: support for a fundamental value", "abstract": "This study back-tests a marginal cost of production model proposed to value the digital currency bitcoin. Results from both conventional regression and vector autoregression (VAR) models show that the marginal cost of production plays an important role in explaining bitcoin prices, challenging recent allegations that bitcoins are essentially worthless. Even with markets pricing bitcoin in the thousands of dollars each, the valuation model seems robust. The data show that a price bubble that began in the Fall of 2017 resolved itself in early 2018, converging with the marginal cost model. This suggests that while bubbles may appear in the bitcoin market, prices will tend to this bound and not collapse to zero.", "published": "2018-05-19T15:30:29Z", "authors": ["Adam Hayes"], "categories": ["econ.EM"], "doi": "", "_query": "hedonic house price", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2002.07331v1", "title": "Dynamic Reserve Prices for Repeated Auctions: Learning from Bids", "abstract": "A large fraction of online advertisement is sold via repeated second price auctions. In these auctions, the reserve price is the main tool for the auctioneer to boost revenues. In this work, we investigate the following question: Can changing the reserve prices based on the previous bids improve the revenue of the auction, taking into account the long-term incentives and strategic behavior of the bidders? We show that if the distribution of the valuations is known and satisfies the standard regularity assumptions, then the optimal mechanism has a constant reserve. However, when there is uncertainty in the distribution of the valuations, previous bids can be used to learn the distribution of the valuations and to update the reserve price. We present a simple, approximately incentive-compatible, and asymptotically optimal dynamic reserve mechanism that can significantly improve the revenue over the best static reserve. The paper is from July 2014 (our submission to WINE 2014), posted later here on the arxiv to complement the 1-page abstract in the WINE 2014 proceedings.", "published": "2020-02-18T01:53:01Z", "authors": ["Yash Kanoria", "Hamid Nazerzadeh"], "categories": ["cs.GT", "econ.TH"], "doi": "10.1007/978-3-319-13129-0_17", "_query": "hedonic house price", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/1807.07120v2", "title": "A Holistic Approach to Forecasting Wholesale Energy Market Prices", "abstract": "Electricity market price predictions enable energy market participants to shape their consumption or supply while meeting their economic and environmental objectives. By utilizing the basic properties of the supply-demand matching process performed by grid operators, known as Optimal Power Flow (OPF), we develop a methodology to recover energy market's structure and predict the resulting nodal prices by using only publicly available data, specifically grid-wide generation type mix, system load, and historical prices. Our methodology uses the latest advancements in statistical learning to cope with high dimensional and sparse real power grid topologies, as well as scarce, public market data, while exploiting structural characteristics of the underlying OPF mechanism. Rigorous validations using the Southwest Power Pool (SPP) market data reveal a strong correlation between the grid level mix and corresponding market prices, resulting in accurate day-ahead predictions of real time prices. The proposed approach demonstrates remarkable proximity to the state-of-the-art industry benchmark while assuming a fully decentralized, market-participant perspective. Finally, we recognize the limitations of the proposed and other evaluated methodologies in predicting large price spike values.", "published": "2018-07-18T19:44:25Z", "authors": ["Ana Radovanovic", "Tommaso Nesti", "Bokan Chen"], "categories": ["stat.AP", "eess.SY"], "doi": "", "_query": "hedonic house price", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/1905.05730v2", "title": "Asset Pricing with Heterogeneous Beliefs and Illiquidity", "abstract": "This paper studies the equilibrium price of an asset that is traded in continuous time between N agents who have heterogeneous beliefs about the state process underlying the asset's payoff. We propose a tractable model where agents maximize expected returns under quadratic costs on inventories and trading rates. The unique equilibrium price is characterized by a weakly coupled system of linear parabolic equations which shows that holding and liquidity costs play dual roles. We derive the leading-order asymptotics for small transaction and holding costs which give further insight into the equilibrium and the consequences of illiquidity.", "published": "2019-05-14T17:19:21Z", "authors": ["Johannes Muhle-Karbe", "Marcel Nutz", "Xiaowei Tan"], "categories": ["q-fin.MF", "q-fin.GN", "q-fin.PR"], "doi": "", "_query": "hedonic house price", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/1004.3884v1", "title": "Oil Price Trackers Inspired by Immune Memory", "abstract": "We outline initial concepts for an immune inspired algorithm to evaluate and predict oil price time series data. The proposed solution evolves a short term pool of trackers dynamically, with each member attempting to map trends and anticipate future price movements. Successful trackers feed into a long term memory pool that can generalise across repeating trend patterns. The resulting sequence of trackers, ordered in time, can be used as a forecasting tool. Examination of the pool of evolving trackers also provides valuable insight into the properties of the crude oil market.", "published": "2010-04-22T10:24:37Z", "authors": ["WIlliam Wilson", "Phil Birkin", "Uwe Aickelin"], "categories": ["cs.AI", "cs.NE"], "doi": "", "_query": "hedonic house price", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2108.09014v1", "title": "Bermudan option pricing by quantum amplitude estimation and Chebyshev interpolation", "abstract": "Pricing of financial derivatives, in particular early exercisable options such as Bermudan options, is an important but heavy numerical task in financial institutions, and its speed-up will provide a large business impact. Recently, applications of quantum computing to financial problems have been started to be investigated. In this paper, we first propose a quantum algorithm for Bermudan option pricing. This method performs the approximation of the continuation value, which is a crucial part of Bermudan option pricing, by Chebyshev interpolation, using the values at interpolation nodes estimated by quantum amplitude estimation. In this method, the number of calls to the oracle to generate underlying asset price paths scales as $\\widetilde{O}(ε^{-1})$, where $ε$ is the error tolerance of the option price. This means the quadratic speed-up compared with classical Monte Carlo-based methods such as least-squares Monte Carlo, in which the oracle call number is $\\widetilde{O}(ε^{-2})$.", "published": "2021-08-20T06:01:57Z", "authors": ["Koichi Miyamoto"], "categories": ["quant-ph", "q-fin.CP", "q-fin.PR"], "doi": "", "_query": "hedonic house price", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/1711.09193v4", "title": "Option Pricing with Orthogonal Polynomial Expansions", "abstract": "We derive analytic series representations for European option prices in polynomial stochastic volatility models. This includes the Jacobi, Heston, Stein-Stein, and Hull-White models, for which we provide numerical case studies. We find that our polynomial option price series expansion performs as efficiently and accurately as the Fourier transform based method in the nested affine cases. We also derive and numerically validate series representations for option Greeks. We depict an extension of our approach to exotic options whose payoffs depend on a finite number of prices.", "published": "2017-11-25T05:30:43Z", "authors": ["Damien Ackerer", "Damir Filipovic"], "categories": ["q-fin.MF", "q-fin.CP", "q-fin.PR"], "doi": "", "_query": "hedonic house price", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2607.04027v1", "title": "Sequential Pricing with Deadlines and Correlated Buyers", "abstract": "We study sequential posted pricing for selling a single item under an exogenous deadline: a seller makes take-it-or-leave-it price offers to buyers one at a time, stopping when some buyer accepts or the selling opportunity expires. We consider both deterministic deadlines and random deadlines drawn from a known distribution, and allow buyer valuations to be either independent or arbitrarily correlated. Despite its practical relevance, this collection of deadline-constrained sequential pricing models has received limited algorithmic study beyond special cases. We develop the first approximation algorithms for revenue maximization across these settings.", "published": "2026-07-04T20:53:58Z", "authors": ["Shruti Penumatsa", "Rajan Udwani"], "categories": ["math.OC"], "doi": "", "_query": "hedonic house price", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2604.22895v1", "title": "Price Cap vs. Per-Unit Subsidies: Selection, Pricing, and Cross Subsidization", "abstract": "We evaluate subsidy mechanisms in the FCC's Rural Health Care program using administrative data covering the full population of participants. The original price-cap mechanism removes cost-containment incentives for health care providers. An ad valorem mechanism introduced in 2014 addresses this flaw by making providers bear 35% of costs. However, allowing consortium applications creates a new distortion: cross-subsidization from eligible to ineligible members. We develop theoretical models predicting these effects and estimate treatment effects using an extension of the two-way fixed effects framework with continuous treatments. We find that the ad valorem mechanism substantially reduces program spending relative to the price cap, while the consortium option significantly inflates it. Enforcement records and an inverted U-shaped relationship between cross-subsidization intensity and ineligible member share corroborate the findings.", "published": "2026-04-24T11:29:02Z", "authors": ["Ram Sewak Dubey", "Maysam Rabbani", "Rodrigo Pinto"], "categories": ["econ.GN"], "doi": "", "_query": "hedonic house price", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/1912.11166v1", "title": "A Gated Recurrent Unit Approach to Bitcoin Price Prediction", "abstract": "In today's era of big data, deep learning and artificial intelligence have formed the backbone for cryptocurrency portfolio optimization. Researchers have investigated various state of the art machine learning models to predict Bitcoin price and volatility. Machine learning models like recurrent neural network (RNN) and long short-term memory (LSTM) have been shown to perform better than traditional time series models in cryptocurrency price prediction. However, very few studies have applied sequence models with robust feature engineering to predict future pricing. in this study, we investigate a framework with a set of advanced machine learning methods with a fixed set of exogenous and endogenous factors to predict daily Bitcoin prices. We study and compare different approaches using the root mean squared error (RMSE). Experimental results show that gated recurring unit (GRU) model with recurrent dropout performs better better than popular existing models. We also show that simple trading strategies, when implemented with our proposed GRU model and with proper learning, can lead to financial gain.", "published": "2019-12-24T01:23:29Z", "authors": ["Aniruddha Dutta", "Saket Kumar", "Meheli Basu"], "categories": ["q-fin.PR", "q-fin.MF"], "doi": "10.3390/jrfm13020023", "_query": "hedonic house price", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2209.12199v1", "title": "RC100: Rotation Curves of 100 Massive Star-Forming Galaxies at z=0.6-2.5 Reveal Little Dark Matter on Galactic Scales", "abstract": "We analyze Ha or CO rotation curves (RCs) extending out to several galaxy effective radii for 100 massive, large, star-forming disk galaxies (SFGs) across the peak of cosmic galaxy star formation (z~0.6-2.5), more than doubling the previous sample presented by Genzel et al. (2020) and Price et al. (2021). The observations were taken with SINFONI and KMOS integral-field spectrographs at ESO-VLT, LUCI at LBT, NOEMA at IRAM, and ALMA. We fit the major axis kinematics with beam-convolved, forward models of turbulent rotating disks with bulges embedded in dark matter (DM) halos, including the effects of pressure support. The fraction of dark to total matter within the disk effective radius ($R_e ~ 5 kpc$), $f_DM (R_e)=V_{DM}^2 (R_e)/V_{circ}^2 (R_e)$, decreases with redshift: At z~1 (z~2) the median DM fraction is $0.38\\pm 0.23$ ($0.27\\pm 0.18$), and a third (half) of all galaxies are \"maximal\" disks with $f_{DM} (R_e)<0.28$. Dark matter fractions correlate inversely with the baryonic surface density, and the low DM fractions require a flattened, or cored, inner DM density distribution. At z~2 there is ~40% less dark matter mass on average within $R_e$ compared to expected values based on cosmological stellar-mass halo-mass relations. The DM deficit is more evident at high star formation rate (SFR) surface densities ($Σ_{SFR}>2.5 M_{\\odot} yr^{-1} kpc^{-2}$) and galaxies with massive bulges ($M_{bulge}>10^{10} M_{\\odot}$). A combination of stellar or active galactic nucleus (AGN) feedback, and/or heating due to dynamical friction, either from satellite accretion or clump migration, may drive the DM from cuspy into cored mass distributions. The observations plausibly indicate an efficient build-up of massive bulges and central black holes at z~2 SFGs.", "published": "2022-09-25T11:30:23Z", "authors": ["A. Nestor Shachar", "S. H. Price", "N. M. Förster Schreiber", "R. Genzel", "T. T. Shimizu", "L. J. Tacconi", "H. Übler", "A. Burkert", "R. I. Davies", "A. Deke", "R. Herrera-Camus", "L. L. Lee", "D. Liu", "D. Lutz", "T. Naab", "R. Neri", "A. Renzini", "R. Saglia", "K. Schuster", "A. Sternberg", "E. Wisnioski", "S. Wuyts"], "categories": ["astro-ph.GA"], "doi": "10.3847/1538-4357/aca9cf", "_query": "hedonic house price", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2303.13295v3", "title": "Irrelevance of personalized pricing under strategic market segmentation", "abstract": "A multiproduct seller is more informed than consumers about the value of her products to consumers. The seller posts a price list and segments the market through cheap-talk communication. We find that when both seller's and consumers' incentive-compatibility constraints are satisfied, the seller cannot benefit from personalized pricing (i.e., third-degree price discrimination). Based on that observation, we provide a tractable characterization of seller's maximum equilibrium profits. We apply our analysis to a credence-good setup and discuss when the credence goods seller benefits from communication. The irrelevance result breaks down when we relax seller's incentive-compatibility constraints.", "published": "2023-03-23T14:26:58Z", "authors": ["Xiaoxiao Hu", "Haoran Lei"], "categories": ["econ.TH"], "doi": "", "_query": "hedonic house price", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2203.05719v1", "title": "Analytical Pricing of 2 Factor Structural PDE model for a Puttable Bond with Credit Risk", "abstract": "In this paper is proposed a 2 factor structural PDE model of pricing puttable bond with credit risk and derived the analytical pricing formula. To this end, first, a 2 factor structural (PDE) model of pricing zero coupon bond with credit risk is provided, the analytical pricing formula is derived under some conditions for default boundary and default recovery, and the strict monotonicity of the bond price function with respect to the firm value variable is proved. Then a (2 factor) pricing model of the option on zero coupon bond with credit risk is provided and under some condition on the exercise price its analytical pricing formula is derived by transforming the 2 factor model into a terminal boundary value problem for Black-Scholes equation with time dependent coefficient using zero coupon bond as numeraire. Using it, we provide the pricing formulae of the puttable and callable bonds with credit risk.", "published": "2022-03-11T02:22:25Z", "authors": ["Hyong Chol O", "Dae Song Choe", "Gyong-Dok Rim"], "categories": ["q-fin.PR", "math.AP"], "doi": "", "_query": "hedonic house price", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/1912.01139v2", "title": "Event Ticket Price Prediction with Deep Neural Network on Spatial-Temporal Sparse Data", "abstract": "Event ticket price prediction is important to marketing strategy for any sports team or musical ensemble. An accurate prediction model can help the marketing team to make promotion plan more effectively and efficiently. However, given all the historical transaction records, it is challenging to predict the sale price of the remaining seats at any future timestamp, not only because that the sale price is relevant to a lot of features (seat locations, date-to-event of the transaction, event date, team performance, etc.), but also because of the temporal and spatial sparsity in the dataset. For a game/concert, the ticket selling price of one seat is only observable once at the time of sale. Furthermore, some seats may not even be purchased (therefore no record available). In fact, data sparsity is commonly encountered in many prediction problems. Here, we propose a bi-level optimizing deep neural network to address the curse of spatio-temporal sparsity. Specifically, we introduce coarsening and refining layers, and design a bi-level loss function to integrate different level of loss for better prediction accuracy. Our model can discover the interrelations among ticket sale price, seat locations, selling time, event information, etc. Experiments show that our proposed model outperforms other benchmark methods in real-world ticket selling price prediction.", "published": "2019-12-03T00:53:57Z", "authors": ["Fei Huang", "Hao Huang"], "categories": ["cs.LG", "stat.ML"], "doi": "", "_query": "hedonic house price", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2609.05499v1", "title": "A 1.283 Price-of-Anarchy Bound for the Repeated Virtual First-Price Auction", "abstract": "We study the repeated allocation of a single indivisible resource among $n$ strategic players. Each player $i$ has a privately known value distribution $D_i$, and values are drawn independently across players and periods. The goal is to find fair and efficient mechanisms. We apply the repeated first-price auction with equal initial endowments of virtual money. We show that each player can asymptotically secure the same fair-floor guarantee $f(D_i)$ as in Csóka 2026; consequently, the mechanism is $1.283$-optimal. This provides a simpler and more robust alternative mechanism for this special case and may also help derive sharper upper bounds on the price of anarchy.", "published": "2026-08-28T15:26:43Z", "authors": ["Endre Csóka"], "categories": ["cs.GT", "econ.TH"], "doi": "", "_query": "hedonic house price", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2608.08690v1", "title": "Price Responses of Rwandan Tungsten Exports under Conflict Minerals Regulation", "abstract": "Section 1502 of the Dodd--Frank Act, enacted in 2010, requires U.S.-listed companies using tin, tantalum, tungsten, and gold (3TG) from the Democratic Republic of the Congo and adjoining countries to disclose information on the minerals' origins. Concerns have been raised that the regulation may have induced a de facto embargo through avoidance of sourcing from the covered region. However, how the price responsiveness of mineral exports evolved under changing institutional and market conditions remains insufficiently understood. Since tungsten production in the covered region is concentrated almost entirely in Rwanda, this study examines the price responsiveness of Rwandan tungsten exports from January 2009 to December 2023. Because missing export quantity data prevent continuous observation of export unit values, we apply the identification approach of Nakano and Nishimura (2025), combining monthly mirror trade data from UN Comtrade with exchange rates and a world average price. An importer fixed-effects model is estimated using export value as the dependent variable, with the sample divided into four periods according to changes in the institutional and market environment. The results reveal substantial temporal variation in price responsiveness. A statistically significant negative price response is observed in Period 1 ($η=-20.814$, $p<0.01$), disappears in Period 2 ($η=1.814$, $p>0.10$), reappears in Period 3 ($η=-5.277$, $p<0.01$), and disappears again in Period 4 ($η=0.440$, $p>0.10$). Coefficient-difference tests confirm significant changes between Periods 1 and 2 ($p=0.0021$) and between Periods 3 and 4 ($p=0.0006$). These findings suggest that the price responsiveness of Rwandan tungsten exports varied substantially over time rather than following a uniform trajectory after the regulation.", "published": "2026-08-09T13:12:09Z", "authors": ["Haruka Nagamori", "Kazuhiko Nishimura"], "categories": ["econ.GN"], "doi": "", "_query": "hedonic house price", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2003.11857v2", "title": "Simultaneous 2nd Price Item Auctions with No-Underbidding", "abstract": "The literature on the Price of Anarchy (PoA) of simple auctions employs a no-overbidding assumption but has completely overlooked the no-underbidding phenomenon, which is evident in empirical studies on variants of the second price auction. In this work, we provide a theoretical foundation for the no-underbidding phenomenon. We study the PoA of simultaneous 2nd price auctions (S2PA) under a new natural condition of {\\em no underbidding}, meaning that agents never bid on items less than their marginal values. We establish improved (mostly tight) bounds on the PoA of S2PA under no underbidding for different valuation classes (including unit-demand, submodular, XOS, subadditive, and general monotone valuations), in both full-information and incomplete information settings. To derive our results, we introduce a new parameterized property of auctions, termed $(γ,δ)$-revenue guaranteed, which implies a PoA of at least $γ/(1+δ)$. Via extension theorems, this guarantee extends to coarse correlated equilibria (CCE) in full information settings, and to Bayesian PoA (BPoA) in settings with incomplete information and arbitrary (correlated) distributions. We then show that S2PA are $(1,1)$-revenue guaranteed with respect to bids satisfying no underbidding. This implies a PoA of at least $1/2$ for general monotone valuation, which extends to BPOA with arbitrary correlated distributions. Moreover, we show that $(λ,μ)$-smoothness combined with $(γ,δ)$-revenue guaranteed guarantees a PoA of at least $(γ+λ)/(1+δ+μ)$. This implies a host of results, such as a tight PoA of $2/3$ for S2PA with submodular (or XOS) valuations, under no overbidding and no underbidding. Beyond establishing improved bounds for S2PA, the no underbidding assumption sheds new light on the performance of S2PA relative to simultaneous 1st price auctions.", "published": "2020-03-26T12:17:34Z", "authors": ["Michal Feldman", "Galia Shabtai"], "categories": ["cs.GT"], "doi": "", "_query": "hedonic house price", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2607.10385v1", "title": "Prices and Competition in Vertically Integrated Launch Markets", "abstract": "Over the last 15 years the number of U.S. orbital launches has grown by roughly an order of magnitude. About three-quarters of those launches were on SpaceX's Falcon 9 vehicle, and roughly three-fifths of those Falcon launches deployed SpaceX's own Starlink constellation. A back-of-envelope Wright's law calculation suggests this increase in experience should have driven the Falcon 9's real launch cost down by roughly 70\\% over 2012--2026. Yet over the same period the advertised price fell by less than 6\\% in real terms. Why? I develop a simple model of competition and vertical integration between launchers and constellations. The launch market is Bertrand; the constellation services market is Cournot; one launcher is integrated with its captive constellation. Three results follow. First, the removal of double marginalization raises the captive constellation's equilibrium size. If the integrated launcher obtains cost reductions from this experience, they are captured as capacity rent rather than passed through to external buyers. Second, the integrated launcher prices launches to be indifferent between serving internal and external demand, leaving more residual demand for a competing launcher to monopolize and pushing the equilibrium launch price up. Third, the same capacity rent that holds the equilibrium launch price up can attract entry to the launch segment, while the expansion of the captive constellation deters entry on the constellation side.", "published": "2026-07-11T16:28:19Z", "authors": ["Akhil Rao"], "categories": ["econ.GN"], "doi": "", "_query": "hedonic house price", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/1910.08693v7", "title": "Online Pricing with Offline Data: Phase Transition and Inverse Square Law", "abstract": "This paper investigates the impact of pre-existing offline data on online learning, in the context of dynamic pricing. We study a single-product dynamic pricing problem over a selling horizon of $T$ periods. The demand in each period is determined by the price of the product according to a linear demand model with unknown parameters. We assume that before the start of the selling horizon, the seller already has some pre-existing offline data. The offline data set contains $n$ samples, each of which is an input-output pair consisting of a historical price and an associated demand observation. The seller wants to utilize both the pre-existing offline data and the sequential online data to minimize the regret of the online learning process. We characterize the joint effect of the size, location and dispersion of the offline data on the optimal regret of the online learning process. Specifically, the size, location and dispersion of the offline data are measured by the number of historical samples $n$, the distance between the average historical price and the optimal price $δ$, and the standard deviation of the historical prices $σ$, respectively. We show that the optimal regret is $\\widetilde Θ\\left(\\sqrt{T}\\wedge \\frac{T}{(n\\wedge T)δ^2+nσ^2}\\right)$, and design a learning algorithm based on the \"optimism in the face of uncertainty\" principle, whose regret is optimal up to a logarithmic factor. Our results reveal surprising transformations of the optimal regret rate with respect to the size of the offline data, which we refer to as phase transitions. In addition, our results demonstrate that the location and dispersion of the offline data also have an intrinsic effect on the optimal regret, and we quantify this effect via the inverse-square law.", "published": "2019-10-19T03:36:05Z", "authors": ["Jinzhi Bu", "David Simchi-Levi", "Yunzong Xu"], "categories": ["cs.LG", "stat.ML"], "doi": "", "_query": "hedonic house price", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/cond-mat/0209522v1", "title": "A master equation approach to option pricing", "abstract": "A master equation approach to the numerical solution of option pricing models is developed. The basic idea of the approach is to consider the Black--Scholes equation as the macroscopic equation of an underlying mesoscopic stochastic option price variable. The dynamics of the latter is constructed and formulated in terms of a master equation. The numerical efficiency of the approach is demonstrated by means of stochastic simulation of the mesoscopic process for both European and American options.", "published": "2002-09-23T15:24:23Z", "authors": ["Daniel Faller", "Francesco Petruccione"], "categories": ["cond-mat.stat-mech", "q-fin.PR"], "doi": "10.1016/S0378-4371(02)01530-3", "_query": "hedonic house price", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/1706.06709v3", "title": "Singular Fourier-Padé Series Expansion of European Option Prices", "abstract": "We apply a new numerical method, the singular Fourier-Padé (SFP) method invented by Driscoll and Fornberg (2001, 2011), to price European-type options in Lévy and affine processes. The motivation behind this application is to reduce the inefficiency of current Fourier techniques when they are used to approximate piecewise continuous (non-smooth) probability density functions. When techniques such as fast Fourier transforms and Fourier series are applied to price and hedge options with non-smooth probability density functions, they cause the Gibbs phenomenon, accordingly, the techniques converge slowly for density functions with jumps in value or derivatives. This seriously adversely affects the efficiency and accuracy of these techniques. In this paper, we derive pricing formulae and their option Greeks using the SFP method to resolve the Gibbs phenomenon and restore the global spectral convergence rate. Moreover, we show that our method requires a small number of terms to yield fast error convergence, and it is able to accurately price any European-type option deep in/out of the money and with very long/short maturities. Furthermore, we conduct an error-bound analysis of the SFP method in option pricing. This new method performs favourably in numerical experiments compared with existing techniques.", "published": "2017-06-21T00:15:01Z", "authors": ["Tat Lung Chan"], "categories": ["q-fin.CP"], "doi": "", "_query": "hedonic house price", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2503.15634v1", "title": "Homogeneous Algorithms Can Reduce Competition in Personalized Pricing", "abstract": "Firms' algorithm development practices are often homogeneous. Whether firms train algorithms on similar data, aim at similar benchmarks, or rely on similar pre-trained models, the result is correlated predictions. We model the impact of correlated algorithms on competition in the context of personalized pricing. Our analysis reveals that (1) higher correlation diminishes consumer welfare and (2) as consumers become more price sensitive, firms are increasingly incentivized to compromise on the accuracy of their predictions in exchange for coordination. We demonstrate our theoretical results in a stylized empirical study where two firms compete using personalized pricing algorithms. Our results underscore the ease with which algorithms facilitate price correlation without overt communication, which raises concerns about a new frontier of anti-competitive behavior. We analyze the implications of our results on the application and interpretation of US antitrust law.", "published": "2025-03-19T18:43:36Z", "authors": ["Nathanael Jo", "Kathleen Creel", "Ashia Wilson", "Manish Raghavan"], "categories": ["cs.GT", "cs.CY"], "doi": "", "_query": "hedonic house price", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/1602.02527v2", "title": "Bounds on the Price of Anarchy for a More General Class of Directed Graphs in Opinion Formation Games", "abstract": "In opinion formation games with directed graphs, a bounded price of anarchy is only known for weighted Eulerian graphs. Thus, we bound the price of anarchy for a more general class of directed graphs with conditions intuitively meaning that each node does not influence the others more than she is influenced, where the bounds depend on such difference (in a ratio). We also show that there exists an example just slightly violating the conditions with an unbounded price of anarchy.", "published": "2016-02-08T11:12:18Z", "authors": ["Po-An Chen", "Yi-Le Chen", "Chi-Jen Lu"], "categories": ["cs.GT", "cs.SI"], "doi": "", "_query": "hedonic house price", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2509.00999v1", "title": "Pricing American Options Time-Capped by a Drawdown Event", "abstract": "This paper presents a derivation of the explicit price for the perpetual American put option in the Black-Scholes model, time-capped by the first drawdown epoch beyond a predefined level. We demonstrate that the optimal exercise strategy involves executing the option when the asset price first falls below a specified threshold. The proof relies on martingale arguments and the fluctuation theory of Lévy processes. To complement the theoretical findings, we provide numerical analysis.", "published": "2025-08-31T21:23:07Z", "authors": ["Zbigniew Palmowski", "Paweł Stȩpniak"], "categories": ["q-fin.MF", "math.PR"], "doi": "", "_query": "hedonic house price", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/1709.00632v1", "title": "On concavity of the monopolist's problem facing consumers with nonlinear price preferences", "abstract": "A monopolist wishes to maximize her profits by finding an optimal price policy. After she announces a menu of products and prices, each agent $x$ will choose to buy that product $y(x)$ which maximizes his own utility, if positive. The principal's profits are the sum of the net earnings produced by each product sold. These are determined by the costs of production and the distribution of products sold, which in turn are based on the distribution of anonymous agents and the choices they make in response to the principal's price menu. In this paper, we provide a necessary and sufficient condition for the convexity or concavity of the principal's (bilevel) optimization problem, assuming each agent's disutility is a strictly increasing but not necessarily affine (i.e. quasilinear) function of the price paid. Concavity when present, makes the problem more amenable to computational and theoretical analysis; it is key to obtaining uniqueness and stability results for the principal's strategy in particular. Even in the quasilinear case, our analysis goes beyond previous work by addressing convexity as well as concavity, by establishing conditions which are not only sufficient but necessary, and by requiring fewer hypotheses on the agents' preferences.", "published": "2017-09-02T21:01:21Z", "authors": ["Robert J. McCann", "Kelvin Shuangjian Zhang"], "categories": ["math.OC", "math.AP"], "doi": "10.1002/cpa.21817", "_query": "hedonic house price", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2207.02832v2", "title": "Distributional neural networks for electricity price forecasting", "abstract": "We present a novel approach to probabilistic electricity price forecasting which utilizes distributional neural networks. The model structure is based on a deep neural network that contains a so-called probability layer. The network's output is a parametric distribution with 2 (normal) or 4 (Johnson's SU) parameters. In a forecasting study involving day-ahead electricity prices in the German market, our approach significantly outperforms state-of-the-art benchmarks, including LASSO-estimated regressions and deep neural networks combined with Quantile Regression Averaging. The obtained results not only emphasize the importance of higher moments when modeling volatile electricity prices, but also -- given that probabilistic forecasting is the essence of risk management -- provide important implications for managing portfolios in the power sector.", "published": "2022-07-06T17:42:52Z", "authors": ["Grzegorz Marcjasz", "Michał Narajewski", "Rafał Weron", "Florian Ziel"], "categories": ["q-fin.ST", "stat.AP", "stat.ML"], "doi": "10.1016/j.eneco.2023.106843", "_query": "hedonic house price", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2205.13423v3", "title": "Do price trajectory data increase the efficiency of market impact estimation?", "abstract": "Market impact is an important problem faced by large institutional investor and active market participant. In this paper, we rigorously investigate whether price trajectory data from the metaorder increases the efficiency of estimation, from an asymptotic view of statistical estimation. We show that, for popular market impact models, estimation methods based on partial price trajectory data, especially those containing early trade prices, can outperform established estimation methods (e.g., VWAP-based) asymptotically. We discuss theoretical and empirical implications of such phenomenon, and how they could be readily incorporated into practice.", "published": "2022-05-26T15:28:46Z", "authors": ["Fengpei Li", "Vitalii Ihnatiuk", "Ryan Kinnear", "Anderson Schneider", "Yuriy Nevmyvaka"], "categories": ["q-fin.TR"], "doi": "", "_query": "hedonic house price", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/1202.5025v1", "title": "The Price of Anarchy for Network Formation in an Adversary Model", "abstract": "We study network formation with n players and link cost α> 0. After the network is built, an adversary randomly deletes one link according to a certain probability distribution. Cost for player v incorporates the expected number of players to which v will become disconnected. We show existence of equilibria and a price of stability of 1+o(1) under moderate assumptions on the adversary and n \\geq 9. As the main result, we prove bounds on the price of anarchy for two special adversaries: one removes a link chosen uniformly at random, while the other removes a link that causes a maximum number of player pairs to be separated. For unilateral link formation we show a bound of O(1) on the price of anarchy for both adversaries, the constant being bounded by 10+o(1) and 8+o(1), respectively. For bilateral link formation we show O(1+\\sqrt{n/α}) for one adversary (if α> 1/2), and Θ(n) for the other (if α> 2 considered constant and n \\geq 9). The latter is the worst that can happen for any adversary in this model (if α= Ω(1)). This points out substantial differences between unilateral and bilateral link formation.", "published": "2012-02-22T20:21:02Z", "authors": ["Lasse Kliemann"], "categories": ["cs.GT"], "doi": "", "_query": "hedonic house price", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2308.08554v1", "title": "AI-Assisted Investigation of On-Chain Parameters: Risky Cryptocurrencies and Price Factors", "abstract": "Cryptocurrencies have become a popular and widely researched topic of interest in recent years for investors and scholars. In order to make informed investment decisions, it is essential to comprehend the factors that impact cryptocurrency prices and to identify risky cryptocurrencies. This paper focuses on analyzing historical data and using artificial intelligence algorithms on on-chain parameters to identify the factors affecting a cryptocurrency's price and to find risky cryptocurrencies. We conducted an analysis of historical cryptocurrencies' on-chain data and measured the correlation between the price and other parameters. In addition, we used clustering and classification in order to get a better understanding of a cryptocurrency and classify it as risky or not. The analysis revealed that a significant proportion of cryptocurrencies (39%) disappeared from the market, while only a small fraction (10%) survived for more than 1000 days. Our analysis revealed a significant negative correlation between cryptocurrency price and maximum and total supply, as well as a weak positive correlation between price and 24-hour trading volume. Moreover, we clustered cryptocurrencies into five distinct groups using their on-chain parameters, which provides investors with a more comprehensive understanding of a cryptocurrency when compared to those clustered with it. Finally, by implementing multiple classifiers to predict whether a cryptocurrency is risky or not, we obtained the best f1-score of 76% using K-Nearest Neighbor.", "published": "2023-08-11T09:20:28Z", "authors": ["Abdulrezzak Zekiye", "Semih Utku", "Fadi Amroush", "Oznur Ozkasap"], "categories": ["q-fin.ST", "cs.AI", "cs.CR", "cs.LG"], "doi": "", "_query": "hedonic house price", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2605.09318v1", "title": "Price Distortions in Korea's Electricity Market: Barriers to Renewable Integration and Reform Pathways", "abstract": "Structural distortions in price signals within the Korean electricity market, governed by a cost-based pool (CBP) and a uniform pricing mechanism, fundamentally undermine the nation's energy transition goals. The current market design fails to reflect transmission constraints, real-time supply and demand dynamics, and generator-specific costs, leading to inefficient resource allocation and hindering long-term investments in renewable energy and grid flexibility. This paper identifies the key drivers of these distortions and proposes a holistic reform package to enhance market efficiency. The package includes four key reforms: \\stepcounter{excep}(\\roman{excep}) introducing a locational marginal pricing system to manage transmission constraints; \\stepcounter{excep}(\\roman{excep}) establishing a real-time market to reflect temporal value; \\stepcounter{excep}(\\roman{excep}) integrating market and system operations to resolve inconsistencies; and \\stepcounter{excep}(\\roman{excep}) transitioning from CBP to a price-based bidding system. Each reform targets a distinct source of inefficiency. The broader contribution of this study, however, lies in showing that, under the current Korean market design, the market cannot readily provide effective price signals. These reforms therefore need to be implemented jointly to establish a coherent market design in which price signals are aligned with Korea's energy policy objectives.", "published": "2026-05-10T04:32:55Z", "authors": ["Ryungyeong Lee", "Minhan Yoon", "Hunyoung Shin"], "categories": ["eess.SY"], "doi": "", "_query": "hedonic house price", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/1112.4534v1", "title": "An application of the method of moments to volatility estimation using daily high, low, opening and closing prices", "abstract": "We use the expectation of the range of an arithmetic Brownian motion and the method of moments on the daily high, low, opening and closing prices to estimate the volatility of the stock price. The daily price jump at the opening is considered to be the result of the unobserved evolution of an after-hours virtual trading day.The annualized volatility is used to calculate Black-Scholes prices for European options, and a trading strategy is devised to profit when these prices differ flagrantly from the market prices.", "published": "2011-12-20T00:04:36Z", "authors": ["Cristin Buescu", "Michael Taksar", "Fatoumata J. Koné"], "categories": ["q-fin.ST", "math.PR", "q-fin.PR", "stat.AP"], "doi": "", "_query": "hedonic house price", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/1502.07265v1", "title": "Estimation of Several Political Action Effects of Energy Prices", "abstract": "One important effect of price shocks in the United States has been increased political attention paid to the structure and performance of oil and natural gas markets, along with some governmental support for energy conservation. This paper describes how price changes helped lead the emergence of a political agenda accompanied by several interventions, as revealed through Granger causality tests on change in the legislative agenda.", "published": "2015-02-25T17:31:05Z", "authors": ["Andrew B. Whitford"], "categories": ["q-fin.GN", "physics.soc-ph"], "doi": "", "_query": "hedonic house price", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2310.05322v1", "title": "Market Crowds' Trading Behaviors, Agreement Prices, and the Implications of Trading Volume", "abstract": "It has been long that literature in financial academics focuses mainly on price and return but much less on trading volume. In the past twenty years, it has already linked both price and trading volume to economic fundamentals, and explored the behavioral implications of trading volume such as investor's attitude toward risks, overconfidence, disagreement, and attention etc. However, what is surprising is how little we really know about trading volume. Here we show that trading volume probability represents the frequency of market crowd's trading action in terms of behavior analysis, and test two adaptive hypotheses relevant to the volume uncertainty associated with price in China stock market. The empirical work reveals that market crowd trade a stock in efficient adaptation except for simple heuristics, gradually tend to achieve agreement on an outcome or an asset price widely on a trading day, and generate such a stationary equilibrium price very often in interaction and competition among themselves no matter whether it is highly overestimated or underestimated. This suggests that asset prices include not only a fundamental value but also private information, speculative, sentiment, attention, gamble, and entertainment values etc. Moreover, market crowd adapt to gain and loss by trading volume increase or decrease significantly in interaction with environment in any two consecutive trading days. Our results demonstrate how interaction between information and news, the trading action, and return outcomes in the three-term feedback loop produces excessive trading volume which includes various internal and external causes.", "published": "2023-10-09T00:59:24Z", "authors": ["Leilei Shi", "Bing Han", "Yingzi Zhu", "Liyan Han", "Yiwen Wang", "Yan Piao"], "categories": ["q-fin.GN"], "doi": "", "_query": "hedonic house price", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2509.08166v1", "title": "A Linear Pricing Mechanism for Load Management in Day-Ahead Retail Energy Markets", "abstract": "Regulators and utilities have been exploring hourly retail electricity pricing, with several existing programs providing day-ahead hourly pricing schedules. At the same time, customers are deploying distributed energy resources and smart energy management systems that have significant flexibility and can optimally follow price signals. In aggregate, these optimally controlled loads can create congestion management issues for distribution system operators (DSOs). In this paper, we describe a new linear pricing mechanism for day-ahead retail electricity pricing that provides a signal for customers to follow to mitigate over-consumption while still consuming energy at hours that are preferential for system performance. We show that by broadcasting a linear price designed for price-signal control of cost-optimizing loads, we can shape customer load profiles to provide congestion management without the need for bi-directional communication or customer bidding programs.", "published": "2025-09-09T22:00:56Z", "authors": ["Phillippe K. Phanivong", "Duncan S. Callaway"], "categories": ["eess.SY", "econ.GN"], "doi": "", "_query": "hedonic house price", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2103.00766v1", "title": "Computing Prices for Target Profits in Contracts", "abstract": "Price discrimination for maximizing expected profit is a well-studied concept in economics and there are various methods that achieve the maximum given the user type distribution and the budget constraints. In many applications, particularly with regards to engineering and computing, it is often the case than the user type distribution is unknown or not accurately known. In this paper, we therefore propose and study a mathematical framework for price discrimination with \\emph{target} profits under the contract-theoretic model. We first consider service providers with a given user type profile and determine sufficient conditions for achieving a target profit. Our proof is constructive in that it also provides a method to compute the quality-price tag menu. Next we consider a dual scenario where the offered service qualities are predetermined and describe an iterative method to obtain nominal demand values that best match the qualities offered by the service provider while achieving a target profit-user satisfaction margin. We also illustrate our methods with design examples in both cases.", "published": "2021-03-01T05:41:45Z", "authors": ["Ghurumuruhan Ganesan"], "categories": ["econ.GN", "cs.IT"], "doi": "", "_query": "hedonic house price", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2201.01026v4", "title": "How Does Risk Hedging Impact Operations? Insights from a Price-Setting Newsvendor Model", "abstract": "If a financial asset's price movement impacts a firm's product demand, the firm can respond to the impact by adjusting its operational decisions. For example, in the automotive industry, car makers decrease the selling prices of fuel-inefficient cars when the oil price rises. Meanwhile, the firm can implement a risk-hedging strategy using the financial asset jointly with its operational decisions. Motivated by this, we develop and solve a general risk-management model integrating risk hedging into a price-setting newsvendor. The optimal hedging strategy is calculated analytically, which leads to an explicit objective function for optimizing price and ``virtual production quantity'' (VPQ). (The latter determines the service level, i.e., the demand fulfillment probability.) We find that hedging generally reduces the optimal price {when the firm sets the target mean return as its production-only maximum expected profit. With the same condition on the target mean return}, hedging also reduces the optimal VPQ when the asset price trend positively impacts product demand; meanwhile, it may increase the VPQ by a small margin when the impact is negative. We construct the return-risk efficient frontier that characterizes the optimal return-risk trade-off. Our numerical study using data from a prominent automotive manufacturer shows that the markdowns in price and reduction in VPQ are small under our model and that the hedging strategy substantially reduces risk without materially reducing operational profit.", "published": "2022-01-04T08:10:20Z", "authors": ["Liao Wang", "Jin Yao", "Xiaowei Zhang"], "categories": ["q-fin.RM", "math.OC"], "doi": "", "_query": "hedonic house price", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2610.17387v1", "title": "Cross-Market Meta-Learning for Electricity Price Forecasting", "abstract": "Day-ahead (DA) electricity price forecasting is usually posed per market, with a separate model fitted to each grid's own history. This caps accuracy: statistical and tree-based baselines saturate, while higher-capacity sequence models need more training data than a single market's few years of hourly prices can supply. Such methods also treat each node as an independent series, discarding the spatial dependencies induced by network topology and congestion. Because price formation across independent system operators (ISOs) is driven by shared physics and largely common market design, we instead treat cross-market history as a pretraining corpus. We present MetaGrid, a topology-aware transformer, meta-trained across markets so that a single zone-count-independent parameter set adapts to an unseen grid. On a leak-free benchmark of five U.S. ISOs, where every model receives the same prices, DA load forecasts and calendar features, MetaGrid attains the lowest average MAE, improving on the best baseline by 3.1\\% and on the same architecture trained from scratch by 7.7\\%.", "published": "2026-10-12T20:48:53Z", "authors": ["Delaram Pirhayatifard", "Terrence Alsup"], "categories": ["cs.LG", "cs.AI"], "doi": "", "_query": "hedonic house price", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/cond-mat/0401422v1", "title": "The Opinion Game: Stock price evolution from microscopic market modelling", "abstract": "We propose a class of Markovian agent based models for the time evolution of a share price in an interactive market. The models rely on a microscopic description of a market of buyers and sellers who change their opinion about the stock value in a stochastic way. The actual price is determined in realistic way by matching (clearing) offers until no further transactions can be performed. Some analytic results for a non-interacting model are presented. We also propose basic interaction mechanisms and show in simulations that these already reproduce certain particular features of prices in real stock markets.", "published": "2004-01-22T10:48:55Z", "authors": ["Anton Bovier", "Jiri Cerny", "Ostap Hryniv"], "categories": ["cond-mat.other", "q-fin.TR"], "doi": "", "_query": "hedonic house price", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/1611.00389v6", "title": "Option pricing in exponential Lévy models with transaction costs", "abstract": "We present an approach for pricing European call options in presence of proportional transaction costs, when the stock price follows a general exponential Lévy process. The model is a generalization of the celebrated work of Davis, Panas and Zariphopoulou (1993), where the value of the option is defined as the utility indifference price. This approach requires the solution of two stochastic singular control problems in finite horizon, satisfying the same Hamilton-Jacobi-Bellman equation, with different terminal conditions. We introduce a general formulation for these portfolio selection problems, and then we focus on the special case in which the probability of default is ignored. We solve numerically the optimization problems using the Markov chain approximation method and show results for diffusion, Merton and Variance Gamma processes. Option prices are computed for both the writer and the buyer.", "published": "2016-11-01T21:02:34Z", "authors": ["Nicola Cantarutti", "João Guerra", "Manuel Guerra", "Maria do Rosário Grossinho"], "categories": ["q-fin.MF", "q-fin.GN", "q-fin.PM", "q-fin.PR"], "doi": "10.21314/JCF.2020.384", "_query": "hedonic house price", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/1511.06017v2", "title": "Rate of Price Discovery in Iterative Combinatorial Auctions", "abstract": "We study a class of iterative combinatorial auctions which can be viewed as subgradient descent methods for the problem of pricing bundles to balance supply and demand. We provide concrete convergence rates for auctions in this class, bounding the number of auction rounds needed to reach clearing prices. Our analysis allows for a variety of pricing schemes, including item, bundle, and polynomial pricing, and the respective convergence rates confirm that more expressive pricing schemes come at the cost of slower convergence. We consider two models of bidder behavior. In the first model, bidders behave stochastically according to a random utility model, which includes standard best-response bidding as a special case. In the second model, bidders behave arbitrarily (even adversarially), and meaningful convergence relies on properly designed activity rules.", "published": "2015-11-18T22:59:54Z", "authors": ["Jacob Abernethy", "Sébastien Lahaie", "Matus Telgarsky"], "categories": ["cs.GT"], "doi": "", "_query": "hedonic house price", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/1411.2036v2", "title": "Price Competition, Fluctuations, and Welfare Guarantees", "abstract": "In various markets where sellers compete in price, price oscillations are observed rather than convergence to equilibrium. Such fluctuations have been empirically observed in the retail market for gasoline, in airline pricing and in the online sale of consumer goods. Motivated by this, we study a model of price competition in which an equilibrium rarely exists. We seek to analyze the welfare, despite the nonexistence of an equilibrium, and present welfare guarantees as a function of the market power of the sellers. We first study best response dynamics in markets with sellers that provide a homogeneous good, and show that except for a modest number of initial rounds, the welfare is guaranteed to be high. We consider two variations: in the first the sellers have full information about the valuation of the buyer. Here we show that if there are $n$ items available across all sellers and $n_{\\max}$ is the maximum number of items controlled by any given seller, the ratio of the optimal welfare to the achieved welfare will be at most $\\log(\\frac{n}{n-n_{\\max}+1})+1$. As the market power of the largest seller diminishes, the welfare becomes closer to optimal. In the second variation we consider an extended model where sellers have uncertainty about the buyer's valuation. Here we similarly show that the welfare improves as the market power of the largest seller decreases, yet with a worse ratio of $\\frac{n}{n-n_{\\max}+1}$. The exponential gap in welfare between the two variations quantifies the value of accurately learning the buyer valuation. Finally, we show that extending our results to heterogeneous goods in general is not possible. Even for the simple class of $k$-additive valuations, there exists a setting where the welfare approximates the optimal welfare within any non-zero factor only for $O(1/s)$ fraction of the time, where $s$ is the number of sellers.", "published": "2014-11-07T21:16:06Z", "authors": ["Moshe Babaioff", "Renato Paes Leme", "Balasubramanian Sivan"], "categories": ["cs.GT"], "doi": "", "_query": "hedonic house price", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2211.12964v1", "title": "Petroleum prices prediction using data mining techniques -- A Review", "abstract": "Over the past 20 years, Kenya's demand for petroleum products has proliferated. This is mainly because this particular commodity is used in many sectors of the country's economy. Exchange rates are impacted by constantly shifting prices, which also impact Kenya's industrial output of commodities. The cost of other items produced and even the expansion of the economy is significantly impacted by any change in the price of petroleum products. Therefore, accurate petroleum price forecasting is critical for devising policies that are suitable to curb fuel-related shocks. Data mining techniques are the tools used to find valuable patterns in data. Data mining techniques used in petroleum price prediction, including artificial neural networks (ANNs), support vector machines (SVMs), and intelligent optimization techniques like the genetic algorithm (GA), have grown increasingly popular. This study provides a comprehensive review of the existing data mining techniques for making predictions on petroleum prices. The data mining techniques are classified into regression models, deep neural network models, fuzzy sets and logic, and hybrid models. A detailed discussion of how these models are developed and the accuracy of the models is provided.", "published": "2022-11-20T19:33:02Z", "authors": ["Kiplang'at Weldon", "John Ngechu", "Ngatho Everlyne", "Nancy Njambi", "Kinyua Gikunda"], "categories": ["cs.LG", "cs.AI"], "doi": "", "_query": "hedonic house price", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/1907.01846v1", "title": "Option pricing in fractional Heston-type model", "abstract": "In this paper, we consider option pricing in a framework of the fractional Heston-type model with $H>1/2$. As it is impossible to obtain an explicit formula for the expectation $\\mathbb E f(S_T)$ in this case, where $S_T$ is the asset price at maturity time and $f$ is a payoff function, we provide a discretization schemes $\\hat Y^n$ and $\\hat S^n$ for volatility and price processes correspondingly and study convergence $\\mathbb E f(\\hat S^n_T) \\to \\mathbb E f(S_T)$ as the mesh of the partition tends to zero. The rate of convergence is calculated. As we allow $f$ to have discontinuities of the first kind which can cause errors in straightforward Monte-Carlo estimation of the expectation, we use Malliavin calculus techniques to provide an alternative formula for $\\mathbb E f(S_T)$ with smooth functional under the expectation.", "published": "2019-07-03T10:51:27Z", "authors": ["Yuliya Mishura", "Anton Yurchenko-Tytarenko"], "categories": ["math.PR"], "doi": "", "_query": "hedonic house price", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2603.29261v1", "title": "Monodense Deep Neural Model for Determining Item Price Elasticity", "abstract": "Item Price Elasticity is used to quantify the responsiveness of consumer demand to changes in item prices, enabling businesses to create pricing strategies and optimize revenue management. Sectors such as store retail, e-commerce, and consumer goods rely on elasticity information derived from historical sales and pricing data. This elasticity provides an understanding of purchasing behavior across different items, consumer discount sensitivity, and demand elastic departments. This information is particularly valuable for competitive markets and resource-constrained businesses decision making which aims to maximize profitability and market share. Price elasticity also uncovers historical shifts in consumer responsiveness over time. In this paper, we model item-level price elasticity using large-scale transactional datasets, by proposing a novel elasticity estimation framework which has the capability to work in an absence of treatment control setting. We test this framework by using Machine learning based algorithms listed below, including our newly proposed Monodense deep neural network. (1) Monodense-DL network -- Hybrid neural network architecture combining embedding, dense, and Monodense layers (2) DML -- Double machine learning setting using regression models (3) LGBM -- Light Gradient Boosting Model We evaluate our model on multi-category retail data spanning millions of transactions using a back testing framework. Experimental results demonstrate the superiority of our proposed neural network model within the framework compared to other prevalent ML based methods listed above.", "published": "2026-03-31T04:50:51Z", "authors": ["Lakshya Garg", "Sai Yaswanth", "Deep Narayan Mishra", "Karthik Kumaran", "Anupriya Sharma", "Mayank Uniyal"], "categories": ["cs.LG", "cs.AI"], "doi": "10.1109/AAIML67890.2026.11498150", "_query": "hedonic house price", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/1809.05840v1", "title": "A Cloud Controller for Performance-Based Pricing", "abstract": "New dynamic cloud pricing options are emerging with cloud providers offering resources as a wide range of CPU frequencies and matching prices that can be switched at runtime. On the other hand, cloud providers are facing the problem of growing operational energy costs. This raises a trade-off problem between energy savings and revenue loss when performing actions such as CPU frequency scaling. Although existing cloud con- trollers for managing cloud resources deploy frequency scaling, they only consider fixed virtual machine (VM) pricing. In this paper we propose a performance-based pricing model adapted for VMs with different CPU-boundedness properties. We present a cloud controller that scales CPU frequencies to achieve energy cost savings that exceed service revenue losses. We evaluate the approach in a simulation based on real VM workload, electricity price and temperature traces, estimating energy cost savings up to 32% in certain scenarios.", "published": "2018-09-16T08:57:14Z", "authors": ["Dražen Lučanin", "Ilia Pietri", "Ivona Brandic", "Rizos Sakellariou"], "categories": ["cs.DC"], "doi": "10.1109/CLOUD.2015.30", "_query": "hedonic house price", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2407.21409v2", "title": "Price formation without fuel costs: the interaction of demand elasticity with storage bidding", "abstract": "Studies looking at electricity market designs for very high shares of wind and solar often conclude that the energy-only market will break down. Without fuel costs, it is said that there is nothing to set prices. Symptoms of breakdown include long phases of zero prices, scarcity prices too high to be politically acceptable, prices that collapse under small perturbations of capacities from the long-term equilibrium, cost recovery that is impossible due to low market values, high variability of revenue between different weather years, and difficulty operating long-term storage with limited foresight. We argue that all these problems are an artefact of modelling with perfectly inelastic demand. If short-term elasticity to reflect today's flexible demand (-5%) is implemented, these problems are reduced. The interaction of demand willingness to pay and storage opportunity costs is enough to produce stable pricing. This behavior is illustrated by a model with wind, solar, batteries, and hydrogen-based storage, where the price duration curve is smoothed with a piecewise linear demand curve. This removes high price peaks, reduces the fraction of zero-price hours from 90% to around 30%, and guarantees more price stability for perturbations of capacity and different weather years. Furthermore, we show that with demand elasticity, the long-term optimisation model exactly reproduces the prices of the short-term model with the same capacities. We then use insights from the long-term model to derive simple bidding strategies for storage so that we can also run the short-term model with limited operational foresight. We demonstrate this short-term operation in a model optimised using 35 years of weather data and then tested on another 35 years of unseen data. We conclude that the energy-only market can still play a key role in coordinating dispatch and investment in the future.", "published": "2024-07-31T07:57:00Z", "authors": ["Tom Brown", "Fabian Neumann", "Iegor Riepin"], "categories": ["econ.GN"], "doi": "10.1016/j.eneco.2025.108483", "_query": "hedonic house price", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2101.01576v2", "title": "On the price of explainability for some clustering problems", "abstract": "The price of explainability for a clustering task can be defined as the unavoidable loss,in terms of the objective function, if we force the final partition to be explainable. Here, we study this price for the following clustering problems: $k$-means, $k$-medians, $k$-centers and maximum-spacing. We provide upper and lower bounds for a natural model where explainability is achieved via decision trees. For the $k$-means and $k$-medians problems our upper bounds improve those obtained by [Moshkovitz et. al, ICML 20] for low dimensions. Another contribution is a simple and efficient algorithm for building explainable clusterings for the $k$-means problem. We provide empirical evidence that its performance is better than the current state of the art for decision-tree based explainable clustering.", "published": "2021-01-05T15:08:25Z", "authors": ["Eduardo Laber", "Lucas Murtinho"], "categories": ["cs.LG", "cs.DS"], "doi": "", "_query": "hedonic house price", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/1609.00599v4", "title": "Optimal Execution in a Multiplayer Model of Transient Price Impact", "abstract": "Trading algorithms that execute large orders are susceptible to exploitation by order anticipation strategies. This paper studies the influence of order anticipation strategies in a multi-investor model of optimal execution under transient price impact. Existence and uniqueness of a Nash equilibrium is established under the assumption that trading incurs quadratic transaction costs. A closed-form representation of the Nash equilibrium is derived for exponential decay kernels. With this representation, it is shown that while order anticipation strategies raise the execution costs of a large order significantly, they typically do not cause price overshooting in the sense of Brunnermeier and Pedersen.", "published": "2016-09-02T13:46:33Z", "authors": ["Elias Strehle"], "categories": ["q-fin.TR"], "doi": "10.1142/S2382626618500077", "_query": "hedonic house price", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/1003.0168v1", "title": "Order flow dynamics around extreme price changes on an emerging stock market", "abstract": "We study the dynamics of order flows around large intraday price changes using ultra-high-frequency data from the Shenzhen Stock Exchange. We find a significant reversal of price for both intraday price decreases and increases with a permanent price impact. The volatility, the volume of different types of orders, the bid-ask spread, and the volume imbalance increase before the extreme events and decay slowly as a power law, which forms a well-established peak. The volume of buy market orders increases faster and the corresponding peak appears earlier than for sell market orders around positive events, while the volume peak of sell market orders leads buy market orders in the magnitude and time around negative events. When orders are divided into four groups according to their aggressiveness, we find that the behaviors of order volume and order number are similar, except for buy limit orders and canceled orders that the peak of order number postpones two minutes later after the peak of order volume, implying that investors placing large orders are more informed and play a central role in large price fluctuations. We also study the relative rates of different types of orders and find differences in the dynamics of relative rates between buy orders and sell orders and between individual investors and institutional investors. There is evidence showing that institutions behave very differently from individuals and that they have more aggressive strategies. Combing these findings, we conclude that institutional investors are more informed and play a more influential role in driving large price fluctuations.", "published": "2010-02-28T07:51:53Z", "authors": ["Guo-Hua Mu", "Wei-Xing Zhou", "Wei Chen", "Janos Kertesz"], "categories": ["q-fin.TR", "q-fin.ST"], "doi": "10.1088/1367-2630/12/7/075037", "_query": "hedonic house price", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2606.19038v1", "title": "Collective completeness and pricing-hedging duality II", "abstract": "This paper complements and extends Doldi, Frittelli and Maggis, Collective completeness and pricing-hedging duality, Math. Finan. Econ. 19, 757-784 (2025), by studying collective pricing and hedging when admissible risk exchanges form a finitely generated convex cone. The collective First Fundamental Theorem of Asset Pricing and the collective pricing-hedging duality are extended to this setting. A key contribution is a closedness result showing that no collective arbitrage implies the closedness of the aggregate feasibility cone combining infinite-dimensional trading opportunities with finite-dimensional exchanges. The paper also proves that no-collective-arbitrage prices for vectors of contingent claims form a relatively open convex set. Finally, strong collective replicability is introduced and shown to be equivalent to price uniqueness. This leads to an enhanced collective Second Fundamental Theorem of Asset Pricing, providing equivalent characterizations of collective completeness and strong collective completeness in terms of the uniqueness of the collective equivalent martingale measure. We highlight that several core aspects of the theory are substantially altered when exchanges belong to a convex cone rather than a vector space.", "published": "2026-06-17T13:07:10Z", "authors": ["Alessandro Doldi", "Marco Frittelli", "Marco Maggis"], "categories": ["q-fin.MF"], "doi": "", "_query": "hedonic house price", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/cond-mat/0407321v2", "title": "Pricing Exotic Options in a Path Integral Approach", "abstract": "In the framework of Black-Scholes-Merton model of financial derivatives, a path integral approach to option pricing is presented. A general formula to price European path dependent options on multidimensional assets is obtained and implemented by means of various flexible and efficient algorithms. As an example, we detail the cases of Asian, barrier knock out, reverse cliquet and basket call options, evaluating prices and Greeks. The numerical results are compared with those obtained with other procedures used in quantitative finance and found to be in good agreement. In particular, when pricing at-the-money and out-of-the-money options, the path integral approach exhibits competitive performances.", "published": "2004-07-13T11:04:28Z", "authors": ["G. Bormetti", "G. Montagna", "N. Moreni", "O. Nicrosini"], "categories": ["cond-mat.other", "q-fin.PR"], "doi": "", "_query": "hedonic house price", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/1810.05884v2", "title": "Mid-price estimation for European corporate bonds: a particle filtering approach", "abstract": "In most illiquid markets, there is no obvious proxy for the market price of an asset. The European corporate bond market is an archetypal example of such an illiquid market where mid-prices can only be estimated with a statistical model. In this OTC market, dealers / market makers only have access, indeed, to partial information about the market. In real time, they know the price associated with their trades on the dealer-to-dealer (D2D) and dealer-to-client (D2C) markets, they know the result of the requests for quotes (RFQ) they answered, and they have access to composite prices (e.g., Bloomberg CBBT). This paper presents a Bayesian method for estimating the mid-price of corporate bonds by using the real-time information available to a dealer. This method relies on recent ideas coming from the particle filtering / sequential Monte-Carlo literature.", "published": "2018-10-13T16:30:32Z", "authors": ["Olivier Guéant", "Jiang Pu"], "categories": ["q-fin.TR"], "doi": "", "_query": "hedonic house price", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2308.05442v1", "title": "Optimal chromatic bound for ($P_3\\cup P_2$, house)-free graphs", "abstract": "Let $G$ and $H$ be two vertex disjoint graphs. The {\\em union} $G\\cup H$ is the graph with $V(G\\cup H)=V(G)\\cup V(H)$ and $E(G\\cup H)=E(G)\\cup E(H)$. We use $P_k$ to denote a {\\em path} on $k$ vertices, use {\\em house} to denote the complement of $P_5$. In this paper, we show that $χ(G)\\le2ω(G)$ if $G$ is ($P_3\\cup P_2$, house)-free. Moreover, this bound is optimal when $ω(G)\\ge2$.", "published": "2023-08-10T08:58:12Z", "authors": ["Rui Li", "Di Wu", "Jinfeng Li"], "categories": ["math.CO"], "doi": "", "_query": "hedonic house price", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2112.01592v1", "title": "Online Search With Best-Price and Query-Based Predictions", "abstract": "In the online (time-series) search problem, a player is presented with a sequence of prices which are revealed in an online manner. In the standard definition of the problem, for each revealed price, the player must decide irrevocably whether to accept or reject it, without knowledge of future prices (other than an upper and a lower bound on their extreme values), and the objective is to minimize the competitive ratio, namely the worst-case ratio between the maximum price in the sequence and the one selected by the player. The problem formulates several applications of decision-making in the face of uncertainty on the revealed samples. Previous work on this problem has largely assumed extreme scenarios in which either the player has almost no information about the input, or the player is provided with some powerful, and error-free advice. In this work, we study learning-augmented algorithms, in which there is a potentially erroneous prediction concerning the input. Specifically, we consider two different settings: the setting in which the prediction is related to the maximum price in the sequence, as well as the setting in which the prediction is obtained as a response to a number of binary queries. For both settings, we provide tight, or near-tight upper and lower bounds on the worst-case performance of search algorithms as a function of the prediction error. We also provide experimental results on data obtained from stock exchange markets that confirm the theoretical analysis, and explain how our techniques can be applicable to other learning-augmented applications.", "published": "2021-12-02T20:18:37Z", "authors": ["Spyros Angelopoulos", "Shahin Kamali", "Dehou Zhang"], "categories": ["cs.DS", "cs.AI", "cs.LG"], "doi": "", "_query": "hedonic house price", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2409.01180v1", "title": "Price effects and pass-through of a VAT increase on restaurants in Germany: causal evidence for the first months and a mega sports event", "abstract": "This paper analyses the price effects and tax pass-through of a VAT increase from 7% to 19% on restaurant services in Germany as of January 1, 2024. The Synthetic Control Method (SCM) is used to identify the causal effects of this reform using prices of goods and services unaffected by the tax change as a counterfactual for restaurant prices. Immediately in January, 31% of the tax increase was passed on to consumer prices. Pass-through increased to 58% in the following six months, which corresponds to a causal consumer price increase of about 6.5%. The presumed increase in demand for gastronomy services due to hosting the UEFA Euro 2024 tournament did not alter the path of price adjustments compared to previous months.", "published": "2024-09-02T11:26:52Z", "authors": ["Matthias Firgo"], "categories": ["econ.GN"], "doi": "", "_query": "hedonic house price", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2607.27814v1", "title": "Pricing and Semi-static Hedging of Green Pay-as-produced Power Purchase Agreements", "abstract": "Pay-as-produced power purchase agreements (PPAs) expose buyers and sellers to the joint risk of power prices and renewable production. This paper develops a theoretical framework for hedging this exposure using a semi-static strategy: liquid futures hedge traded price risk dynamically, while a fixed portfolio of renewable-linked claims targets residual volume and covariance risk. The pricing and hedging decomposition is model-free, whereas the empirical implementation for German wind and solar generation uses a calibrated stochastic model. Conditional on a valuation measure, the fair strike is a production-weighted expected spot price. We show that it decomposes exactly into the baseload forward level, a deterministic production-profile correction, and a stochastic price-volume covariance correction, where the covariance term measures the pricing effect of renewable cannibalisation. The static hedge is selected through a finite-dimensional variance projection onto claims linked to renewable volume, delivery-period average prices, and price-volume covariance. We estimate a Lévy-driven bivariate MCARMA state-space model with state-dependent price spikes using hourly German data for 2023-2024 and apply it to monthly PPAs over the January-December 2025 delivery horizon. The results distinguish deterministic profile risk from stochastic covariance risk and show how sparse static overlays reduce residual exposures that fixed-volume futures cannot hedge. The selected portfolios also indicate which claim types are most effective for hedging residual renewable shape risk.", "published": "2026-07-30T07:53:46Z", "authors": ["Konstantinos Chatziandreou", "Sven Karbach"], "categories": ["q-fin.MF", "q-fin.RM"], "doi": "", "_query": "hedonic house price", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/1911.06963v1", "title": "Optimal Storage Control for Dynamic Pricing", "abstract": "Renewable energy brings huge uncertainties to the power system, which challenges the traditional power system operation with limited flexible resources. One promising solution is to introduce dynamic pricing to more consumers, which, if designed properly, could enable an active demand side. To further exploit flexibility, in this work, we seek to advice the consumers an optimal online control policy to utilize their storage devices facing dynamic pricing. Towards designing a more adaptive control policy, we devise a data-driven approach to estimating the price distribution. Simulation studies verify the optimality of our proposed schemes.", "published": "2019-11-16T05:28:10Z", "authors": ["Jiaman Wu", "Zhiqi Wang", "Yang Yu", "Chenye Wu"], "categories": ["eess.SY", "math.OC"], "doi": "", "_query": "hedonic house price", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/1503.08119v1", "title": "About the decomposition of pricing formulas under stochastic volatility models", "abstract": "We obtain a decomposition of the call option price for a very general stochastic volatility diffusion model extending the decomposition obtained by E. Alòs in [2] for the Heston model. We realize that a new term arises when the stock price does not follow an exponential model. The techniques used are non anticipative. In particular, we see also that equivalent results can be obtained using Functional Itô Calculus. Using the same generalizing ideas we also extend to non exponential models the alternative call option price decompostion formula obtained in [1] and [3] written in terms of the Malliavin derivative of the volatility process. Finally, we give a general expression for the derivative of the implied volatility under both, the anticipative and the non anticipative case.", "published": "2015-03-27T16:03:57Z", "authors": ["Raul Merino", "Josep Vives"], "categories": ["q-fin.MF"], "doi": "", "_query": "hedonic house price", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2103.04781v1", "title": "District Wise Price Forecasting of Wheat in Pakistan using Deep Learning", "abstract": "Wheat is the main agricultural crop of Pakistan and is a staple food requirement of almost every Pakistani household making it the main strategic commodity of the country whose availability and affordability is the government's main priority. Wheat food availability can be vastly affected by multiple factors included but not limited to the production, consumption, financial crisis, inflation, or volatile market. The government ensures food security by particular policy and monitory arrangements, which keeps up purchase parity for the poor. Such arrangements can be made more effective if a dynamic analysis is carried out to estimate the future yield based on certain current factors. Future planning of commodity pricing is achievable by forecasting their future price anticipated by the current circumstances. This paper presents a wheat price forecasting methodology, which uses the price, weather, production, and consumption trends for wheat prices taken over the past few years and analyzes them with the help of advance neural networks architecture Long Short Term Memory (LSTM) networks. The proposed methodology presented significantly improved results versus other conventional machine learning and statistical time series analysis methods.", "published": "2021-03-05T06:13:51Z", "authors": ["Ahmed Rasheed", "Muhammad Shahzad Younis", "Farooq Ahmad", "Junaid Qadir", "Muhammad Kashif"], "categories": ["cs.LG", "cs.AI"], "doi": "", "_query": "hedonic house price", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2510.07101v2", "title": "Data as Commodity: a Game-Theoretic Principle for Information Pricing", "abstract": "Data is the central commodity of the digital economy. Unlike physical goods, data exhibits properties that defy the standard theory of supply and demand: it is non-rival (the same dataset can be sold to multiple buyers without degradation), it is replicable at near-zero cost, and it is traded under heterogeneous licensing rules that restrict lawful use. Determining a new pricing principle to attach a fair price tag to datasets is therefore a difficult but central problem. We propose a game-theoretic framework in which the value of a data string emerges from strategic competition among $N$ players betting on a stochastic process with asymmetric information about past outcomes. A better-informed player may either exploit her advantage or sell part of her dataset to less informed competitors. By analytically deriving the Nash equilibrium, we identify the price range for a mutually beneficial trade. The model reveals market dynamics that depart from textbook intuition: informed players may compete or jointly exploit the least informed; data can be shared even at zero price without reducing the seller`s utility; rivalry among well-informed players can benefit uninformed ones; and trades infeasible in small markets can be viable in larger ones. These findings establish a theoretical foundation for the pricing of intangible goods in interacting digital markets, which are in need of robust valuation principles.", "published": "2025-10-08T14:58:14Z", "authors": ["Pasquale Casaburi", "Giovanni Piccioli", "Pierpaolo Vivo"], "categories": ["physics.soc-ph", "cond-mat.stat-mech", "cs.GT"], "doi": "", "_query": "hedonic house price", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2502.15974v1", "title": "Sticky information and price controls: Evidence from a natural experiment", "abstract": "We test the predictions of the sticky information model using a survey dataset by comparing shoppers accuracy in recalling the prices of regulated and comparable unregulated products. Because regulated product prices are capped, they are sold more than comparable unregulated products, while their prices change less frequently and vary less across stores and between brands, than the prices of comparable unregulated products. Therefore, shoppers would be expected to recall the regulated product prices more accurately. However, we find that shoppers are better at recalling the prices of unregulated products, in line with the sticky information model which predicts that shoppers will be more attentive to prices that change more frequently.", "published": "2025-02-21T22:21:59Z", "authors": ["Doron Sayag", "Avichai Snir", "Daniel Levy"], "categories": ["econ.GN"], "doi": "10.1016/j.econlet.2025.112245", "_query": "hedonic house price", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/cond-mat/0003357v1", "title": "A dynamical model describing stock market price distributions", "abstract": "High frequency data in finance have led to a deeper understanding on probability distributions of market prices. Several facts seem to be well stablished by empirical evidence. Specifically, probability distributions have the following properties: (i) They are not Gaussian and their center is well adjusted by Levy distributions. (ii) They are long-tailed but have finite moments of any order. (iii) They are self-similar on many time scales. Finally, (iv) at small time scales, price volatility follows a non-diffusive behavior. We extend Merton's ideas on speculative price formation and present a dynamical model resulting in a characteristic function that explains in a natural way all of the above features. The knowledge of such distribution opens a new and useful way of quantifying financial risk. The results of the model agree -with high degree of accuracy- with empirical data taken from historical records of the Standard & Poor's 500 cash index.", "published": "2000-03-22T14:26:14Z", "authors": ["Jaume Masoliver", "Miquel Montero", "Josep M. Porra"], "categories": ["cond-mat.stat-mech", "q-fin.ST"], "doi": "10.1016/S0378-4371(00)00117-5", "_query": "hedonic house price", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2203.05673v1", "title": "Fusion of Sentiment and Asset Price Predictions for Portfolio Optimization", "abstract": "The fusion of public sentiment data in the form of text with stock price prediction is a topic of increasing interest within the financial community. However, the research literature seldom explores the application of investor sentiment in the Portfolio Selection problem. This paper aims to unpack and develop an enhanced understanding of the sentiment aware portfolio selection problem. To this end, the study uses a Semantic Attention Model to predict sentiment towards an asset. We select the optimal portfolio through a sentiment-aware Long Short Term Memory (LSTM) recurrent neural network for price prediction and a mean-variance strategy. Our sentiment portfolio strategies achieved on average a significant increase in revenue above the non-sentiment aware models. However, the results show that our strategy does not outperform traditional portfolio allocation strategies from a stability perspective. We argue that an improved fusion of sentiment prediction with a combination of price prediction and portfolio optimization leads to an enhanced portfolio selection strategy.", "published": "2022-03-10T23:21:12Z", "authors": ["Mufhumudzi Muthivhi", "Terence L. van Zyl"], "categories": ["q-fin.PM", "cs.LG"], "doi": "", "_query": "hedonic house price", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/1711.00307v1", "title": "Pricing of commodity derivatives on processes with memory", "abstract": "Spot option prices, forwards and options on forwards relevant for the commodity markets are computed when the underlying process S is modelled as an exponential of a process ξ with memory as e.g. a Lévy semi-stationary process. Moreover a risk premium \\r{ho} representing storage costs, illiquidity, convenience yield or insurance costs is explicitly modelled as an Ornstein-Uhlenbeck type of dynamics with a mean level that depends on the same memory term as the commodity. Also the interest rate is assumed to be stochastic. To show the existence of an equivalent pricing measure Q for S we relate the stochastic differential equation for ξ to the generalised Langevin equation. When the interest rate is deterministic the process (ξ; \\r{ho}) has an affine structure under the pricing measure Q and an explicit expression for the option price is derived in terms of the Fourier transform of the payoff function.", "published": "2017-11-01T12:22:19Z", "authors": ["Fred Espen Benth", "Asma Khedher", "Michèle Vanmaele"], "categories": ["q-fin.PR", "math.PR"], "doi": "", "_query": "hedonic house price", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2208.02610v2", "title": "Twitter Attribute Classification with Q-Learning on Bitcoin Price Prediction", "abstract": "Aspiring to achieve an accurate Bitcoin price prediction based on people's opinions on Twitter usually requires millions of tweets, using different text mining techniques (preprocessing, tokenization, stemming, stop word removal), and developing a machine learning model to perform the prediction. These attempts lead to the employment of a significant amount of computer power, central processing unit (CPU) utilization, random-access memory (RAM) usage, and time. To address this issue, in this paper, we consider a classification of tweet attributes that effects on price changes and computer resource usage levels while obtaining an accurate price prediction. To classify tweet attributes having a high effect on price movement, we collect all Bitcoin-related tweets posted in a certain period and divide them into four categories based on the following tweet attributes: $(i)$ the number of followers of the tweet poster, $(ii)$ the number of comments on the tweet, $(iii)$ the number of likes, and $(iv)$ the number of retweets. We separately train and test by using the Q-learning model with the above four categorized sets of tweets and find the best accurate prediction among them. Especially, we design several reward functions to improve the prediction accuracy of the Q-leaning. We compare our approach with a classic approach where all Bitcoin-related tweets are used as input data for the model, by analyzing the CPU workloads, RAM usage, memory, time, and prediction accuracy. The results show that tweets posted by users with the most followers have the most influence on a future price, and their utilization leads to spending 80\\% less time, 88.8\\% less CPU consumption, and 12.5\\% more accurate predictions compared with the classic approach.", "published": "2022-08-04T12:18:07Z", "authors": ["Sattarov Otabek", "Jaeyoung Choi"], "categories": ["cs.SI"], "doi": "", "_query": "hedonic house price", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2506.00044v1", "title": "Probabilistic intraday electricity price forecasting using generative machine learning", "abstract": "The growing importance of intraday electricity trading in Europe calls for improved price forecasting and tailored decision-support tools. In this paper, we propose a novel generative neural network model to generate probabilistic path forecasts for intraday electricity prices and use them to construct effective trading strategies for Germany's continuous-time intraday market. Our method demonstrates competitive performance in terms of statistical evaluation metrics compared to two state-of-the-art statistical benchmark approaches. To further assess its economic value, we consider a realistic fixed-volume trading scenario and propose various strategies for placing market sell orders based on the path forecasts. Among the different trading strategies, the price paths generated by our generative model lead to higher profit gains than the benchmark methods. Our findings highlight the potential of generative machine learning tools in electricity price forecasting and underscore the importance of economic evaluation.", "published": "2025-05-28T11:41:46Z", "authors": ["Jieyu Chen", "Sebastian Lerch", "Melanie Schienle", "Tomasz Serafin", "Rafał Weron"], "categories": ["stat.AP", "cs.LG", "stat.ML"], "doi": "", "_query": "hedonic house price", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2009.00557v2", "title": "The SINC way: A fast and accurate approach to Fourier pricing", "abstract": "The goal of this paper is to investigate the method outlined by one of us (PR) in Cherubini et al. (2009) to compute option prices. We name it the SINC approach. While the COS method by Fang and Osterlee (2009) leverages the Fourier-cosine expansion of truncated densities, the SINC approach builds on the Shannon Sampling Theorem revisited for functions with bounded support. We provide several results which were missing in the early derivation: i) a rigorous proof of the convergence of the SINC formula to the correct option price when the support grows and the number of Fourier frequencies increases; ii) ready to implement formulas for put, Cash-or-Nothing, and Asset-or-Nothing options; iii) a systematic comparison with the COS formula for several log-price models; iv) a numerical challenge against alternative Fast Fourier specifications, such as Carr and Madan (1999) and Lewis (2000); v) an extensive pricing exercise under the rough Heston model of Jaisson and Rosenbaum (2015); vi) formulas to evaluate numerically the moments of a truncated density. The advantages of the SINC approach are numerous. When compared to benchmark methodologies, SINC provides the most accurate and fast pricing computation. The method naturally lends itself to price all options in a smile concurrently by means of Fast Fourier techniques, boosting fast calibration. Pricing requires to resort only to odd moments in the Fourier space. A previous version of this manuscript circulated with the title `Rough Heston: The SINC way'.", "published": "2020-09-01T16:43:07Z", "authors": ["Fabio Baschetti", "Giacomo Bormetti", "Silvia Romagnoli", "Pietro Rossi"], "categories": ["q-fin.PR", "q-fin.CP"], "doi": "", "_query": "hedonic house price", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/math/0703076v1", "title": "Game pricing and double sequence of random variables", "abstract": "In this paper, we study a game with positive or plus infinite expectation and determine the optimal proportion of investment for maximizing the limit expectation of growth rate per attempt. With this objective, we introduce a new pricing method in which the price is different from that obtained by the Black-Scholes formula for a European option.", "published": "2007-03-03T02:51:48Z", "authors": ["Yukio Hirashita"], "categories": ["math.OC"], "doi": "", "_query": "hedonic house price", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2608.19217v1", "title": "CAT Bond Pricing with Kolmogorov--Arnold Networks", "abstract": "We study the approximation of CAT bond prices under a compound Poisson loss model with lognormal severities using Kolmogorov--Arnold Networks (KANs). Building on a baseline-plus-residual learning pipeline, we train a KAN on the deviation from a closed-form lognormal baseline price and extract an interpretable symbolic pricing formula. The extracted formula achieves an average relative pricing error of 0.483% on a fully disjoint holdout sample of 90,000 simulated prices. In contrast to purely empirical modelling, we also analyse structural properties of the true CAT bond pricing map, including monotonicity with respect to the catastrophe arrival intensity lambda, the initial short rate r0, and the trigger threshold D. We derive sufficient conditions on KAN edge functions that guarantee these monotonicities are preserved by the learned model, and formulate a monotonicity-constrained training objective with a convergence guarantee. Our results suggest that symbolic KAN surrogates provide a practical compromise between accuracy, computational speed, and interpretability for CAT bond valuation.", "published": "2026-07-10T09:07:58Z", "authors": ["Sean Seow Cheng Hong"], "categories": ["q-fin.CP"], "doi": "", "_query": "hedonic house price", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/1901.03843v1", "title": "Fuzzy Profit Shifting: A Model for Optimal Tax-induced Transfer Pricing with Fuzzy Arm's Length Parameter", "abstract": "This paper proposes a model of optimal tax-induced transfer pricing with a fuzzy arm's length parameter. Fuzzy numbers provide a suitable structure for modelling the ambiguity that is intrinsic to the arm's length parameter. For the usual conditions regarding the anti-shifting mechanisms, the optimal transfer price becomes a maximising $α$-cut of the fuzzy arm's length parameter. Nonetheless, we show that it is profitable for firms to choose any maximising transfer price if the probability of tax audit is sufficiently low, even if the chosen price is considered a completely non-arm's length price by tax authorities. In this case, we derive the necessary and sufficient conditions to prevent this extreme shifting strategy", "published": "2019-01-12T10:43:52Z", "authors": ["Alex A. T. Rathke"], "categories": ["econ.GN"], "doi": "", "_query": "hedonic house price", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/0901.1776v1", "title": "Efficient swaptions price in Hull-White one factor model", "abstract": "The Hull-White one factor model is used to price interest rate options. The parameters of the model are often calibrated to simple liquid instruments, in particular European swaptions. It is therefore very important to have very efficient pricing formula for simple instruments. Such a formula is proposed here for European swaption. Based on a very efficient corrector type approximation the approximation is efficient both in term of precision and in term of spped. In our implementation the approximation is more than ten time faster than the direct pricing formula and more than twenty time faster than the Jamshidian trick.", "published": "2009-01-13T12:30:45Z", "authors": ["Marc Henrard"], "categories": ["q-fin.PR", "q-fin.CP"], "doi": "", "_query": "hedonic house price", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2209.02407v1", "title": "Predict stock prices with ARIMA and LSTM", "abstract": "MAE, MSE and RMSE performance indicators are used to analyze the performance of different stocks predicted by LSTM and ARIMA models in this paper. 50 listed company stocks from finance.yahoo.com are selected as the research object in the experiments. The dataset used in this work consists of the highest price on transaction days, corresponding to the period from 01 January 2010 to 31 December 2018. For LSTM model, the data from 01 January 2010 to 31 December 2015 are selected as the training set, the data from 01 January 2016 to 31 December 2017 as the validation set and the data from 01 January 2018 to 31 December 2018 as the test set. In term of ARIMA model, the data from 01 January 2016 to 31 December 2017 are selected as the training set, and the data from 01 January 2018 to 31 December 2018 as the test set. For both models, 60 days of data are used to predict the next day. After analysis, it is suggested that both ARIMA and LSTM models can predict stock prices, and the prediction results are generally consistent with the actual results;and LSTM has better performance in predicting stock prices(especially in expressing stock price changes), while the application of ARIMA is more convenient.", "published": "2022-08-31T17:05:44Z", "authors": ["Ruochen Xiao", "Yingying Feng", "Lei Yan", "Yihan Ma"], "categories": ["q-fin.ST", "cs.CE"], "doi": "", "_query": "hedonic house price", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2610.18563v1", "title": "Shadow Pricing in Transient Stability-Constrained Optimal Power Flow", "abstract": "Transient stability constraints alter the economic dispatch of power systems, while the resulting price signals and their implications for market settlement remain insufficiently characterized. Existing approaches identify stability-related components in locational marginal prices, without deriving shadow prices for individual assets, such as generators and loads, or analyzing the resulting settlement. To address this gap, this paper derives shadow prices from a transient stability-constrained optimal power flow in which the differential-algebraic equations governing the system dynamics are embedded through direct discretization. Each shadow price decomposes into a nodal energy component and a stability component, the latter formed by the dual variables of the dynamic equations in which the power of the asset appears. Shadow prices are therefore asset-specific: co-located generators and loads are assigned the same nodal energy component, but can be subject to different stability components when their transient behaviors differ. Case studies on the WECC 9-bus system and a modified IEEE 39-bus system show that settlement at the proposed shadow prices allocates the cost of the stability constraints among the assets without any side payment. An asset that reduces the stability margin is charged its stability component, whereas an asset that improves it is paid, and no generator is dispatched at a loss.", "published": "2026-10-13T15:33:00Z", "authors": ["Alex Coelho", "Araceli Hernandez", "Luis Badesa"], "categories": ["eess.SY"], "doi": "", "_query": "hedonic house price", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/math/0403521v1", "title": "Stochastic arbitrage return and its implications for option pricing", "abstract": "The purpose of this work is to explore the role that arbitrage opportunities play in pricing financial derivatives. We use a non-equilibrium model to set up a stochastic portfolio, and for the random arbitrage return, we choose a stationary ergodic random process rapidly varying in time. We exploit the fact that option price and random arbitrage returns change on different time scales which allows us to develop an asymptotic pricing theory involving the central limit theorem for random processes. We restrict ourselves to finding pricing bands for options rather than exact prices. The resulting pricing bands are shown to be independent of the detailed statistical characteristics of the arbitrage return. We find that the volatility \"smile\" can also be explained in terms of random arbitrage opportunities.", "published": "2004-03-30T15:54:18Z", "authors": ["Sergei Fedotov", "Stephanos Panayides"], "categories": ["math.GM"], "doi": "10.1016/j.physa.2004.07.028", "_query": "hedonic house price", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2009.04171v1", "title": "A Framework for Crop Price Forecasting in Emerging Economies by Analyzing the Quality of Time-series Data", "abstract": "Accuracy of crop price forecasting techniques is important because it enables the supply chain planners and government bodies to take appropriate actions by estimating market factors such as demand and supply. In emerging economies such as India, the crop prices at marketplaces are manually entered every day, which can be prone to human-induced errors like the entry of incorrect data or entry of no data for many days. In addition to such human prone errors, the fluctuations in the prices itself make the creation of stable and robust forecasting solution a challenging task. Considering such complexities in crop price forecasting, in this paper, we present techniques to build robust crop price prediction models considering various features such as (i) historical price and market arrival quantity of crops, (ii) historical weather data that influence crop production and transportation, (iii) data quality-related features obtained by performing statistical analysis. We additionally propose a framework for context-based model selection and retraining considering factors such as model stability, data quality metrics, and trend analysis of crop prices. To show the efficacy of the proposed approach, we show experimental results on two crops - Tomato and Maize for 14 marketplaces in India and demonstrate that the proposed approach not only improves accuracy metrics significantly when compared against the standard forecasting techniques but also provides robust models.", "published": "2020-09-09T09:06:07Z", "authors": ["Ayush Jain", "Smit Marvaniya", "Shantanu Godbole", "Vitobha Munigala"], "categories": ["stat.AP", "econ.EM"], "doi": "", "_query": "hedonic house price", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/1508.03853v1", "title": "Transfer pricing manipulation, tax penalty cost and the impact of foreign profit taxation", "abstract": "This paper analizes the optimal level of transfer pricing manipulation when the expected tax penalty is a function of the tax enforcement and the market price parameter. The arm's length principle implies the existence of a range of acceptable prices shaped by market, and firms can manipulate transfer prices more freely if market price range is wide, or if its delimitations are difficult to determine. Home taxation of foreign profits can reduce income shifting incentive, depending on the portion of repatriation for tax purposes. We find that the limited tax credit rule tends to be a less efficient measure, nonetheless it is the most widely adopted rule by countries, so to spark the perspective of more powerful approaches for taxation of foreign profits.", "published": "2015-08-16T18:11:55Z", "authors": ["Alex Augusto Timm Rathke"], "categories": ["econ.GN"], "doi": "", "_query": "hedonic house price", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/1212.0781v2", "title": "Analytical Pricing of American Bond Options in the Heath-Jarrow-Morton Model", "abstract": "We study the optimal stopping problem of pricing an American Put option on a Zero Coupon Bond (ZCB) in the Musiela's parametrization of the Heath-Jarrow-Morton (HJM) model for forward interest rates. First we show regularity properties of the price function by probabilistic methods. Then we find an infinite dimensional variational formulation of the pricing problem by approximating the original optimal stopping problem by finite dimensional ones, after a suitable smoothing of the payoff. As expected, the first time the price of the American bond option equals the payoff is shown to be optimal.", "published": "2012-12-04T16:24:23Z", "authors": ["Maria B. Chiarolla", "Tiziano De Angelis"], "categories": ["q-fin.PR", "math.OC", "math.PR"], "doi": "", "_query": "hedonic house price", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/1607.07105v3", "title": "Revenue Gaps for Static and Dynamic Posted Pricing of Homogeneous Goods", "abstract": "We consider the problem of maximizing the expected revenue from selling $k$ homogeneous goods to $n$ unit-demand buyers who arrive sequentially with independent and identically distributed valuations. In this setting the optimal posted prices are dynamic in the sense that they depend on the remaining numbers of goods and buyers. We investigate how much revenue is lost when a single static price is used instead for all buyers and goods, and prove upper bounds on the ratio between the maximum revenue from dynamic prices and that from static prices. These bounds are tight for all values of $k$ and $n$ and vary depending on a regularity property of the underlying distribution. For general distributions we obtain a ratio of $2-k/n$, for regular distributions a ratio that increases in $n$ and is bounded from above by $1/(1-k^k/(e^{k}k!))$, which is roughly $1/(1-1/(\\sqrt{2πk}))$. The lower bounds hold for the revenue gap between dynamic and static prices. The upper bounds are obtained via an ex-ante relaxation of the revenue maximization problem, as a consequence the tight bounds of $2-k/n$ in the general case and of $1/(1-1/(\\sqrt{2πk}))$ in the regular case apply also to the potentially larger revenue gap between the optimal incentive-compatible mechanism and the optimal static price. Our results imply that for regular distributions the benefit of dynamic prices vanishes while for non-regular distributions dynamic prices may achieve up to twice the revenue of static prices.", "published": "2016-07-24T22:27:12Z", "authors": ["Paul Dütting", "Felix Fischer", "Max Klimm"], "categories": ["cs.GT"], "doi": "", "_query": "hedonic house price", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/1707.08029v1", "title": "Price and Profit Awareness in Recommender Systems", "abstract": "Academic research in the field of recommender systems mainly focuses on the problem of maximizing the users' utility by trying to identify the most relevant items for each user. However, such items are not necessarily the ones that maximize the utility of the service provider (e.g., an online retailer) in terms of the business value, such as profit. One approach to increasing the providers' utility is to incorporate purchase-oriented information, e.g., the price, sales probabilities, and the resulting profit, into the recommendation algorithms. In this paper we specifically focus on price- and profit-aware recommender systems. We provide a brief overview of the relevant literature and use numerical simulations to illustrate the potential business benefit of such approaches.", "published": "2017-07-25T15:07:33Z", "authors": ["Dietmar Jannach", "Gediminas Adomavicius"], "categories": ["cs.IR", "cs.AI"], "doi": "", "_query": "hedonic house price", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/1809.02433v1", "title": "Dealing with the Dimensionality Curse in Dynamic Pricing Competition: Using Frequent Repricing to Compensate Imperfect Market Anticipations", "abstract": "Most sales applications are characterized by competition and limited demand information. For successful pricing strategies, frequent price adjustments as well as anticipation of market dynamics are crucial. Both effects are challenging as competitive markets are complex and computations of optimized pricing adjustments can be time-consuming. We analyze stochastic dynamic pricing models under oligopoly competition for the sale of perishable goods. To circumvent the curse of dimensionality, we propose a heuristic approach to efficiently compute price adjustments. To demonstrate our strategy's applicability even if the number of competitors is large and their strategies are unknown, we consider different competitive settings in which competitors frequently and strategically adjust their prices. For all settings, we verify that our heuristic strategy yields promising results. We compare the performance of our heuristic against upper bounds, which are obtained by optimal strategies that take advantage of perfect price anticipations. We find that price adjustment frequencies can have a larger impact on expected profits than price anticipations. Finally, our approach has been applied on Amazon for the sale of used books. We have used a seller's historical market data to calibrate our model. Sales results show that our data-driven strategy outperforms the rule-based strategy of an experienced seller by a profit increase of more than 20%.", "published": "2018-09-07T12:20:50Z", "authors": ["Rainer Schlosser", "Martin Boissier"], "categories": ["cs.GT", "q-fin.TR"], "doi": "10.1016/j.cor.2018.07.011", "_query": "hedonic house price", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2610.16588v1", "title": "Demand Under a Mechanical Price: Elasticity and Welfare in Ethereum's Data-Availability Market", "abstract": "Ethereum sells data-availability capacity to rollups through \"blobs\" priced by a controller: the blob fee rises and falls exponentially with cumulative usage above or below a target, and no auction sets it. We measure what this mechanical price costs. The Pectra upgrade of 7 May 2025 doubled blob capacity on a date fixed weeks in advance, while the market was congested, and the blob fee fell from 0.82 mETH per blob (about USD 1.40) to its 1-wei floor. Rollups' blob purchases rose 24%. The 95% interval, built from the same estimator at 516 placebo dates, runs from 8% to 40%, so removing the entire price raised purchases by at most 40%. The implied elasticity at the pre-shock price is -0.19, and no value below -0.31 is consistent with the interval. The estimate includes any rise in L2 activity that coincided with the fork, so it bounds the price response from above. With demand this inelastic, the controller works mainly as a transfer. Rollups paid USD 14.8 million in blob fees under the first blob regime, 67% of it in two quarters when capacity was full and any market-clearing mechanism would have collected comparable scarcity rents. Where capacity was slack, the reserve price introduced in December 2025 cost at most 2.6 ETH (USD 8,039) in deadweight loss under the most elastic demand the data allow. Mempool records for 5.4 million blob transactions show that when the fee sits at its floor, capacity is slack: transactions miss about one block and are almost never priced out.", "published": "2026-10-12T11:10:21Z", "authors": ["Farbod Ghasemlu"], "categories": ["q-fin.TR"], "doi": "", "_query": "hedonic house price", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2107.00261v1", "title": "Price change prediction of ultra high frequency financial data based on temporal convolutional network", "abstract": "Through in-depth analysis of ultra high frequency (UHF) stock price change data, more reasonable discrete dynamic distribution models are constructed in this paper. Firstly, we classify the price changes into several categories. Then, temporal convolutional network (TCN) is utilized to predict the conditional probability for each category. Furthermore, attention mechanism is added into the TCN architecture to model the time-varying distribution for stock price change data. Empirical research on constituent stocks of Chinese Shenzhen Stock Exchange 100 Index (SZSE 100) found that the TCN framework model and the TCN (attention) framework have a better overall performance than GARCH family models and the long short-term memory (LSTM) framework model for the description of the dynamic process of the UHF stock price change sequence. In addition, the scale of the dataset reached nearly 10 million, to the best of our knowledge, there has been no previous attempt to apply TCN to such a large-scale UHF transaction price dataset in Chinese stock market.", "published": "2021-07-01T07:31:37Z", "authors": ["Wei Dai", "Yuan An", "Wen Long"], "categories": ["q-fin.CP"], "doi": "", "_query": "hedonic house price", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/1702.08029v2", "title": "The short-term price impact of trades is universal", "abstract": "We analyze a proprietary dataset of trades by a single asset manager, comparing their price impact with that of the trades of the rest of the market. In the context of a linear propagator model we find no significant difference between the two, suggesting that both the magnitude and time dependence of impact are universal in anonymous, electronic markets. This result is important as optimal execution policies often rely on propagators calibrated on anonymous data. We also find evidence that in the wake of a trade the order flow of other market participants first adds further copy-cat trades enhancing price impact on very short time scales. The induced order flow then quickly inverts, thereby contributing to impact decay.", "published": "2017-02-26T13:16:32Z", "authors": ["Bence Toth", "Zoltan Eisler", "Jean-Philippe Bouchaud"], "categories": ["q-fin.TR", "cond-mat.stat-mech"], "doi": "", "_query": "hedonic house price", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2602.00037v2", "title": "Bitcoin Price Prediction using Machine Learning and Combinatorial Fusion Analysis", "abstract": "In this work, we propose to apply a new model fusion and learning paradigm, known as Combinatorial Fusion Analysis (CFA), to the field of Bitcoin price prediction. Price prediction of financial product has always been a big topic in finance, as the successful prediction of the price can yield significant profit. Every machine learning model has its own strength and weakness, which hinders progress toward robustness. CFA has been used to enhance models by leveraging rank-score characteristic (RSC) function and cognitive diversity in the combination of a moderate set of diverse and relatively well-performed models. Our method utilizes both score and rank combinations as well as other weighted combination techniques. Key metrics such as RMSE and MAPE are used to evaluate our methodology performance. Our proposal presents a notable MAPE performance of 0.19\\%. The proposed method greatly improves upon individual model performance, as well as outperforms other Bitcoin price prediction models.", "published": "2026-01-19T02:41:43Z", "authors": ["Yuanhong Wu", "Wei Ye", "Jingyan Xu", "D. Frank Hsu"], "categories": ["q-fin.ST", "cs.AI", "cs.CE", "cs.LG"], "doi": "", "_query": "hedonic house price", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/1912.01832v2", "title": "Covariate-dependent control limits for the detection of abnormal price changes in scanner data", "abstract": "Currently, large-scale sales data for consumer goods, called scanner data, are obtained by scanning the bar codes of individual products at the points of sale of retail outlets. Many national statistical offices use scanner data to build consumer price statistics. In this process, as in other statistical procedures, the detection of abnormal transactions in sales prices is an important step in the analysis. Popular methods for conducting such outlier detection are the quartile method, the Hidiroglou-Berthelot method, the resistant fences method, and the Tukey algorithm. These methods are based solely on information about price changes and not on any of the other covariates (e.g., sales volume or types of retail shops) that are also available from scanner data. In this paper, we propose a new method to detect abnormal price changes that takes into account an additional covariate, namely, sales volume. We assume that the variance of the log of the price change is a smooth function of the sales volume and estimate the function from previously observed data. We numerically show the advantages of the new method over existing methods. We also apply the methods to real scanner data collected at weekly intervals by the Korean Chamber of Commerce and Industry between 2013 and 2014 and compare their performance.", "published": "2019-12-04T07:27:37Z", "authors": ["Youngrae Kim", "Sangkyun Kim", "Johan Lim", "Sungim Lee", "Won Son", "Heejin Hwang"], "categories": ["stat.ME"], "doi": "", "_query": "hedonic house price", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/1907.03577v1", "title": "The evolving liaisons between the transaction networks of Bitcoin and its price dynamics", "abstract": "Cryptocurrencies are distributed systems that allow exchanges of native tokens among participants, or the exchange of such tokens for fiat currencies in markets external to these public ledgers. The availability of their complete historical bookkeeping opens up the possibility of understanding the relationship between aggregated users' behaviour and the cryptocurrency pricing in exchange markets. This paper analyses the properties of the transaction network of Bitcoin. We consider four different representations of it, over a period of nine years since the Bitcoin creation and involving 16 million users and 283 million transactions. By analysing these networks, we show the existence of causal relationships between Bitcoin price movements and changes of its transaction network topology. Our results reveal the interplay between structural quantities, indicative of the collective behaviour of Bitcoin users, and price movements, showing that, during price drops, the system is characterised by a larger heterogeneity of nodes activity.", "published": "2019-07-08T12:59:56Z", "authors": ["Alexandre Bovet", "Carlo Campajola", "Francesco Mottes", "Valerio Restocchi", "Nicolò Vallarano", "Tiziano Squartini", "Claudio J. Tessone"], "categories": ["q-fin.GN", "physics.soc-ph"], "doi": "10.7566/JPSCP.40.011002", "_query": "hedonic house price", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2003.03035v5", "title": "A Mean Field Game Approach to Equilibrium Pricing with Market Clearing Condition", "abstract": "In this work, we study an equilibrium-based continuous asset pricing problem which seeks to form a price process endogenously by requiring it to balance the flow of sales-and-purchase orders in the exchange market, where a large number of agents are interacting through the market price. Adopting a mean field game (MFG) approach, we find a special form of forward-backward stochastic differential equations of McKean-Vlasov type with common noise whose solution provides a good approximate of the market price. We show the convergence of the net order flow to zero in the large N-limit and get the order of convergence in N under some conditions. We also extend the model to a setup with multiple populations where the agents within each population share the same cost and coefficient functions but they can be different population by population.", "published": "2020-03-06T05:13:47Z", "authors": ["Masaaki Fujii", "Akihiko Takahashi"], "categories": ["q-fin.MF", "q-fin.TR"], "doi": "", "_query": "hedonic house price", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/math/0703074v1", "title": "Bid-Ask Dynamic Pricing in Financial Markets with Transaction Costs and Liquidity Risk", "abstract": "We introduce, in continuous time, an axiomatic approach to assign to any financial position a dynamic ask (resp. bid) price process. Taking into account both transaction costs and liquidity risk this leads to the convexity (resp. concavity) of the ask (resp. bid) price. Time consistency is a crucial property for dynamic pricing. Generalizing the result of Jouini and Kallal, we prove that the No Free Lunch condition for a time consistent dynamic pricing procedure (TCPP) is equivalent to the existence of an equivalent probability measure $R$ that transforms a process between the bid process and the ask process of any financial instrument into a martingale. Furthermore we prove that the ask price process associated with any financial instrument is then a $R$-supermartingale process which has a cadlag modification. Finally we show that time consistent dynamic pricing allows both to extend the dynamics of some reference assets and to be consistent with any observed bid ask spreads that one wants to take into account. It then provides new bounds reducing the bid ask spreads for the other financial instruments.", "published": "2007-03-05T20:03:03Z", "authors": ["Jocelyne Bion-Nadal"], "categories": ["math.PR", "q-fin.PR"], "doi": "", "_query": "hedonic house price", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/1010.4989v1", "title": "On using shadow prices in portfolio optimization with transaction costs", "abstract": "In frictionless markets, utility maximization problems are typically solved either by stochastic control or by martingale methods. Beginning with the seminal paper of Davis and Norman [Math. Oper. Res. 15 (1990) 676--713], stochastic control theory has also been used to solve various problems of this type in the presence of proportional transaction costs. Martingale methods, on the other hand, have so far only been used to derive general structural results. These apply the duality theory for frictionless markets typically to a fictitious shadow price process lying within the bid-ask bounds of the real price process. In this paper, we show that this dual approach can actually be used for both deriving a candidate solution and verification in Merton's problem with logarithmic utility and proportional transaction costs. In particular, we determine the shadow price process.", "published": "2010-10-21T11:18:23Z", "authors": ["J. Kallsen", "J. Muhle-Karbe"], "categories": ["q-fin.CP", "math.PR", "q-fin.PM"], "doi": "10.1214/09-AAP648", "_query": "hedonic house price", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2508.20108v2", "title": "Mitigating Distribution Shift in Stock Price Data via Return-Volatility Normalization for Accurate Prediction", "abstract": "How can we address distribution shifts in stock price data to improve stock price prediction accuracy? Stock price prediction has attracted attention from both academia and industry, driven by its potential to uncover complex market patterns and enhance decisionmaking. However, existing methods often fail to handle distribution shifts effectively, focusing on scaling or representation adaptation without fully addressing distributional discrepancies and shape misalignments between training and test data. We propose ReVol (Return-Volatility Normalization for Mitigating Distribution Shift in Stock Price Data), a robust method for stock price prediction that explicitly addresses the distribution shift problem. ReVol leverages three key strategies to mitigate these shifts: (1) normalizing price features to remove sample-specific characteristics, including return, volatility, and price scale, (2) employing an attention-based module to estimate these characteristics accurately, thereby reducing the influence of market anomalies, and (3) reintegrating the sample characteristics into the predictive process, restoring the traits lost during normalization. Additionally, ReVol combines geometric Brownian motion for long-term trend modeling with neural networks for short-term pattern recognition, unifying their complementary strengths. Extensive experiments on real-world datasets demonstrate that ReVol enhances the performance of the state-of-the-art backbone models in most cases, achieving an average improvement of more than 0.03 in IC and over 0.7 in SR across various settings.", "published": "2025-08-13T15:20:13Z", "authors": ["Hyunwoo Lee", "Jihyeong Jeon", "Jaemin Hong", "U Kang"], "categories": ["q-fin.ST", "cs.LG"], "doi": "", "_query": "hedonic house price", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2208.02842v1", "title": "Subgame perfect Nash equilibrium for dynamic pricing competition with finite planning horizon", "abstract": "Having fixed capacities, homogeneous products and price sensitive customer purchase decision are primary distinguishing characteristics of numerous revenue management systems. Even with two or three rivals, competition is still highly fierce. This paper studies sub-game perfect Nash equilibrium of a price competition in an oligopoly market with perishable assets. Sellers each has one unit of a good that cannot be replenished, and they compete in setting prices to sell their good over a finite sales horizon. Each period, buyers desire one unit of the good and the number of buyers coming to the market in each period is random. All sellers' prices are accessible for buyers, and search is costless. Using stochastic dynamic programming methods, the best response of sellers can be obtained from a one-shot price competition game regarding remained periods and the current-time demand structure. Assuming a binary demand model, we demonstrate that the duopoly model has a unique Nash equilibrium and the oligopoly model does not reveal price dispersion with respect to a particular metric. We illustrate that, when considering a generalized demand model, the duopoly model has a unique mixed strategy Nash equilibrium while the oligopoly model has a unique symmetric mixed strategy Nash equilibrium.", "published": "2022-08-04T18:31:52Z", "authors": ["Niloofar Fadavi"], "categories": ["econ.TH"], "doi": "", "_query": "hedonic house price", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/1609.04620v1", "title": "Price impact without order book: A study of the OTC credit index market", "abstract": "We present a study of price impact in the over-the-counter credit index market, where no limit order book is used. Contracts are traded via dealers, that compete for the orders of clients. Despite this distinct microstructure, we successfully apply the propagator technique to estimate the price impact of individual transactions. Because orders are typically split less than in multilateral markets, impact is observed to be mainly permanent, in line with theoretical expectations. A simple method is presented to correct for errors in our classification of trades between buying and selling. We find a very significant, temporary increase in order flow correlations during late 2015 and early 2016, which we attribute to increased order splitting or herding among investors. We also find indications that orders advertised to less dealers may have lower price impact. Quantitative results are compatible with earlier findings in other more classical markets, further supporting the argument that price impact is a universal phenomenon, to a large degree independent of market microstructure.", "published": "2016-09-15T13:11:34Z", "authors": ["Zoltan Eisler", "Jean-Philippe Bouchaud"], "categories": ["q-fin.TR"], "doi": "", "_query": "hedonic house price", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/0708.1146v1", "title": "Stochastic Knapsack Problem Revisited: Switch-Over Policies and Dynamic Pricing", "abstract": "The stochastic knapsack has been used as a model in wide ranging applications from dynamic resource allocation to admission control in telecommunication. In recent years, a variation of the model has become a basic tool in studying problems that arise in revenue management and dynamic/flexible pricing; and it is in this context that our study is undertaken. Based on a dynamic programming formulation and associated properties of the value function, we study in this paper a class of control that we call switch-over policies -- start from accepting only orders of the highest price, and switch to including lower prices as time goes by, with the switch-over times optimally decided via convex programming. We establish the asymptotic optimality of the switch-over policy, and develop pricing models based on this policy to optimize the price reductions over the decision horizon.", "published": "2007-08-08T16:38:04Z", "authors": ["Grace Lin", "Yingdong Lu", "David Yao"], "categories": ["q-fin.PR", "math.OC", "math.PR"], "doi": "", "_query": "hedonic house price", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/cond-mat/9811114v1", "title": "Reaction-Diffusion-Branching Models of Stock Price Fluctuations", "abstract": "Several models of stock trading [P. Bak et al, Physica A {\\bf 246}, 430 (1997)] are analyzed in analogy with one-dimensional, two-species reaction-diffusion-branching processes. Using heuristic and scaling arguments, we show that the short-time market price variation is subdiffusive with a Hurst exponent $H=1/4$. Biased diffusion towards the market price and blind-eyed copying lead to crossovers to the empirically observed random-walk behavior ($H=1/2$) at long times. The calculated crossover forms and diffusion constants are shown to agree well with simulation data.", "published": "1998-11-09T10:45:26Z", "authors": ["Lei-Han Tang", "Guang-Shan Tian"], "categories": ["cond-mat.stat-mech", "q-fin.TR"], "doi": "10.1016/S0378-4371(98)00549-4", "_query": "hedonic house price", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2603.20582v1", "title": "Generative Diffusion Model for Risk-Neutral Derivative Pricing", "abstract": "Denoising diffusion probabilistic models (DDPMs) have emerged as powerful generative models for complex distributions, yet their use in arbitrage-free derivative pricing remains largely unexplored. Financial asset prices are naturally modeled by stochastic differential equations (SDEs), whose forward and reverse density evolution closely parallels the forward noising and reverse denoising structure of diffusion models. In this paper, we develop a framework for using DDPMs to generate risk-neutral asset price dynamics for derivative valuation. Starting from log-return dynamics under the physical measure, we analyze the associated forward diffusion and derive the reverse-time SDE. We show that the change of measure from the physical to the risk-neutral measure induces an additive shift in the score function, which translates into a closed-form risk-neutral epsilon shift in the DDPM reverse dynamics. This correction enforces the risk-neutral drift while preserving the learned variance and higher-order structure, yielding an explicit bridge between diffusion-based generative modeling and classical risk-neutral SDE-based pricing. We show that the resulting discounted price paths satisfy the martingale condition under the risk-neutral measure. Empirically, the method reproduces the risk-neutral terminal distribution and accurately prices both European and path-dependent derivatives, including arithmetic Asian options, under a GBM benchmark. These results demonstrate that diffusion-based generative models provide a flexible and principled approach to simulation-based derivative pricing.", "published": "2026-03-21T00:44:25Z", "authors": ["Nilay Tiwari"], "categories": ["q-fin.MF", "stat.ML"], "doi": "", "_query": "hedonic house price", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2604.06227v1", "title": "A Benchmark of Classical and Deep Learning Models for Agricultural Commodity Price Forecasting on A Novel Bangladeshi Market Price Dataset", "abstract": "Accurate short-term forecasting of agricultural commodity prices is critical for food security planning and smallholder income stabilisation in developing economies, yet machine-learning-ready datasets for this purpose remain scarce in South Asia. This paper makes two contributions. First, we introduce AgriPriceBD, a benchmark dataset of 1,779 daily retail mid-prices for five Bangladeshi commodities - garlic, chickpea, green chilli, cucumber, and sweet pumpkin - spanning July 2020 to June 2025, extracted from government reports via an LLM-assisted digitisation pipeline. Second, we evaluate seven forecasting approaches spanning classical models - naïve persistence, SARIMA, and Prophet - and deep learning architectures - BiLSTM, Transformer, Time2Vec-enhanced Transformer, and Informer - with Diebold-Mariano statistical significance tests. Commodity price forecastability is fundamentally heterogeneous: naïve persistence dominates on near-random-walk commodities. Time2Vec temporal encoding provides no statistically significant advantage over fixed sinusoidal encoding and causes catastrophic degradation on green chilli (+146.1% MAE, p<0.001). Prophet fails systematically, attributable to discrete step-function price dynamics incompatible with its smooth decomposition assumptions. Informer produces erratic predictions (variance up to 50x ground-truth), confirming sparse-attention Transformers require substantially larger training sets than small agricultural datasets provide. All code, models, and data are released publicly to support replication and future forecasting research on agricultural commodity markets in Bangladesh and similar developing economies.", "published": "2026-03-27T20:21:52Z", "authors": ["Tashreef Muhammad", "Tahsin Ahmed", "Meherun Farzana", "Md. Mahmudul Hasan", "Abrar Eyasir", "Md. Emon Khan", "Mahafuzul Islam Shawon", "Ferdous Mondol", "Mahmudul Hasan", "Muhammad Ibrahim"], "categories": ["cs.LG", "econ.EM"], "doi": "", "_query": "hedonic house price", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2512.17508v1", "title": "Most certainly certain? The Impact of Contract for Difference Design on Renewables' Strike Prices and Electricity Market Risks", "abstract": "Weather, technological and regulatory uncertainties expose actors in highly renewable electricity markets to substantial price and volume risks. Two-way Contracts for Difference (CfDs) can mitigate these risks. They stipulate payments between the government and generators of renewable electricity based on the difference of a strike and a reference price, whose definition and unit of payment differ between CfD designs. We study the effect of three different CfD designs on wind power profit and consumer price volatility under the consideration of uncertain market outcomes in a highly renewable, sector-coupled electricity market. First, we analytically derive optimal strike prices under uncertainty. Second, we numerically determine optimal strike prices based on market expectations retrieved from optimising a set of 36 market scenarios in an energy system model. Third, we study the distribution of ex post market revenues, CfD payments and consumer prices across all 36 scenarios. Compared to purely market-based consumer prices and investor profits, we find all CfDs to significantly reduce volatility. For consumer prices, results show no substantial differences between CfD designs. For investor profits, we identify the highest volatility reduction under a capacity-based CfD with a reference price similar to power plants' individual market revenues. Since such a CfD design is known to diminish the effect of price signals on investment decisions, our results reveal a trade-off between incentivising system-friendliness and reducing investor risk.", "published": "2025-12-19T12:17:20Z", "authors": ["Silke Johanndeiter", "Jonas Finke", "Justus Heuer"], "categories": ["econ.GN"], "doi": "", "_query": "hedonic house price", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/1608.00230v1", "title": "Application of Malliavin calculus to exact and approximate option pricing under stochastic volatility", "abstract": "The article is devoted to models of financial markets with stochastic volatility, which is defined by a functional of Ornstein-Uhlenbeck process or Cox-Ingersoll-Ross process. We study the question of exact price of European option. The form of the density function of the random variable, which expresses the average of the volatility over time to maturity is established using Malliavin calculus.The result allows calculate the price of the option with respect to minimum martingale measure when the Wiener process driving the evolution of asset price and the Wiener process, which defines volatility, are uncorrelated.", "published": "2016-07-31T15:20:58Z", "authors": ["S. Kuchuk-Iatsenko", "Y. Mishura", "Y. Munchak"], "categories": ["q-fin.PR", "math.PR"], "doi": "", "_query": "hedonic house price", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2608.12363v1", "title": "EU-ETS under attack? The impact of carbon price suppression on the decarbonization of the power sector", "abstract": "European countries are debating policies to mitigate the increased energy costs caused by renewed geopolitical tensions, while pursuing decarbonization and electrification. A notable example is Italy's 2026 Decreto Bollette package, which proposes to remove the carbon price equivalent from the bids of certain gas-driven power plants to wholesale electricity markets, among other provisions. We use this as a case study to assess the long-term implications of suppressing the carbon price signal in the electricity market for investment, emissions, and consumer costs. We employ a stylized Italian power system using MARLEY, a multi-agent reinforcement learning framework focused on long-term electricity market assessments. In this framework, we test this policy across configurations with varying levels of support for green investment, resource adequacy, and flexibility. Results show that partial suppression of the carbon price signal yields short-term cost reductions but only a minor long-term effect on total system costs, as the deferred emissions are ultimately repaid by consumers. CO$_2$ emissions rise across most configurations since suppressing the price signal erodes incentives for renewable and storage investment. Only the most ambitious configurations for supporting green investment avoid this outcome, but they do so by marginalizing the wholesale price signal itself, thereby requiring a commitment to a hybrid market paradigm that is in contradiction with the rationale of the proposed price intervention.", "published": "2026-07-06T14:34:26Z", "authors": ["Javier Gonzalez-Ruiz", "Carlos Rodriguez-Pardo", "Alice Di Bella", "Paolo Mastropietro", "Jose Pablo Chavez-Avila", "Massimo Tavoni"], "categories": ["econ.GN", "cs.AI", "cs.CY", "cs.LG", "cs.MA", "eess.SY"], "doi": "", "_query": "hedonic house price", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/1401.1891v2", "title": "Dynamical Models of Stock Prices Based on Technical Trading Rules Part II: Analysis of the Models", "abstract": "In Part II of this paper, we concentrate our analysis on the price dynamical model with the moving average rules developed in Part I of this paper. By decomposing the excessive demand function, we reveal that it is the interplay between trend-following and contrarian actions that generates the price chaos, and give parameter ranges for the price series to change from divergence to chaos and to oscillation. We prove that the price dynamical model has an infinite number of equilibrium points but all these equilibrium points are unstable. We demonstrate the short-term predictability of the return volatility and derive the detailed formula of the Lyapunov exponent as function of the model parameters. We show that although the price is chaotic, the volatility converges to some constant very quickly at the rate of the Lyapunov exponent. We extract the formula relating the converged volatility to the model parameters based on Monte-Carlo simulations. We explore the circumstances under which the returns show independency and illustrate in details how the independency index changes with the model parameters. Finally, we plot the strange attractor and return distribution of the chaotic price model to illustrate the complex structure and fat-tailed distribution of the returns.", "published": "2014-01-09T04:49:30Z", "authors": ["Li-Xin Wang"], "categories": ["q-fin.TR", "cs.CE", "q-fin.ST"], "doi": "10.1109/TFUZZ.2014.2346244", "_query": "hedonic house price", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2605.27781v3", "title": "Day-Ahead Electricity Price Forecasting Using a Multivariate Group Lasso Method", "abstract": "Electricity price signals in modern power systems exhibit complex dependence structures that render forecasting inherently challenging. Our analysis of real-world electricity pricing signals reveals complex temporal group effects, whereby the influence of explanatory variables on electricity prices persists across consecutive blocks of time due to underlying economic, system, and operational drivers. In response, we propose a multivariate statistical method based on a Group Lasso formulation to jointly forecast the vector of day-ahead electricity prices (h = 1, ..., 24), by leveraging multi-feature temporal group effects. Our approach is evaluated on two full years of electricity prices from the California Independent System Operator (CAISO), demonstrating considerable improvements in point and probabilistic forecast metrics compared to a wide array of statistical and deep learning methods. Empirical analyses confirm the effectiveness of the proposed approach in modeling realistic group effects, maintaining both interpretability and low computational complexity. When retrospectively evaluated on test data from a recent international electricity price forecasting challenge, the proposed method ranked in second place, despite having access to significantly less information than competing approaches. Finally, the proposed method is independently validated against two operational electricity price forecasting systems in CAISO, demonstrating competitive predictive performance and practical relevance.", "published": "2026-05-27T00:08:44Z", "authors": ["Keyi Wang", "Jiaxiang Ji", "Mahan Mansouri", "Ahmed Aziz Ezzat"], "categories": ["stat.AP", "eess.SY"], "doi": "", "_query": "hedonic house price", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2303.04242v1", "title": "MEV in fixed gas price blockchains: Terra Classic as a case of study", "abstract": "Maximum extractable value (MEV) has been extensively studied. In most papers, the researchers have worked with the Ethereum blockchain almost exclusively. Even though, Ethereum and other blockchains have dynamic gas prices this is not the case for all blockchains; many of them have fixed gas prices. Extending the research to other blockchains with fixed gas price could broaden the scope of the existing studies on MEV. To our knowledge, there is not a vast understanding of MEV in fixed gas price blockchains. Therefore, we propose to study Terra Classic as an example to understand how MEV activities affect blockchains with fixed gas price. We first analysed the data from Terra Classic before the UST de-peg event in May 2022 and described the nature of the exploited arbitrage opportunities. We found more than 188K successful arbitrages, and most of them used UST as the initial token. The capital to perform the arbitrage was less than 1K UST in 50% of the cases, and 80% of the arbitrages had less than four swaps. Then, we explored the characteristics that attribute to higher MEV. We found that searchers who use more complex mechanisms, i.e. different contracts and accounts, made higher profits. Finally, we concluded that the most profitable searchers used a strategy of running bots in a multi-instance environment, i.e. running bots with different virtual machines. We measured the importance of the geographic distribution of the virtual machines that run the bots. We found that having good geographic coverage makes the difference between winning or losing the arbitrage opportunities. That is because, unlike MEV extraction in Ethereum, bots in fixed gas price blockchains are not battling a gas war; they are fighting in a latency war.", "published": "2023-03-07T21:28:13Z", "authors": ["Facundo Carrillo", "Elaine Hu"], "categories": ["cs.CR"], "doi": "", "_query": "hedonic house price", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2609.06005v1", "title": "Price Dislocations, News Citations, and Epistemic Leverage on Polymarket", "abstract": "Prediction-market probabilities increasingly appear in news coverage, yet little is known about which market movements become news or how much trading money sits behind the numbers journalists quote. Unlike a poll, a market price can be moved by anyone willing to trade, so the cost of manufacturing a number that circulates as news bears directly on the information environment. We link 173.7 million signed Polymarket trades to news coverage from 2024-2025. From 6,990 articles mentioning prediction-market venues, an LLM-based, human-validated matcher extracts 1,582 sentences quoting market odds and attributes 918 to the specific market whose price they cite. We then detect 44,976 price dislocations, movements of at least five percentage points backed by concentrated one-sided trading, and ask whether a market is cited more often afterward. In the days after a dislocation, a market's citation rate is about 33% higher than its matched baseline (log citation-rate ratio $τ_{\\mathrm{cite}}=0.283$, permutation $p=0.001$), robust to binary and Poisson count outcomes. Yet move size is not the strongest predictor of citation: prominence dominates (standardized $β=0.610$ vs. $β=0.159$ for move size). Finally, we combine the dollar flow behind a given price change with observed citation rates into a metric we call epistemic leverage, the dollars needed to move a market five points and have the move cited. It stays near \\$0.7-1.0 million across prominence quintiles, because cheaper-to-move markets are proportionally less likely to be cited. The implied threat model centers not on the long tail of cheaply moved markets but on the few prominent markets newsrooms treat as informational infrastructure, where a seven-figure price of influence sits within the budgets of actors with a large stake in the quoted number. We release aggregate event-study data and validation materials.", "published": "2026-09-05T10:14:50Z", "authors": ["Hazem Ibrahim", "Yasir Zaki"], "categories": ["physics.soc-ph", "cs.CY", "cs.SI"], "doi": "", "_query": "hedonic house price", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/1302.3319v8", "title": "The Pricing of Multiple-Expiry Exotics", "abstract": "In this paper we extend Buchen's method to develop a new technique for pricing of some exotic options with several expiry dates(more than 3 expiry dates) using a concept of higher order binary option. At first we introduce the concept of higher order binary option and then provide the pricing formulae of $n$-th order binaries using PDE method. After that, we apply them to pricing of some multiple-expiry exotic options such as Bermudan option, multi time extendable option, multi shout option and etc. Here, when calculating the price of concrete multiple-expiry exotic options, we do not try to get the formal solution to corresponding initial-boundary problem of the Black-Scholes equation, but explain how to express the expiry payoffs of the exotic options as a combination of the payoffs of some class of higher order binary options. Once the expiry payoffs are expressed as a linear combination of the payoffs of some class of higher order binary options, in order to avoid arbitrage, the exotic option prices are obtained by static replication with respect to this family of higher order binaries.", "published": "2013-02-14T06:29:47Z", "authors": ["Hyong-Chol O", "Mun-Chol KiM"], "categories": ["q-fin.PR", "q-fin.CP"], "doi": "", "_query": "hedonic house price", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2609.19094v1", "title": "Quadratic G-BSDEs for bond pricing with endogenous short-rate feedback", "abstract": "We study robust bond valuation with endogenous short-rate feedback under volatility uncertainty. Within the $G$-expectation framework, the dependence of the short rate on the bond price yields a nonlinear fixed-point problem, represented by a quadratic $G$-BSDE for the logarithmic price. Under suitable assumptions, we establish existence, uniqueness, comparison, and stability for bounded finite-horizon solutions. An additional strict monotonicity condition yields a unique bounded infinite-horizon solution and exponential convergence of finite-horizon approximations on compact time intervals. We apply these results to inverse short-rate design, constructing discount-rate coefficients that reproduce admissible smooth bond-price targets at a fixed maturity. For long maturities, we construct feedback rules under which the compensated logarithmic price converges exponentially to a prescribed bounded state-dependent profile, while the asymptotic yield equals a specified target.", "published": "2026-09-16T17:29:23Z", "authors": ["Jaehyun Kim", "Hyungbin Park"], "categories": ["q-fin.MF", "math.PR"], "doi": "", "_query": "hedonic house price", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/0805.3087v1", "title": "Price dynamics in a strategic model of trade between two regions", "abstract": "This paper develops a strategic model of trade between two regions in which, depending on the relation among output, financial resources and transportation costs, the adjustment of prices towards an equilibrium is studied. We derive conditions on the relations among output and financial resources which produce different types of Nash equilibria. The paths obtained in the process of converging toward a steady state for prices under discrete-time and continuous-time dynamics are derived and compared. It turns out that the results in the two cases differ substantially. Some of the effects of random disturbances on the price dynamics in continuous time are also studied.", "published": "2008-05-20T14:13:05Z", "authors": ["Iordan V. Iordanov", "Stoyan V. Stoyanov", "Andrey A. Vassilev"], "categories": ["math.OC"], "doi": "", "_query": "hedonic house price", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/1310.4282v1", "title": "A Nash Equilibrium Need Not Exist in the Locational Marginal Pricing Mechanism", "abstract": "Locational marginal pricing (LMP) is a widely employed method for pricing electricity in the wholesale electricity market. Although it is well known that the LMP mechanism is vulnerable to market manipulation, there is little literature providing a systematic analysis of this phenomenon. In the first part of this paper, we investigate the economic dispatch outcomes of the LMP mechanism with strategic agents. We show via counterexamples, that contrary to popular belief, a Nash equilibrium may not exist. And when it exists, the price of anarchy may be arbitrarily large. We then provide two sufficient conditions under either of which an efficient Nash equilibria exists. Last, we propose a new market mechanism for electricity markets, the Power Network Second Price (PNSP) mechanism that always induces an efficient Nash equilibrium. We briefly address the extensions on the demand side.", "published": "2013-10-16T06:51:14Z", "authors": ["Wenyuan Tang", "Rahul Jain"], "categories": ["cs.GT", "math.OC"], "doi": "", "_query": "hedonic house price", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2007.01903v2", "title": "Model Distillation for Revenue Optimization: Interpretable Personalized Pricing", "abstract": "Data-driven pricing strategies are becoming increasingly common, where customers are offered a personalized price based on features that are predictive of their valuation of a product. It is desirable for this pricing policy to be simple and interpretable, so it can be verified, checked for fairness, and easily implemented. However, efforts to incorporate machine learning into a pricing framework often lead to complex pricing policies which are not interpretable, resulting in slow adoption in practice. We present a customized, prescriptive tree-based algorithm that distills knowledge from a complex black-box machine learning algorithm, segments customers with similar valuations and prescribes prices in such a way that maximizes revenue while maintaining interpretability. We quantify the regret of a resulting policy and demonstrate its efficacy in applications with both synthetic and real-world datasets.", "published": "2020-07-03T18:33:23Z", "authors": ["Max Biggs", "Wei Sun", "Markus Ettl"], "categories": ["stat.ML", "cs.LG", "stat.AP"], "doi": "", "_query": "hedonic house price", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2209.04620v1", "title": "A semi-Markovian approach to model the tick-by-tick dynamics of stock price", "abstract": "We model the stock price dynamics through a semi-Markov process obtained using a Poisson random measure. We establish the existence and uniqueness of the classical solution of a non-homogeneous terminal value problem and we show that the expected value of stock price at horizon can be obtained as a classical solution of a linear partial differential equation that is a special case of the terminal value problem studied in this paper. We further analyze the market making problem using the point of view of an agent who posts the limit orders at the best price available. We use the dynamic programming principle to obtain a HJB equation. In no-risk aversion case, we obtain the value function as a classical solution of a linear pde and derive the expressions for optimal controls by solving the HJB equation.", "published": "2022-09-10T08:33:10Z", "authors": ["Garima Agrawal", "Anindya Goswami"], "categories": ["q-fin.MF", "math.OC"], "doi": "", "_query": "hedonic house price", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2302.01295v1", "title": "Ditto in the House: Building Articulation Models of Indoor Scenes through Interactive Perception", "abstract": "Virtualizing the physical world into virtual models has been a critical technique for robot navigation and planning in the real world. To foster manipulation with articulated objects in everyday life, this work explores building articulation models of indoor scenes through a robot's purposeful interactions in these scenes. Prior work on articulation reasoning primarily focuses on siloed objects of limited categories. To extend to room-scale environments, the robot has to efficiently and effectively explore a large-scale 3D space, locate articulated objects, and infer their articulations. We introduce an interactive perception approach to this task. Our approach, named Ditto in the House, discovers possible articulated objects through affordance prediction, interacts with these objects to produce articulated motions, and infers the articulation properties from the visual observations before and after each interaction. It tightly couples affordance prediction and articulation inference to improve both tasks. We demonstrate the effectiveness of our approach in both simulation and real-world scenes. Code and additional results are available at https://ut-austin-rpl.github.io/HouseDitto/", "published": "2023-02-02T18:22:00Z", "authors": ["Cheng-Chun Hsu", "Zhenyu Jiang", "Yuke Zhu"], "categories": ["cs.RO", "cs.AI", "cs.CV"], "doi": "", "_query": "hedonic house price", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/1512.04017v1", "title": "The price of anarchy and stability in general noisy best-response dynamics", "abstract": "Logit-response dynamics (Alos-Ferrer and Netzer, Games and Economic Behavior 2010) are a rich and natural class of noisy best-response dynamics. In this work we revise the price of anarchy and the price of stability by considering the quality of long-run equilibria in these dynamics. Our results show that prior studies on simpler dynamics of this type can strongly depend on a synchronous schedule of the players' moves. In particular, a small noise by itself is not enough to improve the quality of equilibria as soon as other very natural schedules are used.", "published": "2015-12-13T08:48:34Z", "authors": ["Paolo Penna"], "categories": ["cs.GT"], "doi": "", "_query": "hedonic house price", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2206.10295v2", "title": "Dynamic Reserve Price Design with Distributed Solving Algorithm", "abstract": "Unexpected advertising items in sponsored search may reduce users' reliance on organic search, resulting in hidden cost for the e-commerce platform. To address this problem and promote sustainable growth, we propose a dynamic reserve price design that incorporates the hidden cost into the auction mechanism to determine whether to sell the traffic, thereby ensuring a balanced relationship between revenue and user experience. Our dynamic reserve price design framework optimizes traffic sales by minimizing impacts on user experience while maintaining long-term incentives for advertisers to reveal their valuations truthfully. Furthermore, we introduce a distributed algorithm capable of computing reserve prices with billion-scale data in the production environment. Experiments involving offline evaluations and online A/B testing demonstrate that this method is simple and efficient, making it suitable for use in industrial production. This method has already been fully deployed in the production environment.", "published": "2022-06-21T12:20:09Z", "authors": ["Mang Li"], "categories": ["cs.GT", "cs.LG", "stat.ML"], "doi": "10.1145/3746252.3760806", "_query": "hedonic house price", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2609.04618v2", "title": "Pricing rides as option contracts: guarantees and memberships under travel-time uncertainty", "abstract": "Modern ride-share platforms must commit to a price before a trip is taken, yet the realized fare depends on travel time that is uncertain at the moment of sale, which can occur days in advance. The upfront price thus decomposes into the expected fare and the premium on an insurance claim whose payoff is the shortfall between the realized and promised prices. This work computes the fair premium for such a claim, following from contingent-claim pricing. Recent advances in travel-time distribution modeling cast the asymptotic distribution of travel time as a Brownian motion on a metric graph carrying a cyclostationary travel-time process. We thus derive closed-form premiums under both a population and a route-conditional travel-time law. A single pricing equation then covers products of increasing informational difficulty, such as on-demand and scheduled rides and capped-membership and commuter products. To price on sparse data, we develop route and travel-time samplers and validate them out of sample on 2014 Quebec City GPS trips, where they reproduce the empirical trip-time distribution. We show that guarantee premiums are inexpensive, typically under 5% of the fare. Conditioning on the realized route roughly halves the premium while reducing tail risk. Analyzing membership abuse, we show that adverse selection acts precisely along the dimension the price fails to condition on, yielding a design principle: subsidies additive in the fare, such as a fixed per-ride discount, leave marginal incentives unchanged and are abuse-proof by construction, with deterministic closed-form cost.", "published": "2026-09-04T01:36:47Z", "authors": ["Mohamad Elmasri", "Mingze Li", "Yunran Wei"], "categories": ["stat.AP"], "doi": "", "_query": "hedonic house price", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/1710.01567v3", "title": "Cloud/fog computing resource management and pricing for blockchain networks", "abstract": "The mining process in blockchain requires solving a proof-of-work puzzle, which is resource expensive to implement in mobile devices due to the high computing power and energy needed. In this paper, we, for the first time, consider edge computing as an enabler for mobile blockchain. In particular, we study edge computing resource management and pricing to support mobile blockchain applications in which the mining process of miners can be offloaded to an edge computing service provider. We formulate a two-stage Stackelberg game to jointly maximize the profit of the edge computing service provider and the individual utilities of the miners. In the first stage, the service provider sets the price of edge computing nodes. In the second stage, the miners decide on the service demand to purchase based on the observed prices. We apply the backward induction to analyze the sub-game perfect equilibrium in each stage for both uniform and discriminatory pricing schemes. For the uniform pricing where the same price is applied to all miners, the existence and uniqueness of Stackelberg equilibrium are validated by identifying the best response strategies of the miners. For the discriminatory pricing where the different prices are applied to different miners, the Stackelberg equilibrium is proved to exist and be unique by capitalizing on the Variational Inequality theory. Further, the real experimental results are employed to justify our proposed model.", "published": "2017-10-04T12:21:45Z", "authors": ["Zehui Xiong", "Shaohan Feng", "Wenbo Wang", "Dusit Niyato", "Ping Wang", "Zhu Han"], "categories": ["cs.CR", "cs.GT"], "doi": "", "_query": "hedonic house price", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/1908.03137v2", "title": "Fast Pricing of Energy Derivatives with Mean-reverting Jump-diffusion Processes", "abstract": "Most energy and commodity markets exhibit mean-reversion and occasional distinctive price spikes, which results in demand for derivative products which protect the holder against high prices. To this end, in this paper we present exact and fast methodologies for the simulation of the spot price dynamics modeled as the exponential of the sum of an Ornstein-Uhlenbeck and an independent pure jump process, where the latter one is driven by a compound Poisson process with (bilateral) exponentially distributed jumps. These methodologies are finally applied to the pricing of Asian options, gas storages and swings under different combinations of jump-diffusion market models, and the apparent computational advantages of the proposed procedures are emphasized.", "published": "2019-08-08T16:06:08Z", "authors": ["Nicola Cufaro Petroni", "Piergiacomo Sabino"], "categories": ["q-fin.CP", "math.PR"], "doi": "10.1080/1350486X.2021.1909488", "_query": "hedonic house price", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2203.01740v1", "title": "Exact Price of Anarchy for Weighted Congestion Games with Two Players", "abstract": "This paper gives a complete analysis of worst-case equilibria for various versions of weighted congestion games with two players and affine cost functions. The results are exact price of anarchy bounds which are parametric in the weights of the two players, and establish exactly how the primitives of the game enter into the quality of equilibria. Interestingly, some of the worst-cases are attained when the players' weights only differ slightly. Our findings also show that sequential play improves the price of anarchy in all cases, however, this effect vanishes with an increasing difference in the players' weights. Methodologically, we obtain exact price of anarchy bounds by a duality based proof mechanism, based on a compact linear programming formulation that computes worst-case instances. This mechanism yields duality-based optimality certificates which can eventually be turned into purely algebraic proofs.", "published": "2022-03-03T14:32:05Z", "authors": ["Joran van den Bosse", "Marc Uetz", "Matthias Walter"], "categories": ["cs.GT", "cs.DM"], "doi": "", "_query": "hedonic house price", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2405.10584v1", "title": "A Hybrid Deep Learning Framework for Stock Price Prediction Considering the Investor Sentiment of Online Forum Enhanced by Popularity", "abstract": "Stock price prediction has always been a difficult task for forecasters. Using cutting-edge deep learning techniques, stock price prediction based on investor sentiment extracted from online forums has become feasible. We propose a novel hybrid deep learning framework for predicting stock prices. The framework leverages the XLNET model to analyze the sentiment conveyed in user posts on online forums, combines these sentiments with the post popularity factor to compute daily group sentiments, and integrates this information with stock technical indicators into an improved BiLSTM-highway model for stock price prediction. Through a series of comparative experiments involving four stocks on the Chinese stock market, it is demonstrated that the hybrid framework effectively predicts stock prices. This study reveals the necessity of analyzing investors' textual views for stock price prediction.", "published": "2024-05-17T07:18:08Z", "authors": ["Huiyu Li", "Junhua Hu"], "categories": ["cs.LG", "cs.CL", "q-fin.ST"], "doi": "10.1007/s10614-025-11268-6", "_query": "hedonic house price", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2409.04903v2", "title": "Semi-analytical pricing of options written on SOFR futures", "abstract": "In this paper, we propose a semi-analytical approach to pricing options on SOFR futures where the underlying SOFR follows a time-dependent CEV model. By definition, these options change their type at the beginning of the reference period: before this time, this is an American option written on a SOFR forward price as an underlying, and after this point, this is an arithmetic Asian option with an American style exercise written on the daily SOFR rates. We develop a new version of the GIT method and solve both problems semi-analytically, obtaining the option price, the exercise boundary, and the option Greeks. This work is intended to address the concern that the transfer from LIBOR to SOFR has resulted in a situation in which the options of the key money market (i.e., futures on the reference rate) are options without any pricing model available. Therefore, the trading in options on 3M SOFR futures currently ends before their reference quarter starts, to eliminate the final metamorphosis into exotic options.", "published": "2024-09-07T20:13:44Z", "authors": ["Andrey Itkin", "Yerkin Kitapbayev"], "categories": ["q-fin.CP", "q-fin.MF", "q-fin.PR"], "doi": "", "_query": "hedonic house price", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/1502.01253v2", "title": "Prices Matter for the Parameterized Complexity of Shift Bribery", "abstract": "In the Shift Bribery problem, we are given an election (based on preference orders), a preferred candidate $p$, and a budget. The goal is to ensure that $p$ wins by shifting $p$ higher in some voters' preference orders. However, each such shift request comes at a price (depending on the voter and on the extent of the shift) and we must not exceed the given budget. We study the parameterized computational complexity of Shift Bribery with respect to a number of parameters (pertaining to the nature of the solution sought and the size of the election) and several classes of price functions. When we parameterize Shift Bribery by the number of affected voters, then for each of our voting rules (Borda, Maximin, Copeland) the problem is W[2]-hard. If, instead, we parameterize by the number of positions by which $p$ is shifted in total,then the problem is fixed-parameter tractable for Borda and Maximin,and is W[1]-hard for Copeland. If we parameterize by the budget, then the results depend on the price function class. We also show that Shift Bribery tends to be tractable when parameterized by the number of voters, but that the results for the number of candidates are more enigmatic.", "published": "2015-02-04T16:40:25Z", "authors": ["Robert Bredereck", "Jiehua Chen", "Piotr Faliszewski", "André Nichterlein", "Rolf Niedermeier"], "categories": ["cs.MA"], "doi": "", "_query": "hedonic house price", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/0704.0773v2", "title": "Collective behavior of stock price movements in an emerging market", "abstract": "To investigate the universality of the structure of interactions in different markets, we analyze the cross-correlation matrix C of stock price fluctuations in the National Stock Exchange (NSE) of India. We find that this emerging market exhibits strong correlations in the movement of stock prices compared to developed markets, such as the New York Stock Exchange (NYSE). This is shown to be due to the dominant influence of a common market mode on the stock prices. By comparison, interactions between related stocks, e.g., those belonging to the same business sector, are much weaker. This lack of distinct sector identity in emerging markets is explicitly shown by reconstructing the network of mutually interacting stocks. Spectral analysis of C for NSE reveals that, the few largest eigenvalues deviate from the bulk of the spectrum predicted by random matrix theory, but they are far fewer in number compared to, e.g., NYSE. We show this to be due to the relative weakness of intra-sector interactions between stocks, compared to the market mode, by modeling stock price dynamics with a two-factor model. Our results suggest that the emergence of an internal structure comprising multiple groups of strongly coupled components is a signature of market development.", "published": "2007-04-05T19:27:38Z", "authors": ["Raj Kumar Pan", "Sitabhra Sinha"], "categories": ["q-fin.ST", "cond-mat.other", "physics.data-an", "physics.soc-ph"], "doi": "10.1103/PhysRevE.76.046116", "_query": "hedonic house price", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2303.01111v1", "title": "Predicting Stock Price Movement as an Image Classification Problem", "abstract": "The paper studies intraday price movement of stocks that is considered as an image classification problem. Using a CNN-based model we make a compelling case for the high-level relationship between the first hour of trading and the close. The algorithm managed to adequately separate between the two opposing classes and investing according to the algorithm's predictions outperformed all alternative constructs but the theoretical maximum. To support the thesis, we ran several additional tests. The findings in the paper highlight the suitability of computer vision techniques for studying financial markets and in particular prediction of stock price movements.", "published": "2023-03-02T09:47:14Z", "authors": ["Matej Steinbacher"], "categories": ["q-fin.PR", "cs.CE", "cs.LG", "q-fin.CP", "q-fin.TR"], "doi": "10.1007/s10614-025-10887-3", "_query": "hedonic house price", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2001.04283v2", "title": "Electricity prices and tariffs to keep everyone happy: a framework for fixed and nodal prices coexistence in distribution grids with optimal tariffs for investment cost recovery", "abstract": "Some consumers, particularly households, are unwilling to face volatile electricity prices, and they can perceive as unfair price differentiation in the same local area. For these reasons, nodal prices in distribution networks are rarely employed. However, the increasing availability of renewable resources and emerging price-elastic behaviours pave the way for the effective introduction of marginal nodal pricing schemes in distribution networks. The aim of the proposed framework is to show how traditional non-flexible consumers can coexist with flexible users in a local distribution area. Flexible users will pay nodal prices, whereas non-flexible consumers will be charged a fixed price derived from the underlying nodal prices. Moreover, the developed approach shows how a distribution system operator should manage the local grid by optimally determining the lines to be expanded, and the collected network tariff levied on grid users, while accounting for both congestion rent and investment costs. The proposed model is formulated as a non-linear integer bilevel program, which is then recast as an equivalent single optimization problem, by using integer algebra and complementarity relations. The power flows in the distribution area are modelled by resorting to a second-order cone relaxation, whose solution is exact for radial networks under mild assumptions. The final model results in a mixed-integer quadratically constrained program, which can be solved with off-the-shelf solvers. Numerical test cases based on both 5-bus and 33-bus networks are reported to show the effectiveness of the proposed method.", "published": "2020-01-08T21:17:22Z", "authors": ["Iacopo Savelli", "Thomas Morstyn"], "categories": ["econ.GN", "eess.SY"], "doi": "10.1016/j.omega.2021.102450", "_query": "hedonic house price", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2510.21951v2", "title": "Pricing Problems in Adoption of New Technologies", "abstract": "We propose a generalization of the Bass diffusion model in discrete-time that explicitly models the effect of price in adoption. Our model is different from earlier price-incorporated models and fits well to adoption data for various products. We then utilize this model to study two decision-making problems. First, we provide a series of structural results on optimal pricing strategies to maximize profits from product sales by a monopolist over a finite horizon. We fully characterize the optimal pricing strategy in the single-period problem, and establish several structural properties of the same for the multi-period counterpart. Second, we study a Stackelberg game between a policy-maker and a monopolist, where the former seeks to maximize adoption through rebates, while the latter focuses on profits. For this problem, we analytically characterize crucial properties of the equilibrium path of the single-period game, and demonstrate how they carry over to the multi-period variant.", "published": "2025-10-24T18:25:38Z", "authors": ["Yijin Wang", "Subhonmesh Bose"], "categories": ["eess.SY"], "doi": "", "_query": "hedonic house price", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2002.10202v1", "title": "Representation of Exchange Option Prices under Stochastic Volatility Jump-Diffusion Dynamics", "abstract": "In this article, we provide representations of European and American exchange option prices under stochastic volatility jump-diffusion (SVJD) dynamics following models by Merton (1976), Heston (1993), and Bates (1996). A Radon-Nikodym derivative process is also introduced to facilitate the shift from the objective market measure to other equivalent probability measures, including the equivalent martingale measure. Under the equivalent martingale measure, we derive the integro-partial differential equation that characterizes the exchange option prices. We also derive representations of the European exchange option price using the change-of-numeraire technique proposed by Geman et al. (1995) and the Fourier inversion formula derived by Caldana and Fusai (2013), and show that these two representations are comparable. Lastly, we show that the American exchange option price can be decomposed into the price of the European exchange option and an early exercise premium.", "published": "2020-02-24T12:32:17Z", "authors": ["Gerald H. L. Cheang", "Len Patrick Dominic M. Garces"], "categories": ["q-fin.MF", "q-fin.PR"], "doi": "10.1080/14697688.2019.1655785", "_query": "hedonic house price", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2106.11691v2", "title": "Two Price Regimes in Limit Order Books: Liquidity Cushion and Fragmented Distant Field", "abstract": "The distribution of liquidity within the limit order book is essential for the impact of market orders on the stock price and the emergence of price shocks. Limit orders are characterized by stylized facts: The number of inserted limit orders declines with the price distance from the quotes following a power law and limit order lifetimes and volumes are power law distributed. Strong dependencies among these quantities add to the complexity of limit order books. Here we analyze the limit order book in the dimensions of price, time, limit order lifetime and volume altogether. This allows us to identify regularities which are not visible in marginal distributions. Particularly we find that the limit order book is divided into two regimes. Around the quotes we find a densely filled regime with mostly short living limit orders closely adapting to the price. Far away from the quotes we find a sparse filling with long living limit orders, mostly inserted at particular times of the day being prone to flash crashes. We determine the characteristics of those two regimes and point out the main differences. Based on our research we propose a model for simulating the regime around the quotes.", "published": "2021-06-22T11:51:28Z", "authors": ["Sebastian M. Krause", "Edgar Jungblut", "Thomas Guhr"], "categories": ["q-fin.ST", "q-fin.TR"], "doi": "10.1088/1742-5468/ac4517", "_query": "hedonic house price", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2105.12852v2", "title": "Identifying Brexit voting patterns in the British House of Commons: an analysis based on Bayesian mixture models with flexible concomitant covariate effects", "abstract": "Brexit and its implications are an ongoing topic of interest since the Brexit referendum in 2016. In 2019 the House of commons held a number of \"indicative\" and \"meaningful\" votes as part of the Brexit approval process. The voting behaviour of members of the parliament in these votes is investigated to gain insight into the Brexit approval process. In particular, a mixture model with concomitant covariates is developed to identify groups of members of parliament who share similar voting behaviour while also considering characteristics of the members of parliament. The novelty of the method lies in the flexible structure used to model the effect of concomitant covariates on the component weights of the mixture, with the (potentially nonlinear) terms represented as a smooth function of the covariates. Results show this approach allows to quantify the effect of the age of members of parliament, as well as preferences and competitiveness in the constituencies they represent, on their position towards Brexit. This helps grouping the aforementioned politicians into homogeous clusters, whose composition departs sensibly from that of the parties.", "published": "2021-05-26T21:22:16Z", "authors": ["Marco Berrettini", "Giuliano Galimberti", "Saverio Ranciati", "Thomas Brendan Murphy"], "categories": ["stat.ME", "stat.AP"], "doi": "10.1093/jrsssc/qlae004", "_query": "hedonic house price", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/1205.4008v4", "title": "Price manipulation in a market impact model with dark pool", "abstract": "For a market impact model, price manipulation and related notions play a role that is similar to the role of arbitrage in a derivatives pricing model. Here, we give a systematic investigation into such regularity issues when orders can be executed both at a traditional exchange and in a dark pool. To this end, we focus on a class of dark-pool models whose market impact at the exchange is described by an Almgren--Chriss model. Conditions for the absence of price manipulation for all Almgren--Chriss models include the absence of temporary cross-venue impact, the presence of full permanent cross-venue impact, and the additional penalization of orders executed in the dark pool. When a particular Almgren--Chriss model has been fixed, we show by a number of examples that the regularity of the dark-pool model hinges in a subtle way on the interplay of all model parameters and on the liquidation time constraint. The paper can also be seen as a case study for the regularity of market impact models in general.", "published": "2012-05-17T19:02:37Z", "authors": ["Florian Klöck", "Alexander Schied", "Yuemeng Sun"], "categories": ["q-fin.TR"], "doi": "", "_query": "hedonic house price", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2112.11968v2", "title": "An Efficient Unified Approach for Spread Option Pricing in a Copula Market Model", "abstract": "In this study, we propose a new formula for spread option pricing with the dependence of two assets described by a copula function. The advantage of the proposed method is that it requires only the numerical evaluation of a one-dimensional integral. Any univariate stock price process, admitting an affine characteristic function, can be used in our formula to get an efficient numerical procedure for computing spread option prices. In the numerical analysis we present a comparison with Monte Carlo simulation methods to assess the performance of our approach, assuming that the univariate stock price follows three widely applied models: Variance Gamma, Heston's Stochastic Volatility and Affine Heston Nandi GARCH(1,1) model.", "published": "2021-12-22T15:40:20Z", "authors": ["Edoardo Berton", "Lorenzo Mercuri"], "categories": ["q-fin.PR"], "doi": "10.1007/s10479-023-05549-2", "_query": "hedonic house price", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/1608.01103v1", "title": "Fluctuation of USA Gold Price - Revisited with Chaos-based Complex Network Method", "abstract": "We give emphasis on the use of chaos-based rigorous nonlinear technique called Visibility Graph Analysis, to study one economic time series - gold price of USA. This method can offer reliable results with fiinite data. This paper reports the result of such an analysis on the times series depicting the fluctuation of gold price of USA for the span of 25 years(1990 - 2013). This analysis reveals that a quantitative parameter from the theory can explain satisfactorily the real life nature of fluctuation of gold price of USA and hence building a strong database in terms of a quantitative parameter which can eventually be used for forecasting purpose.", "published": "2016-08-03T08:05:35Z", "authors": ["Susmita Bhaduri", "Dipak Ghosh", "Subhadeep Ghosh"], "categories": ["q-fin.ST", "physics.data-an"], "doi": "", "_query": "hedonic house price", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2001.01302v1", "title": "A Locational Price for Power Injection Fluctuations of Variable Generation and Load", "abstract": "In this paper we calculate the incremental system production cost associated with a measure of locational power injection uncertainty that can be interpreted as a locational price for tracking power fluctuations. This Locational Price of Variability (LPV) can be used to allocate charges for regulation reserves by location, and hence, can also be used to value distributed energy storage employed to mitigate such fluctuations. We consider policy changes that could enable the implementation of the LPV.", "published": "2020-01-05T20:01:05Z", "authors": ["Adria E. Brooks", "Bernard C. Lesieutre"], "categories": ["eess.SY"], "doi": "", "_query": "hedonic house price", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/cond-mat/9708178v1", "title": "A Path Integral Approach to Option Pricing with Stochastic Volatility: Some Exact Results", "abstract": "The Black-Scholes formula for pricing options on stocks and other securities has been generalized by Merton and Garman to the case when stock volatility is stochastic. The derivation of the price of a security derivative with stochastic volatility is reviewed starting from the first principles of finance. The equation of Merton and Garman is then recast using the path integration technique of theoretical physics. The price of the stock option is shown to be the analogue of the Schrodinger wavefuction of quantum mechacnics and the exact Hamiltonian and Lagrangian of the system is obtained. The results of Hull and White are generalized results for pricing stock options for the general correlated case are derived.", "published": "1997-08-22T04:22:01Z", "authors": ["B. E. Baaquie"], "categories": ["cond-mat", "hep-th"], "doi": "10.1051/jp1:1997167", "_query": "hedonic house price", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/1203.4177v2", "title": "Strict linear prices in non-convex European day-ahead electricity markets", "abstract": "The European power grid can be divided into several market areas where the price of electricity is determined in a day-ahead auction. Market participants can provide continuous hourly bid curves and combinatorial bids with associated quantities given the prices. The goal of our auction is to maximize the economic surplus of all participants subject to quantity constraints and price constraints. The price constraints ensure that no one incurs a loss. Only traders who submitted a combinatorial bid might miss a not-realized profit. The resulting problem is a large scale mathematical program with equilibrium constraints (MPEC) and binary variables that cannot be solved efficiently by standard solvers. We present an exact algorithm and a fast heuristic for this type of problem. Both algorithms decompose the MPEC into a master problem (a MIQP) and pricing subproblems (LPs). The modeling technique and the algorithms are applicable to a wide variety of combinatorial auctions that are based on mixed integer programs.", "published": "2012-03-19T17:22:28Z", "authors": ["Alexander Martin", "Johannes C. Müller", "Sebastian Pokutta"], "categories": ["math.OC", "math.CO"], "doi": "10.1080/10556788.2013.823544", "_query": "hedonic house price", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2007.14162v1", "title": "Insider Trading with Temporary Price Impact", "abstract": "We model an informed agent with information about the future value of an asset trying to maximize profits when subjected to a transaction cost as well as a market maker tasked with setting fair transaction prices. In a single auction model, equilibrium is characterized by the unique root of a particular polynomial. Analysis of this polynomial with small levels of risk-aversion and transaction costs reveal a dimensionless parameter which captures several orders of asymptotic accuracy of the equilibrium behaviour. In a continuous time analogue of the single auction model, incorporation of a transaction costs allows the informed agent's optimal trading strategy to be obtained in feedback form. Linear equilibrium is characterized by the unique solution to a system of two ordinary differential equations, of which one is forward in time and one is backward. When transaction costs are in effect, the price set by the market maker in equilibrium is not fully revealing of the informed agent's private signal, leaving an information gap at the end of the trading interval. When considering vanishing transaction costs, the equilibrium trading strategy and pricing rules converge to their frictionless counterparts.", "published": "2020-07-28T12:38:26Z", "authors": ["Weston Barger", "Ryan Donnelly"], "categories": ["q-fin.TR"], "doi": "", "_query": "hedonic house price", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2010.13630v1", "title": "Derivatives Pricing in Non-Arbitrage Market", "abstract": "The general method is proposed for constructing a family of martingale measures for a wide class of evolution of risky assets. The sufficient conditions are formulated for the evolution of risky assets under which the family of equivalent martingale measures to the original measure is a non-empty set. The set of martingale measures is constructed from a set of strictly nonneg ative random variables, satisfying certain conditions. The inequalities are obtained for the non-negative random variables satisfying certain conditions. Using these inequalities, a new simple proof of optional decomposition theorem for the nonnegative super-martingale is proposed. The family of spot measures is introduced and the representation is found for them. The conditions are found under which each martingale measure is an integral over the set of spot measures. On the basis of nonlinear processes such as ARCH and GARCH, the parametric family of random processes is introduced for which the interval of non-arbitrage prices are found. The formula is obtained for the fair price of the contract with option of European type for the considered parametric processes. The parameters of the introduced random processes are estimated and the estimate is found at which the fair price of contract with option is the least.", "published": "2020-10-26T14:46:03Z", "authors": ["N. S. Gonchar"], "categories": ["q-fin.PR", "q-fin.MF"], "doi": "", "_query": "hedonic house price", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2512.24520v2", "title": "Optimal Carbon Prices in an Unequal World: The Role of Regional Welfare Weights", "abstract": "How should nations price carbon? This paper examines how the treatment of global inequality, captured by regional welfare weights, affects optimal carbon prices. I develop theory to identify the conditions under which accounting for differences in marginal utilities of consumption across countries leads to more stringent global climate policy in the absence of international transfers. I further establish a connection between the optimal uniform carbon prices implied by different welfare weights and heterogeneous regional preferences over climate policy stringency. In calibrated simulations, I find that accounting for global inequality reduces optimal global emissions relative to an inequality-insensitive benchmark. This holds both when carbon prices are regionally differentiated, with emissions 21% lower, and when they are constrained to be globally uniform, with the uniform carbon price 15% higher.", "published": "2025-12-30T23:46:31Z", "authors": ["Simon F. Lang"], "categories": ["econ.GN"], "doi": "", "_query": "hedonic house price", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2303.10253v2", "title": "Pricing for Multi-modal Pickup and Delivery Problems with Heterogeneous Users", "abstract": "In this paper, we study the pickup and delivery problem with multiple transportation modalities, and address the challenge of efficiently allocating transportation resources while price matching users with their desired delivery modes. More precisely, we consider that orders are demanded by a heterogeneous population of users with varying trade-offs between price and latency. To capture how prices affect the behavior of heterogeneous selfish users choosing between multiple delivery modes, we construct a congestion game taking place over a form of star network, where each source-sink pair is composed of parallel links connecting users with their preferred delivery method. Using the unique geometry of this network, we prove that one can set prices explicitly to induce any desired network flow, i.e, given a desired allocation strategy, we have a closed-form solution for the delivery prices. We conclude by performing a case study on a meal delivery problem with multiple courier modalities using data from real world instances.", "published": "2023-03-17T21:21:56Z", "authors": ["Mark Beliaev", "Negar Mehr", "Ramtin Pedarsani"], "categories": ["eess.SY"], "doi": "", "_query": "hedonic house price", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2403.11093v2", "title": "Learning-Based Pricing and Matching for Two-Sided Queues", "abstract": "We consider a dynamic system with multiple types of customers and servers. Each type of waiting customer or server joins a separate queue, forming a bipartite graph with customer-side queues and server-side queues. The platform can match the servers and customers if their types are compatible. The matched pairs then leave the system. The platform will charge a customer a price according to their type when they arrive and will pay a server a price according to their type. The arrival rate of each queue is determined by the price according to some unknown demand or supply functions. Our goal is to design pricing and matching algorithms to maximize the profit of the platform with unknown demand and supply functions, while keeping queue lengths of both customers and servers below a predetermined threshold. This system can be used to model two-sided markets such as ride-sharing markets with passengers and drivers. The difficulties of the problem include simultaneous learning and decision making, and the tradeoff between maximizing profit and minimizing queue length. We use a longest-queue-first matching algorithm and propose a learning-based pricing algorithm, which combines gradient-free stochastic projected gradient ascent with bisection search. We prove that our proposed algorithm yields a sublinear regret $\\tilde{O}(T^{5/6})$ and anytime queue-length bound $\\tilde{O}(T^{1/6})$, where $T$ is the time horizon. We further establish a tradeoff between the regret bound and the queue-length bound: $\\tilde{O}(T^{1-γ})$ versus $\\tilde{O}(T^γ)$ for $γ\\in (0, 1/6].$", "published": "2024-03-17T05:07:04Z", "authors": ["Zixian Yang", "Lei Ying"], "categories": ["math.OC", "cs.LG", "math.PR"], "doi": "", "_query": "hedonic house price", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2206.13860v2", "title": "Detection and Forecasting of Extreme event in Stock Price Triggered by Fundamental, Technical, and External Factors", "abstract": "The sporadic large fluctuations are seen in the stock market due to changes in fundamental parameters, technical setups, and external factors. These large fluctuations are termed as Extreme Events (EE). The EEs may be positive or negative depending on the impact of these factors. During such events, the stock price time series is found to be nonstationary. Hence, the Hilbert-Huang transformation (HHT) is used to identify EEs based on their high instantaneous energy ($IE$) concentration. The analysis shows that the $IE$ concentration in the stock price is very high during both positive and negative EE with $IE>E_μ+4σ,$ where $E_μ$ and $σ$ are the mean energy and standard deviation of energy, respectively. Further, support vector regression is used to predict the stock price during an EE, with the close price being the most helpful input than the open-high-low-close (OHLC) inputs. The maximum prediction accuracy for one step using close price and OHLC prices are 95.98\\% and 95.64\\% respectively. Whereas, for the two steps prediction, the accuracies are 94.09\\% and 93.58\\% respectively. The EEs found from the predicted time series shows similar statistical characteristics that were obtained from the original data. The analysis emphasizes the importance of monitoring factors that lead to EEs for a compelling entry or exit strategy as investors can gain or lose significant amounts of capital due to these events.", "published": "2022-06-28T09:55:57Z", "authors": ["Anish Rai", "Salam Rabindrajit Luwang", "Md Nurujjaman", "Chittaranjan Hens", "Pratyay Kuila", "Kanish Debnath"], "categories": ["q-fin.ST"], "doi": "10.1016/j.chaos.2023.113716", "_query": "hedonic house price", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/1811.07166v4", "title": "Pacing Equilibrium in First-Price Auction Markets", "abstract": "Mature internet advertising platforms offer high-level campaign management tools to help advertisers run their campaigns, often abstracting away the intricacies of how each ad is placed and focusing on aggregate metrics of interest to advertisers. On such platforms, advertisers often participate in auctions through a proxy bidder, so the standard incentive analyses that are common in the literature do not apply directly. In this paper, we take the perspective of a budget management system that surfaces aggregated incentives -- instead of individual auctions -- and compare first and second price auctions. We show that theory offers surprising endorsement for using a first price auction to sell individual impressions. In particular, first price auctions guarantee uniqueness of the steady-state equilibrium of the budget management system, monotonicity, and other desirable properties, as well as efficient computation through the solution to the well-studied Eisenberg-Gale convex program. Contrary to what one can expect from first price auctions, we show that incentives issues are not a barrier that undermines the system. Using realistic instances generated from data collected at real-world auction platforms, we show that bidders have small regret with respect to their optimal ex-post strategy, and they do not have a big incentive to misreport when they can influence equilibria directly by giving inputs strategically. Finally, budget-constrained bidders, who have significant prevalence in real-world platforms, tend to have smaller regrets. Our computations indicate that bidder budgets, pacing multipliers and regrets all have a positive association in statistical terms.", "published": "2018-11-17T14:24:32Z", "authors": ["Vincent Conitzer", "Christian Kroer", "Debmalya Panigrahi", "Okke Schrijvers", "Eric Sodomka", "Nicolas E. Stier-Moses", "Chris Wilkens"], "categories": ["cs.GT"], "doi": "10.1145/3328526.3329600", "_query": "hedonic house price", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2106.04689v1", "title": "Learning to Price Against a Moving Target", "abstract": "In the Learning to Price setting, a seller posts prices over time with the goal of maximizing revenue while learning the buyer's valuation. This problem is very well understood when values are stationary (fixed or iid). Here we study the problem where the buyer's value is a moving target, i.e., they change over time either by a stochastic process or adversarially with bounded variation. In either case, we provide matching upper and lower bounds on the optimal revenue loss. Since the target is moving, any information learned soon becomes out-dated, which forces the algorithms to keep switching between exploring and exploiting phases.", "published": "2021-06-08T20:57:11Z", "authors": ["Renato Paes Leme", "Balasubramanian Sivan", "Yifeng Teng", "Pratik Worah"], "categories": ["cs.GT", "cs.LG"], "doi": "", "_query": "hedonic house price", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2508.01419v1", "title": "Cryptocurrency Price Forecasting Using Machine Learning: Building Intelligent Financial Prediction Models", "abstract": "Cryptocurrency markets are experiencing rapid growth, but this expansion comes with significant challenges, particularly in predicting cryptocurrency prices for traders in the U.S. In this study, we explore how deep learning and machine learning models can be used to forecast the closing prices of the XRP/USDT trading pair. While many existing cryptocurrency prediction models focus solely on price and volume patterns, they often overlook market liquidity, a crucial factor in price predictability. To address this, we introduce two important liquidity proxy metrics: the Volume-To-Volatility Ratio (VVR) and the Volume-Weighted Average Price (VWAP). These metrics provide a clearer understanding of market stability and liquidity, ultimately enhancing the accuracy of our price predictions. We developed four machine learning models, Linear Regression, Random Forest, XGBoost, and LSTM neural networks, using historical data without incorporating the liquidity proxy metrics, and evaluated their performance. We then retrained the models, including the liquidity proxy metrics, and reassessed their performance. In both cases (with and without the liquidity proxies), the LSTM model consistently outperformed the others. These results underscore the importance of considering market liquidity when predicting cryptocurrency closing prices. Therefore, incorporating these liquidity metrics is essential for more accurate forecasting models. Our findings offer valuable insights for traders and developers seeking to create smarter and more risk-aware strategies in the U.S. digital assets market.", "published": "2025-08-02T15:54:41Z", "authors": ["Md Zahidul Islam", "Md Shafiqur Rahman", "Md Sumsuzoha", "Babul Sarker", "Md Rafiqul Islam", "Mahfuz Alam", "Sanjib Kumar Shil"], "categories": ["cs.LG"], "doi": "", "_query": "hedonic house price", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2605.06941v2", "title": "Constrained Triple-Head Price Optimization (C3PO) Network for Discrete-Choice Models", "abstract": "We propose a Constrained Triple-Head Price Optimization (C3PO) network for real-time bi-level decision making in discrete-choice environments. In these settings, a seller selects product or service offerings to achieve business objectives, while heterogeneous users make personalized acceptance or rejection decisions based on their preferences, ultimately determining seller outcomes. Existing approaches that fine-tune pre-trained tabular foundation models on synthetic data are limited by their imitation-learning paradigm and inability to enforce common pricing rules, including cross-product and global constraints. C3PO addresses these limitations through a novel transformer-based architecture that jointly integrates price imitation learning, multi-task revenue prediction, and in-context elasticity learning to generate robust pricing recommendations across domains without retraining. A lightweight differentiable constraint layer enables generated prices to satisfy business constraints while optimizing the revenue objective. We demonstrate strong out-of-distribution performance on simulated, synthetic, and real-world datasets. Across simulated benchmarks where the calibrated MNL model is misspecified relative to the ground-truth choice model, C3PO improves expected revenue by 1.4 to 7.9 percentage points over a calibrated MNL baseline. The constraint layer reduces average absolute constraint violations by more than 50\\%. We further deploy the model in real-world applications, including healthcare tender pricing and airline ancillary pricing, where C3PO improves directional pricing effectiveness, measured by price-increase and price-decrease recall, by 18--34 percentage points depending on the baselines considered, including calibrated MNL, classical optimization, and off-the-shelf tabular foundation-models.", "published": "2026-05-07T20:55:47Z", "authors": ["Shivaram Subramanian", "Zhengliang Xue", "Markus Ettl", "Yingdong Lu", "Jayant Kalagnanam"], "categories": ["cs.LG", "math.OC"], "doi": "", "_query": "hedonic house price", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/1707.01600v1", "title": "Option Pricing with Delayed Information", "abstract": "We propose a model to study the effects of delayed information on option pricing. We first talk about the absence of arbitrage in our model, and then discuss super replication with delayed information in a binomial model, notably, we present a closed form formula for the price of convex contingent claims. Also, we address the convergence problem as the time-step and delay length tend to zero and introduce analogous results in the continuous time framework. Finally, we explore how delayed information exaggerates the volatility smile.", "published": "2017-07-06T00:16:32Z", "authors": ["Tomoyuki Ichiba", "Seyyed Mostafa Mousavi"], "categories": ["q-fin.MF"], "doi": "", "_query": "hedonic house price", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/1704.05511v1", "title": "Optimal Posted Prices for Online Cloud Resource Allocation", "abstract": "We study online resource allocation in a cloud computing platform, through a posted pricing mechanism: The cloud provider publishes a unit price for each resource type, which may vary over time; upon arrival at the cloud system, a cloud user either takes the current prices, renting resources to execute its job, or refuses the prices without running its job there. We design pricing functions based on the current resource utilization ratios, in a wide array of demand-supply relationships and resource occupation durations, and prove worst-case competitive ratios of the pricing functions in terms of social welfare. In the basic case of a single-type, non-recycled resource (i.e., allocated resources are not later released for reuse), we prove that our pricing function design is optimal, in that any other pricing function can only lead to a worse competitive ratio. Insights obtained from the basic cases are then used to generalize the pricing functions to more realistic cloud systems with multiple types of resources, where a job occupies allocated resources for a number of time slots till completion, upon which time the resources are returned back to the cloud resource pool.", "published": "2017-04-18T20:11:27Z", "authors": ["Zijun Zhang", "Zongpeng Li", "Chuan Wu"], "categories": ["cs.NI", "cs.GT"], "doi": "", "_query": "hedonic house price", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/0905.4171v1", "title": "A Prediction Market for Toxic Assets Prices", "abstract": "We propose the development of a prediction market for forecasting prices for \"toxic assets\" to be transferred from Irish banks to the National Asset Management Agency (NAMA). Such a market allows market participants to assume a stake in a security whose value is tied to a future event. We propose that securities are created whose value hinges on the transfer amount paid for loans from NAMA to a bank. In essence, bets are accepted on whether the price is higher or lower than a certain quoted figure. The prices of the securities represent transfer prices for toxic assets increases or decreases in line with market opinion. Prediction markets offer a proven means of aggregating distributed knowledge pertaining to fair market values in a scalable and transparent manner. They are incentive compatible (i.e. induce truthful reporting) and robust to strategic manipulation. We propose that a prediction market is run in parallel with the pricing procedure recommended by the European Commission. This procedure need not necessarily take heed of the prediction markets view in all cases but it may offer guidance and a means of anomaly detection. An online prediction market would offer everybody an opportunity to \"have their say\" in an open and transparent manner.", "published": "2009-05-26T11:32:43Z", "authors": ["Alan Holland"], "categories": ["q-fin.GN"], "doi": "", "_query": "hedonic house price", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2311.08392v1", "title": "Iterative Network Pricing for Ridesharing Platforms", "abstract": "Ridesharing platforms match riders and drivers, using dynamic pricing to balance supply and demand. The origin-based \"surge pricing\", however, does not take into consideration market conditions at trip destinations, leading to inefficient driver flows in space and incentivizes drivers to strategize. In this work, we introduce the Iterative Network Pricing mechanism, addressing a main challenge in the practical implementation of optimal origin-destination (OD) based prices, that the model for rider demand is hard to estimate. Assuming that the platform's surge algorithm clears the market for each origin in real-time, our mechanism updates the OD-based price adjustments week-over-week, using only information immediately observable during the same time window in the prior weeks. For stationary market conditions, we prove that our mechanism converges to an outcome that is approximately welfare-optimal. Using data from the City of Chicago, we illustrate (via simulation) the iterative updates under our mechanism for morning rush hours, demonstrating substantial welfare improvements despite significant fluctuations of market conditions from early 2019 through the end of 2020.", "published": "2023-11-14T18:52:50Z", "authors": ["Chenkai Yu", "Hongyao Ma"], "categories": ["cs.GT", "math.OC"], "doi": "", "_query": "hedonic house price", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/1810.09670v1", "title": "Cliquet option pricing in a jump-diffusion Lévy model", "abstract": "We investigate the pricing of cliquet options in a jump-diffusion model. The considered option is of monthly sum cap style while the underlying stock price model is driven by a drifted Lévy process entailing a Brownian diffusion component as well as compound Poisson jumps. We also derive representations for the density and distribution function of the emerging Lévy process. In this setting, we infer semi-analytic expressions for the cliquet option price by two different approaches. The first one involves the probability distribution function of the driving Lévy process whereas the second draws upon Fourier transform techniques. With view on sensitivity analysis and hedging purposes, we eventually deduce representations for several Greeks while putting emphasis on the Vega.", "published": "2018-10-23T05:55:24Z", "authors": ["Markus Hess"], "categories": ["q-fin.PR", "math.PR"], "doi": "10.15559/18-VMSTA107", "_query": "hedonic house price", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/1712.02735v1", "title": "Variance and Volatility Swaps and Futures Pricing for Stochastic Volatility Models", "abstract": "In this chapter, we consider volatility swap, variance swap and VIX future pricing under different stochastic volatility models and jump diffusion models which are commonly used in financial market. We use convexity correction approximation technique and Laplace transform method to evaluate volatility strikes and estimate VIX future prices. In empirical study, we use Markov chain Monte Carlo algorithm for model calibration based on S&P 500 historical data, evaluate the effect of adding jumps into asset price processes on volatility derivatives pricing, and compare the performance of different pricing approaches.", "published": "2017-12-07T17:28:36Z", "authors": ["Anatoliy Swishchuk", "Zijia Wang"], "categories": ["q-fin.MF"], "doi": "", "_query": "hedonic house price", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2609.36589v1", "title": "Non-Linear Pricing Restores Tractability for a Data Seller", "abstract": "We consider a data seller who designs pricing mechanisms over multiple datasets to maximize revenue from budget-constrained buyers. The seller offers multiple datasets and assigns each a pricing function that maps the quantity purchased to a total payment. The goal is to design these pricing functions to maximize revenue, anticipating that buyers---who trade off accuracy gains against cost---choose bundles optimally subject to their budget constraints. Prior work [Chaudhury et al., 2026] studies such optimal pricing under the restriction that each dataset is assigned a linear price, and shows that computing optimal linear prices is computationally intractable. In contrast, we allow each dataset to be priced via a general function and show that this additional flexibility can not only increase the revenue but also restore tractability, yielding a surprising simultaneous improvement in economic performance and computational efficiency. Even when pricing functions are only required to be monotone and lower-continuous, optimal pricing admits a highly structured and simple form: each pricing function is piecewise linear and convex (PLC), and the optimal solution can be computed in polynomial time. Moreover, the total number of kinks across all pricing functions is bounded by the number of buyers. Consequently, when datasets significantly outnumber buyers, most pricing functions are effectively linear. We further empirically study the structure of optimal pricing by analyzing the number of kinks and the revenue gap between optimal nonlinear pricing and optimal linear pricing on simulations generated from a real dataset.", "published": "2026-09-29T03:05:55Z", "authors": ["Bhaskar Ray Chaudhury", "Jugal Garg", "Eklavya Sharma", "Jiaxin Song"], "categories": ["cs.GT"], "doi": "", "_query": "hedonic house price", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/1211.6695v2", "title": "Unstable Price Dynamics as a Result of Information Absorption in Speculative Markets", "abstract": "In speculative markets, risk-free profit opportunities are eliminated by traders exploiting them. Markets are therefore often described as \"informationally efficient\", rapidly removing predictable price changes, and leaving only residual unpredictable fluctuations. This classical view of markets absorbing information and otherwise operating close to an equilibrium is challenged by extreme price fluctuations, in particular since they occur far more frequently than can be accounted for by external news. Here we show that speculative markets which absorb mainly self-generated information can exhibit both: evolution towards efficient equilibrium states as well as their subsequent destabilization. This peculiar dynamics, a generic instability arising from an adaptive control which annihilates predictable information, is realized in a minimal agent-based market model where the impacts of agents' strategies adapt according to their trading success. This adaptation implements a learning rule for the market as a whole minimizing predictable price changes. The model reproduces stylized statistical properties of price changes in quantitative detail, including heavy tailed log return distributions and volatility clusters. Our results demonstrate that the perpetual occurrence of market instabilities can be a direct consequence of the very mechanisms that lead to market efficiency.", "published": "2012-11-28T18:27:52Z", "authors": ["Felix Patzelt", "Klaus R. Pawelzik"], "categories": ["q-fin.TR", "nlin.AO", "physics.soc-ph"], "doi": "10.1038/srep02784", "_query": "hedonic house price", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/1001.0656v2", "title": "A Security Price Volatile Trading Conditioning Model", "abstract": "We develop a theoretical trading conditioning model subject to price volatility and return information in terms of market psychological behavior, based on analytical transaction volume-price probability wave distributions in which we use transaction volume probability to describe price volatility uncertainty and intensity. Applying the model to high frequent data test in China stock market, we have main findings as follows: 1) there is, in general, significant positive correlation between the rate of mean return and that of change in trading conditioning intensity; 2) it lacks significance in spite of positive correlation in two time intervals right before and just after bubble crashes; and 3) it shows, particularly, significant negative correlation in a time interval when SSE Composite Index is rising during bull market. Our model and findings can test both disposition effect and herd behavior simultaneously, and explain excessive trading (volume) and other anomalies in stock market.", "published": "2010-01-05T09:31:27Z", "authors": ["Leilei Shi", "Yiwen Wang", "Ding Chen", "Liyan Han", "Yan Piao", "Chengling Gou"], "categories": ["q-fin.TR", "q-fin.GN"], "doi": "", "_query": "hedonic house price", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/physics/0608115v1", "title": "Analysis of price diffusion in financial markets using PUCK model", "abstract": "Based on the new type of random walk process called the Potentials of Unbalanced Complex Kinetics (PUCK) model, we theoretically show that the price diffusion in large scales is amplified 2/(2 + b) times, where b is the coefficient of quadratic term of the potential. In short time scales the price diffusion depends on the size M of the super moving average. Both numerical simulations and real data analysis of Yen-Dollar rates are consistent with theoretical analysis.", "published": "2006-08-10T12:45:28Z", "authors": ["Takayuki Mizuno", "Hideki Takayasu", "Misako Takayasu"], "categories": ["physics.soc-ph", "physics.data-an", "q-fin.ST"], "doi": "10.1016/j.physa.2007.02.049", "_query": "hedonic house price", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2302.10485v2", "title": "Optimal investment with a noisy signal of future stock prices", "abstract": "We consider an investor who is dynamically informed about the future evolution of one of the independent Brownian motions driving a stock's price fluctuations. With linear temporary price impact the resulting optimal investment problem with exponential utility turns out to be not only well posed, but it even allows for a closed-form solution. We describe this solution and the resulting problem value for this stochastic control problem with partial observation by solving its convex-analytic dual problem.", "published": "2023-02-21T07:19:53Z", "authors": ["Peter Bank", "Yan Dolinsky"], "categories": ["q-fin.MF", "math.OC"], "doi": "", "_query": "hedonic house price", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/1107.0034v1", "title": "Price Prediction in a Trading Agent Competition", "abstract": "The 2002 Trading Agent Competition (TAC) presented a challenging market game in the domain of travel shopping. One of the pivotal issues in this domain is uncertainty about hotel prices, which have a significant influence on the relative cost of alternative trip schedules. Thus, virtually all participants employ some method for predicting hotel prices. We survey approaches employed in the tournament, finding that agents apply an interesting diversity of techniques, taking into account differing sources of evidence bearing on prices. Based on data provided by entrants on their agents' actual predictions in the TAC-02 finals and semifinals, we analyze the relative efficacy of these approaches. The results show that taking into account game-specific information about flight prices is a major distinguishing factor. Machine learning methods effectively induce the relationship between flight and hotel prices from game data, and a purely analytical approach based on competitive equilibrium analysis achieves equal accuracy with no historical data. Employing a new measure of prediction quality, we relate absolute accuracy to bottom-line performance in the game.", "published": "2011-06-30T20:35:25Z", "authors": ["K. M. Lochner", "D. M. Reeves", "Y. Vorobeychik", "M. P. Wellman"], "categories": ["cs.AI"], "doi": "10.1613/jair.1333", "_query": "hedonic house price", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2608.12674v1", "title": "Lines and Ladders: A Context-Aware Multi-Agent Framework for Large-Scale Retail Price Taxonomy", "abstract": "Maintaining price consistency and executing an Every Day Low Price strategy is critical for global retailers. However, with catalogs spanning millions of active items, manual governance of price relationships is infeasible. Inconsistent pricing across item variants distorts customer value perception and cannibalizes sales. To address this, we present a scalable, context-aware Multi-Agent Framework designed to automate the construction of \"Lines and Ladders\" pricing taxonomies. Our framework employs specialized LLM agents to construct these coherent pricing structures by identifying key attributes, extracting multi-modal values, and applying hierarchical grouping logic. Evaluated on real-world enterprise data and deployed in production, our 3-Agent system achieves an F1-score of 0.83 for Lines, outperforming single-agent baselines by mitigating cognitive overload. The system achieves >90% precision and >75% recall in Food & Consumables, and 80.2% assignment accuracy in the unstructured General Merchandise catalog.", "published": "2026-08-13T00:22:22Z", "authors": ["Ravi Teja Chunduri", "Srikaran Reddy Boya", "Deep Narayan Mishra", "Ajay Kumar B", "Karthik Kumaran", "Pranay Kona"], "categories": ["cs.AI"], "doi": "", "_query": "hedonic house price", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2402.16401v1", "title": "A Stationary Equilibrium Model of Green Technology Adoption with Endogenous Carbon Price", "abstract": "This paper proposes and analyzes a stationary equilibrium model for a competitive industry which endogenously determines the carbon price necessary to achieve a given emission target. In the model, firms are identified by their level of technology and make production, entry, and abatement decisions. Polluting firms are subject to a carbon price and abatement is formulated as an irreversible investment, which entails a sunk cost and results in the firms switching to a carbon neutral technology. In equilibrium, we identify a carbon price and a stationary distribution of incumbent, polluting firms, that guarantee the compliance with a certain emission target. Our general theoretical framework is complemented with a case study with Brownian technology shocks, in which we discuss some implications of our model. We observe that a carbon pricing system alongside installation subsidies and tax benefits for green firms trigger earlier investment, while higher income taxes for polluting firms may be distorting. Moreover, we discuss the role of a welfare maximizing regulator, who, by optimally setting the emission target, may mitigate or revert some parameters' effects observed in the model with fixed limit.", "published": "2024-02-26T08:53:29Z", "authors": ["Felix Dammann", "Giorgio Ferrari"], "categories": ["q-fin.MF", "econ.TH"], "doi": "", "_query": "hedonic house price", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2305.03245v2", "title": "How does node centrality in a financial network affect asset price prediction?", "abstract": "In complex financial networks, systemically important nodes usually play crucial roles. Asset price forecasting is important for describing the evolution of a financial network. Naturally, the question arises as to whether node centrality impacts the effectiveness of price forecasting. To explore this, we examine networks composed of major global assets and investigate how node centrality affects price forecasting using a hybrid random forest algorithm. Our findings reveal two counterintuitive phenomena: (i) factors with low centrality usually have better forecasting ability, and (ii) nodes with low centrality can be predicted more accurately in direction. These unexpected observations can be explained from the perspective of information theory. Moreover, our research suggests a criterion for factor selection: when predicting an asset price in a complex system, factors with low centrality should be selected rather than only factors with high centrality. Finally, we verify the robustness of our results using an alternative deep learning method.", "published": "2023-05-05T02:08:29Z", "authors": ["Yuhong Xu", "Xinyao Zhao"], "categories": ["physics.soc-ph", "cs.SI"], "doi": "", "_query": "hedonic house price", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2409.08205v2", "title": "A market resilient data-driven approach to option pricing", "abstract": "In this paper, we present a data-driven ensemble approach for option price prediction whose derivation is based on the no-arbitrage theory of option pricing. Using the theoretical treatment, we derive a common representation space for achieving domain adaptation. The success of an implementation of this idea is shown using some real data. Then we report several experimental results for critically examining the performance of the derived pricing models.", "published": "2024-09-12T16:46:07Z", "authors": ["Anindya Goswami", "Nimit Rana"], "categories": ["q-fin.MF"], "doi": "", "_query": "hedonic house price", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/1804.04550v2", "title": "Locational Marginal Price Variability at Distribution Level: A Regional Study", "abstract": "As distribution systems move towards being more actively managed there is increased potential for regional markets and the application of locational marginal prices (LMPs) to capture spatial variation in the marginal cost of electricity at distribution level. However, with this increased network visibility can come increased price volatility and uncertainty to investors. This paper studies the variation in LMPs in a section of the south west of England distribution network for current and future installed capacity of distributed generation. It has been shown that in an unconstrained network, spatial LMP variation (due to losses) is minimal compared to the temporal variation. In a constrained network, a significant increase in LMP volatility was observed, both spatially and temporally. This could bring risk for generators particularly if they become stranded in low price areas, or flexible demands facing a drop-off in return when constraints are removed.", "published": "2018-04-12T14:47:33Z", "authors": ["Calum Edmunds", "Waqquas Bukhsh", "Simon Gill", "Stuart Galloway"], "categories": ["cs.CE"], "doi": "10.1109/ISGTEurope.2018.8571664", "_query": "hedonic house price", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/1504.08073v1", "title": "ESO Valuation with Job Termination Risk and Jumps in Stock Price", "abstract": "Employee stock options (ESOs) are American-style call options that can be terminated early due to employment shock. This paper studies an ESO valuation framework that accounts for job termination risk and jumps in the company stock price. Under general Lévy stock price dynamics, we show that a higher job termination risk induces the ESO holder to voluntarily accelerate exercise, which in turn reduces the cost to the company. The holder's optimal exercise boundary and ESO cost are determined by solving an inhomogeneous partial integro-differential variational inequality (PIDVI). We apply Fourier transform to simplify the variational inequality and develop accurate numerical methods. Furthermore, when the stock price follows a geometric Brownian motion, we provide closed-form formulas for both the vested and unvested perpetual ESOs. Our model is also applied to evaluate the probabilities of understating ESO expenses and contract termination.", "published": "2015-04-30T03:35:49Z", "authors": ["Tim Leung", "Haohua Wan"], "categories": ["q-fin.PR"], "doi": "", "_query": "hedonic house price", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2006.04599v6", "title": "Disparate Impact of Artificial Intelligence Bias in Ridehailing Economy's Price Discrimination Algorithms", "abstract": "Ridehailing applications that collect mobility data from individuals to inform smart city planning predict each trip's fare pricing with automated algorithms that rely on artificial intelligence (AI). This type of AI algorithm, namely a price discrimination algorithm, is widely used in the industry's black box systems for dynamic individualized pricing. Lacking transparency, studying such AI systems for fairness and disparate impact has not been possible without access to data used in generating the outcomes of price discrimination algorithms. Recently, in an effort to enhance transparency in city planning, the city of Chicago regulation mandated that transportation providers publish anonymized data on ridehailing. As a result, we present the first large-scale measurement of the disparate impact of price discrimination algorithms used by ridehailing applications. The application of random effects models from the meta-analysis literature combines the city-level effects of AI bias on fare pricing from census tract attributes, aggregated from the American Community Survey. An analysis of 100 million ridehailing samples from the city of Chicago indicates a significant disparate impact in fare pricing of neighborhoods due to AI bias learned from ridehailing utilization patterns associated with demographic attributes. Neighborhoods with larger non-white populations, higher poverty levels, younger residents, and high education levels are significantly associated with higher fare prices, with combined effect sizes, measured in Cohen's d, of -0.32, -0.28, 0.69, and 0.24 for each demographic, respectively. Further, our methods hold promise for identifying and addressing the sources of disparate impact in AI algorithms learning from datasets that contain U.S. geolocations.", "published": "2020-06-08T13:51:03Z", "authors": ["Akshat Pandey", "Aylin Caliskan"], "categories": ["cs.CY", "cs.AI", "cs.LG"], "doi": "10.1145/3461702.3462561", "_query": "hedonic house price", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2308.04419v1", "title": "Stock Market Price Prediction: A Hybrid LSTM and Sequential Self-Attention based Approach", "abstract": "One of the most enticing research areas is the stock market, and projecting stock prices may help investors profit by making the best decisions at the correct time. Deep learning strategies have emerged as a critical technique in the field of the financial market. The stock market is impacted due to two aspects, one is the geo-political, social and global events on the bases of which the price trends could be affected. Meanwhile, the second aspect purely focuses on historical price trends and seasonality, allowing us to forecast stock prices. In this paper, our aim is to focus on the second aspect and build a model that predicts future prices with minimal errors. In order to provide better prediction results of stock price, we propose a new model named Long Short-Term Memory (LSTM) with Sequential Self-Attention Mechanism (LSTM-SSAM). Finally, we conduct extensive experiments on the three stock datasets: SBIN, HDFCBANK, and BANKBARODA. The experimental results prove the effectiveness and feasibility of the proposed model compared to existing models. The experimental findings demonstrate that the root-mean-squared error (RMSE), and R-square (R2) evaluation indicators are giving the best results.", "published": "2023-08-07T14:21:05Z", "authors": ["Karan Pardeshi", "Sukhpal Singh Gill", "Ahmed M. Abdelmoniem"], "categories": ["cs.AI", "cs.LG"], "doi": "10.1201/9781003467199-11", "_query": "hedonic house price", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2312.16637v3", "title": "Price predictability at ultra-high frequency: Entropy-based randomness test", "abstract": "We use the statistical properties of Shannon entropy estimator and Kullback-Leibler divergence to study the predictability of ultra-high frequency financial data. We develop a statistical test for the predictability of a sequence based on empirical frequencies. We show that the degree of randomness grows with the increase of aggregation level in transaction time. We also find that predictable days are usually characterized by high trading activity, i.e., days with unusually high trading volumes and the number of price changes. We find a group of stocks for which predictability is caused by a frequent change of price direction. We study stylized facts that cause price predictability such as persistence of order signs, autocorrelation of returns, and volatility clustering. We perform multiple testing for sub-intervals of days to identify whether there is predictability at a specific time period during the day.", "published": "2023-12-27T16:48:07Z", "authors": ["Andrey Shternshis", "Stefano Marmi"], "categories": ["q-fin.ST", "q-fin.CP"], "doi": "", "_query": "hedonic house price", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2502.11205v1", "title": "Deep Contrastive Learning for Feature Alignment: Insights from Housing-Household Relationship Inference", "abstract": "Housing and household characteristics are key determinants of social and economic well-being, yet our understanding of their interrelationships remains limited. This study addresses this knowledge gap by developing a deep contrastive learning (DCL) model to infer housing-household relationships using the American Community Survey (ACS) Public Use Microdata Sample (PUMS). More broadly, the proposed model is suitable for a class of problems where the goal is to learn joint relationships between two distinct entities without explicitly labeled ground truth data. Our proposed dual-encoder DCL approach leverages co-occurrence patterns in PUMS and introduces a bisect K-means clustering method to overcome the absence of ground truth labels. The dual-encoder DCL architecture is designed to handle the semantic differences between housing (building) and household (people) features while mitigating noise introduced by clustering. To validate the model, we generate a synthetic ground truth dataset and conduct comprehensive evaluations. The model further demonstrates its superior performance in capturing housing-household relationships in Delaware compared to state-of-the-art methods. A transferability test in North Carolina confirms its generalizability across diverse sociodemographic and geographic contexts. Finally, the post-hoc explainable AI analysis using SHAP values reveals that tenure status and mortgage information play a more significant role in housing-household matching than traditionally emphasized factors such as the number of persons and rooms.", "published": "2025-02-16T17:06:06Z", "authors": ["Xiao Qian", "Shangjia Dong", "Rachel Davidson"], "categories": ["cs.LG", "cs.CY"], "doi": "", "_query": "hedonic house price", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2606.06694v2", "title": "The Geography of Algorithmic Judgment: LLM Intermediaries, Place Identity, and Racial Steering in Housing Search", "abstract": "Large language models (LLMs) are rapidly assuming an intermediary role in housing search through the integration of listing platforms within conversational interfaces, mediating access to information, search, and recommendations within urban settings. We expand on prior work on racial steering in LLMs by conducting a behavioral audit of seven open-weight and closed-source LLMs across four U.S. cities, testing location recommendations across three iterative prompting conditions that progressively add lifestyle preference context and reflect fair housing paired-testing methodologies. We find that steering is an emergent behavior of the model's interpretive license rather than primarily a static property. Steering results from the interaction of a user's identity, preference articulation, and the spatial logic that a model has internalized about learned representations of place, preference, and opportunity in a given city, and how different types of users relate to it. While steering was present, it was not uniform in direction or magnitude across evaluated conditions. Preference-conditioned testing often increased or reconfigured the number of models that exhibited steering behaviors relative to baseline conditions, suggesting that LLMs may interpret what the same housing preference means differently depending on the racial identity of the user. Our findings also demonstrate that the city is not a neutral testing unit for LLM evaluation in place-based sectors, and results from one local market cannot be assumed to generalize to another. Local and domain expertise will be required in the housing sector to ensure that legal and institutional commitments to fair housing are not undermined while adopting AI tools that mediate spatial access.", "published": "2026-06-04T20:17:58Z", "authors": ["Hana Samad", "Trung Lam", "Christoph Mügge-Durum", "Michael Akinwumi"], "categories": ["cs.LG", "cs.AI", "cs.CY"], "doi": "10.1609/aies.v9i4.43303", "_query": "hedonic house price", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2411.05788v1", "title": "News-Driven Stock Price Forecasting in Indian Markets: A Comparative Study of Advanced Deep Learning Models", "abstract": "Forecasting stock market prices remains a complex challenge for traders, analysts, and engineers due to the multitude of factors that influence price movements. Recent advancements in artificial intelligence (AI) and natural language processing (NLP) have significantly enhanced stock price prediction capabilities. AI's ability to process vast and intricate data sets has led to more sophisticated forecasts. However, achieving consistently high accuracy in stock price forecasting remains elusive. In this paper, we leverage 30 years of historical data from national banks in India, sourced from the National Stock Exchange, to forecast stock prices. Our approach utilizes state-of-the-art deep learning models, including multivariate multi-step Long Short-Term Memory (LSTM), Facebook Prophet with LightGBM optimized through Optuna, and Seasonal Auto-Regressive Integrated Moving Average (SARIMA). We further integrate sentiment analysis from tweets and reliable financial sources such as Business Standard and Reuters, acknowledging their crucial influence on stock price fluctuations.", "published": "2024-10-14T15:30:06Z", "authors": ["Kaushal Attaluri", "Mukesh Tripathi", "Srinithi Reddy", " Shivendra"], "categories": ["q-fin.ST", "cs.LG"], "doi": "", "_query": "hedonic house price", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2103.15096v1", "title": "Accurate Stock Price Forecasting Using Robust and Optimized Deep Learning Models", "abstract": "Designing robust frameworks for precise prediction of future prices of stocks has always been considered a very challenging research problem. The advocates of the classical efficient market hypothesis affirm that it is impossible to accurately predict the future prices in an efficiently operating market due to the stochastic nature of the stock price variables. However, numerous propositions exist in the literature with varying degrees of sophistication and complexity that illustrate how algorithms and models can be designed for making efficient, accurate, and robust predictions of stock prices. We present a gamut of ten deep learning models of regression for precise and robust prediction of the future prices of the stock of a critical company in the auto sector of India. Using a very granular stock price collected at 5 minutes intervals, we train the models based on the records from 31st Dec, 2012 to 27th Dec, 2013. The testing of the models is done using records from 30th Dec, 2013 to 9th Jan 2015. We explain the design principles of the models and analyze the results of their performance based on accuracy in forecasting and speed of execution.", "published": "2021-03-28T09:52:29Z", "authors": ["Jaydip Sen", "Sidra Mehtab"], "categories": ["q-fin.ST", "cs.LG", "q-fin.MF"], "doi": "10.1109/CONIT51480.2021.9498565", "_query": "hedonic house price", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/1906.10121v3", "title": "Metaheuristics optimized feedforward neural networks for efficient stock price prediction", "abstract": "The prediction of stock prices is an important task in economics, investment and making financial decisions. This has, for decades, spurred the interest of many researchers to make focused contributions to the design of accurate stock price predictive models; of which some have been utilized to predict the next day opening and closing prices of the stock indices. This paper proposes the design and implementation of a hybrid symbiotic organisms search trained feedforward neural network model for effective and accurate stock price prediction. The symbiotic organisms search algorithm is used as an efficient optimization technique to train the feedforward neural networks, while the resulting training process is used to build a better stock price prediction model. Furthermore, the study also presents a comparative performance evaluation of three different stock price forecasting models; namely, the particle swarm optimization trained feedforward neural network model, the genetic algorithm trained feedforward neural network model and the well-known ARIMA model. The system developed in support of this study utilizes sixteen stock indices as time series datasets for training and testing purpose. Three statistical evaluation measures are used to compare the results of the implemented models, namely the root mean squared error, the mean absolute percentage error and the mean absolution deviation. The computational results obtained reveal that the symbiotic organisms search trained feedforward neural network model exhibits outstanding predictive performance compared to the other models. However, the performance study shows that the three metaheuristics trained feedforward neural network models have promising predictive competence for solving problems of high dimensional nonlinear time series data, which are difficult to capture by traditional models.", "published": "2019-06-23T11:31:52Z", "authors": ["Bradley J. Pillay", "Absalom E. Ezugwu"], "categories": ["q-fin.ST", "cs.LG", "stat.ML"], "doi": "", "_query": "hedonic house price", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2311.09972v4", "title": "Inference in Auctions with Many Bidders Using Transaction Prices", "abstract": "This paper studies inference in first-price and second-price sealed-bid auctions with many bidders, using an asymptotic framework where the number of bidders increases while the number of auctions remains fixed. Our approach enables asymptotically exact inference on key features, such as the winner's expected utility, the seller's expected revenue, and the tail of the valuation distribution, using only transaction price data. Our simulations demonstrate the accuracy of the methods in finite samples. We apply our methods to Hong Kong vehicle license auctions, focusing on high-priced, single-letter plates. Other relevant applications include online and art auctions.", "published": "2023-11-16T15:47:30Z", "authors": ["Federico A. Bugni", "Yulong Wang"], "categories": ["econ.EM"], "doi": "", "_query": "hedonic house price", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2112.03031v1", "title": "Complexity and Persistence of Price Time Series of the European Electricity Spot Market", "abstract": "The large variability of renewable power sources is a central challenge in the transition to a sustainable energy system. Electricity markets are central for the coordination of electric power generation. These markets rely evermore on short-term trading to facilitate the balancing of power generation and demand and to enable systems integration of small producers. Electricity prices in these spot markets show pronounced fluctuations, featuring extreme peaks as well as occasional negative prices. In this article, we analyse electricity price time series from the European EPEX market, in particular the hourly day-ahead, hourly intraday, and 15-min intraday market prices. We quantify the fluctuations, correlations, and extreme events and reveal different time scales in the dynamics of the market. The short-term fluctuations show remarkably different characteristics for time scales below and above 12 hours. Fluctuations are strongly correlated and persistent below 12 hours, which contributes to extreme price events and a strong multifractal behaviour. On longer time scales, they get anti-correlated and price time series revert to their mean, witnessed by a stark decrease of the Hurst coefficient after 12 hours. The long-term behaviour is strongly influenced by the evolution of a large-scale weather patterns with a typical time scale of four days. We elucidate this dependence in detail using a classification into circulation weather types. The separation in time scales enables a superstatistical treatment, which confirms the characteristic time scale of four days, and motivates the use of $q$-Gaussian distributions as the best fit to the empiric distribution of electricity prices.", "published": "2021-12-02T03:07:27Z", "authors": ["Chengyuan Han", "Hannes Hilger", "Eva Mix", "Philipp C. Böttcher", "Mark Reyers", "Christian Beck", "Dirk Witthaut", "Leonardo Rydin Gorjão"], "categories": ["q-fin.ST", "nlin.AO", "physics.soc-ph"], "doi": "", "_query": "hedonic house price", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2506.06446v2", "title": "Tokenization Multiplicity Leads to Arbitrary Price Variation in LLM-as-a-service", "abstract": "Providers of LLM-as-a-service have predominantly adopted a simple pricing model: users pay a fixed price per token. Consequently, one may think that the price two different users would pay for the same output string under the same input prompt is the same. In our work, we show that, surprisingly, this is not (always) true. We find empirical evidence that, particularly for non-english outputs, both proprietary and open-weights LLMs often generate the same (output) string with multiple different tokenizations, even under the same input prompt, and this in turn leads to arbitrary price variation. To address the problem of tokenization multiplicity, we introduce canonical generation, a type of constrained generation that restricts LLMs to only generate canonical tokenizations -- the unique tokenization in which each string is tokenized during the training process of an LLM. Further, we introduce an efficient sampling algorithm for canonical generation based on the Gumbel-Max trick. Experiments on a variety of natural language tasks demonstrate that our sampling algorithm for canonical generation is comparable to standard sampling in terms of performance and runtime, and it solves the problem of tokenization multiplicity.", "published": "2025-06-06T18:09:10Z", "authors": ["Ivi Chatzi", "Nina Corvelo Benz", "Stratis Tsirtsis", "Manuel Gomez-Rodriguez"], "categories": ["cs.CL", "cs.AI", "cs.LG", "stat.ML"], "doi": "", "_query": "hedonic house price", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2410.21109v1", "title": "Dual-Agent Deep Reinforcement Learning for Dynamic Pricing and Replenishment", "abstract": "We study the dynamic pricing and replenishment problems under inconsistent decision frequencies. Different from the traditional demand assumption, the discreteness of demand and the parameter within the Poisson distribution as a function of price introduce complexity into analyzing the problem property. We demonstrate the concavity of the single-period profit function with respect to product price and inventory within their respective domains. The demand model is enhanced by integrating a decision tree-based machine learning approach, trained on comprehensive market data. Employing a two-timescale stochastic approximation scheme, we address the discrepancies in decision frequencies between pricing and replenishment, ensuring convergence to local optimum. We further refine our methodology by incorporating deep reinforcement learning (DRL) techniques and propose a fast-slow dual-agent DRL algorithm. In this approach, two agents handle pricing and inventory and are updated on different scales. Numerical results from both single and multiple products scenarios validate the effectiveness of our methods.", "published": "2024-10-28T15:12:04Z", "authors": ["Yi Zheng", "Zehao Li", "Peng Jiang", "Yijie Peng"], "categories": ["cs.LG", "econ.GN"], "doi": "", "_query": "hedonic house price", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2001.01641v1", "title": "Housing Investment, Stock Market Participation and Household Portfolio choice: Evidence from China's Urban Areas", "abstract": "This paper employs the survey data of CHFS (2013) to investigate the impact of housing investment on household stock market participation and portfolio choice. The results show that larger housing investment encourages the household participation in the stock market, but reduces the proportion of their stockholding. The above conclusion remains true even when the endogeneity problem is controlled with risk attitude classification, Heckman model test and subsample regression. This study shows that the growth in the housing market will not lead to stock market development because of lack of household financial literacy and the low expected yield on stock market.", "published": "2020-01-06T16:07:04Z", "authors": ["Huirong Liu"], "categories": ["econ.GN"], "doi": "", "_query": "hedonic house price", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/1802.08286v1", "title": "Reliability and Market Price of Energy in the Presence of Intermittent and Non-Dispatchable Renewable Energies", "abstract": "The intermittent nature of the renewable energies increases the operation costs of conventional generators. As the share of energy supplied by renewable sources increases, these costs also increase. In this paper, we quantify these costs by developing a market clearing price of energy in the presence of renewable energy and congestion constraints. We consider an electricity market where generators propose their asking price per unit of energy to an independent system operator (ISO). The ISO solve an optimization problem to dispatch energy from each generator to minimize the total cost of energy purchased on behalf of the consumers. To ensure that the generators are able to meet the load within a desired confidence level, we incorporate the notion of load variance using the Conditional Value-at-Risk (CVAR) measure in an electricity market and we derive the amount of committed power and market clearing price of energy as a function of CVAR. It is shown that a higher penetration of renewable energies may increase the committed power, market clearing price of energy and consumer cost of energy due to renewable generation uncertainties. We also obtain an upper-bound on the amount that congestion constraints can affect the committed power. We present descriptive simulations to illustrate the impact of renewable energy penetration and reliability levels on committed power by the non-renewable generators, difference between the dispatched and committed power, market price of energy and profit of renewable and non-renewable generators.", "published": "2018-02-05T19:22:23Z", "authors": ["Ashkan Zeinalzadeh", "Donya Ghavidel", "Vijay Gupta"], "categories": ["eess.SY", "stat.AP"], "doi": "", "_query": "hedonic house price", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2409.00769v2", "title": "Not All Oil Price Shocks Are Alike. A Replication of Kilian (American Economic Review, 2009)", "abstract": "The price of oil can rise because of a disruption to supply or an increase in demand. The nature of the price change determines the dynamic effects. As Kilian (2009) put it: \"not all oil price shocks are alike.\" Using the latest available data, we extend Kilian's (2009) analysis using the R ecosystem and provide more evidence for Kilian's (2009) conclusions. Inference based on unknown conditional heteroskedasticity strengthens the conclusions. With the updated shocks, we assess how a local economy responds to the global oil market, an application that is relevant to policymakers concerned with the transition away from fossil fuels.", "published": "2024-09-01T16:25:24Z", "authors": ["Rich Ryan", "Nyakundi Michieka"], "categories": ["econ.GN"], "doi": "", "_query": "hedonic house price", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/1801.08215v1", "title": "Target volatility option pricing in lognormal fractional SABR model", "abstract": "We examine in this article the pricing of target volatility options in the lognormal fractional SABR model. A decomposition formula by Ito's calculus yields a theoretical replicating strategy for the target volatility option, assuming the accessibilities of all variance swaps and swaptions. The same formula also suggests an approximation formula for the price of target volatility option in small time by the technique of freezing the coefficient. Alternatively, we also derive closed formed expressions for a small volatility of volatility expansion of the price of target volatility option. Numerical experiments show accuracy of the approximations in a reasonably wide range of parameters.", "published": "2018-01-24T22:07:12Z", "authors": ["Elisa Alos", "Rupak Chatterjee", "Sebastian Tudor", "Tai-Ho Wang"], "categories": ["q-fin.CP", "q-fin.MF"], "doi": "", "_query": "hedonic house price", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/1502.00674v3", "title": "An equilibrium model for spot and forward prices of commodities", "abstract": "We consider a market model that consists of financial investors and producers of a commodity. Producers optionally store some production for future sale and go short on forward contracts to hedge the uncertainty of the future commodity price. Financial investors take positions in these contracts in order to diversify their portfolios. The spot and forward equilibrium commodity prices are endogenously derived as the outcome of the interaction between producers and investors. Assuming that both are utility maximizers, we first prove the existence of an equilibrium in an abstract setting. Then, in a framework where the consumers' demand and the exogenously priced financial market are correlated, we provide semi-explicit expressions for the equilibrium prices and analyze their dependence on the model parameters. The model can explain why increased investors' participation in forward commodity markets and higher correlation between the commodity and the stock market could result in higher spot prices and lower forward premia.", "published": "2015-02-02T22:26:21Z", "authors": ["Michail Anthropelos", "Michael Kupper", "Antonis Papapantoleon"], "categories": ["econ.GN", "math.OC", "q-fin.PR"], "doi": "", "_query": "hedonic house price", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/1005.1917v1", "title": "Two-sided estimates for stock price distribution densities in jump-diffusion models", "abstract": "We consider uncorrelated Stein-Stein, Heston, and Hull-White models and their perturbations by compound Poisson processes with jump amplitudes distributed according to a double exponential law. Similar perturbations of the Black-Scholes model were studied by S. Kou. For perturbed stochastic volatility models, we obtain two-sided estimates for the stock price distribution density and compare the tail behavior of this density before and after perturbation. It is shown that if the value of the parameter, characterizing the right tail of the double exponential law, is small, then the stock price density in the perturbed model decays slower than the density in the original model. On the other hand, if the value of this parameter is large, then there are no significant changes in the behavior of the stock price distribution density.", "published": "2010-05-11T19:27:18Z", "authors": ["Archil Gulisashvili", "Josep Vives"], "categories": ["q-fin.GN"], "doi": "", "_query": "hedonic house price", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2411.19763v1", "title": "Forecasting Foreign Exchange Market Prices Using Technical Indicators with Deep Learning and Attention Mechanism", "abstract": "Accurate prediction of price behavior in the foreign exchange market is crucial. This paper proposes a novel approach that leverages technical indicators and deep neural networks. The proposed architecture consists of a Long Short-Term Memory (LSTM) and Convolutional Neural Network (CNN), and attention mechanism. Initially, trend and oscillation technical indicators are employed to extract statistical features from Forex currency pair data, providing insights into price trends, market volatility, relative price strength, and overbought and oversold conditions. Subsequently, the LSTM and CNN networks are utilized in parallel to predict future price movements, leveraging the strengths of both recurrent and convolutional architectures. The LSTM network captures long-term dependencies and temporal patterns in the data, while the CNN network extracts local patterns. The outputs of the parallel LSTM and CNN networks are then fed into an attention mechanism, which learns to weigh the importance of each feature and temporal dependency, generating a context-aware representation of the input data. The attention-weighted output is then used to predict future price movements, enabling the model to focus on the most relevant features and temporal dependencies. Through a comprehensive evaluation of the proposed approach on multiple Forex currency pairs, we demonstrate its effectiveness in predicting price behavior and outperforming benchmark models.", "published": "2024-11-29T15:07:44Z", "authors": ["Sahabeh Saadati", "Mohammad Manthouri"], "categories": ["cs.LG", "cs.AI"], "doi": "", "_query": "hedonic house price", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/1610.08143v1", "title": "Optimal Risk-Averse Timing of an Asset Sale: Trending vs Mean-Reverting Price Dynamics", "abstract": "This paper studies the optimal risk-averse timing to sell a risky asset. The investor's risk preference is described by the exponential, power, or log utility. Two stochastic models are considered for the asset price -- the geometric Brownian motion and exponential Ornstein-Uhlenbeck models -- to account for, respectively, the trending and mean-reverting price dynamics. In all cases, we derive the optimal thresholds and certainty equivalents to sell the asset, and compare them across models and utilities, with emphasis on their dependence on asset price, risk aversion, and quantity. We find that the timing option may render the investor's value function and certainty equivalent non-concave in price. Numerical results are provided to illustrate the investor's strategies and the premium associated with optimally timing to sell.", "published": "2016-10-26T01:55:13Z", "authors": ["Tim Leung", "Zheng Wang"], "categories": ["q-fin.MF", "q-fin.RM", "q-fin.TR"], "doi": "", "_query": "hedonic house price", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/1306.1116v1", "title": "Instability and bifurcation in a trend depending price formation model", "abstract": "A well-known model due to J.-M. Lasry and P.L. Lions that presents the evolution of prices in a market as the evolution of a free boundary in a diffusion equation is suggested to be modified in order to show instabilities for some values of the parameters. This loss of stability is associated to the appearance of new types of solutions, namely periodic solutions, due to a Hopf bifurcation and representing price oscillations, and traveling waves, that represent either inflationary or deflationary behavior.", "published": "2013-06-05T14:23:05Z", "authors": ["María del Mar González", "Maria Pia Gualdani", "Joan Solà-Morales"], "categories": ["math.AP"], "doi": "", "_query": "hedonic house price", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2509.24879v3", "title": "Pixels to Prices: Visual Traits, Market Cycles, and the Economics of NFT Valuation", "abstract": "Pixels and market cycles both move NFT prices. Non-fungible tokens (NFTs) are unique digital assets, often used to represent ownership of digital art, collectibles, and other media, secured on blockchain networks like Ethereum. The rise of NFTs has led to the creation of a multi-billion-dollar market for digital art and collectibles, making it a key area of interest for researchers, artists, and investors. Using 94,039 transactions from 26 major generative Ethereum collections, this study extracts 196 machine-quantified image descriptors - color, composition, palette structure, geometry, texture, and deep-learning embeddings - and applies a three-stage filter to identify stable predictors for hedonic regression. A static mixed-effects model shows that market sentiment and transparent, interpretable image traits have significant and independent pricing power: higher focal saturation, tighter compositional concentration, and greater curvature are rewarded, while clutter, heavy line work, and dispersed palettes are discounted; deep embeddings add limited incremental value once explicit traits are included. To assess state dependence, a Bayesian dynamic mixed-effects panel with cycle effects is estimated, allowing Composition Focus - Saturation - the ratio of saturation in the central region to the whole image, capturing vividness and concentration at the focal area - to vary across market regimes. Collection-level heterogeneity (brand premia) is absorbed by random effects. The time-varying coefficients exhibit clear regime sensitivity, with stronger premia in expansionary phases and weaker or negative loadings in downturns, while the grand-mean effect is small on average. Overall, NFT prices reflect both observable digital product characteristics and market regimes, and the framework offers a cycle-aware tool for asset pricing, platform strategy, and market design in digital art markets.", "published": "2025-09-29T14:56:40Z", "authors": ["Samiha Tariq"], "categories": ["econ.GN"], "doi": "", "_query": "hedonic house price", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/1804.00048v1", "title": "Convex Hull, IP and European Electricity Pricing in a European Power Exchanges setting with efficient computation of Convex Hull Prices", "abstract": "This paper introduces a computationally efficient comparative approach to classical pricing rules for day-ahead electricity markets, namely Convex Hull Pricing, IP Pricing and European-like market rules, in a Power Exchange setting with non-convex demand bids. These demand bids can, for example, be useful to large industrial consumers, and extend demand block orders in use by European Power Exchanges. For this purpose, we show that Convex Hull Prices can be efficiently computed using continuous relaxations for bidding products involving start-up costs, minimum power output levels and ramp constraints, or analogous versions on the demand side. Relying on existing efficient algorithmic approaches to handle European-like market rules for such bidding products, we provide comparative numerical experiments using realistic data, which, together with stylized examples, elucidates the relative merits of each pricing rule from economic and computational perspectives. The motivation for this work is the prospective need for mid-term evolution of day-ahead markets in Europe and in the US, as well as the importance of day-ahead price signals, since these (spot) prices are used as reference prices for many power derivatives. The datasets, models and algorithms programmed in Julia/JuMP are provided in an online Git repository.", "published": "2018-03-30T20:32:41Z", "authors": ["Mehdi Madani", "Carlos Ruiz", "Sauleh Siddiqui", "Mathieu Van Vyve"], "categories": ["math.OC"], "doi": "", "_query": "hedonic house price", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/0801.0003v2", "title": "Short-time behaviour of demand and price viewed through an exactly solvable model for heterogeneous interacting market agents", "abstract": "We introduce a stochastic heterogeneous interacting-agent model for the short-time non-equilibrium evolution of excess demand and price in a stylized asset market. We consider a combination of social interaction within peer groups and individually heterogeneous fundamentalist trading decisions which take into account the market price and the perceived fundamental value of the asset. The resulting excess demand is coupled to the market price. Rigorous analysis reveals that this feedback may lead to price oscillations, a single bounce, or monotonic price behaviour. The model is a rare example of an analytically tractable interacting-agent model which allows us to deduce in detail the origin of these different collective patterns. For a natural choice of initial distribution the results are independent of the graph structure that models the peer network of agents whose decisions influence each other.", "published": "2007-12-28T21:01:30Z", "authors": ["Gunter M. Schütz", "Fernando Pigeard de Almeida Prado", "Rosemary J. Harris", "Vladimir Belitsky"], "categories": ["q-fin.GN", "cond-mat.stat-mech", "physics.soc-ph"], "doi": "10.1016/j.physa.2009.06.025", "_query": "hedonic house price", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2502.06080v1", "title": "Compounding Effect of Harsh Climate and Societal Disruptions on Food Prices in Early Modern Europe", "abstract": "The complex interplay between famine, warfare, and climate constitutes a multifaceted and context-dependent relationship that has profoundly influenced human history, particularly in early modern Europe. This study advances the literature on climate-economy interactions by leveraging multi-scale statistical techniques to quantify the compounded effects of climate variability and socio-political factors on food prices, offering novel model-based insights into the historical dynamics of climate and economic systems. Using Canonical Correlation Analysis (CCA), we investigate the influence of temperature fluctuations and drought severity on food prices across 14 European cities from 1565 to 1785. Our findings confirm a persistent negative relationship between temperature and food prices over the long term, while the relationship between drought severity and price dynamics appears positive yet inconsistent. Extending our analysis to higher-frequency patterns, we demonstrate that cold anomalies are strongly associated with food price that caused large-scale famines of the 1590s and 1690s. Likewise, we show that the severe and consecutive droughts of 1634 to 1636, coinciding with the Thirty Years' War, significantly amplified food price volatility, illustrating how climatic shocks can compound socio-economic and political crises. Furthermore, we identify years characterized by the simultaneous occurrence of extreme cold and drought as periods of heightened price instability, underscoring the compounded impact of concurrent climatic stressors on food prices during the early modern period.", "published": "2025-02-10T00:30:18Z", "authors": ["Emile Esmaili", "Michael J. Puma", "Francis Ludlow", "Eva Jobbova"], "categories": ["econ.GN"], "doi": "", "_query": "hedonic house price", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2106.16177v3", "title": "\"Stabilizer\" or \"catalyst\"? How green technology innovation affects the risk of stock price crashes: an analysis based on the quantity and quality of patents", "abstract": "To explore the relationship between corporate green technological innovation and the risk of stock price crashes, we first analyzed the data of listed companies in China from 2008 to 2018 and constructed indicators for the quantity and quality of corporate green technology innovation. The study found that the quantity of green technology innovation is not related to the risk of stock price crashes, while the quality of green technology innovation is negatively related to the risk of stock price crashes. Second, we studied the impact of corporate ownership on the relationship between the quality of green technological innovation and the risk of stock price crashes and found that in nonstate-owned enterprises, the quality of green technological innovation is negatively correlated with the risk of a stock price collapse, while in state-owned enterprises, the quality of green technological innovation and the risk of a stock price collapse are positive and not significant. Furthermore, we studied the mediating effect of the number of negative news reports in the media of listed companies on the relationship between the quality of corporate green technology innovation and the stock price crash and found that the quality of green technology innovation is positively correlated with the number of negative news reports in the media of listed companies, while the number of negative news reports in the media of listed companies is positively correlated with the risk of a stock price collapse. Finally, we conducted a DID regression by using the impact of exogenous policy shocks on the quality of green technology innovation, and the main results passed the robustness test.", "published": "2021-06-30T16:12:19Z", "authors": ["Ge-zhi Wu", "Da-ming You"], "categories": ["econ.GN"], "doi": "", "_query": "hedonic house price", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2208.09898v1", "title": "Fair pricing and hedging under small perturbations of the numéraire on a finite probability space", "abstract": "We consider the problem of fair pricing and hedging under small perturbations of the numéraire. We show that for replicable claims, the change of numéraire affects neither the fair price nor the hedging strategy. For non-replicable claims, we demonstrate that is not the case. By reformulating the key stochastic control problem in a more tractable form, we show that both the fair price and optimal strategy are stable with respect to small perturbations of the numéraire. Further, our approach allows for explicit asymptotic formulas describing the fair price and hedging strategy's leading order correction terms. Mathematically, our results constitute stability and asymptotic analysis of a stochastic control problem under certain perturbations of the integrator of the controlled process, where constraints make this problem hard to analyze.", "published": "2022-08-21T14:53:52Z", "authors": ["William Busching", "Delphine Hintz", "Oleksii Mostovyi", "Alexey Pozdnyakov"], "categories": ["q-fin.PR", "q-fin.PM"], "doi": "", "_query": "hedonic house price", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/cond-mat/0403022v2", "title": "A Non-Gaussian Option Pricing Model with Skew", "abstract": "Closed form option pricing formulae explaining skew and smile are obtained within a parsimonious non-Gaussian framework. We extend the non-Gaussian option pricing model of L. Borland (Quantitative Finance, {\\bf 2}, 415-431, 2002) to include volatility-stock correlations consistent with the leverage effect. A generalized Black-Scholes partial differential equation for this model is obtained, together with closed-form approximate solutions for the fair price of a European call option. In certain limits, the standard Black-Scholes model is recovered, as is the Constant Elasticity of Variance (CEV) model of Cox and Ross. Alternative methods of solution to that model are thereby also discussed. The model parameters are partially fit from empirical observations of the distribution of the underlying. The option pricing model then predicts European call prices which fit well to empirical market data over several maturities.", "published": "2004-02-29T22:06:05Z", "authors": ["L. Borland", "J. P. Bouchaud"], "categories": ["cond-mat.other", "q-fin.PR"], "doi": "", "_query": "hedonic house price", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2603.13599v2", "title": "Dynamic Wholesale Pricing under Censored-Demand Learning", "abstract": "This paper studies dynamic wholesale pricing and ordering in a two-tier supply chain where firms share POS data and learn about demand from censored demand data. When stockouts occur, unmet demand is unobserved, so the retailer's order quantity affects not only current profits but also the informativeness of future demand signals. This creates a strategic interaction between pricing, ordering, and learning: the manufacturer can influence the pace of learning through wholesale prices, whereas the retailer internalizes the effect of inventory decisions on future information. We analyze a finite-horizon dynamic game in which a manufacturer sets a wholesale price, the retailer then chooses an order quantity, demand is realized, and both firms observe sales. For Weibull demand with a conjugate prior, we extend a dimensionality-reduction approach from single-agent inventory learning models to a strategic supply-chain setting and use it to establish the existence of a Markov perfect equilibrium. For exponential demand, we further show that the equilibrium is unique and admits a recursive characterization. Our numerical analysis shows that public learning can create conflicting incentives in the supply chain: In order to induce larger orders and reduce future censoring, the manufacturer chooses a wholesale price that is lower than a myopic benchmark. By contrast, because of its forward-looking ordering incentive, the retailer may prefer slower learning to avoid strengthening the manufacturer's future wholesale-pricing position.", "published": "2026-03-13T21:17:59Z", "authors": ["Michalis Deligiannis", "Marco Scarsini", "Xavier Venel"], "categories": ["cs.GT", "econ.TH", "math.OC"], "doi": "", "_query": "hedonic house price", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2202.00783v1", "title": "Modeling ventilation in a low-income house in Dhaka, Bangladesh", "abstract": "According to UNICEF, pneumonia is the leading cause of death in children under 5. 70% of worldwide pneumonia deaths occur in only 15 countries, including Bangladesh. Previous research has indicated a potential association between the incidence of pneumonia and the presence of cross-ventilation in slum housing in Dhaka, Bangladesh. The objective of this research is to establish a validated computational framework that can predict ventilation rates in slum homes to support further studies investigating this correlation. To achieve this objective we employ a building thermal model (BTM) in combination with uncertainty quantification (UQ). The BTM solves for the time-evolution of volume-averaged temperatures in a typical home, considering different ventilation configurations. The UQ method propagates uncertainty in model parameters, weather inputs, and physics models to predict mean values and 95% confidence intervals for the quantities of interest, namely temperatures and ventilation rates in terms of air changes per hour (ACH). The model predictions are compared to on-site field measurements of air and thermal mass temperatures, and of ACH. The results indicate that the use of standard cross- or single-sided ventilation models limits the accuracy of the ACH predictions; in contrast, a model based on a similarity relationship informed by the available ACH measurements can produce more accurate predictions with confidence intervals that encompass the measurements for 12 of the 17 available data points.", "published": "2022-01-30T05:33:21Z", "authors": ["Yunjae Hwang", " Laura", " Kwong", "Mohammad Saeed Munim", "Fosiul Alam Nizame", "Stephen Luby", "Catherine Gorlé"], "categories": ["cs.CE"], "doi": "", "_query": "hedonic house price", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2607.16622v1", "title": "Proof-of-Stake Dynamics: The Elusive Price Anchor and Endogenous Volatility Harvesting", "abstract": "In this paper, we develop an open-economy macroeconomic model of a Proof-of-Stake network to analyze nominal token-price dynamics and the systemic effects of speculative capital. We first consider a network populated solely by active utility users, who finance network activity through a steady exogenous inflow of fiat currency. We prove the existence of a unique, globally asymptotically stable steady-state equilibrium with a well-defined nominal token price and derive a closed-form expression for the network's relaxation time. Calibrating the model using parameters representative of the current Ethereum network, we estimate a relaxation half-life of approximately 46 years. This extreme macroeconomic inertia implies that the token price may remain persistently displaced from its evolving steady-state benchmark, producing sustained price overshooting as the network adjusts to changing fundamentals. We then introduce an Investor class to examine the effects of passive and active speculative capital. We show that passive institutional staking compresses the native staking yield and creates a structural imbalance that systematically raises the nominal token price while shifting consensus ownership away from active utility users. Active speculative capital has a qualitatively different effect. In response to capital shocks, the Consumer class's rigid preference for fiat-denominated consumption generates an endogenous constant-value strategy. This mechanism shifts staked-token ownership from the Investor class toward active utility users, with potentially favorable implications for consensus decentralization.", "published": "2026-07-18T03:47:16Z", "authors": ["Mikhail Perepelitsa"], "categories": ["econ.GN", "q-fin.MF", "q-fin.TR"], "doi": "", "_query": "hedonic house price", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/1612.06665v2", "title": "Subdiffusive fractional Brownian motion regime for pricing currency options under transaction costs", "abstract": "A new framework for pricing the European currency option is developed in the case where the spot exchange rate fellows a time-changed fractional Brownian motion. An analytic formula for pricing European foreign currency option is proposed by a mean self-financing delta-hedging argument in a discrete time setting. The minimal price of a currency option under transaction costs is obtained as time-step $Δt=\\left(\\frac{t^{β-1}}{Γ(β)}\\right)^{-1}\\left(\\frac{2}π\\right)^{\\frac{1}{2H}}\\left(\\fracασ\\right)^{\\frac{1}{H}}$ , which can be used as the actual price of an option. In addition, we also show that time-step and long-range dependence have a significant impact on option pricing.", "published": "2016-12-20T14:00:15Z", "authors": ["Foad Shokrollahi"], "categories": ["q-fin.PR"], "doi": "", "_query": "hedonic house price", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2109.02872v2", "title": "Moment Matching Method for Pricing Spread Options with Mean-Variance Mixture Lévy Motions", "abstract": "The paper Borovkova et al. [4] uses moment matching method to obtain closed form formulas for spread and basket call option prices under log normal models. In this note, we also use moment matching method to obtain semi-closed form formulas for the price of spread options under exponential Lévy models with mean-variance mixture. Unlike the semi-closed form formulas in Caldana and Fusai [5], where spread prices were expressed by using Fourier inversion formula for general price dynamics, our formula expresses spread prices in terms of the mixing distribution. Numerical tests show that our formulas give accurate spread prices also", "published": "2021-09-07T05:16:05Z", "authors": ["Dongdong Hu", "Hasanjan Sayit", "Svetlozar T. Rachev"], "categories": ["q-fin.PR"], "doi": "", "_query": "hedonic house price", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2308.04947v1", "title": "Methods for Acquiring and Incorporating Knowledge into Stock Price Prediction: A Survey", "abstract": "Predicting stock prices presents a challenging research problem due to the inherent volatility and non-linear nature of the stock market. In recent years, knowledge-enhanced stock price prediction methods have shown groundbreaking results by utilizing external knowledge to understand the stock market. Despite the importance of these methods, there is a scarcity of scholarly works that systematically synthesize previous studies from the perspective of external knowledge types. Specifically, the external knowledge can be modeled in different data structures, which we group into non-graph-based formats and graph-based formats: 1) non-graph-based knowledge captures contextual information and multimedia descriptions specifically associated with an individual stock; 2) graph-based knowledge captures interconnected and interdependent information in the stock market. This survey paper aims to provide a systematic and comprehensive description of methods for acquiring external knowledge from various unstructured data sources and then incorporating it into stock price prediction models. We also explore fusion methods for combining external knowledge with historical price features. Moreover, this paper includes a compilation of relevant datasets and delves into potential future research directions in this domain.", "published": "2023-08-09T13:28:00Z", "authors": ["Liping Wang", "Jiawei Li", "Lifan Zhao", "Zhizhuo Kou", "Xiaohan Wang", "Xinyi Zhu", "Hao Wang", "Yanyan Shen", "Lei Chen"], "categories": ["q-fin.ST", "cs.AI", "cs.LG"], "doi": "", "_query": "hedonic house price", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2210.15453v1", "title": "Approximate Pricing of Derivatives Under Fractional Stochastic Volatility Model", "abstract": "We investigate the problem of pricing derivatives under a fractional stochastic volatility model. We obtain an approximate expression of the derivative price where the stochastic volatility can be composed of deterministic functions of time and fractional Ornstein-Uhlenbeck process. Numerical simulations are given to illustrate the feasibility and operability of the approximation, and also demonstrate the effect of long-range on derivative prices.", "published": "2022-10-21T02:31:49Z", "authors": ["Yuecai Han", "Xudong Zheng"], "categories": ["q-fin.PR", "math.PR"], "doi": "", "_query": "hedonic house price", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/1401.3261v2", "title": "General indifference pricing with small transaction costs", "abstract": "We study the utility indifference price of a European option in the context of small transaction costs. Considering the general setup allowing consumption and a general utility function at final time T, we obtain an asymptotic expansion of the utility indifference price as a function of the asymptotic expansions of the utility maximization problems with and without the European contingent claim. We use the tools developed in [54] and [48] based on homogenization and viscosity solutions to characterize these expansions. Finally we study more precisely the example of exponential utilities, in particular recovering under weaker assumptions the results of [6].", "published": "2014-01-14T17:15:04Z", "authors": ["Dylan Possamaï", "Guillaume Royer"], "categories": ["math.OC", "math.AP", "q-fin.PM"], "doi": "", "_query": "hedonic house price", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2003.09390v1", "title": "A Regression Analysis Study Examining the Price of Drones", "abstract": "Objective of this study is to examine the impact of twelve specifications namely No. of Rotors, Max Flight Time(min.), Operating Range (m), Wingspan(mm), Weight(g), Payload Capacity(g), Max Speed(m/s), Max Flying Altitude(m), GPS Compatibility, Autonomous Flight, Collision Avoidance, Flight Battery (mAh) on the price of the drones. Using Stepwise Multiple Linear Regression analysis, our study shows that at α=0.05; Max Flight Time (min.) (p-value=<0.001), Wingspan (mm) (p-value=<0.001), and Autonomous Flight capability (p-value=0.006) are the significant impact factors, while GPS Compatibility (p-value=0.057), and Collision Avoidance (p-value=0.077) are marginally significant factors affecting the price of drones.", "published": "2020-03-18T16:44:23Z", "authors": ["Burchan Aydin", "Subroto Singha"], "categories": ["eess.SP"], "doi": "", "_query": "hedonic house price", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/cs/0102003v1", "title": "Fast Pricing of European Asian Options with Provable Accuracy: Single-stock and Basket Options", "abstract": "This paper develops three polynomial-time pricing techniques for European Asian options with provably small errors, where the stock prices follow binomial trees or trees of higher-degree. The first technique is the first known Monte Carlo algorithm with analytical error bounds suitable for pricing single-stock options with meaningful confidence and speed. The second technique is a general recursive bucketing-based scheme that can use the Aingworth-Motwani-Oldham aggregation algorithm, Monte-Carlo simulation and possibly others as the base-case subroutine. This scheme enables robust trade-offs between accuracy and time over subtrees of different sizes. For long-term options or high frequency price averaging, it can price single-stock options with smaller errors in less time than the base-case algorithms themselves. The third technique combines Fast Fourier Transform with bucketing-based schemes for pricing basket options. This technique takes polynomial time in the number of days and the number of stocks, and does not add any errors to those already incurred in the companion bucketing scheme. This technique assumes that the price of each underlying stock moves independently.", "published": "2001-02-02T20:52:36Z", "authors": ["Karhan Akcoglu", "Ming-Yang Kao", "Shuba Raghavan"], "categories": ["cs.CE"], "doi": "", "_query": "hedonic house price", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2410.00063v1", "title": "Uniform price auctions with pre-announced revenue targets: Evidence from China's SEOs", "abstract": "This study explores the performance of auctions in China's seasoned equity offering (SEO) market, both theoretically and empirically. In these auctions, issuers must commit to a pre-announced revenue target and a maximum number of shares available for auction. We use a common value framework to analyze this auction mechanism, detailing its operation, share allocation, and pricing. The theoretical findings suggest that when buyers bid truthfully, the seller's optimal strategy is to set the total share quantity equal to the target revenue divided by the reserve price. We demonstrate that committing to a target revenue results in a higher level of truthful bidding compared to a standard uniform-price auction without any revenue commitment. We empirically test our theoretical findings using data from China's SEO markets. First, we assess the impact of various issuer strategies on firm-level SEO discounts, categorizing scenarios based on share availability and target revenue. We find that the scenario where the reserve price times the share quantity matches the target revenue is the most optimal for sellers. Second, we examine bidding behavior and auction performance, showing that China's SEO uniform price auction performs exceptionally well. Specifically, the actual issue prices are only 0.029 below the truthful case prices, indicating that the revenue raised is still close to what would have been achieved with truthful bids.", "published": "2024-09-29T23:49:17Z", "authors": ["Shenghao Gao", "Peyman Khezr", "Armin Pourkhanali"], "categories": ["econ.GN"], "doi": "", "_query": "hedonic house price", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/1802.09954v4", "title": "Price Impact Under Heterogeneous Beliefs and Restricted Participation", "abstract": "We consider a financial market in which traders potentially face restrictions in trading some of the available securities. Traders are heterogeneous with respect to their beliefs and risk profiles, and the market is assumed thin: traders strategically trade against their price impacts. We prove existence and uniqueness of a corresponding equilibrium, and provide an efficient algorithm to numerically obtain the equilibrium prices and allocations given market's inputs. We find that restrictions may increase the market's welfare if traders have different views regarding the covariance matrix of securities returns. The latter heterogeneity regarding covariance matrix disagreement is essential in modelling; for instance, when traders agree on the covariance matrix, restricting participation in some securities for some traders leaves equilibrium prices unaltered in the unrestricted securities, a certainly undesirable model effect.", "published": "2018-02-27T15:07:19Z", "authors": ["Michail Anthropelos", "Constantinos Kardaras"], "categories": ["econ.GN"], "doi": "", "_query": "hedonic house price", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/1308.5019v1", "title": "A Taylor series approach to pricing and implied vol for LSV models", "abstract": "Using classical Taylor series techniques, we develop a unified approach to pricing and implied volatility for European-style options in a general local-stochastic volatility setting. Our price approximations require only a normal CDF and our implied volatility approximations are fully explicit (ie, they require no special functions, no infinite series and no numerical integration). As such, approximate prices can be computed as efficiently as Black-Scholes prices, and approximate implied volatilities can be computed nearly instantaneously.", "published": "2013-08-22T23:58:48Z", "authors": ["Matthew Lorig", "Stefano Pagliarani", "Andrea Pascucci"], "categories": ["q-fin.CP"], "doi": "", "_query": "hedonic house price", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/1201.5621v2", "title": "Fixed and Market Pricing for Cloud Services", "abstract": "We study a model of congestible resources, where pricing and scheduling are intertwined. Motivated by the problem of pricing cloud instances, we model a cloud computing service as linked $GI/GI/\\cdot$ queuing systems where the provider chooses to offer a fixed pricing service, a dynamic market based service, or a hybrid of both, where jobs can be preempted in the market-based service. Users (jobs), who are heterogeneous in both the value they place on service and their cost for waiting, then choose between the services offered. Combining insights from auction theory with queuing theory we are able to characterize user equilibrium behavior, and show its insensitivity to the precise market design mechanism used. We then provide theoretical and simulation based evidence suggesting that a fixed price typically, though not always, generates a higher expected revenue than the hybrid system for the provider.", "published": "2012-01-26T19:59:45Z", "authors": ["Vineet Abhishek", "Ian A. Kash", "Peter Key"], "categories": ["cs.GT"], "doi": "", "_query": "hedonic house price", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2608.14760v1", "title": "The Price of Submission", "abstract": "Economics journals ration access to peer review with time and money. A July 2026 census of 500 RePEc-ranked economics journals accepting unsolicited submissions finds that 113 (22.6 per cent) charge a fee, with incidence falling from 68 per cent in the top 50 to 2 per cent in the bottom 50. A sequential-submission model shows that waiting pays part of the capacity-clearing price, leaving the fee as a residual. Because rejected manuscripts move down the ranking, a fee reduces system-wide referee demand only when authors stop, shorten their submission chains or switch to journals requiring fewer reports.", "published": "2026-08-14T09:24:59Z", "authors": ["Johan Fourie"], "categories": ["econ.GN"], "doi": "", "_query": "hedonic house price", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/1403.5302v1", "title": "Asymptotic analysis of stock price densities and implied volatilities in mixed stochastic models", "abstract": "In this paper, we obtain sharp asymptotic formulas with error estimates for the Mellin convolution of functions, and use these formulas to characterize the asymptotic behavior of marginal distribution densities of stock price processes in mixed stochastic models. Special examples of mixed models are jump-diffusion models and stochastic volatility models with jumps. We apply our general results to the Heston model with double exponential jumps, and make a detailed analysis of the asymptotic behavior of the stock price density, the call option pricing function, and the implied volatility in this model. We also obtain similar results for the Heston model with jumps distributed according to the NIG law.", "published": "2014-03-20T21:28:21Z", "authors": ["Archil Gulisashvili", "Josep Vives"], "categories": ["q-fin.PR"], "doi": "", "_query": "hedonic house price", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2512.20932v2", "title": "Guardrailed Elasticity Pricing: A Churn-Aware Forecasting Playbook for Subscription Strategy", "abstract": "This paper presents a marketing analytics framework that operationalizes subscription pricing as a dynamic, guardrailed decision system, uniting multivariate demand forecasting, segment-level price elasticity, and churn propensity to optimize revenue, margin, and retention. The approach blends seasonal time-series models with tree-based learners, runs Monte Carlo scenario tests to map risk envelopes, and solves a constrained optimization that enforces business guardrails on customer experience, margin floors, and allowable churn. Validated across heterogeneous SaaS portfolios, the method consistently outperforms static tiers and uniform uplifts by reallocating price moves toward segments with higher willingness-to-pay while protecting price-sensitive cohorts. The system is designed for real-time recalibration via modular APIs and includes model explainability for governance and compliance. Managerially, the framework functions as a strategy playbook that clarifies when to shift from flat to dynamic pricing, how to align pricing with CLV and MRR targets, and how to embed ethical guardrails, enabling durable growth without eroding customer trust.", "published": "2025-12-24T04:25:31Z", "authors": ["Deepit Sapru"], "categories": ["cs.LG", "cs.AI"], "doi": "10.1109/ESIC68176.2026.11496127", "_query": "hedonic house price", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/cond-mat/0406696v1", "title": "Short-term market reaction after extreme price changes of liquid stocks", "abstract": "In our empirical study, we examine the price of liquid stocks after experiencing a large intraday price change using data from the NYSE and the NASDAQ. We find significant reversal for both intraday price decreases and increases. The results are stable against varying parameters. While on the NYSE the large widening of the bid-ask spread eliminates most of the profits that can be achieved by a contrarian strategy, on the NASDAQ the bid-ask spread stays almost constant yielding significant short-term abnormal profits. Furthermore, volatility, volume, and in case of the NYSE the bid-ask spread, which increase sharply at the event, decay according to a power-law and stay significantly high over days afterwards.", "published": "2004-06-28T19:36:00Z", "authors": ["Adam G. Zawadowski", "Gyorgy Andor", "Janos Kertesz"], "categories": ["cond-mat.other", "q-fin.ST"], "doi": "", "_query": "hedonic house price", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/1604.08824v1", "title": "A new structural stochastic volatility model of asset pricing and its stylized facts", "abstract": "Building on a prominent agent-based model, we present a new structural stochastic volatility asset pricing model of fundamentalists vs. chartists where the prices are determined based on excess demand. Specifically, this allows for modelling stochastic interactions between agents, based on a herding process corrected by a price misalignment, and incorporating strong noise components in the agents' demand. The model's parameters are estimated using the method of simulated moments, where the moments reflect the basic properties of the daily returns of a stock market index. In addition, for the first time we apply a (parametric) bootstrap method in a setting where the switching between strategies is modelled using a discrete choice approach. As we demonstrate, the resulting dynamics replicate a rich set of the stylized facts of the daily financial data including: heavy tails, volatility clustering, long memory in absolute returns, as well as the absence of autocorrelation in raw returns, volatility-volume correlations, aggregate Gaussianity, concave price impact and extreme price events.", "published": "2016-04-29T13:36:37Z", "authors": ["Radu T. Pruna", "Maria Polukarov", "Nicholas R. Jennings"], "categories": ["econ.GN", "q-fin.ST"], "doi": "", "_query": "hedonic house price", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2104.04817v5", "title": "Limit theorems for prices of options written on semi-Markov processes", "abstract": "We consider plain vanilla European options written on an underlying asset that follows a continuous time semi-Markov multiplicative process. We derive a formula and a renewal type equation for the martingale option price. In the case in which intertrade times follow the Mittag-Leffler distribution, under appropriate scaling, we prove that these option prices converge to the price of an option written on geometric Brownian motion time-changed with the inverse stable subordinator. For geometric Brownian motion time changed with an inverse subordinator, in the more general case when the subordinator's Laplace exponent is a special Bernstein function, we derive a time-fractional generalization of the equation of Black and Scholes.", "published": "2021-04-10T17:02:46Z", "authors": ["Enrico Scalas", "Bruno Toaldo"], "categories": ["math.PR"], "doi": "", "_query": "hedonic house price", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/1501.03701v1", "title": "A New Approach to Model Free Option Pricing", "abstract": "In this paper we introduce a new approach to model-free path-dependent option pricing. We first introduce a general duality result for linear optimisation problems over signed measures introduced in [3] and show how the the problem of model-free option pricing can be formulated in the new framework. We then introduce a model to solve the problem numerically when the only information provided is the market data of vanilla call or put option prices. Compared to the common approaches in the literature, e.g. [4], the model does not require the marginal distributions of the stock price for different maturities. Though the experiments are carried out for simple path-dependent options on a single stock, the model is easy to generalise for multi-asset framework.", "published": "2015-01-15T14:51:37Z", "authors": ["Raphael Hauser", "Sergey Shahverdyan"], "categories": ["q-fin.PR"], "doi": "", "_query": "hedonic house price", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2503.01780v2", "title": "Price Impact of Insurance", "abstract": "This paper analyzes optimal insurance design when the insurer internalizes the effect of coverage on third-party service prices. A monopolistic insurer contracts with risk-averse agents who have sequential two-dimensional private information and preferences represented by Yaari's dual utility. Insurance contracts shape service demand and, through a market-clearing condition, determine equilibrium third-party prices. We characterize the structure of optimal contracts and show they take simple forms: either full coverage after a deductible is paid or limited coverage with an out-of-pocket maximum, closely mirroring real-world insurance plans. Technically, we formulate the problem as a sequential screening model and solve it using tools from optimal transport theory.", "published": "2025-03-03T18:10:22Z", "authors": ["Andrea Di Giovan Paolo", "Jose Higueras"], "categories": ["econ.TH"], "doi": "", "_query": "hedonic house price", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2204.08334v1", "title": "Time Series Clustering for Grouping Products Based on Price and Sales Patterns", "abstract": "Developing technology and changing lifestyles have made online grocery delivery applications an indispensable part of urban life. Since the beginning of the COVID-19 pandemic, the demand for such applications has dramatically increased, creating new competitors that disrupt the market. An increasing level of competition might prompt companies to frequently restructure their marketing and product pricing strategies. Therefore, identifying the change patterns in product prices and sales volumes would provide a competitive advantage for the companies in the marketplace. In this paper, we investigate alternative clustering methodologies to group the products based on the price patterns and sales volumes. We propose a novel distance metric that takes into account how product prices and sales move together rather than calculating the distance using numerical values. We compare our approach with traditional clustering algorithms, which typically rely on generic distance metrics such as Euclidean distance, and image clustering approaches that aim to group data by capturing its visual patterns. We evaluate the performances of different clustering algorithms using our custom evaluation metric as well as Calinski Harabasz and Davies Bouldin indices, which are commonly used internal validity metrics. We conduct our numerical study using a propriety price dataset from an online food and grocery delivery company, and the publicly available Favorita sales dataset. We find that our proposed clustering approach and image clustering both perform well for finding the products with similar price and sales patterns within large datasets.", "published": "2022-04-18T14:24:42Z", "authors": ["Aysun Bozanta", "Sean Berry", "Mucahit Cevik", "Beste Bulut", "Deniz Yigit", "Fahrettin F. Gonen", "Ayşe Başar"], "categories": ["cs.LG"], "doi": "", "_query": "hedonic house price", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/1305.5963v2", "title": "Note on multidimensional Breeden-Litzenberger representation for state price densities", "abstract": "In this note, we consider European options of type $h(X^1_T, X^2_T,\\ldots, X^n_T)$ depending on several underlying assets. We give a multidimensional version of the result of Breeden and Litzenberger \\cite{Breeden} on the relation between derivatives of the call price and the risk-neutral density of the underlying asset. The pricing measure is assumed to be absolutely continuous with respect to the Lebesgue measure on the state space.", "published": "2013-05-25T19:45:54Z", "authors": ["Jarno Talponen", "Lauri Viitasaari"], "categories": ["math.PR", "q-fin.PR"], "doi": "", "_query": "hedonic house price", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2204.01071v2", "title": "Improved Robust Price Bounds for Multi-Asset Derivatives under Market-Implied Dependence Information", "abstract": "We show how inter-asset dependence information derived from market prices of options can lead to improved model-free price bounds for multi-asset derivatives. Depending on the type of the traded option, we either extract correlation information or we derive restrictions on the set of admissible copulas that capture the inter-asset dependencies. To compute the resultant price bounds for some multi-asset options of interest, we apply a modified martingale optimal transport approach. Several examples based on simulated and real market data illustrate the improvement of the obtained price bounds and thus provide evidence for the relevance and tractability of our approach.", "published": "2022-04-03T13:25:15Z", "authors": ["Jonathan Ansari", "Eva Lütkebohmert", "Ariel Neufeld", "Julian Sester"], "categories": ["q-fin.MF", "math.PR", "q-fin.CP", "q-fin.PR"], "doi": "", "_query": "hedonic house price", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2609.32949v1", "title": "Optimal Nonparametric Dynamic Pricing with Censored Demand and Adversarial Inventory", "abstract": "We study online dynamic pricing with censored demand, where an arbitrary inventory level is revealed before pricing and may adapt to past observations, while demand follows an unknown, price-dependent distribution that is stationary over time. For a horizon of $T$ rounds, Xu et al. [2026] achieved $\\widetilde{\\mathcal{O}}(\\sqrt{T})$ regret under restrictive structural assumptions including linear demand, price-independent additive noise, and conditions relating inventory levels to the noise support. Our first contribution is to extend this framework to a substantially more general and statistically harder nonparametric setting, requiring only the natural assumption that expected sales are nonincreasing in price and allowing nonlinear demand curves and price-dependent noise. For this model, we first propose a simple baseline, Double-Grid-UCB, which discretizes both price and inventory and achieves $\\widetilde{\\mathcal{O}}(T^{3/4})$ expected regret using separate revenue estimates for each price-inventory grid pair. Then, we develop Threshold-UCB, which improves the expected regret to $\\widetilde{\\mathcal{O}}(T^{2/3})$. Unlike Double-Grid-UCB, Threshold-UCB reuses sales observations across inventory levels through shared estimates of demand-tail probabilities, allowing the same data to support revenue upper bounds for multiple inventories rather than a single inventory bin. We also complement this upper bound with an $Ω(T^{2/3})$ lower bound via a reduction from stochastic posted pricing, establishing its minimax optimality. Finally, extensive experiments across inventory processes, demand functions, and noise models demonstrate consistently superior performance of Threshold-UCB over benchmark algorithms.", "published": "2026-09-26T21:20:45Z", "authors": ["Mengxiao Zhang", "Yingfei Wang", "Haipeng Luo"], "categories": ["cs.LG"], "doi": "", "_query": "hedonic house price", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2305.10678v1", "title": "Option pricing under jump diffusion model", "abstract": "We provide an European option pricing formula written in the form of an infinite series of Black Scholes type terms under double Levy jumps model, where both the interest rate and underlying price are driven by Levy process. The series solution converges with a radius of convergence, and it is complemented by some numerical experiments to demonstrate its speed of convergence.", "published": "2023-05-18T03:30:06Z", "authors": ["Qian Li", "Li Wang"], "categories": ["q-fin.PR", "math.PR"], "doi": "", "_query": "hedonic house price", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/1503.03739v1", "title": "Price of Stability in Games of Incomplete Information", "abstract": "We address the question of whether price of stability results (existence of equilibria with low social cost) are robust to incomplete information. We show that this is the case in potential games, if the underlying algorithmic social cost minimization problem admits a constant factor approximation algorithm via strict cost-sharing schemes. Roughly, if the existence of an $α$-approximate equilibrium in the complete information setting was proven via the potential method, then there also exists a $α\\cdot β$-approximate Bayes-Nash equilibrium in the incomplete information setting, where $β$ is the approximation factor of the strict-cost sharing scheme algorithm. We apply our approach to Bayesian versions of the archetypal, in the price of stability analysis, network design models and show the existence of $O(\\log(n))$-approximate Bayes-Nash equilibria in several games whose complete information counterparts have been well-studied, such as undirected network design games, multi-cast games and covering games.", "published": "2015-03-12T14:27:27Z", "authors": ["Vasilis Syrgkanis"], "categories": ["cs.GT"], "doi": "", "_query": "hedonic house price", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2101.04280v1", "title": "Quantum option pricing using Wick rotated imaginary time evolution", "abstract": "In this paper we reformulate the problem of pricing options in a quantum setting. Our proposed algorithm involves preparing an initial state, representing the option price, and then evolving it using existing imaginary time simulation algorithms. This way of pricing options boils down to mapping an initial option price to a quantum state and then simulating the time dependence in Wick's imaginary time space. We numerically verify our algorithm for European options using a particular imaginary time evolution algorithm as proof of concept and show how it can be extended to path dependent options like Asian options. As the proposed method uses a hybrid variational algorithm, it is bound to be relevant for near-term quantum computers.", "published": "2021-01-12T03:39:43Z", "authors": ["Santosh Kumar Radha"], "categories": ["quant-ph", "q-fin.PR"], "doi": "", "_query": "hedonic house price", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/1403.1715v1", "title": "Do Google Trend data contain more predictability than price returns?", "abstract": "Using non-linear machine learning methods and a proper backtest procedure, we critically examine the claim that Google Trends can predict future price returns. We first review the many potential biases that may influence backtests with this kind of data positively, the choice of keywords being by far the greatest culprit. We then argue that the real question is whether such data contain more predictability than price returns themselves: our backtest yields a performance of about 17bps per week which only weakly depends on the kind of data on which predictors are based, i.e. either past price returns or Google Trends data, or both.", "published": "2014-03-07T10:42:43Z", "authors": ["Damien Challet", "Ahmed Bel Hadj Ayed"], "categories": ["q-fin.TR", "physics.soc-ph"], "doi": "", "_query": "hedonic house price", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/1503.02516v1", "title": "Optimal Pricing is Hard", "abstract": "We show that computing the revenue-optimal deterministic auction in unit-demand single-buyer Bayesian settings, i.e. the optimal item-pricing, is computationally hard even in single-item settings where the buyer's value distribution is a sum of independently distributed attributes, or multi-item settings where the buyer's values for the items are independent. We also show that it is intractable to optimally price the grand bundle of multiple items for an additive bidder whose values for the items are independent. These difficulties stem from implicit definitions of a value distribution. We provide three instances of how different properties of implicit distributions can lead to intractability: the first is a #P-hardness proof, while the remaining two are reductions from the SQRT-SUM problem of Garey, Graham, and Johnson. While simple pricing schemes can oftentimes approximate the best scheme in revenue, they can have drastically different underlying structure. We argue therefore that either the specification of the input distribution must be highly restricted in format, or it is necessary for the goal to be mere approximation to the optimal scheme's revenue instead of computing properties of the scheme itself.", "published": "2015-03-09T15:18:15Z", "authors": ["Constantinos Daskalakis", "Alan Deckelbaum", "Christos Tzamos"], "categories": ["cs.GT"], "doi": "", "_query": "hedonic house price", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2609.31752v3", "title": "The Price of a Familiar Perspective", "abstract": "Access to past reports changes how consumers learn about competing information providers. A subscriber who has learned to interpret a familiar source may find an equally competent rival less useful. We study how opening the rival's archive changes renewal prices and the effect of outcome feedback. Better feedback strengthens the familiar source's advantage when rival reports are inaccessible, but can weaken it once some rival records are available. We derive the exact threshold in a Gaussian learning model with differentiated price competition. Opening further records narrows the renewal premium and raises consumer surplus. When the familiar source initially serves a sufficiently large share, however, partial opening can reduce aggregate forecasting quality and total surplus by drawing consumers toward a rival that remains less informative. Complete access improves both outcomes relative to every incomplete archive. These results concern a covered interior renewal market with inherited customer histories, before implementation costs. The results distinguish the quality of feedback from access to the records with which consumers combine it.", "published": "2026-09-23T09:42:06Z", "authors": ["Georgy Lukyanov", "Ilya Pototskiy"], "categories": ["econ.TH"], "doi": "", "_query": "hedonic house price", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2503.11053v1", "title": "Pricing American Parisian Options under General Time-Inhomogeneous Markov Models", "abstract": "This paper develops general approaches for pricing various types of American-style Parisian options (down-in/-out, perpetual/finite-maturity) with general payoff functions based on continuous-time Markov chain (CTMC) approximation under general 1D time-inhomogeneous Markov models. For the down-in types, by conditioning on the Parisian stopping time, we reduce the pricing problem to that of a series of vanilla American options with different maturities and their prices integrated with the distribution function of the Parisian stopping time yield the American Parisian down-in option price. This facilitates an efficient application of CTMC approximation to obtain the approximate option price by calculating the required quantities. For the perpetual down-in cases under time-homogeneous models, significant computational cost can be reduced. The down-out cases are more complicated, for which we use the state augmentation approach to record the excursion duration and then the approximate option price is obtained by solving a series of variational inequalities recursively with the Lemke's pivoting method. We show the convergence of CTMC approximation for all the types of American Parisian options under general time-inhomogeneous Markov models, and the accuracy and efficiency of our algorithms are confirmed with extensive numerical experiments.", "published": "2025-03-14T03:45:18Z", "authors": ["Yuhao Liu", "Nian Yang", "Gongqiu Zhang"], "categories": ["q-fin.CP"], "doi": "", "_query": "hedonic house price", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2008.11817v2", "title": "Spatial Privacy Pricing: The Interplay between Privacy, Utility and Price in Geo-Marketplaces", "abstract": "A geo-marketplace allows users to be paid for their location data. Users concerned about privacy may want to charge more for data that pinpoints their location accurately, but may charge less for data that is more vague. A buyer would prefer to minimize data costs, but may have to spend more to get the necessary level of accuracy. We call this interplay between privacy, utility, and price \\emph{spatial privacy pricing}. We formalize the issues mathematically with an example problem of a buyer deciding whether or not to open a restaurant by purchasing location data to determine if the potential number of customers is sufficient to open. The problem is expressed as a sequential decision making problem, where the buyer first makes a series of decisions about which data to buy and concludes with a decision about opening the restaurant or not. We present two algorithms to solve this problem, including experiments that show they perform better than baselines.", "published": "2020-08-25T06:28:02Z", "authors": ["Kien Nguyen", "John Krumm", "Cyrus Shahabi"], "categories": ["cs.CR", "cs.AI"], "doi": "10.1145/3397536.3422213", "_query": "hedonic house price", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/1805.09763v6", "title": "A self-organized criticality participative pricing mechanism for selling zero-marginal cost products", "abstract": "In today's economy, selling a new zero-marginal cost product is a real challenge, as it is difficult to determine a product's \"correct\" sales price based on its profit and dissemination. As an example, think of the price of a new app or video game. New sales mechanisms for selling this type of product need to be designed, in particular ones that consider consumer preferences and reality. Current auction mechanisms establish a time deadline for the auction to take place. This deadline is set to increase the number of bidders and thus the final offering price. Consumers want to obtain the product as quickly as possible from the moment they become interested in it, and this time does not always coincide with the seller's deadline. Naturally, consumers also want to pay a price they consider \"fair\". Here we introduce an auction model where buyers continuously place bids and the challenge is to decide quickly whether or not to accept them. The model does not include a deadline for placing bids, and exhibits self-organized criticality; it presents a critical price from which a bid is accepted with probability one, and avalanches of sales above this value are observed. This model is of particular interest for startup companies interested in profit as well as making the product known on the market.", "published": "2018-05-24T16:28:32Z", "authors": ["Daniel Fraiman"], "categories": ["q-fin.TR", "q-fin.GN"], "doi": "", "_query": "hedonic house price", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/0908.2834v1", "title": "On Revenue Maximization in Second-Price Ad Auctions", "abstract": "Most recent papers addressing the algorithmic problem of allocating advertisement space for keywords in sponsored search auctions assume that pricing is done via a first-price auction, which does not realistically model the Generalized Second Price (GSP) auction used in practice. Towards the goal of more realistically modeling these auctions, we introduce the Second-Price Ad Auctions problem, in which bidders' payments are determined by the GSP mechanism. We show that the complexity of the Second-Price Ad Auctions problem is quite different than that of the more studied First-Price Ad Auctions problem. First, unlike the first-price variant, for which small constant-factor approximations are known, it is NP-hard to approximate the Second-Price Ad Auctions problem to any non-trivial factor. Second, this discrepancy extends even to the 0-1 special case that we call the Second-Price Matching problem (2PM). In particular, offline 2PM is APX-hard, and for online 2PM there is no deterministic algorithm achieving a non-trivial competitive ratio and no randomized algorithm achieving a competitive ratio better than 2. This stands in contrast to the results for the analogous special case in the first-price model, the standard bipartite matching problem, which is solvable in polynomial time and which has deterministic and randomized online algorithms achieving better competitive ratios. On the positive side, we provide a 2-approximation for offline 2PM and a 5.083-competitive randomized algorithm for online 2PM. The latter result makes use of a new generalization of a classic result on the performance of the \"Ranking\" algorithm for online bipartite matching.", "published": "2009-08-19T23:46:02Z", "authors": ["Yossi Azar", "Benjamin Birnbaum", "Anna R. Karlin", "C. Thach Nguyen"], "categories": ["cs.DS"], "doi": "", "_query": "hedonic house price", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/1810.02109v1", "title": "District heating systems under high CO2 emission prices: the role of the pass-through from emission cost to electricity prices", "abstract": "Low CO2 prices have prompted discussion about political measures aimed at increasing the cost of carbon dioxide emissions. These costs affect, inter alia, integrated district heating system operators (DHSO), often owned by municipalities with some political influence, that use a variety of (CO2 emis- sion intense) heat generation technologies. We examine whether DHSOs have an incentive to support measures that increase CO2 emission prices in the short term. Therefore, we (i) develop a simplified analytical framework to analyse optimal decisions of a district heating operator, and (ii) investigate the market-wide effects of increasing emission prices, in particular the pass- through from emission costs to electricity prices. Using a numerical model of the common Austrian and German power system, we estimate a pass-through from CO2 emission prices to power prices between 0.69 and 0.53 as of 2017, depending on the absolute emission price level. We find the CO2 emission cost pass-through to be sufficiently high so that low-emission district heating systems operating at least moderately efficient generation units benefit from rising CO2 emission prices in the short term.", "published": "2018-10-04T09:12:53Z", "authors": ["Sebastian Wehrle", "Johannes Schmidt"], "categories": ["econ.GN"], "doi": "", "_query": "hedonic house price", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2604.01416v1", "title": "Pay-Per-Crawl Pricing for AI: The LM-Tree Agent", "abstract": "As AI systems shift from directing users to content toward consuming it directly, publishers need a new revenue model: charging AI crawlers for content access. This model, called pay-per-crawl, must solve a problem of mechanism selection at scale: content is too heterogeneous for a fixed pricing framework. Different sub-types warrant not only different price levels but different pricing rules based on different unstructured features, and there are too many to enumerate or design by hand. We propose the LM Tree, an adaptive pricing agent that grows a segmentation tree over the content library, using LLMs to discover what distinguishes high-value from low-value items and apply those attributes at scale, from binary purchase feedback alone. We evaluate the LM Tree on real content from a major German technology publisher, using 8,939 articles and 80,451 buyer queries with willingness-to-pay calibrated from actual AI crawler traffic. The LM Tree achieves a 65% revenue gain over a single static price and a 47% gain over two-category pricing, outperforming even the publisher's own 8-segment editorial taxonomy by 40% -- recovering content distinctions the publisher's own categories miss.", "published": "2026-04-01T21:30:03Z", "authors": ["Richard Archer", "Soheil Ghili", "Nima Haghpanah"], "categories": ["econ.GN"], "doi": "", "_query": "hedonic house price", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/cs/0110016v1", "title": "Limits To Certainty in QoS Pricing and Bandwidth", "abstract": "Advanced services require more reliable bandwidth than currently provided by the Internet Protocol, even with the reliability enhancements provided by TCP. More reliable bandwidth will be provided through QoS (quality of service), as currently discussed widely. Yet QoS has some implications beyond providing ubiquitous access to advance Internet service, which are of interest from a policy perspective. In particular, what are the implications for price of Internet services? Further, how will these changes impact demand and universal service for the Internet. This paper explores the relationship between certainty of bandwidth and certainty of price for Internet services over a statistically shared network and finds that these are mutually exclusive goals.", "published": "2001-10-03T19:32:08Z", "authors": ["Carolyn Gideon", "L Jean Camp"], "categories": ["cs.CY", "cs.HC"], "doi": "", "_query": "hedonic house price", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/1801.08341v1", "title": "The Price of Indivisibility in Cake Cutting", "abstract": "We consider the problem of envy-free cake cutting, which is the distribution of a continuous heterogeneous resource among self interested players such that nobody prefers what somebody else receives to what they get. Existing work has focused on two distinct classes of solutions to this problem - allocations which give each player a continuous piece of cake and allocations which give each player arbitrarily many disjoint pieces of cake. Our aim is to investigate allocations between these two extremes by parameterizing the maximum number of disjoint pieces each player may receive. We characterize the Price of Indivisibility (POI) as the gain achieved in social welfare (utilitarian and egalitarian), by moving from allocations which give each player a continuous piece of cake to allocations that may give each player up to $k$ disjoint pieces of cake. Our results contain bounds for the Price of Indivisibility for utilitarian as well as egalitarian social welfare, and for envy-free cake cutting as well as cake cutting without any fairness constraints.", "published": "2018-01-25T10:35:19Z", "authors": ["Eshwar Ram Arunachaleswaran", "Ragavendran Gopalakrishnan"], "categories": ["cs.GT", "math.CO"], "doi": "", "_query": "hedonic house price", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2311.06397v4", "title": "Predicting Stock Price of Construction Companies using Weighted Ensemble Learning", "abstract": "Modeling the behavior of stock price data has always been one of the challengeous applications of Artificial Intelligence (AI) and Machine Learning (ML) due to its high complexity and dependence on various conditions. Recent studies show that this will be difficult to do with just one learning model. The problem can be more complex for companies of construction section, due to the dependency of their behavior on more conditions. This study aims to provide a hybrid model for improving the accuracy of prediction for stock price index of companies in construction section. The contribution of this paper can be considered as follows: First, a combination of several prediction models is used to predict stock price, so that learning models can cover each other's error. In this research, an ensemble model based on Artificial Neural Network (ANN), Gaussian Process Regression (GPR) and Classification and Regression Tree (CART) is presented for predicting stock price index. Second, the optimization technique is used to determine the effect of each learning model on the prediction result. For this purpose, first all three mentioned algorithms process the data simultaneously and perform the prediction operation. Then, using the Cuckoo Search (CS) algorithm, the output weight of each algorithm is determined as a coefficient. Finally, using the ensemble technique, these results are combined and the final output is generated through weighted averaging on optimal coefficients. The results showed that using CS optimization in the proposed ensemble system is highly effective in reducing prediction error. Comparing the evaluation results of the proposed system with similar algorithms, indicates that our model is more accurate and can be useful for predicting stock price index in real-world scenarios.", "published": "2023-11-10T20:59:56Z", "authors": ["Xinyuan Song"], "categories": ["stat.AP"], "doi": "", "_query": "hedonic house price", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2407.04776v2", "title": "Quantifying Privacy Risks of Public Statistics to Residents of Subsidized Housing", "abstract": "As the U.S. Census Bureau implements its controversial new disclosure avoidance system, researchers and policymakers debate the necessity of new privacy protections for public statistics. With experiments on both public statistics and synthetic microdata, we explore a particular privacy concern: respondents in subsidized housing may deliberately not mention unauthorized children and other household members for fear of being discovered and evicted. By combining public statistics from the Decennial Census and the Department of Housing and Urban Development, we demonstrate a simple, inexpensive reconstruction attack that could identify subsidized households living in violation of occupancy guidelines in 2010. Experiments on synthetic data suggest that a random swapping mechanism similar to the Census Bureau's 2010 disclosure avoidance measures does not significantly reduce the precision of this attack, while a differentially private mechanism similar to the 2020 disclosure avoidance system does. Our results provide a valuable example for policymakers seeking trustworthy public statistics.", "published": "2024-07-05T18:00:02Z", "authors": ["Ryan Steed", "Diana Qing", "Zhiwei Steven Wu"], "categories": ["cs.CY"], "doi": "10.1162/99608f92.39d8bfa4", "_query": "hedonic house price", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/1410.0448v2", "title": "Fair and profitable bilateral prices under funding costs and collateralization", "abstract": "Bielecki and Rutkowski (2014) introduced and studied a generic nonlinear market model, which includes several risky assets, multiple funding accounts and margin accounts. In this paper, we examine the pricing and hedging of contract both from the perspective of the hedger and the counterparty with arbitrary initial endowments. We derive inequalities for unilateral prices and we give the range for either fair bilateral prices or bilaterally profitable prices. We also study the monotonicity of a unilateral price with respect to the initial endowment. Our study hinges on results for BSDE driven by continuous martingales obtained in Nie and Rutkowski (2014), but we also derive the pricing PDEs for path-independent contingent claims of European style in a Markovian framework.", "published": "2014-10-02T04:07:36Z", "authors": ["Tianyang Nie", "Marek Rutkowski"], "categories": ["q-fin.MF", "q-fin.PR"], "doi": "", "_query": "hedonic house price", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2404.14953v1", "title": "Dynamic pricing with Bayesian updates from online reviews", "abstract": "When launching new products, firms face uncertainty about market reception. Online reviews provide valuable information not only to consumers but also to firms, allowing firms to adjust the product characteristics, including its selling price. In this paper, we consider a pricing model with online reviews in which the quality of the product is uncertain, and both the seller and the buyers Bayesianly update their beliefs to make purchasing & pricing decisions. We model the seller's pricing problem as a basic bandits' problem and show a close connection with the celebrated Catalan numbers, allowing us to efficiently compute the overall future discounted reward of the seller. With this tool, we analyze and compare the optimal static and dynamic pricing strategies in terms of the probability of effectively learning the quality of the product.", "published": "2024-04-23T11:55:20Z", "authors": ["José Correa", "Mathieu Mari", "Andrew Xia"], "categories": ["cs.LG"], "doi": "", "_query": "hedonic house price", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2205.04687v3", "title": "Optimal Price Discrimination for Randomized Mechanisms", "abstract": "We study the power of price discrimination via an intermediary in bilateral trade, when there is a revenue-maximizing seller selling an item to a buyer with a private value drawn from a prior. Between the seller and the buyer, there is an intermediary that can segment the market by releasing information about the true values to the seller. This is termed signaling, and enables the seller to price discriminate. In this setting, Bergemann et al. showed the existence of a signaling scheme that simultaneously raises the optimal consumer surplus, guarantees the item always sells, and ensures the seller's revenue does not increase. Our work extends the positive result of Bergemann et al. to settings where the type space is larger, and where optimal auction is randomized, possibly over a menu that can be exponentially large. In particular, we consider two settings motivated by budgets: The first is when there is a publicly known budget constraint on the price the seller can charge and the second is the FedEx problem where the buyer has a private deadline or service level (equivalently, a private budget that is guaranteed to never bind). For both settings, we present a novel signaling scheme and its analysis via a continuous construction process that recreates the optimal consumer surplus guarantee of Bergemann et al. The settings we consider are special cases of the more general problem where the buyer has a private budget constraint in addition to a private value. We finally show that our positive results do not extend to this more general setting. Here, we show that any efficient signaling scheme necessarily transfers almost all the surplus to the seller instead of the buyer.", "published": "2022-05-10T05:49:20Z", "authors": ["Shao-Heng Ko", "Kamesh Munagala"], "categories": ["cs.GT"], "doi": "", "_query": "hedonic house price", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/1805.01136v3", "title": "Nonparametric Pricing Analytics with Customer Covariates", "abstract": "Personalized pricing analytics is becoming an essential tool in retailing. Upon observing the personalized information of each arriving customer, the firm needs to set a price accordingly based on the covariates such as income, education background, past purchasing history to extract more revenue. For new entrants of the business, the lack of historical data may severely limit the power and profitability of personalized pricing. We propose a nonparametric pricing policy to simultaneously learn the preference of customers based on the covariates and maximize the expected revenue over a finite horizon. The policy does not depend on any prior assumptions on how the personalized information affects consumers' preferences (such as linear models). It is adaptively splits the covariate space into smaller bins (hyper-rectangles) and clusters customers based on their covariates and preferences, offering similar prices for customers who belong to the same cluster trading off granularity and accuracy. We show that the algorithm achieves a regret of order $O(\\log(T)^2 T^{(2+d)/(4+d)})$, where $T$ is the length of the horizon and $d$ is the dimension of the covariate. It improves the current regret in the literature \\citep{slivkins2014contextual}, under mild technical conditions in the pricing context (smoothness and local concavity). We also prove that no policy can achieve a regret less than $O(T^{(2+d)/(4+d)})$ for a particular instance and thus demonstrate the near optimality of the proposed policy.", "published": "2018-05-03T06:42:27Z", "authors": ["Ningyuan Chen", "Guillermo Gallego"], "categories": ["cs.LG", "stat.ML"], "doi": "", "_query": "hedonic house price", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/1305.2322v1", "title": "Simulation of a typical house in the region of Antananarivo, Madagascar. Determination of passive solutions using local materials", "abstract": "This paper deals with new proposals for the design of passive solutions adapted to the climate of the highlands of Madagascar. While the strongest population density is located in the central highlands, the problem of thermal comfort in buildings occurs mainly during winter time. Currently, people use raw wood to warm the poorly designed houses. This leads to a large scale deforestation of the areas and causes erosion and environmental problems. The methodology used consisted of the identification of a typical building and of a typical meteorological year. Simulations were carried out using a thermal and airflow software (CODYRUN) to improve each building component (roof, walls, windows, and soil) in such a way as to estimate the influence of some technical solutions on each component in terms of thermal comfort. The proposed solutions also took into account the use of local materials and the standard of living of the country.", "published": "2013-05-10T12:16:41Z", "authors": ["Harimalala Razanamanampisoa", "Zely Arivelo Randriamanantany", "Hery Tiana Rakotondramiarana", "François Garde", "Harry Boyer"], "categories": ["cs.CE"], "doi": "", "_query": "hedonic house price", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/cond-mat/0112033v1", "title": "The effect of non-ideal market conditions on option pricing", "abstract": "Option pricing is mainly based on ideal market conditions which are well represented by the Geometric Brownian Motion (GBM) as market model. We study the effect of non-ideal market conditions on the price of the option. We focus our attention on two crucial aspects appearing in real markets: The influence of heavy tails and the effect of colored noise. We will see that both effects have opposite consequences on option pricing.", "published": "2001-12-03T16:32:33Z", "authors": ["Josep Perello", "Jaume Masoliver"], "categories": ["cond-mat", "physics.soc-ph"], "doi": "10.1016/S0378-4371(02)00627-1", "_query": "hedonic house price", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/0706.1460v1", "title": "Uncertainty in the Fluctuations of the Price of Stocks", "abstract": "We report on a study of the Tehran Price Index (TEPIX) from 2001 to 2006 as an emerging market that has been affected by several political crises during the recent years, and analyze the non-Gaussian probability density function (PDF) of the log returns of the stocks' prices. We show that while the average of the index did not fall very much over the time period of the study, its day-to-day fluctuations strongly increased due to the crises. Using an approach based on multiplicative processes with a detrending procedure, we study the scale-dependence of the non-Gaussian PDFs, and show that the temporal dependence of their tails indicates a gradual and systematic increase in the probability of the appearance of large increments in the returns on approaching distinct critical time scales over which the TEPIX has exhibited maximum uncertainty.", "published": "2007-06-11T12:39:42Z", "authors": ["G. R. Jafari", "M. Sadegh Movahed", "P. Noroozzadeh", "A. Bahraminasab", "Muhammad Sahimi", "F. Ghasemi", "M. Reza Rahimi Tabar"], "categories": ["q-fin.ST", "physics.data-an", "physics.soc-ph"], "doi": "10.1142/S0129183107011662", "_query": "hedonic house price", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/1904.01226v1", "title": "Pricing Traffic Networks with Mixed Vehicle Autonomy", "abstract": "In a traffic network, vehicles normally select their routes selfishly. Consequently, traffic networks normally operate at an equilibrium characterized by Wardrop conditions. However, it is well known that equilibria are inefficient in general. In addition to the intrinsic inefficiency of equilibria, the authors recently showed that, in mixed-autonomy networks in which autonomous vehicles maintain a shorter headway than human-driven cars, increasing the fraction of autonomous vehicles in the network may increase the inefficiency of equilibria. In this work, we study the possibility of obviating the inefficiency of equilibria in mixed-autonomy traffic networks via pricing mechanisms. In particular, we study assigning prices to network links such that the overall or social delay of the resulting equilibria is minimum. First, we study the possibility of inducing such optimal equilibria by imposing a set of undifferentiated prices, i.e. a set of prices that treat both human-driven and autonomous vehicles similarly at each link. We provide an example which demonstrates that undifferentiated pricing is not sufficient for achieving minimum social delay. Then, we study differentiated pricing where the price of traversing each link may depend on whether vehicles are human-driven or autonomous. Under differentiated pricing, we prove that link prices obtained from the marginal cost taxation of links will induce equilibria with minimum social delay if the degree of road capacity asymmetry (i.e. the ratio between the road capacity when all vehicles are human-driven and the road capacity when all vehicles are autonomous) is homogeneous among network links.", "published": "2019-04-02T05:58:17Z", "authors": ["Negar Mehr", "Roberto Horowitz"], "categories": ["cs.GT", "eess.SY", "math.OC"], "doi": "", "_query": "hedonic house price", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/1612.03161v2", "title": "Prophet Inequalities Made Easy: Stochastic Optimization by Pricing Non-Stochastic Inputs", "abstract": "We present a general framework for stochastic online maximization problems with combinatorial feasibility constraints. The framework establishes prophet inequalities by constructing price-based online approximation algorithms, a natural extension of threshold algorithms for settings beyond binary selection. Our analysis takes the form of an extension theorem: we derive sufficient conditions on prices when all weights are known in advance, then prove that the resulting approximation guarantees extend directly to stochastic settings. Our framework unifies and simplifies much of the existing literature on prophet inequalities and posted price mechanisms, and is used to derive new and improved results for combinatorial markets (with and without complements), multi-dimensional matroids, and sparse packing problems. Finally, we highlight a surprising connection between the smoothness framework for bounding the price of anarchy of mechanisms and our framework, and show that many smooth mechanisms can be recast as posted price mechanisms with comparable performance guarantees.", "published": "2016-12-09T20:50:32Z", "authors": ["Paul Dütting", "Michal Feldman", "Thomas Kesselheim", "Brendan Lucier"], "categories": ["cs.GT", "cs.DS"], "doi": "", "_query": "hedonic house price", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/1602.04848v2", "title": "Option Pricing in Markets with Unknown Stochastic Dynamics", "abstract": "We consider arbitrage free valuation of European options in Black-Scholes and Merton markets, where the general structure of the market is known, however the specific parameters are not known. In order to reflect this subjective uncertainty of a market participant, we follow a Bayesian approach to option pricing. Here we use historic discrete or continuous observations of the market to set up posterior distributions for the future market. Given a subjective physical measure for the market dynamics, we derive the existence of arbitrage free pricing rules by constructing subjective option pricing measures. The non-uniqueness of such measures can be proven using the freedom of choice of prior distributions. The subjective market measure thus turns out to model an incomplete market. In addition, for the Black-Scholes market we prove that in the high frequency limit (or the long time limit) of observations, Bayesian option prices converge to the standard BS-Option price with the true volatility. In contrast to this, in the Merton market with normally distributed jumps Bayesian prices do not converge to standard Merton prices with the true parameters, as only a finite number of jump events can be observed in finite time. However, we prove that this convergence holds true in the limit of long observation times.", "published": "2016-02-15T21:43:19Z", "authors": ["Hanno Gottschalk", "Elpida Nizami", "Marius Schubert"], "categories": ["q-fin.MF", "q-fin.PR"], "doi": "", "_query": "hedonic house price", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2512.05868v1", "title": "Predicting Price Movements in High-Frequency Financial Data with Spiking Neural Networks", "abstract": "Modern high-frequency trading (HFT) environments are characterized by sudden price spikes that present both risk and opportunity, but conventional financial models often fail to capture the required fine temporal structure. Spiking Neural Networks (SNNs) offer a biologically inspired framework well-suited to these challenges due to their natural ability to process discrete events and preserve millisecond-scale timing. This work investigates the application of SNNs to high-frequency price-spike forecasting, enhancing performance via robust hyperparameter tuning with Bayesian Optimization (BO). This work converts high-frequency stock data into spike trains and evaluates three architectures: an established unsupervised STDP-trained SNN, a novel SNN with explicit inhibitory competition, and a supervised backpropagation network. BO was driven by a novel objective, Penalized Spike Accuracy (PSA), designed to ensure a network's predicted price spike rate aligns with the empirical rate of price events. Simulated trading demonstrated that models optimized with PSA consistently outperformed their Spike Accuracy (SA)-tuned counterparts and baselines. Specifically, the extended SNN model with PSA achieved the highest cumulative return (76.8%) in simple backtesting, significantly surpassing the supervised alternative (42.54% return). These results validate the potential of spiking networks, when robustly tuned with task-specific objectives, for effective price spike forecasting in HFT.", "published": "2025-12-05T16:44:43Z", "authors": ["Brian Ezinwoke", "Oliver Rhodes"], "categories": ["cs.LG", "q-fin.CP"], "doi": "", "_query": "hedonic house price", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/1804.06890v1", "title": "Accounting Noise and the Pricing of CoCos", "abstract": "Contingent Convertible bonds (CoCos) are debt instruments that convert into equity or are written down in times of distress. Existing pricing models assume conversion triggers based on market prices and on the assumption that markets can always observe all relevant firm information. But all Cocos issued so far have triggers based on accounting ratios and/or regulatory intervention. We incorporate that markets receive information through noisy accounting reports issued at discrete time instants, which allows us to distinguish between market and accounting values, and between automatic triggers and regulator-mandated conversions. Our second contribution is to incorporate that coupon payments are contingent too: their payment is conditional on the Maximum Distributable Amount not being exceeded. We examine the impact of CoCo design parameters, asset volatility and accounting noise on the price of a CoCo; and investigate the interaction between CoCo design features, the capital structure of the issuing bank and their implications for risk taking and investment incentives. Finally, we use our model to explain the crash in CoCo prices after Deutsche Bank's profit warning in February 2016.", "published": "2018-04-18T19:41:35Z", "authors": ["Mike Derksen", "Peter Spreij", "Sweder van Wijnbergen"], "categories": ["q-fin.PR"], "doi": "10.1142/S0219024922500285", "_query": "hedonic house price", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/1607.07108v2", "title": "Model-Independent Price Bounds for Catastrophic Mortality Bonds", "abstract": "In this paper, we are concerned with the valuation of Catastrophic Mortality Bonds and, in particular, we examine the case of the Swiss Re Mortality Bond 2003 as a primary example of this class of assets. This bond was the first Catastrophic Mortality Bond to be launched in the market and encapsulates the behaviour of a well-defined mortality index to generate payoffs for bondholders. Pricing these type of bonds is a challenging task and no closed form solution exists in the literature. In our approach, we express the payoff of such a bond in terms of the payoff of an Asian put option and present a new approach to derive model-independent bounds exploiting comonotonic theory as illustrated in \\cite{prime1}, \\cite{2} and \\cite{Simon} for the pricing of Asian options. We carry out Monte Carlo simulations to estimate the bond price and illustrate the quality of the bounds.", "published": "2016-07-24T22:53:55Z", "authors": ["Raj Kumari Bahl", "Sotirios Sabanis"], "categories": ["q-fin.PR", "math.PR"], "doi": "", "_query": "hedonic house price", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2306.07731v3", "title": "A Comparative Study of Factor Models for Different Periods of the Electricity Spot Price Market", "abstract": "Due to major shifts in European energy supply, a structural change can be observed in Austrian electricity spot price data starting from the second quarter of the year 2021 onward. In this work we study the performance of two different factor models for the electricity spot price in three different time periods. To this end, we consider three samples of EEX data for the Austrian base load electricity spot price, one from the pre-crises from 2018 to 2021, the second from the time of the crisis from 2021 to 2023 and the whole data from 2018 to 2023. For each of these samples, we investigate the fit of a classical 3-factor model with a Gaussian base signal and one positive and one negative jump signal and compare it with a 4-factor model to assess the effect of adding a second Gaussian base signal to the model. For the calibration of the models we develop a tailor-made Markov Chain Monte Carlo method based on Gibbs sampling. To evaluate the model adequacy, we provide simulations of the spot price as well as a posterior predictive check for the 3- and the 4-factor model. We find that the 4-factor model outperforms the 3-factor model in times of non-crises. In times of crises, the second Gaussian base signal does not lead to a better fit of the model. To the best of our knowledge, this is the first study regarding stochastic electricity spot price models in this new market environment. Hence, it serves as a solid base for future research.", "published": "2023-06-13T12:38:15Z", "authors": ["Christian Laudagé", "Florian Aichinger", "Sascha Desmettre"], "categories": ["q-fin.MF", "q-fin.RM"], "doi": "", "_query": "hedonic house price", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/1303.4274v1", "title": "A note on pricing of contingent claims under G-expectation", "abstract": "In this paper, we study the pricing of contingent claims under G-expectation. In order to accomodate volatility uncertainty, the price of the risky security is supposed to governed by a general linear stochastic differential equation (SDE) driven by G-Brownian motion. Utilizing the recently developed results of Backward SDE driven by G-Brownian motion, we obtain the superhedging and suberhedging prices of a given contingent claim. Explicit results in the Markovian case are also derived.", "published": "2013-03-18T14:58:37Z", "authors": ["Mingshang Hu", "Shaolin Ji"], "categories": ["math.PR", "q-fin.PR"], "doi": "", "_query": "hedonic house price", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2004.01499v1", "title": "Deep Recurrent Modelling of Stationary Bitcoin Price Formation Using the Order Flow", "abstract": "In this paper we propose a deep recurrent model based on the order flow for the stationary modelling of the high-frequency directional prices movements. The order flow is the microsecond stream of orders arriving at the exchange, driving the formation of prices seen on the price chart of a stock or currency. To test the stationarity of our proposed model we train our model on data before the 2017 Bitcoin bubble period and test our model during and after the bubble. We show that without any retraining, the proposed model is temporally stable even as Bitcoin trading shifts into an extremely volatile \"bubble trouble\" period. The significance of the result is shown by benchmarking against existing state-of-the-art models in the literature for modelling price formation using deep learning.", "published": "2020-03-31T18:13:04Z", "authors": ["Ye-Sheen Lim", "Denise Gorse"], "categories": ["q-fin.ST", "cs.LG", "q-fin.TR", "stat.ML"], "doi": "", "_query": "hedonic house price", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/1407.1601v5", "title": "Deadline Differentiated Pricing of Deferrable Electric Loads", "abstract": "A large fraction of the total electric load is comprised of end-use devices whose demand for energy is inherently deferrable in time. Of interest is the potential to leverage on such latent flexibility in demand to absorb variability in power supplied from intermittent renewable generation. The challenge, however, lies in designing incentives to reliably induce the desired response in demand. With an eye to electric vehicle charging, we propose a novel forward market for differentiated electric power services, where consumers consent to deferred service of pre-specified loads in exchange for a reduced per-unit price for energy. The longer a consumer is willing to defer, the larger the reduction in price. The proposed forward contract provides a guarantee on the aggregate quantity of energy to be delivered by a consumer-specified deadline. Under the earliest-deadline-first (EDF) scheduling policy, which is shown to be optimal for the supplier, we explicitly characterize a non-discriminatory, deadline-differentiated pricing scheme that yields an efficient competitive equilibrium between the supplier and consumers. We further show that this efficient pricing scheme, in combination with EDF scheduling, is incentive compatible (IC) in that every consumer would like to reveal her true deadline to the supplier, regardless of the actions taken by other consumers.", "published": "2014-07-07T07:01:55Z", "authors": ["Eilyan Bitar", "Yunjian Xu"], "categories": ["math.OC", "cs.GT"], "doi": "", "_query": "hedonic house price", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/physics/0604179v1", "title": "Socioeconophysics: Opinion Dynamics for number of transactions and price, a trader based model", "abstract": "Involving effects of media, opinion leader and other agents on the opinion of individuals of market society, a trader based model is developed and utilized to simulate price via supply and demand. Pronounced effects are considered with several weights and some personal differences between traders are taken into account. Resulting time series and probabilty distribution function involving a power law for price come out similar to the real ones.", "published": "2006-04-21T17:52:53Z", "authors": ["Caglar Tuncay"], "categories": ["physics.soc-ph"], "doi": "10.1142/S0129183106009667", "_query": "hedonic house price", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2308.07577v2", "title": "Interest Rate Dynamics and Commodity Prices", "abstract": "In economic studies and popular media, interest rates are routinely cited as a major factor behind commodity price fluctuations. At the same time, the transmission channels are far from transparent, leading to long-running debates on the sign and magnitude of interest rate effects. Purely empirical studies struggle to address these issues because of the complex interactions between interest rates, prices, supply changes, and aggregate demand. To move this debate to a solid footing, we extend the competitive storage model to include stochastically evolving interest rates. We establish general conditions for existence and uniqueness of solutions and provide a systematic theoretical and quantitative analysis of the interactions between interest rates and prices.", "published": "2023-08-15T05:10:35Z", "authors": ["Christophe Gouel", "Qingyin Ma", "John Stachurski"], "categories": ["econ.TH", "math.NA", "math.OC"], "doi": "", "_query": "hedonic house price", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2106.12961v1", "title": "Next-Day Bitcoin Price Forecast Based on Artificial intelligence Methods", "abstract": "In recent years, Bitcoin price prediction has attracted the interest of researchers and investors. However, the accuracy of previous studies is not well enough. Machine learning and deep learning methods have been proved to have strong prediction ability in this area. This paper proposed a method combined with Ensemble Empirical Mode Decomposition (EEMD) and a deep learning method called long short-term memory (LSTM) to research the problem of next-day Bitcoin price forecast.", "published": "2021-06-21T04:45:59Z", "authors": ["Liping Yang"], "categories": ["q-fin.ST", "cs.LG"], "doi": "", "_query": "hedonic house price", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2508.06497v1", "title": "Forecasting Commodity Price Shocks Using Temporal and Semantic Fusion of Prices Signals and Agentic Generative AI Extracted Economic News", "abstract": "Accurate forecasting of commodity price spikes is vital for countries with limited economic buffers, where sudden increases can strain national budgets, disrupt import-reliant sectors, and undermine food and energy security. This paper introduces a hybrid forecasting framework that combines historical commodity price data with semantic signals derived from global economic news, using an agentic generative AI pipeline. The architecture integrates dual-stream Long Short-Term Memory (LSTM) networks with attention mechanisms to fuse structured time-series inputs with semantically embedded, fact-checked news summaries collected from 1960 to 2023. The model is evaluated on a 64-year dataset comprising normalized commodity price series and temporally aligned news embeddings. Results show that the proposed approach achieves a mean AUC of 0.94 and an overall accuracy of 0.91 substantially outperforming traditional baselines such as logistic regression (AUC = 0.34), random forest (AUC = 0.57), and support vector machines (AUC = 0.47). Additional ablation studies reveal that the removal of attention or dimensionality reduction leads to moderate declines in performance, while eliminating the news component causes a steep drop in AUC to 0.46, underscoring the critical value of incorporating real-world context through unstructured text. These findings demonstrate that integrating agentic generative AI with deep learning can meaningfully improve early detection of commodity price shocks, offering a practical tool for economic planning and risk mitigation in volatile market environments while saving the very high costs of operating a full generative AI agents pipeline.", "published": "2025-07-24T20:52:47Z", "authors": ["Mohammed-Khalil Ghali", "Cecil Pang", "Oscar Molina", "Carlos Gershenson-Garcia", "Daehan Won"], "categories": ["q-fin.CP", "cs.AI", "cs.LG"], "doi": "", "_query": "hedonic house price", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/1804.08184v1", "title": "Exchange of Renewable Energy among Prosumers using Blockchain with Dynamic Pricing", "abstract": "We consider users which may have renewable energy harvesting devices, or distributed generators. Such users can behave as consumer or producer (hence, we denote them as prosumers) at different time instances. A prosumer may sell the energy to other prosumers in exchange of money. We consider a demand response model, where the price of conventional energy depends on the total demand of all the prosumers at a certain time. A prosumer depending on its own utility has to select the amount of energy it wants to buy either from the grid or from other prosumers, or the amount of excess energy it wants to sell to other prosumers. However, the strategy, and the payoff of a prosumer inherently depends on the strategy of other prosumers as a prosumer can only buy if the other prosumers are willing to sell. We formulate the problem as a coupled constrained game, and seek to obtain the generalized Nash equilibrium. We show that the game is a concave potential game and show that there exists a unique generalized Nash equilibrium. We consider that a platform will set the price for distributed interchange of energy among the prosumers in order to minimize the consumption of the conventional energy. We propose a distributed algorithm where the platform sets a price to each prosumer, and then each prosumer at a certain time only optimizes its own payoff. The prosumer then updates the price depending on the supply and demand for each prosumer. We show that the algorithm converges to an optimal generalized Nash equilibrium. The distributed algorithm also provides an optimal price for the exchange market.", "published": "2018-04-22T22:19:16Z", "authors": ["Arnob Ghosh", "Vaneet Aggarwal", "Hong Wan"], "categories": ["cs.GT", "math.OC"], "doi": "", "_query": "hedonic house price", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2410.16745v1", "title": "Characterizing the top trading cycles rule for housing markets with lexicographic preferences", "abstract": "We consider a housing market model with limited externalities where agents care both about their own consumption via demand preferences and about the agent who receives their endowment via supply preferences (we extend the associated lexicographic preference domains introduced in Klaus and Meo, 2023). If preferences are demand lexicographic, then our model extends the classical Shapley-Scarf housing market (Shapley and Scarf, 1974) with strict preferences model. Our main result is a characterization of the corresponding top trading cycles (TTC) rule by individual rationality, pair efficiency, and strategy-proofness (Theorem 1), which extends that of Ekici (2024) from classical Shapley-Scarf housing markets with strict preferences to our model. Two further characterizations are immediately obtained by strengthening pair efficiency to either Pareto efficiency or pairwise stability (Corollaries 1 and 2). Finally, we show that as soon as we extend the preference domain to include demand lexicographic as well as supply lexicographic preferences (e.g., when preferences are separable), no rule satisfying individual rationality, pair efficiency, and strategy-proofness exists (Theorem 2).", "published": "2024-10-22T06:58:48Z", "authors": ["Bettina Klaus"], "categories": ["econ.TH", "cs.GT"], "doi": "10.1007/s00355-024-01556-9", "_query": "hedonic house price", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2604.24788v1", "title": "Liquid Neural Network Models for Natural Gas Spot Price Time-Series Forecasting", "abstract": "Natural gas is undoubtedly an essential component of the global energy system. Accurate short-term forecasting of natural gas price is challenging due to pronounced volatility driven by seasonal demand patterns, geopolitical developments, and shifting macroeconomic conditions. The nonlinear dynamics and frequent regime changes can limit the effectiveness of traditional time-series models. In this study, we explore the use of Liquid Neural Networks (LNNs) for short-horizon forecasting of the Henry Hub spot price, a primary benchmark for pricing. LNNs are designed to adapt continuously to evolving temporal patterns through dynamic internal state updates, making them well suited for nonstationary price behavior. By improving forecast accuracy in volatile market conditions, this work aims to reduce uncertainty and enhance decision support across energy trading and power market applications.", "published": "2026-04-24T21:37:36Z", "authors": ["Yiqian Liu", "Jiayi Niu", "Adam Kelleher", "Subhabrata Das"], "categories": ["cs.LG", "cs.AI"], "doi": "", "_query": "hedonic house price", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/1812.02433v1", "title": "Using published bid/ask curves to error dress spot electricity price forecasts", "abstract": "Accurate forecasts of electricity spot prices are essential to the daily operational and planning decisions made by power producers and distributors. Typically, point forecasts of these quantities suffice, particularly in the Nord Pool market where the large quantity of hydro power leads to price stability. However, when situations become irregular, deviations on the price scale can often be extreme and difficult to pinpoint precisely, which is a result of the highly varying marginal costs of generating facilities at the edges of the load curve. In these situations it is useful to supplant a point forecast of price with a distributional forecast, in particular one whose tails are adaptive to the current production regime. This work outlines a methodology for leveraging published bid/ask information from the Nord Pool market to construct such adaptive predictive distributions. Our methodology is a non-standard application of the concept of error-dressing, which couples a feature driven error distribution in volume space with a non-linear transformation via the published bid/ask curves to obtain highly non-symmetric, adaptive price distributions. Using data from the Nord Pool market, we show that our method outperforms more standard forms of distributional modeling. We further show how such distributions can be used to render `warning systems' that issue reliable probabilities of prices exceeding various important thresholds.", "published": "2018-12-06T10:09:35Z", "authors": ["Gunnhildur H. Steinbakk", "Alex Lenkoski", "Ragnar Bang Huseby", "Anders Løland", "Tor Arne Øigård"], "categories": ["q-fin.ST", "stat.AP"], "doi": "", "_query": "hedonic house price", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2511.10718v7", "title": "Online Price Competition under Generalized Linear Demands", "abstract": "We study a sequential price competition among $N$ sellers, each influenced by the pricing decisions of their rivals. Specifically, the demand function for each seller $i$ follows the single index model $λ_i(\\mathbf p) = μ_i(\\langle \\boldsymbol θ_{i,0}, \\mathbf p \\rangle)$, with known increasing link $μ_i$ and unknown parameter $\\boldsymbol θ_{i,0}$, where the vector $\\mathbf{p}$ denotes the vector of prices offered by all the sellers simultaneously at a given instant. Each seller observes only their own realized demand - unobservable to competitors - and the prices set by rivals. We propose a novel decentralized policy, PML-GLUCB, that combines penalized MLE with an upper-confidence pricing rule. Our approach (i) \\emph{removes the need for coordinated front-loaded exploration phases across sellers} - which is integral to previous models - making our method aligned with realistic market conditions; (ii) generalizes existing approaches that focus solely on linear demand models; (iii) accommodates both binary and real-valued demand observations. Relative to a dynamic benchmark policy, each seller achieves $\\widetilde{O}(\\sqrt{T})$ regret, which matches the optimal rate known in the linear setting.", "published": "2025-11-13T18:06:21Z", "authors": ["Daniele Bracale", "Moulinath Banerjee", "Cong Shi", "Yuekai Sun"], "categories": ["cs.GT", "math.ST", "stat.ME"], "doi": "", "_query": "hedonic house price", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/physics/0606213v3", "title": "Self-organization of price fluctuation distribution in evolving markets", "abstract": "Financial markets can be seen as complex systems in non-equilibrium steady state, one of whose most important properties is the distribution of price fluctuations. Recently, there have been assertions that this distribution is qualitatively different in emerging markets as compared to developed markets. Here we analyse both high-frequency tick-by-tick as well as daily closing price data to show that the price fluctuations in the Indian stock market, one of the largest emerging markets, have a distribution that is identical to that observed for developed markets (e.g., NYSE). In particular, the cumulative distribution has a long tail described by a power law with an exponent $α\\approx 3$. Also, we study the historical evolution of this distribution over the period of existence of the National Stock Exchange (NSE) of India, which coincided with the rapid transformation of the Indian economy due to liberalization, and show that this power law tail has been present almost throughout. We conclude that the ``inverse cubic law'' is a truly universal feature of a financial market, independent of its stage of development or the condition of the underlying economy.", "published": "2006-06-26T09:23:01Z", "authors": ["Raj Kumar Pan", "Sitabhra Sinha"], "categories": ["physics.soc-ph", "cond-mat.other", "q-fin.ST"], "doi": "10.1209/0295-5075/77/58004", "_query": "hedonic house price", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/1604.04157v1", "title": "Note on VCG vs. Price Raising for Matching Markets", "abstract": "In \\cite{EK10} the use of VCG in matching markets is motivated by saying that in order to compute market clearing prices in a matching market, the auctioneer needs to know the true valuations of the bidders. Hence VCG and corresponding personalized prices are proposed as an incentive compatible mechanism. The same line of argument pops up in several lecture sheets and other documents related to courses based on Easley and Kleinberg's book, seeming to suggest that computing market clearing prices and corresponding assignments were \\emph{not} incentive compatible. Main purpose of our note is to observe that, in contrast, assignments based on buyer optimal market clearing prices are indeed incentive compatible.", "published": "2016-04-14T13:54:10Z", "authors": ["Walter Kern", "Bodo Manthey", "Marc Uetz"], "categories": ["cs.GT"], "doi": "", "_query": "hedonic house price", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2503.06582v2", "title": "Marketplace Operators Can Induce Competitive Pricing", "abstract": "As e-commerce marketplaces continue to grow in popularity, it has become increasingly important to understand the role and impact of marketplace operators on competition and social welfare. We model a marketplace operator as an entity that not only facilitates third-party sales but can also choose to directly participate in the market as a competing seller. We formalize this market structure as a price-quantity Stackelberg duopoly in which the leader is a marketplace operator and the follower is an independent seller who shares a fraction of their revenue with the marketplace operator for the privilege of selling on the platform. The objective of the marketplace operator is to maximize a weighted sum of profit and a term capturing positive customer experience, whereas the independent seller seeks solely to maximize their own profit. We derive the subgame-perfect Nash equilibrium and find that it is often optimal for the marketplace operator to induce competition by offering the product at a low price to incentivize the independent seller to match their price.", "published": "2025-03-09T12:21:23Z", "authors": ["Tiffany Ding", "Dominique Perrault-Joncas", "Orit Ronen", "Michael I. Jordan", "Dirk Bergemann", "Dean Foster", "Omer Gottesman"], "categories": ["econ.TH", "cs.GT"], "doi": "", "_query": "hedonic house price", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2308.11117v1", "title": "Meta-Stock: Task-Difficulty-Adaptive Meta-learning for Sub-new Stock Price Prediction", "abstract": "Sub-new stock price prediction, forecasting the price trends of stocks listed less than one year, is crucial for effective quantitative trading. While deep learning methods have demonstrated effectiveness in predicting old stock prices, they require large training datasets unavailable for sub-new stocks. In this paper, we propose Meta-Stock: a task-difficulty-adaptive meta-learning approach for sub-new stock price prediction. Leveraging prediction tasks formulated by old stocks, our meta-learning method aims to acquire the fast generalization ability that can be further adapted to sub-new stock price prediction tasks, thereby solving the data scarcity of sub-new stocks. Moreover, we enhance the meta-learning process by incorporating an adaptive learning strategy sensitive to varying task difficulties. Through wavelet transform, we extract high-frequency coefficients to manifest stock price volatility. This allows the meta-learning model to assign gradient weights based on volatility-quantified task difficulty. Extensive experiments on datasets collected from three stock markets spanning twenty-two years prove that our Meta-Stock significantly outperforms previous methods and manifests strong applicability in real-world stock trading. Besides, we evaluate the reasonability of the task difficulty quantification and the effectiveness of the adaptive learning strategy.", "published": "2023-08-22T01:54:11Z", "authors": ["Linghao Wang", "Zhen Liu", "Peitian Ma", "Qianli Ma"], "categories": ["cs.CE"], "doi": "", "_query": "hedonic house price", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/1711.03506v1", "title": "Measuring Price Discovery between Nearby and Deferred Contracts in Storable and Non-Storable Commodity Futures Markets", "abstract": "Futures market contracts with varying maturities are traded concurrently and the speed at which they process information is of value in understanding the pricing discovery process. Using price discovery measures, including Putnins (2013) information leadership share and intraday data, we quantify the proportional contribution of price discovery between nearby and deferred contracts in the corn and live cattle futures markets. Price discovery is more systematic in the corn than in the live cattle market. On average, nearby contracts lead all deferred contracts in price discovery in the corn market, but have a relatively less dominant role in the live cattle market. In both markets, the nearby contract loses dominance when its relative volume share dips below 50%, which occurs about 2-3 weeks before expiration in corn and 5-6 weeks before expiration in live cattle. Regression results indicate that the share of price discovery is most closely linked to trading volume but is also affected, to far less degree, by time to expiration, backwardation, USDA announcements and market crashes. The effects of these other factors vary between the markets which likely reflect the difference in storability as well as other market-related characteristics.", "published": "2017-11-09T18:12:05Z", "authors": ["Zhepeng Hu", "Mindy Mallory", "Teresa Serra", "Philip Garcia"], "categories": ["econ.EM"], "doi": "", "_query": "hedonic house price", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/math-ph/0412071v3", "title": "Pricing of options on stocks driven by multi-dimensional operator stable Levy processes", "abstract": "We model the price of a stock via a Langévin equation with multi-dimensional fluctuations coupled in the price and in time. We generalize previous models in that we assume that the fluctuations conditioned on the time step are compound Poisson processes with operator stable jump intensities. We derive exact relations for Fourier transforms of the jump intensity in case of different scaling indices $\\underline{\\underline{E}}$ of the process. We express the Fourier transform of the joint probability density of the process to attain given values at several different times and to attain a given maximal value in a given time period through Fourier transforms of the jump intensity. Then we consider a portfolio composed of stocks and of options on stocks and we derive the Fourier transform of a random variable $\\mathfrak{D}_t$ (deviation of the portfolio) that is defined as a small temporal change of the portfolio diminished by the the compound interest earned. We show that if the price of the option at time $t$ satisfies a certain functional equation specified in text then the deviation of the portfolio has a zero mean $E[ \\mathfrak{D}_t ] = 0$ and the option pricing problem may have a solution. We compare our approach to other approaches that assumed the log-characteristic function of the fluctuations that drive the stock price to be an analytic function.", "published": "2004-12-20T19:24:56Z", "authors": ["Przemyslaw Repetowicz", "Peter Richmond"], "categories": ["math-ph", "q-fin.PR"], "doi": "", "_query": "hedonic house price", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/1204.2716v5", "title": "Drift dependence of optimal trade execution strategies under transient price impact", "abstract": "We give a complete solution to the problem of minimizing the expected liquidity costs in presence of a general drift when the underlying market impact model has linear transient price impact with exponential resilience. It turns out that this problem is well-posed only if the drift is absolutely continuous. Optimal strategies often do not exist, and when they do, they depend strongly on the derivative of the drift. Our approach uses elements from singular stochastic control, even though the problem is essentially non-Markovian due to the transience of price impact and the lack in Markovian structure of the underlying price process. As a corollary, we give a complete solution to the minimization of a certain cost-risk criterion in our setting.", "published": "2012-04-12T13:31:00Z", "authors": ["Christopher Lorenz", "Alexander Schied"], "categories": ["q-fin.TR", "math.OC", "math.PR"], "doi": "", "_query": "hedonic house price", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2307.15982v1", "title": "Vehicle Price Prediction By Aggregating decision tree model With Boosting Model", "abstract": "Predicting the price of used vehicles is a more interesting and needed problem by many users. Vehicle price prediction can be a challenging task due to the high number of attributes that should be considered for accurate prediction. The major step in the prediction process is the collection and pre-processing of the data. In this project, python scripts were built to normalize, standardize, and clean data to avoid unnecessary noise for machine learning algorithms. The data set used in this project can be very valuable in conducting similar research using different prediction techniques. Many assumptions were made on the basis of the data set. The proposed system uses a Decision tree model and Gradient boosting predictive model, which are combined in other to get closed to accurate prediction, the proposed model was evaluated and it gives a promising performance. The future price prediction of used vehicles with the help of the same data set will comprise different models.", "published": "2023-07-29T13:07:57Z", "authors": ["Auwal Tijjani Amshi"], "categories": ["cs.LG"], "doi": "", "_query": "hedonic house price", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2304.02362v1", "title": "A network-based strategy of price correlations for optimal cryptocurrency portfolios", "abstract": "A cryptocurrency is a digital asset maintained by a decentralised system using cryptography. Investors in this emerging digital market are exploring the profitability potential of portfolios in place of single coins. Portfolios are particularly useful given that price forecasting in such a volatile market is challenging. The crypto market is a self-organised complex system where the complex inter-dependencies between the cryptocurrencies may be exploited to understand the market dynamics and build efficient portfolios. In this letter, we use network methods to identify highly decorrelated cryptocurrencies to create diversified portfolios using the Markowitz Portfolio Theory agnostic to future market behaviour. The performance of our network-based portfolios is optimal with 46 coins and superior to benchmarks up to an investment horizon of 14 days, reaching up to 1,066% average expected return within 1 day, with reasonable associated risks. We also show that popular cryptocurrencies are typically not included in the optimal portfolios. Past price correlations reduce risk and may improve the performance of crypto portfolios in comparison to methodologies based exclusively on price auto-correlations. Short-term crypto investments may be competitive to traditional high-risk investments such as the stock market or commodity market but call for caution given the high variability of prices.", "published": "2023-04-05T10:56:01Z", "authors": ["Ruixue Jing", "Luis Enrique Correa Rocha"], "categories": ["physics.soc-ph", "physics.pop-ph", "q-fin.PM"], "doi": "", "_query": "hedonic house price", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/1211.6525v1", "title": "The Pricing Mechanism of Contingent Claims and its Generating Function", "abstract": "In this paper we study dynamic pricing mechanism of contingent claims. A typical model of such pricing mechanism is the so-called g-expectation $E^g_{s,t}[X]$ defined by the solution of the backward stochastic differential equation with generator g and with the contingent claim X as terminal condition. The generating function g this BSDE. We also provide examples of determining the price generating function $g=g(y,z)$ by testing. The main result of this paper is as follows: if a given dynamic pricing mechanism is $E^{g_μ}$-dominated, i.e., the criteria (A5) $E_{s,t}[X]-E_{s,t}[X']\\leq E^{g_μ}_{s,t}[X-X']$ is satisfied for a large enough $μ> 0$, where $g_μ=g_μ(|y|+|z|)$, then $E_{s,t}$ is a g-pricing mechanism. This domination condition was statistically tested using CME data documents. The result of test is significantly positive.", "published": "2012-11-28T05:46:19Z", "authors": ["Shige Peng"], "categories": ["q-fin.PR"], "doi": "", "_query": "hedonic house price", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2501.14155v1", "title": "Learning to Price with Resource Constraints: From Full Information to Machine-Learned Prices", "abstract": "We study the dynamic pricing problem with knapsack, addressing the challenge of balancing exploration and exploitation under resource constraints. We introduce three algorithms tailored to different informational settings: a Boundary Attracted Re-solve Method for full information, an online learning algorithm for scenarios with no prior information, and an estimate-then-select re-solve algorithm that leverages machine-learned informed prices with known upper bound of estimation errors. The Boundary Attracted Re-solve Method achieves logarithmic regret without requiring the non-degeneracy condition, while the online learning algorithm attains an optimal $O(\\sqrt{T})$ regret. Our estimate-then-select approach bridges the gap between these settings, providing improved regret bounds when reliable offline data is available. Numerical experiments validate the effectiveness and robustness of our algorithms across various scenarios. This work advances the understanding of online resource allocation and dynamic pricing, offering practical solutions adaptable to different informational structures.", "published": "2025-01-24T00:46:52Z", "authors": ["Ruicheng Ao", "Jiashuo Jiang", "David Simchi-Levi"], "categories": ["math.OC", "cs.LG"], "doi": "", "_query": "hedonic house price", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2010.13411v1", "title": "Options Pricing for Two Stocks by Black Sholes Time Fractional Order NonLinear Partial Differential Equation", "abstract": "The BS equations with fractional order two asset price models give a better prediction of options pricing in the monetary market. In this paper, the changed form of BS-condition with two asset price models dependent on the Liovelle-Caputo derivative for good predictions of options prices are utilized. The analytical solution is demonstrated in form of convergent infinite series and obtained by the properties of Samudu Transform.", "published": "2020-10-26T08:15:09Z", "authors": ["Kamran Zakaria", "Saeed Hafeez"], "categories": ["q-fin.PR", "math.AP"], "doi": "10.1109/iCoMET48670.2020.9073866", "_query": "hedonic house price", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/cs/0510027v2", "title": "A Market Test for the Positivity of Arrow-Debreu Prices", "abstract": "We derive tractable necessary and sufficient conditions for the absence of buy-and-hold arbitrage opportunities in a perfectly liquid, one period market. We formulate the positivity of Arrow-Debreu prices as a generalized moment problem to show that this no arbitrage condition is equivalent to the positive semidefiniteness of matrices formed by the market price of tradeable securities and their products. We apply this result to a market with multiple assets and basket call options.", "published": "2005-10-11T13:40:17Z", "authors": ["Alexandre d'Aspremont"], "categories": ["cs.CE"], "doi": "", "_query": "hedonic house price", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/1208.5700v1", "title": "Dynamic Pricing of Power in Smart-Grid Networks", "abstract": "In this paper we introduce the problem of dynamic pricing of power for smart-grid networks. This is studied within a network utility maximization (NUM) framework in a deterministic setting with a single provider, multiple users and a finite horizon. The provider produces power or buys power in a (deterministic) spot market, and determines a dynamic price to charge the users. The users then adjust their demand in response to the time-varying prices. This is typically categorized as the demand response problem, and we study a progression of related models by focusing on two aspects: 1) the characterization of the structure of the optimal dynamic prices in the Smart Grid and the optimal demand and supply under various interaction with a spot market; 2) a greedy approach to facilitate the solution process of the aggregate NUM problem and the optimality gap between the greedy solution and the optimal one.", "published": "2012-08-28T16:12:18Z", "authors": ["Qingsi Wang", "Mingyan Liu", "Rahul Jain"], "categories": ["eess.SY"], "doi": "", "_query": "hedonic house price", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2004.01498v1", "title": "Deep Probabilistic Modelling of Price Movements for High-Frequency Trading", "abstract": "In this paper we propose a deep recurrent architecture for the probabilistic modelling of high-frequency market prices, important for the risk management of automated trading systems. Our proposed architecture incorporates probabilistic mixture models into deep recurrent neural networks. The resulting deep mixture models simultaneously address several practical challenges important in the development of automated high-frequency trading strategies that were previously neglected in the literature: 1) probabilistic forecasting of the price movements; 2) single objective prediction of both the direction and size of the price movements. We train our models on high-frequency Bitcoin market data and evaluate them against benchmark models obtained from the literature. We show that our model outperforms the benchmark models in both a metric-based test and in a simulated trading scenario", "published": "2020-03-31T19:25:40Z", "authors": ["Ye-Sheen Lim", "Denise Gorse"], "categories": ["q-fin.ST", "cs.LG", "q-fin.TR", "stat.ML"], "doi": "", "_query": "hedonic house price", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/0708.3198v2", "title": "Universal price impact functions of individual trades in an order-driven market", "abstract": "The trade size $ω$ has direct impact on the price formation of the stock traded. Econophysical analyses of transaction data for the US and Australian stock markets have uncovered market-specific scaling laws, where a master curve of price impact can be obtained in each market when stock capitalization $C$ is included as an argument in the scaling relation. However, the rationale of introducing stock capitalization in the scaling is unclear and the anomalous negative correlation between price change $r$ and trade size $ω$ for small trades is unexplained. Here we show that these issues can be addressed by taking into account the aggressiveness of orders that result in trades together with a proper normalization technique. Using order book data from the Chinese market, we show that trades from filled and partially filled limit orders have very different price impact. The price impact of trades from partially filled orders is constant when the volume is not too large, while that of filled orders shows power-law behavior $r\\sim ω^α$ with $α\\approx2/3$. When returns and volumes are normalized by stock-dependent averages, capitalization-independent scaling laws emerge for both types of trades. However, no scaling relation in terms of stock capitalization can be constructed. In addition, the relation $α=α_ω/α_r$ is verified, where $α_ω$ and $α_r$ are the tail exponents of trade sizes and returns. These observations also enable us to explain the anomalous negative correlation between $r$ and $ω$ for small-size trades. We anticipate that these regularities may hold in other order-driven markets.", "published": "2007-08-23T16:55:34Z", "authors": ["Wei-Xing Zhou"], "categories": ["q-fin.TR", "physics.soc-ph"], "doi": "10.1080/14697688.2010.504733", "_query": "hedonic house price", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/cond-mat/0008147v1", "title": "Hedged Monte-Carlo: low variance derivative pricing with objective probabilities", "abstract": "We propose a new `hedged' Monte-Carlo (HMC) method to price financial derivatives, which allows to determine simultaneously the optimal hedge. The inclusion of the optimal hedging strategy allows one to reduce the financial risk associated with option trading, and for the very same reason reduces considerably the variance of our HMC scheme as compared to previous methods. The explicit accounting of the hedging cost naturally converts the objective probability into the `risk-neutral' one. This allows a consistent use of purely historical time series to price derivatives and obtain their residual risk. The method can be used to price a large class of exotic options, including those with path dependent and early exercise features.", "published": "2000-08-09T10:10:29Z", "authors": ["Marc Potters", "Jean-Philippe Bouchaud", "Dragan Sestovic"], "categories": ["cond-mat"], "doi": "10.1016/S0378-4371(00)00554-9", "_query": "hedonic house price", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/1503.08969v1", "title": "Indifference Pricing and Hedging in a Multiple-Priors Model with Trading Constraints", "abstract": "This paper considers utility indifference valuation of derivatives under model uncertainty and trading constraints, where the utility is formulated as an additive stochastic differential utility of both intertemporal consumption and terminal wealth, and the uncertain prospects are ranked according to a multiple-priors model of Chen and Epstein (2002). The price is determined by two optimal stochastic control problems (mixed with optimal stopping time in the case of American option) of forward-backward stochastic differential equations. By means of backward stochastic differential equation and partial differential equation methods, we show that both bid and ask prices are closely related to the Black-Scholes risk-neutral price with modified dividend rates. The two prices will actually coincide with each other if there is no trading constraint or the model uncertainty disappears. Finally, two applications to European option and American option are discussed.", "published": "2015-03-31T09:06:50Z", "authors": ["Huiwen Yan", "Gechun Liang", "Zhou Yang"], "categories": ["q-fin.MF"], "doi": "10.1007/s11425-014-4885-0", "_query": "hedonic house price", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/1912.13469v4", "title": "Pricing Multi-Interval Dispatch under Uncertainty Part II: Generalization and Performance", "abstract": "Pricing multi-interval economic dispatch of electric power under operational uncertainty is considered in this two-part paper. Part I investigates dispatch-following incentives for generators under the locational marginal pricing (LMP) and temporal locational marginal pricing (TLMP) policies. Extending the theoretical results developed in Part I, Part II evaluates a broader set of performance measures under a general network model. For networks with power flow constraints, TLMP is shown to have an energy-congestion-ramping price decomposition. Under the one-shot dispatch and pricing model, this decomposition leads to a nonnegative merchandising surplus equal to the sum of congestion and ramping surpluses. It is also shown that, comparing with LMP, TLMP imposes a penalty on generators with limited ramping capabilities, thus giving incentives for generators to reveal their ramping limits truthfully and improve their ramping capacities. Several benchmark pricing mechanisms are evaluated under the rolling-window dispatch and pricing models. The performance measures considered are the level of out-of-the-market uplifts, the revenue adequacy of the system operator, consumer payment, generator profit, level of discriminative payment, and price volatility.", "published": "2019-12-31T18:13:06Z", "authors": ["Cong Chen", "Ye Guo", "Lang Tong"], "categories": ["eess.SY"], "doi": "", "_query": "hedonic house price", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/1205.2299v1", "title": "Electricity price modeling and asset valuation: a multi-fuel structural approach", "abstract": "We introduce a new and highly tractable structural model for spot and derivative prices in electricity markets. Using a stochastic model of the bid stack, we translate the demand for power and the prices of generating fuels into electricity spot prices. The stack structure allows for a range of generator efficiencies per fuel type and for the possibility of future changes in the merit order of the fuels. The derived spot price process captures important stylized facts of historical electricity prices, including both spikes and the complex dependence upon its underlying supply and demand drivers. Furthermore, under mild and commonly used assumptions on the distributions of the input factors, we obtain closed-form formulae for electricity forward contracts and for spark and dark spread options. As merit order dynamics and fuel forward prices are embedded into the model, we capture a much richer and more realistic dependence structure than can be achieved by classical reduced-form models. We illustrate these advantages by comparing with Margrabe's formula and a simple cointegration model, and highlight important implications for the valuation of power plants.", "published": "2012-05-10T15:58:24Z", "authors": ["Rene Carmona", "Michael Coulon", "Daniel Schwarz"], "categories": ["q-fin.PR"], "doi": "10.1007/s11579-012-0091-4", "_query": "hedonic house price", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/1503.05655v2", "title": "Option Pricing Beyond Black-Scholes Based on Double-Fractional Diffusion", "abstract": "We show how the prices of options can be determined with the help of double-fractional differential equation in such a way that their inclusion in a portfolio of stocks provides a more reliable hedge against dramatic price drops that the use of options whose prices were fixed by the Black-Scholes formula.", "published": "2015-03-19T06:20:57Z", "authors": ["Hagen Kleinert", "Jan Korbel"], "categories": ["q-fin.RM"], "doi": "10.1016/j.physa.2015.12.125", "_query": "hedonic house price", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/1305.5220v1", "title": "Pricing bonds with optional sinking feature using Markov Decision Processes", "abstract": "An efficient method to price bonds with optional sinking feature is presented. Such instruments equip their issuer with the option (but not the obligation) to redeem parts of the notional prior to maturity, therefore the future cash flows are random. In a one-factor model for the issuer's default intensity we show that the pricing algorithm can be formulated as a Markov Decision Process, which is both accurate and quick. The method is demonstrated using a 1.5-factor credit-equity model which defines the default intensity in a reciprocal relationship to the issuer's stock price process, termed jump-to-default extended model with constant elasticity of variance (JDCEV).", "published": "2013-05-22T18:30:50Z", "authors": ["Jan-Frederik Mai", "Marc Wittlinger"], "categories": ["q-fin.PR"], "doi": "", "_query": "hedonic house price", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/1603.07682v3", "title": "Descending Price Optimally Coordinates Search", "abstract": "Investigating potential purchases is often a substantial investment under uncertainty. Standard market designs, such as simultaneous or English auctions, compound this with uncertainty about the price a bidder will have to pay in order to win. As a result they tend to confuse the process of search both by leading to wasteful information acquisition on goods that have already found a good purchaser and by discouraging needed investigations of objects, potentially eliminating all gains from trade. In contrast, we show that the Dutch auction preserves all of its properties from a standard setting without information costs because it guarantees, at the time of information acquisition, a price at which the good can be purchased. Calibrations to start-up acquisition and timber auctions suggest that in practice the social losses through poor search coordination in standard formats are an order of magnitude or two larger than the (negligible) inefficiencies arising from ex-ante bidder asymmetries.", "published": "2016-03-24T17:49:24Z", "authors": ["Robert Kleinberg", "Bo Waggoner", "E. Glen Weyl"], "categories": ["cs.GT", "econ.GN"], "doi": "", "_query": "hedonic house price", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/1304.4688v1", "title": "On the pricing and hedging of options for highly volatile periods", "abstract": "Option pricing is an integral part of modern financial risk management. The well-known Black and Scholes (1973) formula is commonly used for this purpose. This paper is an attempt to extend their work to a situation in which the unconditional volatility of the original asset is increasing during a certain period of time. We consider a market suffering from a financial crisis. We provide the solution for the equation of the underlying asset price as well as finding the hedging strategy. In addition, a closed formula of the pricing problem is proved for a particular case. The suggested formulas are expected to make the valuation of options and the underlying hedging strategies during financial crisis more precise.", "published": "2013-04-17T04:51:47Z", "authors": ["Youssef El-Khatib", "Abdulnasser Hatemi-J"], "categories": ["q-fin.PR"], "doi": "", "_query": "hedonic house price", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/1910.13286v1", "title": "A Self-Exciting Modelling Framework for Forward Prices in Power Markets", "abstract": "We propose and investigate two model classes for forward power price dynamics, based on continuous branching processes with immigration, and on Hawkes processes with exponential kernel, respectively. The models proposed exhibit jumps clustering features. Models of this kind have been already proposed for the spot price dynamics, but the main purpose of the present work is to investigate the performances of such models in describing the forward dynamics. We adopt a Heath-Jarrow-Morton approach in order to capture the whole forward curve evolution. By examining daily data in the French power market, we perform a goodness-of-fit test and we present our conclusions about the adequacy of these models in describing the forward prices evolution.", "published": "2019-10-29T14:25:43Z", "authors": ["Giorgia Callegaro", "Andrea Mazzoran", "Carlo Sgarra"], "categories": ["q-fin.ST"], "doi": "", "_query": "hedonic house price", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2610.02256v2", "title": "TRACE: A Reproducible Benchmark for Electricity Price Forecasting with Official Operational Text", "abstract": "Electricity price forecasting (EPF) supports scheduling, bidding, and risk management in electricity markets, yet existing benchmarks focus mainly on numerical inputs, leaving the forecasting value of forecast-time textual context insufficiently evaluated. We introduce TRACE, a reproducible benchmark of 7,300 zone--day instances pairing prices from five zones in a major U.S. market with official operational text available at the forecast cutoff. TRACE reconstructs official operational text at each cutoff, preventing post-cutoff information leakage. We evaluate TRACE for semantic alignment and forecasting value. Semantic assessments align with central movement and both tail risks in ground-truth prices, most consistently for upper-tail price risk. Forecasting value is reflected in a median 7.4\\% reduction in upper-tail pinball loss across time-series foundation models. A controlled cross-day text-mismatch ablation reverses the gains, falling below the no-text baseline.", "published": "2026-09-30T20:44:33Z", "authors": ["Xinyi Yi", "Zhangdie Yuan", "Ioannis Lestas"], "categories": ["cs.LG", "stat.ML"], "doi": "", "_query": "hedonic house price", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/physics/0205053v2", "title": "A Quantum Approach to Stock Price Fluctuations", "abstract": "A simple quantum model explains the Levy-unstable distributions for individual stock returns observed by ref.[1]. The probability density function of the returns is written as the squared modulus of an amplitude. For short time intervals this amplitude is proportional to a Cauchy-distribution and satisfies the Schroedinger equation with a non-hermitian Hamiltonian. The observed power law tails of the return fluctuations imply that the \"decay rate\", $γ(q)$ asymptotically is proportional to $|q|$, for large $|q|$. The wave number, the Fourier-conjugate variable to the return, is interpreted as a quantitative measure of \"market sentiment\". On a time scale of less than a few weeks, the distribution of returns in this quantum model is shape stable and scales. The model quantitatively reproduces the observed cumulative distribution for the short-term normalized returns over 7 orders of magnitude without adjustable parameters. The return fluctuations over large time periods ultimately become Gaussian if $γ(q\\sim 0)\\propto q^2$. The ansatz $γ(q)=b_T\\sqrt{m^2+q^2}$ is found to describe the positive part of the observed historic probability of normalized returns for time periods between T=5 min and $T\\sim 4$ years over more than 4 orders of magnitude in terms of one adjustable parameter $s_T=m b_T\\propto T$. The Sharpe ratio of a stock in this model has a finite limit as the investment horizon $T\\to 0$. Implications for short-term investments are discussed.", "published": "2002-05-20T02:30:34Z", "authors": ["Martin Schaden"], "categories": ["physics.soc-ph", "physics.data-an", "q-fin.ST"], "doi": "", "_query": "hedonic house price", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2502.06830v5", "title": "OrderFusion: Encoding Orderbook for End-to-End Probabilistic Intraday Electricity Price Forecasting", "abstract": "Probabilistic intraday electricity price forecasting is becoming increasingly important for short-term power-system operation. With increasing renewable generation, demand-side flexibility, and storage assets, market participants need to adjust their positions under uncertainty closer to delivery. Continuous intraday (CID) markets support this process by providing updated price signals, helping participants manage imbalance exposure and operational risk. Unlike auction markets, CID trading in many jurisdictions is characterized by the continuous posting of buy and sell orders. This dynamic orderbook microstructure of price formation presents special challenges for price forecasting. Conventional methods represent the orderbook via domain features aggregated from buy and sell trades, or by treating it as a multivariate time series, but such representations neglect the full buy-sell interaction structure of the orderbook. This research therefore develops a new order fusion methodology, which is an end-to-end and parameter-efficient probabilistic forecasting model that learns a interaction-aware representation of the buy-sell dynamics. Furthermore, as quantile crossing is often a problem in probabilistic forecasting, this approach hierarchically estimates the quantiles with non-crossing constraints. Extensive experiments on CID price indices across high- and low-liquidity European markets demonstrate consistent improvements over conventional baselines, and ablation studies highlight the contributions of the main components.The methodology is available at: https://runyao-yu.github.io/OrderFusion/.", "published": "2025-02-05T15:37:21Z", "authors": ["Runyao Yu", "Yuchen Tao", "Fabian Leimgruber", "Tara Esterl", "Jochen Stiasny", "Derek W. Bunn", "Qingsong Wen", "Hongye Guo", "Jochen L. Cremer"], "categories": ["q-fin.CP", "cs.AI", "cs.LG"], "doi": "", "_query": "hedonic house price", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2602.15722v1", "title": "Pricing Discrete and Nonlinear Markets With Semidefinite Relaxations", "abstract": "Nonconvexities in markets with discrete decisions and nonlinear constraints make efficient pricing challenging, often necessitating subsidies. A prime example is the unit commitment (UC) problem in electricity markets, where costly subsidies are commonly required. We propose a new pricing scheme for nonconvex markets with both discreteness and nonlinearity, by convexifying nonconvex structures through a semidefinite programming (SDP) relaxation and deriving prices from the relaxation's dual variables. When the choice set is bounded, we establish strong duality for the SDP, which allows us to extend the envelope theorem to the value function of the relaxation. This extension yields a marginal price signal for demand, which we use as our pricing mechanism. We demonstrate that under certain conditions-for instance, when the relaxation's right hand sides are linear in demand-the resulting lost opportunity cost is bounded by the relaxation's optimality gap. This result highlights the importance of achieving tight relaxations. The proposed framework applies to nonconvex electricity market problems, including for both direct current and alternating current UC. Our numerical experiments indicate that the SDP relaxations are often tight, reinforcing the effectiveness of the proposed pricing scheme. Across a suite of IEEE benchmark instances, the lost opportunity cost under our pricing scheme is, on average, 46% lower than that of the commonly used fixed-binary pricing scheme.", "published": "2026-02-17T16:55:48Z", "authors": ["Cheng Guo", "Lauren Henderson", "Ryan Cory-Wright", "Boshi Yang"], "categories": ["math.OC", "econ.GN"], "doi": "", "_query": "hedonic house price", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2512.00733v1", "title": "Price of Anarchy of Multi-Stage Machine Scheduling Games", "abstract": "In this paper, we extend the discussion of the price of anarchy of machine scheduling games to a multi-stage machine setting. The multi-stage setting arises naturally in manufacturing pipelines and distributed computing workflows, when each job must traverse a fixed sequence of processing stages. While the classical makespan price of anarchy of $2 - \\frac{1}{m}$ has been established for sequential scheduling on identical machines, the efficiency loss in multi-stage scheduling has, to the best of our knowledge, not been previously analyzed. We assume that each task follows a greedy strategy and gets assigned to the least-loaded machine upon arrival at each stage. Notably, we observe that in multi-stage environments, greedy behavior generally does not coincide with a subgame perfect Nash equilibrium. We continue with analyzing the equilibrium under greedy choices, since it is logical for modeling selfish agents with limited computational power, and may also model a central scheduler performing the common least-load scheduling heuristics. Under this model, we first show that in single-stage scheduling, greedy choice again yields an exact price of anarchy of $2 - \\frac{1}{m}$. In multi-stage scheduling, we show that the completion time from one stage to the next increases by at most two times the maximum job execution time. Using this relationship, we derived the price of anarchy of multistage scheduling under greedy choices to lie within $[2 - \\frac{1}{m}, 3 - \\frac{1}{m}]$, where $m$ denote the maximum number of machines in one stage.", "published": "2025-11-30T05:05:28Z", "authors": ["Ho-Lin Chen", "Pin-Ju Huang"], "categories": ["cs.GT"], "doi": "", "_query": "hedonic house price", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/1707.03746v3", "title": "Modeling the price of Bitcoin with geometric fractional Brownian motion: a Monte Carlo approach", "abstract": "The long-term dependence of Bitcoin (BTC), manifesting itself through a Hurst exponent $H>0.5$, is exploited in order to predict future BTC/USD price. A Monte Carlo simulation with $10^4$ geometric fractional Brownian motion realisations is performed as extensions of historical data. The accuracy of statistical inferences is 10\\%. The most probable Bitcoin price at the beginning of 2018 is 6358 USD.", "published": "2017-07-12T14:47:37Z", "authors": ["Mariusz Tarnopolski"], "categories": ["q-fin.CP", "econ.GN", "q-fin.ST", "stat.AP"], "doi": "", "_query": "hedonic house price", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2604.23908v1", "title": "Machine Learning and Deep Learning Models for Short Term Electricity Price Forecasting in Australia's National Electricity Market", "abstract": "Short term electricity price forecast is essential in competitive power markets, yet electricity price series exhibit high volatility, irregularity, and non-stationarity. This phenomenon is pronounced in the South Australian region of the National Electricity Market, where high renewable penetration drives price volatility and frequent negative price intervals, while structural changes such as the transition to five-minute settlement further complicate forecast. To address these challenges, this study develops a unified benchmark framework. Under identical data preprocessing, feature engineering with lag features, rolling statistics, cyclic temporal encodings, and so on, and an 85% to 15% chronological train test split, six algorithms are systematically compared, including AWMLSTM, CatBoost, GBRT, LSTM, LightGBM, and SVR. The results show that for price prediction, tree-based models, especially GBRT with an R squared value of 0.88, generally outperform LSTM and SVR. However, all models achieve a mean absolute percentage error above 90%, and more than 65% of GBRT predictions have relative errors above 10%, which highlights the inherent difficulty of price forecast. For demand prediction, all models perform substantially better than in price prediction. AWMLSTM and GBRT achieve an R2 value of 0.96 with mean absolute percentage error below 32%, and GBRT has 74.37% of samples within 5% error, while LSTM and SVR perform less accurately in both tasks. Future improvements should focus on hybrid models such as tree plus transformers, data augmentation for extreme events, and error correction to better capture price spikes.", "published": "2026-04-26T22:57:39Z", "authors": ["Wei Lu", "Jay Wang", "Dingli Duan", "Ding Mao", "Caiyi Song", "John Huang"], "categories": ["cs.LG", "eess.SY"], "doi": "", "_query": "hedonic house price", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2605.08788v1", "title": "The Phase Structure of Metallic Money: An MPTT Framework for the Spanish Price Revolution", "abstract": "The Spanish Price Revolution is usually treated as a classic case in which American bullion inflows expanded the money supply and generated inflation. This view captures the first phase of the episode but fails to explain why the same monetary expansion did not continue to produce proportional price growth after 1600. We develop a two-phase Money Phase Transition Theory (MPTT) model in which the classical monetary relation is recovered before a transition point, while a second-phase correction term modifies the money-price transmission coefficient after the transition. Using annual Spanish CPI and reconstructed money-supply data, we show that 1500-1600 was a high-transmission metallic inflationary phase: CPI increased approximately 3.35-fold while money supply increased approximately 3.73-fold. After 1600, money supply continued to rise, increasing approximately 1.82-fold during 1600-1650, while CPI rose only approximately 1.22-fold. A classical one-phase model fitted on 1500-1600, therefore, overpredicts post-1600 prices when extrapolated forward. The MPTT two-phase model with transition point tau=1600 estimates beta_1=0.949, gamma=-0.812, and beta_2=beta_1+gamma=0.137, indicating a sharp post-transition weakening of monetary transmission. An unrestricted break scan identifies a deeper BIC-minimizing break around 1636. These results suggest that the Spanish Price Revolution was not a single monotonic bullion-inflation process but the rise and exhaustion of high-transmission metallic money inflation.", "published": "2026-05-09T08:17:11Z", "authors": ["Ran Huang"], "categories": ["econ.GN", "physics.data-an"], "doi": "", "_query": "hedonic house price", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2004.01504v1", "title": "Machine Learning Algorithms for Financial Asset Price Forecasting", "abstract": "This research paper explores the performance of Machine Learning (ML) algorithms and techniques that can be used for financial asset price forecasting. The prediction and forecasting of asset prices and returns remains one of the most challenging and exciting problems for quantitative finance and practitioners alike. The massive increase in data generated and captured in recent years presents an opportunity to leverage Machine Learning algorithms. This study directly compares and contrasts state-of-the-art implementations of modern Machine Learning algorithms on high performance computing (HPC) infrastructures versus the traditional and highly popular Capital Asset Pricing Model (CAPM) on U.S equities data. The implemented Machine Learning models - trained on time series data for an entire stock universe (in addition to exogenous macroeconomic variables) significantly outperform the CAPM on out-of-sample (OOS) test data.", "published": "2020-03-31T18:14:18Z", "authors": ["Philip Ndikum"], "categories": ["q-fin.ST", "cs.LG", "econ.EM", "stat.ML"], "doi": "", "_query": "hedonic house price", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2301.06087v2", "title": "Near-optimal Online Algorithms for Joint Pricing and Scheduling in EV Charging Networks", "abstract": "With the rapid acceleration of transportation electrification, public charging stations are becoming vital infrastructure in a smart sustainable city to provide on-demand electric vehicle (EV) charging services. As more consumers seek to utilize public charging services, the pricing and scheduling of such services will become vital, complementary tools to mediate competition for charging resources. However, determining the right prices to charge is difficult due to the online nature of EV arrivals. This paper studies a joint pricing and scheduling problem for the operator of EV charging networks with limited charging capacity and time-varying energy cost. Upon receiving a charging request, the operator offers a price, and the EV decides whether to admit the offer based on its own value and the posted price. The operator then schedules the real-time charging process to satisfy the charging request if the EV admits the offer. We propose an online pricing algorithm that can determine the posted price and EV charging schedule to maximize social welfare, i.e., the total value of EVs minus the energy cost of charging stations. Theoretically, we prove the devised algorithm can achieve the order-optimal competitive ratio under the competitive analysis framework. Practically, we show the empirical performance of our algorithm outperforms other benchmark algorithms in experiments using real EV charging data.", "published": "2023-01-11T03:40:44Z", "authors": ["Roozbeh Bostandoost", "Bo Sun", "Carlee Joe-Wong", "Mohammad Hajiesmaili"], "categories": ["cs.GT"], "doi": "", "_query": "hedonic house price", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2502.07168v1", "title": "Robust pricing for cloud computing", "abstract": "We study the robust sequential screening problem of a monopolist seller of multiple cloud computing services facing a buyer who has private information about his demand distribution for these services. At the time of contracting, the buyer knows the distribution of his demand of various services and the seller simply knows the mean of the buyer's total demand. We show that a simple \"committed spend mechanism\" is robustly optimal: it provides the seller with the highest profit guarantee against all demand distributions that have the known total mean demand. This mechanism requires the buyer to commit to a minimum total usage and a corresponding base payment; the buyer can choose the individual quantities of each service and is free to consume additional units (over the committed total usage) at a fixed marginal price. This result provides theoretical support for prevalent cloud computing pricing practices while highlighting the robustness of simple pricing schemes in environments with complex uncertainty.", "published": "2025-02-11T01:25:23Z", "authors": ["Dirk Bergemann", "Rahul Deb"], "categories": ["econ.TH"], "doi": "", "_query": "hedonic house price", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/1711.01049v1", "title": "Optimal Pricing-Based Edge Computing Resource Management in Mobile Blockchain", "abstract": "As the core issue of blockchain, the mining requires solving a proof-of-work puzzle, which is resource expensive to implement in mobile devices due to high computing power needed. Thus, the development of blockchain in mobile applications is restricted. In this paper, we consider the edge computing as the network enabler for mobile blockchain. In particular, we study optimal pricing-based edge computing resource management to support mobile blockchain applications where the mining process can be offloaded to an Edge computing Service Provider (ESP). We adopt a two-stage Stackelberg game to jointly maximize the profit of the ESP and the individual utilities of different miners. In Stage I, the ESP sets the price of edge computing services. In Stage II, the miners decide on the service demand to purchase based on the observed prices. We apply the backward induction to analyze the sub-game perfect equilibrium in each stage for uniform and discriminatory pricing schemes. Further, the existence and uniqueness of Stackelberg game are validated for both pricing schemes. At last, the performance evaluation shows that the ESP intends to set the maximum possible value as the optimal price for profit maximization under uniform pricing. In addition, the discriminatory pricing helps the ESP encourage higher total service demand from miners and achieve greater profit correspondingly.", "published": "2017-11-03T07:56:48Z", "authors": ["Zehui Xiong", "Shaohan Feng", "Dusit Niyato", "Ping Wang", "Zhu Han"], "categories": ["cs.GT", "cs.CR"], "doi": "", "_query": "hedonic house price", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2505.11890v1", "title": "LLM-Enhanced Feature Engineering for Multi-Factor Electricity Price Predictions", "abstract": "Accurately forecasting electricity price volatility is crucial for effective risk management and decision-making. Traditional forecasting models often fall short in capturing the complex, non-linear dynamics of electricity markets, particularly when external factors like weather conditions and market volatility are involved. These limitations hinder their ability to provide reliable predictions in markets with high volatility, such as the New South Wales (NSW) electricity market. To address these challenges, we introduce FAEP, a Feature-Augmented Electricity Price Prediction framework. FAEP leverages Large Language Models (LLMs) combined with advanced feature engineering to enhance prediction accuracy. By incorporating external features such as weather data and price volatility jumps, and utilizing Retrieval-Augmented Generation (RAG) for effective feature extraction, FAEP overcomes the shortcomings of traditional approaches. A hybrid XGBoost-LSTM model in FAEP further refines these augmented features, resulting in a more robust prediction framework. Experimental results demonstrate that FAEP achieves state-of-art (SOTA) performance compared to other electricity price prediction models in the Australian New South Wale electricity market, showcasing the efficiency of LLM-enhanced feature engineering and hybrid machine learning architectures.", "published": "2025-05-17T07:56:01Z", "authors": ["Haochen Xue", "Chenghao Liu", "Chong Zhang", "Yuxuan Chen", "Angxiao Zong", "Zhaodong Wu", "Yulong Li", "Jiayi Liu", "Kaiyu Liang", "Zhixiang Lu", "Ruobing Li", "Jionglong Su"], "categories": ["cs.CE"], "doi": "", "_query": "hedonic house price", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2212.13815v2", "title": "Fundamental theorem for quantum asset pricing", "abstract": "Quantum computers have the potential to provide an advantage for financial pricing problems by the use of quantum estimation. In a broader context, it is reasonable to ask about situations where the market and the assets traded on the market themselves have quantum properties. In this work, we consider a financial setting where instead of by classical probabilities the market is described by a pure quantum state or, more generally, a quantum density operator. This setting naturally leads to a new asset class, which we call quantum assets. Under the assumption that such assets have a price and can be traded, we develop an extended definition of arbitrage to quantify gains without the corresponding risk. Our main result is a quantum version of the first fundamental theorem of asset pricing. If and only if there is no arbitrage, there exists a risk-free density operator under which all assets are martingales. This density operator is used for the pricing of quantum derivatives. To prove the theorem, we study the density operator version of the Radon-Nikodym measure change. We provide examples to illustrate the theory.", "published": "2022-12-28T13:25:49Z", "authors": ["Jinge Bao", "Patrick Rebentrost"], "categories": ["quant-ph", "q-fin.MF"], "doi": "", "_query": "hedonic house price", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/1605.05496v1", "title": "The Design of a Drag-Free CubeSat and the Housing for its Gravitational Reference Sensor", "abstract": "A Drag-Free CubeSat mission has been proposed to demonstrate the feasibility of a Gravitational Reference Sensor (GRS) with an optical readout for a 3 units (3U) spacecraft. A purely drag-free object is defined by the absence of all external forces other than gravity, which are shielded by the spacecraft. In a real case, the TM will still be affected by disturbances. Several of them are passively reduced by the design of the TM housing. This system is a thick-walled aluminium box that holds the shadow sensors and shields the TM. The housing has an effect on the mechanical, thermal and magnetic environment around the TM. All of them have been analysed. The mechanical vibrations have to fit the launch environment and the modes have to be outside of the measurement range (0.0001 - 1 Hz). The magnetic field has to be reduced by a 0.01 factor. The temperature difference between internal opposing surfaces, determining pressure on the TM, has to be below 10^-3(1 mHz/f)1/3 K Hz^-1/2. The housing, together with the TM, the sensors and the UV LEDs for charging control, constitutes the GRS, which would then fit into a 1U. The other 2Us are occupied by the caging mechanism that constraints the TM during launch, the thrusters, the Attitude Determination And Control System (ADACS) and the electronics. The Drag-Free CubeSat will be the result of the combined efforts of Stanford, University of Florida, KACST and NASA and will be the first drag-free mission with an optical readout and the first GRS designed within the limits of a 3U small satellite. In the first section, this paper briefly updates on the main characteristics and systems of the project. Particular emphasis is then given to the recently designed housing, its expected performance and the open issues.", "published": "2016-05-18T09:41:18Z", "authors": ["Carlo Zanoni", "Abdul Alfauwaz", "Ahmad Aljadaan", "Salman Althubiti", "Karthik Balakrishnan", "Sasha Buchman", "Robert L. Byer", "John W. Conklin", "Grant D. Cutler", "Dan DeBra", "Eric Hultgren", "John A. Lipa", "Shailendhar Saraf", "Andreas Zoellner"], "categories": ["astro-ph.IM", "physics.ins-det"], "doi": "", "_query": "hedonic house price", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2502.04935v1", "title": "Conformal Prediction for Electricity Price Forecasting in the Day-Ahead and Real-Time Balancing Market", "abstract": "The integration of renewable energy into electricity markets poses significant challenges to price stability and increases the complexity of market operations. Accurate and reliable electricity price forecasting is crucial for effective market participation, where price dynamics can be significantly more challenging to predict. Probabilistic forecasting, through prediction intervals, efficiently quantifies the inherent uncertainties in electricity prices, supporting better decision-making for market participants. This study explores the enhancement of probabilistic price prediction using Conformal Prediction (CP) techniques, specifically Ensemble Batch Prediction Intervals and Sequential Predictive Conformal Inference. These methods provide precise and reliable prediction intervals, outperforming traditional models in validity metrics. We propose an ensemble approach that combines the efficiency of quantile regression models with the robust coverage properties of time series adapted CP techniques. This ensemble delivers both narrow prediction intervals and high coverage, leading to more reliable and accurate forecasts. We further evaluate the practical implications of CP techniques through a simulated trading algorithm applied to a battery storage system. The ensemble approach demonstrates improved financial returns in energy trading in both the Day-Ahead and Balancing Markets, highlighting its practical benefits for market participants.", "published": "2025-02-07T13:57:47Z", "authors": ["Ciaran O'Connor", "Mohamed Bahloul", "Roberto Rossi", "Steven Prestwich", "Andrea Visentin"], "categories": ["cs.LG", "cs.AI"], "doi": "", "_query": "hedonic house price", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/cond-mat/0203157v1", "title": "Stochastic Calculus for Assets with Non-Gaussian Price Fluctuations", "abstract": "From the path integral formalism for price fluctuations with non-Gaussian distributions I derive the appropriate stochastic calculus replacing Ito's calculus for stochastic fluctuations.", "published": "2002-03-07T09:41:42Z", "authors": ["Hagen Kleinert"], "categories": ["cond-mat"], "doi": "10.1016/S0378-4371(02)00803-8", "_query": "hedonic house price", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/1811.04144v1", "title": "Simulation of the energy efficiency auction prices in Brazil", "abstract": "The electricity consumption behavior in Brazil has been extensively investigated over the years due to financial and social problems. In this context, it is important to simulate the energy prices of the energy efficiency auctions in the Brazilian regulated environment. This paper presents an approach to generate samples of auction energy prices in energy efficiency market, using Markov chain Monte Carlo method, through the Metropolis-Hastings algorithm. The obtained results show that this approach can be used to generate energy price samples.", "published": "2018-11-09T21:45:30Z", "authors": ["Javier L. L. Gonzales", "Rodrigo F. Calili", "Reinaldo C. Souza", "Felipe L. Coelho da Silva"], "categories": ["stat.AP"], "doi": "", "_query": "hedonic house price", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/1808.02547v1", "title": "The economic value of neighborhoods: Predicting real estate prices from the urban environment", "abstract": "Housing costs have a significant impact on individuals, families, businesses, and governments. Recently, online companies such as Zillow have developed proprietary systems that provide automated estimates of housing prices without the immediate need of professional appraisers. Yet, our understanding of what drives the value of houses is very limited. In this paper, we use multiple sources of data to entangle the economic contribution of the neighborhood's characteristics such as walkability and security perception. We also develop and release a framework able to now-cast housing prices from Open data, without the need for historical transactions. Experiments involving 70,000 houses in 8 Italian cities highlight that the neighborhood's vitality and walkability seem to drive more than 20% of the housing value. Moreover, the use of this information improves the nowcast by 60%. Hence, the use of property's surroundings' characteristics can be an invaluable resource to appraise the economic and social value of houses after neighborhood changes and, potentially, anticipate gentrification.", "published": "2018-08-07T20:42:39Z", "authors": ["Marco De Nadai", "Bruno Lepri"], "categories": ["cs.CY"], "doi": "", "_query": "hedonic house price", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/1407.4694v2", "title": "Distributed Pricing-Based User Association for Downlink Heterogeneous Cellular Networks", "abstract": "This paper considers the optimization of the user and base-station (BS) association in a wireless downlink heterogeneous cellular network under the proportional fairness criterion. We first consider the case where each BS has a single antenna and transmits at fixed power, and propose a distributed price update strategy for a pricing-based user association scheme, in which the users are assigned to the BS based on the value of a utility function minus a price. The proposed price update algorithm is based on a coordinate descent method for solving the dual of the network utility maximization problem, and it has a rigorous performance guarantee. The main advantage of the proposed algorithm as compared to the existing subgradient method for price update is that the proposed algorithm is independent of parameter choices and can be implemented asynchronously. Further, this paper considers the joint user association and BS power control problem, and proposes an iterative dual coordinate descent and the power optimization algorithm that significantly outperforms existing approaches. Finally, this paper considers the joint user association and BS beamforming problem for the case where the BSs are equipped with multiple antennas and spatially multiplex multiple users. We incorporate dual coordinate descent with the weighted minimum mean-squared error (WMMSE) algorithm, and show that it achieves nearly the same performance as a computationally more complex benchmark algorithm (which applies the WMMSE algorithm on the entire network for BS association), while avoiding excessive BS handover.", "published": "2014-07-17T14:59:53Z", "authors": ["Kaiming Shen", "Wei Yu"], "categories": ["cs.NI", "cs.IT"], "doi": "", "_query": "hedonic house price", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2610.01562v2", "title": "Social welfare and price discovery in double auction markets", "abstract": "The tendency of the double auction mechanism to drive prices to competitive equilibrium has been well documented in laboratory experiments, but the phenomenon has lacked a theoretical explanation. This paper studies dynamic double auctions in a pure exchange economy where agents bid their indifference prices implied by their current holdings and preferences. We show that Walras equilibria coincide with the fixed points of the double auction and that repeated double auctions generate bounded sequences of allocations and prices whose cluster points are Walras equilibria with transfers.", "published": "2026-10-01T12:28:15Z", "authors": ["Teemu Pennanen"], "categories": ["q-fin.TR"], "doi": "", "_query": "hedonic house price", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2508.14813v2", "title": "Pricing Options on Forwards in Function-Valued Affine Stochastic Volatility Models", "abstract": "We study the pricing of European-style options written on forward contracts within function-valued infinite-dimensional affine stochastic volatility models. The dynamics of the underlying forward price curves are modeled within the Heath-Jarrow-Morton-Musiela framework as solution to a stochastic partial differential equation modulated by a stochastic volatility process. We analyze two classes of affine stochastic volatility models: (i) a Gaussian model governed by a finite-rank Wishart process, and (ii) a pure-jump affine model extending the Barndorff--Nielsen--Shephard framework with state-dependent jumps in the covariance component. For both models, we derive conditions for the existence of exponential moments and develop semi-closed Fourier-based pricing formulas for vanilla call and put options written on forward price curves. Our approach allows for tractable pricing in models with infinitely many risk factors, thereby capturing maturity-specific and term structure risk essential in forward markets.", "published": "2025-08-20T16:04:56Z", "authors": ["Jian He", "Sven Karbach", "Asma Khedher"], "categories": ["q-fin.MF", "math.PR", "q-fin.CP"], "doi": "", "_query": "hedonic house price", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2607.11210v1", "title": "Decoupling Corruption and Horizon in Robust Contextual Pricing", "abstract": "We study robust repeated contextual pricing, where valuations depends linearly on the features. At each round $t\\in[T]$, a seller observes a context, posts a price, and receives only a possibly corrupted binary sale feedback. The seller knows an upper bound $C$ on the number of corrupted rounds. We design an algorithm with regret $\\mathcal O(Cd+d^2\\log T)$, where $d$ is the context dimension. This is the first guarantee for robust contextual pricing that separates the dependence on the corruption budget $C$ from the horizon $T$, closing the problem left open by Gupta, Guruganesh, Paes Leme, and Schneider (2025).", "published": "2026-07-13T08:02:18Z", "authors": ["Matteo Castiglioni", "Francesco Emanuele Stradi"], "categories": ["cs.GT"], "doi": "", "_query": "hedonic house price", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/1405.5230v4", "title": "A Functional Limit Theorem for Limit Order Books with State Dependent Price Dynamics", "abstract": "We consider a stochastic model for the dynamics of the two-sided limit order book (LOB). Our model is flexible enough to allow for a dependence of the price dynamics on volumes. For the joint dynamics of best bid and ask prices and the standing buy and sell volume densities, we derive a functional limit theorem, which states that our LOB model converges in distribution to a fully coupled SDE-SPDE system when the order arrival rates tend to infinity and the impact of an individual order arrival on the book as well as the tick size tends to zero. The SDE describes the bid/ask price dynamics while the SPDE describes the volume dynamics.", "published": "2014-05-20T20:20:06Z", "authors": ["Christian Bayer", "Ulrich Horst", "Jinniao Qiu"], "categories": ["q-fin.MF", "q-fin.TR"], "doi": "", "_query": "hedonic house price", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/quant-ph/0510121v1", "title": "Quantum extension of European option pricing based on the Ornstein-Uhlenbeck process", "abstract": "In this work we propose a option pricing model based on the Ornstein-Uhlenbeck process. It is a new look at the Black-Scholes formula which is based on the quantum game theory. We show the differences between a classical look which is price changing by a Wiener process and the pricing is supported by a quantum model.", "published": "2005-10-16T09:31:15Z", "authors": ["Edward W. Piotrowski", "Malgorzata Schroeder", "Anna Zambrzycka"], "categories": ["quant-ph"], "doi": "10.1016/j.physa.2005.12.021", "_query": "hedonic house price", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2007.08804v2", "title": "Pricing equity-linked life insurance contracts with multiple risk factors by neural networks", "abstract": "This paper considers the pricing of equity-linked life insurance contracts with death and survival benefits in a general model with multiple stochastic risk factors: interest rate, equity, volatility, unsystematic and systematic mortality. We price the equity-linked contracts by assuming that the insurer hedges the risks to reduce the local variance of the net asset value process and requires a compensation for the non-hedgeable part of the liability in the form of an instantaneous standard deviation risk margin. The price can then be expressed as the solution of a system of non-linear partial differential equations. We reformulate the problem as a backward stochastic differential equation with jumps and solve it numerically by the use of efficient neural networks. Sensitivity analysis is performed with respect to initial parameters and an analysis of the accuracy of the approximation of the true price with our neural networks is provided.", "published": "2020-07-17T07:59:32Z", "authors": ["Karim Barigou", "Lukasz Delong"], "categories": ["q-fin.PR"], "doi": "", "_query": "hedonic house price", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/1712.03566v1", "title": "Enhancing Binomial and Trinomial Equity Option Pricing Models", "abstract": "We extend the classical Cox-Ross-Rubinstein binomial model in two ways. We first develop a binomial model with time-dependent parameters that equate all moments of the pricing tree increments with the corresponding moments of the increments of the limiting Itô price process. Second, we introduce a new trinomial model in the natural (historical) world, again fitting all moments of the pricing tree increments to the corresponding geometric Brownian motion. We introduce the risk-neutral trinomial tree and derive a hedging strategy based on an additional perpetual derivative used as a second asset for hedging in any node of the trinomial pricing tree.", "published": "2017-12-10T17:57:03Z", "authors": ["Yong Shin Kim", "Stoyan Stoyanov", "Svetlozar Rachev", "Frank J. Fabozzi"], "categories": ["q-fin.MF"], "doi": "", "_query": "hedonic house price", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2510.11261v4", "title": "Mean-Field Price Formation on Trees with Multi-Population and Non-Rational Agents", "abstract": "This work solves the equilibrium price formation problem for the risky stock by combining mean-field game theory with the binomial tree framework, adapting the classic approach of Cox, Ross & Rubinstein. For agents with exponential and recursive utilities of exponential-type, we prove the existence of a unique mean-field market-clearing equilibrium and derive an explicit analytic formula for equilibrium transition probabilities of the stock price on the binomial lattice. The agents face stochastic terminal liabilities and incremental endowments that depend on unhedgeable common and idiosyncratic factors, in addition to the stock price path. We also incorporate an external order flow. Furthermore, the analytic tractability of the proposed approach allows us to extend the framework in two important directions: First, we incorporate multi-population heterogeneity, allowing agents to differ in functional forms for their liabilities, endowments, and risk coefficients. Second, we relax the rational expectations hypothesis by modeling agents operating under subjective probability measures which induce stochastically biased views on the stock transition probabilities. Our numerical examples illustrate the qualitative effects of these components on the equilibrium price distribution.", "published": "2025-10-13T10:51:56Z", "authors": ["Masaaki Fujii"], "categories": ["q-fin.MF", "econ.GN", "q-fin.PM"], "doi": "", "_query": "hedonic house price", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/1105.6272v1", "title": "Life time of correlation between stocks prices on established and emerging markets", "abstract": "The correlation coefficient between stocks depends on price history and includes information on hierarchical structure in financial markets. It is useful for portfolio selection and estimation of risk. I introduce the Life Time of Correlation between stocks prices to know how far we should investigate the price history to obtain the optimal durability of correlation. I carry out my research on emerging (Poland) and established markets (in the USA, Great Britain and Germany). Other methods, including the Minimum Spanning Trees, tree half-life, decomposition of correlations and the Epps effect are also discussed.", "published": "2011-05-31T13:30:38Z", "authors": ["Andrzej Buda"], "categories": ["q-fin.GN", "stat.AP"], "doi": "", "_query": "hedonic house price", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2202.04616v4", "title": "Sequentially Optimal Pricing under Informational Robustness", "abstract": "A seller sells an object over time but is uncertain how the buyer learns their willingness-to-pay. We consider informational robustness under \\textit{limited commitment}, where the seller offers a price \\textit{each period} to maximize expected continuation profit against worst-case learning. Our formulation considers the worst case \\textit{sequentially}. We characterize an essentially unique equilibrium under general conditions. We further show that, under mild conditions on the prior distribution, the equilibrium profit coincides exactly with the profit guaranteed by the equilibrium price path even under arbitrary (unrestricted) learning processes.", "published": "2022-02-09T18:22:21Z", "authors": ["Zihao Li", "Jonathan Libgober", "Xiaosheng Mu"], "categories": ["econ.TH"], "doi": "", "_query": "hedonic house price", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/1401.1479v1", "title": "Multilevel Pricing Schemes in a Deregulated Wireless Network Market", "abstract": "Typically the cost of a product, a good or a service has many components. Those components come from different complex steps in the supply chain of the product from sourcing to distribution. This economic point of view also takes place in the determination of goods and services in wireless networks. Indeed, before transmitting customer data, a network operator has to lease some frequency range from a spectrum owner and also has to establish agreements with electricity suppliers. The goal of this paper is to compare two pricing schemes, namely a power-based and a flat rate, and give a possible explanation why flat rate pricing schemes are more common than power based pricing ones in a deregulated wireless market. We suggest a hierarchical game-theoretical model of a three level supply chain: the end users, the service provider and the spectrum owner. The end users intend to transmit data on a wireless network. The amount of traffic sent by the end users depends on the available frequency bandwidth as well as the price they have to pay for their transmission. A natural question arises for the service provider: how to design an efficient pricing scheme in order to maximize his profit. Moreover he has to take into account the lease charge he has to pay to the spectrum owner and how many frequency bandwidth to rent. The spectrum owner itself also looks for maximizing its profit and has to determine the lease price to the service provider. The equilibrium at each level of our supply chain model are established and several properties are investigated. In particular, in the case of a power-based pricing scheme, the service provider and the spectrum owner tend to share the gross provider profit. Whereas, considering the flat rate pricing scheme, if the end users are going to exploit the network intensively, then the tariffs of the suppliers (spectrum owner and service provider) explode.", "published": "2014-01-07T19:08:01Z", "authors": ["Andrey Garnaev", "Yezekael Hayel", "Eitan Altman"], "categories": ["cs.GT"], "doi": "", "_query": "hedonic house price", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2106.12747v1", "title": "Automated Agriculture Commodity Price Prediction System with Machine Learning Techniques", "abstract": "The intention of this research is to study and design an automated agriculture commodity price prediction system with novel machine learning techniques. Due to the increasing large amounts historical data of agricultural commodity prices and the need of performing accurate prediction of price fluctuations, the solution has largely shifted from statistical methods to machine learning area. However, the selection of proper set from historical data for forecasting still has limited consideration. On the other hand, when implementing machine learning techniques, finding a suitable model with optimal parameters for global solution, nonlinearity and avoiding curse of dimensionality are still biggest challenges, therefore machine learning strategies study are needed. In this research, we propose a web-based automated system to predict agriculture commodity price. In the two series experiments, five popular machine learning algorithms, ARIMA, SVR, Prophet, XGBoost and LSTM have been compared with large historical datasets in Malaysia and the most optimal algorithm, LSTM model with an average of 0.304 mean-square error has been selected as the prediction engine of the proposed system.", "published": "2021-06-24T03:10:25Z", "authors": ["Zhiyuan Chen", "Howe Seng Goh", "Kai Ling Sin", "Kelly Lim", "Nicole Ka Hei Chung", "Xin Yu Liew"], "categories": ["cs.LG"], "doi": "", "_query": "hedonic house price", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/1912.01387v2", "title": "Pricing FX Options under Intermediate Currency", "abstract": "We suggest an intermediate currency approach that allows us to price options on all FX markets simultaneously under the same risk-neutral measure which ensures consistency of FX option prices across all markets. In particular, it is sufficient to calibrate a model to the volatility smile on the domestic market as, due to the consistency of pricing formulas, the model automatically reproduces the correct smile for the inverse pair (the foreign market). We first consider the case of two currencies and then the multi-currency setting. We illustrate the intermediate currency approach by applying it to the Heston and SABR stochastic volatility models, to the model in which exchange rates are described by an extended skewed normal distribution, and also to the model-free approach of option pricing", "published": "2019-12-03T14:20:02Z", "authors": ["S. Maurer", "T. E. Sharp", "M. V. Tretyakov"], "categories": ["q-fin.MF", "math.PR", "q-fin.PR"], "doi": "", "_query": "hedonic house price", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2204.10154v1", "title": "From point forecasts to multivariate probabilistic forecasts: The Schaake shuffle for day-ahead electricity price forecasting", "abstract": "Modeling price risks is crucial for economic decision making in energy markets. Besides the risk of a single price, the dependence structure of multiple prices is often relevant. We therefore propose a generic and easy-to-implement method for creating multivariate probabilistic forecasts based on univariate point forecasts of day-ahead electricity prices. While each univariate point forecast refers to one of the day's 24 hours, the multivariate forecast distribution models dependencies across hours. The proposed method is based on simple copula techniques and an optional time series component. We illustrate the method for five benchmark data sets recently provided by Lago et al. (2020). Furthermore, we demonstrate an example for constructing realistic prediction intervals for the weighted sum of consecutive electricity prices, as, e.g., needed for pricing individual load profiles.", "published": "2022-04-21T15:04:39Z", "authors": ["Oliver Grothe", "Fabian Kächele", "Fabian Krüger"], "categories": ["econ.EM", "stat.AP"], "doi": "10.1016/j.eneco.2023.106602", "_query": "hedonic house price", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2204.01326v3", "title": "Price Optimal Routing in Public Transportation", "abstract": "We consider the price-optimal earliest arrival problem in public transit (POEAP) in which we aim to calculate the Pareto-set of journeys with respect to ticket price and arrival time in a public transportation network. Public transit fare structures are often a combination of various fare strategies such as, e.g., distance-based fares, zone-based fares or flat fares. The rules that determine the actual ticket price are often very complex. Accordingly, fare structures are notoriously difficult to model, as it is in general not sufficient to simply assign costs to arcs in a routing graph. Research into POEAP is scarce and usually either relies on heuristics or only considers restrictive fare models that are too limited to cover the full scope of most real-world applications. We therefore introduce conditional fare networks (CFNs), the first framework for representing a large number of real-world fare structures. We show that by relaxing label domination criteria, CFNs can be used as a building block in label-setting multi-objective shortest path algorithms. By the nature of their extensive modeling capabilities, optimizing over CFNs is NP-hard. However, we demonstrate that adapting the multi-criteria RAPTOR (MCRAP) algorithm for CFNs yields an algorithm capable of solving POEAP to optimality in less than 400 ms on average on a real-world data set. By restricting the size of the Pareto-set, running times are further reduced to below 10 ms.", "published": "2022-04-04T08:57:48Z", "authors": ["Ricardo Euler", "Niels Lindner", "Ralf Borndörfer"], "categories": ["math.OC", "cs.DM"], "doi": "10.1016/j.ejtl.2024.100128", "_query": "hedonic house price", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2511.19842v3", "title": "Strategy-robust Online Learning in Contextual Pricing", "abstract": "Learning effective pricing strategies is crucial in digital marketplaces, especially when buyers' valuations are unknown and must be inferred through interaction. We study the online contextual pricing problem, where a seller observes a stream of context-valuation pairs and dynamically sets prices. Moreover, departing from traditional online learning frameworks, we consider a strategic setting in which buyers may misreport valuations to influence future prices, a challenge known as strategic overfitting (Amin et al. 2013). We introduce a strategy-robust notion of regret for multi-buyer online environments, capturing worst-case strategic behavior in the spirit of the Price of Anarchy. Our first contribution is a polynomial-time approximation scheme (PTAS) for learning linear pricing policies in adversarial, adaptive environments, enabled by a novel online sketching technique. Building on this result, we propose our main construction: the Sparse Update Mechanism (SUM), a simple yet effective sequential mechanism that ensures robustness to all Nash equilibria among buyers. Moreover, our construction yields a black-box reduction from online expert algorithms to strategy-robust learners.", "published": "2025-11-25T02:16:36Z", "authors": ["Joon Suk Huh", "Kirthevasan Kandasamy"], "categories": ["cs.GT"], "doi": "", "_query": "hedonic house price", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/1007.0199v5", "title": "Optimal execution strategy in the presence of permanent price impact and fixed transaction cost", "abstract": "We study a single risky financial asset model subject to price impact and transaction cost over an infinite horizon. An investor needs to execute a long position in the asset affecting the price of the asset and possibly incurring in fixed transaction cost. The objective is to maximize the discounted revenue obtained by this transaction. This problem is formulated first as an impulse control problem and we characterize the value function using the viscosity solutions framework. We also analyze the case where there is no transaction cost and how this formulation relates with a singular control problem. A viscosity solution characterization is provided in this case as well. We also establish a connection between both formulations with zero fixed transaction cost. Numerical examples with different types of price impact conclude the discussion.", "published": "2010-07-01T15:45:58Z", "authors": ["Mauricio Junca"], "categories": ["q-fin.TR", "eess.SY", "math.OC", "math.PR"], "doi": "", "_query": "hedonic house price", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2412.15707v3", "title": "Online Optimization Algorithms in Repeated Price Competition: Equilibrium Learning and Algorithmic Collusion", "abstract": "This paper investigates whether online learning algorithms in pricing produce competitive outcomes or tacit collusion. This issue has drawn considerable attention from competition regulators as algorithmic pricing becomes more common in digital markets. Understanding when such algorithms lead to equilibrium or supra-competitive prices is critical for buyers, sellers, and policymakers. We study the behavior of multi-armed bandit (MAB) online learning algorithms in repeated price competition. These algorithms require little information to learn, making them realistic models of automated pricing. Our analysis shows that mean-based algorithms, a special variant of online learning algorithms, converge to correlated rationalizable actions. In the Bertrand environments considered, this implies convergence to the Nash equilibrium or adjacent prices. Numerical experiments reveal that most MAB algorithms, including those that are not mean-based, also converge. We observe supra-competitive prices only in specific cases where all sellers implement the same symmetric version of certain algorithms, such as UCB. This effect diminishes as the number of competitors increases. Our results suggest that, even in a stylized repeated Bertrand competition, sustained supra-competitive prices may be less of a concern when independent agents use different online learning algorithms. Our insights are relevant for regulators and managers considering the use of algorithmic pricing algorithms.", "published": "2024-12-20T09:29:57Z", "authors": ["Martin Bichler", "Julius Durmann", "Matthias Oberlechner"], "categories": ["cs.GT"], "doi": "", "_query": "hedonic house price", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2609.23598v1", "title": "OrderFusion+: Probabilistic Buy--Sell Price Trajectory Forecasting in Intraday Electricity Markets", "abstract": "Intraday electricity markets enable participants to adjust energy positions close to delivery, with price forecasts necessary to support trading and the scheduling of flexible electricity resources as well as increasingly responsive consumers. Forecasting model specifications have progressed from using macro-features, such as renewable generation and load, to the micro-features of continuous orderbooks. A recent advanced deep learning model, OrderFusion, explicitly models micro-level buy-sell orderbook interactions. However, despite its superior comparative forecasting performance, it considers only one delivery product at a time, ignoring neighboring-product information. Moreover, when forecasting aggregated price indices such as the liquid German ID3, ID2, and ID1 products, it omits the information contained in price trajectories. In contrast, pretrained time-series foundation models have shown success in financial, renewable-energy, and day-ahead electricity price forecasting. However, their performance on intraday orderbook data remains an open research question of considerable practical importance. In this paper, we propose OrderFusion+, an open-source deep learning model that combines historical orders from the target and neighboring delivery products to forecast probabilistic buy-sell price trajectories. We benchmark OrderFusion+ against forecasting baselines and pretrained foundation models, and investigate dynamic market conditions through the designed dynamic masking mechanism, revealing insights into market efficiency. The implementation and forecasts can be found at: https://runyao-yu.com/OrderFusion/", "published": "2026-09-20T12:33:57Z", "authors": ["Runyao Yu", "Derek W. Bunn"], "categories": ["q-fin.CP"], "doi": "", "_query": "hedonic house price", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/1508.00632v4", "title": "Robust replication of barrier-style claims on price and volatility", "abstract": "We show how to price and replicate a variety of barrier-style claims written on the $\\log$ price $X$ and quadratic variation $\\langle X \\rangle$ of a risky asset. Our framework assumes no arbitrage, frictionless markets and zero interest rates. We model the risky asset as a strictly positive continuous semimartingale with an independent volatility process. The volatility process may exhibit jumps and may be non-Markovian. As hedging instruments, we use only the underlying risky asset, zero-coupon bonds, and European calls and puts with the same maturity as the barrier-style claim. We consider knock-in, knock-out and rebate claims in single and double barrier varieties.", "published": "2015-08-04T01:10:47Z", "authors": ["Peter Carr", "Roger Lee", "Matthew Lorig"], "categories": ["q-fin.MF"], "doi": "", "_query": "hedonic house price", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2111.08390v1", "title": "Price Stability of Cryptocurrencies as a Medium of Exchange", "abstract": "We present positive evidence of price stability of cryptocurrencies as a medium of exchange. For the sample years from 2016 to 2020, the prices of major cryptocurrencies are found to be stable, relative to major financial assets. Specifically, after filtering out the less-than-one-month cycles, we investigate the daily returns in US dollars of the major cryptocurrencies (i.e., Bitcoin, Ethereum, and Ripple) as well as their comparators (i.e., major legal tenders, the Euro and Japanese yen, and the major stock indexes, S&P 500 and MSCI World Index). We examine the stability of the filtered daily returns using three different measures. First, the Pearson correlations increased in later years in our sample. Second, based on the dynamic time-warping method that allows lags and leads in relations, the similarities in the daily returns of cryptocurrencies with their comparators have been present even since 2016. Third, we check whether the cumulative sum of errors to predict cryptocurrency prices, assuming stable relations with comparators' daily returns, does not exceeds the bounds implied by the Black-Scholes model. This test, in other words, does not reject the efficient market hypothesis.", "published": "2021-11-16T11:41:57Z", "authors": ["Tatsuru Kikuchi", "Toranosuke Onishi", "Kenichi Ueda"], "categories": ["econ.GN", "q-fin.PR", "q-fin.ST"], "doi": "10.7566/JPSCP.36.011001", "_query": "hedonic house price", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2310.06079v4", "title": "Anomalous diffusion and price impact in the fluid-limit of an order book", "abstract": "We extend a Discrete Time Random Walk (DTRW) numerical scheme to simulate the anomalous diffusion of financial market orders in a simulated order book. Here using random walks with Sibuya waiting times to include a time-dependent stochastic forcing function with non-uniformly sampled times between order book events in the setting of fractional diffusion. This models the fluid limit of an order book by modelling the continuous arrival, cancellation and diffusion of orders in the presence of information shocks. We study the impulse response and stylised facts of orders undergoing anomalous diffusion for different forcing functions and model parameters. Concretely, we demonstrate the price impact for flash limit-orders and market orders and show how the numerical method generate kinks in the price impact. We use cubic spline interpolation to generate smoothed price impact curves. The work promotes the use of non-uniform sampling in the presence of diffusive dynamics as the preferred simulation method.", "published": "2023-10-09T18:36:08Z", "authors": ["Derick Diana", "Tim Gebbie"], "categories": ["q-fin.CP", "cs.CE", "nlin.AO", "q-fin.TR"], "doi": "10.1016/j.cam.2024.116202", "_query": "hedonic house price", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2605.26901v1", "title": "Load Management of Distribution Systems via Online Dynamic Pricing", "abstract": "The growing adoption of electric vehicles (EVs) is increasing peak demand in distribution systems, which can threaten grid stability and reduce operational efficiency. Dynamic electricity pricing is a promising means of mitigating these peaks by shifting flexible demand. However, most existing approaches rely on detailed user-level consumption data and behavioral models, which are often difficult to obtain in practice and may raise privacy concerns. This paper proposes an Online Feedback Optimization (OFO) algorithm for day-ahead price design with limited data, where only aggregate loads are observed. OFO updates prices iteratively using aggregate load measurements, enabling effective peak reduction without access to individual user data. The formulation also includes a term that penalizes deviations in total electricity cost relative to a reference tariff. Although relying only on aggregate load measurements, the OFO price updates efficiently converge to the optimal price. In finite-horizon simulations, OFO achieves peak reduction close to that of the Stackelberg benchmark with full model information. Meanwhile, its computational effort is substantially lower. Additional tests under multiple initial conditions and delayed charging-window mismatch further confirm the robustness of the proposed method. Overall, these results show that OFO is a scalable and computationally efficient approach for peak-demand management in distribution systems with limited observability.", "published": "2026-05-26T12:01:42Z", "authors": ["Jiarui Yu", "Zhiyu He", "Wenbin Wang", "Colin N. Jones", "Florian Dörfler", "Hanmin Cai"], "categories": ["eess.SY"], "doi": "", "_query": "hedonic house price", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2606.07014v1", "title": "Networked Spatial Effects in European Electricity Price Forecasting", "abstract": "As European bidding zones are highly interconnected by physical transmission lines, spatial influences propagate across neighboring nodes through a network. It is reflected in the day-ahead electricity prices across European bidding zones, as the auction algorithm also uses information beyond each bidding zone's geographic boundary. To capture how this interconnection affects the electricity prices in neighboring bidding zones, we have used a metric graph to map the spatial coverage of information using a well-defined neighborhood measure. We propose the Networked Spatio-Temporal Model (NSTM), which maps irregular spatial nodes into an ordered network, enabling the systematic incorporation of neighborhood information. We implement the NSTM across 39 bidding zones covering the majority of European electricity markets in a high-resolution, streaming-forecasting setup. The model uses autoregressive, cross-hour, and seasonal effects, along with fuel and emission prices and day-ahead forecasts of fundamentals, as interconnected information to predict the day-ahead prices for each bidding zone. A Europe-wide study presented in this paper shows that the NSTM consistently outperforms traditional island-based pure local models. This paper provides a framework that demonstrates the critical role the networked structure plays in propagating information across interconnected markets and its vast implications for day-ahead electricity price forecasting.", "published": "2026-06-05T07:56:46Z", "authors": ["Sultan Mahmud Chomon", "Florian Ziel"], "categories": ["stat.AP"], "doi": "", "_query": "hedonic house price", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/1810.07832v3", "title": "Scaling Limits for Super--replication with Transient Price Impact", "abstract": "We prove a scaling limit theorem for the super-replication cost of options in a Cox--Ross--Rubinstein binomial model with transient price impact. The correct scaling turns out to keep the market depth parameter constant while resilience over fixed periods of time grows in inverse proportion with the duration between trading times. For vanilla options, the scaling limit is found to coincide with the one obtained by PDE methods in [12] for models with purely temporary price impact. These models are a special case of our framework and so our probabilistic scaling limit argument allows one to expand the scope of the scaling limit result to path-dependent options.", "published": "2018-10-17T23:18:56Z", "authors": ["Peter Bank", "Yan Dolinsky"], "categories": ["q-fin.MF"], "doi": "", "_query": "hedonic house price", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/1410.2640v1", "title": "Optimal Lower Bound for Itemset Frequency Indicator Sketches", "abstract": "Given a database, a common problem is to find the pairs or $k$-tuples of items that frequently co-occur. One specific problem is to create a small space \"sketch\" of the data that records which $k$-tuples appear in more than an $ε$ fraction of rows of the database. We improve the lower bound of Liberty, Mitzenmacher, and Thaler [LMT14], showing that $Ω(\\frac{1}εd \\log (εd))$ bits are necessary even in the case of $k=2$. This matches the sampling upper bound for all $ε\\geq 1/d^{.99}$, and (in the case of $k=2$) another trivial upper bound for $ε= 1/d$.", "published": "2014-10-09T22:07:01Z", "authors": ["Eric Price"], "categories": ["cs.DS"], "doi": "", "_query": "hedonic house price", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/1111.4298v5", "title": "Time Consistent Bid-Ask Dynamic Pricing Mechanisms for Contingent Claims and Its Numerical Simulations Under Uncertainty", "abstract": "We study time consistent dynamic pricing mechanisms of European contingent claims under uncertainty by using G framework introduced by Peng ([24]). We consider a financial market consisting of a riskless asset and a risky stock with price process modelled by a geometric generalized G-Brownian motion, which features the drift uncertainty and volatility uncertainty of the stock price process. Using the techniques on G-framework we show that the risk premium of the asset is uncertain and distributed with maximum distribution. A time consistent G-expectation is defined by the viscosity solution of the G-heat equation. Using the time consistent G-expectation we define the G dynamic pricing mechanism for the claim. We prove that G dynamic pricing mechanism is the bid-ask Markovian dynamic pricing mechanism. The full nonlinear PDE is derived to describe the bid (resp. ask) price process of the claim. Monotone implicit characteristic finite difference schemes for the nonlinear PDE are given, nonlinear iterative schemes are constructed, and the simulations of the bid (resp. ask) prices of contingent claims under uncertainty are implemented.", "published": "2011-11-18T08:31:41Z", "authors": ["Wei Chen"], "categories": ["q-fin.PR"], "doi": "", "_query": "hedonic house price", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/1405.4490v1", "title": "Quantum spatial-periodic harmonic model for daily price-limited stock markets", "abstract": "We investigate the behavior of stocks in daily price-limited stock markets by purposing a quantum spatial-periodic harmonic model. The stock price is presumed to oscillate and damp in a quantum spatial-periodic harmonic oscillator potential well. Complicated non-linear relations including inter-band positive correlation and intra-band negative correlation between the volatility and the trading volume of stocks are derived by considering the energy band structure of the model. The validity of price limitation is then examined and abnormal phenomena of a price-limited stock market (Shanghai Stock Exchange) of China are studied by applying our quantum model.", "published": "2014-05-18T11:07:29Z", "authors": ["Xiangyi Meng", "Jian-Wei Zhang", "Jingjing Xu", "Hong Guo"], "categories": ["q-fin.GN", "physics.soc-ph"], "doi": "10.1016/j.physa.2015.06.041", "_query": "hedonic house price", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2402.10132v1", "title": "Quantum option pricing via the Karhunen-Loève expansion", "abstract": "We consider the problem of pricing discretely monitored Asian options over $T$ monitoring points where the underlying asset is modeled by a geometric Brownian motion. We provide two quantum algorithms with complexity poly-logarithmic in $T$ and polynomial in $1/ε$, where $ε$ is the additive approximation error. Our algorithms are obtained respectively by using an $O(\\log T)$-qubit semi-digital quantum encoding of the Brownian motion that allows for exponentiation of the stochastic process and by analyzing classical Monte Carlo algorithms inspired by the semi-digital encodings. The best quantum algorithm obtained using this approach has complexity $\\widetilde{O}(1/ε^{3})$ where the $\\widetilde{O}$ suppresses factors poly-logarithmic in $T$ and $1/ε$. The methods proposed in this work generalize to pricing options where the underlying asset price is modeled by a smooth function of a sub-Gaussian process and the payoff is dependent on the weighted time-average of the underlying asset price.", "published": "2024-02-15T17:37:23Z", "authors": ["Anupam Prakash", "Yue Sun", "Shouvanik Chakrabarti", "Charlie Che", "Aditi Dandapani", "Dylan Herman", "Niraj Kumar", "Shree Hari Sureshbabu", "Ben Wood", "Iordanis Kerenidis", "Marco Pistoia"], "categories": ["quant-ph"], "doi": "", "_query": "hedonic house price", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/1807.09873v2", "title": "Formalizing the Cox-Ross-Rubinstein pricing of European derivatives in Isabelle/HOL", "abstract": "We formalize in the proof assistant Isabelle essential basic notions and results in financial mathematics. We provide generic formal definitions of concepts such as markets, portfolios, derivative products, arbitrages or fair prices, and we show that, under the usual no-arbitrage condition, the existence of a replicating portfolio for a derivative implies that the latter admits a unique fair price. Then, we provide a formalization of the Cox-Rubinstein model and we show that the market is complete in this model, i.e., that every derivative product admits a replicating portfolio. This entails that in this model, every derivative product admits a unique fair price.", "published": "2018-07-25T21:39:33Z", "authors": ["Mnacho Echenim", "Hervé Guiol", "Nicolas Peltier"], "categories": ["cs.LO", "q-fin.PR"], "doi": "", "_query": "hedonic house price", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2607.08785v1", "title": "DaDaDa: A Dataset for Data Pricing in Data Marketplaces", "abstract": "High-quality data drives machine learning advances across industries. Recognizing the value of data, data transactions are increasingly common, giving rise to many data marketplaces, e.g., AWS Marketplace, Databricks, and Datarade. However, determining the appropriate prices for data products remains a significant challenge due to the unique properties of data products. Traditional pricing methods in economics can be categorized into the cost approach, the income approach, and the sales comparison approach. The cost approach fails in data pricing due to near-zero marginal cost from data replication, and the income approach fails due to inherently unpredictable data revenue. The sales comparison approach remains viable, yet its application is hindered by the absence of standardized pricing benchmarks for data products across marketplaces. To address this challenge, we introduce \\texttt{DaDaDa}, the first dataset for data product pricing, containing metadata for 16,147 data products from 9 major data marketplaces worldwide. \\texttt{DaDaDa} enables the training of pricing models, thereby establishing price benchmarks for new data products. In addition, \\texttt{DaDaDa} can be utilized for other important tasks in data markets, such as data product classification and retrieval. Experiments and a retrieval prototype demonstrate the effectiveness of \\texttt{DaDaDa} for pricing, classification, and retrieval of data products. The dataset and code are available at https://github.com/ZJU-DIVER/DaDaDa.", "published": "2026-06-13T12:19:37Z", "authors": ["Qiheng Sun", "Hongwei Zhang", "Junxu Liu", "Xiaokai Mao", "Jinfei Liu", "Kui Ren", "Haibo Hu"], "categories": ["cs.LG", "cs.AI"], "doi": "", "_query": "hedonic house price", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2010.09637v2", "title": "Price of Fairness in Budget Division for Egalitarian Social Welfare", "abstract": "We study a participatory budgeting problem of aggregating the preferences of agents and dividing a budget over the projects. A budget division solution is a probability distribution over the projects. The main purpose of our study concerns the comparison between the system optimum solution and a fair solution. We are interested in assessing the quality of fair solutions, i.e., in measuring the system efficiency loss under a fair allocation compared to the one that maximizes (egalitarian) social welfare. This indicator is called the price of fairness. We are also interested in the performance of several aggregation rules. Asymptotically tight bounds are provided both for the price of fairness and the efficiency guarantee of aggregation rules.", "published": "2020-10-19T16:26:33Z", "authors": ["Zhongzheng Tang", "Chenhao Wang", "Mengqi Zhang"], "categories": ["cs.GT"], "doi": "", "_query": "hedonic house price", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/cs/0610053v1", "title": "Towards a Bayesian framework for option pricing", "abstract": "In this paper, we describe a general method for constructing the posterior distribution of an option price. Our framework takes as inputs the prior distributions of the parameters of the stochastic process followed by the underlying, as well as the likelihood function implied by the observed price history for the underlying. Our work extends that of Karolyi (1993) and Darsinos and Satchell (2001), but with the crucial difference that the likelihood function we use for inference is that which is directly implied by the underlying, rather than imposed in an ad hoc manner via the introduction of a function representing \"measurement error.\" As such, an important problem still relevant for our method is that of model risk, and we address this issue by describing how to perform a Bayesian averaging of parameter inferences based on the different models considered using our framework.", "published": "2006-10-10T19:37:37Z", "authors": ["Henryk Gzyl", "Enrique ter Horst", "Samuel Malone"], "categories": ["cs.CE", "q-fin.PR"], "doi": "", "_query": "hedonic house price", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/1804.10869v1", "title": "Application of Probabilistic Graphical Models in Forecasting Crude Oil Price", "abstract": "The dissertation investigates the application of Probabilistic Graphical Models (PGMs) in forecasting the price of Crude Oil. This research is important because crude oil plays a very pivotal role in the global economy hence is a very critical macroeconomic indicator of the industrial growth. Given the vast amount of macroeconomic factors affecting the price of crude oil such as supply of oil from OPEC countries, demand of oil from OECD countries, geopolitical and geoeconomic changes among many other variables - probabilistic graphical models (PGMs) allow us to understand by learning the graphical structure. This dissertation proposes condensing data numerous Crude Oil factors into a graphical model in the attempt of creating a accurate forecast of the price of crude oil. The research project experiments with using different libraries in Python in order to construct models of the crude oil market. The experiments in this thesis investigate three main challenges commonly presented while trading oil in the financial markets. The first challenge it investigates is the process of learning the structure of the oil markets; thus allowing crude oil traders to understand the different physical market factors and macroeconomic indicators affecting crude oil markets and how they are \\textit{causally} related. The second challenge it solves is the exploration and exploitation of the available data and the learnt structure in predicting the behaviour of the oil markets. The third challenge it investigates is how to validate the performance and reliability of the constructed model in order for it to be deployed in the financial markets. A design and implementation of a probabilistic framework for forecasting the price of crude oil is also presented as part of the research.", "published": "2018-04-29T04:42:16Z", "authors": ["Danish A. Alvi"], "categories": ["q-fin.TR"], "doi": "", "_query": "hedonic house price", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/1409.6645v1", "title": "Calculation of a power price equilibrium", "abstract": "In this paper we propose a tractable quadratic programming formulation for calculating the equilibrium term structure of electricity prices. We rely on a theoretical model described in [21], but extend it so that it reflects actually traded electricity contracts, transaction costs and liquidity considerations. Our numerical simulations examine the properties of the term structure and its dependence on various parameters of the model. The proposed quadratic programming formulation is applied to calculate the equilibrium term structure of electricity prices in the UK power grid consisting of a few hundred power plants. The impact of ramp up and ramp down constraints are also studied.", "published": "2014-09-23T15:52:01Z", "authors": ["Miha Troha", "Raphael Hauser"], "categories": ["q-fin.PR", "math.OC"], "doi": "", "_query": "hedonic house price", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2609.00541v1", "title": "Optimal Uniform Pricing for Multi-Interval Dispatch without Make-Whole Uplifts", "abstract": "In a network with ramp-limited generators and inaccurate net-demand forecasts, practical rolling-window dispatch can drive locational marginal prices (LMPs) below generators' bid-in offers. In such cases, out-of-market (OOM) settlements are used to compensate generators and maintain dispatch-following incentives, but OOM can have negative consequences, including nontransparent real-time price signals, discriminatory compensation, and incentives for untruthful bidding. This paper presents an optimal uniform pricing rule that minimizes demand payments, eliminates OOM make-whole payments, preserves LMP-based congestion charges, and ensures revenue adequacy. We derive the proposed pricing rule in closed form and relate it to existing pricing schemes. Numerical comparisons demonstrate favorable generator profits and reduced price volatility. However, higher generator profits are accompanied by increased demand payments, reflecting the in-market, uniform allocation of ramping costs while preserving the LMP-based congestion charges widely used in real-time market settlements. The numerical results also show that, under LMP with OOM settlement, a price-taking generator has an incentive to inflate its offer, whereas this incentive is absent under the proposed pricing rule within the tested bid range.", "published": "2026-09-01T01:28:40Z", "authors": ["Valentina Norambuena-Guzman", "Cong Chen", "Lang Tong", "Timothy D. Mount"], "categories": ["eess.SY", "econ.EM"], "doi": "", "_query": "hedonic house price", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/1610.05562v1", "title": "Do Price Ranges Increase Click-throughs?", "abstract": "An important goal of online comparison shopping services is to \"convert\" a viewer from general product category pages (for example product groups such as \"smartphones\" or \"air-conditioners\") to detailed product pages and ultimately to order pages. Comparison shopping websites provide a familiar web interface as well as a chance for consumers to purchase items at competitive prices. In return for providing access to a large market of potential consumers, the comparison shopping service usually receives financial compensation for product clicks and orders. This study looked at 2.5 million product listing visits at price.com.hk to determine whether a modification in the way prices are displayed on general category pages resulted in more \"conversions\" to product detail pages. We found a statistically significant improvement over-all as a result of the new price display resulting in 3.6% more product clicks over all categories. Additional analysis showed that the effect is heterogeneous among different categories, and in a few cases there may be some categories negatively affected by the display modification.", "published": "2016-05-15T03:58:06Z", "authors": ["Patrick Ng", "Drew Plant", "Yiran Sheng"], "categories": ["cs.CY"], "doi": "", "_query": "hedonic house price", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/physics/0608271v3", "title": "Mechanical vs. informational components of price impact", "abstract": "We study the problem of what causes prices to change. We define the mechanical impact of a trading order as the change in future prices in the absence of any future changes in decision making, and its it informational impact as the remainder of the total impact once mechanical impact is removed. We introduce a method of measuring mechanical impact and apply it to order book data from the London Stock Exchange. The average mechanical impact of a market order decays to zero as a function of time, at an asymptotic rate that is consistent with a power law with an exponent of roughly 1.7. In contrast the average informational impact builds to approach a constant value. Initially the impact is entirely mechanical, and is about half as big as the asymptotic informational impact. The size of the informational impact is positively correlated to mechanical impact. For cases where the mechanical impact is zero for all times, we find that the informational impact is negative, i.e. buy market orders that have no mechanical impact at all generate strong negative price responses.", "published": "2006-08-27T19:57:12Z", "authors": ["J. Doyne Farmer", "Neda Zamani"], "categories": ["physics.soc-ph", "q-fin.TR"], "doi": "10.1140/epjb/e2006-00384-5", "_query": "hedonic house price", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2403.02785v2", "title": "A Fully-discrete Semi-Lagrangian scheme for a price formation MFG model", "abstract": "Here, we examine a fully-discrete Semi-Lagrangian scheme for a mean-field game price formation model. We show the existence of the solution of the discretized problem and that it is monotone as a multivalued operator. Moreover, we show that the limit of the discretization converges to the weak solution of the continuous price formation mean-field game using monotonicity methods. Numerical simulations demonstrate that this scheme can provide results efficiently, comparing favorably with other methods in the examples we tested.", "published": "2024-03-05T08:58:04Z", "authors": ["Yuri Ashrafyan", "Diogo Gomes"], "categories": ["math.NA"], "doi": "", "_query": "hedonic house price", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/1708.02170v1", "title": "Long time thermal asymptotics of nonlinear Luttinger liquid from inverse scattering", "abstract": "I derive a Fredholm determinant for the thermal correlator of vertex operators with a conformally invariant density matrix and nonlinear time evolution. Using the method of nonlinear steepest descent I find the long time asymptotics at finite temperature. The asymptotics display exponentially small corrections in temperature to the finite temperature mobile impurity phenomenology.", "published": "2017-08-07T15:41:24Z", "authors": ["Tom Price"], "categories": ["cond-mat.quant-gas"], "doi": "", "_query": "hedonic house price", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2609.24394v1", "title": "Climate Variability Modulates the Impact of Price Spikes on Food Insecurity", "abstract": "Climate variability influences whether a market disruption escalates into a food crisis, yet broad climate patterns like El Niño, tracked months before they alter hydro-climatic conditions, are still not incorporated as an early-warning component in food-security responses. We address this gap by introducing sensitivity regimes, a stratification of regions by the direction and strength of their vegetation response to the El Niño Southern Oscillation, and using them to estimate how food price spikes affect acute food insecurity across sub-Saharan Africa. Integrating remote sensing, socioeconomic data, and causal machine learning, we find that in regions where ENSO systematically suppresses vegetation, a price spike raises the share of the population at acute risk by 5.4 percentage points in the following month. In regions where vegetation is unaffected by or positively linked to ENSO, the estimated effect is smaller (around 2 percentage points) and statistically insignificant. These results demonstrate that climate context is critical for understanding food security vulnerabilities. Sensitivity regimes can be combined with operational price-spike triggers to stage anticipatory action: the ENSO state flags vulnerable regions months ahead, and a pre-positioned response in those regions to a price spike would avert the largest jump in acute food insecurity.", "published": "2026-09-21T10:40:58Z", "authors": ["Jordi Cerdà-Bautista", "Vasileios Sitokonstantinou", "Homer Durand", "Gherardo Varando", "Michele Ronco", "Gustau Camps-Valls"], "categories": ["cs.LG"], "doi": "", "_query": "hedonic house price", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/1507.02615v1", "title": "Optimal Auctions vs. Anonymous Pricing", "abstract": "For selling a single item to agents with independent but non-identically distributed values, the revenue optimal auction is complex. With respect to it, Hartline and Roughgarden (2009) showed that the approximation factor of the second-price auction with an anonymous reserve is between two and four. We consider the more demanding problem of approximating the revenue of the ex ante relaxation of the auction problem by posting an anonymous price (while supplies last) and prove that their worst-case ratio is e. As a corollary, the upper-bound of anonymous pricing or anonymous reserves versus the optimal auction improves from four to $e$. We conclude that, up to an $e$ factor, discrimination and simultaneity are unimportant for driving revenue in single-item auctions.", "published": "2015-07-09T17:42:37Z", "authors": ["Saeed Alaei", "Jason Hartline", "Rad Niazadeh", "Emmanouil Pountourakis", "Yang Yuan"], "categories": ["cs.GT", "cs.DS"], "doi": "10.1109/FOCS.2015.92", "_query": "hedonic house price", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2502.06168v2", "title": "Dynamic Pricing with Adversarially-Censored Demands", "abstract": "We study an online dynamic pricing problem where the potential demand at each time period $t=1,2,\\ldots, T$ is stochastic and dependent on the price. However, a perishable inventory is imposed at the beginning of each time $t$, censoring the potential demand if it exceeds the inventory level. To address this problem, we introduce a pricing algorithm based on the optimistic estimates of derivatives. We show that our algorithm achieves $\\tilde{O}(\\sqrt{T})$ optimal regret even with adversarial inventory series. Our findings advance the state-of-the-art in online decision-making problems with censored feedback, offering a theoretically optimal solution against adversarial observations.", "published": "2025-02-10T05:37:39Z", "authors": ["Jianyu Xu", "Yining Wang", "Xi Chen", "Yu-Xiang Wang"], "categories": ["stat.ML", "cs.LG", "econ.EM", "math.OC"], "doi": "", "_query": "hedonic house price", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2601.01706v1", "title": "Semantic Non-Fungibility and Violations of the Law of One Price in Prediction Markets", "abstract": "Prediction markets are designed to aggregate dispersed information about future events, yet today's ecosystem is fragmented across heterogeneous operator-run platforms and blockchain-based protocols that independently list economically identical events. In the absence of a shared notion of event identity, liquidity fails to pool across venues, arbitrage becomes capital-intensive or unenforceable, and prices systematically violate the Law of One Price. As a result, market prices reflect platform-local beliefs rather than a single, globally aggregated probability, undermining the core information-aggregation function of prediction markets. We address this gap by introducing a semantic alignment framework that makes cross-platform event identity explicit through joint analysis of natural-language descriptions, resolution semantics, and temporal scope. Applying this framework, we construct the first human-validated, cross-platform dataset of aligned prediction markets, covering over 100 000 events across ten major venues from 2018 to 2025. Using this dataset, we show that roughly 6% of all events are concurrently listed across platforms and that semantically equivalent markets exhibit persistent execution-aware price deviations of 2-4% on average, even in highly liquid and information-rich settings. These mispricings give rise to persistent cross-platform arbitrage opportunities driven by structural frictions rather than informational disagreement. Overall, our results demonstrate that semantic non-fungibility is a fundamental barrier to price convergence, and that resolving event identity is a prerequisite for prediction markets to aggregate information at a global scale.", "published": "2026-01-05T01:01:52Z", "authors": ["Jonas Gebele", "Florian Matthes"], "categories": ["cs.CE"], "doi": "", "_query": "hedonic house price", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2412.00062v1", "title": "Deep Learning-Based Electricity Price Forecast for Virtual Bidding in Wholesale Electricity Market", "abstract": "Virtual bidding plays an important role in two-settlement electric power markets, as it can reduce discrepancies between day-ahead and real-time markets. Renewable energy penetration increases volatility in electricity prices, making accurate forecasting critical for virtual bidders, reducing uncertainty and maximizing profits. This study presents a Transformer-based deep learning model to forecast the price spread between real-time and day-ahead electricity prices in the ERCOT (Electric Reliability Council of Texas) market. The proposed model leverages various time-series features, including load forecasts, solar and wind generation forecasts, and temporal attributes. The model is trained under realistic constraints and validated using a walk-forward approach by updating the model every week. Based on the price spread prediction results, several trading strategies are proposed and the most effective strategy for maximizing cumulative profit under realistic market conditions is identified through backtesting. The results show that the strategy of trading only at the peak hour with a precision score of over 50% produces nearly consistent profit over the test period. The proposed method underscores the importance of an accurate electricity price forecasting model and introduces a new method of evaluating the price forecast model from a virtual bidder's perspective, providing valuable insights for future research.", "published": "2024-11-25T20:04:16Z", "authors": ["Xuesong Wang", "Sharaf K. Magableh", "Oraib Dawaghreh", "Caisheng Wang", "Jiaxuan Gong", "Zhongyang Zhao", "Michael H. Liao"], "categories": ["cs.LG", "q-fin.CP"], "doi": "", "_query": "hedonic house price", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2303.16314v2", "title": "A multifractional option pricing formula", "abstract": "Fractional Brownian motion has become a standard tool to address long-range dependence in financial time series. However, a constant memory parameter is too restrictive to address different market conditions. Here we model the price fluctuations using a multifractional Brownian motion assuming that the Hurst exponent is a time-deterministic function. Through the multifractional Ito calculus, both the related transition density function and the analytical European Call option pricing formula are obtained. The empirical performance of the multifractional Black-Scholes model is tested by calibration of option market quotes for the SPX index and offers best fit than its counterparts based on standard and fractional Brownian motions.", "published": "2023-03-28T21:25:38Z", "authors": ["Axel A. Araneda"], "categories": ["q-fin.MF"], "doi": "10.1142/S0219477524500603", "_query": "hedonic house price", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2307.04059v1", "title": "Exploring Dynamic Asset Pricing within Bachelier Market Model", "abstract": "This paper delves into the dynamics of asset pricing within Bachelier market model, elucidating the representation of risky asset price dynamics and the definition of riskless assets.", "published": "2023-07-08T23:38:50Z", "authors": ["Nancy Asare Nyarko", "Bhathiya Divelgama", "Jagdish Gnawali", "Blessing Omotade", "Svetlozar Rachev", "Peter Yegon"], "categories": ["q-fin.GN"], "doi": "", "_query": "hedonic house price", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2206.04424v6", "title": "Instrument-Free Demand Estimation Using Relative Prices Variation, with an Application to Railway Transportation", "abstract": "We develop a new identification strategy for demand estimation when cost shifters may not be available and there are substantial variations in demand over time. This approaches relies on a kind of nonlinear difference-in-differences, in which price elasticities are identified by relating changes over time in relative purchases between two goods to changes in their relative prices. We apply this strategy to the context of French railway transportation. Our price elasticity estimates are in line with those on airlines, but substantially higher, in absolute terms, than those generally obtained on railway transportation. We then use our demand estimation to compare the current pricing with several counterfactual pricing strategies. Our results suggest similar or better performance of the actual revenue management compared to optimal uniform pricing, but also substantial losses compared to the optimal pricing strategy. Finally, we highlight the key role of revenue management in acquiring information when demand is uncertain.", "published": "2022-06-09T11:25:47Z", "authors": ["Xavier D'Haultfœuille", "Ao Wang", "Philippe Février", "Lionel Wilner"], "categories": ["econ.GN"], "doi": "", "_query": "hedonic house price", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/1805.07403v2", "title": "Asset Price Bubbles: An Option-based Indicator", "abstract": "We construct a statistical indicator for the detection of short-term asset price bubbles based on the information content of bid and ask market quotes for plain vanilla put and call options. Our construction makes use of the martingale theory of asset price bubbles and the fact that such scenarios where the price for an asset exceeds its fundamental value can in principle be detected by analysis of the asymptotic behavior of the implied volatility surface. For extrapolating this implied volatility, we choose the SABR model, mainly because of its decent fit to real option market quotes for a broad range of maturities and its ease of calibration. As main theoretical result, we show that under lognormal SABR dynamics, we can compute a simple yet powerful closed-form martingale defect indicator by solving an ill-posed inverse calibration problem. In order to cope with the ill-posedness and to quantify the uncertainty which is inherent to such an indicator, we adopt a Bayesian statistical parameter estimation perspective. We probe the resulting posterior densities with a combination of optimization and adaptive Markov chain Monte Carlo methods, thus providing a full-blown uncertainty estimation of all the underlying parameters and the martingale defect indicator. Finally, we provide real-market tests of the proposed option-based indicator with focus on tech stocks due to increasing concerns about a tech bubble 2.0.", "published": "2018-05-18T19:10:21Z", "authors": ["Petteri Piiroinen", "Lassi Roininen", "Tobias Schoden", "Martin Simon"], "categories": ["q-fin.PR", "math.PR"], "doi": "", "_query": "hedonic house price", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/1202.2877v2", "title": "Improving the Price of Anarchy for Selfish Routing via Coordination Mechanisms", "abstract": "We reconsider the well-studied Selfish Routing game with affine latency functions. The Price of Anarchy for this class of games takes maximum value 4/3; this maximum is attained already for a simple network of two parallel links, known as Pigou's network. We improve upon the value 4/3 by means of Coordination Mechanisms. We increase the latency functions of the edges in the network, i.e., if $\\ell_e(x)$ is the latency function of an edge $e$, we replace it by $\\hat{\\ell}_e(x)$ with $\\ell_e(x) \\le \\hat{\\ell}_e(x)$ for all $x$. Then an adversary fixes a demand rate as input. The engineered Price of Anarchy of the mechanism is defined as the worst-case ratio of the Nash social cost in the modified network over the optimal social cost in the original network. Formally, if $\\CM(r)$ denotes the cost of the worst Nash flow in the modified network for rate $r$ and $\\Copt(r)$ denotes the cost of the optimal flow in the original network for the same rate then [\\ePoA = \\max_{r \\ge 0} \\frac{\\CM(r)}{\\Copt(r)}.] We first exhibit a simple coordination mechanism that achieves for any network of parallel links an engineered Price of Anarchy strictly less than 4/3. For the case of two parallel links our basic mechanism gives 5/4 = 1.25. Then, for the case of two parallel links, we describe an optimal mechanism; its engineered Price of Anarchy lies between 1.191 and 1.192.", "published": "2012-02-13T21:41:11Z", "authors": ["George Christodoulou", "Kurt Mehlhorn", "Evangelia Pyrga"], "categories": ["cs.GT"], "doi": "", "_query": "hedonic house price", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2403.06278v1", "title": "Pre- and Post-Auction Discounts in First-Price Auctions", "abstract": "One method to offer some bidders a discount in a first-price auction is to augment their bids when selecting a winner but only charge them their original bids should they win. Another method is to use their original bids to select a winner, then charge them a discounted price that is lower than their bid should they win. We show that the two methods have equivalent auction outcomes, for equal additive discounts and for multiplicative ones with appropriate adjustments to discount amounts. As a result, they have corresponding equilibria when equilibria exist. We also show that with the same level of multiplicative adjustments, bidders with discounts should prefer an augmented bid to a discounted price. Then we estimate optimal bid functions for valuation distributions based on data from online advertising auctions, and show how different discount levels affect auction outcomes for those bid functions.", "published": "2024-03-10T18:09:21Z", "authors": ["Miguel Alcobendas", "Eric Bax"], "categories": ["cs.GT"], "doi": "", "_query": "hedonic house price", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2001.11585v1", "title": "Housing Search in the Age of Big Data: Smarter Cities or the Same Old Blind Spots?", "abstract": "Housing scholars stress the importance of the information environment in shaping housing search behavior and outcomes. Rental listings have increasingly moved online over the past two decades and, in turn, online platforms like Craigslist are now central to the search process. Do these technology platforms serve as information equalizers or do they reflect traditional information inequalities that correlate with neighborhood sociodemographics? We synthesize and extend analyses of millions of US Craigslist rental listings and find they supply significantly different volumes, quality, and types of information in different communities. Technology platforms have the potential to broaden, diversify, and equalize housing search information, but they rely on landlord behavior and, in turn, likely will not reach this potential without a significant redesign or policy intervention. Smart cities advocates hoping to build better cities through technology must critically interrogate technology platforms and big data for systematic biases.", "published": "2020-01-30T22:10:29Z", "authors": ["Geoff Boeing", "Max Besbris", "Ariela Schachter", "John Kuk"], "categories": ["stat.AP", "cs.CY", "econ.GN"], "doi": "10.1080/10511482.2019.1684336", "_query": "hedonic house price", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2610.24388v1", "title": "Hidden urban exposure to energy-price shocks: spreading burdens and shifting hotspots", "abstract": "Cities need to anticipate where energy-price shocks could intensify hardship beyond established hotspots. How income distributions and fuel-price balances jointly shape this hidden exposure remains unclear. Here we address this gap by stress-testing nearly one million properties in six English local authorities, combining required-energy costs for existing housing with modelled local income distributions. Under gas-led crisis prices, 41% of neighbourhoods in the highest local exposure quarter lie outside the highest official fuel-poverty quarter. Electricity-led stress changes this hidden geography: when electricity unit prices alone triple from 2021, comparable to the unmitigated late-2022 cap increase, 38% of the resulting hidden-exposure group are absent from the crisis-based group. Entering neighbourhoods have newer, more efficient housing, more flats and greater electricity dependence than retained areas. Crisis preparedness should extend household protection beyond current hardship hotspots, using fuel-specific stress tests to anticipate support needs as fuel prices and heating systems change.", "published": "2026-10-17T02:42:06Z", "authors": ["Yunfei Bai", "Aaron Tesfa Tsion", "Raúl Rosales", "Barbara Shollock", "Wei He"], "categories": ["physics.soc-ph"], "doi": "", "_query": "hedonic house price", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2512.14431v2", "title": "Precise Predictions for $μ^{\\pm}e^-\\rightarrowμ^{\\pm}e^-$ at the MUonE Experiment", "abstract": "The proposed fixed-target experiment, MUonE, at CERN will aim to measure the hadronic contribution to the running of the QED coupling by analysing the scattering of muons on electrons. Here we present state-of-the-art predictions for the process $μ^{\\pm}e^-\\rightarrowμ^{\\pm}e^-$, where for the first time an all-order resummation of soft and soft-collinear logarithms has been performed. Further, we match this resummation with the complete next-to-leading and the dominant next-to-next-to-leading higher-order corrections. We find that the resummation has a dominant effect in the signal region, while the systematic matching significantly reduces the perturbative uncertainty.", "published": "2025-12-16T14:20:15Z", "authors": ["Alan Price"], "categories": ["hep-ph", "hep-ex"], "doi": "", "_query": "hedonic house price", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2409.08281v1", "title": "StockTime: A Time Series Specialized Large Language Model Architecture for Stock Price Prediction", "abstract": "The stock price prediction task holds a significant role in the financial domain and has been studied for a long time. Recently, large language models (LLMs) have brought new ways to improve these predictions. While recent financial large language models (FinLLMs) have shown considerable progress in financial NLP tasks compared to smaller pre-trained language models (PLMs), challenges persist in stock price forecasting. Firstly, effectively integrating the modalities of time series data and natural language to fully leverage these capabilities remains complex. Secondly, FinLLMs focus more on analysis and interpretability, which can overlook the essential features of time series data. Moreover, due to the abundance of false and redundant information in financial markets, models often produce less accurate predictions when faced with such input data. In this paper, we introduce StockTime, a novel LLM-based architecture designed specifically for stock price data. Unlike recent FinLLMs, StockTime is specifically designed for stock price time series data. It leverages the natural ability of LLMs to predict the next token by treating stock prices as consecutive tokens, extracting textual information such as stock correlations, statistical trends and timestamps directly from these stock prices. StockTime then integrates both textual and time series data into the embedding space. By fusing this multimodal data, StockTime effectively predicts stock prices across arbitrary look-back periods. Our experiments demonstrate that StockTime outperforms recent LLMs, as it gives more accurate predictions while reducing memory usage and runtime costs.", "published": "2024-08-25T00:50:33Z", "authors": ["Shengkun Wang", "Taoran Ji", "Linhan Wang", "Yanshen Sun", "Shang-Ching Liu", "Amit Kumar", "Chang-Tien Lu"], "categories": ["q-fin.ST", "cs.AI", "cs.CE", "cs.LG"], "doi": "", "_query": "hedonic house price", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/0711.2550v1", "title": "Effective multifractal features and l-variability diagrams of high-frequency price fluctuations time series", "abstract": "In this manuscript we present a comprehensive study on the multifractal properties of high-frequency price fluctuations and instantaneous volatility of the equities that compose Dow Jones Industrial Average. The analysis consists about quantification of dependence and non-Gaussianity on the multifractal character of financial quantities. Our results point out an equivalent influence of dependence and non-Gaussianity on the multifractality of time series. Moreover, we analyse l-diagrams of price fluctuations. In the latter case, we show that the fractal dimension of these maps is basically independent of the lag between price fluctuations that we assume.", "published": "2007-11-16T15:44:22Z", "authors": ["Jeferson de Souza", "Silvio M. Duarte Queiros"], "categories": ["q-fin.ST", "physics.data-an"], "doi": "", "_query": "hedonic house price", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2106.09664v1", "title": "Design and Analysis of Robust Deep Learning Models for Stock Price Prediction", "abstract": "Building predictive models for robust and accurate prediction of stock prices and stock price movement is a challenging research problem to solve. The well-known efficient market hypothesis believes in the impossibility of accurate prediction of future stock prices in an efficient stock market as the stock prices are assumed to be purely stochastic. However, numerous works proposed by researchers have demonstrated that it is possible to predict future stock prices with a high level of precision using sophisticated algorithms, model architectures, and the selection of appropriate variables in the models. This chapter proposes a collection of predictive regression models built on deep learning architecture for robust and precise prediction of the future prices of a stock listed in the diversified sectors in the National Stock Exchange (NSE) of India. The Metastock tool is used to download the historical stock prices over a period of two years (2013- 2014) at 5 minutes intervals. While the records for the first year are used to train the models, the testing is carried out using the remaining records. The design approaches of all the models and their performance results are presented in detail. The models are also compared based on their execution time and accuracy of prediction.", "published": "2021-06-17T17:15:02Z", "authors": ["Jaydip Sen", "Sidra Mehtab"], "categories": ["q-fin.ST", "cs.LG"], "doi": "10.5772/intechopen.99982", "_query": "hedonic house price", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/1611.07605v1", "title": "Optimal Pricing for Submodular Valuations with Bounded Curvature", "abstract": "The optimal pricing problem is a fundamental problem that arises in combinatorial auctions. Suppose that there is one seller who has indivisible items and multiple buyers who want to purchase a combination of the items. The seller wants to sell his items for the highest possible prices, and each buyer wants to maximize his utility (i.e., valuation minus payment) as long as his payment does not exceed his budget. The optimal pricing problem seeks a price of each item and an assignment of items to buyers such that every buyer achieves the maximum utility under the prices. The goal of the problem is to maximize the total payment from buyers. In this paper, we consider the case that the valuations are submodular. We show that the problem is computationally hard even if there exists only one buyer. Then we propose approximation algorithms for the unlimited budget case. We also extend the algorithm for the limited budget case when there exists one buyer and multiple buyers collaborate with each other.", "published": "2016-11-23T02:18:10Z", "authors": ["Takanori Maehara", "Yasushi Kawase", "Hanna Sumita", "Katsuya Tono", "Ken-ichi Kawarabayashi"], "categories": ["cs.GT", "cs.DS"], "doi": "", "_query": "hedonic house price", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2001.08906v1", "title": "Pricing commodity swing options", "abstract": "In commodity and energy markets swing options allow the buyer to hedge against futures price fluctuations and to select its preferred delivery strategy within daily or periodic constraints, possibly fixed by observing quoted futures contracts. In this paper we focus on the natural gas market and we present a dynamical model for commodity futures prices able to calibrate liquid market quotes and to imply the volatility smile for futures contracts with different delivery periods. We implement the numerical problem by means of a least-square Monte Carlo simulation and we investigate alternative approaches based on reinforcement learning algorithms.", "published": "2020-01-24T08:03:05Z", "authors": ["Roberto Daluiso", "Emanuele Nastasi", "Andrea Pallavicini", "Giulio Sartorelli"], "categories": ["q-fin.PR", "q-fin.CP"], "doi": "", "_query": "hedonic house price", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/1711.10241v8", "title": "The Price of Quota-based Diversity in Assignment Problems", "abstract": "We introduce and analyze an extension to the matching problem on a weighted bipartite graph: Assignment with Type Constraints. The two parts of the graph are partitioned into subsets called types and blocks; we seek a matching with the largest sum of weights under the constraint that there is a pre-specified cap on the number of vertices matched in every type-block pair. Our primary motivation stems from the public housing program of Singapore, accounting for over 70% of its residential real estate. To promote ethnic diversity within its housing projects, Singapore imposes ethnicity quotas: each new housing development comprises blocks of flats and each ethnicity-based group in the population must not own more than a certain percentage of flats in a block. Other domains using similar hard capacity constraints include matching prospective students to schools or medical residents to hospitals. Limiting agents' choices for ensuring diversity in this manner naturally entails some welfare loss. One of our goals is to study the trade-off between diversity and social welfare in such settings. We first show that, while the classic assignment program is polynomial-time computable, adding diversity constraints makes it computationally intractable; however, we identify a $\\tfrac{1}{2}$-approximation algorithm, as well as reasonable assumptions on the weights that permit poly-time algorithms. Next, we provide two upper bounds on the price of diversity -- a measure of the loss in welfare incurred by imposing diversity constraints -- as functions of natural problem parameters. We conclude the paper with simulations based on publicly available data from two diversity-constrained allocation problems -- Singapore Public Housing and Chicago School Choice -- which shed light on how the constrained maximization as well as lottery-based variants perform in practice.", "published": "2017-11-28T11:58:54Z", "authors": ["Nawal Benabbou", "Mithun Chakraborty", "Vinh Ho Xuan", "Jakub Sliwinski", "Yair Zick"], "categories": ["cs.AI"], "doi": "10.1145/3411513", "_query": "hedonic house price", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/1910.01034v2", "title": "Stationarity of the detrended price return in stock markets", "abstract": "This paper proposes a governing equation for stock market indexes that accounts for non-stationary effects. This is a linear Fokker-Planck equation (FPE) that describes the time evolution of the probability distribution function (PDF) of the price return. By applying Ito's lemma, this FPE is associated with a stochastic differential equation (SDE) that models the time evolution of the price return in a fashion different from the classical Black-Scholes equation. Both FPE and SDE equations account for a deterministic part or trend, and a stationary, stochastic part as a q-Gaussian noise. The model is validated using the S\\&P500 index's data. After removing the trend from the index, we show that the detrended part is stationary by evaluating the Hurst exponent of the multifractal time series, its power spectrum, and its autocorrelation.", "published": "2019-10-02T15:39:33Z", "authors": ["Karina Arias-Calluari", "Morteza. N. Najafi", "Michael S. Harré", "Fernando Alonso-Marroquin"], "categories": ["q-fin.ST"], "doi": "", "_query": "hedonic house price", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/1312.3211v1", "title": "Barrier Option Pricing", "abstract": "We use Lie symmetry methods to price certain types of barrier options. Usually Lie symmetry methods cannot be used to solve the Black-Scholes equation for options because the function defining the maturity condition for an option is not smooth. However, for barrier options, this restriction can be accommodated and a symmetry analysis utilised to find new solutions.", "published": "2013-12-11T15:33:33Z", "authors": ["A. H. Davison", "T. Sidogi"], "categories": ["math.AP", "q-fin.CP"], "doi": "", "_query": "hedonic house price", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/0812.2440v2", "title": "Liquidity Risk, Price Impacts and the Replication Problem", "abstract": "We extend a linear version of the liquidity risk model of Cetin et al. (2004) to allow for price impacts. We show that the impact of a market order on prices depends on the size of the transaction and the level of liquidity. We obtain a simple characterization of self-financing trading strategies and a sufficient condition for no arbitrage. We consider a stochastic volatility model in which the volatility is partly correlated with the liquidity process and show that, with the use of variance swaps, contingent claims whose payoffs depend on the value of the asset can be approximately replicated in this setting. The replicating costs of such payoffs are obtained from the solutions of BSDEs with quadratic growth and analytical properties of these solutions are investigated.", "published": "2008-12-12T17:57:43Z", "authors": ["Alexandre F. Roch"], "categories": ["math.PR", "q-fin.PR", "q-fin.TR"], "doi": "", "_query": "hedonic house price", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/0911.1502v1", "title": "A Round-based Pricing Scheme for Maximizing Service Provider's Revenue in P2PTV Networks", "abstract": "In this paper, we analyze a round-based pricing scheme that encourages favorable behavior from users of real-time P2P applications like P2PTV. In the design of pricing schemes, we consider price to be a function of usage and capacity of download/upload streams, and quality of content served. Users are consumers and servers at the same time in such networks, and often exhibit behavior that is unfavorable towards maximization of social benefits. Traditionally, network designers have overcome this difficulty by building-in traffic latencies. However, using simulations, we show that appropriate pricing schemes and usage terms can enable designers to limit required traffic latencies, and be able to earn nearly 30% extra revenue from providing P2PTV services. The service provider adjusts the prices of individual programs incrementally within rounds, while making relatively large-scale adjustments at the end of each round. Through simulations, we show that it is most beneficial for the service provider to carry out 5 such rounds of price adjustments for maximizing his average profit and minimizing the associated standard deviation at the same time.", "published": "2009-11-08T10:04:54Z", "authors": ["Gitanjali Bhutani"], "categories": ["cs.NI"], "doi": "", "_query": "hedonic house price", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/1702.07444v1", "title": "Bandits with Movement Costs and Adaptive Pricing", "abstract": "We extend the model of Multi-armed Bandit with unit switching cost to incorporate a metric between the actions. We consider the case where the metric over the actions can be modeled by a complete binary tree, and the distance between two leaves is the size of the subtree of their least common ancestor, which abstracts the case that the actions are points on the continuous interval $[0,1]$ and the switching cost is their distance. In this setting, we give a new algorithm that establishes a regret of $\\widetilde{O}(\\sqrt{kT} + T/k)$, where $k$ is the number of actions and $T$ is the time horizon. When the set of actions corresponds to whole $[0,1]$ interval we can exploit our method for the task of bandit learning with Lipschitz loss functions, where our algorithm achieves an optimal regret rate of $\\widetildeΘ(T^{2/3})$, which is the same rate one obtains when there is no penalty for movements. As our main application, we use our new algorithm to solve an adaptive pricing problem. Specifically, we consider the case of a single seller faced with a stream of patient buyers. Each buyer has a private value and a window of time in which they are interested in buying, and they buy at the lowest price in the window, if it is below their value. We show that with an appropriate discretization of the prices, the seller can achieve a regret of $\\widetilde{O}(T^{2/3})$ compared to the best fixed price in hindsight, which outperform the previous regret bound of $\\widetilde{O}(T^{3/4})$ for the problem.", "published": "2017-02-24T01:51:48Z", "authors": ["Tomer Koren", "Roi Livni", "Yishay Mansour"], "categories": ["cs.LG", "cs.GT"], "doi": "", "_query": "hedonic house price", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/1808.09807v2", "title": "Continuous-time Duality for Super-replication with Transient Price Impact", "abstract": "We establish a super-replication duality in a continuous-time financial model where an investor's trades adversely affect bid- and ask-prices for a risky asset and where market resilience drives the resulting spread back towards zero at an exponential rate. Similar to the literature on models with a constant spread, our dual description of super-replication prices involves the construction of suitable absolutely continuous measures with martingales close to the unaffected reference price. A novel feature in our duality is a liquidity weighted $L^2$-norm that enters as a measurement of this closeness and that accounts for strategy dependent spreads. As applications, we establish optimality of buy-and-hold strategies for the super-replication of call options and we prove a verification theorem for utility maximizing investment strategies.", "published": "2018-08-29T13:34:11Z", "authors": ["Peter Bank", "Yan Dolinsky"], "categories": ["q-fin.PR", "math.OC"], "doi": "", "_query": "hedonic house price", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/1912.11060v3", "title": "Pricing and hedging American-style options with deep learning", "abstract": "In this paper we introduce a deep learning method for pricing and hedging American-style options. It first computes a candidate optimal stopping policy. From there it derives a lower bound for the price. Then it calculates an upper bound, a point estimate and confidence intervals. Finally, it constructs an approximate dynamic hedging strategy. We test the approach on different specifications of a Bermudan max-call option. In all cases it produces highly accurate prices and dynamic hedging strategies with small replication errors.", "published": "2019-12-23T19:01:02Z", "authors": ["Sebastian Becker", "Patrick Cheridito", "Arnulf Jentzen"], "categories": ["q-fin.CP"], "doi": "10.3390/jrfm13070158", "_query": "hedonic house price", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/1909.08047v3", "title": "Option pricing under normal dynamics with stochastic volatility", "abstract": "In this paper, we derive the price of a European call option of an asset following a normal process assuming stochastic volatility. The volatility is assumed to follow the Cox Ingersoll Ross (CIR) process. We then use the fast Fourier transform (FFT) to evaluate the option price given we know the characteristic function of the return analytically. We compare the results of fast Fourier transform with the Monte Carlo simulation results of our process. Further, we present a numerical example to understand the normal implied volatility of the model.", "published": "2019-09-17T19:35:03Z", "authors": ["Matta Uma Maheswara Reddy"], "categories": ["q-fin.PR", "q-fin.MF"], "doi": "", "_query": "hedonic house price", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/1407.5699v1", "title": "Feasibility of Using Discriminate Pricing Schemes for Energy Trading in Smart Grid", "abstract": "This paper investigates the feasibility of using a discriminate pricing scheme to offset the inconvenience that is experienced by an energy user (EU) in trading its energy with an energy controller in smart grid. The main objective is to encourage EUs with small distributed energy resources (DERs), or with high sensitivity to their inconvenience, to take part in the energy trading via providing incentive to them with relatively higher payment at the same time as reducing the total cost to the energy controller. The proposed scheme is modeled through a two-stage Stackelberg game that describes the energy trading between a shared facility authority (SFA) and EUs in a smart community. A suitable cost function is proposed for the SFA to leverage the generation of discriminate pricing according to the inconvenience experienced by each EU. It is shown that the game has a unique sub-game perfect equilibrium (SPE), under the certain condition at which the SFA's total cost is minimized, and that each EU receives its best utility according to its associated inconvenience for the given price. A backward induction technique is used to derive a closed form expression for the price function at SPE, and thus the dependency of price on an EU's different decision parameters is explained for the studied system. Numerical examples are provided to show the beneficial properties of the proposed scheme.", "published": "2014-07-22T00:32:32Z", "authors": ["Wayes Tushar", "Chau Yuen", "Bo Chai", "David B. Smith", "H. Vincent Poor"], "categories": ["eess.SY"], "doi": "", "_query": "hedonic house price", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2502.15893v2", "title": "Pricing Valid Cuts for Price-Match Equilibria", "abstract": "We use valid inequalities (cuts) of the binary integer program for winner determination in a combinatorial auction (CA) as \"artificial items\" that can be interpreted intuitively and priced to generate Artificial Walrasian Equilibria. We thus provide a method for converting a CA problem that admits only non-anonymous, nonlinear bundle prices into one that admits anonymous linear prices over the augmented item space, forestalling ex-post bidder complaints about opaque and strongly discriminatory pricing. To this end, we introduce a refinement of the Walrasian equilibrium which we call a \"price-match equilibrium\" (PME) in which all prices are justified by providing an iso-revenue reallocation for the hypothetical removal of any single bidder. We prove the existence of PME for any CA and characterize their economic properties and computation. We implement minimally artificial PME rules and compare them with other prominent CA payment rules in the literature.", "published": "2025-02-21T19:27:06Z", "authors": ["Robert Day", "Benjamin Lubin"], "categories": ["econ.TH"], "doi": "", "_query": "hedonic house price", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/1603.04786v1", "title": "Parabolic free boundary price formation models under market size fluctuations", "abstract": "In this paper we propose an extension of the Lasry-Lions price formation model which includes fluctuations of the numbers of buyers and vendors. We analyze the model in the case of deterministic and stochastic market size fluctuations and present results on the long time asymptotic behavior and numerical evidence and conjectures on periodic, almost periodic and stochastic fluctuations. The numerical simulations extend the theoretical statements and give further insights into price formation dynamics.", "published": "2016-03-15T18:00:26Z", "authors": ["Peter A. Markowich", "Josef Teichmann", "Marie-Therese Wolfram"], "categories": ["math.AP"], "doi": "", "_query": "hedonic house price", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2201.09790v1", "title": "Linear Laws of Markov Chains with an Application for Anomaly Detection in Bitcoin Prices", "abstract": "The goals of this paper are twofold: (1) to present a new method that is able to find linear laws governing the time evolution of Markov chains and (2) to apply this method for anomaly detection in Bitcoin prices. To accomplish these goals, first, the linear laws of Markov chains are derived by using the time embedding of their (categorical) autocorrelation function. Then, a binary series is generated from the first difference of Bitcoin exchange rate (against the United States Dollar). Finally, the minimum number of parameters describing the linear laws of this series is identified through stepped time windows. Based on the results, linear laws typically became more complex (containing an additional third parameter that indicates hidden Markov property) in two periods: before the crash of cryptocurrency markets inducted by the COVID-19 pandemic (12 March 2020), and before the record-breaking surge in the price of Bitcoin (Q4 2020 - Q1 2021). In addition, the locally high values of this third parameter are often related to short-term price peaks, which suggests price manipulation.", "published": "2022-01-24T16:40:04Z", "authors": ["Marcell T. Kurbucz", "Péter Pósfay", "Antal Jakovác"], "categories": ["q-fin.ST", "cs.LG"], "doi": "", "_query": "hedonic house price", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/1602.03402v1", "title": "Pricing options on forwards in energy markets: the role of mean reversion's speed", "abstract": "Consider the problem of pricing options on forwards in energy markets, when spot prices follow a geometric multi-factor model in which several rates of mean reversion appear. In this paper we investigate the role played by slow mean reversion when pricing and hedging options. In particular, we determine both upper and lower bounds for the error one makes neglecting low rates of mean reversion in the spot price dynamics.", "published": "2016-02-10T15:12:51Z", "authors": ["Maren Diane Schmeck"], "categories": ["q-fin.MF", "q-fin.PR"], "doi": "", "_query": "hedonic house price", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/0905.2366v1", "title": "Emergence of Price Divergence in a Model Short-Term Electric Power Market", "abstract": "A minimal model of a market of myopic non-cooperative agents who trade bilaterally with random bids reproduces qualitative features of short-term electric power markets, such as those in California and New England. Each agent knows its own budget and preferences but not those of any other agent. The near-equilibrium price established mid-way through the trading session diverges to both much higher and much lower prices towards the end of the trading session. This price divergence emerges in the model without any possibility that the agents could have conspired to \"game\" the market. The results were weakly sensitive to the endowments but strongly sensitive to the nature of the agent's preferences and budget constraints.", "published": "2009-05-14T16:04:33Z", "authors": ["Randall A. LaViolette", "Lory A. Ellebracht", "Kevin L. Stamber", "Charles J. Gieseler", "Benjamin K. Cook"], "categories": ["q-fin.TR"], "doi": "", "_query": "hedonic house price", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2609.37903v1", "title": "From Intraday Orderbook to Imbalance Price: Understanding Cross-Market Interaction", "abstract": "Power systems with increasing variable renewable generation face greater uncertainty in scheduling and balancing. Intraday and balancing electricity markets facilitate position adjustments and real-time balancing close to delivery. As delivery approaches, continuous intraday market participants exposed to imbalance settlement adjust their positions by trading additional volumes to reduce their imbalance exposure. We conjecture that positions remaining open after intraday trading, together with demand and supply uncertainties affecting physical market participants, influence price formation in the balancing market. This cross-market interaction is, however, rarely studied. To understand this interaction, this paper uses probabilistic modeling to examine how intraday orderbook information reflects subsequent imbalance price formation in Germany and Austria. We compare orderbook representations based on open, high, low, close, and volume, Volume-Weighted Average Price (VWAP), and last mid price across multiple horizons. Each representation is evaluated using the self product, neighboring products, and the product from the neighboring country. We then compare the best orderbook setting with fundamental feature sets and their combinations, followed by an ablation study of the available training history. We show that VWAP with neighboring products provides the best performance in both countries. Combining orderbook and fundamental information reduces testing loss in Germany but increases it in Austria. Using all available observations provides the best overall performance, while excluding 2022 can reduce loss for extreme price samples. These results reveal and help explain country-dependent interactions between the intraday and balancing markets.", "published": "2026-09-29T16:02:35Z", "authors": ["Runyao Yu", "Jochen L. Cremer", "Pierre Pinson", "Jalal Kazempour", "Leo Semmelmann", "Takuji Matsumoto", "Derek W. Bunn"], "categories": ["q-fin.CP", "cs.CE"], "doi": "", "_query": "hedonic house price", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/1511.00848v1", "title": "A backward Monte Carlo approach to exotic option pricing", "abstract": "We propose a novel algorithm which allows to sample paths from an underlying price process in a local volatility model and to achieve a substantial variance reduction when pricing exotic options. The new algorithm relies on the construction of a discrete multinomial tree. The crucial feature of our approach is that -- in a similar spirit to the Brownian Bridge -- each random path runs backward from a terminal fixed point to the initial spot price. We characterize the tree in two alternative ways: in terms of the optimal grids originating from the Recursive Marginal Quantization algorithm and following an approach inspired by the finite difference approximation of the diffusion's infinitesimal generator. We assess the reliability of the new methodology comparing the performance of both approaches and benchmarking them with competitor Monte Carlo methods.", "published": "2015-11-03T10:35:08Z", "authors": ["Giacomo Bormetti", "Giorgia Callegaro", "Giulia Livieri", "Andrea Pallavicini"], "categories": ["q-fin.CP"], "doi": "", "_query": "hedonic house price", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2202.02367v1", "title": "The end of 'set it and forget it' pricing? Opportunities for market-based freight contracts", "abstract": "In the for-hire truckload market, firms often experience unexpected transportation cost increases due to contracted transportation service provider (carrier) load rejections. The dominant procurement strategy results in long-term, fixed-price contracts that become obsolete as transportation providers' networks change and freight markets fluctuate between times of over and under supply. We build behavioral models of the contracted carrier's load acceptance decision under two distinct freight market conditions based on empirical load transaction data. With the results, we quantify carriers' likelihood of sticking to the contract as their best known alternative priced load options increase and become more attractive; in other words, carriers' contract price stickiness. Finally, we explore carriers' contract price stickiness for different lane, freight, and carrier segments and offer insights for shippers to identify where they can expect to see substantial improvement in contracted carrier load acceptance as they consider alternative, market-based pricing strategies.", "published": "2022-02-04T20:06:05Z", "authors": ["Angela Acocella", "Chris Caplice", "Yossi Sheffi"], "categories": ["econ.GN"], "doi": "", "_query": "hedonic house price", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/1712.04990v2", "title": "Series representation of the pricing formula for the European option driven by space-time fractional diffusion", "abstract": "In this paper, we show that the price of an European call option, whose underlying asset price is driven by the space-time fractional diffusion, can be expressed in terms of rapidly convergent double-series. The series formula can be obtained from the Mellin-Barnes representation of the option price with help of residue summation in $\\mathbb{C}^2$. We also derive the series representation for the associated risk-neutral factors, obtained by Esscher transform of the space-time fractional Green functions.", "published": "2017-12-13T20:10:11Z", "authors": ["Jean-Philippe Aguilar", "Cyril Coste", "Jan Korbel"], "categories": ["q-fin.MF", "q-fin.PR"], "doi": "10.1515/fca-2018-0054", "_query": "hedonic house price", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2601.13911v1", "title": "Designing sustainable barn-type houses: Optimal shapes for minimal envelope and energy use", "abstract": "Barn-type houses have become one of the most popular single-family housing typologies in Poland and across Europe due to their simplicity, functionality, and potential for energy efficiency. Despite their widespread use, systematic methods for optimizing their geometry in terms of envelope area and energy performance remain limited. This paper develops a rigorous mathematical framework for determining the optimal proportions of barn-type houses with respect to minimizing the external surface area while satisfying constraints of either fixed volume or fixed floor area. Closed-form solutions for the optimal width, length, and height are derived as explicit functions of the roof slope, together with formulas for the minimal achievable surface. A recently introduced dimensionless compactness measure is also calculated, allowing quantitative assessment of how far a given design deviates from the theoretical optimum. The methodology is applied to case studies of three existing houses, showing that while some designs deviate significantly from optimal compactness, others already closely approximate it. The results confirm that theoretical optimization can lead to meaningful reductions in construction costs and energy demand. To support practical implementation, two original freely available software tools were developed, enabling architects and engineers to perform optimization analyses.", "published": "2026-01-20T12:40:24Z", "authors": ["Ewa Rokita-Magdziarz", "Barbara Gronostajska", "Marcin Magdziarz"], "categories": ["math.OC"], "doi": "", "_query": "hedonic house price", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2311.06278v1", "title": "Boosting Stock Price Prediction with Anticipated Macro Policy Changes", "abstract": "Prediction of stock prices plays a significant role in aiding the decision-making of investors. Considering its importance, a growing literature has emerged trying to forecast stock prices with improved accuracy. In this study, we introduce an innovative approach for forecasting stock prices with greater accuracy. We incorporate external economic environment-related information along with stock prices. In our novel approach, we improve the performance of stock price prediction by taking into account variations due to future expected macroeconomic policy changes as investors adjust their current behavior ahead of time based on expected future macroeconomic policy changes. Furthermore, we incorporate macroeconomic variables along with historical stock prices to make predictions. Results from this strongly support the inclusion of future economic policy changes along with current macroeconomic information. We confirm the supremacy of our method over the conventional approach using several tree-based machine-learning algorithms. Results are strongly conclusive across various machine learning models. Our preferred model outperforms the conventional approach with an RMSE value of 1.61 compared to an RMSE value of 1.75 from the conventional approach.", "published": "2023-10-27T04:57:45Z", "authors": ["Md Sabbirul Haque", "Md Shahedul Amin", "Jonayet Miah", "Duc Minh Cao", "Ashiqul Haque Ahmed"], "categories": ["q-fin.ST", "cs.AI", "cs.LG"], "doi": "10.32996/jmss", "_query": "hedonic house price", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2001.03064v1", "title": "Option pricing in time-changed Lévy models with compound Poisson jumps", "abstract": "The problem of European-style option pricing in time-changed Lévy models in the presence of compound Poisson jumps is considered. These jumps relate to sudden large drops in stock prices induced by political or economical hits. As the time-changed Lévy models, the variance-gamma and the normal-inverse Gaussian models are discussed. Exact formulas are given for the price of digital asset-or-nothing call option on extra asset in foreign currency. The prices of simpler options can be derived as corollaries of our results and examples are presented. Various types of dependencies between stock prices are mentioned.", "published": "2020-01-09T15:34:58Z", "authors": ["Roman V. Ivanov", "Katsunori Ano"], "categories": ["math.PR"], "doi": "10.15559/18-VMSTA124", "_query": "hedonic house price", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/1811.07294v1", "title": "CVA and vulnerable options pricing by correlation expansions", "abstract": "We consider the problem of computing the Credit Value Adjustment ({CVA}) of a European option in presence of the Wrong Way Risk ({WWR}) in a default intensity setting. Namely we model the asset price evolution as solution to a linear equation that might depend on different stochastic factors and we provide an approximate evaluation of the option's price, by exploiting a correlation expansion approach, introduced in \\cite{AS}. We compare the numerical performance of such a method with that recently proposed by Brigo et al. (\\cite{BR18}, \\cite{BRH18}) in the case of a call option driven by a GBM correlated with the CIR default intensity. We additionally report some numerical evaluations obtained by other methods.", "published": "2018-11-18T08:10:02Z", "authors": ["Fabio Antonelli", "Alessandro Ramponi", "Sergio Scarlatti"], "categories": ["q-fin.CP"], "doi": "", "_query": "hedonic house price", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2210.11064v1", "title": "Competitive Equilibrium for Dynamic Multi-Agent Systems: Social Shaping and Price Trajectories", "abstract": "In this paper, we consider dynamic multi-agent systems (MAS) for decentralized resource allocation. The MAS operates at a competitive equilibrium to ensure supply and demand are balanced. First, we investigate the MAS over a finite horizon. The utility functions of agents are parameterized to incorporate individual preferences. We shape individual preferences through a set of utility functions to guarantee the resource price at a competitive equilibrium remains socially acceptable, i.e., the price is upper-bounded by an affordability threshold. We show this problem is solvable at the conceptual level. Next, we consider quadratic MAS and formulate the associated social shaping problem as a multi-agent linear quadratic regulator (LQR) problem which enables us to propose explicit utility sets using quadratic programming and dynamic programming. Then, a numerical algorithm is presented for calculating a tight range of the preference function parameters which guarantees a socially accepted price. We investigate the properties of a competitive equilibrium over an infinite horizon. Considering general utility functions, we show that under feasibility assumptions, any competitive equilibrium maximizes the social welfare. Then, we prove that for sufficiently small initial conditions, the social welfare maximization solution constitutes a competitive equilibrium with zero price. We also prove for general feasible initial conditions, there exists a time instant after which the optimal price, corresponding to a competitive equilibrium, becomes zero. Finally, we specifically focus on quadratic MAS and propose explicit results.", "published": "2022-10-20T07:37:22Z", "authors": ["Zeinab Salehi", "Yijun Chen", "Elizabeth L. Ratnam", "Ian R. Petersen", "Guodong Shi"], "categories": ["eess.SY"], "doi": "", "_query": "hedonic house price", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/1805.00109v2", "title": "Quantum computational finance: Monte Carlo pricing of financial derivatives", "abstract": "Financial derivatives are contracts that can have a complex payoff dependent upon underlying benchmark assets. In this work, we present a quantum algorithm for the Monte Carlo pricing of financial derivatives. We show how the relevant probability distributions can be prepared in quantum superposition, the payoff functions can be implemented via quantum circuits, and the price of financial derivatives can be extracted via quantum measurements. We show how the amplitude estimation algorithm can be applied to achieve a quadratic quantum speedup in the number of steps required to obtain an estimate for the price with high confidence. This work provides a starting point for further research at the interface of quantum computing and finance.", "published": "2018-04-30T21:41:23Z", "authors": ["Patrick Rebentrost", "Brajesh Gupt", "Thomas R. Bromley"], "categories": ["quant-ph"], "doi": "10.1103/PhysRevA.98.022321", "_query": "hedonic house price", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2507.02762v1", "title": "Contextual Online Pricing with (Biased) Offline Data", "abstract": "We study contextual online pricing with biased offline data. For the scalar price elasticity case, we identify the instance-dependent quantity $δ^2$ that measures how far the offline data lies from the (unknown) online optimum. We show that the time length $T$, bias bound $V$, size $N$ and dispersion $λ_{\\min}(\\hatΣ)$ of the offline data, and $δ^2$ jointly determine the statistical complexity. An Optimism-in-the-Face-of-Uncertainty (OFU) policy achieves a minimax-optimal, instance-dependent regret bound $\\tilde{\\mathcal{O}}\\big(d\\sqrt{T} \\wedge (V^2T + \\frac{dT}{λ_{\\min}(\\hatΣ) + (N \\wedge T) δ^2})\\big)$. For general price elasticity, we establish a worst-case, minimax-optimal rate $\\tilde{\\mathcal{O}}\\big(d\\sqrt{T} \\wedge (V^2T + \\frac{dT }{λ_{\\min}(\\hatΣ)})\\big)$ and provide a generalized OFU algorithm that attains it. When the bias bound $V$ is unknown, we design a robust variant that always guarantees sub-linear regret and strictly improves on purely online methods whenever the exact bias is small. These results deliver the first tight regret guarantees for contextual pricing in the presence of biased offline data. Our techniques also transfer verbatim to stochastic linear bandits with biased offline data, yielding analogous bounds.", "published": "2025-07-03T16:21:49Z", "authors": ["Yixuan Zhang", "Ruihao Zhu", "Qiaomin Xie"], "categories": ["cs.LG", "stat.ML"], "doi": "", "_query": "hedonic house price", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/1706.02408v2", "title": "Most-likely-path in Asian option pricing under local volatility models", "abstract": "This article addresses the problem of approximating the price of options on discrete and continuous arithmetic average of the underlying, i.e. discretely and continuously monitored Asian options, in local volatility models. A path-integral-type expression for option prices is obtained using a Brownian bridge representation for the transition density between consecutive sampling times and a Laplace asymptotic formula. In the limit where the sampling time window approaches zero, the option price is found to be approximated by a constrained variational problem on paths in time-price space. We refer to the optimizing path as the most-likely path (MLP). Approximation for the implied normal volatility follows accordingly. The small-time asymptotics and the existence of the MLP are also recovered rigorously using large deviation theory.", "published": "2017-06-07T23:07:33Z", "authors": ["Louis-Pierre Arguin", "Nien-Lin Liu", "Tai-Ho Wang"], "categories": ["q-fin.CP"], "doi": "", "_query": "hedonic house price", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/1801.10583v1", "title": "Short- to Mid-term Day-Ahead Electricity Price Forecasting Using Futures", "abstract": "Due to the liberalization of markets, the change in the energy mix and the surrounding energy laws, electricity research is a dynamically altering field with steadily changing challenges. One challenge especially for investment decisions is to provide reliable short to mid-term forecasts despite high variation in the time series of electricity prices. This paper tackles this issue in a promising and novel approach. By combining the precision of econometric autoregressive models in the short-run with the expectations of market participants reflected in future prices for the short- and mid-run we show that the forecasting performance can be vastly increased while maintaining hourly precision. We investigate the day-ahead electricity price of the EPEX Spot for Germany and Austria and setup a model which incorporates the Phelix future of the EEX for Germany and Austria. The model can be considered as an AR24-X model with one distinct model for each hour of the day. We are able to show that future data contains relevant price information for future time periods of the day-ahead electricity price. We show that relying only on deterministic external regressors can provide stability for forecast horizons of multiple weeks. By implementing a fast and efficient lasso estimation approach we demonstrate that our model can outperform several other models in the literature.", "published": "2018-01-31T18:09:20Z", "authors": ["Rick Steinert", "Florian Ziel"], "categories": ["q-fin.ST"], "doi": "10.5547/01956574.40.1.rste", "_query": "hedonic house price", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2605.08290v1", "title": "Toward Optimal Regret in Robust Pricing: Decoupling Corruption and Time", "abstract": "We design the first regret guarantees for robust dynamic pricing that decouple the dependence on the corruption $C$ and the time horizon $T$. In dynamic pricing, a seller with unlimited supply of a good interacts with a stream of buyers over \\( T \\) rounds, with the goal of maximizing revenue. At each round $t$, the seller posts a price $p_t$, and the buyer purchases the good only if their unknown valuation $v^\\star$ exceeds this price. The seller observes only the binary feedback $\\mathbb{I} \\left\\{ p_t \\leq v^\\star \\right\\}$, indicating whether a sale occurred. In the \\emph{robust} pricing setting, a malicious adversary is allowed to corrupt this feedback in at most $C$ rounds. Even if the learner knows the corruption $C$, the best known regret bound is $\\mathcal{O}(C\\log\\log T)$ by Gupta et al. [2025]. This leaves as an open problem to ``decouple'' the dependence on $C$ and $T$. In this work, we resolve this open problem. In particular, we develop a robust variant of binary search that achieves regret $\\mathcal{O}(C+\\log T)$ when the corruption $C$ is known and $\\mathcal{O}(C+\\log^2 T)$ when the corruption is unknown.", "published": "2026-05-08T08:22:43Z", "authors": ["Kalana Kalupahana", "Francesco Emanuele Stradi", "Matteo Castiglioni", "Alberto Marchesi"], "categories": ["cs.LG", "cs.AI"], "doi": "", "_query": "hedonic house price", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2511.22027v1", "title": "Local unanimity in Shapley-Scarf housing markets", "abstract": "In the housing market model introduced by Shapley and Scarf (1974), we propose a new axiom, local unanimity, that extends the unanimity condition widely used in social choice theory. It applies the unanimity condition to any subset of agents in the model who unanimously agree on the best exchange of their endowments. Building on this axiom, we provide several concise characterizations of the Top Trading Cycles (TTC) mechanism under both strict and weak preference domains.", "published": "2025-11-27T02:19:06Z", "authors": ["Xinquan Hu", "Jun Zhang"], "categories": ["econ.TH"], "doi": "", "_query": "hedonic house price", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/1912.00454v1", "title": "On Extensions of the Barone-Adesi & Whaley Method to Price American-Type Options", "abstract": "The present article provides an efficient and accurate hybrid method to price American standard options in certain jump-diffusion models as well as American barrier-type options under the Black & Scholes framework. Our method generalizes the quadratic approximation scheme of Barone-Adesi & Whaley (1987) and several of its extensions. Using perturbative arguments, we decompose the early exercise pricing problem into sub-problems of different orders and solve these sub-problems successively. The obtained solutions are combined to recover approximations to the original pricing problem of multiple orders, with the 0-th order version matching the general Barone-Adesi & Whaley ansatz. We test the accuracy and efficiency of the approximations via numerical simulations. The results show a clear dominance of higher order approximations over their respective 0-th order version and reveal that significantly more pricing accuracy can be obtained by relying on approximations of the first few orders. Additionally, they suggest that increasing the order of any approximation by one generally refines the pricing precision, however that this happens at the expense of greater computational costs.", "published": "2019-12-01T18:02:00Z", "authors": ["Ludovic Mathys"], "categories": ["q-fin.MF", "q-fin.CP", "q-fin.PR"], "doi": "", "_query": "hedonic house price", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2306.04158v1", "title": "Bachelier's Market Model for ESG Asset Pricing", "abstract": "Environmental, Social, and Governance (ESG) finance is a cornerstone of modern finance and investment, as it changes the classical return-risk view of investment by incorporating an additional dimension of investment performance: the ESG score of the investment. We define the ESG price process and integrate it into an extension of Bachelier's market model in both discrete and continuous time, enabling option pricing valuation.", "published": "2023-06-07T05:14:33Z", "authors": ["Svetlozar Rachev", "Nancy Asare Nyarko", "Blessing Omotade", "Peter Yegon"], "categories": ["q-fin.MF"], "doi": "", "_query": "hedonic house price", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2602.20356v1", "title": "Prevalence of Food and Housing Insecurity among Direct Support Professionals in New York", "abstract": "Background. Low earnings are associated with household insecurity. Direct Support Professionals (DSPs) provide support for people with intellectual and developmental disabilities, typically for wages close to state minimums, and may experience insecurity. Objective. The purpose of the study was to evaluate the prevalence of food and housing insecurity among DSPs. Methods. We conducted a statewide, cross-sectional survey of DSPs in New York State (2022-2023). Measures included detailed questions about food and housing insecurity. We used chi-square analyses and logistic regressions to examine relationships between insecurity and demographic characteristics as proxies for social determinants of health. A total of 4,503 DSPs responded to the survey. The analytic sample contained 2,766 respondents with complete data for all relevant variables. Results. Overall, 62.6% experienced food and/or housing insecurity, with over half of those respondents experiencing both. Insecurity was highest among DSPs with a disability (76.2%), DSPs of color (75.7%), and those with lower income (72.4%), but over 50% of DSPs across demographic groups experienced insecurity. Conclusions. The widespread insecurity this study demonstrates is an occupational hazard that reduces worker welfare. At the macro-level, household insecurity is a critical threat to the stability of the care and support delivery system. The human services sector is projected to grow rapidly in the future. If growth continues along low wage lines, it implies an equally rapid expansion of worker insecurity. Government action to raise pay and interventions that enhance food and housing security are needed to support workers in the care delivery system for people with intellectual and developmental disabilities.", "published": "2026-02-23T21:03:31Z", "authors": ["Jennifer Cohen", "Yana Rodgers"], "categories": ["econ.GN"], "doi": "10.1016/j.dhjo.2024.101769", "_query": "hedonic house price", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/1707.09609v1", "title": "Explicit expressions for European option pricing under a generalized skew normal distribution", "abstract": "Under a generalized skew normal distribution we consider the problem of European option pricing. Existence of the martingale measure is proved. An explicit expression for a given European option price is presented in terms of the cumulative distribution function of the univariate skew normal and the bivariate standard normal distributions. Some special cases are investigated in a greater detail. To carry out the sensitivity of the option price to the skew parameters, numerical methods are applied. Some concluding remarks and further works are given. The results obtained are extensions of the results provided by [4].", "published": "2017-07-30T11:03:40Z", "authors": ["Mahdi Doostparast"], "categories": ["q-fin.PR", "stat.AP"], "doi": "", "_query": "hedonic house price", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2608.15518v1", "title": "A Lifecycle-Oriented Detection and Defense Framework for Price Manipulation Attacks in DeFi", "abstract": "Aiming at frequent oracle price manipulation attacks in Decentralized Finance (DeFi), this paper investigates attack patterns, vulnerability types, risk assessment, automated detection, and defense strategies. Based on Oracle lifecycle theory, a three-layer attack tree covering the physical, protocol, and application layers is constructed to analyze the attack chain and identify the data election stage as a key intrusion point. Four major contract-level vulnerabilities are summarized: insufficient validation of price data flow, oracle call risks, uncontrolled cross-contract calls, and defects in AMM price reading logic. Fuzzy-AHP and Value-at-Risk (VaR) models are combined to quantify risk factors and construct a risk matrix covering technology, market, governance, and contract risks. An automated detection tool based on an extended Slither framework is developed using taint tracking and pattern matching. Finally, a multi-layer defense strategy is proposed, integrating trusted execution environments at the data source layer, TWAP smoothing and adaptive circuit breakers at the smart contract layer, and optimized kernel network configurations at the operating system layer. Experiments show that the proposed approach reduces attack-induced price deviation from 55.56% to below 5%. The detection tool achieves 94.38% accuracy and 92.31% recall, outperforming existing open-source tools.", "published": "2026-08-16T04:01:42Z", "authors": ["Xingyu Xiong", "Chang Liu", "Xiaoqi Li"], "categories": ["cs.CR"], "doi": "", "_query": "hedonic house price", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2605.12794v2", "title": "Dynamic Transaction Scheduling and Pricing in the Ethereum Mempool", "abstract": "The Ethereum blockchain utilizes the EIP-1559 algorithm to manage transaction inclusion and block assembly. However, EIP-1559 and much of the existing literature study this problem from a static perspective, focusing on price evolution without modelling transaction dynamics within the mempool. Motivated by this limitation, we study a dynamic transaction scheduling problem in which transactions with heterogeneous sizes and per-unit values arrive over time and remain in the mempool until scheduled. To capture the stochastic mempool evolution, we formulate the problem as a Markov Decision Process (MDP) whose state represents the mempool configuration and whose actions correspond to block prices. We first provide a primal-dual interpretation of the static EIP-1559 mechanism, showing that block prices arise naturally as dual variables of a social-welfare maximization problem. Building on this perspective, we extend the framework to the dynamic setting and formulate an objective that maximizes long-run discounted reward while incorporating holding costs and overshoot penalties. We then employ a Natural Policy Gradient (NPG) algorithm to compute the optimal policy. Our results show that dynamic pricing stabilizes the mempool while maximizing long-run discounted reward. In particular, as the overshoot penalty increases, the average scheduled transaction volume converges to the target block capacity, and the resulting NPG updates closely resemble the EIP-1559 price update rule. Finally, we study two special cases of the MDP formulation: homogeneous transactions and uniform arrivals. In the homogeneous setting, where the protocol directly controls scheduled volume, we show that the optimal policy has a threshold structure. We then propose a bang-bang pricing mechanism for uniform arrivals and derive a lower bound on the block capacity needed to ensure system stability.", "published": "2026-05-12T22:20:29Z", "authors": ["Fatemeh Fardno", "S. Rasoul Etesami"], "categories": ["cs.GT", "cs.CR", "cs.DC", "cs.NI", "eess.SY"], "doi": "", "_query": "hedonic house price", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/1609.05286v2", "title": "From quantum mechanics to finance: Microfoundations for jumps, spikes and high volatility phases in diffusion price processes", "abstract": "We present an agent behavior based microscopic model that induces jumps, spikes and high volatility phases in the price process of a traded asset. We transfer dynamics of thermally activated jumps of an unexcited/ excited two state system discussed in the context of quantum mechanics to agent socio-economic behavior and provide microfoundations. After we link the endogenous agent behavior to price dynamics we establish the circumstances under which the dynamics converge to an Itô-diffusion price processes in the large market limit.", "published": "2016-09-17T06:19:27Z", "authors": ["Christof Henkel"], "categories": ["q-fin.TR", "econ.GN"], "doi": "10.1016/j.physa.2016.11.125", "_query": "hedonic house price", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2601.04220v1", "title": "Constrained Assortment and Price Optimization under Generalized Nested Logit Models", "abstract": "We study assortment and price optimization under the generalized nested logit (GNL) model, one of the most general and flexible modeling frameworks in discrete choice modeling. Despite its modeling advantages, optimization under GNL is highly challenging: even the pure assortment problem is NP-hard, and existing approaches rely on approximation schemes or are limited to simple cardinality constraints. In this paper, we develop the first exact and near-exact algorithms for constrained assortment and joint assortment--pricing optimization (JAP) under GNL. Our approach reformulates the problem into bilinear and exponential-cone convex programs and exploits convexity, concavity, and submodularity properties to generate strong cutting planes within a Branch-and-Cut framework (B\\&C). We further extend this framework to the mixed GNL (MGNL) model, capturing heterogeneous customer segments, and to JAP with discrete prices. For the continuous pricing case, we propose a near-exact algorithm based on piecewise-linear approximation (PWLA) that achieves arbitrarily high precision under general linear constraints. Extensive computational experiments demonstrate that our methods substantially outperform state-of-the-art approximation approaches in both solution quality and scalability. In particular, we are able to solve large-scale instances with up to 1000 products and 20 nests, and to obtain near-optimal solutions for continuous pricing problems with negligible optimality gaps. To the best of our knowledge, this work resolves several open problems in assortment and price optimization under GNL.", "published": "2025-12-25T17:18:44Z", "authors": ["Hoang Giang Pham", "Tien Mai"], "categories": ["econ.GN", "math.OC"], "doi": "", "_query": "hedonic house price", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2609.33213v2", "title": "Finance-Informed Operator Learning for Option Pricing with Quantum-Compatible Realizations", "abstract": "Pricing European options under local volatility requires repeatedly solving a PDE whose coefficients change with recalibration, while practitioners need both prices and sensitivities across spot-time surfaces. Neural surrogates can amortize these solves, but near expiry the solution loses regularity, making curvature difficult to learn and allowing violations of no-arbitrage bounds. We propose a finance-informed Deep Operator Network (FI-DeepONet) that decomposes the pricing operator into an input-dependent Black-Scholes carrier and a learned correction. The carrier uses strike-line integrated variance, capturing the leading near-expiry curvature singularity, while a smooth monotone Financial Admissibility Layer enforces pointwise price bounds. We derive a short-maturity estimate and differentiated asymptotics for the exact correction, together with identities linking correction error to price, sensitivity, and PDE-residual errors. On in-distribution tests, FI-DeepONet reduces global relative price error by nearly an order of magnitude versus vanilla and physics-informed DeepONet baselines. On parameter-shift OOD tests within the same local-volatility family, generalization is moderate. On index-option data, the frozen model remains accurate without market-specific retraining, though it does not outperform the same-input analytic formula. We also give a quantum-compatible realization in which selected linear maps are exactly compiled or approximated by a restricted diagonal-orthogonal family before supervised adaptation.", "published": "2026-09-27T04:52:43Z", "authors": ["Jiarui Feng", "Bingyang Hu", "Yu Jiang", "Changhong Mou", "Yeyu Zhang"], "categories": ["math.NA"], "doi": "", "_query": "hedonic house price", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2409.08411v2", "title": "Social Equity Based Optimal Power Flow Framework to Hedge Against Price Events", "abstract": "With the increasing frequency of high impact low probability events, electricity markets are experiencing significant price spikes more often. This paper proposes a novel social equity driven optimal power flow framework to mitigate the adverse effects of price events that lead to such price spikes. The framework integrates social welfare optimization with socioeconomic considerations by including a socioeconomic score that quantifies the energy burden and socioeconomic status of consumers. By incorporating both supply cost and consumer satisfaction, the model aims to achieve a balanced and fair distribution of resources during price events, while considering resource scarcity and possible load curtailment. The proposed framework is tested for convergence on modified versions of the PJM 5-bus system and IEEE 24-bus reliability test system, discussing its potential effectiveness in enhancing social equity and optimizing power flow under system security constraints. Sensitivity analysis further highlights the impact of socioeconomic score on social welfare, providing insights for future improvements.", "published": "2024-09-12T21:53:48Z", "authors": ["Sachinth Viththarachchige", "Demy Alexander", "Sarangan Rajendran", "Visvakumar Aravinthan"], "categories": ["eess.SY"], "doi": "10.1109/NAPS61145.2024.10741650", "_query": "hedonic house price", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2403.19847v1", "title": "Strategic complementarities as stochastic control under sticky price", "abstract": "We examine how monetary shocks spread throughout an economic model characterized by sticky prices and general equilibrium, where the pricing strategies of firms are interlinked, fostering a mutually beneficial relationship. In this dynamic equilibrium, pricing choices of firms are influenced by overall economic factors, which are themselves affected by these decisions. We approach this situation using a path integral control method, yielding several important insights. We confirm the presence and uniqueness of the equilibrium and scrutinize the impulse response function (IRF) of output subsequent to a shock affecting the entire economy.", "published": "2024-03-28T21:48:18Z", "authors": ["Lambert Dong"], "categories": ["econ.TH", "math.OC"], "doi": "", "_query": "hedonic house price", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2501.17251v1", "title": "Demand Analysis under Price Rigidity and Endogenous Assortment: An Application to China's Tobacco Industry", "abstract": "We observe nominal price rigidity in tobacco markets across China. The monopolistic seller responds by adjusting product assortments, which remain unobserved by the analyst. We develop and estimate a logit demand model that incorporates assortment discrimination and nominal price rigidity. We find that consumers are significantly more responsive to price changes than conventional models predict. Simulated tax increases reveal that neglecting the role of endogenous assortments results in underestimations of the decline in higher-tier product sales, incorrect directional predictions of lower-tier product sales, and overestimation of tax revenue by more than 50%. Finally, we extend our methodology to settings with competition and random coefficient models.", "published": "2025-01-28T19:16:45Z", "authors": ["Hui Liu", "Yao Luo"], "categories": ["econ.EM"], "doi": "", "_query": "hedonic house price", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/1506.02719v1", "title": "Non-parametric Revenue Optimization for Generalized Second Price Auctions", "abstract": "We present an extensive analysis of the key problem of learning optimal reserve prices for generalized second price auctions. We describe two algorithms for this task: one based on density estimation, and a novel algorithm benefiting from solid theoretical guarantees and with a very favorable running-time complexity of $O(n S \\log (n S))$, where $n$ is the sample size and $S$ the number of slots. Our theoretical guarantees are more favorable than those previously presented in the literature. Additionally, we show that even if bidders do not play at an equilibrium, our second algorithm is still well defined and minimizes a quantity of interest. To our knowledge, this is the first attempt to apply learning algorithms to the problem of reserve price optimization in GSP auctions. Finally, we present the first convergence analysis of empirical equilibrium bidding functions to the unique symmetric Bayesian-Nash equilibrium of a GSP.", "published": "2015-06-08T22:45:30Z", "authors": ["Mehryar Mohri", "Andres Munoz Medina"], "categories": ["cs.LG", "cs.GT"], "doi": "", "_query": "hedonic house price", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2406.02878v1", "title": "The test of investors' behavioral bias through the price discovery process in cryptoasset exchange\" Transactional-level evidence from Thailand", "abstract": "Analyzing investors' trading behavior in cryptoasset markets provides new evidence supporting the theory that retail investors likely exhibit behavioral biases. We investigate the price discovery process between Thailand's most highly liquid exchange and the global exchange. Under the no-arbitrage assumption, bid-offer quotes in the local exchange should quickly move to match the new price levels in the global exchange, as the price process of cryptoassets in the local exchange does not contain new information that can lead the price dynamic in the global exchange. We analyze intraday bid-offer quotes and investors' portfolio positions and find that investors exhibit the disposition effect by attempting to sell their profitable positions during market upturns. The rate of bid-offer movement is significantly slow to match a new global price level only in situations when most investors in the market are in profit. These insights are crucial as they suggest that the risk-return characteristics of asset prices between the bull and bear market may differ, resulting from investors' behavioral biases.", "published": "2024-06-05T02:53:02Z", "authors": ["Roongkiat Ratanabanchuen", "Kanis Saengchote", "Voraprapa Nakavachara", "Thitiphong Amonthumniyom", "Pongsathon Parinyavuttichai", "Polpatt Vinaibodee"], "categories": ["econ.GN"], "doi": "", "_query": "hedonic house price", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2010.12300v1", "title": "Perturbed Pricing", "abstract": "We propose a simple randomized rule for the optimization of prices in revenue management with contextual information. It is known that the certainty equivalent pricing rule, albeit popular, is sub-optimal. We show that, by allowing a small amount of randomization around these certainty equivalent prices, the benefits of optimal pricing and low regret are achievable.", "published": "2020-10-23T11:12:32Z", "authors": ["Neil Walton", "Yuqing Zhang"], "categories": ["cs.GT", "math.OC", "math.ST", "stat.AP"], "doi": "", "_query": "hedonic house price", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/1106.2193v1", "title": "Randall-Sundrum Models and Holography", "abstract": "This Master's thesis will introduce the Randall-Sundrum RSI model and overview its general features and basic phenomenology, with the goal of understanding its 5-dimensional geometric structure in terms of its 4-dimensional AdS/CFT dual theory. We discuss mode expansions, boundary terms, field content, and supersymmetry, leading up to a discussion of the AdS/CFT correspondence in RSI models. Low-energy 4d Yukawa hierarchies and gauge couplings are understood in terms of the bulk 5d theory. A few simple Standard Model and MSSM-like constructions are considered, and their 4d dual theories are discussed. One new result is presented: the 5d bulk interpretation of the Nelson-Strassler mechanism of generating Yukawa hierarchies through renormalization group flow.", "published": "2011-06-11T03:27:06Z", "authors": ["Jeremy Price"], "categories": ["hep-ph"], "doi": "", "_query": "hedonic house price", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/physics/0402040v1", "title": "The Thermodynamic Arrow: Puzzles and Pseudo-puzzles", "abstract": "For more than a century, physics has known of a puzzling conflict between the T-asymmetry of thermodynamic phenomena and the T-symmetry of the underlying microphysics on which these phenomena depend. This paper provides a guide to the current status of this puzzle, distinguishing the central issue from various issues with which it may be confused. It is shown that there are two competing conceptions of what is needed to resolve the puzzle of the thermodynamic asymmetry, which differ with respect to the number of distinct T-asymmetries they take to be manifest in the physical world. On the preferable one-asymmetry conception, the remaining puzzle concerns the ordered distribution of matter in the early universe. The puzzle of the thermodynamic arrow thus becomes a puzzle for cosmology.", "published": "2004-02-06T23:32:39Z", "authors": ["Huw Price"], "categories": ["physics.class-ph", "physics.hist-ph", "physics.pop-ph"], "doi": "", "_query": "hedonic house price", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/1502.04521v1", "title": "A dynamic optimal execution strategy under stochastic price recovery", "abstract": "In the present paper, we study the optimal execution problem under stochastic price recovery based on limit order book dynamics. We model price recovery after execution of a large order by accelerating the arrival of the refilling order, which is defined as a Cox process whose intensity increases by the degree of the market impact. We include not only the market order but also the limit order in our strategy in a restricted fashion. We formulate the problem as a combined stochastic control problem over a finite time horizon. The corresponding Hamilton-Jacobi-Bellman quasi-variational inequality is solved numerically. The optimal strategy obtained consists of three components: (i) the initial large trade; (ii) the unscheduled small trades during the period; (iii) the terminal large trade. The size and timing of the trade is governed by the tolerance for market impact depending on the state at each time step, and hence the strategy behaves dynamically. We also provide competitive results due to inclusion of the limit order, even though a limit order is allowed under conservative evaluation of the execution price.", "published": "2015-02-16T13:11:56Z", "authors": ["Masashi Ieda"], "categories": ["q-fin.TR", "q-fin.CP"], "doi": "", "_query": "hedonic house price", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/1505.03587v2", "title": "Pricing complexity options", "abstract": "We consider options that pay the complexity deficiency of a sequence of up and down ticks of a stock upon exercise. We study the price of European and American versions of this option numerically for automatic complexity, and theoretically for Kolmogorov complexity. We also consider run complexity, which is a restricted form of automatic complexity.", "published": "2015-05-14T01:01:51Z", "authors": ["Malihe Alikhani", "Bjørn Kjos-Hanssen", "Amirarsalan Pakravan", "Babak Saadat"], "categories": ["q-fin.PR", "cs.CC", "cs.FL", "math.LO"], "doi": "10.3233/AF-150050", "_query": "hedonic house price", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/1303.2044v4", "title": "Bubbles, Jumps, and Scaling from Properly Anticipated Prices", "abstract": "Prices in financial markets exhibit extreme jumps far more often than can be accounted for by external news. Further, magnitudes of price changes are correlated over long times. These so called stylized facts are quantified by scaling laws similar to, for example, turbulent fluids. They are believed to reflect the complex interactions of heterogenous agents which give rise to irrational herding. Therefore, the stylized facts have been argued to provide evidence against the efficient market hypothesis which states that prices rapidly reflect available information and therefore are described by a martingale. Here we show, that in very simple bidding processes efficiency is not opposed to, but causative to scaling properties observed in real markets. Thereby, we link the stylized facts not only to price efficiency, but also to the economic theory of rational bubbles. We then demonstrate effects predicted from our normative model in the dynamics of groups of real human subjects playing a modified minority game. An extended version of the latter can be played online at seesaw.neuro.uni-bremen.de.", "published": "2013-03-08T16:23:10Z", "authors": ["Felix Patzelt", "Klaus Pawelzik"], "categories": ["q-fin.TR", "physics.soc-ph"], "doi": "10.1007/978-3-319-27635-9", "_query": "hedonic house price", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/1707.01237v2", "title": "Option Pricing in a Regime Switching Stochastic Volatility Model", "abstract": "In the classical model of stock prices which is assumed to be Geometric Brownian motion, the drift and the volatility of the prices are held constant. However, in reality, the volatility does vary. In quantitative finance, the Heston model has been successfully used where the volatility is expressed as a stochastic differential equation. In addition, we consider a regime switching model where the stock volatility dynamics depends on an underlying process which is possibly a non-Markov pure jump process. Under this model assumption, we find the locally risk minimizing pricing of European type vanilla options. The price function is shown to satisfy a Heston type PDE.", "published": "2017-07-05T07:20:22Z", "authors": ["Arunangshu Biswas", "Anindya Goswami", "Ludger Overbeck"], "categories": ["q-fin.PR", "math.PR", "q-fin.MF"], "doi": "10.1016/j.spl.2018.02.056", "_query": "hedonic house price", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2509.12286v1", "title": "Prediction of Stocks Index Price using Quantum GANs", "abstract": "This paper investigates the application of Quantum Generative Adversarial Networks (QGANs) for stock price prediction. Financial markets are inherently complex, marked by high volatility and intricate patterns that traditional models often fail to capture. QGANs, leveraging the power of quantum computing, offer a novel approach by combining the strengths of generative models with quantum machine learning techniques. We implement a QGAN model tailored for stock price prediction and evaluate its performance using historical stock market data. Our results demonstrate that QGANs can generate synthetic data closely resembling actual market behavior, leading to enhanced prediction accuracy. The experiment was conducted using the Stocks index price data and the AWS Braket SV1 simulator for training the QGAN circuits. The quantum-enhanced model outperforms classical Long Short-Term Memory (LSTM) and GAN models in terms of convergence speed and prediction accuracy. This research represents a key step toward integrating quantum computing in financial forecasting, offering potential advantages in speed and precision over traditional methods. The findings suggest important implications for traders, financial analysts, and researchers seeking advanced tools for market analysis.", "published": "2025-09-14T20:28:24Z", "authors": ["Sangram Deshpande", "Gopal Ramesh Dahale", "Sai Nandan Morapakula", "Uday Wad"], "categories": ["cs.LG", "quant-ph"], "doi": "10.1609/aaaiss.v7i1.36904", "_query": "hedonic house price", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2308.04717v1", "title": "Fully Decentralized Peer-to-Peer Community Grid with Dynamic and Congestion Pricing", "abstract": "Peer-to-peer (P2P) electricity markets enable prosumers to minimize their costs, which has been extensively studied in recent research. However, there are several challenges with P2P trading when physical network constraints are also included. Moreover, most studies use fixed prices for grid power prices without considering dynamic grid pricing, and equity for all participants. This policy may negatively affect the long-term development of the market if prosumers with low demand are not treated fairly. An initial step towards addressing these problems is the design of a new decentralized P2P electricity market with two dynamic grid pricing schemes that are determined by consumer demand. Futhermore, we consider a decentralized system with physical constraints for optimizing power flow in networks without compromising privacy. We propose a dynamic congestion price to effectively address congestion and then prove the convergence and global optimality of the proposed method. Our experiments show that P2P energy trade decreases generation cost of main grid by 56.9% compared with previous works. Consumers reduce grid trading by 57.3% while the social welfare of consumers is barely affected by the increase of grid price.", "published": "2023-08-09T05:43:06Z", "authors": ["Hien Thanh Doan", "Truong Hoang Bao Huy", "Daehee Kim", "Hongseok Kim"], "categories": ["eess.SY"], "doi": "", "_query": "hedonic house price", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2303.10043v1", "title": "Optimal liquidation with temporary and permanent price impact, an application to cryptocurrencies", "abstract": "This paper studies the optimal liquidation of stocks in the presence of temporary and permanent price impacts, and we focus in the case of cryptocurrencies. We start by presenting analytical solutions to the problem with linear temporary impact, and linear and quadratic permanent impact. Then, using data from the order book of the BNB cryptocurrency, we estimate the functional form of the temporary and permanent price impact in three different scenarios: underestimation, overestimation and average estimation, finding different functional forms for each scenario. Using finite differences and optimal policy iteration, we solve the problem numerically and observe interesting changes in the optimal liquidation policy when applying calibrated linear and power forms for the temporary and permanent price impacts. Then, with these optimal policies, we identify optimal liquidation trajectories and simulate the liquidation of initial inventories to compare the performance among the optimal strategies under different parametrizations and against a naive strategy. Finally, we characterize the optimal policies based on the functional form of the inventory and find that policies generating the highest revenue are those starting with a low trading rate and increasing it as time passes.", "published": "2023-03-17T15:12:51Z", "authors": ["Hugo E. Ramirez", "Julián Fernando Sanchez"], "categories": ["q-fin.TR"], "doi": "", "_query": "hedonic house price", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/1601.03418v1", "title": "Pointwise Schauder estimates of parabolic equations in Carnot groups", "abstract": "Schauder estimates were a historical stepping stone for establishing uniqueness and smoothness of solutions for certain classes of partial differential equations. Since that time, they have remained an essential tool in the field. Roughly speaking, the estimates state that the Hölder continuity of the coefficient functions and inhomogeneous term implies the Hölder continuity of the solution and its derivatives. This document establishes pointwise Schauder estimates for second order \"parabolic\" equations of the form $$\\partial_{t}u(x,t)-\\sum_{i,j=1}^{m_1} a_{ij}(x,t)X_iX_ju(x,t)=f(x,t)$$ where $X_{1},\\ldots ,X_{m_1}$ generate the first layer of the Lie algebra stratification for a Carnot group. The Schauder estimates are shown by means of Campanato spaces. These spaces make the pointwise nature of the estimates possible by comparing solutions to their Taylor polynomials. As a prerequisite device, a version of both the mean value theorem and Taylor inequality are established with the parabolic distance incorporated.", "published": "2016-01-13T21:42:18Z", "authors": ["Heather Price"], "categories": ["math.AP"], "doi": "", "_query": "hedonic house price", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/1310.5665v3", "title": "Learning Theory and Algorithms for Revenue Optimization in Second-Price Auctions with Reserve", "abstract": "Second-price auctions with reserve play a critical role for modern search engine and popular online sites since the revenue of these companies often directly de- pends on the outcome of such auctions. The choice of the reserve price is the main mechanism through which the auction revenue can be influenced in these electronic markets. We cast the problem of selecting the reserve price to optimize revenue as a learning problem and present a full theoretical analysis dealing with the complex properties of the corresponding loss function. We further give novel algorithms for solving this problem and report the results of several experiments in both synthetic and real data demonstrating their effectiveness.", "published": "2013-10-21T18:27:25Z", "authors": ["Mehryar Mohri", "Andres Muñoz Medina"], "categories": ["cs.LG"], "doi": "", "_query": "hedonic house price", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2412.07787v1", "title": "Anomaly Detection in California Electricity Price Forecasting: Enhancing Accuracy and Reliability Using Principal Component Analysis", "abstract": "Accurate and reliable electricity price forecasting has significant practical implications for grid management, renewable energy integration, power system planning, and price volatility management. This study focuses on enhancing electricity price forecasting in California's grid, addressing challenges from complex generation data and heteroskedasticity. Utilizing principal component analysis (PCA), we analyze CAISO's hourly electricity prices and demand from 2016-2021 to improve day-ahead forecasting accuracy. Initially, we apply traditional outlier analysis with the interquartile range method, followed by robust PCA (RPCA) for more effective outlier elimination. This approach improves data symmetry and reduces skewness. We then construct multiple linear regression models using both raw and PCA-transformed features. The model with transformed features, refined through traditional and SAS Sparse Matrix outlier removal methods, shows superior forecasting performance. The SAS Sparse Matrix method, in particular, significantly enhances model accuracy. Our findings demonstrate that PCA-based methods are key in advancing electricity price forecasting, supporting renewable integration and grid management in day-ahead markets. Keywords: Electricity price forecasting, principal component analysis (PCA), power system planning, heteroskedasticity, renewable energy integration.", "published": "2024-11-25T20:55:25Z", "authors": ["Joseph Nyangon", "Ruth Akintunde"], "categories": ["econ.EM", "cs.AI", "cs.ET", "cs.LG", "eess.SY"], "doi": "10.1002/wene.504", "_query": "hedonic house price", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2202.06541v2", "title": "Pathway: a protocol for algorithmic pricing of a DAO governance token", "abstract": "In this paper, we will consider a governance token pricing algorithm that conducts liquidity operations on AMM (CPMM) DEXs (automated market maker decentralized exchanges) with liquidity that belongs to a decentralized autonomous organization (DAO), also called protocol-owned liquidity (POL). The primary aim of the protocol is maintaining a price peg by determining algorithmically when and how to carry out interventions that consist of two steps: extracting liquidity from an AMM liquidity pool and conducting \"token swap\" operations. We will cover setting up an optimal peg function as a weighted sum of certain normalized factors, which are to be determined collectively by the DAO. In particular, we will review various arithmetic invariants of liquidity intervention, which brings the price to a peg while leaving total liquidity intact, and show how such interventions can be substituted in practice by so-called PMM (proactive market maker) protocols.", "published": "2022-02-14T08:24:33Z", "authors": ["Aleksei Pupyshev", "Ilya Sapranidi", "Shamil Khalilov"], "categories": ["cs.CR"], "doi": "", "_query": "hedonic house price", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/1810.01372v5", "title": "Pricing of debt and equity in a financial network with comonotonic endowments", "abstract": "In this paper we present formulas for the valuation of debt and equity of firms in a financial network under comonotonic endowments. We demonstrate that the comonotonic setting provides a lower bound and Jensen's inequality provides an upper bound to the price of debt under Eisenberg-Noe financial networks with bankruptcy costs. Such financial networks encode the interconnection of firms through debt claims. The proposed pricing formulas consider the realized, endogenous, recovery rate on debt claims. Special consideration is given to the CAPM setting in which firms invest in correlated portfolios so as to provide analytical stress testing formulas. We endogenously construct the comonotonic endowment setting from a equity maximizing standpoint with capital transfers. We conclude by, numerically, comparing the network valuation problem with two single firm baseline heuristics which can, respectively, approximate the price of debt and equity.", "published": "2018-10-02T17:05:33Z", "authors": ["Tathagata Banerjee", "Zachary Feinstein"], "categories": ["q-fin.MF", "q-fin.RM"], "doi": "", "_query": "hedonic house price", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2302.09700v2", "title": "Leveraging Reviews: Learning to Price with Buyer and Seller Uncertainty", "abstract": "In online marketplaces, customers have access to hundreds of reviews for a single product. Buyers often use reviews from other customers that share their type -- such as height for clothing, skin type for skincare products, and location for outdoor furniture -- to estimate their values, which they may not know a priori. Customers with few relevant reviews may hesitate to make a purchase except at a low price, so for the seller, there is a tension between setting high prices and ensuring that there are enough reviews so that buyers can confidently estimate their values. Simultaneously, sellers may use reviews to gauge the demand for items they wish to sell. In this work, we study this pricing problem in an online setting where the seller interacts with a set of buyers of finitely many types, one by one, over a series of $T$ rounds. At each round, the seller first sets a price. Then a buyer arrives and examines the reviews of the previous buyers with the same type, which reveal those buyers' ex-post values. Based on the reviews, the buyer decides to purchase if they have good reason to believe that their ex-ante utility is positive. Crucially, the seller does not know the buyer's type when setting the price, nor even the distribution over types. We provide a no-regret algorithm that the seller can use to obtain high revenue. When there are $d$ types, after $T$ rounds, our algorithm achieves a problem-independent $\\tilde O(T^{2/3}d^{1/3})$ regret bound. However, when the smallest probability $q_{\\text{min}}$ that any given type appears is large, specifically when $q_{\\text{min}} \\in Ω(d^{-2/3}T^{-1/3})$, then the same algorithm achieves a $\\tilde O(T^{1/2}q_{\\text{min}}^{-1/2})$ regret bound. We complement these upper bounds with matching lower bounds in both regimes, showing that our algorithm is minimax optimal up to lower-order terms.", "published": "2023-02-20T00:15:45Z", "authors": ["Wenshuo Guo", "Nika Haghtalab", "Kirthevasan Kandasamy", "Ellen Vitercik"], "categories": ["cs.GT", "cs.LG"], "doi": "", "_query": "hedonic house price", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2307.04510v2", "title": "An analysis of linear regression and neural networks approximation for the pricing of swing options", "abstract": "Linear regression, firstly introduced for the pricing of American-style options, has since been expanded to include swing options pricing. Swing options price may be viewed as the solution to a Backward Dynamic Programming Principle, which involves a conditional expectation known as the continuation value. The approximation of the continuation value using linear regression involves two levels of approximation. First, the continuation value is replaced by an orthogonal projection over a subspace spanned by a finite set of $m$ squared-integrable functions yielding a first approximation $V^m$ of the swing value function. In this paper, we prove that, with well-chosen regression functions, $V^m$ converges to the swing actual price $V$ as $m \\to + \\infty$. A similar result is proved when classic regression functions are replaced by neural networks. For both methods (linear regression and neural networks), we analyze the second level of approximation involving practical computation of the swing price using Monte Carlo simulations and yielding an approximation $V^{m, N}$ (where $N$ denotes the Monte Carlo sample size). Especially, we prove that $V^{m, N} \\to V^m$ as $N \\to + \\infty$ for both methods and using a Hilbert basis assumption in the linear regression. Besides, a convergence rate of order $\\mathcal{O}\\big(\\frac{1}{\\sqrt{N}} \\big)$ is proved in the linear regression case.", "published": "2023-07-10T12:12:42Z", "authors": ["Christian Yeo"], "categories": ["q-fin.MF"], "doi": "", "_query": "hedonic house price", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2005.07482v5", "title": "An exact algorithm for the static pricing problem under discrete mixed logit demand", "abstract": "Price differentiation is a common strategy in many markets. In this paper, we study a static multiproduct price optimization problem with demand given by a discrete mixed multinomial logit model. By considering a mixed logit model that includes individual-specific variables in the utility specification, our pricing problem reflects well the discrete choice models used in practice. To solve this pricing problem, we design an efficient iterative optimization algorithm that asymptotically converges to the optimal solution. To this end, a linear optimization (LO) problem is formulated, based on the trust-region approach, to find a \"good\" feasible solution and approximate the problem from below. Another LO problem is designed using piecewise linear relaxations to approximate the optimization problem from above. Then, we develop a new branching method to tighten the optimality gap. The effectiveness of our algorithm is demonstrated on a parking services pricing case, and a benchmark against solvers and existing contributions in the literature are performed.", "published": "2020-05-15T12:03:16Z", "authors": ["Ahmadreza Marandi", "Virginie Lurkin"], "categories": ["math.OC"], "doi": "", "_query": "hedonic house price", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2605.00406v3", "title": "Bell Correlations and Selection Bias", "abstract": "Methods of selecting samples from larger populations may produce bias, in either direction: inducing correlations between variables independent in the full population, or masking correlations between variables dependent in the full population. Here we propose a surprising application of these familiar ideas. We argue that they are relevant to puzzling correlations uncovered in quantum theory by John Stewart Bell (Bell 1964). In the light of Bell's work and subsequent experiments it is widely believed that the quantum world is 'nonlocal', in apparent tension with relativity. Many hold that the only alternative is to abandon 'realism', the view that there is an objective world independent of measurement. We propose instead that Bell's correlations are selection artefacts, in tension neither with relativity nor realism. In standard two-particle Bell experiments the relevant selection is a preselection, achieved by the preparation of the initial state. Again, selection bias via preselection is familiar elsewhere in science, but it doesn't seem to have been noticed that it is applicable in this case.", "published": "2026-05-01T04:54:54Z", "authors": ["Huw Price"], "categories": ["quant-ph", "physics.hist-ph"], "doi": "", "_query": "hedonic house price", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/1701.04208v1", "title": "Developing and Deploying a Taxi Price Comparison Mobile App in the Wild: Insights and Challenges", "abstract": "As modern transportation systems become more complex, there is need for mobile applications that allow travelers to navigate efficiently in cities. In taxi transport the recent proliferation of Uber has introduced new norms including a flexible pricing scheme where journey costs can change rapidly depending on passenger demand and driver supply. To make informed choices on the most appropriate provider for their journeys, travelers need access to knowledge about provider pricing in real time. To this end, we developed OpenStreetcab a mobile application that offers advice on taxi transport comparing provider prices. We describe its development and deployment in two cities, London and New York, and analyse thousands of user journey queries to compare the price patterns of Uber against major local taxi providers. We have observed large heterogeneity across the taxi transport markets in the two cities. This motivated us to perform a price validation and measurement experiment on the ground comparing Uber and Black Cabs in London. The experimental results reveal interesting insights: not only they confirm feedback on pricing and service quality received by professional drivers users, but also they reveal the tradeoffs between prices and journey times between taxi providers. With respect to journey times in particular, we show how experienced taxi drivers, in the majority of the cases, are able to navigate faster to a destination compared to drivers who rely on modern navigation systems. We provide evidence that this advantage becomes stronger in the centre of a city where urban density is high.", "published": "2017-01-16T09:15:38Z", "authors": ["Anastasios Noulas", "Vsevolod Salnikov", "Desislava Hristova", "Cecilia Mascolo", "Renaud Lambiotte"], "categories": ["cs.CY"], "doi": "", "_query": "hedonic house price", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2309.00073v2", "title": "Diffusion Variational Autoencoder for Tackling Stochasticity in Multi-Step Regression Stock Price Prediction", "abstract": "Multi-step stock price prediction over a long-term horizon is crucial for forecasting its volatility, allowing financial institutions to price and hedge derivatives, and banks to quantify the risk in their trading books. Additionally, most financial regulators also require a liquidity horizon of several days for institutional investors to exit their risky assets, in order to not materially affect market prices. However, the task of multi-step stock price prediction is challenging, given the highly stochastic nature of stock data. Current solutions to tackle this problem are mostly designed for single-step, classification-based predictions, and are limited to low representation expressiveness. The problem also gets progressively harder with the introduction of the target price sequence, which also contains stochastic noise and reduces generalizability at test-time. To tackle these issues, we combine a deep hierarchical variational-autoencoder (VAE) and diffusion probabilistic techniques to do seq2seq stock prediction through a stochastic generative process. The hierarchical VAE allows us to learn the complex and low-level latent variables for stock prediction, while the diffusion probabilistic model trains the predictor to handle stock price stochasticity by progressively adding random noise to the stock data. Our Diffusion-VAE (D-Va) model is shown to outperform state-of-the-art solutions in terms of its prediction accuracy and variance. More importantly, the multi-step outputs can also allow us to form a stock portfolio over the prediction length. We demonstrate the effectiveness of our model outputs in the portfolio investment task through the Sharpe ratio metric and highlight the importance of dealing with different types of prediction uncertainties.", "published": "2023-08-18T16:21:15Z", "authors": ["Kelvin J. L. Koa", "Yunshan Ma", "Ritchie Ng", "Tat-Seng Chua"], "categories": ["q-fin.ST", "cs.LG", "q-fin.CP"], "doi": "10.1145/3583780.3614844", "_query": "hedonic house price", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/1803.06707v1", "title": "An Improved Welfare Guarantee for First Price Auctions", "abstract": "This paper proves that the welfare of the first price auction in Bayes-Nash equilibrium is at least a $.743$-fraction of the welfare of the optimal mechanism assuming agents' values are independently distributed. The previous best bound was $1-1/e \\approx .63$, derived in Syrgkanis and Tardos (2013) using smoothness, the standard technique for reasoning about welfare of games in equilibrium. In the worst known example (from Hartline et al. (2014)), the first price auction achieves a $\\approx .869$-fraction of the optimal welfare, far better than the theoretical guarantee. Despite this large gap, it was unclear whether the $1-1/e \\approx .63$ bound was tight. We prove that it is not. Our analysis eschews smoothness, and instead uses the independence assumption on agents' value distributions to give a more careful accounting of the welfare contribution of agents who win despite not having the highest value.", "published": "2018-03-18T18:13:59Z", "authors": ["Darrell Hoy", "Sam Taggart", "Zihe Wang"], "categories": ["cs.GT"], "doi": "", "_query": "hedonic house price", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2306.04338v1", "title": "Changing Data Sources in the Age of Machine Learning for Official Statistics", "abstract": "Data science has become increasingly essential for the production of official statistics, as it enables the automated collection, processing, and analysis of large amounts of data. With such data science practices in place, it enables more timely, more insightful and more flexible reporting. However, the quality and integrity of data-science-driven statistics rely on the accuracy and reliability of the data sources and the machine learning techniques that support them. In particular, changes in data sources are inevitable to occur and pose significant risks that are crucial to address in the context of machine learning for official statistics. This paper gives an overview of the main risks, liabilities, and uncertainties associated with changing data sources in the context of machine learning for official statistics. We provide a checklist of the most prevalent origins and causes of changing data sources; not only on a technical level but also regarding ownership, ethics, regulation, and public perception. Next, we highlight the repercussions of changing data sources on statistical reporting. These include technical effects such as concept drift, bias, availability, validity, accuracy and completeness, but also the neutrality and potential discontinuation of the statistical offering. We offer a few important precautionary measures, such as enhancing robustness in both data sourcing and statistical techniques, and thorough monitoring. In doing so, machine learning-based official statistics can maintain integrity, reliability, consistency, and relevance in policy-making, decision-making, and public discourse.", "published": "2023-06-07T11:08:12Z", "authors": ["Cedric De Boom", "Michael Reusens"], "categories": ["stat.ML", "cs.LG"], "doi": "", "_query": "house price prediction machine learning", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2006.16189v4", "title": "DOME: Recommendations for supervised machine learning validation in biology", "abstract": "Modern biology frequently relies on machine learning to provide predictions and improve decision processes. There have been recent calls for more scrutiny on machine learning performance and possible limitations. Here we present a set of community-wide recommendations aiming to help establish standards of supervised machine learning validation in biology. Adopting a structured methods description for machine learning based on data, optimization, model, evaluation (DOME) will aim to help both reviewers and readers to better understand and assess the performance and limitations of a method or outcome. The recommendations are formulated as questions to anyone wishing to pursue implementation of a machine learning algorithm. Answers to these questions can be easily included in the supplementary material of published papers.", "published": "2020-06-25T12:01:39Z", "authors": ["Ian Walsh", "Dmytro Fishman", "Dario Garcia-Gasulla", "Tiina Titma", "Gianluca Pollastri", "The ELIXIR Machine Learning focus group", "Jen Harrow", "Fotis E. Psomopoulos", "Silvio C. E. Tosatto"], "categories": ["q-bio.OT", "cs.LG"], "doi": "", "_query": "house price prediction machine learning", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2201.12150v2", "title": "Learning Curves for Decision Making in Supervised Machine Learning: A Survey", "abstract": "Learning curves are a concept from social sciences that has been adopted in the context of machine learning to assess the performance of a learning algorithm with respect to a certain resource, e.g., the number of training examples or the number of training iterations. Learning curves have important applications in several machine learning contexts, most notably in data acquisition, early stopping of model training, and model selection. For instance, learning curves can be used to model the performance of the combination of an algorithm and its hyperparameter configuration, providing insights into their potential suitability at an early stage and often expediting the algorithm selection process. Various learning curve models have been proposed to use learning curves for decision making. Some of these models answer the binary decision question of whether a given algorithm at a certain budget will outperform a certain reference performance, whereas more complex models predict the entire learning curve of an algorithm. We contribute a framework that categorises learning curve approaches using three criteria: the decision-making situation they address, the intrinsic learning curve question they answer and the type of resources they use. We survey papers from the literature and classify them into this framework.", "published": "2022-01-28T14:34:32Z", "authors": ["Felix Mohr", "Jan N. van Rijn"], "categories": ["cs.LG"], "doi": "10.1007/s10994-024-06619-7", "_query": "house price prediction machine learning", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2302.08893v4", "title": "Active learning for data streams: a survey", "abstract": "Online active learning is a paradigm in machine learning that aims to select the most informative data points to label from a data stream. The problem of minimizing the cost associated with collecting labeled observations has gained a lot of attention in recent years, particularly in real-world applications where data is only available in an unlabeled form. Annotating each observation can be time-consuming and costly, making it difficult to obtain large amounts of labeled data. To overcome this issue, many active learning strategies have been proposed in the last decades, aiming to select the most informative observations for labeling in order to improve the performance of machine learning models. These approaches can be broadly divided into two categories: static pool-based and stream-based active learning. Pool-based active learning involves selecting a subset of observations from a closed pool of unlabeled data, and it has been the focus of many surveys and literature reviews. However, the growing availability of data streams has led to an increase in the number of approaches that focus on online active learning, which involves continuously selecting and labeling observations as they arrive in a stream. This work aims to provide an overview of the most recently proposed approaches for selecting the most informative observations from data streams in real time. We review the various techniques that have been proposed and discuss their strengths and limitations, as well as the challenges and opportunities that exist in this area of research.", "published": "2023-02-17T14:24:13Z", "authors": ["Davide Cacciarelli", "Murat Kulahci"], "categories": ["stat.ML", "cs.LG", "stat.ME"], "doi": "10.1007/s10994-023-06454-2", "_query": "house price prediction machine learning", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2303.15563v1", "title": "Privacy-preserving machine learning for healthcare: open challenges and future perspectives", "abstract": "Machine Learning (ML) has recently shown tremendous success in modeling various healthcare prediction tasks, ranging from disease diagnosis and prognosis to patient treatment. Due to the sensitive nature of medical data, privacy must be considered along the entire ML pipeline, from model training to inference. In this paper, we conduct a review of recent literature concerning Privacy-Preserving Machine Learning (PPML) for healthcare. We primarily focus on privacy-preserving training and inference-as-a-service, and perform a comprehensive review of existing trends, identify challenges, and discuss opportunities for future research directions. The aim of this review is to guide the development of private and efficient ML models in healthcare, with the prospects of translating research efforts into real-world settings.", "published": "2023-03-27T19:20:51Z", "authors": ["Alejandro Guerra-Manzanares", "L. Julian Lechuga Lopez", "Michail Maniatakos", "Farah E. Shamout"], "categories": ["cs.LG", "cs.CR"], "doi": "10.1007/978-3-031-39539-0_3", "_query": "house price prediction machine learning", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2304.02381v2", "title": "Physics-Inspired Interpretability Of Machine Learning Models", "abstract": "The ability to explain decisions made by machine learning models remains one of the most significant hurdles towards widespread adoption of AI in highly sensitive areas such as medicine, cybersecurity or autonomous driving. Great interest exists in understanding which features of the input data prompt model decision making. In this contribution, we propose a novel approach to identify relevant features of the input data, inspired by methods from the energy landscapes field, developed in the physical sciences. By identifying conserved weights within groups of minima of the loss landscapes, we can identify the drivers of model decision making. Analogues to this idea exist in the molecular sciences, where coordinate invariants or order parameters are employed to identify critical features of a molecule. However, no such approach exists for machine learning loss landscapes. We will demonstrate the applicability of energy landscape methods to machine learning models and give examples, both synthetic and from the real world, for how these methods can help to make models more interpretable.", "published": "2023-04-05T11:35:17Z", "authors": ["Maximilian P Niroomand", "David J Wales"], "categories": ["cs.LG", "cs.AI"], "doi": "", "_query": "house price prediction machine learning", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2112.04906v3", "title": "Enhancing Column Generation by a Machine-Learning-Based Pricing Heuristic for Graph Coloring", "abstract": "Column Generation (CG) is an effective method for solving large-scale optimization problems. CG starts by solving a sub-problem with a subset of columns (i.e., variables) and gradually includes new columns that can improve the solution of the current subproblem. The new columns are generated as needed by repeatedly solving a pricing problem, which is often NP-hard and is a bottleneck of the CG approach. To tackle this, we propose a Machine-Learning-based Pricing Heuristic (MLPH)that can generate many high-quality columns efficiently. In each iteration of CG, our MLPH leverages an ML model to predict the optimal solution of the pricing problem, which is then used to guide a sampling method to efficiently generate multiple high-quality columns. Using the graph coloring problem, we empirically show that MLPH significantly enhancesCG as compared to six state-of-the-art methods, and the improvement in CG can lead to substantially better performance of the branch-and-price exact method.", "published": "2021-12-08T03:58:25Z", "authors": ["Yunzhuang Shen", "Yuan Sun", "Xiaodong Li", "Andrew Eberhard", "Andreas Ernst"], "categories": ["math.OC", "cs.AI", "cs.LG"], "doi": "", "_query": "house price prediction machine learning", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/1905.04749v2", "title": "A Benchmark Study of Machine Learning Models for Online Fake News Detection", "abstract": "The proliferation of fake news and its propagation on social media has become a major concern due to its ability to create devastating impacts. Different machine learning approaches have been suggested to detect fake news. However, most of those focused on a specific type of news (such as political) which leads us to the question of dataset-bias of the models used. In this research, we conducted a benchmark study to assess the performance of different applicable machine learning approaches on three different datasets where we accumulated the largest and most diversified one. We explored a number of advanced pre-trained language models for fake news detection along with the traditional and deep learning ones and compared their performances from different aspects for the first time to the best of our knowledge. We find that BERT and similar pre-trained models perform the best for fake news detection, especially with very small dataset. Hence, these models are significantly better option for languages with limited electronic contents, i.e., training data. We also carried out several analysis based on the models' performance, article's topic, article's length, and discussed different lessons learned from them. We believe that this benchmark study will help the research community to explore further and news sites/blogs to select the most appropriate fake news detection method.", "published": "2019-05-12T17:15:11Z", "authors": ["Junaed Younus Khan", "Md. Tawkat Islam Khondaker", "Sadia Afroz", "Gias Uddin", "Anindya Iqbal"], "categories": ["cs.CL", "cs.IR", "cs.LG", "stat.ML"], "doi": "10.1016/j.mlwa.2021.100032", "_query": "house price prediction machine learning", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2602.11092v2", "title": "MerLin: A Discovery Engine for Photonic and Hybrid Quantum Machine Learning", "abstract": "Identifying where quantum models may offer practical benefits in near term quantum machine learning (QML) requires moving beyond isolated algorithmic proposals toward systematic and empirical exploration across models, datasets, and hardware constraints. We introduce MerLin, an open-source framework designed as a discovery engine for photonic and hybrid quantum machine learning. MerLin integrates optimized strong simulation of linear optical circuits into standard PyTorch and scikit learn workflows, enabling end-to-end differentiable training of quantum layers. MerLin is designed around systematic benchmarking and reproducibility. As an initial contribution, we reproduce eighteen state-of-the-art photonic and hybrid QML works spanning kernel methods, reservoir computing, convolutional and recurrent architectures, generative models, and modern training paradigms. These reproductions are released as reusable, modular experiments that can be directly extended and adapted, establishing a shared experimental baseline consistent with empirical benchmarking methodologies widely adopted in modern artificial intelligence. By embedding photonic quantum models within established machine learning ecosystems, MerLin allows practitioners to leverage existing tooling for ablation studies, cross-modality comparisons, and hybrid classical-quantum workflows. The framework already implements hardware-aware features, allowing tests on available quantum hardware while enabling exploration beyond its current capabilities, positioning MerLin as a forward-looking co-design tool linking algorithms, benchmarks, and hardware.", "published": "2026-02-11T18:00:01Z", "authors": ["Cassandre Notton", "Benjamin Stott", "Philippe Schoeb", "Anthony Walsh", "Grégoire Leboucher", "Vincent Espitalier", "Vassilis Apostolou", "Louis-Félix Vigneux", "Alexia Salavrakos", "Jean Senellart"], "categories": ["cs.LG", "cs.PL", "quant-ph"], "doi": "", "_query": "house price prediction machine learning", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/1907.12665v1", "title": "Airbnb Price Prediction Using Machine Learning and Sentiment Analysis", "abstract": "Pricing a rental property on Airbnb is a challenging task for the owner as it determines the number of customers for the place. On the other hand, customers have to evaluate an offered price with minimal knowledge of an optimal value for the property. This paper aims to develop a reliable price prediction model using machine learning, deep learning, and natural language processing techniques to aid both the property owners and the customers with price evaluation given minimal available information about the property. Features of the rentals, owner characteristics, and the customer reviews will comprise the predictors, and a range of methods from linear regression to tree-based models, support-vector regression (SVR), K-means Clustering (KMC), and neural networks (NNs) will be used for creating the prediction model.", "published": "2019-07-29T21:45:42Z", "authors": ["Pouya Rezazadeh Kalehbasti", "Liubov Nikolenko", "Hoormazd Rezaei"], "categories": ["cs.LG", "stat.ML"], "doi": "10.1007/978-3-030-84060-0_11", "_query": "house price prediction machine learning", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/1705.05172v1", "title": "Emotion in Reinforcement Learning Agents and Robots: A Survey", "abstract": "This article provides the first survey of computational models of emotion in reinforcement learning (RL) agents. The survey focuses on agent/robot emotions, and mostly ignores human user emotions. Emotions are recognized as functional in decision-making by influencing motivation and action selection. Therefore, computational emotion models are usually grounded in the agent's decision making architecture, of which RL is an important subclass. Studying emotions in RL-based agents is useful for three research fields. For machine learning (ML) researchers, emotion models may improve learning efficiency. For the interactive ML and human-robot interaction (HRI) community, emotions can communicate state and enhance user investment. Lastly, it allows affective modelling (AM) researchers to investigate their emotion theories in a successful AI agent class. This survey provides background on emotion theory and RL. It systematically addresses 1) from what underlying dimensions (e.g., homeostasis, appraisal) emotions can be derived and how these can be modelled in RL-agents, 2) what types of emotions have been derived from these dimensions, and 3) how these emotions may either influence the learning efficiency of the agent or be useful as social signals. We also systematically compare evaluation criteria, and draw connections to important RL sub-domains like (intrinsic) motivation and model-based RL. In short, this survey provides both a practical overview for engineers wanting to implement emotions in their RL agents, and identifies challenges and directions for future emotion-RL research.", "published": "2017-05-15T11:49:56Z", "authors": ["Thomas M. Moerland", "Joost Broekens", "Catholijn M. Jonker"], "categories": ["cs.LG", "cs.AI", "cs.HC", "cs.RO", "stat.ML"], "doi": "10.1007/s10994-017-5666-0", "_query": "house price prediction machine learning", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2509.08759v3", "title": "Fourier Learning Machines: Nonharmonic Fourier-Based Neural Networks for Scientific Machine Learning", "abstract": "We introduce the Fourier Learning Machine (FLM), a neural network (NN) architecture designed to represent a multidimensional nonharmonic Fourier series. The FLM uses a simple feedforward structure with cosine activation functions to learn the frequencies, amplitudes, and phase shifts of the series as trainable parameters. This design allows the model to create a problem-specific spectral basis adaptable to both periodic and nonperiodic functions. Unlike previous Fourier-inspired NN models, the FLM is the first architecture able to represent a multidimensional Fourier series with a complete set of basis functions in separable form, doing so by using a standard Multilayer Perceptron-like architecture. A one-to-one correspondence between the Fourier coefficients and amplitudes and phase-shifts is demonstrated, allowing for the translation between a full, separable basis form and the cosine phase-shifted one. Additionally, we evaluate the performance of FLMs on several scientific computing problems, including benchmark Partial Differential Equations (PDEs) and a family of Optimal Control Problems (OCPs). Computational experiments show that the performance of FLMs is comparable, and often superior, to that of established architectures like SIREN and vanilla feedforward NNs.", "published": "2025-09-10T16:49:20Z", "authors": ["Mominul Rubel", "Adam Meyers", "Gabriel Nicolosi"], "categories": ["cs.LG", "math.OC"], "doi": "", "_query": "house price prediction machine learning", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/1812.02852v1", "title": "Automatically Explaining Machine Learning Prediction Results: A Demonstration on Type 2 Diabetes Risk Prediction", "abstract": "Background: Predictive modeling is a key component of solutions to many healthcare problems. Among all predictive modeling approaches, machine learning methods often achieve the highest prediction accuracy, but suffer from a long-standing open problem precluding their widespread use in healthcare. Most machine learning models give no explanation for their prediction results, whereas interpretability is essential for a predictive model to be adopted in typical healthcare settings. Methods: This paper presents the first complete method for automatically explaining results for any machine learning predictive model without degrading accuracy. We did a computer coding implementation of the method. Using the electronic medical record data set from the Practice Fusion diabetes classification competition containing patient records from all 50 states in the United States, we demonstrated the method on predicting type 2 diabetes diagnosis within the next year. Results: For the champion machine learning model of the competition, our method explained prediction results for 87.4% of patients who were correctly predicted by the model to have type 2 diabetes diagnosis within the next year. Conclusions: Our demonstration showed the feasibility of automatically explaining results for any machine learning predictive model without degrading accuracy.", "published": "2018-12-06T23:22:15Z", "authors": ["Gang Luo"], "categories": ["cs.LG", "stat.ML"], "doi": "10.1186/s13755-016-0015-4", "_query": "house price prediction machine learning", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2404.12511v1", "title": "Generalizing Machine Learning Evaluation through the Integration of Shannon Entropy and Rough Set Theory", "abstract": "This research paper delves into the innovative integration of Shannon entropy and rough set theory, presenting a novel approach to generalize the evaluation approach in machine learning. The conventional application of entropy, primarily focused on information uncertainty, is extended through its combination with rough set theory to offer a deeper insight into data's intrinsic structure and the interpretability of machine learning models. We introduce a comprehensive framework that synergizes the granularity of rough set theory with the uncertainty quantification of Shannon entropy, applied across a spectrum of machine learning algorithms. Our methodology is rigorously tested on various datasets, showcasing its capability to not only assess predictive performance but also to illuminate the underlying data complexity and model robustness. The results underscore the utility of this integrated approach in enhancing the evaluation landscape of machine learning, offering a multi-faceted perspective that balances accuracy with a profound understanding of data attributes and model dynamics. This paper contributes a groundbreaking perspective to machine learning evaluation, proposing a method that encapsulates a holistic view of model performance, thereby facilitating more informed decision-making in model selection and application.", "published": "2024-04-18T21:22:42Z", "authors": ["Olga Cherednichenko", "Dmytro Chernyshov", "Dmytro Sytnikov", "Polina Sytnikova"], "categories": ["cs.LG"], "doi": "", "_query": "house price prediction machine learning", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2209.06346v2", "title": "Prediction of the outcome of a Twenty-20 Cricket Match : A Machine Learning Approach", "abstract": "Twenty20 cricket, sometimes written Twenty-20, and often abbreviated to T20, is a short form of cricket. In a Twenty20 game the two teams of 11 players have a single innings each, which is restricted to a maximum of 20 overs. This version of cricket is especially unpredictable and is one of the reasons it has gained popularity over recent times. However, in this paper we try four different machine learning approaches for predicting the results of T20 Cricket Matches. Specifically we take in to account: previous performance statistics of the players involved in the competing teams, ratings of players obtained from reputed cricket statistics websites, clustering the players' with similar performance statistics and propose a novel method using an ELO based approach to rate players. We compare the performances of each of these feature engineering approaches by using different ML algorithms, including logistic regression, support vector machines, bayes network, decision tree, random forest.", "published": "2022-09-13T23:43:36Z", "authors": ["Ashish V Shenoy", "Arjun Singhvi", "Shruthi Racha", "Srinivas Tunuguntla"], "categories": ["cs.LG"], "doi": "", "_query": "house price prediction machine learning", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/1906.01101v1", "title": "MEMe: An Accurate Maximum Entropy Method for Efficient Approximations in Large-Scale Machine Learning", "abstract": "Efficient approximation lies at the heart of large-scale machine learning problems. In this paper, we propose a novel, robust maximum entropy algorithm, which is capable of dealing with hundreds of moments and allows for computationally efficient approximations. We showcase the usefulness of the proposed method, its equivalence to constrained Bayesian variational inference and demonstrate its superiority over existing approaches in two applications, namely, fast log determinant estimation and information-theoretic Bayesian optimisation.", "published": "2019-06-03T22:10:52Z", "authors": ["Diego Granziol", "Binxin Ru", "Stefan Zohren", "Xiaowen Doing", "Michael Osborne", "Stephen Roberts"], "categories": ["stat.ML", "cs.LG"], "doi": "10.3390/e21060551", "_query": "house price prediction machine learning", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2111.07407v1", "title": "A Machine Learning Approach for Recruitment Prediction in Clinical Trial Design", "abstract": "Significant advancements have been made in recent years to optimize patient recruitment for clinical trials, however, improved methods for patient recruitment prediction are needed to support trial site selection and to estimate appropriate enrollment timelines in the trial design stage. In this paper, using data from thousands of historical clinical trials, we explore machine learning methods to predict the number of patients enrolled per month at a clinical trial site over the course of a trial's enrollment duration. We show that these methods can reduce the error that is observed with current industry standards and propose opportunities for further improvement.", "published": "2021-11-14T18:24:11Z", "authors": ["Jingshu Liu", "Patricia J Allen", "Luke Benz", "Daniel Blickstein", "Evon Okidi", "Xiao Shi"], "categories": ["cs.LG", "stat.AP", "stat.ML"], "doi": "", "_query": "house price prediction machine learning", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2505.01591v2", "title": "Machine Learning Fairness in House Price Prediction: A Case Study of America's Expanding Metropolises", "abstract": "As a basic human need, housing plays a key role in enhancing health, well-being, and educational outcome in society, and the housing market is a major factor for promoting quality of life and ensuring social equity. To improve the housing conditions, there has been extensive research on building Machine Learning (ML)-driven house price prediction solutions to accurately forecast the future conditions, and help inform actions and policies in the field. In spite of their success in developing high-accuracy models, there is a gap in our understanding of the extent to which various ML-driven house price prediction approaches show ethnic and/or racial bias, which in turn is essential for the responsible use of ML, and ensuring that the ML-driven solutions do not exacerbate inequity. To fill this gap, this paper develops several ML models from a combination of structural and neighborhood-level attributes, and conducts comprehensive assessments on the fairness of ML models under various definitions of privileged groups. As a result, it finds that the ML-driven house price prediction models show various levels of bias towards protected attributes (i.e., race and ethnicity in this study). Then, it investigates the performance of different bias mitigation solutions, and the experimental results show their various levels of effectiveness on different ML-driven methods. However, in general, the in-processing bias mitigation approach tends to be more effective than the pre-processing one in this problem domain. Our code is available at https://github.com/wahab1412/housing_fairness.", "published": "2025-05-02T21:26:21Z", "authors": ["Abdalwahab Almajed", "Maryam Tabar", "Peyman Najafirad"], "categories": ["cs.LG"], "doi": "", "_query": "house price prediction machine learning", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2402.01393v3", "title": "ALERT-Transformer: Bridging Asynchronous and Synchronous Machine Learning for Real-Time Event-based Spatio-Temporal Data", "abstract": "We seek to enable classic processing of continuous ultra-sparse spatiotemporal data generated by event-based sensors with dense machine learning models. We propose a novel hybrid pipeline composed of asynchronous sensing and synchronous processing that combines several ideas: (1) an embedding based on PointNet models -- the ALERT module -- that can continuously integrate new and dismiss old events thanks to a leakage mechanism, (2) a flexible readout of the embedded data that allows to feed any downstream model with always up-to-date features at any sampling rate, (3) exploiting the input sparsity in a patch-based approach inspired by Vision Transformer to optimize the efficiency of the method. These embeddings are then processed by a transformer model trained for object and gesture recognition. Using this approach, we achieve performances at the state-of-the-art with a lower latency than competitors. We also demonstrate that our asynchronous model can operate at any desired sampling rate.", "published": "2024-02-02T13:17:19Z", "authors": ["Carmen Martin-Turrero", "Maxence Bouvier", "Manuel Breitenstein", "Pietro Zanuttigh", "Vincent Parret"], "categories": ["cs.CV", "cs.LG", "cs.NE"], "doi": "", "_query": "house price prediction machine learning", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2111.07508v1", "title": "Public Policymaking for International Agricultural Trade using Association Rules and Ensemble Machine Learning", "abstract": "International economics has a long history of improving our understanding of factors causing trade, and the consequences of free flow of goods and services across countries. The recent shocks to the free trade regime, especially trade disputes among major economies, as well as black swan events, such as trade wars and pandemics, raise the need for improved predictions to inform policy decisions. AI methods are allowing economists to solve such prediction problems in new ways. In this manuscript, we present novel methods that predict and associate food and agricultural commodities traded internationally. Association Rules (AR) analysis has been deployed successfully for economic scenarios at the consumer or store level, such as for market basket analysis. In our work however, we present analysis of imports and exports associations and their effects on commodity trade flows. Moreover, Ensemble Machine Learning methods are developed to provide improved agricultural trade predictions, outlier events' implications, and quantitative pointers to policy makers.", "published": "2021-11-15T02:58:03Z", "authors": ["Feras A. Batarseh", "Munisamy Gopinath", "Anderson Monken", "Zhengrong Gu"], "categories": ["cs.LG", "cs.AI", "econ.GN"], "doi": "10.1016/j.mlwa.2021.100046", "_query": "house price prediction machine learning", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2006.10092v1", "title": "Housing Market Prediction Problem using Different Machine Learning Algorithms: A Case Study", "abstract": "Developing an accurate prediction model for housing prices is always needed for socio-economic development and well-being of citizens. In this paper, a diverse set of machine learning algorithms such as XGBoost, CatBoost, Random Forest, Lasso, Voting Regressor, and others, are being employed to predict the housing prices using public available datasets. The housing datasets of 62,723 records from January 2015 to November 2019 are obtained from Florida Volusia County Property Appraiser website. The records are publicly available and include the real estate or economic database, maps, and other associated information. The database is usually updated weekly according to the State of Florida regulations. Then, the housing price prediction models using machine learning techniques are developed and their regression model performances are compared. Finally, an improved housing price prediction model for assisting the housing market is proposed. Particularly, a house seller or buyer, or a real estate broker can get insight in making better-informed decisions considering the housing price prediction. The empirical results illustrate that based on prediction model performance, Coefficient of Determination (R2), Mean Square Error (MSE), Mean Absolute Error (MAE), and computational time, the XGBoost algorithm performs superior to the other models to predict the housing price.", "published": "2020-06-17T18:16:24Z", "authors": ["Shashi Bhushan Jha", "Radu F. Babiceanu", "Vijay Pandey", "Rajesh Kumar Jha"], "categories": ["cs.LG", "stat.ML"], "doi": "", "_query": "house price prediction machine learning", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2405.17505v1", "title": "Predicting Rental Price of Lane Houses in Shanghai with Machine Learning Methods and Large Language Models", "abstract": "Housing has emerged as a crucial concern among young individuals residing in major cities, including Shanghai. Given the unprecedented surge in property prices in this metropolis, young people have increasingly resorted to the rental market to address their housing needs. This study utilizes five traditional machine learning methods: multiple linear regression (MLR), ridge regression (RR), lasso regression (LR), decision tree (DT), and random forest (RF), along with a Large Language Model (LLM) approach using ChatGPT, for predicting the rental prices of lane houses in Shanghai. It applies these methods to examine a public data sample of about 2,609 lane house rental transactions in 2021 in Shanghai, and then compares the results of these methods. In terms of predictive power, RF has achieved the best performance among the traditional methods. However, the LLM approach, particularly in the 10-shot scenario, shows promising results that surpass traditional methods in terms of R-Squared value. The three performance metrics: mean squared error (MSE), mean absolute error (MAE), and R-Squared, are used to evaluate the models. Our conclusion is that while traditional machine learning models offer robust techniques for rental price prediction, the integration of LLM such as ChatGPT holds significant potential for enhancing predictive accuracy.", "published": "2024-05-26T07:01:33Z", "authors": ["Tingting Chen", "Shijing Si"], "categories": ["cs.LG", "cs.CL"], "doi": "", "_query": "house price prediction machine learning", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/1812.09225v4", "title": "Learning Representations from Dendrograms", "abstract": "We propose unsupervised representation learning and feature extraction from dendrograms. The commonly used Minimax distance measures correspond to building a dendrogram with single linkage criterion, with defining specific forms of a level function and a distance function over that. Therefore, we extend this method to arbitrary dendrograms. We develop a generalized framework wherein different distance measures and representations can be inferred from different types of dendrograms, level functions and distance functions. Via an appropriate embedding, we compute a vector-based representation of the inferred distances, in order to enable many numerical machine learning algorithms to employ such distances. Then, to address the model selection problem, we study the aggregation of different dendrogram-based distances respectively in solution space and in representation space in the spirit of deep representations. In the first approach, for example for the clustering problem, we build a graph with positive and negative edge weights according to the consistency of the clustering labels of different objects among different solutions, in the context of ensemble methods. Then, we use an efficient variant of correlation clustering to produce the final clusters. In the second approach, we investigate the combination of different distances and features sequentially in the spirit of multi-layered architectures to obtain the final features. Finally, we demonstrate the effectiveness of our approach via several numerical studies.", "published": "2018-12-21T16:11:00Z", "authors": ["Morteza Haghir Chehreghani", "Mostafa Haghir Chehreghani"], "categories": ["cs.LG", "stat.ML"], "doi": "10.1007/s10994-020-05895-3", "_query": "house price prediction machine learning", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/1904.13223v3", "title": "Unsupervised Representation Learning with Minimax Distance Measures", "abstract": "We investigate the use of Minimax distances to extract in a nonparametric way the features that capture the unknown underlying patterns and structures in the data. We develop a general-purpose and computationally efficient framework to employ Minimax distances with many machine learning methods that perform on numerical data. We study both computing the pairwise Minimax distances for all pairs of objects and as well as computing the Minimax distances of all the objects to/from a fixed (test) object. We first efficiently compute the pairwise Minimax distances between the objects, using the equivalence of Minimax distances over a graph and over a minimum spanning tree constructed on that. Then, we perform an embedding of the pairwise Minimax distances into a new vector space, such that their squared Euclidean distances in the new space equal to the pairwise Minimax distances in the original space. We also study the case of having multiple pairwise Minimax matrices, instead of a single one. Thereby, we propose an embedding via first summing up the centered matrices and then performing an eigenvalue decomposition to obtain the relevant features. In the following, we study computing Minimax distances from a fixed (test) object which can be used for instance in K-nearest neighbor search. Similar to the case of all-pair pairwise Minimax distances, we develop an efficient and general-purpose algorithm that is applicable with any arbitrary base distance measure. Moreover, we investigate in detail the edges selected by the Minimax distances and thereby explore the ability of Minimax distances in detecting outlier objects. Finally, for each setting, we perform several experiments to demonstrate the effectiveness of our framework.", "published": "2019-04-27T16:13:08Z", "authors": ["Morteza Haghir Chehreghani"], "categories": ["cs.LG", "cs.AI", "stat.ML"], "doi": "10.1007/s10994-020-05886-4", "_query": "house price prediction machine learning", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2409.05335v1", "title": "A Multi-Modal Deep Learning Based Approach for House Price Prediction", "abstract": "Accurate prediction of house price, a vital aspect of the residential real estate sector, is of substantial interest for a wide range of stakeholders. However, predicting house prices is a complex task due to the significant variability influenced by factors such as house features, location, neighborhood, and many others. Despite numerous attempts utilizing a wide array of algorithms, including recent deep learning techniques, to predict house prices accurately, existing approaches have fallen short of considering a wide range of factors such as textual and visual features. This paper addresses this gap by comprehensively incorporating attributes, such as features, textual descriptions, geo-spatial neighborhood, and house images, typically showcased in real estate listings in a house price prediction system. Specifically, we propose a multi-modal deep learning approach that leverages different types of data to learn more accurate representation of the house. In particular, we learn a joint embedding of raw house attributes, geo-spatial neighborhood, and most importantly from textual description and images representing the house; and finally use a downstream regression model to predict the house price from this jointly learned embedding vector. Our experimental results with a real-world dataset show that the text embedding of the house advertisement description and image embedding of the house pictures in addition to raw attributes and geo-spatial embedding, can significantly improve the house price prediction accuracy. The relevant source code and dataset are publicly accessible at the following URL: https://github.com/4P0N/mhpp", "published": "2024-09-09T05:26:33Z", "authors": ["Md Hasebul Hasan", "Md Abid Jahan", "Mohammed Eunus Ali", "Yuan-Fang Li", "Timos Sellis"], "categories": ["cs.LG", "cs.AI", "cs.CV"], "doi": "", "_query": "house price prediction machine learning", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/1206.6443v2", "title": "Isoelastic Agents and Wealth Updates in Machine Learning Markets", "abstract": "Recently, prediction markets have shown considerable promise for developing flexible mechanisms for machine learning. In this paper, agents with isoelastic utilities are considered. It is shown that the costs associated with homogeneous markets of agents with isoelastic utilities produce equilibrium prices corresponding to alpha-mixtures, with a particular form of mixing component relating to each agent's wealth. We also demonstrate that wealth accumulation for logarithmic and other isoelastic agents (through payoffs on prediction of training targets) can implement both Bayesian model updates and mixture weight updates by imposing different market payoff structures. An iterative algorithm is given for market equilibrium computation. We demonstrate that inhomogeneous markets of agents with isoelastic utilities outperform state of the art aggregate classifiers such as random forests, as well as single classifiers (neural networks, decision trees) on a number of machine learning benchmarks, and show that isoelastic combination methods are generally better than their logarithmic counterparts.", "published": "2012-06-27T19:59:59Z", "authors": ["Amos Storkey", "Jono Millin", "Krzysztof Geras"], "categories": ["cs.LG", "cs.GT", "stat.ML"], "doi": "", "_query": "house price prediction machine learning", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/1706.01513v2", "title": "Beyond Volume: The Impact of Complex Healthcare Data on the Machine Learning Pipeline", "abstract": "From medical charts to national census, healthcare has traditionally operated under a paper-based paradigm. However, the past decade has marked a long and arduous transformation bringing healthcare into the digital age. Ranging from electronic health records, to digitized imaging and laboratory reports, to public health datasets, today, healthcare now generates an incredible amount of digital information. Such a wealth of data presents an exciting opportunity for integrated machine learning solutions to address problems across multiple facets of healthcare practice and administration. Unfortunately, the ability to derive accurate and informative insights requires more than the ability to execute machine learning models. Rather, a deeper understanding of the data on which the models are run is imperative for their success. While a significant effort has been undertaken to develop models able to process the volume of data obtained during the analysis of millions of digitalized patient records, it is important to remember that volume represents only one aspect of the data. In fact, drawing on data from an increasingly diverse set of sources, healthcare data presents an incredibly complex set of attributes that must be accounted for throughout the machine learning pipeline. This chapter focuses on highlighting such challenges, and is broken down into three distinct components, each representing a phase of the pipeline. We begin with attributes of the data accounted for during preprocessing, then move to considerations during model building, and end with challenges to the interpretation of model output. For each component, we present a discussion around data as it relates to the healthcare domain and offer insight into the challenges each may impose on the efficiency of machine learning techniques.", "published": "2017-06-01T20:34:41Z", "authors": ["Keith Feldman", "Louis Faust", "Xian Wu", "Chao Huang", "Nitesh V. Chawla"], "categories": ["cs.CY", "cs.LG", "stat.ML"], "doi": "10.1007/978-3-319-69775-8_9", "_query": "house price prediction machine learning", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/1905.10345v1", "title": "Automatic Machine Learning by Pipeline Synthesis using Model-Based Reinforcement Learning and a Grammar", "abstract": "Automatic machine learning is an important problem in the forefront of machine learning. The strongest AutoML systems are based on neural networks, evolutionary algorithms, and Bayesian optimization. Recently AlphaD3M reached state-of-the-art results with an order of magnitude speedup using reinforcement learning with self-play. In this work we extend AlphaD3M by using a pipeline grammar and a pre-trained model which generalizes from many different datasets and similar tasks. Our results demonstrate improved performance compared with our earlier work and existing methods on AutoML benchmark datasets for classification and regression tasks. In the spirit of reproducible research we make our data, models, and code publicly available.", "published": "2019-05-24T17:27:10Z", "authors": ["Iddo Drori", "Yamuna Krishnamurthy", "Raoni Lourenco", "Remi Rampin", "Kyunghyun Cho", "Claudio Silva", "Juliana Freire"], "categories": ["cs.LG", "stat.ML"], "doi": "", "_query": "house price prediction machine learning", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/1910.08922v1", "title": "Predicting ice flow using machine learning", "abstract": "Though machine learning has achieved notable success in modeling sequential and spatial data for speech recognition and in computer vision, applications to remote sensing and climate science problems are seldom considered. In this paper, we demonstrate techniques from unsupervised learning of future video frame prediction, to increase the accuracy of ice flow tracking in multi-spectral satellite images. As the volume of cryosphere data increases in coming years, this is an interesting and important opportunity for machine learning to address a global challenge for climate change, risk management from floods, and conserving freshwater resources. Future frame prediction of ice melt and tracking the optical flow of ice dynamics presents modeling difficulties, due to uncertainties in global temperature increase, changing precipitation patterns, occlusion from cloud cover, rapid melting and glacier retreat due to black carbon aerosol deposition, from wildfires or human fossil emissions. We show the adversarial learning method helps improve the accuracy of tracking the optical flow of ice dynamics compared to existing methods in climate science. We present a dataset, IceNet, to encourage machine learning research and to help facilitate further applications in the areas of cryospheric science and climate change.", "published": "2019-10-20T07:56:18Z", "authors": ["Yimeng Min", "S. Karthik Mukkavilli", "Yoshua Bengio"], "categories": ["cs.LG", "physics.ao-ph", "physics.geo-ph", "stat.ML"], "doi": "", "_query": "house price prediction machine learning", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/1910.02390v4", "title": "Migration through Machine Learning Lens -- Predicting Sexual and Reproductive Health Vulnerability of Young Migrants", "abstract": "In this paper, we have discussed initial findings and results of our experiment to predict sexual and reproductive health vulnerabilities of migrants in a data-constrained environment. Notwithstanding the limited research and data about migrants and migration cities, we propose a solution that simultaneously focuses on data gathering from migrants, augmenting awareness of the migrants to reduce mishaps, and setting up a mechanism to present insights to the key stakeholders in migration to act upon. We have designed a webapp for the stakeholders involved in migration: migrants, who would participate in data gathering process and can also use the app for getting to know safety and awareness tips based on analysis of the data received; public health workers, who would have an access to the database of migrants on the app; policy makers, who would have a greater understanding of the ground reality, and of the patterns of migration through machine-learned analysis. Finally, we have experimented with different machine learning models on an artificially curated dataset. We have shown, through experiments, how machine learning can assist in predicting the migrants at risk and can also help in identifying the critical factors that make migration dangerous for migrants. The results for identifying vulnerable migrants through machine learning algorithms are statistically significant at an alpha of 0.05.", "published": "2019-10-06T07:09:13Z", "authors": ["Amber Nigam", "Pragati Jaiswal", "Uma Girkar", "Teertha Arora", "Leo A. Celi"], "categories": ["cs.LG", "cs.CY", "stat.ML"], "doi": "", "_query": "house price prediction machine learning", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/1810.13348v4", "title": "Multimodal Machine Learning for Automated ICD Coding", "abstract": "This study presents a multimodal machine learning model to predict ICD-10 diagnostic codes. We developed separate machine learning models that can handle data from different modalities, including unstructured text, semi-structured text and structured tabular data. We further employed an ensemble method to integrate all modality-specific models to generate ICD-10 codes. Key evidence was also extracted to make our prediction more convincing and explainable. We used the Medical Information Mart for Intensive Care III (MIMIC -III) dataset to validate our approach. For ICD code prediction, our best-performing model (micro-F1 = 0.7633, micro-AUC = 0.9541) significantly outperforms other baseline models including TF-IDF (micro-F1 = 0.6721, micro-AUC = 0.7879) and Text-CNN model (micro-F1 = 0.6569, micro-AUC = 0.9235). For interpretability, our approach achieves a Jaccard Similarity Coefficient (JSC) of 0.1806 on text data and 0.3105 on tabular data, where well-trained physicians achieve 0.2780 and 0.5002 respectively.", "published": "2018-10-31T15:39:32Z", "authors": ["Keyang Xu", "Mike Lam", "Jingzhi Pang", "Xin Gao", "Charlotte Band", "Piyush Mathur", "Frank Papay", "Ashish K. Khanna", "Jacek B. Cywinski", "Kamal Maheshwari", "Pengtao Xie", "Eric Xing"], "categories": ["cs.LG", "stat.ML"], "doi": "", "_query": "house price prediction machine learning", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2501.06241v1", "title": "Predicting House Rental Prices in Ghana Using Machine Learning", "abstract": "This study investigates the efficacy of machine learning models for predicting house rental prices in Ghana, addressing the need for accurate and accessible housing market information. Utilising a comprehensive dataset of rental listings, we trained and evaluated various models, including CatBoost, XGBoost, and Random Forest. CatBoost emerged as the best-performing model, achieving an $R^2$ of 0.876, demonstrating its ability to effectively capture complex relationships within the housing market. Feature importance analysis revealed that location-based features, number of bedrooms, bathrooms, and furnishing status are key drivers of rental prices. Our findings provide valuable insights for stakeholders, including real estate professionals, investors, and policymakers, while also highlighting opportunities for future research, such as incorporating temporal data and exploring regional variations.", "published": "2025-01-08T15:40:46Z", "authors": ["Philip Adzanoukpe"], "categories": ["cs.LG", "stat.AP"], "doi": "10.20944/preprints202412.1927.v1", "_query": "house price prediction machine learning", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/1911.07679v1", "title": "Towards Quantification of Bias in Machine Learning for Healthcare: A Case Study of Renal Failure Prediction", "abstract": "As machine learning (ML) models, trained on real-world datasets, become common practice, it is critical to measure and quantify their potential biases. In this paper, we focus on renal failure and compare a commonly used traditional risk score, Tangri, with a more powerful machine learning model, which has access to a larger variable set and trained on 1.6 million patients' EHR data. We will compare and discuss the generalization and applicability of these two models, in an attempt to quantify biases of status quo clinical practice, compared to ML-driven models.", "published": "2019-11-18T15:04:31Z", "authors": ["Josie Williams", "Narges Razavian"], "categories": ["cs.LG", "stat.AP", "stat.ML"], "doi": "", "_query": "house price prediction machine learning", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2402.04082v1", "title": "An Optimal House Price Prediction Algorithm: XGBoost", "abstract": "An accurate prediction of house prices is a fundamental requirement for various sectors including real estate and mortgage lending. It is widely recognized that a property value is not solely determined by its physical attributes but is significantly influenced by its surrounding neighbourhood. Meeting the diverse housing needs of individuals while balancing budget constraints is a primary concern for real estate developers. To this end, we addressed the house price prediction problem as a regression task and thus employed various machine learning techniques capable of expressing the significance of independent variables. We made use of the housing dataset of Ames City in Iowa, USA to compare support vector regressor, random forest regressor, XGBoost, multilayer perceptron and multiple linear regression algorithms for house price prediction. Afterwards, we identified the key factors that influence housing costs. Our results show that XGBoost is the best performing model for house price prediction.", "published": "2024-02-06T15:36:06Z", "authors": ["Hemlata Sharma", "Hitesh Harsora", "Bayode Ogunleye"], "categories": ["cs.LG", "cs.AI", "stat.AP", "stat.ME"], "doi": "10.3390/analytics3010003", "_query": "house price prediction machine learning", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/1811.10455v1", "title": "A Framework for Implementing Machine Learning on Omics Data", "abstract": "The potential benefits of applying machine learning methods to -omics data are becoming increasingly apparent, especially in clinical settings. However, the unique characteristics of these data are not always well suited to machine learning techniques. These data are often generated across different technologies in different labs, and frequently with high dimensionality. In this paper we present a framework for combining -omics data sets, and for handling high dimensional data, making -omics research more accessible to machine learning applications. We demonstrate the success of this framework through integration and analysis of multi-analyte data for a set of 3,533 breast cancers. We then use this data-set to predict breast cancer patient survival for individuals at risk of an impending event, with higher accuracy and lower variance than methods trained on individual data-sets. We hope that our pipelines for data-set generation and transformation will open up -omics data to machine learning researchers. We have made these freely available for noncommercial use at www.ccg.ai.", "published": "2018-11-26T15:35:57Z", "authors": ["Geoffroy Dubourg-Felonneau", "Timothy Cannings", "Fergal Cotter", "Hannah Thompson", "Nirmesh Patel", "John W Cassidy", "Harry W Clifford"], "categories": ["cs.LG", "cs.AI", "q-bio.GN", "stat.ML"], "doi": "", "_query": "house price prediction machine learning", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2205.10250v2", "title": "Explanatory machine learning for sequential human teaching", "abstract": "The topic of comprehensibility of machine-learned theories has recently drawn increasing attention. Inductive Logic Programming (ILP) uses logic programming to derive logic theories from small data based on abduction and induction techniques. Learned theories are represented in the form of rules as declarative descriptions of obtained knowledge. In earlier work, the authors provided the first evidence of a measurable increase in human comprehension based on machine-learned logic rules for simple classification tasks. In a later study, it was found that the presentation of machine-learned explanations to humans can produce both beneficial and harmful effects in the context of game learning. We continue our investigation of comprehensibility by examining the effects of the ordering of concept presentations on human comprehension. In this work, we examine the explanatory effects of curriculum order and the presence of machine-learned explanations for sequential problem-solving. We show that 1) there exist tasks A and B such that learning A before B has a better human comprehension with respect to learning B before A and 2) there exist tasks A and B such that the presence of explanations when learning A contributes to improved human comprehension when subsequently learning B. We propose a framework for the effects of sequential teaching on comprehension based on an existing definition of comprehensibility and provide evidence for support from data collected in human trials. Empirical results show that sequential teaching of concepts with increasing complexity a) has a beneficial effect on human comprehension and b) leads to human re-discovery of divide-and-conquer problem-solving strategies, and c) studying machine-learned explanations allows adaptations of human problem-solving strategy with better performance.", "published": "2022-05-20T15:23:46Z", "authors": ["Lun Ai", "Johannes Langer", "Stephen H. Muggleton", "Ute Schmid"], "categories": ["cs.AI", "cs.LG"], "doi": "10.1007/s10994-023-06351-8", "_query": "house price prediction machine learning", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2605.14067v1", "title": "Comparative Evaluation of Machine Learning Approaches for Minority-Class Financial Distress Prediction Under Class Imbalance Constraints", "abstract": "Financial distress prediction remains a significant challenge in enterprise risk analysis due to the highly imbalanced nature of real-world financial datasets, where bankrupt or distressed firms typically constitute only a small minority of observations. This paper presents a comparative evaluation of classical statistical methods, ensemble learning approaches, and exploratory neural models for minority-class financial distress prediction under class imbalance constraints. The study incorporates structured preprocessing, imbalance mitigation using the Synthetic Minority Oversampling Technique (SMOTE), comparative evaluation across ensemble learning architectures including XGBoost, CatBoost, LightGBM, Random Forest, and explainability analysis using SHAP-based feature attribution methods. Experimental evaluation demonstrates that gradient-boosting approaches achieved improved minority-class sensitivity relative to baseline statistical classifiers under severe imbalance conditions. The workflow additionally emphasises reproducibility, interpretability, auditability, and governance-oriented machine learning evaluation within enterprise financial risk environments. The work is positioned as an applied engineering evaluation intended to support reproducible and interpretable machine learning workflows for financial distress prediction under severe class imbalance constraints.", "published": "2026-05-13T19:44:25Z", "authors": ["Karan Sehgal", "Khawar Naveed Bhatti"], "categories": ["cs.LG"], "doi": "", "_query": "house price prediction machine learning", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2210.06261v1", "title": "Predicting housing prices and analyzing real estate market in the Chicago suburbs using Machine Learning", "abstract": "The pricing of housing properties is determined by a variety of factors. However, post-pandemic markets have experienced volatility in the Chicago suburb area, which have affected house prices greatly. In this study, analysis was done on the Naperville/Bolingbrook real estate market to predict property prices based on these housing attributes through machine learning models, and to evaluate the effectiveness of such models in a volatile market space. Gathering data from Redfin, a real estate website, sales data from 2018 up until the summer season of 2022 were collected for research. By analyzing these sales in this range of time, we can also look at the state of the housing market and identify trends in price. For modeling the data, the models used were linear regression, support vector regression, decision tree regression, random forest regression, and XGBoost regression. To analyze results, comparison was made on the MAE, RMSE, and R-squared values for each model. It was found that the XGBoost model performs the best in predicting house prices despite the additional volatility sponsored by post-pandemic conditions. After modeling, Shapley Values (SHAP) were used to evaluate the weights of the variables in constructing models.", "published": "2022-10-12T14:41:53Z", "authors": ["Kevin Xu", "Hieu Nguyen"], "categories": ["cs.LG"], "doi": "", "_query": "house price prediction machine learning", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/1907.01463v1", "title": "Reproducibility in Machine Learning for Health", "abstract": "Machine learning algorithms designed to characterize, monitor, and intervene on human health (ML4H) are expected to perform safely and reliably when operating at scale, potentially outside strict human supervision. This requirement warrants a stricter attention to issues of reproducibility than other fields of machine learning. In this work, we conduct a systematic evaluation of over 100 recently published ML4H research papers along several dimensions related to reproducibility. We find that the field of ML4H compares poorly to more established machine learning fields, particularly concerning data and code accessibility. Finally, drawing from success in other fields of science, we propose recommendations to data providers, academic publishers, and the ML4H research community in order to promote reproducible research moving forward.", "published": "2019-07-02T15:46:46Z", "authors": ["Matthew B. A. McDermott", "Shirly Wang", "Nikki Marinsek", "Rajesh Ranganath", "Marzyeh Ghassemi", "Luca Foschini"], "categories": ["cs.LG", "cs.CY", "stat.ML"], "doi": "", "_query": "house price prediction machine learning", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/1807.05351v1", "title": "ML-Schema: Exposing the Semantics of Machine Learning with Schemas and Ontologies", "abstract": "The ML-Schema, proposed by the W3C Machine Learning Schema Community Group, is a top-level ontology that provides a set of classes, properties, and restrictions for representing and interchanging information on machine learning algorithms, datasets, and experiments. It can be easily extended and specialized and it is also mapped to other more domain-specific ontologies developed in the area of machine learning and data mining. In this paper we overview existing state-of-the-art machine learning interchange formats and present the first release of ML-Schema, a canonical format resulted of more than seven years of experience among different research institutions. We argue that exposing semantics of machine learning algorithms, models, and experiments through a canonical format may pave the way to better interpretability and to realistically achieve the full interoperability of experiments regardless of platform or adopted workflow solution.", "published": "2018-07-14T08:07:31Z", "authors": ["Gustavo Correa Publio", "Diego Esteves", "Agnieszka Ławrynowicz", "Panče Panov", "Larisa Soldatova", "Tommaso Soru", "Joaquin Vanschoren", "Hamid Zafar"], "categories": ["cs.LG", "cs.DB", "cs.IR", "stat.ML"], "doi": "", "_query": "house price prediction machine learning", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/1811.10154v3", "title": "Stop Explaining Black Box Machine Learning Models for High Stakes Decisions and Use Interpretable Models Instead", "abstract": "Black box machine learning models are currently being used for high stakes decision-making throughout society, causing problems throughout healthcare, criminal justice, and in other domains. People have hoped that creating methods for explaining these black box models will alleviate some of these problems, but trying to \\textit{explain} black box models, rather than creating models that are \\textit{interpretable} in the first place, is likely to perpetuate bad practices and can potentially cause catastrophic harm to society. There is a way forward -- it is to design models that are inherently interpretable. This manuscript clarifies the chasm between explaining black boxes and using inherently interpretable models, outlines several key reasons why explainable black boxes should be avoided in high-stakes decisions, identifies challenges to interpretable machine learning, and provides several example applications where interpretable models could potentially replace black box models in criminal justice, healthcare, and computer vision.", "published": "2018-11-26T03:00:25Z", "authors": ["Cynthia Rudin"], "categories": ["stat.ML", "cs.LG"], "doi": "", "_query": "house price prediction machine learning", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2108.08712v1", "title": "Teaching Uncertainty Quantification in Machine Learning through Use Cases", "abstract": "Uncertainty in machine learning is not generally taught as general knowledge in Machine Learning course curricula. In this paper we propose a short curriculum for a course about uncertainty in machine learning, and complement the course with a selection of use cases, aimed to trigger discussion and let students play with the concepts of uncertainty in a programming setting. Our use cases cover the concept of output uncertainty, Bayesian neural networks and weight distributions, sources of uncertainty, and out of distribution detection. We expect that this curriculum and set of use cases motivates the community to adopt these important concepts into courses for safety in AI.", "published": "2021-08-19T14:22:17Z", "authors": ["Matias Valdenegro-Toro"], "categories": ["cs.LG", "stat.ML"], "doi": "", "_query": "house price prediction machine learning", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2510.16898v2", "title": "Adaptive Online Learning with LSTM Networks for Energy Price Prediction", "abstract": "Accurate prediction of electricity prices is crucial for stakeholders in the energy market, particularly for grid operators, energy producers, and consumers. This study focuses on developing a predictive model leveraging Long Short-Term Memory (LSTM) networks to forecast day-ahead electricity prices in the California energy market. The model incorporates a variety of features, including historical price data, weather conditions, and the energy generation mix. A novel custom loss function that integrates Mean Absolute Error (MAE), Jensen-Shannon Divergence (JSD), and a smoothness penalty is introduced to enhance the prediction accuracy and interpretability. Additionally, an adaptive online learning framework is implemented to allow the model to adapt to new data incrementally, ensuring continuous relevance and accuracy. The results demonstrate that the custom loss function can improve the model's performance, aligning predicted prices more closely with actual values, particularly during peak intervals. The adaptive online learning framework achieves the best overall performance, reducing MSE, MAE, and RMSE by approximately 23.0%, 3.4%, and 12.2%, respectively, compared with the next-best model, namely the static model, which is trained once and kept fixed during testing. The inclusion of the energy generation mix further enhances the model's predictive capabilities, highlighting the importance of comprehensive feature integration. This research provides a robust framework for electricity price forecasting, which offers valuable insights and tools for better decision-making in dynamic electricity markets.", "published": "2025-10-19T15:48:38Z", "authors": ["Salih Salihoglu", "Ibrahim Ahmed", "Afshin Asadi"], "categories": ["cs.LG", "cs.AI"], "doi": "10.1007/s41060-026-01251-1", "_query": "house price prediction machine learning", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/1811.12583v1", "title": "Rethinking clinical prediction: Why machine learning must consider year of care and feature aggregation", "abstract": "Machine learning for healthcare often trains models on de-identified datasets with randomly-shifted calendar dates, ignoring the fact that data were generated under hospital operation practices that change over time. These changing practices induce definitive changes in observed data which confound evaluations which do not account for dates and limit the generalisability of date-agnostic models. In this work, we establish the magnitude of this problem on MIMIC, a public hospital dataset, and showcase a simple solution. We augment MIMIC with the year in which care was provided and show that a model trained using standard feature representations will significantly degrade in quality over time. We find a deterioration of 0.3 AUC when evaluating mortality prediction on data from 10 years later. We find a similar deterioration of 0.15 AUC for length-of-stay. In contrast, we demonstrate that clinically-oriented aggregates of raw features significantly mitigate future deterioration. Our suggested aggregated representations, when retrained yearly, have prediction quality comparable to year-agnostic models.", "published": "2018-11-30T02:30:10Z", "authors": ["Bret Nestor", "Matthew B. A. McDermott", "Geeticka Chauhan", "Tristan Naumann", "Michael C. Hughes", "Anna Goldenberg", "Marzyeh Ghassemi"], "categories": ["cs.LG", "stat.ML"], "doi": "", "_query": "house price prediction machine learning", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/1904.10922v1", "title": "The Scientific Method in the Science of Machine Learning", "abstract": "In the quest to align deep learning with the sciences to address calls for rigor, safety, and interpretability in machine learning systems, this contribution identifies key missing pieces: the stages of hypothesis formulation and testing, as well as statistical and systematic uncertainty estimation -- core tenets of the scientific method. This position paper discusses the ways in which contemporary science is conducted in other domains and identifies potentially useful practices. We present a case study from physics and describe how this field has promoted rigor through specific methodological practices, and provide recommendations on how machine learning researchers can adopt these practices into the research ecosystem. We argue that both domain-driven experiments and application-agnostic questions of the inner workings of fundamental building blocks of machine learning models ought to be examined with the tools of the scientific method, to ensure we not only understand effect, but also begin to understand cause, which is the raison d'être of science.", "published": "2019-04-24T17:01:43Z", "authors": ["Jessica Zosa Forde", "Michela Paganini"], "categories": ["cs.LG", "stat.ML"], "doi": "", "_query": "house price prediction machine learning", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2009.10819v1", "title": "Stock Price Prediction Using Machine Learning and LSTM-Based Deep Learning Models", "abstract": "Prediction of stock prices has been an important area of research for a long time. While supporters of the efficient market hypothesis believe that it is impossible to predict stock prices accurately, there are formal propositions demonstrating that accurate modeling and designing of appropriate variables may lead to models using which stock prices and stock price movement patterns can be very accurately predicted. In this work, we propose an approach of hybrid modeling for stock price prediction building different machine learning and deep learning-based models. For the purpose of our study, we have used NIFTY 50 index values of the National Stock Exchange (NSE) of India, during the period December 29, 2014 till July 31, 2020. We have built eight regression models using the training data that consisted of NIFTY 50 index records during December 29, 2014 till December 28, 2018. Using these regression models, we predicted the open values of NIFTY 50 for the period December 31, 2018 till July 31, 2020. We, then, augment the predictive power of our forecasting framework by building four deep learning-based regression models using long-and short-term memory (LSTM) networks with a novel approach of walk-forward validation. We exploit the power of LSTM regression models in forecasting the future NIFTY 50 open values using four different models that differ in their architecture and in the structure of their input data. Extensive results are presented on various metrics for the all the regression models. The results clearly indicate that the LSTM-based univariate model that uses one-week prior data as input for predicting the next week open value of the NIFTY 50 time series is the most accurate model.", "published": "2020-09-20T20:32:33Z", "authors": ["Sidra Mehtab", "Jaydip Sen", "Abhishek Dutta"], "categories": ["q-fin.ST", "cs.LG"], "doi": "10.1007/978-981-16-0419-5_8", "_query": "house price prediction machine learning", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/1911.00623v2", "title": "On-Device Machine Learning: An Algorithms and Learning Theory Perspective", "abstract": "The predominant paradigm for using machine learning models on a device is to train a model in the cloud and perform inference using the trained model on the device. However, with increasing number of smart devices and improved hardware, there is interest in performing model training on the device. Given this surge in interest, a comprehensive survey of the field from a device-agnostic perspective sets the stage for both understanding the state-of-the-art and for identifying open challenges and future avenues of research. However, on-device learning is an expansive field with connections to a large number of related topics in AI and machine learning (including online learning, model adaptation, one/few-shot learning, etc.). Hence, covering such a large number of topics in a single survey is impractical. This survey finds a middle ground by reformulating the problem of on-device learning as resource constrained learning where the resources are compute and memory. This reformulation allows tools, techniques, and algorithms from a wide variety of research areas to be compared equitably. In addition to summarizing the state-of-the-art, the survey also identifies a number of challenges and next steps for both the algorithmic and theoretical aspects of on-device learning.", "published": "2019-11-02T01:16:02Z", "authors": ["Sauptik Dhar", "Junyao Guo", "Jiayi Liu", "Samarth Tripathi", "Unmesh Kurup", "Mohak Shah"], "categories": ["cs.LG", "cs.DC", "stat.ML"], "doi": "", "_query": "house price prediction machine learning", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/1811.07216v2", "title": "Machine Learning for Health (ML4H) Workshop at NeurIPS 2018", "abstract": "This volume represents the accepted submissions from the Machine Learning for Health (ML4H) workshop at the conference on Neural Information Processing Systems (NeurIPS) 2018, held on December 8, 2018 in Montreal, Canada.", "published": "2018-11-17T20:14:43Z", "authors": ["Natalia Antropova", "Andrew L. Beam", "Brett K. Beaulieu-Jones", "Irene Chen", "Corey Chivers", "Adrian Dalca", "Sam Finlayson", "Madalina Fiterau", "Jason Alan Fries", "Marzyeh Ghassemi", "Mike Hughes", "Bruno Jedynak", "Jasvinder S. Kandola", "Matthew McDermott", "Tristan Naumann", "Peter Schulam", "Farah Shamout", "Alexandre Yahi"], "categories": ["cs.LG", "stat.ML"], "doi": "", "_query": "house price prediction machine learning", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/1912.05796v1", "title": "Automatic Layout Generation with Applications in Machine Learning Engine Evaluation", "abstract": "Machine learning-based lithography hotspot detection has been deeply studied recently, from varies feature extraction techniques to efficient learning models. It has been observed that such machine learning-based frameworks are providing satisfactory metal layer hotspot prediction results on known public metal layer benchmarks. In this work, we seek to evaluate how these machine learning-based hotspot detectors generalize to complicated patterns. We first introduce a automatic layout generation tool that can synthesize varies layout patterns given a set of design rules. The tool currently supports both metal layer and via layer generation. As a case study, we conduct hotspot detection on the generated via layer layouts with representative machine learning-based hotspot detectors, which shows that continuous study on model robustness and generality is necessary to prototype and integrate the learning engines in DFM flows. The source code of the layout generation tool will be available at https://github. com/phdyang007/layout-generation.", "published": "2019-12-12T06:52:12Z", "authors": ["Haoyu Yang", "Wen Chen", "Piyush Pathak", "Frank Gennari", "Ya-Chieh Lai", "Bei Yu"], "categories": ["cs.LG", "cs.AI", "stat.ML"], "doi": "", "_query": "house price prediction machine learning", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2108.07380v2", "title": "InfoGram and Admissible Machine Learning", "abstract": "We have entered a new era of machine learning (ML), where the most accurate algorithm with superior predictive power may not even be deployable, unless it is admissible under the regulatory constraints. This has led to great interest in developing fair, transparent and trustworthy ML methods. The purpose of this article is to introduce a new information-theoretic learning framework (admissible machine learning) and algorithmic risk-management tools (InfoGram, L-features, ALFA-testing) that can guide an analyst to redesign off-the-shelf ML methods to be regulatory compliant, while maintaining good prediction accuracy. We have illustrated our approach using several real-data examples from financial sectors, biomedical research, marketing campaigns, and the criminal justice system.", "published": "2021-08-17T00:04:38Z", "authors": ["Subhadeep Mukhopadhyay"], "categories": ["stat.ML", "cs.AI", "cs.LG", "econ.EM"], "doi": "", "_query": "house price prediction machine learning", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/1811.12234v1", "title": "Machine Learning on Electronic Health Records: Models and Features Usages to predict Medication Non-Adherence", "abstract": "Adherence can be defined as \"the extent to which patients take their medications as prescribed by their healthcare providers\"[Osterberg and Blaschke, 2005]. World Health Organization's reports point out that, in developed countries, only about 50% of patients with chronic diseases correctly follow their treatments. This severely compromises the efficiency of long-term therapy and increases the cost of health services. We propose in this paper different models of patient drug consumption in breast cancer treatments. The aim of these different approaches is to predict medication non-adherence while giving insights to doctors of the underlying reasons of these illegitimate drop-outs. Working with oncologists, we show the interest of Machine- Learning algorithms fined tune by the feedback of experts to estimate a risk score of a patient's non-adherence and thus improve support throughout their care path.", "published": "2018-11-29T15:08:55Z", "authors": ["Thomas Janssoone", "Clémence Bic", "Dorra Kanoun", "Pierre Hornus", "Pierre Rinder"], "categories": ["cs.LG", "cs.AI", "stat.ML"], "doi": "", "_query": "house price prediction machine learning", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2007.15769v2", "title": "Instrument variable detection with graph learning : an application to high dimensional GIS-census data for house pricing", "abstract": "Endogeneity bias and instrument variable validation have always been important topics in statistics and econometrics. In the era of big data, such issues typically combine with dimensionality issues and, hence, require even more attention. In this paper, we merge two well-known tools from machine learning and biostatistics---variable selection algorithms and probablistic graphs---to estimate house prices and the corresponding causal structure using 2010 data on Sydney. The estimation uses a 200-gigabyte ultrahigh dimensional database consisting of local school data, GIS information, census data, house characteristics and other socio-economic records. Using \"big data\", we show that it is possible to perform a data-driven instrument selection efficiently and purge out the invalid instruments. Our approach improves the sparsity of variable selection, stability and robustness in the presence of high dimensionality, complicated causal structures and the consequent multicollinearity, and recovers a sparse and intuitive causal structure. The approach also reveals an efficiency and effectiveness in endogeneity detection, instrument validation, weak instrument pruning and the selection of valid instruments. From the perspective of machine learning, the estimation results both align with and confirms the facts of Sydney house market, the classical economic theories and the previous findings of simultaneous equations modeling. Moreover, the estimation results are consistent with and supported by classical econometric tools such as two-stage least square regression and different instrument tests. All the code may be found at \\url{https://github.com/isaac2math/solar_graph_learning}.", "published": "2020-07-30T23:11:54Z", "authors": ["Ning Xu", "Timothy C. G. Fisher", "Jian Hong"], "categories": ["stat.ML", "cs.LG", "stat.AP"], "doi": "", "_query": "house price prediction machine learning", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/1901.09697v5", "title": "Bayesian Differential Privacy for Machine Learning", "abstract": "Traditional differential privacy is independent of the data distribution. However, this is not well-matched with the modern machine learning context, where models are trained on specific data. As a result, achieving meaningful privacy guarantees in ML often excessively reduces accuracy. We propose Bayesian differential privacy (BDP), which takes into account the data distribution to provide more practical privacy guarantees. We also derive a general privacy accounting method under BDP, building upon the well-known moments accountant. Our experiments demonstrate that in-distribution samples in classic machine learning datasets, such as MNIST and CIFAR-10, enjoy significantly stronger privacy guarantees than postulated by DP, while models maintain high classification accuracy.", "published": "2019-01-28T14:37:17Z", "authors": ["Aleksei Triastcyn", "Boi Faltings"], "categories": ["cs.LG", "cs.CR", "stat.ML"], "doi": "", "_query": "house price prediction machine learning", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2007.01135v1", "title": "Student-Teacher Curriculum Learning via Reinforcement Learning: Predicting Hospital Inpatient Admission Location", "abstract": "Accurate and reliable prediction of hospital admission location is important due to resource-constraints and space availability in a clinical setting, particularly when dealing with patients who come from the emergency department. In this work we propose a student-teacher network via reinforcement learning to deal with this specific problem. A representation of the weights of the student network is treated as the state and is fed as an input to the teacher network. The teacher network's action is to select the most appropriate batch of data to train the student network on from a training set sorted according to entropy. By validating on three datasets, not only do we show that our approach outperforms state-of-the-art methods on tabular data and performs competitively on image recognition, but also that novel curricula are learned by the teacher network. We demonstrate experimentally that the teacher network can actively learn about the student network and guide it to achieve better performance than if trained alone.", "published": "2020-07-01T15:00:43Z", "authors": ["Rasheed el-Bouri", "David Eyre", "Peter Watkinson", "Tingting Zhu", "David Clifton"], "categories": ["cs.LG", "cs.CV", "stat.ML"], "doi": "", "_query": "house price prediction machine learning", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/1905.06549v2", "title": "TapNet: Neural Network Augmented with Task-Adaptive Projection for Few-Shot Learning", "abstract": "Handling previously unseen tasks after given only a few training examples continues to be a tough challenge in machine learning. We propose TapNets, neural networks augmented with task-adaptive projection for improved few-shot learning. Here, employing a meta-learning strategy with episode-based training, a network and a set of per-class reference vectors are learned across widely varying tasks. At the same time, for every episode, features in the embedding space are linearly projected into a new space as a form of quick task-specific conditioning. The training loss is obtained based on a distance metric between the query and the reference vectors in the projection space. Excellent generalization results in this way. When tested on the Omniglot, miniImageNet and tieredImageNet datasets, we obtain state of the art classification accuracies under various few-shot scenarios.", "published": "2019-05-16T06:21:28Z", "authors": ["Sung Whan Yoon", "Jun Seo", "Jaekyun Moon"], "categories": ["cs.LG", "stat.ML"], "doi": "", "_query": "house price prediction machine learning", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2501.09687v1", "title": "U-Fair: Uncertainty-based Multimodal Multitask Learning for Fairer Depression Detection", "abstract": "Machine learning bias in mental health is becoming an increasingly pertinent challenge. Despite promising efforts indicating that multitask approaches often work better than unitask approaches, there is minimal work investigating the impact of multitask learning on performance and fairness in depression detection nor leveraged it to achieve fairer prediction outcomes. In this work, we undertake a systematic investigation of using a multitask approach to improve performance and fairness for depression detection. We propose a novel gender-based task-reweighting method using uncertainty grounded in how the PHQ-8 questionnaire is structured. Our results indicate that, although a multitask approach improves performance and fairness compared to a unitask approach, the results are not always consistent and we see evidence of negative transfer and a reduction in the Pareto frontier, which is concerning given the high-stake healthcare setting. Our proposed approach of gender-based reweighting with uncertainty improves performance and fairness and alleviates both challenges to a certain extent. Our findings on each PHQ-8 subitem task difficulty are also in agreement with the largest study conducted on the PHQ-8 subitem discrimination capacity, thus providing the very first tangible evidence linking ML findings with large-scale empirical population studies conducted on the PHQ-8.", "published": "2025-01-16T17:39:25Z", "authors": ["Jiaee Cheong", "Aditya Bangar", "Sinan Kalkan", "Hatice Gunes"], "categories": ["cs.LG"], "doi": "", "_query": "house price prediction machine learning", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2107.02363v4", "title": "Asymptotics of Network Embeddings Learned via Subsampling", "abstract": "Network data are ubiquitous in modern machine learning, with tasks of interest including node classification, node clustering and link prediction. A frequent approach begins by learning an Euclidean embedding of the network, to which algorithms developed for vector-valued data are applied. For large networks, embeddings are learned using stochastic gradient methods where the sub-sampling scheme can be freely chosen. Despite the strong empirical performance of such methods, they are not well understood theoretically. Our work encapsulates representation methods using a subsampling approach, such as node2vec, into a single unifying framework. We prove, under the assumption that the graph is exchangeable, that the distribution of the learned embedding vectors asymptotically decouples. Moreover, we characterize the asymptotic distribution and provided rates of convergence, in terms of the latent parameters, which includes the choice of loss function and the embedding dimension. This provides a theoretical foundation to understand what the embedding vectors represent and how well these methods perform on downstream tasks. Notably, we observe that typically used loss functions may lead to shortcomings, such as a lack of Fisher consistency.", "published": "2021-07-06T02:54:53Z", "authors": ["Andrew Davison", "Morgane Austern"], "categories": ["stat.ML", "cs.LG", "math.ST"], "doi": "", "_query": "house price prediction machine learning", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2002.04555v2", "title": "Predicting drug properties with parameter-free machine learning: Pareto-Optimal Embedded Modeling (POEM)", "abstract": "The prediction of absorption, distribution, metabolism, excretion, and toxicity (ADMET) of small molecules from their molecular structure is a central problem in medicinal chemistry with great practical importance in drug discovery. Creating predictive models conventionally requires substantial trial-and-error for the selection of molecular representations, machine learning (ML) algorithms, and hyperparameter tuning. A generally applicable method that performs well on all datasets without tuning would be of great value but is currently lacking. Here, we describe Pareto-Optimal Embedded Modeling (POEM), a similarity-based method for predicting molecular properties. POEM is a non-parametric, supervised ML algorithm developed to generate reliable predictive models without need for optimization. POEMs predictive strength is obtained by combining multiple different representations of molecular structures in a context-specific manner, while maintaining low dimensionality. We benchmark POEM relative to industry-standard ML algorithms and published results across 17 classifications tasks. POEM performs well in all cases and reduces the risk of overfitting.", "published": "2020-02-11T17:20:28Z", "authors": ["Andrew E. Brereton", "Stephen MacKinnon", "Zhaleh Safikhani", "Shawn Reeves", "Sana Alwash", "Vijay Shahani", "Andreas Windemuth"], "categories": ["cs.LG", "stat.ML"], "doi": "10.1088/2632-2153/ab891b", "_query": "house price prediction machine learning", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2002.04803v2", "title": "Machine Learning in Python: Main developments and technology trends in data science, machine learning, and artificial intelligence", "abstract": "Smarter applications are making better use of the insights gleaned from data, having an impact on every industry and research discipline. At the core of this revolution lies the tools and the methods that are driving it, from processing the massive piles of data generated each day to learning from and taking useful action. Deep neural networks, along with advancements in classical ML and scalable general-purpose GPU computing, have become critical components of artificial intelligence, enabling many of these astounding breakthroughs and lowering the barrier to adoption. Python continues to be the most preferred language for scientific computing, data science, and machine learning, boosting both performance and productivity by enabling the use of low-level libraries and clean high-level APIs. This survey offers insight into the field of machine learning with Python, taking a tour through important topics to identify some of the core hardware and software paradigms that have enabled it. We cover widely-used libraries and concepts, collected together for holistic comparison, with the goal of educating the reader and driving the field of Python machine learning forward.", "published": "2020-02-12T05:20:59Z", "authors": ["Sebastian Raschka", "Joshua Patterson", "Corey Nolet"], "categories": ["cs.LG", "stat.ML"], "doi": "", "_query": "house price prediction machine learning", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/1606.05685v2", "title": "Using Visual Analytics to Interpret Predictive Machine Learning Models", "abstract": "It is commonly believed that increasing the interpretability of a machine learning model may decrease its predictive power. However, inspecting input-output relationships of those models using visual analytics, while treating them as black-box, can help to understand the reasoning behind outcomes without sacrificing predictive quality. We identify a space of possible solutions and provide two examples of where such techniques have been successfully used in practice.", "published": "2016-06-17T21:56:43Z", "authors": ["Josua Krause", "Adam Perer", "Enrico Bertini"], "categories": ["stat.ML", "cs.LG"], "doi": "", "_query": "house price prediction machine learning", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/1810.10731v3", "title": "Law and Adversarial Machine Learning", "abstract": "When machine learning systems fail because of adversarial manipulation, how should society expect the law to respond? Through scenarios grounded in adversarial ML literature, we explore how some aspects of computer crime, copyright, and tort law interface with perturbation, poisoning, model stealing and model inversion attacks to show how some attacks are more likely to result in liability than others. We end with a call for action to ML researchers to invest in transparent benchmarks of attacks and defenses; architect ML systems with forensics in mind and finally, think more about adversarial machine learning in the context of civil liberties. The paper is targeted towards ML researchers who have no legal background.", "published": "2018-10-25T06:17:34Z", "authors": ["Ram Shankar Siva Kumar", "David R. O'Brien", "Kendra Albert", "Salome Vilojen"], "categories": ["cs.LG", "cs.CR", "cs.CY", "stat.ML"], "doi": "", "_query": "house price prediction machine learning", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2502.07977v3", "title": "RESIST: Resilient Decentralized Learning Using Consensus Gradient Descent", "abstract": "Empirical risk minimization (ERM) is a cornerstone of modern machine learning. This paper focuses on the man-in-the-middle (MITM) attack, wherein adversaries exploit communication vulnerabilities between devices to inject malicious updates during training. To address this challenge, we propose RESIST (Resilient dEcentralized learning using conSensus gradIent deScenT), an optimization algorithm designed to be robust against adversarially compromised communication links. RESIST uses a multistep consensus gradient descent framework with robust-statistics-based screening of neighbor messages. It has a design parameter $J$ that controls the frequency of local gradient computation: each local gradient update is preceded by $J-1$ communication and robust aggregation rounds. Compared with methods that perform both communication and a local gradient update at every iteration, RESIST with $J>2$ uses fewer local gradient updates at the same communication budget. We establish geometric algorithmic convergence guarantees for strongly convex and Polyak-Lojasiewicz ERM problems, and sublinear and finite-horizon guarantees for smooth nonconvex ERM problems. For heterogeneous local objectives, these guarantees quantify neighborhoods for the relevant iterate, objective-value, and stationarity errors. In the strongly convex homogeneous case, convergence becomes exact. We also establish statistical learning-rate guarantees under common-population independent and identically distributed sampling assumptions, including regimes in which the statistical error vanishes as the sample size grows. Experimental results demonstrate the robustness of RESIST across diverse attack strategies, screening methods, and loss functions. The $J$-ablation experiments further show that larger $J$ can improve convergence and reduce the number of local gradient updates at a fixed communication budget.", "published": "2025-02-11T21:48:10Z", "authors": ["Cheng Fang", "Rishabh Dixit", "Waheed U. Bajwa", "Mert Gürbüzbalaban"], "categories": ["cs.LG", "math.OC", "stat.ML"], "doi": "", "_query": "house price prediction machine learning", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/1606.05386v1", "title": "Model-Agnostic Interpretability of Machine Learning", "abstract": "Understanding why machine learning models behave the way they do empowers both system designers and end-users in many ways: in model selection, feature engineering, in order to trust and act upon the predictions, and in more intuitive user interfaces. Thus, interpretability has become a vital concern in machine learning, and work in the area of interpretable models has found renewed interest. In some applications, such models are as accurate as non-interpretable ones, and thus are preferred for their transparency. Even when they are not accurate, they may still be preferred when interpretability is of paramount importance. However, restricting machine learning to interpretable models is often a severe limitation. In this paper we argue for explaining machine learning predictions using model-agnostic approaches. By treating the machine learning models as black-box functions, these approaches provide crucial flexibility in the choice of models, explanations, and representations, improving debugging, comparison, and interfaces for a variety of users and models. We also outline the main challenges for such methods, and review a recently-introduced model-agnostic explanation approach (LIME) that addresses these challenges.", "published": "2016-06-16T23:39:41Z", "authors": ["Marco Tulio Ribeiro", "Sameer Singh", "Carlos Guestrin"], "categories": ["stat.ML", "cs.LG"], "doi": "", "_query": "house price prediction machine learning", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2511.18225v2", "title": "Adaptive Conformal Prediction for Quantum Machine Learning", "abstract": "Quantum machine learning seeks to leverage quantum computers to improve upon classical machine learning algorithms. Currently, robust uncertainty quantification methods remain underdeveloped in the quantum domain, despite the critical need for reliable and trustworthy predictions. Recent work has introduced quantum conformal prediction, a framework that produces prediction sets that are guaranteed to contain the true outcome with a user-specified probability. In this work, we formalise how the time-varying noise inherent in quantum processors can undermine conformal guarantees, even when calibration and test data are exchangeable. To address this challenge, we draw on Adaptive Conformal Inference, a method which maintains validity over time via repeated recalibration. We introduce Adaptive Quantum Conformal Prediction (AQCP), an algorithm which provides asymptotic average coverage guarantees under arbitrary hardware noise conditions. Empirical studies on an IBM quantum processor demonstrate that AQCP achieves the target coverage level and exhibits greater stability than quantum conformal prediction.", "published": "2025-11-23T00:04:03Z", "authors": ["Douglas Spencer", "Samual Nicholls", "Michele Caprio"], "categories": ["cs.LG", "stat.ML", "stat.OT"], "doi": "", "_query": "house price prediction machine learning", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/1811.04380v1", "title": "ReSet: Learning Recurrent Dynamic Routing in ResNet-like Neural Networks", "abstract": "Neural Network is a powerful Machine Learning tool that shows outstanding performance in Computer Vision, Natural Language Processing, and Artificial Intelligence. In particular, recently proposed ResNet architecture and its modifications produce state-of-the-art results in image classification problems. ResNet and most of the previously proposed architectures have a fixed structure and apply the same transformation to all input images. In this work, we develop a ResNet-based model that dynamically selects Computational Units (CU) for each input object from a learned set of transformations. Dynamic selection allows the network to learn a sequence of useful transformations and apply only required units to predict the image label. We compare our model to ResNet-38 architecture and achieve better results than the original ResNet on CIFAR-10.1 test set. While examining the produced paths, we discovered that the network learned different routes for images from different classes and similar routes for similar images.", "published": "2018-11-11T09:45:41Z", "authors": ["Iurii Kemaev", "Daniil Polykovskiy", "Dmitry Vetrov"], "categories": ["stat.ML", "cs.LG"], "doi": "", "_query": "house price prediction machine learning", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/1810.00619v3", "title": "SmartChoices: Hybridizing Programming and Machine Learning", "abstract": "We present SmartChoices, an approach to making machine learning (ML) a first class citizen in programming languages which we see as one way to lower the entrance cost to applying ML to problems in new domains. There is a growing divide in approaches to building systems: on the one hand, programming leverages human experts to define a system while on the other hand behavior is learned from data in machine learning. We propose to hybridize these two by providing a 3-call API which we expose through an object called SmartChoice. We describe the SmartChoices-interface, how it can be used in programming with minimal code changes, and demonstrate that it is an easy to use but still powerful tool by demonstrating improvements over not using ML at all on three algorithmic problems: binary search, QuickSort, and caches. In these three examples, we replace the commonly used heuristics with an ML model entirely encapsulated within a SmartChoice and thus requiring minimal code changes. As opposed to previous work applying ML to algorithmic problems, our proposed approach does not require to drop existing implementations but seamlessly integrates into the standard software development workflow and gives full control to the software developer over how ML methods are applied. Our implementation relies on standard Reinforcement Learning (RL) methods. To learn faster, we use the heuristic function, which they are replacing, as an initial function. We show how this initial function can be used to speed up and stabilize learning while providing a safety net that prevents performance to become substantially worse -- allowing for a safe deployment in critical applications in real life.", "published": "2018-10-01T11:14:22Z", "authors": ["Victor Carbune", "Thierry Coppey", "Alexander Daryin", "Thomas Deselaers", "Nikhil Sarda", "Jay Yagnik"], "categories": ["cs.LG", "cs.PL", "stat.ML"], "doi": "", "_query": "house price prediction machine learning", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2003.05155v2", "title": "Towards CRISP-ML(Q): A Machine Learning Process Model with Quality Assurance Methodology", "abstract": "Machine learning is an established and frequently used technique in industry and academia but a standard process model to improve success and efficiency of machine learning applications is still missing. Project organizations and machine learning practitioners have a need for guidance throughout the life cycle of a machine learning application to meet business expectations. We therefore propose a process model for the development of machine learning applications, that covers six phases from defining the scope to maintaining the deployed machine learning application. The first phase combines business and data understanding as data availability oftentimes affects the feasibility of the project. The sixth phase covers state-of-the-art approaches for monitoring and maintenance of a machine learning applications, as the risk of model degradation in a changing environment is eminent. With each task of the process, we propose quality assurance methodology that is suitable to adress challenges in machine learning development that we identify in form of risks. The methodology is drawn from practical experience and scientific literature and has proven to be general and stable. The process model expands on CRISP-DM, a data mining process model that enjoys strong industry support but lacks to address machine learning specific tasks. Our work proposes an industry and application neutral process model tailored for machine learning applications with focus on technical tasks for quality assurance.", "published": "2020-03-11T08:25:49Z", "authors": ["Stefan Studer", "Thanh Binh Bui", "Christian Drescher", "Alexander Hanuschkin", "Ludwig Winkler", "Steven Peters", "Klaus-Robert Mueller"], "categories": ["cs.LG", "cs.SE", "stat.ML"], "doi": "", "_query": "house price prediction machine learning", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2306.04748v2", "title": "Analysis, Identification and Prediction of Parkinson Disease Sub-Types and Progression through Machine Learning", "abstract": "This paper represents a groundbreaking advancement in Parkinson disease (PD) research by employing a novel machine learning framework to categorize PD into distinct subtypes and predict its progression. Utilizing a comprehensive dataset encompassing both clinical and neurological parameters, the research applies advanced supervised and unsupervised learning techniques. This innovative approach enables the identification of subtle, yet critical, patterns in PD manifestation, which traditional methodologies often miss. Significantly, this research offers a path toward personalized treatment strategies, marking a major stride in the precision medicine domain and showcasing the transformative potential of integrating machine learning into medical research.", "published": "2023-06-07T19:54:56Z", "authors": ["Ashwin Ram"], "categories": ["cs.LG"], "doi": "10.4236/oalib.1111135", "_query": "house price prediction machine learning", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2012.03787v1", "title": "A predictive model for kidney transplant graft survival using machine learning", "abstract": "Kidney transplantation is the best treatment for end-stage renal failure patients. The predominant method used for kidney quality assessment is the Cox regression-based, kidney donor risk index. A machine learning method may provide improved prediction of transplant outcomes and help decision-making. A popular tree-based machine learning method, random forest, was trained and evaluated with the same data originally used to develop the risk index (70,242 observations from 1995-2005). The random forest successfully predicted an additional 2,148 transplants than the risk index with equal type II error rates of 10%. Predicted results were analyzed with follow-up survival outcomes up to 240 months after transplant using Kaplan-Meier analysis and confirmed that the random forest performed significantly better than the risk index (p<0.05). The random forest predicted significantly more successful and longer-surviving transplants than the risk index. Random forests and other machine learning models may improve transplant decisions.", "published": "2020-12-07T15:29:51Z", "authors": ["Eric S. Pahl", "W. Nick Street", "Hans J. Johnson", "Alan I. Reed"], "categories": ["cs.LG", "stat.ML"], "doi": "10.5121/csit.2020.101609", "_query": "house price prediction machine learning", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/1911.02455v1", "title": "Unfairness towards subjective opinions in Machine Learning", "abstract": "Despite the high interest for Machine Learning (ML) in academia and industry, many issues related to the application of ML to real-life problems are yet to be addressed. Here we put forward one limitation which arises from a lack of adaptation of ML models and datasets to specific applications. We formalise a new notion of unfairness as exclusion of opinions. We propose ways to quantify this unfairness, and aid understanding its causes through visualisation. These insights into the functioning of ML-based systems hint at methods to mitigate unfairness.", "published": "2019-11-06T16:11:41Z", "authors": ["Agathe Balayn", "Alessandro Bozzon", "Zoltan Szlavik"], "categories": ["cs.LG", "cs.CY", "cs.HC", "stat.ML"], "doi": "", "_query": "house price prediction machine learning", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2211.07076v1", "title": "Learning predictive checklists from continuous medical data", "abstract": "Checklists, while being only recently introduced in the medical domain, have become highly popular in daily clinical practice due to their combined effectiveness and great interpretability. Checklists are usually designed by expert clinicians that manually collect and analyze available evidence. However, the increasing quantity of available medical data is calling for a partially automated checklist design. Recent works have taken a step in that direction by learning predictive checklists from categorical data. In this work, we propose to extend this approach to accomodate learning checklists from continuous medical data using mixed-integer programming approach. We show that this extension outperforms a range of explainable machine learning baselines on the prediction of sepsis from intensive care clinical trajectories.", "published": "2022-11-14T02:51:04Z", "authors": ["Yukti Makhija", "Edward De Brouwer", "Rahul G. Krishnan"], "categories": ["cs.LG"], "doi": "", "_query": "house price prediction machine learning", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/1905.12278v6", "title": "An Inertial Newton Algorithm for Deep Learning", "abstract": "We introduce a new second-order inertial optimization method for machine learning called INNA. It exploits the geometry of the loss function while only requiring stochastic approximations of the function values and the generalized gradients. This makes INNA fully implementable and adapted to large-scale optimization problems such as the training of deep neural networks. The algorithm combines both gradient-descent and Newton-like behaviors as well as inertia. We prove the convergence of INNA for most deep learning problems. To do so, we provide a well-suited framework to analyze deep learning loss functions involving tame optimization in which we study a continuous dynamical system together with its discrete stochastic approximations. We prove sublinear convergence for the continuous-time differential inclusion which underlies our algorithm. Additionally, we also show how standard optimization mini-batch methods applied to non-smooth non-convex problems can yield a certain type of spurious stationary points never discussed before. We address this issue by providing a theoretical framework around the new idea of $D$-criticality; we then give a simple asymptotic analysis of INNA. Our algorithm allows for using an aggressive learning rate of $o(1/\\log k)$. From an empirical viewpoint, we show that INNA returns competitive results with respect to state of the art (stochastic gradient descent, ADAGRAD, ADAM) on popular deep learning benchmark problems.", "published": "2019-05-29T09:00:49Z", "authors": ["Camille Castera", "Jérôme Bolte", "Cédric Févotte", "Edouard Pauwels"], "categories": ["cs.LG", "math.OC", "stat.ML"], "doi": "", "_query": "house price prediction machine learning", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2409.14508v2", "title": "Evaluating Machine Learning Models for Supernova Gravitational Wave Signal Classification", "abstract": "We investigate the potential of using gravitational wave (GW) signals from rotating core-collapse supernovae to probe the equation of state (EOS) of nuclear matter. By generating GW signals from simulations with various EOSs, we train machine learning models to classify them and evaluate their performance. Our study builds on previous work by examining how different machine learning models, parameters, and data preprocessing techniques impact classification accuracy. We test convolutional and recurrent neural networks, as well as six classical algorithms: random forest, support vector machines, naïve Bayes, logistic regression, $k$-nearest neighbors, and eXtreme gradient boosting. All models, except naïve Bayes, achieve over 90 per cent accuracy on our dataset. Additionally, we assess the impact of approximating the GW signal using the general relativistic effective potential (GREP) on EOS classification. We find that models trained on GREP data exhibit low classification accuracy. However, normalizing time by the peak signal frequency, which partially compensates for the absence of the time dilation effect in GREP, leads to a notable improvement in accuracy. Despite this, the accuracy does not exceed 70 per cent, suggesting that GREP lacks the precision necessary for EOS classification. Finally, our study has several limitations, including the omission of detector noise and the focus on a single progenitor mass model, which will be addressed in future works.", "published": "2024-09-22T16:11:25Z", "authors": ["Y. Sultan Abylkairov", "Matthew C. Edwards", "Daniil Orel", "Ayan Mitra", "Bekdaulet Shukirgaliyev", "Ernazar Abdikamalov"], "categories": ["astro-ph.HE", "gr-qc"], "doi": "10.1088/2632-2153/ada33a", "_query": "house price prediction machine learning", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/1905.07435v1", "title": "Alpha MAML: Adaptive Model-Agnostic Meta-Learning", "abstract": "Model-agnostic meta-learning (MAML) is a meta-learning technique to train a model on a multitude of learning tasks in a way that primes the model for few-shot learning of new tasks. The MAML algorithm performs well on few-shot learning problems in classification, regression, and fine-tuning of policy gradients in reinforcement learning, but comes with the need for costly hyperparameter tuning for training stability. We address this shortcoming by introducing an extension to MAML, called Alpha MAML, to incorporate an online hyperparameter adaptation scheme that eliminates the need to tune meta-learning and learning rates. Our results with the Omniglot database demonstrate a substantial reduction in the need to tune MAML training hyperparameters and improvement to training stability with less sensitivity to hyperparameter choice.", "published": "2019-05-17T18:45:25Z", "authors": ["Harkirat Singh Behl", "Atılım Güneş Baydin", "Philip H. S. Torr"], "categories": ["cs.LG", "cs.AI", "stat.ML"], "doi": "", "_query": "house price prediction machine learning", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2105.06060v1", "title": "House Price Prediction using Satellite Imagery", "abstract": "In this paper we show how using satellite images can improve the accuracy of housing price estimation models. Using Los Angeles County's property assessment dataset, by transferring learning from an Inception-v3 model pretrained on ImageNet, we could achieve an improvement of ~10% in R-squared score compared to two baseline models that only use non-image features of the house.", "published": "2021-05-13T03:25:32Z", "authors": ["Sina Jandaghi Semnani", "Hoormazd Rezaei"], "categories": ["cs.LG", "stat.ML"], "doi": "", "_query": "house price prediction machine learning", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2112.09429v2", "title": "Federated Learning with Superquantile Aggregation for Heterogeneous Data", "abstract": "We present a federated learning framework that is designed to robustly deliver good predictive performance across individual clients with heterogeneous data. The proposed approach hinges upon a superquantile-based learning objective that captures the tail statistics of the error distribution over heterogeneous clients. We present a stochastic training algorithm that interleaves differentially private client filtering with federated averaging steps. We prove finite time convergence guarantees for the algorithm: $O(1/\\sqrt{T})$ in the nonconvex case in $T$ communication rounds and $O(\\exp(-T/κ^{3/2}) + κ/T)$ in the strongly convex case with local condition number $κ$. Experimental results on benchmark datasets for federated learning demonstrate that our approach is competitive with classical ones in terms of average error and outperforms them in terms of tail statistics of the error.", "published": "2021-12-17T11:00:23Z", "authors": ["Krishna Pillutla", "Yassine Laguel", "Jérôme Malick", "Zaid Harchaoui"], "categories": ["cs.LG", "math.OC", "stat.ML"], "doi": "10.1007/s10994-023-06332-x", "_query": "house price prediction machine learning", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2402.00054v1", "title": "Predicting loss-of-function impact of genetic mutations: a machine learning approach", "abstract": "The innovation of next-generation sequencing (NGS) techniques has significantly reduced the price of genome sequencing, lowering barriers to future medical research; it is now feasible to apply genome sequencing to studies where it would have previously been cost-inefficient. Identifying damaging or pathogenic mutations in vast amounts of complex, high-dimensional genome sequencing data may be of particular interest to researchers. Thus, this paper's aims were to train machine learning models on the attributes of a genetic mutation to predict LoFtool scores (which measure a gene's intolerance to loss-of-function mutations). These attributes included, but were not limited to, the position of a mutation on a chromosome, changes in amino acids, and changes in codons caused by the mutation. Models were built using the univariate feature selection technique f-regression combined with K-nearest neighbors (KNN), Support Vector Machine (SVM), Random Sample Consensus (RANSAC), Decision Trees, Random Forest, and Extreme Gradient Boosting (XGBoost). These models were evaluated using five-fold cross-validated averages of r-squared, mean squared error, root mean squared error, mean absolute error, and explained variance. The findings of this study include the training of multiple models with testing set r-squared values of 0.97.", "published": "2024-01-26T19:27:38Z", "authors": ["Arshmeet Kaur", "Morteza Sarmadi"], "categories": ["q-bio.GN", "cs.LG", "stat.AP"], "doi": "10.54364/AAIML.2024.41119", "_query": "house price prediction machine learning", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2007.15745v3", "title": "On Hyperparameter Optimization of Machine Learning Algorithms: Theory and Practice", "abstract": "Machine learning algorithms have been used widely in various applications and areas. To fit a machine learning model into different problems, its hyper-parameters must be tuned. Selecting the best hyper-parameter configuration for machine learning models has a direct impact on the model's performance. It often requires deep knowledge of machine learning algorithms and appropriate hyper-parameter optimization techniques. Although several automatic optimization techniques exist, they have different strengths and drawbacks when applied to different types of problems. In this paper, optimizing the hyper-parameters of common machine learning models is studied. We introduce several state-of-the-art optimization techniques and discuss how to apply them to machine learning algorithms. Many available libraries and frameworks developed for hyper-parameter optimization problems are provided, and some open challenges of hyper-parameter optimization research are also discussed in this paper. Moreover, experiments are conducted on benchmark datasets to compare the performance of different optimization methods and provide practical examples of hyper-parameter optimization. This survey paper will help industrial users, data analysts, and researchers to better develop machine learning models by identifying the proper hyper-parameter configurations effectively.", "published": "2020-07-30T21:11:01Z", "authors": ["Li Yang", "Abdallah Shami"], "categories": ["cs.LG", "stat.ML"], "doi": "10.1016/j.neucom.2020.07.061", "_query": "house price prediction machine learning", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2311.07511v3", "title": "Uncertainty estimation of machine learning spatial precipitation predictions from satellite data", "abstract": "Merging satellite and gauge data with machine learning produces high-resolution precipitation datasets, but uncertainty estimates are often missing. We addressed the gap of how to optimally provide such estimates by benchmarking six algorithms, mostly novel even for the more general task of quantifying predictive uncertainty in spatial prediction settings. On 15 years of monthly data from over the contiguous United States (CONUS), we compared quantile regression (QR), quantile regression forests (QRF), generalized random forests (GRF), gradient boosting machines (GBM), light gradient boosting machine (LightGBM), and quantile regression neural networks (QRNN). Their ability to issue predictive precipitation quantiles at nine quantile levels (0.025, 0.050, 0.100, 0.250, 0.500, 0.750, 0.900, 0.950, 0.975), approximating the full probability distribution, was evaluated using quantile scoring functions and the quantile scoring rule. Predictors at a site were nearby values from two satellite precipitation retrievals, namely PERSIANN (Precipitation Estimation from Remotely Sensed Information using Artificial Neural Networks) and IMERG (Integrated Multi-satellitE Retrievals), and the site's elevation. The dependent variable was the monthly mean gauge precipitation. With respect to QR, LightGBM showed improved performance in terms of the quantile scoring rule by 11.10%, also surpassing QRF (7.96%), GRF (7.44%), GBM (4.64%) and QRNN (1.73%). Notably, LightGBM outperformed all random forest variants, the current standard in spatial prediction with machine learning. To conclude, we propose a suite of machine learning algorithms for estimating uncertainty in spatial data prediction, supported with a formal evaluation framework based on scoring functions and scoring rules.", "published": "2023-11-13T17:55:28Z", "authors": ["Georgia Papacharalampous", "Hristos Tyralis", "Nikolaos Doulamis", "Anastasios Doulamis"], "categories": ["stat.ML", "cs.LG", "physics.ao-ph", "stat.AP", "stat.ME"], "doi": "10.1088/2632-2153/ad63f3", "_query": "house price prediction machine learning", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/1905.05461v2", "title": "Learning Generative Models across Incomparable Spaces", "abstract": "Generative Adversarial Networks have shown remarkable success in learning a distribution that faithfully recovers a reference distribution in its entirety. However, in some cases, we may want to only learn some aspects (e.g., cluster or manifold structure), while modifying others (e.g., style, orientation or dimension). In this work, we propose an approach to learn generative models across such incomparable spaces, and demonstrate how to steer the learned distribution towards target properties. A key component of our model is the Gromov-Wasserstein distance, a notion of discrepancy that compares distributions relationally rather than absolutely. While this framework subsumes current generative models in identically reproducing distributions, its inherent flexibility allows application to tasks in manifold learning, relational learning and cross-domain learning.", "published": "2019-05-14T08:56:12Z", "authors": ["Charlotte Bunne", "David Alvarez-Melis", "Andreas Krause", "Stefanie Jegelka"], "categories": ["cs.LG", "stat.ML"], "doi": "", "_query": "house price prediction machine learning", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2408.09840v2", "title": "Machine Learning with Physics Knowledge for Prediction: A Survey", "abstract": "This survey examines the broad suite of methods and models for combining machine learning with physics knowledge for prediction and forecast, with a focus on partial differential equations. These methods have attracted significant interest due to their potential impact on advancing scientific research and industrial practices by improving predictive models with small- or large-scale datasets and expressive predictive models with useful inductive biases. The survey has two parts. The first considers incorporating physics knowledge on an architectural level through objective functions, structured predictive models, and data augmentation. The second considers data as physics knowledge, which motivates looking at multi-task, meta, and contextual learning as an alternative approach to incorporating physics knowledge in a data-driven fashion. Finally, we also provide an industrial perspective on the application of these methods and a survey of the open-source ecosystem for physics-informed machine learning.", "published": "2024-08-19T09:36:07Z", "authors": ["Joe Watson", "Chen Song", "Oliver Weeger", "Theo Gruner", "An T. Le", "Kay Pompetzki", "Ahmed Hendawy", "Oleg Arenz", "Will Trojak", "Miles Cranmer", "Carlo D'Eramo", "Fabian Bülow", "Tanmay Goyal", "Jan Peters", "Martin W. Hoffman"], "categories": ["cs.LG", "math.NA", "physics.comp-ph"], "doi": "", "_query": "house price prediction machine learning", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2003.03384v2", "title": "AutoML-Zero: Evolving Machine Learning Algorithms From Scratch", "abstract": "Machine learning research has advanced in multiple aspects, including model structures and learning methods. The effort to automate such research, known as AutoML, has also made significant progress. However, this progress has largely focused on the architecture of neural networks, where it has relied on sophisticated expert-designed layers as building blocks---or similarly restrictive search spaces. Our goal is to show that AutoML can go further: it is possible today to automatically discover complete machine learning algorithms just using basic mathematical operations as building blocks. We demonstrate this by introducing a novel framework that significantly reduces human bias through a generic search space. Despite the vastness of this space, evolutionary search can still discover two-layer neural networks trained by backpropagation. These simple neural networks can then be surpassed by evolving directly on tasks of interest, e.g. CIFAR-10 variants, where modern techniques emerge in the top algorithms, such as bilinear interactions, normalized gradients, and weight averaging. Moreover, evolution adapts algorithms to different task types: e.g., dropout-like techniques appear when little data is available. We believe these preliminary successes in discovering machine learning algorithms from scratch indicate a promising new direction for the field.", "published": "2020-03-06T19:00:04Z", "authors": ["Esteban Real", "Chen Liang", "David R. So", "Quoc V. Le"], "categories": ["cs.LG", "cs.NE", "stat.ML"], "doi": "", "_query": "house price prediction machine learning", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2301.11257v1", "title": "A Benchmark Study by using various Machine Learning Models for Predicting Covid-19 trends", "abstract": "Machine learning and deep learning play vital roles in predicting diseases in the medical field. Machine learning algorithms are widely classified as supervised, unsupervised, and reinforcement learning. This paper contains a detailed description of our experimental research work in that we used a supervised machine-learning algorithm to build our model for outbreaks of the novel Coronavirus that has spread over the whole world and caused many deaths, which is one of the most disastrous Pandemics in the history of the world. The people suffered physically and economically to survive in this lockdown. This work aims to understand better how machine learning, ensemble, and deep learning models work and are implemented in the real dataset. In our work, we are going to analyze the current trend or pattern of the coronavirus and then predict the further future of the covid-19 confirmed cases or new cases by training the past Covid-19 dataset by using the machine learning algorithm such as Linear Regression, Polynomial Regression, K-nearest neighbor, Decision Tree, Support Vector Machine and Random forest algorithm are used to train the model. The decision tree and the Random Forest algorithm perform better than SVR in this work. The performance of SVR and lasso regression are low in all prediction areas Because the SVR is challenging to separate the data using the hyperplane for this type of problem. So SVR mostly gives a lower performance in this problem. Ensemble (Voting, Bagging, and Stacking) and deep learning models(ANN) also predict well. After the prediction, we evaluated the model using MAE, MSE, RMSE, and MAPE. This work aims to find the trend/pattern of the covid-19.", "published": "2023-01-26T17:49:05Z", "authors": ["D. Kamelesun", "R. Saranya", "P. Kathiravan"], "categories": ["cs.LG"], "doi": "", "_query": "house price prediction machine learning", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2204.12833v3", "title": "Transfer Learning with Pre-trained Conditional Generative Models", "abstract": "Transfer learning is crucial in training deep neural networks on new target tasks. Current transfer learning methods always assume at least one of (i) source and target task label spaces overlap, (ii) source datasets are available, and (iii) target network architectures are consistent with source ones. However, holding these assumptions is difficult in practical settings because the target task rarely has the same labels as the source task, the source dataset access is restricted due to storage costs and privacy, and the target architecture is often specialized to each task. To transfer source knowledge without these assumptions, we propose a transfer learning method that uses deep generative models and is composed of the following two stages: pseudo pre-training (PP) and pseudo semi-supervised learning (P-SSL). PP trains a target architecture with an artificial dataset synthesized by using conditional source generative models. P-SSL applies SSL algorithms to labeled target data and unlabeled pseudo samples, which are generated by cascading the source classifier and generative models to condition them with target samples. Our experimental results indicate that our method can outperform the baselines of scratch training and knowledge distillation.", "published": "2022-04-27T10:36:32Z", "authors": ["Shin'ya Yamaguchi", "Sekitoshi Kanai", "Atsutoshi Kumagai", "Daiki Chijiwa", "Hisashi Kashima"], "categories": ["cs.LG", "cs.AI", "stat.ML"], "doi": "10.1007/s10994-025-06748-7", "_query": "house price prediction machine learning", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2405.18095v2", "title": "Is machine learning good or bad for the natural sciences?", "abstract": "Machine learning (ML) methods are having a huge impact across all of the sciences. However, ML has a strong ontology - in which only the data exist - and a strong epistemology - in which a model is considered good if it performs well on held-out training data. These philosophies are in strong conflict with both standard practices and key philosophies in the natural sciences. Here we identify some locations for ML in the natural sciences at which the ontology and epistemology are valuable. For example, when an expressive machine learning model is used in a causal inference to represent the effects of confounders, such as foregrounds, backgrounds, or instrument calibration parameters, the model capacity and loose philosophy of ML can make the results more trustworthy. We also show that there are contexts in which the introduction of ML introduces strong, unwanted statistical biases. For one, when ML models are used to emulate physical (or first-principles) simulations, they amplify confirmation biases. For another, when expressive regressions are used to label datasets, those labels cannot be used in downstream joint or ensemble analyses without taking on uncontrolled biases. The question in the title is being asked of all of the natural sciences; that is, we are calling on the scientific communities to take a step back and consider the role and value of ML in their fields; the (partial) answers we give here come from the particular perspective of physics.", "published": "2024-05-28T12:01:52Z", "authors": ["David W. Hogg", "Soledad Villar"], "categories": ["stat.ML", "astro-ph.IM", "cs.LG", "physics.data-an"], "doi": "", "_query": "house price prediction machine learning", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2005.00478v3", "title": "DriveML: An R Package for Driverless Machine Learning", "abstract": "In recent years, the concept of automated machine learning has become very popular. Automated Machine Learning (AutoML) mainly refers to the automated methods for model selection and hyper-parameter optimization of various algorithms such as random forests, gradient boosting, neural networks, etc. In this paper, we introduce a new package i.e. DriveML for automated machine learning. DriveML helps in implementing some of the pillars of an automated machine learning pipeline such as automated data preparation, feature engineering, model building and model explanation by running the function instead of writing lengthy R codes. The DriveML package is available in CRAN. We compare the DriveML package with other relevant packages in CRAN/Github and find that DriveML performs the best across different parameters. We also provide an illustration by applying the DriveML package with default configuration on a real world dataset. Overall, the main benefits of DriveML are in development time savings, reduce developer's errors, optimal tuning of machine learning models and reproducibility.", "published": "2020-05-01T16:40:25Z", "authors": ["Sayan Putatunda", "Dayananda Ubrangala", "Kiran Rama", "Ravi Kondapalli"], "categories": ["cs.LG", "stat.ML"], "doi": "", "_query": "house price prediction machine learning", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2505.16287v1", "title": "Machine learning approach to stock price crash risk", "abstract": "In this study, we propose a novel machine-learning-based measure for stock price crash risk, utilizing the minimum covariance determinant methodology. Employing this newly introduced dependent variable, we predict stock price crash risk through cross-sectional regression analysis. The findings confirm that the proposed method effectively captures stock price crash risk, with the model demonstrating strong performance in terms of both statistical significance and economic relevance. Furthermore, leveraging a newly developed firm-specific investor sentiment index, the analysis identifies a positive correlation between stock price crash risk and firm-specific investor sentiment. Specifically, higher levels of sentiment are associated with an increased likelihood of stock price crash risk. This relationship remains robust across different firm sizes and when using the detoned version of the firm-specific investor sentiment index, further validating the reliability of the proposed approach.", "published": "2025-05-22T06:33:35Z", "authors": ["Abdullah Karasan", "Ozge Sezgin Alp", "Gerhard-Wilhelm Weber"], "categories": ["q-fin.CP"], "doi": "10.1007/s10479-025-06596-7", "_query": "house price prediction machine learning", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2304.05366v3", "title": "The No Free Lunch Theorem, Kolmogorov Complexity, and the Role of Inductive Biases in Machine Learning", "abstract": "No free lunch theorems for supervised learning state that no learner can solve all problems or that all learners achieve exactly the same accuracy on average over a uniform distribution on learning problems. Accordingly, these theorems are often referenced in support of the notion that individual problems require specially tailored inductive biases. While virtually all uniformly sampled datasets have high complexity, real-world problems disproportionately generate low-complexity data, and we argue that neural network models share this same preference, formalized using Kolmogorov complexity. Notably, we show that architectures designed for a particular domain, such as computer vision, can compress datasets on a variety of seemingly unrelated domains. Our experiments show that pre-trained and even randomly initialized language models prefer to generate low-complexity sequences. Whereas no free lunch theorems seemingly indicate that individual problems require specialized learners, we explain how tasks that often require human intervention such as picking an appropriately sized model when labeled data is scarce or plentiful can be automated into a single learning algorithm. These observations justify the trend in deep learning of unifying seemingly disparate problems with an increasingly small set of machine learning models.", "published": "2023-04-11T17:22:22Z", "authors": ["Micah Goldblum", "Marc Finzi", "Keefer Rowan", "Andrew Gordon Wilson"], "categories": ["cs.LG", "stat.ML"], "doi": "", "_query": "house price prediction machine learning", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2108.13387v1", "title": "Ovarian Cancer Prediction from Ovarian Cysts Based on TVUS Using Machine Learning Algorithms", "abstract": "Ovarian Cancer (OC) is type of female reproductive malignancy which can be found among young girls and mostly the women in their fertile or reproductive. There are few number of cysts are dangerous and may it cause cancer. So, it is very important to predict and it can be from different types of screening are used for this detection using Transvaginal Ultrasonography (TVUS) screening. In this research, we employed an actual datasets called PLCO with TVUS screening and three machine learning (ML) techniques, respectively Random Forest KNN, and XGBoost within three target variables. We obtained a best performance from this algorithms as far as accuracy, recall, f1 score and precision with the approximations of 99.50%, 99.50%, 99.49% and 99.50% individually. The AUC score of 99.87%, 98.97% and 99.88% are observed in these Random Forest, KNN and XGB algorithms .This approach helps assist physicians and suspects in identifying ovarian risks early on, reducing ovarian malignancy-related complications and deaths.", "published": "2021-08-30T17:16:00Z", "authors": ["Laboni Akter", "Nasrin Akhter"], "categories": ["cs.LG", "eess.IV", "q-bio.BM", "stat.ML"], "doi": "", "_query": "house price prediction machine learning", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/1812.10426v3", "title": "Stochastic Trust Region Inexact Newton Method for Large-scale Machine Learning", "abstract": "Nowadays stochastic approximation methods are one of the major research direction to deal with the large-scale machine learning problems. From stochastic first order methods, now the focus is shifting to stochastic second order methods due to their faster convergence and availability of computing resources. In this paper, we have proposed a novel Stochastic Trust RegiOn Inexact Newton method, called as STRON, to solve large-scale learning problems which uses conjugate gradient (CG) to inexactly solve trust region subproblem. The method uses progressive subsampling in the calculation of gradient and Hessian values to take the advantage of both, stochastic and full-batch regimes. We have extended STRON using existing variance reduction techniques to deal with the noisy gradients and using preconditioned conjugate gradient (PCG) as subproblem solver, and empirically proved that they do not work as expected, for the large-scale learning problems. Finally, our empirical results prove efficacy of the proposed method against existing methods with bench marked datasets.", "published": "2018-12-26T17:33:43Z", "authors": ["Vinod Kumar Chauhan", "Anuj Sharma", "Kalpana Dahiya"], "categories": ["cs.LG", "stat.ML"], "doi": "10.1007/s13042-019-01055-9", "_query": "house price prediction machine learning", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2210.00053v2", "title": "Kernel Normalized Convolutional Networks for Privacy-Preserving Machine Learning", "abstract": "Normalization is an important but understudied challenge in privacy-related application domains such as federated learning (FL), differential privacy (DP), and differentially private federated learning (DP-FL). While the unsuitability of batch normalization for these domains has already been shown, the impact of other normalization methods on the performance of federated or differentially private models is not well-known. To address this, we draw a performance comparison among layer normalization (LayerNorm), group normalization (GroupNorm), and the recently proposed kernel normalization (KernelNorm) in FL, DP, and DP-FL settings. Our results indicate LayerNorm and GroupNorm provide no performance gain compared to the baseline (i.e. no normalization) for shallow models in FL and DP. They, on the other hand, considerably enhance the performance of shallow models in DP-FL and deeper models in FL and DP. KernelNorm, moreover, significantly outperforms its competitors in terms of accuracy and convergence rate (or communication efficiency) for both shallow and deeper models in all considered learning environments. Given these key observations, we propose a kernel normalized ResNet architecture called KNResNet-13 for differentially private learning. Using the proposed architecture, we provide new state-of-the-art accuracy values on the CIFAR-10 and Imagenette datasets, when trained from scratch.", "published": "2022-09-30T19:33:53Z", "authors": ["Reza Nasirigerdeh", "Javad Torkzadehmahani", "Daniel Rueckert", "Georgios Kaissis"], "categories": ["cs.LG", "cs.CR"], "doi": "", "_query": "house price prediction machine learning", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2009.00254v1", "title": "Boosting House Price Predictions using Geo-Spatial Network Embedding", "abstract": "Real estate contributes significantly to all major economies around the world. In particular, house prices have a direct impact on stakeholders, ranging from house buyers to financing companies. Thus, a plethora of techniques have been developed for real estate price prediction. Most of the existing techniques rely on different house features to build a variety of prediction models to predict house prices. Perceiving the effect of spatial dependence on house prices, some later works focused on introducing spatial regression models for improving prediction performance. However, they fail to take into account the geo-spatial context of the neighborhood amenities such as how close a house is to a train station, or a highly-ranked school, or a shopping center. Such contextual information may play a vital role in users' interests in a house and thereby has a direct influence on its price. In this paper, we propose to leverage the concept of graph neural networks to capture the geo-spatial context of the neighborhood of a house. In particular, we present a novel method, the Geo-Spatial Network Embedding (GSNE), that learns the embeddings of houses and various types of Points of Interest (POIs) in the form of multipartite networks, where the houses and the POIs are represented as attributed nodes and the relationships between them as edges. Extensive experiments with a large number of regression techniques show that the embeddings produced by our proposed GSNE technique consistently and significantly improve the performance of the house price prediction task regardless of the downstream regression model.", "published": "2020-09-01T06:17:21Z", "authors": ["Sarkar Snigdha Sarathi Das", "Mohammed Eunus Ali", "Yuan-Fang Li", "Yong-Bin Kang", "Timos Sellis"], "categories": ["cs.LG", "stat.ML"], "doi": "", "_query": "house price prediction machine learning", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/1806.09710v1", "title": "Why Interpretability in Machine Learning? An Answer Using Distributed Detection and Data Fusion Theory", "abstract": "As artificial intelligence is increasingly affecting all parts of society and life, there is growing recognition that human interpretability of machine learning models is important. It is often argued that accuracy or other similar generalization performance metrics must be sacrificed in order to gain interpretability. Such arguments, however, fail to acknowledge that the overall decision-making system is composed of two entities: the learned model and a human who fuses together model outputs with his or her own information. As such, the relevant performance criteria should be for the entire system, not just for the machine learning component. In this work, we characterize the performance of such two-node tandem data fusion systems using the theory of distributed detection. In doing so, we work in the population setting and model interpretable learned models as multi-level quantizers. We prove that under our abstraction, the overall system of a human with an interpretable classifier outperforms one with a black box classifier.", "published": "2018-06-25T21:37:21Z", "authors": ["Kush R. Varshney", "Prashant Khanduri", "Pranay Sharma", "Shan Zhang", "Pramod K. Varshney"], "categories": ["stat.ML", "cs.IT", "cs.LG"], "doi": "", "_query": "house price prediction machine learning", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2001.09734v1", "title": "One Explanation Does Not Fit All: The Promise of Interactive Explanations for Machine Learning Transparency", "abstract": "The need for transparency of predictive systems based on Machine Learning algorithms arises as a consequence of their ever-increasing proliferation in the industry. Whenever black-box algorithmic predictions influence human affairs, the inner workings of these algorithms should be scrutinised and their decisions explained to the relevant stakeholders, including the system engineers, the system's operators and the individuals whose case is being decided. While a variety of interpretability and explainability methods is available, none of them is a panacea that can satisfy all diverse expectations and competing objectives that might be required by the parties involved. We address this challenge in this paper by discussing the promises of Interactive Machine Learning for improved transparency of black-box systems using the example of contrastive explanations -- a state-of-the-art approach to Interpretable Machine Learning. Specifically, we show how to personalise counterfactual explanations by interactively adjusting their conditional statements and extract additional explanations by asking follow-up \"What if?\" questions. Our experience in building, deploying and presenting this type of system allowed us to list desired properties as well as potential limitations, which can be used to guide the development of interactive explainers. While customising the medium of interaction, i.e., the user interface comprising of various communication channels, may give an impression of personalisation, we argue that adjusting the explanation itself and its content is more important. To this end, properties such as breadth, scope, context, purpose and target of the explanation have to be considered, in addition to explicitly informing the explainee about its limitations and caveats...", "published": "2020-01-27T13:10:12Z", "authors": ["Kacper Sokol", "Peter Flach"], "categories": ["cs.LG", "cs.AI", "stat.ML"], "doi": "10.1007/s13218-020-00637-y", "_query": "house price prediction machine learning", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2204.09050v1", "title": "House Price Prediction Based On Deep Learning", "abstract": "Since ancient times, what Chinese people have been pursuing is very simple, which is nothing more than \"to live and work happily, to eat and dress comfortable\". Today, more than 40 years after the reform and opening, people have basically solved the problem of food and clothing, and the urgent problem is housing. Nowadays, due to the storm of long-term rental apartment intermediary platforms such as eggshell, increasing the sense of insecurity of renters, as well as the urbanization in recent years and the scramble for people in major cities, this will make the future real estate market competition more intense. In order to better grasp the real estate price, let consumers buy a house reasonably, and provide a reference for the government to formulate policies, this paper summarizes the existing methods of house price prediction and proposes a house price prediction method based on mixed depth vision and text features.", "published": "2022-04-19T20:12:16Z", "authors": ["Yuying Wu", "Youshan Zhang"], "categories": ["cs.LG"], "doi": "", "_query": "house price prediction machine learning", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/1207.4676v2", "title": "Proceedings of the 29th International Conference on Machine Learning (ICML-12)", "abstract": "This is an index to the papers that appear in the Proceedings of the 29th International Conference on Machine Learning (ICML-12). The conference was held in Edinburgh, Scotland, June 27th - July 3rd, 2012.", "published": "2012-07-19T14:08:22Z", "authors": ["John Langford", "Joelle Pineau"], "categories": ["cs.LG", "stat.ML"], "doi": "", "_query": "house price prediction machine learning", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2510.07569v2", "title": "Automated Machine Learning for Unsupervised Tabular Tasks", "abstract": "In this work, we present LOTUS (Learning to Learn with Optimal Transport for Unsupervised Scenarios), a simple yet effective method to perform model selection for multiple unsupervised machine learning(ML) tasks such as outlier detection and clustering. Our intuition behind this work is that a machine learning pipeline will perform well in a new dataset if it previously worked well on datasets with a similar underlying data distribution. We use Optimal Transport distances to find this similarity between unlabeled tabular datasets and recommend machine learning pipelines with one unified single method on two downstream unsupervised tasks: outlier detection and clustering. We present the effectiveness of our approach with experiments against strong baselines and show that LOTUS is a very promising first step toward model selection for multiple unsupervised ML tasks.", "published": "2025-10-08T21:31:22Z", "authors": ["Prabhant Singh", "Pieter Gijsbers", "Elif Ceren Gok Yildirim", "Murat Onur Yildirim", "Joaquin Vanschoren"], "categories": ["cs.LG"], "doi": "", "_query": "house price prediction machine learning", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2002.04881v3", "title": "Learning Flat Latent Manifolds with VAEs", "abstract": "Measuring the similarity between data points often requires domain knowledge, which can in parts be compensated by relying on unsupervised methods such as latent-variable models, where similarity/distance is estimated in a more compact latent space. Prevalent is the use of the Euclidean metric, which has the drawback of ignoring information about similarity of data stored in the decoder, as captured by the framework of Riemannian geometry. We propose an extension to the framework of variational auto-encoders allows learning flat latent manifolds, where the Euclidean metric is a proxy for the similarity between data points. This is achieved by defining the latent space as a Riemannian manifold and by regularising the metric tensor to be a scaled identity matrix. Additionally, we replace the compact prior typically used in variational auto-encoders with a recently presented, more expressive hierarchical one---and formulate the learning problem as a constrained optimisation problem. We evaluate our method on a range of data-sets, including a video-tracking benchmark, where the performance of our unsupervised approach nears that of state-of-the-art supervised approaches, while retaining the computational efficiency of straight-line-based approaches.", "published": "2020-02-12T09:54:52Z", "authors": ["Nutan Chen", "Alexej Klushyn", "Francesco Ferroni", "Justin Bayer", "Patrick van der Smagt"], "categories": ["stat.ML", "cs.LG"], "doi": "", "_query": "house price prediction machine learning", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2312.05933v1", "title": "Temporal Supervised Contrastive Learning for Modeling Patient Risk Progression", "abstract": "We consider the problem of predicting how the likelihood of an outcome of interest for a patient changes over time as we observe more of the patient data. To solve this problem, we propose a supervised contrastive learning framework that learns an embedding representation for each time step of a patient time series. Our framework learns the embedding space to have the following properties: (1) nearby points in the embedding space have similar predicted class probabilities, (2) adjacent time steps of the same time series map to nearby points in the embedding space, and (3) time steps with very different raw feature vectors map to far apart regions of the embedding space. To achieve property (3), we employ a nearest neighbor pairing mechanism in the raw feature space. This mechanism also serves as an alternative to data augmentation, a key ingredient of contrastive learning, which lacks a standard procedure that is adequately realistic for clinical tabular data, to our knowledge. We demonstrate that our approach outperforms state-of-the-art baselines in predicting mortality of septic patients (MIMIC-III dataset) and tracking progression of cognitive impairment (ADNI dataset). Our method also consistently recovers the correct synthetic dataset embedding structure across experiments, a feat not achieved by baselines. Our ablation experiments show the pivotal role of our nearest neighbor pairing.", "published": "2023-12-10T16:43:15Z", "authors": ["Shahriar Noroozizadeh", "Jeremy C. Weiss", "George H. Chen"], "categories": ["cs.LG", "cs.AI", "stat.ML"], "doi": "", "_query": "house price prediction machine learning", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/1912.03905v2", "title": "ChainerRL: A Deep Reinforcement Learning Library", "abstract": "In this paper, we introduce ChainerRL, an open-source deep reinforcement learning (DRL) library built using Python and the Chainer deep learning framework. ChainerRL implements a comprehensive set of DRL algorithms and techniques drawn from state-of-the-art research in the field. To foster reproducible research, and for instructional purposes, ChainerRL provides scripts that closely replicate the original papers' experimental settings and reproduce published benchmark results for several algorithms. Lastly, ChainerRL offers a visualization tool that enables the qualitative inspection of trained agents. The ChainerRL source code can be found on GitHub: https://github.com/chainer/chainerrl.", "published": "2019-12-09T08:59:15Z", "authors": ["Yasuhiro Fujita", "Prabhat Nagarajan", "Toshiki Kataoka", "Takahiro Ishikawa"], "categories": ["cs.LG", "cs.AI", "stat.ML"], "doi": "", "_query": "house price prediction machine learning", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/1908.09038v1", "title": "Identification of Pediatric Sepsis Subphenotypes for Enhanced Machine Learning Predictive Performance: A Latent Profile Analysis", "abstract": "Background: While machine learning (ML) models are rapidly emerging as promising screening tools in critical care medicine, the identification of homogeneous subphenotypes within populations with heterogeneous conditions such as pediatric sepsis may facilitate attainment of high-predictive performance of these prognostic algorithms. This study is aimed to identify subphenotypes of pediatric sepsis and demonstrate the potential value of partitioned data/subtyping-based training. Methods: This was a retrospective study of clinical data extracted from medical records of 6,446 pediatric patients that were admitted at a major hospital system in the DC area. Vitals and labs associated with patients meeting the diagnostic criteria for sepsis were used to perform latent profile analysis. Modern ML algorithms were used to explore the predictive performance benefits of reduced training data heterogeneity via label profiling. Results: In total 134 (2.1%) patients met the diagnostic criteria for sepsis in this cohort and latent profile analysis identified four profiles/subphenotypes of pediatric sepsis. Profiles 1 and 3 had the lowest mortality and included pediatric patients from different age groups. Profile 2 were characterized by respiratory dysfunction; profile 4 by neurological dysfunction and highest mortality rate (22.2%). Machine learning experiments comparing the predictive performance of models derived without training data profiling against profile targeted models suggest statistically significant improved performance of prediction can be obtained. For example, area under ROC curve (AUC) obtained to predict profile 4 with 24-hour data (AUC = .998, p < .0001) compared favorably with the AUC obtained from the model considering all profiles as a single homogeneous group (AUC = .918) with 24-hour data.", "published": "2019-08-23T20:59:42Z", "authors": ["Tom Velez", "Tony Wang", "Ioannis Koutroulis", "James Chamberlain", "Amit Uppal", "Seife Yohannes", "Tim Tschampel", "Emilia Apostolova"], "categories": ["stat.ML", "cs.LG", "q-bio.QM", "stat.AP"], "doi": "", "_query": "house price prediction machine learning", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2109.04318v1", "title": "Estimation of Corporate Greenhouse Gas Emissions via Machine Learning", "abstract": "As an important step to fulfill the Paris Agreement and achieve net-zero emissions by 2050, the European Commission adopted the most ambitious package of climate impact measures in April 2021 to improve the flow of capital towards sustainable activities. For these and other international measures to be successful, reliable data is key. The ability to see the carbon footprint of companies around the world will be critical for investors to comply with the measures. However, with only a small portion of companies volunteering to disclose their greenhouse gas (GHG) emissions, it is nearly impossible for investors to align their investment strategies with the measures. By training a machine learning model on disclosed GHG emissions, we are able to estimate the emissions of other companies globally who do not disclose their emissions. In this paper, we show that our model provides accurate estimates of corporate GHG emissions to investors such that they are able to align their investments with the regulatory measures and achieve net-zero goals.", "published": "2021-09-09T14:50:26Z", "authors": ["You Han", "Achintya Gopal", "Liwen Ouyang", "Aaron Key"], "categories": ["cs.LG", "stat.ML"], "doi": "", "_query": "house price prediction machine learning", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/1811.11400v2", "title": "FADL:Federated-Autonomous Deep Learning for Distributed Electronic Health Record", "abstract": "Electronic health record (EHR) data is collected by individual institutions and often stored across locations in silos. Getting access to these data is difficult and slow due to security, privacy, regulatory, and operational issues. We show, using ICU data from 58 different hospitals, that machine learning models to predict patient mortality can be trained efficiently without moving health data out of their silos using a distributed machine learning strategy. We propose a new method, called Federated-Autonomous Deep Learning (FADL) that trains part of the model using all data sources in a distributed manner and other parts using data from specific data sources. We observed that FADL outperforms traditional federated learning strategy and conclude that balance between global and local training is an important factor to consider when design distributed machine learning methods , especially in healthcare.", "published": "2018-11-28T06:06:38Z", "authors": ["Dianbo Liu", "Timothy Miller", "Raheel Sayeed", "Kenneth D. Mandl"], "categories": ["cs.CY", "cs.LG"], "doi": "", "_query": "house price prediction machine learning", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2509.02391v3", "title": "Gaming and Cooperation in Federated Learning: What Can Happen and How to Monitor It", "abstract": "The success of federated learning (FL) ultimately depends on how strategic participants behave under partial observability, yet most formulations still treat FL as a static optimization problem. We instead view FL deployments as governed strategic systems and develop an analytical framework that separates welfare-improving behavior from metric gaming. Within this framework, we introduce indices that quantify manipulability, the price of gaming, and the price of cooperation, and we use them to study how rules, information disclosure, evaluation metrics, and aggregator-switching policies reshape incentives and cooperation patterns. We derive threshold conditions for deterring harmful gaming while preserving benign cooperation, and for triggering auto-switch rules when early-warning indicators become critical. Building on these results, we construct a design toolkit including a governance checklist and a simple audit-budget allocation algorithm with a provable performance guarantee. Simulations across diverse stylized environments and a federated learning case study consistently match the qualitative and quantitative patterns predicted by our framework. Taken together, our results provide design principles and operational guidelines for reducing metric gaming while sustaining stable, high-welfare cooperation in FL platforms.", "published": "2025-09-02T14:55:01Z", "authors": ["Dongseok Kim", "Hyoungsun Choi", "Mohamed Jismy Aashik Rasool", "Gisung Oh"], "categories": ["cs.LG", "cs.GT", "stat.ML"], "doi": "", "_query": "house price prediction machine learning", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/1510.08231v3", "title": "Operator-valued Kernels for Learning from Functional Response Data", "abstract": "In this paper we consider the problems of supervised classification and regression in the case where attributes and labels are functions: a data is represented by a set of functions, and the label is also a function. We focus on the use of reproducing kernel Hilbert space theory to learn from such functional data. Basic concepts and properties of kernel-based learning are extended to include the estimation of function-valued functions. In this setting, the representer theorem is restated, a set of rigorously defined infinite-dimensional operator-valued kernels that can be valuably applied when the data are functions is described, and a learning algorithm for nonlinear functional data analysis is introduced. The methodology is illustrated through speech and audio signal processing experiments.", "published": "2015-10-28T09:18:50Z", "authors": ["Hachem Kadri", "Emmanuel Duflos", "Philippe Preux", "Stéphane Canu", "Alain Rakotomamonjy", "Julien Audiffren"], "categories": ["cs.LG", "stat.ML"], "doi": "", "_query": "house price prediction machine learning", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/1909.12475v2", "title": "Hidden Stratification Causes Clinically Meaningful Failures in Machine Learning for Medical Imaging", "abstract": "Machine learning models for medical image analysis often suffer from poor performance on important subsets of a population that are not identified during training or testing. For example, overall performance of a cancer detection model may be high, but the model still consistently misses a rare but aggressive cancer subtype. We refer to this problem as hidden stratification, and observe that it results from incompletely describing the meaningful variation in a dataset. While hidden stratification can substantially reduce the clinical efficacy of machine learning models, its effects remain difficult to measure. In this work, we assess the utility of several possible techniques for measuring and describing hidden stratification effects, and characterize these effects on multiple medical imaging datasets. We find evidence that hidden stratification can occur in unidentified imaging subsets with low prevalence, low label quality, subtle distinguishing features, or spurious correlates, and that it can result in relative performance differences of over 20% on clinically important subsets. Finally, we explore the clinical implications of our findings, and suggest that evaluation of hidden stratification should be a critical component of any machine learning deployment in medical imaging.", "published": "2019-09-27T02:42:58Z", "authors": ["Luke Oakden-Rayner", "Jared Dunnmon", "Gustavo Carneiro", "Christopher Ré"], "categories": ["cs.LG", "stat.ML"], "doi": "", "_query": "house price prediction machine learning", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/1911.12868v1", "title": "Machine Learning for a Low-cost Air Pollution Network", "abstract": "Data collection in economically constrained countries often necessitates using approximate and biased measurements due to the low-cost of the sensors used. This leads to potentially invalid predictions and poor policies or decision making. This is especially an issue if methods from resource-rich regions are applied without handling these additional constraints. In this paper we show, through the use of an air pollution network example, how using probabilistic machine learning can mitigate some of the technical constraints. Specifically we experiment with modelling the calibration for individual sensors as either distributions or Gaussian processes over time, and discuss the wider issues around the decision process.", "published": "2019-11-28T21:32:59Z", "authors": ["Michael T. Smith", "Joel Ssematimba", "Mauricio A. Alvarez", "Engineer Bainomugisha"], "categories": ["cs.LG", "stat.AP", "stat.ML"], "doi": "", "_query": "house price prediction machine learning", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/1808.05054v1", "title": "Shedding Light on Black Box Machine Learning Algorithms: Development of an Axiomatic Framework to Assess the Quality of Methods that Explain Individual Predictions", "abstract": "From self-driving vehicles and back-flipping robots to virtual assistants who book our next appointment at the hair salon or at that restaurant for dinner - machine learning systems are becoming increasingly ubiquitous. The main reason for this is that these methods boast remarkable predictive capabilities. However, most of these models remain black boxes, meaning that it is very challenging for humans to follow and understand their intricate inner workings. Consequently, interpretability has suffered under this ever-increasing complexity of machine learning models. Especially with regards to new regulations, such as the General Data Protection Regulation (GDPR), the necessity for plausibility and verifiability of predictions made by these black boxes is indispensable. Driven by the needs of industry and practice, the research community has recognised this interpretability problem and focussed on developing a growing number of so-called explanation methods over the past few years. These methods explain individual predictions made by black box machine learning models and help to recover some of the lost interpretability. With the proliferation of these explanation methods, it is, however, often unclear, which explanation method offers a higher explanation quality, or is generally better-suited for the situation at hand. In this thesis, we thus propose an axiomatic framework, which allows comparing the quality of different explanation methods amongst each other. Through experimental validation, we find that the developed framework is useful to assess the explanation quality of different explanation methods and reach conclusions that are consistent with independent research.", "published": "2018-08-15T12:25:02Z", "authors": ["Milo Honegger"], "categories": ["cs.LG", "stat.ML"], "doi": "", "_query": "house price prediction machine learning", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/1805.05052v17", "title": "Machine Learning: The Basics", "abstract": "Machine learning (ML) has become a commodity in our every-day lives. We routinely ask ML empowered smartphones to suggest lovely food places or to guide us through a strange place. ML methods have also become standard tools in many fields of science and engineering. A plethora of ML applications transform human lives at unprecedented pace and scale. This book portrays ML as the combination of three basic components: data, model and loss. ML methods combine these three components within computationally efficient implementations of the basic scientific principle \"trial and error\". This principle consists of the continuous adaptation of a hypothesis about a phenomenon that generates data. ML methods use a hypothesis to compute predictions for future events. We believe that thinking about ML as combinations of three components given by data, model, and loss helps to navigate the steadily growing offer for ready-to-use ML methods. Our three-component picture of ML allows a unified treatment of a wide range of concepts and techniques which seem quite unrelated at first sight. The regularization effect of early stopping in iterative methods is due to the shrinking of the effective hypothesis space. Privacy-preserving ML is obtained by particular choices for the features of data points. Explainable ML methods are characterized by particular choices for the hypothesis space. To make good use of ML tools it is instrumental to understand its underlying principles at different levels of detail. On a lower level, this tutorial helps ML engineers to choose suitable methods for the application at hand. The book also offers a higher-level view on the implementation of ML methods which is typically required to manage a team of ML engineers and data scientists.", "published": "2018-05-14T08:08:33Z", "authors": ["Alexander Jung"], "categories": ["cs.LG", "stat.ML"], "doi": "", "_query": "house price prediction machine learning", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2007.08216v1", "title": "Graph topology inference benchmarks for machine learning", "abstract": "Graphs are nowadays ubiquitous in the fields of signal processing and machine learning. As a tool used to express relationships between objects, graphs can be deployed to various ends: I) clustering of vertices, II) semi-supervised classification of vertices, III) supervised classification of graph signals, and IV) denoising of graph signals. However, in many practical cases graphs are not explicitly available and must therefore be inferred from data. Validation is a challenging endeavor that naturally depends on the downstream task for which the graph is learnt. Accordingly, it has often been difficult to compare the efficacy of different algorithms. In this work, we introduce several ease-to-use and publicly released benchmarks specifically designed to reveal the relative merits and limitations of graph inference methods. We also contrast some of the most prominent techniques in the literature.", "published": "2020-07-16T09:40:32Z", "authors": ["Carlos Lassance", "Vincent Gripon", "Gonzalo Mateos"], "categories": ["cs.LG", "stat.ML"], "doi": "", "_query": "house price prediction machine learning", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2609.02984v1", "title": "From Euclidean to Graph-Structured Data: A Survey of Collaborative Learning", "abstract": "The conventional approach to machine learning, that is, collecting data, training models, and performing inference in a single location, faces fundamental limitations, including scalability and privacy, that restrict its applicability. To address these challenges, recent research has explored collaborative learning approaches, including federated learning and decentralized learning, where individual agents perform training and inference locally, with limited collaboration. Most collaborative learning research focuses on Euclidean data with regular, grid-like structure (e.g., images, text). However, these approaches fail to capture the relational patterns in many real-world applications, best represented by graphs. Learning on graphs relies on message-passing mechanisms to propagate information between connected nodes, making it conceptually well-suited for collaborative environments where agents must exchange information. Yet, the opportunities and challenges of learning on graph-structured data in collaborative settings remain largely underexplored. This survey provides a comprehensive investigation of collaborative learning from Euclidean to graph-structured data, aiming to consolidate this emerging field. We begin by reviewing its foundational principles for Euclidean data, organizing them along three core dimensions: learning effectiveness, efficiency, and privacy preservation. We then extend the discussion to graph-structured data, introducing a taxonomy of graph distribution scenarios, characterizing associated statistical heterogeneities, and developing standardized problem formulations and algorithmic frameworks. Finally, we systematically identify open challenges and promising research directions.", "published": "2026-09-02T13:49:46Z", "authors": ["Rémi Bourgerie", "Šarūnas Girdzijauskas", "Viktoria Fodor"], "categories": ["cs.LG", "cs.MA", "cs.SI"], "doi": "", "_query": "house price prediction machine learning", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2301.08028v4", "title": "A Tutorial on Meta-Reinforcement Learning", "abstract": "While deep reinforcement learning (RL) has fueled multiple high-profile successes in machine learning, it is held back from more widespread adoption by its often poor data efficiency and the limited generality of the policies it produces. A promising approach for alleviating these limitations is to cast the development of better RL algorithms as a machine learning problem itself in a process called meta-RL. Meta-RL is most commonly studied in a problem setting where, given a distribution of tasks, the goal is to learn a policy that is capable of adapting to any new task from the task distribution with as little data as possible. In this survey, we describe the meta-RL problem setting in detail as well as its major variations. We discuss how, at a high level, meta-RL research can be clustered based on the presence of a task distribution and the learning budget available for each individual task. Using these clusters, we then survey meta-RL algorithms and applications. We conclude by presenting the open problems on the path to making meta-RL part of the standard toolbox for a deep RL practitioner.", "published": "2023-01-19T12:01:41Z", "authors": ["Jacob Beck", "Risto Vuorio", "Evan Zheran Liu", "Zheng Xiong", "Luisa Zintgraf", "Chelsea Finn", "Shimon Whiteson"], "categories": ["cs.LG"], "doi": "10.1561/2200000080", "_query": "house price prediction machine learning", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2007.01977v1", "title": "Lale: Consistent Automated Machine Learning", "abstract": "Automated machine learning makes it easier for data scientists to develop pipelines by searching over possible choices for hyperparameters, algorithms, and even pipeline topologies. Unfortunately, the syntax for automated machine learning tools is inconsistent with manual machine learning, with each other, and with error checks. Furthermore, few tools support advanced features such as topology search or higher-order operators. This paper introduces Lale, a library of high-level Python interfaces that simplifies and unifies automated machine learning in a consistent way.", "published": "2020-07-04T00:55:41Z", "authors": ["Guillaume Baudart", "Martin Hirzel", "Kiran Kate", "Parikshit Ram", "Avraham Shinnar"], "categories": ["cs.LG", "cs.AI"], "doi": "", "_query": "house price prediction machine learning", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/1709.08432v1", "title": "House Price Prediction Using LSTM", "abstract": "In this paper, we use the house price data ranging from January 2004 to October 2016 to predict the average house price of November and December in 2016 for each district in Beijing, Shanghai, Guangzhou and Shenzhen. We apply Autoregressive Integrated Moving Average model to generate the baseline while LSTM networks to build prediction model. These algorithms are compared in terms of Mean Squared Error. The result shows that the LSTM model has excellent properties with respect to predict time series. Also, stateful LSTM networks and stack LSTM networks are employed to further study the improvement of accuracy of the house prediction model.", "published": "2017-09-25T11:31:50Z", "authors": ["Xiaochen Chen", "Lai Wei", "Jiaxin Xu"], "categories": ["cs.LG", "stat.ML"], "doi": "", "_query": "house price prediction machine learning", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2307.08386v1", "title": "Tabular Machine Learning Methods for Predicting Gas Turbine Emissions", "abstract": "Predicting emissions for gas turbines is critical for monitoring harmful pollutants being released into the atmosphere. In this study, we evaluate the performance of machine learning models for predicting emissions for gas turbines. We compare an existing predictive emissions model, a first principles-based Chemical Kinetics model, against two machine learning models we developed based on SAINT and XGBoost, to demonstrate improved predictive performance of nitrogen oxides (NOx) and carbon monoxide (CO) using machine learning techniques. Our analysis utilises a Siemens Energy gas turbine test bed tabular dataset to train and validate the machine learning models. Additionally, we explore the trade-off between incorporating more features to enhance the model complexity, and the resulting presence of increased missing values in the dataset.", "published": "2023-07-17T10:50:09Z", "authors": ["Rebecca Potts", "Rick Hackney", "Georgios Leontidis"], "categories": ["cs.LG"], "doi": "10.3390/make5030055", "_query": "house price prediction machine learning", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2409.04365v1", "title": "Leveraging Machine Learning for Official Statistics: A Statistical Manifesto", "abstract": "It is important for official statistics production to apply ML with statistical rigor, as it presents both opportunities and challenges. Although machine learning has enjoyed rapid technological advances in recent years, its application does not possess the methodological robustness necessary to produce high quality statistical results. In order to account for all sources of error in machine learning models, the Total Machine Learning Error (TMLE) is presented as a framework analogous to the Total Survey Error Model used in survey methodology. As a means of ensuring that ML models are both internally valid as well as externally valid, the TMLE model addresses issues such as representativeness and measurement errors. There are several case studies presented, illustrating the importance of applying more rigor to the application of machine learning in official statistics.", "published": "2024-09-06T15:57:25Z", "authors": ["Marco Puts", "David Salgado", "Piet Daas"], "categories": ["stat.ML", "cs.LG", "stat.ME"], "doi": "", "_query": "house price prediction machine learning", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/1811.10799v2", "title": "What is Interpretable? Using Machine Learning to Design Interpretable Decision-Support Systems", "abstract": "Recent efforts in Machine Learning (ML) interpretability have focused on creating methods for explaining black-box ML models. However, these methods rely on the assumption that simple approximations, such as linear models or decision-trees, are inherently human-interpretable, which has not been empirically tested. Additionally, past efforts have focused exclusively on comprehension, neglecting to explore the trust component necessary to convince non-technical experts, such as clinicians, to utilize ML models in practice. In this paper, we posit that reinforcement learning (RL) can be used to learn what is interpretable to different users and, consequently, build their trust in ML models. To validate this idea, we first train a neural network to provide risk assessments for heart failure patients. We then design a RL-based clinical decision-support system (DSS) around the neural network model, which can learn from its interactions with users. We conduct an experiment involving a diverse set of clinicians from multiple institutions in three different countries. Our results demonstrate that ML experts cannot accurately predict which system outputs will maximize clinicians' confidence in the underlying neural network model, and suggest additional findings that have broad implications to the future of research into ML interpretability and the use of ML in medicine.", "published": "2018-11-27T04:26:36Z", "authors": ["Owen Lahav", "Nicholas Mastronarde", "Mihaela van der Schaar"], "categories": ["cs.LG", "stat.ML"], "doi": "", "_query": "house price prediction machine learning", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2502.17993v1", "title": "A Perspective on Symbolic Machine Learning in Physical Sciences", "abstract": "Machine learning is rapidly making its pathway across all of the natural sciences, including physical sciences. The rate at which ML is impacting non-scientific disciplines is incomparable to that in the physical sciences. This is partly due to the uninterpretable nature of deep neural networks. Symbolic machine learning stands as an equal and complementary partner to numerical machine learning in speeding up scientific discovery in physics. This perspective discusses the main differences between the ML and scientific approaches. It stresses the need to develop and apply symbolic machine learning to physics problems equally, in parallel to numerical machine learning, because of the dual nature of physics research.", "published": "2025-02-25T09:02:02Z", "authors": ["Nour Makke", "Sanjay Chawla"], "categories": ["cs.LG", "hep-ph", "hep-th"], "doi": "", "_query": "house price prediction machine learning", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2510.19579v1", "title": "Multi-modal Co-learning for Earth Observation: Enhancing single-modality models via modality collaboration", "abstract": "Multi-modal co-learning is emerging as an effective paradigm in machine learning, enabling models to collaboratively learn from different modalities to enhance single-modality predictions. Earth Observation (EO) represents a quintessential domain for multi-modal data analysis, wherein diverse remote sensors collect data to sense our planet. This unprecedented volume of data introduces novel challenges. Specifically, the access to the same sensor modalities at both training and inference stages becomes increasingly complex based on real-world constraints affecting remote sensing platforms. In this context, multi-modal co-learning presents a promising strategy to leverage the vast amount of sensor-derived data available at the training stage to improve single-modality models for inference-time deployment. Most current research efforts focus on designing customized solutions for either particular downstream tasks or specific modalities available at the inference stage. To address this, we propose a novel multi-modal co-learning framework capable of generalizing across various tasks without targeting a specific modality for inference. Our approach combines contrastive and modality discriminative learning together to guide single-modality models to structure the internal model manifold into modality-shared and modality-specific information. We evaluate our framework on four EO benchmarks spanning classification and regression tasks across different sensor modalities, where only one of the modalities available during training is accessible at inference time. Our results demonstrate consistent predictive improvements over state-of-the-art approaches from the recent machine learning and computer vision literature, as well as EO-specific methods. The obtained findings validate our framework in the single-modality inference scenarios across a diverse range of EO applications.", "published": "2025-10-22T13:29:32Z", "authors": ["Francisco Mena", "Dino Ienco", "Cassio F. Dantas", "Roberto Interdonato", "Andreas Dengel"], "categories": ["cs.CV", "cs.AI", "cs.LG"], "doi": "10.1007/s10994-025-06903-0", "_query": "house price prediction machine learning", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2109.09307v4", "title": "Assisted Learning for Organizations with Limited Imbalanced Data", "abstract": "In the era of big data, many big organizations are integrating machine learning into their work pipelines to facilitate data analysis. However, the performance of their trained models is often restricted by limited and imbalanced data available to them. In this work, we develop an assisted learning framework for assisting organizations to improve their learning performance. The organizations have sufficient computation resources but are subject to stringent data-sharing and collaboration policies. Their limited imbalanced data often cause biased inference and sub-optimal decision-making. In assisted learning, an organizational learner purchases assistance service from an external service provider and aims to enhance its model performance within only a few assistance rounds. We develop effective stochastic training algorithms for both assisted deep learning and assisted reinforcement learning. Different from existing distributed algorithms that need to frequently transmit gradients or models, our framework allows the learner to only occasionally share information with the service provider, but still obtain a model that achieves near-oracle performance as if all the data were centralized.", "published": "2021-09-20T05:57:52Z", "authors": ["Cheng Chen", "Jiaying Zhou", "Jie Ding", "Yi Zhou"], "categories": ["cs.LG"], "doi": "", "_query": "house price prediction machine learning", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/1905.13613v2", "title": "Regression Networks for Meta-Learning Few-Shot Classification", "abstract": "We propose regression networks for the problem of few-shot classification, where a classifier must generalize to new classes not seen in the training set, given only a small number of examples of each class. In high dimensional embedding spaces the direction of data generally contains richer information than magnitude. Next to this, state-of-the-art few-shot metric methods that compare distances with aggregated class representations, have shown superior performance. Combining these two insights, we propose to meta-learn classification of embedded points by regressing the closest approximation in every class subspace while using the regression error as a distance metric. Similarly to recent approaches for few-shot learning, regression networks reflect a simple inductive bias that is beneficial in this limited-data regime and they achieve excellent results, especially when more aggregate class representations can be formed with multiple shots.", "published": "2019-05-31T13:35:41Z", "authors": ["Arnout Devos", "Matthias Grossglauser"], "categories": ["cs.LG", "cs.CV", "stat.ML"], "doi": "", "_query": "house price prediction machine learning", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2002.03425v3", "title": "Cyclic Boosting -- an explainable supervised machine learning algorithm", "abstract": "Supervised machine learning algorithms have seen spectacular advances and surpassed human level performance in a wide range of specific applications. However, using complex ensemble or deep learning algorithms typically results in black box models, where the path leading to individual predictions cannot be followed in detail. In order to address this issue, we propose the novel \"Cyclic Boosting\" machine learning algorithm, which allows to efficiently perform accurate regression and classification tasks while at the same time allowing a detailed understanding of how each individual prediction was made.", "published": "2020-02-09T18:52:42Z", "authors": ["Felix Wick", "Ulrich Kerzel", "Michael Feindt"], "categories": ["cs.LG", "stat.ML"], "doi": "10.1109/ICMLA.2019.00067", "_query": "house price prediction machine learning", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2410.23450v2", "title": "Return Augmented Decision Transformer for Off-Dynamics Reinforcement Learning", "abstract": "We study offline off-dynamics reinforcement learning (RL) to utilize data from an easily accessible source domain to enhance policy learning in a target domain with limited data. Our approach centers on return-conditioned supervised learning (RCSL), particularly focusing on Decision Transformer (DT) type frameworks, which can predict actions conditioned on desired return guidance and complete trajectory history. Previous works address the dynamics shift problem by augmenting the reward in the trajectory from the source domain to match the optimal trajectory in the target domain. However, this strategy can not be directly applicable in RCSL owing to (1) the unique form of the RCSL policy class, which explicitly depends on the return, and (2) the absence of a straightforward representation of the optimal trajectory distribution. We propose the Return Augmented (REAG) method for DT type frameworks, where we augment the return in the source domain by aligning its distribution with that in the target domain. We provide the theoretical analysis demonstrating that the RCSL policy learned from REAG achieves the same level of suboptimality as would be obtained without a dynamics shift. We introduce two practical implementations REAG$_\\text{Dara}^{*}$ and REAG$_\\text{MV}^{*}$ respectively. Thorough experiments on D4RL datasets and various DT-type baselines demonstrate that our methods consistently enhance the performance of DT type frameworks in off-dynamics RL.", "published": "2024-10-30T20:46:26Z", "authors": ["Ruhan Wang", "Yu Yang", "Zhishuai Liu", "Dongruo Zhou", "Pan Xu"], "categories": ["cs.LG", "cs.AI", "cs.RO", "stat.ML"], "doi": "", "_query": "house price prediction machine learning", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/1811.00620v1", "title": "Efficient Online Hyperparameter Optimization for Kernel Ridge Regression with Applications to Traffic Time Series Prediction", "abstract": "Computational efficiency is an important consideration for deploying machine learning models for time series prediction in an online setting. Machine learning algorithms adjust model parameters automatically based on the data, but often require users to set additional parameters, known as hyperparameters. Hyperparameters can significantly impact prediction accuracy. Traffic measurements, typically collected online by sensors, are serially correlated. Moreover, the data distribution may change gradually. A typical adaptation strategy is periodically re-tuning the model hyperparameters, at the cost of computational burden. In this work, we present an efficient and principled online hyperparameter optimization algorithm for Kernel Ridge regression applied to traffic prediction problems. In tests with real traffic measurement data, our approach requires as little as one-seventh of the computation time of other tuning methods, while achieving better or similar prediction accuracy.", "published": "2018-11-01T20:14:49Z", "authors": ["Hongyuan Zhan", "Gabriel Gomes", "Xiaoye S. Li", "Kamesh Madduri", "Kesheng Wu"], "categories": ["cs.LG", "stat.ML"], "doi": "", "_query": "house price prediction machine learning", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/1303.7093v3", "title": "Relevance As a Metric for Evaluating Machine Learning Algorithms", "abstract": "In machine learning, the choice of a learning algorithm that is suitable for the application domain is critical. The performance metric used to compare different algorithms must also reflect the concerns of users in the application domain under consideration. In this work, we propose a novel probability-based performance metric called Relevance Score for evaluating supervised learning algorithms. We evaluate the proposed metric through empirical analysis on a dataset gathered from an intelligent lighting pilot installation. In comparison to the commonly used Classification Accuracy metric, the Relevance Score proves to be more appropriate for a certain class of applications.", "published": "2013-03-28T11:01:53Z", "authors": ["Aravind Kota Gopalakrishna", "Tanir Ozcelebi", "Antonio Liotta", "Johan J. Lukkien"], "categories": ["stat.ML", "cs.LG"], "doi": "10.1007/978-3-642-39712-7_15", "_query": "house price prediction machine learning", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/1806.03256v1", "title": "Incorporating Features Learned by an Enhanced Deep Knowledge Tracing Model for STEM/Non-STEM Job Prediction", "abstract": "The 2017 ASSISTments Data Mining competition aims to use data from a longitudinal study for predicting a brand-new outcome of students which had never been studied before by the educational data mining research community. Specifically, it facilitates research in developing predictive models that predict whether the first job of a student out of college belongs to a STEM (the acronym for science, technology, engineering, and mathematics) field. This is based on the student's learning history on the ASSISTments blended learning platform in the form of extensive clickstream data gathered during the middle school years. To tackle this challenge, we first estimate the expected knowledge state of students with respect to different mathematical skills using a deep knowledge tracing (DKT) model and an enhanced DKT (DKT+) model. We then combine the features corresponding to the DKT/DKT+ expected knowledge state with other features extracted directly from the student profile in the dataset to train several machine learning models for the STEM/non-STEM job prediction. Our experiments show that models trained with the combined features generally perform better than the models trained with the student profile alone. Detailed analysis of the student's knowledge state reveals that, when compared with non-STEM students, STEM students generally show a higher mastery level and a higher learning gain in mathematics.", "published": "2018-06-06T13:57:47Z", "authors": ["Chun-kit Yeung", "Zizheng Lin", "Kai Yang", "Dit-yan Yeung"], "categories": ["cs.CY", "cs.AI", "cs.LG"], "doi": "", "_query": "house price prediction machine learning", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2007.05479v1", "title": "Impact of Legal Requirements on Explainability in Machine Learning", "abstract": "The requirements on explainability imposed by European laws and their implications for machine learning (ML) models are not always clear. In that perspective, our research analyzes explanation obligations imposed for private and public decision-making, and how they can be implemented by machine learning techniques.", "published": "2020-07-10T16:57:18Z", "authors": ["Adrien Bibal", "Michael Lognoul", "Alexandre de Streel", "Benoît Frénay"], "categories": ["cs.AI", "cs.CY", "cs.LG"], "doi": "", "_query": "house price prediction machine learning", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2406.04897v3", "title": "From Link Prediction to Forecasting: Addressing Challenges in Batch-based Temporal Graph Learning", "abstract": "Dynamic link prediction is an important problem considered in many recent works that propose approaches for learning temporal edge patterns. To assess their efficacy, models are evaluated on continuous-time and discrete-time temporal graph datasets, typically using a traditional batch-oriented evaluation setup. However, as we show in this work, a batch-oriented evaluation is often unsuitable and can cause several issues. Grouping edges into fixed-sized batches regardless of their occurrence time leads to information loss or leakage, depending on the temporal granularity of the data. Furthermore, fixed-size batches create time windows with different durations, resulting in an inconsistent dynamic link prediction task. In this work, we empirically show how traditional batch-based evaluation leads to skewed model performance and hinders the fair comparison of methods. We mitigate this problem by reformulating dynamic link prediction as a link forecasting task that better accounts for temporal information present in the data.", "published": "2024-06-07T12:45:12Z", "authors": ["Moritz Lampert", "Christopher Blöcker", "Ingo Scholtes"], "categories": ["cs.LG"], "doi": "", "_query": "house price prediction machine learning", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2204.00887v2", "title": "Dimensionless machine learning: Imposing exact units equivariance", "abstract": "Units equivariance (or units covariance) is the exact symmetry that follows from the requirement that relationships among measured quantities of physics relevance must obey self-consistent dimensional scalings. Here, we express this symmetry in terms of a (non-compact) group action, and we employ dimensional analysis and ideas from equivariant machine learning to provide a methodology for exactly units-equivariant machine learning: For any given learning task, we first construct a dimensionless version of its inputs using classic results from dimensional analysis, and then perform inference in the dimensionless space. Our approach can be used to impose units equivariance across a broad range of machine learning methods which are equivariant to rotations and other groups. We discuss the in-sample and out-of-sample prediction accuracy gains one can obtain in contexts like symbolic regression and emulation, where symmetry is important. We illustrate our approach with simple numerical examples involving dynamical systems in physics and ecology.", "published": "2022-04-02T15:46:20Z", "authors": ["Soledad Villar", "Weichi Yao", "David W. Hogg", "Ben Blum-Smith", "Bianca Dumitrascu"], "categories": ["stat.ML", "cs.LG", "physics.data-an"], "doi": "", "_query": "house price prediction machine learning", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2112.13398v6", "title": "Long Story Short: Omitted Variable Bias in Causal Machine Learning", "abstract": "We develop a general theory of omitted variable bias for a wide range of common causal parameters, including (but not limited to) averages of potential outcomes, average treatment effects, average causal derivatives, and policy effects from covariate shifts. Our theory applies to nonparametric models, while naturally allowing for (semi-)parametric restrictions (such as partial linearity) when such assumptions are made. We show how simple plausibility judgments on the maximum explanatory power of omitted variables are sufficient to bound the magnitude of the bias, thus facilitating sensitivity analysis in otherwise complex, nonlinear models. Finally, we provide flexible and efficient statistical inference methods for the bounds, which can leverage modern machine learning algorithms for estimation. These results allow empirical researchers to perform sensitivity analyses in a flexible class of machine-learned causal models using very simple, and interpretable, tools. We demonstrate the utility of our approach with two empirical examples.", "published": "2021-12-26T15:38:23Z", "authors": ["Victor Chernozhukov", "Carlos Cinelli", "Whitney Newey", "Amit Sharma", "Vasilis Syrgkanis"], "categories": ["econ.EM", "cs.LG", "stat.ME", "stat.ML"], "doi": "10.1162/REST.a.1705", "_query": "house price prediction machine learning", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2603.29261v1", "title": "Monodense Deep Neural Model for Determining Item Price Elasticity", "abstract": "Item Price Elasticity is used to quantify the responsiveness of consumer demand to changes in item prices, enabling businesses to create pricing strategies and optimize revenue management. Sectors such as store retail, e-commerce, and consumer goods rely on elasticity information derived from historical sales and pricing data. This elasticity provides an understanding of purchasing behavior across different items, consumer discount sensitivity, and demand elastic departments. This information is particularly valuable for competitive markets and resource-constrained businesses decision making which aims to maximize profitability and market share. Price elasticity also uncovers historical shifts in consumer responsiveness over time. In this paper, we model item-level price elasticity using large-scale transactional datasets, by proposing a novel elasticity estimation framework which has the capability to work in an absence of treatment control setting. We test this framework by using Machine learning based algorithms listed below, including our newly proposed Monodense deep neural network. (1) Monodense-DL network -- Hybrid neural network architecture combining embedding, dense, and Monodense layers (2) DML -- Double machine learning setting using regression models (3) LGBM -- Light Gradient Boosting Model We evaluate our model on multi-category retail data spanning millions of transactions using a back testing framework. Experimental results demonstrate the superiority of our proposed neural network model within the framework compared to other prevalent ML based methods listed above.", "published": "2026-03-31T04:50:51Z", "authors": ["Lakshya Garg", "Sai Yaswanth", "Deep Narayan Mishra", "Karthik Kumaran", "Anupriya Sharma", "Mayank Uniyal"], "categories": ["cs.LG", "cs.AI"], "doi": "10.1109/AAIML67890.2026.11498150", "_query": "house price prediction machine learning", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2410.22854v3", "title": "Hyperparameter Optimization in Machine Learning", "abstract": "Hyperparameters are configuration variables controlling the behavior of machine learning algorithms. They are ubiquitous in machine learning and artificial intelligence and the choice of their values determines the effectiveness of systems based on these technologies. Manual hyperparameter search is often time-consuming and becomes infeasible when the number of hyperparameters is large. Automating the search is an important step towards advancing, streamlining, and systematizing machine learning, freeing researchers and practitioners alike from the burden of finding a good set of hyperparameters by trial and error. In this survey, we present a unified treatment of hyperparameter optimization, providing the reader with examples, insights into the state-of-the-art, and numerous links to further reading. We cover the main families of techniques to automate hyperparameter search, often referred to as hyperparameter optimization or tuning, including random and quasi-random search, bandit-, model-, population-, and gradient-based approaches. We further discuss extensions, including online, constrained, and multi-objective formulations, touch upon connections with other fields, such as meta-learning and neural architecture search, and conclude with open questions and future research directions.", "published": "2024-10-30T09:39:22Z", "authors": ["Luca Franceschi", "Michele Donini", "Valerio Perrone", "Aaron Klein", "Cédric Archambeau", "Matthias Seeger", "Massimiliano Pontil", "Paolo Frasconi"], "categories": ["stat.ML", "cs.LG"], "doi": "10.1561/2200000088", "_query": "house price prediction machine learning", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2208.02670v1", "title": "Development and Validation of ML-DQA -- a Machine Learning Data Quality Assurance Framework for Healthcare", "abstract": "The approaches by which the machine learning and clinical research communities utilize real world data (RWD), including data captured in the electronic health record (EHR), vary dramatically. While clinical researchers cautiously use RWD for clinical investigations, ML for healthcare teams consume public datasets with minimal scrutiny to develop new algorithms. This study bridges this gap by developing and validating ML-DQA, a data quality assurance framework grounded in RWD best practices. The ML-DQA framework is applied to five ML projects across two geographies, different medical conditions, and different cohorts. A total of 2,999 quality checks and 24 quality reports were generated on RWD gathered on 247,536 patients across the five projects. Five generalizable practices emerge: all projects used a similar method to group redundant data element representations; all projects used automated utilities to build diagnosis and medication data elements; all projects used a common library of rules-based transformations; all projects used a unified approach to assign data quality checks to data elements; and all projects used a similar approach to clinical adjudication. An average of 5.8 individuals, including clinicians, data scientists, and trainees, were involved in implementing ML-DQA for each project and an average of 23.4 data elements per project were either transformed or removed in response to ML-DQA. This study demonstrates the importance role of ML-DQA in healthcare projects and provides teams a framework to conduct these essential activities.", "published": "2022-08-04T13:58:36Z", "authors": ["Mark Sendak", "Gaurav Sirdeshmukh", "Timothy Ochoa", "Hayley Premo", "Linda Tang", "Kira Niederhoffer", "Sarah Reed", "Kaivalya Deshpande", "Emily Sterrett", "Melissa Bauer", "Laurie Snyder", "Afreen Shariff", "David Whellan", "Jeffrey Riggio", "David Gaieski", "Kristin Corey", "Megan Richards", "Michael Gao", "Marshall Nichols", "Bradley Heintze", "William Knechtle", "William Ratliff", "Suresh Balu"], "categories": ["stat.ML", "cs.LG"], "doi": "", "_query": "house price prediction machine learning", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2311.03037v1", "title": "Validity problems in clinical machine learning by indirect data labeling using consensus definitions", "abstract": "We demonstrate a validity problem of machine learning in the vital application area of disease diagnosis in medicine. It arises when target labels in training data are determined by an indirect measurement, and the fundamental measurements needed to determine this indirect measurement are included in the input data representation. Machine learning models trained on this data will learn nothing else but to exactly reconstruct the known target definition. Such models show perfect performance on similarly constructed test data but will fail catastrophically on real-world examples where the defining fundamental measurements are not or only incompletely available. We present a general procedure allowing identification of problematic datasets and black-box machine learning models trained on them, and exemplify our detection procedure on the task of early prediction of sepsis.", "published": "2023-11-06T11:14:48Z", "authors": ["Michael Hagmann", "Shigehiko Schamoni", "Stefan Riezler"], "categories": ["cs.LG", "q-bio.QM", "stat.AP", "stat.ML"], "doi": "", "_query": "house price prediction machine learning", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2604.03772v1", "title": "Debiased Machine Learning for Conformal Prediction of Counterfactual Outcomes Under Runtime Confounding", "abstract": "Data-driven decision making frequently relies on predicting counterfactual outcomes. In practice, researchers commonly train counterfactual prediction models on a source dataset to inform decisions on a possibly separate target population. Conformal prediction has arisen as a popular method for producing assumption-lean prediction intervals for counterfactual outcomes that would arise under different treatment decisions in the target population of interest. However, existing methods require that every confounding factor of the treatment-outcome relationship used for training on the source data is additionally measured in the target population, risking miscoverage if important confounders are unmeasured in the target population. In this paper, we introduce a computationally efficient debiased machine learning framework that allows for valid prediction intervals when only a subset of confounders is measured in the target population, a common challenge referred to as runtime confounding. Grounded in semiparametric efficiency theory, we show the resulting prediction intervals achieve desired coverage rates with faster convergence compared to standard methods. Through numerous synthetic and semi-synthetic experiments, we demonstrate the utility of our proposed method.", "published": "2026-04-04T15:49:41Z", "authors": ["Keith Barnatchez", "Kevin P. Josey", "Rachel C. Nethery", "Giovanni Parmigiani"], "categories": ["stat.ML", "cs.LG"], "doi": "", "_query": "house price prediction machine learning", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2303.06871v3", "title": "Physics-driven machine learning models coupling PyTorch and Firedrake", "abstract": "Partial differential equations (PDEs) are central to describing and modelling complex physical systems that arise in many disciplines across science and engineering. However, in many realistic applications PDE modelling provides an incomplete description of the physics of interest. PDE-based machine learning techniques are designed to address this limitation. In this approach, the PDE is used as an inductive bias enabling the coupled model to rely on fundamental physical laws while requiring less training data. The deployment of high-performance simulations coupling PDEs and machine learning to complex problems necessitates the composition of capabilities provided by machine learning and PDE-based frameworks. We present a simple yet effective coupling between the machine learning framework PyTorch and the PDE system Firedrake that provides researchers, engineers and domain specialists with a high productive way of specifying coupled models while only requiring trivial changes to existing code.", "published": "2023-03-13T05:42:58Z", "authors": ["Nacime Bouziani", "David A. Ham"], "categories": ["cs.LG", "cs.MS", "math.NA", "physics.comp-ph"], "doi": "", "_query": "house price prediction machine learning", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2106.12747v1", "title": "Automated Agriculture Commodity Price Prediction System with Machine Learning Techniques", "abstract": "The intention of this research is to study and design an automated agriculture commodity price prediction system with novel machine learning techniques. Due to the increasing large amounts historical data of agricultural commodity prices and the need of performing accurate prediction of price fluctuations, the solution has largely shifted from statistical methods to machine learning area. However, the selection of proper set from historical data for forecasting still has limited consideration. On the other hand, when implementing machine learning techniques, finding a suitable model with optimal parameters for global solution, nonlinearity and avoiding curse of dimensionality are still biggest challenges, therefore machine learning strategies study are needed. In this research, we propose a web-based automated system to predict agriculture commodity price. In the two series experiments, five popular machine learning algorithms, ARIMA, SVR, Prophet, XGBoost and LSTM have been compared with large historical datasets in Malaysia and the most optimal algorithm, LSTM model with an average of 0.304 mean-square error has been selected as the prediction engine of the proposed system.", "published": "2021-06-24T03:10:25Z", "authors": ["Zhiyuan Chen", "Howe Seng Goh", "Kai Ling Sin", "Kelly Lim", "Nicole Ka Hei Chung", "Xin Yu Liew"], "categories": ["cs.LG"], "doi": "", "_query": "house price prediction machine learning", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2503.01884v2", "title": "Contextual Quantum Neural Networks for Stock Price Prediction", "abstract": "In this paper, we apply quantum machine learning (QML) to predict the stock prices of multiple assets using a contextual quantum neural network. Our approach captures recent trends to predict future stock price distributions, moving beyond traditional models that focus on entire historical data, enhancing adaptability and precision. Utilizing the principles of quantum superposition, we introduce a new training technique called the quantum batch gradient update (QBGU), which accelerates the standard stochastic gradient descent (SGD) in quantum applications and improves convergence. Consequently, we propose a quantum multi-task learning (QMTL) architecture, specifically, the share-and-specify ansatz, that integrates task-specific operators controlled by quantum labels, enabling the simultaneous and efficient training of multiple assets on the same quantum circuit as well as enabling efficient portfolio representation with logarithmic overhead in the number of qubits. This architecture represents the first of its kind in quantum finance, offering superior predictive power and computational efficiency for multi-asset stock price forecasting. Through extensive experimentation on S\\&P 500 data for Apple, Google, Microsoft, and Amazon stocks, we demonstrate that our approach not only outperforms quantum single-task learning (QSTL) models but also effectively captures inter-asset correlations, leading to enhanced prediction accuracy. Our findings highlight the transformative potential of QML in financial applications, paving the way for more advanced, resource-efficient quantum algorithms in stock price prediction and other complex financial modeling tasks.", "published": "2025-02-26T22:39:23Z", "authors": ["Sharan Mourya", "Hannes Leipold", "Bibhas Adhikari"], "categories": ["cs.LG", "cs.AI"], "doi": "10.1038/s41598-025-34413-5", "_query": "house price prediction machine learning", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/1408.0055v1", "title": "Thurstonian Boltzmann Machines: Learning from Multiple Inequalities", "abstract": "We introduce Thurstonian Boltzmann Machines (TBM), a unified architecture that can naturally incorporate a wide range of data inputs at the same time. Our motivation rests in the Thurstonian view that many discrete data types can be considered as being generated from a subset of underlying latent continuous variables, and in the observation that each realisation of a discrete type imposes certain inequalities on those variables. Thus learning and inference in TBM reduce to making sense of a set of inequalities. Our proposed TBM naturally supports the following types: Gaussian, intervals, censored, binary, categorical, muticategorical, ordinal, (in)-complete rank with and without ties. We demonstrate the versatility and capacity of the proposed model on three applications of very different natures; namely handwritten digit recognition, collaborative filtering and complex social survey analysis.", "published": "2014-08-01T00:32:32Z", "authors": ["Truyen Tran", "Dinh Phung", "Svetha Venkatesh"], "categories": ["stat.ML", "cs.LG", "stat.ME"], "doi": "", "_query": "house price prediction machine learning", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2304.04839v2", "title": "MHfit: Mobile Health Data for Predicting Athletics Fitness Using Machine Learning", "abstract": "Mobile phones and other electronic gadgets or devices have aided in collecting data without the need for data entry. This paper will specifically focus on Mobile health data. Mobile health data use mobile devices to gather clinical health data and track patient vitals in real-time. Our study is aimed to give decisions for small or big sports teams on whether one athlete good fit or not for a particular game with the compare several machine learning algorithms to predict human behavior and health using the data collected from mobile devices and sensors placed on patients. In this study, we have obtained the dataset from a similar study done on mhealth. The dataset contains vital signs recordings of ten volunteers from different backgrounds. They had to perform several physical activities with a sensor placed on their bodies. Our study used 5 machine learning algorithms (XGBoost, Naive Bayes, Decision Tree, Random Forest, and Logistic Regression) to analyze and predict human health behavior. XGBoost performed better compared to the other machine learning algorithms and achieved 95.2% accuracy, 99.5% in sensitivity, 99.5% in specificity, and 99.66% in F1 score. Our research indicated a promising future in mhealth being used to predict human behavior and further research and exploration need to be done for it to be available for commercial use specifically in the sports industry.", "published": "2023-04-10T19:53:03Z", "authors": ["Jonayet Miah", "Muntasir Mamun", "Md Minhazur Rahman", "Md Ishtyaq Mahmud", "Sabbir Ahmed", "Md Hasan Bin Nasir"], "categories": ["cs.LG", "cs.CY"], "doi": "", "_query": "house price prediction machine learning", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2306.11715v2", "title": "Multi-Fidelity Active Learning with GFlowNets", "abstract": "In the last decades, the capacity to generate large amounts of data in science and engineering applications has been growing steadily. Meanwhile, machine learning has progressed to become a suitable tool to process and utilise the available data. Nonetheless, many relevant scientific and engineering problems present challenges where current machine learning methods cannot yet efficiently leverage the available data and resources. For example, in scientific discovery, we are often faced with the problem of exploring very large, structured and high-dimensional spaces. Moreover, the high fidelity, black-box objective function is often very expensive to evaluate. Progress in machine learning methods that can efficiently tackle such challenges would help accelerate currently crucial areas such as drug and materials discovery. In this paper, we propose a multi-fidelity active learning algorithm with GFlowNets as a sampler, to efficiently discover diverse, high-scoring candidates where multiple approximations of the black-box function are available at lower fidelity and cost. Our evaluation on molecular discovery tasks shows that multi-fidelity active learning with GFlowNets can discover high-scoring candidates at a fraction of the budget of its single-fidelity counterpart while maintaining diversity, unlike RL-based alternatives. These results open new avenues for multi-fidelity active learning to accelerate scientific discovery and engineering design.", "published": "2023-06-20T17:43:42Z", "authors": ["Alex Hernandez-Garcia", "Nikita Saxena", "Moksh Jain", "Cheng-Hao Liu", "Yoshua Bengio"], "categories": ["cs.LG", "q-bio.BM"], "doi": "", "_query": "house price prediction machine learning", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2411.02709v1", "title": "Carbon price fluctuation prediction using blockchain information A new hybrid machine learning approach", "abstract": "In this study, the novel hybrid machine learning approach is proposed in carbon price fluctuation prediction. Specifically, a research framework integrating DILATED Convolutional Neural Networks (CNN) and Long Short-Term Memory (LSTM) neural network algorithm is proposed. The advantage of the combined framework is that it can make feature extraction more efficient. Then, based on the DILATED CNN-LSTM framework, the L1 and L2 parameter norm penalty as regularization method is adopted to predict. Referring to the characteristics of high correlation between energy indicator price and blockchain information in previous literature, and we primarily includes indicators related to blockchain information through regularization process. Based on the above methods, this paper uses a dataset containing an amount of data to carry out the carbon price prediction. The experimental results show that the DILATED CNN-LSTM framework is superior to the traditional CNN-LSTM architecture. Blockchain information can effectively predict the price. Since parameter norm penalty as regularization, Ridge Regression (RR) as L2 regularization is better than Smoothly Clipped Absolute Deviation Penalty (SCAD) as L1 regularization in price forecasting. Thus, the proposed RR-DILATED CNN-LSTM approach can effectively and accurately predict the fluctuation trend of the carbon price. Therefore, the new forecasting methods and theoretical ecology proposed in this study provide a new basis for trend prediction and evaluating digital assets policy represented by the carbon price for both the academia and practitioners.", "published": "2024-11-05T01:12:17Z", "authors": ["H. Wang", "Y. Pang", "D. Shang"], "categories": ["cs.LG", "stat.ML"], "doi": "", "_query": "house price prediction machine learning", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/1606.07163v1", "title": "Interpretable Machine Learning Models for the Digital Clock Drawing Test", "abstract": "The Clock Drawing Test (CDT) is a rapid, inexpensive, and popular neuropsychological screening tool for cognitive conditions. The Digital Clock Drawing Test (dCDT) uses novel software to analyze data from a digitizing ballpoint pen that reports its position with considerable spatial and temporal precision, making possible the analysis of both the drawing process and final product. We developed methodology to analyze pen stroke data from these drawings, and computed a large collection of features which were then analyzed with a variety of machine learning techniques. The resulting scoring systems were designed to be more accurate than the systems currently used by clinicians, but just as interpretable and easy to use. The systems also allow us to quantify the tradeoff between accuracy and interpretability. We created automated versions of the CDT scoring systems currently used by clinicians, allowing us to benchmark our models, which indicated that our machine learning models substantially outperformed the existing scoring systems.", "published": "2016-06-23T02:08:58Z", "authors": ["William Souillard-Mandar", "Randall Davis", "Cynthia Rudin", "Rhoda Au", "Dana Penney"], "categories": ["stat.ML", "cs.LG"], "doi": "", "_query": "house price prediction machine learning", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2006.06592v3", "title": "The Backbone Method for Ultra-High Dimensional Sparse Machine Learning", "abstract": "We present the backbone method, a generic framework that enables sparse and interpretable supervised machine learning methods to scale to ultra-high dimensional problems. We solve sparse regression problems with $10^7$ features in minutes and $10^8$ features in hours, as well as decision tree problems with $10^5$ features in minutes.The proposed method operates in two phases: we first determine the backbone set, consisting of potentially relevant features, by solving a number of tractable subproblems; then, we solve a reduced problem, considering only the backbone features. For the sparse regression problem, our theoretical analysis shows that, under certain assumptions and with high probability, the backbone set consists of the truly relevant features. Numerical experiments on both synthetic and real-world datasets demonstrate that our method outperforms or competes with state-of-the-art methods in ultra-high dimensional problems, and competes with optimal solutions in problems where exact methods scale, both in terms of recovering the truly relevant features and in its out-of-sample predictive performance.", "published": "2020-06-11T16:43:02Z", "authors": ["Dimitris Bertsimas", "Vassilis Digalakis"], "categories": ["cs.LG", "stat.ML"], "doi": "10.1007/s10994-021-06123-2", "_query": "house price prediction machine learning", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/1805.09921v4", "title": "Meta-Learning Probabilistic Inference For Prediction", "abstract": "This paper introduces a new framework for data efficient and versatile learning. Specifically: 1) We develop ML-PIP, a general framework for Meta-Learning approximate Probabilistic Inference for Prediction. ML-PIP extends existing probabilistic interpretations of meta-learning to cover a broad class of methods. 2) We introduce VERSA, an instance of the framework employing a flexible and versatile amortization network that takes few-shot learning datasets as inputs, with arbitrary numbers of shots, and outputs a distribution over task-specific parameters in a single forward pass. VERSA substitutes optimization at test time with forward passes through inference networks, amortizing the cost of inference and relieving the need for second derivatives during training. 3) We evaluate VERSA on benchmark datasets where the method sets new state-of-the-art results, handles arbitrary numbers of shots, and for classification, arbitrary numbers of classes at train and test time. The power of the approach is then demonstrated through a challenging few-shot ShapeNet view reconstruction task.", "published": "2018-05-24T22:08:27Z", "authors": ["Jonathan Gordon", "John Bronskill", "Matthias Bauer", "Sebastian Nowozin", "Richard E. Turner"], "categories": ["stat.ML", "cs.LG"], "doi": "", "_query": "house price prediction machine learning", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2410.10908v2", "title": "The State of Julia for Scientific Machine Learning", "abstract": "Julia has been heralded as a potential successor to Python for scientific machine learning and numerical computing, boasting ergonomic and performance improvements. Since Julia's inception in 2012 and declaration of language goals in 2017, its ecosystem and language-level features have grown tremendously. In this paper, we take a modern look at Julia's features and ecosystem, assess the current state of the language, and discuss its viability and pitfalls as a replacement for Python as the de-facto scientific machine learning language. We call for the community to address Julia's language-level issues that are preventing further adoption.", "published": "2024-10-14T01:43:23Z", "authors": ["Edward Berman", "Jacob Ginesin"], "categories": ["cs.LG", "cs.MS", "cs.PL"], "doi": "", "_query": "house price prediction machine learning", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/1910.07640v1", "title": "A Combined Deep Learning-Gradient Boosting Machine Framework for Fluid Intelligence Prediction", "abstract": "The ABCD Neurocognitive Prediction Challenge is a community driven competition asking competitors to develop algorithms to predict fluid intelligence score from T1-w MRIs. In this work, we propose a deep learning combined with gradient boosting machine framework to solve this task. We train a convolutional neural network to compress the high dimensional MRI data and learn meaningful image features by predicting the 123 continuous-valued derived data provided with each MRI. These extracted features are then used to train a gradient boosting machine that predicts the residualized fluid intelligence score. Our approach achieved mean square error (MSE) scores of 18.4374, 68.7868, and 96.1806 for the training, validation, and test set respectively.", "published": "2019-10-16T22:32:13Z", "authors": ["Yeeleng S. Vang", "Yingxin Cao", "Xiaohui Xie"], "categories": ["cs.CV", "q-bio.NC"], "doi": "10.1007/978-3-030-31901-4_1", "_query": "house price prediction machine learning", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2411.10754v1", "title": "Integrated Machine Learning and Survival Analysis Modeling for Enhanced Chronic Kidney Disease Risk Stratification", "abstract": "Chronic kidney disease (CKD) is a significant public health challenge, often progressing to end-stage renal disease (ESRD) if not detected and managed early. Early intervention, warranted by silent disease progression, can significantly reduce associated morbidity, mortality, and financial burden. In this study, we propose a novel approach to modeling CKD progression using a combination of machine learning techniques and classical statistical models. Building on the work of Liu et al. (2023), we evaluate linear models, tree-based methods, and deep learning models to extract novel predictors for CKD progression, with feature importance assessed using Shapley values. These newly identified predictors, integrated with established clinical features from the Kidney Failure Risk Equation, are then applied within the framework of Cox proportional hazards models to predict CKD progression.", "published": "2024-11-16T09:22:06Z", "authors": ["Zachary Dana", "Ahmed Ammar Naseer", "Botros Toro", "Sumanth Swaminathan"], "categories": ["cs.LG", "cs.AI", "stat.CO", "stat.ML"], "doi": "", "_query": "house price prediction machine learning", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/1607.01224v1", "title": "Machine Learning for Antimicrobial Resistance", "abstract": "Biological datasets amenable to applied machine learning are more available today than ever before, yet they lack adequate representation in the Data-for-Good community. Here we present a work in progress case study performing analysis on antimicrobial resistance (AMR) using standard ensemble machine learning techniques and note the successes and pitfalls such work entails. Broadly, applied machine learning (AML) techniques are well suited to AMR, with classification accuracies ranging from mid-90% to low- 80% depending on sample size. Additionally, these techniques prove successful at identifying gene regions known to be associated with the AMR phenotype. We believe that the extensive amount of biological data available, the plethora of problems presented, and the global impact of such work merits the consideration of the Data- for-Good community.", "published": "2016-07-05T12:42:01Z", "authors": ["John W. Santerre", "James J. Davis", "Fangfang Xia", "Rick Stevens"], "categories": ["stat.ML", "q-bio.QM"], "doi": "", "_query": "house price prediction machine learning", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/1607.01400v1", "title": "An Aggregate and Iterative Disaggregate Algorithm with Proven Optimality in Machine Learning", "abstract": "We propose a clustering-based iterative algorithm to solve certain optimization problems in machine learning, where we start the algorithm by aggregating the original data, solving the problem on aggregated data, and then in subsequent steps gradually disaggregate the aggregated data. We apply the algorithm to common machine learning problems such as the least absolute deviation regression problem, support vector machines, and semi-supervised support vector machines. We derive model-specific data aggregation and disaggregation procedures. We also show optimality, convergence, and the optimality gap of the approximated solution in each iteration. A computational study is provided.", "published": "2016-07-05T20:04:57Z", "authors": ["Young Woong Park", "Diego Klabjan"], "categories": ["stat.ML", "cs.LG"], "doi": "10.1007/s10994-016-5562-z", "_query": "house price prediction machine learning", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2207.13596v2", "title": "Fairness and Randomness in Machine Learning: Statistical Independence and Relativization", "abstract": "Fair Machine Learning endeavors to prevent unfairness arising in the context of machine learning applications embedded in society. Despite the variety of definitions of fairness and proposed \"fair algorithms\", there remain unresolved conceptual problems regarding fairness. In this paper, we dissect the role of statistical independence in fairness and randomness notions regularly used in machine learning. Thereby, we are led to a suprising hypothesis: randomness and fairness can be considered equivalent concepts in machine learning. In particular, we obtain a relativized notion of randomness expressed as statistical independence by appealing to Von Mises' century-old foundations for probability. This notion turns out to be \"orthogonal\" in an abstract sense to the commonly used i.i.d.-randomness. Using standard fairness notions in machine learning, which are defined via statistical independence, we then link the ex ante randomness assumptions about the data to the ex post requirements for fair predictions. This connection proves fruitful: we use it to argue that randomness and fairness are essentially relative and that both concepts should reflect their nature as modeling assumptions in machine learning.", "published": "2022-07-27T15:55:05Z", "authors": ["Rabanus Derr", "Robert C. Williamson"], "categories": ["cs.LG", "cs.CY"], "doi": "", "_query": "house price prediction machine learning", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2208.01220v2", "title": "GeoECG: Data Augmentation via Wasserstein Geodesic Perturbation for Robust Electrocardiogram Prediction", "abstract": "There has been an increased interest in applying deep neural networks to automatically interpret and analyze the 12-lead electrocardiogram (ECG). The current paradigms with machine learning methods are often limited by the amount of labeled data. This phenomenon is particularly problematic for clinically-relevant data, where labeling at scale can be time-consuming and costly in terms of the specialized expertise and human effort required. Moreover, deep learning classifiers may be vulnerable to adversarial examples and perturbations, which could have catastrophic consequences, for example, when applied in the context of medical treatment, clinical trials, or insurance claims. In this paper, we propose a physiologically-inspired data augmentation method to improve performance and increase the robustness of heart disease detection based on ECG signals. We obtain augmented samples by perturbing the data distribution towards other classes along the geodesic in Wasserstein space. To better utilize domain-specific knowledge, we design a ground metric that recognizes the difference between ECG signals based on physiologically determined features. Learning from 12-lead ECG signals, our model is able to distinguish five categories of cardiac conditions. Our results demonstrate improvements in accuracy and robustness, reflecting the effectiveness of our data augmentation method.", "published": "2022-08-02T03:14:13Z", "authors": ["Jiacheng Zhu", "Jielin Qiu", "Zhuolin Yang", "Douglas Weber", "Michael A. Rosenberg", "Emerson Liu", "Bo Li", "Ding Zhao"], "categories": ["stat.ML", "cs.LG", "eess.SP"], "doi": "", "_query": "house price prediction machine learning", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2104.03220v2", "title": "DoubleML -- An Object-Oriented Implementation of Double Machine Learning in Python", "abstract": "DoubleML is an open-source Python library implementing the double machine learning framework of Chernozhukov et al. (2018) for a variety of causal models. It contains functionalities for valid statistical inference on causal parameters when the estimation of nuisance parameters is based on machine learning methods. The object-oriented implementation of DoubleML provides a high flexibility in terms of model specifications and makes it easily extendable. The package is distributed under the MIT license and relies on core libraries from the scientific Python ecosystem: scikit-learn, numpy, pandas, scipy, statsmodels and joblib. Source code, documentation and an extensive user guide can be found at https://github.com/DoubleML/doubleml-for-py and https://docs.doubleml.org.", "published": "2021-04-07T16:16:39Z", "authors": ["Philipp Bach", "Victor Chernozhukov", "Malte S. Kurz", "Martin Spindler"], "categories": ["stat.ML", "cs.LG", "econ.EM"], "doi": "", "_query": "house price prediction machine learning", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/1403.0745v1", "title": "EnsembleSVM: A Library for Ensemble Learning Using Support Vector Machines", "abstract": "EnsembleSVM is a free software package containing efficient routines to perform ensemble learning with support vector machine (SVM) base models. It currently offers ensemble methods based on binary SVM models. Our implementation avoids duplicate storage and evaluation of support vectors which are shared between constituent models. Experimental results show that using ensemble approaches can drastically reduce training complexity while maintaining high predictive accuracy. The EnsembleSVM software package is freely available online at http://esat.kuleuven.be/stadius/ensemblesvm.", "published": "2014-03-04T11:28:59Z", "authors": ["Marc Claesen", "Frank De Smet", "Johan Suykens", "Bart De Moor"], "categories": ["stat.ML", "cs.LG"], "doi": "", "_query": "house price prediction machine learning", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/1806.05009v3", "title": "Tree Edit Distance Learning via Adaptive Symbol Embeddings", "abstract": "Metric learning has the aim to improve classification accuracy by learning a distance measure which brings data points from the same class closer together and pushes data points from different classes further apart. Recent research has demonstrated that metric learning approaches can also be applied to trees, such as molecular structures, abstract syntax trees of computer programs, or syntax trees of natural language, by learning the cost function of an edit distance, i.e. the costs of replacing, deleting, or inserting nodes in a tree. However, learning such costs directly may yield an edit distance which violates metric axioms, is challenging to interpret, and may not generalize well. In this contribution, we propose a novel metric learning approach for trees which we call embedding edit distance learning (BEDL) and which learns an edit distance indirectly by embedding the tree nodes as vectors, such that the Euclidean distance between those vectors supports class discrimination. We learn such embeddings by reducing the distance to prototypical trees from the same class and increasing the distance to prototypical trees from different classes. In our experiments, we show that BEDL improves upon the state-of-the-art in metric learning for trees on six benchmark data sets, ranging from computer science over biomedical data to a natural-language processing data set containing over 300,000 nodes.", "published": "2018-06-13T13:08:16Z", "authors": ["Benjamin Paaßen", "Claudio Gallicchio", "Alessio Micheli", "Barbara Hammer"], "categories": ["cs.LG", "stat.ML"], "doi": "", "_query": "house price prediction machine learning", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2012.03115v2", "title": "Bayesian optimization assisted unsupervised learning for efficient intra-tumor partitioning in MRI and survival prediction for glioblastoma patients", "abstract": "Glioblastoma is profoundly heterogeneous in microstructure and vasculature, which may lead to tumor regional diversity and distinct treatment response. Although successful in tumor sub-region segmentation and survival prediction, radiomics based on machine learning algorithms, is challenged by its robustness, due to the vague intermediate process and track changes. Also, the weak interpretability of the model poses challenges to clinical application. Here we proposed a machine learning framework to semi-automatically fine-tune the clustering algorithms and quantitatively identify stable sub-regions for reliable clinical survival prediction. Hyper-parameters are automatically determined by the global minimum of the trained Gaussian Process (GP) surrogate model through Bayesian optimization(BO) to alleviate the difficulty of tuning parameters for clinical researchers. To enhance the interpretability of the survival prediction model, we incorporated the prior knowledge of intra-tumoral heterogeneity, by segmenting tumor sub-regions and extracting sub-regional features. The results demonstrated that the global minimum of the trained GP surrogate can be used as sub-optimal hyper-parameter solutions for efficient. The sub-regions segmented based on physiological MRI can be applied to predict patient survival, which could enhance the clinical interpretability for the machine learning model.", "published": "2020-12-05T20:29:53Z", "authors": ["Yifan Li", "Chao Li", "Stephen Price", "Carola-Bibiane Schönlieb", "Xi Chen"], "categories": ["eess.IV", "cs.LG", "eess.SP"], "doi": "", "_query": "house price prediction machine learning", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/1711.07831v4", "title": "On Breast Cancer Detection: An Application of Machine Learning Algorithms on the Wisconsin Diagnostic Dataset", "abstract": "This paper presents a comparison of six machine learning (ML) algorithms: GRU-SVM (Agarap, 2017), Linear Regression, Multilayer Perceptron (MLP), Nearest Neighbor (NN) search, Softmax Regression, and Support Vector Machine (SVM) on the Wisconsin Diagnostic Breast Cancer (WDBC) dataset (Wolberg, Street, & Mangasarian, 1992) by measuring their classification test accuracy and their sensitivity and specificity values. The said dataset consists of features which were computed from digitized images of FNA tests on a breast mass (Wolberg, Street, & Mangasarian, 1992). For the implementation of the ML algorithms, the dataset was partitioned in the following fashion: 70% for training phase, and 30% for the testing phase. The hyper-parameters used for all the classifiers were manually assigned. Results show that all the presented ML algorithms performed well (all exceeded 90% test accuracy) on the classification task. The MLP algorithm stands out among the implemented algorithms with a test accuracy of ~99.04%.", "published": "2017-11-20T06:33:34Z", "authors": ["Abien Fred Agarap"], "categories": ["cs.LG", "stat.ML"], "doi": "10.1145/3184066.3184080", "_query": "house price prediction machine learning", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/1602.03943v5", "title": "Second-Order Stochastic Optimization for Machine Learning in Linear Time", "abstract": "First-order stochastic methods are the state-of-the-art in large-scale machine learning optimization owing to efficient per-iteration complexity. Second-order methods, while able to provide faster convergence, have been much less explored due to the high cost of computing the second-order information. In this paper we develop second-order stochastic methods for optimization problems in machine learning that match the per-iteration cost of gradient based methods, and in certain settings improve upon the overall running time over popular first-order methods. Furthermore, our algorithm has the desirable property of being implementable in time linear in the sparsity of the input data.", "published": "2016-02-12T01:38:05Z", "authors": ["Naman Agarwal", "Brian Bullins", "Elad Hazan"], "categories": ["stat.ML", "cs.LG"], "doi": "", "_query": "house price prediction machine learning", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2310.18646v1", "title": "Predicting Agricultural Commodities Prices with Machine Learning: A Review of Current Research", "abstract": "Agricultural price prediction is crucial for farmers, policymakers, and other stakeholders in the agricultural sector. However, it is a challenging task due to the complex and dynamic nature of agricultural markets. Machine learning algorithms have the potential to revolutionize agricultural price prediction by improving accuracy, real-time prediction, customization, and integration. This paper reviews recent research on machine learning algorithms for agricultural price prediction. We discuss the importance of agriculture in developing countries and the problems associated with crop price falls. We then identify the challenges of predicting agricultural prices and highlight how machine learning algorithms can support better prediction. Next, we present a comprehensive analysis of recent research, discussing the strengths and weaknesses of various machine learning techniques. We conclude that machine learning has the potential to revolutionize agricultural price prediction, but further research is essential to address the limitations and challenges associated with this approach.", "published": "2023-10-28T09:09:29Z", "authors": ["Nhat-Quang Tran", "Anna Felipe", "Thanh Nguyen Ngoc", "Tom Huynh", "Quang Tran", "Arthur Tang", "Thuy Nguyen"], "categories": ["cs.AI", "cs.LG"], "doi": "", "_query": "house price prediction machine learning", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2511.09573v1", "title": "Group Averaging for Physics Applications: Accuracy Improvements at Zero Training Cost", "abstract": "Many machine learning tasks in the natural sciences are precisely equivariant to particular symmetries. Nonetheless, equivariant methods are often not employed, perhaps because training is perceived to be challenging, or the symmetry is expected to be learned, or equivariant implementations are seen as hard to build. Group averaging is an available technique for these situations. It happens at test time; it can make any trained model precisely equivariant at a (often small) cost proportional to the size of the group; it places no requirements on model structure or training. It is known that, under mild conditions, the group-averaged model will have a provably better prediction accuracy than the original model. Here we show that an inexpensive group averaging can improve accuracy in practice. We take well-established benchmark machine learning models of differential equations in which certain symmetries ought to be obeyed. At evaluation time, we average the models over a small group of symmetries. Our experiments show that this procedure always decreases the average evaluation loss, with improvements of up to 37\\% in terms of the VRMSE. The averaging produces visually better predictions for continuous dynamics. This short paper shows that, under certain common circumstances, there are no disadvantages to imposing exact symmetries; the ML4PS community should consider group averaging as a cheap and simple way to improve model accuracy.", "published": "2025-11-11T21:10:09Z", "authors": ["Valentino F. Foit", "David W. Hogg", "Soledad Villar"], "categories": ["cs.LG", "stat.ML"], "doi": "", "_query": "house price prediction machine learning", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/1907.04708v1", "title": "Learning a Behavior Model of Hybrid Systems Through Combining Model-Based Testing and Machine Learning (Full Version)", "abstract": "Models play an essential role in the design process of cyber-physical systems. They form the basis for simulation and analysis and help in identifying design problems as early as possible. However, the construction of models that comprise physical and digital behavior is challenging. Therefore, there is considerable interest in learning such hybrid behavior by means of machine learning which requires sufficient and representative training data covering the behavior of the physical system adequately. In this work, we exploit a combination of automata learning and model-based testing to generate sufficient training data fully automatically. Experimental results on a platooning scenario show that recurrent neural networks learned with this data achieved significantly better results compared to models learned from randomly generated data. In particular, the classification error for crash detection is reduced by a factor of five and a similar F1-score is obtained with up to three orders of magnitude fewer training samples.", "published": "2019-07-10T13:22:32Z", "authors": ["Bernhard K. Aichernig", "Roderick Bloem", "Masoud Ebrahimi", "Martin Horn", "Franz Pernkopf", "Wolfgang Roth", "Astrid Rupp", "Martin Tappler", "Markus Tranninger"], "categories": ["cs.LG", "stat.ML"], "doi": "", "_query": "house price prediction machine learning", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2509.17208v3", "title": "Active Learning for Machine Learning Driven Molecular Dynamics", "abstract": "Machine-learned coarse-grained (CG) potentials are fast, but degrade over time when simulations reach under-sampled bio-molecular conformations, and generating widespread all-atom (AA) data to combat this is computationally infeasible. We propose a novel active learning (AL) framework for CG neural network potentials in molecular dynamics (MD). Building on the CGSchNet model, our method employs root mean squared deviation (RMSD)-based frame selection from MD simulations in order to generate data on-the-fly by querying an oracle during the training of a neural network potential. This framework preserves CG-level efficiency while correcting the model at precise, RMSD-identified coverage gaps. By training CGSchNet, a coarse-grained neural network potential, we empirically show that our framework explores previously unseen configurations and trains the model on unexplored regions of conformational space. Our active learning framework enables a CGSchNet model trained on the Chignolin protein to achieve a 33.05\\% improvement in the Wasserstein-1 (W1) metric in Time-lagged Independent Component Analysis (TICA) space on an in-house benchmark suite.", "published": "2025-09-21T19:26:32Z", "authors": ["Kevin Bachelor", "Sanya Murdeshwar", "Daniel Sabo", "Razvan Marinescu"], "categories": ["cs.LG", "physics.atm-clus"], "doi": "", "_query": "house price prediction machine learning", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2003.08561v2", "title": "XtarNet: Learning to Extract Task-Adaptive Representation for Incremental Few-Shot Learning", "abstract": "Learning novel concepts while preserving prior knowledge is a long-standing challenge in machine learning. The challenge gets greater when a novel task is given with only a few labeled examples, a problem known as incremental few-shot learning. We propose XtarNet, which learns to extract task-adaptive representation (TAR) for facilitating incremental few-shot learning. The method utilizes a backbone network pretrained on a set of base categories while also employing additional modules that are meta-trained across episodes. Given a new task, the novel feature extracted from the meta-trained modules is mixed with the base feature obtained from the pretrained model. The process of combining two different features provides TAR and is also controlled by meta-trained modules. The TAR contains effective information for classifying both novel and base categories. The base and novel classifiers quickly adapt to a given task by utilizing the TAR. Experiments on standard image datasets indicate that XtarNet achieves state-of-the-art incremental few-shot learning performance. The concept of TAR can also be used in conjunction with existing incremental few-shot learning methods; extensive simulation results in fact show that applying TAR enhances the known methods significantly.", "published": "2020-03-19T04:02:44Z", "authors": ["Sung Whan Yoon", "Do-Yeon Kim", "Jun Seo", "Jaekyun Moon"], "categories": ["cs.LG", "cs.AI", "cs.NE"], "doi": "", "_query": "house price prediction machine learning", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2104.08690v3", "title": "Rethinking Image-Scaling Attacks: The Interplay Between Vulnerabilities in Machine Learning Systems", "abstract": "As real-world images come in varying sizes, the machine learning model is part of a larger system that includes an upstream image scaling algorithm. In this paper, we investigate the interplay between vulnerabilities of the image scaling procedure and machine learning models in the decision-based black-box setting. We propose a novel sampling strategy to make a black-box attack exploit vulnerabilities in scaling algorithms, scaling defenses, and the final machine learning model in an end-to-end manner. Based on this scaling-aware attack, we reveal that most existing scaling defenses are ineffective under threat from downstream models. Moreover, we empirically observe that standard black-box attacks can significantly improve their performance by exploiting the vulnerable scaling procedure. We further demonstrate this problem on a commercial Image Analysis API with decision-based black-box attacks.", "published": "2021-04-18T03:19:15Z", "authors": ["Yue Gao", "Ilia Shumailov", "Kassem Fawaz"], "categories": ["cs.LG", "cs.CR"], "doi": "", "_query": "house price prediction machine learning", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/1906.01827v3", "title": "Coresets for Data-efficient Training of Machine Learning Models", "abstract": "Incremental gradient (IG) methods, such as stochastic gradient descent and its variants are commonly used for large scale optimization in machine learning. Despite the sustained effort to make IG methods more data-efficient, it remains an open question how to select a training data subset that can theoretically and practically perform on par with the full dataset. Here we develop CRAIG, a method to select a weighted subset (or coreset) of training data that closely estimates the full gradient by maximizing a submodular function. We prove that applying IG to this subset is guaranteed to converge to the (near)optimal solution with the same convergence rate as that of IG for convex optimization. As a result, CRAIG achieves a speedup that is inversely proportional to the size of the subset. To our knowledge, this is the first rigorous method for data-efficient training of general machine learning models. Our extensive set of experiments show that CRAIG, while achieving practically the same solution, speeds up various IG methods by up to 6x for logistic regression and 3x for training deep neural networks.", "published": "2019-06-05T05:10:37Z", "authors": ["Baharan Mirzasoleiman", "Jeff Bilmes", "Jure Leskovec"], "categories": ["cs.LG", "cs.AI", "stat.ML"], "doi": "", "_query": "house price prediction machine learning", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2011.01925v1", "title": "Comparison of pharmacist evaluation of medication orders with predictions of a machine learning model", "abstract": "The objective of this work was to assess the clinical performance of an unsupervised machine learning model aimed at identifying unusual medication orders and pharmacological profiles. We conducted a prospective study between April 2020 and August 2020 where 25 clinical pharmacists dichotomously (typical or atypical) rated 12,471 medication orders and 1,356 pharmacological profiles. Based on AUPR, performance was poor for orders, but satisfactory for profiles. Pharmacists considered the model a useful screening tool.", "published": "2020-11-03T18:57:47Z", "authors": ["Sophie-Camille Hogue", "Flora Chen", "Geneviève Brassard", "Denis Lebel", "Jean-François Bussières", "Audrey Durand", "Maxime Thibault"], "categories": ["cs.LG"], "doi": "", "_query": "house price prediction machine learning", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2012.14406v2", "title": "dalex: Responsible Machine Learning with Interactive Explainability and Fairness in Python", "abstract": "The increasing amount of available data, computing power, and the constant pursuit for higher performance results in the growing complexity of predictive models. Their black-box nature leads to opaqueness debt phenomenon inflicting increased risks of discrimination, lack of reproducibility, and deflated performance due to data drift. To manage these risks, good MLOps practices ask for better validation of model performance and fairness, higher explainability, and continuous monitoring. The necessity of deeper model transparency appears not only from scientific and social domains, but also emerging laws and regulations on artificial intelligence. To facilitate the development of responsible machine learning models, we showcase dalex, a Python package which implements the model-agnostic interface for interactive model exploration. It adopts the design crafted through the development of various tools for responsible machine learning; thus, it aims at the unification of the existing solutions. This library's source code and documentation are available under open license at https://python.drwhy.ai/.", "published": "2020-12-28T18:39:59Z", "authors": ["Hubert Baniecki", "Wojciech Kretowicz", "Piotr Piatyszek", "Jakub Wisniewski", "Przemyslaw Biecek"], "categories": ["cs.LG", "cs.HC", "cs.SE", "stat.ML"], "doi": "", "_query": "house price prediction machine learning", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/1709.03239v2", "title": "On better training the infinite restricted Boltzmann machines", "abstract": "The infinite restricted Boltzmann machine (iRBM) is an extension of the classic RBM. It enjoys a good property of automatically deciding the size of the hidden layer according to specific training data. With sufficient training, the iRBM can achieve a competitive performance with that of the classic RBM. However, the convergence of learning the iRBM is slow, due to the fact that the iRBM is sensitive to the ordering of its hidden units, the learned filters change slowly from the left-most hidden unit to right. To break this dependency between neighboring hidden units and speed up the convergence of training, a novel training strategy is proposed. The key idea of the proposed training strategy is randomly regrouping the hidden units before each gradient descent step. Potentially, a mixing of infinite many iRBMs with different permutations of the hidden units can be achieved by this learning method, which has a similar effect of preventing the model from over-fitting as the dropout. The original iRBM is also modified to be capable of carrying out discriminative training. To evaluate the impact of our method on convergence speed of learning and the model's generalization ability, several experiments have been performed on the binarized MNIST and CalTech101 Silhouettes datasets. Experimental results indicate that the proposed training strategy can greatly accelerate learning and enhance generalization ability of iRBMs.", "published": "2017-09-11T04:41:06Z", "authors": ["Xuan Peng", "Xunzhang Gao", "Xiang Li"], "categories": ["cs.LG", "cs.AI", "stat.ML"], "doi": "10.1007/s10994-018-5696-2", "_query": "house price prediction machine learning", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2004.05005v1", "title": "Adversarial Attacks on Machine Learning Cybersecurity Defences in Industrial Control Systems", "abstract": "The proliferation and application of machine learning based Intrusion Detection Systems (IDS) have allowed for more flexibility and efficiency in the automated detection of cyber attacks in Industrial Control Systems (ICS). However, the introduction of such IDSs has also created an additional attack vector; the learning models may also be subject to cyber attacks, otherwise referred to as Adversarial Machine Learning (AML). Such attacks may have severe consequences in ICS systems, as adversaries could potentially bypass the IDS. This could lead to delayed attack detection which may result in infrastructure damages, financial loss, and even loss of life. This paper explores how adversarial learning can be used to target supervised models by generating adversarial samples using the Jacobian-based Saliency Map attack and exploring classification behaviours. The analysis also includes the exploration of how such samples can support the robustness of supervised models using adversarial training. An authentic power system dataset was used to support the experiments presented herein. Overall, the classification performance of two widely used classifiers, Random Forest and J48, decreased by 16 and 20 percentage points when adversarial samples were present. Their performances improved following adversarial training, demonstrating their robustness towards such attacks.", "published": "2020-04-10T12:05:33Z", "authors": ["Eirini Anthi", "Lowri Williams", "Matilda Rhode", "Pete Burnap", "Adam Wedgbury"], "categories": ["cs.LG", "cs.CR", "eess.SP", "stat.ML"], "doi": "", "_query": "house price prediction machine learning", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2004.03722v1", "title": "Challenges in Vessel Behavior and Anomaly Detection: From Classical Machine Learning to Deep Learning", "abstract": "The global expansion of maritime activities and the development of the Automatic Identification System (AIS) have driven the advances in maritime monitoring systems in the last decade. Monitoring vessel behavior is fundamental to safeguard maritime operations, protecting other vessels sailing the ocean and the marine fauna and flora. Given the enormous volume of vessel data continually being generated, real-time analysis of vessel behaviors is only possible because of decision support systems provided with event and anomaly detection methods. However, current works on vessel event detection are ad-hoc methods able to handle only a single or a few predefined types of vessel behavior. Most of the existing approaches do not learn from the data and require the definition of queries and rules for describing each behavior. In this paper, we discuss challenges and opportunities in classical machine learning and deep learning for vessel event and anomaly detection. We hope to motivate the research of novel methods and tools, since addressing these challenges is an essential step towards actual intelligent maritime monitoring systems.", "published": "2020-04-07T21:25:12Z", "authors": ["Lucas May Petry", "Amilcar Soares", "Vania Bogorny", "Bruno Brandoli", "Stan Matwin"], "categories": ["cs.LG", "stat.ML"], "doi": "", "_query": "house price prediction machine learning", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/1811.05660v1", "title": "MT-CGCNN: Integrating Crystal Graph Convolutional Neural Network with Multitask Learning for Material Property Prediction", "abstract": "Developing accurate, transferable and computationally inexpensive machine learning models can rapidly accelerate the discovery and development of new materials. Some of the major challenges involved in developing such models are, (i) limited availability of materials data as compared to other fields, (ii) lack of universal descriptor of materials to predict its various properties. The limited availability of materials data can be addressed through transfer learning, while the generic representation was recently addressed by Xie and Grossman [1], where they developed a crystal graph convolutional neural network (CGCNN) that provides a unified representation of crystals. In this work, we develop a new model (MT-CGCNN) by integrating CGCNN with transfer learning based on multi-task (MT) learning. We demonstrate the effectiveness of MT-CGCNN by simultaneous prediction of various material properties such as Formation Energy, Band Gap and Fermi Energy for a wide range of inorganic crystals (46774 materials). MT-CGCNN is able to reduce the test error when employed on correlated properties by upto 8%. The model prediction has lower test error compared to CGCNN, even when the training data is reduced by 10%. We also demonstrate our model's better performance through prediction of end user scenario related to metal/non-metal classification. These results encourage further development of machine learning approaches which leverage multi-task learning to address the aforementioned challenges in the discovery of new materials. We make MT-CGCNN's source code available to encourage reproducible research.", "published": "2018-11-14T06:13:29Z", "authors": ["Soumya Sanyal", "Janakiraman Balachandran", "Naganand Yadati", "Abhishek Kumar", "Padmini Rajagopalan", "Suchismita Sanyal", "Partha Talukdar"], "categories": ["cs.LG", "cond-mat.mtrl-sci", "stat.ML"], "doi": "", "_query": "house price prediction machine learning", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2106.07214v4", "title": "Backdoor Learning Curves: Explaining Backdoor Poisoning Beyond Influence Functions", "abstract": "Backdoor attacks inject poisoning samples during training, with the goal of forcing a machine learning model to output an attacker-chosen class when presented a specific trigger at test time. Although backdoor attacks have been demonstrated in a variety of settings and against different models, the factors affecting their effectiveness are still not well understood. In this work, we provide a unifying framework to study the process of backdoor learning under the lens of incremental learning and influence functions. We show that the effectiveness of backdoor attacks depends on: (i) the complexity of the learning algorithm, controlled by its hyperparameters; (ii) the fraction of backdoor samples injected into the training set; and (iii) the size and visibility of the backdoor trigger. These factors affect how fast a model learns to correlate the presence of the backdoor trigger with the target class. Our analysis unveils the intriguing existence of a region in the hyperparameter space in which the accuracy on clean test samples is still high while backdoor attacks are ineffective, thereby suggesting novel criteria to improve existing defenses.", "published": "2021-06-14T08:00:48Z", "authors": ["Antonio Emanuele Cinà", "Kathrin Grosse", "Sebastiano Vascon", "Ambra Demontis", "Battista Biggio", "Fabio Roli", "Marcello Pelillo"], "categories": ["cs.LG", "cs.CR"], "doi": "10.1007/s13042-024-02363-5", "_query": "house price prediction machine learning", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/1703.10121v1", "title": "The Top 10 Topics in Machine Learning Revisited: A Quantitative Meta-Study", "abstract": "Which topics of machine learning are most commonly addressed in research? This question was initially answered in 2007 by doing a qualitative survey among distinguished researchers. In our study, we revisit this question from a quantitative perspective. Concretely, we collect 54K abstracts of papers published between 2007 and 2016 in leading machine learning journals and conferences. We then use machine learning in order to determine the top 10 topics in machine learning. We not only include models, but provide a holistic view across optimization, data, features, etc. This quantitative approach allows reducing the bias of surveys. It reveals new and up-to-date insights into what the 10 most prolific topics in machine learning research are. This allows researchers to identify popular topics as well as new and rising topics for their research.", "published": "2017-03-29T16:29:04Z", "authors": ["Patrick Glauner", "Manxing Du", "Victor Paraschiv", "Andrey Boytsov", "Isabel Lopez Andrade", "Jorge Meira", "Petko Valtchev", "Radu State"], "categories": ["cs.LG", "cs.AI", "stat.ML"], "doi": "", "_query": "house price prediction machine learning", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/1901.01774v1", "title": "Location-Centered House Price Prediction: A Multi-Task Learning Approach", "abstract": "Accurate house prediction is of great significance to various real estate stakeholders such as house owners, buyers, investors, and agents. We propose a location-centered prediction framework that differs from existing work in terms of data profiling and prediction model. Regarding data profiling, we define and capture a fine-grained location profile powered by a diverse range of location data sources, such as transportation profile (e.g., distance to nearest train station), education profile (e.g., school zones and ranking), suburb profile based on census data, facility profile (e.g., nearby hospitals, supermarkets). Regarding the choice of prediction model, we observe that a variety of approaches either consider the entire house data for modeling, or split the entire data and model each partition independently. However, such modeling ignores the relatedness between partitions, and for all prediction scenarios, there may not be sufficient training samples per partition for the latter approach. We address this problem by conducting a careful study of exploiting the Multi-Task Learning (MTL) model. Specifically, we map the strategies for splitting the entire house data to the ways the tasks are defined in MTL, and each partition obtained is aligned with a task. Furthermore, we select specific MTL-based methods with different regularization terms to capture and exploit the relatedness between tasks. Based on real-world house transaction data collected in Melbourne, Australia. We design extensive experimental evaluations, and the results indicate a significant superiority of MTL-based methods over state-of-the-art approaches. Meanwhile, we conduct an in-depth analysis on the impact of task definitions and method selections in MTL on the prediction performance, and demonstrate that the impact of task definitions on prediction performance far exceeds that of method selections.", "published": "2019-01-07T12:36:57Z", "authors": ["Guangliang Gao", "Zhifeng Bao", "Jie Cao", "A. K. Qin", "Timos Sellis", "Zhiang Wu"], "categories": ["cs.LG", "stat.ML"], "doi": "", "_query": "house price prediction machine learning", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2605.12165v1", "title": "Machine Learning for neutron source distributions", "abstract": "In light of the recent advancements in machine learning, we propose a novel approach to neutron source distribution estimation through the utilisation of probabilistic generative models. The estimation is based on a Monte Carlo particle list, which is only required during the training stage of the machine learning model. Once the source distribution has been learned, the model is independent of the original particle list, allowing for further sampling in an efficient, rapid, and memory-costless manner. The performance of various generative models is evaluated, including a variational autoencoder, a normalizing flow, a generative adversarial network, and a denoising diffusion model. These approaches are then compared to existing source distribution estimations, and the advantages and disadvantages of each approach are discussed. The results demonstrate that source distributions can be modeled through the use of probabilistic generative models, which paves the way for further advancements in this field.", "published": "2026-05-12T14:13:38Z", "authors": ["Jose Ignacio Robledo", "Norberto Schmidt", "Klaus Lieutenant", "Jingjing Li", "Stefan Kesselheim", "Paul Zakalek"], "categories": ["physics.ins-det", "cs.LG", "physics.comp-ph"], "doi": "", "_query": "house price prediction machine learning", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2402.00849v5", "title": "Score-based Causal Representation Learning: Linear and General Transformations", "abstract": "This paper addresses intervention-based causal representation learning (CRL) under a general nonparametric latent causal model and an unknown transformation that maps the latent variables to the observed variables. Linear and general transformations are investigated. The paper addresses both the identifiability and achievability aspects. Identifiability refers to determining algorithm-agnostic conditions that ensure the recovery of the true latent causal variables and the underlying latent causal graph. Achievability refers to the algorithmic aspects and addresses designing algorithms that achieve identifiability guarantees. By drawing novel connections between score functions (i.e., the gradients of the logarithm of density functions) and CRL, this paper designs a score-based class of algorithms that ensures both identifiability and achievability. First, the paper focuses on linear transformations and shows that one stochastic hard intervention per node suffices to guarantee identifiability. It also provides partial identifiability guarantees for soft interventions, including identifiability up to mixing with parents for general causal models and perfect recovery of the latent graph for sufficiently nonlinear causal models. Secondly, it focuses on general transformations and demonstrates that two stochastic hard interventions per node are sufficient for identifiability. This is achieved by defining a differentiable loss function whose global optima ensure identifiability for general CRL. Notably, one does not need to know which pair of interventional environments has the same node intervened. Finally, the theoretical results are empirically validated via experiments on structured synthetic data and image data.", "published": "2024-02-01T18:40:03Z", "authors": ["Burak Varıcı", "Emre Acartürk", "Karthikeyan Shanmugam", "Abhishek Kumar", "Ali Tajer"], "categories": ["cs.LG", "stat.ML"], "doi": "", "_query": "house price prediction machine learning", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/1206.4656v1", "title": "Machine Learning that Matters", "abstract": "Much of current machine learning (ML) research has lost its connection to problems of import to the larger world of science and society. From this perspective, there exist glaring limitations in the data sets we investigate, the metrics we employ for evaluation, and the degree to which results are communicated back to their originating domains. What changes are needed to how we conduct research to increase the impact that ML has? We present six Impact Challenges to explicitly focus the field?s energy and attention, and we discuss existing obstacles that must be addressed. We aim to inspire ongoing discussion and focus on ML that matters.", "published": "2012-06-18T15:26:13Z", "authors": ["Kiri Wagstaff"], "categories": ["cs.LG", "cs.AI", "stat.ML"], "doi": "", "_query": "house price prediction machine learning", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/1305.2505v1", "title": "On the Generalization Ability of Online Learning Algorithms for Pairwise Loss Functions", "abstract": "In this paper, we study the generalization properties of online learning based stochastic methods for supervised learning problems where the loss function is dependent on more than one training sample (e.g., metric learning, ranking). We present a generic decoupling technique that enables us to provide Rademacher complexity-based generalization error bounds. Our bounds are in general tighter than those obtained by Wang et al (COLT 2012) for the same problem. Using our decoupling technique, we are further able to obtain fast convergence rates for strongly convex pairwise loss functions. We are also able to analyze a class of memory efficient online learning algorithms for pairwise learning problems that use only a bounded subset of past training samples to update the hypothesis at each step. Finally, in order to complement our generalization bounds, we propose a novel memory efficient online learning algorithm for higher order learning problems with bounded regret guarantees.", "published": "2013-05-11T13:52:37Z", "authors": ["Purushottam Kar", "Bharath K Sriperumbudur", "Prateek Jain", "Harish C Karnick"], "categories": ["cs.LG", "stat.ML"], "doi": "", "_query": "house price prediction machine learning", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2111.13180v4", "title": "Variational Gibbs Inference for Statistical Model Estimation from Incomplete Data", "abstract": "Statistical models are central to machine learning with broad applicability across a range of downstream tasks. The models are controlled by free parameters that are typically estimated from data by maximum-likelihood estimation or approximations thereof. However, when faced with real-world data sets many of the models run into a critical issue: they are formulated in terms of fully-observed data, whereas in practice the data sets are plagued with missing data. The theory of statistical model estimation from incomplete data is conceptually similar to the estimation of latent-variable models, where powerful tools such as variational inference (VI) exist. However, in contrast to standard latent-variable models, parameter estimation with incomplete data often requires estimating exponentially-many conditional distributions of the missing variables, hence making standard VI methods intractable. We address this gap by introducing variational Gibbs inference (VGI), a new general-purpose method to estimate the parameters of statistical models from incomplete data. We validate VGI on a set of synthetic and real-world estimation tasks, estimating important machine learning models such as variational autoencoders and normalising flows from incomplete data. The proposed method, whilst general-purpose, achieves competitive or better performance than existing model-specific estimation methods.", "published": "2021-11-25T17:22:22Z", "authors": ["Vaidotas Simkus", "Benjamin Rhodes", "Michael U. Gutmann"], "categories": ["cs.LG", "stat.ME", "stat.ML"], "doi": "", "_query": "house price prediction machine learning", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/1905.02342v3", "title": "Machine Learning Cryptanalysis of a Quantum Random Number Generator", "abstract": "Random number generators (RNGs) that are crucial for cryptographic applications have been the subject of adversarial attacks. These attacks exploit environmental information to predict generated random numbers that are supposed to be truly random and unpredictable. Though quantum random number generators (QRNGs) are based on the intrinsic indeterministic nature of quantum properties, the presence of classical noise in the measurement process compromises the integrity of a QRNG. In this paper, we develop a predictive machine learning (ML) analysis to investigate the impact of deterministic classical noise in different stages of an optical continuous variable QRNG. Our ML model successfully detects inherent correlations when the deterministic noise sources are prominent. After appropriate filtering and randomness extraction processes are introduced, our QRNG system, in turn, demonstrates its robustness against ML. We further demonstrate the robustness of our ML approach by applying it to uniformly distributed random numbers from the QRNG and a congruential RNG. Hence, our result shows that ML has potentials in benchmarking the quality of RNG devices.", "published": "2019-05-07T03:42:04Z", "authors": ["Nhan Duy Truong", "Jing Yan Haw", "Syed Muhamad Assad", "Ping Koy Lam", "Omid Kavehei"], "categories": ["cs.LG", "cs.CR", "quant-ph", "stat.ML"], "doi": "10.1109/TIFS.2018.2850770", "_query": "house price prediction machine learning", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2204.11719v1", "title": "On the Performance of Machine Learning Methods for Breakthrough Curve Prediction", "abstract": "Reactive flows are important part of numerous technical and environmental processes. Often monitoring the flow and species concentrations within the domain is not possible or is expensive, in contrast, outlet concentration is straightforward to measure. In connection with reactive flows in porous media, the term breakthrough curve is used to denote the time dependency of the outlet concentration with prescribed conditions at the inlet. In this work we apply several machine learning methods to predict breakthrough curves from the given set of parameters. In our case the parameters are the Damköhler and Peclet numbers. We perform a thorough analysis for the one-dimensional case and also provide the results for the three-dimensional case.", "published": "2022-04-25T15:27:03Z", "authors": ["Daria Fokina", "Oleg Iliev", "Pavel Toktaliev", "Ivan Oseledets", "Felix Schindler"], "categories": ["physics.flu-dyn", "cs.LG", "physics.comp-ph"], "doi": "", "_query": "house price prediction machine learning", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/1911.11251v8", "title": "Hexagonal Image Processing in the Context of Machine Learning: Conception of a Biologically Inspired Hexagonal Deep Learning Framework", "abstract": "Inspired by the human visual perception system, hexagonal image processing in the context of machine learning deals with the development of image processing systems that combine the advantages of evolutionary motivated structures based on biological models. While conventional state-of-the-art image processing systems of recording and output devices almost exclusively utilize square arranged methods, their hexagonal counterparts offer a number of key advantages that can benefit both researchers and users. This contribution serves as a general application-oriented approach the synthesis of the therefore designed hexagonal image processing framework, called Hexnet, the processing steps of hexagonal image transformation, and dependent methods. The results of our created test environment show that the realized framework surpasses current approaches of hexagonal image processing systems, while hexagonal artificial neural networks can benefit from the implemented hexagonal architecture. As hexagonal lattice format based deep neural networks, also called H-DNN, can be compared to their square counterparts by transforming classical square lattice based data sets into their hexagonal representation, they can also result in a reduction of trainable parameters as well as result in increased training and test rates.", "published": "2019-11-25T21:58:31Z", "authors": ["Tobias Schlosser", "Michael Friedrich", "Danny Kowerko"], "categories": ["cs.LG", "cs.CV", "eess.IV", "stat.ML"], "doi": "10.1109/ICMLA.2019.00300", "_query": "house price prediction machine learning", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/0904.3664v1", "title": "Introduction to Machine Learning: Class Notes 67577", "abstract": "Introduction to Machine learning covering Statistical Inference (Bayes, EM, ML/MaxEnt duality), algebraic and spectral methods (PCA, LDA, CCA, Clustering), and PAC learning (the Formal model, VC dimension, Double Sampling theorem).", "published": "2009-04-23T11:40:57Z", "authors": ["Amnon Shashua"], "categories": ["cs.LG"], "doi": "", "_query": "house price prediction machine learning", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/1612.04108v1", "title": "Corporate Disruption in the Science of Machine Learning", "abstract": "This MSc dissertation considers the effects of the current corporate interest on researchers in the field of machine learning. Situated within the field's cyclical history of academic, public and corporate interest, this dissertation investigates how current researchers view recent developments and negotiate their own research practices within an environment of increased commercial interest and funding. The original research consists of in-depth interviews with 12 machine learning researchers working in both academia and industry. Building on theory from science, technology and society studies, this dissertation problematizes the traditional narratives of the neoliberalization of academic research by allowing the researchers themselves to discuss how their career choices, working environments and interactions with others in the field have been affected by the reinvigorated corporate interest of recent years.", "published": "2016-12-13T11:54:22Z", "authors": ["Sam Work"], "categories": ["cs.CY", "cs.LG"], "doi": "", "_query": "house price prediction machine learning", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2303.02060v2", "title": "Spectral learning of Bernoulli linear dynamical systems models", "abstract": "Latent linear dynamical systems with Bernoulli observations provide a powerful modeling framework for identifying the temporal dynamics underlying binary time series data, which arise in a variety of contexts such as binary decision-making and discrete stochastic processes (e.g., binned neural spike trains). Here we develop a spectral learning method for fast, efficient fitting of probit-Bernoulli latent linear dynamical system (LDS) models. Our approach extends traditional subspace identification methods to the Bernoulli setting via a transformation of the first and second sample moments. This results in a robust, fixed-cost estimator that avoids the hazards of local optima and the long computation time of iterative fitting procedures like the expectation-maximization (EM) algorithm. In regimes where data is limited or assumptions about the statistical structure of the data are not met, we demonstrate that the spectral estimate provides a good initialization for Laplace-EM fitting. Finally, we show that the estimator provides substantial benefits to real world settings by analyzing data from mice performing a sensory decision-making task.", "published": "2023-03-03T16:29:12Z", "authors": ["Iris R. Stone", "Yotam Sagiv", "Il Memming Park", "Jonathan W. Pillow"], "categories": ["stat.ML", "cs.LG"], "doi": "", "_query": "house price prediction machine learning", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/1904.05381v1", "title": "ReinBo: Machine Learning pipeline search and configuration with Bayesian Optimization embedded Reinforcement Learning", "abstract": "Machine learning pipeline potentially consists of several stages of operations like data preprocessing, feature engineering and machine learning model training. Each operation has a set of hyper-parameters, which can become irrelevant for the pipeline when the operation is not selected. This gives rise to a hierarchical conditional hyper-parameter space. To optimize this mixed continuous and discrete conditional hierarchical hyper-parameter space, we propose an efficient pipeline search and configuration algorithm which combines the power of Reinforcement Learning and Bayesian Optimization. Empirical results show that our method performs favorably compared to state of the art methods like Auto-sklearn , TPOT, Tree Parzen Window, and Random Search.", "published": "2019-04-10T18:26:16Z", "authors": ["Xudong Sun", "Jiali Lin", "Bernd Bischl"], "categories": ["cs.LG", "cs.AI", "stat.ML"], "doi": "", "_query": "house price prediction machine learning", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2510.23191v2", "title": "The Benchmarking Epistemology: Validity Theory for Evaluating Machine Learning Models", "abstract": "Predictive benchmarking, evaluating machine learning models based on predictive performance and competitive ranking, is central to machine learning research and scientific inquiry. However, benchmark scores at best measure performance relative to a specific dataset and learning problem. Drawing substantial scientific inferences requires additional assumptions. Adapting ideas from psychological validity theory, we propose validity conditions that make these assumptions explicit. In two case studies---ImageNet and the Fragile Families Challenge---we show how benchmark results can support inferences about research progress and limits of predictability, situating predictive benchmarking as a distinct epistemic practice in machine learning.", "published": "2025-10-27T10:30:30Z", "authors": ["Timo Freiesleben", "Sebastian Zezulka"], "categories": ["cs.LG", "stat.ML"], "doi": "10.1017/psa.2026.10280", "_query": "house price prediction machine learning", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/1906.10652v2", "title": "Monte Carlo Gradient Estimation in Machine Learning", "abstract": "This paper is a broad and accessible survey of the methods we have at our disposal for Monte Carlo gradient estimation in machine learning and across the statistical sciences: the problem of computing the gradient of an expectation of a function with respect to parameters defining the distribution that is integrated; the problem of sensitivity analysis. In machine learning research, this gradient problem lies at the core of many learning problems, in supervised, unsupervised and reinforcement learning. We will generally seek to rewrite such gradients in a form that allows for Monte Carlo estimation, allowing them to be easily and efficiently used and analysed. We explore three strategies--the pathwise, score function, and measure-valued gradient estimators--exploring their historical development, derivation, and underlying assumptions. We describe their use in other fields, show how they are related and can be combined, and expand on their possible generalisations. Wherever Monte Carlo gradient estimators have been derived and deployed in the past, important advances have followed. A deeper and more widely-held understanding of this problem will lead to further advances, and it is these advances that we wish to support.", "published": "2019-06-25T16:46:04Z", "authors": ["Shakir Mohamed", "Mihaela Rosca", "Michael Figurnov", "Andriy Mnih"], "categories": ["stat.ML", "cs.LG", "math.OC"], "doi": "", "_query": "house price prediction machine learning", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2011.06158v3", "title": "Mostly Harmless Machine Learning: Learning Optimal Instruments in Linear IV Models", "abstract": "We offer straightforward theoretical results that justify incorporating machine learning in the standard linear instrumental variable setting. The key idea is to use machine learning, combined with sample-splitting, to predict the treatment variable from the instrument and any exogenous covariates, and then use this predicted treatment and the covariates as technical instruments to recover the coefficients in the second-stage. This allows the researcher to extract non-linear co-variation between the treatment and instrument that may dramatically improve estimation precision and robustness by boosting instrument strength. Importantly, we constrain the machine-learned predictions to be linear in the exogenous covariates, thus avoiding spurious identification arising from non-linear relationships between the treatment and the covariates. We show that this approach delivers consistent and asymptotically normal estimates under weak conditions and that it may be adapted to be semiparametrically efficient (Chamberlain, 1992). Our method preserves standard intuitions and interpretations of linear instrumental variable methods, including under weak identification, and provides a simple, user-friendly upgrade to the applied economics toolbox. We illustrate our method with an example in law and criminal justice, examining the causal effect of appellate court reversals on district court sentencing decisions.", "published": "2020-11-12T01:55:11Z", "authors": ["Jiafeng Chen", "Daniel L. Chen", "Greg Lewis"], "categories": ["econ.EM", "cs.LG", "stat.ME"], "doi": "", "_query": "house price prediction machine learning", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2410.21339v1", "title": "Machine Learning and Quantum Intelligence for Health Data Scenarios", "abstract": "The advent of quantum computing has opened new possibilities in data science, offering unique capabilities for addressing complex, data-intensive problems. Traditional machine learning algorithms often face challenges in high-dimensional or limited-quality datasets, which are common in healthcare. Quantum Machine Learning leverages quantum properties, such as superposition and entanglement, to enhance pattern recognition and classification, potentially surpassing classical approaches. This paper explores QML's application in healthcare, focusing on quantum kernel methods and hybrid quantum-classical networks for heart disease prediction and COVID-19 detection, assessing their feasibility and performance.", "published": "2024-10-28T01:04:43Z", "authors": ["Sanjeev Naguleswaran"], "categories": ["quant-ph", "cs.AI", "cs.LG"], "doi": "", "_query": "house price prediction machine learning", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2002.05810v1", "title": "RNA Secondary Structure Prediction By Learning Unrolled Algorithms", "abstract": "In this paper, we propose an end-to-end deep learning model, called E2Efold, for RNA secondary structure prediction which can effectively take into account the inherent constraints in the problem. The key idea of E2Efold is to directly predict the RNA base-pairing matrix, and use an unrolled algorithm for constrained programming as the template for deep architectures to enforce constraints. With comprehensive experiments on benchmark datasets, we demonstrate the superior performance of E2Efold: it predicts significantly better structures compared to previous SOTA (especially for pseudoknotted structures), while being as efficient as the fastest algorithms in terms of inference time.", "published": "2020-02-13T23:21:25Z", "authors": ["Xinshi Chen", "Yu Li", "Ramzan Umarov", "Xin Gao", "Le Song"], "categories": ["cs.LG", "stat.ML"], "doi": "", "_query": "house price prediction machine learning", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2503.08636v2", "title": "Birds look like cars: Adversarial analysis of intrinsically interpretable deep learning", "abstract": "A common belief is that intrinsically interpretable deep learning models ensure a correct, intuitive understanding of their behavior and offer greater robustness against accidental errors or intentional manipulation. However, these beliefs have not been comprehensively verified, and growing evidence casts doubt on them. In this paper, we highlight the risks related to overreliance and susceptibility to adversarial manipulation of these so-called \"intrinsically (aka inherently) interpretable\" models by design. We introduce two strategies for adversarial analysis with prototype manipulation and backdoor attacks against prototype-based networks, and discuss how concept bottleneck models defend against these attacks. Fooling the model's reasoning by exploiting its use of latent prototypes manifests the inherent uninterpretability of deep neural networks, leading to a false sense of security reinforced by a visual confirmation bias. The reported limitations of part-prototype networks put their trustworthiness and applicability into question, motivating further work on the robustness and alignment of (deep) interpretable models.", "published": "2025-03-11T17:24:33Z", "authors": ["Hubert Baniecki", "Przemyslaw Biecek"], "categories": ["cs.LG", "cs.CV"], "doi": "10.1007/s10994-025-06896-w", "_query": "house price prediction machine learning", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/1910.00189v2", "title": "The Non-IID Data Quagmire of Decentralized Machine Learning", "abstract": "Many large-scale machine learning (ML) applications need to perform decentralized learning over datasets generated at different devices and locations. Such datasets pose a significant challenge to decentralized learning because their different contexts result in significant data distribution skew across devices/locations. In this paper, we take a step toward better understanding this challenge by presenting a detailed experimental study of decentralized DNN training on a common type of data skew: skewed distribution of data labels across devices/locations. Our study shows that: (i) skewed data labels are a fundamental and pervasive problem for decentralized learning, causing significant accuracy loss across many ML applications, DNN models, training datasets, and decentralized learning algorithms; (ii) the problem is particularly challenging for DNN models with batch normalization; and (iii) the degree of data skew is a key determinant of the difficulty of the problem. Based on these findings, we present SkewScout, a system-level approach that adapts the communication frequency of decentralized learning algorithms to the (skew-induced) accuracy loss between data partitions. We also show that group normalization can recover much of the accuracy loss of batch normalization.", "published": "2019-10-01T03:52:47Z", "authors": ["Kevin Hsieh", "Amar Phanishayee", "Onur Mutlu", "Phillip B. Gibbons"], "categories": ["cs.LG", "stat.ML"], "doi": "", "_query": "house price prediction machine learning", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2303.16310v1", "title": "Crime Prediction Using Machine Learning and Deep Learning: A Systematic Review and Future Directions", "abstract": "Predicting crime using machine learning and deep learning techniques has gained considerable attention from researchers in recent years, focusing on identifying patterns and trends in crime occurrences. This review paper examines over 150 articles to explore the various machine learning and deep learning algorithms applied to predict crime. The study provides access to the datasets used for crime prediction by researchers and analyzes prominent approaches applied in machine learning and deep learning algorithms to predict crime, offering insights into different trends and factors related to criminal activities. Additionally, the paper highlights potential gaps and future directions that can enhance the accuracy of crime prediction. Finally, the comprehensive overview of research discussed in this paper on crime prediction using machine learning and deep learning approaches serves as a valuable reference for researchers in this field. By gaining a deeper understanding of crime prediction techniques, law enforcement agencies can develop strategies to prevent and respond to criminal activities more effectively.", "published": "2023-03-28T21:07:42Z", "authors": ["Varun Mandalapu", "Lavanya Elluri", "Piyush Vyas", "Nirmalya Roy"], "categories": ["cs.LG", "cs.AI", "cs.CV", "cs.CY", "cs.DB"], "doi": "10.1109/ACCESS.2023.3286344", "_query": "house price prediction machine learning", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2506.22450v1", "title": "Arnoldi Singular Vector perturbations for machine learning weather prediction", "abstract": "Since weather forecasts are fundamentally uncertain, reliable decision making requires information on the likelihoods of future weather scenarios. We explore the sensitivity of machine learning weather prediction (MLWP) using the 24h Pangu Weather ML model of Huawei to errors in the initial conditions with a specific kind of Singular Vector (SV) perturbations. Our Arnoldi-SV (A-SV) method does not need linear nor adjoint model versions and is applicable to numerical weather prediction (NWP) as well as MLWP. It observes error growth within a given optimization time window by iteratively applying a forecast model to perturbed model states. This creates a Krylov subspace, implicitly based on a matrix operator, which approximates the local error growth. Each iteration adds new dimensions to the Krylov space and its leading right SVs are expected to turn into directions of growing errors. We show that A-SV indeed finds dynamically meaningful perturbation patterns for the 24h Pangu Weather model, which grow right from the beginning of the forecast rollout. These perturbations describe local unstable modes and could be a basis to initialize MLWP ensembles. Since we start A-SV from random noise perturbations, the algorithm transforms noise into perturbations conditioned on a given reference state - a process that is akin to the denoising process of the generic diffusion based ML model of GenCast, therefor we briefly discuss similarities and differences.", "published": "2025-06-13T08:52:20Z", "authors": ["Jens Winkler", "Michael Denhard"], "categories": ["physics.ao-ph", "cs.LG"], "doi": "", "_query": "house price prediction machine learning", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2101.06536v6", "title": "Deep Cox Mixtures for Survival Regression", "abstract": "Survival analysis is a challenging variation of regression modeling because of the presence of censoring, where the outcome measurement is only partially known, due to, for example, loss to follow up. Such problems come up frequently in medical applications, making survival analysis a key endeavor in biostatistics and machine learning for healthcare, with Cox regression models being amongst the most commonly employed models. We describe a new approach for survival analysis regression models, based on learning mixtures of Cox regressions to model individual survival distributions. We propose an approximation to the Expectation Maximization algorithm for this model that does hard assignments to mixture groups to make optimization efficient. In each group assignment, we fit the hazard ratios within each group using deep neural networks, and the baseline hazard for each mixture component non-parametrically. We perform experiments on multiple real world datasets, and look at the mortality rates of patients across ethnicity and gender. We emphasize the importance of calibration in healthcare settings and demonstrate that our approach outperforms classical and modern survival analysis baselines, both in terms of discriminative performance and calibration, with large gains in performance on the minority demographics.", "published": "2021-01-16T22:41:22Z", "authors": ["Chirag Nagpal", "Steve Yadlowsky", "Negar Rostamzadeh", "Katherine Heller"], "categories": ["cs.LG", "stat.ME", "stat.ML"], "doi": "", "_query": "house price prediction machine learning", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2307.14453v1", "title": "Predictive Maintenance of Armoured Vehicles using Machine Learning Approaches", "abstract": "Armoured vehicles are specialized and complex pieces of machinery designed to operate in high-stress environments, often in combat or tactical situations. This study proposes a predictive maintenance-based ensemble system that aids in predicting potential maintenance needs based on sensor data collected from these vehicles. The proposed model's architecture involves various models such as Light Gradient Boosting, Random Forest, Decision Tree, Extra Tree Classifier and Gradient Boosting to predict the maintenance requirements of the vehicles accurately. In addition, K-fold cross validation, along with TOPSIS analysis, is employed to evaluate the proposed ensemble model's stability. The results indicate that the proposed system achieves an accuracy of 98.93%, precision of 99.80% and recall of 99.03%. The algorithm can effectively predict maintenance needs, thereby reducing vehicle downtime and improving operational efficiency. Through comparisons between various algorithms and the suggested ensemble, this study highlights the potential of machine learning-based predictive maintenance solutions.", "published": "2023-07-26T18:50:32Z", "authors": ["Prajit Sengupta", "Anant Mehta", "Prashant Singh Rana"], "categories": ["cs.LG"], "doi": "", "_query": "house price prediction machine learning", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2506.09781v2", "title": "On the Similarities of Embeddings in Contrastive Learning", "abstract": "Contrastive learning operates on a simple yet effective principle: Embeddings of positive pairs are pulled together, while those of negative pairs are pushed apart. In this paper, we propose a unified framework for understanding contrastive learning through the lens of cosine similarity, and present two key theoretical insights derived from this framework. First, in full-batch settings, we show that perfect alignment of positive pairs is unattainable when negative-pair similarities fall below a threshold, and this misalignment can be mitigated by incorporating within-view negative pairs into the objective. Second, in mini-batch settings, smaller batch sizes induce stronger separation among negative pairs in the embedding space, i.e., higher variance in their similarities, which in turn degrades the quality of learned representations compared to full-batch settings. To address this, we propose an auxiliary loss that reduces the variance of negative-pair similarities in mini-batch settings. Empirical results show that incorporating the proposed loss improves performance in small-batch settings.", "published": "2025-06-11T14:21:05Z", "authors": ["Chungpa Lee", "Sehee Lim", "Kibok Lee", "Jy-yong Sohn"], "categories": ["cs.LG", "stat.ML"], "doi": "", "_query": "house price prediction machine learning", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2103.03122v1", "title": "Machine Learning using Stata/Python", "abstract": "We present two related Stata modules, r_ml_stata and c_ml_stata, for fitting popular Machine Learning (ML) methods both in regression and classification settings. Using the recent Stata/Python integration platform (sfi) of Stata 16, these commands provide hyper-parameters' optimal tuning via K-fold cross-validation using greed search. More specifically, they make use of the Python Scikit-learn API to carry out both cross-validation and outcome/label prediction.", "published": "2021-03-03T10:31:44Z", "authors": ["Giovanni Cerulli"], "categories": ["stat.CO", "cs.LG", "cs.MS"], "doi": "", "_query": "house price prediction machine learning", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/0911.1386v1", "title": "Machine Learning: When and Where the Horses Went Astray?", "abstract": "Machine Learning is usually defined as a subfield of AI, which is busy with information extraction from raw data sets. Despite of its common acceptance and widespread recognition, this definition is wrong and groundless. Meaningful information does not belong to the data that bear it. It belongs to the observers of the data and it is a shared agreement and a convention among them. Therefore, this private information cannot be extracted from the data by any means. Therefore, all further attempts of Machine Learning apologists to justify their funny business are inappropriate.", "published": "2009-11-07T02:52:53Z", "authors": ["Emanuel Diamant"], "categories": ["cs.AI", "cs.LG"], "doi": "", "_query": "house price prediction machine learning", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/1201.0490v4", "title": "Scikit-learn: Machine Learning in Python", "abstract": "Scikit-learn is a Python module integrating a wide range of state-of-the-art machine learning algorithms for medium-scale supervised and unsupervised problems. This package focuses on bringing machine learning to non-specialists using a general-purpose high-level language. Emphasis is put on ease of use, performance, documentation, and API consistency. It has minimal dependencies and is distributed under the simplified BSD license, encouraging its use in both academic and commercial settings. Source code, binaries, and documentation can be downloaded from http://scikit-learn.org.", "published": "2012-01-02T16:42:40Z", "authors": ["Fabian Pedregosa", "Gaël Varoquaux", "Alexandre Gramfort", "Vincent Michel", "Bertrand Thirion", "Olivier Grisel", "Mathieu Blondel", "Andreas Müller", "Joel Nothman", "Gilles Louppe", "Peter Prettenhofer", "Ron Weiss", "Vincent Dubourg", "Jake Vanderplas", "Alexandre Passos", "David Cournapeau", "Matthieu Brucher", "Matthieu Perrot", "Édouard Duchesnay"], "categories": ["cs.LG", "cs.MS"], "doi": "", "_query": "house price prediction machine learning", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2302.09438v1", "title": "Does Machine Learning Amplify Pricing Errors in the Housing Market? -- The Economics of Machine Learning Feedback Loops", "abstract": "Machine learning algorithms are increasingly employed to price or value homes for sale, properties for rent, rides for hire, and various other goods and services. Machine learning-based prices are typically generated by complex algorithms trained on historical sales data. However, displaying these prices to consumers anchors the realized sales prices, which will in turn become training samples for future iterations of the algorithms. The economic implications of this machine learning \"feedback loop\" - an indirect human-algorithm interaction - remain relatively unexplored. In this work, we develop an analytical model of machine learning feedback loops in the context of the housing market. We show that feedback loops lead machine learning algorithms to become overconfident in their own accuracy (by underestimating its error), and leads home sellers to over-rely on possibly erroneous algorithmic prices. As a consequence at the feedback loop equilibrium, sale prices can become entirely erratic (relative to true consumer preferences in absence of ML price interference). We then identify conditions (choice of ML models, seller characteristics and market characteristics) where the economic payoffs for home sellers at the feedback loop equilibrium is worse off than no machine learning. We also empirically validate primitive building blocks of our analytical model using housing market data from Zillow. We conclude by prescribing algorithmic corrective strategies to mitigate the effects of machine learning feedback loops, discuss the incentives for platforms to adopt these strategies, and discuss the role of policymakers in regulating the same.", "published": "2023-02-18T23:20:57Z", "authors": ["Nikhil Malik", "Emaad Manzoor"], "categories": ["econ.TH", "cs.CY"], "doi": "", "_query": "house price prediction machine learning", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/1811.11479v2", "title": "Communication-Efficient On-Device Machine Learning: Federated Distillation and Augmentation under Non-IID Private Data", "abstract": "On-device machine learning (ML) enables the training process to exploit a massive amount of user-generated private data samples. To enjoy this benefit, inter-device communication overhead should be minimized. With this end, we propose federated distillation (FD), a distributed model training algorithm whose communication payload size is much smaller than a benchmark scheme, federated learning (FL), particularly when the model size is large. Moreover, user-generated data samples are likely to become non-IID across devices, which commonly degrades the performance compared to the case with an IID dataset. To cope with this, we propose federated augmentation (FAug), where each device collectively trains a generative model, and thereby augments its local data towards yielding an IID dataset. Empirical studies demonstrate that FD with FAug yields around 26x less communication overhead while achieving 95-98% test accuracy compared to FL.", "published": "2018-11-28T10:16:18Z", "authors": ["Eunjeong Jeong", "Seungeun Oh", "Hyesung Kim", "Jihong Park", "Mehdi Bennis", "Seong-Lyun Kim"], "categories": ["cs.LG", "cs.NI", "stat.ML"], "doi": "", "_query": "house price prediction machine learning", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2201.09196v2", "title": "Learning to Predict Gradients for Semi-Supervised Continual Learning", "abstract": "A key challenge for machine intelligence is to learn new visual concepts without forgetting the previously acquired knowledge. Continual learning is aimed towards addressing this challenge. However, there is a gap between existing supervised continual learning and human-like intelligence, where human is able to learn from both labeled and unlabeled data. How unlabeled data affects learning and catastrophic forgetting in the continual learning process remains unknown. To explore these issues, we formulate a new semi-supervised continual learning method, which can be generically applied to existing continual learning models. Specifically, a novel gradient learner learns from labeled data to predict gradients on unlabeled data. Hence, the unlabeled data could fit into the supervised continual learning method. Different from conventional semi-supervised settings, we do not hypothesize that the underlying classes, which are associated to the unlabeled data, are known to the learning process. In other words, the unlabeled data could be very distinct from the labeled data. We evaluate the proposed method on mainstream continual learning, adversarial continual learning, and semi-supervised learning tasks. The proposed method achieves state-of-the-art performance on classification accuracy and backward transfer in the continual learning setting while achieving desired performance on classification accuracy in the semi-supervised learning setting. This implies that the unlabeled images can enhance the generalizability of continual learning models on the predictive ability on unseen data and significantly alleviate catastrophic forgetting. The code is available at \\url{https://github.com/luoyan407/grad_prediction.git}.", "published": "2022-01-23T06:45:47Z", "authors": ["Yan Luo", "Yongkang Wong", "Mohan Kankanhalli", "Qi Zhao"], "categories": ["cs.LG", "cs.CV"], "doi": "", "_query": "house price prediction machine learning", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2407.06390v2", "title": "JANET: Joint Adaptive predictioN-region Estimation for Time-series", "abstract": "Conformal prediction provides machine learning models with prediction sets that offer theoretical guarantees, but the underlying assumption of exchangeability limits its applicability to time series data. Furthermore, existing approaches struggle to handle multi-step ahead prediction tasks, where uncertainty estimates across multiple future time points are crucial. We propose JANET (Joint Adaptive predictioN-region Estimation for Time-series), a novel framework for constructing conformal prediction regions that are valid for both univariate and multivariate time series. JANET generalises the inductive conformal framework and efficiently produces joint prediction regions with controlled K-familywise error rates, enabling flexible adaptation to specific application needs. Our empirical evaluation demonstrates JANET's superior performance in multi-step prediction tasks across diverse time series datasets, highlighting its potential for reliable and interpretable uncertainty quantification in sequential data.", "published": "2024-07-08T21:03:15Z", "authors": ["Eshant English", "Eliot Wong-Toi", "Matteo Fontana", "Stephan Mandt", "Padhraic Smyth", "Christoph Lippert"], "categories": ["stat.ML", "cs.LG", "stat.ME"], "doi": "", "_query": "house price prediction machine learning", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/1812.05551v3", "title": "Exploration Conscious Reinforcement Learning Revisited", "abstract": "The Exploration-Exploitation tradeoff arises in Reinforcement Learning when one cannot tell if a policy is optimal. Then, there is a constant need to explore new actions instead of exploiting past experience. In practice, it is common to resolve the tradeoff by using a fixed exploration mechanism, such as $ε$-greedy exploration or by adding Gaussian noise, while still trying to learn an optimal policy. In this work, we take a different approach and study exploration-conscious criteria, that result in optimal policies with respect to the exploration mechanism. Solving these criteria, as we establish, amounts to solving a surrogate Markov Decision Process. We continue and analyze properties of exploration-conscious optimal policies and characterize two general approaches to solve such criteria. Building on the approaches, we apply simple changes in existing tabular and deep Reinforcement Learning algorithms and empirically demonstrate superior performance relatively to their non-exploration-conscious counterparts, both for discrete and continuous action spaces.", "published": "2018-12-13T18:08:04Z", "authors": ["Lior Shani", "Yonathan Efroni", "Shie Mannor"], "categories": ["cs.LG", "stat.ML"], "doi": "", "_query": "house price prediction machine learning", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2110.07151v1", "title": "Machine Learning, Deep Learning, and Hedonic Methods for Real Estate Price Prediction", "abstract": "In recent years several complaints about racial discrimination in appraising home values have been accumulating. For several decades, to estimate the sale price of the residential properties, appraisers have been walking through the properties, observing the property, collecting data, and making use of the hedonic pricing models. However, this method bears some costs and by nature is subjective and biased. To minimize human involvement and the biases in the real estate appraisals and boost the accuracy of the real estate market price prediction models, in this research we design data-efficient learning machines capable of learning and extracting the relation or patterns between the inputs (features for the house) and output (value of the houses). We compare the performance of some machine learning and deep learning algorithms, specifically artificial neural networks, random forest, and k nearest neighbor approaches to that of hedonic method on house price prediction in the city of Boulder, Colorado. Even though this study has been done over the houses in the city of Boulder it can be generalized to the housing market in any cities. The results indicate non-linear association between the dwelling features and dwelling prices. In light of these findings, this study demonstrates that random forest and artificial neural networks algorithms can be better alternatives over the hedonic regression analysis for prediction of the house prices in the city of Boulder, Colorado.", "published": "2021-10-14T04:43:04Z", "authors": ["Mahdieh Yazdani"], "categories": ["econ.EM"], "doi": "", "_query": "house price prediction machine learning", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2604.05057v1", "title": "Blind-Spot Mass: A Good-Turing Framework for Quantifying Deployment Coverage Risk in Machine Learning Systems", "abstract": "Blind-spot mass is a Good-Turing framework for quantifying deployment coverage risk in machine learning. In modern ML systems, operational state distributions are often heavy-tailed, implying that a long tail of valid but rare states is structurally under-supported in finite training and evaluation data. This creates a form of 'coverage blindness': models can appear accurate on standard test sets yet remain unreliable across large regions of the deployment state space. We propose blind-spot mass B_n(tau), a deployment metric estimating the total probability mass assigned to states whose empirical support falls below a threshold tau. B_n(tau) is computed using Good-Turing unseen-species estimation and yields a principled estimate of how much of the operational distribution lies in reliability-critical, under-supported regimes. We further derive a coverage-imposed accuracy ceiling, decomposing overall performance into supported and blind components and separating capacity limits from data limits. We validate the framework in wearable human activity recognition (HAR) using wrist-worn inertial data. We then replicate the same analysis in the MIMIC-IV hospital database with 275 admissions, where the blind-spot mass curve converges to the same 95% at tau = 5 across clinical state abstractions. This replication across structurally independent domains - differing in modality, feature space, label space, and application - shows that blind-spot mass is a general ML methodology for quantifying combinatorial coverage risk, not an application-specific artifact. Blind-spot decomposition identifies which activities or clinical regimes dominate risk, providing actionable guidance for industrial practitioners on targeted data collection, normalization/renormalization, and physics- or domain-informed constraints for safer deployment.", "published": "2026-04-06T18:06:38Z", "authors": ["Biplab Pal", "Santanu Bhattacharya", "Madanjit Singh"], "categories": ["cs.LG", "stat.ML"], "doi": "", "_query": "house price prediction machine learning", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2505.17756v1", "title": "Qiskit Machine Learning: an open-source library for quantum machine learning tasks at scale on quantum hardware and classical simulators", "abstract": "We present Qiskit Machine Learning (ML), a high-level Python library that combines elements of quantum computing with traditional machine learning. The API abstracts Qiskit's primitives to facilitate interactions with classical simulators and quantum hardware. Qiskit ML started as a proof-of-concept code in 2019 and has since been developed to be a modular, intuitive tool for non-specialist users while allowing extensibility and fine-tuning controls for quantum computational scientists and developers. The library is available as a public, open-source tool and is distributed under the Apache version 2.0 license.", "published": "2025-05-23T11:27:03Z", "authors": ["M. Emre Sahin", "Edoardo Altamura", "Oscar Wallis", "Stephen P. Wood", "Anton Dekusar", "Declan A. Millar", "Takashi Imamichi", "Atsushi Matsuo", "Stefano Mensa"], "categories": ["quant-ph", "cs.ET", "cs.LG", "physics.comp-ph"], "doi": "", "_query": "house price prediction machine learning", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2105.06314v1", "title": "Explainable Machine Learning for Fraud Detection", "abstract": "The application of machine learning to support the processing of large datasets holds promise in many industries, including financial services. However, practical issues for the full adoption of machine learning remain with the focus being on understanding and being able to explain the decisions and predictions made by complex models. In this paper, we explore explainability methods in the domain of real-time fraud detection by investigating the selection of appropriate background datasets and runtime trade-offs on both supervised and unsupervised models.", "published": "2021-05-13T14:12:02Z", "authors": ["Ismini Psychoula", "Andreas Gutmann", "Pradip Mainali", "S. H. Lee", "Paul Dunphy", "Fabien A. P. Petitcolas"], "categories": ["cs.LG", "cs.AI"], "doi": "", "_query": "house price prediction machine learning", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2304.02539v2", "title": "Multi-annotator Deep Learning: A Probabilistic Framework for Classification", "abstract": "Solving complex classification tasks using deep neural networks typically requires large amounts of annotated data. However, corresponding class labels are noisy when provided by error-prone annotators, e.g., crowdworkers. Training standard deep neural networks leads to subpar performances in such multi-annotator supervised learning settings. We address this issue by presenting a probabilistic training framework named multi-annotator deep learning (MaDL). A downstream ground truth and an annotator performance model are jointly trained in an end-to-end learning approach. The ground truth model learns to predict instances' true class labels, while the annotator performance model infers probabilistic estimates of annotators' performances. A modular network architecture enables us to make varying assumptions regarding annotators' performances, e.g., an optional class or instance dependency. Further, we learn annotator embeddings to estimate annotators' densities within a latent space as proxies of their potentially correlated annotations. Together with a weighted loss function, we improve the learning from correlated annotation patterns. In a comprehensive evaluation, we examine three research questions about multi-annotator supervised learning. Our findings show MaDL's state-of-the-art performance and robustness against many correlated, spamming annotators.", "published": "2023-04-05T16:00:42Z", "authors": ["Marek Herde", "Denis Huseljic", "Bernhard Sick"], "categories": ["cs.LG"], "doi": "", "_query": "house price prediction machine learning", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2211.02578v3", "title": "Data Models for Dataset Drift Controls in Machine Learning With Optical Images", "abstract": "Camera images are ubiquitous in machine learning research. They also play a central role in the delivery of important services spanning medicine and environmental surveying. However, the application of machine learning models in these domains has been limited because of robustness concerns. A primary failure mode are performance drops due to differences between the training and deployment data. While there are methods to prospectively validate the robustness of machine learning models to such dataset drifts, existing approaches do not account for explicit models of the primary object of interest: the data. This limits our ability to study and understand the relationship between data generation and downstream machine learning model performance in a physically accurate manner. In this study, we demonstrate how to overcome this limitation by pairing traditional machine learning with physical optics to obtain explicit and differentiable data models. We demonstrate how such data models can be constructed for image data and used to control downstream machine learning model performance related to dataset drift. The findings are distilled into three applications. First, drift synthesis enables the controlled generation of physically faithful drift test cases to power model selection and targeted generalization. Second, the gradient connection between machine learning task model and data model allows advanced, precise tolerancing of task model sensitivity to changes in the data generation. These drift forensics can be used to precisely specify the acceptable data environments in which a task model may be run. Third, drift optimization opens up the possibility to create drifts that can help the task model learn better faster, effectively optimizing the data generating process itself. A guide to access the open code and datasets is available at https://github.com/aiaudit-org/raw2logit.", "published": "2022-11-04T16:50:10Z", "authors": ["Luis Oala", "Marco Aversa", "Gabriel Nobis", "Kurt Willis", "Yoan Neuenschwander", "Michèle Buck", "Christian Matek", "Jerome Extermann", "Enrico Pomarico", "Wojciech Samek", "Roderick Murray-Smith", "Christoph Clausen", "Bruno Sanguinetti"], "categories": ["cs.LG", "cs.AI", "cs.CV"], "doi": "", "_query": "house price prediction machine learning", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2508.10836v2", "title": "SoK: Data Minimization in Machine Learning", "abstract": "Data minimization (DM) describes the principle of collecting only the data strictly necessary for a given task. It is a foundational principle across major data protection regulations like GDPR and CPRA. Violations of this principle have substantial real-world consequences, with regulatory actions resulting in fines reaching hundreds of millions of dollars. Notably, the relevance of data minimization is particularly pronounced in machine learning (ML) applications, which typically rely on large datasets, resulting in an emerging research area known as Data Minimization in Machine Learning (DMML). At the same time, existing work on other ML privacy and security topics often addresses concerns relevant to DMML without explicitly acknowledging the connection. This disconnect leads to confusion among practitioners, complicating their efforts to implement DM principles and interpret the terminology, metrics, and evaluation criteria used across different research communities. To address this gap, we present the first systematization of knowledge (SoK) for DMML. We introduce a general framework for DMML, encompassing a unified data pipeline, adversarial models, and points of minimization. This framework allows us to systematically review data minimization literature as well as DM-adjacent methodologies whose link to DM was often overlooked. Our structured overview is designed to help practitioners and researchers effectively adopt and apply DM principles in ML, by helping them identify relevant techniques and understand underlying assumptions and trade-offs through a DM-centric lens.", "published": "2025-08-14T17:00:13Z", "authors": ["Robin Staab", "Nikola Jovanović", "Kimberly Mai", "Prakhar Ganesh", "Martin Vechev", "Ferdinando Fioretto", "Matthew Jagielski"], "categories": ["cs.LG", "cs.CR"], "doi": "", "_query": "house price prediction machine learning", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2407.19078v1", "title": "Practical Marketplace Optimization at Uber Using Causally-Informed Machine Learning", "abstract": "Budget allocation of marketplace levers, such as incentives for drivers and promotions for riders, has long been a technical and business challenge at Uber; understanding lever budget changes' impact and estimating cost efficiency to achieve predefined budgets is crucial, with the goal of optimal allocations that maximize business value; we introduce an end-to-end machine learning and optimization procedure to automate budget decision-making for cities, relying on feature store, model training and serving, optimizers, and backtesting; proposing state-of-the-art deep learning (DL) estimator based on S-Learner and a novel tensor B-Spline regression model, we solve high-dimensional optimization with ADMM and primal-dual interior point convex optimization, substantially improving Uber's resource allocation efficiency.", "published": "2024-07-26T20:51:37Z", "authors": ["Bobby Chen", "Siyu Chen", "Jason Dowlatabadi", "Yu Xuan Hong", "Vinayak Iyer", "Uday Mantripragada", "Rishabh Narang", "Apoorv Pandey", "Zijun Qin", "Abrar Sheikh", "Hongtao Sun", "Jiaqi Sun", "Matthew Walker", "Kaichen Wei", "Chen Xu", "Jingnan Yang", "Allen T. Zhang", "Guoqing Zhang"], "categories": ["cs.LG", "stat.ML"], "doi": "", "_query": "house price prediction machine learning", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2406.04344v3", "title": "Verbalized Machine Learning: Revisiting Machine Learning with Language Models", "abstract": "Motivated by the progress made by large language models (LLMs), we introduce the framework of verbalized machine learning (VML). In contrast to conventional machine learning (ML) models that are typically optimized over a continuous parameter space, VML constrains the parameter space to be human-interpretable natural language. Such a constraint leads to a new perspective of function approximation, where an LLM with a text prompt can be viewed as a function parameterized by the text prompt. Guided by this perspective, we revisit classical ML problems, such as regression and classification, and find that these problems can be solved by an LLM-parameterized learner and optimizer. The major advantages of VML include (1) easy encoding of inductive bias: prior knowledge about the problem and hypothesis class can be encoded in natural language and fed into the LLM-parameterized learner; (2) automatic model class selection: the optimizer can automatically select a model class based on data and verbalized prior knowledge, and it can update the model class during training; and (3) interpretable learner updates: the LLM-parameterized optimizer can provide explanations for why an update is performed. We empirically verify the effectiveness of VML, and hope that VML can serve as a stepping stone to stronger interpretability.", "published": "2024-06-06T17:59:56Z", "authors": ["Tim Z. Xiao", "Robert Bamler", "Bernhard Schölkopf", "Weiyang Liu"], "categories": ["cs.LG", "cs.CL", "cs.CV"], "doi": "", "_query": "house price prediction machine learning", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2404.11754v4", "title": "Differentiated Aggregation to Improve Generalization in Federated Learning", "abstract": "This paper focuses on reducing the communication cost of federated learning by exploring generalization bounds and representation learning. We first characterize a tighter generalization bound for one-round federated learning based on local clients' generalizations and heterogeneity of data distribution (non-iid scenario). We also characterize a generalization bound in R-round federated learning and its relation to the number of local updates (local stochastic gradient descents (SGDs)). Then, based on our generalization bound analysis and its interpretation through representation learning, we infer that less frequent aggregations for the representation extractor (typically corresponds to initial layers) compared to the head (usually the final layers) leads to the creation of more generalizable models, particularly in non-iid scenarios. We design a novel Federated Learning with Adaptive Local Steps (FedALS) algorithm based on our generalization bound and representation learning analysis. FedALS employs varying aggregation frequencies for different parts of the model, so reduces the communication cost. The paper is followed with experimental results showing the effectiveness of FedALS. Our codes are available at for reproducibility.", "published": "2024-04-17T21:17:48Z", "authors": ["Peyman Gholami", "Hulya Seferoglu"], "categories": ["cs.LG", "cs.AI"], "doi": "", "_query": "house price prediction machine learning", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/1904.00324v1", "title": "SysML'19 demo: customizable and reusable Collective Knowledge pipelines to automate and reproduce machine learning experiments", "abstract": "Reproducing, comparing and reusing results from machine learning and systems papers is a very tedious, ad hoc and time-consuming process. I will demonstrate how to automate this process using open-source, portable, customizable and CLI-based Collective Knowledge workflows and pipelines developed by the community. I will help participants run several real-world non-virtualized CK workflows from the SysML'19 conference, companies (General Motors, Arm) and MLPerf benchmark to automate benchmarking and co-design of efficient software/hardware stacks for machine learning workloads. I hope that our approach will help authors reduce their effort when sharing reusable and extensible research artifacts while enabling artifact evaluators to automatically validate experimental results from published papers in a standard and portable way.", "published": "2019-03-31T02:39:33Z", "authors": ["Grigori Fursin"], "categories": ["cs.LG"], "doi": "", "_query": "house price prediction machine learning", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2107.04983v1", "title": "Leveraging Domain Adaptation for Low-Resource Geospatial Machine Learning", "abstract": "Machine learning in remote sensing has matured alongside a proliferation in availability and resolution of geospatial imagery, but its utility is bottlenecked by the need for labeled data. What's more, many labeled geospatial datasets are specific to certain regions, instruments, or extreme weather events. We investigate the application of modern domain-adaptation to multiple proposed geospatial benchmarks, uncovering unique challenges and proposing solutions to them.", "published": "2021-07-11T06:47:20Z", "authors": ["Jack Lynch", "Sam Wookey"], "categories": ["cs.LG", "cs.CV"], "doi": "", "_query": "house price prediction machine learning", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2409.12642v1", "title": "Deep generative models as an adversarial attack strategy for tabular machine learning", "abstract": "Deep Generative Models (DGMs) have found application in computer vision for generating adversarial examples to test the robustness of machine learning (ML) systems. Extending these adversarial techniques to tabular ML presents unique challenges due to the distinct nature of tabular data and the necessity to preserve domain constraints in adversarial examples. In this paper, we adapt four popular tabular DGMs into adversarial DGMs (AdvDGMs) and evaluate their effectiveness in generating realistic adversarial examples that conform to domain constraints.", "published": "2024-09-19T10:41:23Z", "authors": ["Salijona Dyrmishi", "Mihaela Cătălina Stoian", "Eleonora Giunchiglia", "Maxime Cordy"], "categories": ["cs.LG", "cs.AI"], "doi": "", "_query": "house price prediction machine learning", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2112.06192v1", "title": "Housing Price Prediction Model Selection Based on Lorenz and Concentration Curves: Empirical Evidence from Tehran Housing Market", "abstract": "This study contributes a house price prediction model selection in Tehran City based on the area between Lorenz curve (LC) and concentration curve (CC) of the predicted price by using 206,556 observed transaction data over the period from March 21, 2018, to February 19, 2021. Several different methods such as generalized linear models (GLM) and recursive partitioning and regression trees (RPART), random forests (RF) regression models, and neural network (NN) models were examined house price prediction. We used 90% of all data samples which were chosen randomly to estimate the parameters of pricing models and 10% of remaining datasets to test the accuracy of prediction. Results showed that the area between the LC and CC curves (which are known as ABC criterion) of real and predicted prices in the test data sample of the random forest regression model was less than by other models under study. The comparison of the calculated ABC criteria leads us to conclude that the nonlinear regression models such as RF regression models give an accurate prediction of house prices in Tehran City.", "published": "2021-12-12T09:44:28Z", "authors": ["Mohammad Mirbagherijam"], "categories": ["econ.EM"], "doi": "", "_query": "house price prediction machine learning", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2211.14617v1", "title": "Mixture of Decision Trees for Interpretable Machine Learning", "abstract": "This work introduces a novel interpretable machine learning method called Mixture of Decision Trees (MoDT). It constitutes a special case of the Mixture of Experts ensemble architecture, which utilizes a linear model as gating function and decision trees as experts. Our proposed method is ideally suited for problems that cannot be satisfactorily learned by a single decision tree, but which can alternatively be divided into subproblems. Each subproblem can then be learned well from a single decision tree. Therefore, MoDT can be considered as a method that improves performance while maintaining interpretability by making each of its decisions understandable and traceable to humans. Our work is accompanied by a Python implementation, which uses an interpretable gating function, a fast learning algorithm, and a direct interface to fine-tuned interpretable visualization methods. The experiments confirm that the implementation works and, more importantly, show the superiority of our approach compared to single decision trees and random forests of similar complexity.", "published": "2022-11-26T17:09:51Z", "authors": ["Simeon Brüggenjürgen", "Nina Schaaf", "Pascal Kerschke", "Marco F. Huber"], "categories": ["cs.LG"], "doi": "", "_query": "house price prediction machine learning", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2510.01658v1", "title": "Learning Time-Series Representations by Hierarchical Uniformity-Tolerance Latent Balancing", "abstract": "We propose TimeHUT, a novel method for learning time-series representations by hierarchical uniformity-tolerance balancing of contrastive representations. Our method uses two distinct losses to learn strong representations with the aim of striking an effective balance between uniformity and tolerance in the embedding space. First, TimeHUT uses a hierarchical setup to learn both instance-wise and temporal information from input time-series. Next, we integrate a temperature scheduler within the vanilla contrastive loss to balance the uniformity and tolerance characteristics of the embeddings. Additionally, a hierarchical angular margin loss enforces instance-wise and temporal contrast losses, creating geometric margins between positive and negative pairs of temporal sequences. This approach improves the coherence of positive pairs and their separation from the negatives, enhancing the capture of temporal dependencies within a time-series sample. We evaluate our approach on a wide range of tasks, namely 128 UCR and 30 UAE datasets for univariate and multivariate classification, as well as Yahoo and KPI datasets for anomaly detection. The results demonstrate that TimeHUT outperforms prior methods by considerable margins on classification, while obtaining competitive results for anomaly detection. Finally, detailed sensitivity and ablation studies are performed to evaluate different components and hyperparameters of our method.", "published": "2025-10-02T04:30:13Z", "authors": ["Amin Jalali", "Milad Soltany", "Michael Greenspan", "Ali Etemad"], "categories": ["cs.LG", "cs.AI"], "doi": "", "_query": "house price prediction machine learning", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2010.12797v1", "title": "Collaborative Machine Learning with Incentive-Aware Model Rewards", "abstract": "Collaborative machine learning (ML) is an appealing paradigm to build high-quality ML models by training on the aggregated data from many parties. However, these parties are only willing to share their data when given enough incentives, such as a guaranteed fair reward based on their contributions. This motivates the need for measuring a party's contribution and designing an incentive-aware reward scheme accordingly. This paper proposes to value a party's reward based on Shapley value and information gain on model parameters given its data. Subsequently, we give each party a model as a reward. To formally incentivize the collaboration, we define some desirable properties (e.g., fairness and stability) which are inspired by cooperative game theory but adapted for our model reward that is uniquely freely replicable. Then, we propose a novel model reward scheme to satisfy fairness and trade off between the desirable properties via an adjustable parameter. The value of each party's model reward determined by our scheme is attained by injecting Gaussian noise to the aggregated training data with an optimized noise variance. We empirically demonstrate interesting properties of our scheme and evaluate its performance using synthetic and real-world datasets.", "published": "2020-10-24T06:20:55Z", "authors": ["Rachael Hwee Ling Sim", "Yehong Zhang", "Mun Choon Chan", "Bryan Kian Hsiang Low"], "categories": ["cs.LG", "cs.GT", "cs.MA", "stat.ML"], "doi": "", "_query": "house price prediction machine learning", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2208.04221v1", "title": "Uncertain Bayesian Networks: Learning from Incomplete Data", "abstract": "When the historical data are limited, the conditional probabilities associated with the nodes of Bayesian networks are uncertain and can be empirically estimated. Second order estimation methods provide a framework for both estimating the probabilities and quantifying the uncertainty in these estimates. We refer to these cases as uncer tain or second-order Bayesian networks. When such data are complete, i.e., all variable values are observed for each instantiation, the conditional probabilities are known to be Dirichlet-distributed. This paper improves the current state-of-the-art approaches for handling uncertain Bayesian networks by enabling them to learn distributions for their parameters, i.e., conditional probabilities, with incomplete data. We extensively evaluate various methods to learn the posterior of the parameters through the desired and empirically derived strength of confidence bounds for various queries.", "published": "2022-08-08T15:46:44Z", "authors": ["Conrad D. Hougen", "Lance M. Kaplan", "Federico Cerutti", "Alfred O. Hero"], "categories": ["stat.ML", "cs.AI", "cs.LG"], "doi": "10.1109/MLSP52302.2021.9596205", "_query": "house price prediction machine learning", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2204.10334v1", "title": "Machine Learning Algebraic Geometry for Physics", "abstract": "We review some recent applications of machine learning to algebraic geometry and physics. Since problems in algebraic geometry can typically be reformulated as mappings between tensors, this makes them particularly amenable to supervised learning. Additionally, unsupervised methods can provide insight into the structure of such geometrical data. At the heart of this programme is the question of how geometry can be machine learned, and indeed how AI helps one to do mathematics. This is a chapter contribution to the book Machine learning and Algebraic Geometry, edited by A. Kasprzyk et al.", "published": "2022-04-21T18:00:03Z", "authors": ["Jiakang Bao", "Yang-Hui He", "Elli Heyes", "Edward Hirst"], "categories": ["hep-th", "math.AG", "stat.ML"], "doi": "", "_query": "house price prediction machine learning", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/1812.00596v1", "title": "Deep Learning Approach for Predicting 30 Day Readmissions after Coronary Artery Bypass Graft Surgery", "abstract": "Hospital Readmissions within 30 days after discharge following Coronary Artery Bypass Graft (CABG) Surgery are substantial contributors to healthcare costs. Many predictive models were developed to identify risk factors for readmissions. However, majority of the existing models use statistical analysis techniques with data available at discharge. We propose an ensembled model to predict CABG readmissions using pre-discharge perioperative data and machine learning survival analysis techniques. Firstly, we applied fifty one potential readmission risk variables to Cox Proportional Hazard (CPH) survival regression univariate analysis. Fourteen of them turned out to be significant (with p value < 0.05), contributing to readmissions. Subsequently, we applied these 14 predictors to multivariate CPH model and Deep Learning Neural Network (NN) representation of the CPH model, DeepSurv. We validated this new ensembled model with 453 isolated adult CABG cases. Nine of the fourteen perioperative risk variables were identified as the most significant with Hazard Ratios (HR) of greater than 1.0. The concordance index metrics for CPH, DeepSurv, and ensembled models were then evaluated with training and validation datasets. Our ensembled model yielded promising results in terms of c-statistics, as we raised the the number of iterations and data set sizes. 30 day all-cause readmissions among isolated CABG patients can be predicted more effectively with perioperative pre-discharge data, using machine learning survival analysis techniques. Prediction accuracy levels could be improved further with deep learning algorithms.", "published": "2018-12-03T08:20:36Z", "authors": ["Ramesh B. Manyam", "Yanqing Zhang", "William B. Keeling", "Jose Binongo", "Michael Kayatta", "Seth Carter"], "categories": ["cs.LG", "q-bio.QM", "stat.ML"], "doi": "", "_query": "house price prediction machine learning", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2501.05646v1", "title": "Efficient Representations for High-Cardinality Categorical Variables in Machine Learning", "abstract": "High\\-cardinality categorical variables pose significant challenges in machine learning, particularly in terms of computational efficiency and model interpretability. Traditional one\\-hot encoding often results in high\\-dimensional sparse feature spaces, increasing the risk of overfitting and reducing scalability. This paper introduces novel encoding techniques, including means encoding, low\\-rank encoding, and multinomial logistic regression encoding, to address these challenges. These methods leverage sufficient representations to generate compact and informative embeddings of categorical data. We conduct rigorous theoretical analyses and empirical validations on diverse datasets, demonstrating significant improvements in model performance and computational efficiency compared to baseline methods. The proposed techniques are particularly effective in domains requiring scalable solutions for large datasets, paving the way for more robust and efficient applications in machine learning.", "published": "2025-01-10T01:25:01Z", "authors": ["Zixuan Liang"], "categories": ["cs.LG", "cs.AI"], "doi": "", "_query": "house price prediction machine learning", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/1411.6725v1", "title": "Accelerated Parallel Optimization Methods for Large Scale Machine Learning", "abstract": "The growing amount of high dimensional data in different machine learning applications requires more efficient and scalable optimization algorithms. In this work, we consider combining two techniques, parallelism and Nesterov's acceleration, to design faster algorithms for L1-regularized loss. We first simplify BOOM, a variant of gradient descent, and study it in a unified framework, which allows us to not only propose a refined measurement of sparsity to improve BOOM, but also show that BOOM is provably slower than FISTA. Moving on to parallel coordinate descent methods, we then propose an efficient accelerated version of Shotgun, improving the convergence rate from $O(1/t)$ to $O(1/t^2)$. Our algorithm enjoys a concise form and analysis compared to previous work, and also allows one to study several connected work in a unified way.", "published": "2014-11-25T04:36:35Z", "authors": ["Haipeng Luo", "Patrick Haffner", "Jean-Francois Paiement"], "categories": ["cs.LG"], "doi": "", "_query": "house price prediction machine learning", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2502.05334v2", "title": "Geometric Machine Learning on EEG Signals", "abstract": "Brain-computer interfaces (BCIs) offer transformative potential, but decoding neural signals presents significant challenges. The core premise of this paper is built around demonstrating methods to elucidate the underlying low-dimensional geometric structure present in high-dimensional brainwave data in order to assist in downstream BCI-related neural classification tasks. We demonstrate two pipelines related to electroencephalography (EEG) signal processing: (1) a preliminary pipeline removing noise from individual EEG channels, and (2) a downstream manifold learning pipeline uncovering geometric structure across networks of EEG channels. We conduct preliminary validation using two EEG datasets and situate our demonstration in the context of the BCI-relevant imagined digit decoding problem. Our preliminary pipeline uses an attention-based EEG filtration network to extract clean signal from individual EEG channels. Our primary pipeline uses a fast Fourier transform, a Laplacian eigenmap, a discrete analog of Ricci flow via Ollivier's notion of Ricci curvature, and a graph convolutional network to perform dimensionality reduction on high-dimensional multi-channel EEG data in order to enable regularizable downstream classification. Our system achieves competitive performance with existing signal processing and classification benchmarks; we demonstrate a mean test correlation coefficient of >0.95 at 2 dB on semi-synthetic neural denoising and a downstream EEG-based classification accuracy of 0.97 on distinguishing digit- versus non-digit- thoughts. Results are preliminary and our geometric machine learning pipeline should be validated by more extensive follow-up studies; generalizing these results to larger inter-subject sample sizes, different hardware systems, and broader use cases will be crucial.", "published": "2025-02-07T21:14:48Z", "authors": ["Benjamin J. Choi"], "categories": ["cs.LG"], "doi": "", "_query": "house price prediction machine learning", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2609.24117v1", "title": "PAC-Bayesian Meta-Learning for Few-Shot Identification of Linear Dynamical Systems", "abstract": "Identifying linear time-invariant (LTI) dynamical systems is challenging when trajectories are short, noisy, or high-dimensional. Traditional system identification typically treats each system independently and cannot exploit shared structure across related systems. We propose PBML-LTI, a PAC-Bayesian meta-learning framework for few-shot LTI system identification that learns a transferable prior over task-specific dynamics while preserving task heterogeneity. Each task corresponds to an unknown LTI system, and the meta-learner uses training trajectories to learn a data-dependent prior over transition matrices. For a new system with limited data, PBML-LTI performs Bayesian adaptation under this prior to obtain a task-specific posterior, providing accurate estimates and principled uncertainty quantification. A key challenge is temporal dependence, since LTI trajectories violate the i.i.d. assumptions underlying most PAC-Bayes meta-learning analyses. We address this with a martingale PAC-Bayes analysis for dependent trajectory losses and derive a support-query predictive-risk bound that motivates a fit-KL meta-training objective. The bound clarifies the roles of empirical fit, posterior complexity, and prior quality in few-shot adaptation under sequential dependence. We further derive corollaries for transition-matrix recovery and multi-step trajectory prediction, connecting uncertainty-aware meta-identification with finite-sample guarantees for dependent dynamical data.", "published": "2026-09-21T05:12:58Z", "authors": ["Chenfeng Huang", "George Michailidis"], "categories": ["cs.LG"], "doi": "", "_query": "house price prediction machine learning", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2012.08275v1", "title": "High throughput screening with machine learning", "abstract": "This study assesses the efficiency of several popular machine learning approaches in the prediction of molecular binding affinity: CatBoost, Graph Attention Neural Network, and Bidirectional Encoder Representations from Transformers. The models were trained to predict binding affinities in terms of inhibition constants $K_i$ for pairs of proteins and small organic molecules. First two approaches use thoroughly selected physico-chemical features, while the third one is based on textual molecular representations - it is one of the first attempts to apply Transformer-based predictors for the binding affinity. We also discuss the visualization of attention layers within the Transformer approach in order to highlight the molecular sites responsible for interactions. All approaches are free from atomic spatial coordinates thus avoiding bias from known structures and being able to generalize for compounds with unknown conformations. The achieved accuracy for all suggested approaches prove their potential in high throughput screening.", "published": "2020-12-15T13:19:03Z", "authors": ["Oleksandr Gurbych", "Maksym Druchok", "Dzvenymyra Yarish", "Sofiya Garkot"], "categories": ["cs.LG", "physics.chem-ph"], "doi": "", "_query": "house price prediction machine learning", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2405.06582v3", "title": "The Role of Learning Algorithms in Collective Action", "abstract": "Collective action in machine learning is the study of the control that a coordinated group can have over machine learning algorithms. While previous research has concentrated on assessing the impact of collectives against Bayes (sub-)optimal classifiers, this perspective is limited in that it does not account for the choice of learning algorithm. Since classifiers seldom behave like Bayes classifiers and are influenced by the choice of learning algorithms along with their inherent biases, in this work we initiate the study of how the choice of the learning algorithm plays a role in the success of a collective in practical settings. Specifically, we focus on distributionally robust optimization (DRO), popular for improving a worst group error, and on the ubiquitous stochastic gradient descent (SGD), due to its inductive bias for \"simpler\" functions. Our empirical results, supported by a theoretical foundation, show that the effective size and success of the collective are highly dependent on properties of the learning algorithm. This highlights the necessity of taking the learning algorithm into account when studying the impact of collective action in machine learning.", "published": "2024-05-10T16:36:59Z", "authors": ["Omri Ben-Dov", "Jake Fawkes", "Samira Samadi", "Amartya Sanyal"], "categories": ["cs.LG", "cs.CY", "stat.ML"], "doi": "", "_query": "house price prediction machine learning", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2305.15410v1", "title": "Machine learning-assisted close-set X-ray diffraction phase identification of transition metals", "abstract": "Machine learning has been applied to the problem of X-ray diffraction phase prediction with promising results. In this paper, we describe a method for using machine learning to predict crystal structure phases from X-ray diffraction data of transition metals and their oxides. We evaluate the performance of our method and compare the variety of its settings. Our results demonstrate that the proposed machine learning framework achieves competitive performance. This demonstrates the potential for machine learning to significantly impact the field of X-ray diffraction and crystal structure determination. Open-source implementation: https://github.com/maxnygma/NeuralXRD.", "published": "2023-04-28T09:29:10Z", "authors": ["Maksim Zhdanov", "Andrey Zhdanov"], "categories": ["cond-mat.mtrl-sci", "cs.AI", "cs.LG"], "doi": "", "_query": "house price prediction machine learning", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2004.12076v2", "title": "Quantum machine learning and quantum biomimetics: A perspective", "abstract": "Quantum machine learning has emerged as an exciting and promising paradigm inside quantum technologies. It may permit, on the one hand, to carry out more efficient machine learning calculations by means of quantum devices, while, on the other hand, to employ machine learning techniques to better control quantum systems. Inside quantum machine learning, quantum reinforcement learning aims at developing \"intelligent\" quantum agents that may interact with the outer world and adapt to it, with the strategy of achieving some final goal. Another paradigm inside quantum machine learning is that of quantum autoencoders, which may allow one for employing fewer resources in a quantum device via a training process. Moreover, the field of quantum biomimetics aims at establishing analogies between biological and quantum systems, to look for previously inadvertent connections that may enable useful applications. Two recent examples are the concepts of quantum artificial life, as well as of quantum memristors. In this Perspective, we give an overview of these topics, describing the related research carried out by the scientific community.", "published": "2020-04-25T07:45:20Z", "authors": ["Lucas Lamata"], "categories": ["quant-ph", "cond-mat.mes-hall", "cs.LG"], "doi": "10.1088/2632-2153/ab9803", "_query": "house price prediction machine learning", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2109.05439v3", "title": "Concave Utility Reinforcement Learning with Zero-Constraint Violations", "abstract": "We consider the problem of tabular infinite horizon concave utility reinforcement learning (CURL) with convex constraints. For this, we propose a model-based learning algorithm that also achieves zero constraint violations. Assuming that the concave objective and the convex constraints have a solution interior to the set of feasible occupation measures, we solve a tighter optimization problem to ensure that the constraints are never violated despite the imprecise model knowledge and model stochasticity. We use Bellman error-based analysis for tabular infinite-horizon setups which allows analyzing stochastic policies. Combining the Bellman error-based analysis and tighter optimization equation, for $T$ interactions with the environment, we obtain a high-probability regret guarantee for objective which grows as $\\Tilde{O}(1/\\sqrt{T})$, excluding other factors. The proposed method can be applied for optimistic algorithms to obtain high-probability regret bounds and also be used for posterior sampling algorithms to obtain a loose Bayesian regret bounds but with significant improvement in computational complexity.", "published": "2021-09-12T06:13:33Z", "authors": ["Mridul Agarwal", "Qinbo Bai", "Vaneet Aggarwal"], "categories": ["cs.LG", "cs.AI"], "doi": "", "_query": "house price prediction machine learning", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2303.16158v4", "title": "Behavioral Machine Learning? Regularization and Forecast Bias", "abstract": "Standard forecast efficiency tests interpret violations as evidence of behavioral bias. We show theoretically and empirically that rational forecasters using optimal regularization systematically violate these tests. Machine learning forecasts show near zero bias at one year horizon, but strong overreaction at two years, consistent with predictions from a model of regularization and measurement noise. We provide three complementary tests: experimental variation in regularization parameters, cross-sectional heterogeneity in firm signal quality, and quasi-experimental evidence from ML adoption around 2013. Technically trained analysts shift sharply toward overreaction post-2013. Our findings suggest reported violations may reflect statistical sophistication rather than cognitive failure.", "published": "2023-03-25T03:06:43Z", "authors": ["Murray Z. Frank", "Jing Gao", "Keer Yang"], "categories": ["q-fin.ST", "cs.LG", "econ.GN", "q-fin.GN"], "doi": "", "_query": "house price prediction machine learning", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2208.05844v2", "title": "Adaptive Identification of Populations with Treatment Benefit in Clinical Trials: Machine Learning Challenges and Solutions", "abstract": "We study the problem of adaptively identifying patient subpopulations that benefit from a given treatment during a confirmatory clinical trial. This type of adaptive clinical trial has been thoroughly studied in biostatistics, but has been allowed only limited adaptivity so far. Here, we aim to relax classical restrictions on such designs and investigate how to incorporate ideas from the recent machine learning literature on adaptive and online experimentation to make trials more flexible and efficient. We find that the unique characteristics of the subpopulation selection problem -- most importantly that (i) one is usually interested in finding subpopulations with any treatment benefit (and not necessarily the single subgroup with largest effect) given a limited budget and that (ii) effectiveness only has to be demonstrated across the subpopulation on average -- give rise to interesting challenges and new desiderata when designing algorithmic solutions. Building on these findings, we propose AdaGGI and AdaGCPI, two meta-algorithms for subpopulation construction. We empirically investigate their performance across a range of simulation scenarios and derive insights into their (dis)advantages across different settings.", "published": "2022-08-11T14:27:49Z", "authors": ["Alicia Curth", "Alihan Hüyük", "Mihaela van der Schaar"], "categories": ["stat.ML", "cs.LG"], "doi": "", "_query": "house price prediction machine learning", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2202.01602v6", "title": "The Disagreement Problem in Explainable Machine Learning: A Practitioner's Perspective", "abstract": "As various post hoc explanation methods are increasingly being leveraged to explain complex models in high-stakes settings, it becomes critical to develop a deeper understanding of whether and when the explanations output by these methods disagree with each other, and how such disagreements are resolved in practice. However, there is little to no research that provides answers to these critical questions. In this work, we formalize and study the disagreement problem in explainable machine learning. More specifically, we define the notion of disagreement between explanations, analyze how often such disagreements occur in practice, and how practitioners resolve these disagreements. We first conduct interviews with data scientists to understand what constitutes disagreement between explanations generated by different methods for the same model prediction, and introduce a novel quantitative framework to formalize this understanding. We then leverage this framework to carry out a rigorous empirical analysis with four real-world datasets, six state-of-the-art post hoc explanation methods, and six different predictive models, to measure the extent of disagreement between the explanations generated by various popular explanation methods. In addition, we carry out an online user study with data scientists to understand how they resolve the aforementioned disagreements. Our results indicate that (1) state-of-the-art explanation methods often disagree in terms of the explanations they output, and (2) machine learning practitioners often employ ad hoc heuristics when resolving such disagreements. These findings suggest that practitioners may be relying on misleading explanations when making consequential decisions. They also underscore the importance of developing principled frameworks for effectively evaluating and comparing explanations output by various explanation techniques.", "published": "2022-02-03T14:19:23Z", "authors": ["Satyapriya Krishna", "Tessa Han", "Alex Gu", "Steven Wu", "Shahin Jabbari", "Himabindu Lakkaraju"], "categories": ["cs.LG", "cs.AI"], "doi": "", "_query": "house price prediction machine learning", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/1810.11383v2", "title": "Some Requests for Machine Learning Research from the East African Tech Scene", "abstract": "Based on 46 in-depth interviews with scientists, engineers, and CEOs, this document presents a list of concrete machine research problems, progress on which would directly benefit tech ventures in East Africa.", "published": "2018-10-25T02:53:14Z", "authors": ["Milan Cvitkovic"], "categories": ["cs.LG", "cs.CY", "stat.ML"], "doi": "", "_query": "house price prediction machine learning", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/1706.08001v1", "title": "Temporal-related Convolutional-Restricted-Boltzmann-Machine capable of learning relational order via reinforcement learning procedure?", "abstract": "In this article, we extend the conventional framework of convolutional-Restricted-Boltzmann-Machine to learn highly abstract features among abitrary number of time related input maps by constructing a layer of multiplicative units, which capture the relations among inputs. In many cases, more than two maps are strongly related, so it is wise to make multiplicative unit learn relations among more input maps, in other words, to find the optimal relational-order of each unit. In order to enable our machine to learn relational order, we developed a reinforcement-learning method whose optimality is proven to train the network.", "published": "2017-06-24T20:56:27Z", "authors": ["Zizhuang Wang"], "categories": ["cs.AI", "cs.LG", "stat.ML"], "doi": "", "_query": "house price prediction machine learning", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2410.23482v1", "title": "Multi-fidelity Machine Learning for Uncertainty Quantification and Optimization", "abstract": "In system analysis and design optimization, multiple computational models are typically available to represent a given physical system. These models can be broadly classified as high-fidelity models, which provide highly accurate predictions but require significant computational resources, and low-fidelity models, which are computationally efficient but less accurate. Multi-fidelity methods integrate high- and low-fidelity models to balance computational cost and predictive accuracy. This perspective paper provides an in-depth overview of the emerging field of machine learning-based multi-fidelity methods, with a particular emphasis on uncertainty quantification and optimization. For uncertainty quantification, a particular focus is on multi-fidelity graph neural networks, compared with multi-fidelity polynomial chaos expansion. For optimization, our emphasis is on multi-fidelity Bayesian optimization, offering a unified perspective on multi-fidelity priors and proposing an application strategy when the objective function is an integral or a weighted sum. We highlight the current state of the art, identify critical gaps in the literature, and outline key research opportunities in this evolving field.", "published": "2024-10-30T22:22:07Z", "authors": ["Ruda Zhang", "Negin Alemazkoor"], "categories": ["cs.LG", "math.OC", "stat.ML"], "doi": "10.1615/JMachLearnModelComput.2024055786", "_query": "house price prediction machine learning", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2304.07687v4", "title": "MLRegTest: A Benchmark for the Machine Learning of Regular Languages", "abstract": "Synthetic datasets constructed from formal languages allow fine-grained examination of the learning and generalization capabilities of machine learning systems for sequence classification. This article presents a new benchmark for machine learning systems on sequence classification called MLRegTest, which contains training, development, and test sets from 1,800 regular languages. Different kinds of formal languages represent different kinds of long-distance dependencies, and correctly identifying long-distance dependencies in sequences is a known challenge for ML systems to generalize successfully. MLRegTest organizes its languages according to their logical complexity (monadic second order, first order, propositional, or monomial expressions) and the kind of logical literals (string, tier-string, subsequence, or combinations thereof). The logical complexity and choice of literal provides a systematic way to understand different kinds of long-distance dependencies in regular languages, and therefore to understand the capacities of different ML systems to learn such long-distance dependencies. Finally, the performance of different neural networks (simple RNN, LSTM, GRU, transformer) on MLRegTest is examined. The main conclusion is that performance depends significantly on the kind of test set, the class of language, and the neural network architecture.", "published": "2023-04-16T03:49:50Z", "authors": ["Sam van der Poel", "Dakotah Lambert", "Kalina Kostyszyn", "Tiantian Gao", "Rahul Verma", "Derek Andersen", "Joanne Chau", "Emily Peterson", "Cody St. Clair", "Paul Fodor", "Chihiro Shibata", "Jeffrey Heinz"], "categories": ["cs.LG", "cs.CL", "cs.FL"], "doi": "", "_query": "house price prediction machine learning", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2211.03179v1", "title": "Personalizing Sustainable Agriculture with Causal Machine Learning", "abstract": "To fight climate change and accommodate the increasing population, global crop production has to be strengthened. To achieve the \"sustainable intensification\" of agriculture, transforming it from carbon emitter to carbon sink is a priority, and understanding the environmental impact of agricultural management practices is a fundamental prerequisite to that. At the same time, the global agricultural landscape is deeply heterogeneous, with differences in climate, soil, and land use inducing variations in how agricultural systems respond to farmer actions. The \"personalization\" of sustainable agriculture with the provision of locally adapted management advice is thus a necessary condition for the efficient uplift of green metrics, and an integral development in imminent policies. Here, we formulate personalized sustainable agriculture as a Conditional Average Treatment Effect estimation task and use Causal Machine Learning for tackling it. Leveraging climate data, land use information and employing Double Machine Learning, we estimate the heterogeneous effect of sustainable practices on the field-level Soil Organic Carbon content in Lithuania. We thus provide a data-driven perspective for targeting sustainable practices and effectively expanding the global carbon sink.", "published": "2022-11-06T17:14:14Z", "authors": ["Georgios Giannarakis", "Vasileios Sitokonstantinou", "Roxanne Suzette Lorilla", "Charalampos Kontoes"], "categories": ["cs.LG", "cs.AI"], "doi": "", "_query": "house price prediction machine learning", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2004.11697v2", "title": "A Time Series Analysis-Based Stock Price Prediction Using Machine Learning and Deep Learning Models", "abstract": "Prediction of future movement of stock prices has always been a challenging task for the researchers. While the advocates of the efficient market hypothesis (EMH) believe that it is impossible to design any predictive framework that can accurately predict the movement of stock prices, there are seminal work in the literature that have clearly demonstrated that the seemingly random movement patterns in the time series of a stock price can be predicted with a high level of accuracy. Design of such predictive models requires choice of appropriate variables, right transformation methods of the variables, and tuning of the parameters of the models. In this work, we present a very robust and accurate framework of stock price prediction that consists of an agglomeration of statistical, machine learning and deep learning models. We use the daily stock price data, collected at five minutes interval of time, of a very well known company that is listed in the National Stock Exchange (NSE) of India. The granular data is aggregated into three slots in a day, and the aggregated data is used for building and training the forecasting models. We contend that the agglomerative approach of model building that uses a combination of statistical, machine learning, and deep learning approaches, can very effectively learn from the volatile and random movement patterns in a stock price data. We build eight classification and eight regression models based on statistical and machine learning approaches. In addition to these models, a deep learning regression model using a long-and-short-term memory (LSTM) network is also built. Extensive results have been presented on the performance of these models, and the results are critically analyzed.", "published": "2020-04-17T19:41:22Z", "authors": ["Sidra Mehtab", "Jaydip Sen"], "categories": ["q-fin.ST", "cs.LG", "stat.ML"], "doi": "10.1504/IJBFMI.2020.115691", "_query": "house price prediction machine learning", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2006.03361v1", "title": "Learning to Rank Learning Curves", "abstract": "Many automated machine learning methods, such as those for hyperparameter and neural architecture optimization, are computationally expensive because they involve training many different model configurations. In this work, we present a new method that saves computational budget by terminating poor configurations early on in the training. In contrast to existing methods, we consider this task as a ranking and transfer learning problem. We qualitatively show that by optimizing a pairwise ranking loss and leveraging learning curves from other datasets, our model is able to effectively rank learning curves without having to observe many or very long learning curves. We further demonstrate that our method can be used to accelerate a neural architecture search by a factor of up to 100 without a significant performance degradation of the discovered architecture. In further experiments we analyze the quality of ranking, the influence of different model components as well as the predictive behavior of the model.", "published": "2020-06-05T10:49:52Z", "authors": ["Martin Wistuba", "Tejaswini Pedapati"], "categories": ["cs.LG", "cs.CV", "stat.ML"], "doi": "", "_query": "house price prediction machine learning", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2406.06714v2", "title": "Coprocessor Actor Critic: A Model-Based Reinforcement Learning Approach For Adaptive Brain Stimulation", "abstract": "Adaptive brain stimulation can treat neurological conditions such as Parkinson's disease and post-stroke motor deficits by influencing abnormal neural activity. Because of patient heterogeneity, each patient requires a unique stimulation policy to achieve optimal neural responses. Model-free reinforcement learning (MFRL) holds promise in learning effective policies for a variety of similar control tasks, but is limited in domains like brain stimulation by a need for numerous costly environment interactions. In this work we introduce Coprocessor Actor Critic, a novel, model-based reinforcement learning (MBRL) approach for learning neural coprocessor policies for brain stimulation. Our key insight is that coprocessor policy learning is a combination of learning how to act optimally in the world and learning how to induce optimal actions in the world through stimulation of an injured brain. We show that our approach overcomes the limitations of traditional MFRL methods in terms of sample efficiency and task success and outperforms baseline MBRL approaches in a neurologically realistic model of an injured brain.", "published": "2024-06-10T18:23:03Z", "authors": ["Michelle Pan", "Mariah Schrum", "Vivek Myers", "Erdem Bıyık", "Anca Dragan"], "categories": ["cs.LG", "cs.AI", "cs.HC"], "doi": "", "_query": "house price prediction machine learning", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2211.05409v1", "title": "Radiomics-enhanced Deep Multi-task Learning for Outcome Prediction in Head and Neck Cancer", "abstract": "Outcome prediction is crucial for head and neck cancer patients as it can provide prognostic information for early treatment planning. Radiomics methods have been widely used for outcome prediction from medical images. However, these methods are limited by their reliance on intractable manual segmentation of tumor regions. Recently, deep learning methods have been proposed to perform end-to-end outcome prediction so as to remove the reliance on manual segmentation. Unfortunately, without segmentation masks, these methods will take the whole image as input, such that makes them difficult to focus on tumor regions and potentially unable to fully leverage the prognostic information within the tumor regions. In this study, we propose a radiomics-enhanced deep multi-task framework for outcome prediction from PET/CT images, in the context of HEad and neCK TumOR segmentation and outcome prediction challenge (HECKTOR 2022). In our framework, our novelty is to incorporate radiomics as an enhancement to our recently proposed Deep Multi-task Survival model (DeepMTS). The DeepMTS jointly learns to predict the survival risk scores of patients and the segmentation masks of tumor regions. Radiomics features are extracted from the predicted tumor regions and combined with the predicted survival risk scores for final outcome prediction, through which the prognostic information in tumor regions can be further leveraged. Our method achieved a C-index of 0.681 on the testing set, placing the 2nd on the leaderboard with only 0.00068 lower in C-index than the 1st place.", "published": "2022-11-10T08:28:56Z", "authors": ["Mingyuan Meng", "Lei Bi", "Dagan Feng", "Jinman Kim"], "categories": ["eess.IV", "cs.CV", "cs.LG"], "doi": "10.1007/978-3-031-27420-6_14", "_query": "house price prediction machine learning", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2405.06148v1", "title": "Detecting Moving Objects With Machine Learning", "abstract": "The scientific study of the Solar System's minor bodies ultimately starts with a search for those bodies. This chapter presents a review of the use of machine learning techniques to find moving objects, both natural and artificial, in astronomical imagery. After a short review of the classical non-machine learning techniques that are historically used, I review the relatively nascent machine learning literature, which can broadly be summarized into three categories: streak detection, detection of moving point sources in image sequences, and detection of moving sources in shift and stack searches. In most cases, convolutional neural networks are utilized, which is the obvious choice given the imagery nature of the inputs. In this chapter I present two example networks: a Residual Network I designed which is in use in various shift and stack searches, and a convolutional neural network that was designed for prediction of source brightnesses and their uncertainties in those same shift-stacks. In discussion of the literature and example networks, I discuss various pitfalls with the use of machine learning techniques, including a discussion on the important issue of overfitting. I discuss various pitfall associated with the use of machine learning techniques, and what I consider best practices to follow in the application of machine learning to a new problem, including methods for the creation of robust training sets, validation, and training to avoid overfitting.", "published": "2024-05-10T00:13:39Z", "authors": ["Wesley C. Fraser"], "categories": ["astro-ph.EP", "astro-ph.IM", "cs.LG"], "doi": "", "_query": "house price prediction machine learning", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/1906.07315v3", "title": "Evolutionary Reinforcement Learning for Sample-Efficient Multiagent Coordination", "abstract": "Many cooperative multiagent reinforcement learning environments provide agents with a sparse team-based reward, as well as a dense agent-specific reward that incentivizes learning basic skills. Training policies solely on the team-based reward is often difficult due to its sparsity. Furthermore, relying solely on the agent-specific reward is sub-optimal because it usually does not capture the team coordination objective. A common approach is to use reward shaping to construct a proxy reward by combining the individual rewards. However, this requires manual tuning for each environment. We introduce Multiagent Evolutionary Reinforcement Learning (MERL), a split-level training platform that handles the two objectives separately through two optimization processes. An evolutionary algorithm maximizes the sparse team-based objective through neuroevolution on a population of teams. Concurrently, a gradient-based optimizer trains policies to only maximize the dense agent-specific rewards. The gradient-based policies are periodically added to the evolutionary population as a way of information transfer between the two optimization processes. This enables the evolutionary algorithm to use skills learned via the agent-specific rewards toward optimizing the global objective. Results demonstrate that MERL significantly outperforms state-of-the-art methods, such as MADDPG, on a number of difficult coordination benchmarks.", "published": "2019-06-18T00:25:27Z", "authors": ["Shauharda Khadka", "Somdeb Majumdar", "Santiago Miret", "Stephen McAleer", "Kagan Tumer"], "categories": ["cs.LG", "cs.AI", "cs.MA", "stat.ML"], "doi": "", "_query": "house price prediction machine learning", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2508.03156v1", "title": "Unveiling Location-Specific Price Drivers: A Two-Stage Cluster Analysis for Interpretable House Price Predictions", "abstract": "House price valuation remains challenging due to localized market variations. Existing approaches often rely on black-box machine learning models, which lack interpretability, or simplistic methods like linear regression (LR), which fail to capture market heterogeneity. To address this, we propose a machine learning approach that applies two-stage clustering, first grouping properties based on minimal location-based features before incorporating additional features. Each cluster is then modeled using either LR or a generalized additive model (GAM), balancing predictive performance with interpretability. Constructing and evaluating our models on 43,309 German house property listings from 2023, we achieve a 36% improvement for the GAM and 58% for LR in mean absolute error compared to models without clustering. Additionally, graphical analyses unveil pattern shifts between clusters. These findings emphasize the importance of cluster-specific insights, enhancing interpretability and offering practical value for buyers, sellers, and real estate analysts seeking more reliable property valuations.", "published": "2025-08-05T07:00:42Z", "authors": ["Paul Gümmer", "Julian Rosenberger", "Mathias Kraus", "Patrick Zschech", "Nico Hambauer"], "categories": ["cs.LG"], "doi": "", "_query": "house price prediction machine learning", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2508.01419v1", "title": "Cryptocurrency Price Forecasting Using Machine Learning: Building Intelligent Financial Prediction Models", "abstract": "Cryptocurrency markets are experiencing rapid growth, but this expansion comes with significant challenges, particularly in predicting cryptocurrency prices for traders in the U.S. In this study, we explore how deep learning and machine learning models can be used to forecast the closing prices of the XRP/USDT trading pair. While many existing cryptocurrency prediction models focus solely on price and volume patterns, they often overlook market liquidity, a crucial factor in price predictability. To address this, we introduce two important liquidity proxy metrics: the Volume-To-Volatility Ratio (VVR) and the Volume-Weighted Average Price (VWAP). These metrics provide a clearer understanding of market stability and liquidity, ultimately enhancing the accuracy of our price predictions. We developed four machine learning models, Linear Regression, Random Forest, XGBoost, and LSTM neural networks, using historical data without incorporating the liquidity proxy metrics, and evaluated their performance. We then retrained the models, including the liquidity proxy metrics, and reassessed their performance. In both cases (with and without the liquidity proxies), the LSTM model consistently outperformed the others. These results underscore the importance of considering market liquidity when predicting cryptocurrency closing prices. Therefore, incorporating these liquidity metrics is essential for more accurate forecasting models. Our findings offer valuable insights for traders and developers seeking to create smarter and more risk-aware strategies in the U.S. digital assets market.", "published": "2025-08-02T15:54:41Z", "authors": ["Md Zahidul Islam", "Md Shafiqur Rahman", "Md Sumsuzoha", "Babul Sarker", "Md Rafiqul Islam", "Mahfuz Alam", "Sanjib Kumar Shil"], "categories": ["cs.LG"], "doi": "", "_query": "house price prediction machine learning", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2210.14396v4", "title": "FeDXL: Provable Federated Learning for Deep X-Risk Optimization", "abstract": "In this paper, we tackle a novel federated learning (FL) problem for optimizing a family of X-risks, to which no existing FL algorithms are applicable. In particular, the objective has the form of $\\mathbb E_{z\\sim S_1} f(\\mathbb E_{z'\\sim S_2} \\ell(w; z, z'))$, where two sets of data $S_1, S_2$ are distributed over multiple machines, $\\ell(\\cdot)$ is a pairwise loss that only depends on the prediction outputs of the input data pairs $(z, z')$, and $f(\\cdot)$ is possibly a non-linear non-convex function. This problem has important applications in machine learning, e.g., AUROC maximization with a pairwise loss, and partial AUROC maximization with a compositional loss. The challenges for designing an FL algorithm for X-risks lie in the non-decomposability of the objective over multiple machines and the interdependency between different machines. To this end, we propose an active-passive decomposition framework that decouples the gradient's components with two types, namely active parts and passive parts, where the active parts depend on local data that are computed with the local model and the passive parts depend on other machines that are communicated/computed based on historical models and samples. Under this framework, we develop two provable FL algorithms (FeDXL) for handling linear and nonlinear $f$, respectively, based on federated averaging and merging. We develop a novel theoretical analysis to combat the latency of the passive parts and the interdependency between the local model parameters and the involved data for computing local gradient estimators. We establish both iteration and communication complexities and show that using the historical samples and models for computing the passive parts do not degrade the complexities. We conduct empirical studies of FeDXL for deep AUROC and partial AUROC maximization, and demonstrate their performance compared with several baselines.", "published": "2022-10-26T00:23:36Z", "authors": ["Zhishuai Guo", "Rong Jin", "Jiebo Luo", "Tianbao Yang"], "categories": ["cs.LG", "cs.DC", "math.OC", "stat.ML"], "doi": "", "_query": "house price prediction machine learning", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/1904.07204v1", "title": "Tutorial: Safe and Reliable Machine Learning", "abstract": "This document serves as a brief overview of the \"Safe and Reliable Machine Learning\" tutorial given at the 2019 ACM Conference on Fairness, Accountability, and Transparency (FAT* 2019). The talk slides can be found here: https://bit.ly/2Gfsukp, while a video of the talk is available here: https://youtu.be/FGLOCkC4KmE, and a complete list of references for the tutorial here: https://bit.ly/2GdLPme.", "published": "2019-04-15T17:28:50Z", "authors": ["Suchi Saria", "Adarsh Subbaswamy"], "categories": ["cs.LG", "cs.AI"], "doi": "", "_query": "house price prediction machine learning", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2505.05577v2", "title": "PyTDC: A multimodal machine learning training, evaluation, and inference platform for biomedical foundation models", "abstract": "Existing biomedical benchmarks do not provide end-to-end infrastructure for training, evaluation, and inference of models that integrate multimodal biological data and a broad range of machine learning tasks in therapeutics. We present PyTDC, an open-source machine-learning platform providing streamlined training, evaluation, and inference software for multimodal biological AI models. PyTDC unifies distributed, heterogeneous, continuously updated data sources and model weights and standardizes benchmarking and inference endpoints. This paper discusses the components of PyTDC's architecture and, to our knowledge, the first-of-its-kind case study on the introduced single-cell drug-target nomination ML task. We find state-of-the-art methods in graph representation learning and domain-specific methods from graph theory perform poorly on this task. Though we find a context-aware geometric deep learning method that outperforms the evaluated SoTA and domain-specific baseline methods, the model is unable to generalize to unseen cell types or incorporate additional modalities, highlighting PyTDC's capacity to facilitate an exciting avenue of research developing multimodal, context-aware, foundation models for open problems in biomedical AI.", "published": "2025-05-08T18:15:38Z", "authors": ["Alejandro Velez-Arce", "Jesus Caraballo", "Marinka Zitnik"], "categories": ["cs.LG", "cs.AI"], "doi": "", "_query": "house price prediction machine learning", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2511.19491v1", "title": "OpenCML: End-to-End Framework of Open-world Machine Learning to Learn Unknown Classes Incrementally", "abstract": "Open-world machine learning is an emerging technique in artificial intelligence, where conventional machine learning models often follow closed-world assumptions, which can hinder their ability to retain previously learned knowledge for future tasks. However, automated intelligence systems must learn about novel classes and previously known tasks. The proposed model offers novel learning classes in an open and continuous learning environment. It consists of two different but connected tasks. First, it discovers unknown classes in the data and creates novel classes; next, it learns how to perform class incrementally for each new class. Together, they enable continual learning, allowing the system to expand its understanding of the data and improve over time. The proposed model also outperformed existing approaches in open-world learning. Furthermore, it demonstrated strong performance in continuous learning, achieving a highest average accuracy of 82.54% over four iterations and a minimum accuracy of 65.87%.", "published": "2025-11-23T10:27:19Z", "authors": ["Jitendra Parmar", "Praveen Singh Thakur"], "categories": ["cs.LG"], "doi": "", "_query": "house price prediction machine learning", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/1803.04383v2", "title": "Delayed Impact of Fair Machine Learning", "abstract": "Fairness in machine learning has predominantly been studied in static classification settings without concern for how decisions change the underlying population over time. Conventional wisdom suggests that fairness criteria promote the long-term well-being of those groups they aim to protect. We study how static fairness criteria interact with temporal indicators of well-being, such as long-term improvement, stagnation, and decline in a variable of interest. We demonstrate that even in a one-step feedback model, common fairness criteria in general do not promote improvement over time, and may in fact cause harm in cases where an unconstrained objective would not. We completely characterize the delayed impact of three standard criteria, contrasting the regimes in which these exhibit qualitatively different behavior. In addition, we find that a natural form of measurement error broadens the regime in which fairness criteria perform favorably. Our results highlight the importance of measurement and temporal modeling in the evaluation of fairness criteria, suggesting a range of new challenges and trade-offs.", "published": "2018-03-12T17:20:56Z", "authors": ["Lydia T. Liu", "Sarah Dean", "Esther Rolf", "Max Simchowitz", "Moritz Hardt"], "categories": ["cs.LG", "stat.ML"], "doi": "", "_query": "house price prediction machine learning", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2503.14621v1", "title": "Reducing False Ventricular Tachycardia Alarms in ICU Settings: A Machine Learning Approach", "abstract": "False arrhythmia alarms in intensive care units (ICUs) are a significant challenge, contributing to alarm fatigue and potentially compromising patient safety. Ventricular tachycardia (VT) alarms are particularly difficult to detect accurately due to their complex nature. This paper presents a machine learning approach to reduce false VT alarms using the VTaC dataset, a benchmark dataset of annotated VT alarms from ICU monitors. We extract time-domain and frequency-domain features from waveform data, preprocess the data, and train deep learning models to classify true and false VT alarms. Our results demonstrate high performance, with ROC-AUC scores exceeding 0.96 across various training configurations. This work highlights the potential of machine learning to improve the accuracy of VT alarm detection in clinical settings.", "published": "2025-03-18T18:18:38Z", "authors": ["Grace Funmilayo Farayola", "Akinyemi Sadeeq Akintola", "Oluwole Fagbohun", "Chukwuka Michael Oforgu", "Bisola Faith Kayode", "Christian Chimezie", "Temitope Kadri", "Abiola Oludotun", "Nelson Ogbeide", "Mgbame Michael", "Adeseye Ifaturoti", "Toyese Oloyede"], "categories": ["cs.LG", "cs.AI"], "doi": "", "_query": "house price prediction machine learning", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/1612.07640v1", "title": "Deep Learning and Its Applications to Machine Health Monitoring: A Survey", "abstract": "Since 2006, deep learning (DL) has become a rapidly growing research direction, redefining state-of-the-art performances in a wide range of areas such as object recognition, image segmentation, speech recognition and machine translation. In modern manufacturing systems, data-driven machine health monitoring is gaining in popularity due to the widespread deployment of low-cost sensors and their connection to the Internet. Meanwhile, deep learning provides useful tools for processing and analyzing these big machinery data. The main purpose of this paper is to review and summarize the emerging research work of deep learning on machine health monitoring. After the brief introduction of deep learning techniques, the applications of deep learning in machine health monitoring systems are reviewed mainly from the following aspects: Auto-encoder (AE) and its variants, Restricted Boltzmann Machines and its variants including Deep Belief Network (DBN) and Deep Boltzmann Machines (DBM), Convolutional Neural Networks (CNN) and Recurrent Neural Networks (RNN). Finally, some new trends of DL-based machine health monitoring methods are discussed.", "published": "2016-12-16T04:56:30Z", "authors": ["Rui Zhao", "Ruqiang Yan", "Zhenghua Chen", "Kezhi Mao", "Peng Wang", "Robert X. Gao"], "categories": ["cs.LG", "stat.ML"], "doi": "", "_query": "house price prediction machine learning", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2407.12267v1", "title": "Generating 3D House Wireframes with Semantics", "abstract": "We present a new approach for generating 3D house wireframes with semantic enrichment using an autoregressive model. Unlike conventional generative models that independently process vertices, edges, and faces, our approach employs a unified wire-based representation for improved coherence in learning 3D wireframe structures. By re-ordering wire sequences based on semantic meanings, we facilitate seamless semantic integration during sequence generation. Our two-phase technique merges a graph-based autoencoder with a transformer-based decoder to learn latent geometric tokens and generate semantic-aware wireframes. Through iterative prediction and decoding during inference, our model produces detailed wireframes that can be easily segmented into distinct components, such as walls, roofs, and rooms, reflecting the semantic essence of the shape. Empirical results on a comprehensive house dataset validate the superior accuracy, novelty, and semantic fidelity of our model compared to existing generative models. More results and details can be found on https://vcc.tech/research/2024/3DWire.", "published": "2024-07-17T02:33:34Z", "authors": ["Xueqi Ma", "Yilin Liu", "Wenjun Zhou", "Ruowei Wang", "Hui Huang"], "categories": ["cs.CV", "cs.GR"], "doi": "", "_query": "house price prediction machine learning", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2501.01696v2", "title": "Guaranteed Nonconvex Low-Rank Tensor Estimation via Scaled Gradient Descent", "abstract": "Tensors, which give a faithful and effective representation to deliver the intrinsic structure of multi-dimensional data, play a crucial role in an increasing number of signal processing and machine learning problems. However, tensor data are often accompanied by arbitrary signal corruptions, including missing entries and sparse noise. A fundamental challenge is to reliably extract the meaningful information from corrupted tensor data in a statistically and computationally efficient manner. This paper develops a scaled gradient descent (ScaledGD) algorithm to directly estimate the tensor factors with tailored spectral initializations under the tensor-tensor product (t-product) and tensor singular value decomposition (t-SVD) framework. With tailored variants for tensor robust principal component analysis, (robust) tensor completion and tensor regression, we theoretically show that ScaledGD achieves linear convergence at a constant rate that is independent of the condition number of the ground truth low-rank tensor, while maintaining the low per-iteration cost of gradient descent. To the best of our knowledge, ScaledGD is the first algorithm that provably has such properties for low-rank tensor estimation with the t-SVD. Finally, numerical examples are provided to demonstrate the efficacy of ScaledGD in accelerating the convergence rate of ill-conditioned low-rank tensor estimation in a number of applications.", "published": "2025-01-03T08:26:01Z", "authors": ["Tong Wu"], "categories": ["stat.ML", "cs.IT", "cs.LG"], "doi": "", "_query": "house price prediction machine learning", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/1812.07858v3", "title": "Machine Learning in Cyber-Security - Problems, Challenges and Data Sets", "abstract": "We present cyber-security problems of high importance. We show that in order to solve these cyber-security problems, one must cope with certain machine learning challenges. We provide novel data sets representing the problems in order to enable the academic community to investigate the problems and suggest methods to cope with the challenges. We also present a method to generate labels via pivoting, providing a solution to common problems of lack of labels in cyber-security.", "published": "2018-12-19T10:19:25Z", "authors": ["Idan Amit", "John Matherly", "William Hewlett", "Zhi Xu", "Yinnon Meshi", "Yigal Weinberger"], "categories": ["cs.LG", "cs.CR", "stat.ML"], "doi": "", "_query": "house price prediction machine learning", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2512.06785v1", "title": "Angular Regularization for Positive-Unlabeled Learning on the Hypersphere", "abstract": "Positive-Unlabeled (PU) learning addresses classification problems where only a subset of positive examples is labeled and the remaining data is unlabeled, making explicit negative supervision unavailable. Existing PU methods often rely on negative-risk estimation or pseudo-labeling, which either require strong distributional assumptions or can collapse in high-dimensional settings. We propose AngularPU, a novel PU framework that operates on the unit hypersphere using cosine similarity and angular margin. In our formulation, the positive class is represented by a learnable prototype vector, and classification reduces to thresholding the cosine similarity between an embedding and this prototype-eliminating the need for explicit negative modeling. To counteract the tendency of unlabeled embeddings to cluster near the positive prototype, we introduce an angular regularizer that encourages dispersion of the unlabeled set over the hypersphere, improving separation. We provide theoretical guarantees on the Bayes-optimality of the angular decision rule, consistency of the learned prototype, and the effect of the regularizer on the unlabeled distribution. Experiments on benchmark datasets demonstrate that AngularPU achieves competitive or superior performance compared to state-of-the-art PU methods, particularly in settings with scarce positives and high-dimensional embeddings, while offering geometric interpretability and scalability.", "published": "2025-12-07T10:59:35Z", "authors": ["Vasileios Sevetlidis", "George Pavlidis", "Antonios Gasteratos"], "categories": ["cs.LG", "cs.AI"], "doi": "", "_query": "house price prediction machine learning", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2310.08133v1", "title": "Multi Level Dense Layer Neural Network Model for Housing Price Prediction", "abstract": "Predicting the price of a house remains a challenging issue that needs to be addressed. Research has attempted to establish a model with different methods and algorithms to predict the housing price, from the traditional hedonic model to a neural network algorithm. However, many existing algorithms in the literature are proposed without any finetuning and customization in the model. In this paper, the author attempted to propose a novel neural network-based model to improve the performance of housing price prediction. Inspired by the modular neural network, the proposed model consists of a three-level neural network that is capable to process information in parallel. The author compared several state-of-the-art algorithms available in the literature on the Boston housing dataset to evaluate the effectiveness of the proposed model. The results show that the proposed model provides better accuracy and outperforms existing algorithms in different evaluation metrics. The code for the implementation is available https://github.com/wijayarobert/MultiLevelDenseLayerNN", "published": "2023-10-12T08:46:26Z", "authors": ["Robert Wijaya"], "categories": ["cs.CY"], "doi": "", "_query": "house price prediction machine learning", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2509.21460v2", "title": "Forecasting House Prices", "abstract": "This article identifies the factors that drove house prices in 13 advanced countries over the past 35 years. It does so based on Breiman s (2001) random forest model. Shapley values indicate that annual house price growth across countries is explained first and foremost by price momentum, initial valuations (proxied by price to rent ratios) and household credit growth. Partial effects of explanatory variables are also elicited and suggest important non-linearities, for instance as to what concerns the effects of CPI inflation on house price growth. The out-of-sample forecast test reveals that the random forest model delivers 44% lower house price variation root square mean errors (RMSEs) and 45% lower mean absolute errors (MAEs) when compared to an OLS model that uses the same set of 10 pre-determined explanatory variables. Notably, the same model works well for all countries, as the random forest attributes minimal values to country fixed effects.", "published": "2025-09-25T19:27:37Z", "authors": ["Emanuel Kohlscheen"], "categories": ["econ.GN"], "doi": "", "_query": "house price prediction machine learning", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/1904.11203v3", "title": "Reviewing Data Access Patterns and Computational Redundancy for Machine Learning Algorithms", "abstract": "Machine learning (ML) is probably the first and foremost used technique to deal with the size and complexity of the new generation of data. In this paper, we analyze one of the means to increase the performances of ML algorithms which is exploiting data locality. Data locality and access patterns are often at the heart of performance issues in computing systems due to the use of certain hardware techniques to improve performance. Altering the access patterns to increase locality can dramatically increase performance of a given algorithm. Besides, repeated data access can be seen as redundancy in data movement. Similarly, there can also be redundancy in the repetition of calculations. This work also identifies some of the opportunities for avoiding these redundancies by directly reusing computation results. We document the possibilities of such reuse in some selected machine learning algorithms and give initial indicative results from our first experiments on data access improvement and algorithm redesign.", "published": "2019-04-25T08:26:19Z", "authors": ["Imen Chakroun", "Tom Vander Aa", "Tom Ashby"], "categories": ["cs.LG", "stat.ML"], "doi": "", "_query": "house price prediction machine learning", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2111.03788v2", "title": "d3rlpy: An Offline Deep Reinforcement Learning Library", "abstract": "In this paper, we introduce d3rlpy, an open-sourced offline deep reinforcement learning (RL) library for Python. d3rlpy supports a set of offline deep RL algorithms as well as off-policy online algorithms via a fully documented plug-and-play API. To address a reproducibility issue, we conduct a large-scale benchmark with D4RL and Atari 2600 dataset to ensure implementation quality and provide experimental scripts and full tables of results. The d3rlpy source code can be found on GitHub: \\url{https://github.com/takuseno/d3rlpy}.", "published": "2021-11-06T03:09:39Z", "authors": ["Takuma Seno", "Michita Imai"], "categories": ["cs.LG", "cs.AI"], "doi": "", "_query": "house price prediction machine learning", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2608.20315v1", "title": "Explainable Transformer Models for Clinical Prediction Tasks on Structured Electronic Health Records", "abstract": "Predictive models over structured electronic health records (EHRs) remain central to machine learning for healthcare, but few have jointly emphasized quantitative laboratory information and interpretability with respect to input medical events. We present BERT-LER, a BERT-style model for coded EHR timelines pretrained and fine-tuned from a de-identified EHR dataset of 75 million patients, that encodes laboratory test results as discrete tokens while retaining graded information through percentile-based binning, paired with Integrated Gradients for token-level attributions grounded in the input EHR sequence. We benchmark our approach on the public EHRShot benchmark suite and on an asthma severity progression study based on real-world data. This addresses a methodological gap in EHR foundation-style modeling by unifying laboratory value representation and explainability in a single framework, while assessing whether both predictive performance and explanations generalize beyond standard clinical prediction tasks. Across EHRShot and asthma tasks, BERT-LER achieves predictive performance that is competitive with, and on laboratory-related tasks often exceeds, publicly available benchmark models, and provides attributions that align with clinically known risk factors. Our architecture and explainability approach can be applied to many therapeutic areas and prediction tasks using language models trained on structured EHRs.", "published": "2026-08-20T17:54:17Z", "authors": ["Jun Ni Du", "Lukas Adamek", "Maxim Kryukov", "Flavio Dormont", "Ziv Bar-Joseph", "Sven Jager", "Brandon Rufino"], "categories": ["cs.LG"], "doi": "", "_query": "house price prediction machine learning", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/1712.07897v1", "title": "Non-convex Optimization for Machine Learning", "abstract": "A vast majority of machine learning algorithms train their models and perform inference by solving optimization problems. In order to capture the learning and prediction problems accurately, structural constraints such as sparsity or low rank are frequently imposed or else the objective itself is designed to be a non-convex function. This is especially true of algorithms that operate in high-dimensional spaces or that train non-linear models such as tensor models and deep networks. The freedom to express the learning problem as a non-convex optimization problem gives immense modeling power to the algorithm designer, but often such problems are NP-hard to solve. A popular workaround to this has been to relax non-convex problems to convex ones and use traditional methods to solve the (convex) relaxed optimization problems. However this approach may be lossy and nevertheless presents significant challenges for large scale optimization. On the other hand, direct approaches to non-convex optimization have met with resounding success in several domains and remain the methods of choice for the practitioner, as they frequently outperform relaxation-based techniques - popular heuristics include projected gradient descent and alternating minimization. However, these are often poorly understood in terms of their convergence and other properties. This monograph presents a selection of recent advances that bridge a long-standing gap in our understanding of these heuristics. The monograph will lead the reader through several widely used non-convex optimization techniques, as well as applications thereof. The goal of this monograph is to both, introduce the rich literature in this area, as well as equip the reader with the tools and techniques needed to analyze these simple procedures for non-convex problems.", "published": "2017-12-21T12:05:40Z", "authors": ["Prateek Jain", "Purushottam Kar"], "categories": ["stat.ML", "cs.LG", "math.OC"], "doi": "10.1561/2200000058", "_query": "house price prediction machine learning", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/1506.06962v1", "title": "Graphs in machine learning: an introduction", "abstract": "Graphs are commonly used to characterise interactions between objects of interest. Because they are based on a straightforward formalism, they are used in many scientific fields from computer science to historical sciences. In this paper, we give an introduction to some methods relying on graphs for learning. This includes both unsupervised and supervised methods. Unsupervised learning algorithms usually aim at visualising graphs in latent spaces and/or clustering the nodes. Both focus on extracting knowledge from graph topologies. While most existing techniques are only applicable to static graphs, where edges do not evolve through time, recent developments have shown that they could be extended to deal with evolving networks. In a supervised context, one generally aims at inferring labels or numerical values attached to nodes using both the graph and, when they are available, node characteristics. Balancing the two sources of information can be challenging, especially as they can disagree locally or globally. In both contexts, supervised and un-supervised, data can be relational (augmented with one or several global graphs) as described above, or graph valued. In this latter case, each object of interest is given as a full graph (possibly completed by other characteristics). In this context, natural tasks include graph clustering (as in producing clusters of graphs rather than clusters of nodes in a single graph), graph classification, etc. 1 Real networks One of the first practical studies on graphs can be dated back to the original work of Moreno [51] in the 30s. Since then, there has been a growing interest in graph analysis associated with strong developments in the modelling and the processing of these data. Graphs are now used in many scientific fields. In Biology [54, 2, 7], for instance, metabolic networks can describe pathways of biochemical reactions [41], while in social sciences networks are used to represent relation ties between actors [66, 56, 36, 34]. Other examples include powergrids [71] and the web [75]. Recently, networks have also been considered in other areas such as geography [22] and history [59, 39]. In machine learning, networks are seen as powerful tools to model problems in order to extract information from data and for prediction purposes. This is the object of this paper. For more complete surveys, we refer to [28, 62, 49, 45]. In this section, we introduce notations and highlight properties shared by most real networks. In Section 2, we then consider methods aiming at extracting information from a unique network. We will particularly focus on clustering methods where the goal is to find clusters of vertices. Finally, in Section 3, techniques that take a series of networks into account, where each network is", "published": "2015-06-23T12:12:45Z", "authors": ["Pierre Latouche", "Fabrice Rossi"], "categories": ["stat.ML", "cs.LG", "cs.SI", "physics.soc-ph"], "doi": "", "_query": "house price prediction machine learning", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/1310.6536v1", "title": "Randomized co-training: from cortical neurons to machine learning and back again", "abstract": "Despite its size and complexity, the human cortex exhibits striking anatomical regularities, suggesting there may simple meta-algorithms underlying cortical learning and computation. We expect such meta-algorithms to be of interest since they need to operate quickly, scalably and effectively with little-to-no specialized assumptions. This note focuses on a specific question: How can neurons use vast quantities of unlabeled data to speed up learning from the comparatively rare labels provided by reward systems? As a partial answer, we propose randomized co-training as a biologically plausible meta-algorithm satisfying the above requirements. As evidence, we describe a biologically-inspired algorithm, Correlated Nystrom Views (XNV) that achieves state-of-the-art performance in semi-supervised learning, and sketch work in progress on a neuronal implementation.", "published": "2013-10-24T09:33:17Z", "authors": ["David Balduzzi"], "categories": ["cs.LG", "q-bio.NC", "stat.ML"], "doi": "", "_query": "house price prediction machine learning", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2304.09872v3", "title": "Depth Functions for Partial Orders with a Descriptive Analysis of Machine Learning Algorithms", "abstract": "We propose a framework for descriptively analyzing sets of partial orders based on the concept of depth functions. Despite intensive studies of depth functions in linear and metric spaces, there is very little discussion on depth functions for non-standard data types such as partial orders. We introduce an adaptation of the well-known simplicial depth to the set of all partial orders, the union-free generic (ufg) depth. Moreover, we utilize our ufg depth for a comparison of machine learning algorithms based on multidimensional performance measures. Concretely, we analyze the distribution of different classifier performances over a sample of standard benchmark data sets. Our results promisingly demonstrate that our approach differs substantially from existing benchmarking approaches and, therefore, adds a new perspective to the vivid debate on the comparison of classifiers.", "published": "2023-04-19T10:10:26Z", "authors": ["Hannah Blocher", "Georg Schollmeyer", "Christoph Jansen", "Malte Nalenz"], "categories": ["cs.LG", "stat.ME"], "doi": "", "_query": "house price prediction machine learning", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/1909.11201v4", "title": "Matrix Sketching for Secure Collaborative Machine Learning", "abstract": "Collaborative learning allows participants to jointly train a model without data sharing. To update the model parameters, the central server broadcasts model parameters to the clients, and the clients send updating directions such as gradients to the server. While data do not leave a client device, the communicated gradients and parameters will leak a client's privacy. Attacks that infer clients' privacy from gradients and parameters have been developed by prior work. Simple defenses such as dropout and differential privacy either fail to defend the attacks or seriously hurt test accuracy. We propose a practical defense which we call Double-Blind Collaborative Learning (DBCL). The high-level idea is to apply random matrix sketching to the parameters (aka weights) and re-generate random sketching after each iteration. DBCL prevents clients from conducting gradient-based privacy inferences which are the most effective attacks. DBCL works because from the attacker's perspective, sketching is effectively random noise that outweighs the signal. Notably, DBCL does not much increase computation and communication costs and does not hurt test accuracy at all.", "published": "2019-09-24T21:55:26Z", "authors": ["Mengjiao Zhang", "Shusen Wang"], "categories": ["cs.LG", "cs.CR", "stat.ML"], "doi": "", "_query": "house price prediction machine learning", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2504.07322v1", "title": "Bregman-Hausdorff divergence: strengthening the connections between computational geometry and machine learning", "abstract": "The purpose of this paper is twofold. On a technical side, we propose an extension of the Hausdorff distance from metric spaces to spaces equipped with asymmetric distance measures. Specifically, we focus on the family of Bregman divergences, which includes the popular Kullback--Leibler divergence (also known as relative entropy). As a proof of concept, we use the resulting Bregman--Hausdorff divergence to compare two collections of probabilistic predictions produced by different machine learning models trained using the relative entropy loss. The algorithms we propose are surprisingly efficient even for large inputs with hundreds of dimensions. In addition to the introduction of this technical concept, we provide a survey. It outlines the basics of Bregman geometry, as well as computational geometry algorithms. We focus on algorithms that are compatible with this geometry and are relevant for machine learning.", "published": "2025-04-09T22:42:29Z", "authors": ["Tuyen Pham", "Hana Dal Poz Kouřimská", "Hubert Wagner"], "categories": ["cs.LG", "cs.CG", "cs.IT"], "doi": "", "_query": "house price prediction machine learning", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2411.04285v1", "title": "Robust Real-Time Mortality Prediction in the Intensive Care Unit using Temporal Difference Learning", "abstract": "The task of predicting long-term patient outcomes using supervised machine learning is a challenging one, in part because of the high variance of each patient's trajectory, which can result in the model over-fitting to the training data. Temporal difference (TD) learning, a common reinforcement learning technique, may reduce variance by generalising learning to the pattern of state transitions rather than terminal outcomes. However, in healthcare this method requires several strong assumptions about patient states, and there appears to be limited literature evaluating the performance of TD learning against traditional supervised learning methods for long-term health outcome prediction tasks. In this study, we define a framework for applying TD learning to real-time irregularly sampled time series data using a Semi-Markov Reward Process. We evaluate the model framework in predicting intensive care mortality and show that TD learning under this framework can result in improved model robustness compared to standard supervised learning methods. and that this robustness is maintained even when validated on external datasets. This approach may offer a more reliable method when learning to predict patient outcomes using high-variance irregular time series data.", "published": "2024-11-06T22:11:20Z", "authors": ["Thomas Frost", "Kezhi Li", "Steve Harris"], "categories": ["cs.LG", "cs.AI"], "doi": "", "_query": "house price prediction machine learning", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2511.06681v1", "title": "An Adaptive Machine Learning Triage Framework for Predicting Alzheimer's Disease Progression", "abstract": "Accurate predictions of conversion from mild cognitive impairment (MCI) to Alzheimer's disease (AD) can enable effective personalized therapy. While cognitive tests and clinical data are routinely collected, they lack the predictive power of PET scans and CSF biomarker analysis, which are prohibitively expensive to obtain for every patient. To address this cost-accuracy dilemma, we design a two-stage machine learning framework that selectively obtains advanced, costly features based on their predicted \"value of information\". We apply our framework to predict AD progression for MCI patients using data from the Alzheimer's Disease Neuroimaging Initiative (ADNI). Our framework reduces the need for advanced testing by 20% while achieving a test AUROC of 0.929, comparable to the model that uses both basic and advanced features (AUROC=0.915, p=0.1010). We also provide an example interpretability analysis showing how one may explain the triage decision. Our work presents an interpretable, data-driven framework that optimizes AD diagnostic pathways and balances accuracy with cost, representing a step towards making early, reliable AD prediction more accessible in real-world practice. Future work should consider multiple categories of advanced features and larger-scale validation.", "published": "2025-11-10T03:57:56Z", "authors": ["Richard Hou", "Shengpu Tang", "Wei Jin"], "categories": ["cs.LG"], "doi": "", "_query": "house price prediction machine learning", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2601.22531v2", "title": "Learn from A Rationalist: Distilling Intermediate Interpretable Rationales", "abstract": "Because of the pervasive use of deep neural networks (DNNs), especially in high-stakes domains, the interpretability of DNNs has received increased attention. The general idea of rationale extraction (RE) is to provide an interpretable-by-design framework for DNNs via a select-predict architecture where two neural networks learn jointly to perform feature selection and prediction, respectively. Given only the remote supervision from the final task prediction, the process of learning to select subsets of features (or rationales) requires searching in the space of all possible feature combinations, which is computationally challenging and even harder when the base neural networks are not sufficiently capable. To improve the predictive performance of RE models that are based on less capable or smaller neural networks (i.e., the students), we propose REKD (Rationale Extraction with Knowledge Distillation) where a student RE model learns from the rationales and predictions of a teacher (i.e., a rationalist) in addition to the student's own RE optimization. This structural adjustment to RE aligns well with how humans could learn effectively from interpretable and verifiable knowledge. Because of the neural-model agnostic nature of the method, any black-box neural network could be integrated as a backbone model. To demonstrate the viability of REKD, we conduct experiments with multiple variants of BERT and vision transformer (ViT) models. Our experiments across language and vision classification datasets (i.e., IMDB movie reviews, CIFAR 10 and CIFAR 100) show that REKD significantly improves the predictive performance of the student RE models.", "published": "2026-01-30T04:07:47Z", "authors": ["Jiayi Dai", "Randy Goebel"], "categories": ["cs.LG", "cs.AI"], "doi": "", "_query": "house price prediction machine learning", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/1106.6186v1", "title": "IBSEAD: - A Self-Evolving Self-Obsessed Learning Algorithm for Machine Learning", "abstract": "We present IBSEAD or distributed autonomous entity systems based Interaction - a learning algorithm for the computer to self-evolve in a self-obsessed manner. This learning algorithm will present the computer to look at the internal and external environment in series of independent entities, which will interact with each other, with and/or without knowledge of the computer's brain. When a learning algorithm interacts, it does so by detecting and understanding the entities in the human algorithm. However, the problem with this approach is that the algorithm does not consider the interaction of the third party or unknown entities, which may be interacting with each other. These unknown entities in their interaction with the non-computer entities make an effect in the environment that influences the information and the behaviour of the computer brain. Such details and the ability to process the dynamic and unsettling nature of these interactions are absent in the current learning algorithm such as the decision tree learning algorithm. IBSEAD is able to evaluate and consider such algorithms and thus give us a better accuracy in simulation of the highly evolved nature of the human brain. Processes such as dreams, imagination and novelty, that exist in humans are not fully simulated by the existing learning algorithms. Also, Hidden Markov models (HMM) are useful in finding \"hidden\" entities, which may be known or unknown. However, this model fails to consider the case of unknown entities which maybe unclear or unknown. IBSEAD is better because it considers three types of entities- known, unknown and invisible. We present our case with a comparison of existing algorithms in known environments and cases and present the results of the experiments using dry run of the simulated runs of the existing machine learning algorithms versus IBSEAD.", "published": "2011-06-30T11:08:35Z", "authors": ["Jitesh Dundas", "David Chik"], "categories": ["cs.LG"], "doi": "", "_query": "house price prediction machine learning", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2210.00973v2", "title": "NCVX: A General-Purpose Optimization Solver for Constrained Machine and Deep Learning", "abstract": "Imposing explicit constraints is relatively new but increasingly pressing in deep learning, stimulated by, e.g., trustworthy AI that performs robust optimization over complicated perturbation sets and scientific applications that need to respect physical laws and constraints. However, it can be hard to reliably solve constrained deep learning problems without optimization expertise. The existing deep learning frameworks do not admit constraints. General-purpose optimization packages can handle constraints but do not perform auto-differentiation and have trouble dealing with nonsmoothness. In this paper, we introduce a new software package called NCVX, whose initial release contains the solver PyGRANSO, a PyTorch-enabled general-purpose optimization package for constrained machine/deep learning problems, the first of its kind. NCVX inherits auto-differentiation, GPU acceleration, and tensor variables from PyTorch, and is built on freely available and widely used open-source frameworks. NCVX is available at https://ncvx.org, with detailed documentation and numerous examples from machine/deep learning and other fields.", "published": "2022-10-03T14:41:26Z", "authors": ["Buyun Liang", "Tim Mitchell", "Ju Sun"], "categories": ["cs.LG", "cs.CV", "cs.MS", "eess.SP", "math.OC"], "doi": "", "_query": "house price prediction machine learning", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2610.02615v1", "title": "Quantifying the Value of Constructive Induction, Knowledge, and Noise Filtering on Inductive Learning", "abstract": "Learning research, as one of its central goals, tries to measure, model, and understand how learning-problem properties affect average-case learning performance. For example, we would like to quantify the value of constructive induction, noise filtering, and background knowledge. This paper describes the effective dimension, a new learning measure that helps link problem properties to learning performance. Like the Vapnik-Chervonenkis (VC) dimension, the effective dimension is often in a simple linear relation with problem properties. Unlike the VC dimension, the effective dimension can be estimated empirically and makes average-case predictions. It is therefore more widely applicable to machine and human learning research. The measure is demonstrated on several learning systems including Backpropagation. Finally, the measure is used to precisely predict the benefit of using FRINGE, a feature construction system. The benefit is found to decrease as the complexity of the target concept increases.", "published": "2026-10-02T00:14:56Z", "authors": ["Carl M. Kadie"], "categories": ["cs.LG"], "doi": "10.1016/B978-1-55860-200-7.50034-9", "_query": "house price prediction machine learning", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/1803.10254v3", "title": "Disease-Atlas: Navigating Disease Trajectories with Deep Learning", "abstract": "Joint models for longitudinal and time-to-event data are commonly used in longitudinal studies to forecast disease trajectories over time. While there are many advantages to joint modeling, the standard forms suffer from limitations that arise from a fixed model specification, and computational difficulties when applied to high-dimensional datasets. In this paper, we propose a deep learning approach to address these limitations, enhancing existing methods with the inherent flexibility and scalability of deep neural networks, while retaining the benefits of joint modeling. Using longitudinal data from a real-world medical dataset, we demonstrate improvements in performance and scalability, as well as robustness in the presence of irregularly sampled data.", "published": "2018-03-27T18:03:02Z", "authors": ["Bryan Lim", "Mihaela van der Schaar"], "categories": ["stat.ML", "cs.LG"], "doi": "", "_query": "house price prediction machine learning", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/1810.10777v2", "title": "Efficient Learning of Restricted Boltzmann Machines Using Covariance Estimates", "abstract": "Learning RBMs using standard algorithms such as CD(k) involves gradient descent on the negative log-likelihood. One of the terms in the gradient, which involves expectation w.r.t. the model distribution, is intractable and is obtained through an MCMC estimate. In this work we show that the Hessian of the log-likelihood can be written in terms of covariances of hidden and visible units and hence, all elements of the Hessian can also be estimated using the same MCMC samples with small extra computational costs. Since inverting the Hessian may be computationally expensive, we propose an algorithm that uses inverse of the diagonal approximation of the Hessian, instead. This essentially results in parameter-specific adaptive learning rates for the gradient descent process and improves the efficiency of learning RBMs compared to the standard methods. Specifically we show that using the inverse of diagonal approximation of Hessian in the stochastic DC (difference of convex functions) program approach results in very efficient learning of RBMs.", "published": "2018-10-25T08:51:19Z", "authors": ["Vidyadhar Upadhya", "P. S. Sastry"], "categories": ["cs.LG", "stat.ML"], "doi": "", "_query": "house price prediction machine learning", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2506.14929v2", "title": "FedOne: Query-Efficient Federated Learning for Black-box Discrete Prompt Learning", "abstract": "Black-Box Discrete Prompt Learning is a prompt-tuning method that optimizes discrete prompts without accessing model parameters or gradients, making the prompt tuning on a cloud-based Large Language Model (LLM) feasible. Adapting federated learning to BDPL could further enhance prompt tuning performance by leveraging data from diverse sources. However, all previous research on federated black-box prompt tuning had neglected the substantial query cost associated with the cloud-based LLM service. To address this gap, we conducted a theoretical analysis of query efficiency within the context of federated black-box prompt tuning. Our findings revealed that degrading FedAvg to activate only one client per round, a strategy we called \\textit{FedOne}, enabled optimal query efficiency in federated black-box prompt learning. Building on this insight, we proposed the FedOne framework, a federated black-box discrete prompt learning method designed to maximize query efficiency when interacting with cloud-based LLMs. We conducted numerical experiments on various aspects of our framework, demonstrating a significant improvement in query efficiency, which aligns with our theoretical results.", "published": "2025-06-17T19:21:22Z", "authors": ["Ganyu Wang", "Jinjie Fang", "Maxwell J. Yin", "Bin Gu", "Xi Chen", "Boyu Wang", "Yi Chang", "Charles Ling"], "categories": ["cs.LG"], "doi": "", "_query": "house price prediction machine learning", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2106.15590v2", "title": "The Values Encoded in Machine Learning Research", "abstract": "Machine learning currently exerts an outsized influence on the world, increasingly affecting institutional practices and impacted communities. It is therefore critical that we question vague conceptions of the field as value-neutral or universally beneficial, and investigate what specific values the field is advancing. In this paper, we first introduce a method and annotation scheme for studying the values encoded in documents such as research papers. Applying the scheme, we analyze 100 highly cited machine learning papers published at premier machine learning conferences, ICML and NeurIPS. We annotate key features of papers which reveal their values: their justification for their choice of project, which attributes of their project they uplift, their consideration of potential negative consequences, and their institutional affiliations and funding sources. We find that few of the papers justify how their project connects to a societal need (15\\%) and far fewer discuss negative potential (1\\%). Through line-by-line content analysis, we identify 59 values that are uplifted in ML research, and, of these, we find that the papers most frequently justify and assess themselves based on Performance, Generalization, Quantitative evidence, Efficiency, Building on past work, and Novelty. We present extensive textual evidence and identify key themes in the definitions and operationalization of these values. Notably, we find systematic textual evidence that these top values are being defined and applied with assumptions and implications generally supporting the centralization of power.Finally, we find increasingly close ties between these highly cited papers and tech companies and elite universities.", "published": "2021-06-29T17:24:14Z", "authors": ["Abeba Birhane", "Pratyusha Kalluri", "Dallas Card", "William Agnew", "Ravit Dotan", "Michelle Bao"], "categories": ["cs.LG", "cs.AI", "cs.CY"], "doi": "", "_query": "house price prediction machine learning", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/1905.09368v2", "title": "Outlier Robust Extreme Learning Machine for Multi-Target Regression", "abstract": "The popularity of algorithms based on Extreme Learning Machine (ELM), which can be used to train Single Layer Feedforward Neural Networks (SLFN), has increased in the past years. They have been successfully applied to a wide range of classification and regression tasks. The most commonly used methods are the ones based on minimizing the $\\ell_2$ norm of the error, which is not suitable to deal with outliers, essentially in regression tasks. The use of $\\ell_1$ norm was proposed in Outlier Robust ELM (OR-ELM), which is defined to one-dimensional outputs. In this paper, we generalize OR-ELM to deal with multi-target regression problems, using the error $\\ell_{2,1}$ norm and the Elastic Net theory, which can result in a more sparse network, resulting in our method, Generalized Outlier Robust ELM (GOR-ELM). We use Alternating Direction Method of Multipliers (ADMM) to solve the resulting optimization problem. An incremental version of GOR-ELM is also proposed. We chose 15 public real-world multi-target regression datasets to test our methods. Our conducted experiments show that they are statistically better than other ELM-based techniques, when considering data contaminated with outliers, and equivalent to them, otherwise.", "published": "2019-05-22T21:16:55Z", "authors": ["Bruno Légora Souza da Silva", "Fernando Kentaro Inaba", "Evandro Ottoni Teatini Salles", "Patrick Marques Ciarelli"], "categories": ["cs.LG", "stat.ML"], "doi": "", "_query": "house price prediction machine learning", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2211.14401v2", "title": "Elements of effective machine learning datasets in astronomy", "abstract": "In this work, we identify elements of effective machine learning datasets in astronomy and present suggestions for their design and creation. Machine learning has become an increasingly important tool for analyzing and understanding the large-scale flood of data in astronomy. To take advantage of these tools, datasets are required for training and testing. However, building machine learning datasets for astronomy can be challenging. Astronomical data is collected from instruments built to explore science questions in a traditional fashion rather than to conduct machine learning. Thus, it is often the case that raw data, or even downstream processed data is not in a form amenable to machine learning. We explore the construction of machine learning datasets and we ask: what elements define effective machine learning datasets? We define effective machine learning datasets in astronomy to be formed with well-defined data points, structure, and metadata. We discuss why these elements are important for astronomical applications and ways to put them in practice. We posit that these qualities not only make the data suitable for machine learning, they also help to foster usable, reusable, and replicable science practices.", "published": "2022-11-25T23:37:24Z", "authors": ["Bernie Boscoe", "Tuan Do", "Evan Jones", "Yunqi Li", "Kevin Alfaro", "Christy Ma"], "categories": ["astro-ph.IM", "cs.LG"], "doi": "", "_query": "house price prediction machine learning", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/1207.6083v4", "title": "Determinantal point processes for machine learning", "abstract": "Determinantal point processes (DPPs) are elegant probabilistic models of repulsion that arise in quantum physics and random matrix theory. In contrast to traditional structured models like Markov random fields, which become intractable and hard to approximate in the presence of negative correlations, DPPs offer efficient and exact algorithms for sampling, marginalization, conditioning, and other inference tasks. We provide a gentle introduction to DPPs, focusing on the intuitions, algorithms, and extensions that are most relevant to the machine learning community, and show how DPPs can be applied to real-world applications like finding diverse sets of high-quality search results, building informative summaries by selecting diverse sentences from documents, modeling non-overlapping human poses in images or video, and automatically building timelines of important news stories.", "published": "2012-07-25T18:45:43Z", "authors": ["Alex Kulesza", "Ben Taskar"], "categories": ["stat.ML", "cs.IR", "cs.LG"], "doi": "10.1561/2200000044", "_query": "house price prediction machine learning", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2303.06021v4", "title": "Machine learning for sports betting: should model selection be based on accuracy or calibration?", "abstract": "Sports betting's recent federal legalisation in the USA coincides with the golden age of machine learning. If bettors can leverage data to reliably predict the probability of an outcome, they can recognise when the bookmaker's odds are in their favour. As sports betting is a multi-billion dollar industry in the USA alone, identifying such opportunities could be extremely lucrative. Many researchers have applied machine learning to the sports outcome prediction problem, generally using accuracy to evaluate the performance of predictive models. We hypothesise that for the sports betting problem, model calibration is more important than accuracy. To test this hypothesis, we train models on NBA data over several seasons and run betting experiments on a single season, using published odds. We show that using calibration, rather than accuracy, as the basis for model selection leads to greater returns, on average (return on investment of $+34.69\\%$ versus $-35.17\\%$) and in the best case ($+36.93\\%$ versus $+5.56\\%$). These findings suggest that for sports betting (or any probabilistic decision-making problem), calibration is a more important metric than accuracy. Sports bettors who wish to increase profits should therefore select their predictive model based on calibration, rather than accuracy.", "published": "2023-03-10T16:22:38Z", "authors": ["Conor Walsh", "Alok Joshi"], "categories": ["cs.LG"], "doi": "", "_query": "house price prediction machine learning", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/1812.00475v4", "title": "Multiple Instance Learning for ECG Risk Stratification", "abstract": "Patients who suffer an acute coronary syndrome are at elevated risk for adverse cardiovascular events such as myocardial infarction and cardiovascular death. Accurate assessment of this risk is crucial to their course of care. We focus on estimating a patient's risk of cardiovascular death after an acute coronary syndrome based on a patient's raw electrocardiogram (ECG) signal. Learning from this signal is challenging for two reasons: 1) positive examples signifying a downstream cardiovascular event are scarce, causing drastic class imbalance, and 2) each patient's ECG signal consists of thousands of heartbeats, accompanied by a single label for the downstream outcome. Machine learning has been previously applied to this task, but most approaches rely on hand-crafted features and domain knowledge. We propose a method that learns a representation from the raw ECG signal by using a multiple instance learning framework. We present a learned risk score for cardiovascular death that outperforms existing risk metrics in predicting cardiovascular death within 30, 60, 90, and 365 days on a dataset of 5000 patients.", "published": "2018-12-02T21:55:53Z", "authors": ["Divya Shanmugam", "Davis Blalock", "John Guttag"], "categories": ["cs.LG", "stat.ML"], "doi": "", "_query": "house price prediction machine learning", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/1910.09700v2", "title": "Quantifying the Carbon Emissions of Machine Learning", "abstract": "From an environmental standpoint, there are a few crucial aspects of training a neural network that have a major impact on the quantity of carbon that it emits. These factors include: the location of the server used for training and the energy grid that it uses, the length of the training procedure, and even the make and model of hardware on which the training takes place. In order to approximate these emissions, we present our Machine Learning Emissions Calculator, a tool for our community to better understand the environmental impact of training ML models. We accompany this tool with an explanation of the factors cited above, as well as concrete actions that individual practitioners and organizations can take to mitigate their carbon emissions.", "published": "2019-10-21T23:57:32Z", "authors": ["Alexandre Lacoste", "Alexandra Luccioni", "Victor Schmidt", "Thomas Dandres"], "categories": ["cs.CY", "cs.LG"], "doi": "", "_query": "house price prediction machine learning", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2406.14775v2", "title": "Machine Learning Global Simulation of Nonlocal Gravity Wave Propagation", "abstract": "Global climate models typically operate at a grid resolution of hundreds of kilometers and fail to resolve atmospheric mesoscale processes, e.g., clouds, precipitation, and gravity waves (GWs). Model representation of these processes and their sources is essential to the global circulation and planetary energy budget, but subgrid scale contributions from these processes are often only approximately represented in models using parameterizations. These parameterizations are subject to approximations and idealizations, which limit their capability and accuracy. The most drastic of these approximations is the \"single-column approximation\" which completely neglects the horizontal evolution of these processes, resulting in key biases in current climate models. With a focus on atmospheric GWs, we present the first-ever global simulation of atmospheric GW fluxes using machine learning (ML) models trained on the WINDSET dataset to emulate global GW emulation in the atmosphere, as an alternative to traditional single-column parameterizations. Using an Attention U-Net-based architecture trained on globally resolved GW momentum fluxes, we illustrate the importance and effectiveness of global nonlocality, when simulating GWs using data-driven schemes.", "published": "2024-06-20T22:57:38Z", "authors": ["Aman Gupta", "Aditi Sheshadri", "Sujit Roy", "Vishal Gaur", "Manil Maskey", "Rahul Ramachandran"], "categories": ["physics.ao-ph", "cs.LG", "physics.flu-dyn", "physics.geo-ph"], "doi": "", "_query": "house price prediction machine learning", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/1206.3713v4", "title": "Learning the Structure and Parameters of Large-Population Graphical Games from Behavioral Data", "abstract": "We consider learning, from strictly behavioral data, the structure and parameters of linear influence games (LIGs), a class of parametric graphical games introduced by Irfan and Ortiz (2014). LIGs facilitate causal strategic inference (CSI): Making inferences from causal interventions on stable behavior in strategic settings. Applications include the identification of the most influential individuals in large (social) networks. Such tasks can also support policy-making analysis. Motivated by the computational work on LIGs, we cast the learning problem as maximum-likelihood estimation (MLE) of a generative model defined by pure-strategy Nash equilibria (PSNE). Our simple formulation uncovers the fundamental interplay between goodness-of-fit and model complexity: good models capture equilibrium behavior within the data while controlling the true number of equilibria, including those unobserved. We provide a generalization bound establishing the sample complexity for MLE in our framework. We propose several algorithms including convex loss minimization (CLM) and sigmoidal approximations. We prove that the number of exact PSNE in LIGs is small, with high probability; thus, CLM is sound. We illustrate our approach on synthetic data and real-world U.S. congressional voting records. We briefly discuss our learning framework's generality and potential applicability to general graphical games.", "published": "2012-06-16T23:20:09Z", "authors": ["Jean Honorio", "Luis Ortiz"], "categories": ["cs.LG", "cs.GT", "stat.ML"], "doi": "", "_query": "house price prediction machine learning", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2605.11989v1", "title": "A Transfer Learning Evaluation of Deep Neural Networks for Image Classification", "abstract": "Transfer learning is a machine learning technique that uses previously acquired knowledge from a source domain to enhance learning in a target domain by reusing learned weights. This technique is ubiquitous because of its great advantages in achieving high performance while saving training time, memory, and effort in network design. In this paper, we investigate how to select the best pre-trained model that meets the target domain requirements for image classification tasks. In our study, we refined the output layers and general network parameters to apply the knowledge of eleven image processing models, pre-trained on ImageNet, to five different target domain datasets. We measured the accuracy, accuracy density, training time, and model size to evaluate the pre-trained models both in training sessions in one episode and with ten episodes.", "published": "2026-05-12T11:40:49Z", "authors": ["Nermeen Abou Baker", "Nico Zengeler", "Uwe Handmann"], "categories": ["cs.CV", "cs.AI"], "doi": "10.3390/make4010002", "_query": "house price prediction machine learning", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2008.02852v1", "title": "Learning Insulin-Glucose Dynamics in the Wild", "abstract": "We develop a new model of insulin-glucose dynamics for forecasting blood glucose in type 1 diabetics. We augment an existing biomedical model by introducing time-varying dynamics driven by a machine learning sequence model. Our model maintains a physiologically plausible inductive bias and clinically interpretable parameters -- e.g., insulin sensitivity -- while inheriting the flexibility of modern pattern recognition algorithms. Critical to modeling success are the flexible, but structured representations of subject variability with a sequence model. In contrast, less constrained models like the LSTM fail to provide reliable or physiologically plausible forecasts. We conduct an extensive empirical study. We show that allowing biomedical model dynamics to vary in time improves forecasting at long time horizons, up to six hours, and produces forecasts consistent with the physiological effects of insulin and carbohydrates.", "published": "2020-08-06T19:47:00Z", "authors": ["Andrew C. Miller", "Nicholas J. Foti", "Emily Fox"], "categories": ["stat.ML", "cs.LG", "stat.AP"], "doi": "", "_query": "house price prediction machine learning", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/1711.06350v1", "title": "Towards Deep Learning Models for Psychological State Prediction using Smartphone Data: Challenges and Opportunities", "abstract": "There is an increasing interest in exploiting mobile sensing technologies and machine learning techniques for mental health monitoring and intervention. Researchers have effectively used contextual information, such as mobility, communication and mobile phone usage patterns for quantifying individuals' mood and wellbeing. In this paper, we investigate the effectiveness of neural network models for predicting users' level of stress by using the location information collected by smartphones. We characterize the mobility patterns of individuals using the GPS metrics presented in the literature and employ these metrics as input to the network. We evaluate our approach on the open-source StudentLife dataset. Moreover, we discuss the challenges and trade-offs involved in building machine learning models for digital mental health and highlight potential future work in this direction.", "published": "2017-11-16T23:18:03Z", "authors": ["Gatis Mikelsons", "Matthew Smith", "Abhinav Mehrotra", "Mirco Musolesi"], "categories": ["cs.LG", "cs.AI", "stat.ML"], "doi": "", "_query": "house price prediction machine learning", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2405.08498v3", "title": "Learning Decision Policies with Instrumental Variables through Double Machine Learning", "abstract": "A common issue in learning decision-making policies in data-rich settings is spurious correlations in the offline dataset, which can be caused by hidden confounders. Instrumental variable (IV) regression, which utilises a key unconfounded variable known as the instrument, is a standard technique for learning causal relationships between confounded action, outcome, and context variables. Most recent IV regression algorithms use a two-stage approach, where a deep neural network (DNN) estimator learnt in the first stage is directly plugged into the second stage, in which another DNN is used to estimate the causal effect. Naively plugging the estimator can cause heavy bias in the second stage, especially when regularisation bias is present in the first stage estimator. We propose DML-IV, a non-linear IV regression method that reduces the bias in two-stage IV regressions and effectively learns high-performing policies. We derive a novel learning objective to reduce bias and design the DML-IV algorithm following the double/debiased machine learning (DML) framework. The learnt DML-IV estimator has strong convergence rate and $O(N^{-1/2})$ suboptimality guarantees that match those when the dataset is unconfounded. DML-IV outperforms state-of-the-art IV regression methods on IV regression benchmarks and learns high-performing policies in the presence of instruments.", "published": "2024-05-14T10:55:04Z", "authors": ["Daqian Shao", "Ashkan Soleymani", "Francesco Quinzan", "Marta Kwiatkowska"], "categories": ["cs.LG", "stat.ML"], "doi": "", "_query": "house price prediction machine learning", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2210.04001v2", "title": "Don't Waste Data: Transfer Learning to Leverage All Data for Machine-Learnt Climate Model Emulation", "abstract": "How can we learn from all available data when training machine-learnt climate models, without incurring any extra cost at simulation time? Typically, the training data comprises coarse-grained high-resolution data. But only keeping this coarse-grained data means the rest of the high-resolution data is thrown out. We use a transfer learning approach, which can be applied to a range of machine learning models, to leverage all the high-resolution data. We use three chaotic systems to show it stabilises training, gives improved generalisation performance and results in better forecasting skill. Our code is at https://github.com/raghul-parthipan/dont_waste_data", "published": "2022-10-08T11:51:12Z", "authors": ["Raghul Parthipan", "Damon J. Wischik"], "categories": ["cs.LG", "nlin.CD"], "doi": "", "_query": "house price prediction machine learning", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2403.07843v1", "title": "A Machine learning and Empirical Bayesian Approach for Predictive Buying in B2B E-commerce", "abstract": "In the context of developing nations like India, traditional business to business (B2B) commerce heavily relies on the establishment of robust relationships, trust, and credit arrangements between buyers and sellers. Consequently, ecommerce enterprises frequently. Established in 2016 with a vision to revolutionize trade in India through technology, Udaan is the countrys largest business to business ecommerce platform. Udaan operates across diverse product categories, including lifestyle, electronics, home and employ telecallers to cultivate buyer relationships, streamline order placement procedures, and promote special promotions. The accurate anticipation of buyer order placement behavior emerges as a pivotal factor for attaining sustainable growth, heightening competitiveness, and optimizing the efficiency of these telecallers. To address this challenge, we have employed an ensemble approach comprising XGBoost and a modified version of Poisson Gamma model to predict customer order patterns with precision. This paper provides an in-depth exploration of the strategic fusion of machine learning and an empirical Bayesian approach, bolstered by the judicious selection of pertinent features. This innovative approach has yielded a remarkable 3 times increase in customer order rates, show casing its potential for transformative impact in the ecommerce industry.", "published": "2024-03-12T17:32:52Z", "authors": ["Tuhin Subhra De", "Pranjal Singh", "Alok Patel"], "categories": ["cs.LG"], "doi": "10.1145/3647750.3647754", "_query": "house price prediction machine learning", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2310.08748v3", "title": "Evolutionary Dynamic Optimization and Machine Learning", "abstract": "Evolutionary Computation (EC) has emerged as a powerful field of Artificial Intelligence, inspired by nature's mechanisms of gradual development. However, EC approaches often face challenges such as stagnation, diversity loss, computational complexity, population initialization, and premature convergence. To overcome these limitations, researchers have integrated learning algorithms with evolutionary techniques. This integration harnesses the valuable data generated by EC algorithms during iterative searches, providing insights into the search space and population dynamics. Similarly, the relationship between evolutionary algorithms and Machine Learning (ML) is reciprocal, as EC methods offer exceptional opportunities for optimizing complex ML tasks characterized by noisy, inaccurate, and dynamic objective functions. These hybrid techniques, known as Evolutionary Machine Learning (EML), have been applied at various stages of the ML process. EC techniques play a vital role in tasks such as data balancing, feature selection, and model training optimization. Moreover, ML tasks often require dynamic optimization, for which Evolutionary Dynamic Optimization (EDO) is valuable. This paper presents the first comprehensive exploration of reciprocal integration between EDO and ML. The study aims to stimulate interest in the evolutionary learning community and inspire innovative contributions in this domain.", "published": "2023-10-12T22:28:53Z", "authors": ["Abdennour Boulesnane"], "categories": ["cs.NE", "cs.LG"], "doi": "10.1007/978-981-99-9718-3_3", "_query": "house price prediction machine learning", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/1911.04289v1", "title": "Relevance Vector Machines for harmonization of MRI brain volumes using image descriptors", "abstract": "With the increased need for multi-center magnetic resonance imaging studies, problems arise related to differences in hardware and software between centers. Namely, current algorithms for brain volume quantification are unreliable for the longitudinal assessment of volume changes in this type of setting. Currently most methods attempt to decrease this issue by regressing the scanner- and/or center-effects from the original data. In this work, we explore a novel approach to harmonize brain volume measurements by using only image descriptors. First, we explore the relationships between volumes and image descriptors. Then, we train a Relevance Vector Machine (RVM) model over a large multi-site dataset of healthy subjects to perform volume harmonization. Finally, we validate the method over two different datasets: i) a subset of unseen healthy controls; and ii) a test-retest dataset of multiple sclerosis (MS) patients. The method decreases scanner and center variability while preserving measurements that did not require correction in MS patient data. We show that image descriptors can be used as input to a machine learning algorithm to improve the reliability of longitudinal volumetric studies.", "published": "2019-11-08T14:37:14Z", "authors": ["Maria Ines Meyer", "Ezequiel de la Rosa", "Koen Van Leemput", "Diana M. Sima"], "categories": ["eess.IV", "cs.LG", "stat.ML"], "doi": "10.1007/978-3-030-32695-1_9", "_query": "house price prediction machine learning", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2207.13887v1", "title": "Adaptive Second Order Coresets for Data-efficient Machine Learning", "abstract": "Training machine learning models on massive datasets incurs substantial computational costs. To alleviate such costs, there has been a sustained effort to develop data-efficient training methods that can carefully select subsets of the training examples that generalize on par with the full training data. However, existing methods are limited in providing theoretical guarantees for the quality of the models trained on the extracted subsets, and may perform poorly in practice. We propose AdaCore, a method that leverages the geometry of the data to extract subsets of the training examples for efficient machine learning. The key idea behind our method is to dynamically approximate the curvature of the loss function via an exponentially-averaged estimate of the Hessian to select weighted subsets (coresets) that provide a close approximation of the full gradient preconditioned with the Hessian. We prove rigorous guarantees for the convergence of various first and second-order methods applied to the subsets chosen by AdaCore. Our extensive experiments show that AdaCore extracts coresets with higher quality compared to baselines and speeds up training of convex and non-convex machine learning models, such as logistic regression and neural networks, by over 2.9x over the full data and 4.5x over random subsets.", "published": "2022-07-28T05:43:09Z", "authors": ["Omead Pooladzandi", "David Davini", "Baharan Mirzasoleiman"], "categories": ["cs.LG"], "doi": "", "_query": "house price prediction machine learning", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2608.11156v1", "title": "Conditional Independence Tests for Constraint-Based Causal Discovery: A Survey", "abstract": "Conditional Independence (CI) tests are the statistical engine of constraint-based causal discovery: in algorithms such as PC (Peter-Clark) and FCI (Fast Causal Inference), skeleton pruning and key orientations follow directly from CI decisions. This survey reviews CI testing with emphasis on assumptions, robustness, and scalability in high-dimensional and mixed-type settings common in biomedical domains. The survey organizes widely used CI methods into six families: partial-correlation, contingency-table, regression, nearest-neighbor, kernel, and machine-learning-based. Special emphasis is provided on the robustness layers that address the limitations of these families. For each family, the survey examines when CI decisions reflect the data-generating distribution and when they fail. By this, we link test-level properties, including power decay with conditioning set size and asymmetric type I/II error consequences, to graph-level errors in skeleton recovery and v-structure orientation. The survey also compares adoption across major R and Python libraries and summarizes open challenges, including mixed-type CI testing without discretization, small-sample error control, and strategies for improving scalability of CI-testing.", "published": "2026-08-11T17:13:52Z", "authors": ["Pavel Averin", "Theodoros Moysiadis", "Ioannis Katakis"], "categories": ["stat.ML", "cs.LG"], "doi": "", "_query": "house price prediction machine learning", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2207.10896v2", "title": "Privacy and Transparency in Graph Machine Learning: A Unified Perspective", "abstract": "Graph Machine Learning (GraphML), whereby classical machine learning is generalized to irregular graph domains, has enjoyed a recent renaissance, leading to a dizzying array of models and their applications in several domains. With its growing applicability to sensitive domains and regulations by governmental agencies for trustworthy AI systems, researchers have started looking into the issues of transparency and privacy of graph learning. However, these topics have been mainly investigated independently. In this position paper, we provide a unified perspective on the interplay of privacy and transparency in GraphML. In particular, we describe the challenges and possible research directions for a formal investigation of privacy-transparency tradeoffs in GraphML.", "published": "2022-07-22T06:18:33Z", "authors": ["Megha Khosla"], "categories": ["cs.LG", "cs.CR"], "doi": "", "_query": "house price prediction machine learning", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/1708.02363v1", "title": "Beyond the technical challenges for deploying Machine Learning solutions in a software company", "abstract": "Recently software development companies started to embrace Machine Learning (ML) techniques for introducing a series of advanced functionality in their products such as personalisation of the user experience, improved search, content recommendation and automation. The technical challenges for tackling these problems are heavily researched in literature. A less studied area is a pragmatic approach to the role of humans in a complex modern industrial environment where ML based systems are developed. Key stakeholders affect the system from inception and up to operation and maintenance. Product managers want to embed \"smart\" experiences for their users and drive the decisions on what should be built next; software engineers are challenged to build or utilise ML software tools that require skills that are well outside of their comfort zone; legal and risk departments may influence design choices and data access; operations teams are requested to maintain ML systems which are non-stationary in their nature and change behaviour over time; and finally ML practitioners should communicate with all these stakeholders to successfully build a reliable system. This paper discusses some of the challenges we faced in Atlassian as we started investing more in the ML space.", "published": "2017-08-08T03:59:09Z", "authors": ["Ilias Flaounas"], "categories": ["cs.HC", "cs.AI", "cs.SE", "stat.ML"], "doi": "", "_query": "house price prediction machine learning", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2602.00037v2", "title": "Bitcoin Price Prediction using Machine Learning and Combinatorial Fusion Analysis", "abstract": "In this work, we propose to apply a new model fusion and learning paradigm, known as Combinatorial Fusion Analysis (CFA), to the field of Bitcoin price prediction. Price prediction of financial product has always been a big topic in finance, as the successful prediction of the price can yield significant profit. Every machine learning model has its own strength and weakness, which hinders progress toward robustness. CFA has been used to enhance models by leveraging rank-score characteristic (RSC) function and cognitive diversity in the combination of a moderate set of diverse and relatively well-performed models. Our method utilizes both score and rank combinations as well as other weighted combination techniques. Key metrics such as RMSE and MAPE are used to evaluate our methodology performance. Our proposal presents a notable MAPE performance of 0.19\\%. The proposed method greatly improves upon individual model performance, as well as outperforms other Bitcoin price prediction models.", "published": "2026-01-19T02:41:43Z", "authors": ["Yuanhong Wu", "Wei Ye", "Jingyan Xu", "D. Frank Hsu"], "categories": ["q-fin.ST", "cs.AI", "cs.CE", "cs.LG"], "doi": "", "_query": "house price prediction machine learning", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2002.04640v1", "title": "Debugging Machine Learning Pipelines", "abstract": "Machine learning tasks entail the use of complex computational pipelines to reach quantitative and qualitative conclusions. If some of the activities in a pipeline produce erroneous or uninformative outputs, the pipeline may fail or produce incorrect results. Inferring the root cause of failures and unexpected behavior is challenging, usually requiring much human thought, and is both time-consuming and error-prone. We propose a new approach that makes use of iteration and provenance to automatically infer the root causes and derive succinct explanations of failures. Through a detailed experimental evaluation, we assess the cost, precision, and recall of our approach compared to the state of the art. Our source code and experimental data will be available for reproducibility and enhancement.", "published": "2020-02-11T19:13:12Z", "authors": ["Raoni Lourenço", "Juliana Freire", "Dennis Shasha"], "categories": ["cs.LG", "cs.DB", "stat.ML"], "doi": "10.1145/3329486.3329489", "_query": "house price prediction machine learning", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2502.16344v1", "title": "Machine Learning-Based Cloud Computing Compliance Process Automation", "abstract": "Cloud computing adoption across industries has revolutionized enterprise operations while introducing significant challenges in compliance management. Organizations must continuously meet evolving regulatory requirements such as GDPR and ISO 27001, yet traditional manual review processes have become increasingly inadequate for modern business scales. This paper presents a novel machine learning-based framework for automating cloud computing compliance processes, addressing critical challenges including resource-intensive manual reviews, extended compliance cycles, and delayed risk identification. Our proposed framework integrates multiple machine learning technologies, including BERT-based document processing (94.5% accuracy), One-Class SVM for anomaly detection (88.7% accuracy), and an improved CNN-LSTM architecture for sequential compliance data analysis (90.2% accuracy). Implementation results demonstrate significant improvements: reducing compliance process duration from 7 days to 1.5 days, improving accuracy from 78% to 93%, and decreasing manual effort by 73.3%. A real-world deployment at a major securities firm validated these results, processing 800,000 daily transactions with 94.2% accuracy in risk identification.", "published": "2025-02-22T20:18:21Z", "authors": ["Yuqing Wang", "Xiao Yang"], "categories": ["cs.LG", "cs.AI", "cs.CY", "cs.DC"], "doi": "10.23977/autml.2025.060105", "_query": "house price prediction machine learning", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2402.14694v1", "title": "A Quick Introduction to Quantum Machine Learning for Non-Practitioners", "abstract": "This paper provides an introduction to quantum machine learning, exploring the potential benefits of using quantum computing principles and algorithms that may improve upon classical machine learning approaches. Quantum computing utilizes particles governed by quantum mechanics for computational purposes, leveraging properties like superposition and entanglement for information representation and manipulation. Quantum machine learning applies these principles to enhance classical machine learning models, potentially reducing network size and training time on quantum hardware. The paper covers basic quantum mechanics principles, including superposition, phase space, and entanglement, and introduces the concept of quantum gates that exploit these properties. It also reviews classical deep learning concepts, such as artificial neural networks, gradient descent, and backpropagation, before delving into trainable quantum circuits as neural networks. An example problem demonstrates the potential advantages of quantum neural networks, and the appendices provide detailed derivations. The paper aims to help researchers new to quantum mechanics and machine learning develop their expertise more efficiently.", "published": "2024-02-22T16:48:17Z", "authors": ["Ethan N. Evans", "Dominic Byrne", "Matthew G. Cook"], "categories": ["quant-ph", "cs.ET", "cs.LG"], "doi": "", "_query": "house price prediction machine learning", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2001.07455v1", "title": "Designing for the Long Tail of Machine Learning", "abstract": "Recent technical advances has made machine learning (ML) a promising component to include in end user facing systems. However, user experience (UX) practitioners face challenges in relating ML to existing user-centered design processes and how to navigate the possibilities and constraints of this design space. Drawing on our own experience, we characterize designing within this space as navigating trade-offs between data gathering, model development and designing valuable interactions for a given model performance. We suggest that the theoretical description of how machine learning performance scales with training data can guide designers in these trade-offs as well as having implications for prototyping. We exemplify the learning curve's usage by arguing that a useful pattern is to design an initial system in a bootstrap phase that aims to exploit the training effect of data collected at increasing orders of magnitude.", "published": "2020-01-21T11:53:28Z", "authors": ["Martin Lindvall", "Jesper Molin"], "categories": ["cs.HC", "cs.AI", "cs.LG"], "doi": "", "_query": "house price prediction machine learning", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/1009.5557v1", "title": "A Mobile Application for Smart House Remote Control System", "abstract": "At the start of the second decade of 21th century, the time has come to make the Smart Houses a reality for regular use. The different parts of a Smart House are researched but there are still distances from an applicable system, using the modern technology. In this paper we present an overview of the Smart House subsystems necessary for controlling the house using a mobile application efficiently and securely. The sequence diagram of the mobile application connecting to the server application and also the use-cases possible are presented. The challenges faced in designing the mobile application and illustrating the updated house top plane view in that application, are discussed and solutions are adapted for it. Finally the designed mobile application was implemented and the important sections of it were described, such as the interactive house top view map which indicates the status of the devices using predefined icons. The facilities to manage the scheduled tasks and defined rules are also implemented in this mobile application that was developed for use in Windows Mobile platform. This application has the capability of connecting to the main server using GPRS mobile internet and SMS. This system is expected to be an important step towards a unified system structure that can be used efficiently in near future regular houses.", "published": "2010-09-27T18:13:37Z", "authors": ["Amir Rajabzadeh", "Ali Reza Manashty", "Zahra Forootan Jahromi"], "categories": ["cs.OH"], "doi": "", "_query": "house price prediction machine learning", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/1106.4509v1", "title": "Machine Learning Markets", "abstract": "Prediction markets show considerable promise for developing flexible mechanisms for machine learning. Here, machine learning markets for multivariate systems are defined, and a utility-based framework is established for their analysis. This differs from the usual approach of defining static betting functions. It is shown that such markets can implement model combination methods used in machine learning, such as product of expert and mixture of expert approaches as equilibrium pricing models, by varying agent utility functions. They can also implement models composed of local potentials, and message passing methods. Prediction markets also allow for more flexible combinations, by combining multiple different utility functions. Conversely, the market mechanisms implement inference in the relevant probabilistic models. This means that market mechanism can be utilized for implementing parallelized model building and inference for probabilistic modelling.", "published": "2011-06-22T17:12:42Z", "authors": ["Amos Storkey"], "categories": ["cs.AI", "cs.MA", "cs.NE", "q-fin.TR", "stat.ML"], "doi": "", "_query": "house price prediction machine learning", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2112.02655v2", "title": "Quantum Machine Learning for Radio Astronomy", "abstract": "In this work we introduce a novel approach to the pulsar classification problem in time-domain radio astronomy using a Born machine, often referred to as a quantum neural network. Using a single-qubit architecture, we show that the pulsar classification problem maps well to the Bloch sphere and that comparable accuracies to more classical machine learning approaches are achievable. We introduce a novel single-qubit encoding for the pulsar data used in this work and show that this performs comparably to a multi-qubit QAOA encoding.", "published": "2021-12-05T19:05:08Z", "authors": ["Mohammad Kordzanganeh", "Aydin Utting", "Anna Scaife"], "categories": ["quant-ph", "astro-ph.HE", "stat.ML"], "doi": "", "_query": "house price prediction machine learning", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/1809.03834v1", "title": "House Price Modeling with Digital Census", "abstract": "Urban house prices are strongly associated with local socioeconomic factors. In literature, house price modeling is based on socioeconomic variables from traditional census, which is not real-time, dynamic and comprehensive. Inspired by the emerging concept of \"digital census\" - using large-scale digital records of human activities to measure urban population dynamics and socioeconomic conditions, we introduce three typical datasets, namely 311 complaints, crime complaints and taxi trips, into house price modeling. Based on the individual housing sales data in New York City, we provide comprehensive evidence that these digital census datasets can substantially improve the modeling performances on both house price levels and changes, regardless whether traditional census is included or not. Hence, digital census can serve as both effective alternatives and complements to traditional census for house price modeling.", "published": "2018-08-29T16:13:58Z", "authors": ["Enwei Zhu", "Stanislav Sobolevsky"], "categories": ["econ.EM", "physics.soc-ph"], "doi": "", "_query": "house price prediction machine learning", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2512.19527v1", "title": "Deep Learning for Unrelated-Machines Scheduling: Handling Variable Dimensions", "abstract": "Deep learning has been effectively applied to many discrete optimization problems. However, learning-based scheduling on unrelated parallel machines remains particularly difficult to design. Not only do the numbers of jobs and machines vary, but each job-machine pair has a unique processing time, dynamically altering feature dimensions. We propose a novel approach with a neural network tailored for offline deterministic scheduling of arbitrary sizes on unrelated machines. The goal is to minimize a complex objective function that includes the makespan and the weighted tardiness of jobs and machines. Unlike existing online approaches, which process jobs sequentially, our method generates a complete schedule considering the entire input at once. The key contribution of this work lies in the sophisticated architecture of our model. By leveraging various NLP-inspired architectures, it effectively processes any number of jobs and machines with varying feature dimensions imposed by unrelated processing times. Our approach enables supervised training on small problem instances while demonstrating strong generalization to much larger scheduling environments. Trained and tested on instances with 8 jobs and 4 machines, costs were only 2.51% above optimal. Across all tested configurations of up to 100 jobs and 10 machines, our network consistently outperformed an advanced dispatching rule, which incurred 22.22% higher costs on average. As our method allows fast retraining with simulated data and adaptation to various scheduling conditions, we believe it has the potential to become a standard approach for learning-based scheduling on unrelated machines and similar problem environments.", "published": "2025-12-22T16:18:29Z", "authors": ["Diego Hitzges", "Guillaume Sagnol"], "categories": ["cs.LG", "cs.DM"], "doi": "", "_query": "house price prediction machine learning", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/1703.02527v2", "title": "Online Learning to Rank in Stochastic Click Models", "abstract": "Online learning to rank is a core problem in information retrieval and machine learning. Many provably efficient algorithms have been recently proposed for this problem in specific click models. The click model is a model of how the user interacts with a list of documents. Though these results are significant, their impact on practice is limited, because all proposed algorithms are designed for specific click models and lack convergence guarantees in other models. In this work, we propose BatchRank, the first online learning to rank algorithm for a broad class of click models. The class encompasses two most fundamental click models, the cascade and position-based models. We derive a gap-dependent upper bound on the $T$-step regret of BatchRank and evaluate it on a range of web search queries. We observe that BatchRank outperforms ranked bandits and is more robust than CascadeKL-UCB, an existing algorithm for the cascade model.", "published": "2017-03-07T18:53:58Z", "authors": ["Masrour Zoghi", "Tomas Tunys", "Mohammad Ghavamzadeh", "Branislav Kveton", "Csaba Szepesvari", "Zheng Wen"], "categories": ["cs.LG", "stat.ML"], "doi": "", "_query": "house price prediction machine learning", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2107.05598v1", "title": "Nonlinear Least Squares for Large-Scale Machine Learning using Stochastic Jacobian Estimates", "abstract": "For large nonlinear least squares loss functions in machine learning we exploit the property that the number of model parameters typically exceeds the data in one batch. This implies a low-rank structure in the Hessian of the loss, which enables effective means to compute search directions. Using this property, we develop two algorithms that estimate Jacobian matrices and perform well when compared to state-of-the-art methods.", "published": "2021-07-12T17:29:08Z", "authors": ["Johannes J. Brust"], "categories": ["cs.LG", "math.NA", "stat.ML"], "doi": "", "_query": "house price prediction machine learning", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2410.12461v1", "title": "Challenges, Methods, Data -- a Survey of Machine Learning in Water Distribution Networks", "abstract": "Research on methods for planning and controlling water distribution networks gains increasing relevance as the availability of drinking water will decrease as a consequence of climate change. So far, the majority of approaches is based on hydraulics and engineering expertise. However, with the increasing availability of sensors, machine learning techniques constitute a promising tool. This work presents the main tasks in water distribution networks, discusses how they relate to machine learning and analyses how the particularities of the domain pose challenges to and can be leveraged by machine learning approaches. Besides, it provides a technical toolkit by presenting evaluation benchmarks and a structured survey of the exemplary task of leakage detection and localization.", "published": "2024-10-16T11:21:07Z", "authors": ["Valerie Vaquet", "Fabian Hinder", "André Artelt", "Inaam Ashraf", "Janine Strotherm", "Jonas Vaquet", "Johannes Brinkrolf", "Barbara Hammer"], "categories": ["cs.LG"], "doi": "10.1007/978-3-031-72356-8_11", "_query": "house price prediction machine learning", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/1809.04933v2", "title": "Identifying Real Estate Opportunities using Machine Learning", "abstract": "The real estate market is exposed to many fluctuations in prices because of existing correlations with many variables, some of which cannot be controlled or might even be unknown. Housing prices can increase rapidly (or in some cases, also drop very fast), yet the numerous listings available online where houses are sold or rented are not likely to be updated that often. In some cases, individuals interested in selling a house (or apartment) might include it in some online listing, and forget about updating the price. In other cases, some individuals might be interested in deliberately setting a price below the market price in order to sell the home faster, for various reasons. In this paper, we aim at developing a machine learning application that identifies opportunities in the real estate market in real time, i.e., houses that are listed with a price substantially below the market price. This program can be useful for investors interested in the housing market. We have focused in a use case considering real estate assets located in the Salamanca district in Madrid (Spain) and listed in the most relevant Spanish online site for home sales and rentals. The application is formally implemented as a regression problem that tries to estimate the market price of a house given features retrieved from public online listings. For building this application, we have performed a feature engineering stage in order to discover relevant features that allows for attaining a high predictive performance. Several machine learning algorithms have been tested, including regression trees, k-nearest neighbors, support vector machines and neural networks, identifying advantages and handicaps of each of them.", "published": "2018-09-13T13:19:23Z", "authors": ["Alejandro Baldominos", "Iván Blanco", "Antonio José Moreno", "Rubén Iturrarte", "Óscar Bernárdez", "Carlos Afonso"], "categories": ["stat.AP", "cs.LG", "stat.ML"], "doi": "10.3390/app8112321", "_query": "house price prediction machine learning", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2509.09146v1", "title": "Peering Partner Recommendation for ISPs using Machine Learning", "abstract": "Internet service providers (ISPs) need to connect with other ISPs to provide global connectivity services to their users. To ensure global connectivity, ISPs can either use transit service(s) or establish direct peering relationships between themselves via Internet exchange points (IXPs). Peering offers more room for ISP-specific optimizations and is preferred, but it often involves a lengthy and complex process. Automating peering partner selection can enhance efficiency in the global Internet ecosystem. We explore the use of publicly available data on ISPs to develop a machine learning (ML) model that can predict whether an ISP pair should peer or not. At first, we explore public databases, e.g., PeeringDB, CAIDA, etc., to gather data on ISPs. Then, we evaluate the performance of three broad types of ML models for predicting peering relationships: tree-based, neural network-based, and transformer-based. Among these, we observe that tree-based models achieve the highest accuracy and efficiency in our experiments. The XGBoost model trained with publicly available data showed promising performance, with a 98% accuracy rate in predicting peering partners. In addition, the model demonstrated great resilience to variations in time, space, and missing data. We envision that ISPs can adopt our method to fully automate the peering partner selection process, thus transitioning to a more efficient and optimized Internet ecosystem.", "published": "2025-09-11T04:43:31Z", "authors": ["Md Ibrahim Ibne Alam", "Ankur Senapati", "Anindo Mahmood", "Murat Yuksel", "Koushik Kar"], "categories": ["cs.LG"], "doi": "", "_query": "house price prediction machine learning", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2412.00864v2", "title": "Explicit and data-Efficient Encoding via Gradient Flow", "abstract": "The autoencoder model typically uses an encoder to map data to a lower dimensional latent space and a decoder to reconstruct it. However, relying on an encoder for inversion can lead to suboptimal representations, particularly limiting in physical sciences where precision is key. We introduce a decoder-only method using gradient flow to directly encode data into the latent space, defined by ordinary differential equations (ODEs). This approach eliminates the need for approximate encoder inversion. We train the decoder via the adjoint method and show that costly integrals can be avoided with minimal accuracy loss. Additionally, we propose a $2^{nd}$ order ODE variant, approximating Nesterov's accelerated gradient descent for faster convergence. To handle stiff ODEs, we use an adaptive solver that prioritizes loss minimization, improving robustness. Compared to traditional autoencoders, our method demonstrates explicit encoding and superior data efficiency, which is crucial for data-scarce scenarios in the physical sciences. Furthermore, this work paves the way for integrating machine learning into scientific workflows, where precise and efficient encoding is critical. \\footnote{The code for this work is available at \\url{https://github.com/k-flouris/gfe}.}", "published": "2024-12-01T15:54:50Z", "authors": ["Kyriakos Flouris", "Anna Volokitin", "Gustav Bredell", "Ender Konukoglu"], "categories": ["stat.ML", "cs.AI", "cs.LG", "math.OC", "physics.comp-ph"], "doi": "", "_query": "house price prediction machine learning", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2503.11973v1", "title": "Machine Learning-Based Model for Postoperative Stroke Prediction in Coronary Artery Disease", "abstract": "Coronary artery disease remains one of the leading causes of mortality globally. Despite advances in revascularization treatments like PCI and CABG, postoperative stroke is inevitable. This study aims to develop and evaluate a sophisticated machine learning prediction model to assess postoperative stroke risk in coronary revascularization patients.This research employed data from the MIMIC-IV database, consisting of a cohort of 7023 individuals. Study data included clinical, laboratory, and comorbidity variables. To reduce multicollinearity, variables with over 30% missing values and features with a correlation coefficient larger than 0.9 were deleted. The dataset has 70% training and 30% test. The Random Forest technique interpolated residual dataset missing values. Numerical values were normalized, whereas categorical variables were one-hot encoded. LASSO regularization selected features, and grid search found model hyperparameters. Finally, Logistic Regression, XGBoost, SVM, and CatBoost were employed for predictive modeling, and SHAP analysis assessed stroke risk for each variable. AUC of 0.855 (0.829-0.878) showed that SVM model outperformed logistic regression and CatBoost models in prior research. SHAP research showed that the Charlson Comorbidity Index (CCI), diabetes, chronic kidney disease, and heart failure are significant prognostic factors for postoperative stroke. This study shows that improved machine learning reduces overfitting and improves model predictive accuracy. Models using the CCI alone cannot predict postoperative stroke risk as accurately as those using independent comorbidity variables. The suggested technique provides a more thorough and individualized risk assessment by encompassing a wider range of clinically relevant characteristics, making it a better reference for preoperative risk assessments and targeted intervention.", "published": "2025-03-15T02:50:32Z", "authors": ["Haonan Pan", "Shuheng Chen", "Elham Pishgar", "Kamiar Alaei", "Greg Placencia", "Maryam Pishgar"], "categories": ["cs.LG"], "doi": "", "_query": "house price prediction machine learning", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2203.14132v1", "title": "A comparative analysis of Graph Neural Networks and commonly used machine learning algorithms on fake news detection", "abstract": "Fake news on social media is increasingly regarded as one of the most concerning issues. Low cost, simple accessibility via social platforms, and a plethora of low-budget online news sources are some of the factors that contribute to the spread of false news. Most of the existing fake news detection algorithms are solely focused on the news content only but engaged users prior posts or social activities provide a wealth of information about their views on news and have significant ability to improve fake news identification. Graph Neural Networks are a form of deep learning approach that conducts prediction on graph-described data. Social media platforms are followed graph structure in their representation, Graph Neural Network are special types of neural networks that could be usually applied to graphs, making it much easier to execute edge, node, and graph-level prediction. Therefore, in this paper, we present a comparative analysis among some commonly used machine learning algorithms and Graph Neural Networks for detecting the spread of false news on social media platforms. In this study, we take the UPFD dataset and implement several existing machine learning algorithms on text data only. Besides this, we create different GNN layers for fusing graph-structured news propagation data and the text data as the node feature in our GNN models. GNNs provide the best solutions to the dilemma of identifying false news in our research.", "published": "2022-03-26T18:40:03Z", "authors": ["Fahim Belal Mahmud", "Mahi Md. Sadek Rayhan", "Mahdi Hasan Shuvo", "Islam Sadia", "Md. Kishor Morol"], "categories": ["cs.LG", "cs.CL", "cs.SI"], "doi": "", "_query": "house price prediction machine learning", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2509.17971v2", "title": "Intra-Cluster Mixup: An Effective Data Augmentation Technique for Complementary-Label Learning", "abstract": "In this paper, we investigate the challenges of complementary-label learning (CLL), a specialized form of weakly-supervised learning (WSL) where models are trained with labels indicating classes to which instances do not belong, rather than standard ordinary labels. This alternative supervision is appealing because collecting complementary labels is generally cheaper and less labor-intensive. Although most existing research in CLL emphasizes the development of novel loss functions, the potential of data augmentation in this domain remains largely underexplored. In this work, we uncover that the widely-used Mixup data augmentation technique is ineffective when directly applied to CLL. Through in-depth analysis, we identify that the complementary-label noise generated by Mixup negatively impacts the performance of CLL models. We then propose an improved technique called Intra-Cluster Mixup (ICM), which only synthesizes augmented data from nearby examples, to mitigate the noise effect. ICM carries the benefits of encouraging complementary label sharing of nearby examples, and leads to substantial performance improvements across synthetic and real-world labeled datasets. In particular, our wide spectrum of experimental results on both balanced and imbalanced CLL settings justifies the potential of ICM in allying with state-of-the-art CLL algorithms, achieving significant accuracy increases of 30% and 10% on MNIST and CIFAR datasets, respectively.", "published": "2025-09-22T16:20:41Z", "authors": ["Tan-Ha Mai", "Hsuan-Tien Lin"], "categories": ["cs.LG", "cs.AI", "cs.CV"], "doi": "", "_query": "house price prediction machine learning", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/1903.05895v2", "title": "Learning Fast Algorithms for Linear Transforms Using Butterfly Factorizations", "abstract": "Fast linear transforms are ubiquitous in machine learning, including the discrete Fourier transform, discrete cosine transform, and other structured transformations such as convolutions. All of these transforms can be represented by dense matrix-vector multiplication, yet each has a specialized and highly efficient (subquadratic) algorithm. We ask to what extent hand-crafting these algorithms and implementations is necessary, what structural priors they encode, and how much knowledge is required to automatically learn a fast algorithm for a provided structured transform. Motivated by a characterization of fast matrix-vector multiplication as products of sparse matrices, we introduce a parameterization of divide-and-conquer methods that is capable of representing a large class of transforms. This generic formulation can automatically learn an efficient algorithm for many important transforms; for example, it recovers the $O(N \\log N)$ Cooley-Tukey FFT algorithm to machine precision, for dimensions $N$ up to $1024$. Furthermore, our method can be incorporated as a lightweight replacement of generic matrices in machine learning pipelines to learn efficient and compressible transformations. On a standard task of compressing a single hidden-layer network, our method exceeds the classification accuracy of unconstrained matrices on CIFAR-10 by 3.9 points -- the first time a structured approach has done so -- with 4X faster inference speed and 40X fewer parameters.", "published": "2019-03-14T10:20:38Z", "authors": ["Tri Dao", "Albert Gu", "Matthew Eichhorn", "Atri Rudra", "Christopher Ré"], "categories": ["cs.LG", "stat.ML"], "doi": "", "_query": "house price prediction machine learning", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2603.13051v1", "title": "Causal Cellular Context Transfer Learning (C3TL): An Efficient Architecture for Prediction of Unseen Perturbation Effects", "abstract": "Predicting the effects of chemical and genetic perturbations on quantitative cell states is a central challenge in computational biology, molecular medicine and drug discovery. Recent work has leveraged large-scale single-cell data and massive foundation models to address this task. However, such computational resources and extensive datasets are not always accessible in academic or clinical settings, hence limiting utility. Here we propose a lightweight framework for perturbation effect prediction that exploits the structured nature of biological interventions and specific inductive biases/invariances. Our approach leverages available information concerning perturbation effects to allow generalization to novel contexts and requires only widely-available bulk molecular data. Extensive testing, comparing predictions of context-specific perturbation effects against real, large-scale interventional experiments, demonstrates accurate prediction in new contexts. The proposed approach is competitive with SOTA foundation models but requires simpler data, much smaller model sizes and less time. Focusing on robust bulk signals and efficient architectures, we show that accurate prediction of perturbation effects is possible without proprietary hardware or very large models, hence opening up ways to leverage causal learning approaches in biomedicine generally.", "published": "2026-03-13T15:02:49Z", "authors": ["Michael Scholkemper", "Sach Mukherjee"], "categories": ["cs.LG", "q-bio.QM"], "doi": "", "_query": "house price prediction machine learning", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/1912.01277v2", "title": "Make Thunderbolts Less Frightening -- Predicting Extreme Weather Using Deep Learning", "abstract": "Forecasting severe weather conditions is still a very challenging and computationally expensive task due to the enormous amount of data and the complexity of the underlying physics. Machine learning approaches and especially deep learning have however shown huge improvements in many research areas dealing with large datasets in recent years. In this work, we tackle one specific sub-problem of weather forecasting, namely the prediction of thunderstorms and lightning. We propose the use of a convolutional neural network architecture inspired by UNet++ and ResNet to predict thunderstorms as a binary classification problem based on satellite images and lightnings recorded in the past. We achieve a probability of detection of more than 94% for lightnings within the next 15 minutes while at the same time minimizing the false alarm ratio compared to previous approaches.", "published": "2019-12-03T10:17:48Z", "authors": ["Christian Schön", "Jens Dittrich"], "categories": ["cs.LG", "stat.ML"], "doi": "", "_query": "house price prediction machine learning", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/1911.05861v1", "title": "Federated and Differentially Private Learning for Electronic Health Records", "abstract": "The use of collaborative and decentralized machine learning techniques such as federated learning have the potential to enable the development and deployment of clinical risk predictions models in low-resource settings without requiring sensitive data be shared or stored in a central repository. This process necessitates communication of model weights or updates between collaborating entities, but it is unclear to what extent patient privacy is compromised as a result. To gain insight into this question, we study the efficacy of centralized versus federated learning in both private and non-private settings. The clinical prediction tasks we consider are the prediction of prolonged length of stay and in-hospital mortality across thirty one hospitals in the eICU Collaborative Research Database. We find that while it is straightforward to apply differentially private stochastic gradient descent to achieve strong privacy bounds when training in a centralized setting, it is considerably more difficult to do so in the federated setting.", "published": "2019-11-13T23:42:06Z", "authors": ["Stephen R. Pfohl", "Andrew M. Dai", "Katherine Heller"], "categories": ["cs.LG", "stat.ML"], "doi": "", "_query": "house price prediction machine learning", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2010.14236v2", "title": "Scientific intuition inspired by machine learning generated hypotheses", "abstract": "Machine learning with application to questions in the physical sciences has become a widely used tool, successfully applied to classification, regression and optimization tasks in many areas. Research focus mostly lies in improving the accuracy of the machine learning models in numerical predictions, while scientific understanding is still almost exclusively generated by human researchers analysing numerical results and drawing conclusions. In this work, we shift the focus on the insights and the knowledge obtained by the machine learning models themselves. In particular, we study how it can be extracted and used to inspire human scientists to increase their intuitions and understanding of natural systems. We apply gradient boosting in decision trees to extract human interpretable insights from big data sets from chemistry and physics. In chemistry, we not only rediscover widely know rules of thumb but also find new interesting motifs that tell us how to control solubility and energy levels of organic molecules. At the same time, in quantum physics, we gain new understanding on experiments for quantum entanglement. The ability to go beyond numerics and to enter the realm of scientific insight and hypothesis generation opens the door to use machine learning to accelerate the discovery of conceptual understanding in some of the most challenging domains of science.", "published": "2020-10-27T12:12:12Z", "authors": ["Pascal Friederich", "Mario Krenn", "Isaac Tamblyn", "Alan Aspuru-Guzik"], "categories": ["cs.LG", "cs.AI", "cs.CE", "physics.chem-ph", "quant-ph"], "doi": "10.1088/2632-2153/abda08", "_query": "house price prediction machine learning", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2109.08618v3", "title": "A review and experimental evaluation of deep learning methods for MRI reconstruction", "abstract": "Following the success of deep learning in a wide range of applications, neural network-based machine-learning techniques have received significant interest for accelerating magnetic resonance imaging (MRI) acquisition and reconstruction strategies. A number of ideas inspired by deep learning techniques for computer vision and image processing have been successfully applied to nonlinear image reconstruction in the spirit of compressed sensing for accelerated MRI. Given the rapidly growing nature of the field, it is imperative to consolidate and summarize the large number of deep learning methods that have been reported in the literature, to obtain a better understanding of the field in general. This article provides an overview of the recent developments in neural-network based approaches that have been proposed specifically for improving parallel imaging. A general background and introduction to parallel MRI is also given from a classical view of k-space based reconstruction methods. Image domain based techniques that introduce improved regularizers are covered along with k-space based methods which focus on better interpolation strategies using neural networks. While the field is rapidly evolving with plenty of papers published each year, in this review, we attempt to cover broad categories of methods that have shown good performance on publicly available data sets. Limitations and open problems are also discussed and recent efforts for producing open data sets and benchmarks for the community are examined.", "published": "2021-09-17T15:50:51Z", "authors": ["Arghya Pal", "Yogesh Rathi"], "categories": ["eess.IV", "cs.CV", "cs.LG"], "doi": "", "_query": "house price prediction machine learning", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/1911.11746v1", "title": "Defending Against Adversarial Machine Learning", "abstract": "An Adversarial System to attack and an Authorship Attribution System (AAS) to defend itself against the attacks are analyzed. Defending a system against attacks from an adversarial machine learner can be done by randomly switching between models for the system, by detecting and reacting to changes in the distribution of normal inputs, or by using other methods. Adversarial machine learning is used to identify a system that is being used to map system inputs to outputs. Three types of machine learners are using for the model that is being attacked. The machine learners that are used to model the system being attacked are a Radial Basis Function Support Vector Machine, a Linear Support Vector Machine, and a Feedforward Neural Network. The feature masks are evolved using accuracy as the fitness measure. The system defends itself against adversarial machine learning attacks by identifying inputs that do not match the probability distribution of normal inputs. The system also defends itself against adversarial attacks by randomly switching between the feature masks being used to map system inputs to outputs.", "published": "2019-11-26T18:28:47Z", "authors": ["Alison Jenkins"], "categories": ["cs.CR", "cs.AI", "cs.LG"], "doi": "", "_query": "house price prediction machine learning", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2405.11651v1", "title": "Movie Revenue Prediction using Machine Learning Models", "abstract": "In the contemporary film industry, accurately predicting a movie's earnings is paramount for maximizing profitability. This project aims to develop a machine learning model for predicting movie earnings based on input features like the movie name, the MPAA rating of the movie, the genre of the movie, the year of release of the movie, the IMDb Rating, the votes by the watchers, the director, the writer and the leading cast, the country of production of the movie, the budget of the movie, the production company and the runtime of the movie. Through a structured methodology involving data collection, preprocessing, analysis, model selection, evaluation, and improvement, a robust predictive model is constructed. Linear Regression, Decision Trees, Random Forest Regression, Bagging, XGBoosting and Gradient Boosting have been trained and tested. Model improvement strategies include hyperparameter tuning and cross-validation. The resulting model offers promising accuracy and generalization, facilitating informed decision-making in the film industry to maximize profits.", "published": "2024-05-19T19:32:12Z", "authors": ["Vikranth Udandarao", "Pratyush Gupta"], "categories": ["cs.LG"], "doi": "", "_query": "house price prediction machine learning", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2506.00969v1", "title": "Data Heterogeneity Modeling for Trustworthy Machine Learning", "abstract": "Data heterogeneity plays a pivotal role in determining the performance of machine learning (ML) systems. Traditional algorithms, which are typically designed to optimize average performance, often overlook the intrinsic diversity within datasets. This oversight can lead to a myriad of issues, including unreliable decision-making, inadequate generalization across different domains, unfair outcomes, and false scientific inferences. Hence, a nuanced approach to modeling data heterogeneity is essential for the development of dependable, data-driven systems. In this survey paper, we present a thorough exploration of heterogeneity-aware machine learning, a paradigm that systematically integrates considerations of data heterogeneity throughout the entire ML pipeline -- from data collection and model training to model evaluation and deployment. By applying this approach to a variety of critical fields, including healthcare, agriculture, finance, and recommendation systems, we demonstrate the substantial benefits and potential of heterogeneity-aware ML. These applications underscore how a deeper understanding of data diversity can enhance model robustness, fairness, and reliability and help model diagnosis and improvements. Moreover, we delve into future directions and provide research opportunities for the whole data mining community, aiming to promote the development of heterogeneity-aware ML.", "published": "2025-06-01T11:36:56Z", "authors": ["Jiashuo Liu", "Peng Cui"], "categories": ["cs.LG", "cs.AI"], "doi": "10.1145/3711896.3736560", "_query": "house price prediction machine learning", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/1308.3750v1", "title": "Comment on \"robustness and regularization of support vector machines\" by H. Xu, et al., (Journal of Machine Learning Research, vol. 10, pp. 1485-1510, 2009, arXiv:0803.3490)", "abstract": "This paper comments on the published work dealing with robustness and regularization of support vector machines (Journal of Machine Learning Research, vol. 10, pp. 1485-1510, 2009) [arXiv:0803.3490] by H. Xu, etc. They proposed a theorem to show that it is possible to relate robustness in the feature space and robustness in the sample space directly. In this paper, we propose a counter example that rejects their theorem.", "published": "2013-08-17T03:56:03Z", "authors": ["Yahya Forghani", "Hadi Sadoghi Yazdi"], "categories": ["cs.LG"], "doi": "", "_query": "house price prediction machine learning", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2510.12934v2", "title": "Learning at the Speed of Physics: Equilibrium Propagation on Oscillator Ising Machines", "abstract": "Physical systems that naturally perform energy descent offer a direct route to accelerating machine learning. Oscillator Ising Machines (OIMs) exemplify this idea: their GHz-frequency dynamics mirror both the optimization of energy-based models (EBMs) and gradient descent on loss landscapes, while intrinsic noise corresponds to Langevin dynamics - supporting sampling as well as optimization. Equilibrium Propagation (EP) unifies these processes into descent on a single total energy landscape, enabling local learning rules without global backpropagation. We show that EP on OIMs achieves competitive accuracy ($\\sim 97.2 \\pm 0.1 \\%$ on MNIST, $\\sim 88.0 \\pm 0.1 \\%$ on Fashion-MNIST), while maintaining robustness under realistic hardware constraints such as parameter quantization and phase noise. These results establish OIMs as a fast, energy-efficient substrate for neuromorphic learning, and suggest that EBMs - often bottlenecked by conventional processors - may find practical realization on physical hardware whose dynamics directly perform their optimization.", "published": "2025-10-14T19:15:49Z", "authors": ["Alex Gower"], "categories": ["cs.LG"], "doi": "", "_query": "house price prediction machine learning", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/1206.6418v1", "title": "Learning Invariant Representations with Local Transformations", "abstract": "Learning invariant representations is an important problem in machine learning and pattern recognition. In this paper, we present a novel framework of transformation-invariant feature learning by incorporating linear transformations into the feature learning algorithms. For example, we present the transformation-invariant restricted Boltzmann machine that compactly represents data by its weights and their transformations, which achieves invariance of the feature representation via probabilistic max pooling. In addition, we show that our transformation-invariant feature learning framework can also be extended to other unsupervised learning methods, such as autoencoders or sparse coding. We evaluate our method on several image classification benchmark datasets, such as MNIST variations, CIFAR-10, and STL-10, and show competitive or superior classification performance when compared to the state-of-the-art. Furthermore, our method achieves state-of-the-art performance on phone classification tasks with the TIMIT dataset, which demonstrates wide applicability of our proposed algorithms to other domains.", "published": "2012-06-27T19:59:59Z", "authors": ["Kihyuk Sohn", "Honglak Lee"], "categories": ["cs.LG", "cs.CV", "stat.ML"], "doi": "", "_query": "house price prediction machine learning", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/1610.08251v1", "title": "Quantum-enhanced machine learning", "abstract": "The emerging field of quantum machine learning has the potential to substantially aid in the problems and scope of artificial intelligence. This is only enhanced by recent successes in the field of classical machine learning. In this work we propose an approach for the systematic treatment of machine learning, from the perspective of quantum information. Our approach is general and covers all three main branches of machine learning: supervised, unsupervised and reinforcement learning. While quantum improvements in supervised and unsupervised learning have been reported, reinforcement learning has received much less attention. Within our approach, we tackle the problem of quantum enhancements in reinforcement learning as well, and propose a systematic scheme for providing improvements. As an example, we show that quadratic improvements in learning efficiency, and exponential improvements in performance over limited time periods, can be obtained for a broad class of learning problems.", "published": "2016-10-26T09:35:11Z", "authors": ["Vedran Dunjko", "Jacob M. Taylor", "Hans J. Briegel"], "categories": ["quant-ph", "cs.AI", "cs.LG"], "doi": "10.1103/PhysRevLett.117.130501", "_query": "house price prediction machine learning", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/1807.01619v2", "title": "Ensemble learning with Conformal Predictors: Targeting credible predictions of conversion from Mild Cognitive Impairment to Alzheimer's Disease", "abstract": "Most machine learning classifiers give predictions for new examples accurately, yet without indicating how trustworthy predictions are. In the medical domain, this hampers their integration in decision support systems, which could be useful in the clinical practice. We use a supervised learning approach that combines Ensemble learning with Conformal Predictors to predict conversion from Mild Cognitive Impairment to Alzheimer's Disease. Our goal is to enhance the classification performance (Ensemble learning) and complement each prediction with a measure of credibility (Conformal Predictors). Our results showed the superiority of the proposed approach over a similar ensemble framework with standard classifiers.", "published": "2018-07-04T14:43:57Z", "authors": ["Telma Pereira", "Sandra Cardoso", "Dina Silva", "Manuela Guerreiro", "Alexandre de Mendonça", "Sara C. Madeira"], "categories": ["cs.LG", "stat.ML"], "doi": "", "_query": "house price prediction machine learning", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2411.18612v2", "title": "Robust Offline Reinforcement Learning with Linearly Structured f-Divergence Regularization", "abstract": "The Robust Regularized Markov Decision Process (RRMDP) is proposed to learn policies robust to dynamics shifts by adding regularization to the transition dynamics in the value function. Existing methods mostly use unstructured regularization, potentially leading to conservative policies under unrealistic transitions. To address this limitation, we propose a novel framework, the $d$-rectangular linear RRMDP ($d$-RRMDP), which introduces latent structures into both transition kernels and regularization. We focus on offline reinforcement learning, where an agent learns policies from a precollected dataset in the nominal environment. We develop the Robust Regularized Pessimistic Value Iteration (R2PVI) algorithm that employs linear function approximation for robust policy learning in $d$-RRMDPs with $f$-divergence based regularization terms on transition kernels. We provide instance-dependent upper bounds on the suboptimality gap of R2PVI policies, demonstrating that these bounds are influenced by how well the dataset covers state-action spaces visited by the optimal robust policy under robustly admissible transitions. We establish information-theoretic lower bounds to verify that our algorithm is near-optimal. Finally, numerical experiments validate that R2PVI learns robust policies and exhibits superior computational efficiency compared to baseline methods.", "published": "2024-11-27T18:57:03Z", "authors": ["Cheng Tang", "Zhishuai Liu", "Pan Xu"], "categories": ["cs.LG", "cs.AI", "cs.RO", "stat.ML"], "doi": "", "_query": "house price prediction machine learning", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2405.09037v2", "title": "SSFL: Discovering Sparse Unified Subnetworks at Initialization for Efficient Federated Learning", "abstract": "In this work, we propose Salient Sparse Federated Learning (SSFL), a streamlined approach for sparse federated learning with efficient communication. SSFL identifies a sparse subnetwork prior to training, leveraging parameter saliency scores computed separately on local client data in non-IID scenarios, and then aggregated, to determine a global mask. Only the sparse model weights are trained and communicated each round between the clients and the server. On standard benchmarks including CIFAR-10, CIFAR-100, and Tiny-ImageNet, SSFL consistently improves the accuracy sparsity trade off, achieving more than 20\\% relative error reduction on CIFAR-10 compared to the strongest sparse baseline, while reducing communication costs by $2 \\times$ relative to dense FL. Finally, in a real-world federated learning deployment, SSFL delivers over $2.3 \\times$ faster communication time, underscoring its practical efficiency.", "published": "2024-05-15T02:13:51Z", "authors": ["Riyasat Ohib", "Bishal Thapaliya", "Gintare Karolina Dziugaite", "Jingyu Liu", "Vince Calhoun", "Sergey Plis"], "categories": ["cs.LG", "cs.AI", "cs.DC"], "doi": "", "_query": "house price prediction machine learning", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2307.13466v1", "title": "Integrating processed-based models and machine learning for crop yield prediction", "abstract": "Crop yield prediction typically involves the utilization of either theory-driven process-based crop growth models, which have proven to be difficult to calibrate for local conditions, or data-driven machine learning methods, which are known to require large datasets. In this work we investigate potato yield prediction using a hybrid meta-modeling approach. A crop growth model is employed to generate synthetic data for (pre)training a convolutional neural net, which is then fine-tuned with observational data. When applied in silico, our meta-modeling approach yields better predictions than a baseline comprising a purely data-driven approach. When tested on real-world data from field trials (n=303) and commercial fields (n=77), the meta-modeling approach yields competitive results with respect to the crop growth model. In the latter set, however, both models perform worse than a simple linear regression with a hand-picked feature set and dedicated preprocessing designed by domain experts. Our findings indicate the potential of meta-modeling for accurate crop yield prediction; however, further advancements and validation using extensive real-world datasets is recommended to solidify its practical effectiveness.", "published": "2023-07-25T12:51:25Z", "authors": ["Michiel G. J. Kallenberg", "Bernardo Maestrini", "Ron van Bree", "Paul Ravensbergen", "Christos Pylianidis", "Frits van Evert", "Ioannis N. Athanasiadis"], "categories": ["cs.LG", "cs.AI"], "doi": "", "_query": "house price prediction machine learning", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2106.04008v2", "title": "Widening Access to Applied Machine Learning with TinyML", "abstract": "Broadening access to both computational and educational resources is critical to diffusing machine-learning (ML) innovation. However, today, most ML resources and experts are siloed in a few countries and organizations. In this paper, we describe our pedagogical approach to increasing access to applied ML through a massive open online course (MOOC) on Tiny Machine Learning (TinyML). We suggest that TinyML, ML on resource-constrained embedded devices, is an attractive means to widen access because TinyML both leverages low-cost and globally accessible hardware, and encourages the development of complete, self-contained applications, from data collection to deployment. To this end, a collaboration between academia (Harvard University) and industry (Google) produced a four-part MOOC that provides application-oriented instruction on how to develop solutions using TinyML. The series is openly available on the edX MOOC platform, has no prerequisites beyond basic programming, and is designed for learners from a global variety of backgrounds. It introduces pupils to real-world applications, ML algorithms, data-set engineering, and the ethical considerations of these technologies via hands-on programming and deployment of TinyML applications in both the cloud and their own microcontrollers. To facilitate continued learning, community building, and collaboration beyond the courses, we launched a standalone website, a forum, a chat, and an optional course-project competition. We also released the course materials publicly, hoping they will inspire the next generation of ML practitioners and educators and further broaden access to cutting-edge ML technologies.", "published": "2021-06-07T23:31:47Z", "authors": ["Vijay Janapa Reddi", "Brian Plancher", "Susan Kennedy", "Laurence Moroney", "Pete Warden", "Anant Agarwal", "Colby Banbury", "Massimo Banzi", "Matthew Bennett", "Benjamin Brown", "Sharad Chitlangia", "Radhika Ghosal", "Sarah Grafman", "Rupert Jaeger", "Srivatsan Krishnan", "Maximilian Lam", "Daniel Leiker", "Cara Mann", "Mark Mazumder", "Dominic Pajak", "Dhilan Ramaprasad", "J. Evan Smith", "Matthew Stewart", "Dustin Tingley"], "categories": ["cs.LG"], "doi": "", "_query": "house price prediction machine learning", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2308.06929v1", "title": "Predicting Listing Prices In Dynamic Short Term Rental Markets Using Machine Learning Models", "abstract": "Our research group wanted to take on the difficult task of predicting prices in a dynamic market. And short term rentals such as Airbnb listings seemed to be the perfect proving ground to do such a thing. Airbnb has revolutionized the travel industry by providing a platform for homeowners to rent out their properties to travelers. The pricing of Airbnb rentals is prone to high fluctuations, with prices changing frequently based on demand, seasonality, and other factors. Accurate prediction of Airbnb rental prices is crucial for hosts to optimize their revenue and for travelers to make informed booking decisions. In this project, we aim to predict the prices of Airbnb rentals using a machine learning modeling approach. Our project expands on earlier research in the area of analyzing Airbnb rental prices by taking a methodical machine learning approach as well as incorporating sentiment analysis into our feature engineering. We intend to gain a deeper understanding on periodic changes of Airbnb rental prices. The primary objective of this study is to construct an accurate machine learning model for predicting Airbnb rental prices specifically in Austin, Texas. Our project's secondary objective is to identify the key factors that drive Airbnb rental prices and to investigate how these factors vary across different locations and property types.", "published": "2023-08-14T04:15:09Z", "authors": ["Sam Chapman", "Seifey Mohammad", "Kimberly Villegas"], "categories": ["cs.LG"], "doi": "", "_query": "house price prediction machine learning", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2006.14473v1", "title": "Real-Time Prediction of BITCOIN Price using Machine Learning Techniques and Public Sentiment Analysis", "abstract": "Bitcoin is the first digital decentralized cryptocurrency that has shown a significant increase in market capitalization in recent years. The objective of this paper is to determine the predictable price direction of Bitcoin in USD by machine learning techniques and sentiment analysis. Twitter and Reddit have attracted a great deal of attention from researchers to study public sentiment. We have applied sentiment analysis and supervised machine learning principles to the extracted tweets from Twitter and Reddit posts, and we analyze the correlation between bitcoin price movements and sentiments in tweets. We explored several algorithms of machine learning using supervised learning to develop a prediction model and provide informative analysis of future market prices. Due to the difficulty of evaluating the exact nature of a Time Series(ARIMA) model, it is often very difficult to produce appropriate forecasts. Then we continue to implement Recurrent Neural Networks (RNN) with long short-term memory cells (LSTM). Thus, we analyzed the time series model prediction of bitcoin prices with greater efficiency using long short-term memory (LSTM) techniques and compared the predictability of bitcoin price and sentiment analysis of bitcoin tweets to the standard method (ARIMA). The RMSE (Root-mean-square error) of LSTM are 198.448 (single feature) and 197.515 (multi-feature) whereas the ARIMA model RMSE is 209.263 which shows that LSTM with multi feature shows the more accurate result.", "published": "2020-06-18T15:40:11Z", "authors": ["S M Raju", "Ali Mohammad Tarif"], "categories": ["q-fin.ST", "cs.LG"], "doi": "", "_query": "house price prediction machine learning", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2412.18507v1", "title": "An Empirical Analysis of Federated Learning Models Subject to Label-Flipping Adversarial Attack", "abstract": "In this paper, we empirically analyze adversarial attacks on selected federated learning models. The specific learning models considered are Multinominal Logistic Regression (MLR), Support Vector Classifier (SVC), Multilayer Perceptron (MLP), Convolution Neural Network (CNN), %Recurrent Neural Network (RNN), Random Forest, XGBoost, and Long Short-Term Memory (LSTM). For each model, we simulate label-flipping attacks, experimenting extensively with 10 federated clients and 100 federated clients. We vary the percentage of adversarial clients from 10% to 100% and, simultaneously, the percentage of labels flipped by each adversarial client is also varied from 10% to 100%. Among other results, we find that models differ in their inherent robustness to the two vectors in our label-flipping attack, i.e., the percentage of adversarial clients, and the percentage of labels flipped by each adversarial client. We discuss the potential practical implications of our results.", "published": "2024-12-24T15:47:25Z", "authors": ["Kunal Bhatnagar", "Sagana Chattanathan", "Angela Dang", "Bhargav Eranki", "Ronnit Rana", "Charan Sridhar", "Siddharth Vedam", "Angie Yao", "Mark Stamp"], "categories": ["cs.LG"], "doi": "10.1007/978-3-031-83157-7_15", "_query": "house price prediction machine learning", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/1806.11189v1", "title": "A hybrid deep learning approach for medical relation extraction", "abstract": "Mining relationships between treatment(s) and medical problem(s) is vital in the biomedical domain. This helps in various applications, such as decision support system, safety surveillance, and new treatment discovery. We propose a deep learning approach that utilizes both word level and sentence-level representations to extract the relationships between treatment and problem. While deep learning techniques demand a large amount of data for training, we make use of a rule-based system particularly for relationship classes with fewer samples. Our final relations are derived by jointly combining the results from deep learning and rule-based models. Our system achieved a promising performance on the relationship classes of I2b2 2010 relation extraction task.", "published": "2018-06-26T06:38:01Z", "authors": ["Veera Raghavendra Chikka", "Kamalakar Karlapalem"], "categories": ["cs.LG", "cs.IR", "stat.ML"], "doi": "", "_query": "house price prediction machine learning", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2411.17570v1", "title": "Learning Explainable Treatment Policies with Clinician-Informed Representations: A Practical Approach", "abstract": "Digital health interventions (DHIs) and remote patient monitoring (RPM) have shown great potential in improving chronic disease management through personalized care. However, barriers like limited efficacy and workload concerns hinder adoption of existing DHIs; while limited sample sizes and lack of interpretability limit the effectiveness and adoption of purely black-box algorithmic DHIs. In this paper, we address these challenges by developing a pipeline for learning explainable treatment policies for RPM-enabled DHIs. We apply our approach in the real-world setting of RPM using a DHI to improve glycemic control of youth with type 1 diabetes. Our main contribution is to reveal the importance of clinical domain knowledge in developing state and action representations for effective, efficient, and interpretable targeting policies. We observe that policies learned from clinician-informed representations are significantly more efficacious and efficient than policies learned from black-box representations. This work emphasizes the importance of collaboration between ML researchers and clinicians for developing effective DHIs in the real world.", "published": "2024-11-26T16:32:08Z", "authors": ["Johannes O. Ferstad", "Emily B. Fox", "David Scheinker", "Ramesh Johari"], "categories": ["cs.LG", "cs.AI", "stat.AP", "stat.ML"], "doi": "", "_query": "house price prediction machine learning", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2608.30442v1", "title": "Confounding Masquerading as Improvement: A Systematic Evaluation of Offline Reinforcement Learning for Stroke Antithrombotic Treatment in a 129,000-Patient Registry", "abstract": "Recent offline reinforcement learning (RL) studies report policies that outperform physician decisions on clinical outcomes. We conduct a systematic, partially crossed evaluation of five offline RL algorithm families and 14 reward designs in 44,894 post-2018 acute ischemic stroke patients from a nationwide registry (N = 129,033). Standard Fitted Q-Evaluation (FQE) yields an apparent policy-improvement estimate of +0.0069; adding an Early Neurological Deterioration penalty increases it to +0.0101. We identify reward-embedded confounding, in which a proxy terminal reward encodes baseline severity and prognosis as well as treatment efficacy. A 2 x 2 factorial analysis finds that terminal reward confounding accounts for 218.6% of the observed signal change, so its removal overshoots the null. After DML-inspired GBM reward residualization, the FQE estimate attenuates to +0.0033 (p = 0.132), and full deconfounding yields +0.0025 (p = 0.291). FQE-based diagnostics, T-learner analyses, and direct recurrence analyses converge away from a clinically meaningful aggregate improvement. A 1-year mRS factorial analysis replicates the attenuation. We provide an empirically motivated six-step evaluation checklist. NIHSS-stratified heterogeneity is hypothesis-generating for prospective trial design; hospital-level disagreement does not persist after full reward deconfounding.", "published": "2026-08-31T08:32:27Z", "authors": ["Kihun Rhee"], "categories": ["cs.LG", "stat.ML"], "doi": "", "_query": "house price prediction machine learning", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2604.14519v1", "title": "CI-CBM: Class-Incremental Concept Bottleneck Model for Interpretable Continual Learning", "abstract": "Catastrophic forgetting remains a fundamental challenge in continual learning, in which models often forget previous knowledge when fine-tuned on a new task. This issue is especially pronounced in class incremental learning (CIL), which is the most challenging setting in continual learning. Existing methods to address catastrophic forgetting often sacrifice either model interpretability or accuracy. To address this challenge, we introduce ClassIncremental Concept Bottleneck Model (CI-CBM), which leverage effective techniques, including concept regularization and pseudo-concept generation to maintain interpretable decision processes throughout incremental learning phases. Through extensive evaluation on seven datasets, CI-CBM achieves comparable performance to black-box models and outperforms previous interpretable approaches in CIL, with an average 36% accuracy gain. CICBM provides interpretable decisions on individual inputs and understandable global decision rules, as shown in our experiments, thereby demonstrating that human understandable concepts can be maintained during incremental learning without compromising model performance. Our approach is effective in both pretrained and non-pretrained scenarios; in the latter, the backbone is trained from scratch during the first learning phase. Code is publicly available at github.com/importAmir/CI-CBM.", "published": "2026-04-16T01:20:33Z", "authors": ["Amirhosein Javadi", "Tuomas Oikarinen", "Tara Javidi", "Tsui-Wei Weng"], "categories": ["cs.LG", "cs.CV"], "doi": "", "_query": "house price prediction machine learning", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2609.17968v1", "title": "Prediction of the maximum penetration of a circular intruder in a two-dimensional granular bed from its early trajectory using Machine Learning", "abstract": "We test whether the maximum penetration depth of a circular intruder into a two-dimensional granular bed can be predicted from its early trajectory using machine learning, without giving the intruder density. From 100 DEM simulations with varying density, we extract kinematic features from the first 50 ms. Ridge and Random Forest predict the maximum penetration. A single feature (the intruder height at 50 ms) concentrates almost all predictive power, showing that density information is already encoded in the early impact response.", "published": "2026-09-16T00:49:58Z", "authors": ["Patricia Altshuler"], "categories": ["cond-mat.soft"], "doi": "", "_query": "house price prediction machine learning", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2101.02033v1", "title": "Boarding House Renting Price Prediction Using Deep Neural Network Regression on Mobile Apps", "abstract": "Boarding house is the most important requirement, especially for college students who live far away from the city, place of his origin or house. However, the problem we see now is the uneven distribution of study places in Indonesia which 75% of the best top educational institutions come from the island of Java. So, students who are looking for boarding houses rent requires more effort in comparing the various aspects desired. They need to survey one by one to the boarding house they want, even though they can survey online, it still requires more effort to pay attention to the desired facilities one by one. Therefore, we then created an Mobile Application that can predict prices based on student needs by comparing several variables, namely city, area, type of boarding house, and facilities. So, students can easily estimate the ideal price. The results of this study prove that we have succeeded in predicting prices for boarding houses rent well based on the variables we have determined, and modeling that variables using Deep Neural Network Regression.", "published": "2020-12-31T15:12:10Z", "authors": ["Malik Abdul Aziz", "Fahmi Nurrahim", "Prastyo Eko Susanto", "Yurio Windiatmoko"], "categories": ["cs.CY", "cs.LG"], "doi": "", "_query": "house price prediction machine learning", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/1703.03121v2", "title": "Coordinated Multi-Agent Imitation Learning", "abstract": "We study the problem of imitation learning from demonstrations of multiple coordinating agents. One key challenge in this setting is that learning a good model of coordination can be difficult, since coordination is often implicit in the demonstrations and must be inferred as a latent variable. We propose a joint approach that simultaneously learns a latent coordination model along with the individual policies. In particular, our method integrates unsupervised structure learning with conventional imitation learning. We illustrate the power of our approach on a difficult problem of learning multiple policies for fine-grained behavior modeling in team sports, where different players occupy different roles in the coordinated team strategy. We show that having a coordination model to infer the roles of players yields substantially improved imitation loss compared to conventional baselines.", "published": "2017-03-09T03:45:42Z", "authors": ["Hoang M. Le", "Yisong Yue", "Peter Carr", "Patrick Lucey"], "categories": ["cs.LG"], "doi": "", "_query": "house price prediction machine learning", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/1607.00279v1", "title": "Meaningful Models: Utilizing Conceptual Structure to Improve Machine Learning Interpretability", "abstract": "The last decade has seen huge progress in the development of advanced machine learning models; however, those models are powerless unless human users can interpret them. Here we show how the mind's construction of concepts and meaning can be used to create more interpretable machine learning models. By proposing a novel method of classifying concepts, in terms of 'form' and 'function', we elucidate the nature of meaning and offer proposals to improve model understandability. As machine learning begins to permeate daily life, interpretable models may serve as a bridge between domain-expert authors and non-expert users.", "published": "2016-07-01T15:07:52Z", "authors": ["Nick Condry"], "categories": ["stat.ML", "cs.AI"], "doi": "", "_query": "house price prediction machine learning", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2502.08728v2", "title": "A Comparative Study of Machine Learning Algorithms for Stock Price Prediction Using Insider Trading Data", "abstract": "The research paper empirically investigates several machine learning algorithms to forecast stock prices depending on insider trading information. Insider trading offers special insights into market sentiment, pointing to upcoming changes in stock prices. This study examines the effectiveness of algorithms like decision trees, random forests, support vector machines (SVM) with different kernels, and K-Means Clustering using a dataset of Tesla stock transactions. Examining past data from April 2020 to March 2023, this study focuses on how well these algorithms identify trends and forecast stock price fluctuations. The paper uses Recursive Feature Elimination (RFE) and feature importance analysis to optimize the feature set and, hence, increase prediction accuracy. While it requires substantially greater processing time than other models, SVM with the Radial Basis Function (RBF) kernel displays the best accuracy. This paper highlights the trade-offs between accuracy and efficiency in machine learning models and proposes the possibility of pooling multiple data sources to raise prediction performance. The results of this paper aim to help financial analysts and investors in choosing strong algorithms to optimize investment strategies.", "published": "2025-02-12T19:03:09Z", "authors": ["Amitabh Chakravorty", "Nelly Elsayed"], "categories": ["cs.LG"], "doi": "", "_query": "house price prediction machine learning", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2301.04404v3", "title": "A prediction and behavioural analysis of machine learning methods for modelling travel mode choice", "abstract": "The emergence of a variety of Machine Learning (ML) approaches for travel mode choice prediction poses an interesting question to transport modellers: which models should be used for which applications? The answer to this question goes beyond simple predictive performance, and is instead a balance of many factors, including behavioural interpretability and explainability, computational complexity, and data efficiency. There is a growing body of research which attempts to compare the predictive performance of different ML classifiers with classical random utility models. However, existing studies typically analyse only the disaggregate predictive performance, ignoring other aspects affecting model choice. Furthermore, many studies are affected by technical limitations, such as the use of inappropriate validation schemes, incorrect sampling for hierarchical data, lack of external validation, and the exclusive use of discrete metrics. We address these limitations by conducting a systematic comparison of different modelling approaches, across multiple modelling problems, in terms of the key factors likely to affect model choice (out-of-sample predictive performance, accuracy of predicted market shares, extraction of behavioural indicators, and computational efficiency). We combine several real world datasets with synthetic datasets, where the data generation function is known. The results indicate that the models with the highest disaggregate predictive performance (namely extreme gradient boosting and random forests) provide poorer estimates of behavioural indicators and aggregate mode shares, and are more expensive to estimate, than other models, including deep neural networks and Multinomial Logit (MNL). It is further observed that the MNL model performs robustly in a variety of situations, though ML techniques can improve the estimates of behavioural indices such as Willingness to Pay.", "published": "2023-01-11T11:10:32Z", "authors": ["José Ángel Martín-Baos", "Julio Alberto López-Gómez", "Luis Rodriguez-Benitez", "Tim Hillel", "Ricardo García-Ródenas"], "categories": ["cs.LG"], "doi": "10.1016/j.trc.2023.104318", "_query": "house price prediction machine learning", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2510.26100v1", "title": "Applications of Machine Learning in Polymer Materials: Property Prediction, Material Design, and Systematic Processes", "abstract": "This paper systematically reviews the research progress and application prospects of machine learning technologies in the field of polymer materials. Currently, machine learning methods are developing rapidly in polymer material research; although they have significantly accelerated material prediction and design, their complexity has also caused difficulties in understanding and application for researchers in traditional fields. In response to the above issues, this paper first analyzes the inherent challenges in the research and development of polymer materials, including structural complexity and the limitations of traditional trial-and-error methods. To address these problems, it focuses on introducing key basic technologies such as molecular descriptors and feature representation, data standardization and cleaning, and records a number of high-quality polymer databases. Subsequently, it elaborates on the key role of machine learning in polymer property prediction and material design, covering the specific applications of algorithms such as traditional machine learning, deep learning, and transfer learning; further, it deeply expounds on data-driven design strategies, such as reverse design, high-throughput virtual screening, and multi-objective optimization. The paper also systematically introduces the complete process of constructing high-reliability machine learning models and summarizes effective experimental verification, model evaluation, and optimization methods. Finally, it summarizes the current technical challenges in research, such as data quality and model generalization ability, and looks forward to future development trends including multi-scale modeling, physics-informed machine learning, standardized data sharing, and interpretable machine learning.", "published": "2025-10-30T03:26:04Z", "authors": ["Hongtao Guo Shuai Li Shu Li"], "categories": ["cond-mat.mtrl-sci"], "doi": "", "_query": "house price prediction machine learning", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2606.11746v1", "title": "Time Series Analysis in Machine Learning", "abstract": "Time series analysis is a fundamental component of machine learning, especially in astrophysics and cosmology where temporal data abound. This chapter provides a pedagogical review of time series analysis techniques from a machine learning perspective. We cover the basic concepts of time series (stationarity, autocorrelation, seasonality), classical statistical models (autoregressive, moving average, ARIMA, exponential smoothing, state-space models), and modern machine learning approaches. In particular, we discuss how traditional statistical methods lay the groundwork, and then explore machine learning methods for time series, including feature-based regression, tree-based ensemble methods, hidden Markov models, Gaussian processes, and deep learning models (recurrent neural networks, convolutional networks, transformers). Throughout, we illustrate with examples drawn from multiple domains (e.g. astronomy, weather forecasting, finance) to emphasize common principles. The goal is to equip readers with both the theoretical understanding and practical context to apply machine learning techniques for time series analysis in their research.", "published": "2026-06-10T07:22:12Z", "authors": ["Antonio Pagliaro", "Anna Anzalone"], "categories": ["astro-ph.IM", "stat.ML"], "doi": "", "_query": "house price prediction machine learning", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2405.11431v2", "title": "Review of deep learning models for crypto price prediction: implementation and evaluation", "abstract": "There has been much interest in accurate cryptocurrency price forecast models by investors and researchers. Deep Learning models are prominent machine learning techniques that have transformed various fields and have shown potential for finance and economics. Although various deep learning models have been explored for cryptocurrency price forecasting, it is not clear which models are suitable due to high market volatility. In this study, we review the literature about deep learning for cryptocurrency price forecasting and evaluate novel deep learning models for cryptocurrency stock price prediction. Our deep learning models include variants of long short-term memory (LSTM) recurrent neural networks, variants of convolutional neural networks (CNNs), and the Transformer model. We evaluate univariate and multivariate approaches for multi-step ahead predicting of cryptocurrencies close-price. We also carry out volatility analysis on the four cryptocurrencies which reveals significant fluctuations in their prices throughout the COVID-19 pandemic. Additionally, we investigate the prediction accuracy of two scenarios identified by different training sets for the models. First, we use the pre-COVID-19 datasets to model cryptocurrency close-price forecasting during the early period of COVID-19. Secondly, we utilise data from the COVID-19 period to predict prices for 2023 to 2024. Our results show that the convolutional LSTM with a multivariate approach provides the best prediction accuracy in two major experimental settings. Our results also indicate that the multivariate deep learning models exhibit better performance in forecasting four different cryptocurrencies when compared to the univariate models.", "published": "2024-05-19T03:15:27Z", "authors": ["Jingyang Wu", "Xinyi Zhang", "Fangyixuan Huang", "Haochen Zhou", "Rohtiash Chandra"], "categories": ["cs.LG", "q-fin.ST", "stat.ML"], "doi": "", "_query": "house price prediction machine learning", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2503.11120v2", "title": "A Multi-Objective Evaluation Framework for Analyzing Utility-Fairness Trade-Offs in Machine Learning Systems", "abstract": "The evaluation of fairness models in Machine Learning involves complex challenges, such as defining appropriate metrics, balancing trade-offs between utility and fairness, and there are still gaps in this stage. This work presents a novel multi-objective evaluation framework that enables the analysis of utility-fairness trade-offs in Machine Learning systems. The framework was developed using criteria from Multi-Objective Optimization that collect comprehensive information regarding this complex evaluation task. The assessment of multiple Machine Learning systems is summarized, both quantitatively and qualitatively, in a straightforward manner through a radar chart and a measurement table encompassing various aspects such as convergence, system capacity, and diversity. The framework's compact representation of performance facilitates the comparative analysis of different Machine Learning strategies for decision-makers, in real-world applications, with single or multiple fairness requirements. In particular, this study focuses on the medical imaging domain, where fairness considerations are crucial due to the potential impact of biased diagnostic systems on patient outcomes. The proposed framework enables a systematic evaluation of multiple fairness constraints helping to identify and mitigate disparities among demographic groups while maintaining diagnostic performance. The framework is model-agnostic and flexible to be adapted to any kind of Machine Learning systems, that is, black- or white-box, any kind and quantity of evaluation metrics, including multidimensional fairness criteria. The functionality and effectiveness of the proposed framework is shown with different simulations, and an empirical study conducted on three real-world medical imaging datasets with various Machine Learning systems. Our evaluation framework is publicly available at https://pypi.org/project/fairical.", "published": "2025-03-14T06:32:42Z", "authors": ["Gökhan Özbulak", "Oscar Jimenez-del-Toro", "Maíra Fatoretto", "Lilian Berton", "André Anjos"], "categories": ["cs.LG", "cs.CV"], "doi": "10.59275/j.melba.2025-ab9a", "_query": "house price prediction machine learning", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2203.03810v1", "title": "Towards Efficient Data-Centric Robust Machine Learning with Noise-based Augmentation", "abstract": "The data-centric machine learning aims to find effective ways to build appropriate datasets which can improve the performance of AI models. In this paper, we mainly focus on designing an efficient data-centric scheme to improve robustness for models towards unforeseen malicious inputs in the black-box test settings. Specifically, we introduce a noised-based data augmentation method which is composed of Gaussian Noise, Salt-and-Pepper noise, and the PGD adversarial perturbations. The proposed method is built on lightweight algorithms and proved highly effective based on comprehensive evaluations, showing good efficiency on computation cost and robustness enhancement. In addition, we share our insights about the data-centric robust machine learning gained from our experiments.", "published": "2022-03-08T02:05:40Z", "authors": ["Xiaogeng Liu", "Haoyu Wang", "Yechao Zhang", "Fangzhou Wu", "Shengshan Hu"], "categories": ["cs.LG", "cs.AI", "cs.CR"], "doi": "", "_query": "house price prediction machine learning", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2401.12708v2", "title": "Deep Neural Network Benchmarks for Selective Classification", "abstract": "With the increasing deployment of machine learning models in many socially sensitive tasks, there is a growing demand for reliable and trustworthy predictions. One way to accomplish these requirements is to allow a model to abstain from making a prediction when there is a high risk of making an error. This requires adding a selection mechanism to the model, which selects those examples for which the model will provide a prediction. The selective classification framework aims to design a mechanism that balances the fraction of rejected predictions (i.e., the proportion of examples for which the model does not make a prediction) versus the improvement in predictive performance on the selected predictions. Multiple selective classification frameworks exist, most of which rely on deep neural network architectures. However, the empirical evaluation of the existing approaches is still limited to partial comparisons among methods and settings, providing practitioners with little insight into their relative merits. We fill this gap by benchmarking 18 baselines on a diverse set of 44 datasets that includes both image and tabular data. Moreover, there is a mix of binary and multiclass tasks. We evaluate these approaches using several criteria, including selective error rate, empirical coverage, distribution of rejected instance's classes, and performance on out-of-distribution instances. The results indicate that there is not a single clear winner among the surveyed baselines, and the best method depends on the users' objectives.", "published": "2024-01-23T12:15:47Z", "authors": ["Andrea Pugnana", "Lorenzo Perini", "Jesse Davis", "Salvatore Ruggieri"], "categories": ["cs.LG", "cs.AI", "stat.ML"], "doi": "", "_query": "house price prediction machine learning", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/1906.03685v1", "title": "Novelty Detection via Network Saliency in Visual-based Deep Learning", "abstract": "Machine-learning driven safety-critical autonomous systems, such as self-driving cars, must be able to detect situations where its trained model is not able to make a trustworthy prediction. Often viewed as a black-box, it is non-obvious to determine when a model will make a safe decision and when it will make an erroneous, perhaps life-threatening one. Prior work on novelty detection deal with highly structured data and do not translate well to dynamic, real-world situations. This paper proposes a multi-step framework for the detection of novel scenarios in vision-based autonomous systems by leveraging information learned by the trained prediction model and a new image similarity metric. We demonstrate the efficacy of this method through experiments on a real-world driving dataset as well as on our in-house indoor racing environment.", "published": "2019-06-09T18:23:57Z", "authors": ["Valerie Chen", "Man-Ki Yoon", "Zhong Shao"], "categories": ["cs.LG", "cs.CV", "stat.ML"], "doi": "", "_query": "house price prediction machine learning", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2509.05922v1", "title": "Predicting Market Troughs: A Machine Learning Approach with Causal Interpretation", "abstract": "This paper provides robust, new evidence on the causal drivers of market troughs. We demonstrate that conclusions about these triggers are critically sensitive to model specification, moving beyond restrictive linear models with a flexible DML average partial effect causal machine learning framework. Our robust estimates identify the volatility of options-implied risk appetite and market liquidity as key causal drivers, relationships misrepresented or obscured by simpler models. These findings provide high-frequency empirical support for intermediary asset pricing theories. This causal analysis is enabled by a high-performance nowcasting model that accurately identifies capitulation events in real-time.", "published": "2025-09-07T04:38:40Z", "authors": ["Peilin Rao", "Randall R. Rojas"], "categories": ["q-fin.ST", "econ.EM", "stat.ML"], "doi": "", "_query": "house price prediction machine learning", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/1909.09246v1", "title": "Machine Learning for Clinical Predictive Analytics", "abstract": "In this chapter, we provide a brief overview of applying machine learning techniques for clinical prediction tasks. We begin with a quick introduction to the concepts of machine learning and outline some of the most common machine learning algorithms. Next, we demonstrate how to apply the algorithms with appropriate toolkits to conduct machine learning experiments for clinical prediction tasks. The objectives of this chapter are to (1) understand the basics of machine learning techniques and the reasons behind why they are useful for solving clinical prediction problems, (2) understand the intuition behind some machine learning models, including regression, decision trees, and support vector machines, and (3) understand how to apply these models to clinical prediction problems using publicly available datasets via case studies.", "published": "2019-09-19T22:02:00Z", "authors": ["Wei-Hung Weng"], "categories": ["cs.LG", "stat.ML"], "doi": "", "_query": "house price prediction machine learning", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2306.04646v1", "title": "Improve State-Level Wheat Yield Forecasts in Kazakhstan on GEOGLAM's EO Data by Leveraging A Simple Spatial-Aware Technique", "abstract": "Accurate yield forecasting is essential for making informed policies and long-term decisions for food security. Earth Observation (EO) data and machine learning algorithms play a key role in providing a comprehensive and timely view of crop conditions from field to national scales. However, machine learning algorithms' prediction accuracy is often harmed by spatial heterogeneity caused by exogenous factors not reflected in remote sensing data, such as differences in crop management strategies. In this paper, we propose and investigate a simple technique called state-wise additive bias to explicitly address the cross-region yield heterogeneity in Kazakhstan. Compared to baseline machine learning models (Random Forest, CatBoost, XGBoost), our method reduces the overall RMSE by 8.9\\% and the highest state-wise RMSE by 28.37\\%. The effectiveness of state-wise additive bias indicates machine learning's performance can be significantly improved by explicitly addressing the spatial heterogeneity, motivating future work on spatial-aware machine learning algorithms for yield forecasts as well as for general geospatial forecasting problems.", "published": "2023-06-01T19:35:13Z", "authors": ["Anh Nhat Nhu", "Ritvik Sahajpal", "Christina Justice", "Inbal Becker-Reshef"], "categories": ["cs.LG", "cs.CY"], "doi": "", "_query": "house price prediction machine learning", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/1804.01882v3", "title": "Hyperbolic Entailment Cones for Learning Hierarchical Embeddings", "abstract": "Learning graph representations via low-dimensional embeddings that preserve relevant network properties is an important class of problems in machine learning. We here present a novel method to embed directed acyclic graphs. Following prior work, we first advocate for using hyperbolic spaces which provably model tree-like structures better than Euclidean geometry. Second, we view hierarchical relations as partial orders defined using a family of nested geodesically convex cones. We prove that these entailment cones admit an optimal shape with a closed form expression both in the Euclidean and hyperbolic spaces, and they canonically define the embedding learning process. Experiments show significant improvements of our method over strong recent baselines both in terms of representational capacity and generalization.", "published": "2018-04-03T19:25:10Z", "authors": ["Octavian-Eugen Ganea", "Gary Bécigneul", "Thomas Hofmann"], "categories": ["cs.LG", "stat.ML"], "doi": "", "_query": "house price prediction machine learning", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2205.14550v5", "title": "Machine Learning for Microcontroller-Class Hardware: A Review", "abstract": "The advancements in machine learning opened a new opportunity to bring intelligence to the low-end Internet-of-Things nodes such as microcontrollers. Conventional machine learning deployment has high memory and compute footprint hindering their direct deployment on ultra resource-constrained microcontrollers. This paper highlights the unique requirements of enabling onboard machine learning for microcontroller class devices. Researchers use a specialized model development workflow for resource-limited applications to ensure the compute and latency budget is within the device limits while still maintaining the desired performance. We characterize a closed-loop widely applicable workflow of machine learning model development for microcontroller class devices and show that several classes of applications adopt a specific instance of it. We present both qualitative and numerical insights into different stages of model development by showcasing several use cases. Finally, we identify the open research challenges and unsolved questions demanding careful considerations moving forward.", "published": "2022-05-29T00:59:38Z", "authors": ["Swapnil Sayan Saha", "Sandeep Singh Sandha", "Mani Srivastava"], "categories": ["cs.LG"], "doi": "10.1109/JSEN.2022.3210773", "_query": "house price prediction machine learning", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2411.10764v1", "title": "ML$^2$Tuner: Efficient Code Tuning via Multi-Level Machine Learning Models", "abstract": "The increasing complexity of deep learning models necessitates specialized hardware and software optimizations, particularly for deep learning accelerators. Existing autotuning methods often suffer from prolonged tuning times due to profiling invalid configurations, which can cause runtime errors. We introduce ML$^2$Tuner, a multi-level machine learning tuning technique that enhances autotuning efficiency by incorporating a validity prediction model to filter out invalid configurations and an advanced performance prediction model utilizing hidden features from the compilation process. Experimental results on an extended VTA accelerator demonstrate that ML$^2$Tuner achieves equivalent performance improvements using only 12.3% of the samples required with a similar approach as TVM and reduces invalid profiling attempts by an average of 60.8%, Highlighting its potential to enhance autotuning performance by filtering out invalid configurations", "published": "2024-11-16T10:10:12Z", "authors": ["JooHyoung Cha", "Munyoung Lee", "Jinse Kwon", "Jubin Lee", "Jemin Lee", "Yongin Kwon"], "categories": ["cs.LG"], "doi": "", "_query": "house price prediction machine learning", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/1104.2719v1", "title": "An autoregressive approach to house price modeling", "abstract": "A statistical model for predicting individual house prices and constructing a house price index is proposed utilizing information regarding sale price, time of sale and location (ZIP code). This model is composed of a fixed time effect and a random ZIP (postal) code effect combined with an autoregressive component. The former two components are applied to all home sales, while the latter is applied only to homes sold repeatedly. The time effect can be converted into a house price index. To evaluate the proposed model and the resulting index, single-family home sales for twenty US metropolitan areas from July 1985 through September 2004 are analyzed. The model is shown to have better predictive abilities than the benchmark S&P/Case--Shiller model, which is a repeat sales model, and a conventional mixed effects model. Finally, Los Angeles, CA, is used to illustrate a historical housing market downturn.", "published": "2011-04-14T10:54:29Z", "authors": ["Chaitra H. Nagaraja", "Lawrence D. Brown", "Linda H. Zhao"], "categories": ["stat.AP"], "doi": "10.1214/10-AOAS380", "_query": "house price prediction machine learning", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2205.01875v2", "title": "Machine Learning based Framework for Robust Price-Sensitivity Estimation with Application to Airline Pricing", "abstract": "We consider the problem of dynamic pricing of a product in the presence of feature-dependent price sensitivity. Developing practical algorithms that can estimate price elasticities robustly, especially when information about no purchases (losses) is not available, to drive such automated pricing systems is a challenge faced by many industries. Based on the Poisson semi-parametric approach, we construct a flexible yet interpretable demand model where the price related part is parametric while the remaining (nuisance) part of the model is non-parametric and can be modeled via sophisticated machine learning (ML) techniques. The estimation of price-sensitivity parameters of this model via direct one-stage regression techniques may lead to biased estimates due to regularization. To address this concern, we propose a two-stage estimation methodology which makes the estimation of the price-sensitivity parameters robust to biases in the estimators of the nuisance parameters of the model. In the first-stage we construct estimators of observed purchases and prices given the feature vector using sophisticated ML estimators such as deep neural networks. Utilizing the estimators from the first-stage, in the second-stage we leverage a Bayesian dynamic generalized linear model to estimate the price-sensitivity parameters. We test the performance of the proposed estimation schemes on simulated and real sales transaction data from the Airline industry. Our numerical studies demonstrate that our proposed two-stage approach reduces the estimation error in price-sensitivity parameters from 25\\% to 4\\% in realistic simulation settings. The two-stage estimation techniques proposed in this work allows practitioners to leverage modern ML techniques to robustly estimate price-sensitivities while still maintaining interpretability and allowing ease of validation of its various constituent parts.", "published": "2022-05-04T03:35:12Z", "authors": ["Ravi Kumar", "Shahin Boluki", "Karl Isler", "Jonas Rauch", "Darius Walczak"], "categories": ["stat.ML", "cs.LG", "econ.EM", "stat.AP"], "doi": "", "_query": "house price prediction machine learning", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2104.04059v1", "title": "Machine Learning on the COVID-19 Pandemic, Human Mobility and Air Quality: A Review", "abstract": "The ongoing COVID-19 global pandemic is affecting every facet of human lives (e.g., public health, education, economy, transportation, and the environment). This novel pandemic and citywide implemented lockdown measures are affecting virus transmission, people's travel patterns, and air quality. Many studies have been conducted to predict the COVID-19 diffusion, assess the impacts of the pandemic on human mobility and air quality, and assess the impacts of lockdown measures on viral spread with a range of Machine Learning (ML) techniques. This review study aims to analyze results from past research to understand the interactions among the COVID-19 pandemic, lockdown measures, human mobility, and air quality. The critical review of prior studies indicates that urban form, people's socioeconomic and physical conditions, social cohesion, and social distancing measures significantly affect human mobility and COVID-19 transmission. during the COVID-19 pandemic, many people are inclined to use private transportation for necessary travel purposes to mitigate coronavirus-related health problems. This review study also noticed that COVID-19 related lockdown measures significantly improve air quality by reducing the concentration of air pollutants, which in turn improves the COVID-19 situation by reducing respiratory-related sickness and deaths of people. It is argued that ML is a powerful, effective, and robust analytic paradigm to handle complex and wicked problems such as a global pandemic. This study also discusses policy implications, which will be helpful for policymakers to take prompt actions to moderate the severity of the pandemic and improve urban environments by adopting data-driven analytic methods.", "published": "2021-03-13T10:08:24Z", "authors": ["Md. Mokhlesur Rahman", "Kamal Chandra Paul", "Md. Amjad Hossain", "G. G. Md. NawazAli", "Md. Shahinoor Rahman", "Jean-Claude Thill"], "categories": ["cs.CY", "cs.LG"], "doi": "10.1109/ACCESS.2021.3079121", "_query": "house price prediction machine learning", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2406.02584v4", "title": "A Scoping Review of Earth Observation and Machine Learning for Causal Inference: Implications for the Geography of Poverty", "abstract": "Earth observation (EO) data such as satellite imagery can have far-reaching impacts on our understanding of the geography of poverty, especially when coupled with machine learning (ML) and computer vision. Early research used computer vision to predict living conditions in areas with limited data, but recent studies increasingly focus on causal analysis. Despite this shift, the use of EO-ML methods for causal inference lacks thorough documentation, and best practices are still developing. Through a comprehensive scoping review, we catalog the current literature on EO-ML methods in causal analysis. We synthesize five principal approaches to incorporating EO data in causal workflows: (1) outcome imputation for downstream causal analysis, (2) EO image deconfounding, (3) EO-based treatment effect heterogeneity, (4) EO-based transportability analysis, and (5) image-informed causal discovery. Building on these findings, we provide a detailed protocol guiding researchers in integrating EO data into causal analysis -- covering data requirements, computer vision model selection, and evaluation metrics. While our focus centers on health and living conditions outcomes, our protocol is adaptable to other sustainable development domains utilizing EO data.", "published": "2024-05-30T20:48:10Z", "authors": ["Kazuki Sakamoto", "Connor T. Jerzak", "Adel Daoud"], "categories": ["cs.LG", "cs.CV", "stat.ME", "stat.ML"], "doi": "", "_query": "house price prediction machine learning", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2610.16275v1", "title": "A Survey of Flow Matching in Reinforcement Learning", "abstract": "Flow Matching (FM) has recently emerged as a principled and efficient generative modeling framework for reinforcement learning (RL), enabling expressive, multimodal policy parameterizations via deterministic probability transport. Compared to diffusion-based policies that rely on stochastic denoising chains, FM uses sampling based on ordinary differential equations (ODEs), with learned velocity fields, which can substantially reduce inference latency and simplify the incorporation of RL objectives. As research in flow-based RL rapidly accelerates across offline continuous control, online fine-tuning, and foundation model alignment, the literature has become highly fragmented. In this survey, we provide a comprehensive taxonomy of flow-matching approaches in reinforcement learning. We organize the literature along two axes: the target distribution being modeled (e.g., action policies, value critics, transition dynamics) and the mechanism of RL signal integration (e.g., energy-weighted regression, flow-based policy gradients, and group relative policy optimization). Furthermore, we survey emerging frontiers such as discrete and non-Euclidean action spaces, provide a systematic comparative analysis against Gaussian and diffusion baselines, and outline critical open problems. Ultimately, this survey serves as a foundational roadmap for the next generation of generative reinforcement learning and alignment.", "published": "2026-10-12T06:55:17Z", "authors": ["Nabuat Zaman Nahim", "Fairoz Nower Khan", "Peizhong Ju"], "categories": ["cs.LG", "cs.AI"], "doi": "", "_query": "house price prediction machine learning", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/1811.11674v1", "title": "Interlacing Personal and Reference Genomes for Machine Learning Disease-Variant Detection", "abstract": "DNA sequencing to identify genetic variants is becoming increasingly valuable in clinical settings. Assessment of variants in such sequencing data is commonly implemented through Bayesian heuristic algorithms. Machine learning has shown great promise in improving on these variant calls, but the input for these is still a standardized \"pile-up\" image, which is not always best suited. In this paper, we present a novel method for generating images from DNA sequencing data, which interlaces the human reference genome with personalized sequencing output, to maximize usage of sequencing reads and improve machine learning algorithm performance. We demonstrate the success of this in improving standard germline variant calling. We also furthered this approach to include somatic variant calling across tumor/normal data with Siamese networks. These approaches can be used in machine learning applications on sequencing data with the hope of improving clinical outcomes, and are freely available for noncommercial use at www.ccg.ai.", "published": "2018-11-26T15:38:29Z", "authors": ["Luke R Harries", "Suyi Zhang", "Geoffroy Dubourg-Felonneau", "James H R Farmery", "Jonathan Sinai", "Belle Taylor", "Nirmesh Patel", "John W Cassidy", "John Shawe-Taylor", "Harry W Clifford"], "categories": ["q-bio.GN", "cs.LG", "stat.ML"], "doi": "", "_query": "house price prediction machine learning", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2604.20078v1", "title": "Improved large-scale graph learning through ridge spectral sparsification", "abstract": "Graph-based techniques and spectral graph theory have enriched the field of machine learning with a variety of critical advances. A central object in the analysis is the graph Laplacian L, which encodes the structure of the graph. We consider the problem of learning over this Laplacian in a distributed streaming setting, where new edges of the graph are observed in real time by a network of workers. In this setting, it is hard to learn quickly or approximately while keeping a distributed representation of L. To address this challenge, we present a novel algorithm, GSQUEAK, which efficiently sparsifies the Laplacian by maintaining a small subset of effective resistances. We show that our algorithm produces sparsifiers with strong spectral approximation guarantees, all while processing edges in a single pass and in a distributed fashion.", "published": "2026-04-22T00:49:31Z", "authors": ["Daniele Calandriello", "Ioannis Koutis", "Alessandro Lazaric", "Michal Valko"], "categories": ["cs.LG", "stat.ML"], "doi": "", "_query": "house price prediction machine learning", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2411.05856v1", "title": "Evaluating the Economic Implications of Using Machine Learning in Clinical Psychiatry", "abstract": "With the growing interest in using AI and machine learning (ML) in medicine, there is an increasing number of literature covering the application and ethics of using AI and ML in areas of medicine such as clinical psychiatry. The problem is that there is little literature covering the economic aspects associated with using ML in clinical psychiatry. This study addresses this gap by specifically studying the economic implications of using ML in clinical psychiatry. In this paper, we evaluate the economic implications of using ML in clinical psychiatry through using three problem-oriented case studies, literature on economics, socioeconomic and medical AI, and two types of health economic evaluations. In addition, we provide details on fairness, legal, ethics and other considerations for ML in clinical psychiatry.", "published": "2024-11-07T01:57:06Z", "authors": ["Soaad Hossain", "James Rasalingam", "Arhum Waheed", "Fatah Awil", "Rachel Kandiah", "Syed Ishtiaque Ahmed"], "categories": ["cs.CY", "cs.AI", "cs.CE", "cs.HC", "cs.LG"], "doi": "", "_query": "house price prediction machine learning", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/1412.1074v1", "title": "Learning interpretable models of phenotypes from whole genome sequences with the Set Covering Machine", "abstract": "The increased affordability of whole genome sequencing has motivated its use for phenotypic studies. We address the problem of learning interpretable models for discrete phenotypes from whole genomes. We propose a general approach that relies on the Set Covering Machine and a k-mer representation of the genomes. We show results for the problem of predicting the resistance of Pseudomonas Aeruginosa, an important human pathogen, against 4 antibiotics. Our results demonstrate that extremely sparse models which are biologically relevant can be learnt using this approach.", "published": "2014-12-02T13:26:50Z", "authors": ["Alexandre Drouin", "Sébastien Giguère", "Vladana Sagatovich", "Maxime Déraspe", "François Laviolette", "Mario Marchand", "Jacques Corbeil"], "categories": ["q-bio.GN", "cs.CE", "cs.LG", "stat.ML"], "doi": "", "_query": "house price prediction machine learning", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/1707.05499v1", "title": "A Machine Learning Approach for Evaluating Creative Artifacts", "abstract": "Much work has been done in understanding human creativity and defining measures to evaluate creativity. This is necessary mainly for the reason of having an objective and automatic way of quantifying creative artifacts. In this work, we propose a regression-based learning framework which takes into account quantitatively the essential criteria for creativity like novelty, influence, value and unexpectedness. As it is often the case with most creative domains, there is no clear ground truth available for creativity. Our proposed learning framework is applicable to all creative domains; yet we evaluate it on a dataset of movies created from IMDb and Rotten Tomatoes due to availability of audience and critic scores, which can be used as proxy ground truth labels for creativity. We report promising results and observations from our experiments in the following ways : 1) Correlation of creative criteria with critic scores, 2) Improvement in movie rating prediction with inclusion of various creative criteria, and 3) Identification of creative movies.", "published": "2017-07-18T06:59:45Z", "authors": ["Disha Shrivastava", "Saneem Ahmed CG", "Anirban Laha", "Karthik Sankaranarayanan"], "categories": ["cs.LG", "cs.AI", "stat.ML"], "doi": "", "_query": "house price prediction machine learning", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2511.10540v2", "title": "Edge Machine Learning for Cluster Counting in Next-Generation Drift Chambers", "abstract": "Drift chambers have long been central to collider tracking, but future machines like a Higgs factory motivate higher granularity and cluster counting for particle ID, posing new data processing challenges. Machine learning (ML) at the \"edge\", or in cell-level readout, can dramatically reduce the off-detector data rate for high-granularity drift chambers by performing cluster counting at-source. We present machine learning algorithms for cluster counting in real-time readout of future drift chambers. These algorithms outperform traditional derivative-based techniques based on achievable pion-kaon separation. When synthesized to FPGA resources, they can achieve latencies consistent with real-time operation in a future Higgs factory scenario, thus advancing both R&D for future collider detectors as well as hardware-based ML for edge applications in high energy physics.", "published": "2025-11-13T17:39:22Z", "authors": ["Deniz Yilmaz", "Liangyu Wu", "Julia Gonski", "Dylan Rankin", "Christian Herwig"], "categories": ["physics.ins-det", "cs.LG"], "doi": "", "_query": "house price prediction machine learning", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2412.06087v1", "title": "Ethnography and Machine Learning: Synergies and New Directions", "abstract": "Ethnography (social scientific methods that illuminate how people understand, navigate and shape the real world contexts in which they live their lives) and machine learning (computational techniques that use big data and statistical learning models to perform quantifiable tasks) are each core to contemporary social science. Yet these tools have remained largely separate in practice. This chapter draws on a growing body of scholarship that argues that ethnography and machine learning can be usefully combined, particularly for large comparative studies. Specifically, this paper (a) explains the value (and challenges) of using machine learning alongside qualitative field research for certain types of projects, (b) discusses recent methodological trends to this effect, (c) provides examples that illustrate workflow drawn from several large projects, and (d) concludes with a roadmap for enabling productive coevolution of field methods and machine learning.", "published": "2024-12-08T22:28:05Z", "authors": ["Zhuofan Li", "Corey M. Abramson"], "categories": ["cs.LG", "cs.AI", "cs.CY", "stat.ME"], "doi": "10.1093/oxfordhb/9780197653609.013.36", "_query": "house price prediction machine learning", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2210.17484v1", "title": "The Open MatSci ML Toolkit: A Flexible Framework for Machine Learning in Materials Science", "abstract": "We present the Open MatSci ML Toolkit: a flexible, self-contained, and scalable Python-based framework to apply deep learning models and methods on scientific data with a specific focus on materials science and the OpenCatalyst Dataset. Our toolkit provides: 1. A scalable machine learning workflow for materials science leveraging PyTorch Lightning, which enables seamless scaling across different computation capabilities (laptop, server, cluster) and hardware platforms (CPU, GPU, XPU). 2. Deep Graph Library (DGL) support for rapid graph neural network prototyping and development. By publishing and sharing this toolkit with the research community via open-source release, we hope to: 1. Lower the entry barrier for new machine learning researchers and practitioners that want to get started with the OpenCatalyst dataset, which presently comprises the largest computational materials science dataset. 2. Enable the scientific community to apply advanced machine learning tools to high-impact scientific challenges, such as modeling of materials behavior for clean energy applications. We demonstrate the capabilities of our framework by enabling three new equivariant neural network models for multiple OpenCatalyst tasks and arrive at promising results for compute scaling and model performance.", "published": "2022-10-31T17:11:36Z", "authors": ["Santiago Miret", "Kin Long Kelvin Lee", "Carmelo Gonzales", "Marcel Nassar", "Matthew Spellings"], "categories": ["cs.LG", "cond-mat.mtrl-sci", "cs.AI"], "doi": "", "_query": "house price prediction machine learning", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2007.02817v3", "title": "Faster Graph Embeddings via Coarsening", "abstract": "Graph embeddings are a ubiquitous tool for machine learning tasks, such as node classification and link prediction, on graph-structured data. However, computing the embeddings for large-scale graphs is prohibitively inefficient even if we are interested only in a small subset of relevant vertices. To address this, we present an efficient graph coarsening approach, based on Schur complements, for computing the embedding of the relevant vertices. We prove that these embeddings are preserved exactly by the Schur complement graph that is obtained via Gaussian elimination on the non-relevant vertices. As computing Schur complements is expensive, we give a nearly-linear time algorithm that generates a coarsened graph on the relevant vertices that provably matches the Schur complement in expectation in each iteration. Our experiments involving prediction tasks on graphs demonstrate that computing embeddings on the coarsened graph, rather than the entire graph, leads to significant time savings without sacrificing accuracy.", "published": "2020-07-06T15:22:25Z", "authors": ["Matthew Fahrbach", "Gramoz Goranci", "Richard Peng", "Sushant Sachdeva", "Chi Wang"], "categories": ["cs.LG", "cs.DS", "stat.ML"], "doi": "", "_query": "house price prediction machine learning", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2002.05842v2", "title": "Graph Prolongation Convolutional Networks: Explicitly Multiscale Machine Learning on Graphs with Applications to Modeling of Cytoskeleton", "abstract": "We define a novel type of ensemble Graph Convolutional Network (GCN) model. Using optimized linear projection operators to map between spatial scales of graph, this ensemble model learns to aggregate information from each scale for its final prediction. We calculate these linear projection operators as the infima of an objective function relating the structure matrices used for each GCN. Equipped with these projections, our model (a Graph Prolongation-Convolutional Network) outperforms other GCN ensemble models at predicting the potential energy of monomer subunits in a coarse-grained mechanochemical simulation of microtubule bending. We demonstrate these performance gains by measuring an estimate of the FLOPs spent to train each model, as well as wall-clock time. Because our model learns at multiple scales, it is possible to train at each scale according to a predetermined schedule of coarse vs. fine training. We examine several such schedules adapted from the Algebraic Multigrid (AMG) literature, and quantify the computational benefit of each. We also compare this model to another model which features an optimized coarsening of the input graph. Finally, we derive backpropagation rules for the input of our network model with respect to its output, and discuss how our method may be extended to very large graphs.", "published": "2020-02-14T01:56:17Z", "authors": ["C. B. Scott", "Eric Mjolsness"], "categories": ["cs.LG", "stat.ML"], "doi": "", "_query": "house price prediction machine learning", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2405.05235v2", "title": "RACH Traffic Prediction in Massive Machine Type Communications", "abstract": "Traffic pattern prediction has emerged as a promising approach for efficiently managing and mitigating the impacts of event-driven bursty traffic in massive machine-type communication (mMTC) networks. However, achieving accurate predictions of bursty traffic remains a non-trivial task due to the inherent randomness of events, and these challenges intensify within live network environments. Consequently, there is a compelling imperative to design a lightweight and agile framework capable of assimilating continuously collected data from the network and accurately forecasting bursty traffic in mMTC networks. This paper addresses these challenges by presenting a machine learning-based framework tailored for forecasting bursty traffic in multi-channel slotted ALOHA networks. The proposed machine learning network comprises long-term short-term memory (LSTM) and a DenseNet with feed-forward neural network (FFNN) layers, where the residual connections enhance the training ability of the machine learning network in capturing complicated patterns. Furthermore, we develop a new low-complexity online prediction algorithm that updates the states of the LSTM network by leveraging frequently collected data from the mMTC network. Simulation results and complexity analysis demonstrate the superiority of our proposed algorithm in terms of both accuracy and complexity, making it well-suited for time-critical live scenarios. We evaluate the performance of the proposed framework in a network with a single base station and thousands of devices organized into groups with distinct traffic-generating characteristics. Comprehensive evaluations and simulations indicate that our proposed machine learning approach achieves a remarkable $52\\%$ higher accuracy in long-term predictions compared to traditional methods, without imposing additional processing load on the system.", "published": "2024-05-08T17:28:07Z", "authors": ["Hossein Mehri", "Hao Chen", "Hani Mehrpouyan"], "categories": ["eess.SY", "cs.LG"], "doi": "10.1109/TMLCN.2025.3542760", "_query": "house price prediction machine learning", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/1812.03057v1", "title": "Open Problems in Engineering and Quality Assurance of Safety Critical Machine Learning Systems", "abstract": "Fatal accidents are a major issue hindering the wide acceptance of safety-critical systems using machine-learning and deep-learning models, such as automated-driving vehicles. Quality assurance frameworks are required for such machine learning systems, but there are no widely accepted and established quality-assurance concepts and techniques. At the same time, open problems and the relevant technical fields are not organized. To establish standard quality assurance frameworks, it is necessary to visualize and organize these open problems in an interdisciplinary way, so that the experts from many different technical fields may discuss these problems in depth and develop solutions. In the present study, we identify, classify, and explore the open problems in quality assurance of safety-critical machine-learning systems, and their relevant corresponding industry and technological trends, using automated-driving vehicles as an example. Our results show that addressing these open problems requires incorporating knowledge from several different technological and industrial fields, including the automobile industry, statistics, software engineering, and machine learning.", "published": "2018-12-07T15:02:40Z", "authors": ["Hiroshi Kuwajima", "Hirotoshi Yasuoka", "Toshihiro Nakae"], "categories": ["cs.CY", "cs.LG", "stat.ML"], "doi": "", "_query": "house price prediction machine learning", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2212.00414v1", "title": "A Comprehensive Study on Machine Learning Methods to Increase the Prediction Accuracy of Classifiers and Reduce the Number of Medical Tests Required to Diagnose Alzheimer'S Disease", "abstract": "Alzheimer's patients gradually lose their ability to think, behave, and interact with others. Medical history, laboratory tests, daily activities, and personality changes can all be used to diagnose the disorder. A series of time-consuming and expensive tests are used to diagnose the illness. The most effective way to identify Alzheimer's disease is using a Random-forest classifier in this study, along with various other Machine Learning techniques. The main goal of this study is to fine-tune the classifier to detect illness with fewer tests while maintaining a reasonable disease discovery accuracy. We successfully identified the condition in almost 94% of cases using four of the thirty frequently utilized indicators.", "published": "2022-12-01T10:34:11Z", "authors": ["Md. Sharifur Rahman", "Professor Girijesh Prasad"], "categories": ["cs.LG", "cs.AI"], "doi": "", "_query": "house price prediction machine learning", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2511.05396v1", "title": "Sample Complexity of Distributionally Robust Off-Dynamics Reinforcement Learning with Online Interaction", "abstract": "Off-dynamics reinforcement learning (RL), where training and deployment transition dynamics are different, can be formulated as learning in a robust Markov decision process (RMDP) where uncertainties in transition dynamics are imposed. Existing literature mostly assumes access to generative models allowing arbitrary state-action queries or pre-collected datasets with a good state coverage of the deployment environment, bypassing the challenge of exploration. In this work, we study a more realistic and challenging setting where the agent is limited to online interaction with the training environment. To capture the intrinsic difficulty of exploration in online RMDPs, we introduce the supremal visitation ratio, a novel quantity that measures the mismatch between the training dynamics and the deployment dynamics. We show that if this ratio is unbounded, online learning becomes exponentially hard. We propose the first computationally efficient algorithm that achieves sublinear regret in online RMDPs with $f$-divergence based transition uncertainties. We also establish matching regret lower bounds, demonstrating that our algorithm achieves optimal dependence on both the supremal visitation ratio and the number of interaction episodes. Finally, we validate our theoretical results through comprehensive numerical experiments.", "published": "2025-11-07T16:24:22Z", "authors": ["Yiting He", "Zhishuai Liu", "Weixin Wang", "Pan Xu"], "categories": ["cs.LG", "cs.AI", "cs.RO", "stat.ML"], "doi": "", "_query": "house price prediction machine learning", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2503.15177v1", "title": "Food Delivery Time Prediction in Indian Cities Using Machine Learning Models", "abstract": "Accurate prediction of food delivery times significantly impacts customer satisfaction, operational efficiency, and profitability in food delivery services. However, existing studies primarily utilize static historical data and often overlook dynamic, real-time contextual factors crucial for precise prediction, particularly in densely populated Indian cities. This research addresses these gaps by integrating real-time contextual variables such as traffic density, weather conditions, local events, and geospatial data (restaurant and delivery location coordinates) into predictive models. We systematically compare various machine learning algorithms, including Linear Regression, Decision Trees, Bagging, Random Forest, XGBoost, and LightGBM, on a comprehensive food delivery dataset specific to Indian urban contexts. Rigorous data preprocessing and feature selection significantly enhanced model performance. Experimental results demonstrate that the LightGBM model achieves superior predictive accuracy, with an R2 score of 0.76 and Mean Squared Error (MSE) of 20.59, outperforming traditional baseline approaches. Our study thus provides actionable insights for improving logistics strategies in complex urban environments. The complete methodology and code are publicly available for reproducibility and further research.", "published": "2025-03-19T13:02:23Z", "authors": ["Ananya Garg", "Mohmmad Ayaan", "Swara Parekh", "Vikranth Udandarao"], "categories": ["cs.LG"], "doi": "", "_query": "house price prediction machine learning", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/1909.12732v4", "title": "Alleviating Privacy Attacks via Causal Learning", "abstract": "Machine learning models, especially deep neural networks have been shown to be susceptible to privacy attacks such as membership inference where an adversary can detect whether a data point was used for training a black-box model. Such privacy risks are exacerbated when a model's predictions are used on an unseen data distribution. To alleviate privacy attacks, we demonstrate the benefit of predictive models that are based on the causal relationships between input features and the outcome. We first show that models learnt using causal structure generalize better to unseen data, especially on data from different distributions than the train distribution. Based on this generalization property, we establish a theoretical link between causality and privacy: compared to associational models, causal models provide stronger differential privacy guarantees and are more robust to membership inference attacks. Experiments on simulated Bayesian networks and the colored-MNIST dataset show that associational models exhibit upto 80% attack accuracy under different test distributions and sample sizes whereas causal models exhibit attack accuracy close to a random guess.", "published": "2019-09-27T15:06:42Z", "authors": ["Shruti Tople", "Amit Sharma", "Aditya Nori"], "categories": ["cs.LG", "stat.ML"], "doi": "", "_query": "house price prediction machine learning", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2207.07958v1", "title": "FastML Science Benchmarks: Accelerating Real-Time Scientific Edge Machine Learning", "abstract": "Applications of machine learning (ML) are growing by the day for many unique and challenging scientific applications. However, a crucial challenge facing these applications is their need for ultra low-latency and on-detector ML capabilities. Given the slowdown in Moore's law and Dennard scaling, coupled with the rapid advances in scientific instrumentation that is resulting in growing data rates, there is a need for ultra-fast ML at the extreme edge. Fast ML at the edge is essential for reducing and filtering scientific data in real-time to accelerate science experimentation and enable more profound insights. To accelerate real-time scientific edge ML hardware and software solutions, we need well-constrained benchmark tasks with enough specifications to be generically applicable and accessible. These benchmarks can guide the design of future edge ML hardware for scientific applications capable of meeting the nanosecond and microsecond level latency requirements. To this end, we present an initial set of scientific ML benchmarks, covering a variety of ML and embedded system techniques.", "published": "2022-07-16T14:30:15Z", "authors": ["Javier Duarte", "Nhan Tran", "Ben Hawks", "Christian Herwig", "Jules Muhizi", "Shvetank Prakash", "Vijay Janapa Reddi"], "categories": ["cs.LG", "physics.comp-ph", "physics.ins-det"], "doi": "", "_query": "house price prediction machine learning", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/1912.09090v1", "title": "Per-sample Prediction Intervals for Extreme Learning Machines", "abstract": "Prediction intervals in supervised Machine Learning bound the region where the true outputs of new samples may fall. They are necessary in the task of separating reliable predictions of a trained model from near random guesses, minimizing the rate of False Positives, and other problem-specific tasks in applied Machine Learning. Many real problems have heteroscedastic stochastic outputs, which explains the need of input-dependent prediction intervals. This paper proposes to estimate the input-dependent prediction intervals by a separate Extreme Learning Machine model, using variance of its predictions as a correction term accounting for the model uncertainty. The variance is estimated from the model's linear output layer with a weighted Jackknife method. The methodology is very fast, robust to heteroscedastic outputs, and handles both extremely large datasets and insufficient amount of training data.", "published": "2019-12-19T09:50:04Z", "authors": ["Anton Akusok", "Yoan Miche", "Kaj-Mikael Björk", "Amaury Lendasse"], "categories": ["cs.LG", "stat.ML"], "doi": "", "_query": "house price prediction machine learning", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/1306.0239v4", "title": "Deep Learning using Linear Support Vector Machines", "abstract": "Recently, fully-connected and convolutional neural networks have been trained to achieve state-of-the-art performance on a wide variety of tasks such as speech recognition, image classification, natural language processing, and bioinformatics. For classification tasks, most of these \"deep learning\" models employ the softmax activation function for prediction and minimize cross-entropy loss. In this paper, we demonstrate a small but consistent advantage of replacing the softmax layer with a linear support vector machine. Learning minimizes a margin-based loss instead of the cross-entropy loss. While there have been various combinations of neural nets and SVMs in prior art, our results using L2-SVMs show that by simply replacing softmax with linear SVMs gives significant gains on popular deep learning datasets MNIST, CIFAR-10, and the ICML 2013 Representation Learning Workshop's face expression recognition challenge.", "published": "2013-06-02T18:46:58Z", "authors": ["Yichuan Tang"], "categories": ["cs.LG", "stat.ML"], "doi": "", "_query": "house price prediction machine learning", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2008.03937v1", "title": "Feature Ranking for Semi-supervised Learning", "abstract": "The data made available for analysis are becoming more and more complex along several directions: high dimensionality, number of examples and the amount of labels per example. This poses a variety of challenges for the existing machine learning methods: coping with dataset with a large number of examples that are described in a high-dimensional space and not all examples have labels provided. For example, when investigating the toxicity of chemical compounds there are a lot of compounds available, that can be described with information rich high-dimensional representations, but not all of the compounds have information on their toxicity. To address these challenges, we propose semi-supervised learning of feature ranking. The feature rankings are learned in the context of classification and regression as well as in the context of structured output prediction (multi-label classification, hierarchical multi-label classification and multi-target regression). To the best of our knowledge, this is the first work that treats the task of feature ranking within the semi-supervised structured output prediction context. More specifically, we propose two approaches that are based on tree ensembles and the Relief family of algorithms. The extensive evaluation across 38 benchmark datasets reveals the following: Random Forests perform the best for the classification-like tasks, while for the regression-like tasks Extra-PCTs perform the best, Random Forests are the most efficient method considering induction times across all tasks, and semi-supervised feature rankings outperform their supervised counterpart across a majority of the datasets from the different tasks.", "published": "2020-08-10T07:50:50Z", "authors": ["Matej Petković", "Sašo Džeroski", "Dragi Kocev"], "categories": ["cs.LG", "stat.ML"], "doi": "", "_query": "house price prediction machine learning", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2011.07396v1", "title": "Cost-Sensitive Machine Learning Classification for Mass Tuberculosis Verbal Screening", "abstract": "Score-based algorithms for tuberculosis (TB) verbal screening perform poorly, causing misclassification that leads to missed cases and unnecessary costly laboratory tests for false positives. We compared score-based classification defined by clinicians to machine learning classification such as SVM-RBF, logistic regression, and XGBoost. We restricted our analyses to data from adults, the population most affected by TB, and investigated the difference between untuned and unweighted classifiers to the cost-sensitive ones. Predictions were compared with the corresponding GeneXpert MTB/Rif results. After adjusting the weight of the positive class to 40 for XGBoost, we achieved 96.64% sensitivity and 35.06% specificity. As such, the sensitivity of our identifier increased by 1.26% while specificity increased by 13.19% in absolute value compared to the traditional score-based method defined by our clinicians. Our approach further demonstrated that only 2000 data points were sufficient to enable the model to converge. The results indicate that even with limited data we can actually devise a better method to identify TB suspects from verbal screening.", "published": "2020-11-14T21:41:29Z", "authors": ["Ali Akbar Septiandri", " Aditiawarman", "Roy Tjiong", "Erlina Burhan", "Anuraj Shankar"], "categories": ["cs.LG", "cs.CY"], "doi": "", "_query": "house price prediction machine learning", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2610.21554v1", "title": "Uncertainty Quantification in Pairwise Difference Learning for Classification", "abstract": "Instead of learning a mapping from instances to outcomes in the standard way, the key idea of pairwise difference learning (PDL) is to learn a function that takes two instances as input and predicts the difference between the respective outcomes. Given a function of this kind, predictions for a query instance are derived from every training example and then averaged. We propose a version of PDL for the task of classification and propose a suitable meta-learning technique for this purpose. Moreover, we address the problem of uncertainty quantification, i.e., of representing and quantifying the uncertainty of a PDL classifier's predictions in a faithful manner. Building on recent work on the decomposition of total uncertainty into aleatoric and epistemic components, we propose different ways of extending corresponding measures to the setting of PDL, depending on how and to what extent uncertainty is represented by the underlying base learner. In large-scale empirical studies, we analyze both the predictive accuracy and the uncertainty awareness of our PDL classifier.", "published": "2026-10-15T09:15:08Z", "authors": ["Mohamed Karim Belaid", "Maximilian Rabus", "Eyke Hüllermeier"], "categories": ["cs.LG", "cs.AI"], "doi": "10.1007/s10994-025-06949-0", "_query": "house price prediction machine learning", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2004.01504v1", "title": "Machine Learning Algorithms for Financial Asset Price Forecasting", "abstract": "This research paper explores the performance of Machine Learning (ML) algorithms and techniques that can be used for financial asset price forecasting. The prediction and forecasting of asset prices and returns remains one of the most challenging and exciting problems for quantitative finance and practitioners alike. The massive increase in data generated and captured in recent years presents an opportunity to leverage Machine Learning algorithms. This study directly compares and contrasts state-of-the-art implementations of modern Machine Learning algorithms on high performance computing (HPC) infrastructures versus the traditional and highly popular Capital Asset Pricing Model (CAPM) on U.S equities data. The implemented Machine Learning models - trained on time series data for an entire stock universe (in addition to exogenous macroeconomic variables) significantly outperform the CAPM on out-of-sample (OOS) test data.", "published": "2020-03-31T18:14:18Z", "authors": ["Philip Ndikum"], "categories": ["q-fin.ST", "cs.LG", "econ.EM", "stat.ML"], "doi": "", "_query": "house price prediction machine learning", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/1812.00524v1", "title": "Generalization in anti-causal learning", "abstract": "The ability to learn and act in novel situations is still a prerogative of animate intelligence, as current machine learning methods mostly fail when moving beyond the standard i.i.d. setting. What is the reason for this discrepancy? Most machine learning tasks are anti-causal, i.e., we infer causes (labels) from effects (observations). Typically, in supervised learning we build systems that try to directly invert causal mechanisms. Instead, in this paper we argue that strong generalization capabilities crucially hinge on searching and validating meaningful hypotheses, requiring access to a causal model. In such a framework, we want to find a cause that leads to the observed effect. Anti-causal models are used to drive this search, but a causal model is required for validation. We investigate the fundamental differences between causal and anti-causal tasks, discuss implications for topics ranging from adversarial attacks to disentangling factors of variation, and provide extensive evidence from the literature to substantiate our view. We advocate for incorporating causal models in supervised learning to shift the paradigm from inference only, to search and validation.", "published": "2018-12-03T02:26:35Z", "authors": ["Niki Kilbertus", "Giambattista Parascandolo", "Bernhard Schölkopf"], "categories": ["cs.LG", "stat.ML"], "doi": "", "_query": "house price prediction machine learning", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/1305.2532v1", "title": "Learning Policies for Contextual Submodular Prediction", "abstract": "Many prediction domains, such as ad placement, recommendation, trajectory prediction, and document summarization, require predicting a set or list of options. Such lists are often evaluated using submodular reward functions that measure both quality and diversity. We propose a simple, efficient, and provably near-optimal approach to optimizing such prediction problems based on no-regret learning. Our method leverages a surprising result from online submodular optimization: a single no-regret online learner can compete with an optimal sequence of predictions. Compared to previous work, which either learn a sequence of classifiers or rely on stronger assumptions such as realizability, we ensure both data-efficiency as well as performance guarantees in the fully agnostic setting. Experiments validate the efficiency and applicability of the approach on a wide range of problems including manipulator trajectory optimization, news recommendation and document summarization.", "published": "2013-05-11T18:09:52Z", "authors": ["Stephane Ross", "Jiaji Zhou", "Yisong Yue", "Debadeepta Dey", "J. Andrew Bagnell"], "categories": ["cs.LG", "stat.ML"], "doi": "", "_query": "house price prediction machine learning", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/1905.07822v2", "title": "Minimal Achievable Sufficient Statistic Learning", "abstract": "We introduce Minimal Achievable Sufficient Statistic (MASS) Learning, a training method for machine learning models that attempts to produce minimal sufficient statistics with respect to a class of functions (e.g. deep networks) being optimized over. In deriving MASS Learning, we also introduce Conserved Differential Information (CDI), an information-theoretic quantity that - unlike standard mutual information - can be usefully applied to deterministically-dependent continuous random variables like the input and output of a deep network. In a series of experiments, we show that deep networks trained with MASS Learning achieve competitive performance on supervised learning and uncertainty quantification benchmarks.", "published": "2019-05-19T22:46:23Z", "authors": ["Milan Cvitkovic", "Günther Koliander"], "categories": ["cs.LG", "stat.ML"], "doi": "", "_query": "house price prediction machine learning", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/1512.01274v1", "title": "MXNet: A Flexible and Efficient Machine Learning Library for Heterogeneous Distributed Systems", "abstract": "MXNet is a multi-language machine learning (ML) library to ease the development of ML algorithms, especially for deep neural networks. Embedded in the host language, it blends declarative symbolic expression with imperative tensor computation. It offers auto differentiation to derive gradients. MXNet is computation and memory efficient and runs on various heterogeneous systems, ranging from mobile devices to distributed GPU clusters. This paper describes both the API design and the system implementation of MXNet, and explains how embedding of both symbolic expression and tensor operation is handled in a unified fashion. Our preliminary experiments reveal promising results on large scale deep neural network applications using multiple GPU machines.", "published": "2015-12-03T22:49:21Z", "authors": ["Tianqi Chen", "Mu Li", "Yutian Li", "Min Lin", "Naiyan Wang", "Minjie Wang", "Tianjun Xiao", "Bing Xu", "Chiyuan Zhang", "Zheng Zhang"], "categories": ["cs.DC", "cs.LG", "cs.MS", "cs.NE"], "doi": "", "_query": "house price prediction machine learning", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2006.11332v2", "title": "Quantum Geometric Machine Learning for Quantum Circuits and Control", "abstract": "The application of machine learning techniques to solve problems in quantum control together with established geometric methods for solving optimisation problems leads naturally to an exploration of how machine learning approaches can be used to enhance geometric approaches to solving problems in quantum information processing. In this work, we review and extend the application of deep learning to quantum geometric control problems. Specifically, we demonstrate enhancements in time-optimal control in the context of quantum circuit synthesis problems by applying novel deep learning algorithms in order to approximate geodesics (and thus minimal circuits) along Lie group manifolds relevant to low-dimensional multi-qubit systems, such as SU(2), SU(4) and SU(8). We demonstrate the superior performance of greybox models, which combine traditional blackbox algorithms with prior domain knowledge of quantum mechanics, as means of learning underlying quantum circuit distributions of interest. Our results demonstrate how geometric control techniques can be used to both (a) verify the extent to which geometrically synthesised quantum circuits lie along geodesic, and thus time-optimal, routes and (b) synthesise those circuits. Our results are of interest to researchers in quantum control and quantum information theory seeking to combine machine learning and geometric techniques for time-optimal control problems.", "published": "2020-06-19T19:12:14Z", "authors": ["Elija Perrier", "Christopher Ferrie", "Dacheng Tao"], "categories": ["quant-ph", "cs.LG", "math.DG"], "doi": "10.1088/1367-2630/abbf6b", "_query": "house price prediction machine learning", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/1612.05730v2", "title": "Towards Wide Learning: Experiments in Healthcare", "abstract": "In this paper, a Wide Learning architecture is proposed that attempts to automate the feature engineering portion of the machine learning (ML) pipeline. Feature engineering is widely considered as the most time consuming and expert knowledge demanding portion of any ML task. The proposed feature recommendation approach is tested on 3 healthcare datasets: a) PhysioNet Challenge 2016 dataset of phonocardiogram (PCG) signals, b) MIMIC II blood pressure classification dataset of photoplethysmogram (PPG) signals and c) an emotion classification dataset of PPG signals. While the proposed method beats the state of the art techniques for 2nd and 3rd dataset, it reaches 94.38% of the accuracy level of the winner of PhysioNet Challenge 2016. In all cases, the effort to reach a satisfactory performance was drastically less (a few days) than manual feature engineering.", "published": "2016-12-17T11:00:49Z", "authors": ["Snehasis Banerjee", "Tanushyam Chattopadhyay", "Swagata Biswas", "Rohan Banerjee", "Anirban Dutta Choudhury", "Arpan Pal", "Utpal Garain"], "categories": ["stat.ML", "cs.LG"], "doi": "", "_query": "house price prediction machine learning", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2507.20993v4", "title": "Annotation-Assisted Learning of Treatment Policies From Multimodal Electronic Health Records", "abstract": "We study how to learn treatment policies from multimodal electronic health records (EHRs) that consist of tabular data and clinical text. These policies can help physicians make better treatment decisions and allocate healthcare resources more efficiently. Causal policy learning methods prioritize patients with the largest expected treatment benefit. Yet, existing estimators are designed for tabular covariates under causal assumptions that may be hard to justify in the multimodal setting. A pragmatic alternative is to apply causal estimators directly to multimodal representations, but this can produce biased treatment effect estimates when the representations do not preserve the relevant confounding information. As a result, predictive models of baseline risk are commonly used in practice to guide treatment decisions, although they are not designed to identify which patients benefit most from treatment. We propose AACE (Annotation-Assisted Coarsened Effects), an annotation-assisted approach to causal policy learning for multimodal EHRs. The method uses expert-provided annotations during training to support confounding adjustment, and then predicts treatment benefit from only multimodal representations at inference. We show that the proposed method achieves strong empirical performance across synthetic, semi-synthetic, and real-world EHR datasets, outperforming risk-based and representation-based causal baselines, and offering practical insights for applying causal machine learning in clinical practice.", "published": "2025-07-28T16:52:31Z", "authors": ["Henri Arno", "Thomas Demeester"], "categories": ["cs.LG", "cs.AI", "stat.ML"], "doi": "", "_query": "house price prediction machine learning", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/1612.05730v2", "title": "Towards Wide Learning: Experiments in Healthcare", "abstract": "In this paper, a Wide Learning architecture is proposed that attempts to automate the feature engineering portion of the machine learning (ML) pipeline. Feature engineering is widely considered as the most time consuming and expert knowledge demanding portion of any ML task. The proposed feature recommendation approach is tested on 3 healthcare datasets: a) PhysioNet Challenge 2016 dataset of phonocardiogram (PCG) signals, b) MIMIC II blood pressure classification dataset of photoplethysmogram (PPG) signals and c) an emotion classification dataset of PPG signals. While the proposed method beats the state of the art techniques for 2nd and 3rd dataset, it reaches 94.38% of the accuracy level of the winner of PhysioNet Challenge 2016. In all cases, the effort to reach a satisfactory performance was drastically less (a few days) than manual feature engineering.", "published": "2016-12-17T11:00:49Z", "authors": ["Snehasis Banerjee", "Tanushyam Chattopadhyay", "Swagata Biswas", "Rohan Banerjee", "Anirban Dutta Choudhury", "Arpan Pal", "Utpal Garain"], "categories": ["stat.ML", "cs.LG"], "doi": "", "_query": "house price prediction machine learning", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2312.01624v1", "title": "GVFs in the Real World: Making Predictions Online for Water Treatment", "abstract": "In this paper we investigate the use of reinforcement-learning based prediction approaches for a real drinking-water treatment plant. Developing such a prediction system is a critical step on the path to optimizing and automating water treatment. Before that, there are many questions to answer about the predictability of the data, suitable neural network architectures, how to overcome partial observability and more. We first describe this dataset, and highlight challenges with seasonality, nonstationarity, partial observability, and heterogeneity across sensors and operation modes of the plant. We then describe General Value Function (GVF) predictions -- discounted cumulative sums of observations -- and highlight why they might be preferable to classical n-step predictions common in time series prediction. We discuss how to use offline data to appropriately pre-train our temporal difference learning (TD) agents that learn these GVF predictions, including how to select hyperparameters for online fine-tuning in deployment. We find that the TD-prediction agent obtains an overall lower normalized mean-squared error than the n-step prediction agent. Finally, we show the importance of learning in deployment, by comparing a TD agent trained purely offline with no online updating to a TD agent that learns online. This final result is one of the first to motivate the importance of adapting predictions in real-time, for non-stationary high-volume systems in the real world.", "published": "2023-12-04T04:49:10Z", "authors": ["Muhammad Kamran Janjua", "Haseeb Shah", "Martha White", "Erfan Miahi", "Marlos C. Machado", "Adam White"], "categories": ["cs.LG", "cs.AI"], "doi": "", "_query": "house price prediction machine learning", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/1506.03410v6", "title": "Sparse Projection Oblique Randomer Forests", "abstract": "Decision forests, including Random Forests and Gradient Boosting Trees, have recently demonstrated state-of-the-art performance in a variety of machine learning settings. Decision forests are typically ensembles of axis-aligned decision trees; that is, trees that split only along feature dimensions. In contrast, many recent extensions to decision forests are based on axis-oblique splits. Unfortunately, these extensions forfeit one or more of the favorable properties of decision forests based on axis-aligned splits, such as robustness to many noise dimensions, interpretability, or computational efficiency. We introduce yet another decision forest, called \"Sparse Projection Oblique Randomer Forests\" (SPORF). SPORF uses very sparse random projections, i.e., linear combinations of a small subset of features. SPORF significantly improves accuracy over existing state-of-the-art algorithms on a standard benchmark suite for classification with >100 problems of varying dimension, sample size, and number of classes. To illustrate how SPORF addresses the limitations of both axis-aligned and existing oblique decision forest methods, we conduct extensive simulated experiments. SPORF typically yields improved performance over existing decision forests, while mitigating computational efficiency and scalability and maintaining interpretability. SPORF can easily be incorporated into other ensemble methods such as boosting to obtain potentially similar gains.", "published": "2015-06-10T17:55:51Z", "authors": ["Tyler M. Tomita", "James Browne", "Cencheng Shen", "Jaewon Chung", "Jesse L. Patsolic", "Benjamin Falk", "Jason Yim", "Carey E. Priebe", "Randal Burns", "Mauro Maggioni", "Joshua T. Vogelstein"], "categories": ["stat.ML", "cs.LG"], "doi": "", "_query": "house price prediction machine learning", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/1710.03184v3", "title": "On Formalizing Fairness in Prediction with Machine Learning", "abstract": "Machine learning algorithms for prediction are increasingly being used in critical decisions affecting human lives. Various fairness formalizations, with no firm consensus yet, are employed to prevent such algorithms from systematically discriminating against people based on certain attributes protected by law. The aim of this article is to survey how fairness is formalized in the machine learning literature for the task of prediction and present these formalizations with their corresponding notions of distributive justice from the social sciences literature. We provide theoretical as well as empirical critiques of these notions from the social sciences literature and explain how these critiques limit the suitability of the corresponding fairness formalizations to certain domains. We also suggest two notions of distributive justice which address some of these critiques and discuss avenues for prospective fairness formalizations.", "published": "2017-10-09T16:39:31Z", "authors": ["Pratik Gajane", "Mykola Pechenizkiy"], "categories": ["cs.LG", "cs.AI", "stat.ML"], "doi": "", "_query": "house price prediction machine learning", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2509.23462v2", "title": "Generative Evolutionary Meta-Solver (GEMS): Scalable Surrogate-Free Multi-Agent Reinforcement Learning", "abstract": "Scalable multi-agent reinforcement learning (MARL) remains a central challenge for AI. Existing population-based methods, like Policy-Space Response Oracles, PSRO, require storing explicit policy populations and constructing full payoff matrices, incurring quadratic computation and linear memory costs. We present Generative Evolutionary Meta-Solver (GEMS), a surrogate-free framework that replaces explicit populations with a compact set of latent anchors and a single amortized generator. Instead of exhaustively constructing the payoff matrix, GEMS relies on unbiased Monte Carlo rollouts, multiplicative-weights meta-dynamics, and a model-free empirical-Bernstein UCB oracle to adaptively expand the policy set. Best responses are trained within the generator using an advantage-based trust-region objective, eliminating the need to store and train separate actors. We evaluated GEMS in a variety of Two-player and Multi-Player games such as the Deceptive Messages Game, Kuhn Poker and Multi-Particle environment. We find that GEMS is up to ~$\\mathbf{6\\times}$ faster, has $\\mathbf{1.3\\times}$ less memory usage than PSRO, while also reaps higher rewards simultaneously. These results demonstrate that GEMS retains the game theoretic guarantees of PSRO, while overcoming its fundamental inefficiencies, hence enabling scalable multi-agent learning in multiple domains.", "published": "2025-09-27T19:23:38Z", "authors": ["Alakh Sharma", "Gaurish Trivedi", "Kartikey Singh Bhandari", "Yash Sinha", "Dhruv Kumar", "Pratik Narang", "Jagat Sesh Challa"], "categories": ["cs.LG", "cs.AI"], "doi": "", "_query": "house price prediction machine learning", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2412.04426v4", "title": "Towards Fast Safe Online Reinforcement Learning via Policy Finetuning", "abstract": "The high costs and risks involved in extensive environment interactions hinder the practical application of current online safe reinforcement learning (RL) methods. While offline safe RL addresses this by learning policies from static datasets, the performance therein is usually limited due to reliance on data quality and challenges with out-of-distribution (OOD) actions. Inspired by recent successes in offline-to-online (O2O) RL, it is crucial to explore whether offline safe RL can be leveraged to facilitate faster and safer online policy learning, a direction that has yet to be fully investigated. To fill this gap, we first demonstrate that naively applying existing O2O algorithms from standard RL would not work well in the safe RL setting due to two unique challenges: \\emph{erroneous Q-estimations}, resulted from offline-online objective mismatch and offline cost sparsity, and \\emph{Lagrangian mismatch}, resulted from difficulties in aligning Lagrange multipliers between offline and online policies. To address these challenges, we introduce \\textbf{Marvel}, a novel framework for O2O safe RL, comprising two key components that work in concert: \\emph{Value Pre-Alignment} to align the Q-functions with the underlying truth before online learning, and \\emph{Adaptive PID Control} to effectively adjust the Lagrange multipliers during online finetuning. Extensive experiments demonstrate that Marvel significantly outperforms existing baselines in both reward maximization and safety constraint satisfaction. By introducing the first policy-finetuning based framework for O2O safe RL, which is compatible with many offline and online safe RL methods, our work has the great potential to advance the field towards more efficient and practical safe RL solutions.", "published": "2024-12-05T18:51:18Z", "authors": ["Keru Chen", "Honghao Wei", "Zhigang Deng", "Sen Lin"], "categories": ["cs.LG", "cs.AI"], "doi": "", "_query": "house price prediction machine learning", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2509.02927v2", "title": "P-DRUM: Post-hoc Descriptor-based Residual Uncertainty Modeling for Machine Learning Potentials", "abstract": "Ensemble method is considered the gold standard for uncertainty quantification (UQ) in machine learning interatomic potentials (MLIPs). However, their high computational cost can limit its practicality. Alternative techniques, such as Monte Carlo dropout and deep kernel learning, have been proposed to improve computational efficiency; however, some of these methods cannot be applied to already trained models and may affect the prediction accuracy. In this paper, we propose a simple and efficient post-hoc framework for UQ that leverages the descriptor of a trained graph neural network potential to estimate residual errors. We refer to this method as post-hoc descriptor-based residual uncertainty modeling (P-DRUM). P-DRUM models the discrepancy between MLIP predictions and ground truth values, allowing these residuals to act as proxies for prediction uncertainty. We explore multiple variants of P-DRUM and benchmark them against established UQ methods, evaluating both their effectiveness and limitations.", "published": "2025-09-03T01:36:27Z", "authors": ["Shih-Peng Huang", "Nontawat Charoenphakdee", "Yuta Tsuboi", "Yong-Bin Zhuang", "Wenwen Li"], "categories": ["cs.LG", "cond-mat.mtrl-sci"], "doi": "", "_query": "house price prediction machine learning", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2408.10265v3", "title": "Distributed and Secure Kernel-Based Quantum Machine Learning", "abstract": "Quantum computing promises to revolutionize machine learning, offering significant efficiency gains in tasks such as clustering and distance estimation. Additionally, it provides enhanced security through fundamental principles like the measurement postulate and the no-cloning theorem, enabling secure protocols such as quantum teleportation and quantum key distribution. While advancements in secure quantum machine learning are notable, the development of secure and distributed quantum analogues of kernel-based machine learning techniques remains underexplored. In this work, we present a novel approach for securely computing common kernels, including polynomial, radial basis function (RBF), and Laplacian kernels, when data is distributed, using quantum feature maps. Our methodology introduces a robust framework that leverages quantum teleportation to ensure secure and distributed kernel learning. The proposed architecture is validated using IBM's Qiskit Aer Simulator on various public datasets.", "published": "2024-08-16T06:31:45Z", "authors": ["Arjhun Swaminathan", "Mete Akgün"], "categories": ["quant-ph", "cs.LG"], "doi": "10.5281/zenodo.15482311", "_query": "house price prediction machine learning", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2106.04757v2", "title": "Fair Machine Learning under Limited Demographically Labeled Data", "abstract": "Research has shown that, machine learning models might inherit and propagate undesired social biases encoded in the data. To address this problem, fair training algorithms are developed. However, most algorithms assume we know demographic/sensitive data features such as gender and race. This assumption falls short in scenarios where collecting demographic information is not feasible due to privacy concerns, and data protection policies. A recent line of work develops fair training methods that can function without any demographic feature on the data, that are collectively referred as Rawlsian methods. Yet, we show in experiments that, Rawlsian methods tend to exhibit relatively high bias. Given this, we look at the middle ground between the previous approaches, and consider a setting where we know the demographic attributes for only a small subset of our data. In such a setting, we design fair training algorithms which exhibit both good utility, and low bias. In particular, we show that our techniques can train models to significantly outperform Rawlsian approaches even when 0.1% of demographic attributes are available in the training data. Furthermore, our main algorithm can accommodate multiple training objectives easily. We expand our main algorithm to achieve robustness to label noise in addition to fairness in the limited demographics setting to highlight that property as well.", "published": "2021-06-03T22:36:17Z", "authors": ["Mustafa Safa Ozdayi", "Murat Kantarcioglu", "Rishabh Iyer"], "categories": ["cs.LG", "cs.CY"], "doi": "", "_query": "house price prediction machine learning", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2106.06235v2", "title": "Knowledge Enhanced Machine Learning Pipeline against Diverse Adversarial Attacks", "abstract": "Despite the great successes achieved by deep neural networks (DNNs), recent studies show that they are vulnerable against adversarial examples, which aim to mislead DNNs by adding small adversarial perturbations. Several defenses have been proposed against such attacks, while many of them have been adaptively attacked. In this work, we aim to enhance the ML robustness from a different perspective by leveraging domain knowledge: We propose a Knowledge Enhanced Machine Learning Pipeline (KEMLP) to integrate domain knowledge (i.e., logic relationships among different predictions) into a probabilistic graphical model via first-order logic rules. In particular, we develop KEMLP by integrating a diverse set of weak auxiliary models based on their logical relationships to the main DNN model that performs the target task. Theoretically, we provide convergence results and prove that, under mild conditions, the prediction of KEMLP is more robust than that of the main DNN model. Empirically, we take road sign recognition as an example and leverage the relationships between road signs and their shapes and contents as domain knowledge. We show that compared with adversarial training and other baselines, KEMLP achieves higher robustness against physical attacks, $\\mathcal{L}_p$ bounded attacks, unforeseen attacks, and natural corruptions under both whitebox and blackbox settings, while still maintaining high clean accuracy.", "published": "2021-06-11T08:37:53Z", "authors": ["Nezihe Merve Gürel", "Xiangyu Qi", "Luka Rimanic", "Ce Zhang", "Bo Li"], "categories": ["cs.LG"], "doi": "", "_query": "house price prediction machine learning", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2505.05295v2", "title": "Performance Estimation in Binary Classification Using Calibrated Confidence", "abstract": "Model monitoring is a critical component of the machine learning lifecycle, safeguarding against undetected drops in the model's performance after deployment. Traditionally, performance monitoring has required access to ground truth labels, which are not always readily available. This can result in unacceptable latency or render performance monitoring altogether impossible. Recently, methods designed to estimate the accuracy of classifier models without access to labels have shown promising results. However, there are various other metrics that might be more suitable for assessing model performance in many cases. Until now, none of these important metrics has received similar interest from the scientific community. In this work, we address this gap by presenting CBPE, a novel method that can estimate any binary classification metric defined using the confusion matrix. In particular, we choose four metrics from this large family: accuracy, precision, recall, and F$_1$, to demonstrate our method. CBPE treats the elements of the confusion matrix as random variables and leverages calibrated confidence scores of the model to estimate their distributions. The desired metric is then also treated as a random variable, whose full probability distribution can be derived from the estimated confusion matrix. CBPE is shown to produce estimates that come with strong theoretical guarantees and valid confidence intervals.", "published": "2025-05-08T14:34:44Z", "authors": ["Juhani Kivimäki", "Jakub Białek", "Wojtek Kuberski", "Jukka K. Nurminen"], "categories": ["cs.LG"], "doi": "10.1007/s10994-025-06970-3", "_query": "house price prediction machine learning", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2002.08596v2", "title": "Interpretability of machine learning based prediction models in healthcare", "abstract": "There is a need of ensuring machine learning models that are interpretable. Higher interpretability of the model means easier comprehension and explanation of future predictions for end-users. Further, interpretable machine learning models allow healthcare experts to make reasonable and data-driven decisions to provide personalized decisions that can ultimately lead to higher quality of service in healthcare. Generally, we can classify interpretability approaches in two groups where the first focuses on personalized interpretation (local interpretability) while the second summarizes prediction models on a population level (global interpretability). Alternatively, we can group interpretability methods into model-specific techniques, which are designed to interpret predictions generated by a specific model, such as a neural network, and model-agnostic approaches, which provide easy-to-understand explanations of predictions made by any machine learning model. Here, we give an overview of interpretability approaches and provide examples of practical interpretability of machine learning in different areas of healthcare, including prediction of health-related outcomes, optimizing treatments or improving the efficiency of screening for specific conditions. Further, we outline future directions for interpretable machine learning and highlight the importance of developing algorithmic solutions that can enable machine-learning driven decision making in high-stakes healthcare problems.", "published": "2020-02-20T07:23:22Z", "authors": ["Gregor Stiglic", "Primoz Kocbek", "Nino Fijacko", "Marinka Zitnik", "Katrien Verbert", "Leona Cilar"], "categories": ["cs.LG", "stat.ML"], "doi": "10.1002/widm.1379", "_query": "house price prediction machine learning", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2111.01137v1", "title": "Stock Price Prediction Using Time Series, Econometric, Machine Learning, and Deep Learning Models", "abstract": "For a long-time, researchers have been developing a reliable and accurate predictive model for stock price prediction. According to the literature, if predictive models are correctly designed and refined, they can painstakingly and faithfully estimate future stock values. This paper demonstrates a set of time series, econometric, and various learning-based models for stock price prediction. The data of Infosys, ICICI, and SUN PHARMA from the period of January 2004 to December 2019 was used here for training and testing the models to know which model performs best in which sector. One time series model (Holt-Winters Exponential Smoothing), one econometric model (ARIMA), two machine Learning models (Random Forest and MARS), and two deep learning-based models (simple RNN and LSTM) have been included in this paper. MARS has been proved to be the best performing machine learning model, while LSTM has proved to be the best performing deep learning model. But overall, for all three sectors - IT (on Infosys data), Banking (on ICICI data), and Health (on SUN PHARMA data), MARS has proved to be the best performing model in sales forecasting.", "published": "2021-11-01T17:17:52Z", "authors": ["Ananda Chatterjee", "Hrisav Bhowmick", "Jaydip Sen"], "categories": ["q-fin.ST", "cs.LG", "econ.EM"], "doi": "10.1109/MysuruCon52639.2021.9641610", "_query": "house price prediction machine learning", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2203.09410v4", "title": "A Framework and Benchmark for Deep Batch Active Learning for Regression", "abstract": "The acquisition of labels for supervised learning can be expensive. To improve the sample efficiency of neural network regression, we study active learning methods that adaptively select batches of unlabeled data for labeling. We present a framework for constructing such methods out of (network-dependent) base kernels, kernel transformations, and selection methods. Our framework encompasses many existing Bayesian methods based on Gaussian process approximations of neural networks as well as non-Bayesian methods. Additionally, we propose to replace the commonly used last-layer features with sketched finite-width neural tangent kernels and to combine them with a novel clustering method. To evaluate different methods, we introduce an open-source benchmark consisting of 15 large tabular regression data sets. Our proposed method outperforms the state-of-the-art on our benchmark, scales to large data sets, and works out-of-the-box without adjusting the network architecture or training code. We provide open-source code that includes efficient implementations of all kernels, kernel transformations, and selection methods, and can be used for reproducing our results.", "published": "2022-03-17T16:11:36Z", "authors": ["David Holzmüller", "Viktor Zaverkin", "Johannes Kästner", "Ingo Steinwart"], "categories": ["stat.ML", "cs.LG", "cs.NE"], "doi": "", "_query": "house price prediction machine learning", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/1911.05121v1", "title": "Detecting Patterns of Physiological Response to Hemodynamic Stress via Unsupervised Deep Learning", "abstract": "Monitoring physiological responses to hemodynamic stress can help in determining appropriate treatment and ensuring good patient outcomes. Physicians' intuition suggests that the human body has a number of physiological response patterns to hemorrhage which escalate as blood loss continues, however the exact etiology and phenotypes of such responses are not well known or understood only at a coarse level. Although previous research has shown that machine learning models can perform well in hemorrhage detection and survival prediction, it is unclear whether machine learning could help to identify and characterize the underlying physiological responses in raw vital sign data. We approach this problem by first transforming the high-dimensional vital sign time series into a tractable, lower-dimensional latent space using a dilated, causal convolutional encoder model trained purely unsupervised. Second, we identify informative clusters in the embeddings. By analyzing the clusters of latent embeddings and visualizing them over time, we hypothesize that the clusters correspond to the physiological response patterns that match physicians' intuition. Furthermore, we attempt to evaluate the latent embeddings using a variety of methods, such as predicting the cluster labels using explainable features.", "published": "2019-11-12T19:55:16Z", "authors": ["Chufan Gao", "Fabian Falck", "Mononito Goswami", "Anthony Wertz", "Michael R. Pinsky", "Artur Dubrawski"], "categories": ["cs.LG", "stat.ML"], "doi": "", "_query": "house price prediction machine learning", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2211.07867v1", "title": "Machine Learning Methods Applied to Cortico-Cortical Evoked Potentials Aid in Localizing Seizure Onset Zones", "abstract": "Epilepsy affects millions of people, reducing quality of life and increasing risk of premature death. One-third of epilepsy cases are drug-resistant and require surgery for treatment, which necessitates localizing the seizure onset zone (SOZ) in the brain. Attempts have been made to use cortico-cortical evoked potentials (CCEPs) to improve SOZ localization but none have been successful enough for clinical adoption. Here, we compare the performance of ten machine learning classifiers in localizing SOZ from CCEP data. This preliminary study validates a novel application of machine learning, and the results establish our approach as a promising line of research that warrants further investigation. This work also serves to facilitate discussion and collaboration with fellow machine learning and/or epilepsy researchers.", "published": "2022-11-15T03:18:37Z", "authors": ["Ian G. Malone", "Kaleb E. Smith", "Morgan E. Urdaneta", "Tyler S. Davis", "Daria Nesterovich Anderson", "Brian J. Phillip", "John D. Rolston", "Christopher R. Butson"], "categories": ["cs.LG", "eess.SP", "q-bio.NC"], "doi": "", "_query": "house price prediction machine learning", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2410.15772v1", "title": "Mislabeled examples detection viewed as probing machine learning models: concepts, survey and extensive benchmark", "abstract": "Mislabeled examples are ubiquitous in real-world machine learning datasets, advocating the development of techniques for automatic detection. We show that most mislabeled detection methods can be viewed as probing trained machine learning models using a few core principles. We formalize a modular framework that encompasses these methods, parameterized by only 4 building blocks, as well as a Python library that demonstrates that these principles can actually be implemented. The focus is on classifier-agnostic concepts, with an emphasis on adapting methods developed for deep learning models to non-deep classifiers for tabular data. We benchmark existing methods on (artificial) Completely At Random (NCAR) as well as (realistic) Not At Random (NNAR) labeling noise from a variety of tasks with imperfect labeling rules. This benchmark provides new insights as well as limitations of existing methods in this setup.", "published": "2024-10-21T08:32:02Z", "authors": ["Thomas George", "Pierre Nodet", "Alexis Bondu", "Vincent Lemaire"], "categories": ["cs.LG"], "doi": "", "_query": "house price prediction machine learning", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/1810.06339v1", "title": "Deep Reinforcement Learning", "abstract": "We discuss deep reinforcement learning in an overview style. We draw a big picture, filled with details. We discuss six core elements, six important mechanisms, and twelve applications, focusing on contemporary work, and in historical contexts. We start with background of artificial intelligence, machine learning, deep learning, and reinforcement learning (RL), with resources. Next we discuss RL core elements, including value function, policy, reward, model, exploration vs. exploitation, and representation. Then we discuss important mechanisms for RL, including attention and memory, unsupervised learning, hierarchical RL, multi-agent RL, relational RL, and learning to learn. After that, we discuss RL applications, including games, robotics, natural language processing (NLP), computer vision, finance, business management, healthcare, education, energy, transportation, computer systems, and, science, engineering, and art. Finally we summarize briefly, discuss challenges and opportunities, and close with an epilogue.", "published": "2018-10-15T13:20:56Z", "authors": ["Yuxi Li"], "categories": ["cs.LG", "stat.ML"], "doi": "", "_query": "house price prediction machine learning", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2602.09182v1", "title": "One RNG to Rule Them All: How Randomness Becomes an Attack Vector in Machine Learning", "abstract": "Machine learning relies on randomness as a fundamental component in various steps such as data sampling, data augmentation, weight initialization, and optimization. Most machine learning frameworks use pseudorandom number generators as the source of randomness. However, variations in design choices and implementations across different frameworks, software dependencies, and hardware backends along with the lack of statistical validation can lead to previously unexplored attack vectors on machine learning systems. Such attacks on randomness sources can be extremely covert, and have a history of exploitation in real-world systems. In this work, we examine the role of randomness in the machine learning development pipeline from an adversarial point of view, and analyze the implementations of PRNGs in major machine learning frameworks. We present RNGGuard to help machine learning engineers secure their systems with low effort. RNGGuard statically analyzes a target library's source code and identifies instances of random functions and modules that use them. At runtime, RNGGuard enforces secure execution of random functions by replacing insecure function calls with RNGGuard's implementations that meet security specifications. Our evaluations show that RNGGuard presents a practical approach to close existing gaps in securing randomness sources in machine learning systems.", "published": "2026-02-09T20:41:05Z", "authors": ["Kotekar Annapoorna Prabhu", "Andrew Gan", "Zahra Ghodsi"], "categories": ["cs.CR", "cs.LG"], "doi": "", "_query": "house price prediction machine learning", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2510.01345v2", "title": "Self-Supervised Representation Learning as Mutual Information Maximization", "abstract": "Self-supervised representation learning (SSRL) has demonstrated remarkable empirical success, yet its underlying principles remain insufficiently understood. While recent works attempt to unify SSRL methods by examining their information-theoretic objectives or summarizing their heuristics for preventing representation collapse, architectural elements like predictor networks, stop-gradient operations, and statistical regularizers are often viewed as empirically motivated additions. In this paper, we adopt a first-principles approach and investigate whether the learning objective of an SSRL algorithm dictates its possible optimization strategies and model design choices. In particular, by starting from a variational mutual information (MI) lower bound, we derive two training paradigms, namely Self-Distillation MI (SDMI) and Joint MI (JMI), each imposing distinct structural constraints and covering a set of existing SSRL algorithms. SDMI relies on alternating optimization, in which stop-gradient operations serve as a principled mechanism for realizing the alternating updates. In contrast, JMI admits joint optimization through symmetric architectures without such components. Under the proposed formulation, predictor networks in SDMI and statistical regularizers in JMI emerge as tractable surrogates for the MI objective. We show that many existing SSRL methods are specific instances or approximations of these two paradigms. This paper provides a theoretical explanation for the choices of different architectural components of existing SSRL methods, going beyond heuristic conveniences.", "published": "2025-10-01T18:18:14Z", "authors": ["Akhlaqur Rahman Sabby", "Yi Sui", "Tongzi Wu", "Jesse C. Cresswell", "Ga Wu"], "categories": ["cs.LG"], "doi": "", "_query": "house price prediction machine learning", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2401.04491v1", "title": "SpiNNaker2: A Large-Scale Neuromorphic System for Event-Based and Asynchronous Machine Learning", "abstract": "The joint progress of artificial neural networks (ANNs) and domain specific hardware accelerators such as GPUs and TPUs took over many domains of machine learning research. This development is accompanied by a rapid growth of the required computational demands for larger models and more data. Concurrently, emerging properties of foundation models such as in-context learning drive new opportunities for machine learning applications. However, the computational cost of such applications is a limiting factor of the technology in data centers, and more importantly in mobile devices and edge systems. To mediate the energy footprint and non-trivial latency of contemporary systems, neuromorphic computing systems deeply integrate computational principles of neurobiological systems by leveraging low-power analog and digital technologies. SpiNNaker2 is a digital neuromorphic chip developed for scalable machine learning. The event-based and asynchronous design of SpiNNaker2 allows the composition of large-scale systems involving thousands of chips. This work features the operating principles of SpiNNaker2 systems, outlining the prototype of novel machine learning applications. These applications range from ANNs over bio-inspired spiking neural networks to generalized event-based neural networks. With the successful development and deployment of SpiNNaker2, we aim to facilitate the advancement of event-based and asynchronous algorithms for future generations of machine learning systems.", "published": "2024-01-09T11:07:48Z", "authors": ["Hector A. Gonzalez", "Jiaxin Huang", "Florian Kelber", "Khaleelulla Khan Nazeer", "Tim Langer", "Chen Liu", "Matthias Lohrmann", "Amirhossein Rostami", "Mark Schöne", "Bernhard Vogginger", "Timo C. Wunderlich", "Yexin Yan", "Mahmoud Akl", "Christian Mayr"], "categories": ["cs.ET", "cs.LG", "cs.NE"], "doi": "", "_query": "house price prediction machine learning", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/1812.02855v1", "title": "Progressive Sampling-Based Bayesian Optimization for Efficient and Automatic Machine Learning Model Selection", "abstract": "Purpose: Machine learning is broadly used for clinical data analysis. Before training a model, a machine learning algorithm must be selected. Also, the values of one or more model parameters termed hyper-parameters must be set. Selecting algorithms and hyper-parameter values requires advanced machine learning knowledge and many labor-intensive manual iterations. To lower the bar to machine learning, miscellaneous automatic selection methods for algorithms and/or hyper-parameter values have been proposed. Existing automatic selection methods are inefficient on large data sets. This poses a challenge for using machine learning in the clinical big data era. Methods: To address the challenge, this paper presents progressive sampling-based Bayesian optimization, an efficient and automatic selection method for both algorithms and hyper-parameter values. Results: We report an implementation of the method. We show that compared to a state of the art automatic selection method, our method can significantly reduce search time, classification error rate, and standard deviation of error rate due to randomization. Conclusions: This is major progress towards enabling fast turnaround in identifying high-quality solutions required by many machine learning-based clinical data analysis tasks.", "published": "2018-12-06T23:46:15Z", "authors": ["Xueqiang Zeng", "Gang Luo"], "categories": ["cs.LG", "stat.ML"], "doi": "10.1007/s13755-017-0023-z", "_query": "house price prediction machine learning", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2312.06616v1", "title": "The built environment and induced transport CO2 emissions: A double machine learning approach to account for residential self-selection", "abstract": "Understanding why travel behavior differs between residents of urban centers and suburbs is key to sustainable urban planning. Especially in light of rapid urban growth, identifying housing locations that minimize travel demand and induced CO2 emissions is crucial to mitigate climate change. While the built environment plays an important role, the precise impact on travel behavior is obfuscated by residential self-selection. To address this issue, we propose a double machine learning approach to obtain unbiased, spatially-explicit estimates of the effect of the built environment on travel-related CO2 emissions for each neighborhood by controlling for residential self-selection. We examine how socio-demographics and travel-related attitudes moderate the effect and how it decomposes across the 5Ds of the built environment. Based on a case study for Berlin and the travel diaries of 32,000 residents, we find that the built environment causes household travel-related CO2 emissions to differ by a factor of almost two between central and suburban neighborhoods in Berlin. To highlight the practical importance for urban climate mitigation, we evaluate current plans for 64,000 new residential units in terms of total induced transport CO2 emissions. Our findings underscore the significance of spatially differentiated compact development to decarbonize the transport sector.", "published": "2023-12-07T22:04:27Z", "authors": ["Florian Nachtigall", "Felix Wagner", "Peter Berrill", "Felix Creutzig"], "categories": ["cs.LG", "physics.soc-ph", "stat.ME"], "doi": "", "_query": "house price prediction machine learning", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2106.03300v2", "title": "Sum of Ranked Range Loss for Supervised Learning", "abstract": "In forming learning objectives, one oftentimes needs to aggregate a set of individual values to a single output. Such cases occur in the aggregate loss, which combines individual losses of a learning model over each training sample, and in the individual loss for multi-label learning, which combines prediction scores over all class labels. In this work, we introduce the sum of ranked range (SoRR) as a general approach to form learning objectives. A ranked range is a consecutive sequence of sorted values of a set of real numbers. The minimization of SoRR is solved with the difference of convex algorithm (DCA). We explore two applications in machine learning of the minimization of the SoRR framework, namely the AoRR aggregate loss for binary/multi-class classification at the sample level and the TKML individual loss for multi-label/multi-class classification at the label level. A combination loss of AoRR and TKML is proposed as a new learning objective for improving the robustness of multi-label learning in the face of outliers in sample and labels alike. Our empirical results highlight the effectiveness of the proposed optimization frameworks and demonstrate the applicability of proposed losses using synthetic and real data sets.", "published": "2021-06-07T02:11:27Z", "authors": ["Shu Hu", "Yiming Ying", "Xin Wang", "Siwei Lyu"], "categories": ["cs.LG", "stat.ML"], "doi": "", "_query": "house price prediction machine learning", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/1705.01142v1", "title": "Machine Learning for Better Models for Predicting Bond Prices", "abstract": "Bond prices are a reflection of extremely complex market interactions and policies, making prediction of future prices difficult. This task becomes even more challenging due to the dearth of relevant information, and accuracy is not the only consideration--in trading situations, time is of the essence. Thus, machine learning in the context of bond price predictions should be both fast and accurate. In this course project, we use a dataset describing the previous 10 trades of a large number of bonds among other relevant descriptive metrics to predict future bond prices. Each of 762,678 bonds in the dataset is described by a total of 61 attributes, including a ground truth trade price. We evaluate the performance of various supervised learning algorithms for regression followed by ensemble methods, with feature and model selection considerations being treated in detail. We further evaluate all methods on both accuracy and speed. Finally, we propose a novel hybrid time-series aided machine learning method that could be applied to such datasets in future work.", "published": "2017-03-31T15:12:49Z", "authors": ["Swetava Ganguli", "Jared Dunnmon"], "categories": ["q-fin.ST", "cs.CE"], "doi": "", "_query": "house price prediction machine learning", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2010.02087v1", "title": "Machine learning on Crays to optimise petrophysical workflows in oil and gas exploration", "abstract": "The oil and gas industry is awash with sub-surface data, which is used to characterize the rock and fluid properties beneath the seabed. This in turn drives commercial decision making and exploration, but the industry currently relies upon highly manual workflows when processing data. A key question is whether this can be improved using machine learning to complement the activities of petrophysicists searching for hydrocarbons. In this paper we present work done, in collaboration with Rock Solid Images (RSI), using supervised machine learning on a Cray XC30 to train models that streamline the manual data interpretation process. With a general aim of decreasing the petrophysical interpretation time down from over 7 days to 7 minutes, in this paper we describe the use of mathematical models that have been trained using raw well log data, for completing each of the four stages of a petrophysical interpretation workflow, along with initial data cleaning. We explore how the predictions from these models compare against the interpretations of human petrophysicists, along with numerous options and techniques that were used to optimise the prediction of our models. The power provided by modern supercomputers such as Cray machines is crucial here, but some popular machine learning framework are unable to take full advantage of modern HPC machines. As such we will also explore the suitability of the machine learning tools we have used, and describe steps we took to work round their limitations. The result of this work is the ability, for the first time, to use machine learning for the entire petrophysical workflow. Whilst there are numerous challenges, limitations and caveats, we demonstrate that machine learning has an important role to play in the processing of sub-surface data.", "published": "2020-10-01T10:14:58Z", "authors": ["Nick Brown", "Anna Roubickova", "Ioanna Lampaki", "Lucy MacGregor", "Michelle Ellis", "Paola Vera de Newton"], "categories": ["physics.geo-ph", "cs.LG"], "doi": "10.1002/cpe.5655", "_query": "house price prediction machine learning", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2405.18434v1", "title": "Modeling the Feedback of AI Price Estimations on Actual Market Values", "abstract": "Public availability of Artificial Intelligence generated information can change the markets forever, and its factoring into economical dynamics may take economists by surprise, out-dating models and schools of thought. Real estate hyper-inflation is not a new phenomenon but its consistent and almost monotonous persistence over 12 years, coinciding with prominence of public estimation information from Zillow, a successful Mass Real Estate Estimator (MREE), could not escape unobserved. What we model is a repetitive theoretical game between the MREE and the home owners, where each player has secret information and expertise. If the intention is to keep housing affordable and maintain old American lifestyle with broad home-ownership, new challenges are defined. Simulations show that a simple restriction of MREE-style price estimation availability to opt-in properties may help partially reduce feedback loop by acting on its likely causes, as suggested by experimental simulation models. The conjecture that the MREE pressure on real estate inflation rate is correlated with the absolute MREE estimation errors, which is logically explainable, is then validated in simulations.", "published": "2024-03-13T03:44:13Z", "authors": ["Viorel Silaghi", "Zobaida Alssadi", "Ben Mathew", "Majed Alotaibi", "Ali Alqarni", "Marius Silaghi"], "categories": ["econ.GN", "cs.AI", "cs.CE", "cs.CY", "cs.GT"], "doi": "", "_query": "house price prediction machine learning", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/1909.04436v1", "title": "The Prevalence of Errors in Machine Learning Experiments", "abstract": "Context: Conducting experiments is central to research machine learning research to benchmark, evaluate and compare learning algorithms. Consequently it is important we conduct reliable, trustworthy experiments. Objective: We investigate the incidence of errors in a sample of machine learning experiments in the domain of software defect prediction. Our focus is simple arithmetical and statistical errors. Method: We analyse 49 papers describing 2456 individual experimental results from a previously undertaken systematic review comparing supervised and unsupervised defect prediction classifiers. We extract the confusion matrices and test for relevant constraints, e.g., the marginal probabilities must sum to one. We also check for multiple statistical significance testing errors. Results: We find that a total of 22 out of 49 papers contain demonstrable errors. Of these 7 were statistical and 16 related to confusion matrix inconsistency (one paper contained both classes of error). Conclusions: Whilst some errors may be of a relatively trivial nature, e.g., transcription errors their presence does not engender confidence. We strongly urge researchers to follow open science principles so errors can be more easily be detected and corrected, thus as a community reduce this worryingly high error rate with our computational experiments.", "published": "2019-09-10T12:32:00Z", "authors": ["Martin Shepperd", "Yuchen Guo", "Ning Li", "Mahir Arzoky", "Andrea Capiluppi", "Steve Counsell", "Giuseppe Destefanis", "Stephen Swift", "Allan Tucker", "Leila Yousefi"], "categories": ["cs.LG", "cs.AI", "stat.AP", "stat.ML"], "doi": "", "_query": "house price prediction machine learning", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2212.07836v1", "title": "Spatially-resolved Thermometry from Line-of-Sight Emission Spectroscopy via Machine Learning", "abstract": "A methodology is proposed, which addresses the caveat that line-of-sight emission spectroscopy presents in that it cannot provide spatially resolved temperature measurements in nonhomogeneous temperature fields. The aim of this research is to explore the use of data-driven models in measuring temperature distributions in a spatially resolved manner using emission spectroscopy data. Two categories of data-driven methods are analyzed: (i) Feature engineering and classical machine learning algorithms, and (ii) end-to-end convolutional neural networks (CNN). In total, combinations of fifteen feature groups and fifteen classical machine learning models, and eleven CNN models are considered and their performances explored. The results indicate that the combination of feature engineering and machine learning provides better performance than the direct use of CNN. Notably, feature engineering which is comprised of physics-guided transformation, signal representation-based feature extraction and Principal Component Analysis is found to be the most effective. Moreover, it is shown that when using the extracted features, the ensemble-based, light blender learning model offers the best performance with RMSE, RE, RRMSE and R values of 64.3, 0.017, 0.025 and 0.994, respectively. The proposed method, based on feature engineering and the light blender model, is capable of measuring nonuniform temperature distributions from low-resolution spectra, even when the species concentration distribution in the gas mixtures is unknown.", "published": "2022-12-15T13:46:15Z", "authors": ["Ruiyuan Kang", "Dimitrios C. Kyritsis", "Panos Liatsis"], "categories": ["cs.LG", "eess.SP"], "doi": "", "_query": "house price prediction machine learning", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/1206.6476v1", "title": "Similarity Learning for Provably Accurate Sparse Linear Classification", "abstract": "In recent years, the crucial importance of metrics in machine learning algorithms has led to an increasing interest for optimizing distance and similarity functions. Most of the state of the art focus on learning Mahalanobis distances (requiring to fulfill a constraint of positive semi-definiteness) for use in a local k-NN algorithm. However, no theoretical link is established between the learned metrics and their performance in classification. In this paper, we make use of the formal framework of good similarities introduced by Balcan et al. to design an algorithm for learning a non PSD linear similarity optimized in a nonlinear feature space, which is then used to build a global linear classifier. We show that our approach has uniform stability and derive a generalization bound on the classification error. Experiments performed on various datasets confirm the effectiveness of our approach compared to state-of-the-art methods and provide evidence that (i) it is fast, (ii) robust to overfitting and (iii) produces very sparse classifiers.", "published": "2012-06-27T19:59:59Z", "authors": ["Aurelien Bellet", "Amaury Habrard", "Marc Sebban"], "categories": ["cs.LG", "cs.AI", "stat.ML"], "doi": "", "_query": "house price prediction machine learning", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2305.08887v1", "title": "Covariate-distance Weighted Regression (CWR): A Case Study for Estimation of House Prices", "abstract": "Geographically weighted regression (GWR) is a popular tool for modeling spatial heterogeneity in a regression model. However, the current weighting function used in GWR only considers the geographical distance, while the attribute similarity is totally ignored. In this study, we proposed a covariate weighting function that combines the geographical distance and attribute distance. The covariate-distance weighted regression (CWR) is the extension of GWR including geographical distance and attribute distance. House prices are affected by numerous factors, such as house age, floor area, and land use. Prediction model is used to help understand the characteristics of regional house prices. The CWR was used to understand the relationship between the house price and controlling factors. The CWR can consider the geological and attribute distances, and produce accurate estimates of house price that preserve the weight matrix for geological and attribute distance functions. Results show that the house attributes/conditions and the characteristics of the house, such as floor area and house age, might affect the house price. After factor selection, in which only house age and floor area of a building are considered, the RMSE of the CWR model can be improved by 2.9%-26.3% for skyscrapers when compared to the GWR. CWR can effectively reduce estimation errors from traditional spatial regression models and provide novel and feasible models for spatial estimation.", "published": "2023-05-15T03:05:57Z", "authors": ["Hone-Jay Chu", "Po-Hung Chen", "Sheng-Mao Chang", "Muhammad Zeeshan Ali", "Sumriti Ranjan Patra"], "categories": ["cs.LG"], "doi": "", "_query": "house price prediction machine learning", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2510.14190v3", "title": "Contrastive Diffusion Alignment: Learning Structured Latents for Controllable Generation", "abstract": "Diffusion models excel at generation, but their latent spaces are high dimensional and not explicitly organized for interpretation or control. We introduce ConDA (Contrastive Diffusion Alignment), a plug-and-play geometry layer that applies contrastive learning to pretrained diffusion latents using auxiliary variables (e.g., time, stimulation parameters, facial action units). ConDA learns a low-dimensional embedding whose directions align with underlying dynamical factors, consistent with recent contrastive learning results on structured and disentangled representations. In this embedding, simple nonlinear trajectories support smooth interpolation, extrapolation, and counterfactual editing while rendering remains in the original diffusion space. ConDA separates editing and rendering by lifting embedding trajectories back to diffusion latents with a neighborhood-preserving kNN decoder and is robust across inversion solvers. Across fluid dynamics, neural calcium imaging, therapeutic neurostimulation, facial expression dynamics, and monkey motor cortex activity, ConDA yields more interpretable and controllable latent structure than linear traversals and conditioning-based baselines, indicating that diffusion latents encode dynamics-relevant structure that can be exploited by an explicit contrastive geometry layer.", "published": "2025-10-16T00:48:05Z", "authors": ["Ruchi Sandilya", "Sumaira Perez", "Charles Lynch", "Lindsay Victoria", "Benjamin Zebley", "Derrick Matthew Buchanan", "Mahendra T. Bhati", "Nolan Williams", "Timothy J. Spellman", "Faith M. Gunning", "Conor Liston", "Logan Grosenick"], "categories": ["cs.LG"], "doi": "", "_query": "house price prediction machine learning", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2607.24057v1", "title": "Constrained Reinforcement Learning Using Successor Representations", "abstract": "Real-world Reinforcement Learning depends on the ability to formulate safety constraints into a policy. A common way to model such constraints is to introduce an additional cost signal in the Markov Decision Process, which notifies the agent of unwanted behavior independently of the reward signal. Unfortunately, current methods are hard to adapt to changes in the cost function introduced by, e.g., domain shift or obstacles moving over time. The lack of adaptability means that policies are too unflexible to deal with complex real-world conditions. We propose the Safe Deep Successor Representation (SafeDSR), a novel method that allows quick retraining of policies towards new cost structures. SafeDSR extends the Deep Successor Representation (Kulkarni et al., 2016) to Constrained Reinforcement Learning by introducing a single learnable weight matrix to decouple the learned value function across dynamics, rewards, and costs. This matrix can be updated in a supervised manner instead of having to adapt the whole network if the cost structure of the environment changes. We demonstrate this ability in a freely configurable two-dimensional navigation environment and show that our method is competitive on a simple navigation task while being considerably more flexible", "published": "2026-07-27T06:57:01Z", "authors": ["Michael Girstl", "Alexander Mattick", "Christopher Mutschler"], "categories": ["cs.LG"], "doi": "", "_query": "house price prediction machine learning", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/1910.13827v1", "title": "Predicting Rainfall using Machine Learning Techniques", "abstract": "Rainfall prediction is one of the challenging and uncertain tasks which has a significant impact on human society. Timely and accurate predictions can help to proactively reduce human and financial loss. This study presents a set of experiments which involve the use of prevalent machine learning techniques to build models to predict whether it is going to rain tomorrow or not based on weather data for that particular day in major cities of Australia. This comparative study is conducted concentrating on three aspects: modeling inputs, modeling methods, and pre-processing techniques. The results provide a comparison of various evaluation metrics of these machine learning techniques and their reliability to predict the rainfall by analyzing the weather data.", "published": "2019-10-29T01:42:15Z", "authors": ["Nikhil Oswal"], "categories": ["cs.LG", "physics.ao-ph", "stat.ML"], "doi": "", "_query": "house price prediction machine learning", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2006.16179v1", "title": "Legal Risks of Adversarial Machine Learning Research", "abstract": "Adversarial Machine Learning is booming with ML researchers increasingly targeting commercial ML systems such as those used in Facebook, Tesla, Microsoft, IBM, Google to demonstrate vulnerabilities. In this paper, we ask, \"What are the potential legal risks to adversarial ML researchers when they attack ML systems?\" Studying or testing the security of any operational system potentially runs afoul the Computer Fraud and Abuse Act (CFAA), the primary United States federal statute that creates liability for hacking. We claim that Adversarial ML research is likely no different. Our analysis show that because there is a split in how CFAA is interpreted, aspects of adversarial ML attacks, such as model inversion, membership inference, model stealing, reprogramming the ML system and poisoning attacks, may be sanctioned in some jurisdictions and not penalized in others. We conclude with an analysis predicting how the US Supreme Court may resolve some present inconsistencies in the CFAA's application in Van Buren v. United States, an appeal expected to be decided in 2021. We argue that the court is likely to adopt a narrow construction of the CFAA, and that this will actually lead to better adversarial ML security outcomes in the long term.", "published": "2020-06-29T16:45:15Z", "authors": ["Ram Shankar Siva Kumar", "Jonathon Penney", "Bruce Schneier", "Kendra Albert"], "categories": ["cs.CY", "cs.CR", "cs.LG"], "doi": "", "_query": "house price prediction machine learning", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2411.11497v2", "title": "Physics Encoded Blocks in Residual Neural Network Architectures for Digital Twin Models", "abstract": "Physics Informed Machine Learning has emerged as a popular approach for modeling and simulation in digital twins, enabling the generation of accurate models of processes and behaviors in real-world systems. However, existing methods either rely on simple loss regularizations that offer limited physics integration or employ highly specialized architectures that are difficult to generalize across diverse physical systems. This paper presents a generic approach based on a novel physics-encoded residual neural network (PERNN) architecture that seamlessly combines data-driven and physics-based analytical models to overcome these limitations. Our method integrates differentiable physics blocks-implementing mathematical operators from physics-based models with feed-forward learning blocks, while intermediate residual blocks ensure stable gradient flow during training. Consequently, the model naturally adheres to the underlying physical principles even when prior physics knowledge is incomplete, thereby improving generalizability with low data requirements and reduced model complexity. We investigate our approach in two application domains. The first is a steering model for autonomous vehicles in a simulation environment, and the second is a digital twin for climate modeling using an ordinary differential equation (ODE)-based model of Net Ecosystem Exchange (NEE) to enable gap-filling in flux tower data. In both cases, our method outperforms conventional neural network approaches as well as state-of-the-art Physics Informed Machine Learning methods.", "published": "2024-11-18T11:58:20Z", "authors": ["Muhammad Saad Zia", "Ashiq Anjum", "Lu Liu", "Anthony Conway", "Anasol Pena Rios"], "categories": ["cs.LG", "cs.RO"], "doi": "10.1007/s10994-025-06808-y", "_query": "house price prediction machine learning", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2202.05146v4", "title": "EquiBind: Geometric Deep Learning for Drug Binding Structure Prediction", "abstract": "Predicting how a drug-like molecule binds to a specific protein target is a core problem in drug discovery. An extremely fast computational binding method would enable key applications such as fast virtual screening or drug engineering. Existing methods are computationally expensive as they rely on heavy candidate sampling coupled with scoring, ranking, and fine-tuning steps. We challenge this paradigm with EquiBind, an SE(3)-equivariant geometric deep learning model performing direct-shot prediction of both i) the receptor binding location (blind docking) and ii) the ligand's bound pose and orientation. EquiBind achieves significant speed-ups and better quality compared to traditional and recent baselines. Further, we show extra improvements when coupling it with existing fine-tuning techniques at the cost of increased running time. Finally, we propose a novel and fast fine-tuning model that adjusts torsion angles of a ligand's rotatable bonds based on closed-form global minima of the von Mises angular distance to a given input atomic point cloud, avoiding previous expensive differential evolution strategies for energy minimization.", "published": "2022-02-07T16:26:05Z", "authors": ["Hannes Stärk", "Octavian-Eugen Ganea", "Lagnajit Pattanaik", "Regina Barzilay", "Tommi Jaakkola"], "categories": ["q-bio.BM", "cs.LG"], "doi": "", "_query": "house price prediction machine learning", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2307.09469v2", "title": "Graph Representation of the Magnetic Field Topology in High-Fidelity Plasma Simulations for Machine Learning Applications", "abstract": "Topological analysis of the magnetic field in simulated plasmas allows the study of various physical phenomena in a wide range of settings. One such application is magnetic reconnection, a phenomenon related to the dynamics of the magnetic field topology, which is difficult to detect and characterize in three dimensions. We propose a scalable pipeline for topological data analysis and spatiotemporal graph representation of three-dimensional magnetic vector fields. We demonstrate our methods on simulations of the Earth's magnetosphere produced by Vlasiator, a supercomputer-scale Vlasov theory-based simulation for near-Earth space. The purpose of this work is to challenge the machine learning community to explore graph-based machine learning approaches to address a largely open scientific problem with wide-ranging potential impact.", "published": "2023-07-10T18:08:05Z", "authors": ["Ioanna Bouri", "Fanni Franssila", "Markku Alho", "Giulia Cozzani", "Ivan Zaitsev", "Minna Palmroth", "Teemu Roos"], "categories": ["physics.plasm-ph", "cs.LG"], "doi": "", "_query": "house price prediction machine learning", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2604.18605v1", "title": "Exploring Drivers of Extreme Housing Prices in Australia", "abstract": "In recent years Australia has observed a growing, unexplained resilience of increasing house price trends. Here, we seek to understand what is driving Australia's indestructible asset using insights from market experts. We construct a differential equation model of house price to develop intuition for its historical behaviour and responsiveness to changes in mortgage rates. Using this model, we identify a point of 'decoupling' between house price and mortgage rate in the system with supply limitations found to be the main driver for this change. From there, modern extreme value techniques are implemented on real-world data to investigate how the effectiveness of mortgage rate in moderating extreme house price has changed before and after this historical decoupling. We find that without an increase in the housing supply chain, through either deregulation or reduced competition with government building, an 11\\% increase in mortgage rate will be needed to slow extreme housing costs.", "published": "2026-04-10T01:43:04Z", "authors": ["Grace Burtenshaw", "Ashley Burtenshaw", "Meagan Carney"], "categories": ["q-fin.GN", "math.PR", "stat.AP"], "doi": "", "_query": "house price prediction machine learning", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2402.13256v1", "title": "Deep Learning Based Event Reconstruction for Cyclotron Radiation Emission Spectroscopy", "abstract": "The objective of the Cyclotron Radiation Emission Spectroscopy (CRES) technology is to build precise particle energy spectra. This is achieved by identifying the start frequencies of charged particle trajectories which, when exposed to an external magnetic field, leave semi-linear profiles (called tracks) in the time-frequency plane. Due to the need for excellent instrumental energy resolution in application, highly efficient and accurate track reconstruction methods are desired. Deep learning convolutional neural networks (CNNs) - particularly suited to deal with information-sparse data and which offer precise foreground localization - may be utilized to extract track properties from measured CRES signals (called events) with relative computational ease. In this work, we develop a novel machine learning based model which operates a CNN and a support vector machine in tandem to perform this reconstruction. A primary application of our method is shown on simulated CRES signals which mimic those of the Project 8 experiment - a novel effort to extract the unknown absolute neutrino mass value from a precise measurement of tritium $β^-$-decay energy spectrum. When compared to a point-clustering based technique used as a baseline, we show a relative gain of 24.1% in event reconstruction efficiency and comparable performance in accuracy of track parameter reconstruction.", "published": "2024-01-05T15:55:27Z", "authors": ["A. Ashtari Esfahani", "S. Böser", "N. Buzinsky", "M. C. Carmona-Benitez", "R. Cervantes", "C. Claessens", "L. de Viveiros", "M. Fertl", "J. A. Formaggio", "J. K. Gaison", "L. Gladstone", "M. Grando", "M. Guigue", "J. Hartse", "K. M. Heeger", "X. Huyan", "A. M. Jones", "K. Kazkaz", "M. Li", "A. Lindman", "A. Marsteller", "C. Matthé", "R. Mohiuddin", "B. Monreal", "E. C. Morrison", "R. Mueller", "J. A. Nikkel", "E. Novitski", "N. S. Oblath", "J. I. Peña", "W. Pettus", "R. Reimann", "R. G. H. Robertson", "L. Saldaña", "M. Schram", "P. L. Slocum", "J. Stachurska", "Y. -H. Sun", "P. T. Surukuchi", "A. B. Telles", "F. Thomas", "M. Thomas", "L. A. Thorne", "T. Thümmler", "L. Tvrznikova", "W. Van De Pontseele", "B. A. VanDevender", "T. E. Weiss", "T. Wendler", "E. Zayas", "A. Ziegler"], "categories": ["physics.ins-det", "nucl-ex"], "doi": "10.1088/2632-2153/ad3ee3", "_query": "house price prediction machine learning", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2106.09664v1", "title": "Design and Analysis of Robust Deep Learning Models for Stock Price Prediction", "abstract": "Building predictive models for robust and accurate prediction of stock prices and stock price movement is a challenging research problem to solve. The well-known efficient market hypothesis believes in the impossibility of accurate prediction of future stock prices in an efficient stock market as the stock prices are assumed to be purely stochastic. However, numerous works proposed by researchers have demonstrated that it is possible to predict future stock prices with a high level of precision using sophisticated algorithms, model architectures, and the selection of appropriate variables in the models. This chapter proposes a collection of predictive regression models built on deep learning architecture for robust and precise prediction of the future prices of a stock listed in the diversified sectors in the National Stock Exchange (NSE) of India. The Metastock tool is used to download the historical stock prices over a period of two years (2013- 2014) at 5 minutes intervals. While the records for the first year are used to train the models, the testing is carried out using the remaining records. The design approaches of all the models and their performance results are presented in detail. The models are also compared based on their execution time and accuracy of prediction.", "published": "2021-06-17T17:15:02Z", "authors": ["Jaydip Sen", "Sidra Mehtab"], "categories": ["q-fin.ST", "cs.LG"], "doi": "10.5772/intechopen.99982", "_query": "house price prediction machine learning", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2308.08494v1", "title": "Conceptualizing Machine Learning for Dynamic Information Retrieval of Electronic Health Record Notes", "abstract": "The large amount of time clinicians spend sifting through patient notes and documenting in electronic health records (EHRs) is a leading cause of clinician burnout. By proactively and dynamically retrieving relevant notes during the documentation process, we can reduce the effort required to find relevant patient history. In this work, we conceptualize the use of EHR audit logs for machine learning as a source of supervision of note relevance in a specific clinical context, at a particular point in time. Our evaluation focuses on the dynamic retrieval in the emergency department, a high acuity setting with unique patterns of information retrieval and note writing. We show that our methods can achieve an AUC of 0.963 for predicting which notes will be read in an individual note writing session. We additionally conduct a user study with several clinicians and find that our framework can help clinicians retrieve relevant information more efficiently. Demonstrating that our framework and methods can perform well in this demanding setting is a promising proof of concept that they will translate to other clinical settings and data modalities (e.g., labs, medications, imaging).", "published": "2023-08-09T21:04:19Z", "authors": ["Sharon Jiang", "Shannon Shen", "Monica Agrawal", "Barbara Lam", "Nicholas Kurtzman", "Steven Horng", "David Karger", "David Sontag"], "categories": ["cs.IR", "cs.CL", "cs.LG"], "doi": "", "_query": "house price prediction machine learning", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2004.11238v1", "title": "Learning Constrained Dynamics with Gauss Principle adhering Gaussian Processes", "abstract": "The identification of the constrained dynamics of mechanical systems is often challenging. Learning methods promise to ease an analytical analysis, but require considerable amounts of data for training. We propose to combine insights from analytical mechanics with Gaussian process regression to improve the model's data efficiency and constraint integrity. The result is a Gaussian process model that incorporates a priori constraint knowledge such that its predictions adhere to Gauss' principle of least constraint. In return, predictions of the system's acceleration naturally respect potentially non-ideal (non-)holonomic equality constraints. As corollary results, our model enables to infer the acceleration of the unconstrained system from data of the constrained system and enables knowledge transfer between differing constraint configurations.", "published": "2020-04-23T15:26:51Z", "authors": ["A. Rene Geist", "Sebastian Trimpe"], "categories": ["cs.LG", "cs.RO", "eess.SY", "stat.ML"], "doi": "", "_query": "house price prediction machine learning", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2311.16172v1", "title": "Evolutionary Machine Learning and Games", "abstract": "Evolutionary machine learning (EML) has been applied to games in multiple ways, and for multiple different purposes. Importantly, AI research in games is not only about playing games; it is also about generating game content, modeling players, and many other applications. Many of these applications pose interesting problems for EML. We will structure this chapter on EML for games based on whether evolution is used to augment machine learning (ML) or ML is used to augment evolution. For completeness, we also briefly discuss the usage of ML and evolution separately in games.", "published": "2023-11-20T13:21:39Z", "authors": ["Julian Togelius", "Ahmed Khalifa", "Sam Earle", "Michael Cerny Green", "Lisa Soros"], "categories": ["cs.NE", "cs.AI", "cs.LG"], "doi": "", "_query": "house price prediction machine learning", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/1812.00050v2", "title": "Learning Interpretable Rules for Multi-label Classification", "abstract": "Multi-label classification (MLC) is a supervised learning problem in which, contrary to standard multiclass classification, an instance can be associated with several class labels simultaneously. In this chapter, we advocate a rule-based approach to multi-label classification. Rule learning algorithms are often employed when one is not only interested in accurate predictions, but also requires an interpretable theory that can be understood, analyzed, and qualitatively evaluated by domain experts. Ideally, by revealing patterns and regularities contained in the data, a rule-based theory yields new insights in the application domain. Recently, several authors have started to investigate how rule-based models can be used for modeling multi-label data. Discussing this task in detail, we highlight some of the problems that make rule learning considerably more challenging for MLC than for conventional classification. While mainly focusing on our own previous work, we also provide a short overview of related work in this area.", "published": "2018-11-30T20:48:12Z", "authors": ["Eneldo Loza Mencía", "Johannes Fürnkranz", "Eyke Hüllermeier", "Michael Rapp"], "categories": ["cs.LG", "stat.ML"], "doi": "10.1007/978-3-319-98131-4_4", "_query": "house price prediction machine learning", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/1103.0890v2", "title": "Efficient Multi-Template Learning for Structured Prediction", "abstract": "Conditional random field (CRF) and Structural Support Vector Machine (Structural SVM) are two state-of-the-art methods for structured prediction which captures the interdependencies among output variables. The success of these methods is attributed to the fact that their discriminative models are able to account for overlapping features on the whole input observations. These features are usually generated by applying a given set of templates on labeled data, but improper templates may lead to degraded performance. To alleviate this issue, in this paper, we propose a novel multiple template learning paradigm to learn structured prediction and the importance of each template simultaneously, so that hundreds of arbitrary templates could be added into the learning model without caution. This paradigm can be formulated as a special multiple kernel learning problem with exponential number of constraints. Then we introduce an efficient cutting plane algorithm to solve this problem in the primal, and its convergence is presented. We also evaluate the proposed learning paradigm on two widely-studied structured prediction tasks, \\emph{i.e.} sequence labeling and dependency parsing. Extensive experimental results show that the proposed method outperforms CRFs and Structural SVMs due to exploiting the importance of each template. Our complexity analysis and empirical results also show that our proposed method is more efficient than OnlineMKL on very sparse and high-dimensional data. We further extend this paradigm for structured prediction using generalized $p$-block norm regularization with $p>1$, and experiments show competitive performances when $p \\in [1,2)$.", "published": "2011-03-04T13:08:59Z", "authors": ["Qi Mao", "Ivor W. Tsang"], "categories": ["cs.LG", "cs.CL"], "doi": "", "_query": "house price prediction machine learning", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2006.12694v2", "title": "Limits of Transfer Learning", "abstract": "Transfer learning involves taking information and insight from one problem domain and applying it to a new problem domain. Although widely used in practice, theory for transfer learning remains less well-developed. To address this, we prove several novel results related to transfer learning, showing the need to carefully select which sets of information to transfer and the need for dependence between transferred information and target problems. Furthermore, we prove how the degree of probabilistic change in an algorithm using transfer learning places an upper bound on the amount of improvement possible. These results build on the algorithmic search framework for machine learning, allowing the results to apply to a wide range of learning problems using transfer.", "published": "2020-06-23T01:48:23Z", "authors": ["Jake Williams", "Abel Tadesse", "Tyler Sam", "Huey Sun", "George D. Montanez"], "categories": ["stat.ML", "cs.AI", "cs.LG"], "doi": "", "_query": "house price prediction machine learning", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/1805.00878v1", "title": "Modelling tourism demand to Spain with machine learning techniques. The impact of forecast horizon on model selection", "abstract": "This study assesses the influence of the forecast horizon on the forecasting performance of several machine learning techniques. We compare the fo recast accuracy of Support Vector Regression (SVR) to Neural Network (NN) models, using a linear model as a benchmark. We focus on international tourism demand to all seventeen regions of Spain. The SVR with a Gaussian radial basis function kernel outperforms the rest of the models for the longest forecast horizons. We also find that machine learning methods improve their forecasting accuracy with respect to linear models as forecast horizons increase. This result shows the suitability of SVR for medium and long term forecasting.", "published": "2018-05-02T15:48:11Z", "authors": ["Oscar Claveria", "Enric Monte", "Salvador Torra"], "categories": ["stat.ML", "cs.LG"], "doi": "", "_query": "house price prediction machine learning", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/1603.00751v2", "title": "Equity forecast: Predicting long term stock price movement using machine learning", "abstract": "Long term investment is one of the major investment strategies. However, calculating intrinsic value of some company and evaluating shares for long term investment is not easy, since analyst have to care about a large number of financial indicators and evaluate them in a right manner. So far, little help in predicting the direction of the company value over the longer period of time has been provided from the machines. In this paper we present a machine learning aided approach to evaluate the equity's future price over the long time. Our method is able to correctly predict whether some company's value will be 10% higher or not over the period of one year in 76.5% of cases.", "published": "2016-03-02T15:33:30Z", "authors": ["Nikola Milosevic"], "categories": ["cs.LG", "q-fin.GN"], "doi": "10.1453/jel.v3i2.750", "_query": "house price prediction machine learning", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/1103.5971v1", "title": "Housing risk and return: Evidence from a housing asset-pricing model", "abstract": "This paper investigates the risk-return relationship in determination of housing asset pricing. In so doing, the paper evaluates behavioral hypotheses advanced by Case and Shiller (1988, 2002, 2009) in studies of boom and post-boom housing markets. The paper specifies and tests a multi-factor housing asset pricing model. In that model, we evaluate whether the market factor as well as other measures of risk, including idiosyncratic risk, momentum, and MSA size effects, have explanatory power for metropolitan-specific housing returns. Further, we test the robustness of the asset pricing results to inclusion of controls for socioeconomic variables commonly represented in the house price literature, including changes in employment, affordability, and foreclosure incidence. We find a sizable and statistically significant influence of the market factor on MSA house price returns. Moreover we show that market betas have varied substantially over time. Also, results are largely robust to the inclusion of other explanatory variables, including standard measures of risk and other housing market fundamentals. Additional tests of model validity using the Fama-MacBeth framework offer further strong support of a positive risk and return relationship in housing. Our findings are supportive of the application of a housing investment risk-return framework in explanation of variation in metro-area cross-section and time-series US house price returns. Further, results strongly corroborate Case-Shiller survey research indicating the importance of speculative forces in the determination of U.S. housing returns.", "published": "2011-03-30T15:55:56Z", "authors": ["Karl Case", "John Cotter", "Stuart Gabriel"], "categories": ["q-fin.PM", "q-fin.PR", "q-fin.ST"], "doi": "", "_query": "house price prediction machine learning", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2012.02007v1", "title": "A Novel index-based multidimensional data organization model that enhances the predictability of the machine learning algorithms", "abstract": "Learning from the multidimensional data has been an interesting concept in the field of machine learning. However, such learning can be difficult, complex, expensive because of expensive data processing, manipulations as the number of dimension increases. As a result, we have introduced an ordered index-based data organization model as the ordered data set provides easy and efficient access than the unordered one and finally, such organization can improve the learning. The ordering maps the multidimensional dataset in the reduced space and ensures that the information associated with the learning can be retrieved back and forth efficiently. We have found that such multidimensional data storage can enhance the predictability for both the unsupervised and supervised machine learning algorithms.", "published": "2020-12-03T15:41:13Z", "authors": ["Mahbubur Rahman"], "categories": ["cs.LG", "cs.AI"], "doi": "10.5121/csit.2020.101210", "_query": "house price prediction machine learning", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2208.01127v1", "title": "Disparate Censorship & Undertesting: A Source of Label Bias in Clinical Machine Learning", "abstract": "As machine learning (ML) models gain traction in clinical applications, understanding the impact of clinician and societal biases on ML models is increasingly important. While biases can arise in the labels used for model training, the many sources from which these biases arise are not yet well-studied. In this paper, we highlight disparate censorship (i.e., differences in testing rates across patient groups) as a source of label bias that clinical ML models may amplify, potentially causing harm. Many patient risk-stratification models are trained using the results of clinician-ordered diagnostic and laboratory tests of labels. Patients without test results are often assigned a negative label, which assumes that untested patients do not experience the outcome. Since orders are affected by clinical and resource considerations, testing may not be uniform in patient populations, giving rise to disparate censorship. Disparate censorship in patients of equivalent risk leads to undertesting in certain groups, and in turn, more biased labels for such groups. Using such biased labels in standard ML pipelines could contribute to gaps in model performance across patient groups. Here, we theoretically and empirically characterize conditions in which disparate censorship or undertesting affect model performance across subgroups. Our findings call attention to disparate censorship as a source of label bias in clinical ML models.", "published": "2022-08-01T20:15:31Z", "authors": ["Trenton Chang", "Michael W. Sjoding", "Jenna Wiens"], "categories": ["cs.LG", "cs.CY"], "doi": "", "_query": "house price prediction machine learning", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2606.30999v1", "title": "Estimating Supply Incrementality in Two-sided Marketplaces: A Causal Machine Learning Approach", "abstract": "In two-sided marketplaces with heterogeneous products, it is important to understand the causal relationship between additional supply and marketplace outcomes, such as the total quantity transacted or transaction value in the marketplace. This paper studies a causal machine learning approach to estimating this relationship across product segments. We use the Airbnb marketplace as an example, focusing on the impact of additional listing supply on total bookings, but the methodology applies to other two-sided marketplaces. Our approach combines double/debiased machine learning with a hierarchical Bayesian framework that leverages pre-existing knowledge as priors. We construct tractable and informative features for the model by leveraging measures of product segment similarity from the geospatial literature. We find that such a model provides plausible estimates of the marketplace returns to additional supply and strong out of sample performance.", "published": "2026-06-30T00:23:00Z", "authors": ["Yufei Wu", "Daniel Schmierer", "Dan Zylberglejd"], "categories": ["cs.LG", "econ.EM", "stat.AP", "stat.ME"], "doi": "", "_query": "house price prediction machine learning", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2409.07132v2", "title": "LLM-based feature generation from text for interpretable machine learning", "abstract": "Existing text representations such as embeddings and bag-of-words are not suitable for rule learning due to their high dimensionality and absent or questionable feature-level interpretability. This article explores whether large language models (LLMs) could address this by extracting a small number of interpretable features from text. We demonstrate this process on two datasets (CORD-19 and M17+) containing several thousand scientific articles from multiple disciplines and a target being a proxy for research impact. An evaluation based on testing for the statistically significant correlation with research impact has shown that LLama 2-generated features are semantically meaningful. We consequently used these generated features in text classification to predict the binary target variable representing the citation rate for the CORD-19 dataset and the ordinal 5-class target representing an expert-awarded grade in the M17+ dataset. Machine-learning models trained on the LLM-generated features provided similar predictive performance to the state-of-the-art embedding model SciBERT for scientific text. The LLM used only 62 features compared to 768 features in SciBERT embeddings, and these features were directly interpretable, corresponding to notions such as article methodological rigor, novelty, or grammatical correctness. As the final step, we extract a small number of well-interpretable action rules. Consistently competitive results obtained with the same LLM feature set across both thematically diverse datasets show that this approach generalizes across domains.", "published": "2024-09-11T09:29:28Z", "authors": ["Vojtěch Balek", "Lukáš Sýkora", "Vilém Sklenák", "Tomáš Kliegr"], "categories": ["cs.LG", "cs.CL"], "doi": "10.1007/s10994-025-06867-1", "_query": "house price prediction machine learning", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2202.03156v1", "title": "Comparative Study of Machine Learning Models for Stock Price Prediction", "abstract": "In this work, we apply machine learning techniques to historical stock prices to forecast future prices. To achieve this, we use recursive approaches that are appropriate for handling time series data. In particular, we apply a linear Kalman filter and different varieties of long short-term memory (LSTM) architectures to historical stock prices over a 10-year range (1/1/2011 - 1/1/2021). We quantify the results of these models by computing the error of the predicted values versus the historical values of each stock. We find that of the algorithms we investigated, a simple linear Kalman filter can predict the next-day value of stocks with low-volatility (e.g., Microsoft) surprisingly well. However, in the case of high-volatility stocks (e.g., Tesla) the more complex LSTM algorithms significantly outperform the Kalman filter. Our results show that we can classify different types of stocks and then train an LSTM for each stock type. This method could be used to automate portfolio generation for a target return rate.", "published": "2022-01-31T17:16:27Z", "authors": ["Ogulcan E. Orsel", "Sasha S. Yamada"], "categories": ["q-fin.ST", "cs.LG"], "doi": "", "_query": "house price prediction machine learning", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/1008.1376v1", "title": "House Price Distributions of Taiwan: A Preliminary Study", "abstract": "The house price distributions of Taiwan are analyzed. The tail of the cumulative distribution function (CDF) follows an approximate power law with an exponent equals to -2.4 while the distribution of the house price per unit area displays a lognormal distribution. Implications of the results are also discussed.", "published": "2010-08-08T01:23:28Z", "authors": ["Chung-I Chou", "S. P. Li"], "categories": ["physics.soc-ph", "physics.data-an"], "doi": "", "_query": "house price prediction machine learning", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2107.01979v1", "title": "Machine Learning for Fraud Detection in E-Commerce: A Research Agenda", "abstract": "Fraud detection and prevention play an important part in ensuring the sustained operation of any e-commerce business. Machine learning (ML) often plays an important role in these anti-fraud operations, but the organizational context in which these ML models operate cannot be ignored. In this paper, we take an organization-centric view on the topic of fraud detection by formulating an operational model of the anti-fraud departments in e-commerce organizations. We derive 6 research topics and 12 practical challenges for fraud detection from this operational model. We summarize the state of the literature for each research topic, discuss potential solutions to the practical challenges, and identify 22 open research challenges.", "published": "2021-07-05T12:37:29Z", "authors": ["Niek Tax", "Kees Jan de Vries", "Mathijs de Jong", "Nikoleta Dosoula", "Bram van den Akker", "Jon Smith", "Olivier Thuong", "Lucas Bernardi"], "categories": ["cs.LG", "cs.CR", "stat.AP"], "doi": "", "_query": "house price prediction machine learning", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2504.04303v1", "title": "Using ensemble methods of machine learning to predict real estate prices", "abstract": "In recent years, machine learning (ML) techniques have become a powerful tool for improving the accuracy of predictions and decision-making. Machine learning technologies have begun to penetrate all areas, including the real estate sector. Correct forecasting of real estate value plays an important role in the buyer-seller chain, because it ensures reasonableness of price expectations based on the offers available in the market and helps to avoid financial risks for both parties of the transaction. Accurate forecasting is also important for real estate investors to make an informed decision on a specific property. This study helps to gain a deeper understanding of how effective and accurate ensemble machine learning methods are in predicting real estate values. The results obtained in the work are quite accurate, as can be seen from the coefficient of determination (R^2), root mean square error (RMSE) and mean absolute error (MAE) calculated for each model. The Gradient Boosting Regressor model provides the highest accuracy, the Extra Trees Regressor, Hist Gradient Boosting Regressor and Random Forest Regressor models give good results. In general, ensemble machine learning techniques can be effectively used to solve real estate valuation. This work forms ideas for future research, which consist in the preliminary processing of the data set by searching and extracting anomalous values, as well as the practical implementation of the obtained results.", "published": "2025-04-05T23:53:38Z", "authors": ["Oleh Pastukh", "Viktor Khomyshyn"], "categories": ["cs.LG"], "doi": "", "_query": "house price prediction machine learning", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2103.11430v2", "title": "Knowledge Discovery in Surveys using Machine Learning: A Case Study of Women in Entrepreneurship in UAE", "abstract": "Knowledge Discovery plays a very important role in analyzing data and getting insights from them to drive better business decisions. Entrepreneurship in a Knowledge based economy contributes greatly to the development of a country's economy. In this paper, we analyze surveys that were conducted on women in entrepreneurship in UAE. Relevant insights are extracted from the data that can help us to better understand the current landscape of women in entrepreneurship and predict the future as well. The features are analyzed using machine learning to drive better business decisions in the future.", "published": "2021-03-21T16:24:52Z", "authors": ["Syed Farhan Ahmad", "Amrah Hermayen", "Ganga Bhavani"], "categories": ["cs.LG"], "doi": "", "_query": "house price prediction machine learning", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2408.17016v2", "title": "Error-controlled non-additive interaction discovery in machine learning models", "abstract": "Machine learning (ML) models are powerful tools for detecting complex patterns within data, yet their \"black box\" nature limits their interpretability, hindering their use in critical domains like healthcare and finance. To address this challenge, interpretable ML methods have been developed to explain how features influence model predictions. However, these methods often focus on univariate feature importance, overlooking the complex interactions between features that ML models are capable of capturing. Recognizing this limitation, recent efforts have aimed to extend these methods to discover feature interactions, but existing approaches struggle with robustness and error control, especially under data perturbations. In this study, we introduce Diamond, a novel method for trustworthy feature interaction discovery. Diamond uniquely integrates the model-X knockoffs framework to control the false discovery rate (FDR), ensuring that the proportion of falsely discovered interactions remains low. A key innovation in Diamond is its non-additivity distillation procedure, which refines existing interaction importance measures to distill non-additive interaction effects, ensuring that FDR control is maintained. This approach addresses the limitations of off-the-shelf interaction measures, which, when used naively, can lead to inaccurate discoveries. Diamond's applicability spans a wide range of ML models, including deep neural networks, transformer models, tree-based models, and factorization-based models. Our empirical evaluations on both simulated and real datasets across various biomedical studies demonstrate Diamond's utility in enabling more reliable data-driven scientific discoveries. This method represents a significant step forward in the deployment of ML models for scientific innovation and hypothesis generation.", "published": "2024-08-30T05:13:11Z", "authors": ["Winston Chen", "Yifan Jiang", "William Stafford Noble", "Yang Young Lu"], "categories": ["cs.LG", "stat.AP", "stat.ML"], "doi": "", "_query": "house price prediction machine learning", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2411.09468v2", "title": "Harnessing Machine Learning for Single-Shot Measurement of Free Electron Laser Pulse Power", "abstract": "Electron beam accelerators are essential in many scientific and technological fields. Their operation relies heavily on the stability and precision of the electron beam. Traditional diagnostic techniques encounter difficulties in addressing the complex and dynamic nature of electron beams. Particularly in the context of free-electron lasers (FELs), it is fundamentally impossible to measure the lasing-on and lasingoff electron power profiles for a single electron bunch. This is a crucial hurdle in the exact reconstruction of the photon pulse profile. To overcome this hurdle, we developed a machine learning model that predicts the temporal power profile of the electron bunch in the lasing-off regime using machine parameters that can be obtained when lasing is on. The model was statistically validated and showed superior predictions compared to the state-of-the-art batch calibrations. The work we present here is a critical element for a virtual pulse reconstruction diagnostic (VPRD) tool designed to reconstruct the power profile of individual photon pulses without requiring repeated measurements in the lasing-off regime. This promises to significantly enhance the diagnostic capabilities in FELs at large.", "published": "2024-11-14T14:16:50Z", "authors": ["Till Korten", "Vladimir Rybnikov", "Mathias Vogt", "Juliane Roensch-Schulenburg", "Peter Steinbach", "Najmeh Mirian"], "categories": ["cs.LG", "physics.acc-ph"], "doi": "", "_query": "house price prediction machine learning", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/1909.01268v1", "title": "Are Bitcoins price predictable? Evidence from machine learning techniques using technical indicators", "abstract": "The uncertainties in future Bitcoin price make it difficult to accurately predict the price of Bitcoin. Accurately predicting the price for Bitcoin is therefore important for decision-making process of investors and market players in the cryptocurrency market. Using historical data from 01/01/2012 to 16/08/2019, machine learning techniques (Generalized linear model via penalized maximum likelihood, random forest, support vector regression with linear kernel, and stacking ensemble) were used to forecast the price of Bitcoin. The prediction models employed key and high dimensional technical indicators as the predictors. The performance of these techniques were evaluated using mean absolute percentage error (MAPE), root mean square error (RMSE), mean absolute error (MAE), and coefficient of determination (R-squared). The performance metrics revealed that the stacking ensemble model with two base learner (random forest and generalized linear model via penalized maximum likelihood) and support vector regression with linear kernel as meta-learner was the optimal model for forecasting Bitcoin price. The MAPE, RMSE, MAE, and R-squared values for the stacking ensemble model were 0.0191%, 15.5331 USD, 124.5508 USD, and 0.9967 respectively. These values show a high degree of reliability in predicting the price of Bitcoin using the stacking ensemble model. Accurately predicting the future price of Bitcoin will yield significant returns for investors and market players in the cryptocurrency market.", "published": "2019-09-03T16:03:13Z", "authors": ["Samuel Asante Gyamerah"], "categories": ["q-fin.ST", "econ.EM", "stat.AP", "stat.ML"], "doi": "", "_query": "house price prediction machine learning", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2304.14735v1", "title": "Benchmarking Automated Machine Learning Methods for Price Forecasting Applications", "abstract": "Price forecasting for used construction equipment is a challenging task due to spatial and temporal price fluctuations. It is thus of high interest to automate the forecasting process based on current market data. Even though applying machine learning (ML) to these data represents a promising approach to predict the residual value of certain tools, it is hard to implement for small and medium-sized enterprises due to their insufficient ML expertise. To this end, we demonstrate the possibility of substituting manually created ML pipelines with automated machine learning (AutoML) solutions, which automatically generate the underlying pipelines. We combine AutoML methods with the domain knowledge of the companies. Based on the CRISP-DM process, we split the manual ML pipeline into a machine learning and non-machine learning part. To take all complex industrial requirements into account and to demonstrate the applicability of our new approach, we designed a novel metric named method evaluation score, which incorporates the most important technical and non-technical metrics for quality and usability. Based on this metric, we show in a case study for the industrial use case of price forecasting, that domain knowledge combined with AutoML can weaken the dependence on ML experts for innovative small and medium-sized enterprises which are interested in conducting such solutions.", "published": "2023-04-28T10:27:38Z", "authors": ["Horst Stühler", "Marc-André Zöller", "Dennis Klau", "Alexandre Beiderwellen-Bedrikow", "Christian Tutschku"], "categories": ["cs.LG", "cs.AI"], "doi": "10.5220/0012051400003541", "_query": "house price prediction machine learning", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/1502.02215v1", "title": "Real World Applications of Machine Learning Techniques over Large Mobile Subscriber Datasets", "abstract": "Communication Service Providers (CSPs) are in a unique position to utilize their vast transactional data assets generated from interactions of subscribers with network elements as well as with other subscribers. CSPs could leverage its data assets for a gamut of applications such as service personalization, predictive offer management, loyalty management, revenue forecasting, network capacity planning, product bundle optimization and churn management to gain significant competitive advantage. However, due to the sheer data volume, variety, velocity and veracity of mobile subscriber datasets, sophisticated data analytics techniques and frameworks are necessary to derive actionable insights in a useable timeframe. In this paper, we describe our journey from a relational database management system (RDBMS) based campaign management solution which allowed data scientists and marketers to use hand-written rules for service personalization and targeted promotions to a distributed Big Data Analytics platform, capable of performing large scale machine learning and data mining to deliver real time service personalization, predictive modelling and product optimization. Our work involves a careful blend of technology, processes and best practices, which facilitate man-machine collaboration and continuous experimentation to derive measurable economic value from data. Our platform has a reach of more than 500 million mobile subscribers worldwide, delivering over 1 billion personalized recommendations annually, processing a total data volume of 64 Petabytes, corresponding to 8.5 trillion events.", "published": "2015-02-08T06:18:55Z", "authors": ["Jobin Wilson", "Chitharanj Kachappilly", "Rakesh Mohan", "Prateek Kapadia", "Arun Soman", "Santanu Chaudhury"], "categories": ["cs.LG", "cs.CY", "cs.SE"], "doi": "", "_query": "house price prediction machine learning", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2405.09557v2", "title": "Machine Learning in Short-Reach Optical Systems: A Comprehensive Survey", "abstract": "In recent years, extensive research has been conducted to explore the utilization of machine learning algorithms in various direct-detected and self-coherent short-reach communication applications. These applications encompass a wide range of tasks, including bandwidth request prediction, signal quality monitoring, fault detection, traffic prediction, and digital signal processing (DSP)-based equalization. As a versatile approach, machine learning demonstrates the ability to address stochastic phenomena in optical systems networks where deterministic methods may fall short. However, when it comes to DSP equalization algorithms, their performance improvements are often marginal, and their complexity is prohibitively high, especially in cost-sensitive short-reach communications scenarios such as passive optical networks (PONs). They excel in capturing temporal dependencies, handling irregular or nonlinear patterns effectively, and accommodating variable time intervals. Within this extensive survey, we outline the application of machine learning techniques in short-reach communications, specifically emphasizing their utilization in high-bandwidth demanding PONs. Notably, we introduce a novel taxonomy for time-series methods employed in machine learning signal processing, providing a structured classification framework. Our taxonomy categorizes current time series methods into four distinct groups: traditional methods, Fourier convolution-based methods, transformer-based models, and time-series convolutional networks. Finally, we highlight prospective research directions within this rapidly evolving field and outline specific solutions to mitigate the complexity associated with hardware implementations. We aim to pave the way for more practical and efficient deployment of machine learning approaches in short-reach optical communication systems by addressing complexity concerns.", "published": "2024-05-02T16:04:30Z", "authors": ["Chen Shao", "Elias Giacoumidis", "Syed Moktacim Billah", "Shi Li", "Jialei Li", "Prashasti Sahu", "Andre Richter", "Tobias Kaefer", "Michael Faerber"], "categories": ["eess.SP", "cs.LG"], "doi": "", "_query": "house price prediction machine learning", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2605.11134v2", "title": "Spurious Correlation Learning in Preference Optimization: Mechanisms, Consequences, and Mitigation via Tie Training", "abstract": "Preference learning methods like Direct Preference Optimization (DPO) are known to induce reliance on spurious correlations, leading to sycophancy and length bias in today's language models and potentially severe goal misgeneralization in future systems. In this work, we provide a unified theoretical analysis of this phenomenon, characterizing the mechanisms of spurious learning, its consequences on deployment, and a provable mitigation strategy. Focusing on log-linear policies, we show that standard preference-learning objectives induce reliance on spurious features at the population level through two channels: mean spurious bias and causal-spurious correlation leakage. We then show that this reliance creates an irreducible vulnerability to distribution shift: more data from the same training distribution fails to reduce the model's dependence on spurious features. To address this, we propose tie training, a data augmentation strategy using ties (equal-utility preference pairs) to introduce data-driven regularization. We demonstrate that this approach selectively reduces spurious learning without degrading causal learning. Finally, we validate our theory on log-linear models and provide empirical evidence that both the spurious learning mechanisms and the benefits of tie training persist for neural networks and large language models.", "published": "2026-05-11T18:41:12Z", "authors": ["Christian Moya", "Alex Semendinger", "Guang Lin", "Elliott Thornley"], "categories": ["cs.LG", "cs.AI"], "doi": "", "_query": "house price prediction machine learning", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2502.04140v2", "title": "Synthetic Datasets for Machine Learning on Spatio-Temporal Graphs using PDEs", "abstract": "Many physical processes can be expressed through partial differential equations (PDEs). Real-world measurements of such processes are often collected at irregularly distributed points in space, which can be effectively represented as graphs; however, there are currently only a few existing datasets. Our work aims to make advancements in the field of PDE-modeling accessible to the temporal graph machine learning community, while addressing the data scarcity problem, by creating and utilizing datasets based on PDEs. In this work, we create and use synthetic datasets based on PDEs to support spatio-temporal graph modeling in machine learning for different applications. More precisely, we showcase three equations to model different types of disasters and hazards in the fields of epidemiology, atmospheric particles, and tsunami waves. Further, we show how such created datasets can be used by benchmarking several machine learning models on the epidemiological dataset. Additionally, we show how pre-training on this dataset can improve model performance on real-world epidemiological data. The presented methods enable others to create datasets and benchmarks customized to individual requirements. The source code for our methodology and the three created datasets can be found on https://github.com/github-usr-ano/Temporal_Graph_Data_PDEs.", "published": "2025-02-06T15:20:32Z", "authors": ["Jost Arndt", "Utku Isil", "Michael Detzel", "Wojciech Samek", "Jackie Ma"], "categories": ["cs.LG", "cs.AI"], "doi": "", "_query": "house price prediction machine learning", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2108.07915v1", "title": "Data Pricing in Machine Learning Pipelines", "abstract": "Machine learning is disruptive. At the same time, machine learning can only succeed by collaboration among many parties in multiple steps naturally as pipelines in an eco-system, such as collecting data for possible machine learning applications, collaboratively training models by multiple parties and delivering machine learning services to end users. Data is critical and penetrating in the whole machine learning pipelines. As machine learning pipelines involve many parties and, in order to be successful, have to form a constructive and dynamic eco-system, marketplaces and data pricing are fundamental in connecting and facilitating those many parties. In this article, we survey the principles and the latest research development of data pricing in machine learning pipelines. We start with a brief review of data marketplaces and pricing desiderata. Then, we focus on pricing in three important steps in machine learning pipelines. To understand pricing in the step of training data collection, we review pricing raw data sets and data labels. We also investigate pricing in the step of collaborative training of machine learning models, and overview pricing machine learning models for end users in the step of machine learning deployment. We also discuss a series of possible future directions.", "published": "2021-08-18T00:57:06Z", "authors": ["Zicun Cong", "Xuan Luo", "Pei Jian", "Feida Zhu", "Yong Zhang"], "categories": ["cs.LG"], "doi": "", "_query": "house price prediction machine learning", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2602.06229v1", "title": "SR4-Fit: An Interpretable and Informative Classification Algorithm Applied to Prediction of U.S. House of Representatives Elections", "abstract": "The growth of machine learning demands interpretable models for critical applications, yet most high-performing models are ``black-box'' systems that obscure input-output relationships, while traditional rule-based algorithms like RuleFit suffer from a lack of predictive power and instability despite their simplicity. This motivated our development of Sparse Relaxed Regularized Regression Rule-Fit (SR4-Fit), a novel interpretable classification algorithm that addresses these limitations while maintaining superior classification performance. Using demographic characteristics of U.S. congressional districts from the Census Bureau's American Community Survey, we demonstrate that SR4-Fit can predict House election party outcomes with unprecedented accuracy and interpretability. Our results show that while the majority party remains the strongest predictor, SR4-Fit has revealed intrinsic combinations of demographic factors that affect prediction outcomes that were unable to be interpreted in black-box algorithms such as random forests. The SR4-Fit algorithm surpasses both black-box models and existing interpretable rule-based algorithms such as RuleFit with respect to accuracy, simplicity, and robustness, generating stable and interpretable rule sets while maintaining superior predictive performance, thus addressing the traditional trade-off between model interpretability and predictive capability in electoral forecasting. To further validate SR4-Fit's performance, we also apply it to six additional publicly available classification datasets, like the breast cancer, Ecoli, page blocks, Pima Indians, vehicle, and yeast datasets, and find similar results.", "published": "2026-02-05T22:18:43Z", "authors": ["Shyam Sundar Murali Krishnan", "Dean Frederick Hougen"], "categories": ["cs.LG", "cs.AI"], "doi": "", "_query": "house price prediction machine learning", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2407.08250v3", "title": "Gradient Boosting Reinforcement Learning", "abstract": "We present Gradient Boosting Reinforcement Learning (GBRL), a framework that adapts the strengths of gradient boosting trees (GBT) to reinforcement learning (RL) tasks. While neural networks (NNs) have become the de facto choice for RL, they face significant challenges with structured and categorical features and tend to generalize poorly to out-of-distribution samples. These are challenges for which GBTs have traditionally excelled in supervised learning. However, GBT's application in RL has been limited. The design of traditional GBT libraries is optimized for static datasets with fixed labels, making them incompatible with RL's dynamic nature, where both state distributions and reward signals evolve during training. GBRL overcomes this limitation by continuously interleaving tree construction with environment interaction. Through extensive experiments, we demonstrate that GBRL outperforms NNs in domains with structured observations and categorical features while maintaining competitive performance on standard continuous control benchmarks. Like its supervised learning counterpart, GBRL demonstrates superior robustness to out-of-distribution samples and better handles irregular state-action relationships.", "published": "2024-07-11T07:52:33Z", "authors": ["Benjamin Fuhrer", "Chen Tessler", "Gal Dalal"], "categories": ["cs.LG", "cs.AI"], "doi": "", "_query": "house price prediction machine learning", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2202.04005v2", "title": "Improved Convergence Rates for Sparse Approximation Methods in Kernel-Based Learning", "abstract": "Kernel-based models such as kernel ridge regression and Gaussian processes are ubiquitous in machine learning applications for regression and optimization. It is well known that a major downside for kernel-based models is the high computational cost; given a dataset of $n$ samples, the cost grows as $\\mathcal{O}(n^3)$. Existing sparse approximation methods can yield a significant reduction in the computational cost, effectively reducing the actual cost down to as low as $\\mathcal{O}(n)$ in certain cases. Despite this remarkable empirical success, significant gaps remain in the existing results for the analytical bounds on the error due to approximation. In this work, we provide novel confidence intervals for the Nyström method and the sparse variational Gaussian process approximation method, which we establish using novel interpretations of the approximate (surrogate) posterior variance of the models. Our confidence intervals lead to improved performance bounds in both regression and optimization problems.", "published": "2022-02-08T17:22:09Z", "authors": ["Sattar Vakili", "Jonathan Scarlett", "Da-shan Shiu", "Alberto Bernacchia"], "categories": ["cs.LG", "stat.ML"], "doi": "", "_query": "house price prediction machine learning", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/1803.04479v1", "title": "Machine Learning Harnesses Molecular Dynamics to Discover New $μ$ Opioid Chemotypes", "abstract": "Computational chemists typically assay drug candidates by virtually screening compounds against crystal structures of a protein despite the fact that some targets, like the $μ$ Opioid Receptor and other members of the GPCR family, traverse many non-crystallographic states. We discover new conformational states of $μOR$ with molecular dynamics simulation and then machine learn ligand-structure relationships to predict opioid ligand function. These artificial intelligence models identified a novel $μ$ opioid chemotype.", "published": "2018-03-12T19:32:21Z", "authors": ["Evan N. Feinberg", "Amir Barati Farimani", "Rajendra Uprety", "Amanda Hunkele", "Gavril W. Pasternak", "Susruta Majumdar", "Vijay S. Pande"], "categories": ["q-bio.BM", "stat.ML"], "doi": "", "_query": "house price prediction machine learning", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2302.14567v1", "title": "Active Learning with Combinatorial Coverage", "abstract": "Active learning is a practical field of machine learning that automates the process of selecting which data to label. Current methods are effective in reducing the burden of data labeling but are heavily model-reliant. This has led to the inability of sampled data to be transferred to new models as well as issues with sampling bias. Both issues are of crucial concern in machine learning deployment. We propose active learning methods utilizing combinatorial coverage to overcome these issues. The proposed methods are data-centric, as opposed to model-centric, and through our experiments we show that the inclusion of coverage in active learning leads to sampling data that tends to be the best in transferring to better performing models and has a competitive sampling bias compared to benchmark methods.", "published": "2023-02-28T13:43:23Z", "authors": ["Sai Prathyush Katragadda", "Tyler Cody", "Peter Beling", "Laura Freeman"], "categories": ["cs.LG", "stat.ML"], "doi": "", "_query": "house price prediction machine learning", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/1811.08840v1", "title": "Integrating Reinforcement Learning to Self Training for Pulmonary Nodule Segmentation in Chest X-rays", "abstract": "Machine learning applications in medical imaging are frequently limited by the lack of quality labeled data. In this paper, we explore the self training method, a form of semi-supervised learning, to address the labeling burden. By integrating reinforcement learning, we were able to expand the application of self training to complex segmentation networks without any further human annotation. The proposed approach, reinforced self training (ReST), fine tunes a semantic segmentation networks by introducing a policy network that learns to generate pseudolabels. We incorporate an expert demonstration network, based on inverse reinforcement learning, to enhance clinical validity and convergence of the policy network. The model was tested on a pulmonary nodule segmentation task in chest X-rays and achieved the performance of a standard U-Net while using only 50% of the labeled data, by exploiting unlabeled data. When the same number of labeled data was used, a moderate to significant cross validation accuracy improvement was achieved depending on the absolute number of labels used.", "published": "2018-11-21T17:37:22Z", "authors": ["Sejin Park", "Woochan Hwang", "Kyu-Hwan Jung"], "categories": ["cs.LG", "stat.ML"], "doi": "", "_query": "house price prediction machine learning", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2003.06428v1", "title": "Towards a Resilient Machine Learning Classifier -- a Case Study of Ransomware Detection", "abstract": "The damage caused by crypto-ransomware, due to encryption, is difficult to revert and cause data losses. In this paper, a machine learning (ML) classifier was built to early detect ransomware (called crypto-ransomware) that uses cryptography by program behavior. If a signature-based detection was missed, a behavior-based detector can be the last line of defense to detect and contain the damages. We find that input/output activities of ransomware and the file-content entropy are unique traits to detect crypto-ransomware. A deep-learning (DL) classifier can detect ransomware with a high accuracy and a low false positive rate. We conduct an adversarial research against the models generated. We use simulated ransomware programs to launch a gray-box analysis to probe the weakness of ML classifiers and to improve model robustness. In addition to accuracy and resiliency, trustworthiness is the other key criteria for a quality detector. Making sure that the correct information was used for inference is important for a security application. The Integrated Gradient method was used to explain the deep learning model and also to reveal why false negatives evade the detection. The approaches to build and to evaluate a real-world detector were demonstrated and discussed.", "published": "2020-03-13T18:02:19Z", "authors": ["Chih-Yuan Yang", "Ravi Sahita"], "categories": ["cs.CR", "cs.LG", "stat.ML"], "doi": "", "_query": "house price prediction machine learning", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/1806.04245v1", "title": "Learning to Speed Up Structured Output Prediction", "abstract": "Predicting structured outputs can be computationally onerous due to the combinatorially large output spaces. In this paper, we focus on reducing the prediction time of a trained black-box structured classifier without losing accuracy. To do so, we train a speedup classifier that learns to mimic a black-box classifier under the learning-to-search approach. As the structured classifier predicts more examples, the speedup classifier will operate as a learned heuristic to guide search to favorable regions of the output space. We present a mistake bound for the speedup classifier and identify inference situations where it can independently make correct judgments without input features. We evaluate our method on the task of entity and relation extraction and show that the speedup classifier outperforms even greedy search in terms of speed without loss of accuracy.", "published": "2018-06-11T21:06:51Z", "authors": ["Xingyuan Pan", "Vivek Srikumar"], "categories": ["cs.LG", "stat.ML"], "doi": "", "_query": "house price prediction machine learning", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2209.08307v2", "title": "A review of predictive uncertainty estimation with machine learning", "abstract": "Predictions and forecasts of machine learning models should take the form of probability distributions, aiming to increase the quantity of information communicated to end users. Although applications of probabilistic prediction and forecasting with machine learning models in academia and industry are becoming more frequent, related concepts and methods have not been formalized and structured under a holistic view of the entire field. Here, we review the topic of predictive uncertainty estimation with machine learning algorithms, as well as the related metrics (consistent scoring functions and proper scoring rules) for assessing probabilistic predictions. The review covers a time period spanning from the introduction of early statistical (linear regression and time series models, based on Bayesian statistics or quantile regression) to recent machine learning algorithms (including generalized additive models for location, scale and shape, random forests, boosting and deep learning algorithms) that are more flexible by nature. The review of the progress in the field, expedites our understanding on how to develop new algorithms tailored to users' needs, since the latest advancements are based on some fundamental concepts applied to more complex algorithms. We conclude by classifying the material and discussing challenges that are becoming a hot topic of research.", "published": "2022-09-17T10:36:30Z", "authors": ["Hristos Tyralis", "Georgia Papacharalampous"], "categories": ["stat.ML", "cs.LG", "math.ST"], "doi": "10.1007/s10462-023-10698-8", "_query": "house price prediction machine learning", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2110.03224v3", "title": "Darts: User-Friendly Modern Machine Learning for Time Series", "abstract": "We present Darts, a Python machine learning library for time series, with a focus on forecasting. Darts offers a variety of models, from classics such as ARIMA to state-of-the-art deep neural networks. The emphasis of the library is on offering modern machine learning functionalities, such as supporting multidimensional series, meta-learning on multiple series, training on large datasets, incorporating external data, ensembling models, and providing a rich support for probabilistic forecasting. At the same time, great care goes into the API design to make it user-friendly and easy to use. For instance, all models can be used using fit()/predict(), similar to scikit-learn.", "published": "2021-10-07T07:18:57Z", "authors": ["Julien Herzen", "Francesco Lässig", "Samuele Giuliano Piazzetta", "Thomas Neuer", "Léo Tafti", "Guillaume Raille", "Tomas Van Pottelbergh", "Marek Pasieka", "Andrzej Skrodzki", "Nicolas Huguenin", "Maxime Dumonal", "Jan Kościsz", "Dennis Bader", "Frédérick Gusset", "Mounir Benheddi", "Camila Williamson", "Michal Kosinski", "Matej Petrik", "Gaël Grosch"], "categories": ["cs.LG", "stat.CO"], "doi": "", "_query": "house price prediction machine learning", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2209.09638v1", "title": "Machine Learning Models Evaluation and Feature Importance Analysis on NPL Dataset", "abstract": "Predicting the probability of non-performing loans for individuals has a vital and beneficial role for banks to decrease credit risk and make the right decisions before giving the loan. The trend to make these decisions are based on credit study and in accordance with generally accepted standards, loan payment history, and demographic data of the clients. In this work, we evaluate how different Machine learning models such as Random Forest, Decision tree, KNN, SVM, and XGBoost perform on the dataset provided by a private bank in Ethiopia. Further, motivated by this evaluation we explore different feature selection methods to state the important features for the bank. Our findings show that XGBoost achieves the highest F1 score on the KMeans SMOTE over-sampled data. We also found that the most important features are the age of the applicant, years of employment, and total income of the applicant rather than collateral-related features in evaluating credit risk.", "published": "2022-08-28T17:09:44Z", "authors": ["Rufael Fekadu", "Anteneh Getachew", "Yishak Tadele", "Nuredin Ali", "Israel Goytom"], "categories": ["cs.LG", "cs.AI"], "doi": "", "_query": "house price prediction machine learning", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/1502.05767v4", "title": "Automatic differentiation in machine learning: a survey", "abstract": "Derivatives, mostly in the form of gradients and Hessians, are ubiquitous in machine learning. Automatic differentiation (AD), also called algorithmic differentiation or simply \"autodiff\", is a family of techniques similar to but more general than backpropagation for efficiently and accurately evaluating derivatives of numeric functions expressed as computer programs. AD is a small but established field with applications in areas including computational fluid dynamics, atmospheric sciences, and engineering design optimization. Until very recently, the fields of machine learning and AD have largely been unaware of each other and, in some cases, have independently discovered each other's results. Despite its relevance, general-purpose AD has been missing from the machine learning toolbox, a situation slowly changing with its ongoing adoption under the names \"dynamic computational graphs\" and \"differentiable programming\". We survey the intersection of AD and machine learning, cover applications where AD has direct relevance, and address the main implementation techniques. By precisely defining the main differentiation techniques and their interrelationships, we aim to bring clarity to the usage of the terms \"autodiff\", \"automatic differentiation\", and \"symbolic differentiation\" as these are encountered more and more in machine learning settings.", "published": "2015-02-20T04:20:47Z", "authors": ["Atilim Gunes Baydin", "Barak A. Pearlmutter", "Alexey Andreyevich Radul", "Jeffrey Mark Siskind"], "categories": ["cs.SC", "cs.LG", "stat.ML"], "doi": "", "_query": "house price prediction machine learning", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/physics/0606064v1", "title": "A Roof over your Head; House Price Peaks in the UK and Ireland", "abstract": "We analyse, following recent work of Roehner, changes in house prices for both the UK and Ireland. We conclude that prices in London have reached a tipping point and prices relative to inflation are set to fall over the next few years. If inflation does not rise then a hard landing seems likely. House prices in the Irish Republic are shown to have broken away from the moderate rise still to be found in Northern Ireland and Dublin has emerged as another global 'hot spot'. An evolution of Dublin house prices similar to that in London can be anticipated. Keywords: Econophysics, house prices, real estate, prediction PACS: 89.65.Gh, 89.90.+n", "published": "2006-06-07T14:07:28Z", "authors": ["Peter Richmond"], "categories": ["physics.soc-ph", "physics.pop-ph"], "doi": "10.1016/j.physa.2006.09.003", "_query": "house price prediction machine learning", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2005.04612v1", "title": "A machine learning based heuristic to predict the efficacy of online sale", "abstract": "It is difficult to decide upon the efficacy of an online sale simply from the discount offered on commodities. Different features have different influence on the price of a product which must be taken into consideration when determining the significance of a discount. In this paper we have proposed a machine learning based heuristic to quantify the \\textit{\"significance\"} of the discount offered on any commodity. Our proposed technique can quantify the significance of the discount based on features and the original price, and hence can guide a buyer during a sale season by predicting the efficacy of the sale. We have applied this technique on the Flipkart Summer Sale dataset using Support Vector Machine, which predicts the efficacy of the sale with an accuracy of 91.11\\%. Our result shows that very few mobile phones have a significant discount during the Flipkart Summer Sale.", "published": "2020-05-10T09:31:54Z", "authors": ["Aditya Vikram Singhania", "Saronyo Lal Mukherjee", "Ritajit Majumdar", "Akash Mehta", "Priyanka Banerjee", "Debasmita Bhoumik"], "categories": ["cs.LG", "stat.ML"], "doi": "", "_query": "house price prediction machine learning", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/1812.02497v1", "title": "Active Learning Methods based on Statistical Leverage Scores", "abstract": "In many real-world machine learning applications, unlabeled data are abundant whereas class labels are expensive and scarce. An active learner aims to obtain a model of high accuracy with as few labeled instances as possible by effectively selecting useful examples for labeling. We propose a new selection criterion that is based on statistical leverage scores and present two novel active learning methods based on this criterion: ALEVS for querying single example at each iteration and DBALEVS for querying a batch of examples. To assess the representativeness of the examples in the pool, ALEVS and DBALEVS use the statistical leverage scores of the kernel matrices computed on the examples of each class. Additionally, DBALEVS selects a diverse a set of examples that are highly representative but are dissimilar to already labeled examples through maximizing a submodular set function defined with the statistical leverage scores and the kernel matrix computed on the pool of the examples. The submodularity property of the set scoring function let us identify batches with a constant factor approximate to the optimal batch in an efficient manner. Our experiments on diverse datasets show that querying based on leverage scores is a powerful strategy for active learning.", "published": "2018-12-06T12:38:22Z", "authors": ["Cem Orhan", "Oznur Tastan"], "categories": ["cs.LG", "stat.ML"], "doi": "", "_query": "house price prediction machine learning", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/1210.6293v1", "title": "MLPACK: A Scalable C++ Machine Learning Library", "abstract": "MLPACK is a state-of-the-art, scalable, multi-platform C++ machine learning library released in late 2011 offering both a simple, consistent API accessible to novice users and high performance and flexibility to expert users by leveraging modern features of C++. MLPACK provides cutting-edge algorithms whose benchmarks exhibit far better performance than other leading machine learning libraries. MLPACK version 1.0.3, licensed under the LGPL, is available at http://www.mlpack.org.", "published": "2012-10-23T17:15:03Z", "authors": ["Ryan R. Curtin", "James R. Cline", "N. P. Slagle", "William B. March", "Parikshit Ram", "Nishant A. Mehta", "Alexander G. Gray"], "categories": ["cs.MS", "cs.CV", "cs.LG"], "doi": "", "_query": "house price prediction machine learning", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2107.10650v1", "title": "Read, Attend, and Code: Pushing the Limits of Medical Codes Prediction from Clinical Notes by Machines", "abstract": "Prediction of medical codes from clinical notes is both a practical and essential need for every healthcare delivery organization within current medical systems. Automating annotation will save significant time and excessive effort spent by human coders today. However, the biggest challenge is directly identifying appropriate medical codes out of several thousands of high-dimensional codes from unstructured free-text clinical notes. In the past three years, with Convolutional Neural Networks (CNN) and Long Short-Term Memory (LTSM) networks, there have been vast improvements in tackling the most challenging benchmark of the MIMIC-III-full-label inpatient clinical notes dataset. This progress raises the fundamental question of how far automated machine learning (ML) systems are from human coders' working performance. We assessed the baseline of human coders' performance on the same subsampled testing set. We also present our Read, Attend, and Code (RAC) model for learning the medical code assignment mappings. By connecting convolved embeddings with self-attention and code-title guided attention modules, combined with sentence permutation-based data augmentations and stochastic weight averaging training, RAC establishes a new state of the art (SOTA), considerably outperforming the current best Macro-F1 by 18.7%, and reaches past the human-level coding baseline. This new milestone marks a meaningful step toward fully autonomous medical coding (AMC) in machines reaching parity with human coders' performance in medical code prediction.", "published": "2021-07-10T06:01:58Z", "authors": ["Byung-Hak Kim", "Varun Ganapathi"], "categories": ["cs.CL", "cs.LG"], "doi": "", "_query": "house price prediction machine learning", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/1206.6389v3", "title": "Poisoning Attacks against Support Vector Machines", "abstract": "We investigate a family of poisoning attacks against Support Vector Machines (SVM). Such attacks inject specially crafted training data that increases the SVM's test error. Central to the motivation for these attacks is the fact that most learning algorithms assume that their training data comes from a natural or well-behaved distribution. However, this assumption does not generally hold in security-sensitive settings. As we demonstrate, an intelligent adversary can, to some extent, predict the change of the SVM's decision function due to malicious input and use this ability to construct malicious data. The proposed attack uses a gradient ascent strategy in which the gradient is computed based on properties of the SVM's optimal solution. This method can be kernelized and enables the attack to be constructed in the input space even for non-linear kernels. We experimentally demonstrate that our gradient ascent procedure reliably identifies good local maxima of the non-convex validation error surface, which significantly increases the classifier's test error.", "published": "2012-06-27T19:59:59Z", "authors": ["Battista Biggio", "Blaine Nelson", "Pavel Laskov"], "categories": ["cs.LG", "cs.CR", "stat.ML"], "doi": "", "_query": "house price prediction machine learning", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2112.09612v1", "title": "Inorganic Synthesis Reaction Condition Prediction with Generative Machine Learning", "abstract": "Data-driven synthesis planning with machine learning is a key step in the design and discovery of novel inorganic compounds with desirable properties. Inorganic materials synthesis is often guided by chemists' prior knowledge and experience, built upon experimental trial-and-error that is both time and resource consuming. Recent developments in natural language processing (NLP) have enabled large-scale text mining of scientific literature, providing open source databases of synthesis information of synthesized compounds, material precursors, and reaction conditions (temperatures, times). In this work, we employ a conditional variational autoencoder (CVAE) to predict suitable inorganic reaction conditions for the crucial inorganic synthesis steps of calcination and sintering. We find that the CVAE model is capable of learning subtle differences in target material composition, precursor compound identities, and choice of synthesis route (solid-state, sol-gel) that are present in the inorganic synthesis space. Moreover, the CVAE can generalize well to unseen chemical entities and shows promise for predicting reaction conditions for previously unsynthesized compounds of interest.", "published": "2021-12-17T16:38:15Z", "authors": ["Christopher Karpovich", "Zach Jensen", "Vineeth Venugopal", "Elsa Olivetti"], "categories": ["cond-mat.mtrl-sci"], "doi": "", "_query": "house price prediction machine learning", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2407.16907v1", "title": "Research on Education Big Data for Students Academic Performance Analysis based on Machine Learning", "abstract": "The application of the Internet in the field of education is becoming more and more popular, and a large amount of educational data is generated in the process. How to effectively use these data has always been a key issue in the field of educational data mining. In this work, a machine learning model based on Long Short-Term Memory Network (LSTM) was used to conduct an in-depth analysis of educational big data to evaluate student performance. The LSTM model efficiently processes time series data, allowing us to capture time-dependent and long-term trends in students' learning activities. This approach is particularly useful for analyzing student progress, engagement, and other behavioral patterns to support personalized education. In an experimental analysis, we verified the effectiveness of the deep learning method in predicting student performance by comparing the performance of different models. Strict cross-validation techniques are used to ensure the accuracy and generalization of experimental results.", "published": "2024-06-25T01:19:22Z", "authors": ["Chun Wang", "Jiexiao Chen", "Ziyang Xie", "Jianke Zou"], "categories": ["cs.CY", "cs.LG"], "doi": "", "_query": "house price prediction machine learning", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2303.05155v2", "title": "Aux-Drop: Handling Haphazard Inputs in Online Learning Using Auxiliary Dropouts", "abstract": "Many real-world applications based on online learning produce streaming data that is haphazard in nature, i.e., contains missing features, features becoming obsolete in time, the appearance of new features at later points in time and a lack of clarity on the total number of input features. These challenges make it hard to build a learnable system for such applications, and almost no work exists in deep learning that addresses this issue. In this paper, we present Aux-Drop, an auxiliary dropout regularization strategy for online learning that handles the haphazard input features in an effective manner. Aux-Drop adapts the conventional dropout regularization scheme for the haphazard input feature space ensuring that the final output is minimally impacted by the chaotic appearance of such features. It helps to prevent the co-adaptation of especially the auxiliary and base features, as well as reduces the strong dependence of the output on any of the auxiliary inputs of the model. This helps in better learning for scenarios where certain features disappear in time or when new features are to be modelled. The efficacy of Aux-Drop has been demonstrated through extensive numerical experiments on SOTA benchmarking datasets that include Italy Power Demand, HIGGS, SUSY and multiple UCI datasets. The code is available at https://github.com/Rohit102497/Aux-Drop.", "published": "2023-03-09T10:15:03Z", "authors": ["Rohit Agarwal", "Deepak Gupta", "Alexander Horsch", "Dilip K. Prasad"], "categories": ["cs.LG", "cs.AI"], "doi": "", "_query": "house price prediction machine learning", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/1801.08640v4", "title": "Considerations When Learning Additive Explanations for Black-Box Models", "abstract": "Many methods to explain black-box models, whether local or global, are additive. In this paper, we study global additive explanations for non-additive models, focusing on four explanation methods: partial dependence, Shapley explanations adapted to a global setting, distilled additive explanations, and gradient-based explanations. We show that different explanation methods characterize non-additive components in a black-box model's prediction function in different ways. We use the concepts of main and total effects to anchor additive explanations, and quantitatively evaluate additive and non-additive explanations. Even though distilled explanations are generally the most accurate additive explanations, non-additive explanations such as tree explanations that explicitly model non-additive components tend to be even more accurate. Despite this, our user study showed that machine learning practitioners were better able to leverage additive explanations for various tasks. These considerations should be taken into account when considering which explanation to trust and use to explain black-box models.", "published": "2018-01-26T00:23:20Z", "authors": ["Sarah Tan", "Giles Hooker", "Paul Koch", "Albert Gordo", "Rich Caruana"], "categories": ["stat.ML", "cs.LG"], "doi": "10.1007/s10994-023-06335-8", "_query": "house price prediction machine learning", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2009.09756v2", "title": "Demand Prediction Using Machine Learning Methods and Stacked Generalization", "abstract": "Supply and demand are two fundamental concepts of sellers and customers. Predicting demand accurately is critical for organizations in order to be able to make plans. In this paper, we propose a new approach for demand prediction on an e-commerce web site. The proposed model differs from earlier models in several ways. The business model used in the e-commerce web site, for which the model is implemented, includes many sellers that sell the same product at the same time at different prices where the company operates a market place model. The demand prediction for such a model should consider the price of the same product sold by competing sellers along the features of these sellers. In this study we first applied different regression algorithms for specific set of products of one department of a company that is one of the most popular online e-commerce companies in Turkey. Then we used stacked generalization or also known as stacking ensemble learning to predict demand. Finally, all the approaches are evaluated on a real world data set obtained from the e-commerce company. The experimental results show that some of the machine learning methods do produce almost as good results as the stacked generalization method.", "published": "2020-09-21T10:58:07Z", "authors": ["Resul Tugay", "Sule Gunduz Oguducu"], "categories": ["cs.LG", "cs.AI", "stat.ML"], "doi": "", "_query": "house price prediction machine learning", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2512.11133v1", "title": "Machine Learning", "abstract": "This chapter gives an overview of the core concepts of machine learning (ML) -- the use of algorithms that learn from data, identify patterns, and make predictions or decisions without being explicitly programmed -- that are relevant to particle physics with some examples of applications to the energy, intensity, cosmic, and accelerator frontiers.", "published": "2025-12-11T21:50:11Z", "authors": ["Javier M. Duarte", "Uros Seljak", "Kazu Terao"], "categories": ["physics.data-an", "hep-ex", "hep-ph", "hep-th"], "doi": "", "_query": "house price prediction machine learning", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2508.14311v2", "title": "Online Learning with Multiple Fairness Regularizers via Graph-Structured Feedback", "abstract": "There is an increasing need to enforce multiple, often competing, measures of fairness within automated decision systems. The appropriate weighting of these fairness objectives is typically unknown a priori, may change over time and, in our setting, must be learned adaptively through sequential interactions. In this work, we address this challenge in a bandit setting, where decisions are made with graph-structured feedback.", "published": "2025-08-19T23:32:26Z", "authors": ["Quan Zhou", "Jakub Marecek", "Robert Shorten"], "categories": ["cs.LG", "cs.AI"], "doi": "", "_query": "house price prediction machine learning", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2312.08999v2", "title": "Conformalised data synthesis", "abstract": "With the proliferation of increasingly complicated Deep Learning architectures, data synthesis is a highly promising technique to address the demand of data-hungry models. However, reliably assessing the quality of a 'synthesiser' model's output is an open research question with significant associated risks for high-stake domains. To address this challenge, we propose a unique synthesis algorithm that generates data from high-confidence feature space regions based on the Conformal Prediction framework. We support our proposed algorithm with a comprehensive exploration of the core parameter's influence, an in-depth discussion of practical advice, and an extensive empirical evaluation of five benchmark datasets. To show our approach's versatility on ubiquitous real-world challenges, the datasets were carefully selected for their variety of difficult characteristics: low sample count, class imbalance, and non-separability. In all trials, training sets extended with our confident synthesised data performed at least as well as the original set and frequently significantly improved Deep Learning performance by up to 61 percentage points F1-score.", "published": "2023-12-14T14:44:08Z", "authors": ["Julia A. Meister", "Khuong An Nguyen"], "categories": ["cs.LG", "stat.ML"], "doi": "10.1007/s10994-024-06701-0", "_query": "house price prediction machine learning", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2409.03204v1", "title": "Pricing American Options using Machine Learning Algorithms", "abstract": "This study investigates the application of machine learning algorithms, particularly in the context of pricing American options using Monte Carlo simulations. Traditional models, such as the Black-Scholes-Merton framework, often fail to adequately address the complexities of American options, which include the ability for early exercise and non-linear payoff structures. By leveraging Monte Carlo methods in conjunction Least Square Method machine learning was used. This research aims to improve the accuracy and efficiency of option pricing. The study evaluates several machine learning models, including neural networks and decision trees, highlighting their potential to outperform traditional approaches. The results from applying machine learning algorithm in LSM indicate that integrating machine learning with Monte Carlo simulations can enhance pricing accuracy and provide more robust predictions, offering significant insights into quantitative finance by merging classical financial theories with modern computational techniques. The dataset was split into features and the target variable representing bid prices, with an 80-20 train-validation split. LSTM and GRU models were constructed using TensorFlow's Keras API, each with four hidden layers of 200 neurons and an output layer for bid price prediction, optimized with the Adam optimizer and MSE loss function. The GRU model outperformed the LSTM model across all evaluated metrics, demonstrating lower mean absolute error, mean squared error, and root mean squared error, along with greater stability and efficiency in training.", "published": "2024-09-05T02:52:11Z", "authors": ["Prudence Djagba", "Callixte Ndizihiwe"], "categories": ["cs.LG", "q-fin.CP"], "doi": "", "_query": "house price prediction machine learning", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2406.17098v2", "title": "Learning Temporal Distances: Contrastive Successor Features Can Provide a Metric Structure for Decision-Making", "abstract": "Temporal distances lie at the heart of many algorithms for planning, control, and reinforcement learning that involve reaching goals, allowing one to estimate the transit time between two states. However, prior attempts to define such temporal distances in stochastic settings have been stymied by an important limitation: these prior approaches do not satisfy the triangle inequality. This is not merely a definitional concern, but translates to an inability to generalize and find shortest paths. In this paper, we build on prior work in contrastive learning and quasimetrics to show how successor features learned by contrastive learning (after a change of variables) form a temporal distance that does satisfy the triangle inequality, even in stochastic settings. Importantly, this temporal distance is computationally efficient to estimate, even in high-dimensional and stochastic settings. Experiments in controlled settings and benchmark suites demonstrate that an RL algorithm based on these new temporal distances exhibits combinatorial generalization (i.e., \"stitching\") and can sometimes learn more quickly than prior methods, including those based on quasimetrics.", "published": "2024-06-24T19:36:45Z", "authors": ["Vivek Myers", "Chongyi Zheng", "Anca Dragan", "Sergey Levine", "Benjamin Eysenbach"], "categories": ["cs.LG", "cs.AI"], "doi": "", "_query": "house price prediction machine learning", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/1908.06040v2", "title": "Performing Deep Recurrent Double Q-Learning for Atari Games", "abstract": "Currently, many applications in Machine Learning are based on define new models to extract more information about data, In this case Deep Reinforcement Learning with the most common application in video games like Atari, Mario, and others causes an impact in how to computers can learning by himself with only information called rewards obtained from any action. There is a lot of algorithms modeled and implemented based on Deep Recurrent Q-Learning proposed by DeepMind used in AlphaZero and Go. In this document, We proposed Deep Recurrent Double Q-Learning that is an implementation of Deep Reinforcement Learning using Double Q-Learning algorithms and Recurrent Networks like LSTM and DRQN.", "published": "2019-08-16T15:56:16Z", "authors": ["Felipe Moreno-Vera"], "categories": ["cs.LG", "cs.AI", "cs.NE", "stat.ML"], "doi": "", "_query": "house price prediction machine learning", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/1907.06260v1", "title": "Counterfactual Reasoning for Fair Clinical Risk Prediction", "abstract": "The use of machine learning systems to support decision making in healthcare raises questions as to what extent these systems may introduce or exacerbate disparities in care for historically underrepresented and mistreated groups, due to biases implicitly embedded in observational data in electronic health records. To address this problem in the context of clinical risk prediction models, we develop an augmented counterfactual fairness criteria to extend the group fairness criteria of equalized odds to an individual level. We do so by requiring that the same prediction be made for a patient, and a counterfactual patient resulting from changing a sensitive attribute, if the factual and counterfactual outcomes do not differ. We investigate the extent to which the augmented counterfactual fairness criteria may be applied to develop fair models for prolonged inpatient length of stay and mortality with observational electronic health records data. As the fairness criteria is ill-defined without knowledge of the data generating process, we use a variational autoencoder to perform counterfactual inference in the context of an assumed causal graph. While our technique provides a means to trade off maintenance of fairness with reduction in predictive performance in the context of a learned generative model, further work is needed to assess the generality of this approach.", "published": "2019-07-14T18:44:09Z", "authors": ["Stephen Pfohl", "Tony Duan", "Daisy Yi Ding", "Nigam H. Shah"], "categories": ["cs.LG", "cs.CY", "stat.ML"], "doi": "", "_query": "house price prediction machine learning", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2412.07244v1", "title": "Developing a Dataset-Adaptive, Normalized Metric for Machine Learning Model Assessment: Integrating Size, Complexity, and Class Imbalance", "abstract": "Traditional metrics like accuracy, F1-score, and precision are frequently used to evaluate machine learning models, however they may not be sufficient for evaluating performance on tiny, unbalanced, or high-dimensional datasets. A dataset-adaptive, normalized metric that incorporates dataset characteristics like size, feature dimensionality, class imbalance, and signal-to-noise ratio is presented in this study. Early insights into the model's performance potential in challenging circumstances are provided by the suggested metric, which offers a scalable and adaptable evaluation framework. The metric's capacity to accurately forecast model scalability and performance is demonstrated via experimental validation spanning classification, regression, and clustering tasks, guaranteeing solid assessments in settings with limited data. This method has important ramifications for effective resource allocation and model optimization in machine learning workflows.", "published": "2024-12-10T07:10:00Z", "authors": ["Serzhan Ossenov"], "categories": ["cs.LG", "cs.LO"], "doi": "", "_query": "house price prediction machine learning", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2511.20591v3", "title": "Attention Trajectories as a Diagnostic Axis for Deep Reinforcement Learning", "abstract": "The emergence and evolution of feature reliance in deep reinforcement learning agents remain poorly understood. Here, we introduce a methodological framework for analyzing the learning process through quantitative analysis of saliency maps. This approach aggregates saliency information at the object and modality level into hierarchical attention profiles, quantifying how agents allocate attention over time, thereby forming attention trajectories throughout training. These profiles are then compared across controlled conditions, connected to behavioral measurements and reproduced with different saliency methods to assess the robustness of the findings. Applied to Atari 2600 benchmarks, custom Pong environments, and biomechanical user simulations in visuomotor tasks, this framework uncovers algorithm-specific attention biases, diagnosed unintended reward-driven strategies, and overfitting to redundant sensory channels. These patterns correspond to measurable behavioral differences, demonstrating empirical links between attention profiles, learning dynamics, and agent behavior. The results establish attention trajectories as a promising diagnostic axis for tracing how feature reliance develops during training and for identifying biases and vulnerabilities invisible to performance metrics alone.", "published": "2025-11-25T18:20:42Z", "authors": ["Charlotte Beylier", "Hannah Selder", "Arthur Fleig", "Simon M. Hofmann", "Nico Scherf"], "categories": ["cs.LG"], "doi": "", "_query": "house price prediction machine learning", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/1812.00786v2", "title": "Generating Material Maps to Map Informal Settlements", "abstract": "Detecting and mapping informal settlements encompasses several of the United Nations sustainable development goals. This is because informal settlements are home to the most socially and economically vulnerable people on the planet. Thus, understanding where these settlements are is of paramount importance to both government and non-government organizations (NGOs), such as the United Nations Children's Fund (UNICEF), who can use this information to deliver effective social and economic aid. We propose a method that detects and maps the locations of informal settlements using only freely available, Sentinel-2 low-resolution satellite spectral data and socio-economic data. This is in contrast to previous studies that only use costly very-high resolution (VHR) satellite and aerial imagery. We show how we can detect informal settlements by combining both domain knowledge and machine learning techniques, to build a classifier that looks for known roofing materials used in informal settlements. Please find additional material at https://frontierdevelopmentlab.github.io/informal-settlements/.", "published": "2018-11-30T09:09:41Z", "authors": ["Patrick Helber", "Bradley Gram-Hansen", "Indhu Varatharajan", "Faiza Azam", "Alejandro Coca-Castro", "Veronika Kopackova", "Piotr Bilinski"], "categories": ["cs.LG", "cs.AI", "cs.CV", "stat.ML"], "doi": "", "_query": "house price prediction machine learning", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/1907.09452v1", "title": "Mid-price Prediction Based on Machine Learning Methods with Technical and Quantitative Indicators", "abstract": "Stock price prediction is a challenging task, but machine learning methods have recently been used successfully for this purpose. In this paper, we extract over 270 hand-crafted features (factors) inspired by technical and quantitative analysis and tested their validity on short-term mid-price movement prediction. We focus on a wrapper feature selection method using entropy, least-mean squares, and linear discriminant analysis. We also build a new quantitative feature based on adaptive logistic regression for online learning, which is constantly selected first among the majority of the proposed feature selection methods. This study examines the best combination of features using high frequency limit order book data from Nasdaq Nordic. Our results suggest that sorting methods and classifiers can be used in such a way that one can reach the best performance with a combination of only very few advanced hand-crafted features.", "published": "2019-07-13T13:26:47Z", "authors": ["Adamantios Ntakaris", "Juho Kanniainen", "Moncef Gabbouj", "Alexandros Iosifidis"], "categories": ["q-fin.ST", "cs.LG", "stat.ML"], "doi": "10.1371/journal.pone.0234107", "_query": "house price prediction machine learning", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2406.08267v2", "title": "A deep cut into Split Federated Self-supervised Learning", "abstract": "Collaborative self-supervised learning has recently become feasible in highly distributed environments by dividing the network layers between client devices and a central server. However, state-of-the-art methods, such as MocoSFL, are optimized for network division at the initial layers, which decreases the protection of the client data and increases communication overhead. In this paper, we demonstrate that splitting depth is crucial for maintaining privacy and communication efficiency in distributed training. We also show that MocoSFL suffers from a catastrophic quality deterioration for the minimal communication overhead. As a remedy, we introduce Momentum-Aligned contrastive Split Federated Learning (MonAcoSFL), which aligns online and momentum client models during training procedure. Consequently, we achieve state-of-the-art accuracy while significantly reducing the communication overhead, making MonAcoSFL more practical in real-world scenarios.", "published": "2024-06-12T14:35:13Z", "authors": ["Marcin Przewięźlikowski", "Marcin Osial", "Bartosz Zieliński", "Marek Śmieja"], "categories": ["cs.LG", "cs.AI", "cs.DC"], "doi": "10.1007/978-3-031-70344-7_26", "_query": "house price prediction machine learning", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2312.03675v2", "title": "GeoShapley: A Game Theory Approach to Measuring Spatial Effects in Machine Learning Models", "abstract": "This paper introduces GeoShapley, a game theory approach to measuring spatial effects in machine learning models. GeoShapley extends the Nobel Prize-winning Shapley value framework in game theory by conceptualizing location as a player in a model prediction game, which enables the quantification of the importance of location and the synergies between location and other features in a model. GeoShapley is a model-agnostic approach and can be applied to statistical or black-box machine learning models in various structures. The interpretation of GeoShapley is directly linked with spatially varying coefficient models for explaining spatial effects and additive models for explaining non-spatial effects. Using simulated data, GeoShapley values are validated against known data-generating processes and are used for cross-comparison of seven statistical and machine learning models. An empirical example of house price modeling is used to illustrate GeoShapley's utility and interpretation with real world data. The method is available as an open-source Python package named geoshapley.", "published": "2023-12-06T18:39:29Z", "authors": ["Ziqi Li"], "categories": ["cs.LG", "stat.ML"], "doi": "10.1080/24694452.2024.2350982", "_query": "house price prediction machine learning", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2208.03112v1", "title": "Explanation of Machine Learning Models of Colon Cancer Using SHAP Considering Interaction Effects", "abstract": "When using machine learning techniques in decision-making processes, the interpretability of the models is important. Shapley additive explanation (SHAP) is one of the most promising interpretation methods for machine learning models. Interaction effects occur when the effect of one variable depends on the value of another variable. Even if each variable has little effect on the outcome, its combination can have an unexpectedly large impact on the outcome. Understanding interactions is important for understanding machine learning models; however, naive SHAP analysis cannot distinguish between the main effect and interaction effects. In this paper, we introduce the Shapley-Taylor index as an interpretation method for machine learning models using SHAP considering interaction effects. We apply the method to the cancer cohort data of Kyushu University Hospital (N=29,080) to analyze what combination of factors contributes to the risk of colon cancer.", "published": "2022-08-05T11:54:30Z", "authors": ["Yasunobu Nohara", "Toyoshi Inoguchi", "Chinatsu Nojiri", "Naoki Nakashima"], "categories": ["cs.LG"], "doi": "", "_query": "house price prediction machine learning", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2501.17634v2", "title": "Federated Learning With Individualized Privacy Through Client Sampling", "abstract": "With growing concerns about user data collection, individualized privacy has emerged as a promising solution to balance protection and utility by accounting for diverse user privacy preferences. Instead of enforcing a uniform level of anonymization for all users, this approach allows individuals to choose privacy settings that align with their comfort levels. Building on this idea, we propose an adapted method for enabling Individualized Differential Privacy (IDP) in Federated Learning (FL) by handling clients according to their personal privacy preferences. By extending the SAMPLE algorithm from centralized settings to FL, we calculate client-specific sampling rates based on their heterogeneous privacy budgets and integrate them into a modified IDP-FedAvg algorithm. We test this method under realistic privacy distributions and multiple datasets. The experimental results demonstrate that our approach achieves clear improvements over uniform DP baselines, reducing the trade-off between privacy and utility. Compared to the alternative SCALE method in related work, which assigns differing noise scales to clients, our method performs notably better. However, challenges remain for complex tasks with non-i.i.d. data, primarily stemming from the constraints of the decentralized setting.", "published": "2025-01-29T13:11:21Z", "authors": ["Lucas Lange", "Ole Borchardt", "Erhard Rahm"], "categories": ["cs.LG", "cs.AI", "cs.CR", "cs.CV"], "doi": "10.1109/ICMLT65785.2025.11193238", "_query": "house price prediction machine learning", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2301.13816v4", "title": "Execution-based Code Generation using Deep Reinforcement Learning", "abstract": "The utilization of programming language (PL) models, pre-trained on large-scale code corpora, as a means of automating software engineering processes has demonstrated considerable potential in streamlining various code generation tasks such as code completion, code translation, and program synthesis. However, current approaches mainly rely on supervised fine-tuning objectives borrowed from text generation, neglecting unique sequence-level characteristics of code, including but not limited to compilability as well as syntactic and functional correctness. To address this limitation, we propose PPOCoder, a new framework for code generation that synergistically combines pre-trained PL models with Proximal Policy Optimization (PPO) which is a widely used deep reinforcement learning technique. By utilizing non-differentiable feedback from code execution and structure alignment, PPOCoder seamlessly integrates external code-specific knowledge into the model optimization process. It's important to note that PPOCoder is a task-agnostic and model-agnostic framework that can be used across different code generation tasks and PLs. Extensive experiments on three code generation tasks demonstrate the effectiveness of our proposed approach compared to SOTA methods, achieving significant improvements in compilation success rates and functional correctness across different PLs.", "published": "2023-01-31T18:02:26Z", "authors": ["Parshin Shojaee", "Aneesh Jain", "Sindhu Tipirneni", "Chandan K. Reddy"], "categories": ["cs.LG", "cs.AI", "cs.CL", "cs.PL"], "doi": "", "_query": "house price prediction machine learning", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2512.22261v1", "title": "The Physics Constraint Paradox: When Removing Explicit Constraints Improves Physics-Informed Data for Machine Learning", "abstract": "Physics-constrained data generation is essential for machine learning in scientific domains where real data are scarce; however, existing approaches often over-constrain models without identifying which physical components are necessary. We present a systematic ablation study of a physics-informed grating coupler spectrum generator that maps five geometric parameters to 100-point spectral responses. By selectively removing explicit energy conservation enforcement, Fabry-Perot oscillations, bandwidth variation, and noise, we uncover a physics constraint paradox: explicit energy conservation enforcement is mathematically redundant when the underlying equations are physically consistent, with constrained and unconstrained variants achieving identical conservation accuracy (mean error approximately 7 x 10^-9). In contrast, Fabry-Perot oscillations dominate threshold-based bandwidth variability, accounting for a 72 percent reduction in half-maximum bandwidth spread when removed (with bandwidth spread reduced from 132.3 nm to 37.4 nm). We further identify a subtle pitfall: standard noise-addition-plus-renormalization pipelines introduce 0.5 percent unphysical negative absorption values. The generator operates at 200 samples per second, enabling high-throughput data generation and remaining orders of magnitude faster than typical full-wave solvers reported in the literature. Finally, downstream machine learning evaluation reveals a clear physics-learnability trade-off: while central wavelength prediction remains unaffected, removing Fabry-Perot oscillations improves bandwidth prediction accuracy by 31.3 percent in R-squared and reduces RMSE by 73.8 percent. These findings provide actionable guidance for physics-informed dataset design and highlight machine learning performance as a diagnostic tool for assessing constraint relevance.", "published": "2025-12-24T14:34:52Z", "authors": ["Rahul D Ray"], "categories": ["cs.LG"], "doi": "", "_query": "house price prediction machine learning", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/1711.10915v1", "title": "Causality Refined Diagnostic Prediction", "abstract": "Applying machine learning in the health care domain has shown promising results in recent years. Interpretable outputs from learning algorithms are desirable for decision making by health care personnel. In this work, we explore the possibility of utilizing causal relationships to refine diagnostic prediction. We focus on the task of diagnostic prediction using discomfort drawings, and explore two ways to employ causal identification to improve the diagnostic results. Firstly, we use causal identification to infer the causal relationships among diagnostic labels which, by itself, provides interpretable results to aid the decision making and training of health care personnel. Secondly, we suggest a post-processing approach where the inferred causal relationships are used to refine the prediction accuracy of a multi-view probabilistic model. Experimental results show firstly that causal identification is capable of detecting the causal relationships among diagnostic labels correctly, and secondly that there is potential for improving pain diagnostics prediction accuracy using the causal relationships.", "published": "2017-11-29T15:28:20Z", "authors": ["Marcus Klasson", "Kun Zhang", "Bo C. Bertilson", "Cheng Zhang", "Hedvig Kjellström"], "categories": ["cs.LG", "stat.ML"], "doi": "", "_query": "house price prediction machine learning", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/1812.01074v1", "title": "Distilling Information from a Flood: A Possibility for the Use of Meta-Analysis and Systematic Review in Machine Learning Research", "abstract": "The current flood of information in all areas of machine learning research, from computer vision to reinforcement learning, has made it difficult to make aggregate scientific inferences. It can be challenging to distill a myriad of similar papers into a set of useful principles, to determine which new methodologies to use for a particular application, and to be confident that one has compared against all relevant related work when developing new ideas. However, such a rapidly growing body of research literature is a problem that other fields have already faced - in particular, medicine and epidemiology. In those fields, systematic reviews and meta-analyses have been used exactly for dealing with these issues and it is not uncommon for entire journals to be dedicated to such analyses. Here, we suggest the field of machine learning might similarly benefit from meta-analysis and systematic review, and we encourage further discussion and development along this direction.", "published": "2018-12-03T20:37:21Z", "authors": ["Peter Henderson", "Emma Brunskill"], "categories": ["cs.DL", "cs.LG", "stat.ML"], "doi": "", "_query": "house price prediction machine learning", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/1910.08215v1", "title": "A Deep Learning-based Framework for the Detection of Schools of Herring in Echograms", "abstract": "Tracking the abundance of underwater species is crucial for understanding the effects of climate change on marine ecosystems. Biologists typically monitor underwater sites with echosounders and visualize data as 2D images (echograms); they interpret these data manually or semi-automatically, which is time-consuming and prone to inconsistencies. This paper proposes a deep learning framework for the automatic detection of schools of herring from echograms. Experiments demonstrated that our approach outperforms a traditional machine learning algorithm using hand-crafted features. Our framework could easily be expanded to detect more species of interest to sustainable fisheries.", "published": "2019-10-18T01:12:46Z", "authors": ["Alireza Rezvanifar", "Tunai Porto Marques", "Melissa Cote", "Alexandra Branzan Albu", "Alex Slonimer", "Thomas Tolhurst", "Kaan Ersahin", "Todd Mudge", "Stephane Gauthier"], "categories": ["cs.LG", "cs.CV", "eess.IV", "stat.ML"], "doi": "", "_query": "house price prediction machine learning", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2512.22189v1", "title": "Physics-Informed Machine Learning for Transformer Condition Monitoring -- Part II: Physics-Informed Neural Networks and Uncertainty Quantification", "abstract": "The integration of physics-based knowledge with machine learning models is increasingly shaping the monitoring, diagnostics, and prognostics of electrical transformers. In this two-part series, the first paper introduced the foundations of Neural Networks (NNs) and their variants for health assessment tasks. This second paper focuses on integrating physics and uncertainty into the learning process. We begin with the fundamentals of Physics-Informed Neural Networks (PINNs), applied to spatiotemporal thermal modeling and solid insulation ageing. Building on this, we present Bayesian PINNs as a principled framework to quantify epistemic uncertainty and deliver robust predictions under sparse data. Finally, we outline emerging research directions that highlight the potential of physics-aware and trustworthy machine learning for critical power assets.", "published": "2025-12-20T10:09:21Z", "authors": ["Jose I. Aizpurua"], "categories": ["cs.LG"], "doi": "10.23919/ARWtr66130.2025.11261232", "_query": "house price prediction machine learning", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/1502.06309v3", "title": "Learning with Differential Privacy: Stability, Learnability and the Sufficiency and Necessity of ERM Principle", "abstract": "While machine learning has proven to be a powerful data-driven solution to many real-life problems, its use in sensitive domains has been limited due to privacy concerns. A popular approach known as **differential privacy** offers provable privacy guarantees, but it is often observed in practice that it could substantially hamper learning accuracy. In this paper we study the learnability (whether a problem can be learned by any algorithm) under Vapnik's general learning setting with differential privacy constraint, and reveal some intricate relationships between privacy, stability and learnability. In particular, we show that a problem is privately learnable **if an only if** there is a private algorithm that asymptotically minimizes the empirical risk (AERM). In contrast, for non-private learning AERM alone is not sufficient for learnability. This result suggests that when searching for private learning algorithms, we can restrict the search to algorithms that are AERM. In light of this, we propose a conceptual procedure that always finds a universally consistent algorithm whenever the problem is learnable under privacy constraint. We also propose a generic and practical algorithm and show that under very general conditions it privately learns a wide class of learning problems. Lastly, we extend some of the results to the more practical $(ε,δ)$-differential privacy and establish the existence of a phase-transition on the class of problems that are approximately privately learnable with respect to how small $δ$ needs to be.", "published": "2015-02-23T03:52:08Z", "authors": ["Yu-Xiang Wang", "Jing Lei", "Stephen E. Fienberg"], "categories": ["stat.ML", "cs.CR", "cs.LG"], "doi": "", "_query": "house price prediction machine learning", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2005.08582v2", "title": "Quantum Machine Learning in High Energy Physics", "abstract": "Machine learning has been used in high energy physics for a long time, primarily at the analysis level with supervised classification. Quantum computing was postulated in the early 1980s as way to perform computations that would not be tractable with a classical computer. With the advent of noisy intermediate-scale quantum computing devices, more quantum algorithms are being developed with the aim at exploiting the capacity of the hardware for machine learning applications. An interesting question is whether there are ways to apply quantum machine learning to High Energy Physics. This paper reviews the first generation of ideas that use quantum machine learning on problems in high energy physics and provide an outlook on future applications.", "published": "2020-05-18T10:48:39Z", "authors": ["Wen Guan", "Gabriel Perdue", "Arthur Pesah", "Maria Schuld", "Koji Terashi", "Sofia Vallecorsa", "Jean-Roch Vlimant"], "categories": ["quant-ph", "hep-ph"], "doi": "10.1088/2632-2153/abc17d", "_query": "house price prediction machine learning", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/1806.07377v6", "title": "Transfer Learning for Related Reinforcement Learning Tasks via Image-to-Image Translation", "abstract": "Despite the remarkable success of Deep RL in learning control policies from raw pixels, the resulting models do not generalize. We demonstrate that a trained agent fails completely when facing small visual changes, and that fine-tuning---the common transfer learning paradigm---fails to adapt to these changes, to the extent that it is faster to re-train the model from scratch. We show that by separating the visual transfer task from the control policy we achieve substantially better sample efficiency and transfer behavior, allowing an agent trained on the source task to transfer well to the target tasks. The visual mapping from the target to the source domain is performed using unaligned GANs, resulting in a control policy that can be further improved using imitation learning from imperfect demonstrations. We demonstrate the approach on synthetic visual variants of the Breakout game, as well as on transfer between subsequent levels of Road Fighter, a Nintendo car-driving game. A visualization of our approach can be seen in https://youtu.be/4mnkzYyXMn4 and https://youtu.be/KCGTrQi6Ogo .", "published": "2018-05-31T09:25:29Z", "authors": ["Shani Gamrian", "Yoav Goldberg"], "categories": ["cs.CV", "cs.AI", "cs.LG"], "doi": "", "_query": "house price prediction machine learning", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/1609.08399v1", "title": "House price estimation from visual and textual features", "abstract": "Most existing automatic house price estimation systems rely only on some textual data like its neighborhood area and the number of rooms. The final price is estimated by a human agent who visits the house and assesses it visually. In this paper, we propose extracting visual features from house photographs and combining them with the house's textual information. The combined features are fed to a fully connected multilayer Neural Network (NN) that estimates the house price as its single output. To train and evaluate our network, we have collected the first houses dataset (to our knowledge) that combines both images and textual attributes. The dataset is composed of 535 sample houses from the state of California, USA. Our experiments showed that adding the visual features increased the R-value by a factor of 3 and decreased the Mean Square Error (MSE) by one order of magnitude compared with textual-only features. Additionally, when trained on the benchmark textual-only features housing dataset, our proposed NN still outperformed the existing model published results.", "published": "2016-09-27T13:15:31Z", "authors": ["Eman Ahmed", "Mohamed Moustafa"], "categories": ["cs.CV"], "doi": "", "_query": "house price prediction machine learning", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/1610.07187v3", "title": "Learning Deep Architectures for Interaction Prediction in Structure-based Virtual Screening", "abstract": "We introduce a deep learning architecture for structure-based virtual screening that generates fixed-sized fingerprints of proteins and small molecules by applying learnable atom convolution and softmax operations to each compound separately. These fingerprints are further transformed non-linearly, their inner-product is calculated and used to predict the binding potential. Moreover, we show that widely used benchmark datasets may be insufficient for testing structure-based virtual screening methods that utilize machine learning. Therefore, we introduce a new benchmark dataset, which we constructed based on DUD-E and PDBBind databases.", "published": "2016-10-23T15:51:46Z", "authors": ["Adam Gonczarek", "Jakub M. Tomczak", "Szymon Zaręba", "Joanna Kaczmar", "Piotr Dąbrowski", "Michał J. Walczak"], "categories": ["stat.ML", "cs.LG"], "doi": "10.1016/j.compbiomed.2017.09.007", "_query": "house price prediction machine learning", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2111.12840v1", "title": "Explaining machine-learned particle-flow reconstruction", "abstract": "The particle-flow (PF) algorithm is used in general-purpose particle detectors to reconstruct a comprehensive particle-level view of the collision by combining information from different subdetectors. A graph neural network (GNN) model, known as the machine-learned particle-flow (MLPF) algorithm, has been developed to substitute the rule-based PF algorithm. However, understanding the model's decision making is not straightforward, especially given the complexity of the set-to-set prediction task, dynamic graph building, and message-passing steps. In this paper, we adapt the layerwise-relevance propagation technique for GNNs and apply it to the MLPF algorithm to gauge the relevant nodes and features for its predictions. Through this process, we gain insight into the model's decision-making.", "published": "2021-11-24T23:20:03Z", "authors": ["Farouk Mokhtar", "Raghav Kansal", "Daniel Diaz", "Javier Duarte", "Joosep Pata", "Maurizio Pierini", "Jean-Roch Vlimant"], "categories": ["physics.data-an", "cs.LG", "hep-ex", "physics.ins-det"], "doi": "", "_query": "house price prediction machine learning", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/1812.01662v1", "title": "Feed-Forward Neural Networks Need Inductive Bias to Learn Equality Relations", "abstract": "Basic binary relations such as equality and inequality are fundamental to relational data structures. Neural networks should learn such relations and generalise to new unseen data. We show in this study, however, that this generalisation fails with standard feed-forward networks on binary vectors. Even when trained with maximal training data, standard networks do not reliably detect equality.We introduce differential rectifier (DR) units that we add to the network in different configurations. The DR units create an inductive bias in the networks, so that they do learn to generalise, even from small numbers of examples and we have not found any negative effect of their inclusion in the network. Given the fundamental nature of these relations, we hypothesize that feed-forward neural network learning benefits from inductive bias in other relations as well. Consequently, the further development of suitable inductive biases will be beneficial to many tasks in relational learning with neural networks.", "published": "2018-12-04T20:02:38Z", "authors": ["Tillman Weyde", "Radha Manisha Kopparti"], "categories": ["cs.LG", "stat.ML"], "doi": "", "_query": "house price prediction machine learning", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2105.03918v2", "title": "Opening the Blackbox: Accelerating Neural Differential Equations by Regularizing Internal Solver Heuristics", "abstract": "Democratization of machine learning requires architectures that automatically adapt to new problems. Neural Differential Equations (NDEs) have emerged as a popular modeling framework by removing the need for ML practitioners to choose the number of layers in a recurrent model. While we can control the computational cost by choosing the number of layers in standard architectures, in NDEs the number of neural network evaluations for a forward pass can depend on the number of steps of the adaptive ODE solver. But, can we force the NDE to learn the version with the least steps while not increasing the training cost? Current strategies to overcome slow prediction require high order automatic differentiation, leading to significantly higher training time. We describe a novel regularization method that uses the internal cost heuristics of adaptive differential equation solvers combined with discrete adjoint sensitivities to guide the training process towards learning NDEs that are easier to solve. This approach opens up the blackbox numerical analysis behind the differential equation solver's algorithm and directly uses its local error estimates and stiffness heuristics as cheap and accurate cost estimates. We incorporate our method without any change in the underlying NDE framework and show that our method extends beyond Ordinary Differential Equations to accommodate Neural Stochastic Differential Equations. We demonstrate how our approach can halve the prediction time and, unlike other methods which can increase the training time by an order of magnitude, we demonstrate similar reduction in training times. Together this showcases how the knowledge embedded within state-of-the-art equation solvers can be used to enhance machine learning.", "published": "2021-05-09T12:03:03Z", "authors": ["Avik Pal", "Yingbo Ma", "Viral Shah", "Christopher Rackauckas"], "categories": ["cs.LG", "math.NA"], "doi": "", "_query": "house price prediction machine learning", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/1810.08305v2", "title": "Open Vocabulary Learning on Source Code with a Graph-Structured Cache", "abstract": "Machine learning models that take computer program source code as input typically use Natural Language Processing (NLP) techniques. However, a major challenge is that code is written using an open, rapidly changing vocabulary due to, e.g., the coinage of new variable and method names. Reasoning over such a vocabulary is not something for which most NLP methods are designed. We introduce a Graph-Structured Cache to address this problem; this cache contains a node for each new word the model encounters with edges connecting each word to its occurrences in the code. We find that combining this graph-structured cache strategy with recent Graph-Neural-Network-based models for supervised learning on code improves the models' performance on a code completion task and a variable naming task --- with over $100\\%$ relative improvement on the latter --- at the cost of a moderate increase in computation time.", "published": "2018-10-18T23:33:11Z", "authors": ["Milan Cvitkovic", "Badal Singh", "Anima Anandkumar"], "categories": ["cs.LG", "stat.ML"], "doi": "", "_query": "house price prediction machine learning", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2409.02332v1", "title": "Double Machine Learning at Scale to Predict Causal Impact of Customer Actions", "abstract": "Causal Impact (CI) of customer actions are broadly used across the industry to inform both short- and long-term investment decisions of various types. In this paper, we apply the double machine learning (DML) methodology to estimate the CI values across 100s of customer actions of business interest and 100s of millions of customers. We operationalize DML through a causal ML library based on Spark with a flexible, JSON-driven model configuration approach to estimate CI at scale (i.e., across hundred of actions and millions of customers). We outline the DML methodology and implementation, and associated benefits over the traditional potential outcomes based CI model. We show population-level as well as customer-level CI values along with confidence intervals. The validation metrics show a 2.2% gain over the baseline methods and a 2.5X gain in the computational time. Our contribution is to advance the scalable application of CI, while also providing an interface that allows faster experimentation, cross-platform support, ability to onboard new use cases, and improves accessibility of underlying code for partner teams.", "published": "2024-09-03T23:13:04Z", "authors": ["Sushant More", "Priya Kotwal", "Sujith Chappidi", "Dinesh Mandalapu", "Chris Khawand"], "categories": ["cs.LG", "econ.EM", "stat.AP", "stat.ME"], "doi": "10.1007/978-3-031-43427-3_31", "_query": "house price prediction machine learning", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2006.09319v3", "title": "A Survey of Constrained Gaussian Process Regression: Approaches and Implementation Challenges", "abstract": "Gaussian process regression is a popular Bayesian framework for surrogate modeling of expensive data sources. As part of a broader effort in scientific machine learning, many recent works have incorporated physical constraints or other a priori information within Gaussian process regression to supplement limited data and regularize the behavior of the model. We provide an overview and survey of several classes of Gaussian process constraints, including positivity or bound constraints, monotonicity and convexity constraints, differential equation constraints provided by linear PDEs, and boundary condition constraints. We compare the strategies behind each approach as well as the differences in implementation, concluding with a discussion of the computational challenges introduced by constraints.", "published": "2020-06-16T17:03:36Z", "authors": ["Laura Swiler", "Mamikon Gulian", "Ari Frankel", "Cosmin Safta", "John Jakeman"], "categories": ["cs.LG", "math.ST", "stat.ML"], "doi": "10.1615/JMachLearnModelComput.2020035155", "_query": "house price prediction machine learning", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2009.04587v2", "title": "A brief history on Homomorphic learning: A privacy-focused approach to machine learning", "abstract": "Cryptography and data science research grew exponential with the internet boom. Legacy encryption techniques force users to make a trade-off between usability, convenience, and security. Encryption makes valuable data inaccessible, as it needs to be decrypted each time to perform any operation. Billions of dollars could be saved, and millions of people could benefit from cryptography methods that don't compromise between usability, convenience, and security. Homomorphic encryption is one such paradigm that allows running arbitrary operations on encrypted data. It enables us to run any sophisticated machine learning algorithm without access to the underlying raw data. Thus, homomorphic learning provides the ability to gain insights from sensitive data that has been neglected due to various governmental and organization privacy rules. In this paper, we trace back the ideas of homomorphic learning formally posed by Ronald L. Rivest and Len Alderman as \"Can we compute upon encrypted data?\" in their 1978 paper. Then we gradually follow the ideas sprouting in the brilliant minds of Shafi Goldwasser, Kristin Lauter, Dan Bonch, Tomas Sander, Donald Beaver, and Craig Gentry to address that vital question. It took more than 30 years of collective effort to finally find the answer \"yes\" to that important question.", "published": "2020-09-09T21:57:47Z", "authors": ["Aadesh Neupane"], "categories": ["cs.CR", "cs.LG"], "doi": "", "_query": "house price prediction machine learning", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/1910.12191v1", "title": "Federated Uncertainty-Aware Learning for Distributed Hospital EHR Data", "abstract": "Recent works have shown that applying Machine Learning to Electronic Health Records (EHR) can strongly accelerate precision medicine. This requires developing models based on diverse EHR sources. Federated Learning (FL) has enabled predictive modeling using distributed training which lifted the need of sharing data and compromising privacy. Since models are distributed in FL, it is attractive to devise ensembles of Deep Neural Networks that also assess model uncertainty. We propose a new FL model called Federated Uncertainty-Aware Learning Algorithm (FUALA) that improves on Federated Averaging (FedAvg) in the context of EHR. FUALA embeds uncertainty information in two ways: It reduces the contribution of models with high uncertainty in the aggregated model. It also introduces model ensembling at prediction time by keeping the last layers of each hospital from the final round. In FUALA, the Federator (central node) sends at each round the average model to all hospitals as well as a randomly assigned hospital model update to estimate its generalization on that hospital own data. Each hospital sends back its model update as well a generalization estimation of the assigned model. At prediction time, the model outputs C predictions for each sample where C is the number of hospital models. The experimental analysis conducted on a cohort of 87K deliveries for the task of preterm-birth prediction showed that the proposed approach outperforms FedAvg when evaluated on out-of-distribution data. We illustrated how uncertainty could be measured using the proposed approach.", "published": "2019-10-27T06:33:34Z", "authors": ["Sabri Boughorbel", "Fethi Jarray", "Neethu Venugopal", "Shabir Moosa", "Haithum Elhadi", "Michel Makhlouf"], "categories": ["cs.LG", "cs.AI", "stat.ML"], "doi": "", "_query": "house price prediction machine learning", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/1712.04248v2", "title": "Decision-Based Adversarial Attacks: Reliable Attacks Against Black-Box Machine Learning Models", "abstract": "Many machine learning algorithms are vulnerable to almost imperceptible perturbations of their inputs. So far it was unclear how much risk adversarial perturbations carry for the safety of real-world machine learning applications because most methods used to generate such perturbations rely either on detailed model information (gradient-based attacks) or on confidence scores such as class probabilities (score-based attacks), neither of which are available in most real-world scenarios. In many such cases one currently needs to retreat to transfer-based attacks which rely on cumbersome substitute models, need access to the training data and can be defended against. Here we emphasise the importance of attacks which solely rely on the final model decision. Such decision-based attacks are (1) applicable to real-world black-box models such as autonomous cars, (2) need less knowledge and are easier to apply than transfer-based attacks and (3) are more robust to simple defences than gradient- or score-based attacks. Previous attacks in this category were limited to simple models or simple datasets. Here we introduce the Boundary Attack, a decision-based attack that starts from a large adversarial perturbation and then seeks to reduce the perturbation while staying adversarial. The attack is conceptually simple, requires close to no hyperparameter tuning, does not rely on substitute models and is competitive with the best gradient-based attacks in standard computer vision tasks like ImageNet. We apply the attack on two black-box algorithms from Clarifai.com. The Boundary Attack in particular and the class of decision-based attacks in general open new avenues to study the robustness of machine learning models and raise new questions regarding the safety of deployed machine learning systems. An implementation of the attack is available as part of Foolbox at https://github.com/bethgelab/foolbox .", "published": "2017-12-12T11:36:26Z", "authors": ["Wieland Brendel", "Jonas Rauber", "Matthias Bethge"], "categories": ["stat.ML", "cs.CR", "cs.CV", "cs.LG", "cs.NE"], "doi": "", "_query": "house price prediction machine learning", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2006.01927v1", "title": "Incorporating Physical Knowledge into Machine Learning for Planetary Space Physics", "abstract": "Recent improvements in data collection volume from planetary and space physics missions have allowed the application of novel data science techniques. The Cassini mission for example collected over 600 gigabytes of scientific data from 2004 to 2017. This represents a surge of data on the Saturn system. Machine learning can help scientists work with data on this larger scale. Unlike many applications of machine learning, a primary use in planetary space physics applications is to infer behavior about the system itself. This raises three concerns: first, the performance of the machine learning model, second, the need for interpretable applications to answer scientific questions, and third, how characteristics of spacecraft data change these applications. In comparison to these concerns, uses of black box or un-interpretable machine learning methods tend toward evaluations of performance only either ignoring the underlying physical process or, less often, providing misleading explanations for it. We build off a previous effort applying a semi-supervised physics-based classification of plasma instabilities in Saturn's magnetosphere. We then use this previous effort in comparison to other machine learning classifiers with varying data size access, and physical information access. We show that incorporating knowledge of these orbiting spacecraft data characteristics improves the performance and interpretability of machine learning methods, which is essential for deriving scientific meaning. Building on these findings, we present a framework on incorporating physics knowledge into machine learning problems targeting semi-supervised classification for space physics data in planetary environments. These findings present a path forward for incorporating physical knowledge into space physics and planetary mission data analyses for scientific discovery.", "published": "2020-06-02T20:31:29Z", "authors": ["A. R. Azari", "J. W. Lockhart", "M. W. Liemohn", "X. Jia"], "categories": ["physics.space-ph", "astro-ph.IM", "stat.ML"], "doi": "10.3389/fspas.2020.00036", "_query": "house price prediction machine learning", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2205.08443v3", "title": "On the (In)security of Peer-to-Peer Decentralized Machine Learning", "abstract": "In this work, we carry out the first, in-depth, privacy analysis of Decentralized Learning -- a collaborative machine learning framework aimed at addressing the main limitations of federated learning. We introduce a suite of novel attacks for both passive and active decentralized adversaries. We demonstrate that, contrary to what is claimed by decentralized learning proposers, decentralized learning does not offer any security advantage over federated learning. Rather, it increases the attack surface enabling any user in the system to perform privacy attacks such as gradient inversion, and even gain full control over honest users' local model. We also show that, given the state of the art in protections, privacy-preserving configurations of decentralized learning require fully connected networks, losing any practical advantage over the federated setup and therefore completely defeating the objective of the decentralized approach.", "published": "2022-05-17T15:36:50Z", "authors": ["Dario Pasquini", "Mathilde Raynal", "Carmela Troncoso"], "categories": ["cs.CR", "cs.LG"], "doi": "", "_query": "house price prediction machine learning", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2105.09107v1", "title": "Mill.jl and JsonGrinder.jl: automated differentiable feature extraction for learning from raw JSON data", "abstract": "Learning from raw data input, thus limiting the need for manual feature engineering, is one of the key components of many successful applications of machine learning methods. While machine learning problems are often formulated on data that naturally translate into a vector representation suitable for classifiers, there are data sources, for example in cybersecurity, that are naturally represented in diverse files with a unifying hierarchical structure, such as XML, JSON, and Protocol Buffers. Converting this data to vector (tensor) representation is generally done by manual feature engineering, which is laborious, lossy, and prone to human bias about the importance of particular features. Mill and JsonGrinder is a tandem of libraries, which fully automates the conversion. Starting with an arbitrary set of JSON samples, they create a differentiable machine learning model capable of infer from further JSON samples in their raw form.", "published": "2021-05-19T13:02:10Z", "authors": ["Simon Mandlik", "Matej Racinsky", "Viliam Lisy", "Tomas Pevny"], "categories": ["stat.ML", "cs.LG", "cs.MS"], "doi": "", "_query": "house price prediction machine learning", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2411.09776v2", "title": "Combining Machine Learning Defenses without Conflicts", "abstract": "Machine learning (ML) defenses protect against various risks to security, privacy, and fairness. Real-life models need simultaneous protection against multiple different risks which necessitates combining multiple defenses. But combining defenses with conflicting interactions in an ML model can be ineffective, incurring a significant drop in the effectiveness of one or more defenses being combined. Practitioners need a way to determine if a given combination can be effective. Experimentally identifying effective combinations can be time-consuming and expensive, particularly when multiple defenses need to be combined. We need an inexpensive, easy-to-use combination technique to identify effective combinations. Ideally, a combination technique should be (a) accurate (correctly identifies whether a combination is effective or not), (b) scalable (allows combining multiple defenses), (c) non-invasive (requires no change to the defenses being combined), and (d) general (is applicable to different types of defenses). Prior works have identified several ad-hoc techniques but none satisfy all the requirements above. We propose a principled combination technique, Def\\Con, to identify effective defense combinations. Def\\Con meets all requirements, achieving 90% accuracy on eight combinations explored in prior work and 81% in 30 previously unexplored combinations that we empirically evaluate in this paper.", "published": "2024-11-14T19:41:51Z", "authors": ["Vasisht Duddu", "Rui Zhang", "N. Asokan"], "categories": ["cs.CR", "cs.LG"], "doi": "", "_query": "house price prediction machine learning", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2312.14325v1", "title": "Exploring Distributions of House Prices and House Price Indices", "abstract": "We use house prices (HP) and house price indices (HPI) as a proxy to income distribution. Specifically, we analyze sale prices in the 1970-2010 window of over 116,000 single-family homes in Hamilton County, Ohio, including Cincinnati metro area of about 2.2 million people. We also analyze HPI, published by Federal Housing Finance Agency (FHFA), for nearly 18,000 US ZIP codes that cover a period of over 40 years starting in 1980's. If HP can be viewed as a first derivative of income, HPI can be viewed as its second derivative. We use generalized beta (GB) family of functions to fit distributions of HP and HPI since GB naturally arises from the models of economic exchange described by stochastic differential equations. Our main finding is that HP and multi-year HPI exhibit a negative Dragon King (nDK) behavior, wherein power-law distribution tail gives way to an abrupt decay to a finite upper limit value, which is similar to our recent findings for realized volatility of S\\&P500 index in the US stock market. This type of tail behavior is best fitted by a modified GB (mGB) distribution. Tails of single-year HPI appear to show more consistency with power-law behavior, which is better described by a GB Prime (GB2) distribution. We supplement full distribution fits by mGB and GB2 with direct linear fits (LF) of the tails. Our numerical procedure relies on evaluation of confidence intervals (CI) of the fits, as well as of p-values that give the likelihood that data come from the fitted distributions.", "published": "2023-12-21T22:38:24Z", "authors": ["Jiong Liu", "Hamed Farahani", "R. A. Serota"], "categories": ["econ.EM", "econ.TH"], "doi": "10.3390/economies12020047", "_query": "house price prediction machine learning", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2010.04651v2", "title": "Wildfire Smoke and Air Quality: How Machine Learning Can Guide Forest Management", "abstract": "Prescribed burns are currently the most effective method of reducing the risk of widespread wildfires, but a largely missing component in forest management is knowing which fuels one can safely burn to minimize exposure to toxic smoke. Here we show how machine learning, such as spectral clustering and manifold learning, can provide interpretable representations and powerful tools for differentiating between smoke types, hence providing forest managers with vital information on effective strategies to reduce climate-induced wildfires while minimizing production of harmful smoke.", "published": "2020-10-09T15:49:38Z", "authors": ["Lorenzo Tomaselli", "Coty Jen", "Ann B. Lee"], "categories": ["stat.AP", "stat.ML"], "doi": "", "_query": "house price prediction machine learning", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/1812.10619v1", "title": "Predicting the Stock Price of Frontier Markets Using Modified Black-Scholes Option Pricing Model and Machine Learning", "abstract": "The Black-Scholes Option pricing model (BSOPM) has long been in use for valuation of equity options to find the prices of stocks. In this work, using BSOPM, we have come up with a comparative analytical approach and numerical technique to find the price of call option and put option and considered these two prices as buying price and selling price of stocks of frontier markets so that we can predict the stock price (close price). Changes have been made to the model to find the parameters strike price and the time of expiration for calculating stock price of frontier markets. To verify the result obtained using modified BSOPM we have used machine learning approach using the software Rapidminer, where we have adopted different algorithms like the decision tree, ensemble learning method and neural network. It has been observed that, the prediction of close price using machine learning is very similar to the one obtained using BSOPM. Machine learning approach stands out to be a better predictor over BSOPM, because Black-Scholes-Merton equation includes risk and dividend parameter, which changes continuously. We have also numerically calculated volatility. As the prices of the stocks goes high due to overpricing, volatility increases at a tremendous rate and when volatility becomes very high market tends to fall, which can be observed and determined using our modified BSOPM. The proposed modified BSOPM has also been explained based on the analogy of Schrodinger equation (and heat equation) of quantum physics.", "published": "2018-12-27T04:34:18Z", "authors": ["Reaz Chowdhury", "M. R. C. Mahdy", "Tanisha Nourin Alam", "Golam Dastegir Al Quaderi"], "categories": ["q-fin.ST", "q-fin.MF"], "doi": "", "_query": "house price prediction machine learning", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/1206.6451v1", "title": "The Greedy Miser: Learning under Test-time Budgets", "abstract": "As machine learning algorithms enter applications in industrial settings, there is increased interest in controlling their cpu-time during testing. The cpu-time consists of the running time of the algorithm and the extraction time of the features. The latter can vary drastically when the feature set is diverse. In this paper, we propose an algorithm, the Greedy Miser, that incorporates the feature extraction cost during training to explicitly minimize the cpu-time during testing. The algorithm is a straightforward extension of stage-wise regression and is equally suitable for regression or multi-class classification. Compared to prior work, it is significantly more cost-effective and scales to larger data sets.", "published": "2012-06-27T19:59:59Z", "authors": ["Zhixiang Xu", "Kilian Weinberger", "Olivier Chapelle"], "categories": ["cs.LG", "stat.ML"], "doi": "", "_query": "house price prediction machine learning", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/1707.09050v1", "title": "A Shared Task on Bandit Learning for Machine Translation", "abstract": "We introduce and describe the results of a novel shared task on bandit learning for machine translation. The task was organized jointly by Amazon and Heidelberg University for the first time at the Second Conference on Machine Translation (WMT 2017). The goal of the task is to encourage research on learning machine translation from weak user feedback instead of human references or post-edits. On each of a sequence of rounds, a machine translation system is required to propose a translation for an input, and receives a real-valued estimate of the quality of the proposed translation for learning. This paper describes the shared task's learning and evaluation setup, using services hosted on Amazon Web Services (AWS), the data and evaluation metrics, and the results of various machine translation architectures and learning protocols.", "published": "2017-07-27T21:16:41Z", "authors": ["Artem Sokolov", "Julia Kreutzer", "Kellen Sunderland", "Pavel Danchenko", "Witold Szymaniak", "Hagen Fürstenau", "Stefan Riezler"], "categories": ["cs.CL", "stat.ML"], "doi": "", "_query": "house price prediction machine learning", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/1105.1033v2", "title": "Adaptively Learning the Crowd Kernel", "abstract": "We introduce an algorithm that, given n objects, learns a similarity matrix over all n^2 pairs, from crowdsourced data alone. The algorithm samples responses to adaptively chosen triplet-based relative-similarity queries. Each query has the form \"is object 'a' more similar to 'b' or to 'c'?\" and is chosen to be maximally informative given the preceding responses. The output is an embedding of the objects into Euclidean space (like MDS); we refer to this as the \"crowd kernel.\" SVMs reveal that the crowd kernel captures prominent and subtle features across a number of domains, such as \"is striped\" among neckties and \"vowel vs. consonant\" among letters.", "published": "2011-05-05T11:03:03Z", "authors": ["Omer Tamuz", "Ce Liu", "Serge Belongie", "Ohad Shamir", "Adam Tauman Kalai"], "categories": ["cs.LG"], "doi": "", "_query": "house price prediction machine learning", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2008.09922v1", "title": "Machine Learning Approaches to Real Estate Market Prediction Problem: A Case Study", "abstract": "Home sale prices are formed given the transaction actors economic interests, which include government, real estate dealers, and the general public who buy or sell properties. Generating an accurate property price prediction model is a major challenge for the real estate market. This work develops a property price classification model using a ten year actual dataset, from January 2010 to November 2019. The real estate dataset is publicly available and was retrieved from Volusia County Property Appraiser of Florida website. In addition, socio-economic factors such as Gross Domestic Product, Consumer Price Index, Producer Price Index, House Price Index, and Effective Federal Funds Rate are collected and used in the prediction model. To solve this case study problem, several powerful machine learning algorithms, namely, Logistic Regression, Random Forest, Voting Classifier, and XGBoost, are employed. They are integrated with target encoding to develop an accurate property sale price prediction model with the aim to predict whether the closing sale price is greater than or less than the listing sale price. To assess the performance of the models, the accuracy, precision, recall, classification F1 score, and error rate of the models are determined. Among the four studied machine learning algorithms, XGBoost delivers superior results and robustness of the model compared to other models. The developed model can facilitate real estate investors, mortgage lenders and financial institutions to make better informed decisions.", "published": "2020-08-22T22:28:58Z", "authors": ["Shashi Bhushan Jha", "Vijay Pandey", "Rajesh Kumar Jha", "Radu F. Babiceanu"], "categories": ["cs.LG"], "doi": "", "_query": "house price prediction machine learning", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2311.09329v1", "title": "A Comparative Analysis of Machine Learning Models for Early Detection of Hospital-Acquired Infections", "abstract": "As more and more infection-specific machine learning models are developed and planned for clinical deployment, simultaneously running predictions from different models may provide overlapping or even conflicting information. It is important to understand the concordance and behavior of parallel models in deployment. In this study, we focus on two models for the early detection of hospital-acquired infections (HAIs): 1) the Infection Risk Index (IRI) and 2) the Ventilator-Associated Pneumonia (VAP) prediction model. The IRI model was built to predict all HAIs, whereas the VAP model identifies patients at risk of developing ventilator-associated pneumonia. These models could make important improvements in patient outcomes and hospital management of infections through early detection of infections and in turn, enable early interventions. The two models vary in terms of infection label definition, cohort selection, and prediction schema. In this work, we present a comparative analysis between the two models to characterize concordances and confusions in predicting HAIs by these models. The learnings from this study will provide important findings for how to deploy multiple concurrent disease-specific models in the future.", "published": "2023-11-15T19:36:12Z", "authors": ["Ethan Harvey", "Junzi Dong", "Erina Ghosh", "Ali Samadani"], "categories": ["cs.LG"], "doi": "", "_query": "house price prediction machine learning", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/1908.00698v2", "title": "Soccer Team Vectors", "abstract": "In this work we present STEVE - Soccer TEam VEctors, a principled approach for learning real valued vectors for soccer teams where similar teams are close to each other in the resulting vector space. STEVE only relies on freely available information about the matches teams played in the past. These vectors can serve as input to various machine learning tasks. Evaluating on the task of team market value estimation, STEVE outperforms all its competitors. Moreover, we use STEVE for similarity search and to rank soccer teams.", "published": "2019-07-30T13:46:16Z", "authors": ["Robert Müller", "Stefan Langer", "Fabian Ritz", "Christoph Roch", "Steffen Illium", "Claudia Linnhoff-Popien"], "categories": ["cs.LG", "cs.AI", "stat.ML"], "doi": "10.1007/978-3-030-43887-6_19", "_query": "house price prediction machine learning", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2502.20089v3", "title": "RIZE: Adaptive Regularization for Imitation Learning", "abstract": "We propose a novel Inverse Reinforcement Learning (IRL) method that mitigates the rigidity of fixed reward structures and the limited flexibility of implicit reward regularization. Building on the Maximum Entropy IRL framework, our approach incorporates a squared temporal-difference (TD) regularizer with adaptive targets that evolve dynamically during training, thereby imposing adaptive bounds on recovered rewards and promoting robust decision-making. To capture richer return information, we integrate distributional RL into the learning process. Empirically, our method achieves expert-level performance on complex MuJoCo and Adroit environments, surpassing baseline methods on the Humanoid-v2 task with limited expert demonstrations. Extensive experiments and ablation studies further validate the effectiveness of the approach and provide insights into reward dynamics in imitation learning. Our source code is available at https://github.com/adibka/RIZE.", "published": "2025-02-27T13:47:29Z", "authors": ["Adib Karimi", "Mohammad Mehdi Ebadzadeh"], "categories": ["cs.LG", "cs.AI", "cs.RO"], "doi": "", "_query": "house price prediction machine learning", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2605.08253v2", "title": "Path-Coupled Bellman Flows for Distributional Reinforcement Learning", "abstract": "Distributional reinforcement learning (DRL) models the full return distribution, but existing finite-support or quantile-based methods rely on projections, while recent flow-based approaches can suffer from \\emph{boundary mismatch} at the flow source or from \\emph{high-variance} bootstrapping when current and successor noises are independent. We propose Path-Coupled Bellman Flows (PCBF), a continuous-time DRL method that learns return distributions with flow matching using \\textbf{source-consistent Bellman-coupled paths}: the current path starts from the required base prior at $t{=}0$, reaches the Bellman target at $t{=}1$, and maintains a pathwise affine relation to the successor flow at intermediate times (without requiring time-$t$ marginals to satisfy a distributional Bellman fixed point for all $t$). PCBF couples current and successor return flows through shared base noise and uses a $λ$-parameterized control-variate target: $λ{=}0$ recovers an unbiased sample Bellman target, while $λ{>}0$ trades controlled bias for variance reduction. Experiments on analytically tractable MRPs, OGBench, and D4RL show improved distributional fidelity and training stability, and competitive offline RL performance.", "published": "2026-05-07T19:05:01Z", "authors": ["Boyang Xu", "Qing Zou", "Siqin Yang", "Hao Yan"], "categories": ["cs.LG", "cs.AI"], "doi": "", "_query": "house price prediction machine learning", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/1808.08121v1", "title": "An Improvement of Data Classification Using Random Multimodel Deep Learning (RMDL)", "abstract": "The exponential growth in the number of complex datasets every year requires more enhancement in machine learning methods to provide robust and accurate data classification. Lately, deep learning approaches have achieved surpassing results in comparison to previous machine learning algorithms. However, finding the suitable structure for these models has been a challenge for researchers. This paper introduces Random Multimodel Deep Learning (RMDL): a new ensemble, deep learning approach for classification. RMDL solves the problem of finding the best deep learning structure and architecture while simultaneously improving robustness and accuracy through ensembles of deep learning architectures. In short, RMDL trains multiple randomly generated models of Deep Neural Network (DNN), Convolutional Neural Network (CNN) and Recurrent Neural Network (RNN) in parallel and combines their results to produce better result of any of those models individually. In this paper, we describe RMDL model and compare the results for image and text classification as well as face recognition. We used MNIST and CIFAR-10 datasets as ground truth datasets for image classification and WOS, Reuters, IMDB, and 20newsgroup datasets for text classification. Lastly, we used ORL dataset to compare the model performance on face recognition task.", "published": "2018-08-23T00:38:14Z", "authors": ["Mojtaba Heidarysafa", "Kamran Kowsari", "Donald E. Brown", "Kiana Jafari Meimandi", "Laura E. Barnes"], "categories": ["cs.LG", "cs.CV", "cs.IR", "cs.NE", "stat.ML"], "doi": "10.18178/ijmlc.2018.8.4.703", "_query": "house price prediction machine learning", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2402.08871v3", "title": "Position: Topological Deep Learning is the New Frontier for Relational Learning", "abstract": "Topological deep learning (TDL) is a rapidly evolving field that uses topological features to understand and design deep learning models. This paper posits that TDL is the new frontier for relational learning. TDL may complement graph representation learning and geometric deep learning by incorporating topological concepts, and can thus provide a natural choice for various machine learning settings. To this end, this paper discusses open problems in TDL, ranging from practical benefits to theoretical foundations. For each problem, it outlines potential solutions and future research opportunities. At the same time, this paper serves as an invitation to the scientific community to actively participate in TDL research to unlock the potential of this emerging field.", "published": "2024-02-14T00:35:10Z", "authors": ["Theodore Papamarkou", "Tolga Birdal", "Michael Bronstein", "Gunnar Carlsson", "Justin Curry", "Yue Gao", "Mustafa Hajij", "Roland Kwitt", "Pietro Liò", "Paolo Di Lorenzo", "Vasileios Maroulas", "Nina Miolane", "Farzana Nasrin", "Karthikeyan Natesan Ramamurthy", "Bastian Rieck", "Simone Scardapane", "Michael T. Schaub", "Petar Veličković", "Bei Wang", "Yusu Wang", "Guo-Wei Wei", "Ghada Zamzmi"], "categories": ["cs.LG", "stat.ML"], "doi": "", "_query": "house price prediction machine learning", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2409.13940v1", "title": "Learning Recourse Costs from Pairwise Feature Comparisons", "abstract": "This paper presents a novel technique for incorporating user input when learning and inferring user preferences. When trying to provide users of black-box machine learning models with actionable recourse, we often wish to incorporate their personal preferences about the ease of modifying each individual feature. These recourse finding algorithms usually require an exhaustive set of tuples associating each feature to its cost of modification. Since it is hard to obtain such costs by directly surveying humans, in this paper, we propose the use of the Bradley-Terry model to automatically infer feature-wise costs using non-exhaustive human comparison surveys. We propose that users only provide inputs comparing entire recourses, with all candidate feature modifications, determining which recourses are easier to implement relative to others, without explicit quantification of their costs. We demonstrate the efficient learning of individual feature costs using MAP estimates, and show that these non-exhaustive human surveys, which do not necessarily contain data for each feature pair comparison, are sufficient to learn an exhaustive set of feature costs, where each feature is associated with a modification cost.", "published": "2024-09-20T23:04:08Z", "authors": ["Kaivalya Rawal", "Himabindu Lakkaraju"], "categories": ["cs.LG", "cs.AI", "cs.CY", "stat.ML"], "doi": "", "_query": "house price prediction machine learning", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2011.12288v1", "title": "Machine Learning (ML) In a 5G Standalone (SA) Self Organizing Network (SON)", "abstract": "Machine learning (ML) is included in Self-organizing Networks (SONs) that are key drivers for enhancing the Operations, Administration, and Maintenance (OAM) activities. It is included in the 5G Standalone (SA) system is one of the 5G communication tracks that transforms 4G networking to next-generation technology that is based on mobile applications. The research's main aim is to an overview of machine learning (ML) in 5G standalone core networks. 5G Standalone is considered a key enabler by the service providers as it improves the efficacy of the throughput that edges the network. It also assists in advancing new cellular use cases like ultra-reliable low latency communications (URLLC) that supports combinations of frequencies.", "published": "2020-11-24T18:57:40Z", "authors": ["Srinivasan Sridharan"], "categories": ["cs.NI", "cs.LG"], "doi": "10.14445/22312803/IJCTT-V68I11P105", "_query": "house price prediction machine learning", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2512.01810v1", "title": "DeepCAVE: A Visualization and Analysis Tool for Automated Machine Learning", "abstract": "Hyperparameter optimization (HPO), as a central paradigm of AutoML, is crucial for leveraging the full potential of machine learning (ML) models; yet its complexity poses challenges in understanding and debugging the optimization process. We present DeepCAVE, a tool for interactive visualization and analysis, providing insights into HPO. Through an interactive dashboard, researchers, data scientists, and ML engineers can explore various aspects of the HPO process and identify issues, untouched potentials, and new insights about the ML model being tuned. By empowering users with actionable insights, DeepCAVE contributes to the interpretability of HPO and ML on a design level and aims to foster the development of more robust and efficient methodologies in the future.", "published": "2025-12-01T15:45:30Z", "authors": ["Sarah Segel", "Helena Graf", "Edward Bergman", "Kristina Thieme", "Marcel Wever", "Alexander Tornede", "Frank Hutter", "Marius Lindauer"], "categories": ["cs.LG"], "doi": "", "_query": "house price prediction machine learning", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2101.04520v2", "title": "A Unified Framework for Online Trip Destination Prediction", "abstract": "Trip destination prediction is an area of increasing importance in many applications such as trip planning, autonomous driving and electric vehicles. Even though this problem could be naturally addressed in an online learning paradigm where data is arriving in a sequential fashion, the majority of research has rather considered the offline setting. In this paper, we present a unified framework for trip destination prediction in an online setting, which is suitable for both online training and online prediction. For this purpose, we develop two clustering algorithms and integrate them within two online prediction models for this problem. We investigate the different configurations of clustering algorithms and prediction models on a real-world dataset. We demonstrate that both the clustering and the entire framework yield consistent results compared to the offline setting. Finally, we propose a novel regret metric for evaluating the entire online framework in comparison to its offline counterpart. This metric makes it possible to relate the source of erroneous predictions to either the clustering or the prediction model. Using this metric, we show that the proposed methods converge to a probability distribution resembling the true underlying distribution with a lower regret than all of the baselines.", "published": "2021-01-12T14:45:27Z", "authors": ["Victor Eberstein", "Jonas Sjöblom", "Nikolce Murgovski", "Morteza Haghir Chehreghani"], "categories": ["cs.LG", "cs.AI"], "doi": "10.1007/s10994-022-06175-y", "_query": "house price prediction machine learning", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2011.07200v1", "title": "Deep Spatial Learning with Molecular Vibration", "abstract": "Machine learning over-fitting caused by data scarcity greatly limits the application of machine learning for molecules. Due to manufacturing processes difference, big data is not always rendered available through computational chemistry methods for some tasks, causing data scarcity problem for machine learning algorithms. Here we propose to extract the natural features of molecular structures and rationally distort them to augment the data availability. This method allows a machine learning project to leverage the powerful fit of physics-informed augmentation for providing significant boost to predictive accuracy. Successfully verified by the prediction of rejection rate and flux of thin film polyamide nanofiltration membranes, with the relative error dropping from 16.34% to 6.71% and the coefficient of determination rising from 0.16 to 0.75, the proposed deep spatial learning with molecular vibration is widely instructive for molecular science. Experimental comparison unequivocally demonstrates its superiority over common learning algorithms.", "published": "2020-11-14T02:46:43Z", "authors": ["Ziyang Zhang", "Yingtao Luo"], "categories": ["cs.LG", "physics.chem-ph"], "doi": "", "_query": "house price prediction machine learning", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2008.12065v1", "title": "Propensity-to-Pay: Machine Learning for Estimating Prediction Uncertainty", "abstract": "Predicting a customer's propensity-to-pay at an early point in the revenue cycle can provide organisations many opportunities to improve the customer experience, reduce hardship and reduce the risk of impaired cash flow and occurrence of bad debt. With the advancements in data science; machine learning techniques can be used to build models to accurately predict a customer's propensity-to-pay. Creating effective machine learning models without access to large and detailed datasets presents some significant challenges. This paper presents a case-study, conducted on a dataset from an energy organisation, to explore the uncertainty around the creation of machine learning models that are able to predict residential customers entering financial hardship which then reduces their ability to pay energy bills. Incorrect predictions can result in inefficient resource allocation and vulnerable customers not being proactively identified. This study investigates machine learning models' ability to consider different contexts and estimate the uncertainty in the prediction. Seven models from four families of machine learning algorithms are investigated for their novel utilisation. A novel concept of utilising a Baysian Neural Network to the binary classification problem of propensity-to-pay energy bills is proposed and explored for deployment.", "published": "2020-08-27T11:49:25Z", "authors": ["Md Abul Bashar", "Astin-Walmsley Kieren", "Heath Kerina", "Richi Nayak"], "categories": ["cs.LG", "cs.AI", "cs.CV", "stat.ML"], "doi": "10.1016/j.iswa.2023.200176", "_query": "house price prediction machine learning", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2008.04699v2", "title": "Byzantine Fault-Tolerant Distributed Machine Learning Using Stochastic Gradient Descent (SGD) and Norm-Based Comparative Gradient Elimination (CGE)", "abstract": "This paper considers the Byzantine fault-tolerance problem in distributed stochastic gradient descent (D-SGD) method - a popular algorithm for distributed multi-agent machine learning. In this problem, each agent samples data points independently from a certain data-generating distribution. In the fault-free case, the D-SGD method allows all the agents to learn a mathematical model best fitting the data collectively sampled by all agents. We consider the case when a fraction of agents may be Byzantine faulty. Such faulty agents may not follow a prescribed algorithm correctly, and may render traditional D-SGD method ineffective by sharing arbitrary incorrect stochastic gradients. We propose a norm-based gradient-filter, named comparative gradient elimination (CGE), that robustifies the D-SGD method against Byzantine agents. We show that the CGE gradient-filter guarantees fault-tolerance against a bounded fraction of Byzantine agents under standard stochastic assumptions, and is computationally simpler compared to many existing gradient-filters such as multi-KRUM, geometric median-of-means, and the spectral filters. We empirically show, by simulating distributed learning on neural networks, that the fault-tolerance of CGE is comparable to that of existing gradient-filters. We also empirically show that exponential averaging of stochastic gradients improves the fault-tolerance of a generic gradient-filter.", "published": "2020-08-11T13:51:16Z", "authors": ["Nirupam Gupta", "Shuo Liu", "Nitin H. Vaidya"], "categories": ["cs.LG", "cs.DC", "stat.ML"], "doi": "", "_query": "house price prediction machine learning", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/1901.10534v1", "title": "Investigating Limit Order Book Characteristics for Short Term Price Prediction: a Machine Learning Approach", "abstract": "With the proliferation of algorithmic high-frequency trading in financial markets, the Limit Order Book has generated increased research interest. Research is still at an early stage and there is much we do not understand about the dynamics of Limit Order Books. In this paper, we employ a machine learning approach to investigate Limit Order Book features and their potential to predict short term price movements. This is an initial broad-based investigation that results in some novel observations about LOB dynamics and identifies several promising directions for further research. Furthermore, we obtain prediction results that are significantly superior to a baseline predictor.", "published": "2018-12-20T05:47:10Z", "authors": ["Faisal I Qureshi"], "categories": ["q-fin.TR", "cs.LG"], "doi": "", "_query": "house price prediction machine learning", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2602.07496v2", "title": "A Behavior-first approach to Multi-Intention Inverse Reinforcement Learning", "abstract": "Inverse Reinforcement Learning (IRL) seeks to infer reward functions from expert demonstrations. When demonstrations originate from multiple experts with different intentions, the problem is known as Multi-Intention IRL (MI-IRL). Recent deep generative MI-IRL approaches couple behavior clustering and reward learning, but typically require prior knowledge of the number of true behavioral modes $K^*$. This reliance on expert knowledge limits their adaptability to new behaviors, and only enables analysis related to the learned rewards, and not across the behavior modes used to train them. We propose Contrastive Multi-Intention IRL (CoMI-IRL), a transformer-based unsupervised framework that decouples behavior representation and clustering from downstream reward learning. Our experiments show that CoMI-IRL outperforms existing approaches without a priori knowledge of $K^*$ or labels, while allowing for visual interpretation of behavior relationships and adaptation to unseen behavior without full retraining.", "published": "2026-02-07T11:14:34Z", "authors": ["Antonio Mone", "Frans A. Oliehoek", "Luciano Cavalcante Siebert"], "categories": ["cs.LG"], "doi": "", "_query": "house price prediction machine learning", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2409.18574v2", "title": "Climate Adaptation with Reinforcement Learning: Experiments with Flooding and Transportation in Copenhagen", "abstract": "Due to climate change the frequency and intensity of extreme rainfall events, which contribute to urban flooding, are expected to increase in many places. These floods can damage transport infrastructure and disrupt mobility, highlighting the need for cities to adapt to escalating risks. Reinforcement learning (RL) serves as a powerful tool for uncovering optimal adaptation strategies, determining how and where to deploy adaptation measures effectively, even under significant uncertainty. In this study, we leverage RL to identify the most effective timing and locations for implementing measures, aiming to reduce both direct and indirect impacts of flooding. Our framework integrates climate change projections of future rainfall events and floods, models city-wide motorized trips, and quantifies direct and indirect impacts on infrastructure and mobility. Preliminary results suggest that our RL-based approach can significantly enhance decision-making by prioritizing interventions in specific urban areas and identifying the optimal periods for their implementation. Our framework is publicly available: \\url{https://github.com/MLSM-at-DTU/floods_transport_rl}.", "published": "2024-09-27T09:18:57Z", "authors": ["Miguel Costa", "Morten W. Petersen", "Arthur Vandervoort", "Martin Drews", "Karyn Morrissey", "Francisco C. Pereira"], "categories": ["cs.LG"], "doi": "", "_query": "house price prediction machine learning", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2106.06508v2", "title": "Preferential Temporal Difference Learning", "abstract": "Temporal-Difference (TD) learning is a general and very useful tool for estimating the value function of a given policy, which in turn is required to find good policies. Generally speaking, TD learning updates states whenever they are visited. When the agent lands in a state, its value can be used to compute the TD-error, which is then propagated to other states. However, it may be interesting, when computing updates, to take into account other information than whether a state is visited or not. For example, some states might be more important than others (such as states which are frequently seen in a successful trajectory). Or, some states might have unreliable value estimates (for example, due to partial observability or lack of data), making their values less desirable as targets. We propose an approach to re-weighting states used in TD updates, both when they are the input and when they provide the target for the update. We prove that our approach converges with linear function approximation and illustrate its desirable empirical behaviour compared to other TD-style methods.", "published": "2021-06-11T17:05:15Z", "authors": ["Nishanth Anand", "Doina Precup"], "categories": ["cs.LG", "cs.AI"], "doi": "", "_query": "house price prediction machine learning", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/1804.06620v3", "title": "Visualizing the Feature Importance for Black Box Models", "abstract": "In recent years, a large amount of model-agnostic methods to improve the transparency, trustability and interpretability of machine learning models have been developed. We introduce local feature importance as a local version of a recent model-agnostic global feature importance method. Based on local feature importance, we propose two visual tools: partial importance (PI) and individual conditional importance (ICI) plots which visualize how changes in a feature affect the model performance on average, as well as for individual observations. Our proposed methods are related to partial dependence (PD) and individual conditional expectation (ICE) plots, but visualize the expected (conditional) feature importance instead of the expected (conditional) prediction. Furthermore, we show that averaging ICI curves across observations yields a PI curve, and integrating the PI curve with respect to the distribution of the considered feature results in the global feature importance. Another contribution of our paper is the Shapley feature importance, which fairly distributes the overall performance of a model among the features according to the marginal contributions and which can be used to compare the feature importance across different models.", "published": "2018-04-18T09:35:38Z", "authors": ["Giuseppe Casalicchio", "Christoph Molnar", "Bernd Bischl"], "categories": ["stat.ML", "cs.AI", "cs.LG"], "doi": "10.1007/978-3-030-10925-7_40", "_query": "house price prediction machine learning", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/1912.04977v3", "title": "Advances and Open Problems in Federated Learning", "abstract": "Federated learning (FL) is a machine learning setting where many clients (e.g. mobile devices or whole organizations) collaboratively train a model under the orchestration of a central server (e.g. service provider), while keeping the training data decentralized. FL embodies the principles of focused data collection and minimization, and can mitigate many of the systemic privacy risks and costs resulting from traditional, centralized machine learning and data science approaches. Motivated by the explosive growth in FL research, this paper discusses recent advances and presents an extensive collection of open problems and challenges.", "published": "2019-12-10T20:55:41Z", "authors": ["Peter Kairouz", "H. Brendan McMahan", "Brendan Avent", "Aurélien Bellet", "Mehdi Bennis", "Arjun Nitin Bhagoji", "Kallista Bonawitz", "Zachary Charles", "Graham Cormode", "Rachel Cummings", "Rafael G. L. D'Oliveira", "Hubert Eichner", "Salim El Rouayheb", "David Evans", "Josh Gardner", "Zachary Garrett", "Adrià Gascón", "Badih Ghazi", "Phillip B. Gibbons", "Marco Gruteser", "Zaid Harchaoui", "Chaoyang He", "Lie He", "Zhouyuan Huo", "Ben Hutchinson", "Justin Hsu", "Martin Jaggi", "Tara Javidi", "Gauri Joshi", "Mikhail Khodak", "Jakub Konečný", "Aleksandra Korolova", "Farinaz Koushanfar", "Sanmi Koyejo", "Tancrède Lepoint", "Yang Liu", "Prateek Mittal", "Mehryar Mohri", "Richard Nock", "Ayfer Özgür", "Rasmus Pagh", "Mariana Raykova", "Hang Qi", "Daniel Ramage", "Ramesh Raskar", "Dawn Song", "Weikang Song", "Sebastian U. Stich", "Ziteng Sun", "Ananda Theertha Suresh", "Florian Tramèr", "Praneeth Vepakomma", "Jianyu Wang", "Li Xiong", "Zheng Xu", "Qiang Yang", "Felix X. Yu", "Han Yu", "Sen Zhao"], "categories": ["cs.LG", "cs.CR", "stat.ML"], "doi": "", "_query": "house price prediction machine learning", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2309.03831v1", "title": "Uncovering Drift in Textual Data: An Unsupervised Method for Detecting and Mitigating Drift in Machine Learning Models", "abstract": "Drift in machine learning refers to the phenomenon where the statistical properties of data or context, in which the model operates, change over time leading to a decrease in its performance. Therefore, maintaining a constant monitoring process for machine learning model performance is crucial in order to proactively prevent any potential performance regression. However, supervised drift detection methods require human annotation and consequently lead to a longer time to detect and mitigate the drift. In our proposed unsupervised drift detection method, we follow a two step process. Our first step involves encoding a sample of production data as the target distribution, and the model training data as the reference distribution. In the second step, we employ a kernel-based statistical test that utilizes the maximum mean discrepancy (MMD) distance metric to compare the reference and target distributions and estimate any potential drift. Our method also identifies the subset of production data that is the root cause of the drift. The models retrained using these identified high drift samples show improved performance on online customer experience quality metrics.", "published": "2023-09-07T16:45:42Z", "authors": ["Saeed Khaki", "Akhouri Abhinav Aditya", "Zohar Karnin", "Lan Ma", "Olivia Pan", "Samarth Marudheri Chandrashekar"], "categories": ["cs.CL", "cs.AI", "cs.LG"], "doi": "", "_query": "house price prediction machine learning", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2509.20008v2", "title": "Learning Robust Penetration Testing Policies under Partial Observability: A systematic evaluation", "abstract": "Penetration testing, the simulation of cyberattacks to identify security vulnerabilities, presents a sequential decision-making problem well-suited for reinforcement learning (RL) automation. Like many applications of RL to real-world problems, partial observability presents a major challenge, as it invalidates the Markov property present in Markov Decision Processes (MDPs). Partially Observable MDPs require history aggregation or belief state estimation to learn successful policies. We investigate stochastic, partially observable penetration testing scenarios over host networks of varying size, aiming to better reflect real-world complexity through more challenging and representative benchmarks. This approach leads to the development of more robust and transferable policies, which are crucial for ensuring reliable performance across diverse and unpredictable real-world environments. Using vanilla Proximal Policy Optimization (PPO) as a baseline, we compare a selection of PPO-based variants designed to mitigate partial observability, including frame-stacking, augmenting observations with historical information, and employing LSTM or TrXL architectures. We conduct a systematic empirical analysis of these algorithms across different host network sizes. We find that this task greatly benefits from history aggregation. Converging up to four times faster than other approaches. Manual inspection of the learned policies by the algorithms reveals clear distinctions and provides insights that go beyond quantitative results.", "published": "2025-09-24T11:27:54Z", "authors": ["Raphael Simon", "Pieter Libin", "Wim Mees"], "categories": ["cs.LG", "cs.CR"], "doi": "", "_query": "house price prediction machine learning", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2204.07005v1", "title": "Interpretability of Machine Learning Methods Applied to Neuroimaging", "abstract": "Deep learning methods have become very popular for the processing of natural images, and were then successfully adapted to the neuroimaging field. As these methods are non-transparent, interpretability methods are needed to validate them and ensure their reliability. Indeed, it has been shown that deep learning models may obtain high performance even when using irrelevant features, by exploiting biases in the training set. Such undesirable situations can potentially be detected by using interpretability methods. Recently, many methods have been proposed to interpret neural networks. However, this domain is not mature yet. Machine learning users face two major issues when aiming to interpret their models: which method to choose, and how to assess its reliability? Here, we aim at providing answers to these questions by presenting the most common interpretability methods and metrics developed to assess their reliability, as well as their applications and benchmarks in the neuroimaging context. Note that this is not an exhaustive survey: we aimed to focus on the studies which we found to be the most representative and relevant.", "published": "2022-04-14T14:56:31Z", "authors": ["Elina Thibeau-Sutre", "Sasha Collin", "Ninon Burgos", "Olivier Colliot"], "categories": ["cs.CV", "cs.AI", "cs.LG", "q-bio.NC", "q-bio.QM"], "doi": "", "_query": "house price prediction machine learning", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2502.04121v3", "title": "First-Passage Approach to Optimizing Perturbations for Improved Training of Machine Learning Models", "abstract": "Machine learning models have become indispensable tools in applications across the physical sciences. Their training is often time-consuming, vastly exceeding the inference timescales. Several protocols have been developed to perturb the learning process and improve the training, such as shrink and perturb, warm restarts, and stochastic resetting. For classifiers, these perturbations have been shown to result in enhanced speedups or improved generalization. However, the design of such perturbations is usually done ad hoc by intuition and trial and error. To rationally optimize training protocols, we frame them as first-passage processes and consider their response to perturbations. We show that if the unperturbed learning process reaches a quasi-steady state, the response at a single perturbation frequency can predict the behavior at a wide range of frequencies. We employ this approach to a CIFAR-10 classifier using the ResNet-18 model and identify a useful perturbation and frequency among several possibilities. We demonstrate the transferability of the approach to other datasets, architectures, optimizers and even tasks (regression instead of classification). Our work allows optimization of perturbations for improving the training of machine learning models using a first-passage approach.", "published": "2025-02-06T14:53:21Z", "authors": ["Sagi Meir", "Tommer D. Keidar", "Shlomi Reuveni", "Barak Hirshberg"], "categories": ["cs.LG", "cond-mat.dis-nn", "physics.chem-ph"], "doi": "10.1088/2632-2153/add8df", "_query": "house price prediction machine learning", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/1612.08486v1", "title": "Understanding the Impacts of Dark Pools on Price Discovery", "abstract": "This paper investigates the impact of dark pools on price discovery (the efficiency of prices on stock exchanges to aggregate information). Assets are traded in either an exchange or a dark pool, with the dark pool offering better prices but lower execution rates. Informed traders receive noisy and heterogeneous signals about an asset's fundamental. We find that informed traders use dark pools to mitigate their information risk and there is a sorting effect: in equilibrium, traders with strong signals trade in exchanges, traders with moderate signals trade in dark pools, and traders with weak signals do not trade. As a result, dark pools have an amplification effect on price discovery. That is, when information precision is high (information risk is low), the majority of informed traders trade in the exchange hence adding a dark pool enhances price discovery, whereas when information precision is low (information risk is high), the majority of the informed traders trade in the dark pool hence adding a dark pool impairs price discovery. The paper reconciles the conflicting empirical evidence and produces novel empirical predictions. The paper also provides regulatory suggestions with dark pools on current equity markets and in emerging markets.", "published": "2016-12-27T03:26:29Z", "authors": ["Linlin Ye"], "categories": ["q-fin.GN", "q-fin.TR"], "doi": "", "_query": "house price prediction machine learning", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/1901.02256v2", "title": "Artificial Intelligence and Machine Learning to Predict and Improve Efficiency in Manufacturing Industry", "abstract": "The overall equipment effectiveness (OEE) is a performance measurement metric widely used. Its calculation provides to the managers the possibility to identify the main losses that reduce the machine effectiveness and then take the necessary decisions in order to improve the situation. However, this calculation is done a-posterior which is often too late. In the present research, we implemented different Machine Learning algorithms namely; Support vector machine, Optimized Support vector Machine (using Genetic Algorithm), Random Forest, XGBoost and Deep Learning to predict the estimate OEE value. The data used to train our models was provided by an automotive cable production industry. The results show that the Deep Learning and Random Forest are more accurate and present better performance for the prediction of the overall equipment effectiveness in our case study.", "published": "2019-01-08T11:12:37Z", "authors": ["Ibtissam El Hassani", "Choumicha El Mazgualdi", "Tawfik Masrour"], "categories": ["cs.LG", "stat.ML"], "doi": "", "_query": "house price prediction machine learning", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2511.15753v1", "title": "Prediction of Retention Time in Larger Antisense Oligonucleotide Datasets using Machine Learning", "abstract": "Antisense oligonucleotides (ASOs) are nucleic acid molecules with transformative therapeutic potential, especially for diseases that are untreatable by traditional drugs. However, the production and purification of ASOs remain challenging due to the presence of unwanted impurities. One tool successfully used to separate an ASO compound from the impurities is ion pair liquid chromatography (IPC). It is a critical step in separation, where each compound is identified by its retention time (tR) in the IPC. Due to the complex sequence-dependent behavior of ASOs and variability in chromatographic conditions, the accurate prediction of tR is a difficult task. This study addresses this challenge by applying machine learning (ML) to predict tR based on the sequence characteristics of ASOs. Four ML models Gradient Boosting, Random Forest, Decision Tree, and Support Vector Regression were evaluated on three large ASO datasets with different gradient times. Through feature engineering and grid search optimization, key predictors were identified and compared for model accuracy using root mean square error, coefficient of determination R-squared, and run time. The results showed that Gradient Boost performance competes with the Support Vector Machine in two of the three datasets, but is 3.94 times faster to tune. Additionally, newly proposed features representing the sulfur count and the nucleotides residing at the first and last positions of a sequence were found to improve the predictive power of the models. This study demonstrates the advantages of ML-based tR prediction at scale and provides insights into interpretable and efficient utilization of ML in chromatographic applications.", "published": "2025-11-19T05:45:42Z", "authors": ["Manal Rahal", "Bestoun S. Ahmed", "Christoph A. Bauer", "Johan Ulander", "Jorgen Samuelsson"], "categories": ["q-bio.OT"], "doi": "", "_query": "house price prediction machine learning", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/1812.00490v1", "title": "Improving Clinical Predictions through Unsupervised Time Series Representation Learning", "abstract": "In this work, we investigate unsupervised representation learning on medical time series, which bears the promise of leveraging copious amounts of existing unlabeled data in order to eventually assist clinical decision making. By evaluating on the prediction of clinically relevant outcomes, we show that in a practical setting, unsupervised representation learning can offer clear performance benefits over end-to-end supervised architectures. We experiment with using sequence-to-sequence (Seq2Seq) models in two different ways, as an autoencoder and as a forecaster, and show that the best performance is achieved by a forecasting Seq2Seq model with an integrated attention mechanism, proposed here for the first time in the setting of unsupervised learning for medical time series.", "published": "2018-12-02T23:59:36Z", "authors": ["Xinrui Lyu", "Matthias Hueser", "Stephanie L. Hyland", "George Zerveas", "Gunnar Raetsch"], "categories": ["cs.LG", "stat.ML"], "doi": "", "_query": "house price prediction machine learning", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2602.04861v2", "title": "From Evaluation to Design: Using Potential Energy Surface Smoothness Metrics to Guide Machine Learning Interatomic Potential Architectures", "abstract": "Machine Learning Interatomic Potentials (MLIPs) sometimes fail to reproduce the physical smoothness of the quantum potential energy surface (PES), leading to erroneous behavior in downstream simulations that standard energy and force regression evaluations can miss. Existing evaluations, such as microcanonical molecular dynamics (MD), are computationally expensive and primarily probe near-equilibrium states. To improve evaluation metrics for MLIPs, we introduce the Bond Smoothness Characterization Test (BSCT). This efficient benchmark probes the PES via controlled bond deformations and detects non-smoothness, including discontinuities, artificial minima, and spurious forces, both near and far from equilibrium. We show that BSCT correlates strongly with MD stability while requiring a fraction of the cost of MD. To demonstrate how BSCT can guide iterative model design, we utilize an unconstrained Transformer backbone as a testbed, illustrating how refinements such as a new differentiable $k$-nearest neighbors algorithm and temperature-controlled attention reduce artifacts identified by our metric. By optimizing model design systematically based on BSCT, the resulting MLIP simultaneously achieves a low conventional E/F regression error, stable MD simulations, and robust atomistic property predictions. Our results establish BSCT as both a validation metric for practitioners to assess MLIP utility and as an \"in-the-loop\" model design proxy that alerts MLIP developers to physical challenges that cannot be efficiently evaluated by current MLIP benchmarks. The BSCT dataset and evaluation are available on https://github.com/ryanliu30/bsct.git", "published": "2026-02-04T18:50:10Z", "authors": ["Ryan Liu", "Eric Qu", "Tobias Kreiman", "Samuel M. Blau", "Aditi S. Krishnapriyan"], "categories": ["cs.LG", "cond-mat.mtrl-sci", "cs.AI", "physics.chem-ph"], "doi": "", "_query": "house price prediction machine learning", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2311.13028v2", "title": "DMLR: Data-centric Machine Learning Research -- Past, Present and Future", "abstract": "Drawing from discussions at the inaugural DMLR workshop at ICML 2023 and meetings prior, in this report we outline the relevance of community engagement and infrastructure development for the creation of next-generation public datasets that will advance machine learning science. We chart a path forward as a collective effort to sustain the creation and maintenance of these datasets and methods towards positive scientific, societal and business impact.", "published": "2023-11-21T22:29:25Z", "authors": ["Luis Oala", "Manil Maskey", "Lilith Bat-Leah", "Alicia Parrish", "Nezihe Merve Gürel", "Tzu-Sheng Kuo", "Yang Liu", "Rotem Dror", "Danilo Brajovic", "Xiaozhe Yao", "Max Bartolo", "William A Gaviria Rojas", "Ryan Hileman", "Rainier Aliment", "Michael W. Mahoney", "Meg Risdal", "Matthew Lease", "Wojciech Samek", "Debojyoti Dutta", "Curtis G Northcutt", "Cody Coleman", "Braden Hancock", "Bernard Koch", "Girmaw Abebe Tadesse", "Bojan Karlaš", "Ahmed Alaa", "Adji Bousso Dieng", "Natasha Noy", "Vijay Janapa Reddi", "James Zou", "Praveen Paritosh", "Mihaela van der Schaar", "Kurt Bollacker", "Lora Aroyo", "Ce Zhang", "Joaquin Vanschoren", "Isabelle Guyon", "Peter Mattson"], "categories": ["cs.LG", "cs.AI", "cs.DC", "eess.SP"], "doi": "", "_query": "house price prediction machine learning", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2106.08443v1", "title": "Reproducing Kernel Hilbert Space, Mercer's Theorem, Eigenfunctions, Nyström Method, and Use of Kernels in Machine Learning: Tutorial and Survey", "abstract": "This is a tutorial and survey paper on kernels, kernel methods, and related fields. We start with reviewing the history of kernels in functional analysis and machine learning. Then, Mercer kernel, Hilbert and Banach spaces, Reproducing Kernel Hilbert Space (RKHS), Mercer's theorem and its proof, frequently used kernels, kernel construction from distance metric, important classes of kernels (including bounded, integrally positive definite, universal, stationary, and characteristic kernels), kernel centering and normalization, and eigenfunctions are explained in detail. Then, we introduce types of use of kernels in machine learning including kernel methods (such as kernel support vector machines), kernel learning by semi-definite programming, Hilbert-Schmidt independence criterion, maximum mean discrepancy, kernel mean embedding, and kernel dimensionality reduction. We also cover rank and factorization of kernel matrix as well as the approximation of eigenfunctions and kernels using the Nystr{ö}m method. This paper can be useful for various fields of science including machine learning, dimensionality reduction, functional analysis in mathematics, and mathematical physics in quantum mechanics.", "published": "2021-06-15T21:29:12Z", "authors": ["Benyamin Ghojogh", "Ali Ghodsi", "Fakhri Karray", "Mark Crowley"], "categories": ["stat.ML", "cs.LG", "math.FA"], "doi": "", "_query": "house price prediction machine learning", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2502.07409v5", "title": "MGPATH: Vision-Language Model with Multi-Granular Prompt Learning for Few-Shot WSI Classification", "abstract": "Whole slide pathology image classification presents challenges due to gigapixel image sizes and limited annotation labels, hindering model generalization. This paper introduces a prompt learning method to adapt large vision-language models for few-shot pathology classification. We first extend the Prov-GigaPath vision foundation model, pre-trained on 1.3 billion pathology image tiles, into a vision-language model by adding adaptors and aligning it with medical text encoders via contrastive learning on 923K image-text pairs. The model is then used to extract visual features and text embeddings from few-shot annotations and fine-tunes with learnable prompt embeddings. Unlike prior methods that combine prompts with frozen features using prefix embeddings or self-attention, we propose multi-granular attention that compares interactions between learnable prompts with individual image patches and groups of them. This approach improves the model's ability to capture both fine-grained details and broader context, enhancing its recognition of complex patterns across sub-regions. To further improve accuracy, we leverage (unbalanced) optimal transport-based visual-text distance to secure model robustness by mitigating perturbations that might occur during the data augmentation process. Empirical experiments on lung, kidney, and breast pathology modalities validate the effectiveness of our approach; thereby, we surpass several of the latest competitors and consistently improve performance across diverse architectures, including CLIP, PLIP, and Prov-GigaPath integrated PLIP.", "published": "2025-02-11T09:42:13Z", "authors": ["Anh-Tien Nguyen", "Duy Minh Ho Nguyen", "Nghiem Tuong Diep", "Trung Quoc Nguyen", "Nhat Ho", "Jacqueline Michelle Metsch", "Miriam Cindy Maurer", "Daniel Sonntag", "Hanibal Bohnenberger", "Anne-Christin Hauschild"], "categories": ["cs.CV", "cs.LG"], "doi": "", "_query": "house price prediction machine learning", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2104.05413v1", "title": "Financial Markets Prediction with Deep Learning", "abstract": "Financial markets are difficult to predict due to its complex systems dynamics. Although there have been some recent studies that use machine learning techniques for financial markets prediction, they do not offer satisfactory performance on financial returns. We propose a novel one-dimensional convolutional neural networks (CNN) model to predict financial market movement. The customized one-dimensional convolutional layers scan financial trading data through time, while different types of data, such as prices and volume, share parameters (kernels) with each other. Our model automatically extracts features instead of using traditional technical indicators and thus can avoid biases caused by selection of technical indicators and pre-defined coefficients in technical indicators. We evaluate the performance of our prediction model with strictly backtesting on historical trading data of six futures from January 2010 to October 2017. The experiment results show that our CNN model can effectively extract more generalized and informative features than traditional technical indicators, and achieves more robust and profitable financial performance than previous machine learning approaches.", "published": "2021-04-05T19:36:48Z", "authors": ["Jia Wang", "Tong Sun", "Benyuan Liu", "Yu Cao", "Degang Wang"], "categories": ["q-fin.ST", "cs.LG", "q-fin.CP", "q-fin.TR"], "doi": "10.1109/ICMLA.2018.00022", "_query": "house price prediction machine learning", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2201.05445v1", "title": "Machine Learning of polymer types from the spectral signature of Raman spectroscopy microplastics data", "abstract": "The tools and technology that are currently used to analyze chemical compound structures that identify polymer types in microplastics are not well-calibrated for environmentally weathered microplastics. Microplastics that have been degraded by environmental weathering factors can offer less analytic certainty than samples of microplastics that have not been exposed to weathering processes. Machine learning tools and techniques allow us to better calibrate the research tools for certainty in microplastics analysis. In this paper, we investigate whether the signatures (Raman shift values) are distinct enough such that well studied machine learning (ML) algorithms can learn to identify polymer types using a relatively small amount of labeled input data when the samples have not been impacted by environmental degradation. Several ML models were trained on a well-known repository, Spectral Libraries of Plastic Particles (SLOPP), that contain Raman shift and intensity results for a range of plastic particles, then tested on environmentally aged plastic particles (SloPP-E) consisting of 22 polymer types. After extensive preprocessing and augmentation, the trained random forest model was then tested on the SloPP-E dataset resulting in an improvement in classification accuracy of 93.81% from 89%.", "published": "2022-01-14T13:34:03Z", "authors": ["Sheela Ramanna", "Danila Morozovskii", "Sam Swanson", "Jennifer Bruneau"], "categories": ["cs.LG"], "doi": "10.54364/AAIML.2023.1144", "_query": "house price prediction machine learning", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2005.04176v3", "title": "In Pursuit of Interpretable, Fair and Accurate Machine Learning for Criminal Recidivism Prediction", "abstract": "Objectives: We study interpretable recidivism prediction using machine learning (ML) models and analyze performance in terms of prediction ability, sparsity, and fairness. Unlike previous works, this study trains interpretable models that output probabilities rather than binary predictions, and uses quantitative fairness definitions to assess the models. This study also examines whether models can generalize across geographic locations. Methods: We generated black-box and interpretable ML models on two different criminal recidivism datasets from Florida and Kentucky. We compared predictive performance and fairness of these models against two methods that are currently used in the justice system to predict pretrial recidivism: the Arnold PSA and COMPAS. We evaluated predictive performance of all models on predicting six different types of crime over two time spans. Results: Several interpretable ML models can predict recidivism as well as black-box ML models and are more accurate than COMPAS or the Arnold PSA. These models are potentially useful in practice. Similar to the Arnold PSA, some of these interpretable models can be written down as a simple table. Others can be displayed using a set of visualizations. Our geographic analysis indicates that ML models should be trained separately for separate locations and updated over time. We also present a fairness analysis for the interpretable models. Conclusions: Interpretable machine learning models can perform just as well as non-interpretable methods and currently-used risk assessment scales, in terms of both prediction accuracy and fairness. Machine learning models might be more accurate when trained separately for distinct locations and kept up-to-date.", "published": "2020-05-08T17:16:31Z", "authors": ["Caroline Wang", "Bin Han", "Bhrij Patel", "Cynthia Rudin"], "categories": ["stat.ML", "cs.LG", "stat.AP"], "doi": "", "_query": "house price prediction machine learning", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2102.07655v2", "title": "Dense for the Price of Sparse: Improved Performance of Sparsely Initialized Networks via a Subspace Offset", "abstract": "That neural networks may be pruned to high sparsities and retain high accuracy is well established. Recent research efforts focus on pruning immediately after initialization so as to allow the computational savings afforded by sparsity to extend to the training process. In this work, we introduce a new `DCT plus Sparse' layer architecture, which maintains information propagation and trainability even with as little as 0.01% trainable kernel parameters remaining. We show that standard training of networks built with these layers, and pruned at initialization, achieves state-of-the-art accuracy for extreme sparsities on a variety of benchmark network architectures and datasets. Moreover, these results are achieved using only simple heuristics to determine the locations of the trainable parameters in the network, and thus without having to initially store or compute with the full, unpruned network, as is required by competing prune-at-initialization algorithms. Switching from standard sparse layers to DCT plus Sparse layers does not increase the storage footprint of a network and incurs only a small additional computational overhead.", "published": "2021-02-12T00:05:02Z", "authors": ["Ilan Price", "Jared Tanner"], "categories": ["cs.LG"], "doi": "", "_query": "house price prediction machine learning", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2504.13459v1", "title": "The impact of institutional quality on the relation between FDI and house prices in ASEAN emerging countries", "abstract": "This study investigates the relationship between house prices and capital inflows in ASEAN emerging economies, and the impact of institutional quality on that relation. Using a unique balanced panel data set of six emerging countries in ASEAN from 2009 to 2019, we employ various econometric techniques to examine the impact of foreign direct investment (FDI) on the house price index. Our findings indicate a long-run relationship and Granger causality from FDI to the house price index in these markets, and we also find evidence of co-movement between the stock price index and the house price index. Additionally, our results suggest that better institutions reduce the impact of FDI on host country housing markets in the context of ASEAN emerging economies. This is one of the first studies to shed light on the role of institutional quality in the effect of FDIs on housing prices in this region.", "published": "2025-04-18T04:33:07Z", "authors": ["Hoang-Anh Le"], "categories": ["econ.GN"], "doi": "", "_query": "house price prediction machine learning", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2512.01287v1", "title": "milearn: A Python Package for Multi-Instance Machine Learning", "abstract": "We introduce milearn, a Python package for multi-instance learning (MIL) that follows the familiar scikit-learn fit/predict interface while providing a unified framework for both classical and neural-network-based MIL algorithms for regression and classification. The package also includes built-in hyperparameter optimization designed specifically for small MIL datasets, enabling robust model selection in data-scarce scenarios. We demonstrate the versatility of milearn across a broad range of synthetic MIL benchmark datasets, including digit classification and regression, molecular property prediction, and protein-protein interaction (PPI) prediction. Special emphasis is placed on the key instance detection (KID) problem, for which the package provides dedicated support.", "published": "2025-12-01T05:15:28Z", "authors": ["Dmitry Zankov", "Pavlo Polishchuk", "Michal Sobieraj", "Mario Barbatti"], "categories": ["cs.LG"], "doi": "", "_query": "house price prediction machine learning", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2001.08791v1", "title": "Machine learning based co-creative design framework", "abstract": "We propose a flexible, co-creative framework bringing together multiple machine learning techniques to assist human users to efficiently produce effective creative designs. We demonstrate its potential with a perfume bottle design case study, including human evaluation and quantitative and qualitative analyses.", "published": "2020-01-23T20:18:44Z", "authors": ["Brian Quanz", "Wei Sun", "Ajay Deshpande", "Dhruv Shah", "Jae-eun Park"], "categories": ["cs.HC", "cs.AI", "cs.LG"], "doi": "", "_query": "house price prediction machine learning", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2011.09350v1", "title": "Asymmetric Private Set Intersection with Applications to Contact Tracing and Private Vertical Federated Machine Learning", "abstract": "We present a multi-language, cross-platform, open-source library for asymmetric private set intersection (PSI) and PSI-Cardinality (PSI-C). Our protocol combines traditional DDH-based PSI and PSI-C protocols with compression based on Bloom filters that helps reduce communication in the asymmetric setting. Currently, our library supports C++, C, Go, WebAssembly, JavaScript, Python, and Rust, and runs on both traditional hardware (x86) and browser targets. We further apply our library to two use cases: (i) a privacy-preserving contact tracing protocol that is compatible with existing approaches, but improves their privacy guarantees, and (ii) privacy-preserving machine learning on vertically partitioned data.", "published": "2020-11-18T15:38:59Z", "authors": ["Nick Angelou", "Ayoub Benaissa", "Bogdan Cebere", "William Clark", "Adam James Hall", "Michael A. Hoeh", "Daniel Liu", "Pavlos Papadopoulos", "Robin Roehm", "Robert Sandmann", "Phillipp Schoppmann", "Tom Titcombe"], "categories": ["cs.CR", "cs.LG"], "doi": "", "_query": "house price prediction machine learning", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/1504.07865v1", "title": "ASTROMLSKIT: A New Statistical Machine Learning Toolkit: A Platform for Data Analytics in Astronomy", "abstract": "Astroinformatics is a new impact area in the world of astronomy, occasionally called the final frontier, where several astrophysicists, statisticians and computer scientists work together to tackle various data intensive astronomical problems. Exponential growth in the data volume and increased complexity of the data augments difficult questions to the existing challenges. Classical problems in Astronomy are compounded by accumulation of astronomical volume of complex data, rendering the task of classification and interpretation incredibly laborious. The presence of noise in the data makes analysis and interpretation even more arduous. Machine learning algorithms and data analytic techniques provide the right platform for the challenges posed by these problems. A diverse range of open problem like star-galaxy separation, detection and classification of exoplanets, classification of supernovae is discussed. The focus of the paper is the applicability and efficacy of various machine learning algorithms like K Nearest Neighbor (KNN), random forest (RF), decision tree (DT), Support Vector Machine (SVM), Naïve Bayes and Linear Discriminant Analysis (LDA) in analysis and inference of the decision theoretic problems in Astronomy. The machine learning algorithms, integrated into ASTROMLSKIT, a toolkit developed in the course of the work, have been used to analyze HabCat data and supernovae data. Accuracy has been found to be appreciably good.", "published": "2015-04-29T14:06:18Z", "authors": ["Snehanshu Saha", "Surbhi Agrawal", "Manikandan. R", "Kakoli Bora", "Swati Routh", "Anand Narasimhamurthy"], "categories": ["cs.CE", "astro-ph.IM", "cs.LG"], "doi": "", "_query": "house price prediction machine learning", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2411.09821v4", "title": "Towards Scalable Newborn Screening: Automated General Movement Assessment in Uncontrolled Settings", "abstract": "General movements (GMs) are spontaneous, coordinated body movements in infants that offer valuable insights into the developing nervous system. Assessed through the Prechtl GM Assessment (GMA), GMs are reliable predictors for neurodevelopmental disorders. However, GMA requires specifically trained clinicians, who are limited in number. To scale up newborn screening, there is a need for an algorithm that can automatically classify GMs from infant video recordings. This data poses challenges, including variability in recording length, device type, and setting, with each video coarsely annotated for overall movement quality. In this work, we introduce a tool for extracting features from these recordings and explore various machine learning techniques for automated GM classification.", "published": "2024-11-14T21:53:46Z", "authors": ["Daphné Chopard", "Sonia Laguna", "Kieran Chin-Cheong", "Annika Dietz", "Anna Badura", "Sven Wellmann", "Julia E. Vogt"], "categories": ["cs.LG", "cs.CV"], "doi": "", "_query": "house price prediction machine learning", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2006.05082v1", "title": "Learning to Stop While Learning to Predict", "abstract": "There is a recent surge of interest in designing deep architectures based on the update steps in traditional algorithms, or learning neural networks to improve and replace traditional algorithms. While traditional algorithms have certain stopping criteria for outputting results at different iterations, many algorithm-inspired deep models are restricted to a ``fixed-depth'' for all inputs. Similar to algorithms, the optimal depth of a deep architecture may be different for different input instances, either to avoid ``over-thinking'', or because we want to compute less for operations converged already. In this paper, we tackle this varying depth problem using a steerable architecture, where a feed-forward deep model and a variational stopping policy are learned together to sequentially determine the optimal number of layers for each input instance. Training such architecture is very challenging. We provide a variational Bayes perspective and design a novel and effective training procedure which decomposes the task into an oracle model learning stage and an imitation stage. Experimentally, we show that the learned deep model along with the stopping policy improves the performances on a diverse set of tasks, including learning sparse recovery, few-shot meta learning, and computer vision tasks.", "published": "2020-06-09T07:22:01Z", "authors": ["Xinshi Chen", "Hanjun Dai", "Yu Li", "Xin Gao", "Le Song"], "categories": ["cs.LG", "stat.ML"], "doi": "", "_query": "house price prediction machine learning", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/1704.04463v2", "title": "On Generalized Bellman Equations and Temporal-Difference Learning", "abstract": "We consider off-policy temporal-difference (TD) learning in discounted Markov decision processes, where the goal is to evaluate a policy in a model-free way by using observations of a state process generated without executing the policy. To curb the high variance issue in off-policy TD learning, we propose a new scheme of setting the $λ$-parameters of TD, based on generalized Bellman equations. Our scheme is to set $λ$ according to the eligibility trace iterates calculated in TD, thereby easily keeping these traces in a desired bounded range. Compared with prior work, this scheme is more direct and flexible, and allows much larger $λ$ values for off-policy TD learning with bounded traces. As to its soundness, using Markov chain theory, we prove the ergodicity of the joint state-trace process under nonrestrictive conditions, and we show that associated with our scheme is a generalized Bellman equation (for the policy to be evaluated) that depends on both the evolution of $λ$ and the unique invariant probability measure of the state-trace process. These results not only lead immediately to a characterization of the convergence behavior of least-squares based implementation of our scheme, but also prepare the ground for further analysis of gradient-based implementations.", "published": "2017-04-14T16:01:18Z", "authors": ["Huizhen Yu", "A. Rupam Mahmood", "Richard S. Sutton"], "categories": ["cs.LG", "math.OC"], "doi": "", "_query": "house price prediction machine learning", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2311.05128v2", "title": "Exploring and Analyzing Wildland Fire Data Via Machine Learning Techniques", "abstract": "This research project investigated the correlation between a 10 Hz time series of thermocouple temperatures and turbulent kinetic energy (TKE) computed from wind speeds collected from a small experimental prescribed burn at the Silas Little Experimental Forest in New Jersey, USA. The primary objective of this project was to explore the potential for using thermocouple temperatures as predictors for estimating the TKE produced by a wildland fire. Machine learning models, including Deep Neural Networks, Random Forest Regressor, Gradient Boosting, and Gaussian Process Regressor, are employed to assess the potential for thermocouple temperature perturbations to predict TKE values. Data visualization and correlation analyses reveal patterns and relationships between thermocouple temperatures and TKE, providing insight into the underlying dynamics. The project achieves high accuracy in predicting TKE by employing various machine learning models despite a weak correlation between the predictors and the target variable. The results demonstrate significant success, particularly from regression models, in accurately estimating the TKE. The research findings contribute to fire behavior and smoke modeling science, emphasizing the importance of incorporating machine learning approaches and identifying complex relationships between fine-scale fire behavior and turbulence. Accurate TKE estimation using thermocouple temperatures allows for the refinement of models that can inform decision-making in fire management strategies, facilitate effective risk mitigation, and optimize fire management efforts. This project highlights the valuable role of machine learning techniques in analyzing wildland fire data, showcasing their potential to advance fire research and management practices.", "published": "2023-11-09T03:47:49Z", "authors": ["Dipak Dulal", "Joseph J. Charney", "Michael Gallagher", "Carmeliza Navasca", "Nicholas Skowronski"], "categories": ["cs.LG"], "doi": "", "_query": "house price prediction machine learning", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/1810.06443v1", "title": "Hedging Algorithms and Repeated Matrix Games", "abstract": "Playing repeated matrix games (RMG) while maximizing the cumulative returns is a basic method to evaluate multi-agent learning (MAL) algorithms. Previous work has shown that $UCB$, $M3$, $S$ or $Exp3$ algorithms have good behaviours on average in RMG. Besides, hedging algorithms have been shown to be effective on prediction problems. An hedging algorithm is made up with a top-level algorithm and a set of basic algorithms. To make its decision, an hedging algorithm uses its top-level algorithm to choose a basic algorithm, and the chosen algorithm makes the decision. This paper experimentally shows that well-selected hedging algorithms are better on average than all previous MAL algorithms on the task of playing RMG against various players. $S$ is a very good top-level algorithm, and $UCB$ and $M3$ are very good basic algorithms. Furthermore, two-level hedging algorithms are more effective than one-level hedging algorithms, and three levels are not better than two levels.", "published": "2018-10-15T15:05:21Z", "authors": ["Bruno Bouzy", "Marc Métivier", "Damien Pellier"], "categories": ["cs.LG", "cs.GT", "cs.MA", "stat.ML"], "doi": "", "_query": "house price prediction machine learning", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2210.02144v2", "title": "SECOE: Alleviating Sensors Failure in Machine Learning-Coupled IoT Systems", "abstract": "Machine learning (ML) applications continue to revolutionize many domains. In recent years, there has been considerable research interest in building novel ML applications for a variety of Internet of Things (IoT) domains, such as precision agriculture, smart cities, and smart manufacturing. IoT domains are characterized by continuous streams of data originating from diverse, geographically distributed sensors, and they often require a real-time or semi-real-time response. IoT characteristics pose several fundamental challenges to designing and implementing effective ML applications. Sensor/network failures that result in data stream interruptions is one such challenge. Unfortunately, the performance of many ML applications quickly degrades when faced with data incompleteness. Current techniques to handle data incompleteness are based upon data imputation ( i.e., they try to fill-in missing data). Unfortunately, these techniques may fail, especially when multiple sensors' data streams become concurrently unavailable (due to simultaneous sensor failures). With the aim of building robust IoT-coupled ML applications, this paper proposes SECOE, a unique, proactive approach for alleviating potentially simultaneous sensor failures. The fundamental idea behind SECOE is to create a carefully chosen ensemble of ML models in which each model is trained assuming a set of failed sensors (i.e., the training set omits corresponding values). SECOE includes a novel technique to minimize the number of models in the ensemble by harnessing the correlations among sensors. We demonstrate the efficacy of the SECOE approach through a series of experiments involving three distinct datasets. The experimental findings reveal that SECOE effectively preserves prediction accuracy in the presence of sensor failures.", "published": "2022-10-05T10:58:39Z", "authors": ["Yousef AlShehri", "Lakshmish Ramaswamy"], "categories": ["cs.LG", "cs.NI"], "doi": "", "_query": "house price prediction machine learning", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2511.15960v1", "title": "Machine Learning vs. Randomness: Challenges in Predicting Binary Options Movements", "abstract": "Binary options trading is often marketed as a field where predictive models can generate consistent profits. However, the inherent randomness and stochastic nature of binary options make price movements highly unpredictable, posing significant challenges for any forecasting approach. This study demonstrates that machine learning algorithms struggle to outperform a simple baseline in predicting binary options movements. Using a dataset of EUR/USD currency pairs from 2021 to 2023, we tested multiple models, including Random Forest, Logistic Regression, Gradient Boosting, and k-Nearest Neighbors (kNN), both before and after hyperparameter optimization. Furthermore, several neural network architectures, including Multi-Layer Perceptrons (MLP) and a Long Short-Term Memory (LSTM) network, were evaluated under different training conditions. Despite these exhaustive efforts, none of the models surpassed the ZeroR baseline accuracy, highlighting the inherent randomness of binary options. These findings reinforce the notion that binary options lack predictable patterns, making them unsuitable for machine learning-based forecasting.", "published": "2025-11-20T01:19:43Z", "authors": ["Gabriel M. Arantes", "Richard F. Pinto", "Bruno L. Dalmazo", "Eduardo N. Borges", "Giancarlo Lucca", "Viviane L. D. de Mattos", "Fabian C. Cardoso", "Rafael A. Berri"], "categories": ["q-fin.CP", "cs.LG"], "doi": "10.1007/978-3-032-10486-1_43", "_query": "house price prediction machine learning", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2103.03635v2", "title": "Autocalibration and Tweedie-dominance for Insurance Pricing with Machine Learning", "abstract": "Boosting techniques and neural networks are particularly effective machine learning methods for insurance pricing. Often in practice, there are nevertheless endless debates about the choice of the right loss function to be used to train the machine learning model, as well as about the appropriate metric to assess the performances of competing models. Also, the sum of fitted values can depart from the observed totals to a large extent and this often confuses actuarial analysts. The lack of balance inherent to training models by minimizing deviance outside the familiar GLM with canonical link setting has been empirically documented in Wüthrich (2019, 2020) who attributes it to the early stopping rule in gradient descent methods for model fitting. The present paper aims to further study this phenomenon when learning proceeds by minimizing Tweedie deviance. It is shown that minimizing deviance involves a trade-off between the integral of weighted differences of lower partial moments and the bias measured on a specific scale. Autocalibration is then proposed as a remedy. This new method to correct for bias adds an extra local GLM step to the analysis. Theoretically, it is shown that it implements the autocalibration concept in pure premium calculation and ensures that balance also holds on a local scale, not only at portfolio level as with existing bias-correction techniques. The convex order appears to be the natural tool to compare competing models, putting a new light on the diagnostic graphs and associated metrics proposed by Denuit et al. (2019).", "published": "2021-03-05T12:40:30Z", "authors": ["Michel Denuit", "Arthur Charpentier", "Julien Trufin"], "categories": ["stat.ML", "cs.LG", "econ.EM"], "doi": "", "_query": "house price prediction machine learning", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2511.17624v1", "title": "QML-HCS: A Hypercausal Quantum Machine Learning Framework for Non-Stationary Environments", "abstract": "QML-HCS is a research-grade framework for constructing and analyzing quantum-inspired machine learning models operating under hypercausal feedback dynamics. Hypercausal refers to AI systems that leverage extended, deep, or nonlinear causal relationships (expanded causality) to reason, predict, and infer states beyond the capabilities of traditional causal models. Current machine learning and quantum-inspired systems struggle in non-stationary environments, where data distributions drift and models lack mechanisms for continuous adaptation, causal stability, and coherent state updating. QML-HCS addresses this limitation through a unified computational architecture that integrates quantum-inspired superposition principles, dynamic causal feedback, and deterministic-stochastic hybrid execution to enable adaptive behavior in changing environments. The framework implements a hypercausal processing core capable of reversible transformations, multipath causal propagation, and evaluation of alternative states under drift. Its architecture incorporates continuous feedback to preserve causal consistency and adjust model behavior without requiring full retraining. QML-HCS provides a reproducible and extensible Python interface backed by efficient computational routines, enabling experimentation in quantum-inspired learning, causal reasoning, and hybrid computation without the need for specialized hardware. A minimal simulation demonstrates how a hypercausal model adapts to a sudden shift in the input distribution while preserving internal coherence. This initial release establishes the foundational architecture for future theoretical extensions, benchmarking studies, and integration with classical and quantum simulation platforms.", "published": "2025-11-18T17:50:49Z", "authors": ["Hector E Mozo"], "categories": ["cs.LG"], "doi": "", "_query": "house price prediction machine learning", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2310.11335v3", "title": "Reinforcement learning with non-ergodic reward increments: robustness via ergodicity transformations", "abstract": "Envisioned application areas for reinforcement learning (RL) include autonomous driving, precision agriculture, and finance, which all require RL agents to make decisions in the real world. A significant challenge hindering the adoption of RL methods in these domains is the non-robustness of conventional algorithms. In particular, the focus of RL is typically on the expected value of the return. The expected value is the average over the statistical ensemble of infinitely many trajectories, which can be uninformative about the performance of the average individual. For instance, when we have a heavy-tailed return distribution, the ensemble average can be dominated by rare extreme events. Consequently, optimizing the expected value can lead to policies that yield exceptionally high returns with a probability that approaches zero but almost surely result in catastrophic outcomes in single long trajectories. In this paper, we develop an algorithm that lets RL agents optimize the long-term performance of individual trajectories. The algorithm enables the agents to learn robust policies, which we show in an instructive example with a heavy-tailed return distribution and standard RL benchmarks. The key element of the algorithm is a transformation that we learn from data. This transformation turns the time series of collected returns into one for whose increments expected value and the average over a long trajectory coincide. Optimizing these increments results in robust policies.", "published": "2023-10-17T15:13:33Z", "authors": ["Dominik Baumann", "Erfaun Noorani", "James Price", "Ole Peters", "Colm Connaughton", "Thomas B. Schön"], "categories": ["cs.LG"], "doi": "", "_query": "house price prediction machine learning", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2502.15905v1", "title": "Improving ex ante accuracy assessment in predicting house price dispersion: evidence from the USA", "abstract": "The study focuses on improving the ex ante prediction accuracy assessment in the case of forecasting various house price dispersion measures in the USA. It addresses a critical gap in real estate market forecasting by proposing a novel method for assessing ex ante prediction accuracy under unanticipated shocks. The proposal is based on a parametric bootstrap approach under a misspecified model, allowing for the simulation of future values and estimation of prediction errors in case of unexpected price changes. The study highlights the limitations of the traditional approach that fails to account for unforeseen market events and provides a more in-depth understanding of how prediction accuracy changes under unexpected scenarios. The proposed methods offers valuable insights for real estate market management by enabling more robust risk assessment and decision-making in the face of unexpected market fluctuations. Real data application is based on longitudinal U.S. data on real estate transactions.", "published": "2025-02-21T19:45:29Z", "authors": ["Adam Chwila", "Monika Hadaś-Dyduch", "Małgorzata Krzciuk", "Tomasz Stachurski", "Alicja Wolny-Dominiak", "Tomasz Żądło"], "categories": ["stat.ME", "stat.AP"], "doi": "", "_query": "house price prediction machine learning", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/1711.06779v1", "title": "Machine Learning Approaches for Traffic Volume Forecasting: A Case Study of the Moroccan Highway Network", "abstract": "In this paper, we aim to illustrate different approaches we followed while developing a forecasting tool for highway traffic in Morocco. Two main approaches were adopted: Statistical Analysis as a step of data exploration and data wrangling. Therefore, a beta model is carried out for a better understanding of traffic behavior. Next, we moved to Machine Learning where we worked with a bunch of algorithms such as Random Forest, Artificial Neural Networks, Extra Trees, etc. yet, we were convinced that this field of study is still considered under state of the art models, so, we were also covering an application of Long Short-Term Memory Neural Networks.", "published": "2017-11-18T00:26:44Z", "authors": ["Abderrahim Khalifa", "Younes Idsouguou", "Loubna Benabbou", "Mourad Zirari"], "categories": ["cs.CY", "cs.LG"], "doi": "", "_query": "house price prediction machine learning", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/1902.06525v1", "title": "Prediction of Porosity and Permeability Alteration based on Machine Learning Algorithms", "abstract": "The objective of this work is to study the applicability of various Machine Learning algorithms for prediction of some rock properties which geoscientists usually define due to special lab analysis. We demonstrate that these special properties can be predicted only basing on routine core analysis (RCA) data. To validate the approach core samples from the reservoir with soluble rock matrix components (salts) were tested within 100+ laboratory experiments. The challenge of the experiments was to characterize the rate of salts in cores and alteration of porosity and permeability after reservoir desalination due to drilling mud or water injection. For these three measured characteristics, we developed the relevant predictive models, which were based on the results of RCA and data on coring depth and top and bottom depths of productive horizons. To select the most accurate Machine Learning algorithm a comparative analysis has been performed. It was shown that different algorithms work better in different models. However, two hidden layers Neural network has demonstrated the best predictive ability and generalizability for all three rock characteristics jointly. The other algorithms, such as Support Vector Machine and Linear Regression, also worked well on the dataset, but in particular cases. Overall, the applied approach allows predicting the alteration of porosity and permeability during desalination in porous rocks and also evaluating salt concentration without direct measurements in a laboratory. This work also shows that developed approaches could be applied for prediction of other rock properties (residual brine and oil saturations, relative permeability, capillary pressure, and others), which laboratory measurements are time-consuming and expensive.", "published": "2019-02-18T11:37:53Z", "authors": ["Andrei Erofeev", "Denis Orlov", "Alexey Ryzhov", "Dmitry Koroteev"], "categories": ["cs.LG", "physics.data-an", "stat.ML"], "doi": "", "_query": "house price prediction machine learning", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/1803.00919v1", "title": "House Price Modeling over Heterogeneous Regions with Hierarchical Spatial Functional Analysis", "abstract": "Online real-estate information systems such as Zillow and Trulia have gained increasing popularity in recent years. One important feature offered by these systems is the online home price estimate through automated data-intensive computation based on housing information and comparative market value analysis. State-of-the-art approaches model house prices as a combination of a latent land desirability surface and a regression from house features. However, by using uniformly damping kernels, they are unable to handle irregularly shaped regions or capture land value discontinuities within the same region due to the existence of implicit sub-communities, which are common in real-world scenarios. In this paper, we explore the novel application of recent advances in spatial functional analysis to house price modeling and propose the Hierarchical Spatial Functional Model (HSFM), which decomposes house values into land desirability at both the global scale and hidden local scales as well as the feature regression component. We propose statistical learning algorithms based on finite-element spatial functional analysis and spatial constrained clustering to train our model. Extensive evaluations based on housing data in a major Canadian city show that our proposed approach can reduce the mean relative house price estimation error down to 6.60%.", "published": "2018-03-01T03:41:55Z", "authors": ["Bang Liu", "Borislav Mavrin", "Di Niu", "Linglong Kong"], "categories": ["cs.CE"], "doi": "10.1109/ICDM.2016.0134", "_query": "house price prediction machine learning", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2012.07583v1", "title": "Machine Learning and Data Analytics for Design and Manufacturing of High-Entropy Materials Exhibiting Mechanical or Fatigue Properties of Interest", "abstract": "This chapter presents an innovative framework for the application of machine learning and data analytics for the identification of alloys or composites exhibiting certain desired properties of interest. The main focus is on alloys and composites with large composition spaces for structural materials. Such alloys or composites are referred to as high-entropy materials (HEMs) and are here presented primarily in context of structural applications. For each output property of interest, the corresponding driving (input) factors are identified. These input factors may include the material composition, heat treatment, manufacturing process, microstructure, temperature, strain rate, environment, or testing mode. The framework assumes the selection of an optimization technique suitable for the application at hand and the data available. Physics-based models are presented, such as for predicting the ultimate tensile strength (UTS) or fatigue resistance. We devise models capable of accounting for physics-based dependencies. We factor such dependencies into the models as a priori information. In case that an artificial neural network (ANN) is deemed suitable for the applications at hand, it is suggested to employ custom kernel functions consistent with the underlying physics, for the purpose of attaining tighter coupling, better prediction, and for extracting the most out of the - usually limited - input data available.", "published": "2020-12-05T19:32:39Z", "authors": ["Baldur Steingrimsson", "Xuesong Fan", "Anand Kulkarni", "Michael C. Gao", "Peter K. Liaw"], "categories": ["cond-mat.mtrl-sci", "cs.LG"], "doi": "", "_query": "house price prediction machine learning", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2412.07415v1", "title": "Machine Learning Algorithms for Detecting Mental Stress in College Students", "abstract": "In today's world, stress is a big problem that affects people's health and happiness. More and more people are feeling stressed out, which can lead to lots of health issues like breathing problems, feeling overwhelmed, heart attack, diabetes, etc. This work endeavors to forecast stress and non-stress occurrences among college students by applying various machine learning algorithms: Decision Trees, Random Forest, Support Vector Machines, AdaBoost, Naive Bayes, Logistic Regression, and K-nearest Neighbors. The primary objective of this work is to leverage a research study to predict and mitigate stress and non-stress based on the collected questionnaire dataset. We conducted a workshop with the primary goal of studying the stress levels found among the students. This workshop was attended by Approximately 843 students aged between 18 to 21 years old. A questionnaire was given to the students validated under the guidance of the experts from the All India Institute of Medical Sciences (AIIMS) Raipur, Chhattisgarh, India, on which our dataset is based. The survey consists of 28 questions, aiming to comprehensively understand the multidimensional aspects of stress, including emotional well-being, physical health, academic performance, relationships, and leisure. This work finds that Support Vector Machines have a maximum accuracy for Stress, reaching 95\\%. The study contributes to a deeper understanding of stress determinants. It aims to improve college student's overall quality of life and academic success, addressing the multifaceted nature of stress.", "published": "2024-12-10T11:07:37Z", "authors": ["Ashutosh Singh", "Khushdeep Singh", "Amit Kumar", "Abhishek Shrivastava", "Santosh Kumar"], "categories": ["cs.LG", "cs.CY"], "doi": "10.1109/I2CT61223.2024.10544243", "_query": "house price prediction machine learning", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2011.12216v3", "title": "Energy-Based Models for Continual Learning", "abstract": "We motivate Energy-Based Models (EBMs) as a promising model class for continual learning problems. Instead of tackling continual learning via the use of external memory, growing models, or regularization, EBMs change the underlying training objective to cause less interference with previously learned information. Our proposed version of EBMs for continual learning is simple, efficient, and outperforms baseline methods by a large margin on several benchmarks. Moreover, our proposed contrastive divergence-based training objective can be combined with other continual learning methods, resulting in substantial boosts in their performance. We further show that EBMs are adaptable to a more general continual learning setting where the data distribution changes without the notion of explicitly delineated tasks. These observations point towards EBMs as a useful building block for future continual learning methods.", "published": "2020-11-24T17:08:13Z", "authors": ["Shuang Li", "Yilun Du", "Gido M. van de Ven", "Igor Mordatch"], "categories": ["cs.LG", "cs.AI", "stat.ML"], "doi": "", "_query": "house price prediction machine learning", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2410.02605v5", "title": "Policy Gradients for Cumulative Prospect Theory in Reinforcement Learning", "abstract": "We derive a policy gradient theorem for Cumulative Prospect Theory (CPT) objectives in finite-horizon Reinforcement Learning (RL), generalizing the standard policy gradient theorem and encompassing distortion-based risk objectives as special cases. Motivated by behavioral economics, CPT combines an asymmetric utility transformation around a reference point with probability distortion. Building on our theorem, we design a first-order policy gradient algorithm for CPT-RL using a Monte Carlo gradient estimator based on order statistics. We establish statistical guarantees for the estimator and prove asymptotic convergence of the resulting algorithm to first-order stationary points of the (generally nonconvex) CPT objective. We complement our asymptotic analysis with a non-asymptotic total sample complexity analysis to reach an approximate first-order stationary policy. Simulations illustrate qualitative behaviors induced by CPT and compare our first-order approach to existing zeroth-order methods.", "published": "2024-10-03T15:45:39Z", "authors": ["Olivier Lepel", "Anas Barakat"], "categories": ["cs.LG", "cs.AI"], "doi": "", "_query": "house price prediction machine learning", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/1905.06147v1", "title": "Embeddings and Representation Learning for Structured Data", "abstract": "Performing machine learning on structured data is complicated by the fact that such data does not have vectorial form. Therefore, multiple approaches have emerged to construct vectorial representations of structured data, from kernel and distance approaches to recurrent, recursive, and convolutional neural networks. Recent years have seen heightened attention in this demanding field of research and several new approaches have emerged, such as metric learning on structured data, graph convolutional neural networks, and recurrent decoder networks for structured data. In this contribution, we provide an high-level overview of the state-of-the-art in representation learning and embeddings for structured data across a wide range of machine learning fields.", "published": "2019-05-15T12:57:54Z", "authors": ["Benjamin Paaßen", "Claudio Gallicchio", "Alessio Micheli", "Alessandro Sperduti"], "categories": ["cs.LG", "stat.ML"], "doi": "", "_query": "house price prediction machine learning", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/1901.08558v1", "title": "Quantifying Interpretability and Trust in Machine Learning Systems", "abstract": "Decisions by Machine Learning (ML) models have become ubiquitous. Trusting these decisions requires understanding how algorithms take them. Hence interpretability methods for ML are an active focus of research. A central problem in this context is that both the quality of interpretability methods as well as trust in ML predictions are difficult to measure. Yet evaluations, comparisons and improvements of trust and interpretability require quantifiable measures. Here we propose a quantitative measure for the quality of interpretability methods. Based on that we derive a quantitative measure of trust in ML decisions. Building on previous work we propose to measure intuitive understanding of algorithmic decisions using the information transfer rate at which humans replicate ML model predictions. We provide empirical evidence from crowdsourcing experiments that the proposed metric robustly differentiates interpretability methods. The proposed metric also demonstrates the value of interpretability for ML assisted human decision making: in our experiments providing explanations more than doubled productivity in annotation tasks. However unbiased human judgement is critical for doctors, judges, policy makers and others. Here we derive a trust metric that identifies when human decisions are overly biased towards ML predictions. Our results complement existing qualitative work on trust and interpretability by quantifiable measures that can serve as objectives for further improving methods in this field of research.", "published": "2019-01-20T18:46:39Z", "authors": ["Philipp Schmidt", "Felix Biessmann"], "categories": ["cs.LG", "stat.ML"], "doi": "", "_query": "house price prediction machine learning", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2212.06370v4", "title": "Dual Accuracy-Quality-Driven Neural Network for Prediction Interval Generation", "abstract": "Accurate uncertainty quantification is necessary to enhance the reliability of deep learning models in real-world applications. In the case of regression tasks, prediction intervals (PIs) should be provided along with the deterministic predictions of deep learning models. Such PIs are useful or \"high-quality\" as long as they are sufficiently narrow and capture most of the probability density. In this paper, we present a method to learn prediction intervals for regression-based neural networks automatically in addition to the conventional target predictions. In particular, we train two companion neural networks: one that uses one output, the target estimate, and another that uses two outputs, the upper and lower bounds of the corresponding PI. Our main contribution is the design of a novel loss function for the PI-generation network that takes into account the output of the target-estimation network and has two optimization objectives: minimizing the mean prediction interval width and ensuring the PI integrity using constraints that maximize the prediction interval probability coverage implicitly. Furthermore, we introduce a self-adaptive coefficient that balances both objectives within the loss function, which alleviates the task of fine-tuning. Experiments using a synthetic dataset, eight benchmark datasets, and a real-world crop yield prediction dataset showed that our method was able to maintain a nominal probability coverage and produce significantly narrower PIs without detriment to its target estimation accuracy when compared to those PIs generated by three state-of-the-art neural-network-based methods. In other words, our method was shown to produce higher-quality PIs.", "published": "2022-12-13T05:03:16Z", "authors": ["Giorgio Morales", "John W. Sheppard"], "categories": ["cs.LG", "stat.ML"], "doi": "10.1109/TNNLS.2023.3339470", "_query": "house price prediction machine learning", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/1812.01063v1", "title": "A Hybrid Instance-based Transfer Learning Method", "abstract": "In recent years, supervised machine learning models have demonstrated tremendous success in a variety of application domains. Despite the promising results, these successful models are data hungry and their performance relies heavily on the size of training data. However, in many healthcare applications it is difficult to collect sufficiently large training datasets. Transfer learning can help overcome this issue by transferring the knowledge from readily available datasets (source) to a new dataset (target). In this work, we propose a hybrid instance-based transfer learning method that outperforms a set of baselines including state-of-the-art instance-based transfer learning approaches. Our method uses a probabilistic weighting strategy to fuse information from the source domain to the model learned in the target domain. Our method is generic, applicable to multiple source domains, and robust with respect to negative transfer. We demonstrate the effectiveness of our approach through extensive experiments for two different applications.", "published": "2018-12-03T20:15:05Z", "authors": ["Azin Asgarian", "Parinaz Sobhani", "Ji Chao Zhang", "Madalin Mihailescu", "Ariel Sibilia", "Ahmed Bilal Ashraf", "Babak Taati"], "categories": ["cs.LG", "cs.AI", "cs.CV", "stat.ML"], "doi": "", "_query": "house price prediction machine learning", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2311.00983v1", "title": "Optimizing Inventory Routing: A Decision-Focused Learning Approach using Neural Networks", "abstract": "Inventory Routing Problem (IRP) is a crucial challenge in supply chain management as it involves optimizing efficient route selection while considering the uncertainty of inventory demand planning. To solve IRPs, usually a two-stage approach is employed, where demand is predicted using machine learning techniques first, and then an optimization algorithm is used to minimize routing costs. Our experiment shows machine learning models fall short of achieving perfect accuracy because inventory levels are influenced by the dynamic business environment, which, in turn, affects the optimization problem in the next stage, resulting in sub-optimal decisions. In this paper, we formulate and propose a decision-focused learning-based approach to solving real-world IRPs. This approach directly integrates inventory prediction and routing optimization within an end-to-end system potentially ensuring a robust supply chain strategy.", "published": "2023-11-02T04:05:28Z", "authors": ["MD Shafikul Islam", "Azmine Toushik Wasi"], "categories": ["cs.LG", "cs.AI", "eess.SY", "math.OC", "stat.ML"], "doi": "", "_query": "house price prediction machine learning", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/1707.04868v1", "title": "Forecasting the U.S. Real House Price Index", "abstract": "The 2006 sudden and immense downturn in U.S. House Prices sparked the 2007 global financial crisis and revived the interest about forecasting such imminent threats for economic stability. In this paper we propose a novel hybrid forecasting methodology that combines the Ensemble Empirical Mode Decomposition (EEMD) from the field of signal processing with the Support Vector Regression (SVR) methodology that originates from machine learning. We test the forecasting ability of the proposed model against a Random Walk (RW) model, a Bayesian Autoregressive and a Bayesian Vector Autoregressive model. The proposed methodology outperforms all the competing models with half the error of the RW model with and without drift in out-of-sample forecasting. Finally, we argue that this new methodology can be used as an early warning system for forecasting sudden house prices drops with direct policy implications.", "published": "2017-07-16T11:36:16Z", "authors": ["Vasilios Plakandaras", "Rangan Gupta", "Periklis Gogas", "Theophilos Papadimitriou"], "categories": ["q-fin.CP"], "doi": "", "_query": "house price prediction machine learning", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/1901.10310v2", "title": "Robust Learning from Untrusted Sources", "abstract": "Modern machine learning methods often require more data for training than a single expert can provide. Therefore, it has become a standard procedure to collect data from external sources, e.g. via crowdsourcing. Unfortunately, the quality of these sources is not always guaranteed. As additional complications, the data might be stored in a distributed way, or might even have to remain private. In this work, we address the question of how to learn robustly in such scenarios. Studying the problem through the lens of statistical learning theory, we derive a procedure that allows for learning from all available sources, yet automatically suppresses irrelevant or corrupted data. We show by extensive experiments that our method provides significant improvements over alternative approaches from robust statistics and distributed optimization.", "published": "2019-01-29T14:33:42Z", "authors": ["Nikola Konstantinov", "Christoph Lampert"], "categories": ["cs.LG", "stat.ML"], "doi": "", "_query": "house price prediction machine learning", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/1606.05798v1", "title": "Interpretable Two-level Boolean Rule Learning for Classification", "abstract": "As a contribution to interpretable machine learning research, we develop a novel optimization framework for learning accurate and sparse two-level Boolean rules. We consider rules in both conjunctive normal form (AND-of-ORs) and disjunctive normal form (OR-of-ANDs). A principled objective function is proposed to trade classification accuracy and interpretability, where we use Hamming loss to characterize accuracy and sparsity to characterize interpretability. We propose efficient procedures to optimize these objectives based on linear programming (LP) relaxation, block coordinate descent, and alternating minimization. Experiments show that our new algorithms provide very good tradeoffs between accuracy and interpretability.", "published": "2016-06-18T19:37:26Z", "authors": ["Guolong Su", "Dennis Wei", "Kush R. Varshney", "Dmitry M. Malioutov"], "categories": ["stat.ML", "cs.LG"], "doi": "", "_query": "house price prediction machine learning", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/1512.02673v3", "title": "Speeding Up Distributed Machine Learning Using Codes", "abstract": "Codes are widely used in many engineering applications to offer robustness against noise. In large-scale systems there are several types of noise that can affect the performance of distributed machine learning algorithms -- straggler nodes, system failures, or communication bottlenecks -- but there has been little interaction cutting across codes, machine learning, and distributed systems. In this work, we provide theoretical insights on how coded solutions can achieve significant gains compared to uncoded ones. We focus on two of the most basic building blocks of distributed learning algorithms: matrix multiplication and data shuffling. For matrix multiplication, we use codes to alleviate the effect of stragglers, and show that if the number of homogeneous workers is $n$, and the runtime of each subtask has an exponential tail, coded computation can speed up distributed matrix multiplication by a factor of $\\log n$. For data shuffling, we use codes to reduce communication bottlenecks, exploiting the excess in storage. We show that when a constant fraction $α$ of the data matrix can be cached at each worker, and $n$ is the number of workers, \\emph{coded shuffling} reduces the communication cost by a factor of $(α+ \\frac{1}{n})γ(n)$ compared to uncoded shuffling, where $γ(n)$ is the ratio of the cost of unicasting $n$ messages to $n$ users to multicasting a common message (of the same size) to $n$ users. For instance, $γ(n) \\simeq n$ if multicasting a message to $n$ users is as cheap as unicasting a message to one user. We also provide experiment results, corroborating our theoretical gains of the coded algorithms.", "published": "2015-12-08T21:54:04Z", "authors": ["Kangwook Lee", "Maximilian Lam", "Ramtin Pedarsani", "Dimitris Papailiopoulos", "Kannan Ramchandran"], "categories": ["cs.DC", "cs.IT", "cs.LG", "cs.PF"], "doi": "10.1109/TIT.2017.2736066", "_query": "house price prediction machine learning", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2107.13671v1", "title": "Deeper Learning By Doing: Integrating Hands-On Research Projects Into a Machine Learning Course", "abstract": "Machine learning has seen a vast increase of interest in recent years, along with an abundance of learning resources. While conventional lectures provide students with important information and knowledge, we also believe that additional project-based learning components can motivate students to engage in topics more deeply. In addition to incorporating project-based learning in our courses, we aim to develop project-based learning components aligned with real-world tasks, including experimental design and execution, report writing, oral presentation, and peer-reviewing. This paper describes the organization of our project-based machine learning courses with a particular emphasis on the class project components and shares our resources with instructors who would like to include similar elements in their courses.", "published": "2021-07-28T23:41:27Z", "authors": ["Sebastian Raschka"], "categories": ["cs.CY", "cs.LG"], "doi": "", "_query": "house price prediction machine learning", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2402.13699v5", "title": "Automation of Quantum Dot Measurement Analysis via Explainable Machine Learning", "abstract": "The rapid development of quantum dot (QD) devices for quantum computing has necessitated more efficient and automated methods for device characterization and tuning. This work demonstrates the feasibility and advantages of applying explainable machine learning techniques to the analysis of quantum dot measurements, paving the way for further advances in automated and transparent QD device tuning. Many of the measurements acquired during the tuning process come in the form of images that need to be properly analyzed to guide the subsequent tuning steps. By design, features present in such images capture certain behaviors or states of the measured QD devices. When considered carefully, such features can aid the control and calibration of QD devices. An important example of such images are so-called $\\textit{triangle plots}$, which visually represent current flow and reveal characteristics important for QD device calibration. While image-based classification tools, such as convolutional neural networks (CNNs), can be used to verify whether a given measurement is $\\textit{good}$ and thus warrants the initiation of the next phase of tuning, they do not provide any insights into how the device should be adjusted in the case of $\\textit{bad}$ images. This is because CNNs sacrifice prediction and model intelligibility for high accuracy. To ameliorate this trade-off, a recent study introduced an image vectorization approach that relies on the Gabor wavelet transform (Schug $\\textit{et al.}$ 2024 $\\textit{Proc. XAI4Sci: Explainable Machine Learning for Sciences Workshop (AAAI 2024) (Vancouver, Canada)}$ pp 1-6). Here we propose an alternative vectorization method that involves mathematical modeling of synthetic triangles to mimic the experimental data. Using explainable boosting machines, we show that this new method offers superior explainability of model prediction without sacrificing accuracy.", "published": "2024-02-21T11:00:23Z", "authors": ["Daniel Schug", "Tyler J. Kovach", "M. A. Wolfe", "Jared Benson", "Sanghyeok Park", "J. P. Dodson", "J. Corrigan", "M. A. Eriksson", "Justyna P. Zwolak"], "categories": ["cs.CV", "cond-mat.mes-hall", "cs.LG"], "doi": "10.1088/2632-2153/ada087", "_query": "house price prediction machine learning", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2005.08854v2", "title": "Scaling-up Distributed Processing of Data Streams for Machine Learning", "abstract": "Emerging applications of machine learning in numerous areas involve continuous gathering of and learning from streams of data. Real-time incorporation of streaming data into the learned models is essential for improved inference in these applications. Further, these applications often involve data that are either inherently gathered at geographically distributed entities or that are intentionally distributed across multiple machines for memory, computational, and/or privacy reasons. Training of models in this distributed, streaming setting requires solving stochastic optimization problems in a collaborative manner over communication links between the physical entities. When the streaming data rate is high compared to the processing capabilities of compute nodes and/or the rate of the communications links, this poses a challenging question: how can one best leverage the incoming data for distributed training under constraints on computing capabilities and/or communications rate? A large body of research has emerged in recent decades to tackle this and related problems. This paper reviews recently developed methods that focus on large-scale distributed stochastic optimization in the compute- and bandwidth-limited regime, with an emphasis on convergence analysis that explicitly accounts for the mismatch between computation, communication and streaming rates. In particular, it focuses on methods that solve: (i) distributed stochastic convex problems, and (ii) distributed principal component analysis, which is a nonconvex problem with geometric structure that permits global convergence. For such methods, the paper discusses recent advances in terms of distributed algorithmic designs when faced with high-rate streaming data. Further, it reviews guarantees underlying these methods, which show there exist regimes in which systems can learn from distributed, streaming data at order-optimal rates.", "published": "2020-05-18T16:28:54Z", "authors": ["Matthew Nokleby", "Haroon Raja", "Waheed U. Bajwa"], "categories": ["cs.LG", "cs.DC", "eess.SP", "math.OC", "stat.ML"], "doi": "10.1109/JPROC.2020.3021381", "_query": "house price prediction machine learning", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2111.08011v2", "title": "Advantage of Machine Learning over Maximum Likelihood in Limited-Angle Low-Photon X-Ray Tomography", "abstract": "Limited-angle X-ray tomography reconstruction is an ill-conditioned inverse problem in general. Especially when the projection angles are limited and the measurements are taken in a photon-limited condition, reconstructions from classical algorithms such as filtered backprojection may lose fidelity and acquire artifacts due to the missing-cone problem. To obtain satisfactory reconstruction results, prior assumptions, such as total variation minimization and nonlocal image similarity, are usually incorporated within the reconstruction algorithm. In this work, we introduce deep neural networks to determine and apply a prior distribution in the reconstruction process. Our neural networks learn the prior directly from synthetic training samples. The neural nets thus obtain a prior distribution that is specific to the class of objects we are interested in reconstructing. In particular, we used deep generative models with 3D convolutional layers and 3D attention layers which are trained on 3D synthetic integrated circuit (IC) data from a model dubbed CircuitFaker. We demonstrate that, when the projection angles and photon budgets are limited, the priors from our deep generative models can dramatically improve the IC reconstruction quality on synthetic data compared with maximum likelihood estimation. Training the deep generative models with synthetic IC data from CircuitFaker illustrates the capabilities of the learned prior from machine learning. We expect that if the process were reproduced with experimental data, the advantage of the machine learning would persist. The advantages of machine learning in limited angle X-ray tomography may further enable applications in low-photon nanoscale imaging.", "published": "2021-11-15T16:24:12Z", "authors": ["Zhen Guo", "Jung Ki Song", "George Barbastathis", "Michael E. Glinsky", "Courtenay T. Vaughan", "Kurt W. Larson", "Bradley K. Alpert", "Zachary H. Levine"], "categories": ["eess.IV", "cs.LG", "eess.SP"], "doi": "", "_query": "house price prediction machine learning", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/1905.08444v1", "title": "Predicting and Forecasting the Price of Constituents and Index of Cryptocurrency Using Machine Learning", "abstract": "At present, cryptocurrencies have become a global phenomenon in financial sectors as it is one of the most traded financial instruments worldwide. Cryptocurrency is not only one of the most complicated and abstruse fields among financial instruments, but it is also deemed as a perplexing problem in finance due to its high volatility. This paper makes an attempt to apply machine learning techniques on the index and constituents of cryptocurrency with a goal to predict and forecast prices thereof. In particular, the purpose of this paper is to predict and forecast the close (closing) price of the cryptocurrency index 30 and nine constituents of cryptocurrencies using machine learning algorithms and models so that, it becomes easier for people to trade these currencies. We have used several machine learning techniques and algorithms and compared the models with each other to get the best output. We believe that our work will help reduce the challenges and difficulties faced by people, who invest in cryptocurrencies. Moreover, the obtained results can play a major role in cryptocurrency portfolio management and in observing the fluctuations in the prices of constituents of cryptocurrency market. We have also compared our approach with similar state of the art works from the literature, where machine learning approaches are considered for predicting and forecasting the prices of these currencies. In the sequel, we have found that our best approach presents better and competitive results than the best works from the literature thereby advancing the state of the art. Using such prediction and forecasting methods, people can easily understand the trend and it would be even easier for them to trade in a difficult and challenging financial instrument like cryptocurrency.", "published": "2019-05-21T05:12:48Z", "authors": ["Reaz Chowdhury", "M. Arifur Rahman", "M. Sohel Rahman", "M. R. C. Mahdy"], "categories": ["q-fin.ST"], "doi": "10.1016/j.physa.2020.124569", "_query": "house price prediction machine learning", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/1711.09223v1", "title": "Malaria Likelihood Prediction By Effectively Surveying Households Using Deep Reinforcement Learning", "abstract": "We build a deep reinforcement learning (RL) agent that can predict the likelihood of an individual testing positive for malaria by asking questions about their household. The RL agent learns to determine which survey question to ask next and when to stop to make a prediction about their likelihood of malaria based on their responses hitherto. The agent incurs a small penalty for each question asked, and a large reward/penalty for making the correct/wrong prediction; it thus has to learn to balance the length of the survey with the accuracy of its final predictions. Our RL agent is a Deep Q-network that learns a policy directly from the responses to the questions, with an action defined for each possible survey question and for each possible prediction class. We focus on Kenya, where malaria is a massive health burden, and train the RL agent on a dataset of 6481 households from the Kenya Malaria Indicator Survey 2015. To investigate the importance of having survey questions be adaptive to responses, we compare our RL agent to a supervised learning (SL) baseline that fixes its set of survey questions a priori. We evaluate on prediction accuracy and on the number of survey questions asked on a holdout set and find that the RL agent is able to predict with 80% accuracy, using only 2.5 questions on average. In addition, the RL agent learns to survey adaptively to responses and is able to match the SL baseline in prediction accuracy while significantly reducing survey length.", "published": "2017-11-25T09:33:05Z", "authors": ["Pranav Rajpurkar", "Vinaya Polamreddi", "Anusha Balakrishnan"], "categories": ["cs.LG", "stat.ML"], "doi": "", "_query": "house price prediction machine learning", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2601.09455v1", "title": "On the Hardness of Computing Counterfactual and Semifactual Explanations in XAI", "abstract": "Providing clear explanations to the choices of machine learning models is essential for these models to be deployed in crucial applications. Counterfactual and semi-factual explanations have emerged as two mechanisms for providing users with insights into the outputs of their models. We provide an overview of the computational complexity results in the literature for generating these explanations, finding that in many cases, generating explanations is computationally hard. We strengthen the argument for this considerably by further contributing our own inapproximability results showing that not only are explanations often hard to generate, but under certain assumptions, they are also hard to approximate. We discuss the implications of these complexity results for the XAI community and for policymakers seeking to regulate explanations in AI.", "published": "2026-01-14T13:02:24Z", "authors": ["André Artelt", "Martin Olsen", "Kevin Tierney"], "categories": ["cs.LG", "cs.AI"], "doi": "", "_query": "house price prediction machine learning", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2211.16116v1", "title": "Machine learning emulation of a local-scale UK climate model", "abstract": "Climate change is causing the intensification of rainfall extremes. Precipitation projections with high spatial resolution are important for society to prepare for these changes, e.g. to model flooding impacts. Physics-based simulations for creating such projections are very computationally expensive. This work demonstrates the effectiveness of diffusion models, a form of deep generative models, for generating much more cheaply realistic high resolution rainfall samples for the UK conditioned on data from a low resolution simulation. We show for the first time a machine learning model that is able to produce realistic samples of high-resolution rainfall based on a physical model that resolves atmospheric convection, a key process behind extreme rainfall. By adding self-learnt, location-specific information to low resolution relative vorticity, quantiles and time-mean of the samples match well their counterparts from the high-resolution simulation.", "published": "2022-11-29T11:44:35Z", "authors": ["Henry Addison", "Elizabeth Kendon", "Suman Ravuri", "Laurence Aitchison", "Peter AG Watson"], "categories": ["physics.ao-ph", "cs.LG"], "doi": "", "_query": "house price prediction machine learning", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/1612.00525v2", "title": "A Noise-Filtering Approach for Cancer Drug Sensitivity Prediction", "abstract": "Accurately predicting drug responses to cancer is an important problem hindering oncologists' efforts to find the most effective drugs to treat cancer, which is a core goal in precision medicine. The scientific community has focused on improving this prediction based on genomic, epigenomic, and proteomic datasets measured in human cancer cell lines. Real-world cancer cell lines contain noise, which degrades the performance of machine learning algorithms. This problem is rarely addressed in the existing approaches. In this paper, we present a noise-filtering approach that integrates techniques from numerical linear algebra and information retrieval targeted at filtering out noisy cancer cell lines. By filtering out noisy cancer cell lines, we can train machine learning algorithms on better quality cancer cell lines. We evaluate the performance of our approach and compare it with an existing approach using the Area Under the ROC Curve (AUC) on clinical trial data. The experimental results show that our proposed approach is stable and also yields the highest AUC at a statistically significant level.", "published": "2016-12-02T00:41:11Z", "authors": ["Turki Turki", "Zhi Wei"], "categories": ["cs.LG", "q-bio.GN", "stat.ML"], "doi": "", "_query": "house price prediction machine learning", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2206.03326v2", "title": "Compilation and Optimizations for Efficient Machine Learning on Embedded Systems", "abstract": "Deep Neural Networks (DNNs) have achieved great success in a variety of machine learning (ML) applications, delivering high-quality inferencing solutions in computer vision, natural language processing, and virtual reality, etc. However, DNN-based ML applications also bring much increased computational and storage requirements, which are particularly challenging for embedded systems with limited compute/storage resources, tight power budgets, and small form factors. Challenges also come from the diverse application-specific requirements, including real-time responses, high-throughput performance, and reliable inference accuracy. To address these challenges, we introduce a series of effective design methodologies, including efficient ML model designs, customized hardware accelerator designs, and hardware/software co-design strategies to enable efficient ML applications on embedded systems.", "published": "2022-06-06T02:54:05Z", "authors": ["Xiaofan Zhang", "Yao Chen", "Cong Hao", "Sitao Huang", "Yuhong Li", "Deming Chen"], "categories": ["cs.LG", "cs.AR"], "doi": "", "_query": "house price prediction machine learning", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2412.06472v1", "title": "Food for thought: How can machine learning help better predict and understand changes in food prices?", "abstract": "In this work, we address a lack of systematic understanding of fluctuations in food affordability in Canada. Canada's Food Price Report (CPFR) is an annual publication that predicts food inflation over the next calendar year. The published predictions are a collaborative effort between forecasting teams that each employ their own approach at Canadian Universities: Dalhousie University, the University of British Columbia, the University of Saskatchewan, and the University of Guelph/Vector Institute. While the University of Guelph/Vector Institute forecasting team has leveraged machine learning (ML) in previous reports, the most recent editions (2024--2025) have also included a human-in-the-loop approach. For the 2025 report, this focus was expanded to evaluate several different data-centric approaches to improve forecast accuracy. In this study, we evaluate how different types of forecasting models perform when estimating food price fluctuations. We also examine the sensitivity of models that curate time series data representing key factors in food pricing.", "published": "2024-12-09T13:17:28Z", "authors": ["Kristina L. Kupferschmidt", "James Requiema", "Mya Simpson", "Zohrah Varsallay", "Ethan Jackson", "Cody Kupferschmidt", "Sara El-Shawa", "Graham W. Taylor"], "categories": ["cs.LG"], "doi": "", "_query": "house price prediction machine learning", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2508.20335v1", "title": "Dynamic Synthetic Controls vs. Panel-Aware Double Machine Learning for Geo-Level Marketing Impact Estimation", "abstract": "Accurately quantifying geo-level marketing lift in two-sided marketplaces is challenging: the Synthetic Control Method (SCM) often exhibits high power yet systematically under-estimates effect size, while panel-style Double Machine Learning (DML) is seldom benchmarked against SCM. We build an open, fully documented simulator that mimics a typical large-scale geo roll-out: N_unit regional markets are tracked for T_pre weeks before launch and for a further T_post-week campaign window, allowing all key parameters to be varied by the user and probe both families under five stylized stress tests: 1) curved baseline trends, 2) heterogeneous response lags, 3) treated-biased shocks, 4) a non-linear outcome link, and 5) a drifting control group trend. Seven estimators are evaluated: three standard Augmented SCM (ASC) variants and four panel-DML flavors (TWFE, CRE/Mundlak, first-difference, and within-group). Across 100 replications per scenario, ASC models consistently demonstrate severe bias and near-zero coverage in challenging scenarios involving nonlinearities or external shocks. By contrast, panel-DML variants dramatically reduce this bias and restore nominal 95%-CI coverage, proving far more robust. The results indicate that while ASC provides a simple baseline, it is unreliable in common, complex situations. We therefore propose a 'diagnose-first' framework where practitioners first identify the primary business challenge (e.g., nonlinear trends, response lags) and then select the specific DML model best suited for that scenario, providing a more robust and reliable blueprint for analyzing geo-experiments.", "published": "2025-08-28T00:33:06Z", "authors": ["Sang Su Lee", "Vineeth Loganathan", "Vijay Raghavan"], "categories": ["cs.LG"], "doi": "", "_query": "house price prediction machine learning", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2107.02613v1", "title": "Machine Learning-Assisted High-Throughput Semi-empirical Search of OFET Molecular Materials", "abstract": "Machine learning has been widely verified and applied in chemoinformatics, and have achieved outstanding results in the prediction, modification, and optimization of luminescence, magnetism, and electrode materials. Here, we propose a deepth first search traversal (DFST) approach combined with lightGBM machine learning model to search the classic Organic field-effect transistor (OFET) functional molecules chemical space, which is simple but effective. Totally 2820588 molecules of different structure within two certain types of skeletons are generated successfully, which shows the searching efficiency of the DFST strategy. With the simplified molecular-input line-entry system (SMILES) utilized, the generation of alphanumeric strings that describe molecules directly tackle the inverse design problem, for the generation set has 100% chemical validity. Light Gradient Boosting Machine (LightGBM) model's intrinsic Distributed and efficient features enables much faster training process and higher training efficiency, which means better model performance with less amount of data. 184 out of 2.8 million molecules are finally screened out with density functional theory (DFT) calculation carried out to verify the accuracy of the prediction.", "published": "2021-07-06T13:38:19Z", "authors": ["Zhenyu Chen", "Jiahao Li", "Yuzhi Xu"], "categories": ["cond-mat.mtrl-sci"], "doi": "", "_query": "house price prediction machine learning", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/1811.03179v4", "title": "How Well Generative Adversarial Networks Learn Distributions", "abstract": "This paper studies the rates of convergence for learning distributions implicitly with the adversarial framework and Generative Adversarial Networks (GANs), which subsume Wasserstein, Sobolev, MMD GAN, and Generalized/Simulated Method of Moments (GMM/SMM) as special cases. We study a wide range of parametric and nonparametric target distributions under a host of objective evaluation metrics. We investigate how to obtain valid statistical guarantees for GANs through the lens of regularization. On the nonparametric end, we derive the optimal minimax rates for distribution estimation under the adversarial framework. On the parametric end, we establish a theory for general neural network classes (including deep leaky ReLU networks) that characterizes the interplay on the choice of generator and discriminator pair. We discover and isolate a new notion of regularization, called the generator-discriminator-pair regularization, that sheds light on the advantage of GANs compared to classical parametric and nonparametric approaches for explicit distribution estimation. We develop novel oracle inequalities as the main technical tools for analyzing GANs, which are of independent interest.", "published": "2018-11-07T23:14:45Z", "authors": ["Tengyuan Liang"], "categories": ["math.ST", "cs.LG", "stat.ML"], "doi": "", "_query": "house price prediction machine learning", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/1504.06329v1", "title": "Analysis of Stopping Active Learning based on Stabilizing Predictions", "abstract": "Within the natural language processing (NLP) community, active learning has been widely investigated and applied in order to alleviate the annotation bottleneck faced by developers of new NLP systems and technologies. This paper presents the first theoretical analysis of stopping active learning based on stabilizing predictions (SP). The analysis has revealed three elements that are central to the success of the SP method: (1) bounds on Cohen's Kappa agreement between successively trained models impose bounds on differences in F-measure performance of the models; (2) since the stop set does not have to be labeled, it can be made large in practice, helping to guarantee that the results transfer to previously unseen streams of examples at test/application time; and (3) good (low variance) sample estimates of Kappa between successive models can be obtained. Proofs of relationships between the level of Kappa agreement and the difference in performance between consecutive models are presented. Specifically, if the Kappa agreement between two models exceeds a threshold T (where $T>0$), then the difference in F-measure performance between those models is bounded above by $\\frac{4(1-T)}{T}$ in all cases. If precision of the positive conjunction of the models is assumed to be $p$, then the bound can be tightened to $\\frac{4(1-T)}{(p+1)T}$.", "published": "2015-04-23T20:07:01Z", "authors": ["Michael Bloodgood", "John Grothendieck"], "categories": ["cs.LG", "cs.CL", "stat.ML"], "doi": "", "_query": "house price prediction machine learning", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/1606.05110v1", "title": "Machine Learning meets Data-Driven Journalism: Boosting International Understanding and Transparency in News Coverage", "abstract": "Migration crisis, climate change or tax havens: Global challenges need global solutions. But agreeing on a joint approach is difficult without a common ground for discussion. Public spheres are highly segmented because news are mainly produced and received on a national level. Gain- ing a global view on international debates about important issues is hindered by the enormous quantity of news and by language barriers. Media analysis usually focuses only on qualitative re- search. In this position statement, we argue that it is imperative to pool methods from machine learning, journalism studies and statistics to help bridging the segmented data of the international public sphere, using the Transatlantic Trade and Investment Partnership (TTIP) as a case study.", "published": "2016-06-16T09:31:12Z", "authors": ["Elena Erdmann", "Karin Boczek", "Lars Koppers", "Gerret von Nordheim", "Christian Pölitz", "Alejandro Molina", "Katharina Morik", "Henrik Müller", "Jörg Rahnenführer", "Kristian Kersting"], "categories": ["stat.ML", "cs.CY"], "doi": "", "_query": "house price prediction machine learning", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/1912.07700v1", "title": "A Robust Predictive Model for Stock Price Prediction Using Deep Learning and Natural Language Processing", "abstract": "Prediction of future movement of stock prices has been a subject matter of many research work. There is a gamut of literature of technical analysis of stock prices where the objective is to identify patterns in stock price movements and derive profit from it. Improving the prediction accuracy remains the single most challenge in this area of research. We propose a hybrid approach for stock price movement prediction using machine learning, deep learning, and natural language processing. We select the NIFTY 50 index values of the National Stock Exchange of India, and collect its daily price movement over a period of three years (2015 to 2017). Based on the data of 2015 to 2017, we build various predictive models using machine learning, and then use those models to predict the closing value of NIFTY 50 for the period January 2018 till June 2019 with a prediction horizon of one week. For predicting the price movement patterns, we use a number of classification techniques, while for predicting the actual closing price of the stock, various regression models have been used. We also build a Long and Short-Term Memory - based deep learning network for predicting the closing price of the stocks and compare the prediction accuracies of the machine learning models with the LSTM model. We further augment the predictive model by integrating a sentiment analysis module on twitter data to correlate the public sentiment of stock prices with the market sentiment. This has been done using twitter sentiment and previous week closing values to predict stock price movement for the next week. We tested our proposed scheme using a cross validation method based on Self Organizing Fuzzy Neural Networks and found extremely interesting results.", "published": "2019-12-09T20:50:07Z", "authors": ["Sidra Mehtab", "Jaydip Sen"], "categories": ["q-fin.ST", "cs.LG"], "doi": "10.36227/techrxiv.15023361.v1", "_query": "house price prediction machine learning", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2410.06074v3", "title": "Scalable Mechanistic Neural Networks for Differential Equations and Machine Learning", "abstract": "We propose Scalable Mechanistic Neural Network (S-MNN), an enhanced neural network framework designed for scientific machine learning applications involving long temporal sequences. By reformulating the original Mechanistic Neural Network (MNN) (Pervez et al., 2024), we reduce the computational time and space complexities from cubic and quadratic with respect to the sequence length, respectively, to linear. This significant improvement enables efficient modeling of long-term dynamics without sacrificing accuracy or interpretability. Extensive experiments demonstrate that S-MNN matches the original MNN in precision while substantially reducing computational resources. Consequently, S-MNN can drop-in replace the original MNN in applications, providing a practical and efficient tool for integrating mechanistic bottlenecks into neural network models of complex dynamical systems. Source code is available at https://github.com/IST-DASLab/ScalableMNN.", "published": "2024-10-08T14:27:28Z", "authors": ["Jiale Chen", "Dingling Yao", "Adeel Pervez", "Dan Alistarh", "Francesco Locatello"], "categories": ["cs.LG", "math.NA"], "doi": "", "_query": "house price prediction machine learning", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2601.11626v2", "title": "Concatenated Matrix SVD: Compression Bounds, Incremental Approximation, and Error-Constrained Clustering", "abstract": "Large collections of matrices arise throughout modern machine learning, signal processing, and scientific computing, where they are commonly compressed by concatenation followed by truncated singular value decomposition (SVD). This strategy enables parameter sharing and efficient reconstruction and has been widely adopted across domains ranging from multi-view learning and signal processing to neural network compression. However, it leaves a fundamental question unanswered: which matrices can be safely concatenated and compressed together under explicit reconstruction error constraints? Existing approaches rely on heuristic or architecture-specific grouping and provide no principled guarantees on the resulting SVD approximation error. In the present work, we introduce a theory-driven framework for compression-aware clustering of matrices under SVD compression constraints. Our analysis establishes new spectral bounds for horizontally concatenated matrices, deriving global upper bounds on the optimal rank-$r$ SVD reconstruction error from lower bounds on singular value growth. The first bound follows from Weyl-type monotonicity under blockwise extensions, while the second leverages singular values of incremental residuals to yield tighter, per-block guarantees. We further develop an efficient approximate estimator based on incremental truncated SVD that tracks dominant singular values without forming the full concatenated matrix. Therefore, we propose three clustering algorithms that merge matrices only when their predicted joint SVD compression error remains below a user-specified threshold. The algorithms span a trade-off between speed, provable accuracy, and scalability, enabling compression-aware clustering with explicit error control.", "published": "2026-01-12T18:15:53Z", "authors": ["Maksym Shamrai"], "categories": ["math.NA", "cs.LG"], "doi": "", "_query": "house price prediction machine learning", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/1408.0047v1", "title": "Cumulative Restricted Boltzmann Machines for Ordinal Matrix Data Analysis", "abstract": "Ordinal data is omnipresent in almost all multiuser-generated feedback - questionnaires, preferences etc. This paper investigates modelling of ordinal data with Gaussian restricted Boltzmann machines (RBMs). In particular, we present the model architecture, learning and inference procedures for both vector-variate and matrix-variate ordinal data. We show that our model is able to capture latent opinion profile of citizens around the world, and is competitive against state-of-art collaborative filtering techniques on large-scale public datasets. The model thus has the potential to extend application of RBMs to diverse domains such as recommendation systems, product reviews and expert assessments.", "published": "2014-07-31T23:54:16Z", "authors": ["Truyen Tran", "Dinh Phung", "Svetha Venkatesh"], "categories": ["stat.ML", "cs.IR", "cs.LG", "stat.AP", "stat.ME"], "doi": "", "_query": "house price prediction machine learning", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2602.18592v1", "title": "A Roof Over Risk: A House Price-at-Risk Framework for Hungary", "abstract": "This paper develops a House Price-at-Risk framework to examine how housing subsidies, credit conditions, and supply factors influence the distribution of house price growth in Hungary. Using quantile regression with adaptive LASSO variable selection, we identify variables driving downside versus upside risks across multiple horizons. Financial stress dominates the lower tail at short horizons, while unemployment and affordability constraints become the primary drivers of downside risk at longer horizons. Housing subsidies exhibit pro-cyclical characteristics, concentrating significant positive effects on the upper quantiles while leaving the lower tail largely unaffected. Supply-side variables display horizon-dependent sign reversals, with construction permits exerting upward pressure on prices in the short run but moderating them as supply materialises. Uncertainty decomposition reveals persistent left-tail dominance across all horizons. These findings suggest that macroprudential frameworks should account for the distributional effects of housing subsidies, particularly their pro-cyclical influence on house price growth.", "published": "2026-02-20T19:57:51Z", "authors": ["Tibor Szendrei", "Nikolett Vágó", "Katalin Varga"], "categories": ["econ.EM"], "doi": "", "_query": "house price prediction machine learning", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/1803.03672v1", "title": "Competitive Machine Learning: Best Theoretical Prediction vs Optimization", "abstract": "Machine learning is often used in competitive scenarios: Participants learn and fit static models, and those models compete in a shared platform. The common assumption is that in order to win a competition one has to have the best predictive model, i.e., the model with the smallest out-sample error. Is that necessarily true? Does the best theoretical predictive model for a target always yield the best reward in a competition? If not, can one take the best model and purposefully change it into a theoretically inferior model which in practice results in a higher competitive edge? How does that modification look like? And finally, if all participants modify their prediction models towards the best practical performance, who benefits the most? players with inferior models, or those with theoretical superiority? The main theme of this paper is to raise these important questions and propose a theoretical model to answer them. We consider a study case where two linear predictive models compete over a shared target. The model with the closest estimate gets the whole reward, which is equal to the absolute value of the target. We characterize the reward function of each model, and using a basic game theoretic approach, demonstrate that the inferior competitor can significantly improve his performance by choosing optimal model coefficients that are different from the best theoretical prediction. This is a preliminary study that emphasizes the fact that in many applications where predictive machine learning is at the service of competition, much can be gained from practical (back-testing) optimization of the model compared to static prediction improvement.", "published": "2018-03-09T19:42:54Z", "authors": ["Amin Khajehnejad", "Shima Hajimirza"], "categories": ["cs.LG", "stat.ML"], "doi": "", "_query": "house price prediction machine learning", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/1807.07155v2", "title": "Take a Look Around: Using Street View and Satellite Images to Estimate House Prices", "abstract": "When an individual purchases a home, they simultaneously purchase its structural features, its accessibility to work, and the neighborhood amenities. Some amenities, such as air quality, are measurable while others, such as the prestige or the visual impression of a neighborhood, are difficult to quantify. Despite the well-known impacts intangible housing features have on house prices, limited attention has been given to systematically quantifying these difficult to measure amenities. Two issues have led to this neglect. Not only do few quantitative methods exist that can measure the urban environment, but that the collection of such data is both costly and subjective. We show that street image and satellite image data can capture these urban qualities and improve the estimation of house prices. We propose a pipeline that uses a deep neural network model to automatically extract visual features from images to estimate house prices in London, UK. We make use of traditional housing features such as age, size, and accessibility as well as visual features from Google Street View images and Bing aerial images in estimating the house price model. We find encouraging results where learning to characterize the urban quality of a neighborhood improves house price prediction, even when generalizing to previously unseen London boroughs. We explore the use of non-linear vs. linear methods to fuse these cues with conventional models of house pricing, and show how the interpretability of linear models allows us to directly extract proxy variables for visual desirability of neighborhoods that are both of interest in their own right, and could be used as inputs to other econometric methods. This is particularly valuable as once the network has been trained with the training data, it can be applied elsewhere, allowing us to generate vivid dense maps of the visual appeal of London streets.", "published": "2018-07-18T21:10:08Z", "authors": ["Stephen Law", "Brooks Paige", "Chris Russell"], "categories": ["econ.EM", "cs.CV"], "doi": "10.1145/3342240", "_query": "house price prediction machine learning", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/1911.02673v2", "title": "Towards the Use of Neural Networks for Influenza Prediction at Multiple Spatial Resolutions", "abstract": "We introduce the use of a Gated Recurrent Unit (GRU) for influenza prediction at the state- and city-level in the US, and experiment with the inclusion of real-time flu-related Internet search data. We find that a GRU has lower prediction error than current state-of-the-art methods for data-driven influenza prediction at time horizons of over two weeks. In contrast with other machine learning approaches, the inclusion of real-time Internet search data does not improve GRU predictions.", "published": "2019-11-06T23:14:53Z", "authors": ["Emily L. Aiken", "Andre T. Nguyen", "Mauricio Santillana"], "categories": ["cs.LG", "cs.CY", "stat.AP", "stat.ML"], "doi": "", "_query": "house price prediction machine learning", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/1811.04820v3", "title": "Learning from positive and unlabeled data: a survey", "abstract": "Learning from positive and unlabeled data or PU learning is the setting where a learner only has access to positive examples and unlabeled data. The assumption is that the unlabeled data can contain both positive and negative examples. This setting has attracted increasing interest within the machine learning literature as this type of data naturally arises in applications such as medical diagnosis and knowledge base completion. This article provides a survey of the current state of the art in PU learning. It proposes seven key research questions that commonly arise in this field and provides a broad overview of how the field has tried to address them.", "published": "2018-11-12T16:00:36Z", "authors": ["Jessa Bekker", "Jesse Davis"], "categories": ["cs.LG", "stat.ML"], "doi": "10.1007/s10994-020-05877-5", "_query": "house price prediction machine learning", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2107.01835v2", "title": "Fast Rate Learning in Stochastic First Price Bidding", "abstract": "First-price auctions have largely replaced traditional bidding approaches based on Vickrey auctions in programmatic advertising. As far as learning is concerned, first-price auctions are more challenging because the optimal bidding strategy does not only depend on the value of the item but also requires some knowledge of the other bids. They have already given rise to several works in sequential learning, many of which consider models for which the value of the buyer or the opponents' maximal bid is chosen in an adversarial manner. Even in the simplest settings, this gives rise to algorithms whose regret grows as $\\sqrt{T}$ with respect to the time horizon $T$. Focusing on the case where the buyer plays against a stationary stochastic environment, we show how to achieve significantly lower regret: when the opponents' maximal bid distribution is known we provide an algorithm whose regret can be as low as $\\log^2(T)$; in the case where the distribution must be learnt sequentially, a generalization of this algorithm can achieve $T^{1/3+ ε}$ regret, for any $ε>0$. To obtain these results, we introduce two novel ideas that can be of interest in their own right. First, by transposing results obtained in the posted price setting, we provide conditions under which the first-price biding utility is locally quadratic around its optimum. Second, we leverage the observation that, on small sub-intervals, the concentration of the variations of the empirical distribution function may be controlled more accurately than by using the classical Dvoretzky-Kiefer-Wolfowitz inequality. Numerical simulations confirm that our algorithms converge much faster than alternatives proposed in the literature for various bid distributions, including for bids collected on an actual programmatic advertising platform.", "published": "2021-07-05T07:48:52Z", "authors": ["Juliette Achddou", "Olivier Cappé", "Aurélien Garivier"], "categories": ["cs.LG", "stat.ML"], "doi": "", "_query": "house price prediction machine learning", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2409.08054v2", "title": "Predicting and Accelerating Nanomaterials Synthesis Using Machine Learning Featurization", "abstract": "Materials synthesis optimization is constrained by serial feedback processes that rely on manual tools and intuition across multiple siloed modes of characterization. We automate and generalize feature extraction of reflection high-energy electron diffraction (RHEED) data with machine learning to establish quantitatively predictive relationships in small sets (\\~10) of expert-labeled data, saving significant time on subsequently grown samples. These predictive relationships are evaluated in a representative material system (\\ce{W_{1-x}V_xSe2} on c-plane sapphire (0001)) with two aims: 1) predicting grain alignment of the deposited film using pre-growth substrate data, and 2) estimating vanadium dopant concentration using in-situ RHEED as a proxy for ex-situ methods (e.g. x-ray photoelectron spectroscopy). Both tasks are accomplished using the same materials-agnostic features, avoiding specific system retraining and leading to a potential 80\\% time saving over a 100-sample synthesis campaign. These predictions provide guidance to avoid doomed trials, reduce follow-on characterization, and improve control resolution for materials synthesis.", "published": "2024-09-12T14:03:55Z", "authors": ["Christopher C. Price", "Yansong Li", "Guanyu Zhou", "Rehan Younas", "Spencer S. Zeng", "Tim H. Scanlon", "Jason M. Munro", "Christopher L. Hinkle"], "categories": ["cond-mat.mtrl-sci", "cs.LG"], "doi": "", "_query": "house price prediction machine learning", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2402.13984v3", "title": "Stability-Aware Training of Machine Learning Force Fields with Differentiable Boltzmann Estimators", "abstract": "Machine learning force fields (MLFFs) are an attractive alternative to ab-initio methods for molecular dynamics (MD) simulations. However, they can produce unstable simulations, limiting their ability to model phenomena occurring over longer timescales and compromising the quality of estimated observables. To address these challenges, we present Stability-Aware Boltzmann Estimator (StABlE) Training, a multi-modal training procedure which leverages joint supervision from reference quantum-mechanical calculations and system observables. StABlE Training iteratively runs many MD simulations in parallel to seek out unstable regions, and corrects the instabilities via supervision with a reference observable. We achieve efficient end-to-end automatic differentiation through MD simulations using our Boltzmann Estimator, a generalization of implicit differentiation techniques to a broader class of stochastic algorithms. Unlike existing techniques based on active learning, our approach requires no additional ab-initio energy and forces calculations to correct instabilities. We demonstrate our methodology across organic molecules, tetrapeptides, and condensed phase systems, using three modern MLFF architectures. StABlE-trained models achieve significant improvements in simulation stability, data efficiency, and agreement with reference observables. The stability improvements cannot be matched by reducing the simulation timestep; thus, StABlE Training effectively allows for larger timesteps. By incorporating observables into the training process alongside first-principles calculations, StABlE Training can be viewed as a general semi-empirical framework applicable across MLFF architectures and systems. This makes it a powerful tool for training stable and accurate MLFFs, particularly in the absence of large reference datasets. Our code is available at https://github.com/ASK-Berkeley/StABlE-Training.", "published": "2024-02-21T18:12:07Z", "authors": ["Sanjeev Raja", "Ishan Amin", "Fabian Pedregosa", "Aditi S. Krishnapriyan"], "categories": ["cs.LG", "cond-mat.dis-nn", "cond-mat.mtrl-sci", "physics.chem-ph", "physics.comp-ph"], "doi": "", "_query": "house price prediction machine learning", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/1705.07205v2", "title": "Machine learning modeling for time series problem: Predicting flight ticket prices", "abstract": "Machine learning has been used in all kinds of fields. In this article, we introduce how machine learning can be applied into time series problem. Especially, we use the airline ticket prediction problem as our specific problem. Airline companies use many different variables to determine the flight ticket prices: indicator whether the travel is during the holidays, the number of free seats in the plane etc. Some of the variables are observed, but some of them are hidden. Based on the data over a 103 day period, we trained our models, getting the best model - which is AdaBoost-Decision Tree Classification. This algorithm has best performance over the observed 8 routes which has 61.35$\\%$ better performance than the random purchase strategy, and relatively small variance over these routes. And we also considered the situation that we cannot get too much historical datas for some routes (for example the route is new and does not have historical data) or we do not want to train historical data to predict to buy or wait quickly, in which problem, we used HMM Sequence Classification based AdaBoost-Decision Tree Classification to perform our prediction on 12 new routes. Finally, we got 31.71$\\%$ better performance than the random purchase strategy.", "published": "2017-05-19T21:59:02Z", "authors": ["Jun Lu"], "categories": ["cs.LG"], "doi": "", "_query": "house price prediction machine learning", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2211.13314v2", "title": "CoMadOut -- A Robust Outlier Detection Algorithm based on CoMAD", "abstract": "Unsupervised learning methods are well established in the area of anomaly detection and achieve state of the art performances on outlier datasets. Outliers play a significant role, since they bear the potential to distort the predictions of a machine learning algorithm on a given dataset. Especially among PCA-based methods, outliers have an additional destructive potential regarding the result: they may not only distort the orientation and translation of the principal components, they also make it more complicated to detect outliers. To address this problem, we propose the robust outlier detection algorithm CoMadOut, which satisfies two required properties: (1) being robust towards outliers and (2) detecting them. Our CoMadOut outlier detection variants using comedian PCA define, dependent on its variant, an inlier region with a robust noise margin by measures of in-distribution (variant CMO) and optimized scores by measures of out-of-distribution (variants CMO*), e.g. kurtosis-weighting by CMO+k. These measures allow distribution based outlier scoring for each principal component, and thus, an appropriate alignment of the degree of outlierness between normal and abnormal instances. Experiments comparing CoMadOut with traditional, deep and other comparable robust outlier detection methods showed that the performance of the introduced CoMadOut approach is competitive to well established methods related to average precision (AP), area under the precision recall curve (AUPRC) and area under the receiver operating characteristic (AUROC) curve. In summary our approach can be seen as a robust alternative for outlier detection tasks.", "published": "2022-11-23T21:33:34Z", "authors": ["Andreas Lohrer", "Daniyal Kazempour", "Maximilian Hünemörder", "Peer Kröger"], "categories": ["cs.LG"], "doi": "10.1007/s10994-024-06521-2", "_query": "house price prediction machine learning", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2503.18670v1", "title": "Deep learning-based identification of precipitation clouds from all-sky camera data for observatory safety", "abstract": "For monitoring the night sky conditions, wide-angle all-sky cameras are used in most astronomical observatories to monitor the sky cloudiness. In this manuscript, we apply a deep-learning approach for automating the identification of precipitation clouds in all-sky camera data as a cloud warning system. We construct our original training and test sets using the all-sky camera image archive of the Iranian National Observatory (INO). The training and test set images are labeled manually based on their potential rainfall and their distribution in the sky. We train our model on a set of roughly 2445 images taken by the INO all-sky camera through the deep learning method based on the EfficientNet network. Our model reaches an average accuracy of 99\\% in determining the cloud rainfall's potential and an accuracy of 96\\% for cloud coverage. To enable a comprehensive comparison and evaluate the performance of alternative architectures for the task, we additionally trained three models LeNet, DeiT, and AlexNet. This approach can be used for early warning of incoming dangerous clouds toward telescopes and harnesses the power of deep learning to automatically analyze vast amounts of all-sky camera data and accurately identify precipitation clouds formations. Our trained model can be deployed for real-time analysis, enabling the rapid identification of potential threats, and offering a scaleable solution that can improve our ability to safeguard telescopes and instruments in observatories. This is important now that numerous small and medium-sized telescopes are increasingly integrated with smart control systems to reduce manual operation.", "published": "2025-03-24T13:40:51Z", "authors": ["Mohammad H. Zhoolideh Haghighi", "Alireza Ghasrimanesh", "Habib Khosroshahi"], "categories": ["astro-ph.IM", "physics.data-an", "stat.ML"], "doi": "10.1016/j.mlwa.2025.100640", "_query": "house price prediction machine learning", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2312.04174v1", "title": "Coherent energy and force uncertainty in deep learning force fields", "abstract": "In machine learning energy potentials for atomic systems, forces are commonly obtained as the negative derivative of the energy function with respect to atomic positions. To quantify aleatoric uncertainty in the predicted energies, a widely used modeling approach involves predicting both a mean and variance for each energy value. However, this model is not differentiable under the usual white noise assumption, so energy uncertainty does not naturally translate to force uncertainty. In this work we propose a machine learning potential energy model in which energy and force aleatoric uncertainty are linked through a spatially correlated noise process. We demonstrate our approach on an equivariant messages passing neural network potential trained on energies and forces on two out-of-equilibrium molecular datasets. Furthermore, we also show how to obtain epistemic uncertainties in this setting based on a Bayesian interpretation of deep ensemble models.", "published": "2023-12-07T09:49:05Z", "authors": ["Peter Bjørn Jørgensen", "Jonas Busk", "Ole Winther", "Mikkel N. Schmidt"], "categories": ["stat.ML", "cs.LG", "physics.comp-ph"], "doi": "", "_query": "house price prediction machine learning", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2411.04209v2", "title": "Machine Learning Mutation-Acyclicity of Quivers", "abstract": "Machine learning (ML) has emerged as a powerful tool in mathematical research in recent years. This paper applies ML techniques to the study of quivers -- a type of directed multigraph with significant relevance in algebra, combinatorics, computer science, and mathematical physics. Specifically, we focus on the challenging problem of determining the mutation-acyclicity of a quiver on 4 vertices, a property that is pivotal since mutation-acyclicity is often a necessary condition for theorems involving path algebras and cluster algebras. Although this classification is known for quivers with at most 3 vertices, little is known about quivers on more than 3 vertices. We give a computer-assisted proof of a theorem to prove that mutation-acyclicity is decidable for quivers on 4 vertices with edge weight at most 2. By leveraging neural networks (NNs) and support vector machines (SVMs), we then accurately classify more general 4-vertex quivers as mutation-acyclic or non-mutation-acyclic. Our results demonstrate that ML models can efficiently detect mutation-acyclicity, providing a promising computational approach to this combinatorial problem, from which the trained SVM equation provides a starting point to guide future theoretical development.", "published": "2024-11-06T19:08:30Z", "authors": ["Kymani T. K. Armstrong-Williams", "Edward Hirst", "Blake Jackson", "Kyu-Hwan Lee"], "categories": ["math.CO", "cs.LG", "hep-th", "math.RT"], "doi": "10.1016/j.jaca.2025.100040", "_query": "house price prediction machine learning", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2503.21241v1", "title": "Feature-Enhanced Machine Learning for All-Cause Mortality Prediction in Healthcare Data", "abstract": "Accurate patient mortality prediction enables effective risk stratification, leading to personalized treatment plans and improved patient outcomes. However, predicting mortality in healthcare remains a significant challenge, with existing studies often focusing on specific diseases or limited predictor sets. This study evaluates machine learning models for all-cause in-hospital mortality prediction using the MIMIC-III database, employing a comprehensive feature engineering approach. Guided by clinical expertise and literature, we extracted key features such as vital signs (e.g., heart rate, blood pressure), laboratory results (e.g., creatinine, glucose), and demographic information. The Random Forest model achieved the highest performance with an AUC of 0.94, significantly outperforming other machine learning and deep learning approaches. This demonstrates Random Forest's robustness in handling high-dimensional, noisy clinical data and its potential for developing effective clinical decision support tools. Our findings highlight the importance of careful feature engineering for accurate mortality prediction. We conclude by discussing implications for clinical adoption and propose future directions, including enhancing model robustness and tailoring prediction models for specific diseases.", "published": "2025-03-27T08:04:42Z", "authors": ["HyeYoung Lee", "Pavel Tsoi"], "categories": ["cs.LG", "cs.AI", "stat.ML"], "doi": "", "_query": "house price prediction machine learning", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2506.07459v5", "title": "ProteinZero: Self-Improving Protein Generation via Online Reinforcement Learning", "abstract": "Protein generative models have shown remarkable promise in protein design, yet their success rates remain constrained by reliance on curated sequence-structure datasets and by misalignment between supervised objectives and real design goals. We present ProteinZero, an online reinforcement learning framework for inverse folding models that enables scalable, automated, and continuous self-improvement with computationally efficient feedback. ProteinZero employs a reward pipeline that combines structural guidance from ESMFold with a novel self-derived ddG predictor, providing stable multi-objective signals while avoiding the prohibitive cost of physics-based methods. To ensure robustness in online RL, we further introduce a novel embedding-level diversity regularizer that mitigates mode collapse and promotes sequence-level diversity among generated designs. Within a general RL formulation balancing multi-reward optimization, KL-divergence from a reference model, and diversity regularization, ProteinZero achieves robust improvements across designability, predicted stability, recovery, and diversity. On the CATH-4.3 benchmark, it consistently outperforms state-of-the-art baselines including ProteinMPNN, ESM-IF, and InstructPLM, reducing design failure rates by 36-48% and achieving success rates above 90% across diverse folds. Importantly, a complete RL run can be executed on a single 8xGPU node within three days, including reward computation and data generation. These results indicate that efficient online RL fine-tuning can complement supervised pretraining by allowing protein generative models to evolve continuously from their own outputs and optimize multiple design objectives without labeled data, opening new possibilities for exploring the vast protein design space. Code and model checkpoints are available at https://github.com/ziwenwang28/ProteinZero.", "published": "2025-06-09T06:08:59Z", "authors": ["Ziwen Wang", "Jiajun Fan", "Ruihan Guo", "Thao Nguyen", "Heng Ji", "Ge Liu"], "categories": ["cs.LG", "q-bio.QM"], "doi": "", "_query": "house price prediction machine learning", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2502.02861v4", "title": "Algorithms with Calibrated Machine Learning Predictions", "abstract": "The field of algorithms with predictions incorporates machine learning advice in the design of online algorithms to improve real-world performance. A central consideration is the extent to which predictions can be trusted -- while existing approaches often require users to specify an aggregate trust level, modern machine learning models can provide estimates of prediction-level uncertainty. In this paper, we propose calibration as a principled and practical tool to bridge this gap, demonstrating the benefits of calibrated advice through two case studies: the ski rental and online job scheduling problems. For ski rental, we design an algorithm that achieves near-optimal prediction-dependent performance and prove that, in high-variance settings, calibrated advice offers more effective guidance than alternative methods for uncertainty quantification. For job scheduling, we demonstrate that using a calibrated predictor leads to significant performance improvements over existing methods. Evaluations on real-world data validate our theoretical findings, highlighting the practical impact of calibration for algorithms with predictions.", "published": "2025-02-05T03:41:18Z", "authors": ["Judy Hanwen Shen", "Ellen Vitercik", "Anders Wikum"], "categories": ["stat.ML", "cs.DS", "cs.LG"], "doi": "", "_query": "house price prediction machine learning", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2510.03003v1", "title": "From high-frequency sensors to noon reports: Using transfer learning for shaft power prediction in maritime", "abstract": "With the growth of global maritime transportation, energy optimization has become crucial for reducing costs and ensuring operational efficiency. Shaft power is the mechanical power transmitted from the engine to the shaft and directly impacts fuel consumption, making its accurate prediction a paramount step in optimizing vessel performance. Power consumption is highly correlated with ship parameters such as speed and shaft rotation per minute, as well as weather and sea conditions. Frequent access to this operational data can improve prediction accuracy. However, obtaining high-quality sensor data is often infeasible and costly, making alternative sources such as noon reports a viable option. In this paper, we propose a transfer learning-based approach for predicting vessels shaft power, where a model is initially trained on high-frequency data from a vessel and then fine-tuned with low-frequency daily noon reports from other vessels. We tested our approach on sister vessels (identical dimensions and configurations), a similar vessel (slightly larger with a different engine), and a different vessel (distinct dimensions and configurations). The experiments showed that the mean absolute percentage error decreased by 10.6 percent for sister vessels, 3.6 percent for a similar vessel, and 5.3 percent for a different vessel, compared to the model trained solely on noon report data.", "published": "2025-10-03T13:47:16Z", "authors": ["Akriti Sharma", "Dogan Altan", "Dusica Marijan", "Arnbjørn Maressa"], "categories": ["cs.LG", "cs.AI"], "doi": "", "_query": "house price prediction machine learning", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/1312.0286v2", "title": "Efficient Learning and Planning with Compressed Predictive States", "abstract": "Predictive state representations (PSRs) offer an expressive framework for modelling partially observable systems. By compactly representing systems as functions of observable quantities, the PSR learning approach avoids using local-minima prone expectation-maximization and instead employs a globally optimal moment-based algorithm. Moreover, since PSRs do not require a predetermined latent state structure as an input, they offer an attractive framework for model-based reinforcement learning when agents must plan without a priori access to a system model. Unfortunately, the expressiveness of PSRs comes with significant computational cost, and this cost is a major factor inhibiting the use of PSRs in applications. In order to alleviate this shortcoming, we introduce the notion of compressed PSRs (CPSRs). The CPSR learning approach combines recent advancements in dimensionality reduction, incremental matrix decomposition, and compressed sensing. We show how this approach provides a principled avenue for learning accurate approximations of PSRs, drastically reducing the computational costs associated with learning while also providing effective regularization. Going further, we propose a planning framework which exploits these learned models. And we show that this approach facilitates model-learning and planning in large complex partially observable domains, a task that is infeasible without the principled use of compression.", "published": "2013-12-01T23:17:06Z", "authors": ["William L. Hamilton", "Mahdi Milani Fard", "Joelle Pineau"], "categories": ["cs.LG", "stat.ML"], "doi": "", "_query": "house price prediction machine learning", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2003.01176v3", "title": "Deep Survival Machines: Fully Parametric Survival Regression and Representation Learning for Censored Data with Competing Risks", "abstract": "We describe a new approach to estimating relative risks in time-to-event prediction problems with censored data in a fully parametric manner. Our approach does not require making strong assumptions of constant proportional hazard of the underlying survival distribution, as required by the Cox-proportional hazard model. By jointly learning deep nonlinear representations of the input covariates, we demonstrate the benefits of our approach when used to estimate survival risks through extensive experimentation on multiple real world datasets with different levels of censoring. We further demonstrate advantages of our model in the competing risks scenario. To the best of our knowledge, this is the first work involving fully parametric estimation of survival times with competing risks in the presence of censoring.", "published": "2020-03-02T20:21:59Z", "authors": ["Chirag Nagpal", "Xinyu Rachel Li", "Artur Dubrawski"], "categories": ["cs.LG", "stat.AP", "stat.ML"], "doi": "", "_query": "house price prediction machine learning", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2202.13916v1", "title": "Machine Learning Wavefunction", "abstract": "This chapter introduces the main ideas and the most important methods for representing the electronic wavefunction through machine learning models. The wavefunction of a N-electron system is an incredibly complicated mathematical object, and models thereof require enough flexibility to properly describe the complex interactions between the particles, but at the same time a sufficiently compact representation to be useful in practice. Machine learning techniques offer an ideal mathematical framework to satisfy these requirements, and provide algorithms for their optimization in both supervised and unsupervised fashions. In this chapter, various examples of machine learning wavefunctions are presented and their strengths and weaknesses with respect to traditional quantum chemical approaches are discussed; first in theory, and then in practice with two case studies.", "published": "2022-02-28T16:11:32Z", "authors": ["Stefano Battaglia"], "categories": ["physics.chem-ph", "physics.comp-ph"], "doi": "10.1016/B978-0-323-90049-2.00003-2", "_query": "house price prediction machine learning", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2306.04255v1", "title": "Accounting For Informative Sampling When Learning to Forecast Treatment Outcomes Over Time", "abstract": "Machine learning (ML) holds great potential for accurately forecasting treatment outcomes over time, which could ultimately enable the adoption of more individualized treatment strategies in many practical applications. However, a significant challenge that has been largely overlooked by the ML literature on this topic is the presence of informative sampling in observational data. When instances are observed irregularly over time, sampling times are typically not random, but rather informative -- depending on the instance's characteristics, past outcomes, and administered treatments. In this work, we formalize informative sampling as a covariate shift problem and show that it can prohibit accurate estimation of treatment outcomes if not properly accounted for. To overcome this challenge, we present a general framework for learning treatment outcomes in the presence of informative sampling using inverse intensity-weighting, and propose a novel method, TESAR-CDE, that instantiates this framework using Neural CDEs. Using a simulation environment based on a clinical use case, we demonstrate the effectiveness of our approach in learning under informative sampling.", "published": "2023-06-07T08:51:06Z", "authors": ["Toon Vanderschueren", "Alicia Curth", "Wouter Verbeke", "Mihaela van der Schaar"], "categories": ["stat.ML", "cs.LG"], "doi": "", "_query": "house price prediction machine learning", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2406.19621v1", "title": "Machine-Learning-Driven Runtime Optimization of BLAS Level 3 on Modern Multi-Core Systems", "abstract": "BLAS Level 3 operations are essential for scientific computing, but finding the optimal number of threads for multi-threaded implementations on modern multi-core systems is challenging. We present an extension to the Architecture and Data-Structure Aware Linear Algebra (ADSALA) library that uses machine learning to optimize the runtime of all BLAS Level 3 operations. Our method predicts the best number of threads for each operation based on the matrix dimensions and the system architecture. We test our method on two HPC platforms with Intel and AMD processors, using MKL and BLIS as baseline BLAS implementations. We achieve speedups of 1.5 to 3.0 for all operations, compared to using the maximum number of threads. We also analyze the runtime patterns of different BLAS operations and explain the sources of speedup. Our work shows the effectiveness and generality of the ADSALA approach for optimizing BLAS routines on modern multi-core systems.", "published": "2024-06-28T03:07:53Z", "authors": ["Yufan Xia", "Giuseppe Maria Junior Barca"], "categories": ["cs.DC", "cs.LG"], "doi": "", "_query": "house price prediction machine learning", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2006.01509v2", "title": "Detection of gravitational-wave signals from binary neutron star mergers using machine learning", "abstract": "As two neutron stars merge, they emit gravitational waves that can potentially be detected by earth bound detectors. Matched-filtering based algorithms have traditionally been used to extract quiet signals embedded in noise. We introduce a novel neural-network based machine learning algorithm that uses time series strain data from gravitational-wave detectors to detect signals from non-spinning binary neutron star mergers. For the Advanced LIGO design sensitivity, our network has an average sensitive distance of 130 Mpc at a false-alarm rate of 10 per month. Compared to other state-of-the-art machine learning algorithms, we find an improvement by a factor of 6 in sensitivity to signals with signal-to-noise ratio below 25. However, this approach is not yet competitive with traditional matched-filtering based methods. A conservative estimate indicates that our algorithm introduces on average 10.2 s of latency between signal arrival and generating an alert. We give an exact description of our testing procedure, which can not only be applied to machine learning based algorithms but all other search algorithms as well. We thereby improve the ability to compare machine learning and classical searches.", "published": "2020-06-02T10:20:11Z", "authors": ["Marlin B. Schäfer", "Frank Ohme", "Alexander H. Nitz"], "categories": ["astro-ph.HE", "astro-ph.IM", "cs.LG", "gr-qc"], "doi": "10.1103/PhysRevD.102.063015", "_query": "house price prediction machine learning", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/1501.02592v1", "title": "Photonic Delay Systems as Machine Learning Implementations", "abstract": "Nonlinear photonic delay systems present interesting implementation platforms for machine learning models. They can be extremely fast, offer great degrees of parallelism and potentially consume far less power than digital processors. So far they have been successfully employed for signal processing using the Reservoir Computing paradigm. In this paper we show that their range of applicability can be greatly extended if we use gradient descent with backpropagation through time on a model of the system to optimize the input encoding of such systems. We perform physical experiments that demonstrate that the obtained input encodings work well in reality, and we show that optimized systems perform significantly better than the common Reservoir Computing approach. The results presented here demonstrate that common gradient descent techniques from machine learning may well be applicable on physical neuro-inspired analog computers.", "published": "2015-01-12T10:25:31Z", "authors": ["Michiel Hermans", "Miguel Soriano", "Joni Dambre", "Peter Bienstman", "Ingo Fischer"], "categories": ["cs.NE", "cs.LG"], "doi": "", "_query": "house price prediction machine learning", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/1009.4791v2", "title": "Multi-parametric Solution-path Algorithm for Instance-weighted Support Vector Machines", "abstract": "An instance-weighted variant of the support vector machine (SVM) has attracted considerable attention recently since they are useful in various machine learning tasks such as non-stationary data analysis, heteroscedastic data modeling, transfer learning, learning to rank, and transduction. An important challenge in these scenarios is to overcome the computational bottleneck---instance weights often change dynamically or adaptively, and thus the weighted SVM solutions must be repeatedly computed. In this paper, we develop an algorithm that can efficiently and exactly update the weighted SVM solutions for arbitrary change of instance weights. Technically, this contribution can be regarded as an extension of the conventional solution-path algorithm for a single regularization parameter to multiple instance-weight parameters. However, this extension gives rise to a significant problem that breakpoints (at which the solution path turns) have to be identified in high-dimensional space. To facilitate this, we introduce a parametric representation of instance weights. We also provide a geometric interpretation in weight space using a notion of critical region: a polyhedron in which the current affine solution remains to be optimal. Then we find breakpoints at intersections of the solution path and boundaries of polyhedrons. Through extensive experiments on various practical applications, we demonstrate the usefulness of the proposed algorithm.", "published": "2010-09-24T09:53:32Z", "authors": ["Masayuki Karasuyama", "Naoyuki Harada", "Masashi Sugiyama", "Ichiro Takeuchi"], "categories": ["cs.LG"], "doi": "", "_query": "house price prediction machine learning", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2006.13309v4", "title": "Fast Deep Mixtures of Gaussian Process Experts", "abstract": "Mixtures of experts have become an indispensable tool for flexible modelling in a supervised learning context, allowing not only the mean function but the entire density of the output to change with the inputs. Sparse Gaussian processes (GP) have shown promise as a leading candidate for the experts in such models, and in this article, we propose to design the gating network for selecting the experts from such mixtures of sparse GPs using a deep neural network (DNN). Furthermore, a fast one pass algorithm called Cluster-Classify-Regress (CCR) is leveraged to approximate the maximum a posteriori (MAP) estimator extremely quickly. This powerful combination of model and algorithm together delivers a novel method which is flexible, robust, and extremely efficient. In particular, the method is able to outperform competing methods in terms of accuracy and uncertainty quantification. The cost is competitive on low-dimensional and small data sets, but is significantly lower for higher-dimensional and big data sets. Iteratively maximizing the distribution of experts given allocations and allocations given experts does not provide significant improvement, which indicates that the algorithm achieves a good approximation to the local MAP estimator very fast. This insight can be useful also in the context of other mixture of experts models.", "published": "2020-06-11T18:52:34Z", "authors": ["Clement Etienam", "Kody Law", "Sara Wade", "Vitaly Zankin"], "categories": ["cs.LG", "stat.ML"], "doi": "10.1007/s10994-023-06491-x", "_query": "house price prediction machine learning", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2108.12978v3", "title": "Private Multi-Task Learning: Formulation and Applications to Federated Learning", "abstract": "Many problems in machine learning rely on multi-task learning (MTL), in which the goal is to solve multiple related machine learning tasks simultaneously. MTL is particularly relevant for privacy-sensitive applications in areas such as healthcare, finance, and IoT computing, where sensitive data from multiple, varied sources are shared for the purpose of learning. In this work, we formalize notions of client-level privacy for MTL via joint differential privacy (JDP), a relaxation of differential privacy for mechanism design and distributed optimization. We then propose an algorithm for mean-regularized MTL, an objective commonly used for applications in personalized federated learning, subject to JDP. We analyze our objective and solver, providing certifiable guarantees on both privacy and utility. Empirically, we find that our method provides improved privacy/utility trade-offs relative to global baselines across common federated learning benchmarks.", "published": "2021-08-30T03:37:36Z", "authors": ["Shengyuan Hu", "Zhiwei Steven Wu", "Virginia Smith"], "categories": ["cs.LG", "cs.CR"], "doi": "", "_query": "house price prediction machine learning", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/1207.2600v1", "title": "Efficient Prediction of DNA-Binding Proteins Using Machine Learning", "abstract": "DNA-binding proteins are a class of proteins which have a specific or general affinity to DNA and include three important components: transcription factors; nucleases, and histones. DNA-binding proteins also perform important roles in many types of cellular activities. In this paper we describe machine learning systems for the prediction of DNA- binding proteins where a Support Vector Machine and a Cascade Correlation Neural Network are optimized and then compared to determine the learning algorithm that achieves the best prediction performance. The information used for classification is derived from characteristics that include overall charge, patch size and amino acids composition. In total 121 DNA- binding proteins and 238 non-binding proteins are used to build and evaluate the system. For SVM using the ANOVA Kernel with Jack-knife evaluation, an accuracy of 86.7% has been achieved with 91.1% for sensitivity and 85.3% for specificity. For CCNN optimized over the entire dataset with Jack knife evaluation we report an accuracy of 75.4%, while the values of specificity and sensitivity achieved were 72.3% and 82.6%, respectively.", "published": "2012-07-11T11:28:57Z", "authors": ["Sokyna Qatawneh", "Afaf Alneaimi", "Thamer Rawashdeh", "Mmohammad Muhairat", "Rami Qahwaji", "Stan Ipson"], "categories": ["cs.CV", "q-bio.QM"], "doi": "", "_query": "house price prediction machine learning", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2608.14894v1", "title": "Can Neural Networks Learn by Experimenting on Themselves? Self-Interventional Learning from Functional Consequences to Predictive Self-Knowledge", "abstract": "Machine-learning systems usually model external data, while their internal functional organization is analyzed by external observers. This work introduces Self-Interventional Learning (SIL), in which a neural system perturbs its own functional structure, observes consequences, learns a predictive self-model, generalizes to unexecuted interventions, and uses predictions to guide later structural action. In a construction-known synthetic system, SIL recovered critical structure, redundancy, and replaceability, while synergy was not reliably recovered. Across 30 fresh confirmatory seeds, increasing the pairwise intervention budget from 4 to 56 reduced held-out prediction error from 0.0335 to 0.0148 and increased Spearman correlation from 0.629 to 0.883. In a matched ablation, preserving the correct intervention--consequence mapping reduced prospective prediction error by 81.3%, while using the same learned self-model for action reduced normalized regret by 31.7% relative to ignoring it. However, model-guided action did not significantly outperform a direct empirical-memory policy, and powered CIFAR-10/ResNet validation showed no robustness advantage over equal-budget direct repair search. These results support SIL as an intervention-driven framework for learning predictive knowledge about a network's own functional organization, while showing that the self-model remains incomplete and is not universally superior to simpler direct strategies.", "published": "2026-08-14T21:01:33Z", "authors": ["Michał Tomaszewski"], "categories": ["cs.LG"], "doi": "", "_query": "house price prediction machine learning", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2109.09463v2", "title": "Predicting Visual Improvement after Macular Hole Surgery: a Cautionary Tale on Deep Learning with Very Limited Data", "abstract": "We investigate the potential of machine learning models for the prediction of visual improvement after macular hole surgery from preoperative data (retinal images and clinical features). Collecting our own data for the task, we end up with only 121 total samples, putting our work in the very limited data regime. We explore a variety of deep learning methods for limited data to train deep computer vision models, finding that all tested deep vision models are outperformed by a simple regression model on the clinical features. We believe this is compelling evidence of the extreme difficulty of using deep learning on very limited data.", "published": "2021-09-20T12:23:04Z", "authors": ["M. Godbout", "A. Lachance", "F. Antaki", "A. Dirani", "A. Durand"], "categories": ["eess.IV", "cs.CV", "cs.LG"], "doi": "", "_query": "house price prediction machine learning", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/1709.03413v1", "title": "Gigamachine: incremental machine learning on desktop computers", "abstract": "We present a concrete design for Solomonoff's incremental machine learning system suitable for desktop computers. We use R5RS Scheme and its standard library with a few omissions as the reference machine. We introduce a Levin Search variant based on a stochastic Context Free Grammar together with new update algorithms that use the same grammar as a guiding probability distribution for incremental machine learning. The updates include adjusting production probabilities, re-using previous solutions, learning programming idioms and discovery of frequent subprograms. The issues of extending the a priori probability distribution and bootstrapping are discussed. We have implemented a good portion of the proposed algorithms. Experiments with toy problems show that the update algorithms work as expected.", "published": "2017-09-08T17:39:26Z", "authors": ["Eray Özkural"], "categories": ["cs.AI", "cs.LG"], "doi": "", "_query": "house price prediction machine learning", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2501.12299v3", "title": "Sublinear Variational Optimization of Gaussian Mixture Models with Millions to Billions of Parameters", "abstract": "Gaussian Mixture Models (GMMs) range among the most frequently used models in machine learning. However, training large, general GMMs becomes computationally prohibitive for data sets that have many data points $N$ of high-dimensionality $D$. For GMMs with arbitrary covariances, we here derive a highly efficient variational approximation, which is then integrated with mixtures of factor analyzers (MFAs). For GMMs with $C$ components, our proposed algorithm substantially reduces runtime complexity from $\\mathcal{O}(NCD^2)$ per iteration to a complexity scaling linearly with $D$ and sublinearly with $NC$. In numerical experiments, we first validate that the complexity reduction results in a sublinear scaling for the entire GMM optimization process. Second, we show on large-scale benchmarks that the sublinear algorithm results in speed-ups of an order-of-magnitude compared to the state-of-the-art. Third, as a proof of concept, we finally train GMMs with over 10 billion parameters on about 100 million images, observing training times of less than nine hours on a single state-of-the-art CPU. Finally, and fourth, we demonstrate the effectiveness of large-scale GMMs on the task of zero-shot image denoising, where sublinear training results in state-of-the-art denoising times while competitive denoising performance is maintained.", "published": "2025-01-21T17:11:25Z", "authors": ["Sebastian Salwig", "Till Kahlke", "Florian Hirschberger", "Dennis Forster", "Jörg Lücke"], "categories": ["stat.ML", "cs.CV", "cs.LG"], "doi": "", "_query": "house price prediction machine learning", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/1812.00546v3", "title": "Learning the progression and clinical subtypes of Alzheimer's disease from longitudinal clinical data", "abstract": "Alzheimer's disease (AD) is a degenerative brain disease impairing a person's ability to perform day to day activities. The clinical manifestations of Alzheimer's disease are characterized by heterogeneity in age, disease span, progression rate, impairment of memory and cognitive abilities. Due to these variabilities, personalized care and treatment planning, as well as patient counseling about their individual progression is limited. Recent developments in machine learning to detect hidden patterns in complex, multi-dimensional datasets provides significant opportunities to address this critical need. In this work, we use unsupervised and supervised machine learning approaches for subtype identification and prediction. We apply machine learning methods to the extensive clinical observations available at the Alzheimer's Disease Neuroimaging Initiative (ADNI) data set to identify patient subtypes and to predict disease progression. Our analysis depicts the progression space for the Alzheimer's disease into low, moderate and high disease progression zones. The proposed work will enable early detection and characterization of distinct disease subtypes based on clinical heterogeneity. We anticipate that our models will enable patient counseling, clinical trial design, and ultimately individualized clinical care.", "published": "2018-12-03T04:09:20Z", "authors": ["Vipul Satone", "Rachneet Kaur", "Faraz Faghri", "Mike A Nalls", "Andrew B Singleton", "Roy H Campbell"], "categories": ["cs.LG", "q-bio.QM", "stat.ML"], "doi": "", "_query": "house price prediction machine learning", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/1808.07412v2", "title": "Ithemal: Accurate, Portable and Fast Basic Block Throughput Estimation using Deep Neural Networks", "abstract": "Predicting the number of clock cycles a processor takes to execute a block of assembly instructions in steady state (the throughput) is important for both compiler designers and performance engineers. Building an analytical model to do so is especially complicated in modern x86-64 Complex Instruction Set Computer (CISC) machines with sophisticated processor microarchitectures in that it is tedious, error prone, and must be performed from scratch for each processor generation. In this paper we present Ithemal, the first tool which learns to predict the throughput of a set of instructions. Ithemal uses a hierarchical LSTM--based approach to predict throughput based on the opcodes and operands of instructions in a basic block. We show that Ithemal is more accurate than state-of-the-art hand-written tools currently used in compiler backends and static machine code analyzers. In particular, our model has less than half the error of state-of-the-art analytical models (LLVM's llvm-mca and Intel's IACA). Ithemal is also able to predict these throughput values just as fast as the aforementioned tools, and is easily ported across a variety of processor microarchitectures with minimal developer effort.", "published": "2018-08-21T03:40:21Z", "authors": ["Charith Mendis", "Alex Renda", "Saman Amarasinghe", "Michael Carbin"], "categories": ["cs.DC", "cs.LG", "stat.ML"], "doi": "", "_query": "house price prediction machine learning", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2305.19505v2", "title": "M3ICRO: Machine Learning-Enabled Compact Photonic Tensor Core based on PRogrammable Multi-Operand Multimode Interference", "abstract": "Photonic computing shows promise for transformative advancements in machine learning (ML) acceleration, offering ultra-fast speed, massive parallelism, and high energy efficiency. However, current photonic tensor core (PTC) designs based on standard optical components hinder scalability and compute density due to their large spatial footprint. To address this, we propose an ultra-compact PTC using customized programmable multi-operand multimode interference (MOMMI) devices, named M3ICRO. The programmable MOMMI leverages the intrinsic light propagation principle, providing a single-device programmable matrix unit beyond the conventional computing paradigm of one multiply-accumulate (MAC) operation per device. To overcome the optimization difficulty of customized devices that often requires time-consuming simulation, we apply ML for optics to predict the device behavior and enable a differentiable optimization flow. We thoroughly investigate the reconfigurability and matrix expressivity of our customized PTC, and introduce a novel block unfolding method to fully exploit the computing capabilities of a complex-valued PTC for near-universal real-valued linear transformations. Extensive evaluations demonstrate that M3ICRO achieves a 3.4-9.6x smaller footprint, 1.6-4.4x higher speed, 10.6-42x higher compute density, 3.7-12x higher system throughput, and superior noise robustness compared to state-of-the-art coherent PTC designs, while maintaining close-to-digital task accuracy across various ML benchmarks. Our code is open-sourced at https://github.com/JeremieMelo/M3ICRO-MOMMI.", "published": "2023-05-31T02:34:36Z", "authors": ["Jiaqi Gu", "Hanqing Zhu", "Chenghao Feng", "Zixuan Jiang", "Ray T. Chen", "David Z. Pan"], "categories": ["cs.ET", "cs.LG", "physics.optics"], "doi": "", "_query": "house price prediction machine learning", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2301.01222v1", "title": "A Multi-Source Information Learning Framework for Airbnb Price Prediction", "abstract": "With the development of technology and sharing economy, Airbnb as a famous short-term rental platform, has become the first choice for many young people to select. The issue of Airbnb's pricing has always been a problem worth studying. While the previous studies achieve promising results, there are exists deficiencies to solve. Such as, (1) the feature attributes of rental are not rich enough; (2) the research on rental text information is not deep enough; (3) there are few studies on predicting the rental price combined with the point of interest(POI) around the house. To address the above challenges, we proposes a multi-source information embedding(MSIE) model to predict the rental price of Airbnb. Specifically, we first selects the statistical feature to embed the original rental data. Secondly, we generates the word feature vector and emotional score combination of three different text information to form the text feature embedding. Thirdly, we uses the points of interest(POI) around the rental house information generates a variety of spatial network graphs, and learns the embedding of the network to obtain the spatial feature embedding. Finally, this paper combines the three modules into multi source rental representations, and uses the constructed fully connected neural network to predict the price. The analysis of the experimental results shows the effectiveness of our proposed model.", "published": "2023-01-01T11:07:55Z", "authors": ["Lu Jiang", "Yuanhan Li", "Na Luo", "Jianan Wang", "Qiao Ning"], "categories": ["cs.LG", "cs.IR"], "doi": "", "_query": "house price prediction machine learning", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/1912.03277v3", "title": "Preserving Causal Constraints in Counterfactual Explanations for Machine Learning Classifiers", "abstract": "To construct interpretable explanations that are consistent with the original ML model, counterfactual examples---showing how the model's output changes with small perturbations to the input---have been proposed. This paper extends the work in counterfactual explanations by addressing the challenge of feasibility of such examples. For explanations of ML models in critical domains such as healthcare and finance, counterfactual examples are useful for an end-user only to the extent that perturbation of feature inputs is feasible in the real world. We formulate the problem of feasibility as preserving causal relationships among input features and present a method that uses (partial) structural causal models to generate actionable counterfactuals. When feasibility constraints cannot be easily expressed, we consider an alternative mechanism where people can label generated CF examples on feasibility: whether it is feasible to intervene and realize the candidate CF example from the original input. To learn from this labelled feasibility data, we propose a modified variational auto encoder loss for generating CF examples that optimizes for feasibility as people interact with its output. Our experiments on Bayesian networks and the widely used ''Adult-Income'' dataset show that our proposed methods can generate counterfactual explanations that better satisfy feasibility constraints than existing methods.. Code repository can be accessed here: \\textit{https://github.com/divyat09/cf-feasibility}", "published": "2019-12-06T18:16:29Z", "authors": ["Divyat Mahajan", "Chenhao Tan", "Amit Sharma"], "categories": ["cs.LG", "cs.AI", "stat.ML"], "doi": "", "_query": "house price prediction machine learning", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2105.04026v2", "title": "The Modern Mathematics of Deep Learning", "abstract": "We describe the new field of mathematical analysis of deep learning. This field emerged around a list of research questions that were not answered within the classical framework of learning theory. These questions concern: the outstanding generalization power of overparametrized neural networks, the role of depth in deep architectures, the apparent absence of the curse of dimensionality, the surprisingly successful optimization performance despite the non-convexity of the problem, understanding what features are learned, why deep architectures perform exceptionally well in physical problems, and which fine aspects of an architecture affect the behavior of a learning task in which way. We present an overview of modern approaches that yield partial answers to these questions. For selected approaches, we describe the main ideas in more detail.", "published": "2021-05-09T21:30:42Z", "authors": ["Julius Berner", "Philipp Grohs", "Gitta Kutyniok", "Philipp Petersen"], "categories": ["cs.LG", "stat.ML"], "doi": "10.1017/9781009025096.002", "_query": "house price prediction machine learning", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2307.14397v3", "title": "A Survey on Generative Modeling with Limited Data, Few Shots, and Zero Shot", "abstract": "Generative modeling in machine learning aims to synthesize new data samples that are statistically similar to those observed during training. While conventional generative models such as GANs and diffusion models typically assume access to large and diverse datasets, many real-world applications (e.g. in medicine, satellite imaging, and artistic domains) operate under limited data availability and strict constraints. In this survey, we examine Generative Modeling under Data Constraint (GM-DC), which includes limited-data, few-shot, and zero-shot settings. We present a unified perspective on the key challenges in GM-DC, including overfitting, frequency bias, and incompatible knowledge transfer, and discuss how these issues impact model performance. To systematically analyze this growing field, we introduce two novel taxonomies: one categorizing GM-DC tasks (e.g. unconditional vs. conditional generation, cross-domain adaptation, and subject-driven modeling), and another organizing methodological approaches (e.g. transfer learning, data augmentation, meta-learning, and frequency-aware modeling). Our study reviews over 230 papers, offering a comprehensive view across generative model types and constraint scenarios. We further analyze task-approach-method interactions using a Sankey diagram and highlight promising directions for future work, including adaptation of foundation models, holistic evaluation frameworks, and data-centric strategies for sample selection. This survey provides a timely and practical roadmap for researchers and practitioners aiming to advance generative modeling under limited data. Project website: https://sutd-visual-computing-group.github.io/gmdc-survey/.", "published": "2023-07-26T12:05:08Z", "authors": ["Milad Abdollahzadeh", "Guimeng Liu", "Touba Malekzadeh", "Christopher T. H. Teo", "Keshigeyan Chandrasegaran", "Ngai-Man Cheung"], "categories": ["cs.CV", "cs.LG"], "doi": "", "_query": "house price prediction machine learning", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2206.07438v3", "title": "Multi-Objective Hyperparameter Optimization in Machine Learning -- An Overview", "abstract": "Hyperparameter optimization constitutes a large part of typical modern machine learning workflows. This arises from the fact that machine learning methods and corresponding preprocessing steps often only yield optimal performance when hyperparameters are properly tuned. But in many applications, we are not only interested in optimizing ML pipelines solely for predictive accuracy; additional metrics or constraints must be considered when determining an optimal configuration, resulting in a multi-objective optimization problem. This is often neglected in practice, due to a lack of knowledge and readily available software implementations for multi-objective hyperparameter optimization. In this work, we introduce the reader to the basics of multi-objective hyperparameter optimization and motivate its usefulness in applied ML. Furthermore, we provide an extensive survey of existing optimization strategies, both from the domain of evolutionary algorithms and Bayesian optimization. We illustrate the utility of MOO in several specific ML applications, considering objectives such as operating conditions, prediction time, sparseness, fairness, interpretability and robustness.", "published": "2022-06-15T10:23:19Z", "authors": ["Florian Karl", "Tobias Pielok", "Julia Moosbauer", "Florian Pfisterer", "Stefan Coors", "Martin Binder", "Lennart Schneider", "Janek Thomas", "Jakob Richter", "Michel Lang", "Eduardo C. Garrido-Merchán", "Juergen Branke", "Bernd Bischl"], "categories": ["cs.LG", "stat.ML"], "doi": "10.1145/3610536", "_query": "house price prediction machine learning", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2410.08355v3", "title": "Metalic: Meta-Learning In-Context with Protein Language Models", "abstract": "Predicting the biophysical and functional properties of proteins is essential for in silico protein design. Machine learning has emerged as a promising technique for such prediction tasks. However, the relative scarcity of in vitro annotations means that these models often have little, or no, specific data on the desired fitness prediction task. As a result of limited data, protein language models (PLMs) are typically trained on general protein sequence modeling tasks, and then fine-tuned, or applied zero-shot, to protein fitness prediction. When no task data is available, the models make strong assumptions about the correlation between the protein sequence likelihood and fitness scores. In contrast, we propose meta-learning over a distribution of standard fitness prediction tasks, and demonstrate positive transfer to unseen fitness prediction tasks. Our method, called Metalic (Meta-Learning In-Context), uses in-context learning and fine-tuning, when data is available, to adapt to new tasks. Crucially, fine-tuning enables considerable generalization, even though it is not accounted for during meta-training. Our fine-tuned models achieve strong results with 18 times fewer parameters than state-of-the-art models. Moreover, our method sets a new state-of-the-art in low-data settings on ProteinGym, an established fitness-prediction benchmark. Due to data scarcity, we believe meta-learning will play a pivotal role in advancing protein engineering.", "published": "2024-10-10T20:19:35Z", "authors": ["Jacob Beck", "Shikha Surana", "Manus McAuliffe", "Oliver Bent", "Thomas D. Barrett", "Juan Jose Garau Luis", "Paul Duckworth"], "categories": ["cs.LG"], "doi": "", "_query": "house price prediction machine learning", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2508.03898v2", "title": "Calibrating Biophysical Models for Grape Phenology Prediction via Multi-Task Learning", "abstract": "Accurate prediction of grape phenology is essential for timely vineyard management decisions, such as scheduling irrigation and fertilization, to maximize crop yield and quality. While traditional biophysical models calibrated on historical field data can be used for season-long predictions, they lack the precision required for fine-grained vineyard management. Deep learning methods are a compelling alternative but their performance is hindered by sparse phenology datasets, particularly at the cultivar level. We propose a hybrid modeling approach that combines multi-task learning with a recurrent neural network to parameterize a differentiable biophysical model. By using multi-task learning to predict the parameters of the biophysical model, our approach enables shared learning across cultivars while preserving biological structure, thereby improving the robustness and accuracy of predictions. Empirical evaluation using real-world and synthetic datasets demonstrates that our method significantly outperforms both conventional biophysical models and baseline deep learning approaches in predicting phenological stages, as well as other crop state variables such as cold-hardiness and wheat yield.", "published": "2025-08-05T20:36:11Z", "authors": ["William Solow", "Sandhya Saisubramanian"], "categories": ["cs.LG", "cs.AI"], "doi": "", "_query": "house price prediction machine learning", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2211.15739v1", "title": "CWD: A Machine Learning based Approach to Detect Unknown Cloud Workloads", "abstract": "Workloads in modern cloud data centers are becoming increasingly complex. The number of workloads running in cloud data centers has been growing exponentially for the last few years, and cloud service providers (CSP) have been supporting on-demand services in real-time. Realizing the growing complexity of cloud environment and cloud workloads, hardware vendors such as Intel and AMD are increasingly introducing cloud-specific workload acceleration features in their CPU platforms. These features are typically targeted towards popular and commonly-used cloud workloads. Nonetheless, uncommon, customer-specific workloads (unknown workloads), if their characteristics are different from common workloads (known workloads), may not realize the potential of the underlying platform. To address this problem of realizing the full potential of the underlying platform, we develop a machine learning based technique to characterize, profile and predict workloads running in the cloud environment. Experimental evaluation of our technique demonstrates good prediction performance. We also develop techniques to analyze the performance of the model in a standalone manner.", "published": "2022-11-28T19:41:56Z", "authors": ["Mohammad Hossain", "Derssie Mebratu", "Niranjan Hasabnis", "Jun Jin", "Gaurav Chaudhary", "Noah Shen"], "categories": ["cs.DC", "cs.LG"], "doi": "", "_query": "house price prediction machine learning", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2405.07456v1", "title": "Boosting House Price Estimations with Multi-Head Gated Attention", "abstract": "Evaluating house prices is crucial for various stakeholders, including homeowners, investors, and policymakers. However, traditional spatial interpolation methods have limitations in capturing the complex spatial relationships that affect property values. To address these challenges, we have developed a new method called Multi-Head Gated Attention for spatial interpolation. Our approach builds upon attention-based interpolation models and incorporates multiple attention heads and gating mechanisms to capture spatial dependencies and contextual information better. Importantly, our model produces embeddings that reduce the dimensionality of the data, enabling simpler models like linear regression to outperform complex ensembling models. We conducted extensive experiments to compare our model with baseline methods and the original attention-based interpolation model. The results show a significant improvement in the accuracy of house price predictions, validating the effectiveness of our approach. This research advances the field of spatial interpolation and provides a robust tool for more precise house price evaluation. Our GitHub repository.contains the data and code for all datasets, which are available for researchers and practitioners interested in replicating or building upon our work.", "published": "2024-05-13T04:12:03Z", "authors": ["Zakaria Abdellah Sellam", "Cosimo Distante", "Abdelmalik Taleb-Ahmed", "Pier Luigi Mazzeo"], "categories": ["cs.LG"], "doi": "", "_query": "house price prediction machine learning", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/1612.04340v1", "title": "End-to-End Deep Reinforcement Learning for Lane Keeping Assist", "abstract": "Reinforcement learning is considered to be a strong AI paradigm which can be used to teach machines through interaction with the environment and learning from their mistakes, but it has not yet been successfully used for automotive applications. There has recently been a revival of interest in the topic, however, driven by the ability of deep learning algorithms to learn good representations of the environment. Motivated by Google DeepMind's successful demonstrations of learning for games from Breakout to Go, we will propose different methods for autonomous driving using deep reinforcement learning. This is of particular interest as it is difficult to pose autonomous driving as a supervised learning problem as it has a strong interaction with the environment including other vehicles, pedestrians and roadworks. As this is a relatively new area of research for autonomous driving, we will formulate two main categories of algorithms: 1) Discrete actions category, and 2) Continuous actions category. For the discrete actions category, we will deal with Deep Q-Network Algorithm (DQN) while for the continuous actions category, we will deal with Deep Deterministic Actor Critic Algorithm (DDAC). In addition to that, We will also discover the performance of these two categories on an open source car simulator for Racing called (TORCS) which stands for The Open Racing car Simulator. Our simulation results demonstrate learning of autonomous maneuvering in a scenario of complex road curvatures and simple interaction with other vehicles. Finally, we explain the effect of some restricted conditions, put on the car during the learning phase, on the convergence time for finishing its learning phase.", "published": "2016-12-13T20:19:42Z", "authors": ["Ahmad El Sallab", "Mohammed Abdou", "Etienne Perot", "Senthil Yogamani"], "categories": ["stat.ML", "cs.LG", "cs.RO"], "doi": "", "_query": "house price prediction machine learning", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2410.01580v3", "title": "Learning-Augmented Robust Algorithmic Recourse", "abstract": "Algorithmic recourse provides individuals who receive undesirable outcomes from machine learning systems with minimum-cost improvements to achieve a desirable outcome. However, machine learning models often get updated, so the recourse may not lead to the desired outcome. The robust recourse framework chooses recourses that are less sensitive to adversarial model changes, but this comes at a higher cost. To address this, we initiate the study of learning-augmented algorithmic recourse and evaluate the extent to which a designer equipped with a prediction of the future model can reduce the cost of recourse when the prediction is accurate (consistency) while also limiting the cost even when the prediction is inaccurate (robustness). We propose a novel algorithm, study the robustness-consistency trade-off, and analyze how prediction accuracy affects performance.", "published": "2024-10-02T14:15:32Z", "authors": ["Kshitij Kayastha", "Vasilis Gkatzelis", "Shahin Jabbari"], "categories": ["cs.LG"], "doi": "", "_query": "house price prediction machine learning", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/1310.5035v2", "title": "Linearized Alternating Direction Method with Parallel Splitting and Adaptive Penalty for Separable Convex Programs in Machine Learning", "abstract": "Many problems in machine learning and other fields can be (re)for-mulated as linearly constrained separable convex programs. In most of the cases, there are multiple blocks of variables. However, the traditional alternating direction method (ADM) and its linearized version (LADM, obtained by linearizing the quadratic penalty term) are for the two-block case and cannot be naively generalized to solve the multi-block case. So there is great demand on extending the ADM based methods for the multi-block case. In this paper, we propose LADM with parallel splitting and adaptive penalty (LADMPSAP) to solve multi-block separable convex programs efficiently. When all the component objective functions have bounded subgradients, we obtain convergence results that are stronger than those of ADM and LADM, e.g., allowing the penalty parameter to be unbounded and proving the sufficient and necessary conditions} for global convergence. We further propose a simple optimality measure and reveal the convergence rate of LADMPSAP in an ergodic sense. For programs with extra convex set constraints, with refined parameter estimation we devise a practical version of LADMPSAP for faster convergence. Finally, we generalize LADMPSAP to handle programs with more difficult objective functions by linearizing part of the objective function as well. LADMPSAP is particularly suitable for sparse representation and low-rank recovery problems because its subproblems have closed form solutions and the sparsity and low-rankness of the iterates can be preserved during the iteration. It is also highly parallelizable and hence fits for parallel or distributed computing. Numerical experiments testify to the advantages of LADMPSAP in speed and numerical accuracy.", "published": "2013-10-18T14:31:08Z", "authors": ["Zhouchen Lin", "Risheng Liu", "Huan Li"], "categories": ["math.NA", "cs.LG", "math.OC", "stat.ML"], "doi": "", "_query": "house price prediction machine learning", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/1902.10028v1", "title": "Performance Analysis of Machine Learning Techniques to Predict Diabetes Mellitus", "abstract": "Diabetes mellitus is a common disease of human body caused by a group of metabolic disorders where the sugar levels over a prolonged period is very high. It affects different organs of the human body which thus harm a large number of the body's system, in particular the blood veins and nerves. Early prediction in such disease can be controlled and save human life. To achieve the goal, this research work mainly explores various risk factors related to this disease using machine learning techniques. Machine learning techniques provide efficient result to extract knowledge by constructing predicting models from diagnostic medical datasets collected from the diabetic patients. Extracting knowledge from such data can be useful to predict diabetic patients. In this work, we employ four popular machine learning algorithms, namely Support Vector Machine (SVM), Naive Bayes (NB), K-Nearest Neighbor (KNN) and C4.5 Decision Tree, on adult population data to predict diabetic mellitus. Our experimental results show that C4.5 decision tree achieved higher accuracy compared to other machine learning techniques.", "published": "2019-01-10T14:53:27Z", "authors": ["Md. Faisal Faruque", " Asaduzzaman", "Iqbal H. Sarker"], "categories": ["cs.LG", "stat.ML"], "doi": "", "_query": "house price prediction machine learning", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/1908.08783v5", "title": "Learning Fitness Functions for Machine Programming", "abstract": "The problem of automatic software generation is known as Machine Programming. In this work, we propose a framework based on genetic algorithms to solve this problem. Although genetic algorithms have been used successfully for many problems, one criticism is that hand-crafting its fitness function, the test that aims to effectively guide its evolution, can be notably challenging. Our framework presents a novel approach to learn the fitness function using neural networks to predict values of ideal fitness functions. We also augment the evolutionary process with a minimally intrusive search heuristic. This heuristic improves the framework's ability to discover correct programs from ones that are approximately correct and does so with negligible computational overhead. We compare our approach with several state-of-the-art program synthesis methods and demonstrate that it finds more correct programs with fewer candidate program generations.", "published": "2019-08-22T17:47:34Z", "authors": ["Shantanu Mandal", "Todd A. Anderson", "Javier S. Turek", "Justin Gottschlich", "Shengtian Zhou", "Abdullah Muzahid"], "categories": ["cs.NE", "cs.LG", "stat.ML"], "doi": "", "_query": "house price prediction machine learning", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/1904.05268v2", "title": "Active Learning for Decision-Making from Imbalanced Observational Data", "abstract": "Machine learning can help personalized decision support by learning models to predict individual treatment effects (ITE). This work studies the reliability of prediction-based decision-making in a task of deciding which action $a$ to take for a target unit after observing its covariates $\\tilde{x}$ and predicted outcomes $\\hat{p}(\\tilde{y} \\mid \\tilde{x}, a)$. An example case is personalized medicine and the decision of which treatment to give to a patient. A common problem when learning these models from observational data is imbalance, that is, difference in treated/control covariate distributions, which is known to increase the upper bound of the expected ITE estimation error. We propose to assess the decision-making reliability by estimating the ITE model's Type S error rate, which is the probability of the model inferring the sign of the treatment effect wrong. Furthermore, we use the estimated reliability as a criterion for active learning, in order to collect new (possibly expensive) observations, instead of making a forced choice based on unreliable predictions. We demonstrate the effectiveness of this decision-making aware active learning in two decision-making tasks: in simulated data with binary outcomes and in a medical dataset with synthetic and continuous treatment outcomes.", "published": "2019-04-10T16:10:32Z", "authors": ["Iiris Sundin", "Peter Schulam", "Eero Siivola", "Aki Vehtari", "Suchi Saria", "Samuel Kaski"], "categories": ["stat.ML", "cs.LG"], "doi": "", "_query": "house price prediction machine learning", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/1911.08856v1", "title": "Learning Generalized Quasi-Geostrophic Models Using Deep Neural Numerical Models", "abstract": "We introduce a new strategy designed to help physicists discover hidden laws governing dynamical systems. We propose to use machine learning automatic differentiation libraries to develop hybrid numerical models that combine components based on prior physical knowledge with components based on neural networks. In these architectures, named Deep Neural Numerical Models (DNNMs), the neural network components are used as building-blocks then deployed for learning hidden variables of underlying physical laws governing dynamical systems. In this paper, we illustrate an application of DNNMs to upper ocean dynamics, more precisely the dynamics of a sea surface tracer, the Sea Surface Height (SSH). We develop an advection-based fully differentiable numerical scheme, where parts of the computations can be replaced with learnable ConvNets, and make connections with the single-layer Quasi-Geostrophic (QG) model, a baseline theory in physical oceanography developed decades ago.", "published": "2019-11-20T12:30:58Z", "authors": ["Redouane Lguensat", "Julien Le Sommer", "Sammy Metref", "Emmanuel Cosme", "Ronan Fablet"], "categories": ["cs.LG", "physics.ao-ph", "stat.ML"], "doi": "", "_query": "house price prediction machine learning", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/1804.04888v2", "title": "Scalable and Interpretable One-class SVMs with Deep Learning and Random Fourier features", "abstract": "One-class support vector machine (OC-SVM) for a long time has been one of the most effective anomaly detection methods and extensively adopted in both research as well as industrial applications. The biggest issue for OC-SVM is yet the capability to operate with large and high-dimensional datasets due to optimization complexity. Those problems might be mitigated via dimensionality reduction techniques such as manifold learning or autoencoder. However, previous work often treats representation learning and anomaly prediction separately. In this paper, we propose autoencoder based one-class support vector machine (AE-1SVM) that brings OC-SVM, with the aid of random Fourier features to approximate the radial basis kernel, into deep learning context by combining it with a representation learning architecture and jointly exploit stochastic gradient descent to obtain end-to-end training. Interestingly, this also opens up the possible use of gradient-based attribution methods to explain the decision making for anomaly detection, which has ever been challenging as a result of the implicit mappings between the input space and the kernel space. To the best of our knowledge, this is the first work to study the interpretability of deep learning in anomaly detection. We evaluate our method on a wide range of unsupervised anomaly detection tasks in which our end-to-end training architecture achieves a performance significantly better than the previous work using separate training.", "published": "2018-04-13T11:24:33Z", "authors": ["Minh-Nghia Nguyen", "Ngo Anh Vien"], "categories": ["cs.LG", "stat.ML"], "doi": "", "_query": "house price prediction machine learning", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/1108.3298v1", "title": "A Machine Learning Perspective on Predictive Coding with PAQ", "abstract": "PAQ8 is an open source lossless data compression algorithm that currently achieves the best compression rates on many benchmarks. This report presents a detailed description of PAQ8 from a statistical machine learning perspective. It shows that it is possible to understand some of the modules of PAQ8 and use this understanding to improve the method. However, intuitive statistical explanations of the behavior of other modules remain elusive. We hope the description in this report will be a starting point for discussions that will increase our understanding, lead to improvements to PAQ8, and facilitate a transfer of knowledge from PAQ8 to other machine learning methods, such a recurrent neural networks and stochastic memoizers. Finally, the report presents a broad range of new applications of PAQ to machine learning tasks including language modeling and adaptive text prediction, adaptive game playing, classification, and compression using features from the field of deep learning.", "published": "2011-08-16T18:06:29Z", "authors": ["Byron Knoll", "Nando de Freitas"], "categories": ["cs.LG", "cs.AI", "cs.CV", "cs.IR", "stat.ML"], "doi": "", "_query": "house price prediction machine learning", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2404.05178v1", "title": "Estimating granular house price distributions in the Australian market using Gaussian mixtures", "abstract": "A new methodology is proposed to approximate the time-dependent house price distribution at a fine regional scale using Gaussian mixtures. The means, variances and weights of the mixture components are related to time, location and dwelling type through a non linear function trained by a deep functional approximator. Price indices are derived as means, medians, quantiles or other functions of the estimated distributions. Price densities for larger regions, such as a city, are calculated via a weighted sum of the component density functions. The method is applied to a data set covering all of Australia at a fine spatial and temporal resolution. In addition to enabling a detailed exploration of the data, the proposed index yields lower prediction errors in the practical task of individual dwelling price projection from previous sales values within the three major Australian cities. The estimated quantiles are also found to be well calibrated empirically, capturing the complexity of house price distributions.", "published": "2024-04-08T04:01:43Z", "authors": ["Willem P Sijp", "Anastasios Panagiotelis"], "categories": ["econ.EM"], "doi": "", "_query": "house price prediction machine learning", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2103.03703v5", "title": "Semi-Supervised Federated Peer Learning for Skin Lesion Classification", "abstract": "Globally, Skin carcinoma is among the most lethal diseases. Millions of people are diagnosed with this cancer every year. Sill, early detection can decrease the medication cost and mortality rate substantially. The recent improvement in automated cancer classification using deep learning methods has reached a human-level performance requiring a large amount of annotated data assembled in one location, yet, finding such conditions usually is not feasible. Recently, federated learning (FL) has been proposed to train decentralized models in a privacy-preserved fashion depending on labeled data at the client-side, which is usually not available and costly. To address this, we propose \\verb!FedPerl!, a semi-supervised federated learning method. Our method is inspired by peer learning from educational psychology and ensemble averaging from committee machines. FedPerl builds communities based on clients' similarities. Then it encourages communities members to learn from each other to generate more accurate pseudo labels for the unlabeled data. We also proposed the peer anonymization (PA) technique to anonymize clients. As a core component of our method, PA is orthogonal to other methods without additional complexity and reduces the communication cost while enhancing performance. Finally, we propose a dynamic peer-learning policy that controls the learning stream to avoid any degradation in the performance, especially for individual clients. Our experimental setup consists of 71,000 skin lesion images collected from 5 publicly available datasets. We test our method in four different scenarios in SSFL. With few annotated data, FedPerl is on par with a state-of-the-art method in skin lesion classification in the standard setup while outperforming SSFLs and the baselines by 1.8% and 15.8%, respectively. Also, it generalizes better to unseen clients while being less sensitive to noisy ones.", "published": "2021-03-05T14:26:15Z", "authors": ["Tariq Bdair", "Nassir Navab", "Shadi Albarqouni"], "categories": ["cs.CV"], "doi": "", "_query": "house price prediction machine learning", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2412.13847v2", "title": "A Concept-Centric Approach to Multi-Modality Learning", "abstract": "Humans possess a remarkable ability to acquire knowledge efficiently and apply it across diverse modalities through a coherent and shared understanding of the world. Inspired by this cognitive capability, we introduce a concept-centric multi-modality learning framework built around a modality-agnostic concept space that captures structured, abstract knowledge, alongside a set of modality-specific projection models that map raw inputs onto this shared space. The concept space is decoupled from any specific modality and serves as a repository of universally applicable knowledge. Once learned, the knowledge embedded in the concept space enables more efficient adaptation to new modalities, as projection models can align with existing conceptual representations rather than learning from scratch. This efficiency is empirically validated in our experiments, where the proposed framework exhibits faster convergence compared to baseline models. In addition, the framework's modular design supports seamless integration of new modalities, since projection models are trained independently yet produce unified outputs within the shared concept space. We evaluate the framework on two representative downstream tasks. While the focus is not on task-specific optimization, the framework attains comparable results with a smaller training footprint, no task-specific fine-tuning, and inference performed entirely within a shared space of learned concepts that offers interpretability. These findings point toward a promising direction for developing learning systems that operate in a manner more consistent with human cognitive processes.", "published": "2024-12-18T13:40:21Z", "authors": ["Yuchong Geng", "Ao Tang"], "categories": ["cs.AI", "cs.LG"], "doi": "", "_query": "house price prediction machine learning", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2608.27124v1", "title": "TRACE-CRC: Trajectory-Adaptive Conformal Risk Control for Multi-Step Channel State Information Prediction", "abstract": "Reliable prediction of time-varying channel state information (CSI) is essential for efficient wireless communication. Each CSI frame is a matrix-valued representation of the wireless channel response, and a sequence of CSI frames forms a temporal channel trajectory. Modern deep learning-based CSI predictors, however, often provide only point predictions and lack calibrated uncertainty estimates. This limitation is particularly problematic in multi-step CSI prediction, where the target is a sequence of future CSI matrices, and downstream decisions such as beamforming or scheduling may fail if any part of the predicted trajectory is unreliable. We propose trajectory-adaptive calibration and error profiling with conformal risk control (TRACE-CRC), a method for trajectory-aware uncertainty quantification in multi-step CSI prediction. TRACE-CRC constructs Frobenius-norm uncertainty balls around predicted CSI matrices and controls the risk that at least one future frame is uncovered. Instead of calibrating each future step independently, TRACE-CRC combines future-step-dependent error profiling, trajectory difficulty stratification, and learn-then-test (LTT) risk control. Empirically, TRACE-CRC achieves reliable trajectory-level coverage with substantially smaller uncertainty balls than conservative multi-step corrections, while avoiding the trajectory undercoverage of compact stepwise and adaptive conformal baselines.", "published": "2026-08-27T13:39:00Z", "authors": ["Kiarash Rezaei", "Mehdi Sattari", "Javad Aliakbari", "Tommy Svensson", "Paolo Monti", "Carlos Natalino"], "categories": ["cs.LG", "stat.ML"], "doi": "", "_query": "house price prediction machine learning", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2101.11105v2", "title": "A Taylor Based Sampling Scheme for Machine Learning in Computational Physics", "abstract": "Machine Learning (ML) is increasingly used to construct surrogate models for physical simulations. We take advantage of the ability to generate data using numerical simulations programs to train ML models better and achieve accuracy gain with no performance cost. We elaborate a new data sampling scheme based on Taylor approximation to reduce the error of a Deep Neural Network (DNN) when learning the solution of an ordinary differential equations (ODE) system.", "published": "2021-01-20T12:56:09Z", "authors": ["Paul Novello", "Gaël Poëtte", "David Lugato", "Pietro Congedo"], "categories": ["physics.comp-ph", "math-ph", "stat.ML"], "doi": "", "_query": "house price prediction machine learning", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/1903.00092v2", "title": "Optimal Algorithms for Ski Rental with Soft Machine-Learned Predictions", "abstract": "We consider a variant of the classic Ski Rental online algorithm with applications to machine learning. In our variant, we allow the skier access to a black-box machine-learning algorithm that provides an estimate of the probability that there will be at most a threshold number of ski-days. We derive a class of optimal randomized algorithms to determine the strategy that minimizes the worst-case expected competitive ratio for the skier given a prediction from the machine learning algorithm,and analyze the performance and robustness of these algorithms.", "published": "2019-02-28T22:34:46Z", "authors": ["Rohan Kodialam"], "categories": ["cs.LG", "cs.DS", "stat.ML"], "doi": "", "_query": "house price prediction machine learning", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2105.13599v2", "title": "Short-Term Stock Price-Trend Prediction Using Meta-Learning", "abstract": "Although conventional machine learning algorithms have been widely adopted for stock-price predictions in recent years, the massive volume of specific labeled data required are not always available. In contrast, meta-learning technology uses relatively small amounts of training data, called fast learners. Such methods are beneficial under conditions of limited data availability, which often obtain for trend prediction based on time-series data limited by sparse information. In this study, we consider short-term stock price prediction using a meta-learning framework with several convolutional neural networks, including the temporal convolution network, fully convolutional network, and residual neural network. We propose a sliding time horizon to label stocks according to their predicted price trends, referred to as called slope-detection labeling, using prediction labels including \"rise plus,\" \"rise,\" \"fall,\" and \"fall plus\". The effectiveness of the proposed meta-learning framework was evaluated by application to the S&P500. The experimental results show that the inclusion of the proposed meta-learning framework significantly improved both regular and balanced prediction accuracy and profitability.", "published": "2021-05-28T06:03:05Z", "authors": ["Shin-Hung Chang", "Cheng-Wen Hsu", "Hsing-Ying Li", "Wei-Sheng Zeng", "Jan-Ming Ho"], "categories": ["cs.LG"], "doi": "10.1109/SMC52423.2021.9658607", "_query": "house price prediction machine learning", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/1907.01992v1", "title": "Learning with Known Operators reduces Maximum Training Error Bounds", "abstract": "We describe an approach for incorporating prior knowledge into machine learning algorithms. We aim at applications in physics and signal processing in which we know that certain operations must be embedded into the algorithm. Any operation that allows computation of a gradient or sub-gradient towards its inputs is suited for our framework. We derive a maximal error bound for deep nets that demonstrates that inclusion of prior knowledge results in its reduction. Furthermore, we also show experimentally that known operators reduce the number of free parameters. We apply this approach to various tasks ranging from CT image reconstruction over vessel segmentation to the derivation of previously unknown imaging algorithms. As such the concept is widely applicable for many researchers in physics, imaging, and signal processing. We assume that our analysis will support further investigation of known operators in other fields of physics, imaging, and signal processing.", "published": "2019-07-03T15:35:16Z", "authors": ["Andreas K. Maier", "Christopher Syben", "Bernhard Stimpel", "Tobias Würfl", "Mathis Hoffmann", "Frank Schebesch", "Weilin Fu", "Leonid Mill", "Lasse Kling", "Silke Christiansen"], "categories": ["cs.LG", "cs.CV", "physics.med-ph", "stat.ML"], "doi": "10.1038/s42256-019-0077-5", "_query": "house price prediction machine learning", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2010.08162v2", "title": "SidechainNet: An All-Atom Protein Structure Dataset for Machine Learning", "abstract": "Despite recent advancements in deep learning methods for protein structure prediction and representation, little focus has been directed at the simultaneous inclusion and prediction of protein backbone and sidechain structure information. We present SidechainNet, a new dataset that directly extends the ProteinNet dataset. SidechainNet includes angle and atomic coordinate information capable of describing all heavy atoms of each protein structure. In this paper, we provide background information on the availability of protein structure data and the significance of ProteinNet. Thereafter, we argue for the potentially beneficial inclusion of sidechain information through SidechainNet, describe the process by which we organize SidechainNet, and provide a software package (https://github.com/jonathanking/sidechainnet) for data manipulation and training with machine learning models.", "published": "2020-10-16T04:38:28Z", "authors": ["Jonathan E. King", "David Ryan Koes"], "categories": ["q-bio.BM", "cs.LG"], "doi": "", "_query": "house price prediction machine learning", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/1910.06521v1", "title": "Machine Learning for Generalizable Prediction of Flood Susceptibility", "abstract": "Flooding is a destructive and dangerous hazard and climate change appears to be increasing the frequency of catastrophic flooding events around the world. Physics-based flood models are costly to calibrate and are rarely generalizable across different river basins, as model outputs are sensitive to site-specific parameters and human-regulated infrastructure. In contrast, statistical models implicitly account for such factors through the data on which they are trained. Such models trained primarily from remotely-sensed Earth observation data could reduce the need for extensive in-situ measurements. In this work, we develop generalizable, multi-basin models of river flooding susceptibility using geographically-distributed data from the USGS stream gauge network. Machine learning models are trained in a supervised framework to predict two measures of flood susceptibility from a mix of river basin attributes, impervious surface cover information derived from satellite imagery, and historical records of rainfall and stream height. We report prediction performance of multiple models using precision-recall curves, and compare with performance of naive baselines. This work on multi-basin flood prediction represents a step in the direction of making flood prediction accessible to all at-risk communities.", "published": "2019-10-15T04:31:39Z", "authors": ["Chelsea Sidrane", "Dylan J Fitzpatrick", "Andrew Annex", "Diane O'Donoghue", "Yarin Gal", "Piotr Biliński"], "categories": ["cs.LG", "stat.AP", "stat.ML"], "doi": "", "_query": "house price prediction machine learning", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/1909.09804v1", "title": "Challenges of Privacy-Preserving Machine Learning in IoT", "abstract": "The Internet of Things (IoT) will be a main data generation infrastructure for achieving better system intelligence. However, the extensive data collection and processing in IoT also engender various privacy concerns. This paper provides a taxonomy of the existing privacy-preserving machine learning approaches developed in the context of cloud computing and discusses the challenges of applying them in the context of IoT. Moreover, we present a privacy-preserving inference approach that runs a lightweight neural network at IoT objects to obfuscate the data before transmission and a deep neural network in the cloud to classify the obfuscated data. Evaluation based on the MNIST dataset shows satisfactory performance.", "published": "2019-09-21T10:12:48Z", "authors": ["Mengyao Zheng", "Dixing Xu", "Linshan Jiang", "Chaojie Gu", "Rui Tan", "Peng Cheng"], "categories": ["cs.CR", "cs.LG", "stat.ML"], "doi": "10.1145/3363347.3363357", "_query": "house price prediction machine learning", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2512.04396v1", "title": "Sarcasm Detection on Reddit Using Classical Machine Learning and Feature Engineering", "abstract": "Sarcasm is common in online discussions, yet difficult for machines to identify because the intended meaning often contradicts the literal wording. In this work, I study sarcasm detection using only classical machine learning methods and explicit feature engineering, without relying on neural networks or context from parent comments. Using a 100,000-comment subsample of the Self-Annotated Reddit Corpus (SARC 2.0), I combine word-level and character-level TF-IDF features with simple stylistic indicators. Four models are evaluated: logistic regression, a linear SVM, multinomial Naive Bayes, and a random forest. Naive Bayes and logistic regression perform the strongest, achieving F1-scores around 0.57 for sarcastic comments. Although the lack of conversational context limits performance, the results offer a clear and reproducible baseline for sarcasm detection using lightweight and interpretable methods.", "published": "2025-12-04T02:41:08Z", "authors": ["Subrata Karmaker"], "categories": ["cs.CL", "cs.LG"], "doi": "10.18517/ijods.6.2.85-93.2025", "_query": "house price prediction machine learning", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2012.00419v1", "title": "Machine Learning Systems in the IoT: Trustworthiness Trade-offs for Edge Intelligence", "abstract": "Machine learning systems (MLSys) are emerging in the Internet of Things (IoT) to provision edge intelligence, which is paving our way towards the vision of ubiquitous intelligence. However, despite the maturity of machine learning systems and the IoT, we are facing severe challenges when integrating MLSys and IoT in practical context. For instance, many machine learning systems have been developed for large-scale production (e.g., cloud environments), but IoT introduces additional demands due to heterogeneous and resource-constrained devices and decentralized operation environment. To shed light on this convergence of MLSys and IoT, this paper analyzes the trade-offs by covering the latest developments (up to 2020) on scaling and distributing ML across cloud, edge, and IoT devices. We position machine learning systems as a component of the IoT, and edge intelligence as a socio-technical system. On the challenges of designing trustworthy edge intelligence, we advocate a holistic design approach that takes multi-stakeholder concerns, design requirements and trade-offs into consideration, and highlight the future research opportunities in edge intelligence.", "published": "2020-12-01T11:42:34Z", "authors": ["Wiebke Toussaint", "Aaron Yi Ding"], "categories": ["cs.LG", "cs.CY"], "doi": "10.1109/CogMI50398.2020.00030", "_query": "house price prediction machine learning", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/1811.02659v2", "title": "Machine Learning Algorithms for Classification of Microcirculation Images from Septic and Non-Septic Patients", "abstract": "Sepsis is a life-threatening disease and one of the major causes of death in hospitals. Imaging of microcirculatory dysfunction is a promising approach for automated diagnosis of sepsis. We report a machine learning classifier capable of distinguishing non-septic and septic images from dark field microcirculation videos of patients. The classifier achieves an accuracy of 89.45%. The area under the receiver operating characteristics of the classifier was 0.92, the precision was 0.92 and the recall was 0.84. Codes representing the learned feature space of trained classifier were visualized using t-SNE embedding and were separable and distinguished between images from critically ill and non-septic patients. Using an unsupervised convolutional autoencoder, independent of the clinical diagnosis, we also report clustering of learned features from a compressed representation associated with healthy images and those with microcirculatory dysfunction. The feature space used by our trained classifier to distinguish between images from septic and non-septic patients has potential diagnostic application.", "published": "2018-10-24T15:34:18Z", "authors": ["Perikumar Javia", "Aman Rana", "Nathan Shapiro", "Pratik Shah"], "categories": ["cs.CV", "cs.LG", "stat.ML"], "doi": "10.1109/ICMLA.2018.00097", "_query": "house price prediction machine learning", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/1711.00001v2", "title": "Gene Ontology (GO) Prediction using Machine Learning Methods", "abstract": "We applied machine learning to predict whether a gene is involved in axon regeneration. We extracted 31 features from different databases and trained five machine learning models. Our optimal model, a Random Forest Classifier with 50 submodels, yielded a test score of 85.71%, which is 4.1% higher than the baseline score. We concluded that our models have some predictive capability. Similar methodology and features could be applied to predict other Gene Ontology (GO) terms.", "published": "2017-10-30T19:02:13Z", "authors": ["Haoze Wu", "Yangyu Zhou"], "categories": ["cs.LG", "cs.CE", "q-bio.QM", "stat.ML"], "doi": "", "_query": "house price prediction machine learning", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2512.01212v1", "title": "A Comparative Study of Machine Learning Algorithms for Electricity Price Forecasting with LIME-Based Interpretability", "abstract": "With the rapid development of electricity markets, price volatility has significantly increased, making accurate forecasting crucial for power system operations and market decisions. Traditional linear models cannot capture the complex nonlinear characteristics of electricity pricing, necessitating advanced machine learning approaches. This study compares eight machine learning models using Spanish electricity market data, integrating consumption, generation, and meteorological variables. The models evaluated include linear regression, ridge regression, decision tree, KNN, random forest, gradient boosting, SVR, and XGBoost. Results show that KNN achieves the best performance with R^2 of 0.865, MAE of 3.556, and RMSE of 5.240. To enhance interpretability, LIME analysis reveals that meteorological factors and supply-demand indicators significantly influence price fluctuations through nonlinear relationships. This work demonstrates the effectiveness of machine learning models in electricity price forecasting while improving decision transparency through interpretability analysis.", "published": "2025-12-01T02:51:41Z", "authors": ["Xuanyi Zhao", "Jiawen Ding", "Xueting Huang", "Yibo Zhang"], "categories": ["cs.LG"], "doi": "", "_query": "house price prediction machine learning", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2006.06945v1", "title": "Smartphone Transportation Mode Recognition Using a Hierarchical Machine Learning Classifier and Pooled Features From Time and Frequency Domains", "abstract": "This paper develops a novel two-layer hierarchical classifier that increases the accuracy of traditional transportation mode classification algorithms. This paper also enhances classification accuracy by extracting new frequency domain features. Many researchers have obtained these features from global positioning system data; however, this data was excluded in this paper, as the system use might deplete the smartphone's battery and signals may be lost in some areas. Our proposed two-layer framework differs from previous classification attempts in three distinct ways: 1) the outputs of the two layers are combined using Bayes' rule to choose the transportation mode with the largest posterior probability; 2) the proposed framework combines the new extracted features with traditionally used time domain features to create a pool of features; and 3) a different subset of extracted features is used in each layer based on the classified modes. Several machine learning techniques were used, including k-nearest neighbor, classification and regression tree, support vector machine, random forest, and a heterogeneous framework of random forest and support vector machine. Results show that the classification accuracy of the proposed framework outperforms traditional approaches. Transforming the time domain features to the frequency domain also adds new features in a new space and provides more control on the loss of information. Consequently, combining the time domain and the frequency domain features in a large pool and then choosing the best subset results in higher accuracy than using either domain alone. The proposed two-layer classifier obtained a maximum classification accuracy of 97.02%.", "published": "2020-06-12T04:56:52Z", "authors": ["Huthaifa I. Ashqar", "Mohammed H. Almannaa", "Mohammed Elhenawy", "Hesham A. Rakha", "Leanna House"], "categories": ["cs.LG", "cs.CY", "physics.soc-ph", "stat.ML"], "doi": "10.1109/TITS.2018.2817658", "_query": "house price prediction machine learning", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/1912.12557v3", "title": "Active Learning in Video Tracking", "abstract": "Active learning methods, like uncertainty sampling, combined with probabilistic prediction techniques have achieved success in various problems like image classification and text classification. For more complex multivariate prediction tasks, the relationships between labels play an important role in designing structured classifiers with better performance. However, computational time complexity limits prevalent probabilistic methods from effectively supporting active learning. Specifically, while non-probabilistic methods based on structured support vector machines can be tractably applied to predicting bipartite matchings, conditional random fields are intractable for these structures. We propose an adversarial approach for active learning with structured prediction domains that is tractable for matching. We evaluate this approach algorithmically in an important structured prediction problems: object tracking in videos. We demonstrate better accuracy and computational efficiency for our proposed method.", "published": "2019-12-29T00:42:06Z", "authors": ["Sima Behpour"], "categories": ["cs.LG", "cs.CV", "stat.ML"], "doi": "", "_query": "house price prediction machine learning", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/1606.05572v1", "title": "Learning Interpretable Musical Compositional Rules and Traces", "abstract": "Throughout music history, theorists have identified and documented interpretable rules that capture the decisions of composers. This paper asks, \"Can a machine behave like a music theorist?\" It presents MUS-ROVER, a self-learning system for automatically discovering rules from symbolic music. MUS-ROVER performs feature learning via $n$-gram models to extract compositional rules --- statistical patterns over the resulting features. We evaluate MUS-ROVER on Bach's (SATB) chorales, demonstrating that it can recover known rules, as well as identify new, characteristic patterns for further study. We discuss how the extracted rules can be used in both machine and human composition.", "published": "2016-06-17T15:58:24Z", "authors": ["Haizi Yu", "Lav R. Varshney", "Guy E. Garnett", "Ranjitha Kumar"], "categories": ["stat.ML", "cs.LG"], "doi": "", "_query": "house price prediction machine learning", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/1611.07634v1", "title": "Interpretation of Prediction Models Using the Input Gradient", "abstract": "State of the art machine learning algorithms are highly optimized to provide the optimal prediction possible, naturally resulting in complex models. While these models often outperform simpler more interpretable models by order of magnitudes, in terms of understanding the way the model functions, we are often facing a \"black box\". In this paper we suggest a simple method to interpret the behavior of any predictive model, both for regression and classification. Given a particular model, the information required to interpret it can be obtained by studying the partial derivatives of the model with respect to the input. We exemplify this insight by interpreting convolutional and multi-layer neural networks in the field of natural language processing.", "published": "2016-11-23T04:21:51Z", "authors": ["Yotam Hechtlinger"], "categories": ["stat.ML", "cs.LG"], "doi": "", "_query": "house price prediction machine learning", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2409.10046v1", "title": "Global Lightning-Ignited Wildfires Prediction and Climate Change Projections based on Explainable Machine Learning Models", "abstract": "Wildfires pose a significant natural disaster risk to populations and contribute to accelerated climate change. As wildfires are also affected by climate change, extreme wildfires are becoming increasingly frequent. Although they occur less frequently globally than those sparked by human activities, lightning-ignited wildfires play a substantial role in carbon emissions and account for the majority of burned areas in certain regions. While existing computational models, especially those based on machine learning, aim to predict lightning-ignited wildfires, they are typically tailored to specific regions with unique characteristics, limiting their global applicability. In this study, we present machine learning models designed to characterize and predict lightning-ignited wildfires on a global scale. Our approach involves classifying lightning-ignited versus anthropogenic wildfires, and estimating with high accuracy the probability of lightning to ignite a fire based on a wide spectrum of factors such as meteorological conditions and vegetation. Utilizing these models, we analyze seasonal and spatial trends in lightning-ignited wildfires shedding light on the impact of climate change on this phenomenon. We analyze the influence of various features on the models using eXplainable Artificial Intelligence (XAI) frameworks. Our findings highlight significant global differences between anthropogenic and lightning-ignited wildfires. Moreover, we demonstrate that, even over a short time span of less than a decade, climate changes have steadily increased the global risk of lightning-ignited wildfires. This distinction underscores the imperative need for dedicated predictive models and fire weather indices tailored specifically to each type of wildfire.", "published": "2024-09-16T07:19:08Z", "authors": ["Assaf Shmuel", "Teddy Lazebnik", "Oren Glickman", "Eyal Heifetz", "Colin Price"], "categories": ["cs.LG", "cs.IR", "physics.ao-ph"], "doi": "", "_query": "house price prediction machine learning", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2206.15079v1", "title": "Prediction of Dilatory Behavior in eLearning: A Comparison of Multiple Machine Learning Models", "abstract": "Procrastination, the irrational delay of tasks, is a common occurrence in online learning. Potential negative consequences include higher risk of drop-outs, increased stress, and reduced mood. Due to the rise of learning management systems and learning analytics, indicators of such behavior can be detected, enabling predictions of future procrastination and other dilatory behavior. However, research focusing on such predictions is scarce. Moreover, studies involving different types of predictors and comparisons between the predictive performance of various methods are virtually non-existent. In this study, we aim to fill these research gaps by analyzing the performance of multiple machine learning algorithms when predicting the delayed or timely submission of online assignments in a higher education setting with two categories of predictors: subjective, questionnaire-based variables and objective, log-data based indicators extracted from a learning management system. The results show that models with objective predictors consistently outperform models with subjective predictors, and a combination of both variable types perform slightly better. For each of these three options, a different approach prevailed (Gradient Boosting Machines for the subjective, Bayesian multilevel models for the objective, and Random Forest for the combined predictors). We conclude that careful attention should be paid to the selection of predictors and algorithms before implementing such models in learning management systems.", "published": "2022-06-30T07:24:08Z", "authors": ["Christof Imhof", "Ioan-Sorin Comsa", "Martin Hlosta", "Behnam Parsaeifard", "Ivan Moser", "Per Bergamin"], "categories": ["stat.ML", "cs.CY", "cs.LG"], "doi": "", "_query": "house price prediction machine learning", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/1907.11882v6", "title": "Selective Machine Learning of the Average Treatment Effect with an Invalid Instrumental Variable", "abstract": "Instrumental variable methods have been widely used to identify causal effects in the presence of unmeasured confounding. A key identification condition known as the exclusion restriction states that the instrument cannot have a direct effect on the outcome which is not mediated by the exposure in view. In the health and social sciences, such an assumption is often not credible. To address this concern, we consider identification conditions of the population average treatment effect with an invalid instrumental variable which does not satisfy the exclusion restriction, and derive the efficient influence function targeting the identifying functional under a nonparametric observed data model. We propose a novel multiply robust locally efficient estimator of the average treatment effect that is consistent in the union of multiple parametric nuisance models, as well as a multiply debiased machine learning estimator for which the nuisance parameters are estimated using generic machine learning methods, that effectively exploit various forms of linear or nonlinear structured sparsity in the nuisance parameter space. When one cannot be confident that any of these machine learners is consistent at sufficiently fast rates to ensure $\\surd{n}$-consistency for the average treatment effect, we introduce a new criteria for selective machine learning which leverages the multiple robustness property in order to ensure small bias. The proposed methods are illustrated through extensive simulations and a data analysis evaluating the causal effect of 401(k) participation on savings.", "published": "2019-07-27T09:44:48Z", "authors": ["Baoluo Sun", "Yifan Cui", "Eric Tchetgen Tchetgen"], "categories": ["stat.ME"], "doi": "", "_query": "house price prediction machine learning", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2010.07259v1", "title": "Privacy-Preserving Object Detection & Localization Using Distributed Machine Learning: A Case Study of Infant Eyeblink Conditioning", "abstract": "Distributed machine learning is becoming a popular model-training method due to privacy, computational scalability, and bandwidth capacities. In this work, we explore scalable distributed-training versions of two algorithms commonly used in object detection. A novel distributed training algorithm using Mean Weight Matrix Aggregation (MWMA) is proposed for Linear Support Vector Machine (L-SVM) object detection based in Histogram of Orientated Gradients (HOG). In addition, a novel Weighted Bin Aggregation (WBA) algorithm is proposed for distributed training of Ensemble of Regression Trees (ERT) landmark localization. Both algorithms do not restrict the location of model aggregation and allow custom architectures for model distribution. For this work, a Pool-Based Local Training and Aggregation (PBLTA) architecture for both algorithms is explored. The application of both algorithms in the medical field is examined using a paradigm from the fields of psychology and neuroscience - eyeblink conditioning with infants - where models need to be trained on facial images while protecting participant privacy. Using distributed learning, models can be trained without sending image data to other nodes. The custom software has been made available for public use on GitHub: https://github.com/SLWZwaard/DMT. Results show that the aggregation of models for the HOG algorithm using MWMA not only preserves the accuracy of the model but also allows for distributed learning with an accuracy increase of 0.9% compared with traditional learning. Furthermore, WBA allows for ERT model aggregation with an accuracy increase of 8% when compared to single-node models.", "published": "2020-10-14T17:33:28Z", "authors": ["Stefan Zwaard", "Henk-Jan Boele", "Hani Alers", "Christos Strydis", "Casey Lew-Williams", "Zaid Al-Ars"], "categories": ["cs.LG", "cs.CR", "cs.CV"], "doi": "", "_query": "house price prediction machine learning", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2112.01079v1", "title": "Who will dropout from university? Academic risk prediction based on interpretable machine learning", "abstract": "In the institutional research mode, in order to explore which characteristics are the best indicators for predicting academic risk from the student behavior data sets that have high-dimensional, unbalanced classified small sample, it transforms the academic risk prediction of college students into a binary classification task. It predicts academic risk based on the LightGBM model and the interpretable machine learning method of Shapley value. The simulation results show that from the global perspective of the prediction model, characteristics such as the quality of academic partners, the seating position in classroom, the dormitory study atmosphere, the English scores of the college entrance examination, the quantity of academic partners, the addiction level of video games, the mobility of academic partners, and the degree of truancy are the best 8 predictors for academic risk. It is contrary to intuition that characteristics such as living in campus or not, work-study, lipstick addiction, student leader or not, lover amount, and smoking have little correlation with university academic risk in this experiment. From the local perspective of the sample, the factors affecting academic risk vary from person to person. It can perform personalized interpretable analysis through Shapley values, which cannot be done by traditional mathematical statistical prediction models. The academic contributions of this research are mainly in two aspects: First, the learning interaction networks is proposed for the first time, so that social behavior can be used to compensate for the one-sided individual behavior and improve the performance of academic risk prediction. Second, the introduction of Shapley value calculation makes machine learning that lacks a clear reasoning process visualized, and provides intuitive decision support for education managers.", "published": "2021-12-02T09:43:31Z", "authors": ["Shudong Yang"], "categories": ["cs.LG", "stat.ML"], "doi": "", "_query": "house price prediction machine learning", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/1911.02792v1", "title": "Machine learning for molecular simulation", "abstract": "Machine learning (ML) is transforming all areas of science. The complex and time-consuming calculations in molecular simulations are particularly suitable for a machine learning revolution and have already been profoundly impacted by the application of existing ML methods. Here we review recent ML methods for molecular simulation, with particular focus on (deep) neural networks for the prediction of quantum-mechanical energies and forces, coarse-grained molecular dynamics, the extraction of free energy surfaces and kinetics and generative network approaches to sample molecular equilibrium structures and compute thermodynamics. To explain these methods and illustrate open methodological problems, we review some important principles of molecular physics and describe how they can be incorporated into machine learning structures. Finally, we identify and describe a list of open challenges for the interface between ML and molecular simulation.", "published": "2019-11-07T08:14:18Z", "authors": ["Frank Noé", "Alexandre Tkatchenko", "Klaus-Robert Müller", "Cecilia Clementi"], "categories": ["physics.chem-ph", "cs.LG", "physics.comp-ph", "quant-ph"], "doi": "10.1146/annurev-physchem-042018-052331", "_query": "house price prediction machine learning", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2405.06660v1", "title": "AI and Machine Learning for Next Generation Science Assessments", "abstract": "This chapter focuses on the transformative role of Artificial Intelligence (AI) and Machine Learning (ML) in science assessments. The paper begins with a discussion of the Framework for K-12 Science Education, which calls for a shift from conceptual learning to knowledge-in-use. This shift necessitates the development of new types of assessments that align with the Framework's three dimensions: science and engineering practices, disciplinary core ideas, and crosscutting concepts. The paper further highlights the limitations of traditional assessment methods like multiple-choice questions, which often fail to capture the complexities of scientific thinking and three-dimensional learning in science. It emphasizes the need for performance-based assessments that require students to engage in scientific practices like modeling, explanation, and argumentation. The paper achieves three major goals: reviewing the current state of ML-based assessments in science education, introducing a framework for scoring accuracy in ML-based automatic assessments, and discussing future directions and challenges. It delves into the evolution of ML-based automatic scoring systems, discussing various types of ML, like supervised, unsupervised, and semi-supervised learning. These systems can provide timely and objective feedback, thus alleviating the burden on teachers. The paper concludes by exploring pre-trained models like BERT and finetuned ChatGPT, which have shown promise in assessing students' written responses effectively.", "published": "2024-04-23T01:39:20Z", "authors": ["Xiaoming Zhai"], "categories": ["physics.ed-ph", "cs.AI", "cs.CY", "cs.LG"], "doi": "", "_query": "house price prediction machine learning", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2005.09787v1", "title": "Self-Updating Models with Error Remediation", "abstract": "Many environments currently employ machine learning models for data processing and analytics that were built using a limited number of training data points. Once deployed, the models are exposed to significant amounts of previously-unseen data, not all of which is representative of the original, limited training data. However, updating these deployed models can be difficult due to logistical, bandwidth, time, hardware, and/or data sensitivity constraints. We propose a framework, Self-Updating Models with Error Remediation (SUMER), in which a deployed model updates itself as new data becomes available. SUMER uses techniques from semi-supervised learning and noise remediation to iteratively retrain a deployed model using intelligently-chosen predictions from the model as the labels for new training iterations. A key component of SUMER is the notion of error remediation as self-labeled data can be susceptible to the propagation of errors. We investigate the use of SUMER across various data sets and iterations. We find that self-updating models (SUMs) generally perform better than models that do not attempt to self-update when presented with additional previously-unseen data. This performance gap is accentuated in cases where there is only limited amounts of initial training data. We also find that the performance of SUMER is generally better than the performance of SUMs, demonstrating a benefit in applying error remediation. Consequently, SUMER can autonomously enhance the operational capabilities of existing data processing systems by intelligently updating models in dynamic environments.", "published": "2020-05-19T23:09:38Z", "authors": ["Justin E. Doak", "Michael R. Smith", "Joey B. Ingram"], "categories": ["cs.LG", "stat.ML"], "doi": "10.1117/12.2563843", "_query": "house price prediction machine learning", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2107.12808v2", "title": "Open-Ended Learning Leads to Generally Capable Agents", "abstract": "In this work we create agents that can perform well beyond a single, individual task, that exhibit much wider generalisation of behaviour to a massive, rich space of challenges. We define a universe of tasks within an environment domain and demonstrate the ability to train agents that are generally capable across this vast space and beyond. The environment is natively multi-agent, spanning the continuum of competitive, cooperative, and independent games, which are situated within procedurally generated physical 3D worlds. The resulting space is exceptionally diverse in terms of the challenges posed to agents, and as such, even measuring the learning progress of an agent is an open research problem. We propose an iterative notion of improvement between successive generations of agents, rather than seeking to maximise a singular objective, allowing us to quantify progress despite tasks being incomparable in terms of achievable rewards. We show that through constructing an open-ended learning process, which dynamically changes the training task distributions and training objectives such that the agent never stops learning, we achieve consistent learning of new behaviours. The resulting agent is able to score reward in every one of our humanly solvable evaluation levels, with behaviour generalising to many held-out points in the universe of tasks. Examples of this zero-shot generalisation include good performance on Hide and Seek, Capture the Flag, and Tag. Through analysis and hand-authored probe tasks we characterise the behaviour of our agent, and find interesting emergent heuristic behaviours such as trial-and-error experimentation, simple tool use, option switching, and cooperation. Finally, we demonstrate that the general capabilities of this agent could unlock larger scale transfer of behaviour through cheap finetuning.", "published": "2021-07-27T13:30:07Z", "authors": [" Open Ended Learning Team", "Adam Stooke", "Anuj Mahajan", "Catarina Barros", "Charlie Deck", "Jakob Bauer", "Jakub Sygnowski", "Maja Trebacz", "Max Jaderberg", "Michael Mathieu", "Nat McAleese", "Nathalie Bradley-Schmieg", "Nathaniel Wong", "Nicolas Porcel", "Roberta Raileanu", "Steph Hughes-Fitt", "Valentin Dalibard", "Wojciech Marian Czarnecki"], "categories": ["cs.LG", "cs.AI", "cs.MA"], "doi": "", "_query": "house price prediction machine learning", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2012.02530v2", "title": "Logic Synthesis Meets Machine Learning: Trading Exactness for Generalization", "abstract": "Logic synthesis is a fundamental step in hardware design whose goal is to find structural representations of Boolean functions while minimizing delay and area. If the function is completely-specified, the implementation accurately represents the function. If the function is incompletely-specified, the implementation has to be true only on the care set. While most of the algorithms in logic synthesis rely on SAT and Boolean methods to exactly implement the care set, we investigate learning in logic synthesis, attempting to trade exactness for generalization. This work is directly related to machine learning where the care set is the training set and the implementation is expected to generalize on a validation set. We present learning incompletely-specified functions based on the results of a competition conducted at IWLS 2020. The goal of the competition was to implement 100 functions given by a set of care minterms for training, while testing the implementation using a set of validation minterms sampled from the same function. We make this benchmark suite available and offer a detailed comparative analysis of the different approaches to learning", "published": "2020-12-04T11:23:01Z", "authors": ["Shubham Rai", "Walter Lau Neto", "Yukio Miyasaka", "Xinpei Zhang", "Mingfei Yu", "Qingyang Yi Masahiro Fujita", "Guilherme B. Manske", "Matheus F. Pontes", "Leomar S. da Rosa Junior", "Marilton S. de Aguiar", "Paulo F. Butzen", "Po-Chun Chien", "Yu-Shan Huang", "Hoa-Ren Wang", "Jie-Hong R. Jiang", "Jiaqi Gu", "Zheng Zhao", "Zixuan Jiang", "David Z. Pan", "Brunno A. de Abreu", "Isac de Souza Campos", "Augusto Berndt", "Cristina Meinhardt", "Jonata T. Carvalho", "Mateus Grellert", "Sergio Bampi", "Aditya Lohana", "Akash Kumar", "Wei Zeng", "Azadeh Davoodi", "Rasit O. Topaloglu", "Yuan Zhou", "Jordan Dotzel", "Yichi Zhang", "Hanyu Wang", "Zhiru Zhang", "Valerio Tenace", "Pierre-Emmanuel Gaillardon", "Alan Mishchenko", "Satrajit Chatterjee"], "categories": ["cs.LG"], "doi": "", "_query": "house price prediction machine learning", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/1612.04898v2", "title": "Efficient Distributed Semi-Supervised Learning using Stochastic Regularization over Affinity Graphs", "abstract": "We describe a computationally efficient, stochastic graph-regularization technique that can be utilized for the semi-supervised training of deep neural networks in a parallel or distributed setting. We utilize a technique, first described in [13] for the construction of mini-batches for stochastic gradient descent (SGD) based on synthesized partitions of an affinity graph that are consistent with the graph structure, but also preserve enough stochasticity for convergence of SGD to good local minima. We show how our technique allows a graph-based semi-supervised loss function to be decomposed into a sum over objectives, facilitating data parallelism for scalable training of machine learning models. Empirical results indicate that our method significantly improves classification accuracy compared to the fully-supervised case when the fraction of labeled data is low, and in the parallel case, achieves significant speed-up in terms of wall-clock time to convergence. We show the results for both sequential and distributed-memory semi-supervised DNN training on a speech corpus.", "published": "2016-12-15T01:00:23Z", "authors": ["Sunil Thulasidasan", "Jeffrey Bilmes", "Garrett Kenyon"], "categories": ["stat.ML", "cs.DC", "cs.LG"], "doi": "", "_query": "house price prediction machine learning", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2106.05799v1", "title": "Hybrid Machine Learning Forecasts for the UEFA EURO 2020", "abstract": "Three state-of-the-art statistical ranking methods for forecasting football matches are combined with several other predictors in a hybrid machine learning model. Namely an ability estimate for every team based on historic matches; an ability estimate for every team based on bookmaker consensus; average plus-minus player ratings based on their individual performances in their home clubs and national teams; and further team covariates (e.g., market value, team structure) and country-specific socio-economic factors (population, GDP). The proposed combined approach is used for learning the number of goals scored in the matches from the four previous UEFA EUROs 2004-2016 and then applied to current information to forecast the upcoming UEFA EURO 2020. Based on the resulting estimates, the tournament is simulated repeatedly and winning probabilities are obtained for all teams. A random forest model favors the current World Champion France with a winning probability of 14.8% before England (13.5%) and Spain (12.3%). Additionally, we provide survival probabilities for all teams and at all tournament stages.", "published": "2021-06-07T21:56:49Z", "authors": ["Andreas Groll", "Lars Magnus Hvattum", "Christophe Ley", "Franziska Popp", "Gunther Schauberger", "Hans Van Eetvelde", "Achim Zeileis"], "categories": ["cs.LG", "stat.AP"], "doi": "", "_query": "house price prediction machine learning", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2306.11157v2", "title": "Human Limits in Machine Learning: Prediction of Plant Phenotypes Using Soil Microbiome Data", "abstract": "The preservation of soil health is a critical challenge in the 21st century due to its significant impact on agriculture, human health, and biodiversity. We provide the first deep investigation of the predictive potential of machine learning models to understand the connections between soil and biological phenotypes. We investigate an integrative framework performing accurate machine learning-based prediction of plant phenotypes from biological, chemical, and physical properties of the soil via two models: random forest and Bayesian neural network. We show that prediction is improved when incorporating environmental features like soil physicochemical properties and microbial population density into the models, in addition to the microbiome information. Exploring various data preprocessing strategies confirms the significant impact of human decisions on predictive performance. We show that the naive total sum scaling normalization that is commonly used in microbiome research is not the optimal strategy to maximize predictive power. Also, we find that accurately defined labels are more important than normalization, taxonomic level or model characteristics. In cases where humans are unable to classify samples accurately, machine learning model performance is limited. Lastly, we provide domain scientists via a full model selection decision tree to identify the human choices that optimize model prediction power. Our work is accompanied by open source reproducible scripts (https://github.com/solislemuslab/soil-microbiome-nn) for maximum outreach among the microbiome research community.", "published": "2023-06-19T20:52:37Z", "authors": ["Rosa Aghdam", "Xudong Tang", "Shan Shan", "Richard Lankau", "Claudia Solís-Lemus"], "categories": ["stat.ML", "cs.LG", "stat.AP"], "doi": "", "_query": "house price prediction machine learning", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2605.00839v1", "title": "2026 Roadmap on Artificial Intelligence and Machine Learning for Smart Manufacturing", "abstract": "The evolution of artificial intelligence (AI) and machine learning (ML) is reshaping smart manufacturing by providing new capabilities for efficiency, adaptability, and autonomy across industrial value chains. However, the deployment of AI and ML in industrial settings still faces critical challenges, including the complexity of industrial big data, effective data management, integration with heterogeneous sensing and control systems, and the demand for trustworthy, explainable, and reliable operation in high-stakes industrial environments. In this roadmap, we present a comprehensive perspective on the foundations, applications, and emerging directions of AI and ML in smart manufacturing. It is structured in three parts. The first highlights the foundations and trends that frame the evolution of AI in smart manufacturing. The second focuses on key topics where AI is already enabling advances, including industrial big data analytics, advanced sensing and perception, autonomous systems, additive and laser-based manufacturing, digital twins, robotics, supply chain and logistics optimization, and sustainable manufacturing. The third section explores non-traditional ML approaches that are opening new frontiers, such as physics-informed AI, generative AI, semantic AI, advanced digital twins, explainable AI, RAMS, data-centric metrology, LLMs, and foundation models for highly connected and complex manufacturing systems. By identifying both opportunities and remaining barriers across these areas, this roadmap outlines the advances needed in methods, integration strategies, and industrial adoption. We hope this roadmap will serve as a guide for researchers, engineers, and practitioners to accelerate innovation, align academic and industrial priorities, and ensure that AI-driven smart manufacturing delivers reliable, sustainable, and scalable impact for the future of manufacturing ecosystems.", "published": "2026-04-05T17:34:26Z", "authors": ["Jay Lee", "Hanqi Su", "Marco Macchi", "Adalberto Polenghi", "Wei Wu", "Zhiheng Zhao", "George Q. Huang", "Kiva Allgood", "Devendra Jain", "Benedikt Gieger", "Vibhor Pandhare", "Soumyabrata Bhattacharjee", "Ram Mohril", "Lingbao Kong", "Qiyuan Wang", "Xinlan Tang", "Sungjong Kim", "Chan Hee Park", "Byeng D. Youn", "Guo Dong Goh", "Xi Huang", "Wai Yee Yeong", "Yung C Shin", "He Zhang", "Zitong Wang", "Fei Tao", "Jagjit Singh Srai", "Satyandra K. Gupta", "Byung Gun Joung", "Albin John", "John W. Sutherland", "Sang Won Lee", "Olga Fink", "Vinay Sharma", "Faez Ahmed", "Wei Chen", "Mark Fuge", "Arild Waaler", "Martin G. Skjæveland", "Dimitris Kyritsis", "Wei Chen", "VispiNevile Karkaria", "Yi-Ping Chen", "Ying-Kuan Tsai", "Joseph Cohen", "Xun Huan", "Jing Lin", "Liangwei Zhang", "Gregory W. Vogl", "Aaron W. Cornelius", "Xiaodong Jia", "Dai-Yan Ji", "Takanobu Minami", "Ruoxin Wang"], "categories": ["cs.AI", "cs.LG"], "doi": "10.1088/3049-4761/ae5967", "_query": "house price prediction machine learning", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2505.15638v2", "title": "Bayesian Ensembling: Insights from Online Optimization and Empirical Bayes", "abstract": "We revisit the classical problem of Bayesian ensembles and address the challenge of learning optimal combinations of Bayesian models in an online, continual learning setting. To this end, we reinterpret existing approaches such as Bayesian model averaging (BMA) and Bayesian stacking through a novel empirical Bayes lens, shedding new light on the limitations and pathologies of BMA. Further motivated by insights from online optimization, we propose Online Bayesian Stacking (OBS), a method that optimizes the log-score over predictive distributions to adaptively combine Bayesian models. A key contribution of our work is establishing a novel connection between OBS and portfolio selection, bridging Bayesian ensemble learning with a rich, well-studied theoretical framework that offers efficient algorithms and extensive regret analysis. We further clarify the relationship between OBS and online BMA, showing that they optimize related but distinct cost functions. Through theoretical analysis and empirical evaluation, we identify scenarios where OBS outperforms online BMA and provide principled methods and guidance on when practitioners should prefer one approach over the other.", "published": "2025-05-21T15:19:08Z", "authors": ["Daniel Waxman", "Fernando Llorente", "Petar M. Djurić"], "categories": ["cs.LG", "stat.CO", "stat.ME", "stat.ML"], "doi": "", "_query": "house price prediction machine learning", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/1309.0238v1", "title": "API design for machine learning software: experiences from the scikit-learn project", "abstract": "Scikit-learn is an increasingly popular machine learning li- brary. Written in Python, it is designed to be simple and efficient, accessible to non-experts, and reusable in various contexts. In this paper, we present and discuss our design choices for the application programming interface (API) of the project. In particular, we describe the simple and elegant interface shared by all learning and processing units in the library and then discuss its advantages in terms of composition and reusability. The paper also comments on implementation details specific to the Python ecosystem and analyzes obstacles faced by users and developers of the library.", "published": "2013-09-01T16:22:48Z", "authors": ["Lars Buitinck", "Gilles Louppe", "Mathieu Blondel", "Fabian Pedregosa", "Andreas Mueller", "Olivier Grisel", "Vlad Niculae", "Peter Prettenhofer", "Alexandre Gramfort", "Jaques Grobler", "Robert Layton", "Jake Vanderplas", "Arnaud Joly", "Brian Holt", "Gaël Varoquaux"], "categories": ["cs.LG", "cs.MS"], "doi": "", "_query": "house price prediction machine learning", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2112.03111v1", "title": "Ethics and Creativity in Computer Vision", "abstract": "This paper offers a retrospective of what we learnt from organizing the workshop *Ethical Considerations in Creative applications of Computer Vision* at CVPR 2021 conference and, prior to that, a series of workshops on *Computer Vision for Fashion, Art and Design* at ECCV 2018, ICCV 2019, and CVPR 2020. We hope this reflection will bring artists and machine learning researchers into conversation around the ethical and social dimensions of creative applications of computer vision.", "published": "2021-12-06T15:23:08Z", "authors": ["Negar Rostamzadeh", "Emily Denton", "Linda Petrini"], "categories": ["cs.CV", "cs.CY", "cs.LG"], "doi": "", "_query": "house price prediction machine learning", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2304.01203v7", "title": "Optimal Goal-Reaching Reinforcement Learning via Quasimetric Learning", "abstract": "In goal-reaching reinforcement learning (RL), the optimal value function has a particular geometry, called quasimetric structure. This paper introduces Quasimetric Reinforcement Learning (QRL), a new RL method that utilizes quasimetric models to learn optimal value functions. Distinct from prior approaches, the QRL objective is specifically designed for quasimetrics, and provides strong theoretical recovery guarantees. Empirically, we conduct thorough analyses on a discretized MountainCar environment, identifying properties of QRL and its advantages over alternatives. On offline and online goal-reaching benchmarks, QRL also demonstrates improved sample efficiency and performance, across both state-based and image-based observations.", "published": "2023-04-03T17:59:58Z", "authors": ["Tongzhou Wang", "Antonio Torralba", "Phillip Isola", "Amy Zhang"], "categories": ["cs.LG"], "doi": "", "_query": "house price prediction machine learning", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/1212.1100v1", "title": "Making Early Predictions of the Accuracy of Machine Learning Applications", "abstract": "The accuracy of machine learning systems is a widely studied research topic. Established techniques such as cross-validation predict the accuracy on unseen data of the classifier produced by applying a given learning method to a given training data set. However, they do not predict whether incurring the cost of obtaining more data and undergoing further training will lead to higher accuracy. In this paper we investigate techniques for making such early predictions. We note that when a machine learning algorithm is presented with a training set the classifier produced, and hence its error, will depend on the characteristics of the algorithm, on training set's size, and also on its specific composition. In particular we hypothesise that if a number of classifiers are produced, and their observed error is decomposed into bias and variance terms, then although these components may behave differently, their behaviour may be predictable. We test our hypothesis by building models that, given a measurement taken from the classifier created from a limited number of samples, predict the values that would be measured from the classifier produced when the full data set is presented. We create separate models for bias, variance and total error. Our models are built from the results of applying ten different machine learning algorithms to a range of data sets, and tested with \"unseen\" algorithms and datasets. We analyse the results for various numbers of initial training samples, and total dataset sizes. Results show that our predictions are very highly correlated with the values observed after undertaking the extra training. Finally we consider the more complex case where an ensemble of heterogeneous classifiers is trained, and show how we can accurately estimate an upper bound on the accuracy achievable after further training.", "published": "2012-12-05T17:07:39Z", "authors": ["J. E. Smith", "P. Caleb-Solly", "M. A. Tahir", "D. Sannen", "H. van-Brussel"], "categories": ["cs.LG", "cs.AI", "stat.ML"], "doi": "", "_query": "house price prediction machine learning", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2012.15816v1", "title": "Fairness in Machine Learning", "abstract": "Machine learning based systems are reaching society at large and in many aspects of everyday life. This phenomenon has been accompanied by concerns about the ethical issues that may arise from the adoption of these technologies. ML fairness is a recently established area of machine learning that studies how to ensure that biases in the data and model inaccuracies do not lead to models that treat individuals unfavorably on the basis of characteristics such as e.g. race, gender, disabilities, and sexual or political orientation. In this manuscript, we discuss some of the limitations present in the current reasoning about fairness and in methods that deal with it, and describe some work done by the authors to address them. More specifically, we show how causal Bayesian networks can play an important role to reason about and deal with fairness, especially in complex unfairness scenarios. We describe how optimal transport theory can be used to develop methods that impose constraints on the full shapes of distributions corresponding to different sensitive attributes, overcoming the limitation of most approaches that approximate fairness desiderata by imposing constraints on the lower order moments or other functions of those distributions. We present a unified framework that encompasses methods that can deal with different settings and fairness criteria, and that enjoys strong theoretical guarantees. We introduce an approach to learn fair representations that can generalize to unseen tasks. Finally, we describe a technique that accounts for legal restrictions about the use of sensitive attributes.", "published": "2020-12-31T18:38:58Z", "authors": ["Luca Oneto", "Silvia Chiappa"], "categories": ["cs.LG", "cs.CY", "stat.ML"], "doi": "10.1007/978-3-030-43883-8_7", "_query": "house price prediction machine learning", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2503.08962v3", "title": "Explainable Quantum Machine Learning for Multispectral Images Segmentation: Case Study", "abstract": "The emergence of Big Data changed how we approach information systems engineering. Nowadays, when we can use remote sensing techniques for Big Data acquisition, the issues such data introduce are as important as ever. One of those concerns is the processing of the data. Classical methods often fail to address that problem or are incapable of processing the data in a reasonable time. With that in mind information system engineers are required to investigate different approaches to the data processing. The recent advancements in noisy intermediate-scale quantum (NISQ) devices implementation allow us to investigate their application to real-life computational problem. This field of study is called quantum (information) systems engineering and usually focuses on technical problems with the contemporary devices. However, hardware challenges are not the only ones that hinder our quantum computation capabilities. Software limitations are the other, less explored side of this medal. Using multispectral image segmentation as a task example, we investigated how difficult it is to run a hybrid quantum-classical model on a real, publicly available quantum device. To quantify how and explain why the performance of our model changed when ran on a real device, we propose new explainability metrics. These metrics introduce new meaning to the explainable quantum machine learning; the explanation of the performance issue comes from the quantum device behavior. We also analyzed the expected money costs of running similar experiment on contemporary quantum devices using standard market prices.", "published": "2025-03-11T23:55:10Z", "authors": ["Tomasz Rybotycki", "Manish K. Gupta", "Piotr Gawron"], "categories": ["quant-ph", "cs.CV", "cs.LG"], "doi": "", "_query": "house price prediction machine learning", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2102.12178v1", "title": "Learning to Generate Wasserstein Barycenters", "abstract": "Optimal transport is a notoriously difficult problem to solve numerically, with current approaches often remaining intractable for very large scale applications such as those encountered in machine learning. Wasserstein barycenters -- the problem of finding measures in-between given input measures in the optimal transport sense -- is even more computationally demanding as it requires to solve an optimization problem involving optimal transport distances. By training a deep convolutional neural network, we improve by a factor of 60 the computational speed of Wasserstein barycenters over the fastest state-of-the-art approach on the GPU, resulting in milliseconds computational times on $512\\times512$ regular grids. We show that our network, trained on Wasserstein barycenters of pairs of measures, generalizes well to the problem of finding Wasserstein barycenters of more than two measures. We demonstrate the efficiency of our approach for computing barycenters of sketches and transferring colors between multiple images.", "published": "2021-02-24T10:13:48Z", "authors": ["Julien Lacombe", "Julie Digne", "Nicolas Courty", "Nicolas Bonneel"], "categories": ["cs.LG", "stat.ML"], "doi": "", "_query": "house price prediction machine learning", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2012.09712v1", "title": "Deep Molecular Dreaming: Inverse machine learning for de-novo molecular design and interpretability with surjective representations", "abstract": "Computer-based de-novo design of functional molecules is one of the most prominent challenges in cheminformatics today. As a result, generative and evolutionary inverse designs from the field of artificial intelligence have emerged at a rapid pace, with aims to optimize molecules for a particular chemical property. These models 'indirectly' explore the chemical space; by learning latent spaces, policies, distributions or by applying mutations on populations of molecules. However, the recent development of the SELFIES string representation of molecules, a surjective alternative to SMILES, have made possible other potential techniques. Based on SELFIES, we therefore propose PASITHEA, a direct gradient-based molecule optimization that applies inceptionism techniques from computer vision. PASITHEA exploits the use of gradients by directly reversing the learning process of a neural network, which is trained to predict real-valued chemical properties. Effectively, this forms an inverse regression model, which is capable of generating molecular variants optimized for a certain property. Although our results are preliminary, we observe a shift in distribution of a chosen property during inverse-training, a clear indication of PASITHEA's viability. A striking property of inceptionism is that we can directly probe the model's understanding of the chemical space it was trained on. We expect that extending PASITHEA to larger datasets, molecules and more complex properties will lead to advances in the design of new functional molecules as well as the interpretation and explanation of machine learning models.", "published": "2020-12-17T16:34:59Z", "authors": ["Cynthia Shen", "Mario Krenn", "Sagi Eppel", "Alan Aspuru-Guzik"], "categories": ["cs.LG", "cs.AI", "physics.chem-ph"], "doi": "10.1088/2632-2153/ac09d6", "_query": "house price prediction machine learning", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2607.12171v1", "title": "Self-Consistent Flow: Unifying Velocity and Endpoint Prediction for Rectified Flow Models", "abstract": "In rectified-flow-based generative models, the neural network can be trained to predict two different targets, such as the instantaneous velocity or the data endpoint, to perform denoising. Although prior work shows that these parameterizations lead to different empirical behaviors, the mechanisms underlying their respective advantages remain to be underexplored, and how to combine them effectively is still unclear. In this work, we analyze how learning errors from different parameterizations affect the generation performance. We show that predicting the data endpoint has a clear training signal that stabilizes training, whereas predicting the velocity maintains stable sampling dynamics near the data manifold. Motivated by these insights, we propose Self-Consistent Flow (SC-Flow), a new method that unifies the benefits of both parameterizations. By employing a lightweight consistency loss, SC-Flow jointly trains a single network to predict both the local velocity and the data endpoint, and the consistency between the two predictions improves the model's performance. The method requires no major architectural changes and adds minimal computational overhead. Extensive experiments on image generation tasks demonstrate that SC-Flow substantially stabilizes optimization and improves the straightness of generation paths, leading to significant gains in generation quality over standard rectified-flow baselines.", "published": "2026-07-13T21:31:45Z", "authors": ["Xu Han", "Jiajing Hu", "Li-Ping Liu"], "categories": ["cs.CV", "cs.AI", "cs.LG"], "doi": "", "_query": "house price prediction machine learning", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2404.03054v3", "title": "Goal Recognition Design for General Behavioral Agents using Machine Learning", "abstract": "Goal recognition design (GRD) aims to make limited modifications to decision-making environments to make it easier to infer the goals of agents acting within those environments. Although various research efforts have been made in goal recognition design, existing approaches are computationally demanding and often assume that agents are (near-)optimal in their decision-making. To address these limitations, we leverage machine learning methods for goal recognition design that can both improve run-time efficiency and account for agents with general behavioral models. Following existing literature, we use worst-case distinctiveness (wcd) as a measure of the difficulty in inferring the goal of an agent in a decision-making environment. Our approach begins by training a machine learning model to predict the wcd for a given environment and the agent behavior model. We then propose a gradient-based optimization framework that accommodates various constraints to optimize decision-making environments for enhanced goal recognition. Through extensive simulations, we demonstrate that our approach outperforms existing methods in reducing wcd and enhances runtime efficiency. Moreover, our approach also adapts to settings in which existing approaches do not apply, such as those involving flexible budget constraints, more complex environments, and suboptimal agent behavior. Finally, we conducted human-subject experiments that demonstrate that our method creates environments that facilitate efficient goal recognition from human decision-makers.", "published": "2024-04-03T20:38:22Z", "authors": ["Robert Kasumba", "Guanghui Yu", "Chien-Ju Ho", "Sarah Keren", "William Yeoh"], "categories": ["cs.AI", "cs.LG"], "doi": "", "_query": "house price prediction machine learning", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2508.14137v2", "title": "Learning to Learn the Macroscopic Fundamental Diagram using Physics-Informed and meta Machine Learning techniques", "abstract": "The Macroscopic Fundamental Diagram is a popular tool used to describe traffic dynamics in an aggregated way, with applications ranging from traffic control to incident analysis. However, estimating the MFD for a given network requires large numbers of loop detectors, which is not always available in practise. This article proposes a framework to alleviate the data scarcity challenge harnessing Meta-Learning, a subcategory of Machine Learning that trains models to understand and adapt to new tasks on their own. We use Meta-Learning to identify and exploit transferable patterns from data-rich cities to cities where not enough data is available to estimate the MFD. The developed model is trained and tested by leveraging data from multiple cities and exploiting it to model the MFD of other cities with different shares of detectors and topological structures. The proposed Meta-Learning framework is applied to an ad-hoc Multi-Task Physics-Informed Neural Network, specifically designed to estimate the MFD. Results show an average MAE improvement in flow prediction of around 50% across cities (depending on the subset of loop detectors tested). The Meta-Learning framework thus successfully generalises across diverse urban settings and improves performance on cities with limited data, demonstrating the potential of using Meta-Learning when a limited number of detectors is available. We directly test this assumption by applying the Meta-Learning outputs to unseen cities to simulate a real-life application scenario and the wide applicability of the proposed methodology. Finally, the proposed framework is validated against traditional Transfer Learning approaches and tested with FitFun, a model for FD estimation from the literature, to prove its transferability.", "published": "2025-08-19T12:59:58Z", "authors": ["Amalie Roark", "Serio Agriesti", "Francisco Camara Pereira", "Guido Cantelmo"], "categories": ["cs.LG"], "doi": "10.1016/j.trc.2026.105707", "_query": "house price prediction machine learning", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/1905.11681v2", "title": "Validating the Validation: Reanalyzing a large-scale comparison of Deep Learning and Machine Learning models for bioactivity prediction", "abstract": "Machine learning methods may have the potential to significantly accelerate drug discovery. However, the increasing rate of new methodological approaches being published in the literature raises the fundamental question of how models should be benchmarked and validated. We reanalyze the data generated by a recently published large-scale comparison of machine learning models for bioactivity prediction and arrive at a somewhat different conclusion. We show that the performance of support vector machines is competitive with that of deep learning methods. Additionally, using a series of numerical experiments, we question the relevance of area under the receiver operating characteristic curve as a metric in virtual screening, and instead suggest that area under the precision-recall curve should be used in conjunction with the receiver operating characteristic. Our numerical experiments also highlight challenges in estimating the uncertainty in model performance via scaffold-split nested cross validation.", "published": "2019-05-28T08:43:06Z", "authors": ["Matthew C. Robinson", "Robert C. Glen", "Alpha A. Lee"], "categories": ["cs.LG", "physics.chem-ph", "stat.ML"], "doi": "10.1007/s10822-019-00274-0", "_query": "house price prediction machine learning", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/1402.1349v1", "title": "Dissimilarity-based Ensembles for Multiple Instance Learning", "abstract": "In multiple instance learning, objects are sets (bags) of feature vectors (instances) rather than individual feature vectors. In this paper we address the problem of how these bags can best be represented. Two standard approaches are to use (dis)similarities between bags and prototype bags, or between bags and prototype instances. The first approach results in a relatively low-dimensional representation determined by the number of training bags, while the second approach results in a relatively high-dimensional representation, determined by the total number of instances in the training set. In this paper a third, intermediate approach is proposed, which links the two approaches and combines their strengths. Our classifier is inspired by a random subspace ensemble, and considers subspaces of the dissimilarity space, defined by subsets of instances, as prototypes. We provide guidelines for using such an ensemble, and show state-of-the-art performances on a range of multiple instance learning problems.", "published": "2014-02-06T13:35:01Z", "authors": ["Veronika Cheplygina", "David M. J. Tax", "Marco Loog"], "categories": ["stat.ML", "cs.LG"], "doi": "10.1109/TNNLS.2015.2424254", "_query": "house price prediction machine learning", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2402.17120v3", "title": "LCEN: A Nonlinear, Interpretable Feature Selection and Machine Learning Algorithm", "abstract": "Interpretable models can have advantages over black-box models, and interpretability is essential for the application of machine learning in critical settings, such as aviation or medicine. This article introduces the LASSO-Clip-EN (LCEN) algorithm for nonlinear, interpretable feature selection and machine learning modeling. In a wide variety of artificial and empirical datasets, LCEN constructed sparse and frequently more accurate models than other methods, including sparse, nonlinear methods, on tested datasets. LCEN was empirically observed to be robust against many issues typically present in datasets and modeling, including noise, multicollinearity, and data scarcity. As a feature selection algorithm, LCEN matched or surpassed the thresholded elastic net but was, on average, 10.3-fold faster based on our experiments. LCEN for feature selection can also rediscover multiple physical laws from empirical data. As a machine learning algorithm, when tested on processes with no known physical laws, LCEN achieved better results than many other dense and sparse methods -- including being comparable to or better than ANNs on multiple datasets.", "published": "2024-02-27T01:26:48Z", "authors": ["Pedro Seber", "Richard D. Braatz"], "categories": ["cs.LG"], "doi": "", "_query": "house price prediction machine learning", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/1905.12226v1", "title": "Address Instance-level Label Prediction in Multiple Instance Learning", "abstract": "\\textit{Multiple Instance Learning} (MIL) is concerned with learning from bags of instances, where only bag labels are given and instance labels are unknown. Existent approaches in this field were mainly designed for the bag-level label prediction (predict labels for bags) but not the instance-level (predict labels for instances), with the task loss being only defined at the bag level. This restricts their application in many tasks, where the instance-level labels are more interested. In this paper, we propose a novel algorithm, whose loss is specifically defined at the instance level, to address instance-level label prediction in MIL. We prove that the loss of this algorithm can be unbiasedly and consistently estimated without using instance labels, under the i.i.d assumption. Empirical study validates the above statements and shows that the proposed algorithm can achieve superior instance-level and comparative bag-level performance, compared to state-of-the-art MIL methods. In addition, it shows that the proposed method can achieve similar results as the fully supervised model (trained with instance labels) for label prediction at the instance level.", "published": "2019-05-29T05:36:06Z", "authors": ["Minlong Peng", "Qi Zhang"], "categories": ["cs.LG", "stat.ML"], "doi": "", "_query": "house price prediction machine learning", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2210.16850v1", "title": "Medical Codes Prediction from Clinical Notes: From Human Coders to Machines", "abstract": "Prediction of medical codes from clinical notes is a practical and essential need for every healthcare delivery organization within current medical systems. Automating annotation will save significant time and excessive effort that human coders spend today. However, the biggest challenge is directly identifying appropriate medical codes from several thousands of high-dimensional codes from unstructured free-text clinical notes. This complex medical codes prediction problem from clinical notes has received substantial interest in the NLP community, and several recent studies have shown the state-of-the-art code prediction results of full-fledged deep learning-based methods. This progress raises the fundamental question of how far automated machine learning systems are from human coders' working performance, as well as the important question of how well current explainability methods apply to advanced neural network models such as transformers. This is to predict correct codes and present references in clinical notes that support code prediction, as this level of explainability and accuracy of the prediction outcomes is critical to gaining trust from professional medical coders.", "published": "2022-10-30T14:24:13Z", "authors": ["Byung-Hak Kim"], "categories": ["cs.LG", "cs.CL"], "doi": "", "_query": "house price prediction machine learning", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2006.07021v2", "title": "A benchmark study on reliable molecular supervised learning via Bayesian learning", "abstract": "Virtual screening aims to find desirable compounds from chemical library by using computational methods. For this purpose with machine learning, model outputs that can be interpreted as predictive probability will be beneficial, in that a high prediction score corresponds to high probability of correctness. In this work, we present a study on the prediction performance and reliability of graph neural networks trained with the recently proposed Bayesian learning algorithms. Our work shows that Bayesian learning algorithms allow well-calibrated predictions for various GNN architectures and classification tasks. Also, we show the implications of reliable predictions on virtual screening, where Bayesian learning may lead to higher success in finding hit compounds.", "published": "2020-06-12T09:06:16Z", "authors": ["Doyeong Hwang", "Grace Lee", "Hanseok Jo", "Seyoul Yoon", "Seongok Ryu"], "categories": ["cs.LG", "stat.ML"], "doi": "", "_query": "house price prediction machine learning", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2112.10327v2", "title": "Classifier Calibration: A survey on how to assess and improve predicted class probabilities", "abstract": "This paper provides both an introduction to and a detailed overview of the principles and practice of classifier calibration. A well-calibrated classifier correctly quantifies the level of uncertainty or confidence associated with its instance-wise predictions. This is essential for critical applications, optimal decision making, cost-sensitive classification, and for some types of context change. Calibration research has a rich history which predates the birth of machine learning as an academic field by decades. However, a recent increase in the interest on calibration has led to new methods and the extension from binary to the multiclass setting. The space of options and issues to consider is large, and navigating it requires the right set of concepts and tools. We provide both introductory material and up-to-date technical details of the main concepts and methods, including proper scoring rules and other evaluation metrics, visualisation approaches, a comprehensive account of post-hoc calibration methods for binary and multiclass classification, and several advanced topics.", "published": "2021-12-20T03:50:55Z", "authors": ["Telmo Silva Filho", "Hao Song", "Miquel Perello-Nieto", "Raul Santos-Rodriguez", "Meelis Kull", "Peter Flach"], "categories": ["cs.LG", "stat.ML"], "doi": "10.1007/s10994-023-06336-7", "_query": "house price prediction machine learning", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2008.06388v4", "title": "Common pitfalls and recommendations for using machine learning to detect and prognosticate for COVID-19 using chest radiographs and CT scans", "abstract": "Machine learning methods offer great promise for fast and accurate detection and prognostication of COVID-19 from standard-of-care chest radiographs (CXR) and computed tomography (CT) images. Many articles have been published in 2020 describing new machine learning-based models for both of these tasks, but it is unclear which are of potential clinical utility. In this systematic review, we search EMBASE via OVID, MEDLINE via PubMed, bioRxiv, medRxiv and arXiv for published papers and preprints uploaded from January 1, 2020 to October 3, 2020 which describe new machine learning models for the diagnosis or prognosis of COVID-19 from CXR or CT images. Our search identified 2,212 studies, of which 415 were included after initial screening and, after quality screening, 61 studies were included in this systematic review. Our review finds that none of the models identified are of potential clinical use due to methodological flaws and/or underlying biases. This is a major weakness, given the urgency with which validated COVID-19 models are needed. To address this, we give many recommendations which, if followed, will solve these issues and lead to higher quality model development and well documented manuscripts.", "published": "2020-08-14T14:25:21Z", "authors": ["Michael Roberts", "Derek Driggs", "Matthew Thorpe", "Julian Gilbey", "Michael Yeung", "Stephan Ursprung", "Angelica I. Aviles-Rivero", "Christian Etmann", "Cathal McCague", "Lucian Beer", "Jonathan R. Weir-McCall", "Zhongzhao Teng", "Effrossyni Gkrania-Klotsas", "James H. F. Rudd", "Evis Sala", "Carola-Bibiane Schönlieb"], "categories": ["cs.LG", "cs.CV", "eess.IV", "stat.ML"], "doi": "10.1038/s42256-021-00307-0", "_query": "house price prediction machine learning", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2008.10679v2", "title": "Machine learning for weather and climate are worlds apart", "abstract": "Modern weather and climate models share a common heritage, and often even components, however they are used in different ways to answer fundamentally different questions. As such, attempts to emulate them using machine learning should reflect this. While the use of machine learning to emulate weather forecast models is a relatively new endeavour there is a rich history of climate model emulation. This is primarily because while weather modelling is an initial condition problem which intimately depends on the current state of the atmosphere, climate modelling is predominantly a boundary condition problem. In order to emulate the response of the climate to different drivers therefore, representation of the full dynamical evolution of the atmosphere is neither necessary, or in many cases, desirable. Climate scientists are typically interested in different questions also. Indeed emulating the steady-state climate response has been possible for many years and provides significant speed increases that allow solving inverse problems for e.g. parameter estimation. Nevertheless, the large datasets, non-linear relationships and limited training data make Climate a domain which is rich in interesting machine learning challenges. Here I seek to set out the current state of climate model emulation and demonstrate how, despite some challenges, recent advances in machine learning provide new opportunities for creating useful statistical models of the climate.", "published": "2020-08-24T19:51:51Z", "authors": ["Duncan Watson-Parris"], "categories": ["physics.ao-ph", "stat.AP"], "doi": "10.1098/rsta.2020.0098", "_query": "house price prediction machine learning", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/1806.08716v2", "title": "Learning Qualitatively Diverse and Interpretable Rules for Classification", "abstract": "There has been growing interest in developing accurate models that can also be explained to humans. Unfortunately, if there exist multiple distinct but accurate models for some dataset, current machine learning methods are unlikely to find them: standard techniques will likely recover a complex model that combines them. In this work, we introduce a way to identify a maximal set of distinct but accurate models for a dataset. We demonstrate empirically that, in situations where the data supports multiple accurate classifiers, we tend to recover simpler, more interpretable classifiers rather than more complex ones.", "published": "2018-06-22T15:10:41Z", "authors": ["Andrew Slavin Ross", "Weiwei Pan", "Finale Doshi-Velez"], "categories": ["cs.LG", "stat.ML"], "doi": "", "_query": "house price prediction machine learning", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2102.06004v3", "title": "Fairness-Aware PAC Learning from Corrupted Data", "abstract": "Addressing fairness concerns about machine learning models is a crucial step towards their long-term adoption in real-world automated systems. While many approaches have been developed for training fair models from data, little is known about the robustness of these methods to data corruption. In this work we consider fairness-aware learning under worst-case data manipulations. We show that an adversary can in some situations force any learner to return an overly biased classifier, regardless of the sample size and with or without degrading accuracy, and that the strength of the excess bias increases for learning problems with underrepresented protected groups in the data. We also prove that our hardness results are tight up to constant factors. To this end, we study two natural learning algorithms that optimize for both accuracy and fairness and show that these algorithms enjoy guarantees that are order-optimal in terms of the corruption ratio and the protected groups frequencies in the large data limit.", "published": "2021-02-11T13:48:41Z", "authors": ["Nikola Konstantinov", "Christoph H. Lampert"], "categories": ["cs.LG", "stat.ML"], "doi": "", "_query": "house price prediction machine learning", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2208.02301v1", "title": "HiCu: Leveraging Hierarchy for Curriculum Learning in Automated ICD Coding", "abstract": "There are several opportunities for automation in healthcare that can improve clinician throughput. One such example is assistive tools to document diagnosis codes when clinicians write notes. We study the automation of medical code prediction using curriculum learning, which is a training strategy for machine learning models that gradually increases the hardness of the learning tasks from easy to difficult. One of the challenges in curriculum learning is the design of curricula -- i.e., in the sequential design of tasks that gradually increase in difficulty. We propose Hierarchical Curriculum Learning (HiCu), an algorithm that uses graph structure in the space of outputs to design curricula for multi-label classification. We create curricula for multi-label classification models that predict ICD diagnosis and procedure codes from natural language descriptions of patients. By leveraging the hierarchy of ICD codes, which groups diagnosis codes based on various organ systems in the human body, we find that our proposed curricula improve the generalization of neural network-based predictive models across recurrent, convolutional, and transformer-based architectures. Our code is available at https://github.com/wren93/HiCu-ICD.", "published": "2022-08-03T18:39:27Z", "authors": ["Weiming Ren", "Ruijing Zeng", "Tongzi Wu", "Tianshu Zhu", "Rahul G. Krishnan"], "categories": ["cs.LG"], "doi": "", "_query": "house price prediction machine learning", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2006.15069v1", "title": "Machine learning-based clinical prediction modeling -- A practical guide for clinicians", "abstract": "In the emerging era of big data, larger available clinical datasets and computational advances have sparked a massive interest in machine learning-based approaches. The number of manuscripts related to machine learning or artificial intelligence has exponentially increased over the past years. As analytical machine learning tools become readily available for clinicians to use, the understanding of key concepts and the awareness of analytical pitfalls are increasingly required for clinicians, investigators, reviewers and editors, who even as experts in their clinical field, sometimes find themselves insufficiently equipped to evaluate machine learning methodologies. In the first section, we provide explanations on the general principles of machine learning, as well as analytical steps required for successful machine learning-based predictive modelling - which is the focus of this series. In further sections, we review the importance of resampling, overfitting and model generalizability as well as feature reduction and selection (Part II), strategies for model evaluation, reporting and discussion of common caveats and other points of significance (Part III), as well as offer a practical guide to classification (Part IV) and regression modelling (Part V), with a complete coding pipeline. Methodological rigor and clarity as well as understanding of the underlying reasoning of the internal workings of a machine learning approach are required, otherwise predictive applications despite being strong analytical tools are not well accepted into the clinical routine. Going forward, machine learning and artificial intelligence shape and influence modern medicine across disciplines including the field of neurosurgery.", "published": "2020-06-23T20:11:37Z", "authors": ["Julius M. Kernbach", "Victor E. Staartjes"], "categories": ["stat.ML", "cs.CY", "cs.LG"], "doi": "", "_query": "house price prediction machine learning", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2505.02889v1", "title": "Early Prediction of Sepsis: Feature-Aligned Transfer Learning", "abstract": "Sepsis is a life threatening medical condition that occurs when the body has an extreme response to infection, leading to widespread inflammation, organ failure, and potentially death. Because sepsis can worsen rapidly, early detection is critical to saving lives. However, current diagnostic methods often identify sepsis only after significant damage has already occurred. Our project aims to address this challenge by developing a machine learning based system to predict sepsis in its early stages, giving healthcare providers more time to intervene. A major problem with existing models is the wide variability in the patient information or features they use, such as heart rate, temperature, and lab results. This inconsistency makes models difficult to compare and limits their ability to work across different hospitals and settings. To solve this, we propose a method called Feature Aligned Transfer Learning (FATL), which identifies and focuses on the most important and commonly reported features across multiple studies, ensuring the model remains consistent and clinically relevant. Most existing models are trained on narrow patient groups, leading to population bias. FATL addresses this by combining knowledge from models trained on diverse populations, using a weighted approach that reflects each models contribution. This makes the system more generalizable and effective across different patient demographics and clinical environments. FATL offers a practical and scalable solution for early sepsis detection, particularly in hospitals with limited resources, and has the potential to improve patient outcomes, reduce healthcare costs, and support more equitable healthcare delivery.", "published": "2025-05-05T17:59:34Z", "authors": ["Oyindolapo O. Komolafe", "Zhimin Mei", "David Morales Zarate", "Gregory William Spangenberg"], "categories": ["cs.LG", "cs.AI"], "doi": "", "_query": "house price prediction machine learning", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2108.02442v1", "title": "House Price Determinants and Market Segmentation in Boulder, Colorado: A Hedonic Price Approach", "abstract": "In this research we perform hedonic regression model to examine the residential property price determinants in the city of Boulder in the state of Colorado, USA. The urban housing markets are too compounded to be considered as homogeneous markets. The heterogeneity of an urban property market requires creation of market segmentation. To test whether residential properties in the real estate market in the city of Boulder are analyzed and predicted in the disaggregate level or at an aggregate level we stratify the housing market based on both property types and location and estimate separate hedonic price models for each submarket. The results indicate that the implicit values of the property characteristics are not identical across property types and locations in the city of Boulder and market segmentation exists.", "published": "2021-08-05T08:19:48Z", "authors": ["Mahdieh Yazdani"], "categories": ["econ.GN"], "doi": "", "_query": "house price prediction machine learning", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2407.19724v1", "title": "Constructing artificial life and materials scientists with accelerated AI using Deep AndersoNN", "abstract": "Deep AndersoNN accelerates AI by exploiting the continuum limit as the number of explicit layers in a neural network approaches infinity and can be taken as a single implicit layer, known as a deep equilibrium model. Solving for deep equilibrium model parameters reduces to a nonlinear fixed point iteration problem, enabling the use of vector-to-vector iterative solvers and windowing techniques, such as Anderson extrapolation, for accelerating convergence to the fixed point deep equilibrium. Here we show that Deep AndersoNN achieves up to an order of magnitude of speed-up in training and inference. The method is demonstrated on density functional theory results for industrial applications by constructing artificial life and materials `scientists' capable of classifying drugs as strongly or weakly polar, metal-organic frameworks by pore size, and crystalline materials as metals, semiconductors, and insulators, using graph images of node-neighbor representations transformed from atom-bond networks. Results exhibit accuracy up to 98\\% and showcase synergy between Deep AndersoNN and machine learning capabilities of modern computing architectures, such as GPUs, for accelerated computational life and materials science by quickly identifying structure-property relationships. This paves the way for saving up to 90\\% of compute required for AI, reducing its carbon footprint by up to 60 gigatons per year by 2030, and scaling above memory limits of explicit neural networks in life and materials science, and beyond.", "published": "2024-07-29T06:12:47Z", "authors": ["Saleem Abdul Fattah Ahmed Al Dajani", "David Keyes"], "categories": ["cs.LG", "physics.app-ph"], "doi": "", "_query": "house price prediction machine learning", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2010.04742v2", "title": "Machine Learning approach to muon spectroscopy analysis", "abstract": "In recent years, Artificial Intelligence techniques have proved to be very successful when applied to problems in physical sciences. Here we apply an unsupervised Machine Learning (ML) algorithm called Principal Component Analysis (PCA) as a tool to analyse the data from muon spectroscopy experiments. Specifically, we apply the ML technique to detect phase transitions in various materials. The measured quantity in muon spectroscopy is an asymmetry function, which may hold information about the distribution of the intrinsic magnetic field in combination with the dynamics of the sample. Sharp changes of shape of asymmetry functions - measured at different temperatures - might indicate a phase transition. Existing methods of processing the muon spectroscopy data are based on regression analysis, but choosing the right fitting function requires knowledge about the underlying physics of the probed material. Conversely, Principal Component Analysis focuses on small differences in the asymmetry curves and works without any prior assumptions about the studied samples. We discovered that the PCA method works well in detecting phase transitions in muon spectroscopy experiments and can serve as an alternative to current analysis, especially if the physics of the studied material are not entirely known. Additionally, we found out that our ML technique seems to work best with large numbers of measurements, regardless of whether the algorithm takes data only for a single material or whether the analysis is performed simultaneously for many materials with different physical properties.", "published": "2020-10-09T18:01:11Z", "authors": ["T. Tula", "G. Möller", "J. Quintanilla", "S. R. Giblin", "A. D. Hillier", "E. E. McCabe", "S. Ramos", "D. S. Barker", "S. Gibson"], "categories": ["cond-mat.mtrl-sci", "stat.ML"], "doi": "10.1088/1361-648X/abe39e", "_query": "house price prediction machine learning", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2307.00928v2", "title": "Learning Differentiable Logic Programs for Abstract Visual Reasoning", "abstract": "Visual reasoning is essential for building intelligent agents that understand the world and perform problem-solving beyond perception. Differentiable forward reasoning has been developed to integrate reasoning with gradient-based machine learning paradigms. However, due to the memory intensity, most existing approaches do not bring the best of the expressivity of first-order logic, excluding a crucial ability to solve abstract visual reasoning, where agents need to perform reasoning by using analogies on abstract concepts in different scenarios. To overcome this problem, we propose NEUro-symbolic Message-pAssiNg reasoNer (NEUMANN), which is a graph-based differentiable forward reasoner, passing messages in a memory-efficient manner and handling structured programs with functors. Moreover, we propose a computationally-efficient structure learning algorithm to perform explanatory program induction on complex visual scenes. To evaluate, in addition to conventional visual reasoning tasks, we propose a new task, visual reasoning behind-the-scenes, where agents need to learn abstract programs and then answer queries by imagining scenes that are not observed. We empirically demonstrate that NEUMANN solves visual reasoning tasks efficiently, outperforming neural, symbolic, and neuro-symbolic baselines.", "published": "2023-07-03T11:02:40Z", "authors": ["Hikaru Shindo", "Viktor Pfanschilling", "Devendra Singh Dhami", "Kristian Kersting"], "categories": ["cs.LG", "cs.AI", "cs.CV"], "doi": "", "_query": "house price prediction machine learning", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/1911.02210v2", "title": "Machine Learning using the Variational Predictive Information Bottleneck with a Validation Set", "abstract": "Zellner (1988) modeled statistical inference in terms of information processing and postulated the Information Conservation Principle (ICP) between the input and output of the information processing block, showing that this yielded Bayesian inference as the optimum information processing rule. Recently, Alemi (2019) reviewed Zellner's work in the context of machine learning and showed that the ICP could be seen as a special case of a more general optimum information processing criterion, namely the Predictive Information Bottleneck Objective. However, Alemi modeled the model training step in machine learning as using training and test data sets only, and did not account for the use of a validation data set during training. The present note is an attempt to extend Alemi's information processing formulation of machine learning, and the predictive information bottleneck objective for model training, to the widely-used scenario where training utilizes not only a training but also a validation data set.", "published": "2019-11-06T05:31:59Z", "authors": ["Sayandev Mukherjee"], "categories": ["cs.LG", "cs.IT", "stat.ML"], "doi": "", "_query": "house price prediction machine learning", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/1206.6407v1", "title": "Large-Scale Feature Learning With Spike-and-Slab Sparse Coding", "abstract": "We consider the problem of object recognition with a large number of classes. In order to overcome the low amount of labeled examples available in this setting, we introduce a new feature learning and extraction procedure based on a factor model we call spike-and-slab sparse coding (S3C). Prior work on S3C has not prioritized the ability to exploit parallel architectures and scale S3C to the enormous problem sizes needed for object recognition. We present a novel inference procedure for appropriate for use with GPUs which allows us to dramatically increase both the training set size and the amount of latent factors that S3C may be trained with. We demonstrate that this approach improves upon the supervised learning capabilities of both sparse coding and the spike-and-slab Restricted Boltzmann Machine (ssRBM) on the CIFAR-10 dataset. We use the CIFAR-100 dataset to demonstrate that our method scales to large numbers of classes better than previous methods. Finally, we use our method to win the NIPS 2011 Workshop on Challenges In Learning Hierarchical Models? Transfer Learning Challenge.", "published": "2012-06-27T19:59:59Z", "authors": ["Ian Goodfellow", "Aaron Courville", "Yoshua Bengio"], "categories": ["cs.LG", "stat.ML"], "doi": "", "_query": "house price prediction machine learning", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2112.07273v1", "title": "Urban Housing Prices and Migration's Fertility Intentions: Based on the 2018 China Migrants' Dynamic Survey", "abstract": "While the size of China's mobile population continues to expand, the fertility rate is significantly lower than the stable generation replacement level of the population, and the structural imbalance of human resource supply has attracted widespread attention. This paper uses LPM and Probit models to estimate the impact of house prices on the fertility intentions of the mobile population based on data from the 2018 National Mobile Population Dynamics Monitoring Survey. The lagged land sales price is used as an instrumental variable of house price to mitigate the potential endogeneity problem. The results show that for every 100\\% increase in the ratio of house price to household income of mobile population, the fertility intention of the female mobile population of working age at the inflow location will decrease by 4.42\\%, and the marginal effect of relative house price on labor force fertility intention is EXP(-0.222); the sensitivity of mobile population fertility intention to house price is affected by the moderating effect of infrastructure construction at the inflow location. The willingness to have children in the inflow area is higher for female migrants of working age with lower age, smaller family size and higher education. Based on the above findings, the study attempts to provide a new practical perspective for the mainline institutional change and balanced economic development in China's economic transition phase.", "published": "2021-12-14T10:09:08Z", "authors": ["Jingwen Tan", "Shixi Kang"], "categories": ["econ.GN"], "doi": "", "_query": "house price prediction machine learning", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/1706.09976v2", "title": "The Authority of \"Fair\" in Machine Learning", "abstract": "In this paper, we argue for the adoption of a normative definition of fairness within the machine learning community. After characterizing this definition, we review the current literature of Fair ML in light of its implications. We end by suggesting ways to incorporate a broader community and generate further debate around how to decide what is fair in ML.", "published": "2017-06-29T23:37:34Z", "authors": ["Michael Skirpan", "Micha Gorelick"], "categories": ["cs.CY"], "doi": "", "_query": "house price prediction machine learning", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/1306.3531v1", "title": "The convergence of regional house prices in the USA in the context of the stress testing of financial institutions", "abstract": "I studied the convergence of regional house prices to national prices in USA by analyzing time-series of house price indices of 9 Census Divisions. I found the evidence of the convergence in some parts of the country using asymmetric unit root tests. The fact that the evidence of the convergence is not present in large parts of the country raises an issue of execution and interpretation of results of Federal Reserve Bank's annual stress testing of the US banking system.", "published": "2013-06-14T23:57:54Z", "authors": ["Argyn Kuketayev"], "categories": ["q-fin.RM", "stat.AP"], "doi": "", "_query": "house price prediction machine learning", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/1809.07861v2", "title": "Machine Learning for Forecasting Mid Price Movement using Limit Order Book Data", "abstract": "Forecasting the movements of stock prices is one the most challenging problems in financial markets analysis. In this paper, we use Machine Learning (ML) algorithms for the prediction of future price movements using limit order book data. Two different sets of features are combined and evaluated: handcrafted features based on the raw order book data and features extracted by ML algorithms, resulting in feature vectors with highly variant dimensionalities. Three classifiers are evaluated using combinations of these sets of features on two different evaluation setups and three prediction scenarios. Even though the large scale and high frequency nature of the limit order book poses several challenges, the scope of the conducted experiments and the significance of the experimental results indicate that Machine Learning highly befits this task carving the path towards future research in this field.", "published": "2018-09-19T10:05:30Z", "authors": ["Paraskevi Nousi", "Avraam Tsantekidis", "Nikolaos Passalis", "Adamantios Ntakaris", "Juho Kanniainen", "Anastasios Tefas", "Moncef Gabbouj", "Alexandros Iosifidis"], "categories": ["cs.CE", "cs.LG", "stat.ML"], "doi": "", "_query": "house price prediction machine learning", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2409.02136v2", "title": "Large Language Models versus Classical Machine Learning: Performance in COVID-19 Mortality Prediction Using High-Dimensional Tabular Data", "abstract": "This study compared the performance of classical feature-based machine learning models (CMLs) and large language models (LLMs) in predicting COVID-19 mortality using high-dimensional tabular data from 9,134 patients across four hospitals. Seven CML models, including XGBoost and random forest (RF), were evaluated alongside eight LLMs, such as GPT-4 and Mistral-7b, which performed zero-shot classification on text-converted structured data. Additionally, Mistral- 7b was fine-tuned using the QLoRA approach. XGBoost and RF demonstrated superior performance among CMLs, achieving F1 scores of 0.87 and 0.83 for internal and external validation, respectively. GPT-4 led the LLM category with an F1 score of 0.43, while fine-tuning Mistral-7b significantly improved its recall from 1% to 79%, yielding a stable F1 score of 0.74 during external validation. Although LLMs showed moderate performance in zero-shot classification, fine-tuning substantially enhanced their effectiveness, potentially bridging the gap with CML models. However, CMLs still outperformed LLMs in handling high-dimensional tabular data tasks. This study highlights the potential of both CMLs and fine-tuned LLMs in medical predictive modeling, while emphasizing the current superiority of CMLs for structured data analysis.", "published": "2024-09-02T14:51:12Z", "authors": ["Mohammadreza Ghaffarzadeh-Esfahani", "Mahdi Ghaffarzadeh-Esfahani", "Arian Salahi-Niri", "Hossein Toreyhi", "Zahra Atf", "Amirali Mohsenzadeh-Kermani", "Mahshad Sarikhani", "Zohreh Tajabadi", "Fatemeh Shojaeian", "Mohammad Hassan Bagheri", "Aydin Feyzi", "Mohammadamin Tarighatpayma", "Narges Gazmeh", "Fateme Heydari", "Hossein Afshar", "Amirreza Allahgholipour", "Farid Alimardani", "Ameneh Salehi", "Naghmeh Asadimanesh", "Mohammad Amin Khalafi", "Hadis Shabanipour", "Ali Moradi", "Sajjad Hossein Zadeh", "Omid Yazdani", "Romina Esbati", "Moozhan Maleki", "Danial Samiei Nasr", "Amirali Soheili", "Hossein Majlesi", "Saba Shahsavan", "Alireza Soheilipour", "Nooshin Goudarzi", "Erfan Taherifard", "Hamidreza Hatamabadi", "Jamil S Samaan", "Thomas Savage", "Ankit Sakhuja", "Ali Soroush", "Girish Nadkarni", "Ilad Alavi Darazam", "Mohamad Amin Pourhoseingholi", "Seyed Amir Ahmad Safavi-Naini"], "categories": ["cs.LG", "cs.AI", "cs.CL"], "doi": "10.1038/s41598-025-26705-7", "_query": "house price prediction machine learning", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/1911.07652v1", "title": "Information-Theoretic Perspective of Federated Learning", "abstract": "An approach to distributed machine learning is to train models on local datasets and aggregate these models into a single, stronger model. A popular instance of this form of parallelization is federated learning, where the nodes periodically send their local models to a coordinator that aggregates them and redistributes the aggregation back to continue training with it. The most frequently used form of aggregation is averaging the model parameters, e.g., the weights of a neural network. However, due to the non-convexity of the loss surface of neural networks, averaging can lead to detrimental effects and it remains an open question under which conditions averaging is beneficial. In this paper, we study this problem from the perspective of information theory: We measure the mutual information between representation and inputs as well as representation and labels in local models and compare it to the respective information contained in the representation of the averaged model. Our empirical results confirm previous observations about the practical usefulness of averaging for neural networks, even if local dataset distributions vary strongly. Furthermore, we obtain more insights about the impact of the aggregation frequency on the information flow and thus on the success of distributed learning. These insights will be helpful both in improving the current synchronization process and in further understanding the effects of model aggregation.", "published": "2019-11-15T13:51:27Z", "authors": ["Linara Adilova", "Julia Rosenzweig", "Michael Kamp"], "categories": ["cs.LG", "cs.DC", "cs.IT", "stat.ML"], "doi": "", "_query": "house price prediction machine learning", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2110.11999v1", "title": "Machine Learning in Finance-Emerging Trends and Challenges", "abstract": "The paradigm of machine learning and artificial intelligence has pervaded our everyday life in such a way that it is no longer an area for esoteric academics and scientists putting their effort to solve a challenging research problem. The evolution is quite natural rather than accidental. With the exponential growth in processing speed and with the emergence of smarter algorithms for solving complex and challenging problems, organizations have found it possible to harness a humongous volume of data in realizing solutions that have far-reaching business values. This introductory chapter highlights some of the challenges and barriers that organizations in the financial services sector at the present encounter in adopting machine learning and artificial intelligence-based models and applications in their day-to-day operations.", "published": "2021-10-08T09:14:06Z", "authors": ["Jaydip Sen", "Rajdeep Sen", "Abhishek Dutta"], "categories": ["q-fin.ST", "cs.LG"], "doi": "", "_query": "house price prediction machine learning", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2503.00549v1", "title": "The Uncertainty of Machine Learning Predictions in Asset Pricing", "abstract": "Machine learning in asset pricing typically predicts expected returns as point estimates, ignoring uncertainty. We develop new methods to construct forecast confidence intervals for expected returns obtained from neural networks. We show that neural network forecasts of expected returns share the same asymptotic distribution as classic nonparametric methods, enabling a closed-form expression for their standard errors. We also propose a computationally feasible bootstrap to obtain the asymptotic distribution. We incorporate these forecast confidence intervals into an uncertainty-averse investment framework. This provides an economic rationale for shrinkage implementations of portfolio selection. Empirically, our methods improve out-of-sample performance.", "published": "2025-03-01T16:32:00Z", "authors": ["Yuan Liao", "Xinjie Ma", "Andreas Neuhierl", "Linda Schilling"], "categories": ["econ.EM", "stat.ML"], "doi": "", "_query": "house price prediction machine learning", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2405.15950v2", "title": "A Systematic Bias of Machine Learning Regression Models and Its Correction: an Application to Imaging-based Brain Age Prediction", "abstract": "Machine learning models for continuous outcomes often yield systematically biased predictions, particularly for values that largely deviate from the mean. Specifically, predictions for large-valued outcomes tend to be negatively biased (underestimating actual values), while those for small-valued outcomes are positively biased (overestimating actual values). We refer to this linear central tendency warped bias as the \"systematic bias of machine learning regression\". In this paper, we first demonstrate that this systematic prediction bias persists across various machine learning regression models, and then delve into its theoretical underpinnings. To address this issue, we propose a general constrained optimization approach designed to correct this bias and develop computationally efficient implementation algorithms. Simulation results indicate that our correction method effectively eliminates the bias from the predicted outcomes. We apply the proposed approach to the prediction of brain age using neuroimaging data. In comparison to competing machine learning regression models, our method effectively addresses the longstanding issue of \"systematic bias of machine learning regression\" in neuroimaging-based brain age calculation, yielding unbiased predictions of brain age.", "published": "2024-05-24T21:34:16Z", "authors": ["Hwiyoung Lee", "Shuo Chen"], "categories": ["stat.ML", "cs.LG", "stat.ME"], "doi": "", "_query": "house price prediction machine learning", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/cs/0611011v1", "title": "Hedging predictions in machine learning", "abstract": "Recent advances in machine learning make it possible to design efficient prediction algorithms for data sets with huge numbers of parameters. This paper describes a new technique for \"hedging\" the predictions output by many such algorithms, including support vector machines, kernel ridge regression, kernel nearest neighbours, and by many other state-of-the-art methods. The hedged predictions for the labels of new objects include quantitative measures of their own accuracy and reliability. These measures are provably valid under the assumption of randomness, traditional in machine learning: the objects and their labels are assumed to be generated independently from the same probability distribution. In particular, it becomes possible to control (up to statistical fluctuations) the number of erroneous predictions by selecting a suitable confidence level. Validity being achieved automatically, the remaining goal of hedged prediction is efficiency: taking full account of the new objects' features and other available information to produce as accurate predictions as possible. This can be done successfully using the powerful machinery of modern machine learning.", "published": "2006-11-02T18:44:49Z", "authors": ["Alexander Gammerman", "Vladimir Vovk"], "categories": ["cs.LG"], "doi": "10.1093/comjnl/bxl065", "_query": "house price prediction machine learning", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2208.04365v1", "title": "Gradient Flows for L2 Support Vector Machine Training", "abstract": "We explore the merits of training of support vector machines for binary classification by means of solving systems of ordinary differential equations. We thus assume a continuous time perspective on a machine learning problem which may be of interest for implementations on (re)emerging hardware platforms such as analog- or quantum computers.", "published": "2022-08-08T18:56:47Z", "authors": ["Christian Bauckhage", "Helen Schneider", "Benjamin Wulff", "Rafet Sifa"], "categories": ["cs.LG"], "doi": "", "_query": "house price prediction machine learning", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/1705.00470v2", "title": "Learning Multimodal Transition Dynamics for Model-Based Reinforcement Learning", "abstract": "In this paper we study how to learn stochastic, multimodal transition dynamics in reinforcement learning (RL) tasks. We focus on evaluating transition function estimation, while we defer planning over this model to future work. Stochasticity is a fundamental property of many task environments. However, discriminative function approximators have difficulty estimating multimodal stochasticity. In contrast, deep generative models do capture complex high-dimensional outcome distributions. First we discuss why, amongst such models, conditional variational inference (VI) is theoretically most appealing for model-based RL. Subsequently, we compare different VI models on their ability to learn complex stochasticity on simulated functions, as well as on a typical RL gridworld with multimodal dynamics. Results show VI successfully predicts multimodal outcomes, but also robustly ignores these for deterministic parts of the transition dynamics. In summary, we show a robust method to learn multimodal transitions using function approximation, which is a key preliminary for model-based RL in stochastic domains.", "published": "2017-05-01T11:06:04Z", "authors": ["Thomas M. Moerland", "Joost Broekens", "Catholijn M. Jonker"], "categories": ["stat.ML", "cs.LG"], "doi": "", "_query": "house price prediction machine learning", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2106.04739v1", "title": "Learning subtree pattern importance for Weisfeiler-Lehmanbased graph kernels", "abstract": "Graph is an usual representation of relational data, which are ubiquitous in manydomains such as molecules, biological and social networks. A popular approach to learningwith graph structured data is to make use of graph kernels, which measure the similaritybetween graphs and are plugged into a kernel machine such as a support vector machine.Weisfeiler-Lehman (WL) based graph kernels, which employ WL labeling scheme to extract subtree patterns and perform node embedding, are demonstrated to achieve great performance while being efficiently computable. However, one of the main drawbacks of ageneral kernel is the decoupling of kernel construction and learning process. For moleculargraphs, usual kernels such as WL subtree, based on substructures of the molecules, consider all available substructures having the same importance, which might not be suitable inpractice. In this paper, we propose a method to learn the weights of subtree patterns in the framework of WWL kernels, the state of the art method for graph classification task [14]. To overcome the computational issue on large scale data sets, we present an efficient learning algorithm and also derive a generalization gap bound to show its convergence. Finally, through experiments on synthetic and real-world data sets, we demonstrate the effectiveness of our proposed method for learning the weights of subtree patterns.", "published": "2021-06-08T23:47:44Z", "authors": ["Dai Hai Nguyen", "Canh Hao Nguyen", "Hiroshi Mamitsuka"], "categories": ["cs.LG"], "doi": "10.1007/S10994-021-05991-Y", "_query": "house price prediction machine learning", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2002.10904v3", "title": "Reward Shaping for Human Learning via Inverse Reinforcement Learning", "abstract": "Humans are spectacular reinforcement learners, constantly learning from and adjusting to experience and feedback. Unfortunately, this doesn't necessarily mean humans are fast learners. When tasks are challenging, learning can become unacceptably slow. Fortunately, humans do not have to learn tabula rasa, and learning speed can be greatly increased with learning aids. In this work we validate a new type of learning aid -- reward shaping for humans via inverse reinforcement learning (IRL). The goal of this aid is to increase the speed with which humans can learn good policies for specific tasks. Furthermore this approach compliments alternative machine learning techniques such as safety features that try to prevent individuals from making poor decisions. To achieve our results we first extend a well known IRL algorithm via kernel methods. Afterwards we conduct two human subjects experiments using an online game where players have limited time to learn a good policy. We show with statistical significance that players who receive our learning aid are able to approach desired policies more quickly than the control group.", "published": "2020-02-25T14:44:25Z", "authors": ["Mark A. Rucker", "Layne T. Watson", "Matthew S. Gerber", "Laura E. Barnes"], "categories": ["cs.LG", "stat.ML"], "doi": "", "_query": "house price prediction machine learning", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2308.12120v1", "title": "An Open-Source ML-Based Full-Stack Optimization Framework for Machine Learning Accelerators", "abstract": "Parameterizable machine learning (ML) accelerators are the product of recent breakthroughs in ML. To fully enable their design space exploration (DSE), we propose a physical-design-driven, learning-based prediction framework for hardware-accelerated deep neural network (DNN) and non-DNN ML algorithms. It adopts a unified approach that combines backend power, performance, and area (PPA) analysis with frontend performance simulation, thereby achieving a realistic estimation of both backend PPA and system metrics such as runtime and energy. In addition, our framework includes a fully automated DSE technique, which optimizes backend and system metrics through an automated search of architectural and backend parameters. Experimental studies show that our approach consistently predicts backend PPA and system metrics with an average 7% or less prediction error for the ASIC implementation of two deep learning accelerator platforms, VTA and VeriGOOD-ML, in both a commercial 12 nm process and a research-oriented 45 nm process.", "published": "2023-08-23T13:16:31Z", "authors": ["Hadi Esmaeilzadeh", "Soroush Ghodrati", "Andrew B. Kahng", "Joon Kyung Kim", "Sean Kinzer", "Sayak Kundu", "Rohan Mahapatra", "Susmita Dey Manasi", "Sachin Sapatnekar", "Zhiang Wang", "Ziqing Zeng"], "categories": ["cs.LG", "cs.AR"], "doi": "", "_query": "house price prediction machine learning", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/1907.02644v5", "title": "PathologyGAN: Learning deep representations of cancer tissue", "abstract": "Histopathological images of tumors contain abundant information about how tumors grow and how they interact with their micro-environment. Better understanding of tissue phenotypes in these images could reveal novel determinants of pathological processes underlying cancer, and in turn improve diagnosis and treatment options. Advances of Deep learning makes it ideal to achieve those goals, however, its application is limited by the cost of high quality labels from patients data. Unsupervised learning, in particular, deep generative models with representation learning properties provides an alternative path to further understand cancer tissue phenotypes, capturing tissue morphologies. In this paper, we develop a framework which allows GANs to capture key tissue features and uses these characteristics to give structure to its latent space. To this end, we trained our model on two different datasets, an H&E colorectal cancer tissue from the National Center for Tumor diseases (NCT) and an H&E breast cancer tissue from the Netherlands Cancer Institute (NKI) and Vancouver General Hospital (VGH). Composed of 86 slide images and 576 TMAs respectively. We show that our model generates high quality images, with a FID of 16.65 (breast cancer) and 32.05 (colorectal cancer). We further assess the quality of the images with cancer tissue characteristics (e.g. count of cancer, lymphocytes, or stromal cells), using quantitative information to calculate the FID and showing consistent performance of 9.86. Additionally, the latent space of our model shows an interpretable structure and allows semantic vector operations that translate into tissue feature transformations. Furthermore, ratings from two expert pathologists found no significant difference between our generated tissue images from real ones. The code, images, and pretrained models are available at https://github.com/AdalbertoCq/Pathology-GAN", "published": "2019-07-04T15:27:22Z", "authors": ["Adalberto Claudio Quiros", "Roderick Murray-Smith", "Ke Yuan"], "categories": ["eess.IV", "cs.LG", "stat.ML"], "doi": "", "_query": "house price prediction machine learning", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2103.02768v1", "title": "Learning to Predict with Supporting Evidence: Applications to Clinical Risk Prediction", "abstract": "The impact of machine learning models on healthcare will depend on the degree of trust that healthcare professionals place in the predictions made by these models. In this paper, we present a method to provide people with clinical expertise with domain-relevant evidence about why a prediction should be trusted. We first design a probabilistic model that relates meaningful latent concepts to prediction targets and observed data. Inference of latent variables in this model corresponds to both making a prediction and providing supporting evidence for that prediction. We present a two-step process to efficiently approximate inference: (i) estimating model parameters using variational learning, and (ii) approximating maximum a posteriori estimation of latent variables in the model using a neural network, trained with an objective derived from the probabilistic model. We demonstrate the method on the task of predicting mortality risk for patients with cardiovascular disease. Specifically, using electrocardiogram and tabular data as input, we show that our approach provides appropriate domain-relevant supporting evidence for accurate predictions.", "published": "2021-03-04T00:26:32Z", "authors": ["Aniruddh Raghu", "John Guttag", "Katherine Young", "Eugene Pomerantsev", "Adrian V. Dalca", "Collin M. Stultz"], "categories": ["cs.LG"], "doi": "", "_query": "house price prediction machine learning", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2009.10990v2", "title": "Accurate and Interpretable Machine Learning for Transparent Pricing of Health Insurance Plans", "abstract": "Health insurance companies cover half of the United States population through commercial employer-sponsored health plans and pay 1.2 trillion US dollars every year to cover medical expenses for their members. The actuary and underwriter roles at a health insurance company serve to assess which risks to take on and how to price those risks to ensure profitability of the organization. While Bayesian hierarchical models are the current standard in the industry to estimate risk, interest in machine learning as a way to improve upon these existing methods is increasing. Lumiata, a healthcare analytics company, ran a study with a large health insurance company in the United States. We evaluated the ability of machine learning models to predict the per member per month cost of employer groups in their next renewal period, especially those groups who will cost less than 95\\% of what an actuarial model predicts (groups with \"concession opportunities\"). We developed a sequence of two models, an individual patient-level and an employer-group-level model, to predict the annual per member per month allowed amount for employer groups, based on a population of 14 million patients. Our models performed 20\\% better than the insurance carrier's existing pricing model, and identified 84\\% of the concession opportunities. This study demonstrates the application of a machine learning system to compute an accurate and fair price for health insurance products and analyzes how explainable machine learning models can exceed actuarial models' predictive accuracy while maintaining interpretability.", "published": "2020-09-23T08:07:33Z", "authors": ["Rohun Kshirsagar", "Li-Yen Hsu", "Vatshank Chaturvedi", "Charles H. Greenberg", "Matthew McClelland", "Anushadevi Mohan", "Wideet Shende", "Nicolas P. Tilmans", "Renzo Frigato", "Min Guo", "Ankit Chheda", "Meredith Trotter", "Shonket Ray", "Arnold Lee", "Miguel Alvarado"], "categories": ["cs.CY", "cs.LG", "stat.ML"], "doi": "", "_query": "house price prediction machine learning", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/1904.07686v1", "title": "Predicting Time-to-Failure of Plasma Etching Equipment using Machine Learning", "abstract": "Predicting unscheduled breakdowns of plasma etching equipment can reduce maintenance costs and production losses in the semiconductor industry. However, plasma etching is a complex procedure and it is hard to capture all relevant equipment properties and behaviors in a single physical model. Machine learning offers an alternative for predicting upcoming machine failures based on relevant data points. In this paper, we describe three different machine learning tasks that can be used for that purpose: (i) predicting Time-To-Failure (TTF), (ii) predicting health state, and (iii) predicting TTF intervals of an equipment. Our results show that trained machine learning models can outperform benchmarks resembling human judgments in all three tasks. This suggests that machine learning offers a viable alternative to currently deployed plasma etching equipment maintenance strategies and decision making processes.", "published": "2019-04-16T14:07:17Z", "authors": ["Anahid Jalali", "Clemens Heistracher", "Alexander Schindler", "Bernhard Haslhofer", "Tanja Nemeth", "Robert Glawar", "Wilfried Sihn", "Peter De Boer"], "categories": ["cs.LG", "stat.ML"], "doi": "", "_query": "house price prediction machine learning", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2307.07332v1", "title": "Nuclear Physics in the Era of Quantum Computing and Quantum Machine Learning", "abstract": "In this paper, the application of quantum simulations and quantum machine learning to solve low-energy nuclear physics problems is explored. The use of quantum computing to deal with nuclear physics problems is, in general, in its infancy and, in particular, the use of quantum machine learning in the realm of nuclear physics at low energy is almost nonexistent. We present here three specific examples where the use of quantum computing and quantum machine learning provides, or could provide in the future, a possible computational advantage: i) the determination of the phase/shape in schematic nuclear models, ii) the calculation of the ground state energy of a nuclear shell model-type Hamiltonian and iii) the identification of particles or the determination of trajectories in nuclear physics experiments.", "published": "2023-07-14T13:18:32Z", "authors": ["J. E. García-Ramos", "A. Sáiz", "J. M. Arias", "L. Lamata", "P. Pérez-Fernández"], "categories": ["quant-ph", "nucl-ex", "nucl-th"], "doi": "", "_query": "house price prediction machine learning", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/1901.02324v2", "title": "Learning with Fenchel-Young Losses", "abstract": "Over the past decades, numerous loss functions have been been proposed for a variety of supervised learning tasks, including regression, classification, ranking, and more generally structured prediction. Understanding the core principles and theoretical properties underpinning these losses is key to choose the right loss for the right problem, as well as to create new losses which combine their strengths. In this paper, we introduce Fenchel-Young losses, a generic way to construct a convex loss function for a regularized prediction function. We provide an in-depth study of their properties in a very broad setting, covering all the aforementioned supervised learning tasks, and revealing new connections between sparsity, generalized entropies, and separation margins. We show that Fenchel-Young losses unify many well-known loss functions and allow to create useful new ones easily. Finally, we derive efficient predictive and training algorithms, making Fenchel-Young losses appealing both in theory and practice.", "published": "2019-01-08T14:37:56Z", "authors": ["Mathieu Blondel", "André F. T. Martins", "Vlad Niculae"], "categories": ["stat.ML", "cs.LG"], "doi": "", "_query": "house price prediction machine learning", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2407.07668v3", "title": "How to Leverage Predictive Uncertainty Estimates for Reducing Catastrophic Forgetting in Online Continual Learning", "abstract": "Many real-world applications require machine-learning models to be able to deal with non-stationary data distributions and thus learn autonomously over an extended period of time, often in an online setting. One of the main challenges in this scenario is the so-called catastrophic forgetting (CF) for which the learning model tends to focus on the most recent tasks while experiencing predictive degradation on older ones. In the online setting, the most effective solutions employ a fixed-size memory buffer to store old samples used for replay when training on new tasks. Many approaches have been presented to tackle this problem. However, it is not clear how predictive uncertainty information for memory management can be leveraged in the most effective manner and conflicting strategies are proposed to populate the memory. Are the easiest-to-forget or the easiest-to-remember samples more effective in combating CF? Starting from the intuition that predictive uncertainty provides an idea of the samples' location in the decision space, this work presents an in-depth analysis of different uncertainty estimates and strategies for populating the memory. The investigation provides a better understanding of the characteristics data points should have for alleviating CF. Then, we propose an alternative method for estimating predictive uncertainty via the generalised variance induced by the negative log-likelihood. Finally, we demonstrate that the use of predictive uncertainty measures helps in reducing CF in different settings.", "published": "2024-07-10T13:51:15Z", "authors": ["Giuseppe Serra", "Ben Werner", "Florian Buettner"], "categories": ["cs.LG", "cs.AI"], "doi": "", "_query": "house price prediction machine learning", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2104.07820v2", "title": "Machine Learning Approaches for Type 2 Diabetes Prediction and Care Management", "abstract": "Prediction of diabetes and its various complications has been studied in a number of settings, but a comprehensive overview of problem setting for diabetes prediction and care management has not been addressed in the literature. In this document we seek to remedy this omission in literature with an encompassing overview of diabetes complication prediction as well as situating this problem in the context of real world healthcare management. We illustrate various problems encountered in real world clinical scenarios via our own experience with building and deploying such models. In this manuscript we illustrate a Machine Learning (ML) framework for addressing the problem of predicting Type 2 Diabetes Mellitus (T2DM) together with a solution for risk stratification, intervention and management. These ML models align with how physicians think about disease management and mitigation, which comprises these four steps: Identify, Stratify, Engage, Measure.", "published": "2021-04-15T23:38:39Z", "authors": ["Aloysius Lim", "Ashish Singh", "Jody Chiam", "Carly Eckert", "Vikas Kumar", "Muhammad Aurangzeb Ahmad", "Ankur Teredesai"], "categories": ["cs.LG", "stat.ML"], "doi": "", "_query": "house price prediction machine learning", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2308.12175v1", "title": "Unsupervised anomalies detection in IIoT edge devices networks using federated learning", "abstract": "In a connection of many IoT devices that each collect data, normally training a machine learning model would involve transmitting the data to a central server which requires strict privacy rules. However, some owners are reluctant of availing their data out of the company due to data security concerns. Federated learning(FL) as a distributed machine learning approach performs training of a machine learning model on the device that gathered the data itself. In this scenario, data is not share over the network for training purpose. Fedavg as one of FL algorithms permits a model to be copied to participating devices during a training session. The devices could be chosen at random, and a device can be aborted. The resulting models are sent to the coordinating server and then average models from the devices that finished training. The process is repeated until a desired model accuracy is achieved. By doing this, FL approach solves the privacy problem for IoT/ IIoT devices that held sensitive data for the owners. In this paper, we leverage the benefits of FL and implemented Fedavg algorithm on a recent dataset that represent the modern IoT/ IIoT device networks. The results were almost the same as the centralized machine learning approach. We also evaluated some shortcomings of Fedavg such as unfairness that happens during the training when struggling devices do not participate for every stage of training. This inefficient training of local or global model could lead in a high number of false alarms in intrusion detection systems for IoT/IIoT gadgets developed using Fedavg. Hence, after evaluating the FedAv deep auto encoder with centralized deep auto encoder ML, we further proposed and designed a Fair Fedavg algorithm that will be evaluated in the future work.", "published": "2023-08-23T14:53:38Z", "authors": ["Niyomukiza Thamar", "Hossam Samy Elsaid Sharara"], "categories": ["cs.LG", "cs.AI", "cs.CR", "cs.DC"], "doi": "", "_query": "house price prediction machine learning", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/1807.07215v4", "title": "Machine Learning Classifiers Do Not Improve the Prediction of Academic Risk: Evidence from Australia", "abstract": "Machine learning methods tend to outperform traditional statistical models at prediction. In the prediction of academic achievement, ML models have not shown substantial improvement over logistic regression. So far, these results have almost entirely focused on college achievement, due to the availability of administrative datasets, and have contained relatively small sample sizes by ML standards. In this article we apply popular machine learning models to a large dataset ($n=1.2$ million) containing primary and middle school performance on a standardized test given annually to Australian students. We show that machine learning models do not outperform logistic regression for detecting students who will perform in the `below standard' band of achievement upon sitting their next test, even in a large-$n$ setting.", "published": "2018-07-19T02:01:36Z", "authors": ["Sarah Cornell-Farrow", "Robert Garrard"], "categories": ["stat.ML", "cs.LG", "econ.EM"], "doi": "10.1080/23737484.2020.1752849", "_query": "house price prediction machine learning", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/1911.04374v2", "title": "Combining human cell line transcriptome analysis and Bayesian inference to build trustworthy machine learning models for prediction of animal toxicity in drug development", "abstract": "Biomedical data, particularly in the field of genomics, has characteristics which make it challenging for machine learning applications - it can be sparse, high dimensional and noisy. Biomedical applications also present challenges to model selection - whilst powerful, accurate predictions are necessary, they alone are not sufficient for a model to be deemed useful. Due to the nature of the predictions, a model must also be trustworthy and transparent, empowering a practitioner with confidence that its use is appropriate and reliable. In this paper, we propose that this can be achieved through the use of judiciously built feature sets coupled with Bayesian models, specifically Gaussian processes. We apply Gaussian processes to drug discovery, using inexpensive transcriptomic profiles from human cell lines to predict animal kidney and liver toxicity after treatment with specific chemical compounds. This approach has the potential to reduce invasive and expensive animal testing during clinical trials if in vitro human cell line analysis can accurately predict model animal phenotypes. We compare results across a range of feature sets and models, to highlight model importance for medical applications.", "published": "2019-11-11T16:32:02Z", "authors": ["Laura-Jayne Gardiner", "Anna Paola Carrieri", "Jenny Wilshaw", "Stephen Checkley", "Edward O Pyzer-Knapp", "Ritesh Krishna"], "categories": ["q-bio.GN", "q-bio.QM"], "doi": "", "_query": "house price prediction machine learning", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/1506.00852v2", "title": "Peer Grading in a Course on Algorithms and Data Structures: Machine Learning Algorithms do not Improve over Simple Baselines", "abstract": "Peer grading is the process of students reviewing each others' work, such as homework submissions, and has lately become a popular mechanism used in massive open online courses (MOOCs). Intrigued by this idea, we used it in a course on algorithms and data structures at the University of Hamburg. Throughout the whole semester, students repeatedly handed in submissions to exercises, which were then evaluated both by teaching assistants and by a peer grading mechanism, yielding a large dataset of teacher and peer grades. We applied different statistical and machine learning methods to aggregate the peer grades in order to come up with accurate final grades for the submissions (supervised and unsupervised, methods based on numeric scores and ordinal rankings). Surprisingly, none of them improves over the baseline of using the mean peer grade as the final grade. We discuss a number of possible explanations for these results and present a thorough analysis of the generated dataset.", "published": "2015-06-02T12:03:30Z", "authors": ["Mehdi S. M. Sajjadi", "Morteza Alamgir", "Ulrike von Luxburg"], "categories": ["cs.LG", "stat.ML"], "doi": "", "_query": "house price prediction machine learning", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2412.06837v1", "title": "Innovative Sentiment Analysis and Prediction of Stock Price Using FinBERT, GPT-4 and Logistic Regression: A Data-Driven Approach", "abstract": "This study explores the comparative performance of cutting-edge AI models, i.e., Finaance Bidirectional Encoder representations from Transsformers (FinBERT), Generatice Pre-trained Transformer GPT-4, and Logistic Regression, for sentiment analysis and stock index prediction using financial news and the NGX All-Share Index data label. By leveraging advanced natural language processing models like GPT-4 and FinBERT, alongside a traditional machine learning model, Logistic Regression, we aim to classify market sentiment, generate sentiment scores, and predict market price movements. This research highlights global AI advancements in stock markets, showcasing how state-of-the-art language models can contribute to understanding complex financial data. The models were assessed using metrics such as accuracy, precision, recall, F1 score, and ROC AUC. Results indicate that Logistic Regression outperformed the more computationally intensive FinBERT and predefined approach of versatile GPT-4, with an accuracy of 81.83% and a ROC AUC of 89.76%. The GPT-4 predefined approach exhibited a lower accuracy of 54.19% but demonstrated strong potential in handling complex data. FinBERT, while offering more sophisticated analysis, was resource-demanding and yielded a moderate performance. Hyperparameter optimization using Optuna and cross-validation techniques ensured the robustness of the models. This study highlights the strengths and limitations of the practical applications of AI approaches in stock market prediction and presents Logistic Regression as the most efficient model for this task, with FinBERT and GPT-4 representing emerging tools with potential for future exploration and innovation in AI-driven financial analytics", "published": "2024-12-07T05:20:31Z", "authors": ["Olamilekan Shobayo", "Sidikat Adeyemi-Longe", "Olusogo Popoola", "Bayode Ogunleye"], "categories": ["cs.LG", "cs.AI", "q-fin.ST", "stat.AP", "stat.CO"], "doi": "10.3390/bdcc8110143", "_query": "house price prediction machine learning", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2607.02166v1", "title": "Dynamic Neural Graph Encoding of Inference Processes in Deep Weight Space", "abstract": "The rapid advancements in using neural networks as implicit data representations have attracted significant interest in developing machine learning methods that analyze and process the weight spaces of other neural networks. However, efficiently handling these highdimensional weight spaces remains challenging. Existing methods often overlook the sequential nature of layer-by-layer processing in neural network inference. In this work, we propose a novel approach using dynamic graphs to represent neural network parameters, capturing the temporal dynamics of inference. Our Dynamic Neural Graph Encoder (DNG-Encoder) processes these graphs, preserving the sequential nature of neural processing. Additionally, we also leverage DNG-Encoder to develop INR2JLS (Implicit Neural Representation to Joint Latent Space) for facilitate downstream applications, such as classifying Implicit Neural Representations (INRs). Our approach demonstrates significant improvements across multiple tasks, surpassing the state-of-the-art INR classification accuracy by approximately 10% on the CIFAR-100-INR.", "published": "2026-07-02T13:36:19Z", "authors": ["Di Wu", "Huan Liu", "Zhixiang Chi", "Yuanhao Yu", "Konstantinos N. Plataniotis", "Yang Wang"], "categories": ["cs.LG", "cs.AI"], "doi": "", "_query": "house price prediction machine learning", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2107.06124v3", "title": "On Doubly Robust Inference for Double Machine Learning in Semiparametric Regression", "abstract": "Due to concerns about parametric model misspecification, there is interest in using machine learning to adjust for confounding when evaluating the causal effect of an exposure on an outcome. Unfortunately, exposure effect estimators that rely on machine learning predictions are generally subject to so-called plug-in bias, which can render naive $p$-values and confidence intervals invalid. Progress has been made via proposals like targeted minimum loss estimation and more recently double machine learning, which rely on learning the conditional mean of both the outcome and exposure. Valid inference can then be obtained so long as both predictions converge (sufficiently fast) to the truth. Focusing on partially linear regression models, we show that a specific implementation of the machine learning techniques can yield exposure effect estimators that have small bias even when one of the first-stage predictions does not converge to the truth. The resulting tests and confidence intervals are doubly robust. We also show that the proposed estimators may fail to be regular when only one nuisance parameter is consistently estimated; nevertheless, we observe in simulation studies that our proposal can lead to reduced bias and improved confidence interval coverage in moderate-to-large samples.", "published": "2021-07-13T14:24:33Z", "authors": ["Oliver Dukes", "Stijn Vansteelandt", "David Whitney"], "categories": ["stat.ME"], "doi": "", "_query": "house price prediction machine learning", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/1604.02485v1", "title": "Machine Learning for Visual Navigation of Unmanned Ground Vehicles", "abstract": "The use of visual information for the navigation of unmanned ground vehicles in a cross-country environment recently received great attention. However, until now, the use of textural information has been somewhat less effective than color or laser range information. This manuscript reviews the recent achievements in cross-country scene segmentation and addresses their shortcomings. It then describes a problem related to classification of high dimensional texture features. Finally, it compares three machine learning algorithms aimed at resolving this problem. The experimental results for each machine learning algorithm with the discussion of comparisons are given at the end of the manuscript.", "published": "2016-04-08T21:04:36Z", "authors": ["Artem A. Lenskiy", "Jong-Soo Lee"], "categories": ["cs.CV"], "doi": "10.4018/978-1-60960-818-7.ch418", "_query": "house price prediction machine learning", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2009.11087v1", "title": "Probabilistic Machine Learning for Healthcare", "abstract": "Machine learning can be used to make sense of healthcare data. Probabilistic machine learning models help provide a complete picture of observed data in healthcare. In this review, we examine how probabilistic machine learning can advance healthcare. We consider challenges in the predictive model building pipeline where probabilistic models can be beneficial including calibration and missing data. Beyond predictive models, we also investigate the utility of probabilistic machine learning models in phenotyping, in generative models for clinical use cases, and in reinforcement learning.", "published": "2020-09-23T12:14:05Z", "authors": ["Irene Y. Chen", "Shalmali Joshi", "Marzyeh Ghassemi", "Rajesh Ranganath"], "categories": ["stat.ML", "cs.CY", "cs.LG"], "doi": "", "_query": "house price prediction machine learning", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/1805.08196v1", "title": "Learning Maximum-A-Posteriori Perturbation Models for Structured Prediction in Polynomial Time", "abstract": "MAP perturbation models have emerged as a powerful framework for inference in structured prediction. Such models provide a way to efficiently sample from the Gibbs distribution and facilitate predictions that are robust to random noise. In this paper, we propose a provably polynomial time randomized algorithm for learning the parameters of perturbed MAP predictors. Our approach is based on minimizing a novel Rademacher-based generalization bound on the expected loss of a perturbed MAP predictor, which can be computed in polynomial time. We obtain conditions under which our randomized learning algorithm can guarantee generalization to unseen examples.", "published": "2018-05-21T17:43:37Z", "authors": ["Asish Ghoshal", "Jean Honorio"], "categories": ["cs.LG", "stat.ML"], "doi": "", "_query": "house price prediction machine learning", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2312.02074v1", "title": "Federated Learning is Better with Non-Homomorphic Encryption", "abstract": "Traditional AI methodologies necessitate centralized data collection, which becomes impractical when facing problems with network communication, data privacy, or storage capacity. Federated Learning (FL) offers a paradigm that empowers distributed AI model training without collecting raw data. There are different choices for providing privacy during FL training. One of the popular methodologies is employing Homomorphic Encryption (HE) - a breakthrough in privacy-preserving computation from Cryptography. However, these methods have a price in the form of extra computation and memory footprint. To resolve these issues, we propose an innovative framework that synergizes permutation-based compressors with Classical Cryptography, even though employing Classical Cryptography was assumed to be impossible in the past in the context of FL. Our framework offers a way to replace HE with cheaper Classical Cryptography primitives which provides security for the training process. It fosters asynchronous communication and provides flexible deployment options in various communication topologies.", "published": "2023-12-04T17:37:41Z", "authors": ["Konstantin Burlachenko", "Abdulmajeed Alrowithi", "Fahad Ali Albalawi", "Peter Richtarik"], "categories": ["cs.CR", "cs.LG", "math.OC"], "doi": "10.1145/3630048.3630182", "_query": "house price prediction machine learning", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2205.15752v2", "title": "Hierarchies of Reward Machines", "abstract": "Reward machines (RMs) are a recent formalism for representing the reward function of a reinforcement learning task through a finite-state machine whose edges encode subgoals of the task using high-level events. The structure of RMs enables the decomposition of a task into simpler and independently solvable subtasks that help tackle long-horizon and/or sparse reward tasks. We propose a formalism for further abstracting the subtask structure by endowing an RM with the ability to call other RMs, thus composing a hierarchy of RMs (HRM). We exploit HRMs by treating each call to an RM as an independently solvable subtask using the options framework, and describe a curriculum-based method to learn HRMs from traces observed by the agent. Our experiments reveal that exploiting a handcrafted HRM leads to faster convergence than with a flat HRM, and that learning an HRM is feasible in cases where its equivalent flat representation is not.", "published": "2022-05-31T12:39:24Z", "authors": ["Daniel Furelos-Blanco", "Mark Law", "Anders Jonsson", "Krysia Broda", "Alessandra Russo"], "categories": ["cs.LG", "cs.AI"], "doi": "", "_query": "house price prediction machine learning", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/1808.04928v1", "title": "Deep EHR: Chronic Disease Prediction Using Medical Notes", "abstract": "Early detection of preventable diseases is important for better disease management, improved inter-ventions, and more efficient health-care resource allocation. Various machine learning approacheshave been developed to utilize information in Electronic Health Record (EHR) for this task. Majorityof previous attempts, however, focus on structured fields and lose the vast amount of information inthe unstructured notes. In this work we propose a general multi-task framework for disease onsetprediction that combines both free-text medical notes and structured information. We compareperformance of different deep learning architectures including CNN, LSTM and hierarchical models.In contrast to traditional text-based prediction models, our approach does not require disease specificfeature engineering, and can handle negations and numerical values that exist in the text. Ourresults on a cohort of about 1 million patients show that models using text outperform modelsusing just structured data, and that models capable of using numerical values and negations in thetext, in addition to the raw text, further improve performance. Additionally, we compare differentvisualization methods for medical professionals to interpret model predictions.", "published": "2018-08-15T00:10:55Z", "authors": ["Jingshu Liu", "Zachariah Zhang", "Narges Razavian"], "categories": ["cs.LG", "cs.CL", "stat.ML"], "doi": "", "_query": "house price prediction machine learning", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2102.09024v1", "title": "Deep Learning Approaches for Forecasting Strawberry Yields and Prices Using Satellite Images and Station-Based Soil Parameters", "abstract": "Computational tools for forecasting yields and prices for fresh produce have been based on traditional machine learning approaches or time series modelling. We propose here an alternate approach based on deep learning algorithms for forecasting strawberry yields and prices in Santa Barbara county, California. Building the proposed forecasting model comprises three stages: first, the station-based ensemble model (ATT-CNN-LSTM-SeriesNet_Ens) with its compound deep learning components, SeriesNet with Gated Recurrent Unit (GRU) and Convolutional Neural Network LSTM with Attention layer (Att-CNN-LSTM), are trained and tested using the station-based soil temperature and moisture data of SantaBarbara as input and the corresponding strawberry yields or prices as output. Secondly, the remote sensing ensemble model (SIM_CNN-LSTM_Ens), which is an ensemble model of Convolutional NeuralNetwork LSTM (CNN-LSTM) models, is trained and tested using satellite images of the same county as input mapped to the same yields and prices as output. These two ensembles forecast strawberry yields and prices with minimal forecasting errors and highest model correlation for five weeks ahead forecasts.Finally, the forecasts of these two models are ensembled to have a final forecasted value for yields and prices by introducing a voting ensemble. Based on an aggregated performance measure (AGM), it is found that this voting ensemble not only enhances the forecasting performance by 5% compared to its best performing component model but also outperforms the Deep Learning (DL) ensemble model found in literature by 33% for forecasting yields and 21% for forecasting prices", "published": "2021-02-17T20:54:34Z", "authors": ["Mohita Chaudhary", "Mohamed Sadok Gastli", "Lobna Nassar", "Fakhri Karray"], "categories": ["cs.LG", "cs.AI"], "doi": "", "_query": "house price prediction machine learning", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2309.07992v2", "title": "An Automated Machine Learning Approach for Detecting Anomalous Peak Patterns in Time Series Data from a Research Watershed in the Northeastern United States Critical Zone", "abstract": "This paper presents an automated machine learning framework designed to assist hydrologists in detecting anomalies in time series data generated by sensors in a research watershed in the northeastern United States critical zone. The framework specifically focuses on identifying peak-pattern anomalies, which may arise from sensor malfunctions or natural phenomena. However, the use of classification methods for anomaly detection poses challenges, such as the requirement for labeled data as ground truth and the selection of the most suitable deep learning model for the given task and dataset. To address these challenges, our framework generates labeled datasets by injecting synthetic peak patterns into synthetically generated time series data and incorporates an automated hyperparameter optimization mechanism. This mechanism generates an optimized model instance with the best architectural and training parameters from a pool of five selected models, namely Temporal Convolutional Network (TCN), InceptionTime, MiniRocket, Residual Networks (ResNet), and Long Short-Term Memory (LSTM). The selection is based on the user's preferences regarding anomaly detection accuracy and computational cost. The framework employs Time-series Generative Adversarial Networks (TimeGAN) as the synthetic dataset generator. The generated model instances are evaluated using a combination of accuracy and computational cost metrics, including training time and memory, during the anomaly detection process. Performance evaluation of the framework was conducted using a dataset from a watershed, demonstrating consistent selection of the most fitting model instance that satisfies the user's preferences.", "published": "2023-09-14T19:07:50Z", "authors": ["Ijaz Ul Haq", "Byung Suk Lee", "Donna M. Rizzo", "Julia N Perdrial"], "categories": ["cs.LG", "cs.AI"], "doi": "", "_query": "house price prediction machine learning", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2406.10787v3", "title": "Evidential Uncertainty Sets in Deep Classifiers Using Conformal Prediction", "abstract": "In this paper, we propose Evidential Conformal Prediction (ECP) method for image classifiers to generate the conformal prediction sets. Our method is designed based on a non-conformity score function that has its roots in Evidential Deep Learning (EDL) as a method of quantifying model (epistemic) uncertainty in DNN classifiers. We use evidence that are derived from the logit values of target labels to compute the components of our non-conformity score function: the heuristic notion of uncertainty in CP, uncertainty surprisal, and expected utility. Our extensive experimental evaluation demonstrates that ECP outperforms three state-of-the-art methods for generating CP sets, in terms of their set sizes and adaptivity while maintaining the coverage of true labels.", "published": "2024-06-16T03:00:16Z", "authors": ["Hamed Karimi", "Reza Samavi"], "categories": ["cs.LG", "cs.AI", "cs.CV", "stat.ML"], "doi": "", "_query": "house price prediction machine learning", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/1811.12560v2", "title": "An Introduction to Deep Reinforcement Learning", "abstract": "Deep reinforcement learning is the combination of reinforcement learning (RL) and deep learning. This field of research has been able to solve a wide range of complex decision-making tasks that were previously out of reach for a machine. Thus, deep RL opens up many new applications in domains such as healthcare, robotics, smart grids, finance, and many more. This manuscript provides an introduction to deep reinforcement learning models, algorithms and techniques. Particular focus is on the aspects related to generalization and how deep RL can be used for practical applications. We assume the reader is familiar with basic machine learning concepts.", "published": "2018-11-30T00:57:30Z", "authors": ["Vincent Francois-Lavet", "Peter Henderson", "Riashat Islam", "Marc G. Bellemare", "Joelle Pineau"], "categories": ["cs.LG", "cs.AI", "stat.ML"], "doi": "10.1561/2200000071", "_query": "house price prediction machine learning", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2001.09769v1", "title": "Stock Price Prediction Using Convolutional Neural Networks on a Multivariate Timeseries", "abstract": "Prediction of future movement of stock prices has been a subject matter of many research work. In this work, we propose a hybrid approach for stock price prediction using machine learning and deep learning-based methods. We select the NIFTY 50 index values of the National Stock Exchange of India, over a period of four years, from January 2015 till December 2019. Based on the NIFTY data during the said period, we build various predictive models using machine learning approaches, and then use those models to predict the Close value of NIFTY 50 for the year 2019, with a forecast horizon of one week. For predicting the NIFTY index movement patterns, we use a number of classification methods, while for forecasting the actual Close values of NIFTY index, various regression models are built. We, then, augment our predictive power of the models by building a deep learning-based regression model using Convolutional Neural Network with a walk-forward validation. The CNN model is fine-tuned for its parameters so that the validation loss stabilizes with increasing number of iterations, and the training and validation accuracies converge. We exploit the power of CNN in forecasting the future NIFTY index values using three approaches which differ in number of variables used in forecasting, number of sub-models used in the overall models and, size of the input data for training the models. Extensive results are presented on various metrics for all classification and regression models. The results clearly indicate that CNN-based multivariate forecasting model is the most effective and accurate in predicting the movement of NIFTY index values with a weekly forecast horizon.", "published": "2020-01-10T03:27:08Z", "authors": ["Sidra Mehtab", "Jaydip Sen"], "categories": ["q-fin.ST", "cs.LG", "q-fin.CP"], "doi": "10.36227/techrxiv.15088734.v1", "_query": "house price prediction machine learning", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2205.13462v4", "title": "FedBR: Improving Federated Learning on Heterogeneous Data via Local Learning Bias Reduction", "abstract": "Federated Learning (FL) is a way for machines to learn from data that is kept locally, in order to protect the privacy of clients. This is typically done using local SGD, which helps to improve communication efficiency. However, such a scheme is currently constrained by slow and unstable convergence due to the variety of data on different clients' devices. In this work, we identify three under-explored phenomena of biased local learning that may explain these challenges caused by local updates in supervised FL. As a remedy, we propose FedBR, a novel unified algorithm that reduces the local learning bias on features and classifiers to tackle these challenges. FedBR has two components. The first component helps to reduce bias in local classifiers by balancing the output of the models. The second component helps to learn local features that are similar to global features, but different from those learned from other data sources. We conducted several experiments to test \\algopt and found that it consistently outperforms other SOTA FL methods. Both of its components also individually show performance gains. Our code is available at https://github.com/lins-lab/fedbr.", "published": "2022-05-26T16:08:01Z", "authors": ["Yongxin Guo", "Xiaoying Tang", "Tao Lin"], "categories": ["cs.LG"], "doi": "", "_query": "house price prediction machine learning", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2110.03861v3", "title": "QTN-VQC: An End-to-End Learning framework for Quantum Neural Networks", "abstract": "The advent of noisy intermediate-scale quantum (NISQ) computers raises a crucial challenge to design quantum neural networks for fully quantum learning tasks. To bridge the gap, this work proposes an end-to-end learning framework named QTN-VQC, by introducing a trainable quantum tensor network (QTN) for quantum embedding on a variational quantum circuit (VQC). The architecture of QTN is composed of a parametric tensor-train network for feature extraction and a tensor product encoding for quantum embedding. We highlight the QTN for quantum embedding in terms of two perspectives: (1) we theoretically characterize QTN by analyzing its representation power of input features; (2) QTN enables an end-to-end parametric model pipeline, namely QTN-VQC, from the generation of quantum embedding to the output measurement. Our experiments on the MNIST dataset demonstrate the advantages of QTN for quantum embedding over other quantum embedding approaches.", "published": "2021-10-06T14:44:51Z", "authors": ["Jun Qi", "Chao-Han Huck Yang", "Pin-Yu Chen"], "categories": ["quant-ph", "cs.AI", "cs.CL", "cs.CV", "cs.LG", "cs.NE"], "doi": "", "_query": "house price prediction machine learning", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2610.01641v1", "title": "MCIR: A Feature Dependence-Aware Explainability Method with Reliability Guarantees", "abstract": "Modern machine-learning models often contain strongly dependent or redundant features, making feature attribution difficult because shared predictive information can be distributed across correlated predictors. Existing methods such as SHAP, LIME, HSIC, MI/CMI, and SAGE may therefore produce unstable rankings under multicollinearity or near-duplicate predictors. We propose the Mutual Correlation Impact Ratio Method (MCIR-M), a dependence-aware global feature-importance approach that quantifies the unique predictive information contributed by each feature beyond a selected dependence neighbourhood. MCIR-M introduces the Mutual Correlation Impact Ratio (MCIR), which conditions each feature on strongly dependent neighbours and computes a normalized ratio of conditional to block-level information. The population score lies in [0,1] and equals zero under exact conditional redundancy. We also introduce a lightweight estimation procedure that computes MCIR using a fraction of the available data and evaluates agreement with full-data explanations. Across controlled synthetic redundancy experiments and the UCI HAR benchmark, MCIR shows dependence-aware ranking behaviour, with its clearest advantage under injected near-duplicate predictors. Comparisons with independent and conditional SHAP, SAGE, HSIC, MI-based scores, and CIR-family baselines are mixed across real-data criteria. Reduced explanation samples lower computational burden in the evaluated configurations, while agreement with full-data explanations is assessed separately through ranking, head-set, and faithfulness diagnostics. Overall, MCIR-M provides a practical dependence-aware diagnostic for global explanation under strong feature dependence.", "published": "2026-10-01T13:07:01Z", "authors": ["Poushali Sengupta", "Sabita Maharjan", "Frank Eliassen", "Shashi Raj Pandey", "Yan Zhang"], "categories": ["cs.LG", "cs.AI", "stat.CO"], "doi": "", "_query": "house price prediction machine learning", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/1804.01825v2", "title": "Evaluating Hospital Case Cost Prediction Models Using Azure Machine Learning Studio", "abstract": "Ability for accurate hospital case cost modelling and prediction is critical for efficient health care financial management and budgetary planning. A variety of regression machine learning algorithms are known to be effective for health care cost predictions. The purpose of this experiment was to build an Azure Machine Learning Studio tool for rapid assessment of multiple types of regression models. The tool offers environment for comparing 14 types of regression models in a unified experiment: linear regression, Bayesian linear regression, decision forest regression, boosted decision tree regression, neural network regression, Poisson regression, Gaussian processes for regression, gradient boosted machine, nonlinear least squares regression, projection pursuit regression, random forest regression, robust regression, robust regression with mm-type estimators, support vector regression. The tool presents assessment results arranged by model accuracy in a single table using five performance metrics. Evaluation of regression machine learning models for performing hospital case cost prediction demonstrated advantage of robust regression model, boosted decision tree regression and decision forest regression. The operational tool has been published to the web and openly available for experiments and extensions.", "published": "2018-04-04T02:40:43Z", "authors": ["Alexei Botchkarev"], "categories": ["cs.LG", "econ.GN", "stat.ML"], "doi": "", "_query": "house price prediction machine learning", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2303.06028v1", "title": "Sleep Quality Prediction from Wearables using Convolution Neural Networks and Ensemble Learning", "abstract": "Sleep is among the most important factors affecting one's daily performance, well-being, and life quality. Nevertheless, it became possible to measure it in daily life in an unobtrusive manner with wearable devices. Rather than camera recordings and extraction of the state from the images, wrist-worn devices can measure directly via accelerometer, heart rate, and heart rate variability sensors. Some measured features can be as follows: time to bed, time out of bed, bedtime duration, minutes to fall asleep, and minutes after wake-up. There are several studies in the literature regarding sleep quality and stage prediction. However, they use only wearable data to predict or focus on the sleep stage. In this study, we use the NetHealth dataset, which is collected from 698 college students' via wearables, as well as surveys. Recently, there has been an advancement in deep learning algorithms, and they generally perform better than conventional machine learning techniques. Among them, Convolutional Neural Networks (CNN) have high performances. Thus, in this study, we apply different CNN architectures that have already performed well in the human activity recognition domain and compare their results. We also apply Random Forest (RF) since it performs best among the conventional methods. In future studies, we will compare them with other deep learning algorithms.", "published": "2023-03-08T18:08:08Z", "authors": ["Ozan Kılıç", "Berrenur Saylam", "Özlem Durmaz İncel"], "categories": ["eess.SP", "cs.LG"], "doi": "", "_query": "house price prediction machine learning", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2102.04419v2", "title": "Analysis of the Effectiveness of Face-Coverings on the Death Ratio of COVID-19 Using Machine Learning", "abstract": "The recent outbreak of the COVID-19 led to the death of millions of people worldwide. To stave off the spread of the virus, the authorities in the US employed different strategies, including the mask mandate order issued by the states' governors. In the current work, we defined a parameter called the average death ratio as the monthly average of the number of daily deaths to the monthly average number of daily cases. We utilized survey data to quantify people's abidance by the mask mandate order. Additionally, we implicitly addressed the extent to which people abide by the mask mandate order that may depend on some parameters like population, income, and education level. Using different machine learning classification algorithms, we investigated how the decrease or increase in death ratio for the counties in the US West Coast correlates with the input parameters. The results showed that for most counties there, the mask mandate order decreased the death ratio reflecting the effectiveness of this preventive measure on the West Coast. Additionally, the changes in the death ratio demonstrated a noticeable correlation with the socio-economic condition of each county. Moreover, the results showed a promising classification accuracy score as high as around 90%.", "published": "2021-02-08T18:26:30Z", "authors": ["Ali Lafzi", "Miad Boodaghi", "Siavash Zamani", "Niyousha Mohammadshafie", "Veeraraghava Raju Hasti"], "categories": ["stat.ML", "cs.LG"], "doi": "10.1038/s41598-021-01005-y", "_query": "house price prediction machine learning", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/1409.5165v1", "title": "A Method for Stopping Active Learning Based on Stabilizing Predictions and the Need for User-Adjustable Stopping", "abstract": "A survey of existing methods for stopping active learning (AL) reveals the needs for methods that are: more widely applicable; more aggressive in saving annotations; and more stable across changing datasets. A new method for stopping AL based on stabilizing predictions is presented that addresses these needs. Furthermore, stopping methods are required to handle a broad range of different annotation/performance tradeoff valuations. Despite this, the existing body of work is dominated by conservative methods with little (if any) attention paid to providing users with control over the behavior of stopping methods. The proposed method is shown to fill a gap in the level of aggressiveness available for stopping AL and supports providing users with control over stopping behavior.", "published": "2014-09-17T23:28:59Z", "authors": ["Michael Bloodgood", "K. Vijay-Shanker"], "categories": ["cs.LG", "cs.CL", "stat.ML"], "doi": "", "_query": "house price prediction machine learning", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2310.20025v3", "title": "GOPlan: Goal-conditioned Offline Reinforcement Learning by Planning with Learned Models", "abstract": "Offline Goal-Conditioned RL (GCRL) offers a feasible paradigm for learning general-purpose policies from diverse and multi-task offline datasets. Despite notable recent progress, the predominant offline GCRL methods, mainly model-free, face constraints in handling limited data and generalizing to unseen goals. In this work, we propose Goal-conditioned Offline Planning (GOPlan), a novel model-based framework that contains two key phases: (1) pretraining a prior policy capable of capturing multi-modal action distribution within the multi-goal dataset; (2) employing the reanalysis method with planning to generate imagined trajectories for funetuning policies. Specifically, we base the prior policy on an advantage-weighted conditioned generative adversarial network, which facilitates distinct mode separation, mitigating the pitfalls of out-of-distribution (OOD) actions. For further policy optimization, the reanalysis method generates high-quality imaginary data by planning with learned models for both intra-trajectory and inter-trajectory goals. With thorough experimental evaluations, we demonstrate that GOPlan achieves state-of-the-art performance on various offline multi-goal navigation and manipulation tasks. Moreover, our results highlight the superior ability of GOPlan to handle small data budgets and generalize to OOD goals.", "published": "2023-10-30T21:19:52Z", "authors": ["Mianchu Wang", "Rui Yang", "Xi Chen", "Hao Sun", "Meng Fang", "Giovanni Montana"], "categories": ["cs.LG", "cs.AI"], "doi": "", "_query": "house price prediction machine learning", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2307.04461v2", "title": "Multi-modal Graph Learning over UMLS Knowledge Graphs", "abstract": "Clinicians are increasingly looking towards machine learning to gain insights about patient evolutions. We propose a novel approach named Multi-Modal UMLS Graph Learning (MMUGL) for learning meaningful representations of medical concepts using graph neural networks over knowledge graphs based on the unified medical language system. These representations are aggregated to represent entire patient visits and then fed into a sequence model to perform predictions at the granularity of multiple hospital visits of a patient. We improve performance by incorporating prior medical knowledge and considering multiple modalities. We compare our method to existing architectures proposed to learn representations at different granularities on the MIMIC-III dataset and show that our approach outperforms these methods. The results demonstrate the significance of multi-modal medical concept representations based on prior medical knowledge.", "published": "2023-07-10T10:16:57Z", "authors": ["Manuel Burger", "Gunnar Rätsch", "Rita Kuznetsova"], "categories": ["cs.LG"], "doi": "", "_query": "house price prediction machine learning", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2312.16341v2", "title": "Harnessing the Power of Federated Learning in Federated Contextual Bandits", "abstract": "Federated learning (FL) has demonstrated great potential in revolutionizing distributed machine learning, and tremendous efforts have been made to extend it beyond the original focus on supervised learning. Among many directions, federated contextual bandits (FCB), a pivotal integration of FL and sequential decision-making, has garnered significant attention in recent years. Despite substantial progress, existing FCB approaches have largely employed their tailored FL components, often deviating from the canonical FL framework. Consequently, even renowned algorithms like FedAvg remain under-utilized in FCB, let alone other FL advancements. Motivated by this disconnection, this work takes one step towards building a tighter relationship between the canonical FL study and the investigations on FCB. In particular, a novel FCB design, termed FedIGW, is proposed to leverage a regression-based CB algorithm, i.e., inverse gap weighting. Compared with existing FCB approaches, the proposed FedIGW design can better harness the entire spectrum of FL innovations, which is concretely reflected as (1) flexible incorporation of (both existing and forthcoming) FL protocols; (2) modularized plug-in of FL analyses in performance guarantees; (3) seamless integration of FL appendages (such as personalization, robustness, and privacy). We substantiate these claims through rigorous theoretical analyses and empirical evaluations.", "published": "2023-12-26T21:44:09Z", "authors": ["Chengshuai Shi", "Ruida Zhou", "Kun Yang", "Cong Shen"], "categories": ["stat.ML", "cs.IT", "cs.LG", "cs.MA"], "doi": "", "_query": "house price prediction machine learning", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2403.20033v1", "title": "A novel decision fusion approach for sale price prediction using Elastic Net and MOPSO", "abstract": "Price prediction algorithms propose prices for every product or service according to market trends, projected demand, and other characteristics, including government rules, international transactions, and speculation and expectation. As the dependent variable in price prediction, it is affected by several independent and correlated variables which may challenge the price prediction. To overcome this challenge, machine learning algorithms allow more accurate price prediction without explicitly modeling the relatedness between variables. However, as inputs increase, it challenges the existing machine learning approaches regarding computing efficiency and prediction effectiveness. Hence, this study introduces a novel decision level fusion approach to select informative variables in price prediction. The suggested metaheuristic algorithm balances two competitive objective functions, which are defined to improve the prediction utilized variables and reduce the error rate simultaneously. To generate Pareto optimal solutions, an Elastic net approach is employed to eliminate unrelated and redundant variables to increase the accuracy. Afterward, we propose a novel method for combining solutions and ensuring that a subset of features is optimal. Two various real datasets evaluate the proposed price prediction method. The results support the suggested superiority of the model concerning its relative root mean square error and adjusted correlation coefficient.", "published": "2024-03-29T07:59:33Z", "authors": ["Amir Eshaghi Chaleshtori"], "categories": ["stat.ML", "cs.LG", "cs.NE"], "doi": "", "_query": "house price prediction machine learning", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2008.13146v1", "title": "Deep Learning Techniques for Geospatial Data Analysis", "abstract": "Consumer electronic devices such as mobile handsets, goods tagged with RFID labels, location and position sensors are continuously generating a vast amount of location enriched data called geospatial data. Conventionally such geospatial data is used for military applications. In recent times, many useful civilian applications have been designed and deployed around such geospatial data. For example, a recommendation system to suggest restaurants or places of attraction to a tourist visiting a particular locality. At the same time, civic bodies are harnessing geospatial data generated through remote sensing devices to provide better services to citizens such as traffic monitoring, pothole identification, and weather reporting. Typically such applications are leveraged upon non-hierarchical machine learning techniques such as Naive-Bayes Classifiers, Support Vector Machines, and decision trees. Recent advances in the field of deep-learning showed that Neural Network-based techniques outperform conventional techniques and provide effective solutions for many geospatial data analysis tasks such as object recognition, image classification, and scene understanding. The chapter presents a survey on the current state of the applications of deep learning techniques for analyzing geospatial data. The chapter is organized as below: (i) A brief overview of deep learning algorithms. (ii)Geospatial Analysis: a Data Science Perspective (iii) Deep-learning techniques for Remote Sensing data analytics tasks (iv) Deep-learning techniques for GPS data analytics(iv) Deep-learning techniques for RFID data analytics.", "published": "2020-08-30T11:51:10Z", "authors": ["Arvind W. Kiwelekar", "Geetanjali S. Mahamunkar", "Laxman D. Netak", "Valmik B Nikam"], "categories": ["cs.AI"], "doi": "10.1007/978-3-030-49724-8_3", "_query": "house price prediction machine learning", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2310.11435v1", "title": "Uncertainty Quantification For Turbulent Flows with Machine Learning", "abstract": "Turbulent flows are of central importance across applications in science and engineering problems. For design and analysis, scientists and engineers use Computational Fluid Dynamics (CFD) simulations using turbulence models. Turbulent models are limited approximations, introducing epistemic uncertainty in CFD results. For reliable design and analysis, we require quantification of these uncertainties. The Eigenspace Perturbation Method (EPM) is the preeminent physics based approach for turbulence model UQ, but often leads to overly conservative uncertainty bounds. In this study, we use Machine Learning (ML) models to moderate the EPM perturbations and introduce our physics constrained machine learning framework for turbulence model UQ. We test this framework in multiple problems to show that it leads to improved calibration of the uncertainty estimates.", "published": "2023-10-17T17:45:56Z", "authors": ["Minghan Chu", "Weicheng Qian"], "categories": ["physics.flu-dyn"], "doi": "", "_query": "house price prediction machine learning", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2404.09114v1", "title": "Intelligent Chemical Purification Technique Based on Machine Learning", "abstract": "We present an innovative of artificial intelligence with column chromatography, aiming to resolve inefficiencies and standardize data collection in chemical separation and purification domain. By developing an automated platform for precise data acquisition and employing advanced machine learning algorithms, we constructed predictive models to forecast key separation parameters, thereby enhancing the efficiency and quality of chromatographic processes. The application of transfer learning allows the model to adapt across various column specifications, broadening its utility. A novel metric, separation probability ($S_p$), quantifies the likelihood of effective compound separation, validated through experimental verification. This study signifies a significant step forward int the application of AI in chemical research, offering a scalable solution to traditional chromatography challenges and providing a foundation for future technological advancements in chemical analysis and purification.", "published": "2024-04-14T01:44:58Z", "authors": ["Wenchao Wu", "Hao Xu", "Dongxiao Zhang", "Fanyang Mo"], "categories": ["cs.LG", "cs.AI", "physics.chem-ph"], "doi": "", "_query": "house price prediction machine learning", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2006.15412v6", "title": "Submodular Combinatorial Information Measures with Applications in Machine Learning", "abstract": "Information-theoretic quantities like entropy and mutual information have found numerous uses in machine learning. It is well known that there is a strong connection between these entropic quantities and submodularity since entropy over a set of random variables is submodular. In this paper, we study combinatorial information measures that generalize independence, (conditional) entropy, (conditional) mutual information, and total correlation defined over sets of (not necessarily random) variables. These measures strictly generalize the corresponding entropic measures since they are all parameterized via submodular functions that themselves strictly generalize entropy. Critically, we show that, unlike entropic mutual information in general, the submodular mutual information is actually submodular in one argument, holding the other fixed, for a large class of submodular functions whose third-order partial derivatives satisfy a non-negativity property. This turns out to include a number of practically useful cases such as the facility location and set-cover functions. We study specific instantiations of the submodular information measures on these, as well as the probabilistic coverage, graph-cut, and saturated coverage functions, and see that they all have mathematically intuitive and practically useful expressions. Regarding applications, we connect the maximization of submodular (conditional) mutual information to problems such as mutual-information-based, query-based, and privacy-preserving summarization -- and we connect optimizing the multi-set submodular mutual information to clustering and robust partitioning.", "published": "2020-06-27T17:45:32Z", "authors": ["Rishabh Iyer", "Ninad Khargonkar", "Jeff Bilmes", "Himanshu Asnani"], "categories": ["cs.LG", "cs.DS", "cs.IT", "math.OC", "stat.ML"], "doi": "", "_query": "house price prediction machine learning", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/1809.00542v2", "title": "Machine learning for predicting thermal power consumption of the Mars Express Spacecraft", "abstract": "The thermal subsystem of the Mars Express (MEX) spacecraft keeps the on-board equipment within its pre-defined operating temperatures range. To plan and optimize the scientific operations of MEX, its operators need to estimate in advance, as accurately as possible, the power consumption of the thermal subsystem. The remaining power can then be allocated for scientific purposes. We present a machine learning pipeline for efficiently constructing accurate predictive models for predicting the power of the thermal subsystem on board MEX. In particular, we employ state-of-the-art feature engineering approaches for transforming raw telemetry data, in turn used for constructing accurate models with different state-of-the-art machine learning methods. We show that the proposed pipeline considerably improve our previous (competition-winning) work in terms of time efficiency and predictive performance. Moreover, while achieving superior predictive performance, the constructed models also provide important insight into the spacecraft's behavior, allowing for further analyses and optimal planning of MEX's operation.", "published": "2018-09-03T10:43:02Z", "authors": ["Matej Petković", "Redouane Boumghar", "Martin Breskvar", "Sašo Džeroski", "Dragi Kocev", "Jurica Levatić", "Luke Lucas", "Aljaž Osojnik", "Bernard Ženko", "Nikola Simidjievski"], "categories": ["cs.LG", "stat.ML"], "doi": "10.1109/MAES.2019.2915456", "_query": "house price prediction machine learning", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/1911.05109v2", "title": "Harmonic Mean Point Processes: Proportional Rate Error Minimization for Obtundation Prediction", "abstract": "In healthcare, the highest risk individuals for morbidity and mortality are rarely those with the greatest modifiable risk. By contrast, many machine learning formulations implicitly attend to the highest risk individuals. We focus on this problem in point processes, a popular modeling technique for the analysis of the temporal event sequences in electronic health records (EHR) data with applications in risk stratification and risk score systems. We show that optimization of the log-likelihood function also gives disproportionate attention to high risk individuals and leads to poor prediction results for low risk individuals compared to ones at high risk. We characterize the problem and propose an adjusted log-likelihood formulation as a new objective for point processes. We demonstrate the benefits of our method in simulations and in EHR data of patients admitted to the critical care unit for intracerebral hemorrhage.", "published": "2019-11-12T19:19:36Z", "authors": ["Yoonjung Kim", "Jeremy C. Weiss"], "categories": ["stat.ML", "cs.AI", "cs.LG"], "doi": "", "_query": "house price prediction machine learning", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2107.04562v4", "title": "The Bayesian Learning Rule", "abstract": "We show that many machine-learning algorithms are specific instances of a single algorithm called the \\emph{Bayesian learning rule}. The rule, derived from Bayesian principles, yields a wide-range of algorithms from fields such as optimization, deep learning, and graphical models. This includes classical algorithms such as ridge regression, Newton's method, and Kalman filter, as well as modern deep-learning algorithms such as stochastic-gradient descent, RMSprop, and Dropout. The key idea in deriving such algorithms is to approximate the posterior using candidate distributions estimated by using natural gradients. Different candidate distributions result in different algorithms and further approximations to natural gradients give rise to variants of those algorithms. Our work not only unifies, generalizes, and improves existing algorithms, but also helps us design new ones.", "published": "2021-07-09T17:28:55Z", "authors": ["Mohammad Emtiyaz Khan", "Håvard Rue"], "categories": ["stat.ML", "cs.LG"], "doi": "", "_query": "house price prediction machine learning", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2503.01433v1", "title": "Reconstruction of muon bundles in KM3NeT detectors using machine learning methods", "abstract": "The KM3NeT Collaboration is installing the ARCA and ORCA neutrino detectors at the bottom of the Mediterranean Sea. The focus of ARCA is neutrino astronomy, while ORCA is optimised for neutrino oscillation studies. Both detectors are already operational in their intermediate states and collect valuable data, including the measurements of the muons produced by cosmic ray interactions in the atmosphere. This work explores the potential of machine learning models for the reconstruction of muon bundles, which are multi-muon events. For this, data collected with intermediate detector configurations of ARCA and ORCA was used in addition to simulated data from the envisaged final configurations of those detectors. Prediction of the total number of muons in a bundle as well as their total energy and even the energy of the primary cosmic ray is presented.", "published": "2025-03-03T11:37:38Z", "authors": ["Piotr Kalaczyński"], "categories": ["hep-ex", "cs.LG"], "doi": "", "_query": "house price prediction machine learning", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2512.05895v2", "title": "A Machine Learning Framework for Predicting Glass-Forming Ability in Ternary Alloy Systems", "abstract": "Predicting the glass-forming ability (GFA) of chemical compositions remains a fundamental challenge in materials science, especially for oxide glasses with broad compositional diversity. Traditional empirical and thermodynamic approaches often fail to capture the complex, nonlinear factors governing vitrification. In this study, we applied two ensemble machine learning algorithms-Random Forest (RF) and Extreme Gradient Boosting (XGB)-to the glass_ternary_hipt dataset to predict the GFA of ternary oxide glasses directly from composition-derived descriptors. Both models achieved excellent predictive accuracy (R^2 > 0.92, MAE < 0.04), confirming that GFA is learnable from compositional features alone. Feature importance analysis revealed that electronegativity variance, atomic size mismatch, and valence electron descriptors are the most influential factors, while cohesive energy and ionic radius provided secondary contributions. These chemically interpretable features align with established theories of glass formation, thereby bridging predictive performance with physical understanding. The novelty of this work lies in systematically extending ML-based predictive modeling to ternary oxide glasses, a class less studied compared to metallic and binary systems. Our results demonstrate that ensemble learning not only enables accurate GFA prediction but also provides actionable insights for designing new glass compositions with enhanced stability.", "published": "2025-12-05T17:15:54Z", "authors": ["Fatemeh Mahmoudi"], "categories": ["cond-mat.mtrl-sci"], "doi": "", "_query": "house price prediction machine learning", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2402.04054v2", "title": "More Flexible PAC-Bayesian Meta-Learning by Learning Learning Algorithms", "abstract": "We introduce a new framework for studying meta-learning methods using PAC-Bayesian theory. Its main advantage over previous work is that it allows for more flexibility in how the transfer of knowledge between tasks is realized. For previous approaches, this could only happen indirectly, by means of learning prior distributions over models. In contrast, the new generalization bounds that we prove express the process of meta-learning much more directly as learning the learning algorithm that should be used for future tasks. The flexibility of our framework makes it suitable to analyze a wide range of meta-learning mechanisms and even design new mechanisms. Other than our theoretical contributions we also show empirically that our framework improves the prediction quality in practical meta-learning mechanisms.", "published": "2024-02-06T15:00:08Z", "authors": ["Hossein Zakerinia", "Amin Behjati", "Christoph H. Lampert"], "categories": ["cs.LG", "stat.ML"], "doi": "", "_query": "house price prediction machine learning", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2203.03366v1", "title": "Improvements to Gradient Descent Methods for Quantum Tensor Network Machine Learning", "abstract": "Tensor networks have demonstrated significant value for machine learning in a myriad of different applications. However, optimizing tensor networks using standard gradient descent has proven to be difficult in practice. Tensor networks suffer from initialization problems resulting in exploding or vanishing gradients and require extensive hyperparameter tuning. Efforts to overcome these problems usually depend on specific network architectures, or ad hoc prescriptions. In this paper we address the problems of initialization and hyperparameter tuning, making it possible to train tensor networks using established machine learning techniques. We introduce a `copy node' method that successfully initializes arbitrary tensor networks, in addition to a gradient based regularization technique for bond dimensions. We present numerical results that show that the combination of techniques presented here produces quantum inspired tensor network models with far fewer parameters, while improving generalization performance.", "published": "2022-03-03T19:00:40Z", "authors": ["Fergus Barratt", "James Dborin", "Lewis Wright"], "categories": ["cs.LG", "cond-mat.str-el", "quant-ph"], "doi": "", "_query": "house price prediction machine learning", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/1703.04730v3", "title": "Understanding Black-box Predictions via Influence Functions", "abstract": "How can we explain the predictions of a black-box model? In this paper, we use influence functions -- a classic technique from robust statistics -- to trace a model's prediction through the learning algorithm and back to its training data, thereby identifying training points most responsible for a given prediction. To scale up influence functions to modern machine learning settings, we develop a simple, efficient implementation that requires only oracle access to gradients and Hessian-vector products. We show that even on non-convex and non-differentiable models where the theory breaks down, approximations to influence functions can still provide valuable information. On linear models and convolutional neural networks, we demonstrate that influence functions are useful for multiple purposes: understanding model behavior, debugging models, detecting dataset errors, and even creating visually-indistinguishable training-set attacks.", "published": "2017-03-14T21:07:01Z", "authors": ["Pang Wei Koh", "Percy Liang"], "categories": ["stat.ML", "cs.AI", "cs.LG"], "doi": "", "_query": "house price prediction machine learning", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/1910.12748v2", "title": "A Study of Machine Learning Models in Predicting the Intention of Adolescents to Smoke Cigarettes", "abstract": "The use of electronic cigarette (e-cigarette) is increasing among adolescents. This is problematic since consuming nicotine at an early age can cause harmful effects in developing teenager's brain and health. Additionally, the use of e-cigarette has a possibility of leading to the use of cigarettes, which is more severe. There were many researches about e-cigarette and cigarette that mostly focused on finding and analyzing causes of smoking using conventional statistics. However, there is a lack of research on developing prediction models, which is more applicable to anti-smoking campaign, about e-cigarette and cigarette. In this paper, we research the prediction models that can be used to predict an individual e-cigarette user's (including non-e-cigarette users) intention to smoke cigarettes, so that one can be early informed about the risk of going down the path of smoking cigarettes. To construct the prediction models, five machine learning (ML) algorithms are exploited and tested for their accuracy in predicting the intention to smoke cigarettes among never smokers using data from the 2018 National Youth Tobacco Survey (NYTS). In our investigation, the Gradient Boosting Classifier, one of the prediction models, shows the highest accuracy out of all the other models. Also, with the best prediction model, we made a public website that enables users to input information to predict their intentions of smoking cigarettes.", "published": "2019-10-28T15:17:24Z", "authors": ["Seung Joon Nam", "Han Min Kim", "Thomas Kang", "Cheol Young Park"], "categories": ["cs.LG", "stat.ML"], "doi": "", "_query": "house price prediction machine learning", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2110.04328v2", "title": "Distinguishing rule- and exemplar-based generalization in learning systems", "abstract": "Machine learning systems often do not share the same inductive biases as humans and, as a result, extrapolate or generalize in ways that are inconsistent with our expectations. The trade-off between exemplar- and rule-based generalization has been studied extensively in cognitive psychology; in this work, we present a protocol inspired by these experimental approaches to probe the inductive biases that control this tradeoff in category-learning systems. We isolate two such inductive biases: feature-level bias (differences in which features are more readily learned) and exemplar or rule bias (differences in how these learned features are used for generalization). We find that standard neural network models are feature-biased and exemplar-based, and discuss the implications of these findings for machine learning research on systematic generalization, fairness, and data augmentation.", "published": "2021-10-08T18:37:59Z", "authors": ["Ishita Dasgupta", "Erin Grant", "Thomas L. Griffiths"], "categories": ["cs.LG"], "doi": "", "_query": "house price prediction machine learning", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2202.04358v1", "title": "House Price Valuation Model Based on Geographically Neural Network Weighted Regression: The Case Study of Shenzhen, China", "abstract": "Confronted with the spatial heterogeneity of real estate market, some traditional research utilized Geographically Weighted Regression (GWR) to estimate the house price. However, its kernel function is non-linear, elusive, and complex to opt bandwidth, the predictive power could also be improved. Consequently, a novel technique, Geographical Neural Network Weighted Regression (GNNWR), has been applied to improve the accuracy of real estate appraisal with the help of neural networks. Based on Shenzhen house price dataset, this work conspicuously captures the weight distribution of different variants at Shenzhen real estate market, which GWR is difficult to materialize. Moreover, we focus on the performance of GNNWR, verify its robustness and superiority, refine the experiment process with 10-fold cross-validation, extend its application area from natural to socioeconomic geospatial data. It's a practical and trenchant way to assess house price, and we demonstrate the effectiveness of GNNWR on a complex socioeconomic dataset.", "published": "2022-02-09T09:40:54Z", "authors": ["Zimo Wang", "Yicheng Wang", "Sensen Wu"], "categories": ["stat.AP", "cs.LG"], "doi": "", "_query": "house price prediction machine learning", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2210.07237v2", "title": "Forces are not Enough: Benchmark and Critical Evaluation for Machine Learning Force Fields with Molecular Simulations", "abstract": "Molecular dynamics (MD) simulation techniques are widely used for various natural science applications. Increasingly, machine learning (ML) force field (FF) models begin to replace ab-initio simulations by predicting forces directly from atomic structures. Despite significant progress in this area, such techniques are primarily benchmarked by their force/energy prediction errors, even though the practical use case would be to produce realistic MD trajectories. We aim to fill this gap by introducing a novel benchmark suite for learned MD simulation. We curate representative MD systems, including water, organic molecules, a peptide, and materials, and design evaluation metrics corresponding to the scientific objectives of respective systems. We benchmark a collection of state-of-the-art (SOTA) ML FF models and illustrate, in particular, how the commonly benchmarked force accuracy is not well aligned with relevant simulation metrics. We demonstrate when and how selected SOTA methods fail, along with offering directions for further improvement. Specifically, we identify stability as a key metric for ML models to improve. Our benchmark suite comes with a comprehensive open-source codebase for training and simulation with ML FFs to facilitate future work.", "published": "2022-10-13T17:59:03Z", "authors": ["Xiang Fu", "Zhenghao Wu", "Wujie Wang", "Tian Xie", "Sinan Keten", "Rafael Gomez-Bombarelli", "Tommi Jaakkola"], "categories": ["physics.comp-ph", "cs.LG", "physics.chem-ph"], "doi": "", "_query": "house price prediction machine learning", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/1810.07159v1", "title": "Packaging and Sharing Machine Learning Models via the Acumos AI Open Platform", "abstract": "Applying Machine Learning (ML) to business applications for automation usually faces difficulties when integrating diverse ML dependencies and services, mainly because of the lack of a common ML framework. In most cases, the ML models are developed for applications which are targeted for specific business domain use cases, leading to duplicated effort, and making reuse impossible. This paper presents Acumos, an open platform capable of packaging ML models into portable containerized microservices which can be easily shared via the platform's catalog, and can be integrated into various business applications. We present a case study of packaging sentiment analysis and classification ML models via the Acumos platform, permitting easy sharing with others. We demonstrate that the Acumos platform reduces the technical burden on application developers when applying machine learning models to their business applications. Furthermore, the platform allows the reuse of readily available ML microservices in various business domains.", "published": "2018-10-16T17:39:53Z", "authors": ["Shuai Zhao", "Manoop Talasila", "Guy Jacobson", "Cristian Borcea", "Syed Anwar Aftab", "John F Murray"], "categories": ["cs.AI", "cs.SE"], "doi": "", "_query": "house price prediction machine learning", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2503.18332v2", "title": "Regional House Price Dynamics in Australia: Insights into Lifestyle and Mining Dynamics through PCA", "abstract": "This report applies Principal Component Analysis (PCA) to regional house price indexes to uncover dominant trends in Australia's housing market. Regions are assigned PCA-derived scores that reveal which underlying market forces are most influential in each area, enabling broad classification of local housing markets. The approach highlights where price movements tend to align across regions, even those geographically distant. The three most dominant trends are described in detail and, together with the regional scores, provide objective tools for policymakers, researchers, and real estate professionals.", "published": "2025-03-24T04:31:17Z", "authors": ["Willem Sijp"], "categories": ["econ.GN"], "doi": "", "_query": "house price prediction machine learning", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2207.04455v1", "title": "The Price of Stability for First Price Auction", "abstract": "This paper establishes the Price of Stability (PoS) for First Price Auctions, for all equilibrium concepts that have been studied in the literature: Bayes Nash Equilibrium $\\subsetneq$ Bayes Correlated Equilibrium $\\subsetneq$ Bayes Coarse Correlated Equilibrium} $\\bullet$ Bayes Nash Equilibrium: For independent valuations, the tight PoS is $1 - 1/ e^{2} \\approx 0.8647$, matching the counterpart Price of Anarchy (PoA) bound \\cite{JL22}. For correlated valuations, the tight $\\PoS$ is $1 - 1 / e \\approx 0.6321$, matching the counterpart PoA bound \\cite{ST13,S14}. This result indicates that, in the worst cases, efficiency degradation depends not on different selections among Bayes Nash Equilibria. $\\bullet$ Bayesian Coarse Correlated Equilibrium: For independent or correlated valuations, the tight PoS is always $1 = 100\\%$, i.e., no efficiency degradation, different from the counterpart PoA bound $1 - 1 / e \\approx 0.6321$ \\cite{ST13,S14}. This result indicates that First Price Auctions can be fully efficient when we allow the more general equilibrium concepts.", "published": "2022-07-10T12:49:36Z", "authors": ["Yaonan Jin", "Pinyan Lu"], "categories": ["cs.GT"], "doi": "", "_query": "house price prediction machine learning", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2001.05948v1", "title": "Machine learning for total cloud cover prediction", "abstract": "Accurate and reliable forecasting of total cloud cover (TCC) is vital for many areas such as astronomy, energy demand and production, or agriculture. Most meteorological centres issue ensemble forecasts of TCC, however, these forecasts are often uncalibrated and exhibit worse forecast skill than ensemble forecasts of other weather variables. Hence, some form of post-processing is strongly required to improve predictive performance. As TCC observations are usually reported on a discrete scale taking just nine different values called oktas, statistical calibration of TCC ensemble forecasts can be considered a classification problem with outputs given by the probabilities of the oktas. This is a classical area where machine learning methods are applied. We investigate the performance of post-processing using multilayer perceptron (MLP) neural networks, gradient boosting machines (GBM) and random forest (RF) methods. Based on the European Centre for Medium-Range Weather Forecasts global TCC ensemble forecasts for 2002-2014 we compare these approaches with the proportional odds logistic regression (POLR) and multiclass logistic regression (MLR) models, as well as the raw TCC ensemble forecasts. We further assess whether improvements in forecast skill can be obtained by incorporating ensemble forecasts of precipitation as additional predictor. Compared to the raw ensemble, all calibration methods result in a significant improvement in forecast skill. RF models provide the smallest increase in predictive performance, while MLP, POLR and GBM approaches perform best. The use of precipitation forecast data leads to further improvements in forecast skill and except for very short lead times the extended MLP model shows the best overall performance.", "published": "2020-01-16T17:13:37Z", "authors": ["Ágnes Baran", "Sebastian Lerch", "Mehrez El Ayari", "Sándor Baran"], "categories": ["stat.ML", "cs.LG", "stat.AP"], "doi": "10.1007/s00521-020-05139-4", "_query": "house price prediction machine learning", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2305.13689v1", "title": "Know Your Self-supervised Learning: A Survey on Image-based Generative and Discriminative Training", "abstract": "Although supervised learning has been highly successful in improving the state-of-the-art in the domain of image-based computer vision in the past, the margin of improvement has diminished significantly in recent years, indicating that a plateau is in sight. Meanwhile, the use of self-supervised learning (SSL) for the purpose of natural language processing (NLP) has seen tremendous successes during the past couple of years, with this new learning paradigm yielding powerful language models. Inspired by the excellent results obtained in the field of NLP, self-supervised methods that rely on clustering, contrastive learning, distillation, and information-maximization, which all fall under the banner of discriminative SSL, have experienced a swift uptake in the area of computer vision. Shortly afterwards, generative SSL frameworks that are mostly based on masked image modeling, complemented and surpassed the results obtained with discriminative SSL. Consequently, within a span of three years, over $100$ unique general-purpose frameworks for generative and discriminative SSL, with a focus on imaging, were proposed. In this survey, we review a plethora of research efforts conducted on image-oriented SSL, providing a historic view and paying attention to best practices as well as useful software packages. While doing so, we discuss pretext tasks for image-based SSL, as well as techniques that are commonly used in image-based SSL. Lastly, to aid researchers who aim at contributing to image-focused SSL, we outline a number of promising research directions.", "published": "2023-05-23T04:54:09Z", "authors": ["Utku Ozbulak", "Hyun Jung Lee", "Beril Boga", "Esla Timothy Anzaku", "Homin Park", "Arnout Van Messem", "Wesley De Neve", "Joris Vankerschaver"], "categories": ["cs.CV", "cs.AI"], "doi": "", "_query": "house price prediction machine learning", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/1012.3039v2", "title": "London house prices are power-law distributed", "abstract": "We explore the house price distributions for the English cities of London, Manchester, Bristol, Newcastle, Birmingham and Leeds. We find Pareto (power law) behaviour in their upper tails, which is clearly distinct from lognormal and gamma distributions in the cases of London, Manchester and Newcastle. For London, the city with the lowest power, this is a striking match with that found in the wealth distribution of the super-rich. We propose an index of Housing Wealth Inequality based on the Pareto exponent and analogous to the Gini coefficient, and comment on its possible uses.", "published": "2010-12-14T14:24:45Z", "authors": ["Niall MacKay"], "categories": ["physics.soc-ph"], "doi": "", "_query": "house price prediction machine learning", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/cond-mat/0010423v1", "title": "Hierarchical learning in polynomial Support Vector Machines", "abstract": "We study the typical properties of polynomial Support Vector Machines within a Statistical Mechanics approach that allows us to analyze the effect of different normalizations of the features. If the normalization is adecuately chosen, there is a hierarchical learning of features of increasing order as a function of the training set size.", "published": "2000-10-26T15:11:01Z", "authors": ["Sebastian Risau-Gusman", "Mirta B. Gordon"], "categories": ["cond-mat.dis-nn"], "doi": "", "_query": "house price prediction machine learning", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/1602.04511v2", "title": "Learning Granger Causality for Hawkes Processes", "abstract": "Learning Granger causality for general point processes is a very challenging task. In this paper, we propose an effective method, learning Granger causality, for a special but significant type of point processes --- Hawkes process. We reveal the relationship between Hawkes process's impact function and its Granger causality graph. Specifically, our model represents impact functions using a series of basis functions and recovers the Granger causality graph via group sparsity of the impact functions' coefficients. We propose an effective learning algorithm combining a maximum likelihood estimator (MLE) with a sparse-group-lasso (SGL) regularizer. Additionally, the flexibility of our model allows to incorporate the clustering structure event types into learning framework. We analyze our learning algorithm and propose an adaptive procedure to select basis functions. Experiments on both synthetic and real-world data show that our method can learn the Granger causality graph and the triggering patterns of the Hawkes processes simultaneously.", "published": "2016-02-14T21:14:07Z", "authors": ["Hongteng Xu", "Mehrdad Farajtabar", "Hongyuan Zha"], "categories": ["cs.LG", "stat.ML"], "doi": "", "_query": "house price prediction machine learning", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2104.06648v3", "title": "Root-finding Approaches for Computing Conformal Prediction Set", "abstract": "Conformal prediction constructs a confidence set for an unobserved response of a feature vector based on previous identically distributed and exchangeable observations of responses and features. It has a coverage guarantee at any nominal level without additional assumptions on their distribution. Its computation deplorably requires a refitting procedure for all replacement candidates of the target response. In regression settings, this corresponds to an infinite number of model fits. Apart from relatively simple estimators that can be written as pieces of linear function of the response, efficiently computing such sets is difficult, and is still considered as an open problem. We exploit the fact that, \\emph{often}, conformal prediction sets are intervals whose boundaries can be efficiently approximated by classical root-finding algorithms. We investigate how this approach can overcome many limitations of formerly used strategies; we discuss its complexity and drawbacks.", "published": "2021-04-14T06:41:12Z", "authors": ["Eugene Ndiaye", "Ichiro Takeuchi"], "categories": ["stat.ML", "cs.LG", "stat.CO"], "doi": "10.1007/s10994-022-06233-5", "_query": "house price prediction machine learning", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2207.02337v1", "title": "Federated and Transfer Learning: A Survey on Adversaries and Defense Mechanisms", "abstract": "The advent of federated learning has facilitated large-scale data exchange amongst machine learning models while maintaining privacy. Despite its brief history, federated learning is rapidly evolving to make wider use more practical. One of the most significant advancements in this domain is the incorporation of transfer learning into federated learning, which overcomes fundamental constraints of primary federated learning, particularly in terms of security. This chapter performs a comprehensive survey on the intersection of federated and transfer learning from a security point of view. The main goal of this study is to uncover potential vulnerabilities and defense mechanisms that might compromise the privacy and performance of systems that use federated and transfer learning.", "published": "2022-07-05T22:07:26Z", "authors": ["Ehsan Hallaji", "Roozbeh Razavi-Far", "Mehrdad Saif"], "categories": ["cs.LG", "cs.AI", "cs.CR", "cs.CV", "cs.DC"], "doi": "10.1007/978-3-031-11748-0_3", "_query": "house price prediction machine learning", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2510.16897v1", "title": "DeepChem Equivariant: SE(3)-Equivariant Support in an Open-Source Molecular Machine Learning Library", "abstract": "Neural networks that incorporate geometric relationships respecting SE(3) group transformations (e.g. rotations and translations) are increasingly important in molecular applications, such as molecular property prediction, protein structure modeling, and materials design. These models, known as SE(3)-equivariant neural networks, ensure outputs transform predictably with input coordinate changes by explicitly encoding spatial atomic positions. Although libraries such as E3NN [4] and SE(3)-TRANSFORMER [3 ] offer powerful implementations, they often require substantial deep learning or mathematical prior knowledge and lack complete training pipelines. We extend DEEPCHEM [ 13] with support for ready-to-use equivariant models, enabling scientists with minimal deep learning background to build, train, and evaluate models, such as SE(3)-Transformer and Tensor Field Networks. Our implementation includes equivariant models, complete training pipelines, and a toolkit of equivariant utilities, supported with comprehensive tests and documentation, to facilitate both application and further development of SE(3)-equivariant models.", "published": "2025-10-19T15:45:57Z", "authors": ["Jose Siguenza", "Bharath Ramsundar"], "categories": ["cs.LG"], "doi": "", "_query": "house price prediction machine learning", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/1712.09208v1", "title": "Machine Learning Cosmic Expansion History", "abstract": "We use the machine learning techniques, for the first time, to study the background evolution of the universe in light of 30 cosmic chronometers. From 7 machine learning algorithms, using the principle of mean squared error minimization on testing set, we find that Bayesian ridge regression is the optimal method to extract the information from cosmic chronometers. By use of a power-law polynomial expansion, we obtain the first Hubble constant estimation $H_0=65.95^{+6.98}_{-6.36}$ km s$^{-1}$ Mpc$^{-1}$ from machine learning. From the view of machine learning, we may rule out a large number of cosmological models, the number of physical parameters of which containing $H_0$ is larger than 3. Very importantly and interestingly, we find that the parameter spaces of 3 specific cosmological models can all be clearly compressed by considering both their explanation and generalization abilities.", "published": "2017-12-26T08:37:39Z", "authors": ["Deng Wang", "Wei Zhang"], "categories": ["astro-ph.CO", "astro-ph.HE", "gr-qc"], "doi": "", "_query": "house price prediction machine learning", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2203.07922v1", "title": "How informative is the Order Book Beyond the Best Levels? Machine Learning Perspective", "abstract": "Research on limit order book markets has been rapidly growing and nowadays high-frequency full order book data is widely available for researchers and practitioners. However, it is common that research papers use the best level data only, which motivates us to ask whether the exclusion of the quotes deeper in the book over multiple price levels causes performance degradation. In this paper, we address this question by using modern Machine Learning (ML) techniques to predict mid-price movements without assuming that limit order book markets represent a linear system. We provide a number of results that are robust across ML prediction models, feature selection algorithms, data sets, and prediction horizons. We find that the best bid and ask levels are systematically identified not only as the most informative levels in the order books, but also to carry most of the information needed for good prediction performance. On the other hand, even if the top-of-the-book levels contain most of the relevant information, to maximize models' performance one should use all data across all the levels. Additionally, the informativeness of the order book levels clearly decreases from the first to the fourth level while the rest of the levels are approximately equally important.", "published": "2022-03-15T14:04:01Z", "authors": ["Dat Thanh Tran", "Juho Kanniainen", "Alexandros Iosifidis"], "categories": ["cs.CE"], "doi": "", "_query": "house price prediction machine learning", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/1812.01735v1", "title": "Learning Cheap and Novel Flight Itineraries", "abstract": "We consider the problem of efficiently constructing cheap and novel round trip flight itineraries by combining legs from different airlines. We analyse the factors that contribute towards the price of such itineraries and find that many result from the combination of just 30% of airlines and that the closer the departure of such itineraries is to the user's search date the more likely they are to be cheaper than the tickets from one airline. We use these insights to formulate the problem as a trade-off between the recall of cheap itinerary constructions and the costs associated with building them. We propose a supervised learning solution with location embeddings which achieves an AUC=80.48, a substantial improvement over simpler baselines. We discuss various practical considerations for dealing with the staleness and the stability of the model and present the design of the machine learning pipeline. Finally, we present an analysis of the model's performance in production and its impact on Skyscanner's users.", "published": "2018-12-04T22:48:27Z", "authors": ["Dmytro Karamshuk", "David Matthews"], "categories": ["cs.LG", "stat.ML"], "doi": "", "_query": "house price prediction machine learning", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2011.12443v1", "title": "The Human Effect Requires Affect: Addressing Social-Psychological Factors of Climate Change with Machine Learning", "abstract": "Machine learning has the potential to aid in mitigating the human effects of climate change. Previous applications of machine learning to tackle the human effects in climate change include approaches like informing individuals of their carbon footprint and strategies to reduce it. For these methods to be the most effective they must consider relevant social-psychological factors for each individual. Of social-psychological factors at play in climate change, affect has been previously identified as a key element in perceptions and willingness to engage in mitigative behaviours. In this work, we propose an investigation into how affect could be incorporated to enhance machine learning based interventions for climate change. We propose using affective agent-based modelling for climate change as well as the use of a simulated climate change social dilemma to explore the potential benefits of affective machine learning interventions. Behavioural and informational interventions can be a powerful tool in helping humans adopt mitigative behaviours. We expect that utilizing affective ML can make interventions an even more powerful tool and help mitigative behaviours become widely adopted.", "published": "2020-11-24T23:34:54Z", "authors": ["Kyle Tilbury", "Jesse Hoey"], "categories": ["cs.AI"], "doi": "", "_query": "house price prediction machine learning", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2408.05212v2", "title": "Preserving Privacy in Large Language Models: A Survey on Current Threats and Solutions", "abstract": "Large Language Models (LLMs) represent a significant advancement in artificial intelligence, finding applications across various domains. However, their reliance on massive internet-sourced datasets for training brings notable privacy issues, which are exacerbated in critical domains (e.g., healthcare). Moreover, certain application-specific scenarios may require fine-tuning these models on private data. This survey critically examines the privacy threats associated with LLMs, emphasizing the potential for these models to memorize and inadvertently reveal sensitive information. We explore current threats by reviewing privacy attacks on LLMs and propose comprehensive solutions for integrating privacy mechanisms throughout the entire learning pipeline. These solutions range from anonymizing training datasets to implementing differential privacy during training or inference and machine unlearning after training. Our comprehensive review of existing literature highlights ongoing challenges, available tools, and future directions for preserving privacy in LLMs. This work aims to guide the development of more secure and trustworthy AI systems by providing a thorough understanding of privacy preservation methods and their effectiveness in mitigating risks.", "published": "2024-08-10T05:41:19Z", "authors": ["Michele Miranda", "Elena Sofia Ruzzetti", "Andrea Santilli", "Fabio Massimo Zanzotto", "Sébastien Bratières", "Emanuele Rodolà"], "categories": ["cs.CR", "cs.AI", "cs.CL", "cs.LG"], "doi": "", "_query": "house price prediction machine learning", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/1909.12227v1", "title": "Stock Prices Prediction using Deep Learning Models", "abstract": "Financial markets have a vital role in the development of modern society. They allow the deployment of economic resources. Changes in stock prices reflect changes in the market. In this study, we focus on predicting stock prices by deep learning model. This is a challenge task, because there is much noise and uncertainty in information that is related to stock prices. So this work uses sparse autoencoders with one-dimension (1-D) residual convolutional networks which is a deep learning model, to de-noise the data. Long-short term memory (LSTM) is then used to predict the stock price. The prices, indices and macroeconomic variables in past are the features used to predict the next day's price. Experiment results show that 1-D residual convolutional networks can de-noise data and extract deep features better than a model that combines wavelet transforms (WT) and stacked autoencoders (SAEs). In addition, we compare the performances of model with two different forecast targets of stock price: absolute stock price and price rate of change. The results show that predicting stock price through price rate of change is better than predicting absolute prices directly.", "published": "2019-09-25T11:38:25Z", "authors": ["Jialin Liu", "Fei Chao", "Yu-Chen Lin", "Chih-Min Lin"], "categories": ["cs.LG"], "doi": "", "_query": "house price prediction machine learning", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2409.15261v1", "title": "Identification and Localization of Cometary Activity in Solar System Objects with Machine Learning", "abstract": "In this chapter, we will discuss the use of Machine Learning methods for the identification and localization of cometary activity for Solar System objects in ground and in space-based wide-field all-sky surveys. We will begin the chapter by discussing the challenges of identifying known and unknown active, extended Solar System objects in the presence of stellar-type sources and the application of classical pre-ML identification techniques and their limitations. We will then transition to the discussion of implementing ML techniques to address the challenge of extended object identification. We will finish with prospective future methods and the application to future surveys such as the Vera C. Rubin Observatory.", "published": "2024-09-23T17:56:32Z", "authors": ["Bryce T. Bolin", "Michael W. Coughlin"], "categories": ["astro-ph.EP", "astro-ph.IM", "cs.AI", "cs.LG"], "doi": "", "_query": "house price prediction machine learning", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2304.05655v4", "title": "Localisation of Regularised and Multiview Support Vector Machine Learning", "abstract": "We prove a few representer theorems for a localised version of the regularised and multiview support vector machine learning problem introduced by H.Q. Minh, L. Bazzani, and V. Murino, Journal of Machine Learning Research, 17(2016) 1-72, that involves operator valued positive semidefinite kernels and their reproducing kernel Hilbert spaces. The results concern general cases when convex or nonconvex loss functions and finite or infinite dimensional input spaces are considered. We show that the general framework allows infinite dimensional input spaces and nonconvex loss functions for some special cases, in particular in case the loss functions are Gateaux differentiable. Detailed calculations are provided for the exponential least squares loss functions that lead to systems of partially nonlinear equations for which a particular different types of Newton's approximation methods based on the interior point method can be used. Some numerical experiments are performed on a toy model that illustrate the tractability of the methods that we propose.", "published": "2023-04-12T07:19:02Z", "authors": ["Aurelian Gheondea", "Cankat Tilki"], "categories": ["math.FA", "cs.LG"], "doi": "", "_query": "house price prediction machine learning", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2308.02796v1", "title": "OBESEYE: Interpretable Diet Recommender for Obesity Management using Machine Learning and Explainable AI", "abstract": "Obesity, the leading cause of many non-communicable diseases, occurs mainly for eating more than our body requirements and lack of proper activity. So, being healthy requires heathy diet plans, especially for patients with comorbidities. But it is difficult to figure out the exact quantity of each nutrient because nutrients requirement varies based on physical and disease conditions. In our study we proposed a novel machine learning based system to predict the amount of nutrients one individual requires for being healthy. We applied different machine learning algorithms: linear regression, support vector machine (SVM), decision tree, random forest, XGBoost, LightGBM on fluid and 3 other major micronutrients: carbohydrate, protein, fat consumption prediction. We achieved high accuracy with low root mean square error (RMSE) by using linear regression in fluid prediction, random forest in carbohydrate prediction and LightGBM in protein and fat prediction. We believe our diet recommender system, OBESEYE, is the only of its kind which recommends diet with the consideration of comorbidities and physical conditions and promote encouragement to get rid of obesity.", "published": "2023-08-05T06:02:28Z", "authors": ["Mrinmoy Roy", "Srabonti Das", "Anica Tasnim Protity"], "categories": ["cs.LG"], "doi": "", "_query": "house price prediction machine learning", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2007.05724v2", "title": "Learning Randomly Perturbed Structured Predictors for Direct Loss Minimization", "abstract": "Direct loss minimization is a popular approach for learning predictors over structured label spaces. This approach is computationally appealing as it replaces integration with optimization and allows to propagate gradients in a deep net using loss-perturbed prediction. Recently, this technique was extended to generative models, while introducing a randomized predictor that samples a structure from a randomly perturbed score function. In this work, we learn the variance of these randomized structured predictors and show that it balances better between the learned score function and the randomized noise in structured prediction. We demonstrate empirically the effectiveness of learning the balance between the signal and the random noise in structured discrete spaces.", "published": "2020-07-11T08:59:11Z", "authors": ["Hedda Cohen Indelman", "Tamir Hazan"], "categories": ["stat.ML", "cs.LG"], "doi": "", "_query": "house price prediction machine learning", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/1503.05550v1", "title": "Club Convergence of House Prices: Evidence from China's Ten Key Cities", "abstract": "The latest global financial tsunami and its follow-up global economic recession has uncovered the crucial impact of housing markets on financial and economic systems. The Chinese stock market experienced a markedly fall during the global financial tsunami and China's economy has also slowed down by about 2\\%-3\\% when measured in GDP. Nevertheless, the housing markets in diverse Chinese cities seemed to continue the almost nonstop mania for more than ten years. However, the structure and dynamics of the Chinese housing market are less studied. Here we perform an extensive study of the Chinese housing market by analyzing ten representative key cities based on both linear and nonlinear econophysical and econometric methods. We identify a common collective driving force which accounts for 96.5\\% of the house price growth, indicating very high systemic risk in the Chinese housing market. The ten key cities can be categorized into clubs and the house prices of the cities in the same club exhibit an evident convergence. These findings from different methods are basically consistent with each other. The identified city clubs are also consistent with the conventional classification of city tiers. The house prices of the first-tier cities grow the fastest, and those of the third- and fourth-tier cities rise the slowest, which illustrates the possible presence of a ripple effect in the diffusion of house prices in different cities.", "published": "2015-03-18T03:46:56Z", "authors": ["Hao Meng", "Wen-Jie Xie", "Wei-Xing Zhou"], "categories": ["q-fin.ST", "physics.soc-ph"], "doi": "10.1142/S0217979215501817", "_query": "house price prediction machine learning", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/1908.02781v1", "title": "Flood Prediction Using Machine Learning Models: Literature Review", "abstract": "Floods are among the most destructive natural disasters, which are highly complex to model. The research on the advancement of flood prediction models contributed to risk reduction, policy suggestion, minimization of the loss of human life, and reduction the property damage associated with floods. To mimic the complex mathematical expressions of physical processes of floods, during the past two decades, machine learning (ML) methods contributed highly in the advancement of prediction systems providing better performance and cost-effective solutions. Due to the vast benefits and potential of ML, its popularity dramatically increased among hydrologists. Researchers through introducing novel ML methods and hybridizing of the existing ones aim at discovering more accurate and efficient prediction models. The main contribution of this paper is to demonstrate the state of the art of ML models in flood prediction and to give insight into the most suitable models. In this paper, the literature where ML models were benchmarked through a qualitative analysis of robustness, accuracy, effectiveness, and speed are particularly investigated to provide an extensive overview on the various ML algorithms used in the field. The performance comparison of ML models presents an in-depth understanding of the different techniques within the framework of a comprehensive evaluation and discussion. As a result, this paper introduces the most promising prediction methods for both long-term and short-term floods. Furthermore, the major trends in improving the quality of the flood prediction models are investigated. Among them, hybridization, data decomposition, algorithm ensemble, and model optimization are reported as the most effective strategies for the improvement of ML methods.", "published": "2019-08-07T18:05:45Z", "authors": ["Amir Mosavi", "Pinar Ozturk", "Kwok-wing Chau"], "categories": ["cs.LG", "stat.ML"], "doi": "10.3390/w10111536", "_query": "house price prediction machine learning", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2312.14426v1", "title": "Room Occupancy Prediction: Exploring the Power of Machine Learning and Temporal Insights", "abstract": "Energy conservation in buildings is a paramount concern to combat greenhouse gas emissions and combat climate change. The efficient management of room occupancy, involving actions like lighting control and climate adjustment, is a pivotal strategy to curtail energy consumption. In contexts where surveillance technology isn't viable, non-intrusive sensors are employed to estimate room occupancy. In this study, we present a predictive framework for room occupancy that leverages a diverse set of machine learning models, with Random Forest consistently achieving the highest predictive accuracy. Notably, this dataset encompasses both temporal and spatial dimensions, revealing a wealth of information. Intriguingly, our framework demonstrates robust performance even in the absence of explicit temporal modeling. These findings underscore the remarkable predictive power of traditional machine learning models. The success can be attributed to the presence of feature redundancy, the simplicity of linear spatial and temporal patterns, and the advantages of high-frequency data sampling. While these results are compelling, it's essential to remain open to the possibility that explicitly modeling the temporal dimension could unlock deeper insights or further enhance predictive capabilities in specific scenarios. In summary, our research not only validates the effectiveness of our prediction framework for continuous and classification tasks but also underscores the potential for improvements through the inclusion of temporal aspects. The study highlights the promise of machine learning in shaping energy-efficient practices and room occupancy management.", "published": "2023-12-22T04:16:34Z", "authors": ["Siqi Mao", "Yaping Yuan", "Yinpu Li", "Ziren Wang", "Yuanxin Yao", "Yixin Kang"], "categories": ["cs.LG", "stat.ML"], "doi": "", "_query": "house price prediction machine learning", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/1810.04227v1", "title": "Rethinking multiscale cardiac electrophysiology with machine learning and predictive modelling", "abstract": "We review some of the latest approaches to analysing cardiac electrophysiology data using machine learning and predictive modelling. Cardiac arrhythmias, particularly atrial fibrillation, are a major global healthcare challenge. Treatment is often through catheter ablation, which involves the targeted localized destruction of regions of the myocardium responsible for initiating or perpetuating the arrhythmia. Ablation targets are either anatomically defined, or identified based on their functional properties as determined through the analysis of contact intracardiac electrograms acquired with increasing spatial density by modern electroanatomic mapping systems. While numerous quantitative approaches have been investigated over the past decades for identifying these critical curative sites, few have provided a reliable and reproducible advance in success rates. Machine learning techniques, including recent deep-learning approaches, offer a potential route to gaining new insight from this wealth of highly complex spatio-temporal information that existing methods struggle to analyse. Coupled with predictive modelling, these techniques offer exciting opportunities to advance the field and produce more accurate diagnoses and robust personalised treatment. We outline some of these methods and illustrate their use in making predictions from the contact electrogram and augmenting predictive modelling tools, both by more rapidly predicting future states of the system and by inferring the parameters of these models from experimental observations.", "published": "2018-10-09T20:09:45Z", "authors": ["Chris D. Cantwell", "Yumnah Mohamied", "Konstantinos N. Tzortzis", "Stef Garasto", "Charles Houston", "Rasheda A. Chowdhury", "Fu Siong Ng", "Anil A. Bharath", "Nicholas S. Peters"], "categories": ["cs.LG", "math.DS", "q-bio.TO", "stat.ML"], "doi": "10.1016/j.compbiomed.2018.10.015", "_query": "house price prediction machine learning", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2407.02737v1", "title": "Development of Machine Learning Classifiers for Blood-based Diagnosis and Prognosis of Suspected Acute Infections and Sepsis", "abstract": "We applied machine learning to the unmet medical need of rapid and accurate diagnosis and prognosis of acute infections and sepsis in emergency departments. Our solution consists of a Myrna (TM) Instrument and embedded TriVerity (TM) classifiers. The instrument measures abundances of 29 messenger RNAs in patient's blood, subsequently used as features for machine learning. The classifiers convert the input features to an intuitive test report comprising the separate likelihoods of (1) a bacterial infection (2) a viral infection, and (3) severity (need for Intensive Care Unit-level care). In internal validation, the system achieved AUROC = 0.83 on the three-class disease diagnosis (bacterial, viral, or non-infected) and AUROC = 0.77 on binary prognosis of disease severity. The Myrna, TriVerity system was granted breakthrough device designation by the United States Food and Drug Administration (FDA). This engineering manuscript teaches the standard and novel machine learning methods used to translate an academic research concept to a clinical product aimed at improving patient care, and discusses lessons learned.", "published": "2024-07-03T01:20:26Z", "authors": ["Ljubomir Buturovic", "Michael Mayhew", "Roland Luethy", "Kirindi Choi", "Uros Midic", "Nandita Damaraju", "Yehudit Hasin-Brumshtein", "Amitesh Pratap", "Rhys M. Adams", "Joao Fonseca", "Ambika Srinath", "Paul Fleming", "Claudia Pereira", "Oliver Liesenfeld", "Purvesh Khatri", "Timothy Sweeney"], "categories": ["q-bio.QM", "cs.LG"], "doi": "", "_query": "house price prediction machine learning", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2510.17503v2", "title": "Stochastic Difference-of-Convex Optimization with Momentum", "abstract": "We study the online stochastic difference-of-convex (DC) program $\\min_{x \\in R^n} F(x) = G(x) - H(x) + r_1(x) - r_2(x)$, which covers sparse learning with nonconvex penalties, robust regression with smooth robust losses, and non-negative positive--unlabeled learning. We propose \\emph{momentum-based DCA} (MDCA), which combines a damped DCA step with a momentum estimator for $\\nabla H$ and uses a constant per-iteration batch size. With exact solutions of the convex subproblems, MDCA with Polyak momentum uses $\\mathcal{O}( ε^{-4})$ stochastic gradients of $H$ to reach $ε$-stationarity, and momentum-based variance reduction (MVR) uses $\\mathcal{O}(ε^{-3})$ under averaged smoothness, with a single $\\mathcal O(ε^{-1})$ warm-up batch in place of the periodic large batches of DCA-PAGE, whose $ε$-dependence it matches. We also introduce a single-loop variant (S-MDCA) that replaces the subproblem solve by one stochastic prox-gradient step; for every fixed smoothing parameter $γ$ for which the separately smoothed surrogate $F_γ$ is bounded below, it reaches $ε$-stationarity of $F_γ$ in $\\mathcal{O}(ε^{-4})$ iterations, which does not by itself imply stationarity of $F$. A variance--drift trade-off lemma describes how the momentum parameter trades estimator variance against iterate-drift bias in steady-state upper bounds. Guarantees are given for two criteria: the critical-distance measure, and a DCA fixed-point residual that requires no smoothness of $r_2$ and vanishes exactly at DC-critical points; both give the same $ε$-dependence. Experiments on two LIBSVM classification datasets illustrate the methods' behaviour.", "published": "2025-10-20T13:00:32Z", "authors": ["El Mahdi Chayti", "Martin Jaggi"], "categories": ["cs.LG", "math.OC", "stat.ML"], "doi": "", "_query": "house price prediction machine learning", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2005.01917v3", "title": "Learning selection strategies in Buchberger's algorithm", "abstract": "Studying the set of exact solutions of a system of polynomial equations largely depends on a single iterative algorithm, known as Buchberger's algorithm. Optimized versions of this algorithm are crucial for many computer algebra systems (e.g., Mathematica, Maple, Sage). We introduce a new approach to Buchberger's algorithm that uses reinforcement learning agents to perform S-pair selection, a key step in the algorithm. We then study how the difficulty of the problem depends on the choices of domain and distribution of polynomials, about which little is known. Finally, we train a policy model using proximal policy optimization (PPO) to learn S-pair selection strategies for random systems of binomial equations. In certain domains, the trained model outperforms state-of-the-art selection heuristics in total number of polynomial additions performed, which provides a proof-of-concept that recent developments in machine learning have the potential to improve performance of algorithms in symbolic computation.", "published": "2020-05-05T02:27:00Z", "authors": ["Dylan Peifer", "Michael Stillman", "Daniel Halpern-Leistner"], "categories": ["cs.LG", "cs.SC", "math.AC", "math.AG", "stat.ML"], "doi": "", "_query": "house price prediction machine learning", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/1706.09249v3", "title": "Logics and practices of transparency and opacity in real-world applications of public sector machine learning", "abstract": "Machine learning systems are increasingly used to support public sector decision-making across a variety of sectors. Given concerns around accountability in these domains, and amidst accusations of intentional or unintentional bias, there have been increased calls for transparency of these technologies. Few, however, have considered how logics and practices concerning transparency have been understood by those involved in the machine learning systems already being piloted and deployed in public bodies today. This short paper distils insights about transparency on the ground from interviews with 27 such actors, largely public servants and relevant contractors, across 5 OECD countries. Considering transparency and opacity in relation to trust and buy-in, better decision-making, and the avoidance of gaming, it seeks to provide useful insights for those hoping to develop socio-technical approaches to transparency that might be useful to practitioners on-the-ground. An extended, archival version of this paper is available as Veale M., Van Kleek M., & Binns R. (2018). `Fairness and accountability design needs for algorithmic support in high-stakes public sector decision-making' Proceedings of the 2018 CHI Conference on Human Factors in Computing Systems (CHI'18), http://doi.org/10.1145/3173574.3174014.", "published": "2017-06-19T16:32:20Z", "authors": ["Michael Veale"], "categories": ["cs.CY", "cs.LG"], "doi": "", "_query": "house price prediction machine learning", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2605.09842v1", "title": "Yield Curve Forecasting using Machine Learning and Econometrics: A Comparative Analysis", "abstract": "While machine learning has revolutionized many fields such as natural language processing (NLP) and computer vision, its impact on time-series forecasting is still widely disputed, especially in the finance domain. This paper compares forecasting performance on U.S. Treasury yield curve data across econometrics/time-series analysis, classical machine learning, and deep learning methods, using daily data over 47 years. The Treasury yield curve is important because it is widely used by every participant in the bond markets, which are larger than equity markets. We examine a variety of methods that have not been tested on yield curve forecasting, especially deep learning algorithms. The algorithms include the Autoregressive Integrated Moving Average (ARIMA) model and its extensions, naive benchmarks, ensemble methods, Recurrent Neural Networks (RNNs), and multiple transformers built for forecasting. ARIMA and naive econometric models outperform other models overall, except in one time block. Of the machine learning methods, TimeGPT, LGBM and RNNs perform the best. Furthermore, the paper explores whether stationary or nonstationary data are more appropriate as input to deep learning models.", "published": "2026-05-11T00:53:26Z", "authors": ["Aman Singh", "Tokunbo Ogunfunmi", "Sanjiv Das"], "categories": ["cs.AI"], "doi": "", "_query": "house price prediction machine learning", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2211.13956v2", "title": "Learning General Audio Representations with Large-Scale Training of Patchout Audio Transformers", "abstract": "The success of supervised deep learning methods is largely due to their ability to learn relevant features from raw data. Deep Neural Networks (DNNs) trained on large-scale datasets are capable of capturing a diverse set of features, and learning a representation that can generalize onto unseen tasks and datasets that are from the same domain. Hence, these models can be used as powerful feature extractors, in combination with shallower models as classifiers, for smaller tasks and datasets where the amount of training data is insufficient for learning an end-to-end model from scratch. During the past years, Convolutional Neural Networks (CNNs) have largely been the method of choice for audio processing. However, recently attention-based transformer models have demonstrated great potential in supervised settings, outperforming CNNs. In this work, we investigate the use of audio transformers trained on large-scale datasets to learn general-purpose representations. We study how the different setups in these audio transformers affect the quality of their embeddings. We experiment with the models' time resolution, extracted embedding level, and receptive fields in order to see how they affect performance on a variety of tasks and datasets, following the HEAR 2021 NeurIPS challenge evaluation setup. Our results show that representations extracted by audio transformers outperform CNN representations. Furthermore, we will show that transformers trained on Audioset can be extremely effective representation extractors for a wide range of downstream tasks.", "published": "2022-11-25T08:39:12Z", "authors": ["Khaled Koutini", "Shahed Masoudian", "Florian Schmid", "Hamid Eghbal-zadeh", "Jan Schlüter", "Gerhard Widmer"], "categories": ["cs.SD", "cs.LG", "eess.AS"], "doi": "", "_query": "house price prediction machine learning", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2501.14155v1", "title": "Learning to Price with Resource Constraints: From Full Information to Machine-Learned Prices", "abstract": "We study the dynamic pricing problem with knapsack, addressing the challenge of balancing exploration and exploitation under resource constraints. We introduce three algorithms tailored to different informational settings: a Boundary Attracted Re-solve Method for full information, an online learning algorithm for scenarios with no prior information, and an estimate-then-select re-solve algorithm that leverages machine-learned informed prices with known upper bound of estimation errors. The Boundary Attracted Re-solve Method achieves logarithmic regret without requiring the non-degeneracy condition, while the online learning algorithm attains an optimal $O(\\sqrt{T})$ regret. Our estimate-then-select approach bridges the gap between these settings, providing improved regret bounds when reliable offline data is available. Numerical experiments validate the effectiveness and robustness of our algorithms across various scenarios. This work advances the understanding of online resource allocation and dynamic pricing, offering practical solutions adaptable to different informational structures.", "published": "2025-01-24T00:46:52Z", "authors": ["Ruicheng Ao", "Jiashuo Jiang", "David Simchi-Levi"], "categories": ["math.OC", "cs.LG"], "doi": "", "_query": "house price prediction machine learning", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/1209.0738v3", "title": "Sparse coding for multitask and transfer learning", "abstract": "We investigate the use of sparse coding and dictionary learning in the context of multitask and transfer learning. The central assumption of our learning method is that the tasks parameters are well approximated by sparse linear combinations of the atoms of a dictionary on a high or infinite dimensional space. This assumption, together with the large quantity of available data in the multitask and transfer learning settings, allows a principled choice of the dictionary. We provide bounds on the generalization error of this approach, for both settings. Numerical experiments on one synthetic and two real datasets show the advantage of our method over single task learning, a previous method based on orthogonal and dense representation of the tasks and a related method learning task grouping.", "published": "2012-09-04T19:06:51Z", "authors": ["Andreas Maurer", "Massimiliano Pontil", "Bernardino Romera-Paredes"], "categories": ["cs.LG", "stat.ML"], "doi": "", "_query": "house price prediction machine learning", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/1809.00357v1", "title": "Trivial Transfer Learning for Low-Resource Neural Machine Translation", "abstract": "Transfer learning has been proven as an effective technique for neural machine translation under low-resource conditions. Existing methods require a common target language, language relatedness, or specific training tricks and regimes. We present a simple transfer learning method, where we first train a \"parent\" model for a high-resource language pair and then continue the training on a lowresource pair only by replacing the training corpus. This \"child\" model performs significantly better than the baseline trained for lowresource pair only. We are the first to show this for targeting different languages, and we observe the improvements even for unrelated languages with different alphabets.", "published": "2018-09-02T15:24:15Z", "authors": ["Tom Kocmi", "Ondřej Bojar"], "categories": ["cs.CL"], "doi": "10.18653/v1/W18-6325", "_query": "house price prediction machine learning", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2609.24112v1", "title": "Causal Bayesian Optimization: Foundations, Methods, and Applications", "abstract": "Causal Bayesian Optimization (CBO) combines causal inference with Bayesian optimization to enable sample-efficient intervention selection in systems with causal structure. This survey provides a systematic review of CBO through a unified BO-loop perspective, showing how causal assumptions shape intervention search spaces, surrogate models, acquisition functions, and decision policies. We organize existing methods by graph and system-knowledge assumptions, environment, intervention representation, surrogate architecture, and decision rule, and connect CBO to causal bandits, Bayesian experimental design, safe optimization, policy search, and causal abstraction. We also introduce a reproducibility-oriented benchmark spanning hard- and soft-intervention settings, with standardized GAP and a new trajectory-aware Path-Aware GAP (PA-GAP), evaluating seven CBO methods and a non-causal BO baseline across thirteen datasets, three budgets, and two metrics. Results show that no method dominates uniformly: rankings depend on dataset, budget, metric, and how causal information is used, while strong non-causal baselines remain competitive in several settings. Controlled graph-misspecification and omitted-variable stress tests further show that rankings can change substantially when learner-side causal information is perturbed. We conclude by identifying key open challenges, including robustness to causal-assumption violations, scalable unknown-graph optimization, mixed intervention types, realistic cost models, stronger theoretical guarantees, and integration with modern representation learning and causal abstractions.", "published": "2026-09-21T04:52:01Z", "authors": ["Chenfeng Huang", "Thuy T. Le", "Zixuan Ma", "Hien Tran"], "categories": ["stat.ML", "cs.LG"], "doi": "", "_query": "house price prediction machine learning", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2204.06518v1", "title": "A pipeline and comparative study of 12 machine learning models for text classification", "abstract": "Text-based communication is highly favoured as a communication method, especially in business environments. As a result, it is often abused by sending malicious messages, e.g., spam emails, to deceive users into relaying personal information, including online accounts credentials or banking details. For this reason, many machine learning methods for text classification have been proposed and incorporated into the services of most email providers. However, optimising text classification algorithms and finding the right tradeoff on their aggressiveness is still a major research problem. We present an updated survey of 12 machine learning text classifiers applied to a public spam corpus. A new pipeline is proposed to optimise hyperparameter selection and improve the models' performance by applying specific methods (based on natural language processing) in the preprocessing stage. Our study aims to provide a new methodology to investigate and optimise the effect of different feature sizes and hyperparameters in machine learning classifiers that are widely used in text classification problems. The classifiers are tested and evaluated on different metrics including F-score (accuracy), precision, recall, and run time. By analysing all these aspects, we show how the proposed pipeline can be used to achieve a good accuracy towards spam filtering on the Enron dataset, a widely used public email corpus. Statistical tests and explainability techniques are applied to provide a robust analysis of the proposed pipeline and interpret the classification outcomes of the 12 machine learning models, also identifying words that drive the classification results. Our analysis shows that it is possible to identify an effective machine learning model to classify the Enron dataset with an F-score of 94%.", "published": "2022-04-04T23:51:22Z", "authors": ["Annalisa Occhipinti", "Louis Rogers", "Claudio Angione"], "categories": ["cs.IR", "cs.CL", "cs.LG"], "doi": "", "_query": "house price prediction machine learning", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2102.02392v1", "title": "Lapsing Quickly into Fatalism: Bell on Backward Causation", "abstract": "This is a dialogue between Huw Price and Travis Norsen, loosely inspired by a letter that Price received from J. S. Bell in 1988. The main topic of discussion is Bell's views about retrocausal approaches to quantum theory, and their relevance to contemporary issues.", "published": "2021-02-04T03:22:50Z", "authors": ["Travis Norsen", "Huw Price"], "categories": ["quant-ph", "physics.hist-ph"], "doi": "", "_query": "house price prediction machine learning", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2102.11107v1", "title": "Towards Causal Representation Learning", "abstract": "The two fields of machine learning and graphical causality arose and developed separately. However, there is now cross-pollination and increasing interest in both fields to benefit from the advances of the other. In the present paper, we review fundamental concepts of causal inference and relate them to crucial open problems of machine learning, including transfer and generalization, thereby assaying how causality can contribute to modern machine learning research. This also applies in the opposite direction: we note that most work in causality starts from the premise that the causal variables are given. A central problem for AI and causality is, thus, causal representation learning, the discovery of high-level causal variables from low-level observations. Finally, we delineate some implications of causality for machine learning and propose key research areas at the intersection of both communities.", "published": "2021-02-22T15:26:57Z", "authors": ["Bernhard Schölkopf", "Francesco Locatello", "Stefan Bauer", "Nan Rosemary Ke", "Nal Kalchbrenner", "Anirudh Goyal", "Yoshua Bengio"], "categories": ["cs.LG", "cs.AI"], "doi": "", "_query": "house price prediction machine learning", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2108.05916v1", "title": "Alzheimer's Disease Diagnosis via Deep Factorization Machine Models", "abstract": "The current state-of-the-art deep neural networks (DNNs) for Alzheimer's Disease diagnosis use different biomarker combinations to classify patients, but do not allow extracting knowledge about the interactions of biomarkers. However, to improve our understanding of the disease, it is paramount to extract such knowledge from the learned model. In this paper, we propose a Deep Factorization Machine model that combines the ability of DNNs to learn complex relationships and the ease of interpretability of a linear model. The proposed model has three parts: (i) an embedding layer to deal with sparse categorical data, (ii) a Factorization Machine to efficiently learn pairwise interactions, and (iii) a DNN to implicitly model higher order interactions. In our experiments on data from the Alzheimer's Disease Neuroimaging Initiative, we demonstrate that our proposed model classifies cognitive normal, mild cognitive impaired, and demented patients more accurately than competing models. In addition, we show that valuable knowledge about the interactions among biomarkers can be obtained.", "published": "2021-08-12T18:39:04Z", "authors": ["Raphael Ronge", "Kwangsik Nho", "Christian Wachinger", "Sebastian Pölsterl"], "categories": ["cs.LG", "cs.CV"], "doi": "10.1007/978-3-030-87589-3_64", "_query": "house price prediction machine learning", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/1507.02592v2", "title": "Fast rates in statistical and online learning", "abstract": "The speed with which a learning algorithm converges as it is presented with more data is a central problem in machine learning --- a fast rate of convergence means less data is needed for the same level of performance. The pursuit of fast rates in online and statistical learning has led to the discovery of many conditions in learning theory under which fast learning is possible. We show that most of these conditions are special cases of a single, unifying condition, that comes in two forms: the central condition for 'proper' learning algorithms that always output a hypothesis in the given model, and stochastic mixability for online algorithms that may make predictions outside of the model. We show that under surprisingly weak assumptions both conditions are, in a certain sense, equivalent. The central condition has a re-interpretation in terms of convexity of a set of pseudoprobabilities, linking it to density estimation under misspecification. For bounded losses, we show how the central condition enables a direct proof of fast rates and we prove its equivalence to the Bernstein condition, itself a generalization of the Tsybakov margin condition, both of which have played a central role in obtaining fast rates in statistical learning. Yet, while the Bernstein condition is two-sided, the central condition is one-sided, making it more suitable to deal with unbounded losses. In its stochastic mixability form, our condition generalizes both a stochastic exp-concavity condition identified by Juditsky, Rigollet and Tsybakov and Vovk's notion of mixability. Our unifying conditions thus provide a substantial step towards a characterization of fast rates in statistical learning, similar to how classical mixability characterizes constant regret in the sequential prediction with expert advice setting.", "published": "2015-07-09T16:53:30Z", "authors": ["Tim van Erven", "Peter D. Grünwald", "Nishant A. Mehta", "Mark D. Reid", "Robert C. Williamson"], "categories": ["cs.LG", "stat.ML"], "doi": "", "_query": "house price prediction machine learning", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2102.05242v2", "title": "Patterns, predictions, and actions: A story about machine learning", "abstract": "This graduate textbook on machine learning tells a story of how patterns in data support predictions and consequential actions. Starting with the foundations of decision making, we cover representation, optimization, and generalization as the constituents of supervised learning. A chapter on datasets as benchmarks examines their histories and scientific bases. Self-contained introductions to causality, the practice of causal inference, sequential decision making, and reinforcement learning equip the reader with concepts and tools to reason about actions and their consequences. Throughout, the text discusses historical context and societal impact. We invite readers from all backgrounds; some experience with probability, calculus, and linear algebra suffices.", "published": "2021-02-10T03:42:03Z", "authors": ["Moritz Hardt", "Benjamin Recht"], "categories": ["cs.LG", "stat.ML"], "doi": "", "_query": "house price prediction machine learning", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/1608.03530v1", "title": "Semi-Supervised Prediction of Gene Regulatory Networks Using Machine Learning Algorithms", "abstract": "Use of computational methods to predict gene regulatory networks (GRNs) from gene expression data is a challenging task. Many studies have been conducted using unsupervised methods to fulfill the task; however, such methods usually yield low prediction accuracies due to the lack of training data. In this article, we propose semi-supervised methods for GRN prediction by utilizing two machine learning algorithms, namely support vector machines (SVM) and random forests (RF). The semi-supervised methods make use of unlabeled data for training. We investigate inductive and transductive learning approaches, both of which adopt an iterative procedure to obtain reliable negative training data from the unlabeled data. We then apply our semi-supervised methods to gene expression data of Escherichia coli and Saccharomyces cerevisiae, and evaluate the performance of our methods using the expression data. Our analysis indicated that the transductive learning approach outperformed the inductive learning approach for both organisms. However, there was no conclusive difference identified in the performance of SVM and RF. Experimental results also showed that the proposed semi-supervised methods performed better than existing supervised methods for both organisms.", "published": "2016-08-11T16:52:03Z", "authors": ["Nihir Patel", "Jason T. L. Wang"], "categories": ["cs.LG", "q-bio.QM", "stat.ML"], "doi": "", "_query": "house price prediction machine learning", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/1910.02545v1", "title": "Early Prediction of 30-day ICU Re-admissions Using Natural Language Processing and Machine Learning", "abstract": "ICU readmission is associated with longer hospitalization, mortality and adverse outcomes. An early recognition of ICU re-admission can help prevent patients from worse situation and lower treatment cost. As the abundance of Electronics Health Records (EHR), it is popular to design clinical decision tools with machine learning technique manipulating on healthcare large scale data. We designed data-driven predictive models to estimate the risk of ICU readmission. The discharge summary of each hospital admission was carefully represented by natural language processing techniques. Unified Medical Language System (UMLS) was further used to standardize inconsistency of discharge summaries. 5 machine learning classifiers were adopted to construct predictive models. The best configuration yielded a competitive AUC of 0.748. Our work suggests that natural language processing of discharge summaries is capable to send clinicians warning of unplanned 30-day readmission upon discharge.", "published": "2019-10-06T22:54:00Z", "authors": ["Zhiheng Li", "Xinyue Xing", "Bingzhang Lu", "Zhixiang Li"], "categories": ["cs.LG", "cs.CL", "stat.ML"], "doi": "", "_query": "house price prediction machine learning", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2107.14759v1", "title": "Tiny Machine Learning for Concept Drift", "abstract": "Tiny Machine Learning (TML) is a new research area whose goal is to design machine and deep learning techniques able to operate in Embedded Systems and IoT units, hence satisfying the severe technological constraints on memory, computation, and energy characterizing these pervasive devices. Interestingly, the related literature mainly focused on reducing the computational and memory demand of the inference phase of machine and deep learning models. At the same time, the training is typically assumed to be carried out in Cloud or edge computing systems (due to the larger memory and computational requirements). This assumption results in TML solutions that might become obsolete when the process generating the data is affected by concept drift (e.g., due to periodicity or seasonality effect, faults or malfunctioning affecting sensors or actuators, or changes in the users' behavior), a common situation in real-world application scenarios. For the first time in the literature, this paper introduces a Tiny Machine Learning for Concept Drift (TML-CD) solution based on deep learning feature extractors and a k-nearest neighbors classifier integrating a hybrid adaptation module able to deal with concept drift affecting the data-generating process. This adaptation module continuously updates (in a passive way) the knowledge base of TML-CD and, at the same time, employs a Change Detection Test to inspect for changes (in an active way) to quickly adapt to concept drift by removing the obsolete knowledge. Experimental results on both image and audio benchmarks show the effectiveness of the proposed solution, whilst the porting of TML-CD on three off-the-shelf micro-controller units shows the feasibility of what is proposed in real-world pervasive systems.", "published": "2021-07-30T17:02:04Z", "authors": ["Simone Disabato", "Manuel Roveri"], "categories": ["cs.LG"], "doi": "", "_query": "house price prediction machine learning", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2606.19134v1", "title": "Pareto Q-Learning with Reward Machines", "abstract": "We present Pareto Q-Learning with Reward Machines (PQLRM), a multi-objective reinforcement learning algorithm for tasks whose reward structure is specified by a set of reward machines (RMs). PQLRM combines Pareto Q-Learning (PQL), which maintains sets of vector-valued Q-estimates to approximate the Pareto front, with enhancements from Q-Learning with Reward Machines (QRM), which exploits the factored automaton structure of the reward signal. This yields a multi-policy algorithm that remains sample-efficient under non-Markovian, RM-encoded rewards. Experimental trials show that PQLRM converges faster than a naive PQL baseline applied to the cross-product MDP and can synthesize Pareto-optimal policies that QRM cannot.", "published": "2026-06-17T14:44:31Z", "authors": ["Arnaud Lequen", "Clément Legrand-Lixon", "Léo Saulières"], "categories": ["cs.LG", "cs.AI"], "doi": "", "_query": "house price prediction machine learning", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2504.02221v1", "title": "Learning and Improving Backgammon Strategy", "abstract": "A novel approach to learning is presented, combining features of on-line and off-line methods to achieve considerable performance in the task of learning a backgammon value function in a process that exploits the processing power of parallel supercomputers. The off-line methods comprise a set of techniques for parallelizing neural network training and $TD(λ)$ reinforcement learning; here Monte-Carlo ``Rollouts'' are introduced as a massively parallel on-line policy improvement technique which applies resources to the decision points encountered during the search of the game tree to further augment the learned value function estimate. A level of play roughly as good as, or possibly better than, the current champion human and computer backgammon players has been achieved in a short period of learning.", "published": "2025-04-03T02:27:22Z", "authors": ["Gregory R. Galperin"], "categories": ["cs.LG", "cs.AI", "cs.NE"], "doi": "", "_query": "house price prediction machine learning", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2002.01441v2", "title": "A Generalized Flow for B2B Sales Predictive Modeling: An Azure Machine Learning Approach", "abstract": "Predicting the outcome of sales opportunities is a core part of successful business management. Conventionally, making this prediction has relied mostly on subjective human evaluations in the process of sales decision making. In this paper, we addressed the problem of forecasting the outcome of business to business (B2B) sales by proposing a thorough data-driven Machine Learning (ML) workflow on a cloud-based computing platform: Microsoft Azure Machine Learning Service (Azure ML). This workflow consists of two pipelines: (1) An ML pipeline to train probabilistic predictive models on the historical sales opportunities data. In this pipeline, data is enriched with an extensive feature enhancement step and then used to train an ensemble of ML classification models in parallel. (2) A prediction pipeline to utilize the trained ML model and infer the likelihood of winning new sales opportunities along with calculating optimal decision boundaries. The effectiveness of the proposed workflow was evaluated on a real sales dataset of a major global B2B consulting firm. Our results implied that decision-making based on the ML predictions is more accurate and brings a higher monetary value.", "published": "2020-02-04T18:01:24Z", "authors": ["Alireza Rezazadeh"], "categories": ["cs.LG", "stat.ML"], "doi": "10.3390/forecast2030015", "_query": "house price prediction machine learning", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2203.04768v1", "title": "Explainable Machine Learning for Predicting Homicide Clearance in the United States", "abstract": "Purpose: To explore the potential of Explainable Machine Learning in the prediction and detection of drivers of cleared homicides at the national- and state-levels in the United States. Methods: First, nine algorithmic approaches are compared to assess the best performance in predicting cleared homicides country-wise, using data from the Murder Accountability Project. The most accurate algorithm among all (XGBoost) is then used for predicting clearance outcomes state-wise. Second, SHAP, a framework for Explainable Artificial Intelligence, is employed to capture the most important features in explaining clearance patterns both at the national and state levels. Results: At the national level, XGBoost demonstrates to achieve the best performance overall. Substantial predictive variability is detected state-wise. In terms of explainability, SHAP highlights the relevance of several features in consistently predicting investigation outcomes. These include homicide circumstances, weapons, victims' sex and race, as well as number of involved offenders and victims. Conclusions: Explainable Machine Learning demonstrates to be a helpful framework for predicting homicide clearance. SHAP outcomes suggest a more organic integration of the two theoretical perspectives emerged in the literature. Furthermore, jurisdictional heterogeneity highlights the importance of developing ad hoc state-level strategies to improve police performance in clearing homicides.", "published": "2022-03-09T14:35:12Z", "authors": ["Gian Maria Campedelli"], "categories": ["cs.LG", "cs.AI", "econ.EM", "stat.AP", "stat.ML"], "doi": "10.1016/j.jcrimjus.2022.101898", "_query": "house price prediction machine learning", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2603.13674v1", "title": "Locally Linear Continual Learning for Time Series based on VC-Theoretical Generalization Bounds", "abstract": "Most machine learning methods assume fixed probability distributions, limiting their applicability in nonstationary real-world scenarios. While continual learning methods address this issue, current approaches often rely on black-box models or require extensive user intervention for interpretability. We propose SyMPLER (Systems Modeling through Piecewise Linear Evolving Regression), an explainable model for time series forecasting in nonstationary environments based on dynamic piecewise-linear approximations. Unlike other locally linear models, SyMPLER uses generalization bounds from Statistical Learning Theory to automatically determine when to add new local models based on prediction errors, eliminating the need for explicit clustering of the data. Experiments show that SyMPLER can achieve comparable performance to both black-box and existing explainable models while maintaining a human-interpretable structure that reveals insights about the system's behavior. In this sense, our approach conciliates accuracy and interpretability, offering a transparent and adaptive solution for forecasting nonstationary time series.", "published": "2026-03-14T00:48:02Z", "authors": ["Yan V. G. Ferreira", "Igor B. Lima", "Pedro H. G. Mapa S.", "Felipe V. Campos", "Antonio P. Braga"], "categories": ["cs.LG", "cs.AI", "stat.ML"], "doi": "10.1109/TPAMI.2026.3672726", "_query": "house price prediction machine learning", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2303.13692v1", "title": "Predicting Physical Parameters of Cepheid and RR Lyrae variables in an Instant with Machine Learning", "abstract": "We present a machine learning method to estimate the physical parameters of classical pulsating stars such as RR Lyrae and Cepheid variables based on an automated comparison of their theoretical and observed light curve parameters at multiple wavelengths. We train artificial neural networks (ANNs) on theoretical pulsation models to predict the fundamental parameters (mass, radius, luminosity, and effective temperature) of Cepheid and RR Lyrae stars based on their period and light curve parameters. The fundamental parameters of these stars can be estimated up to 60 percent more accurately when the light curve parameters are taken into consideration. This method was applied to the observations of hundreds of Cepheids and thousands of RR Lyrae in the Magellanic Clouds to produce catalogs of estimated masses, radii, luminosities, and other parameters of these stars.", "published": "2023-03-23T22:05:58Z", "authors": ["Anupam Bhardwaj", "Earl P. Bellinger", "Shashi M. Kanbur", "Marcella Marconi"], "categories": ["astro-ph.SR", "astro-ph.GA"], "doi": "", "_query": "house price prediction machine learning", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/1811.00628v1", "title": "Independent Vector Analysis for Data Fusion Prior to Molecular Property Prediction with Machine Learning", "abstract": "Due to its high computational speed and accuracy compared to ab-initio quantum chemistry and forcefield modeling, the prediction of molecular properties using machine learning has received great attention in the fields of materials design and drug discovery. A main ingredient required for machine learning is a training dataset consisting of molecular features\\textemdash for example fingerprint bits, chemical descriptors, etc. that adequately characterize the corresponding molecules. However, choosing features for any application is highly non-trivial. No \"universal\" method for feature selection exists. In this work, we propose a data fusion framework that uses Independent Vector Analysis to exploit underlying complementary information contained in different molecular featurization methods, bringing us a step closer to automated feature generation. Our approach takes an arbitrary number of individual feature vectors and automatically generates a single, compact (low dimensional) set of molecular features that can be used to enhance the prediction performance of regression models. At the same time our methodology retains the possibility of interpreting the generated features to discover relationships between molecular structures and properties. We demonstrate this on the QM7b dataset for the prediction of several properties such as atomization energy, polarizability, frontier orbital eigenvalues, ionization potential, electron affinity, and excitation energies. In addition, we show how our method helps improve the prediction of experimental binding affinities for a set of human BACE-1 inhibitors. To encourage more widespread use of IVA we have developed the PyIVA Python package, an open source code which is available for download on Github.", "published": "2018-11-01T20:34:31Z", "authors": ["Zois Boukouvalas", "Daniel C. Elton", "Peter W. Chung", "Mark D. Fuge"], "categories": ["stat.ML", "cond-mat.mtrl-sci", "cs.LG"], "doi": "", "_query": "house price prediction machine learning", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/1910.00270v4", "title": "Robust Learning with the Hilbert-Schmidt Independence Criterion", "abstract": "We investigate the use of a non-parametric independence measure, the Hilbert-Schmidt Independence Criterion (HSIC), as a loss-function for learning robust regression and classification models. This loss-function encourages learning models where the distribution of the residuals between the label and the model prediction is statistically independent of the distribution of the instances themselves. This loss-function was first proposed by Mooij et al. (2009) in the context of learning causal graphs. We adapt it to the task of learning for unsupervised covariate shift: learning on a source domain without access to any instances or labels from the unknown target domain, but with the assumption that $p(y|x)$ (the conditional probability of labels given instances) remains the same in the target domain. We show that the proposed loss is expected to give rise to models that generalize well on a class of target domains characterised by the complexity of their description within a reproducing kernel Hilbert space. Experiments on unsupervised covariate shift tasks demonstrate that models learned with the proposed loss-function outperform models learned with standard loss functions, achieving state-of-the-art results on a challenging cell-microscopy unsupervised covariate shift task.", "published": "2019-10-01T09:27:33Z", "authors": ["Daniel Greenfeld", "Uri Shalit"], "categories": ["cs.LG", "stat.ML"], "doi": "", "_query": "house price prediction machine learning", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/1110.2306v1", "title": "Ground Metric Learning", "abstract": "Transportation distances have been used for more than a decade now in machine learning to compare histograms of features. They have one parameter: the ground metric, which can be any metric between the features themselves. As is the case for all parameterized distances, transportation distances can only prove useful in practice when this parameter is carefully chosen. To date, the only option available to practitioners to set the ground metric parameter was to rely on a priori knowledge of the features, which limited considerably the scope of application of transportation distances. We propose to lift this limitation and consider instead algorithms that can learn the ground metric using only a training set of labeled histograms. We call this approach ground metric learning. We formulate the problem of learning the ground metric as the minimization of the difference of two polyhedral convex functions over a convex set of distance matrices. We follow the presentation of our algorithms with promising experimental results on binary classification tasks using GIST descriptors of images taken in the Caltech-256 set.", "published": "2011-10-11T09:04:56Z", "authors": ["Marco Cuturi", "David Avis"], "categories": ["stat.ML", "cs.CV", "cs.LG"], "doi": "", "_query": "house price prediction machine learning", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/1902.02060v3", "title": "On ADMM in Deep Learning: Convergence and Saturation-Avoidance", "abstract": "In this paper, we develop an alternating direction method of multipliers (ADMM) for deep neural networks training with sigmoid-type activation functions (called \\textit{sigmoid-ADMM pair}), mainly motivated by the gradient-free nature of ADMM in avoiding the saturation of sigmoid-type activations and the advantages of deep neural networks with sigmoid-type activations (called deep sigmoid nets) over their rectified linear unit (ReLU) counterparts (called deep ReLU nets) in terms of approximation. In particular, we prove that the approximation capability of deep sigmoid nets is not worse than that of deep ReLU nets by showing that ReLU activation function can be well approximated by deep sigmoid nets with two hidden layers and finitely many free parameters but not vice-verse. We also establish the global convergence of the proposed ADMM for the nonlinearly constrained formulation of the deep sigmoid nets training from arbitrary initial points to a Karush-Kuhn-Tucker (KKT) point at a rate of order ${\\cal O}(1/k)$. Besides sigmoid activation, such a convergence theorem holds for a general class of smooth activations. Compared with the widely used stochastic gradient descent (SGD) algorithm for the deep ReLU nets training (called ReLU-SGD pair), the proposed sigmoid-ADMM pair is practically stable with respect to the algorithmic hyperparameters including the learning rate, initial schemes and the pro-processing of the input data. Moreover, we find that to approximate and learn simple but important functions the proposed sigmoid-ADMM pair numerically outperforms the ReLU-SGD pair.", "published": "2019-02-06T08:22:08Z", "authors": ["Jinshan Zeng", "Shao-Bo Lin", "Yuan Yao", "Ding-Xuan Zhou"], "categories": ["cs.LG", "math.OC", "stat.ML"], "doi": "", "_query": "house price prediction machine learning", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/1809.01898v2", "title": "Propheticus: Machine Learning Framework for the Development of Predictive Models for Reliable and Secure Software", "abstract": "The growing complexity of software calls for innovative solutions that support the deployment of reliable and secure software. Machine Learning (ML) has shown its applicability to various complex problems and is frequently used in the dependability domain, both for supporting systems design and verification activities. However, using ML is complex and highly dependent on the problem in hand, increasing the probability of mistakes that compromise the results. In this paper, we introduce Propheticus, a ML framework that can be used to create predictive models for reliable and secure software systems. Propheticus attempts to abstract the complexity of ML whilst being easy to use and accommodating the needs of the users. To demonstrate its use, we present two case studies (vulnerability prediction and online failure prediction) that show how it can considerably ease and expedite a thorough ML workflow.", "published": "2018-09-06T09:26:03Z", "authors": ["João R. Campos", "Marco Vieira", "Ernesto Costa"], "categories": ["cs.LG", "cs.AI", "stat.ML"], "doi": "10.1109/ISSRE.2019.00026", "_query": "house price prediction machine learning", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/1409.7770v4", "title": "Entanglement-Based Machine Learning on a Quantum Computer", "abstract": "Machine learning, a branch of artificial intelligence, learns from previous experience to optimize performance, which is ubiquitous in various fields such as computer sciences, financial analysis, robotics, and bioinformatics. A challenge is that machine learning with the rapidly growing \"big data\" could become intractable for classical computers. Recently, quantum machine learning algorithms [Lloyd, Mohseni, and Rebentrost, arXiv.1307.0411] was proposed which could offer an exponential speedup over classical algorithms. Here, we report the first experimental entanglement-based classification of 2-, 4-, and 8-dimensional vectors to different clusters using a small-scale photonic quantum computer, which is then used to implement supervised and unsupervised machine learning. The results demonstrate the working principle of using quantum computers to manipulate and classify high-dimensional vectors, the core mathematical routine in machine learning. The method can in principle be scaled to a larger number of qubits, and may provide a new route to accelerate machine learning.", "published": "2014-09-27T06:31:08Z", "authors": ["X. -D. Cai", "D. Wu", "Z. -E. Su", "M. -C. Chen", "X. -L. Wang", "L. Li", "N. -L. Liu", "Chao-Yang Lu", "Jian-Wei Pan"], "categories": ["quant-ph", "cond-mat.other"], "doi": "10.1103/PhysRevLett.114.110504", "_query": "house price prediction machine learning", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2312.09490v1", "title": "Efficient Machine Learning Force Field for Large-Scale Molecular Simulations of Organic Systems", "abstract": "To address the computational challenges of ab initio molecular dynamics and the accuracy limitations of empirical force fields, the introduction of machine learning force fields has proven effective in various systems including metals and inorganic materials. However, in large-scale organic systems, the application of machine learning force fields is often hindered by impediments such as the complexity of long-range intermolecular interactions and molecular conformations, as well as the instability in long-time molecular simulations. Therefore, we propose a universal multiscale higher-order equivariant model combined with active learning techniques, efficiently capturing the complex long-range intermolecular interactions and molecular conformations. Compared to existing equivariant models, our model achieves the highest predictive accuracy, and magnitude-level improvements in computational speed and memory efficiency. In addition, a bond length stretching method is designed to improve the stability of long-time molecular simulations. Utilizing only 901 samples from a dataset with 120 atoms, our model successfully extends high precision to systems with hundreds of thousands of atoms. These achievements guarantee high predictive accuracy, fast simulation speed, minimal memory consumption, and robust simulation stability, satisfying the requirements for high-precision and long-time molecular simulations in large-scale organic systems.", "published": "2023-12-15T02:06:51Z", "authors": ["Junbao Hu", "Liyang Zhou", "Jian Jiang"], "categories": ["cond-mat.soft"], "doi": "", "_query": "house price prediction machine learning", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2605.07364v2", "title": "FlightSense: An End-to-End MLOps Platform for Real-Time Flight Delay Prediction via Rotation-Chain Propagation Features and Agentic Conversational AI", "abstract": "Flight delays impose cascading operational and financial burdens across the aviation network, costing the U.S. economy billions of dollars annually by disrupting interconnected aircraft rotation systems. While prior machine learning approaches have demonstrated strong predictive performance, most treat upstream delays as static input variables rather than explicitly modeling how delays propagate dynamically through aircraft rotation chains, and none have deployed such systems alongside a live weather-aware conversational AI interface for end-user interaction. This paper presents FlightSense, an end-to-end MLOps platform for real-time flight delay prediction built through a progressive three-version feature engineering framework. Version 1 trains an XGBoost classifier on 11 schedule-based features establishing a baseline ROC AUC of 0.732 on 7.07 million BTS 2018 On-Time Performance records. Version 2 introduces 11 delay propagation features derived from aircraft rotation chains via tail-number tracking, yielding the dominant performance gain (AUC 0.732 to 0.875) and surpassing the single-stage XGBoost baseline reported by Zhou (2025). Version 3 integrates five NOAA meteorological features across 10 major U.S. airports, achieving a final test set AUC of 0.879. FlightSense is deployed as a production AWS MLOps pipeline incorporating live weather ingestion via Lambda, real-time SageMaker inference, an interactive Streamlit dashboard, and an Amazon Bedrock Nova Micro conversational assistant answering natural-language delay queries via a tool-use architecture.", "published": "2026-05-08T07:21:00Z", "authors": ["Aditi J. Shelke", "Renuka J. Shelke", "Yash M. Kamerkar", "Nitin P. Hazarani"], "categories": ["cs.LG"], "doi": "", "_query": "house price prediction machine learning", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2206.10303v2", "title": "Prediction of Maneuvering Status for Aerial Vehicles using Supervised Learning Methods", "abstract": "Aerial Vehicles follow a guided approach based on Latitude, Longitude and Altitude. This information can be used for calculating the status of maneuvering for the aerial vehicles along the line of trajectory. This is a binary classification problem and Machine Learning can be leveraged for solving such problem. In this paper we present a methodology for deriving maneuvering status and its prediction using Linear, Distance Metric, Discriminant Analysis and Boosting Ensemble supervised learning methods. We provide various metrics along the line in the results section that give condensed comparison of the appropriate algorithm for prediction of the maneuvering status.", "published": "2022-06-02T05:16:13Z", "authors": ["Abhishek Gupta", "Sarvesh Thustu", "Riti Thakor", "Saniya Patil", "Raunak Joshi", "Ronald Melvin Laban"], "categories": ["cs.RO", "cs.LG"], "doi": "", "_query": "house price prediction machine learning", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2603.14483v2", "title": "Disentangling Dynamical Systems: Causal Representation Learning Meets Local Sparse Attention", "abstract": "Parametric system identification methods estimate the parameters of explicitly defined physical systems from data. Yet, they remain constrained by the need to provide an explicit function space, typically through a predefined library of candidate functions chosen via available domain knowledge. In contrast, deep learning can demonstrably model systems of broad complexity with high fidelity, but black-box function approximation typically fails to yield explicit descriptive or disentangled representations revealing the structure of a system. We develop a novel identifiability theorem, leveraging causal representation learning, to uncover disentangled representations of system parameters without structural assumptions. We derive a graphical criterion specifying when system parameters can be uniquely disentangled from raw trajectory data, up to permutation and diffeomorphism. Crucially, our analysis demonstrates that global causal structures provide a lower bound on the disentanglement guarantees achievable when considering local state-dependent causal structures. We instantiate system parameter identification as a variational inference problem, leveraging a sparsity-regularised transformer to uncover state-dependent causal structures. We empirically validate our approach across four synthetic domains, demonstrating its ability to recover highly disentangled representations that baselines fail to recover. Corroborating our theoretical analysis, our results confirm that enforcing local causal structure is often necessary for full identifiability.", "published": "2026-03-15T17:03:34Z", "authors": ["Markus W. Baumgartner", "Anson Lei", "Joe Watson", "Ingmar Posner"], "categories": ["cs.LG"], "doi": "", "_query": "house price prediction machine learning", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2012.15103v1", "title": "Explanations of Machine Learning predictions: a mandatory step for its application to Operational Processes", "abstract": "In the global economy, credit companies play a central role in economic development, through their activity as money lenders. This important task comes with some drawbacks, mainly the risk of the debtors not being able to repay the provided credit. Therefore, Credit Risk Modelling (CRM), namely the evaluation of the probability that a debtor will not repay the due amount, plays a paramount role. Statistical approaches have been successfully exploited since long, becoming the most used methods for CRM. Recently, also machine and deep learning techniques have been applied to the CRM task, showing an important increase in prediction quality and performances. However, such techniques usually do not provide reliable explanations for the scores they come up with. As a consequence, many machine and deep learning techniques fail to comply with western countries' regulations such as, for example, GDPR. In this paper we suggest to use LIME (Local Interpretable Model-agnostic Explanations) technique to tackle the explainability problem in this field, we show its employment on a real credit-risk dataset and eventually discuss its soundness and the necessary improvements to guarantee its adoption and compliance with the task.", "published": "2020-12-30T10:27:59Z", "authors": ["Giorgio Visani", "Federico Chesani", "Enrico Bagli", "Davide Capuzzo", "Alessandro Poluzzi"], "categories": ["cs.LG", "stat.ML"], "doi": "", "_query": "house price prediction machine learning", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2008.10773v1", "title": "Enabling robust offline active learning for machine learning potentials using simple physics-based priors", "abstract": "Machine learning surrogate models for quantum mechanical simulations has enabled the field to efficiently and accurately study material and molecular systems. Developed models typically rely on a substantial amount of data to make reliable predictions of the potential energy landscape or careful active learning and uncertainty estimates. When starting with small datasets, convergence of active learning approaches is a major outstanding challenge which limited most demonstrations to online active learning. In this work we demonstrate a $Δ$-machine learning approach that enables stable convergence in offline active learning strategies by avoiding unphysical configurations. We demonstrate our framework's capabilities on a structural relaxation, transition state calculation, and molecular dynamics simulation, with the number of first principle calculations being cut down anywhere from 70-90%. The approach is incorporated and developed alongside AMPtorch, an open-source machine learning potential package, along with interactive Google Colab notebook examples.", "published": "2020-08-25T01:14:53Z", "authors": ["Muhammed Shuaibi", "Saurabh Sivakumar", "Rui Qi Chen", "Zachary W. Ulissi"], "categories": ["physics.comp-ph", "cond-mat.mtrl-sci"], "doi": "10.1088/2632-2153/abcc44", "_query": "house price prediction machine learning", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2507.11136v1", "title": "Interpretable Bayesian Tensor Network Kernel Machines with Automatic Rank and Feature Selection", "abstract": "Tensor Network (TN) Kernel Machines speed up model learning by representing parameters as low-rank TNs, reducing computation and memory use. However, most TN-based Kernel methods are deterministic and ignore parameter uncertainty. Further, they require manual tuning of model complexity hyperparameters like tensor rank and feature dimensions, often through trial-and-error or computationally costly methods like cross-validation. We propose Bayesian Tensor Network Kernel Machines, a fully probabilistic framework that uses sparsity-inducing hierarchical priors on TN factors to automatically infer model complexity. This enables automatic inference of tensor rank and feature dimensions, while also identifying the most relevant features for prediction, thereby enhancing model interpretability. All the model parameters and hyperparameters are treated as latent variables with corresponding priors. Given the Bayesian approach and latent variable dependencies, we apply a mean-field variational inference to approximate their posteriors. We show that applying a mean-field approximation to TN factors yields a Bayesian ALS algorithm with the same computational complexity as its deterministic counterpart, enabling uncertainty quantification at no extra computational cost. Experiments on synthetic and real-world datasets demonstrate the superior performance of our model in prediction accuracy, uncertainty quantification, interpretability, and scalability.", "published": "2025-07-15T09:37:49Z", "authors": ["Afra Kilic", "Kim Batselier"], "categories": ["stat.ML", "cs.LG"], "doi": "", "_query": "house price prediction machine learning", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2506.00044v1", "title": "Probabilistic intraday electricity price forecasting using generative machine learning", "abstract": "The growing importance of intraday electricity trading in Europe calls for improved price forecasting and tailored decision-support tools. In this paper, we propose a novel generative neural network model to generate probabilistic path forecasts for intraday electricity prices and use them to construct effective trading strategies for Germany's continuous-time intraday market. Our method demonstrates competitive performance in terms of statistical evaluation metrics compared to two state-of-the-art statistical benchmark approaches. To further assess its economic value, we consider a realistic fixed-volume trading scenario and propose various strategies for placing market sell orders based on the path forecasts. Among the different trading strategies, the price paths generated by our generative model lead to higher profit gains than the benchmark methods. Our findings highlight the potential of generative machine learning tools in electricity price forecasting and underscore the importance of economic evaluation.", "published": "2025-05-28T11:41:46Z", "authors": ["Jieyu Chen", "Sebastian Lerch", "Melanie Schienle", "Tomasz Serafin", "Rafał Weron"], "categories": ["stat.AP", "cs.LG", "stat.ML"], "doi": "", "_query": "house price prediction machine learning", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2006.08352v1", "title": "Modeling bike availability in a bike-sharing system using machine learning", "abstract": "This paper models the availability of bikes at San Francisco Bay Area Bike Share stations using machine learning algorithms. Random Forest (RF) and Least-Squares Boosting (LSBoost) were used as univariate regression algorithms, and Partial Least-Squares Regression (PLSR) was applied as a multivariate regression algorithm. The univariate models were used to model the number of available bikes at each station. PLSR was applied to reduce the number of required prediction models and reflect the spatial correlation between stations in the network. Results clearly show that univariate models have lower error predictions than the multivariate model. However, the multivariate model results are reasonable for networks with a relatively large number of spatially correlated stations. Results also show that station neighbors and the prediction horizon time are significant predictors. The most effective prediction horizon time that produced the least prediction error was 15 minutes.", "published": "2020-06-12T04:49:14Z", "authors": ["Huthaifa I. Ashqar", "Mohammed Elhenawy", "Mohammed H. Almannaa", "Ahmed Ghanem", "Hesham A. Rakha", "Leanna House"], "categories": ["cs.CY", "cs.LG", "physics.soc-ph"], "doi": "10.1109/MTITS.2017.8005700", "_query": "house price prediction machine learning", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2004.09280v4", "title": "Towards a theory of machine learning", "abstract": "We define a neural network as a septuple consisting of (1) a state vector, (2) an input projection, (3) an output projection, (4) a weight matrix, (5) a bias vector, (6) an activation map and (7) a loss function. We argue that the loss function can be imposed either on the boundary (i.e. input and/or output neurons) or in the bulk (i.e. hidden neurons) for both supervised and unsupervised systems. We apply the principle of maximum entropy to derive a canonical ensemble of the state vectors subject to a constraint imposed on the bulk loss function by a Lagrange multiplier (or an inverse temperature parameter). We show that in an equilibrium the canonical partition function must be a product of two factors: a function of the temperature and a function of the bias vector and weight matrix. Consequently, the total Shannon entropy consists of two terms which represent respectively a thermodynamic entropy and a complexity of the neural network. We derive the first and second laws of learning: during learning the total entropy must decrease until the system reaches an equilibrium (i.e. the second law), and the increment in the loss function must be proportional to the increment in the thermodynamic entropy plus the increment in the complexity (i.e. the first law). We calculate the entropy destruction to show that the efficiency of learning is given by the Laplacian of the total free energy which is to be maximized in an optimal neural architecture, and explain why the optimization condition is better satisfied in a deep network with a large number of hidden layers. The key properties of the model are verified numerically by training a supervised feedforward neural network using the method of stochastic gradient descent. We also discuss a possibility that the entire universe on its most fundamental level is a neural network.", "published": "2020-04-15T00:41:46Z", "authors": ["Vitaly Vanchurin"], "categories": ["cs.LG", "cond-mat.dis-nn", "hep-th", "quant-ph"], "doi": "", "_query": "house price prediction machine learning", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/1611.07054v1", "title": "An Efficient Training Algorithm for Kernel Survival Support Vector Machines", "abstract": "Survival analysis is a fundamental tool in medical research to identify predictors of adverse events and develop systems for clinical decision support. In order to leverage large amounts of patient data, efficient optimisation routines are paramount. We propose an efficient training algorithm for the kernel survival support vector machine (SSVM). We directly optimise the primal objective function and employ truncated Newton optimisation and order statistic trees to significantly lower computational costs compared to previous training algorithms, which require $O(n^4)$ space and $O(p n^6)$ time for datasets with $n$ samples and $p$ features. Our results demonstrate that our proposed optimisation scheme allows analysing data of a much larger scale with no loss in prediction performance. Experiments on synthetic and 5 real-world datasets show that our technique outperforms existing kernel SSVM formulations if the amount of right censoring is high ($\\geq85\\%$), and performs comparably otherwise.", "published": "2016-11-21T21:09:33Z", "authors": ["Sebastian Pölsterl", "Nassir Navab", "Amin Katouzian"], "categories": ["cs.LG", "cs.AI", "stat.ML"], "doi": "", "_query": "house price prediction machine learning", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/1908.10218v1", "title": "Urban flows prediction from spatial-temporal data using machine learning: A survey", "abstract": "Urban spatial-temporal flows prediction is of great importance to traffic management, land use, public safety, etc. Urban flows are affected by several complex and dynamic factors, such as patterns of human activities, weather, events and holidays. Datasets evaluated the flows come from various sources in different domains, e.g. mobile phone data, taxi trajectories data, metro/bus swiping data, bike-sharing data and so on. To summarize these methodologies of urban flows prediction, in this paper, we first introduce four main factors affecting urban flows. Second, in order to further analysis urban flows, a preparation process of multi-sources spatial-temporal data related with urban flows is partitioned into three groups. Third, we choose the spatial-temporal dynamic data as a case study for the urban flows prediction task. Fourth, we analyze and compare some well-known and state-of-the-art flows prediction methods in detail, classifying them into five categories: statistics-based, traditional machine learning-based, deep learning-based, reinforcement learning-based and transfer learning-based methods. Finally, we give open challenges of urban flows prediction and an outlook in the future of this field. This paper will facilitate researchers find suitable methods and open datasets for addressing urban spatial-temporal flows forecast problems.", "published": "2019-08-26T13:50:37Z", "authors": ["Peng Xie", "Tianrui Li", "Jia Liu", "Shengdong Du", "Xin Yang", "Junbo Zhang"], "categories": ["cs.LG", "stat.ML"], "doi": "", "_query": "house price prediction machine learning", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2205.06117v1", "title": "Secure Aggregation for Federated Learning in Flower", "abstract": "Federated Learning (FL) allows parties to learn a shared prediction model by delegating the training computation to clients and aggregating all the separately trained models on the server. To prevent private information being inferred from local models, Secure Aggregation (SA) protocols are used to ensure that the server is unable to inspect individual trained models as it aggregates them. However, current implementations of SA in FL frameworks have limitations, including vulnerability to client dropouts or configuration difficulties. In this paper, we present Salvia, an implementation of SA for Python users in the Flower FL framework. Based on the SecAgg(+) protocols for a semi-honest threat model, Salvia is robust against client dropouts and exposes a flexible and easy-to-use API that is compatible with various machine learning frameworks. We show that Salvia's experimental performance is consistent with SecAgg(+)'s theoretical computation and communication complexities.", "published": "2022-05-12T14:31:54Z", "authors": ["Kwing Hei Li", "Pedro Porto Buarque de Gusmão", "Daniel J. Beutel", "Nicholas D. Lane"], "categories": ["cs.LG", "cs.CR"], "doi": "", "_query": "house price prediction machine learning", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2209.06114v1", "title": "Analysing the Predictivity of Features to Characterise the Search Space", "abstract": "Exploring search spaces is one of the most unpredictable challenges that has attracted the interest of researchers for decades. One way to handle unpredictability is to characterise the search spaces and take actions accordingly. A well-characterised search space can assist in mapping the problem states to a set of operators for generating new problem states. In this paper, a landscape analysis-based set of features has been analysed using the most renown machine learning approaches to determine the optimal feature set. However, in order to deal with problem complexity and induce commonality for transferring experience across domains, the selection of the most representative features remains crucial. The proposed approach analyses the predictivity of a set of features in order to determine the best categorization.", "published": "2022-09-11T20:04:17Z", "authors": ["Rafet Durgut", "Mehmet Emin Aydin", "Hisham Ihshaish", "Abdur Rakib"], "categories": ["cs.LG", "cs.AI", "cs.NE"], "doi": "10.1007/978-3-031-15937-4_1", "_query": "house price prediction machine learning", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2409.14157v1", "title": "Price predictability in limit order book with deep learning model", "abstract": "This study explores the prediction of high-frequency price changes using deep learning models. Although state-of-the-art methods perform well, their complexity impedes the understanding of successful predictions. We found that an inadequately defined target price process may render predictions meaningless by incorporating past information. The commonly used three-class problem in asset price prediction can generally be divided into volatility and directional prediction. When relying solely on the price process, directional prediction performance is not substantial. However, volume imbalance improves directional prediction performance.", "published": "2024-09-21T14:40:13Z", "authors": ["Kyungsub Lee"], "categories": ["q-fin.ST", "q-fin.TR", "stat.ML"], "doi": "", "_query": "house price prediction machine learning", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2202.05702v1", "title": "Machine Learning for Stock Prediction Based on Fundamental Analysis", "abstract": "Application of machine learning for stock prediction is attracting a lot of attention in recent years. A large amount of research has been conducted in this area and multiple existing results have shown that machine learning methods could be successfully used toward stock predicting using stocks historical data. Most of these existing approaches have focused on short term prediction using stocks historical price and technical indicators. In this paper, we prepared 22 years worth of stock quarterly financial data and investigated three machine learning algorithms: Feed-forward Neural Network (FNN), Random Forest (RF) and Adaptive Neural Fuzzy Inference System (ANFIS) for stock prediction based on fundamental analysis. In addition, we applied RF based feature selection and bootstrap aggregation in order to improve model performance and aggregate predictions from different models. Our results show that RF model achieves the best prediction results, and feature selection is able to improve test performance of FNN and ANFIS. Moreover, the aggregated model outperforms all baseline models as well as the benchmark DJIA index by an acceptable margin for the test period. Our findings demonstrate that machine learning models could be used to aid fundamental analysts with decision-making regarding stock investment.", "published": "2022-01-26T18:48:51Z", "authors": ["Yuxuan Huang", "Luiz Fernando Capretz", "Danny Ho"], "categories": ["q-fin.ST", "cs.LG", "cs.NE"], "doi": "10.1109/SSCI50451.2021.9660134", "_query": "house price prediction machine learning", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2405.18765v1", "title": "Large Brain Model for Learning Generic Representations with Tremendous EEG Data in BCI", "abstract": "The current electroencephalogram (EEG) based deep learning models are typically designed for specific datasets and applications in brain-computer interaction (BCI), limiting the scale of the models and thus diminishing their perceptual capabilities and generalizability. Recently, Large Language Models (LLMs) have achieved unprecedented success in text processing, prompting us to explore the capabilities of Large EEG Models (LEMs). We hope that LEMs can break through the limitations of different task types of EEG datasets, and obtain universal perceptual capabilities of EEG signals through unsupervised pre-training. Then the models can be fine-tuned for different downstream tasks. However, compared to text data, the volume of EEG datasets is generally small and the format varies widely. For example, there can be mismatched numbers of electrodes, unequal length data samples, varied task designs, and low signal-to-noise ratio. To overcome these challenges, we propose a unified foundation model for EEG called Large Brain Model (LaBraM). LaBraM enables cross-dataset learning by segmenting the EEG signals into EEG channel patches. Vector-quantized neural spectrum prediction is used to train a semantically rich neural tokenizer that encodes continuous raw EEG channel patches into compact neural codes. We then pre-train neural Transformers by predicting the original neural codes for the masked EEG channel patches. The LaBraMs were pre-trained on about 2,500 hours of various types of EEG signals from around 20 datasets and validated on multiple different types of downstream tasks. Experiments on abnormal detection, event type classification, emotion recognition, and gait prediction show that our LaBraM outperforms all compared SOTA methods in their respective fields. Our code is available at https://github.com/935963004/LaBraM.", "published": "2024-05-29T05:08:16Z", "authors": ["Wei-Bang Jiang", "Li-Ming Zhao", "Bao-Liang Lu"], "categories": ["cs.LG"], "doi": "", "_query": "house price prediction machine learning", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2608.07287v1", "title": "A foundation-model approach to pediatric headache classification from rs-fMRI", "abstract": "Headache is the most common neurological disorder in children and substantially affects quality of life. We investigated whether resting-state functional MRI (rs-fMRI) can support pediatric headache classification using machine learning. We encoded rs-fMRI data using NeuroSTORM, a recent foundation model, and fine-tuned it to distinguish healthy controls from children with headache and subsequently classify headache subtypes. We compared NeuroSTORM with a standard neuroscience approach using functional-connectivity (FC) matrices derived from brain activity as predictors. Using 189 rs-fMRI scans from 110 individuals collected across two visits (prevalence of any headache: 74%), NeuroSTORM achieved an area under the receiver operating characteristic curve (AUROC) of 0.82 (95% CI, 0.82-0.82) and an area under the precision-recall curve (AUPRC) of 0.93 (95% CI, 0.93-0.94) for discriminating headache from non-headache. In contrast, models trained on FC matrices showed lower performance (AUROC, 0.67 [95% CI, 0.67-0.67]; AUPRC, 0.85 [95% CI, 0.85-0.85]). In multiclass classification of healthy controls, chronic migraine, and non-chronic headaches (e.g., post-viral headache, new daily persistent headache, post-traumatic headache), NeuroSTORM achieved a macro-AUROC of 0.69 (95% CI, 0.68-0.69). Results suggest that the approach can distinguish chronic migraine but has difficulty differentiating other headache subtypes from chronic migraine. Overall, under limited-data conditions, NeuroSTORM appears to capture latent rs-fMRI representations that transfer to headache-related tasks without relying on FC features. These findings provide proof of concept for fMRI-based prediction of pediatric headache and highlight potential future utility for subtype identification and individualized treatment strategies.", "published": "2026-08-07T14:46:57Z", "authors": ["Guilherme S. Imai Aldeia", "Clara Moon", "Julie Shulman", "Navil Sethna", "Allison Smith", "Alyssa Lebel", "William G. La Cava", "Scott Holmes"], "categories": ["cs.LG"], "doi": "", "_query": "house price prediction machine learning", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2511.15743v2", "title": "Connecting the Dots: A Machine Learning Ready Dataset for Ionospheric Forecasting Models", "abstract": "Operational forecasting of the ionosphere remains a critical space weather challenge due to sparse observations, complex coupling across geospatial layers, and a growing need for timely, accurate predictions that support Global Navigation Satellite System (GNSS), communications, aviation safety, as well as satellite operations. As part of the 2025 NASA Heliolab, we present a curated, open-access dataset that integrates diverse ionospheric and heliospheric measurements into a coherent, machine learning-ready structure, designed specifically to support next-generation forecasting models and address gaps in current operational frameworks. Our workflow integrates a large selection of data sources comprising Solar Dynamic Observatory data, solar irradiance indices (F10.7), solar wind parameters (velocity and interplanetary magnetic field), geomagnetic activity indices (Kp, AE, SYM-H), and NASA JPL's Global Ionospheric Maps of Total Electron Content (GIM-TEC). We also implement geospatially sparse data such as the TEC derived from the World-Wide GNSS Receiver Network and crowdsourced Android smartphone measurements. This novel heterogeneous dataset is temporally and spatially aligned into a single, modular data structure that supports both physical and data-driven modeling. Leveraging this dataset, we train and benchmark several spatiotemporal machine learning architectures for forecasting vertical TEC under both quiet and geomagnetically active conditions. This work presents an extensive dataset and modeling pipeline that enables exploration of not only ionospheric dynamics but also broader Sun-Earth interactions, supporting both scientific inquiry and operational forecasting efforts.", "published": "2025-11-18T20:13:25Z", "authors": ["Linnea M. Wolniewicz", "Halil S. Kelebek", "Simone Mestici", "Michael D. Vergalla", "Giacomo Acciarini", "Bala Poduval", "Olga Verkhoglyadova", "Madhulika Guhathakurta", "Thomas E. Berger", "Atılım Güneş Baydin", "Frank Soboczenski"], "categories": ["cs.LG", "astro-ph.EP", "astro-ph.IM"], "doi": "", "_query": "house price prediction machine learning", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2605.14374v1", "title": "Optimal Pattern Detection Tree for Symbolic Rule-Based Classification", "abstract": "Pattern discovery in data plays a crucial role across diverse domains, including healthcare, risk assessment, and machinery maintenance. In contrast to black-box deep learning models, symbolic rule discovery emerges as a key data mining task, generating human-interpretable rules that offer both transparency and intuitive explainability. This paper introduces the Optimal Pattern Detection Tree (OPDT), a rule-based machine learning model based on novel mixed-integer programming to discover a single optimal pattern in data through binary classification. To incorporate prior knowledge and compliance requirements, we further introduce the Branching Structure Constraints (BSC) framework, which enables decision makers to encode domain knowledge and constraints directly into the model. This optimization-based approach discovers a hidden underlying pattern in datasets, when it exists, by identifying an optimal rule that maximizes coverage while minimizing the false positive rate due to misclassification. Our computational experiments show that OPDT discovers a pattern with optimality guarantees on moderately sized datasets within reasonable runtime.", "published": "2026-05-14T04:53:18Z", "authors": ["Young-Chae Hong", "Yangho Chen"], "categories": ["cs.LG", "cs.AI", "math.OC"], "doi": "", "_query": "house price prediction machine learning", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2010.13523v2", "title": "Kernel Smoothing, Mean Shift, and Their Learning Theory with Directional Data", "abstract": "Directional data consist of observations distributed on a (hyper)sphere, and appear in many applied fields, such as astronomy, ecology, and environmental science. This paper studies both statistical and computational problems of kernel smoothing for directional data. We generalize the classical mean shift algorithm to directional data, which allows us to identify local modes of the directional kernel density estimator (KDE). The statistical convergence rates of the directional KDE and its derivatives are derived, and the problem of mode estimation is examined. We also prove the ascending property of the directional mean shift algorithm and investigate a general problem of gradient ascent on the unit hypersphere. To demonstrate the applicability of the algorithm, we evaluate it as a mode clustering method on both simulated and real-world data sets.", "published": "2020-10-23T01:53:47Z", "authors": ["Yikun Zhang", "Yen-Chi Chen"], "categories": ["stat.ML", "cs.LG", "stat.ME"], "doi": "", "_query": "house price prediction machine learning", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/1910.02034v1", "title": "Generative Adversarial Networks for Failure Prediction", "abstract": "Prognostics and Health Management (PHM) is an emerging engineering discipline which is concerned with the analysis and prediction of equipment health and performance. One of the key challenges in PHM is to accurately predict impending failures in the equipment. In recent years, solutions for failure prediction have evolved from building complex physical models to the use of machine learning algorithms that leverage the data generated by the equipment. However, failure prediction problems pose a set of unique challenges that make direct application of traditional classification and prediction algorithms impractical. These challenges include the highly imbalanced training data, the extremely high cost of collecting more failure samples, and the complexity of the failure patterns. Traditional oversampling techniques will not be able to capture such complexity and accordingly result in overfitting the training data. This paper addresses these challenges by proposing a novel algorithm for failure prediction using Generative Adversarial Networks (GAN-FP). GAN-FP first utilizes two GAN networks to simultaneously generate training samples and build an inference network that can be used to predict failures for new samples. GAN-FP first adopts an infoGAN to generate realistic failure and non-failure samples, and initialize the weights of the first few layers of the inference network. The inference network is then tuned by optimizing a weighted loss objective using only real failure and non-failure samples. The inference network is further tuned using a second GAN whose purpose is to guarantee the consistency between the generated samples and corresponding labels. GAN-FP can be used for other imbalanced classification problems as well.", "published": "2019-10-04T16:51:55Z", "authors": ["Shuai Zheng", "Ahmed Farahat", "Chetan Gupta"], "categories": ["cs.LG", "cs.CV", "stat.ML"], "doi": "", "_query": "house price prediction machine learning", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/1908.09251v1", "title": "Predicting the Long-Term Outcomes of Biologics in Psoriasis Patients Using Machine Learning", "abstract": "Background. Real-world data show that approximately 50% of psoriasis patients treated with a biologic agent will discontinue the drug because of loss of efficacy. History of previous therapy with another biologic, female sex and obesity were identified as predictors of drug discontinuations, but their individual predictive value is low. Objectives. To determine whether machine learning algorithms can produce models that can accurately predict outcomes of biologic therapy in psoriasis on individual patient level. Results. All tested machine learning algorithms could accurately predict the risk of drug discontinuation and its cause (e.g. lack of efficacy vs adverse event). The learned generalized linear model achieved diagnostic accuracy of 82%, requiring under 2 seconds per patient using the psoriasis patients dataset. Input optimization analysis established a profile of a patient who has best chances of long-term treatment success: biologic-naive patient under 49 years, early-onset plaque psoriasis without psoriatic arthritis, weight < 100 kg, and moderate-to-severe psoriasis activity (DLQI $\\geq$ 16; PASI $\\geq$ 10). Moreover, a different generalized linear model is used to predict the length of treatment for each patient with mean absolute error (MAE) of 4.5 months. However Pearson Correlation Coefficient indicates 0.935 linear dependencies between the actual treatment lengths and predicted ones. Conclusions. Machine learning algorithms predict the risk of drug discontinuation and treatment duration with accuracy exceeding 80%, based on a small set of predictive variables. This approach can be used as a decision-making tool, communicating expected outcomes to the patient, and development of evidence-based guidelines.", "published": "2019-08-25T05:07:49Z", "authors": ["Sepideh Emam", "Amy X. Du", "Philip Surmanowicz", "Simon F. Thomsen", "Russ Greiner", "Robert Gniadecki"], "categories": ["stat.ML", "cs.LG", "q-bio.QM"], "doi": "", "_query": "house price prediction machine learning", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2106.11732v4", "title": "FLEA: Provably Robust Fair Multisource Learning from Unreliable Training Data", "abstract": "Fairness-aware learning aims at constructing classifiers that not only make accurate predictions, but also do not discriminate against specific groups. It is a fast-growing area of machine learning with far-reaching societal impact. However, existing fair learning methods are vulnerable to accidental or malicious artifacts in the training data, which can cause them to unknowingly produce unfair classifiers. In this work we address the problem of fair learning from unreliable training data in the robust multisource setting, where the available training data comes from multiple sources, a fraction of which might not be representative of the true data distribution. We introduce FLEA, a filtering-based algorithm that identifies and suppresses those data sources that would have a negative impact on fairness or accuracy if they were used for training. As such, FLEA is not a replacement of prior fairness-aware learning methods but rather an augmentation that makes any of them robust against unreliable training data. We show the effectiveness of our approach by a diverse range of experiments on multiple datasets. Additionally, we prove formally that -- given enough data -- FLEA protects the learner against corruptions as long as the fraction of affected data sources is less than half. Our source code and documentation are available at https://github.com/ISTAustria-CVML/FLEA.", "published": "2021-06-22T13:09:45Z", "authors": ["Eugenia Iofinova", "Nikola Konstantinov", "Christoph H. Lampert"], "categories": ["cs.LG", "stat.ML"], "doi": "", "_query": "house price prediction machine learning", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/1912.10798v1", "title": "Deep learning predictions of sand dune migration", "abstract": "A dry decade in the Navajo Nation has killed vegetation, dessicated soils, and released once-stable sand into the wind. This sand now covers one-third of the Nation's land, threatening roads, gardens and hundreds of homes. Many arid regions have similar problems: global warming has increased dune movement across farmland in Namibia and Angola, and the southwestern US. Current dune models, unfortunately, do not scale well enough to provide useful forecasts for the $\\sim$5\\% of land surfaces covered by mobile sand. We test the ability of two deep learning algorithms, a GAN and a CNN, to model the motion of sand dunes. The models are trained on simulated data from community-standard cellular automaton model of sand dunes. Preliminary results show the GAN producing reasonable forward predictions of dune migration at ten million times the speed of the existing model.", "published": "2019-12-13T01:17:42Z", "authors": ["Kelly Kochanski", "Divya Mohan", "Jenna Horrall", "Barry Rountree", "Ghaleb Abdulla"], "categories": ["cs.LG", "stat.ML"], "doi": "", "_query": "house price prediction machine learning", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/1701.03220v1", "title": "Predicting the Plant Root-Associated Ecological Niche of 21 Pseudomonas Species Using Machine Learning and Metabolic Modeling", "abstract": "Plants rarely occur in isolated systems. Bacteria can inhabit either the endosphere, the region inside the plant root, or the rhizosphere, the soil region just outside the plant root. Our goal is to understand if using genomic data and media dependent metabolic model information is better for training machine learning of predicting bacterial ecological niche than media independent models or pure genome based species trees. We considered three machine learning techniques: support vector machine, non-negative matrix factorization, and artificial neural networks. In all three machine-learning approaches, the media-based metabolic models and flux balance analyses were more effective at predicting bacterial niche than the genome or PRMT models. Support Vector Machine trained on a minimal media base with Mannose, Proline and Valine was most predictive of all models and media types with an f-score of 0.8 for rhizosphere and 0.97 for endosphere. Thus we can conclude that media-based metabolic modeling provides a holistic view of the metabolome, allowing machine learning algorithms to highlight the differences between and categorize endosphere and rhizosphere bacteria. There was no single media type that best highlighted differences between endosphere and rhizosphere bacteria metabolism and therefore no single enzyme, reaction, or compound that defined whether a bacteria's origin was of the endosphere or rhizosphere.", "published": "2017-01-12T03:31:10Z", "authors": ["Jennifer Chien", "Peter Larsen"], "categories": ["q-bio.GN"], "doi": "", "_query": "house price prediction machine learning", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2110.06611v4", "title": "Machine Learning applied to asteroid dynamics", "abstract": "Machine Learning (ML) is the branch of computer science that studies computer algorithms that can learn from data. It is mainly divided into supervised learning, where the computer is presented with examples of entries, and the goal is to learn a general rule that maps inputs to outputs, and unsupervised learning, where no label is provided to the learning algorithm, leaving it alone to find structures. Deep learning is a branch of machine learning based on numerous layers of artificial neural networks, which are computing systems inspired by the biological neural networks that constitute animal brains. In asteroid dynamics, machine learning methods have been recently used to identify members of asteroid families, and to identify resonant arguments images of asteroids in three-body resonances, among other applications. Here, we will conduct a review of available literature in the field, and classify it in terms of metrics recently used by other authors to assess the state of the art of applications of machine learning in other astronomical subfields. For comparison, applications of machine learning to Solar System bodies, a larger area that includes imaging and spectrophotometry of small bodies, have already reached a state classified as progressing. Research communities and methodologies are more established, and the use of ML led to the discovery of new celestial objects or features. ML applied to asteroid dynamics, however, is still in the emerging phase, with smaller groups, and fewer papers producing discoveries. Large observational surveys, like those conducted at the Vera C. Rubin Observatory, will produce very substantial datasets of orbital and physical properties for asteroids. Applications of ML for clustering, image identification, and anomaly detection, among others, are currently being developed and are expected of being of great help.", "published": "2021-10-13T10:07:56Z", "authors": ["V. Carruba", "S. Aljbaae", "R. C. Domingos", "M. Huaman", "W. Barletta"], "categories": ["astro-ph.EP", "astro-ph.IM"], "doi": "10.1007/s10569-022-10088-2", "_query": "house price prediction machine learning", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2501.17211v1", "title": "MR imaging in the low-field: Leveraging the power of machine learning", "abstract": "Recent innovations in Magnetic Resonance Imaging (MRI) hardware and software have reignited interest in low-field ($<1\\,\\mathrm{T}$) and ultra-low-field MRI ($<0.1\\,\\mathrm{T}$). These technologies offer advantages such as lower power consumption, reduced specific absorption rate, reduced field-inhomogeneities, and cost-effectiveness, presenting a promising alternative for resource-limited and point-of-care settings. However, low-field MRI faces inherent challenges like reduced signal-to-noise ratio and therefore, potentially lower spatial resolution or longer scan times. This chapter examines the challenges and opportunities of low-field and ultra-low-field MRI, with a focus on the role of machine learning (ML) in overcoming these limitations. We provide an overview of deep neural networks and their application in enhancing low-field and ultra-low-field MRI performance. Specific ML-based solutions, including advanced image reconstruction, denoising, and super-resolution algorithms, are discussed. The chapter concludes by exploring how integrating ML with low-field MRI could expand its clinical applications and improve accessibility, potentially revolutionizing its use in diverse healthcare settings.", "published": "2025-01-28T15:06:47Z", "authors": ["Andreas Kofler", "Dongyue Si", "David Schote", "Rene M Botnar", "Christoph Kolbitsch", "Claudia Prieto"], "categories": ["eess.IV", "cs.LG"], "doi": "", "_query": "house price prediction machine learning", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2006.16679v1", "title": "R2-B2: Recursive Reasoning-Based Bayesian Optimization for No-Regret Learning in Games", "abstract": "This paper presents a recursive reasoning formalism of Bayesian optimization (BO) to model the reasoning process in the interactions between boundedly rational, self-interested agents with unknown, complex, and costly-to-evaluate payoff functions in repeated games, which we call Recursive Reasoning-Based BO (R2-B2). Our R2-B2 algorithm is general in that it does not constrain the relationship among the payoff functions of different agents and can thus be applied to various types of games such as constant-sum, general-sum, and common-payoff games. We prove that by reasoning at level 2 or more and at one level higher than the other agents, our R2-B2 agent can achieve faster asymptotic convergence to no regret than that without utilizing recursive reasoning. We also propose a computationally cheaper variant of R2-B2 called R2-B2-Lite at the expense of a weaker convergence guarantee. The performance and generality of our R2-B2 algorithm are empirically demonstrated using synthetic games, adversarial machine learning, and multi-agent reinforcement learning.", "published": "2020-06-30T10:54:06Z", "authors": ["Zhongxiang Dai", "Yizhou Chen", "Kian Hsiang Low", "Patrick Jaillet", "Teck-Hua Ho"], "categories": ["cs.LG", "cs.AI", "cs.GT", "stat.ML"], "doi": "", "_query": "house price prediction machine learning", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/1502.04843v2", "title": "Generalized Gradient Learning on Time Series under Elastic Transformations", "abstract": "The majority of machine learning algorithms assumes that objects are represented as vectors. But often the objects we want to learn on are more naturally represented by other data structures such as sequences and time series. For these representations many standard learning algorithms are unavailable. We generalize gradient-based learning algorithms to time series under dynamic time warping. To this end, we introduce elastic functions, which extend functions on time series to matrix spaces. Necessary conditions are presented under which generalized gradient learning on time series is consistent. We indicate how results carry over to arbitrary elastic distance functions and to sequences consisting of symbolic elements. Specifically, four linear classifiers are extended to time series under dynamic time warping and applied to benchmark datasets. Results indicate that generalized gradient learning via elastic functions have the potential to complement the state-of-the-art in statistical pattern recognition on time series.", "published": "2015-02-17T10:08:48Z", "authors": ["Brijnesh Jain"], "categories": ["cs.LG"], "doi": "", "_query": "house price prediction machine learning", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/1907.04543v4", "title": "An Optimistic Perspective on Offline Reinforcement Learning", "abstract": "Off-policy reinforcement learning (RL) using a fixed offline dataset of logged interactions is an important consideration in real world applications. This paper studies offline RL using the DQN replay dataset comprising the entire replay experience of a DQN agent on 60 Atari 2600 games. We demonstrate that recent off-policy deep RL algorithms, even when trained solely on this fixed dataset, outperform the fully trained DQN agent. To enhance generalization in the offline setting, we present Random Ensemble Mixture (REM), a robust Q-learning algorithm that enforces optimal Bellman consistency on random convex combinations of multiple Q-value estimates. Offline REM trained on the DQN replay dataset surpasses strong RL baselines. Ablation studies highlight the role of offline dataset size and diversity as well as the algorithm choice in our positive results. Overall, the results here present an optimistic view that robust RL algorithms trained on sufficiently large and diverse offline datasets can lead to high quality policies. The DQN replay dataset can serve as an offline RL benchmark and is open-sourced.", "published": "2019-07-10T07:23:27Z", "authors": ["Rishabh Agarwal", "Dale Schuurmans", "Mohammad Norouzi"], "categories": ["cs.LG", "cs.AI", "stat.ML"], "doi": "", "_query": "house price prediction machine learning", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/1901.03896v1", "title": "Personalized Colorectal Cancer Survivability Prediction with Machine Learning Methods", "abstract": "In this work, we investigate the importance of ethnicity in colorectal cancer survivability prediction using machine learning techniques and the SEER cancer incidence database. We compare model performances for 2-year survivability prediction and feature importance rankings between Hispanic, White, and mixed patient populations. Our models consistently perform better on single-ethnicity populations and provide different feature importance rankings when trained in different populations. Additionally, we show our models achieve higher Area Under Curve (AUC) score than the best reported in the literature. We also apply imbalanced classification techniques to improve classification performance when the number of patients who have survived from colorectal cancer is much larger than who have not. These results provide evidence in favor for increased consideration of patient ethnicity in cancer survivability prediction, and for more personalized medicine in general.", "published": "2019-01-12T20:23:29Z", "authors": ["Samuel Li", "Talayeh Razzaghi"], "categories": ["cs.LG", "stat.ML"], "doi": "", "_query": "house price prediction machine learning", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2204.04017v1", "title": "Quantum Machine Learning Framework for Virtual Screening in Drug Discovery: a Prospective Quantum Advantage", "abstract": "Machine Learning (ML) for Ligand Based Virtual Screening (LB-VS) is an important in-silico tool for discovering new drugs in a faster and cost-effective manner, especially for emerging diseases such as COVID-19. In this paper, we propose a general-purpose framework combining a classical Support Vector Classifier (SVC) algorithm with quantum kernel estimation for LB-VS on real-world databases, and we argue in favor of its prospective quantum advantage. Indeed, we heuristically prove that our quantum integrated workflow can, at least in some relevant instances, provide a tangible advantage compared to state-of-art classical algorithms operating on the same datasets, showing strong dependence on target and features selection method. Finally, we test our algorithm on IBM Quantum processors using ADRB2 and COVID-19 datasets, showing that hardware simulations provide results in line with the predicted performances and can surpass classical equivalents.", "published": "2022-04-08T12:05:27Z", "authors": ["Stefano Mensa", "Emre Sahin", "Francesco Tacchino", "Panagiotis Kl. Barkoutsos", "Ivano Tavernelli"], "categories": ["quant-ph", "cs.LG", "physics.chem-ph"], "doi": "10.1088/2632-2153/acb900", "_query": "house price prediction machine learning", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2404.01748v1", "title": "Global Mapping of Exposure and Physical Vulnerability Dynamics in Least Developed Countries using Remote Sensing and Machine Learning", "abstract": "As the world marked the midterm of the Sendai Framework for Disaster Risk Reduction 2015-2030, many countries are still struggling to monitor their climate and disaster risk because of the expensive large-scale survey of the distribution of exposure and physical vulnerability and, hence, are not on track in reducing risks amidst the intensifying effects of climate change. We present an ongoing effort in mapping this vital information using machine learning and time-series remote sensing from publicly available Sentinel-1 SAR GRD and Sentinel-2 Harmonized MSI. We introduce the development of \"OpenSendaiBench\" consisting of 47 countries wherein most are least developed (LDCs), trained ResNet-50 deep learning models, and demonstrated the region of Dhaka, Bangladesh by mapping the distribution of its informal constructions. As a pioneering effort in auditing global disaster risk over time, this paper aims to advance the area of large-scale risk quantification in informing our collective long-term efforts in reducing climate and disaster risk.", "published": "2024-04-02T09:04:56Z", "authors": ["Joshua Dimasaka", "Christian Geiß", "Emily So"], "categories": ["cs.LG", "cs.CV"], "doi": "", "_query": "house price prediction machine learning", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/1703.07915v1", "title": "Perspective: Energy Landscapes for Machine Learning", "abstract": "Machine learning techniques are being increasingly used as flexible non-linear fitting and prediction tools in the physical sciences. Fitting functions that exhibit multiple solutions as local minima can be analysed in terms of the corresponding machine learning landscape. Methods to explore and visualise molecular potential energy landscapes can be applied to these machine learning landscapes to gain new insight into the solution space involved in training and the nature of the corresponding predictions. In particular, we can define quantities analogous to molecular structure, thermodynamics, and kinetics, and relate these emergent properties to the structure of the underlying landscape. This Perspective aims to describe these analogies with examples from recent applications, and suggest avenues for new interdisciplinary research.", "published": "2017-03-23T03:17:14Z", "authors": ["Andrew J. Ballard", "Ritankar Das", "Stefano Martiniani", "Dhagash Mehta", "Levent Sagun", "Jacob D. Stevenson", "David J. Wales"], "categories": ["stat.ML", "cond-mat.dis-nn", "cs.CV", "cs.LG", "hep-th"], "doi": "10.1039/C7CP01108C", "_query": "house price prediction machine learning", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/0908.0050v2", "title": "Online Learning for Matrix Factorization and Sparse Coding", "abstract": "Sparse coding--that is, modelling data vectors as sparse linear combinations of basis elements--is widely used in machine learning, neuroscience, signal processing, and statistics. This paper focuses on the large-scale matrix factorization problem that consists of learning the basis set, adapting it to specific data. Variations of this problem include dictionary learning in signal processing, non-negative matrix factorization and sparse principal component analysis. In this paper, we propose to address these tasks with a new online optimization algorithm, based on stochastic approximations, which scales up gracefully to large datasets with millions of training samples, and extends naturally to various matrix factorization formulations, making it suitable for a wide range of learning problems. A proof of convergence is presented, along with experiments with natural images and genomic data demonstrating that it leads to state-of-the-art performance in terms of speed and optimization for both small and large datasets.", "published": "2009-08-01T06:09:18Z", "authors": ["Julien Mairal", "Francis Bach", "Jean Ponce", "Guillermo Sapiro"], "categories": ["stat.ML", "cs.LG", "math.OC"], "doi": "", "_query": "house price prediction machine learning", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2607.18281v3", "title": "Position: The Inevitable Transition to Machine Learning in Quantum Chemistry", "abstract": "Finding exact solutions to the quantum many-body problem is computationally intractable (QMA-hard). Traditional approximations for electrons in an atom or molecule -- density functional theory and wavefunction methods -- have been indispensable, but their development shows signs of saturation: DFT functionals have proliferated without converging toward the exact functional, and strong correlation remains largely unsolved after decades of effort. This position paper argues that machine learning represents the most promising path forward -- not as a proof of logical necessity, but as a decision-theoretic argument: ML succeeds whether the underlying problems are truly hard or merely lack simple analytical solutions. We reframe recent traditional method development as ``hand-crafted machine learning'' that has exhausted the hypothesis space accessible to human intuition. Significant challenges remain, but these have clear research paths forward, unlike the fundamental barriers facing traditional approaches. ML-based approaches merit strategic priority in quantum chemistry's next phase.", "published": "2026-06-30T02:54:57Z", "authors": ["Karen Sargsyan", "Chao-Ping Hsu"], "categories": ["physics.chem-ph", "cs.LG", "quant-ph"], "doi": "", "_query": "house price prediction machine learning", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2006.11560v1", "title": "Learning Objective Boundaries for Constraint Optimization Problems", "abstract": "Constraint Optimization Problems (COP) are often considered without sufficient knowledge on the boundaries of the objective variable to optimize. When available, tight boundaries are helpful to prune the search space or estimate problem characteristics. Finding close boundaries, that correctly under- and overestimate the optimum, is almost impossible without actually solving the COP. This paper introduces Bion, a novel approach for boundary estimation by learning from previously solved instances of the COP. Based on supervised machine learning, Bion is problem-specific and solver-independent and can be applied to any COP which is repeatedly solved with different data inputs. An experimental evaluation over seven realistic COPs shows that an estimation model can be trained to prune the objective variables' domains by over 80%. By evaluating the estimated boundaries with various COP solvers, we find that Bion improves the solving process for some problems, although the effect of closer bounds is generally problem-dependent.", "published": "2020-06-20T12:09:49Z", "authors": ["Helge Spieker", "Arnaud Gotlieb"], "categories": ["cs.AI"], "doi": "10.1007/978-3-030-64580-9_33", "_query": "house price prediction machine learning", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/1905.10988v3", "title": "Natural Compression for Distributed Deep Learning", "abstract": "Modern deep learning models are often trained in parallel over a collection of distributed machines to reduce training time. In such settings, communication of model updates among machines becomes a significant performance bottleneck and various lossy update compression techniques have been proposed to alleviate this problem. In this work, we introduce a new, simple yet theoretically and practically effective compression technique: natural compression (NC). Our technique is applied individually to all entries of the to-be-compressed update vector and works by randomized rounding to the nearest (negative or positive) power of two, which can be computed in a \"natural\" way by ignoring the mantissa. We show that compared to no compression, NC increases the second moment of the compressed vector by not more than the tiny factor $\\frac{9}{8}$, which means that the effect of NC on the convergence speed of popular training algorithms, such as distributed SGD, is negligible. However, the communications savings enabled by NC are substantial, leading to $3$-$4\\times$ improvement in overall theoretical running time. For applications requiring more aggressive compression, we generalize NC to natural dithering, which we prove is exponentially better than the common random dithering technique. Our compression operators can be used on their own or in combination with existing operators for a more aggressive combined effect and offer new state-of-the-art both in theory and practice.", "published": "2019-05-27T06:10:59Z", "authors": ["Samuel Horvath", "Chen-Yu Ho", "Ludovit Horvath", "Atal Narayan Sahu", "Marco Canini", "Peter Richtarik"], "categories": ["cs.LG", "math.OC", "stat.ML"], "doi": "", "_query": "house price prediction machine learning", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/1903.03759v1", "title": "Machine Learning Based Prediction and Classification of Computational Jobs in Cloud Computing Centers", "abstract": "With the rapid growth of the data volume and the fast increasing of the computational model complexity in the scenario of cloud computing, it becomes an important topic that how to handle users' requests by scheduling computational jobs and assigning the resources in data center. In order to have a better perception of the computing jobs and their requests of resources, we analyze its characteristics and focus on the prediction and classification of the computing jobs with some machine learning approaches. Specifically, we apply LSTM neural network to predict the arrival of the jobs and the aggregated requests for computing resources. Then we evaluate it on Google Cluster dataset and it shows that the accuracy has been improved compared to the current existing methods. Additionally, to have a better understanding of the computing jobs, we use an unsupervised hierarchical clustering algorithm, BIRCH, to make classification and get some interpretability of our results in the computing centers.", "published": "2019-03-09T08:02:18Z", "authors": ["Zheqi Zhu", "Pingyi Fan"], "categories": ["cs.LG", "cs.IT", "cs.NE", "stat.ML"], "doi": "10.1109/IWCMC.2019.8766558", "_query": "house price prediction machine learning", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2312.13953v1", "title": "Subtle Sound Design: Designing for experience blend in a historic house museum", "abstract": "In this article, we present and discuss a user-study prototype, developed for Bakkehuset historic house museum in Copenhagen. We examine how the prototype - a digital sound installation - can expand visitors' experiences of the house and offer encounters with immaterial cultural heritage. Historic house museums often hold back on utilizing digital communication tools inside the houses, since a central purpose of this type of museum is to preserve an original environment. Digital communication tools however hold great potential for facilitating rich encounters with cultural heritage and in particular with the immaterial aspects of museum collections and their histories. In this article we present our design steps and choices, aiming at subtly and seamlessly adding a digital dimension to a historic house. Based on qualitative interviews, we evaluate how the sound installation at Bakkehuset is sensed, interpreted, and used by visitors as part of their museum experience. In turn, we shed light on the historic house museum as a distinct design context for designing hybrid visitor experiences and point to the potentials of digital communication tools in this context.", "published": "2023-12-21T15:40:39Z", "authors": ["Mia F. Yates", "Anders Sundnes Løvlie"], "categories": ["cs.HC"], "doi": "10.1145/3633476", "_query": "house price prediction machine learning", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2307.01177v2", "title": "Neural Hilbert Ladders: Multi-Layer Neural Networks in Function Space", "abstract": "To characterize the function space explored by neural networks (NNs) is an important aspect of learning theory. In this work, noticing that a multi-layer NN generates implicitly a hierarchy of reproducing kernel Hilbert spaces (RKHSs) - named a neural Hilbert ladder (NHL) - we define the function space as an infinite union of RKHSs, which generalizes the existing Barron space theory of two-layer NNs. We then establish several theoretical properties of the new space. First, we prove a correspondence between functions expressed by L-layer NNs and those belonging to L-level NHLs. Second, we prove generalization guarantees for learning an NHL with a controlled complexity measure. Third, we derive a non-Markovian dynamics of random fields that governs the evolution of the NHL which is induced by the training of multi-layer NNs in an infinite-width mean-field limit. Fourth, we show examples of depth separation in NHLs under the ReLU activation function. Finally, we perform numerical experiments to illustrate the feature learning aspect of NN training through the lens of NHLs.", "published": "2023-07-03T17:40:58Z", "authors": ["Zhengdao Chen"], "categories": ["cs.LG", "math.FA", "math.OC", "math.PR", "stat.ML"], "doi": "", "_query": "house price prediction machine learning", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/1306.4152v1", "title": "Bioclimating Modelling: A Machine Learning Perspective", "abstract": "Many machine learning (ML) approaches are widely used to generate bioclimatic models for prediction of geographic range of organism as a function of climate. Applications such as prediction of range shift in organism, range of invasive species influenced by climate change are important parameters in understanding the impact of climate change. However, success of machine learning-based approaches depends on a number of factors. While it can be safely said that no particular ML technique can be effective in all applications and success of a technique is predominantly dependent on the application or the type of the problem, it is useful to understand their behaviour to ensure informed choice of techniques. This paper presents a comprehensive review of machine learning-based bioclimatic model generation and analyses the factors influencing success of such models. Considering the wide use of statistical techniques, in our discussion we also include conventional statistical techniques used in bioclimatic modelling.", "published": "2013-06-18T11:42:03Z", "authors": ["Maumita Bhattacharya"], "categories": ["cs.LG", "stat.ML"], "doi": "", "_query": "house price prediction machine learning", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2208.06997v1", "title": "Combining deep learning and crowdsourcing geo-images to predict housing quality in rural China", "abstract": "Housing quality is an essential proxy for regional wealth, security and health. Understanding the distribution of housing quality is crucial for unveiling rural development status and providing political proposals. However,present rural house quality data highly depends on a top-down, time-consuming survey at the national or provincial level but fails to unpack the housing quality at the village level. To fill the gap between accurately depicting rural housing quality conditions and deficient data,we collect massive rural images and invite users to assess their housing quality at scale. Furthermore, a deep learning framework is proposed to automatically and efficiently predict housing quality based on crowd-sourcing rural images.", "published": "2022-08-15T03:58:03Z", "authors": ["Weipan Xu", "Yu Gu", "Yifan Chen", "Yongtian Wang", "Weihuan Deng", "Xun Li"], "categories": ["cs.LG"], "doi": "", "_query": "house price prediction machine learning", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/1908.05571v1", "title": "Combining Prediction Intervals on Multi-Source Non-Disclosed Regression Datasets", "abstract": "Conformal Prediction is a framework that produces prediction intervals based on the output from a machine learning algorithm. In this paper we explore the case when training data is made up of multiple parts available in different sources that cannot be pooled. We here consider the regression case and propose a method where a conformal predictor is trained on each data source independently, and where the prediction intervals are then combined into a single interval. We call the approach Non-Disclosed Conformal Prediction (NDCP), and we evaluate it on a regression dataset from the UCI machine learning repository using support vector regression as the underlying machine learning algorithm, with varying number of data sources and sizes. The results show that the proposed method produces conservatively valid prediction intervals, and while we cannot retain the same efficiency as when all data is used, efficiency is improved through the proposed approach as compared to predicting using a single arbitrarily chosen source.", "published": "2019-08-15T15:01:55Z", "authors": ["Ola Spjuth", "Robin Carrión Brännström", "Lars Carlsson", "Niharika Gauraha"], "categories": ["stat.ML", "cs.LG"], "doi": "", "_query": "house price prediction machine learning", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/1908.08168v1", "title": "Intra-day Equity Price Prediction using Deep Learning as a Measure of Market Efficiency", "abstract": "In finance, the weak form of the Efficient Market Hypothesis asserts that historic stock price and volume data cannot inform predictions of future prices. In this paper we show that, to the contrary, future intra-day stock prices could be predicted effectively until 2009. We demonstrate this using two different profitable machine learning-based trading strategies. However, the effectiveness of both approaches diminish over time, and neither of them are profitable after 2009. We present our implementation and results in detail for the period 2003-2017 and propose a novel idea: the use of such flexible machine learning methods as an objective measure of relative market efficiency. We conclude with a candidate explanation, comparing our returns over time with high-frequency trading volume, and suggest concrete steps for further investigation.", "published": "2019-08-22T02:06:27Z", "authors": ["David Byrd", "Tucker Hybinette Balch"], "categories": ["q-fin.TR", "cs.LG"], "doi": "", "_query": "house price prediction machine learning", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2109.05629v1", "title": "AdViCE: Aggregated Visual Counterfactual Explanations for Machine Learning Model Validation", "abstract": "Rapid improvements in the performance of machine learning models have pushed them to the forefront of data-driven decision-making. Meanwhile, the increased integration of these models into various application domains has further highlighted the need for greater interpretability and transparency. To identify problems such as bias, overfitting, and incorrect correlations, data scientists require tools that explain the mechanisms with which these model decisions are made. In this paper we introduce AdViCE, a visual analytics tool that aims to guide users in black-box model debugging and validation. The solution rests on two main visual user interface innovations: (1) an interactive visualization design that enables the comparison of decisions on user-defined data subsets; (2) an algorithm and visual design to compute and visualize counterfactual explanations - explanations that depict model outcomes when data features are perturbed from their original values. We provide a demonstration of the tool through a use case that showcases the capabilities and potential limitations of the proposed approach.", "published": "2021-09-12T22:52:12Z", "authors": ["Oscar Gomez", "Steffen Holter", "Jun Yuan", "Enrico Bertini"], "categories": ["cs.HC", "cs.AI", "cs.LG"], "doi": "", "_query": "house price prediction machine learning", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2003.08447v1", "title": "Potential Neutralizing Antibodies Discovered for Novel Corona Virus Using Machine Learning", "abstract": "The fast and untraceable virus mutations take lives of thousands of people before the immune system can produce the inhibitory antibody. Recent outbreak of novel coronavirus infected and killed thousands of people in the world. Rapid methods in finding peptides or antibody sequences that can inhibit the viral epitopes of COVID-19 will save the life of thousands. In this paper, we devised a machine learning (ML) model to predict the possible inhibitory synthetic antibodies for Corona virus. We collected 1933 virus-antibody sequences and their clinical patient neutralization response and trained an ML model to predict the antibody response. Using graph featurization with variety of ML methods, we screened thousands of hypothetical antibody sequences and found 8 stable antibodies that potentially inhibit COVID-19. We combined bioinformatics, structural biology, and Molecular Dynamics (MD) simulations to verify the stability of the candidate antibodies that can inhibit the Corona virus.", "published": "2020-03-18T19:23:32Z", "authors": ["Rishikesh Magar", "Prakarsh Yadav", "Amir Barati Farimani"], "categories": ["q-bio.BM", "cs.LG"], "doi": "", "_query": "house price prediction machine learning", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2603.00192v2", "title": "Diagnostics for Individual-Level Prediction Instability in Machine Learning for Healthcare", "abstract": "In healthcare, predictive models increasingly inform patient-level decisions, yet little attention is paid to the variability in individual risk estimates and its impact on treatment decisions. For overparameterized models, now standard in machine learning, a substantial source of variability often goes undetected. Even when the data and model architecture are held fixed, randomness introduced by optimization and initialization can lead to materially different risk estimates for the same patient. This problem is largely obscured by standard evaluation practices, which rely on aggregate performance metrics (e.g., log-loss, accuracy) that are agnostic to individual-level stability. As a result, models with indistinguishable aggregate performance can nonetheless exhibit substantial procedural arbitrariness, which can undermine clinical trust. We propose an evaluation framework that quantifies individual-level prediction instability by using two complementary diagnostics: empirical prediction interval width (ePIW), which captures variability in continuous risk estimates, and empirical decision flip rate (eDFR), which measures instability in threshold-based clinical decisions. We apply these diagnostics to simulated data and GUSTO-I clinical dataset. Across observed settings, we find that for flexible machine-learning models, randomness arising solely from optimization and initialization can induce individual-level variability comparable to that produced by resampling the entire training dataset. Neural networks exhibit substantially greater instability in individual risk predictions compared to logistic regression models. Risk estimate instability near clinically relevant decision thresholds can alter treatment recommendations. These findings that stability diagnostics should be incorporated into routine model validation for assessing clinical reliability.", "published": "2026-02-27T03:42:28Z", "authors": ["Elizabeth W. Miller", "Jeffrey D. Blume"], "categories": ["cs.LG", "stat.AP", "stat.ML"], "doi": "", "_query": "house price prediction machine learning", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2405.11907v5", "title": "Ensemble and Mixture-of-Experts DeepONets For Operator Learning", "abstract": "We present a novel deep operator network (DeepONet) architecture for operator learning, the ensemble DeepONet, that allows for enriching the trunk network of a single DeepONet with multiple distinct trunk networks. This trunk enrichment allows for greater expressivity and generalization capabilities over a range of operator learning problems. We also present a spatial mixture-of-experts (MoE) DeepONet trunk network architecture that utilizes a partition-of-unity (PoU) approximation to promote spatial locality and model sparsity in the operator learning problem. We first prove that both the ensemble and PoU-MoE DeepONets are universal approximators. We then demonstrate that ensemble DeepONets containing a trunk ensemble of a standard trunk, the PoU-MoE trunk, and/or a proper orthogonal decomposition (POD) trunk can achieve 2-4x lower relative $\\ell_2$ errors than standard DeepONets and POD-DeepONets on both standard and challenging new operator learning problems involving partial differential equations (PDEs) in two and three dimensions. Our new PoU-MoE formulation provides a natural way to incorporate spatial locality and model sparsity into any neural network architecture, while our new ensemble DeepONet provides a powerful and general framework for incorporating basis enrichment in scientific machine learning architectures for operator learning.", "published": "2024-05-20T09:42:44Z", "authors": ["Ramansh Sharma", "Varun Shankar"], "categories": ["cs.LG"], "doi": "", "_query": "house price prediction machine learning", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2209.04693v1", "title": "Reconstruction of Long-Term Historical Demand Data", "abstract": "Long-term planning of a robust power system requires the understanding of changing demand patterns. Electricity demand is highly weather sensitive. Thus, the supply side variation from introducing intermittent renewable sources, juxtaposed with variable demand, will introduce additional challenges in the grid planning process. By understanding the spatial and temporal variability of temperature over the US, the response of demand to natural variability and climate change-related effects on temperature can be separated, especially because the effects due to the former factor are not known. Through this project, we aim to better support the technology & policy development process for power systems by developing machine and deep learning 'back-forecasting' models to reconstruct multidecadal demand records and study the natural variability of temperature and its influence on demand.", "published": "2022-09-10T15:27:10Z", "authors": ["Reshmi Ghosh", "Michael Craig", "H. Scott Matthews", "Constantine Samaras", "Laure Berti-Equille"], "categories": ["cs.LG", "stat.AP"], "doi": "", "_query": "house price prediction machine learning", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2012.02684v2", "title": "Model-Agnostic Learning to Meta-Learn", "abstract": "In this paper, we propose a learning algorithm that enables a model to quickly exploit commonalities among related tasks from an unseen task distribution, before quickly adapting to specific tasks from that same distribution. We investigate how learning with different task distributions can first improve adaptability by meta-finetuning on related tasks before improving goal task generalization with finetuning. Synthetic regression experiments validate the intuition that learning to meta-learn improves adaptability and consecutively generalization. Experiments on more complex image classification, continual regression, and reinforcement learning tasks demonstrate that learning to meta-learn generally improves task-specific adaptation. The methodology, setup, and hypotheses in this proposal were positively evaluated by peer review before conclusive experiments were carried out.", "published": "2020-12-04T15:55:08Z", "authors": ["Arnout Devos", "Yatin Dandi"], "categories": ["cs.LG", "cs.AI", "stat.ML"], "doi": "", "_query": "house price prediction machine learning", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/1811.03392v1", "title": "Transformative Machine Learning", "abstract": "The key to success in machine learning (ML) is the use of effective data representations. Traditionally, data representations were hand-crafted. Recently it has been demonstrated that, given sufficient data, deep neural networks can learn effective implicit representations from simple input representations. However, for most scientific problems, the use of deep learning is not appropriate as the amount of available data is limited, and/or the output models must be explainable. Nevertheless, many scientific problems do have significant amounts of data available on related tasks, which makes them amenable to multi-task learning, i.e. learning many related problems simultaneously. Here we propose a novel and general representation learning approach for multi-task learning that works successfully with small amounts of data. The fundamental new idea is to transform an input intrinsic data representation (i.e., handcrafted features), to an extrinsic representation based on what a pre-trained set of models predict about the examples. This transformation has the dual advantages of producing significantly more accurate predictions, and providing explainable models. To demonstrate the utility of this transformative learning approach, we have applied it to three real-world scientific problems: drug-design (quantitative structure activity relationship learning), predicting human gene expression (across different tissue types and drug treatments), and meta-learning for machine learning (predicting which machine learning methods work best for a given problem). In all three problems, transformative machine learning significantly outperforms the best intrinsic representation.", "published": "2018-11-08T13:09:05Z", "authors": ["Ivan Olier", "Oghenejokpeme I. Orhobor", "Joaquin Vanschoren", "Ross D. King"], "categories": ["cs.LG", "stat.ML"], "doi": "", "_query": "house price prediction machine learning", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2307.07657v3", "title": "Machine learning for option pricing: an empirical investigation of network architectures", "abstract": "We consider the supervised learning problem of learning the price of an option or the implied volatility given appropriate input data (model parameters) and corresponding output data (option prices or implied volatilities). The majority of articles in this literature considers a (plain) feed forward neural network architecture in order to connect the neurons used for learning the function mapping inputs to outputs. In this article, motivated by methods in image classification and recent advances in machine learning methods for PDEs, we investigate empirically whether and how the choice of network architecture affects the accuracy and training time of a machine learning algorithm. We find that the generalized highway network architecture achieves the best performance, when considering the mean squared error and the training time as criteria, within the considered parameter budgets for the Black-Scholes and Heston option pricing problems. Considering the transformed implied volatility problem, a simplified DGM variant achieves the lowest error among the tested architectures. We also carry out a capacity-normalised comparison for completeness, where all architectures are evaluated with an equal number of parameters. Finally, for the implied volatility problem, we additionally include experiments using real market data.", "published": "2023-07-14T23:27:43Z", "authors": ["Serena Della Corte", "Laurens Van Mieghem", "Antonis Papapantoleon", "Jonas Papazoglou-Hennig"], "categories": ["q-fin.CP", "cs.LG"], "doi": "", "_query": "house price prediction machine learning", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2303.07600v1", "title": "Forecasting COVID-19 Infections in Gulf Cooperation Council (GCC) Countries using Machine Learning", "abstract": "COVID-19 has infected more than 68 million people worldwide since it was first detected about a year ago. Machine learning time series models have been implemented to forecast COVID-19 infections. In this paper, we develop time series models for the Gulf Cooperation Council (GCC) countries using the public COVID-19 dataset from Johns Hopkins. The dataset set includes the one-year cumulative COVID-19 cases between 22/01/2020 to 22/01/2021. We developed different models for the countries under study based on the spatial distribution of the infection data. Our experimental results show that the developed models can forecast COVID-19 infections with high precision.", "published": "2023-03-14T02:46:42Z", "authors": ["Leila Ismail", "Huned Materwala", "Alain Hennebelle"], "categories": ["cs.LG", "cs.AI", "cs.CY"], "doi": "10.1145/3474963.3475844", "_query": "house price prediction machine learning", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2007.04074v3", "title": "Auto-Sklearn 2.0: Hands-free AutoML via Meta-Learning", "abstract": "Automated Machine Learning (AutoML) supports practitioners and researchers with the tedious task of designing machine learning pipelines and has recently achieved substantial success. In this paper, we introduce new AutoML approaches motivated by our winning submission to the second ChaLearn AutoML challenge. We develop PoSH Auto-sklearn, which enables AutoML systems to work well on large datasets under rigid time limits by using a new, simple and meta-feature-free meta-learning technique and by employing a successful bandit strategy for budget allocation. However, PoSH Auto-sklearn introduces even more ways of running AutoML and might make it harder for users to set it up correctly. Therefore, we also go one step further and study the design space of AutoML itself, proposing a solution towards truly hands-free AutoML. Together, these changes give rise to the next generation of our AutoML system, Auto-sklearn 2.0. We verify the improvements by these additions in an extensive experimental study on 39 AutoML benchmark datasets. We conclude the paper by comparing to other popular AutoML frameworks and Auto-sklearn 1.0, reducing the relative error by up to a factor of 4.5, and yielding a performance in 10 minutes that is substantially better than what Auto-sklearn 1.0 achieves within an hour.", "published": "2020-07-08T12:41:03Z", "authors": ["Matthias Feurer", "Katharina Eggensperger", "Stefan Falkner", "Marius Lindauer", "Frank Hutter"], "categories": ["cs.LG", "stat.ML"], "doi": "", "_query": "house price prediction machine learning", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/1901.06752v1", "title": "Predicting wind pressures around circular cylinders using machine learning techniques", "abstract": "Numerous studies have been carried out to measure wind pressures around circular cylinders since the early 20th century due to its engineering significance. Consequently, a large amount of wind pressure data sets have accumulated, which presents an excellent opportunity for using machine learning (ML) techniques to train models to predict wind pressures around circular cylinders. Wind pressures around smooth circular cylinders are a function of mainly the Reynolds number (Re), turbulence intensity (Ti) of the incident wind, and circumferential angle of the cylinder. Considering these three parameters as the inputs, this study trained two ML models to predict mean and fluctuating pressures respectively. Three machine learning algorithms including decision tree regressor, random forest, and gradient boosting regression trees (GBRT) were tested. The GBRT models exhibited the best performance for predicting both mean and fluctuating pressures, and they are capable of making accurate predictions for Re ranging from 10^4 to 10^6 and Ti ranging from 0% to 15%. It is believed that the GBRT models provide very efficient and economical alternative to traditional wind tunnel tests and computational fluid dynamic simulations for determining wind pressures around smooth circular cylinders within the studied Re and Ti range.", "published": "2019-01-21T00:04:48Z", "authors": ["Gang Hu", "K. C. S. Kwok"], "categories": ["cs.LG", "physics.flu-dyn", "stat.ML"], "doi": "", "_query": "house price prediction machine learning", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2011.13350v1", "title": "Unsupervised learning for economic risk evaluation in the context of Covid-19 pandemic", "abstract": "Justifying draconian measures during the Covid-19 pandemic was difficult not only because of the restriction of individual rights, but also because of its economic impact. The objective of this work is to present a machine learning approach to identify regions that should implement similar health policies. For that end, we successfully developed a system that gives a notion of economic impact given the prediction of new incidental cases through unsupervised learning and time series forecasting. This system was built taking into account computational restrictions and low maintenance requirements in order to improve the system's resilience. Finally this system was deployed as part of a web application for simulation and data analysis of COVID-19, in Colombia, available at (https://covid19.dis.eafit.edu.co).", "published": "2020-11-26T15:31:06Z", "authors": ["Santiago Cortes", "Yullys M. Quintero"], "categories": ["cs.LG", "cs.CY"], "doi": "", "_query": "house price prediction machine learning", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2004.01144v1", "title": "Predicting Injectable Medication Adherence via a Smart Sharps Bin and Machine Learning", "abstract": "Medication non-adherence is a widespread problem affecting over 50% of people who have chronic illness and need chronic treatment. Non-adherence exacerbates health risks and drives significant increases in treatment costs. In order to address these challenges, the importance of predicting patients' adherence has been recognised. In other words, it is important to improve the efficiency of interventions of the current healthcare system by prioritizing resources to the patients who are most likely to be non-adherent. Our objective in this work is to make predictions regarding individual patients' behaviour in terms of taking their medication on time during their next scheduled medication opportunity. We do this by leveraging a number of machine learning models. In particular, we demonstrate the use of a connected IoT device; a \"Smart Sharps Bin\", invented by HealthBeacon Ltd.; to monitor and track injection disposal of patients in their home environment. Using extensive data collected from these devices, five machine learning models, namely Extra Trees Classifier, Random Forest, XGBoost, Gradient Boosting and Multilayer Perception were trained and evaluated on a large dataset comprising 165,223 historic injection disposal records collected from 5,915 HealthBeacon units over the course of 3 years. The testing work was conducted on real-time data generated by the smart device over a time period after the model training was complete, i.e. true future data. The proposed machine learning approach demonstrated very good predictive performance exhibiting an Area Under the Receiver Operating Characteristic Curve (ROC AUC) of 0.86.", "published": "2020-04-02T17:16:51Z", "authors": ["Yingqi Gu", "Akshay Zalkikar", "Lara Kelly", "Kieran Daly", "Tomas E. Ward"], "categories": ["cs.LG", "stat.ML"], "doi": "", "_query": "house price prediction machine learning", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2004.01608v3", "title": "Learning 2-opt Heuristics for the Traveling Salesman Problem via Deep Reinforcement Learning", "abstract": "Recent works using deep learning to solve the Traveling Salesman Problem (TSP) have focused on learning construction heuristics. Such approaches find TSP solutions of good quality but require additional procedures such as beam search and sampling to improve solutions and achieve state-of-the-art performance. However, few studies have focused on improvement heuristics, where a given solution is improved until reaching a near-optimal one. In this work, we propose to learn a local search heuristic based on 2-opt operators via deep reinforcement learning. We propose a policy gradient algorithm to learn a stochastic policy that selects 2-opt operations given a current solution. Moreover, we introduce a policy neural network that leverages a pointing attention mechanism, which unlike previous works, can be easily extended to more general k-opt moves. Our results show that the learned policies can improve even over random initial solutions and approach near-optimal solutions at a faster rate than previous state-of-the-art deep learning methods.", "published": "2020-04-03T14:51:54Z", "authors": ["Paulo R. de O. da Costa", "Jason Rhuggenaath", "Yingqian Zhang", "Alp Akcay"], "categories": ["cs.LG", "cs.AI", "stat.ML"], "doi": "", "_query": "house price prediction machine learning", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2506.03938v2", "title": "FPGA-Enabled Machine Learning Applications in Earth Observation: A Systematic Review", "abstract": "New UAV technologies and the NewSpace era are transforming Earth Observation missions and data acquisition. Numerous small platforms generate large data volume, straining bandwidth and requiring onboard decision-making to transmit high-quality information in time. While Machine Learning allows real-time autonomous processing, FPGAs balance performance with adaptability to mission-specific requirements, enabling onboard deployment. This review systematically analyzes 68 experiments deploying ML models on FPGAs for Remote Sensing applications. We introduce two distinct taxonomies to capture both efficient model architectures and FPGA implementation strategies. For transparency and reproducibility, we follow PRISMA 2020 guidelines and share all data and code at https://github.com/CedricLeon/Survey_RS-ML-FPGA.", "published": "2025-06-04T13:30:47Z", "authors": ["Cédric Léonard", "Dirk Stober", "Martin Schulz"], "categories": ["cs.LG", "cs.AR"], "doi": "10.1145/3800686", "_query": "house price prediction machine learning", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2405.05185v1", "title": "Machine Learning Assisted Dynamical Classification of Trans-Neptunian Objects", "abstract": "Trans-Neptunian objects (TNOs) are small, icy bodies in the outer solar system. They are observed to have a complex orbital distribution that was shaped by the early dynamical history and migration of the giant planets. Comparisons between the different dynamical classes of modeled and observed TNOs can help constrain the history of the outer solar system. Because of the complex dynamics of TNOs, particularly those in and near mean motion resonances with Neptune, classification has traditionally been done by human inspection of plots of the time evolution of orbital parameters. This is very inefficient. The Vera Rubin Observatory's Legacy Survey of Space and Time (LSST) is expected to increase the number of known TNOs by a factor of $\\sim$10, necessitating a much more automated process. In this chapter we present an improved supervised machine learning classifier for TNOs. Using a large and diverse training set as well as carefully chosen, dynamically motivated data features calculated from numerical integrations of TNO orbits, our classifier returns results that match those of a human classifier 98% of the time, and dynamically relevant classifications 99.7% of the time. This classifier is dramatically more efficient than human classification, and it will improve classification of both observed and modeled TNO data.", "published": "2024-05-08T16:20:47Z", "authors": ["Kathryn Volk", "Renu Malhotra"], "categories": ["astro-ph.EP", "astro-ph.IM", "cs.LG"], "doi": "", "_query": "house price prediction machine learning", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2305.19440v2", "title": "Machine learning with tree tensor networks, CP rank constraints, and tensor dropout", "abstract": "Tensor networks developed in the context of condensed matter physics try to approximate order-$N$ tensors with a reduced number of degrees of freedom that is only polynomial in $N$ and arranged as a network of partially contracted smaller tensors. As we have recently demonstrated in the context of quantum many-body physics, computation costs can be further substantially reduced by imposing constraints on the canonical polyadic (CP) rank of the tensors in such networks [arXiv:2205.15296]. Here, we demonstrate how tree tensor networks (TTN) with CP rank constraints and tensor dropout can be used in machine learning. The approach is found to outperform other tensor-network-based methods in Fashion-MNIST image classification. A low-rank TTN classifier with branching ratio $b=4$ reaches a test set accuracy of 90.3\\% with low computation costs. Consisting of mostly linear elements, tensor network classifiers avoid the vanishing gradient problem of deep neural networks. The CP rank constraints have additional advantages: The number of parameters can be decreased and tuned more freely to control overfitting, improve generalization properties, and reduce computation costs. They allow us to employ trees with large branching ratios, substantially improving the representation power.", "published": "2023-05-30T22:22:24Z", "authors": ["Hao Chen", "Thomas Barthel"], "categories": ["cs.LG", "cond-mat.str-el", "stat.ML"], "doi": "10.1109/TPAMI.2024.3396386", "_query": "house price prediction machine learning", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2108.03762v1", "title": "EVGen: Adversarial Networks for Learning Electric Vehicle Charging Loads and Hidden Representations", "abstract": "The nexus between transportation, the power grid, and consumer behavior is more pronounced than ever before as the race to decarbonize the transportation sector intensifies. Electrification in the transportation sector has led to technology shifts and rapid deployment of electric vehicles (EVs). The potential increase in stochastic and spatially heterogeneous charging load presents a unique challenge that is not well studied, and will have significant impacts on grid operations, emissions, and system reliability if not managed effectively. Realistic scenario generators can help operators prepare, and machine learning can be leveraged to this end. In this work, we develop generative adversarial networks (GANs) to learn distributions of electric vehicle (EV) charging sessions and disentangled representations. We show that this model structure successfully parameterizes unlabeled temporal and power patterns without supervision and is able to generate synthetic data conditioned on these parameters. We benchmark the generation capability of this model with Gaussian Mixture Models (GMMs), and empirically show that our proposed model framework is better at capturing charging distributions and temporal dynamics.", "published": "2021-08-09T00:23:47Z", "authors": ["Robert Buechler", "Emmanuel Balogun", "Arun Majumdar", "Ram Rajagopal"], "categories": ["cs.LG"], "doi": "", "_query": "house price prediction machine learning", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/1705.05067v3", "title": "Robust Frequent Directions with Application in Online Learning", "abstract": "The frequent directions (FD) technique is a deterministic approach for online sketching that has many applications in machine learning. The conventional FD is a heuristic procedure that often outputs rank deficient matrices. To overcome the rank deficiency problem, we propose a new sketching strategy called robust frequent directions (RFD) by introducing a regularization term. RFD can be derived from an optimization problem. It updates the sketch matrix and the regularization term adaptively and jointly. RFD reduces the approximation error of FD without increasing the computational cost. We also apply RFD to online learning and propose an effective hyperparameter-free online Newton algorithm. We derive a regret bound for our online Newton algorithm based on RFD, which guarantees the robustness of the algorithm. The experimental studies demonstrate that the proposed method outperforms state-of-the-art second order online learning algorithms.", "published": "2017-05-15T04:18:29Z", "authors": ["Luo Luo", "Cheng Chen", "Zhihua Zhang", "Wu-Jun Li", "Tong Zhang"], "categories": ["cs.LG"], "doi": "", "_query": "house price prediction machine learning", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2305.17251v2", "title": "Duality in Multi-View Restricted Kernel Machines", "abstract": "We propose a unifying setting that combines existing restricted kernel machine methods into a single primal-dual multi-view framework for kernel principal component analysis in both supervised and unsupervised settings. We derive the primal and dual representations of the framework and relate different training and inference algorithms from a theoretical perspective. We show how to achieve full equivalence in primal and dual formulations by rescaling primal variables. Finally, we experimentally validate the equivalence and provide insight into the relationships between different methods on a number of time series data sets by recursively forecasting unseen test data and visualizing the learned features.", "published": "2023-05-26T20:37:19Z", "authors": ["Sonny Achten", "Arun Pandey", "Hannes De Meulemeester", "Bart De Moor", "Johan A. K. Suykens"], "categories": ["cs.LG"], "doi": "", "_query": "house price prediction machine learning", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2310.07787v1", "title": "Using Spark Machine Learning Models to Perform Predictive Analysis on Flight Ticket Pricing Data", "abstract": "This paper discusses predictive performance and processes undertaken on flight pricing data utilizing r2(r-square) and RMSE that leverages a large dataset, originally from Expedia.com, consisting of approximately 20 million records or 4.68 gigabytes. The project aims to determine the best models usable in the real world to predict airline ticket fares for non-stop flights across the US. Therefore, good generalization capability and optimized processing times are important measures for the model. We will discover key business insights utilizing feature importance and discuss the process and tools used for our analysis. Four regression machine learning algorithms were utilized: Random Forest, Gradient Boost Tree, Decision Tree, and Factorization Machines utilizing Cross Validator and Training Validator functions for assessing performance and generalization capability.", "published": "2023-10-11T18:20:17Z", "authors": ["Philip Wong", "Phue Thant", "Pratiksha Yadav", "Ruta Antaliya", "Jongwook Woo"], "categories": ["cs.LG", "cs.DC"], "doi": "", "_query": "house price prediction machine learning", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2112.09668v1", "title": "Deep Learning for Spatiotemporal Modeling of Urbanization", "abstract": "Urbanization has a strong impact on the health and wellbeing of populations across the world. Predictive spatial modeling of urbanization therefore can be a useful tool for effective public health planning. Many spatial urbanization models have been developed using classic machine learning and numerical modeling techniques. However, deep learning with its proven capacity to capture complex spatiotemporal phenomena has not been applied to urbanization modeling. Here we explore the capacity of deep spatial learning for the predictive modeling of urbanization. We treat numerical geospatial data as images with pixels and channels, and enrich the dataset by augmentation, in order to leverage the high capacity of deep learning. Our resulting model can generate end-to-end multi-variable urbanization predictions, and outperforms a state-of-the-art classic machine learning urbanization model in preliminary comparisons.", "published": "2021-12-17T18:27:52Z", "authors": ["Tang Li", "Jing Gao", "Xi Peng"], "categories": ["cs.LG", "cs.AI", "cs.CV"], "doi": "", "_query": "house price prediction machine learning", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/1808.00525v2", "title": "The impact of imbalanced training data on machine learning for author name disambiguation", "abstract": "In supervised machine learning for author name disambiguation, negative training data are often dominantly larger than positive training data. This paper examines how the ratios of negative to positive training data can affect the performance of machine learning algorithms to disambiguate author names in bibliographic records. On multiple labeled datasets, three classifiers - Logistic Regression, Naïve Bayes, and Random Forest - are trained through representative features such as coauthor names, and title words extracted from the same training data but with various positive-negative training data ratios. Results show that increasing negative training data can improve disambiguation performance but with a few percent of performance gains and sometimes degrade it. Logistic Regression and Naïve Bayes learn optimal disambiguation models even with a base ratio (1:1) of positive and negative training data. Also, the performance improvement by Random Forest tends to quickly saturate roughly after 1:10 ~ 1:15. These findings imply that contrary to the common practice using all training data, name disambiguation algorithms can be trained using part of negative training data without degrading much disambiguation performance while increasing computational efficiency. This study calls for more attention from author name disambiguation scholars to methods for machine learning from imbalanced data.", "published": "2018-07-30T14:29:27Z", "authors": ["Jinseok Kim", "Jenna Kim"], "categories": ["cs.IR", "cs.CL", "cs.DL", "cs.LG", "stat.ML"], "doi": "10.1007/s11192-018-2865-9", "_query": "house price prediction machine learning", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2505.03393v1", "title": "Prediction Models That Learn to Avoid Missing Values", "abstract": "Handling missing values at test time is challenging for machine learning models, especially when aiming for both high accuracy and interpretability. Established approaches often add bias through imputation or excessive model complexity via missingness indicators. Moreover, either method can obscure interpretability, making it harder to understand how the model utilizes the observed variables in predictions. We propose missingness-avoiding (MA) machine learning, a general framework for training models to rarely require the values of missing (or imputed) features at test time. We create tailored MA learning algorithms for decision trees, tree ensembles, and sparse linear models by incorporating classifier-specific regularization terms in their learning objectives. The tree-based models leverage contextual missingness by reducing reliance on missing values based on the observed context. Experiments on real-world datasets demonstrate that MA-DT, MA-LASSO, MA-RF, and MA-GBT effectively reduce the reliance on features with missing values while maintaining predictive performance competitive with their unregularized counterparts. This shows that our framework gives practitioners a powerful tool to maintain interpretability in predictions with test-time missing values.", "published": "2025-05-06T10:16:35Z", "authors": ["Lena Stempfle", "Anton Matsson", "Newton Mwai", "Fredrik D. Johansson"], "categories": ["cs.LG"], "doi": "10.48550/arXiv.2505.03393", "_query": "house price prediction machine learning", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/1803.03242v2", "title": "Probably Approximately Metric-Fair Learning", "abstract": "The seminal work of Dwork {\\em et al.} [ITCS 2012] introduced a metric-based notion of individual fairness. Given a task-specific similarity metric, their notion required that every pair of similar individuals should be treated similarly. In the context of machine learning, however, individual fairness does not generalize from a training set to the underlying population. We show that this can lead to computational intractability even for simple fair-learning tasks. With this motivation in mind, we introduce and study a relaxed notion of {\\em approximate metric-fairness}: for a random pair of individuals sampled from the population, with all but a small probability of error, if they are similar then they should be treated similarly. We formalize the goal of achieving approximate metric-fairness simultaneously with best-possible accuracy as Probably Approximately Correct and Fair (PACF) Learning. We show that approximate metric-fairness {\\em does} generalize, and leverage these generalization guarantees to construct polynomial-time PACF learning algorithms for the classes of linear and logistic predictors.", "published": "2018-03-08T18:35:08Z", "authors": ["Guy N. Rothblum", "Gal Yona"], "categories": ["cs.LG", "cs.DS"], "doi": "", "_query": "house price prediction machine learning", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2006.04258v2", "title": "Investigating Estimated Kolmogorov Complexity as a Means of Regularization for Link Prediction", "abstract": "Link prediction in graphs is an important task in the fields of network science and machine learning. We investigate a flexible means of regularization for link prediction based on an approximation of the Kolmogorov complexity of graphs that is differentiable and compatible with recent advances in link prediction algorithms. Informally, the Kolmogorov complexity of an object is the length of the shortest computer program that produces the object. Complex networks are often generated, in part, by simple mechanisms; for example, many citation networks and social networks are approximately scale-free and can be explained by preferential attachment. A preference for predicting graphs with simpler generating mechanisms motivates our choice of Kolmogorov complexity as a regularization term. In our experiments the regularization method shows good performance on many diverse real-world networks, however we determine that this is likely due to an aggregation method rather than any actual estimation of Kolmogorov complexity.", "published": "2020-06-07T20:40:47Z", "authors": ["Paris D. L. Flood", "Ramon Viñas", "Pietro Liò"], "categories": ["cs.LG", "stat.ML"], "doi": "", "_query": "house price prediction machine learning", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2202.09875v4", "title": "Trying to Outrun Causality with Machine Learning: Limitations of Model Explainability Techniques for Identifying Predictive Variables", "abstract": "Machine Learning explainability techniques have been proposed as a means of `explaining' or interrogating a model in order to understand why a particular decision or prediction has been made. Such an ability is especially important at a time when machine learning is being used to automate decision processes which concern sensitive factors and legal outcomes. Indeed, it is even a requirement according to EU law. Furthermore, researchers concerned with imposing overly restrictive functional form (e.g., as would be the case in a linear regression) may be motivated to use machine learning algorithms in conjunction with explainability techniques, as part of exploratory research, with the goal of identifying important variables which are associated with an outcome of interest. For example, epidemiologists might be interested in identifying `risk factors' - i.e. factors which affect recovery from disease - by using random forests and assessing variable relevance using importance measures. However, and as we demonstrate, machine learning algorithms are not as flexible as they might seem, and are instead incredibly sensitive to the underling causal structure in the data. The consequences of this are that predictors which are, in fact, critical to a causal system and highly correlated with the outcome, may nonetheless be deemed by explainability techniques to be unrelated/unimportant/unpredictive of the outcome. Rather than this being a limitation of explainability techniques per se, we show that it is rather a consequence of the mathematical implications of regression, and the interaction of these implications with the associated conditional independencies of the underlying causal structure. We provide some alternative recommendations for researchers wanting to explore the data for important variables.", "published": "2022-02-20T17:48:54Z", "authors": ["Matthew J. Vowels"], "categories": ["stat.ML", "cs.LG"], "doi": "", "_query": "house price prediction machine learning", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2111.07089v1", "title": "Evaluating Contrastive Learning on Wearable Timeseries for Downstream Clinical Outcomes", "abstract": "Vast quantities of person-generated health data (wearables) are collected but the process of annotating to feed to machine learning models is impractical. This paper discusses ways in which self-supervised approaches that use contrastive losses, such as SimCLR and BYOL, previously applied to the vision domain, can be applied to high-dimensional health signals for downstream classification tasks of various diseases spanning sleep, heart, and metabolic conditions. To this end, we adapt the data augmentation step and the overall architecture to suit the temporal nature of the data (wearable traces) and evaluate on 5 downstream tasks by comparing other state-of-the-art methods including supervised learning and an adversarial unsupervised representation learning method. We show that SimCLR outperforms the adversarial method and a fully-supervised method in the majority of the downstream evaluation tasks, and that all self-supervised methods outperform the fully-supervised methods. This work provides a comprehensive benchmark for contrastive methods applied to the wearable time-series domain, showing the promise of task-agnostic representations for downstream clinical outcomes.", "published": "2021-11-13T10:48:17Z", "authors": ["Kevalee Shah", "Dimitris Spathis", "Chi Ian Tang", "Cecilia Mascolo"], "categories": ["cs.LG", "eess.SP"], "doi": "", "_query": "house price prediction machine learning", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/1912.06166v3", "title": "ABOUT ML: Annotation and Benchmarking on Understanding and Transparency of Machine Learning Lifecycles", "abstract": "We present the \"Annotation and Benchmarking on Understanding and Transparency of Machine Learning Lifecycles\" (ABOUT ML) project as an initiative to operationalize ML transparency and work towards a standard ML documentation practice. We make the case for the project's relevance and effectiveness in consolidating disparate efforts across a variety of stakeholders, as well as bringing in the perspectives of currently missing voices that will be valuable in shaping future conversations. We describe the details of the initiative and the gaps we hope this project will help address.", "published": "2019-12-12T19:23:04Z", "authors": ["Inioluwa Deborah Raji", "Jingying Yang"], "categories": ["cs.CY", "stat.ML"], "doi": "", "_query": "house price prediction machine learning", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2409.06146v2", "title": "Configuration Interaction Guided Sampling with Interpretable Restricted Boltzmann Machine", "abstract": "We propose a data-driven approach using a Restricted Boltzmann Machine (RBM) to solve the Schrödinger equation in configuration space. Traditional Configuration Interaction (CI) methods construct the wavefunction as a linear combination of Slater determinants, but this becomes computationally expensive due to the factorial growth in the number of configurations. Our approach extends the use of a generative model such as the RBM by incorporating a taboo list strategy to enhance efficiency and convergence. The RBM is used to efficiently identify and sample the most significant determinants, thus accelerating convergence and substantially reducing computational cost. This method achieves up to 99.99% of the correlation energy while using up to four orders of magnitude fewer determinants compared to full CI calculations and up to two orders of magnitude fewer than previous state of the art methods. Beyond efficiency, our analysis reveals that the RBM learns electron distributions over molecular orbitals by capturing quantum patterns that resemble Radial Distribution Functions (RDFs) linked to molecular bonding. This suggests that the learned pattern is interpretable, highlighting the potential of machine learning for explainable quantum chemistry", "published": "2024-09-10T01:42:10Z", "authors": ["Jorge I. Hernandez-Martinez", "Andres Mendez-Vazquez", "Gerardo Rodriguez-Hernandez", "Sandra Leticia Juárez-Osorio"], "categories": ["cs.LG", "physics.comp-ph"], "doi": "", "_query": "house price prediction machine learning", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/1910.01570v1", "title": "Prediction of GNSS Phase Scintillations: A Machine Learning Approach", "abstract": "A Global Navigation Satellite System (GNSS) uses a constellation of satellites around the earth for accurate navigation, timing, and positioning. Natural phenomena like space weather introduce irregularities in the Earth's ionosphere, disrupting the propagation of the radio signals that GNSS relies upon. Such disruptions affect both the amplitude and the phase of the propagated waves. No physics-based model currently exists to predict the time and location of these disruptions with sufficient accuracy and at relevant scales. In this paper, we focus on predicting the phase fluctuations of GNSS radio waves, known as phase scintillations. We propose a novel architecture and loss function to predict 1 hour in advance the magnitude of phase scintillations within a time window of plus-minus 5 minutes with state-of-the-art performance.", "published": "2019-10-03T16:15:55Z", "authors": ["Kara Lamb", "Garima Malhotra", "Athanasios Vlontzos", "Edward Wagstaff", "Atılım Günes Baydin", "Anahita Bhiwandiwalla", "Yarin Gal", "Alfredo Kalaitzis", "Anthony Reina", "Asti Bhatt"], "categories": ["cs.LG", "stat.ML"], "doi": "", "_query": "house price prediction machine learning", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2608.21383v1", "title": "Determinants of Starting Salaries for Filipino Graduates: An Explainable Machine Learning Approach", "abstract": "Filipino graduates face a persistent disconnect between educational preparation and labor market outcomes, where starting salary is a key signal of entry-level valuation. Current Philippine research is dominated by descriptive tracer studies that document employment rates but do not explain the determinants of pay. We address this gap using a crowd-sourced survey dataset of graduate responses whose noisy, self-reported nature makes it a challenging prediction target. Applying machine learning to this problem, we identify job role and industry as the dominant determinants of starting salary, significantly outweighing institutional prestige. The strength of this finding is its central contribution: it is corroborated by three independent lines of evidence, namely SHAP attributions, the heavy reliance of the best ensemble on occupational text, and a Natural Language Inference reformulation. These results suggest that career guidance and policy should prioritize sector-specific skills over institutional brand.", "published": "2026-07-17T03:49:00Z", "authors": ["Alexander Gabriel A. Aranes", "John Michael C. Magpantay", "Reginald Neil C. Recario", "Jamlech Iram N. Gojo Cruz", "Rodolfo C. Camaclang"], "categories": ["cs.CY", "cs.AI"], "doi": "", "_query": "house price prediction machine learning", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2004.05839v4", "title": "A Theory of the Risk for Optimization with Relaxation and its Application to Support Vector Machines", "abstract": "In this paper we consider optimization with relaxation, an ample paradigm to make data-driven designs. This approach was previously considered by the same authors of this work in Garatti and Campi (2019), a study that revealed a deep-seated connection between two concepts: risk (probability of not satisfying a new, out-of-sample, constraint) and complexity (according to a definition introduced in paper Garatti and Campi (2019)). This connection was shown to have profound implications in applications because it implied that the risk can be estimated from the complexity, a quantity that can be measured from the data without any knowledge of the data-generation mechanism. In the present work we establish new results. First, we expand the scope of Garatti and Campi (2019) so as to embrace a more general setup that covers various algorithms in machine learning. Then, we study classical support vector methods - including SVM (Support Vector Machine), SVR (Support Vector Regression) and SVDD (Support Vector Data Description) - and derive new results for the ability of these methods to generalize. All results are valid for any finite size of the data set. When the sample size tends to infinity, we establish the unprecedented result that the risk approaches the ratio between the complexity and the cardinality of the data sample, regardless of the value of the complexity.", "published": "2020-04-13T09:38:25Z", "authors": ["Marco C. Campi", "Simone Garatti"], "categories": ["cs.LG", "eess.SY", "math.OC", "stat.ML"], "doi": "", "_query": "house price prediction machine learning", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2002.05551v5", "title": "PACOH: Bayes-Optimal Meta-Learning with PAC-Guarantees", "abstract": "Meta-learning can successfully acquire useful inductive biases from data. Yet, its generalization properties to unseen learning tasks are poorly understood. Particularly if the number of meta-training tasks is small, this raises concerns about overfitting. We provide a theoretical analysis using the PAC-Bayesian framework and derive novel generalization bounds for meta-learning. Using these bounds, we develop a class of PAC-optimal meta-learning algorithms with performance guarantees and a principled meta-level regularization. Unlike previous PAC-Bayesian meta-learners, our method results in a standard stochastic optimization problem which can be solved efficiently and scales well. When instantiating our PAC-optimal hyper-posterior (PACOH) with Gaussian processes and Bayesian Neural Networks as base learners, the resulting methods yield state-of-the-art performance, both in terms of predictive accuracy and the quality of uncertainty estimates. Thanks to their principled treatment of uncertainty, our meta-learners can also be successfully employed for sequential decision problems.", "published": "2020-02-13T15:01:38Z", "authors": ["Jonas Rothfuss", "Vincent Fortuin", "Martin Josifoski", "Andreas Krause"], "categories": ["stat.ML", "cs.LG"], "doi": "", "_query": "house price prediction machine learning", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2211.15476v1", "title": "Meta-analysis of individualized treatment rules via sign-coherency", "abstract": "Medical treatments tailored to a patient's baseline characteristics hold the potential of improving patient outcomes while reducing negative side effects. Learning individualized treatment rules (ITRs) often requires aggregation of multiple datasets(sites); however, current ITR methodology does not take between-site heterogeneity into account, which can hurt model generalizability when deploying back to each site. To address this problem, we develop a method for individual-level meta-analysis of ITRs, which jointly learns site-specific ITRs while borrowing information about feature sign-coherency via a scientifically-motivated directionality principle. We also develop an adaptive procedure for model tuning, using information criteria tailored to the ITR learning problem. We study the proposed methods through numerical experiments to understand their performance under different levels of between-site heterogeneity and apply the methodology to estimate ITRs in a large multi-center database of electronic health records. This work extends several popular methodologies for estimating ITRs (A-learning, weighted learning) to the multiple-sites setting.", "published": "2022-11-28T15:55:55Z", "authors": ["Jay Jojo Cheng", "Jared D. Huling", "Guanhua Chen"], "categories": ["stat.ML", "cs.LG", "stat.ME"], "doi": "", "_query": "house price prediction machine learning", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/1206.6398v2", "title": "Learning Parameterized Skills", "abstract": "We introduce a method for constructing skills capable of solving tasks drawn from a distribution of parameterized reinforcement learning problems. The method draws example tasks from a distribution of interest and uses the corresponding learned policies to estimate the topology of the lower-dimensional piecewise-smooth manifold on which the skill policies lie. This manifold models how policy parameters change as task parameters vary. The method identifies the number of charts that compose the manifold and then applies non-linear regression in each chart to construct a parameterized skill by predicting policy parameters from task parameters. We evaluate our method on an underactuated simulated robotic arm tasked with learning to accurately throw darts at a parameterized target location.", "published": "2012-06-27T19:59:59Z", "authors": ["Bruno Da Silva", "George Konidaris", "Andrew Barto"], "categories": ["cs.LG", "stat.ML"], "doi": "", "_query": "house price prediction machine learning", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/1903.07167v1", "title": "Machine Learning: A Dark Side of Cancer Computing", "abstract": "Cancer analysis and prediction is the utmost important research field for well-being of humankind. The Cancer data are analyzed and predicted using machine learning algorithms. Most of the researcher claims the accuracy of the predicted results within 99%. However, we show that machine learning algorithms can easily predict with an accuracy of 100% on Wisconsin Diagnostic Breast Cancer dataset. We show that the method of gaining accuracy is an unethical approach that we can easily mislead the algorithms. In this paper, we exploit the weakness of Machine Learning algorithms. We perform extensive experiments for the correctness of our results to exploit the weakness of machine learning algorithms. The methods are rigorously evaluated to validate our claim. In addition, this paper focuses on correctness of accuracy. This paper report three key outcomes of the experiments, namely, correctness of accuracies, significance of minimum accuracy, and correctness of machine learning algorithms.", "published": "2019-03-17T20:44:25Z", "authors": ["Ripon Patgiri", "Sabuzima Nayak", "Tanya Akutota", "Bishal Paul"], "categories": ["cs.LG", "stat.ML"], "doi": "", "_query": "house price prediction machine learning", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2108.01867v2", "title": "A Pragmatic Look at Deep Imitation Learning", "abstract": "The introduction of the generative adversarial imitation learning (GAIL) algorithm has spurred the development of scalable imitation learning approaches using deep neural networks. Many of the algorithms that followed used a similar procedure, combining on-policy actor-critic algorithms with inverse reinforcement learning. More recently there have been an even larger breadth of approaches, most of which use off-policy algorithms. However, with the breadth of algorithms, everything from datasets to base reinforcement learning algorithms to evaluation settings can vary, making it difficult to fairly compare them. In this work we re-implement 6 different IL algorithms, updating 3 of them to be off-policy, base them on a common off-policy algorithm (SAC), and evaluate them on a widely-used expert trajectory dataset (D4RL) for the most common benchmark (MuJoCo). After giving all algorithms the same hyperparameter optimisation budget, we compare their results for a range of expert trajectories. In summary, GAIL, with all of its improvements, consistently performs well across a range of sample sizes, AdRIL is a simple contender that performs well with one important hyperparameter to tune, and behavioural cloning remains a strong baseline when data is more plentiful.", "published": "2021-08-04T06:33:10Z", "authors": ["Kai Arulkumaran", "Dan Ogawa Lillrank"], "categories": ["cs.LG", "cs.NE", "stat.ML"], "doi": "", "_query": "house price prediction machine learning", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2103.03923v3", "title": "Surface Warping Incorporating Machine Learning Assisted Domain Likelihood Estimation: A New Paradigm in Mine Geology Modelling and Automation", "abstract": "This paper illustrates an application of machine learning (ML) within a complex system that performs grade estimation. In surface mining, assay measurements taken from production drilling often provide useful information that allows initially inaccurate surfaces created using sparse exploration data to be revised and subsequently improved. Recently, a Bayesian warping technique has been proposed to reshape modeled surfaces using geochemical and spatial constraints imposed by newly acquired blasthole data. This paper focuses on incorporating machine learning into this warping framework to make the likelihood computation generalizable. The technique works by adjusting the position of vertices on the surface to maximize the integrity of modeled geological boundaries with respect to sparse geochemical observations. Its foundation is laid by a Bayesian derivation in which the geological domain likelihood given the chemistry, p(g|c), plays a similar role to p(y(c)|g). This observation allows a manually calibrated process centered around the latter to be automated since ML techniques may be used to estimate the former in a data-driven way. Machine learning performance is evaluated for gradient boosting, neural network, random forest and other classifiers in a binary and multi-class context using precision and recall rates. Once ML likelihood estimators are integrated in the surface warping framework, surface shaping performance is evaluated using unseen data by examining the categorical distribution of test samples located above and below the warped surface. Large-scale validation experiments are performed to assess the overall efficacy of ML assisted surface warping as a fully integrated component within an ore grade estimation system where the posterior mean is obtained via Gaussian Process inference with a Matern 3/2 kernel.", "published": "2021-02-15T10:37:52Z", "authors": ["Raymond Leung", "Mehala Balamurali", "Alexander Lowe"], "categories": ["physics.geo-ph", "cs.CE", "cs.LG"], "doi": "10.1007/s11004-021-09967-5", "_query": "house price prediction machine learning", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2412.05070v1", "title": "Enhancing Fourier pricing with machine learning", "abstract": "Fourier pricing methods such as the Carr-Madan formula or the COS method are classic tools for pricing European options for advanced models such as the Heston model. These methods require tuning parameters such as a damping factor, a truncation range, a number of terms, etc. Estimating these tuning parameters is difficult or computationally expensive. Recently, machine learning techniques have been proposed for fast pricing: they are able to learn the functional relationship between the parameters of the Heston model and the option price. However, machine learning techniques suffer from error control and require retraining for different error tolerances. In this research, we propose to learn the tuning parameters of the Fourier methods (instead of the prices) using machine learning techniques. As a result, we obtain very fast algorithms with full error control: Our approach works with any error tolerance without retraining, as demonstrated in numerical experiments using the Heston model.", "published": "2024-12-06T14:27:59Z", "authors": ["Gero Junike", "Hauke Stier"], "categories": ["q-fin.MF"], "doi": "", "_query": "house price prediction machine learning", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/1912.09848v1", "title": "Prediction of Physical Load Level by Machine Learning Analysis of Heart Activity after Exercises", "abstract": "The assessment of energy expenditure in real life is of great importance for monitoring the current physical state of people, especially in work, sport, elderly care, health care, and everyday life even. This work reports about application of some machine learning methods (linear regression, linear discriminant analysis, k-nearest neighbors, decision tree, random forest, Gaussian naive Bayes, support-vector machine) for monitoring energy expenditures in athletes. The classification problem was to predict the known level of the in-exercise loads (in three categories by calories) by the heart rate activity features measured during the short period of time (1 minute only) after training, i.e by features of the post-exercise load. The results obtained shown that the post-exercise heart activity features preserve the information of the in-exercise training loads and allow us to predict their actual in-exercise levels. The best performance can be obtained by the random forest classifier with all 8 heart rate features (micro-averaged area under curve value AUCmicro = 0.87 and macro-averaged one AUCmacro = 0.88) and the k-nearest neighbors classifier with 4 most important heart rate features (AUCmicro = 0.91 and AUCmacro = 0.89). The limitations and perspectives of the ML methods used are outlined, and some practical advices are proposed as to their improvement and implementation for the better prediction of in-exercise energy expenditures.", "published": "2019-12-20T14:35:49Z", "authors": ["Peng Gang", "Wei Zeng", "Yuri Gordienko", "Oleksandr Rokovyi", "Oleg Alienin", "Sergii Stirenko"], "categories": ["cs.LG", "eess.SP", "stat.ML"], "doi": "", "_query": "house price prediction machine learning", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2310.02074v2", "title": "ACE: A fast, skillful learned global atmospheric model for climate prediction", "abstract": "Existing ML-based atmospheric models are not suitable for climate prediction, which requires long-term stability and physical consistency. We present ACE (AI2 Climate Emulator), a 200M-parameter, autoregressive machine learning emulator of an existing comprehensive 100-km resolution global atmospheric model. The formulation of ACE allows evaluation of physical laws such as the conservation of mass and moisture. The emulator is stable for 100 years, nearly conserves column moisture without explicit constraints and faithfully reproduces the reference model's climate, outperforming a challenging baseline on over 90% of tracked variables. ACE requires nearly 100x less wall clock time and is 100x more energy efficient than the reference model using typically available resources. Without fine-tuning, ACE can stably generalize to a previously unseen historical sea surface temperature dataset.", "published": "2023-10-03T14:15:06Z", "authors": ["Oliver Watt-Meyer", "Gideon Dresdner", "Jeremy McGibbon", "Spencer K. Clark", "Brian Henn", "James Duncan", "Noah D. Brenowitz", "Karthik Kashinath", "Michael S. Pritchard", "Boris Bonev", "Matthew E. Peters", "Christopher S. Bretherton"], "categories": ["physics.ao-ph", "cs.LG"], "doi": "", "_query": "house price prediction machine learning", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2310.11978v2", "title": "Can bin-wise scaling improve consistency and adaptivity of prediction uncertainty for machine learning regression ?", "abstract": "Binwise Variance Scaling (BVS) has recently been proposed as a post hoc recalibration method for prediction uncertainties of machine learning regression problems that is able of more efficient corrections than uniform variance (or temperature) scaling. The original version of BVS uses uncertainty-based binning, which is aimed to improve calibration conditionally on uncertainty, i.e. consistency. I explore here several adaptations of BVS, in particular with alternative loss functions and a binning scheme based on an input-feature (X) in order to improve adaptivity, i.e. calibration conditional on X. The performances of BVS and its proposed variants are tested on a benchmark dataset for the prediction of atomization energies and compared to the results of isotonic regression.", "published": "2023-10-18T14:05:04Z", "authors": ["Pascal Pernot"], "categories": ["stat.ML", "cs.LG", "physics.chem-ph", "physics.data-an"], "doi": "", "_query": "house price prediction machine learning", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/1206.6479v1", "title": "The Landmark Selection Method for Multiple Output Prediction", "abstract": "Conditional modeling x \\to y is a central problem in machine learning. A substantial research effort is devoted to such modeling when x is high dimensional. We consider, instead, the case of a high dimensional y, where x is either low dimensional or high dimensional. Our approach is based on selecting a small subset y_L of the dimensions of y, and proceed by modeling (i) x \\to y_L and (ii) y_L \\to y. Composing these two models, we obtain a conditional model x \\to y that possesses convenient statistical properties. Multi-label classification and multivariate regression experiments on several datasets show that this model outperforms the one vs. all approach as well as several sophisticated multiple output prediction methods.", "published": "2012-06-27T19:59:59Z", "authors": ["Krishnakumar Balasubramanian", "Guy Lebanon"], "categories": ["cs.LG", "stat.ML"], "doi": "", "_query": "house price prediction machine learning", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2502.03143v1", "title": "Machine Learning-Driven Student Performance Prediction for Enhancing Tiered Instruction", "abstract": "Student performance prediction is one of the most important subjects in educational data mining. As a modern technology, machine learning offers powerful capabilities in feature extraction and data modeling, providing essential support for diverse application scenarios, as evidenced by recent studies confirming its effectiveness in educational data mining. However, despite extensive prediction experiments, machine learning methods have not been effectively integrated into practical teaching strategies, hindering their application in modern education. In addition, massive features as input variables for machine learning algorithms often leads to information redundancy, which can negatively impact prediction accuracy. Therefore, how to effectively use machine learning methods to predict student performance and integrate the prediction results with actual teaching scenarios is a worthy research subject. To this end, this study integrates the results of machine learning-based student performance prediction with tiered instruction, aiming to enhance student outcomes in target course, which is significant for the application of educational data mining in contemporary teaching scenarios. Specifically, we collect original educational data and perform feature selection to reduce information redundancy. Then, the performance of five representative machine learning methods is analyzed and discussed with Random Forest showing the best performance. Furthermore, based on the results of the classification of students, tiered instruction is applied accordingly, and different teaching objectives and contents are set for all levels of students. The comparison of teaching outcomes between the control and experimental classes, along with the analysis of questionnaire results, demonstrates the effectiveness of the proposed framework.", "published": "2025-02-05T13:13:25Z", "authors": ["Yawen Chen", "Jiande Sun", "Jinhui Wang", "Liang Zhao", "Xinmin Song", "Linbo Zhai"], "categories": ["cs.LG", "cs.CY"], "doi": "", "_query": "house price prediction machine learning", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2409.07626v2", "title": "Generalization Error Bound for Quantum Machine Learning in NISQ Era -- A Survey", "abstract": "Despite the mounting anticipation for the quantum revolution, the success of Quantum Machine Learning (QML) in the Noisy Intermediate-Scale Quantum (NISQ) era hinges on a largely unexplored factor: the generalization error bound, a cornerstone of robust and reliable machine learning models. Current QML research, while exploring novel algorithms and applications extensively, is predominantly situated in the context of noise-free, ideal quantum computers. However, Quantum Circuit (QC) operations in NISQ-era devices are susceptible to various noise sources and errors. In this article, we conduct a Systematic Mapping Study (SMS) to explore the state-of-the-art generalization bound for supervised QML in NISQ-era and analyze the latest practices in the field. Our study systematically summarizes the existing computational platforms with quantum hardware, datasets, optimization techniques, and the common properties of the bounds found in the literature. We further present the performance accuracy of various approaches in classical benchmark datasets like the MNIST and IRIS datasets. The SMS also highlights the limitations and challenges in QML in the NISQ era and discusses future research directions to advance the field. Using a detailed Boolean operators query in five reliable indexers, we collected 544 papers and filtered them to a small set of 37 relevant articles. This filtration was done following the best practice of SMS with well-defined research questions and inclusion and exclusion criteria.", "published": "2024-09-11T21:17:30Z", "authors": ["Bikram Khanal", "Pablo Rivas", "Arun Sanjel", "Korn Sooksatra", "Ernesto Quevedo", "Alejandro Rodriguez"], "categories": ["quant-ph", "cs.CC", "cs.LG"], "doi": "10.1007/s42484-024-00204-w", "_query": "house price prediction machine learning", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2407.02236v1", "title": "Indian Stock Market Prediction using Augmented Financial Intelligence ML", "abstract": "This paper presents price prediction models using Machine Learning algorithms augmented with Superforecasters predictions, aimed at enhancing investment decisions. Five Machine Learning models are built, including Bidirectional LSTM, ARIMA, a combination of CNN and LSTM, GRU, and a model built using LSTM and GRU algorithms. The models are evaluated using the Mean Absolute Error to determine their predictive accuracy. Additionally, the paper suggests incorporating human intelligence by identifying Superforecasters and tracking their predictions to anticipate unpredictable shifts or changes in stock prices . The predictions made by these users can further enhance the accuracy of stock price predictions when combined with Machine Learning and Natural Language Processing techniques. Predicting the price of any commodity can be a significant task but predicting the price of a stock in the stock market deals with much more uncertainty. Recognising the limited knowledge and exposure to stocks among certain investors, this paper proposes price prediction models using Machine Learning algorithms. In this work, five Machine learning models are built using Bidirectional LSTM, ARIMA, a combination of CNN and LSTM, GRU and the last one is built using LSTM and GRU algorithms. Later these models are assessed using MAE scores to find which model is predicting with the highest accuracy. In addition to this, this paper also suggests the use of human intelligence to closely predict the shift in price patterns in the stock market The main goal is to identify Superforecasters and track their predictions to anticipate unpredictable shifts or changes in stock prices. By leveraging the combined power of Machine Learning and the Human Intelligence, predictive accuracy can be significantly increased.", "published": "2024-07-02T12:58:50Z", "authors": ["Anishka Chauhan", "Pratham Mayur", "Yeshwanth Sai Gokarakonda", "Pooriya Jamie", "Naman Mehrotra"], "categories": ["q-fin.TR", "cs.AI", "cs.CE", "stat.ML"], "doi": "10.2139/ssrn.4697853", "_query": "house price prediction machine learning", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/1610.08210v1", "title": "Price of Transparency in Strategic Machine Learning", "abstract": "Based on the observation that the transparency of an algorithm comes with a cost for the algorithm designer when the users (data providers) are strategic, this paper studies the impact of strategic intent of the users on the design and performance of transparent ML algorithms. We quantitatively study the {\\bf price of transparency} in the context of strategic classification algorithms, by modeling the problem as a nonzero-sum game between the users and the algorithm designer. The cost of having a transparent algorithm is measured by a quantity, named here as price of transparency which is the ratio of the designer cost at the Stackelberg equilibrium, when the algorithm is transparent (which allows users to be strategic) to that of the setting where the algorithm is not transparent.", "published": "2016-10-26T07:17:27Z", "authors": ["Emrah Akyol", "Cedric Langbort", "Tamer Basar"], "categories": ["cs.GT"], "doi": "", "_query": "house price prediction machine learning", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2112.03555v3", "title": "FedDAG: Federated DAG Structure Learning", "abstract": "To date, most directed acyclic graphs (DAGs) structure learning approaches require data to be stored in a central server. However, due to the consideration of privacy protection, data owners gradually refuse to share their personalized raw data to avoid private information leakage, making this task more troublesome by cutting off the first step. Thus, a puzzle arises: \\textit{how do we discover the underlying DAG structure from decentralized data?} In this paper, focusing on the additive noise models (ANMs) assumption of data generation, we take the first step in developing a gradient-based learning framework named FedDAG, which can learn the DAG structure without directly touching the local data and also can naturally handle the data heterogeneity. Our method benefits from a two-level structure of each local model. The first level structure learns the edges and directions of the graph and communicates with the server to get the model information from other clients during the learning procedure, while the second level structure approximates the mechanisms among variables and personally updates on its own data to accommodate the data heterogeneity. Moreover, FedDAG formulates the overall learning task as a continuous optimization problem by taking advantage of an equality acyclicity constraint, which can be solved by gradient descent methods to boost the searching efficiency. Extensive experiments on both synthetic and real-world datasets verify the efficacy of the proposed method.", "published": "2021-12-07T08:04:12Z", "authors": ["Erdun Gao", "Junjia Chen", "Li Shen", "Tongliang Liu", "Mingming Gong", "Howard Bondell"], "categories": ["cs.LG", "stat.ML"], "doi": "", "_query": "house price prediction machine learning", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/1311.1354v3", "title": "How to Center Binary Deep Boltzmann Machines", "abstract": "This work analyzes centered binary Restricted Boltzmann Machines (RBMs) and binary Deep Boltzmann Machines (DBMs), where centering is done by subtracting offset values from visible and hidden variables. We show analytically that (i) centering results in a different but equivalent parameterization for artificial neural networks in general, (ii) the expected performance of centered binary RBMs/DBMs is invariant under simultaneous flip of data and offsets, for any offset value in the range of zero to one, (iii) centering can be reformulated as a different update rule for normal binary RBMs/DBMs, and (iv) using the enhanced gradient is equivalent to setting the offset values to the average over model and data mean. Furthermore, numerical simulations suggest that (i) optimal generative performance is achieved by subtracting mean values from visible as well as hidden variables, (ii) centered RBMs/DBMs reach significantly higher log-likelihood values than normal binary RBMs/DBMs, (iii) centering variants whose offsets depend on the model mean, like the enhanced gradient, suffer from severe divergence problems, (iv) learning is stabilized if an exponentially moving average over the batch means is used for the offset values instead of the current batch mean, which also prevents the enhanced gradient from diverging, (v) centered RBMs/DBMs reach higher LL values than normal RBMs/DBMs while having a smaller norm of the weight matrix, (vi) centering leads to an update direction that is closer to the natural gradient and that the natural gradient is extremly efficient for training RBMs, (vii) centering dispense the need for greedy layer-wise pre-training of DBMs, (viii) furthermore we show that pre-training often even worsen the results independently whether centering is used or not, and (ix) centering is also beneficial for auto encoders.", "published": "2013-11-06T11:25:42Z", "authors": ["Jan Melchior", "Asja Fischer", "Laurenz Wiskott"], "categories": ["stat.ML", "cs.LG"], "doi": "", "_query": "house price prediction machine learning", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/1908.04710v3", "title": "metric-learn: Metric Learning Algorithms in Python", "abstract": "metric-learn is an open source Python package implementing supervised and weakly-supervised distance metric learning algorithms. As part of scikit-learn-contrib, it provides a unified interface compatible with scikit-learn which allows to easily perform cross-validation, model selection, and pipelining with other machine learning estimators. metric-learn is thoroughly tested and available on PyPi under the MIT licence.", "published": "2019-08-13T15:52:31Z", "authors": ["William de Vazelhes", "CJ Carey", "Yuan Tang", "Nathalie Vauquier", "Aurélien Bellet"], "categories": ["cs.LG", "stat.ML"], "doi": "", "_query": "house price prediction machine learning", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2312.01294v4", "title": "Deep sub-ensembles meets quantile regression: uncertainty-aware imputation for time series", "abstract": "Real-world time series data often exhibits substantial missing values, posing challenges for advanced analysis. A common approach to addressing this issue is imputation, where the primary challenge lies in determining the appropriate values to fill in. While previous deep learning methods have proven effective for time series imputation, they often produce overconfident imputations, which poses a potentially overlooked risk to the reliability of the intelligent system. Diffusion methods are proficient in estimating probability distributions but face challenges under a high missing rate and are, moreover, computationally expensive due to the nature of the generative model framework. In this paper, we propose Quantile Sub-Ensembles, a novel method that estimates uncertainty with ensembles of quantile-regression-based task networks and incorporate Quantile Sub-Ensembles into a non-generative time series imputation method. Our method not only produces accurate and reliable imputations, but also remains computationally efficient due to its non-generative framework. We conduct extensive experiments on five real-world datasets, and the results demonstrates superior performance in both deterministic and probabilistic imputation compared to baselines across most experimental settings. The code is available at https://github.com/yingliu-coder/QSE.", "published": "2023-12-03T05:52:30Z", "authors": ["Ying Liu", "Peng Cui", "Wenbo Hu", "Richang Hong"], "categories": ["cs.LG", "stat.ML"], "doi": "10.1007/s10994-025-06922-x", "_query": "house price prediction machine learning", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/1903.12127v1", "title": "Using Latent Class Analysis to Identify ARDS Sub-phenotypes for Enhanced Machine Learning Predictive Performance", "abstract": "In this work, we utilize Machine Learning for early recognition of patients at high risk of acute respiratory distress syndrome (ARDS), which is critical for successful prevention strategies for this devastating syndrome. The difficulty in early ARDS recognition stems from its complex and heterogenous nature. In this study, we integrate knowledge of the heterogeneity of ARDS patients into predictive model building. Using MIMIC-III data, we first apply latent class analysis (LCA) to identify homogeneous sub-groups in the ARDS population, and then build predictive models on the partitioned data. The results indicate that significantly improved performances of prediction can be obtained for two of the three identified sub-phenotypes of ARDS. Experiments suggests that identifying sub-phenotypes is beneficial for building predictive model for ARDS.", "published": "2019-03-28T17:12:35Z", "authors": ["Tony Wang", "Tim Tschampel", "Emilia Apostolova", "Tom Velez"], "categories": ["cs.LG", "stat.AP", "stat.ML"], "doi": "", "_query": "house price prediction machine learning", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2305.09946v3", "title": "AdaMSS: Adaptive Multi-Modality Segmentation-to-Survival Learning for Survival Outcome Prediction from PET/CT Images", "abstract": "Survival prediction is a major concern for cancer management. Deep survival models based on deep learning have been widely adopted to perform end-to-end survival prediction from medical images. Recent deep survival models achieved promising performance by jointly performing tumor segmentation with survival prediction, where the models were guided to extract tumor-related information through Multi-Task Learning (MTL). However, these deep survival models have difficulties in exploring out-of-tumor prognostic information. In addition, existing deep survival models are unable to effectively leverage multi-modality images. Empirically-designed fusion strategies were commonly adopted to fuse multi-modality information via task-specific manually-designed networks, thus limiting the adaptability to different scenarios. In this study, we propose an Adaptive Multi-modality Segmentation-to-Survival model (AdaMSS) for survival prediction from PET/CT images. Instead of adopting MTL, we propose a novel Segmentation-to-Survival Learning (SSL) strategy, where our AdaMSS is trained for tumor segmentation and survival prediction sequentially in two stages. This strategy enables the AdaMSS to focus on tumor regions in the first stage and gradually expand its focus to include other prognosis-related regions in the second stage. We also propose a data-driven strategy to fuse multi-modality information, which realizes adaptive optimization of fusion strategies based on training data during training. With the SSL and data-driven fusion strategies, our AdaMSS is designed as an adaptive model that can self-adapt its focus regions and fusion strategy for different training stages. Extensive experiments with two large clinical datasets show that our AdaMSS outperforms state-of-the-art survival prediction methods.", "published": "2023-05-17T04:56:11Z", "authors": ["Mingyuan Meng", "Bingxin Gu", "Michael Fulham", "Shaoli Song", "Dagan Feng", "Lei Bi", "Jinman Kim"], "categories": ["eess.IV", "cs.CV", "cs.LG"], "doi": "10.1038/s41698-024-00690-y", "_query": "house price prediction machine learning", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2111.05672v1", "title": "Automatically detecting data drift in machine learning classifiers", "abstract": "Classifiers and other statistics-based machine learning (ML) techniques generalize, or learn, based on various statistical properties of the training data. The assumption underlying statistical ML resulting in theoretical or empirical performance guarantees is that the distribution of the training data is representative of the production data distribution. This assumption often breaks; for instance, statistical distributions of the data may change. We term changes that affect ML performance `data drift' or `drift'. Many classification techniques compute a measure of confidence in their results. This measure might not reflect the actual ML performance. A famous example is the Panda picture that is correctly classified as such with a confidence of about 60\\%, but when noise is added it is incorrectly classified as a Gibbon with a confidence of above 99\\%. However, the work we report on here suggests that a classifier's measure of confidence can be used for the purpose of detecting data drift. We propose an approach based solely on classifier suggested labels and its confidence in them, for alerting on data distribution or feature space changes that are likely to cause data drift. Our approach identities degradation in model performance and does not require labeling of data in production which is often lacking or delayed. Our experiments with three different data sets and classifiers demonstrate the effectiveness of this approach in detecting data drift. This is especially encouraging as the classification itself may or may not be correct and no model input data is required. We further explore the statistical approach of sequential change-point tests to automatically determine the amount of data needed in order to identify drift while controlling the false positive rate (Type-1 error).", "published": "2021-11-10T12:34:14Z", "authors": ["Samuel Ackerman", "Orna Raz", "Marcel Zalmanovici", "Aviad Zlotnick"], "categories": ["cs.LG"], "doi": "", "_query": "house price prediction machine learning", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/1310.3101v1", "title": "Deep Multiple Kernel Learning", "abstract": "Deep learning methods have predominantly been applied to large artificial neural networks. Despite their state-of-the-art performance, these large networks typically do not generalize well to datasets with limited sample sizes. In this paper, we take a different approach by learning multiple layers of kernels. We combine kernels at each layer and then optimize over an estimate of the support vector machine leave-one-out error rather than the dual objective function. Our experiments on a variety of datasets show that each layer successively increases performance with only a few base kernels.", "published": "2013-10-11T12:14:00Z", "authors": ["Eric Strobl", "Shyam Visweswaran"], "categories": ["stat.ML", "cs.LG"], "doi": "10.1109/ICMLA.2013.84", "_query": "house price prediction machine learning", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/1810.06115v1", "title": "PRETZEL: Opening the Black Box of Machine Learning Prediction Serving Systems", "abstract": "Machine Learning models are often composed of pipelines of transformations. While this design allows to efficiently execute single model components at training time, prediction serving has different requirements such as low latency, high throughput and graceful performance degradation under heavy load. Current prediction serving systems consider models as black boxes, whereby prediction-time-specific optimizations are ignored in favor of ease of deployment. In this paper, we present PRETZEL, a prediction serving system introducing a novel white box architecture enabling both end-to-end and multi-model optimizations. Using production-like model pipelines, our experiments show that PRETZEL is able to introduce performance improvements over different dimensions; compared to state-of-the-art approaches PRETZEL is on average able to reduce 99th percentile latency by 5.5x while reducing memory footprint by 25x, and increasing throughput by 4.7x.", "published": "2018-10-14T22:21:30Z", "authors": ["Yunseong Lee", "Alberto Scolari", "Byung-Gon Chun", "Marco Domenico Santambrogio", "Markus Weimer", "Matteo Interlandi"], "categories": ["cs.LG", "cs.DC", "stat.ML"], "doi": "", "_query": "house price prediction machine learning", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/1908.08649v3", "title": "Adversary-resilient Distributed and Decentralized Statistical Inference and Machine Learning: An Overview of Recent Advances Under the Byzantine Threat Model", "abstract": "While the last few decades have witnessed a huge body of work devoted to inference and learning in distributed and decentralized setups, much of this work assumes a non-adversarial setting in which individual nodes---apart from occasional statistical failures---operate as intended within the algorithmic framework. In recent years, however, cybersecurity threats from malicious non-state actors and rogue entities have forced practitioners and researchers to rethink the robustness of distributed and decentralized algorithms against adversarial attacks. As a result, we now have a plethora of algorithmic approaches that guarantee robustness of distributed and/or decentralized inference and learning under different adversarial threat models. Driven in part by the world's growing appetite for data-driven decision making, however, securing of distributed/decentralized frameworks for inference and learning against adversarial threats remains a rapidly evolving research area. In this article, we provide an overview of some of the most recent developments in this area under the threat model of Byzantine attacks.", "published": "2019-08-23T03:23:49Z", "authors": ["Zhixiong Yang", "Arpita Gang", "Waheed U. Bajwa"], "categories": ["stat.ML", "cs.CR", "cs.DC", "cs.LG", "eess.SP"], "doi": "10.1109/MSP.2020.2973345", "_query": "house price prediction machine learning", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2406.18990v1", "title": "A Fast Learning-Based Surrogate of Electrical Machines using a Reduced Basis", "abstract": "A surrogate model approximates the outputs of a solver of Partial Differential Equations (PDEs) with a low computational cost. In this article, we propose a method to build learning-based surrogates in the context of parameterized PDEs, which are PDEs that depend on a set of parameters but are also temporal and spatial processes. Our contribution is a method hybridizing the Proper Orthogonal Decomposition and several Support Vector Regression machines. This method is conceived to work in real-time, thus aimed for being used in the context of digital twins, where a user can perform an interactive analysis of results based on the proposed surrogate. We present promising results on two use cases concerning electrical machines. These use cases are not toy examples but are produced an industrial computational code, they use meshes representing non-trivial geometries and contain non-linearities.", "published": "2024-06-27T08:29:04Z", "authors": ["Alejandro Ribés", "Nawfal Benchekroun", "Théo Delagnes"], "categories": ["cs.LG"], "doi": "", "_query": "house price prediction machine learning", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2306.03566v1", "title": "Memory-Based Dual Gaussian Processes for Sequential Learning", "abstract": "Sequential learning with Gaussian processes (GPs) is challenging when access to past data is limited, for example, in continual and active learning. In such cases, errors can accumulate over time due to inaccuracies in the posterior, hyperparameters, and inducing points, making accurate learning challenging. Here, we present a method to keep all such errors in check using the recently proposed dual sparse variational GP. Our method enables accurate inference for generic likelihoods and improves learning by actively building and updating a memory of past data. We demonstrate its effectiveness in several applications involving Bayesian optimization, active learning, and continual learning.", "published": "2023-06-06T10:34:03Z", "authors": ["Paul E. Chang", "Prakhar Verma", "S. T. John", "Arno Solin", "Mohammad Emtiyaz Khan"], "categories": ["cs.LG", "stat.ML"], "doi": "", "_query": "house price prediction machine learning", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2001.11328v2", "title": "Accelerating High-Strain Continuum-Scale Brittle Fracture Simulations with Machine Learning", "abstract": "Failure in brittle materials under dynamic loading conditions is a result of the propagation and coalescence of microcracks. Simulating this mechanism at the continuum level is computationally expensive or, in some cases, intractable. The computational cost is due to the need for highly resolved computational meshes required to capture complex crack growth behavior, such as branching, turning, etc. Typically, continuum-scale models that account for brittle damage evolution homogenize the crack network in some way, which reduces the overall computational cost, but can also neglect key physics of the subgrid crack growth behavior, sacrificing accuracy for efficiency. We have developed an approach using machine learning that overcomes the current inability to represent micro-scale physics at the macro-scale. Our approach leverages damage and stress data from a high-fidelity model that explicitly resolves microcrack behavior to build an inexpensive machine learning emulator, which runs in seconds as opposed to the high-fidelity model, which takes hours. Once trained, the machine learning emulator is used to predict the evolution of crack length statistics. A continuum-scale constitutive model is then informed with these crack statistics, speeding up the workflow by four orders of magnitude. Both the machine learning model and the continuum-scale model are validated against a high-fidelity model and experimental data, respectively, showing excellent agreement. There are two key findings. The first is that we can reduce the dimensionality of the problem, establishing that the machine learning emulator only needs the length of the longest crack and one of the maximum stress components to capture the necessary physics. Another compelling finding is that the emulator can be trained in one experimental setting and transferred successfully to predict behavior in a different setting.", "published": "2020-01-09T03:23:50Z", "authors": ["M. Giselle Fernández-Godino", "Nishant Panda", "Daniel O'Malley", "Kevin Larkin", "Abigail Hunter", "Raphael T. Haftka", "Gowri Srinivasan"], "categories": ["cond-mat.mtrl-sci", "physics.comp-ph", "physics.data-an"], "doi": "10.1016/j.commatsci.2020.109959", "_query": "house price prediction machine learning", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/1806.09351v3", "title": "Multi-objective Model-based Policy Search for Data-efficient Learning with Sparse Rewards", "abstract": "The most data-efficient algorithms for reinforcement learning in robotics are model-based policy search algorithms, which alternate between learning a dynamical model of the robot and optimizing a policy to maximize the expected return given the model and its uncertainties. However, the current algorithms lack an effective exploration strategy to deal with sparse or misleading reward scenarios: if they do not experience any state with a positive reward during the initial random exploration, it is very unlikely to solve the problem. Here, we propose a novel model-based policy search algorithm, Multi-DEX, that leverages a learned dynamical model to efficiently explore the task space and solve tasks with sparse rewards in a few episodes. To achieve this, we frame the policy search problem as a multi-objective, model-based policy optimization problem with three objectives: (1) generate maximally novel state trajectories, (2) maximize the expected return and (3) keep the system in state-space regions for which the model is as accurate as possible. We then optimize these objectives using a Pareto-based multi-objective optimization algorithm. The experiments show that Multi-DEX is able to solve sparse reward scenarios (with a simulated robotic arm) in much lower interaction time than VIME, TRPO, GEP-PG, CMA-ES and Black-DROPS.", "published": "2018-06-25T09:46:47Z", "authors": ["Rituraj Kaushik", "Konstantinos Chatzilygeroudis", "Jean-Baptiste Mouret"], "categories": ["cs.LG", "cs.AI", "cs.NE", "cs.RO", "stat.ML"], "doi": "", "_query": "house price prediction machine learning", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/1505.06813v1", "title": "Surrogate Functions for Maximizing Precision at the Top", "abstract": "The problem of maximizing precision at the top of a ranked list, often dubbed Precision@k (prec@k), finds relevance in myriad learning applications such as ranking, multi-label classification, and learning with severe label imbalance. However, despite its popularity, there exist significant gaps in our understanding of this problem and its associated performance measure. The most notable of these is the lack of a convex upper bounding surrogate for prec@k. We also lack scalable perceptron and stochastic gradient descent algorithms for optimizing this performance measure. In this paper we make key contributions in these directions. At the heart of our results is a family of truly upper bounding surrogates for prec@k. These surrogates are motivated in a principled manner and enjoy attractive properties such as consistency to prec@k under various natural margin/noise conditions. These surrogates are then used to design a class of novel perceptron algorithms for optimizing prec@k with provable mistake bounds. We also devise scalable stochastic gradient descent style methods for this problem with provable convergence bounds. Our proofs rely on novel uniform convergence bounds which require an in-depth analysis of the structural properties of prec@k and its surrogates. We conclude with experimental results comparing our algorithms with state-of-the-art cutting plane and stochastic gradient algorithms for maximizing prec@k.", "published": "2015-05-26T06:01:24Z", "authors": ["Purushottam Kar", "Harikrishna Narasimhan", "Prateek Jain"], "categories": ["stat.ML", "cs.LG"], "doi": "", "_query": "house price prediction machine learning", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2505.06595v3", "title": "Feature Representation Transferring to Lightweight Models via Perception Coherence", "abstract": "In this paper, we propose a method for transferring feature representation to lightweight student models from larger teacher models. We mathematically define a new notion called \\textit{perception coherence}. Based on this notion, we propose a loss function, which takes into account the dissimilarities between data points in feature space through their ranking. At a high level, by minimizing this loss function, the student model learns to mimic how the teacher model \\textit{perceives} inputs. More precisely, our method is motivated by the fact that the representational capacity of the student model is weaker than the teacher model. Hence, we aim to develop a new method allowing for a better relaxation. This means that, the student model does not need to preserve the absolute geometry of the teacher one, while preserving global coherence through dissimilarity ranking. Importantly, while rankings are defined only on finite sets, our notion of \\textit{perception coherence} extends them into a probabilistic form. This formulation depends on the input distribution and applies to general dissimilarity metrics. Our theoretical insights provide a probabilistic perspective on the process of feature representation transfer. Our experiments results show that our method outperforms or achieves on-par performance compared to strong baseline methods for representation transferring.", "published": "2025-05-10T10:55:06Z", "authors": ["Hai-Vy Nguyen", "Fabrice Gamboa", "Sixin Zhang", "Reda Chhaibi", "Serge Gratton", "Thierry Giaccone"], "categories": ["stat.ML", "cs.AI", "cs.CV", "cs.LG", "math.PR"], "doi": "", "_query": "house price prediction machine learning", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/1507.02188v1", "title": "AutoCompete: A Framework for Machine Learning Competition", "abstract": "In this paper, we propose AutoCompete, a highly automated machine learning framework for tackling machine learning competitions. This framework has been learned by us, validated and improved over a period of more than two years by participating in online machine learning competitions. It aims at minimizing human interference required to build a first useful predictive model and to assess the practical difficulty of a given machine learning challenge. The proposed system helps in identifying data types, choosing a machine learn- ing model, tuning hyper-parameters, avoiding over-fitting and optimization for a provided evaluation metric. We also observe that the proposed system produces better (or comparable) results with less runtime as compared to other approaches.", "published": "2015-07-08T15:07:39Z", "authors": ["Abhishek Thakur", "Artus Krohn-Grimberghe"], "categories": ["stat.ML", "cs.LG"], "doi": "", "_query": "house price prediction machine learning", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/1907.12778v3", "title": "An anomaly prediction framework for financial IT systems using hybrid machine learning methods", "abstract": "In financial field, a robust software system is of vital importance to ensure the smooth operation of financial transactions. However, many financial corporations still depend on operators to identify and eliminate the system failures when financial software systems break down. This traditional operation method is time consuming and extremely inefficient. To improve the efficiency and accuracy of system failure detection and thereby reduce the impact of system failures on financial services, we propose a novel machine learning-based framework to predict the occurrence of system exceptions and failures in a financial software system. In particular, we first extract rich information from system logs and eliminate noises in the data. Then the cleaned data is leveraged as the input of our proposed anomaly prediction framework which consists of three modules: key performance indicator(KPI) data prediction module, anomaly identification module and severity classification module. Notably, we design a hierarchical architecture of alarm classifiers and try to alleviate the influence of class-imbalance problem on the overall performance. Empirically, the experimental results demonstrate the superior performance of our proposed method on a real-world financial software system log data set.", "published": "2019-07-30T08:36:37Z", "authors": ["Jingwen Wang", "Jingxin Liu", "Juntao Pu", "Qinghong Yang", "Zhongchen Miao", "Jian Gao", "You Song"], "categories": ["cs.LG", "stat.ML"], "doi": "", "_query": "house price prediction machine learning", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/1803.04193v1", "title": "Extreme Learning Machine for Graph Signal Processing", "abstract": "In this article, we improve extreme learning machines for regression tasks using a graph signal processing based regularization. We assume that the target signal for prediction or regression is a graph signal. With this assumption, we use the regularization to enforce that the output of an extreme learning machine is smooth over a given graph. Simulation results with real data confirm that such regularization helps significantly when the available training data is limited in size and corrupted by noise.", "published": "2018-03-12T11:12:48Z", "authors": ["Arun Venkitaraman", "Saikat Chatterjee", "Peter Händel"], "categories": ["stat.ML", "cs.LG", "eess.SP"], "doi": "", "_query": "house price prediction machine learning", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2104.05499v3", "title": "L3DAS21 Challenge: Machine Learning for 3D Audio Signal Processing", "abstract": "The L3DAS21 Challenge is aimed at encouraging and fostering collaborative research on machine learning for 3D audio signal processing, with particular focus on 3D speech enhancement (SE) and 3D sound localization and detection (SELD). Alongside with the challenge, we release the L3DAS21 dataset, a 65 hours 3D audio corpus, accompanied with a Python API that facilitates the data usage and results submission stage. Usually, machine learning approaches to 3D audio tasks are based on single-perspective Ambisonics recordings or on arrays of single-capsule microphones. We propose, instead, a novel multichannel audio configuration based multiple-source and multiple-perspective Ambisonics recordings, performed with an array of two first-order Ambisonics microphones. To the best of our knowledge, it is the first time that a dual-mic Ambisonics configuration is used for these tasks. We provide baseline models and results for both tasks, obtained with state-of-the-art architectures: FaSNet for SE and SELDNet for SELD. This report is aimed at providing all needed information to participate in the L3DAS21 Challenge, illustrating the details of the L3DAS21 dataset, the challenge tasks and the baseline models.", "published": "2021-04-12T14:29:54Z", "authors": ["Eric Guizzo", "Riccardo F. Gramaccioni", "Saeid Jamili", "Christian Marinoni", "Edoardo Massaro", "Claudia Medaglia", "Giuseppe Nachira", "Leonardo Nucciarelli", "Ludovica Paglialunga", "Marco Pennese", "Sveva Pepe", "Enrico Rocchi", "Aurelio Uncini", "Danilo Comminiello"], "categories": ["eess.AS", "cs.LG", "cs.SD", "eess.SP"], "doi": "10.1109/MLSP52302.2021.9596248", "_query": "house price prediction machine learning", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/cond-mat/9801297v1", "title": "Statistical Mechanics of Online Learning of Drifting Concepts : A Variational Approach", "abstract": "We review the application of Statistical Mechanics methods to the study of online learning of a drifting concept in the limit of large systems. The model where a feed-forward network learns from examples generated by a time dependent teacher of the same architecture is analyzed. The best possible generalization ability is determined exactly, through the use of a variational method. The constructive variational method also suggests a learning algorithm. It depends, however, on some unavailable quantities, such as the present performance of the student. The construction of estimators for these quantities permits the implementation of a very effective, highly adaptive algorithm. Several other algorithms are also studied for comparison with the optimal bound and the adaptive algorithm, for different types of time evolution of the rule.", "published": "1998-01-28T16:30:40Z", "authors": ["Renato Vicente", "Osame Kinouchi", "Nestor Caticha"], "categories": ["cond-mat.dis-nn", "cond-mat.stat-mech"], "doi": "", "_query": "house price prediction machine learning", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/1611.05817v1", "title": "Nothing Else Matters: Model-Agnostic Explanations By Identifying Prediction Invariance", "abstract": "At the core of interpretable machine learning is the question of whether humans are able to make accurate predictions about a model's behavior. Assumed in this question are three properties of the interpretable output: coverage, precision, and effort. Coverage refers to how often humans think they can predict the model's behavior, precision to how accurate humans are in those predictions, and effort is either the up-front effort required in interpreting the model, or the effort required to make predictions about a model's behavior. In this work, we propose anchor-LIME (aLIME), a model-agnostic technique that produces high-precision rule-based explanations for which the coverage boundaries are very clear. We compare aLIME to linear LIME with simulated experiments, and demonstrate the flexibility of aLIME with qualitative examples from a variety of domains and tasks.", "published": "2016-11-17T19:07:00Z", "authors": ["Marco Tulio Ribeiro", "Sameer Singh", "Carlos Guestrin"], "categories": ["stat.ML", "cs.AI", "cs.LG"], "doi": "", "_query": "house price prediction machine learning", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2407.15905v1", "title": "A divide-and-conquer approach for spatio-temporal analysis of large house price data from Greater London", "abstract": "Statistical research in real estate markets, particularly in understanding the spatio-temporal dynamics of house prices, has garnered significant attention in recent times. Although Bayesian methods are common in spatio-temporal modeling, standard Markov chain Monte Carlo (MCMC) techniques are usually slow for large datasets such as house price data. To tackle this problem, we propose a divide-and-conquer spatio-temporal modeling approach. This method involves partitioning the data into multiple subsets and applying an appropriate Gaussian process model to each subset in parallel. The results from each subset are then combined using the Wasserstein barycenter technique to obtain the global parameters for the original problem. The proposed methodology allows for multiple observations per spatial and time unit, thereby offering added benefits for practitioners. As a real-life application, we analyze house price data of more than 0.6 million transactions from 983 middle layer super output areas in London over a period of eight years. The methodology provides insightful findings about the effects of various amenities, trend patterns, and the relationship between prices and carbon emissions. Furthermore, as demonstrated through a cross-validation study, it shows good predictive accuracy while balancing computational efficiency.", "published": "2024-07-22T14:29:13Z", "authors": ["Kapil Gupta", "Soudeep Deb"], "categories": ["stat.AP"], "doi": "", "_query": "house price prediction machine learning", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2201.05236v1", "title": "A Method for Controlling Extrapolation when Visualizing and Optimizing the Prediction Profiles of Statistical and Machine Learning Models", "abstract": "We present a novel method for controlling extrapolation in the prediction profiler in the JMP software. The prediction profiler is a graphical tool for exploring high dimensional prediction surfaces for statistical and machine learning models. The profiler contains interactive cross-sectional views, or profile traces, of the prediction surface of a model. Our method helps users avoid exploring predictions that should be considered extrapolation. It also performs optimization over a constrained factor region that avoids extrapolation using a genetic algorithm. In simulations and real world examples, we demonstrate how optimal factor settings without constraint in the profiler are frequently extrapolated, and how extrapolation control helps avoid these solutions with invalid factor settings that may not be useful to the user.", "published": "2022-01-13T22:31:32Z", "authors": ["Jeremy Ash", "Laura Lancaster", "Chris Gotwalt"], "categories": ["stat.ML", "cs.LG", "stat.AP"], "doi": "", "_query": "house price prediction machine learning", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/1808.10724v3", "title": "Learning Data-adaptive Nonparametric Kernels", "abstract": "In this paper, we propose a data-adaptive non-parametric kernel learning framework in margin based kernel methods. In model formulation, given an initial kernel matrix, a data-adaptive matrix with two constraints is imposed in an entry-wise scheme. Learning this data-adaptive matrix in a formulation-free strategy enlarges the margin between classes and thus improves the model flexibility. The introduced two constraints are imposed either exactly (on small data sets) or approximately (on large data sets) in our model, which provides a controllable trade-off between model flexibility and complexity with theoretical demonstration. In algorithm optimization, the objective function of our learning framework is proven to be gradient-Lipschitz continuous. Thereby, kernel and classifier/regressor learning can be efficiently optimized in a unified framework via Nesterov's acceleration. For the scalability issue, we study a decomposition-based approach to our model in the large sample case. The effectiveness of this approximation is illustrated by both empirical studies and theoretical guarantees. Experimental results on various classification and regression benchmark data sets demonstrate that our non-parametric kernel learning framework achieves good performance when compared with other representative kernel learning based algorithms.", "published": "2018-08-31T13:16:31Z", "authors": ["Fanghui Liu", "Xiaolin Huang", "Chen Gong", "Jie Yang", "Li Li"], "categories": ["cs.LG", "stat.ML"], "doi": "", "_query": "house price prediction machine learning", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2108.05955v1", "title": "Using Machine Learning to Predict Engineering Technology Students' Success with Computer Aided Design", "abstract": "Computer-aided design (CAD) programs are essential to engineering as they allow for better designs through low-cost iterations. While CAD programs are typically taught to undergraduate students as a job skill, such software can also help students learn engineering concepts. A current limitation of CAD programs (even those that are specifically designed for educational purposes) is that they are not capable of providing automated real-time help to students. To encourage CAD programs to build in assistance to students, we used data generated from students using a free, open source CAD software called Aladdin to demonstrate how student data combined with machine learning techniques can predict how well a particular student will perform in a design task. We challenged students to design a house that consumed zero net energy as part of an introductory engineering technology undergraduate course. Using data from 128 students, along with the scikit-learn Python machine learning library, we tested our models using both total counts of design actions and sequences of design actions as inputs. We found that our models using early design sequence actions are particularly valuable for prediction. Our logistic regression model achieved a >60% chance of predicting if a student would succeed in designing a zero net energy house. Our results suggest that it would be feasible for Aladdin to provide useful feedback to students when they are approximately halfway through their design. Further improvements to these models could lead to earlier predictions and thus provide students feedback sooner to enhance their learning.", "published": "2021-08-12T20:24:54Z", "authors": ["Jasmine Singh", "Viranga Perera", "Alejandra J. Magana", "Brittany Newell", "Jin Wei-Kocsis", "Ying Ying Seah", "Greg J. Strimel", "Charles Xie"], "categories": ["cs.LG", "physics.ed-ph"], "doi": "", "_query": "house price prediction machine learning", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2307.07881v2", "title": "Graph Embedded Intuitionistic Fuzzy Random Vector Functional Link Neural Network for Class Imbalance Learning", "abstract": "The domain of machine learning is confronted with a crucial research area known as class imbalance learning, which presents considerable hurdles in precise classification of minority classes. This issue can result in biased models where the majority class takes precedence in the training process, leading to the underrepresentation of the minority class. The random vector functional link (RVFL) network is a widely used and effective learning model for classification due to its good generalization performance and efficiency. However, it suffers when dealing with imbalanced datasets. To overcome this limitation, we propose a novel graph embedded intuitionistic fuzzy RVFL for class imbalance learning (GE-IFRVFL-CIL) model incorporating a weighting mechanism to handle imbalanced datasets. The proposed GE-IFRVFL-CIL model offers plethora of benefits: $(i)$ leveraging graph embedding to preserve the inherent topological structure of the datasets, $(ii)$ employing intuitionistic fuzzy theory to handle uncertainty and imprecision in the data, $(iii)$ and the most important, it tackles class imbalance learning. The amalgamation of a weighting scheme, graph embedding, and intuitionistic fuzzy sets leads to the superior performance of the proposed models on KEEL benchmark imbalanced datasets with and without Gaussian noise. Furthermore, we implemented the proposed GE-IFRVFL-CIL on the ADNI dataset and achieved promising results, demonstrating the model's effectiveness in real-world applications. The proposed GE-IFRVFL-CIL model offers a promising solution to address the class imbalance issue, mitigates the detrimental effect of noise and outliers, and preserves the inherent geometrical structures of the dataset.", "published": "2023-07-15T20:45:45Z", "authors": ["M. A. Ganaie", "M. Sajid", "A. K. Malik", "M. Tanveer"], "categories": ["cs.LG"], "doi": "10.1109/TNNLS.2024.3353531", "_query": "house price prediction machine learning", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2110.09304v3", "title": "Prediction of Occurrence of Extreme Events using Machine Learning", "abstract": "Machine learning models play a vital role in the prediction task in several fields of study. In this work, we utilize the ability of machine learning algorithms to predict the occurrence of extreme events in a nonlinear mechanical system. Extreme events are rare events that occur ubiquitously in nature. We consider four machine learning models, namely Logistic Regression, Support Vector Machine, Random Forest and Multi-Layer Perceptron in our prediction task. We train these four machine learning models using training set data and compute the performance of each model using the test set data. We show that the Multi-Layer Perceptron model performs better among the four models in the prediction of extreme events in the considered system. The persistent behaviour of the considered machine learning models is cross-checked with randomly shuffled training set and test set data.", "published": "2021-10-11T19:34:40Z", "authors": ["J. Meiyazhagan", "S. Sudharsan", "A. Venkatasen", "M. Senthilvelan"], "categories": ["cs.LG", "nlin.CD"], "doi": "", "_query": "house price prediction machine learning", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/1904.05328v1", "title": "PropTech for Proactive Pricing of Houses in Classified Advertisements in the Indian Real Estate Market", "abstract": "Property Technology (PropTech) is the next big thing that is going to disrupt the real estate market. Nowadays, we see applications of Machine Learning (ML) and Artificial Intelligence (AI) in almost all the domains but for a long time the real estate industry was quite slow in adopting data science and machine learning for problem solving and improving their processes. However, things are changing quite fast as we see a lot of adoption of AI and ML in the US and European real estate markets. But the Indian real estate market has to catch-up a lot. This paper proposes a machine learning approach for solving the house price prediction problem in the classified advertisements. This study focuses on the Indian real estate market. We apply advanced machine learning algorithms such as Random forest, Gradient boosting and Artificial neural networks on a real world dataset and compare the performance of these methods. We find that the Random forest method is the best performer in terms of prediction accuracy.", "published": "2019-03-28T03:38:05Z", "authors": ["Sayan Putatunda"], "categories": ["cs.SI"], "doi": "", "_query": "house price prediction machine learning", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2103.04046v6", "title": "Simplicial Complex Representation Learning", "abstract": "Simplicial complexes form an important class of topological spaces that are frequently used in many application areas such as computer-aided design, computer graphics, and simulation. Representation learning on graphs, which are just 1-d simplicial complexes, has witnessed a great attention in recent years. However, there has not been enough effort to extend representation learning to higher dimensional simplicial objects due to the additional complexity these objects hold, especially when it comes to entire-simplicial complex representation learning. In this work, we propose a method for simplicial complex-level representation learning that embeds a simplicial complex to a universal embedding space in a way that complex-to-complex proximity is preserved. Our method uses our novel geometric message passing schemes to learn an entire simplicial complex representation in an end-to-end fashion. We demonstrate the proposed model on publicly available mesh dataset. To the best of our knowledge, this work presents the first method for learning simplicial complex-level representation.", "published": "2021-03-06T06:33:04Z", "authors": ["Mustafa Hajij", "Ghada Zamzmi", "Theodore Papamarkou", "Vasileios Maroulas", "Xuanting Cai"], "categories": ["cs.LG", "cs.CG", "cs.CV", "math.AT", "stat.ML"], "doi": "", "_query": "house price prediction machine learning", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2007.06229v1", "title": "Deep Claim: Payer Response Prediction from Claims Data with Deep Learning", "abstract": "Each year, almost 10% of claims are denied by payers (i.e., health insurance plans). With the cost to recover these denials and underpayments, predicting payer response (likelihood of payment) from claims data with a high degree of accuracy and precision is anticipated to improve healthcare staffs' performance productivity and drive better patient financial experience and satisfaction in the revenue cycle (Barkholz, 2017). However, constructing advanced predictive analytics models has been considered challenging in the last twenty years. That said, we propose a (low-level) context-dependent compact representation of patients' historical claim records by effectively learning complicated dependencies in the (high-level) claim inputs. Built on this new latent representation, we demonstrate that a deep learning-based framework, Deep Claim, can accurately predict various responses from multiple payers using 2,905,026 de-identified claims data from two US health systems. Deep Claim's improvements over carefully chosen baselines in predicting claim denials are most pronounced as 22.21% relative recall gain (at 95% precision) on Health System A, which implies Deep Claim can find 22.21% more denials than the best baseline system.", "published": "2020-07-13T08:05:17Z", "authors": ["Byung-Hak Kim", "Seshadri Sridharan", "Andy Atwal", "Varun Ganapathi"], "categories": ["cs.LG", "cs.CY", "stat.ML"], "doi": "", "_query": "house price prediction machine learning", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2004.02396v1", "title": "A Learning Framework for n-bit Quantized Neural Networks toward FPGAs", "abstract": "The quantized neural network (QNN) is an efficient approach for network compression and can be widely used in the implementation of FPGAs. This paper proposes a novel learning framework for n-bit QNNs, whose weights are constrained to the power of two. To solve the gradient vanishing problem, we propose a reconstructed gradient function for QNNs in back-propagation algorithm that can directly get the real gradient rather than estimating an approximate gradient of the expected loss. We also propose a novel QNN structure named n-BQ-NN, which uses shift operation to replace the multiply operation and is more suitable for the inference on FPGAs. Furthermore, we also design a shift vector processing element (SVPE) array to replace all 16-bit multiplications with SHIFT operations in convolution operation on FPGAs. We also carry out comparable experiments to evaluate our framework. The experimental results show that the quantized models of ResNet, DenseNet and AlexNet through our learning framework can achieve almost the same accuracies with the original full-precision models. Moreover, when using our learning framework to train our n-BQ-NN from scratch, it can achieve state-of-the-art results compared with typical low-precision QNNs. Experiments on Xilinx ZCU102 platform show that our n-BQ-NN with our SVPE can execute 2.9 times faster than with the vector processing element (VPE) in inference. As the SHIFT operation in our SVPE array will not consume Digital Signal Processings (DSPs) resources on FPGAs, the experiments have shown that the use of SVPE array also reduces average energy consumption to 68.7% of the VPE array with 16-bit.", "published": "2020-04-06T04:21:24Z", "authors": ["Jun Chen", "Liang Liu", "Yong Liu", "Xianfang Zeng"], "categories": ["cs.LG", "eess.SP", "stat.ML"], "doi": "10.1109/TNNLS.2020.2980041", "_query": "house price prediction machine learning", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/1806.03286v2", "title": "Regression with Comparisons: Escaping the Curse of Dimensionality with Ordinal Information", "abstract": "In supervised learning, we typically leverage a fully labeled dataset to design methods for function estimation or prediction. In many practical situations, we are able to obtain alternative feedback, possibly at a low cost. A broad goal is to understand the usefulness of, and to design algorithms to exploit, this alternative feedback. In this paper, we consider a semi-supervised regression setting, where we obtain additional ordinal (or comparison) information for the unlabeled samples. We consider ordinal feedback of varying qualities where we have either a perfect ordering of the samples, a noisy ordering of the samples or noisy pairwise comparisons between the samples. We provide a precise quantification of the usefulness of these types of ordinal feedback in both nonparametric and linear regression, showing that in many cases it is possible to accurately estimate an underlying function with a very small labeled set, effectively \\emph{escaping the curse of dimensionality}. We also present lower bounds, that establish fundamental limits for the task and show that our algorithms are optimal in a variety of settings. Finally, we present extensive experiments on new datasets that demonstrate the efficacy and practicality of our algorithms and investigate their robustness to various sources of noise and model misspecification.", "published": "2018-06-08T17:33:43Z", "authors": ["Yichong Xu", "Sivaraman Balakrishnan", "Aarti Singh", "Artur Dubrawski"], "categories": ["stat.ML", "cs.LG"], "doi": "", "_query": "house price prediction machine learning", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/1802.06403v2", "title": "RadialGAN: Leveraging multiple datasets to improve target-specific predictive models using Generative Adversarial Networks", "abstract": "Training complex machine learning models for prediction often requires a large amount of data that is not always readily available. Leveraging these external datasets from related but different sources is therefore an important task if good predictive models are to be built for deployment in settings where data can be rare. In this paper we propose a novel approach to the problem in which we use multiple GAN architectures to learn to translate from one dataset to another, thereby allowing us to effectively enlarge the target dataset, and therefore learn better predictive models than if we simply used the target dataset. We show the utility of such an approach, demonstrating that our method improves the prediction performance on the target domain over using just the target dataset and also show that our framework outperforms several other benchmarks on a collection of real-world medical datasets.", "published": "2018-02-18T16:55:15Z", "authors": ["Jinsung Yoon", "James Jordon", "Mihaela van der Schaar"], "categories": ["cs.LG", "stat.ML"], "doi": "", "_query": "house price prediction machine learning", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2202.03881v3", "title": "Robust Hybrid Learning With Expert Augmentation", "abstract": "Hybrid modelling reduces the misspecification of expert models by combining them with machine learning (ML) components learned from data. Similarly to many ML algorithms, hybrid model performance guarantees are limited to the training distribution. Leveraging the insight that the expert model is usually valid even outside the training domain, we overcome this limitation by introducing a hybrid data augmentation strategy termed \\textit{expert augmentation}. Based on a probabilistic formalization of hybrid modelling, we demonstrate that expert augmentation, which can be incorporated into existing hybrid systems, improves generalization. We empirically validate the expert augmentation on three controlled experiments modelling dynamical systems with ordinary and partial differential equations. Finally, we assess the potential real-world applicability of expert augmentation on a dataset of a real double pendulum.", "published": "2022-02-08T14:11:04Z", "authors": ["Antoine Wehenkel", "Jens Behrmann", "Hsiang Hsu", "Guillermo Sapiro", "Gilles Louppe", "Jörn-Henrik Jacobsen"], "categories": ["cs.LG", "stat.ML"], "doi": "", "_query": "house price prediction machine learning", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2411.06919v1", "title": "Understanding Generalization in Quantum Machine Learning with Margins", "abstract": "Understanding and improving generalization capabilities is crucial for both classical and quantum machine learning (QML). Recent studies have revealed shortcomings in current generalization theories, particularly those relying on uniform bounds, across both classical and quantum settings. In this work, we present a margin-based generalization bound for QML models, providing a more reliable framework for evaluating generalization. Our experimental studies on the quantum phase recognition (QPR) dataset demonstrate that margin-based metrics are strong predictors of generalization performance, outperforming traditional metrics like parameter count. By connecting this margin-based metric to quantum information theory, we demonstrate how to enhance the generalization performance of QML through a classical-quantum hybrid approach when applied to classical data.", "published": "2024-11-11T12:22:18Z", "authors": ["Tak Hur", "Daniel K. Park"], "categories": ["quant-ph", "cs.LG"], "doi": "", "_query": "house price prediction machine learning", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2410.14532v1", "title": "Using Sentiment and Technical Analysis to Predict Bitcoin with Machine Learning", "abstract": "Cryptocurrencies have gained significant attention in recent years due to their decentralized nature and potential for financial innovation. Thus, the ability to accurately predict its price has become a subject of great interest for investors, traders, and researchers. Some works in the literature show how Bitcoin's market sentiment correlates with its price fluctuations in the market. However, papers that consider the sentiment of the market associated with financial Technical Analysis indicators in order to predict Bitcoin's price are still scarce. In this paper, we present a novel approach for predicting Bitcoin price movements by combining the Fear & Greedy Index, a measure of market sentiment, Technical Analysis indicators, and the potential of Machine Learning algorithms. This work represents a preliminary study on the importance of sentiment metrics in cryptocurrency forecasting. Our initial experiments demonstrate promising results considering investment returns, surpassing the Buy & Hold baseline, and offering valuable insights about the combination of indicators of sentiment and market in a cryptocurrency prediction model.", "published": "2024-10-18T15:13:07Z", "authors": ["Arthur Emanuel de Oliveira Carosia"], "categories": ["cs.LG", "cs.CE"], "doi": "", "_query": "house price prediction machine learning", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/1811.01134v1", "title": "A Marauder's Map of Security and Privacy in Machine Learning", "abstract": "There is growing recognition that machine learning (ML) exposes new security and privacy vulnerabilities in software systems, yet the technical community's understanding of the nature and extent of these vulnerabilities remains limited but expanding. In this talk, we explore the threat model space of ML algorithms through the lens of Saltzer and Schroeder's principles for the design of secure computer systems. This characterization of the threat space prompts an investigation of current and future research directions. We structure our discussion around three of these directions, which we believe are likely to lead to significant progress. The first encompasses a spectrum of approaches to verification and admission control, which is a prerequisite to enable fail-safe defaults in machine learning systems. The second seeks to design mechanisms for assembling reliable records of compromise that would help understand the degree to which vulnerabilities are exploited by adversaries, as well as favor psychological acceptability of machine learning applications. The third pursues formal frameworks for security and privacy in machine learning, which we argue should strive to align machine learning goals such as generalization with security and privacy desiderata like robustness or privacy. Key insights resulting from these three directions pursued both in the ML and security communities are identified and the effectiveness of approaches are related to structural elements of ML algorithms and the data used to train them. We conclude by systematizing best practices in our community.", "published": "2018-11-03T00:25:50Z", "authors": ["Nicolas Papernot"], "categories": ["cs.CR"], "doi": "", "_query": "house price prediction machine learning", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2505.09620v1", "title": "The impact of economic policies on housing prices. Approximations and predictions in the UK, the US, France, and Switzerland from the 1980s to today", "abstract": "I show that house prices can be modeled using machine learning (kNN and tree-bagging) and a small dataset composed of macro-economic factors (MEF), including an inflation metric (CPI), US treasury rates (10-yr), Gross Domestic Product (GDP), and portfolio size of central banks (ECB, FED). This set of parameters covers all the parties involved in a transaction (buyer, seller, and financing facility) while ignoring the intrinsic properties of each asset and encompassing local (inflation) and liquidity issues that may impede each transaction composing a market. The model here takes the point of view of a real estate trader who is interested in both the financing and the price of the transaction. Machine Learning allows for the discrimination of two periods within the dataset. Unconventional policies of central banks may have allowed some institutional investors to arbitrage between real estate returns and other bond markets (sovereign and corporate). Finally, to assess the models' relative performances, I performed various sensitivity tests, which tend to constrain the possibilities of each approach for each need. I also show that some models can predict the evolution of prices over the next 4 quarters with uncertainties that outperform existing index uncertainties.", "published": "2025-04-10T12:46:54Z", "authors": ["Nicolas Houlié"], "categories": ["q-fin.ST"], "doi": "", "_query": "house price prediction machine learning", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2112.00423v3", "title": "Controlling Wasserstein Distances by Kernel Norms with Application to Compressive Statistical Learning", "abstract": "Comparing probability distributions is at the crux of many machine learning algorithms. Maximum Mean Discrepancies (MMD) and Wasserstein distances are two classes of distances between probability distributions that have attracted abundant attention in past years. This paper establishes some conditions under which the Wasserstein distance can be controlled by MMD norms. Our work is motivated by the compressive statistical learning (CSL) theory, a general framework for resource-efficient large scale learning in which the training data is summarized in a single vector (called sketch) that captures the information relevant to the considered learning task. Inspired by existing results in CSL, we introduce the Hölder Lower Restricted Isometric Property and show that this property comes with interesting guarantees for compressive statistical learning. Based on the relations between the MMD and the Wasserstein distances, we provide guarantees for compressive statistical learning by introducing and studying the concept of Wasserstein regularity of the learning task, that is when some task-specific metric between probability distributions can be bounded by a Wasserstein distance.", "published": "2021-12-01T11:19:25Z", "authors": ["Titouan Vayer", "Rémi Gribonval"], "categories": ["stat.ML", "cs.LG"], "doi": "", "_query": "house price prediction machine learning", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2404.08592v3", "title": "Scarce Resource Allocations That Rely On Machine Learning Should Be Randomized", "abstract": "Contrary to traditional deterministic notions of algorithmic fairness, this paper argues that fairly allocating scarce resources using machine learning often requires randomness. We address why, when, and how to randomize by proposing stochastic procedures that more adequately account for all of the claims that individuals have to allocations of social goods or opportunities.", "published": "2024-04-12T16:40:29Z", "authors": ["Shomik Jain", "Kathleen Creel", "Ashia Wilson"], "categories": ["cs.CY"], "doi": "", "_query": "house price prediction machine learning", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2502.01654v1", "title": "Predicting concentration levels of air pollutants by transfer learning and recurrent neural network", "abstract": "Air pollution (AP) poses a great threat to human health, and people are paying more attention than ever to its prediction. Accurate prediction of AP helps people to plan for their outdoor activities and aids protecting human health. In this paper, long-short term memory (LSTM) recurrent neural networks (RNNs) have been used to predict the future concentration of air pollutants (APS) in Macau. Additionally, meteorological data and data on the concentration of APS have been utilized. Moreover, in Macau, some air quality monitoring stations (AQMSs) have less observed data in quantity, and, at the same time, some AQMSs recorded less observed data of certain types of APS. Therefore, the transfer learning and pre-trained neural networks have been employed to assist AQMSs with less observed data to build a neural network with high prediction accuracy. The experimental sample covers a period longer than 12-year and includes daily measurements from several APS as well as other more classical meteorological values. Records from five stations, four out of them are AQMSs and the remaining one is an automatic weather station, have been prepared from the aforesaid period and eventually underwent to computational intelligence techniques to build and extract a prediction knowledge-based system. As shown by experimentation, LSTM RNNs initialized with transfer learning methods have higher prediction accuracy; it incurred shorter training time than randomly initialized recurrent neural networks.", "published": "2025-01-30T23:39:19Z", "authors": ["Iat Hang Fong", "Tengyue Li", "Simon Fong", "Raymond K. Wong", "Antonio J. Tallón-Ballesteros"], "categories": ["cs.LG", "cs.NE", "physics.ao-ph"], "doi": "", "_query": "house price prediction machine learning", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2506.12226v1", "title": "Learning Causality for Modern Machine Learning", "abstract": "In the past decades, machine learning with Empirical Risk Minimization (ERM) has demonstrated great capability in learning and exploiting the statistical patterns from data, or even surpassing humans. Despite the success, ERM avoids the modeling of causality the way of understanding and handling changes, which is fundamental to human intelligence. When deploying models beyond the training environment, distribution shifts are everywhere. For example, an autopilot system often needs to deal with new weather conditions that have not been seen during training, An Al-aided drug discovery system needs to predict the biochemical properties of molecules with respect to new viruses such as COVID-19. It renders the problem of Out-of-Distribution (OOD) generalization challenging to conventional machine learning. In this thesis, we investigate how to incorporate and realize the causality for broader tasks in modern machine learning. In particular, we exploit the invariance implied by the principle of independent causal mechanisms (ICM), that is, the causal mechanisms generating the effects from causes do not inform or influence each other. Therefore, the conditional distribution between the target variable given its causes is invariant under distribution shifts. With the causal invariance principle, we first instantiate it to graphs -- a general data structure ubiquitous in many real-world industry and scientific applications, such as financial networks and molecules. Then, we shall see how learning the causality benefits many of the desirable properties of modern machine learning, in terms of (i) OOD generalization capability; (ii) interpretability; and (iii) robustness to adversarial attacks. Realizing the causality in machine learning, on the other hand, raises a dilemma for optimization in conventional machine learning, as it often contradicts the objective of ERM...", "published": "2025-06-13T21:03:49Z", "authors": ["Yongqiang Chen"], "categories": ["cs.LG", "stat.ML"], "doi": "", "_query": "house price prediction machine learning", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2406.05064v3", "title": "Pretraining Decision Transformers with Reward Prediction for In-Context Multi-task Structured Bandit Learning", "abstract": "We study learning to learn for the multi-task structured bandit problem where the goal is to learn a near-optimal algorithm that minimizes cumulative regret. The tasks share a common structure and an algorithm should exploit the shared structure to minimize the cumulative regret for an unseen but related test task. We use a transformer as a decision-making algorithm to learn this shared structure from data collected by a demonstrator on a set of training task instances. Our objective is to devise a training procedure such that the transformer will learn to outperform the demonstrator's learning algorithm on unseen test task instances. Prior work on pretraining decision transformers either requires privileged information like access to optimal arms or cannot outperform the demonstrator. Going beyond these approaches, we introduce a pre-training approach that trains a transformer network to learn a near-optimal policy in-context. This approach leverages the shared structure across tasks, does not require access to optimal actions, and can outperform the demonstrator. We validate these claims over a wide variety of structured bandit problems to show that our proposed solution is general and can quickly identify expected rewards on unseen test tasks to support effective exploration.", "published": "2024-06-07T16:34:31Z", "authors": ["Subhojyoti Mukherjee", "Josiah P. Hanna", "Qiaomin Xie", "Robert Nowak"], "categories": ["cs.LG"], "doi": "", "_query": "house price prediction machine learning", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2007.10306v3", "title": "An Empirical Characterization of Fair Machine Learning For Clinical Risk Prediction", "abstract": "The use of machine learning to guide clinical decision making has the potential to worsen existing health disparities. Several recent works frame the problem as that of algorithmic fairness, a framework that has attracted considerable attention and criticism. However, the appropriateness of this framework is unclear due to both ethical as well as technical considerations, the latter of which include trade-offs between measures of fairness and model performance that are not well-understood for predictive models of clinical outcomes. To inform the ongoing debate, we conduct an empirical study to characterize the impact of penalizing group fairness violations on an array of measures of model performance and group fairness. We repeat the analyses across multiple observational healthcare databases, clinical outcomes, and sensitive attributes. We find that procedures that penalize differences between the distributions of predictions across groups induce nearly-universal degradation of multiple performance metrics within groups. On examining the secondary impact of these procedures, we observe heterogeneity of the effect of these procedures on measures of fairness in calibration and ranking across experimental conditions. Beyond the reported trade-offs, we emphasize that analyses of algorithmic fairness in healthcare lack the contextual grounding and causal awareness necessary to reason about the mechanisms that lead to health disparities, as well as about the potential of algorithmic fairness methods to counteract those mechanisms. In light of these limitations, we encourage researchers building predictive models for clinical use to step outside the algorithmic fairness frame and engage critically with the broader sociotechnical context surrounding the use of machine learning in healthcare.", "published": "2020-07-20T17:46:31Z", "authors": ["Stephen R. Pfohl", "Agata Foryciarz", "Nigam H. Shah"], "categories": ["stat.ML", "cs.CY", "cs.LG", "stat.AP"], "doi": "10.1016/j.jbi.2020.103621", "_query": "house price prediction machine learning", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2411.08374v1", "title": "Federated Graph Learning with Graphless Clients", "abstract": "Federated Graph Learning (FGL) is tasked with training machine learning models, such as Graph Neural Networks (GNNs), for multiple clients, each with its own graph data. Existing methods usually assume that each client has both node features and graph structure of its graph data. In real-world scenarios, however, there exist federated systems where only a part of the clients have such data while other clients (i.e. graphless clients) may only have node features. This naturally leads to a novel problem in FGL: how to jointly train a model over distributed graph data with graphless clients? In this paper, we propose a novel framework FedGLS to tackle the problem in FGL with graphless clients. In FedGLS, we devise a local graph learner on each graphless client which learns the local graph structure with the structure knowledge transferred from other clients. To enable structure knowledge transfer, we design a GNN model and a feature encoder on each client. During local training, the feature encoder retains the local graph structure knowledge together with the GNN model via knowledge distillation, and the structure knowledge is transferred among clients in global update. Our extensive experiments demonstrate the superiority of the proposed FedGLS over five baselines.", "published": "2024-11-13T06:54:05Z", "authors": ["Xingbo Fu", "Song Wang", "Yushun Dong", "Binchi Zhang", "Chen Chen", "Jundong Li"], "categories": ["cs.LG", "cs.DC"], "doi": "", "_query": "house price prediction machine learning", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/1910.01716v4", "title": "False Data Injection Attacks in Internet of Things and Deep Learning enabled Predictive Analytics", "abstract": "Industry 4.0 is the latest industrial revolution primarily merging automation with advanced manufacturing to reduce direct human effort and resources. Predictive maintenance (PdM) is an industry 4.0 solution, which facilitates predicting faults in a component or a system powered by state-of-the-art machine learning (ML) algorithms and the Internet-of-Things (IoT) sensors. However, IoT sensors and deep learning (DL) algorithms, both are known for their vulnerabilities to cyber-attacks. In the context of PdM systems, such attacks can have catastrophic consequences as they are hard to detect due to the nature of the attack. To date, the majority of the published literature focuses on the accuracy of DL enabled PdM systems and often ignores the effect of such attacks. In this paper, we demonstrate the effect of IoT sensor attacks on a PdM system. At first, we use three state-of-the-art DL algorithms, specifically, Long Short-Term Memory (LSTM), Gated Recurrent Unit (GRU), and Convolutional Neural Network (CNN) for predicting the Remaining Useful Life (RUL) of a turbofan engine using NASA's C-MAPSS dataset. The obtained results show that the GRU-based PdM model outperforms some of the recent literature on RUL prediction using the C-MAPSS dataset. Afterward, we model two different types of false data injection attacks (FDIA) on turbofan engine sensor data and evaluate their impact on CNN, LSTM, and GRU-based PdM systems. The obtained results demonstrate that FDI attacks on even a few IoT sensors can strongly defect the RUL prediction. However, the GRU-based PdM model performs better in terms of accuracy and resiliency. Lastly, we perform a study on the GRU-based PdM model using four different GRU networks with different sequence lengths. Our experiments reveal an interesting relationship between the accuracy, resiliency and sequence length for the GRU-based PdM models.", "published": "2019-10-03T20:50:08Z", "authors": ["Gautam Raj Mode", "Prasad Calyam", "Khaza Anuarul Hoque"], "categories": ["cs.LG", "cs.PF", "eess.SP", "stat.ML"], "doi": "", "_query": "house price prediction machine learning", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2402.02230v1", "title": "Federated Learning with Differential Privacy", "abstract": "Federated learning (FL), as a type of distributed machine learning, is capable of significantly preserving client's private data from being shared among different parties. Nevertheless, private information can still be divulged by analyzing uploaded parameter weights from clients. In this report, we showcase our empirical benchmark of the effect of the number of clients and the addition of differential privacy (DP) mechanisms on the performance of the model on different types of data. Our results show that non-i.i.d and small datasets have the highest decrease in performance in a distributed and differentially private setting.", "published": "2024-02-03T18:21:38Z", "authors": ["Adrien Banse", "Jan Kreischer", "Xavier Oliva i Jürgens"], "categories": ["cs.LG", "cs.AI", "cs.DC"], "doi": "", "_query": "house price prediction machine learning", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/1811.07143v1", "title": "High Quality Prediction of Protein Q8 Secondary Structure by Diverse Neural Network Architectures", "abstract": "We tackle the problem of protein secondary structure prediction using a common task framework. This lead to the introduction of multiple ideas for neural architectures based on state of the art building blocks, used in this task for the first time. We take a principled machine learning approach, which provides genuine, unbiased performance measures, correcting longstanding errors in the application domain. We focus on the Q8 resolution of secondary structure, an active area for continuously improving methods. We use an ensemble of strong predictors to achieve accuracy of 70.7% (on the CB513 test set using the CB6133filtered training set). These results are statistically indistinguishable from those of the top existing predictors. In the spirit of reproducible research we make our data, models and code available, aiming to set a gold standard for purity of training and testing sets. Such good practices lower entry barriers to this domain and facilitate reproducible, extendable research.", "published": "2018-11-17T10:47:22Z", "authors": ["Iddo Drori", "Isht Dwivedi", "Pranav Shrestha", "Jeffrey Wan", "Yueqi Wang", "Yunchu He", "Anthony Mazza", "Hugh Krogh-Freeman", "Dimitri Leggas", "Kendal Sandridge", "Linyong Nan", "Kaveri Thakoor", "Chinmay Joshi", "Sonam Goenka", "Chen Keasar", "Itsik Pe'er"], "categories": ["cs.LG", "q-bio.QM", "stat.ML"], "doi": "", "_query": "house price prediction machine learning", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/1902.09154v1", "title": "Deep Bayesian Multi-Target Learning for Recommender Systems", "abstract": "With the increasing variety of services that e-commerce platforms provide, criteria for evaluating their success become also increasingly multi-targeting. This work introduces a multi-target optimization framework with Bayesian modeling of the target events, called Deep Bayesian Multi-Target Learning (DBMTL). In this framework, target events are modeled as forming a Bayesian network, in which directed links are parameterized by hidden layers, and learned from training samples. The structure of Bayesian network is determined by model selection. We applied the framework to Taobao live-streaming recommendation, to simultaneously optimize (and strike a balance) on targets including click-through rate, user stay time in live room, purchasing behaviors and interactions. Significant improvement has been observed for the proposed method over other MTL frameworks and the non-MTL model. Our practice shows that with an integrated causality structure, we can effectively make the learning of a target benefit from other targets, creating significant synergy effects that improve all targets. The neural network construction guided by DBMTL fits in with the general probabilistic model connecting features and multiple targets, taking weaker assumption than the other methods discussed in this paper. This theoretical generality brings about practical generalization power over various targets distributions, including sparse targets and continuous-value ones.", "published": "2019-02-25T09:11:53Z", "authors": ["Qi Wang", "Zhihui Ji", "Huasheng Liu", "Binqiang Zhao"], "categories": ["cs.LG", "cs.IR", "stat.ML"], "doi": "", "_query": "house price prediction machine learning", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2309.06082v1", "title": "A Machine Learning Framework to Deconstruct the Primary Drivers for Electricity Market Price Events", "abstract": "Power grids are moving towards 100% renewable energy source bulk power grids, and the overall dynamics of power system operations and electricity markets are changing. The electricity markets are not only dispatching resources economically but also taking into account various controllable actions like renewable curtailment, transmission congestion mitigation, and energy storage optimization to ensure grid reliability. As a result, price formations in electricity markets have become quite complex. Traditional root cause analysis and statistical approaches are rendered inapplicable to analyze and infer the main drivers behind price formation in the modern grid and markets with variable renewable energy (VRE). In this paper, we propose a machine learning-based analysis framework to deconstruct the primary drivers for price spike events in modern electricity markets with high renewable energy. The outcomes can be utilized for various critical aspects of market design, renewable dispatch and curtailment, operations, and cyber-security applications. The framework can be applied to any ISO or market data; however, in this paper, it is applied to open-source publicly available datasets from California Independent System Operator (CAISO) and ISO New England (ISO-NE).", "published": "2023-09-12T09:24:21Z", "authors": ["Milan Jain", "Xueqing Sun", "Sohom Datta", "Abhishek Somani"], "categories": ["cs.LG", "cs.AI", "cs.CY"], "doi": "", "_query": "house price prediction machine learning", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/1505.06279v2", "title": "The Benefit of Multitask Representation Learning", "abstract": "We discuss a general method to learn data representations from multiple tasks. We provide a justification for this method in both settings of multitask learning and learning-to-learn. The method is illustrated in detail in the special case of linear feature learning. Conditions on the theoretical advantage offered by multitask representation learning over independent task learning are established. In particular, focusing on the important example of half-space learning, we derive the regime in which multitask representation learning is beneficial over independent task learning, as a function of the sample size, the number of tasks and the intrinsic data dimensionality. Other potential applications of our results include multitask feature learning in reproducing kernel Hilbert spaces and multilayer, deep networks.", "published": "2015-05-23T06:37:17Z", "authors": ["Andreas Maurer", "Massimiliano Pontil", "Bernardino Romera-Paredes"], "categories": ["stat.ML", "cs.LG"], "doi": "", "_query": "house price prediction machine learning", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/1801.00681v1", "title": "A novel improved fuzzy support vector machine based stock price trend forecast model", "abstract": "Application of fuzzy support vector machine in stock price forecast. Support vector machine is a new type of machine learning method proposed in 1990s. It can deal with classification and regression problems very successfully. Due to the excellent learning performance of support vector machine, the technology has become a hot research topic in the field of machine learning, and it has been successfully applied in many fields. However, as a new technology, there are many limitations to support vector machines. There is a large amount of fuzzy information in the objective world. If the training of support vector machine contains noise and fuzzy information, the performance of the support vector machine will become very weak and powerless. As the complexity of many factors influence the stock price prediction, the prediction results of traditional support vector machine cannot meet people with precision, this study improved the traditional support vector machine fuzzy prediction algorithm is proposed to improve the new model precision. NASDAQ Stock Market, Standard & Poor's (S&P) Stock market are considered. Novel advanced- fuzzy support vector machine (NA-FSVM) is the proposed methodology.", "published": "2018-01-02T15:17:37Z", "authors": ["Shuheng Wang", "Guohao Li", "Yifan Bao"], "categories": ["stat.ML", "q-fin.ST"], "doi": "", "_query": "house price prediction machine learning", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/1704.06439v4", "title": "Unified Representation of Molecules and Crystals for Machine Learning", "abstract": "Accurate simulations of atomistic systems from first principles are limited by computational cost. In high-throughput settings, machine learning can reduce these costs significantly by accurately interpolating between reference calculations. For this, kernel learning approaches crucially require a representation that accommodates arbitrary atomistic systems. We introduce a many-body tensor representation that is invariant to translations, rotations, and nuclear permutations of same elements, unique, differentiable, can represent molecules and crystals, and is fast to compute. Empirical evidence for competitive energy and force prediction errors is presented for changes in molecular structure, crystal chemistry, and molecular dynamics using kernel regression and symmetric gradient-domain machine learning as models. Applicability is demonstrated for phase diagrams of Pt-group/transition-metal binary systems.", "published": "2017-04-21T08:30:25Z", "authors": ["Haoyan Huo", "Matthias Rupp"], "categories": ["physics.chem-ph", "cond-mat.mtrl-sci"], "doi": "10.1088/2632-2153/aca005", "_query": "house price prediction machine learning", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/1908.00868v2", "title": "Machine Learning as Ecology", "abstract": "Machine learning methods have had spectacular success on numerous problems. Here we show that a prominent class of learning algorithms - including Support Vector Machines (SVMs) -- have a natural interpretation in terms of ecological dynamics. We use these ideas to design new online SVM algorithms that exploit ecological invasions, and benchmark performance using the MNIST dataset. Our work provides a new ecological lens through which we can view statistical learning and opens the possibility of designing ecosystems for machine learning. Supplemental code is found at https://github.com/owenhowell20/EcoSVM.", "published": "2019-08-02T14:08:17Z", "authors": ["Owen Howell", "Cui Wenping", "Robert Marsland", "Pankaj Mehta"], "categories": ["cs.LG", "cond-mat.stat-mech", "stat.ML"], "doi": "10.1088/1751-8121/ab956e", "_query": "house price prediction machine learning", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2010.10070v1", "title": "Real-Time Optimisation for Online Learning in Auctions", "abstract": "In display advertising, a small group of sellers and bidders face each other in up to 10 12 auctions a day. In this context, revenue maximisation via monopoly price learning is a high-value problem for sellers. By nature, these auctions are online and produce a very high frequency stream of data. This results in a computational strain that requires algorithms be real-time. Unfortunately, existing methods inherited from the batch setting suffer O($\\sqrt t$) time/memory complexity at each update, prohibiting their use. In this paper, we provide the first algorithm for online learning of monopoly prices in online auctions whose update is constant in time and memory.", "published": "2020-10-20T06:58:46Z", "authors": ["Lorenzo Croissant", "Marc Abeille", "Clément Calauzènes"], "categories": ["cs.LG", "cs.GT", "stat.ML"], "doi": "", "_query": "house price prediction machine learning", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2409.02668v2", "title": "Introduction to Machine Learning", "abstract": "This book introduces the mathematical foundations and techniques that lead to the development and analysis of many of the algorithms that are used in machine learning. It starts with an introductory chapter that describes notation used throughout the book and serve at a reminder of basic concepts in calculus, linear algebra and probability and also introduces some measure theoretic terminology, which can be used as a reading guide for the sections that use these tools. The introductory chapters also provide background material on matrix analysis and optimization. The latter chapter provides theoretical support to many algorithms that are used in the book, including stochastic gradient descent, proximal methods, etc. After discussing basic concepts for statistical prediction, the book includes an introduction to reproducing kernel theory and Hilbert space techniques, which are used in many places, before addressing the description of various algorithms for supervised statistical learning, including linear methods, support vector machines, decision trees, boosting, or neural networks. The subject then switches to generative methods, starting with a chapter that presents sampling methods and an introduction to the theory of Markov chains. The following chapter describe the theory of graphical models, an introduction to variational methods for models with latent variables, and to deep-learning based generative models. The next chapters focus on unsupervised learning methods, for clustering, factor analysis and manifold learning. The final chapter of the book is theory-oriented and discusses concentration inequalities and generalization bounds.", "published": "2024-09-04T12:51:41Z", "authors": ["Laurent Younes"], "categories": ["stat.ML", "cs.LG"], "doi": "", "_query": "house price prediction machine learning", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/1803.00590v2", "title": "Hierarchical Imitation and Reinforcement Learning", "abstract": "We study how to effectively leverage expert feedback to learn sequential decision-making policies. We focus on problems with sparse rewards and long time horizons, which typically pose significant challenges in reinforcement learning. We propose an algorithmic framework, called hierarchical guidance, that leverages the hierarchical structure of the underlying problem to integrate different modes of expert interaction. Our framework can incorporate different combinations of imitation learning (IL) and reinforcement learning (RL) at different levels, leading to dramatic reductions in both expert effort and cost of exploration. Using long-horizon benchmarks, including Montezuma's Revenge, we demonstrate that our approach can learn significantly faster than hierarchical RL, and be significantly more label-efficient than standard IL. We also theoretically analyze labeling cost for certain instantiations of our framework.", "published": "2018-03-01T19:12:27Z", "authors": ["Hoang M. Le", "Nan Jiang", "Alekh Agarwal", "Miroslav Dudík", "Yisong Yue", "Hal Daumé"], "categories": ["cs.LG", "cs.AI", "stat.ML"], "doi": "", "_query": "house price prediction machine learning", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/1803.08355v2", "title": "Structured Output Learning with Abstention: Application to Accurate Opinion Prediction", "abstract": "Motivated by Supervised Opinion Analysis, we propose a novel framework devoted to Structured Output Learning with Abstention (SOLA). The structure prediction model is able to abstain from predicting some labels in the structured output at a cost chosen by the user in a flexible way. For that purpose, we decompose the problem into the learning of a pair of predictors, one devoted to structured abstention and the other, to structured output prediction. To compare fully labeled training data with predictions potentially containing abstentions, we define a wide class of asymmetric abstention-aware losses. Learning is achieved by surrogate regression in an appropriate feature space while prediction with abstention is performed by solving a new pre-image problem. Thus, SOLA extends recent ideas about Structured Output Prediction via surrogate problems and calibration theory and enjoys statistical guarantees on the resulting excess risk. Instantiated on a hierarchical abstention-aware loss, SOLA is shown to be relevant for fine-grained opinion mining and gives state-of-the-art results on this task. Moreover, the abstention-aware representations can be used to competitively predict user-review ratings based on a sentence-level opinion predictor.", "published": "2018-03-22T13:48:30Z", "authors": ["Alexandre Garcia", "Slim Essid", "Chloé Clavel", "Florence d'Alché-Buc"], "categories": ["cs.LG", "cs.AI", "stat.ML"], "doi": "", "_query": "house price prediction machine learning", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2301.00944v3", "title": "Temporal Difference Learning with Compressed Updates: Error-Feedback meets Reinforcement Learning", "abstract": "In large-scale distributed machine learning, recent works have studied the effects of compressing gradients in stochastic optimization to alleviate the communication bottleneck. These works have collectively revealed that stochastic gradient descent (SGD) is robust to structured perturbations such as quantization, sparsification, and delays. Perhaps surprisingly, despite the surge of interest in multi-agent reinforcement learning, almost nothing is known about the analogous question: Are common reinforcement learning (RL) algorithms also robust to similar perturbations? We investigate this question by studying a variant of the classical temporal difference (TD) learning algorithm with a perturbed update direction, where a general compression operator is used to model the perturbation. Our work makes three important technical contributions. First, we prove that compressed TD algorithms, coupled with an error-feedback mechanism used widely in optimization, exhibit the same non-asymptotic theoretical guarantees as their SGD counterparts. Second, we show that our analysis framework extends seamlessly to nonlinear stochastic approximation schemes that subsume Q-learning. Third, we prove that for multi-agent TD learning, one can achieve linear convergence speedups with respect to the number of agents while communicating just $\\tilde{O}(1)$ bits per iteration. Notably, these are the first finite-time results in RL that account for general compression operators and error-feedback in tandem with linear function approximation and Markovian sampling. Our proofs hinge on the construction of novel Lyapunov functions that capture the dynamics of a memory variable introduced by error-feedback.", "published": "2023-01-03T04:09:38Z", "authors": ["Aritra Mitra", "George J. Pappas", "Hamed Hassani"], "categories": ["cs.LG", "cs.AI", "eess.SY", "math.OC"], "doi": "", "_query": "house price prediction machine learning", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2412.14624v2", "title": "The Diffusive Nature of Housing Prices", "abstract": "We analyze the French housing market prices in the period 1970-2022, with high-resolution data from 2018 to 2022. The spatial correlation of the observed price field exhibits logarithmic decay characteristic of the two-dimensional random diffusion equation -- local interactions may create long-range correlations. We introduce a stylized model, used in the past to model spatial regularities in voting patterns, that accounts for both spatial and temporal correlations with reasonable values of parameters. Our analysis reveals that price shocks are persistent in time and their amplitude is strongly heterogeneous in space. Our study confirms and quantifies the diffusive nature of housing prices that was anticipated long ago (Clapp et al. 1994, Pollakowski et al. 1997), albeit on much restricted, local data sets.", "published": "2024-12-19T08:21:51Z", "authors": ["Antoine-Cyrus Becharat", "Michael Benzaquen", "Jean-Philippe Bouchaud"], "categories": ["cond-mat.stat-mech", "econ.GN", "physics.soc-ph"], "doi": "", "_query": "house price prediction machine learning", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2302.13034v1", "title": "Does Noise Affect Housing Prices? A Case Study in the Urban Area of Thessaloniki", "abstract": "Real estate markets depend on various methods to predict housing prices, including models that have been trained on datasets of residential or commercial properties. Most studies endeavor to create more accurate machine learning models by utilizing data such as basic property characteristics as well as urban features like distances from amenities and road accessibility. Even though environmental factors like noise pollution can potentially affect prices, the research around this topic is limited. One of the reasons is the lack of data. In this paper, we reconstruct and make publicly available a general purpose noise pollution dataset based on published studies conducted by the Hellenic Ministry of Environment and Energy for the city of Thessaloniki, Greece. Then, we train ensemble machine learning models, like XGBoost, on property data for different areas of Thessaloniki to investigate the way noise influences prices through interpretability evaluation techniques. Our study provides a new noise pollution dataset that not only demonstrates the impact noise has on housing prices, but also indicates that the influence of noise on prices significantly varies among different areas of the same city.", "published": "2023-02-25T09:11:57Z", "authors": ["Georgios Kamtziridis", "Dimitris Vrakas", "Grigorios Tsoumakas"], "categories": ["cs.LG"], "doi": "", "_query": "house price prediction machine learning", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2506.10180v1", "title": "A Comparative Study of Machine Learning Techniques for Early Prediction of Diabetes", "abstract": "In many nations, diabetes is becoming a significant health problem, and early identification and control are crucial. Using machine learning algorithms to predict diabetes has yielded encouraging results. Using the Pima Indians Diabetes dataset, this study attempts to evaluate the efficacy of several machine-learning methods for diabetes prediction. The collection includes information on 768 patients, such as their ages, BMIs, and glucose levels. The techniques assessed are Logistic Regression, Decision Tree, Random Forest, k-Nearest Neighbors, Naive Bayes, Support Vector Machine, Gradient Boosting, and Neural Network. The findings indicate that the Neural Network algorithm performed the best, with an accuracy of 78.57 percent, followed by the Random Forest method, with an accuracy of 76.30 percent. The study implies that machine learning algorithms can aid diabetes prediction and be an efficient early detection tool.", "published": "2025-06-11T21:12:28Z", "authors": ["Mowafaq Salem Alzboon", "Mohammad Al-Batah", "Muhyeeddin Alqaraleh", "Ahmad Abuashour", "Ahmad Fuad Bader"], "categories": ["cs.LG", "cs.AI"], "doi": "10.1109/ComNet60156.2023.10366688", "_query": "house price prediction machine learning", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2011.04128v1", "title": "Stable predictions for health related anticausal prediction tasks affected by selection biases: the need to deconfound the test set features", "abstract": "In health related machine learning applications, the training data often corresponds to a non-representative sample from the target populations where the learners will be deployed. In anticausal prediction tasks, selection biases often make the associations between confounders and the outcome variable unstable across different target environments. As a consequence, the predictions from confounded learners are often unstable, and might fail to generalize in shifted test environments. Stable prediction approaches aim to solve this problem by producing predictions that are stable across unknown test environments. These approaches, however, are sometimes applied to the training data alone with the hope that training an unconfounded model will be enough to generate stable predictions in shifted test sets. Here, we show that this is insufficient, and that improved stability can be achieved by deconfounding the test set features as well. We illustrate these observations using both synthetic data and real world data from a mobile health study.", "published": "2020-11-09T01:17:58Z", "authors": ["Elias Chaibub Neto", "Phil Snyder", "Solveig K Sieberts", "Larsson Omberg"], "categories": ["stat.ML", "cs.LG"], "doi": "", "_query": "house price prediction machine learning", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2405.01365v1", "title": "Dynamic Online Ensembles of Basis Expansions", "abstract": "Practical Bayesian learning often requires (1) online inference, (2) dynamic models, and (3) ensembling over multiple different models. Recent advances have shown how to use random feature approximations to achieve scalable, online ensembling of Gaussian processes with desirable theoretical properties and fruitful applications. One key to these methods' success is the inclusion of a random walk on the model parameters, which makes models dynamic. We show that these methods can be generalized easily to any basis expansion model and that using alternative basis expansions, such as Hilbert space Gaussian processes, often results in better performance. To simplify the process of choosing a specific basis expansion, our method's generality also allows the ensembling of several entirely different models, for example, a Gaussian process and polynomial regression. Finally, we propose a novel method to ensemble static and dynamic models together.", "published": "2024-05-02T15:09:59Z", "authors": ["Daniel Waxman", "Petar M. Djurić"], "categories": ["cs.LG", "eess.SP", "stat.ML"], "doi": "", "_query": "house price prediction machine learning", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2502.01813v1", "title": "Machine Learning-Driven Analytical Models for Threshold Displacement Energy Prediction in Materials", "abstract": "Understanding the behavior of materials under irradiation is crucial for the design and safety of nuclear reactors, spacecraft, and other radiation environments. The threshold displacement energy (Ed) is a critical parameter for understanding radiation damage in materials, yet its determination often relies on costly experiments or simulations. This work leverages the machine learning-based Sure Independence Screening and Sparsifying Operator (SISSO) method to derive accurate, analytical models for predicting Ed using fundamental material properties. The models outperform traditional approaches for monoatomic materials, capturing key trends with high accuracy. While predictions for polyatomic materials highlight challenges due to dataset complexity, they reveal opportunities for improvement with expanded data. This study identifies cohesive energy and melting temperature as key factors influencing Ed, offering a robust framework for efficient, data-driven predictions of radiation damage in diverse materials.", "published": "2025-02-03T20:43:13Z", "authors": ["Rosty B. Martinez Duque", "Arman Duha", "Mario F. Borunda"], "categories": ["cond-mat.mtrl-sci", "physics.comp-ph"], "doi": "", "_query": "house price prediction machine learning", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2512.22190v1", "title": "Physics-Informed Machine Learning for Transformer Condition Monitoring -- Part I: Basic Concepts, Neural Networks, and Variants", "abstract": "Power transformers are critical assets in power networks, whose reliability directly impacts grid resilience and stability. Traditional condition monitoring approaches, often rule-based or purely physics-based, struggle with uncertainty, limited data availability, and the complexity of modern operating conditions. Recent advances in machine learning (ML) provide powerful tools to complement and extend these methods, enabling more accurate diagnostics, prognostics, and control. In this two-part series, we examine the role of Neural Networks (NNs) and their extensions in transformer condition monitoring and health management tasks. This first paper introduces the basic concepts of NNs, explores Convolutional Neural Networks (CNNs) for condition monitoring using diverse data modalities, and discusses the integration of NN concepts within the Reinforcement Learning (RL) paradigm for decision-making and control. Finally, perspectives on emerging research directions are also provided.", "published": "2025-12-20T10:10:25Z", "authors": ["Jose I. Aizpurua"], "categories": ["cs.LG"], "doi": "10.23919/ARWtr66130.2025.11261289", "_query": "house price prediction machine learning", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2008.02790v4", "title": "Decoupling Exploration and Exploitation for Meta-Reinforcement Learning without Sacrifices", "abstract": "The goal of meta-reinforcement learning (meta-RL) is to build agents that can quickly learn new tasks by leveraging prior experience on related tasks. Learning a new task often requires both exploring to gather task-relevant information and exploiting this information to solve the task. In principle, optimal exploration and exploitation can be learned end-to-end by simply maximizing task performance. However, such meta-RL approaches struggle with local optima due to a chicken-and-egg problem: learning to explore requires good exploitation to gauge the exploration's utility, but learning to exploit requires information gathered via exploration. Optimizing separate objectives for exploration and exploitation can avoid this problem, but prior meta-RL exploration objectives yield suboptimal policies that gather information irrelevant to the task. We alleviate both concerns by constructing an exploitation objective that automatically identifies task-relevant information and an exploration objective to recover only this information. This avoids local optima in end-to-end training, without sacrificing optimal exploration. Empirically, DREAM substantially outperforms existing approaches on complex meta-RL problems, such as sparse-reward 3D visual navigation. Videos of DREAM: https://ezliu.github.io/dream/", "published": "2020-08-06T17:57:36Z", "authors": ["Evan Zheran Liu", "Aditi Raghunathan", "Percy Liang", "Chelsea Finn"], "categories": ["cs.LG", "cs.AI", "stat.ML"], "doi": "", "_query": "house price prediction machine learning", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2412.04121v1", "title": "DeepFEA: Deep Learning for Prediction of Transient Finite Element Analysis Solutions", "abstract": "Finite Element Analysis (FEA) is a powerful but computationally intensive method for simulating physical phenomena. Recent advancements in machine learning have led to surrogate models capable of accelerating FEA. Yet there are still limitations in developing surrogates of transient FEA models that can simultaneously predict the solutions for both nodes and elements with applicability on both the 2D and 3D domains. Motivated by this research gap, this study proposes DeepFEA, a deep learning-based framework that leverages a multilayer Convolutional Long Short-Term Memory (ConvLSTM) network branching into two parallel convolutional neural networks to predict the solutions for both nodes and elements of FEA models. The proposed network is optimized using a novel adaptive learning algorithm, called Node-Element Loss Optimization (NELO). NELO minimizes the error occurring at both branches of the network enabling the prediction of solutions for transient FEA simulations. The experimental evaluation of DeepFEA is performed on three datasets in the context of structural mechanics, generated to serve as publicly available reference datasets. The results show that DeepFEA can achieve less than 3% normalized mean and root mean squared error for 2D and 3D simulation scenarios, and inference times that are two orders of magnitude faster than FEA. In contrast, relevant state-of-the-art methods face challenges with multi-dimensional output and dynamic input prediction. Furthermore, DeepFEA's robustness was demonstrated in a real-life biomedical scenario, confirming its suitability for accurate and efficient predictions of FEA simulations.", "published": "2024-12-05T12:46:18Z", "authors": ["Georgios Triantafyllou", "Panagiotis G. Kalozoumis", "George Dimas", "Dimitris K. Iakovidis"], "categories": ["cs.LG", "cs.AI", "cs.CE"], "doi": "10.1016/j.eswa.2024.126343", "_query": "house price prediction machine learning", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2201.10523v1", "title": "Interpretability in Convolutional Neural Networks for Building Damage Classification in Satellite Imagery", "abstract": "Natural disasters ravage the world's cities, valleys, and shores on a regular basis. Deploying precise and efficient computational mechanisms for assessing infrastructure damage is essential to channel resources and minimize the loss of life. Using a dataset that includes labeled pre- and post- disaster satellite imagery, we take a machine learning-based remote sensing approach and train multiple convolutional neural networks (CNNs) to assess building damage on a per-building basis. We present a novel methodology of interpretable deep learning that seeks to explicitly investigate the most useful modalities of information in the training data to create an accurate classification model. We also investigate which loss functions best optimize these models. Our findings include that ordinal-cross entropy loss is the most optimal criterion for optimization to use and that including the type of disaster that caused the damage in combination with pre- and post-disaster training data most accurately predicts the level of damage caused. Further, we make progress in the qualitative representation of which parts of the images that the model is using to predict damage levels, through gradient-weighted class activation mapping (Grad-CAM). Our research seeks to computationally contribute to aiding in this ongoing and growing humanitarian crisis, heightened by anthropogenic climate change.", "published": "2022-01-24T16:55:56Z", "authors": ["Thomas Y. Chen"], "categories": ["cs.CV", "cs.AI", "cs.LG", "physics.geo-ph"], "doi": "", "_query": "house price prediction machine learning", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2405.00697v2", "title": "Unveiling Nonlinear Dynamics in Catastrophe Bond Pricing: A Machine Learning Perspective", "abstract": "This paper explores the implications of using machine learning models in the pricing of catastrophe (CAT) bonds. By integrating advanced machine learning techniques, our approach uncovers nonlinear relationships and complex interactions between key risk factors and CAT bond spreads -- dynamics that are often overlooked by traditional linear regression models. Using primary market CAT bond transaction records between January 1999 and March 2021, our findings demonstrate that machine learning models not only enhance the accuracy of CAT bond pricing but also provide a deeper understanding of how various risk factors interact and influence bond prices in a nonlinear way. These findings suggest that investors and issuers can benefit from incorporating machine learning to better capture the intricate interplay between risk factors when pricing CAT bonds. The results also highlight the potential for machine learning models to refine our understanding of asset pricing in markets characterized by complex risk structures.", "published": "2024-04-10T11:20:52Z", "authors": ["Xiaowei Chen", "Hong Li", "Yufan Lu", "Rui Zhou"], "categories": ["q-fin.CP", "cs.LG", "q-fin.PR", "stat.AP"], "doi": "", "_query": "house price prediction machine learning", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2604.27563v1", "title": "Bayesian policy gradient and actor-critic algorithms", "abstract": "Policy gradient methods are reinforcement learning algorithms that adapt a parameterized policy by following a performance gradient estimate. Conventional policy gradient methods use Monte-Carlo techniques to estimate the gradient, which tend to have high variance, requiring many samples and resulting in slow convergence. We first propose a Bayesian framework for policy gradient, based on modeling the policy gradient as a Gaussian process. This reduces the number of samples needed to obtain accurate gradient estimates. Moreover, estimates of the natural gradient and a measure of the uncertainty in the gradient estimates, namely, the gradient covariance, are provided at little extra cost. Since the proposed framework considers system trajectories as its basic observable unit, it does not require the dynamics within trajectories to be of any particular form, and can be extended to partially observable problems. On the downside, it cannot exploit the Markov property when the system is Markovian. To address this, we supplement our Bayesian policy gradient framework with a new actor-critic learning model in which a Bayesian class of non-parametric critics, based on Gaussian process temporal difference learning, is used. Such critics model the action-value function as a Gaussian process, allowing Bayes rule to be used to compute the posterior distribution over action-value functions, conditioned on the observed data. Appropriate choices of the policy parameterization and of the prior covariance (kernel) between action-values yield closed-form expressions for the posterior of the gradient of the expected return with respect to the policy parameters. We perform detailed experimental comparisons of the proposed Bayesian policy gradient and actor-critic algorithms with classic Monte-Carlo based policy gradient methods, on a number of reinforcement learning problems.", "published": "2026-04-30T08:14:45Z", "authors": ["Mohammad Ghavamzadeh", "Yaakov Engel", "Michal Valko"], "categories": ["cs.LG", "stat.ML"], "doi": "", "_query": "house price prediction machine learning", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/1909.05151v1", "title": "Validating Weak-form Market Efficiency in United States Stock Markets with Trend Deterministic Price Data and Machine Learning", "abstract": "The Efficient Market Hypothesis has been a staple of economics research for decades. In particular, weak-form market efficiency -- the notion that past prices cannot predict future performance -- is strongly supported by econometric evidence. In contrast, machine learning algorithms implemented to predict stock price have been touted, to varying degrees, as successful. Moreover, some data scientists boast the ability to garner above-market returns using price data alone. This study endeavors to connect existing econometric research on weak-form efficient markets with data science innovations in algorithmic trading. First, a traditional exploration of stationarity in stock index prices over the past decade is conducted with Augmented Dickey-Fuller and Variance Ratio tests. Then, an algorithmic trading platform is implemented with the use of five machine learning algorithms. Econometric findings identify potential stationarity, hinting technical evaluation may be possible, though algorithmic trading results find little predictive power in any machine learning model, even when using trend-specific metrics. Accounting for transaction costs and risk, no system achieved above-market returns consistently. Our findings reinforce the validity of weak-form market efficiency.", "published": "2019-09-11T15:39:49Z", "authors": ["Samuel Showalter", "Jeffrey Gropp"], "categories": ["q-fin.ST", "cs.CE", "cs.LG", "econ.EM"], "doi": "", "_query": "house price prediction machine learning", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/1907.08908v1", "title": "Techniques for Automated Machine Learning", "abstract": "Automated machine learning (AutoML) aims to find optimal machine learning solutions automatically given a machine learning problem. It could release the burden of data scientists from the multifarious manual tuning process and enable the access of domain experts to the off-the-shelf machine learning solutions without extensive experience. In this paper, we review the current developments of AutoML in terms of three categories, automated feature engineering (AutoFE), automated model and hyperparameter learning (AutoMHL), and automated deep learning (AutoDL). State-of-the-art techniques adopted in the three categories are presented, including Bayesian optimization, reinforcement learning, evolutionary algorithm, and gradient-based approaches. We summarize popular AutoML frameworks and conclude with current open challenges of AutoML.", "published": "2019-07-21T04:03:36Z", "authors": ["Yi-Wei Chen", "Qingquan Song", "Xia Hu"], "categories": ["cs.LG", "cs.AI", "stat.ML"], "doi": "", "_query": "house price prediction machine learning", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/1910.02578v3", "title": "Differential Privacy-enabled Federated Learning for Sensitive Health Data", "abstract": "Leveraging real-world health data for machine learning tasks requires addressing many practical challenges, such as distributed data silos, privacy concerns with creating a centralized database from person-specific sensitive data, resource constraints for transferring and integrating data from multiple sites, and risk of a single point of failure. In this paper, we introduce a federated learning framework that can learn a global model from distributed health data held locally at different sites. The framework offers two levels of privacy protection. First, it does not move or share raw data across sites or with a centralized server during the model training process. Second, it uses a differential privacy mechanism to further protect the model from potential privacy attacks. We perform a comprehensive evaluation of our approach on two healthcare applications, using real-world electronic health data of 1 million patients. We demonstrate the feasibility and effectiveness of the federated learning framework in offering an elevated level of privacy and maintaining utility of the global model.", "published": "2019-10-07T02:10:30Z", "authors": ["Olivia Choudhury", "Aris Gkoulalas-Divanis", "Theodoros Salonidis", "Issa Sylla", "Yoonyoung Park", "Grace Hsu", "Amar Das"], "categories": ["cs.LG", "cs.CR"], "doi": "", "_query": "house price prediction machine learning", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2212.04458v2", "title": "General-Purpose In-Context Learning by Meta-Learning Transformers", "abstract": "Modern machine learning requires system designers to specify aspects of the learning pipeline, such as losses, architectures, and optimizers. Meta-learning, or learning-to-learn, instead aims to learn those aspects, and promises to unlock greater capabilities with less manual effort. One particularly ambitious goal of meta-learning is to train general-purpose in-context learning algorithms from scratch, using only black-box models with minimal inductive bias. Such a model takes in training data, and produces test-set predictions across a wide range of problems, without any explicit definition of an inference model, training loss, or optimization algorithm. In this paper we show that Transformers and other black-box models can be meta-trained to act as general-purpose in-context learners. We characterize transitions between algorithms that generalize, algorithms that memorize, and algorithms that fail to meta-train at all, induced by changes in model size, number of tasks, and meta-optimization. We further show that the capabilities of meta-trained algorithms are bottlenecked by the accessible state size (memory) determining the next prediction, unlike standard models which are thought to be bottlenecked by parameter count. Finally, we propose practical interventions such as biasing the training distribution that improve the meta-training and meta-generalization of general-purpose in-context learning algorithms.", "published": "2022-12-08T18:30:22Z", "authors": ["Louis Kirsch", "James Harrison", "Jascha Sohl-Dickstein", "Luke Metz"], "categories": ["cs.LG", "cs.AI", "cs.NE", "stat.ML"], "doi": "", "_query": "house price prediction machine learning", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2002.10268v1", "title": "Using Machine Learning to predict extreme events in the Hénon map", "abstract": "Machine Learning (ML) inspired algorithms provide a flexible set of tools for analyzing and forecasting chaotic dynamical systems. We here analyze the performance of one algorithm for the prediction of extreme events in the two-dimensional Hénon map at the classical parameters. The task is to determine whether a trajectory will exceed a threshold after a set number of time steps into the future. This task has a geometric interpretation within the dynamics of the Hénon map, which we use to gauge the performance of the neural networks that are used in this work. We analyze the dependence of the success rate of the ML models on the prediction time $T$ , the number of training samples $N_T$ and the size of the network $N_p$. We observe that in order to maintain a certain accuracy, $N_T \\propto exp(2 h T)$ and $N_p \\propto exp(hT)$, where $h$ is the topological entropy. Similar relations between the intrinsic chaotic properties of the dynamics and ML parameters might be observable in other systems as well.", "published": "2020-02-20T15:56:20Z", "authors": ["Martin Lellep", "Jonathan Prexl", "Moritz Linkmann", "Bruno Eckhardt"], "categories": ["cs.LG", "nlin.CD", "stat.ML"], "doi": "10.1063/1.5121844", "_query": "house price prediction machine learning", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2408.05628v1", "title": "Forecasting Day-Ahead Electricity Prices in the Integrated Single Electricity Market: Addressing Volatility with Comparative Machine Learning Methods", "abstract": "This paper undertakes a comprehensive investigation of electricity price forecasting methods, focused on the Irish Integrated Single Electricity Market, particularly on changes during recent periods of high volatility. The primary objective of this research is to evaluate and compare the performance of various forecasting models, ranging from traditional machine learning models to more complex neural networks, as well as the impact of different lengths of training periods. The performance metrics, mean absolute error, root mean square error, and relative mean absolute error, are utilized to assess and compare the accuracy of each model. A comprehensive set of input features was investigated and selected from data recorded between October 2018 and September 2022. The paper demonstrates that the daily EU Natural Gas price is a more useful feature for electricity price forecasting in Ireland than the daily Henry Hub Natural Gas price. This study also shows that the correlation of features to the day-ahead market price has changed in recent years. The price of natural gas on the day and the amount of wind energy on the grid that hour are significantly more important than any other features. More specifically speaking, the input fuel for electricity has become a more important driver of the price of it, than the total generation or demand. In addition, it can be seen that System Non-Synchronous Penetration (SNSP) is highly correlated with the day-ahead market price, and that renewables are pushing down the price of electricity.", "published": "2024-08-10T20:43:21Z", "authors": ["Ben Harkin", "Xueqin Liu"], "categories": ["cs.LG", "cs.AI", "eess.SY"], "doi": "", "_query": "house price prediction machine learning", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2306.12965v2", "title": "Improved Financial Forecasting via Quantum Machine Learning", "abstract": "Quantum algorithms have the potential to enhance machine learning across a variety of domains and applications. In this work, we show how quantum machine learning can be used to improve financial forecasting. First, we use classical and quantum Determinantal Point Processes to enhance Random Forest models for churn prediction, improving precision by almost 6%. Second, we design quantum neural network architectures with orthogonal and compound layers for credit risk assessment, which match classical performance with significantly fewer parameters. Our results demonstrate that leveraging quantum ideas can effectively enhance the performance of machine learning, both today as quantum-inspired classical ML solutions, and even more in the future, with the advent of better quantum hardware.", "published": "2023-05-31T14:57:05Z", "authors": ["Sohum Thakkar", "Skander Kazdaghli", "Natansh Mathur", "Iordanis Kerenidis", "André J. Ferreira-Martins", "Samurai Brito"], "categories": ["q-fin.ST", "cs.LG", "quant-ph"], "doi": "", "_query": "house price prediction machine learning", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2507.02690v1", "title": "RLHGNN: Reinforcement Learning-driven Heterogeneous Graph Neural Network for Next Activity Prediction in Business Processes", "abstract": "Next activity prediction represents a fundamental challenge for optimizing business processes in service-oriented architectures such as microservices environments, distributed enterprise systems, and cloud-native platforms, which enables proactive resource allocation and dynamic service composition. Despite the prevalence of sequence-based methods, these approaches fail to capture non-sequential relationships that arise from parallel executions and conditional dependencies. Even though graph-based approaches address structural preservation, they suffer from homogeneous representations and static structures that apply uniform modeling strategies regardless of individual process complexity characteristics. To address these limitations, we introduce RLHGNN, a novel framework that transforms event logs into heterogeneous process graphs with three distinct edge types grounded in established process mining theory. Our approach creates four flexible graph structures by selectively combining these edges to accommodate different process complexities, and employs reinforcement learning formulated as a Markov Decision Process to automatically determine the optimal graph structure for each specific process instance. RLHGNN then applies heterogeneous graph convolution with relation-specific aggregation strategies to effectively predict the next activity. This adaptive methodology enables precise modeling of both sequential and non-sequential relationships in service interactions. Comprehensive evaluation on six real-world datasets demonstrates that RLHGNN consistently outperforms state-of-the-art approaches. Furthermore, it maintains an inference latency of approximately 1 ms per prediction, representing a highly practical solution suitable for real-time business process monitoring applications. The source code is available at https://github.com/Joker3993/RLHGNN.", "published": "2025-07-03T15:01:08Z", "authors": ["Jiaxing Wang", "Yifeng Yu", "Jiahan Song", "Bin Cao", "Jing Fan", "Ji Zhang"], "categories": ["cs.SE", "cs.LG"], "doi": "", "_query": "house price prediction machine learning", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2606.04279v1", "title": "Derivative Informed Learning of Exchange-Correlation Functionals", "abstract": "Machine-learned (ML) exchange-correlation (XC) functionals aim to replace human-designed density functional approximations by learning directly from reference data, but they still do not consistently outperform traditional $\\mathcal{O}(N^4)$-scaling hybrid functionals. We study a hybrid-distillation setting in which $\\mathcal{O}(N^3)$-scaling ML-XC functionals are trained to reproduce B3LYP/def2-SVP targets. We introduce Derivative Informed XC-Loss (DI-Loss), a loss that incorporates additional information from the reference hybrid functional by supervising first and second derivatives of the energy on the Grassmannian of admissible density matrices. Rather than only matching the self-consistent fixed point, DI-Loss aligns the local first- and second-order response of the learned functional with that of the target functional. Across four evaluated architectures, DI-Loss consistently improves the main energy metrics. Averaged uniformly across architectures, the total-energy MAE decreases by 66% relative to energy and density supervision alone. The density-sensitive mean-field energy metric $E_ρ$ improves from $1.2$ to $0.8$ mEh on average, while dipole and $\\mathcal{L}_2$ density errors do not improve uniformly. We further show that densities from the distilled functionals reduce hybrid-functional SCF iterations by up to 50%. In downstream TDDFT calculations, Hessian supervision improves excited-state predictions, with XCdiff reducing the mean excitation-energy MAE by 19 - 35%.", "published": "2026-06-02T23:07:54Z", "authors": ["Eike S. Eberhard", "Luca A. Thiede", "Abdul Aldossary", "Andreas Burger", "Nicholas Gao", "Vignesh Bhethanabotla", "Alán Aspuru-Guzik", "Stephan Günnemann"], "categories": ["cs.LG", "quant-ph"], "doi": "", "_query": "house price prediction machine learning", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2211.15338v1", "title": "Learning Integrable Dynamics with Action-Angle Networks", "abstract": "Machine learning has become increasingly popular for efficiently modelling the dynamics of complex physical systems, demonstrating a capability to learn effective models for dynamics which ignore redundant degrees of freedom. Learned simulators typically predict the evolution of the system in a step-by-step manner with numerical integration techniques. However, such models often suffer from instability over long roll-outs due to the accumulation of both estimation and integration error at each prediction step. Here, we propose an alternative construction for learned physical simulators that are inspired by the concept of action-angle coordinates from classical mechanics for describing integrable systems. We propose Action-Angle Networks, which learn a nonlinear transformation from input coordinates to the action-angle space, where evolution of the system is linear. Unlike traditional learned simulators, Action-Angle Networks do not employ any higher-order numerical integration methods, making them extremely efficient at modelling the dynamics of integrable physical systems.", "published": "2022-11-24T17:37:20Z", "authors": ["Ameya Daigavane", "Arthur Kosmala", "Miles Cranmer", "Tess Smidt", "Shirley Ho"], "categories": ["cs.LG", "physics.comp-ph"], "doi": "", "_query": "house price prediction machine learning", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2205.07739v5", "title": "The Role of Pseudo-labels in Self-training Linear Classifiers on High-dimensional Gaussian Mixture Data", "abstract": "Self-training (ST) is a simple yet effective semi-supervised learning method. However, why and how ST improves generalization performance by using potentially erroneous pseudo-labels is still not well understood. To deepen the understanding of ST, we derive and analyze a sharp characterization of the behavior of iterative ST when training a linear classifier by minimizing the ridge-regularized convex loss on binary Gaussian mixtures, in the asymptotic limit where input dimension and data size diverge proportionally. The results show that ST improves generalization in different ways depending on the number of iterations. When the number of iterations is small, ST improves generalization performance by fitting the model to relatively reliable pseudo-labels and updating the model parameters by a large amount at each iteration. This suggests that ST works intuitively. On the other hand, with many iterations, ST can gradually improve the direction of the classification plane by updating the model parameters incrementally, using soft labels and small regularization. It is argued that this is because the small update of ST can extract information from the data in an almost noiseless way. However, in the presence of label imbalance, the generalization performance of ST underperforms supervised learning with true labels. To overcome this, two heuristics are proposed to enable ST to achieve nearly compatible performance with supervised learning even with significant label imbalance.", "published": "2022-05-16T15:02:44Z", "authors": ["Takashi Takahashi"], "categories": ["stat.ML", "cond-mat.dis-nn", "cond-mat.stat-mech", "cs.LG", "math.ST"], "doi": "", "_query": "house price prediction machine learning", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2006.15680v1", "title": "Modeling Generalization in Machine Learning: A Methodological and Computational Study", "abstract": "As machine learning becomes more and more available to the general public, theoretical questions are turning into pressing practical issues. Possibly, one of the most relevant concerns is the assessment of our confidence in trusting machine learning predictions. In many real-world cases, it is of utmost importance to estimate the capabilities of a machine learning algorithm to generalize, i.e., to provide accurate predictions on unseen data, depending on the characteristics of the target problem. In this work, we perform a meta-analysis of 109 publicly-available classification data sets, modeling machine learning generalization as a function of a variety of data set characteristics, ranging from number of samples to intrinsic dimensionality, from class-wise feature skewness to $F1$ evaluated on test samples falling outside the convex hull of the training set. Experimental results demonstrate the relevance of using the concept of the convex hull of the training data in assessing machine learning generalization, by emphasizing the difference between interpolated and extrapolated predictions. Besides several predictable correlations, we observe unexpectedly weak associations between the generalization ability of machine learning models and all metrics related to dimensionality, thus challenging the common assumption that the \\textit{curse of dimensionality} might impair generalization in machine learning.", "published": "2020-06-28T19:06:16Z", "authors": ["Pietro Barbiero", "Giovanni Squillero", "Alberto Tonda"], "categories": ["cs.LG", "stat.ML"], "doi": "", "_query": "house price prediction machine learning", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2406.07398v2", "title": "Visual Representation Learning with Stochastic Frame Prediction", "abstract": "Self-supervised learning of image representations by predicting future frames is a promising direction but still remains a challenge. This is because of the under-determined nature of frame prediction; multiple potential futures can arise from a single current frame. To tackle this challenge, in this paper, we revisit the idea of stochastic video generation that learns to capture uncertainty in frame prediction and explore its effectiveness for representation learning. Specifically, we design a framework that trains a stochastic frame prediction model to learn temporal information between frames. Moreover, to learn dense information within each frame, we introduce an auxiliary masked image modeling objective along with a shared decoder architecture. We find this architecture allows for combining both objectives in a synergistic and compute-efficient manner. We demonstrate the effectiveness of our framework on a variety of tasks from video label propagation and vision-based robot learning domains, such as video segmentation, pose tracking, vision-based robotic locomotion, and manipulation tasks. Code is available on the project webpage: https://sites.google.com/view/2024rsp.", "published": "2024-06-11T16:05:15Z", "authors": ["Huiwon Jang", "Dongyoung Kim", "Junsu Kim", "Jinwoo Shin", "Pieter Abbeel", "Younggyo Seo"], "categories": ["cs.CV", "cs.AI", "cs.LG", "cs.RO"], "doi": "", "_query": "house price prediction machine learning", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2005.09052v3", "title": "Underestimation Bias and Underfitting in Machine Learning", "abstract": "Often, what is termed algorithmic bias in machine learning will be due to historic bias in the training data. But sometimes the bias may be introduced (or at least exacerbated) by the algorithm itself. The ways in which algorithms can actually accentuate bias has not received a lot of attention with researchers focusing directly on methods to eliminate bias - no matter the source. In this paper we report on initial research to understand the factors that contribute to bias in classification algorithms. We believe this is important because underestimation bias is inextricably tied to regularization, i.e. measures to address overfitting can accentuate bias.", "published": "2020-05-18T20:01:56Z", "authors": ["Padraig Cunningham", "Sarah Jane Delany"], "categories": ["cs.LG", "stat.ML"], "doi": "10.1007/978-3-030-73959-1_2", "_query": "house price prediction machine learning", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2002.12462v2", "title": "LEEP: A New Measure to Evaluate Transferability of Learned Representations", "abstract": "We introduce a new measure to evaluate the transferability of representations learned by classifiers. Our measure, the Log Expected Empirical Prediction (LEEP), is simple and easy to compute: when given a classifier trained on a source data set, it only requires running the target data set through this classifier once. We analyze the properties of LEEP theoretically and demonstrate its effectiveness empirically. Our analysis shows that LEEP can predict the performance and convergence speed of both transfer and meta-transfer learning methods, even for small or imbalanced data. Moreover, LEEP outperforms recently proposed transferability measures such as negative conditional entropy and H scores. Notably, when transferring from ImageNet to CIFAR100, LEEP can achieve up to 30% improvement compared to the best competing method in terms of the correlations with actual transfer accuracy.", "published": "2020-02-27T22:02:20Z", "authors": ["Cuong V. Nguyen", "Tal Hassner", "Matthias Seeger", "Cedric Archambeau"], "categories": ["cs.LG", "cs.CV", "stat.ML"], "doi": "", "_query": "house price prediction machine learning", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/1902.00183v2", "title": "Learning Action Representations for Reinforcement Learning", "abstract": "Most model-free reinforcement learning methods leverage state representations (embeddings) for generalization, but either ignore structure in the space of actions or assume the structure is provided a priori. We show how a policy can be decomposed into a component that acts in a low-dimensional space of action representations and a component that transforms these representations into actual actions. These representations improve generalization over large, finite action sets by allowing the agent to infer the outcomes of actions similar to actions already taken. We provide an algorithm to both learn and use action representations and provide conditions for its convergence. The efficacy of the proposed method is demonstrated on large-scale real-world problems.", "published": "2019-02-01T04:58:40Z", "authors": ["Yash Chandak", "Georgios Theocharous", "James Kostas", "Scott Jordan", "Philip S. Thomas"], "categories": ["cs.LG", "stat.ML"], "doi": "", "_query": "house price prediction machine learning", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2210.00161v1", "title": "Machine learning phases of active matter", "abstract": "Recent years have witnessed a growing interest in using machine learning to predict and identify phase transitions in various systems. Here we adopt convolutional neural networks (CNNs) to study the phase transitions of Vicsek model, solving the problem that traditional order parameters are insufficiently able to do. Within the large-scale simulations, there are four phases, and we confirm that all the phase transitions between two neighboring phases are first-order. We have successfully classified the phase by using CNNs with a high accuracy and identified the phase transition points, while traditional approaches using various order parameters fail to obtain. These results indicate that the great potential of machine learning approach in understanding the complexities in collective behaviors, and in related complex systems in general.", "published": "2022-10-01T01:41:10Z", "authors": ["Tingting Xue", "Xu Li", "Xiaosong Chen", "Li Chen", "Zhangang Han"], "categories": ["physics.bio-ph", "cond-mat.stat-mech"], "doi": "10.1088/2632-2153/acc007", "_query": "house price prediction machine learning", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/1707.06742v3", "title": "Machine Teaching: A New Paradigm for Building Machine Learning Systems", "abstract": "The current processes for building machine learning systems require practitioners with deep knowledge of machine learning. This significantly limits the number of machine learning systems that can be created and has led to a mismatch between the demand for machine learning systems and the ability for organizations to build them. We believe that in order to meet this growing demand for machine learning systems we must significantly increase the number of individuals that can teach machines. We postulate that we can achieve this goal by making the process of teaching machines easy, fast and above all, universally accessible. While machine learning focuses on creating new algorithms and improving the accuracy of \"learners\", the machine teaching discipline focuses on the efficacy of the \"teachers\". Machine teaching as a discipline is a paradigm shift that follows and extends principles of software engineering and programming languages. We put a strong emphasis on the teacher and the teacher's interaction with data, as well as crucial components such as techniques and design principles of interaction and visualization. In this paper, we present our position regarding the discipline of machine teaching and articulate fundamental machine teaching principles. We also describe how, by decoupling knowledge about machine learning algorithms from the process of teaching, we can accelerate innovation and empower millions of new uses for machine learning models.", "published": "2017-07-21T02:37:04Z", "authors": ["Patrice Y. Simard", "Saleema Amershi", "David M. Chickering", "Alicia Edelman Pelton", "Soroush Ghorashi", "Christopher Meek", "Gonzalo Ramos", "Jina Suh", "Johan Verwey", "Mo Wang", "John Wernsing"], "categories": ["cs.LG", "cs.AI", "cs.HC", "cs.SE", "stat.ML"], "doi": "", "_query": "house price prediction machine learning", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2203.12094v1", "title": "Learning curves for the multi-class teacher-student perceptron", "abstract": "One of the most classical results in high-dimensional learning theory provides a closed-form expression for the generalisation error of binary classification with the single-layer teacher-student perceptron on i.i.d. Gaussian inputs. Both Bayes-optimal estimation and empirical risk minimisation (ERM) were extensively analysed for this setting. At the same time, a considerable part of modern machine learning practice concerns multi-class classification. Yet, an analogous analysis for the corresponding multi-class teacher-student perceptron was missing. In this manuscript we fill this gap by deriving and evaluating asymptotic expressions for both the Bayes-optimal and ERM generalisation errors in the high-dimensional regime. For Gaussian teacher weights, we investigate the performance of ERM with both cross-entropy and square losses, and explore the role of ridge regularisation in approaching Bayes-optimality. In particular, we observe that regularised cross-entropy minimisation yields close-to-optimal accuracy. Instead, for a binary teacher we show that a first-order phase transition arises in the Bayes-optimal performance.", "published": "2022-03-22T23:16:36Z", "authors": ["Elisabetta Cornacchia", "Francesca Mignacco", "Rodrigo Veiga", "Cédric Gerbelot", "Bruno Loureiro", "Lenka Zdeborová"], "categories": ["stat.ML", "cond-mat.dis-nn", "cs.LG"], "doi": "10.1088/2632-2153/acb428", "_query": "house price prediction machine learning", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2409.03697v1", "title": "Classification and Prediction of Heart Diseases using Machine Learning Algorithms", "abstract": "Heart disease is a serious worldwide health issue because it claims the lives of many people who might have been treated if the disease had been identified earlier. The leading cause of death in the world is cardiovascular disease, usually referred to as heart disease. Creating reliable, effective, and precise predictions for these diseases is one of the biggest issues facing the medical world today. Although there are tools for predicting heart diseases, they are either expensive or challenging to apply for determining a patient's risk. The best classifier for foretelling and spotting heart disease was the aim of this research. This experiment examined a range of machine learning approaches, including Logistic Regression, K-Nearest Neighbor, Support Vector Machine, and Artificial Neural Networks, to determine which machine learning algorithm was most effective at predicting heart diseases. One of the most often utilized data sets for this purpose, the UCI heart disease repository provided the data set for this study. The K-Nearest Neighbor technique was shown to be the most effective machine learning algorithm for determining whether a patient has heart disease. It will be beneficial to conduct further studies on the application of additional machine learning algorithms for heart disease prediction.", "published": "2024-09-05T16:52:20Z", "authors": ["Akua Sekyiwaa Osei-Nkwantabisa", "Redeemer Ntumy"], "categories": ["cs.LG"], "doi": "10.48550/arXiv.2409.03697", "_query": "house price prediction machine learning", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2208.08300v1", "title": "Transformer-Based Deep Learning Model for Stock Price Prediction: A Case Study on Bangladesh Stock Market", "abstract": "In modern capital market the price of a stock is often considered to be highly volatile and unpredictable because of various social, financial, political and other dynamic factors. With calculated and thoughtful investment, stock market can ensure a handsome profit with minimal capital investment, while incorrect prediction can easily bring catastrophic financial loss to the investors. This paper introduces the application of a recently introduced machine learning model - the Transformer model, to predict the future price of stocks of Dhaka Stock Exchange (DSE), the leading stock exchange in Bangladesh. The transformer model has been widely leveraged for natural language processing and computer vision tasks, but, to the best of our knowledge, has never been used for stock price prediction task at DSE. Recently the introduction of time2vec encoding to represent the time series features has made it possible to employ the transformer model for the stock price prediction. This paper concentrates on the application of transformer-based model to predict the price movement of eight specific stocks listed in DSE based on their historical daily and weekly data. Our experiments demonstrate promising results and acceptable root mean squared error on most of the stocks.", "published": "2022-08-17T14:03:28Z", "authors": ["Tashreef Muhammad", "Anika Bintee Aftab", "Md. Mainul Ahsan", "Maishameem Meherin Muhu", "Muhammad Ibrahim", "Shahidul Islam Khan", "Mohammad Shafiul Alam"], "categories": ["q-fin.ST", "cs.LG"], "doi": "10.1142/S146902682350013X", "_query": "house price prediction machine learning", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2304.09761v1", "title": "An innovative Deep Learning Based Approach for Accurate Agricultural Crop Price Prediction", "abstract": "Accurate prediction of agricultural crop prices is a crucial input for decision-making by various stakeholders in agriculture: farmers, consumers, retailers, wholesalers, and the Government. These decisions have significant implications including, most importantly, the economic well-being of the farmers. In this paper, our objective is to accurately predict crop prices using historical price information, climate conditions, soil type, location, and other key determinants of crop prices. This is a technically challenging problem, which has been attempted before. In this paper, we propose an innovative deep learning based approach to achieve increased accuracy in price prediction. The proposed approach uses graph neural networks (GNNs) in conjunction with a standard convolutional neural network (CNN) model to exploit geospatial dependencies in prices. Our approach works well with noisy legacy data and produces a performance that is at least 20% better than the results available in the literature. We are able to predict prices up to 30 days ahead. We choose two vegetables, potato (stable price behavior) and tomato (volatile price behavior) and work with noisy public data available from Indian agricultural markets.", "published": "2023-04-15T10:54:57Z", "authors": ["Mayank Ratan Bhardwaj", "Jaydeep Pawar", "Abhijnya Bhat", " Deepanshu", "Inavamsi Enaganti", "Kartik Sagar", "Y. Narahari"], "categories": ["cs.LG", "cs.AI", "q-fin.ST"], "doi": "", "_query": "house price prediction machine learning", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2604.25271v1", "title": "Online learning with Erdős-Rényi side-observation graphs", "abstract": "We consider adversarial multi-armed bandit problems where the learner is allowed to observe losses of a number of arms beside the arm that it actually chose. We study the case where all non-chosen arms reveal their loss with a fixed but unknown probability $r$, independently of each other and the action of the learner. We propose two algorithms that work for different ranges of $r$. We show that after $T$ rounds in a bandit problem with $N$ arms, the expected regret of our first algorithm is $O(\\sqrt{(T /r) \\log N })$ whenever $r\\ge(\\log T)/(2N)$, while our second algorithm achieves a regret of $O(\\sqrt{(T/r) \\log (N+T)})$ for smaller values of $r$. We also give a quick estimation procedure that decides the range of~$r$. All our bounds are within logarithmic factors of the best achievable performance of any algorithm that is even allowed to know~$r$.", "published": "2026-04-28T06:29:13Z", "authors": ["Tomáš Kocák", "Gergely Neu", "Michal Valko"], "categories": ["stat.ML", "cs.LG"], "doi": "", "_query": "house price prediction machine learning", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/1910.04879v1", "title": "Predicting Auction Price of Vehicle License Plate with Deep Residual Learning", "abstract": "Due to superstition, license plates with desirable combinations of characters are highly sought after in China, fetching prices that can reach into the millions in government-held auctions. Despite the high stakes involved, there has been essentially no attempt to provide price estimates for license plates. We present an end-to-end neural network model that simultaneously predict the auction price, gives the distribution of prices and produces latent feature vectors. While both types of neural network architectures we consider outperform simpler machine learning methods, convolutional networks outperform recurrent networks for comparable training time or model complexity. The resulting model powers our online price estimator and search engine.", "published": "2019-10-08T16:38:06Z", "authors": ["Vinci Chow"], "categories": ["cs.CV", "cs.LG", "econ.GN"], "doi": "10.1007/978-3-030-26142-9_16", "_query": "house price prediction machine learning", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2310.11011v2", "title": "From Identifiable Causal Representations to Controllable Counterfactual Generation: A Survey on Causal Generative Modeling", "abstract": "Deep generative models have shown tremendous capability in data density estimation and data generation from finite samples. While these models have shown impressive performance by learning correlations among features in the data, some fundamental shortcomings are their lack of explainability, tendency to induce spurious correlations, and poor out-of-distribution extrapolation. To remedy such challenges, recent work has proposed a shift toward causal generative models. Causal models offer several beneficial properties to deep generative models, such as distribution shift robustness, fairness, and interpretability. Structural causal models (SCMs) describe data-generating processes and model complex causal relationships and mechanisms among variables in a system. Thus, SCMs can naturally be combined with deep generative models. We provide a technical survey on causal generative modeling categorized into causal representation learning and controllable counterfactual generation methods. We focus on fundamental theory, methodology, drawbacks, datasets, and metrics. Then, we cover applications of causal generative models in fairness, privacy, out-of-distribution generalization, precision medicine, and biological sciences. Lastly, we discuss open problems and fruitful research directions for future work in the field.", "published": "2023-10-17T05:45:32Z", "authors": ["Aneesh Komanduri", "Xintao Wu", "Yongkai Wu", "Feng Chen"], "categories": ["cs.LG", "cs.AI", "stat.ML"], "doi": "", "_query": "house price prediction machine learning", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/1601.04126v1", "title": "Engineering Safety in Machine Learning", "abstract": "Machine learning algorithms are increasingly influencing our decisions and interacting with us in all parts of our daily lives. Therefore, just like for power plants, highways, and myriad other engineered sociotechnical systems, we must consider the safety of systems involving machine learning. In this paper, we first discuss the definition of safety in terms of risk, epistemic uncertainty, and the harm incurred by unwanted outcomes. Then we examine dimensions, such as the choice of cost function and the appropriateness of minimizing the empirical average training cost, along which certain real-world applications may not be completely amenable to the foundational principle of modern statistical machine learning: empirical risk minimization. In particular, we note an emerging dichotomy of applications: ones in which safety is important and risk minimization is not the complete story (we name these Type A applications), and ones in which safety is not so critical and risk minimization is sufficient (we name these Type B applications). Finally, we discuss how four different strategies for achieving safety in engineering (inherently safe design, safety reserves, safe fail, and procedural safeguards) can be mapped to the machine learning context through interpretability and causality of predictive models, objectives beyond expected prediction accuracy, human involvement for labeling difficult or rare examples, and user experience design of software.", "published": "2016-01-16T05:46:57Z", "authors": ["Kush R. Varshney"], "categories": ["stat.ML", "cs.AI", "cs.CY", "cs.LG"], "doi": "", "_query": "house price prediction machine learning", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2504.03793v1", "title": "Outlook Towards Deployable Continual Learning for Particle Accelerators", "abstract": "Particle Accelerators are high power complex machines. To ensure uninterrupted operation of these machines, thousands of pieces of equipment need to be synchronized, which requires addressing many challenges including design, optimization and control, anomaly detection and machine protection. With recent advancements, Machine Learning (ML) holds promise to assist in more advance prognostics, optimization, and control. While ML based solutions have been developed for several applications in particle accelerators, only few have reached deployment and even fewer to long term usage, due to particle accelerator data distribution drifts caused by changes in both measurable and non-measurable parameters. In this paper, we identify some of the key areas within particle accelerators where continual learning can allow maintenance of ML model performance with distribution drifts. Particularly, we first discuss existing applications of ML in particle accelerators, and their limitations due to distribution drift. Next, we review existing continual learning techniques and investigate their potential applications to address data distribution drifts in accelerators. By identifying the opportunities and challenges in applying continual learning, this paper seeks to open up the new field and inspire more research efforts towards deployable continual learning for particle accelerators.", "published": "2025-04-04T03:34:39Z", "authors": ["Kishansingh Rajput", "Sen Lin", "Auralee Edelen", "Willem Blokland", "Malachi Schram"], "categories": ["cs.LG", "cs.AI"], "doi": "", "_query": "house price prediction machine learning", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2409.10203v1", "title": "Efficient Milling Quality Prediction with Explainable Machine Learning", "abstract": "This paper presents an explainable machine learning (ML) approach for predicting surface roughness in milling. Utilizing a dataset from milling aluminum alloy 2017A, the study employs random forest regression models and feature importance techniques. The key contributions include developing ML models that accurately predict various roughness values and identifying redundant sensors, particularly those for measuring normal cutting force. Our experiments show that removing certain sensors can reduce costs without sacrificing predictive accuracy, highlighting the potential of explainable machine learning to improve cost-effectiveness in machining.", "published": "2024-09-16T11:52:17Z", "authors": ["Dennis Gross", "Helge Spieker", "Arnaud Gotlieb", "Ricardo Knoblauch", "Mohamed Elmansori"], "categories": ["cs.LG"], "doi": "", "_query": "house price prediction machine learning", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2506.11501v1", "title": "Diabetes Prediction and Management Using Machine Learning Approaches", "abstract": "Diabetes has emerged as a significant global health issue, especially with the increasing number of cases in many countries. This trend Underlines the need for a greater emphasis on early detection and proactive management to avert or mitigate the severe health complications of this disease. Over recent years, machine learning algorithms have shown promising potential in predicting diabetes risk and are beneficial for practitioners. Objective: This study highlights the prediction capabilities of statistical and non-statistical machine learning methods over Diabetes risk classification in 768 samples from the Pima Indians Diabetes Database. It consists of the significant demographic and clinical features of age, body mass index (BMI) and blood glucose levels that greatly depend on the vulnerability against Diabetes. The experimentation assesses the various types of machine learning algorithms in terms of accuracy and effectiveness regarding diabetes prediction. These algorithms include Logistic Regression, Decision Tree, Random Forest, K-Nearest Neighbors, Naive Bayes, Support Vector Machine, Gradient Boosting and Neural Network Models. The results show that the Neural Network algorithm gained the highest predictive accuracy with 78,57 %, and then the Random Forest algorithm had the second position with 76,30 % accuracy. These findings show that machine learning techniques are not just highly effective. Still, they also can potentially act as early screening tools in predicting Diabetes within a data-driven fashion with valuable information on who is more likely to get affected. In addition, this study can help to realize the potential of machine learning for timely intervention over the longer term, which is a step towards reducing health outcomes and disease burden attributable to Diabetes on healthcare systems", "published": "2025-06-13T06:58:19Z", "authors": ["Mowafaq Salem Alzboon", "Muhyeeddin Alqaraleh", "Mohammad Subhi Al-Batah"], "categories": ["cs.LG", "cs.AI"], "doi": "10.56294/dm2025545", "_query": "house price prediction machine learning", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/1803.10311v2", "title": "How Developers Iterate on Machine Learning Workflows -- A Survey of the Applied Machine Learning Literature", "abstract": "Machine learning workflow development is anecdotally regarded to be an iterative process of trial-and-error with humans-in-the-loop. However, we are not aware of quantitative evidence corroborating this popular belief. A quantitative characterization of iteration can serve as a benchmark for machine learning workflow development in practice, and can aid the development of human-in-the-loop machine learning systems. To this end, we conduct a small-scale survey of the applied machine learning literature from five distinct application domains. We collect and distill statistics on the role of iteration within machine learning workflow development, and report preliminary trends and insights from our investigation, as a starting point towards this benchmark. Based on our findings, we finally describe desiderata for effective and versatile human-in-the-loop machine learning systems that can cater to users in diverse domains.", "published": "2018-03-27T20:38:05Z", "authors": ["Doris Xin", "Litian Ma", "Shuchen Song", "Aditya Parameswaran"], "categories": ["cs.LG", "cs.DB", "cs.HC", "stat.ML"], "doi": "", "_query": "house price prediction machine learning", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2309.16014v3", "title": "Graph-level Representation Learning with Joint-Embedding Predictive Architectures", "abstract": "Joint-Embedding Predictive Architectures (JEPAs) have recently emerged as a novel and powerful technique for self-supervised representation learning. They aim to learn an energy-based model by predicting the latent representation of a target signal y from the latent representation of a context signal x. JEPAs bypass the need for negative and positive samples, traditionally required by contrastive learning while avoiding the overfitting issues associated with generative pretraining. In this paper, we show that graph-level representations can be effectively modeled using this paradigm by proposing a Graph Joint-Embedding Predictive Architecture (Graph-JEPA). In particular, we employ masked modeling and focus on predicting the latent representations of masked subgraphs starting from the latent representation of a context subgraph. To endow the representations with the implicit hierarchy that is often present in graph-level concepts, we devise an alternative prediction objective that consists of predicting the coordinates of the encoded subgraphs on the unit hyperbola in the 2D plane. Through multiple experimental evaluations, we show that Graph-JEPA can learn highly semantic and expressive representations, as shown by the downstream performance in graph classification, regression, and distinguishing non-isomorphic graphs. The code is available at https://github.com/geriskenderi/graph-jepa.", "published": "2023-09-27T20:42:02Z", "authors": ["Geri Skenderi", "Hang Li", "Jiliang Tang", "Marco Cristani"], "categories": ["cs.LG"], "doi": "", "_query": "house price prediction machine learning", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/1611.07567v1", "title": "Feature Importance Measure for Non-linear Learning Algorithms", "abstract": "Complex problems may require sophisticated, non-linear learning methods such as kernel machines or deep neural networks to achieve state of the art prediction accuracies. However, high prediction accuracies are not the only objective to consider when solving problems using machine learning. Instead, particular scientific applications require some explanation of the learned prediction function. Unfortunately, most methods do not come with out of the box straight forward interpretation. Even linear prediction functions are not straight forward to explain if features exhibit complex correlation structure. In this paper, we propose the Measure of Feature Importance (MFI). MFI is general and can be applied to any arbitrary learning machine (including kernel machines and deep learning). MFI is intrinsically non-linear and can detect features that by itself are inconspicuous and only impact the prediction function through their interaction with other features. Lastly, MFI can be used for both --- model-based feature importance and instance-based feature importance (i.e, measuring the importance of a feature for a particular data point).", "published": "2016-11-22T22:36:31Z", "authors": ["Marina M. -C. Vidovic", "Nico Görnitz", "Klaus-Robert Müller", "Marius Kloft"], "categories": ["cs.AI", "cs.LG", "stat.ML"], "doi": "", "_query": "house price prediction machine learning", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2403.14385v2", "title": "Estimating Causal Effects with Double Machine Learning -- A Method Evaluation", "abstract": "The estimation of causal effects with observational data continues to be a very active research area. In recent years, researchers have developed new frameworks which use machine learning to relax classical assumptions necessary for the estimation of causal effects. In this paper, we review one of the most prominent methods - \"double/debiased machine learning\" (DML) - and empirically evaluate it by comparing its performance on simulated data relative to more traditional statistical methods, before applying it to real-world data. Our findings indicate that the application of a suitably flexible machine learning algorithm within DML improves the adjustment for various nonlinear confounding relationships. This advantage enables a departure from traditional functional form assumptions typically necessary in causal effect estimation. However, we demonstrate that the method continues to critically depend on standard assumptions about causal structure and identification. When estimating the effects of air pollution on housing prices in our application, we find that DML estimates are consistently larger than estimates of less flexible methods. From our overall results, we provide actionable recommendations for specific choices researchers must make when applying DML in practice.", "published": "2024-03-21T13:21:33Z", "authors": ["Jonathan Fuhr", "Philipp Berens", "Dominik Papies"], "categories": ["stat.ML", "cs.LG", "econ.EM", "stat.ME"], "doi": "", "_query": "house price prediction machine learning", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/1910.08842v1", "title": "Machine Learning for AC Optimal Power Flow", "abstract": "We explore machine learning methods for AC Optimal Powerflow (ACOPF) - the task of optimizing power generation in a transmission network according while respecting physical and engineering constraints. We present two formulations of ACOPF as a machine learning problem: 1) an end-to-end prediction task where we directly predict the optimal generator settings, and 2) a constraint prediction task where we predict the set of active constraints in the optimal solution. We validate these approaches on two benchmark grids.", "published": "2019-10-19T20:47:13Z", "authors": ["Neel Guha", "Zhecheng Wang", "Matt Wytock", "Arun Majumdar"], "categories": ["cs.LG", "eess.SP", "stat.ML"], "doi": "", "_query": "house price prediction machine learning", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/1806.05250v1", "title": "What About Applied Fairness?", "abstract": "Machine learning practitioners are often ambivalent about the ethical aspects of their products. We believe anything that gets us from that current state to one in which our systems are achieving some degree of fairness is an improvement that should be welcomed. This is true even when that progress does not get us 100% of the way to the goal of \"complete\" fairness or perfectly align with our personal belief on which measure of fairness is used. Some measure of fairness being built would still put us in a better position than the status quo. Impediments to getting fairness and ethical concerns applied in real applications, whether they are abstruse philosophical debates or technical overhead such as the introduction of ever more hyper-parameters, should be avoided. In this paper we further elaborate on our argument for this viewpoint and its importance.", "published": "2018-06-13T20:15:28Z", "authors": ["Jared Sylvester", "Edward Raff"], "categories": ["cs.AI", "cs.LG", "stat.ML"], "doi": "", "_query": "house price prediction machine learning", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/1812.03472v1", "title": "Theory of Curriculum Learning, with Convex Loss Functions", "abstract": "Curriculum Learning - the idea of teaching by gradually exposing the learner to examples in a meaningful order, from easy to hard, has been investigated in the context of machine learning long ago. Although methods based on this concept have been empirically shown to improve performance of several learning algorithms, no theoretical analysis has been provided even for simple cases. To address this shortfall, we start by formulating an ideal definition of difficulty score - the loss of the optimal hypothesis at a given datapoint. We analyze the possible contribution of curriculum learning based on this score in two convex problems - linear regression, and binary classification by hinge loss minimization. We show that in both cases, the expected convergence rate decreases monotonically with the ideal difficulty score, in accordance with earlier empirical results. We also prove that when the ideal difficulty score is fixed, the convergence rate is monotonically increasing with respect to the loss of the current hypothesis at each point. We discuss how these results bring to term two apparently contradicting heuristics: curriculum learning on the one hand, and hard data mining on the other.", "published": "2018-12-09T12:35:11Z", "authors": ["Daphna Weinshall", "Dan Amir"], "categories": ["cs.LG", "stat.ML"], "doi": "", "_query": "house price prediction machine learning", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2508.20207v2", "title": "Operator learning meets inverse problems: A probabilistic perspective", "abstract": "Operator learning offers a robust framework for approximating mappings between infinite-dimensional function spaces. It has also become a powerful tool for solving inverse problems in the computational sciences. This chapter surveys methodological and theoretical developments at the intersection of operator learning and inverse problems. It begins by summarizing the probabilistic and deterministic approaches to inverse problems, and pays special attention to emerging measure-centric formulations that treat observed data or unknown parameters as probability distributions. The discussion then turns to operator learning by covering essential components such as data generation, loss functions, and widely used architectures for representing function-to-function maps. The core of the chapter centers on the end-to-end inverse operator learning paradigm, which aims to directly map observed data to the solution of the inverse problem without requiring explicit knowledge of the forward map. It highlights the unique challenge that noise plays in this data-driven inversion setting, presents structure-aware architectures for both point predictions and posterior estimates, and surveys relevant theory for linear and nonlinear inverse problems. The chapter also discusses the estimation of priors and regularizers, where operator learning is used more selectively within classical inversion algorithms.", "published": "2025-08-27T18:34:40Z", "authors": ["Nicholas H. Nelsen", "Yunan Yang"], "categories": ["math.NA", "cs.LG", "math.ST"], "doi": "10.1016/bs.hna.2026.03.010", "_query": "house price prediction machine learning", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2208.03520v1", "title": "Recurrent networks, hidden states and beliefs in partially observable environments", "abstract": "Reinforcement learning aims to learn optimal policies from interaction with environments whose dynamics are unknown. Many methods rely on the approximation of a value function to derive near-optimal policies. In partially observable environments, these functions depend on the complete sequence of observations and past actions, called the history. In this work, we show empirically that recurrent neural networks trained to approximate such value functions internally filter the posterior probability distribution of the current state given the history, called the belief. More precisely, we show that, as a recurrent neural network learns the Q-function, its hidden states become more and more correlated with the beliefs of state variables that are relevant to optimal control. This correlation is measured through their mutual information. In addition, we show that the expected return of an agent increases with the ability of its recurrent architecture to reach a high mutual information between its hidden states and the beliefs. Finally, we show that the mutual information between the hidden states and the beliefs of variables that are irrelevant for optimal control decreases through the learning process. In summary, this work shows that in its hidden states, a recurrent neural network approximating the Q-function of a partially observable environment reproduces a sufficient statistic from the history that is correlated to the relevant part of the belief for taking optimal actions.", "published": "2022-08-06T13:56:16Z", "authors": ["Gaspard Lambrechts", "Adrien Bolland", "Damien Ernst"], "categories": ["cs.LG", "stat.ML"], "doi": "", "_query": "house price prediction machine learning", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2206.01088v2", "title": "Machine Learning-based Lung and Colon Cancer Detection using Deep Feature Extraction and Ensemble Learning", "abstract": "Cancer is a fatal disease caused by a combination of genetic diseases and a variety of biochemical abnormalities. Lung and colon cancer have emerged as two of the leading causes of death and disability in humans. The histopathological detection of such malignancies is usually the most important component in determining the best course of action. Early detection of the ailment on either front considerably decreases the likelihood of mortality. Machine learning and deep learning techniques can be utilized to speed up such cancer detection, allowing researchers to study a large number of patients in a much shorter amount of time and at a lower cost. In this research work, we introduced a hybrid ensemble feature extraction model to efficiently identify lung and colon cancer. It integrates deep feature extraction and ensemble learning with high-performance filtering for cancer image datasets. The model is evaluated on histopathological (LC25000) lung and colon datasets. According to the study findings, our hybrid model can detect lung, colon, and (lung and colon) cancer with accuracy rates of 99.05%, 100%, and 99.30%, respectively. The study's findings show that our proposed strategy outperforms existing models significantly. Thus, these models could be applicable in clinics to support the doctor in the diagnosis of cancers.", "published": "2022-06-02T15:14:41Z", "authors": ["Md. Alamin Talukder", "Md. Manowarul Islam", "Md Ashraf Uddin", "Arnisha Akhter", "Khondokar Fida Hasan", "Mohammad Ali Moni"], "categories": ["eess.IV", "cs.CV", "cs.LG"], "doi": "", "_query": "house price prediction machine learning", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/1911.00108v2", "title": "RankML: a Meta Learning-Based Approach for Pre-Ranking Machine Learning Pipelines", "abstract": "The explosion of digital data has created multiple opportunities for organizations and individuals to leverage machine learning (ML) to transform the way they operate. However, the shortage of experts in the field of machine learning -- data scientists -- is often a setback to the use of ML. In an attempt to alleviate this shortage, multiple approaches for the automation of machine learning have been proposed in recent years. While these approaches are effective, they often require a great deal of time and computing resources. In this study, we propose RankML, a meta-learning based approach for predicting the performance of whole machine learning pipelines. Given a previously-unseen dataset, a performance metric, and a set of candidate pipelines, RankML immediately produces a ranked list of all pipelines based on their predicted performance. Extensive evaluation on 244 datasets, both in regression and classification tasks, shows that our approach either outperforms or is comparable to state-of-the-art, computationally heavy approaches while requiring a fraction of the time and computational cost.", "published": "2019-10-31T21:05:24Z", "authors": ["Doron Laadan", "Roman Vainshtein", "Yarden Curiel", "Gilad Katz", "Lior Rokach"], "categories": ["cs.LG", "stat.ML"], "doi": "", "_query": "house price prediction machine learning", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2506.08169v1", "title": "Federated Learning on Stochastic Neural Networks", "abstract": "Federated learning is a machine learning paradigm that leverages edge computing on client devices to optimize models while maintaining user privacy by ensuring that local data remains on the device. However, since all data is collected by clients, federated learning is susceptible to latent noise in local datasets. Factors such as limited measurement capabilities or human errors may introduce inaccuracies in client data. To address this challenge, we propose the use of a stochastic neural network as the local model within the federated learning framework. Stochastic neural networks not only facilitate the estimation of the true underlying states of the data but also enable the quantification of latent noise. We refer to our federated learning approach, which incorporates stochastic neural networks as local models, as Federated stochastic neural networks. We will present numerical experiments demonstrating the performance and effectiveness of our method, particularly in handling non-independent and identically distributed data.", "published": "2025-06-09T19:28:39Z", "authors": ["Jingqiao Tang", "Ryan Bausback", "Feng Bao", "Richard Archibald"], "categories": ["cs.LG", "cs.DC"], "doi": "", "_query": "house price prediction machine learning", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2308.03037v1", "title": "Machine Learning for Infectious Disease Risk Prediction: A Survey", "abstract": "Infectious diseases, either emerging or long-lasting, place numerous people at risk and bring heavy public health burdens worldwide. In the process against infectious diseases, predicting the epidemic risk by modeling the disease transmission plays an essential role in assisting with preventing and controlling disease transmission in a more effective way. In this paper, we systematically describe how machine learning can play an essential role in quantitatively characterizing disease transmission patterns and accurately predicting infectious disease risks. First, we introduce the background and motivation of using machine learning for infectious disease risk prediction. Next, we describe the development and components of various machine learning models for infectious disease risk prediction. Specifically, existing models fall into three categories: Statistical prediction, data-driven machine learning, and epidemiology-inspired machine learning. Subsequently, we discuss challenges encountered when dealing with model inputs, designing task-oriented objectives, and conducting performance evaluation. Finally, we conclude with a discussion of open questions and future directions.", "published": "2023-08-06T06:57:11Z", "authors": ["Mutong Liu", "Yang Liu", "Jiming Liu"], "categories": ["cs.LG"], "doi": "", "_query": "house price prediction machine learning", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2410.08770v1", "title": "Causal machine learning for predicting treatment outcomes", "abstract": "Causal machine learning (ML) offers flexible, data-driven methods for predicting treatment outcomes including efficacy and toxicity, thereby supporting the assessment and safety of drugs. A key benefit of causal ML is that it allows for estimating individualized treatment effects, so that clinical decision-making can be personalized to individual patient profiles. Causal ML can be used in combination with both clinical trial data and real-world data, such as clinical registries and electronic health records, but caution is needed to avoid biased or incorrect predictions. In this Perspective, we discuss the benefits of causal ML (relative to traditional statistical or ML approaches) and outline the key components and steps. Finally, we provide recommendations for the reliable use of causal ML and effective translation into the clinic.", "published": "2024-10-11T12:39:08Z", "authors": ["Stefan Feuerriegel", "Dennis Frauen", "Valentyn Melnychuk", "Jonas Schweisthal", "Konstantin Hess", "Alicia Curth", "Stefan Bauer", "Niki Kilbertus", "Isaac S. Kohane", "Mihaela van der Schaar"], "categories": ["cs.LG", "stat.AP", "stat.ML"], "doi": "10.1038/s41591-024-02902-1", "_query": "house price prediction machine learning", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2103.15933v1", "title": "Learning Under Adversarial and Interventional Shifts", "abstract": "Machine learning models are often trained on data from one distribution and deployed on others. So it becomes important to design models that are robust to distribution shifts. Most of the existing work focuses on optimizing for either adversarial shifts or interventional shifts. Adversarial methods lack expressivity in representing plausible shifts as they consider shifts to joint distributions in the data. Interventional methods allow more expressivity but provide robustness to unbounded shifts, resulting in overly conservative models. In this work, we combine the complementary strengths of the two approaches and propose a new formulation, RISe, for designing robust models against a set of distribution shifts that are at the intersection of adversarial and interventional shifts. We employ the distributionally robust optimization framework to optimize the resulting objective in both supervised and reinforcement learning settings. Extensive experimentation with synthetic and real world datasets from healthcare demonstrate the efficacy of the proposed approach.", "published": "2021-03-29T20:10:51Z", "authors": ["Harvineet Singh", "Shalmali Joshi", "Finale Doshi-Velez", "Himabindu Lakkaraju"], "categories": ["cs.LG", "stat.ML"], "doi": "", "_query": "house price prediction machine learning", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/1812.09452v1", "title": "The Price of BitCoin: GARCH Evidence from High Frequency Data", "abstract": "This is the first paper that estimates the price determinants of BitCoin in a Generalised Autoregressive Conditional Heteroscedasticity framework using high frequency data. Derived from a theoretical model, we estimate BitCoin transaction demand and speculative demand equations in a GARCH framework using hourly data for the period 2013-2018. In line with the theoretical model, our empirical results confirm that both the BitCoin transaction demand and speculative demand have a statistically significant impact on the BitCoin price formation. The BitCoin price responds negatively to the BitCoin velocity, whereas positive shocks to the BitCoin stock, interest rate and the size of the BitCoin economy exercise an upward pressure on the BitCoin price.", "published": "2018-12-22T05:25:15Z", "authors": ["Pavel Ciaian", "d'Artis Kancs", "Miroslava Rajcaniova"], "categories": ["q-fin.ST", "econ.GN"], "doi": "", "_query": "house price prediction machine learning", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/1404.0879v1", "title": "Utility indifference pricing of derivatives written on industrial loss indexes", "abstract": "We consider the problem of pricing derivatives written on some industrial loss index via utility indifference pricing. The industrial loss index is modelled by a compound Poisson process and the insurer can adjust her portfolio by choosing the risk loading, which in turn determines the demand. We compute the price of a CAT(spread) option written on that index using utility indifference pricing.", "published": "2014-04-03T12:34:01Z", "authors": ["Gunther Leobacher", "Philip Ngare"], "categories": ["q-fin.PR", "q-fin.PM", "q-fin.RM"], "doi": "", "_query": "house price prediction machine learning", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2402.14015v2", "title": "Corrective Machine Unlearning", "abstract": "Machine Learning models increasingly face data integrity challenges due to the use of large-scale training datasets drawn from the Internet. We study what model developers can do if they detect that some data was manipulated or incorrect. Such manipulated data can cause adverse effects including vulnerability to backdoored samples, systemic biases, and reduced accuracy on certain input domains. Realistically, all manipulated training samples cannot be identified, and only a small, representative subset of the affected data can be flagged. We formalize Corrective Machine Unlearning as the problem of mitigating the impact of data affected by unknown manipulations on a trained model, only having identified a subset of the corrupted data. We demonstrate that the problem of corrective unlearning has significantly different requirements from traditional privacy-oriented unlearning. We find most existing unlearning methods, including retraining-from-scratch without the deletion set, require most of the manipulated data to be identified for effective corrective unlearning. However, one approach, Selective Synaptic Dampening, achieves limited success, unlearning adverse effects with just a small portion of the manipulated samples in our setting, which shows encouraging signs for future progress. We hope our work spurs research towards developing better methods for corrective unlearning and offers practitioners a new strategy to handle data integrity challenges arising from web-scale training. Code is available at https://github.com/drimpossible/corrective-unlearning-bench.", "published": "2024-02-21T18:54:37Z", "authors": ["Shashwat Goel", "Ameya Prabhu", "Philip Torr", "Ponnurangam Kumaraguru", "Amartya Sanyal"], "categories": ["cs.LG", "cs.AI", "cs.CR", "cs.CV"], "doi": "", "_query": "house price prediction machine learning", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2112.07893v3", "title": "Graph-based Ensemble Machine Learning for Student Performance Prediction", "abstract": "Student performance prediction is a critical research problem to understand the students' needs, present proper learning opportunities/resources, and develop the teaching quality. However, traditional machine learning methods fail to produce stable and accurate prediction results. In this paper, we propose a graph-based ensemble machine learning method that aims to improve the stability of single machine learning methods via the consensus of multiple methods. To be specific, we leverage both supervised prediction methods and unsupervised clustering methods, build an iterative approach that propagates in a bipartite graph as well as converges to more stable and accurate prediction results. Extensive experiments demonstrate the effectiveness of our proposed method in predicting more accurate student performance. Specifically, our model outperforms the best traditional machine learning algorithms by up to 14.8% in prediction accuracy.", "published": "2021-12-15T05:19:46Z", "authors": ["Yinkai Wang", "Aowei Ding", "Kaiyi Guan", "Shixi Wu", "Yuanqi Du"], "categories": ["cs.LG", "cs.AI", "cs.CY"], "doi": "", "_query": "house price prediction machine learning", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/1806.00037v1", "title": "MULTIGRAIN: Simulating mixtures of multiple dust grains and gas with SPH", "abstract": "We present MULTIGRAIN, an algorithm for simulating multiple phases of small dust grains embedded in a gas, building on our earlier work in simulating two-phase mixtures of gas and dust in SPH (Laibe & Price 2012a,b; Price & Laibe 2015). The MULTIGRAIN method (Hutchison, Price & Laibe 2018) is more accurate than single-phase simulations because the gas experiences a backreaction from each dust phase and communicates this change to the other phases, thereby indirectly coupling the dust phases together. The MULTIGRAIN method is fast, explicit and low storage, requiring only an array of dust fractions and their derivatives defined for each resolution element. We demonstrate the MULTIGRAIN algorithm on test problems related to the settling of dust in the discs of gas around young stars, where solar systems are born. Finally I will discuss possible extensions of the method to incorporate both large and small grains, together with recent improvements in our numerical techniques for gas and dust mixtures. In particular, I will show how the 'overdamping' problem identified by Laibe & Price (2012a) can be solved.", "published": "2018-05-31T18:22:21Z", "authors": ["Daniel J. Price", "Mark Hutchison", "Guillaume Laibe"], "categories": ["astro-ph.IM"], "doi": "", "_query": "house price prediction machine learning", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2301.02033v1", "title": "Physics-informed self-supervised deep learning reconstruction for accelerated first-pass perfusion cardiac MRI", "abstract": "First-pass perfusion cardiac magnetic resonance (FPP-CMR) is becoming an essential non-invasive imaging method for detecting deficits of myocardial blood flow, allowing the assessment of coronary heart disease. Nevertheless, acquisitions suffer from relatively low spatial resolution and limited heart coverage. Compressed sensing (CS) methods have been proposed to accelerate FPP-CMR and achieve higher spatial resolution. However, the long reconstruction times have limited the widespread clinical use of CS in FPP-CMR. Deep learning techniques based on supervised learning have emerged as alternatives for speeding up reconstructions. However, these approaches require fully sampled data for training, which is not possible to obtain, particularly high-resolution FPP-CMR images. Here, we propose a physics-informed self-supervised deep learning FPP-CMR reconstruction approach for accelerating FPP-CMR scans and hence facilitate high spatial resolution imaging. The proposed method provides high-quality FPP-CMR images from 10x undersampled data without using fully sampled reference data.", "published": "2023-01-05T12:11:17Z", "authors": ["Elena Martín-González", "Ebraham Alskaf", "Amedeo Chiribiri", "Pablo Casaseca-de-la-Higuera", "Carlos Alberola-López", "Rita G Nunes", "Teresa M Correia"], "categories": ["eess.IV", "cs.LG", "physics.med-ph"], "doi": "10.1007/978-3-030-88552-6_9", "_query": "house price prediction machine learning", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2008.03911v1", "title": "A Survey on Large-scale Machine Learning", "abstract": "Machine learning can provide deep insights into data, allowing machines to make high-quality predictions and having been widely used in real-world applications, such as text mining, visual classification, and recommender systems. However, most sophisticated machine learning approaches suffer from huge time costs when operating on large-scale data. This issue calls for the need of {Large-scale Machine Learning} (LML), which aims to learn patterns from big data with comparable performance efficiently. In this paper, we offer a systematic survey on existing LML methods to provide a blueprint for the future developments of this area. We first divide these LML methods according to the ways of improving the scalability: 1) model simplification on computational complexities, 2) optimization approximation on computational efficiency, and 3) computation parallelism on computational capabilities. Then we categorize the methods in each perspective according to their targeted scenarios and introduce representative methods in line with intrinsic strategies. Lastly, we analyze their limitations and discuss potential directions as well as open issues that are promising to address in the future.", "published": "2020-08-10T06:07:52Z", "authors": ["Meng Wang", "Weijie Fu", "Xiangnan He", "Shijie Hao", "Xindong Wu"], "categories": ["cs.LG", "stat.ML"], "doi": "", "_query": "house price prediction machine learning", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2301.13724v2", "title": "Towards fully covariant machine learning", "abstract": "Any representation of data involves arbitrary investigator choices. Because those choices are external to the data-generating process, each choice leads to an exact symmetry, corresponding to the group of transformations that takes one possible representation to another. These are the passive symmetries; they include coordinate freedom, gauge symmetry, and units covariance, all of which have led to important results in physics. In machine learning, the most visible passive symmetry is the relabeling or permutation symmetry of graphs. Our goal is to understand the implications for machine learning of the many passive symmetries in play. We discuss dos and don'ts for machine learning practice if passive symmetries are to be respected. We discuss links to causal modeling, and argue that the implementation of passive symmetries is particularly valuable when the goal of the learning problem is to generalize out of sample. This paper is conceptual: It translates among the languages of physics, mathematics, and machine-learning. We believe that consideration and implementation of passive symmetries might help machine learning in the same ways that it transformed physics in the twentieth century.", "published": "2023-01-31T16:01:12Z", "authors": ["Soledad Villar", "David W. Hogg", "Weichi Yao", "George A. Kevrekidis", "Bernhard Schölkopf"], "categories": ["stat.ML", "astro-ph.IM", "cs.LG", "math-ph", "physics.data-an"], "doi": "", "_query": "house price prediction machine learning", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2609.16082v2", "title": "Predicting Social Media Engagement using Machine Learning", "abstract": "Social media platforms are popular channels for disseminating information, owing to their large user bases and ease of access. Companies also use social media as an important aspect of the advertising process. By creating high-quality posts, companies can strengthen their engagement metrics and increase their follower count. While a growing body of research has examined social media engagement, fewer studies have jointly examined the visual, textual, and temporal features of image posts, even though these features collectively determine the performance of content on social media. To understand the important drivers of social media engagement, we collect image posts of furniture firms on Facebook and extract visual, temporal, and textual features from them using text and image analytics methods. We evaluate several machine learning models--including Random Forest, Light Gradient Boosting Machine (LightGBM), and eXtreme Gradient Boosting (XGBoost)--to assess the drivers and the prediction power of social media engagement using the features from our data. Our primary model, LightGBM, explains 81.8% of the variance in the engagement score, 2.3 times that of our baseline model. We observed that brand-relative deviation features, which capture how far a post departs from its brand's norms, are the strongest predictors, accounting for 51.3% of total importance. Our research quantifies the extent to which these features are associated with interactions and provides recommendations that organizations may consider.", "published": "2026-09-14T00:12:26Z", "authors": ["Ritwik Singh", "Mayukh Majumdar", "Subodha Kumar"], "categories": ["cs.SI", "cs.LG"], "doi": "", "_query": "house price prediction machine learning", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2202.13890v2", "title": "Pessimistic Q-Learning for Offline Reinforcement Learning: Towards Optimal Sample Complexity", "abstract": "Offline or batch reinforcement learning seeks to learn a near-optimal policy using history data without active exploration of the environment. To counter the insufficient coverage and sample scarcity of many offline datasets, the principle of pessimism has been recently introduced to mitigate high bias of the estimated values. While pessimistic variants of model-based algorithms (e.g., value iteration with lower confidence bounds) have been theoretically investigated, their model-free counterparts -- which do not require explicit model estimation -- have not been adequately studied, especially in terms of sample efficiency. To address this inadequacy, we study a pessimistic variant of Q-learning in the context of finite-horizon Markov decision processes, and characterize its sample complexity under the single-policy concentrability assumption which does not require the full coverage of the state-action space. In addition, a variance-reduced pessimistic Q-learning algorithm is proposed to achieve near-optimal sample complexity. Altogether, this work highlights the efficiency of model-free algorithms in offline RL when used in conjunction with pessimism and variance reduction.", "published": "2022-02-28T15:39:36Z", "authors": ["Laixi Shi", "Gen Li", "Yuting Wei", "Yuxin Chen", "Yuejie Chi"], "categories": ["cs.LG", "stat.ML"], "doi": "", "_query": "house price prediction machine learning", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2006.11184v2", "title": "Poisson Learning: Graph Based Semi-Supervised Learning At Very Low Label Rates", "abstract": "We propose a new framework, called Poisson learning, for graph based semi-supervised learning at very low label rates. Poisson learning is motivated by the need to address the degeneracy of Laplacian semi-supervised learning in this regime. The method replaces the assignment of label values at training points with the placement of sources and sinks, and solves the resulting Poisson equation on the graph. The outcomes are provably more stable and informative than those of Laplacian learning. Poisson learning is efficient and simple to implement, and we present numerical experiments showing the method is superior to other recent approaches to semi-supervised learning at low label rates on MNIST, FashionMNIST, and Cifar-10. We also propose a graph-cut enhancement of Poisson learning, called Poisson MBO, that gives higher accuracy and can incorporate prior knowledge of relative class sizes.", "published": "2020-06-19T15:21:04Z", "authors": ["Jeff Calder", "Brendan Cook", "Matthew Thorpe", "Dejan Slepcev"], "categories": ["cs.LG", "cs.CV", "math.AP", "math.NA", "stat.ML"], "doi": "", "_query": "house price prediction machine learning", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2201.10859v2", "title": "Visualizing the Diversity of Representations Learned by Bayesian Neural Networks", "abstract": "Explainable Artificial Intelligence (XAI) aims to make learning machines less opaque, and offers researchers and practitioners various tools to reveal the decision-making strategies of neural networks. In this work, we investigate how XAI methods can be used for exploring and visualizing the diversity of feature representations learned by Bayesian Neural Networks (BNNs). Our goal is to provide a global understanding of BNNs by making their decision-making strategies a) visible and tangible through feature visualizations and b) quantitatively measurable with a distance measure learned by contrastive learning. Our work provides new insights into the \\emph{posterior} distribution in terms of human-understandable feature information with regard to the underlying decision making strategies. The main findings of our work are the following: 1) global XAI methods can be applied to explain the diversity of decision-making strategies of BNN instances, 2) Monte Carlo dropout with commonly used Dropout rates exhibit increased diversity in feature representations compared to the multimodal posterior approximation of MultiSWAG, 3) the diversity of learned feature representations highly correlates with the uncertainty estimate for the output and 4) the inter-mode diversity of the multimodal posterior decreases as the network width increases, while the intra mode diversity increases. These findings are consistent with the recent Deep Neural Networks theory, providing additional intuitions about what the theory implies in terms of humanly understandable concepts.", "published": "2022-01-26T10:40:55Z", "authors": ["Dennis Grinwald", "Kirill Bykov", "Shinichi Nakajima", "Marina M. -C. Höhne"], "categories": ["cs.LG", "cs.AI", "cs.CV", "stat.ML"], "doi": "", "_query": "house price prediction machine learning", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/1911.12976v2", "title": "Learning and Planning for Time-Varying MDPs Using Maximum Likelihood Estimation", "abstract": "This paper proposes a formal approach to online learning and planning for agents operating in a priori unknown, time-varying environments. The proposed method computes the maximally likely model of the environment, given the observations about the environment made by an agent earlier in the system run and assuming knowledge of a bound on the maximal rate of change of system dynamics. Such an approach generalizes the estimation method commonly used in learning algorithms for unknown Markov decision processes with time-invariant transition probabilities, but is also able to quickly and correctly identify the system dynamics following a change. Based on the proposed method, we generalize the exploration bonuses used in learning for time-invariant Markov decision processes by introducing a notion of uncertainty in a learned time-varying model, and develop a control policy for time-varying Markov decision processes based on the exploitation and exploration trade-off. We demonstrate the proposed methods on four numerical examples: a patrolling task with a change in system dynamics, a two-state MDP with periodically changing outcomes of actions, a wind flow estimation task, and a multi-armed bandit problem with periodically changing probabilities of different rewards.", "published": "2019-11-29T07:02:14Z", "authors": ["Melkior Ornik", "Ufuk Topcu"], "categories": ["cs.LG", "eess.SY", "math.OC", "stat.ML"], "doi": "", "_query": "house price prediction machine learning", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2004.06277v1", "title": "A Demonstration of Issues with Value-Based Multiobjective Reinforcement Learning Under Stochastic State Transitions", "abstract": "We report a previously unidentified issue with model-free, value-based approaches to multiobjective reinforcement learning in the context of environments with stochastic state transitions. An example multiobjective Markov Decision Process (MOMDP) is used to demonstrate that under such conditions these approaches may be unable to discover the policy which maximises the Scalarised Expected Return, and in fact may converge to a Pareto-dominated solution. We discuss several alternative methods which may be more suitable for maximising SER in MOMDPs with stochastic transitions.", "published": "2020-04-14T02:55:12Z", "authors": ["Peter Vamplew", "Cameron Foale", "Richard Dazeley"], "categories": ["cs.LG", "cs.MA", "stat.ML"], "doi": "10.1007/s00521-021-05859-1", "_query": "house price prediction machine learning", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2202.11416v1", "title": "Price formation in financial markets: a game-theoretic perspective", "abstract": "We propose two novel frameworks to study the price formation of an asset negotiated in an order book. Specifically, we develop a game-theoretic model in many-person games and mean-field games, considering costs stemming from limited liquidity. We derive analytical formulas for the formed price in terms of the realized order flow. We also identify appropriate conditions that ensure the convergence of the price we find in the finite population game to that of its mean-field counterpart. We numerically assess our results with a large experiment using high-frequency data from ten stocks listed in the NASDAQ, a stock listed in B3 in Brazil, and a cryptocurrency listed in Binance.", "published": "2022-02-23T10:59:35Z", "authors": ["David Evangelista", "Yuri Saporito", "Yuri Thamsten"], "categories": ["q-fin.TR"], "doi": "", "_query": "house price prediction machine learning", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2509.23544v2", "title": "End-to-End Deep Learning for Predicting Metric Space-Valued Outputs", "abstract": "Many modern applications involve predicting structured, non-Euclidean outputs such as probability distributions, networks, and symmetric positive-definite matrices. These outputs are naturally modeled as elements of general metric spaces, where classical regression techniques that rely on vector space structure no longer apply. We introduce E2M (End-to-End Metric regression), a deep learning framework for predicting metric space-valued outputs. E2M performs prediction via weighted Fréchet means over training outputs, where the weights are learned by a neural network conditioned on the input. This construction provides a principled mechanism for geometry-aware prediction that avoids surrogate embeddings and restrictive parametric assumptions, while fully preserving the intrinsic geometry of the output space. We establish theoretical guarantees, including a universal approximation theorem that characterizes the expressive capacity of the model and a convergence analysis of the entropy-regularized training objective. Through extensive simulations involving probability distributions, networks, and symmetric positive-definite matrices, we show that E2M consistently achieves state-of-the-art performance, with its advantages becoming more pronounced at larger sample sizes. Applications to human mortality distributions and New York City taxi networks further demonstrate the flexibility and practical utility of this framework.", "published": "2025-09-28T00:46:12Z", "authors": ["Yidong Zhou", "Su I Iao", "Hans-Georg Müller"], "categories": ["stat.ML", "cs.AI", "cs.LG", "stat.ME"], "doi": "", "_query": "house price prediction machine learning", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2112.11429v3", "title": "Machine Learning Emulation of Urban Land Surface Processes", "abstract": "Can we improve the modeling of urban land surface processes with machine learning (ML)? A prior comparison of urban land surface models (ULSMs) found that no single model is 'best' at predicting all common surface fluxes. Here, we develop an urban neural network (UNN) trained on the mean predicted fluxes from 22 ULSMs at one site. The UNN emulates the mean output of ULSMs accurately. When compared to a reference ULSM (Town Energy Balance; TEB), the UNN has greater accuracy relative to flux observations, less computational cost, and requires fewer input parameters. When coupled to the Weather Research Forecasting (WRF) model using TensorFlow bindings, WRF-UNN is stable and more accurate than the reference WRF-TEB. Although the application is currently constrained by the training data (1 site), we show a novel approach to improve the modeling of surface fluxes by combining the strengths of several ULSMs into one using ML.", "published": "2021-12-21T18:47:46Z", "authors": ["David Meyer", "Sue Grimmond", "Peter Dueben", "Robin Hogan", "Maarten van Reeuwijk"], "categories": ["cs.LG", "physics.ao-ph"], "doi": "10.1029/2021MS002744", "_query": "house price prediction machine learning", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2207.00391v4", "title": "A Theoretical Analysis of the Learning Dynamics under Class Imbalance", "abstract": "Data imbalance is a common problem in machine learning that can have a critical effect on the performance of a model. Various solutions exist but their impact on the convergence of the learning dynamics is not understood. Here, we elucidate the significant negative impact of data imbalance on learning, showing that the learning curves for minority and majority classes follow sub-optimal trajectories when training with a gradient-based optimizer. This slowdown is related to the imbalance ratio and can be traced back to a competition between the optimization of different classes. Our main contribution is the analysis of the convergence of full-batch (GD) and stochastic gradient descent (SGD), and of variants that renormalize the contribution of each per-class gradient. We find that GD is not guaranteed to decrease the loss for each class but that this problem can be addressed by performing a per-class normalization of the gradient. With SGD, class imbalance has an additional effect on the direction of the gradients: the minority class suffers from a higher directional noise, which reduces the effectiveness of the per-class gradient normalization. Our findings not only allow us to understand the potential and limitations of strategies involving the per-class gradients, but also the reason for the effectiveness of previously used solutions for class imbalance such as oversampling.", "published": "2022-07-01T12:54:38Z", "authors": ["Emanuele Francazi", "Marco Baity-Jesi", "Aurelien Lucchi"], "categories": ["stat.ML", "cs.LG"], "doi": "", "_query": "house price prediction machine learning", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2207.03576v2", "title": "Robustness Evaluation of Deep Unsupervised Learning Algorithms for Intrusion Detection Systems", "abstract": "Recently, advances in deep learning have been observed in various fields, including computer vision, natural language processing, and cybersecurity. Machine learning (ML) has demonstrated its ability as a potential tool for anomaly detection-based intrusion detection systems to build secure computer networks. Increasingly, ML approaches are widely adopted than heuristic approaches for cybersecurity because they learn directly from data. Data is critical for the development of ML systems, and becomes potential targets for attackers. Basically, data poisoning or contamination is one of the most common techniques used to fool ML models through data. This paper evaluates the robustness of six recent deep learning algorithms for intrusion detection on contaminated data. Our experiments suggest that the state-of-the-art algorithms used in this study are sensitive to data contamination and reveal the importance of self-defense against data perturbation when developing novel models, especially for intrusion detection systems.", "published": "2022-06-25T02:28:39Z", "authors": ["D'Jeff Kanda Nkashama", "Arian Soltani", "Jean-Charles Verdier", "Marc Frappier", "Pierre-Martin Tardif", "Froduald Kabanza"], "categories": ["cs.CR", "cs.LG", "cs.NI"], "doi": "", "_query": "house price prediction machine learning", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/1907.03423v7", "title": "On-Policy Robot Imitation Learning from a Converging Supervisor", "abstract": "Existing on-policy imitation learning algorithms, such as DAgger, assume access to a fixed supervisor. However, there are many settings where the supervisor may evolve during policy learning, such as a human performing a novel task or an improving algorithmic controller. We formalize imitation learning from a \"converging supervisor\" and provide sublinear static and dynamic regret guarantees against the best policy in hindsight with labels from the converged supervisor, even when labels during learning are only from intermediate supervisors. We then show that this framework is closely connected to a class of reinforcement learning (RL) algorithms known as dual policy iteration (DPI), which alternate between training a reactive learner with imitation learning and a model-based supervisor with data from the learner. Experiments suggest that when this framework is applied with the state-of-the-art deep model-based RL algorithm PETS as an improving supervisor, it outperforms deep RL baselines on continuous control tasks and provides up to an 80-fold speedup in policy evaluation.", "published": "2019-07-08T07:02:57Z", "authors": ["Ashwin Balakrishna", "Brijen Thananjeyan", "Jonathan Lee", "Felix Li", "Arsh Zahed", "Joseph E. Gonzalez", "Ken Goldberg"], "categories": ["cs.LG", "cs.AI", "cs.RO"], "doi": "", "_query": "house price prediction machine learning", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/1906.07248v1", "title": "Iterative Model-Based Reinforcement Learning Using Simulations in the Differentiable Neural Computer", "abstract": "We propose a lifelong learning architecture, the Neural Computer Agent (NCA), where a Reinforcement Learning agent is paired with a predictive model of the environment learned by a Differentiable Neural Computer (DNC). The agent and DNC model are trained in conjunction iteratively. The agent improves its policy in simulations generated by the DNC model and rolls out the policy to the live environment, collecting experiences in new portions or tasks of the environment for further learning. Experiments in two synthetic environments show that DNC models can continually learn from pixels alone to simulate new tasks as they are encountered by the agent, while the agents can be successfully trained to solve the tasks using Proximal Policy Optimization entirely in simulations.", "published": "2019-06-17T20:15:02Z", "authors": ["Adeel Mufti", "Svetlin Penkov", "Subramanian Ramamoorthy"], "categories": ["cs.LG", "stat.ML"], "doi": "", "_query": "house price prediction machine learning", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2403.03741v1", "title": "SUPClust: Active Learning at the Boundaries", "abstract": "Active learning is a machine learning paradigm designed to optimize model performance in a setting where labeled data is expensive to acquire. In this work, we propose a novel active learning method called SUPClust that seeks to identify points at the decision boundary between classes. By targeting these points, SUPClust aims to gather information that is most informative for refining the model's prediction of complex decision regions. We demonstrate experimentally that labeling these points leads to strong model performance. This improvement is observed even in scenarios characterized by strong class imbalance.", "published": "2024-03-06T14:30:09Z", "authors": ["Yuta Ono", "Till Aczel", "Benjamin Estermann", "Roger Wattenhofer"], "categories": ["cs.LG", "cs.AI", "cs.CV"], "doi": "", "_query": "house price prediction machine learning", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2202.11985v1", "title": "Can deep neural networks learn process model structure? An assessment framework and analysis", "abstract": "Predictive process monitoring concerns itself with the prediction of ongoing cases in (business) processes. Prediction tasks typically focus on remaining time, outcome, next event or full case suffix prediction. Various methods using machine and deep learning havebeen proposed for these tasks in recent years. Especially recurrent neural networks (RNNs) such as long short-term memory nets (LSTMs) have gained in popularity. However, no research focuses on whether such neural network-based models can truly learn the structure of underlying process models. For instance, can such neural networks effectively learn parallel behaviour or loops? Therefore, in this work, we propose an evaluation scheme complemented with new fitness, precision, and generalisation metrics, specifically tailored towards measuring the capacity of deep learning models to learn process model structure. We apply this framework to several process models with simple control-flow behaviour, on the task of next-event prediction. Our results show that, even for such simplistic models, careful tuning of overfitting countermeasures is required to allow these models to learn process model structure.", "published": "2022-02-24T09:44:13Z", "authors": ["Jari Peeperkorn", "Seppe vanden Broucke", "Jochen De Weerdt"], "categories": ["cs.LG"], "doi": "10.1007/978-3-030-98581-3_10", "_query": "house price prediction machine learning", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2401.13744v3", "title": "Conformal Prediction Sets Improve Human Decision Making", "abstract": "In response to everyday queries, humans explicitly signal uncertainty and offer alternative answers when they are unsure. Machine learning models that output calibrated prediction sets through conformal prediction mimic this human behaviour; larger sets signal greater uncertainty while providing alternatives. In this work, we study the usefulness of conformal prediction sets as an aid for human decision making by conducting a pre-registered randomized controlled trial with conformal prediction sets provided to human subjects. With statistical significance, we find that when humans are given conformal prediction sets their accuracy on tasks improves compared to fixed-size prediction sets with the same coverage guarantee. The results show that quantifying model uncertainty with conformal prediction is helpful for human-in-the-loop decision making and human-AI teams.", "published": "2024-01-24T19:01:22Z", "authors": ["Jesse C. Cresswell", "Yi Sui", "Bhargava Kumar", "Noël Vouitsis"], "categories": ["cs.LG", "cs.HC", "stat.ML"], "doi": "", "_query": "house price prediction machine learning", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/1910.09457v3", "title": "Aleatoric and Epistemic Uncertainty in Machine Learning: An Introduction to Concepts and Methods", "abstract": "The notion of uncertainty is of major importance in machine learning and constitutes a key element of machine learning methodology. In line with the statistical tradition, uncertainty has long been perceived as almost synonymous with standard probability and probabilistic predictions. Yet, due to the steadily increasing relevance of machine learning for practical applications and related issues such as safety requirements, new problems and challenges have recently been identified by machine learning scholars, and these problems may call for new methodological developments. In particular, this includes the importance of distinguishing between (at least) two different types of uncertainty, often referred to as aleatoric and epistemic. In this paper, we provide an introduction to the topic of uncertainty in machine learning as well as an overview of attempts so far at handling uncertainty in general and formalizing this distinction in particular.", "published": "2019-10-21T15:41:28Z", "authors": ["Eyke Hüllermeier", "Willem Waegeman"], "categories": ["cs.LG", "stat.ML"], "doi": "10.1007/s10994-021-05946-3", "_query": "house price prediction machine learning", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2601.00593v1", "title": "Uncertainty-Adjusted Sorting for Asset Pricing with Machine Learning", "abstract": "Machine learning is central to empirical asset pricing, but portfolio construction still relies on point predictions and largely ignores asset-specific estimation uncertainty. We propose a simple change: sort assets using uncertainty-adjusted prediction bounds instead of point predictions alone. Across a broad set of ML models and a U.S. equity panel, this approach improves portfolio performance relative to point-prediction sorting. These gains persist even when bounds are built from partial or misspecified uncertainty information. They arise mainly from reduced volatility and are strongest for flexible machine learning models. Identification and robustness exercises show that these improvements are driven by asset-level rather than time or aggregate predictive uncertainty.", "published": "2026-01-02T06:53:47Z", "authors": ["Yan Liu", "Ye Luo", "Zigan Wang", "Xiaowei Zhang"], "categories": ["q-fin.PM", "stat.ML"], "doi": "", "_query": "house price prediction machine learning", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/1701.06452v1", "title": "Learning what to look in chest X-rays with a recurrent visual attention model", "abstract": "X-rays are commonly performed imaging tests that use small amounts of radiation to produce pictures of the organs, tissues, and bones of the body. X-rays of the chest are used to detect abnormalities or diseases of the airways, blood vessels, bones, heart, and lungs. In this work we present a stochastic attention-based model that is capable of learning what regions within a chest X-ray scan should be visually explored in order to conclude that the scan contains a specific radiological abnormality. The proposed model is a recurrent neural network (RNN) that learns to sequentially sample the entire X-ray and focus only on informative areas that are likely to contain the relevant information. We report on experiments carried out with more than $100,000$ X-rays containing enlarged hearts or medical devices. The model has been trained using reinforcement learning methods to learn task-specific policies.", "published": "2017-01-23T15:29:47Z", "authors": ["Petros-Pavlos Ypsilantis", "Giovanni Montana"], "categories": ["stat.ML", "cs.CV", "cs.LG"], "doi": "", "_query": "house price prediction machine learning", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/1512.09327v4", "title": "Distributed Bayesian Learning with Stochastic Natural-gradient Expectation Propagation and the Posterior Server", "abstract": "This paper makes two contributions to Bayesian machine learning algorithms. Firstly, we propose stochastic natural gradient expectation propagation (SNEP), a novel alternative to expectation propagation (EP), a popular variational inference algorithm. SNEP is a black box variational algorithm, in that it does not require any simplifying assumptions on the distribution of interest, beyond the existence of some Monte Carlo sampler for estimating the moments of the EP tilted distributions. Further, as opposed to EP which has no guarantee of convergence, SNEP can be shown to be convergent, even when using Monte Carlo moment estimates. Secondly, we propose a novel architecture for distributed Bayesian learning which we call the posterior server. The posterior server allows scalable and robust Bayesian learning in cases where a data set is stored in a distributed manner across a cluster, with each compute node containing a disjoint subset of data. An independent Monte Carlo sampler is run on each compute node, with direct access only to the local data subset, but which targets an approximation to the global posterior distribution given all data across the whole cluster. This is achieved by using a distributed asynchronous implementation of SNEP to pass messages across the cluster. We demonstrate SNEP and the posterior server on distributed Bayesian learning of logistic regression and neural networks. Keywords: Distributed Learning, Large Scale Learning, Deep Learning, Bayesian Learn- ing, Variational Inference, Expectation Propagation, Stochastic Approximation, Natural Gradient, Markov chain Monte Carlo, Parameter Server, Posterior Server.", "published": "2015-12-31T17:30:45Z", "authors": ["Leonard Hasenclever", "Stefan Webb", "Thibaut Lienart", "Sebastian Vollmer", "Balaji Lakshminarayanan", "Charles Blundell", "Yee Whye Teh"], "categories": ["cs.LG", "stat.ML"], "doi": "", "_query": "house price prediction machine learning", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2506.03068v1", "title": "Causal Explainability of Machine Learning in Heart Failure Prediction from Electronic Health Records", "abstract": "The importance of clinical variables in the prognosis of the disease is explained using statistical correlation or machine learning (ML). However, the predictive importance of these variables may not represent their causal relationships with diseases. This paper uses clinical variables from a heart failure (HF) patient cohort to investigate the causal explainability of important variables obtained in statistical and ML contexts. Due to inherent regression modeling, popular causal discovery methods strictly assume that the cause and effect variables are numerical and continuous. This paper proposes a new computational framework to enable causal structure discovery (CSD) and score the causal strength of mixed-type (categorical, numerical, binary) clinical variables for binary disease outcomes. In HF classification, we investigate the association between the importance rank order of three feature types: correlated features, features important for ML predictions, and causal features. Our results demonstrate that CSD modeling for nonlinear causal relationships is more meaningful than its linear counterparts. Feature importance obtained from nonlinear classifiers (e.g., gradient-boosting trees) strongly correlates with the causal strength of variables without differentiating cause and effect variables. Correlated variables can be causal for HF, but they are rarely identified as effect variables. These results can be used to add the causal explanation of variables important for ML-based prediction modeling.", "published": "2025-06-03T16:46:13Z", "authors": ["Yina Hou", "Shourav B. Rabbani", "Liang Hong", "Norou Diawara", "Manar D. Samad"], "categories": ["stat.ML", "cs.CY", "cs.LG"], "doi": "", "_query": "house price prediction machine learning", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2307.10780v2", "title": "Learned Thresholds Token Merging and Pruning for Vision Transformers", "abstract": "Vision transformers have demonstrated remarkable success in a wide range of computer vision tasks over the last years. However, their high computational costs remain a significant barrier to their practical deployment. In particular, the complexity of transformer models is quadratic with respect to the number of input tokens. Therefore techniques that reduce the number of input tokens that need to be processed have been proposed. This paper introduces Learned Thresholds token Merging and Pruning (LTMP), a novel approach that leverages the strengths of both token merging and token pruning. LTMP uses learned threshold masking modules that dynamically determine which tokens to merge and which to prune. We demonstrate our approach with extensive experiments on vision transformers on the ImageNet classification task. Our results demonstrate that LTMP achieves state-of-the-art accuracy across reduction rates while requiring only a single fine-tuning epoch, which is an order of magnitude faster than previous methods. Code is available at https://github.com/Mxbonn/ltmp .", "published": "2023-07-20T11:30:12Z", "authors": ["Maxim Bonnaerens", "Joni Dambre"], "categories": ["cs.CV"], "doi": "", "_query": "house price prediction machine learning", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2607.03998v5", "title": "Directional Curvature from Armijo Backtracking: A Low-Cost Sharpness Probe and a Calibration-Free Learning-Rate Safeguard for Adam", "abstract": "Local sharpness, defined by the largest Hessian eigenvalue $λ_1$, sets the maximum stable gradient update size, but its computation would usually require running Lanczos or Hessian-vector products. However, we notice that even a single Armijo backtracking line search already contains this information with just a few forward passes, as the accepted step $α$ determines the directional curvature along the search direction up to the multiplicative band set by the backtracking factor. The correlation between $\\logα$ and $\\logλ_1$ on CIFAR-10, Fashion-MNIST and Imagenette reaches $-0.91$ to $-0.95$ in Pearson correlation, and even after removing the trend per run the correlation remains at $-0.60$ to $-0.70$. This allows for a cheap online Edge-of-Stability estimate of the slow sharpness component. The employed probing mechanism searches along Adam's first-step update direction, at initialisation and nine times over the course of the first 50 optimiser steps. The learning-rate cap is set as twice the smallest observed step size, and in the studied learning-rate ranges ($10^{-3}$ to $3.0$) and GPT-2 pretraining experiments all capped runs avoid divergence; in the general architecture analysis, one MLP architecture is still sensitive to the initial batch order. This probing protocol incurs an approximately one percent overhead, and using a non-binding cap means that the optimiser's state and first update are bit-identical. There is no fine-tuning of any of the protocol parameters to any specific architecture; this is the sense in which this is a calibration-free safeguard. It is meant as a way of avoiding divergence, not achieving accuracy. At GPT-2 scale, multiple measurements also illustrate why an initialisation-only cap is insufficient: directional curvature rises substantially in the first five optimiser steps, which motivates the short probationary window.", "published": "2026-07-04T19:49:01Z", "authors": ["Ashmitha R", "Jörg Frochte"], "categories": ["cs.LG"], "doi": "", "_query": "house price prediction machine learning", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2405.08033v1", "title": "Predicting Ship Responses in Different Seaways using a Generalizable Force Correcting Machine Learning Method", "abstract": "A machine learning (ML) method is generalizable if it can make predictions on inputs which differ from the training dataset. For predictions of wave-induced ship responses, generalizability is an important consideration if ML methods are to be useful in design evaluations. Furthermore, the size of the training dataset has a significant impact on the practicality of a method, especially when training data is generated using high-fidelity numerical tools which are expensive. This paper considers a hybrid machine learning method which corrects the force in a low-fidelity equation of motion. The method is applied to two different case studies: the nonlinear responses of a Duffing equation subject to irregular excitation, and high-fidelity heave and pitch response data of a Fast Displacement Ship (FDS) in head seas. The generalizability of the method is determined in both cases by making predictions of the response in irregular wave conditions that differ from those in the training dataset. The influence that low-fidelity physics-based terms in the hybrid model have on generalizability is also investigated. The predictions are compared to two benchmarks: a linear physics-based model and a data-driven LSTM model. It is found that the hybrid method offers an improvement in prediction accuracy and generalizability when trained on a small dataset.", "published": "2024-05-13T01:04:27Z", "authors": ["Kyle E. Marlantes", "Piotr J. Bandyk", "Kevin J. Maki"], "categories": ["cs.LG", "physics.flu-dyn", "stat.ML"], "doi": "", "_query": "house price prediction machine learning", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2007.06918v1", "title": "Lifelong Learning using Eigentasks: Task Separation, Skill Acquisition, and Selective Transfer", "abstract": "We introduce the eigentask framework for lifelong learning. An eigentask is a pairing of a skill that solves a set of related tasks, paired with a generative model that can sample from the skill's input space. The framework extends generative replay approaches, which have mainly been used to avoid catastrophic forgetting, to also address other lifelong learning goals such as forward knowledge transfer. We propose a wake-sleep cycle of alternating task learning and knowledge consolidation for learning in our framework, and instantiate it for lifelong supervised learning and lifelong RL. We achieve improved performance over the state-of-the-art in supervised continual learning, and show evidence of forward knowledge transfer in a lifelong RL application in the game Starcraft2.", "published": "2020-07-14T09:06:13Z", "authors": ["Aswin Raghavan", "Jesse Hostetler", "Indranil Sur", "Abrar Rahman", "Ajay Divakaran"], "categories": ["cs.LG", "cs.AI", "cs.CV", "stat.ML"], "doi": "", "_query": "house price prediction machine learning", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2406.00793v1", "title": "Is In-Context Learning in Large Language Models Bayesian? A Martingale Perspective", "abstract": "In-context learning (ICL) has emerged as a particularly remarkable characteristic of Large Language Models (LLM): given a pretrained LLM and an observed dataset, LLMs can make predictions for new data points from the same distribution without fine-tuning. Numerous works have postulated ICL as approximately Bayesian inference, rendering this a natural hypothesis. In this work, we analyse this hypothesis from a new angle through the martingale property, a fundamental requirement of a Bayesian learning system for exchangeable data. We show that the martingale property is a necessary condition for unambiguous predictions in such scenarios, and enables a principled, decomposed notion of uncertainty vital in trustworthy, safety-critical systems. We derive actionable checks with corresponding theory and test statistics which must hold if the martingale property is satisfied. We also examine if uncertainty in LLMs decreases as expected in Bayesian learning when more data is observed. In three experiments, we provide evidence for violations of the martingale property, and deviations from a Bayesian scaling behaviour of uncertainty, falsifying the hypothesis that ICL is Bayesian.", "published": "2024-06-02T16:20:30Z", "authors": ["Fabian Falck", "Ziyu Wang", "Chris Holmes"], "categories": ["stat.ML", "cs.LG"], "doi": "", "_query": "house price prediction machine learning", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/1701.07102v2", "title": "A Comprehensive Physics-Informed Machine Learning Framework for Predictive Turbulence Modeling", "abstract": "Although an increased availability of computational resources has enabled high-fidelity simulations of turbulent flows, the RANS models are still the dominant tools for industrial applications. However, the predictive capabilities of RANS models are limited by potential inaccuracy driven by hypotheses in the Reynolds stress closure. Recently, a Physics-Informed Machine Learning (PIML) approach has been proposed to learn the functional form of Reynolds stress discrepancy in RANS simulations based on available data. It has been demonstrated that the learned discrepancy function can be used to improve Reynolds stresses in different flows where data are not available. However, owing to a number of challenges, the improvements have been demonstrated only in the Reynolds stress prediction but not in the corresponding propagated quantities of interest. In this work, we introduce the procedures toward a complete PIML framework for predictive turbulence modeling, including learning Reynolds stress discrepancy function, predicting Reynolds stresses in different flows, and propagating to mean flow fields. The process of Reynolds stress propagation and predictive accuracy of the propagated velocity field are investigated. To improve the learning-prediction performance, the input features are enriched based on an integrity basis of invariants. The fully developed turbulent flow in a square duct is used as the test case. The discrepancy model is trained on flow fields obtained from several Reynolds numbers and evaluated on a duct flow at a Reynolds number higher than any of the training cases. The predicted Reynolds stresses are propagated to velocity field through RANS equations. Numerical results show excellent predictive performances in both Reynolds stresses and their propagated velocities, demonstrating the merits of the PIML approach in predictive turbulence modeling.", "published": "2017-01-24T23:04:32Z", "authors": ["Jian-Xun Wang", "Jinlong Wu", "Julia Ling", "Gianluca Iaccarino", "Heng Xiao"], "categories": ["physics.flu-dyn"], "doi": "10.1103/PhysRevFluids.3.074602", "_query": "house price prediction machine learning", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/1412.3919v1", "title": "Machine Learning for Neuroimaging with Scikit-Learn", "abstract": "Statistical machine learning methods are increasingly used for neuroimaging data analysis. Their main virtue is their ability to model high-dimensional datasets, e.g. multivariate analysis of activation images or resting-state time series. Supervised learning is typically used in decoding or encoding settings to relate brain images to behavioral or clinical observations, while unsupervised learning can uncover hidden structures in sets of images (e.g. resting state functional MRI) or find sub-populations in large cohorts. By considering different functional neuroimaging applications, we illustrate how scikit-learn, a Python machine learning library, can be used to perform some key analysis steps. Scikit-learn contains a very large set of statistical learning algorithms, both supervised and unsupervised, and its application to neuroimaging data provides a versatile tool to study the brain.", "published": "2014-12-12T08:38:35Z", "authors": ["Alexandre Abraham", "Fabian Pedregosa", "Michael Eickenberg", "Philippe Gervais", "Andreas Muller", "Jean Kossaifi", "Alexandre Gramfort", "Bertrand Thirion", "Gäel Varoquaux"], "categories": ["cs.LG", "cs.CV", "stat.ML"], "doi": "", "_query": "house price prediction machine learning", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2206.03493v2", "title": "DeepCAVE: An Interactive Analysis Tool for Automated Machine Learning", "abstract": "Automated Machine Learning (AutoML) is used more than ever before to support users in determining efficient hyperparameters, neural architectures, or even full machine learning pipelines. However, users tend to mistrust the optimization process and its results due to a lack of transparency, making manual tuning still widespread. We introduce DeepCAVE, an interactive framework to analyze and monitor state-of-the-art optimization procedures for AutoML easily and ad hoc. By aiming for full and accessible transparency, DeepCAVE builds a bridge between users and AutoML and contributes to establishing trust. Our framework's modular and easy-to-extend nature provides users with automatically generated text, tables, and graphic visualizations. We show the value of DeepCAVE in an exemplary use-case of outlier detection, in which our framework makes it easy to identify problems, compare multiple runs and interpret optimization processes. The package is freely available on GitHub https://github.com/automl/DeepCAVE.", "published": "2022-06-07T12:59:39Z", "authors": ["René Sass", "Eddie Bergman", "André Biedenkapp", "Frank Hutter", "Marius Lindauer"], "categories": ["cs.LG"], "doi": "", "_query": "house price prediction machine learning", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2111.08749v4", "title": "SMACE: A New Method for the Interpretability of Composite Decision Systems", "abstract": "Interpretability is a pressing issue for decision systems. Many post hoc methods have been proposed to explain the predictions of a single machine learning model. However, business processes and decision systems are rarely centered around a unique model. These systems combine multiple models that produce key predictions, and then apply decision rules to generate the final decision. To explain such decisions, we propose the Semi-Model-Agnostic Contextual Explainer (SMACE), a new interpretability method that combines a geometric approach for decision rules with existing interpretability methods for machine learning models to generate an intuitive feature ranking tailored to the end user. We show that established model-agnostic approaches produce poor results on tabular data in this setting, in particular giving the same importance to several features, whereas SMACE can rank them in a meaningful way.", "published": "2021-11-16T19:37:35Z", "authors": ["Gianluigi Lopardo", "Damien Garreau", "Frederic Precioso", "Greger Ottosson"], "categories": ["cs.LG", "cs.AI"], "doi": "10.1007/978-3-031-26387-3_20", "_query": "house price prediction machine learning", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/0906.4258v1", "title": "The Feature Importance Ranking Measure", "abstract": "Most accurate predictions are typically obtained by learning machines with complex feature spaces (as e.g. induced by kernels). Unfortunately, such decision rules are hardly accessible to humans and cannot easily be used to gain insights about the application domain. Therefore, one often resorts to linear models in combination with variable selection, thereby sacrificing some predictive power for presumptive interpretability. Here, we introduce the Feature Importance Ranking Measure (FIRM), which by retrospective analysis of arbitrary learning machines allows to achieve both excellent predictive performance and superior interpretation. In contrast to standard raw feature weighting, FIRM takes the underlying correlation structure of the features into account. Thereby, it is able to discover the most relevant features, even if their appearance in the training data is entirely prevented by noise. The desirable properties of FIRM are investigated analytically and illustrated in simulations.", "published": "2009-06-23T13:45:10Z", "authors": ["Alexander Zien", "Nicole Kraemer", "Soeren Sonnenburg", "Gunnar Raetsch"], "categories": ["stat.ML"], "doi": "", "_query": "house price prediction machine learning", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2412.05333v1", "title": "Learning Symmetry-Independent Jet Representations via Jet-Based Joint Embedding Predictive Architecture", "abstract": "In high energy physics, self-supervised learning (SSL) methods have the potential to aid in the creation of machine learning models without the need for labeled datasets for a variety of tasks, including those related to jets -- narrow sprays of particles produced by quarks and gluons in high energy particle collisions. This study introduces an approach to learning jet representations without hand-crafted augmentations using a jet-based joint embedding predictive architecture (J-JEPA), which aims to predict various physical targets from an informative context. As our method does not require hand-crafted augmentation like other common SSL techniques, J-JEPA avoids introducing biases that could harm downstream tasks. Since different tasks generally require invariance under different augmentations, this training without hand-crafted augmentation enables versatile applications, offering a pathway toward a cross-task foundation model. We finetune the representations learned by J-JEPA for jet tagging and benchmark them against task-specific representations.", "published": "2024-12-05T19:00:14Z", "authors": ["Subash Katel", "Haoyang Li", "Zihan Zhao", "Raghav Kansal", "Farouk Mokhtar", "Javier Duarte"], "categories": ["hep-ph", "cs.LG", "hep-ex", "physics.data-an"], "doi": "", "_query": "house price prediction machine learning", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2303.18087v1", "title": "Evaluation Challenges for Geospatial ML", "abstract": "As geospatial machine learning models and maps derived from their predictions are increasingly used for downstream analyses in science and policy, it is imperative to evaluate their accuracy and applicability. Geospatial machine learning has key distinctions from other learning paradigms, and as such, the correct way to measure performance of spatial machine learning outputs has been a topic of debate. In this paper, I delineate unique challenges of model evaluation for geospatial machine learning with global or remotely sensed datasets, culminating in concrete takeaways to improve evaluations of geospatial model performance.", "published": "2023-03-31T14:24:06Z", "authors": ["Esther Rolf"], "categories": ["cs.LG", "stat.ML"], "doi": "", "_query": "house price prediction machine learning", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2406.16032v2", "title": "Effect of Random Learning Rate: Theoretical Analysis of SGD Dynamics in Non-Convex Optimization via Stationary Distribution", "abstract": "We consider a variant of the stochastic gradient descent (SGD) with a random learning rate and reveal its convergence properties. SGD is a widely used stochastic optimization algorithm in machine learning, especially deep learning. Numerous studies reveal the convergence properties of SGD and its theoretically favorable variants. Among these, the analysis of convergence using a stationary distribution of updated parameters provides generalizable results. However, to obtain a stationary distribution, the update direction of the parameters must not degenerate, which limits the applicable variants of SGD. In this study, we consider a novel SGD variant, Poisson SGD, which has degenerated parameter update directions and instead utilizes a random learning rate. Consequently, we demonstrate that a distribution of a parameter updated by Poisson SGD converges to a stationary distribution under weak assumptions on a loss function. Based on this, we further show that Poisson SGD finds global minima in non-convex optimization problems and also evaluate the generalization error using this method. As a proof technique, we approximate the distribution by Poisson SGD with that of the bouncy particle sampler (BPS) and derive its stationary distribution, using the theoretical advance of the piece-wise deterministic Markov process (PDMP).", "published": "2024-06-23T06:52:33Z", "authors": ["Naoki Yoshida", "Shogo Nakakita", "Masaaki Imaizumi"], "categories": ["stat.ML", "cs.LG"], "doi": "", "_query": "house price prediction machine learning", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2405.10584v1", "title": "A Hybrid Deep Learning Framework for Stock Price Prediction Considering the Investor Sentiment of Online Forum Enhanced by Popularity", "abstract": "Stock price prediction has always been a difficult task for forecasters. Using cutting-edge deep learning techniques, stock price prediction based on investor sentiment extracted from online forums has become feasible. We propose a novel hybrid deep learning framework for predicting stock prices. The framework leverages the XLNET model to analyze the sentiment conveyed in user posts on online forums, combines these sentiments with the post popularity factor to compute daily group sentiments, and integrates this information with stock technical indicators into an improved BiLSTM-highway model for stock price prediction. Through a series of comparative experiments involving four stocks on the Chinese stock market, it is demonstrated that the hybrid framework effectively predicts stock prices. This study reveals the necessity of analyzing investors' textual views for stock price prediction.", "published": "2024-05-17T07:18:08Z", "authors": ["Huiyu Li", "Junhua Hu"], "categories": ["cs.LG", "cs.CL", "q-fin.ST"], "doi": "10.1007/s10614-025-11268-6", "_query": "house price prediction machine learning", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/1910.13327v1", "title": "Machine Learning-Based Analysis of Sperm Videos and Participant Data for Male Fertility Prediction", "abstract": "Methods for automatic analysis of clinical data are usually targeted towards a specific modality and do not make use of all relevant data available. In the field of male human reproduction, clinical and biological data are not used to its fullest potential. Manual evaluation of a semen sample using a microscope is time-consuming and requires extensive training. Furthermore, the validity of manual semen analysis has been questioned due to limited reproducibility, and often high inter-personnel variation. The existing computer-aided sperm analyzer systems are not recommended for routine clinical use due to methodological challenges caused by the consistency of the semen sample. Thus, there is a need for an improved methodology. We use modern and classical machine learning techniques together with a dataset consisting of 85 videos of human semen samples and related participant data to automatically predict sperm motility. Used techniques include simple linear regression and more sophisticated methods using convolutional neural networks. Our results indicate that sperm motility prediction based on deep learning using sperm motility videos is rapid to perform and consistent. The algorithms performed worse when participant data was added. In conclusion, machine learning-based automatic analysis may become a valuable tool in male infertility investigation and research.", "published": "2019-10-29T15:38:47Z", "authors": ["Steven A. Hicks", "Jorunn M. Andersen", "Oliwia Witczak", "Vajira Thambawita", "Påll Halvorsen", "Hugo L. Hammer", "Trine B. Haugen", "Michael A. Riegler"], "categories": ["cs.LG", "cs.CV", "eess.IV", "stat.ML"], "doi": "", "_query": "house price prediction machine learning", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2606.02795v1", "title": "Recovering Direct Price Effects of Environmental Amenities in Housing Markets: Regression and Causal Machine Learning Model Assessment with Empirical Monte Carlo Simulation", "abstract": "Hedonic price models are widely used to assess how environmental amenities affect property values, yet methodological guidance for estimating direct price effects remains sparse. We conduct an empirical Monte Carlo simulation to evaluate the performance of traditional and causal machine learning approaches for estimating the direct unmediated price effect of spatially delineated amenities on treated properties (DUET), a conservative lower-bound approximation for welfare changes with direct applications to benefit-cost analysis. Where previous simulations rely on parametric assumptions, we retain the actual data-generating process underlying over 1 million property transactions from upstate New York (1990--2024). By randomly assigning \"treatment locations\" across iterations we establish a \"ground truth\" that allows us to precisely measure estimation error. Our results demonstrate that generalized difference-in-differences (DID) regression consistently outperforms baseline DID and two-way fixed effects models across all scenarios. Causal Machine Learning (CML) methods, particularly causal forest DID, achieve comparable performance to generalized DID in most scenarios. In larger samples (above 3,000 treated) increasingly common in contemporary hedonic studies, CML approaches offer substantial advantages when properly specified. Based on empirical simulation results, we provide a set of method-specific best practice recommendations for both traditional regression and causal machine learning approaches.", "published": "2026-06-01T18:59:44Z", "authors": ["Zhenshan Chen", "Klaus Moeltner", "Matthew Mair"], "categories": ["econ.EM", "stat.ML"], "doi": "", "_query": "house price prediction machine learning", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/1808.00033v3", "title": "Techniques for Interpretable Machine Learning", "abstract": "Interpretable machine learning tackles the important problem that humans cannot understand the behaviors of complex machine learning models and how these models arrive at a particular decision. Although many approaches have been proposed, a comprehensive understanding of the achievements and challenges is still lacking. We provide a survey covering existing techniques to increase the interpretability of machine learning models. We also discuss crucial issues that the community should consider in future work such as designing user-friendly explanations and developing comprehensive evaluation metrics to further push forward the area of interpretable machine learning.", "published": "2018-07-31T19:14:39Z", "authors": ["Mengnan Du", "Ninghao Liu", "Xia Hu"], "categories": ["cs.LG", "cs.AI", "stat.ML"], "doi": "", "_query": "house price prediction machine learning", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2104.11009v1", "title": "Enhancing predictive skills in physically-consistent way: Physics Informed Machine Learning for Hydrological Processes", "abstract": "Current modeling approaches for hydrological modeling often rely on either physics-based or data-science methods, including Machine Learning (ML) algorithms. While physics-based models tend to rigid structure resulting in unrealistic parameter values in certain instances, ML algorithms establish the input-output relationship while ignoring the constraints imposed by well-known physical processes. While there is a notion that the physics model enables better process understanding and ML algorithms exhibit better predictive skills, scientific knowledge that does not add to predictive ability may be deceptive. Hence, there is a need for a hybrid modeling approach to couple ML algorithms and physics-based models in a synergistic manner. Here we develop a Physics Informed Machine Learning (PIML) model that combines the process understanding of conceptual hydrological model with predictive abilities of state-of-the-art ML models. We apply the proposed model to predict the monthly time series of the target (streamflow) and intermediate variables (actual evapotranspiration) in the Narmada river basin in India. Our results show the capability of the PIML model to outperform a purely conceptual model ($abcd$ model) and ML algorithms while ensuring the physical consistency in outputs validated through water balance analysis. The systematic approach for combining conceptual model structure with ML algorithms could be used to improve the predictive accuracy of crucial hydrological processes important for flood risk assessment.", "published": "2021-04-22T12:13:42Z", "authors": ["Pravin Bhasme", "Jenil Vagadiya", "Udit Bhatia"], "categories": ["stat.ML", "cs.LG", "stat.AP"], "doi": "", "_query": "house price prediction machine learning", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2309.01592v3", "title": "Les Houches Lectures on Deep Learning at Large & Infinite Width", "abstract": "These lectures, presented at the 2022 Les Houches Summer School on Statistical Physics and Machine Learning, focus on the infinite-width limit and large-width regime of deep neural networks. Topics covered include various statistical and dynamical properties of these networks. In particular, the lecturers discuss properties of random deep neural networks; connections between trained deep neural networks, linear models, kernels, and Gaussian processes that arise in the infinite-width limit; and perturbative and non-perturbative treatments of large but finite-width networks, at initialization and after training.", "published": "2023-09-04T13:21:18Z", "authors": ["Yasaman Bahri", "Boris Hanin", "Antonin Brossollet", "Vittorio Erba", "Christian Keup", "Rosalba Pacelli", "James B. Simon"], "categories": ["stat.ML", "cs.AI", "cs.LG", "hep-th", "math.PR"], "doi": "", "_query": "house price prediction machine learning", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/1904.11711v2", "title": "Robust Metric Learning based on the Rescaled Hinge Loss", "abstract": "Distance/Similarity learning is a fundamental problem in machine learning. For example, kNN classifier or clustering methods are based on a distance/similarity measure. Metric learning algorithms enhance the efficiency of these methods by learning an optimal distance function from data. Most metric learning methods need training information in the form of pair or triplet sets. Nowadays, this training information often is obtained from the Internet via crowdsourcing methods. Therefore, this information may contain label noise or outliers leading to the poor performance of the learned metric. It is even possible that the learned metric functions perform worse than the general metrics such as Euclidean distance. To address this challenge, this paper presents a new robust metric learning method based on the Rescaled Hinge loss. This loss function is a general case of the popular Hinge loss and initially introduced in (Xu et al. 2017) to develop a new robust SVM algorithm. In this paper, we formulate the metric learning problem using the Rescaled Hinge loss function and then develop an efficient algorithm based on HQ (Half-Quadratic) to solve the problem. Experimental results on a variety of both real and synthetic datasets confirm that our new robust algorithm considerably outperforms state-of-the-art metric learning methods in the presence of label noise and outliers.", "published": "2019-04-26T08:20:59Z", "authors": ["Sumia Abdulhussien Razooqi Al-Obaidi", "Davood Zabihzadeh", "Hamideh Hajiabadi"], "categories": ["cs.LG", "stat.ML"], "doi": "10.1007/s13042-020-01137-z", "_query": "house price prediction machine learning", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2508.10178v3", "title": "Estimating carbon pools in the European Shelf sea environment: replacing reanalysis by model-informed machine learning?", "abstract": "Shelf seas are important for the economy and the carbon cycle, but shelf sea observations for carbon pools are often sparse, or highly uncertain. An alternative can be provided by carbon reanalyses (whether assimilating proxy variables, such as chlorophyll-$a$, or directly carbon), but these are often expensive to run. We propose to use a computationally cheap ensemble of neural networks (i.e. deep ensemble) to learn the relationship between the directly observable (atmospheric, riverine and ocean) variables and marine carbon pools from a coupled physics-biogeochemistry model. The deep ensemble was trained on a North-West European Shelf (NWES) physical-biogeochemistry model free run simulation. After training, the deep ensemble was run using inputs from the NWES reanalysis instead of the free run, demonstrating that it can efficiently predict several NWES carbon pools (e.g., detritus, zooplankton, heterotrophic bacteria) in much better agreement with the reanalysis than the free run, while also providing uncertainty information. We further show that the deep ensemble performs similarly well when it is driven directly by the observations assimilated into the reanalysis, with the limitation that carbon pools can then be predicted only at the observed locations and times. We focus on explainability of the results and demonstrate potential use of the deep ensembles for future climate what-if scenarios. We suggest that model-informed machine learning presents a viable alternative to expensive reanalyses and could complement observations, wherever they are missing and/or highly uncertain.", "published": "2025-08-13T20:30:42Z", "authors": ["Jozef Skakala"], "categories": ["q-bio.QM", "cs.LG"], "doi": "10.1029/2026JH001326", "_query": "house price prediction machine learning", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2007.14390v5", "title": "Flower: A Friendly Federated Learning Research Framework", "abstract": "Federated Learning (FL) has emerged as a promising technique for edge devices to collaboratively learn a shared prediction model, while keeping their training data on the device, thereby decoupling the ability to do machine learning from the need to store the data in the cloud. However, FL is difficult to implement realistically, both in terms of scale and systems heterogeneity. Although there are a number of research frameworks available to simulate FL algorithms, they do not support the study of scalable FL workloads on heterogeneous edge devices. In this paper, we present Flower -- a comprehensive FL framework that distinguishes itself from existing platforms by offering new facilities to execute large-scale FL experiments and consider richly heterogeneous FL device scenarios. Our experiments show Flower can perform FL experiments up to 15M in client size using only a pair of high-end GPUs. Researchers can then seamlessly migrate experiments to real devices to examine other parts of the design space. We believe Flower provides the community with a critical new tool for FL study and development.", "published": "2020-07-28T17:59:07Z", "authors": ["Daniel J. Beutel", "Taner Topal", "Akhil Mathur", "Xinchi Qiu", "Javier Fernandez-Marques", "Yan Gao", "Lorenzo Sani", "Kwing Hei Li", "Titouan Parcollet", "Pedro Porto Buarque de Gusmão", "Nicholas D. Lane"], "categories": ["cs.LG", "cs.CV", "stat.ML"], "doi": "", "_query": "house price prediction machine learning", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2110.04126v4", "title": "3D Infomax improves GNNs for Molecular Property Prediction", "abstract": "Molecular property prediction is one of the fastest-growing applications of deep learning with critical real-world impacts. Including 3D molecular structure as input to learned models improves their performance for many molecular tasks. However, this information is infeasible to compute at the scale required by several real-world applications. We propose pre-training a model to reason about the geometry of molecules given only their 2D molecular graphs. Using methods from self-supervised learning, we maximize the mutual information between 3D summary vectors and the representations of a Graph Neural Network (GNN) such that they contain latent 3D information. During fine-tuning on molecules with unknown geometry, the GNN still generates implicit 3D information and can use it to improve downstream tasks. We show that 3D pre-training provides significant improvements for a wide range of properties, such as a 22% average MAE reduction on eight quantum mechanical properties. Moreover, the learned representations can be effectively transferred between datasets in different molecular spaces.", "published": "2021-10-08T13:30:49Z", "authors": ["Hannes Stärk", "Dominique Beaini", "Gabriele Corso", "Prudencio Tossou", "Christian Dallago", "Stephan Günnemann", "Pietro Liò"], "categories": ["cs.LG", "cs.AI", "q-bio.BM"], "doi": "", "_query": "house price prediction machine learning", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/1903.00817v1", "title": "Cultural evolution in Vietnam's early 20th century: a Bayesian networks analysis of Franco-Chinese house designs", "abstract": "The study of cultural evolution has taken on an increasingly interdisciplinary and diverse approach in explicating phenomena of cultural transmission and adoptions. Inspired by this computational movement, this study uses Bayesian networks analysis, combining both the frequentist and the Hamiltonian Markov chain Monte Carlo (MCMC) approach, to investigate the highly representative elements in the cultural evolution of a Vietnamese city's architecture in the early 20th century. With a focus on the façade design of 68 old houses in Hanoi's Old Quarter (based on 78 data lines extracted from 248 photos), the study argues that it is plausible to look at the aesthetics, architecture and designs of the house façade to find traces of cultural evolution in Vietnam, which went through more than six decades of French colonization and centuries of sociocultural influence from China. The in-depth technical analysis, though refuting the presumed model on the probabilistic dependency among the variables, yields several results, the most notable of which is the strong influence of Buddhism over the decorations of the house façade. Particularly, in the top 5 networks with the best Bayesian Information criterion (BIC) scores and p<0.05, the variable for decorations (DC) always has a direct probabilistic dependency on the variable B for Buddhism. The paper then checks the robustness of these models using Hamiltonian MCMC method and find the posterior distributions of the models' coefficients all satisfy the technical requirement. Finally, this study suggests integrating Bayesian statistics in social sciences in general and for study of cultural evolution and architectural transformation in particular.", "published": "2019-03-03T03:47:11Z", "authors": ["Quan-Hoang Vuong", "Quang-Khiem Bui", "Viet-Phuong La", "Thu-Trang Vuong", "Manh-Toan Ho", "Hong-Kong T. Nguyen", "Hong-Ngoc Nguyen", "Kien-Cuong P. Nghiem", "Manh-Tung Ho"], "categories": ["stat.AP"], "doi": "10.1016/j.ssaho.2019.100001", "_query": "house price prediction machine learning", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2006.09430v2", "title": "Wasserstein Embedding for Graph Learning", "abstract": "We present Wasserstein Embedding for Graph Learning (WEGL), a novel and fast framework for embedding entire graphs in a vector space, in which various machine learning models are applicable for graph-level prediction tasks. We leverage new insights on defining similarity between graphs as a function of the similarity between their node embedding distributions. Specifically, we use the Wasserstein distance to measure the dissimilarity between node embeddings of different graphs. Unlike prior work, we avoid pairwise calculation of distances between graphs and reduce the computational complexity from quadratic to linear in the number of graphs. WEGL calculates Monge maps from a reference distribution to each node embedding and, based on these maps, creates a fixed-sized vector representation of the graph. We evaluate our new graph embedding approach on various benchmark graph-property prediction tasks, showing state-of-the-art classification performance while having superior computational efficiency. The code is available at https://github.com/navid-naderi/WEGL.", "published": "2020-06-16T18:23:00Z", "authors": ["Soheil Kolouri", "Navid Naderializadeh", "Gustavo K. Rohde", "Heiko Hoffmann"], "categories": ["cs.LG", "stat.ML"], "doi": "", "_query": "house price prediction machine learning", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/1910.03081v1", "title": "On the Interpretability and Evaluation of Graph Representation Learning", "abstract": "With the rising interest in graph representation learning, a variety of approaches have been proposed to effectively capture a graph's properties. While these approaches have improved performance in graph machine learning tasks compared to traditional graph techniques, they are still perceived as techniques with limited insight into the information encoded in these representations. In this work, we explore methods to interpret node embeddings and propose the creation of a robust evaluation framework for comparing graph representation learning algorithms and hyperparameters. We test our methods on graphs with different properties and investigate the relationship between embedding training parameters and the ability of the produced embedding to recover the structure of the original graph in a downstream task.", "published": "2019-10-07T21:02:13Z", "authors": ["Antonia Gogoglou", "C. Bayan Bruss", "Keegan E. Hines"], "categories": ["cs.LG", "stat.ML"], "doi": "", "_query": "house price prediction machine learning", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/1811.00542v1", "title": "Pymc-learn: Practical Probabilistic Machine Learning in Python", "abstract": "$\\textit{Pymc-learn}$ is a Python package providing a variety of state-of-the-art probabilistic models for supervised and unsupervised machine learning. It is inspired by $\\textit{scikit-learn}$ and focuses on bringing probabilistic machine learning to non-specialists. It uses a general-purpose high-level language that mimics $\\textit{scikit-learn}$. Emphasis is put on ease of use, productivity, flexibility, performance, documentation, and an API consistent with $\\textit{scikit-learn}$. It depends on $\\textit{scikit-learn}$ and $\\textit{pymc3}$ and is distributed under the new BSD-3 license, encouraging its use in both academia and industry. Source code, binaries, and documentation are available on http://github.com/pymc-learn/pymc-learn.", "published": "2018-10-31T22:54:12Z", "authors": ["Daniel Emaasit"], "categories": ["stat.ML", "cs.LG"], "doi": "", "_query": "house price prediction machine learning", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/1806.07552v1", "title": "Interpretable to Whom? A Role-based Model for Analyzing Interpretable Machine Learning Systems", "abstract": "Several researchers have argued that a machine learning system's interpretability should be defined in relation to a specific agent or task: we should not ask if the system is interpretable, but to whom is it interpretable. We describe a model intended to help answer this question, by identifying different roles that agents can fulfill in relation to the machine learning system. We illustrate the use of our model in a variety of scenarios, exploring how an agent's role influences its goals, and the implications for defining interpretability. Finally, we make suggestions for how our model could be useful to interpretability researchers, system developers, and regulatory bodies auditing machine learning systems.", "published": "2018-06-20T04:52:33Z", "authors": ["Richard Tomsett", "Dave Braines", "Dan Harborne", "Alun Preece", "Supriyo Chakraborty"], "categories": ["cs.AI"], "doi": "", "_query": "house price prediction machine learning", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2211.09781v2", "title": "Monitoring machine learning (ML)-based risk prediction algorithms in the presence of confounding medical interventions", "abstract": "Performance monitoring of machine learning (ML)-based risk prediction models in healthcare is complicated by the issue of confounding medical interventions (CMI): when an algorithm predicts a patient to be at high risk for an adverse event, clinicians are more likely to administer prophylactic treatment and alter the very target that the algorithm aims to predict. A simple approach is to ignore CMI and monitor only the untreated patients, whose outcomes remain unaltered. In general, ignoring CMI may inflate Type I error because (i) untreated patients disproportionally represent those with low predicted risk and (ii) evolution in both the model and clinician trust in the model can induce complex dependencies that violate standard assumptions. Nevertheless, we show that valid inference is still possible if one monitors conditional performance and if either conditional exchangeability or time-constant selection bias hold. Specifically, we develop a new score-based cumulative sum (CUSUM) monitoring procedure with dynamic control limits. Through simulations, we demonstrate the benefits of combining model updating with monitoring and investigate how over-trust in a prediction model may delay detection of performance deterioration. Finally, we illustrate how these monitoring methods can be used to detect calibration decay of an ML-based risk calculator for postoperative nausea and vomiting during the COVID-19 pandemic.", "published": "2022-11-17T18:54:34Z", "authors": ["Jean Feng", "Alexej Gossmann", "Gene Pennello", "Nicholas Petrick", "Berkman Sahiner", "Romain Pirracchio"], "categories": ["stat.ML", "cs.CY", "cs.LG"], "doi": "", "_query": "house price prediction machine learning", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/1806.04360v1", "title": "MSplit LBI: Realizing Feature Selection and Dense Estimation Simultaneously in Few-shot and Zero-shot Learning", "abstract": "It is one typical and general topic of learning a good embedding model to efficiently learn the representation coefficients between two spaces/subspaces. To solve this task, $L_{1}$ regularization is widely used for the pursuit of feature selection and avoiding overfitting, and yet the sparse estimation of features in $L_{1}$ regularization may cause the underfitting of training data. $L_{2}$ regularization is also frequently used, but it is a biased estimator. In this paper, we propose the idea that the features consist of three orthogonal parts, \\emph{namely} sparse strong signals, dense weak signals and random noise, in which both strong and weak signals contribute to the fitting of data. To facilitate such novel decomposition, \\emph{MSplit} LBI is for the first time proposed to realize feature selection and dense estimation simultaneously. We provide theoretical and simulational verification that our method exceeds $L_{1}$ and $L_{2}$ regularization, and extensive experimental results show that our method achieves state-of-the-art performance in the few-shot and zero-shot learning.", "published": "2018-06-12T07:07:44Z", "authors": ["Bo Zhao", "Xinwei Sun", "Yanwei Fu", "Yuan Yao", "Yizhou Wang"], "categories": ["cs.CV"], "doi": "", "_query": "house price prediction machine learning", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2502.15962v2", "title": "Towards Efficient Contrastive PAC Learning", "abstract": "We study contrastive learning under the PAC learning framework. While a series of recent works have shown statistical results for learning under contrastive loss, based either on the VC-dimension or Rademacher complexity, their algorithms are inherently inefficient or not implying PAC guarantees. In this paper, we consider contrastive learning of the fundamental concept of linear representations. Surprisingly, even under such basic setting, the existence of efficient PAC learners is largely open. We first show that the problem of contrastive PAC learning of linear representations is intractable to solve in general. We then show that it can be relaxed to a semi-definite program when the distance between contrastive samples is measured by the $\\ell_2$-norm. We then establish generalization guarantees based on Rademacher complexity, and connect it to PAC guarantees under certain contrastive large-margin conditions. To the best of our knowledge, this is the first efficient PAC learning algorithm for contrastive learning.", "published": "2025-02-21T21:51:01Z", "authors": ["Jie Shen"], "categories": ["cs.LG", "cs.DS", "stat.ML"], "doi": "", "_query": "house price prediction machine learning", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/1502.02127v2", "title": "Hyperparameter Search in Machine Learning", "abstract": "We introduce the hyperparameter search problem in the field of machine learning and discuss its main challenges from an optimization perspective. Machine learning methods attempt to build models that capture some element of interest based on given data. Most common learning algorithms feature a set of hyperparameters that must be determined before training commences. The choice of hyperparameters can significantly affect the resulting model's performance, but determining good values can be complex; hence a disciplined, theoretically sound search strategy is essential.", "published": "2015-02-07T11:46:22Z", "authors": ["Marc Claesen", "Bart De Moor"], "categories": ["cs.LG", "stat.ML"], "doi": "", "_query": "house price prediction machine learning", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/1910.04956v2", "title": "Central Server Free Federated Learning over Single-sided Trust Social Networks", "abstract": "Federated learning has become increasingly important for modern machine learning, especially for data privacy-sensitive scenarios. Existing federated learning mostly adopts the central server-based architecture or centralized architecture. However, in many social network scenarios, centralized federated learning is not applicable (e.g., a central agent or server connecting all users may not exist, or the communication cost to the central server is not affordable). In this paper, we consider a generic setting: 1) the central server may not exist, and 2) the social network is unidirectional or of single-sided trust (i.e., user A trusts user B but user B may not trust user A). We propose a central server free federated learning algorithm, named Online Push-Sum (OPS) method, to handle this challenging but generic scenario. A rigorous regret analysis is also provided, which shows very interesting results on how users can benefit from communication with trusted users in the federated learning scenario. This work builds upon the fundamental algorithm framework and theoretical guarantees for federated learning in the generic social network scenario.", "published": "2019-10-11T03:36:53Z", "authors": ["Chaoyang He", "Conghui Tan", "Hanlin Tang", "Shuang Qiu", "Ji Liu"], "categories": ["cs.LG", "cs.SI", "stat.ML"], "doi": "", "_query": "house price prediction machine learning", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/1901.00770v2", "title": "Personalized explanation in machine learning: A conceptualization", "abstract": "Explanation in machine learning and related fields such as artificial intelligence aims at making machine learning models and their decisions understandable to humans. Existing work suggests that personalizing explanations might help to improve understandability. In this work, we derive a conceptualization of personalized explanation by defining and structuring the problem based on prior work on machine learning explanation, personalization (in machine learning) and concepts and techniques from other domains such as privacy and knowledge elicitation. We perform a categorization of explainee data used in the process of personalization as well as describing means to collect this data. We also identify three key explanation properties that are amendable to personalization: complexity, decision information and presentation. We also enhance existing work on explanation by introducing additional desiderata and measures to quantify the quality of personalized explanations.", "published": "2019-01-03T14:37:04Z", "authors": ["Johanes Schneider", "Joshua Handali"], "categories": ["cs.LG", "stat.ML"], "doi": "", "_query": "house price prediction machine learning", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2112.14332v5", "title": "Adaptive Client Sampling in Federated Learning via Online Learning with Bandit Feedback", "abstract": "Due to the high cost of communication, federated learning (FL) systems need to sample a subset of clients that are involved in each round of training. As a result, client sampling plays an important role in FL systems as it affects the convergence rate of optimization algorithms used to train machine learning models. Despite its importance, there is limited work on how to sample clients effectively. In this paper, we cast client sampling as an online learning task with bandit feedback, which we solve with an online stochastic mirror descent (OSMD) algorithm designed to minimize the sampling variance. We then theoretically show how our sampling method can improve the convergence speed of federated optimization algorithms over the widely used uniform sampling. Through both simulated and real data experiments, we empirically illustrate the advantages of the proposed client sampling algorithm over uniform sampling and existing online learning-based sampling strategies. The proposed adaptive sampling procedure is applicable beyond the FL problem studied here and can be used to improve the performance of stochastic optimization procedures such as stochastic gradient descent and stochastic coordinate descent.", "published": "2021-12-28T23:50:52Z", "authors": ["Boxin Zhao", "Lingxiao Wang", "Ziqi Liu", "Zhiqiang Zhang", "Jun Zhou", "Chaochao Chen", "Mladen Kolar"], "categories": ["cs.LG"], "doi": "", "_query": "house price prediction machine learning", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2210.15070v2", "title": "Visual explanations of machine learning model estimating charge states in quantum dots", "abstract": "Charge state recognition in quantum dot devices is important in the preparation of quantum bits for quantum information processing. Toward auto-tuning of larger-scale quantum devices, automatic charge state recognition by machine learning has been demonstrated. For further development of this technology, an understanding of the operation of the machine learning model, which is usually a black box, will be useful. In this study, we analyze the explainability of the machine learning model estimating charge states in quantum dots by gradient-weighted class activation mapping, which identified class-discriminative regions for the predictions. The model predicts the state based on the change transition lines, indicating that human-like recognition is realized. We also demonstrate improvements of the model by utilizing feedback from the mapping results. Due to the simplicity of our simulation and pre-processing methods, our approach offers scalability without significant additional simulation costs, demonstrating its suitability for future quantum dot system expansions.", "published": "2022-10-26T22:53:59Z", "authors": ["Yui Muto", "Takumi Nakaso", "Motoya Shinozaki", "Takumi Aizawa", "Takahito Kitada", "Takashi Nakajima", "Matthieu R. Delbecq", "Jun Yoneda", "Kenta Takeda", "Akito Noiri", "Arne Ludwig", "Andreas D. Wieck", "Seigo Tarucha", "Atsunori Kanemura", "Motoki Shiga", "Tomohiro Otsuka"], "categories": ["cond-mat.mes-hall"], "doi": "10.1063/5.0193621", "_query": "house price prediction machine learning", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2310.16996v1", "title": "Towards Continually Learning Application Performance Models", "abstract": "Machine learning-based performance models are increasingly being used to build critical job scheduling and application optimization decisions. Traditionally, these models assume that data distribution does not change as more samples are collected over time. However, owing to the complexity and heterogeneity of production HPC systems, they are susceptible to hardware degradation, replacement, and/or software patches, which can lead to drift in the data distribution that can adversely affect the performance models. To this end, we develop continually learning performance models that account for the distribution drift, alleviate catastrophic forgetting, and improve generalizability. Our best model was able to retain accuracy, regardless of having to learn the new distribution of data inflicted by system changes, while demonstrating a 2x improvement in the prediction accuracy of the whole data sequence in comparison to the naive approach.", "published": "2023-10-25T20:48:46Z", "authors": ["Ray A. O. Sinurat", "Anurag Daram", "Haryadi S. Gunawi", "Robert B. Ross", "Sandeep Madireddy"], "categories": ["cs.LG", "cs.DC"], "doi": "", "_query": "house price prediction machine learning", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2107.02847v2", "title": "Transfer Learning in Information Criteria-based Feature Selection", "abstract": "This paper investigates the effectiveness of transfer learning based on Mallows' Cp. We propose a procedure that combines transfer learning with Mallows' Cp (TLCp) and prove that it outperforms the conventional Mallows' Cp criterion in terms of accuracy and stability. Our theoretical results indicate that, for any sample size in the target domain, the proposed TLCp estimator performs better than the Cp estimator by the mean squared error (MSE) metric in the case of orthogonal predictors, provided that i) the dissimilarity between the tasks from source domain and target domain is small, and ii) the procedure parameters (complexity penalties) are tuned according to certain explicit rules. Moreover, we show that our transfer learning framework can be extended to other feature selection criteria, such as the Bayesian information criterion. By analyzing the solution of the orthogonalized Cp, we identify an estimator that asymptotically approximates the solution of the Cp criterion in the case of non-orthogonal predictors. Similar results are obtained for the non-orthogonal TLCp. Finally, simulation studies and applications with real data demonstrate the usefulness of the TLCp scheme.", "published": "2021-07-06T19:12:15Z", "authors": ["Shaohan Chen", "Nikolaos V. Sahinidis", "Chuanhou Gao"], "categories": ["stat.ML", "cs.LG"], "doi": "", "_query": "house price prediction machine learning", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2202.02003v1", "title": "Capturing and incorporating expert knowledge into machine learning models for quality prediction in manufacturing", "abstract": "Increasing digitalization enables the use of machine learning methods for analyzing and optimizing manufacturing processes. A main application of machine learning is the construction of quality prediction models, which can be used, among other things, for documentation purposes, as assistance systems for process operators, or for adaptive process control. The quality of such machine learning models typically strongly depends on the amount and the quality of data used for training. In manufacturing, the size of available datasets before start of production is often limited. In contrast to data, expert knowledge commonly is available in manufacturing. Therefore, this study introduces a general methodology for building quality prediction models with machine learning methods on small datasets by integrating shape expert knowledge, that is, prior knowledge about the shape of the input-output relationship to be learned. The proposed methodology is applied to a brushing process with $125$ data points for predicting the surface roughness as a function of five process variables. As opposed to conventional machine learning methods for small datasets, the proposed methodology produces prediction models that strictly comply with all the expert knowledge specified by the involved process specialists. In particular, the direct involvement of process experts in the training of the models leads to a very clear interpretation and, by extension, to a high acceptance of the models. Another merit of the proposed methodology is that, in contrast to most conventional machine learning methods, it involves no time-consuming and often heuristic hyperparameter tuning or model selection step.", "published": "2022-02-04T07:22:29Z", "authors": ["Patrick Link", "Miltiadis Poursanidis", "Jochen Schmid", "Rebekka Zache", "Martin von Kurnatowski", "Uwe Teicher", "Steffen Ihlenfeldt"], "categories": ["cs.LG"], "doi": "", "_query": "house price prediction machine learning", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2403.10368v1", "title": "Conformal Predictions for Probabilistically Robust Scalable Machine Learning Classification", "abstract": "Conformal predictions make it possible to define reliable and robust learning algorithms. But they are essentially a method for evaluating whether an algorithm is good enough to be used in practice. To define a reliable learning framework for classification from the very beginning of its design, the concept of scalable classifier was introduced to generalize the concept of classical classifier by linking it to statistical order theory and probabilistic learning theory. In this paper, we analyze the similarities between scalable classifiers and conformal predictions by introducing a new definition of a score function and defining a special set of input variables, the conformal safety set, which can identify patterns in the input space that satisfy the error coverage guarantee, i.e., that the probability of observing the wrong (possibly unsafe) label for points belonging to this set is bounded by a predefined $\\varepsilon$ error level. We demonstrate the practical implications of this framework through an application in cybersecurity for identifying DNS tunneling attacks. Our work contributes to the development of probabilistically robust and reliable machine learning models.", "published": "2024-03-15T14:59:24Z", "authors": ["Alberto Carlevaro", "Teodoro Alamo Cantarero", "Fabrizio Dabbene", "Maurizio Mongelli"], "categories": ["stat.ML", "cs.CR", "cs.LG"], "doi": "", "_query": "house price prediction machine learning", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2203.13534v2", "title": "Generalization bounds for learning under graph-dependence: A survey", "abstract": "Traditional statistical learning theory relies on the assumption that data are identically and independently distributed (i.i.d.). However, this assumption often does not hold in many real-life applications. In this survey, we explore learning scenarios where examples are dependent and their dependence relationship is described by a dependency graph, a commonly utilized model in probability and combinatorics. We collect various graph-dependent concentration bounds, which are then used to derive Rademacher complexity and stability generalization bounds for learning from graph-dependent data. We illustrate this paradigm through practical learning tasks and provide some research directions for future work. To our knowledge, this survey is the first of this kind on this subject.", "published": "2022-03-25T09:33:49Z", "authors": ["Rui-Ray Zhang", "Massih-Reza Amini"], "categories": ["cs.LG", "stat.ML"], "doi": "10.1007/s10994-024-06536-9", "_query": "house price prediction machine learning", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/1602.03146v2", "title": "DCM Bandits: Learning to Rank with Multiple Clicks", "abstract": "A search engine recommends to the user a list of web pages. The user examines this list, from the first page to the last, and clicks on all attractive pages until the user is satisfied. This behavior of the user can be described by the dependent click model (DCM). We propose DCM bandits, an online learning variant of the DCM where the goal is to maximize the probability of recommending satisfactory items, such as web pages. The main challenge of our learning problem is that we do not observe which attractive item is satisfactory. We propose a computationally-efficient learning algorithm for solving our problem, dcmKL-UCB; derive gap-dependent upper bounds on its regret under reasonable assumptions; and also prove a matching lower bound up to logarithmic factors. We evaluate our algorithm on synthetic and real-world problems, and show that it performs well even when our model is misspecified. This work presents the first practical and regret-optimal online algorithm for learning to rank with multiple clicks in a cascade-like click model.", "published": "2016-02-09T20:03:30Z", "authors": ["Sumeet Katariya", "Branislav Kveton", "Csaba Szepesvári", "Zheng Wen"], "categories": ["cs.LG", "stat.ML"], "doi": "", "_query": "house price prediction machine learning", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2007.07981v1", "title": "Differential Replication in Machine Learning", "abstract": "When deployed in the wild, machine learning models are usually confronted with data and requirements that constantly vary, either because of changes in the generating distribution or because external constraints change the environment where the model operates. To survive in such an ecosystem, machine learning models need to adapt to new conditions by evolving over time. The idea of model adaptability has been studied from different perspectives. In this paper, we propose a solution based on reusing the knowledge acquired by the already deployed machine learning models and leveraging it to train future generations. This is the idea behind differential replication of machine learning models.", "published": "2020-07-15T20:26:49Z", "authors": ["Irene Unceta", "Jordi Nin", "Oriol Pujol"], "categories": ["cs.LG", "stat.ML"], "doi": "", "_query": "house price prediction machine learning", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/1902.00562v1", "title": "The Spatially-Conscious Machine Learning Model", "abstract": "Successfully predicting gentrification could have many social and commercial applications; however, real estate sales are difficult to predict because they belong to a chaotic system comprised of intrinsic and extrinsic characteristics, perceived value, and market speculation. Using New York City real estate as our subject, we combine modern techniques of data science and machine learning with traditional spatial analysis to create robust real estate prediction models for both classification and regression tasks. We compare several cutting edge machine learning algorithms across spatial, semi-spatial and non-spatial feature engineering techniques, and we empirically show that spatially-conscious machine learning models outperform non-spatial models when married with advanced prediction techniques such as feed-forward artificial neural networks and gradient boosting machine models.", "published": "2019-02-01T20:58:34Z", "authors": ["Timothy J. Kiely", "Nathaniel D. Bastian"], "categories": ["stat.ML", "cs.LG"], "doi": "", "_query": "house price prediction machine learning", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2002.05212v3", "title": "Estimating Uncertainty Intervals from Collaborating Networks", "abstract": "Effective decision making requires understanding the uncertainty inherent in a prediction. In regression, this uncertainty can be estimated by a variety of methods; however, many of these methods are laborious to tune, generate overconfident uncertainty intervals, or lack sharpness (give imprecise intervals). We address these challenges by proposing a novel method to capture predictive distributions in regression by defining two neural networks with two distinct loss functions. Specifically, one network approximates the cumulative distribution function, and the second network approximates its inverse. We refer to this method as Collaborating Networks (CN). Theoretical analysis demonstrates that a fixed point of the optimization is at the idealized solution, and that the method is asymptotically consistent to the ground truth distribution. Empirically, learning is straightforward and robust. We benchmark CN against several common approaches on two synthetic and six real-world datasets, including forecasting A1c values in diabetic patients from electronic health records, where uncertainty is critical. In the synthetic data, the proposed approach essentially matches ground truth. In the real-world datasets, CN improves results on many performance metrics, including log-likelihood estimates, mean absolute errors, coverage estimates, and prediction interval widths.", "published": "2020-02-12T20:10:27Z", "authors": ["Tianhui Zhou", "Yitong Li", "Yuan Wu", "David Carlson"], "categories": ["stat.ML", "cs.LG"], "doi": "", "_query": "house price prediction machine learning", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2108.10826v2", "title": "S&P 500 Stock Price Prediction Using Technical, Fundamental and Text Data", "abstract": "We summarized both common and novel predictive models used for stock price prediction and combined them with technical indices, fundamental characteristics and text-based sentiment data to predict S&P stock prices. A 66.18% accuracy in S&P 500 index directional prediction and 62.09% accuracy in individual stock directional prediction was achieved by combining different machine learning models such as Random Forest and LSTM together into state-of-the-art ensemble models. The data we use contains weekly historical prices, finance reports, and text information from news items associated with 518 different common stocks issued by current and former S&P 500 large-cap companies, from January 1, 2000 to December 31, 2019. Our study's innovation includes utilizing deep language models to categorize and infer financial news item sentiment; fusing different models containing different combinations of variables and stocks to jointly make predictions; and overcoming the insufficient data problem for machine learning models in time series by using data across different stocks.", "published": "2021-08-24T16:18:52Z", "authors": ["Shan Zhong", "David B. Hitchcock"], "categories": ["stat.ML", "cs.LG"], "doi": "10.19139/soic-2310-5070-1362", "_query": "house price prediction machine learning", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2301.02738v1", "title": "LS-DYNA Machine Learning-based Multiscale Method for Nonlinear Modeling of Short Fiber-Reinforced Composites", "abstract": "Short-fiber-reinforced composites (SFRC) are high-performance engineering materials for lightweight structural applications in the automotive and electronics industries. Typically, SFRC structures are manufactured by injection molding, which induces heterogeneous microstructures, and the resulting nonlinear anisotropic behaviors are challenging to predict by conventional micromechanical analyses. In this work, we present a machine learning-based multiscale method by integrating injection molding-induced microstructures, material homogenization, and Deep Material Network (DMN) in the finite element simulation software LS-DYNA for structural analysis of SFRC. DMN is a physics-embedded machine learning model that learns the microscale material morphologies hidden in representative volume elements of composites through offline training. By coupling DMN with finite elements, we have developed a highly accurate and efficient data-driven approach, which predicts nonlinear behaviors of composite materials and structures at a computational speed orders-of-magnitude faster than the high-fidelity direct numerical simulation. To model industrial-scale SFRC products, transfer learning is utilized to generate a unified DMN database, which effectively captures the effects of injection molding-induced fiber orientations and volume fractions on the overall composite properties. Numerical examples are presented to demonstrate the promising performance of this LS-DYNA machine learning-based multiscale method for SFRC modeling.", "published": "2023-01-06T22:33:19Z", "authors": ["Haoyan Wei", "C. T. Wu", "Wei Hu", "Tung-Huan Su", "Hitoshi Oura", "Masato Nishi", "Tadashi Naito", "Stan Chung", "Leo Shen"], "categories": ["cs.CE", "cs.AI", "cs.LG", "math.NA"], "doi": "10.1061/JENMDT.EMENG-6945", "_query": "house price prediction machine learning", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/1912.11762v1", "title": "The Application of Machine Learning Techniques for Predicting Results in Team Sport: A Review", "abstract": "Over the past two decades, Machine Learning (ML) techniques have been increasingly utilized for the purpose of predicting outcomes in sport. In this paper, we provide a review of studies that have used ML for predicting results in team sport, covering studies from 1996 to 2019. We sought to answer five key research questions while extensively surveying papers in this field. This paper offers insights into which ML algorithms have tended to be used in this field, as well as those that are beginning to emerge with successful outcomes. Our research highlights defining characteristics of successful studies and identifies robust strategies for evaluating accuracy results in this application domain. Our study considers accuracies that have been achieved across different sports and explores the notion that outcomes of some team sports could be inherently more difficult to predict than others. Finally, our study uncovers common themes of future research directions across all surveyed papers, looking for gaps and opportunities, while proposing recommendations for future researchers in this domain.", "published": "2019-12-26T03:12:21Z", "authors": ["Rory Bunker", "Teo Susnjak"], "categories": ["cs.LG", "stat.AP", "stat.ML"], "doi": "10.1613/jair.1.13509", "_query": "house price prediction machine learning", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2102.00837v1", "title": "Machine learning pipeline for battery state of health estimation", "abstract": "Lithium-ion batteries are ubiquitous in modern day applications ranging from portable electronics to electric vehicles. Irrespective of the application, reliable real-time estimation of battery state of health (SOH) by on-board computers is crucial to the safe operation of the battery, ultimately safeguarding asset integrity. In this paper, we design and evaluate a machine learning pipeline for estimation of battery capacity fade - a metric of battery health - on 179 cells cycled under various conditions. The pipeline estimates battery SOH with an associated confidence interval by using two parametric and two non-parametric algorithms. Using segments of charge voltage and current curves, the pipeline engineers 30 features, performs automatic feature selection and calibrates the algorithms. When deployed on cells operated under the fast-charging protocol, the best model achieves a root mean squared percent error of 0.45\\%. This work provides insights into the design of scalable data-driven models for battery SOH estimation, emphasising the value of confidence bounds around the prediction. The pipeline methodology combines experimental data with machine learning modelling and can be generalized to other critical components that require real-time estimation of SOH.", "published": "2021-02-01T13:50:56Z", "authors": ["Darius Roman", "Saurabh Saxena", "Valentin Robu", "Michael Pecht", "David Flynn"], "categories": ["cs.LG"], "doi": "", "_query": "house price prediction machine learning", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/1905.07026v2", "title": "Fairness in Machine Learning with Tractable Models", "abstract": "Machine Learning techniques have become pervasive across a range of different applications, and are now widely used in areas as disparate as recidivism prediction, consumer credit-risk analysis and insurance pricing. The prevalence of machine learning techniques has raised concerns about the potential for learned algorithms to become biased against certain groups. Many definitions have been proposed in the literature, but the fundamental task of reasoning about probabilistic events is a challenging one, owing to the intractability of inference. The focus of this paper is taking steps towards the application of tractable models to fairness. Tractable probabilistic models have emerged that guarantee that conditional marginal can be computed in time linear in the size of the model. In particular, we show that sum product networks (SPNs) enable an effective technique for determining the statistical relationships between protected attributes and other training variables. If a subset of these training variables are found by the SPN to be independent of the training attribute then they can be considered `safe' variables, from which we can train a classification model without concern that the resulting classifier will result in disparate outcomes for different demographic groups. Our initial experiments on the `German Credit' data set indicate that this processing technique significantly reduces disparate treatment of male and female credit applicants, with a small reduction in classification accuracy compared to state of the art. We will also motivate the concept of \"fairness through percentile equivalence\", a new definition predicated on the notion that individuals at the same percentile of their respective distributions should be treated equivalently, and this prevents unfair penalisation of those individuals who lie at the extremities of their respective distributions.", "published": "2019-05-16T20:31:26Z", "authors": ["Michael Varley", "Vaishak Belle"], "categories": ["cs.LG", "cs.AI", "cs.SC", "stat.ML"], "doi": "", "_query": "house price prediction machine learning", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2606.00512v1", "title": "Semi-Supervised Learning with Noisy Proxy Covariates: Generalization Bounds and Distribution Regression", "abstract": "In many modern machine learning pipelines, abundant pretrained representations serve as noisy proxy covariates, while task-specific labels remain scarce. We study semi-supervised regression in this setting, and propose a simple two stage estimator that learns kernel eigenfeatures from all proxy covariates and fits a ridge predictor on labeled data. We derive finite sample bounds showing that fast labeled sample rates are recovered when proxy perturbation is controlled and unlabeled proxy covariates are sufficiently abundant. We also show that distribution regression is a direct special case, with analogous guarantees when the finite bag size is large enough. Experiments show consistent gains over supervised and semi-supervised baselines, especially in low label regimes.", "published": "2026-05-30T04:01:14Z", "authors": ["Kwangho Kim", "Jisu Kim"], "categories": ["cs.LG", "cs.IT", "stat.ML"], "doi": "", "_query": "house price prediction machine learning", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2101.03788v1", "title": "Second Hand Price Prediction for Tesla Vehicles", "abstract": "The Tesla vehicles became very popular in the car industry as it was affordable in the consumer market and it left no carbon footprint. Due to the large decline in the stock prices of Tesla Inc. at the beginning of 2019, Tesla owners started selling their vehicles in the used car market. These used car prices depended on attributes such as the model of the vehicle, year of production, miles driven, and the battery used for the vehicle. Prices were different for a specific vehicle in different months. In this paper, it is discussed how a machine learning technique is being implemented in order to develop a second-hand Teslavehicle price prediction system. To reach this goal, different machine learning techniques such as decision trees, support vector machine (SVM), random forest, and deep learning were investigated and finally was implemented with boosted decision tree regression. I the future, it is intended to use a more sophisticated algorithm for better accuracy.", "published": "2021-01-11T09:54:13Z", "authors": ["Sayed Erfan Arefin"], "categories": ["cs.LG"], "doi": "", "_query": "house price prediction machine learning", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2106.00058v5", "title": "Stable and Interpretable Unrolled Dictionary Learning", "abstract": "The dictionary learning problem, representing data as a combination of a few atoms, has long stood as a popular method for learning representations in statistics and signal processing. The most popular dictionary learning algorithm alternates between sparse coding and dictionary update steps, and a rich literature has studied its theoretical convergence. The success of dictionary learning relies on access to a \"good\" initial estimate of the dictionary and the ability of the sparse coding step to provide an unbiased estimate of the code. The growing popularity of unrolled sparse coding networks has led to the empirical finding that backpropagation through such networks performs dictionary learning. We offer the theoretical analysis of these empirical results through PUDLE, a Provable Unrolled Dictionary LEarning method. We provide conditions on the network initialization and data distribution sufficient to recover and preserve the support of the latent code. Additionally, we address two challenges; first, the vanilla unrolled sparse coding computes a biased code estimate, and second, gradients during backpropagated learning can become unstable. We show approaches to reduce the bias of the code estimate in the forward pass, and that of the dictionary estimate in the backward pass. We propose strategies to resolve the learning instability by tuning network parameters and modifying the loss function. Overall, we highlight the impact of loss, unrolling, and backpropagation on convergence. We complement our findings through synthetic and image denoising experiments. Finally, we demonstrate PUDLE's interpretability, a driving factor in designing deep networks based on iterative optimizations, by building a mathematical relation between network weights, its output, and the training set.", "published": "2021-05-31T18:49:58Z", "authors": ["Bahareh Tolooshams", "Demba Ba"], "categories": ["cs.LG", "eess.SP", "stat.ML"], "doi": "", "_query": "house price prediction machine learning", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/1901.01686v1", "title": "Ten ways to fool the masses with machine learning", "abstract": "If you want to tell people the truth, make them laugh, otherwise they'll kill you. (source unclear) Machine learning and deep learning are the technologies of the day for developing intelligent automatic systems. However, a key hurdle for progress in the field is the literature itself: we often encounter papers that report results that are difficult to reconstruct or reproduce, results that mis-represent the performance of the system, or contain other biases that limit their validity. In this semi-humorous article, we discuss issues that arise in running and reporting results of machine learning experiments. The purpose of the article is to provide a list of watch out points for researchers to be aware of when developing machine learning models or writing and reviewing machine learning papers.", "published": "2019-01-07T07:27:11Z", "authors": ["Fayyaz Minhas", "Amina Asif", "Asa Ben-Hur"], "categories": ["cs.LG", "stat.ML"], "doi": "", "_query": "house price prediction machine learning", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/1808.03753v1", "title": "MARVIN: An Open Machine Learning Corpus and Environment for Automated Machine Learning Primitive Annotation and Execution", "abstract": "In this demo paper, we introduce the DARPA D3M program for automatic machine learning (ML) and JPL's MARVIN tool that provides an environment to locate, annotate, and execute machine learning primitives for use in ML pipelines. MARVIN is a web-based application and associated back-end interface written in Python that enables composition of ML pipelines from hundreds of primitives from the world of Scikit-Learn, Keras, DL4J and other widely used libraries. MARVIN allows for the creation of Docker containers that run on Kubernetes clusters within DARPA to provide an execution environment for automated machine learning. MARVIN currently contains over 400 datasets and challenge problems from a wide array of ML domains including routine classification and regression to advanced video/image classification and remote sensing.", "published": "2018-08-11T05:05:26Z", "authors": ["Chris A. Mattmann", "Sujen Shah", "Brian Wilson"], "categories": ["cs.LG", "stat.ML"], "doi": "", "_query": "house price prediction machine learning", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2106.09756v1", "title": "PyKale: Knowledge-Aware Machine Learning from Multiple Sources in Python", "abstract": "Machine learning is a general-purpose technology holding promises for many interdisciplinary research problems. However, significant barriers exist in crossing disciplinary boundaries when most machine learning tools are developed in different areas separately. We present Pykale - a Python library for knowledge-aware machine learning on graphs, images, texts, and videos to enable and accelerate interdisciplinary research. We formulate new green machine learning guidelines based on standard software engineering practices and propose a novel pipeline-based application programming interface (API). PyKale focuses on leveraging knowledge from multiple sources for accurate and interpretable prediction, thus supporting multimodal learning and transfer learning (particularly domain adaptation) with latest deep learning and dimensionality reduction models. We build PyKale on PyTorch and leverage the rich PyTorch ecosystem. Our pipeline-based API design enforces standardization and minimalism, embracing green machine learning concepts via reducing repetitions and redundancy, reusing existing resources, and recycling learning models across areas. We demonstrate its interdisciplinary nature via examples in bioinformatics, knowledge graph, image/video recognition, and medical imaging.", "published": "2021-06-17T18:35:37Z", "authors": ["Haiping Lu", "Xianyuan Liu", "Robert Turner", "Peizhen Bai", "Raivo E Koot", "Shuo Zhou", "Mustafa Chasmai", "Lawrence Schobs"], "categories": ["cs.LG", "cs.AI", "cs.CV", "stat.ML"], "doi": "", "_query": "house price prediction machine learning", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/1112.4632v4", "title": "The Price of Matching Selfish Vertices", "abstract": "We analyze the setting of minimum-cost perfect matchings with selfish vertices through the price of anarchy (PoA) and price of stability (PoS) lens. The underlying solution concept used for this analysis is the Gale-Shapley stable matching notion, where the preferences are determined so that each player (vertex) wishes to minimize the cost of her own matching edge.", "published": "2011-12-20T09:57:00Z", "authors": ["Yuval Emek", "Tobias Langner", "Roger Wattenhofer"], "categories": ["cs.CG"], "doi": "", "_query": "house price prediction machine learning", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/1905.09972v1", "title": "Generative Adversarial Networks for Mitigating Biases in Machine Learning Systems", "abstract": "In this paper, we propose a new framework for mitigating biases in machine learning systems. The problem of the existing mitigation approaches is that they are model-oriented in the sense that they focus on tuning the training algorithms to produce fair results, while overlooking the fact that the training data can itself be the main reason for biased outcomes. Technically speaking, two essential limitations can be found in such model-based approaches: 1) the mitigation cannot be achieved without degrading the accuracy of the machine learning models, and 2) when the data used for training are largely biased, the training time automatically increases so as to find suitable learning parameters that help produce fair results. To address these shortcomings, we propose in this work a new framework that can largely mitigate the biases and discriminations in machine learning systems while at the same time enhancing the prediction accuracy of these systems. The proposed framework is based on conditional Generative Adversarial Networks (cGANs), which are used to generate new synthetic fair data with selective properties from the original data. We also propose a framework for analyzing data biases, which is important for understanding the amount and type of data that need to be synthetically sampled and labeled for each population group. Experimental results show that the proposed solution can efficiently mitigate different types of biases, while at the same time enhancing the prediction accuracy of the underlying machine learning model.", "published": "2019-05-23T23:49:19Z", "authors": ["Adel Abusitta", "Esma Aïmeur", "Omar Abdel Wahab"], "categories": ["cs.LG", "stat.ML"], "doi": "", "_query": "house price prediction machine learning", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/1809.10780v3", "title": "Morpho-MNIST: Quantitative Assessment and Diagnostics for Representation Learning", "abstract": "Revealing latent structure in data is an active field of research, having introduced exciting technologies such as variational autoencoders and adversarial networks, and is essential to push machine learning towards unsupervised knowledge discovery. However, a major challenge is the lack of suitable benchmarks for an objective and quantitative evaluation of learned representations. To address this issue we introduce Morpho-MNIST, a framework that aims to answer: \"to what extent has my model learned to represent specific factors of variation in the data?\" We extend the popular MNIST dataset by adding a morphometric analysis enabling quantitative comparison of trained models, identification of the roles of latent variables, and characterisation of sample diversity. We further propose a set of quantifiable perturbations to assess the performance of unsupervised and supervised methods on challenging tasks such as outlier detection and domain adaptation. Data and code are available at https://github.com/dccastro/Morpho-MNIST.", "published": "2018-09-27T22:05:03Z", "authors": ["Daniel C. Castro", "Jeremy Tan", "Bernhard Kainz", "Ender Konukoglu", "Ben Glocker"], "categories": ["cs.LG", "stat.ML"], "doi": "", "_query": "house price prediction machine learning", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2009.13384v1", "title": "Transparency, Auditability and eXplainability of Machine Learning Models in Credit Scoring", "abstract": "A major requirement for credit scoring models is to provide a maximally accurate risk prediction. Additionally, regulators demand these models to be transparent and auditable. Thus, in credit scoring, very simple predictive models such as logistic regression or decision trees are still widely used and the superior predictive power of modern machine learning algorithms cannot be fully leveraged. Significant potential is therefore missed, leading to higher reserves or more credit defaults. This paper works out different dimensions that have to be considered for making credit scoring models understandable and presents a framework for making ``black box'' machine learning models transparent, auditable and explainable. Following this framework, we present an overview of techniques, demonstrate how they can be applied in credit scoring and how results compare to the interpretability of score cards. A real world case study shows that a comparable degree of interpretability can be achieved while machine learning techniques keep their ability to improve predictive power.", "published": "2020-09-28T15:00:13Z", "authors": ["Michael Bücker", "Gero Szepannek", "Alicja Gosiewska", "Przemyslaw Biecek"], "categories": ["stat.ML", "cs.LG", "econ.GN", "stat.AP", "stat.ME"], "doi": "10.1080/01605682.2021.1922098", "_query": "house price prediction machine learning", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2503.09330v2", "title": "Group-robust Machine Unlearning", "abstract": "Machine unlearning is an emerging paradigm to remove the influence of specific training data (i.e., the forget set) from a model while preserving its knowledge of the rest of the data (i.e., the retain set). Previous approaches assume the forget data to be uniformly distributed from all training datapoints. However, if the data to unlearn is dominant in one group (e.g., ethnicity, gender), we empirically show that performance for this group degrades, leading to fairness issues. To perform unlearning while preserving fairness, this work addresses the overlooked problem of non-uniformly distributed forget sets, which we refer to as group-robust machine unlearning. We formalize the problem and present a simple and effective exact unlearning strategy that mitigates the performance loss in dominant groups via sample distribution reweighting. Moreover, we present MIU (Mutual Information-aware Machine Unlearning), the first approach for group robustness in approximate machine unlearning. MIU minimizes the mutual information between model features and group information, achieving unlearning while reducing performance degradation in the dominant group of the forget set. Additionally, MIU exploits sample distribution reweighting and mutual information calibration with the original model to preserve group robustness. We conduct experiments on three datasets and show that MIU outperforms standard methods, achieving unlearning without compromising model robustness. Source code available at https://github.com/tdemin16/group-robust_machine_unlearning", "published": "2025-03-12T12:24:05Z", "authors": ["Thomas De Min", "Subhankar Roy", "Stéphane Lathuilière", "Elisa Ricci", "Massimiliano Mancini"], "categories": ["cs.LG", "cs.AI"], "doi": "", "_query": "house price prediction machine learning", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2006.16789v2", "title": "Causality Learning: A New Perspective for Interpretable Machine Learning", "abstract": "Recent years have witnessed the rapid growth of machine learning in a wide range of fields such as image recognition, text classification, credit scoring prediction, recommendation system, etc. In spite of their great performance in different sectors, researchers still concern about the mechanism under any machine learning (ML) techniques that are inherently black-box and becoming more complex to achieve higher accuracy. Therefore, interpreting machine learning model is currently a mainstream topic in the research community. However, the traditional interpretable machine learning focuses on the association instead of the causality. This paper provides an overview of causal analysis with the fundamental background and key concepts, and then summarizes most recent causal approaches for interpretable machine learning. The evaluation techniques for assessing method quality, and open problems in causal interpretability are also discussed in this paper.", "published": "2020-06-27T13:01:28Z", "authors": ["Guandong Xu", "Tri Dung Duong", "Qian Li", "Shaowu Liu", "Xianzhi Wang"], "categories": ["cs.LG", "cs.AI", "stat.ML"], "doi": "", "_query": "house price prediction machine learning", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2007.12475v1", "title": "Predicting and Mapping of Soil Organic Carbon Using Machine Learning Algorithms in Northern Iran", "abstract": "Estimation of the soil organic carbon content is of utmost importance in understanding the chemical, physical, and biological functions of the soil. This study proposes machine learning algorithms of support vector machines, artificial neural networks, regression tree, random forest, extreme gradient boosting, and conventional deep neural network for advancing prediction models of SOC. Models are trained with 1879 composite surface soil samples, and 105 auxiliary data as predictors. The genetic algorithm is used as a feature selection approach to identify effective variables. The results indicate that precipitation is the most important predictor driving 15 percent of SOC spatial variability followed by the normalized difference vegetation index, day temperature index of moderate resolution imaging spectroradiometer, multiresolution valley bottom flatness and land use, respectively. Based on 10 fold cross validation, the DNN model reported as a superior algorithm with the lowest prediction error and uncertainty. In terms of accuracy, DNN yielded a mean absolute error of 59 percent, a root mean squared error of 75 percent, a coefficient of determination of 0.65, and Lins concordance correlation coefficient of 0.83. The SOC content was the highest in udic soil moisture regime class with mean values of 4 percent, followed by the aquic and xeric classes, respectively. Soils in dense forestlands had the highest SOC contents, whereas soils of younger geological age and alluvial fans had lower SOC. The proposed DNN is a promising algorithm for handling large numbers of auxiliary data at a province scale, and due to its flexible structure and the ability to extract more information from the auxiliary data surrounding the sampled observations, it had high accuracy for the prediction of the SOC baseline map and minimal uncertainty.", "published": "2020-07-12T08:23:24Z", "authors": ["Mostafa Emadi", "Ruhollah Taghizadeh-Mehrjardi", "Ali Cherati", "Majid Danesh", "Amir Mosavi", "Thomas Scholten"], "categories": ["cs.LG", "stat.ML"], "doi": "10.3390/rs12142234", "_query": "house price prediction machine learning", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2002.04317v4", "title": "Machine Learning Approaches For Motor Learning: A Short Review", "abstract": "Machine learning approaches have seen considerable applications in human movement modeling, but remain limited for motor learning. Motor learning requires accounting for motor variability, and poses new challenges as the algorithms need to be able to differentiate between new movements and variation of known ones. In this short review, we outline existing machine learning models for motor learning and their adaptation capabilities. We identify and describe three types of adaptation: Parameter adaptation in probabilistic models, Transfer and meta-learning in deep neural networks, and Planning adaptation by reinforcement learning. To conclude, we discuss challenges for applying these models in the domain of motor learning support systems.", "published": "2020-02-11T11:11:26Z", "authors": ["Baptiste Caramiaux", "Jules Françoise", "Wanyu Liu", "Téo Sanchez", "Frédéric Bevilacqua"], "categories": ["cs.LG", "cs.HC", "cs.RO", "stat.ML"], "doi": "", "_query": "house price prediction machine learning", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/1812.06591v1", "title": "SMART: An Open Source Data Labeling Platform for Supervised Learning", "abstract": "SMART is an open source web application designed to help data scientists and research teams efficiently build labeled training data sets for supervised machine learning tasks. SMART provides users with an intuitive interface for creating labeled data sets, supports active learning to help reduce the required amount of labeled data, and incorporates inter-rater reliability statistics to provide insight into label quality. SMART is designed to be platform agnostic and easily deployable to meet the needs of as many different research teams as possible. The project website contains links to the code repository and extensive user documentation.", "published": "2018-12-11T22:49:14Z", "authors": ["Rob Chew", "Michael Wenger", "Caroline Kery", "Jason Nance", "Keith Richards", "Emily Hadley", "Peter Baumgartner"], "categories": ["stat.ML", "cs.LG"], "doi": "", "_query": "house price prediction machine learning", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/1811.06418v1", "title": "Adversarial Examples from Cryptographic Pseudo-Random Generators", "abstract": "In our recent work (Bubeck, Price, Razenshteyn, arXiv:1805.10204) we argued that adversarial examples in machine learning might be due to an inherent computational hardness of the problem. More precisely, we constructed a binary classification task for which (i) a robust classifier exists; yet no non-trivial accuracy can be obtained with an efficient algorithm in (ii) the statistical query model. In the present paper we significantly strengthen both (i) and (ii): we now construct a task which admits (i') a maximally robust classifier (that is it can tolerate perturbations of size comparable to the size of the examples themselves); and moreover we prove computational hardness of learning this task under (ii') a standard cryptographic assumption.", "published": "2018-11-15T15:08:12Z", "authors": ["Sébastien Bubeck", "Yin Tat Lee", "Eric Price", "Ilya Razenshteyn"], "categories": ["cs.LG", "cs.CC", "cs.CR", "stat.ML"], "doi": "", "_query": "house price prediction machine learning", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2106.07263v3", "title": "Machine Learning for Variance Reduction in Online Experiments", "abstract": "We consider the problem of variance reduction in randomized controlled trials, through the use of covariates correlated with the outcome but independent of the treatment. We propose a machine learning regression-adjusted treatment effect estimator, which we call MLRATE. MLRATE uses machine learning predictors of the outcome to reduce estimator variance. It employs cross-fitting to avoid overfitting biases, and we prove consistency and asymptotic normality under general conditions. MLRATE is robust to poor predictions from the machine learning step: if the predictions are uncorrelated with the outcomes, the estimator performs asymptotically no worse than the standard difference-in-means estimator, while if predictions are highly correlated with outcomes, the efficiency gains are large. In A/A tests, for a set of 48 outcome metrics commonly monitored in Facebook experiments the estimator has over 70% lower variance than the simple difference-in-means estimator, and about 19% lower variance than the common univariate procedure which adjusts only for pre-experiment values of the outcome.", "published": "2021-06-14T09:35:54Z", "authors": ["Yongyi Guo", "Dominic Coey", "Mikael Konutgan", "Wenting Li", "Chris Schoener", "Matt Goldman"], "categories": ["stat.ML", "cs.LG"], "doi": "", "_query": "house price prediction machine learning", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2107.07997v1", "title": "Uncertainty Prediction for Machine Learning Models of Material Properties", "abstract": "Uncertainty quantification in Artificial Intelligence (AI)-based predictions of material properties is of immense importance for the success and reliability of AI applications in material science. While confidence intervals are commonly reported for machine learning (ML) models, prediction intervals, i.e., the evaluation of the uncertainty on each prediction, are seldomly available. In this work we compare 3 different approaches to obtain such individual uncertainty, testing them on 12 ML-physical properties. Specifically, we investigated using the Quantile loss function, machine learning the prediction intervals directly and using Gaussian Processes. We identify each approachs advantages and disadvantages and end up slightly favoring the modeling of the individual uncertainties directly, as it is the easiest to fit and, in most cases, minimizes over-and under-estimation of the predicted errors. All data for training and testing were taken from the publicly available JARVIS-DFT database, and the codes developed for computing the prediction intervals are available through JARVIS-Tools.", "published": "2021-07-16T16:33:55Z", "authors": ["Francesca Tavazza", "Brian De Cost", "Kamal Choudhary"], "categories": ["cs.LG", "cond-mat.mtrl-sci"], "doi": "10.1021/acsomega.1c03752", "_query": "house price prediction machine learning", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2211.00246v2", "title": "Batch Active Learning from the Perspective of Sparse Approximation", "abstract": "Active learning enables efficient model training by leveraging interactions between machine learning agents and human annotators. We study and propose a novel framework that formulates batch active learning from the sparse approximation's perspective. Our active learning method aims to find an informative subset from the unlabeled data pool such that the corresponding training loss function approximates its full data pool counterpart. We realize the framework as sparsity-constrained discontinuous optimization problems, which explicitly balance uncertainty and representation for large-scale applications and could be solved by greedy or proximal iterative hard thresholding algorithms. The proposed method can adapt to various settings, including both Bayesian and non-Bayesian neural networks. Numerical experiments show that our work achieves competitive performance across different settings with lower computational complexity.", "published": "2022-11-01T03:20:28Z", "authors": ["Maohao Shen", "Bowen Jiang", "Jacky Yibo Zhang", "Oluwasanmi Koyejo"], "categories": ["cs.LG", "stat.ML"], "doi": "", "_query": "house price prediction machine learning", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/1807.11398v2", "title": "Preference-based Online Learning with Dueling Bandits: A Survey", "abstract": "In machine learning, the notion of multi-armed bandits refers to a class of online learning problems, in which an agent is supposed to simultaneously explore and exploit a given set of choice alternatives in the course of a sequential decision process. In the standard setting, the agent learns from stochastic feedback in the form of real-valued rewards. In many applications, however, numerical reward signals are not readily available -- instead, only weaker information is provided, in particular relative preferences in the form of qualitative comparisons between pairs of alternatives. This observation has motivated the study of variants of the multi-armed bandit problem, in which more general representations are used both for the type of feedback to learn from and the target of prediction. The aim of this paper is to provide a survey of the state of the art in this field, referred to as preference-based multi-armed bandits or dueling bandits. To this end, we provide an overview of problems that have been considered in the literature as well as methods for tackling them. Our taxonomy is mainly based on the assumptions made by these methods about the data-generating process and, related to this, the properties of the preference-based feedback.", "published": "2018-07-30T15:40:54Z", "authors": ["Viktor Bengs", "Robert Busa-Fekete", "Adil El Mesaoudi-Paul", "Eyke Hüllermeier"], "categories": ["cs.LG", "stat.ML"], "doi": "", "_query": "house price prediction machine learning", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2508.11210v1", "title": "Borrowing From the Future: Enhancing Early Risk Assessment through Contrastive Learning", "abstract": "Risk assessments for a pediatric population are often conducted across multiple stages. For example, clinicians may evaluate risks prenatally, at birth, and during Well-Child visits. Although predictions made at later stages typically achieve higher precision, it is clinically desirable to make reliable risk assessments as early as possible. Therefore, this study focuses on improving prediction performance in early-stage risk assessments. Our solution, \\textbf{Borrowing From the Future (BFF)}, is a contrastive multi-modal framework that treats each time window as a distinct modality. In BFF, a model is trained on all available data throughout the time while performing a risk assessment using up-to-date information. This contrastive framework allows the model to ``borrow'' informative signals from later stages (e.g., Well-Child visits) to implicitly supervise the learning at earlier stages (e.g., prenatal/birth stages). We validate BFF on two real-world pediatric outcome prediction tasks, demonstrating consistent improvements in early risk assessments. The code is available at https://github.com/scotsun/bff.", "published": "2025-08-15T04:40:21Z", "authors": ["Minghui Sun", "Matthew M. Engelhard", "Benjamin A. Goldstein"], "categories": ["cs.LG", "stat.ML"], "doi": "", "_query": "house price prediction machine learning", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2008.02369v1", "title": "QUBO Formulations for Training Machine Learning Models", "abstract": "Training machine learning models on classical computers is usually a time and compute intensive process. With Moore's law coming to an end and ever increasing demand for large-scale data analysis using machine learning, we must leverage non-conventional computing paradigms like quantum computing to train machine learning models efficiently. Adiabatic quantum computers like the D-Wave 2000Q can approximately solve NP-hard optimization problems, such as the quadratic unconstrained binary optimization (QUBO), faster than classical computers. Since many machine learning problems are also NP-hard, we believe adiabatic quantum computers might be instrumental in training machine learning models efficiently in the post Moore's law era. In order to solve a problem on adiabatic quantum computers, it must be formulated as a QUBO problem, which is a challenging task in itself. In this paper, we formulate the training problems of three machine learning models---linear regression, support vector machine (SVM) and equal-sized k-means clustering---as QUBO problems so that they can be trained on adiabatic quantum computers efficiently. We also analyze the time and space complexities of our formulations and compare them to the state-of-the-art classical algorithms for training these machine learning models. We show that the time and space complexities of our formulations are better (in the case of SVM and equal-sized k-means clustering) or equivalent (in case of linear regression) to their classical counterparts.", "published": "2020-08-05T21:16:05Z", "authors": ["Prasanna Date", "Davis Arthur", "Lauren Pusey-Nazzaro"], "categories": ["cs.LG", "physics.data-an", "stat.ML"], "doi": "10.1038/s41598-021-89461-4", "_query": "house price prediction machine learning", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2608.27205v2", "title": "Accurate prediction is not profitable advice: profit-based evaluation of machine learning nitrogen recommendations in winter wheat", "abstract": "Nitrogen rates for winter wheat are set before the season, under unknown prices and weather. The standard UK advice does not respond to prices, yet recent price swings moved the most profitable rate by tens of kilograms per hectare. Machine learning is often proposed as the fix. However, it is usually judged on prediction accuracy, and accurate prediction does not by itself make the recommended rate more profitable. Our insight is to score nitrogen advice directly by the profit it forgoes on measured yield response curves. We build a test bench on 892 such curves from two long running UK experiments, and sweep the nitrogen to grain price ratio to cover all price scenarios. On this bench, machine learning fails as a predictor. No model recovers the best rate within farm tolerance, and the benchmark noise shows none can. At normal prices, every model also loses to the standard advice on profit. The gain sits elsewhere. A simple correction step applied after the model cuts profit losses by a quarter, while better models and extra features give no gain. The same frozen correction cuts losses by 43% at the second site without any retraining. A hybrid of standard advice plus a damped correction removes bias and trims rare large losses. The same price sweep also prices emission cuts, at a cost comparable to current carbon prices. Machine learning therefore pays as a profit scored correction to standard advice, not as its replacement.", "published": "2026-08-27T14:52:02Z", "authors": ["Xulong Wang", "Po Yang"], "categories": ["cs.LG"], "doi": "", "_query": "house price prediction machine learning", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/1803.08118v3", "title": "Seglearn: A Python Package for Learning Sequences and Time Series", "abstract": "Seglearn is an open-source python package for machine learning time series or sequences using a sliding window segmentation approach. The implementation provides a flexible pipeline for tackling classification, regression, and forecasting problems with multivariate sequence and contextual data. This package is compatible with scikit-learn and is listed under scikit-learn Related Projects. The package depends on numpy, scipy, and scikit-learn. Seglearn is distributed under the BSD 3-Clause License. Documentation includes a detailed API description, user guide, and examples. Unit tests provide a high degree of code coverage.", "published": "2018-03-21T20:30:34Z", "authors": ["David M. Burns", "Cari M. Whyne"], "categories": ["stat.ML", "cs.LG"], "doi": "", "_query": "house price prediction machine learning", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/1912.09789v1", "title": "A Survey on Distributed Machine Learning", "abstract": "The demand for artificial intelligence has grown significantly over the last decade and this growth has been fueled by advances in machine learning techniques and the ability to leverage hardware acceleration. However, in order to increase the quality of predictions and render machine learning solutions feasible for more complex applications, a substantial amount of training data is required. Although small machine learning models can be trained with modest amounts of data, the input for training larger models such as neural networks grows exponentially with the number of parameters. Since the demand for processing training data has outpaced the increase in computation power of computing machinery, there is a need for distributing the machine learning workload across multiple machines, and turning the centralized into a distributed system. These distributed systems present new challenges, first and foremost the efficient parallelization of the training process and the creation of a coherent model. This article provides an extensive overview of the current state-of-the-art in the field by outlining the challenges and opportunities of distributed machine learning over conventional (centralized) machine learning, discussing the techniques used for distributed machine learning, and providing an overview of the systems that are available.", "published": "2019-12-20T12:24:25Z", "authors": ["Joost Verbraeken", "Matthijs Wolting", "Jonathan Katzy", "Jeroen Kloppenburg", "Tim Verbelen", "Jan S. Rellermeyer"], "categories": ["cs.LG", "cs.DC", "stat.ML"], "doi": "10.1145/3377454", "_query": "house price prediction machine learning", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2406.04896v2", "title": "Stabilizing Extreme Q-learning by Maclaurin Expansion", "abstract": "In offline reinforcement learning, in-sample learning methods have been widely used to prevent performance degradation caused by evaluating out-of-distribution actions from the dataset. Extreme Q-learning (XQL) employs a loss function based on the assumption that Bellman error follows a Gumbel distribution, enabling it to model the soft optimal value function in an in-sample manner. It has demonstrated strong performance in both offline and online reinforcement learning settings. However, issues remain, such as the instability caused by the exponential term in the loss function and the risk of the error distribution deviating from the Gumbel distribution. Therefore, we propose Maclaurin Expanded Extreme Q-learning to enhance stability. In this method, applying Maclaurin expansion to the loss function in XQL enhances stability against large errors. This approach involves adjusting the modeled value function between the value function under the behavior policy and the soft optimal value function, thus achieving a trade-off between stability and optimality depending on the order of expansion. It also enables adjustment of the error distribution assumption from a normal distribution to a Gumbel distribution. Our method significantly stabilizes learning in online RL tasks from DM Control, where XQL was previously unstable. Additionally, it improves performance in several offline RL tasks from D4RL.", "published": "2024-06-07T12:43:17Z", "authors": ["Motoki Omura", "Takayuki Osa", "Yusuke Mukuta", "Tatsuya Harada"], "categories": ["cs.LG", "cs.AI"], "doi": "10.5281/zenodo.13899776", "_query": "house price prediction machine learning", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/1801.09373v2", "title": "Search Based Code Generation for Machine Learning Programs", "abstract": "Machine Learning (ML) has revamped every domain of life as it provides powerful tools to build complex systems that learn and improve from experience and data. Our key insight is that to solve a machine learning problem, data scientists do not invent a new algorithm each time, but evaluate a range of existing models with different configurations and select the best one. This task is laborious, error-prone, and drains a large chunk of project budget and time. In this paper we present a novel framework inspired by programming by Sketching and Partial Evaluation to minimize human intervention in developing ML solutions. We templatize machine learning algorithms to expose configuration choices as holes to be searched. We share code and computation between different algorithms, and only partially evaluate configuration space of algorithms based on information gained from initial algorithm evaluations. We also employ hierarchical and heuristic based pruning to reduce the search space. Our initial findings indicate that our approach can generate highly accurate ML models. Interviews with data scientists show that they feel our framework can eliminate sources of common errors and significantly reduce development time.", "published": "2018-01-29T06:28:47Z", "authors": ["Muhammad Zubair Malik", "Muhammad Nawaz", "Nimrah Mustafa", "Junaid Haroon Siddiqui"], "categories": ["cs.SE", "cs.AI", "cs.PL"], "doi": "", "_query": "house price prediction machine learning", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/1703.00564v3", "title": "MoleculeNet: A Benchmark for Molecular Machine Learning", "abstract": "Molecular machine learning has been maturing rapidly over the last few years. Improved methods and the presence of larger datasets have enabled machine learning algorithms to make increasingly accurate predictions about molecular properties. However, algorithmic progress has been limited due to the lack of a standard benchmark to compare the efficacy of proposed methods; most new algorithms are benchmarked on different datasets making it challenging to gauge the quality of proposed methods. This work introduces MoleculeNet, a large scale benchmark for molecular machine learning. MoleculeNet curates multiple public datasets, establishes metrics for evaluation, and offers high quality open-source implementations of multiple previously proposed molecular featurization and learning algorithms (released as part of the DeepChem open source library). MoleculeNet benchmarks demonstrate that learnable representations are powerful tools for molecular machine learning and broadly offer the best performance. However, this result comes with caveats. Learnable representations still struggle to deal with complex tasks under data scarcity and highly imbalanced classification. For quantum mechanical and biophysical datasets, the use of physics-aware featurizations can be more important than choice of particular learning algorithm.", "published": "2017-03-02T00:39:53Z", "authors": ["Zhenqin Wu", "Bharath Ramsundar", "Evan N. Feinberg", "Joseph Gomes", "Caleb Geniesse", "Aneesh S. Pappu", "Karl Leswing", "Vijay Pande"], "categories": ["cs.LG", "physics.chem-ph", "stat.ML"], "doi": "", "_query": "house price prediction machine learning", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2406.12785v1", "title": "In-Context Learning of Energy Functions", "abstract": "In-context learning is a powerful capability of certain machine learning models that arguably underpins the success of today's frontier AI models. However, in-context learning is critically limited to settings where the in-context distribution of interest $p_θ^{ICL}( x|\\mathcal{D})$ can be straightforwardly expressed and/or parameterized by the model; for instance, language modeling relies on expressing the next-token distribution as a categorical distribution parameterized by the network's output logits. In this work, we present a more general form of in-context learning without such a limitation that we call \\textit{in-context learning of energy functions}. The idea is to instead learn the unconstrained and arbitrary in-context energy function $E_θ^{ICL}(x|\\mathcal{D})$ corresponding to the in-context distribution $p_θ^{ICL}(x|\\mathcal{D})$. To do this, we use classic ideas from energy-based modeling. We provide preliminary evidence that our method empirically works on synthetic data. Interestingly, our work contributes (to the best of our knowledge) the first example of in-context learning where the input space and output space differ from one another, suggesting that in-context learning is a more-general capability than previously realized.", "published": "2024-06-18T16:54:43Z", "authors": ["Rylan Schaeffer", "Mikail Khona", "Sanmi Koyejo"], "categories": ["cs.LG"], "doi": "", "_query": "house price prediction machine learning", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/1912.03135v1", "title": "Pairwise Neural Machine Translation Evaluation", "abstract": "We present a novel framework for machine translation evaluation using neural networks in a pairwise setting, where the goal is to select the better translation from a pair of hypotheses, given the reference translation. In this framework, lexical, syntactic and semantic information from the reference and the two hypotheses is compacted into relatively small distributed vector representations, and fed into a multi-layer neural network that models the interaction between each of the hypotheses and the reference, as well as between the two hypotheses. These compact representations are in turn based on word and sentence embeddings, which are learned using neural networks. The framework is flexible, allows for efficient learning and classification, and yields correlation with humans that rivals the state of the art.", "published": "2019-12-05T05:17:05Z", "authors": ["Francisco Guzman", "Shafiq Joty", "Lluis Marquez", "Preslav Nakov"], "categories": ["cs.CL", "cs.IR", "cs.LG"], "doi": "", "_query": "house price prediction machine learning", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2306.04201v1", "title": "Improving Hyperparameter Learning under Approximate Inference in Gaussian Process Models", "abstract": "Approximate inference in Gaussian process (GP) models with non-conjugate likelihoods gets entangled with the learning of the model hyperparameters. We improve hyperparameter learning in GP models and focus on the interplay between variational inference (VI) and the learning target. While VI's lower bound to the marginal likelihood is a suitable objective for inferring the approximate posterior, we show that a direct approximation of the marginal likelihood as in Expectation Propagation (EP) is a better learning objective for hyperparameter optimization. We design a hybrid training procedure to bring the best of both worlds: it leverages conjugate-computation VI for inference and uses an EP-like marginal likelihood approximation for hyperparameter learning. We compare VI, EP, Laplace approximation, and our proposed training procedure and empirically demonstrate the effectiveness of our proposal across a wide range of data sets.", "published": "2023-06-07T07:15:08Z", "authors": ["Rui Li", "ST John", "Arno Solin"], "categories": ["cs.LG", "stat.ML"], "doi": "", "_query": "house price prediction machine learning", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2012.05013v1", "title": "Machine Learning for Glacier Monitoring in the Hindu Kush Himalaya", "abstract": "Glacier mapping is key to ecological monitoring in the hkh region. Climate change poses a risk to individuals whose livelihoods depend on the health of glacier ecosystems. In this work, we present a machine learning based approach to support ecological monitoring, with a focus on glaciers. Our approach is based on semi-automated mapping from satellite images. We utilize readily available remote sensing data to create a model to identify and outline both clean ice and debris-covered glaciers from satellite imagery. We also release data and develop a web tool that allows experts to visualize and correct model predictions, with the ultimate aim of accelerating the glacier mapping process.", "published": "2020-12-09T12:48:06Z", "authors": ["Shimaa Baraka", "Benjamin Akera", "Bibek Aryal", "Tenzing Sherpa", "Finu Shresta", "Anthony Ortiz", "Kris Sankaran", "Juan Lavista Ferres", "Mir Matin", "Yoshua Bengio"], "categories": ["cs.CV"], "doi": "", "_query": "house price prediction machine learning", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2306.05764v1", "title": "Fair yet Asymptotically Equal Collaborative Learning", "abstract": "In collaborative learning with streaming data, nodes (e.g., organizations) jointly and continuously learn a machine learning (ML) model by sharing the latest model updates computed from their latest streaming data. For the more resourceful nodes to be willing to share their model updates, they need to be fairly incentivized. This paper explores an incentive design that guarantees fairness so that nodes receive rewards commensurate to their contributions. Our approach leverages an explore-then-exploit formulation to estimate the nodes' contributions (i.e., exploration) for realizing our theoretically guaranteed fair incentives (i.e., exploitation). However, we observe a \"rich get richer\" phenomenon arising from the existing approaches to guarantee fairness and it discourages the participation of the less resourceful nodes. To remedy this, we additionally preserve asymptotic equality, i.e., less resourceful nodes achieve equal performance eventually to the more resourceful/\"rich\" nodes. We empirically demonstrate in two settings with real-world streaming data: federated online incremental learning and federated reinforcement learning, that our proposed approach outperforms existing baselines in fairness and learning performance while remaining competitive in preserving equality.", "published": "2023-06-09T08:57:14Z", "authors": ["Xiaoqiang Lin", "Xinyi Xu", "See-Kiong Ng", "Chuan-Sheng Foo", "Bryan Kian Hsiang Low"], "categories": ["cs.LG", "cs.AI", "cs.GT", "cs.MA"], "doi": "", "_query": "house price prediction machine learning", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2006.04732v1", "title": "A Semiparametric Approach to Interpretable Machine Learning", "abstract": "Black box models in machine learning have demonstrated excellent predictive performance in complex problems and high-dimensional settings. However, their lack of transparency and interpretability restrict the applicability of such models in critical decision-making processes. In order to combat this shortcoming, we propose a novel approach to trading off interpretability and performance in prediction models using ideas from semiparametric statistics, allowing us to combine the interpretability of parametric regression models with performance of nonparametric methods. We achieve this by utilizing a two-piece model: the first piece is interpretable and parametric, to which a second, uninterpretable residual piece is added. The performance of the overall model is optimized using methods from the sufficient dimension reduction literature. Influence function based estimators are derived and shown to be doubly robust. This allows for use of approaches such as double Machine Learning in estimating our model parameters. We illustrate the utility of our approach via simulation studies and a data application based on predicting the length of stay in the intensive care unit among surgery patients.", "published": "2020-06-08T16:38:15Z", "authors": ["Numair Sani", "Jaron Lee", "Razieh Nabi", "Ilya Shpitser"], "categories": ["cs.LG", "stat.ML"], "doi": "", "_query": "house price prediction machine learning", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/1606.07129v1", "title": "Explainable Restricted Boltzmann Machines for Collaborative Filtering", "abstract": "Most accurate recommender systems are black-box models, hiding the reasoning behind their recommendations. Yet explanations have been shown to increase the user's trust in the system in addition to providing other benefits such as scrutability, meaning the ability to verify the validity of recommendations. This gap between accuracy and transparency or explainability has generated an interest in automated explanation generation methods. Restricted Boltzmann Machines (RBM) are accurate models for CF that also lack interpretability. In this paper, we focus on RBM based collaborative filtering recommendations, and further assume the absence of any additional data source, such as item content or user attributes. We thus propose a new Explainable RBM technique that computes the top-n recommendation list from items that are explainable. Experimental results show that our method is effective in generating accurate and explainable recommendations.", "published": "2016-06-22T22:24:30Z", "authors": ["Behnoush Abdollahi", "Olfa Nasraoui"], "categories": ["stat.ML", "cs.LG"], "doi": "", "_query": "house price prediction machine learning", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2408.12308v3", "title": "Deep Learning with CNNs: A Compact Holistic Tutorial with Focus on Supervised Regression (Preprint)", "abstract": "In this tutorial, we present a compact and holistic discussion of Deep Learning with a focus on Convolutional Neural Networks (CNNs) and supervised regression. While there are numerous books and articles on the individual topics we cover, comprehensive and detailed tutorials that address Deep Learning from a foundational yet rigorous and accessible perspective are rare. Most resources on CNNs are either too advanced, focusing on cutting-edge architectures, or too narrow, addressing only specific applications like image classification.This tutorial not only summarizes the most relevant concepts but also provides an in-depth exploration of each, offering a complete yet agile set of ideas. Moreover, we highlight the powerful synergy between learning theory, statistic, and machine learning, which together underpin the Deep Learning and CNN frameworks. We aim for this tutorial to serve as an optimal resource for students, professors, and anyone interested in understanding the foundations of Deep Learning. Upon acceptance we will provide an accompanying repository under \\href{https://github.com/neoglez/deep-learning-tutorial}{https://github.com/neoglez/deep-learning-tutorial} Keywords: Tutorial, Deep Learning, Convolutional Neural Networks, Machine Learning.", "published": "2024-08-22T11:34:34Z", "authors": ["Yansel Gonzalez Tejeda", "Helmut A. Mayer"], "categories": ["cs.AI", "cs.LG"], "doi": "10.3390/make6040132", "_query": "house price prediction machine learning", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/1910.13372v3", "title": "Predicting gait events from tibial acceleration in rearfoot running: a structured machine learning approach", "abstract": "Gait event detection of the initial contact and toe off is essential for running gait analysis, allowing the derivation of parameters such as stance time. Heuristic-based methods exist to estimate these key gait events from tibial accelerometry. However, these methods are tailored to very specific acceleration profiles, which may offer complications when dealing with larger data sets and inherent biological variability. Therefore, this paper investigates whether a structured machine learning approach can achieve a more accurate prediction of running gait event timings from tibial accelerometry. Force-based event detection acted as the criterion measure in order to assess the accuracy, repeatability and sensitivity of the predicted gait events. A heuristic method and two structured machine learning methods were employed to derive initial contact, toe off and stance time from tibial acceleration signals. Both a structured perceptron model (median absolute error of stance time estimation: 10.00 $\\pm$ 8.73 ms) and a structured recurrent neural network model (median absolute error of stance time estimation: 6.50 $\\pm$ 5.74 ms) significantly outperformed the existing heuristic approach (median absolute error of stance time estimation: 11.25 $\\pm$ 9.52 ms) on data from 93 rearfoot runners. Thus, results indicate that a structured recurrent neural network machine learning model offers the most accurate and consistent estimation of the gait events and its derived stance time during level overground running. The machine learning methods seem less affected by intra- and inter-subject variation within the data, allowing for accurate and efficient automated data output during rearfoot overground running. Furthermore offering possibilities for real-time monitoring and biofeedback during prolonged measurements, even outside the laboratory.", "published": "2019-10-29T16:36:04Z", "authors": ["Pieter Robberechts", "Rud Derie", "Pieter Van den Berghe", "Joeri Gerlo", "Dirk De Clercq", "Veerle Segers", "Jesse Davis"], "categories": ["cs.LG", "stat.ML"], "doi": "10.1016/j.gaitpost.2020.10.035", "_query": "house price prediction machine learning", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/1711.03654v1", "title": "Poverty Prediction with Public Landsat 7 Satellite Imagery and Machine Learning", "abstract": "Obtaining detailed and reliable data about local economic livelihoods in developing countries is expensive, and data are consequently scarce. Previous work has shown that it is possible to measure local-level economic livelihoods using high-resolution satellite imagery. However, such imagery is relatively expensive to acquire, often not updated frequently, and is mainly available for recent years. We train CNN models on free and publicly available multispectral daytime satellite images of the African continent from the Landsat 7 satellite, which has collected imagery with global coverage for almost two decades. We show that despite these images' lower resolution, we can achieve accuracies that exceed previous benchmarks.", "published": "2017-11-10T00:21:54Z", "authors": ["Anthony Perez", "Christopher Yeh", "George Azzari", "Marshall Burke", "David Lobell", "Stefano Ermon"], "categories": ["stat.ML", "cs.CV", "cs.LG"], "doi": "", "_query": "house price prediction machine learning", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/1610.07647v2", "title": "Learning to Reason With Adaptive Computation", "abstract": "Multi-hop inference is necessary for machine learning systems to successfully solve tasks such as Recognising Textual Entailment and Machine Reading. In this work, we demonstrate the effectiveness of adaptive computation for learning the number of inference steps required for examples of different complexity and that learning the correct number of inference steps is difficult. We introduce the first model involving Adaptive Computation Time which provides a small performance benefit on top of a similar model without an adaptive component as well as enabling considerable insight into the reasoning process of the model.", "published": "2016-10-24T20:48:04Z", "authors": ["Mark Neumann", "Pontus Stenetorp", "Sebastian Riedel"], "categories": ["cs.CL", "cs.NE", "stat.ML"], "doi": "", "_query": "house price prediction machine learning", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2405.03574v1", "title": "ILILT: Implicit Learning of Inverse Lithography Technologies", "abstract": "Lithography, transferring chip design masks to the silicon wafer, is the most important phase in modern semiconductor manufacturing flow. Due to the limitations of lithography systems, Extensive design optimizations are required to tackle the design and silicon mismatch. Inverse lithography technology (ILT) is one of the promising solutions to perform pre-fabrication optimization, termed mask optimization. Because of mask optimization problems' constrained non-convexity, numerical ILT solvers rely heavily on good initialization to avoid getting stuck on sub-optimal solutions. Machine learning (ML) techniques are hence proposed to generate mask initialization for ILT solvers with one-shot inference, targeting faster and better convergence during ILT. This paper addresses the question of \\textit{whether ML models can directly generate high-quality optimized masks without engaging ILT solvers in the loop}. We propose an implicit learning ILT framework: ILILT, which leverages the implicit layer learning method and lithography-conditioned inputs to ground the model. Trained to understand the ILT optimization procedure, ILILT can outperform the state-of-the-art machine learning solutions, significantly improving efficiency and quality.", "published": "2024-05-06T15:49:46Z", "authors": ["Haoyu Yang", "Haoxing Ren"], "categories": ["cs.LG"], "doi": "", "_query": "house price prediction machine learning", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/1711.10057v2", "title": "Predicting Adolescent Suicide Attempts with Neural Networks", "abstract": "Though suicide is a major public health problem in the US, machine learning methods are not commonly used to predict an individual's risk of attempting/committing suicide. In the present work, starting with an anonymized collection of electronic health records for 522,056 unique, California-resident adolescents, we develop neural network models to predict suicide attempts. We frame the problem as a binary classification problem in which we use a patient's data from 2006-2009 to predict either the presence (1) or absence (0) of a suicide attempt in 2010. After addressing issues such as severely imbalanced classes and the variable length of a patient's history, we build neural networks with depths varying from two to eight hidden layers. For test set observations where we have at least five ED/hospital visits' worth of data on a patient, our depth-4 model achieves a sensitivity of 0.703, specificity of 0.980, and AUC of 0.958.", "published": "2017-11-28T00:08:33Z", "authors": ["Harish S. Bhat", "Sidra J. Goldman-Mellor"], "categories": ["stat.ML", "cs.LG"], "doi": "", "_query": "house price prediction machine learning", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2101.10790v1", "title": "The Consequences of the Framing of Machine Learning Risk Prediction Models: Evaluation of Sepsis in General Wards", "abstract": "Objectives: To evaluate the consequences of the framing of machine learning risk prediction models. We evaluate how framing affects model performance and model learning in four different approaches previously applied in published artificial-intelligence (AI) models. Setting and participants: We analysed structured secondary healthcare data from 221,283 citizens from four Danish municipalities who were 18 years of age or older. Results: The four models had similar population level performance (a mean area under the receiver operating characteristic curve of 0.73 to 0.82), in contrast to the mean average precision, which varied greatly from 0.007 to 0.385. Correspondingly, the percentage of missing values also varied between framing approaches. The on-clinical-demand framing, which involved samples for each time the clinicians made an early warning score assessment, showed the lowest percentage of missing values among the vital sign parameters, and this model was also able to learn more temporal dependencies than the others. The Shapley additive explanations demonstrated opposing interpretations of SpO2 in the prediction of sepsis as a consequence of differentially framed models. Conclusions: The profound consequences of framing mandate attention from clinicians and AI developers, as the understanding and reporting of framing are pivotal to the successful development and clinical implementation of future AI technology. Model framing must reflect the expected clinical environment. The importance of proper problem framing is by no means exclusive to sepsis prediction and applies to most clinical risk prediction models.", "published": "2021-01-26T14:00:05Z", "authors": ["Simon Meyer Lauritsen", "Bo Thiesson", "Marianne Johansson Jørgensen", "Anders Hammerich Riis", "Ulrick Skipper Espelund", "Jesper Bo Weile", "Jeppe Lange"], "categories": ["cs.LG", "cs.AI", "stat.ML"], "doi": "", "_query": "house price prediction machine learning", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/1204.4539v3", "title": "Supervised Feature Selection in Graphs with Path Coding Penalties and Network Flows", "abstract": "We consider supervised learning problems where the features are embedded in a graph, such as gene expressions in a gene network. In this context, it is of much interest to automatically select a subgraph with few connected components; by exploiting prior knowledge, one can indeed improve the prediction performance or obtain results that are easier to interpret. Regularization or penalty functions for selecting features in graphs have recently been proposed, but they raise new algorithmic challenges. For example, they typically require solving a combinatorially hard selection problem among all connected subgraphs. In this paper, we propose computationally feasible strategies to select a sparse and well-connected subset of features sitting on a directed acyclic graph (DAG). We introduce structured sparsity penalties over paths on a DAG called \"path coding\" penalties. Unlike existing regularization functions that model long-range interactions between features in a graph, path coding penalties are tractable. The penalties and their proximal operators involve path selection problems, which we efficiently solve by leveraging network flow optimization. We experimentally show on synthetic, image, and genomic data that our approach is scalable and leads to more connected subgraphs than other regularization functions for graphs.", "published": "2012-04-20T06:24:37Z", "authors": ["Julien Mairal", "Bin Yu"], "categories": ["stat.ML", "cs.LG", "math.OC"], "doi": "", "_query": "house price prediction machine learning", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2101.10790v1", "title": "The Consequences of the Framing of Machine Learning Risk Prediction Models: Evaluation of Sepsis in General Wards", "abstract": "Objectives: To evaluate the consequences of the framing of machine learning risk prediction models. We evaluate how framing affects model performance and model learning in four different approaches previously applied in published artificial-intelligence (AI) models. Setting and participants: We analysed structured secondary healthcare data from 221,283 citizens from four Danish municipalities who were 18 years of age or older. Results: The four models had similar population level performance (a mean area under the receiver operating characteristic curve of 0.73 to 0.82), in contrast to the mean average precision, which varied greatly from 0.007 to 0.385. Correspondingly, the percentage of missing values also varied between framing approaches. The on-clinical-demand framing, which involved samples for each time the clinicians made an early warning score assessment, showed the lowest percentage of missing values among the vital sign parameters, and this model was also able to learn more temporal dependencies than the others. The Shapley additive explanations demonstrated opposing interpretations of SpO2 in the prediction of sepsis as a consequence of differentially framed models. Conclusions: The profound consequences of framing mandate attention from clinicians and AI developers, as the understanding and reporting of framing are pivotal to the successful development and clinical implementation of future AI technology. Model framing must reflect the expected clinical environment. The importance of proper problem framing is by no means exclusive to sepsis prediction and applies to most clinical risk prediction models.", "published": "2021-01-26T14:00:05Z", "authors": ["Simon Meyer Lauritsen", "Bo Thiesson", "Marianne Johansson Jørgensen", "Anders Hammerich Riis", "Ulrick Skipper Espelund", "Jesper Bo Weile", "Jeppe Lange"], "categories": ["cs.LG", "cs.AI", "stat.ML"], "doi": "", "_query": "house price prediction machine learning", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/1812.00210v1", "title": "Measuring the Stability of EHR- and EKG-based Predictive Models", "abstract": "Databases of electronic health records (EHRs) are increasingly used to inform clinical decisions. Machine learning methods can find patterns in EHRs that are predictive of future adverse outcomes. However, statistical models may be built upon patterns of health-seeking behavior that vary across patient subpopulations, leading to poor predictive performance when training on one patient population and predicting on another. This note proposes two tests to better measure and understand model generalization. We use these tests to compare models derived from two data sources: (i) historical medical records, and (ii) electrocardiogram (EKG) waveforms. In a predictive task, we show that EKG-based models can be more stable than EHR-based models across different patient populations.", "published": "2018-12-01T14:32:06Z", "authors": ["Andrew C. Miller", "Ziad Obermeyer", "Sendhil Mullainathan"], "categories": ["stat.ML", "cs.LG"], "doi": "", "_query": "house price prediction machine learning", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/1204.4539v3", "title": "Supervised Feature Selection in Graphs with Path Coding Penalties and Network Flows", "abstract": "We consider supervised learning problems where the features are embedded in a graph, such as gene expressions in a gene network. In this context, it is of much interest to automatically select a subgraph with few connected components; by exploiting prior knowledge, one can indeed improve the prediction performance or obtain results that are easier to interpret. Regularization or penalty functions for selecting features in graphs have recently been proposed, but they raise new algorithmic challenges. For example, they typically require solving a combinatorially hard selection problem among all connected subgraphs. In this paper, we propose computationally feasible strategies to select a sparse and well-connected subset of features sitting on a directed acyclic graph (DAG). We introduce structured sparsity penalties over paths on a DAG called \"path coding\" penalties. Unlike existing regularization functions that model long-range interactions between features in a graph, path coding penalties are tractable. The penalties and their proximal operators involve path selection problems, which we efficiently solve by leveraging network flow optimization. We experimentally show on synthetic, image, and genomic data that our approach is scalable and leads to more connected subgraphs than other regularization functions for graphs.", "published": "2012-04-20T06:24:37Z", "authors": ["Julien Mairal", "Bin Yu"], "categories": ["stat.ML", "cs.LG", "math.OC"], "doi": "", "_query": "house price prediction machine learning", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/1912.09722v1", "title": "Robust Data Preprocessing for Machine-Learning-Based Disk Failure Prediction in Cloud Production Environments", "abstract": "To provide proactive fault tolerance for modern cloud data centers, extensive studies have proposed machine learning (ML) approaches to predict imminent disk failures for early remedy and evaluated their approaches directly on public datasets (e.g., Backblaze SMART logs). However, in real-world production environments, the data quality is imperfect (e.g., inaccurate labeling, missing data samples, and complex failure types), thereby degrading the prediction accuracy. We present RODMAN, a robust data preprocessing pipeline that refines data samples before feeding them into ML models. We start with a large-scale trace-driven study of over three million disks from Alibaba Cloud's data centers, and motivate the practical challenges in ML-based disk failure prediction. We then design RODMAN with three data preprocessing echniques, namely failure-type filtering, spline-based data filling, and automated pre-failure backtracking, that are applicable for general ML models. Evaluation on both the Alibaba and Backblaze datasets shows that RODMAN improves the prediction accuracy compared to without data preprocessing under various settings.", "published": "2019-12-20T09:43:25Z", "authors": ["Shujie Han", "Jun Wu", "Erci Xu", "Cheng He", "Patrick P. C. Lee", "Yi Qiang", "Qixing Zheng", "Tao Huang", "Zixi Huang", "Rui Li"], "categories": ["cs.LG", "cs.DC", "stat.ML"], "doi": "", "_query": "house price prediction machine learning", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/1901.05639v4", "title": "Machine learning with neural networks", "abstract": "These are lecture notes for a course on machine learning with neural networks for scientists and engineers that I have given at Gothenburg University and Chalmers Technical University in Gothenburg, Sweden. The material is organised into three parts: Hopfield networks, supervised learning of labeled data, and learning algorithms for unlabeled data sets. Part I introduces stochastic recurrent networks: Hopfield networks and Boltzmann machines. The analysis of their learning rules sets the scene for the later parts. Part II describes supervised learning with multilayer perceptrons and convolutional neural networks. This part starts with a simple geometrical interpretation of the learning rule and leads to the recent successes of convolutional networks in object recognition, recurrent networks in language processing, and reservoir computers in time-series analysis. Part III explains what neural networks can learn about data that is not labeled. This part begins with a description of unsupervised learning techniques for clustering of data, non-linear projections, and embeddings. A section on autoencoders explains how to learn without labels using convolutional networks, and the last chapter is dedicated to reinforcement learning. The overall goal of the course is to explain the fundamental principles that allow neural networks to learn, emphasising ideas and concepts that are common to all three parts. The present version does not contain exercises (copyright owned by Cambridge University Press). The complete book is available at https://www.cambridge.org/gb/academic/subjects/physics/statistical-physics/machine-learning-neural-networks-introduction-scientists-and-engineers?format=HB.", "published": "2019-01-17T06:26:32Z", "authors": ["B. Mehlig"], "categories": ["cs.LG", "cond-mat.stat-mech", "stat.ML"], "doi": "10.1017/9781108860604", "_query": "house price prediction machine learning", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2604.24911v1", "title": "Learning with Embedded Linear Equality Constraints via Variational Bayesian Inference", "abstract": "Machine Learning is becoming more prevalent in science and engineering, but many approaches do not provide meaningful uncertainty estimates and predictions may also violate known physical knowledge. We propose a Bayesian framework to embed linear relationships across inputs and outputs into the learning process, whilst characterizing full predictive uncertainty over both the model parameters and the domain knowledge. We evaluated our method on learning the single particle battery model subject to voltage and energy balances, showing its ability to provide reduced credible intervals and constraint violations compared to standard Bayesian neural networks based on variational inference.", "published": "2026-04-27T18:47:13Z", "authors": ["Matthew Marsh", "Benoît Chachuat", "Antonio del Rio Chanona"], "categories": ["cs.LG", "cs.AI"], "doi": "", "_query": "house price prediction machine learning", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/1812.11477v1", "title": "Machine learning in resting-state fMRI analysis", "abstract": "Machine learning techniques have gained prominence for the analysis of resting-state functional Magnetic Resonance Imaging (rs-fMRI) data. Here, we present an overview of various unsupervised and supervised machine learning applications to rs-fMRI. We present a methodical taxonomy of machine learning methods in resting-state fMRI. We identify three major divisions of unsupervised learning methods with regard to their applications to rs-fMRI, based on whether they discover principal modes of variation across space, time or population. Next, we survey the algorithms and rs-fMRI feature representations that have driven the success of supervised subject-level predictions. The goal is to provide a high-level overview of the burgeoning field of rs-fMRI from the perspective of machine learning applications.", "published": "2018-12-30T06:37:46Z", "authors": ["Meenakshi Khosla", "Keith Jamison", "Gia H. Ngo", "Amy Kuceyeski", "Mert R. Sabuncu"], "categories": ["cs.LG", "cs.CV", "q-bio.QM", "stat.ML"], "doi": "", "_query": "house price prediction machine learning", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2006.15690v1", "title": "Lookahead-Bounded Q-Learning", "abstract": "We introduce the lookahead-bounded Q-learning (LBQL) algorithm, a new, provably convergent variant of Q-learning that seeks to improve the performance of standard Q-learning in stochastic environments through the use of ``lookahead'' upper and lower bounds. To do this, LBQL employs previously collected experience and each iteration's state-action values as dual feasible penalties to construct a sequence of sampled information relaxation problems. The solutions to these problems provide estimated upper and lower bounds on the optimal value, which we track via stochastic approximation. These quantities are then used to constrain the iterates to stay within the bounds at every iteration. Numerical experiments on benchmark problems show that LBQL exhibits faster convergence and more robustness to hyperparameters when compared to standard Q-learning and several related techniques. Our approach is particularly appealing in problems that require expensive simulations or real-world interactions.", "published": "2020-06-28T19:50:55Z", "authors": ["Ibrahim El Shar", "Daniel R. Jiang"], "categories": ["cs.LG", "math.OC", "stat.ML"], "doi": "", "_query": "house price prediction machine learning", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2307.12158v1", "title": "DIP-RL: Demonstration-Inferred Preference Learning in Minecraft", "abstract": "In machine learning for sequential decision-making, an algorithmic agent learns to interact with an environment while receiving feedback in the form of a reward signal. However, in many unstructured real-world settings, such a reward signal is unknown and humans cannot reliably craft a reward signal that correctly captures desired behavior. To solve tasks in such unstructured and open-ended environments, we present Demonstration-Inferred Preference Reinforcement Learning (DIP-RL), an algorithm that leverages human demonstrations in three distinct ways, including training an autoencoder, seeding reinforcement learning (RL) training batches with demonstration data, and inferring preferences over behaviors to learn a reward function to guide RL. We evaluate DIP-RL in a tree-chopping task in Minecraft. Results suggest that the method can guide an RL agent to learn a reward function that reflects human preferences and that DIP-RL performs competitively relative to baselines. DIP-RL is inspired by our previous work on combining demonstrations and pairwise preferences in Minecraft, which was awarded a research prize at the 2022 NeurIPS MineRL BASALT competition, Learning from Human Feedback in Minecraft. Example trajectory rollouts of DIP-RL and baselines are located at https://sites.google.com/view/dip-rl.", "published": "2023-07-22T20:05:31Z", "authors": ["Ellen Novoseller", "Vinicius G. Goecks", "David Watkins", "Josh Miller", "Nicholas Waytowich"], "categories": ["cs.LG", "cs.AI", "cs.HC"], "doi": "", "_query": "house price prediction machine learning", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/1811.04422v1", "title": "An Optimal Control View of Adversarial Machine Learning", "abstract": "I describe an optimal control view of adversarial machine learning, where the dynamical system is the machine learner, the input are adversarial actions, and the control costs are defined by the adversary's goals to do harm and be hard to detect. This view encompasses many types of adversarial machine learning, including test-item attacks, training-data poisoning, and adversarial reward shaping. The view encourages adversarial machine learning researcher to utilize advances in control theory and reinforcement learning.", "published": "2018-11-11T14:28:34Z", "authors": ["Xiaojin Zhu"], "categories": ["cs.LG", "stat.ML"], "doi": "", "_query": "house price prediction machine learning", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2209.03936v2", "title": "Shift in house price estimates during COVID-19 reveals effect of crisis on collective speculation", "abstract": "We exploit a city-level panel comprised of individual house price estimates to estimate the impact of COVID-19 on both small and big real-estate markets in California USA. Descriptive analysis of spot house price estimates, including contemporaneous price uncertainty and 30-day price change for individual properties listed on the online real-estate platform Zillow.com, together facilitate quantifying both the excess valuation and valuation confidence attributable to this global socio-economic shock. Our quasi-experimental pre-/post-COVID-19 design spans several years around 2020 and leverages contemporaneous price estimates of rental properties - i.e., real estate entering the habitation market, just not for purchase (off-market) and hence free of speculation - as an appropriate counterfactual to properties listed for sale, which are subject to on-market speculation. Combining unit-level matching and multivariate difference-in-difference regression approaches, we obtain consistent estimates regarding the sign and magnitude of excess price growth observed after the pandemic onset. Specifically, our results indicate that properties listed for sale appreciated an additional 1% per month above what would be expected in the absence of the pandemic. This corresponds to an excess annual price growth of roughly 12.7 percentage points, which accounts for more than half of the actual annual price growth in 2021 observed across the studied regions. Simultaneously, uncertainty in price estimates decreased, signaling the irrational confidence characteristic of prior asset bubbles. We explore how these two trends are related to market size, local market supply and borrowing costs, which altogether lend support for the counterintuitive roles of uncertainty and interruptions in decision-making.", "published": "2022-07-21T17:47:20Z", "authors": ["Alexander M. Petersen"], "categories": ["q-fin.GN", "physics.soc-ph"], "doi": "10.1140/epjds/s13688-024-00488-9", "_query": "house price prediction machine learning", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2007.06081v1", "title": "VAFL: a Method of Vertical Asynchronous Federated Learning", "abstract": "Horizontal Federated learning (FL) handles multi-client data that share the same set of features, and vertical FL trains a better predictor that combine all the features from different clients. This paper targets solving vertical FL in an asynchronous fashion, and develops a simple FL method. The new method allows each client to run stochastic gradient algorithms without coordination with other clients, so it is suitable for intermittent connectivity of clients. This method further uses a new technique of perturbed local embedding to ensure data privacy and improve communication efficiency. Theoretically, we present the convergence rate and privacy level of our method for strongly convex, nonconvex and even nonsmooth objectives separately. Empirically, we apply our method to FL on various image and healthcare datasets. The results compare favorably to centralized and synchronous FL methods.", "published": "2020-07-12T20:09:25Z", "authors": ["Tianyi Chen", "Xiao Jin", "Yuejiao Sun", "Wotao Yin"], "categories": ["cs.LG", "cs.DC", "math.OC", "stat.ML"], "doi": "", "_query": "house price prediction machine learning", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2306.01549v1", "title": "Evaluating Machine Translation Quality with Conformal Predictive Distributions", "abstract": "This paper presents a new approach for assessing uncertainty in machine translation by simultaneously evaluating translation quality and providing a reliable confidence score. Our approach utilizes conformal predictive distributions to produce prediction intervals with guaranteed coverage, meaning that for any given significance level $ε$, we can expect the true quality score of a translation to fall out of the interval at a rate of $1-ε$. In this paper, we demonstrate how our method outperforms a simple, but effective baseline on six different language pairs in terms of coverage and sharpness. Furthermore, we validate that our approach requires the data exchangeability assumption to hold for optimal performance.", "published": "2023-06-02T13:56:30Z", "authors": ["Patrizio Giovannotti"], "categories": ["cs.CL", "stat.ML"], "doi": "", "_query": "house price prediction machine learning", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2407.07664v2", "title": "A Coding-Theoretic Analysis of Hyperspherical Prototypical Learning Geometry", "abstract": "Hyperspherical Prototypical Learning (HPL) is a supervised approach to representation learning that designs class prototypes on the unit hypersphere. The prototypes bias the representations to class separation in a scale invariant and known geometry. Previous approaches to HPL have either of the following shortcomings: (i) they follow an unprincipled optimisation procedure; or (ii) they are theoretically sound, but are constrained to only one possible latent dimension. In this paper, we address both shortcomings. To address (i), we present a principled optimisation procedure whose solution we show is optimal. To address (ii), we construct well-separated prototypes in a wide range of dimensions using linear block codes. Additionally, we give a full characterisation of the optimal prototype placement in terms of achievable and converse bounds, showing that our proposed methods are near-optimal.", "published": "2024-07-10T13:44:19Z", "authors": ["Martin Lindström", "Borja Rodríguez-Gálvez", "Ragnar Thobaben", "Mikael Skoglund"], "categories": ["cs.LG", "cs.AI", "cs.CV", "eess.SP", "stat.ML"], "doi": "", "_query": "house price prediction machine learning", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2610.02574v2", "title": "DISSOLVR: An Interpretable and Fast Framework for Aqueous and Organic Solubility Prediction", "abstract": "High-fidelity solubility prediction is fundamental to pharmaceutical development and environmental partitioning, where accurate modeling must couple molecular structure with thermodynamic behavior across diverse chemical environments. However, recent advancements have been dominated by deep learning architectures that often sacrifice physical interpretability for predictive power. We challenge this trend by showing that state-of-the-art performance does not require such non-transparent architectures. To address this, we introduce DISSOLVR, a transparent framework for molecular solubility prediction. In addition, we perform a comprehensive literature review and a benchmarking study against various methods. We show that DISSOLVR approaches the aleatoric limit of experimental uncertainty and achieves OOD generalization through structural invariance, derived by mapping molecules to physically-grounded descriptors. Then, we present an LLM-assisted post-hoc explanation pipeline that bridges the gap between symbolic model artifacts and chemically grounded narratives. Finally, a comparative benchmark of a survey involving 22 expert chemists reveals that expert evaluators provide deep insights.", "published": "2026-10-01T23:10:05Z", "authors": ["Vansh Ramani", "Har Ashish Arora", "Dhairya Kuchhal", "Sayan Ranu", "Tarak Karmakar"], "categories": ["cs.LG", "physics.chem-ph"], "doi": "", "_query": "house price prediction machine learning", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2205.15614v3", "title": "Communication-Efficient Distributionally Robust Decentralized Learning", "abstract": "Decentralized learning algorithms empower interconnected devices to share data and computational resources to collaboratively train a machine learning model without the aid of a central coordinator. In the case of heterogeneous data distributions at the network nodes, collaboration can yield predictors with unsatisfactory performance for a subset of the devices. For this reason, in this work, we consider the formulation of a distributionally robust decentralized learning task and we propose a decentralized single loop gradient descent/ascent algorithm (AD-GDA) to directly solve the underlying minimax optimization problem. We render our algorithm communication-efficient by employing a compressed consensus scheme and we provide convergence guarantees for smooth convex and non-convex loss functions. Finally, we corroborate the theoretical findings with empirical results that highlight AD-GDA's ability to provide unbiased predictors and to greatly improve communication efficiency compared to existing distributionally robust algorithms.", "published": "2022-05-31T09:00:37Z", "authors": ["Matteo Zecchin", "Marios Kountouris", "David Gesbert"], "categories": ["cs.LG", "cs.AI", "eess.SP"], "doi": "", "_query": "house price prediction machine learning", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2201.09750v3", "title": "Online AutoML: An adaptive AutoML framework for online learning", "abstract": "Automated Machine Learning (AutoML) has been used successfully in settings where the learning task is assumed to be static. In many real-world scenarios, however, the data distribution will evolve over time, and it is yet to be shown whether AutoML techniques can effectively design online pipelines in dynamic environments. This study aims to automate pipeline design for online learning while continuously adapting to data drift. For this purpose, we design an adaptive Online Automated Machine Learning (OAML) system, searching the complete pipeline configuration space of online learners, including preprocessing algorithms and ensembling techniques. This system combines the inherent adaptation capabilities of online learners with the fast automated pipeline (re)optimization capabilities of AutoML. Focusing on optimization techniques that can adapt to evolving objectives, we evaluate asynchronous genetic programming and asynchronous successive halving to optimize these pipelines continually. We experiment on real and artificial data streams with varying types of concept drift to test the performance and adaptation capabilities of the proposed system. The results confirm the utility of OAML over popular online learning algorithms and underscore the benefits of continuous pipeline redesign in the presence of data drift.", "published": "2022-01-24T15:37:20Z", "authors": ["Bilge Celik", "Prabhant Singh", "Joaquin Vanschoren"], "categories": ["cs.LG", "cs.AI"], "doi": "10.1007/s10994-022-06262-0", "_query": "house price prediction machine learning", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2602.15751v2", "title": "Enabling Low-Latency Machine learning on Radiation-Hard FPGAs with hls4ml", "abstract": "This paper presents an end-to-end demonstration of a viable, ultra-fast, radiation-hard machine learning (ML) application on FPGAs, which could be used in future high-energy physics experiments. We present a three-fold contribution, with the PicoCal calorimeter, planned for the LHCb Upgrade II experiment, used as a test case. First, we develop a lightweight autoencoder to compress a 32-sample timing readout, representative of that of the PicoCal, into a two-dimensional latent space. Second, we introduce a systematic, hardware-aware quantization strategy and show that the model can be reduced to 10-bit weights with minimal performance loss. Third, as a barrier to the adoption of on-detector ML is the lack of support for radiation-hard FPGAs in the High-Energy Physics community's standard ML synthesis tool, hls4ml, we develop a new backend for this library. This new back-end enables the automatic translation of ML models into High-Level Synthesis (HLS) projects for the Microchip PolarFire family of FPGAs, one of the few commercially available and radiation hard FPGAs. We present the synthesis of the autoencoder on a target PolarFire FPGA, which indicates that a latency of 25 ns can be achieved. We show that the resources utilized are low enough that the model can be placed within the inherently protected logic of the FPGA. Our extension to hls4ml is a significant contribution, paving the way for broader adoption of ML on FPGAs in high-radiation environments.", "published": "2026-02-17T17:30:28Z", "authors": ["Katya Govorkova", "Julian Garcia Pardinas", "Vladimir Loncar", "Victoria Nguyen", "Sebastian Schmitt", "Marco Pizzichemi", "Loris Martinazzoli", "Eluned Anne Smith"], "categories": ["hep-ex", "cs.LG"], "doi": "10.1088/2632-2153/ae8218", "_query": "house price prediction machine learning", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2606.17450v2", "title": "A Machine-Learned Comorbidity Index", "abstract": "Traditional comorbidity scores (e.g., Charlson and Elixhauser) are widely used for risk adjustment and patient stratification, but they have two key limitations: (i) they are largely mortality-centric and do not align well with other clinical outcomes, and (ii) their linear, rule-based structure cannot capture nonlinear, outcome-specific risk relationships. We propose a Machine-Learned Comorbidity Index (MLCI) that maps diagnosis codes to a single scalar by maximizing the normalized Hilbert-Schmidt Independence Criterion (nHSIC) between the learned score and multiple clinical outcomes. MLCI captures nonlinear risk-outcome dependence and is supported by a theory that characterizes when a unified, informative admission-level ordering can be achieved across outcomes. Empirical results on multiple benchmark electronic health record (EHR) datasets show that MLCI outperforms strong baselines across multiple evaluation metrics.", "published": "2026-06-16T03:07:20Z", "authors": ["Suleman Baloch", "Kishlay Jha", "Alberto M. Segre", "Philip M. Polgreen", "Bijaya Adhikari"], "categories": ["cs.AI"], "doi": "", "_query": "house price prediction machine learning", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/1206.6417v1", "title": "Learning Task Grouping and Overlap in Multi-task Learning", "abstract": "In the paradigm of multi-task learning, mul- tiple related prediction tasks are learned jointly, sharing information across the tasks. We propose a framework for multi-task learn- ing that enables one to selectively share the information across the tasks. We assume that each task parameter vector is a linear combi- nation of a finite number of underlying basis tasks. The coefficients of the linear combina- tion are sparse in nature and the overlap in the sparsity patterns of two tasks controls the amount of sharing across these. Our model is based on on the assumption that task pa- rameters within a group lie in a low dimen- sional subspace but allows the tasks in differ- ent groups to overlap with each other in one or more bases. Experimental results on four datasets show that our approach outperforms competing methods.", "published": "2012-06-27T19:59:59Z", "authors": ["Abhishek Kumar", "Hal Daume"], "categories": ["cs.LG", "stat.ML"], "doi": "", "_query": "house price prediction machine learning", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/1406.3726v1", "title": "Evaluation of Machine Learning Techniques for Green Energy Prediction", "abstract": "We evaluate the following Machine Learning techniques for Green Energy (Wind, Solar) Prediction: Bayesian Inference, Neural Networks, Support Vector Machines, Clustering techniques (PCA). Our objective is to predict green energy using weather forecasts, predict deviations from forecast green energy, find correlation amongst different weather parameters and green energy availability, recover lost or missing energy (/ weather) data. We use historical weather data and weather forecasts for the same.", "published": "2014-06-14T13:08:30Z", "authors": ["Ankur Sahai"], "categories": ["cs.LG"], "doi": "", "_query": "house price prediction machine learning", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2106.09424v1", "title": "Interpretable Machine Learning Classifiers for Brain Tumour Survival Prediction", "abstract": "Prediction of survival in patients diagnosed with a brain tumour is challenging because of heterogeneous tumour behaviours and responses to treatment. Better estimations of prognosis would support treatment planning and patient support. Advances in machine learning have informed development of clinical predictive models, but their integration into clinical practice is almost non-existent. One reasons for this is the lack of interpretability of models. In this paper, we use a novel brain tumour dataset to compare two interpretable rule list models against popular machine learning approaches for brain tumour survival prediction. All models are quantitatively evaluated using standard performance metrics. The rule lists are also qualitatively assessed for their interpretability and clinical utility. The interpretability of the black box machine learning models is evaluated using two post-hoc explanation techniques, LIME and SHAP. Our results show that the rule lists were only slightly outperformed by the black box models. We demonstrate that rule list algorithms produced simple decision lists that align with clinical expertise. By comparison, post-hoc interpretability methods applied to black box models may produce unreliable explanations of local model predictions. Model interpretability is essential for understanding differences in predictive performance and for integration into clinical practice.", "published": "2021-06-17T12:17:10Z", "authors": ["Colleen E. Charlton", "Michael Tin Chung Poon", "Paul M. Brennan", "Jacques D. Fleuriot"], "categories": ["cs.LG", "cs.AI"], "doi": "", "_query": "house price prediction machine learning", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2403.07669v1", "title": "Machine Learning for Soccer Match Result Prediction", "abstract": "Machine learning has become a common approach to predicting the outcomes of soccer matches, and the body of literature in this domain has grown substantially in the past decade and a half. This chapter discusses available datasets, the types of models and features, and ways of evaluating model performance in this application domain. The aim of this chapter is to give a broad overview of the current state and potential future developments in machine learning for soccer match results prediction, as a resource for those interested in conducting future studies in the area. Our main findings are that while gradient-boosted tree models such as CatBoost, applied to soccer-specific ratings such as pi-ratings, are currently the best-performing models on datasets containing only goals as the match features, there needs to be a more thorough comparison of the performance of deep learning models and Random Forest on a range of datasets with different types of features. Furthermore, new rating systems using both player- and team-level information and incorporating additional information from, e.g., spatiotemporal tracking and event data, could be investigated further. Finally, the interpretability of match result prediction models needs to be enhanced for them to be more useful for team management.", "published": "2024-03-12T14:00:50Z", "authors": ["Rory Bunker", "Calvin Yeung", "Keisuke Fujii"], "categories": ["cs.LG"], "doi": "", "_query": "house price prediction machine learning", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2110.00207v1", "title": "Contraction-Based Methods for Stable Identification and Robust Machine Learning: a Tutorial", "abstract": "This tutorial paper provides an introduction to recently developed tools for machine learning, especially learning dynamical systems (system identification), with stability and robustness constraints. The main ideas are drawn from contraction analysis and robust control, but adapted to problems in which large-scale models can be learnt with behavioural guarantees. We illustrate the methods with applications in robust image recognition and system identification.", "published": "2021-10-01T04:28:49Z", "authors": ["Ian R. Manchester", "Max Revay", "Ruigang Wang"], "categories": ["eess.SY", "math.OC"], "doi": "", "_query": "house price prediction machine learning", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2608.18188v1", "title": "A systematic review of machine learning techniques to address diagnosis and treatment of autism: challenges and opportunities", "abstract": "Autism spectrum disorder (ASD) is a developmental disability characterized by challenges in social interaction and communication. As the causes of ASD remain unclear, identifying relevant features and hidden correlations is crucial for early diagnosis. This systematic review evaluates 55 studies from 2017 to 2023 on the application of machine learning (ML) techniques to ASD. The primary objective is to examine recent ML applications in ASD research, identifying trends, techniques, and datasets that enhance diagnosis and treatment. Supervised learning methods dominate, as they align well with ASD diagnostic needs; however, the role of deep learning is expanding with greater data availability. Emerging techniques based on hybrid methods, where unsupervised, deep learning, and fuzzy logic could be included, will be interesting to observe in the future. The review highlights key challenges and opportunities, particularly the need for models that can integrate complex data -such as genetic and clinical information- to improve diagnostic accuracy and treatment outcomes. Additionally, incorporating innovative data sources, like wearable devices and biometric sensors, could enable continuous and non-intrusive monitoring, providing a more holistic understanding of ASD. Findings emphasize that addressing current challenges requires interdisciplinary collaboration and expanded datasets tailored to ASD. Future ML models will benefit from broader multimodal data integration, enabling researchers to more comprehensively address the complexities of ASD.", "published": "2026-08-18T11:27:05Z", "authors": ["Rafael Muñoz-Terol", "Jesús Peral", "Sandra Amador", "David Gil"], "categories": ["cs.LG", "cs.AI"], "doi": "10.1016/j.heliyon.2025.e44359", "_query": "house price prediction machine learning", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/1711.06813v1", "title": "Household poverty classification in data-scarce environments: a machine learning approach", "abstract": "We describe a method to identify poor households in data-scarce countries by leveraging information contained in nationally representative household surveys. It employs standard statistical learning techniques---cross-validation and parameter regularization---which together reduce the extent to which the model is over-fitted to match the idiosyncracies of observed survey data. The automated framework satisfies three important constraints of this development setting: i) The prediction model uses at most ten questions, which limits the costs of data collection; ii) No computation beyond simple arithmetic is needed to calculate the probability that a given household is poor, immediately after data on the ten indicators is collected; and iii) One specification of the model (i.e. one scorecard) is used to predict poverty throughout a country that may be characterized by significant sub-national differences. Using survey data from Zambia, the model's out-of-sample predictions distinguish poor households from non-poor households using information contained in ten questions.", "published": "2017-11-18T04:57:05Z", "authors": ["Varun Kshirsagar", "Jerzy Wieczorek", "Sharada Ramanathan", "Rachel Wells"], "categories": ["stat.ML", "stat.AP"], "doi": "", "_query": "house price prediction machine learning", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2601.09693v3", "title": "Contrastive Geometric Learning Unlocks Unified Structure- and Ligand-Based Drug Design", "abstract": "Structure-based and ligand-based computational drug design have traditionally relied on disjoint data sources and modeling assumptions, limiting their joint use at scale. In this work, we introduce Contrastive Geometric Learning for Unified Computational Drug Design (ConGLUDe), a single contrastive geometric model that unifies structure- and ligand-based training. ConGLUDe couples a geometric protein encoder that produces whole-protein representations and implicit embeddings of predicted binding sites with a fast ligand encoder, removing the need for predefined pockets. By aligning ligands with both global protein representations and multiple candidate binding sites through contrastive learning, ConGLUDe supports ligand-conditioned pocket prediction in addition to virtual screening and target fishing, while being trained jointly on protein-ligand complexes and large-scale bioactivity data. Across diverse benchmarks, ConGLUDe achieves competitive zero-shot virtual screening performance, substantially outperforms existing methods on a challenging target fishing task, and demonstrates state-of-the-art ligand-conditioned pocket selection. These results highlight the advantages of unified structure-ligand training and position ConGLUDe as a step toward general-purpose foundation models for drug discovery.", "published": "2026-01-14T18:45:08Z", "authors": ["Lisa Schneckenreiter", "Sohvi Luukkonen", "Lukas Friedrich", "Daniel Kuhn", "Günter Klambauer"], "categories": ["cs.LG", "stat.ML"], "doi": "", "_query": "house price prediction machine learning", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2410.08886v1", "title": "Bank Loan Prediction Using Machine Learning Techniques", "abstract": "Banks are important for the development of economies in any financial ecosystem through consumer and business loans. Lending, however, presents risks; thus, banks have to determine the applicant's financial position to reduce the probabilities of default. A number of banks have currently, therefore, adopted data analytics and state-of-the-art technology to arrive at better decisions in the process. The probability of payback is prescribed by a predictive modeling technique in which machine learning algorithms are applied. In this research project, we will apply several machine learning methods to further improve the accuracy and efficiency of loan approval processes. Our work focuses on the prediction of bank loan approval; we have worked on a dataset of 148,670 instances and 37 attributes using machine learning methods. The target property segregates the loan applications into \"Approved\" and \"Denied\" groups. various machine learning techniques have been used, namely, Decision Tree Categorization, AdaBoosting, Random Forest Classifier, SVM, and GaussianNB. Following that, the models were trained and evaluated. Among these, the best-performing algorithm was AdaBoosting, which achieved an incredible accuracy of 99.99%. The results therefore show how ensemble learning works effectively to improve the prediction skills of loan approval decisions. The presented work points to the possibility of achieving extremely accurate and efficient loan prediction models that provide useful insights for applying machine learning to financial domains.", "published": "2024-10-11T15:01:47Z", "authors": ["F M Ahosanul Haque", "Md. Mahedi Hassan"], "categories": ["cs.LG", "cs.AI"], "doi": "", "_query": "house price prediction machine learning", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/1808.01095v1", "title": "Helix: Accelerating Human-in-the-loop Machine Learning", "abstract": "Data application developers and data scientists spend an inordinate amount of time iterating on machine learning (ML) workflows -- by modifying the data pre-processing, model training, and post-processing steps -- via trial-and-error to achieve the desired model performance. Existing work on accelerating machine learning focuses on speeding up one-shot execution of workflows, failing to address the incremental and dynamic nature of typical ML development. We propose Helix, a declarative machine learning system that accelerates iterative development by optimizing workflow execution end-to-end and across iterations. Helix minimizes the runtime per iteration via program analysis and intelligent reuse of previous results, which are selectively materialized -- trading off the cost of materialization for potential future benefits -- to speed up future iterations. Additionally, Helix offers a graphical interface to visualize workflow DAGs and compare versions to facilitate iterative development. Through two ML applications, in classification and in structured prediction, attendees will experience the succinctness of Helix programming interface and the speed and ease of iterative development using Helix. In our evaluations, Helix achieved up to an order of magnitude reduction in cumulative run time compared to state-of-the-art machine learning tools.", "published": "2018-08-03T06:02:46Z", "authors": ["Doris Xin", "Litian Ma", "Jialin Liu", "Stephen Macke", "Shuchen Song", "Aditya Parameswaran"], "categories": ["cs.LG", "cs.DB", "stat.ML"], "doi": "10.14778/3229863.3236234", "_query": "house price prediction machine learning", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2406.01098v1", "title": "Learning Decision Trees and Forests with Algorithmic Recourse", "abstract": "This paper proposes a new algorithm for learning accurate tree-based models while ensuring the existence of recourse actions. Algorithmic Recourse (AR) aims to provide a recourse action for altering the undesired prediction result given by a model. Typical AR methods provide a reasonable action by solving an optimization task of minimizing the required effort among executable actions. In practice, however, such actions do not always exist for models optimized only for predictive performance. To alleviate this issue, we formulate the task of learning an accurate classification tree under the constraint of ensuring the existence of reasonable actions for as many instances as possible. Then, we propose an efficient top-down greedy algorithm by leveraging the adversarial training techniques. We also show that our proposed algorithm can be applied to the random forest, which is known as a popular framework for learning tree ensembles. Experimental results demonstrated that our method successfully provided reasonable actions to more instances than the baselines without significantly degrading accuracy and computational efficiency.", "published": "2024-06-03T08:33:42Z", "authors": ["Kentaro Kanamori", "Takuya Takagi", "Ken Kobayashi", "Yuichi Ike"], "categories": ["cs.LG", "stat.ML"], "doi": "", "_query": "house price prediction machine learning", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2002.00727v2", "title": "Distance Metric Learning for Graph Structured Data", "abstract": "Graphs are versatile tools for representing structured data. As a result, a variety of machine learning methods have been studied for graph data analysis. Although many such learning methods depend on the measurement of differences between input graphs, defining an appropriate distance metric for graphs remains a controversial issue. Hence, we propose a supervised distance metric learning method for the graph classification problem. Our method, named interpretable graph metric learning (IGML), learns discriminative metrics in a subgraph-based feature space, which has a strong graph representation capability. By introducing a sparsity-inducing penalty on the weight of each subgraph, IGML can identify a small number of important subgraphs that can provide insight into the given classification task. Because our formulation has a large number of optimization variables, an efficient algorithm that uses pruning techniques based on safe screening and working set selection methods is also proposed. An important property of IGML is that solution optimality is guaranteed because the problem is formulated as a convex problem and our pruning strategies only discard unnecessary subgraphs. Furthermore, we show that IGML is also applicable to other structured data such as itemset and sequence data, and that it can incorporate vertex-label similarity by using a transportation-based subgraph feature. We empirically evaluate the computational efficiency and classification performance of IGML on several benchmark datasets and provide some illustrative examples of how IGML identifies important subgraphs from a given graph dataset.", "published": "2020-02-03T13:42:43Z", "authors": ["Tomoki Yoshida", "Ichiro Takeuchi", "Masayuki Karasuyama"], "categories": ["stat.ML", "cs.LG"], "doi": "10.1007/s10994-021-06009-3", "_query": "house price prediction machine learning", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/1909.03013v1", "title": "Approaching Machine Learning Fairness through Adversarial Network", "abstract": "Fairness is becoming a rising concern w.r.t. machine learning model performance. Especially for sensitive fields such as criminal justice and loan decision, eliminating the prediction discrimination towards a certain group of population (characterized by sensitive features like race and gender) is important for enhancing the trustworthiness of model. In this paper, we present a new general framework to improve machine learning fairness. The goal of our model is to minimize the influence of sensitive feature from the perspectives of both the data input and the predictive model. In order to achieve this goal, we reformulate the data input by removing the sensitive information and strengthen model fairness by minimizing the marginal contribution of the sensitive feature. We propose to learn the non-sensitive input via sampling among features and design an adversarial network to minimize the dependence between the reformulated input and the sensitive information. Extensive experiments on three benchmark datasets suggest that our model achieve better results than related state-of-the-art methods with respect to both fairness metrics and prediction performance.", "published": "2019-09-06T16:55:05Z", "authors": ["Xiaoqian Wang", "Heng Huang"], "categories": ["cs.LG", "stat.ML"], "doi": "", "_query": "house price prediction machine learning", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2008.07363v1", "title": "Predicting Account Receivables with Machine Learning", "abstract": "Being able to predict when invoices will be paid is valuable in multiple industries and supports decision-making processes in most financial workflows. However, due to the complexity of data related to invoices and the fact that the decision-making process is not registered in the accounts receivable system, performing this prediction becomes a challenge. In this paper, we present a prototype able to support collectors in predicting the payment of invoices. This prototype is part of a solution developed in partnership with a multinational bank and it has reached up to 81% of prediction accuracy, which improved the prioritization of customers and supported the daily work of collectors. Our simulations show that the adoption of our model to prioritize the work o collectors saves up to ~1.75 million dollars per month. The methodology and results presented in this paper will allow researchers and practitioners in dealing with the problem of invoice payment prediction, providing insights and examples of how to tackle issues present in real data.", "published": "2020-08-11T11:53:24Z", "authors": ["Ana Paula Appel", "Gabriel Louzada Malfatti", "Renato Luiz de Freitas Cunha", "Bruno Lima", "Rogerio de Paula"], "categories": ["cs.LG"], "doi": "", "_query": "house price prediction machine learning", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2302.09656v5", "title": "Credal Bayesian Deep Learning", "abstract": "Uncertainty quantification and robustness to distribution shifts are important goals in machine learning and artificial intelligence. Although Bayesian Neural Networks (BNNs) allow for uncertainty in the predictions to be assessed, different sources of predictive uncertainty cannot be distinguished properly. We present Credal Bayesian Deep Learning (CBDL). Heuristically, CBDL allows to train an (uncountably) infinite ensemble of BNNs, using only finitely many elements. This is possible thanks to prior and likelihood finitely generated credal sets (FGCSs), a concept from the imprecise probability literature. Intuitively, convex combinations of a finite collection of prior-likelihood pairs are able to represent infinitely many such pairs. After training, CBDL outputs a set of posteriors on the parameters of the neural network. At inference time, such posterior set is used to derive a set of predictive distributions that is in turn utilized to distinguish between (predictive) aleatoric and epistemic uncertainties, and to quantify them. The predictive set also produces either (i) a collection of outputs enjoying desirable probabilistic guarantees, or (ii) the single output that is deemed the best, that is, the one having the highest predictive lower probability -- another imprecise-probabilistic concept. CBDL is more robust than single BNNs to prior and likelihood misspecification, and to distribution shift. We show that CBDL is better at quantifying and disentangling different types of (predictive) uncertainties than single BNNs and ensemble of BNNs. In addition, we apply CBDL to two case studies to demonstrate its downstream tasks capabilities: one, for motion prediction in autonomous driving scenarios, and two, to model blood glucose and insulin dynamics for artificial pancreas control. We show that CBDL performs better when compared to an ensemble of BNNs baseline.", "published": "2023-02-19T19:03:26Z", "authors": ["Michele Caprio", "Souradeep Dutta", "Kuk Jin Jang", "Vivian Lin", "Radoslav Ivanov", "Oleg Sokolsky", "Insup Lee"], "categories": ["cs.LG", "stat.ML"], "doi": "", "_query": "house price prediction machine learning", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/1810.03642v4", "title": "Fast Context Adaptation via Meta-Learning", "abstract": "We propose CAVIA for meta-learning, a simple extension to MAML that is less prone to meta-overfitting, easier to parallelise, and more interpretable. CAVIA partitions the model parameters into two parts: context parameters that serve as additional input to the model and are adapted on individual tasks, and shared parameters that are meta-trained and shared across tasks. At test time, only the context parameters are updated, leading to a low-dimensional task representation. We show empirically that CAVIA outperforms MAML for regression, classification, and reinforcement learning. Our experiments also highlight weaknesses in current benchmarks, in that the amount of adaptation needed in some cases is small.", "published": "2018-10-08T18:11:01Z", "authors": ["Luisa M Zintgraf", "Kyriacos Shiarlis", "Vitaly Kurin", "Katja Hofmann", "Shimon Whiteson"], "categories": ["cs.LG", "stat.ML"], "doi": "", "_query": "house price prediction machine learning", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2110.09360v1", "title": "Prediction of liquid fuel properties using machine learning models with Gaussian processes and probabilistic conditional generative learning", "abstract": "Accurate determination of fuel properties of complex mixtures over a wide range of pressure and temperature conditions is essential to utilizing alternative fuels. The present work aims to construct cheap-to-compute machine learning (ML) models to act as closure equations for predicting the physical properties of alternative fuels. Those models can be trained using the database from MD simulations and/or experimental measurements in a data-fusion-fidelity approach. Here, Gaussian Process (GP) and probabilistic generative models are adopted. GP is a popular non-parametric Bayesian approach to build surrogate models mainly due to its capacity to handle the aleatory and epistemic uncertainties. Generative models have shown the ability of deep neural networks employed with the same intent. In this work, ML analysis is focused on a particular property, the fuel density, but it can also be extended to other physicochemical properties. This study explores the versatility of the ML models to handle multi-fidelity data. The results show that ML models can predict accurately the fuel properties of a wide range of pressure and temperature conditions.", "published": "2021-10-18T14:43:50Z", "authors": ["Rodolfo S. M. Freitas", "Ágatha P. F. Lima", "Cheng Chen", "Fernando A. Rochinha", "Daniel Mira", "Xi Jiang"], "categories": ["stat.ML", "cs.LG"], "doi": "", "_query": "house price prediction machine learning", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2001.00629v1", "title": "A Loss-Function for Causal Machine-Learning", "abstract": "Causal machine-learning is about predicting the net-effect (true-lift) of treatments. Given the data of a treatment group and a control group, it is similar to a standard supervised-learning problem. Unfortunately, there is no similarly well-defined loss function due to the lack of point-wise true values in the data. Many advances in modern machine-learning are not directly applicable due to the absence of such loss function. We propose a novel method to define a loss function in this context, which is equal to mean-square-error (MSE) in a standard regression problem. Our loss function is universally applicable, thus providing a general standard to evaluate the quality of any model/strategy that predicts the true-lift. We demonstrate that despite its novel definition, one can still perform gradient descent directly on this loss function to find the best fit. This leads to a new way to train any parameter-based model, such as deep neural networks, to solve causal machine-learning problems without going through the meta-learner strategy.", "published": "2020-01-02T21:22:18Z", "authors": ["I-Sheng Yang"], "categories": ["cs.LG", "stat.ML"], "doi": "", "_query": "house price prediction machine learning", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2105.13809v1", "title": "Distribution Matching for Machine Teaching", "abstract": "Machine teaching is an inverse problem of machine learning that aims at steering the student learner towards its target hypothesis, in which the teacher has already known the student's learning parameters. Previous studies on machine teaching focused on balancing the teaching risk and cost to find those best teaching examples deriving the student model. This optimization solver is in general ineffective when the student learner does not disclose any cue of the learning parameters. To supervise such a teaching scenario, this paper presents a distribution matching-based machine teaching strategy. Specifically, this strategy backwardly and iteratively performs the halving operation on the teaching cost to find a desired teaching set. Technically, our strategy can be expressed as a cost-controlled optimization process that finds the optimal teaching examples without further exploring in the parameter distribution of the student learner. Then, given any a limited teaching cost, the training examples will be closed-form. Theoretical analysis and experiment results demonstrate this strategy.", "published": "2021-05-06T09:32:57Z", "authors": ["Xiaofeng Cao", "Ivor W. Tsang"], "categories": ["cs.LG"], "doi": "", "_query": "house price prediction machine learning", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2310.06148v1", "title": "Understanding Transfer Learning and Gradient-Based Meta-Learning Techniques", "abstract": "Deep neural networks can yield good performance on various tasks but often require large amounts of data to train them. Meta-learning received considerable attention as one approach to improve the generalization of these networks from a limited amount of data. Whilst meta-learning techniques have been observed to be successful at this in various scenarios, recent results suggest that when evaluated on tasks from a different data distribution than the one used for training, a baseline that simply finetunes a pre-trained network may be more effective than more complicated meta-learning techniques such as MAML, which is one of the most popular meta-learning techniques. This is surprising as the learning behaviour of MAML mimics that of finetuning: both rely on re-using learned features. We investigate the observed performance differences between finetuning, MAML, and another meta-learning technique called Reptile, and show that MAML and Reptile specialize for fast adaptation in low-data regimes of similar data distribution as the one used for training. Our findings show that both the output layer and the noisy training conditions induced by data scarcity play important roles in facilitating this specialization for MAML. Lastly, we show that the pre-trained features as obtained by the finetuning baseline are more diverse and discriminative than those learned by MAML and Reptile. Due to this lack of diversity and distribution specialization, MAML and Reptile may fail to generalize to out-of-distribution tasks whereas finetuning can fall back on the diversity of the learned features.", "published": "2023-10-09T20:51:49Z", "authors": ["Mike Huisman", "Aske Plaat", "Jan N. van Rijn"], "categories": ["cs.LG", "cs.AI", "stat.ML"], "doi": "", "_query": "house price prediction machine learning", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2509.00479v2", "title": "A Novel Method to Determine Total Oxidant Concentration Produced by Non-Thermal Plasma Based on Image Processing and Machine Learning", "abstract": "Accurate determination of total oxidant concentration [Ox]tot in nonthermal plasma treated aqueous systems remains a critical challenge due to the transient nature of reactive oxygen and nitrogen species and the subjectivity of conventional titration methods used for [Ox]tot determination. This study introduces a color based computer analysis method that integrates advanced image processing with machine learning to quantify colorimetric changes in potassium iodide solutions during oxidation. A custom built visual acquisition system recorded high resolution video of the color transitions occurring during plasma treatment while the change in oxidant concentration was simultaneously monitored using a standard titrimetric method. Extracted image frames were processed through a structured pipeline to obtain RGB, HSV, and Lab color features. Statistical analysis revealed strong linear relationships between selected color features and measured oxidant concentrations, particularly for HSV saturation, Lab a and b channels, and the blue component of RGB. These features were subsequently used to train and validate multiple machine learning models including linear regression, ridge regression, random forest, gradient boosting, and neural networks. Linear regression and gradient boosting demonstrated the highest predictive accuracy with R2 values exceeding 0.99. Dimensionality reduction from nine features to smaller feature subsets preserved predictive performance while improving computational efficiency. Comparison with experimental titration measurements showed that the proposed system predicts total oxidant concentration in potassium iodide solution with very high accuracy, achieving R2 values above 0.998 even under reduced feature conditions.", "published": "2025-08-30T12:28:52Z", "authors": ["Mirkan Emir Sancak", "Unal Sen", "Ulker Diler Keris-Sen"], "categories": ["eess.IV", "cs.AI", "cs.LG"], "doi": "", "_query": "house price prediction machine learning", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/1803.00158v1", "title": "Modeling reverse thinking for machine learning", "abstract": "Human inertial thinking schemes can be formed through learning, which are then applied to quickly solve similar problems later. However, when problems are significantly different, inertial thinking generally presents the solutions that are definitely imperfect. In such cases, people will apply creative thinking, such as reverse thinking, to solve problems. Similarly, machine learning methods also form inertial thinking schemes through learning the knowledge from a large amount of data. However, when the testing data are vastly difference, the formed inertial thinking schemes will inevitably generate errors. This kind of inertial thinking is called illusion inertial thinking. Because all machine learning methods do not consider illusion inertial thinking, in this paper we propose a new method that uses reverse thinking to correct illusion inertial thinking, which increases the generalization ability of machine learning methods. Experimental results on benchmark datasets are used to validate the proposed method.", "published": "2018-03-01T01:45:30Z", "authors": ["Li Huihui", "Wen Guihua"], "categories": ["cs.LG", "cs.AI", "stat.ML"], "doi": "", "_query": "house price prediction machine learning", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2110.07661v2", "title": "Distribution-Free Federated Learning with Conformal Predictions", "abstract": "Federated learning has attracted considerable interest for collaborative machine learning in healthcare to leverage separate institutional datasets while maintaining patient privacy. However, additional challenges such as poor calibration and lack of interpretability may also hamper widespread deployment of federated models into clinical practice, leading to user distrust or misuse of ML tools in high-stakes clinical decision-making. In this paper, we propose to address these challenges by incorporating an adaptive conformal framework into federated learning to ensure distribution-free prediction sets that provide coverage guarantees. Importantly, these uncertainty estimates can be obtained without requiring any additional modifications to the model. Empirical results on the MedMNIST medical imaging benchmark demonstrate our federated method provides tighter coverage over local conformal predictions on 6 different medical imaging datasets for 2D and 3D multi-class classification tasks. Furthermore, we correlate class entropy with prediction set size to assess task uncertainty.", "published": "2021-10-14T18:41:17Z", "authors": ["Charles Lu", "Jayasheree Kalpathy-Cramer"], "categories": ["cs.LG", "eess.IV"], "doi": "", "_query": "house price prediction machine learning", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2211.16782v1", "title": "Panel Discussion: Practical Problem Solving for Machine Learning", "abstract": "Machine Learning is a powerful tool for astrophysicists, which has already had significant uptake in the community. But there remain some barriers to entry, relating to proper understanding, the difficulty of interpretability, and the lack of cohesive training. In this discussion session we addressed some of these questions, and suggest how the field may move forward.", "published": "2022-11-30T06:44:27Z", "authors": ["Guillermo Cabrera", "Sungwook E. Hong", "Lilianne Nakazono", "David Parkinson", "Yuan-Sen Ting"], "categories": ["astro-ph.IM"], "doi": "10.1017/S1743921322003647", "_query": "house price prediction machine learning", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/1107.0194v1", "title": "Law of Connectivity in Machine Learning", "abstract": "We present in this paper our law that there is always a connection present between two entities, with a selfconnection being present at least in each node. An entity is an object, physical or imaginary, that is connected by a path (or connection) and which is important for achieving the desired result of the scenario. In machine learning, we state that for any scenario, a subject entity is always, directly or indirectly, connected and affected by single or multiple independent / dependent entities, and their impact on the subject entity is dependent on various factors falling into the categories such as the existenc", "published": "2011-07-01T11:08:32Z", "authors": ["Jitesh Dundas"], "categories": ["cs.AI"], "doi": "", "_query": "house price prediction machine learning", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/1907.02260v3", "title": "On Explaining Machine Learning Models by Evolving Crucial and Compact Features", "abstract": "Feature construction can substantially improve the accuracy of Machine Learning (ML) algorithms. Genetic Programming (GP) has been proven to be effective at this task by evolving non-linear combinations of input features. GP additionally has the potential to improve ML explainability since explicit expressions are evolved. Yet, in most GP works the complexity of evolved features is not explicitly bound or minimized though this is arguably key for explainability. In this article, we assess to what extent GP still performs favorably at feature construction when constructing features that are (1) Of small-enough number, to enable visualization of the behavior of the ML model; (2) Of small-enough size, to enable interpretability of the features themselves; (3) Of sufficient informative power, to retain or even improve the performance of the ML algorithm. We consider a simple feature construction scheme using three different GP algorithms, as well as random search, to evolve features for five ML algorithms, including support vector machines and random forest. Our results on 21 datasets pertaining to classification and regression problems show that constructing only two compact features can be sufficient to rival the use of the entire original feature set. We further find that a modern GP algorithm, GP-GOMEA, performs best overall. These results, combined with examples that we provide of readable constructed features and of 2D visualizations of ML behavior, lead us to positively conclude that GP-based feature construction still works well when explicitly searching for compact features, making it extremely helpful to explain ML models.", "published": "2019-07-04T07:52:23Z", "authors": ["Marco Virgolin", "Tanja Alderliesten", "Peter A. N. Bosman"], "categories": ["cs.NE", "cs.LG"], "doi": "10.1016/j.swevo.2019.100640", "_query": "house price prediction machine learning", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2205.11446v2", "title": "Overfitting in quantum machine learning and entangling dropout", "abstract": "The ultimate goal in machine learning is to construct a model function that has a generalization capability for unseen dataset, based on given training dataset. If the model function has too much expressibility power, then it may overfit to the training data and as a result lose the generalization capability. To avoid such overfitting issue, several techniques have been developed in the classical machine learning regime, and the dropout is one such effective method. This paper proposes a straightforward analogue of this technique in the quantum machine learning regime, the entangling dropout, meaning that some entangling gates in a given parametrized quantum circuit are randomly removed during the training process to reduce the expressibility of the circuit. Some simple case studies are given to show that this technique actually suppresses the overfitting.", "published": "2022-05-23T16:35:46Z", "authors": ["Masahiro Kobayashi", "Kouhei Nakaji", "Naoki Yamamoto"], "categories": ["quant-ph", "cs.LG"], "doi": "10.1007/s42484-022-00087-9", "_query": "house price prediction machine learning", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/1903.04797v3", "title": "Elements of Sequential Monte Carlo", "abstract": "A core problem in statistics and probabilistic machine learning is to compute probability distributions and expectations. This is the fundamental problem of Bayesian statistics and machine learning, which frames all inference as expectations with respect to the posterior distribution. The key challenge is to approximate these intractable expectations. In this tutorial, we review sequential Monte Carlo (SMC), a random-sampling-based class of methods for approximate inference. First, we explain the basics of SMC, discuss practical issues, and review theoretical results. We then examine two of the main user design choices: the proposal distributions and the so called intermediate target distributions. We review recent results on how variational inference and amortization can be used to learn efficient proposals and target distributions. Next, we discuss the SMC estimate of the normalizing constant, how this can be used for pseudo-marginal inference and inference evaluation. Throughout the tutorial we illustrate the use of SMC on various models commonly used in machine learning, such as stochastic recurrent neural networks, probabilistic graphical models, and probabilistic programs.", "published": "2019-03-12T09:28:05Z", "authors": ["Christian A. Naesseth", "Fredrik Lindsten", "Thomas B. Schön"], "categories": ["stat.ML", "cs.LG", "stat.CO"], "doi": "", "_query": "house price prediction machine learning", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2308.14343v1", "title": "Buy when? Survival machine learning model comparison for purchase timing", "abstract": "The value of raw data is unlocked by converting it into information and knowledge that drives decision-making. Machine Learning (ML) algorithms are capable of analysing large datasets and making accurate predictions. Market segmentation, client lifetime value, and marketing techniques have all made use of machine learning. This article examines marketing machine learning techniques such as Support Vector Machines, Genetic Algorithms, Deep Learning, and K-Means. ML is used to analyse consumer behaviour, propose items, and make other customer choices about whether or not to purchase a product or service, but it is seldom used to predict when a person will buy a product or a basket of products. In this paper, the survival models Kernel SVM, DeepSurv, Survival Random Forest, and MTLR are examined to predict tine-purchase individual decisions. Gender, Income, Location, PurchaseHistory, OnlineBehavior, Interests, PromotionsDiscounts and CustomerExperience all have an influence on purchasing time, according to the analysis. The study shows that the DeepSurv model predicted purchase completion the best. These insights assist marketers in increasing conversion rates.", "published": "2023-08-28T06:40:02Z", "authors": ["Diego Vallarino"], "categories": ["stat.ML", "cs.LG"], "doi": "", "_query": "house price prediction machine learning", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2107.08066v2", "title": "LeanML: A Design Pattern To Slash Avoidable Wastes in Machine Learning Projects", "abstract": "We introduce the first application of the lean methodology to machine learning projects. Similar to lean startups and lean manufacturing, we argue that lean machine learning (LeanML) can drastically slash avoidable wastes in commercial machine learning projects, reduce the business risk in investing in machine learning capabilities and, in so doing, further democratize access to machine learning. The lean design pattern we propose in this paper is based on two realizations. First, it is possible to estimate the best performance one may achieve when predicting an outcome $y \\in \\mathcal{Y}$ using a given set of explanatory variables $x \\in \\mathcal{X}$, for a wide range of performance metrics, and without training any predictive model. Second, doing so is considerably easier, faster, and cheaper than learning the best predictive model. We derive formulae expressing the best $R^2$, MSE, classification accuracy, and log-likelihood per observation achievable when using $x$ to predict $y$ as a function of the mutual information $I\\left(y; x\\right)$, and possibly a measure of the variability of $y$ (e.g. its Shannon entropy in the case of classification accuracy, and its variance in the case regression MSE). We illustrate the efficacy of the LeanML design pattern on a wide range of regression and classification problems, synthetic and real-life.", "published": "2021-07-16T18:16:48Z", "authors": ["Yves-Laurent Kom Samo"], "categories": ["cs.LG", "cs.SE", "stat.ML"], "doi": "", "_query": "house price prediction machine learning", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2202.08566v1", "title": "Efficient and Reliable Probabilistic Interactive Learning with Structured Outputs", "abstract": "In this position paper, we study interactive learning for structured output spaces, with a focus on active learning, in which labels are unknown and must be acquired, and on skeptical learning, in which the labels are noisy and may need relabeling. These scenarios require expressive models that guarantee reliable and efficient computation of probabilistic quantities to measure uncertainty. We identify conditions under which a class of probabilistic models -- which we denote CRISPs -- meet all of these conditions, thus delivering tractable computation of the above quantities while preserving expressiveness. Building on prior work on tractable probabilistic circuits, we illustrate how CRISPs enable robust and efficient active and skeptical learning in large structured output spaces.", "published": "2022-02-17T10:29:32Z", "authors": ["Stefano Teso", "Antonio Vergari"], "categories": ["cs.LG", "stat.ML"], "doi": "", "_query": "house price prediction machine learning", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2106.07329v1", "title": "On-Policy Deep Reinforcement Learning for the Average-Reward Criterion", "abstract": "We develop theory and algorithms for average-reward on-policy Reinforcement Learning (RL). We first consider bounding the difference of the long-term average reward for two policies. We show that previous work based on the discounted return (Schulman et al., 2015; Achiam et al., 2017) results in a non-meaningful bound in the average-reward setting. By addressing the average-reward criterion directly, we then derive a novel bound which depends on the average divergence between the two policies and Kemeny's constant. Based on this bound, we develop an iterative procedure which produces a sequence of monotonically improved policies for the average reward criterion. This iterative procedure can then be combined with classic DRL (Deep Reinforcement Learning) methods, resulting in practical DRL algorithms that target the long-run average reward criterion. In particular, we demonstrate that Average-Reward TRPO (ATRPO), which adapts the on-policy TRPO algorithm to the average-reward criterion, significantly outperforms TRPO in the most challenging MuJuCo environments.", "published": "2021-06-14T12:12:09Z", "authors": ["Yiming Zhang", "Keith W. Ross"], "categories": ["cs.LG", "cs.AI", "stat.ML"], "doi": "", "_query": "house price prediction machine learning", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2103.09603v6", "title": "DoubleML -- An Object-Oriented Implementation of Double Machine Learning in R", "abstract": "The R package DoubleML implements the double/debiased machine learning framework of Chernozhukov et al. (2018). It provides functionalities to estimate parameters in causal models based on machine learning methods. The double machine learning framework consist of three key ingredients: Neyman orthogonality, high-quality machine learning estimation and sample splitting. Estimation of nuisance components can be performed by various state-of-the-art machine learning methods that are available in the mlr3 ecosystem. DoubleML makes it possible to perform inference in a variety of causal models, including partially linear and interactive regression models and their extensions to instrumental variable estimation. The object-oriented implementation of DoubleML enables a high flexibility for the model specification and makes it easily extendable. This paper serves as an introduction to the double machine learning framework and the R package DoubleML. In reproducible code examples with simulated and real data sets, we demonstrate how DoubleML users can perform valid inference based on machine learning methods.", "published": "2021-03-17T12:42:41Z", "authors": ["Philipp Bach", "Victor Chernozhukov", "Malte S. Kurz", "Martin Spindler", "Sven Klaassen"], "categories": ["stat.ML", "cs.LG", "econ.EM"], "doi": "10.18637/jss.v108.i03", "_query": "house price prediction machine learning", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/1303.5685v2", "title": "Sparse Factor Analysis for Learning and Content Analytics", "abstract": "We develop a new model and algorithms for machine learning-based learning analytics, which estimate a learner's knowledge of the concepts underlying a domain, and content analytics, which estimate the relationships among a collection of questions and those concepts. Our model represents the probability that a learner provides the correct response to a question in terms of three factors: their understanding of a set of underlying concepts, the concepts involved in each question, and each question's intrinsic difficulty. We estimate these factors given the graded responses to a collection of questions. The underlying estimation problem is ill-posed in general, especially when only a subset of the questions are answered. The key observation that enables a well-posed solution is the fact that typical educational domains of interest involve only a small number of key concepts. Leveraging this observation, we develop both a bi-convex maximum-likelihood and a Bayesian solution to the resulting SPARse Factor Analysis (SPARFA) problem. We also incorporate user-defined tags on questions to facilitate the interpretability of the estimated factors. Experiments with synthetic and real-world data demonstrate the efficacy of our approach. Finally, we make a connection between SPARFA and noisy, binary-valued (1-bit) dictionary learning that is of independent interest.", "published": "2013-03-22T18:44:56Z", "authors": ["Andrew S. Lan", "Andrew E. Waters", "Christoph Studer", "Richard G. Baraniuk"], "categories": ["stat.ML", "cs.LG", "math.OC", "stat.AP"], "doi": "", "_query": "house price prediction machine learning", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2006.02482v6", "title": "Explaining the Behavior of Black-Box Prediction Algorithms with Causal Learning", "abstract": "Causal approaches to post-hoc explainability for black-box prediction models (e.g., deep neural networks trained on image pixel data) have become increasingly popular. However, existing approaches have two important shortcomings: (i) the \"explanatory units\" are micro-level inputs into the relevant prediction model, e.g., image pixels, rather than interpretable macro-level features that are more useful for understanding how to possibly change the algorithm's behavior, and (ii) existing approaches assume there exists no unmeasured confounding between features and target model predictions, which fails to hold when the explanatory units are macro-level variables. Our focus is on the important setting where the analyst has no access to the inner workings of the target prediction algorithm, rather only the ability to query the output of the model in response to a particular input. To provide causal explanations in such a setting, we propose to learn causal graphical representations that allow for arbitrary unmeasured confounding among features. We demonstrate the resulting graph can differentiate between interpretable features that causally influence model predictions versus those that are merely associated with model predictions due to confounding. Our approach is motivated by a counterfactual theory of causal explanation wherein good explanations point to factors that are \"difference-makers\" in an interventionist sense.", "published": "2020-06-03T19:02:34Z", "authors": ["Numair Sani", "Daniel Malinsky", "Ilya Shpitser"], "categories": ["cs.LG", "cs.AI", "stat.ML"], "doi": "", "_query": "house price prediction machine learning", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2204.05783v1", "title": "Stock Price Prediction using Sentiment Analysis and Deep Learning for Indian Markets", "abstract": "Stock market prediction has been an active area of research for a considerable period. Arrival of computing, followed by Machine Learning has upgraded the speed of research as well as opened new avenues. As part of this research study, we aimed to predict the future stock movement of shares using the historical prices aided with availability of sentiment data. Two models were used as part of the exercise, LSTM was the first model with historical prices as the independent variable. Sentiment Analysis captured using Intensity Analyzer was used as the major parameter for Random Forest Model used for the second part, some macro parameters like Gold, Oil prices, USD exchange rate and Indian Govt. Securities yields were also added to the model for improved accuracy of the model. As the end product, prices of 4 stocks viz. Reliance, HDFC Bank, TCS and SBI were predicted using the aforementioned two models. The results were evaluated using RMSE metric.", "published": "2022-04-07T12:09:39Z", "authors": ["Narayana Darapaneni", "Anwesh Reddy Paduri", "Himank Sharma", "Milind Manjrekar", "Nutan Hindlekar", "Pranali Bhagat", "Usha Aiyer", "Yogesh Agarwal"], "categories": ["q-fin.ST", "cs.LG"], "doi": "", "_query": "house price prediction machine learning", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2001.08980v1", "title": "Machine Learning and value generation in Software Development: a survey", "abstract": "Machine Learning (ML) has become a ubiquitous tool for predicting and classifying data and has found application in several problem domains, including Software Development (SD). This paper reviews the literature between 2000 and 2019 on the use the learning models that have been employed for programming effort estimation, predicting risks and identifying and detecting defects. This work is meant to serve as a starting point for practitioners willing to add ML to their software development toolbox. It categorises recent literature and identifies trends and limitations. The survey shows as some authors have agreed that industrial applications of ML for SD have not been as popular as the reported results would suggest. The conducted investigation shows that, despite having promising findings for a variety of SD tasks, most of the studies yield vague results, in part due to the lack of comprehensive datasets in this problem domain. The paper ends with concluding remarks and suggestions for future research.", "published": "2020-01-23T11:56:10Z", "authors": ["Barakat. J. Akinsanya", "Luiz J. P. Araújo", "Mariia Charikova", "Susanna Gimaeva", "Alexandr Grichshenko", "Adil Khan", "Manuel Mazzara", "Ozioma Okonicha N", "Daniil Shilintsev"], "categories": ["cs.SE"], "doi": "10.13140/RG.2.2.35867.62243", "_query": "house price prediction machine learning", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/1904.05384v3", "title": "Feature Engineering for Mid-Price Prediction with Deep Learning", "abstract": "Mid-price movement prediction based on limit order book (LOB) data is a challenging task due to the complexity and dynamics of the LOB. So far, there have been very limited attempts for extracting relevant features based on LOB data. In this paper, we address this problem by designing a new set of handcrafted features and performing an extensive experimental evaluation on both liquid and illiquid stocks. More specifically, we implement a new set of econometrical features that capture statistical properties of the underlying securities for the task of mid-price prediction. Moreover, we develop a new experimental protocol for online learning that treats the task as a multi-objective optimization problem and predicts i) the direction of the next price movement and ii) the number of order book events that occur until the change takes place. In order to predict the mid-price movement, the features are fed into nine different deep learning models based on multi-layer perceptrons (MLP), convolutional neural networks (CNN) and long short-term memory (LSTM) neural networks. The performance of the proposed method is then evaluated on liquid and illiquid stocks, which are based on TotalView-ITCH US and Nordic stocks, respectively. For some stocks, results suggest that the correct choice of a feature set and a model can lead to the successful prediction of how long it takes to have a stock price movement.", "published": "2019-04-10T18:40:20Z", "authors": ["Adamantios Ntakaris", "Giorgio Mirone", "Juho Kanniainen", "Moncef Gabbouj", "Alexandros Iosifidis"], "categories": ["q-fin.ST", "cs.LG"], "doi": "", "_query": "house price prediction machine learning", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2510.14837v1", "title": "Reinforcement Learning with Stochastic Reward Machines", "abstract": "Reward machines are an established tool for dealing with reinforcement learning problems in which rewards are sparse and depend on complex sequences of actions. However, existing algorithms for learning reward machines assume an overly idealized setting where rewards have to be free of noise. To overcome this practical limitation, we introduce a novel type of reward machines, called stochastic reward machines, and an algorithm for learning them. Our algorithm, based on constraint solving, learns minimal stochastic reward machines from the explorations of a reinforcement learning agent. This algorithm can easily be paired with existing reinforcement learning algorithms for reward machines and guarantees to converge to an optimal policy in the limit. We demonstrate the effectiveness of our algorithm in two case studies and show that it outperforms both existing methods and a naive approach for handling noisy reward functions.", "published": "2025-10-16T16:12:04Z", "authors": ["Jan Corazza", "Ivan Gavran", "Daniel Neider"], "categories": ["cs.LG", "cs.AI"], "doi": "10.1609/aaai.v36i6.20594", "_query": "house price prediction machine learning", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/1705.07538v2", "title": "Infrastructure for Usable Machine Learning: The Stanford DAWN Project", "abstract": "Despite incredible recent advances in machine learning, building machine learning applications remains prohibitively time-consuming and expensive for all but the best-trained, best-funded engineering organizations. This expense comes not from a need for new and improved statistical models but instead from a lack of systems and tools for supporting end-to-end machine learning application development, from data preparation and labeling to productionization and monitoring. In this document, we outline opportunities for infrastructure supporting usable, end-to-end machine learning applications in the context of the nascent DAWN (Data Analytics for What's Next) project at Stanford.", "published": "2017-05-22T02:28:19Z", "authors": ["Peter Bailis", "Kunle Olukotun", "Christopher Re", "Matei Zaharia"], "categories": ["cs.LG", "cs.DB", "stat.ML"], "doi": "", "_query": "house price prediction machine learning", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2409.13786v1", "title": "Physics-informed kernel learning", "abstract": "Physics-informed machine learning typically integrates physical priors into the learning process by minimizing a loss function that includes both a data-driven term and a partial differential equation (PDE) regularization. Building on the formulation of the problem as a kernel regression task, we use Fourier methods to approximate the associated kernel, and propose a tractable estimator that minimizes the physics-informed risk function. We refer to this approach as physics-informed kernel learning (PIKL). This framework provides theoretical guarantees, enabling the quantification of the physical prior's impact on convergence speed. We demonstrate the numerical performance of the PIKL estimator through simulations, both in the context of hybrid modeling and in solving PDEs. In particular, we show that PIKL can outperform physics-informed neural networks in terms of both accuracy and computation time. Additionally, we identify cases where PIKL surpasses traditional PDE solvers, particularly in scenarios with noisy boundary conditions.", "published": "2024-09-20T06:55:20Z", "authors": ["Nathan Doumèche", "Francis Bach", "Gérard Biau", "Claire Boyer"], "categories": ["stat.ML", "cs.LG", "math.ST"], "doi": "", "_query": "house price prediction machine learning", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/1812.01025v1", "title": "Accelerating Photovoltaic Materials Development via High-Throughput Experiments and Machine-Learning-Assisted Diagnosis", "abstract": "Accelerating the experimental cycle for new materials development is vital for addressing the grand energy challenges of the 21st century. We fabricate and characterize 75 unique halide perovskite-inspired solution-based thin-film materials within a two-month period, with 87% exhibiting band gaps between 1.2 eV and 2.4 eV that are of interest for energy-harvesting applications. This increased throughput is enabled by streamlining experimental workflows, developing a set of precursors amenable to high-throughput synthesis, and developing machine-learning assisted diagnosis. We utilize a deep neural network to classify compounds based on experimental X-ray diffraction data into 0D, 2D, and 3D structures more than 10 times faster than human analysis and with 90% accuracy. We validate our methods using lead-halide perovskites and extend the application to novel lead-free compositions. The wider synthesis window and faster cycle of learning enables three noteworthy scientific findings: (1) we realize four inorganic layered perovskites, A3B2Br9 (A = Cs, Rb; B = Bi, Sb) in thin-film form via one-step liquid deposition; (2) we report a multi-site lead-free alloy series that was not previously described in literature, Cs3(Bi1-xSbx)2(I1-xBrx)9; and (3) we reveal the effect on bandgap (reduction to <2 eV) and structure upon simultaneous alloying on the B-site and X-site of Cs3Bi2I9 with Sb and Br. This study demonstrates that combining an accelerated experimental cycle of learning and machine-learning based diagnosis represents an important step toward realizing fully-automated laboratories for materials discovery and development.", "published": "2018-11-25T06:29:23Z", "authors": ["Shijing Sun", "Noor T. P. Hartono", "Zekun D. Ren", "Felipe Oviedo", "Antonio M. Buscemi", "Mariya Layurova", "De Xin Chen", "Tofunmi Ogunfunmi", "Janak Thapa", "Savitha Ramasamy", "Charles Settens", "Brian L. DeCost", "Aaron Gilad Kusne", "Zhe Liu", "Siyu I. P. Tian", "I. Marius Peters", "Juan-Pablo Correa-Baena", "Tonio Buonassisi"], "categories": ["physics.app-ph", "cond-mat.mtrl-sci"], "doi": "10.1016/j.joule.2019.05.014", "_query": "house price prediction machine learning", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2302.09826v4", "title": "On the Expressivity of Persistent Homology in Graph Learning", "abstract": "Persistent homology, a technique from computational topology, has recently shown strong empirical performance in the context of graph classification. Being able to capture long range graph properties via higher-order topological features, such as cycles of arbitrary length, in combination with multi-scale topological descriptors, has improved predictive performance for data sets with prominent topological structures, such as molecules. At the same time, the theoretical properties of persistent homology have not been formally assessed in this context. This paper intends to bridge the gap between computational topology and graph machine learning by providing a brief introduction to persistent homology in the context of graphs, as well as a theoretical discussion and empirical analysis of its expressivity for graph learning tasks.", "published": "2023-02-20T08:19:19Z", "authors": ["Rubén Ballester", "Bastian Rieck"], "categories": ["cs.LG", "math.AT", "stat.ML"], "doi": "", "_query": "house price prediction machine learning", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2303.17475v4", "title": "Learning distributed representations with efficient SoftMax normalization", "abstract": "Learning distributed representations, or embeddings, that encode the relational similarity patterns among objects is a relevant task in machine learning. A popular method to learn the embedding matrices $X, Y$ is optimizing a loss function of the term ${\\rm SoftMax}(XY^T)$. The complexity required to calculate this term, however, runs quadratically with the problem size, making it a computationally heavy solution. In this article, we propose a linear-time heuristic approximation to compute the normalization constants of ${\\rm SoftMax}(XY^T)$ for embedding vectors with bounded norms. We show on some pre-trained embedding datasets that the proposed estimation method achieves higher or comparable accuracy with competing methods. From this result, we design an efficient and task-agnostic algorithm that learns the embeddings by optimizing the cross entropy between the softmax and a set of probability distributions given as inputs. The proposed algorithm is interpretable and easily adapted to arbitrary embedding problems. We consider a few use cases and observe similar or higher performances and a lower computational time than similar ``2Vec'' algorithms.", "published": "2023-03-30T15:48:26Z", "authors": ["Lorenzo Dall'Amico", "Enrico Maria Belliardo"], "categories": ["cs.LG", "cs.CL", "stat.ML"], "doi": "", "_query": "house price prediction machine learning", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2501.15893v2", "title": "Benchmarking Quantum Reinforcement Learning", "abstract": "Benchmarking and establishing proper statistical validation metrics for reinforcement learning (RL) remain ongoing challenges, where no consensus has been established yet. The emergence of quantum computing and its potential applications in quantum reinforcement learning (QRL) further complicate benchmarking efforts. To enable valid performance comparisons and to streamline current research in this area, we propose a novel benchmarking methodology, which is based on a statistical estimator for sample complexity and a definition of statistical outperformance. Furthermore, considering QRL, our methodology casts doubt on some previous claims regarding its superiority. We conducted experiments on a novel benchmarking environment with flexible levels of complexity. While we still identify possible advantages, our findings are more nuanced overall. We discuss the potential limitations of these results and explore their implications for empirical research on quantum advantage in QRL.", "published": "2025-01-27T09:40:18Z", "authors": ["Nico Meyer", "Christian Ufrecht", "George Yammine", "Georgios Kontes", "Christopher Mutschler", "Daniel D. Scherer"], "categories": ["quant-ph", "cs.LG"], "doi": "", "_query": "house price prediction machine learning", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2412.21046v1", "title": "Mind the truncation gap: challenges of learning on dynamic graphs with recurrent architectures", "abstract": "Systems characterized by evolving interactions, prevalent in social, financial, and biological domains, are effectively modeled as continuous-time dynamic graphs (CTDGs). To manage the scale and complexity of these graph datasets, machine learning (ML) approaches have become essential. However, CTDGs pose challenges for ML because traditional static graph methods do not naturally account for event timings. Newer approaches, such as graph recurrent neural networks (GRNNs), are inherently time-aware and offer advantages over static methods for CTDGs. However, GRNNs face another issue: the short truncation of backpropagation-through-time (BPTT), whose impact has not been properly examined until now. In this work, we demonstrate that this truncation can limit the learning of dependencies beyond a single hop, resulting in reduced performance. Through experiments on a novel synthetic task and real-world datasets, we reveal a performance gap between full backpropagation-through-time (F-BPTT) and the truncated backpropagation-through-time (T-BPTT) commonly used to train GRNN models. We term this gap the \"truncation gap\" and argue that understanding and addressing it is essential as the importance of CTDGs grows, discussing potential future directions for research in this area.", "published": "2024-12-30T16:07:41Z", "authors": ["João Bravo", "Jacopo Bono", "Pedro Saleiro", "Hugo Ferreira", "Pedro Bizarro"], "categories": ["cs.LG"], "doi": "", "_query": "house price prediction machine learning", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2407.04712v1", "title": "Sensing technologies and machine learning methods for emotion recognition in autism: Systematic review", "abstract": "Background: Human Emotion Recognition (HER) has been a popular field of study in the past years. Despite the great progresses made so far, relatively little attention has been paid to the use of HER in autism. People with autism are known to face problems with daily social communication and the prototypical interpretation of emotional responses, which are most frequently exerted via facial expressions. This poses significant practical challenges to the application of regular HER systems, which are normally developed for and by neurotypical people. Objective: This study reviews the literature on the use of HER systems in autism, particularly with respect to sensing technologies and machine learning methods, as to identify existing barriers and possible future directions. Methods: We conducted a systematic review of articles published between January 2011 and June 2023 according to the 2020 PRISMA guidelines. Manuscripts were identified through searching Web of Science and Scopus databases. Manuscripts were included when related to emotion recognition, used sensors and machine learning techniques, and involved children with autism, young, or adults. Results: The search yielded 346 articles. A total of 65 publications met the eligibility criteria and were included in the review. Conclusions: Studies predominantly used facial expression techniques as the emotion recognition method. Consequently, video cameras were the most widely used devices across studies, although a growing trend in the use of physiological sensors was observed lately. Happiness, sadness, anger, fear, disgust, and surprise were most frequently addressed. Classical supervised machine learning techniques were primarily used at the expense of unsupervised approaches or more recent deep learning models.", "published": "2024-05-15T19:48:04Z", "authors": ["Oresti Banos", "Zhoe Comas-González", "Javier Medina", "Aurora Polo-Rodríguez", "David Gil", "Jesús Peral", "Sandra Amador", "Claudia Villalonga"], "categories": ["cs.CV", "cs.AI", "cs.LG"], "doi": "10.1016/j.ijmedinf.2024.105469", "_query": "house price prediction machine learning", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2406.08748v2", "title": "Learning in Feature Spaces via Coupled Covariances: Asymmetric Kernel SVD and Nyström method", "abstract": "In contrast with Mercer kernel-based approaches as used e.g., in Kernel Principal Component Analysis (KPCA), it was previously shown that Singular Value Decomposition (SVD) inherently relates to asymmetric kernels and Asymmetric Kernel Singular Value Decomposition (KSVD) has been proposed. However, the existing formulation to KSVD cannot work with infinite-dimensional feature mappings, the variational objective can be unbounded, and needs further numerical evaluation and exploration towards machine learning. In this work, i) we introduce a new asymmetric learning paradigm based on coupled covariance eigenproblem (CCE) through covariance operators, allowing infinite-dimensional feature maps. The solution to CCE is ultimately obtained from the SVD of the induced asymmetric kernel matrix, providing links to KSVD. ii) Starting from the integral equations corresponding to a pair of coupled adjoint eigenfunctions, we formalize the asymmetric Nyström method through a finite sample approximation to speed up training. iii) We provide the first empirical evaluations verifying the practical utility and benefits of KSVD and compare with methods resorting to symmetrization or linear SVD across multiple tasks.", "published": "2024-06-13T02:12:18Z", "authors": ["Qinghua Tao", "Francesco Tonin", "Alex Lambert", "Yingyi Chen", "Panagiotis Patrinos", "Johan A. K. Suykens"], "categories": ["cs.LG", "cs.AI", "stat.ML"], "doi": "", "_query": "house price prediction machine learning", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2608.09166v1", "title": "Particle-Based Conformal Prediction for Contact-Aware Uncertainty Calibration in Stratified Configuration Spaces", "abstract": "Reliable uncertainty representation is essential for deploying autonomous systems that interact with their environment, as robots must reason about how uncertainty arising from both stochasticity and model mismatch is impacted by contacts with obstacles (e.g., when navigating through a cluttered environment or inserting a part into an assembly). We propose Calibrated Particle-sets for Trans-dimensional Uncertainty Representation (CaPTURe), a geometry-aware, conformal prediction-based algorithm that generates probabilistically valid prediction regions of the unknown future system configuration using particle-based models of arbitrary fidelity. While calibrated uncertainty predictions are essential for safe and efficient planning, analytical or learned motion models are often inaccurate - due to limited data, simplifying assumptions, unmodeled effects, etc. - which can lead to unsafe executions or task failure. Additionally, when a robot contacts an obstacle, the distribution of its future configurations can become multimodal or disjoint, or lie along manifolds of lower intrinsic dimension than the space of possible robot configurations. Our method uses a calibration dataset of system transitions to locally calibrate motion uncertainty estimates, constructing regions guaranteed to contain the future robot configuration at a user-set probability. Our calibration procedure captures how motion uncertainty varies between contact-rich and contactless motions, leading to sufficient coverage in both cases. We evaluate our method on two simulated planning tasks: controlling a marble around a labyrinth and performing tight-tolerance peg-in-hole insertion with a manipulator. Compared to relevant baselines, CaPTURe achieves the user-specified coverage requirement both in and out of contact and achieves up to a 30% absolute improvement in task success rate over the best baseline.", "published": "2026-08-10T06:22:10Z", "authors": ["Luís Marques", "Kristian Popov", "Dmitry Berenson"], "categories": ["cs.RO", "cs.LG", "stat.ME"], "doi": "", "_query": "house price prediction machine learning", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2006.08669v1", "title": "On Adversarial Bias and the Robustness of Fair Machine Learning", "abstract": "Optimizing prediction accuracy can come at the expense of fairness. Towards minimizing discrimination against a group, fair machine learning algorithms strive to equalize the behavior of a model across different groups, by imposing a fairness constraint on models. However, we show that giving the same importance to groups of different sizes and distributions, to counteract the effect of bias in training data, can be in conflict with robustness. We analyze data poisoning attacks against group-based fair machine learning, with the focus on equalized odds. An adversary who can control sampling or labeling for a fraction of training data, can reduce the test accuracy significantly beyond what he can achieve on unconstrained models. Adversarial sampling and adversarial labeling attacks can also worsen the model's fairness gap on test data, even though the model satisfies the fairness constraint on training data. We analyze the robustness of fair machine learning through an empirical evaluation of attacks on multiple algorithms and benchmark datasets.", "published": "2020-06-15T18:17:44Z", "authors": ["Hongyan Chang", "Ta Duy Nguyen", "Sasi Kumar Murakonda", "Ehsan Kazemi", "Reza Shokri"], "categories": ["stat.ML", "cs.CR", "cs.CY", "cs.LG"], "doi": "", "_query": "house price prediction machine learning", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2002.05674v3", "title": "What Would You Ask the Machine Learning Model? Identification of User Needs for Model Explanations Based on Human-Model Conversations", "abstract": "Recently we see a rising number of methods in the field of eXplainable Artificial Intelligence. To our surprise, their development is driven by model developers rather than a study of needs for human end users. The analysis of needs, if done, takes the form of an A/B test rather than a study of open questions. To answer the question \"What would a human operator like to ask the ML model?\" we propose a conversational system explaining decisions of the predictive model. In this experiment, we developed a chatbot called dr_ant to talk about machine learning model trained to predict survival odds on Titanic. People can talk with dr_ant about different aspects of the model to understand the rationale behind its predictions. Having collected a corpus of 1000+ dialogues, we analyse the most common types of questions that users would like to ask. To our knowledge, it is the first study which uses a conversational system to collect the needs of human operators from the interactive and iterative dialogue explorations of a predictive model.", "published": "2020-02-07T15:59:49Z", "authors": ["Michał Kuźba", "Przemysław Biecek"], "categories": ["cs.CY", "cs.CL", "cs.HC", "cs.LG", "stat.ML"], "doi": "10.1007/978-3-030-65965-3_30", "_query": "house price prediction machine learning", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2101.04904v4", "title": "EEC: Learning to Encode and Regenerate Images for Continual Learning", "abstract": "The two main impediments to continual learning are catastrophic forgetting and memory limitations on the storage of data. To cope with these challenges, we propose a novel, cognitively-inspired approach which trains autoencoders with Neural Style Transfer to encode and store images. During training on a new task, reconstructed images from encoded episodes are replayed in order to avoid catastrophic forgetting. The loss function for the reconstructed images is weighted to reduce its effect during classifier training to cope with image degradation. When the system runs out of memory the encoded episodes are converted into centroids and covariance matrices, which are used to generate pseudo-images during classifier training, keeping classifier performance stable while using less memory. Our approach increases classification accuracy by 13-17% over state-of-the-art methods on benchmark datasets, while requiring 78% less storage space.", "published": "2021-01-13T06:43:10Z", "authors": ["Ali Ayub", "Alan R. Wagner"], "categories": ["cs.CV", "cs.AI", "cs.LG"], "doi": "", "_query": "house price prediction machine learning", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2111.06723v1", "title": "Mobility prediction Based on Machine Learning Algorithms", "abstract": "Nowadays mobile communication is growing fast in the 5G communication industry. With the increasing capacity requirements and requirements for quality of experience, mobility prediction has been widely applied to mobile communication and has becoming one of the key enablers that utilizes historical traffic information to predict future locations of traffic users, Since accurate mobility prediction can help enable efficient radio resource management, assist route planning, guide vehicle dispatching, or mitigate traffic congestion. However, mobility prediction is a challenging problem due to the complicated traffic network. In the past few years, plenty of researches have been done in this area, including Non-Machine-Learning (Non-ML)- based and Machine-Learning (ML)-based mobility prediction. In this paper, firstly we introduce the state of the art technologies for mobility prediction. Then, we selected Support Vector Machine (SVM) algorithm, the ML algorithm for practical traffic date training. Lastly, we analyse the simulation results for mobility prediction and introduce a future work plan where mobility prediction will be applied for improving mobile communication.", "published": "2021-11-12T13:49:29Z", "authors": ["Donglin Wang", "Qiuheng Zhou", "Sanket Partani", "Anjie Qiu", "Hans D. Schotten"], "categories": ["cs.LG", "cs.NI"], "doi": "", "_query": "house price prediction machine learning", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2606.14416v1", "title": "Federated Learning for Feature Generalization with Convex Constraints", "abstract": "Federated learning (FL) often struggles with generalization due to heterogeneous client data. Local models are prone to overfitting their local data distributions, and even transferable features can be distorted during aggregation. To address these challenges, we propose FedCONST, an approach that adaptively modulates update magnitudes based on the parameter strength of the global model. This prevents over-emphasizing well-learned parameters while reinforcing underdeveloped ones. Specifically, FedCONST employs linear convex constraints to ensure training stability and preserve locally learned generalization capabilities during aggregation. A Gradient Signal to Noise Ratio (GSNR) analysis further validates the effectiveness of FedCONST in enhancing feature transferability and robustness. As a result, FedCONST effectively aligns local and global objectives, mitigating overfitting and promoting stronger generalization across diverse FL environments, achieving state-of-the-art performance.", "published": "2026-06-12T12:50:33Z", "authors": ["Dongwon Kim", "Donghee Kim", "Sung Kuk Shyn", "Kwangsu Kim"], "categories": ["cs.LG", "stat.ML"], "doi": "", "_query": "house price prediction machine learning", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2401.16407v2", "title": "Is K-fold cross validation the best model selection method for Machine Learning?", "abstract": "As a technique that can compactly represent complex patterns, machine learning has significant potential for predictive inference. K-fold cross-validation (CV) is the most common approach to ascertaining the likelihood that a machine learning outcome is generated by chance, and it frequently outperforms conventional hypothesis testing. This improvement uses measures directly obtained from machine learning classifications, such as accuracy, that do not have a parametric description. To approach a frequentist analysis within machine learning pipelines, a permutation test or simple statistics from data partitions (i.e., folds) can be added to estimate confidence intervals. Unfortunately, neither parametric nor non-parametric tests solve the inherent problems of partitioning small sample-size datasets and learning from heterogeneous data sources. The fact that machine learning strongly depends on the learning parameters and the distribution of data across folds recapitulates familiar difficulties around excess false positives and replication. A novel statistical test based on K-fold CV and the Upper Bound of the actual risk (K-fold CUBV) is proposed, where uncertain predictions of machine learning with CV are bounded by the worst case through the evaluation of concentration inequalities. Probably Approximately Correct-Bayesian upper bounds for linear classifiers in combination with K-fold CV are derived and used to estimate the actual risk. The performance with simulated and neuroimaging datasets suggests that K-fold CUBV is a robust criterion for detecting effects and validating accuracy values obtained from machine learning and classical CV schemes, while avoiding excess false positives.", "published": "2024-01-29T18:46:53Z", "authors": ["Juan M Gorriz", "R. Martin Clemente", "F Segovia", "J Ramirez", "A Ortiz", "J. Suckling"], "categories": ["stat.ML", "cs.LG", "eess.IV", "eess.SP"], "doi": "10.1016/j.inffus.2026.104404", "_query": "house price prediction machine learning", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/1808.09856v1", "title": "Application of Machine Learning in Rock Facies Classification with Physics-Motivated Feature Augmentation", "abstract": "With recent progress in algorithms and the availability of massive amounts of computation power, application of machine learning techniques is becoming a hot topic in the oil and gas industry. One of the most promising aspects to apply machine learning to the upstream field is the rock facies classification in reservoir characterization, which is crucial in determining the net pay thickness of reservoirs, thus a definitive factor in drilling decision making process. For complex machine learning tasks like facies classification, feature engineering is often critical. This paper shows the inclusion of physics-motivated feature interaction in feature augmentation can further improve the capability of machine learning in rock facies classification. We demonstrate this approach with the SEG 2016 machine learning contest dataset and the top winning algorithms. The improvement is roboust and can be $\\sim5\\%$ better than current existing best F-1 score, where F-1 is an evaluation metric used to quantify average prediction accuracy.", "published": "2018-08-29T14:43:12Z", "authors": ["Jie Chen", "Yu Zeng"], "categories": ["stat.ML", "cs.LG", "physics.geo-ph"], "doi": "", "_query": "house price prediction machine learning", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2602.16352v1", "title": "Machine Learning in Epidemiology", "abstract": "In the age of digital epidemiology, epidemiologists are faced by an increasing amount of data of growing complexity and dimensionality. Machine learning is a set of powerful tools that can help to analyze such enormous amounts of data. This chapter lays the methodological foundations for successfully applying machine learning in epidemiology. It covers the principles of supervised and unsupervised learning and discusses the most important machine learning methods. Strategies for model evaluation and hyperparameter optimization are developed and interpretable machine learning is introduced. All these theoretical parts are accompanied by code examples in R, where an example dataset on heart disease is used throughout the chapter.", "published": "2026-02-18T10:35:18Z", "authors": ["Marvin N. Wright", "Lukas Burk", "Pegah Golchian", "Jan Kapar", "Niklas Koenen", "Sophie Hanna Langbein"], "categories": ["stat.ML", "cs.CY", "cs.LG"], "doi": "10.1007/978-1-4614-6625-3_81-1", "_query": "house price prediction machine learning", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/1106.1770v3", "title": "Reinforcement learning based sensing policy optimization for energy efficient cognitive radio networks", "abstract": "This paper introduces a machine learning based collaborative multi-band spectrum sensing policy for cognitive radios. The proposed sensing policy guides secondary users to focus the search of unused radio spectrum to those frequencies that persistently provide them high data rate. The proposed policy is based on machine learning, which makes it adaptive with the temporally and spatially varying radio spectrum. Furthermore, there is no need for dynamic modeling of the primary activity since it is implicitly learned over time. Energy efficiency is achieved by minimizing the number of assigned sensors per each subband under a constraint on miss detection probability. It is important to control the missed detections because they cause collisions with primary transmissions and lead to retransmissions at both the primary and secondary user. Simulations show that the proposed machine learning based sensing policy improves the overall throughput of the secondary network and improves the energy efficiency while controlling the miss detection probability.", "published": "2011-06-09T10:40:08Z", "authors": ["Jan Oksanen", "Jarmo Lundén", "Visa Koivunen"], "categories": ["cs.LG"], "doi": "", "_query": "house price prediction machine learning", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2301.12636v2", "title": "Exploring Image Augmentations for Siamese Representation Learning with Chest X-Rays", "abstract": "Image augmentations are quintessential for effective visual representation learning across self-supervised learning techniques. While augmentation strategies for natural imaging have been studied extensively, medical images are vastly different from their natural counterparts. Thus, it is unknown whether common augmentation strategies employed in Siamese representation learning generalize to medical images and to what extent. To address this challenge, in this study, we systematically assess the effect of various augmentations on the quality and robustness of the learned representations. We train and evaluate Siamese Networks for abnormality detection on chest X-Rays across three large datasets (MIMIC-CXR, CheXpert and VinDR-CXR). We investigate the efficacy of the learned representations through experiments involving linear probing, fine-tuning, zero-shot transfer, and data efficiency. Finally, we identify a set of augmentations that yield robust representations that generalize well to both out-of-distribution data and diseases, while outperforming supervised baselines using just zero-shot transfer and linear probes by up to 20%. Our code is available at https://github.com/StanfordMIMI/siaug.", "published": "2023-01-30T03:42:02Z", "authors": ["Rogier van der Sluijs", "Nandita Bhaskhar", "Daniel Rubin", "Curtis Langlotz", "Akshay Chaudhari"], "categories": ["eess.IV", "cs.AI", "cs.CV", "cs.LG"], "doi": "", "_query": "house price prediction machine learning", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2506.22055v1", "title": "crypto price prediction using lstm+xgboost", "abstract": "The volatility and complex dynamics of cryptocurrency markets present unique challenges for accurate price forecasting. This research proposes a hybrid deep learning and machine learning model that integrates Long Short-Term Memory (LSTM) networks and Extreme Gradient Boosting (XGBoost) for cryptocurrency price prediction. The LSTM component captures temporal dependencies in historical price data, while XGBoost enhances prediction by modeling nonlinear relationships with auxiliary features such as sentiment scores and macroeconomic indicators. The model is evaluated on historical datasets of Bitcoin, Ethereum, Dogecoin, and Litecoin, incorporating both global and localized exchange data. Comparative analysis using Mean Absolute Percentage Error (MAPE) and Min-Max Normalized Root Mean Square Error (MinMax RMSE) demonstrates that the LSTM+XGBoost hybrid consistently outperforms standalone models and traditional forecasting methods. This study underscores the potential of hybrid architectures in financial forecasting and provides insights into model adaptability across different cryptocurrencies and market contexts.", "published": "2025-06-27T09:49:25Z", "authors": ["Mehul Gautam"], "categories": ["cs.LG"], "doi": "", "_query": "house price prediction machine learning", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2102.12723v1", "title": "On Interpretability and Similarity in Concept-Based Machine Learning", "abstract": "Machine Learning (ML) provides important techniques for classification and predictions. Most of these are black-box models for users and do not provide decision-makers with an explanation. For the sake of transparency or more validity of decisions, the need to develop explainable/interpretable ML-methods is gaining more and more importance. Certain questions need to be addressed: How does an ML procedure derive the class for a particular entity? Why does a particular clustering emerge from a particular unsupervised ML procedure? What can we do if the number of attributes is very large? What are the possible reasons for the mistakes for concrete cases and models? For binary attributes, Formal Concept Analysis (FCA) offers techniques in terms of intents of formal concepts, and thus provides plausible reasons for model prediction. However, from the interpretable machine learning viewpoint, we still need to provide decision-makers with the importance of individual attributes to the classification of a particular object, which may facilitate explanations by experts in various domains with high-cost errors like medicine or finance. We discuss how notions from cooperative game theory can be used to assess the contribution of individual attributes in classification and clustering processes in concept-based machine learning. To address the 3rd question, we present some ideas on how to reduce the number of attributes using similarities in large contexts.", "published": "2021-02-25T07:57:28Z", "authors": ["Léonard Kwuida", "Dmitry I. Ignatov"], "categories": ["cs.LG", "cs.AI", "cs.DM", "math.CO", "stat.ML"], "doi": "", "_query": "house price prediction machine learning", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2001.03998v2", "title": "Towards causality-aware predictions in static anticausal machine learning tasks: the linear structural causal model case", "abstract": "We propose a counterfactual approach to train ``causality-aware\" predictive models that are able to leverage causal information in static anticausal machine learning tasks (i.e., prediction tasks where the outcome influences the features). In applications plagued by confounding, the approach can be used to generate predictions that are free from the influence of observed confounders. In applications involving observed mediators, the approach can be used to generate predictions that only capture the direct or the indirect causal influences. Mechanistically, we train supervised learners on (counterfactually) simulated features which retain only the associations generated by the causal relations of interest. We focus on linear models, where analytical results connecting covariances, causal effects, and prediction mean squared errors are readily available. Quite importantly, we show that our approach does not require knowledge of the full causal graph. It suffices to know which variables represent potential confounders and/or mediators. We discuss the stability of the method with respect to dataset shifts generated by selection biases and validate the approach using synthetic data experiments.", "published": "2020-01-12T20:49:07Z", "authors": ["Elias Chaibub Neto"], "categories": ["stat.AP"], "doi": "", "_query": "house price prediction machine learning", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2306.11771v1", "title": "Designing Explainable Predictive Machine Learning Artifacts: Methodology and Practical Demonstration", "abstract": "Prediction-oriented machine learning is becoming increasingly valuable to organizations, as it may drive applications in crucial business areas. However, decision-makers from companies across various industries are still largely reluctant to employ applications based on modern machine learning algorithms. We ascribe this issue to the widely held view on advanced machine learning algorithms as \"black boxes\" whose complexity does not allow for uncovering the factors that drive the output of a corresponding system. To contribute to overcome this adoption barrier, we argue that research in information systems should devote more attention to the design of prototypical prediction-oriented machine learning applications (i.e., artifacts) whose predictions can be explained to human decision-makers. However, despite the recent emergence of a variety of tools that facilitate the development of such artifacts, there has so far been little research on their development. We attribute this research gap to the lack of methodological guidance to support the creation of these artifacts. For this reason, we develop a methodology which unifies methodological knowledge from design science research and predictive analytics with state-of-the-art approaches to explainable artificial intelligence. Moreover, we showcase the methodology using the example of price prediction in the sharing economy (i.e., on Airbnb).", "published": "2023-06-20T15:11:26Z", "authors": ["Giacomo Welsch", "Peter Kowalczyk"], "categories": ["cs.SE", "cs.AI", "cs.LG"], "doi": "", "_query": "house price prediction machine learning", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2505.15728v1", "title": "Are machine learning interpretations reliable? A stability study on global interpretations", "abstract": "As machine learning systems are increasingly used in high-stakes domains, there is a growing emphasis placed on making them interpretable to improve trust in these systems. In response, a range of interpretable machine learning (IML) methods have been developed to generate human-understandable insights into otherwise black box models. With these methods, a fundamental question arises: Are these interpretations reliable? Unlike with prediction accuracy or other evaluation metrics for supervised models, the proximity to the true interpretation is difficult to define. Instead, we ask a closely related question that we argue is a prerequisite for reliability: Are these interpretations stable? We define stability as findings that are consistent or reliable under small random perturbations to the data or algorithms. In this study, we conduct the first systematic, large-scale empirical stability study on popular machine learning global interpretations for both supervised and unsupervised tasks on tabular data. Our findings reveal that popular interpretation methods are frequently unstable, notably less stable than the predictions themselves, and that there is no association between the accuracy of machine learning predictions and the stability of their associated interpretations. Moreover, we show that no single method consistently provides the most stable interpretations across a range of benchmark datasets. Overall, these results suggest that interpretability alone does not warrant trust, and underscores the need for rigorous evaluation of interpretation stability in future work. To support these principles, we have developed and released an open source IML dashboard and Python package to enable researchers to assess the stability and reliability of their own data-driven interpretations and discoveries.", "published": "2025-05-21T16:34:11Z", "authors": ["Luqin Gan", "Tarek M. Zikry", "Genevera I. Allen"], "categories": ["stat.ML", "cs.LG", "stat.AP"], "doi": "", "_query": "house price prediction machine learning", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2311.00235v1", "title": "Implicit biases in multitask and continual learning from a backward error analysis perspective", "abstract": "Using backward error analysis, we compute implicit training biases in multitask and continual learning settings for neural networks trained with stochastic gradient descent. In particular, we derive modified losses that are implicitly minimized during training. They have three terms: the original loss, accounting for convergence, an implicit flatness regularization term proportional to the learning rate, and a last term, the conflict term, which can theoretically be detrimental to both convergence and implicit regularization. In multitask, the conflict term is a well-known quantity, measuring the gradient alignment between the tasks, while in continual learning the conflict term is a new quantity in deep learning optimization, although a basic tool in differential geometry: The Lie bracket between the task gradients.", "published": "2023-11-01T02:37:32Z", "authors": ["Benoit Dherin"], "categories": ["stat.ML", "cs.AI", "cs.LG"], "doi": "", "_query": "house price prediction machine learning", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2103.07268v1", "title": "Adversarial Machine Learning Security Problems for 6G: mmWave Beam Prediction Use-Case", "abstract": "6G is the next generation for the communication systems. In recent years, machine learning algorithms have been applied widely in various fields such as health, transportation, and the autonomous car. The predictive algorithms will be used in 6G problems. With the rapid developments of deep learning techniques, it is critical to take the security concern into account to apply the algorithms. While machine learning offers significant advantages for 6G, AI models' security is ignored. Since it has many applications in the real world, security is a vital part of the algorithms. This paper has proposed a mitigation method for adversarial attacks against proposed 6G machine learning models for the millimeter-wave (mmWave) beam prediction with adversarial learning. The main idea behind adversarial attacks against machine learning models is to produce faulty results by manipulating trained deep learning models for 6G applications for mmWave beam prediction use case. We have also presented the adversarial learning mitigation method's performance for 6G security in millimeter-wave beam prediction application with fast gradient sign method attack. The mean square errors of the defended model and undefended model are very close.", "published": "2021-03-12T13:42:25Z", "authors": ["Evren Catak", "Ferhat Ozgur Catak", "Arild Moldsvor"], "categories": ["cs.LG", "cs.AI"], "doi": "", "_query": "house price prediction machine learning", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/1808.00198v1", "title": "Towards Machine Learning on data from Professional Cyclists", "abstract": "Professional sports are developing towards increasingly scientific training methods with increasing amounts of data being collected from laboratory tests, training sessions and competitions. In cycling, it is standard to equip bicycles with small computers recording data from sensors such as power-meters, in addition to heart-rate, speed, altitude etc. Recently, machine learning techniques have provided huge success in a wide variety of areas where large amounts of data (big data) is available. In this paper, we perform a pilot experiment on machine learning to model physical response in elite cyclists. As a first experiment, we show that it is possible to train a LSTM machine learning algorithm to predict the heart-rate response of a cyclist during a training session. This work is a promising first step towards developing more elaborate models based on big data and machine learning to capture performance aspects of athletes.", "published": "2018-08-01T07:13:16Z", "authors": ["Agrin Hilmkil", "Oscar Ivarsson", "Moa Johansson", "Dan Kuylenstierna", "Teun van Erp"], "categories": ["cs.LG", "stat.ML"], "doi": "", "_query": "house price prediction machine learning", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2309.16521v2", "title": "Generating Personalized Insulin Treatments Strategies with Deep Conditional Generative Time Series Models", "abstract": "We propose a novel framework that combines deep generative time series models with decision theory for generating personalized treatment strategies. It leverages historical patient trajectory data to jointly learn the generation of realistic personalized treatment and future outcome trajectories through deep generative time series models. In particular, our framework enables the generation of novel multivariate treatment strategies tailored to the personalized patient history and trained for optimal expected future outcomes based on conditional expected utility maximization. We demonstrate our framework by generating personalized insulin treatment strategies and blood glucose predictions for hospitalized diabetes patients, showcasing the potential of our approach for generating improved personalized treatment strategies. Keywords: deep generative model, probabilistic decision support, personalized treatment generation, insulin and blood glucose prediction", "published": "2023-09-28T15:27:28Z", "authors": ["Manuel Schürch", "Xiang Li", "Ahmed Allam", "Giulia Rathmes", "Amina Mollaysa", "Claudia Cavelti-Weder", "Michael Krauthammer"], "categories": ["stat.ML", "cs.LG"], "doi": "", "_query": "house price prediction machine learning", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/1807.05527v2", "title": "Learning Probabilistic Logic Programs in Continuous Domains", "abstract": "The field of statistical relational learning aims at unifying logic and probability to reason and learn from data. Perhaps the most successful paradigm in the field is probabilistic logic programming: the enabling of stochastic primitives in logic programming, which is now increasingly seen to provide a declarative background to complex machine learning applications. While many systems offer inference capabilities, the more significant challenge is that of learning meaningful and interpretable symbolic representations from data. In that regard, inductive logic programming and related techniques have paved much of the way for the last few decades. Unfortunately, a major limitation of this exciting landscape is that much of the work is limited to finite-domain discrete probability distributions. Recently, a handful of systems have been extended to represent and perform inference with continuous distributions. The problem, of course, is that classical solutions for inference are either restricted to well-known parametric families (e.g., Gaussians) or resort to sampling strategies that provide correct answers only in the limit. When it comes to learning, moreover, inducing representations remains entirely open, other than \"data-fitting\" solutions that force-fit points to aforementioned parametric families. In this paper, we take the first steps towards inducing probabilistic logic programs for continuous and mixed discrete-continuous data, without being pigeon-holed to a fixed set of distribution families. Our key insight is to leverage techniques from piecewise polynomial function approximation theory, yielding a principled way to learn and compositionally construct density functions. We test the framework and discuss the learned representations.", "published": "2018-07-15T11:00:00Z", "authors": ["Stefanie Speichert", "Vaishak Belle"], "categories": ["cs.LG", "cs.AI", "stat.ML"], "doi": "", "_query": "house price prediction machine learning", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/1906.00532v2", "title": "Efficient 8-Bit Quantization of Transformer Neural Machine Language Translation Model", "abstract": "In this work, we quantize a trained Transformer machine language translation model leveraging INT8/VNNI instructions in the latest Intel$^\\circledR$ Xeon$^\\circledR$ Cascade Lake processors to improve inference performance while maintaining less than 0.5$\\%$ drop in accuracy. To the best of our knowledge, this is the first attempt in the industry to quantize the Transformer model. This has high impact as it clearly demonstrates the various complexities of quantizing the language translation model. We present novel quantization techniques directly in TensorFlow to opportunistically replace 32-bit floating point (FP32) computations with 8-bit integers (INT8) and transform the FP32 computational graph. We also present a bin-packing parallel batching technique to maximize CPU utilization. Overall, our optimizations with INT8/VNNI deliver 1.5X improvement over the best FP32 performance. Furthermore, it reveals the opportunities and challenges to boost performance of quantized deep learning inference and establishes best practices to run inference with high efficiency on Intel CPUs.", "published": "2019-06-03T02:29:22Z", "authors": ["Aishwarya Bhandare", "Vamsi Sripathi", "Deepthi Karkada", "Vivek Menon", "Sun Choi", "Kushal Datta", "Vikram Saletore"], "categories": ["cs.LG"], "doi": "", "_query": "house price prediction machine learning", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/1906.06821v2", "title": "A Survey of Optimization Methods from a Machine Learning Perspective", "abstract": "Machine learning develops rapidly, which has made many theoretical breakthroughs and is widely applied in various fields. Optimization, as an important part of machine learning, has attracted much attention of researchers. With the exponential growth of data amount and the increase of model complexity, optimization methods in machine learning face more and more challenges. A lot of work on solving optimization problems or improving optimization methods in machine learning has been proposed successively. The systematic retrospect and summary of the optimization methods from the perspective of machine learning are of great significance, which can offer guidance for both developments of optimization and machine learning research. In this paper, we first describe the optimization problems in machine learning. Then, we introduce the principles and progresses of commonly used optimization methods. Next, we summarize the applications and developments of optimization methods in some popular machine learning fields. Finally, we explore and give some challenges and open problems for the optimization in machine learning.", "published": "2019-06-17T02:54:51Z", "authors": ["Shiliang Sun", "Zehui Cao", "Han Zhu", "Jing Zhao"], "categories": ["cs.LG", "math.OC", "stat.ML"], "doi": "", "_query": "house price prediction machine learning", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/1905.08883v3", "title": "Explainable Machine Learning for Scientific Insights and Discoveries", "abstract": "Machine learning methods have been remarkably successful for a wide range of application areas in the extraction of essential information from data. An exciting and relatively recent development is the uptake of machine learning in the natural sciences, where the major goal is to obtain novel scientific insights and discoveries from observational or simulated data. A prerequisite for obtaining a scientific outcome is domain knowledge, which is needed to gain explainability, but also to enhance scientific consistency. In this article we review explainable machine learning in view of applications in the natural sciences and discuss three core elements which we identified as relevant in this context: transparency, interpretability, and explainability. With respect to these core elements, we provide a survey of recent scientific works that incorporate machine learning and the way that explainable machine learning is used in combination with domain knowledge from the application areas.", "published": "2019-05-21T21:56:18Z", "authors": ["Ribana Roscher", "Bastian Bohn", "Marco F. Duarte", "Jochen Garcke"], "categories": ["cs.LG", "stat.ML"], "doi": "10.1109/ACCESS.2020.2976199", "_query": "house price prediction machine learning", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2008.06738v1", "title": "Reducing Sampling Error in Batch Temporal Difference Learning", "abstract": "Temporal difference (TD) learning is one of the main foundations of modern reinforcement learning. This paper studies the use of TD(0), a canonical TD algorithm, to estimate the value function of a given policy from a batch of data. In this batch setting, we show that TD(0) may converge to an inaccurate value function because the update following an action is weighted according to the number of times that action occurred in the batch -- not the true probability of the action under the given policy. To address this limitation, we introduce \\textit{policy sampling error corrected}-TD(0) (PSEC-TD(0)). PSEC-TD(0) first estimates the empirical distribution of actions in each state in the batch and then uses importance sampling to correct for the mismatch between the empirical weighting and the correct weighting for updates following each action. We refine the concept of a certainty-equivalence estimate and argue that PSEC-TD(0) is a more data efficient estimator than TD(0) for a fixed batch of data. Finally, we conduct an empirical evaluation of PSEC-TD(0) on three batch value function learning tasks, with a hyperparameter sensitivity analysis, and show that PSEC-TD(0) produces value function estimates with lower mean squared error than TD(0).", "published": "2020-08-15T15:30:06Z", "authors": ["Brahma Pavse", "Ishan Durugkar", "Josiah Hanna", "Peter Stone"], "categories": ["cs.LG", "cs.AI", "stat.ML"], "doi": "", "_query": "house price prediction machine learning", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2509.13464v1", "title": "LIGHT-HIDS: A Lightweight and Effective Machine Learning-Based Framework for Robust Host Intrusion Detection", "abstract": "The expansion of edge computing has increased the attack surface, creating an urgent need for robust, real-time machine learning (ML)-based host intrusion detection systems (HIDS) that balance accuracy and efficiency. In such settings, inference latency poses a critical security risk, as delays may provide exploitable opportunities for attackers. However, many state-of-the-art ML-based HIDS solutions rely on computationally intensive architectures with high inference costs, limiting their practical deployment. This paper proposes LIGHT-HIDS, a lightweight machine learning framework that combines a compressed neural network feature extractor trained via Deep Support Vector Data Description (DeepSVDD) with an efficient novelty detection model. This hybrid approach enables the learning of compact, meaningful representations of normal system call behavior for accurate anomaly detection. Experimental results on multiple datasets demonstrate that LIGHT-HIDS consistently enhances detection accuracy while reducing inference time by up to 75x compared to state-of-the-art methods. These findings highlight its effectiveness and scalability as a machine learning-based solution for real-time host intrusion detection.", "published": "2025-09-16T19:02:23Z", "authors": ["Onat Gungor", "Ishaan Kale", "Jiasheng Zhou", "Tajana Rosing"], "categories": ["cs.CR"], "doi": "", "_query": "house price prediction machine learning", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/1806.05454v1", "title": "Low-rank geometric mean metric learning", "abstract": "We propose a low-rank approach to learning a Mahalanobis metric from data. Inspired by the recent geometric mean metric learning (GMML) algorithm, we propose a low-rank variant of the algorithm. This allows to jointly learn a low-dimensional subspace where the data reside and the Mahalanobis metric that appropriately fits the data. Our results show that we compete effectively with GMML at lower ranks.", "published": "2018-06-14T10:35:21Z", "authors": ["Mukul Bhutani", "Pratik Jawanpuria", "Hiroyuki Kasai", "Bamdev Mishra"], "categories": ["cs.LG", "stat.ML"], "doi": "", "_query": "house price prediction machine learning", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2212.07295v2", "title": "Maximal Initial Learning Rates in Deep ReLU Networks", "abstract": "Training a neural network requires choosing a suitable learning rate, which involves a trade-off between speed and effectiveness of convergence. While there has been considerable theoretical and empirical analysis of how large the learning rate can be, most prior work focuses only on late-stage training. In this work, we introduce the maximal initial learning rate $η^{\\ast}$ - the largest learning rate at which a randomly initialized neural network can successfully begin training and achieve (at least) a given threshold accuracy. Using a simple approach to estimate $η^{\\ast}$, we observe that in constant-width fully-connected ReLU networks, $η^{\\ast}$ behaves differently from the maximum learning rate later in training. Specifically, we find that $η^{\\ast}$ is well predicted as a power of depth $\\times$ width, provided that (i) the width of the network is sufficiently large compared to the depth, and (ii) the input layer is trained at a relatively small learning rate. We further analyze the relationship between $η^{\\ast}$ and the sharpness $λ_{1}$ of the network at initialization, indicating they are closely though not inversely related. We formally prove bounds for $λ_{1}$ in terms of depth $\\times$ width that align with our empirical results.", "published": "2022-12-14T15:58:37Z", "authors": ["Gaurav Iyer", "Boris Hanin", "David Rolnick"], "categories": ["stat.ML", "cs.LG"], "doi": "", "_query": "house price prediction machine learning", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/1706.08605v1", "title": "Developing Bug-Free Machine Learning Systems With Formal Mathematics", "abstract": "Noisy data, non-convex objectives, model misspecification, and numerical instability can all cause undesired behaviors in machine learning systems. As a result, detecting actual implementation errors can be extremely difficult. We demonstrate a methodology in which developers use an interactive proof assistant to both implement their system and to state a formal theorem defining what it means for their system to be correct. The process of proving this theorem interactively in the proof assistant exposes all implementation errors since any error in the program would cause the proof to fail. As a case study, we implement a new system, Certigrad, for optimizing over stochastic computation graphs, and we generate a formal (i.e. machine-checkable) proof that the gradients sampled by the system are unbiased estimates of the true mathematical gradients. We train a variational autoencoder using Certigrad and find the performance comparable to training the same model in TensorFlow.", "published": "2017-06-26T21:30:02Z", "authors": ["Daniel Selsam", "Percy Liang", "David L. Dill"], "categories": ["cs.SE", "cs.AI"], "doi": "", "_query": "house price prediction machine learning", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2410.10523v3", "title": "Machine Learning for Inverse Problems and Data Assimilation", "abstract": "The aim of this book is to demonstrate the potential for ideas in machine learning to impact on the fields of inverse problems and data assimilation. The perspective is one that is primarily aimed at researchers from inverse problems and/or data assimilation who wish to see a mathematical presentation of machine learning as it pertains to their fields. As a by-product, we include a succinct mathematical treatment of various fundamental underpinning topics in machine learning, and adjacent areas of (computational) mathematics.", "published": "2024-10-14T14:01:35Z", "authors": ["Eviatar Bach", "Ricardo Baptista", "Daniel Sanz-Alonso", "Andrew Stuart"], "categories": ["stat.ML", "cs.LG", "math.OC"], "doi": "", "_query": "house price prediction machine learning", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/1912.02631v2", "title": "Trident: Efficient 4PC Framework for Privacy Preserving Machine Learning", "abstract": "Machine learning has started to be deployed in fields such as healthcare and finance, which propelled the need for and growth of privacy-preserving machine learning (PPML). We propose an actively secure four-party protocol (4PC), and a framework for PPML, showcasing its applications on four of the most widely-known machine learning algorithms -- Linear Regression, Logistic Regression, Neural Networks, and Convolutional Neural Networks. Our 4PC protocol tolerating at most one malicious corruption is practically efficient as compared to the existing works. We use the protocol to build an efficient mixed-world framework (Trident) to switch between the Arithmetic, Boolean, and Garbled worlds. Our framework operates in the offline-online paradigm over rings and is instantiated in an outsourced setting for machine learning. Also, we propose conversions especially relevant to privacy-preserving machine learning. The highlights of our framework include using a minimal number of expensive circuits overall as compared to ABY3. This can be seen in our technique for truncation, which does not affect the online cost of multiplication and removes the need for any circuits in the offline phase. Our B2A conversion has an improvement of $\\mathbf{7} \\times$ in rounds and $\\mathbf{18} \\times$ in the communication complexity. The practicality of our framework is argued through improvements in the benchmarking of the aforementioned algorithms when compared with ABY3. All the protocols are implemented over a 64-bit ring in both LAN and WAN settings. Our improvements go up to $\\mathbf{187} \\times$ for the training phase and $\\mathbf{158} \\times$ for the prediction phase when observed over LAN and WAN.", "published": "2019-12-05T15:06:39Z", "authors": ["Harsh Chaudhari", "Rahul Rachuri", "Ajith Suresh"], "categories": ["cs.LG", "cs.CR", "stat.ML"], "doi": "10.14722/ndss.2020.23005", "_query": "house price prediction machine learning", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2407.08086v2", "title": "The GeometricKernels Package: Heat and Matérn Kernels for Geometric Learning on Manifolds, Meshes, and Graphs", "abstract": "Kernels are a fundamental technical primitive in machine learning. In recent years, kernel-based methods such as Gaussian processes are becoming increasingly important in applications where quantifying uncertainty is of key interest. In settings that involve structured data defined on graphs, meshes, manifolds, or other related spaces, defining kernels with good uncertainty-quantification behavior, and computing their value numerically, is less straightforward than in the Euclidean setting. To address this difficulty, we present GeometricKernels, a Python software package which implements the geometric analogs of classical Euclidean squared exponential - also known as heat - and Matérn kernels, which are widely-used in settings where uncertainty is of key interest. As a byproduct, we obtain the ability to compute Fourier-feature-type expansions, which are widely used in their own right, on a wide set of geometric spaces. Our implementation supports automatic differentiation in every major current framework simultaneously via a backend-agnostic design. In this companion paper to the package and its documentation, we outline the capabilities of the package and present an illustrated example of its interface. We also include a brief overview of the theory the package is built upon and provide some historic context in the appendix.", "published": "2024-07-10T23:09:23Z", "authors": ["Peter Mostowsky", "Vincent Dutordoir", "Iskander Azangulov", "Noémie Jaquier", "Michael John Hutchinson", "Aditya Ravuri", "Leonel Rozo", "Alexander Terenin", "Viacheslav Borovitskiy"], "categories": ["cs.LG", "stat.CO", "stat.ML"], "doi": "", "_query": "house price prediction machine learning", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2302.11709v1", "title": "Bayes meets Bernstein at the Meta Level: an Analysis of Fast Rates in Meta-Learning with PAC-Bayes", "abstract": "Bernstein's condition is a key assumption that guarantees fast rates in machine learning. For example, the Gibbs algorithm with prior $π$ has an excess risk in $O(d_π/n)$, as opposed to the standard $O(\\sqrt{d_π/n})$, where $n$ denotes the number of observations and $d_π$ is a complexity parameter which depends on the prior $π$. In this paper, we examine the Gibbs algorithm in the context of meta-learning, i.e., when learning the prior $π$ from $T$ tasks (with $n$ observations each) generated by a meta distribution. Our main result is that Bernstein's condition always holds at the meta level, regardless of its validity at the observation level. This implies that the additional cost to learn the Gibbs prior $π$, which will reduce the term $d_π$ across tasks, is in $O(1/T)$, instead of the expected $O(1/\\sqrt{T})$. We further illustrate how this result improves on standard rates in three different settings: discrete priors, Gaussian priors and mixture of Gaussians priors.", "published": "2023-02-23T00:07:03Z", "authors": ["Charles Riou", "Pierre Alquier", "Badr-Eddine Chérief-Abdellatif"], "categories": ["stat.ML", "cs.LG"], "doi": "", "_query": "house price prediction machine learning", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/1805.00020v4", "title": "A Guide to Constraining Effective Field Theories with Machine Learning", "abstract": "We develop, discuss, and compare several inference techniques to constrain theory parameters in collider experiments. By harnessing the latent-space structure of particle physics processes, we extract extra information from the simulator. This augmented data can be used to train neural networks that precisely estimate the likelihood ratio. The new methods scale well to many observables and high-dimensional parameter spaces, do not require any approximations of the parton shower and detector response, and can be evaluated in microseconds. Using weak-boson-fusion Higgs production as an example process, we compare the performance of several techniques. The best results are found for likelihood ratio estimators trained with extra information about the score, the gradient of the log likelihood function with respect to the theory parameters. The score also provides sufficient statistics that contain all the information needed for inference in the neighborhood of the Standard Model. These methods enable us to put significantly stronger bounds on effective dimension-six operators than the traditional approach based on histograms. They also outperform generic machine learning methods that do not make use of the particle physics structure, demonstrating their potential to substantially improve the new physics reach of the LHC legacy results.", "published": "2018-04-30T18:00:01Z", "authors": ["Johann Brehmer", "Kyle Cranmer", "Gilles Louppe", "Juan Pavez"], "categories": ["hep-ph", "physics.data-an", "stat.ML"], "doi": "10.1103/PhysRevD.98.052004", "_query": "house price prediction machine learning", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2312.17210v1", "title": "Continual Learning via Sequential Function-Space Variational Inference", "abstract": "Sequential Bayesian inference over predictive functions is a natural framework for continual learning from streams of data. However, applying it to neural networks has proved challenging in practice. Addressing the drawbacks of existing techniques, we propose an optimization objective derived by formulating continual learning as sequential function-space variational inference. In contrast to existing methods that regularize neural network parameters directly, this objective allows parameters to vary widely during training, enabling better adaptation to new tasks. Compared to objectives that directly regularize neural network predictions, the proposed objective allows for more flexible variational distributions and more effective regularization. We demonstrate that, across a range of task sequences, neural networks trained via sequential function-space variational inference achieve better predictive accuracy than networks trained with related methods while depending less on maintaining a set of representative points from previous tasks.", "published": "2023-12-28T18:44:32Z", "authors": ["Tim G. J. Rudner", "Freddie Bickford Smith", "Qixuan Feng", "Yee Whye Teh", "Yarin Gal"], "categories": ["stat.ML", "cs.AI", "cs.LG"], "doi": "", "_query": "house price prediction machine learning", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2211.00024v2", "title": "A robust estimator of mutual information for deep learning interpretability", "abstract": "We develop the use of mutual information (MI), a well-established metric in information theory, to interpret the inner workings of deep learning models. To accurately estimate MI from a finite number of samples, we present GMM-MI (pronounced $``$Jimmie$\"$), an algorithm based on Gaussian mixture models that can be applied to both discrete and continuous settings. GMM-MI is computationally efficient, robust to the choice of hyperparameters and provides the uncertainty on the MI estimate due to the finite sample size. We extensively validate GMM-MI on toy data for which the ground truth MI is known, comparing its performance against established mutual information estimators. We then demonstrate the use of our MI estimator in the context of representation learning, working with synthetic data and physical datasets describing highly non-linear processes. We train deep learning models to encode high-dimensional data within a meaningful compressed (latent) representation, and use GMM-MI to quantify both the level of disentanglement between the latent variables, and their association with relevant physical quantities, thus unlocking the interpretability of the latent representation. We make GMM-MI publicly available.", "published": "2022-10-31T18:00:02Z", "authors": ["Davide Piras", "Hiranya V. Peiris", "Andrew Pontzen", "Luisa Lucie-Smith", "Ningyuan Guo", "Brian Nord"], "categories": ["physics.data-an", "astro-ph.IM", "cs.LG"], "doi": "10.1088/2632-2153/acc444", "_query": "house price prediction machine learning", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2206.05255v1", "title": "Interactively Learning Preference Constraints in Linear Bandits", "abstract": "We study sequential decision-making with known rewards and unknown constraints, motivated by situations where the constraints represent expensive-to-evaluate human preferences, such as safe and comfortable driving behavior. We formalize the challenge of interactively learning about these constraints as a novel linear bandit problem which we call constrained linear best-arm identification. To solve this problem, we propose the Adaptive Constraint Learning (ACOL) algorithm. We provide an instance-dependent lower bound for constrained linear best-arm identification and show that ACOL's sample complexity matches the lower bound in the worst-case. In the average case, ACOL's sample complexity bound is still significantly tighter than bounds of simpler approaches. In synthetic experiments, ACOL performs on par with an oracle solution and outperforms a range of baselines. As an application, we consider learning constraints to represent human preferences in a driving simulation. ACOL is significantly more sample efficient than alternatives for this application. Further, we find that learning preferences as constraints is more robust to changes in the driving scenario than encoding the preferences directly in the reward function.", "published": "2022-06-10T17:52:58Z", "authors": ["David Lindner", "Sebastian Tschiatschek", "Katja Hofmann", "Andreas Krause"], "categories": ["cs.LG", "cs.AI", "stat.ML"], "doi": "", "_query": "house price prediction machine learning", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/1606.04838v3", "title": "Optimization Methods for Large-Scale Machine Learning", "abstract": "This paper provides a review and commentary on the past, present, and future of numerical optimization algorithms in the context of machine learning applications. Through case studies on text classification and the training of deep neural networks, we discuss how optimization problems arise in machine learning and what makes them challenging. A major theme of our study is that large-scale machine learning represents a distinctive setting in which the stochastic gradient (SG) method has traditionally played a central role while conventional gradient-based nonlinear optimization techniques typically falter. Based on this viewpoint, we present a comprehensive theory of a straightforward, yet versatile SG algorithm, discuss its practical behavior, and highlight opportunities for designing algorithms with improved performance. This leads to a discussion about the next generation of optimization methods for large-scale machine learning, including an investigation of two main streams of research on techniques that diminish noise in the stochastic directions and methods that make use of second-order derivative approximations.", "published": "2016-06-15T16:15:53Z", "authors": ["Léon Bottou", "Frank E. Curtis", "Jorge Nocedal"], "categories": ["stat.ML", "cs.LG", "math.OC"], "doi": "", "_query": "house price prediction machine learning", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/1112.1937v1", "title": "Bootstrapping Intrinsically Motivated Learning with Human Demonstrations", "abstract": "This paper studies the coupling of internally guided learning and social interaction, and more specifically the improvement owing to demonstrations of the learning by intrinsic motivation. We present Socially Guided Intrinsic Motivation by Demonstration (SGIM-D), an algorithm for learning in continuous, unbounded and non-preset environments. After introducing social learning and intrinsic motivation, we describe the design of our algorithm, before showing through a fishing experiment that SGIM-D efficiently combines the advantages of social learning and intrinsic motivation to gain a wide repertoire while being specialised in specific subspaces.", "published": "2011-12-08T20:27:31Z", "authors": ["Sao Mai Nguyen", "Adrien Baranes", "Pierre-Yves Oudeyer"], "categories": ["cs.LG", "cs.AI", "cs.RO"], "doi": "10.1109/DEVLRN.2011.6037329", "_query": "house price prediction machine learning", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2410.05638v2", "title": "Time Series Classification of Supraglacial Lakes Evolution over Greenland Ice Sheet", "abstract": "The Greenland Ice Sheet (GrIS) has emerged as a significant contributor to global sea level rise, primarily due to increased meltwater runoff. Supraglacial lakes, which form on the ice sheet surface during the summer months, can impact ice sheet dynamics and mass loss; thus, better understanding these lakes' seasonal evolution and dynamics is an important task. This study presents a computationally efficient time series classification approach that uses Gaussian Mixture Models (GMMs) of the Reconstructed Phase Spaces (RPSs) to identify supraglacial lakes based on their seasonal evolution: 1) those that refreeze at the end of the melt season, 2) those that drain during the melt season, and 3) those that become buried, remaining liquid insulated a few meters beneath the surface. Our approach uses time series data from the Sentinel-1 and Sentinel-2 satellites, which utilize microwave and visible radiation, respectively. Evaluated on a GrIS-wide dataset, the RPS-GMM model, trained on a single representative sample per class, achieves 85.46% accuracy with Sentinel-1 data alone and 89.70% with combined Sentinel-1 and Sentinel-2 data. This performance significantly surpasses existing machine learning and deep learning models which require a large training data. The results demonstrate the robustness of the RPS-GMM model in capturing the complex temporal dynamics of supraglacial lakes with minimal training data.", "published": "2024-10-08T02:42:15Z", "authors": ["Emam Hossain", "Md Osman Gani", "Devon Dunmire", "Aneesh Subramanian", "Hammad Younas"], "categories": ["cs.LG"], "doi": "10.1109/ICMLA61862.2024.00072", "_query": "house price prediction machine learning", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2211.06034v1", "title": "Does Deep Learning REALLY Outperform Non-deep Machine Learning for Clinical Prediction on Physiological Time Series?", "abstract": "Machine learning has been widely used in healthcare applications to approximate complex models, for clinical diagnosis, prognosis, and treatment. As deep learning has the outstanding ability to extract information from time series, its true capabilities on sparse, irregularly sampled, multivariate, and imbalanced physiological data are not yet fully explored. In this paper, we systematically examine the performance of machine learning models for the clinical prediction task based on the EHR, especially physiological time series. We choose Physionet 2019 challenge public dataset to predict Sepsis outcomes in ICU units. Ten baseline machine learning models are compared, including 3 deep learning methods and 7 non-deep learning methods, commonly used in the clinical prediction domain. Nine evaluation metrics with specific clinical implications are used to assess the performance of models. Besides, we sub-sample training dataset sizes and use learning curve fit to investigate the impact of the training dataset size on the performance of the machine learning models. We also propose the general pre-processing method for the physiology time-series data and use Dice Loss to deal with the dataset imbalanced problem. The results show that deep learning indeed outperforms non-deep learning, but with certain conditions: firstly, evaluating with some particular evaluation metrics (AUROC, AUPRC, Sensitivity, and FNR), but not others; secondly, the training dataset size is large enough (with an estimation of a magnitude of thousands).", "published": "2022-11-11T07:09:49Z", "authors": ["Ke Liao", "Wei Wang", "Armagan Elibol", "Lingzhong Meng", "Xu Zhao", "Nak Young Chong"], "categories": ["cs.LG", "cs.AI"], "doi": "", "_query": "house price prediction machine learning", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/1402.6076v1", "title": "Machine Learning at Scale", "abstract": "It takes skill to build a meaningful predictive model even with the abundance of implementations of modern machine learning algorithms and readily available computing resources. Building a model becomes challenging if hundreds of terabytes of data need to be processed to produce the training data set. In a digital advertising technology setting, we are faced with the need to build thousands of such models that predict user behavior and power advertising campaigns in a 24/7 chaotic real-time production environment. As data scientists, we also have to convince other internal departments critical to implementation success, our management, and our customers that our machine learning system works. In this paper, we present the details of the design and implementation of an automated, robust machine learning platform that impacts billions of advertising impressions monthly. This platform enables us to continuously optimize thousands of campaigns over hundreds of millions of users, on multiple continents, against varying performance objectives.", "published": "2014-02-25T07:50:50Z", "authors": ["Sergei Izrailev", "Jeremy M. Stanley"], "categories": ["cs.LG", "cs.MS", "stat.ML"], "doi": "", "_query": "house price prediction machine learning", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2008.04216v3", "title": "Using Experts' Opinions in Machine Learning Tasks", "abstract": "In machine learning tasks, especially in the tasks of prediction, scientists tend to rely solely on available historical data and disregard unproven insights, such as experts' opinions, polls, and betting odds. In this paper, we propose a general three-step framework for utilizing experts' insights in machine learning tasks and build four concrete models for a sports game prediction case study. For the case study, we have chosen the task of predicting NCAA Men's Basketball games, which has been the focus of a group of Kaggle competitions in recent years. Results highly suggest that the good performance and high scores of the past models are a result of chance, and not because of a good-performing and stable model. Furthermore, our proposed models can achieve more steady results with lower log loss average (best at 0.489) compared to the top solutions of the 2019 competition (>0.503), and reach the top 1%, 10% and 1% in the 2017, 2018 and 2019 leaderboards, respectively.", "published": "2020-08-10T15:48:49Z", "authors": ["Jafar Habibi", "Amir Fazelinia", "Issa Annamoradnejad"], "categories": ["cs.LG", "cs.CY", "stat.ML"], "doi": "", "_query": "house price prediction machine learning", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2310.09028v1", "title": "Subspace Adaptation Prior for Few-Shot Learning", "abstract": "Gradient-based meta-learning techniques aim to distill useful prior knowledge from a set of training tasks such that new tasks can be learned more efficiently with gradient descent. While these methods have achieved successes in various scenarios, they commonly adapt all parameters of trainable layers when learning new tasks. This neglects potentially more efficient learning strategies for a given task distribution and may be susceptible to overfitting, especially in few-shot learning where tasks must be learned from a limited number of examples. To address these issues, we propose Subspace Adaptation Prior (SAP), a novel gradient-based meta-learning algorithm that jointly learns good initialization parameters (prior knowledge) and layer-wise parameter subspaces in the form of operation subsets that should be adaptable. In this way, SAP can learn which operation subsets to adjust with gradient descent based on the underlying task distribution, simultaneously decreasing the risk of overfitting when learning new tasks. We demonstrate that this ability is helpful as SAP yields superior or competitive performance in few-shot image classification settings (gains between 0.1% and 3.9% in accuracy). Analysis of the learned subspaces demonstrates that low-dimensional operations often yield high activation strengths, indicating that they may be important for achieving good few-shot learning performance. For reproducibility purposes, we publish all our research code publicly.", "published": "2023-10-13T11:40:18Z", "authors": ["Mike Huisman", "Aske Plaat", "Jan N. van Rijn"], "categories": ["cs.LG", "cs.AI", "stat.ML"], "doi": "", "_query": "house price prediction machine learning", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/1810.07339v2", "title": "Security Matters: A Survey on Adversarial Machine Learning", "abstract": "Adversarial machine learning is a fast growing research area, which considers the scenarios when machine learning systems may face potential adversarial attackers, who intentionally synthesize input data to make a well-trained model to make mistake. It always involves a defending side, usually a classifier, and an attacking side that aims to cause incorrect output. The earliest studies on the adversarial examples for machine learning algorithms start from the information security area, which considers a much wider varieties of attacking methods. But recent research focus that popularized by the deep learning community places strong emphasis on how the \"imperceivable\" perturbations on the normal inputs may cause dramatic mistakes by the deep learning with supposed super-human accuracy. This paper serves to give a comprehensive introduction to a range of aspects of the adversarial deep learning topic, including its foundations, typical attacking and defending strategies, and some extended studies.", "published": "2018-10-16T09:06:26Z", "authors": ["Guofu Li", "Pengjia Zhu", "Jin Li", "Zhemin Yang", "Ning Cao", "Zhiyi Chen"], "categories": ["cs.LG", "cs.CR", "stat.ML"], "doi": "", "_query": "house price prediction machine learning", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/1811.10775v2", "title": "Machine learning-guided directed evolution for protein engineering", "abstract": "Machine learning (ML)-guided directed evolution is a new paradigm for biological design that enables optimization of complex functions. ML methods use data to predict how sequence maps to function without requiring a detailed model of the underlying physics or biological pathways. To demonstrate ML-guided directed evolution, we introduce the steps required to build ML sequence-function models and use them to guide engineering, making recommendations at each stage. This review covers basic concepts relevant to using ML for protein engineering as well as the current literature and applications of this new engineering paradigm. ML methods accelerate directed evolution by learning from information contained in all measured variants and using that information to select sequences that are likely to be improved. We then provide two case studies that demonstrate the ML-guided directed evolution process. We also look to future opportunities where ML will enable discovery of new protein functions and uncover the relationship between protein sequence and function.", "published": "2018-11-27T02:17:19Z", "authors": ["Kevin K. Yang", "Zachary Wu", "Frances H. Arnold"], "categories": ["q-bio.BM"], "doi": "", "_query": "house price prediction machine learning", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/1411.5497v1", "title": "Time-warped growth processes, with applications to the modeling of boom-bust cycles in house prices", "abstract": "House price increases have been steady over much of the last 40 years, but there have been occasional declines, most notably in the recent housing bust that started around 2007, on the heels of the preceding housing bubble. We introduce a novel growth model that is motivated by time-warping models in functional data analysis and includes a nonmonotone time-warping component that allows the inclusion and description of boom-bust cycles and facilitates insights into the dynamics of asset bubbles. The underlying idea is to model longitudinal growth trajectories for house prices and other phenomena, where temporal setbacks and deflation may be encountered, by decomposing such trajectories into two components. A first component corresponds to underlying steady growth driven by inflation that anchors the observed trajectories on a simple first order linear differential equation, while a second boom-bust component is implemented as time warping. Time warping is a commonly encountered phenomenon and reflects random variation along the time axis. Our approach to time warping is more general than previous approaches by admitting the inclusion of nonmonotone warping functions. The anchoring of the trajectories on an underlying linear dynamic system also makes the time-warping component identifiable and enables straightforward estimation procedures for all model components. The application to the dynamics of housing prices as observed for 19 metropolitan areas in the U.S. from December 1998 to July 2013 reveals that the time setbacks corresponding to nonmonotone time warping vary substantially across markets and we find indications that they are related to market-specific growth rates.", "published": "2014-11-20T10:42:29Z", "authors": ["Jie Peng", "Debashis Paul", "Hans-Georg Müller"], "categories": ["stat.AP"], "doi": "10.1214/14-AOAS740", "_query": "house price prediction machine learning", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2508.16097v1", "title": "Machine Learning for Medicine Must Be Interpretable, Shareable, Reproducible and Accountable by Design", "abstract": "This paper claims that machine learning models deployed in high stakes domains such as medicine must be interpretable, shareable, reproducible and accountable. We argue that these principles should form the foundational design criteria for machine learning algorithms dealing with critical medical data, including survival analysis and risk prediction tasks. Black box models, while often highly accurate, struggle to gain trust and regulatory approval in health care due to a lack of transparency. We discuss how intrinsically interpretable modeling approaches (such as kernel methods with sparsity, prototype-based learning, and deep kernel models) can serve as powerful alternatives to opaque deep networks, providing insight into biomedical predictions. We then examine accountability in model development, calling for rigorous evaluation, fairness, and uncertainty quantification to ensure models reliably support clinical decisions. Finally, we explore how generative AI and collaborative learning paradigms (such as federated learning and diffusion-based data synthesis) enable reproducible research and cross-institutional integration of heterogeneous biomedical data without compromising privacy, hence shareability. By rethinking machine learning foundations along these axes, we can develop medical AI that is not only accurate but also transparent, trustworthy, and translatable to real-world clinical settings.", "published": "2025-08-22T05:23:34Z", "authors": ["Ayyüce Begüm Bektaş", "Mithat Gönen"], "categories": ["cs.LG", "stat.ML"], "doi": "", "_query": "house price prediction machine learning", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/1909.06434v1", "title": "Adaptive Scheduling for Multi-Task Learning", "abstract": "To train neural machine translation models simultaneously on multiple tasks (languages), it is common to sample each task uniformly or in proportion to dataset sizes. As these methods offer little control over performance trade-offs, we explore different task scheduling approaches. We first consider existing non-adaptive techniques, then move on to adaptive schedules that over-sample tasks with poorer results compared to their respective baseline. As explicit schedules can be inefficient, especially if one task is highly over-sampled, we also consider implicit schedules, learning to scale learning rates or gradients of individual tasks instead. These techniques allow training multilingual models that perform better for low-resource language pairs (tasks with small amount of data), while minimizing negative effects on high-resource tasks.", "published": "2019-09-13T20:23:40Z", "authors": ["Sébastien Jean", "Orhan Firat", "Melvin Johnson"], "categories": ["cs.LG", "cs.CL", "stat.ML"], "doi": "", "_query": "house price prediction machine learning", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/1906.10343v2", "title": "Exploring Self-Supervised Regularization for Supervised and Semi-Supervised Learning", "abstract": "Recent advances in semi-supervised learning have shown tremendous potential in overcoming a major barrier to the success of modern machine learning algorithms: access to vast amounts of human-labeled training data. Previous algorithms based on consistency regularization can harness the abundance of unlabeled data to produce impressive results on a number of semi-supervised benchmarks, approaching the performance of strong supervised baselines using only a fraction of the available labeled data. In this work, we challenge the long-standing success of consistency regularization by introducing self-supervised regularization as the basis for combining semantic feature representations from unlabeled data. We perform extensive comparative experiments to demonstrate the effectiveness of self-supervised regularization for supervised and semi-supervised image classification on SVHN, CIFAR-10, and CIFAR-100 benchmark datasets. We present two main results: (1) models augmented with self-supervised regularization significantly improve upon traditional supervised classifiers without the need for unlabeled data; (2) together with unlabeled data, our models yield semi-supervised performance competitive with, and in many cases exceeding, prior state-of-the-art consistency baselines. Lastly, our models have the practical utility of being efficiently trained end-to-end and require no additional hyper-parameters to tune for optimal performance beyond the standard set for training neural networks. Reference code and data are available at https://github.com/vuptran/sesemi", "published": "2019-06-25T06:42:05Z", "authors": ["Phi Vu Tran"], "categories": ["cs.LG", "cs.CV", "stat.ML"], "doi": "", "_query": "house price prediction machine learning", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2602.19761v1", "title": "Ensemble Machine Learning and Statistical Procedures for Dynamic Predictions of Time-to-Event Outcomes", "abstract": "Dynamic predictions for longitudinal and time-to-event outcomes have become a versatile tool in precision medicine. Our work is motivated by the application of dynamic predictions in the decision-making process for primary biliary cholangitis patients. For these patients, serial biomarker measurements (e.g., bilirubin and alkaline phosphatase levels) are routinely collected to inform treating physicians of the risk of liver failure and guide clinical decision-making. Two popular statistical approaches to derive dynamic predictions are joint modelling and landmarking. However, recently, machine learning techniques have also been proposed. Each approach has its merits, and no single method exists to outperform all others. Consequently, obtaining the best possible survival estimates is challenging. Therefore, we extend the Super Learner framework to combine dynamic predictions from different models and procedures. Super Learner is an ensemble learning technique that allows users to combine different prediction algorithms to improve predictive accuracy and flexibility. It uses cross-validation and different objective functions of performance (e.g., squared loss) that suit specific applications to build the optimally weighted combination of predictions from a library of candidate algorithms. In our work, we pay special attention to appropriate objective functions for Super Learner to obtain the most optimal weighted combination of dynamic predictions. In our primary biliary cholangitis application, Super Learner presented unique benefits due to its ability to flexibly combine outputs from a diverse set of models with varying assumptions for equal or better predictive performance than any model fit separately.", "published": "2026-02-23T12:12:13Z", "authors": ["Nina van Gerwen", "Sten Willemsen", "Bettina E. Hansen", "Christophe Corpechot", "Marco Carbone", "Cynthia Levy", "Maria-Carlota Londõno", "Atsushi Tanaka", "Palak Trivedi", "Alejandra Villamil", "Gideon Hirschfield", "Dimitris Rizopoulos"], "categories": ["stat.ML", "cs.LG", "stat.AP"], "doi": "", "_query": "house price prediction machine learning", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2204.13625v1", "title": "Standardized Evaluation of Machine Learning Methods for Evolving Data Streams", "abstract": "Due to the unspecified and dynamic nature of data streams, online machine learning requires powerful and flexible solutions. However, evaluating online machine learning methods under realistic conditions is difficult. Existing work therefore often draws on different heuristics and simulations that do not necessarily produce meaningful and reliable results. Indeed, in the absence of common evaluation standards, it often remains unclear how online learning methods will perform in practice or in comparison to similar work. In this paper, we propose a comprehensive set of properties for high-quality machine learning in evolving data streams. In particular, we discuss sensible performance measures and evaluation strategies for online predictive modelling, online feature selection and concept drift detection. As one of the first works, we also look at the interpretability of online learning methods. The proposed evaluation standards are provided in a new Python framework called float. Float is completely modular and allows the simultaneous integration of common libraries, such as scikit-multiflow or river, with custom code. Float is open-sourced and can be accessed at https://github.com/haugjo/float. In this sense, we hope that our work will contribute to more standardized, reliable and realistic testing and comparison of online machine learning methods.", "published": "2022-04-28T16:40:33Z", "authors": ["Johannes Haug", "Effi Tramountani", "Gjergji Kasneci"], "categories": ["cs.LG", "stat.ML"], "doi": "", "_query": "house price prediction machine learning", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2502.15755v2", "title": "Physics-consistent machine learning: output projection onto physical manifolds", "abstract": "Data-driven machine learning models often require extensive datasets, which can be costly or inaccessible, and their predictions may fail to comply with established physical laws. Current approaches for incorporating physical priors mitigate these issues by penalizing deviations from known physical laws, as in physics-informed neural networks, or by designing architectures that automatically satisfy specific invariants. However, penalization approaches do not guarantee compliance with physical constraints for unseen inputs, and invariant-based methods lack flexibility and generality. We propose a novel physics-consistent machine learning method that directly enforces compliance with physical principles by projecting model outputs onto the manifold defined by these laws. This procedure ensures that predictions inherently adhere to the chosen physical constraints, improving reliability and interpretability. Our method is demonstrated on two systems: a spring-mass system and a low-temperature reactive plasma. Compared to purely data-driven models, our approach significantly reduces errors in physical law compliance, enhances predictive accuracy of physical quantities, and outperforms alternatives when working with simpler models or limited datasets. The proposed projection-based technique is versatile and can function independently or in conjunction with existing physics-informed neural networks, offering a powerful, general, and scalable solution for developing fast and reliable surrogate models of complex physical systems, particularly in resource-constrained scenarios.", "published": "2025-02-11T13:18:19Z", "authors": ["Matilde Valente", "Tiago C. Dias", "Vasco Guerra", "Rodrigo Ventura"], "categories": ["cs.LG", "cs.AI", "physics.plasm-ph"], "doi": "10.1038/s42005-025-02329-1", "_query": "house price prediction machine learning", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2208.02048v2", "title": "Centroids Matching: an efficient Continual Learning approach operating in the embedding space", "abstract": "Catastrophic forgetting (CF) occurs when a neural network loses the information previously learned while training on a set of samples from a different distribution, i.e., a new task. Existing approaches have achieved remarkable results in mitigating CF, especially in a scenario called task incremental learning. However, this scenario is not realistic, and limited work has been done to achieve good results on more realistic scenarios. In this paper, we propose a novel regularization method called Centroids Matching, that, inspired by meta-learning approaches, fights CF by operating in the feature space produced by the neural network, achieving good results while requiring a small memory footprint. Specifically, the approach classifies the samples directly using the feature vectors produced by the neural network, by matching those vectors with the centroids representing the classes from the current task, or all the tasks up to that point. Centroids Matching is faster than competing baselines, and it can be exploited to efficiently mitigate CF, by preserving the distances between the embedding space produced by the model when past tasks were over, and the one currently produced, leading to a method that achieves high accuracy on all the tasks, without using an external memory when operating on easy scenarios, or using a small one for more realistic ones. Extensive experiments demonstrate that Centroids Matching achieves accuracy gains on multiple datasets and scenarios.", "published": "2022-08-03T13:17:16Z", "authors": ["Jary Pomponi", "Simone Scardapane", "Aurelio Uncini"], "categories": ["cs.LG", "stat.ML"], "doi": "", "_query": "house price prediction machine learning", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2306.08400v1", "title": "Simple Embodied Language Learning as a Byproduct of Meta-Reinforcement Learning", "abstract": "Whereas machine learning models typically learn language by directly training on language tasks (e.g., next-word prediction), language emerges in human children as a byproduct of solving non-language tasks (e.g., acquiring food). Motivated by this observation, we ask: can embodied reinforcement learning (RL) agents also indirectly learn language from non-language tasks? Learning to associate language with its meaning requires a dynamic environment with varied language. Therefore, we investigate this question in a multi-task environment with language that varies across the different tasks. Specifically, we design an office navigation environment, where the agent's goal is to find a particular office, and office locations differ in different buildings (i.e., tasks). Each building includes a floor plan with a simple language description of the goal office's location, which can be visually read as an RGB image when visited. We find RL agents indeed are able to indirectly learn language. Agents trained with current meta-RL algorithms successfully generalize to reading floor plans with held-out layouts and language phrases, and quickly navigate to the correct office, despite receiving no direct language supervision.", "published": "2023-06-14T09:48:48Z", "authors": ["Evan Zheran Liu", "Sahaana Suri", "Tong Mu", "Allan Zhou", "Chelsea Finn"], "categories": ["cs.CL", "cs.AI", "cs.LG"], "doi": "", "_query": "house price prediction machine learning", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/1904.05270v1", "title": "Google Street View image of a house predicts car accident risk of its resident", "abstract": "Road traffic injuries are a leading cause of death worldwide. Proper estimation of car accident risk is critical for appropriate allocation of resources in healthcare, insurance, civil engineering, and other industries. We show how images of houses are predictive of car accidents. We analyze 20,000 addresses of insurance company clients, collect a corresponding house image using Google Street View, and annotate house features such as age, type, and condition. We find that this information substantially improves car accident risk prediction compared to the state-of-the-art risk model of the insurance company and could be used for price discrimination. From this perspective, public availability of house images raises legal and social concerns, as they can be a proxy of ethnicity, religion and other sensitive data.", "published": "2019-04-10T16:16:31Z", "authors": ["Kinga Kita", "Łukasz Kidziński"], "categories": ["stat.AP"], "doi": "", "_query": "house price prediction machine learning", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/1905.11719v5", "title": "Machine Learning on data with sPlot background subtraction", "abstract": "Data analysis in high energy physics often deals with data samples consisting of a mixture of signal and background events. The sPlot technique is a common method to subtract the contribution of the background by assigning weights to events. Part of the weights are by design negative. Negative weights lead to the divergence of some machine learning algorithms training due to absence of the lower bound in the loss function. In this paper we propose a mathematically rigorous way to train machine learning algorithms on data samples with background described by sPlot to obtain signal probabilities conditioned on observables, without encountering negative event weight at all. This allows usage of any out-of-the-box machine learning methods on such data.", "published": "2019-05-28T10:09:52Z", "authors": ["Maxim Borisyak", "Nikita Kazeev"], "categories": ["cs.LG", "hep-ex", "physics.data-an", "stat.ML"], "doi": "10.1088/1748-0221/14/08/P08020", "_query": "house price prediction machine learning", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/1208.3561v3", "title": "Efficient Active Learning of Halfspaces: an Aggressive Approach", "abstract": "We study pool-based active learning of half-spaces. We revisit the aggressive approach for active learning in the realizable case, and show that it can be made efficient and practical, while also having theoretical guarantees under reasonable assumptions. We further show, both theoretically and experimentally, that it can be preferable to mellow approaches. Our efficient aggressive active learner of half-spaces has formal approximation guarantees that hold when the pool is separable with a margin. While our analysis is focused on the realizable setting, we show that a simple heuristic allows using the same algorithm successfully for pools with low error as well. We further compare the aggressive approach to the mellow approach, and prove that there are cases in which the aggressive approach results in significantly better label complexity compared to the mellow approach. We demonstrate experimentally that substantial improvements in label complexity can be achieved using the aggressive approach, for both realizable and low-error settings.", "published": "2012-08-17T09:49:31Z", "authors": ["Alon Gonen", "Sivan Sabato", "Shai Shalev-Shwartz"], "categories": ["cs.LG"], "doi": "", "_query": "house price prediction machine learning", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2102.05639v1", "title": "Energy-Harvesting Distributed Machine Learning", "abstract": "This paper provides a first study of utilizing energy harvesting for sustainable machine learning in distributed networks. We consider a distributed learning setup in which a machine learning model is trained over a large number of devices that can harvest energy from the ambient environment, and develop a practical learning framework with theoretical convergence guarantees. We demonstrate through numerical experiments that the proposed framework can significantly outperform energy-agnostic benchmarks. Our framework is scalable, requires only local estimation of the energy statistics, and can be applied to a wide range of distributed training settings, including machine learning in wireless networks, edge computing, and mobile internet of things.", "published": "2021-02-10T18:53:51Z", "authors": ["Basak Guler", "Aylin Yener"], "categories": ["cs.LG", "cs.IT", "stat.ML"], "doi": "", "_query": "house price prediction machine learning", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2605.25353v2", "title": "PDEInvBench: A Comprehensive Dataset and Design Space Exploration of Neural Networks for PDE Inverse Problems", "abstract": "Inverse problems in partial differential equations (PDEs) involve estimating the physical parameters of a system from observed spatiotemporal solution fields. Neural networks are well-suited for PDE parameter estimation due to their capability to model function-to-function space transformations. While existing benchmarks of machine learning methods for PDEs primarily focus on the forward problem, there are no similar comprehensive studies and benchmark datasets on PDE inverse problems, i.e., mapping solution fields to underlying physical parameters. We fill this gap by introducing PDEInvBench, a comprehensive benchmark dataset consisting of numerical simulations for both time-dependent and time-independent PDEs across a wide range of physical behaviors and parameters. Our dataset includes evaluation splits that assess performance in both in-distribution and various out-of-distribution settings. Using our benchmark dataset, we comprehensively explore the design space of neural networks for PDE inverse problems along three key dimensions: (1) optimization procedures, analyzing the role of supervised, self-supervised, and test-time training objectives on performance, (2) problem representations, where we study the value of architectural choices with different inductive biases and various conditioning strategies, and (3) scaling, which we perform with respect to both model and data size. Our experiments reveal several practical insights: 1) neural networks perform best with a two-stage training procedure: initial supervision with PDE parameters followed by test-time fine-tuning using the PDE residual, 2) incorporating PDE derivatives as input features consistently improves accuracy, and 3) increasing the diversity of initial conditions in the training data yields greater performance gains than expanding the range of PDE parameters. We make our dataset and codebase publicly available.", "published": "2026-05-25T02:13:33Z", "authors": ["Divyam Goel", "Nithin Chalapathi", "Sanjeev Raja", "Aditi S. Krishnapriyan"], "categories": ["cs.LG", "cs.CV", "physics.comp-ph"], "doi": "", "_query": "house price prediction machine learning", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2407.05526v1", "title": "Can Machines Learn the True Probabilities?", "abstract": "When there exists uncertainty, AI machines are designed to make decisions so as to reach the best expected outcomes. Expectations are based on true facts about the objective environment the machines interact with, and those facts can be encoded into AI models in the form of true objective probability functions. Accordingly, AI models involve probabilistic machine learning in which the probabilities should be objectively interpreted. We prove under some basic assumptions when machines can learn the true objective probabilities, if any, and when machines cannot learn them.", "published": "2024-07-08T00:19:43Z", "authors": ["Jinsook Kim"], "categories": ["cs.LG", "cs.AI", "stat.ML"], "doi": "", "_query": "house price prediction machine learning", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/1810.02513v2", "title": "Learning To Simulate", "abstract": "Simulation is a useful tool in situations where training data for machine learning models is costly to annotate or even hard to acquire. In this work, we propose a reinforcement learning-based method for automatically adjusting the parameters of any (non-differentiable) simulator, thereby controlling the distribution of synthesized data in order to maximize the accuracy of a model trained on that data. In contrast to prior art that hand-crafts these simulation parameters or adjusts only parts of the available parameters, our approach fully controls the simulator with the actual underlying goal of maximizing accuracy, rather than mimicking the real data distribution or randomly generating a large volume of data. We find that our approach (i) quickly converges to the optimal simulation parameters in controlled experiments and (ii) can indeed discover good sets of parameters for an image rendering simulator in actual computer vision applications.", "published": "2018-10-05T04:11:25Z", "authors": ["Nataniel Ruiz", "Samuel Schulter", "Manmohan Chandraker"], "categories": ["cs.LG", "cs.CV", "stat.ML"], "doi": "", "_query": "house price prediction machine learning", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2512.17678v2", "title": "You Only Train Once: Differentiable Subset Selection for Omics Data", "abstract": "Selecting compact and informative gene subsets from single-cell transcriptomic data is essential for biomarker discovery, improving interpretability, and cost-effective profiling. However, most existing feature selection approaches either operate as multi-stage pipelines or rely on post hoc feature attribution, making selection and prediction weakly coupled. In this work, we present YOTO (you only train once), an end-to-end framework that jointly identifies discrete gene subsets and performs prediction within a single differentiable architecture. In our model, the prediction task directly guides which genes are selected, while the learned subsets, in turn, shape the predictive representation. This closed feedback loop enables the model to iteratively refine both what it selects and how it predicts during training. Unlike existing approaches, YOTO enforces sparsity so that only the selected genes contribute to inference, eliminating the need to train additional downstream classifiers. Through a multi-task learning design, the model learns shared representations across related objectives, allowing partially labeled datasets to inform one another, and discovering gene subsets that generalize across tasks without additional training steps. We evaluate YOTO on two representative single-cell RNA-seq datasets, showing that it consistently outperforms state-of-the-art baselines. These results demonstrate that sparse, end-to-end, multi-task gene subset selection improves predictive performance and yields compact and meaningful gene subsets, advancing biomarker discovery and single-cell analysis.", "published": "2025-12-19T15:17:34Z", "authors": ["Daphné Chopard", "Jorge da Silva Gonçalves", "Irene Cannistraci", "Thomas M. Sutter", "Julia E. Vogt"], "categories": ["cs.LG", "cs.AI"], "doi": "", "_query": "house price prediction machine learning", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/1907.01674v3", "title": "Machine Learning based Prediction of Hierarchical Classification of Transposable Elements", "abstract": "Transposable Elements (TEs) or jumping genes are the DNA sequences that have an intrinsic capability to move within a host genome from one genomic location to another. Studies show that the presence of a TE within or adjacent to a functional gene may alter its expression. TEs can also cause an increase in the rate of mutation and can even mediate duplications and large insertions and deletions in the genome, promoting gross genetic rearrangements. Thus, the proper classification of the identified jumping genes is essential to understand their genetic and evolutionary effects in the genome. While computational methods have been developed that perform either binary classification or multi-label classification of TEs, few studies have focused on their hierarchical classification. The state-of-the-art machine learning classification method utilizes a Multi-Layer Perceptron (MLP), a class of neural network, for hierarchical classification of TEs. However, the existing methods have limited accuracy in classifying TEs. A more effective classifier, which can explain the role of TEs in germline and somatic evolution, is needed. In this study, we examine the performance of a variety of machine learning (ML) methods. And eventually, propose a robust approach for the hierarchical classification of TEs, with higher accuracy, using Support Vector Machines (SVM).", "published": "2019-07-02T23:09:57Z", "authors": ["Manisha Panta", "Avdesh Mishra", "Md Tamjidul Hoque", "Joel Atallah"], "categories": ["cs.LG", "q-bio.GN", "stat.ML"], "doi": "", "_query": "house price prediction machine learning", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/1707.06353v4", "title": "Machine Learning for Large-Scale Quality Control of 3D Shape Models in Neuroimaging", "abstract": "As very large studies of complex neuroimaging phenotypes become more common, human quality assessment of MRI-derived data remains one of the last major bottlenecks. Few attempts have so far been made to address this issue with machine learning. In this work, we optimize predictive models of quality for meshes representing deep brain structure shapes. We use standard vertex-wise and global shape features computed homologously across 19 cohorts and over 7500 human-rated subjects, training kernelized Support Vector Machine and Gradient Boosted Decision Trees classifiers to detect meshes of failing quality. Our models generalize across datasets and diseases, reducing human workload by 30-70\\%, or equivalently hundreds of human rater hours for datasets of comparable size, with recall rates approaching inter-rater reliability.", "published": "2017-07-20T02:57:28Z", "authors": ["Dmitry Petrov", "Boris A. Gutman", " Shih-Hua", " Yu", "Theo G. M. van Erp", "Jessica A. Turner", "Lianne Schmaal", "Dick Veltman", "Lei Wang", "Kathryn Alpert", "Dmitry Isaev", "Artemis Zavaliangos-Petropulu", "Christopher R. K. Ching", "Vince Calhoun", "David Glahn", "Theodore D. Satterthwaite", "Ole Andreas Andreasen", "Stefan Borgwardt", "Fleur Howells", "Nynke Groenewold", "Aristotle Voineskos", "Joaquim Radua", "Steven G. Potkin", "Benedicto Crespo-Facorro", "Diana Tordesillas-Gutierrez", "Li Shen", "Irina Lebedeva", "Gianfranco Spalletta", "Gary Donohoe", "Peter Kochunov", "Pedro G. P. Rosa", "Anthony James", "Udo Dannlowski", "Bernhard T. Baune", "Andre Aleman", "Ian H. Gotlib", "Henrik Walter", "Martin Walter", "Jair C. Soares", "Stefan Ehrlich", "Ruben C. Gur", "N. Trung Doan", "Ingrid Agartz", "Lars T. Westlye", "Fabienne Harrisberger", "Anita Riecher-Rossler", "Anne Uhlmann", "Dan J. Stein", "Erin W. Dickie", "Edith Pomarol-Clotet", "Paola Fuentes-Claramonte", "Erick Jorge Canales-Rodriguez", "Raymond Salvador", "Alexander J. Huang", "Roberto Roiz-Santianez", "Shan Cong", "Alexander Tomyshev", "Fabrizio Piras", "Daniela Vecchio", "Nerisa Banaj", "Valentina Ciullo", "Elliot Hong", "Geraldo Busatto", "Marcus V. Zanetti", "Mauricio H. Serpa", "Simon Cervenka", "Sinead Kelly", "Dominik Grotegerd", "Matthew D. Sacchet", "Ilya M. Veer", "Meng Li", "Mon-Ju Wu", "Benson Irungu", "Esther Walton", "Paul M. Thompson"], "categories": ["q-bio.QM"], "doi": "", "_query": "house price prediction machine learning", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2609.35335v1", "title": "Large Language Models for Automated Cross-Domain Machine Learning Task Type Identification: A Benchmark Dataset and Evaluation", "abstract": "Machine learning task type identification is essential for constructing valid ML pipelines, yet in practice it is typically specified manually. We investigate whether large language models (LLMs) can infer both the data domain and the downstream prediction task directly from dataset-level information when only the target feature is provided by the user. Together with our LLM-based system we also release an annotated benchmark comprising 625 public tabular and time series datasets. We evaluate the proposed approach in three settings: (i) tabular datasets in comparison with established AutoML heuristics, (ii) cross-domain evaluation across tabular and time series datasets, and (iii) a practical deployment scenario using smaller local models. The results show consistent advantages for LLM-based task type identification, with increasing difficulty in heterogeneous and resource-constrained settings. LLM-based approaches outperform AutoGluon in the tabular setting, reaching 0.98 F1 macro compared to 0.93. In the cross-domain setting, the best model achieves 0.90 F1 macro, while smaller locally deployable models reach 0.75, indicating a trade-off between deployment feasibility and accuracy.", "published": "2026-09-28T14:55:59Z", "authors": ["Petros Tsialis", "Steffen Limmer", "Tobias Rodemann", "Martin Heckmann"], "categories": ["cs.LG", "cs.AI"], "doi": "", "_query": "house price prediction machine learning", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2006.05468v3", "title": "Variational Auto-Regressive Gaussian Processes for Continual Learning", "abstract": "Through sequential construction of posteriors on observing data online, Bayes' theorem provides a natural framework for continual learning. We develop Variational Auto-Regressive Gaussian Processes (VAR-GPs), a principled posterior updating mechanism to solve sequential tasks in continual learning. By relying on sparse inducing point approximations for scalable posteriors, we propose a novel auto-regressive variational distribution which reveals two fruitful connections to existing results in Bayesian inference, expectation propagation and orthogonal inducing points. Mean predictive entropy estimates show VAR-GPs prevent catastrophic forgetting, which is empirically supported by strong performance on modern continual learning benchmarks against competitive baselines. A thorough ablation study demonstrates the efficacy of our modeling choices.", "published": "2020-06-09T19:23:57Z", "authors": ["Sanyam Kapoor", "Theofanis Karaletsos", "Thang D. Bui"], "categories": ["stat.ML", "cs.LG"], "doi": "", "_query": "house price prediction machine learning", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2406.18781v1", "title": "Learning to Remove Cuts in Integer Linear Programming", "abstract": "Cutting plane methods are a fundamental approach for solving integer linear programs (ILPs). In each iteration of such methods, additional linear constraints (cuts) are introduced to the constraint set with the aim of excluding the previous fractional optimal solution while not affecting the optimal integer solution. In this work, we explore a novel approach within cutting plane methods: instead of only adding new cuts, we also consider the removal of previous cuts introduced at any of the preceding iterations of the method under a learnable parametric criteria. We demonstrate that in fundamental combinatorial optimization settings such cut removal policies can lead to significant improvements over both human-based and machine learning-guided cut addition policies even when implemented with simple models.", "published": "2024-06-26T22:50:43Z", "authors": ["Pol Puigdemont", "Stratis Skoulakis", "Grigorios Chrysos", "Volkan Cevher"], "categories": ["math.OC", "cs.DM", "cs.LG"], "doi": "", "_query": "house price prediction machine learning", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/1811.04463v1", "title": "Machine Learning with Abstention for Automated Liver Disease Diagnosis", "abstract": "This paper presents a novel approach for detection of liver abnormalities in an automated manner using ultrasound images. For this purpose, we have implemented a machine learning model that can not only generate labels (normal and abnormal) for a given ultrasound image but it can also detect when its prediction is likely to be incorrect. The proposed model abstains from generating the label of a test example if it is not confident about its prediction. Such behavior is commonly practiced by medical doctors who, when given insufficient information or a difficult case, can chose to carry out further clinical or diagnostic tests before generating a diagnosis. However, existing machine learning models are designed in a way to always generate a label for a given example even when the confidence of their prediction is low. We have proposed a novel stochastic gradient based solver for the learning with abstention paradigm and use it to make a practical, state of the art method for liver disease classification. The proposed method has been benchmarked on a data set of approximately 100 patients from MINAR, Multan, Pakistan and our results show that the proposed scheme offers state of the art classification performance.", "published": "2018-11-11T19:37:40Z", "authors": ["Kanza Hamid", "Amina Asif", "Wajid Abbasi", "Durre Sabih", "Fayyaz Minhas"], "categories": ["cs.LG", "cs.CV", "eess.IV", "stat.ML"], "doi": "10.1109/FIT.2017.00070", "_query": "house price prediction machine learning", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/1904.06950v1", "title": "Human-Guided Learning of Column Networks: Augmenting Deep Learning with Advice", "abstract": "Recently, deep models have been successfully applied in several applications, especially with low-level representations. However, sparse, noisy samples and structured domains (with multiple objects and interactions) are some of the open challenges in most deep models. Column Networks, a deep architecture, can succinctly capture such domain structure and interactions, but may still be prone to sub-optimal learning from sparse and noisy samples. Inspired by the success of human-advice guided learning in AI, especially in data-scarce domains, we propose Knowledge-augmented Column Networks that leverage human advice/knowledge for better learning with noisy/sparse samples. Our experiments demonstrate that our approach leads to either superior overall performance or faster convergence (i.e., both effective and efficient).", "published": "2019-04-15T10:23:10Z", "authors": ["Mayukh Das", "Yang Yu", "Devendra Singh Dhami", "Gautam Kunapuli", "Sriraam Natarajan"], "categories": ["cs.LG", "cs.AI", "stat.ML"], "doi": "", "_query": "house price prediction machine learning", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/1803.03234v3", "title": "Improving Optimization for Models With Continuous Symmetry Breaking", "abstract": "Many loss functions in representation learning are invariant under a continuous symmetry transformation. For example, the loss function of word embeddings (Mikolov et al., 2013) remains unchanged if we simultaneously rotate all word and context embedding vectors. We show that representation learning models for time series possess an approximate continuous symmetry that leads to slow convergence of gradient descent. We propose a new optimization algorithm that speeds up convergence using ideas from gauge theory in physics. Our algorithm leads to orders of magnitude faster convergence and to more interpretable representations, as we show for dynamic extensions of matrix factorization and word embedding models. We further present an example application of our proposed algorithm that translates modern words into their historic equivalents.", "published": "2018-03-08T18:07:40Z", "authors": ["Robert Bamler", "Stephan Mandt"], "categories": ["stat.ML", "cs.LG"], "doi": "", "_query": "house price prediction machine learning", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/1806.00388v4", "title": "A Review of Challenges and Opportunities in Machine Learning for Health", "abstract": "Modern electronic health records (EHRs) provide data to answer clinically meaningful questions. The growing data in EHRs makes healthcare ripe for the use of machine learning. However, learning in a clinical setting presents unique challenges that complicate the use of common machine learning methodologies. For example, diseases in EHRs are poorly labeled, conditions can encompass multiple underlying endotypes, and healthy individuals are underrepresented. This article serves as a primer to illuminate these challenges and highlights opportunities for members of the machine learning community to contribute to healthcare.", "published": "2018-06-01T15:12:20Z", "authors": ["Marzyeh Ghassemi", "Tristan Naumann", "Peter Schulam", "Andrew L. Beam", "Irene Y. Chen", "Rajesh Ranganath"], "categories": ["cs.LG", "cs.CY", "stat.ML"], "doi": "", "_query": "house price prediction machine learning", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2104.07469v1", "title": "A comparative study of Different Machine Learning Regressors For Stock Market Prediction", "abstract": "For the development of successful share trading strategies, forecasting the course of action of the stock market index is important. Effective prediction of closing stock prices could guarantee investors attractive benefits. Machine learning algorithms have the ability to process and forecast almost reliable closing prices for historical stock patterns. In this article, we intensively studied NASDAQ stock market and targeted to choose the portfolio of ten different companies belongs to different sectors. The objective is to compute opening price of next day stock using historical data. To fulfill this task nine different Machine Learning regressor applied on this data and evaluated using MSE and R2 as performance metric.", "published": "2021-04-14T15:37:33Z", "authors": ["Nazish Ashfaq", "Zubair Nawaz", "Muhammad Ilyas"], "categories": ["q-fin.ST", "cs.LG"], "doi": "", "_query": "house price prediction machine learning", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/1909.01866v1", "title": "Understanding Bias in Machine Learning", "abstract": "Bias is known to be an impediment to fair decisions in many domains such as human resources, the public sector, health care etc. Recently, hope has been expressed that the use of machine learning methods for taking such decisions would diminish or even resolve the problem. At the same time, machine learning experts warn that machine learning models can be biased as well. In this article, our goal is to explain the issue of bias in machine learning from a technical perspective and to illustrate the impact that biased data can have on a machine learning model. To reach such a goal, we develop interactive plots to visualizing the bias learned from synthetic data.", "published": "2019-09-02T20:36:19Z", "authors": ["Jindong Gu", "Daniela Oelke"], "categories": ["cs.LG", "stat.ML"], "doi": "", "_query": "house price prediction machine learning", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2011.01464v2", "title": "Autoencoding Features for Aviation Machine Learning Problems", "abstract": "The current practice of manually processing features for high-dimensional and heterogeneous aviation data is labor-intensive, does not scale well to new problems, and is prone to information loss, affecting the effectiveness and maintainability of machine learning (ML) procedures. This research explored an unsupervised learning method, autoencoder, to extract effective features for aviation machine learning problems. The study explored variants of autoencoders with the aim of forcing the learned representations of the input to assume useful properties. A flight track anomaly detection autoencoder was developed to demonstrate the versatility of the technique. The research results show that the autoencoder can not only automatically extract effective features for the flight track data, but also efficiently deep clean data, thereby reducing the workload of data scientists. Moreover, the research leveraged transfer learning to efficiently train models for multiple airports. Transfer learning can reduce model training times from days to hours, as well as improving model performance. The developed applications and techniques are shared with the whole aviation community to improve effectiveness of ongoing and future machine learning studies.", "published": "2020-11-03T04:09:34Z", "authors": ["Liya Wang", "Panta Lucic", "Keith Campbell", "Craig Wanke"], "categories": ["cs.LG", "stat.ML"], "doi": "", "_query": "house price prediction machine learning", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/1804.07193v3", "title": "Lipschitz Continuity in Model-based Reinforcement Learning", "abstract": "We examine the impact of learning Lipschitz continuous models in the context of model-based reinforcement learning. We provide a novel bound on multi-step prediction error of Lipschitz models where we quantify the error using the Wasserstein metric. We go on to prove an error bound for the value-function estimate arising from Lipschitz models and show that the estimated value function is itself Lipschitz. We conclude with empirical results that show the benefits of controlling the Lipschitz constant of neural-network models.", "published": "2018-04-19T14:29:41Z", "authors": ["Kavosh Asadi", "Dipendra Misra", "Michael L. Littman"], "categories": ["cs.LG", "cs.AI", "stat.ML"], "doi": "", "_query": "house price prediction machine learning", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2406.09322v2", "title": "Active Inference Meeting Energy-Efficient Control of Parallel and Identical Machines", "abstract": "We investigate the application of active inference in developing energy-efficient control agents for manufacturing systems. Active inference, rooted in neuroscience, provides a unified probabilistic framework integrating perception, learning, and action, with inherent uncertainty quantification elements. Our study explores deep active inference, an emerging field that combines deep learning with the active inference decision-making framework. Leveraging a deep active inference agent, we focus on controlling parallel and identical machine workstations to enhance energy efficiency. We address challenges posed by the problem's stochastic nature and delayed policy response by introducing tailored enhancements to existing agent architectures. Specifically, we introduce multi-step transition and hybrid horizon methods to mitigate the need for complex planning. Our experimental results demonstrate the effectiveness of these enhancements and highlight the potential of the active inference-based approach.", "published": "2024-06-13T17:00:30Z", "authors": ["Yavar Taheri Yeganeh", "Mohsen Jafari", "Andrea Matta"], "categories": ["cs.LG", "cs.AI"], "doi": "10.1007/978-3-031-82481-4_33", "_query": "house price prediction machine learning", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2209.02057v3", "title": "Applying Machine Learning to Life Insurance: some knowledge sharing to master it", "abstract": "Machine Learning permeates many industries, which brings new source of benefits for companies. However within the life insurance industry, Machine Learning is not widely used in practice as over the past years statistical models have shown their efficiency for risk assessment. Thus insurers may face difficulties to assess the value of the artificial intelligence. Focusing on the modification of the life insurance industry over time highlights the stake of using Machine Learning for insurers and benefits that it can bring by unleashing data value. This paper reviews traditional actuarial methodologies for survival modeling and extends them with Machine Learning techniques. It points out differences with regular machine learning models and emphasizes importance of specific implementations to face censored data with machine learning models family. In complement to this article, a Python library has been developed. Different open-source Machine Learning algorithms have been adjusted to adapt the specificities of life insurance data, namely censoring and truncation. Such models can be easily applied from this SCOR library to accurately model life insurance risks.", "published": "2022-09-05T17:09:03Z", "authors": ["Antoine Chancel", "Laura Bradier", "Antoine Ly", "Razvan Ionescu", "Laurene Martin", "Marguerite Sauce"], "categories": ["stat.ML", "cs.CY", "cs.LG", "stat.AP"], "doi": "", "_query": "house price prediction machine learning", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/1806.03461v1", "title": "TAPAS: Tricks to Accelerate (encrypted) Prediction As a Service", "abstract": "Machine learning methods are widely used for a variety of prediction problems. \\emph{Prediction as a service} is a paradigm in which service providers with technological expertise and computational resources may perform predictions for clients. However, data privacy severely restricts the applicability of such services, unless measures to keep client data private (even from the service provider) are designed. Equally important is to minimize the amount of computation and communication required between client and server. Fully homomorphic encryption offers a possible way out, whereby clients may encrypt their data, and on which the server may perform arithmetic computations. The main drawback of using fully homomorphic encryption is the amount of time required to evaluate large machine learning models on encrypted data. We combine ideas from the machine learning literature, particularly work on binarization and sparsification of neural networks, together with algorithmic tools to speed-up and parallelize computation using encrypted data.", "published": "2018-06-09T11:40:22Z", "authors": ["Amartya Sanyal", "Matt J. Kusner", "Adrià Gascón", "Varun Kanade"], "categories": ["cs.CR", "cs.LG", "stat.ML"], "doi": "", "_query": "house price prediction machine learning", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2608.20255v1", "title": "Transfer Learning in Nonparametric Regression with Deep ReLU Networks", "abstract": "This paper develops a general transfer learning framework for nonparametric regression with data consisting of multiple groups. Under the assumption that groups share a common structure along with group-specific deviations in additive form, the proposed method employs a two-stage offset learning procedure: the first stage pools data from all groups to estimate an overall mean function, and the second stage estimates offsets for each group, yielding final group-level estimators through additive combination. Upper bounds on the $\\mathcal L_2$ error are established for the proposed framework, covering a broad class of nonparametric estimators under mild complexity and noise conditions. When instantiated with deep ReLU networks, explicit convergence rates are derived under hierarchical composition models, demonstrating the ability to overcome the curse of dimensionality. Conditions that enable positive transfer with faster rates are considered, including learning with simpler functions and data augmentation through pooling samples across groups. Various simulations and real-data experiments further validate the effectiveness of the proposed method.", "published": "2026-08-20T16:53:00Z", "authors": ["Junpeng Ren", "Carlos Misael Madrid Padilla", "Yanzhen Chen", "Oscar Hernan Madrid Padilla"], "categories": ["stat.ML", "cs.LG", "stat.ME"], "doi": "", "_query": "house price prediction machine learning", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/1206.6486v1", "title": "Flexible Modeling of Latent Task Structures in Multitask Learning", "abstract": "Multitask learning algorithms are typically designed assuming some fixed, a priori known latent structure shared by all the tasks. However, it is usually unclear what type of latent task structure is the most appropriate for a given multitask learning problem. Ideally, the \"right\" latent task structure should be learned in a data-driven manner. We present a flexible, nonparametric Bayesian model that posits a mixture of factor analyzers structure on the tasks. The nonparametric aspect makes the model expressive enough to subsume many existing models of latent task structures (e.g, mean-regularized tasks, clustered tasks, low-rank or linear/non-linear subspace assumption on tasks, etc.). Moreover, it can also learn more general task structures, addressing the shortcomings of such models. We present a variational inference algorithm for our model. Experimental results on synthetic and real-world datasets, on both regression and classification problems, demonstrate the effectiveness of the proposed method.", "published": "2012-06-27T19:59:59Z", "authors": ["Alexandre Passos", "Piyush Rai", "Jacques Wainer", "Hal Daume"], "categories": ["cs.LG", "stat.ML"], "doi": "", "_query": "house price prediction machine learning", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2403.20233v4", "title": "Functional Bilevel Optimization for Machine Learning", "abstract": "In this paper, we introduce a new functional point of view on bilevel optimization problems for machine learning, where the inner objective is minimized over a function space. These types of problems are most often solved by using methods developed in the parametric setting, where the inner objective is strongly convex with respect to the parameters of the prediction function. The functional point of view does not rely on this assumption and notably allows using over-parameterized neural networks as the inner prediction function. We propose scalable and efficient algorithms for the functional bilevel optimization problem and illustrate the benefits of our approach on instrumental regression and reinforcement learning tasks.", "published": "2024-03-29T15:22:03Z", "authors": ["Ieva Petrulionyte", "Julien Mairal", "Michael Arbel"], "categories": ["stat.ML", "cs.LG"], "doi": "", "_query": "house price prediction machine learning", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/1810.03530v1", "title": "Effective Parallelisation for Machine Learning", "abstract": "We present a novel parallelisation scheme that simplifies the adaptation of learning algorithms to growing amounts of data as well as growing needs for accurate and confident predictions in critical applications. In contrast to other parallelisation techniques, it can be applied to a broad class of learning algorithms without further mathematical derivations and without writing dedicated code, while at the same time maintaining theoretical performance guarantees. Moreover, our parallelisation scheme is able to reduce the runtime of many learning algorithms to polylogarithmic time on quasi-polynomially many processing units. This is a significant step towards a general answer to an open question on the efficient parallelisation of machine learning algorithms in the sense of Nick's Class (NC). The cost of this parallelisation is in the form of a larger sample complexity. Our empirical study confirms the potential of our parallelisation scheme with fixed numbers of processors and instances in realistic application scenarios.", "published": "2018-10-08T15:35:07Z", "authors": ["Michael Kamp", "Mario Boley", "Olana Missura", "Thomas Gärtner"], "categories": ["cs.LG", "cs.AI", "cs.DC", "stat.ML"], "doi": "", "_query": "house price prediction machine learning", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2405.02082v1", "title": "A comparative study of conformal prediction methods for valid uncertainty quantification in machine learning", "abstract": "In the past decades, most work in the area of data analysis and machine learning was focused on optimizing predictive models and getting better results than what was possible with existing models. To what extent the metrics with which such improvements were measured were accurately capturing the intended goal, whether the numerical differences in the resulting values were significant, or whether uncertainty played a role in this study and if it should have been taken into account, was of secondary importance. Whereas probability theory, be it frequentist or Bayesian, used to be the gold standard in science before the advent of the supercomputer, it was quickly replaced in favor of black box models and sheer computing power because of their ability to handle large data sets. This evolution sadly happened at the expense of interpretability and trustworthiness. However, while people are still trying to improve the predictive power of their models, the community is starting to realize that for many applications it is not so much the exact prediction that is of importance, but rather the variability or uncertainty. The work in this dissertation tries to further the quest for a world where everyone is aware of uncertainty, of how important it is and how to embrace it instead of fearing it. A specific, though general, framework that allows anyone to obtain accurate uncertainty estimates is singled out and analysed. Certain aspects and applications of the framework -- dubbed `conformal prediction' -- are studied in detail. Whereas many approaches to uncertainty quantification make strong assumptions about the data, conformal prediction is, at the time of writing, the only framework that deserves the title `distribution-free'. No parametric assumptions have to be made and the nonparametric results also hold without having to resort to the law of large numbers in the asymptotic regime.", "published": "2024-05-03T13:19:33Z", "authors": ["Nicolas Dewolf"], "categories": ["stat.ML", "cs.AI", "cs.LG", "math.ST"], "doi": "", "_query": "house price prediction machine learning", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/1711.01661v2", "title": "Provenance and Pseudo-Provenance for Seeded Learning-Based Automated Test Generation", "abstract": "Many methods for automated software test generation, including some that explicitly use machine learning (and some that use ML more broadly conceived) derive new tests from existing tests (often referred to as seeds). Often, the seed tests from which new tests are derived are manually constructed, or at least simpler than the tests that are produced as the final outputs of such test generators. We propose annotation of generated tests with a provenance (trail) showing how individual generated tests of interest (especially failing tests) derive from seed tests, and how the population of generated tests relates to the original seed tests. In some cases, post-processing of generated tests can invalidate provenance information, in which case we also propose a method for attempting to construct \"pseudo-provenance\" describing how the tests could have been (partly) generated from seeds.", "published": "2017-11-05T21:08:01Z", "authors": ["Alex Groce", "Josie Holmes"], "categories": ["stat.ML", "cs.SE"], "doi": "", "_query": "house price prediction machine learning", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2007.01503v1", "title": "Mathematical Perspective of Machine Learning", "abstract": "We take a closer look at some theoretical challenges of Machine Learning as a function approximation, gradient descent as the default optimization algorithm, limitations of fixed length and width networks and a different approach to RNNs from a mathematical perspective.", "published": "2020-07-03T05:26:02Z", "authors": ["Yarema Boryshchak"], "categories": ["cs.LG", "stat.ML"], "doi": "", "_query": "house price prediction machine learning", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2002.06975v1", "title": "Cross-sectional Stock Price Prediction using Deep Learning for Actual Investment Management", "abstract": "Stock price prediction has been an important research theme both academically and practically. Various methods to predict stock prices have been studied until now. The feature that explains the stock price by a cross-section analysis is called a \"factor\" in the field of finance. Many empirical studies in finance have identified which stocks having features in the cross-section relatively increase and which decrease in terms of price. Recently, stock price prediction methods using machine learning, especially deep learning, have been proposed since the relationship between these factors and stock prices is complex and non-linear. However, there are no practical examples for actual investment management. In this paper, therefore, we present a cross-sectional daily stock price prediction framework using deep learning for actual investment management. For example, we build a portfolio with information available at the time of market closing and invest at the time of market opening the next day. We perform empirical analysis in the Japanese stock market and confirm the profitability of our framework.", "published": "2020-02-17T14:28:42Z", "authors": ["Masaya Abe", "Kei Nakagawa"], "categories": ["q-fin.PM"], "doi": "10.1145/3399871.3399889", "_query": "house price prediction machine learning", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/1910.04371v1", "title": "Active Learning with Importance Sampling", "abstract": "We consider an active learning setting where the algorithm has access to a large pool of unlabeled data and a small pool of labeled data. In each iteration, the algorithm chooses few unlabeled data points and obtains their labels from an oracle. In this paper, we consider a probabilistic querying procedure to choose the points to be labeled. We propose an algorithm for Active Learning with Importance Sampling (ALIS), and derive upper bounds on the true loss incurred by the algorithm for any arbitrary probabilistic sampling procedure. Further, we propose an optimal sampling distribution that minimizes the upper bound on the true loss.", "published": "2019-10-10T05:11:08Z", "authors": ["Muni Sreenivas Pydi", "Vishnu Suresh Lokhande"], "categories": ["cs.LG", "stat.ML"], "doi": "", "_query": "house price prediction machine learning", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2404.00203v3", "title": "No-Regret Learning for Stackelberg Equilibrium Computation in Newsvendor Pricing Games", "abstract": "We introduce the application of online learning in a Stackelberg game pertaining to a system with two learning agents in a dyadic exchange network, consisting of a supplier and retailer, specifically where the parameters of the demand function are unknown. In this game, the supplier is the first-moving leader, and must determine the optimal wholesale price of the product. Subsequently, the retailer who is the follower, must determine both the optimal procurement amount and selling price of the product. In the perfect information setting, this is known as the classical price-setting Newsvendor problem, and we prove the existence of a unique Stackelberg equilibrium when extending this to a two-player pricing game. In the framework of online learning, the parameters of the reward function for both the follower and leader must be learned, under the assumption that the follower will best respond with optimism under uncertainty. A novel algorithm based on contextual linear bandits with a measurable uncertainty set is used to provide a confidence bound on the parameters of the stochastic demand. Consequently, optimal finite time regret bounds on the Stackelberg regret, along with convergence guarantees to an approximate Stackelberg equilibrium, are provided.", "published": "2024-03-30T00:31:02Z", "authors": ["Larkin Liu", "Yuming Rong"], "categories": ["cs.CE", "cs.MA"], "doi": "", "_query": "house price prediction machine learning", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/1910.13016v1", "title": "bLIMEy: Surrogate Prediction Explanations Beyond LIME", "abstract": "Surrogate explainers of black-box machine learning predictions are of paramount importance in the field of eXplainable Artificial Intelligence since they can be applied to any type of data (images, text and tabular), are model-agnostic and are post-hoc (i.e., can be retrofitted). The Local Interpretable Model-agnostic Explanations (LIME) algorithm is often mistakenly unified with a more general framework of surrogate explainers, which may lead to a belief that it is the solution to surrogate explainability. In this paper we empower the community to \"build LIME yourself\" (bLIMEy) by proposing a principled algorithmic framework for building custom local surrogate explainers of black-box model predictions, including LIME itself. To this end, we demonstrate how to decompose the surrogate explainers family into algorithmically independent and interoperable modules and discuss the influence of these component choices on the functional capabilities of the resulting explainer, using the example of LIME.", "published": "2019-10-29T00:21:31Z", "authors": ["Kacper Sokol", "Alexander Hepburn", "Raul Santos-Rodriguez", "Peter Flach"], "categories": ["cs.LG", "stat.ML"], "doi": "", "_query": "house price prediction machine learning", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/1905.12200v2", "title": "A Topology Layer for Machine Learning", "abstract": "Topology applied to real world data using persistent homology has started to find applications within machine learning, including deep learning. We present a differentiable topology layer that computes persistent homology based on level set filtrations and edge-based filtrations. We present three novel applications: the topological layer can (i) regularize data reconstruction or the weights of machine learning models, (ii) construct a loss on the output of a deep generative network to incorporate topological priors, and (iii) perform topological adversarial attacks on deep networks trained with persistence features. The code (www.github.com/bruel-gabrielsson/TopologyLayer) is publicly available and we hope its availability will facilitate the use of persistent homology in deep learning and other gradient based applications.", "published": "2019-05-29T03:46:32Z", "authors": ["Rickard Brüel-Gabrielsson", "Bradley J. Nelson", "Anjan Dwaraknath", "Primoz Skraba", "Leonidas J. Guibas", "Gunnar Carlsson"], "categories": ["cs.LG", "math.AT", "stat.ML"], "doi": "", "_query": "house price prediction machine learning", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2312.03218v1", "title": "Accelerated Gradient Algorithms with Adaptive Subspace Search for Instance-Faster Optimization", "abstract": "Gradient-based minimax optimal algorithms have greatly promoted the development of continuous optimization and machine learning. One seminal work due to Yurii Nesterov [Nes83a] established $\\tilde{\\mathcal{O}}(\\sqrt{L/μ})$ gradient complexity for minimizing an $L$-smooth $μ$-strongly convex objective. However, an ideal algorithm would adapt to the explicit complexity of a particular objective function and incur faster rates for simpler problems, triggering our reconsideration of two defeats of existing optimization modeling and analysis. (i) The worst-case optimality is neither the instance optimality nor such one in reality. (ii) Traditional $L$-smoothness condition may not be the primary abstraction/characterization for modern practical problems. In this paper, we open up a new way to design and analyze gradient-based algorithms with direct applications in machine learning, including linear regression and beyond. We introduce two factors $(α, τ_α)$ to refine the description of the degenerated condition of the optimization problems based on the observation that the singular values of Hessian often drop sharply. We design adaptive algorithms that solve simpler problems without pre-known knowledge with reduced gradient or analogous oracle accesses. The algorithms also improve the state-of-art complexities for several problems in machine learning, thereby solving the open problem of how to design faster algorithms in light of the known complexity lower bounds. Specially, with the $\\mathcal{O}(1)$-nuclear norm bounded, we achieve an optimal $\\tilde{\\mathcal{O}}(μ^{-1/3})$ (v.s. $\\tilde{\\mathcal{O}}(μ^{-1/2})$) gradient complexity for linear regression. We hope this work could invoke the rethinking for understanding the difficulty of modern problems in optimization.", "published": "2023-12-06T01:16:10Z", "authors": ["Yuanshi Liu", "Hanzhen Zhao", "Yang Xu", "Pengyun Yue", "Cong Fang"], "categories": ["cs.LG", "cs.CC", "stat.ML"], "doi": "", "_query": "house price prediction machine learning", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/1605.06636v2", "title": "Deep Transfer Learning with Joint Adaptation Networks", "abstract": "Deep networks have been successfully applied to learn transferable features for adapting models from a source domain to a different target domain. In this paper, we present joint adaptation networks (JAN), which learn a transfer network by aligning the joint distributions of multiple domain-specific layers across domains based on a joint maximum mean discrepancy (JMMD) criterion. Adversarial training strategy is adopted to maximize JMMD such that the distributions of the source and target domains are made more distinguishable. Learning can be performed by stochastic gradient descent with the gradients computed by back-propagation in linear-time. Experiments testify that our model yields state of the art results on standard datasets.", "published": "2016-05-21T12:56:14Z", "authors": ["Mingsheng Long", "Han Zhu", "Jianmin Wang", "Michael I. Jordan"], "categories": ["cs.LG", "stat.ML"], "doi": "", "_query": "house price prediction machine learning", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2307.16164v3", "title": "Adaptive learning of density ratios in RKHS", "abstract": "Estimating the ratio of two probability densities from finitely many observations of the densities is a central problem in machine learning and statistics with applications in two-sample testing, divergence estimation, generative modeling, covariate shift adaptation, conditional density estimation, and novelty detection. In this work, we analyze a large class of density ratio estimation methods that minimize a regularized Bregman divergence between the true density ratio and a model in a reproducing kernel Hilbert space (RKHS). We derive new finite-sample error bounds, and we propose a Lepskii type parameter choice principle that minimizes the bounds without knowledge of the regularity of the density ratio. In the special case of quadratic loss, our method adaptively achieves a minimax optimal error rate. A numerical illustration is provided.", "published": "2023-07-30T08:18:39Z", "authors": ["Werner Zellinger", "Stefan Kindermann", "Sergei V. Pereverzyev"], "categories": ["cs.LG", "math.ST", "stat.ML"], "doi": "", "_query": "house price prediction machine learning", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/1803.01035v1", "title": "Machine Learning Entanglement Freedom Or: How I Learned to Stop Worrying and Love Linear Regression", "abstract": "Quantum many-body systems realise many different phases of matter characterised by their exotic emergent phenomena. While some simple versions of these properties can occur in systems of free fermions, their occurrence generally implies that the physics is dictated by an interacting Hamiltonian. The interaction distance has been successfully used to quantify the effect of interactions in a variety of states of matter via the entanglement spectrum [Nat. Commun. 8, 14926 (2017), arXiv:1705.09983]. The computation of the interaction distance reduces to a global optimisation problem whose goal is to search for the free-fermion entanglement spectrum closest to the given entanglement spectrum. In this work, we employ techniques from machine learning in order to perform this same task. In a supervised learning setting, we use labelled data obtained by computing the interaction distance and predict its value via linear regression. Moving to a semi-supervised setting, we train an auto-encoder to estimate an alternative measure to the interaction distance, and we show that it behaves in a similar manner.", "published": "2018-03-02T20:34:01Z", "authors": ["Samuel Spillard", "Christopher J. Turner", "Konstantinos Meichanetzidis"], "categories": ["cond-mat.str-el", "cond-mat.dis-nn"], "doi": "10.1142/S0219749918400026", "_query": "house price prediction machine learning", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/1404.7456v1", "title": "Automatic Differentiation of Algorithms for Machine Learning", "abstract": "Automatic differentiation---the mechanical transformation of numeric computer programs to calculate derivatives efficiently and accurately---dates to the origin of the computer age. Reverse mode automatic differentiation both antedates and generalizes the method of backwards propagation of errors used in machine learning. Despite this, practitioners in a variety of fields, including machine learning, have been little influenced by automatic differentiation, and make scant use of available tools. Here we review the technique of automatic differentiation, describe its two main modes, and explain how it can benefit machine learning practitioners. To reach the widest possible audience our treatment assumes only elementary differential calculus, and does not assume any knowledge of linear algebra.", "published": "2014-04-28T17:19:25Z", "authors": ["Atilim Gunes Baydin", "Barak A. Pearlmutter"], "categories": ["cs.LG", "cs.SC", "stat.ML"], "doi": "", "_query": "house price prediction machine learning", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/1802.07051v2", "title": "On Learning Causal Structures from Non-Experimental Data without Any Faithfulness Assumption", "abstract": "Consider the problem of learning, from non-experimental data, the causal (Markov equivalence) structure of the true, unknown causal Bayesian network (CBN) on a given, fixed set of (categorical) variables. This learning problem is known to be so hard that there is no learning algorithm that converges to the truth for all possible CBNs (on the given set of variables). So the convergence property has to be sacrificed for some CBNs---but for which? In response, the standard practice has been to design and employ learning algorithms that secure the convergence property for at least all the CBNs that satisfy the famous faithfulness condition, which implies sacrificing the convergence property for some CBNs that violate the faithfulness condition (Spirtes et al. 2000). This standard design practice can be justified by assuming---that is, accepting on faith---that the true, unknown CBN satisfies the faithfulness condition. But the real question is this: Is it possible to explain, without assuming the faithfulness condition or any of its weaker variants, why it is mandatory rather than optional to follow the standard design practice? This paper aims to answer the above question in the affirmative. We first define an array of modes of convergence to the truth as desiderata that might or might not be achieved by a causal learning algorithm. Those modes of convergence concern (i) how pervasive the domain of convergence is on the space of all possible CBNs and (ii) how uniformly the convergence happens. Then we prove a result to the following effect: for any learning algorithm that tackles the causal learning problem in question, if it achieves the best achievable mode of convergence (considered in this paper), then it must follow the standard design practice of converging to the truth for at least all CBNs that satisfy the faithfulness condition---it is a requirement, not an option.", "published": "2018-02-20T10:46:12Z", "authors": ["Hanti Lin", "Jiji Zhang"], "categories": ["stat.ML", "cs.LG", "stat.ME"], "doi": "", "_query": "house price prediction machine learning", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2207.11417v1", "title": "Multiscale Neural Operator: Learning Fast and Grid-independent PDE Solvers", "abstract": "Numerical simulations in climate, chemistry, or astrophysics are computationally too expensive for uncertainty quantification or parameter-exploration at high-resolution. Reduced-order or surrogate models are multiple orders of magnitude faster, but traditional surrogates are inflexible or inaccurate and pure machine learning (ML)-based surrogates too data-hungry. We propose a hybrid, flexible surrogate model that exploits known physics for simulating large-scale dynamics and limits learning to the hard-to-model term, which is called parametrization or closure and captures the effect of fine- onto large-scale dynamics. Leveraging neural operators, we are the first to learn grid-independent, non-local, and flexible parametrizations. Our \\textit{multiscale neural operator} is motivated by a rich literature in multiscale modeling, has quasilinear runtime complexity, is more accurate or flexible than state-of-the-art parametrizations and demonstrated on the chaotic equation multiscale Lorenz96.", "published": "2022-07-23T05:01:03Z", "authors": ["Björn Lütjens", "Catherine H. Crawford", "Campbell D Watson", "Christopher Hill", "Dava Newman"], "categories": ["cs.LG", "cs.AI", "cs.CE", "cs.DC"], "doi": "", "_query": "house price prediction machine learning", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2607.16329v2", "title": "Lipschitz Continuity in Deep Learning: A Systematic Review of Theoretical Foundations, Estimation Methods, Regularization Approaches, and Certifiable Robustness", "abstract": "Lipschitz continuity is a fundamental property of neural networks that characterizes their sensitivity to input perturbations. It plays a pivotal role in deep learning, governing \\textbf{robustness}, \\textbf{generalization} and \\textbf{optimization dynamics}. Despite its importance, research on Lipschitz continuity is scattered across various domains, lacking a unified perspective. This paper addresses this gap by providing a systematic review of Lipschitz continuity in deep learning. We explore its \\textbf{theoretical foundations}, \\textbf{estimation methods}, \\textbf{regularization approaches}, and \\textbf{certifiable robustness}. By reviewing existing research through the lens of Lipschitz continuity, this survey serves as a comprehensive reference for researchers and practitioners seeking a deeper understanding of Lipschitz continuity and its implications in deep learning.", "published": "2026-07-15T16:04:37Z", "authors": ["Róisín Luo", "James McDermott", "Colm O'Riordan"], "categories": ["stat.ML", "cs.LG"], "doi": "", "_query": "house price prediction machine learning", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2307.15982v1", "title": "Vehicle Price Prediction By Aggregating decision tree model With Boosting Model", "abstract": "Predicting the price of used vehicles is a more interesting and needed problem by many users. Vehicle price prediction can be a challenging task due to the high number of attributes that should be considered for accurate prediction. The major step in the prediction process is the collection and pre-processing of the data. In this project, python scripts were built to normalize, standardize, and clean data to avoid unnecessary noise for machine learning algorithms. The data set used in this project can be very valuable in conducting similar research using different prediction techniques. Many assumptions were made on the basis of the data set. The proposed system uses a Decision tree model and Gradient boosting predictive model, which are combined in other to get closed to accurate prediction, the proposed model was evaluated and it gives a promising performance. The future price prediction of used vehicles with the help of the same data set will comprise different models.", "published": "2023-07-29T13:07:57Z", "authors": ["Auwal Tijjani Amshi"], "categories": ["cs.LG"], "doi": "", "_query": "house price prediction machine learning", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2305.06442v1", "title": "Data, Trees, and Forests -- Decision Tree Learning in K-12 Education", "abstract": "As a consequence of the increasing influence of machine learning on our lives, everyone needs competencies to understand corresponding phenomena, but also to get involved in shaping our world and making informed decisions regarding the influences on our society. Therefore, in K-12 education, students need to learn about core ideas and principles of machine learning. However, for this target group, achieving all of the aforementioned goals presents an enormous challenge. To this end, we present a teaching concept that combines a playful and accessible unplugged approach focusing on conceptual understanding with empowering students to actively apply machine learning methods and reflect their influence on society, building upon decision tree learning.", "published": "2023-05-10T20:24:38Z", "authors": ["Tilman Michaeli", "Stefan Seegerer", "Lennard Kerber", "Ralf Romeike"], "categories": ["cs.CY", "cs.LG"], "doi": "", "_query": "house price prediction machine learning", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2302.02406v1", "title": "Pre-screening breast cancer with machine learning and deep learning", "abstract": "We suggest that deep learning can be used for pre-screening cancer by analyzing demographic and anthropometric information of patients, as well as biological markers obtained from routine blood samples and relative risks obtained from meta-analysis and international databases. We applied feature selection algorithms to a database of 116 women, including 52 healthy women and 64 women diagnosed with breast cancer, to identify the best pre-screening predictors of cancer. We utilized the best predictors to perform k-fold Monte Carlo cross-validation experiments that compare deep learning against traditional machine learning algorithms. Our results indicate that a deep learning model with an input-layer architecture that is fine-tuned using feature selection can effectively distinguish between patients with and without cancer. Additionally, compared to machine learning, deep learning has the lowest uncertainty in its predictions. These findings suggest that deep learning algorithms applied to cancer pre-screening offer a radiation-free, non-invasive, and affordable complement to screening methods based on imagery. The implementation of deep learning algorithms in cancer pre-screening offer opportunities to identify individuals who may require imaging-based screening, can encourage self-examination, and decrease the psychological externalities associated with false positives in cancer screening. The integration of deep learning algorithms for both screening and pre-screening will ultimately lead to earlier detection of malignancy, reducing the healthcare and societal burden associated to cancer treatment.", "published": "2023-02-05T15:27:50Z", "authors": ["Rolando Gonzales Martinez", "Daan-Max van Dongen"], "categories": ["stat.ML", "cs.LG"], "doi": "", "_query": "house price prediction machine learning", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2010.15240v1", "title": "Test Set Optimization by Machine Learning Algorithms", "abstract": "Diagnosis results are highly dependent on the volume of test set. To derive the most efficient test set, we propose several machine learning based methods to predict the minimum amount of test data that produces relatively accurate diagnosis. By collecting outputs from failing circuits, the feature matrix and label vector are generated, which involves the inference information of the test termination point. Thus we develop a prediction model to fit the data and determine when to terminate testing. The considered methods include LASSO and Support Vector Machine(SVM) where the relationship between goals(label) and predictors(feature matrix) are considered to be linear in LASSO and nonlinear in SVM. Numerical results show that SVM reaches a diagnosis accuracy of 90.4% while deducting the volume of test set by 35.24%.", "published": "2020-10-28T21:24:06Z", "authors": ["Kaiming Fu", "Yulu Jin", "Zhousheng Chen"], "categories": ["cs.LG", "stat.ML"], "doi": "", "_query": "house price prediction machine learning", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/1612.01356v1", "title": "Diagnostic Prediction Using Discomfort Drawings", "abstract": "In this paper, we explore the possibility to apply machine learning to make diagnostic predictions using discomfort drawings. A discomfort drawing is an intuitive way for patients to express discomfort and pain related symptoms. These drawings have proven to be an effective method to collect patient data and make diagnostic decisions in real-life practice. A dataset from real-world patient cases is collected for which medical experts provide diagnostic labels. Next, we extend a factorized multimodal topic model, Inter-Battery Topic Model (IBTM), to train a system that can make diagnostic predictions given an unseen discomfort drawing. Experimental results show reasonable predictions of diagnostic labels given an unseen discomfort drawing. The positive result indicates a significant potential of machine learning to be used for parts of the pain diagnostic process and to be a decision support system for physicians and other health care personnel.", "published": "2016-12-05T14:11:20Z", "authors": ["Cheng Zhang", "Hedvig Kjellstrom", "Bo C. Bertilson"], "categories": ["cs.LG"], "doi": "", "_query": "house price prediction machine learning", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2202.13733v2", "title": "On the Benefits of Large Learning Rates for Kernel Methods", "abstract": "This paper studies an intriguing phenomenon related to the good generalization performance of estimators obtained by using large learning rates within gradient descent algorithms. First observed in the deep learning literature, we show that a phenomenon can be precisely characterized in the context of kernel methods, even though the resulting optimization problem is convex. Specifically, we consider the minimization of a quadratic objective in a separable Hilbert space, and show that with early stopping, the choice of learning rate influences the spectral decomposition of the obtained solution on the Hessian's eigenvectors. This extends an intuition described by Nakkiran (2020) on a two-dimensional toy problem to realistic learning scenarios such as kernel ridge regression. While large learning rates may be proven beneficial as soon as there is a mismatch between the train and test objectives, we further explain why it already occurs in classification tasks without assuming any particular mismatch between train and test data distributions.", "published": "2022-02-28T13:01:04Z", "authors": ["Gaspard Beugnot", "Julien Mairal", "Alessandro Rudi"], "categories": ["stat.ML", "cs.LG", "math.OC"], "doi": "", "_query": "house price prediction machine learning", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2005.08679v1", "title": "An Overview of Privacy in Machine Learning", "abstract": "Over the past few years, providers such as Google, Microsoft, and Amazon have started to provide customers with access to software interfaces allowing them to easily embed machine learning tasks into their applications. Overall, organizations can now use Machine Learning as a Service (MLaaS) engines to outsource complex tasks, e.g., training classifiers, performing predictions, clustering, etc. They can also let others query models trained on their data. Naturally, this approach can also be used (and is often advocated) in other contexts, including government collaborations, citizen science projects, and business-to-business partnerships. However, if malicious users were able to recover data used to train these models, the resulting information leakage would create serious issues. Likewise, if the inner parameters of the model are considered proprietary information, then access to the model should not allow an adversary to learn such parameters. In this document, we set to review privacy challenges in this space, providing a systematic review of the relevant research literature, also exploring possible countermeasures. More specifically, we provide ample background information on relevant concepts around machine learning and privacy. Then, we discuss possible adversarial models and settings, cover a wide range of attacks that relate to private and/or sensitive information leakage, and review recent results attempting to defend against such attacks. Finally, we conclude with a list of open problems that require more work, including the need for better evaluations, more targeted defenses, and the study of the relation to policy and data protection efforts.", "published": "2020-05-18T13:05:17Z", "authors": ["Emiliano De Cristofaro"], "categories": ["cs.LG", "cs.AI", "cs.CR", "cs.CY", "stat.ML"], "doi": "", "_query": "house price prediction machine learning", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2010.02006v7", "title": "Interpretable Machine Learning for COVID-19: An Empirical Study on Severity Prediction Task", "abstract": "The black-box nature of machine learning models hinders the deployment of some high-accuracy models in medical diagnosis. It is risky to put one's life in the hands of models that medical researchers do not fully understand. However, through model interpretation, black-box models can promptly reveal significant biomarkers that medical practitioners may have overlooked due to the surge of infected patients in the COVID-19 pandemic. This research leverages a database of 92 patients with confirmed SARS-CoV-2 laboratory tests between 18th Jan. 2020 and 5th Mar. 2020, in Zhuhai, China, to identify biomarkers indicative of severity prediction. Through the interpretation of four machine learning models, decision tree, random forests, gradient boosted trees, and neural networks using permutation feature importance, Partial Dependence Plot (PDP), Individual Conditional Expectation (ICE), Accumulated Local Effects (ALE), Local Interpretable Model-agnostic Explanations (LIME), and Shapley Additive Explanation (SHAP), we identify an increase in N-Terminal pro-Brain Natriuretic Peptide (NTproBNP), C-Reaction Protein (CRP), and lactic dehydrogenase (LDH), a decrease in lymphocyte (LYM) is associated with severe infection and an increased risk of death, which is consistent with recent medical research on COVID-19 and other research using dedicated models. We further validate our methods on a large open dataset with 5644 confirmed patients from the Hospital Israelita Albert Einstein, at São Paulo, Brazil from Kaggle, and unveil leukocytes, eosinophils, and platelets as three indicative biomarkers for COVID-19.", "published": "2020-09-30T16:13:41Z", "authors": ["Han Wu", "Wenjie Ruan", "Jiangtao Wang", "Dingchang Zheng", "Bei Liu", "Yayuan Gen", "Xiangfei Chai", "Jian Chen", "Kunwei Li", "Shaolin Li", "Sumi Helal"], "categories": ["cs.LG", "stat.ML"], "doi": "10.1109/TAI.2021.3092698", "_query": "house price prediction machine learning", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2507.04493v1", "title": "Machine Learning-Based Prediction of Metal-Organic Framework Materials: A Comparative Analysis of Multiple Models", "abstract": "Metal-organic frameworks (MOFs) have emerged as promising materials for various applications due to their unique structural properties and versatile functionalities. This study presents a comprehensive investigation of machine learning approaches for predicting MOF material properties. We employed five different machine learning models: Random Forest, XGBoost, LightGBM, Support Vector Machine, and Neural Network, to analyze and predict MOF characteristics using a dataset from the Kaggle platform. The models were evaluated using multiple performance metrics, including RMSE, R^2, MAE, and cross-validation scores. Results demonstrated that the Random Forest model achieved superior performance with an R^2 value of 0.891 and RMSE of 0.152, significantly outperforming other models. LightGBM showed remarkable computational efficiency, completing training in 25.7 seconds while maintaining high accuracy. Our comparative analysis revealed that ensemble learning methods generally exhibited better performance than traditional single models in MOF property prediction. This research provides valuable insights into the application of machine learning in materials science and establishes a robust framework for future MOF material design and property prediction.", "published": "2025-07-06T18:10:00Z", "authors": ["Zhuo Zheng", "Keyan Liu", "Xiyuan Zhu"], "categories": ["cs.LG"], "doi": "", "_query": "house price prediction machine learning", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2506.14110v2", "title": "Universal rates of ERM for agnostic learning", "abstract": "The universal learning framework has been developed to obtain guarantees on the learning rates that hold for any fixed distribution, which can be much faster than the ones uniformly hold over all the distributions. Given that the Empirical Risk Minimization (ERM) principle being fundamental in the PAC theory and ubiquitous in practical machine learning, the recent work of arXiv:2412.02810 studied the universal rates of ERM for binary classification under the realizable setting. However, the assumption of realizability is too restrictive to hold in practice. Indeed, the majority of the literature on universal learning has focused on the realizable case, leaving the non-realizable case barely explored. In this paper, we consider the problem of universal learning by ERM for binary classification under the agnostic setting, where the ''learning curve\" reflects the decay of the excess risk as the sample size increases. We explore the possibilities of agnostic universal rates and reveal a compact trichotomy: there are three possible agnostic universal rates of ERM, being either $e^{-n}$, $o(n^{-1/2})$, or arbitrarily slow. We provide a complete characterization of which concept classes fall into each of these categories. Moreover, we also establish complete characterizations for the target-dependent universal rates as well as the Bayes-dependent universal rates.", "published": "2025-06-17T02:02:11Z", "authors": ["Steve Hanneke", "Mingyue Xu"], "categories": ["stat.ML", "cs.LG"], "doi": "", "_query": "house price prediction machine learning", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2405.14503v1", "title": "Radial analysis and scaling of housing prices in French urban areas", "abstract": "Urban scaling laws summarize how urban attributes evolve with city size. Recent criticism questions notably the aggregate view of this approach, which leads to neglecting the internal structure of cities. This is all the more relevant for housing prices due to their important variations across space. Based on a dataset compiling millions of real estate transactions over the period 2017-2021, we investigate the regularities of the radial (center-periphery) profiles of housing prices across cities, with respect to their size. Results are threefold. First, they corroborate prior findings in the urban scaling literature stating that largest cities agglomerate higher housing prices. Second, we find that housing price radial profiles scale in three dimensions with the power 1/5 of city population. After rescaling, great regularities between radial profiles can be observed, although some locational amenities have a significant impact on prices. Third, it appears that our rescaled profiles approach fails to explain housing price variations in the city center across cities. In fact, prices near the city center rise much faster with city size than those in the periphery. This has strong implications for low-income households seeking homeownership, because prohibitive prices in the center may contribute to pushing them out into peripheral locations.", "published": "2024-05-23T12:38:20Z", "authors": ["Gaëtan Laziou", "Rémi Lemoy", "Marion Le Texier"], "categories": ["physics.soc-ph"], "doi": "", "_query": "house price prediction machine learning", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2602.12375v1", "title": "Value Bonuses using Ensemble Errors for Exploration in Reinforcement Learning", "abstract": "Optimistic value estimates provide one mechanism for directed exploration in reinforcement learning (RL). The agent acts greedily with respect to an estimate of the value plus what can be seen as a value bonus. The value bonus can be learned by estimating a value function on reward bonuses, propagating local uncertainties around rewards. However, this approach only increases the value bonus for an action retroactively, after seeing a higher reward bonus from that state and action. Such an approach does not encourage the agent to visit a state and action for the first time. In this work, we introduce an algorithm for exploration called Value Bonuses with Ensemble errors (VBE), that maintains an ensemble of random action-value functions (RQFs). VBE uses the errors in the estimation of these RQFs to design value bonuses that provide first-visit optimism and deep exploration. The key idea is to design the rewards for these RQFs in such a way that the value bonus can decrease to zero. We show that VBE outperforms Bootstrap DQN and two reward bonus approaches (RND and ACB) on several classic environments used to test exploration and provide demonstrative experiments that it can scale easily to more complex environments like Atari.", "published": "2026-02-12T20:12:17Z", "authors": ["Abdul Wahab", "Raksha Kumaraswamy", "Martha White"], "categories": ["cs.LG", "cs.AI"], "doi": "", "_query": "house price prediction machine learning", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2306.15487v1", "title": "Quadtree features for machine learning on CMDs", "abstract": "The upcoming facilities like the Vera C. Rubin Observatory will provide extremely deep photometry of thousands of star clusters to the edge of the Galaxy and beyond, which will require adequate tools for automatic analysis, capable of performing tasks such as the characterization of a star cluster through the analysis of color-magnitude diagrams (CMDs). The latter are essentially point clouds in N-dimensional space, with the number of dimensions corresponding to the photometric bands employed. In this context, machine learning techniques suitable for tabular data are not immediately applicable to CMDs because the number of stars included in a given CMD is variable, and equivariance for permutations is required. To address this issue without introducing ad-hoc manipulations that would require human oversight, here we present a new CMD featurization procedure that summarizes a CMD by means of a quadtree-like structure through iterative partitions of the color-magnitude plane, extracting a fixed number of meaningful features of the relevant subregion from any given CMD. The present approach is robust to photometric noise and contamination and it shows that a simple linear regression on our features predicts distance modulus (metallicity) with a scatter of 0.33 dex (0.16 dex) in cross-validation.", "published": "2023-06-27T14:08:28Z", "authors": ["Jose Schiappacasse-Ulloa", "Mario Pasquato", "Sara Lucatello"], "categories": ["astro-ph.IM", "astro-ph.GA", "astro-ph.SR"], "doi": "", "_query": "house price prediction machine learning", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/1712.07704v1", "title": "Unsupervised learning of dynamical and molecular similarity using variance minimization", "abstract": "In this report, we present an unsupervised machine learning method for determining groups of molecular systems according to similarity in their dynamics or structures using Ward's minimum variance objective function. We first apply the minimum variance clustering to a set of simulated tripeptides using the information theoretic Jensen-Shannon divergence between Markovian transition matrices in order to gain insight into how point mutations affect protein dynamics. Then, we extend the method to partition two chemoinformatic datasets according to structural similarity to motivate a train/validation/test split for supervised learning that avoids overfitting.", "published": "2017-12-20T20:56:12Z", "authors": ["Brooke E. Husic", "Vijay S. Pande"], "categories": ["physics.bio-ph", "q-bio.BM", "q-bio.QM", "stat.ML"], "doi": "", "_query": "house price prediction machine learning", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2407.03194v5", "title": "Prediction Instability in Machine Learning Ensembles", "abstract": "In machine learning ensembles predictions from multiple models are aggregated. Despite widespread use and strong performance of ensembles in applied problems little is known about the mathematical properties of aggregating models and associated consequences for safe, explainable use of such models. In this paper we prove a theorem that shows that any ensemble will exhibit at least one of the following forms of prediction instability. It will either ignore agreement among all underlying models, change its mind when none of the underlying models have done so, or be manipulable through inclusion or exclusion of options it would never actually predict. As a consequence, ensemble aggregation procedures will always need to balance the benefits of information use against the risk of these prediction instabilities. This analysis also sheds light on what specific forms of prediction instability to expect from particular ensemble algorithms; for example popular tree ensembles like random forest, or xgboost will violate basic, intuitive fairness properties. Finally, we show that this can be ameliorated by using consistent models in asymptotic conditions.", "published": "2024-07-03T15:26:02Z", "authors": ["Jeremy Kedziora"], "categories": ["cs.LG"], "doi": "", "_query": "house price prediction machine learning", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2510.01446v1", "title": "Can Machine Learning Algorithms Outperform Traditional Models for Option Pricing?", "abstract": "This study investigates the application of machine learning techniques, specifically Neural Networks, Random Forests, and CatBoost for option pricing, in comparison to traditional models such as Black-Scholes and Heston Model. Using both synthetically generated data and real market option data, each model is evaluated in predicting the option price. The results show that machine learning models can capture complex, non-linear relationships in option prices and, in several cases, outperform both Black-Scholes and Heston models. These findings highlight the potential of data-driven methods to improve pricing accuracy and better reflect market dynamics.", "published": "2025-10-01T20:37:18Z", "authors": ["Georgy Milyushkov"], "categories": ["q-fin.CP"], "doi": "", "_query": "house price prediction machine learning", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2607.14190v1", "title": "A Temporal Machine Learning-Based Time-to-Event Model for Predicting ALS Progression and Healthcare Utilization", "abstract": "Amyotrophic lateral sclerosis (ALS) is a progressive and heterogeneous neurodegenerative disease in which predicting clinically meaningful milestones, such as assistive device use, remains challenging. We developed a time-to-event, digital-twin-inspired framework that integrates longitudinal ALS Functional Rating Scale-Revised (ALSFRS-R) trajectories with survival modeling to support individualized prediction of functional decline and assistive device utilization. We constructed a harmonized longitudinal dataset by integrating diagnosis records, ALSFRS-R assessments, activities of daily living, and demographic information, followed by preprocessing to ensure data quality, temporal alignment, and cohort consistency. Correlation-based clustering identified coherent functional domains spanning bulbar, upper limb, axial, lower limb, and respiratory systems. Generalized additive mixed models characterized nonlinear, domain-specific functional decline across all domains. In addition, a temporal machine learning model was developed to predict longitudinal functional decline and capture stage-dependent disease progression. Cox proportional hazards modeling further identified lower limb function, particularly walking and stair climbing, as the strongest predictors of earlier wheelchair access. Building on these results, we implemented a digital twin-inspired temporal machine learning-based time-to-event (TTE) model that generates individualized survival curves and dynamically predicts wheelchair-free survival. This framework provides a scalable, interpretable, and clinically actionable approach for linking ALS progression with personalized decision support, with applications in proactive care planning, clinical trial stratification, and precision medicine.", "published": "2026-07-15T16:11:23Z", "authors": ["Zongliang Yue", "Qi Li", "Terry Heiman-Patterson", "Frank Bearoff", "Zhaohui Qin", "Huanmei Wu"], "categories": ["cs.LG", "cs.IR", "stat.ML"], "doi": "", "_query": "house price prediction machine learning", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/1903.00402v1", "title": "Machine learning in policy evaluation: new tools for causal inference", "abstract": "While machine learning (ML) methods have received a lot of attention in recent years, these methods are primarily for prediction. Empirical researchers conducting policy evaluations are, on the other hand, pre-occupied with causal problems, trying to answer counterfactual questions: what would have happened in the absence of a policy? Because these counterfactuals can never be directly observed (described as the \"fundamental problem of causal inference\") prediction tools from the ML literature cannot be readily used for causal inference. In the last decade, major innovations have taken place incorporating supervised ML tools into estimators for causal parameters such as the average treatment effect (ATE). This holds the promise of attenuating model misspecification issues, and increasing of transparency in model selection. One particularly mature strand of the literature include approaches that incorporate supervised ML approaches in the estimation of the ATE of a binary treatment, under the \\textit{unconfoundedness} and positivity assumptions (also known as exchangeability and overlap assumptions). This article reviews popular supervised machine learning algorithms, including the Super Learner. Then, some specific uses of machine learning for treatment effect estimation are introduced and illustrated, namely (1) to create balance among treated and control groups, (2) to estimate so-called nuisance models (e.g. the propensity score, or conditional expectations of the outcome) in semi-parametric estimators that target causal parameters (e.g. targeted maximum likelihood estimation or the double ML estimator), and (3) the use of machine learning for variable selection in situations with a high number of covariates.", "published": "2019-03-01T16:52:51Z", "authors": ["Noemi Kreif", "Karla DiazOrdaz"], "categories": ["stat.ML", "cs.LG"], "doi": "", "_query": "house price prediction machine learning", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/1406.2721v1", "title": "Learning Latent Variable Gaussian Graphical Models", "abstract": "Gaussian graphical models (GGM) have been widely used in many high-dimensional applications ranging from biological and financial data to recommender systems. Sparsity in GGM plays a central role both statistically and computationally. Unfortunately, real-world data often does not fit well to sparse graphical models. In this paper, we focus on a family of latent variable Gaussian graphical models (LVGGM), where the model is conditionally sparse given latent variables, but marginally non-sparse. In LVGGM, the inverse covariance matrix has a low-rank plus sparse structure, and can be learned in a regularized maximum likelihood framework. We derive novel parameter estimation error bounds for LVGGM under mild conditions in the high-dimensional setting. These results complement the existing theory on the structural learning, and open up new possibilities of using LVGGM for statistical inference.", "published": "2014-06-10T21:03:22Z", "authors": ["Zhaoshi Meng", "Brian Eriksson", "Alfred O. Hero"], "categories": ["stat.ML", "cs.LG", "math.ST"], "doi": "", "_query": "house price prediction machine learning", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2002.01244v1", "title": "Machine Learning Techniques to Detect and Characterise Whistler Radio Waves", "abstract": "Lightning strokes create powerful electromagnetic pulses that routinely cause very low frequency (VLF) waves to propagate across hemispheres along geomagnetic field lines. VLF antenna receivers can be used to detect these whistler waves generated by these lightning strokes. The particular time/frequency dependence of the received whistler wave enables the estimation of electron density in the plasmasphere region of the magnetosphere. Therefore the identification and characterisation of whistlers are important tasks to monitor the plasmasphere in real-time and to build large databases of events to be used for statistical studies. The current state of the art in detecting whistler is the Automatic Whistler Detection (AWD) method developed by Lichtenberger (2009). This method is based on image correlation in 2 dimensions and requires significant computing hardware situated at the VLF receiver antennas (e.g. in Antarctica). The aim of this work is to develop a machine learning-based model capable of automatically detecting whistlers in the data provided by the VLF receivers. The approach is to use a combination of image classification and localisation on the spectrogram data generated by the VLF receivers to identify and localise each whistler. The data at hand has around 2300 events identified by AWD at SANAE and Marion and will be used as training, validation, and testing data. Three detector designs have been proposed. The first one using a similar method to AWD, the second using image classification on regions of interest extracted from a spectrogram, and the last one using YOLO, the current state of the art in object detection. It has been shown that these detectors can achieve a misdetection and false alarm of less than 15% on Marion's dataset.", "published": "2020-02-04T12:05:44Z", "authors": ["Othniel J. E. Y. Konan", "Amit Kumar Mishra", "Stefan Lotz"], "categories": ["eess.SP", "cs.LG", "stat.ML"], "doi": "", "_query": "house price prediction machine learning", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2407.05520v1", "title": "A Theory of Machine Learning", "abstract": "We critically review three major theories of machine learning and provide a new theory according to which machines learn a function when the machines successfully compute it. We show that this theory challenges common assumptions in the statistical and the computational learning theories, for it implies that learning true probabilities is equivalent neither to obtaining a correct calculation of the true probabilities nor to obtaining an almost-sure convergence to them. We also briefly discuss some case studies from natural language processing and macroeconomics from the perspective of the new theory.", "published": "2024-07-07T23:57:10Z", "authors": ["Jinsook Kim", "Jinho Kang"], "categories": ["cs.LG", "stat.ML"], "doi": "", "_query": "house price prediction machine learning", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/1901.03678v1", "title": "Machine Learning Automation Toolbox (MLaut)", "abstract": "In this paper we present MLaut (Machine Learning AUtomation Toolbox) for the python data science ecosystem. MLaut automates large-scale evaluation and benchmarking of machine learning algorithms on a large number of datasets. MLaut provides a high-level workflow interface to machine algorithm algorithms, implements a local back-end to a database of dataset collections, trained algorithms, and experimental results, and provides easy-to-use interfaces to the scikit-learn and keras modelling libraries. Experiments are easy to set up with default settings in a few lines of code, while remaining fully customizable to the level of hyper-parameter tuning, pipeline composition, or deep learning architecture. As a principal test case for MLaut, we conducted a large-scale supervised classification study in order to benchmark the performance of a number of machine learning algorithms - to our knowledge also the first larger-scale study on standard supervised learning data sets to include deep learning algorithms. While corroborating a number of previous findings in literature, we found (within the limitations of our study) that deep neural networks do not perform well on basic supervised learning, i.e., outside the more specialized, image-, audio-, or text-based tasks.", "published": "2019-01-11T18:06:05Z", "authors": ["Viktor Kazakov", "Franz J. Király"], "categories": ["cs.LG", "cs.AI", "stat.ML"], "doi": "", "_query": "house price prediction machine learning", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2505.16136v1", "title": "Interpretable Machine Learning for Macro Alpha: A News Sentiment Case Study", "abstract": "This study introduces an interpretable machine learning (ML) framework to extract macroeconomic alpha from global news sentiment. We process the Global Database of Events, Language, and Tone (GDELT) Project's worldwide news feed using FinBERT -- a Bidirectional Encoder Representations from Transformers (BERT) based model pretrained on finance-specific language -- to construct daily sentiment indices incorporating mean tone, dispersion, and event impact. These indices drive an XGBoost classifier, benchmarked against logistic regression, to predict next-day returns for EUR/USD, USD/JPY, and 10-year U.S. Treasury futures (ZN). Rigorous out-of-sample (OOS) backtesting (5-fold expanding-window cross-validation, OOS period: c. 2017-April 2025) demonstrates exceptional, cost-adjusted performance for the XGBoost strategy: Sharpe ratios achieve 5.87 (EUR/USD), 4.65 (USD/JPY), and 4.65 (Treasuries), with respective compound annual growth rates (CAGRs) exceeding 50% in Foreign Exchange (FX) and 22% in bonds. Shapley Additive Explanations (SHAP) affirm that sentiment dispersion and article impact are key predictive features. Our findings establish that integrating domain-specific Natural Language Processing (NLP) with interpretable ML offers a potent and explainable source of macro alpha.", "published": "2025-05-22T02:24:45Z", "authors": ["Yuke Zhang"], "categories": ["q-fin.CP", "cs.AI", "cs.LG", "q-fin.TR"], "doi": "", "_query": "house price prediction machine learning", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2011.01103v1", "title": "Generating Knowledge Graphs by Employing Natural Language Processing and Machine Learning Techniques within the Scholarly Domain", "abstract": "The continuous growth of scientific literature brings innovations and, at the same time, raises new challenges. One of them is related to the fact that its analysis has become difficult due to the high volume of published papers for which manual effort for annotations and management is required. Novel technological infrastructures are needed to help researchers, research policy makers, and companies to time-efficiently browse, analyse, and forecast scientific research. Knowledge graphs i.e., large networks of entities and relationships, have proved to be effective solution in this space. Scientific knowledge graphs focus on the scholarly domain and typically contain metadata describing research publications such as authors, venues, organizations, research topics, and citations. However, the current generation of knowledge graphs lacks of an explicit representation of the knowledge presented in the research papers. As such, in this paper, we present a new architecture that takes advantage of Natural Language Processing and Machine Learning methods for extracting entities and relationships from research publications and integrates them in a large-scale knowledge graph. Within this research work, we i) tackle the challenge of knowledge extraction by employing several state-of-the-art Natural Language Processing and Text Mining tools, ii) describe an approach for integrating entities and relationships generated by these tools, iii) show the advantage of such an hybrid system over alternative approaches, and vi) as a chosen use case, we generated a scientific knowledge graph including 109,105 triples, extracted from 26,827 abstracts of papers within the Semantic Web domain. As our approach is general and can be applied to any domain, we expect that it can facilitate the management, analysis, dissemination, and processing of scientific knowledge.", "published": "2020-10-28T08:31:40Z", "authors": ["Danilo Dessì", "Francesco Osborne", "Diego Reforgiato Recupero", "Davide Buscaldi", "Enrico Motta"], "categories": ["cs.CL", "cs.AI", "cs.LG"], "doi": "10.1016/j.future.2020.10.026", "_query": "house price prediction machine learning", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2004.11794v2", "title": "A Review of Privacy-preserving Federated Learning for the Internet-of-Things", "abstract": "The Internet-of-Things (IoT) generates vast quantities of data, much of it attributable to individuals' activity and behaviour. Gathering personal data and performing machine learning tasks on this data in a central location presents a significant privacy risk to individuals as well as challenges with communicating this data to the cloud. However, analytics based on machine learning and in particular deep learning benefit greatly from large amounts of data to develop high-performance predictive models. This work reviews federated learning as an approach for performing machine learning on distributed data with the goal of protecting the privacy of user-generated data as well as reducing communication costs associated with data transfer. We survey a wide variety of papers covering communication-efficiency, client heterogeneity and privacy preserving methods that are crucial for federated learning in the context of the IoT. Throughout this review, we identify the strengths and weaknesses of different methods applied to federated learning and finally, we outline future directions for privacy preserving federated learning research, particularly focusing on IoT applications.", "published": "2020-04-24T15:27:23Z", "authors": ["Christopher Briggs", "Zhong Fan", "Peter Andras"], "categories": ["cs.LG", "cs.CR", "stat.ML"], "doi": "", "_query": "house price prediction machine learning", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2004.05366v2", "title": "In-Machine-Learning Database: Reimagining Deep Learning with Old-School SQL", "abstract": "In-database machine learning has been very popular, almost being a cliche. However, can we do it the other way around? In this work, we say \"yes\" by applying plain old SQL to deep learning, in a sense implementing deep learning algorithms with SQL. Most deep learning frameworks, as well as generic machine learning ones, share a de facto standard of multidimensional array operations, underneath fancier infrastructure such as automatic differentiation. As SQL tables can be regarded as generalisations of (multi-dimensional) arrays, we have found a way to express common deep learning operations in SQL, encouraging a different way of thinking and thus potentially novel models. In particular, one of the latest trend in deep learning was the introduction of sparsity in the name of graph convolutional networks, whereas we take sparsity almost for granted in the database world. As both databases and machine learning involve transformation of datasets, we hope this work can inspire further works utilizing the large body of existing wisdom, algorithms and technologies in the database field to advance the state of the art in machine learning, rather than merely integerating machine learning into databases.", "published": "2020-04-11T11:00:26Z", "authors": ["Len Du"], "categories": ["cs.LG", "cs.DB", "stat.ML"], "doi": "", "_query": "house price prediction machine learning", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2002.11511v1", "title": "A Comparative Study of Machine Learning Models for Predicting the State of Reactive Mixing", "abstract": "Accurate predictions of reactive mixing are critical for many Earth and environmental science problems. To investigate mixing dynamics over time under different scenarios, a high-fidelity, finite-element-based numerical model is built to solve the fast, irreversible bimolecular reaction-diffusion equations to simulate a range of reactive-mixing scenarios. A total of 2,315 simulations are performed using different sets of model input parameters comprising various spatial scales of vortex structures in the velocity field, time-scales associated with velocity oscillations, the perturbation parameter for the vortex-based velocity, anisotropic dispersion contrast, and molecular diffusion. Outputs comprise concentration profiles of the reactants and products. The inputs and outputs of these simulations are concatenated into feature and label matrices, respectively, to train 20 different machine learning (ML) emulators to approximate system behavior. The 20 ML emulators based on linear methods, Bayesian methods, ensemble learning methods, and multilayer perceptron (MLP), are compared to assess these models. The ML emulators are specifically trained to classify the state of mixing and predict three quantities of interest (QoIs) characterizing species production, decay, and degree of mixing. Linear classifiers and regressors fail to reproduce the QoIs; however, ensemble methods (classifiers and regressors) and the MLP accurately classify the state of reactive mixing and the QoIs. Among ensemble methods, random forest and decision-tree-based AdaBoost faithfully predict the QoIs. At run time, trained ML emulators are $\\approx10^5$ times faster than the high-fidelity numerical simulations. Speed and accuracy of the ensemble and MLP models facilitate uncertainty quantification, which usually requires 1,000s of model run, to estimate the uncertainty bounds on the QoIs.", "published": "2020-02-24T22:50:19Z", "authors": ["B. Ahmmed", "M. K. Mudunuru", "S. Karra", "S. C. James", "V. V. Vesselinov"], "categories": ["stat.ML", "cs.DC", "cs.LG", "math.NA"], "doi": "10.1016/j.jcp.2021.110147", "_query": "house price prediction machine learning", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/1904.07990v2", "title": "Machine learning for early prediction of circulatory failure in the intensive care unit", "abstract": "Intensive care clinicians are presented with large quantities of patient information and measurements from a multitude of monitoring systems. The limited ability of humans to process such complex information hinders physicians to readily recognize and act on early signs of patient deterioration. We used machine learning to develop an early warning system for circulatory failure based on a high-resolution ICU database with 240 patient years of data. This automatic system predicts 90.0% of circulatory failure events (prevalence 3.1%), with 81.8% identified more than two hours in advance, resulting in an area under the receiver operating characteristic curve of 94.0% and area under the precision-recall curve of 63.0%. The model was externally validated in a large independent patient cohort.", "published": "2019-04-16T21:18:17Z", "authors": ["Stephanie L. Hyland", "Martin Faltys", "Matthias Hüser", "Xinrui Lyu", "Thomas Gumbsch", "Cristóbal Esteban", "Christian Bock", "Max Horn", "Michael Moor", "Bastian Rieck", "Marc Zimmermann", "Dean Bodenham", "Karsten Borgwardt", "Gunnar Rätsch", "Tobias M. Merz"], "categories": ["cs.LG", "stat.AP", "stat.ML"], "doi": "", "_query": "house price prediction machine learning", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2311.06397v4", "title": "Predicting Stock Price of Construction Companies using Weighted Ensemble Learning", "abstract": "Modeling the behavior of stock price data has always been one of the challengeous applications of Artificial Intelligence (AI) and Machine Learning (ML) due to its high complexity and dependence on various conditions. Recent studies show that this will be difficult to do with just one learning model. The problem can be more complex for companies of construction section, due to the dependency of their behavior on more conditions. This study aims to provide a hybrid model for improving the accuracy of prediction for stock price index of companies in construction section. The contribution of this paper can be considered as follows: First, a combination of several prediction models is used to predict stock price, so that learning models can cover each other's error. In this research, an ensemble model based on Artificial Neural Network (ANN), Gaussian Process Regression (GPR) and Classification and Regression Tree (CART) is presented for predicting stock price index. Second, the optimization technique is used to determine the effect of each learning model on the prediction result. For this purpose, first all three mentioned algorithms process the data simultaneously and perform the prediction operation. Then, using the Cuckoo Search (CS) algorithm, the output weight of each algorithm is determined as a coefficient. Finally, using the ensemble technique, these results are combined and the final output is generated through weighted averaging on optimal coefficients. The results showed that using CS optimization in the proposed ensemble system is highly effective in reducing prediction error. Comparing the evaluation results of the proposed system with similar algorithms, indicates that our model is more accurate and can be useful for predicting stock price index in real-world scenarios.", "published": "2023-11-10T20:59:56Z", "authors": ["Xinyuan Song"], "categories": ["stat.AP"], "doi": "", "_query": "house price prediction machine learning", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2601.06729v1", "title": "Predicting Student Success with Heterogeneous Graph Deep Learning and Machine Learning Models", "abstract": "Early identification of student success is crucial for enabling timely interventions, reducing dropout rates, and promoting on time graduation. In educational settings, AI powered systems have become essential for predicting student performance due to their advanced analytical capabilities. However, effectively leveraging diverse student data to uncover latent and complex patterns remains a key challenge. While prior studies have explored this area, the potential of dynamic data features and multi category entities has been largely overlooked. To address this gap, we propose a framework that integrates heterogeneous graph deep learning models to enhance early and continuous student performance prediction, using traditional machine learning algorithms for comparison. Our approach employs a graph metapath structure and incorporates dynamic assessment features, which progressively influence the student success prediction task. Experiments on the Open University Learning Analytics (OULA) dataset demonstrate promising results, achieving a 68.6% validation F1 score with only 7% of the semester completed, and reaching up to 89.5% near the semester's end. Our approach outperforms top machine learning models by 4.7% in validation F1 score during the critical early 7% of the semester, underscoring the value of dynamic features and heterogeneous graph representations in student success prediction.", "published": "2026-01-11T00:46:44Z", "authors": ["Anca Muresan", "Mihaela Cardei", "Ionut Cardei"], "categories": ["cs.LG"], "doi": "10.5281/zenodo.15870191", "_query": "house price prediction machine learning", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/1304.5678v1", "title": "Analytic Feature Selection for Support Vector Machines", "abstract": "Support vector machines (SVMs) rely on the inherent geometry of a data set to classify training data. Because of this, we believe SVMs are an excellent candidate to guide the development of an analytic feature selection algorithm, as opposed to the more commonly used heuristic methods. We propose a filter-based feature selection algorithm based on the inherent geometry of a feature set. Through observation, we identified six geometric properties that differ between optimal and suboptimal feature sets, and have statistically significant correlations to classifier performance. Our algorithm is based on logistic and linear regression models using these six geometric properties as predictor variables. The proposed algorithm achieves excellent results on high dimensional text data sets, with features that can be organized into a handful of feature types; for example, unigrams, bigrams or semantic structural features. We believe this algorithm is a novel and effective approach to solving the feature selection problem for linear SVMs.", "published": "2013-04-20T23:38:45Z", "authors": ["Carly Stambaugh", "Hui Yang", "Felix Breuer"], "categories": ["cs.LG", "stat.ML"], "doi": "", "_query": "house price prediction machine learning", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2401.11462v1", "title": "Frost Prediction Using Machine Learning Methods in Fars Province", "abstract": "One of the common hazards and issues in meteorology and agriculture is the problem of frost, chilling or freezing. This event occurs when the minimum ambient temperature falls below a certain value. This phenomenon causes a lot of damage to the country, especially Fars province. Solving this problem requires that, in addition to predicting the minimum temperature, we can provide enough time to implement the necessary measures. Empirical methods have been provided by the Food and Agriculture Organization (FAO), which can predict the minimum temperature, but not in time. In addition to this, we can use machine learning methods to model the minimum temperature. In this study, we have used three methods Gated Recurrent Unit (GRU), Temporal Convolutional Network (TCN) as deep learning methods, and Gradient Boosting (XGBoost). A customized loss function designed for methods based on deep learning, which can be effective in reducing prediction errors. With methods based on deep learning models, not only do we observe a reduction in RMSE error compared to empirical methods but also have more time to predict minimum temperature. Thus, we can model the minimum temperature for the next 24 hours by having the current 24 hours. With the gradient boosting model (XGBoost) we can keep the prediction time as deep learning and RMSE error reduced. Finally, we experimentally concluded that machine learning methods work better than empirical methods and XGBoost model can have better performance in this problem among other implemented.", "published": "2024-01-21T11:02:00Z", "authors": ["Milad Barooni", "Koorush Ziarati", "Ali Barooni"], "categories": ["cs.LG"], "doi": "10.1109/CSICC58665.2023.10105391", "_query": "house price prediction machine learning", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/1407.5528v1", "title": "Arbitrage-free prediction of the implied volatility smile", "abstract": "This paper gives an arbitrage-free prediction for future prices of an arbitrary co-terminal set of options with a given maturity, based on the observed time series of these option prices. The statistical analysis of such a multi-dimensional time series of option prices corresponding to $n$ strikes (with $n$ large, e.g. $n\\geq 40$) and the same maturity, is a difficult task due to the fact that option prices at any moment in time satisfy non-linear and non-explicit no-arbitrage restrictions. Hence any $n$-dimensional time series model also has to satisfy these implicit restrictions at each time step, a condition that is impossible to meet since the model innovations can take arbitrary values. We solve this problem for any $n\\in\\NN$ in the context of Foreign Exchange (FX) by first encoding the option prices at each time step in terms of the parameters of the corresponding risk-neutral measure and then performing the time series analysis in the parameter space. The option price predictions are obtained from the predicted risk-neutral measure by effectively integrating it against the corresponding option payoffs. The non-linear transformation between option prices and the risk-neutral parameters applied here is \\textit{not} arbitrary: it is the standard mapping used by market makers in the FX option markets (the SABR parameterisation) and is given explicitly in closed form. Our method is not restricted to the FX asset class nor does it depend on the type of parameterisation used. Statistical analysis of FX market data illustrates that our arbitrage-free predictions outperform the naive random walk forecasts, suggesting a potential for building management strategies for portfolios of derivative products, akin to the ones widely used in the underlying equity and futures markets.", "published": "2014-07-21T15:29:33Z", "authors": ["Petros Dellaportas", "Aleksandar Mijatović"], "categories": ["q-fin.PR", "q-fin.ST"], "doi": "", "_query": "house price prediction machine learning", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2108.13367v1", "title": "Survival Prediction of Heart Failure Patients using Stacked Ensemble Machine Learning Algorithm", "abstract": "Cardiovascular disease, especially heart failure is one of the major health hazard issues of our time and is a leading cause of death worldwide. Advancement in data mining techniques using machine learning (ML) models is paving promising prediction approaches. Data mining is the process of converting massive volumes of raw data created by the healthcare institutions into meaningful information that can aid in making predictions and crucial decisions. Collecting various follow-up data from patients who have had heart failures, analyzing those data, and utilizing several ML models to predict the survival possibility of cardiovascular patients is the key aim of this study. Due to the imbalance of the classes in the dataset, Synthetic Minority Oversampling Technique (SMOTE) has been implemented. Two unsupervised models (K-Means and Fuzzy C-Means clustering) and three supervised classifiers (Random Forest, XGBoost and Decision Tree) have been used in our study. After thorough investigation, our results demonstrate a superior performance of the supervised ML algorithms over unsupervised models. Moreover, we designed and propose a supervised stacked ensemble learning model that can achieve an accuracy, precision, recall and F1 score of 99.98%. Our study shows that only certain attributes collected from the patients are imperative to successfully predict the surviving possibility post heart failure, using supervised ML algorithms.", "published": "2021-08-30T16:42:27Z", "authors": ["S. M Mehedi Zaman", "Wasay Mahmood Qureshi", "Md. Mohsin Sarker Raihan", "Ocean Monjur", "Abdullah Bin Shams"], "categories": ["cs.LG", "cs.AI", "q-bio.BM", "stat.ML"], "doi": "", "_query": "house price prediction machine learning", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2506.01177v2", "title": "Bridging Quantum and Classical Computing in Drug Design: Architecture Principles for Improved Molecule Generation", "abstract": "Hybrid quantum-classical machine learning offers a path to leverage noisy intermediate-scale quantum (NISQ) devices for drug discovery, but optimal model architectures remain unclear. We systematically optimize the quantum-classical bridge architecture of generative adversarial networks (GANs) for molecule discovery using multi-objective Bayesian optimization. Our optimized model (BO-QGAN) significantly improves performance, achieving a 2.27-fold higher Drug Candidate Score (DCS) than prior quantum-hybrid benchmarks and 2.21-fold higher than the classical baseline, while reducing parameter count by more than 60%. Key findings favor layering multiple (3-4) shallow (4-8 qubit) quantum circuits sequentially, while classical architecture shows less sensitivity above a minimum capacity. This work provides the first empirically-grounded architectural guidelines for hybrid models, enabling more effective integration of current quantum computers into pharmaceutical research pipelines.", "published": "2025-06-01T21:24:43Z", "authors": ["Andrew Smith", "Erhan Guven"], "categories": ["cs.LG", "cs.AI", "q-bio.BM"], "doi": "", "_query": "house price prediction machine learning", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2509.25216v1", "title": "Evaluating Double Descent in Machine Learning: Insights from Tree-Based Models Applied to a Genomic Prediction Task", "abstract": "Classical learning theory describes a well-characterised U-shaped relationship between model complexity and prediction error, reflecting a transition from underfitting in underparameterised regimes to overfitting as complexity grows. Recent work, however, has introduced the notion of a second descent in test error beyond the interpolation threshold-giving rise to the so-called double descent phenomenon. While double descent has been studied extensively in the context of deep learning, it has also been reported in simpler models, including decision trees and gradient boosting. In this work, we revisit these claims through the lens of classical machine learning applied to a biological classification task: predicting isoniazid resistance in Mycobacterium tuberculosis using whole-genome sequencing data. We systematically vary model complexity along two orthogonal axes-learner capacity (e.g., Pleaf, Pboost) and ensemble size (i.e., Pens)-and show that double descent consistently emerges only when complexity is scaled jointly across these axes. When either axis is held fixed, generalisation behaviour reverts to classical U- or L-shaped patterns. These results are replicated on a synthetic benchmark and support the unfolding hypothesis, which attributes double descent to the projection of distinct generalisation regimes onto a single complexity axis. Our findings underscore the importance of treating model complexity as a multidimensional construct when analysing generalisation behaviour. All code and reproducibility materials are available at: https://github.com/guillermocomesanacimadevila/Demystifying-Double-Descent-in-ML.", "published": "2025-09-22T16:41:31Z", "authors": ["Guillermo Comesaña Cimadevila"], "categories": ["cs.LG", "stat.ML"], "doi": "", "_query": "house price prediction machine learning", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2005.10242v10", "title": "Understanding Contrastive Representation Learning through Alignment and Uniformity on the Hypersphere", "abstract": "Contrastive representation learning has been outstandingly successful in practice. In this work, we identify two key properties related to the contrastive loss: (1) alignment (closeness) of features from positive pairs, and (2) uniformity of the induced distribution of the (normalized) features on the hypersphere. We prove that, asymptotically, the contrastive loss optimizes these properties, and analyze their positive effects on downstream tasks. Empirically, we introduce an optimizable metric to quantify each property. Extensive experiments on standard vision and language datasets confirm the strong agreement between both metrics and downstream task performance. Remarkably, directly optimizing for these two metrics leads to representations with comparable or better performance at downstream tasks than contrastive learning. Project Page: https://tongzhouwang.info/hypersphere Code: https://github.com/SsnL/align_uniform , https://github.com/SsnL/moco_align_uniform", "published": "2020-05-20T17:59:57Z", "authors": ["Tongzhou Wang", "Phillip Isola"], "categories": ["cs.LG", "cs.CV", "stat.ML"], "doi": "", "_query": "house price prediction machine learning", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2402.03220v3", "title": "The Benefits of Reusing Batches for Gradient Descent in Two-Layer Networks: Breaking the Curse of Information and Leap Exponents", "abstract": "We investigate the training dynamics of two-layer neural networks when learning multi-index target functions. We focus on multi-pass gradient descent (GD) that reuses the batches multiple times and show that it significantly changes the conclusion about which functions are learnable compared to single-pass gradient descent. In particular, multi-pass GD with finite stepsize is found to overcome the limitations of gradient flow and single-pass GD given by the information exponent (Ben Arous et al., 2021) and leap exponent (Abbe et al., 2023) of the target function. We show that upon re-using batches, the network achieves in just two time steps an overlap with the target subspace even for functions not satisfying the staircase property (Abbe et al., 2021). We characterize the (broad) class of functions efficiently learned in finite time. The proof of our results is based on the analysis of the Dynamical Mean-Field Theory (DMFT). We further provide a closed-form description of the dynamical process of the low-dimensional projections of the weights, and numerical experiments illustrating the theory.", "published": "2024-02-05T17:30:42Z", "authors": ["Yatin Dandi", "Emanuele Troiani", "Luca Arnaboldi", "Luca Pesce", "Lenka Zdeborová", "Florent Krzakala"], "categories": ["stat.ML", "cs.LG"], "doi": "", "_query": "house price prediction machine learning", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/1407.5528v1", "title": "Arbitrage-free prediction of the implied volatility smile", "abstract": "This paper gives an arbitrage-free prediction for future prices of an arbitrary co-terminal set of options with a given maturity, based on the observed time series of these option prices. The statistical analysis of such a multi-dimensional time series of option prices corresponding to $n$ strikes (with $n$ large, e.g. $n\\geq 40$) and the same maturity, is a difficult task due to the fact that option prices at any moment in time satisfy non-linear and non-explicit no-arbitrage restrictions. Hence any $n$-dimensional time series model also has to satisfy these implicit restrictions at each time step, a condition that is impossible to meet since the model innovations can take arbitrary values. We solve this problem for any $n\\in\\NN$ in the context of Foreign Exchange (FX) by first encoding the option prices at each time step in terms of the parameters of the corresponding risk-neutral measure and then performing the time series analysis in the parameter space. The option price predictions are obtained from the predicted risk-neutral measure by effectively integrating it against the corresponding option payoffs. The non-linear transformation between option prices and the risk-neutral parameters applied here is \\textit{not} arbitrary: it is the standard mapping used by market makers in the FX option markets (the SABR parameterisation) and is given explicitly in closed form. Our method is not restricted to the FX asset class nor does it depend on the type of parameterisation used. Statistical analysis of FX market data illustrates that our arbitrage-free predictions outperform the naive random walk forecasts, suggesting a potential for building management strategies for portfolios of derivative products, akin to the ones widely used in the underlying equity and futures markets.", "published": "2014-07-21T15:29:33Z", "authors": ["Petros Dellaportas", "Aleksandar Mijatović"], "categories": ["q-fin.PR", "q-fin.ST"], "doi": "", "_query": "house price prediction machine learning", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2211.01296v1", "title": "Machine Learning for Metasurfaces Design and Their Applications", "abstract": "Metasurfaces (MTSs) are increasingly emerging as enabling technologies to meet the demands for multi-functional, small form-factor, efficient, reconfigurable, tunable, and low-cost radio-frequency (RF) components because of their ability to manipulate waves in a sub-wavelength thickness through modified boundary conditions. They enable the design of reconfigurable intelligent surfaces (RISs) for adaptable wireless channels and smart radio environments, wherein the inherently stochastic nature of the wireless environment is transformed into a programmable propagation channel. In particular, space-limited RF applications, such as communications and radar, that have strict radiation requirements are currently being investigated for potential RIS deployment. The RIS comprises sub-wavelength units or meta-atoms, which are independently controlled and whose geometry and material determine the spectral response of the RIS. Conventionally, designing RIS to yield the desired EM response requires trial and error by iteratively investigating a large possibility of various geometries and materials through thousands of full-wave EM simulations. In this context, machine/deep learning (ML/DL) techniques are proving critical in reducing the computational cost and time of RIS inverse design. Instead of explicitly solving Maxwell's equations, DL models learn physics-based relationships through supervised training data. The ML/DL techniques also aid in RIS deployment for numerous wireless applications, which requires dealing with multiple channel links between the base station (BS) and the users. As a result, the BS and RIS beamformers require a joint design, wherein the RIS elements must be rapidly reconfigured. This chapter provides a synopsis of DL techniques for both inverse RIS design and RIS-assisted wireless systems.", "published": "2022-11-02T17:19:37Z", "authors": ["Kumar Vijay Mishra", "Ahmet M. Elbir", "Amir I. Zaghloul"], "categories": ["physics.app-ph", "eess.SP", "stat.ML"], "doi": "", "_query": "house price prediction machine learning", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/1907.00038v1", "title": "The Practical Challenges of Active Learning: Lessons Learned from Live Experimentation", "abstract": "We tested in a live setting the use of active learning for selecting text sentences for human annotations used in training a Thai segmentation machine learning model. In our study, two concurrent annotated samples were constructed, one through random sampling of sentences from a text corpus, and the other through model-based scoring and ranking of sentences from the same corpus. In the course of the experiment, we observed the effect of significant changes to the learning environment which are likely to occur in real-world learning tasks. We describe how our active learning strategy interacted with these events and discuss other practical challenges encountered in using active learning in the live setting.", "published": "2019-06-28T18:56:20Z", "authors": ["Jean-François Kagy", "Tolga Kayadelen", "Ji Ma", "Afshin Rostamizadeh", "Jana Strnadova"], "categories": ["cs.LG", "stat.ML"], "doi": "", "_query": "house price prediction machine learning", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2105.10148v2", "title": "On Instrumental Variable Regression for Deep Offline Policy Evaluation", "abstract": "We show that the popular reinforcement learning (RL) strategy of estimating the state-action value (Q-function) by minimizing the mean squared Bellman error leads to a regression problem with confounding, the inputs and output noise being correlated. Hence, direct minimization of the Bellman error can result in significantly biased Q-function estimates. We explain why fixing the target Q-network in Deep Q-Networks and Fitted Q Evaluation provides a way of overcoming this confounding, thus shedding new light on this popular but not well understood trick in the deep RL literature. An alternative approach to address confounding is to leverage techniques developed in the causality literature, notably instrumental variables (IV). We bring together here the literature on IV and RL by investigating whether IV approaches can lead to improved Q-function estimates. This paper analyzes and compares a wide range of recent IV methods in the context of offline policy evaluation (OPE), where the goal is to estimate the value of a policy using logged data only. By applying different IV techniques to OPE, we are not only able to recover previously proposed OPE methods such as model-based techniques but also to obtain competitive new techniques. We find empirically that state-of-the-art OPE methods are closely matched in performance by some IV methods such as AGMM, which were not developed for OPE. We open-source all our code and datasets at https://github.com/liyuan9988/IVOPEwithACME.", "published": "2021-05-21T06:22:34Z", "authors": ["Yutian Chen", "Liyuan Xu", "Caglar Gulcehre", "Tom Le Paine", "Arthur Gretton", "Nando de Freitas", "Arnaud Doucet"], "categories": ["cs.LG", "stat.ML"], "doi": "", "_query": "house price prediction machine learning", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2208.11080v2", "title": "SurvSHAP(t): Time-dependent explanations of machine learning survival models", "abstract": "Machine and deep learning survival models demonstrate similar or even improved time-to-event prediction capabilities compared to classical statistical learning methods yet are too complex to be interpreted by humans. Several model-agnostic explanations are available to overcome this issue; however, none directly explain the survival function prediction. In this paper, we introduce SurvSHAP(t), the first time-dependent explanation that allows for interpreting survival black-box models. It is based on SHapley Additive exPlanations with solid theoretical foundations and a broad adoption among machine learning practitioners. The proposed methods aim to enhance precision diagnostics and support domain experts in making decisions. Experiments on synthetic and medical data confirm that SurvSHAP(t) can detect variables with a time-dependent effect, and its aggregation is a better determinant of the importance of variables for a prediction than SurvLIME. SurvSHAP(t) is model-agnostic and can be applied to all models with functional output. We provide an accessible implementation of time-dependent explanations in Python at http://github.com/MI2DataLab/survshap.", "published": "2022-08-23T17:01:14Z", "authors": ["Mateusz Krzyziński", "Mikołaj Spytek", "Hubert Baniecki", "Przemysław Biecek"], "categories": ["cs.LG", "cs.AI", "stat.ML"], "doi": "10.1016/j.knosys.2022.110234", "_query": "house price prediction machine learning", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2010.01062v3", "title": "Exploration in Approximate Hyper-State Space for Meta Reinforcement Learning", "abstract": "To rapidly learn a new task, it is often essential for agents to explore efficiently -- especially when performance matters from the first timestep. One way to learn such behaviour is via meta-learning. Many existing methods however rely on dense rewards for meta-training, and can fail catastrophically if the rewards are sparse. Without a suitable reward signal, the need for exploration during meta-training is exacerbated. To address this, we propose HyperX, which uses novel reward bonuses for meta-training to explore in approximate hyper-state space (where hyper-states represent the environment state and the agent's task belief). We show empirically that HyperX meta-learns better task-exploration and adapts more successfully to new tasks than existing methods.", "published": "2020-10-02T15:43:31Z", "authors": ["Luisa Zintgraf", "Leo Feng", "Cong Lu", "Maximilian Igl", "Kristian Hartikainen", "Katja Hofmann", "Shimon Whiteson"], "categories": ["cs.LG", "cs.AI", "stat.ML"], "doi": "", "_query": "house price prediction machine learning", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/1505.06812v1", "title": "Optimizing Non-decomposable Performance Measures: A Tale of Two Classes", "abstract": "Modern classification problems frequently present mild to severe label imbalance as well as specific requirements on classification characteristics, and require optimizing performance measures that are non-decomposable over the dataset, such as F-measure. Such measures have spurred much interest and pose specific challenges to learning algorithms since their non-additive nature precludes a direct application of well-studied large scale optimization methods such as stochastic gradient descent. In this paper we reveal that for two large families of performance measures that can be expressed as functions of true positive/negative rates, it is indeed possible to implement point stochastic updates. The families we consider are concave and pseudo-linear functions of TPR, TNR which cover several popularly used performance measures such as F-measure, G-mean and H-mean. Our core contribution is an adaptive linearization scheme for these families, using which we develop optimization techniques that enable truly point-based stochastic updates. For concave performance measures we propose SPADE, a stochastic primal dual solver; for pseudo-linear measures we propose STAMP, a stochastic alternate maximization procedure. Both methods have crisp convergence guarantees, demonstrate significant speedups over existing methods - often by an order of magnitude or more, and give similar or more accurate predictions on test data.", "published": "2015-05-26T05:59:33Z", "authors": ["Harikrishna Narasimhan", "Purushottam Kar", "Prateek Jain"], "categories": ["stat.ML", "cs.LG"], "doi": "", "_query": "house price prediction machine learning", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2108.05620v1", "title": "FreaAI: Automated extraction of data slices to test machine learning models", "abstract": "Machine learning (ML) solutions are prevalent. However, many challenges exist in making these solutions business-grade. One major challenge is to ensure that the ML solution provides its expected business value. In order to do that, one has to bridge the gap between the way ML model performance is measured and the solution requirements. In previous work (Barash et al, \"Bridging the gap...\") we demonstrated the effectiveness of utilizing feature models in bridging this gap. Whereas ML performance metrics, such as the accuracy or F1-score of a classifier, typically measure the average ML performance, feature models shed light on explainable data slices that are too far from that average, and therefore might indicate unsatisfied requirements. For example, the overall accuracy of a bank text terms classifier may be very high, say $98\\% \\pm 2\\%$, yet it might perform poorly for terms that include short descriptions and originate from commercial accounts. A business requirement, which may be implicit in the training data, may be to perform well regardless of the type of account and length of the description. Therefore, the under-performing data slice that includes short descriptions and commercial accounts suggests poorly-met requirements. In this paper we show the feasibility of automatically extracting feature models that result in explainable data slices over which the ML solution under-performs. Our novel technique, IBM FreaAI aka FreaAI, extracts such slices from structured ML test data or any other labeled data. We demonstrate that FreaAI can automatically produce explainable and statistically-significant data slices over seven open datasets.", "published": "2021-08-12T09:21:16Z", "authors": ["Samuel Ackerman", "Orna Raz", "Marcel Zalmanovici"], "categories": ["cs.LG", "stat.AP"], "doi": "10.1007/978-3-030-62144-5_6", "_query": "house price prediction machine learning", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2301.01885v2", "title": "Enhancement attacks in biomedical machine learning", "abstract": "The prevalence of machine learning in biomedical research is rapidly growing, yet the trustworthiness of such research is often overlooked. While some previous works have investigated the ability of adversarial attacks to degrade model performance in medical imaging, the ability to falsely improve performance via recently-developed \"enhancement attacks\" may be a greater threat to biomedical machine learning. In the spirit of developing attacks to better understand trustworthiness, we developed two techniques to drastically enhance prediction performance of classifiers with minimal changes to features: 1) general enhancement of prediction performance, and 2) enhancement of a particular method over another. Our enhancement framework falsely improved classifiers' accuracy from 50% to almost 100% while maintaining high feature similarities between original and enhanced data (Pearson's r's>0.99). Similarly, the method-specific enhancement framework was effective in falsely improving the performance of one method over another. For example, a simple neural network outperformed logistic regression by 17% on our enhanced dataset, although no performance differences were present in the original dataset. Crucially, the original and enhanced data were still similar (r=0.99). Our results demonstrate the feasibility of minor data manipulations to achieve any desired prediction performance, which presents an interesting ethical challenge for the future of biomedical machine learning. These findings emphasize the need for more robust data provenance tracking and other precautionary measures to ensure the integrity of biomedical machine learning research.", "published": "2023-01-05T03:03:28Z", "authors": ["Matthew Rosenblatt", "Javid Dadashkarimi", "Dustin Scheinost"], "categories": ["stat.ML", "cs.CR", "cs.LG", "q-bio.NC"], "doi": "", "_query": "house price prediction machine learning", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2112.05000v1", "title": "The Peril of Popular Deep Learning Uncertainty Estimation Methods", "abstract": "Uncertainty estimation (UE) techniques -- such as the Gaussian process (GP), Bayesian neural networks (BNN), Monte Carlo dropout (MCDropout) -- aim to improve the interpretability of machine learning models by assigning an estimated uncertainty value to each of their prediction outputs. However, since too high uncertainty estimates can have fatal consequences in practice, this paper analyzes the above techniques. Firstly, we show that GP methods always yield high uncertainty estimates on out of distribution (OOD) data. Secondly, we show on a 2D toy example that both BNNs and MCDropout do not give high uncertainty estimates on OOD samples. Finally, we show empirically that this pitfall of BNNs and MCDropout holds on real world datasets as well. Our insights (i) raise awareness for the more cautious use of currently popular UE methods in Deep Learning, (ii) encourage the development of UE methods that approximate GP-based methods -- instead of BNNs and MCDropout, and (iii) our empirical setups can be used for verifying the OOD performances of any other UE method. The source code is available at https://github.com/epfml/uncertainity-estimation.", "published": "2021-12-09T15:47:21Z", "authors": ["Yehao Liu", "Matteo Pagliardini", "Tatjana Chavdarova", "Sebastian U. Stich"], "categories": ["cs.LG", "stat.ML"], "doi": "", "_query": "house price prediction machine learning", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/1812.09138v1", "title": "Ecological Data Analysis Based on Machine Learning Algorithms", "abstract": "Classification is an important supervised machine learning method, which is necessary and challenging issue for ecological research. It offers a way to classify a dataset into subsets that share common patterns. Notably, there are many classification algorithms to choose from, each making certain assumptions about the data and about how classification should be formed. In this paper, we applied eight machine learning classification algorithms such as Decision Trees, Random Forest, Artificial Neural Network, Support Vector Machine, Linear Discriminant Analysis, k-nearest neighbors, Logistic Regression and Naive Bayes on ecological data. The goal of this study is to compare different machine learning classification algorithms in ecological dataset. In this analysis we have checked the accuracy test among the algorithms. In our study we conclude that Linear Discriminant Analysis and k-nearest neighbors are the best methods among all other methods", "published": "2018-12-21T14:18:56Z", "authors": ["Md. Siraj-Ud-Doula", "Md. Ashad Alam"], "categories": ["stat.ML", "cs.LG"], "doi": "", "_query": "house price prediction machine learning", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2512.12735v1", "title": "Limits To (Machine) Learning", "abstract": "Machine learning (ML) methods are highly flexible, but their ability to approximate the true data-generating process is fundamentally constrained by finite samples. We characterize a universal lower bound, the Limits-to-Learning Gap (LLG), quantifying the unavoidable discrepancy between a model's empirical fit and the population benchmark. Recovering the true population $R^2$, therefore, requires correcting observed predictive performance by this bound. Using a broad set of variables, including excess returns, yields, credit spreads, and valuation ratios, we find that the implied LLGs are large. This indicates that standard ML approaches can substantially understate true predictability in financial data. We also derive LLG-based refinements to the classic Hansen and Jagannathan (1991) bounds, analyze implications for parameter learning in general-equilibrium settings, and show that the LLG provides a natural mechanism for generating excess volatility.", "published": "2025-12-14T15:18:45Z", "authors": ["Zhimin Chen", "Bryan Kelly", "Semyon Malamud"], "categories": ["stat.ML", "cs.LG"], "doi": "", "_query": "house price prediction machine learning", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2506.15713v1", "title": "An application of machine learning to the motion response prediction of floating assets", "abstract": "The real-time prediction of floating offshore asset behavior under stochastic metocean conditions remains a significant challenge in offshore engineering. While traditional empirical and frequency-domain methods work well in benign conditions, they struggle with both extreme sea states and nonlinear responses. This study presents a supervised machine learning approach using multivariate regression to predict the nonlinear motion response of a turret-moored vessel in 400 m water depth. We developed a machine learning workflow combining a gradient-boosted ensemble method with a custom passive weathervaning solver, trained on approximately $10^6$ samples spanning 100 features. The model achieved mean prediction errors of less than 5% for critical mooring parameters and vessel heading accuracy to within 2.5 degrees across diverse metocean conditions, significantly outperforming traditional frequency-domain methods. The framework has been successfully deployed on an operational facility, demonstrating its efficacy for real-time vessel monitoring and operational decision-making in offshore environments.", "published": "2025-05-31T08:10:12Z", "authors": ["Michael T. M. B. Morris-Thomas", "Marius Martens"], "categories": ["cs.LG", "physics.data-an", "physics.flu-dyn"], "doi": "", "_query": "house price prediction machine learning", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/1905.05715v2", "title": "Machine Learning at Microsoft with ML .NET", "abstract": "Machine Learning is transitioning from an art and science into a technology available to every developer. In the near future, every application on every platform will incorporate trained models to encode data-based decisions that would be impossible for developers to author. This presents a significant engineering challenge, since currently data science and modeling are largely decoupled from standard software development processes. This separation makes incorporating machine learning capabilities inside applications unnecessarily costly and difficult, and furthermore discourage developers from embracing ML in first place. In this paper we present ML .NET, a framework developed at Microsoft over the last decade in response to the challenge of making it easy to ship machine learning models in large software applications. We present its architecture, and illuminate the application demands that shaped it. Specifically, we introduce DataView, the core data abstraction of ML .NET which allows it to capture full predictive pipelines efficiently and consistently across training and inference lifecycles. We close the paper with a surprisingly favorable performance study of ML .NET compared to more recent entrants, and a discussion of some lessons learned.", "published": "2019-05-14T16:43:16Z", "authors": ["Zeeshan Ahmed", "Saeed Amizadeh", "Mikhail Bilenko", "Rogan Carr", "Wei-Sheng Chin", "Yael Dekel", "Xavier Dupre", "Vadim Eksarevskiy", "Eric Erhardt", "Costin Eseanu", "Senja Filipi", "Tom Finley", "Abhishek Goswami", "Monte Hoover", "Scott Inglis", "Matteo Interlandi", "Shon Katzenberger", "Najeeb Kazmi", "Gleb Krivosheev", "Pete Luferenko", "Ivan Matantsev", "Sergiy Matusevych", "Shahab Moradi", "Gani Nazirov", "Justin Ormont", "Gal Oshri", "Artidoro Pagnoni", "Jignesh Parmar", "Prabhat Roy", "Sarthak Shah", "Mohammad Zeeshan Siddiqui", "Markus Weimer", "Shauheen Zahirazami", "Yiwen Zhu"], "categories": ["cs.LG", "stat.ML"], "doi": "10.1145/3292500.3330667", "_query": "house price prediction machine learning", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2512.19184v2", "title": "Operator-Based Generalization Bound for Deep Learning: Insights on Multi-Task Learning", "abstract": "This paper presents novel generalization bounds for vector-valued neural networks and deep kernel methods, focusing on multi-task learning through an operator-theoretic framework. Our key development lies in strategically combining a Koopman based approach with existing techniques, achieving tighter generalization guarantees compared to traditional norm-based bounds. To mitigate computational challenges associated with Koopman-based methods, we introduce sketching techniques applicable to vector valued neural networks. These techniques yield excess risk bounds under generic Lipschitz losses, providing performance guarantees for applications including robust and multiple quantile regression. Furthermore, we propose a novel deep learning framework, deep vector-valued reproducing kernel Hilbert spaces (vvRKHS), leveraging Perron Frobenius (PF) operators to enhance deep kernel methods. We derive a new Rademacher generalization bound for this framework, explicitly addressing underfitting and overfitting through kernel refinement strategies. This work offers novel insights into the generalization properties of multitask learning with deep learning architectures, an area that has been relatively unexplored until recent developments.", "published": "2025-12-22T09:18:30Z", "authors": ["Mahdi Mohammadigohari", "Giuseppe Di Fatta", "Giuseppe Nicosia", "Panos M. Pardalos"], "categories": ["cs.LG", "cs.AI"], "doi": "10.1007/978-3-032-21480-5_9", "_query": "house price prediction machine learning", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2102.05960v1", "title": "Comparative Analysis of Machine Learning Approaches to Analyze and Predict the Covid-19 Outbreak", "abstract": "Background. Forecasting the time of forthcoming pandemic reduces the impact of diseases by taking precautionary steps such as public health messaging and raising the consciousness of doctors. With the continuous and rapid increase in the cumulative incidence of COVID-19, statistical and outbreak prediction models including various machine learning (ML) models are being used by the research community to track and predict the trend of the epidemic, and also in developing appropriate strategies to combat and manage its spread. Methods. In this paper, we present a comparative analysis of various ML approaches including Support Vector Machine, Random Forest, K-Nearest Neighbor and Artificial Neural Network in predicting the COVID-19 outbreak in the epidemiological domain. We first apply the autoregressive distributed lag (ARDL) method to identify and model the short and long-run relationships of the time-series COVID-19 datasets. That is, we determine the lags between a response variable and its respective explanatory time series variables as independent variables. Then, the resulting significant variables concerning their lags are used in the regression model selected by the ARDL for predicting and forecasting the trend of the epidemic. Results. Statistical measures i.e., Root Mean Square Error (RMSE), Mean Absolute Error (MAE) and Mean Absolute Percentage Error (MAPE) are used for model accuracy. The values of MAPE for the best selected models for confirmed, recovered and deaths cases are 0.407, 0.094 and 0.124 respectively, which falls under the category of highly accurate forecasts. In addition, we computed fifteen days ahead forecast for the daily deaths, recover, and confirm patients and the cases fluctuated across time in all aspects. Besides, the results reveal the advantages of ML algorithms for supporting decision making of evolving short term policies.", "published": "2021-02-11T11:57:33Z", "authors": ["Muhammad Naeem", "Jian Yu", "Muhammad Aamir", "Sajjad Ahmad Khan", "Olayinka Adeleye", "Zardad Khan"], "categories": ["stat.ML", "cs.AI", "cs.LG"], "doi": "", "_query": "house price prediction machine learning", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2006.09529v1", "title": "Quality Management of Machine Learning Systems", "abstract": "In the past decade, Artificial Intelligence (AI) has become a part of our daily lives due to major advances in Machine Learning (ML) techniques. In spite of an explosive growth in the raw AI technology and in consumer facing applications on the internet, its adoption in business applications has conspicuously lagged behind. For business/mission-critical systems, serious concerns about reliability and maintainability of AI applications remain. Due to the statistical nature of the output, software 'defects' are not well defined. Consequently, many traditional quality management techniques such as program debugging, static code analysis, functional testing, etc. have to be reevaluated. Beyond the correctness of an AI model, many other new quality attributes, such as fairness, robustness, explainability, transparency, etc. become important in delivering an AI system. The purpose of this paper is to present a view of a holistic quality management framework for ML applications based on the current advances and identify new areas of software engineering research to achieve a more trustworthy AI.", "published": "2020-06-16T21:34:44Z", "authors": ["P. Santhanam"], "categories": ["cs.SE", "cs.AI"], "doi": "", "_query": "house price prediction machine learning", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/1904.05961v3", "title": "Robust Coreset Construction for Distributed Machine Learning", "abstract": "Coreset, which is a summary of the original dataset in the form of a small weighted set in the same sample space, provides a promising approach to enable machine learning over distributed data. Although viewed as a proxy of the original dataset, each coreset is only designed to approximate the cost function of a specific machine learning problem, and thus different coresets are often required to solve different machine learning problems, increasing the communication overhead. We resolve this dilemma by developing robust coreset construction algorithms that can support a variety of machine learning problems. Motivated by empirical evidence that suitably-weighted k-clustering centers provide a robust coreset, we harden the observation by establishing theoretical conditions under which the coreset provides a guaranteed approximation for a broad range of machine learning problems, and developing both centralized and distributed algorithms to generate coresets satisfying the conditions. The robustness of the proposed algorithms is verified through extensive experiments on diverse datasets with respect to both supervised and unsupervised learning problems.", "published": "2019-04-11T21:39:10Z", "authors": ["Hanlin Lu", "Ming-Ju Li", "Ting He", "Shiqiang Wang", "Vijaykrishnan Narayanan", "Kevin S Chan"], "categories": ["cs.LG", "cs.DS", "stat.ML"], "doi": "", "_query": "house price prediction machine learning", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/1405.4980v2", "title": "Convex Optimization: Algorithms and Complexity", "abstract": "This monograph presents the main complexity theorems in convex optimization and their corresponding algorithms. Starting from the fundamental theory of black-box optimization, the material progresses towards recent advances in structural optimization and stochastic optimization. Our presentation of black-box optimization, strongly influenced by Nesterov's seminal book and Nemirovski's lecture notes, includes the analysis of cutting plane methods, as well as (accelerated) gradient descent schemes. We also pay special attention to non-Euclidean settings (relevant algorithms include Frank-Wolfe, mirror descent, and dual averaging) and discuss their relevance in machine learning. We provide a gentle introduction to structural optimization with FISTA (to optimize a sum of a smooth and a simple non-smooth term), saddle-point mirror prox (Nemirovski's alternative to Nesterov's smoothing), and a concise description of interior point methods. In stochastic optimization we discuss stochastic gradient descent, mini-batches, random coordinate descent, and sublinear algorithms. We also briefly touch upon convex relaxation of combinatorial problems and the use of randomness to round solutions, as well as random walks based methods.", "published": "2014-05-20T07:50:56Z", "authors": ["Sébastien Bubeck"], "categories": ["math.OC", "cs.CC", "cs.LG", "math.NA", "stat.ML"], "doi": "", "_query": "house price prediction machine learning", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2403.17637v3", "title": "PeersimGym: An Environment for Solving the Task Offloading Problem with Reinforcement Learning", "abstract": "Task offloading, crucial for balancing computational loads across devices in networks such as the Internet of Things, poses significant optimization challenges, including minimizing latency and energy usage under strict communication and storage constraints. While traditional optimization falls short in scalability; and heuristic approaches lack in achieving optimal outcomes, Reinforcement Learning (RL) offers a promising avenue by enabling the learning of optimal offloading strategies through iterative interactions. However, the efficacy of RL hinges on access to rich datasets and custom-tailored, realistic training environments. To address this, we introduce PeersimGym, an open-source, customizable simulation environment tailored for developing and optimizing task offloading strategies within computational networks. PeersimGym supports a wide range of network topologies and computational constraints and integrates a \\textit{PettingZoo}-based interface for RL agent deployment in both solo and multi-agent setups. Furthermore, we demonstrate the utility of the environment through experiments with Deep Reinforcement Learning agents, showcasing the potential of RL-based approaches to significantly enhance offloading strategies in distributed computing settings. PeersimGym thus bridges the gap between theoretical RL models and their practical applications, paving the way for advancements in efficient task offloading methodologies.", "published": "2024-03-26T12:12:44Z", "authors": ["Frederico Metelo", "Stevo Racković", "Pedro Ákos Costa", "Cláudia Soares"], "categories": ["cs.LG", "cs.AI"], "doi": "10.1007/978-3-031-70378-2_3", "_query": "house price prediction machine learning", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2104.12516v1", "title": "Evaluating the performance of personal, social, health-related, biomarker and genetic data for predicting an individuals future health using machine learning: A longitudinal analysis", "abstract": "As we gain access to a greater depth and range of health-related information about individuals, three questions arise: (1) Can we build better models to predict individual-level risk of ill health? (2) How much data do we need to effectively predict ill health? (3) Are new methods required to process the added complexity that new forms of data bring? The aim of the study is to apply a machine learning approach to identify the relative contribution of personal, social, health-related, biomarker and genetic data as predictors of future health in individuals. Using longitudinal data from 6830 individuals in the UK from Understanding Society (2010-12 to 2015-17), the study compares the predictive performance of five types of measures: personal (e.g. age, sex), social (e.g. occupation, education), health-related (e.g. body weight, grip strength), biomarker (e.g. cholesterol, hormones) and genetic single nucleotide polymorphisms (SNPs). The predicted outcome variable was limiting long-term illness one and five years from baseline. Two machine learning approaches were used to build predictive models: deep learning via neural networks and XGBoost (gradient boosting decision trees). Model fit was compared to traditional logistic regression models. Results found that health-related measures had the strongest prediction of future health status, with genetic data performing poorly. Machine learning models only offered marginal improvements in model accuracy when compared to logistic regression models, but also performed well on other metrics e.g. neural networks were best on AUC and XGBoost on precision. The study suggests that increasing complexity of data and methods does not necessarily translate to improved understanding of the determinants of health or performance of predictive models of ill health.", "published": "2021-04-26T12:31:40Z", "authors": ["Mark Green"], "categories": ["stat.ML", "cs.LG", "stat.AP"], "doi": "", "_query": "house price prediction machine learning", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/1810.09965v1", "title": "Using Deep Learning for price prediction by exploiting stationary limit order book features", "abstract": "The recent surge in Deep Learning (DL) research of the past decade has successfully provided solutions to many difficult problems. The field of quantitative analysis has been slowly adapting the new methods to its problems, but due to problems such as the non-stationary nature of financial data, significant challenges must be overcome before DL is fully utilized. In this work a new method to construct stationary features, that allows DL models to be applied effectively, is proposed. These features are thoroughly tested on the task of predicting mid price movements of the Limit Order Book. Several DL models are evaluated, such as recurrent Long Short Term Memory (LSTM) networks and Convolutional Neural Networks (CNN). Finally a novel model that combines the ability of CNNs to extract useful features and the ability of LSTMs' to analyze time series, is proposed and evaluated. The combined model is able to outperform the individual LSTM and CNN models in the prediction horizons that are tested.", "published": "2018-10-23T16:53:03Z", "authors": ["Avraam Tsantekidis", "Nikolaos Passalis", "Anastasios Tefas", "Juho Kanniainen", "Moncef Gabbouj", "Alexandros Iosifidis"], "categories": ["cs.LG", "q-fin.ST", "stat.ML"], "doi": "", "_query": "house price prediction machine learning", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/1910.09356v1", "title": "Supervised Machine Learning based Ensemble Model for Accurate Prediction of Type 2 Diabetes", "abstract": "According to the American Diabetes Association(ADA), 30.3 million people in the United States have diabetes, but only 7.2 million may be undiagnosed and unaware of their condition. Type 2 diabetes is usually diagnosed for most patients later on in life whereas the less common Type 1 diabetes is diagnosed early on in life. People can live healthy and happy lives while living with diabetes, but early detection produces a better overall outcome on most patient's health. Thus, to test the accurate prediction of Type 2 diabetes, we use the patients' information from an electronic health records company called Practice Fusion, which has about 10,000 patient records from 2009 to 2012. This data contains individual key biometrics, including age, diastolic and systolic blood pressure, gender, height, and weight. We use this data on popular machine learning algorithms and for each algorithm, we evaluate the performance of every model based on their classification accuracy, precision, sensitivity, specificity/recall, negative predictive value, and F1 score. In our study, we find that all algorithms other than Naive Bayes suffered from very low precision. Hence, we take a step further and incorporate all the algorithms into a weighted average or soft voting ensemble model where each algorithm will count towards a majority vote towards the decision outcome of whether a patient has diabetes or not. The accuracy of the Ensemble model on Practice Fusion is 85\\%, by far our ensemble approach is new in this space. We firmly believe that the weighted average ensemble model not only performed well in overall metrics but also helped to recover wrong predictions and aid in accurate prediction of Type 2 diabetes. Our accurate novel model can be used as an alert for the patients to seek medical evaluation in time.", "published": "2019-10-18T01:30:25Z", "authors": ["Ramya Akula", "Ni Nguyen", "Ivan Garibay"], "categories": ["cs.LG", "stat.ML"], "doi": "", "_query": "house price prediction machine learning", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/1903.09296v1", "title": "Patient Clustering Improves Efficiency of Federated Machine Learning to predict mortality and hospital stay time using distributed Electronic Medical Records", "abstract": "Electronic medical records (EMRs) supports the development of machine learning algorithms for predicting disease incidence, patient response to treatment, and other healthcare events. But insofar most algorithms have been centralized, taking little account of the decentralized, non-identically independently distributed (non-IID), and privacy-sensitive characteristics of EMRs that can complicate data collection, sharing and learning. To address this challenge, we introduced a community-based federated machine learning (CBFL) algorithm and evaluated it on non-IID ICU EMRs. Our algorithm clustered the distributed data into clinically meaningful communities that captured similar diagnoses and geological locations, and learnt one model for each community. Throughout the learning process, the data was kept local on hospitals, while locally-computed results were aggregated on a server. Evaluation results show that CBFL outperformed the baseline FL algorithm in terms of Area Under the Receiver Operating Characteristic Curve (ROC AUC), Area Under the Precision-Recall Curve (PR AUC), and communication cost between hospitals and the server. Furthermore, communities' performance difference could be explained by how dissimilar one community was to others.", "published": "2019-03-22T01:49:30Z", "authors": ["Li Huang", "Dianbo Liu"], "categories": ["cs.LG", "stat.ML"], "doi": "", "_query": "house price prediction machine learning", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2006.14709v3", "title": "The Gaussian equivalence of generative models for learning with shallow neural networks", "abstract": "Understanding the impact of data structure on the computational tractability of learning is a key challenge for the theory of neural networks. Many theoretical works do not explicitly model training data, or assume that inputs are drawn component-wise independently from some simple probability distribution. Here, we go beyond this simple paradigm by studying the performance of neural networks trained on data drawn from pre-trained generative models. This is possible due to a Gaussian equivalence stating that the key metrics of interest, such as the training and test errors, can be fully captured by an appropriately chosen Gaussian model. We provide three strands of rigorous, analytical and numerical evidence corroborating this equivalence. First, we establish rigorous conditions for the Gaussian equivalence to hold in the case of single-layer generative models, as well as deterministic rates for convergence in distribution. Second, we leverage this equivalence to derive a closed set of equations describing the generalisation performance of two widely studied machine learning problems: two-layer neural networks trained using one-pass stochastic gradient descent, and full-batch pre-learned features or kernel methods. Finally, we perform experiments demonstrating how our theory applies to deep, pre-trained generative models. These results open a viable path to the theoretical study of machine learning models with realistic data.", "published": "2020-06-25T21:20:09Z", "authors": ["Sebastian Goldt", "Bruno Loureiro", "Galen Reeves", "Florent Krzakala", "Marc Mézard", "Lenka Zdeborová"], "categories": ["stat.ML", "cond-mat.dis-nn", "cond-mat.stat-mech", "cs.LG"], "doi": "", "_query": "house price prediction machine learning", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/1603.09035v1", "title": "Towards Geo-Distributed Machine Learning", "abstract": "Latency to end-users and regulatory requirements push large companies to build data centers all around the world. The resulting data is \"born\" geographically distributed. On the other hand, many machine learning applications require a global view of such data in order to achieve the best results. These types of applications form a new class of learning problems, which we call Geo-Distributed Machine Learning (GDML). Such applications need to cope with: 1) scarce and expensive cross-data center bandwidth, and 2) growing privacy concerns that are pushing for stricter data sovereignty regulations. Current solutions to learning from geo-distributed data sources revolve around the idea of first centralizing the data in one data center, and then training locally. As machine learning algorithms are communication-intensive, the cost of centralizing the data is thought to be offset by the lower cost of intra-data center communication during training. In this work, we show that the current centralized practice can be far from optimal, and propose a system for doing geo-distributed training. Furthermore, we argue that the geo-distributed approach is structurally more amenable to dealing with regulatory constraints, as raw data never leaves the source data center. Our empirical evaluation on three real datasets confirms the general validity of our approach, and shows that GDML is not only possible but also advisable in many scenarios.", "published": "2016-03-30T04:05:29Z", "authors": ["Ignacio Cano", "Markus Weimer", "Dhruv Mahajan", "Carlo Curino", "Giovanni Matteo Fumarola"], "categories": ["cs.LG", "cs.DC", "stat.ML"], "doi": "", "_query": "house price prediction machine learning", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2303.07875v1", "title": "Solar Power Prediction Using Machine Learning", "abstract": "This paper presents a machine learning-based approach for predicting solar power generation with high accuracy using a 99% AUC (Area Under the Curve) metric. The approach includes data collection, pre-processing, feature selection, model selection, training, evaluation, and deployment. High-quality data from multiple sources, including weather data, solar irradiance data, and historical solar power generation data, are collected and pre-processed to remove outliers, handle missing values, and normalize the data. Relevant features such as temperature, humidity, wind speed, and solar irradiance are selected for model training. Support Vector Machines (SVM), Random Forest, and Gradient Boosting are used as machine learning algorithms to produce accurate predictions. The models are trained on a large dataset of historical solar power generation data and other relevant features. The performance of the models is evaluated using AUC and other metrics such as precision, recall, and F1-score. The trained machine learning models are then deployed in a production environment, where they can be used to make real-time predictions about solar power generation. The results show that the proposed approach achieves a 99% AUC for solar power generation prediction, which can help energy companies better manage their solar power systems, reduce costs, and improve energy efficiency.", "published": "2023-03-11T06:31:46Z", "authors": ["E. Subramanian", "M. Mithun Karthik", "G Prem Krishna", "D. Vaisnav Prasath", "V. Sukesh Kumar"], "categories": ["cs.LG"], "doi": "", "_query": "house price prediction machine learning", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/1512.08836v2", "title": "Learning to Filter with Predictive State Inference Machines", "abstract": "Latent state space models are a fundamental and widely used tool for modeling dynamical systems. However, they are difficult to learn from data and learned models often lack performance guarantees on inference tasks such as filtering and prediction. In this work, we present the PREDICTIVE STATE INFERENCE MACHINE (PSIM), a data-driven method that considers the inference procedure on a dynamical system as a composition of predictors. The key idea is that rather than first learning a latent state space model, and then using the learned model for inference, PSIM directly learns predictors for inference in predictive state space. We provide theoretical guarantees for inference, in both realizable and agnostic settings, and showcase practical performance on a variety of simulated and real world robotics benchmarks.", "published": "2015-12-30T03:17:00Z", "authors": ["Wen Sun", "Arun Venkatraman", "Byron Boots", "J. Andrew Bagnell"], "categories": ["cs.LG"], "doi": "", "_query": "house price prediction machine learning", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2007.04391v1", "title": "A Critical Evaluation of Open-World Machine Learning", "abstract": "Open-world machine learning (ML) combines closed-world models trained on in-distribution data with out-of-distribution (OOD) detectors, which aim to detect and reject OOD inputs. Previous works on open-world ML systems usually fail to test their reliability under diverse, and possibly adversarial conditions. Therefore, in this paper, we seek to understand how resilient are state-of-the-art open-world ML systems to changes in system components? With our evaluation across 6 OOD detectors, we find that the choice of in-distribution data, model architecture and OOD data have a strong impact on OOD detection performance, inducing false positive rates in excess of $70\\%$. We further show that OOD inputs with 22 unintentional corruptions or adversarial perturbations render open-world ML systems unusable with false positive rates of up to $100\\%$. To increase the resilience of open-world ML, we combine robust classifiers with OOD detection techniques and uncover a new trade-off between OOD detection and robustness.", "published": "2020-07-08T19:40:07Z", "authors": ["Liwei Song", "Vikash Sehwag", "Arjun Nitin Bhagoji", "Prateek Mittal"], "categories": ["cs.LG", "cs.CR"], "doi": "", "_query": "house price prediction machine learning", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2301.11695v3", "title": "LegendreTron: Uprising Proper Multiclass Loss Learning", "abstract": "Loss functions serve as the foundation of supervised learning and are often chosen prior to model development. To avoid potentially ad hoc choices of losses, statistical decision theory describes a desirable property for losses known as \\emph{properness}, which asserts that Bayes' rule is optimal. Recent works have sought to \\emph{learn losses} and models jointly. Existing methods do this by fitting an inverse canonical link function which monotonically maps $\\mathbb{R}$ to $[0,1]$ to estimate probabilities for binary problems. In this paper, we extend monotonicity to maps between $\\mathbb{R}^{C-1}$ and the projected probability simplex $\\tildeΔ^{C-1}$ by using monotonicity of gradients of convex functions. We present {\\sc LegendreTron} as a novel and practical method that jointly learns \\emph{proper canonical losses} and probabilities for multiclass problems. Tested on a benchmark of domains with up to 1,000 classes, our experimental results show that our method consistently outperforms the natural multiclass baseline under a $t$-test at 99% significance on all datasets with greater than 10 classes.", "published": "2023-01-27T13:10:45Z", "authors": ["Kevin Lam", "Christian Walder", "Spiridon Penev", "Richard Nock"], "categories": ["stat.ML", "cs.LG"], "doi": "", "_query": "house price prediction machine learning", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/1912.04661v1", "title": "Adaptive Dynamic Model Averaging with an Application to House Price Forecasting", "abstract": "Dynamic model averaging (DMA) combines the forecasts of a large number of dynamic linear models (DLMs) to predict the future value of a time series. The performance of DMA critically depends on the appropriate choice of two forgetting factors. The first of these controls the speed of adaptation of the coefficient vector of each DLM, while the second enables time variation in the model averaging stage. In this paper we develop a novel, adaptive dynamic model averaging (ADMA) methodology. The proposed methodology employs a stochastic optimisation algorithm that sequentially updates the forgetting factor of each DLM, and uses a state-of-the-art non-parametric model combination algorithm from the prediction with expert advice literature, which offers finite-time performance guarantees. An empirical application to quarterly UK house price data suggests that ADMA produces more accurate forecasts than the benchmark autoregressive model, as well as competing DMA specifications.", "published": "2019-12-10T12:03:58Z", "authors": ["Alisa Yusupova", "Nicos G. Pavlidis", "Efthymios G. Pavlidis"], "categories": ["econ.EM", "stat.ME"], "doi": "", "_query": "house price prediction machine learning", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2212.01415v1", "title": "Measuring Competency of Machine Learning Systems and Enforcing Reliability", "abstract": "We explore the impact of environmental conditions on the competency of machine learning agents and how real-time competency assessments improve the reliability of ML agents. We learn a representation of conditions which impact the strategies and performance of the ML agent enabling determination of actions the agent can make to maintain operator expectations in the case of a convolutional neural network that leverages visual imagery to aid in the obstacle avoidance task of a simulated self-driving vehicle.", "published": "2022-12-02T19:37:37Z", "authors": ["M. Planer", "J. M. Sierchio", "for BAE Systems"], "categories": ["cs.LG"], "doi": "", "_query": "house price prediction machine learning", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/1206.6485v1", "title": "Greedy Algorithms for Sparse Reinforcement Learning", "abstract": "Feature selection and regularization are becoming increasingly prominent tools in the efforts of the reinforcement learning (RL) community to expand the reach and applicability of RL. One approach to the problem of feature selection is to impose a sparsity-inducing form of regularization on the learning method. Recent work on $L_1$ regularization has adapted techniques from the supervised learning literature for use with RL. Another approach that has received renewed attention in the supervised learning community is that of using a simple algorithm that greedily adds new features. Such algorithms have many of the good properties of the $L_1$ regularization methods, while also being extremely efficient and, in some cases, allowing theoretical guarantees on recovery of the true form of a sparse target function from sampled data. This paper considers variants of orthogonal matching pursuit (OMP) applied to reinforcement learning. The resulting algorithms are analyzed and compared experimentally with existing $L_1$ regularized approaches. We demonstrate that perhaps the most natural scenario in which one might hope to achieve sparse recovery fails; however, one variant, OMP-BRM, provides promising theoretical guarantees under certain assumptions on the feature dictionary. Another variant, OMP-TD, empirically outperforms prior methods both in approximation accuracy and efficiency on several benchmark problems.", "published": "2012-06-27T19:59:59Z", "authors": ["Christopher Painter-Wakefield", "Ronald Parr"], "categories": ["cs.LG", "stat.ML"], "doi": "", "_query": "house price prediction machine learning", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2206.14987v2", "title": "Lookback for Learning to Branch", "abstract": "The expressive and computationally inexpensive bipartite Graph Neural Networks (GNN) have been shown to be an important component of deep learning based Mixed-Integer Linear Program (MILP) solvers. Recent works have demonstrated the effectiveness of such GNNs in replacing the branching (variable selection) heuristic in branch-and-bound (B&B) solvers. These GNNs are trained, offline and on a collection of MILPs, to imitate a very good but computationally expensive branching heuristic, strong branching. Given that B&B results in a tree of sub-MILPs, we ask (a) whether there are strong dependencies exhibited by the target heuristic among the neighboring nodes of the B&B tree, and (b) if so, whether we can incorporate them in our training procedure. Specifically, we find that with the strong branching heuristic, a child node's best choice was often the parent's second-best choice. We call this the \"lookback\" phenomenon. Surprisingly, the typical branching GNN of Gasse et al. (2019) often misses this simple \"answer\". To imitate the target behavior more closely by incorporating the lookback phenomenon in GNNs, we propose two methods: (a) target smoothing for the standard cross-entropy loss function, and (b) adding a Parent-as-Target (PAT) Lookback regularizer term. Finally, we propose a model selection framework to incorporate harder-to-formulate objectives such as solving time in the final models. Through extensive experimentation on standard benchmark instances, we show that our proposal results in up to 22% decrease in the size of the B&B tree and up to 15% improvement in the solving times.", "published": "2022-06-30T02:33:32Z", "authors": ["Prateek Gupta", "Elias B. Khalil", "Didier Chetélat", "Maxime Gasse", "Yoshua Bengio", "Andrea Lodi", "M. Pawan Kumar"], "categories": ["cs.LG", "math.OC", "stat.ML"], "doi": "", "_query": "house price prediction machine learning", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2112.05332v1", "title": "Dispersive qubit readout with machine learning", "abstract": "Open quantum systems can undergo dissipative phase transitions, and their critical behavior can be exploited in sensing applications. For example, it can be used to enhance the fidelity of superconducting qubit readout measurements, a central problem toward the creation of reliable quantum hardware. A recently introduced measurement protocol, named ``critical parametric quantum sensing'', uses the parametric (two-photon driven) Kerr resonator's driven-dissipative phase transition to reach single-qubit detection fidelity of 99.9\\% [arXiv:2107.04503]. In this work, we improve upon the previous protocol by using machine learning-based classification algorithms to \\textit{efficiently and rapidly} extract information from this critical dynamics, which has so far been neglected to focus only on stationary properties. These classification algorithms are applied to the time series data of weak quantum measurements (homodyne detection) of a circuit-QED implementation of the Kerr resonator coupled to a superconducting qubit. This demonstrates how machine learning methods enable a faster and more reliable measurement protocol in critical open quantum systems.", "published": "2021-12-10T04:25:43Z", "authors": ["Enrico Rinaldi", "Roberto Di Candia", "Simone Felicetti", "Fabrizio Minganti"], "categories": ["quant-ph"], "doi": "", "_query": "house price prediction machine learning", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2407.20158v3", "title": "Machine Learning for Predicting Chaotic Systems", "abstract": "Predicting chaotic dynamical systems is critical in many scientific fields, such as weather forecasting, but challenging due to the characteristic sensitive dependence on initial conditions. Traditional modeling approaches require extensive domain knowledge, often leading to a shift towards data-driven methods using machine learning. However, existing research provides inconclusive results on which machine learning methods are best suited for predicting chaotic systems. In this paper, we compare different lightweight and heavyweight machine learning architectures using extensive existing benchmark databases, as well as a newly introduced database that allows for uncertainty quantification in the benchmark results. In addition to state-of-the-art methods from the literature, we also present new advantageous variants of established methods. Hyperparameter tuning is adjusted based on computational cost, with more tuning allocated to less costly methods. Furthermore, we introduce the cumulative maximum error, a novel metric that combines desirable properties of traditional metrics and is tailored for chaotic systems. Our results show that well-tuned simple methods, as well as untuned baseline methods, often outperform state-of-the-art deep learning models, but their performance can vary significantly with different experimental setups. These findings highlight the importance of aligning prediction methods with data characteristics and caution against the indiscriminate use of overly complex models.", "published": "2024-07-29T16:34:47Z", "authors": ["Christof Schötz", "Alistair White", "Maximilian Gelbrecht", "Niklas Boers"], "categories": ["cs.LG", "nlin.CD"], "doi": "", "_query": "house price prediction machine learning", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/1901.09613v1", "title": "Hybrid Machine Learning Approach to Popularity Prediction of Newly Released Contents for Online Video Streaming Service", "abstract": "In the industry of video content providers such as VOD and IPTV, predicting the popularity of video contents in advance is critical not only from a marketing perspective but also from a network optimization perspective. By predicting whether the content will be successful or not in advance, the content file, which is large, is efficiently deployed in the proper service providing server, leading to network cost optimization. Many previous studies have done view count prediction research to do this. However, the studies have been making predictions based on historical view count data from users. In this case, the contents had been published to the users and already deployed on a service server. These approaches make possible to efficiently deploy a content already published but are impossible to use for a content that is not be published. To address the problems, this research proposes a hybrid machine learning approach to the classification model for the popularity prediction of newly video contents which is not published. In this paper, we create a new variable based on the related content of the specific content and divide entire dataset by the characteristics of the contents. Next, the prediction is performed using XGBoosting and deep neural net based model according to the data characteristics of the cluster. Our model uses metadata for contents for prediction, so we use categorical embedding techniques to solve the sparsity of categorical variables and make them learn efficiently for the deep neural net model. As well, we use the FTRL-proximal algorithm to solve the problem of the view-count volatility of video content. We achieve overall better performance than the previous standalone method with a dataset from one of the top streaming service company.", "published": "2019-01-28T11:42:34Z", "authors": ["Hongjun Jeon", "Wonchul Seo", "Eunjeong Lucy Park", "Sungchul Choi"], "categories": ["cs.LG", "cs.IR", "stat.ML"], "doi": "", "_query": "house price prediction machine learning", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2007.05872v1", "title": "Is Machine Learning Speaking my Language? A Critical Look at the NLP-Pipeline Across 8 Human Languages", "abstract": "Natural Language Processing (NLP) is increasingly used as a key ingredient in critical decision-making systems such as resume parsers used in sorting a list of job candidates. NLP systems often ingest large corpora of human text, attempting to learn from past human behavior and decisions in order to produce systems that will make recommendations about our future world. Over 7000 human languages are being spoken today and the typical NLP pipeline underrepresents speakers of most of them while amplifying the voices of speakers of other languages. In this paper, a team including speakers of 8 languages - English, Chinese, Urdu, Farsi, Arabic, French, Spanish, and Wolof - takes a critical look at the typical NLP pipeline and how even when a language is technically supported, substantial caveats remain to prevent full participation. Despite huge and admirable investments in multilingual support in many tools and resources, we are still making NLP-guided decisions that systematically and dramatically underrepresent the voices of much of the world.", "published": "2020-07-11T22:56:37Z", "authors": ["Esma Wali", "Yan Chen", "Christopher Mahoney", "Thomas Middleton", "Marzieh Babaeianjelodar", "Mariama Njie", "Jeanna Neefe Matthews"], "categories": ["cs.CL", "cs.LG"], "doi": "", "_query": "house price prediction machine learning", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2011.02832v2", "title": "Pitfalls in Machine Learning Research: Reexamining the Development Cycle", "abstract": "Machine learning has the potential to fuel further advances in data science, but it is greatly hindered by an ad hoc design process, poor data hygiene, and a lack of statistical rigor in model evaluation. Recently, these issues have begun to attract more attention as they have caused public and embarrassing issues in research and development. Drawing from our experience as machine learning researchers, we follow the machine learning process from algorithm design to data collection to model evaluation, drawing attention to common pitfalls and providing practical recommendations for improvements. At each step, case studies are introduced to highlight how these pitfalls occur in practice, and where things could be improved.", "published": "2020-11-04T17:58:18Z", "authors": ["Stella Biderman", "Walter J. Scheirer"], "categories": ["cs.LG", "stat.ME", "stat.ML"], "doi": "", "_query": "house price prediction machine learning", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2301.09042v1", "title": "The Shape of Explanations: A Topological Account of Rule-Based Explanations in Machine Learning", "abstract": "Rule-based explanations provide simple reasons explaining the behavior of machine learning classifiers at given points in the feature space. Several recent methods (Anchors, LORE, etc.) purport to generate rule-based explanations for arbitrary or black-box classifiers. But what makes these methods work in general? We introduce a topological framework for rule-based explanation methods and provide a characterization of explainability in terms of the definability of a classifier relative to an explanation scheme. We employ this framework to consider various explanation schemes and argue that the preferred scheme depends on how much the user knows about the domain and the probability measure over the feature space.", "published": "2023-01-22T02:58:00Z", "authors": ["Brett Mullins"], "categories": ["cs.LG"], "doi": "", "_query": "house price prediction machine learning", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2306.14070v2", "title": "SuperBench: A Super-Resolution Benchmark Dataset for Scientific Machine Learning", "abstract": "Super-resolution (SR) techniques aim to enhance data resolution, enabling the retrieval of finer details, and improving the overall quality and fidelity of the data representation. There is growing interest in applying SR methods to complex spatiotemporal systems within the Scientific Machine Learning (SciML) community, with the hope of accelerating numerical simulations and/or improving forecasts in weather, climate, and related areas. However, the lack of standardized benchmark datasets for comparing and validating SR methods hinders progress and adoption in SciML. To address this, we introduce SuperBench, the first benchmark dataset featuring high-resolution datasets, including data from fluid flows, cosmology, and weather. Here, we focus on validating spatial SR performance from data-centric and physics-preserved perspectives, as well as assessing robustness to data degradation tasks. While deep learning-based SR methods (developed in the computer vision community) excel on certain tasks, despite relatively limited prior physics information, we identify limitations of these methods in accurately capturing intricate fine-scale features and preserving fundamental physical properties and constraints in scientific data. These shortcomings highlight the importance and subtlety of incorporating domain knowledge into ML models. We anticipate that SuperBench will help to advance SR methods for science.", "published": "2023-06-24T22:39:33Z", "authors": ["Pu Ren", "N. Benjamin Erichson", "Junyi Guo", "Shashank Subramanian", "Omer San", "Zarija Lukic", "Michael W. Mahoney"], "categories": ["cs.CV", "eess.IV", "physics.comp-ph"], "doi": "", "_query": "house price prediction machine learning", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2007.03051v1", "title": "Carbontracker: Tracking and Predicting the Carbon Footprint of Training Deep Learning Models", "abstract": "Deep learning (DL) can achieve impressive results across a wide variety of tasks, but this often comes at the cost of training models for extensive periods on specialized hardware accelerators. This energy-intensive workload has seen immense growth in recent years. Machine learning (ML) may become a significant contributor to climate change if this exponential trend continues. If practitioners are aware of their energy and carbon footprint, then they may actively take steps to reduce it whenever possible. In this work, we present Carbontracker, a tool for tracking and predicting the energy and carbon footprint of training DL models. We propose that energy and carbon footprint of model development and training is reported alongside performance metrics using tools like Carbontracker. We hope this will promote responsible computing in ML and encourage research into energy-efficient deep neural networks.", "published": "2020-07-06T20:24:31Z", "authors": ["Lasse F. Wolff Anthony", "Benjamin Kanding", "Raghavendra Selvan"], "categories": ["cs.CY", "cs.LG", "eess.SP", "stat.ML"], "doi": "", "_query": "house price prediction machine learning", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/1902.04699v2", "title": "Differential Description Length for Hyperparameter Selection in Machine Learning", "abstract": "This paper introduces a new method for model selection and more generally hyperparameter selection in machine learning. Minimum description length (MDL) is an established method for model selection, which is however not directly aimed at minimizing generalization error, which is often the primary goal in machine learning. The paper demonstrates a relationship between generalization error and a difference of description lengths of the training data; we call this difference differential description length (DDL). This allows prediction of generalization error from the training data alone by performing encoding of the training data. DDL can then be used for model selection by choosing the model with the smallest predicted generalization error. We show how this method can be used for linear regression and neural networks and deep learning. Experimental results show that DDL leads to smaller generalization error than cross-validation and traditional MDL and Bayes methods.", "published": "2019-02-13T01:48:58Z", "authors": ["Mojtaba Abolfazli", "Anders Host-Madsen", "June Zhang"], "categories": ["cs.LG", "cs.IT", "stat.ML"], "doi": "", "_query": "house price prediction machine learning", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2609.07897v1", "title": "The Accuracy Paradox: Empirical Diagnostic of Default Decision Thresholds in Multi-Label Enzyme Commission Prediction [With Code]", "abstract": "Automated prediction of Enzyme Commission (EC) numbers plays a central role in functional annotation and computational drug discovery. However, standard multi-label machine learning pipelines frequently rely on default decision thresholds (t=0.50), assuming balanced prior distributions across target heads. In this study, we present a systematic empirical diagnostic of uncalibrated fixed decision boundaries operating under severe class imbalance across N = 14,096 annotated compounds categorized into six primary EC classes (EC1-EC6). Our results highlight a pronounced Accuracy Paradox: while the multi-label system achieves a deceivingly high mean accuracy of 77.16%, the macro F1-score (0.3976) and macro recall (0.3872) reveal severe predictive breakdown. Majority target classes suffer from hyper-sensitivity and over-prediction, whereas minority classes exhibit sharp recall decay, culminating in a total decision boundary collapse for EC6 (Recall = 0.00%) despite underlying discriminative power (ROC-AUC = 0.5857). Feature correlation analysis further reveals high linear redundancy among topological indices relative to fingerprint density metrics. Ultimately, this diagnostic study demonstrates that standard point predictions mask critical errors in bioinformatics workflows. We establish target-specific threshold optimization and post-hoc conformal calibration as essential, open-source post-processing safeguards for reliable applied machine learning and deep learning architectures.", "published": "2026-09-07T19:00:17Z", "authors": ["Bilal Ahmad", "Rajed Mehmood"], "categories": ["cs.LG", "cs.AI", "q-bio.QM"], "doi": "", "_query": "house price prediction machine learning", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2609.16155v1", "title": "LLMs as Master Forgers: Generating Synthetic Time Series Data for Manufacturing", "abstract": "This paper presents a novel framework leveraging Large Language Models (LLMs) to generate synthetic time series data for manufacturing processes. Motivated by the scarcity of labeled time-series data in real-world manufacturing settings, which hinders the development of robust machine learning models, we explore the potential of LLMs to learn complex temporal dependencies and generate realistic synthetic data. Our approach involves fine-tuning pre-trained LLMs on manufacturing process instructions and employing a Retrieval Augmented Generation (RAG) technique to enhance data diversity and realism. We evaluate our method against traditional time series modeling techniques like ARIMA and LSTMs, using quantitative metrics, PCA analysis, and downstream task performance (anomaly detection). Results demonstrate that our LLM-driven framework outperforms these baselines, generating high-quality synthetic time series data that effectively captures temporal dependencies and statistical properties of real manufacturing data, leading to improvements in downstream task performance.", "published": "2026-09-14T18:01:29Z", "authors": ["Mantek Singh", "Jeshwanth Challagundla", "Prateek Karnal", "Gagan Ganapathy", "Vineet Shah", "Ridam Arora"], "categories": ["cs.LG", "cs.AI"], "doi": "10.1109/ICICML63543.2024.10958017", "_query": "house price prediction machine learning", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2002.08053v3", "title": "Progressive Identification of True Labels for Partial-Label Learning", "abstract": "Partial-label learning (PLL) is a typical weakly supervised learning problem, where each training instance is equipped with a set of candidate labels among which only one is the true label. Most existing methods elaborately designed learning objectives as constrained optimizations that must be solved in specific manners, making their computational complexity a bottleneck for scaling up to big data. The goal of this paper is to propose a novel framework of PLL with flexibility on the model and optimization algorithm. More specifically, we propose a novel estimator of the classification risk, theoretically analyze the classifier-consistency, and establish an estimation error bound. Then we propose a progressive identification algorithm for approximately minimizing the proposed risk estimator, where the update of the model and identification of true labels are conducted in a seamless manner. The resulting algorithm is model-independent and loss-independent, and compatible with stochastic optimization. Thorough experiments demonstrate it sets the new state of the art.", "published": "2020-02-19T08:35:15Z", "authors": ["Jiaqi Lv", "Miao Xu", "Lei Feng", "Gang Niu", "Xin Geng", "Masashi Sugiyama"], "categories": ["cs.LG", "stat.ML"], "doi": "", "_query": "house price prediction machine learning", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2605.11638v1", "title": "Learning U-Statistics with Active Inference", "abstract": "$U$-statistics play a central role in statistical inference. In many modern applications, however, acquiring the labels required for $U$-statistics is costly. Motivated by recent advances in active inference, we develop an active inference framework for $U$-statistics that selectively queries informative labels to improve estimation efficiency under a fixed labeling budget, while preserving valid statistical inference. Our approach is built on the augmented inverse probability weighting $U$-statistic, which is designed to incorporate the sampling rule and machine learning predictions. We characterize the optimal sampling rule that minimizes its variance and design practical sampling strategies. We further extend the framework to $U$-statistic-based empirical risk minimization. Experiments on real datasets demonstrate substantial gains in estimation efficiency over baseline methods, while maintaining target coverage.", "published": "2026-05-12T06:59:36Z", "authors": ["Xiaoning Wang", "Yuyang Huo", "Liuhua Peng", "Changliang Zou"], "categories": ["stat.ML", "cs.LG"], "doi": "", "_query": "house price prediction machine learning", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2602.16600v1", "title": "Predicting The Cop Number Using Machine Learning", "abstract": "Cops and Robbers is a pursuit evasion game played on a graph, first introduced independently by Quilliot \\cite{quilliot1978jeux} and Nowakowski and Winkler \\cite{NOWAKOWSKI1983235} over four decades ago. A main interest in recent the literature is identifying the cop number of graph families. The cop number of a graph, $c(G)$, is defined as the minimum number of cops required to guarantee capture of the robber. Determining the cop number is computationally difficult and exact algorithms for this are typically restricted to small graph families. This paper investigates whether classical machine learning methods and graph neural networks can accurately predict a graph's cop number from its structural properties and identify which properties most strongly influence this prediction. Of the classical machine learning models, tree-based models achieve high accuracy in prediction despite class imbalance, whereas graph neural networks achieve comparable results without explicit feature engineering. The interpretability analysis shows that the most predictive features are related to node connectivity, clustering, clique structure, and width parameters, which aligns with known theoretical results. Our findings suggest that machine learning approaches can be used in complement with existing cop number algorithms by offering scalable approximations where computation is infeasible.", "published": "2026-02-18T16:52:46Z", "authors": ["Meagan Mann", "Christian Muise", "Erin Meger"], "categories": ["cs.LG"], "doi": "", "_query": "house price prediction machine learning", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2006.05604v1", "title": "Machine Learning and Control Theory", "abstract": "We survey in this article the connections between Machine Learning and Control Theory. Control Theory provide useful concepts and tools for Machine Learning. Conversely Machine Learning can be used to solve large control problems. In the first part of the paper, we develop the connections between reinforcement learning and Markov Decision Processes, which are discrete time control problems. In the second part, we review the concept of supervised learning and the relation with static optimization. Deep learning which extends supervised learning, can be viewed as a control problem. In the third part, we present the links between stochastic gradient descent and mean-field theory. Conversely, in the fourth and fifth parts, we review machine learning approaches to stochastic control problems, and focus on the deterministic case, to explain, more easily, the numerical algorithms.", "published": "2020-06-10T01:47:34Z", "authors": ["Alain Bensoussan", "Yiqun Li", "Dinh Phan Cao Nguyen", "Minh-Binh Tran", "Sheung Chi Phillip Yam", "Xiang Zhou"], "categories": ["cs.LG", "math.OC", "stat.ML"], "doi": "", "_query": "house price prediction machine learning", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/1905.12588v2", "title": "Meta-Learning Representations for Continual Learning", "abstract": "A continual learning agent should be able to build on top of existing knowledge to learn on new data quickly while minimizing forgetting. Current intelligent systems based on neural network function approximators arguably do the opposite---they are highly prone to forgetting and rarely trained to facilitate future learning. One reason for this poor behavior is that they learn from a representation that is not explicitly trained for these two goals. In this paper, we propose OML, an objective that directly minimizes catastrophic interference by learning representations that accelerate future learning and are robust to forgetting under online updates in continual learning. We show that it is possible to learn naturally sparse representations that are more effective for online updating. Moreover, our algorithm is complementary to existing continual learning strategies, such as MER and GEM. Finally, we demonstrate that a basic online updating strategy on representations learned by OML is competitive with rehearsal based methods for continual learning. We release an implementation of our method at https://github.com/khurramjaved96/mrcl .", "published": "2019-05-29T17:09:31Z", "authors": ["Khurram Javed", "Martha White"], "categories": ["cs.LG", "cs.AI", "stat.ML"], "doi": "", "_query": "house price prediction machine learning", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/1901.11303v3", "title": "Hyperbox based machine learning algorithms: A comprehensive survey", "abstract": "With the rapid development of digital information, the data volume generated by humans and machines is growing exponentially. Along with this trend, machine learning algorithms have been formed and evolved continuously to discover new information and knowledge from different data sources. Learning algorithms using hyperboxes as fundamental representational and building blocks are a branch of machine learning methods. These algorithms have enormous potential for high scalability and online adaptation of predictors built using hyperbox data representations to the dynamically changing environments and streaming data. This paper aims to give a comprehensive survey of literature on hyperbox-based machine learning models. In general, according to the architecture and characteristic features of the resulting models, the existing hyperbox-based learning algorithms may be grouped into three major categories: fuzzy min-max neural networks, hyperbox-based hybrid models, and other algorithms based on hyperbox representations. Within each of these groups, this paper shows a brief description of the structure of models, associated learning algorithms, and an analysis of their advantages and drawbacks. Main applications of these hyperbox-based models to the real-world problems are also described in this paper. Finally, we discuss some open problems and identify potential future research directions in this field.", "published": "2019-01-31T11:10:29Z", "authors": ["Thanh Tung Khuat", "Dymitr Ruta", "Bogdan Gabrys"], "categories": ["cs.LG", "stat.ML"], "doi": "", "_query": "house price prediction machine learning", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2609.13919v1", "title": "Machine Learning in Fish Farming", "abstract": "This chapter explores how machine learning (ML) is transforming aquaculture, with a particular focus on enhancing decision-making processes and improving operational efficiency. The chapter is structured to first introduce the challenges in aquaculture and the role of AI and then provide an overview of ML techniques in the context of aquaculture, followed by applications, emerging trends, future directions, and case studies. The focus is on real-world applications of ML techniques, including Random Forest, Convolutional Neural Networks (CNNs), and Recurrent Neural Networks (RNNs), as well as emerging technologies such as Graph Neural Networks (GNNs) and large language models (LLMs). Key applications include biomass estimation, species recognition, behavioural analysis, and environmental forecasting. The chapter also highlights the synergy between ML and the Internet of Things (IoT) for real-time monitoring and decision support. Ultimately, ML-driven innovations have the potential to revolutionise fish farming, leading to more efficient, sustainable, and productive practices in the aquaculture industry.", "published": "2026-09-12T13:05:16Z", "authors": ["Fearghal O'Donncha", "Nikos Papandroulakis", "Jennie Korus", "Abigail Langbridge", "Alexander Timms", "Konstantinos Topouzelis", "Abdul Baseer Khan", "Shree Rama Kamal Kumar Vegu", "Mahtab Sarvmaili", "Ryan Mowat", "Rhanna Turberville", "Tyler Sclodnick", "Christopher Whidden"], "categories": ["cs.LG"], "doi": "10.1007/978-3-032-10362-8_3", "_query": "house price prediction machine learning", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/1605.01116v1", "title": "An evaluation of randomized machine learning methods for redundant data: Predicting short and medium-term suicide risk from administrative records and risk assessments", "abstract": "Accurate prediction of suicide risk in mental health patients remains an open problem. Existing methods including clinician judgments have acceptable sensitivity, but yield many false positives. Exploiting administrative data has a great potential, but the data has high dimensionality and redundancies in the recording processes. We investigate the efficacy of three most effective randomized machine learning techniques random forests, gradient boosting machines, and deep neural nets with dropout in predicting suicide risk. Using a cohort of mental health patients from a regional Australian hospital, we compare the predictive performance with popular traditional approaches clinician judgments based on a checklist, sparse logistic regression and decision trees. The randomized methods demonstrated robustness against data redundancies and superior predictive performance on AUC and F-measure.", "published": "2016-05-03T23:46:48Z", "authors": ["Thuong Nguyen", "Truyen Tran", "Shivapratap Gopakumar", "Dinh Phung", "Svetha Venkatesh"], "categories": ["stat.ML", "cs.LG"], "doi": "", "_query": "house price prediction machine learning", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2212.11729v1", "title": "Federated Learning -- Methods, Applications and beyond", "abstract": "In recent years the applications of machine learning models have increased rapidly, due to the large amount of available data and technological progress.While some domains like web analysis can benefit from this with only minor restrictions, other fields like in medicine with patient data are strongerregulated. In particular \\emph{data privacy} plays an important role as recently highlighted by the trustworthy AI initiative of the EU or general privacy regulations in legislation. Another major challenge is, that the required training \\emph{data is} often \\emph{distributed} in terms of features or samples and unavailable for classicalbatch learning approaches. In 2016 Google came up with a framework, called \\emph{Federated Learning} to solve both of these problems. We provide a brief overview on existing Methods and Applications in the field of vertical and horizontal \\emph{Federated Learning}, as well as \\emph{Fderated Transfer Learning}.", "published": "2022-12-22T14:25:44Z", "authors": ["Moritz Heusinger", "Christoph Raab", "Fabrice Rossi", "Frank-Michael Schleif"], "categories": ["cs.LG", "stat.ML"], "doi": "10.14428/esann/2021.ES2021-4", "_query": "house price prediction machine learning", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/1909.13340v2", "title": "Learning Everywhere: A Taxonomy for the Integration of Machine Learning and Simulations", "abstract": "We present a taxonomy of research on Machine Learning (ML) applied to enhance simulations together with a catalog of some activities. We cover eight patterns for the link of ML to the simulations or systems plus three algorithmic areas: particle dynamics, agent-based models and partial differential equations. The patterns are further divided into three action areas: Improving simulation with Configurations and Integration of Data, Learn Structure, Theory and Model for Simulation, and Learn to make Surrogates.", "published": "2019-09-29T18:52:30Z", "authors": ["Geoffrey Fox", "Shantenu Jha"], "categories": ["cs.LG", "stat.ML"], "doi": "", "_query": "house price prediction machine learning", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2209.14991v3", "title": "Machine learning and invariant theory", "abstract": "Inspired by constraints from physical law, equivariant machine learning restricts the learning to a hypothesis class where all the functions are equivariant with respect to some group action. Irreducible representations or invariant theory are typically used to parameterize the space of such functions. In this article, we introduce the topic and explain a couple of methods to explicitly parameterize equivariant functions that are being used in machine learning applications. In particular, we explicate a general procedure, attributed to Malgrange, to express all polynomial maps between linear spaces that are equivariant under the action of a group $G$, given a characterization of the invariant polynomials on a bigger space. The method also parametrizes smooth equivariant maps in the case that $G$ is a compact Lie group.", "published": "2022-09-29T17:52:17Z", "authors": ["Ben Blum-Smith", "Soledad Villar"], "categories": ["stat.ML", "cs.LG"], "doi": "10.1090/noti2760", "_query": "house price prediction machine learning", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/1911.08603v1", "title": "Forbidden knowledge in machine learning -- Reflections on the limits of research and publication", "abstract": "Certain research strands can yield \"forbidden knowledge\". This term refers to knowledge that is considered too sensitive, dangerous or taboo to be produced or shared. Discourses about such publication restrictions are already entrenched in scientific fields like IT security, synthetic biology or nuclear physics research. This paper makes the case for transferring this discourse to machine learning research. Some machine learning applications can very easily be misused and unfold harmful consequences, for instance with regard to generative video or text synthesis, personality analysis, behavior manipulation, software vulnerability detection and the like. Up to now, the machine learning research community embraces the idea of open access. However, this is opposed to precautionary efforts to prevent the malicious use of machine learning applications. Information about or from such applications may, if improperly disclosed, cause harm to people, organizations or whole societies. Hence, the goal of this work is to outline norms that can help to decide whether and when the dissemination of such information should be prevented. It proposes review parameters for the machine learning community to establish an ethical framework on how to deal with forbidden knowledge and dual-use applications.", "published": "2019-11-19T21:43:06Z", "authors": ["Thilo Hagendorff"], "categories": ["cs.LG", "cs.AI", "cs.CY", "stat.ML"], "doi": "10.1007/s00146-020-01045-4", "_query": "house price prediction machine learning", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2303.04156v1", "title": "Computing with Categories in Machine Learning", "abstract": "Category theory has been successfully applied in various domains of science, shedding light on universal principles unifying diverse phenomena and thereby enabling knowledge transfer between them. Applications to machine learning have been pursued recently, and yet there is still a gap between abstract mathematical foundations and concrete applications to machine learning tasks. In this paper we introduce DisCoPyro as a categorical structure learning framework, which combines categorical structures (such as symmetric monoidal categories and operads) with amortized variational inference, and can be applied, e.g., in program learning for variational autoencoders. We provide both mathematical foundations and concrete applications together with comparison of experimental performance with other models (e.g., neuro-symbolic models). We speculate that DisCoPyro could ultimately contribute to the development of artificial general intelligence.", "published": "2023-03-07T17:26:18Z", "authors": ["Eli Sennesh", "Tom Xu", "Yoshihiro Maruyama"], "categories": ["cs.LG", "cs.AI", "math.CT", "stat.ML"], "doi": "", "_query": "house price prediction machine learning", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2405.08917v1", "title": "Feature Importance and Explainability in Quantum Machine Learning", "abstract": "Many Machine Learning (ML) models are referred to as black box models, providing no real insights into why a prediction is made. Feature importance and explainability are important for increasing transparency and trust in ML models, particularly in settings such as healthcare and finance. With quantum computing's unique capabilities, such as leveraging quantum mechanical phenomena like superposition, which can be combined with ML techniques to create the field of Quantum Machine Learning (QML), and such techniques may be applied to QML models. This article explores feature importance and explainability insights in QML compared to Classical ML models. Utilizing the widely recognized Iris dataset, classical ML algorithms such as SVM and Random Forests, are compared against hybrid quantum counterparts, implemented via IBM's Qiskit platform: the Variational Quantum Classifier (VQC) and Quantum Support Vector Classifier (QSVC). This article aims to provide a comparison of the insights generated in ML by employing permutation and leave one out feature importance methods, alongside ALE (Accumulated Local Effects) and SHAP (SHapley Additive exPlanations) explainers.", "published": "2024-05-14T19:12:32Z", "authors": ["Luke Power", "Krishnendu Guha"], "categories": ["cs.LG", "math.QA", "stat.ML"], "doi": "", "_query": "house price prediction machine learning", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2201.06610v1", "title": "A Brief Survey of Machine Learning Methods for Emotion Prediction using Physiological Data", "abstract": "Emotion prediction is a key emerging research area that focuses on identifying and forecasting the emotional state of a human from multiple modalities. Among other data sources, physiological data can serve as an indicator for emotions with an added advantage that it cannot be masked/tampered by the individual and can be easily collected. This paper surveys multiple machine learning methods that deploy smartphone and physiological data to predict emotions in real-time, using self-reported ecological momentary assessments (EMA) scores as ground-truth. Comparing regression, long short-term memory (LSTM) networks, convolutional neural networks (CNN), reinforcement online learning (ROL), and deep belief networks (DBN), we showcase the variability of machine learning methods employed to achieve accurate emotion prediction. We compare the state-of-the-art methods and highlight that experimental performance is still not very good. The performance can be improved in future works by considering the following issues: improving scalability and generalizability, synchronizing multimodal data, optimizing EMA sampling, integrating adaptability with sequence prediction, collecting unbiased data, and leveraging sophisticated feature engineering techniques.", "published": "2022-01-17T19:46:12Z", "authors": ["Maryam Khalid", "Emily Willis"], "categories": ["cs.LG"], "doi": "", "_query": "house price prediction machine learning", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/1812.02240v1", "title": "Efficient and Robust Machine Learning for Real-World Systems", "abstract": "While machine learning is traditionally a resource intensive task, embedded systems, autonomous navigation and the vision of the Internet-of-Things fuel the interest in resource efficient approaches. These approaches require a carefully chosen trade-off between performance and resource consumption in terms of computation and energy. On top of this, it is crucial to treat uncertainty in a consistent manner in all but the simplest applications of machine learning systems. In particular, a desideratum for any real-world system is to be robust in the presence of outliers and corrupted data, as well as being `aware' of its limits, i.e.\\ the system should maintain and provide an uncertainty estimate over its own predictions. These complex demands are among the major challenges in current machine learning research and key to ensure a smooth transition of machine learning technology into every day's applications. In this article, we provide an overview of the current state of the art of machine learning techniques facilitating these real-world requirements. First we provide a comprehensive review of resource-efficiency in deep neural networks with focus on techniques for model size reduction, compression and reduced precision. These techniques can be applied during training or as post-processing and are widely used to reduce both computational complexity and memory footprint. As most (practical) neural networks are limited in their ways to treat uncertainty, we contrast them with probabilistic graphical models, which readily serve these desiderata by means of probabilistic inference. In that way, we provide an extensive overview of the current state-of-the-art of robust and efficient machine learning for real-world systems.", "published": "2018-12-05T21:47:23Z", "authors": ["Franz Pernkopf", "Wolfgang Roth", "Matthias Zoehrer", "Lukas Pfeifenberger", "Guenther Schindler", "Holger Froening", "Sebastian Tschiatschek", "Robert Peharz", "Matthew Mattina", "Zoubin Ghahramani"], "categories": ["cs.LG", "stat.ML"], "doi": "", "_query": "house price prediction machine learning", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2012.00152v1", "title": "Every Model Learned by Gradient Descent Is Approximately a Kernel Machine", "abstract": "Deep learning's successes are often attributed to its ability to automatically discover new representations of the data, rather than relying on handcrafted features like other learning methods. We show, however, that deep networks learned by the standard gradient descent algorithm are in fact mathematically approximately equivalent to kernel machines, a learning method that simply memorizes the data and uses it directly for prediction via a similarity function (the kernel). This greatly enhances the interpretability of deep network weights, by elucidating that they are effectively a superposition of the training examples. The network architecture incorporates knowledge of the target function into the kernel. This improved understanding should lead to better learning algorithms.", "published": "2020-11-30T23:02:47Z", "authors": ["Pedro Domingos"], "categories": ["cs.LG", "cs.NE", "stat.ML"], "doi": "", "_query": "house price prediction machine learning", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/1805.07507v1", "title": "Reconciled Polynomial Machine: A Unified Representation of Shallow and Deep Learning Models", "abstract": "In this paper, we aim at introducing a new machine learning model, namely reconciled polynomial machine, which can provide a unified representation of existing shallow and deep machine learning models. Reconciled polynomial machine predicts the output by computing the inner product of the feature kernel function and variable reconciling function. Analysis of several concrete models, including Linear Models, FM, MVM, Perceptron, MLP and Deep Neural Networks, will be provided in this paper, which can all be reduced to the reconciled polynomial machine representations. Detailed analysis of the learning error by these models will also be illustrated in this paper based on their reduced representations from the function approximation perspective.", "published": "2018-05-19T03:40:52Z", "authors": ["Jiawei Zhang", "Limeng Cui", "Fisher B. Gouza"], "categories": ["cs.LG", "cs.NE", "stat.ML"], "doi": "", "_query": "house price prediction machine learning", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2410.01259v1", "title": "Revisiting Optimism and Model Complexity in the Wake of Overparameterized Machine Learning", "abstract": "Common practice in modern machine learning involves fitting a large number of parameters relative to the number of observations. These overparameterized models can exhibit surprising generalization behavior, e.g., ``double descent'' in the prediction error curve when plotted against the raw number of model parameters, or another simplistic notion of complexity. In this paper, we revisit model complexity from first principles, by first reinterpreting and then extending the classical statistical concept of (effective) degrees of freedom. Whereas the classical definition is connected to fixed-X prediction error (in which prediction error is defined by averaging over the same, nonrandom covariate points as those used during training), our extension of degrees of freedom is connected to random-X prediction error (in which prediction error is averaged over a new, random sample from the covariate distribution). The random-X setting more naturally embodies modern machine learning problems, where highly complex models, even those complex enough to interpolate the training data, can still lead to desirable generalization performance under appropriate conditions. We demonstrate the utility of our proposed complexity measures through a mix of conceptual arguments, theory, and experiments, and illustrate how they can be used to interpret and compare arbitrary prediction models.", "published": "2024-10-02T06:09:57Z", "authors": ["Pratik Patil", "Jin-Hong Du", "Ryan J. Tibshirani"], "categories": ["stat.ML", "cs.LG", "math.ST"], "doi": "", "_query": "house price prediction machine learning", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/1811.11668v1", "title": "Racial categories in machine learning", "abstract": "Controversies around race and machine learning have sparked debate among computer scientists over how to design machine learning systems that guarantee fairness. These debates rarely engage with how racial identity is embedded in our social experience, making for sociological and psychological complexity. This complexity challenges the paradigm of considering fairness to be a formal property of supervised learning with respect to protected personal attributes. Racial identity is not simply a personal subjective quality. For people labeled \"Black\" it is an ascribed political category that has consequences for social differentiation embedded in systemic patterns of social inequality achieved through both social and spatial segregation. In the United States, racial classification can best be understood as a system of inherently unequal status categories that places whites as the most privileged category while signifying the Negro/black category as stigmatized. Social stigma is reinforced through the unequal distribution of societal rewards and goods along racial lines that is reinforced by state, corporate, and civic institutions and practices. This creates a dilemma for society and designers: be blind to racial group disparities and thereby reify racialized social inequality by no longer measuring systemic inequality, or be conscious of racial categories in a way that itself reifies race. We propose a third option. By preceding group fairness interventions with unsupervised learning to dynamically detect patterns of segregation, machine learning systems can mitigate the root cause of social disparities, social segregation and stratification, without further anchoring status categories of disadvantage.", "published": "2018-11-28T16:47:36Z", "authors": ["Sebastian Benthall", "Bruce D. Haynes"], "categories": ["cs.LG", "stat.ML"], "doi": "10.1145/3287560.3287575", "_query": "house price prediction machine learning", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/1807.04162v3", "title": "TherML: Thermodynamics of Machine Learning", "abstract": "In this work we offer a framework for reasoning about a wide class of existing objectives in machine learning. We develop a formal correspondence between this work and thermodynamics and discuss its implications.", "published": "2018-07-11T14:39:17Z", "authors": ["Alexander A. Alemi", "Ian Fischer"], "categories": ["cs.LG", "cond-mat.stat-mech", "stat.ML"], "doi": "", "_query": "house price prediction machine learning", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/1809.02869v3", "title": "Machine Teaching of Active Sequential Learners", "abstract": "Machine teaching addresses the problem of finding the best training data that can guide a learning algorithm to a target model with minimal effort. In conventional settings, a teacher provides data that are consistent with the true data distribution. However, for sequential learners which actively choose their queries, such as multi-armed bandits and active learners, the teacher can only provide responses to the learner's queries, not design the full data. In this setting, consistent teachers can be sub-optimal for finite horizons. We formulate this sequential teaching problem, which current techniques in machine teaching do not address, as a Markov decision process, with the dynamics nesting a model of the learner and the actions being the teacher's responses. Furthermore, we address the complementary problem of learning from a teacher that plans: to recognise the teaching intent of the responses, the learner is endowed with a model of the teacher. We test the formulation with multi-armed bandit learners in simulated experiments and a user study. The results show that learning is improved by (i) planning teaching and (ii) the learner having a model of the teacher. The approach gives tools to taking into account strategic (planning) behaviour of users of interactive intelligent systems, such as recommendation engines, by considering them as boundedly optimal teachers.", "published": "2018-09-08T20:39:31Z", "authors": ["Tomi Peltola", "Mustafa Mert Çelikok", "Pedram Daee", "Samuel Kaski"], "categories": ["cs.LG", "cs.AI", "cs.HC", "stat.ML"], "doi": "", "_query": "house price prediction machine learning", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2604.09635v1", "title": "Leveraging Machine Learning Techniques to Investigate Media and Information Literacy Competence in Tackling Disinformation", "abstract": "This study develops machine learning models to assess Media and Information Literacy (MIL) skills specifically in the context of disinformation among students, particularly future educators and communicators. While the digital revolution has expanded access to information, it has also amplified the spread of false and misleading content, making MIL essential for fostering critical thinking and responsible media engagement. Despite its relevance, predictive modeling of MIL in relation to disinformation remains underexplored. To address this gap, a quantitative study was conducted with 723 students in education and communication programs using a validated survey. Classification and regression algorithms were applied to predict MIL competencies and identify key influencing factors. Results show that complex models outperform simpler approaches, with variables such as academic year and prior training significantly improving prediction accuracy. These findings can inform the design of targeted educational interventions and personalized strategies to enhance students' ability to critically navigate and respond to disinformation in digital environments.", "published": "2026-03-20T00:08:01Z", "authors": ["José Manuel Alcalde-Llergo", "Mariana Buenestado Fernández", "Carlos Enrique George-Reyes", "Andrea Zingoni", "Enrique Yeguas-Bolívar"], "categories": ["cs.CY", "cs.AI", "cs.LG"], "doi": "10.3390/info16110929", "_query": "house price prediction machine learning", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2502.09214v1", "title": "Machine-Learning Detection of the Berezinskii-Kosterlitz-Thouless Transitions", "abstract": "The Berezinskii-Kosterlitz-Thouless (BKT) transition is a typical topological phase transition defined between binding and unbinding states of vortices and antivortices, which is not accompanied by spontaneous symmetry breaking. It is known that the BKT transition is difficult to detect from thermodynamic quantities such as specific heat and magnetic susceptibility because of the absence of anomaly in free energy and significant finite-size effects. Therefore, methods based on statistical mechanics which are commonly used to discuss phase transitions cannot be easily applied to the BKT transition. In recent years, several attempts to detect the BKT transition using machine-learning methods based on image recognition techniques have been reported. However, it has turned out that the detection is difficult even for machine learning methods because of the absence of trivial order parameters and symmetry breaking. Most of the methods proposed so far require prior knowledge about the models and/or preprocessing of input data for feature engineering, which is problematic in terms of the general applicability. In this article, we introduce recent development of the machine-learning methods to detect the BKT transitions in several spin models. Specifically, we demonstrate the success of two new methods named temperature-identification method and phase-classification method for detecting the BKT transitions in the q-state clock model and the XXZ model. This progress is expected to sublimate the machine-learning-based study of spin models for exploring new physics beyond simple benchmark test.", "published": "2025-02-13T11:48:52Z", "authors": ["Masahito Mochizuki", "Yusuke Miyajima"], "categories": ["cond-mat.stat-mech", "cond-mat.dis-nn", "cond-mat.str-el"], "doi": "10.7566/JPSJ.94.031003", "_query": "house price prediction machine learning", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2104.04569v1", "title": "Patient Contrastive Learning: a Performant, Expressive, and Practical Approach to ECG Modeling", "abstract": "Supervised machine learning applications in health care are often limited due to a scarcity of labeled training data. To mitigate this effect of small sample size, we introduce a pre-training approach, Patient Contrastive Learning of Representations (PCLR), which creates latent representations of ECGs from a large number of unlabeled examples. The resulting representations are expressive, performant, and practical across a wide spectrum of clinical tasks. We develop PCLR using a large health care system with over 3.2 million 12-lead ECGs, and demonstrate substantial improvements across multiple new tasks when there are fewer than 5,000 labels. We release our model to extract ECG representations at https://github.com/broadinstitute/ml4h/tree/master/model_zoo/PCLR.", "published": "2021-04-09T18:58:08Z", "authors": ["Nathaniel Diamant", "Erik Reinertsen", "Steven Song", "Aaron Aguirre", "Collin Stultz", "Puneet Batra"], "categories": ["cs.LG", "eess.SP"], "doi": "10.1371/journal.pcbi.1009862", "_query": "house price prediction machine learning", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2012.05940v1", "title": "A Simplistic Machine Learning Approach to Contact Tracing", "abstract": "This report is based on the modified NIST challenge, Too Close For Too Long, provided by the SFI Centre for Machine Learning (ML-Labs). The modified challenge excludes the time calculation (too long) aspect. By handcrafting features from phone instrumental data we develop two machine learning models, a GBM and an MLP, to estimate distance between two phones. Our method is able to outperform the leading NIST challenge result by the Hong Kong University of Science and Technology (HKUST) by a significant margin.", "published": "2020-12-10T19:34:48Z", "authors": ["Carlos Gómez", "Niamh Belton", "Boi Quach", "Jack Nicholls", "Devanshu Anand"], "categories": ["cs.LG", "stat.ML"], "doi": "", "_query": "house price prediction machine learning", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/1405.7292v2", "title": "An Easy to Use Repository for Comparing and Improving Machine Learning Algorithm Usage", "abstract": "The results from most machine learning experiments are used for a specific purpose and then discarded. This results in a significant loss of information and requires rerunning experiments to compare learning algorithms. This also requires implementation of another algorithm for comparison, that may not always be correctly implemented. By storing the results from previous experiments, machine learning algorithms can be compared easily and the knowledge gained from them can be used to improve their performance. The purpose of this work is to provide easy access to previous experimental results for learning and comparison. These stored results are comprehensive -- storing the prediction for each test instance as well as the learning algorithm, hyperparameters, and training set that were used. Previous results are particularly important for meta-learning, which, in a broad sense, is the process of learning from previous machine learning results such that the learning process is improved. While other experiment databases do exist, one of our focuses is on easy access to the data. We provide meta-learning data sets that are ready to be downloaded for meta-learning experiments. In addition, queries to the underlying database can be made if specific information is desired. We also differ from previous experiment databases in that our databases is designed at the instance level, where an instance is an example in a data set. We store the predictions of a learning algorithm trained on a specific training set for each instance in the test set. Data set level information can then be obtained by aggregating the results from the instances. The instance level information can be used for many tasks such as determining the diversity of a classifier or algorithmically determining the optimal subset of training instances for a learning algorithm.", "published": "2014-05-28T16:08:32Z", "authors": ["Michael R. Smith", "Andrew White", "Christophe Giraud-Carrier", "Tony Martinez"], "categories": ["stat.ML", "cs.LG"], "doi": "", "_query": "house price prediction machine learning", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2508.04478v2", "title": "Who cuts emissions, who turns up the heat? causal machine learning estimates of energy efficiency interventions", "abstract": "Reducing domestic energy demand is central to climate mitigation and fuel poverty strategies, yet the impact of energy efficiency interventions is highly heterogeneous. Using a causal machine learning model trained on nationally representative data of the English housing stock, we estimate average and conditional treatment effects of wall insulation on gas consumption, focusing on distributional effects across energy burden subgroups. While interventions reduce gas demand on average (by as much as 19 percent), low energy burden groups achieve substantial savings, whereas those experiencing high energy burdens see little to no reduction. This pattern reflects a behaviourally-driven mechanism: households constrained by high costs-to-income ratios (e.g. more than 0.1) reallocate savings toward improved thermal comfort rather than lowering consumption. Far from wasteful, such responses represent rational adjustments in contexts of prior deprivation, with potential co-benefits for health and well-being. These findings call for a broader evaluation framework that accounts for both climate impacts and the equity implications of domestic energy policy.", "published": "2025-08-06T14:29:38Z", "authors": ["Bernardino D'Amico", "Francesco Pomponi", "Jay H. Arehart", "Lina Khaddour"], "categories": ["cs.LG"], "doi": "10.1016/j.enbuild.2025.116613", "_query": "house price prediction machine learning", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/1904.02773v1", "title": "Adaptive Sequential Machine Learning", "abstract": "A framework previously introduced in [3] for solving a sequence of stochastic optimization problems with bounded changes in the minimizers is extended and applied to machine learning problems such as regression and classification. The stochastic optimization problems arising in these machine learning problems is solved using algorithms such as stochastic gradient descent (SGD). A method based on estimates of the change in the minimizers and properties of the optimization algorithm is introduced for adaptively selecting the number of samples at each time step to ensure that the excess risk, i.e., the expected gap between the loss achieved by the approximate minimizer produced by the optimization algorithm and the exact minimizer, does not exceed a target level. A bound is developed to show that the estimate of the change in the minimizers is non-trivial provided that the excess risk is small enough. Extensions relevant to the machine learning setting are considered, including a cost-based approach to select the number of samples with a cost budget over a fixed horizon, and an approach to applying cross-validation for model selection. Finally, experiments with synthetic and real data are used to validate the algorithms.", "published": "2019-04-04T20:03:46Z", "authors": ["Craig Wilson", "Yuheng Bu", "Venugopal Veeravalli"], "categories": ["cs.LG", "stat.ML"], "doi": "", "_query": "house price prediction machine learning", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2507.08641v1", "title": "Pricing and hedging the prepayment option of mortgages under stochastic housing market activity", "abstract": "Prepayment risk embedded in fixed-rate mortgages forms a significant fraction of a financial institution's exposure. The embedded prepayment option bears the same interest rate risk as an exotic interest rate swap with a suitable stochastic notional. Focusing on penalty-free prepayment because of the contract owner's relocation to a new house, we model the prepayment option value as an European-type interest rate receiver swaption with stochastic maturity matching the stochastic time of relocation. This is a convenient representation since it allows us to compute the prepayment option value in terms of well-known pricing formulas for European-type swaptions. We investigate the effect of a stochastic housing market activity as the explanatory variable for the distribution of the relocation time, as opposed to the conventional assumption of a deterministic housing market activity. We prove that the housing market covariance drives the prepayment option price difference between the stochastic setting and its deterministic counterpart. The prepayment option exposure is hedged using market instruments based on Delta-Gamma replication. Furthermore, since the housing market activity is a non-tradable risk factor, we perform non-standard actuarial hedging focusing on controlling the prepayment option exposure yield by risky housing market scenarios.", "published": "2025-07-11T14:43:02Z", "authors": ["Leonardo Perotti", "Lech A. Grzelak", "Cornelis W. Oosterlee"], "categories": ["q-fin.PR"], "doi": "", "_query": "house price prediction machine learning", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/1508.06845v1", "title": "Encrypted statistical machine learning: new privacy preserving methods", "abstract": "We present two new statistical machine learning methods designed to learn on fully homomorphic encrypted (FHE) data. The introduction of FHE schemes following Gentry (2009) opens up the prospect of privacy preserving statistical machine learning analysis and modelling of encrypted data without compromising security constraints. We propose tailored algorithms for applying extremely random forests, involving a new cryptographic stochastic fraction estimator, and naïve Bayes, involving a semi-parametric model for the class decision boundary, and show how they can be used to learn and predict from encrypted data. We demonstrate that these techniques perform competitively on a variety of classification data sets and provide detailed information about the computational practicalities of these and other FHE methods.", "published": "2015-08-27T13:06:55Z", "authors": ["Louis J. M. Aslett", "Pedro M. Esperança", "Chris C. Holmes"], "categories": ["stat.ML", "cs.CR", "cs.LG", "stat.ME"], "doi": "", "_query": "house price prediction machine learning", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/1206.6428v1", "title": "A Binary Classification Framework for Two-Stage Multiple Kernel Learning", "abstract": "With the advent of kernel methods, automating the task of specifying a suitable kernel has become increasingly important. In this context, the Multiple Kernel Learning (MKL) problem of finding a combination of pre-specified base kernels that is suitable for the task at hand has received significant attention from researchers. In this paper we show that Multiple Kernel Learning can be framed as a standard binary classification problem with additional constraints that ensure the positive definiteness of the learned kernel. Framing MKL in this way has the distinct advantage that it makes it easy to leverage the extensive research in binary classification to develop better performing and more scalable MKL algorithms that are conceptually simpler, and, arguably, more accessible to practitioners. Experiments on nine data sets from different domains show that, despite its simplicity, the proposed technique compares favorably with current leading MKL approaches.", "published": "2012-06-27T19:59:59Z", "authors": ["Abhishek Kumar", "Alexandru Niculescu-Mizil", "Koray Kavukcuoglu", "Hal Daume"], "categories": ["cs.LG", "stat.ML"], "doi": "", "_query": "house price prediction machine learning", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2408.12476v3", "title": "Predicting Solar Energy Generation with Machine Learning based on AQI and Weather Features", "abstract": "This paper addresses the pressing need for an accurate solar energy prediction model, which is crucial for efficient grid integration. We explore the influence of the Air Quality Index and weather features on solar energy generation, employing advanced Machine Learning and Deep Learning techniques. Our methodology uses time series modeling and makes novel use of power transform normalization and zero-inflated modeling. Various Machine Learning algorithms and Conv2D Long Short-Term Memory model based Deep Learning models are applied to these transformations for precise predictions. Results underscore the effectiveness of our approach, demonstrating enhanced prediction accuracy with Air Quality Index and weather features. We achieved a 0.9691 $R^2$ Score, 0.18 MAE, 0.10 RMSE with Conv2D Long Short-Term Memory model, showcasing the power transform technique's innovation in enhancing time series forecasting for solar energy generation. Such results help our research contribute valuable insights to the synergy between Air Quality Index, weather features, and Deep Learning techniques for solar energy prediction.", "published": "2024-08-22T15:13:44Z", "authors": ["Arjun Shah", "Varun Viswanath", "Kashish Gandhi", "Nilesh Madhukar Patil"], "categories": ["cs.LG", "cs.AI"], "doi": "10.21203/rs.3.rs-3178713/v1", "_query": "house price prediction machine learning", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2007.00611v4", "title": "Gradient Temporal-Difference Learning with Regularized Corrections", "abstract": "It is still common to use Q-learning and temporal difference (TD) learning-even though they have divergence issues and sound Gradient TD alternatives exist-because divergence seems rare and they typically perform well. However, recent work with large neural network learning systems reveals that instability is more common than previously thought. Practitioners face a difficult dilemma: choose an easy to use and performant TD method, or a more complex algorithm that is more sound but harder to tune and all but unexplored with non-linear function approximation or control. In this paper, we introduce a new method called TD with Regularized Corrections (TDRC), that attempts to balance ease of use, soundness, and performance. It behaves as well as TD, when TD performs well, but is sound in cases where TD diverges. We empirically investigate TDRC across a range of problems, for both prediction and control, and for both linear and non-linear function approximation, and show, potentially for the first time, that gradient TD methods could be a better alternative to TD and Q-learning.", "published": "2020-07-01T16:56:56Z", "authors": ["Sina Ghiassian", "Andrew Patterson", "Shivam Garg", "Dhawal Gupta", "Adam White", "Martha White"], "categories": ["cs.LG", "cs.AI", "stat.ML"], "doi": "", "_query": "house price prediction machine learning", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2506.06454v2", "title": "LETS Forecast: Learning Embedology for Time Series Forecasting", "abstract": "Real-world time series are often governed by complex nonlinear dynamics. Understanding these underlying dynamics is crucial for precise future prediction. While deep learning has achieved major success in time series forecasting, many existing approaches do not explicitly model the dynamics. To bridge this gap, we introduce DeepEDM, a framework that integrates nonlinear dynamical systems modeling with deep neural networks. Inspired by empirical dynamic modeling (EDM) and rooted in Takens' theorem, DeepEDM presents a novel deep model that learns a latent space from time-delayed embeddings, and employs kernel regression to approximate the underlying dynamics, while leveraging efficient implementation of softmax attention and allowing for accurate prediction of future time steps. To evaluate our method, we conduct comprehensive experiments on synthetic data of nonlinear dynamical systems as well as real-world time series across domains. Our results show that DeepEDM is robust to input noise, and outperforms state-of-the-art methods in forecasting accuracy. Our code is available at: https://abrarmajeedi.github.io/deep_edm.", "published": "2025-06-06T18:24:12Z", "authors": ["Abrar Majeedi", "Viswanatha Reddy Gajjala", "Satya Sai Srinath Namburi GNVV", "Nada Magdi Elkordi", "Yin Li"], "categories": ["cs.LG", "cs.AI", "stat.ML"], "doi": "", "_query": "house price prediction machine learning", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2201.12428v1", "title": "Systematic Training and Testing for Machine Learning Using Combinatorial Interaction Testing", "abstract": "This paper demonstrates the systematic use of combinatorial coverage for selecting and characterizing test and training sets for machine learning models. The presented work adapts combinatorial interaction testing, which has been successfully leveraged in identifying faults in software testing, to characterize data used in machine learning. The MNIST hand-written digits data is used to demonstrate that combinatorial coverage can be used to select test sets that stress machine learning model performance, to select training sets that lead to robust model performance, and to select data for fine-tuning models to new domains. Thus, the results posit combinatorial coverage as a holistic approach to training and testing for machine learning. In contrast to prior work which has focused on the use of coverage in regard to the internal of neural networks, this paper considers coverage over simple features derived from inputs and outputs. Thus, this paper addresses the case where the supplier of test and training sets for machine learning models does not have intellectual property rights to the models themselves. Finally, the paper addresses prior criticism of combinatorial coverage and provides a rebuttal which advocates the use of coverage metrics in machine learning applications.", "published": "2022-01-28T21:33:31Z", "authors": ["Tyler Cody", "Erin Lanus", "Daniel D. Doyle", "Laura Freeman"], "categories": ["cs.LG", "cs.SE", "stat.ML"], "doi": "", "_query": "house price prediction machine learning", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2508.07556v2", "title": "Uncertainty-Driven Reliability: Selective Prediction and Trustworthy Deployment in Modern Machine Learning", "abstract": "Machine learning (ML) systems are increasingly deployed in high-stakes domains where reliability is paramount. This thesis investigates how uncertainty estimation can enhance the safety and trustworthiness of ML, focusing on selective prediction -- where models abstain when confidence is low. We first show that a model's training trajectory contains rich uncertainty signals that can be exploited without altering its architecture or loss. By ensembling predictions from intermediate checkpoints, we propose a lightweight, post-hoc abstention method that works across tasks, avoids the cost of deep ensembles, and achieves state-of-the-art selective prediction performance. Crucially, this approach is fully compatible with differential privacy (DP), allowing us to study how privacy noise affects uncertainty quality. We find that while many methods degrade under DP, our trajectory-based approach remains robust, and we introduce a framework for isolating the privacy-uncertainty trade-off. Next, we then develop a finite-sample decomposition of the selective classification gap -- the deviation from the oracle accuracy-coverage curve -- identifying five interpretable error sources and clarifying which interventions can close the gap. This explains why calibration alone cannot fix ranking errors, motivating methods that improve uncertainty ordering. Finally, we show that uncertainty signals can be adversarially manipulated to hide errors or deny service while maintaining high accuracy, and we design defenses combining calibration audits with verifiable inference. Together, these contributions advance reliable ML by improving, evaluating, and safeguarding uncertainty estimation, enabling models that not only make accurate predictions -- but also know when to say \"I do not know\".", "published": "2025-08-11T02:33:53Z", "authors": ["Stephan Rabanser"], "categories": ["cs.LG", "cs.AI", "cs.CY", "stat.ML"], "doi": "", "_query": "house price prediction machine learning", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2008.01132v3", "title": "Accuracy and Fairness Trade-offs in Machine Learning: A Stochastic Multi-Objective Approach", "abstract": "In the application of machine learning to real-life decision-making systems, e.g., credit scoring and criminal justice, the prediction outcomes might discriminate against people with sensitive attributes, leading to unfairness. The commonly used strategy in fair machine learning is to include fairness as a constraint or a penalization term in the minimization of the prediction loss, which ultimately limits the information given to decision-makers. In this paper, we introduce a new approach to handle fairness by formulating a stochastic multi-objective optimization problem for which the corresponding Pareto fronts uniquely and comprehensively define the accuracy-fairness trade-offs. We have then applied a stochastic approximation-type method to efficiently obtain well-spread and accurate Pareto fronts, and by doing so we can handle training data arriving in a streaming way.", "published": "2020-08-03T18:51:24Z", "authors": ["Suyun Liu", "Luis Nunes Vicente"], "categories": ["cs.LG", "stat.ML"], "doi": "", "_query": "house price prediction machine learning", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2008.08353v1", "title": "DECE: Decision Explorer with Counterfactual Explanations for Machine Learning Models", "abstract": "With machine learning models being increasingly applied to various decision-making scenarios, people have spent growing efforts to make machine learning models more transparent and explainable. Among various explanation techniques, counterfactual explanations have the advantages of being human-friendly and actionable -- a counterfactual explanation tells the user how to gain the desired prediction with minimal changes to the input. Besides, counterfactual explanations can also serve as efficient probes to the models' decisions. In this work, we exploit the potential of counterfactual explanations to understand and explore the behavior of machine learning models. We design DECE, an interactive visualization system that helps understand and explore a model's decisions on individual instances and data subsets, supporting users ranging from decision-subjects to model developers. DECE supports exploratory analysis of model decisions by combining the strengths of counterfactual explanations at instance- and subgroup-levels. We also introduce a set of interactions that enable users to customize the generation of counterfactual explanations to find more actionable ones that can suit their needs. Through three use cases and an expert interview, we demonstrate the effectiveness of DECE in supporting decision exploration tasks and instance explanations.", "published": "2020-08-19T09:44:47Z", "authors": ["Furui Cheng", "Yao Ming", "Huamin Qu"], "categories": ["cs.LG", "cs.HC", "stat.ML"], "doi": "", "_query": "house price prediction machine learning", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2211.09856v1", "title": "Machine Learning-Assisted Recurrence Prediction for Early-Stage Non-Small-Cell Lung Cancer Patients", "abstract": "Background: Stratifying cancer patients according to risk of relapse can personalize their care. In this work, we provide an answer to the following research question: How to utilize machine learning to estimate probability of relapse in early-stage non-small-cell lung cancer patients? Methods: For predicting relapse in 1,387 early-stage (I-II), non-small-cell lung cancer (NSCLC) patients from the Spanish Lung Cancer Group data (65.7 average age, 24.8% females, 75.2% males) we train tabular and graph machine learning models. We generate automatic explanations for the predictions of such models. For models trained on tabular data, we adopt SHAP local explanations to gauge how each patient feature contributes to the predicted outcome. We explain graph machine learning predictions with an example-based method that highlights influential past patients. Results: Machine learning models trained on tabular data exhibit a 76% accuracy for the Random Forest model at predicting relapse evaluated with a 10-fold cross-validation (model was trained 10 times with different independent sets of patients in test, train and validation sets, the reported metrics are averaged over these 10 test sets). Graph machine learning reaches 68% accuracy over a 200-patient, held-out test set, calibrated on a held-out set of 100 patients. Conclusions: Our results show that machine learning models trained on tabular and graph data can enable objective, personalised and reproducible prediction of relapse and therefore, disease outcome in patients with early-stage NSCLC. With further prospective and multisite validation, and additional radiological and molecular data, this prognostic model could potentially serve as a predictive decision support tool for deciding the use of adjuvant treatments in early-stage lung cancer. Keywords: Non-Small-Cell Lung Cancer, Tumor Recurrence Prediction, Machine Learning", "published": "2022-11-17T19:34:16Z", "authors": ["Adrianna Janik", "Maria Torrente", "Luca Costabello", "Virginia Calvo", "Brian Walsh", "Carlos Camps", "Sameh K. Mohamed", "Ana L. Ortega", "Vít Nováček", "Bartomeu Massutí", "Pasquale Minervini", "M. Rosario Garcia Campelo", "Edel del Barco", "Joaquim Bosch-Barrera", "Ernestina Menasalvas", "Mohan Timilsina", "Mariano Provencio"], "categories": ["cs.LG", "q-bio.QM"], "doi": "", "_query": "house price prediction machine learning", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2011.01715v1", "title": "Brain Predictability toolbox: a Python library for neuroimaging based machine learning", "abstract": "Summary Brain Predictability toolbox (BPt) represents a unified framework of machine learning (ML) tools designed to work with both tabulated data (in particular brain, psychiatric, behavioral, and physiological variables) and neuroimaging specific derived data (e.g., brain volumes and surfaces). This package is suitable for investigating a wide range of different neuroimaging based ML questions, in particular, those queried from large human datasets. Availability and Implementation BPt has been developed as an open-source Python 3.6+ package hosted at https://github.com/sahahn/BPt under MIT License, with documentation provided at https://bpt.readthedocs.io/en/latest/, and continues to be actively developed. The project can be downloaded through the github link provided. A web GUI interface based on the same code is currently under development and can be set up through docker with instructions at https://github.com/sahahn/BPt_app. Contact Please contact Sage Hahn at sahahn@uvm.edu", "published": "2020-11-03T14:06:43Z", "authors": ["Sage Hahn", "Dekang Yuan", "Wesley Thompson", "Max M Owens", "Nicholas Allgaier", "Hugh Garavan"], "categories": ["cs.LG", "stat.ML"], "doi": "", "_query": "house price prediction machine learning", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2007.05056v4", "title": "Multimodal price prediction", "abstract": "Price prediction is one of the examples related to forecasting tasks and is a project based on data science. Price prediction analyzes data and predicts the cost of new products. The goal of this research is to achieve an arrangement to predict the price of a cellphone based on its specifications. So, five deep learning models are proposed to predict the price range of a cellphone, one unimodal and four multimodal approaches. The multimodal methods predict the prices based on the graphical and non-graphical features of cellphones that have an important effect on their valorizations. Also, to evaluate the efficiency of the proposed methods, a cellphone dataset has been gathered from GSMArena. The experimental results show 88.3% F1-score, which confirms that multimodal learning leads to more accurate predictions than state-of-the-art techniques.", "published": "2020-07-09T20:46:13Z", "authors": ["Aidin Zehtab-Salmasi", "Ali-Reza Feizi-Derakhshi", "Narjes Nikzad-Khasmakhi", "Meysam Asgari-Chenaghlu", "Saeideh Nabipour"], "categories": ["cs.CV", "cs.LG"], "doi": "10.1007/s40745-021-00326-z", "_query": "house price prediction machine learning", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/1906.04304v1", "title": "Meta-Learning Neural Bloom Filters", "abstract": "There has been a recent trend in training neural networks to replace data structures that have been crafted by hand, with an aim for faster execution, better accuracy, or greater compression. In this setting, a neural data structure is instantiated by training a network over many epochs of its inputs until convergence. In applications where inputs arrive at high throughput, or are ephemeral, training a network from scratch is not practical. This motivates the need for few-shot neural data structures. In this paper we explore the learning of approximate set membership over a set of data in one-shot via meta-learning. We propose a novel memory architecture, the Neural Bloom Filter, which is able to achieve significant compression gains over classical Bloom Filters and existing memory-augmented neural networks.", "published": "2019-06-10T22:23:14Z", "authors": ["Jack W Rae", "Sergey Bartunov", "Timothy P Lillicrap"], "categories": ["cs.LG", "cs.DB", "cs.DS", "stat.ML"], "doi": "", "_query": "house price prediction machine learning", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/1906.10742v2", "title": "Machine Learning Testing: Survey, Landscapes and Horizons", "abstract": "This paper provides a comprehensive survey of Machine Learning Testing (ML testing) research. It covers 144 papers on testing properties (e.g., correctness, robustness, and fairness), testing components (e.g., the data, learning program, and framework), testing workflow (e.g., test generation and test evaluation), and application scenarios (e.g., autonomous driving, machine translation). The paper also analyses trends concerning datasets, research trends, and research focus, concluding with research challenges and promising research directions in ML testing.", "published": "2019-06-19T22:39:19Z", "authors": ["Jie M. Zhang", "Mark Harman", "Lei Ma", "Yang Liu"], "categories": ["cs.LG", "cs.AI", "cs.SE", "stat.ML"], "doi": "", "_query": "house price prediction machine learning", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/1002.0650v2", "title": "Magnetic fields and radiative feedback in the star formation process", "abstract": "Star formation is a complex process involving the interplay of many physical effects, including gravity, turbulent gas dynamics, magnetic fields and radiation. Our understanding of the process has improved substantially in recent years, primarily as a result of our increased ability to incorporate the relevant physics in numerical calculations of the star formation process. In this contribution we present an overview of our recent studies of star cluster formation in turbulent, magnetised clouds using self-gravitating radiation-magnetohydrodynamics calculations (Price and Bate 2008, 2009). Our incorporation of magnetic fields and radiative transfer into the Smoothed Particle Hydrodynamics method are discussed. We highlight how magnetic fields and radiative heating of the gas around newborn stars can solve several of the key puzzles in star formation, including an explanation for why star formation is such a slow and inefficient process. However, the presence of magnetic fields at observed strengths in collapsing protostellar cores also leads to problems on smaller scales, including a difficulty in forming protostellar discs and binary stars (Price and Bate 2007, Hennebelle and Teyssier 2008), which suggests that our understanding of the role of magnetic fields in star formation is not yet complete.", "published": "2010-02-03T05:55:54Z", "authors": ["Daniel J. Price", "Matthew R. Bate"], "categories": ["astro-ph.GA", "astro-ph.SR"], "doi": "10.1063/1.3460126", "_query": "house price prediction machine learning", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2106.00660v1", "title": "Markpainting: Adversarial Machine Learning meets Inpainting", "abstract": "Inpainting is a learned interpolation technique that is based on generative modeling and used to populate masked or missing pieces in an image; it has wide applications in picture editing and retouching. Recently, inpainting started being used for watermark removal, raising concerns. In this paper we study how to manipulate it using our markpainting technique. First, we show how an image owner with access to an inpainting model can augment their image in such a way that any attempt to edit it using that model will add arbitrary visible information. We find that we can target multiple different models simultaneously with our technique. This can be designed to reconstitute a watermark if the editor had been trying to remove it. Second, we show that our markpainting technique is transferable to models that have different architectures or were trained on different datasets, so watermarks created using it are difficult for adversaries to remove. Markpainting is novel and can be used as a manipulation alarm that becomes visible in the event of inpainting.", "published": "2021-06-01T17:45:52Z", "authors": ["David Khachaturov", "Ilia Shumailov", "Yiren Zhao", "Nicolas Papernot", "Ross Anderson"], "categories": ["cs.LG", "cs.AI", "cs.CR", "cs.CV", "cs.CY"], "doi": "", "_query": "house price prediction machine learning", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2301.12548v1", "title": "Global Flood Prediction: a Multimodal Machine Learning Approach", "abstract": "Flooding is one of the most destructive and costly natural disasters, and climate changes would further increase risks globally. This work presents a novel multimodal machine learning approach for multi-year global flood risk prediction, combining geographical information and historical natural disaster dataset. Our multimodal framework employs state-of-the-art processing techniques to extract embeddings from each data modality, including text-based geographical data and tabular-based time-series data. Experiments demonstrate that a multimodal approach, that is combining text and statistical data, outperforms a single-modality approach. Our most advanced architecture, employing embeddings extracted using transfer learning upon DistilBert model, achieves 75\\%-77\\% ROCAUC score in predicting the next 1-5 year flooding event in historically flooded locations. This work demonstrates the potentials of using machine learning for long-term planning in natural disaster management.", "published": "2023-01-29T21:39:39Z", "authors": ["Cynthia Zeng", "Dimitris Bertsimas"], "categories": ["cs.LG", "cs.CY"], "doi": "", "_query": "house price prediction machine learning", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2509.10074v1", "title": "Prototypical Contrastive Learning For Improved Few-Shot Audio Classification", "abstract": "Few-shot learning has emerged as a powerful paradigm for training models with limited labeled data, addressing challenges in scenarios where large-scale annotation is impractical. While extensive research has been conducted in the image domain, few-shot learning in audio classification remains relatively underexplored. In this work, we investigate the effect of integrating supervised contrastive loss into prototypical few shot training for audio classification. In detail, we demonstrate that angular loss further improves the performance compared to the standard contrastive loss. Our method leverages SpecAugment followed by a self-attention mechanism to encapsulate diverse information of augmented input versions into one unified embedding. We evaluate our approach on MetaAudio, a benchmark including five datasets with predefined splits, standardized preprocessing, and a comprehensive set of few-shot learning models for comparison. The proposed approach achieves state-of-the-art performance in a 5-way, 5-shot setting.", "published": "2025-09-12T09:10:55Z", "authors": ["Christos Sgouropoulos", "Christos Nikou", "Stefanos Vlachos", "Vasileios Theiou", "Christos Foukanelis", "Theodoros Giannakopoulos"], "categories": ["cs.SD", "cs.LG"], "doi": "10.1109/MLSP62443.2025.11204215", "_query": "house price prediction machine learning", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/1206.6452v1", "title": "Smoothness and Structure Learning by Proxy", "abstract": "As data sets grow in size, the ability of learning methods to find structure in them is increasingly hampered by the time needed to search the large spaces of possibilities and generate a score for each that takes all of the observed data into account. For instance, Bayesian networks, the model chosen in this paper, have a super-exponentially large search space for a fixed number of variables. One possible method to alleviate this problem is to use a proxy, such as a Gaussian Process regressor, in place of the true scoring function, training it on a selection of sampled networks. We prove here that the use of such a proxy is well-founded, as we can bound the smoothness of a commonly-used scoring function for Bayesian network structure learning. We show here that, compared to an identical search strategy using the network?s exact scores, our proxy-based search is able to get equivalent or better scores on a number of data sets in a fraction of the time.", "published": "2012-06-27T19:59:59Z", "authors": ["Benjamin Yackley", "Terran Lane"], "categories": ["cs.LG", "math.OC", "stat.ML"], "doi": "", "_query": "house price prediction machine learning", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2212.12303v1", "title": "Introduction to Machine Learning for Physicians: A Survival Guide for Data Deluge", "abstract": "Many modern research fields increasingly rely on collecting and analysing massive, often unstructured, and unwieldy datasets. Consequently, there is growing interest in machine learning and artificial intelligence applications that can harness this `data deluge'. This broad nontechnical overview provides a gentle introduction to machine learning with a specific focus on medical and biological applications. We explain the common types of machine learning algorithms and typical tasks that can be solved, illustrating the basics with concrete examples from healthcare. Lastly, we provide an outlook on open challenges, limitations, and potential impacts of machine-learning-powered medicine.", "published": "2022-12-23T13:08:59Z", "authors": ["Ričards Marcinkevičs", "Ece Ozkan", "Julia E. Vogt"], "categories": ["cs.LG", "stat.ML"], "doi": "", "_query": "house price prediction machine learning", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2202.12008v3", "title": "A Fair Pricing Model via Adversarial Learning", "abstract": "At the core of insurance business lies classification between risky and non-risky insureds, actuarial fairness meaning that risky insureds should contribute more and pay a higher premium than non-risky or less-risky ones. Actuaries, therefore, use econometric or machine learning techniques to classify, but the distinction between a fair actuarial classification and \"discrimination\" is subtle. For this reason, there is a growing interest about fairness and discrimination in the actuarial community Lindholm, Richman, Tsanakas, and Wuthrich (2022). Presumably, non-sensitive characteristics can serve as substitutes or proxies for protected attributes. For example, the color and model of a car, combined with the driver's occupation, may lead to an undesirable gender bias in the prediction of car insurance prices. Surprisingly, we will show that debiasing the predictor alone may be insufficient to maintain adequate accuracy (1). Indeed, the traditional pricing model is currently built in a two-stage structure that considers many potentially biased components such as car or geographic risks. We will show that this traditional structure has significant limitations in achieving fairness. For this reason, we have developed a novel pricing model approach. Recently some approaches have Blier-Wong, Cossette, Lamontagne, and Marceau (2021); Wuthrich and Merz (2021) shown the value of autoencoders in pricing. In this paper, we will show that (2) this can be generalized to multiple pricing factors (geographic, car type), (3) it perfectly adapted for a fairness context (since it allows to debias the set of pricing components): We extend this main idea to a general framework in which a single whole pricing model is trained by generating the geographic and car pricing components needed to predict the pure premium while mitigating the unwanted bias according to the desired metric.", "published": "2022-02-24T10:42:20Z", "authors": ["Vincent Grari", "Arthur Charpentier", "Marcin Detyniecki"], "categories": ["stat.ML", "cs.AI", "cs.CY", "cs.LG", "stat.AP"], "doi": "", "_query": "house price prediction machine learning", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2007.06299v1", "title": "Monitoring and explainability of models in production", "abstract": "The machine learning lifecycle extends beyond the deployment stage. Monitoring deployed models is crucial for continued provision of high quality machine learning enabled services. Key areas include model performance and data monitoring, detecting outliers and data drift using statistical techniques, and providing explanations of historic predictions. We discuss the challenges to successful implementation of solutions in each of these areas with some recent examples of production ready solutions using open source tools.", "published": "2020-07-13T10:37:05Z", "authors": ["Janis Klaise", "Arnaud Van Looveren", "Clive Cox", "Giovanni Vacanti", "Alexandru Coca"], "categories": ["stat.ML", "cs.LG"], "doi": "", "_query": "house price prediction machine learning", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2206.13446v1", "title": "Pen and Paper Exercises in Machine Learning", "abstract": "This is a collection of (mostly) pen-and-paper exercises in machine learning. The exercises are on the following topics: linear algebra, optimisation, directed graphical models, undirected graphical models, expressive power of graphical models, factor graphs and message passing, inference for hidden Markov models, model-based learning (including ICA and unnormalised models), sampling and Monte-Carlo integration, and variational inference.", "published": "2022-06-27T16:53:18Z", "authors": ["Michael U. Gutmann"], "categories": ["cs.LG", "stat.ML"], "doi": "", "_query": "house price prediction machine learning", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2104.14847v1", "title": "Active WeaSuL: Improving Weak Supervision with Active Learning", "abstract": "The availability of labelled data is one of the main limitations in machine learning. We can alleviate this using weak supervision: a framework that uses expert-defined rules $\\boldsymbolλ$ to estimate probabilistic labels $p(y|\\boldsymbolλ)$ for the entire data set. These rules, however, are dependent on what experts know about the problem, and hence may be inaccurate or may fail to capture important parts of the problem-space. To mitigate this, we propose Active WeaSuL: an approach that incorporates active learning into weak supervision. In Active WeaSuL, experts do not only define rules, but they also iteratively provide the true label for a small set of points where the weak supervision model is most likely to be mistaken, which are then used to better estimate the probabilistic labels. In this way, the weak labels provide a warm start, which active learning then improves upon. We make two contributions: 1) a modification of the weak supervision loss function, such that the expert-labelled data inform and improve the combination of weak labels; and 2) the maxKL divergence sampling strategy, which determines for which data points expert labelling is most beneficial. Our experiments show that when the budget for labelling data is limited (e.g. $\\leq 60$ data points), Active WeaSuL outperforms weak supervision, active learning, and competing strategies, with only a handful of labelled data points. This makes Active WeaSuL ideal for situations where obtaining labelled data is difficult.", "published": "2021-04-30T08:58:26Z", "authors": ["Samantha Biegel", "Rafah El-Khatib", "Luiz Otavio Vilas Boas Oliveira", "Max Baak", "Nanne Aben"], "categories": ["cs.LG", "stat.ML"], "doi": "", "_query": "house price prediction machine learning", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/1807.00154v1", "title": "AI in Education needs interpretable machine learning: Lessons from Open Learner Modelling", "abstract": "Interpretability of the underlying AI representations is a key raison d'être for Open Learner Modelling (OLM) -- a branch of Intelligent Tutoring Systems (ITS) research. OLMs provide tools for 'opening' up the AI models of learners' cognition and emotions for the purpose of supporting human learning and teaching. Over thirty years of research in ITS (also known as AI in Education) produced important work, which informs about how AI can be used in Education to best effects and, through the OLM research, what are the necessary considerations to make it interpretable and explainable for the benefit of learning. We argue that this work can provide a valuable starting point for a framework of interpretable AI, and as such is of relevance to the application of both knowledge-based and machine learning systems in other high-stakes contexts, beyond education.", "published": "2018-06-30T11:28:17Z", "authors": ["Cristina Conati", "Kaska Porayska-Pomsta", "Manolis Mavrikis"], "categories": ["cs.AI", "cs.CY"], "doi": "", "_query": "house price prediction machine learning", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2506.22504v2", "title": "Patch2Loc: Learning to Localize Patches for Unsupervised Brain Lesion Detection", "abstract": "Detecting brain lesions as abnormalities observed in magnetic resonance imaging (MRI) is essential for diagnosis and treatment. In the search of abnormalities, such as tumors and malformations, radiologists may benefit from computer-aided diagnostics that use computer vision systems trained with machine learning to segment normal tissue from abnormal brain tissue. While supervised learning methods require annotated lesions, we propose a new unsupervised approach (Patch2Loc) that learns from normal patches taken from structural MRI. We train a neural network model to map a patch back to its spatial location within a slice of the brain volume. During inference, abnormal patches are detected by the relatively higher error and/or variance of the location prediction. This generates a heatmap that can be integrated into pixel-wise methods to achieve finer-grained segmentation. We demonstrate the ability of our model to segment abnormal brain tissues by applying our approach to the detection of tumor tissues in MRI on T2-weighted images from BraTS2021 and MSLUB datasets and T1-weighted images from ATLAS and WMH datasets. We show that it outperforms the state-of-the art in unsupervised segmentation. The implementation for this work can be found on our \\href{https://github.com/bakerhassan/Patch2Loc}{GitHub page}. This paper has been accepted at AISTATS 2026.", "published": "2025-06-25T16:00:12Z", "authors": ["Hassan Baker", "Austin J. Brockmeier"], "categories": ["cs.CV", "cs.LG"], "doi": "", "_query": "house price prediction machine learning", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/1807.00905v2", "title": "Learning under selective labels in the presence of expert consistency", "abstract": "We explore the problem of learning under selective labels in the context of algorithm-assisted decision making. Selective labels is a pervasive selection bias problem that arises when historical decision making blinds us to the true outcome for certain instances. Examples of this are common in many applications, ranging from predicting recidivism using pre-trial release data to diagnosing patients. In this paper we discuss why selective labels often cannot be effectively tackled by standard methods for adjusting for sample selection bias, even if there are no unobservables. We propose a data augmentation approach that can be used to either leverage expert consistency to mitigate the partial blindness that results from selective labels, or to empirically validate whether learning under such framework may lead to unreliable models prone to systemic discrimination.", "published": "2018-07-02T21:48:59Z", "authors": ["Maria De-Arteaga", "Artur Dubrawski", "Alexandra Chouldechova"], "categories": ["cs.LG", "stat.ML"], "doi": "", "_query": "house price prediction machine learning", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2112.05254v1", "title": "Addressing Deep Learning Model Uncertainty in Long-Range Climate Forecasting with Late Fusion", "abstract": "Global warming leads to the increase in frequency and intensity of climate extremes that cause tremendous loss of lives and property. Accurate long-range climate prediction allows more time for preparation and disaster risk management for such extreme events. Although machine learning approaches have shown promising results in long-range climate forecasting, the associated model uncertainties may reduce their reliability. To address this issue, we propose a late fusion approach that systematically combines the predictions from multiple models to reduce the expected errors of the fused results. We also propose a network architecture with the novel denormalization layer to gain the benefits of data normalization without actually normalizing the data. The experimental results on long-range 2m temperature forecasting show that the framework outperforms the 30-year climate normals, and the accuracy can be improved by increasing the number of models.", "published": "2021-12-10T00:00:09Z", "authors": ["Ken C. L. Wong", "Hongzhi Wang", "Etienne E. Vos", "Bianca Zadrozny", "Campbell D. Watson", "Tanveer Syeda-Mahmood"], "categories": ["cs.LG"], "doi": "", "_query": "house price prediction machine learning", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/1901.02046v3", "title": "A New Perspective on Machine Learning: How to do Perfect Supervised Learning", "abstract": "In this work, we introduce the concept of bandlimiting into the theory of machine learning because all physical processes are bandlimited by nature, including real-world machine learning tasks. After the bandlimiting constraint is taken into account, our theoretical analysis has shown that all practical machine learning tasks are asymptotically solvable in a perfect sense. Furthermore, the key towards this solvability almost solely relies on two factors: i) a sufficiently large amount of training samples beyond a threshold determined by a difficulty measurement of the underlying task; ii) a sufficiently complex and bandlimited model. Moreover, for some special cases, we have derived new error bounds for perfect learning, which can quantify the difficulty of learning. These generalization bounds are not only asymptotically convergent but also irrelevant to model complexity. Our new results on generalization have provided a new perspective to explain the recent successes of large-scale supervised learning using complex models like neural networks.", "published": "2019-01-07T20:10:55Z", "authors": ["Hui Jiang"], "categories": ["cs.LG", "cs.AI", "stat.ML"], "doi": "", "_query": "house price prediction machine learning", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/1802.05688v4", "title": "Simulation assisted machine learning", "abstract": "Motivation: In a predictive modeling setting, if sufficient details of the system behavior are known, one can build and use a simulation for making predictions. When sufficient system details are not known, one typically turns to machine learning, which builds a black-box model of the system using a large dataset of input sample features and outputs. We consider a setting which is between these two extremes: some details of the system mechanics are known but not enough for creating simulations that can be used to make high quality predictions. In this context we propose using approximate simulations to build a kernel for use in kernelized machine learning methods, such as support vector machines. The results of multiple simulations (under various uncertainty scenarios) are used to compute similarity measures between every pair of samples: sample pairs are given a high similarity score if they behave similarly under a wide range of simulation parameters. These similarity values, rather than the original high dimensional feature data, are used to build the kernel. Results: We demonstrate and explore the simulation based kernel (SimKern) concept using four synthetic complex systems--three biologically inspired models and one network flow optimization model. We show that, when the number of training samples is small compared to the number of features, the SimKern approach dominates over no-prior-knowledge methods. This approach should be applicable in all disciplines where predictive models are sought and informative yet approximate simulations are available. Availability: The Python SimKern software, the demonstration models (in MATLAB, R), and the datasets are available at https://github.com/davidcraft/SimKern.", "published": "2018-02-15T18:04:34Z", "authors": ["Timo M. Deist", "Andrew Patti", "Zhaoqi Wang", "David Krane", "Taylor Sorenson", "David Craft"], "categories": ["stat.ML", "cs.LG", "q-bio.QM"], "doi": "10.1093/bioinformatics/btz199", "_query": "house price prediction machine learning", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/1310.4260v1", "title": "A Switch for Artificial Resistivity and Other Dissipation Terms", "abstract": "We describe a new switch to reduce dissipation from artificial resistivity in Smoothed Particle Magnetohydrodynamics simulations. The switch utilises the gradient of the magnetic field to detect shocks, setting alpha_B = h |gradB| / |B|. This measures the relative degree of discontinuity, and the switch is not dependent on the absolute field strength. We present results comparing the new resistivity switch to the switch of Price & Monaghan (2005), showing that it is more robust in capturing shocks (especially in weak fields), while leading to less overall dissipation. The design of this switch is generalised to create similar switches for artificial viscosity and thermal conduction, with proof of concept tests conducted on a Sod shock tube and Kelvin-Helmholtz instabilities.", "published": "2013-10-16T04:07:02Z", "authors": ["Terrence Tricco", "Daniel Price"], "categories": ["astro-ph.IM", "physics.comp-ph"], "doi": "", "_query": "house price prediction machine learning", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2206.00885v1", "title": "Coordinated Double Machine Learning", "abstract": "Double machine learning is a statistical method for leveraging complex black-box models to construct approximately unbiased treatment effect estimates given observational data with high-dimensional covariates, under the assumption of a partially linear model. The idea is to first fit on a subset of the samples two non-linear predictive models, one for the continuous outcome of interest and one for the observed treatment, and then to estimate a linear coefficient for the treatment using the remaining samples through a simple orthogonalized regression. While this methodology is flexible and can accommodate arbitrary predictive models, typically trained independently of one another, this paper argues that a carefully coordinated learning algorithm for deep neural networks may reduce the estimation bias. The improved empirical performance of the proposed method is demonstrated through numerical experiments on both simulated and real data.", "published": "2022-06-02T05:56:21Z", "authors": ["Nitai Fingerhut", "Matteo Sesia", "Yaniv Romano"], "categories": ["stat.ML", "cs.LG", "stat.ME"], "doi": "", "_query": "house price prediction machine learning", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/1910.02544v1", "title": "Using Deep Learning and Machine Learning to Detect Epileptic Seizure with Electroencephalography (EEG) Data", "abstract": "The prediction of epileptic seizure has always been extremely challenging in medical domain. However, as the development of computer technology, the application of machine learning introduced new ideas for seizure forecasting. Applying machine learning model onto the predication of epileptic seizure could help us obtain a better result and there have been plenty of scientists who have been doing such works so that there are sufficient medical data provided for researchers to do training of machine learning models.", "published": "2019-10-06T22:53:28Z", "authors": ["Haotian Liu", "Lin Xi", "Ying Zhao", "Zhixiang Li"], "categories": ["cs.LG", "eess.SP", "stat.ML"], "doi": "", "_query": "house price prediction machine learning", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2412.00464v1", "title": "On the Conditions for Domain Stability for Machine Learning: a Mathematical Approach", "abstract": "This work proposes a mathematical approach that (re)defines a property of Machine Learning models named stability and determines sufficient conditions to validate it. Machine Learning models are represented as functions, and the characteristics in scope depend upon the domain of the function, what allows us to adopt topological and metric spaces theory as a basis. Finally, this work provides some equivalences useful to prove and test stability in Machine Learning models. The results suggest that whenever stability is aligned with the notion of function smoothness, then the stability of Machine Learning models primarily depends upon certain topological, measurable properties of the classification sets within the ML model domain.", "published": "2024-11-30T12:57:07Z", "authors": ["Gabriel Pedroza"], "categories": ["cs.LG", "cs.AI", "stat.ML"], "doi": "", "_query": "house price prediction machine learning", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/1909.09223v1", "title": "InterpretML: A Unified Framework for Machine Learning Interpretability", "abstract": "InterpretML is an open-source Python package which exposes machine learning interpretability algorithms to practitioners and researchers. InterpretML exposes two types of interpretability - glassbox models, which are machine learning models designed for interpretability (ex: linear models, rule lists, generalized additive models), and blackbox explainability techniques for explaining existing systems (ex: Partial Dependence, LIME). The package enables practitioners to easily compare interpretability algorithms by exposing multiple methods under a unified API, and by having a built-in, extensible visualization platform. InterpretML also includes the first implementation of the Explainable Boosting Machine, a powerful, interpretable, glassbox model that can be as accurate as many blackbox models. The MIT licensed source code can be downloaded from github.com/microsoft/interpret.", "published": "2019-09-19T20:22:32Z", "authors": ["Harsha Nori", "Samuel Jenkins", "Paul Koch", "Rich Caruana"], "categories": ["cs.LG", "stat.ML"], "doi": "", "_query": "house price prediction machine learning", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/1906.10019v4", "title": "Machine Learning Construction: implications to cybersecurity", "abstract": "Statistical learning is the process of estimating an unknown probabilistic input-output relationship of a system using a limited number of observations. A statistical learning machine (SLM) is the algorithm, function, model, or rule, that learns such a process; and machine learning (ML) is the conventional name of this field. ML and its applications are ubiquitous in the modern world. Systems such as Automatic target recognition (ATR) in military applications, computer aided diagnosis (CAD) in medical imaging, DNA microarrays in genomics, optical character recognition (OCR), speech recognition (SR), spam email filtering, stock market prediction, etc., are few examples and applications for ML; diverse fields but one theory. In particular, ML has gained a lot of attention in the field of cyberphysical security, especially in the last decade. It is of great importance to this field to design detection algorithms that have the capability of learning from security data to be able to hunt threats, achieve better monitoring, master the complexity of the threat intelligence feeds, and achieve timely remediation of security incidents. The field of ML can be decomposed into two basic subfields: \\textit{construction} and \\textit{assessment}. We mean by \\textit{construction} designing or inventing an appropriate algorithm that learns from the input data and achieves a good performance according to some optimality criterion. We mean by \\textit{assessment} attributing some performance measures to the constructed ML algorithm, along with their estimators, to objectively assess this algorithm. \\textit{Construction} and \\textit{assessment} of a ML algorithm require familiarity with different other fields: probability, statistics, matrix theory, optimization, algorithms, and programming, among others.f", "published": "2019-06-24T15:15:21Z", "authors": ["Waleed A. Yousef"], "categories": ["stat.ML", "cs.LG"], "doi": "10.1007/978-3-031-16237-4_2", "_query": "house price prediction machine learning", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2006.09481v1", "title": "Efficient nonparametric statistical inference on population feature importance using Shapley values", "abstract": "The true population-level importance of a variable in a prediction task provides useful knowledge about the underlying data-generating mechanism and can help in deciding which measurements to collect in subsequent experiments. Valid statistical inference on this importance is a key component in understanding the population of interest. We present a computationally efficient procedure for estimating and obtaining valid statistical inference on the Shapley Population Variable Importance Measure (SPVIM). Although the computational complexity of the true SPVIM scales exponentially with the number of variables, we propose an estimator based on randomly sampling only $Θ(n)$ feature subsets given $n$ observations. We prove that our estimator converges at an asymptotically optimal rate. Moreover, by deriving the asymptotic distribution of our estimator, we construct valid confidence intervals and hypothesis tests. Our procedure has good finite-sample performance in simulations, and for an in-hospital mortality prediction task produces similar variable importance estimates when different machine learning algorithms are applied.", "published": "2020-06-16T19:47:11Z", "authors": ["Brian D. Williamson", "Jean Feng"], "categories": ["stat.ME", "stat.ML"], "doi": "", "_query": "house price prediction machine learning", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/1206.6419v1", "title": "Cross-Domain Multitask Learning with Latent Probit Models", "abstract": "Learning multiple tasks across heterogeneous domains is a challenging problem since the feature space may not be the same for different tasks. We assume the data in multiple tasks are generated from a latent common domain via sparse domain transforms and propose a latent probit model (LPM) to jointly learn the domain transforms, and the shared probit classifier in the common domain. To learn meaningful task relatedness and avoid over-fitting in classification, we introduce sparsity in the domain transforms matrices, as well as in the common classifier. We derive theoretical bounds for the estimation error of the classifier in terms of the sparsity of domain transforms. An expectation-maximization algorithm is derived for learning the LPM. The effectiveness of the approach is demonstrated on several real datasets.", "published": "2012-06-27T19:59:59Z", "authors": ["Shaobo Han", "Xuejun Liao", "Lawrence Carin"], "categories": ["cs.LG", "stat.ML"], "doi": "", "_query": "house price prediction machine learning", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2212.00557v1", "title": "Deep Kernel Learning for Mortality Prediction in the Face of Temporal Shift", "abstract": "Neural models, with their ability to provide novel representations, have shown promising results in prediction tasks in healthcare. However, patient demographics, medical technology, and quality of care change over time. This often leads to drop in the performance of neural models for prospective patients, especially in terms of their calibration. The deep kernel learning (DKL) framework may be robust to such changes as it combines neural models with Gaussian processes, which are aware of prediction uncertainty. Our hypothesis is that out-of-distribution test points will result in probabilities closer to the global mean and hence prevent overconfident predictions. This in turn, we hypothesise, will result in better calibration on prospective data. This paper investigates DKL's behaviour when facing a temporal shift, which was naturally introduced when an information system that feeds a cohort database was changed. We compare DKL's performance to that of a neural baseline based on recurrent neural networks. We show that DKL indeed produced superior calibrated predictions. We also confirm that the DKL's predictions were indeed less sharp. In addition, DKL's discrimination ability was even improved: its AUC was 0.746 (+- 0.014 std), compared to 0.739 (+- 0.028 std) for the baseline. The paper demonstrated the importance of including uncertainty in neural computing, especially for their prospective use.", "published": "2022-12-01T15:04:05Z", "authors": ["Miguel Rios", "Ameen Abu-Hanna"], "categories": ["cs.LG"], "doi": "10.1007/978-3-030-77211-6_22", "_query": "house price prediction machine learning", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2208.13499v1", "title": "Generalization In Multi-Objective Machine Learning", "abstract": "Modern machine learning tasks often require considering not just one but multiple objectives. For example, besides the prediction quality, this could be the efficiency, robustness or fairness of the learned models, or any of their combinations. Multi-objective learning offers a natural framework for handling such problems without having to commit to early trade-offs. Surprisingly, statistical learning theory so far offers almost no insight into the generalization properties of multi-objective learning. In this work, we make first steps to fill this gap: we establish foundational generalization bounds for the multi-objective setting as well as generalization and excess bounds for learning with scalarizations. We also provide the first theoretical analysis of the relation between the Pareto-optimal sets of the true objectives and the Pareto-optimal sets of their empirical approximations from training data. In particular, we show a surprising asymmetry: all Pareto-optimal solutions can be approximated by empirically Pareto-optimal ones, but not vice versa.", "published": "2022-08-29T11:06:39Z", "authors": ["Peter Súkeník", "Christoph H. Lampert"], "categories": ["cs.LG", "cs.AI", "stat.ML"], "doi": "", "_query": "house price prediction machine learning", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/1505.06807v1", "title": "MLlib: Machine Learning in Apache Spark", "abstract": "Apache Spark is a popular open-source platform for large-scale data processing that is well-suited for iterative machine learning tasks. In this paper we present MLlib, Spark's open-source distributed machine learning library. MLlib provides efficient functionality for a wide range of learning settings and includes several underlying statistical, optimization, and linear algebra primitives. Shipped with Spark, MLlib supports several languages and provides a high-level API that leverages Spark's rich ecosystem to simplify the development of end-to-end machine learning pipelines. MLlib has experienced a rapid growth due to its vibrant open-source community of over 140 contributors, and includes extensive documentation to support further growth and to let users quickly get up to speed.", "published": "2015-05-26T05:12:23Z", "authors": ["Xiangrui Meng", "Joseph Bradley", "Burak Yavuz", "Evan Sparks", "Shivaram Venkataraman", "Davies Liu", "Jeremy Freeman", "DB Tsai", "Manish Amde", "Sean Owen", "Doris Xin", "Reynold Xin", "Michael J. Franklin", "Reza Zadeh", "Matei Zaharia", "Ameet Talwalkar"], "categories": ["cs.LG", "cs.DC", "cs.MS", "stat.ML"], "doi": "", "_query": "house price prediction machine learning", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2402.11701v2", "title": "Explaining the Machine Learning Solution of the Ising Model", "abstract": "As powerful as machine learning (ML) techniques are in solving problems involving data with large dimensionality, explaining the results from the fitted parameters remains a challenging task of utmost importance, especially in physics applications. This work shows how this can be accomplished for the ferromagnetic Ising model, the main target of several ML studies in statistical physics. Here it is demonstrated that the successful unsupervised identification of the phases and order parameter by principal component analysis, a common method in those studies, detects that the magnetization per spin has its greatest variation with the temperature, the actual control parameter of the phase transition. Then, by using a neural network (NN) without hidden layers (the simplest possible) and informed by the symmetry of the Hamiltonian, an explanation is provided for the strategy used in finding the supervised learning solution for the critical temperature of the model's continuous phase transition. This allows the prediction of the minimal extension of the NN to solve the problem when the symmetry is not known, which becomes also explainable. These results pave the way to a physics-informed explainable generalized framework, enabling the extraction of physical laws and principles from the parameters of the models.", "published": "2024-02-18T20:47:33Z", "authors": ["Roberto C. Alamino"], "categories": ["cond-mat.dis-nn", "cs.LG", "physics.comp-ph"], "doi": "", "_query": "house price prediction machine learning", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2102.04961v2", "title": "Morphology of three-body quantum states from machine learning", "abstract": "The relative motion of three impenetrable particles on a ring, in our case two identical fermions and one impurity, is isomorphic to a triangular quantum billiard. Depending on the ratio $κ$ of the impurity and fermion masses, the billiards can be integrable or non-integrable (also referred to in the main text as chaotic). To set the stage, we first investigate the energy level distributions of the billiards as a function of $1/κ\\in [0,1]$ and find no evidence of integrable cases beyond the limiting values $1/κ=1$ and $1/κ=0$. Then, we use machine learning tools to analyze properties of probability distributions of individual quantum states. We find that convolutional neural networks can correctly classify integrable and non-integrable states.The decisive features of the wave functions are the normalization and a large number of zero elements, corresponding to the existence of a nodal line. The network achieves typical accuracies of 97%, suggesting that machine learning tools can be used to analyze and classify the morphology of probability densities obtained in theory or experiment.", "published": "2021-02-09T17:23:08Z", "authors": ["David Huber", "Oleksandr V. Marchukov", "Hans-Werner Hammer", "Artem G. Volosniev"], "categories": ["quant-ph", "cond-mat.quant-gas", "nlin.SI", "nucl-th"], "doi": "10.1088/1367-2630/ac0576", "_query": "house price prediction machine learning", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/1910.07178v1", "title": "Generative Learning of Counterfactual for Synthetic Control Applications in Econometrics", "abstract": "A common statistical problem in econometrics is to estimate the impact of a treatment on a treated unit given a control sample with untreated outcomes. Here we develop a generative learning approach to this problem, learning the probability distribution of the data, which can be used for downstream tasks such as post-treatment counterfactual prediction and hypothesis testing. We use control samples to transform the data to a Gaussian and homoschedastic form and then perform Gaussian process analysis in Fourier space, evaluating the optimal Gaussian kernel via non-parametric power spectrum estimation. We combine this Gaussian prior with the data likelihood given by the pre-treatment data of the single unit, to obtain the synthetic prediction of the unit post-treatment, which minimizes the error variance of synthetic prediction. Given the generative model the minimum variance counterfactual is unique, and comes with an associated error covariance matrix. We extend this basic formalism to include correlations of primary variable with other covariates of interest. Given the probabilistic description of generative model we can compare synthetic data prediction with real data to address the question of whether the treatment had a statistically significant impact. For this purpose we develop a hypothesis testing approach and evaluate the Bayes factor. We apply the method to the well studied example of California (CA) tobacco sales tax of 1988. We also perform a placebo analysis using control states to validate our methodology. Our hypothesis testing method suggests 5.8:1 odds in favor of CA tobacco sales tax having an impact on the tobacco sales, a value that is at least three times higher than any of the 38 control states.", "published": "2019-10-16T05:50:14Z", "authors": ["Chirag Modi", "Uros Seljak"], "categories": ["stat.ML", "cs.LG"], "doi": "", "_query": "house price prediction machine learning", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2002.08599v4", "title": "On Learning Sets of Symmetric Elements", "abstract": "Learning from unordered sets is a fundamental learning setup, recently attracting increasing attention. Research in this area has focused on the case where elements of the set are represented by feature vectors, and far less emphasis has been given to the common case where set elements themselves adhere to their own symmetries. That case is relevant to numerous applications, from deblurring image bursts to multi-view 3D shape recognition and reconstruction. In this paper, we present a principled approach to learning sets of general symmetric elements. We first characterize the space of linear layers that are equivariant both to element reordering and to the inherent symmetries of elements, like translation in the case of images. We further show that networks that are composed of these layers, called Deep Sets for Symmetric Elements (DSS) layers, are universal approximators of both invariant and equivariant functions, and that these networks are strictly more expressive than Siamese networks. DSS layers are also straightforward to implement. Finally, we show that they improve over existing set-learning architectures in a series of experiments with images, graphs, and point-clouds.", "published": "2020-02-20T07:29:20Z", "authors": ["Haggai Maron", "Or Litany", "Gal Chechik", "Ethan Fetaya"], "categories": ["cs.LG", "stat.ML"], "doi": "", "_query": "house price prediction machine learning", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/1811.08067v2", "title": "Reinforcement Learning of Active Vision for Manipulating Objects under Occlusions", "abstract": "We consider artificial agents that learn to jointly control their gripperand camera in order to reinforcement learn manipulation policies in the presenceof occlusions from distractor objects. Distractors often occlude the object of in-terest and cause it to disappear from the field of view. We propose hand/eye con-trollers that learn to move the camera to keep the object within the field of viewand visible, in coordination to manipulating it to achieve the desired goal, e.g.,pushing it to a target location. We incorporate structural biases of object-centricattention within our actor-critic architectures, which our experiments suggest tobe a key for good performance. Our results further highlight the importance ofcurriculum with regards to environment difficulty. The resulting active vision /manipulation policies outperform static camera setups for a variety of clutteredenvironments.", "published": "2018-11-20T04:24:38Z", "authors": ["Ricson Cheng", "Arpit Agarwal", "Katerina Fragkiadaki"], "categories": ["cs.RO", "cs.CV", "cs.LG"], "doi": "", "_query": "house price prediction machine learning", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2003.05996v2", "title": "Meta-Learning GNN Initializations for Low-Resource Molecular Property Prediction", "abstract": "Building in silico models to predict chemical properties and activities is a crucial step in drug discovery. However, limited labeled data often hinders the application of deep learning in this setting. Meanwhile advances in meta-learning have enabled state-of-the-art performances in few-shot learning benchmarks, naturally prompting the question: Can meta-learning improve deep learning performance in low-resource drug discovery projects? In this work, we assess the transferability of graph neural networks initializations learned by the Model-Agnostic Meta-Learning (MAML) algorithm - and its variants FO-MAML and ANIL - for chemical properties and activities tasks. Using the ChEMBL20 dataset to emulate low-resource settings, our benchmark shows that meta-initializations perform comparably to or outperform multi-task pre-training baselines on 16 out of 20 in-distribution tasks and on all out-of-distribution tasks, providing an average improvement in AUPRC of 11.2% and 26.9% respectively. Finally, we observe that meta-initializations consistently result in the best performing models across fine-tuning sets with $k \\in \\{16, 32, 64, 128, 256\\}$ instances.", "published": "2020-03-12T19:49:57Z", "authors": ["Cuong Q. Nguyen", "Constantine Kreatsoulas", "Kim M. Branson"], "categories": ["cs.LG", "physics.chem-ph", "stat.ML"], "doi": "", "_query": "house price prediction machine learning", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2302.12538v2", "title": "UnbiasedNets: A Dataset Diversification Framework for Robustness Bias Alleviation in Neural Networks", "abstract": "Performance of trained neural network (NN) models, in terms of testing accuracy, has improved remarkably over the past several years, especially with the advent of deep learning. However, even the most accurate NNs can be biased toward a specific output classification due to the inherent bias in the available training datasets, which may propagate to the real-world implementations. This paper deals with the robustness bias, i.e., the bias exhibited by the trained NN by having a significantly large robustness to noise for a certain output class, as compared to the remaining output classes. The bias is shown to result from imbalanced datasets, i.e., the datasets where all output classes are not equally represented. Towards this, we propose the UnbiasedNets framework, which leverages K-means clustering and the NN's noise tolerance to diversify the given training dataset, even from relatively smaller datasets. This generates balanced datasets and reduces the bias within the datasets themselves. To the best of our knowledge, this is the first framework catering to the robustness bias problem in NNs. We use real-world datasets to demonstrate the efficacy of the UnbiasedNets for data diversification, in case of both binary and multi-label classifiers. The results are compared to well-known tools aimed at generating balanced datasets, and illustrate how existing works have limited success while addressing the robustness bias. In contrast, UnbiasedNets provides a notable improvement over existing works, while even reducing the robustness bias significantly in some cases, as observed by comparing the NNs trained on the diversified and original datasets.", "published": "2023-02-24T09:49:43Z", "authors": ["Mahum Naseer", "Bharath Srinivas Prabakaran", "Osman Hasan", "Muhammad Shafique"], "categories": ["cs.LG"], "doi": "10.1007/s10994-023-06314-z", "_query": "house price prediction machine learning", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/1809.00175v3", "title": "Hyperparameter Learning for Conditional Kernel Mean Embeddings with Rademacher Complexity Bounds", "abstract": "Conditional kernel mean embeddings are nonparametric models that encode conditional expectations in a reproducing kernel Hilbert space. While they provide a flexible and powerful framework for probabilistic inference, their performance is highly dependent on the choice of kernel and regularization hyperparameters. Nevertheless, current hyperparameter tuning methods predominantly rely on expensive cross validation or heuristics that is not optimized for the inference task. For conditional kernel mean embeddings with categorical targets and arbitrary inputs, we propose a hyperparameter learning framework based on Rademacher complexity bounds to prevent overfitting by balancing data fit against model complexity. Our approach only requires batch updates, allowing scalable kernel hyperparameter tuning without invoking kernel approximations. Experiments demonstrate that our learning framework outperforms competing methods, and can be further extended to incorporate and learn deep neural network weights to improve generalization.", "published": "2018-09-01T13:33:31Z", "authors": ["Kelvin Hsu", "Richard Nock", "Fabio Ramos"], "categories": ["stat.ML", "cs.LG"], "doi": "", "_query": "house price prediction machine learning", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2010.06010v1", "title": "Machine Learning for Material Characterization with an Application for Predicting Mechanical Properties", "abstract": "Currently, the growth of material data from experiments and simulations is expanding beyond processable amounts. This makes the development of new data-driven methods for the discovery of patterns among multiple lengthscales and time-scales and structure-property relationships essential. These data-driven approaches show enormous promise within materials science. The following review covers machine learning applications for metallic material characterization. Many parameters associated with the processing and the structure of materials affect the properties and the performance of manufactured components. Thus, this study is an attempt to investigate the usefulness of machine learning methods for material property prediction. Material characteristics such as strength, toughness, hardness, brittleness or ductility are relevant to categorize a material or component according to their quality. In industry, material tests like tensile tests, compression tests or creep tests are often time consuming and expensive to perform. Therefore, the application of machine learning approaches is considered helpful for an easier generation of material property information. This study also gives an application of machine learning methods on small punch test data for the determination of the property ultimate tensile strength for various materials. A strong correlation between small punch test data and tensile test data was found which ultimately allows to replace more costly tests by simple and fast tests in combination with machine learning.", "published": "2020-10-12T20:30:27Z", "authors": ["Anke Stoll", "Peter Benner"], "categories": ["cs.LG"], "doi": "", "_query": "house price prediction machine learning", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/1909.07218v4", "title": "Learning to Tune XGBoost with XGBoost", "abstract": "In this short paper we investigate whether meta-learning techniques can be used to more effectively tune the hyperparameters of machine learning models using successive halving (SH). We propose a novel variant of the SH algorithm (MeSH), that uses meta-regressors to determine which candidate configurations should be eliminated at each round. We apply MeSH to the problem of tuning the hyperparameters of a gradient-boosted decision tree model. By training and tuning our meta-regressors using existing tuning jobs from 95 datasets, we demonstrate that MeSH can often find a superior solution to both SH and random search.", "published": "2019-09-16T14:09:55Z", "authors": ["Johanna Sommer", "Dimitrios Sarigiannis", "Thomas Parnell"], "categories": ["cs.LG", "stat.ML"], "doi": "", "_query": "house price prediction machine learning", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/1703.09146v1", "title": "GPU Activity Prediction using Representation Learning", "abstract": "GPU activity prediction is an important and complex problem. This is due to the high level of contention among thousands of parallel threads. This problem was mostly addressed using heuristics. We propose a representation learning approach to address this problem. We model any performance metric as a temporal function of the executed instructions with the intuition that the flow of instructions can be identified as distinct activities of the code. Our experiments show high accuracy and non-trivial predictive power of representation learning on a benchmark.", "published": "2017-03-27T15:31:02Z", "authors": ["Aswin Raghavan", "Mohamed Amer", "Timothy Shields", "David Zhang", "Sek Chai"], "categories": ["cs.LG"], "doi": "", "_query": "house price prediction machine learning", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/1711.10781v1", "title": "Introduction to Tensor Decompositions and their Applications in Machine Learning", "abstract": "Tensors are multidimensional arrays of numerical values and therefore generalize matrices to multiple dimensions. While tensors first emerged in the psychometrics community in the $20^{\\text{th}}$ century, they have since then spread to numerous other disciplines, including machine learning. Tensors and their decompositions are especially beneficial in unsupervised learning settings, but are gaining popularity in other sub-disciplines like temporal and multi-relational data analysis, too. The scope of this paper is to give a broad overview of tensors, their decompositions, and how they are used in machine learning. As part of this, we are going to introduce basic tensor concepts, discuss why tensors can be considered more rigid than matrices with respect to the uniqueness of their decomposition, explain the most important factorization algorithms and their properties, provide concrete examples of tensor decomposition applications in machine learning, conduct a case study on tensor-based estimation of mixture models, talk about the current state of research, and provide references to available software libraries.", "published": "2017-11-29T11:29:09Z", "authors": ["Stephan Rabanser", "Oleksandr Shchur", "Stephan Günnemann"], "categories": ["stat.ML", "cs.LG"], "doi": "", "_query": "house price prediction machine learning", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2002.04258v3", "title": "Learning to Switch Among Agents in a Team via 2-Layer Markov Decision Processes", "abstract": "Reinforcement learning agents have been mostly developed and evaluated under the assumption that they will operate in a fully autonomous manner -- they will take all actions. In this work, our goal is to develop algorithms that, by learning to switch control between agents, allow existing reinforcement learning agents to operate under different automation levels. To this end, we first formally define the problem of learning to switch control among agents in a team via a 2-layer Markov decision process. Then, we develop an online learning algorithm that uses upper confidence bounds on the agents' policies and the environment's transition probabilities to find a sequence of switching policies. The total regret of our algorithm with respect to the optimal switching policy is sublinear in the number of learning steps and, whenever multiple teams of agents operate in a similar environment, our algorithm greatly benefits from maintaining shared confidence bounds for the environments' transition probabilities and it enjoys a better regret bound than problem-agnostic algorithms. Simulation experiments in an obstacle avoidance task illustrate our theoretical findings and demonstrate that, by exploiting the specific structure of the problem, our proposed algorithm is superior to problem-agnostic algorithms.", "published": "2020-02-11T08:50:52Z", "authors": ["Vahid Balazadeh", "Abir De", "Adish Singla", "Manuel Gomez-Rodriguez"], "categories": ["cs.LG", "cs.CY", "cs.HC", "eess.SY", "stat.ML"], "doi": "", "_query": "house price prediction machine learning", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/1904.06387v5", "title": "Extrapolating Beyond Suboptimal Demonstrations via Inverse Reinforcement Learning from Observations", "abstract": "A critical flaw of existing inverse reinforcement learning (IRL) methods is their inability to significantly outperform the demonstrator. This is because IRL typically seeks a reward function that makes the demonstrator appear near-optimal, rather than inferring the underlying intentions of the demonstrator that may have been poorly executed in practice. In this paper, we introduce a novel reward-learning-from-observation algorithm, Trajectory-ranked Reward EXtrapolation (T-REX), that extrapolates beyond a set of (approximately) ranked demonstrations in order to infer high-quality reward functions from a set of potentially poor demonstrations. When combined with deep reinforcement learning, T-REX outperforms state-of-the-art imitation learning and IRL methods on multiple Atari and MuJoCo benchmark tasks and achieves performance that is often more than twice the performance of the best demonstration. We also demonstrate that T-REX is robust to ranking noise and can accurately extrapolate intention by simply watching a learner noisily improve at a task over time.", "published": "2019-04-12T19:34:43Z", "authors": ["Daniel S. Brown", "Wonjoon Goo", "Prabhat Nagarajan", "Scott Niekum"], "categories": ["cs.LG", "stat.ML"], "doi": "", "_query": "house price prediction machine learning", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2002.08095v2", "title": "Logarithmic Regret for Learning Linear Quadratic Regulators Efficiently", "abstract": "We consider the problem of learning in Linear Quadratic Control systems whose transition parameters are initially unknown. Recent results in this setting have demonstrated efficient learning algorithms with regret growing with the square root of the number of decision steps. We present new efficient algorithms that achieve, perhaps surprisingly, regret that scales only (poly)logarithmically with the number of steps in two scenarios: when only the state transition matrix $A$ is unknown, and when only the state-action transition matrix $B$ is unknown and the optimal policy satisfies a certain non-degeneracy condition. On the other hand, we give a lower bound that shows that when the latter condition is violated, square root regret is unavoidable.", "published": "2020-02-19T10:09:26Z", "authors": ["Asaf Cassel", "Alon Cohen", "Tomer Koren"], "categories": ["cs.LG", "stat.ML"], "doi": "", "_query": "house price prediction machine learning", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2010.04053v1", "title": "Fairness in Machine Learning: A Survey", "abstract": "As Machine Learning technologies become increasingly used in contexts that affect citizens, companies as well as researchers need to be confident that their application of these methods will not have unexpected social implications, such as bias towards gender, ethnicity, and/or people with disabilities. There is significant literature on approaches to mitigate bias and promote fairness, yet the area is complex and hard to penetrate for newcomers to the domain. This article seeks to provide an overview of the different schools of thought and approaches to mitigating (social) biases and increase fairness in the Machine Learning literature. It organises approaches into the widely accepted framework of pre-processing, in-processing, and post-processing methods, subcategorizing into a further 11 method areas. Although much of the literature emphasizes binary classification, a discussion of fairness in regression, recommender systems, unsupervised learning, and natural language processing is also provided along with a selection of currently available open source libraries. The article concludes by summarising open challenges articulated as four dilemmas for fairness research.", "published": "2020-10-04T21:01:34Z", "authors": ["Simon Caton", "Christian Haas"], "categories": ["cs.LG", "stat.ML"], "doi": "10.1145/3616865", "_query": "house price prediction machine learning", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2311.18356v2", "title": "Towards Comparable Active Learning", "abstract": "Active Learning has received significant attention in the field of machine learning for its potential in selecting the most informative samples for labeling, thereby reducing data annotation costs. However, we show that the reported lifts in recent literature generalize poorly to other domains leading to an inconclusive landscape in Active Learning research. Furthermore, we highlight overlooked problems for reproducing AL experiments that can lead to unfair comparisons and increased variance in the results. This paper addresses these issues by providing an Active Learning framework for a fair comparison of algorithms across different tasks and domains, as well as a fast and performant oracle algorithm for evaluation. To the best of our knowledge, we propose the first AL benchmark that tests algorithms in 3 major domains: Tabular, Image, and Text. We report empirical results for 6 widely used algorithms on 7 real-world and 2 synthetic datasets and aggregate them into a domain-specific ranking of AL algorithms.", "published": "2023-11-30T08:54:32Z", "authors": ["Thorben Werner", "Johannes Burchert", "Lars Schmidt-Thieme"], "categories": ["cs.LG", "stat.ML"], "doi": "", "_query": "house price prediction machine learning", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/1912.11603v2", "title": "Image Enhanced Rotation Prediction for Self-Supervised Learning", "abstract": "The rotation prediction (Rotation) is a simple pretext-task for self-supervised learning (SSL), where models learn useful representations for target vision tasks by solving pretext-tasks. Although Rotation captures information of object shapes, it hardly captures information of textures. To tackle this problem, we introduce a novel pretext-task called image enhanced rotation prediction (IE-Rot) for SSL. IE-Rot simultaneously solves Rotation and another pretext-task based on image enhancement (e.g., sharpening and solarizing) while maintaining simplicity. Through the simultaneous prediction of rotation and image enhancement, models learn representations to capture the information of not only object shapes but also textures. Our experimental results show that IE-Rot models outperform Rotation on various standard benchmarks including ImageNet classification, PASCAL-VOC detection, and COCO detection/segmentation.", "published": "2019-12-25T06:11:35Z", "authors": ["Shin'ya Yamaguchi", "Sekitoshi Kanai", "Tetsuya Shioda", "Shoichiro Takeda"], "categories": ["stat.ML", "cs.CV", "cs.LG"], "doi": "", "_query": "house price prediction machine learning", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2212.13881v3", "title": "Mechanism of feature learning in deep fully connected networks and kernel machines that recursively learn features", "abstract": "In recent years neural networks have achieved impressive results on many technological and scientific tasks. Yet, the mechanism through which these models automatically select features, or patterns in data, for prediction remains unclear. Identifying such a mechanism is key to advancing performance and interpretability of neural networks and promoting reliable adoption of these models in scientific applications. In this paper, we identify and characterize the mechanism through which deep fully connected neural networks learn features. We posit the Deep Neural Feature Ansatz, which states that neural feature learning occurs by implementing the average gradient outer product to up-weight features strongly related to model output. Our ansatz sheds light on various deep learning phenomena including emergence of spurious features and simplicity biases and how pruning networks can increase performance, the \"lottery ticket hypothesis.\" Moreover, the mechanism identified in our work leads to a backpropagation-free method for feature learning with any machine learning model. To demonstrate the effectiveness of this feature learning mechanism, we use it to enable feature learning in classical, non-feature learning models known as kernel machines and show that the resulting models, which we refer to as Recursive Feature Machines, achieve state-of-the-art performance on tabular data.", "published": "2022-12-28T15:50:58Z", "authors": ["Adityanarayanan Radhakrishnan", "Daniel Beaglehole", "Parthe Pandit", "Mikhail Belkin"], "categories": ["cs.LG", "cs.AI", "stat.ML"], "doi": "", "_query": "house price prediction machine learning", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2309.07982v1", "title": "Uncertainty quantification for learned ISTA", "abstract": "Model-based deep learning solutions to inverse problems have attracted increasing attention in recent years as they bridge state-of-the-art numerical performance with interpretability. In addition, the incorporated prior domain knowledge can make the training more efficient as the smaller number of parameters allows the training step to be executed with smaller datasets. Algorithm unrolling schemes stand out among these model-based learning techniques. Despite their rapid advancement and their close connection to traditional high-dimensional statistical methods, they lack certainty estimates and a theory for uncertainty quantification is still elusive. This work provides a step towards closing this gap proposing a rigorous way to obtain confidence intervals for the LISTA estimator.", "published": "2023-09-14T18:39:07Z", "authors": ["Frederik Hoppe", "Claudio Mayrink Verdun", "Felix Krahmer", "Hannah Laus", "Holger Rauhut"], "categories": ["stat.ML", "cs.IT", "cs.LG", "eess.IV", "eess.SP"], "doi": "", "_query": "house price prediction machine learning", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2003.11059v2", "title": "Integrating Physiological Time Series and Clinical Notes with Deep Learning for Improved ICU Mortality Prediction", "abstract": "Intensive Care Unit Electronic Health Records (ICU EHRs) store multimodal data about patients including clinical notes, sparse and irregularly sampled physiological time series, lab results, and more. To date, most methods designed to learn predictive models from ICU EHR data have focused on a single modality. In this paper, we leverage the recently proposed interpolation-prediction deep learning architecture(Shukla and Marlin 2019) as a basis for exploring how physiological time series data and clinical notes can be integrated into a unified mortality prediction model. We study both early and late fusion approaches and demonstrate how the relative predictive value of clinical text and physiological data change over time. Our results show that a late fusion approach can provide a statistically significant improvement in mortality prediction performance over using individual modalities in isolation.", "published": "2020-03-24T18:25:49Z", "authors": ["Satya Narayan Shukla", "Benjamin M. Marlin"], "categories": ["cs.LG", "cs.CY", "stat.ML"], "doi": "", "_query": "house price prediction machine learning", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2305.16703v3", "title": "Sources of Uncertainty in Supervised Machine Learning -- A Statisticians' View", "abstract": "Supervised machine learning and predictive models have achieved an impressive standard today, enabling us to answer questions that were inconceivable a few years ago. Besides these successes, it becomes clear, that beyond pure prediction, which is the primary strength of most supervised machine learning algorithms, the quantification of uncertainty is relevant and necessary as well. However, before quantification is possible, types and sources of uncertainty need to be defined precisely. While first concepts and ideas in this direction have emerged in recent years, this paper adopts a conceptual, basic science perspective and examines possible sources of uncertainty. By adopting the viewpoint of a statistician, we discuss the concepts of aleatoric and epistemic uncertainty, which are more commonly associated with machine learning. The paper aims to formalize the two types of uncertainty and demonstrates that sources of uncertainty are miscellaneous and can not always be decomposed into aleatoric and epistemic. Drawing parallels between statistical concepts and uncertainty in machine learning, we emphasise the role of data and their influence on uncertainty.", "published": "2023-05-26T07:44:19Z", "authors": ["Cornelia Gruber", "Patrick Oliver Schenk", "Malte Schierholz", "Frauke Kreuter", "Göran Kauermann"], "categories": ["stat.ML", "cs.LG"], "doi": "", "_query": "house price prediction machine learning", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2103.16910v1", "title": "Trusted Artificial Intelligence: Towards Certification of Machine Learning Applications", "abstract": "Artificial Intelligence is one of the fastest growing technologies of the 21st century and accompanies us in our daily lives when interacting with technical applications. However, reliance on such technical systems is crucial for their widespread applicability and acceptance. The societal tools to express reliance are usually formalized by lawful regulations, i.e., standards, norms, accreditations, and certificates. Therefore, the TÜV AUSTRIA Group in cooperation with the Institute for Machine Learning at the Johannes Kepler University Linz, proposes a certification process and an audit catalog for Machine Learning applications. We are convinced that our approach can serve as the foundation for the certification of applications that use Machine Learning and Deep Learning, the techniques that drive the current revolution in Artificial Intelligence. While certain high-risk areas, such as fully autonomous robots in workspaces shared with humans, are still some time away from certification, we aim to cover low-risk applications with our certification procedure. Our holistic approach attempts to analyze Machine Learning applications from multiple perspectives to evaluate and verify the aspects of secure software development, functional requirements, data quality, data protection, and ethics. Inspired by existing work, we introduce four criticality levels to map the criticality of a Machine Learning application regarding the impact of its decisions on people, environment, and organizations. Currently, the audit catalog can be applied to low-risk applications within the scope of supervised learning as commonly encountered in industry. Guided by field experience, scientific developments, and market demands, the audit catalog will be extended and modified accordingly.", "published": "2021-03-31T08:59:55Z", "authors": ["Philip Matthias Winter", "Sebastian Eder", "Johannes Weissenböck", "Christoph Schwald", "Thomas Doms", "Tom Vogt", "Sepp Hochreiter", "Bernhard Nessler"], "categories": ["stat.ML", "cs.CY", "cs.LG", "cs.SE"], "doi": "", "_query": "house price prediction machine learning", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2607.21702v2", "title": "An Introduction to Bayesian and Frequentist Simulation-Based Inference with Machine Learning", "abstract": "Simulation-based inference (SBI) with machine learning is an increasingly important tool for solving inverse problems in science and engineering, including parameter inference and the inversion of detector effects. We provide an overview of the Bayesian and frequentist statistical frameworks, describe how machine-learning-based SBI methods, such as neural posterior estimation and neural likelihood estimation, can be used for parameter estimation within these frameworks, and show that the same methods can also be applied to Empirical Bayes or unfolding tasks. We also discuss how to validate inference results and the limitations of SBI with machine learning.", "published": "2026-07-23T18:00:01Z", "authors": ["Maximilian Dax", "Theo Heimel", "Gilles Louppe"], "categories": ["cs.LG", "astro-ph.CO", "astro-ph.GA", "hep-ex", "hep-ph", "stat.ML"], "doi": "", "_query": "house price prediction machine learning", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/1908.05725v1", "title": "Trustable and Automated Machine Learning Running with Blockchain and Its Applications", "abstract": "Machine learning algorithms learn from data and use data from databases that are mutable; therefore, the data and the results of machine learning cannot be fully trusted. Also, the machine learning process is often difficult to automate. A unified analytical framework for trustable machine learning has been presented in the literature. It proposed building a trustable machine learning system by using blockchain technology, which can store data in a permanent and immutable way. In addition, smart contracts on blockchain are used to automate the machine learning process. In the proposed framework, a core machine learning algorithm can have three implementations: server layer implementation, streaming layer implementation, and smart contract implementation. However, there are still open questions. First, the streaming layer usually deploys on edge devices and therefore has limited memory and computing power. How can we run machine learning on the streaming layer? Second, most data that are stored on blockchain are financial transactions, for which fraud detection is often needed. However, in some applications, training data are hard to obtain. Can we build good machine learning models to do fraud detection with limited training data? These questions motivated this paper; which makes two contributions. First, it proposes training a machine learning model on the server layer and saving the model with a special binary data format. Then, the streaming layer can take this blob of binary data as input and score incoming data online. The blob of binary data is very compact and can be deployed on edge devices. Second, the paper presents a new method of synthetic data generation that can enrich the training data set. Experiments show that this synthetic data generation is very effective in applications such as fraud detection in financial data.", "published": "2019-08-14T00:09:12Z", "authors": ["Tao Wang", "Xinmin Wu", "Taiping He"], "categories": ["cs.LG", "cs.CR", "stat.ML"], "doi": "", "_query": "house price prediction machine learning", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2306.10189v4", "title": "MOCK: an Algorithm for Learning Nonparametric Differential Equations via Multivariate Occupation Kernel Functions", "abstract": "Learning a nonparametric system of ordinary differential equations from trajectories in a $d$-dimensional state space requires learning $d$ functions of $d$ variables. Explicit formulations often scale quadratically in $d$ unless additional knowledge about system properties, such as sparsity and symmetries, is available. In this work, we propose a linear approach, the multivariate occupation kernel method (MOCK), using the implicit formulation provided by vector-valued reproducing kernel Hilbert spaces. The solution for the vector field relies on multivariate occupation kernel functions associated with the trajectories and scales linearly with the dimension of the state space. We validate through experiments on a variety of simulated and real datasets ranging from 2 to 1024 dimensions. MOCK outperforms all other comparators on 3 of the 9 datasets on full trajectory prediction and 4 out of the 9 datasets on next-point prediction.", "published": "2023-06-16T21:49:36Z", "authors": ["Victor Rielly", "Kamel Lahouel", "Ethan Lew", "Nicholas Fisher", "Vicky Haney", "Michael Wells", "Bruno Jedynak"], "categories": ["stat.ML", "cs.LG"], "doi": "", "_query": "house price prediction machine learning", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2402.07307v3", "title": "Self-Calibrating Conformal Prediction", "abstract": "In machine learning, model calibration and predictive inference are essential for producing reliable predictions and quantifying uncertainty to support decision-making. Recognizing the complementary roles of point and interval predictions, we introduce Self-Calibrating Conformal Prediction, a method that combines Venn-Abers calibration and conformal prediction to deliver calibrated point predictions alongside prediction intervals with finite-sample validity conditional on these predictions. To achieve this, we extend the original Venn-Abers procedure from binary classification to regression. Our theoretical framework supports analyzing conformal prediction methods that involve calibrating model predictions and subsequently constructing conditionally valid prediction intervals on the same data, where the conditioning set or conformity scores may depend on the calibrated predictions. Real-data experiments show that our method improves interval efficiency through model calibration and offers a practical alternative to feature-conditional validity.", "published": "2024-02-11T21:12:21Z", "authors": ["Lars van der Laan", "Ahmed M. Alaa"], "categories": ["stat.ML", "cs.LG", "stat.ME"], "doi": "", "_query": "house price prediction machine learning", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2202.03099v2", "title": "FL_PyTorch: optimization research simulator for federated learning", "abstract": "Federated Learning (FL) has emerged as a promising technique for edge devices to collaboratively learn a shared machine learning model while keeping training data locally on the device, thereby removing the need to store and access the full data in the cloud. However, FL is difficult to implement, test and deploy in practice considering heterogeneity in common edge device settings, making it fundamentally hard for researchers to efficiently prototype and test their optimization algorithms. In this work, our aim is to alleviate this problem by introducing FL_PyTorch : a suite of open-source software written in python that builds on top of one the most popular research Deep Learning (DL) framework PyTorch. We built FL_PyTorch as a research simulator for FL to enable fast development, prototyping and experimenting with new and existing FL optimization algorithms. Our system supports abstractions that provide researchers with a sufficient level of flexibility to experiment with existing and novel approaches to advance the state-of-the-art. Furthermore, FL_PyTorch is a simple to use console system, allows to run several clients simultaneously using local CPUs or GPU(s), and even remote compute devices without the need for any distributed implementation provided by the user. FL_PyTorch also offers a Graphical User Interface. For new methods, researchers only provide the centralized implementation of their algorithm. To showcase the possibilities and usefulness of our system, we experiment with several well-known state-of-the-art FL algorithms and a few of the most common FL datasets.", "published": "2022-02-07T12:18:28Z", "authors": ["Konstantin Burlachenko", "Samuel Horváth", "Peter Richtárik"], "categories": ["cs.LG", "cs.AI", "cs.MS", "math.OC"], "doi": "10.1145/3488659.3493775", "_query": "house price prediction machine learning", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2108.08313v1", "title": "Teaching Machine Learning for the Physical Sciences: A summary of lessons learned and challenges", "abstract": "This paper summarizes some challenges encountered and best practices established in several years of teaching Machine Learning for the Physical Sciences at the undergraduate and graduate level. I discuss motivations for teaching ML to physicists, desirable properties of pedagogical materials, such as accessibility, relevance, and likeness to real-world research problems, and give examples of components of teaching units.", "published": "2021-08-18T18:00:04Z", "authors": ["Viviana Acquaviva"], "categories": ["physics.ed-ph"], "doi": "", "_query": "house price prediction machine learning", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2502.01158v1", "title": "MIND: Modality-Informed Knowledge Distillation Framework for Multimodal Clinical Prediction Tasks", "abstract": "Multimodal fusion leverages information across modalities to learn better feature representations with the goal of improving performance in fusion-based tasks. However, multimodal datasets, especially in medical settings, are typically smaller than their unimodal counterparts, which can impede the performance of multimodal models. Additionally, the increase in the number of modalities is often associated with an overall increase in the size of the multimodal network, which may be undesirable in medical use cases. Utilizing smaller unimodal encoders may lead to sub-optimal performance, particularly when dealing with high-dimensional clinical data. In this paper, we propose the Modality-INformed knowledge Distillation (MIND) framework, a multimodal model compression approach based on knowledge distillation that transfers knowledge from ensembles of pre-trained deep neural networks of varying sizes into a smaller multimodal student. The teacher models consist of unimodal networks, allowing the student to learn from diverse representations. MIND employs multi-head joint fusion models, as opposed to single-head models, enabling the use of unimodal encoders in the case of unimodal samples without requiring imputation or masking of absent modalities. As a result, MIND generates an optimized multimodal model, enhancing both multimodal and unimodal representations. It can also be leveraged to balance multimodal learning during training. We evaluate MIND on binary and multilabel clinical prediction tasks using time series data and chest X-ray images. Additionally, we assess the generalizability of the MIND framework on three non-medical multimodal multiclass datasets. Experimental results demonstrate that MIND enhances the performance of the smaller multimodal network across all five tasks, as well as various fusion methods and multimodal architectures, compared to state-of-the-art baselines.", "published": "2025-02-03T08:50:00Z", "authors": ["Alejandro Guerra-Manzanares", "Farah E. Shamout"], "categories": ["cs.LG", "cs.AI", "cs.CV"], "doi": "", "_query": "house price prediction machine learning", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/1409.2620v1", "title": "Learning Machines Implemented on Non-Deterministic Hardware", "abstract": "This paper highlights new opportunities for designing large-scale machine learning systems as a consequence of blurring traditional boundaries that have allowed algorithm designers and application-level practitioners to stay -- for the most part -- oblivious to the details of the underlying hardware-level implementations. The hardware/software co-design methodology advocated here hinges on the deployment of compute-intensive machine learning kernels onto compute platforms that trade-off determinism in the computation for improvement in speed and/or energy efficiency. To achieve this, we revisit digital stochastic circuits for approximating matrix computations that are ubiquitous in machine learning algorithms. Theoretical and empirical evaluation is undertaken to assess the impact of the hardware-induced computational noise on algorithm performance. As a proof-of-concept, a stochastic hardware simulator is employed for training deep neural networks for image recognition problems.", "published": "2014-09-09T07:35:40Z", "authors": ["Suyog Gupta", "Vikas Sindhwani", "Kailash Gopalakrishnan"], "categories": ["cs.LG", "stat.ML"], "doi": "", "_query": "house price prediction machine learning", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2106.14122v1", "title": "Score-Based Change Detection for Gradient-Based Learning Machines", "abstract": "The widespread use of machine learning algorithms calls for automatic change detection algorithms to monitor their behavior over time. As a machine learning algorithm learns from a continuous, possibly evolving, stream of data, it is desirable and often critical to supplement it with a companion change detection algorithm to facilitate its monitoring and control. We present a generic score-based change detection method that can detect a change in any number of components of a machine learning model trained via empirical risk minimization. This proposed statistical hypothesis test can be readily implemented for such models designed within a differentiable programming framework. We establish the consistency of the hypothesis test and show how to calibrate it to achieve a prescribed false alarm rate. We illustrate the versatility of the approach on synthetic and real data.", "published": "2021-06-27T01:38:11Z", "authors": ["Lang Liu", "Joseph Salmon", "Zaid Harchaoui"], "categories": ["stat.ML", "cs.LG"], "doi": "", "_query": "house price prediction machine learning", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2203.09607v1", "title": "Learning Distributionally Robust Models at Scale via Composite Optimization", "abstract": "To train machine learning models that are robust to distribution shifts in the data, distributionally robust optimization (DRO) has been proven very effective. However, the existing approaches to learning a distributionally robust model either require solving complex optimization problems such as semidefinite programming or a first-order method whose convergence scales linearly with the number of data samples -- which hinders their scalability to large datasets. In this paper, we show how different variants of DRO are simply instances of a finite-sum composite optimization for which we provide scalable methods. We also provide empirical results that demonstrate the effectiveness of our proposed algorithm with respect to the prior art in order to learn robust models from very large datasets.", "published": "2022-03-17T20:47:42Z", "authors": ["Farzin Haddadpour", "Mohammad Mahdi Kamani", "Mehrdad Mahdavi", "Amin Karbasi"], "categories": ["cs.LG", "stat.ML"], "doi": "", "_query": "house price prediction machine learning", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2010.10596v3", "title": "Counterfactual Explanations and Algorithmic Recourses for Machine Learning: A Review", "abstract": "Machine learning plays a role in many deployed decision systems, often in ways that are difficult or impossible to understand by human stakeholders. Explaining, in a human-understandable way, the relationship between the input and output of machine learning models is essential to the development of trustworthy machine learning based systems. A burgeoning body of research seeks to define the goals and methods of explainability in machine learning. In this paper, we seek to review and categorize research on counterfactual explanations, a specific class of explanation that provides a link between what could have happened had input to a model been changed in a particular way. Modern approaches to counterfactual explainability in machine learning draw connections to the established legal doctrine in many countries, making them appealing to fielded systems in high-impact areas such as finance and healthcare. Thus, we design a rubric with desirable properties of counterfactual explanation algorithms and comprehensively evaluate all currently proposed algorithms against that rubric. Our rubric provides easy comparison and comprehension of the advantages and disadvantages of different approaches and serves as an introduction to major research themes in this field. We also identify gaps and discuss promising research directions in the space of counterfactual explainability.", "published": "2020-10-20T20:08:42Z", "authors": ["Sahil Verma", "Varich Boonsanong", "Minh Hoang", "Keegan E. Hines", "John P. Dickerson", "Chirag Shah"], "categories": ["cs.LG", "cs.AI", "stat.ML"], "doi": "", "_query": "house price prediction machine learning", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2504.17112v1", "title": "Physics-informed features in supervised machine learning", "abstract": "Supervised machine learning involves approximating an unknown functional relationship from a limited dataset of features and corresponding labels. The classical approach to feature-based machine learning typically relies on applying linear regression to standardized features, without considering their physical meaning. This may limit model explainability, particularly in scientific applications. This study proposes a physics-informed approach to feature-based machine learning that constructs non-linear feature maps informed by physical laws and dimensional analysis. These maps enhance model interpretability and, when physical laws are unknown, allow for the identification of relevant mechanisms through feature ranking. The method aims to improve both predictive performance in regression tasks and classification skill scores by integrating domain knowledge into the learning process, while also enabling the potential discovery of new physical equations within the context of explainable machine learning.", "published": "2025-04-23T21:45:49Z", "authors": ["Margherita Lampani", "Sabrina Guastavino", "Michele Piana", "Federico Benvenuto"], "categories": ["stat.ML", "cs.LG", "math.NA"], "doi": "", "_query": "house price prediction machine learning", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/1802.07954v1", "title": "The State of the Art in Integrating Machine Learning into Visual Analytics", "abstract": "Visual analytics systems combine machine learning or other analytic techniques with interactive data visualization to promote sensemaking and analytical reasoning. It is through such techniques that people can make sense of large, complex data. While progress has been made, the tactful combination of machine learning and data visualization is still under-explored. This state-of-the-art report presents a summary of the progress that has been made by highlighting and synthesizing select research advances. Further, it presents opportunities and challenges to enhance the synergy between machine learning and visual analytics for impactful future research directions.", "published": "2018-02-22T09:48:56Z", "authors": ["A. Endert", "W. Ribarsky", "C. Turkay", "W Wong", "I. Nabney", "I Díaz Blanco", "Fabrice Rossi"], "categories": ["stat.ML", "cs.HC", "cs.LG"], "doi": "10.1111/cgf.13092", "_query": "house price prediction machine learning", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2010.11743v1", "title": "The Role of Machine Learning for Trajectory Prediction in Cooperative Driving", "abstract": "In this paper, we study the role that machine learning can play in cooperative driving. Given the increasing rate of connectivity in modern vehicles, and road infrastructure, cooperative driving is a promising first step in automated driving. The example scenario we explored in this paper, is coordinated lane merge, with data collection, test and evaluation all conducted in an automotive test track. The assumption is that vehicles are a mix of those equipped with communication units on board, i.e. connected vehicles, and those that are not connected. However, roadside cameras are connected and can capture all vehicles including those without connectivity. We develop a Traffic Orchestrator that suggests trajectories based on these two sources of information, i.e. connected vehicles, and connected roadside cameras. Recommended trajectories are built, which are then communicated back to the connected vehicles. We explore the use of different machine learning techniques in accurately and timely prediction of trajectories.", "published": "2020-10-21T09:25:17Z", "authors": ["Luis Sequeira", "Toktam Mahmoodi"], "categories": ["cs.LG", "cs.RO"], "doi": "10.1145/3397166.3413470", "_query": "house price prediction machine learning", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2405.16971v2", "title": "A Unified Framework for Tabular Generative Modeling: Loss Functions, Benchmarks, and Improved Multi-objective Bayesian Optimization Approaches", "abstract": "Deep learning (DL) models require extensive data to achieve strong performance and generalization. Deep generative models (DGMs) offer a solution by synthesizing data. Yet current approaches for tabular data often fail to preserve feature correlations and distributions during training, struggle with multi-metric hyperparameter selection, and lack comprehensive evaluation protocols. We address this gap with a unified framework that integrates training, hyperparameter tuning, and evaluation. First, we introduce a novel correlation- and distribution-aware loss function that regularizes DGMs, enhancing their ability to generate synthetic tabular data that faithfully represents the underlying data distributions. Theoretical analysis establishes stability and consistency guarantees. To enable principled hyperparameter search via Bayesian optimization (BO), we also propose a new multi-objective aggregation strategy based on iterative objective refinement Bayesian optimization (IORBO), along with a comprehensive statistical testing framework. We validate the proposed approach using a benchmarking framework with twenty real-world datasets and ten established tabular DGM baselines. The correlation-aware loss function significantly improves synthetic data fidelity and downstream machine learning (ML) performance, while IORBO consistently outperforms standard Bayesian optimization (SBO) in hyperparameter selection. The unified framework advances tabular generative modeling beyond isolated method improvements. Code is available at: https://github.com/vuhoangminh/TabGen-Framework", "published": "2024-05-27T09:08:08Z", "authors": ["Minh H. Vu", "Daniel Edler", "Carl Wibom", "Tommy Löfstedt", "Beatrice Melin", "Martin Rosvall"], "categories": ["cs.LG"], "doi": "", "_query": "house price prediction machine learning", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2508.08883v1", "title": "Position: Causal Machine Learning Requires Rigorous Synthetic Experiments for Broader Adoption", "abstract": "Causal machine learning has the potential to revolutionize decision-making by combining the predictive power of machine learning algorithms with the theory of causal inference. However, these methods remain underutilized by the broader machine learning community, in part because current empirical evaluations do not permit assessment of their reliability and robustness, undermining their practical utility. Specifically, one of the principal criticisms made by the community is the extensive use of synthetic experiments. We argue, on the contrary, that synthetic experiments are essential and necessary to precisely assess and understand the capabilities of causal machine learning methods. To substantiate our position, we critically review the current evaluation practices, spotlight their shortcomings, and propose a set of principles for conducting rigorous empirical analyses with synthetic data. Adopting the proposed principles will enable comprehensive evaluations that build trust in causal machine learning methods, driving their broader adoption and impactful real-world use.", "published": "2025-08-12T12:13:13Z", "authors": ["Audrey Poinsot", "Panayiotis Panayiotou", "Alessandro Leite", "Nicolas Chesneau", "Özgür Şimşek", "Marc Schoenauer"], "categories": ["cs.LG", "cs.AI", "stat.ME", "stat.ML"], "doi": "", "_query": "house price prediction machine learning", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/1804.02969v7", "title": "A review of possible effects of cognitive biases on the interpretation of rule-based machine learning models", "abstract": "While the interpretability of machine learning models is often equated with their mere syntactic comprehensibility, we think that interpretability goes beyond that, and that human interpretability should also be investigated from the point of view of cognitive science. The goal of this paper is to discuss to what extent cognitive biases may affect human understanding of interpretable machine learning models, in particular of logical rules discovered from data. Twenty cognitive biases are covered, as are possible debiasing techniques that can be adopted by designers of machine learning algorithms and software. Our review transfers results obtained in cognitive psychology to the domain of machine learning, aiming to bridge the current gap between these two areas. It needs to be followed by empirical studies specifically focused on the machine learning domain.", "published": "2018-04-09T13:28:56Z", "authors": ["Tomáš Kliegr", "Štěpán Bahník", "Johannes Fürnkranz"], "categories": ["stat.ML", "cs.AI", "cs.LG"], "doi": "10.1016/j.artint.2021.103458", "_query": "house price prediction machine learning", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2001.04561v2", "title": "A survey on Machine Learning-based Performance Improvement of Wireless Networks: PHY, MAC and Network layer", "abstract": "This paper provides a systematic and comprehensive survey that reviews the latest research efforts focused on machine learning (ML) based performance improvement of wireless networks, while considering all layers of the protocol stack (PHY, MAC and network). First, the related work and paper contributions are discussed, followed by providing the necessary background on data-driven approaches and machine learning for non-machine learning experts to understand all discussed techniques. Then, a comprehensive review is presented on works employing ML-based approaches to optimize the wireless communication parameters settings to achieve improved network quality-of-service (QoS) and quality-of-experience (QoE). We first categorize these works into: radio analysis, MAC analysis and network prediction approaches, followed by subcategories within each. Finally, open challenges and broader perspectives are discussed.", "published": "2020-01-13T23:33:29Z", "authors": ["Merima Kulin", "Tarik Kazaz", "Ingrid Moerman", "Eli de Poorter"], "categories": ["cs.LG", "cs.NI", "eess.SP", "stat.ML"], "doi": "", "_query": "house price prediction machine learning", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2408.14964v1", "title": "Cross-Modal Learning for Chemistry Property Prediction: Large Language Models Meet Graph Machine Learning", "abstract": "In the field of chemistry, the objective is to create novel molecules with desired properties, facilitating accurate property predictions for applications such as material design and drug screening. However, existing graph deep learning methods face limitations that curb their expressive power. To address this, we explore the integration of vast molecular domain knowledge from Large Language Models (LLMs) with the complementary strengths of Graph Neural Networks (GNNs) to enhance performance in property prediction tasks. We introduce a Multi-Modal Fusion (MMF) framework that synergistically harnesses the analytical prowess of GNNs and the linguistic generative and predictive abilities of LLMs, thereby improving accuracy and robustness in predicting molecular properties. Our framework combines the effectiveness of GNNs in modeling graph-structured data with the zero-shot and few-shot learning capabilities of LLMs, enabling improved predictions while reducing the risk of overfitting. Furthermore, our approach effectively addresses distributional shifts, a common challenge in real-world applications, and showcases the efficacy of learning cross-modal representations, surpassing state-of-the-art baselines on benchmark datasets for property prediction tasks.", "published": "2024-08-27T11:10:39Z", "authors": ["Sakhinana Sagar Srinivas", "Venkataramana Runkana"], "categories": ["cs.LG"], "doi": "", "_query": "house price prediction machine learning", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2003.00484v2", "title": "An Information-Theoretic Approach to Personalized Explainable Machine Learning", "abstract": "Automated decision making is used routinely throughout our everyday life. Recommender systems decide which jobs, movies, or other user profiles might be interesting to us. Spell checkers help us to make good use of language. Fraud detection systems decide if a credit card transactions should be verified more closely. Many of these decision making systems use machine learning methods that fit complex models to massive datasets. The successful deployment of machine learning (ML) methods to many (critical) application domains crucially depends on its explainability. Indeed, humans have a strong desire to get explanations that resolve the uncertainty about experienced phenomena like the predictions and decisions obtained from ML methods. Explainable ML is challenging since explanations must be tailored (personalized) to individual users with varying backgrounds. Some users might have received university-level education in ML, while other users might have no formal training in linear algebra. Linear regression with few features might be perfectly interpretable for the first group but might be considered a black-box by the latter. We propose a simple probabilistic model for the predictions and user knowledge. This model allows to study explainable ML using information theory. Explaining is here considered as the task of reducing the \"surprise\" incurred by a prediction. We quantify the effect of an explanation by the conditional mutual information between the explanation and prediction, given the user background.", "published": "2020-03-01T13:06:29Z", "authors": ["Alexander Jung", "Pedro H. J. Nardelli"], "categories": ["cs.LG", "stat.ML"], "doi": "10.1109/LSP.2020.2993176", "_query": "house price prediction machine learning", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/1910.03466v1", "title": "Can We Distinguish Machine Learning from Human Learning?", "abstract": "What makes a task relatively more or less difficult for a machine compared to a human? Much AI/ML research has focused on expanding the range of tasks that machines can do, with a focus on whether machines can beat humans. Allowing for differences in scale, we can seek interesting (anomalous) pairs of tasks T, T'. We define interesting in this way: The \"harder to learn\" relation is reversed when comparing human intelligence (HI) to AI. While humans seems to be able to understand problems by formulating rules, ML using neural networks does not rely on constructing rules. We discuss a novel approach where the challenge is to \"perform well under rules that have been created by human beings.\" We suggest that this provides a rigorous and precise pathway for understanding the difference between the two kinds of learning. Specifically, we suggest a large and extensible class of learning tasks, formulated as learning under rules. With these tasks, both the AI and HI will be studied with rigor and precision. The immediate goal is to find interesting groundtruth rule pairs. In the long term, the goal will be to understand, in a generalizable way, what distinguishes interesting pairs from ordinary pairs, and to define saliency behind interesting pairs. This may open new ways of thinking about AI, and provide unexpected insights into human learning.", "published": "2019-10-08T15:37:03Z", "authors": ["Vicki Bier", "Paul B. Kantor", "Gary Lupyan", "Xiaojin Zhu"], "categories": ["cs.LG", "cs.AI", "stat.ML"], "doi": "", "_query": "house price prediction machine learning", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2402.10724v2", "title": "Machine Learning based Prediction of Ditching Loads", "abstract": "We present approaches to predict dynamic ditching loads on aircraft fuselages using machine learning. The employed learning procedure is structured into two parts, the reconstruction of the spatial loads using a convolutional autoencoder (CAE) and the transient evolution of these loads in a subsequent part. Different CAE strategies are assessed and combined with either long short-term memory (LSTM) networks or Koopman-operator based methods to predict the transient behaviour. The training data is compiled by an extension of the momentum method of von-Karman and Wagner and the rationale of the training approach is briefly summarised. The application included refers to a full-scale fuselage of a DLR-D150 aircraft for a range of horizontal and vertical approach velocities at 6° incidence. Results indicate a satisfactory level of predictive agreement for all four investigated surrogate models examined, with the combination of an LSTM and a deep decoder CAE showing the best performance.", "published": "2024-02-16T14:30:46Z", "authors": ["Henning Schwarz", "Micha Überrück", "Jens-Peter M. Zemke", "Thomas Rung"], "categories": ["cs.LG", "cs.CE"], "doi": "", "_query": "house price prediction machine learning", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2304.00249v1", "title": "From Conception to Deployment: Intelligent Stroke Prediction Framework using Machine Learning and Performance Evaluation", "abstract": "Stroke is the second leading cause of death worldwide. Machine learning classification algorithms have been widely adopted for stroke prediction. However, these algorithms were evaluated using different datasets and evaluation metrics. Moreover, there is no comprehensive framework for stroke data analytics. This paper proposes an intelligent stroke prediction framework based on a critical examination of machine learning prediction algorithms in the literature. The five most used machine learning algorithms for stroke prediction are evaluated using a unified setup for objective comparison. Comparative analysis and numerical results reveal that the Random Forest algorithm is best suited for stroke prediction.", "published": "2023-04-01T07:11:31Z", "authors": ["Leila Ismail", "Huned Materwala"], "categories": ["cs.LG", "cs.AI"], "doi": "10.1109/COINS54846.2022.9854961", "_query": "house price prediction machine learning", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/1805.07072v1", "title": "Optimizing for Generalization in Machine Learning with Cross-Validation Gradients", "abstract": "Cross-validation is the workhorse of modern applied statistics and machine learning, as it provides a principled framework for selecting the model that maximizes generalization performance. In this paper, we show that the cross-validation risk is differentiable with respect to the hyperparameters and training data for many common machine learning algorithms, including logistic regression, elastic-net regression, and support vector machines. Leveraging this property of differentiability, we propose a cross-validation gradient method (CVGM) for hyperparameter optimization. Our method enables efficient optimization in high-dimensional hyperparameter spaces of the cross-validation risk, the best surrogate of the true generalization ability of our learning algorithm.", "published": "2018-05-18T07:04:37Z", "authors": ["Shane Barratt", "Rishi Sharma"], "categories": ["stat.ML", "cs.LG"], "doi": "", "_query": "house price prediction machine learning", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2304.06412v1", "title": "Quantifying and Explaining Machine Learning Uncertainty in Predictive Process Monitoring: An Operations Research Perspective", "abstract": "This paper introduces a comprehensive, multi-stage machine learning methodology that effectively integrates information systems and artificial intelligence to enhance decision-making processes within the domain of operations research. The proposed framework adeptly addresses common limitations of existing solutions, such as the neglect of data-driven estimation for vital production parameters, exclusive generation of point forecasts without considering model uncertainty, and lacking explanations regarding the sources of such uncertainty. Our approach employs Quantile Regression Forests for generating interval predictions, alongside both local and global variants of SHapley Additive Explanations for the examined predictive process monitoring problem. The practical applicability of the proposed methodology is substantiated through a real-world production planning case study, emphasizing the potential of prescriptive analytics in refining decision-making procedures. This paper accentuates the imperative of addressing these challenges to fully harness the extensive and rich data resources accessible for well-informed decision-making.", "published": "2023-04-13T11:18:22Z", "authors": ["Nijat Mehdiyev", "Maxim Majlatow", "Peter Fettke"], "categories": ["cs.LG", "cs.AI", "math.OC", "stat.ML"], "doi": "", "_query": "house price prediction machine learning", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2303.04183v1", "title": "Robustness-preserving Lifelong Learning via Dataset Condensation", "abstract": "Lifelong learning (LL) aims to improve a predictive model as the data source evolves continuously. Most work in this learning paradigm has focused on resolving the problem of 'catastrophic forgetting,' which refers to a notorious dilemma between improving model accuracy over new data and retaining accuracy over previous data. Yet, it is also known that machine learning (ML) models can be vulnerable in the sense that tiny, adversarial input perturbations can deceive the models into producing erroneous predictions. This motivates the research objective of this paper - specification of a new LL framework that can salvage model robustness (against adversarial attacks) from catastrophic forgetting. Specifically, we propose a new memory-replay LL strategy that leverages modern bi-level optimization techniques to determine the 'coreset' of the current data (i.e., a small amount of data to be memorized) for ease of preserving adversarial robustness over time. We term the resulting LL framework 'Data-Efficient Robustness-Preserving LL' (DERPLL). The effectiveness of DERPLL is evaluated for class-incremental image classification using ResNet-18 over the CIFAR-10 dataset. Experimental results show that DERPLL outperforms the conventional coreset-guided LL baseline and achieves a substantial improvement in both standard accuracy and robust accuracy.", "published": "2023-03-07T19:09:03Z", "authors": ["Jinghan Jia", "Yihua Zhang", "Dogyoon Song", "Sijia Liu", "Alfred Hero"], "categories": ["cs.LG", "cs.CV"], "doi": "", "_query": "house price prediction machine learning", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2601.08957v2", "title": "The Connection Between Monetary Policy and Housing Prices: Public Perception and Expert Communication", "abstract": "We study how the general public perceives the link between monetary policy and housing markets. Using a large-scale, cross-country survey experiment in Austria, Germany, Italy, Sweden, and the United Kingdom, we examine households' understanding of monetary policy, their beliefs about its impact on house prices, and how these beliefs respond to expert information. We find that while most respondents grasp the basic mechanisms of conventional monetary policy and recognize the connection between interest rates and house prices, literacy regarding unconventional monetary policy is very low. Beliefs about the monetary policy-housing nexus are malleable and respond to information, particularly when it is provided by academic economists rather than central bankers. Monetary policy literacy is strongly related to education, gender, age, and experience in housing and mortgage markets. Our results highlight the central role of housing in how households interpret monetary policy and point to the importance of credible and inclusive communication strategies for effective policy transmission.", "published": "2026-01-13T19:51:06Z", "authors": ["Philipp Poyntner", "Sofie R. Waltl"], "categories": ["econ.GN"], "doi": "", "_query": "house price prediction machine learning", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2302.01952v3", "title": "On a continuous time model of gradient descent dynamics and instability in deep learning", "abstract": "The recipe behind the success of deep learning has been the combination of neural networks and gradient-based optimization. Understanding the behavior of gradient descent however, and particularly its instability, has lagged behind its empirical success. To add to the theoretical tools available to study gradient descent we propose the principal flow (PF), a continuous time flow that approximates gradient descent dynamics. To our knowledge, the PF is the only continuous flow that captures the divergent and oscillatory behaviors of gradient descent, including escaping local minima and saddle points. Through its dependence on the eigendecomposition of the Hessian the PF sheds light on the recently observed edge of stability phenomena in deep learning. Using our new understanding of instability we propose a learning rate adaptation method which enables us to control the trade-off between training stability and test set evaluation performance.", "published": "2023-02-03T19:03:10Z", "authors": ["Mihaela Rosca", "Yan Wu", "Chongli Qin", "Benoit Dherin"], "categories": ["stat.ML", "cs.LG"], "doi": "", "_query": "house price prediction machine learning", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2002.02561v7", "title": "Spectrum Dependent Learning Curves in Kernel Regression and Wide Neural Networks", "abstract": "We derive analytical expressions for the generalization performance of kernel regression as a function of the number of training samples using theoretical methods from Gaussian processes and statistical physics. Our expressions apply to wide neural networks due to an equivalence between training them and kernel regression with the Neural Tangent Kernel (NTK). By computing the decomposition of the total generalization error due to different spectral components of the kernel, we identify a new spectral principle: as the size of the training set grows, kernel machines and neural networks fit successively higher spectral modes of the target function. When data are sampled from a uniform distribution on a high-dimensional hypersphere, dot product kernels, including NTK, exhibit learning stages where different frequency modes of the target function are learned. We verify our theory with simulations on synthetic data and MNIST dataset.", "published": "2020-02-07T00:03:40Z", "authors": ["Blake Bordelon", "Abdulkadir Canatar", "Cengiz Pehlevan"], "categories": ["cs.LG", "stat.ML"], "doi": "", "_query": "house price prediction machine learning", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2409.03023v1", "title": "Machine learning of phases and structures for model systems in physics", "abstract": "The detection of phase transitions is a fundamental challenge in condensed matter physics, traditionally addressed through analytical methods and direct numerical simulations. In recent years, machine learning techniques have emerged as powerful tools to complement these standard approaches, offering valuable insights into phase and structure determination. Additionally, they have been shown to enhance the application of traditional methods. In this work, we review recent advancements in this area, with a focus on our contributions to phase and structure determination using supervised and unsupervised learning methods in several systems: (a) 2D site percolation, (b) the 3D Anderson model of localization, (c) the 2D $J_1$-$J_2$ Ising model, and (d) the prediction of large-angle convergent beam electron diffraction patterns.", "published": "2024-09-04T18:28:22Z", "authors": ["Djenabou Bayo", "Burak Çivitcioğlu", "Joseph J Webb", "Andreas Honecker", "Rudolf A. Römer"], "categories": ["cond-mat.dis-nn"], "doi": "10.7566/JPSJ.94.031002", "_query": "house price prediction machine learning", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2302.10612v1", "title": "Tree-Based Machine Learning Methods For Vehicle Insurance Claims Size Prediction", "abstract": "Vehicle insurance claims size prediction needs methods to efficiently handle these claims. Machine learning (ML) is one of the methods that solve this problem. Tree-based ensemble learning algorithms are highly effective and widely used ML methods. This study considers how vehicle insurance providers incorporate ML methods in their companies and explores how the models can be applied to insurance big data. We utilize various tree-based ML methods, such as bagging, random forest, and gradient boosting, to determine the relative importance of predictors in predicting claims size and to explore the relationships between claims size and predictors. Furthermore, we evaluate and compare these models' performances. The results show that tree-based ensemble methods are better than the classical least square method. Keywords: claims size prediction; machine learning; tree-based ensemble methods; vehicle insurance.", "published": "2023-02-21T11:43:01Z", "authors": ["Edossa Merga Terefe"], "categories": ["cs.LG", "stat.AP"], "doi": "", "_query": "house price prediction machine learning", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/1203.3783v1", "title": "Learning Feature Hierarchies with Centered Deep Boltzmann Machines", "abstract": "Deep Boltzmann machines are in principle powerful models for extracting the hierarchical structure of data. Unfortunately, attempts to train layers jointly (without greedy layer-wise pretraining) have been largely unsuccessful. We propose a modification of the learning algorithm that initially recenters the output of the activation functions to zero. This modification leads to a better conditioned Hessian and thus makes learning easier. We test the algorithm on real data and demonstrate that our suggestion, the centered deep Boltzmann machine, learns a hierarchy of increasingly abstract representations and a better generative model of data.", "published": "2012-03-16T19:01:10Z", "authors": ["Grégoire Montavon", "Klaus-Robert Müller"], "categories": ["stat.ML", "cs.AI", "cs.LG"], "doi": "10.1007/978-3-642-35289-8_33", "_query": "house price prediction machine learning", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/1803.00444v3", "title": "Inverse Reinforcement Learning via Nonparametric Spatio-Temporal Subgoal Modeling", "abstract": "Advances in the field of inverse reinforcement learning (IRL) have led to sophisticated inference frameworks that relax the original modeling assumption of observing an agent behavior that reflects only a single intention. Instead of learning a global behavioral model, recent IRL methods divide the demonstration data into parts, to account for the fact that different trajectories may correspond to different intentions, e.g., because they were generated by different domain experts. In this work, we go one step further: using the intuitive concept of subgoals, we build upon the premise that even a single trajectory can be explained more efficiently locally within a certain context than globally, enabling a more compact representation of the observed behavior. Based on this assumption, we build an implicit intentional model of the agent's goals to forecast its behavior in unobserved situations. The result is an integrated Bayesian prediction framework that significantly outperforms existing IRL solutions and provides smooth policy estimates consistent with the expert's plan. Most notably, our framework naturally handles situations where the intentions of the agent change over time and classical IRL algorithms fail. In addition, due to its probabilistic nature, the model can be straightforwardly applied in active learning scenarios to guide the demonstration process of the expert.", "published": "2018-03-01T15:31:28Z", "authors": ["Adrian Šošić", "Elmar Rueckert", "Jan Peters", "Abdelhak M. Zoubir", "Heinz Koeppl"], "categories": ["cs.LG", "cs.AI", "cs.RO", "eess.SY", "stat.ML"], "doi": "", "_query": "house price prediction machine learning", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2005.06562v1", "title": "A solution to the overdamping problem when simulating dust-gas mixtures with smoothed particle hydrodynamics", "abstract": "We present a fix to the overdamping problem found by Laibe & Price (2012) when simulating strongly coupled dust-gas mixtures using two different sets of particles using smoothed particle hydrodynamics. Our solution is to compute the drag at the barycentre between gas and dust particle pairs when computing the drag force by reconstructing the velocity field, similar to the procedure in Godunov-type solvers. This fixes the overdamping problem at negligible computational cost, but with additional memory required to store velocity derivatives. We employ slope limiters to avoid spurious oscillations at shocks, finding the van Leer Monotonized Central limiter most effective.", "published": "2020-05-13T19:56:04Z", "authors": ["Daniel J. Price", "Guillaume Laibe"], "categories": ["astro-ph.IM", "astro-ph.EP", "physics.comp-ph"], "doi": "10.1093/mnras/staa1366", "_query": "house price prediction machine learning", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2110.01258v1", "title": "A Aelf-supervised Tibetan-chinese Vocabulary Alignment Method Based On Adversarial Learning", "abstract": "Tibetan is a low-resource language. In order to alleviate the shortage of parallel corpus between Tibetan and Chinese, this paper uses two monolingual corpora and a small number of seed dictionaries to learn the semi-supervised method with seed dictionaries and self-supervised adversarial training method through the similarity calculation of word clusters in different embedded spaces and puts forward an improved self-supervised adversarial learning method of Tibetan and Chinese monolingual data alignment only. The experimental results are as follows. First, the experimental results of Tibetan syllables Chinese characters are not good, which reflects the weak semantic correlation between Tibetan syllables and Chinese characters; second, the seed dictionary of semi-supervised method made before 10 predicted word accuracy of 66.5 (Tibetan - Chinese) and 74.8 (Chinese - Tibetan) results, to improve the self-supervision methods in both language directions have reached 53.5 accuracy.", "published": "2021-10-04T08:56:33Z", "authors": ["Enshuai Hou", "Jie zhu"], "categories": ["cs.CL"], "doi": "", "_query": "house price prediction machine learning", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/1805.11450v1", "title": "Model-based Pricing for Machine Learning in a Data Marketplace", "abstract": "Data analytics using machine learning (ML) has become ubiquitous in science, business intelligence, journalism and many other domains. While a lot of work focuses on reducing the training cost, inference runtime and storage cost of ML models, little work studies how to reduce the cost of data acquisition, which potentially leads to a loss of sellers' revenue and buyers' affordability and efficiency. In this paper, we propose a model-based pricing (MBP) framework, which instead of pricing the data, directly prices ML model instances. We first formally describe the desired properties of the MBP framework, with a focus on avoiding arbitrage. Next, we show a concrete realization of the MBP framework via a noise injection approach, which provably satisfies the desired formal properties. Based on the proposed framework, we then provide algorithmic solutions on how the seller can assign prices to models under different market scenarios (such as to maximize revenue). Finally, we conduct extensive experiments, which validate that the MBP framework can provide high revenue to the seller, high affordability to the buyer, and also operate on low runtime cost.", "published": "2018-05-26T06:02:40Z", "authors": ["Lingjiao Chen", "Paraschos Koutris", "Arun Kumar"], "categories": ["cs.DB", "cs.GT", "cs.LG", "math.OC", "stat.ML"], "doi": "", "_query": "house price prediction machine learning", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2304.07299v1", "title": "Supervised Machine Learning for Breast Cancer Risk Factors Analysis and Survival Prediction", "abstract": "The choice of the most effective treatment may eventually be influenced by breast cancer survival prediction. To predict the chances of a patient surviving, a variety of techniques were employed, such as statistical, machine learning, and deep learning models. In the current study, 1904 patient records from the METABRIC dataset were utilized to predict a 5-year breast cancer survival using a machine learning approach. In this study, we compare the outcomes of seven classification models to evaluate how well they perform using the following metrics: recall, AUC, confusion matrix, accuracy, precision, false positive rate, and true positive rate. The findings demonstrate that the classifiers for Logistic Regression (LR), Support Vector Machines (SVM), Decision Tree (DT), Random Forest (RD), Extremely Randomized Trees (ET), K-Nearest Neighbor (KNN), and Adaptive Boosting (AdaBoost) can accurately predict the survival rate of the tested samples, which is 75,4\\%, 74,7\\%, 71,5\\%, 75,5\\%, 70,3\\%, and 78 percent.", "published": "2023-04-13T12:32:14Z", "authors": ["Khaoula Chtouki", "Maryem Rhanoui", "Mounia Mikram", "Kamelia Amazian", "Siham Yousfi"], "categories": ["cs.LG", "cs.AI"], "doi": "", "_query": "house price prediction machine learning", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/1911.10500v2", "title": "Causality for Machine Learning", "abstract": "Graphical causal inference as pioneered by Judea Pearl arose from research on artificial intelligence (AI), and for a long time had little connection to the field of machine learning. This article discusses where links have been and should be established, introducing key concepts along the way. It argues that the hard open problems of machine learning and AI are intrinsically related to causality, and explains how the field is beginning to understand them.", "published": "2019-11-24T11:04:56Z", "authors": ["Bernhard Schölkopf"], "categories": ["cs.LG", "cs.AI", "stat.ML"], "doi": "10.1145/3501714.3501755", "_query": "house price prediction machine learning", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2410.13989v1", "title": "Reproducibility study of \"LICO: Explainable Models with Language-Image Consistency\"", "abstract": "The growing reproducibility crisis in machine learning has brought forward a need for careful examination of research findings. This paper investigates the claims made by Lei et al. (2023) regarding their proposed method, LICO, for enhancing post-hoc interpretability techniques and improving image classification performance. LICO leverages natural language supervision from a vision-language model to enrich feature representations and guide the learning process. We conduct a comprehensive reproducibility study, employing (Wide) ResNets and established interpretability methods like Grad-CAM and RISE. We were mostly unable to reproduce the authors' results. In particular, we did not find that LICO consistently led to improved classification performance or improvements in quantitative and qualitative measures of interpretability. Thus, our findings highlight the importance of rigorous evaluation and transparent reporting in interpretability research.", "published": "2024-10-17T19:41:34Z", "authors": ["Luan Fletcher", "Robert van der Klis", "Martin Sedláček", "Stefan Vasilev", "Christos Athanasiadis"], "categories": ["cs.CV"], "doi": "", "_query": "house price prediction machine learning", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/1908.07782v1", "title": "Decentralized Federated Learning: A Segmented Gossip Approach", "abstract": "The emerging concern about data privacy and security has motivated the proposal of federated learning, which allows nodes to only synchronize the locally-trained models instead their own original data. Conventional federated learning architecture, inherited from the parameter server design, relies on highly centralized topologies and the assumption of large nodes-to-server bandwidths. However, in real-world federated learning scenarios the network capacities between nodes are highly uniformly distributed and smaller than that in a datacenter. It is of great challenges for conventional federated learning approaches to efficiently utilize network capacities between nodes. In this paper, we propose a model segment level decentralized federated learning to tackle this problem. In particular, we propose a segmented gossip approach, which not only makes full utilization of node-to-node bandwidth, but also has good training convergence. The experimental results show that even the training time can be highly reduced as compared to centralized federated learning.", "published": "2019-08-21T10:21:43Z", "authors": ["Chenghao Hu", "Jingyan Jiang", "Zhi Wang"], "categories": ["cs.LG", "cs.DC", "cs.NI", "stat.ML"], "doi": "", "_query": "house price prediction machine learning", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/1701.04944v5", "title": "A Machine Learning Alternative to P-values", "abstract": "This paper presents an alternative approach to p-values in regression settings. This approach, whose origins can be traced to machine learning, is based on the leave-one-out bootstrap for prediction error. In machine learning this is called the out-of-bag (OOB) error. To obtain the OOB error for a model, one draws a bootstrap sample and fits the model to the in-sample data. The out-of-sample prediction error for the model is obtained by calculating the prediction error for the model using the out-of-sample data. Repeating and averaging yields the OOB error, which represents a robust cross-validated estimate of the accuracy of the underlying model. By a simple modification to the bootstrap data involving \"noising up\" a variable, the OOB method yields a variable importance (VIMP) index, which directly measures how much a specific variable contributes to the prediction precision of a model. VIMP provides a scientifically interpretable measure of the effect size of a variable, we call the \"predictive effect size\", that holds whether the researcher's model is correct or not, unlike the p-value whose calculation is based on the assumed correctness of the model. We also discuss a marginal VIMP index, also easily calculated, which measures the marginal effect of a variable, or what we call \"the discovery effect\". The OOB procedure can be applied to both parametric and nonparametric regression models and requires only that the researcher can repeatedly fit their model to bootstrap and modified bootstrap data. We illustrate this approach on a survival data set involving patients with systolic heart failure and to a simulated survival data set where the model is incorrectly specified to illustrate its robustness to model misspecification.", "published": "2017-01-18T05:07:03Z", "authors": ["Min Lu", "Hemant Ishwaran"], "categories": ["stat.ML", "cs.LG"], "doi": "", "_query": "house price prediction machine learning", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2308.09766v3", "title": "Time Series Predictions in Unmonitored Sites: A Survey of Machine Learning Techniques in Water Resources", "abstract": "Prediction of dynamic environmental variables in unmonitored sites remains a long-standing challenge for water resources science. The majority of the world's freshwater resources have inadequate monitoring of critical environmental variables needed for management. Yet, the need to have widespread predictions of hydrological variables such as river flow and water quality has become increasingly urgent due to climate and land use change over the past decades, and their associated impacts on water resources. Modern machine learning methods increasingly outperform their process-based and empirical model counterparts for hydrologic time series prediction with their ability to extract information from large, diverse data sets. We review relevant state-of-the art applications of machine learning for streamflow, water quality, and other water resources prediction and discuss opportunities to improve the use of machine learning with emerging methods for incorporating watershed characteristics into deep learning models, transfer learning, and incorporating process knowledge into machine learning models. The analysis here suggests most prior efforts have been focused on deep learning learning frameworks built on many sites for predictions at daily time scales in the United States, but that comparisons between different classes of machine learning methods are few and inadequate. We identify several open questions for time series predictions in unmonitored sites that include incorporating dynamic inputs and site characteristics, mechanistic understanding and spatial context, and explainable AI techniques in modern machine learning frameworks.", "published": "2023-08-18T18:30:33Z", "authors": ["Jared D. Willard", "Charuleka Varadharajan", "Xiaowei Jia", "Vipin Kumar"], "categories": ["cs.LG"], "doi": "10.1017/eds.2024.14", "_query": "house price prediction machine learning", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2005.06182v2", "title": "MLSolv-A: A Novel Machine Learning-Based Prediction of Solvation Free Energies from Pairwise Atomistic Interactions", "abstract": "Recent advances in machine learning and their applications have lead to the development of diverse structure-property relationship models for crucial chemical properties, and the solvation free energy is one of them. Here, we introduce a novel ML-based solvation model, which calculates the solvation energy from pairwise atomistic interactions. The novelty of the proposed model consists of a simple architecture: two encoding functions extract atomic feature vectors from the given chemical structure, while the inner product between two atomistic features calculates their interactions. The results on 6,493 experimental measurements achieve outstanding performance and transferability for enlarging training data due to its solvent-non-specific nature. Analysis of the interaction map shows there is a great potential that our model reproduces group contributions on the solvation energy, which makes us believe that the model not only provides the predicted target property but also gives us more detailed physicochemical insights.", "published": "2020-05-13T06:53:54Z", "authors": ["Hyuntae Lim", "YounJoon Jung"], "categories": ["stat.ML", "cond-mat.soft", "cs.LG", "physics.chem-ph"], "doi": "10.1186/s13321-021-00533-z", "_query": "house price prediction machine learning", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2509.21547v2", "title": "Machine Learning. The Science of Selection under Uncertainty", "abstract": "Learning, whether natural or artificial, is a process of selection. It starts with a set of candidate options and selects the more successful ones. In the case of machine learning the selection is done based on empirical estimates of prediction accuracy of candidate prediction rules on some data. Due to randomness of data sampling the empirical estimates are inherently noisy, leading to selection under uncertainty. The book provides statistical tools to obtain theoretical guarantees on the outcome of selection under uncertainty. We start with concentration of measure inequalities, which are the main statistical instrument for controlling how much an empirical estimate of expectation of a function deviates from the true expectation. The book covers a broad range of inequalities, including Markov's, Chebyshev's, Hoeffding's, Bernstein's, Empirical Bernstein's, Unexpected Bernstein's, kl, and split-kl. We then study the classical (offline) supervised learning and provide a range of tools for deriving generalization bounds, including Occam's razor, Vapnik-Chervonenkis analysis, and PAC-Bayesian analysis. The latter is further applied to derive generalization guarantees for weighted majority votes. After covering the offline setting, we turn our attention to online learning. We present the space of online learning problems characterized by environmental feedback, environmental resistance, and structural complexity. A common performance measure in online learning is regret, which compares performance of an algorithm to performance of the best prediction rule in hindsight, out of a restricted set of prediction rules. We present tools for deriving regret bounds in stochastic and adversarial environments, and under full information and bandit feedback.", "published": "2025-09-25T20:33:13Z", "authors": ["Yevgeny Seldin"], "categories": ["cs.LG", "stat.ML"], "doi": "", "_query": "house price prediction machine learning", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/astro-ph/0310790v1", "title": "Smoothed Particle Magnetohydrodynamics II. Variational principles and variable smoothing length terms", "abstract": "In this paper we show how a Lagrangian variational principle can be used to derive the SPMHD (smoothed particle magnetohydrodynamics) equations for ideal MHD. We also consider the effect of a variable smoothing length in the SPH kernels after which we demonstrate by numerical tests that the consistent treatment of terms relating to the gradient of the smoothing length in the SPMHD equations significantly improves the accuracy of the algorithm. Our results complement those obtained in a companion paper (Price and Monaghan 2003a, paper I) for non ideal MHD where artificial dissipative terms were included to handle shocks.", "published": "2003-10-28T12:31:58Z", "authors": ["D. J. Price", "J. J. Monaghan"], "categories": ["astro-ph"], "doi": "10.1111/j.1365-2966.2004.07346.x", "_query": "house price prediction machine learning", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2210.13206v3", "title": "Post-Selection Confidence Bounds for Prediction Performance", "abstract": "In machine learning, the selection of a promising model from a potentially large number of competing models and the assessment of its generalization performance are critical tasks that need careful consideration. Typically, model selection and evaluation are strictly separated endeavors, splitting the sample at hand into a training, validation, and evaluation set, and only compute a single confidence interval for the prediction performance of the final selected model. We however propose an algorithm how to compute valid lower confidence bounds for multiple models that have been selected based on their prediction performances in the evaluation set by interpreting the selection problem as a simultaneous inference problem. We use bootstrap tilting and a maxT-type multiplicity correction. The approach is universally applicable for any combination of prediction models, any model selection strategy, and any prediction performance measure that accepts weights. We conducted various simulation experiments which show that our proposed approach yields lower confidence bounds that are at least comparably good as bounds from standard approaches, and that reliably reach the nominal coverage probability. In addition, especially when sample size is small, our proposed approach yields better performing prediction models than the default selection of only one model for evaluation does.", "published": "2022-10-24T13:28:43Z", "authors": ["Pascal Rink", "Werner Brannath"], "categories": ["stat.ML", "cs.LG"], "doi": "", "_query": "house price prediction machine learning", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2209.11533v2", "title": "A Unified Perspective on Natural Gradient Variational Inference with Gaussian Mixture Models", "abstract": "Variational inference with Gaussian mixture models (GMMs) enables learning of highly tractable yet multi-modal approximations of intractable target distributions with up to a few hundred dimensions. The two currently most effective methods for GMM-based variational inference, VIPS and iBayes-GMM, both employ independent natural gradient updates for the individual components and their weights. We show for the first time, that their derived updates are equivalent, although their practical implementations and theoretical guarantees differ. We identify several design choices that distinguish both approaches, namely with respect to sample selection, natural gradient estimation, stepsize adaptation, and whether trust regions are enforced or the number of components adapted. We argue that for both approaches, the quality of the learned approximations can heavily suffer from the respective design choices: By updating the individual components using samples from the mixture model, iBayes-GMM often fails to produce meaningful updates to low-weight components, and by using a zero-order method for estimating the natural gradient, VIPS scales badly to higher-dimensional problems. Furthermore, we show that information-geometric trust-regions (used by VIPS) are effective even when using first-order natural gradient estimates, and often outperform the improved Bayesian learning rule (iBLR) update used by iBayes-GMM. We systematically evaluate the effects of design choices and show that a hybrid approach significantly outperforms both prior works. Along with this work, we publish our highly modular and efficient implementation for natural gradient variational inference with Gaussian mixture models, which supports 432 different combinations of design choices, facilitates the reproduction of all our experiments, and may prove valuable for the practitioner.", "published": "2022-09-23T11:43:27Z", "authors": ["Oleg Arenz", "Philipp Dahlinger", "Zihan Ye", "Michael Volpp", "Gerhard Neumann"], "categories": ["cs.LG", "cs.RO", "stat.ML"], "doi": "", "_query": "house price prediction machine learning", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2504.04260v2", "title": "LOGLO-FNO: Efficient Learning of Local and Global Features in Fourier Neural Operators", "abstract": "Modeling high-frequency information is a critical challenge in scientific machine learning. For instance, fully turbulent flow simulations of the Navier-Stokes equations at Reynolds numbers 3500 and above can generate high-frequency signals due to swirling fluid motions caused by eddies and vortices. Faithfully modeling such signals using neural nets depends on the accurate reconstruction of moderate to high frequencies. However, it has been well known that neural nets exhibit spectral or frequency bias towards learning low-frequency components. Meanwhile, Fourier Neural Operators (FNOs) have emerged as a popular class of data-driven models for surrogate modeling and solving PDEs. Although impressive results were achieved on several PDE benchmark problems, FNOs perform poorly in learning non-dominant frequencies characterized by local features. This limitation stems from spectral bias inherent in neural nets and the explicit exclusion of high-frequency modes in FNOs and their variants. Therefore, to mitigate these issues and improve FNO's spectral learning capabilities to represent a broad range of frequency components, we propose two key architectural enhancements: (i) a parallel branch performing local spectral convolution (ii) a high-frequency propagation module. Moreover, we propose a novel frequency-sensitive loss based on radially binned spectral errors. This introduction of a parallel branch for local convolution reduces the trainable parameters by up to 50% while achieving the accuracy of FNO that relies solely on global convolution. Moreover, our findings demonstrate that the proposed model improves stability over longer rollouts. Experiments on six challenging PDEs in fluid mechanics, wave propagation, and biological pattern formation, and the qualitative and spectral analysis of predictions, show the effectiveness of our method over SOTA neural operator families of baselines.", "published": "2025-04-05T19:35:04Z", "authors": ["Marimuthu Kalimuthu", "David Holzmüller", "Mathias Niepert"], "categories": ["cs.LG", "cs.AI", "cs.CV", "physics.geo-ph"], "doi": "", "_query": "house price prediction machine learning", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2112.04807v1", "title": "Effective dimension of machine learning models", "abstract": "Making statements about the performance of trained models on tasks involving new data is one of the primary goals of machine learning, i.e., to understand the generalization power of a model. Various capacity measures try to capture this ability, but usually fall short in explaining important characteristics of models that we observe in practice. In this study, we propose the local effective dimension as a capacity measure which seems to correlate well with generalization error on standard data sets. Importantly, we prove that the local effective dimension bounds the generalization error and discuss the aptness of this capacity measure for machine learning models.", "published": "2021-12-09T10:00:18Z", "authors": ["Amira Abbas", "David Sutter", "Alessio Figalli", "Stefan Woerner"], "categories": ["cs.LG", "stat.ML"], "doi": "", "_query": "house price prediction machine learning", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2104.09325v2", "title": "Modelling the COVID-19 virus evolution with Incremental Machine Learning", "abstract": "The investment of time and resources for better strategies and methodologies to tackle a potential pandemic is key to deal with potential outbreaks of new variants or other viruses in the future. In this work, we recreated the scene of a year ago, 2020, when the pandemic erupted across the world for the fifty countries with more COVID-19 cases reported. We performed some experiments in which we compare state-of-the-art machine learning algorithms, such as LSTM, against online incremental machine learning algorithms to adapt them to the daily changes in the spread of the disease and predict future COVID-19 cases. To compare the methods, we performed three experiments: In the first one, we trained the models using only data from the country we predicted. In the second one, we use data from all fifty countries to train and predict each of them. In the first and second experiment, we used a static hold-out approach for all methods. In the third experiment, we trained the incremental methods sequentially, using a prequential evaluation. This scheme is not suitable for most state-of-the-art machine learning algorithms because they need to be retrained from scratch for every batch of predictions, causing a computational burden. Results show that incremental methods are a promising approach to adapt to changes of the disease over time; they are always up to date with the last state of the data distribution, and they have a significantly lower computational cost than other techniques such as LSTMs.", "published": "2021-04-14T16:08:35Z", "authors": ["Andrés L. Suárez-Cetrulo", "Ankit Kumar", "Luis Miralles-Pechuán"], "categories": ["cs.LG", "stat.ML"], "doi": "", "_query": "house price prediction machine learning", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2408.08629v2", "title": "Navigating Uncertainties in Machine Learning for Structural Dynamics: A Comprehensive Survey of Probabilistic and Non-Probabilistic Approaches in Forward and Inverse Problems", "abstract": "In the era of big data, machine learning (ML) has become a powerful tool in various fields, notably impacting structural dynamics. ML algorithms offer advantages by modeling physical phenomena based on data, even in the absence of underlying mechanisms. However, uncertainties such as measurement noise and modeling errors can compromise the reliability of ML predictions, highlighting the need for effective uncertainty awareness to enhance prediction robustness. This paper presents a comprehensive review on navigating uncertainties in ML, categorizing uncertainty-aware approaches into probabilistic methods (including Bayesian and frequentist perspectives) and non-probabilistic methods (such as interval learning and fuzzy learning). Bayesian neural networks, known for their uncertainty quantification and nonlinear mapping capabilities, are emphasized for their superior performance and potential. The review covers various techniques and methodologies for addressing uncertainties in ML, discussing fundamentals and implementation procedures of each method. While providing a concise overview of fundamental concepts, the paper refrains from in-depth critical explanations. Strengths and limitations of each approach are examined, along with their applications in structural dynamic forward problems like response prediction, sensitivity assessment, and reliability analysis, and inverse problems like system identification, model updating, and damage identification. Additionally, the review identifies research gaps and suggests future directions for investigations, aiming to provide comprehensive insights to the research community. By offering an extensive overview of both probabilistic and non-probabilistic approaches, this review aims to assist researchers and practitioners in making informed decisions when utilizing ML techniques to address uncertainties in structural dynamic problems.", "published": "2024-08-16T09:43:01Z", "authors": ["Wang-Ji Yan", "Lin-Feng Mei", "Jiang Mo", "Costas Papadimitriou", "Ka-Veng Yuen", "Michael Beer"], "categories": ["cs.LG", "math.DS"], "doi": "10.1007/s11831-026-10688-3", "_query": "house price prediction machine learning", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2108.00819v1", "title": "Active Learning in Gaussian Process State Space Model", "abstract": "We investigate active learning in Gaussian Process state-space models (GPSSM). Our problem is to actively steer the system through latent states by determining its inputs such that the underlying dynamics can be optimally learned by a GPSSM. In order that the most informative inputs are selected, we employ mutual information as our active learning criterion. In particular, we present two approaches for the approximation of mutual information for the GPSSM given latent states. The proposed approaches are evaluated in several physical systems where we actively learn the underlying non-linear dynamics represented by the state-space model.", "published": "2021-07-30T16:52:38Z", "authors": ["Hon Sum Alec Yu", "Dingling Yao", "Christoph Zimmer", "Marc Toussaint", "Duy Nguyen-Tuong"], "categories": ["cs.LG", "cs.AI", "stat.ML"], "doi": "", "_query": "house price prediction machine learning", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2107.14330v2", "title": "Developing Open Source Educational Resources for Machine Learning and Data Science", "abstract": "Education should not be a privilege but a common good. It should be openly accessible to everyone, with as few barriers as possible; even more so for key technologies such as Machine Learning (ML) and Data Science (DS). Open Educational Resources (OER) are a crucial factor for greater educational equity. In this paper, we describe the specific requirements for OER in ML and DS and argue that it is especially important for these fields to make source files publicly available, leading to Open Source Educational Resources (OSER). We present our view on the collaborative development of OSER, the challenges this poses, and first steps towards their solutions. We outline how OSER can be used for blended learning scenarios and share our experiences in university education. Finally, we discuss additional challenges such as credit assignment or granting certificates.", "published": "2021-07-28T10:20:20Z", "authors": ["Ludwig Bothmann", "Sven Strickroth", "Giuseppe Casalicchio", "David Rügamer", "Marius Lindauer", "Fabian Scheipl", "Bernd Bischl"], "categories": ["cs.CY", "cs.LG"], "doi": "", "_query": "house price prediction machine learning", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2309.06838v2", "title": "Supervised Machine Learning and Physics based Machine Learning approach for prediction of peak temperature distribution in Additive Friction Stir Deposition of Aluminium Alloy", "abstract": "Additive friction stir deposition (AFSD) is a novel solid-state additive manufacturing technique that circumvents issues of porosity, cracking, and properties anisotropy that plague traditional powder bed fusion and directed energy deposition approaches. However, correlations between process parameters, thermal profiles, and resulting microstructure in AFSD remain poorly understood. This hinders process optimization for properties. This work employs a framework combining supervised machine learning (SML) and physics-informed neural networks (PINNs) to predict peak temperature distribution in AFSD from process parameters. Eight regression algorithms were implemented for SML modeling, while four PINNs leveraged governing equations for transport, wave propagation, heat transfer, and quantum mechanics. Across multiple statistical measures, ensemble techniques like gradient boosting proved superior for SML, with lowest MSE of 165.78. The integrated ML approach was also applied to classify deposition quality from process factors, with logistic regression delivering robust accuracy. By fusing data-driven learning and fundamental physics, this dual methodology provides comprehensive insights into tailoring microstructure through thermal management in AFSD. The work demonstrates the power of bridging statistical and physics-based modeling for elucidating AM process-property relationships.", "published": "2023-09-13T09:39:42Z", "authors": ["Akshansh Mishra"], "categories": ["cs.LG", "math.OC", "stat.ML"], "doi": "", "_query": "house price prediction machine learning", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2307.00280v1", "title": "SysNoise: Exploring and Benchmarking Training-Deployment System Inconsistency", "abstract": "Extensive studies have shown that deep learning models are vulnerable to adversarial and natural noises, yet little is known about model robustness on noises caused by different system implementations. In this paper, we for the first time introduce SysNoise, a frequently occurred but often overlooked noise in the deep learning training-deployment cycle. In particular, SysNoise happens when the source training system switches to a disparate target system in deployments, where various tiny system mismatch adds up to a non-negligible difference. We first identify and classify SysNoise into three categories based on the inference stage; we then build a holistic benchmark to quantitatively measure the impact of SysNoise on 20+ models, comprehending image classification, object detection, instance segmentation and natural language processing tasks. Our extensive experiments revealed that SysNoise could bring certain impacts on model robustness across different tasks and common mitigations like data augmentation and adversarial training show limited effects on it. Together, our findings open a new research topic and we hope this work will raise research attention to deep learning deployment systems accounting for model performance. We have open-sourced the benchmark and framework at https://modeltc.github.io/systemnoise_web.", "published": "2023-07-01T09:22:54Z", "authors": ["Yan Wang", "Yuhang Li", "Ruihao Gong", "Aishan Liu", "Yanfei Wang", "Jian Hu", "Yongqiang Yao", "Yunchen Zhang", "Tianzi Xiao", "Fengwei Yu", "Xianglong Liu"], "categories": ["cs.LG", "cs.AI", "cs.CV"], "doi": "", "_query": "house price prediction machine learning", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2608.18659v1", "title": "Quantum-Logic Tsetlin Machines: Interpretable Quantum Machine Learning with Commuting Projector Clauses", "abstract": "Tsetlin Machines (TMs) learn interpretable Boolean clauses using finite-state automata. We introduce the Quantum-Logic Tsetlin Machine (QL-TM), which replaces Boolean literals with quantum propositions represented by projectors while retaining classical include/exclude automata. Clauses are restricted to commuting measurement contexts and activate through the Born probability of their joint projector. We prove an exact reduction to ordinary Boolean TM clauses in diagonal computational-basis contexts and connect Pauli-projector clauses to stabilizer and syndrome semantics. Controlled experiments on Bell states, phase-flip syndromes, randomized 16-class stabilizer tasks, mixed literal pools, context-budget ablations, and finite-shot noise show that correct non-diagonal contexts recover physically meaningful clauses, while diagonal or wrong contexts lose the relevant phase/syndrome information. The context-budget results closely follow the predicted separability ladder 2^(b-k) as true stabilizer generators are removed. The contribution is a controlled bridge between Tsetlin clause learning and quantum logic, not a claim of quantum advantage.", "published": "2026-08-19T08:04:26Z", "authors": ["Krishna Bhatia"], "categories": ["quant-ph", "cs.LG", "cs.LO"], "doi": "", "_query": "house price prediction machine learning", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2012.04830v4", "title": "Machine Learning for Cataract Classification and Grading on Ophthalmic Imaging Modalities: A Survey", "abstract": "Cataracts are the leading cause of visual impairment and blindness globally. Over the years, researchers have achieved significant progress in developing state-of-the-art machine learning techniques for automatic cataract classification and grading, aiming to prevent cataracts early and improve clinicians' diagnosis efficiency. This survey provides a comprehensive survey of recent advances in machine learning techniques for cataract classification/grading based on ophthalmic images. We summarize existing literature from two research directions: conventional machine learning methods and deep learning methods. This survey also provides insights into existing works of both merits and limitations. In addition, we discuss several challenges of automatic cataract classification/grading based on machine learning techniques and present possible solutions to these challenges for future research.", "published": "2020-12-09T02:37:41Z", "authors": ["Xiaoqing Zhang", "Yan Hu", "Zunjie Xiao", "Jiansheng Fang", "Risa Higashita", "Jiang Liu"], "categories": ["eess.IV", "cs.CV", "cs.LG"], "doi": "", "_query": "house price prediction machine learning", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2012.04830v4", "title": "Machine Learning for Cataract Classification and Grading on Ophthalmic Imaging Modalities: A Survey", "abstract": "Cataracts are the leading cause of visual impairment and blindness globally. Over the years, researchers have achieved significant progress in developing state-of-the-art machine learning techniques for automatic cataract classification and grading, aiming to prevent cataracts early and improve clinicians' diagnosis efficiency. This survey provides a comprehensive survey of recent advances in machine learning techniques for cataract classification/grading based on ophthalmic images. We summarize existing literature from two research directions: conventional machine learning methods and deep learning methods. This survey also provides insights into existing works of both merits and limitations. In addition, we discuss several challenges of automatic cataract classification/grading based on machine learning techniques and present possible solutions to these challenges for future research.", "published": "2020-12-09T02:37:41Z", "authors": ["Xiaoqing Zhang", "Yan Hu", "Zunjie Xiao", "Jiansheng Fang", "Risa Higashita", "Jiang Liu"], "categories": ["eess.IV", "cs.CV", "cs.LG"], "doi": "", "_query": "house price prediction machine learning", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/1801.05062v2", "title": "Multi-Label Learning from Medical Plain Text with Convolutional Residual Models", "abstract": "Predicting diagnoses from Electronic Health Records (EHRs) is an important medical application of multi-label learning. We propose a convolutional residual model for multi-label classification from doctor notes in EHR data. A given patient may have multiple diagnoses, and therefore multi-label learning is required. We employ a Convolutional Neural Network (CNN) to encode plain text into a fixed-length sentence embedding vector. Since diagnoses are typically correlated, a deep residual network is employed on top of the CNN encoder, to capture label (diagnosis) dependencies and incorporate information directly from the encoded sentence vector. A real EHR dataset is considered, and we compare the proposed model with several well-known baselines, to predict diagnoses based on doctor notes. Experimental results demonstrate the superiority of the proposed convolutional residual model.", "published": "2018-01-15T22:59:17Z", "authors": ["Xinyuan Zhang", "Ricardo Henao", "Zhe Gan", "Yitong Li", "Lawrence Carin"], "categories": ["stat.ML", "cs.LG", "stat.AP"], "doi": "", "_query": "house price prediction machine learning", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/1807.02442v1", "title": "Multi-Task Learning with Incomplete Data for Healthcare", "abstract": "Multi-task learning is a type of transfer learning that trains multiple tasks simultaneously and leverages the shared information between related tasks to improve the generalization performance. However, missing features in the input matrix is a much more difficult problem which needs to be carefully addressed. Removing records with missing values can significantly reduce the sample size, which is impractical for datasets with large percentage of missing values. Popular imputation methods often distort the covariance structure of the data, which causes inaccurate inference. In this paper we propose using plug-in covariance matrix estimators to tackle the challenge of missing features. Specifically, we analyze the plug-in estimators under the framework of robust multi-task learning with LASSO and graph regularization, which captures the relatedness between tasks via graph regularization. We use the Alzheimer's disease progression dataset as an example to show how the proposed framework is effective for prediction and model estimation when missing data is present.", "published": "2018-07-06T15:08:26Z", "authors": ["Xin J. Hunt", "Saba Emrani", "Ilknur Kaynar Kabul", "Jorge Silva"], "categories": ["stat.ML", "cs.LG"], "doi": "", "_query": "house price prediction machine learning", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2005.09428v2", "title": "Quantum-Classical Machine learning by Hybrid Tensor Networks", "abstract": "Tensor networks (TN) have found a wide use in machine learning, and in particular, TN and deep learning bear striking similarities. In this work, we propose the quantum-classical hybrid tensor networks (HTN) which combine tensor networks with classical neural networks in a uniform deep learning framework to overcome the limitations of regular tensor networks in machine learning. We first analyze the limitations of regular tensor networks in the applications of machine learning involving the representation power and architecture scalability. We conclude that in fact the regular tensor networks are not competent to be the basic building blocks of deep learning. Then, we discuss the performance of HTN which overcome all the deficiency of regular tensor networks for machine learning. In this sense, we are able to train HTN in the deep learning way which is the standard combination of algorithms such as Back Propagation and Stochastic Gradient Descent. We finally provide two applicable cases to show the potential applications of HTN, including quantum states classification and quantum-classical autoencoder. These cases also demonstrate the great potentiality to design various HTN in deep learning way.", "published": "2020-05-15T10:20:35Z", "authors": ["Ding Liu", "Jiaqi Yao", "Zekun Yao", "Quan Zhang"], "categories": ["cs.LG", "quant-ph", "stat.ML"], "doi": "", "_query": "house price prediction machine learning", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2208.02610v2", "title": "Twitter Attribute Classification with Q-Learning on Bitcoin Price Prediction", "abstract": "Aspiring to achieve an accurate Bitcoin price prediction based on people's opinions on Twitter usually requires millions of tweets, using different text mining techniques (preprocessing, tokenization, stemming, stop word removal), and developing a machine learning model to perform the prediction. These attempts lead to the employment of a significant amount of computer power, central processing unit (CPU) utilization, random-access memory (RAM) usage, and time. To address this issue, in this paper, we consider a classification of tweet attributes that effects on price changes and computer resource usage levels while obtaining an accurate price prediction. To classify tweet attributes having a high effect on price movement, we collect all Bitcoin-related tweets posted in a certain period and divide them into four categories based on the following tweet attributes: $(i)$ the number of followers of the tweet poster, $(ii)$ the number of comments on the tweet, $(iii)$ the number of likes, and $(iv)$ the number of retweets. We separately train and test by using the Q-learning model with the above four categorized sets of tweets and find the best accurate prediction among them. Especially, we design several reward functions to improve the prediction accuracy of the Q-leaning. We compare our approach with a classic approach where all Bitcoin-related tweets are used as input data for the model, by analyzing the CPU workloads, RAM usage, memory, time, and prediction accuracy. The results show that tweets posted by users with the most followers have the most influence on a future price, and their utilization leads to spending 80\\% less time, 88.8\\% less CPU consumption, and 12.5\\% more accurate predictions compared with the classic approach.", "published": "2022-08-04T12:18:07Z", "authors": ["Sattarov Otabek", "Jaeyoung Choi"], "categories": ["cs.SI"], "doi": "", "_query": "house price prediction machine learning", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/1908.10796v2", "title": "Multi-Objective Automatic Machine Learning with AutoxgboostMC", "abstract": "AutoML systems are currently rising in popularity, as they can build powerful models without human oversight. They often combine techniques from many different sub-fields of machine learning in order to find a model or set of models that optimize a user-supplied criterion, such as predictive performance. The ultimate goal of such systems is to reduce the amount of time spent on menial tasks, or tasks that can be solved better by algorithms while leaving decisions that require human intelligence to the end-user. In recent years, the importance of other criteria, such as fairness and interpretability, and many others have become more and more apparent. Current AutoML frameworks either do not allow to optimize such secondary criteria or only do so by limiting the system's choice of models and preprocessing steps. We propose to optimize additional criteria defined by the user directly to guide the search towards an optimal machine learning pipeline. In order to demonstrate the need and usefulness of our approach, we provide a simple multi-criteria AutoML system and showcase an exemplary application.", "published": "2019-08-28T15:52:57Z", "authors": ["Florian Pfisterer", "Stefan Coors", "Janek Thomas", "Bernd Bischl"], "categories": ["stat.ML", "cs.LG"], "doi": "", "_query": "house price prediction machine learning", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/1808.06865v1", "title": "Machine Learning for Spatiotemporal Sequence Forecasting: A Survey", "abstract": "Spatiotemporal systems are common in the real-world. Forecasting the multi-step future of these spatiotemporal systems based on the past observations, or, Spatiotemporal Sequence Forecasting (STSF), is a significant and challenging problem. Although lots of real-world problems can be viewed as STSF and many research works have proposed machine learning based methods for them, no existing work has summarized and compared these methods from a unified perspective. This survey aims to provide a systematic review of machine learning for STSF. In this survey, we define the STSF problem and classify it into three subcategories: Trajectory Forecasting of Moving Point Cloud (TF-MPC), STSF on Regular Grid (STSF-RG) and STSF on Irregular Grid (STSF-IG). We then introduce the two major challenges of STSF: 1) how to learn a model for multi-step forecasting and 2) how to adequately model the spatial and temporal structures. After that, we review the existing works for solving these challenges, including the general learning strategies for multi-step forecasting, the classical machine learning based methods for STSF, and the deep learning based methods for STSF. We also compare these methods and point out some potential research directions.", "published": "2018-08-21T12:17:08Z", "authors": ["Xingjian Shi", "Dit-Yan Yeung"], "categories": ["cs.LG", "stat.ML"], "doi": "", "_query": "house price prediction machine learning", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/1910.02136v2", "title": "Risks of Using Non-verified Open Data: A case study on using Machine Learning techniques for predicting Pregnancy Outcomes in India", "abstract": "Artificial intelligence (AI) has evolved considerably in the last few years. While applications of AI is now becoming more common in fields like retail and marketing, application of AI in solving problems related to developing countries is still an emerging topic. Specially, AI applications in resource-poor settings remains relatively nascent. There is a huge scope of AI being used in such settings. For example, researchers have started exploring AI applications to reduce poverty and deliver a broad range of critical public services. However, despite many promising use cases, there are many dataset related challenges that one has to overcome in such projects. These challenges often take the form of missing data, incorrectly collected data and improperly labeled variables, among other factors. As a result, we can often end up using data that is not representative of the problem we are trying to solve. In this case study, we explore the challenges of using such an open dataset from India, to predict an important health outcome. We highlight how the use of AI without proper understanding of reporting metrics can lead to erroneous conclusions.", "published": "2019-10-04T20:27:20Z", "authors": ["Anusua Trivedi", "Sumit Mukherjee", "Edmund Tse", "Anne Ewing", "Juan Lavista Ferres"], "categories": ["cs.LG", "cs.AI", "stat.ML"], "doi": "", "_query": "house price prediction machine learning", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2212.06663v2", "title": "Quantum Policy Gradient Algorithm with Optimized Action Decoding", "abstract": "Quantum machine learning implemented by variational quantum circuits (VQCs) is considered a promising concept for the noisy intermediate-scale quantum computing era. Focusing on applications in quantum reinforcement learning, we propose a specific action decoding procedure for a quantum policy gradient approach. We introduce a novel quality measure that enables us to optimize the classical post-processing required for action selection, inspired by local and global quantum measurements. The resulting algorithm demonstrates a significant performance improvement in several benchmark environments. With this technique, we successfully execute a full training routine on a 5-qubit hardware device. Our method introduces only negligible classical overhead and has the potential to improve VQC-based algorithms beyond the field of quantum reinforcement learning.", "published": "2022-12-13T15:42:10Z", "authors": ["Nico Meyer", "Daniel D. Scherer", "Axel Plinge", "Christopher Mutschler", "Michael J. Hartmann"], "categories": ["quant-ph", "cs.LG"], "doi": "", "_query": "house price prediction machine learning", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/1206.6421v1", "title": "Structured Learning from Partial Annotations", "abstract": "Structured learning is appropriate when predicting structured outputs such as trees, graphs, or sequences. Most prior work requires the training set to consist of complete trees, graphs or sequences. Specifying such detailed ground truth can be tedious or infeasible for large outputs. Our main contribution is a large margin formulation that makes structured learning from only partially annotated data possible. The resulting optimization problem is non-convex, yet can be efficiently solve by concave-convex procedure (CCCP) with novel speedup strategies. We apply our method to a challenging tracking-by-assignment problem of a variable number of divisible objects. On this benchmark, using only 25% of a full annotation we achieve a performance comparable to a model learned with a full annotation. Finally, we offer a unifying perspective of previous work using the hinge, ramp, or max loss for structured learning, followed by an empirical comparison on their practical performance.", "published": "2012-06-27T19:59:59Z", "authors": ["Xinghua Lou", "Fred Hamprecht"], "categories": ["cs.LG", "stat.ML"], "doi": "", "_query": "house price prediction machine learning", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2002.05929v1", "title": "Optimal Pricing of Internet of Things: A Machine Learning Approach", "abstract": "Internet of things (IoT) produces massive data from devices embedded with sensors. The IoT data allows creating profitable services using machine learning. However, previous research does not address the problem of optimal pricing and bundling of machine learning-based IoT services. In this paper, we define the data value and service quality from a machine learning perspective. We present an IoT market model which consists of data vendors selling data to service providers, and service providers offering IoT services to customers. Then, we introduce optimal pricing schemes for the standalone and bundled selling of IoT services. In standalone service sales, the service provider optimizes the size of bought data and service subscription fee to maximize its profit. For service bundles, the subscription fee and data sizes of the grouped IoT services are optimized to maximize the total profit of cooperative service providers. We show that bundling IoT services maximizes the profit of service providers compared to the standalone selling. For profit sharing of bundled services, we apply the concepts of core and Shapley solutions from cooperative game theory as efficient and fair allocations of payoffs among the cooperative service providers in the bundling coalition.", "published": "2020-02-14T09:17:40Z", "authors": ["Mohammad Abu Alsheikh", "Dinh Thai Hoang", "Dusit Niyato", "Derek Leong", "Ping Wang", "Zhu Han"], "categories": ["cs.NI", "cs.LG"], "doi": "10.1109/JSAC.2020.2971898", "_query": "house price prediction machine learning", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/1611.08699v1", "title": "Machine Learning on Human Connectome Data from MRI", "abstract": "Functional MRI (fMRI) and diffusion MRI (dMRI) are non-invasive imaging modalities that allow in-vivo analysis of a patient's brain network (known as a connectome). Use of these technologies has enabled faster and better diagnoses and treatments of neurological disorders and a deeper understanding of the human brain. Recently, researchers have been exploring the application of machine learning models to connectome data in order to predict clinical outcomes and analyze the importance of subnetworks in the brain. Connectome data has unique properties, which present both special challenges and opportunities when used for machine learning. The purpose of this work is to review the literature on the topic of applying machine learning models to MRI-based connectome data. This field is growing rapidly and now encompasses a large body of research. To summarize the research done to date, we provide a comparative, structured summary of 77 relevant works, tabulated according to different criteria, that represent the majority of the literature on this topic. (We also published a living version of this table online at http://connectomelearning.cs.sfu.ca that the community can continue to contribute to.) After giving an overview of how connectomes are constructed from dMRI and fMRI data, we discuss the variety of machine learning tasks that have been explored with connectome data. We then compare the advantages and drawbacks of different machine learning approaches that have been employed, discussing different feature selection and feature extraction schemes, as well as the learning models and regularization penalties themselves. Throughout this discussion, we focus particularly on how the methods are adapted to the unique nature of graphical connectome data. Finally, we conclude by summarizing the current state of the art and by outlining what we believe are strategic directions for future research.", "published": "2016-11-26T11:14:22Z", "authors": ["Colin J Brown", "Ghassan Hamarneh"], "categories": ["cs.LG", "q-bio.NC", "stat.ML"], "doi": "", "_query": "house price prediction machine learning", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2402.09236v2", "title": "Learning Interpretable Concepts: Unifying Causal Representation Learning and Foundation Models", "abstract": "To build intelligent machine learning systems, there are two broad approaches. One approach is to build inherently interpretable models, as endeavored by the growing field of causal representation learning. The other approach is to build highly-performant foundation models and then invest efforts into understanding how they work. In this work, we relate these two approaches and study how to learn human-interpretable concepts from data. Weaving together ideas from both fields, we formally define a notion of concepts and show that they can be provably recovered from diverse data. Experiments on synthetic data and large language models show the utility of our unified approach.", "published": "2024-02-14T15:23:59Z", "authors": ["Goutham Rajendran", "Simon Buchholz", "Bryon Aragam", "Bernhard Schölkopf", "Pradeep Ravikumar"], "categories": ["cs.LG", "cs.AI", "math.ST", "stat.ML"], "doi": "", "_query": "house price prediction machine learning", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2307.02732v1", "title": "Evaluating the Evaluators: Are Current Few-Shot Learning Benchmarks Fit for Purpose?", "abstract": "Numerous benchmarks for Few-Shot Learning have been proposed in the last decade. However all of these benchmarks focus on performance averaged over many tasks, and the question of how to reliably evaluate and tune models trained for individual tasks in this regime has not been addressed. This paper presents the first investigation into task-level evaluation -- a fundamental step when deploying a model. We measure the accuracy of performance estimators in the few-shot setting, consider strategies for model selection, and examine the reasons for the failure of evaluators usually thought of as being robust. We conclude that cross-validation with a low number of folds is the best choice for directly estimating the performance of a model, whereas using bootstrapping or cross validation with a large number of folds is better for model selection purposes. Overall, we find that existing benchmarks for few-shot learning are not designed in such a way that one can get a reliable picture of how effectively methods can be used on individual tasks.", "published": "2023-07-06T02:31:38Z", "authors": ["Luísa Shimabucoro", "Timothy Hospedales", "Henry Gouk"], "categories": ["cs.LG", "stat.ML"], "doi": "", "_query": "house price prediction machine learning", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2204.06967v1", "title": "JUE Insight: The (Non-)Effect of Opportunity Zones on Housing Prices", "abstract": "Will the Opportunity Zones (OZ) program, America's largest new place-based policy in decades, generate neighborhood change? We compare single-family housing price growth in OZs with price growth in areas that were eligible but not included in the program. We also compare OZs to their nearest geographic neighbors. Our most credible estimates rule out price impacts greater than 0.5 percentage points with 95% confidence, suggesting that, so far, home buyers don't believe that this subsidy will generate major neighborhood change. OZ status reduces prices in areas with little employment, perhaps because buyers think that subsidizing new investment will increase housing supply. Mixed evidence suggests that OZs may have increased residential permitting.", "published": "2022-04-14T13:48:40Z", "authors": ["Jiafeng Chen", "Edward Glaeser", "David Wessel"], "categories": ["econ.GN"], "doi": "10.1016/j.jue.2022.103451", "_query": "house price prediction machine learning", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/1806.10961v3", "title": "Automatic Exploration of Machine Learning Experiments on OpenML", "abstract": "Understanding the influence of hyperparameters on the performance of a machine learning algorithm is an important scientific topic in itself and can help to improve automatic hyperparameter tuning procedures. Unfortunately, experimental meta data for this purpose is still rare. This paper presents a large, free and open dataset addressing this problem, containing results on 38 OpenML data sets, six different machine learning algorithms and many different hyperparameter configurations. Results where generated by an automated random sampling strategy, termed the OpenML Random Bot. Each algorithm was cross-validated up to 20.000 times per dataset with different hyperparameters settings, resulting in a meta dataset of around 2.5 million experiments overall.", "published": "2018-06-28T13:37:10Z", "authors": ["Daniel Kühn", "Philipp Probst", "Janek Thomas", "Bernd Bischl"], "categories": ["stat.ML", "cs.DB", "cs.LG"], "doi": "", "_query": "house price prediction machine learning", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2204.10083v1", "title": "A two-level machine learning framework for predictive maintenance: comparison of learning formulations", "abstract": "Predicting incoming failures and scheduling maintenance based on sensors information in industrial machines is increasingly important to avoid downtime and machine failure. Different machine learning formulations can be used to solve the predictive maintenance problem. However, many of the approaches studied in the literature are not directly applicable to real-life scenarios. Indeed, many of those approaches usually either rely on labelled machine malfunctions in the case of classification and fault detection, or rely on finding a monotonic health indicator on which a prediction can be made in the case of regression and remaining useful life estimation, which is not always feasible. Moreover, the decision-making part of the problem is not always studied in conjunction with the prediction phase. This paper aims to design and compare different formulations for predictive maintenance in a two-level framework and design metrics that quantify both the failure detection performance as well as the timing of the maintenance decision. The first level is responsible for building a health indicator by aggregating features using a learning algorithm. The second level consists of a decision-making system that can trigger an alarm based on this health indicator. Three degrees of refinements are compared in the first level of the framework, from simple threshold-based univariate predictive technique to supervised learning methods based on the remaining time before failure. We choose to use the Support Vector Machine (SVM) and its variations as the common algorithm used in all the formulations. We apply and compare the different strategies on a real-world rotating machine case study and observe that while a simple model can already perform well, more sophisticated refinements enhance the predictions for well-chosen parameters.", "published": "2022-04-21T13:24:28Z", "authors": ["Valentin Hamaide", "Denis Joassin", "Lauriane Castin", "François Glineur"], "categories": ["cs.LG", "eess.SP"], "doi": "", "_query": "house price prediction machine learning", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2410.19241v2", "title": "Enhancing Exchange Rate Forecasting with Explainable Deep Learning Models", "abstract": "Accurate exchange rate prediction is fundamental to financial stability and international trade, positioning it as a critical focus in economic and financial research. Traditional forecasting models often falter when addressing the inherent complexities and non-linearities of exchange rate data. This study explores the application of advanced deep learning models, including LSTM, CNN, and transformer-based architectures, to enhance the predictive accuracy of the RMB/USD exchange rate. Utilizing 40 features across 6 categories, the analysis identifies TSMixer as the most effective model for this task. A rigorous feature selection process emphasizes the inclusion of key economic indicators, such as China-U.S. trade volumes and exchange rates of other major currencies like the euro-RMB and yen-dollar pairs. The integration of grad-CAM visualization techniques further enhances model interpretability, allowing for clearer identification of the most influential features and bolstering the credibility of the predictions. These findings underscore the pivotal role of fundamental economic data in exchange rate forecasting and highlight the substantial potential of machine learning models to deliver more accurate and reliable predictions, thereby serving as a valuable tool for financial analysis and decision-making.", "published": "2024-10-25T01:29:54Z", "authors": ["Shuchen Meng", "Andi Chen", "Chihang Wang", "Mengyao Zheng", "Fangyu Wu", "Xupeng Chen", "Haowei Ni", "Panfeng Li"], "categories": ["cs.LG"], "doi": "10.1109/EIECS63941.2024.10800545", "_query": "house price prediction machine learning", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2103.11251v2", "title": "Interpretable Machine Learning: Fundamental Principles and 10 Grand Challenges", "abstract": "Interpretability in machine learning (ML) is crucial for high stakes decisions and troubleshooting. In this work, we provide fundamental principles for interpretable ML, and dispel common misunderstandings that dilute the importance of this crucial topic. We also identify 10 technical challenge areas in interpretable machine learning and provide history and background on each problem. Some of these problems are classically important, and some are recent problems that have arisen in the last few years. These problems are: (1) Optimizing sparse logical models such as decision trees; (2) Optimization of scoring systems; (3) Placing constraints into generalized additive models to encourage sparsity and better interpretability; (4) Modern case-based reasoning, including neural networks and matching for causal inference; (5) Complete supervised disentanglement of neural networks; (6) Complete or even partial unsupervised disentanglement of neural networks; (7) Dimensionality reduction for data visualization; (8) Machine learning models that can incorporate physics and other generative or causal constraints; (9) Characterization of the \"Rashomon set\" of good models; and (10) Interpretable reinforcement learning. This survey is suitable as a starting point for statisticians and computer scientists interested in working in interpretable machine learning.", "published": "2021-03-20T21:58:27Z", "authors": ["Cynthia Rudin", "Chaofan Chen", "Zhi Chen", "Haiyang Huang", "Lesia Semenova", "Chudi Zhong"], "categories": ["cs.LG", "stat.ML"], "doi": "", "_query": "house price prediction machine learning", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/1903.05202v2", "title": "Continual Learning in Practice", "abstract": "This paper describes a reference architecture for self-maintaining systems that can learn continually, as data arrives. In environments where data evolves, we need architectures that manage Machine Learning (ML) models in production, adapt to shifting data distributions, cope with outliers, retrain when necessary, and adapt to new tasks. This represents continual AutoML or Automatically Adaptive Machine Learning. We describe the challenges and proposes a reference architecture.", "published": "2019-03-12T20:41:36Z", "authors": ["Tom Diethe", "Tom Borchert", "Eno Thereska", "Borja Balle", "Neil Lawrence"], "categories": ["stat.ML", "cs.LG"], "doi": "", "_query": "house price prediction machine learning", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2202.02414v2", "title": "OMLT: Optimization & Machine Learning Toolkit", "abstract": "The optimization and machine learning toolkit (OMLT) is an open-source software package incorporating neural network and gradient-boosted tree surrogate models, which have been trained using machine learning, into larger optimization problems. We discuss the advances in optimization technology that made OMLT possible and show how OMLT seamlessly integrates with the algebraic modeling language Pyomo. We demonstrate how to use OMLT for solving decision-making problems in both computer science and engineering.", "published": "2022-02-04T22:23:45Z", "authors": ["Francesco Ceccon", "Jordan Jalving", "Joshua Haddad", "Alexander Thebelt", "Calvin Tsay", "Carl D. Laird", "Ruth Misener"], "categories": ["stat.ML", "cs.AI", "cs.LG", "math.OC"], "doi": "", "_query": "house price prediction machine learning", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2106.07103v1", "title": "A News-based Machine Learning Model for Adaptive Asset Pricing", "abstract": "The paper proposes a new asset pricing model -- the News Embedding UMAP Selection (NEUS) model, to explain and predict the stock returns based on the financial news. Using a combination of various machine learning algorithms, we first derive a company embedding vector for each basis asset from the financial news. Then we obtain a collection of the basis assets based on their company embedding. After that for each stock, we select the basis assets to explain and predict the stock return with high-dimensional statistical methods. The new model is shown to have a significantly better fitting and prediction power than the Fama-French 5-factor model.", "published": "2021-06-13T22:38:20Z", "authors": ["Liao Zhu", "Haoxuan Wu", "Martin T. Wells"], "categories": ["q-fin.ST", "cs.LG", "stat.ME", "stat.ML"], "doi": "", "_query": "house price prediction machine learning", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/1812.01718v1", "title": "Deep Learning for Classical Japanese Literature", "abstract": "Much of machine learning research focuses on producing models which perform well on benchmark tasks, in turn improving our understanding of the challenges associated with those tasks. From the perspective of ML researchers, the content of the task itself is largely irrelevant, and thus there have increasingly been calls for benchmark tasks to more heavily focus on problems which are of social or cultural relevance. In this work, we introduce Kuzushiji-MNIST, a dataset which focuses on Kuzushiji (cursive Japanese), as well as two larger, more challenging datasets, Kuzushiji-49 and Kuzushiji-Kanji. Through these datasets, we wish to engage the machine learning community into the world of classical Japanese literature. Dataset available at https://github.com/rois-codh/kmnist", "published": "2018-12-03T12:37:31Z", "authors": ["Tarin Clanuwat", "Mikel Bober-Irizar", "Asanobu Kitamoto", "Alex Lamb", "Kazuaki Yamamoto", "David Ha"], "categories": ["cs.CV", "cs.LG", "stat.ML"], "doi": "10.20676/00000341", "_query": "house price prediction machine learning", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/1902.00407v2", "title": "Understanding Impacts of High-Order Loss Approximations and Features in Deep Learning Interpretation", "abstract": "Current methods to interpret deep learning models by generating saliency maps generally rely on two key assumptions. First, they use first-order approximations of the loss function neglecting higher-order terms such as the loss curvatures. Second, they evaluate each feature's importance in isolation, ignoring their inter-dependencies. In this work, we study the effect of relaxing these two assumptions. First, by characterizing a closed-form formula for the Hessian matrix of a deep ReLU network, we prove that, for a classification problem with a large number of classes, if an input has a high confidence classification score, the inclusion of the Hessian term has small impacts in the final solution. We prove this result by showing that in this case the Hessian matrix is approximately of rank one and its leading eigenvector is almost parallel to the gradient of the loss function. Our empirical experiments on ImageNet samples are consistent with our theory. This result can have implications in other related problems such as adversarial examples as well. Second, we compute the importance of group-features in deep learning interpretation by introducing a sparsity regularization term. We use the $L_0-L_1$ relaxation technique along with the proximal gradient descent to have an efficient computation of group feature importance scores. Our empirical results indicate that considering group features can improve deep learning interpretation significantly.", "published": "2019-02-01T15:42:46Z", "authors": ["Sahil Singla", "Eric Wallace", "Shi Feng", "Soheil Feizi"], "categories": ["cs.LG", "stat.ML"], "doi": "", "_query": "house price prediction machine learning", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/1803.03965v1", "title": "BEBP: An Poisoning Method Against Machine Learning Based IDSs", "abstract": "In big data era, machine learning is one of fundamental techniques in intrusion detection systems (IDSs). However, practical IDSs generally update their decision module by feeding new data then retraining learning models in a periodical way. Hence, some attacks that comprise the data for training or testing classifiers significantly challenge the detecting capability of machine learning-based IDSs. Poisoning attack, which is one of the most recognized security threats towards machine learning-based IDSs, injects some adversarial samples into the training phase, inducing data drifting of training data and a significant performance decrease of target IDSs over testing data. In this paper, we adopt the Edge Pattern Detection (EPD) algorithm to design a novel poisoning method that attack against several machine learning algorithms used in IDSs. Specifically, we propose a boundary pattern detection algorithm to efficiently generate the points that are near to abnormal data but considered to be normal ones by current classifiers. Then, we introduce a Batch-EPD Boundary Pattern (BEBP) detection algorithm to overcome the limitation of the number of edge pattern points generated by EPD and to obtain more useful adversarial samples. Based on BEBP, we further present a moderate but effective poisoning method called chronic poisoning attack. Extensive experiments on synthetic and three real network data sets demonstrate the performance of the proposed poisoning method against several well-known machine learning algorithms and a practical intrusion detection method named FMIFS-LSSVM-IDS.", "published": "2018-03-11T14:15:50Z", "authors": ["Pan Li", "Qiang Liu", "Wentao Zhao", "Dongxu Wang", "Siqi Wang"], "categories": ["cs.LG", "cs.CR", "stat.ML"], "doi": "", "_query": "house price prediction machine learning", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2001.11757v2", "title": "Statistical stability indices for LIME: obtaining reliable explanations for Machine Learning models", "abstract": "Nowadays we are witnessing a transformation of the business processes towards a more computation driven approach. The ever increasing usage of Machine Learning techniques is the clearest example of such trend. This sort of revolution is often providing advantages, such as an increase in prediction accuracy and a reduced time to obtain the results. However, these methods present a major drawback: it is very difficult to understand on what grounds the algorithm took the decision. To address this issue we consider the LIME method. We give a general background on LIME then, we focus on the stability issue: employing the method repeated times, under the same conditions, may yield to different explanations. Two complementary indices are proposed, to measure LIME stability. It is important for the practitioner to be aware of the issue, as well as to have a tool for spotting it. Stability guarantees LIME explanations to be reliable, therefore a stability assessment, made through the proposed indices, is crucial. As a case study, we apply both Machine Learning and classical statistical techniques to Credit Risk data. We test LIME on the Machine Learning algorithm and check its stability. Eventually, we examine the goodness of the explanations returned.", "published": "2020-01-31T10:39:46Z", "authors": ["Giorgio Visani", "Enrico Bagli", "Federico Chesani", "Alessandro Poluzzi", "Davide Capuzzo"], "categories": ["cs.LG", "cs.AI", "stat.ML"], "doi": "10.1080/01605682.2020.1865846", "_query": "house price prediction machine learning", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/1812.02734v5", "title": "Learning to Design Circuits", "abstract": "Analog IC design relies on human experts to search for parameters that satisfy circuit specifications with their experience and intuitions, which is highly labor intensive, time consuming and suboptimal. Machine learning is a promising tool to automate this process. However, supervised learning is difficult for this task due to the low availability of training data: 1) Circuit simulation is slow, thus generating large-scale dataset is time-consuming; 2) Most circuit designs are propitiatory IPs within individual IC companies, making it expensive to collect large-scale datasets. We propose Learning to Design Circuits (L2DC) to leverage reinforcement learning that learns to efficiently generate new circuits data and to optimize circuits. We fix the schematic, and optimize the parameters of the transistors automatically by training an RL agent with no prior knowledge about optimizing circuits. After iteratively getting observations, generating a new set of transistor parameters, getting a reward, and adjusting the model, L2DC is able to optimize circuits. We evaluate L2DC on two transimpedance amplifiers. Trained for a day, our RL agent can achieve comparable or better performance than human experts trained for a quarter. It first learns to meet hard-constraints (eg. gain, bandwidth), and then learns to optimize good-to-have targets (eg. area, power). Compared with grid search-aided human design, L2DC can achieve $\\mathbf{250}\\boldsymbol{\\times}$ higher sample efficiency with comparable performance. Under the same runtime constraint, the performance of L2DC is also better than Bayesian Optimization.", "published": "2018-12-05T21:15:42Z", "authors": ["Hanrui Wang", "Jiacheng Yang", "Hae-Seung Lee", "Song Han"], "categories": ["cs.LG"], "doi": "", "_query": "house price prediction machine learning", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2202.13554v1", "title": "A Machine Learning Method for Material Property Prediction: Example Polymer Compatibility", "abstract": "Prediction of material property is a key problem because of its significance to material design and screening. We present a brand-new and general machine learning method for material property prediction. As a representative example, polymer compatibility is chosen to demonstrate the effectiveness of our method. Specifically, we mine data from related literature to build a specific database and give a prediction based on the basic molecular structures of blending polymers and, as auxiliary, the blending composition. Our model obtains at least 75% accuracy on the dataset consisting of thousands of entries. We demonstrate that the relationship between structure and properties can be learned and simulated by machine learning method.", "published": "2022-02-28T05:48:05Z", "authors": ["Zhilong Liang", "Zhiwei Li", "Shuo Zhou", "Yiwen Sun", "Changshui Zhang", "Jinying Yuan"], "categories": ["cs.LG", "cond-mat.mtrl-sci"], "doi": "", "_query": "house price prediction machine learning", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/1206.6458v1", "title": "Batch Active Learning via Coordinated Matching", "abstract": "Most prior work on active learning of classifiers has focused on sequentially selecting one unlabeled example at a time to be labeled in order to reduce the overall labeling effort. In many scenarios, however, it is desirable to label an entire batch of examples at once, for example, when labels can be acquired in parallel. This motivates us to study batch active learning, which iteratively selects batches of $k>1$ examples to be labeled. We propose a novel batch active learning method that leverages the availability of high-quality and efficient sequential active-learning policies by attempting to approximate their behavior when applied for $k$ steps. Specifically, our algorithm first uses Monte-Carlo simulation to estimate the distribution of unlabeled examples selected by a sequential policy over $k$ step executions. The algorithm then attempts to select a set of $k$ examples that best matches this distribution, leading to a combinatorial optimization problem that we term \"bounded coordinated matching\". While we show this problem is NP-hard in general, we give an efficient greedy solution, which inherits approximation bounds from supermodular minimization theory. Our experimental results on eight benchmark datasets show that the proposed approach is highly effective", "published": "2012-06-27T19:59:59Z", "authors": ["Javad Azimi", "Alan Fern", "Xiaoli Zhang-Fern", "Glencora Borradaile", "Brent Heeringa"], "categories": ["cs.LG", "stat.ML"], "doi": "", "_query": "house price prediction machine learning", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2306.16156v2", "title": "Recent Advances in Optimal Transport for Machine Learning", "abstract": "Recently, Optimal Transport has been proposed as a probabilistic framework in Machine Learning for comparing and manipulating probability distributions. This is rooted in its rich history and theory, and has offered new solutions to different problems in machine learning, such as generative modeling and transfer learning. In this survey we explore contributions of Optimal Transport for Machine Learning over the period 2012 -- 2023, focusing on four sub-fields of Machine Learning: supervised, unsupervised, transfer and reinforcement learning. We further highlight the recent development in computational Optimal Transport and its extensions, such as partial, unbalanced, Gromov and Neural Optimal Transport, and its interplay with Machine Learning practice.", "published": "2023-06-28T12:37:23Z", "authors": ["Eduardo Fernandes Montesuma", "Fred Ngolè Mboula", "Antoine Souloumiac"], "categories": ["cs.LG", "math.PR", "stat.ML"], "doi": "", "_query": "house price prediction machine learning", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2106.01782v1", "title": "Machine learning models for DOTA 2 outcomes prediction", "abstract": "Prediction of the real-time multiplayer online battle arena (MOBA) games' match outcome is one of the most important and exciting tasks in Esports analytical research. This research paper predominantly focuses on building predictive machine and deep learning models to identify the outcome of the Dota 2 MOBA game using the new method of multi-forward steps predictions. Three models were investigated and compared: Linear Regression (LR), Neural Networks (NN), and a type of recurrent neural network Long Short-Term Memory (LSTM). In order to achieve the goals, we developed a data collecting python server using Game State Integration (GSI) to track the real-time data of the players. Once the exploratory feature analysis and tuning hyper-parameters were done, our models' experiments took place on different players with dissimilar backgrounds of playing experiences. The achieved accuracy scores depend on the multi-forward prediction parameters, which for the worse case in linear regression 69\\% but on average 82\\%, while in the deep learning models hit the utmost accuracy of prediction on average 88\\% for NN, and 93\\% for LSTM models.", "published": "2021-06-03T12:10:26Z", "authors": ["Kodirjon Akhmedov", "Anh Huy Phan"], "categories": ["cs.LG"], "doi": "", "_query": "house price prediction machine learning", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2211.08507v1", "title": "Decision-Aware Learning for Optimizing Health Supply Chains", "abstract": "We study the problem of allocating limited supply of medical resources in developing countries, in particular, Sierra Leone. We address this problem by combining machine learning (to predict demand) with optimization (to optimize allocations). A key challenge is the need to align the loss function used to train the machine learning model with the decision loss associated with the downstream optimization problem. Traditional solutions have limited flexibility in the model architecture and scale poorly to large datasets. We propose a decision-aware learning algorithm that uses a novel Taylor expansion of the optimal decision loss to derive the machine learning loss. Importantly, our approach only requires a simple re-weighting of the training data, ensuring it is both flexible and scalable, e.g., we incorporate it into a random forest trained using a multitask learning framework. We apply our framework to optimize the distribution of essential medicines in collaboration with policymakers in Sierra Leone; highly uncertain demand and limited budgets currently result in excessive unmet demand. Out-of-sample results demonstrate that our end-to-end approach can significantly reduce unmet demand across 1040 health facilities throughout Sierra Leone.", "published": "2022-11-15T21:03:52Z", "authors": ["Tsai-Hsuan Chung", "Vahid Rostami", "Hamsa Bastani", "Osbert Bastani"], "categories": ["cs.LG"], "doi": "", "_query": "house price prediction machine learning", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2412.12024v1", "title": "Learning to Navigate in Mazes with Novel Layouts using Abstract Top-down Maps", "abstract": "Learning navigation capabilities in different environments has long been one of the major challenges in decision-making. In this work, we focus on zero-shot navigation ability using given abstract $2$-D top-down maps. Like human navigation by reading a paper map, the agent reads the map as an image when navigating in a novel layout, after learning to navigate on a set of training maps. We propose a model-based reinforcement learning approach for this multi-task learning problem, where it jointly learns a hypermodel that takes top-down maps as input and predicts the weights of the transition network. We use the DeepMind Lab environment and customize layouts using generated maps. Our method can adapt better to novel environments in zero-shot and is more robust to noise.", "published": "2024-12-16T17:51:09Z", "authors": ["Linfeng Zhao", "Lawson L. S. Wong"], "categories": ["cs.LG", "cs.AI", "cs.RO"], "doi": "", "_query": "house price prediction machine learning", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/quant-ph/9602020v1", "title": "Locality, Independence and the Pro-Liberty Bell", "abstract": "Construed as an argument against hidden variable theories, Bell's Theorem assumes that hidden variables would be independent of future measurement settings. This Independence Assumption (IA) is rarely questioned. Bell considered relaxing it to avoid non-locality, but thought that the resulting view left no room for free will. However, Bell seems to have failed to distinguish two different strategies for giving up IA. One strategy takes for granted the Principle of the Common Cause, which requires that a correlation between hidden variables and measurement settings be explained by a joint correlation with some unknown factor in their common past. The other strategy rejects the Principle of the Common Cause, and argues that the required correlation might be due to the known interaction between the object system and the measuring device in their common future. Bell and most others who have discussed these issues have focussed on the former strategy, but because the two approaches have not been properly distinguished, it has not been well appreciated that there is a quite different way to relax IA. This paper distinguishes the two strategies, and argues that the latter is considerably more appealing than the former.", "published": "1996-02-28T00:54:30Z", "authors": ["Huw Price"], "categories": ["quant-ph"], "doi": "", "_query": "house price prediction machine learning", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/1909.08329v2", "title": "From Server-Based to Client-Based Machine Learning: A Comprehensive Survey", "abstract": "In recent years, mobile devices have gained increasing development with stronger computation capability and larger storage space. Some of the computation-intensive machine learning tasks can now be run on mobile devices. To exploit the resources available on mobile devices and preserve personal privacy, the concept of client-based machine learning has been proposed. It leverages the users' local hardware and local data to solve machine learning sub-problems on mobile devices and only uploads computation results rather than the original data for the optimization of the global model. Such an architecture can not only relieve computation and storage burdens on servers but also protect the users' sensitive information. Another benefit is the bandwidth reduction because various kinds of local data can be involved in the training process without being uploaded. In this article, we provide a literature review on the progressive development of machine learning from server based to client based. We revisit a number of widely used server-based and client-based machine learning methods and applications. We also extensively discuss the challenges and future directions in this area. We believe that this survey will give a clear overview of client-based machine learning and provide guidelines on applying client-based machine learning to practice.", "published": "2019-09-18T10:09:02Z", "authors": ["Renjie Gu", "Chaoyue Niu", "Fan Wu", "Guihai Chen", "Chun Hu", "Chengfei Lyu", "Zhihua Wu"], "categories": ["cs.LG", "cs.DC", "stat.ML"], "doi": "10.1145/3424660", "_query": "house price prediction machine learning", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2512.13031v1", "title": "Comprehensive Evaluation of Rule-Based, Machine Learning, and Deep Learning in Human Estimation Using Radio Wave Sensing: Accuracy, Spatial Generalization, and Output Granularity Trade-offs", "abstract": "This study presents the first comprehensive comparison of rule-based methods, traditional machine learning models, and deep learning models in radio wave sensing with frequency modulated continuous wave multiple input multiple output radar. We systematically evaluated five approaches in two indoor environments with distinct layouts: a rule-based connected component method; three traditional machine learning models, namely k-nearest neighbors, random forest, and support vector machine; and a deep learning model combining a convolutional neural network and long short term memory. In the training environment, the convolutional neural network long short term memory model achieved the highest accuracy, while traditional machine learning models provided moderate performance. In a new layout, however, all learning based methods showed significant degradation, whereas the rule-based method remained stable. Notably, for binary detection of presence versus absence of people, all models consistently achieved high accuracy across layouts. These results demonstrate that high capacity models can produce fine grained outputs with high accuracy in the same environment, but they are vulnerable to domain shift. In contrast, rule-based methods cannot provide fine grained outputs but exhibit robustness against domain shift. Moreover, regardless of the model type, a clear trade off was revealed between spatial generalization performance and output granularity.", "published": "2025-12-15T07:02:06Z", "authors": ["Tomoya Tanaka", "Tomonori Ikeda", "Ryo Yonemoto"], "categories": ["cs.CV"], "doi": "", "_query": "house price prediction machine learning", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2301.08917v2", "title": "A fast and flexible machine learning approach to data quality monitoring", "abstract": "We present a machine learning based approach for real-time monitoring of particle detectors. The proposed strategy evaluates the compatibility between incoming batches of experimental data and a reference sample representing the data behavior in normal conditions by implementing a likelihood-ratio hypothesis test. The core model is powered by recent large-scale implementations of kernel methods, nonparametric learning algorithms that can approximate any continuous function given enough data. The resulting algorithm is fast, efficient and agnostic about the type of potential anomaly in the data. We show the performance of the model on multivariate data from a drift tube chambers muon detector.", "published": "2023-01-21T08:43:15Z", "authors": ["Gaia Grosso", "Nicolò Lai", "Marco Letizia", "Jacopo Pazzini", "Marco Rando", "Andrea Wulzer", "Marco Zanetti"], "categories": ["hep-ex"], "doi": "", "_query": "house price prediction machine learning", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/1906.09310v1", "title": "A Study of State Aliasing in Structured Prediction with RNNs", "abstract": "End-to-end reinforcement learning agents learn a state representation and a policy at the same time. Recurrent neural networks (RNNs) have been trained successfully as reinforcement learning agents in settings like dialogue that require structured prediction. In this paper, we investigate the representations learned by RNN-based agents when trained with both policy gradient and value-based methods. We show through extensive experiments and analysis that, when trained with policy gradient, recurrent neural networks often fail to learn a state representation that leads to an optimal policy in settings where the same action should be taken at different states. To explain this failure, we highlight the problem of state aliasing, which entails conflating two or more distinct states in the representation space. We demonstrate that state aliasing occurs when several states share the same optimal action and the agent is trained via policy gradient. We characterize this phenomenon through experiments on a simple maze setting and a more complex text-based game, and make recommendations for training RNNs with reinforcement learning.", "published": "2019-06-21T20:16:52Z", "authors": ["Layla El Asri", "Adam Trischler"], "categories": ["cs.LG", "cs.AI", "cs.CL"], "doi": "", "_query": "house price prediction machine learning", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2502.05186v1", "title": "Multimodal Stock Price Prediction", "abstract": "In an era where financial markets are heavily influenced by many static and dynamic factors, it has become increasingly critical to carefully integrate diverse data sources with machine learning for accurate stock price prediction. This paper explores a multimodal machine learning approach for stock price prediction by combining data from diverse sources, including traditional financial metrics, tweets, and news articles. We capture real-time market dynamics and investor mood through sentiment analysis on these textual data using both ChatGPT-4o and FinBERT models. We look at how these integrated data streams augment predictions made with a standard Long Short-Term Memory (LSTM model) to illustrate the extent of performance gains. Our study's results indicate that incorporating the mentioned data sources considerably increases the forecast effectiveness of the reference model by up to 5%. We also provide insights into the individual and combined predictive capacities of these modalities, highlighting the substantial impact of incorporating sentiment analysis from tweets and news articles. This research offers a systematic and effective framework for applying multimodal data analytics techniques in financial time series forecasting that provides a new view for investors to leverage data for decision-making.", "published": "2025-01-23T16:38:46Z", "authors": ["Furkan Karadaş", "Bahaeddin Eravcı", "Ahmet Murat Özbayoğlu"], "categories": ["q-fin.ST", "cs.AI", "cs.LG"], "doi": "10.5220/0013174500003890", "_query": "house price prediction machine learning", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2405.20172v3", "title": "Iterative Feature Boosting for Explainable Speech Emotion Recognition", "abstract": "In speech emotion recognition (SER), using predefined features without considering their practical importance may lead to high dimensional datasets, including redundant and irrelevant information. Consequently, high-dimensional learning often results in decreasing model accuracy while increasing computational complexity. Our work underlines the importance of carefully considering and analyzing features in order to build efficient SER systems. We present a new supervised SER method based on an efficient feature engineering approach. We pay particular attention to the explainability of results to evaluate feature relevance and refine feature sets. This is performed iteratively through feature evaluation loop, using Shapley values to boost feature selection and improve overall framework performance. Our approach allows thus to balance the benefits between model performance and transparency. The proposed method outperforms human-level performance (HLP) and state-of-the-art machine learning methods in emotion recognition on the TESS dataset. The source code of this paper is publicly available at https://github.com/alaaNfissi/Iterative-Feature-Boosting-for-Explainable-Speech-Emotion-Recognition.", "published": "2024-05-30T15:44:27Z", "authors": ["Alaa Nfissi", "Wassim Bouachir", "Nizar Bouguila", "Brian Mishara"], "categories": ["cs.SD", "cs.AI", "cs.CL", "cs.LG", "eess.AS"], "doi": "10.1109/ICMLA58977.2023.00081", "_query": "house price prediction machine learning", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2507.06415v3", "title": "PERK: Long-Context Reasoning as Test-Time Learning", "abstract": "Long-context reasoning requires accurately identifying relevant information in extensive, noisy input contexts. In this work, we propose PERK (Parameter Efficient Reasoning over Knowledge), a scalable approach for learning to encode long contexts using gradient updates at test time. Specifically, PERK employs two nested optimization loops in a meta-training phase. The inner loop rapidly encodes contexts into a low-rank adapter (LoRA) that serves as a parameter-efficient memory module for the base model. Concurrently, the outer loop learns to use the updated adapter to accurately recall and reason over relevant information from the encoded long context. Our evaluations on several long-context reasoning tasks show that PERK significantly outperforms the standard long-context finetuning, achieving average absolute performance gains of up to 20% for Qwen-2.5 (0.5B & 7B) on synthetic and real-world long-context reasoning. PERK also maintains its advantages across model scales and families. Compared to specialized long-context LLMs, PERK matches or surpasses their performance. Finally, our analyses show PERK is more robust to reasoning complexity, length extrapolation, and the positions of relevant information in contexts. https://perk-long-context.web.app", "published": "2025-07-08T21:38:45Z", "authors": ["Zeming Chen", "Angelika Romanou", "Gail Weiss", "Antoine Bosselut"], "categories": ["cs.CL", "cs.LG"], "doi": "", "_query": "house price prediction machine learning", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2006.08350v2", "title": "Societal biases reinforcement through machine learning: A credit scoring perspective", "abstract": "Does machine learning and AI ensure that social biases thrive ? This paper aims to analyse this issue. Indeed, as algorithms are informed by data, if these are corrupted, from a social bias perspective, good machine learning algorithms would learn from the data provided and reverberate the patterns learnt on the predictions related to either the classification or the regression intended. In other words, the way society behaves whether positively or negatively, would necessarily be reflected by the models. In this paper, we analyse how social biases are transmitted from the data into banks loan approvals by predicting either the gender or the ethnicity of the customers using the exact same information provided by customers through their applications.", "published": "2020-06-15T12:40:21Z", "authors": ["Bertrand K. Hassani"], "categories": ["stat.ML", "cs.LG", "stat.AP", "stat.CO"], "doi": "", "_query": "house price prediction machine learning", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2410.08934v4", "title": "Understanding the Statistical Accuracy-Communication Trade-off in Personalized Federated Learning with Minimax Guarantees", "abstract": "Personalized federated learning (PFL) offers a flexible framework for aggregating information across distributed clients with heterogeneous data. This work considers a personalized federated learning setting that simultaneously learns global and local models. While purely local training has no communication cost, collaborative learning among the clients can leverage shared knowledge to improve statistical accuracy, presenting an accuracy-communication trade-off in personalized federated learning. However, the theoretical analysis of how personalization quantitatively influences sample and algorithmic efficiency and their inherent trade-off is largely unexplored. This paper makes a contribution towards filling this gap, by providing a quantitative characterization of the personalization degree on the tradeoff. The results further offers theoretical insights for choosing the personalization degree. As a side contribution, we establish the minimax optimality in terms of statistical accuracy for a widely studied PFL formulation. The theoretical result is validated on both synthetic and real-world datasets and its generalizability is verified in a non-convex setting.", "published": "2024-10-11T16:00:21Z", "authors": ["Xin Yu", "Zelin He", "Ying Sun", "Lingzhou Xue", "Runze Li"], "categories": ["stat.ML", "cs.DC", "cs.LG", "math.ST", "stat.CO"], "doi": "", "_query": "house price prediction machine learning", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2006.06675v2", "title": "Towards Early Diagnosis of Epilepsy from EEG Data", "abstract": "Epilepsy is one of the most common neurological disorders, affecting about 1% of the population at all ages. Detecting the development of epilepsy, i.e., epileptogenesis (EPG), before any seizures occur could allow for early interventions and potentially more effective treatments. Here, we investigate if modern machine learning (ML) techniques can detect EPG from intra-cranial electroencephalography (EEG) recordings prior to the occurrence of any seizures. For this we use a rodent model of epilepsy where EPG is triggered by electrical stimulation of the brain. We propose a ML framework for EPG identification, which combines a deep convolutional neural network (CNN) with a prediction aggregation method to obtain the final classification decision. Specifically, the neural network is trained to distinguish five second segments of EEG recordings taken from either the pre-stimulation period or the post-stimulation period. Due to the gradual development of epilepsy, there is enormous overlap of the EEG patterns before and after the stimulation. Hence, a prediction aggregation process is introduced, which pools predictions over a longer period. By aggregating predictions over one hour, our approach achieves an area under the curve (AUC) of 0.99 on the EPG detection task. This demonstrates the feasibility of EPG prediction from EEG recordings.", "published": "2020-06-11T21:04:54Z", "authors": ["Diyuan Lu", "Sebastian Bauer", "Valentin Neubert", "Lara Sophie Costard", "Felix Rosenow", "Jochen Triesch"], "categories": ["cs.LG", "eess.SP", "stat.ML"], "doi": "", "_query": "house price prediction machine learning", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/1904.03257v3", "title": "MLSys: The New Frontier of Machine Learning Systems", "abstract": "Machine learning (ML) techniques are enjoying rapidly increasing adoption. However, designing and implementing the systems that support ML models in real-world deployments remains a significant obstacle, in large part due to the radically different development and deployment profile of modern ML methods, and the range of practical concerns that come with broader adoption. We propose to foster a new systems machine learning research community at the intersection of the traditional systems and ML communities, focused on topics such as hardware systems for ML, software systems for ML, and ML optimized for metrics beyond predictive accuracy. To do this, we describe a new conference, MLSys, that explicitly targets research at the intersection of systems and machine learning with a program committee split evenly between experts in systems and ML, and an explicit focus on topics at the intersection of the two.", "published": "2019-03-29T12:43:36Z", "authors": ["Alexander Ratner", "Dan Alistarh", "Gustavo Alonso", "David G. Andersen", "Peter Bailis", "Sarah Bird", "Nicholas Carlini", "Bryan Catanzaro", "Jennifer Chayes", "Eric Chung", "Bill Dally", "Jeff Dean", "Inderjit S. Dhillon", "Alexandros Dimakis", "Pradeep Dubey", "Charles Elkan", "Grigori Fursin", "Gregory R. Ganger", "Lise Getoor", "Phillip B. Gibbons", "Garth A. Gibson", "Joseph E. Gonzalez", "Justin Gottschlich", "Song Han", "Kim Hazelwood", "Furong Huang", "Martin Jaggi", "Kevin Jamieson", "Michael I. Jordan", "Gauri Joshi", "Rania Khalaf", "Jason Knight", "Jakub Konečný", "Tim Kraska", "Arun Kumar", "Anastasios Kyrillidis", "Aparna Lakshmiratan", "Jing Li", "Samuel Madden", "H. Brendan McMahan", "Erik Meijer", "Ioannis Mitliagkas", "Rajat Monga", "Derek Murray", "Kunle Olukotun", "Dimitris Papailiopoulos", "Gennady Pekhimenko", "Theodoros Rekatsinas", "Afshin Rostamizadeh", "Christopher Ré", "Christopher De Sa", "Hanie Sedghi", "Siddhartha Sen", "Virginia Smith", "Alex Smola", "Dawn Song", "Evan Sparks", "Ion Stoica", "Vivienne Sze", "Madeleine Udell", "Joaquin Vanschoren", "Shivaram Venkataraman", "Rashmi Vinayak", "Markus Weimer", "Andrew Gordon Wilson", "Eric Xing", "Matei Zaharia", "Ce Zhang", "Ameet Talwalkar"], "categories": ["cs.LG", "cs.DB", "cs.DC", "cs.SE", "stat.ML"], "doi": "", "_query": "house price prediction machine learning", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2009.08868v1", "title": "Review of Machine-Learning Methods for RNA Secondary Structure Prediction", "abstract": "Secondary structure plays an important role in determining the function of non-coding RNAs. Hence, identifying RNA secondary structures is of great value to research. Computational prediction is a mainstream approach for predicting RNA secondary structure. Unfortunately, even though new methods have been proposed over the past 40 years, the performance of computational prediction methods has stagnated in the last decade. Recently, with the increasing availability of RNA structure data, new methods based on machine-learning technologies, especially deep learning, have alleviated the issue. In this review, we provide a comprehensive overview of RNA secondary structure prediction methods based on machine-learning technologies and a tabularized summary of the most important methods in this field. The current pending issues in the field of RNA secondary structure prediction and future trends are also discussed.", "published": "2020-09-01T03:17:15Z", "authors": ["Qi Zhao", "Zheng Zhao", "Xiaoya Fan", "Zhengwei Yuan", "Qian Mao", "Yudong Yao"], "categories": ["q-bio.BM", "cs.LG", "stat.ML"], "doi": "10.1371/journal.pcbi.1009291", "_query": "house price prediction machine learning", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/1910.10685v2", "title": "Machine Learning for Scent: Learning Generalizable Perceptual Representations of Small Molecules", "abstract": "Predicting the relationship between a molecule's structure and its odor remains a difficult, decades-old task. This problem, termed quantitative structure-odor relationship (QSOR) modeling, is an important challenge in chemistry, impacting human nutrition, manufacture of synthetic fragrance, the environment, and sensory neuroscience. We propose the use of graph neural networks for QSOR, and show they significantly out-perform prior methods on a novel data set labeled by olfactory experts. Additional analysis shows that the learned embeddings from graph neural networks capture a meaningful odor space representation of the underlying relationship between structure and odor, as demonstrated by strong performance on two challenging transfer learning tasks. Machine learning has already had a large impact on the senses of sight and sound. Based on these early results with graph neural networks for molecular properties, we hope machine learning can eventually do for olfaction what it has already done for vision and hearing.", "published": "2019-10-23T17:38:47Z", "authors": ["Benjamin Sanchez-Lengeling", "Jennifer N. Wei", "Brian K. Lee", "Richard C. Gerkin", "Alán Aspuru-Guzik", "Alexander B. Wiltschko"], "categories": ["stat.ML", "cs.LG", "physics.chem-ph"], "doi": "", "_query": "house price prediction machine learning", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2305.05080v2", "title": "Scalable Optimal Transport Methods in Machine Learning: A Contemporary Survey", "abstract": "Optimal Transport (OT) is a mathematical framework that first emerged in the eighteenth century and has led to a plethora of methods for answering many theoretical and applied questions. The last decade has been a witness to the remarkable contributions of this classical optimization problem to machine learning. This paper is about where and how optimal transport is used in machine learning with a focus on the question of scalable optimal transport. We provide a comprehensive survey of optimal transport while ensuring an accessible presentation as permitted by the nature of the topic and the context. First, we explain the optimal transport background and introduce different flavors (i.e., mathematical formulations), properties, and notable applications. We then address the fundamental question of how to scale optimal transport to cope with the current demands of big and high dimensional data. We conduct a systematic analysis of the methods used in the literature for scaling OT and present the findings in a unified taxonomy. We conclude with presenting some open challenges and discussing potential future research directions. A live repository of related OT research papers is maintained in https://github.com/abdelwahed/OT_for_big_data.git", "published": "2023-05-08T22:43:16Z", "authors": ["Abdelwahed Khamis", "Russell Tsuchida", "Mohamed Tarek", "Vivien Rolland", "Lars Petersson"], "categories": ["cs.LG", "cs.AI"], "doi": "10.1109/TPAMI.2024.3379571", "_query": "house price prediction machine learning", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2303.05593v1", "title": "Learning the Wrong Lessons: Inserting Trojans During Knowledge Distillation", "abstract": "In recent years, knowledge distillation has become a cornerstone of efficiently deployed machine learning, with labs and industries using knowledge distillation to train models that are inexpensive and resource-optimized. Trojan attacks have contemporaneously gained significant prominence, revealing fundamental vulnerabilities in deep learning models. Given the widespread use of knowledge distillation, in this work we seek to exploit the unlabelled data knowledge distillation process to embed Trojans in a student model without introducing conspicuous behavior in the teacher. We ultimately devise a Trojan attack that effectively reduces student accuracy, does not alter teacher performance, and is efficiently constructible in practice.", "published": "2023-03-09T21:37:50Z", "authors": ["Leonard Tang", "Tom Shlomi", "Alexander Cai"], "categories": ["cs.LG", "cs.AI", "cs.CR"], "doi": "", "_query": "house price prediction machine learning", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2303.11494v1", "title": "FlexVDW: A machine learning approach to account for protein flexibility in ligand docking", "abstract": "Most widely used ligand docking methods assume a rigid protein structure. This leads to problems when the structure of the target protein deforms upon ligand binding. In particular, the ligand's true binding pose is often scored very unfavorably due to apparent clashes between ligand and protein atoms, which lead to extremely high values of the calculated van der Waals energy term. Traditionally, this problem has been addressed by explicitly searching for receptor conformations to account for the flexibility of the receptor in ligand binding. Here we present a deep learning model trained to take receptor flexibility into account implicitly when predicting van der Waals energy. We show that incorporating this machine-learned energy term into a state-of-the-art physics-based scoring function improves small molecule ligand pose prediction results in cases with substantial protein deformation, without degrading performance in cases with minimal protein deformation. This work demonstrates the feasibility of learning effects of protein flexibility on ligand binding without explicitly modeling changes in protein structure.", "published": "2023-03-20T23:19:05Z", "authors": ["Patricia Suriana", "Joseph M. Paggi", "Ron O. Dror"], "categories": ["q-bio.BM", "cs.LG"], "doi": "", "_query": "house price prediction machine learning", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2411.11088v1", "title": "An Investigation of Offline Reinforcement Learning in Factorisable Action Spaces", "abstract": "Expanding reinforcement learning (RL) to offline domains generates promising prospects, particularly in sectors where data collection poses substantial challenges or risks. Pivotal to the success of transferring RL offline is mitigating overestimation bias in value estimates for state-action pairs absent from data. Whilst numerous approaches have been proposed in recent years, these tend to focus primarily on continuous or small-scale discrete action spaces. Factorised discrete action spaces, on the other hand, have received relatively little attention, despite many real-world problems naturally having factorisable actions. In this work, we undertake a formative investigation into offline reinforcement learning in factorisable action spaces. Using value-decomposition as formulated in DecQN as a foundation, we present the case for a factorised approach and conduct an extensive empirical evaluation of several offline techniques adapted to the factorised setting. In the absence of established benchmarks, we introduce a suite of our own comprising datasets of varying quality and task complexity. Advocating for reproducible research and innovation, we make all datasets available for public use alongside our code base.", "published": "2024-11-17T14:31:14Z", "authors": ["Alex Beeson", "David Ireland", "Giovanni Montana"], "categories": ["stat.ML", "cs.LG"], "doi": "", "_query": "house price prediction machine learning", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2112.11806v2", "title": "Lifting Symmetry Breaking Constraints with Inductive Logic Programming", "abstract": "Efficient omission of symmetric solution candidates is essential for combinatorial problem-solving. Most of the existing approaches are instance-specific and focus on the automatic computation of Symmetry Breaking Constraints (SBCs) for each given problem instance. However, the application of such approaches to large-scale instances or advanced problem encodings might be problematic since the computed SBCs are propositional and, therefore, can neither be meaningfully interpreted nor transferred to other instances. As a result, a time-consuming recomputation of SBCs must be done before every invocation of a solver. To overcome these limitations, we introduce a new model-oriented approach for Answer Set Programming that lifts the SBCs of small problem instances into a set of interpretable first-order constraints using the Inductive Logic Programming paradigm. Experiments demonstrate the ability of our framework to learn general constraints from instance-specific SBCs for a collection of combinatorial problems. The obtained results indicate that our approach significantly outperforms a state-of-the-art instance-specific method as well as the direct application of a solver.", "published": "2021-12-22T11:27:48Z", "authors": ["Alice Tarzariol", "Martin Gebser", "Konstantin Schekotihin"], "categories": ["cs.LO", "cs.AI", "cs.LG"], "doi": "10.1007/s10994-022-06146-3", "_query": "house price prediction machine learning", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2003.09758v1", "title": "ARDA: Automatic Relational Data Augmentation for Machine Learning", "abstract": "Automatic machine learning (\\AML) is a family of techniques to automate the process of training predictive models, aiming to both improve performance and make machine learning more accessible. While many recent works have focused on aspects of the machine learning pipeline like model selection, hyperparameter tuning, and feature selection, relatively few works have focused on automatic data augmentation. Automatic data augmentation involves finding new features relevant to the user's predictive task with minimal ``human-in-the-loop'' involvement. We present \\system, an end-to-end system that takes as input a dataset and a data repository, and outputs an augmented data set such that training a predictive model on this augmented dataset results in improved performance. Our system has two distinct components: (1) a framework to search and join data with the input data, based on various attributes of the input, and (2) an efficient feature selection algorithm that prunes out noisy or irrelevant features from the resulting join. We perform an extensive empirical evaluation of different system components and benchmark our feature selection algorithm on real-world datasets.", "published": "2020-03-21T21:55:22Z", "authors": ["Nadiia Chepurko", "Ryan Marcus", "Emanuel Zgraggen", "Raul Castro Fernandez", "Tim Kraska", "David Karger"], "categories": ["cs.LG", "cs.DB", "stat.ML"], "doi": "", "_query": "house price prediction machine learning", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2102.12112v2", "title": "Modeling Price Clustering in High-Frequency Prices", "abstract": "The price clustering phenomenon manifesting itself as an increased occurrence of specific prices is widely observed and well-documented for various financial instruments and markets. In the literature, however, it is rarely incorporated into price models. We consider that there are several types of agents trading only in specific multiples of the tick size resulting in an increased occurrence of these multiples in prices. For example, stocks on the NYSE and NASDAQ exchanges are traded with precision to one cent but multiples of five cents and ten cents occur much more often in prices. To capture this behavior, we propose a discrete price model based on a mixture of double Poisson distributions with dynamic volatility and dynamic proportions of agent types. The model is estimated by the maximum likelihood method. In an empirical study of DJIA stocks, we find that higher instantaneous volatility leads to weaker price clustering at the ultra-high frequency. This is in sharp contrast with results at low frequencies which show that daily realized volatility has a positive impact on price clustering.", "published": "2021-02-24T08:06:36Z", "authors": ["Vladimír Holý", "Petra Tomanová"], "categories": ["q-fin.ST"], "doi": "10.1080/14697688.2022.2050285", "_query": "house price prediction machine learning", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/1709.05379v1", "title": "Road Friction Estimation for Connected Vehicles using Supervised Machine Learning", "abstract": "In this paper, the problem of road friction prediction from a fleet of connected vehicles is investigated. A framework is proposed to predict the road friction level using both historical friction data from the connected cars and data from weather stations, and comparative results from different methods are presented. The problem is formulated as a classification task where the available data is used to train three machine learning models including logistic regression, support vector machine, and neural networks to predict the friction class (slippery or non-slippery) in the future for specific road segments. In addition to the friction values, which are measured by moving vehicles, additional parameters such as humidity, temperature, and rainfall are used to obtain a set of descriptive feature vectors as input to the classification methods. The proposed prediction models are evaluated for different prediction horizons (0 to 120 minutes in the future) where the evaluation shows that the neural networks method leads to more stable results in different conditions.", "published": "2017-09-15T19:52:18Z", "authors": ["Ghazaleh Panahandeh", "Erik Ek", "Nasser Mohammadiha"], "categories": ["cs.LG", "stat.ML"], "doi": "10.1109/IVS.2017.7995885", "_query": "house price prediction machine learning", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/1903.07822v2", "title": "A semi-supervised deep learning algorithm for abnormal EEG identification", "abstract": "Systems that can automatically analyze EEG signals can aid neurologists by reducing heavy workload and delays. However, such systems need to be first trained using a labeled dataset. While large corpuses of EEG data exist, a fraction of them are labeled. Hand-labeling data increases workload for the very neurologists we try to aid. This paper proposes a semi-supervised learning workflow that can not only extract meaningful information from large unlabeled EEG datasets but also make predictions with minimal supervision, using labeled datasets as small as 5 examples.", "published": "2019-03-19T04:23:04Z", "authors": ["Subhrajit Roy", "Kiran Kate", "Martin Hirzel"], "categories": ["cs.LG", "eess.SP", "stat.ML"], "doi": "", "_query": "house price prediction machine learning", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2505.03861v1", "title": "Machine Learning: a Lecture Note", "abstract": "This lecture note is intended to prepare early-year master's and PhD students in data science or a related discipline with foundational ideas in machine learning. It starts with basic ideas in modern machine learning with classification as a main target task. These basic ideas include loss formulation, backpropagation, stochastic gradient descent, generalization, model selection as well as fundamental blocks of artificial neural networks. Based on these basic ideas, the lecture note explores in depth the probablistic approach to unsupervised learning, covering directed latent variable models, product of experts, generative adversarial networks and autoregressive models. Finally, the note ends by covering a diverse set of further topics, such as reinforcement learning, ensemble methods and meta-learning. After reading this lecture note, a student should be ready to embark on studying and researching more advanced topics in machine learning and more broadly artificial intelligence.", "published": "2025-05-06T16:03:41Z", "authors": ["Kyunghyun Cho"], "categories": ["cs.LG", "stat.ML"], "doi": "", "_query": "house price prediction machine learning", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/1704.06885v1", "title": "A General Theory for Training Learning Machine", "abstract": "Though the deep learning is pushing the machine learning to a new stage, basic theories of machine learning are still limited. The principle of learning, the role of the a prior knowledge, the role of neuron bias, and the basis for choosing neural transfer function and cost function, etc., are still far from clear. In this paper, we present a general theoretical framework for machine learning. We classify the prior knowledge into common and problem-dependent parts, and consider that the aim of learning is to maximally incorporate them. The principle we suggested for maximizing the former is the design risk minimization principle, while the neural transfer function, the cost function, as well as pretreatment of samples, are endowed with the role for maximizing the latter. The role of the neuron bias is explained from a different angle. We develop a Monte Carlo algorithm to establish the input-output responses, and we control the input-output sensitivity of a learning machine by controlling that of individual neurons. Applications of function approaching and smoothing, pattern recognition and classification, are provided to illustrate how to train general learning machines based on our theory and algorithm. Our method may in addition induce new applications, such as the transductive inference.", "published": "2017-04-23T05:48:18Z", "authors": ["Hong Zhao"], "categories": ["stat.ML", "cs.AI", "cs.CV", "cs.LG"], "doi": "", "_query": "house price prediction machine learning", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2110.12674v2", "title": "mlr3spatiotempcv: Spatiotemporal resampling methods for machine learning in R", "abstract": "Spatial and spatiotemporal machine-learning models require a suitable framework for their model assessment, model selection, and hyperparameter tuning, in order to avoid error estimation bias and over-fitting. This contribution reviews the state-of-the-art in spatial and spatiotemporal cross-validation, and introduces the {R} package {mlr3spatiotempcv} as an extension package of the machine-learning framework {mlr3}. Currently various {R} packages implementing different spatiotemporal partitioning strategies exist: {blockCV}, {CAST}, {skmeans} and {sperrorest}. The goal of {mlr3spatiotempcv} is to gather the available spatiotemporal resampling methods in {R} and make them available to users through a simple and common interface. This is made possible by integrating the package directly into the {mlr3} machine-learning framework, which already has support for generic non-spatiotemporal resampling methods such as random partitioning. One advantage is the use of a consistent nomenclature in an overarching machine-learning toolkit instead of a varying package-specific syntax, making it easier for users to choose from a variety of spatiotemporal resampling methods. This package avoids giving recommendations which method to use in practice as this decision depends on the predictive task at hand, the autocorrelation within the data, and the spatial structure of the sampling design or geographic objects being studied.", "published": "2021-10-25T06:48:29Z", "authors": ["Patrick Schratz", "Marc Becker", "Michel Lang", "Alexander Brenning"], "categories": ["stat.ML", "cs.LG", "stat.ME"], "doi": "", "_query": "house price prediction machine learning", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2109.09394v1", "title": "Prediction of properties of metal alloy materials based on machine learning", "abstract": "Density functional theory and its optimization algorithm are the main methods to calculate the properties in the field of materials. Although the calculation results are accurate, it costs a lot of time and money. In order to alleviate this problem, we intend to use machine learning to predict material properties. In this paper, we conduct experiments on atomic volume, atomic energy and atomic formation energy of metal alloys, using the open quantum material database. Through the traditional machine learning models, deep learning network and automated machine learning, we verify the feasibility of machine learning in material property prediction. The experimental results show that the machine learning can predict the material properties accurately.", "published": "2021-09-20T09:40:36Z", "authors": ["Houchen Zuo", "Yongquan Jiang", "Yan Yang", "Jie Hu"], "categories": ["cond-mat.mtrl-sci", "cs.LG", "physics.comp-ph"], "doi": "", "_query": "house price prediction machine learning", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2507.23455v1", "title": "Machine learning and machine learned prediction in chest X-ray images", "abstract": "Machine learning and artificial intelligence are fast-growing fields of research in which data is used to train algorithms, learn patterns, and make predictions. This approach helps to solve seemingly intricate problems with significant accuracy without explicit programming by recognizing complex relationships in data. Taking an example of 5824 chest X-ray images, we implement two machine learning algorithms, namely, a baseline convolutional neural network (CNN) and a DenseNet-121, and present our analysis in making machine-learned predictions in predicting patients with ailments. Both baseline CNN and DenseNet-121 perform very well in the binary classification problem presented in this work. Gradient-weighted class activation mapping shows that DenseNet-121 correctly focuses on essential parts of the input chest X-ray images in its decision-making more than the baseline CNN.", "published": "2025-07-31T11:31:25Z", "authors": ["Shereiff Garrett", "Abhinav Adhikari", "Sarina Gautam", "DaShawn Marquis Morris", "Chandra Mani Adhikari"], "categories": ["cs.CV", "cs.AI", "cs.LG"], "doi": "10.3126/jnphyssoc.v11i1.87420", "_query": "house price prediction machine learning", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/1206.6400v1", "title": "Online Bandit Learning against an Adaptive Adversary: from Regret to Policy Regret", "abstract": "Online learning algorithms are designed to learn even when their input is generated by an adversary. The widely-accepted formal definition of an online algorithm's ability to learn is the game-theoretic notion of regret. We argue that the standard definition of regret becomes inadequate if the adversary is allowed to adapt to the online algorithm's actions. We define the alternative notion of policy regret, which attempts to provide a more meaningful way to measure an online algorithm's performance against adaptive adversaries. Focusing on the online bandit setting, we show that no bandit algorithm can guarantee a sublinear policy regret against an adaptive adversary with unbounded memory. On the other hand, if the adversary's memory is bounded, we present a general technique that converts any bandit algorithm with a sublinear regret bound into an algorithm with a sublinear policy regret bound. We extend this result to other variants of regret, such as switching regret, internal regret, and swap regret.", "published": "2012-06-27T19:59:59Z", "authors": ["Raman Arora", "Ofer Dekel", "Ambuj Tewari"], "categories": ["cs.LG", "stat.ML"], "doi": "", "_query": "house price prediction machine learning", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/1912.08136v2", "title": "Direction Concentration Learning: Enhancing Congruency in Machine Learning", "abstract": "One of the well-known challenges in computer vision tasks is the visual diversity of images, which could result in an agreement or disagreement between the learned knowledge and the visual content exhibited by the current observation. In this work, we first define such an agreement in a concepts learning process as congruency. Formally, given a particular task and sufficiently large dataset, the congruency issue occurs in the learning process whereby the task-specific semantics in the training data are highly varying. We propose a Direction Concentration Learning (DCL) method to improve congruency in the learning process, where enhancing congruency influences the convergence path to be less circuitous. The experimental results show that the proposed DCL method generalizes to state-of-the-art models and optimizers, as well as improves the performances of saliency prediction task, continual learning task, and classification task. Moreover, it helps mitigate the catastrophic forgetting problem in the continual learning task. The code is publicly available at https://github.com/luoyan407/congruency.", "published": "2019-12-17T16:58:04Z", "authors": ["Yan Luo", "Yongkang Wong", "Mohan S. Kankanhalli", "Qi Zhao"], "categories": ["cs.LG", "cs.CV", "stat.ML"], "doi": "10.1109/TPAMI.2019.2963387", "_query": "house price prediction machine learning", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2012.07278v1", "title": "Learning how to approve updates to machine learning algorithms in non-stationary settings", "abstract": "Machine learning algorithms in healthcare have the potential to continually learn from real-world data generated during healthcare delivery and adapt to dataset shifts. As such, the FDA is looking to design policies that can autonomously approve modifications to machine learning algorithms while maintaining or improving the safety and effectiveness of the deployed models. However, selecting a fixed approval strategy, a priori, can be difficult because its performance depends on the stationarity of the data and the quality of the proposed modifications. To this end, we investigate a learning-to-approve approach (L2A) that uses accumulating monitoring data to learn how to approve modifications. L2A defines a family of strategies that vary in their \"optimism''---where more optimistic policies have faster approval rates---and searches over this family using an exponentially weighted average forecaster. To control the cumulative risk of the deployed model, we give L2A the option to abstain from making a prediction and incur some fixed abstention cost instead. We derive bounds on the average risk of the model deployed by L2A, assuming the distributional shifts are smooth. In simulation studies and empirical analyses, L2A tailors the level of optimism for each problem-setting: It learns to abstain when performance drops are common and approve beneficial modifications quickly when the distribution is stable.", "published": "2020-12-14T05:54:55Z", "authors": ["Jean Feng"], "categories": ["stat.ML", "cs.LG", "stat.AP"], "doi": "", "_query": "house price prediction machine learning", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/1910.05594v2", "title": "Open-plan Glare Evaluator (OGE): A Demonstration of a New Glare Prediction Approach Using Machine Learning Algorithms", "abstract": "Predicting discomfort glare in open-plan offices is a challenging problem. Although glare research has existed for more than 50 years, all current glare metrics have accuracy limitations, especially in open-plan offices with low lighting levels. Thus, it is crucial to develop a new method to predict glare more accurately. This paper is the first to adopt Machine Learning (ML) approaches in the prediction of glare. This research aims to demonstrate the validity of this approach by comparing the accuracy of the new ML model for open-plan offices (OGE) to the accuracy of the existing glare metrics using local dataset. To utilize and test this approach, Post-Occupancy Evaluation (POE) and High Dynamic Range (HDR) images were collected from 80 occupants (n=80) in four different open-plan offices in Brisbane, Australia. Consequently, various multi-region luminance values, luminance, and glare indices were calculated and examined as input features to train ML models. The accuracy of the ML model was compared to the accuracy of 24 indices which were also evaluated using a Receiver Operating Characteristic (ROC) analysis to identify the best cutoff values (thresholds) for each index in open-plan configurations. Results showed that the ML approach could predict glare with an accuracy of 83.8% (0.80 true positive rate and 0.86 true negative rate), which outperformed the accuracy of the previously developed glare metrics. OGE is applicable for open-plan office situations with low vertical illuminance (200 to 600 lux). However, ML models can be trained with more substantial datasets to achieve global model.", "published": "2019-10-12T16:33:02Z", "authors": ["Ayman Wagdy", "Veronica Garcia-Hansen", "Mohammed Elhenawy", "Gillian Isoardi", "Robin Drogemuller", "Fatma Fathy"], "categories": ["cs.LG", "cs.CV", "stat.ML"], "doi": "", "_query": "house price prediction machine learning", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2510.07822v1", "title": "SIMU: Selective Influence Machine Unlearning", "abstract": "The undesired memorization of sensitive information by Large Language Models (LLMs) has emphasized the need for safety mechanisms that can regulate model behavior. This has led to the development of machine unlearning techniques that enable models to precisely forget sensitive and unwanted information. For machine unlearning, first-order and second-order optimizer-based methods have shown significant progress in enabling LLMs to forget targeted information. However, in doing so, these approaches often compromise the model's original capabilities, resulting in unlearned models that struggle to retain their prior knowledge and overall utility. To address this, we propose Selective Influence Machine Unlearning (SIMU), a two-step framework that enhances second-order optimizer-based unlearning by selectively updating only the critical neurons responsible for encoding the forget-set. By constraining updates to these targeted neurons, SIMU achieves comparable unlearning efficacy while substantially outperforming current methods in retaining the model's original knowledge.", "published": "2025-10-09T06:03:15Z", "authors": ["Anu Agarwal", "Mihir Pamnani", "Dilek Hakkani-Tur"], "categories": ["cs.LG", "cs.AI"], "doi": "", "_query": "house price prediction machine learning", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/1810.09126v3", "title": "Risk-Sensitive Reinforcement Learning via Policy Gradient Search", "abstract": "The objective in a traditional reinforcement learning (RL) problem is to find a policy that optimizes the expected value of a performance metric such as the infinite-horizon cumulative discounted or long-run average cost/reward. In practice, optimizing the expected value alone may not be satisfactory, in that it may be desirable to incorporate the notion of risk into the optimization problem formulation, either in the objective or as a constraint. Various risk measures have been proposed in the literature, e.g., exponential utility, variance, percentile performance, chance constraints, value at risk (quantile), conditional value-at-risk, prospect theory and its later enhancement, cumulative prospect theory. In this book, we consider risk-sensitive RL in two settings: one where the goal is to find a policy that optimizes the usual expected value objective while ensuring that a risk constraint is satisfied, and the other where the risk measure is the objective. We survey some of the recent work in this area specifically where policy gradient search is the solution approach. In the first risk-sensitive RL setting, we cover popular risk measures based on variance, conditional value-at-risk, and chance constraints, and present a template for policy gradient-based risk-sensitive RL algorithms using a Lagrangian formulation. For the setting where risk is incorporated directly into the objective function, we consider an exponential utility formulation, cumulative prospect theory, and coherent risk measures. This non-exhaustive survey aims to give a flavor of the challenges involved in solving risk-sensitive RL problems using policy gradient methods, as well as outlining some potential future research directions.", "published": "2018-10-22T08:01:18Z", "authors": ["Prashanth L. A.", "Michael Fu"], "categories": ["cs.LG", "math.OC", "stat.ML"], "doi": "", "_query": "house price prediction machine learning", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2505.21626v3", "title": "Learning where to learn: Training data distribution optimization for scientific machine learning", "abstract": "In scientific machine learning, models are routinely deployed with parameter values or boundary conditions far from those used in training. This paper studies the learning-where-to-learn problem of designing a training data distribution that minimizes average prediction error across a family of deployment regimes. A theoretical analysis shows how the training distribution shapes deployment accuracy. This motivates two adaptive algorithms based on bilevel or alternating optimization in the space of probability measures. Discretized implementations using parametric distribution classes or nonparametric particle-based gradient flows deliver optimized training distributions that outperform nonadaptive designs. Once trained, the resulting models exhibit improved sample complexity and robustness to distribution shift. This framework unlocks the potential of principled data acquisition for learning functions and solution operators of partial differential equations.", "published": "2025-05-27T18:00:58Z", "authors": ["Nicolas Guerra", "Nicholas H. Nelsen", "Yunan Yang"], "categories": ["cs.LG", "math.OC", "stat.ML"], "doi": "", "_query": "house price prediction machine learning", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/1806.05413v2", "title": "Learning Dynamics of Linear Denoising Autoencoders", "abstract": "Denoising autoencoders (DAEs) have proven useful for unsupervised representation learning, but a thorough theoretical understanding is still lacking of how the input noise influences learning. Here we develop theory for how noise influences learning in DAEs. By focusing on linear DAEs, we are able to derive analytic expressions that exactly describe their learning dynamics. We verify our theoretical predictions with simulations as well as experiments on MNIST and CIFAR-10. The theory illustrates how, when tuned correctly, noise allows DAEs to ignore low variance directions in the inputs while learning to reconstruct them. Furthermore, in a comparison of the learning dynamics of DAEs to standard regularised autoencoders, we show that noise has a similar regularisation effect to weight decay, but with faster training dynamics. We also show that our theoretical predictions approximate learning dynamics on real-world data and qualitatively match observed dynamics in nonlinear DAEs.", "published": "2018-06-14T08:46:36Z", "authors": ["Arnu Pretorius", "Steve Kroon", "Herman Kamper"], "categories": ["stat.ML", "cs.LG"], "doi": "", "_query": "house price prediction machine learning", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2308.01475v1", "title": "Interpretable Machine Learning for Discovery: Statistical Challenges \\& Opportunities", "abstract": "New technologies have led to vast troves of large and complex datasets across many scientific domains and industries. People routinely use machine learning techniques to not only process, visualize, and make predictions from this big data, but also to make data-driven discoveries. These discoveries are often made using Interpretable Machine Learning, or machine learning models and techniques that yield human understandable insights. In this paper, we discuss and review the field of interpretable machine learning, focusing especially on the techniques as they are often employed to generate new knowledge or make discoveries from large data sets. We outline the types of discoveries that can be made using Interpretable Machine Learning in both supervised and unsupervised settings. Additionally, we focus on the grand challenge of how to validate these discoveries in a data-driven manner, which promotes trust in machine learning systems and reproducibility in science. We discuss validation from both a practical perspective, reviewing approaches based on data-splitting and stability, as well as from a theoretical perspective, reviewing statistical results on model selection consistency and uncertainty quantification via statistical inference. Finally, we conclude by highlighting open challenges in using interpretable machine learning techniques to make discoveries, including gaps between theory and practice for validating data-driven-discoveries.", "published": "2023-08-02T23:57:31Z", "authors": ["Genevera I. Allen", "Luqin Gan", "Lili Zheng"], "categories": ["stat.ML", "cs.LG", "stat.ME"], "doi": "", "_query": "house price prediction machine learning", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2311.07636v2", "title": "Attention-based Multi-task Learning for Base Editor Outcome Prediction", "abstract": "Human genetic diseases often arise from point mutations, emphasizing the critical need for precise genome editing techniques. Among these, base editing stands out as it allows targeted alterations at the single nucleotide level. However, its clinical application is hindered by low editing efficiency and unintended mutations, necessitating extensive trial-and-error experimentation in the laboratory. To speed up this process, we present an attention-based two-stage machine learning model that learns to predict the likelihood of all possible editing outcomes for a given genomic target sequence. We further propose a multi-task learning schema to jointly learn multiple base editors (i.e. variants) at once. Our model's predictions consistently demonstrated a strong correlation with the actual experimental results on multiple datasets and base editor variants. These results provide further validation for the models' capacity to enhance and accelerate the process of refining base editing designs.", "published": "2023-11-13T15:16:24Z", "authors": ["Amina Mollaysa", "Ahmed Allam", "Michael Krauthammer"], "categories": ["q-bio.GN", "cs.LG"], "doi": "", "_query": "house price prediction machine learning", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/1711.08621v3", "title": "Counterfactual Learning for Machine Translation: Degeneracies and Solutions", "abstract": "Counterfactual learning is a natural scenario to improve web-based machine translation services by offline learning from feedback logged during user interactions. In order to avoid the risk of showing inferior translations to users, in such scenarios mostly exploration-free deterministic logging policies are in place. We analyze possible degeneracies of inverse and reweighted propensity scoring estimators, in stochastic and deterministic settings, and relate them to recently proposed techniques for counterfactual learning under deterministic logging.", "published": "2017-11-23T08:54:05Z", "authors": ["Carolin Lawrence", "Pratik Gajane", "Stefan Riezler"], "categories": ["stat.ML", "cs.CL", "cs.LG"], "doi": "", "_query": "house price prediction machine learning", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2311.07114v1", "title": "Novel models for fatigue life prediction under wideband random loads based on machine learning", "abstract": "Machine learning as a data-driven solution has been widely applied in the field of fatigue lifetime prediction. In this paper, three models for wideband fatigue life prediction are built based on three machine learning models, i.e. support vector machine (SVM), Gaussian process regression (GPR) and artificial neural network (ANN). The generalization ability of the models is enhanced by employing numerous power spectra samples with different bandwidth parameters and a variety of material properties related to fatigue life. Sufficient Monte Carlo numerical simulations demonstrate that the newly developed machine learning models are superior to the traditional frequency-domain models in terms of life prediction accuracy and the ANN model has the best overall performance among the three developed machine learning models.", "published": "2023-11-13T07:09:45Z", "authors": ["Hong Sun", "Yuanying Qiu", "Jing Li", "Jin Bai", "Ming Peng"], "categories": ["cond-mat.mtrl-sci", "cond-mat.dis-nn", "cs.LG"], "doi": "", "_query": "house price prediction machine learning", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/1902.04622v1", "title": "Learning Theory and Support Vector Machines - a primer", "abstract": "The main goal of statistical learning theory is to provide a fundamental framework for the problem of decision making and model construction based on sets of data. Here, we present a brief introduction to the fundamentals of statistical learning theory, in particular the difference between empirical and structural risk minimization, including one of its most prominent implementations, i.e. the Support Vector Machine.", "published": "2019-02-12T20:28:09Z", "authors": ["Michael Banf"], "categories": ["cs.LG", "stat.ML"], "doi": "", "_query": "house price prediction machine learning", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2004.07984v1", "title": "Spectral Learning on Matrices and Tensors", "abstract": "Spectral methods have been the mainstay in several domains such as machine learning and scientific computing. They involve finding a certain kind of spectral decomposition to obtain basis functions that can capture important structures for the problem at hand. The most common spectral method is the principal component analysis (PCA). It utilizes the top eigenvectors of the data covariance matrix, e.g. to carry out dimensionality reduction. This data pre-processing step is often effective in separating signal from noise. PCA and other spectral techniques applied to matrices have several limitations. By limiting to only pairwise moments, they are effectively making a Gaussian approximation on the underlying data and fail on data with hidden variables which lead to non-Gaussianity. However, in most data sets, there are latent effects that cannot be directly observed, e.g., topics in a document corpus, or underlying causes of a disease. By extending the spectral decomposition methods to higher order moments, we demonstrate the ability to learn a wide range of latent variable models efficiently. Higher-order moments can be represented by tensors, and intuitively, they can encode more information than just pairwise moment matrices. More crucially, tensor decomposition can pick up latent effects that are missed by matrix methods, e.g. uniquely identify non-orthogonal components. Exploiting these aspects turns out to be fruitful for provable unsupervised learning of a wide range of latent variable models. We also outline the computational techniques to design efficient tensor decomposition methods. We introduce Tensorly, which has a simple python interface for expressing tensor operations. It has a flexible back-end system supporting NumPy, PyTorch, TensorFlow and MXNet amongst others, allowing multi-GPU and CPU operations and seamless integration with deep-learning functionalities.", "published": "2020-04-16T22:53:00Z", "authors": ["Majid Janzamin", "Rong Ge", "Jean Kossaifi", "Anima Anandkumar"], "categories": ["cs.LG", "stat.ML"], "doi": "10.1561/2200000057", "_query": "house price prediction machine learning", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2103.11869v2", "title": "Robust Orthogonal Machine Learning of Treatment Effects", "abstract": "Causal learning is the key to obtaining stable predictions and answering \\textit{what if} problems in decision-makings. In causal learning, it is central to seek methods to estimate the average treatment effect (ATE) from observational data. The Double/Debiased Machine Learning (DML) is one of the prevalent methods to estimate ATE. However, the DML estimators can suffer from an \\textit{error-compounding issue} and even give extreme estimates when the propensity scores are close to 0 or 1. Previous studies have overcome this issue through some empirical tricks such as propensity score trimming, yet none of the existing works solves it from a theoretical standpoint. In this paper, we propose a \\textit{Robust Causal Learning (RCL)} method to offset the deficiencies of DML estimators. Theoretically, the RCL estimators i) satisfy the (higher-order) orthogonal condition and are as \\textit{consistent and doubly robust} as the DML estimators, and ii) get rid of the error-compounding issue. Empirically, the comprehensive experiments show that: i) the RCL estimators give more stable estimations of the causal parameters than DML; ii) the RCL estimators outperform traditional estimators and their variants when applying different machine learning models on both simulation and benchmark datasets, and a mimic consumer credit dataset generated by WGAN.", "published": "2021-03-22T14:04:13Z", "authors": ["Yiyan Huang", "Cheuk Hang Leung", "Qi Wu", "Xing Yan"], "categories": ["stat.ML", "cs.LG", "econ.EM", "q-fin.ST"], "doi": "", "_query": "house price prediction machine learning", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2411.10153v3", "title": "A unifying framework for generalised Bayesian online learning in non-stationary environments", "abstract": "We propose a unifying framework for methods that perform probabilistic online learning in non-stationary environments. We call the framework BONE, which stands for generalised (B)ayesian (O)nline learning in (N)on-stationary (E)nvironments. BONE provides a common structure to tackle a variety of problems, including online continual learning, prequential forecasting, and contextual bandits. The framework requires specifying three modelling choices: (i) a model for measurements (e.g., a neural network), (ii) an auxiliary process to model non-stationarity (e.g., the time since the last changepoint), and (iii) a conditional prior over model parameters (e.g., a multivariate Gaussian). The framework also requires two algorithmic choices, which we use to carry out approximate inference under this framework: (i) an algorithm to estimate beliefs (posterior distribution) about the model parameters given the auxiliary variable, and (ii) an algorithm to estimate beliefs about the auxiliary variable. We show how the modularity of our framework allows for many existing methods to be reinterpreted as instances of BONE, and it allows us to propose new methods. We compare experimentally existing methods with our proposed new method on several datasets, providing insights into the situations that make each method more suitable for a specific task. We provide a Jax open source library to facilitate the adoption of this framework.", "published": "2024-11-15T12:52:02Z", "authors": ["Gerardo Duran-Martin", "Leandro Sánchez-Betancourt", "Alexander Y. Shestopaloff", "Kevin Murphy"], "categories": ["stat.ML", "cs.LG"], "doi": "", "_query": "house price prediction machine learning", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2111.01743v1", "title": "Designing Inherently Interpretable Machine Learning Models", "abstract": "Interpretable machine learning (IML) becomes increasingly important in highly regulated industry sectors related to the health and safety or fundamental rights of human beings. In general, the inherently IML models should be adopted because of their transparency and explainability, while black-box models with model-agnostic explainability can be more difficult to defend under regulatory scrutiny. For assessing inherent interpretability of a machine learning model, we propose a qualitative template based on feature effects and model architecture constraints. It provides the design principles for high-performance IML model development, with examples given by reviewing our recent works on ExNN, GAMI-Net, SIMTree, and the Aletheia toolkit for local linear interpretability of deep ReLU networks. We further demonstrate how to design an interpretable ReLU DNN model with evaluation of conceptual soundness for a real case study of predicting credit default in home lending. We hope that this work will provide a practical guide of developing inherently IML models in high risk applications in banking industry, as well as other sectors.", "published": "2021-11-02T17:06:02Z", "authors": ["Agus Sudjianto", "Aijun Zhang"], "categories": ["cs.LG", "stat.ML"], "doi": "", "_query": "house price prediction machine learning", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/1912.12941v3", "title": "A general anomaly detection framework for fleet-based condition monitoring of machines", "abstract": "Machine failures decrease up-time and can lead to extra repair costs or even to human casualties and environmental pollution. Recent condition monitoring techniques use artificial intelligence in an effort to avoid time-consuming manual analysis and handcrafted feature extraction. Many of these only analyze a single machine and require a large historical data set. In practice, this can be difficult and expensive to collect. However, some industrial condition monitoring applications involve a fleet of similar operating machines. In most of these applications, it is safe to assume healthy conditions for the majority of machines. Deviating machine behavior is then an indicator for a machine fault. This work proposes an unsupervised, generic, anomaly detection framework for fleet-based condition monitoring. It uses generic building blocks and offers three key advantages. First, a historical data set is not required due to online fleet-based comparisons. Second, it allows incorporating domain expertise by user-defined comparison measures. Finally, contrary to most black-box artificial intelligence techniques, easy interpretability allows a domain expert to validate the predictions made by the framework. Two use-cases on an electrical machine fleet demonstrate the applicability of the framework to detect a voltage unbalance by means of electrical and vibration signatures.", "published": "2019-12-30T14:35:45Z", "authors": ["Kilian Hendrickx", "Wannes Meert", "Yves Mollet", "Johan Gyselinck", "Bram Cornelis", "Konstantinos Gryllias", "Jesse Davis"], "categories": ["cs.LG", "eess.SP", "stat.ML"], "doi": "10.1016/j.ymssp.2019.106585", "_query": "house price prediction machine learning", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/1902.11175v2", "title": "One-Shot Federated Learning", "abstract": "We present one-shot federated learning, where a central server learns a global model over a network of federated devices in a single round of communication. Our approach - drawing on ensemble learning and knowledge aggregation - achieves an average relative gain of 51.5% in AUC over local baselines and comes within 90.1% of the (unattainable) global ideal. We discuss these methods and identify several promising directions of future work.", "published": "2019-02-28T15:55:18Z", "authors": ["Neel Guha", "Ameet Talwalkar", "Virginia Smith"], "categories": ["cs.LG", "stat.ML"], "doi": "", "_query": "house price prediction machine learning", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/1611.06996v1", "title": "Spatial contrasting for deep unsupervised learning", "abstract": "Convolutional networks have marked their place over the last few years as the best performing model for various visual tasks. They are, however, most suited for supervised learning from large amounts of labeled data. Previous attempts have been made to use unlabeled data to improve model performance by applying unsupervised techniques. These attempts require different architectures and training methods. In this work we present a novel approach for unsupervised training of Convolutional networks that is based on contrasting between spatial regions within images. This criterion can be employed within conventional neural networks and trained using standard techniques such as SGD and back-propagation, thus complementing supervised methods.", "published": "2016-11-21T20:24:58Z", "authors": ["Elad Hoffer", "Itay Hubara", "Nir Ailon"], "categories": ["stat.ML", "cs.LG"], "doi": "", "_query": "house price prediction machine learning", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2412.12213v2", "title": "Finance-Informed Neural Network: Learning the Geometry of Option Pricing", "abstract": "We propose a Finance-Informed Neural Network (FINN) for option pricing and hedging that integrates financial theory directly into machine learning. Instead of training on observed option prices, FINN is learned through a self-supervised replication objective based on dynamic hedging, ensuring economic consistency by construction. We show theoretically that minimizing replication error recovers the arbitrage-free pricing operator and yields economically meaningful sensitivities. Empirically, FINN accurately recovers classical Black--Scholes prices and performs robustly in stochastic volatility environments, including the Heston model, while remaining stable in settings where analytical solutions are unavailable or unreliable. Fundamental pricing relationships such as put--call parity emerge endogenously. When applied to implied-volatility surface reconstruction, FINN produces surfaces that are consistently closer to observed market-implied volatilities than those obtained from Heston calibrations, indicating superior out-of-sample adaptability and reduced structural bias. Importantly, FINN extends beyond liquid option markets: it can be trained directly on historical spot prices to construct coherent option prices and Greeks for assets with no listed options. More broadly, FINN defines a new paradigm for financial pricing, in which prices are learned from replication and risk-control principles rather than inferred from parametric assumptions or direct supervision on option prices. By reframing option pricing as the learning of a pricing operator rather than the fitting of prices, FINN offers practitioners a practical and scalable tool for pricing, hedging, and risk management across both established and emerging financial markets.", "published": "2024-12-15T22:40:40Z", "authors": ["Amine M. Aboussalah", "Xuanze Li", "Cheng Chi", "Raj Patel"], "categories": ["cs.LG", "q-fin.CP", "stat.ML"], "doi": "", "_query": "house price prediction machine learning", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2208.02377v1", "title": "Improving Meta-Learning Generalization with Activation-Based Early-Stopping", "abstract": "Meta-Learning algorithms for few-shot learning aim to train neural networks capable of generalizing to novel tasks using only a few examples. Early-stopping is critical for performance, halting model training when it reaches optimal generalization to the new task distribution. Early-stopping mechanisms in Meta-Learning typically rely on measuring the model performance on labeled examples from a meta-validation set drawn from the training (source) dataset. This is problematic in few-shot transfer learning settings, where the meta-test set comes from a different target dataset (OOD) and can potentially have a large distributional shift with the meta-validation set. In this work, we propose Activation Based Early-stopping (ABE), an alternative to using validation-based early-stopping for meta-learning. Specifically, we analyze the evolution, during meta-training, of the neural activations at each hidden layer, on a small set of unlabelled support examples from a single task of the target tasks distribution, as this constitutes a minimal and justifiably accessible information from the target problem. Our experiments show that simple, label agnostic statistics on the activations offer an effective way to estimate how the target generalization evolves over time. At each hidden layer, we characterize the activation distributions, from their first and second order moments, then further summarized along the feature dimensions, resulting in a compact yet intuitive characterization in a four-dimensional space. Detecting when, throughout training time, and at which layer, the target activation trajectory diverges from the activation trajectory of the source data, allows us to perform early-stopping and improve generalization in a large array of few-shot transfer learning settings, across different algorithms, source and target datasets.", "published": "2022-08-03T22:55:45Z", "authors": ["Simon Guiroy", "Christopher Pal", "Gonçalo Mordido", "Sarath Chandar"], "categories": ["cs.LG", "cs.AI", "stat.ML"], "doi": "", "_query": "house price prediction machine learning", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2001.10655v1", "title": "Regularization Helps with Mitigating Poisoning Attacks: Distributionally-Robust Machine Learning Using the Wasserstein Distance", "abstract": "We use distributionally-robust optimization for machine learning to mitigate the effect of data poisoning attacks. We provide performance guarantees for the trained model on the original data (not including the poison records) by training the model for the worst-case distribution on a neighbourhood around the empirical distribution (extracted from the training dataset corrupted by a poisoning attack) defined using the Wasserstein distance. We relax the distributionally-robust machine learning problem by finding an upper bound for the worst-case fitness based on the empirical sampled-averaged fitness and the Lipschitz-constant of the fitness function (on the data for given model parameters) as regularizer. For regression models, we prove that this regularizer is equal to the dual norm of the model parameters. We use the Wine Quality dataset, the Boston Housing Market dataset, and the Adult dataset for demonstrating the results of this paper.", "published": "2020-01-29T01:16:19Z", "authors": ["Farhad Farokhi"], "categories": ["cs.LG", "cs.CR", "eess.SP", "math.OC", "math.ST", "stat.ML"], "doi": "", "_query": "house price prediction machine learning", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2503.16286v3", "title": "Explainable Graph-theoretical Machine Learning with Application to Alzheimer's Disease Prediction", "abstract": "Dementia affects over 55 million people worldwide, projected to reach 139 million by 2050, with Alzheimer's disease (AD) accounting for 60-70% of cases. AD is associated with disruptions in metabolic brain connectivity. Detecting these disruptions early is crucial for AD management. FDG-PET is a useful tool for identifying such impairments. However, most studies rely on group-level analyses or thresholding, potentially masking individual differences and overlooking weaker yet biologically critical brain connections. Moreover, AD prediction largely focuses on univariate rather than multivariate outcomes. To address this, we introduce explainable graph-theoretical machine learning (XGML), a framework for constructing individual metabolic brain graphs and identifying subgraphs most predictive of multivariate disease-related outcomes. Using Alzheimer's Disease Neuroimaging Initiative (ADNI) FDG-PET data, we compared six graph representations against three non-graph baselines, each with six machine learning models using repeated stratified 3-fold cross-validation (10 repeats). The best configuration combined kernel density estimation with Hellinger distance and random forest. Across eight cognitive scores, it reached an overall Fisher-z-averaged Pearson correlation of r=0.595, with strongest performance for ADAS13 (r=0.67), ADAS11 (r=0.65), and ADASQ4 (r=0.62). We identified key edges that were jointly but differentially predictive across outcomes, suggesting their potential as network biomarkers of cognitive decline. Preliminary external feasibility validation on an OASIS3 cohort yielded weak predictive performance for CDRSB (r=0.26) and MMSE (r=0.18), likely reflecting cohort, protocol, and diagnostic differences. Overall, our results suggest the promise of graph-theoretical machine learning for biomarker discovery, disease prediction, and understanding the neural mechanisms underlying AD.", "published": "2025-03-20T16:13:09Z", "authors": ["Narmina Baghirova", "Duy-Thanh Vũ", "Duy-Cat Can", "Christelle Schneuwly Diaz", "Julien Bodlet", "Guillaume Blanc", "Georgi Hrusanov", "Bernard Ries", "Oliver Y. Chén"], "categories": ["cs.LG"], "doi": "", "_query": "house price prediction machine learning", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/1904.05419v4", "title": "FairVis: Visual Analytics for Discovering Intersectional Bias in Machine Learning", "abstract": "The growing capability and accessibility of machine learning has led to its application to many real-world domains and data about people. Despite the benefits algorithmic systems may bring, models can reflect, inject, or exacerbate implicit and explicit societal biases into their outputs, disadvantaging certain demographic subgroups. Discovering which biases a machine learning model has introduced is a great challenge, due to the numerous definitions of fairness and the large number of potentially impacted subgroups. We present FairVis, a mixed-initiative visual analytics system that integrates a novel subgroup discovery technique for users to audit the fairness of machine learning models. Through FairVis, users can apply domain knowledge to generate and investigate known subgroups, and explore suggested and similar subgroups. FairVis' coordinated views enable users to explore a high-level overview of subgroup performance and subsequently drill down into detailed investigation of specific subgroups. We show how FairVis helps to discover biases in two real datasets used in predicting income and recidivism. As a visual analytics system devoted to discovering bias in machine learning, FairVis demonstrates how interactive visualization may help data scientists and the general public understand and create more equitable algorithmic systems.", "published": "2019-04-10T20:07:35Z", "authors": ["Ángel Alexander Cabrera", "Will Epperson", "Fred Hohman", "Minsuk Kahng", "Jamie Morgenstern", "Duen Horng Chau"], "categories": ["cs.LG", "stat.ML"], "doi": "10.1109/VAST47406.2019.8986948", "_query": "house price prediction machine learning", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2003.00415v1", "title": "Advanced kNN: A Mature Machine Learning Series", "abstract": "k-nearest neighbour (kNN) is one of the most prominent, simple and basic algorithm used in machine learning and data mining. However, kNN has limited prediction ability, i.e., kNN cannot predict any instance correctly if it does not belong to any of the predefined classes in the training data set. The purpose of this paper is to suggest an Advanced kNN (A-kNN) algorithm that will be able to classify an instance as unknown, after verifying that it does not belong to any of the predefined classes. Performance of kNN and A-kNN is compared on three different data sets namely iris plant data set, BUPA liver disorder data set, and Alpha Beta detection data set. Results of A-kNN are significantly accurate for detecting unknown instances.", "published": "2020-03-01T06:11:04Z", "authors": ["Muhammad Asim", "Muaaz Zakria"], "categories": ["cs.LG", "stat.ML"], "doi": "", "_query": "house price prediction machine learning", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2107.02517v1", "title": "An Evaluation of Machine Learning and Deep Learning Models for Drought Prediction using Weather Data", "abstract": "Drought is a serious natural disaster that has a long duration and a wide range of influence. To decrease the drought-caused losses, drought prediction is the basis of making the corresponding drought prevention and disaster reduction measures. While this problem has been studied in the literature, it remains unknown whether drought can be precisely predicted or not with machine learning models using weather data. To answer this question, a real-world public dataset is leveraged in this study and different drought levels are predicted using the last 90 days of 18 meteorological indicators as the predictors. In a comprehensive approach, 16 machine learning models and 16 deep learning models are evaluated and compared. The results show no single model can achieve the best performance for all evaluation metrics simultaneously, which indicates the drought prediction problem is still challenging. As benchmarks for further studies, the code and results are publicly available in a Github repository.", "published": "2021-07-06T10:19:43Z", "authors": ["Weiwei Jiang", "Jiayun Luo"], "categories": ["cs.LG", "cs.AI"], "doi": "10.3233/JIFS-212748", "_query": "house price prediction machine learning", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/1804.02477v3", "title": "Programmatically Interpretable Reinforcement Learning", "abstract": "We present a reinforcement learning framework, called Programmatically Interpretable Reinforcement Learning (PIRL), that is designed to generate interpretable and verifiable agent policies. Unlike the popular Deep Reinforcement Learning (DRL) paradigm, which represents policies by neural networks, PIRL represents policies using a high-level, domain-specific programming language. Such programmatic policies have the benefits of being more easily interpreted than neural networks, and being amenable to verification by symbolic methods. We propose a new method, called Neurally Directed Program Search (NDPS), for solving the challenging nonsmooth optimization problem of finding a programmatic policy with maximal reward. NDPS works by first learning a neural policy network using DRL, and then performing a local search over programmatic policies that seeks to minimize a distance from this neural \"oracle\". We evaluate NDPS on the task of learning to drive a simulated car in the TORCS car-racing environment. We demonstrate that NDPS is able to discover human-readable policies that pass some significant performance bars. We also show that PIRL policies can have smoother trajectories, and can be more easily transferred to environments not encountered during training, than corresponding policies discovered by DRL.", "published": "2018-04-06T22:17:18Z", "authors": ["Abhinav Verma", "Vijayaraghavan Murali", "Rishabh Singh", "Pushmeet Kohli", "Swarat Chaudhuri"], "categories": ["cs.LG", "cs.AI", "cs.PL", "stat.ML"], "doi": "", "_query": "house price prediction machine learning", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/quant-ph/9610036v1", "title": "Time Symmetry in Microphysics", "abstract": "Physics takes for granted that interacting physical systems with no common history are independent, before their interaction. This principle is time-asymmetric, for no such restriction applies to systems with no common future, after an interaction. The time-asymmetry is normally attributed to boundary conditions. I argue that there are two distinct independence principles of this kind at work in contemporary physics, one of which cannot be attributed to boundary conditions, and therefore conflicts with the assumed T (or CPT) symmetry of microphysics. I note that this may have interesting ramifications in quantum mechanics.", "published": "1996-10-22T23:16:19Z", "authors": ["Huw Price"], "categories": ["quant-ph"], "doi": "", "_query": "house price prediction machine learning", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2209.07921v2", "title": "ImDrug: A Benchmark for Deep Imbalanced Learning in AI-aided Drug Discovery", "abstract": "The last decade has witnessed a prosperous development of computational methods and dataset curation for AI-aided drug discovery (AIDD). However, real-world pharmaceutical datasets often exhibit highly imbalanced distribution, which is overlooked by the current literature but may severely compromise the fairness and generalization of machine learning applications. Motivated by this observation, we introduce ImDrug, a comprehensive benchmark with an open-source Python library which consists of 4 imbalance settings, 11 AI-ready datasets, 54 learning tasks and 16 baseline algorithms tailored for imbalanced learning. It provides an accessible and customizable testbed for problems and solutions spanning a broad spectrum of the drug discovery pipeline such as molecular modeling, drug-target interaction and retrosynthesis. We conduct extensive empirical studies with novel evaluation metrics, to demonstrate that the existing algorithms fall short of solving medicinal and pharmaceutical challenges in the data imbalance scenario. We believe that ImDrug opens up avenues for future research and development, on real-world challenges at the intersection of AIDD and deep imbalanced learning.", "published": "2022-09-16T13:35:57Z", "authors": ["Lanqing Li", "Liang Zeng", "Ziqi Gao", "Shen Yuan", "Yatao Bian", "Bingzhe Wu", "Hengtong Zhang", "Yang Yu", "Chan Lu", "Zhipeng Zhou", "Hongteng Xu", "Jia Li", "Peilin Zhao", "Pheng-Ann Heng"], "categories": ["cs.LG", "cs.AI", "q-bio.QM"], "doi": "", "_query": "house price prediction machine learning", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2310.15848v4", "title": "On Responsible Machine Learning Datasets with Fairness, Privacy, and Regulatory Norms", "abstract": "Artificial Intelligence (AI) has made its way into various scientific fields, providing astonishing improvements over existing algorithms for a wide variety of tasks. In recent years, there have been severe concerns over the trustworthiness of AI technologies. The scientific community has focused on the development of trustworthy AI algorithms. However, machine and deep learning algorithms, popular in the AI community today, depend heavily on the data used during their development. These learning algorithms identify patterns in the data, learning the behavioral objective. Any flaws in the data have the potential to translate directly into algorithms. In this study, we discuss the importance of Responsible Machine Learning Datasets and propose a framework to evaluate the datasets through a responsible rubric. While existing work focuses on the post-hoc evaluation of algorithms for their trustworthiness, we provide a framework that considers the data component separately to understand its role in the algorithm. We discuss responsible datasets through the lens of fairness, privacy, and regulatory compliance and provide recommendations for constructing future datasets. After surveying over 100 datasets, we use 60 datasets for analysis and demonstrate that none of these datasets is immune to issues of fairness, privacy preservation, and regulatory compliance. We provide modifications to the ``datasheets for datasets\" with important additions for improved dataset documentation. With governments around the world regularizing data protection laws, the method for the creation of datasets in the scientific community requires revision. We believe this study is timely and relevant in today's era of AI.", "published": "2023-10-24T14:01:53Z", "authors": ["Surbhi Mittal", "Kartik Thakral", "Richa Singh", "Mayank Vatsa", "Tamar Glaser", "Cristian Canton Ferrer", "Tal Hassner"], "categories": ["cs.LG", "cs.CV"], "doi": "", "_query": "house price prediction machine learning", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2606.19138v2", "title": "INDEQS: Informed Neural controlled Differential EQuationS", "abstract": "Neural Controlled Differential Equations (NCDE) provide a powerful continuous-time framework for forecasting time series, but standard graph-based extensions typically learn spatial structure purely from data, even in settings where a directed graph structure is known a priori. We introduce Informed Neural controlled Differential EQuationS (INDEQS), a modification to graph-based NCDE forecasting methods that incorporates prior knowledge of a directed graph at distinct architectural positions. INDEQS separates inner mixing of hidden states across graph nodes from outer mixing between vector field and control, and offers both a lightweight graph-constrained variant and a more expressive variant, learning additional graph connections from data via adaptive graph convolutions. To systematically study when graph informedness is beneficial in forecasting, we devise a continuous advection simulation on directed graphs, yielding synthetic spatio-temporal datasets with known ground-truth flow structure. We then evaluate INDEQS on two real-world tasks: river discharge forecasting on a hydrological network and traffic flow prediction on PeMS08. Across the synthetic and the river-discharge tasks, outer informedness consistently improves mean absolute error over an uninformed NCDE with comparable parameter count, particularly on larger graphs, while inner informedness offers a more parameter-efficient alternative when strict adherence to a known adjacency is desired. A comparison of discrete convolutional and continuous-time decoders further shows that continuous decoders yield better accuracy and greater temporal flexibility on real-world tasks. An implementation of INDEQS and the advection simulation is available at https://github.com/mitchi1/indeqs .", "published": "2026-06-17T14:46:23Z", "authors": ["Michael Detzel", "Gabriel Nobis", "Kristiyan Blagov", "Juri F. Schubert", "Jackie Ma", "Wojciech Samek"], "categories": ["cs.LG", "stat.ML"], "doi": "", "_query": "house price prediction machine learning", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2210.11226v1", "title": "Comparing Machine Learning Techniques for Alfalfa Biomass Yield Prediction", "abstract": "The alfalfa crop is globally important as livestock feed, so highly efficient planting and harvesting could benefit many industries, especially as the global climate changes and traditional methods become less accurate. Recent work using machine learning (ML) to predict yields for alfalfa and other crops has shown promise. Previous efforts used remote sensing, weather, planting, and soil data to train machine learning models for yield prediction. However, while remote sensing works well, the models require large amounts of data and cannot make predictions until the harvesting season begins. Using weather and planting data from alfalfa variety trials in Kentucky and Georgia, our previous work compared feature selection techniques to find the best technique and best feature set. In this work, we trained a variety of machine learning models, using cross validation for hyperparameter optimization, to predict biomass yields, and we showed better accuracy than similar work that employed more complex techniques. Our best individual model was a random forest with a mean absolute error of 0.081 tons/acre and R{$^2$} of 0.941. Next, we expanded this dataset to include Wisconsin and Mississippi, and we repeated our experiments, obtaining a higher best R{$^2$} of 0.982 with a regression tree. We then isolated our testing datasets by state to explore this problem's eligibility for domain adaptation (DA), as we trained on multiple source states and tested on one target state. This Trivial DA (TDA) approach leaves plenty of room for improvement through exploring more complex DA techniques in forthcoming work.", "published": "2022-10-20T13:00:33Z", "authors": ["Jonathan Vance", "Khaled Rasheed", "Ali Missaoui", "Frederick Maier", "Christian Adkins", "Chris Whitmire"], "categories": ["cs.LG", "cs.CV"], "doi": "", "_query": "house price prediction machine learning", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2204.03968v2", "title": "Machine Learning architectures for price formation models", "abstract": "Here, we study machine learning (ML) architectures to solve a mean-field games (MFGs) system arising in price formation models. We formulate a training process that relies on a min-max characterization of the optimal control and price variables. Our main theoretical contribution is the development of a posteriori estimates as a tool to evaluate the convergence of the training process. We illustrate our results with numerical experiments for linear dynamics and both quadratic and non-quadratic models.", "published": "2022-04-08T09:55:48Z", "authors": ["Diogo Gomes", "Julián Gutiérrez", "Mathieu Laurière"], "categories": ["math.OC"], "doi": "", "_query": "house price prediction machine learning", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/1912.09630v1", "title": "Practical Solutions for Machine Learning Safety in Autonomous Vehicles", "abstract": "Autonomous vehicles rely on machine learning to solve challenging tasks in perception and motion planning. However, automotive software safety standards have not fully evolved to address the challenges of machine learning safety such as interpretability, verification, and performance limitations. In this paper, we review and organize practical machine learning safety techniques that can complement engineering safety for machine learning based software in autonomous vehicles. Our organization maps safety strategies to state-of-the-art machine learning techniques in order to enhance dependability and safety of machine learning algorithms. We also discuss security limitations and user experience aspects of machine learning components in autonomous vehicles.", "published": "2019-12-20T03:47:28Z", "authors": ["Sina Mohseni", "Mandar Pitale", "Vasu Singh", "Zhangyang Wang"], "categories": ["cs.LG", "stat.ML"], "doi": "", "_query": "house price prediction machine learning", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2011.06069v2", "title": "Ecole: A Gym-like Library for Machine Learning in Combinatorial Optimization Solvers", "abstract": "We present Ecole, a new library to simplify machine learning research for combinatorial optimization. Ecole exposes several key decision tasks arising in general-purpose combinatorial optimization solvers as control problems over Markov decision processes. Its interface mimics the popular OpenAI Gym library and is both extensible and intuitive to use. We aim at making this library a standardized platform that will lower the bar of entry and accelerate innovation in the field. Documentation and code can be found at https://www.ecole.ai.", "published": "2020-11-11T20:59:36Z", "authors": ["Antoine Prouvost", "Justin Dumouchelle", "Lara Scavuzzo", "Maxime Gasse", "Didier Chételat", "Andrea Lodi"], "categories": ["cs.LG", "math.OC"], "doi": "", "_query": "house price prediction machine learning", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/1510.02533v2", "title": "New Optimisation Methods for Machine Learning", "abstract": "A thesis submitted for the degree of Doctor of Philosophy of The Australian National University. In this work we introduce several new optimisation methods for problems in machine learning. Our algorithms broadly fall into two categories: optimisation of finite sums and of graph structured objectives. The finite sum problem is simply the minimisation of objective functions that are naturally expressed as a summation over a large number of terms, where each term has a similar or identical weight. Such objectives most often appear in machine learning in the empirical risk minimisation framework in the non-online learning setting. The second category, that of graph structured objectives, consists of objectives that result from applying maximum likelihood to Markov random field models. Unlike the finite sum case, all the non-linearity is contained within a partition function term, which does not readily decompose into a summation. For the finite sum problem, we introduce the Finito and SAGA algorithms, as well as variants of each. For graph-structured problems, we take three complementary approaches. We look at learning the parameters for a fixed structure, learning the structure independently, and learning both simultaneously. Specifically, for the combined approach, we introduce a new method for encouraging graph structures with the \"scale-free\" property. For the structure learning problem, we establish SHORTCUT, a O(n^{2.5}) expected time approximate structure learning method for Gaussian graphical models. For problems where the structure is known but the parameters unknown, we introduce an approximate maximum likelihood learning algorithm that is capable of learning a useful subclass of Gaussian graphical models.", "published": "2015-10-09T00:34:12Z", "authors": ["Aaron Defazio"], "categories": ["cs.LG", "stat.ML"], "doi": "", "_query": "house price prediction machine learning", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2208.05144v1", "title": "Machine Learning-based EEG Applications and Markets", "abstract": "This paper addresses both the various EEG applications and the current EEG market ecosystem propelled by machine learning. Increasingly available open medical and health datasets using EEG encourage data-driven research with a promise of improving neurology for patient care through knowledge discovery and machine learning data science algorithm development. This effort leads to various kinds of EEG developments and currently forms a new EEG market. This paper attempts to do a comprehensive survey on the EEG market and covers the six significant applications of EEG, including diagnosis/screening, drug development, neuromarketing, daily health, metaverse, and age/disability assistance. The highlight of this survey is on the compare and contrast between the research field and the business market. Our survey points out the current limitations of EEG and indicates the future direction of research and business opportunity for every EEG application listed above. Based on our survey, more research on machine learning-based EEG applications will lead to a more robust EEG-related market. More companies will use the research technology and apply it to real-life settings. As the EEG-related market grows, the EEG-related devices will collect more EEG data, and there will be more EEG data available for researchers to use in their study, coming back as a virtuous cycle. Our market analysis indicates that research related to the use of EEG data and machine learning in the six applications listed above points toward a clear trend in the growth and development of the EEG ecosystem and machine learning world.", "published": "2022-08-10T04:47:23Z", "authors": ["Weiqing Gu", "Bohan Yang", "Ryan Chang"], "categories": ["cs.LG", "stat.ML"], "doi": "", "_query": "house price prediction machine learning", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/1903.12394v3", "title": "Informed Machine Learning -- A Taxonomy and Survey of Integrating Knowledge into Learning Systems", "abstract": "Despite its great success, machine learning can have its limits when dealing with insufficient training data. A potential solution is the additional integration of prior knowledge into the training process which leads to the notion of informed machine learning. In this paper, we present a structured overview of various approaches in this field. We provide a definition and propose a concept for informed machine learning which illustrates its building blocks and distinguishes it from conventional machine learning. We introduce a taxonomy that serves as a classification framework for informed machine learning approaches. It considers the source of knowledge, its representation, and its integration into the machine learning pipeline. Based on this taxonomy, we survey related research and describe how different knowledge representations such as algebraic equations, logic rules, or simulation results can be used in learning systems. This evaluation of numerous papers on the basis of our taxonomy uncovers key methods in the field of informed machine learning.", "published": "2019-03-29T08:37:40Z", "authors": ["Laura von Rueden", "Sebastian Mayer", "Katharina Beckh", "Bogdan Georgiev", "Sven Giesselbach", "Raoul Heese", "Birgit Kirsch", "Julius Pfrommer", "Annika Pick", "Rajkumar Ramamurthy", "Michal Walczak", "Jochen Garcke", "Christian Bauckhage", "Jannis Schuecker"], "categories": ["stat.ML", "cs.AI", "cs.LG"], "doi": "10.1109/TKDE.2021.3079836", "_query": "house price prediction machine learning", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2309.00380v3", "title": "Learning multi-modal generative models with permutation-invariant encoders and tighter variational objectives", "abstract": "Devising deep latent variable models for multi-modal data has been a long-standing theme in machine learning research. Multi-modal Variational Autoencoders (VAEs) have been a popular generative model class that learns latent representations that jointly explain multiple modalities. Various objective functions for such models have been suggested, often motivated as lower bounds on the multi-modal data log-likelihood or from information-theoretic considerations. To encode latent variables from different modality subsets, Product-of-Experts (PoE) or Mixture-of-Experts (MoE) aggregation schemes have been routinely used and shown to yield different trade-offs, for instance, regarding their generative quality or consistency across multiple modalities. In this work, we consider a variational objective that can tightly approximate the data log-likelihood. We develop more flexible aggregation schemes that avoid the inductive biases in PoE or MoE approaches by combining encoded features from different modalities based on permutation-invariant neural networks. Our numerical experiments illustrate trade-offs for multi-modal variational objectives and various aggregation schemes. We show that our variational objective and more flexible aggregation models can become beneficial when one wants to approximate the true joint distribution over observed modalities and latent variables in identifiable models.", "published": "2023-09-01T10:32:21Z", "authors": ["Marcel Hirt", "Domenico Campolo", "Victoria Leong", "Juan-Pablo Ortega"], "categories": ["stat.ML", "cs.LG"], "doi": "", "_query": "house price prediction machine learning", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2007.04911v2", "title": "GAMA: a General Automated Machine learning Assistant", "abstract": "The General Automated Machine learning Assistant (GAMA) is a modular AutoML system developed to empower users to track and control how AutoML algorithms search for optimal machine learning pipelines, and facilitate AutoML research itself. In contrast to current, often black-box systems, GAMA allows users to plug in different AutoML and post-processing techniques, logs and visualizes the search process, and supports easy benchmarking. It currently features three AutoML search algorithms, two model post-processing steps, and is designed to allow for more components to be added.", "published": "2020-07-09T16:16:25Z", "authors": ["Pieter Gijsbers", "Joaquin Vanschoren"], "categories": ["cs.LG", "stat.ML"], "doi": "10.1007/978-3-030-67670-4_39", "_query": "house price prediction machine learning", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/1906.02287v2", "title": "Automated Machine Learning: State-of-The-Art and Open Challenges", "abstract": "With the continuous and vast increase in the amount of data in our digital world, it has been acknowledged that the number of knowledgeable data scientists can not scale to address these challenges. Thus, there was a crucial need for automating the process of building good machine learning models. In the last few years, several techniques and frameworks have been introduced to tackle the challenge of automating the process of Combined Algorithm Selection and Hyper-parameter tuning (CASH) in the machine learning domain. The main aim of these techniques is to reduce the role of the human in the loop and fill the gap for non-expert machine learning users by playing the role of the domain expert. In this paper, we present a comprehensive survey for the state-of-the-art efforts in tackling the CASH problem. In addition, we highlight the research work of automating the other steps of the full complex machine learning pipeline (AutoML) from data understanding till model deployment. Furthermore, we provide comprehensive coverage for the various tools and frameworks that have been introduced in this domain. Finally, we discuss some of the research directions and open challenges that need to be addressed in order to achieve the vision and goals of the AutoML process.", "published": "2019-06-05T20:07:13Z", "authors": ["Radwa Elshawi", "Mohamed Maher", "Sherif Sakr"], "categories": ["cs.LG", "stat.ML"], "doi": "", "_query": "house price prediction machine learning", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2511.21890v2", "title": "Sparse Multiple Kernel Learning: Alternating Best Response and Semidefinite Relaxations", "abstract": "We study Sparse Multiple Kernel Learning (SMKL), which is the problem of selecting a sparse convex combination of prespecified kernels for support vector binary classification. Unlike prevailing l1 regularized approaches that approximate a sparsifying penalty, we formulate the problem by imposing an explicit cardinality constraint on the kernel weights and add an l2 penalty for robustness. We solve the resulting non-convex minimax problem via an alternating best response algorithm with two subproblems: the alpha subproblem is a standard kernel SVM dual solved via LIBSVM, while the beta subproblem admits an efficient solution via the Greedy Selector and Simplex Projector algorithm. We reformulate SMKL as a mixed integer semidefinite optimization problem and derive a hierarchy of semidefinite convex relaxations which can be used to certify near-optimality of the solutions returned by our best response algorithm and also to warm start it. On ten UCI benchmarks, our method with random initialization outperforms state-of-the-art MKL approaches in out-of-sample prediction accuracy on average by 3.34 percentage points (relative to the best performing benchmark) while selecting a small number of candidate kernels in comparable runtime. With warm starting, our method outperforms the best performing benchmark's out-of-sample prediction accuracy on average by 4.05 percentage points. Our convex relaxations provide a certificate that in several cases, the solution returned by our best response algorithm is the globally optimal solution.", "published": "2025-11-26T20:14:45Z", "authors": ["Dimitris Bertsimas", "Caio de Prospero Iglesias", "Nicholas A. G. Johnson"], "categories": ["stat.ML", "cs.LG"], "doi": "", "_query": "house price prediction machine learning", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/1908.05557v2", "title": "Towards Automated Machine Learning: Evaluation and Comparison of AutoML Approaches and Tools", "abstract": "There has been considerable growth and interest in industrial applications of machine learning (ML) in recent years. ML engineers, as a consequence, are in high demand across the industry, yet improving the efficiency of ML engineers remains a fundamental challenge. Automated machine learning (AutoML) has emerged as a way to save time and effort on repetitive tasks in ML pipelines, such as data pre-processing, feature engineering, model selection, hyperparameter optimization, and prediction result analysis. In this paper, we investigate the current state of AutoML tools aiming to automate these tasks. We conduct various evaluations of the tools on many datasets, in different data segments, to examine their performance, and compare their advantages and disadvantages on different test cases.", "published": "2019-08-15T14:16:09Z", "authors": ["Anh Truong", "Austin Walters", "Jeremy Goodsitt", "Keegan Hines", "C. Bayan Bruss", "Reza Farivar"], "categories": ["cs.LG", "stat.ML"], "doi": "10.1109/ICTAI.2019.00209", "_query": "house price prediction machine learning", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/1906.02664v1", "title": "Machine Learning and Visualization in Clinical Decision Support: Current State and Future Directions", "abstract": "Deep learning, an area of machine learning, is set to revolutionize patient care. But it is not yet part of standard of care, especially when it comes to individual patient care. In fact, it is unclear to what extent data-driven techniques are being used to support clinical decision making (CDS). Heretofore, there has not been a review of ways in which research in machine learning and other types of data-driven techniques can contribute effectively to clinical care and the types of support they can bring to clinicians. In this paper, we consider ways in which two data driven domains - machine learning and data visualizations - can contribute to the next generation of clinical decision support systems. We review the literature regarding the ways heuristic knowledge, machine learning, and visualization are - and can be - applied to three types of CDS. There has been substantial research into the use of predictive modeling for alerts, however current CDS systems are not utilizing these methods. Approaches that leverage interactive visualizations and machine-learning inferences to organize and review patient data are gaining popularity but are still at the prototype stage and are not yet in use. CDS systems that could benefit from prescriptive machine learning (e.g., treatment recommendations for specific patients) have not yet been developed. We discuss potential reasons for the lack of deployment of data-driven methods in CDS and directions for future research.", "published": "2019-06-06T16:03:46Z", "authors": ["Gal Levy-Fix", "Gilad J. Kuperman", "Noémie Elhadad"], "categories": ["cs.LG", "cs.CY", "stat.ML"], "doi": "", "_query": "house price prediction machine learning", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2408.01612v2", "title": "Data-Driven Machine Learning Approaches for Predicting In-Hospital Sepsis Mortality", "abstract": "Sepsis is a severe condition responsible for many deaths in the United States and worldwide, making accurate prediction of outcomes crucial for timely and effective treatment. Previous studies employing machine learning faced limitations in feature selection and model interpretability, reducing their clinical applicability. This research aimed to develop an interpretable and accurate machine learning model to predict in-hospital sepsis mortality, addressing these gaps. Using ICU patient records from the MIMIC-III database, we extracted relevant data through a combination of literature review, clinical input refinement, and Random Forest-based feature selection, identifying the top 35 features. Data preprocessing included cleaning, imputation, standardization, and applying the Synthetic Minority Over-sampling Technique (SMOTE) to address class imbalance, resulting in a dataset of 4,683 patients with 17,429 admissions. Five models-Random Forest, Gradient Boosting, Logistic Regression, Support Vector Machine, and K-Nearest Neighbor-were developed and evaluated. The Random Forest model demonstrated the best performance, achieving an accuracy of 0.90, AUROC of 0.97, precision of 0.93, recall of 0.91, and F1-score of 0.92. These findings underscore the potential of data-driven machine learning approaches to improve critical care, offering clinicians a powerful tool for predicting in-hospital sepsis mortality and enhancing patient outcomes.", "published": "2024-08-03T00:28:25Z", "authors": ["Arseniy Shumilov", "Yueting Zhu", "Negin Ashrafi", "Armin Abdollahi", "Greg Placencia", "Kamiar Alaei", "Maryam Pishgar"], "categories": ["cs.LG"], "doi": "", "_query": "house price prediction machine learning", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2201.00292v4", "title": "Fair Data Representation for Machine Learning at the Pareto Frontier", "abstract": "As machine learning powered decision-making becomes increasingly important in our daily lives, it is imperative to strive for fairness in the underlying data processing. We propose a pre-processing algorithm for fair data representation via which supervised learning results in estimations of the Pareto frontier between prediction error and statistical disparity. Particularly, the present work applies the optimal affine transport to approach the post-processing Wasserstein-2 barycenter characterization of the optimal fair $L^2$-objective supervised learning via a pre-processing data deformation. Furthermore, we show that the Wasserstein-2 geodesics from the conditional (on sensitive information) distributions of the learning outcome to their barycenter characterizes the Pareto frontier between $L^2$-loss and the average pairwise Wasserstein-2 distance among sensitive groups on the learning outcome. Numerical simulations underscore the advantages: (1) the pre-processing step is compositive with arbitrary conditional expectation estimation supervised learning methods and unseen data; (2) the fair representation protects the sensitive information by limiting the inference capability of the remaining data with respect to the sensitive data; (3) the optimal affine maps are computationally efficient even for high-dimensional data.", "published": "2022-01-02T05:05:26Z", "authors": ["Shizhou Xu", "Thomas Strohmer"], "categories": ["stat.ML", "cs.LG", "math.PR"], "doi": "", "_query": "house price prediction machine learning", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2112.03946v1", "title": "Generative Adversarial Network (GAN) and Enhanced Root Mean Square Error (ERMSE): Deep Learning for Stock Price Movement Prediction", "abstract": "The prediction of stock price movement direction is significant in financial circles and academic. Stock price contains complex, incomplete, and fuzzy information which makes it an extremely difficult task to predict its development trend. Predicting and analysing financial data is a nonlinear, time-dependent problem. With rapid development in machine learning and deep learning, this task can be performed more effectively by a purposely designed network. This paper aims to improve prediction accuracy and minimizing forecasting error loss through deep learning architecture by using Generative Adversarial Networks. It was proposed a generic model consisting of Phase-space Reconstruction (PSR) method for reconstructing price series and Generative Adversarial Network (GAN) which is a combination of two neural networks which are Long Short-Term Memory (LSTM) as Generative model and Convolutional Neural Network (CNN) as Discriminative model for adversarial training to forecast the stock market. LSTM will generate new instances based on historical basic indicators information and then CNN will estimate whether the data is predicted by LSTM or is real. It was found that the Generative Adversarial Network (GAN) has performed well on the enhanced root mean square error to LSTM, as it was 4.35% more accurate in predicting the direction and reduced processing time and RMSE by 78 secs and 0.029, respectively. This study provides a better result in the accuracy of the stock index. It seems that the proposed system concentrates on minimizing the root mean square error and processing time and improving the direction prediction accuracy, and provides a better result in the accuracy of the stock index.", "published": "2021-11-30T18:38:59Z", "authors": ["Ashish Kumar", "Abeer Alsadoon", "P. W. C. Prasad", "Salma Abdullah", "Tarik A. Rashid", "Duong Thu Hang Pham", "Tran Quoc Vinh Nguyen"], "categories": ["q-fin.ST", "cs.LG", "cs.NE"], "doi": "10.1007/s11042-021-11670-w", "_query": "house price prediction machine learning", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/1908.04909v1", "title": "Constrained Multi-Objective Optimization for Automated Machine Learning", "abstract": "Automated machine learning has gained a lot of attention recently. Building and selecting the right machine learning models is often a multi-objective optimization problem. General purpose machine learning software that simultaneously supports multiple objectives and constraints is scant, though the potential benefits are great. In this work, we present a framework called Autotune that effectively handles multiple objectives and constraints that arise in machine learning problems. Autotune is built on a suite of derivative-free optimization methods, and utilizes multi-level parallelism in a distributed computing environment for automatically training, scoring, and selecting good models. Incorporation of multiple objectives and constraints in the model exploration and selection process provides the flexibility needed to satisfy trade-offs necessary in practical machine learning applications. Experimental results from standard multi-objective optimization benchmark problems show that Autotune is very efficient in capturing Pareto fronts. These benchmark results also show how adding constraints can guide the search to more promising regions of the solution space, ultimately producing more desirable Pareto fronts. Results from two real-world case studies demonstrate the effectiveness of the constrained multi-objective optimization capability offered by Autotune.", "published": "2019-08-14T01:31:45Z", "authors": ["Steven Gardner", "Oleg Golovidov", "Joshua Griffin", "Patrick Koch", "Wayne Thompson", "Brett Wujek", "Yan Xu"], "categories": ["cs.LG", "cs.DC", "cs.NE", "stat.ML"], "doi": "", "_query": "house price prediction machine learning", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/1206.6412v1", "title": "A Simple Algorithm for Semi-supervised Learning with Improved Generalization Error Bound", "abstract": "In this work, we develop a simple algorithm for semi-supervised regression. The key idea is to use the top eigenfunctions of integral operator derived from both labeled and unlabeled examples as the basis functions and learn the prediction function by a simple linear regression. We show that under appropriate assumptions about the integral operator, this approach is able to achieve an improved regression error bound better than existing bounds of supervised learning. We also verify the effectiveness of the proposed algorithm by an empirical study.", "published": "2012-06-27T19:59:59Z", "authors": ["Ming Ji", "Tianbao Yang", "Binbin Lin", "Rong Jin", "Jiawei Han"], "categories": ["cs.LG", "stat.ML"], "doi": "", "_query": "house price prediction machine learning", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2212.06986v4", "title": "Why entanglement?", "abstract": "In this piece, written for a general audience, we propose a mechanism for quantum entanglement. The key ingredient is collider bias. In the language of causal models, a collider is a variable causally influenced by two or more other variables. Conditioning on a collider typically produces non-causal correlations between its contributing causes. This phenomenon can produce associations analogous to Bell correlations, in suitable post-selected ensembles. Such collider artefacts may become real connections, resembling causality, if a collider is 'constrained' (e.g., by a future boundary condition). We consider the time-reversed analogues of these points in the context of retrocausal models of QM. Retrocausality yields a collider at the source of an EPR-Bell particle pair, and in this case constraint of the collider is possible by normal methods of experimental preparation. It follows that connections resembling causality may emerge across such colliders, from one branch of the experiment to the other. Our hypothesis is that this constrained retrocausal collider bias is the origin of entanglement. This piece is based on a suggestion first made in arXiv:2101.05370v4 [quant-ph], and is an ancestor of an essay now published online in Aeon magazine [Price & Wharton 2023a]. In an updated version of the argument in arXiv:2309.10969 [quant-ph] we (i) demonstrate its application in a real Bell experiment; and (ii) show that we can do without an explicit postulate of retrocausality", "published": "2022-12-14T02:46:22Z", "authors": ["Huw Price", "Ken Wharton"], "categories": ["quant-ph", "physics.hist-ph", "physics.pop-ph"], "doi": "", "_query": "house price prediction machine learning", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2509.12286v1", "title": "Prediction of Stocks Index Price using Quantum GANs", "abstract": "This paper investigates the application of Quantum Generative Adversarial Networks (QGANs) for stock price prediction. Financial markets are inherently complex, marked by high volatility and intricate patterns that traditional models often fail to capture. QGANs, leveraging the power of quantum computing, offer a novel approach by combining the strengths of generative models with quantum machine learning techniques. We implement a QGAN model tailored for stock price prediction and evaluate its performance using historical stock market data. Our results demonstrate that QGANs can generate synthetic data closely resembling actual market behavior, leading to enhanced prediction accuracy. The experiment was conducted using the Stocks index price data and the AWS Braket SV1 simulator for training the QGAN circuits. The quantum-enhanced model outperforms classical Long Short-Term Memory (LSTM) and GAN models in terms of convergence speed and prediction accuracy. This research represents a key step toward integrating quantum computing in financial forecasting, offering potential advantages in speed and precision over traditional methods. The findings suggest important implications for traders, financial analysts, and researchers seeking advanced tools for market analysis.", "published": "2025-09-14T20:28:24Z", "authors": ["Sangram Deshpande", "Gopal Ramesh Dahale", "Sai Nandan Morapakula", "Uday Wad"], "categories": ["cs.LG", "quant-ph"], "doi": "10.1609/aaaiss.v7i1.36904", "_query": "house price prediction machine learning", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2403.04231v1", "title": "Identification of socioeconomic factors influencing global food price security using machine learning", "abstract": "Global concern over food prices and security has been exacerbated by the impacts of armed conflicts such as the Russia Ukraine War, pandemic diseases, and climate change. Traditionally, analyzing global food prices and their associations with socioeconomic factors has relied on static linear regression models. However, the complexity of socioeconomic factors and their implications extend beyond simple linear relationships. By incorporating determinants, critical characteristics identification, and comparative model analysis, this study aimed to identify the critical socioeconomic characteristics and multidimensional relationships associated with the underlying factors of food prices and security. Machine learning tools were used to uncover the socioeconomic factors influencing global food prices from 2000 to 2022. A total of 105 key variables from the World Development Indicators and the Food and Agriculture Organization of the United Nations were selected. Machine learning identified four key dimensions of food price security: economic and population metrics, military spending, health spending, and environmental factors. The top 30 determinants were selected for feature extraction using data mining. The efficiency of the support vector regression model allowed for precise prediction making and correlation analysis. Keywords: environment and growth, global economics, price fluctuation, support vector regression", "published": "2024-03-07T05:24:11Z", "authors": ["Shan Shan"], "categories": ["stat.AP"], "doi": "10.22004/ag.econ.348726", "_query": "house price prediction machine learning", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2609.13701v1", "title": "Leakage-Safe and Scheduler-Aware Machine Learning for Grid Job Runtime Prediction", "abstract": "Accurate job runtime prediction can improve scheduling-aware resource management in grid and distributed computing environments, but prediction models must be evaluated under realistic deployment constraints. This paper revisits CPU burst time prediction on the GWA-T-4 AuverGrid workload trace and reformulates it as leakage-safe pre-execution job runtime prediction. We define the target as job-level runtime, use only submission-time attributes, exclude post-execution variables, and evaluate models under temporal and cold-start settings rather than relying only on random cross-validation. We compare standard regressors, chronological historical baselines, categorical encoding strategies, and CatBoost with native categorical handling. We further add temporal hyperparameter tuning, runtime predictability analysis, feature ablation, error analysis by job length, and a minimal scheduling simulation. After temporal-validation tuning, CatBoost achieves the strongest deployment-oriented result with R^2=0.239, MAE=27,019, RMSE=46,587, and LogMAE=2.646 on the held-out temporal test set. A single-server simulation over all 69,523 held-out temporal test jobs shows that prediction-informed SJF reduces average waiting time by 50.92% relative to FCFS. The results show that random-split evaluation overestimates performance, categorical-native boosting improves temporal generalization, and long-job underestimation remains a scheduler-relevant challenge.", "published": "2026-09-12T04:20:41Z", "authors": ["Ashfaq Ali Shafin", "Khandaker Mamun Ahmed"], "categories": ["cs.LG", "cs.AI"], "doi": "", "_query": "house price prediction machine learning", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/1902.03501v2", "title": "Assessing the Local Interpretability of Machine Learning Models", "abstract": "The increasing adoption of machine learning tools has led to calls for accountability via model interpretability. But what does it mean for a machine learning model to be interpretable by humans, and how can this be assessed? We focus on two definitions of interpretability that have been introduced in the machine learning literature: simulatability (a user's ability to run a model on a given input) and \"what if\" local explainability (a user's ability to correctly determine a model's prediction under local changes to the input, given knowledge of the model's original prediction). Through a user study with 1,000 participants, we test whether humans perform well on tasks that mimic the definitions of simulatability and \"what if\" local explainability on models that are typically considered locally interpretable. To track the relative interpretability of models, we employ a simple metric, the runtime operation count on the simulatability task. We find evidence that as the number of operations increases, participant accuracy on the local interpretability tasks decreases. In addition, this evidence is consistent with the common intuition that decision trees and logistic regression models are interpretable and are more interpretable than neural networks.", "published": "2019-02-09T21:49:36Z", "authors": ["Dylan Slack", "Sorelle A. Friedler", "Carlos Scheidegger", "Chitradeep Dutta Roy"], "categories": ["cs.LG", "cs.HC", "stat.ML"], "doi": "", "_query": "house price prediction machine learning", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2507.05441v1", "title": "Adversarial Machine Learning Attacks on Financial Reporting via Maximum Violated Multi-Objective Attack", "abstract": "Bad actors, primarily distressed firms, have the incentive and desire to manipulate their financial reports to hide their distress and derive personal gains. As attackers, these firms are motivated by potentially millions of dollars and the availability of many publicly disclosed and used financial modeling frameworks. Existing attack methods do not work on this data due to anti-correlated objectives that must both be satisfied for the attacker to succeed. We introduce Maximum Violated Multi-Objective (MVMO) attacks that adapt the attacker's search direction to find $20\\times$ more satisfying attacks compared to standard attacks. The result is that in $\\approx50\\%$ of cases, a company could inflate their earnings by 100-200%, while simultaneously reducing their fraud scores by 15%. By working with lawyers and professional accountants, we ensure our threat model is realistic to how such frauds are performed in practice.", "published": "2025-07-07T19:45:46Z", "authors": ["Edward Raff", "Karen Kukla", "Michel Benaroch", "Joseph Comprix"], "categories": ["cs.LG", "stat.ML"], "doi": "", "_query": "house price prediction machine learning", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/1811.09602v1", "title": "Model-Based Reinforcement Learning for Sepsis Treatment", "abstract": "Sepsis is a dangerous condition that is a leading cause of patient mortality. Treating sepsis is highly challenging, because individual patients respond very differently to medical interventions and there is no universally agreed-upon treatment for sepsis. In this work, we explore the use of continuous state-space model-based reinforcement learning (RL) to discover high-quality treatment policies for sepsis patients. Our quantitative evaluation reveals that by blending the treatment strategy discovered with RL with what clinicians follow, we can obtain improved policies, potentially allowing for better medical treatment for sepsis.", "published": "2018-11-23T18:57:30Z", "authors": ["Aniruddh Raghu", "Matthieu Komorowski", "Sumeetpal Singh"], "categories": ["cs.LG", "cs.AI", "stat.ML"], "doi": "", "_query": "house price prediction machine learning", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/1905.07318v4", "title": "Stochastically Dominant Distributional Reinforcement Learning", "abstract": "We describe a new approach for managing aleatoric uncertainty in the Reinforcement Learning (RL) paradigm. Instead of selecting actions according to a single statistic, we propose a distributional method based on the second-order stochastic dominance (SSD) relation. This compares the inherent dispersion of random returns induced by actions, producing a more comprehensive and robust evaluation of the environment's uncertainty. The necessary conditions for SSD require estimators to predict accurate second moments. To accommodate this, we map the distributional RL problem to a Wasserstein gradient flow, treating the distributional Bellman residual as a potential energy functional. We propose a particle-based algorithm for which we prove optimality and convergence. Our experiments characterize the algorithm performance and demonstrate how uncertainty and performance are better balanced using an \\textsc{ssd} policy than with other risk measures.", "published": "2019-05-17T15:15:08Z", "authors": ["John D. Martin", "Michal Lyskawinski", "Xiaohu Li", "Brendan Englot"], "categories": ["cs.LG", "stat.ML"], "doi": "", "_query": "house price prediction machine learning", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2506.16929v1", "title": "A deep learning and machine learning approach to predict neonatal death in the context of São Paulo", "abstract": "Neonatal death is still a concerning reality for underdeveloped and even some developed countries. Worldwide data indicate that 26.693 babies out of 1,000 births die, according to Macro Trades. To reduce this number, early prediction of endangered babies is crucial. Such prediction enables the opportunity to take ample care of the child and mother so that early child death can be avoided. In this context, machine learning was used to determine whether a newborn baby is at risk. To train the predictive model, historical data of 1.4 million newborns was used. Machine learning and deep learning techniques such as logical regression, K-nearest neighbor, random forest classifier, extreme gradient boosting (XGBoost), convolutional neural network, and long short-term memory (LSTM) were implemented using the dataset to identify the most accurate model for predicting neonatal mortality. Among the machine learning algorithms, XGBoost and random forest classifier achieved the best accuracy with 94%, while among the deep learning models, LSTM delivered the highest accuracy with 99%. Therefore, using LSTM appears to be the most suitable approach to predict whether precautionary measures for a child are necessary.", "published": "2025-06-20T11:44:48Z", "authors": ["Mohon Raihan", "Plabon Kumar Saha", "Rajan Das Gupta", "A Z M Tahmidul Kabir", "Afia Anjum Tamanna", "Md. Harun-Ur-Rashid", "Adnan Bin Abdus Salam", "Md Tanvir Anjum", "A Z M Ahteshamul Kabir"], "categories": ["cs.LG", "cs.AI"], "doi": "10.11591/ijphs.v13i1.22577", "_query": "house price prediction machine learning", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2202.05621v2", "title": "Nonlinear MCMC for Bayesian Machine Learning", "abstract": "We explore the application of a nonlinear MCMC technique first introduced in [1] to problems in Bayesian machine learning. We provide a convergence guarantee in total variation that uses novel results for long-time convergence and large-particle (\"propagation of chaos\") convergence. We apply this nonlinear MCMC technique to sampling problems including a Bayesian neural network on CIFAR10.", "published": "2022-02-11T14:01:53Z", "authors": ["James Vuckovic"], "categories": ["stat.ML", "cs.LG", "math.PR"], "doi": "", "_query": "house price prediction machine learning", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2105.07562v3", "title": "Power-grid stability predictions using transferable machine learning", "abstract": "Complex network analyses have provided clues to improve power-grid stability with the help of numerical models. The high computational cost of numerical simulations, however, has inhibited the approach, especially when it deals with the dynamic properties of power grids such as frequency synchronization. In this study, we investigate machine learning techniques to estimate the stability of power-grid synchronization. We test three different machine learning algorithms -- random forest, support vector machine, and artificial neural network -- training them with two different types of synthetic power grids consisting of homogeneous and heterogeneous input-power distribution, respectively. We find that the three machine learning models better predict the synchronization stability of power-grid nodes when they are trained with the heterogeneous input-power distribution than the homogeneous one. With the real-world power grids of Great Britain, Spain, France, and Germany, we also demonstrate that the machine learning algorithms trained on synthetic power grids are transferable to the stability prediction of the real-world power grids, which implies the prospective applicability of machine learning techniques on power-grid studies.", "published": "2021-05-17T01:19:01Z", "authors": ["Seong-Gyu Yang", "Beom Jun Kim", "Seung-Woo Son", "Heetae Kim"], "categories": ["physics.soc-ph", "cs.LG", "eess.SY"], "doi": "10.1063/5.0058001", "_query": "house price prediction machine learning", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2308.01566v2", "title": "Fast Slate Policy Optimization: Going Beyond Plackett-Luce", "abstract": "An increasingly important building block of large scale machine learning systems is based on returning slates; an ordered lists of items given a query. Applications of this technology include: search, information retrieval and recommender systems. When the action space is large, decision systems are restricted to a particular structure to complete online queries quickly. This paper addresses the optimization of these large scale decision systems given an arbitrary reward function. We cast this learning problem in a policy optimization framework and propose a new class of policies, born from a novel relaxation of decision functions. This results in a simple, yet efficient learning algorithm that scales to massive action spaces. We compare our method to the commonly adopted Plackett-Luce policy class and demonstrate the effectiveness of our approach on problems with action space sizes in the order of millions.", "published": "2023-08-03T07:13:27Z", "authors": ["Otmane Sakhi", "David Rohde", "Nicolas Chopin"], "categories": ["cs.LG", "cs.IR", "stat.ML"], "doi": "", "_query": "house price prediction machine learning", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2505.09812v1", "title": "Comparative Analysis of Stroke Prediction Models Using Machine Learning", "abstract": "Stroke remains one of the most critical global health challenges, ranking as the second leading cause of death and the third leading cause of disability worldwide. This study explores the effectiveness of machine learning algorithms in predicting stroke risk using demographic, clinical, and lifestyle data from the Stroke Prediction Dataset. By addressing key methodological challenges such as class imbalance and missing data, we evaluated the performance of multiple models, including Logistic Regression, Random Forest, and XGBoost. Our results demonstrate that while these models achieve high accuracy, sensitivity remains a limiting factor for real-world clinical applications. In addition, we identify the most influential predictive features and propose strategies to improve machine learning-based stroke prediction. These findings contribute to the development of more reliable and interpretable models for the early assessment of stroke risk.", "published": "2025-05-14T21:27:19Z", "authors": ["Anastasija Tashkova", "Stefan Eftimov", "Bojan Ristov", "Slobodan Kalajdziski"], "categories": ["cs.LG"], "doi": "", "_query": "house price prediction machine learning", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2108.00887v2", "title": "A Machine learning approach for rapid disaster response based on multi-modal data. The case of housing & shelter needs", "abstract": "Along with climate change, more frequent extreme events, such as flooding and tropical cyclones, threaten the livelihoods and wellbeing of poor and vulnerable populations. One of the most immediate needs of people affected by a disaster is finding shelter. While the proliferation of data on disasters is already helping to save lives, identifying damages in buildings, assessing shelter needs, and finding appropriate places to establish emergency shelters or settlements require a wide range of data to be combined rapidly. To address this gap and make a headway in comprehensive assessments, this paper proposes a machine learning workflow that aims to fuse and rapidly analyse multimodal data. This workflow is built around open and online data to ensure scalability and broad accessibility. Based on a database of 19 characteristics for more than 200 disasters worldwide, a fusion approach at the decision level was used. This technique allows the collected multimodal data to share a common semantic space that facilitates the prediction of individual variables. Each fused numerical vector was fed into an unsupervised clustering algorithm called Self-Organizing-Maps (SOM). The trained SOM serves as a predictor for future cases, allowing predicting consequences such as total deaths, total people affected, and total damage, and provides specific recommendations for assessments in the shelter and housing sector. To achieve such prediction, a satellite image from before the disaster and the geographic and demographic conditions are shown to the trained model, which achieved a prediction accuracy of 62 %", "published": "2021-07-29T18:22:34Z", "authors": ["Karla Saldana Ochoa", "Tina Comes"], "categories": ["cs.LG", "cs.AI"], "doi": "", "_query": "house price prediction machine learning", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2006.11645v3", "title": "Accelerating Safe Reinforcement Learning with Constraint-mismatched Policies", "abstract": "We consider the problem of reinforcement learning when provided with (1) a baseline control policy and (2) a set of constraints that the learner must satisfy. The baseline policy can arise from demonstration data or a teacher agent and may provide useful cues for learning, but it might also be sub-optimal for the task at hand, and is not guaranteed to satisfy the specified constraints, which might encode safety, fairness or other application-specific requirements. In order to safely learn from baseline policies, we propose an iterative policy optimization algorithm that alternates between maximizing expected return on the task, minimizing distance to the baseline policy, and projecting the policy onto the constraint-satisfying set. We analyze our algorithm theoretically and provide a finite-time convergence guarantee. In our experiments on five different control tasks, our algorithm consistently outperforms several state-of-the-art baselines, achieving 10 times fewer constraint violations and 40% higher reward on average.", "published": "2020-06-20T20:20:47Z", "authors": ["Tsung-Yen Yang", "Justinian Rosca", "Karthik Narasimhan", "Peter J. Ramadge"], "categories": ["cs.LG", "cs.AI", "cs.RO", "stat.ML"], "doi": "", "_query": "house price prediction machine learning", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2304.04819v2", "title": "Recent Advancements in Machine Learning For Cybercrime Prediction", "abstract": "Cybercrime is a growing threat to organizations and individuals worldwide, with criminals using sophisticated techniques to breach security systems and steal sensitive data. This paper aims to comprehensively survey the latest advancements in cybercrime prediction, highlighting the relevant research. For this purpose, we reviewed more than 150 research articles and discussed 50 most recent and appropriate ones. We start the review with some standard methods cybercriminals use and then focus on the latest machine and deep learning techniques, which detect anomalous behavior and identify potential threats. We also discuss transfer learning, which allows models trained on one dataset to be adapted for use on another dataset. We then focus on active and reinforcement learning as part of early-stage algorithmic research in cybercrime prediction. Finally, we discuss critical innovations, research gaps, and future research opportunities in Cybercrime prediction. This paper presents a holistic view of cutting-edge developments and publicly available datasets.", "published": "2023-04-10T19:00:29Z", "authors": ["Lavanya Elluri", "Varun Mandalapu", "Piyush Vyas", "Nirmalya Roy"], "categories": ["cs.LG", "cs.AI", "cs.CR", "cs.CV"], "doi": "10.1080/08874417.2023.2270457", "_query": "house price prediction machine learning", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2203.01471v2", "title": "Structure Learning of Latent Factors via Clique Search on Correlation Thresholded Graphs", "abstract": "Despite the widespread application of latent factor analysis, existing methods suffer from the following weaknesses: requiring the number of factors to be known, lack of theoretical guarantees for learning the model structure, and nonidentifiability of the parameters due to rotation invariance properties of the likelihood. We address these concerns by proposing a fast correlation thresholding (CT) algorithm that simultaneously learns the number of latent factors and a rotationally identifiable model structure. Our novel approach translates this structure learning problem into the search for so-called independent maximal cliques in a thresholded correlation graph that can be easily constructed from the observed data. Our clique analysis technique scales well up to thousands of variables, while competing methods are not applicable in a reasonable amount of running time. We establish a finite-sample error bound and high-dimensional consistency for the structure learning of our method. Through a series of simulation studies and a real data example, we show that the CT algorithm is an accurate method for learning the structure of factor analysis models and is robust to violations of its assumptions.", "published": "2022-03-03T00:56:03Z", "authors": ["Dale S. Kim", "Qing Zhou"], "categories": ["stat.ME"], "doi": "", "_query": "house price prediction machine learning", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/1902.06497v1", "title": "Differentially Private Continual Learning", "abstract": "Catastrophic forgetting can be a significant problem for institutions that must delete historic data for privacy reasons. For example, hospitals might not be able to retain patient data permanently. But neural networks trained on recent data alone will tend to forget lessons learned on old data. We present a differentially private continual learning framework based on variational inference. We estimate the likelihood of past data given the current model using differentially private generative models of old datasets.", "published": "2019-02-18T10:31:28Z", "authors": ["Sebastian Farquhar", "Yarin Gal"], "categories": ["stat.ML", "cs.LG"], "doi": "", "_query": "house price prediction machine learning", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2402.00809v5", "title": "Position: Bayesian Deep Learning is Needed in the Age of Large-Scale AI", "abstract": "In the current landscape of deep learning research, there is a predominant emphasis on achieving high predictive accuracy in supervised tasks involving large image and language datasets. However, a broader perspective reveals a multitude of overlooked metrics, tasks, and data types, such as uncertainty, active and continual learning, and scientific data, that demand attention. Bayesian deep learning (BDL) constitutes a promising avenue, offering advantages across these diverse settings. This paper posits that BDL can elevate the capabilities of deep learning. It revisits the strengths of BDL, acknowledges existing challenges, and highlights some exciting research avenues aimed at addressing these obstacles. Looking ahead, the discussion focuses on possible ways to combine large-scale foundation models with BDL to unlock their full potential.", "published": "2024-02-01T17:45:26Z", "authors": ["Theodore Papamarkou", "Maria Skoularidou", "Konstantina Palla", "Laurence Aitchison", "Julyan Arbel", "David Dunson", "Maurizio Filippone", "Vincent Fortuin", "Philipp Hennig", "José Miguel Hernández-Lobato", "Aliaksandr Hubin", "Alexander Immer", "Theofanis Karaletsos", "Mohammad Emtiyaz Khan", "Agustinus Kristiadi", "Yingzhen Li", "Stephan Mandt", "Christopher Nemeth", "Michael A. Osborne", "Tim G. J. Rudner", "David Rügamer", "Yee Whye Teh", "Max Welling", "Andrew Gordon Wilson", "Ruqi Zhang"], "categories": ["cs.LG", "stat.ML"], "doi": "", "_query": "house price prediction machine learning", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2412.11257v4", "title": "Prediction-Enhanced Monte Carlo: A Machine Learning View on Control Variate", "abstract": "For many complex simulation tasks spanning areas such as healthcare, engineering, and finance, Monte Carlo (MC) methods are invaluable due to their unbiased estimates and precise error quantification. Nevertheless, Monte Carlo simulations often become computationally prohibitive, especially for nested, multi-level, or path-dependent evaluations lacking effective variance reduction techniques. While machine learning (ML) surrogates appear as natural alternatives, naive replacements typically introduce unquantifiable biases. We address this challenge by introducing Prediction-Enhanced Monte Carlo (PEMC), a framework that leverages modern ML models as learned predictors, using cheap and parallelizable simulation as features, to output unbiased evaluation with reduced variance and runtime. PEMC can also be viewed as a \"modernized\" view of control variates, where we consider the overall computation-cost-aware variance reduction instead of per-replication reduction, while bypassing the closed-form mean function requirement and maintaining the advantageous unbiasedness and uncertainty quantifiability of Monte Carlo. We illustrate PEMC's broader efficacy and versatility through three examples: first, equity derivatives such as variance swaps under stochastic local volatility models; second, interest rate derivatives such as swaption pricing under the Heath-Jarrow-Morton (HJM) interest-rate model. Finally, we showcase PEMC in a socially significant context - ambulance dispatch and hospital load balancing - where accurate mortality rate estimates are key for ethically sensitive decision-making. Across these diverse scenarios, PEMC consistently reduces variance while preserving unbiasedness, highlighting its potential as a powerful enhancement to standard Monte Carlo baselines.", "published": "2024-12-15T17:41:38Z", "authors": ["Fengpei Li", "Haoxian Chen", "Jiahe Lin", "Arkin Gupta", "Xiaowei Tan", "Honglei Zhao", "Gang Xu", "Yuriy Nevmyvaka", "Agostino Capponi", "Henry Lam"], "categories": ["stat.ML", "cs.CE", "cs.LG", "q-fin.PR"], "doi": "", "_query": "house price prediction machine learning", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2002.05514v1", "title": "Combining Machine Learning with Knowledge-Based Modeling for Scalable Forecasting and Subgrid-Scale Closure of Large, Complex, Spatiotemporal Systems", "abstract": "We consider the commonly encountered situation (e.g., in weather forecasting) where the goal is to predict the time evolution of a large, spatiotemporally chaotic dynamical system when we have access to both time series data of previous system states and an imperfect model of the full system dynamics. Specifically, we attempt to utilize machine learning as the essential tool for integrating the use of past data into predictions. In order to facilitate scalability to the common scenario of interest where the spatiotemporally chaotic system is very large and complex, we propose combining two approaches:(i) a parallel machine learning prediction scheme; and (ii) a hybrid technique, for a composite prediction system composed of a knowledge-based component and a machine-learning-based component. We demonstrate that not only can this method combining (i) and (ii) be scaled to give excellent performance for very large systems, but also that the length of time series data needed to train our multiple, parallel machine learning components is dramatically less than that necessary without parallelization. Furthermore, considering cases where computational realization of the knowledge-based component does not resolve subgrid-scale processes, our scheme is able to use training data to incorporate the effect of the unresolved short-scale dynamics upon the resolved longer-scale dynamics (\"subgrid-scale closure\").", "published": "2020-02-10T23:21:50Z", "authors": ["Alexander Wikner", "Jaideep Pathak", "Brian Hunt", "Michelle Girvan", "Troy Arcomano", "Istvan Szunyogh", "Andrew Pomerance", "Edward Ott"], "categories": ["cs.LG", "nlin.CD", "physics.ao-ph", "physics.comp-ph", "stat.ML"], "doi": "10.1063/5.0005541", "_query": "house price prediction machine learning", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2009.02653v3", "title": "A Survey on Machine Learning from Few Samples", "abstract": "Few sample learning (FSL) is significant and challenging in the field of machine learning. The capability of learning and generalizing from very few samples successfully is a noticeable demarcation separating artificial intelligence and human intelligence since humans can readily establish their cognition to novelty from just a single or a handful of examples whereas machine learning algorithms typically entail hundreds or thousands of supervised samples to guarantee generalization ability. Despite the long history dated back to the early 2000s and the widespread attention in recent years with booming deep learning technologies, little surveys or reviews for FSL are available until now. In this context, we extensively review 300+ papers of FSL spanning from the 2000s to 2019 and provide a timely and comprehensive survey for FSL. In this survey, we review the evolution history as well as the current progress on FSL, categorize FSL approaches into the generative model based and discriminative model based kinds in principle, and emphasize particularly on the meta learning based FSL approaches. We also summarize several recently emerging extensional topics of FSL and review the latest advances on these topics. Furthermore, we highlight the important FSL applications covering many research hotspots in computer vision, natural language processing, audio and speech, reinforcement learning and robotic, data analysis, etc. Finally, we conclude the survey with a discussion on promising trends in the hope of providing guidance and insights to follow-up researches.", "published": "2020-09-06T06:13:09Z", "authors": ["Jiang Lu", "Pinghua Gong", "Jieping Ye", "Jianwei Zhang", "Changshui Zhang"], "categories": ["cs.LG", "cs.CV", "stat.ML"], "doi": "", "_query": "house price prediction machine learning", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2002.05426v4", "title": "PHOTONAI -- A Python API for Rapid Machine Learning Model Development", "abstract": "PHOTONAI is a high-level Python API designed to simplify and accelerate machine learning model development. It functions as a unifying framework allowing the user to easily access and combine algorithms from different toolboxes into custom algorithm sequences. It is especially designed to support the iterative model development process and automates the repetitive training, hyperparameter optimization and evaluation tasks. Importantly, the workflow ensures unbiased performance estimates while still allowing the user to fully customize the machine learning analysis. PHOTONAI extends existing solutions with a novel pipeline implementation supporting more complex data streams, feature combinations, and algorithm selection. Metrics and results can be conveniently visualized using the PHOTONAI Explorer and predictive models are shareable in a standardized format for further external validation or application. A growing add-on ecosystem allows researchers to offer data modality specific algorithms to the community and enhance machine learning in the areas of the life sciences. Its practical utility is demonstrated on an exemplary medical machine learning problem, achieving a state-of-the-art solution in few lines of code. Source code is publicly available on Github, while examples and documentation can be found at www.photon-ai.com.", "published": "2020-02-13T10:33:05Z", "authors": ["Ramona Leenings", "Nils Ralf Winter", "Lucas Plagwitz", "Vincent Holstein", "Jan Ernsting", "Jakob Steenweg", "Julian Gebker", "Kelvin Sarink", "Daniel Emden", "Dominik Grotegerd", "Nils Opel", "Benjamin Risse", "Xiaoyi Jiang", "Udo Dannlowski", "Tim Hahn"], "categories": ["cs.LG", "cs.SE", "stat.ML"], "doi": "10.1371/journal.pone.0254062", "_query": "house price prediction machine learning", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2005.04048v1", "title": "Sherpa: Robust Hyperparameter Optimization for Machine Learning", "abstract": "Sherpa is a hyperparameter optimization library for machine learning models. It is specifically designed for problems with computationally expensive, iterative function evaluations, such as the hyperparameter tuning of deep neural networks. With Sherpa, scientists can quickly optimize hyperparameters using a variety of powerful and interchangeable algorithms. Sherpa can be run on either a single machine or in parallel on a cluster. Finally, an interactive dashboard enables users to view the progress of models as they are trained, cancel trials, and explore which hyperparameter combinations are working best. Sherpa empowers machine learning practitioners by automating the more tedious aspects of model tuning. Its source code and documentation are available at https://github.com/sherpa-ai/sherpa.", "published": "2020-05-08T13:52:49Z", "authors": ["Lars Hertel", "Julian Collado", "Peter Sadowski", "Jordan Ott", "Pierre Baldi"], "categories": ["cs.LG", "stat.ML"], "doi": "", "_query": "house price prediction machine learning", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/1901.04592v1", "title": "Interpretable machine learning: definitions, methods, and applications", "abstract": "Machine-learning models have demonstrated great success in learning complex patterns that enable them to make predictions about unobserved data. In addition to using models for prediction, the ability to interpret what a model has learned is receiving an increasing amount of attention. However, this increased focus has led to considerable confusion about the notion of interpretability. In particular, it is unclear how the wide array of proposed interpretation methods are related, and what common concepts can be used to evaluate them. We aim to address these concerns by defining interpretability in the context of machine learning and introducing the Predictive, Descriptive, Relevant (PDR) framework for discussing interpretations. The PDR framework provides three overarching desiderata for evaluation: predictive accuracy, descriptive accuracy and relevancy, with relevancy judged relative to a human audience. Moreover, to help manage the deluge of interpretation methods, we introduce a categorization of existing techniques into model-based and post-hoc categories, with sub-groups including sparsity, modularity and simulatability. To demonstrate how practitioners can use the PDR framework to evaluate and understand interpretations, we provide numerous real-world examples. These examples highlight the often under-appreciated role played by human audiences in discussions of interpretability. Finally, based on our framework, we discuss limitations of existing methods and directions for future work. We hope that this work will provide a common vocabulary that will make it easier for both practitioners and researchers to discuss and choose from the full range of interpretation methods.", "published": "2019-01-14T22:35:26Z", "authors": ["W. James Murdoch", "Chandan Singh", "Karl Kumbier", "Reza Abbasi-Asl", "Bin Yu"], "categories": ["stat.ML", "cs.AI", "cs.LG", "stat.AP"], "doi": "10.1073/pnas.1900654116", "_query": "house price prediction machine learning", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2204.06109v1", "title": "Prediction of motor insurance claims occurrence as an imbalanced machine learning problem", "abstract": "The insurance industry, with its large datasets, is a natural place to use big data solutions. However it must be stressed, that significant number of applications for machine learning in insurance industry, like fraud detection or claim prediction, deals with the problem of machine learning on an imbalanced data set. This is due to the fact that frauds or claims are rare events when compared with the entire population of drivers. The problem of imbalanced learning is often hard to overcome. Therefore, the main goal of this work is to present and apply various methods of dealing with an imbalanced dataset in the context of claim occurrence prediction in car insurance. In addition, the above techniques are used to compare the results of machine learning algorithms in the context of claim occurrence prediction in car insurance. Our study covers the following techniques: logistic-regression, decision tree, random forest, xgBoost, feed-forward network. The problem is the classification one.", "published": "2022-04-12T22:41:47Z", "authors": ["Sebastian Baran", "Przemysław Rola"], "categories": ["q-fin.ST", "cs.LG"], "doi": "", "_query": "house price prediction machine learning", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2309.17370v2", "title": "Graph-based Neural Weather Prediction for Limited Area Modeling", "abstract": "The rise of accurate machine learning methods for weather forecasting is creating radical new possibilities for modeling the atmosphere. In the time of climate change, having access to high-resolution forecasts from models like these is also becoming increasingly vital. While most existing Neural Weather Prediction (NeurWP) methods focus on global forecasting, an important question is how these techniques can be applied to limited area modeling. In this work we adapt the graph-based NeurWP approach to the limited area setting and propose a multi-scale hierarchical model extension. Our approach is validated by experiments with a local model for the Nordic region.", "published": "2023-09-29T16:20:34Z", "authors": ["Joel Oskarsson", "Tomas Landelius", "Fredrik Lindsten"], "categories": ["cs.LG", "stat.ML"], "doi": "", "_query": "house price prediction machine learning", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2501.11293v1", "title": "A Machine Learning Framework for Handling Unreliable Absence Label and Class Imbalance for Marine Stinger Beaching Prediction", "abstract": "Bluebottles (\\textit{Physalia} spp.) are marine stingers resembling jellyfish, whose presence on Australian beaches poses a significant public risk due to their venomous nature. Understanding the environmental factors driving bluebottles ashore is crucial for mitigating their impact, and machine learning tools are to date relatively unexplored. We use bluebottle marine stinger presence/absence data from beaches in Eastern Sydney, Australia, and compare machine learning models (Multilayer Perceptron, Random Forest, and XGBoost) to identify factors influencing their presence. We address challenges such as class imbalance, class overlap, and unreliable absence data by employing data augmentation techniques, including the Synthetic Minority Oversampling Technique (SMOTE), Random Undersampling, and Synthetic Negative Approach that excludes the negative class. Our results show that SMOTE failed to resolve class overlap, but the presence-focused approach effectively handled imbalance, class overlap, and ambiguous absence data. The data attributes such as the wind direction, which is a circular variable, emerged as a key factor influencing bluebottle presence, confirming previous inference studies. However, in the absence of population dynamics, biological behaviours, and life cycles, the best predictive model appears to be Random Forests combined with Synthetic Negative Approach. This research contributes to mitigating the risks posed by bluebottles to beachgoers and provides insights into handling class overlap and unreliable negative class in environmental modelling.", "published": "2025-01-20T06:28:27Z", "authors": ["Amuche Ibenegbu", "Amandine Schaeffer", "Pierre Lafaye de Micheaux", "Rohitash Chandra"], "categories": ["cs.LG", "cs.AI", "stat.ML"], "doi": "", "_query": "house price prediction machine learning", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2510.09493v1", "title": "Performance Analysis of Machine Learning Algorithms in Chronic Kidney Disease Prediction", "abstract": "Kidneys are the filter of the human body. About 10% of the global population is thought to be affected by Chronic Kidney Disease (CKD), which causes kidney function to decline. To protect in danger patients from additional kidney damage, effective risk evaluation of CKD and appropriate CKD monitoring are crucial. Due to quick and precise detection capabilities, Machine Learning models can help practitioners accomplish this goal efficiently; therefore, an enormous number of diagnosis systems and processes in the healthcare sector nowadays are relying on machine learning due to its disease prediction capability. In this study, we designed and suggested disease predictive computer-aided designs for the diagnosis of CKD. The dataset for CKD is attained from the repository of machine learning of UCL, with a few missing values; those are filled in using \"mean-mode\" and \"Random sampling method\" strategies. After successfully achieving the missing data, eight ML techniques (Random Forest, SVM, Naive Bayes, Logistic Regression, KNN, XGBoost, Decision Tree, and AdaBoost) were used to establish models, and the performance evaluation comparisons among the result accuracies are measured by the techniques to find the machine learning models with the highest accuracy. Among them, Random Forest as well as Logistic Regression showed an outstanding 99% accuracy, followed by the Ada Boost, XGBoost, Naive Bayes, Decision Tree, and SVM, whereas the KNN classifier model stands last with an accuracy of 73%.", "published": "2025-10-10T15:54:37Z", "authors": ["Iftekhar Ahmed", "Tanzil Ebad Chowdhury", "Biggo Bushon Routh", "Nafisa Tasmiya", "Shadman Sakib", "Adil Ahmed Chowdhury"], "categories": ["cs.LG", "cs.AI"], "doi": "10.1109/IEMCON56893.2022.9946591", "_query": "house price prediction machine learning", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/1805.08308v2", "title": "geomstats: a Python Package for Riemannian Geometry in Machine Learning", "abstract": "We introduce geomstats, a python package that performs computations on manifolds such as hyperspheres, hyperbolic spaces, spaces of symmetric positive definite matrices and Lie groups of transformations. We provide efficient and extensively unit-tested implementations of these manifolds, together with useful Riemannian metrics and associated Exponential and Logarithm maps. The corresponding geodesic distances provide a range of intuitive choices of Machine Learning loss functions. We also give the corresponding Riemannian gradients. The operations implemented in geomstats are available with different computing backends such as numpy, tensorflow and keras. We have enabled GPU implementation and integrated geomstats manifold computations into keras deep learning framework. This paper also presents a review of manifolds in machine learning and an overview of the geomstats package with examples demonstrating its use for efficient and user-friendly Riemannian geometry.", "published": "2018-05-21T22:24:14Z", "authors": ["Nina Miolane", "Johan Mathe", "Claire Donnat", "Mikael Jorda", "Xavier Pennec"], "categories": ["cs.LG", "cs.MS", "stat.ML"], "doi": "", "_query": "house price prediction machine learning", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2009.10065v1", "title": "Machine learning based forecasting of significant daily returns in foreign exchange markets", "abstract": "Asset value forecasting has always attracted an enormous amount of interest among researchers in quantitative analysis. The advent of modern machine learning models has introduced new tools to tackle this classical problem. In this paper, we apply machine learning algorithms to hitherto unexplored question of forecasting instances of significant fluctuations in currency exchange rates. We perform analysis of nine modern machine learning algorithms using data on four major currency pairs over a 10 year period. A key contribution is the novel use of outlier detection methods for this purpose. Numerical experiments show that outlier detection methods substantially outperform traditional machine learning and finance techniques. In addition, we show that a recently proposed new outlier detection method PKDE produces best overall results. Our findings hold across different currency pairs, significance levels, and time horizons indicating the robustness of the proposed method.", "published": "2020-09-21T17:56:42Z", "authors": ["Firuz Kamalov", "Ikhlaas Gurrib"], "categories": ["cs.LG", "stat.ML"], "doi": "", "_query": "house price prediction machine learning", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/1503.06468v1", "title": "Machine Learning Methods for Attack Detection in the Smart Grid", "abstract": "Attack detection problems in the smart grid are posed as statistical learning problems for different attack scenarios in which the measurements are observed in batch or online settings. In this approach, machine learning algorithms are used to classify measurements as being either secure or attacked. An attack detection framework is provided to exploit any available prior knowledge about the system and surmount constraints arising from the sparse structure of the problem in the proposed approach. Well-known batch and online learning algorithms (supervised and semi-supervised) are employed with decision and feature level fusion to model the attack detection problem. The relationships between statistical and geometric properties of attack vectors employed in the attack scenarios and learning algorithms are analyzed to detect unobservable attacks using statistical learning methods. The proposed algorithms are examined on various IEEE test systems. Experimental analyses show that machine learning algorithms can detect attacks with performances higher than the attack detection algorithms which employ state vector estimation methods in the proposed attack detection framework.", "published": "2015-03-22T19:38:45Z", "authors": ["Mete Ozay", "Inaki Esnaola", "Fatos T. Yarman Vural", "Sanjeev R. Kulkarni", "H. Vincent Poor"], "categories": ["cs.LG", "cs.CR", "eess.SY"], "doi": "10.1109/TNNLS.2015.2404803", "_query": "house price prediction machine learning", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2103.00113v2", "title": "Anomaly Detection on Attributed Networks via Contrastive Self-Supervised Learning", "abstract": "Anomaly detection on attributed networks attracts considerable research interests due to wide applications of attributed networks in modeling a wide range of complex systems. Recently, the deep learning-based anomaly detection methods have shown promising results over shallow approaches, especially on networks with high-dimensional attributes and complex structures. However, existing approaches, which employ graph autoencoder as their backbone, do not fully exploit the rich information of the network, resulting in suboptimal performance. Furthermore, these methods do not directly target anomaly detection in their learning objective and fail to scale to large networks due to the full graph training mechanism. To overcome these limitations, in this paper, we present a novel contrastive self-supervised learning framework for anomaly detection on attributed networks. Our framework fully exploits the local information from network data by sampling a novel type of contrastive instance pair, which can capture the relationship between each node and its neighboring substructure in an unsupervised way. Meanwhile, a well-designed graph neural network-based contrastive learning model is proposed to learn informative embedding from high-dimensional attributes and local structure and measure the agreement of each instance pairs with its outputted scores. The multi-round predicted scores by the contrastive learning model are further used to evaluate the abnormality of each node with statistical estimation. In this way, the learning model is trained by a specific anomaly detection-aware target. Furthermore, since the input of the graph neural network module is batches of instance pairs instead of the full network, our framework can adapt to large networks flexibly. Experimental results show that our proposed framework outperforms the state-of-the-art baseline methods on all seven benchmark datasets.", "published": "2021-02-27T03:17:20Z", "authors": ["Yixin Liu", "Zhao Li", "Shirui Pan", "Chen Gong", "Chuan Zhou", "George Karypis"], "categories": ["cs.LG"], "doi": "10.1109/TNNLS.2021.3068344", "_query": "house price prediction machine learning", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2409.09980v1", "title": "From Bytes to Bites: Using Country Specific Machine Learning Models to Predict Famine", "abstract": "Hunger crises are critical global issues affecting millions, particularly in low-income and developing countries. This research investigates how machine learning can be utilized to predict and inform decisions regarding famine and hunger crises. By leveraging a diverse set of variables (natural, economic, and conflict-related), three machine learning models (Linear Regression, XGBoost, and RandomForestRegressor) were employed to predict food consumption scores, a key indicator of household nutrition. The RandomForestRegressor emerged as the most accurate model, with an average prediction error of 10.6%, though accuracy varied significantly across countries, ranging from 2% to over 30%. Notably, economic indicators were consistently the most significant predictors of average household nutrition, while no single feature dominated across all regions, underscoring the necessity for comprehensive data collection and tailored, country-specific models. These findings highlight the potential of machine learning, particularly Random Forests, to enhance famine prediction, suggesting that continued research and improved data gathering are essential for more effective global hunger forecasting.", "published": "2024-09-16T04:23:06Z", "authors": ["Salloni Kapoor", "Simeon Sayer"], "categories": ["cs.LG"], "doi": "", "_query": "house price prediction machine learning", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/1702.08608v2", "title": "Towards A Rigorous Science of Interpretable Machine Learning", "abstract": "As machine learning systems become ubiquitous, there has been a surge of interest in interpretable machine learning: systems that provide explanation for their outputs. These explanations are often used to qualitatively assess other criteria such as safety or non-discrimination. However, despite the interest in interpretability, there is very little consensus on what interpretable machine learning is and how it should be measured. In this position paper, we first define interpretability and describe when interpretability is needed (and when it is not). Next, we suggest a taxonomy for rigorous evaluation and expose open questions towards a more rigorous science of interpretable machine learning.", "published": "2017-02-28T02:19:20Z", "authors": ["Finale Doshi-Velez", "Been Kim"], "categories": ["stat.ML", "cs.AI", "cs.LG"], "doi": "", "_query": "house price prediction machine learning", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2301.13375v2", "title": "Optimal Transport Perturbations for Safe Reinforcement Learning with Robustness Guarantees", "abstract": "Robustness and safety are critical for the trustworthy deployment of deep reinforcement learning. Real-world decision making applications require algorithms that can guarantee robust performance and safety in the presence of general environment disturbances, while making limited assumptions on the data collection process during training. In order to accomplish this goal, we introduce a safe reinforcement learning framework that incorporates robustness through the use of an optimal transport cost uncertainty set. We provide an efficient implementation based on applying Optimal Transport Perturbations to construct worst-case virtual state transitions, which does not impact data collection during training and does not require detailed simulator access. In experiments on continuous control tasks with safety constraints, our approach demonstrates robust performance while significantly improving safety at deployment time compared to standard safe reinforcement learning.", "published": "2023-01-31T02:39:52Z", "authors": ["James Queeney", "Erhan Can Ozcan", "Ioannis Ch. Paschalidis", "Christos G. Cassandras"], "categories": ["cs.LG", "cs.AI", "stat.ML"], "doi": "", "_query": "house price prediction machine learning", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2010.04430v1", "title": "Large-scale randomized experiment reveals machine learning helps people learn and remember more effectively", "abstract": "Machine learning has typically focused on developing models and algorithms that would ultimately replace humans at tasks where intelligence is required. In this work, rather than replacing humans, we focus on unveiling the potential of machine learning to improve how people learn and remember factual material. To this end, we perform a large-scale randomized controlled trial with thousands of learners from a popular learning app in the area of mobility. After controlling for the length and frequency of study, we find that learners whose study sessions are optimized using machine learning remember the content over $\\sim$67% longer than those whose study sessions are generated using two alternative heuristics. Our randomized controlled trial also reveals that the learners whose study sessions are optimized using machine learning are $\\sim$50% more likely to return to the app within 4-7 days.", "published": "2020-10-09T08:30:15Z", "authors": ["Utkarsh Upadhyay", "Graham Lancashire", "Christoph Moser", "Manuel Gomez-Rodriguez"], "categories": ["cs.LG", "cs.HC", "cs.SI", "stat.ML"], "doi": "", "_query": "house price prediction machine learning", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2004.08889v3", "title": "Sequential hypothesis testing in machine learning, and crude oil price jump size detection", "abstract": "In this paper we present a sequential hypothesis test for the detection of general jump size distrubution. Infinitesimal generators for the corresponding log-likelihood ratios are presented and analyzed. Bounds for infinitesimal generators in terms of super-solutions and sub-solutions are computed. This is shown to be implementable in relation to various classification problems for a crude oil price data set. Machine and deep learning algorithms are implemented to extract a specific deterministic component from the crude oil data set, and the deterministic component is implemented to improve the Barndorff-Nielsen and Shephard model, a commonly used stochastic model for derivative and commodity market analysis.", "published": "2020-04-19T16:02:08Z", "authors": ["Michael Roberts", "Indranil SenGupta"], "categories": ["stat.ME", "q-fin.MF", "stat.ML"], "doi": "10.1080/1350486X.2020.1859943", "_query": "house price prediction machine learning", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/1908.09788v1", "title": "An Introduction to Advanced Machine Learning : Meta Learning Algorithms, Applications and Promises", "abstract": "In [1, 2], we have explored the theoretical aspects of feature extraction optimization processes for solving largescale problems and overcoming machine learning limitations. Majority of optimization algorithms that have been introduced in [1, 2] guarantee the optimal performance of supervised learning, given offline and discrete data, to deal with curse of dimensionality (CoD) problem. These algorithms, however, are not tailored for solving emerging learning problems. One of the important issues caused by online data is lack of sufficient samples per class. Further, traditional machine learning algorithms cannot achieve accurate training based on limited distributed data, as data has proliferated and dispersed significantly. Machine learning employs a strict model or embedded engine to train and predict which still fails to learn unseen classes and sufficiently use online data. In this chapter, we introduce these challenges elaborately. We further investigate Meta-Learning (MTL) algorithm, and their application and promises to solve the emerging problems by answering how autonomous agents can learn to learn?.", "published": "2019-08-26T16:42:33Z", "authors": ["Farid Ghareh Mohammadi", "M. Hadi Amini", "Hamid R. Arabnia"], "categories": ["cs.LG", "stat.ML"], "doi": "", "_query": "house price prediction machine learning", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2007.12620v1", "title": "A Novel Ensemble Deep Learning Model for Stock Prediction Based on Stock Prices and News", "abstract": "In recent years, machine learning and deep learning have become popular methods for financial data analysis, including financial textual data, numerical data, and graphical data. This paper proposes to use sentiment analysis to extract useful information from multiple textual data sources and a blending ensemble deep learning model to predict future stock movement. The blending ensemble model contains two levels. The first level contains two Recurrent Neural Networks (RNNs), one Long-Short Term Memory network (LSTM) and one Gated Recurrent Units network (GRU), followed by a fully connected neural network as the second level model. The RNNs, LSTM, and GRU models can effectively capture the time-series events in the input data, and the fully connected neural network is used to ensemble several individual prediction results to further improve the prediction accuracy. The purpose of this work is to explain our design philosophy and show that ensemble deep learning technologies can truly predict future stock price trends more effectively and can better assist investors in making the right investment decision than other traditional methods.", "published": "2020-07-23T15:25:37Z", "authors": ["Yang Li", "Yi Pan"], "categories": ["q-fin.ST", "cs.LG"], "doi": "", "_query": "house price prediction machine learning", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/1807.00243v3", "title": "chemmodlab: A Cheminformatics Modeling Laboratory for Fitting and Assessing Machine Learning Models", "abstract": "The goal of chemmodlab is to streamline the fitting and assessment pipeline for many machine learning models in R, making it easy for researchers to compare the utility of new models. While focused on implementing methods for model fitting and assessment that have been accepted by experts in the cheminformatics field, all of the methods in chemmodlab have broad utility for the machine learning community. chemmodlab contains several assessment utilities including a plotting function that constructs accumulation curves and a function that computes many performance measures. The most novel feature of chemmodlab is the ease with which statistically significant performance differences for many machine learning models is presented by means of the multiple comparisons similarity plot. Differences are assessed using repeated k-fold cross validation where blocking increases precision and multiplicity adjustments are applied.", "published": "2018-06-30T23:17:41Z", "authors": ["Jeremy R. Ash Jacqueline M. Hughes-Oliver"], "categories": ["stat.ML", "cs.LG", "q-bio.QM"], "doi": "", "_query": "house price prediction machine learning", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2102.01535v3", "title": "Predicting Propensity to Vote with Machine Learning", "abstract": "We demonstrate that machine learning enables the capability to infer an individual's propensity to vote from their past actions and attributes. This is useful for microtargeting voter outreach, voter education and get-out-the-vote (GOVT) campaigns. Political scientists developed increasingly sophisticated techniques for estimating election outcomes since the late 1940s. Two prior studies similarly used machine learning to predict individual future voting behavior. We built a machine learning environment using TensorFlow, obtained voting data from 2004 to 2018, and then ran three experiments. We show positive results with a Matthews correlation coefficient of 0.39.", "published": "2021-02-02T15:04:39Z", "authors": ["Rebecca D. Pollard", "Sara M. Pollard", "Scott Streit"], "categories": ["physics.soc-ph", "cs.LG"], "doi": "", "_query": "house price prediction machine learning", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/1811.07051v2", "title": "Symmetry constrained machine learning", "abstract": "Symmetry, a central concept in understanding the laws of nature, has been used for centuries in physics, mathematics, and chemistry, to help make mathematical models tractable. Yet, despite its power, symmetry has not been used extensively in machine learning, until rather recently. In this article we show a general way to incorporate symmetries into machine learning models. We demonstrate this with a detailed analysis on a rather simple real world machine learning system - a neural network for classifying handwritten digits, lacking bias terms for every neuron. We demonstrate that ignoring symmetries can have dire over-fitting consequences, and that incorporating symmetry into the model reduces over-fitting, while at the same time reducing complexity, ultimately requiring less training data, and taking less time and resources to train.", "published": "2018-11-16T22:25:10Z", "authors": ["Doron L. Bergman"], "categories": ["stat.ML", "cs.LG", "physics.data-an"], "doi": "10.1007/978-3-030-29513-4_37", "_query": "house price prediction machine learning", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/1908.00080v4", "title": "Machine Learning at the Network Edge: A Survey", "abstract": "Resource-constrained IoT devices, such as sensors and actuators, have become ubiquitous in recent years. This has led to the generation of large quantities of data in real-time, which is an appealing target for AI systems. However, deploying machine learning models on such end-devices is nearly impossible. A typical solution involves offloading data to external computing systems (such as cloud servers) for further processing but this worsens latency, leads to increased communication costs, and adds to privacy concerns. To address this issue, efforts have been made to place additional computing devices at the edge of the network, i.e close to the IoT devices where the data is generated. Deploying machine learning systems on such edge computing devices alleviates the above issues by allowing computations to be performed close to the data sources. This survey describes major research efforts where machine learning systems have been deployed at the edge of computer networks, focusing on the operational aspects including compression techniques, tools, frameworks, and hardware used in successful applications of intelligent edge systems.", "published": "2019-07-31T20:23:00Z", "authors": ["M. G. Sarwar Murshed", "Christopher Murphy", "Daqing Hou", "Nazar Khan", "Ganesh Ananthanarayanan", "Faraz Hussain"], "categories": ["cs.LG", "cs.CV", "cs.NI", "stat.ML"], "doi": "10.1145/3469029", "_query": "house price prediction machine learning", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2107.10804v2", "title": "Active Learning in Incomplete Label Multiple Instance Multiple Label Learning", "abstract": "In multiple instance multiple label learning, each sample, a bag, consists of multiple instances. To alleviate labeling complexity, each sample is associated with a set of bag-level labels leaving instances within the bag unlabeled. This setting is more convenient and natural for representing complicated objects, which have multiple semantic meanings. Compared to single instance labeling, this approach allows for labeling larger datasets at an equivalent labeling cost. However, for sufficiently large datasets, labeling all bags may become prohibitively costly. Active learning uses an iterative labeling and retraining approach aiming to provide reasonable classification performance using a small number of labeled samples. To our knowledge, only a few works in the area of active learning in the MIML setting are available. These approaches can provide practical solutions to reduce labeling cost but their efficacy remains unclear. In this paper, we propose a novel bag-class pair based approach for active learning in the MIML setting. Due to the partial availability of bag-level labels, we focus on the incomplete-label MIML setting for the proposed active learning approach. Our approach is based on a discriminative graphical model with efficient and exact inference. For the query process, we adapt active learning criteria to the novel bag-class pair selection strategy. Additionally, we introduce an online stochastic gradient descent algorithm to provide an efficient model update after each query. Numerical experiments on benchmark datasets illustrate the robustness of the proposed approach.", "published": "2021-07-22T17:01:28Z", "authors": ["Tam Nguyen", "Raviv Raich"], "categories": ["cs.LG"], "doi": "", "_query": "house price prediction machine learning", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2107.10541v3", "title": "Qsun: an open-source platform towards practical quantum machine learning applications", "abstract": "Currently, quantum hardware is restrained by noises and qubit numbers. Thus, a quantum virtual machine that simulates operations of a quantum computer on classical computers is a vital tool for developing and testing quantum algorithms before deploying them on real quantum computers. Various variational quantum algorithms have been proposed and tested on quantum virtual machines to surpass the limitations of quantum hardware. Our goal is to exploit further the variational quantum algorithms towards practical applications of quantum machine learning using state-of-the-art quantum computers. This paper first introduces our quantum virtual machine named Qsun, whose operation is underlined by quantum state wave-functions. The platform provides native tools supporting variational quantum algorithms. Especially using the parameter-shift rule, we implement quantum differentiable programming essential for gradient-based optimization. We then report two tests representative of quantum machine learning: quantum linear regression and quantum neural network.", "published": "2021-07-22T09:37:31Z", "authors": ["Chuong Nguyen Quoc", "Le Bin Ho", "Lan Nguyen Tran", "Hung Q. Nguyen"], "categories": ["quant-ph"], "doi": "10.1088/2632-2153/ac5997", "_query": "house price prediction machine learning", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2108.04884v3", "title": "Retiring Adult: New Datasets for Fair Machine Learning", "abstract": "Although the fairness community has recognized the importance of data, researchers in the area primarily rely on UCI Adult when it comes to tabular data. Derived from a 1994 US Census survey, this dataset has appeared in hundreds of research papers where it served as the basis for the development and comparison of many algorithmic fairness interventions. We reconstruct a superset of the UCI Adult data from available US Census sources and reveal idiosyncrasies of the UCI Adult dataset that limit its external validity. Our primary contribution is a suite of new datasets derived from US Census surveys that extend the existing data ecosystem for research on fair machine learning. We create prediction tasks relating to income, employment, health, transportation, and housing. The data span multiple years and all states of the United States, allowing researchers to study temporal shift and geographic variation. We highlight a broad initial sweep of new empirical insights relating to trade-offs between fairness criteria, performance of algorithmic interventions, and the role of distribution shift based on our new datasets. Our findings inform ongoing debates, challenge some existing narratives, and point to future research directions. Our datasets are available at https://github.com/zykls/folktables.", "published": "2021-08-10T19:19:41Z", "authors": ["Frances Ding", "Moritz Hardt", "John Miller", "Ludwig Schmidt"], "categories": ["cs.LG", "stat.ML"], "doi": "", "_query": "house price prediction machine learning", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2603.22320v3", "title": "How Can Machine Learning Emulators Best Support Climate Science?", "abstract": "For decades, physics-based climate models have been used to provide insights for climate decision-making. Their application is, however, constrained by significant computational and technical demands. Machine learning (ML) emulators offer a way to reduce these high computational costs; yet, it remains challenging to use ML emulators effectively in climate research. In practice, climate scientists often bypass emulators altogether, and machine learning researchers frequently develop them as methodological showcases without proving their practical utility. The reasons are diverse, ranging from limited accessibility and a lack of specialized knowledge to broader concerns about the physical grounding of ML methods. Here, we discuss limitations and introduce a framework for guiding emulator development, considering both climate science and machine learning perspectives. We argue that designing easy-to-adopt emulators that address clearly defined tasks and demonstrate their reliability is essential. This offers a promising path towards making machine-learning approaches more relevant and usable for applied climate research.", "published": "2026-03-20T10:41:50Z", "authors": ["Luca Schmidt", "Nina Effenberger", "Vitus Benson", "Philine L. Bommer", "Robert Brunstein", "Mikel N. Legasa", "Maxim Samarin", "Maybritt Schillinger"], "categories": ["cs.LG", "stat.AP", "stat.ML"], "doi": "", "_query": "house price prediction machine learning", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2406.04068v2", "title": "Reassessing How to Compare and Improve the Calibration of Machine Learning Models", "abstract": "A machine learning model is calibrated if its predicted probability for an outcome matches the observed frequency for that outcome conditional on the model prediction. This property has become increasingly important as the impact of machine learning models has continued to spread to various domains. As a result, there are now a dizzying number of recent papers on measuring and improving the calibration of (specifically deep learning) models. In this work, we reassess the reporting of calibration metrics in the recent literature. We show that there exist trivial recalibration approaches that can appear seemingly state-of-the-art unless calibration and prediction metrics (i.e. test accuracy) are accompanied by additional generalization metrics such as negative log-likelihood. We then use a calibration-based decomposition of Bregman divergences to develop a new extension to reliability diagrams that jointly visualizes calibration and generalization error, and show how our visualization can be used to detect trade-offs between calibration and generalization. Along the way, we prove novel results regarding the relationship between full calibration error and confidence calibration error for Bregman divergences. We also establish the consistency of the kernel regression estimator for calibration error used in our visualization approach, which generalizes existing consistency results in the literature.", "published": "2024-06-06T13:33:45Z", "authors": ["Muthu Chidambaram", "Rong Ge"], "categories": ["cs.LG", "math.ST", "stat.ML"], "doi": "", "_query": "house price prediction machine learning", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2003.06795v1", "title": "Towards automated kernel selection in machine learning systems: A SYCL case study", "abstract": "Automated tuning of compute kernels is a popular area of research, mainly focused on finding optimal kernel parameters for a problem with fixed input sizes. This approach is good for deploying machine learning models, where the network topology is constant, but machine learning research often involves changing network topologies and hyperparameters. Traditional kernel auto-tuning has limited impact in this case; a more general selection of kernels is required for libraries to accelerate machine learning research. In this paper we present initial results using machine learning to select kernels in a case study deploying high performance SYCL kernels in libraries that target a range of heterogeneous devices from desktop GPUs to embedded accelerators. The techniques investigated apply more generally and could similarly be integrated with other heterogeneous programming systems. By combining auto-tuning and machine learning these kernel selection processes can be deployed with little developer effort to achieve high performance on new hardware.", "published": "2020-03-15T11:23:36Z", "authors": ["John Lawson"], "categories": ["cs.LG", "cs.PF", "stat.ML"], "doi": "", "_query": "house price prediction machine learning", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2302.04262v3", "title": "Algorithmic Collective Action in Machine Learning", "abstract": "We initiate a principled study of algorithmic collective action on digital platforms that deploy machine learning algorithms. We propose a simple theoretical model of a collective interacting with a firm's learning algorithm. The collective pools the data of participating individuals and executes an algorithmic strategy by instructing participants how to modify their own data to achieve a collective goal. We investigate the consequences of this model in three fundamental learning-theoretic settings: the case of a nonparametric optimal learning algorithm, a parametric risk minimizer, and gradient-based optimization. In each setting, we come up with coordinated algorithmic strategies and characterize natural success criteria as a function of the collective's size. Complementing our theory, we conduct systematic experiments on a skill classification task involving tens of thousands of resumes from a gig platform for freelancers. Through more than two thousand model training runs of a BERT-like language model, we see a striking correspondence emerge between our empirical observations and the predictions made by our theory. Taken together, our theory and experiments broadly support the conclusion that algorithmic collectives of exceedingly small fractional size can exert significant control over a platform's learning algorithm.", "published": "2023-02-08T18:55:49Z", "authors": ["Moritz Hardt", "Eric Mazumdar", "Celestine Mendler-Dünner", "Tijana Zrnic"], "categories": ["cs.LG", "cs.GT", "stat.ML"], "doi": "", "_query": "house price prediction machine learning", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/1101.1057v3", "title": "Sparsity regret bounds for individual sequences in online linear regression", "abstract": "We consider the problem of online linear regression on arbitrary deterministic sequences when the ambient dimension d can be much larger than the number of time rounds T. We introduce the notion of sparsity regret bound, which is a deterministic online counterpart of recent risk bounds derived in the stochastic setting under a sparsity scenario. We prove such regret bounds for an online-learning algorithm called SeqSEW and based on exponential weighting and data-driven truncation. In a second part we apply a parameter-free version of this algorithm to the stochastic setting (regression model with random design). This yields risk bounds of the same flavor as in Dalalyan and Tsybakov (2011) but which solve two questions left open therein. In particular our risk bounds are adaptive (up to a logarithmic factor) to the unknown variance of the noise if the latter is Gaussian. We also address the regression model with fixed design.", "published": "2011-01-05T19:43:37Z", "authors": ["Sébastien Gerchinovitz"], "categories": ["stat.ML", "cs.LG", "math.ST"], "doi": "", "_query": "house price prediction machine learning", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/1510.07129v1", "title": "Bayesian Inference for High Dimensional Changing Linear Regression with Application to Minnesota House Price Index Data", "abstract": "In many applications, the dataset under investigation exhibits heterogeneous regimes that are more appropriately modeled using piece-wise linear models for each of the data segments separated by change-points. Although there have been much work on change point linear regression for the low dimensional case, high-dimensional change point regression is severely underdeveloped. Motivated by the analysis of Minnesota House Price Index data, we propose a fully Bayesian framework for fitting changing linear regression models in high-dimensional settings. Using segment-specific shrinkage and diffusion priors, we deliver full posterior inference for the change points and simultaneously obtain posterior probabilities of variable selection in each segment via an efficient Gibbs sampler. Additionally, our method can detect an unknown number of change points and accommodate different variable selection constraints like grouping or partial selection. We substantiate the accuracy of our method using simulation experiments for a wide range of scenarios. We apply our approach for a macro-economic analysis of Minnesota house price index data. The results strongly favor the change point model over a homogeneous (no change point) high-dimensional regression model.", "published": "2015-10-24T10:28:14Z", "authors": ["Abhirup Datta", "Hui Zou", "Sudipto Banerjee"], "categories": ["math.ST", "stat.AP"], "doi": "", "_query": "house price prediction machine learning", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/1904.12054v5", "title": "Benchmark and Survey of Automated Machine Learning Frameworks", "abstract": "Machine learning (ML) has become a vital part in many aspects of our daily life. However, building well performing machine learning applications requires highly specialized data scientists and domain experts. Automated machine learning (AutoML) aims to reduce the demand for data scientists by enabling domain experts to build machine learning applications automatically without extensive knowledge of statistics and machine learning. This paper is a combination of a survey on current AutoML methods and a benchmark of popular AutoML frameworks on real data sets. Driven by the selected frameworks for evaluation, we summarize and review important AutoML techniques and methods concerning every step in building an ML pipeline. The selected AutoML frameworks are evaluated on 137 data sets from established AutoML benchmark suits.", "published": "2019-04-26T21:42:56Z", "authors": ["Marc-André Zöller", "Marco F. Huber"], "categories": ["cs.LG", "cs.AI", "stat.ML"], "doi": "", "_query": "house price prediction machine learning", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/1907.12953v2", "title": "A real-time iterative machine learning approach for temperature profile prediction in additive manufacturing processes", "abstract": "Additive Manufacturing (AM) is a manufacturing paradigm that builds three-dimensional objects from a computer-aided design model by successively adding material layer by layer. AM has become very popular in the past decade due to its utility for fast prototyping such as 3D printing as well as manufacturing functional parts with complex geometries using processes such as laser metal deposition that would be difficult to create using traditional machining. As the process for creating an intricate part for an expensive metal such as Titanium is prohibitive with respect to cost, computational models are used to simulate the behavior of AM processes before the experimental run. However, as the simulations are computationally costly and time-consuming for predicting multiscale multi-physics phenomena in AM, physics-informed data-driven machine-learning systems for predicting the behavior of AM processes are immensely beneficial. Such models accelerate not only multiscale simulation tools but also empower real-time control systems using in-situ data. In this paper, we design and develop essential components of a scientific framework for developing a data-driven model-based real-time control system. Finite element methods are employed for solving time-dependent heat equations and developing the database. The proposed framework uses extremely randomized trees - an ensemble of bagged decision trees as the regression algorithm iteratively using temperatures of prior voxels and laser information as inputs to predict temperatures of subsequent voxels. The models achieve mean absolute percentage errors below 1% for predicting temperature profiles for AM processes. The code is made available for the research community at https://github.com/paularindam/ml-iter-additive.", "published": "2019-07-28T22:43:02Z", "authors": ["Arindam Paul", "Mojtaba Mozaffar", "Zijiang Yang", "Wei-keng Liao", "Alok Choudhary", "Jian Cao", "Ankit Agrawal"], "categories": ["cs.LG", "stat.ML"], "doi": "", "_query": "house price prediction machine learning", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2007.06557v3", "title": "Prediction-Centric Learning of Independent Cascade Dynamics from Partial Observations", "abstract": "Spreading processes play an increasingly important role in modeling for diffusion networks, information propagation, marketing and opinion setting. We address the problem of learning of a spreading model such that the predictions generated from this model are accurate and could be subsequently used for the optimization, and control of diffusion dynamics. We focus on a challenging setting where full observations of the dynamics are not available, and standard approaches such as maximum likelihood quickly become intractable for large network instances. We introduce a computationally efficient algorithm, based on a scalable dynamic message-passing approach, which is able to learn parameters of the effective spreading model given only limited information on the activation times of nodes in the network. The popular Independent Cascade model is used to illustrate our approach. We show that tractable inference from the learned model generates a better prediction of marginal probabilities compared to the original model. We develop a systematic procedure for learning a mixture of models which further improves the prediction quality.", "published": "2020-07-13T17:58:21Z", "authors": ["Mateusz Wilinski", "Andrey Y. Lokhov"], "categories": ["cs.SI", "cs.LG", "physics.soc-ph", "stat.ML"], "doi": "", "_query": "house price prediction machine learning", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2405.20452v1", "title": "Understanding Encoder-Decoder Structures in Machine Learning Using Information Measures", "abstract": "We present new results to model and understand the role of encoder-decoder design in machine learning (ML) from an information-theoretic angle. We use two main information concepts, information sufficiency (IS) and mutual information loss (MIL), to represent predictive structures in machine learning. Our first main result provides a functional expression that characterizes the class of probabilistic models consistent with an IS encoder-decoder latent predictive structure. This result formally justifies the encoder-decoder forward stages many modern ML architectures adopt to learn latent (compressed) representations for classification. To illustrate IS as a realistic and relevant model assumption, we revisit some known ML concepts and present some interesting new examples: invariant, robust, sparse, and digital models. Furthermore, our IS characterization allows us to tackle the fundamental question of how much performance (predictive expressiveness) could be lost, using the cross entropy risk, when a given encoder-decoder architecture is adopted in a learning setting. Here, our second main result shows that a mutual information loss quantifies the lack of expressiveness attributed to the choice of a (biased) encoder-decoder ML design. Finally, we address the problem of universal cross-entropy learning with an encoder-decoder design where necessary and sufficiency conditions are established to meet this requirement. In all these results, Shannon's information measures offer new interpretations and explanations for representation learning.", "published": "2024-05-30T19:58:01Z", "authors": ["Jorge F. Silva", "Victor Faraggi", "Camilo Ramirez", "Alvaro Egana", "Eduardo Pavez"], "categories": ["cs.LG", "cs.IT", "stat.ML"], "doi": "", "_query": "house price prediction machine learning", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2012.01545v1", "title": "Machine learning prediction of critical transition and system collapse", "abstract": "To predict a critical transition due to parameter drift without relying on model is an outstanding problem in nonlinear dynamics and applied fields. A closely related problem is to predict whether the system is already in or if the system will be in a transient state preceding its collapse. We develop a model free, machine learning based solution to both problems by exploiting reservoir computing to incorporate a parameter input channel. We demonstrate that, when the machine is trained in the normal functioning regime with a chaotic attractor (i.e., before the critical transition), the transition point can be predicted accurately. Remarkably, for a parameter drift through the critical point, the machine with the input parameter channel is able to predict not only that the system will be in a transient state, but also the average transient time before the final collapse.", "published": "2020-12-02T21:38:54Z", "authors": ["Ling-Wei Kong", "Hua-Wei Fan", "Celso Grebogi", "Ying-Cheng Lai"], "categories": ["cs.LG", "cs.AI", "math.DS", "physics.data-an"], "doi": "", "_query": "house price prediction machine learning", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/1911.05268v2", "title": "Adversarial Examples in Modern Machine Learning: A Review", "abstract": "Recent research has found that many families of machine learning models are vulnerable to adversarial examples: inputs that are specifically designed to cause the target model to produce erroneous outputs. In this survey, we focus on machine learning models in the visual domain, where methods for generating and detecting such examples have been most extensively studied. We explore a variety of adversarial attack methods that apply to image-space content, real world adversarial attacks, adversarial defenses, and the transferability property of adversarial examples. We also discuss strengths and weaknesses of various methods of adversarial attack and defense. Our aim is to provide an extensive coverage of the field, furnishing the reader with an intuitive understanding of the mechanics of adversarial attack and defense mechanisms and enlarging the community of researchers studying this fundamental set of problems.", "published": "2019-11-13T03:09:40Z", "authors": ["Rey Reza Wiyatno", "Anqi Xu", "Ousmane Dia", "Archy de Berker"], "categories": ["cs.LG", "cs.AI", "cs.CR", "stat.ML"], "doi": "", "_query": "house price prediction machine learning", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2507.04187v1", "title": "Where to Intervene: Action Selection in Deep Reinforcement Learning", "abstract": "Deep reinforcement learning (RL) has gained widespread adoption in recent years but faces significant challenges, particularly in unknown and complex environments. Among these, high-dimensional action selection stands out as a critical problem. Existing works often require a sophisticated prior design to eliminate redundancy in the action space, relying heavily on domain expert experience or involving high computational complexity, which limits their generalizability across different RL tasks. In this paper, we address these challenges by proposing a general data-driven action selection approach with model-free and computationally friendly properties. Our method not only selects minimal sufficient actions but also controls the false discovery rate via knockoff sampling. More importantly, we seamlessly integrate the action selection into deep RL methods during online training. Empirical experiments validate the established theoretical guarantees, demonstrating that our method surpasses various alternative techniques in terms of both performance in variable selection and overall achieved rewards.", "published": "2025-07-05T23:40:55Z", "authors": ["Wenbo Zhang", "Hengrui Cai"], "categories": ["stat.ML", "cs.LG"], "doi": "", "_query": "house price prediction machine learning", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/1912.13032v1", "title": "Using massive health insurance claims data to predict very high-cost claimants: a machine learning approach", "abstract": "Due to escalating healthcare costs, accurately predicting which patients will incur high costs is an important task for payers and providers of healthcare. High-cost claimants (HiCCs) are patients who have annual costs above $\\$250,000$ and who represent just 0.16% of the insured population but currently account for 9% of all healthcare costs. In this study, we aimed to develop a high-performance algorithm to predict HiCCs to inform a novel care management system. Using health insurance claims from 48 million people and augmented with census data, we applied machine learning to train binary classification models to calculate the personal risk of HiCC. To train the models, we developed a platform starting with 6,006 variables across all clinical and demographic dimensions and constructed over one hundred candidate models. The best model achieved an area under the receiver operating characteristic curve of 91.2%. The model exceeds the highest published performance (84%) and remains high for patients with no prior history of high-cost status (89%), who have less than a full year of enrollment (87%), or lack pharmacy claims data (88%). It attains an area under the precision-recall curve of 23.1%, and precision of 74% at a threshold of 0.99. A care management program enrolling 500 people with the highest HiCC risk is expected to treat 199 true HiCCs and generate a net savings of $\\$7.3$ million per year. Our results demonstrate that high-performing predictive models can be constructed using claims data and publicly available data alone, even for rare high-cost claimants exceeding $\\$250,000$. Our model demonstrates the transformational power of machine learning and artificial intelligence in care management, which would allow healthcare payers and providers to introduce the next generation of care management programs.", "published": "2019-12-30T18:01:30Z", "authors": ["José M. Maisog", "Wenhong Li", "Yanchun Xu", "Brian Hurley", "Hetal Shah", "Ryan Lemberg", "Tina Borden", "Stephen Bandeian", "Melissa Schline", "Roxanna Cross", "Alan Spiro", "Russ Michael", "Alexander Gutfraind"], "categories": ["cs.LG", "stat.ML"], "doi": "", "_query": "house price prediction machine learning", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2512.15720v1", "title": "Hidden Order in Trades Predicts the Size of Price Moves", "abstract": "Financial markets exhibit an apparent paradox: while directional price movements remain largely unpredictable--consistent with weak-form efficiency--the magnitude of price changes displays systematic structure. Here we demonstrate that real-time order-flow entropy, computed from a 15-state Markov transition matrix at second resolution, predicts the magnitude of intraday returns without providing directional information. Analysis of 38.5 million SPY trades over 36 trading days reveals that conditioning on entropy below the 5th percentile increases subsequent 5-minute absolute returns by a factor of 2.89 (t = 12.41, p < 0.0001), while directional accuracy remains at 45.0%--statistically indistinguishable from chance (p = 0.12). This decoupling arises from a fundamental symmetry: entropy is invariant under sign permutation, detecting the presence of informed trading without revealing its direction. Walk-forward validation across five non-overlapping test periods confirms out-of-sample predictability, and label-permutation placebo tests yield z = 14.4 against the null. These findings suggest that information-theoretic measures may serve as volatility state variables in market microstructure, though the limited sample (36 days, single instrument) requires extended validation.", "published": "2025-12-02T23:20:46Z", "authors": ["Mainak Singha"], "categories": ["q-fin.TR", "q-fin.ST", "stat.AP", "stat.ME"], "doi": "", "_query": "house price prediction machine learning", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2207.04994v3", "title": "Uncertainty-Aware Mixed-Variable Machine Learning for Materials Design", "abstract": "Data-driven design shows the promise of accelerating materials discovery but is challenging due to the prohibitive cost of searching the vast design space of chemistry, structure, and synthesis methods. Bayesian Optimization (BO) employs uncertainty-aware machine learning models to select promising designs to evaluate, hence reducing the cost. However, BO with mixed numerical and categorical variables, which is of particular interest in materials design, has not been well studied. In this work, we survey frequentist and Bayesian approaches to uncertainty quantification of machine learning with mixed variables. We then conduct a systematic comparative study of their performances in BO using a popular representative model from each group, the random forest-based Lolo model (frequentist) and the latent variable Gaussian process model (Bayesian). We examine the efficacy of the two models in the optimization of mathematical functions, as well as properties of structural and functional materials, where we observe performance differences as related to problem dimensionality and complexity. By investigating the machine learning models' predictive and uncertainty estimation capabilities, we provide interpretations of the observed performance differences. Our results provide practical guidance on choosing between frequentist and Bayesian uncertainty-aware machine learning models for mixed-variable BO in materials design.", "published": "2022-07-11T16:37:17Z", "authors": ["Hengrui Zhang", "Wei Wayne Chen", "Akshay Iyer", "Daniel W. Apley", "Wei Chen"], "categories": ["stat.ML", "cond-mat.mtrl-sci", "cs.LG"], "doi": "10.1038/s41598-022-23431-2", "_query": "house price prediction machine learning", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2405.18563v2", "title": "No $D_{\\text{train}}$: Model-Agnostic Counterfactual Explanations Using Reinforcement Learning", "abstract": "Machine learning (ML) methods have experienced significant growth in the past decade, yet their practical application in high-impact real-world domains has been hindered by their opacity. When ML methods are responsible for making critical decisions, stakeholders often require insights into how to alter these decisions. Counterfactual explanations (CFEs) have emerged as a solution, offering interpretations of opaque ML models and providing a pathway to transition from one decision to another. However, most existing CFE methods require access to the model's training dataset, few methods can handle multivariate time-series, and none of model-agnostic CFE methods can handle multivariate time-series without training datasets. These limitations can be formidable in many scenarios. In this paper, we present NTD-CFE, a novel model-agnostic CFE method based on reinforcement learning (RL) that generates CFEs when training datasets are unavailable. NTD-CFE is suitable for both static and multivariate time-series datasets with continuous and discrete features. NTD-CFE reduces the CFE search space from a multivariate time-series domain to a lower dimensional space and addresses the problem using RL. Users have the flexibility to specify non-actionable, immutable, and preferred features, as well as causal constraints. We demonstrate the performance of NTD-CFE against four baselines on several datasets and find that, despite not having access to a training dataset, NTD-CFE finds CFEs that make significantly fewer and significantly smaller changes to the input time-series. These properties make CFEs more actionable, as the magnitude of change required to alter an outcome is vastly reduced. The code is available in the supplementary material.", "published": "2024-05-28T20:15:09Z", "authors": ["Xiangyu Sun", "Raquel Aoki", "Kevin H. Wilson"], "categories": ["cs.LG", "stat.ME"], "doi": "", "_query": "house price prediction machine learning", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2307.11332v1", "title": "Beyond Convergence: Identifiability of Machine Learning and Deep Learning Models", "abstract": "Machine learning (ML) and deep learning models are extensively used for parameter optimization and regression problems. However, not all inverse problems in ML are ``identifiable,'' indicating that model parameters may not be uniquely determined from the available data and the data model's input-output relationship. In this study, we investigate the notion of model parameter identifiability through a case study focused on parameter estimation from motion sensor data. Utilizing a bipedal-spring mass human walk dynamics model, we generate synthetic data representing diverse gait patterns and conditions. Employing a deep neural network, we attempt to estimate subject-wise parameters, including mass, stiffness, and equilibrium leg length. The results show that while certain parameters can be identified from the observation data, others remain unidentifiable, highlighting that unidentifiability is an intrinsic limitation of the experimental setup, necessitating a change in data collection and experimental scenarios. Beyond this specific case study, the concept of identifiability has broader implications in ML and deep learning. Addressing unidentifiability requires proven identifiable models (with theoretical support), multimodal data fusion techniques, and advancements in model-based machine learning. Understanding and resolving unidentifiability challenges will lead to more reliable and accurate applications across diverse domains, transcending mere model convergence and enhancing the reliability of machine learning models.", "published": "2023-07-21T03:40:53Z", "authors": ["Reza Sameni"], "categories": ["cs.LG", "stat.ML"], "doi": "", "_query": "house price prediction machine learning", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/1805.05431v1", "title": "Comparison of Classical and Nonlinear Models for Short-Term Electricity Price Prediction", "abstract": "Electricity is bought and sold in wholesale markets at prices that fluctuate significantly. Short-term forecasting of electricity prices is an important endeavor because it helps electric utilities control risk and because it influences competitive strategy for generators. As the \"smart grid\" grows, short-term price forecasts are becoming an important input to bidding and control algorithms for battery operators and demand response aggregators. While the statistics and machine learning literature offers many proposed methods for electricity price prediction, there is no consensus supporting a single best approach. We test two contrasting machine learning approaches for predicting electricity prices, regression decision trees and recurrent neural networks (RNNs), and compare them to a more traditional ARIMA implementation. We conduct the analysis on a challenging dataset of electricity prices from ERCOT, in Texas, where price fluctuation is especially high. We find that regression decision trees in particular achieves high performance compared to the other methods, suggesting that regression trees should be more carefully considered for electricity price forecasting.", "published": "2018-05-02T20:15:36Z", "authors": ["Elaheh Fata", "Igor Kadota", "Ian Schneider"], "categories": ["cs.CY", "cs.LG", "stat.ML"], "doi": "", "_query": "house price prediction machine learning", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2403.18864v1", "title": "Interpretable Machine Learning for Weather and Climate Prediction: A Survey", "abstract": "Advanced machine learning models have recently achieved high predictive accuracy for weather and climate prediction. However, these complex models often lack inherent transparency and interpretability, acting as \"black boxes\" that impede user trust and hinder further model improvements. As such, interpretable machine learning techniques have become crucial in enhancing the credibility and utility of weather and climate modeling. In this survey, we review current interpretable machine learning approaches applied to meteorological predictions. We categorize methods into two major paradigms: 1) Post-hoc interpretability techniques that explain pre-trained models, such as perturbation-based, game theory based, and gradient-based attribution methods. 2) Designing inherently interpretable models from scratch using architectures like tree ensembles and explainable neural networks. We summarize how each technique provides insights into the predictions, uncovering novel meteorological relationships captured by machine learning. Lastly, we discuss research challenges around achieving deeper mechanistic interpretations aligned with physical principles, developing standardized evaluation benchmarks, integrating interpretability into iterative model development workflows, and providing explainability for large foundation models.", "published": "2024-03-24T14:23:35Z", "authors": ["Ruyi Yang", "Jingyu Hu", "Zihao Li", "Jianli Mu", "Tingzhao Yu", "Jiangjiang Xia", "Xuhong Li", "Aritra Dasgupta", "Haoyi Xiong"], "categories": ["physics.ao-ph", "cs.AI", "cs.LG"], "doi": "", "_query": "house price prediction machine learning", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/1808.02213v1", "title": "Importance of the Mathematical Foundations of Machine Learning Methods for Scientific and Engineering Applications", "abstract": "There has been a lot of recent interest in adopting machine learning methods for scientific and engineering applications. This has in large part been inspired by recent successes and advances in the domains of Natural Language Processing (NLP) and Image Classification (IC). However, scientific and engineering problems have their own unique characteristics and requirements raising new challenges for effective design and deployment of machine learning approaches. There is a strong need for further mathematical developments on the foundations of machine learning methods to increase the level of rigor of employed methods and to ensure more reliable and interpretable results. Also as reported in the recent literature on state-of-the-art results and indicated by the No Free Lunch Theorems of statistical learning theory incorporating some form of inductive bias and domain knowledge is essential to success. Consequently, even for existing and widely used methods there is a strong need for further mathematical work to facilitate ways to incorporate prior scientific knowledge and related inductive biases into learning frameworks and algorithms. We briefly discuss these topics and discuss some ideas proceeding in this direction.", "published": "2018-08-07T05:09:13Z", "authors": ["Paul J. Atzberger"], "categories": ["stat.ML", "cs.LG"], "doi": "", "_query": "house price prediction machine learning", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2306.02216v3", "title": "Forgettable Federated Linear Learning with Certified Data Unlearning", "abstract": "Federated Learning (FL) enables collaborative model training across distributed clients while preserving user privacy. Recently, Federated Unlearning (FU) has emerged to address the \"right to be forgotten\" and to remove the influence of poisoned or target clients without retraining the entire FL system. However, many FU methods require communication with retained or target clients, introduce additional security risks, or store historical models, limiting their efficiency and practicality. Moreover, most FU methods for deep neural networks (DNNs) lack theoretical certification due to the complexity of nonlinear models and their training dynamics. In this work, we introduce Forgettable Federated Linear Learning, a training and unlearning framework for DNNs. Our approach uses pre-trained models to linearly approximate DNNs and achieve performance comparable to the original networks through Federated Linear Training. We further present a certified, efficient, and secure unlearning strategy that enables the server to remove a target client's influence without additional client communication or storage. Extensive experiments on small- to large-scale datasets, using both convolutional neural networks and modern foundation models, show that our method balances model accuracy with effective target-client unlearning. This work provides a practical pipeline for efficient and trustworthy FU. Code: https://github.com/Nanboy-Ronan/2F2L-Federated-Unlearning", "published": "2023-06-03T23:53:57Z", "authors": ["Ruinan Jin", "Minghui Chen", "Qiong Zhang", "Xiaoxiao Li"], "categories": ["cs.LG", "cs.CV"], "doi": "10.1109/TNNLS.2026.3683398", "_query": "house price prediction machine learning", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/1505.00641v3", "title": "fastFM: A Library for Factorization Machines", "abstract": "Factorization Machines (FM) are only used in a narrow range of applications and are not part of the standard toolbox of machine learning models. This is a pity, because even though FMs are recognized as being very successful for recommender system type applications they are a general model to deal with sparse and high dimensional features. Our Factorization Machine implementation provides easy access to many solvers and supports regression, classification and ranking tasks. Such an implementation simplifies the use of FM's for a wide field of applications. This implementation has the potential to improve our understanding of the FM model and drive new development.", "published": "2015-05-04T14:06:11Z", "authors": ["Immanuel Bayer"], "categories": ["cs.LG", "cs.IR"], "doi": "", "_query": "house price prediction machine learning", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2206.05703v2", "title": "PAC-Net: A Model Pruning Approach to Inductive Transfer Learning", "abstract": "Inductive transfer learning aims to learn from a small amount of training data for the target task by utilizing a pre-trained model from the source task. Most strategies that involve large-scale deep learning models adopt initialization with the pre-trained model and fine-tuning for the target task. However, when using over-parameterized models, we can often prune the model without sacrificing the accuracy of the source task. This motivates us to adopt model pruning for transfer learning with deep learning models. In this paper, we propose PAC-Net, a simple yet effective approach for transfer learning based on pruning. PAC-Net consists of three steps: Prune, Allocate, and Calibrate (PAC). The main idea behind these steps is to identify essential weights for the source task, fine-tune on the source task by updating the essential weights, and then calibrate on the target task by updating the remaining redundant weights. Under the various and extensive set of inductive transfer learning experiments, we show that our method achieves state-of-the-art performance by a large margin.", "published": "2022-06-12T09:45:16Z", "authors": ["Sanghoon Myung", "In Huh", "Wonik Jang", "Jae Myung Choe", "Jisu Ryu", "Dae Sin Kim", "Kee-Eung Kim", "Changwook Jeong"], "categories": ["cs.LG", "cs.AI", "physics.comp-ph", "stat.AP", "stat.ML"], "doi": "", "_query": "house price prediction machine learning", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/1911.13300v3", "title": "Refinements of Barndorff-Nielsen and Shephard model: an analysis of crude oil price with machine learning", "abstract": "A commonly used stochastic model for derivative and commodity market analysis is the Barndorff-Nielsen and Shephard (BN-S) model. Though this model is very efficient and analytically tractable, it suffers from the absence of long range dependence and many other issues. For this paper, the analysis is restricted to crude oil price dynamics. A simple way of improving the BN-S model with the implementation of various machine learning algorithms is proposed. This refined BN-S model is more efficient and has fewer parameters than other models which are used in practice as improvements of the BN-S model. The procedure and the model show the application of data science for extracting a \"deterministic component\" out of processes that are usually considered to be completely stochastic. Empirical applications validate the efficacy of the proposed model for long range dependence.", "published": "2019-11-29T18:57:09Z", "authors": ["Indranil SenGupta", "William Nganje", "Erik Hanson"], "categories": ["q-fin.ST", "q-fin.MF", "stat.ML"], "doi": "10.1007/s40745-020-00256-2", "_query": "house price prediction machine learning", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/1905.04223v1", "title": "Assuring the Machine Learning Lifecycle: Desiderata, Methods, and Challenges", "abstract": "Machine learning has evolved into an enabling technology for a wide range of highly successful applications. The potential for this success to continue and accelerate has placed machine learning (ML) at the top of research, economic and political agendas. Such unprecedented interest is fuelled by a vision of ML applicability extending to healthcare, transportation, defence and other domains of great societal importance. Achieving this vision requires the use of ML in safety-critical applications that demand levels of assurance beyond those needed for current ML applications. Our paper provides a comprehensive survey of the state-of-the-art in the assurance of ML, i.e. in the generation of evidence that ML is sufficiently safe for its intended use. The survey covers the methods capable of providing such evidence at different stages of the machine learning lifecycle, i.e. of the complex, iterative process that starts with the collection of the data used to train an ML component for a system, and ends with the deployment of that component within the system. The paper begins with a systematic presentation of the ML lifecycle and its stages. We then define assurance desiderata for each stage, review existing methods that contribute to achieving these desiderata, and identify open challenges that require further research.", "published": "2019-05-10T15:44:38Z", "authors": ["Rob Ashmore", "Radu Calinescu", "Colin Paterson"], "categories": ["cs.LG", "cs.SE", "stat.ML"], "doi": "", "_query": "house price prediction machine learning", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2306.11946v1", "title": "Winter Wheat Crop Yield Prediction on Multiple Heterogeneous Datasets using Machine Learning", "abstract": "Winter wheat is one of the most important crops in the United Kingdom, and crop yield prediction is essential for the nation's food security. Several studies have employed machine learning (ML) techniques to predict crop yield on a county or farm-based level. The main objective of this study is to predict winter wheat crop yield using ML models on multiple heterogeneous datasets, i.e., soil and weather on a zone-based level. Experimental results demonstrated their impact when used alone and in combination. In addition, we employ numerous ML algorithms to emphasize the significance of data quality in any machine-learning strategy.", "published": "2023-06-20T23:52:39Z", "authors": ["Yogesh Bansal", "David Lillis", "Mohand Tahar Kechadi"], "categories": ["cs.LG"], "doi": "10.1109/CSCI58124.2022.00040", "_query": "house price prediction machine learning", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2010.00821v3", "title": "Explainable Online Validation of Machine Learning Models for Practical Applications", "abstract": "We present a reformulation of the regression and classification, which aims to validate the result of a machine learning algorithm. Our reformulation simplifies the original problem and validates the result of the machine learning algorithm using the training data. Since the validation of machine learning algorithms must always be explainable, we perform our experiments with the kNN algorithm as well as with an algorithm based on conditional probabilities, which is proposed in this work. For the evaluation of our approach, three publicly available data sets were used and three classification and two regression problems were evaluated. The presented algorithm based on conditional probabilities is also online capable and requires only a fraction of memory compared to the kNN algorithm.", "published": "2020-10-02T07:38:31Z", "authors": ["Wolfgang Fuhl", "Yao Rong", "Thomas Motz", "Michael Scheidt", "Andreas Hartel", "Andreas Koch", "Enkelejda Kasneci"], "categories": ["cs.LG", "cs.AI", "cs.CV", "stat.ML"], "doi": "", "_query": "house price prediction machine learning", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2011.06655v1", "title": "Performance and Power Modeling and Prediction Using MuMMI and Ten Machine Learning Methods", "abstract": "In this paper, we use modeling and prediction tool MuMMI (Multiple Metrics Modeling Infrastructure) and ten machine learning methods to model and predict performance and power and compare their prediction error rates. We use a fault-tolerant linear algebra code and a fault-tolerant heat distribution code to conduct our modeling and prediction study on the Cray XC40 Theta and IBM BG/Q Mira at Argonne National Laboratory and the Intel Haswell cluster Shepard at Sandia National Laboratories. Our experiment results show that the prediction error rates in performance and power using MuMMI are less than 10% for most cases. Based on the models for runtime, node power, CPU power, and memory power, we identify the most significant performance counters for potential optimization efforts associated with the application characteristics and the target architectures, and we predict theoretical outcomes of the potential optimizations. When we compare the prediction accuracy using MuMMI with that using 10 machine learning methods, we observe that MuMMI not only results in more accurate prediction in both performance and power but also presents how performance counters impact the performance and power models. This provides some insights about how to fine-tune the applications and/or systems for energy efficiency.", "published": "2020-11-12T21:24:11Z", "authors": ["Xingfu Wu", "Valerie Taylor", "Zhiling Lan"], "categories": ["cs.LG", "cs.PF"], "doi": "", "_query": "house price prediction machine learning", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/1708.08022v1", "title": "On the Protection of Private Information in Machine Learning Systems: Two Recent Approaches", "abstract": "The recent, remarkable growth of machine learning has led to intense interest in the privacy of the data on which machine learning relies, and to new techniques for preserving privacy. However, older ideas about privacy may well remain valid and useful. This note reviews two recent works on privacy in the light of the wisdom of some of the early literature, in particular the principles distilled by Saltzer and Schroeder in the 1970s.", "published": "2017-08-26T23:23:39Z", "authors": ["Martín Abadi", "Úlfar Erlingsson", "Ian Goodfellow", "H. Brendan McMahan", "Ilya Mironov", "Nicolas Papernot", "Kunal Talwar", "Li Zhang"], "categories": ["stat.ML", "cs.CR", "cs.LG"], "doi": "10.1109/CSF.2017.10", "_query": "house price prediction machine learning", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2609.24370v1", "title": "Prescriptive SVD-Inspired Attention via Spectral Energy Retention", "abstract": "Self-attention is central to modern Transformer architectures, but its dense dot-product formulation makes it difficult to identify which internal directions are structurally important and which can be modified without disrupting the model. SVD-Inspired Attention (SVDA) addresses part of this problem by introducing a learned diagonal spectrum into the query-key score interaction, making latent attention directions explicitly inspectable through indicators such as spectral entropy, effective rank, sparsity, alignment, selectivity, and perturbation response. This paper examines the transition from diagnostic interpretation to operational intervention. A diagnosis--intervention--verification framework is proposed, and one intervention is evaluated: spectral energy retention in the attention-score pathway. Across FashionMNIST, CIFAR-10, CIFAR-100, and Food-101, the $ρ=0.90$ prescription removes 24.5--53.7\\% of score directions, reduces parameters by 2.6--4.3\\%, and reduces estimated MACs by 2.8--5.4\\%. The paired mean accuracy change of the dimension-reduced model ranges from $-0.03$ to $+0.05$ percentage points over three seeds. These results support SVDA as an intrinsically interpretable attention mechanism whose learned spectrum exposes an operational coordinate system for deterministic and verifiable modification of attention-score formation.", "published": "2026-09-21T10:02:37Z", "authors": ["Vasileios Arampatzakis", "Vasileios Sevetlidis", "George Pavlidis"], "categories": ["cs.LG", "cs.CV"], "doi": "", "_query": "house price prediction machine learning", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/1904.02206v1", "title": "Jointly Pre-training with Supervised, Autoencoder, and Value Losses for Deep Reinforcement Learning", "abstract": "Deep Reinforcement Learning (DRL) algorithms are known to be data inefficient. One reason is that a DRL agent learns both the feature and the policy tabula rasa. Integrating prior knowledge into DRL algorithms is one way to improve learning efficiency since it helps to build helpful representations. In this work, we consider incorporating human knowledge to accelerate the asynchronous advantage actor-critic (A3C) algorithm by pre-training a small amount of non-expert human demonstrations. We leverage the supervised autoencoder framework and propose a novel pre-training strategy that jointly trains a weighted supervised classification loss, an unsupervised reconstruction loss, and an expected return loss. The resulting pre-trained model learns more useful features compared to independently training in supervised or unsupervised fashion. Our pre-training method drastically improved the learning performance of the A3C agent in Atari games of Pong and MsPacman, exceeding the performance of the state-of-the-art algorithms at a much smaller number of game interactions. Our method is light-weight and easy to implement in a single machine. For reproducibility, our code is available at github.com/gabrieledcjr/DeepRL/tree/A3C-ALA2019", "published": "2019-04-03T19:14:15Z", "authors": ["Gabriel V. de la Cruz", "Yunshu Du", "Matthew E. Taylor"], "categories": ["cs.LG", "stat.ML"], "doi": "", "_query": "house price prediction machine learning", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2110.07658v1", "title": "Predicting Solar Flares with Remote Sensing and Machine Learning", "abstract": "High energy solar flares and coronal mass ejections have the potential to destroy Earth's ground and satellite infrastructures, causing trillions of dollars in damage and mass human suffering. Destruction of these critical systems would disable power grids and satellites, crippling communications and transportation. This would lead to food shortages and an inability to respond to emergencies. A solution to this impending problem is proposed herein using satellites in solar orbit that continuously monitor the Sun, use artificial intelligence and machine learning to calculate the probability of massive solar explosions from this sensed data, and then signal defense mechanisms that will mitigate the threat. With modern technology there may be only safeguards that can be implemented with enough warning, which is why the best algorithm must be identified and continuously trained with existing and new data to maximize true positive rates while minimizing false negatives. This paper conducts a survey of current machine learning models using open source solar flare prediction data. The rise of edge computing allows machine learning hardware to be placed on the same satellites as the sensor arrays, saving critical time by not having to transmit remote sensing data across the vast distances of space. A system of systems approach will allow enough warning for safety measures to be put into place mitigating the risk of disaster.", "published": "2021-10-14T18:28:28Z", "authors": ["Erik Larsen"], "categories": ["cs.LG", "astro-ph.SR"], "doi": "", "_query": "house price prediction machine learning", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2204.10807v3", "title": "Predicting highway lane-changing maneuvers: A benchmark analysis of machine and ensemble learning algorithms", "abstract": "Understanding and predicting highway lane-change maneuvers is essential for driving modeling and its automation. The development of data-based lane-changing decision-making algorithms is nowadays in full expansion. We compare empirically in this article different machine and ensemble learning classification techniques to the MOBIL rule-based model using trajectory data of European two-lane highways. The analysis relies on instantaneous measurements of up to twenty-four spatial-temporal variables with the four neighboring vehicles on current and adjacent lanes. Preliminary descriptive investigations by principal component and logistic analyses allow identifying main variables intending a driver to change lanes. We predict two types of discretionary lane-change maneuvers: overtaking (from the slow to the fast lane) and fold-down (from the fast to the slow lane). The prediction accuracy is quantified using total, lane-changing and lane-keeping errors and associated receiver operating characteristic curves. The benchmark analysis includes logistic model, linear discriminant, decision tree, naïve Bayes classifier, support vector machine, neural network machine learning algorithms, and up to ten bagging and stacking ensemble learning meta-heuristics. If the rule-based model provides limited predicting accuracy, the data-based algorithms, devoid of modeling bias, allow significant prediction improvements. Cross validations show that selected neural networks and stacking algorithms allow predicting from a single observation both fold-down and overtaking maneuvers up to four seconds in advance with high accuracy.", "published": "2022-04-20T22:55:59Z", "authors": ["Basma Khelfa", "Ibrahima Ba", "Antoine Tordeux"], "categories": ["cs.LG", "eess.SP", "physics.soc-ph"], "doi": "10.1016/j.physa.2023.128471", "_query": "house price prediction machine learning", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/1906.01233v3", "title": "A hybrid machine learning framework for analyzing human decision making through learning preferences", "abstract": "Machine learning has recently been widely adopted to address the managerial decision making problems, in which the decision maker needs to be able to interpret the contributions of individual attributes in an explicit form. However, there is a trade-off between performance and interpretability. Full complexity models are non-traceable black-box, whereas classic interpretable models are usually simplified with lower accuracy. This trade-off limits the application of state-of-the-art machine learning models in management problems, which requires high prediction performance, as well as the understanding of individual attributes' contributions to the model outcome. Multiple criteria decision aiding (MCDA) is a family of analytic approaches to depicting the rationale of human decision. It is also limited by strong assumptions. To meet the decision maker's demand for more interpretable machine learning models, we propose a novel hybrid method, namely Neural Network-based Multiple Criteria Decision Aiding, which combines an additive value model and a fully-connected multilayer perceptron (MLP) to achieve good performance while capturing the explicit relationships between individual attributes and the prediction. NN-MCDA has a linear component to characterize such relationships through providing explicit marginal value functions, and a nonlinear component to capture the implicit high-order interactions between attributes and their complex nonlinear transformations. We demonstrate the effectiveness of NN-MCDA with extensive simulation studies and three real-world datasets. To the best of our knowledge, this research is the first to enhance the interpretability of machine learning models with MCDA techniques. The proposed framework also sheds light on how to use machine learning techniques to free MCDA from strong assumptions.", "published": "2019-06-04T07:06:50Z", "authors": ["Mengzhuo Guo", "Qingpeng Zhang", "Xiuwu Liao", "Frank Youhua Chen", "Daniel Dajun Zeng"], "categories": ["cs.LG", "stat.ML"], "doi": "", "_query": "house price prediction machine learning", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2406.01782v1", "title": "Multi-agent assignment via state augmented reinforcement learning", "abstract": "We address the conflicting requirements of a multi-agent assignment problem through constrained reinforcement learning, emphasizing the inadequacy of standard regularization techniques for this purpose. Instead, we recur to a state augmentation approach in which the oscillation of dual variables is exploited by agents to alternate between tasks. In addition, we coordinate the actions of the multiple agents acting on their local states through these multipliers, which are gossiped through a communication network, eliminating the need to access other agent states. By these means, we propose a distributed multi-agent assignment protocol with theoretical feasibility guarantees that we corroborate in a monitoring numerical experiment.", "published": "2024-06-03T20:56:12Z", "authors": ["Leopoldo Agorio", "Sean Van Alen", "Miguel Calvo-Fullana", "Santiago Paternain", "Juan Andres Bazerque"], "categories": ["eess.SY", "cs.AI", "cs.LG", "cs.MA"], "doi": "", "_query": "house price prediction machine learning", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/1404.2353v1", "title": "Power System Parameters Forecasting Using Hilbert-Huang Transform and Machine Learning", "abstract": "A novel hybrid data-driven approach is developed for forecasting power system parameters with the goal of increasing the efficiency of short-term forecasting studies for non-stationary time-series. The proposed approach is based on mode decomposition and a feature analysis of initial retrospective data using the Hilbert-Huang transform and machine learning algorithms. The random forests and gradient boosting trees learning techniques were examined. The decision tree techniques were used to rank the importance of variables employed in the forecasting models. The Mean Decrease Gini index is employed as an impurity function. The resulting hybrid forecasting models employ the radial basis function neural network and support vector regression. Apart from introduction and references the paper is organized as follows. The section 2 presents the background and the review of several approaches for short-term forecasting of power system parameters. In the third section a hybrid machine learning-based algorithm using Hilbert-Huang transform is developed for short-term forecasting of power system parameters. Fourth section describes the decision tree learning algorithms used for the issue of variables importance. Finally in section six the experimental results in the following electric power problems are presented: active power flow forecasting, electricity price forecasting and for the wind speed and direction forecasting.", "published": "2014-04-09T02:11:17Z", "authors": ["Victor Kurbatsky", "Nikita Tomin", "Vadim Spiryaev", "Paul Leahy", "Denis Sidorov", "Alexei Zhukov"], "categories": ["cs.LG", "stat.ML"], "doi": "", "_query": "house price prediction machine learning", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2211.14666v2", "title": "Synergies between Disentanglement and Sparsity: Generalization and Identifiability in Multi-Task Learning", "abstract": "Although disentangled representations are often said to be beneficial for downstream tasks, current empirical and theoretical understanding is limited. In this work, we provide evidence that disentangled representations coupled with sparse base-predictors improve generalization. In the context of multi-task learning, we prove a new identifiability result that provides conditions under which maximally sparse base-predictors yield disentangled representations. Motivated by this theoretical result, we propose a practical approach to learn disentangled representations based on a sparsity-promoting bi-level optimization problem. Finally, we explore a meta-learning version of this algorithm based on group Lasso multiclass SVM base-predictors, for which we derive a tractable dual formulation. It obtains competitive results on standard few-shot classification benchmarks, while each task is using only a fraction of the learned representations.", "published": "2022-11-26T21:02:09Z", "authors": ["Sébastien Lachapelle", "Tristan Deleu", "Divyat Mahajan", "Ioannis Mitliagkas", "Yoshua Bengio", "Simon Lacoste-Julien", "Quentin Bertrand"], "categories": ["cs.LG", "stat.ML"], "doi": "", "_query": "house price prediction machine learning", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2003.00641v1", "title": "VAE/WGAN-Based Image Representation Learning For Pose-Preserving Seamless Identity Replacement In Facial Images", "abstract": "We present a novel variational generative adversarial network (VGAN) based on Wasserstein loss to learn a latent representation from a face image that is invariant to identity but preserves head-pose information. This facilitates synthesis of a realistic face image with the same head pose as a given input image, but with a different identity. One application of this network is in privacy-sensitive scenarios; after identity replacement in an image, utility, such as head pose, can still be recovered. Extensive experimental validation on synthetic and real human-face image datasets performed under 3 threat scenarios confirms the ability of the proposed network to preserve head pose of the input image, mask the input identity, and synthesize a good-quality realistic face image of a desired identity. We also show that our network can be used to perform pose-preserving identity morphing and identity-preserving pose morphing. The proposed method improves over a recent state-of-the-art method in terms of quantitative metrics as well as synthesized image quality.", "published": "2020-03-02T03:35:59Z", "authors": ["Hiroki Kawai", "Jiawei Chen", "Prakash Ishwar", "Janusz Konrad"], "categories": ["cs.CV", "cs.LG"], "doi": "10.1109/MLSP.2019.8918926", "_query": "house price prediction machine learning", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2404.18909v3", "title": "Sample-Efficient Robust Multi-Agent Reinforcement Learning in the Face of Environmental Uncertainty", "abstract": "To overcome the sim-to-real gap in reinforcement learning (RL), learned policies must maintain robustness against environmental uncertainties. While robust RL has been widely studied in single-agent regimes, in multi-agent environments, the problem remains understudied -- despite the fact that the problems posed by environmental uncertainties are often exacerbated by strategic interactions. This work focuses on learning in distributionally robust Markov games (RMGs), a robust variant of standard Markov games, wherein each agent aims to learn a policy that maximizes its own worst-case performance when the deployed environment deviates within its own prescribed uncertainty set. This results in a set of robust equilibrium strategies for all agents that align with classic notions of game-theoretic equilibria. Assuming a non-adaptive sampling mechanism from a generative model, we propose a sample-efficient model-based algorithm (DRNVI) with finite-sample complexity guarantees for learning robust variants of various notions of game-theoretic equilibria. We also establish an information-theoretic lower bound for solving RMGs, which confirms the near-optimal sample complexity of DRNVI with respect to problem-dependent factors such as the size of the state space, the target accuracy, and the horizon length.", "published": "2024-04-29T17:51:47Z", "authors": ["Laixi Shi", "Eric Mazumdar", "Yuejie Chi", "Adam Wierman"], "categories": ["cs.LG", "cs.MA", "stat.ML"], "doi": "", "_query": "house price prediction machine learning", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2211.01446v2", "title": "Trustworthy Representation Learning via Information Funnels and Bottlenecks", "abstract": "Ensuring trustworthiness in machine learning -- by balancing utility, fairness, and privacy -- remains a critical challenge, particularly in representation learning. In this work, we investigate a family of closely related information-theoretic objectives, including information funnels and bottlenecks, designed to extract invariant representations from data. We introduce the Conditional Privacy Funnel with Side-information (CPFSI), a novel formulation within this family, applicable in both fully and semi-supervised settings. Given the intractability of these objectives, we derive neural-network-based approximations via amortized variational inference. We systematically analyze the trade-offs between utility, invariance, and representation fidelity, offering new insights into the Pareto frontiers of these methods. Our results demonstrate that CPFSI effectively balances these competing objectives and frequently outperforms existing approaches. Furthermore, we show that by intervening on sensitive attributes in CPFSI's predictive posterior enhances fairness while maintaining predictive performance. Finally, we focus on the real-world applicability of these approaches, particularly for learning robust and fair representations from tabular datasets in data scarce-environments -- a modality where these methods are often especially relevant.", "published": "2022-11-02T19:37:55Z", "authors": ["João Machado de Freitas", "Bernhard C. Geiger"], "categories": ["cs.LG"], "doi": "10.1007/s10994-025-06924-9", "_query": "house price prediction machine learning", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2507.02934v1", "title": "Predictive Maintenance Optimization for Smart Vending Machines Using IoT and Machine Learning", "abstract": "The increasing proliferation of vending machines in public and commercial environments has placed a growing emphasis on operational efficiency and customer satisfaction. Traditional maintenance approaches either reactive or time-based preventive are limited in their ability to preempt machine failures, leading to unplanned downtimes and elevated service costs. This research presents a novel predictive maintenance framework tailored for vending machines by leveraging Internet of Things (IoT) sensors and machine learning (ML) algorithms. The proposed system continuously monitors machine components and operating conditions in real time and applies predictive models to forecast failures before they occur. This enables timely maintenance scheduling, minimizing downtime and extending machine lifespan. The framework was validated through simulated fault data and performance evaluation using classification algorithms. Results show a significant improvement in early fault detection and a reduction in redundant service interventions. The findings indicate that predictive maintenance systems, when integrated into vending infrastructure, can transform operational efficiency and service reliability.", "published": "2025-06-26T16:33:58Z", "authors": ["Md. Nisharul Hasan"], "categories": ["cs.LG", "eess.SY"], "doi": "", "_query": "house price prediction machine learning", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2111.10434v3", "title": "Machine Learning for Mechanical Ventilation Control (Extended Abstract)", "abstract": "Mechanical ventilation is one of the most widely used therapies in the ICU. However, despite broad application from anaesthesia to COVID-related life support, many injurious challenges remain. We frame these as a control problem: ventilators must let air in and out of the patient's lungs according to a prescribed trajectory of airway pressure. Industry-standard controllers, based on the PID method, are neither optimal nor robust. Our data-driven approach learns to control an invasive ventilator by training on a simulator itself trained on data collected from the ventilator. This method outperforms popular reinforcement learning algorithms and even controls the physical ventilator more accurately and robustly than PID. These results underscore how effective data-driven methodologies can be for invasive ventilation and suggest that more general forms of ventilation (e.g., non-invasive, adaptive) may also be amenable.", "published": "2021-11-19T20:54:41Z", "authors": ["Daniel Suo", "Naman Agarwal", "Wenhan Xia", "Xinyi Chen", "Udaya Ghai", "Alexander Yu", "Paula Gradu", "Karan Singh", "Cyril Zhang", "Edgar Minasyan", "Julienne LaChance", "Tom Zajdel", "Manuel Schottdorf", "Daniel Cohen", "Elad Hazan"], "categories": ["cs.LG"], "doi": "", "_query": "house price prediction machine learning", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2007.15710v4", "title": "Privacy Enhancing Machine Learning via Removal of Unwanted Dependencies", "abstract": "The rapid rise of IoT and Big Data has facilitated copious data driven applications to enhance our quality of life. However, the omnipresent and all-encompassing nature of the data collection can generate privacy concerns. Hence, there is a strong need to develop techniques that ensure the data serve only the intended purposes, giving users control over the information they share. To this end, this paper studies new variants of supervised and adversarial learning methods, which remove the sensitive information in the data before they are sent out for a particular application. The explored methods optimize privacy preserving feature mappings and predictive models simultaneously in an end-to-end fashion. Additionally, the models are built with an emphasis on placing little computational burden on the user side so that the data can be desensitized on device in a cheap manner. Experimental results on mobile sensing and face datasets demonstrate that our models can successfully maintain the utility performances of predictive models while causing sensitive predictions to perform poorly.", "published": "2020-07-30T19:55:10Z", "authors": ["Mert Al", "Semih Yagli", "Sun-Yuan Kung"], "categories": ["cs.LG", "stat.ML"], "doi": "10.1109/TNNLS.2021.3110831", "_query": "house price prediction machine learning", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2109.08049v2", "title": "A Machine Learning Framework for Automatic Prediction of Human Semen Motility", "abstract": "In this paper, human semen samples from the visem dataset collected by the Simula Research Laboratory are automatically assessed with machine learning methods for their quality in respect to sperm motility. Several regression models are trained to automatically predict the percentage (0 to 100) of progressive, non-progressive, and immotile spermatozoa in a given sample. The video samples are adopted for three different feature extraction methods, in particular custom movement statistics, displacement features, and motility specific statistics have been utilised. Furthermore, four machine learning models, including linear Support Vector Regressor (SVR), Multilayer Perceptron (MLP), Convolutional Neural Network (CNN), and Recurrent Neural Network (RNN), have been trained on the extracted features for the task of automatic motility prediction. Best results for predicting motility are achieved by using the Crocker-Grier algorithm to track sperm cells in an unsupervised way and extracting individual mean squared displacement features for each detected track. These features are then aggregated into a histogram representation applying a Bag-of-Words approach. Finally, a linear SVR is trained on this feature representation. Compared to the best submission of the Medico Multimedia for Medicine challenge, which used the same dataset and splits, the Mean Absolute Error (MAE) could be reduced from 8.83 to 7.31. For the sake of reproducibility, we provide the source code for our experiments on GitHub.", "published": "2021-09-16T15:26:40Z", "authors": ["Sandra Ottl", "Shahin Amiriparian", "Maurice Gerczuk", "Björn Schuller"], "categories": ["cs.LG", "cs.AI", "cs.CV"], "doi": "", "_query": "house price prediction machine learning", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2404.18909v3", "title": "Sample-Efficient Robust Multi-Agent Reinforcement Learning in the Face of Environmental Uncertainty", "abstract": "To overcome the sim-to-real gap in reinforcement learning (RL), learned policies must maintain robustness against environmental uncertainties. While robust RL has been widely studied in single-agent regimes, in multi-agent environments, the problem remains understudied -- despite the fact that the problems posed by environmental uncertainties are often exacerbated by strategic interactions. This work focuses on learning in distributionally robust Markov games (RMGs), a robust variant of standard Markov games, wherein each agent aims to learn a policy that maximizes its own worst-case performance when the deployed environment deviates within its own prescribed uncertainty set. This results in a set of robust equilibrium strategies for all agents that align with classic notions of game-theoretic equilibria. Assuming a non-adaptive sampling mechanism from a generative model, we propose a sample-efficient model-based algorithm (DRNVI) with finite-sample complexity guarantees for learning robust variants of various notions of game-theoretic equilibria. We also establish an information-theoretic lower bound for solving RMGs, which confirms the near-optimal sample complexity of DRNVI with respect to problem-dependent factors such as the size of the state space, the target accuracy, and the horizon length.", "published": "2024-04-29T17:51:47Z", "authors": ["Laixi Shi", "Eric Mazumdar", "Yuejie Chi", "Adam Wierman"], "categories": ["cs.LG", "cs.MA", "stat.ML"], "doi": "", "_query": "house price prediction machine learning", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/1806.00148v1", "title": "Interpreting Deep Learning: The Machine Learning Rorschach Test?", "abstract": "Theoretical understanding of deep learning is one of the most important tasks facing the statistics and machine learning communities. While deep neural networks (DNNs) originated as engineering methods and models of biological networks in neuroscience and psychology, they have quickly become a centerpiece of the machine learning toolbox. Unfortunately, DNN adoption powered by recent successes combined with the open-source nature of the machine learning community, has outpaced our theoretical understanding. We cannot reliably identify when and why DNNs will make mistakes. In some applications like text translation these mistakes may be comical and provide for fun fodder in research talks, a single error can be very costly in tasks like medical imaging. As we utilize DNNs in increasingly sensitive applications, a better understanding of their properties is thus imperative. Recent advances in DNN theory are numerous and include many different sources of intuition, such as learning theory, sparse signal analysis, physics, chemistry, and psychology. An interesting pattern begins to emerge in the breadth of possible interpretations. The seemingly limitless approaches are mostly constrained by the lens with which the mathematical operations are viewed. Ultimately, the interpretation of DNNs appears to mimic a type of Rorschach test --- a psychological test wherein subjects interpret a series of seemingly ambiguous ink-blots. Validation for DNN theory requires a convergence of the literature. We must distinguish between universal results that are invariant to the analysis perspective and those that are specific to a particular network configuration. Simultaneously we must deal with the fact that many standard statistical tools for quantifying generalization or empirically assessing important network features are difficult to apply to DNNs.", "published": "2018-06-01T00:35:32Z", "authors": ["Adam S. Charles"], "categories": ["stat.ML", "cs.LG"], "doi": "", "_query": "house price prediction machine learning", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2106.11053v3", "title": "Leveraging Language to Learn Program Abstractions and Search Heuristics", "abstract": "Inductive program synthesis, or inferring programs from examples of desired behavior, offers a general paradigm for building interpretable, robust, and generalizable machine learning systems. Effective program synthesis depends on two key ingredients: a strong library of functions from which to build programs, and an efficient search strategy for finding programs that solve a given task. We introduce LAPS (Language for Abstraction and Program Search), a technique for using natural language annotations to guide joint learning of libraries and neurally-guided search models for synthesis. When integrated into a state-of-the-art library learning system (DreamCoder), LAPS produces higher-quality libraries and improves search efficiency and generalization on three domains -- string editing, image composition, and abstract reasoning about scenes -- even when no natural language hints are available at test time.", "published": "2021-06-18T15:08:47Z", "authors": ["Catherine Wong", "Kevin Ellis", "Joshua B. Tenenbaum", "Jacob Andreas"], "categories": ["cs.LG", "cs.AI", "cs.CL"], "doi": "", "_query": "house price prediction machine learning", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/0706.3188v1", "title": "A tutorial on conformal prediction", "abstract": "Conformal prediction uses past experience to determine precise levels of confidence in new predictions. Given an error probability $ε$, together with a method that makes a prediction $\\hat{y}$ of a label $y$, it produces a set of labels, typically containing $\\hat{y}$, that also contains $y$ with probability $1-ε$. Conformal prediction can be applied to any method for producing $\\hat{y}$: a nearest-neighbor method, a support-vector machine, ridge regression, etc. Conformal prediction is designed for an on-line setting in which labels are predicted successively, each one being revealed before the next is predicted. The most novel and valuable feature of conformal prediction is that if the successive examples are sampled independently from the same distribution, then the successive predictions will be right $1-ε$ of the time, even though they are based on an accumulating dataset rather than on independent datasets. In addition to the model under which successive examples are sampled independently, other on-line compression models can also use conformal prediction. The widely used Gaussian linear model is one of these. This tutorial presents a self-contained account of the theory of conformal prediction and works through several numerical examples. A more comprehensive treatment of the topic is provided in \"Algorithmic Learning in a Random World\", by Vladimir Vovk, Alex Gammerman, and Glenn Shafer (Springer, 2005).", "published": "2007-06-21T16:40:06Z", "authors": ["Glenn Shafer", "Vladimir Vovk"], "categories": ["cs.LG", "stat.ML"], "doi": "", "_query": "house price prediction machine learning", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2307.02023v1", "title": "Using Random Effects Machine Learning Algorithms to Identify Vulnerability to Depression", "abstract": "Background: Reliable prediction of clinical progression over time can improve the outcomes of depression. Little work has been done integrating various risk factors for depression, to determine the combinations of factors with the greatest utility for identifying which individuals are at the greatest risk. Method: This study demonstrates that data-driven machine learning (ML) methods such as RE-EM (Random Effects/Expectation Maximization) trees and MERF (Mixed Effects Random Forest) can be applied to reliably identify variables that have the greatest utility for classifying subgroups at greatest risk for depression. 185 young adults completed measures of depression risk, including rumination, worry, negative cognitive styles, cognitive and coping flexibilities, and negative life events, along with symptoms of depression. We trained RE-EM trees and MERF algorithms and compared them to traditional linear mixed models (LMMs) predicting depressive symptoms prospectively and concurrently with cross-validation. Results: Our results indicated that the RE-EM tree and MERF methods model complex interactions, identify subgroups of individuals and predict depression severity comparable to LMM. Further, machine learning models determined that brooding, negative life events, negative cognitive styles, and perceived control were the most relevant predictors of future depression levels. Conclusions: Random effects machine learning models have the potential for high clinical utility and can be leveraged for interventions to reduce vulnerability to depression.", "published": "2023-07-05T05:18:30Z", "authors": ["Runa Bhaumik", "Jonathan Stange"], "categories": ["stat.ML", "cs.CY", "cs.LG"], "doi": "", "_query": "house price prediction machine learning", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2508.19381v1", "title": "Towards Quantum Machine Learning for Malicious Code Analysis", "abstract": "Classical machine learning (CML) has been extensively studied for malware classification. With the emergence of quantum computing, quantum machine learning (QML) presents a paradigm-shifting opportunity to improve malware detection, though its application in this domain remains largely unexplored. In this study, we investigate two hybrid quantum-classical models -- a Quantum Multilayer Perceptron (QMLP) and a Quantum Convolutional Neural Network (QCNN), for malware classification. Both models utilize angle embedding to encode malware features into quantum states. QMLP captures complex patterns through full qubit measurement and data re-uploading, while QCNN achieves faster training via quantum convolution and pooling layers that reduce active qubits. We evaluate both models on five widely used malware datasets -- API-Graph, EMBER-Domain, EMBER-Class, AZ-Domain, and AZ-Class, across binary and multiclass classification tasks. Our results show high accuracy for binary classification -- 95-96% on API-Graph, 91-92% on AZ-Domain, and 77% on EMBER-Domain. In multiclass settings, accuracy ranges from 91.6-95.7% on API-Graph, 41.7-93.6% on AZ-Class, and 60.7-88.1% on EMBER-Class. Overall, QMLP outperforms QCNN in complex multiclass tasks, while QCNN offers improved training efficiency at the cost of reduced accuracy.", "published": "2025-08-26T19:20:21Z", "authors": ["Jesus Lopez", "Saeefa Rubaiyet Nowmi", "Viviana Cadena", "Mohammad Saidur Rahman"], "categories": ["cs.LG", "cs.CR"], "doi": "", "_query": "house price prediction machine learning", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/1911.11819v2", "title": "Cryptocurrency Price Prediction and Trading Strategies Using Support Vector Machines", "abstract": "Few assets in financial history have been as notoriously volatile as cryptocurrencies. While the long term outlook for this asset class remains unclear, we are successful in making short term price predictions for several major crypto assets. Using historical data from July 2015 to November 2019, we develop a large number of technical indicators to capture patterns in the cryptocurrency market. We then test various classification methods to forecast short-term future price movements based on these indicators. On both PPV and NPV metrics, our classifiers do well in identifying up and down market moves over the next 1 hour. Beyond evaluating classification accuracy, we also develop a strategy for translating 1-hour-ahead class predictions into trading decisions, along with a backtester that simulates trading in a realistic environment. We find that support vector machines yield the most profitable trading strategies, which outperform the market on average for Bitcoin, Ethereum and Litecoin over the past 22 months, since January 2018.", "published": "2019-11-26T20:26:46Z", "authors": ["David Zhao", "Alessandro Rinaldo", "Christopher Brookins"], "categories": ["q-fin.TR", "q-fin.ST", "stat.ML"], "doi": "", "_query": "house price prediction machine learning", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2010.08168v1", "title": "A Generalizable and Accessible Approach to Machine Learning with Global Satellite Imagery", "abstract": "Combining satellite imagery with machine learning (SIML) has the potential to address global challenges by remotely estimating socioeconomic and environmental conditions in data-poor regions, yet the resource requirements of SIML limit its accessibility and use. We show that a single encoding of satellite imagery can generalize across diverse prediction tasks (e.g. forest cover, house price, road length). Our method achieves accuracy competitive with deep neural networks at orders of magnitude lower computational cost, scales globally, delivers label super-resolution predictions, and facilitates characterizations of uncertainty. Since image encodings are shared across tasks, they can be centrally computed and distributed to unlimited researchers, who need only fit a linear regression to their own ground truth data in order to achieve state-of-the-art SIML performance.", "published": "2020-10-16T05:00:39Z", "authors": ["Esther Rolf", "Jonathan Proctor", "Tamma Carleton", "Ian Bolliger", "Vaishaal Shankar", "Miyabi Ishihara", "Benjamin Recht", "Solomon Hsiang"], "categories": ["cs.LG", "cs.CV"], "doi": "10.1038/s41467-021-24638-z", "_query": "house price prediction machine learning", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/1912.07938v2", "title": "How Personal is Machine Learning Personalization?", "abstract": "Though used extensively, the concept and process of machine learning (ML) personalization have generally received little attention from academics, practitioners, and the general public. We describe the ML approach as relying on the metaphor of the person as a feature vector and contrast this with humanistic views of the person. In light of the recent calls by the IEEE to consider the effects of ML on human well-being, we ask whether ML personalization can be reconciled with these humanistic views of the person, which highlight the importance of moral and social identity. As human behavior increasingly becomes digitized, analyzed, and predicted, to what extent do our subsequent decisions about what to choose, buy, or do, made both by us and others, reflect who we are as persons? This paper first explicates the term personalization by considering ML personalization and highlights its relation to humanistic conceptions of the person, then proposes several dimensions for evaluating the degree of personalization of ML personalized scores. By doing so, we hope to contribute to current debate on the issues of algorithmic bias, transparency, and fairness in machine learning.", "published": "2019-12-17T11:37:19Z", "authors": ["Travis Greene", "Galit Shmueli"], "categories": ["stat.ML", "cs.HC", "cs.LG", "cs.SI"], "doi": "", "_query": "house price prediction machine learning", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2207.08768v3", "title": "Rank-based Decomposable Losses in Machine Learning: A Survey", "abstract": "Recent works have revealed an essential paradigm in designing loss functions that differentiate individual losses vs. aggregate losses. The individual loss measures the quality of the model on a sample, while the aggregate loss combines individual losses/scores over each training sample. Both have a common procedure that aggregates a set of individual values to a single numerical value. The ranking order reflects the most fundamental relation among individual values in designing losses. In addition, decomposability, in which a loss can be decomposed into an ensemble of individual terms, becomes a significant property of organizing losses/scores. This survey provides a systematic and comprehensive review of rank-based decomposable losses in machine learning. Specifically, we provide a new taxonomy of loss functions that follows the perspectives of aggregate loss and individual loss. We identify the aggregator to form such losses, which are examples of set functions. We organize the rank-based decomposable losses into eight categories. Following these categories, we review the literature on rank-based aggregate losses and rank-based individual losses. We describe general formulas for these losses and connect them with existing research topics. We also suggest future research directions spanning unexplored, remaining, and emerging issues in rank-based decomposable losses.", "published": "2022-07-18T17:21:01Z", "authors": ["Shu Hu", "Xin Wang", "Siwei Lyu"], "categories": ["cs.LG"], "doi": "", "_query": "house price prediction machine learning", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2005.00872v1", "title": "How deep the machine learning can be", "abstract": "Today we live in the age of artificial intelligence and machine learning; from small startups to HW or SW giants, everyone wants to build machine intelligence chips, applications. The task, however, is hard: not only because of the size of the problem: the technology one can utilize (and the paradigm it is based upon) strongly degrades the chances to succeed efficiently. Today the single-processor performance practically reached the limits the laws of nature enable. The only feasible way to achieve the needed high computing performance seems to be parallelizing many sequentially working units. The laws of the (massively) parallelized computing, however, are different from those experienced in connection with assembling and utilizing systems comprising just-a-few single processors. As machine learning is mostly based on the conventional computing (processors), we scrutinize the (known, but somewhat faded) laws of the parallel computing, concerning AI. This paper attempts to review some of the caveats, especially concerning scaling the computing performance of the AI solutions.", "published": "2020-05-02T16:06:31Z", "authors": ["János Végh"], "categories": ["cs.DC", "cs.LG"], "doi": "", "_query": "house price prediction machine learning", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/1603.07292v1", "title": "Debugging Machine Learning Tasks", "abstract": "Unlike traditional programs (such as operating systems or word processors) which have large amounts of code, machine learning tasks use programs with relatively small amounts of code (written in machine learning libraries), but voluminous amounts of data. Just like developers of traditional programs debug errors in their code, developers of machine learning tasks debug and fix errors in their data. However, algorithms and tools for debugging and fixing errors in data are less common, when compared to their counterparts for detecting and fixing errors in code. In this paper, we consider classification tasks where errors in training data lead to misclassifications in test points, and propose an automated method to find the root causes of such misclassifications. Our root cause analysis is based on Pearl's theory of causation, and uses Pearl's PS (Probability of Sufficiency) as a scoring metric. Our implementation, Psi, encodes the computation of PS as a probabilistic program, and uses recent work on probabilistic programs and transformations on probabilistic programs (along with gray-box models of machine learning algorithms) to efficiently compute PS. Psi is able to identify root causes of data errors in interesting data sets.", "published": "2016-03-23T18:30:37Z", "authors": ["Aleksandar Chakarov", "Aditya Nori", "Sriram Rajamani", "Shayak Sen", "Deepak Vijaykeerthy"], "categories": ["cs.LG", "cs.AI", "cs.PL", "stat.ML"], "doi": "", "_query": "house price prediction machine learning", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2311.09128v1", "title": "Fast Detection of Phase Transitions with Multi-Task Learning-by-Confusion", "abstract": "Machine learning has been successfully used to study phase transitions. One of the most popular approaches to identifying critical points from data without prior knowledge of the underlying phases is the learning-by-confusion scheme. As input, it requires system samples drawn from a grid of the parameter whose change is associated with potential phase transitions. Up to now, the scheme required training a distinct binary classifier for each possible splitting of the grid into two sides, resulting in a computational cost that scales linearly with the number of grid points. In this work, we propose and showcase an alternative implementation that only requires the training of a single multi-class classifier. Ideally, such multi-task learning eliminates the scaling with respect to the number of grid points. In applications to the Ising model and an image dataset generated with Stable Diffusion, we find significant speedups that closely correspond to the ideal case, with only minor deviations.", "published": "2023-11-15T17:17:49Z", "authors": ["Julian Arnold", "Frank Schäfer", "Niels Lörch"], "categories": ["cs.LG", "cond-mat.dis-nn", "cond-mat.stat-mech"], "doi": "", "_query": "house price prediction machine learning", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/1708.04649v1", "title": "Machine Learning for Survival Analysis: A Survey", "abstract": "Accurately predicting the time of occurrence of an event of interest is a critical problem in longitudinal data analysis. One of the main challenges in this context is the presence of instances whose event outcomes become unobservable after a certain time point or when some instances do not experience any event during the monitoring period. Such a phenomenon is called censoring which can be effectively handled using survival analysis techniques. Traditionally, statistical approaches have been widely developed in the literature to overcome this censoring issue. In addition, many machine learning algorithms are adapted to effectively handle survival data and tackle other challenging problems that arise in real-world data. In this survey, we provide a comprehensive and structured review of the representative statistical methods along with the machine learning techniques used in survival analysis and provide a detailed taxonomy of the existing methods. We also discuss several topics that are closely related to survival analysis and illustrate several successful applications in various real-world application domains. We hope that this paper will provide a more thorough understanding of the recent advances in survival analysis and offer some guidelines on applying these approaches to solve new problems that arise in applications with censored data.", "published": "2017-08-15T19:03:36Z", "authors": ["Ping Wang", "Yan Li", "Chandan K. Reddy"], "categories": ["cs.LG", "stat.ML"], "doi": "", "_query": "house price prediction machine learning", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2403.10250v2", "title": "Interpretable Machine Learning for Survival Analysis", "abstract": "With the spread and rapid advancement of black box machine learning models, the field of interpretable machine learning (IML) or explainable artificial intelligence (XAI) has become increasingly important over the last decade. This is particularly relevant for survival analysis, where the adoption of IML techniques promotes transparency, accountability and fairness in sensitive areas, such as clinical decision making processes, the development of targeted therapies, interventions or in other medical or healthcare related contexts. More specifically, explainability can uncover a survival model's potential biases and limitations and provide more mathematically sound ways to understand how and which features are influential for prediction or constitute risk factors. However, the lack of readily available IML methods may have deterred medical practitioners and policy makers in public health from leveraging the full potential of machine learning for predicting time-to-event data. We present a comprehensive review of the limited existing amount of work on IML methods for survival analysis within the context of the general IML taxonomy. In addition, we formally detail how commonly used IML methods, such as such as individual conditional expectation (ICE), partial dependence plots (PDP), accumulated local effects (ALE), different feature importance measures or Friedman's H-interaction statistics can be adapted to survival outcomes. An application of several IML methods to real data on data on under-5 year mortality of Ghanaian children from the Demographic and Health Surveys (DHS) Program serves as a tutorial or guide for researchers, on how to utilize the techniques in practice to facilitate understanding of model decisions or predictions.", "published": "2024-03-15T12:38:00Z", "authors": ["Sophie Hanna Langbein", "Mateusz Krzyziński", "Mikołaj Spytek", "Hubert Baniecki", "Przemysław Biecek", "Marvin N. Wright"], "categories": ["stat.ML", "cs.LG", "stat.ME"], "doi": "10.1002/bimj.70089", "_query": "house price prediction machine learning", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2211.16736v1", "title": "Understanding transit ridership in an equity context through a comparison of statistical and machine learning algorithms", "abstract": "Building an accurate model of travel behaviour based on individuals' characteristics and built environment attributes is of importance for policy-making and transportation planning. Recent experiments with big data and Machine Learning (ML) algorithms toward a better travel behaviour analysis have mainly overlooked socially disadvantaged groups. Accordingly, in this study, we explore the travel behaviour responses of low-income individuals to transit investments in the Greater Toronto and Hamilton Area, Canada, using statistical and ML models. We first investigate how the model choice affects the prediction of transit use by the low-income group. This step includes comparing the predictive performance of traditional and ML algorithms and then evaluating a transit investment policy by contrasting the predicted activities and the spatial distribution of transit trips generated by vulnerable households after improving accessibility. We also empirically investigate the proposed transit investment by each algorithm and compare it with the city of Brampton's future transportation plan. While, unsurprisingly, the ML algorithms outperform classical models, there are still doubts about using them due to interpretability concerns. Hence, we adopt recent local and global model-agnostic interpretation tools to interpret how the model arrives at its predictions. Our findings reveal the great potential of ML algorithms for enhanced travel behaviour predictions for low-income strata without considerably sacrificing interpretability.", "published": "2022-11-30T04:48:58Z", "authors": ["Elnaz Yousefzadeh Barri", "Steven Farber", "Hadi Jahanshahi", "Eda Beyazit"], "categories": ["cs.LG", "cs.CY", "stat.AP"], "doi": "10.1016/j.jtrangeo.2022.103482", "_query": "house price prediction machine learning", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2412.13665v1", "title": "Time-Reversible Bridges of Data with Machine Learning", "abstract": "The analysis of dynamical systems is a fundamental tool in the natural sciences and engineering. It is used to understand the evolution of systems as large as entire galaxies and as small as individual molecules. With predefined conditions on the evolution of dy-namical systems, the underlying differential equations have to fulfill specific constraints in time and space. This class of problems is known as boundary value problems. This thesis presents novel approaches to learn time-reversible deterministic and stochastic dynamics constrained by initial and final conditions. The dynamics are inferred by machine learning algorithms from observed data, which is in contrast to the traditional approach of solving differential equations by numerical integration. The work in this thesis examines a set of problems of increasing difficulty each of which is concerned with learning a different aspect of the dynamics. Initially, we consider learning deterministic dynamics from ground truth solutions which are constrained by deterministic boundary conditions. Secondly, we study a boundary value problem in discrete state spaces, where the forward dynamics follow a stochastic jump process and the boundary conditions are discrete probability distributions. In particular, the stochastic dynamics of a specific jump process, the Ehrenfest process, is considered and the reverse time dynamics are inferred with machine learning. Finally, we investigate the problem of inferring the dynamics of a continuous-time stochastic process between two probability distributions without any reference information. Here, we propose a novel criterion to learn time-reversible dynamics of two stochastic processes to solve the Schrödinger Bridge Problem.", "published": "2024-12-18T09:47:19Z", "authors": ["Ludwig Winkler"], "categories": ["stat.ML", "cs.LG"], "doi": "", "_query": "house price prediction machine learning", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2411.11099v1", "title": "Mitigating Relative Over-Generalization in Multi-Agent Reinforcement Learning", "abstract": "In decentralized multi-agent reinforcement learning, agents learning in isolation can lead to relative over-generalization (RO), where optimal joint actions are undervalued in favor of suboptimal ones. This hinders effective coordination in cooperative tasks, as agents tend to choose actions that are individually rational but collectively suboptimal. To address this issue, we introduce MaxMax Q-Learning (MMQ), which employs an iterative process of sampling and evaluating potential next states, selecting those with maximal Q-values for learning. This approach refines approximations of ideal state transitions, aligning more closely with the optimal joint policy of collaborating agents. We provide theoretical analysis supporting MMQ's potential and present empirical evaluations across various environments susceptible to RO. Our results demonstrate that MMQ frequently outperforms existing baselines, exhibiting enhanced convergence and sample efficiency.", "published": "2024-11-17T15:00:39Z", "authors": ["Ting Zhu", "Yue Jin", "Jeremie Houssineau", "Giovanni Montana"], "categories": ["cs.LG", "cs.AI", "stat.ML"], "doi": "", "_query": "house price prediction machine learning", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2006.07886v3", "title": "On Disentangled Representations Learned From Correlated Data", "abstract": "The focus of disentanglement approaches has been on identifying independent factors of variation in data. However, the causal variables underlying real-world observations are often not statistically independent. In this work, we bridge the gap to real-world scenarios by analyzing the behavior of the most prominent disentanglement approaches on correlated data in a large-scale empirical study (including 4260 models). We show and quantify that systematically induced correlations in the dataset are being learned and reflected in the latent representations, which has implications for downstream applications of disentanglement such as fairness. We also demonstrate how to resolve these latent correlations, either using weak supervision during training or by post-hoc correcting a pre-trained model with a small number of labels.", "published": "2020-06-14T12:47:34Z", "authors": ["Frederik Träuble", "Elliot Creager", "Niki Kilbertus", "Francesco Locatello", "Andrea Dittadi", "Anirudh Goyal", "Bernhard Schölkopf", "Stefan Bauer"], "categories": ["cs.LG", "stat.ML"], "doi": "", "_query": "house price prediction machine learning", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2204.13819v1", "title": "Automatic Machine Learning for Multi-Receiver CNN Technology Classifiers", "abstract": "Convolutional Neural Networks (CNNs) are one of the most studied family of deep learning models for signal classification, including modulation, technology, detection, and identification. In this work, we focus on technology classification based on raw I/Q samples collected from multiple synchronized receivers. As an example use case, we study protocol identification of Wi-Fi, LTE-LAA, and 5G NR-U technologies that coexist over the 5 GHz Unlicensed National Information Infrastructure (U-NII) bands. Designing and training accurate CNN classifiers involve significant time and effort that goes into fine-tuning a model's architectural settings and determining the appropriate hyperparameter configurations, such as learning rate and batch size. We tackle the former by defining architectural settings themselves as hyperparameters. We attempt to automatically optimize these architectural parameters, along with other preprocessing (e.g., number of I/Q samples within each classifier input) and learning hyperparameters, by forming a Hyperparameter Optimization (HyperOpt) problem, which we solve in a near-optimal fashion using the Hyperband algorithm. The resulting near-optimal CNN (OCNN) classifier is then used to study classification accuracy for OTA as well as simulations datasets, considering various SNR values. We show that the number of receivers to construct multi-channel inputs for CNNs should be defined as a preprocessing hyperparameter to be optimized via Hyperband. OTA results reveal that our OCNN classifiers improve classification accuracy by 24.58% compared to manually tuned CNNs. We also study the effect of min-max normalization of I/Q samples within each classifier's input on generalization accuracy over simulated datasets with SNRs other than training set's SNR and show an average of 108.05% improvement when I/Q samples are normalized.", "published": "2022-04-28T23:41:38Z", "authors": ["Amir-Hossein Yazdani-Abyaneh", "Marwan Krunz"], "categories": ["cs.LG", "cs.NI", "eess.SP"], "doi": "10.1145/3522783.3529524", "_query": "house price prediction machine learning", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2207.00108v2", "title": "Discrimination in machine learning algorithms", "abstract": "Machine learning algorithms are routinely used for business decisions that may directly affect individuals, for example, because a credit scoring algorithm refuses them a loan. It is then relevant from an ethical (and legal) point of view to ensure that these algorithms do not discriminate based on sensitive attributes (like sex or race), which may occur unwittingly and unknowingly by the operator and the management. Statistical tools and methods are then required to detect and eliminate such potential biases.", "published": "2022-06-30T21:35:42Z", "authors": ["Roberta Pappadà", "Francesco Pauli"], "categories": ["stat.ML", "cs.CY", "cs.LG"], "doi": "", "_query": "house price prediction machine learning", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2305.18687v2", "title": "Graph-based Multi-ODE Neural Networks for Spatio-Temporal Traffic Forecasting", "abstract": "There is a recent surge in the development of spatio-temporal forecasting models in the transportation domain. Long-range traffic forecasting, however, remains a challenging task due to the intricate and extensive spatio-temporal correlations observed in traffic networks. Current works primarily rely on road networks with graph structures and learn representations using graph neural networks (GNNs), but this approach suffers from over-smoothing problem in deep architectures. To tackle this problem, recent methods introduced the combination of GNNs with residual connections or neural ordinary differential equations (ODE). However, current graph ODE models face two key limitations in feature extraction: (1) they lean towards global temporal patterns, overlooking local patterns that are important for unexpected events; and (2) they lack dynamic semantic edges in their architectural design. In this paper, we propose a novel architecture called Graph-based Multi-ODE Neural Networks (GRAM-ODE) which is designed with multiple connective ODE-GNN modules to learn better representations by capturing different views of complex local and global dynamic spatio-temporal dependencies. We also add some techniques like shared weights and divergence constraints into the intermediate layers of distinct ODE-GNN modules to further improve their communication towards the forecasting task. Our extensive set of experiments conducted on six real-world datasets demonstrate the superior performance of GRAM-ODE compared with state-of-the-art baselines as well as the contribution of different components to the overall performance. The code is available at https://github.com/zbliu98/GRAM-ODE", "published": "2023-05-30T02:10:42Z", "authors": ["Zibo Liu", "Parshin Shojaee", "Chandan K Reddy"], "categories": ["cs.LG", "cs.AI"], "doi": "", "_query": "house price prediction machine learning", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2006.11385v2", "title": "Quantile-Quantile Embedding for Distribution Transformation and Manifold Embedding with Ability to Choose the Embedding Distribution", "abstract": "We propose a new embedding method, named Quantile-Quantile Embedding (QQE), for distribution transformation and manifold embedding with the ability to choose the embedding distribution. QQE, which uses the concept of quantile-quantile plot from visual statistical tests, can transform the distribution of data to any theoretical desired distribution or empirical reference sample. Moreover, QQE gives the user a choice of embedding distribution in embedding the manifold of data into the low dimensional embedding space. It can also be used for modifying the embedding distribution of other dimensionality reduction methods, such as PCA, t-SNE, and deep metric learning, for better representation or visualization of data. We propose QQE in both unsupervised and supervised forms. QQE can also transform a distribution to either an exact reference distribution or its shape. We show that QQE allows for better discrimination of classes in some cases. Our experiments on different synthetic and image datasets show the effectiveness of the proposed embedding method.", "published": "2020-06-19T21:09:09Z", "authors": ["Benyamin Ghojogh", "Fakhri Karray", "Mark Crowley"], "categories": ["stat.ML", "cs.CV", "cs.LG", "stat.CO"], "doi": "10.1016/j.mlwa.2021.100088", "_query": "house price prediction machine learning", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/1911.00348v7", "title": "Hierarchical Expert Networks for Meta-Learning", "abstract": "The goal of meta-learning is to train a model on a variety of learning tasks, such that it can adapt to new problems within only a few iterations. Here we propose a principled information-theoretic model that optimally partitions the underlying problem space such that specialized expert decision-makers solve the resulting sub-problems. To drive this specialization we impose the same kind of information processing constraints both on the partitioning and the expert decision-makers. We argue that this specialization leads to efficient adaptation to new tasks. To demonstrate the generality of our approach we evaluate three meta-learning domains: image classification, regression, and reinforcement learning.", "published": "2019-10-31T06:23:52Z", "authors": ["Heinke Hihn", "Daniel A. Braun"], "categories": ["stat.ML", "cs.LG"], "doi": "", "_query": "house price prediction machine learning", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2409.04106v1", "title": "CryptoAnalytics: Cryptocoins Price Forecasting with Machine Learning Techniques", "abstract": "This paper introduces CryptoAnalytics, a software toolkit for cryptocoins price forecasting with machine learning (ML) techniques. Cryptocoins are tradable digital assets exchanged for specific trading prices. While history has shown the extreme volatility of such trading prices, the ability to efficiently model and forecast the time series resulting from the exchange price volatility remains an open research challenge. Good results can been achieved with state-of-the-art ML techniques, including Gradient-Boosting Machines (GBMs) and Recurrent Neural Networks (RNNs). CryptoAnalytics is a software toolkit to easily train these models and make inference on up-to-date cryptocoin trading price data, with facilities to fetch datasets from one of the main leading aggregator websites, i.e., CoinMarketCap, train models and infer the future trends. This software is implemented in Python. It relies on PyTorch for the implementation of RNNs (LSTM and GRU), while for GBMs, it leverages on XgBoost, LightGBM and CatBoost.", "published": "2024-09-06T08:20:02Z", "authors": ["Pasquale De Rosa", "Pascal Felber", "Valerio Schiavoni"], "categories": ["cs.CE"], "doi": "10.1016/j.softx.2024.101663", "_query": "house price prediction machine learning", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/1801.04016v1", "title": "Theoretical Impediments to Machine Learning With Seven Sparks from the Causal Revolution", "abstract": "Current machine learning systems operate, almost exclusively, in a statistical, or model-free mode, which entails severe theoretical limits on their power and performance. Such systems cannot reason about interventions and retrospection and, therefore, cannot serve as the basis for strong AI. To achieve human level intelligence, learning machines need the guidance of a model of reality, similar to the ones used in causal inference tasks. To demonstrate the essential role of such models, I will present a summary of seven tasks which are beyond reach of current machine learning systems and which have been accomplished using the tools of causal modeling.", "published": "2018-01-11T23:37:48Z", "authors": ["Judea Pearl"], "categories": ["cs.LG", "cs.AI", "stat.ML"], "doi": "", "_query": "house price prediction machine learning", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2007.06849v1", "title": "Additively Homomorphical Encryption based Deep Neural Network for Asymmetrically Collaborative Machine Learning", "abstract": "The financial sector presents many opportunities to apply various machine learning techniques. Centralized machine learning creates a constraint which limits further applications in finance sectors. Data privacy is a fundamental challenge for a variety of finance and insurance applications that account on learning a model across different sections. In this paper, we define a new practical scheme of collaborative machine learning that one party owns data, but another party owns labels only, and term this \\textbf{Asymmetrically Collaborative Machine Learning}. For this scheme, we propose a novel privacy-preserving architecture where two parties can collaboratively train a deep learning model efficiently while preserving the privacy of each party's data. More specifically, we decompose the forward propagation and backpropagation of the neural network into four different steps and propose a novel protocol to handle information leakage in these steps. Our extensive experiments on different datasets demonstrate not only stable training without accuracy loss, but also more than 100 times speedup compared with the state-of-the-art system.", "published": "2020-07-14T06:43:25Z", "authors": ["Yifei Zhang", "Hao Zhu"], "categories": ["cs.LG", "stat.ML"], "doi": "", "_query": "house price prediction machine learning", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2410.02541v3", "title": "Fair Decentralized Learning", "abstract": "Decentralized learning (DL) is an emerging approach that enables nodes to collaboratively train a machine learning model without sharing raw data. In many application domains, such as healthcare, this approach faces challenges due to the high level of heterogeneity in the training data's feature space. Such feature heterogeneity lowers model utility and negatively impacts fairness, particularly for nodes with under-represented training data. In this paper, we introduce \\textsc{Facade}, a clustering-based DL algorithm specifically designed for fair model training when the training data exhibits several distinct features. The challenge of \\textsc{Facade} is to assign nodes to clusters, one for each feature, based on the similarity in the features of their local data, without requiring individual nodes to know apriori which cluster they belong to. \\textsc{Facade} (1) dynamically assigns nodes to their appropriate clusters over time, and (2) enables nodes to collaboratively train a specialized model for each cluster in a fully decentralized manner. We theoretically prove the convergence of \\textsc{Facade}, implement our algorithm, and compare it against three state-of-the-art baselines. Our experimental results on three datasets demonstrate the superiority of our approach in terms of model accuracy and fairness compared to all three competitors. Compared to the best-performing baseline, \\textsc{Facade} on the CIFAR-10 dataset also reduces communication costs by 32.3\\% to reach a target accuracy when cluster sizes are imbalanced.", "published": "2024-10-03T14:45:23Z", "authors": ["Sayan Biswas", "Anne-Marie Kermarrec", "Rishi Sharma", "Thibaud Trinca", "Martijn de Vos"], "categories": ["cs.LG", "cs.DC"], "doi": "10.1109/SaTML64287.2025.00045", "_query": "house price prediction machine learning", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2602.22527v1", "title": "Predicting Tennis Serve directions with Machine Learning", "abstract": "Serves, especially first serves, are very important in professional tennis. Servers choose their serve directions strategically to maximize their winning chances while trying to be unpredictable. On the other hand, returners try to predict serve directions to make good returns. The mind game between servers and returners is an important part of decision-making in professional tennis matches. To help understand the players' serve decisions, we have developed a machine learning method for predicting professional tennis players' first serve directions. Through feature engineering, our method achieves an average prediction accuracy of around 49\\% for male players and 44\\% for female players. Our analysis provides some evidence that top professional players use a mixed-strategy model in serving decisions and that fatigue might be a factor in choosing serve directions. Our analysis also suggests that contextual information is perhaps more important for returners' anticipatory reactions than previously thought.", "published": "2026-02-26T01:56:40Z", "authors": ["Ying Zhu", "Ruthuparna Naikar"], "categories": ["cs.LG", "cs.AI"], "doi": "10.1007/978-3-031-27527-2_7", "_query": "house price prediction machine learning", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2208.01587v4", "title": "Learning to Incorporate Texture Saliency Adaptive Attention to Image Cartoonization", "abstract": "Image cartoonization is recently dominated by generative adversarial networks (GANs) from the perspective of unsupervised image-to-image translation, in which an inherent challenge is to precisely capture and sufficiently transfer characteristic cartoon styles (e.g., clear edges, smooth color shading, abstract fine structures, etc.). Existing advanced models try to enhance cartoonization effect by learning to promote edges adversarially, introducing style transfer loss, or learning to align style from multiple representation space. This paper demonstrates that more distinct and vivid cartoonization effect could be easily achieved with only basic adversarial loss. Observing that cartoon style is more evident in cartoon-texture-salient local image regions, we build a region-level adversarial learning branch in parallel with the normal image-level one, which constrains adversarial learning on cartoon-texture-salient local patches for better perceiving and transferring cartoon texture features. To this end, a novel cartoon-texture-saliency-sampler (CTSS) module is proposed to dynamically sample cartoon-texture-salient patches from training data. With extensive experiments, we demonstrate that texture saliency adaptive attention in adversarial learning, as a missing ingredient of related methods in image cartoonization, is of significant importance in facilitating and enhancing image cartoon stylization, especially for high-resolution input pictures.", "published": "2022-08-02T16:45:55Z", "authors": ["Xiang Gao", "Yuqi Zhang", "Yingjie Tian"], "categories": ["cs.CV"], "doi": "", "_query": "house price prediction machine learning", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2309.13781v4", "title": "Explainable Machine Learning for ICU Readmission Prediction", "abstract": "The intensive care unit (ICU) comprises a complex hospital environment, where decisions made by clinicians have a high level of risk for the patients' lives. A comprehensive care pathway must then be followed to reduce p complications. Uncertain, competing and unplanned aspects within this environment increase the difficulty in uniformly implementing the care pathway. Readmission contributes to this pathway's difficulty, occurring when patients are admitted again to the ICU in a short timeframe, resulting in high mortality rates and high resource utilisation. Several works have tried to predict readmission through patients' medical information. Although they have some level of success while predicting readmission, those works do not properly assess, characterise and understand readmission prediction. This work proposes a standardised and explainable machine learning pipeline to model patient readmission on a multicentric database (i.e., the eICU cohort with 166,355 patients, 200,859 admissions and 6,021 readmissions) while validating it on monocentric (i.e., the MIMIC IV cohort with 382,278 patients, 523,740 admissions and 5,984 readmissions) and multicentric settings. Our machine learning pipeline achieved predictive performance in terms of the area of the receiver operating characteristic curve (AUC) up to 0.7 with a Random Forest classification model, yielding an overall good calibration and consistency on validation sets. From explanations provided by the constructed models, we could also derive a set of insightful conclusions, primarily on variables related to vital signs and blood tests (e.g., albumin, blood urea nitrogen and hemoglobin levels), demographics (e.g., age, and admission height and weight), and ICU-associated variables (e.g., unit type). These insights provide an invaluable source of information during clinicians' decision-making while discharging ICU patients.", "published": "2023-09-25T00:16:43Z", "authors": ["Alex G. C. de Sá", "Daniel Gould", "Anna Fedyukova", "Mitchell Nicholas", "Lucy Dockrell", "Calvin Fletcher", "David Pilcher", "Daniel Capurro", "David B. Ascher", "Khaled El-Khawas", "Douglas E. V. Pires"], "categories": ["cs.LG", "cs.AI"], "doi": "", "_query": "house price prediction machine learning", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2010.02576v2", "title": "A Note on High-Probability versus In-Expectation Guarantees of Generalization Bounds in Machine Learning", "abstract": "Statistical machine learning theory often tries to give generalization guarantees of machine learning models. Those models naturally underlie some fluctuation, as they are based on a data sample. If we were unlucky, and gathered a sample that is not representative of the underlying distribution, one cannot expect to construct a reliable machine learning model. Following that, statements made about the performance of machine learning models have to take the sampling process into account. The two common approaches for that are to generate statements that hold either in high-probability, or in-expectation, over the random sampling process. In this short note we show how one may transform one statement to another. As a technical novelty we address the case of unbounded loss function, where we use a fairly new assumption, called the witness condition.", "published": "2020-10-06T09:41:35Z", "authors": ["Alexander Mey"], "categories": ["cs.LG", "stat.ML"], "doi": "", "_query": "house price prediction machine learning", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2004.14545v2", "title": "Explainable Deep Learning: A Field Guide for the Uninitiated", "abstract": "Deep neural networks (DNNs) have become a proven and indispensable machine learning tool. As a black-box model, it remains difficult to diagnose what aspects of the model's input drive the decisions of a DNN. In countless real-world domains, from legislation and law enforcement to healthcare, such diagnosis is essential to ensure that DNN decisions are driven by aspects appropriate in the context of its use. The development of methods and studies enabling the explanation of a DNN's decisions has thus blossomed into an active, broad area of research. A practitioner wanting to study explainable deep learning may be intimidated by the plethora of orthogonal directions the field has taken. This complexity is further exacerbated by competing definitions of what it means ``to explain'' the actions of a DNN and to evaluate an approach's ``ability to explain''. This article offers a field guide to explore the space of explainable deep learning aimed at those uninitiated in the field. The field guide: i) Introduces three simple dimensions defining the space of foundational methods that contribute to explainable deep learning, ii) discusses the evaluations for model explanations, iii) places explainability in the context of other related deep learning research areas, and iv) finally elaborates on user-oriented explanation designing and potential future directions on explainable deep learning. We hope the guide is used as an easy-to-digest starting point for those just embarking on research in this field.", "published": "2020-04-30T02:09:02Z", "authors": ["Gabrielle Ras", "Ning Xie", "Marcel van Gerven", "Derek Doran"], "categories": ["cs.LG", "cs.AI", "stat.ML"], "doi": "", "_query": "house price prediction machine learning", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2306.09624v1", "title": "Power-law Dynamic arising from machine learning", "abstract": "We study a kind of new SDE that was arisen from the research on optimization in machine learning, we call it power-law dynamic because its stationary distribution cannot have sub-Gaussian tail and obeys power-law. We prove that the power-law dynamic is ergodic with unique stationary distribution, provided the learning rate is small enough. We investigate its first exist time. In particular, we compare the exit times of the (continuous) power-law dynamic and its discretization. The comparison can help guide machine learning algorithm.", "published": "2023-06-16T04:47:01Z", "authors": ["Wei Chen", "Weitao Du", "Zhi-Ming Ma", "Qi Meng"], "categories": ["stat.ML", "cs.LG"], "doi": "", "_query": "house price prediction machine learning", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/1709.03768v3", "title": "Learning with Bounded Instance- and Label-dependent Label Noise", "abstract": "Instance- and Label-dependent label Noise (ILN) widely exists in real-world datasets but has been rarely studied. In this paper, we focus on Bounded Instance- and Label-dependent label Noise (BILN), a particular case of ILN where the label noise rates -- the probabilities that the true labels of examples flip into the corrupted ones -- have upper bound less than $1$. Specifically, we introduce the concept of distilled examples, i.e. examples whose labels are identical with the labels assigned for them by the Bayes optimal classifier, and prove that under certain conditions classifiers learnt on distilled examples will converge to the Bayes optimal classifier. Inspired by the idea of learning with distilled examples, we then propose a learning algorithm with theoretical guarantees for its robustness to BILN. At last, empirical evaluations on both synthetic and real-world datasets show effectiveness of our algorithm in learning with BILN.", "published": "2017-09-12T09:59:59Z", "authors": ["Jiacheng Cheng", "Tongliang Liu", "Kotagiri Ramamohanarao", "Dacheng Tao"], "categories": ["stat.ML", "cs.LG"], "doi": "", "_query": "house price prediction machine learning", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2208.11512v2", "title": "FedOS: using open-set learning to stabilize training in federated learning", "abstract": "Federated Learning is a recent approach to train statistical models on distributed datasets without violating privacy constraints. The data locality principle is preserved by sharing the model instead of the data between clients and the server. This brings many advantages but also poses new challenges. In this report, we explore this new research area and perform several experiments to deepen our understanding of what these challenges are and how different problem settings affect the performance of the final model. Finally, we present a novel approach to one of these challenges and compare it to other methods found in literature.", "published": "2022-08-22T19:53:39Z", "authors": ["Mohamad Mohamad", "Julian Neubert", "Juan Segundo Argayo"], "categories": ["stat.ML", "cs.LG"], "doi": "", "_query": "house price prediction machine learning", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2309.10010v1", "title": "Machine Learning Approaches to Predict and Detect Early-Onset of Digital Dermatitis in Dairy Cows using Sensor Data", "abstract": "The aim of this study was to employ machine learning algorithms based on sensor behavior data for (1) early-onset detection of digital dermatitis (DD); and (2) DD prediction in dairy cows. With the ultimate goal to set-up early warning tools for DD prediction, which would than allow a better monitoring and management of DD under commercial settings, resulting in a decrease of DD prevalence and severity, while improving animal welfare. A machine learning model that is capable of predicting and detecting digital dermatitis in cows housed under free-stall conditions based on behavior sensor data has been purposed and tested in this exploratory study. The model for DD detection on day 0 of the appearance of the clinical signs has reached an accuracy of 79%, while the model for prediction of DD 2 days prior to the appearance of the first clinical signs has reached an accuracy of 64%. The proposed machine learning models could help to develop a real-time automated tool for monitoring and diagnostic of DD in lactating dairy cows, based on behavior sensor data under conventional dairy environments. Results showed that alterations in behavioral patterns at individual levels can be used as inputs in an early warning system for herd management in order to detect variances in health of individual cows.", "published": "2023-09-18T06:08:26Z", "authors": ["Jennifer Magana", "Dinu Gavojdian", "Yakir Menachem", "Teddy Lazebnik", "Anna Zamansky", "Amber Adams-Progar"], "categories": ["cs.LG", "eess.SP"], "doi": "", "_query": "house price prediction machine learning", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/1808.00196v1", "title": "Manifold: A Model-Agnostic Framework for Interpretation and Diagnosis of Machine Learning Models", "abstract": "Interpretation and diagnosis of machine learning models have gained renewed interest in recent years with breakthroughs in new approaches. We present Manifold, a framework that utilizes visual analysis techniques to support interpretation, debugging, and comparison of machine learning models in a more transparent and interactive manner. Conventional techniques usually focus on visualizing the internal logic of a specific model type (i.e., deep neural networks), lacking the ability to extend to a more complex scenario where different model types are integrated. To this end, Manifold is designed as a generic framework that does not rely on or access the internal logic of the model and solely observes the input (i.e., instances or features) and the output (i.e., the predicted result and probability distribution). We describe the workflow of Manifold as an iterative process consisting of three major phases that are commonly involved in the model development and diagnosis process: inspection (hypothesis), explanation (reasoning), and refinement (verification). The visual components supporting these tasks include a scatterplot-based visual summary that overviews the models' outcome and a customizable tabular view that reveals feature discrimination. We demonstrate current applications of the framework on the classification and regression tasks and discuss other potential machine learning use scenarios where Manifold can be applied.", "published": "2018-08-01T07:04:08Z", "authors": ["Jiawei Zhang", "Yang Wang", "Piero Molino", "Lezhi Li", "David S. Ebert"], "categories": ["cs.LG", "cs.HC", "stat.ML"], "doi": "10.1109/TVCG.2018.2864499", "_query": "house price prediction machine learning", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2110.08265v4", "title": "Knowledge-driven Active Learning", "abstract": "The deployment of Deep Learning (DL) models is still precluded in those contexts where the amount of supervised data is limited. To answer this issue, active learning strategies aim at minimizing the amount of labelled data required to train a DL model. Most active strategies are based on uncertain sample selection, and even often restricted to samples lying close to the decision boundary. These techniques are theoretically sound, but an understanding of the selected samples based on their content is not straightforward, further driving non-experts to consider DL as a black-box. For the first time, here we propose to take into consideration common domain-knowledge and enable non-expert users to train a model with fewer samples. In our Knowledge-driven Active Learning (KAL) framework, rule-based knowledge is converted into logic constraints and their violation is checked as a natural guide for sample selection. We show that even simple relationships among data and output classes offer a way to spot predictions for which the model need supervision. We empirically show that KAL (i) outperforms many active learning strategies, particularly in those contexts where domain knowledge is rich, (ii) it discovers data distribution lying far from the initial training data, (iii) it ensures domain experts that the provided knowledge is acquired by the model, (iv) it is suitable for regression and object recognition tasks unlike uncertainty-based strategies, and (v) its computational demand is low.", "published": "2021-10-15T06:11:53Z", "authors": ["Gabriele Ciravegna", "Frédéric Precioso", "Alessandro Betti", "Kevin Mottin", "Marco Gori"], "categories": ["cs.LG", "cs.AI"], "doi": "10.1007/978-3-031-43412-9_3", "_query": "house price prediction machine learning", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/astro-ph/0310789v1", "title": "Smoothed Particle Magnetohydrodynamics I. Algorithms and tests in one dimension", "abstract": "In this paper we show how the Smoothed Particle Hydrodynamics (SPH) equations for ideal magnetohydrodynamics (MHD) can be written in conservation form with the positivity of the dissipation guaranteed. We call the resulting algorithm Smoothed Particle Magnetohydrodynamics (SPMHD). The equations appear to be accurate, robust and easy to apply and do not suffer from the instabilities known to exist previously in formulations of the SPMHD equations. In addition we formulate our MHD equations such that errors associated with non-zero divergence of the magnetic field are naturally propagated by the flow and should therefore remain small. In this and a companion paper (Price and Monaghan 2003b) we present a wide range of numerical tests in one dimension to show that the algorithm gives very good results for one dimensional flows in both adiabatic and isothermal MHD. For the one dimensional tests the field structure is either two or three dimensional. The algorithm has many astrophysical applications and is particularly suited to star formation problems.", "published": "2003-10-28T12:27:40Z", "authors": ["D. J. Price", "J. J. Monaghan"], "categories": ["astro-ph"], "doi": "10.1111/j.1365-2966.2004.07345.x", "_query": "house price prediction machine learning", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2306.12688v1", "title": "Towards quantum enhanced adversarial robustness in machine learning", "abstract": "Machine learning algorithms are powerful tools for data driven tasks such as image classification and feature detection, however their vulnerability to adversarial examples - input samples manipulated to fool the algorithm - remains a serious challenge. The integration of machine learning with quantum computing has the potential to yield tools offering not only better accuracy and computational efficiency, but also superior robustness against adversarial attacks. Indeed, recent work has employed quantum mechanical phenomena to defend against adversarial attacks, spurring the rapid development of the field of quantum adversarial machine learning (QAML) and potentially yielding a new source of quantum advantage. Despite promising early results, there remain challenges towards building robust real-world QAML tools. In this review we discuss recent progress in QAML and identify key challenges. We also suggest future research directions which could determine the route to practicality for QAML approaches as quantum computing hardware scales up and noise levels are reduced.", "published": "2023-06-22T06:21:45Z", "authors": ["Maxwell T. West", "Shu-Lok Tsang", "Jia S. Low", "Charles D. Hill", "Christopher Leckie", "Lloyd C. L. Hollenberg", "Sarah M. Erfani", "Muhammad Usman"], "categories": ["quant-ph", "cs.AI", "cs.ET", "cs.LG"], "doi": "10.1038/s42256-023-00661-1", "_query": "house price prediction machine learning", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/1910.12024v1", "title": "SUPER Learning: A Supervised-Unsupervised Framework for Low-Dose CT Image Reconstruction", "abstract": "Recent years have witnessed growing interest in machine learning-based models and techniques for low-dose X-ray CT (LDCT) imaging tasks. The methods can typically be categorized into supervised learning methods and unsupervised or model-based learning methods. Supervised learning methods have recently shown success in image restoration tasks. However, they often rely on large training sets. Model-based learning methods such as dictionary or transform learning do not require large or paired training sets and often have good generalization properties, since they learn general properties of CT image sets. Recent works have shown the promising reconstruction performance of methods such as PWLS-ULTRA that rely on clustering the underlying (reconstructed) image patches into a learned union of transforms. In this paper, we propose a new Supervised-UnsuPERvised (SUPER) reconstruction framework for LDCT image reconstruction that combines the benefits of supervised learning methods and (unsupervised) transform learning-based methods such as PWLS-ULTRA that involve highly image-adaptive clustering. The SUPER model consists of several layers, each of which includes a deep network learned in a supervised manner and an unsupervised iterative method that involves image-adaptive components. The SUPER reconstruction algorithms are learned in a greedy manner from training data. The proposed SUPER learning methods dramatically outperform both the constituent supervised learning-based networks and iterative algorithms for LDCT, and use much fewer iterations in the iterative reconstruction modules.", "published": "2019-10-26T09:04:45Z", "authors": ["Zhipeng Li", "Siqi Ye", "Yong Long", "Saiprasad Ravishankar"], "categories": ["cs.LG", "cs.CV", "eess.IV", "eess.SP", "stat.ML"], "doi": "", "_query": "house price prediction machine learning", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2512.07335v1", "title": "Machine learning in an expectation-maximisation framework for nowcasting", "abstract": "Decision making often occurs in the presence of incomplete information, leading to the under- or overestimation of risk. Leveraging the observable information to learn the complete information is called nowcasting. In practice, incomplete information is often a consequence of reporting or observation delays. In this paper, we propose an expectation-maximisation (EM) framework for nowcasting that uses machine learning techniques to model both the occurrence as well as the reporting process of events. We allow for the inclusion of covariate information specific to the occurrence and reporting periods as well as characteristics related to the entity for which events occurred. We demonstrate how the maximisation step and the information flow between EM iterations can be tailored to leverage the predictive power of neural networks and (extreme) gradient boosting machines (XGBoost). With simulation experiments, we show that we can effectively model both the occurrence and reporting of events when dealing with high-dimensional covariate information. In the presence of non-linear effects, we show that our methodology outperforms existing EM-based nowcasting frameworks that use generalised linear models in the maximisation step. Finally, we apply the framework to the reporting of Argentinian Covid-19 cases, where the XGBoost-based approach again is most performant.", "published": "2025-12-08T09:22:22Z", "authors": ["Paul Wilsens", "Katrien Antonio", "Gerda Claeskens"], "categories": ["stat.ML", "cs.LG"], "doi": "", "_query": "house price prediction machine learning", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2009.11479v1", "title": "Theoretical Analysis of the Advantage of Deepening Neural Networks", "abstract": "We propose two new criteria to understand the advantage of deepening neural networks. It is important to know the expressivity of functions computable by deep neural networks in order to understand the advantage of deepening neural networks. Unless deep neural networks have enough expressivity, they cannot have good performance even though learning is successful. In this situation, the proposed criteria contribute to understanding the advantage of deepening neural networks since they can evaluate the expressivity independently from the efficiency of learning. The first criterion shows the approximation accuracy of deep neural networks to the target function. This criterion has the background that the goal of deep learning is approximating the target function by deep neural networks. The second criterion shows the property of linear regions of functions computable by deep neural networks. This criterion has the background that deep neural networks whose activation functions are piecewise linear are also piecewise linear. Furthermore, by the two criteria, we show that to increase layers is more effective than to increase units at each layer on improving the expressivity of deep neural networks.", "published": "2020-09-24T04:10:50Z", "authors": ["Yasushi Esaki", "Yuta Nakahara", "Toshiyasu Matsushima"], "categories": ["cs.LG", "cs.NE", "stat.ML"], "doi": "10.1109/ICMLA51294.2020.00081", "_query": "house price prediction machine learning", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2205.12184v2", "title": "Distributional Hamilton-Jacobi-Bellman Equations for Continuous-Time Reinforcement Learning", "abstract": "Continuous-time reinforcement learning offers an appealing formalism for describing control problems in which the passage of time is not naturally divided into discrete increments. Here we consider the problem of predicting the distribution of returns obtained by an agent interacting in a continuous-time, stochastic environment. Accurate return predictions have proven useful for determining optimal policies for risk-sensitive control, learning state representations, multiagent coordination, and more. We begin by establishing the distributional analogue of the Hamilton-Jacobi-Bellman (HJB) equation for Itô diffusions and the broader class of Feller-Dynkin processes. We then specialize this equation to the setting in which the return distribution is approximated by $N$ uniformly-weighted particles, a common design choice in distributional algorithms. Our derivation highlights additional terms due to statistical diffusivity which arise from the proper handling of distributions in the continuous-time setting. Based on this, we propose a tractable algorithm for approximately solving the distributional HJB based on a JKO scheme, which can be implemented in an online control algorithm. We demonstrate the effectiveness of such an algorithm in a synthetic control problem.", "published": "2022-05-24T16:33:54Z", "authors": ["Harley Wiltzer", "David Meger", "Marc G. Bellemare"], "categories": ["cs.LG", "math.OC", "stat.ML"], "doi": "", "_query": "house price prediction machine learning", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/1409.7085v1", "title": "Semantically-Informed Syntactic Machine Translation: A Tree-Grafting Approach", "abstract": "We describe a unified and coherent syntactic framework for supporting a semantically-informed syntactic approach to statistical machine translation. Semantically enriched syntactic tags assigned to the target-language training texts improved translation quality. The resulting system significantly outperformed a linguistically naive baseline model (Hiero), and reached the highest scores yet reported on the NIST 2009 Urdu-English translation task. This finding supports the hypothesis (posed by many researchers in the MT community, e.g., in DARPA GALE) that both syntactic and semantic information are critical for improving translation quality---and further demonstrates that large gains can be achieved for low-resource languages with different word order than English.", "published": "2014-09-24T20:16:49Z", "authors": ["Kathryn Baker", "Michael Bloodgood", "Chris Callison-Burch", "Bonnie J. Dorr", "Nathaniel W. Filardo", "Lori Levin", "Scott Miller", "Christine Piatko"], "categories": ["cs.CL", "cs.LG", "stat.ML"], "doi": "", "_query": "house price prediction machine learning", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/1911.08382v1", "title": "A model for predicting price polarity of real estate properties using information of real estate market websites", "abstract": "This paper presents a model that uses the information that sellers publish in real estate market websites to predict whether a property has higher or lower price than the average price of its similar properties. The model learns the correlation between price and information (text descriptions and features) of real estate properties through automatic identification of latent semantic content given by a machine learning model based on doc2vec and xgboost. The proposed model was evaluated with a data set of 57,516 publications of real estate properties collected from 2016 to 2018 of Bogotá city. Results show that the accuracy of a classifier that involves text descriptions is slightly higher than a classifier that only uses features of the real estate properties, as text descriptions tends to contain detailed information about the property.", "published": "2019-11-19T16:29:59Z", "authors": ["Vladimir Vargas-Calderón", "Jorge E. Camargo"], "categories": ["cs.LG", "stat.ML"], "doi": "10.1080/01605682.2021.2023672", "_query": "house price prediction machine learning", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2401.09787v2", "title": "Querying Easily Flip-flopped Samples for Deep Active Learning", "abstract": "Active learning is a machine learning paradigm that aims to improve the performance of a model by strategically selecting and querying unlabeled data. One effective selection strategy is to base it on the model's predictive uncertainty, which can be interpreted as a measure of how informative a sample is. The sample's distance to the decision boundary is a natural measure of predictive uncertainty, but it is often intractable to compute, especially for complex decision boundaries formed in multiclass classification tasks. To address this issue, this paper proposes the {\\it least disagree metric} (LDM), defined as the smallest probability of disagreement of the predicted label, and an estimator for LDM proven to be asymptotically consistent under mild assumptions. The estimator is computationally efficient and can be easily implemented for deep learning models using parameter perturbation. The LDM-based active learning is performed by querying unlabeled data with the smallest LDM. Experimental results show that our LDM-based active learning algorithm obtains state-of-the-art overall performance on all considered datasets and deep architectures.", "published": "2024-01-18T08:12:23Z", "authors": ["Seong Jin Cho", "Gwangsu Kim", "Junghyun Lee", "Jinwoo Shin", "Chang D. Yoo"], "categories": ["cs.LG", "cs.AI", "stat.ML"], "doi": "", "_query": "house price prediction machine learning", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/1807.06722v2", "title": "Machine Learning Interpretability: A Science rather than a tool", "abstract": "The term \"interpretability\" is oftenly used by machine learning researchers each with their own intuitive understanding of it. There is no universal well agreed upon definition of interpretability in machine learning. As any type of science discipline is mainly driven by the set of formulated questions rather than by different tools in that discipline, e.g. astrophysics is the discipline that learns the composition of stars, not as the discipline that use the spectroscopes. Similarly, we propose that machine learning interpretability should be a discipline that answers specific questions related to interpretability. These questions can be of statistical, causal and counterfactual nature. Therefore, there is a need to look into the interpretability problem of machine learning in the context of questions that need to be addressed rather than different tools. We discuss about a hypothetical interpretability framework driven by a question based scientific approach rather than some specific machine learning model. Using a question based notion of interpretability, we can step towards understanding the science of machine learning rather than its engineering. This notion will also help us understanding any specific problem more in depth rather than relying solely on machine learning methods.", "published": "2018-07-18T00:50:18Z", "authors": ["Abdul Karim", "Avinash Mishra", "MA Hakim Newton", "Abdul Sattar"], "categories": ["cs.LG", "stat.ML"], "doi": "", "_query": "house price prediction machine learning", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2106.08936v1", "title": "Improved CNN-based Learning of Interpolation Filters for Low-Complexity Inter Prediction in Video Coding", "abstract": "The versatility of recent machine learning approaches makes them ideal for improvement of next generation video compression solutions. Unfortunately, these approaches typically bring significant increases in computational complexity and are difficult to interpret into explainable models, affecting their potential for implementation within practical video coding applications. This paper introduces a novel explainable neural network-based inter-prediction scheme, to improve the interpolation of reference samples needed for fractional precision motion compensation. The approach requires a single neural network to be trained from which a full quarter-pixel interpolation filter set is derived, as the network is easily interpretable due to its linear structure. A novel training framework enables each network branch to resemble a specific fractional shift. This practical solution makes it very efficient to use alongside conventional video coding schemes. When implemented in the context of the state-of-the-art Versatile Video Coding (VVC) test model, 0.77%, 1.27% and 2.25% BD-rate savings can be achieved on average for lower resolution sequences under the random access, low-delay B and low-delay P configurations, respectively, while the complexity of the learned interpolation schemes is significantly reduced compared to the interpolation with full CNNs.", "published": "2021-06-16T16:48:01Z", "authors": ["Luka Murn", "Saverio Blasi", "Alan F. Smeaton", "Marta Mrak"], "categories": ["eess.IV", "cs.CV", "cs.LG", "cs.MM"], "doi": "10.1109/OJSP.2021.3089439", "_query": "house price prediction machine learning", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2210.02631v3", "title": "Data-driven Approaches to Surrogate Machine Learning Model Development", "abstract": "We demonstrate the adaption of three established methods to the field of surrogate machine learning model development. These methods are data augmentation, custom loss functions and transfer learning. Each of these methods have seen widespread use in the field of machine learning, however, here we apply them specifically to surrogate machine learning model development. The machine learning model that forms the basis behind this work was intended to surrogate a traditional engineering model used in the UK nuclear industry. Previous performance of this model has been hampered by poor performance due to limited training data. Here, we demonstrate that through a combination of additional techniques, model performance can be significantly improved. We show that each of the aforementioned techniques have utility in their own right and in combination with one another. However, we see them best applied as part of a transfer learning operation. Five pre-trained surrogate models produced prior to this research were further trained with an augmented dataset and with our custom loss function. Through the combination of all three techniques, we see an improvement of at least $38\\%$ in performance across the five models.", "published": "2022-10-06T01:30:11Z", "authors": ["H. Rhys Jones", "Tingting Mu", "Andrei C. Popescu", "Yusuf Sulehman"], "categories": ["cs.LG", "stat.ML"], "doi": "", "_query": "house price prediction machine learning", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/1911.07391v3", "title": "Justification-Based Reliability in Machine Learning", "abstract": "With the advent of Deep Learning, the field of machine learning (ML) has surpassed human-level performance on diverse classification tasks. At the same time, there is a stark need to characterize and quantify reliability of a model's prediction on individual samples. This is especially true in application of such models in safety-critical domains of industrial control and healthcare. To address this need, we link the question of reliability of a model's individual prediction to the epistemic uncertainty of the model's prediction. More specifically, we extend the theory of Justified True Belief (JTB) in epistemology, created to study the validity and limits of human-acquired knowledge, towards characterizing the validity and limits of knowledge in supervised classifiers. We present an analysis of neural network classifiers linking the reliability of its prediction on an input to characteristics of the support gathered from the input and latent spaces of the network. We hypothesize that the JTB analysis exposes the epistemic uncertainty (or ignorance) of a model with respect to its inference, thereby allowing for the inference to be only as strong as the justification permits. We explore various forms of support (for e.g., k-nearest neighbors (k-NN) and l_p-norm based) generated for an input, using the training data to construct a justification for the prediction with that input. Through experiments conducted on simulated and real datasets, we demonstrate that our approach can provide reliability for individual predictions and characterize regions where such reliability cannot be ascertained.", "published": "2019-11-18T01:15:24Z", "authors": ["Nurali Virani", "Naresh Iyer", "Zhaoyuan Yang"], "categories": ["cs.LG", "stat.ML"], "doi": "", "_query": "house price prediction machine learning", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/1911.07420v1", "title": "A Graph Autoencoder Approach to Causal Structure Learning", "abstract": "Causal structure learning has been a challenging task in the past decades and several mainstream approaches such as constraint- and score-based methods have been studied with theoretical guarantees. Recently, a new approach has transformed the combinatorial structure learning problem into a continuous one and then solved it using gradient-based optimization methods. Following the recent state-of-the-arts, we propose a new gradient-based method to learn causal structures from observational data. The proposed method generalizes the recent gradient-based methods to a graph autoencoder framework that allows nonlinear structural equation models and is easily applicable to vector-valued variables. We demonstrate that on synthetic datasets, our proposed method outperforms other gradient-based methods significantly, especially on large causal graphs. We further investigate the scalability and efficiency of our method, and observe a near linear training time when scaling up the graph size.", "published": "2019-11-18T04:22:00Z", "authors": ["Ignavier Ng", "Shengyu Zhu", "Zhitang Chen", "Zhuangyan Fang"], "categories": ["cs.LG", "stat.ML"], "doi": "", "_query": "house price prediction machine learning", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/1902.08874v4", "title": "Evaluating Differentially Private Machine Learning in Practice", "abstract": "Differential privacy is a strong notion for privacy that can be used to prove formal guarantees, in terms of a privacy budget, $ε$, about how much information is leaked by a mechanism. However, implementations of privacy-preserving machine learning often select large values of $ε$ in order to get acceptable utility of the model, with little understanding of the impact of such choices on meaningful privacy. Moreover, in scenarios where iterative learning procedures are used, differential privacy variants that offer tighter analyses are used which appear to reduce the needed privacy budget but present poorly understood trade-offs between privacy and utility. In this paper, we quantify the impact of these choices on privacy in experiments with logistic regression and neural network models. Our main finding is that there is a huge gap between the upper bounds on privacy loss that can be guaranteed, even with advanced mechanisms, and the effective privacy loss that can be measured using current inference attacks. Current mechanisms for differentially private machine learning rarely offer acceptable utility-privacy trade-offs with guarantees for complex learning tasks: settings that provide limited accuracy loss provide meaningless privacy guarantees, and settings that provide strong privacy guarantees result in useless models. Code for the experiments can be found here: https://github.com/bargavj/EvaluatingDPML", "published": "2019-02-24T01:48:53Z", "authors": ["Bargav Jayaraman", "David Evans"], "categories": ["cs.LG", "cs.CR", "stat.ML"], "doi": "", "_query": "house price prediction machine learning", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/1902.09096v1", "title": "Field-aware Neural Factorization Machine for Click-Through Rate Prediction", "abstract": "Recommendation systems and computing advertisements have gradually entered the field of academic research from the field of commercial applications. Click-through rate prediction is one of the core research issues because the prediction accuracy affects the user experience and the revenue of merchants and platforms. Feature engineering is very important to improve click-through rate prediction. Traditional feature engineering heavily relies on people's experience, and is difficult to construct a feature combination that can describe the complex patterns implied in the data. This paper combines traditional feature combination methods and deep neural networks to automate feature combinations to improve the accuracy of click-through rate prediction. We propose a mechannism named 'Field-aware Neural Factorization Machine' (FNFM). This model can have strong second order feature interactive learning ability like Field-aware Factorization Machine, on this basis, deep neural network is used for higher-order feature combination learning. Experiments show that the model has stronger expression ability than current deep learning feature combination models like the DeepFM, DCN and NFM.", "published": "2019-02-25T05:34:15Z", "authors": ["Li Zhang", "Weichen Shen", "Shijian Li", "Gang Pan"], "categories": ["cs.LG", "stat.ML"], "doi": "", "_query": "house price prediction machine learning", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2402.07586v4", "title": "Unveiling Group-Specific Distributed Concept Drift: A Fairness Imperative in Federated Learning", "abstract": "In the evolving field of machine learning, ensuring group fairness has become a critical concern, prompting the development of algorithms designed to mitigate bias in decision-making processes. Group fairness refers to the principle that a model's decisions should be equitable across different groups defined by sensitive attributes such as gender or race, ensuring that individuals from privileged groups and unprivileged groups are treated fairly and receive similar outcomes. However, achieving fairness in the presence of group-specific concept drift remains an unexplored frontier, and our research represents pioneering efforts in this regard. Group-specific concept drift refers to situations where one group experiences concept drift over time while another does not, leading to a decrease in fairness even if accuracy remains fairly stable. Within the framework of Federated Learning, where clients collaboratively train models, its distributed nature further amplifies these challenges since each client can experience group-specific concept drift independently while still sharing the same underlying concept, creating a complex and dynamic environment for maintaining fairness. The most significant contribution of our research is the formalization and introduction of the problem of group-specific concept drift and its distributed counterpart, shedding light on its critical importance in the field of fairness. Additionally, leveraging insights from prior research, we adapt an existing distributed concept drift adaptation algorithm to tackle group-specific distributed concept drift which uses a multi-model approach, a local group-specific drift detection mechanism, and continuous clustering of models over time. The findings from our experiments highlight the importance of addressing group-specific concept drift and its distributed counterpart to advance fairness in machine learning.", "published": "2024-02-12T11:35:25Z", "authors": ["Teresa Salazar", "João Gama", "Helder Araújo", "Pedro Henriques Abreu"], "categories": ["cs.LG"], "doi": "10.1109/TNNLS.2025.3601834", "_query": "house price prediction machine learning", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2505.02874v1", "title": "Uncertainty Quantification for Machine Learning in Healthcare: A Survey", "abstract": "Uncertainty Quantification (UQ) is pivotal in enhancing the robustness, reliability, and interpretability of Machine Learning (ML) systems for healthcare, optimizing resources and improving patient care. Despite the emergence of ML-based clinical decision support tools, the lack of principled quantification of uncertainty in ML models remains a major challenge. Current reviews have a narrow focus on analyzing the state-of-the-art UQ in specific healthcare domains without systematically evaluating method efficacy across different stages of model development, and despite a growing body of research, its implementation in healthcare applications remains limited. Therefore, in this survey, we provide a comprehensive analysis of current UQ in healthcare, offering an informed framework that highlights how different methods can be integrated into each stage of the ML pipeline including data processing, training and evaluation. We also highlight the most popular methods used in healthcare and novel approaches from other domains that hold potential for future adoption in the medical context. We expect this study will provide a clear overview of the challenges and opportunities of implementing UQ in the ML pipeline for healthcare, guiding researchers and practitioners in selecting suitable techniques to enhance the reliability, safety and trust from patients and clinicians on ML-driven healthcare solutions.", "published": "2025-05-04T16:56:22Z", "authors": ["L. Julián Lechuga López", "Shaza Elsharief", "Dhiyaa Al Jorf", "Firas Darwish", "Congbo Ma", "Farah E. Shamout"], "categories": ["cs.LG", "cs.AI"], "doi": "", "_query": "house price prediction machine learning", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2003.00880v1", "title": "Introducing Fuzzy Layers for Deep Learning", "abstract": "Many state-of-the-art technologies developed in recent years have been influenced by machine learning to some extent. Most popular at the time of this writing are artificial intelligence methodologies that fall under the umbrella of deep learning. Deep learning has been shown across many applications to be extremely powerful and capable of handling problems that possess great complexity and difficulty. In this work, we introduce a new layer to deep learning: the fuzzy layer. Traditionally, the network architecture of neural networks is composed of an input layer, some combination of hidden layers, and an output layer. We propose the introduction of fuzzy layers into the deep learning architecture to exploit the powerful aggregation properties expressed through fuzzy methodologies, such as the Choquet and Sugueno fuzzy integrals. To date, fuzzy approaches taken to deep learning have been through the application of various fusion strategies at the decision level to aggregate outputs from state-of-the-art pre-trained models, e.g., AlexNet, VGG16, GoogLeNet, Inception-v3, ResNet-18, etc. While these strategies have been shown to improve accuracy performance for image classification tasks, none have explored the use of fuzzified intermediate, or hidden, layers. Herein, we present a new deep learning strategy that incorporates fuzzy strategies into the deep learning architecture focused on the application of semantic segmentation using per-pixel classification. Experiments are conducted on a benchmark data set as well as a data set collected via an unmanned aerial system at a U.S. Army test site for the task of automatic road segmentation, and preliminary results are promising.", "published": "2020-02-21T19:33:30Z", "authors": ["Stanton R. Price", "Steven R. Price", "Derek T. Anderson"], "categories": ["cs.CV", "cs.LG", "stat.ML"], "doi": "10.1109/FUZZ-IEEE.2019.8858790", "_query": "house price prediction machine learning", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2407.12288v5", "title": "Information-Theoretic Foundations for Machine Learning", "abstract": "The progress of machine learning over the past decade is undeniable. In retrospect, it is both remarkable and unsettling that this progress was achievable with little to no rigorous theory to guide experimentation. Despite this fact, practitioners have been able to guide their future experimentation via observations from previous large-scale empirical investigations. In this work, we propose a theoretical framework which attempts to provide rigor to existing practices in machine learning. To the theorist, we provide a framework which is mathematically rigorous and leaves open many interesting ideas for future exploration. To the practitioner, we provide a framework whose results are simple, and provide intuition to guide future investigations across a wide range of learning paradigms. Concretely, we provide a theoretical framework rooted in Bayesian statistics and Shannon's information theory which is general enough to unify the analysis of many phenomena in machine learning. Our framework characterizes the performance of an optimal Bayesian learner as it learns from a stream of experience. Unlike existing analyses that weaken with increasing data complexity, our theoretical tools provide accurate insights across diverse machine learning settings. Throughout this work, we derive theoretical results and demonstrate their generality by apply them to derive insights specific to settings. These settings range from learning from data which is independently and identically distributed under an unknown distribution, to data which is sequential, to data which exhibits hierarchical structure amenable to meta-learning, and finally to data which is not fully explainable under the learner's beliefs (misspecification). These results are particularly relevant as we strive to understand and overcome increasingly difficult machine learning challenges in this endlessly complex world.", "published": "2024-07-17T03:18:40Z", "authors": ["Hong Jun Jeon", "Benjamin Van Roy"], "categories": ["stat.ML", "cs.AI", "cs.LG"], "doi": "", "_query": "house price prediction machine learning", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/1910.02217v1", "title": "A Novel Graphical Lasso based approach towards Segmentation Analysis in Energy Game-Theoretic Frameworks", "abstract": "Energy game-theoretic frameworks have emerged to be a successful strategy to encourage energy efficient behavior in large scale by leveraging human-in-the-loop strategy. A number of such frameworks have been introduced over the years which formulate the energy saving process as a competitive game with appropriate incentives for energy efficient players. However, prior works involve an incentive design mechanism which is dependent on knowledge of utility functions for all the players in the game, which is hard to compute especially when the number of players is high, common in energy game-theoretic frameworks. Our research proposes that the utilities of players in such a framework can be grouped together to a relatively small number of clusters, and the clusters can then be targeted with tailored incentives. The key to above segmentation analysis is to learn the features leading to human decision making towards energy usage in competitive environments. We propose a novel graphical lasso based approach to perform such segmentation, by studying the feature correlations in a real-world energy social game dataset. To further improve the explainability of the model, we perform causality study using grangers causality. Proposed segmentation analysis results in characteristic clusters demonstrating different energy usage behaviors. We also present avenues to implement intelligent incentive design using proposed segmentation method.", "published": "2019-10-05T06:04:40Z", "authors": ["Hari Prasanna Das", "Ioannis C. Konstantakopoulos", "Aummul Baneen Manasawala", "Tanya Veeravalli", "Huihan Liu", "Costas J. Spanos"], "categories": ["cs.LG", "stat.ML"], "doi": "10.1109/ICMLA.2019.00277", "_query": "house price prediction machine learning", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2007.16061v1", "title": "Graph signal processing for machine learning: A review and new perspectives", "abstract": "The effective representation, processing, analysis, and visualization of large-scale structured data, especially those related to complex domains such as networks and graphs, are one of the key questions in modern machine learning. Graph signal processing (GSP), a vibrant branch of signal processing models and algorithms that aims at handling data supported on graphs, opens new paths of research to address this challenge. In this article, we review a few important contributions made by GSP concepts and tools, such as graph filters and transforms, to the development of novel machine learning algorithms. In particular, our discussion focuses on the following three aspects: exploiting data structure and relational priors, improving data and computational efficiency, and enhancing model interpretability. Furthermore, we provide new perspectives on future development of GSP techniques that may serve as a bridge between applied mathematics and signal processing on one side, and machine learning and network science on the other. Cross-fertilization across these different disciplines may help unlock the numerous challenges of complex data analysis in the modern age.", "published": "2020-07-31T13:21:33Z", "authors": ["Xiaowen Dong", "Dorina Thanou", "Laura Toni", "Michael Bronstein", "Pascal Frossard"], "categories": ["cs.LG", "cs.SI", "eess.SP", "stat.ML"], "doi": "10.1109/MSP.2020.3014591", "_query": "house price prediction machine learning", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/1902.04811v2", "title": "On Nonconvex Optimization for Machine Learning: Gradients, Stochasticity, and Saddle Points", "abstract": "Gradient descent (GD) and stochastic gradient descent (SGD) are the workhorses of large-scale machine learning. While classical theory focused on analyzing the performance of these methods in convex optimization problems, the most notable successes in machine learning have involved nonconvex optimization, and a gap has arisen between theory and practice. Indeed, traditional analyses of GD and SGD show that both algorithms converge to stationary points efficiently. But these analyses do not take into account the possibility of converging to saddle points. More recent theory has shown that GD and SGD can avoid saddle points, but the dependence on dimension in these analyses is polynomial. For modern machine learning, where the dimension can be in the millions, such dependence would be catastrophic. We analyze perturbed versions of GD and SGD and show that they are truly efficient---their dimension dependence is only polylogarithmic. Indeed, these algorithms converge to second-order stationary points in essentially the same time as they take to converge to classical first-order stationary points.", "published": "2019-02-13T09:44:02Z", "authors": ["Chi Jin", "Praneeth Netrapalli", "Rong Ge", "Sham M. Kakade", "Michael I. Jordan"], "categories": ["cs.LG", "math.OC", "stat.ML"], "doi": "", "_query": "house price prediction machine learning", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/1808.04572v3", "title": "Small Sample Learning in Big Data Era", "abstract": "As a promising area in artificial intelligence, a new learning paradigm, called Small Sample Learning (SSL), has been attracting prominent research attention in the recent years. In this paper, we aim to present a survey to comprehensively introduce the current techniques proposed on this topic. Specifically, current SSL techniques can be mainly divided into two categories. The first category of SSL approaches can be called \"concept learning\", which emphasizes learning new concepts from only few related observations. The purpose is mainly to simulate human learning behaviors like recognition, generation, imagination, synthesis and analysis. The second category is called \"experience learning\", which usually co-exists with the large sample learning manner of conventional machine learning. This category mainly focuses on learning with insufficient samples, and can also be called small data learning in some literatures. More extensive surveys on both categories of SSL techniques are introduced and some neuroscience evidences are provided to clarify the rationality of the entire SSL regime, and the relationship with human learning process. Some discussions on the main challenges and possible future research directions along this line are also presented.", "published": "2018-08-14T08:01:07Z", "authors": ["Jun Shu", "Zongben Xu", "Deyu Meng"], "categories": ["cs.LG", "stat.ML"], "doi": "", "_query": "house price prediction machine learning", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2005.05847v2", "title": "Generalization of Machine Learning for Problem Reduction: A Case Study on Travelling Salesman Problems", "abstract": "Combinatorial optimization plays an important role in real-world problem solving. In the big data era, the dimensionality of a combinatorial optimization problem is usually very large, which poses a significant challenge to existing solution methods. In this paper, we examine the generalization capability of a machine learning model for problem reduction on the classic travelling salesman problems (TSP). We demonstrate that our method can greedily remove decision variables from an optimization problem that are predicted not to be part of an optimal solution. More specifically, we investigate our model's capability to generalize on test instances that have not been seen during the training phase. We consider three scenarios where training and test instances are different in terms of: 1) problem characteristics; 2) problem sizes; and 3) problem types. Our experiments show that this machine learning based technique can generalize reasonably well over a wide range of TSP test instances with different characteristics or sizes. While the accuracy of predicting unused variables naturally deteriorates as a test instance is further away from the training set, we observe that even when tested on a different TSP problem variant, the machine learning model still makes useful predictions about which variables can be eliminated without significantly impacting solution quality.", "published": "2020-05-12T15:09:20Z", "authors": ["Yuan Sun", "Andreas Ernst", "Xiaodong Li", "Jake Weiner"], "categories": ["cs.LG", "stat.ML"], "doi": "10.1007/s00291-020-00604-x", "_query": "house price prediction machine learning", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/1005.5603v3", "title": "On the Relation between Realizable and Nonrealizable Cases of the Sequence Prediction Problem", "abstract": "A sequence $x_1,\\dots,x_n,\\dots$ of discrete-valued observations is generated according to some unknown probabilistic law (measure) $μ$. After observing each outcome, one is required to give conditional probabilities of the next observation. The realizable case is when the measure $μ$ belongs to an arbitrary but known class $\\mathcal C$ of process measures. The non-realizable case is when $μ$ is completely arbitrary, but the prediction performance is measured with respect to a given set $\\mathcal C$ of process measures. We are interested in the relations between these problems and between their solutions, as well as in characterizing the cases when a solution exists and finding these solutions. We show that if the quality of prediction is measured using the total variation distance, then these problems coincide, while if it is measured using the expected average KL divergence, then they are different. For some of the formalizations we also show that when a solution exists, it can be obtained as a Bayes mixture over a countable subset of $\\mathcal C$. We also obtain several characterization of those sets $\\mathcal C$ for which solutions to the considered problems exist. As an illustration to the general results obtained, we show that a solution to the non-realizable case of the sequence prediction problem exists for the set of all finite-memory processes, but does not exist for the set of all stationary processes. It should be emphasized that the framework is completely general: the processes measures considered are not required to be i.i.d., mixing, stationary, or to belong to any parametric family.", "published": "2010-05-31T06:58:11Z", "authors": ["Daniil Ryabko"], "categories": ["cs.LG", "cs.IT", "math.ST"], "doi": "", "_query": "house price prediction machine learning", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/1910.01612v1", "title": "Partial differential equation regularization for supervised machine learning", "abstract": "This article is an overview of supervised machine learning problems for regression and classification. Topics include: kernel methods, training by stochastic gradient descent, deep learning architecture, losses for classification, statistical learning theory, and dimension independent generalization bounds. Implicit regularization in deep learning examples are presented, including data augmentation, adversarial training, and additive noise. These methods are reframed as explicit gradient regularization.", "published": "2019-10-03T17:29:17Z", "authors": ["Adam M Oberman"], "categories": ["cs.LG", "math.AP", "stat.ML"], "doi": "", "_query": "house price prediction machine learning", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/1912.02762v2", "title": "Normalizing Flows for Probabilistic Modeling and Inference", "abstract": "Normalizing flows provide a general mechanism for defining expressive probability distributions, only requiring the specification of a (usually simple) base distribution and a series of bijective transformations. There has been much recent work on normalizing flows, ranging from improving their expressive power to expanding their application. We believe the field has now matured and is in need of a unified perspective. In this review, we attempt to provide such a perspective by describing flows through the lens of probabilistic modeling and inference. We place special emphasis on the fundamental principles of flow design, and discuss foundational topics such as expressive power and computational trade-offs. We also broaden the conceptual framing of flows by relating them to more general probability transformations. Lastly, we summarize the use of flows for tasks such as generative modeling, approximate inference, and supervised learning.", "published": "2019-12-05T17:55:27Z", "authors": ["George Papamakarios", "Eric Nalisnick", "Danilo Jimenez Rezende", "Shakir Mohamed", "Balaji Lakshminarayanan"], "categories": ["stat.ML", "cs.LG"], "doi": "", "_query": "house price prediction machine learning", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2412.07520v1", "title": "Quantifying the Prediction Uncertainty of Machine Learning Models for Individual Data", "abstract": "Machine learning models have exhibited exceptional results in various domains. The most prevalent approach for learning is the empirical risk minimizer (ERM), which adapts the model's weights to reduce the loss on a training set and subsequently leverages these weights to predict the label for new test data. Nonetheless, ERM makes the assumption that the test distribution is similar to the training distribution, which may not always hold in real-world situations. In contrast, the predictive normalized maximum likelihood (pNML) was proposed as a min-max solution for the individual setting where no assumptions are made on the distribution of the tested input. This study investigates pNML's learnability for linear regression and neural networks, and demonstrates that pNML can improve the performance and robustness of these models on various tasks. Moreover, the pNML provides an accurate confidence measure for its output, showcasing state-of-the-art results for out-of-distribution detection, resistance to adversarial attacks, and active learning.", "published": "2024-12-10T13:58:19Z", "authors": ["Koby Bibas"], "categories": ["cs.LG", "cs.IT"], "doi": "", "_query": "house price prediction machine learning", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2409.08359v1", "title": "Participatory Science and Machine Learning Applied to Millions of Sources in the Hobby-Eberly Telescope Dark Energy Experiment", "abstract": "We are merging a large participatory science effort with machine learning to enhance the Hobby-Eberly Telescope Dark Energy Experiment (HETDEX). Our overall goal is to remove false positives, allowing us to use lower signal-to-noise data and sources with low goodness-of-fit. With six million classifications through Dark Energy Explorers, we can confidently determine if a source is not real at over 94% confidence level when classified by at least ten individuals; this confidence level increases for higher signal-to-noise sources. To date, we have only been able to apply this direct analysis to 190,000 sources. The full sample of HETDEX will contain around 2-3M sources, including nearby galaxies ([O II] emitters), distant galaxies (Lyman-alpha emitters or LAEs), false positives, and contamination from instrument issues. We can accommodate this tenfold increase by using machine learning with visually-vetted samples from Dark Energy Explorers. We have already increased by over ten-fold in number of sources that have been visually vetted from our previous pilot study where we only had 14,000 visually vetted LAE candidates. This paper expands on the previous work increasing the visually-vetted sample from 14,000 to 190,000. In addition, using our currently visually-vetted sample, we generate a real or false positive classification for the full candidate sample of 1.2 million LAEs. We currently have approximately 17,000 volunteers from 159 countries around the world. Thus, we are applying participatory or citizen scientist analysis to our full HETDEX dataset, creating a free educational opportunity that requires no prior technical knowledge.", "published": "2024-09-12T18:51:17Z", "authors": ["Lindsay R. House", "Karl Gebhardt", "Keely Finkelstein", "Erin Mentuch Cooper", "Dustin Davis", "Daniel J. Farrow", "Donald P. Schneider"], "categories": ["astro-ph.IM", "astro-ph.CO", "astro-ph.GA", "physics.ed-ph"], "doi": "10.3847/1538-4357/ad782c", "_query": "house price prediction machine learning", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2309.17368v2", "title": "Machine Learning for Practical Quantum Error Mitigation", "abstract": "Quantum computers progress toward outperforming classical supercomputers, but quantum errors remain their primary obstacle. The key to overcoming errors on near-term devices has emerged through the field of quantum error mitigation, enabling improved accuracy at the cost of additional run time. Here, through experiments on state-of-the-art quantum computers using up to 100 qubits, we demonstrate that without sacrificing accuracy machine learning for quantum error mitigation (ML-QEM) drastically reduces the cost of mitigation. We benchmark ML-QEM using a variety of machine learning models -- linear regression, random forests, multi-layer perceptrons, and graph neural networks -- on diverse classes of quantum circuits, over increasingly complex device-noise profiles, under interpolation and extrapolation, and in both numerics and experiments. These tests employ the popular digital zero-noise extrapolation method as an added reference. Finally, we propose a path toward scalable mitigation by using ML-QEM to mimic traditional mitigation methods with superior runtime efficiency. Our results show that classical machine learning can extend the reach and practicality of quantum error mitigation by reducing its overheads and highlight its broader potential for practical quantum computations.", "published": "2023-09-29T16:17:12Z", "authors": ["Haoran Liao", "Derek S. Wang", "Iskandar Sitdikov", "Ciro Salcedo", "Alireza Seif", "Zlatko K. Minev"], "categories": ["quant-ph", "cs.LG"], "doi": "10.1038/s42256-024-00927-2", "_query": "house price prediction machine learning", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2303.16289v2", "title": "Long-term experimental study of price responsive predictive control in a real occupied single-family house with heat pump", "abstract": "The continuous introduction of renewable electricity and increased consumption through electrification of the transport and heating sector challenges grid stability. This study investigates load shifting through demand side management as a solution. We present a four-month experimental study of a low-complexity, hierarchical Model Predictive Control approach for demand side management in a near-zero emission occupied single-family house in Denmark. The control algorithm uses a price signal, weather forecast, a single-zone building model, and a non-linear heat pump efficiency model to generate a space-heating schedule. The weather-compensated, commercial heat pump is made to act smart grid-ready through outdoor temperature input override to enable heat boosting and forced stops to accommodate the heating schedule. The cost reduction from the controller ranged from 2-33% depending on the chosen comfort level. The experiment demonstrates that load shifting is feasible and cost-effective, even without energy storage, and that the current price scheme provides an incentive for Danish end-consumers to shift heating loads. However, issues related to controlling the heat pump through input-manipulation were identified, and the authors propose a more promising path forward involving coordination with manufacturers and regulators to make commercial heat pumps truly smart grid-ready.", "published": "2023-03-28T20:08:20Z", "authors": ["Simon Thorsteinsson", "Alex Arash Sand Kalaee", "Pierre Vogler-Finck", "Henrik Lund Stærmose", "Ivan Katic", "Jan Dimon Bendtsen"], "categories": ["eess.SY"], "doi": "10.1016/j.apenergy.2023.121398", "_query": "house price prediction machine learning", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2402.16131v1", "title": "A VAE-based Framework for Learning Multi-Level Neural Granger-Causal Connectivity", "abstract": "Granger causality has been widely used in various application domains to capture lead-lag relationships amongst the components of complex dynamical systems, and the focus in extant literature has been on a single dynamical system. In certain applications in macroeconomics and neuroscience, one has access to data from a collection of related such systems, wherein the modeling task of interest is to extract the shared common structure that is embedded across them, as well as to identify the idiosyncrasies within individual ones. This paper introduces a Variational Autoencoder (VAE) based framework that jointly learns Granger-causal relationships amongst components in a collection of related-yet-heterogeneous dynamical systems, and handles the aforementioned task in a principled way. The performance of the proposed framework is evaluated on several synthetic data settings and benchmarked against existing approaches designed for individual system learning. The method is further illustrated on a real dataset involving time series data from a neurophysiological experiment and produces interpretable results.", "published": "2024-02-25T16:11:32Z", "authors": ["Jiahe Lin", "Huitian Lei", "George Michailidis"], "categories": ["cs.LG", "stat.ME", "stat.ML"], "doi": "", "_query": "house price prediction machine learning", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2107.06317v3", "title": "Inverse Contextual Bandits: Learning How Behavior Evolves over Time", "abstract": "Understanding a decision-maker's priorities by observing their behavior is critical for transparency and accountability in decision processes, such as in healthcare. Though conventional approaches to policy learning almost invariably assume stationarity in behavior, this is hardly true in practice: Medical practice is constantly evolving as clinical professionals fine-tune their knowledge over time. For instance, as the medical community's understanding of organ transplantations has progressed over the years, a pertinent question is: How have actual organ allocation policies been evolving? To give an answer, we desire a policy learning method that provides interpretable representations of decision-making, in particular capturing an agent's non-stationary knowledge of the world, as well as operating in an offline manner. First, we model the evolving behavior of decision-makers in terms of contextual bandits, and formalize the problem of Inverse Contextual Bandits (ICB). Second, we propose two concrete algorithms as solutions, learning parametric and nonparametric representations of an agent's behavior. Finally, using both real and simulated data for liver transplantations, we illustrate the applicability and explainability of our method, as well as benchmarking and validating its accuracy.", "published": "2021-07-13T18:24:18Z", "authors": ["Alihan Hüyük", "Daniel Jarrett", "Mihaela van der Schaar"], "categories": ["cs.LG", "stat.ML"], "doi": "", "_query": "house price prediction machine learning", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2202.01560v2", "title": "Extending turbulence model uncertainty quantification using machine learning", "abstract": "In order to achieve a more virtual design and certification process of jet engines in aviation industry, the uncertainty bounds for computational fluid dynamics have to be known. This work shows the application of a machine learning methodology to quantify the epistemic uncertainties of turbulence models. The underlying method in order to estimate the uncertainty bounds is based on an eigenspace perturbation of the Reynolds stress tensor in combination with random forests.", "published": "2022-02-03T12:36:32Z", "authors": ["Marcel Matha", "Christian Morsbach"], "categories": ["cs.CE", "physics.flu-dyn"], "doi": "", "_query": "house price prediction machine learning", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2501.09103v1", "title": "Similarity-Quantized Relative Difference Learning for Improved Molecular Activity Prediction", "abstract": "Accurate prediction of molecular activities is crucial for efficient drug discovery, yet remains challenging due to limited and noisy datasets. We introduce Similarity-Quantized Relative Learning (SQRL), a learning framework that reformulates molecular activity prediction as relative difference learning between structurally similar pairs of compounds. SQRL uses precomputed molecular similarities to enhance training of graph neural networks and other architectures, and significantly improves accuracy and generalization in low-data regimes common in drug discovery. We demonstrate its broad applicability and real-world potential through benchmarking on public datasets as well as proprietary industry data. Our findings demonstrate that leveraging similarity-aware relative differences provides an effective paradigm for molecular activity prediction.", "published": "2025-01-15T19:42:18Z", "authors": ["Karina Zadorozhny", "Kangway V. Chuang", "Bharath Sathappan", "Ewan Wallace", "Vishnu Sresht", "Colin A. Grambow"], "categories": ["cs.LG"], "doi": "", "_query": "house price prediction machine learning", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/1609.00804v1", "title": "Randomized Prediction Games for Adversarial Machine Learning", "abstract": "In spam and malware detection, attackers exploit randomization to obfuscate malicious data and increase their chances of evading detection at test time; e.g., malware code is typically obfuscated using random strings or byte sequences to hide known exploits. Interestingly, randomization has also been proposed to improve security of learning algorithms against evasion attacks, as it results in hiding information about the classifier to the attacker. Recent work has proposed game-theoretical formulations to learn secure classifiers, by simulating different evasion attacks and modifying the classification function accordingly. However, both the classification function and the simulated data manipulations have been modeled in a deterministic manner, without accounting for any form of randomization. In this work, we overcome this limitation by proposing a randomized prediction game, namely, a non-cooperative game-theoretic formulation in which the classifier and the attacker make randomized strategy selections according to some probability distribution defined over the respective strategy set. We show that our approach allows one to improve the trade-off between attack detection and false alarms with respect to state-of-the-art secure classifiers, even against attacks that are different from those hypothesized during design, on application examples including handwritten digit recognition, spam and malware detection.", "published": "2016-09-03T09:30:51Z", "authors": ["Samuel Rota Bulò", "Battista Biggio", "Ignazio Pillai", "Marcello Pelillo", "Fabio Roli"], "categories": ["cs.LG", "cs.GT"], "doi": "10.1109/TNNLS.2016.2593488", "_query": "house price prediction machine learning", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/1905.10214v5", "title": "Partially Encrypted Machine Learning using Functional Encryption", "abstract": "Machine learning on encrypted data has received a lot of attention thanks to recent breakthroughs in homomorphic encryption and secure multi-party computation. It allows outsourcing computation to untrusted servers without sacrificing privacy of sensitive data. We propose a practical framework to perform partially encrypted and privacy-preserving predictions which combines adversarial training and functional encryption. We first present a new functional encryption scheme to efficiently compute quadratic functions so that the data owner controls what can be computed but is not involved in the calculation: it provides a decryption key which allows one to learn a specific function evaluation of some encrypted data. We then show how to use it in machine learning to partially encrypt neural networks with quadratic activation functions at evaluation time, and we provide a thorough analysis of the information leaks based on indistinguishability of data items of the same label. Last, since most encryption schemes cannot deal with the last thresholding operation used for classification, we propose a training method to prevent selected sensitive features from leaking, which adversarially optimizes the network against an adversary trying to identify these features. This is interesting for several existing works using partially encrypted machine learning as it comes with little reduction on the model's accuracy and significantly improves data privacy.", "published": "2019-05-24T13:06:53Z", "authors": ["Theo Ryffel", "Edouard Dufour-Sans", "Romain Gay", "Francis Bach", "David Pointcheval"], "categories": ["cs.LG", "cs.CR", "stat.ML"], "doi": "", "_query": "house price prediction machine learning", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/1811.06083v3", "title": "Learning Optimal Personalized Treatment Rules Using Robust Regression Informed K-NN", "abstract": "We develop a prediction-based prescriptive model for learning optimal personalized treatments for patients based on their Electronic Health Records (EHRs). Our approach consists of: (i) predicting future outcomes under each possible therapy using a robustified nonlinear model, and (ii) adopting a randomized prescriptive policy determined by the predicted outcomes. We show theoretical results that guarantee the out-of-sample predictive power of the model, and prove the optimality of the randomized strategy in terms of the expected true future outcome. We apply the proposed methodology to develop optimal therapies for patients with type 2 diabetes or hypertension using EHRs from a major safety-net hospital in New England, and show that our algorithm leads to a larger reduction of the HbA1c, for diabetics, or systolic blood pressure, for patients with hypertension, compared to the alternatives. We demonstrate that our approach outperforms the standard of care under the robustified nonlinear predictive model.", "published": "2018-11-14T21:46:56Z", "authors": ["Ruidi Chen", "Ioannis Paschalidis"], "categories": ["stat.ML", "cs.LG"], "doi": "", "_query": "house price prediction machine learning", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/1902.05009v1", "title": "ATMSeer: Increasing Transparency and Controllability in Automated Machine Learning", "abstract": "To relieve the pain of manually selecting machine learning algorithms and tuning hyperparameters, automated machine learning (AutoML) methods have been developed to automatically search for good models. Due to the huge model search space, it is impossible to try all models. Users tend to distrust automatic results and increase the search budget as much as they can, thereby undermining the efficiency of AutoML. To address these issues, we design and implement ATMSeer, an interactive visualization tool that supports users in refining the search space of AutoML and analyzing the results. To guide the design of ATMSeer, we derive a workflow of using AutoML based on interviews with machine learning experts. A multi-granularity visualization is proposed to enable users to monitor the AutoML process, analyze the searched models, and refine the search space in real time. We demonstrate the utility and usability of ATMSeer through two case studies, expert interviews, and a user study with 13 end users.", "published": "2019-02-13T17:03:33Z", "authors": ["Qianwen Wang", "Yao Ming", "Zhihua Jin", "Qiaomu Shen", "Dongyu Liu", "Micah J. Smith", "Kalyan Veeramachaneni", "Huamin Qu"], "categories": ["cs.LG", "cs.HC", "stat.ML"], "doi": "10.1145/3290605.3300911", "_query": "house price prediction machine learning", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2506.21802v1", "title": "Classification with Reject Option: Distribution-free Error Guarantees via Conformal Prediction", "abstract": "Machine learning (ML) models always make a prediction, even when they are likely to be wrong. This causes problems in practical applications, as we do not know if we should trust a prediction. ML with reject option addresses this issue by abstaining from making a prediction if it is likely to be incorrect. In this work, we formalise the approach to ML with reject option in binary classification, deriving theoretical guarantees on the resulting error rate. This is achieved through conformal prediction (CP), which produce prediction sets with distribution-free validity guarantees. In binary classification, CP can output prediction sets containing exactly one, two or no labels. By accepting only the singleton predictions, we turn CP into a binary classifier with reject option. Here, CP is formally put in the framework of predicting with reject option. We state and prove the resulting error rate, and give finite sample estimates. Numerical examples provide illustrations of derived error rate through several different conformal prediction settings, ranging from full conformal prediction to offline batch inductive conformal prediction. The former has a direct link to sharp validity guarantees, whereas the latter is more fuzzy in terms of validity guarantees but can be used in practice. Error-reject curves illustrate the trade-off between error rate and reject rate, and can serve to aid a user to set an acceptable error rate or reject rate in practice.", "published": "2025-06-26T23:04:25Z", "authors": ["Johan Hallberg Szabadváry", "Tuwe Löfström", "Ulf Johansson", "Cecilia Sönströd", "Ernst Ahlberg", "Lars Carlsson"], "categories": ["stat.ML", "cs.LG"], "doi": "10.1016/j.mlwa.2025.100664", "_query": "house price prediction machine learning", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/1702.01806v2", "title": "Beam Search Strategies for Neural Machine Translation", "abstract": "The basic concept in Neural Machine Translation (NMT) is to train a large Neural Network that maximizes the translation performance on a given parallel corpus. NMT is then using a simple left-to-right beam-search decoder to generate new translations that approximately maximize the trained conditional probability. The current beam search strategy generates the target sentence word by word from left-to- right while keeping a fixed amount of active candidates at each time step. First, this simple search is less adaptive as it also expands candidates whose scores are much worse than the current best. Secondly, it does not expand hypotheses if they are not within the best scoring candidates, even if their scores are close to the best one. The latter one can be avoided by increasing the beam size until no performance improvement can be observed. While you can reach better performance, this has the draw- back of a slower decoding speed. In this paper, we concentrate on speeding up the decoder by applying a more flexible beam search strategy whose candidate size may vary at each time step depending on the candidate scores. We speed up the original decoder by up to 43% for the two language pairs German-English and Chinese-English without losing any translation quality.", "published": "2017-02-06T22:08:46Z", "authors": ["Markus Freitag", "Yaser Al-Onaizan"], "categories": ["cs.CL"], "doi": "10.18653/v1/W17-3207", "_query": "house price prediction machine learning", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2403.10567v3", "title": "Ensemble learning for uncertainty estimation with application to the correction of satellite precipitation products", "abstract": "Predictions in the form of probability distributions are crucial for effective decision-making. Quantile regression enables such predictions within spatial prediction settings that aim to create improved precipitation datasets by merging remote sensing and gauge data. However, ensemble learning of quantile regression algorithms remains unexplored in this context and, at the same time, it has not been substantially developed so far in the broader machine learning research landscape. Here, we introduce nine quantile-based ensemble learners and address the aforementioned gap in precipitation dataset creation by presenting the first application of these learners to large precipitation datasets. We employed a novel feature engineering strategy, which reduces the number of predictors by using distance-weighted satellite precipitation at relevant locations, combined with location elevation. Our ensemble learners include six that are based on stacking ideas and three simple methods (mean, median, best combiner). Each of them combines the following six individual algorithms: quantile regression (QR), quantile regression forests (QRF), generalized random forests (GRF), gradient boosting machines (GBM), light gradient boosting machines (LightGBM), and quantile regression neural networks (QRNN). These algorithms serve as both base learners and combiners within different ensemble learning methods. We evaluated performance against a reference method (i.e., QR) using quantile scoring functions and a large dataset. The latter comprises 15 years of monthly gauge-measured and satellite precipitation in the contiguous United States (CONUS). Ensemble learning with QR and QRNN yielded the best results across the various investigated quantile levels, which range from 0.025 to 0.975, outperforming the reference method by 3.91% to 8.95%...", "published": "2024-03-14T17:45:56Z", "authors": ["Georgia Papacharalampous", "Hristos Tyralis", "Nikolaos Doulamis", "Anastasios Doulamis"], "categories": ["cs.LG", "stat.ME"], "doi": "10.1088/3049-4753/add93b", "_query": "house price prediction machine learning", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2309.13310v1", "title": "An Interpretable Systematic Review of Machine Learning Models for Predictive Maintenance of Aircraft Engine", "abstract": "This paper presents an interpretable review of various machine learning and deep learning models to predict the maintenance of aircraft engine to avoid any kind of disaster. One of the advantages of the strategy is that it can work with modest datasets. In this study, sensor data is utilized to predict aircraft engine failure within a predetermined number of cycles using LSTM, Bi-LSTM, RNN, Bi-RNN GRU, Random Forest, KNN, Naive Bayes, and Gradient Boosting. We explain how deep learning and machine learning can be used to generate predictions in predictive maintenance using a straightforward scenario with just one data source. We applied lime to the models to help us understand why machine learning models did not perform well than deep learning models. An extensive analysis of the model's behavior is presented for several test data to understand the black box scenario of the models. A lucrative accuracy of 97.8%, 97.14%, and 96.42% are achieved by GRU, Bi-LSTM, and LSTM respectively which denotes the capability of the models to predict maintenance at an early stage.", "published": "2023-09-23T08:54:10Z", "authors": ["Abdullah Al Hasib", "Ashikur Rahman", "Mahpara Khabir", "Md. Tanvir Rouf Shawon"], "categories": ["cs.LG"], "doi": "", "_query": "house price prediction machine learning", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/1901.10539v1", "title": "Impact of Data Pruning on Machine Learning Algorithm Performance", "abstract": "Dataset pruning is the process of removing sub-optimal tuples from a dataset to improve the learning of a machine learning model. In this paper, we compared the performance of different algorithms, first on an unpruned dataset and then on an iteratively pruned dataset. The goal was to understand whether an algorithm (say A) on an unpruned dataset performs better than another algorithm (say B), will algorithm B perform better on the pruned data or vice-versa. The dataset chosen for our analysis is a subset of the largest movie ratings database publicly available on the internet, IMDb [1]. The learning objective of the model was to predict the categorical rating of a movie among 5 bins: poor, average, good, very good, excellent. The results indicated that an algorithm that performed better on an unpruned dataset also performed better on a pruned dataset.", "published": "2019-01-11T19:32:09Z", "authors": ["Arun Thundyill Saseendran", "Lovish Setia", "Viren Chhabria", "Debrup Chakraborty", "Aneek Barman Roy"], "categories": ["cs.LG", "stat.ML"], "doi": "", "_query": "house price prediction machine learning", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2312.17584v1", "title": "Interpretable and Explainable Machine Learning Methods for Predictive Process Monitoring: A Systematic Literature Review", "abstract": "This paper presents a systematic literature review (SLR) on the explainability and interpretability of machine learning (ML) models within the context of predictive process mining, using the PRISMA framework. Given the rapid advancement of artificial intelligence (AI) and ML systems, understanding the \"black-box\" nature of these technologies has become increasingly critical. Focusing specifically on the domain of process mining, this paper delves into the challenges of interpreting ML models trained with complex business process data. We differentiate between intrinsically interpretable models and those that require post-hoc explanation techniques, providing a comprehensive overview of the current methodologies and their applications across various application domains. Through a rigorous bibliographic analysis, this research offers a detailed synthesis of the state of explainability and interpretability in predictive process mining, identifying key trends, challenges, and future directions. Our findings aim to equip researchers and practitioners with a deeper understanding of how to develop and implement more trustworthy, transparent, and effective intelligent systems for predictive process analytics.", "published": "2023-12-29T12:43:43Z", "authors": ["Nijat Mehdiyev", "Maxim Majlatow", "Peter Fettke"], "categories": ["cs.LG", "cs.AI", "stat.ML"], "doi": "", "_query": "house price prediction machine learning", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2605.05882v2", "title": "Tuning Derivatives for Causal Fairness in Machine Learning", "abstract": "Artificial-intelligence systems are becoming ubiquitous in society, yet their predictions typically inherit biases with respect to protected attributes such as race, gender, or age. Classical fairness notions, most notably Statistical Parity (SP), demand that predictions be independent of the protected attributes, but are overly restrictive when these attributes influence mediating variables that are considered business necessities. Recent causal formulations relax SP by distinguishing allowed from not-allowed causal paths and by complementing SP with Predictive Parity (PP), requiring the predictor to replicate the legitimate influence of business-necessities. Existing path-based definitions are mainly practical when applied to categorical attributes. This paper introduces a new framework for fairness in structural causal models that is tailored to continuous protected attributes. We formalize SP and PP through path-specific partial derivatives, establish conditions under which these criteria coincide with prior causal definitions, and characterize when a fair predictor, one that satisfies SP along not-allowed paths while achieving PP along allowed paths, exists. Building on this theory, we propose a fair tuning algorithm that either constructs such a predictor or, when not possible, allows for a trade-off between SP and PP. We present experiments on simulated and real data to evaluate our proposal, compare it with previously proposed methods, and show that it performs better when PP is considered.", "published": "2026-05-07T08:53:50Z", "authors": ["Filip Edström", "Guilherme W. F. Barros", "Tetiana Gorbach", "Xavier de Luna"], "categories": ["stat.ML", "cs.AI", "cs.CY", "cs.LG"], "doi": "10.1007/s10994-026-07061-7", "_query": "house price prediction machine learning", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2404.03586v2", "title": "Leveraging Interpolation Models and Error Bounds for Verifiable Scientific Machine Learning", "abstract": "Effective verification and validation techniques for modern scientific machine learning workflows are challenging to devise. Statistical methods are abundant and easily deployed, but often rely on speculative assumptions about the data and methods involved. Error bounds for classical interpolation techniques can provide mathematically rigorous estimates of accuracy, but often are difficult or impractical to determine computationally. In this work, we present a best-of-both-worlds approach to verifiable scientific machine learning by demonstrating that (1) multiple standard interpolation techniques have informative error bounds that can be computed or estimated efficiently; (2) comparative performance among distinct interpolants can aid in validation goals; (3) deploying interpolation methods on latent spaces generated by deep learning techniques enables some interpretability for black-box models. We present a detailed case study of our approach for predicting lift-drag ratios from airfoil images. Code developed for this work is available in a public Github repository.", "published": "2024-04-04T16:52:17Z", "authors": ["Tyler Chang", "Andrew Gillette", "Romit Maulik"], "categories": ["cs.LG", "stat.ML"], "doi": "10.1016/j.jcp.2025.113726", "_query": "house price prediction machine learning", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2003.10933v3", "title": "Learn to Forget: Machine Unlearning via Neuron Masking", "abstract": "Nowadays, machine learning models, especially neural networks, become prevalent in many real-world applications.These models are trained based on a one-way trip from user data: as long as users contribute their data, there is no way to withdraw; and it is well-known that a neural network memorizes its training data. This contradicts the \"right to be forgotten\" clause of GDPR, potentially leading to law violations. To this end, machine unlearning becomes a popular research topic, which allows users to eliminate memorization of their private data from a trained machine learning model.In this paper, we propose the first uniform metric called for-getting rate to measure the effectiveness of a machine unlearning method. It is based on the concept of membership inference and describes the transformation rate of the eliminated data from \"memorized\" to \"unknown\" after conducting unlearning. We also propose a novel unlearning method calledForsaken. It is superior to previous work in either utility or efficiency (when achieving the same forgetting rate). We benchmark Forsaken with eight standard datasets to evaluate its performance. The experimental results show that it can achieve more than 90\\% forgetting rate on average and only causeless than 5\\% accuracy loss.", "published": "2020-03-24T15:46:38Z", "authors": ["Yang Liu", "Zhuo Ma", "Ximeng Liu", "Jian Liu", "Zhongyuan Jiang", "Jianfeng Ma", "Philip Yu", "Kui Ren"], "categories": ["cs.LG", "cs.CR", "stat.ML"], "doi": "", "_query": "house price prediction machine learning", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2003.08500v2", "title": "The Cost of Privacy in Asynchronous Differentially-Private Machine Learning", "abstract": "We consider training machine learning models using Training data located on multiple private and geographically-scattered servers with different privacy settings. Due to the distributed nature of the data, communicating with all collaborating private data owners simultaneously may prove challenging or altogether impossible. In this paper, we develop differentially-private asynchronous algorithms for collaboratively training machine-learning models on multiple private datasets. The asynchronous nature of the algorithms implies that a central learner interacts with the private data owners one-on-one whenever they are available for communication without needing to aggregate query responses to construct gradients of the entire fitness function. Therefore, the algorithm efficiently scales to many data owners. We define the cost of privacy as the difference between the fitness of a privacy-preserving machine-learning model and the fitness of trained machine-learning model in the absence of privacy concerns. We prove that we can forecast the performance of the proposed privacy-preserving asynchronous algorithms. We demonstrate that the cost of privacy has an upper bound that is inversely proportional to the combined size of the training datasets squared and the sum of the privacy budgets squared. We validate the theoretical results with experiments on financial and medical datasets. The experiments illustrate that collaboration among more than 10 data owners with at least 10,000 records with privacy budgets greater than or equal to 1 results in a superior machine-learning model in comparison to a model trained in isolation on only one of the datasets, illustrating the value of collaboration and the cost of the privacy. The number of the collaborating datasets can be lowered if the privacy budget is higher.", "published": "2020-03-18T23:06:28Z", "authors": ["Farhad Farokhi", "Nan Wu", "David Smith", "Mohamed Ali Kaafar"], "categories": ["cs.LG", "cs.CR", "eess.SP", "math.OC", "stat.ML"], "doi": "", "_query": "house price prediction machine learning", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2311.14139v1", "title": "Machine Learning For An Explainable Cost Prediction of Medical Insurance", "abstract": "Predictive modeling in healthcare continues to be an active actuarial research topic as more insurance companies aim to maximize the potential of Machine Learning approaches to increase their productivity and efficiency. In this paper, the authors deployed three regression-based ensemble ML models that combine variations of decision trees through Extreme Gradient Boosting, Gradient-boosting Machine, and Random Forest) methods in predicting medical insurance costs. Explainable Artificial Intelligence methods SHapley Additive exPlanations and Individual Conditional Expectation plots were deployed to discover and explain the key determinant factors that influence medical insurance premium prices in the dataset. The dataset used comprised 986 records and is publicly available in the KAGGLE repository. The models were evaluated using four performance evaluation metrics, including R-squared, Mean Absolute Error, Root Mean Squared Error, and Mean Absolute Percentage Error. The results show that all models produced impressive outcomes; however, the XGBoost model achieved a better overall performance although it also expanded more computational resources, while the RF model recorded a lesser prediction error and consumed far fewer computing resources than the XGBoost model. Furthermore, we compared the outcome of both XAi methods in identifying the key determinant features that influenced the PremiumPrices for each model and whereas both XAi methods produced similar outcomes, we found that the ICE plots showed in more detail the interactions between each variable than the SHAP analysis which seemed to be more high-level. It is the aim of the authors that the contributions of this study will help policymakers, insurers, and potential medical insurance buyers in their decision-making process for selecting the right policies that meet their specific needs.", "published": "2023-11-23T18:13:34Z", "authors": ["Ugochukwu Orji", "Elochukwu Ukwandu"], "categories": ["cs.LG", "cs.AI"], "doi": "", "_query": "house price prediction machine learning", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/1912.07737v2", "title": "On the Explanation of Machine Learning Predictions in Clinical Gait Analysis", "abstract": "Machine learning (ML) is increasingly used to support decision-making in the healthcare sector. While ML approaches provide promising results with regard to their classification performance, most share a central limitation, namely their black-box character. Motivated by the interest to understand the functioning of ML models, methods from the field of Explainable Artificial Intelligence (XAI) have recently become important. This article investigates the usefulness of XAI methods in clinical gait classification. For this purpose, predictions of state-of-the-art classification methods are explained with an established XAI method, i.e., Layer-wise Relevance Propagation (LRP). We propose to evaluate the obtained explanations with two complementary approaches: a statistical analysis of the underlying data using Statistical Parametric Mapping and a qualitative evaluation by a clinical expert. A gait dataset comprising ground reaction force measurements from 132 patients with different lower-body gait disorders and 62 healthy controls is utilized. We investigate several gait classification tasks, employ multiple classification methods, and analyze the impact of data normalization and different signal components for classification performance and explanation quality. Our experiments show that explanations obtained by LRP exhibit promising statistical properties concerning inter-class discriminativity and are also in line with clinically relevant biomechanical gait characteristics.", "published": "2019-12-16T22:34:06Z", "authors": ["Djordje Slijepcevic", "Fabian Horst", "Sebastian Lapuschkin", "Anna-Maria Raberger", "Matthias Zeppelzauer", "Wojciech Samek", "Christian Breiteneder", "Wolfgang I. Schöllhorn", "Brian Horsak"], "categories": ["cs.LG", "stat.ML"], "doi": "10.1145/3474121", "_query": "house price prediction machine learning", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2012.10394v1", "title": "Deep Learning for Climate Model Output Statistics", "abstract": "Climate models are an important tool for the assessment of prospective climate change effects but they suffer from systematic and representation errors, especially for precipitation. Model output statistics (MOS) reduce these errors by fitting the model output to observational data with machine learning. In this work, we explore the feasibility and potential of deep learning with convolutional neural networks (CNNs) for MOS. We propose the CNN architecture ConvMOS specifically designed for reducing errors in climate model outputs and apply it to the climate model REMO. Our results show a considerable reduction of errors and mostly improved performance compared to three commonly used MOS approaches.", "published": "2020-12-09T11:38:37Z", "authors": ["Michael Steininger", "Daniel Abel", "Katrin Ziegler", "Anna Krause", "Heiko Paeth", "Andreas Hotho"], "categories": ["physics.ao-ph", "cs.LG"], "doi": "", "_query": "house price prediction machine learning", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2306.04566v1", "title": "Recent applications of machine learning, remote sensing, and iot approaches in yield prediction: a critical review", "abstract": "The integration of remote sensing and machine learning in agriculture is transforming the industry by providing insights and predictions through data analysis. This combination leads to improved yield prediction and water management, resulting in increased efficiency, better yields, and more sustainable agricultural practices. Achieving the United Nations' Sustainable Development Goals, especially \"zero hunger,\" requires the investigation of crop yield and precipitation gaps, which can be accomplished through, the usage of artificial intelligence (AI), machine learning (ML), remote sensing (RS), and the internet of things (IoT). By integrating these technologies, a robust agricultural mobile or web application can be developed, providing farmers and decision-makers with valuable information and tools for improving crop management and increasing efficiency. Several studies have investigated these new technologies and their potential for diverse tasks such as crop monitoring, yield prediction, irrigation management, etc. Through a critical review, this paper reviews relevant articles that have used RS, ML, cloud computing, and IoT in crop yield prediction. It reviews the current state-of-the-art in this field by critically evaluating different machine-learning approaches proposed in the literature for crop yield prediction and water management. It provides insights into how these methods can improve decision-making in agricultural production systems. This work will serve as a compendium for those interested in yield prediction in terms of primary literature but, most importantly, what approaches can be used for real-time and robust prediction.", "published": "2023-06-07T16:13:16Z", "authors": ["Fatima Zahra Bassine", "Terence Epule Epule", "Ayoub Kechchour", "Abdelghani Chehbouni"], "categories": ["cs.LG", "cs.AI", "cs.NI", "cs.SE"], "doi": "", "_query": "house price prediction machine learning", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2608.03017v1", "title": "Paired Recipient-based Evaluation of Survival Prediction for Deceased Donor Kidney Transplants", "abstract": "There has been significant interest in using machine learning algorithms to predict kidney transplant outcomes, such as the number of years until a graft inevitably fails. These prediction algorithms could possibly be used for pre-transplant donor-recipient matching to identify more compatible donors and recipients and thus improve post-transplant outcomes. In this study, we explore the use of survival prediction models trained on deceased donor kidney transplant data from the Scientific Registry of Transplant Recipients (SRTR). We propose a novel paired recipient-based evaluation framework that compares graft outcomes between two recipients who received kidneys from the same deceased donor, allowing us to evaluate the counterfactual benefit of changing the recipient for a certain donor. We find that five different survival prediction models, ranging in complexity from linear to deep learning-based models, all result in ~60% paired recipient-based accuracy. We further translate this accuracy into an interpretable quantity of post-transplant years gained. We also highlight major limitations of the commonly used concordance index (C-index) metric for evaluating survival prediction accuracy in this setting and demonstrate that our proposed paired recipient-based accuracy metric is more clinically relevant and better reflects real-world allocation settings.", "published": "2026-08-04T02:01:03Z", "authors": ["Misaki Matsuura", "Mohammadreza Nemati", "Dulat Bekbolsynov", "Stanislaw Stepkowski", "Kevin S. Xu"], "categories": ["cs.LG", "cs.CY", "stat.AP"], "doi": "", "_query": "house price prediction machine learning", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2307.04957v2", "title": "Reinforcement Learning with Non-Cumulative Objective", "abstract": "In reinforcement learning, the objective is almost always defined as a \\emph{cumulative} function over the rewards along the process. However, there are many optimal control and reinforcement learning problems in various application fields, especially in communications and networking, where the objectives are not naturally expressed as summations of the rewards. In this paper, we recognize the prevalence of non-cumulative objectives in various problems, and propose a modification to existing algorithms for optimizing such objectives. Specifically, we dive into the fundamental building block for many optimal control and reinforcement learning algorithms: the Bellman optimality equation. To optimize a non-cumulative objective, we replace the original summation operation in the Bellman update rule with a generalized operation corresponding to the objective. Furthermore, we provide sufficient conditions on the form of the generalized operation as well as assumptions on the Markov decision process under which the globally optimal convergence of the generalized Bellman updates can be guaranteed. We demonstrate the idea experimentally with the bottleneck objective, i.e., the objectives determined by the minimum reward along the process, on classical optimal control and reinforcement learning tasks, as well as on two network routing problems on maximizing the flow rates.", "published": "2023-07-11T01:20:09Z", "authors": ["Wei Cui", "Wei Yu"], "categories": ["cs.LG", "cs.AI", "cs.NI", "math.OC", "stat.ML"], "doi": "10.1109/TMLCN.2023.3285543", "_query": "house price prediction machine learning", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2401.10290v1", "title": "Early Prediction of Geomagnetic Storms by Machine Learning Algorithms", "abstract": "Geomagnetic storms (GS) occur when solar winds disrupt Earth's magnetosphere. GS can cause severe damages to satellites, power grids, and communication infrastructures. Estimate of direct economic impacts of a large scale GS exceeds $40 billion a day in the US. Early prediction is critical in preventing and minimizing the hazards. However, current methods either predict several hours ahead but fail to identify all types of GS, or make predictions within short time, e.g., one hour ahead of the occurrence. This work aims to predict all types of geomagnetic storms reliably and as early as possible using big data and machine learning algorithms. By fusing big data collected from multiple ground stations in the world on different aspects of solar measurements and using Random Forests regression with feature selection and downsampling on minor geomagnetic storm instances (which carry majority of the data), we are able to achieve an accuracy of 82.55% on data collected in 2021 when making early predictions three hours in advance. Given that important predictive features such as historic Kp indices are measured every 3 hours and their importance decay quickly with the amount of time in advance, an early prediction of 3 hours ahead of time is believed to be close to the practical limit.", "published": "2024-01-17T05:17:40Z", "authors": ["Iris Yan"], "categories": ["cs.LG"], "doi": "", "_query": "house price prediction machine learning", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2609.00375v1", "title": "Residential Price Modeling using Spatially Validated Machine Learning Methods: A Comparison Across Geographic Contexts", "abstract": "Land use and transportation infrastructure are tightly linked systems, and residential property prices play a central role in transportation planning. Transportation investments also influence property values, making accurate forecasting of both systems an interconnected research challenge. This study examines these interactions and evaluates machine learning methods for modeling residential real estate prices across contrasting urban contexts. We use XGBoost and Random Forest models to assess how land use and transportation infrastructure shape dwelling prices, comparing results between the Rawalpindi and Islamabad Metropolitan Area in Pakistan and the City of Toronto in Canada. We also examine the performance of machine learning models on spatial data by comparing nonspatial and spatial cross validation, and use SHAP values to interpret feature impacts. Despite differences in demographics and economic development, both cities show similar effects of transportation infrastructure and local amenities on prices. Proximity to major urban cores increases sale price, while high quality transit such as subway and bus rapid transit raises prices and conventional bus stop proximity lowers them. Nonspatial cross validation overestimates predictive accuracy for spatial datasets. XGBoost performs slightly better than Random Forest. We recommend careful application of machine learning methods when modeling spatially dependent land price data.", "published": "2026-07-16T19:18:24Z", "authors": ["Usman Ahmed", "Jason Hawkins"], "categories": ["econ.EM"], "doi": "", "_query": "house price prediction machine learning", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2601.05082v1", "title": "Exploring Student Expectations and Confidence in Learning Analytics", "abstract": "Learning Analytics (LA) is nowadays ubiquitous in many educational systems, providing the ability to collect and analyze student data in order to understand and optimize learning and the environments in which it occurs. On the other hand, the collection of data requires to comply with the growing demand regarding privacy legislation. In this paper, we use the Student Expectation of Learning Analytics Questionnaire (SELAQ) to analyze the expectations and confidence of students from different faculties regarding the processing of their data for Learning Analytics purposes. This allows us to identify four clusters of students through clustering algorithms: Enthusiasts, Realists, Cautious and Indifferents. This structured analysis provides valuable insights into the acceptance and criticism of Learning Analytics among students.", "published": "2026-01-08T16:27:09Z", "authors": ["Hayk Asatryan", "Basile Tousside", "Janis Mohr", "Malte Neugebauer", "Hildo Bijl", "Paul Spiegelberg", "Claudia Frohn-Schauf", "Jörg Frochte"], "categories": ["cs.LG", "cs.CY", "cs.HC"], "doi": "10.1145/3636555.3636923", "_query": "house price prediction machine learning", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2212.09240v1", "title": "Probabilistic machine learning based predictive and interpretable digital twin for dynamical systems", "abstract": "A framework for creating and updating digital twins for dynamical systems from a library of physics-based functions is proposed. The sparse Bayesian machine learning is used to update and derive an interpretable expression for the digital twin. Two approaches for updating the digital twin are proposed. The first approach makes use of both the input and output information from a dynamical system, whereas the second approach utilizes output-only observations to update the digital twin. Both methods use a library of candidate functions representing certain physics to infer new perturbation terms in the existing digital twin model. In both cases, the resulting expressions of updated digital twins are identical, and in addition, the epistemic uncertainties are quantified. In the first approach, the regression problem is derived from a state-space model, whereas in the latter case, the output-only information is treated as a stochastic process. The concepts of Itô calculus and Kramers-Moyal expansion are being utilized to derive the regression equation. The performance of the proposed approaches is demonstrated using highly nonlinear dynamical systems such as the crack-degradation problem. Numerical results demonstrated in this paper almost exactly identify the correct perturbation terms along with their associated parameters in the dynamical system. The probabilistic nature of the proposed approach also helps in quantifying the uncertainties associated with updated models. The proposed approaches provide an exact and explainable description of the perturbations in digital twin models, which can be directly used for better cyber-physical integration, long-term future predictions, degradation monitoring, and model-agnostic control.", "published": "2022-12-19T04:25:59Z", "authors": ["Tapas Tripura", "Aarya Sheetal Desai", "Sondipon Adhikari", "Souvik Chakraborty"], "categories": ["stat.ML", "cs.LG"], "doi": "", "_query": "house price prediction machine learning", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2001.03000v1", "title": "Guidelines for enhancing data locality in selected machine learning algorithms", "abstract": "To deal with the complexity of the new bigger and more complex generation of data, machine learning (ML) techniques are probably the first and foremost used. For ML algorithms to produce results in a reasonable amount of time, they need to be implemented efficiently. In this paper, we analyze one of the means to increase the performances of machine learning algorithms which is exploiting data locality. Data locality and access patterns are often at the heart of performance issues in computing systems due to the use of certain hardware techniques to improve performance. Altering the access patterns to increase locality can dramatically increase performance of a given algorithm. Besides, repeated data access can be seen as redundancy in data movement. Similarly, there can also be redundancy in the repetition of calculations. This work also identifies some of the opportunities for avoiding these redundancies by directly reusing computation results. We start by motivating why and how a more efficient implementation can be achieved by exploiting reuse in the memory hierarchy of modern instruction set processors. Next we document the possibilities of such reuse in some selected machine learning algorithms.", "published": "2020-01-09T14:16:40Z", "authors": ["Imen Chakroun", "Tom Vander Aa", "Thomas J. Ashby"], "categories": ["cs.LG", "stat.ML"], "doi": "10.3233/IDA-184287", "_query": "house price prediction machine learning", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2210.08186v1", "title": "Machine Learning Approach for Predicting Students Academic Performance and Study Strategies based on their Motivation", "abstract": "This research aims to develop machine learning models for students academic performance and study strategies prediction which could be generalized to all courses in higher education. Key learning attributes (intrinsic, extrinsic, autonomy, relatedness, competence, and self-esteem) essential for students learning process were used in building the models. Determining the broad effect of these attributes on students' academic performance and study strategy is the center of our interest. To investigate this, we used Scikit-learn in python to build five machine learning models (Decision Tree, K-Nearest Neighbour, Random Forest, Linear/Logistic Regression, and Support Vector Machine) for both regression and classification tasks to perform our analysis. The models were trained, evaluated, and tested for accuracy using 924 university dentistry students' data collected by Chilean authors through quantitative research design. A comparative analysis of the models revealed that the tree-based models such as the random forest (with prediction accuracy of 94.9%) and decision tree show the best results compared to the linear, support vector, and k-nearest neighbours. The models built in this research can be used in predicting student performance and study strategy so that appropriate interventions could be implemented to improve student learning progress. Thus, incorporating strategies that could improve diverse student learning attributes in the design of online educational systems may increase the likelihood of students continuing with their learning tasks as required. Moreover, the results show that the attributes could be modelled together and used to adapt/personalize the learning process.", "published": "2022-10-15T04:09:05Z", "authors": ["Fidelia A. Orji", "Julita Vassileva"], "categories": ["cs.LG", "cs.AI"], "doi": "", "_query": "house price prediction machine learning", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2006.14078v2", "title": "Machine learning the real discriminant locus", "abstract": "Parameterized systems of polynomial equations arise in many applications in science and engineering with the real solutions describing, for example, equilibria of a dynamical system, linkages satisfying design constraints, and scene reconstruction in computer vision. Since different parameter values can have a different number of real solutions, the parameter space is decomposed into regions whose boundary forms the real discriminant locus. This article views locating the real discriminant locus as a supervised classification problem in machine learning where the goal is to determine classification boundaries over the parameter space, with the classes being the number of real solutions. For multidimensional parameter spaces, this article presents a novel sampling method which carefully samples the parameter space. At each sample point, homotopy continuation is used to obtain the number of real solutions to the corresponding polynomial system. Machine learning techniques including nearest neighbor and deep learning are used to efficiently approximate the real discriminant locus. One application of having learned the real discriminant locus is to develop a real homotopy method that only tracks the real solution paths unlike traditional methods which track all~complex~solution~paths. Examples show that the proposed approach can efficiently approximate complicated solution boundaries such as those arising from the equilibria of the Kuramoto model.", "published": "2020-06-24T22:18:08Z", "authors": ["Edgar A. Bernal", "Jonathan D. Hauenstein", "Dhagash Mehta", "Margaret H. Regan", "Tingting Tang"], "categories": ["stat.ML", "cs.LG", "cs.SC", "math.AG", "stat.AP"], "doi": "", "_query": "house price prediction machine learning", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/1911.03030v6", "title": "Certified Data Removal from Machine Learning Models", "abstract": "Good data stewardship requires removal of data at the request of the data's owner. This raises the question if and how a trained machine-learning model, which implicitly stores information about its training data, should be affected by such a removal request. Is it possible to \"remove\" data from a machine-learning model? We study this problem by defining certified removal: a very strong theoretical guarantee that a model from which data is removed cannot be distinguished from a model that never observed the data to begin with. We develop a certified-removal mechanism for linear classifiers and empirically study learning settings in which this mechanism is practical.", "published": "2019-11-08T03:57:41Z", "authors": ["Chuan Guo", "Tom Goldstein", "Awni Hannun", "Laurens van der Maaten"], "categories": ["cs.LG", "stat.ML"], "doi": "", "_query": "house price prediction machine learning", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2211.01551v1", "title": "Crime Prediction using Machine Learning with a Novel Crime Dataset", "abstract": "Crime is an unlawful act that carries legal repercussions. Bangladesh has a high crime rate due to poverty, population growth, and many other socio-economic issues. For law enforcement agencies, understanding crime patterns is essential for preventing future criminal activity. For this purpose, these agencies need structured crime database. This paper introduces a novel crime dataset that contains temporal, geographic, weather, and demographic data about 6574 crime incidents of Bangladesh. We manually gather crime news articles of a seven year time span from a daily newspaper archive. We extract basic features from these raw text. Using these basic features, we then consult standard service-providers of geo-location and weather data in order to garner these information related to the collected crime incidents. Furthermore, we collect demographic information from Bangladesh National Census data. All these information are combined that results in a standard machine learning dataset. Together, 36 features are engineered for the crime prediction task. Five supervised machine learning classification algorithms are then evaluated on this newly built dataset and satisfactory results are achieved. We also conduct exploratory analysis on various aspects the dataset. This dataset is expected to serve as the foundation for crime incidence prediction systems for Bangladesh and other countries. The findings of this study will help law enforcement agencies to forecast and contain crime as well as to ensure optimal resource allocation for crime patrol and prevention.", "published": "2022-11-03T01:55:52Z", "authors": ["Faisal Tareque Shohan", "Abu Ubaida Akash", "Muhammad Ibrahim", "Mohammad Shafiul Alam"], "categories": ["cs.LG", "cs.AI", "cs.CY"], "doi": "", "_query": "house price prediction machine learning", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2002.08207v1", "title": "Inventory effects on the price dynamics of VSTOXX futures quantified via machine learning", "abstract": "The VSTOXX index tracks the expected 30-day volatility of the EURO STOXX 50 equity index. Futures on the VSTOXX index can, therefore, be used to hedge against economic uncertainty. We investigate the effect of trader inventory on the price of VSTOXX futures through a combination of stochastic processes and machine learning methods. We formulate a simple and efficient pricing methodology for VSTOXX futures, which assumes a Heston-type stochastic process for the underlying EURO STOXX 50 market. Under these dynamics, approximate analytical formulas for the implied volatility smile and the VSTOXX index have recently been derived. We use the EURO STOXX 50 option implied volatilities and the VSTOXX index value to estimate the parameters of this Heston model. Following the calibration, we calculate theoretical VSTOXX future prices and compare them to the actual market prices. While theoretical and market prices are usually in line, we also observe time periods, during which the market price does not agree with our Heston model. We collect a variety of market features that could potentially explain the price deviations and calibrate two machine learning models to the price difference: a regularized linear model and a random forest. We find that both models indicate a strong influence of accumulated trader positions on the VSTOXX futures price.", "published": "2020-02-19T14:30:31Z", "authors": ["Daniel Guterding"], "categories": ["q-fin.TR", "q-fin.PR"], "doi": "10.1016/j.jfds.2021.06.001", "_query": "house price prediction machine learning", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2210.05955v2", "title": "Identifiability and Asymptotics in Learning Homogeneous Linear ODE Systems from Discrete Observations", "abstract": "Ordinary Differential Equations (ODEs) have recently gained a lot of attention in machine learning. However, the theoretical aspects, e.g., identifiability and asymptotic properties of statistical estimation are still obscure. This paper derives a sufficient condition for the identifiability of homogeneous linear ODE systems from a sequence of equally-spaced error-free observations sampled from a single trajectory. When observations are disturbed by measurement noise, we prove that under mild conditions, the parameter estimator based on the Nonlinear Least Squares (NLS) method is consistent and asymptotic normal with $n^{-1/2}$ convergence rate. Based on the asymptotic normality property, we construct confidence sets for the unknown system parameters and propose a new method to infer the causal structure of the ODE system, i.e., inferring whether there is a causal link between system variables. Furthermore, we extend the results to degraded observations, including aggregated and time-scaled ones. To the best of our knowledge, our work is the first systematic study of the identifiability and asymptotic properties in learning linear ODE systems. We also construct simulations with various system dimensions to illustrate the established theoretical results.", "published": "2022-10-12T06:46:38Z", "authors": ["Yuanyuan Wang", "Wei Huang", "Mingming Gong", "Xi Geng", "Tongliang Liu", "Kun Zhang", "Dacheng Tao"], "categories": ["stat.ML", "cs.LG"], "doi": "", "_query": "house price prediction machine learning", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/1912.00074v1", "title": "Quadratic Q-network for Learning Continuous Control for Autonomous Vehicles", "abstract": "Reinforcement Learning algorithms have recently been proposed to learn time-sequential control policies in the field of autonomous driving. Direct applications of Reinforcement Learning algorithms with discrete action space will yield unsatisfactory results at the operational level of driving where continuous control actions are actually required. In addition, the design of neural networks often fails to incorporate the domain knowledge of the targeting problem such as the classical control theories in our case. In this paper, we propose a hybrid model by combining Q-learning and classic PID (Proportion Integration Differentiation) controller for handling continuous vehicle control problems under dynamic driving environment. Particularly, instead of using a big neural network as Q-function approximation, we design a Quadratic Q-function over actions with multiple simple neural networks for finding optimal values within a continuous space. We also build an action network based on the domain knowledge of the control mechanism of a PID controller to guide the agent to explore optimal actions more efficiently.We test our proposed approach in simulation under two common but challenging driving situations, the lane change scenario and ramp merge scenario. Results show that the autonomous vehicle agent can successfully learn a smooth and efficient driving behavior in both situations.", "published": "2019-11-29T21:32:32Z", "authors": ["Pin Wang", "Hanhan Li", "Ching-Yao Chan"], "categories": ["cs.LG", "cs.AI", "stat.ML"], "doi": "", "_query": "house price prediction machine learning", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/1901.00246v2", "title": "Natively Interpretable Machine Learning and Artificial Intelligence: Preliminary Results and Future Directions", "abstract": "Machine learning models have become more and more complex in order to better approximate complex functions. Although fruitful in many domains, the added complexity has come at the cost of model interpretability. The once popular k-nearest neighbors (kNN) approach, which finds and uses the most similar data for reasoning, has received much less attention in recent decades due to numerous problems when compared to other techniques. We show that many of these historical problems with kNN can be overcome, and our contribution has applications not only in machine learning but also in online learning, data synthesis, anomaly detection, model compression, and reinforcement learning, without sacrificing interpretability. We introduce a synthesis between kNN and information theory that we hope will provide a clear path towards models that are innately interpretable and auditable. Through this work we hope to gather interest in combining kNN with information theory as a promising path to fully auditable machine learning and artificial intelligence.", "published": "2019-01-02T03:00:21Z", "authors": ["Christopher J. Hazard", "Christopher Fusting", "Michael Resnick", "Michael Auerbach", "Michael Meehan", "Valeri Korobov"], "categories": ["cs.LG", "cs.AI", "stat.ML"], "doi": "", "_query": "house price prediction machine learning", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2203.16797v1", "title": "When Physics Meets Machine Learning: A Survey of Physics-Informed Machine Learning", "abstract": "Physics-informed machine learning (PIML), referring to the combination of prior knowledge of physics, which is the high level abstraction of natural phenomenons and human behaviours in the long history, with data-driven machine learning models, has emerged as an effective way to mitigate the shortage of training data, to increase models' generalizability and to ensure the physical plausibility of results. In this paper, we survey an abundant number of recent works in PIML and summarize them from three aspects: (1) motivations of PIML, (2) physics knowledge in PIML, (3) methods of physics knowledge integration in PIML. We also discuss current challenges and corresponding research opportunities in PIML.", "published": "2022-03-31T04:58:27Z", "authors": ["Chuizheng Meng", "Sungyong Seo", "Defu Cao", "Sam Griesemer", "Yan Liu"], "categories": ["cs.LG", "stat.ML"], "doi": "", "_query": "house price prediction machine learning", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2003.12206v4", "title": "Improving Reproducibility in Machine Learning Research (A Report from the NeurIPS 2019 Reproducibility Program)", "abstract": "One of the challenges in machine learning research is to ensure that presented and published results are sound and reliable. Reproducibility, that is obtaining similar results as presented in a paper or talk, using the same code and data (when available), is a necessary step to verify the reliability of research findings. Reproducibility is also an important step to promote open and accessible research, thereby allowing the scientific community to quickly integrate new findings and convert ideas to practice. Reproducibility also promotes the use of robust experimental workflows, which potentially reduce unintentional errors. In 2019, the Neural Information Processing Systems (NeurIPS) conference, the premier international conference for research in machine learning, introduced a reproducibility program, designed to improve the standards across the community for how we conduct, communicate, and evaluate machine learning research. The program contained three components: a code submission policy, a community-wide reproducibility challenge, and the inclusion of the Machine Learning Reproducibility checklist as part of the paper submission process. In this paper, we describe each of these components, how it was deployed, as well as what we were able to learn from this initiative.", "published": "2020-03-27T02:16:25Z", "authors": ["Joelle Pineau", "Philippe Vincent-Lamarre", "Koustuv Sinha", "Vincent Larivière", "Alina Beygelzimer", "Florence d'Alché-Buc", "Emily Fox", "Hugo Larochelle"], "categories": ["cs.LG", "stat.ML"], "doi": "", "_query": "house price prediction machine learning", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/1906.01279v1", "title": "Graduated Optimization of Black-Box Functions", "abstract": "Motivated by the problem of tuning hyperparameters in machine learning, we present a new approach for gradually and adaptively optimizing an unknown function using estimated gradients. We validate the empirical performance of the proposed idea on both low and high dimensional problems. The experimental results demonstrate the advantages of our approach for tuning high dimensional hyperparameters in machine learning.", "published": "2019-06-04T08:56:11Z", "authors": ["Weijia Shao", "Christian Geißler", "Fikret Sivrikaya"], "categories": ["cs.LG", "math.OC", "stat.ML"], "doi": "", "_query": "house price prediction machine learning", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2505.09907v1", "title": "Avocado Price Prediction Using a Hybrid Deep Learning Model: TCN-MLP-Attention Architecture", "abstract": "With the growing demand for healthy foods, agricultural product price forecasting has become increasingly important. Hass avocados, as a high-value crop, exhibit complex price fluctuations influenced by factors such as seasonality, region, and weather. Traditional prediction models often struggle with highly nonlinear and dynamic data. To address this, we propose a hybrid deep learning model, TCN-MLP-Attention Architecture, combining Temporal Convolutional Networks (TCN) for sequential feature extraction, Multi-Layer Perceptrons (MLP) for nonlinear interactions, and an Attention mechanism for dynamic feature weighting. The dataset used covers over 50,000 records of Hass avocado sales across the U.S. from 2015 to 2018, including variables such as sales volume, average price, time, region, weather, and variety type, collected from point-of-sale systems and the Hass Avocado Board. After systematic preprocessing, including missing value imputation and feature normalization, the proposed model was trained and evaluated. Experimental results demonstrate that the TCN-MLP-Attention model achieves excellent predictive performance, with an RMSE of 1.23 and an MSE of 1.51, outperforming traditional methods. This research provides a scalable and effective approach for time series forecasting in agricultural markets and offers valuable insights for intelligent supply chain management and price strategy optimization.", "published": "2025-05-15T02:26:22Z", "authors": ["Linwei Zhang", " LuFeng", "Ruijia Liang"], "categories": ["cs.LG", "cs.AI", "cs.CE"], "doi": "", "_query": "house price prediction machine learning", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2105.02637v1", "title": "Dataset Bias in the Natural Sciences: A Case Study in Chemical Reaction Prediction and Synthesis Design", "abstract": "Datasets in the Natural Sciences are often curated with the goal of aiding scientific understanding and hence may not always be in a form that facilitates the application of machine learning. In this paper, we identify three trends within the fields of chemical reaction prediction and synthesis design that require a change in direction. First, the manner in which reaction datasets are split into reactants and reagents encourages testing models in an unrealistically generous manner. Second, we highlight the prevalence of mislabelled data, and suggest that the focus should be on outlier removal rather than data fitting only. Lastly, we discuss the problem of reagent prediction, in addition to reactant prediction, in order to solve the full synthesis design problem, highlighting the mismatch between what machine learning solves and what a lab chemist would need. Our critiques are also relevant to the burgeoning field of using machine learning to accelerate progress in experimental Natural Sciences, where datasets are often split in a biased way, are highly noisy, and contextual variables that are not evident from the data strongly influence the outcome of experiments.", "published": "2021-05-06T13:11:56Z", "authors": ["Ryan-Rhys Griffiths", "Philippe Schwaller", "Alpha A. Lee"], "categories": ["physics.chem-ph", "cs.LG"], "doi": "", "_query": "house price prediction machine learning", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2401.10149v1", "title": "Multi-Agent Reinforcement Learning for Maritime Operational Technology Cyber Security", "abstract": "This paper demonstrates the potential for autonomous cyber defence to be applied on industrial control systems and provides a baseline environment to further explore Multi-Agent Reinforcement Learning's (MARL) application to this problem domain. It introduces a simulation environment, IPMSRL, of a generic Integrated Platform Management System (IPMS) and explores the use of MARL for autonomous cyber defence decision-making on generic maritime based IPMS Operational Technology (OT). OT cyber defensive actions are less mature than they are for Enterprise IT. This is due to the relatively brittle nature of OT infrastructure originating from the use of legacy systems, design-time engineering assumptions, and lack of full-scale modern security controls. There are many obstacles to be tackled across the cyber landscape due to continually increasing cyber-attack sophistication and the limitations of traditional IT-centric cyber defence solutions. Traditional IT controls are rarely deployed on OT infrastructure, and where they are, some threats aren't fully addressed. In our experiments, a shared critic implementation of Multi Agent Proximal Policy Optimisation (MAPPO) outperformed Independent Proximal Policy Optimisation (IPPO). MAPPO reached an optimal policy (episode outcome mean of 1) after 800K timesteps, whereas IPPO was only able to reach an episode outcome mean of 0.966 after one million timesteps. Hyperparameter tuning greatly improved training performance. Across one million timesteps the tuned hyperparameters reached an optimal policy whereas the default hyperparameters only managed to win sporadically, with most simulations resulting in a draw. We tested a real-world constraint, attack detection alert success, and found that when alert success probability is reduced to 0.75 or 0.9, the MARL defenders were still able to win in over 97.5% or 99.5% of episodes, respectively.", "published": "2024-01-18T17:22:22Z", "authors": ["Alec Wilson", "Ryan Menzies", "Neela Morarji", "David Foster", "Marco Casassa Mont", "Esin Turkbeyler", "Lisa Gralewski"], "categories": ["cs.LG", "cs.CR", "cs.MA"], "doi": "", "_query": "house price prediction machine learning", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2107.02033v1", "title": "Quality Metrics for Transparent Machine Learning With and Without Humans In the Loop Are Not Correlated", "abstract": "The field explainable artificial intelligence (XAI) has brought about an arsenal of methods to render Machine Learning (ML) predictions more interpretable. But how useful explanations provided by transparent ML methods are for humans remains difficult to assess. Here we investigate the quality of interpretable computer vision algorithms using techniques from psychophysics. In crowdsourced annotation tasks we study the impact of different interpretability approaches on annotation accuracy and task time. We compare these quality metrics with classical XAI, automated quality metrics. Our results demonstrate that psychophysical experiments allow for robust quality assessment of transparency in machine learning. Interestingly the quality metrics computed without humans in the loop did not provide a consistent ranking of interpretability methods nor were they representative for how useful an explanation was for humans. These findings highlight the potential of methods from classical psychophysics for modern machine learning applications. We hope that our results provide convincing arguments for evaluating interpretability in its natural habitat, human-ML interaction, if the goal is to obtain an authentic assessment of interpretability.", "published": "2021-07-01T12:30:51Z", "authors": ["Felix Biessmann", "Dionysius Refiano"], "categories": ["cs.HC", "cs.AI", "cs.CV"], "doi": "", "_query": "house price prediction machine learning", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2302.04143v2", "title": "Predicting Thrombectomy Recanalization from CT Imaging Using Deep Learning Models", "abstract": "For acute ischemic stroke (AIS) patients with large vessel occlusions, clinicians must decide if the benefit of mechanical thrombectomy (MTB) outweighs the risks and potential complications following an invasive procedure. Pre-treatment computed tomography (CT) and angiography (CTA) are widely used to characterize occlusions in the brain vasculature. If a patient is deemed eligible, a modified treatment in cerebral ischemia (mTICI) score will be used to grade how well blood flow is reestablished throughout and following the MTB procedure. An estimation of the likelihood of successful recanalization can support treatment decision-making. In this study, we proposed a fully automated prediction of a patient's recanalization score using pre-treatment CT and CTA imaging. We designed a spatial cross attention network (SCANet) that utilizes vision transformers to localize to pertinent slices and brain regions. Our top model achieved an average cross-validated ROC-AUC of 77.33 $\\pm$ 3.9\\%. This is a promising result that supports future applications of deep learning on CT and CTA for the identification of eligible AIS patients for MTB.", "published": "2023-02-08T15:41:21Z", "authors": ["Haoyue Zhang", "Jennifer S. Polson", "Eric J. Yang", "Kambiz Nael", "William Speier", "Corey W. Arnold"], "categories": ["eess.IV", "cs.CV"], "doi": "", "_query": "house price prediction machine learning", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2107.07511v6", "title": "A Gentle Introduction to Conformal Prediction and Distribution-Free Uncertainty Quantification", "abstract": "Black-box machine learning models are now routinely used in high-risk settings, like medical diagnostics, which demand uncertainty quantification to avoid consequential model failures. Conformal prediction is a user-friendly paradigm for creating statistically rigorous uncertainty sets/intervals for the predictions of such models. Critically, the sets are valid in a distribution-free sense: they possess explicit, non-asymptotic guarantees even without distributional assumptions or model assumptions. One can use conformal prediction with any pre-trained model, such as a neural network, to produce sets that are guaranteed to contain the ground truth with a user-specified probability, such as 90%. It is easy-to-understand, easy-to-use, and general, applying naturally to problems arising in the fields of computer vision, natural language processing, deep reinforcement learning, and so on. This hands-on introduction is aimed to provide the reader a working understanding of conformal prediction and related distribution-free uncertainty quantification techniques with one self-contained document. We lead the reader through practical theory for and examples of conformal prediction and describe its extensions to complex machine learning tasks involving structured outputs, distribution shift, time-series, outliers, models that abstain, and more. Throughout, there are many explanatory illustrations, examples, and code samples in Python. With each code sample comes a Jupyter notebook implementing the method on a real-data example; the notebooks can be accessed and easily run using our codebase.", "published": "2021-07-15T17:59:50Z", "authors": ["Anastasios N. Angelopoulos", "Stephen Bates"], "categories": ["cs.LG", "cs.AI", "math.ST", "stat.ME", "stat.ML"], "doi": "", "_query": "house price prediction machine learning", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2007.12285v2", "title": "MLJ: A Julia package for composable machine learning", "abstract": "MLJ (Machine Learing in Julia) is an open source software package providing a common interface for interacting with machine learning models written in Julia and other languages. It provides tools and meta-algorithms for selecting, tuning, evaluating, composing and comparing those models, with a focus on flexible model composition. In this design overview we detail chief novelties of the framework, together with the clear benefits of Julia over the dominant multi-language alternatives.", "published": "2020-07-23T22:46:33Z", "authors": ["Anthony D. Blaom", "Franz Kiraly", "Thibaut Lienart", "Yiannis Simillides", "Diego Arenas", "Sebastian J. Vollmer"], "categories": ["cs.LG", "stat.ML"], "doi": "10.21105/joss.02704", "_query": "house price prediction machine learning", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2003.00541v1", "title": "Deep Learning for Musculoskeletal Image Analysis", "abstract": "The diagnosis, prognosis, and treatment of patients with musculoskeletal (MSK) disorders require radiology imaging (using computed tomography, magnetic resonance imaging(MRI), and ultrasound) and their precise analysis by expert radiologists. Radiology scans can also help assessment of metabolic health, aging, and diabetes. This study presents how machinelearning, specifically deep learning methods, can be used for rapidand accurate image analysis of MRI scans, an unmet clinicalneed in MSK radiology. As a challenging example, we focus on automatic analysis of knee images from MRI scans and study machine learning classification of various abnormalities including meniscus and anterior cruciate ligament tears. Using widely used convolutional neural network (CNN) based architectures, we comparatively evaluated the knee abnormality classification performances of different neural network architectures under limited imaging data regime and compared single and multi-view imaging when classifying the abnormalities. Promising results indicated the potential use of multi-view deep learning based classification of MSK abnormalities in routine clinical assessment.", "published": "2020-03-01T18:13:59Z", "authors": ["Ismail Irmakci", "Syed Muhammad Anwar", "Drew A. Torigian", "Ulas Bagci"], "categories": ["eess.IV", "cs.LG", "stat.ML"], "doi": "", "_query": "house price prediction machine learning", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/1810.06992v3", "title": "Topographic Representation for Quantum Machine Learning", "abstract": "This paper proposes a brain-inspired approach to quantum machine learning with the goal of circumventing many of the complications of other approaches. The fact that quantum processes are unitary presents both opportunities and challenges. A principal opportunity is that a large number of computations can be carried out in parallel in linear superposition, that is, quantum parallelism. The challenge is that the process is linear, and most approaches to machine learning depend significantly on nonlinear processes. Fortunately, the situation is not hopeless, for we know that nonlinear processes can be embedded in unitary processes, as is familiar from the circuit model of quantum computation. This paper explores an approach to the quantum implementation of machine learning involving nonlinear functions operating on information represented topographically (by computational maps), as common in neural cortex.", "published": "2018-10-13T01:54:08Z", "authors": ["Bruce MacLennan"], "categories": ["cs.LG", "quant-ph", "stat.ML"], "doi": "", "_query": "house price prediction machine learning", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2206.07650v1", "title": "Flexible Raman Amplifier Optimization Based on Machine Learning-aided Physical Stimulated Raman Scattering Model", "abstract": "The problem of Raman amplifier optimization is studied. A differentiable interpolation function is obtained for the Raman gain coefficient using machine learning (ML), which allows for the gradient descent optimization of forward-propagating Raman pumps. Both the frequency and power of an arbitrary number of pumps in a forward pumping configuration are then optimized for an arbitrary data channel load and span length. The forward propagation model is combined with an experimentally-trained ML model of a backward-pumping Raman amplifier to jointly optimize the frequency and power of the forward amplifier's pumps and the powers of the backward amplifier's pumps. The joint forward and backward amplifier optimization is demonstrated for an unrepeatered transmission of 250 km. A gain flatness of $<$ 1~dB over 4 THz is achieved. The optimized amplifiers are validated using a numerical simulator.", "published": "2022-06-13T09:08:18Z", "authors": ["Metodi Plamenov Yankov", "Francesco Da Ros", "Uiara Celine de Moura", "Andrea Carena", "Darko Zibar"], "categories": ["eess.SP", "cs.LG"], "doi": "", "_query": "house price prediction machine learning", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/1712.08238v2", "title": "Interventions over Predictions: Reframing the Ethical Debate for Actuarial Risk Assessment", "abstract": "Actuarial risk assessments might be unduly perceived as a neutral way to counteract implicit bias and increase the fairness of decisions made at almost every juncture of the criminal justice system, from pretrial release to sentencing, parole and probation. In recent times these assessments have come under increased scrutiny, as critics claim that the statistical techniques underlying them might reproduce existing patterns of discrimination and historical biases that are reflected in the data. Much of this debate is centered around competing notions of fairness and predictive accuracy, resting on the contested use of variables that act as \"proxies\" for characteristics legally protected against discrimination, such as race and gender. We argue that a core ethical debate surrounding the use of regression in risk assessments is not simply one of bias or accuracy. Rather, it's one of purpose. If machine learning is operationalized merely in the service of predicting individual future crime, then it becomes difficult to break cycles of criminalization that are driven by the iatrogenic effects of the criminal justice system itself. We posit that machine learning should not be used for prediction, but rather to surface covariates that are fed into a causal model for understanding the social, structural and psychological drivers of crime. We propose an alternative application of machine learning and causal inference away from predicting risk scores to risk mitigation.", "published": "2017-12-21T22:27:39Z", "authors": ["Chelsea Barabas", "Karthik Dinakar", "Joichi Ito", "Madars Virza", "Jonathan Zittrain"], "categories": ["cs.LG", "cs.CY", "stat.AP"], "doi": "", "_query": "house price prediction machine learning", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/1904.00577v2", "title": "Adaptive Bayesian Linear Regression for Automated Machine Learning", "abstract": "To solve a machine learning problem, one typically needs to perform data preprocessing, modeling, and hyperparameter tuning, which is known as model selection and hyperparameter optimization.The goal of automated machine learning (AutoML) is to design methods that can automatically perform model selection and hyperparameter optimization without human interventions for a given dataset. In this paper, we propose a meta-learning method that can search for a high-performance machine learning pipeline from the predefined set of candidate pipelines for supervised classification datasets in an efficient way by leveraging meta-data collected from previous experiments. More specifically, our method combines an adaptive Bayesian regression model with a neural network basis function and the acquisition function from Bayesian optimization. The adaptive Bayesian regression model is able to capture knowledge from previous meta-data and thus make predictions of the performances of machine learning pipelines on a new dataset. The acquisition function is then used to guide the search of possible pipelines based on the predictions.The experiments demonstrate that our approach can quickly identify high-performance pipelines for a range of test datasets and outperforms the baseline methods.", "published": "2019-04-01T06:21:31Z", "authors": ["Weilin Zhou", "Frederic Precioso"], "categories": ["cs.LG", "stat.ML"], "doi": "", "_query": "house price prediction machine learning", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/1811.07901v4", "title": "On Human Predictions with Explanations and Predictions of Machine Learning Models: A Case Study on Deception Detection", "abstract": "Humans are the final decision makers in critical tasks that involve ethical and legal concerns, ranging from recidivism prediction, to medical diagnosis, to fighting against fake news. Although machine learning models can sometimes achieve impressive performance in these tasks, these tasks are not amenable to full automation. To realize the potential of machine learning for improving human decisions, it is important to understand how assistance from machine learning models affects human performance and human agency. In this paper, we use deception detection as a testbed and investigate how we can harness explanations and predictions of machine learning models to improve human performance while retaining human agency. We propose a spectrum between full human agency and full automation, and develop varying levels of machine assistance along the spectrum that gradually increase the influence of machine predictions. We find that without showing predicted labels, explanations alone slightly improve human performance in the end task. In comparison, human performance is greatly improved by showing predicted labels (>20% relative improvement) and can be further improved by explicitly suggesting strong machine performance. Interestingly, when predicted labels are shown, explanations of machine predictions induce a similar level of accuracy as an explicit statement of strong machine performance. Our results demonstrate a tradeoff between human performance and human agency and show that explanations of machine predictions can moderate this tradeoff.", "published": "2018-11-19T19:00:01Z", "authors": ["Vivian Lai", "Chenhao Tan"], "categories": ["cs.AI", "cs.CL", "cs.CY", "physics.soc-ph", "stat.ML"], "doi": "10.1145/3287560.3287590", "_query": "house price prediction machine learning", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/1906.02299v1", "title": "Teaching AI to Explain its Decisions Using Embeddings and Multi-Task Learning", "abstract": "Using machine learning in high-stakes applications often requires predictions to be accompanied by explanations comprehensible to the domain user, who has ultimate responsibility for decisions and outcomes. Recently, a new framework for providing explanations, called TED, has been proposed to provide meaningful explanations for predictions. This framework augments training data to include explanations elicited from domain users, in addition to features and labels. This approach ensures that explanations for predictions are tailored to the complexity expectations and domain knowledge of the consumer. In this paper, we build on this foundational work, by exploring more sophisticated instantiations of the TED framework and empirically evaluate their effectiveness in two diverse domains, chemical odor and skin cancer prediction. Results demonstrate that meaningful explanations can be reliably taught to machine learning algorithms, and in some cases, improving modeling accuracy.", "published": "2019-06-05T20:42:14Z", "authors": ["Noel C. F. Codella", "Michael Hind", "Karthikeyan Natesan Ramamurthy", "Murray Campbell", "Amit Dhurandhar", "Kush R. Varshney", "Dennis Wei", "Aleksandra Mojsilović"], "categories": ["cs.LG", "cs.AI", "stat.ML"], "doi": "", "_query": "house price prediction machine learning", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2412.08016v3", "title": "GLL: A Differentiable Graph Learning Layer for Neural Networks", "abstract": "Standard deep learning architectures used for classification generate label predictions with a projection head and softmax activation function. Although successful, these methods fail to leverage the relational information between samples for generating label predictions. In recent works, graph-based learning techniques, namely Laplace learning, have been heuristically combined with neural networks for both supervised and semi-supervised learning (SSL) tasks. However, prior works approximate the gradient of the loss function with respect to the graph learning algorithm or decouple the processes; end-to-end integration with neural networks is not achieved. In this work, we derive backpropagation equations, via the adjoint method, for inclusion of a general family of graph learning layers into a neural network. The resulting method, distinct from graph neural networks, allows us to precisely integrate similarity graph construction and graph Laplacian-based label propagation into a neural network layer, replacing a projection head and softmax activation function for general classification task. Our experimental results demonstrate smooth label transitions across data, improved generalization and robustness to adversarial attacks, and improved training dynamics compared to a standard softmax-based approach.", "published": "2024-12-11T01:54:29Z", "authors": ["Jason Brown", "Bohan Chen", "Harris Hardiman-Mostow", "Jeff Calder", "Andrea L. Bertozzi"], "categories": ["cs.LG", "stat.ML"], "doi": "", "_query": "house price prediction machine learning", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2106.00065v1", "title": "Using machine learning for quantum annealing accuracy prediction", "abstract": "Quantum annealers, such as the device built by D-Wave Systems, Inc., offer a way to compute solutions of NP-hard problems that can be expressed in Ising or QUBO (quadratic unconstrained binary optimization) form. Although such solutions are typically of very high quality, problem instances are usually not solved to optimality due to imperfections of the current generations quantum annealers. In this contribution, we aim to understand some of the factors contributing to the hardness of a problem instance, and to use machine learning models to predict the accuracy of the D-Wave 2000Q annealer for solving specific problems. We focus on the Maximum Clique problem, a classic NP-hard problem with important applications in network analysis, bioinformatics, and computational chemistry. By training a machine learning classification model on basic problem characteristics such as the number of edges in the graph, or annealing parameters such as D-Wave's chain strength, we are able to rank certain features in the order of their contribution to the solution hardness, and present a simple decision tree which allows to predict whether a problem will be solvable to optimality with the D-Wave 2000Q. We extend these results by training a machine learning regression model that predicts the clique size found by D-Wave.", "published": "2021-05-31T19:14:37Z", "authors": ["Aaron Barbosa", "Elijah Pelofske", "Georg Hahn", "Hristo N. Djidjev"], "categories": ["quant-ph", "cs.ET", "cs.LG"], "doi": "10.3390/a14060187", "_query": "house price prediction machine learning", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/1708.02582v3", "title": "Cascade Adversarial Machine Learning Regularized with a Unified Embedding", "abstract": "Injecting adversarial examples during training, known as adversarial training, can improve robustness against one-step attacks, but not for unknown iterative attacks. To address this challenge, we first show iteratively generated adversarial images easily transfer between networks trained with the same strategy. Inspired by this observation, we propose cascade adversarial training, which transfers the knowledge of the end results of adversarial training. We train a network from scratch by injecting iteratively generated adversarial images crafted from already defended networks in addition to one-step adversarial images from the network being trained. We also propose to utilize embedding space for both classification and low-level (pixel-level) similarity learning to ignore unknown pixel level perturbation. During training, we inject adversarial images without replacing their corresponding clean images and penalize the distance between the two embeddings (clean and adversarial). Experimental results show that cascade adversarial training together with our proposed low-level similarity learning efficiently enhances the robustness against iterative attacks, but at the expense of decreased robustness against one-step attacks. We show that combining those two techniques can also improve robustness under the worst case black box attack scenario.", "published": "2017-08-08T17:58:40Z", "authors": ["Taesik Na", "Jong Hwan Ko", "Saibal Mukhopadhyay"], "categories": ["stat.ML", "cs.LG"], "doi": "", "_query": "house price prediction machine learning", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2606.26497v2", "title": "Learning Probabilistic Filters with Strictly Proper Scoring Rules", "abstract": "Bayesian filtering of partially and noisily observed dynamical systems seeks to infer the evolving conditional distribution of the state of a dynamical system given observations, in an online fashion. This Bayesian filtering distribution is rarely available as a supervised learning target. However, one can often use the forecast model to generate synthetic trajectories, with corresponding synthetic observations. We introduce the proper scoring ensemble filter (PSEF), an ensemble data assimilation method trained using only synthetic trajectories. The analysis step is represented as a permutation-equivariant, transformer-based map. Training is based on strictly proper scoring rules---with the energy score used in our implementation---so that probabilistic accuracy is rewarded over the whole probability distribution. Under a realizability assumption, the population mean-field objective is minimized by the true Bayesian filtering distribution. Our methodology allows the same learned parameters to be shared between different ensemble sizes, subject to an ensemble-dependent fine-tuning. Numerical experiments show that the learned filter accurately approximates challenging filtering distributions, including highly non-Gaussian and multi-modal posteriors, and achieves stronger performance in data assimilation tasks than classical methods or learning-based methods with mean-squared-error objectives.", "published": "2026-06-25T01:04:21Z", "authors": ["Eviatar Bach", "Ricardo Baptista", "Jochen Bröcker", "Bohan Chen", "Andrew Stuart"], "categories": ["cs.LG", "math.DS", "stat.ML"], "doi": "", "_query": "house price prediction machine learning", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2110.14753v1", "title": "Subtleties in the trainability of quantum machine learning models", "abstract": "A new paradigm for data science has emerged, with quantum data, quantum models, and quantum computational devices. This field, called Quantum Machine Learning (QML), aims to achieve a speedup over traditional machine learning for data analysis. However, its success usually hinges on efficiently training the parameters in quantum neural networks, and the field of QML is still lacking theoretical scaling results for their trainability. Some trainability results have been proven for a closely related field called Variational Quantum Algorithms (VQAs). While both fields involve training a parametrized quantum circuit, there are crucial differences that make the results for one setting not readily applicable to the other. In this work we bridge the two frameworks and show that gradient scaling results for VQAs can also be applied to study the gradient scaling of QML models. Our results indicate that features deemed detrimental for VQA trainability can also lead to issues such as barren plateaus in QML. Consequently, our work has implications for several QML proposals in the literature. In addition, we provide theoretical and numerical evidence that QML models exhibit further trainability issues not present in VQAs, arising from the use of a training dataset. We refer to these as dataset-induced barren plateaus. These results are most relevant when dealing with classical data, as here the choice of embedding scheme (i.e., the map between classical data and quantum states) can greatly affect the gradient scaling.", "published": "2021-10-27T20:28:53Z", "authors": ["Supanut Thanasilp", "Samson Wang", "Nhat A. Nghiem", "Patrick J. Coles", "M. Cerezo"], "categories": ["quant-ph", "cs.LG", "stat.ML"], "doi": "10.1007/s42484-023-00103-6", "_query": "house price prediction machine learning", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2001.04029v1", "title": "Tangent-Space Gradient Optimization of Tensor Network for Machine Learning", "abstract": "The gradient-based optimization method for deep machine learning models suffers from gradient vanishing and exploding problems, particularly when the computational graph becomes deep. In this work, we propose the tangent-space gradient optimization (TSGO) for the probabilistic models to keep the gradients from vanishing or exploding. The central idea is to guarantee the orthogonality between the variational parameters and the gradients. The optimization is then implemented by rotating parameter vector towards the direction of gradient. We explain and testify TSGO in tensor network (TN) machine learning, where the TN describes the joint probability distribution as a normalized state $\\left| ψ\\right\\rangle $ in Hilbert space. We show that the gradient can be restricted in the tangent space of $\\left\\langle ψ\\right.\\left| ψ\\right\\rangle = 1$ hyper-sphere. Instead of additional adaptive methods to control the learning rate in deep learning, the learning rate of TSGO is naturally determined by the angle $θ$ as $η= \\tan θ$. Our numerical results reveal better convergence of TSGO in comparison to the off-the-shelf Adam.", "published": "2020-01-10T16:40:40Z", "authors": ["Zheng-zhi Sun", "Shi-ju Ran", "Gang Su"], "categories": ["cs.LG", "cond-mat.dis-nn", "stat.ML"], "doi": "10.1103/PhysRevE.102.012152", "_query": "house price prediction machine learning", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2405.02200v2", "title": "Position: Why We Must Rethink Empirical Research in Machine Learning", "abstract": "We warn against a common but incomplete understanding of empirical research in machine learning that leads to non-replicable results, makes findings unreliable, and threatens to undermine progress in the field. To overcome this alarming situation, we call for more awareness of the plurality of ways of gaining knowledge experimentally but also of some epistemic limitations. In particular, we argue most current empirical machine learning research is fashioned as confirmatory research while it should rather be considered exploratory.", "published": "2024-05-03T15:57:22Z", "authors": ["Moritz Herrmann", "F. Julian D. Lange", "Katharina Eggensperger", "Giuseppe Casalicchio", "Marcel Wever", "Matthias Feurer", "David Rügamer", "Eyke Hüllermeier", "Anne-Laure Boulesteix", "Bernd Bischl"], "categories": ["cs.LG", "stat.ML"], "doi": "", "_query": "house price prediction machine learning", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2012.07994v1", "title": "Binary Black-box Evasion Attacks Against Deep Learning-based Static Malware Detectors with Adversarial Byte-Level Language Model", "abstract": "Anti-malware engines are the first line of defense against malicious software. While widely used, feature engineering-based anti-malware engines are vulnerable to unseen (zero-day) attacks. Recently, deep learning-based static anti-malware detectors have achieved success in identifying unseen attacks without requiring feature engineering and dynamic analysis. However, these detectors are susceptible to malware variants with slight perturbations, known as adversarial examples. Generating effective adversarial examples is useful to reveal the vulnerabilities of such systems. Current methods for launching such attacks require accessing either the specifications of the targeted anti-malware model, the confidence score of the anti-malware response, or dynamic malware analysis, which are either unrealistic or expensive. We propose MalRNN, a novel deep learning-based approach to automatically generate evasive malware variants without any of these restrictions. Our approach features an adversarial example generation process, which learns a language model via a generative sequence-to-sequence recurrent neural network to augment malware binaries. MalRNN effectively evades three recent deep learning-based malware detectors and outperforms current benchmark methods. Findings from applying our MalRNN on a real dataset with eight malware categories are discussed.", "published": "2020-12-14T22:54:53Z", "authors": ["Mohammadreza Ebrahimi", "Ning Zhang", "James Hu", "Muhammad Taqi Raza", "Hsinchun Chen"], "categories": ["cs.CR", "cs.LG"], "doi": "", "_query": "house price prediction machine learning", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/1607.07959v2", "title": "Using Kernel Methods and Model Selection for Prediction of Preterm Birth", "abstract": "We describe an application of machine learning to the problem of predicting preterm birth. We conduct a secondary analysis on a clinical trial dataset collected by the National In- stitute of Child Health and Human Development (NICHD) while focusing our attention on predicting different classes of preterm birth. We compare three approaches for deriving predictive models: a support vector machine (SVM) approach with linear and non-linear kernels, logistic regression with different model selection along with a model based on decision rules prescribed by physician experts for prediction of preterm birth. Our approach highlights the pre-processing methods applied to handle the inherent dynamics, noise and gaps in the data and describe techniques used to handle skewed class distributions. Empirical experiments demonstrate significant improvement in predicting preterm birth compared to past work.", "published": "2016-07-27T04:56:57Z", "authors": ["Ilia Vovsha", "Ansaf Salleb-Aouissi", "Anita Raja", "Thomas Koch", "Alex Rybchuk", "Axinia Radeva", "Ashwath Rajan", "Yiwen Huang", "Hatim Diab", "Ashish Tomar", "Ronald Wapner"], "categories": ["cs.LG", "stat.ML"], "doi": "", "_query": "house price prediction machine learning", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2502.20885v1", "title": "A Fused Gromov-Wasserstein Approach to Subgraph Contrastive Learning", "abstract": "Self-supervised learning has become a key method for training deep learning models when labeled data is scarce or unavailable. While graph machine learning holds great promise across various domains, the design of effective pretext tasks for self-supervised graph representation learning remains challenging. Contrastive learning, a popular approach in graph self-supervised learning, leverages positive and negative pairs to compute a contrastive loss function. However, current graph contrastive learning methods often struggle to fully use structural patterns and node similarities. To address these issues, we present a new method called Fused Gromov Wasserstein Subgraph Contrastive Learning (FOSSIL). Our model integrates node-level and subgraph-level contrastive learning, seamlessly combining a standard node-level contrastive loss with the Fused Gromov-Wasserstein distance. This combination helps our method capture both node features and graph structure together. Importantly, our approach works well with both homophilic and heterophilic graphs and can dynamically create views for generating positive and negative pairs. Through extensive experiments on benchmark graph datasets, we show that FOSSIL outperforms or achieves competitive performance compared to current state-of-the-art methods.", "published": "2025-02-28T09:32:07Z", "authors": ["Amadou S. Sangare", "Nicolas Dunou", "Jhony H. Giraldo", "Fragkiskos D. Malliaros"], "categories": ["cs.LG", "cs.AI"], "doi": "", "_query": "house price prediction machine learning", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2307.02071v1", "title": "A Comparison of Machine Learning Methods for Data with High-Cardinality Categorical Variables", "abstract": "High-cardinality categorical variables are variables for which the number of different levels is large relative to the sample size of a data set, or in other words, there are few data points per level. Machine learning methods can have difficulties with high-cardinality variables. In this article, we empirically compare several versions of two of the most successful machine learning methods, tree-boosting and deep neural networks, and linear mixed effects models using multiple tabular data sets with high-cardinality categorical variables. We find that, first, machine learning models with random effects have higher prediction accuracy than their classical counterparts without random effects, and, second, tree-boosting with random effects outperforms deep neural networks with random effects.", "published": "2023-07-05T07:26:27Z", "authors": ["Fabio Sigrist"], "categories": ["cs.LG", "cs.AI", "stat.ML"], "doi": "", "_query": "house price prediction machine learning", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2004.05849v1", "title": "MLPSVM:A new parallel support vector machine to multi-label learning", "abstract": "Multi-label learning has attracted the attention of the machine learning community. The problem conversion method Binary Relevance converts a familiar single label into a multi-label algorithm. The binary relevance method is widely used because of its simple structure and efficient algorithm. But binary relevance does not consider the links between labels, making it cumbersome to handle some tasks. This paper proposes a multi-label learning algorithm that can also be used for single-label classification. It is based on standard support vector machines and changes the original single decision hyperplane into two parallel decision hyper-planes, which call multi-label parallel support vector machine (MLPSVM). At the end of the article, MLPSVM is compared with other multi-label learning algorithms. The experimental results show that the algorithm performs well on data sets.", "published": "2020-04-13T10:04:25Z", "authors": ["Yanghong Liu", "Jia Lu", "Tingting Li"], "categories": ["cs.LG", "stat.ML"], "doi": "", "_query": "house price prediction machine learning", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2007.08911v3", "title": "Technologies for Trustworthy Machine Learning: A Survey in a Socio-Technical Context", "abstract": "Concerns about the societal impact of AI-based services and systems has encouraged governments and other organisations around the world to propose AI policy frameworks to address fairness, accountability, transparency and related topics. To achieve the objectives of these frameworks, the data and software engineers who build machine-learning systems require knowledge about a variety of relevant supporting tools and techniques. In this paper we provide an overview of technologies that support building trustworthy machine learning systems, i.e., systems whose properties justify that people place trust in them. We argue that four categories of system properties are instrumental in achieving the policy objectives, namely fairness, explainability, auditability and safety & security (FEAS). We discuss how these properties need to be considered across all stages of the machine learning life cycle, from data collection through run-time model inference. As a consequence, we survey in this paper the main technologies with respect to all four of the FEAS properties, for data-centric as well as model-centric stages of the machine learning system life cycle. We conclude with an identification of open research problems, with a particular focus on the connection between trustworthy machine learning technologies and their implications for individuals and society.", "published": "2020-07-17T11:39:20Z", "authors": ["Ehsan Toreini", "Mhairi Aitken", "Kovila P. L. Coopamootoo", "Karen Elliott", "Vladimiro Gonzalez Zelaya", "Paolo Missier", "Magdalene Ng", "Aad van Moorsel"], "categories": ["cs.LG", "cs.AI", "cs.CR", "cs.CY", "stat.ML"], "doi": "", "_query": "house price prediction machine learning", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2003.10146v2", "title": "Julia Language in Machine Learning: Algorithms, Applications, and Open Issues", "abstract": "Machine learning is driving development across many fields in science and engineering. A simple and efficient programming language could accelerate applications of machine learning in various fields. Currently, the programming languages most commonly used to develop machine learning algorithms include Python, MATLAB, and C/C ++. However, none of these languages well balance both efficiency and simplicity. The Julia language is a fast, easy-to-use, and open-source programming language that was originally designed for high-performance computing, which can well balance the efficiency and simplicity. This paper summarizes the related research work and developments in the application of the Julia language in machine learning. It first surveys the popular machine learning algorithms that are developed in the Julia language. Then, it investigates applications of the machine learning algorithms implemented with the Julia language. Finally, it discusses the open issues and the potential future directions that arise in the use of the Julia language in machine learning.", "published": "2020-03-23T09:31:02Z", "authors": ["Kaifeng Gao", "Gang Mei", "Francesco Piccialli", "Salvatore Cuomo", "Jingzhi Tu", "Zenan Huo"], "categories": ["cs.LG", "stat.ML"], "doi": "10.1016/j.cosrev.2020.100254", "_query": "house price prediction machine learning", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2405.04494v2", "title": "Representation Learning of Daily Movement Data Using Text Encoders", "abstract": "Time-series representation learning is a key area of research for remote healthcare monitoring applications. In this work, we focus on a dataset of recordings of in-home activity from people living with Dementia. We design a representation learning method based on converting activity to text strings that can be encoded using a language model fine-tuned to transform data from the same participants within a $30$-day window to similar embeddings in the vector space. This allows for clustering and vector searching over participants and days, and the identification of activity deviations to aid with personalised delivery of care.", "published": "2024-05-07T17:04:21Z", "authors": ["Alexander Capstick", "Tianyu Cui", "Yu Chen", "Payam Barnaghi"], "categories": ["cs.LG"], "doi": "", "_query": "house price prediction machine learning", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2007.07090v1", "title": "Transfer learning extensions for the probabilistic classification vector machine", "abstract": "Transfer learning is focused on the reuse of supervised learning models in a new context. Prominent applications can be found in robotics, image processing or web mining. In these fields, the learning scenarios are naturally changing but often remain related to each other motivating the reuse of existing supervised models. Current transfer learning models are neither sparse nor interpretable. Sparsity is very desirable if the methods have to be used in technically limited environments and interpretability is getting more critical due to privacy regulations. In this work, we propose two transfer learning extensions integrated into the sparse and interpretable probabilistic classification vector machine. They are compared to standard benchmarks in the field and show their relevance either by sparsity or performance improvements.", "published": "2020-07-11T08:35:10Z", "authors": ["Christoph Raab", "Frank-Michael Schleif"], "categories": ["cs.LG", "stat.ML"], "doi": "10.1016/j.neucom.2019.09.104", "_query": "house price prediction machine learning", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2112.01687v1", "title": "Differential Property Prediction: A Machine Learning Approach to Experimental Design in Advanced Manufacturing", "abstract": "Advanced manufacturing techniques have enabled the production of materials with state-of-the-art properties. In many cases however, the development of physics-based models of these techniques lags behind their use in the lab. This means that designing and running experiments proceeds largely via trial and error. This is sub-optimal since experiments are cost-, time-, and labor-intensive. In this work we propose a machine learning framework, differential property classification (DPC), which enables an experimenter to leverage machine learning's unparalleled pattern matching capability to pursue data-driven experimental design. DPC takes two possible experiment parameter sets and outputs a prediction of which will produce a material with a more desirable property specified by the operator. We demonstrate the success of DPC on AA7075 tube manufacturing process and mechanical property data using shear assisted processing and extrusion (ShAPE), a solid phase processing technology. We show that by focusing on the experimenter's need to choose between multiple candidate experimental parameters, we can reframe the challenging regression task of predicting material properties from processing parameters, into a classification task on which machine learning models can achieve good performance.", "published": "2021-12-03T02:51:15Z", "authors": ["Loc Truong", "WoongJo Choi", "Colby Wight", "Lizzy Coda", "Tegan Emerson", "Keerti Kappagantula", "Henry Kvinge"], "categories": ["cs.LG", "cs.AI"], "doi": "", "_query": "house price prediction machine learning", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/1811.12932v2", "title": "Recurrent machines for likelihood-free inference", "abstract": "Likelihood-free inference is concerned with the estimation of the parameters of a non-differentiable stochastic simulator that best reproduce real observations. In the absence of a likelihood function, most of the existing inference methods optimize the simulator parameters through a handcrafted iterative procedure that tries to make the simulated data more similar to the observations. In this work, we explore whether meta-learning can be used in the likelihood-free context, for learning automatically from data an iterative optimization procedure that would solve likelihood-free inference problems. We design a recurrent inference machine that learns a sequence of parameter updates leading to good parameter estimates, without ever specifying some explicit notion of divergence between the simulated data and the real data distributions. We demonstrate our approach on toy simulators, showing promising results both in terms of performance and robustness.", "published": "2018-11-30T18:39:12Z", "authors": ["Arthur Pesah", "Antoine Wehenkel", "Gilles Louppe"], "categories": ["stat.ML", "cs.LG", "hep-ph", "physics.data-an"], "doi": "", "_query": "house price prediction machine learning", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/1904.09448v1", "title": "LIBS2ML: A Library for Scalable Second Order Machine Learning Algorithms", "abstract": "LIBS2ML is a library based on scalable second order learning algorithms for solving large-scale problems, i.e., big data problems in machine learning. LIBS2ML has been developed using MEX files, i.e., C++ with MATLAB/Octave interface to take the advantage of both the worlds, i.e., faster learning using C++ and easy I/O using MATLAB. Most of the available libraries are either in MATLAB/Python/R which are very slow and not suitable for large-scale learning, or are in C/C++ which does not have easy ways to take input and display results. So LIBS2ML is completely unique due to its focus on the scalable second order methods, the hot research topic, and being based on MEX files. Thus it provides researchers a comprehensive environment to evaluate their ideas and it also provides machine learning practitioners an effective tool to deal with the large-scale learning problems. LIBS2ML is an open-source, highly efficient, extensible, scalable, readable, portable and easy to use library. The library can be downloaded from the URL: \\url{https://github.com/jmdvinodjmd/LIBS2ML}.", "published": "2019-04-20T14:41:05Z", "authors": ["Vinod Kumar Chauhan", "Anuj Sharma", "Kalpana Dahiya"], "categories": ["cs.LG", "stat.ML"], "doi": "10.1016/j.simpa.2021.100123", "_query": "house price prediction machine learning", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2607.22853v1", "title": "Machine Learning of Quantum Entanglement from Noisy Measurements", "abstract": "In this work, we investigate the application of Machine Learning (ML) algorithms to the identification and quantitative characterization of quantum entanglement in polarization-entangled photon pairs. The analysis is based on simulated symmetric, informationally complete, positive operator-valued measure (SIC-POVM) measurement data, where each two-qubit state is represented by a 16-dimensional measurement vector corresponding to experimentally accessible coincidence counts. The generated SIC-POVM measurement data include Poissonian shot noise. Several supervised ML algorithms, including Logistic Regression, k-Nearest Neighbors, Decision Trees, Support Vector Machines, and Random Forests, are applied to the classification of separable and entangled states directly from raw measurement data, without explicit density matrix reconstruction or the use of conventional separability criteria. The study additionally explores clustering methods and nonlinear regression techniques for estimating continuous entanglement measures. The obtained results demonstrate that ML methods can achieve very high classification accuracy, even under extremely limited training conditions. These findings indicate that ML may provide an efficient alternative to conventional quantum-state analysis under simulated Poissonian noise conditions.", "published": "2026-07-24T18:51:56Z", "authors": ["Artur Czerwinski", "Maciej Wiśniewski", "Paweł Moszczyński"], "categories": ["quant-ph", "physics.comp-ph", "physics.data-an", "physics.optics"], "doi": "10.3390/electronics15143197", "_query": "house price prediction machine learning", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2506.12030v1", "title": "Impact, Causation and Prediction of Socio-Academic and Economic Factors in Exam-centric Student Evaluation Measures using Machine Learning and Causal Analysis", "abstract": "Understanding socio-academic and economic factors influencing students' performance is crucial for effective educational interventions. This study employs several machine learning techniques and causal analysis to predict and elucidate the impacts of these factors on academic performance. We constructed a hypothetical causal graph and collected data from 1,050 student profiles. Following meticulous data cleaning and visualization, we analyze linear relationships through correlation and variable plots, and perform causal analysis on the hypothetical graph. Regression and classification models are applied for prediction, and unsupervised causality analysis using PC, GES, ICA-LiNGAM, and GRASP algorithms is conducted. Our regression analysis shows that Ridge Regression achieve a Mean Absolute Error (MAE) of 0.12 and a Mean Squared Error (MSE) of 0.024, indicating robustness, while classification models like Random Forest achieve nearly perfect F1-scores. The causal analysis shows significant direct and indirect effects of factors such as class attendance, study hours, and group study on CGPA. These insights are validated through unsupervised causality analysis. By integrating the best regression model into a web application, we are developing a practical tool for students and educators to enhance academic outcomes based on empirical evidence.", "published": "2025-05-22T17:41:05Z", "authors": ["Md. Biplob Hosen", "Sabbir Ahmed", "Bushra Akter", "Mehrin Anannya"], "categories": ["cs.LG", "cs.AI", "stat.ML"], "doi": "", "_query": "house price prediction machine learning", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/1911.07084v1", "title": "Missingness as Stability: Understanding the Structure of Missingness in Longitudinal EHR data and its Impact on Reinforcement Learning in Healthcare", "abstract": "There is an emerging trend in the reinforcement learning for healthcare literature. In order to prepare longitudinal, irregularly sampled, clinical datasets for reinforcement learning algorithms, many researchers will resample the time series data to short, regular intervals and use last-observation-carried-forward (LOCF) imputation to fill in these gaps. Typically, they will not maintain any explicit information about which values were imputed. In this work, we (1) call attention to this practice and discuss its potential implications; (2) propose an alternative representation of the patient state that addresses some of these issues; and (3) demonstrate in a novel but representative clinical dataset that our alternative representation yields consistently better results for achieving optimal control, as measured by off-policy policy evaluation, compared to representations that do not incorporate missingness information.", "published": "2019-11-16T19:40:23Z", "authors": ["Scott L. Fleming", "Kuhan Jeyapragasan", "Tony Duan", "Daisy Ding", "Saurabh Gombar", "Nigam Shah", "Emma Brunskill"], "categories": ["cs.LG", "cs.AI", "stat.ML"], "doi": "", "_query": "house price prediction machine learning", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/1902.06789v2", "title": "Seven Myths in Machine Learning Research", "abstract": "We present seven myths commonly believed to be true in machine learning research, circa Feb 2019. This is an archival copy of the blog post at https://crazyoscarchang.github.io/2019/02/16/seven-myths-in-machine-learning-research/ Myth 1: TensorFlow is a Tensor manipulation library Myth 2: Image datasets are representative of real images found in the wild Myth 3: Machine Learning researchers do not use the test set for validation Myth 4: Every datapoint is used in training a neural network Myth 5: We need (batch) normalization to train very deep residual networks Myth 6: Attention $>$ Convolution Myth 7: Saliency maps are robust ways to interpret neural networks", "published": "2019-02-18T20:38:14Z", "authors": ["Oscar Chang", "Hod Lipson"], "categories": ["cs.LG", "stat.ML"], "doi": "", "_query": "house price prediction machine learning", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2412.20298v2", "title": "An Experimental Study on Fairness-aware Machine Learning for Credit Scoring Problems", "abstract": "The digitalization of credit scoring has become essential for financial institutions and commercial banks, especially in the era of digital transformation. Machine learning techniques are commonly used to evaluate customers' creditworthiness. However, the predicted outcomes of machine learning models can be biased toward protected attributes, such as race or gender. Numerous fairness-aware machine learning models and fairness measures have been proposed. Nevertheless, their performance in the context of credit scoring has not been thoroughly investigated. In this paper, we present a comprehensive experimental study of fairness-aware machine learning in credit scoring. The study explores key aspects of credit scoring, including financial datasets, predictive models, and fairness measures. We also provide a detailed evaluation of fairness-aware predictive models and fairness measures on widely used financial datasets. The experimental results show that fairness-aware models achieve a better balance between predictive accuracy and fairness compared to traditional classification models.", "published": "2024-12-28T23:27:07Z", "authors": ["Huyen Giang Thi Thu", "Thang Viet Doan", "Ha-Bang Ban", "Tai Le Quy"], "categories": ["cs.LG", "cs.CY", "stat.ML"], "doi": "10.1007/s42521-026-00202-6", "_query": "house price prediction machine learning", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2505.06849v1", "title": "Predictive Digital Twins for Thermal Management Using Machine Learning and Reduced-Order Models", "abstract": "Digital twins enable real-time simulation and prediction in engineering systems. This paper presents a novel framework for predictive digital twins of a headlamp heatsink, integrating physics-based reduced-order models (ROMs) from computational fluid dynamics (CFD) with supervised machine learning. A component-based ROM library, derived via proper orthogonal decomposition (POD), captures thermal dynamics efficiently. Machine learning models, including Decision Trees, k-Nearest Neighbors, Support Vector Regression (SVR), and Neural Networks, predict optimal ROM configurations, enabling rapid digital twin updates. The Neural Network achieves a mean absolute error (MAE) of 54.240, outperforming other models. Quantitative comparisons of predicted and original values demonstrate high accuracy. This scalable, interpretable framework advances thermal management in automotive systems, supporting robust design and predictive maintenance.", "published": "2025-05-11T05:20:16Z", "authors": ["Tamilselvan Subramani", "Sebastian Bartscher"], "categories": ["cs.LG"], "doi": "", "_query": "house price prediction machine learning", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2006.16239v2", "title": "An Imitation Learning Approach for Cache Replacement", "abstract": "Program execution speed critically depends on increasing cache hits, as cache hits are orders of magnitude faster than misses. To increase cache hits, we focus on the problem of cache replacement: choosing which cache line to evict upon inserting a new line. This is challenging because it requires planning far ahead and currently there is no known practical solution. As a result, current replacement policies typically resort to heuristics designed for specific common access patterns, which fail on more diverse and complex access patterns. In contrast, we propose an imitation learning approach to automatically learn cache access patterns by leveraging Belady's, an oracle policy that computes the optimal eviction decision given the future cache accesses. While directly applying Belady's is infeasible since the future is unknown, we train a policy conditioned only on past accesses that accurately approximates Belady's even on diverse and complex access patterns, and call this approach Parrot. When evaluated on 13 of the most memory-intensive SPEC applications, Parrot increases cache miss rates by 20% over the current state of the art. In addition, on a large-scale web search benchmark, Parrot increases cache hit rates by 61% over a conventional LRU policy. We release a Gym environment to facilitate research in this area, as data is plentiful, and further advancements can have significant real-world impact.", "published": "2020-06-29T17:58:40Z", "authors": ["Evan Zheran Liu", "Milad Hashemi", "Kevin Swersky", "Parthasarathy Ranganathan", "Junwhan Ahn"], "categories": ["cs.LG", "cs.AR", "stat.ML"], "doi": "", "_query": "house price prediction machine learning", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/1608.00860v2", "title": "Hierarchically Compositional Kernels for Scalable Nonparametric Learning", "abstract": "We propose a novel class of kernels to alleviate the high computational cost of large-scale nonparametric learning with kernel methods. The proposed kernel is defined based on a hierarchical partitioning of the underlying data domain, where the Nyström method (a globally low-rank approximation) is married with a locally lossless approximation in a hierarchical fashion. The kernel maintains (strict) positive-definiteness. The corresponding kernel matrix admits a recursively off-diagonal low-rank structure, which allows for fast linear algebra computations. Suppressing the factor of data dimension, the memory and arithmetic complexities for training a regression or a classifier are reduced from $O(n^2)$ and $O(n^3)$ to $O(nr)$ and $O(nr^2)$, respectively, where $n$ is the number of training examples and $r$ is the rank on each level of the hierarchy. Although other randomized approximate kernels entail a similar complexity, empirical results show that the proposed kernel achieves a matching performance with a smaller $r$. We demonstrate comprehensive experiments to show the effective use of the proposed kernel on data sizes up to the order of millions.", "published": "2016-08-02T15:07:25Z", "authors": ["Jie Chen", "Haim Avron", "Vikas Sindhwani"], "categories": ["cs.LG", "stat.ML"], "doi": "", "_query": "house price prediction machine learning", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2306.11481v1", "title": "Learning Locally Interpretable Rule Ensemble", "abstract": "This paper proposes a new framework for learning a rule ensemble model that is both accurate and interpretable. A rule ensemble is an interpretable model based on the linear combination of weighted rules. In practice, we often face the trade-off between the accuracy and interpretability of rule ensembles. That is, a rule ensemble needs to include a sufficiently large number of weighted rules to maintain its accuracy, which harms its interpretability for human users. To avoid this trade-off and learn an interpretable rule ensemble without degrading accuracy, we introduce a new concept of interpretability, named local interpretability, which is evaluated by the total number of rules necessary to express individual predictions made by the model, rather than to express the model itself. Then, we propose a regularizer that promotes local interpretability and develop an efficient algorithm for learning a rule ensemble with the proposed regularizer by coordinate descent with local search. Experimental results demonstrated that our method learns rule ensembles that can explain individual predictions with fewer rules than the existing methods, including RuleFit, while maintaining comparable accuracy.", "published": "2023-06-20T12:06:56Z", "authors": ["Kentaro Kanamori"], "categories": ["cs.LG", "stat.ML"], "doi": "", "_query": "house price prediction machine learning", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2309.13330v1", "title": "Predicting Temperature of Major Cities Using Machine Learning and Deep Learning", "abstract": "Currently, the issue that concerns the world leaders most is climate change for its effect on agriculture, environment and economies of daily life. So, to combat this, temperature prediction with strong accuracy is vital. So far, the most effective widely used measure for such forecasting is Numerical weather prediction (NWP) which is a mathematical model that needs broad data from different applications to make predictions. This expensive, time and labor consuming work can be minimized through making such predictions using Machine learning algorithms. Using the database made by University of Dayton which consists the change of temperature in major cities we used the Time Series Analysis method where we use LSTM for the purpose of turning existing data into a tool for future prediction. LSTM takes the long-term data as well as any short-term exceptions or anomalies that may have occurred and calculates trend, seasonality and the stationarity of a data. By using models such as ARIMA, SARIMA, Prophet with the concept of RNN and LSTM we can, filter out any abnormalities, preprocess the data compare it with previous trends and make a prediction of future trends. Also, seasonality and stationarity help us analyze the reoccurrence or repeat over one year variable and removes the constrain of time in which the data was dependent so see the general changes that are predicted. By doing so we managed to make prediction of the temperature of different cities during any time in future based on available data and built a method of accurate prediction. This document contains our methodology for being able to make such predictions.", "published": "2023-09-23T10:23:00Z", "authors": ["Wasiou Jaharabi", "MD Ibrahim Al Hossain", "Rownak Tahmid", "Md. Zuhayer Islam", "T. M. Saad Rayhan"], "categories": ["cs.LG"], "doi": "", "_query": "house price prediction machine learning", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2302.11966v2", "title": "Machine Learning for QoS Prediction in Vehicular Communication: Challenges and Solution Approaches", "abstract": "As cellular networks evolve towards the 6th generation, machine learning is seen as a key enabling technology to improve the capabilities of the network. Machine learning provides a methodology for predictive systems, which can make networks become proactive. This proactive behavior of the network can be leveraged to sustain, for example, a specific quality of service requirement. With predictive quality of service, a wide variety of new use cases, both safety- and entertainment-related, are emerging, especially in the automotive sector. Therefore, in this work, we consider maximum throughput prediction enhancing, for example, streaming or high-definition mapping applications. We discuss the entire machine learning workflow highlighting less regarded aspects such as the detailed sampling procedures, the in-depth analysis of the dataset characteristics, the effects of splits in the provided results, and the data availability. Reliable machine learning models need to face a lot of challenges during their lifecycle. We highlight how confidence can be built on machine learning technologies by better understanding the underlying characteristics of the collected data. We discuss feature engineering and the effects of different splits for the training processes, showcasing that random splits might overestimate performance by more than twofold. Moreover, we investigate diverse sets of input features, where network information proved to be most effective, cutting the error by half. Part of our contribution is the validation of multiple machine learning models within diverse scenarios. We also use explainable AI to show that machine learning can learn underlying principles of wireless networks without being explicitly programmed. Our data is collected from a deployed network that was under full control of the measurement team and covered different vehicular scenarios and radio environments.", "published": "2023-02-23T12:29:20Z", "authors": ["Alexandros Palaios", "Christian L. Vielhaus", "Daniel F. Külzer", "Cara Watermann", "Rodrigo Hernangomez", "Sanket Partani", "Philipp Geuer", "Anton Krause", "Raja Sattiraju", "Martin Kasparick", "Gerhard Fettweis", "Frank H. P. Fitzek", "Hans D. Schotten", "Slawomir Stanczak"], "categories": ["cs.NI", "cs.LG"], "doi": "10.1109/ACCESS.2023.3303528", "_query": "house price prediction machine learning", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2304.01220v1", "title": "Evaluating the impact of an explainable machine learning system on the interobserver agreement in chest radiograph interpretation", "abstract": "We conducted a prospective study to measure the clinical impact of an explainable machine learning system on interobserver agreement in chest radiograph interpretation. The AI system, which we call as it VinDr-CXR when used as a diagnosis-supporting tool, significantly improved the agreement between six radiologists with an increase of 1.5% in mean Fleiss' Kappa. In addition, we also observed that, after the radiologists consulted AI's suggestions, the agreement between each radiologist and the system was remarkably increased by 3.3% in mean Cohen's Kappa. This work has been accepted for publication in IEEE Access and this paper is our short version submitted to the Midwest Machine Learning Symposium (MMLS 2023), Chicago, IL, USA.", "published": "2023-04-01T19:14:51Z", "authors": ["Hieu H. Pham", "Ha Q. Nguyen", "Hieu T. Nguyen", "Linh T. Le", "Khanh Lam"], "categories": ["eess.IV", "cs.CV"], "doi": "", "_query": "house price prediction machine learning", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2407.06212v1", "title": "Bias Correction in Machine Learning-based Classification of Rare Events", "abstract": "Online platform businesses can be identified by using web-scraped texts. This is a classification problem that combines elements of natural language processing and rare event detection. Because online platforms are rare, accurately identifying them with Machine Learning algorithms is challenging. Here, we describe the development of a Machine Learning-based text classification approach that reduces the number of false positives as much as possible. It greatly reduces the bias in the estimates obtained by using calibrated probabilities and ensembles.", "published": "2024-07-04T08:02:34Z", "authors": ["Luuk Gubbels", "Marco Puts", "Piet Daas"], "categories": ["cs.LG", "stat.ML"], "doi": "", "_query": "house price prediction machine learning", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2501.00048v2", "title": "Stroke Prediction using Clinical and Social Features in Machine Learning", "abstract": "Every year in the United States, 800,000 individuals suffer a stroke - one person every 40 seconds, with a death occurring every four minutes. While individual factors vary, certain predictors are more prevalent in determining stroke risk. As strokes are the second leading cause of death and disability worldwide, predicting stroke likelihood based on lifestyle factors is crucial. Showing individuals their stroke risk could motivate lifestyle changes, and machine learning offers solutions to this prediction challenge. Neural networks excel at predicting outcomes based on training features like lifestyle factors, however, they're not the only option. Logistic regression models can also effectively compute the likelihood of binary outcomes based on independent variables, making them well-suited for stroke prediction. This analysis will compare both neural networks (dense and convolutional) and logistic regression models for stroke prediction, examining their pros, cons, and differences to develop the most effective predictor that minimizes false negatives.", "published": "2024-12-27T23:05:16Z", "authors": ["Aidan Chadha"], "categories": ["cs.LG", "cs.AI"], "doi": "", "_query": "house price prediction machine learning", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2105.14676v2", "title": "NoiLIn: Improving Adversarial Training and Correcting Stereotype of Noisy Labels", "abstract": "Adversarial training (AT) formulated as the minimax optimization problem can effectively enhance the model's robustness against adversarial attacks. The existing AT methods mainly focused on manipulating the inner maximization for generating quality adversarial variants or manipulating the outer minimization for designing effective learning objectives. However, empirical results of AT always exhibit the robustness at odds with accuracy and the existence of the cross-over mixture problem, which motivates us to study some label randomness for benefiting the AT. First, we thoroughly investigate noisy labels (NLs) injection into AT's inner maximization and outer minimization, respectively and obtain the observations on when NL injection benefits AT. Second, based on the observations, we propose a simple but effective method -- NoiLIn that randomly injects NLs into training data at each training epoch and dynamically increases the NL injection rate once robust overfitting occurs. Empirically, NoiLIn can significantly mitigate the AT's undesirable issue of robust overfitting and even further improve the generalization of the state-of-the-art AT methods. Philosophically, NoiLIn sheds light on a new perspective of learning with NLs: NLs should not always be deemed detrimental, and even in the absence of NLs in the training set, we may consider injecting them deliberately. Codes are available in https://github.com/zjfheart/NoiLIn.", "published": "2021-05-31T02:17:51Z", "authors": ["Jingfeng Zhang", "Xilie Xu", "Bo Han", "Tongliang Liu", "Gang Niu", "Lizhen Cui", "Masashi Sugiyama"], "categories": ["cs.LG"], "doi": "", "_query": "house price prediction machine learning", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2006.12328v2", "title": "Siamese Meta-Learning and Algorithm Selection with 'Algorithm-Performance Personas' [Proposal]", "abstract": "Automated per-instance algorithm selection often outperforms single learners. Key to algorithm selection via meta-learning is often the (meta) features, which sometimes though do not provide enough information to train a meta-learner effectively. We propose a Siamese Neural Network architecture for automated algorithm selection that focuses more on 'alike performing' instances than meta-features. Our work includes a novel performance metric and method for selecting training samples. We introduce further the concept of 'Algorithm Performance Personas' that describe instances for which the single algorithms perform alike. The concept of 'alike performing algorithms' as ground truth for selecting training samples is novel and provides a huge potential as we believe. In this proposal, we outline our ideas in detail and provide the first evidence that our proposed metric is better suitable for training sample selection that standard performance metrics such as absolute errors.", "published": "2020-06-22T15:12:21Z", "authors": ["Joeran Beel", "Bryan Tyrell", "Edward Bergman", "Andrew Collins", "Shahad Nagoor"], "categories": ["cs.LG", "cs.AI", "stat.ML"], "doi": "", "_query": "house price prediction machine learning", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/1610.09423v2", "title": "Computational genomic algorithms for miRNA-based diagnosis of lung cancer: the potential of machine learning", "abstract": "The advent of large scale, high-throughput genomic screening has introduced a wide range of tests for diagnostic purposes. Prominent among them are tests using miRNA expression levels. Genomics and proteomics now provide expression levels of hundreds of miRNAs at a time. However, for actual diagnostic tools to become reality requires the simultaneous development of methods to interpret the large amounts of miRNA expression data that can be generated from a single patient sample. Because these data are in numeric form, quantitative methods must be developed. Statistics such as p-values and log fold change give some insight, but the diagnostic effectiveness of each miRNA test must first be evaluated. Here, the author has developed a traditional, sensitivity- and specificity-based algorithm, as well as a modern machine learning algorithm, and evaluated their diagnostic potential for lung cancer against a publicly available database. The findings suggest that the machine learning algorithm achieves higher accuracy (97% for cancerous and 73% for normal samples), in addition to providing confidence intervals that could provide valuable diagnostic support. The machine learning algorithm also has significant potential for expansion to more complex diagnoses of lung cancer sub-types, to other cancers as well diseases beyond cancer. Both algorithms are available on the Github repo: https://github.com/neerja-g/machine-learning-miRNA.", "published": "2016-10-28T23:03:30Z", "authors": ["Neerja Garikipati"], "categories": ["q-bio.GN"], "doi": "", "_query": "house price prediction machine learning", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/1811.06128v2", "title": "Machine Learning for Combinatorial Optimization: a Methodological Tour d'Horizon", "abstract": "This paper surveys the recent attempts, both from the machine learning and operations research communities, at leveraging machine learning to solve combinatorial optimization problems. Given the hard nature of these problems, state-of-the-art algorithms rely on handcrafted heuristics for making decisions that are otherwise too expensive to compute or mathematically not well defined. Thus, machine learning looks like a natural candidate to make such decisions in a more principled and optimized way. We advocate for pushing further the integration of machine learning and combinatorial optimization and detail a methodology to do so. A main point of the paper is seeing generic optimization problems as data points and inquiring what is the relevant distribution of problems to use for learning on a given task.", "published": "2018-11-15T00:40:32Z", "authors": ["Yoshua Bengio", "Andrea Lodi", "Antoine Prouvost"], "categories": ["cs.LG", "stat.ML"], "doi": "", "_query": "house price prediction machine learning", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2001.04942v2", "title": "Private Machine Learning via Randomised Response", "abstract": "We introduce a general learning framework for private machine learning based on randomised response. Our assumption is that all actors are potentially adversarial and as such we trust only to release a single noisy version of an individual's datapoint. We discuss a general approach that forms a consistent way to estimate the true underlying machine learning model and demonstrate this in the case of logistic regression.", "published": "2020-01-14T17:56:16Z", "authors": ["David Barber"], "categories": ["cs.LG", "stat.ML"], "doi": "", "_query": "house price prediction machine learning", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2310.10117v3", "title": "Federated Learning with Convex Global and Local Constraints", "abstract": "In practice, many machine learning (ML) problems come with constraints, and their applied domains involve distributed sensitive data that cannot be shared with others, e.g., in healthcare. Collaborative learning in such practical scenarios entails federated learning (FL) for ML problems with constraints, or FL with constraints for short. Despite the extensive developments of FL techniques in recent years, these techniques only deal with unconstrained FL problems or FL problems with simple constraints that are amenable to easy projections. There is little work dealing with FL problems with general constraints. To fill this gap, we take the first step toward building an algorithmic framework for solving FL problems with general constraints. In particular, we propose a new FL algorithm for constrained ML problems based on the proximal augmented Lagrangian (AL) method. Assuming convex objective and convex constraints plus other mild conditions, we establish the worst-case complexity of the proposed algorithm. Our numerical experiments show the effectiveness of our algorithm in performing Neyman-Pearson classification and fairness-aware learning with nonconvex constraints, in an FL setting.", "published": "2023-10-16T06:51:32Z", "authors": ["Chuan He", "Le Peng", "Ju Sun"], "categories": ["cs.LG", "math.OC"], "doi": "", "_query": "house price prediction machine learning", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2005.08005v1", "title": "Forecasting electricity prices with machine learning: Predictor sensitivity", "abstract": "Purpose: Trading on electricity markets occurs such that the price settlement takes place before delivery, often day-ahead. In practice, these prices are highly volatile as they largely depend upon a range of variables such as electricity demand and the feed-in from renewable energy sources. Hence, accurate forecasts are demanded. Approach: This paper aims at comparing different predictors stemming from supply-side (solar and wind power generation), demand-side, fuel-related and economic influences. For this reason, we implement a broad range of non-linear models from machine learning and draw upon the information-fusion-based sensitivity analysis. Findings: We disentangle the respective relevance of each predictor. We show that external predictors altogether decrease root mean squared errors by up to 21.96%. A Diebold-Mariano test statistically proves that the forecasting accuracy of the proposed machine learning models is superior. Originality: The benefit of adding further predictors has only recently received traction; however, little is known about how the individual variables contribute to improving forecasts in machine learning.", "published": "2020-05-16T14:45:13Z", "authors": ["Christof Naumzik", "Stefan Feuerriegel"], "categories": ["stat.AP"], "doi": "", "_query": "house price prediction machine learning", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2111.09939v1", "title": "Explainable predictions of different machine learning algorithms used to predict Early Stage diabetes", "abstract": "Machine Learning and Artificial Intelligence can be widely used to diagnose chronic diseases so that necessary precautionary treatment can be done in critical time. Diabetes Mellitus which is one of the major diseases can be easily diagnosed by several Machine Learning algorithms. Early stage diagnosis is crucial to prevent dangerous consequences. In this paper we have made a comparative analysis of several machine learning algorithms viz. Random Forest, Decision Tree, Artificial Neural Networks, K Nearest Neighbor, Support Vector Machine, and XGBoost along with feature attribution using SHAP to identify the most important feature in predicting the diabetes on a dataset collected from Sylhet Hospital. As per the experimental results obtained, the Random Forest algorithm has outperformed all the other algorithms with an accuracy of 99 percent on this particular dataset.", "published": "2021-11-18T20:29:03Z", "authors": ["V. Vakil", "S. Pachchigar", "C. Chavda", "S. Soni"], "categories": ["cs.LG", "cs.AI"], "doi": "", "_query": "house price prediction machine learning", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2008.00524v2", "title": "Interactive Imitation Learning in State-Space", "abstract": "Imitation Learning techniques enable programming the behavior of agents through demonstrations rather than manual engineering. However, they are limited by the quality of available demonstration data. Interactive Imitation Learning techniques can improve the efficacy of learning since they involve teachers providing feedback while the agent executes its task. In this work, we propose a novel Interactive Learning technique that uses human feedback in state-space to train and improve agent behavior (as opposed to alternative methods that use feedback in action-space). Our method titled Teaching Imitative Policies in State-space~(TIPS) enables providing guidance to the agent in terms of `changing its state' which is often more intuitive for a human demonstrator. Through continuous improvement via corrective feedback, agents trained by non-expert demonstrators using TIPS outperformed the demonstrator and conventional Imitation Learning agents.", "published": "2020-08-02T17:23:54Z", "authors": ["Snehal Jauhri", "Carlos Celemin", "Jens Kober"], "categories": ["cs.RO", "cs.LG"], "doi": "", "_query": "house price prediction machine learning", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2411.14665v1", "title": "Double Machine Learning for Adaptive Causal Representation in High-Dimensional Data", "abstract": "Adaptive causal representation learning from observational data is presented, integrated with an efficient sample splitting technique within the semiparametric estimating equation framework. The support points sample splitting (SPSS), a subsampling method based on energy distance, is employed for efficient double machine learning (DML) in causal inference. The support points are selected and split as optimal representative points of the full raw data in a random sample, in contrast to the traditional random splitting, and providing an optimal sub-representation of the underlying data generating distribution. They offer the best representation of a full big dataset, whereas the unit structural information of the underlying distribution via the traditional random data splitting is most likely not preserved. Three machine learning estimators were adopted for causal inference, support vector machine (SVM), deep learning (DL), and a hybrid super learner (SL) with deep learning (SDL), using SPSS. A comparative study is conducted between the proposed SVM, DL, and SDL representations using SPSS, and the benchmark results from Chernozhukov et al. (2018), which employed random forest, neural network, and regression trees with a random k-fold cross-fitting technique on the 401(k)-pension plan real data. The simulations show that DL with SPSS and the hybrid methods of DL and SL with SPSS outperform SVM with SPSS in terms of computational efficiency and the estimation quality, respectively.", "published": "2024-11-22T01:54:53Z", "authors": ["Lynda Aouar", "Han Yu"], "categories": ["stat.ML", "cs.LG", "stat.AP", "stat.CO"], "doi": "", "_query": "house price prediction machine learning", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/1907.07291v1", "title": "Adversarial Security Attacks and Perturbations on Machine Learning and Deep Learning Methods", "abstract": "The ever-growing big data and emerging artificial intelligence (AI) demand the use of machine learning (ML) and deep learning (DL) methods. Cybersecurity also benefits from ML and DL methods for various types of applications. These methods however are susceptible to security attacks. The adversaries can exploit the training and testing data of the learning models or can explore the workings of those models for launching advanced future attacks. The topic of adversarial security attacks and perturbations within the ML and DL domains is a recent exploration and a great interest is expressed by the security researchers and practitioners. The literature covers different adversarial security attacks and perturbations on ML and DL methods and those have their own presentation styles and merits. A need to review and consolidate knowledge that is comprehending of this increasingly focused and growing topic of research; however, is the current demand of the research communities. In this review paper, we specifically aim to target new researchers in the cybersecurity domain who may seek to acquire some basic knowledge on the machine learning and deep learning models and algorithms, as well as some of the relevant adversarial security attacks and perturbations.", "published": "2019-07-17T00:00:07Z", "authors": ["Arif Siddiqi"], "categories": ["cs.LG", "cs.CR", "stat.ML"], "doi": "", "_query": "house price prediction machine learning", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/1906.04859v3", "title": "Reinforcement Learning for Integer Programming: Learning to Cut", "abstract": "Integer programming (IP) is a general optimization framework widely applicable to a variety of unstructured and structured problems arising in, e.g., scheduling, production planning, and graph optimization. As IP models many provably hard to solve problems, modern IP solvers rely on many heuristics. These heuristics are usually human-designed, and naturally prone to suboptimality. The goal of this work is to show that the performance of those solvers can be greatly enhanced using reinforcement learning (RL). In particular, we investigate a specific methodology for solving IPs, known as the Cutting Plane Method. This method is employed as a subroutine by all modern IP solvers. We present a deep RL formulation, network architecture, and algorithms for intelligent adaptive selection of cutting planes (aka cuts). Across a wide range of IP tasks, we show that the trained RL agent significantly outperforms human-designed heuristics, and effectively generalizes to 10X larger instances and across IP problem classes. The trained agent is also demonstrated to benefit the popular downstream application of cutting plane methods in Branch-and-Cut algorithm, which is the backbone of state-of-the-art commercial IP solvers.", "published": "2019-06-11T23:14:46Z", "authors": ["Yunhao Tang", "Shipra Agrawal", "Yuri Faenza"], "categories": ["cs.LG", "math.OC", "stat.ML"], "doi": "", "_query": "house price prediction machine learning", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2311.14028v2", "title": "Continual Learning of Diffusion Models with Generative Distillation", "abstract": "Diffusion models are powerful generative models that achieve state-of-the-art performance in image synthesis. However, training them demands substantial amounts of data and computational resources. Continual learning would allow for incrementally learning new tasks and accumulating knowledge, thus enabling the reuse of trained models for further learning. One potentially suitable continual learning approach is generative replay, where a copy of a generative model trained on previous tasks produces synthetic data that are interleaved with data from the current task. However, standard generative replay applied to diffusion models results in a catastrophic loss in denoising capabilities. In this paper, we propose generative distillation, an approach that distils the entire reverse process of a diffusion model. We demonstrate that our approach substantially improves the continual learning performance of generative replay with only a modest increase in the computational costs.", "published": "2023-11-23T14:33:03Z", "authors": ["Sergi Masip", "Pau Rodriguez", "Tinne Tuytelaars", "Gido M. van de Ven"], "categories": ["cs.LG", "cs.AI", "cs.CV"], "doi": "", "_query": "house price prediction machine learning", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2402.02637v2", "title": "$C^*$-Algebraic Machine Learning: Moving in a New Direction", "abstract": "Machine learning has a long collaborative tradition with several fields of mathematics, such as statistics, probability and linear algebra. We propose a new direction for machine learning research: $C^*$-algebraic ML $-$ a cross-fertilization between $C^*$-algebra and machine learning. The mathematical concept of $C^*$-algebra is a natural generalization of the space of complex numbers. It enables us to unify existing learning strategies, and construct a new framework for more diverse and information-rich data models. We explain why and how to use $C^*$-algebras in machine learning, and provide technical considerations that go into the design of $C^*$-algebraic learning models in the contexts of kernel methods and neural networks. Furthermore, we discuss open questions and challenges in $C^*$-algebraic ML and give our thoughts for future development and applications.", "published": "2024-02-04T23:11:19Z", "authors": ["Yuka Hashimoto", "Masahiro Ikeda", "Hachem Kadri"], "categories": ["cs.LG", "math.OA", "stat.ML"], "doi": "", "_query": "house price prediction machine learning", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/1711.05374v1", "title": "Optimizing Kernel Machines using Deep Learning", "abstract": "Building highly non-linear and non-parametric models is central to several state-of-the-art machine learning systems. Kernel methods form an important class of techniques that induce a reproducing kernel Hilbert space (RKHS) for inferring non-linear models through the construction of similarity functions from data. These methods are particularly preferred in cases where the training data sizes are limited and when prior knowledge of the data similarities is available. Despite their usefulness, they are limited by the computational complexity and their inability to support end-to-end learning with a task-specific objective. On the other hand, deep neural networks have become the de facto solution for end-to-end inference in several learning paradigms. In this article, we explore the idea of using deep architectures to perform kernel machine optimization, for both computational efficiency and end-to-end inferencing. To this end, we develop the DKMO (Deep Kernel Machine Optimization) framework, that creates an ensemble of dense embeddings using Nystrom kernel approximations and utilizes deep learning to generate task-specific representations through the fusion of the embeddings. Intuitively, the filters of the network are trained to fuse information from an ensemble of linear subspaces in the RKHS. Furthermore, we introduce the kernel dropout regularization to enable improved training convergence. Finally, we extend this framework to the multiple kernel case, by coupling a global fusion layer with pre-trained deep kernel machines for each of the constituent kernels. Using case studies with limited training data, and lack of explicit feature sources, we demonstrate the effectiveness of our framework over conventional model inferencing techniques.", "published": "2017-11-15T01:30:58Z", "authors": ["Huan Song", "Jayaraman J. Thiagarajan", "Prasanna Sattigeri", "Andreas Spanias"], "categories": ["stat.ML", "cs.LG"], "doi": "", "_query": "house price prediction machine learning", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/1903.04717v2", "title": "Activation Analysis of a Byte-Based Deep Neural Network for Malware Classification", "abstract": "Feature engineering is one of the most costly aspects of developing effective machine learning models, and that cost is even greater in specialized problem domains, like malware classification, where expert skills are necessary to identify useful features. Recent work, however, has shown that deep learning models can be used to automatically learn feature representations directly from the raw, unstructured bytes of the binaries themselves. In this paper, we explore what these models are learning about malware. To do so, we examine the learned features at multiple levels of resolution, from individual byte embeddings to end-to-end analysis of the model. At each step, we connect these byte-oriented activations to their original semantics through parsing and disassembly of the binary to arrive at human-understandable features. Through our results, we identify several interesting features learned by the model and their connection to manually-derived features typically used by traditional machine learning models. Additionally, we explore the impact of training data volume and regularization on the quality of the learned features and the efficacy of the classifiers, revealing the somewhat paradoxical insight that better generalization does not necessarily result in better performance for byte-based malware classifiers.", "published": "2019-03-12T04:00:42Z", "authors": ["Scott E. Coull", "Christopher Gardner"], "categories": ["cs.LG", "cs.CR", "stat.ML"], "doi": "", "_query": "house price prediction machine learning", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/1809.10210v3", "title": "A Machine Learning Approach to Shipping Box Design", "abstract": "Having the right assortment of shipping boxes in the fulfillment warehouse to pack and ship customer's online orders is an indispensable and integral part of nowadays eCommerce business, as it will not only help maintain a profitable business but also create great experiences for customers. However, it is an extremely challenging operations task to strategically select the best combination of tens of box sizes from thousands of feasible ones to be responsible for hundreds of thousands of orders daily placed on millions of inventory products. In this paper, we present a machine learning approach to tackle the task by formulating the box design problem prescriptively as a generalized version of weighted $k$-medoids clustering problem, where the parameters are estimated through a variety of descriptive analytics. We test this machine learning approach on fulfillment data collected from Walmart U.S. eCommerce, and our approach is shown to be capable of improving the box utilization rate by more than $10\\%$.", "published": "2018-09-26T19:48:45Z", "authors": ["Guang Yang", "Cun Mu"], "categories": ["stat.ML", "cs.LG", "math.OC"], "doi": "", "_query": "house price prediction machine learning", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2007.02407v3", "title": "Adversarial Machine Learning Attacks and Defense Methods in the Cyber Security Domain", "abstract": "In recent years machine learning algorithms, and more specifically deep learning algorithms, have been widely used in many fields, including cyber security. However, machine learning systems are vulnerable to adversarial attacks, and this limits the application of machine learning, especially in non-stationary, adversarial environments, such as the cyber security domain, where actual adversaries (e.g., malware developers) exist. This paper comprehensively summarizes the latest research on adversarial attacks against security solutions based on machine learning techniques and illuminates the risks they pose. First, the adversarial attack methods are characterized based on their stage of occurrence, and the attacker's goals and capabilities. Then, we categorize the applications of adversarial attack and defense methods in the cyber security domain. Finally, we highlight some characteristics identified in recent research and discuss the impact of recent advancements in other adversarial learning domains on future research directions in the cyber security domain. This paper is the first to discuss the unique challenges of implementing end-to-end adversarial attacks in the cyber security domain, map them in a unified taxonomy, and use the taxonomy to highlight future research directions.", "published": "2020-07-05T18:22:40Z", "authors": ["Ihai Rosenberg", "Asaf Shabtai", "Yuval Elovici", "Lior Rokach"], "categories": ["cs.LG", "cs.CR", "stat.ML"], "doi": "", "_query": "house price prediction machine learning", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/1711.07910v3", "title": "Domain Generalization by Marginal Transfer Learning", "abstract": "In the problem of domain generalization (DG), there are labeled training data sets from several related prediction problems, and the goal is to make accurate predictions on future unlabeled data sets that are not known to the learner. This problem arises in several applications where data distributions fluctuate because of environmental, technical, or other sources of variation. We introduce a formal framework for DG, and argue that it can be viewed as a kind of supervised learning problem by augmenting the original feature space with the marginal distribution of feature vectors. While our framework has several connections to conventional analysis of supervised learning algorithms, several unique aspects of DG require new methods of analysis. This work lays the learning theoretic foundations of domain generalization, building on our earlier conference paper where the problem of DG was introduced (Blanchard et al., 2011). We present two formal models of data generation, corresponding notions of risk, and distribution-free generalization error analysis. By focusing our attention on kernel methods, we also provide more quantitative results and a universally consistent algorithm. An efficient implementation is provided for this algorithm, which is experimentally compared to a pooling strategy on one synthetic and three real-world data sets.", "published": "2017-11-21T16:59:43Z", "authors": ["Gilles Blanchard", "Aniket Anand Deshmukh", "Urun Dogan", "Gyemin Lee", "Clayton Scott"], "categories": ["stat.ML"], "doi": "", "_query": "house price prediction machine learning", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/1909.04885v1", "title": "Addressing Algorithmic Bottlenecks in Elastic Machine Learning with Chicle", "abstract": "Distributed machine learning training is one of the most common and important workloads running on data centers today, but it is rarely executed alone. Instead, to reduce costs, computing resources are consolidated and shared by different applications. In this scenario, elasticity and proper load balancing are vital to maximize efficiency, fairness, and utilization. Currently, most distributed training frameworks do not support the aforementioned properties. A few exceptions that do support elasticity, imitate generic distributed frameworks and use micro-tasks. In this paper we illustrate that micro-tasks are problematic for machine learning applications, because they require a high degree of parallelism which hinders the convergence of distributed training at a pure algorithmic level (i.e., ignoring overheads and scalability limitations). To address this, we propose Chicle, a new elastic distributed training framework which exploits the nature of machine learning algorithms to implement elasticity and load balancing without micro-tasks. We use Chicle to train deep neural network as well as generalized linear models, and show that Chicle achieves performance competitive with state of the art rigid frameworks, while efficiently enabling elastic execution and dynamic load balancing.", "published": "2019-09-11T07:37:05Z", "authors": ["Michael Kaufmann", "Kornilios Kourtis", "Celestine Mendler-Dünner", "Adrian Schüpbach", "Thomas Parnell"], "categories": ["cs.LG", "cs.DC", "cs.PF", "stat.ML"], "doi": "", "_query": "house price prediction machine learning", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2312.10633v1", "title": "Some Ideas for Improving Stock Price Prediction Based on Machine Learning", "abstract": "Stock price prediction is a complicated and interesting task. Noisy trends make stock pricing sensitive and complicated while the economical motivation behind, keeps it interesting for researchers and investors. In this paper we are to outline two novel ideas for stock pricing. We also test each of our suggested algorithms for predicting the price of 6 stocks from different sectors. To show the efficiency of our proposed algorithm, we compare the predicted prices with real values and also perform a backtest to verify that the annual returns based on real data and predicted price are almost the same.", "published": "2023-12-17T07:32:16Z", "authors": ["Negin Bagherpour"], "categories": ["math.OC", "cs.CE"], "doi": "", "_query": "house price prediction machine learning", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/1811.09385v2", "title": "On the Importance of Strong Baselines in Bayesian Deep Learning", "abstract": "Like all sub-fields of machine learning Bayesian Deep Learning is driven by empirical validation of its theoretical proposals. Given the many aspects of an experiment it is always possible that minor or even major experimental flaws can slip by both authors and reviewers. One of the most popular experiments used to evaluate approximate inference techniques is the regression experiment on UCI datasets. However, in this experiment, models which have been trained to convergence have often been compared with baselines trained only for a fixed number of iterations. We find that a well-established baseline, Monte Carlo dropout, when evaluated under the same experimental settings shows significant improvements. In fact, the baseline outperforms or performs competitively with methods that claimed to be superior to the very same baseline method when they were introduced. Hence, by exposing this flaw in experimental procedure, we highlight the importance of using identical experimental setups to evaluate, compare, and benchmark methods in Bayesian Deep Learning.", "published": "2018-11-23T08:22:17Z", "authors": ["Jishnu Mukhoti", "Pontus Stenetorp", "Yarin Gal"], "categories": ["cs.LG", "stat.ML"], "doi": "", "_query": "house price prediction machine learning", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2010.07389v1", "title": "Explainability for fair machine learning", "abstract": "As the decisions made or influenced by machine learning models increasingly impact our lives, it is crucial to detect, understand, and mitigate unfairness. But even simply determining what \"unfairness\" should mean in a given context is non-trivial: there are many competing definitions, and choosing between them often requires a deep understanding of the underlying task. It is thus tempting to use model explainability to gain insights into model fairness, however existing explainability tools do not reliably indicate whether a model is indeed fair. In this work we present a new approach to explaining fairness in machine learning, based on the Shapley value paradigm. Our fairness explanations attribute a model's overall unfairness to individual input features, even in cases where the model does not operate on sensitive attributes directly. Moreover, motivated by the linearity of Shapley explainability, we propose a meta algorithm for applying existing training-time fairness interventions, wherein one trains a perturbation to the original model, rather than a new model entirely. By explaining the original model, the perturbation, and the fair-corrected model, we gain insight into the accuracy-fairness trade-off that is being made by the intervention. We further show that this meta algorithm enjoys both flexibility and stability benefits with no loss in performance.", "published": "2020-10-14T20:21:01Z", "authors": ["Tom Begley", "Tobias Schwedes", "Christopher Frye", "Ilya Feige"], "categories": ["cs.LG", "cs.AI", "stat.ML"], "doi": "", "_query": "house price prediction machine learning", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2403.12562v2", "title": "PePR: Performance Per Resource Unit as a Metric to Promote Small-Scale Deep Learning in Medical Image Analysis", "abstract": "The recent advances in deep learning (DL) have been accelerated by access to large-scale data and compute. These large-scale resources have been used to train progressively larger models which are resource intensive in terms of compute, data, energy, and carbon emissions. These costs are becoming a new type of entry barrier to researchers and practitioners with limited access to resources at such scale, particularly in the Global South. In this work, we take a comprehensive look at the landscape of existing DL models for medical image analysis tasks and demonstrate their usefulness in settings where resources are limited. To account for the resource consumption of DL models, we introduce a novel measure to estimate the performance per resource unit, which we call the PePR score. Using a diverse family of 131 unique DL architectures (spanning 1M to 130M trainable parameters) and three medical image datasets, we capture trends about the performance-resource trade-offs. In applications like medical image analysis, we argue that small-scale, specialized models are better than striving for large-scale models. Furthermore, we show that using existing pretrained models that are fine-tuned on new data can significantly reduce the computational resources and data required compared to training models from scratch. We hope this work will encourage the community to focus on improving AI equity by developing methods and models with smaller resource footprints.", "published": "2024-03-19T09:17:18Z", "authors": ["Raghavendra Selvan", "Bob Pepin", "Christian Igel", "Gabrielle Samuel", "Erik B Dam"], "categories": ["cs.LG", "cs.AI", "stat.ML"], "doi": "", "_query": "house price prediction machine learning", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/1803.04779v1", "title": "Hybrid Forecasting of Chaotic Processes: Using Machine Learning in Conjunction with a Knowledge-Based Model", "abstract": "A model-based approach to forecasting chaotic dynamical systems utilizes knowledge of the physical processes governing the dynamics to build an approximate mathematical model of the system. In contrast, machine learning techniques have demonstrated promising results for forecasting chaotic systems purely from past time series measurements of system state variables (training data), without prior knowledge of the system dynamics. The motivation for this paper is the potential of machine learning for filling in the gaps in our underlying mechanistic knowledge that cause widely-used knowledge-based models to be inaccurate. Thus we here propose a general method that leverages the advantages of these two approaches by combining a knowledge-based model and a machine learning technique to build a hybrid forecasting scheme. Potential applications for such an approach are numerous (e.g., improving weather forecasting). We demonstrate and test the utility of this approach using a particular illustrative version of a machine learning known as reservoir computing, and we apply the resulting hybrid forecaster to a low-dimensional chaotic system, as well as to a high-dimensional spatiotemporal chaotic system. These tests yield extremely promising results in that our hybrid technique is able to accurately predict for a much longer period of time than either its machine-learning component or its model-based component alone.", "published": "2018-03-09T21:02:25Z", "authors": ["Jaideep Pathak", "Alexander Wikner", "Rebeckah Fussell", "Sarthak Chandra", "Brian Hunt", "Michelle Girvan", "Edward Ott"], "categories": ["cs.LG", "nlin.CD", "stat.ML"], "doi": "10.1063/1.5028373", "_query": "house price prediction machine learning", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2507.17903v1", "title": "Federated Learning for Large-Scale Cloud Robotic Manipulation: Opportunities and Challenges", "abstract": "Federated Learning (FL) is an emerging distributed machine learning paradigm, where the collaborative training of a model involves dynamic participation of devices to achieve broad objectives. In contrast, classical machine learning (ML) typically requires data to be located on-premises for training, whereas FL leverages numerous user devices to train a shared global model without the need to share private data. Current robotic manipulation tasks are constrained by the individual capabilities and speed of robots due to limited low-latency computing resources. Consequently, the concept of cloud robotics has emerged, allowing robotic applications to harness the flexibility and reliability of computing resources, effectively alleviating their computational demands across the cloud-edge continuum. Undoubtedly, within this distributed computing context, as exemplified in cloud robotic manipulation scenarios, FL offers manifold advantages while also presenting several challenges and opportunities. In this paper, we present fundamental concepts of FL and their connection to cloud robotic manipulation. Additionally, we envision the opportunities and challenges associated with realizing efficient and reliable cloud robotic manipulation at scale through FL, where researchers adopt to design and verify FL models in either centralized or decentralized settings.", "published": "2025-07-23T20:01:36Z", "authors": ["Obaidullah Zaland", "Chanh Nguyen", "Florian T. Pokorny", "Monowar Bhuyan"], "categories": ["cs.LG"], "doi": "", "_query": "house price prediction machine learning", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/1709.02840v3", "title": "A Brief Introduction to Machine Learning for Engineers", "abstract": "This monograph aims at providing an introduction to key concepts, algorithms, and theoretical results in machine learning. The treatment concentrates on probabilistic models for supervised and unsupervised learning problems. It introduces fundamental concepts and algorithms by building on first principles, while also exposing the reader to more advanced topics with extensive pointers to the literature, within a unified notation and mathematical framework. The material is organized according to clearly defined categories, such as discriminative and generative models, frequentist and Bayesian approaches, exact and approximate inference, as well as directed and undirected models. This monograph is meant as an entry point for researchers with a background in probability and linear algebra.", "published": "2017-09-08T19:21:26Z", "authors": ["Osvaldo Simeone"], "categories": ["cs.LG", "cs.IT", "stat.ML"], "doi": "", "_query": "house price prediction machine learning", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/1307.7050v1", "title": "A Comprehensive Evaluation of Machine Learning Techniques for Cancer Class Prediction Based on Microarray Data", "abstract": "Prostate cancer is among the most common cancer in males and its heterogeneity is well known. Its early detection helps making therapeutic decision. There is no standard technique or procedure yet which is full-proof in predicting cancer class. The genomic level changes can be detected in gene expression data and those changes may serve as standard model for any random cancer data for class prediction. Various techniques were implied on prostate cancer data set in order to accurately predict cancer class including machine learning techniques. Huge number of attributes and few number of sample in microarray data leads to poor machine learning, therefore the most challenging part is attribute reduction or non significant gene reduction. In this work we have compared several machine learning techniques for their accuracy in predicting the cancer class. Machine learning is effective when number of attributes (genes) are larger than the number of samples which is rarely possible with gene expression data. Attribute reduction or gene filtering is absolutely required in order to make the data more meaningful as most of the genes do not participate in tumor development and are irrelevant for cancer prediction. Here we have applied combination of statistical techniques such as inter-quartile range and t-test, which has been effective in filtering significant genes and minimizing noise from data. Further we have done a comprehensive evaluation of ten state-of-the-art machine learning techniques for their accuracy in class prediction of prostate cancer. Out of these techniques, Bayes Network out performed with an accuracy of 94.11% followed by Navie Bayes with an accuracy of 91.17%. To cross validate our results, we modified our training dataset in six different way and found that average sensitivity, specificity, precision and accuracy of Bayes Network is highest among all other techniques used.", "published": "2013-07-26T14:44:16Z", "authors": ["Khalid Raza", "Atif N Hasan"], "categories": ["cs.LG", "cs.CE"], "doi": "10.1504/IJBRA.2015.071940", "_query": "house price prediction machine learning", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/1901.10002v5", "title": "A Framework for Understanding Sources of Harm throughout the Machine Learning Life Cycle", "abstract": "As machine learning (ML) increasingly affects people and society, awareness of its potential unwanted consequences has also grown. To anticipate, prevent, and mitigate undesirable downstream consequences, it is critical that we understand when and how harm might be introduced throughout the ML life cycle. In this paper, we provide a framework that identifies seven distinct potential sources of downstream harm in machine learning, spanning data collection, development, and deployment. In doing so, we aim to facilitate more productive and precise communication around these issues, as well as more direct, application-grounded ways to mitigate them.", "published": "2019-01-28T21:00:20Z", "authors": ["Harini Suresh", "John V. Guttag"], "categories": ["cs.LG", "stat.ML"], "doi": "10.1145/3465416.3483305", "_query": "house price prediction machine learning", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2004.02401v1", "title": "Applying Cyclical Learning Rate to Neural Machine Translation", "abstract": "In training deep learning networks, the optimizer and related learning rate are often used without much thought or with minimal tuning, even though it is crucial in ensuring a fast convergence to a good quality minimum of the loss function that can also generalize well on the test dataset. Drawing inspiration from the successful application of cyclical learning rate policy for computer vision related convolutional networks and datasets, we explore how cyclical learning rate can be applied to train transformer-based neural networks for neural machine translation. From our carefully designed experiments, we show that the choice of optimizers and the associated cyclical learning rate policy can have a significant impact on the performance. In addition, we establish guidelines when applying cyclical learning rates to neural machine translation tasks. Thus with our work, we hope to raise awareness of the importance of selecting the right optimizers and the accompanying learning rate policy, at the same time, encourage further research into easy-to-use learning rate policies.", "published": "2020-04-06T04:45:49Z", "authors": ["Choon Meng Lee", "Jianfeng Liu", "Wei Peng"], "categories": ["cs.LG", "cs.CL", "stat.ML"], "doi": "", "_query": "house price prediction machine learning", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2008.00138v1", "title": "Vulnerability Under Adversarial Machine Learning: Bias or Variance?", "abstract": "Prior studies have unveiled the vulnerability of the deep neural networks in the context of adversarial machine learning, leading to great recent attention into this area. One interesting question that has yet to be fully explored is the bias-variance relationship of adversarial machine learning, which can potentially provide deeper insights into this behaviour. The notion of bias and variance is one of the main approaches to analyze and evaluate the generalization and reliability of a machine learning model. Although it has been extensively used in other machine learning models, it is not well explored in the field of deep learning and it is even less explored in the area of adversarial machine learning. In this study, we investigate the effect of adversarial machine learning on the bias and variance of a trained deep neural network and analyze how adversarial perturbations can affect the generalization of a network. We derive the bias-variance trade-off for both classification and regression applications based on two main loss functions: (i) mean squared error (MSE), and (ii) cross-entropy. Furthermore, we perform quantitative analysis with both simulated and real data to empirically evaluate consistency with the derived bias-variance tradeoffs. Our analysis sheds light on why the deep neural networks have poor performance under adversarial perturbation from a bias-variance point of view and how this type of perturbation would change the performance of a network. Moreover, given these new theoretical findings, we introduce a new adversarial machine learning algorithm with lower computational complexity than well-known adversarial machine learning strategies (e.g., PGD) while providing a high success rate in fooling deep neural networks in lower perturbation magnitudes.", "published": "2020-08-01T00:58:54Z", "authors": ["Hossein Aboutalebi", "Mohammad Javad Shafiee", "Michelle Karg", "Christian Scharfenberger", "Alexander Wong"], "categories": ["cs.LG", "cs.NE", "stat.ML"], "doi": "", "_query": "house price prediction machine learning", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2210.03426v1", "title": "Certified machine learning: Rigorous a posteriori error bounds for PDE defined PINNs", "abstract": "Prediction error quantification in machine learning has been left out of most methodological investigations of neural networks, for both purely data-driven and physics-informed approaches. Beyond statistical investigations and generic results on the approximation capabilities of neural networks, we present a rigorous upper bound on the prediction error of physics-informed neural networks. This bound can be calculated without the knowledge of the true solution and only with a priori available information about the characteristics of the underlying dynamical system governed by a partial differential equation. We apply this a posteriori error bound exemplarily to four problems: the transport equation, the heat equation, the Navier-Stokes equation and the Klein-Gordon equation.", "published": "2022-10-07T09:49:18Z", "authors": ["Birgit Hillebrecht", "Benjamin Unger"], "categories": ["cs.LG", "math.NA"], "doi": "10.1109/TNNLS.2023.3335837", "_query": "house price prediction machine learning", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2206.15370v1", "title": "Development of exchange-correlation functionals assisted by machine learning", "abstract": "With the recent rapid progress in the machine-learning (ML), there have emerged a new approach using the ML methods to the exchange-correlation functional of density functional theory. In this chapter, we review how the ML tools are used for this and the performances achieved recently. It is revealed that the ML, not being opposed to the analytical methods, complements the human intuition and advance the development toward the first-principles calculation with desired accuracy.", "published": "2022-06-30T15:44:53Z", "authors": ["Ryo Nagai", "Ryosuke Akashi"], "categories": ["cond-mat.mtrl-sci"], "doi": "", "_query": "house price prediction machine learning", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/1706.00241v1", "title": "Krylov Subspace Recycling for Fast Iterative Least-Squares in Machine Learning", "abstract": "Solving symmetric positive definite linear problems is a fundamental computational task in machine learning. The exact solution, famously, is cubicly expensive in the size of the matrix. To alleviate this problem, several linear-time approximations, such as spectral and inducing-point methods, have been suggested and are now in wide use. These are low-rank approximations that choose the low-rank space a priori and do not refine it over time. While this allows linear cost in the data-set size, it also causes a finite, uncorrected approximation error. Authors from numerical linear algebra have explored ways to iteratively refine such low-rank approximations, at a cost of a small number of matrix-vector multiplications. This idea is particularly interesting in the many situations in machine learning where one has to solve a sequence of related symmetric positive definite linear problems. From the machine learning perspective, such deflation methods can be interpreted as transfer learning of a low-rank approximation across a time-series of numerical tasks. We study the use of such methods for our field. Our empirical results show that, on regression and classification problems of intermediate size, this approach can interpolate between low computational cost and numerical precision.", "published": "2017-06-01T10:17:12Z", "authors": ["Filip de Roos", "Philipp Hennig"], "categories": ["cs.LG", "math.NA", "stat.ML"], "doi": "", "_query": "house price prediction machine learning", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2211.01972v1", "title": "The role of prior information and computational power in Machine Learning", "abstract": "Science consists on conceiving hypotheses, confronting them with empirical evidence, and keeping only hypotheses which have not yet been falsified. Under deductive reasoning they are conceived in view of a theory and confronted with empirical evidence in an attempt to falsify it, and under inductive reasoning they are conceived based on observation, confronted with empirical evidence and a theory is established based on the not falsified hypotheses. When the hypotheses testing can be performed with quantitative data, the confrontation can be achieved with Machine Learning methods, whose quality is highly dependent on the hypotheses' complexity, hence on the proper insertion of prior information into the set of hypotheses seeking to decrease its complexity without loosing good hypotheses. However, Machine Learning tools have been applied under the pragmatic view of instrumentalism, which is concerned only with the performance of the methods and not with the understanding of their behavior, leading to methods which are not fully understood. In this context, we discuss how prior information and computational power can be employed to solve a learning problem, but while prior information and a careful design of the hypotheses space has as advantage the interpretability of the results, employing high computational power has the advantage of a higher performance. We discuss why learning methods which combine both should work better from an understanding and performance perspective, arguing in favor of basic theoretical research on Machine Learning, in special about how properties of classifiers may be identified in parameters of modern learning models.", "published": "2022-10-31T20:39:53Z", "authors": ["Diego Marcondes", "Adilson Simonis", "Junior Barrera"], "categories": ["cs.LG", "stat.ML"], "doi": "", "_query": "house price prediction machine learning", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2412.09686v2", "title": "The Cost of Replicability in Active Learning", "abstract": "Active learning aims to reduce the number of labeled data points required by machine learning algorithms by selectively querying labels from initially unlabeled data. Ensuring replicability, where an algorithm produces consistent outcomes across different runs, is essential for the reliability of machine learning models but often increases sample complexity. This paper investigates the cost of replicability in active learning using two classical disagreement-based methods: the CAL and A^2 algorithms. Leveraging randomized thresholding techniques, we propose two replicable active learning algorithms: one for realizable learning of finite hypothesis classes and another for the agnostic setting. Our theoretical analysis shows that while enforcing replicability increases label complexity, CAL and A^2 still achieve substantial label savings under this constraint. These findings provide insights into balancing efficiency and stability in active learning.", "published": "2024-12-12T19:03:31Z", "authors": ["Rupkatha Hira", "Dominik Kau", "Jessica Sorrell"], "categories": ["cs.LG"], "doi": "", "_query": "house price prediction machine learning", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2607.16250v1", "title": "Benchmarking Machine Learning Models for Multi-Omics-Based Breast Cancer Prediction", "abstract": "Estrogen Receptor (ER) status is a critical biomarker in breast cancer diagnosis, prognosis, and treatment selection. Recent advances in high-throughput sequencing technologies have enabled the generation of multi-omics datasets that provide complementary molecular information for computational prediction tasks. This study presents a systematic benchmarking analysis of classical machine learning models for ER status prediction using transcriptomic (RNA expression), genomic (copy number variation; CNV), and proteomic (RPPA) data from the TCGA-BRCA cohort. A rigorous experimental framework incorporating stratified train-test splitting, stratified five-fold cross-validation, class imbalance handling, and fold-specific feature selection was employed to ensure reliable evaluation and prevent data leakage. Random Forest, XGBoost, LightGBM, CatBoost, Support Vector Machines (SVM), and Logistic Regression were evaluated across single-omic and multi-omic settings. Results demonstrated that RNA expression provided the strongest predictive signal, while multi-omic integration yielded modest but consistent improvements over individual modalities. Among all evaluated approaches, Random Forest achieved the best overall performance in the integrated multi-omic setting, obtaining a balanced accuracy of 90.3\\% and an ROC-AUC of 97.1\\%. Furthermore, recurrent selection of biologically relevant genes, including \\textit{ESR1}, \\textit{PGR}, \\textit{FOXA1}, and \\textit{GATA3}, supported the biological validity of the learned models. These findings indicate that carefully regularized classical machine learning methods remain highly effective for small, high-dimensional genomic datasets and that multi-omic integration provides complementary information for breast cancer ER status prediction.", "published": "2026-06-27T02:46:40Z", "authors": ["Priyanka Paudel", "Madan Baduwal"], "categories": ["cs.LG", "cs.AI"], "doi": "", "_query": "house price prediction machine learning", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2406.08440v2", "title": "Adaptive Swarm Mesh Refinement using Deep Reinforcement Learning with Local Rewards", "abstract": "Simulating physical systems is essential in engineering, but analytical solutions are limited to straightforward problems. Consequently, numerical methods like the Finite Element Method (FEM) are widely used. However, the FEM becomes computationally expensive as problem complexity and accuracy demands increase. Adaptive Mesh Refinement (AMR) improves the FEM by dynamically placing mesh elements on the domain, balancing computational speed and accuracy. Classical AMR depends on heuristics or expensive error estimators, which may lead to suboptimal performance for complex simulations. While AMR methods based on machine learning are promising, they currently only scale to simple problems. In this work, we formulate AMR as a system of collaborating, homogeneous agents that iteratively split into multiple new agents. This agent-wise perspective enables a spatial reward formulation focused on reducing the maximum mesh element error. Our approach, Adaptive Swarm Mesh Refinement++ (ASMR++), offers efficient, stable optimization and generates highly adaptive meshes at user-defined resolution at inference time. Extensive experiments demonstrate that ASMR++ outperforms heuristic approaches and learned baselines, matching the performance of expensive error-based oracle AMR strategies. ASMR additionally generalizes to different domains during inference, and produces meshes that simulate up to 2 orders of magnitude faster than uniform refinements in more demanding settings.", "published": "2024-06-12T17:26:54Z", "authors": ["Niklas Freymuth", "Philipp Dahlinger", "Tobias Würth", "Simon Reisch", "Luise Kärger", "Gerhard Neumann"], "categories": ["cs.LG", "cs.MA"], "doi": "", "_query": "house price prediction machine learning", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2011.03649v3", "title": "Thermal Prediction for Efficient Energy Management of Clouds using Machine Learning", "abstract": "Thermal management in the hyper-scale cloud data centers is a critical problem. Increased host temperature creates hotspots which significantly increases cooling cost and affects reliability. Accurate prediction of host temperature is crucial for managing the resources effectively. Temperature estimation is a non-trivial problem due to thermal variations in the data center. Existing solutions for temperature estimation are inefficient due to their computational complexity and lack of accurate prediction. However, data-driven machine learning methods for temperature prediction is a promising approach. In this regard, we collect and study data from a private cloud and show the presence of thermal variations. We investigate several machine learning models to accurately predict the host temperature. Specifically, we propose a gradient boosting machine learning model for temperature prediction. The experiment results show that our model accurately predicts the temperature with the average RMSE value of 0.05 or an average prediction error of 2.38 degree Celsius, which is 6 degree Celsius less as compared to an existing theoretical model. In addition, we propose a dynamic scheduling algorithm to minimize the peak temperature of hosts. The results show that our algorithm reduces the peak temperature by 6.5 degree Celsius and consumes 34.5% less energy as compared to the baseline algorithm.", "published": "2020-11-07T00:55:47Z", "authors": ["Shashikant Ilager", "Kotagiri Ramamohanarao", "Rajkumar Buyya"], "categories": ["cs.DC", "cs.AI", "cs.LG"], "doi": "", "_query": "house price prediction machine learning", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/1909.00704v1", "title": "Learning Real Estate Automated Valuation Models from Heterogeneous Data Sources", "abstract": "Real estate appraisal is a complex and important task, that can be made more precise and faster with the help of automated valuation tools. Usually the value of some property is determined by taking into account both structural and geographical characteristics. However, while geographical information is easily found, obtaining significant structural information requires the intervention of a real estate expert, a professional appraiser. In this paper we propose a Web data acquisition methodology, and a Machine Learning model, that can be used to automatically evaluate real estate properties. This method uses data from previous appraisal documents, from the advertised prices of similar properties found via Web crawling, and from open data describing the characteristics of a corresponding geographical area. We describe a case study, applicable to the whole Italian territory, and initially trained on a data set of individual homes located in the city of Turin, and analyze prediction and practical applicability.", "published": "2019-09-02T13:16:51Z", "authors": ["Francesco Bergadano", "Roberto Bertilone", "Daniela Paolotti", "Giancarlo Ruffo"], "categories": ["cs.CY", "cs.LG", "stat.ML"], "doi": "10.11113/intrest.v15n1.10", "_query": "real estate valuation deep learning", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2306.11113v2", "title": "Learn to Accumulate Evidence from All Training Samples: Theory and Practice", "abstract": "Evidential deep learning, built upon belief theory and subjective logic, offers a principled and computationally efficient way to turn a deterministic neural network uncertainty-aware. The resultant evidential models can quantify fine-grained uncertainty using the learned evidence. To ensure theoretically sound evidential models, the evidence needs to be non-negative, which requires special activation functions for model training and inference. This constraint often leads to inferior predictive performance compared to standard softmax models, making it challenging to extend them to many large-scale datasets. To unveil the real cause of this undesired behavior, we theoretically investigate evidential models and identify a fundamental limitation that explains the inferior performance: existing evidential activation functions create zero evidence regions, which prevent the model to learn from training samples falling into such regions. A deeper analysis of evidential activation functions based on our theoretical underpinning inspires the design of a novel regularizer that effectively alleviates this fundamental limitation. Extensive experiments over many challenging real-world datasets and settings confirm our theoretical findings and demonstrate the effectiveness of our proposed approach.", "published": "2023-06-19T18:27:12Z", "authors": ["Deep Pandey", "Qi Yu"], "categories": ["cs.LG", "cs.AI", "cs.CV"], "doi": "", "_query": "real estate valuation deep learning", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2105.04026v2", "title": "The Modern Mathematics of Deep Learning", "abstract": "We describe the new field of mathematical analysis of deep learning. This field emerged around a list of research questions that were not answered within the classical framework of learning theory. These questions concern: the outstanding generalization power of overparametrized neural networks, the role of depth in deep architectures, the apparent absence of the curse of dimensionality, the surprisingly successful optimization performance despite the non-convexity of the problem, understanding what features are learned, why deep architectures perform exceptionally well in physical problems, and which fine aspects of an architecture affect the behavior of a learning task in which way. We present an overview of modern approaches that yield partial answers to these questions. For selected approaches, we describe the main ideas in more detail.", "published": "2021-05-09T21:30:42Z", "authors": ["Julius Berner", "Philipp Grohs", "Gitta Kutyniok", "Philipp Petersen"], "categories": ["cs.LG", "stat.ML"], "doi": "10.1017/9781009025096.002", "_query": "real estate valuation deep learning", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/1809.04933v2", "title": "Identifying Real Estate Opportunities using Machine Learning", "abstract": "The real estate market is exposed to many fluctuations in prices because of existing correlations with many variables, some of which cannot be controlled or might even be unknown. Housing prices can increase rapidly (or in some cases, also drop very fast), yet the numerous listings available online where houses are sold or rented are not likely to be updated that often. In some cases, individuals interested in selling a house (or apartment) might include it in some online listing, and forget about updating the price. In other cases, some individuals might be interested in deliberately setting a price below the market price in order to sell the home faster, for various reasons. In this paper, we aim at developing a machine learning application that identifies opportunities in the real estate market in real time, i.e., houses that are listed with a price substantially below the market price. This program can be useful for investors interested in the housing market. We have focused in a use case considering real estate assets located in the Salamanca district in Madrid (Spain) and listed in the most relevant Spanish online site for home sales and rentals. The application is formally implemented as a regression problem that tries to estimate the market price of a house given features retrieved from public online listings. For building this application, we have performed a feature engineering stage in order to discover relevant features that allows for attaining a high predictive performance. Several machine learning algorithms have been tested, including regression trees, k-nearest neighbors, support vector machines and neural networks, identifying advantages and handicaps of each of them.", "published": "2018-09-13T13:19:23Z", "authors": ["Alejandro Baldominos", "Iván Blanco", "Antonio José Moreno", "Rubén Iturrarte", "Óscar Bernárdez", "Carlos Afonso"], "categories": ["stat.AP", "cs.LG", "stat.ML"], "doi": "10.3390/app8112321", "_query": "real estate valuation deep learning", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/1711.00146v1", "title": "A multitask deep learning model for real-time deployment in embedded systems", "abstract": "We propose an approach to Multitask Learning (MTL) to make deep learning models faster and lighter for applications in which multiple tasks need to be solved simultaneously, which is particularly useful in embedded, real-time systems. We develop a multitask model for both Object Detection and Semantic Segmentation and analyze the challenges that appear during its training. Our multitask network is 1.6x faster, lighter and uses less memory than deploying the single-task models in parallel. We conclude that MTL has the potential to give superior performance in exchange of a more complex training process that introduces challenges not present in single-task models.", "published": "2017-10-31T23:59:23Z", "authors": ["Miquel Martí", "Atsuto Maki"], "categories": ["cs.CV", "cs.LG"], "doi": "", "_query": "real estate valuation deep learning", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2301.00942v1", "title": "Deep Learning and Computational Physics (Lecture Notes)", "abstract": "These notes were compiled as lecture notes for a course developed and taught at the University of the Southern California. They should be accessible to a typical engineering graduate student with a strong background in Applied Mathematics. The main objective of these notes is to introduce a student who is familiar with concepts in linear algebra and partial differential equations to select topics in deep learning. These lecture notes exploit the strong connections between deep learning algorithms and the more conventional techniques of computational physics to achieve two goals. First, they use concepts from computational physics to develop an understanding of deep learning algorithms. Not surprisingly, many concepts in deep learning can be connected to similar concepts in computational physics, and one can utilize this connection to better understand these algorithms. Second, several novel deep learning algorithms can be used to solve challenging problems in computational physics. Thus, they offer someone who is interested in modeling a physical phenomena with a complementary set of tools.", "published": "2023-01-03T03:56:19Z", "authors": ["Deep Ray", "Orazio Pinti", "Assad A. Oberai"], "categories": ["cs.LG", "math-ph"], "doi": "", "_query": "real estate valuation deep learning", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2207.04459v1", "title": "A Decentralised Real Estate Transfer Verification Based on Self-Sovereign Identity and Smart Contracts", "abstract": "Since its first introduction in late 90s, the use of marketplaces has continued to grow, today virtually everything from physical assets to services can be purchased on digital marketplaces, real estate is not an exception. Some marketplaces allow acclaimed asset owners to advertise their products, to which the services gets commission/percentage from proceeds of sale/lease. Despite the success recorded in the use of the marketplaces, they are not without limitations which include identity and property fraud, impersonation and the use of centralised technology with trusted parties that are prone to single point of failures (SPOF). Being one of the most valuable assets, real estate has been a target for marketplace fraud as impersonators take pictures of properties they do not own, upload them on marketplace with promising prices that lures innocent or naive buyers. This paper addresses these issues by proposing a self sovereign identity (SSI) and smart contract based framework for identity verification and verified transaction management on secure digital marketplaces. First, the use of SSI technology enable methods for acquiring verified credential (VC) that are verifiable on a decentralised blockchain registry to identify both real estate owner(s) and real estate property. Second, the smart contracts are used to negotiate the secure transfer of real estate property deeds on the marketplace. To assess the viability of our proposal we define an application scenario and compare our work with other approaches", "published": "2022-07-10T13:12:40Z", "authors": ["Abubakar-Sadiq Shehu", "Antonio Pinto", "Manuel E. Correia"], "categories": ["cs.CR"], "doi": "", "_query": "real estate valuation deep learning", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2603.29261v1", "title": "Monodense Deep Neural Model for Determining Item Price Elasticity", "abstract": "Item Price Elasticity is used to quantify the responsiveness of consumer demand to changes in item prices, enabling businesses to create pricing strategies and optimize revenue management. Sectors such as store retail, e-commerce, and consumer goods rely on elasticity information derived from historical sales and pricing data. This elasticity provides an understanding of purchasing behavior across different items, consumer discount sensitivity, and demand elastic departments. This information is particularly valuable for competitive markets and resource-constrained businesses decision making which aims to maximize profitability and market share. Price elasticity also uncovers historical shifts in consumer responsiveness over time. In this paper, we model item-level price elasticity using large-scale transactional datasets, by proposing a novel elasticity estimation framework which has the capability to work in an absence of treatment control setting. We test this framework by using Machine learning based algorithms listed below, including our newly proposed Monodense deep neural network. (1) Monodense-DL network -- Hybrid neural network architecture combining embedding, dense, and Monodense layers (2) DML -- Double machine learning setting using regression models (3) LGBM -- Light Gradient Boosting Model We evaluate our model on multi-category retail data spanning millions of transactions using a back testing framework. Experimental results demonstrate the superiority of our proposed neural network model within the framework compared to other prevalent ML based methods listed above.", "published": "2026-03-31T04:50:51Z", "authors": ["Lakshya Garg", "Sai Yaswanth", "Deep Narayan Mishra", "Karthik Kumaran", "Anupriya Sharma", "Mayank Uniyal"], "categories": ["cs.LG", "cs.AI"], "doi": "10.1109/AAIML67890.2026.11498150", "_query": "real estate valuation deep learning", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/1211.0398v1", "title": "Extending valuations to formal completions", "abstract": "This paper is an extended version of the talk given by Miguel Olalla at the International Conference on Valuation Theory in El Escorial in July 2011. Its purpose is to provide an introduction to our joint paper \"Extending a valuation centered in a local domain to the formal completion\" (Proc. London Math. Soc. (2012) 105 (3), 571-621) without grinding through all of its technical details.", "published": "2012-11-02T09:09:38Z", "authors": ["F. J. Herrera Govantes", "M. A. Olalla Acosta", "M. Spivakovsky", "B. Teissier"], "categories": ["math.AG", "math.RA"], "doi": "10.4171/149-1/12", "_query": "real estate valuation deep learning", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/1903.03040v2", "title": "Deep learning observables in computational fluid dynamics", "abstract": "Many large scale problems in computational fluid dynamics such as uncertainty quantification, Bayesian inversion, data assimilation and PDE constrained optimization are considered very challenging computationally as they require a large number of expensive (forward) numerical solutions of the corresponding PDEs. We propose a machine learning algorithm, based on deep artificial neural networks, that predicts the underlying \\emph{input parameters to observable} map from a few training samples (computed realizations of this map). By a judicious combination of theoretical arguments and empirical observations, we find suitable network architectures and training hyperparameters that result in robust and efficient neural network approximations of the parameters to observable map. Numerical experiments are presented to demonstrate low prediction errors for the trained network networks, even when the network has been trained with a few samples, at a computational cost which is several orders of magnitude lower than the underlying PDE solver. Moreover, we combine the proposed deep learning algorithm with Monte Carlo (MC) and Quasi-Monte Carlo (QMC) methods to efficiently compute uncertainty propagation for nonlinear PDEs. Under the assumption that the underlying neural networks generalize well, we prove that the deep learning MC and QMC algorithms are guaranteed to be faster than the baseline (quasi-) Monte Carlo methods. Numerical experiments demonstrating one to two orders of magnitude speed up over baseline QMC and MC algorithms, for the intricate problem of computing probability distributions of the observable, are also presented.", "published": "2019-03-07T16:57:52Z", "authors": ["Kjetil O. Lye", "Siddhartha Mishra", "Deep Ray"], "categories": ["physics.comp-ph", "cs.LG", "math.NA", "physics.flu-dyn", "stat.ML"], "doi": "10.1016/j.jcp.2020.109339", "_query": "real estate valuation deep learning", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2012.06469v1", "title": "DILIE: Deep Internal Learning for Image Enhancement", "abstract": "We consider the generic deep image enhancement problem where an input image is transformed into a perceptually better-looking image. Recent methods for image enhancement consider the problem by performing style transfer and image restoration. The methods mostly fall into two categories: training data-based and training data-independent (deep internal learning methods). We perform image enhancement in the deep internal learning framework. Our Deep Internal Learning for Image Enhancement framework enhances content features and style features and uses contextual content loss for preserving image context in the enhanced image. We show results on both hazy and noisy image enhancement. To validate the results, we use structure similarity and perceptual error, which is efficient in measuring the unrealistic deformation present in the images. We show that the proposed framework outperforms the relevant state-of-the-art works for image enhancement.", "published": "2020-12-11T16:39:44Z", "authors": ["Indra Deep Mastan", "Shanmuganathan Raman"], "categories": ["cs.CV"], "doi": "", "_query": "real estate valuation deep learning", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2512.23753v1", "title": "Generalized Regularized Evidential Deep Learning Models: Theory and Comprehensive Evaluation", "abstract": "Evidential deep learning (EDL) models, based on Subjective Logic, introduce a principled and computationally efficient way to make deterministic neural networks uncertainty-aware. The resulting evidential models can quantify fine-grained uncertainty using learned evidence. However, the Subjective-Logic framework constrains evidence to be non-negative, requiring specific activation functions whose geometric properties can induce activation-dependent learning-freeze behavior: a regime where gradients become extremely small for samples mapped into low-evidence regions. We theoretically characterize this behavior and analyze how different evidential activations influence learning dynamics. Building on this analysis, we design a general family of activation functions and corresponding evidential regularizers that provide an alternative pathway for consistent evidence updates across activation regimes. Extensive experiments on four benchmark classification problems (MNIST, CIFAR-10, CIFAR-100, and Tiny-ImageNet), two few-shot classification problems, and blind face restoration problem empirically validate the developed theory and demonstrate the effectiveness of the proposed generalized regularized evidential models.", "published": "2025-12-27T11:26:18Z", "authors": ["Deep Shankar Pandey", "Hyomin Choi", "Qi Yu"], "categories": ["cs.LG", "cs.AI"], "doi": "", "_query": "real estate valuation deep learning", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/1903.04717v2", "title": "Activation Analysis of a Byte-Based Deep Neural Network for Malware Classification", "abstract": "Feature engineering is one of the most costly aspects of developing effective machine learning models, and that cost is even greater in specialized problem domains, like malware classification, where expert skills are necessary to identify useful features. Recent work, however, has shown that deep learning models can be used to automatically learn feature representations directly from the raw, unstructured bytes of the binaries themselves. In this paper, we explore what these models are learning about malware. To do so, we examine the learned features at multiple levels of resolution, from individual byte embeddings to end-to-end analysis of the model. At each step, we connect these byte-oriented activations to their original semantics through parsing and disassembly of the binary to arrive at human-understandable features. Through our results, we identify several interesting features learned by the model and their connection to manually-derived features typically used by traditional machine learning models. Additionally, we explore the impact of training data volume and regularization on the quality of the learned features and the efficacy of the classifiers, revealing the somewhat paradoxical insight that better generalization does not necessarily result in better performance for byte-based malware classifiers.", "published": "2019-03-12T04:00:42Z", "authors": ["Scott E. Coull", "Christopher Gardner"], "categories": ["cs.LG", "cs.CR", "stat.ML"], "doi": "", "_query": "real estate valuation deep learning", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2302.04143v2", "title": "Predicting Thrombectomy Recanalization from CT Imaging Using Deep Learning Models", "abstract": "For acute ischemic stroke (AIS) patients with large vessel occlusions, clinicians must decide if the benefit of mechanical thrombectomy (MTB) outweighs the risks and potential complications following an invasive procedure. Pre-treatment computed tomography (CT) and angiography (CTA) are widely used to characterize occlusions in the brain vasculature. If a patient is deemed eligible, a modified treatment in cerebral ischemia (mTICI) score will be used to grade how well blood flow is reestablished throughout and following the MTB procedure. An estimation of the likelihood of successful recanalization can support treatment decision-making. In this study, we proposed a fully automated prediction of a patient's recanalization score using pre-treatment CT and CTA imaging. We designed a spatial cross attention network (SCANet) that utilizes vision transformers to localize to pertinent slices and brain regions. Our top model achieved an average cross-validated ROC-AUC of 77.33 $\\pm$ 3.9\\%. This is a promising result that supports future applications of deep learning on CT and CTA for the identification of eligible AIS patients for MTB.", "published": "2023-02-08T15:41:21Z", "authors": ["Haoyue Zhang", "Jennifer S. Polson", "Eric J. Yang", "Kambiz Nael", "William Speier", "Corey W. Arnold"], "categories": ["eess.IV", "cs.CV"], "doi": "", "_query": "real estate valuation deep learning", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2011.03712v1", "title": "DeepCFL: Deep Contextual Features Learning from a Single Image", "abstract": "Recently, there is a vast interest in developing image feature learning methods that are independent of the training data, such as deep image prior, InGAN, SinGAN, and DCIL. These methods are unsupervised and are used to perform low-level vision tasks such as image restoration, image editing, and image synthesis. In this work, we proposed a new training data-independent framework, called Deep Contextual Features Learning (DeepCFL), to perform image synthesis and image restoration based on the semantics of the input image. The contextual features are simply the high dimensional vectors representing the semantics of the given image. DeepCFL is a single image GAN framework that learns the distribution of the context vectors from the input image. We show the performance of contextual learning in various challenging scenarios: outpainting, inpainting, and restoration of randomly removed pixels. DeepCFL is applicable when the input source image and the generated target image are not aligned. We illustrate image synthesis using DeepCFL for the task of image resizing.", "published": "2020-11-07T06:54:59Z", "authors": ["Indra Deep Mastan", "Shanmuganathan Raman"], "categories": ["cs.CV"], "doi": "", "_query": "real estate valuation deep learning", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2609.01077v1", "title": "JENGA: Exploiting Counter-Based RowHammer Countermeasures to Break Real-Time Predictability", "abstract": "Safety-critical real-time systems must satisfy multiple dependability requirements, notably time predictability and security. In such systems, tasks must complete within bounded and known execution times, typically characterised through Worst-Case Execution Time (WCET) analysis. At the same time, DRAM-based platforms are increasingly sensitive to the RowHammer read-disturbance security vulnerability, which has motivated the development of numerous hardware and software countermeasures in both academia and industry. However, the impact of these defences is generally evaluated in terms of average-case performance, a metric that is insufficient for safetycritical real-time systems, where worst-case behaviour is the primary concern. In this paper, we study the impact of RowHammer countermeasures based on hardware counters on the timing behaviour of real-time systems. We use a Per-Row-Activation-Counter (PRAC) countermeasure as a case study, standardised for recent DDR5 memories, and show that it can introduce significant timing variations. Based on this observation, we introduce JENGA, an attack in which an attacker-controlled task manipulates the internal state of the RowHammer countermeasure mechanism to increase the execution time of a victim real-time task beyond its expected WCET. We implement JENGA in a gem5 and Ramulator 2.0 simulation environment and evaluate its impact on TACLeBench workloads. We show that such an attack can delay tasks up to 200% of their WCET, making the initial timesafety assumptions unsafe. To address this issue, we derive a safe analytical bound that accounts for mitigation-induced delays in WCET analysis for DRAM systems protected by hardware countermeasures, such as PRAC-N.", "published": "2026-09-01T11:08:10Z", "authors": ["Valentin Abgrall", "Marcello Traiola", "Ruben Salvador", "Maria Méndez Real", "Alessandro Palumbo", "Angeliki Kritikakou"], "categories": ["cs.CR", "cs.AR"], "doi": "", "_query": "real estate valuation deep learning", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2403.12562v2", "title": "PePR: Performance Per Resource Unit as a Metric to Promote Small-Scale Deep Learning in Medical Image Analysis", "abstract": "The recent advances in deep learning (DL) have been accelerated by access to large-scale data and compute. These large-scale resources have been used to train progressively larger models which are resource intensive in terms of compute, data, energy, and carbon emissions. These costs are becoming a new type of entry barrier to researchers and practitioners with limited access to resources at such scale, particularly in the Global South. In this work, we take a comprehensive look at the landscape of existing DL models for medical image analysis tasks and demonstrate their usefulness in settings where resources are limited. To account for the resource consumption of DL models, we introduce a novel measure to estimate the performance per resource unit, which we call the PePR score. Using a diverse family of 131 unique DL architectures (spanning 1M to 130M trainable parameters) and three medical image datasets, we capture trends about the performance-resource trade-offs. In applications like medical image analysis, we argue that small-scale, specialized models are better than striving for large-scale models. Furthermore, we show that using existing pretrained models that are fine-tuned on new data can significantly reduce the computational resources and data required compared to training models from scratch. We hope this work will encourage the community to focus on improving AI equity by developing methods and models with smaller resource footprints.", "published": "2024-03-19T09:17:18Z", "authors": ["Raghavendra Selvan", "Bob Pepin", "Christian Igel", "Gabrielle Samuel", "Erik B Dam"], "categories": ["cs.LG", "cs.AI", "stat.ML"], "doi": "", "_query": "real estate valuation deep learning", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2107.02926v2", "title": "Solution of Physics-based Bayesian Inverse Problems with Deep Generative Priors", "abstract": "Inverse problems are ubiquitous in nature, arising in almost all areas of science and engineering ranging from geophysics and climate science to astrophysics and biomechanics. One of the central challenges in solving inverse problems is tackling their ill-posed nature. Bayesian inference provides a principled approach for overcoming this by formulating the inverse problem into a statistical framework. However, it is challenging to apply when inferring fields that have discrete representations of large dimensions (the so-called \"curse of dimensionality\") and/or when prior information is available only in the form of previously acquired solutions. In this work, we present a novel method for efficient and accurate Bayesian inversion using deep generative models. Specifically, we demonstrate how using the approximate distribution learned by a Generative Adversarial Network (GAN) as a prior in a Bayesian update and reformulating the resulting inference problem in the low-dimensional latent space of the GAN, enables the efficient solution of large-scale Bayesian inverse problems. Our statistical framework preserves the underlying physics and is demonstrated to yield accurate results with reliable uncertainty estimates, even in the absence of information about underlying noise model, which is a significant challenge with many existing methods. We demonstrate the effectiveness of proposed method on a variety of inverse problems which include both synthetic as well as experimentally observed data.", "published": "2021-07-06T22:23:27Z", "authors": ["Dhruv V Patel", "Deep Ray", "Assad A Oberai"], "categories": ["stat.ML", "cs.LG"], "doi": "10.1016/j.cma.2022.115428", "_query": "real estate valuation deep learning", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2412.03084v2", "title": "Hybrid deep learning-based strategy for the hepatocellular carcinoma cancer grade classification of H&E stained liver histopathology images", "abstract": "Hepatocellular carcinoma (HCC) is a common type of liver cancer whose early-stage diagnosis is a common challenge, mainly due to the manual assessment of hematoxylin and eosin-stained whole slide images, which is a time-consuming process and may lead to variability in decision-making. For accurate detection of HCC, we propose a hybrid deep learning-based architecture that uses transfer learning to extract the features from pre-trained convolutional neural network (CNN) models and a classifier made up of a sequence of fully connected layers. This study uses a publicly available The Cancer Genome Atlas Hepatocellular Carcinoma (TCGA-LIHC)database (n=491) for model development and database of Kasturba Gandhi Medical College (KMC), India for validation. The pre-processing step involves patch extraction, colour normalization, and augmentation that results in 3920 patches for the TCGA dataset. The developed hybrid deep neural network consisting of a CNN-based pre-trained feature extractor and a customized artificial neural network-based classifier is trained using five-fold cross-validation. For this study, eight different state-of-the-art models are trained and tested as feature extractors for the proposed hybrid model. The proposed hybrid model with ResNet50-based feature extractor provided the sensitivity, specificity, F1-score, accuracy, and AUC of 100.00%, 100.00%, 100.00%, 100.00%, and 1.00, respectively on the TCGA database. On the KMC database, EfficientNetb3 resulted in the optimal choice of the feature extractor giving sensitivity, specificity, F1-score, accuracy, and AUC of 96.97, 98.85, 96.71, 96.71, and 0.99, respectively. The proposed hybrid models showed improvement in accuracy of 2% and 4% over the pre-trained models in TCGA-LIHC and KMC databases.", "published": "2024-12-04T07:26:36Z", "authors": ["Ajinkya Deshpande", "Deep Gupta", "Ankit Bhurane", "Nisha Meshram", "Sneha Singh", "Petia Radeva"], "categories": ["eess.IV", "cs.CV", "cs.LG", "q-bio.QM"], "doi": "", "_query": "real estate valuation deep learning", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2302.00117v1", "title": "Real Estate Property Valuation using Self-Supervised Vision Transformers", "abstract": "The use of Artificial Intelligence (AI) in the real estate market has been growing in recent years. In this paper, we propose a new method for property valuation that utilizes self-supervised vision transformers, a recent breakthrough in computer vision and deep learning. Our proposed algorithm uses a combination of machine learning, computer vision and hedonic pricing models trained on real estate data to estimate the value of a given property. We collected and pre-processed a data set of real estate properties in the city of Boulder, Colorado and used it to train, validate and test our algorithm. Our data set consisted of qualitative images (including house interiors, exteriors, and street views) as well as quantitative features such as the number of bedrooms, bathrooms, square footage, lot square footage, property age, crime rates, and proximity to amenities. We evaluated the performance of our model using metrics such as Root Mean Squared Error (RMSE). Our findings indicate that these techniques are able to accurately predict the value of properties, with a low RMSE. The proposed algorithm outperforms traditional appraisal methods that do not leverage property images and has the potential to be used in real-world applications.", "published": "2023-01-31T21:54:15Z", "authors": ["Mahdieh Yazdani", "Maziar Raissi"], "categories": ["cs.CV", "cs.AI", "cs.LG", "econ.EM"], "doi": "", "_query": "real estate valuation deep learning", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2107.09957v2", "title": "Memorization in Deep Neural Networks: Does the Loss Function matter?", "abstract": "Deep Neural Networks, often owing to the overparameterization, are shown to be capable of exactly memorizing even randomly labelled data. Empirical studies have also shown that none of the standard regularization techniques mitigate such overfitting. We investigate whether the choice of the loss function can affect this memorization. We empirically show, with benchmark data sets MNIST and CIFAR-10, that a symmetric loss function, as opposed to either cross-entropy or squared error loss, results in significant improvement in the ability of the network to resist such overfitting. We then provide a formal definition for robustness to memorization and provide a theoretical explanation as to why the symmetric losses provide this robustness. Our results clearly bring out the role loss functions alone can play in this phenomenon of memorization.", "published": "2021-07-21T09:08:51Z", "authors": ["Deep Patel", "P. S. Sastry"], "categories": ["cs.LG", "cs.CV", "stat.ML"], "doi": "", "_query": "real estate valuation deep learning", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/1912.04229v1", "title": "DCIL: Deep Contextual Internal Learning for Image Restoration and Image Retargeting", "abstract": "Recently, there is a vast interest in developing methods which are independent of the training samples such as deep image prior, zero-shot learning, and internal learning. The methods above are based on the common goal of maximizing image features learning from a single image despite inherent technical diversity. In this work, we bridge the gap between the various unsupervised approaches above and propose a general framework for image restoration and image retargeting. We use contextual feature learning and internal learning to improvise the structure similarity between the source and the target images. We perform image resize application in the following setups: classical image resize using super-resolution, a challenging image resize where the low-resolution image contains noise, and content-aware image resize using image retargeting. We also provide comparisons to the relevant state-of-the-art methods.", "published": "2019-12-09T18:12:49Z", "authors": ["Indra Deep Mastan", "Shanmuganathan Raman"], "categories": ["eess.IV", "cs.CV"], "doi": "", "_query": "real estate valuation deep learning", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2410.08947v2", "title": "Meta-Transfer Learning Powered Temporal Graph Networks for Cross-City Real Estate Appraisal", "abstract": "Real estate appraisal is important for a variety of endeavors such as real estate deals, investment analysis, and real property taxation. Recently, deep learning has shown great promise for real estate appraisal by harnessing substantial online transaction data from web platforms. Nonetheless, deep learning is data-hungry, and thus it may not be trivially applicable to enormous small cities with limited data. To this end, we propose Meta-Transfer Learning Powered Temporal Graph Networks (MetaTransfer) to transfer valuable knowledge from multiple data-rich metropolises to the data-scarce city to improve valuation performance. Specifically, by modeling the ever-growing real estate transactions with associated residential communities as a temporal event heterogeneous graph, we first design an Event-Triggered Temporal Graph Network to model the irregular spatiotemporal correlations between evolving real estate transactions. Besides, we formulate the city-wide real estate appraisal as a multi-task dynamic graph link label prediction problem, where the valuation of each community in a city is regarded as an individual task. A Hypernetwork-Based Multi-Task Learning module is proposed to simultaneously facilitate intra-city knowledge sharing between multiple communities and task-specific parameters generation to accommodate the community-wise real estate price distribution. Furthermore, we propose a Tri-Level Optimization Based Meta- Learning framework to adaptively re-weight training transaction instances from multiple source cities to mitigate negative transfer, and thus improve the cross-city knowledge transfer effectiveness. Finally, extensive experiments based on five real-world datasets demonstrate the significant superiority of MetaTransfer compared with eleven baseline algorithms.", "published": "2024-10-11T16:16:38Z", "authors": ["Weijia Zhang", "Jindong Han", "Hao Liu", "Wei Fan", "Hao Wang", "Hui Xiong"], "categories": ["cs.LG", "cs.AI"], "doi": "", "_query": "real estate valuation deep learning", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/1912.06732v2", "title": "On the approximation of rough functions with deep neural networks", "abstract": "Deep neural networks and the ENO procedure are both efficient frameworks for approximating rough functions. We prove that at any order, the ENO interpolation procedure can be cast as a deep ReLU neural network. This surprising fact enables the transfer of several desirable properties of the ENO procedure to deep neural networks, including its high-order accuracy at approximating Lipschitz functions. Numerical tests for the resulting neural networks show excellent performance for approximating solutions of nonlinear conservation laws and at data compression.", "published": "2019-12-13T22:48:36Z", "authors": ["Tim De Ryck", "Siddhartha Mishra", "Deep Ray"], "categories": ["math.NA", "cs.LG", "stat.ML"], "doi": "", "_query": "real estate valuation deep learning", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2503.22119v1", "title": "Multimodal Machine Learning for Real Estate Appraisal: A Comprehensive Survey", "abstract": "Real estate appraisal has undergone a significant transition from manual to automated valuation and is entering a new phase of evolution. Leveraging comprehensive attention to various data sources, a novel approach to automated valuation, multimodal machine learning, has taken shape. This approach integrates multimodal data to deeply explore the diverse factors influencing housing prices. Furthermore, multimodal machine learning significantly outperforms single-modality or fewer-modality approaches in terms of prediction accuracy, with enhanced interpretability. However, systematic and comprehensive survey work on the application in the real estate domain is still lacking. In this survey, we aim to bridge this gap by reviewing the research efforts. We begin by reviewing the background of real estate appraisal and propose two research questions from the perspecve of performance and fusion aimed at improving the accuracy of appraisal results. Subsequently, we explain the concept of multimodal machine learning and provide a comprehensive classification and definition of modalities used in real estate appraisal for the first time. To ensure clarity, we explore works related to data and techniques, along with their evaluation methods, under the framework of these two research questions. Furthermore, specific application domains are summarized. Finally, we present insights into future research directions including multimodal complementarity, technology and modality contribution.", "published": "2025-03-28T03:47:06Z", "authors": ["Chenya Huang", "Zhidong Li", "Fang Chen", "Bin Liang"], "categories": ["cs.LG"], "doi": "", "_query": "real estate valuation deep learning", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2606.04445v1", "title": "RowNet: A Memory Transformer for Tabular Regression", "abstract": "Real estate valuation is a structured regression problem in which prices are governed by heterogeneous feature types, sparse regional effects, nonlinear interactions, and the practical logic of comparable properties. Standard multilayer perceptrons treat each row as an isolated vector and must learn locality, scale sensitivity, and categorical matching from supervision alone. Gradient-boosted decision trees provide strong tabular baselines, but their feature-centric splitting mechanism does not explicitly model the retrieval of similar historical observations. This paper presents RowNet, a retrieval-based neural architecture for real estate price-per-square-meter prediction. RowNet represents a query property through pairwise similarity features against a memory bank of labeled properties. A first retrieval layer estimates a coarse target from feature-only similarities. A second layer augments the memory comparison with target-consistency features and uses multiple learned attention heads to retrieve complementary comparable sets. A final mixture-of-experts module combines learned gating, residual correction, entropy regularization, and head-diversity regularization to produce the prediction.", "published": "2026-06-03T04:47:38Z", "authors": ["Askat Rakhymbekov", "Gulshat Muhametjanova"], "categories": ["cs.LG", "cs.AI", "math.ST"], "doi": "", "_query": "real estate valuation deep learning", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/1902.02060v3", "title": "On ADMM in Deep Learning: Convergence and Saturation-Avoidance", "abstract": "In this paper, we develop an alternating direction method of multipliers (ADMM) for deep neural networks training with sigmoid-type activation functions (called \\textit{sigmoid-ADMM pair}), mainly motivated by the gradient-free nature of ADMM in avoiding the saturation of sigmoid-type activations and the advantages of deep neural networks with sigmoid-type activations (called deep sigmoid nets) over their rectified linear unit (ReLU) counterparts (called deep ReLU nets) in terms of approximation. In particular, we prove that the approximation capability of deep sigmoid nets is not worse than that of deep ReLU nets by showing that ReLU activation function can be well approximated by deep sigmoid nets with two hidden layers and finitely many free parameters but not vice-verse. We also establish the global convergence of the proposed ADMM for the nonlinearly constrained formulation of the deep sigmoid nets training from arbitrary initial points to a Karush-Kuhn-Tucker (KKT) point at a rate of order ${\\cal O}(1/k)$. Besides sigmoid activation, such a convergence theorem holds for a general class of smooth activations. Compared with the widely used stochastic gradient descent (SGD) algorithm for the deep ReLU nets training (called ReLU-SGD pair), the proposed sigmoid-ADMM pair is practically stable with respect to the algorithmic hyperparameters including the learning rate, initial schemes and the pro-processing of the input data. Moreover, we find that to approximate and learn simple but important functions the proposed sigmoid-ADMM pair numerically outperforms the ReLU-SGD pair.", "published": "2019-02-06T08:22:08Z", "authors": ["Jinshan Zeng", "Shao-Bo Lin", "Yuan Yao", "Ding-Xuan Zhou"], "categories": ["cs.LG", "math.OC", "stat.ML"], "doi": "", "_query": "real estate valuation deep learning", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/1705.03921v1", "title": "Why & When Deep Learning Works: Looking Inside Deep Learnings", "abstract": "The Intel Collaborative Research Institute for Computational Intelligence (ICRI-CI) has been heavily supporting Machine Learning and Deep Learning research from its foundation in 2012. We have asked six leading ICRI-CI Deep Learning researchers to address the challenge of \"Why & When Deep Learning works\", with the goal of looking inside Deep Learning, providing insights on how deep networks function, and uncovering key observations on their expressiveness, limitations, and potential. The output of this challenge resulted in five papers that address different facets of deep learning. These different facets include a high-level understating of why and when deep networks work (and do not work), the impact of geometry on the expressiveness of deep networks, and making deep networks interpretable.", "published": "2017-05-10T18:52:26Z", "authors": ["Ronny Ronen"], "categories": ["cs.LG"], "doi": "", "_query": "real estate valuation deep learning", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2408.06364v1", "title": "Using deep learning to enhance electronic service quality: Application to real estate websites", "abstract": "Electronic service quality (E-SQ) is a strategic metric for successful e-services.Among the service quality dimensions, tangibility is overlooked. However, by incorporating visuals or tangible tools, the intangible nature of e-services can be balanced. Thanks to advancements in Deep Learning for computer vision, tangible visual features can now be leveraged to enhance the browsing and searching experience of electronic services. Users usually have specific search criteria to meet, but most services will not offer flexible search filters. This research emphasizes the importance of integrating visual and descriptive features to improve the tangibility and efficiency of e-services. A prime example of an electronic service that can benefit from this is real-estate websites. Searching for real estate properties that match user preferences is usually demanding and lacks visual filters, such as the Damage Level to the property. The research introduces a novel visual descriptive feature, the Damage Level, which utilizes a deep learning network known as Mask-RCNN to estimate damage in real estate images. Additionally, a model is developed to incorporate the Damage Level as a tangible feature in electronic real estate services, with the aim of enhancing the tangible customer experience.", "published": "2024-07-27T02:02:26Z", "authors": ["Samaa Elnagar"], "categories": ["cs.CV", "cs.LG"], "doi": "10.1016/j.iswa.2024.200330", "_query": "real estate valuation deep learning", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2504.04303v1", "title": "Using ensemble methods of machine learning to predict real estate prices", "abstract": "In recent years, machine learning (ML) techniques have become a powerful tool for improving the accuracy of predictions and decision-making. Machine learning technologies have begun to penetrate all areas, including the real estate sector. Correct forecasting of real estate value plays an important role in the buyer-seller chain, because it ensures reasonableness of price expectations based on the offers available in the market and helps to avoid financial risks for both parties of the transaction. Accurate forecasting is also important for real estate investors to make an informed decision on a specific property. This study helps to gain a deeper understanding of how effective and accurate ensemble machine learning methods are in predicting real estate values. The results obtained in the work are quite accurate, as can be seen from the coefficient of determination (R^2), root mean square error (RMSE) and mean absolute error (MAE) calculated for each model. The Gradient Boosting Regressor model provides the highest accuracy, the Extra Trees Regressor, Hist Gradient Boosting Regressor and Random Forest Regressor models give good results. In general, ensemble machine learning techniques can be effectively used to solve real estate valuation. This work forms ideas for future research, which consist in the preliminary processing of the data set by searching and extracting anomalous values, as well as the practical implementation of the obtained results.", "published": "2025-04-05T23:53:38Z", "authors": ["Oleh Pastukh", "Viktor Khomyshyn"], "categories": ["cs.LG"], "doi": "", "_query": "real estate valuation deep learning", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/1711.03121v1", "title": "Deep Learning for Real-time Gravitational Wave Detection and Parameter Estimation: Results with Advanced LIGO Data", "abstract": "The recent Nobel-prize-winning detections of gravitational waves from merging black holes and the subsequent detection of the collision of two neutron stars in coincidence with electromagnetic observations have inaugurated a new era of multimessenger astrophysics. To enhance the scope of this emergent field of science, we pioneered the use of deep learning with convolutional neural networks, that take time-series inputs, for rapid detection and characterization of gravitational wave signals. This approach, Deep Filtering, was initially demonstrated using simulated LIGO noise. In this article, we present the extension of Deep Filtering using real data from LIGO, for both detection and parameter estimation of gravitational waves from binary black hole mergers using continuous data streams from multiple LIGO detectors. We demonstrate for the first time that machine learning can detect and estimate the true parameters of real events observed by LIGO. Our results show that Deep Filtering achieves similar sensitivities and lower errors compared to matched-filtering while being far more computationally efficient and more resilient to glitches, allowing real-time processing of weak time-series signals in non-stationary non-Gaussian noise with minimal resources, and also enables the detection of new classes of gravitational wave sources that may go unnoticed with existing detection algorithms. This unified framework for data analysis is ideally suited to enable coincident detection campaigns of gravitational waves and their multimessenger counterparts in real-time.", "published": "2017-11-08T19:05:28Z", "authors": ["Daniel George", "E. A. Huerta"], "categories": ["gr-qc", "astro-ph.HE", "astro-ph.IM", "cs.LG", "cs.NE"], "doi": "10.1016/j.physletb.2017.12.053", "_query": "real estate valuation deep learning", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/1708.05866v2", "title": "A Brief Survey of Deep Reinforcement Learning", "abstract": "Deep reinforcement learning is poised to revolutionise the field of AI and represents a step towards building autonomous systems with a higher level understanding of the visual world. Currently, deep learning is enabling reinforcement learning to scale to problems that were previously intractable, such as learning to play video games directly from pixels. Deep reinforcement learning algorithms are also applied to robotics, allowing control policies for robots to be learned directly from camera inputs in the real world. In this survey, we begin with an introduction to the general field of reinforcement learning, then progress to the main streams of value-based and policy-based methods. Our survey will cover central algorithms in deep reinforcement learning, including the deep $Q$-network, trust region policy optimisation, and asynchronous advantage actor-critic. In parallel, we highlight the unique advantages of deep neural networks, focusing on visual understanding via reinforcement learning. To conclude, we describe several current areas of research within the field.", "published": "2017-08-19T15:55:31Z", "authors": ["Kai Arulkumaran", "Marc Peter Deisenroth", "Miles Brundage", "Anil Anthony Bharath"], "categories": ["cs.LG", "cs.AI", "cs.CV", "stat.ML"], "doi": "10.1109/MSP.2017.2743240", "_query": "real estate valuation deep learning", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/1706.09552v1", "title": "Chord Label Personalization through Deep Learning of Integrated Harmonic Interval-based Representations", "abstract": "The increasing accuracy of automatic chord estimation systems, the availability of vast amounts of heterogeneous reference annotations, and insights from annotator subjectivity research make chord label personalization increasingly important. Nevertheless, automatic chord estimation systems are historically exclusively trained and evaluated on a single reference annotation. We introduce a first approach to automatic chord label personalization by modeling subjectivity through deep learning of a harmonic interval-based chord label representation. After integrating these representations from multiple annotators, we can accurately personalize chord labels for individual annotators from a single model and the annotators' chord label vocabulary. Furthermore, we show that chord personalization using multiple reference annotations outperforms using a single reference annotation.", "published": "2017-06-29T02:38:02Z", "authors": ["H. V. Koops", "W. B. de Haas", "J. Bransen", "A. Volk"], "categories": ["cs.SD", "cs.MM", "cs.NE"], "doi": "", "_query": "real estate valuation deep learning", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/1912.03905v2", "title": "ChainerRL: A Deep Reinforcement Learning Library", "abstract": "In this paper, we introduce ChainerRL, an open-source deep reinforcement learning (DRL) library built using Python and the Chainer deep learning framework. ChainerRL implements a comprehensive set of DRL algorithms and techniques drawn from state-of-the-art research in the field. To foster reproducible research, and for instructional purposes, ChainerRL provides scripts that closely replicate the original papers' experimental settings and reproduce published benchmark results for several algorithms. Lastly, ChainerRL offers a visualization tool that enables the qualitative inspection of trained agents. The ChainerRL source code can be found on GitHub: https://github.com/chainer/chainerrl.", "published": "2019-12-09T08:59:15Z", "authors": ["Yasuhiro Fujita", "Prabhat Nagarajan", "Toshiki Kataoka", "Takahiro Ishikawa"], "categories": ["cs.LG", "cs.AI", "stat.ML"], "doi": "", "_query": "real estate valuation deep learning", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2110.05116v2", "title": "Towards Explainable Real Estate Valuation via Evolutionary Algorithms", "abstract": "Human lives are increasingly influenced by algorithms, which therefore need to meet higher standards not only in accuracy but also with respect to explainability. This is especially true for high-stakes areas such as real estate valuation. Unfortunately, the methods applied there often exhibit a trade-off between accuracy and explainability. One explainable approach is case-based reasoning (CBR), where each decision is supported by specific previous cases. However, such methods can be wanting in accuracy. The unexplainable machine learning approaches are often observed to provide higher accuracy but are not scrutable in their decision-making. In this paper, we apply evolutionary algorithms (EAs) to CBR predictors in order to improve their performance. In particular, we deploy EAs to the similarity functions (used in CBR to find comparable cases), which are fitted to the data set at hand. As a consequence, we achieve higher accuracy than state-of-the-art deep neural networks (DNNs), while keeping interpretability and explainability. These results stem from our empirical evaluation on a large data set of real estate offers where we compare known similarity functions, their EA-improved counterparts, and DNNs. Surprisingly, DNNs are only on par with standard CBR techniques. However, using EA-learned similarity functions does yield an improved performance.", "published": "2021-10-11T09:51:18Z", "authors": ["Sebastian Angrick", "Ben Bals", "Niko Hastrich", "Maximilian Kleissl", "Jonas Schmidt", "Vanja Doskoč", "Maximilian Katzmann", "Louise Molitor", "Tobias Friedrich"], "categories": ["cs.NE"], "doi": "", "_query": "real estate valuation deep learning", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2309.00855v3", "title": "DoRA: Domain-Based Self-Supervised Learning Framework for Low-Resource Real Estate Appraisal", "abstract": "The marketplace system connecting demands and supplies has been explored to develop unbiased decision-making in valuing properties. Real estate appraisal serves as one of the high-cost property valuation tasks for financial institutions since it requires domain experts to appraise the estimation based on the corresponding knowledge and the judgment of the market. Existing automated valuation models reducing the subjectivity of domain experts require a large number of transactions for effective evaluation, which is predominantly limited to not only the labeling efforts of transactions but also the generalizability of new developing and rural areas. To learn representations from unlabeled real estate sets, existing self-supervised learning (SSL) for tabular data neglects various important features, and fails to incorporate domain knowledge. In this paper, we propose DoRA, a Domain-based self-supervised learning framework for low-resource Real estate Appraisal. DoRA is pre-trained with an intra-sample geographic prediction as the pretext task based on the metadata of the real estate for equipping the real estate representations with prior domain knowledge. Furthermore, inter-sample contrastive learning is employed to generalize the representations to be robust for limited transactions of downstream tasks. Our benchmark results on three property types of real-world transactions show that DoRA significantly outperforms the SSL baselines for tabular data, the graph-based methods, and the supervised approaches in the few-shot scenarios by at least 7.6% for MAPE, 11.59% for MAE, and 3.34% for HR10%. We expect DoRA to be useful to other financial practitioners with similar marketplace applications who need general models for properties that are newly built and have limited records. The source code is available at https://github.com/wwweiwei/DoRA.", "published": "2023-09-02T08:01:32Z", "authors": ["Wei-Wei Du", "Wei-Yao Wang", "Wen-Chih Peng"], "categories": ["cs.LG"], "doi": "", "_query": "real estate valuation deep learning", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2004.14545v2", "title": "Explainable Deep Learning: A Field Guide for the Uninitiated", "abstract": "Deep neural networks (DNNs) have become a proven and indispensable machine learning tool. As a black-box model, it remains difficult to diagnose what aspects of the model's input drive the decisions of a DNN. In countless real-world domains, from legislation and law enforcement to healthcare, such diagnosis is essential to ensure that DNN decisions are driven by aspects appropriate in the context of its use. The development of methods and studies enabling the explanation of a DNN's decisions has thus blossomed into an active, broad area of research. A practitioner wanting to study explainable deep learning may be intimidated by the plethora of orthogonal directions the field has taken. This complexity is further exacerbated by competing definitions of what it means ``to explain'' the actions of a DNN and to evaluate an approach's ``ability to explain''. This article offers a field guide to explore the space of explainable deep learning aimed at those uninitiated in the field. The field guide: i) Introduces three simple dimensions defining the space of foundational methods that contribute to explainable deep learning, ii) discusses the evaluations for model explanations, iii) places explainability in the context of other related deep learning research areas, and iv) finally elaborates on user-oriented explanation designing and potential future directions on explainable deep learning. We hope the guide is used as an easy-to-digest starting point for those just embarking on research in this field.", "published": "2020-04-30T02:09:02Z", "authors": ["Gabrielle Ras", "Ning Xie", "Marcel van Gerven", "Derek Doran"], "categories": ["cs.LG", "cs.AI", "stat.ML"], "doi": "", "_query": "real estate valuation deep learning", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2607.21152v1", "title": "A physics-assisted deep neural network-based closure framework for velocity gradient dynamics in compressible flows with vibrational non-equilibrium", "abstract": "In this study, we propose a dynamical model for the evolution of velocity gradients in compressible turbulent flows with vibrational non-equilibrium effects, using physics-assisted deep neural networks. Such models provide a powerful framework for understanding the nonlinear physics associated with small-scale structures. In compressible flows, the influence of thermodynamic fields on velocity-gradient dynamics is represented through thermodynamic gradient field (TGF) tensor. The TGF tensor is one of the primary unclosed terms in velocity-gradient evolution equations. The TGF tensor comprises contributions from the pressure-Hessian tensor, $ρ\\boldsymbol{H}$, and the baroclinic tensor, $\\boldsymbol{B}$. Accordingly, the proposed framework incorporates closures for both $\\boldsymbol{H}$ and $\\boldsymbol{B}$ tensor dynamics. Building upon existing phenomenological closures for the $\\boldsymbol{H}$ tensor governing mechanisms, we develop a neural-network-based closure for the inviscid mechanism responsible for generating the $\\boldsymbol{B}$ tensor. Unlike the other recently used tensor bases, the presented work employs a novel tensor basis allowing for the inclusion of non-symmetric features in the model. The framework also incorporates a data-driven closure for vibrational non-equilibrium effects.The resulting framework combines phenomenological and data-driven representations of various $\\boldsymbol{H}$ and $\\boldsymbol{B}$ tensors governing mechanisms, termed as the \\textit{hybrid enhanced homogenized Euler equation} (H-EHEE) model. Model predictions are evaluated across a range of turbulent Mach numbers and compared against direct numerical simulation (DNS) data and existing compressible velocity-gradient models. The H-EHEE model exhibits close agreement with DNS statistics and provides significant improvements over existing models, particularly in highly compressible flow regimes.", "published": "2026-07-23T10:36:05Z", "authors": ["Deep Shikha", "Sawan S Sinha"], "categories": ["physics.flu-dyn"], "doi": "", "_query": "real estate valuation deep learning", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2306.09011v2", "title": "CAD-Estate: Large-scale CAD Model Annotation in RGB Videos", "abstract": "We propose a method for annotating videos of complex multi-object scenes with a globally-consistent 3D representation of the objects. We annotate each object with a CAD model from a database, and place it in the 3D coordinate frame of the scene with a 9-DoF pose transformation. Our method is semi-automatic and works on commonly-available RGB videos, without requiring a depth sensor. Many steps are performed automatically, and the tasks performed by humans are simple, well-specified, and require only limited reasoning in 3D. This makes them feasible for crowd-sourcing and has allowed us to construct a large-scale dataset by annotating real-estate videos from YouTube. Our dataset CAD-Estate offers 101k instances of 12k unique CAD models placed in the 3D representations of 20k videos. In comparison to Scan2CAD, the largest existing dataset with CAD model annotations on real scenes, CAD-Estate has 7x more instances and 4x more unique CAD models. We showcase the benefits of pre-training a Mask2CAD model on CAD-Estate for the task of automatic 3D object reconstruction and pose estimation, demonstrating that it leads to performance improvements on the popular Scan2CAD benchmark. The dataset is available at https://github.com/google-research/cad-estate.", "published": "2023-06-15T10:12:02Z", "authors": ["Kevis-Kokitsi Maninis", "Stefan Popov", "Matthias Nießner", "Vittorio Ferrari"], "categories": ["cs.CV"], "doi": "", "_query": "real estate valuation deep learning", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/1604.08153v3", "title": "Classifying Options for Deep Reinforcement Learning", "abstract": "In this paper we combine one method for hierarchical reinforcement learning - the options framework - with deep Q-networks (DQNs) through the use of different \"option heads\" on the policy network, and a supervisory network for choosing between the different options. We utilise our setup to investigate the effects of architectural constraints in subtasks with positive and negative transfer, across a range of network capacities. We empirically show that our augmented DQN has lower sample complexity when simultaneously learning subtasks with negative transfer, without degrading performance when learning subtasks with positive transfer.", "published": "2016-04-27T17:48:39Z", "authors": ["Kai Arulkumaran", "Nat Dilokthanakul", "Murray Shanahan", "Anil Anthony Bharath"], "categories": ["cs.LG", "cs.AI", "stat.ML"], "doi": "", "_query": "real estate valuation deep learning", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/1801.00904v4", "title": "ScreenerNet: Learning Self-Paced Curriculum for Deep Neural Networks", "abstract": "We propose to learn a curriculum or a syllabus for supervised learning and deep reinforcement learning with deep neural networks by an attachable deep neural network, called ScreenerNet. Specifically, we learn a weight for each sample by jointly training the ScreenerNet and the main network in an end-to-end self-paced fashion. The ScreenerNet neither has sampling bias nor requires to remember the past learning history. We show the networks augmented with the ScreenerNet achieve early convergence with better accuracy than the state-of-the-art curricular learning methods in extensive experiments using three popular vision datasets such as MNIST, CIFAR10 and Pascal VOC2012, and a Cart-pole task using Deep Q-learning. Moreover, the ScreenerNet can extend other curriculum learning methods such as Prioritized Experience Replay (PER) for further accuracy improvement.", "published": "2018-01-03T05:49:37Z", "authors": ["Tae-Hoon Kim", "Jonghyun Choi"], "categories": ["cs.CV"], "doi": "", "_query": "real estate valuation deep learning", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/1811.09385v2", "title": "On the Importance of Strong Baselines in Bayesian Deep Learning", "abstract": "Like all sub-fields of machine learning Bayesian Deep Learning is driven by empirical validation of its theoretical proposals. Given the many aspects of an experiment it is always possible that minor or even major experimental flaws can slip by both authors and reviewers. One of the most popular experiments used to evaluate approximate inference techniques is the regression experiment on UCI datasets. However, in this experiment, models which have been trained to convergence have often been compared with baselines trained only for a fixed number of iterations. We find that a well-established baseline, Monte Carlo dropout, when evaluated under the same experimental settings shows significant improvements. In fact, the baseline outperforms or performs competitively with methods that claimed to be superior to the very same baseline method when they were introduced. Hence, by exposing this flaw in experimental procedure, we highlight the importance of using identical experimental setups to evaluate, compare, and benchmark methods in Bayesian Deep Learning.", "published": "2018-11-23T08:22:17Z", "authors": ["Jishnu Mukhoti", "Pontus Stenetorp", "Yarin Gal"], "categories": ["cs.LG", "stat.ML"], "doi": "", "_query": "real estate valuation deep learning", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/1812.00922v1", "title": "Multi-agent Deep Reinforcement Learning with Extremely Noisy Observations", "abstract": "Multi-agent reinforcement learning systems aim to provide interacting agents with the ability to collaboratively learn and adapt to the behaviour of other agents. In many real-world applications, the agents can only acquire a partial view of the world. Here we consider a setting whereby most agents' observations are also extremely noisy, hence only weakly correlated to the true state of the environment. Under these circumstances, learning an optimal policy becomes particularly challenging, even in the unrealistic case that an agent's policy can be made conditional upon all other agents' observations. To overcome these difficulties, we propose a multi-agent deep deterministic policy gradient algorithm enhanced by a communication medium (MADDPG-M), which implements a two-level, concurrent learning mechanism. An agent's policy depends on its own private observations as well as those explicitly shared by others through a communication medium. At any given point in time, an agent must decide whether its private observations are sufficiently informative to be shared with others. However, our environments provide no explicit feedback informing an agent whether a communication action is beneficial, rather the communication policies must also be learned through experience concurrently to the main policies. Our experimental results demonstrate that the algorithm performs well in six highly non-stationary environments of progressively higher complexity, and offers substantial performance gains compared to the baselines.", "published": "2018-12-03T17:27:41Z", "authors": ["Ozsel Kilinc", "Giovanni Montana"], "categories": ["cs.LG", "stat.ML"], "doi": "", "_query": "real estate valuation deep learning", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2107.05180v2", "title": "MugRep: A Multi-Task Hierarchical Graph Representation Learning Framework for Real Estate Appraisal", "abstract": "Real estate appraisal refers to the process of developing an unbiased opinion for real property's market value, which plays a vital role in decision-making for various players in the marketplace (e.g., real estate agents, appraisers, lenders, and buyers). However, it is a nontrivial task for accurate real estate appraisal because of three major challenges: (1) The complicated influencing factors for property value; (2) The asynchronously spatiotemporal dependencies among real estate transactions; (3) The diversified correlations between residential communities. To this end, we propose a Multi-Task Hierarchical Graph Representation Learning (MugRep) framework for accurate real estate appraisal. Specifically, by acquiring and integrating multi-source urban data, we first construct a rich feature set to comprehensively profile the real estate from multiple perspectives (e.g., geographical distribution, human mobility distribution, and resident demographics distribution). Then, an evolving real estate transaction graph and a corresponding event graph convolution module are proposed to incorporate asynchronously spatiotemporal dependencies among real estate transactions. Moreover, to further incorporate valuable knowledge from the view of residential communities, we devise a hierarchical heterogeneous community graph convolution module to capture diversified correlations between residential communities. Finally, an urban district partitioned multi-task learning module is introduced to generate differently distributed value opinions for real estate. Extensive experiments on two real-world datasets demonstrate the effectiveness of MugRep and its components and features.", "published": "2021-07-12T03:51:44Z", "authors": ["Weijia Zhang", "Hao Liu", "Lijun Zha", "Hengshu Zhu", "Ji Liu", "Dejing Dou", "Hui Xiong"], "categories": ["cs.LG"], "doi": "10.1145/3447548.3467187", "_query": "real estate valuation deep learning", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2104.00606v2", "title": "Model Selection's Disparate Impact in Real-World Deep Learning Applications", "abstract": "Algorithmic fairness has emphasized the role of biased data in automated decision outcomes. Recently, there has been a shift in attention to sources of bias that implicate fairness in other stages in the ML pipeline. We contend that one source of such bias, human preferences in model selection, remains under-explored in terms of its role in disparate impact across demographic groups. Using a deep learning model trained on real-world medical imaging data, we verify our claim empirically and argue that choice of metric for model comparison, especially those that do not take variability into account, can significantly bias model selection outcomes.", "published": "2021-04-01T16:37:01Z", "authors": ["Jessica Zosa Forde", "A. Feder Cooper", "Kweku Kwegyir-Aggrey", "Chris De Sa", "Michael Littman"], "categories": ["cs.LG", "cs.AI", "cs.CY"], "doi": "", "_query": "real estate valuation deep learning", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2112.05000v1", "title": "The Peril of Popular Deep Learning Uncertainty Estimation Methods", "abstract": "Uncertainty estimation (UE) techniques -- such as the Gaussian process (GP), Bayesian neural networks (BNN), Monte Carlo dropout (MCDropout) -- aim to improve the interpretability of machine learning models by assigning an estimated uncertainty value to each of their prediction outputs. However, since too high uncertainty estimates can have fatal consequences in practice, this paper analyzes the above techniques. Firstly, we show that GP methods always yield high uncertainty estimates on out of distribution (OOD) data. Secondly, we show on a 2D toy example that both BNNs and MCDropout do not give high uncertainty estimates on OOD samples. Finally, we show empirically that this pitfall of BNNs and MCDropout holds on real world datasets as well. Our insights (i) raise awareness for the more cautious use of currently popular UE methods in Deep Learning, (ii) encourage the development of UE methods that approximate GP-based methods -- instead of BNNs and MCDropout, and (iii) our empirical setups can be used for verifying the OOD performances of any other UE method. The source code is available at https://github.com/epfml/uncertainity-estimation.", "published": "2021-12-09T15:47:21Z", "authors": ["Yehao Liu", "Matteo Pagliardini", "Tatjana Chavdarova", "Sebastian U. Stich"], "categories": ["cs.LG", "stat.ML"], "doi": "", "_query": "real estate valuation deep learning", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/1912.01715v1", "title": "Human-Robot Collaboration via Deep Reinforcement Learning of Real-World Interactions", "abstract": "We present a robotic setup for real-world testing and evaluation of human-robot and human-human collaborative learning. Leveraging the sample-efficiency of the Soft Actor-Critic algorithm, we have implemented a robotic platform able to learn a non-trivial collaborative task with a human partner, without pre-training in simulation, and using only 30 minutes of real-world interactions. This enables us to study Human-Robot and Human-Human collaborative learning through real-world interactions. We present preliminary results, showing that state-of-the-art deep learning methods can take human-robot collaborative learning a step closer to that of humans interacting with each other.", "published": "2019-12-02T12:07:23Z", "authors": ["Jonas Tjomsland", "Ali Shafti", "A. Aldo Faisal"], "categories": ["cs.RO", "cs.AI", "cs.LG"], "doi": "", "_query": "real estate valuation deep learning", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/1911.08382v1", "title": "A model for predicting price polarity of real estate properties using information of real estate market websites", "abstract": "This paper presents a model that uses the information that sellers publish in real estate market websites to predict whether a property has higher or lower price than the average price of its similar properties. The model learns the correlation between price and information (text descriptions and features) of real estate properties through automatic identification of latent semantic content given by a machine learning model based on doc2vec and xgboost. The proposed model was evaluated with a data set of 57,516 publications of real estate properties collected from 2016 to 2018 of Bogotá city. Results show that the accuracy of a classifier that involves text descriptions is slightly higher than a classifier that only uses features of the real estate properties, as text descriptions tends to contain detailed information about the property.", "published": "2019-11-19T16:29:59Z", "authors": ["Vladimir Vargas-Calderón", "Jorge E. Camargo"], "categories": ["cs.LG", "stat.ML"], "doi": "10.1080/01605682.2021.2023672", "_query": "real estate valuation deep learning", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/1701.07274v6", "title": "Deep Reinforcement Learning: An Overview", "abstract": "We give an overview of recent exciting achievements of deep reinforcement learning (RL). We discuss six core elements, six important mechanisms, and twelve applications. We start with background of machine learning, deep learning and reinforcement learning. Next we discuss core RL elements, including value function, in particular, Deep Q-Network (DQN), policy, reward, model, planning, and exploration. After that, we discuss important mechanisms for RL, including attention and memory, unsupervised learning, transfer learning, multi-agent RL, hierarchical RL, and learning to learn. Then we discuss various applications of RL, including games, in particular, AlphaGo, robotics, natural language processing, including dialogue systems, machine translation, and text generation, computer vision, neural architecture design, business management, finance, healthcare, Industry 4.0, smart grid, intelligent transportation systems, and computer systems. We mention topics not reviewed yet, and list a collection of RL resources. After presenting a brief summary, we close with discussions. Please see Deep Reinforcement Learning, arXiv:1810.06339, for a significant update.", "published": "2017-01-25T11:52:11Z", "authors": ["Yuxi Li"], "categories": ["cs.LG"], "doi": "", "_query": "real estate valuation deep learning", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/1710.05468v9", "title": "Generalization in Deep Learning", "abstract": "This paper provides theoretical insights into why and how deep learning can generalize well, despite its large capacity, complexity, possible algorithmic instability, nonrobustness, and sharp minima, responding to an open question in the literature. We also discuss approaches to provide non-vacuous generalization guarantees for deep learning. Based on theoretical observations, we propose new open problems and discuss the limitations of our results.", "published": "2017-10-16T02:21:24Z", "authors": ["Kenji Kawaguchi", "Leslie Pack Kaelbling", "Yoshua Bengio"], "categories": ["stat.ML", "cs.AI", "cs.LG", "cs.NE"], "doi": "10.1017/9781009025096.003", "_query": "real estate valuation deep learning", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/1902.09154v1", "title": "Deep Bayesian Multi-Target Learning for Recommender Systems", "abstract": "With the increasing variety of services that e-commerce platforms provide, criteria for evaluating their success become also increasingly multi-targeting. This work introduces a multi-target optimization framework with Bayesian modeling of the target events, called Deep Bayesian Multi-Target Learning (DBMTL). In this framework, target events are modeled as forming a Bayesian network, in which directed links are parameterized by hidden layers, and learned from training samples. The structure of Bayesian network is determined by model selection. We applied the framework to Taobao live-streaming recommendation, to simultaneously optimize (and strike a balance) on targets including click-through rate, user stay time in live room, purchasing behaviors and interactions. Significant improvement has been observed for the proposed method over other MTL frameworks and the non-MTL model. Our practice shows that with an integrated causality structure, we can effectively make the learning of a target benefit from other targets, creating significant synergy effects that improve all targets. The neural network construction guided by DBMTL fits in with the general probabilistic model connecting features and multiple targets, taking weaker assumption than the other methods discussed in this paper. This theoretical generality brings about practical generalization power over various targets distributions, including sparse targets and continuous-value ones.", "published": "2019-02-25T09:11:53Z", "authors": ["Qi Wang", "Zhihui Ji", "Huasheng Liu", "Binqiang Zhao"], "categories": ["cs.LG", "cs.IR", "stat.ML"], "doi": "", "_query": "real estate valuation deep learning", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/1911.11285v1", "title": "Biologically inspired architectures for sample-efficient deep reinforcement learning", "abstract": "Deep reinforcement learning requires a heavy price in terms of sample efficiency and overparameterization in the neural networks used for function approximation. In this work, we use tensor factorization in order to learn more compact representation for reinforcement learning policies. We show empirically that in the low-data regime, it is possible to learn online policies with 2 to 10 times less total coefficients, with little to no loss of performance. We also leverage progress in second order optimization, and use the theory of wavelet scattering to further reduce the number of learned coefficients, by foregoing learning the topmost convolutional layer filters altogether. We evaluate our results on the Atari suite against recent baseline algorithms that represent the state-of-the-art in data efficiency, and get comparable results with an order of magnitude gain in weight parsimony.", "published": "2019-11-25T23:59:22Z", "authors": ["Pierre H. Richemond", "Arinbjörn Kolbeinsson", "Yike Guo"], "categories": ["cs.LG", "cs.NE", "stat.ML"], "doi": "", "_query": "real estate valuation deep learning", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/1811.10041v1", "title": "BDLOB: Bayesian Deep Convolutional Neural Networks for Limit Order Books", "abstract": "We showcase how dropout variational inference can be applied to a large-scale deep learning model that predicts price movements from limit order books (LOBs), the canonical data source representing trading and pricing movements. We demonstrate that uncertainty information derived from posterior predictive distributions can be utilised for position sizing, avoiding unnecessary trades and improving profits. Further, we test our models by using millions of observations across several instruments and markets from the London Stock Exchange. Our results suggest that those Bayesian techniques not only deliver uncertainty information that can be used for trading but also improve predictive performance as stochastic regularisers. To the best of our knowledge, we are the first to apply Bayesian networks to LOBs.", "published": "2018-11-25T15:53:31Z", "authors": ["Zihao Zhang", "Stefan Zohren", "Stephen Roberts"], "categories": ["q-fin.CP"], "doi": "", "_query": "real estate valuation deep learning", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/1512.02011v2", "title": "How to Discount Deep Reinforcement Learning: Towards New Dynamic Strategies", "abstract": "Using deep neural nets as function approximator for reinforcement learning tasks have recently been shown to be very powerful for solving problems approaching real-world complexity. Using these results as a benchmark, we discuss the role that the discount factor may play in the quality of the learning process of a deep Q-network (DQN). When the discount factor progressively increases up to its final value, we empirically show that it is possible to significantly reduce the number of learning steps. When used in conjunction with a varying learning rate, we empirically show that it outperforms original DQN on several experiments. We relate this phenomenon with the instabilities of neural networks when they are used in an approximate Dynamic Programming setting. We also describe the possibility to fall within a local optimum during the learning process, thus connecting our discussion with the exploration/exploitation dilemma.", "published": "2015-12-07T12:25:18Z", "authors": ["Vincent François-Lavet", "Raphael Fonteneau", "Damien Ernst"], "categories": ["cs.LG", "cs.AI"], "doi": "", "_query": "real estate valuation deep learning", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/1812.01995v4", "title": "Deep Learning Model for Finding New Superconductors", "abstract": "Exploration of new superconductors still relies on the experience and intuition of experts and is largely a process of experimental trial and error. In one study, only 3% of the candidate materials showed superconductivity. Here, we report the first deep learning model for finding new superconductors. We introduced the method named \"reading periodic table\" which represented the periodic table in a way that allows deep learning to learn to read the periodic table and to learn the law of elements for the purpose of discovering novel superconductors that are outside the training data. It is recognized that it is difficult for deep learning to predict something outside the training data. Although we used only the chemical composition of materials as information, we obtained an $R^{2}$ value of 0.92 for predicting $T_\\text{c}$ for materials in a database of superconductors. We also introduced the method named \"garbage-in\" to create synthetic data of non-superconductors that do not exist. Non-superconductors are not reported, but the data must be required for deep learning to distinguish between superconductors and non-superconductors. We obtained three remarkable results. The deep learning can predict superconductivity for a material with a precision of 62%, which shows the usefulness of the model; it found the recently discovered superconductor CaBi2 and another one Hf0.5Nb0.2V2Zr0.3, neither of which is in the superconductor database; and it found Fe-based high-temperature superconductors (discovered in 2008) from the training data before 2008. These results open the way for the discovery of new high-temperature superconductor families. The candidate materials list, data, and method are openly available from the link https://github.com/tomo835g/Deep-Learning-to-find-Superconductors.", "published": "2018-12-03T05:30:34Z", "authors": ["Tomohiko Konno", "Hodaka Kurokawa", "Fuyuki Nabeshima", "Yuki Sakishita", "Ryo Ogawa", "Iwao Hosako", "Atsutaka Maeda"], "categories": ["cs.LG", "cond-mat.mtrl-sci", "cond-mat.supr-con", "cs.CL", "physics.comp-ph"], "doi": "10.1103/PhysRevB.103.014509", "_query": "real estate valuation deep learning", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/1907.02644v5", "title": "PathologyGAN: Learning deep representations of cancer tissue", "abstract": "Histopathological images of tumors contain abundant information about how tumors grow and how they interact with their micro-environment. Better understanding of tissue phenotypes in these images could reveal novel determinants of pathological processes underlying cancer, and in turn improve diagnosis and treatment options. Advances of Deep learning makes it ideal to achieve those goals, however, its application is limited by the cost of high quality labels from patients data. Unsupervised learning, in particular, deep generative models with representation learning properties provides an alternative path to further understand cancer tissue phenotypes, capturing tissue morphologies. In this paper, we develop a framework which allows GANs to capture key tissue features and uses these characteristics to give structure to its latent space. To this end, we trained our model on two different datasets, an H&E colorectal cancer tissue from the National Center for Tumor diseases (NCT) and an H&E breast cancer tissue from the Netherlands Cancer Institute (NKI) and Vancouver General Hospital (VGH). Composed of 86 slide images and 576 TMAs respectively. We show that our model generates high quality images, with a FID of 16.65 (breast cancer) and 32.05 (colorectal cancer). We further assess the quality of the images with cancer tissue characteristics (e.g. count of cancer, lymphocytes, or stromal cells), using quantitative information to calculate the FID and showing consistent performance of 9.86. Additionally, the latent space of our model shows an interpretable structure and allows semantic vector operations that translate into tissue feature transformations. Furthermore, ratings from two expert pathologists found no significant difference between our generated tissue images from real ones. The code, images, and pretrained models are available at https://github.com/AdalbertoCq/Pathology-GAN", "published": "2019-07-04T15:27:22Z", "authors": ["Adalberto Claudio Quiros", "Roderick Murray-Smith", "Ke Yuan"], "categories": ["eess.IV", "cs.LG", "stat.ML"], "doi": "", "_query": "real estate valuation deep learning", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/1712.00006v2", "title": "Comparing Deep Reinforcement Learning and Evolutionary Methods in Continuous Control", "abstract": "Reinforcement Learning and the Evolutionary Strategy are two major approaches in addressing complicated control problems. Both are strong contenders and have their own devotee communities. Both groups have been very active in developing new advances in their own domain and devising, in recent years, leading-edge techniques to address complex continuous control tasks. Here, in the context of Deep Reinforcement Learning, we formulate a parallelized version of the Proximal Policy Optimization method and a Deep Deterministic Policy Gradient method. Moreover, we conduct a thorough comparison between the state-of-the-art techniques in both camps fro continuous control; evolutionary methods and Deep Reinforcement Learning methods. The results show there is no consistent winner.", "published": "2017-11-30T03:40:06Z", "authors": ["Shangtong Zhang", "Osmar R. Zaiane"], "categories": ["cs.LG", "cs.AI"], "doi": "", "_query": "real estate valuation deep learning", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/1312.5602v1", "title": "Playing Atari with Deep Reinforcement Learning", "abstract": "We present the first deep learning model to successfully learn control policies directly from high-dimensional sensory input using reinforcement learning. The model is a convolutional neural network, trained with a variant of Q-learning, whose input is raw pixels and whose output is a value function estimating future rewards. We apply our method to seven Atari 2600 games from the Arcade Learning Environment, with no adjustment of the architecture or learning algorithm. We find that it outperforms all previous approaches on six of the games and surpasses a human expert on three of them.", "published": "2013-12-19T16:00:08Z", "authors": ["Volodymyr Mnih", "Koray Kavukcuoglu", "David Silver", "Alex Graves", "Ioannis Antonoglou", "Daan Wierstra", "Martin Riedmiller"], "categories": ["cs.LG"], "doi": "", "_query": "real estate valuation deep learning", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2008.02629v1", "title": "Developing a real estate yield investment deviceusing granular data and machine learning", "abstract": "This project aims at creating an investment device to help investors determine which real estate units have a higher return to investment in Madrid. To do so, we gather data from Idealista.com, a real estate web-page with millions of real estate units across Spain, Italy and Portugal. In this preliminary version, we present the road map on how we gather the data; descriptive statistics of the 8,121 real estate units gathered (rental and sale); build a return index based on the difference in prices of rental and sale units(per neighbourhood and size) and introduce machine learning algorithms for rental real estate price prediction.", "published": "2020-06-20T15:42:12Z", "authors": ["Monica Azqueta-Gavaldon", "Gonzalo Azqueta-Gavaldon", "Inigo Azqueta-Gavaldon", "Andres Azqueta-Gavaldon"], "categories": ["q-fin.GN"], "doi": "", "_query": "real estate valuation deep learning", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2110.07151v1", "title": "Machine Learning, Deep Learning, and Hedonic Methods for Real Estate Price Prediction", "abstract": "In recent years several complaints about racial discrimination in appraising home values have been accumulating. For several decades, to estimate the sale price of the residential properties, appraisers have been walking through the properties, observing the property, collecting data, and making use of the hedonic pricing models. However, this method bears some costs and by nature is subjective and biased. To minimize human involvement and the biases in the real estate appraisals and boost the accuracy of the real estate market price prediction models, in this research we design data-efficient learning machines capable of learning and extracting the relation or patterns between the inputs (features for the house) and output (value of the houses). We compare the performance of some machine learning and deep learning algorithms, specifically artificial neural networks, random forest, and k nearest neighbor approaches to that of hedonic method on house price prediction in the city of Boulder, Colorado. Even though this study has been done over the houses in the city of Boulder it can be generalized to the housing market in any cities. The results indicate non-linear association between the dwelling features and dwelling prices. In light of these findings, this study demonstrates that random forest and artificial neural networks algorithms can be better alternatives over the hedonic regression analysis for prediction of the house prices in the city of Boulder, Colorado.", "published": "2021-10-14T04:43:04Z", "authors": ["Mahdieh Yazdani"], "categories": ["econ.EM"], "doi": "", "_query": "real estate valuation deep learning", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2008.04221v4", "title": "Deep learning for photoacoustic imaging: a survey", "abstract": "Machine learning has been developed dramatically and witnessed a lot of applications in various fields over the past few years. This boom originated in 2009, when a new model emerged, that is, the deep artificial neural network, which began to surpass other established mature models on some important benchmarks. Later, it was widely used in academia and industry. Ranging from image analysis to natural language processing, it fully exerted its magic and now become the state-of-the-art machine learning models. Deep neural networks have great potential in medical imaging technology, medical data analysis, medical diagnosis and other healthcare issues, and is promoted in both pre-clinical and even clinical stages. In this review, we performed an overview of some new developments and challenges in the application of machine learning to medical image analysis, with a special focus on deep learning in photoacoustic imaging. The aim of this review is threefold: (i) introducing deep learning with some important basics, (ii) reviewing recent works that apply deep learning in the entire ecological chain of photoacoustic imaging, from image reconstruction to disease diagnosis, (iii) providing some open source materials and other resources for researchers interested in applying deep learning to photoacoustic imaging.", "published": "2020-08-10T15:53:30Z", "authors": ["Changchun Yang", "Hengrong Lan", "Feng Gao", "Fei Gao"], "categories": ["cs.CV", "eess.IV"], "doi": "", "_query": "real estate valuation deep learning", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/1809.01906v2", "title": "Model-Based Regularization for Deep Reinforcement Learning with Transcoder Networks", "abstract": "This paper proposes a new optimization objective for value-based deep reinforcement learning. We extend conventional Deep Q-Networks (DQNs) by adding a model-learning component yielding a transcoder network. The prediction errors for the model are included in the basic DQN loss as additional regularizers. This augmented objective leads to a richer training signal that provides feedback at every time step. Moreover, because learning an environment model shares a common structure with the RL problem, we hypothesize that the resulting objective improves both sample efficiency and performance. We empirically confirm our hypothesis on a range of 20 games from the Atari benchmark attaining superior results over vanilla DQN without model-based regularization.", "published": "2018-09-06T09:49:18Z", "authors": ["Felix Leibfried", "Peter Vrancx"], "categories": ["cs.LG", "stat.ML"], "doi": "", "_query": "real estate valuation deep learning", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/1304.3824v13", "title": "Pricing and Valuation under the Real-World Measure", "abstract": "In general it is not clear which kind of information is supposed to be used for calculating the fair value of a contingent claim. Even if the information is specified, it is not guaranteed that the fair value is uniquely determined by the given information. A further problem is that asset prices are typically expressed in terms of a risk-neutral measure. This makes it difficult to transfer the fundamental results of financial mathematics to econometrics. I show that the aforementioned problems evaporate if the financial market is complete and sensitive. In this case, after an appropriate choice of the numeraire, the discounted price processes turn out to be uniformly integrable martingales under the real-world measure. This leads to a Law of One Price and a simple real-world valuation formula in a model-independent framework where the number of assets as well as the lifetime of the market can be finite or infinite.", "published": "2013-04-13T17:15:13Z", "authors": ["Gabriel Frahm"], "categories": ["q-fin.GN"], "doi": "", "_query": "real estate valuation deep learning", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2603.12986v1", "title": "Retrieval-Enhanced Real Estate Appraisal", "abstract": "The Sales Comparison Approach (SCA) is one of the most popular when it comes to real estate appraisal. Used as a reference in real estate expertise and as one of the major types of Automatic Valuation Models (AVM), it recently gained popularity within machine learning methods. The performance of models able to use data represented as sets and graphs made it possible to adapt this methodology efficiently, yielding substantial results. SCA relies on taking past transactions (comparables) as references, selected according to their similarity with the target property's sale. In this study, we focus on the selection of these comparables for real estate appraisal. We demonstrate that the selection of comparables used in many state-of-the-art algorithms can be significantly improved by learning a selection policy instead of imposing it. Our method relies on a hybrid vector-geographical retrieval module capable of adapting to different datasets and optimized jointly with an estimation module. We further show that the use of carefully selected comparables makes it possible to build models that require fewer comparables and fewer parameters with performance close to state-of-the-art models. All our evaluations are made on five datasets which span areas in the United States, Brazil, and France.", "published": "2026-03-13T13:36:55Z", "authors": ["Simon Popelier", "Matthieu X. B. Sarazin", "Maximilien Bohm", "Mathieu Gierski", "Hanna Mergui", "Matthieu Ospici", "Adrien Bernhardt"], "categories": ["cs.LG"], "doi": "", "_query": "real estate valuation deep learning", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/1510.04609v1", "title": "Layer-Specific Adaptive Learning Rates for Deep Networks", "abstract": "The increasing complexity of deep learning architectures is resulting in training time requiring weeks or even months. This slow training is due in part to vanishing gradients, in which the gradients used by back-propagation are extremely large for weights connecting deep layers (layers near the output layer), and extremely small for shallow layers (near the input layer); this results in slow learning in the shallow layers. Additionally, it has also been shown that in highly non-convex problems, such as deep neural networks, there is a proliferation of high-error low curvature saddle points, which slows down learning dramatically. In this paper, we attempt to overcome the two above problems by proposing an optimization method for training deep neural networks which uses learning rates which are both specific to each layer in the network and adaptive to the curvature of the function, increasing the learning rate at low curvature points. This enables us to speed up learning in the shallow layers of the network and quickly escape high-error low curvature saddle points. We test our method on standard image classification datasets such as MNIST, CIFAR10 and ImageNet, and demonstrate that our method increases accuracy as well as reduces the required training time over standard algorithms.", "published": "2015-10-15T16:31:46Z", "authors": ["Bharat Singh", "Soham De", "Yangmuzi Zhang", "Thomas Goldstein", "Gavin Taylor"], "categories": ["cs.CV", "cs.AI", "cs.LG", "cs.NE"], "doi": "", "_query": "real estate valuation deep learning", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2302.08893v4", "title": "Active learning for data streams: a survey", "abstract": "Online active learning is a paradigm in machine learning that aims to select the most informative data points to label from a data stream. The problem of minimizing the cost associated with collecting labeled observations has gained a lot of attention in recent years, particularly in real-world applications where data is only available in an unlabeled form. Annotating each observation can be time-consuming and costly, making it difficult to obtain large amounts of labeled data. To overcome this issue, many active learning strategies have been proposed in the last decades, aiming to select the most informative observations for labeling in order to improve the performance of machine learning models. These approaches can be broadly divided into two categories: static pool-based and stream-based active learning. Pool-based active learning involves selecting a subset of observations from a closed pool of unlabeled data, and it has been the focus of many surveys and literature reviews. However, the growing availability of data streams has led to an increase in the number of approaches that focus on online active learning, which involves continuously selecting and labeling observations as they arrive in a stream. This work aims to provide an overview of the most recently proposed approaches for selecting the most informative observations from data streams in real time. We review the various techniques that have been proposed and discuss their strengths and limitations, as well as the challenges and opportunities that exist in this area of research.", "published": "2023-02-17T14:24:13Z", "authors": ["Davide Cacciarelli", "Murat Kulahci"], "categories": ["stat.ML", "cs.LG", "stat.ME"], "doi": "10.1007/s10994-023-06454-2", "_query": "real estate valuation deep learning", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2304.02539v2", "title": "Multi-annotator Deep Learning: A Probabilistic Framework for Classification", "abstract": "Solving complex classification tasks using deep neural networks typically requires large amounts of annotated data. However, corresponding class labels are noisy when provided by error-prone annotators, e.g., crowdworkers. Training standard deep neural networks leads to subpar performances in such multi-annotator supervised learning settings. We address this issue by presenting a probabilistic training framework named multi-annotator deep learning (MaDL). A downstream ground truth and an annotator performance model are jointly trained in an end-to-end learning approach. The ground truth model learns to predict instances' true class labels, while the annotator performance model infers probabilistic estimates of annotators' performances. A modular network architecture enables us to make varying assumptions regarding annotators' performances, e.g., an optional class or instance dependency. Further, we learn annotator embeddings to estimate annotators' densities within a latent space as proxies of their potentially correlated annotations. Together with a weighted loss function, we improve the learning from correlated annotation patterns. In a comprehensive evaluation, we examine three research questions about multi-annotator supervised learning. Our findings show MaDL's state-of-the-art performance and robustness against many correlated, spamming annotators.", "published": "2023-04-05T16:00:42Z", "authors": ["Marek Herde", "Denis Huseljic", "Bernhard Sick"], "categories": ["cs.LG"], "doi": "", "_query": "real estate valuation deep learning", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2602.07145v1", "title": "Convex Dominance in Deep Learning I: A Scaling Law of Loss and Learning Rate", "abstract": "Deep learning has non-convex loss landscape and its optimization dynamics is hard to analyze or control. Nevertheless, the dynamics can be empirically convex-like across various tasks, models, optimizers, hyperparameters, etc. In this work, we examine the applicability of convexity and Lipschitz continuity in deep learning, in order to precisely control the loss dynamics via the learning rate schedules. We illustrate that deep learning quickly becomes weakly convex after a short period of training, and the loss is predicable by an upper bound on the last iterate, which further informs the scaling of optimal learning rate. Through the lens of convexity, we build scaling laws of learning rates and losses that extrapolate as much as 80X across training horizons and 70X across model sizes.", "published": "2026-02-06T19:41:59Z", "authors": ["Zhiqi Bu", "Shiyun Xu", "Jialin Mao"], "categories": ["cs.LG", "cs.CL", "math.OC"], "doi": "", "_query": "real estate valuation deep learning", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/1711.10137v2", "title": "One-Shot Reinforcement Learning for Robot Navigation with Interactive Replay", "abstract": "Recently, model-free reinforcement learning algorithms have been shown to solve challenging problems by learning from extensive interaction with the environment. A significant issue with transferring this success to the robotics domain is that interaction with the real world is costly, but training on limited experience is prone to overfitting. We present a method for learning to navigate, to a fixed goal and in a known environment, on a mobile robot. The robot leverages an interactive world model built from a single traversal of the environment, a pre-trained visual feature encoder, and stochastic environmental augmentation, to demonstrate successful zero-shot transfer under real-world environmental variations without fine-tuning.", "published": "2017-11-28T06:03:14Z", "authors": ["Jake Bruce", "Niko Suenderhauf", "Piotr Mirowski", "Raia Hadsell", "Michael Milford"], "categories": ["cs.AI", "cs.LG", "cs.RO"], "doi": "", "_query": "real estate valuation deep learning", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/1905.12278v6", "title": "An Inertial Newton Algorithm for Deep Learning", "abstract": "We introduce a new second-order inertial optimization method for machine learning called INNA. It exploits the geometry of the loss function while only requiring stochastic approximations of the function values and the generalized gradients. This makes INNA fully implementable and adapted to large-scale optimization problems such as the training of deep neural networks. The algorithm combines both gradient-descent and Newton-like behaviors as well as inertia. We prove the convergence of INNA for most deep learning problems. To do so, we provide a well-suited framework to analyze deep learning loss functions involving tame optimization in which we study a continuous dynamical system together with its discrete stochastic approximations. We prove sublinear convergence for the continuous-time differential inclusion which underlies our algorithm. Additionally, we also show how standard optimization mini-batch methods applied to non-smooth non-convex problems can yield a certain type of spurious stationary points never discussed before. We address this issue by providing a theoretical framework around the new idea of $D$-criticality; we then give a simple asymptotic analysis of INNA. Our algorithm allows for using an aggressive learning rate of $o(1/\\log k)$. From an empirical viewpoint, we show that INNA returns competitive results with respect to state of the art (stochastic gradient descent, ADAGRAD, ADAM) on popular deep learning benchmark problems.", "published": "2019-05-29T09:00:49Z", "authors": ["Camille Castera", "Jérôme Bolte", "Cédric Févotte", "Edouard Pauwels"], "categories": ["cs.LG", "math.OC", "stat.ML"], "doi": "", "_query": "real estate valuation deep learning", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2008.09922v1", "title": "Machine Learning Approaches to Real Estate Market Prediction Problem: A Case Study", "abstract": "Home sale prices are formed given the transaction actors economic interests, which include government, real estate dealers, and the general public who buy or sell properties. Generating an accurate property price prediction model is a major challenge for the real estate market. This work develops a property price classification model using a ten year actual dataset, from January 2010 to November 2019. The real estate dataset is publicly available and was retrieved from Volusia County Property Appraiser of Florida website. In addition, socio-economic factors such as Gross Domestic Product, Consumer Price Index, Producer Price Index, House Price Index, and Effective Federal Funds Rate are collected and used in the prediction model. To solve this case study problem, several powerful machine learning algorithms, namely, Logistic Regression, Random Forest, Voting Classifier, and XGBoost, are employed. They are integrated with target encoding to develop an accurate property sale price prediction model with the aim to predict whether the closing sale price is greater than or less than the listing sale price. To assess the performance of the models, the accuracy, precision, recall, classification F1 score, and error rate of the models are determined. Among the four studied machine learning algorithms, XGBoost delivers superior results and robustness of the model compared to other models. The developed model can facilitate real estate investors, mortgage lenders and financial institutions to make better informed decisions.", "published": "2020-08-22T22:28:58Z", "authors": ["Shashi Bhushan Jha", "Vijay Pandey", "Rajesh Kumar Jha", "Radu F. Babiceanu"], "categories": ["cs.LG"], "doi": "", "_query": "real estate valuation deep learning", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2110.00568v2", "title": "Conditional Deep Gaussian Processes: empirical Bayes hyperdata learning", "abstract": "It is desirable to combine the expressive power of deep learning with Gaussian Process (GP) in one expressive Bayesian learning model. Deep kernel learning showed success in adopting a deep network for feature extraction followed by a GP used as function model. Recently,it was suggested that, albeit training with marginal likelihood, the deterministic nature of feature extractor might lead to overfitting while the replacement with a Bayesian network seemed to cure it. Here, we propose the conditional Deep Gaussian Process (DGP) in which the intermediate GPs in hierarchical composition are supported by the hyperdata and the exposed GP remains zero mean. Motivated by the inducing points in sparse GP, the hyperdata also play the role of function supports, but are hyperparameters rather than random variables. We follow our previous moment matching approach to approximate the marginal prior for conditional DGP with a GP carrying an effective kernel. Thus, as in empirical Bayes, the hyperdata are learned by optimizing the approximate marginal likelihood which implicitly depends on the hyperdata via the kernel. We shall show the equivalence with the deep kernel learning in the limit of dense hyperdata in latent space. However, the conditional DGP and the corresponding approximate inference enjoy the benefit of being more Bayesian than deep kernel learning. Preliminary extrapolation results demonstrate expressive power from the depth of hierarchy by exploiting the exact covariance and hyperdata learning, in comparison with GP kernel composition, DGP variational inference and deep kernel learning. We also address the non-Gaussian aspect of our model as well as way of upgrading to a full Bayes inference.", "published": "2021-10-01T17:50:48Z", "authors": ["Chi-Ken Lu", "Patrick Shafto"], "categories": ["cs.LG", "stat.ML"], "doi": "10.3390/e23111387", "_query": "real estate valuation deep learning", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2301.04614v1", "title": "Real-time simulation of viscoelastic tissue behavior with physics-guided deep learning", "abstract": "Finite element methods (FEM) are popular approaches for simulation of soft tissues with elastic or viscoelastic behavior. However, their usage in real-time applications, such as in virtual reality surgical training, is limited by computational cost. In this application scenario, which typically involves transportable simulators, the computing hardware severely constrains the size or the level of details of the simulated scene. To address this limitation, data-driven approaches have been suggested to simulate mechanical deformations by learning the mapping rules from FEM generated datasets. Herein, we propose a deep learning method for predicting displacement fields of soft tissues with viscoelastic properties. The main contribution of this work is the use of a physics-guided loss function for the optimization of the deep learning model parameters. The proposed deep learning model is based on convolutional (CNN) and recurrent layers (LSTM) to predict spatiotemporal variations. It is augmented with a mass conservation law in the lost function to prevent the generation of physically inconsistent results. The deep learning model is trained on a set of FEM datasets that are generated from a commercially available state-of-the-art numerical neurosurgery simulator. The use of the physics-guided loss function in a deep learning model has led to a better generalization in the prediction of deformations in unseen simulation cases. Moreover, the proposed method achieves a better accuracy over the conventional CNN models, where improvements were observed in unseen tissue from 8% to 30% depending on the magnitude of external forces. It is hoped that the present investigation will help in filling the gap in applying deep learning in virtual reality simulators, hence improving their computational performance (compared to FEM simulations) and ultimately their usefulness.", "published": "2023-01-11T18:17:10Z", "authors": ["Mohammad Karami", "Hervé Lombaert", "David Rivest-Hénault"], "categories": ["cs.LG", "cond-mat.soft", "physics.comp-ph", "q-bio.TO"], "doi": "10.1016/j.compmedimag.2022.102165", "_query": "real estate valuation deep learning", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2109.05472v2", "title": "Compute and Energy Consumption Trends in Deep Learning Inference", "abstract": "The progress of some AI paradigms such as deep learning is said to be linked to an exponential growth in the number of parameters. There are many studies corroborating these trends, but does this translate into an exponential increase in energy consumption? In order to answer this question we focus on inference costs rather than training costs, as the former account for most of the computing effort, solely because of the multiplicative factors. Also, apart from algorithmic innovations, we account for more specific and powerful hardware (leading to higher FLOPS) that is usually accompanied with important energy efficiency optimisations. We also move the focus from the first implementation of a breakthrough paper towards the consolidated version of the techniques one or two year later. Under this distinctive and comprehensive perspective, we study relevant models in the areas of computer vision and natural language processing: for a sustained increase in performance we see a much softer growth in energy consumption than previously anticipated. The only caveat is, yet again, the multiplicative factor, as future AI increases penetration and becomes more pervasive.", "published": "2021-09-12T09:40:18Z", "authors": ["Radosvet Desislavov", "Fernando Martínez-Plumed", "José Hernández-Orallo"], "categories": ["cs.LG", "cs.AI"], "doi": "10.1016/j.suscom.2023.100857", "_query": "real estate valuation deep learning", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2008.05730v1", "title": "Iterative Surrogate Model Optimization (ISMO): An active learning algorithm for PDE constrained optimization with deep neural networks", "abstract": "We present a novel active learning algorithm, termed as iterative surrogate model optimization (ISMO), for robust and efficient numerical approximation of PDE constrained optimization problems. This algorithm is based on deep neural networks and its key feature is the iterative selection of training data through a feedback loop between deep neural networks and any underlying standard optimization algorithm. Under suitable hypotheses, we show that the resulting optimizers converge exponentially fast (and with exponentially decaying variance), with respect to increasing number of training samples. Numerical examples for optimal control, parameter identification and shape optimization problems for PDEs are provided to validate the proposed theory and to illustrate that ISMO significantly outperforms a standard deep neural network based surrogate optimization algorithm.", "published": "2020-08-13T07:31:07Z", "authors": ["Kjetil O. Lye", "Siddhartha Mishra", "Deep Ray", "Praveen Chandrasekhar"], "categories": ["math.OC", "cs.LG", "math.NA"], "doi": "10.1016/j.cma.2020.113575", "_query": "real estate valuation deep learning", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2302.06370v1", "title": "Review of Deep Reinforcement Learning for Autonomous Driving", "abstract": "Since deep neural networks' resurgence, reinforcement learning has gradually strengthened and surpassed humans in many conventional games. However, it is not easy to copy these accomplishments to autonomous driving because state spaces are immensely complicated in the real world and action spaces are continuous and fine control is necessary. Besides, autonomous driving systems must also maintain their functionality regardless of the environment's complexity. The deep reinforcement learning domain (DRL) has become a robust learning framework to handle complex policies in high dimensional surroundings with deep representation learning. This research outlines deep, reinforcement learning algorithms (DRL). It presents a nomenclature of autonomous driving in which DRL techniques have been used, thus discussing important computational issues in evaluating autonomous driving agents in the real environment. Instead, it involves similar but not standard RL techniques, adjoining fields such as emulation of actions, modelling imitation, inverse reinforcement learning. The simulators' role in training agents is addressed, as are the methods for validating, checking and robustness of existing RL solutions.", "published": "2023-02-13T14:01:26Z", "authors": ["B. Udugama"], "categories": ["cs.RO"], "doi": "", "_query": "real estate valuation deep learning", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/1708.01867v5", "title": "An Information-Theoretic Optimality Principle for Deep Reinforcement Learning", "abstract": "We methodologically address the problem of Q-value overestimation in deep reinforcement learning to handle high-dimensional state spaces efficiently. By adapting concepts from information theory, we introduce an intrinsic penalty signal encouraging reduced Q-value estimates. The resultant algorithm encompasses a wide range of learning outcomes containing deep Q-networks as a special case. Different learning outcomes can be demonstrated by tuning a Lagrange multiplier accordingly. We furthermore propose a novel scheduling scheme for this Lagrange multiplier to ensure efficient and robust learning. In experiments on Atari, our algorithm outperforms other algorithms (e.g. deep and double deep Q-networks) in terms of both game-play performance and sample complexity. These results remain valid under the recently proposed dueling architecture.", "published": "2017-08-06T09:23:22Z", "authors": ["Felix Leibfried", "Jordi Grau-Moya", "Haitham Bou-Ammar"], "categories": ["cs.AI", "cs.LG", "stat.ML"], "doi": "", "_query": "real estate valuation deep learning", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2206.14373v2", "title": "Theoretical Perspectives on Deep Learning Methods in Inverse Problems", "abstract": "In recent years, there have been significant advances in the use of deep learning methods in inverse problems such as denoising, compressive sensing, inpainting, and super-resolution. While this line of works has predominantly been driven by practical algorithms and experiments, it has also given rise to a variety of intriguing theoretical problems. In this paper, we survey some of the prominent theoretical developments in this line of works, focusing in particular on generative priors, untrained neural network priors, and unfolding algorithms. In addition to summarizing existing results in these topics, we highlight several ongoing challenges and open problems.", "published": "2022-06-29T02:37:50Z", "authors": ["Jonathan Scarlett", "Reinhard Heckel", "Miguel R. D. Rodrigues", "Paul Hand", "Yonina C. Eldar"], "categories": ["stat.ML", "cs.IT", "cs.LG", "eess.SP", "math.ST"], "doi": "", "_query": "real estate valuation deep learning", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2503.08636v2", "title": "Birds look like cars: Adversarial analysis of intrinsically interpretable deep learning", "abstract": "A common belief is that intrinsically interpretable deep learning models ensure a correct, intuitive understanding of their behavior and offer greater robustness against accidental errors or intentional manipulation. However, these beliefs have not been comprehensively verified, and growing evidence casts doubt on them. In this paper, we highlight the risks related to overreliance and susceptibility to adversarial manipulation of these so-called \"intrinsically (aka inherently) interpretable\" models by design. We introduce two strategies for adversarial analysis with prototype manipulation and backdoor attacks against prototype-based networks, and discuss how concept bottleneck models defend against these attacks. Fooling the model's reasoning by exploiting its use of latent prototypes manifests the inherent uninterpretability of deep neural networks, leading to a false sense of security reinforced by a visual confirmation bias. The reported limitations of part-prototype networks put their trustworthiness and applicability into question, motivating further work on the robustness and alignment of (deep) interpretable models.", "published": "2025-03-11T17:24:33Z", "authors": ["Hubert Baniecki", "Przemyslaw Biecek"], "categories": ["cs.LG", "cs.CV"], "doi": "10.1007/s10994-025-06896-w", "_query": "real estate valuation deep learning", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2503.01052v2", "title": "ALinFiK: Learning to Approximate Linearized Future Influence Kernel for Scalable Third-Party LLM Data Valuation", "abstract": "Large Language Models (LLMs) heavily rely on high-quality training data, making data valuation crucial for optimizing model performance, especially when working within a limited budget. In this work, we aim to offer a third-party data valuation approach that benefits both data providers and model developers. We introduce a linearized future influence kernel (LinFiK), which assesses the value of individual data samples in improving LLM performance during training. We further propose ALinFiK, a learning strategy to approximate LinFiK, enabling scalable data valuation. Our comprehensive evaluations demonstrate that this approach surpasses existing baselines in effectiveness and efficiency, demonstrating significant scalability advantages as LLM parameters increase.", "published": "2025-03-02T22:51:12Z", "authors": ["Yanzhou Pan", "Huawei Lin", "Yide Ran", "Jiamin Chen", "Xiaodong Yu", "Weijie Zhao", "Denghui Zhang", "Zhaozhuo Xu"], "categories": ["cs.LG"], "doi": "", "_query": "real estate valuation deep learning", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2212.12190v1", "title": "Look Around! A Neighbor Relation Graph Learning Framework for Real Estate Appraisal", "abstract": "Real estate appraisal is a crucial issue for urban applications, which aims to value the properties on the market. Traditional methods perform appraisal based on the domain knowledge, but suffer from the efforts of hand-crafted design. Recently, several methods have been developed to automatize the valuation process by taking the property trading transaction into account when estimating the property value. However, existing methods only consider the real estate itself, ignoring the relation between the properties. Moreover, naively aggregating the information of neighbors fails to model the relationships between the transactions. To tackle these limitations, we propose a novel Neighbor Relation Graph Learning Framework (ReGram) by incorporating the relation between target transaction and surrounding neighbors with the attention mechanism. To model the influence between communities, we integrate the environmental information and the past price of each transaction from other communities. Moreover, since the target transactions in different regions share some similarities and differences of characteristics, we introduce a dynamic adapter to model the different distributions of the target transactions based on the input-related kernel weights. Extensive experiments on the real-world dataset with various scenarios demonstrate that ReGram robustly outperforms the state-of-the-art methods. Furthermore, comprehensive ablation studies were conducted to examine the effectiveness of each component in ReGram.", "published": "2022-12-23T08:20:19Z", "authors": ["Chih-Chia Li", "Wei-Yao Wang", "Wei-Wei Du", "Wen-Chih Peng"], "categories": ["cs.LG"], "doi": "", "_query": "real estate valuation deep learning", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2402.01460v4", "title": "Deep conditional distribution learning via conditional Föllmer flow", "abstract": "We introduce an ordinary differential equation (ODE) based deep generative method for learning conditional distributions, named Conditional Föllmer Flow. Starting from a standard Gaussian distribution, the proposed flow could approximate the target conditional distribution very well when the time is close to 1. For effective implementation, we discretize the flow with Euler's method where we estimate the velocity field nonparametrically using a deep neural network. Furthermore, we also establish the convergence result for the Wasserstein-2 distance between the distribution of the learned samples and the target conditional distribution, providing the first comprehensive end-to-end error analysis for conditional distribution learning via ODE flow. Our numerical experiments showcase its effectiveness across a range of scenarios, from standard nonparametric conditional density estimation problems to more intricate challenges involving image data, illustrating its superiority over various existing conditional density estimation methods.", "published": "2024-02-02T14:52:10Z", "authors": ["Jinyuan Chang", "Zhao Ding", "Yuling Jiao", "Ruoxuan Li", "Jerry Zhijian Yang"], "categories": ["stat.ML", "cs.LG"], "doi": "", "_query": "real estate valuation deep learning", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2111.03788v2", "title": "d3rlpy: An Offline Deep Reinforcement Learning Library", "abstract": "In this paper, we introduce d3rlpy, an open-sourced offline deep reinforcement learning (RL) library for Python. d3rlpy supports a set of offline deep RL algorithms as well as off-policy online algorithms via a fully documented plug-and-play API. To address a reproducibility issue, we conduct a large-scale benchmark with D4RL and Atari 2600 dataset to ensure implementation quality and provide experimental scripts and full tables of results. The d3rlpy source code can be found on GitHub: \\url{https://github.com/takuseno/d3rlpy}.", "published": "2021-11-06T03:09:39Z", "authors": ["Takuma Seno", "Michita Imai"], "categories": ["cs.LG", "cs.AI"], "doi": "", "_query": "real estate valuation deep learning", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/1804.06309v2", "title": "On Improving Deep Reinforcement Learning for POMDPs", "abstract": "Deep Reinforcement Learning (RL) recently emerged as one of the most competitive approaches for learning in sequential decision making problems with fully observable environments, e.g., computer Go. However, very little work has been done in deep RL to handle partially observable environments. We propose a new architecture called Action-specific Deep Recurrent Q-Network (ADRQN) to enhance learning performance in partially observable domains. Actions are encoded by a fully connected layer and coupled with a convolutional observation to form an action-observation pair. The time series of action-observation pairs are then integrated by an LSTM layer that learns latent states based on which a fully connected layer computes Q-values as in conventional Deep Q-Networks (DQNs). We demonstrate the effectiveness of our new architecture in several partially observable domains, including flickering Atari games.", "published": "2018-04-17T15:08:36Z", "authors": ["Pengfei Zhu", "Xin Li", "Pascal Poupart", "Guanghui Miao"], "categories": ["cs.LG", "stat.ML"], "doi": "", "_query": "real estate valuation deep learning", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/1708.07469v5", "title": "DGM: A deep learning algorithm for solving partial differential equations", "abstract": "High-dimensional PDEs have been a longstanding computational challenge. We propose to solve high-dimensional PDEs by approximating the solution with a deep neural network which is trained to satisfy the differential operator, initial condition, and boundary conditions. Our algorithm is meshfree, which is key since meshes become infeasible in higher dimensions. Instead of forming a mesh, the neural network is trained on batches of randomly sampled time and space points. The algorithm is tested on a class of high-dimensional free boundary PDEs, which we are able to accurately solve in up to $200$ dimensions. The algorithm is also tested on a high-dimensional Hamilton-Jacobi-Bellman PDE and Burgers' equation. The deep learning algorithm approximates the general solution to the Burgers' equation for a continuum of different boundary conditions and physical conditions (which can be viewed as a high-dimensional space). We call the algorithm a \"Deep Galerkin Method (DGM)\" since it is similar in spirit to Galerkin methods, with the solution approximated by a neural network instead of a linear combination of basis functions. In addition, we prove a theorem regarding the approximation power of neural networks for a class of quasilinear parabolic PDEs.", "published": "2017-08-24T15:50:24Z", "authors": ["Justin Sirignano", "Konstantinos Spiliopoulos"], "categories": ["q-fin.MF", "math.NA", "q-fin.CP", "stat.ML"], "doi": "10.1016/j.jcp.2018.08.029", "_query": "real estate valuation deep learning", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2508.02765v1", "title": "The Architecture of Trust: A Framework for AI-Augmented Real Estate Valuation in the Era of Structured Data", "abstract": "The Uniform Appraisal Dataset (UAD) 3.6's mandatory 2026 implementation transforms residential property valuation from narrative reporting to structured, machine-readable formats. This paper provides the first comprehensive analysis of this regulatory shift alongside concurrent AI advances in computer vision, natural language processing, and autonomous systems. We develop a three-layer framework for AI-augmented valuation addressing technical implementation and institutional trust requirements. Our analysis reveals how regulatory standardization converging with AI capabilities enables fundamental market restructuring with profound implications for professional practice, efficiency, and systemic risk. We make four key contributions: (1) documenting institutional failures including inter-appraiser variability and systematic biases undermining valuation reliability; (2) developing an architectural framework spanning physical data acquisition, semantic understanding, and cognitive reasoning that integrates emerging technologies while maintaining professional oversight; (3) addressing trust requirements for high-stakes financial applications including regulatory compliance, algorithmic fairness, and uncertainty quantification; (4) proposing evaluation methodologies beyond generic AI benchmarks toward domain-specific protocols. Our findings indicate successful transformation requires not merely technological sophistication but careful human-AI collaboration, creating systems that augment rather than replace professional expertise while addressing historical biases and information asymmetries in real estate markets.", "published": "2025-08-04T05:24:25Z", "authors": ["Petteri Teikari", "Mike Jarrell", "Maryam Azh", "Harri Pesola"], "categories": ["cs.CY", "cs.AI", "cs.CV"], "doi": "", "_query": "real estate valuation deep learning", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/1910.12154v1", "title": "ZPD Teaching Strategies for Deep Reinforcement Learning from Demonstrations", "abstract": "Learning from demonstrations is a popular tool for accelerating and reducing the exploration requirements of reinforcement learning. When providing expert demonstrations to human students, we know that the demonstrations must fall within a particular range of difficulties called the \"Zone of Proximal Development (ZPD)\". If they are too easy the student learns nothing, but if they are too difficult the student is unable to follow along. This raises the question: Given a set of potential demonstrators, which among them is best suited for teaching any particular learner? Prior work, such as the popular Deep Q-learning from Demonstrations (DQfD) algorithm has generally focused on single demonstrators. In this work we consider the problem of choosing among multiple demonstrators of varying skill levels. Our results align with intuition from human learners: it is not always the best policy to draw demonstrations from the best performing demonstrator (in terms of reward). We show that careful selection of teaching strategies can result in sample efficiency gains in the learner's environment across nine Atari games", "published": "2019-10-26T23:05:43Z", "authors": ["Daniel Seita", "David Chan", "Roshan Rao", "Chen Tang", "Mandi Zhao", "John Canny"], "categories": ["cs.LG", "cs.AI"], "doi": "", "_query": "real estate valuation deep learning", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/1906.00108v1", "title": "ActiveHARNet: Towards On-Device Deep Bayesian Active Learning for Human Activity Recognition", "abstract": "Various health-care applications such as assisted living, fall detection etc., require modeling of user behavior through Human Activity Recognition (HAR). HAR using mobile- and wearable-based deep learning algorithms have been on the rise owing to the advancements in pervasive computing. However, there are two other challenges that need to be addressed: first, the deep learning model should support on-device incremental training (model updation) from real-time incoming data points to learn user behavior over time, while also being resource-friendly; second, a suitable ground truthing technique (like Active Learning) should help establish labels on-the-fly while also selecting only the most informative data points to query from an oracle. Hence, in this paper, we propose ActiveHARNet, a resource-efficient deep ensembled model which supports on-device Incremental Learning and inference, with capabilities to represent model uncertainties through approximations in Bayesian Neural Networks using dropout. This is combined with suitable acquisition functions for active learning. Empirical results on two publicly available wrist-worn HAR and fall detection datasets indicate that ActiveHARNet achieves considerable efficiency boost during inference across different users, with a substantially low number of acquired pool points (at least 60% reduction) during incremental learning on both datasets experimented with various acquisition functions, thus demonstrating deployment and Incremental Learning feasibility.", "published": "2019-05-31T22:28:40Z", "authors": ["Gautham Krishna Gudur", "Prahalathan Sundaramoorthy", "Venkatesh Umaashankar"], "categories": ["cs.LG", "cs.HC", "stat.ML"], "doi": "", "_query": "real estate valuation deep learning", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/1803.10254v3", "title": "Disease-Atlas: Navigating Disease Trajectories with Deep Learning", "abstract": "Joint models for longitudinal and time-to-event data are commonly used in longitudinal studies to forecast disease trajectories over time. While there are many advantages to joint modeling, the standard forms suffer from limitations that arise from a fixed model specification, and computational difficulties when applied to high-dimensional datasets. In this paper, we propose a deep learning approach to address these limitations, enhancing existing methods with the inherent flexibility and scalability of deep neural networks, while retaining the benefits of joint modeling. Using longitudinal data from a real-world medical dataset, we demonstrate improvements in performance and scalability, as well as robustness in the presence of irregularly sampled data.", "published": "2018-03-27T18:03:02Z", "authors": ["Bryan Lim", "Mihaela van der Schaar"], "categories": ["stat.ML", "cs.LG"], "doi": "", "_query": "real estate valuation deep learning", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/1611.08675v1", "title": "Deep Reinforcement Learning for Multi-Domain Dialogue Systems", "abstract": "Standard deep reinforcement learning methods such as Deep Q-Networks (DQN) for multiple tasks (domains) face scalability problems. We propose a method for multi-domain dialogue policy learning---termed NDQN, and apply it to an information-seeking spoken dialogue system in the domains of restaurants and hotels. Experimental results comparing DQN (baseline) versus NDQN (proposed) using simulations report that our proposed method exhibits better scalability and is promising for optimising the behaviour of multi-domain dialogue systems.", "published": "2016-11-26T07:53:22Z", "authors": ["Heriberto Cuayáhuitl", "Seunghak Yu", "Ashley Williamson", "Jacob Carse"], "categories": ["cs.AI", "cs.CL", "cs.LG"], "doi": "", "_query": "real estate valuation deep learning", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/1912.07832v1", "title": "Deep Iterative and Adaptive Learning for Graph Neural Networks", "abstract": "In this paper, we propose an end-to-end graph learning framework, namely Deep Iterative and Adaptive Learning for Graph Neural Networks (DIAL-GNN), for jointly learning the graph structure and graph embeddings simultaneously. We first cast the graph structure learning problem as a similarity metric learning problem and leverage an adapted graph regularization for controlling smoothness, connectivity and sparsity of the generated graph. We further propose a novel iterative method for searching for a hidden graph structure that augments the initial graph structure. Our iterative method dynamically stops when the learned graph structure approaches close enough to the optimal graph. Our extensive experiments demonstrate that the proposed DIAL-GNN model can consistently outperform or match state-of-the-art baselines in terms of both downstream task performance and computational time. The proposed approach can cope with both transductive learning and inductive learning.", "published": "2019-12-17T06:02:59Z", "authors": ["Yu Chen", "Lingfei Wu", "Mohammed J. Zaki"], "categories": ["cs.LG", "stat.ML"], "doi": "", "_query": "real estate valuation deep learning", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2303.13055v1", "title": "Reimagining Application User Interface (UI) Design using Deep Learning Methods: Challenges and Opportunities", "abstract": "In this paper, we present a review of the recent work in deep learning methods for user interface design. The survey encompasses well known deep learning techniques (deep neural networks, convolutional neural networks, recurrent neural networks, autoencoders, and generative adversarial networks) and datasets widely used to design user interface applications. We highlight important problems and emerging research frontiers in this field. We believe that the use of deep learning for user interface design automation tasks could be one of the high potential fields for the advancement of the software development industry.", "published": "2023-03-23T05:59:56Z", "authors": ["Subtain Malik", "Muhammad Tariq Saeed", "Marya Jabeen Zia", "Shahzad Rasool", "Liaquat Ali Khan", "Mian Ilyas Ahmed"], "categories": ["cs.HC", "cs.LG"], "doi": "", "_query": "real estate valuation deep learning", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/1811.01459v3", "title": "Deep Metric Learning by Online Soft Mining and Class-Aware Attention", "abstract": "Deep metric learning aims to learn a deep embedding that can capture the semantic similarity of data points. Given the availability of massive training samples, deep metric learning is known to suffer from slow convergence due to a large fraction of trivial samples. Therefore, most existing methods generally resort to sample mining strategies for selecting nontrivial samples to accelerate convergence and improve performance. In this work, we identify two critical limitations of the sample mining methods, and provide solutions for both of them. First, previous mining methods assign one binary score to each sample, i.e., dropping or keeping it, so they only selects a subset of relevant samples in a mini-batch. Therefore, we propose a novel sample mining method, called Online Soft Mining (OSM), which assigns one continuous score to each sample to make use of all samples in the mini-batch. OSM learns extended manifolds that preserve useful intraclass variances by focusing on more similar positives. Second, the existing methods are easily influenced by outliers as they are generally included in the mined subset. To address this, we introduce Class-Aware Attention (CAA) that assigns little attention to abnormal data samples. Furthermore, by combining OSM and CAA, we propose a novel weighted contrastive loss to learn discriminative embeddings. Extensive experiments on two fine-grained visual categorisation datasets and two video-based person re-identification benchmarks show that our method significantly outperforms the state-of-the-art.", "published": "2018-11-04T23:47:18Z", "authors": ["Xinshao Wang", "Yang Hua", "Elyor Kodirov", "Guosheng Hu", "Neil M. Robertson"], "categories": ["cs.LG", "stat.ML"], "doi": "", "_query": "real estate valuation deep learning", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/1507.04296v2", "title": "Massively Parallel Methods for Deep Reinforcement Learning", "abstract": "We present the first massively distributed architecture for deep reinforcement learning. This architecture uses four main components: parallel actors that generate new behaviour; parallel learners that are trained from stored experience; a distributed neural network to represent the value function or behaviour policy; and a distributed store of experience. We used our architecture to implement the Deep Q-Network algorithm (DQN). Our distributed algorithm was applied to 49 games from Atari 2600 games from the Arcade Learning Environment, using identical hyperparameters. Our performance surpassed non-distributed DQN in 41 of the 49 games and also reduced the wall-time required to achieve these results by an order of magnitude on most games.", "published": "2015-07-15T16:56:56Z", "authors": ["Arun Nair", "Praveen Srinivasan", "Sam Blackwell", "Cagdas Alcicek", "Rory Fearon", "Alessandro De Maria", "Vedavyas Panneershelvam", "Mustafa Suleyman", "Charles Beattie", "Stig Petersen", "Shane Legg", "Volodymyr Mnih", "Koray Kavukcuoglu", "David Silver"], "categories": ["cs.LG", "cs.AI", "cs.DC", "cs.NE"], "doi": "", "_query": "real estate valuation deep learning", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2111.15258v1", "title": "DeepAL: Deep Active Learning in Python", "abstract": "We present DeepAL, a Python library that implements several common strategies for active learning, with a particular emphasis on deep active learning. DeepAL provides a simple and unified framework based on PyTorch that allows users to easily load custom datasets, build custom data handlers, and design custom strategies without much modification of codes. DeepAL is open-source on Github and welcome any contribution.", "published": "2021-11-30T10:17:58Z", "authors": ["Kuan-Hao Huang"], "categories": ["cs.LG"], "doi": "", "_query": "real estate valuation deep learning", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2210.15670v1", "title": "Knowledge-Guided Exploration in Deep Reinforcement Learning", "abstract": "This paper proposes a new method to drastically speed up deep reinforcement learning (deep RL) training for problems that have the property of state-action permissibility (SAP). Two types of permissibility are defined under SAP. The first type says that after an action $a_t$ is performed in a state $s_t$ and the agent has reached the new state $s_{t+1}$, the agent can decide whether $a_t$ is permissible or not permissible in $s_t$. The second type says that even without performing $a_t$ in $s_t$, the agent can already decide whether $a_t$ is permissible or not in $s_t$. An action is not permissible in a state if the action can never lead to an optimal solution and thus should not be tried (over and over again). We incorporate the proposed SAP property and encode action permissibility knowledge into two state-of-the-art deep RL algorithms to guide their state-action exploration together with a virtual stopping strategy. Results show that the SAP-based guidance can markedly speed up RL training.", "published": "2022-10-26T18:39:49Z", "authors": ["Sahisnu Mazumder", "Bing Liu", "Shuai Wang", "Yingxuan Zhu", "Xiaotian Yin", "Lifeng Liu", "Jian Li"], "categories": ["cs.LG", "cs.AI"], "doi": "", "_query": "real estate valuation deep learning", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2006.12878v2", "title": "Direct Feedback Alignment Scales to Modern Deep Learning Tasks and Architectures", "abstract": "Despite being the workhorse of deep learning, the backpropagation algorithm is no panacea. It enforces sequential layer updates, thus preventing efficient parallelization of the training process. Furthermore, its biological plausibility is being challenged. Alternative schemes have been devised; yet, under the constraint of synaptic asymmetry, none have scaled to modern deep learning tasks and architectures. Here, we challenge this perspective, and study the applicability of Direct Feedback Alignment to neural view synthesis, recommender systems, geometric learning, and natural language processing. In contrast with previous studies limited to computer vision tasks, our findings show that it successfully trains a large range of state-of-the-art deep learning architectures, with performance close to fine-tuned backpropagation. At variance with common beliefs, our work supports that challenging tasks can be tackled in the absence of weight transport.", "published": "2020-06-23T10:17:49Z", "authors": ["Julien Launay", "Iacopo Poli", "François Boniface", "Florent Krzakala"], "categories": ["stat.ML", "cs.LG", "cs.NE"], "doi": "", "_query": "real estate valuation deep learning", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/1801.04261v2", "title": "Deep saliency: What is learnt by a deep network about saliency?", "abstract": "Deep convolutional neural networks have achieved impressive performance on a broad range of problems, beating prior art on established benchmarks, but it often remains unclear what are the representations learnt by those systems and how they achieve such performance. This article examines the specific problem of saliency detection, where benchmarks are currently dominated by CNN-based approaches, and investigates the properties of the learnt representation by visualizing the artificial neurons' receptive fields. We demonstrate that fine tuning a pre-trained network on the saliency detection task lead to a profound transformation of the network's deeper layers. Moreover we argue that this transformation leads to the emergence of receptive fields conceptually similar to the centre-surround filters hypothesized by early research on visual saliency.", "published": "2018-01-12T18:32:15Z", "authors": ["Sen He", "Nicolas Pugeault"], "categories": ["cs.CV"], "doi": "", "_query": "real estate valuation deep learning", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/1511.08099v1", "title": "Strategic Dialogue Management via Deep Reinforcement Learning", "abstract": "Artificially intelligent agents equipped with strategic skills that can negotiate during their interactions with other natural or artificial agents are still underdeveloped. This paper describes a successful application of Deep Reinforcement Learning (DRL) for training intelligent agents with strategic conversational skills, in a situated dialogue setting. Previous studies have modelled the behaviour of strategic agents using supervised learning and traditional reinforcement learning techniques, the latter using tabular representations or learning with linear function approximation. In this study, we apply DRL with a high-dimensional state space to the strategic board game of Settlers of Catan---where players can offer resources in exchange for others and they can also reply to offers made by other players. Our experimental results report that the DRL-based learnt policies significantly outperformed several baselines including random, rule-based, and supervised-based behaviours. The DRL-based policy has a 53% win rate versus 3 automated players (`bots'), whereas a supervised player trained on a dialogue corpus in this setting achieved only 27%, versus the same 3 bots. This result supports the claim that DRL is a promising framework for training dialogue systems, and strategic agents with negotiation abilities.", "published": "2015-11-25T15:48:59Z", "authors": ["Heriberto Cuayáhuitl", "Simon Keizer", "Oliver Lemon"], "categories": ["cs.AI", "cs.LG"], "doi": "", "_query": "real estate valuation deep learning", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2601.00417v5", "title": "Deep Delta Learning", "abstract": "Transformer residual streams are updated by addition. A sufficiently expressive residual block can represent content replacement, but the residual update itself has no operation that reads, compares, and replaces content. We introduce Deep Delta Learning (DDL), which applies the delta rule over network depth. Each layer reads the residual state along a learned direction, compares the readout with a learned target, and writes a gated rank-1 correction back along the same direction. A closed gate gives the identity map, and a unit gate overwrites the selected readout with the target. DDL works with the usual vector state or with an expanded state that stores several value channels, while attention and MLP blocks keep the original model width. We pretrain decoder-only models with approximately GPT-2 small and medium sizes on the FineWeb-Edu dataset. At both scales, every DDL variant has lower validation loss and higher average one-shot accuracy than the additive baseline, and the best expanded variants raise that average by 0.91 and 1.18 points.", "published": "2026-01-01T18:11:38Z", "authors": ["Yifan Zhang", "Yifeng Liu", "Mengdi Wang", "Quanquan Gu"], "categories": ["cs.LG", "cs.AI", "cs.CL", "cs.CV"], "doi": "", "_query": "real estate valuation deep learning", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2204.12833v3", "title": "Transfer Learning with Pre-trained Conditional Generative Models", "abstract": "Transfer learning is crucial in training deep neural networks on new target tasks. Current transfer learning methods always assume at least one of (i) source and target task label spaces overlap, (ii) source datasets are available, and (iii) target network architectures are consistent with source ones. However, holding these assumptions is difficult in practical settings because the target task rarely has the same labels as the source task, the source dataset access is restricted due to storage costs and privacy, and the target architecture is often specialized to each task. To transfer source knowledge without these assumptions, we propose a transfer learning method that uses deep generative models and is composed of the following two stages: pseudo pre-training (PP) and pseudo semi-supervised learning (P-SSL). PP trains a target architecture with an artificial dataset synthesized by using conditional source generative models. P-SSL applies SSL algorithms to labeled target data and unlabeled pseudo samples, which are generated by cascading the source classifier and generative models to condition them with target samples. Our experimental results indicate that our method can outperform the baselines of scratch training and knowledge distillation.", "published": "2022-04-27T10:36:32Z", "authors": ["Shin'ya Yamaguchi", "Sekitoshi Kanai", "Atsutoshi Kumagai", "Daiki Chijiwa", "Hisashi Kashima"], "categories": ["cs.LG", "cs.AI", "stat.ML"], "doi": "10.1007/s10994-025-06748-7", "_query": "real estate valuation deep learning", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/1806.11189v1", "title": "A hybrid deep learning approach for medical relation extraction", "abstract": "Mining relationships between treatment(s) and medical problem(s) is vital in the biomedical domain. This helps in various applications, such as decision support system, safety surveillance, and new treatment discovery. We propose a deep learning approach that utilizes both word level and sentence-level representations to extract the relationships between treatment and problem. While deep learning techniques demand a large amount of data for training, we make use of a rule-based system particularly for relationship classes with fewer samples. Our final relations are derived by jointly combining the results from deep learning and rule-based models. Our system achieved a promising performance on the relationship classes of I2b2 2010 relation extraction task.", "published": "2018-06-26T06:38:01Z", "authors": ["Veera Raghavendra Chikka", "Kamalakar Karlapalem"], "categories": ["cs.LG", "cs.IR", "stat.ML"], "doi": "", "_query": "real estate valuation deep learning", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2202.07201v3", "title": "Holistic Adversarial Robustness of Deep Learning Models", "abstract": "Adversarial robustness studies the worst-case performance of a machine learning model to ensure safety and reliability. With the proliferation of deep-learning-based technology, the potential risks associated with model development and deployment can be amplified and become dreadful vulnerabilities. This paper provides a comprehensive overview of research topics and foundational principles of research methods for adversarial robustness of deep learning models, including attacks, defenses, verification, and novel applications.", "published": "2022-02-15T05:30:27Z", "authors": ["Pin-Yu Chen", "Sijia Liu"], "categories": ["cs.LG", "cs.AI", "cs.CR"], "doi": "", "_query": "real estate valuation deep learning", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/1704.04979v1", "title": "Urban Data Streams and Machine Learning: A Case of Swiss Real Estate Market", "abstract": "In this paper, we show how using publicly available data streams and machine learning algorithms one can develop practical data driven services with no input from domain experts as a form of prior knowledge. We report the initial steps toward development of a real estate portal in Switzerland. Based on continuous web crawling of publicly available real estate advertisements and using building data from Open Street Map, we developed a system, where we roughly estimate the rental and sale price indexes of 1.7 million buildings across the country. In addition to these rough estimates, we developed a web based API for accurate automated valuation of rental prices of individual properties and spatial sensitivity analysis of rental market. We tested several established function approximation methods against the test data to check the quality of the rental price estimations and based on our experiments, Random Forest gives very reasonable results with the median absolute relative error of 6.57 percent, which is comparable with the state of the art in the industry. We argue that while recently there have been successful cases of real estate portals, which are based on Big Data, majority of the existing solutions are expensive, limited to certain users and mostly with non-transparent underlying systems. As an alternative we discuss, how using the crawled data sets and other open data sets provided from different institutes it is easily possible to develop data driven services for spatial and temporal sensitivity analysis in the real estate market to be used for different stakeholders. We believe that this kind of digital literacy can disrupt many other existing business concepts across many domains.", "published": "2017-03-30T09:28:55Z", "authors": ["Vahid Moosavi"], "categories": ["cs.CY", "q-fin.GN", "stat.AP"], "doi": "", "_query": "real estate valuation deep learning", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2003.03384v2", "title": "AutoML-Zero: Evolving Machine Learning Algorithms From Scratch", "abstract": "Machine learning research has advanced in multiple aspects, including model structures and learning methods. The effort to automate such research, known as AutoML, has also made significant progress. However, this progress has largely focused on the architecture of neural networks, where it has relied on sophisticated expert-designed layers as building blocks---or similarly restrictive search spaces. Our goal is to show that AutoML can go further: it is possible today to automatically discover complete machine learning algorithms just using basic mathematical operations as building blocks. We demonstrate this by introducing a novel framework that significantly reduces human bias through a generic search space. Despite the vastness of this space, evolutionary search can still discover two-layer neural networks trained by backpropagation. These simple neural networks can then be surpassed by evolving directly on tasks of interest, e.g. CIFAR-10 variants, where modern techniques emerge in the top algorithms, such as bilinear interactions, normalized gradients, and weight averaging. Moreover, evolution adapts algorithms to different task types: e.g., dropout-like techniques appear when little data is available. We believe these preliminary successes in discovering machine learning algorithms from scratch indicate a promising new direction for the field.", "published": "2020-03-06T19:00:04Z", "authors": ["Esteban Real", "Chen Liang", "David R. So", "Quoc V. Le"], "categories": ["cs.LG", "cs.NE", "stat.ML"], "doi": "", "_query": "real estate valuation deep learning", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/1809.03322v1", "title": "Guiding the Creation of Deep Learning-based Object Detectors", "abstract": "Object detection is a computer vision field that has applications in several contexts ranging from biomedicine and agriculture to security. In the last years, several deep learning techniques have greatly improved object detection models. Among those techniques, we can highlight the YOLO approach, that allows the construction of accurate models that can be employed in real-time applications. However, as most deep learning techniques, YOLO has a steep learning curve and creating models using this approach might be challenging for non-expert users. In this work, we tackle this problem by constructing a suite of Jupyter notebooks that democratizes the construction of object detection models using YOLO. The suitability of our approach has been proven with a dataset of stomata images where we have achieved a mAP of 90.91%.", "published": "2018-09-06T07:07:12Z", "authors": ["Ángela Casado", "Jónathan Heras"], "categories": ["cs.CV", "cs.LG", "stat.ML"], "doi": "", "_query": "real estate valuation deep learning", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2008.13146v1", "title": "Deep Learning Techniques for Geospatial Data Analysis", "abstract": "Consumer electronic devices such as mobile handsets, goods tagged with RFID labels, location and position sensors are continuously generating a vast amount of location enriched data called geospatial data. Conventionally such geospatial data is used for military applications. In recent times, many useful civilian applications have been designed and deployed around such geospatial data. For example, a recommendation system to suggest restaurants or places of attraction to a tourist visiting a particular locality. At the same time, civic bodies are harnessing geospatial data generated through remote sensing devices to provide better services to citizens such as traffic monitoring, pothole identification, and weather reporting. Typically such applications are leveraged upon non-hierarchical machine learning techniques such as Naive-Bayes Classifiers, Support Vector Machines, and decision trees. Recent advances in the field of deep-learning showed that Neural Network-based techniques outperform conventional techniques and provide effective solutions for many geospatial data analysis tasks such as object recognition, image classification, and scene understanding. The chapter presents a survey on the current state of the applications of deep learning techniques for analyzing geospatial data. The chapter is organized as below: (i) A brief overview of deep learning algorithms. (ii)Geospatial Analysis: a Data Science Perspective (iii) Deep-learning techniques for Remote Sensing data analytics tasks (iv) Deep-learning techniques for GPS data analytics(iv) Deep-learning techniques for RFID data analytics.", "published": "2020-08-30T11:51:10Z", "authors": ["Arvind W. Kiwelekar", "Geetanjali S. Mahamunkar", "Laxman D. Netak", "Valmik B Nikam"], "categories": ["cs.AI"], "doi": "10.1007/978-3-030-49724-8_3", "_query": "real estate valuation deep learning", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/1912.00327v1", "title": "The Effect of Real Estate Auction Events on Mortality Rate", "abstract": "This study has investigated the mortality rate of parties at real estate auctions compared to that of the overall population in South Korea by using various variables, including age, real estate usage, cumulative number of real estate auction events, disposal of real estate, and appraisal price. In each case, there has been a significant difference between mortality rate of parties at real estate auctions and that of the overall population, which provides a new insight regarding utilization of the information on real estate auctions. Despite the need for further detailed analysis on the correlation between real estate auction events and death, because the result from this study is still meaningful, the result is summarized for informational purposes.", "published": "2019-12-01T05:54:45Z", "authors": ["Cheoljoon Jeong"], "categories": ["stat.AP"], "doi": "", "_query": "real estate valuation deep learning", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/1904.10353v1", "title": "Read classification using semi-supervised deep learning", "abstract": "In this paper, we propose a semi-supervised deep learning method for detecting the specific types of reads that impede the de novo genome assembly process. Instead of dealing directly with sequenced reads, we analyze their coverage graphs converted to 1D-signals. We noticed that specific signal patterns occur in each relevant class of reads. Semi-supervised approach is chosen because manually labelling the data is a very slow and tedious process, so our goal was to facilitate the assembly process with as little labeled data as possible. We tested two models to learn patterns in the coverage graphs: M1+M2 and semi-GAN. We evaluated the performance of each model based on a manually labeled dataset that comprises various reads from multiple reference genomes with respect to the number of labeled examples that were used during the training process. In addition, we embedded our detection in the assembly process which improved the quality of assemblies.", "published": "2019-04-23T14:25:42Z", "authors": ["Tomislav Šebrek", "Jan Tomljanović", "Josip Krapac", "Mile Šikić"], "categories": ["cs.LG", "q-bio.GN", "stat.ML"], "doi": "", "_query": "real estate valuation deep learning", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/1904.10337v1", "title": "MinCall - MinION end2end convolutional deep learning basecaller", "abstract": "The Oxford Nanopore Technologies's MinION is the first portable DNA sequencing device. It is capable of producing long reads, over 100 kBp were reported. However, it has significantly higher error rate than other methods. In this study, we present MinCall, an end2end basecaller model for the MinION. The model is based on deep learning and uses convolutional neural networks (CNN) in its implementation. For extra performance, it uses cutting edge deep learning techniques and architectures, batch normalization and Connectionist Temporal Classification (CTC) loss. The best performing deep learning model achieves 91.4% median match rate on E. Coli dataset using R9 pore chemistry and 1D reads.", "published": "2019-04-22T16:37:00Z", "authors": ["Neven Miculinić", "Marko Ratković", "Mile Šikić"], "categories": ["q-bio.GN", "cs.LG"], "doi": "", "_query": "real estate valuation deep learning", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2004.03357v2", "title": "Deep learning approaches in food recognition", "abstract": "Automatic image-based food recognition is a particularly challenging task. Traditional image analysis approaches have achieved low classification accuracy in the past, whereas deep learning approaches enabled the identification of food types and their ingredients. The contents of food dishes are typically deformable objects, usually including complex semantics, which makes the task of defining their structure very difficult. Deep learning methods have already shown very promising results in such challenges, so this chapter focuses on the presentation of some popular approaches and techniques applied in image-based food recognition. The three main lines of solutions, namely the design from scratch, the transfer learning and the platform-based approaches, are outlined, particularly for the task at hand, and are tested and compared to reveal the inherent strengths and weaknesses. The chapter is complemented with basic background material, a section devoted to the relevant datasets that are crucial in light of the empirical approaches adopted, and some concluding remarks that underline the future directions.", "published": "2020-04-04T20:22:16Z", "authors": ["Chairi Kiourt", "George Pavlidis", "Stella Markantonatou"], "categories": ["cs.CV", "cs.LG", "eess.IV"], "doi": "", "_query": "real estate valuation deep learning", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2506.09781v2", "title": "On the Similarities of Embeddings in Contrastive Learning", "abstract": "Contrastive learning operates on a simple yet effective principle: Embeddings of positive pairs are pulled together, while those of negative pairs are pushed apart. In this paper, we propose a unified framework for understanding contrastive learning through the lens of cosine similarity, and present two key theoretical insights derived from this framework. First, in full-batch settings, we show that perfect alignment of positive pairs is unattainable when negative-pair similarities fall below a threshold, and this misalignment can be mitigated by incorporating within-view negative pairs into the objective. Second, in mini-batch settings, smaller batch sizes induce stronger separation among negative pairs in the embedding space, i.e., higher variance in their similarities, which in turn degrades the quality of learned representations compared to full-batch settings. To address this, we propose an auxiliary loss that reduces the variance of negative-pair similarities in mini-batch settings. Empirical results show that incorporating the proposed loss improves performance in small-batch settings.", "published": "2025-06-11T14:21:05Z", "authors": ["Chungpa Lee", "Sehee Lim", "Kibok Lee", "Jy-yong Sohn"], "categories": ["cs.LG", "stat.ML"], "doi": "", "_query": "real estate valuation deep learning", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2105.04984v1", "title": "A Comparison of Multi-View Learning Strategies for Satellite Image-Based Real Estate Appraisal", "abstract": "In the house credit process, banks and lenders rely on a fast and accurate estimation of a real estate price to determine the maximum loan value. Real estate appraisal is often based on relational data, capturing the hard facts of the property. Yet, models benefit strongly from including image data, capturing additional soft factors. The combination of the different data types requires a multi-view learning method. Therefore, the question arises which strengths and weaknesses different multi-view learning strategies have. In our study, we test multi-kernel learning, multi-view concatenation and multi-view neural networks on real estate data and satellite images from Asheville, NC. Our results suggest that multi-view learning increases the predictive performance up to 13% in MAE. Multi-view neural networks perform best, however result in intransparent black-box models. For users seeking interpretability, hybrid multi-view neural networks or a boosting strategy are a suitable alternative.", "published": "2021-05-11T12:41:22Z", "authors": ["Jan-Peter Kucklick", "Oliver Müller"], "categories": ["cs.CV"], "doi": "", "_query": "real estate valuation deep learning", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2412.18507v1", "title": "An Empirical Analysis of Federated Learning Models Subject to Label-Flipping Adversarial Attack", "abstract": "In this paper, we empirically analyze adversarial attacks on selected federated learning models. The specific learning models considered are Multinominal Logistic Regression (MLR), Support Vector Classifier (SVC), Multilayer Perceptron (MLP), Convolution Neural Network (CNN), %Recurrent Neural Network (RNN), Random Forest, XGBoost, and Long Short-Term Memory (LSTM). For each model, we simulate label-flipping attacks, experimenting extensively with 10 federated clients and 100 federated clients. We vary the percentage of adversarial clients from 10% to 100% and, simultaneously, the percentage of labels flipped by each adversarial client is also varied from 10% to 100%. Among other results, we find that models differ in their inherent robustness to the two vectors in our label-flipping attack, i.e., the percentage of adversarial clients, and the percentage of labels flipped by each adversarial client. We discuss the potential practical implications of our results.", "published": "2024-12-24T15:47:25Z", "authors": ["Kunal Bhatnagar", "Sagana Chattanathan", "Angela Dang", "Bhargav Eranki", "Ronnit Rana", "Charan Sridhar", "Siddharth Vedam", "Angie Yao", "Mark Stamp"], "categories": ["cs.LG"], "doi": "10.1007/978-3-031-83157-7_15", "_query": "real estate valuation deep learning", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2401.15721v2", "title": "A Study of Acquisition Functions for Medical Imaging Deep Active Learning", "abstract": "The Deep Learning revolution has enabled groundbreaking achievements in recent years. From breast cancer detection to protein folding, deep learning algorithms have been at the core of very important advancements. However, these modern advancements are becoming more and more data-hungry, especially on labeled data whose availability is scarce: this is even more prevalent in the medical context. In this work, we show how active learning could be very effective in data scarcity situations, where obtaining labeled data (or annotation budget is very limited). We compare several selection criteria (BALD, MeanSTD, and MaxEntropy) on the ISIC 2016 dataset. We also explored the effect of acquired pool size on the model's performance. Our results suggest that uncertainty is useful to the Melanoma detection task, and confirms the hypotheses of the author of the paper of interest, that \\textit{bald} performs on average better than other acquisition functions. Our extended analyses however revealed that all acquisition functions perform badly on the positive (cancerous) samples, suggesting exploitation of class unbalance, which could be crucial in real-world settings. We finish by suggesting future work directions that would be useful to improve this current work. The code of our implementation is open-sourced at \\url{https://github.com/bonaventuredossou/ece526_course_project}", "published": "2024-01-28T18:09:02Z", "authors": ["Bonaventure F. P. Dossou"], "categories": ["cs.CV", "cs.AI", "cs.HC", "cs.LG"], "doi": "", "_query": "real estate valuation deep learning", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2005.01710v1", "title": "Issues In Disintermediation In The Real Estate Brokerage Sector", "abstract": "This article introduces new models of disintermediation of the real estate broker by the buyer or the seller. The decision to retain a real estate broker is critical in the property purchase/sale process. The existing literature does not contain analysis of: 1) information asymmetry, 2) the conditions under which it will be optimal to disintermediate the broker, 3) social capital and reputation, 4) the impact of different types of real estate brokerage contracts. The article shows that dis-intermediation of the real estate broker by the seller or buyer may be optimal in certain conditions.", "published": "2020-05-01T17:41:06Z", "authors": ["Michael C. Nwogugu"], "categories": ["q-fin.GN"], "doi": "10.1016/j.amc.2006.08.053", "_query": "real estate valuation deep learning", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2312.10046v1", "title": "Deep Metric Learning for Computer Vision: A Brief Overview", "abstract": "Objective functions that optimize deep neural networks play a vital role in creating an enhanced feature representation of the input data. Although cross-entropy-based loss formulations have been extensively used in a variety of supervised deep-learning applications, these methods tend to be less adequate when there is large intra-class variance and low inter-class variance in input data distribution. Deep Metric Learning seeks to develop methods that aim to measure the similarity between data samples by learning a representation function that maps these data samples into a representative embedding space. It leverages carefully designed sampling strategies and loss functions that aid in optimizing the generation of a discriminative embedding space even for distributions having low inter-class and high intra-class variances. In this chapter, we will provide an overview of recent progress in this area and discuss state-of-the-art Deep Metric Learning approaches.", "published": "2023-12-01T21:53:36Z", "authors": ["Deen Dayal Mohan", "Bhavin Jawade", "Srirangaraj Setlur", "Venu Govindaraj"], "categories": ["cs.CV", "cs.AI", "cs.IR", "cs.LG"], "doi": "", "_query": "real estate valuation deep learning", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2106.06097v4", "title": "Neural Optimization Kernel: Towards Robust Deep Learning", "abstract": "Deep neural networks (NN) have achieved great success in many applications. However, why do deep neural networks obtain good generalization at an over-parameterization regime is still unclear. To better understand deep NN, we establish the connection between deep NN and a novel kernel family, i.e., Neural Optimization Kernel (NOK). The architecture of structured approximation of NOK performs monotonic descent updates of implicit regularization problems. We can implicitly choose the regularization problems by employing different activation functions, e.g., ReLU, max pooling, and soft-thresholding. We further establish a new generalization bound of our deep structured approximated NOK architecture. Our unsupervised structured approximated NOK block can serve as a simple plug-in of popular backbones for a good generalization against input noise.", "published": "2021-06-11T00:34:55Z", "authors": ["Yueming Lyu", "Ivor Tsang"], "categories": ["stat.ML", "cs.LG"], "doi": "", "_query": "real estate valuation deep learning", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2303.14433v1", "title": "Deep Active Learning with Contrastive Learning Under Realistic Data Pool Assumptions", "abstract": "Active learning aims to identify the most informative data from an unlabeled data pool that enables a model to reach the desired accuracy rapidly. This benefits especially deep neural networks which generally require a huge number of labeled samples to achieve high performance. Most existing active learning methods have been evaluated in an ideal setting where only samples relevant to the target task, i.e., in-distribution samples, exist in an unlabeled data pool. A data pool gathered from the wild, however, is likely to include samples that are irrelevant to the target task at all and/or too ambiguous to assign a single class label even for the oracle. We argue that assuming an unlabeled data pool consisting of samples from various distributions is more realistic. In this work, we introduce new active learning benchmarks that include ambiguous, task-irrelevant out-of-distribution as well as in-distribution samples. We also propose an active learning method designed to acquire informative in-distribution samples in priority. The proposed method leverages both labeled and unlabeled data pools and selects samples from clusters on the feature space constructed via contrastive learning. Experimental results demonstrate that the proposed method requires a lower annotation budget than existing active learning methods to reach the same level of accuracy.", "published": "2023-03-25T10:46:10Z", "authors": ["Jihyo Kim", "Jeonghyeon Kim", "Sangheum Hwang"], "categories": ["cs.CV", "cs.LG"], "doi": "", "_query": "real estate valuation deep learning", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2105.09266v5", "title": "Copyright in Generative Deep Learning", "abstract": "Machine-generated artworks are now part of the contemporary art scene: they are attracting significant investments and they are presented in exhibitions together with those created by human artists. These artworks are mainly based on generative deep learning techniques, which have seen a formidable development and remarkable refinement in the very recent years. Given the inherent characteristics of these techniques, a series of novel legal problems arise. In this article, we consider a set of key questions in the area of generative deep learning for the arts, including the following: is it possible to use copyrighted works as training set for generative models? How do we legally store their copies in order to perform the training process? Who (if someone) will own the copyright on the generated data? We try to answer these questions considering the law in force in both the United States of America and the European Union, and potential future alternatives. We then extend our analysis to code generation, which is an emerging area of generative deep learning. Finally, we also formulate a set of practical guidelines for artists and developers working on deep learning generated art, as well as some policy suggestions for policymakers.", "published": "2021-05-19T17:22:47Z", "authors": ["Giorgio Franceschelli", "Mirco Musolesi"], "categories": ["cs.CY", "cs.AI", "cs.LG"], "doi": "10.1017/dap.2022.10", "_query": "real estate valuation deep learning", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/1811.12535v1", "title": "The Relevance of Bayesian Layer Positioning to Model Uncertainty in Deep Bayesian Active Learning", "abstract": "One of the main challenges of deep learning tools is their inability to capture model uncertainty. While Bayesian deep learning can be used to tackle the problem, Bayesian neural networks often require more time and computational power to train than deterministic networks. Our work explores whether fully Bayesian networks are needed to successfully capture model uncertainty. We vary the number and position of Bayesian layers in a network and compare their performance on active learning with the MNIST dataset. We found that we can fully capture the model uncertainty by using only a few Bayesian layers near the output of the network, combining the advantages of deterministic and Bayesian networks.", "published": "2018-11-29T23:36:25Z", "authors": ["Jiaming Zeng", "Adam Lesnikowski", "Jose M. Alvarez"], "categories": ["cs.LG", "cs.AI", "stat.ML"], "doi": "", "_query": "real estate valuation deep learning", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2408.12553v1", "title": "Dynamic Pricing for Real Estate", "abstract": "We study a mathematical model for the optimization of the price of real estate (RE). This model can be characterised by a limited amount of goods, fixed sales horizon and presence of intermediate sales and revenue goals. We develop it as an enhancement and upgrade of the model presented by Besbes and Maglaras now also taking into account variable demand, time value of money, and growth of the objective value of Real Estate with the development stage.", "published": "2024-08-22T17:08:23Z", "authors": ["Lev Razumovskiy", "Mariya Gerasimova", "Nikolay Karenin"], "categories": ["q-fin.MF", "econ.TH", "q-fin.CP", "q-fin.TR"], "doi": "", "_query": "real estate valuation deep learning", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/1611.09180v2", "title": "Image Based Appraisal of Real Estate Properties", "abstract": "Real estate appraisal, which is the process of estimating the price for real estate properties, is crucial for both buys and sellers as the basis for negotiation and transaction. Traditionally, the repeat sales model has been widely adopted to estimate real estate price. However, it depends the design and calculation of a complex economic related index, which is challenging to estimate accurately. Today, real estate brokers provide easy access to detailed online information on real estate properties to their clients. We are interested in estimating the real estate price from these large amounts of easily accessed data. In particular, we analyze the prediction power of online house pictures, which is one of the key factors for online users to make a potential visiting decision. The development of robust computer vision algorithms makes the analysis of visual content possible. In this work, we employ a Recurrent Neural Network (RNN) to predict real estate price using the state-of-the-art visual features. The experimental results indicate that our model outperforms several of other state-of-the-art baseline algorithms in terms of both mean absolute error (MAE) and mean absolute percentage error (MAPE).", "published": "2016-11-28T15:23:14Z", "authors": ["Quanzeng You", "Ran Pang", "Liangliang Cao", "Jiebo Luo"], "categories": ["cs.CV", "cs.LG"], "doi": "10.1109/TMM.2017.2710804", "_query": "real estate valuation deep learning", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2106.15292v3", "title": "Adaptive Sample Selection for Robust Learning under Label Noise", "abstract": "Deep Neural Networks (DNNs) have been shown to be susceptible to memorization or overfitting in the presence of noisily-labelled data. For the problem of robust learning under such noisy data, several algorithms have been proposed. A prominent class of algorithms rely on sample selection strategies wherein, essentially, a fraction of samples with loss values below a certain threshold are selected for training. These algorithms are sensitive to such thresholds, and it is difficult to fix or learn these thresholds. Often, these algorithms also require information such as label noise rates which are typically unavailable in practice. In this paper, we propose an adaptive sample selection strategy that relies only on batch statistics of a given mini-batch to provide robustness against label noise. The algorithm does not have any additional hyperparameters for sample selection, does not need any information on noise rates and does not need access to separate data with clean labels. We empirically demonstrate the effectiveness of our algorithm on benchmark datasets.", "published": "2021-06-29T12:10:58Z", "authors": ["Deep Patel", "P. S. Sastry"], "categories": ["cs.LG", "cs.AI", "cs.CV"], "doi": "", "_query": "real estate valuation deep learning", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2002.07493v1", "title": "MapLUR: Exploring a new Paradigm for Estimating Air Pollution using Deep Learning on Map Images", "abstract": "Land-use regression (LUR) models are important for the assessment of air pollution concentrations in areas without measurement stations. While many such models exist, they often use manually constructed features based on restricted, locally available data. Thus, they are typically hard to reproduce and challenging to adapt to areas beyond those they have been developed for. In this paper, we advocate a paradigm shift for LUR models: We propose the Data-driven, Open, Global (DOG) paradigm that entails models based on purely data-driven approaches using only openly and globally available data. Progress within this paradigm will alleviate the need for experts to adapt models to the local characteristics of the available data sources and thus facilitate the generalizability of air pollution models to new areas on a global scale. In order to illustrate the feasibility of the DOG paradigm for LUR, we introduce a deep learning model called MapLUR. It is based on a convolutional neural network architecture and is trained exclusively on globally and openly available map data without requiring manual feature engineering. We compare our model to state-of-the-art baselines like linear regression, random forests and multi-layer perceptrons using a large data set of modeled $\\text{NO}_2$ concentrations in Central London. Our results show that MapLUR significantly outperforms these approaches even though they are provided with manually tailored features. Furthermore, we illustrate that the automatic feature extraction inherent to models based on the DOG paradigm can learn features that are readily interpretable and closely resemble those commonly used in traditional LUR approaches.", "published": "2020-02-18T11:21:55Z", "authors": ["Michael Steininger", "Konstantin Kobs", "Albin Zehe", "Florian Lautenschlager", "Martin Becker", "Andreas Hotho"], "categories": ["cs.LG", "cs.CV", "eess.IV", "stat.ML"], "doi": "10.1145/3380973", "_query": "real estate valuation deep learning", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/1905.07435v1", "title": "Alpha MAML: Adaptive Model-Agnostic Meta-Learning", "abstract": "Model-agnostic meta-learning (MAML) is a meta-learning technique to train a model on a multitude of learning tasks in a way that primes the model for few-shot learning of new tasks. The MAML algorithm performs well on few-shot learning problems in classification, regression, and fine-tuning of policy gradients in reinforcement learning, but comes with the need for costly hyperparameter tuning for training stability. We address this shortcoming by introducing an extension to MAML, called Alpha MAML, to incorporate an online hyperparameter adaptation scheme that eliminates the need to tune meta-learning and learning rates. Our results with the Omniglot database demonstrate a substantial reduction in the need to tune MAML training hyperparameters and improvement to training stability with less sensitivity to hyperparameter choice.", "published": "2019-05-17T18:45:25Z", "authors": ["Harkirat Singh Behl", "Atılım Güneş Baydin", "Philip H. S. Torr"], "categories": ["cs.LG", "cs.AI", "stat.ML"], "doi": "", "_query": "real estate valuation deep learning", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/1707.05489v3", "title": "Vision-based Real Estate Price Estimation", "abstract": "Since the advent of online real estate database companies like Zillow, Trulia and Redfin, the problem of automatic estimation of market values for houses has received considerable attention. Several real estate websites provide such estimates using a proprietary formula. Although these estimates are often close to the actual sale prices, in some cases they are highly inaccurate. One of the key factors that affects the value of a house is its interior and exterior appearance, which is not considered in calculating automatic value estimates. In this paper, we evaluate the impact of visual characteristics of a house on its market value. Using deep convolutional neural networks on a large dataset of photos of home interiors and exteriors, we develop a method for estimating the luxury level of real estate photos. We also develop a novel framework for automated value assessment using the above photos in addition to home characteristics including size, offered price and number of bedrooms. Finally, by applying our proposed method for price estimation to a new dataset of real estate photos and metadata, we show that it outperforms Zillow's estimates.", "published": "2017-07-18T06:29:02Z", "authors": ["Omid Poursaeed", "Tomas Matera", "Serge Belongie"], "categories": ["cs.CV", "cs.LG"], "doi": "10.1007/s00138-018-0922-2", "_query": "real estate valuation deep learning", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2012.09115v1", "title": "Towards robust and speculation-reduction real estate pricing models based on a data-driven strategy", "abstract": "In many countries, real estate appraisal is based on conventional methods that rely on appraisers' abilities to collect data, interpret it and model the price of a real estate property. With the increasing use of real estate online platforms and the large amount of information found therein, there exists the possibility of overcoming many drawbacks of conventional pricing models such as subjectivity, cost, unfairness, among others. In this paper we propose a data-driven real estate pricing model based on machine learning methods to estimate prices reducing human bias. We test the model with 178,865 flats listings from Bogotá, collected from 2016 to 2020. Results show that the proposed state-of-the-art model is robust and accurate in estimating real estate prices. This case study serves as an incentive for local governments from developing countries to discuss and build real estate pricing models based on large data sets that increases fairness for all the real estate market stakeholders and reduces price speculation.", "published": "2020-11-26T15:54:07Z", "authors": ["Vladimir Vargas-Calderón", "Jorge E. Camargo"], "categories": ["econ.GN", "cs.LG"], "doi": "10.1080/01605682.2021.2023672", "_query": "real estate valuation deep learning", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2506.11812v1", "title": "On the Performance of LLMs for Real Estate Appraisal", "abstract": "The real estate market is vital to global economies but suffers from significant information asymmetry. This study examines how Large Language Models (LLMs) can democratize access to real estate insights by generating competitive and interpretable house price estimates through optimized In-Context Learning (ICL) strategies. We systematically evaluate leading LLMs on diverse international housing datasets, comparing zero-shot, few-shot, market report-enhanced, and hybrid prompting techniques. Our results show that LLMs effectively leverage hedonic variables, such as property size and amenities, to produce meaningful estimates. While traditional machine learning models remain strong for pure predictive accuracy, LLMs offer a more accessible, interactive and interpretable alternative. Although self-explanations require cautious interpretation, we find that LLMs explain their predictions in agreement with state-of-the-art models, confirming their trustworthiness. Carefully selected in-context examples based on feature similarity and geographic proximity, significantly enhance LLM performance, yet LLMs struggle with overconfidence in price intervals and limited spatial reasoning. We offer practical guidance for structured prediction tasks through prompt optimization. Our findings highlight LLMs' potential to improve transparency in real estate appraisal and provide actionable insights for stakeholders.", "published": "2025-06-13T14:14:40Z", "authors": ["Margot Geerts", "Manon Reusens", "Bart Baesens", "Seppe vanden Broucke", "Jochen De Weerdt"], "categories": ["cs.AI", "cs.CL", "cs.LG"], "doi": "10.1007/978-3-032-06118-8_12", "_query": "real estate valuation deep learning", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2406.02024v3", "title": "Verifying the Generalization of Deep Learning to Out-of-Distribution Domains", "abstract": "Deep neural networks (DNNs) play a crucial role in the field of machine learning, demonstrating state-of-the-art performance across various application domains. However, despite their success, DNN-based models may occasionally exhibit challenges with generalization, i.e., may fail to handle inputs that were not encountered during training. This limitation is a significant challenge when it comes to deploying deep learning for safety-critical tasks, as well as in real-world settings characterized by substantial variability. We introduce a novel approach for harnessing DNN verification technology to identify DNN-driven decision rules that exhibit robust generalization to previously unencountered input domains. Our method assesses generalization within an input domain by measuring the level of agreement between independently trained deep neural networks for inputs in this domain. We also efficiently realize our approach by using off-the-shelf DNN verification engines, and extensively evaluate it on both supervised and unsupervised DNN benchmarks, including a deep reinforcement learning (DRL) system for Internet congestion control -- demonstrating the applicability of our approach for real-world settings. Moreover, our research introduces a fresh objective for formal verification, offering the prospect of mitigating the challenges linked to deploying DNN-driven systems in real-world scenarios.", "published": "2024-06-04T07:02:59Z", "authors": ["Guy Amir", "Osher Maayan", "Tom Zelazny", "Guy Katz", "Michael Schapira"], "categories": ["cs.LG", "cs.LO"], "doi": "", "_query": "real estate valuation deep learning", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2309.08500v1", "title": "Deep-learning-powered data analysis in plankton ecology", "abstract": "The implementation of deep learning algorithms has brought new perspectives to plankton ecology. Emerging as an alternative approach to established methods, deep learning offers objective schemes to investigate plankton organisms in diverse environments. We provide an overview of deep-learning-based methods including detection and classification of phyto- and zooplankton images, foraging and swimming behaviour analysis, and finally ecological modelling. Deep learning has the potential to speed up the analysis and reduce the human experimental bias, thus enabling data acquisition at relevant temporal and spatial scales with improved reproducibility. We also discuss shortcomings and show how deep learning architectures have evolved to mitigate imprecise readouts. Finally, we suggest opportunities where deep learning is particularly likely to catalyze plankton research. The examples are accompanied by detailed tutorials and code samples that allow readers to apply the methods described in this review to their own data.", "published": "2023-09-15T16:04:11Z", "authors": ["Harshith Bachimanchi", "Matthew I. M. Pinder", "Chloé Robert", "Pierre De Wit", "Jonathan Havenhand", "Alexandra Kinnby", "Daniel Midtvedt", "Erik Selander", "Giovanni Volpe"], "categories": ["physics.bio-ph", "cond-mat.soft", "cs.LG", "q-bio.QM"], "doi": "", "_query": "real estate valuation deep learning", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/1811.00576v3", "title": "Connections between physics, mathematics and deep learning", "abstract": "Starting from the Fermat's principle of least action, which governs classical and quantum mechanics and from the theory of exterior differential forms, which governs the geometry of curved manifolds, we show how to derive the equations governing neural networks in an intrinsic, coordinate invariant way, where the loss function plays the role of the Hamiltonian. To be covariant, these equations imply a layer metric which is instrumental in pretraining and explains the role of conjugation when using complex numbers. The differential formalism also clarifies the relation of the gradient descent optimizer with Aristotelian and Newtonian mechanics and why large learning steps break the logic of the linearization procedure. We hope that this formal presentation of the differential geometry of neural networks will encourage some physicists to dive into deep learning, and reciprocally, that the specialists of deep learning will better appreciate the close interconnection of their subject with the foundations of classical and quantum field theory.", "published": "2018-11-01T18:21:42Z", "authors": ["Jean Thierry-Mieg"], "categories": ["cs.LG", "hep-th", "stat.ML"], "doi": "", "_query": "real estate valuation deep learning", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2207.07859v3", "title": "SenseFi: A Library and Benchmark on Deep-Learning-Empowered WiFi Human Sensing", "abstract": "WiFi sensing has been evolving rapidly in recent years. Empowered by propagation models and deep learning methods, many challenging applications are realized such as WiFi-based human activity recognition and gesture recognition. However, in contrast to deep learning for visual recognition and natural language processing, no sufficiently comprehensive public benchmark exists. In this paper, we review the recent progress on deep learning enabled WiFi sensing, and then propose a benchmark, SenseFi, to study the effectiveness of various deep learning models for WiFi sensing. These advanced models are compared in terms of distinct sensing tasks, WiFi platforms, recognition accuracy, model size, computational complexity, feature transferability, and adaptability of unsupervised learning. It is also regarded as a tutorial for deep learning based WiFi sensing, starting from CSI hardware platform to sensing algorithms. The extensive experiments provide us with experiences in deep model design, learning strategy skills and training techniques for real-world applications. To the best of our knowledge, this is the first benchmark with an open-source library for deep learning in WiFi sensing research. The benchmark codes are available at https://github.com/xyanchen/WiFi-CSI-Sensing-Benchmark.", "published": "2022-07-16T07:23:45Z", "authors": ["Jianfei Yang", "Xinyan Chen", "Dazhuo Wang", "Han Zou", "Chris Xiaoxuan Lu", "Sumei Sun", "Lihua Xie"], "categories": ["cs.LG", "cs.AI", "eess.SP"], "doi": "", "_query": "real estate valuation deep learning", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/1905.05461v2", "title": "Learning Generative Models across Incomparable Spaces", "abstract": "Generative Adversarial Networks have shown remarkable success in learning a distribution that faithfully recovers a reference distribution in its entirety. However, in some cases, we may want to only learn some aspects (e.g., cluster or manifold structure), while modifying others (e.g., style, orientation or dimension). In this work, we propose an approach to learn generative models across such incomparable spaces, and demonstrate how to steer the learned distribution towards target properties. A key component of our model is the Gromov-Wasserstein distance, a notion of discrepancy that compares distributions relationally rather than absolutely. While this framework subsumes current generative models in identically reproducing distributions, its inherent flexibility allows application to tasks in manifold learning, relational learning and cross-domain learning.", "published": "2019-05-14T08:56:12Z", "authors": ["Charlotte Bunne", "David Alvarez-Melis", "Andreas Krause", "Stefanie Jegelka"], "categories": ["cs.LG", "stat.ML"], "doi": "", "_query": "real estate valuation deep learning", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2105.13807v3", "title": "Gym-$μ$RTS: Toward Affordable Full Game Real-time Strategy Games Research with Deep Reinforcement Learning", "abstract": "In recent years, researchers have achieved great success in applying Deep Reinforcement Learning (DRL) algorithms to Real-time Strategy (RTS) games, creating strong autonomous agents that could defeat professional players in StarCraft~II. However, existing approaches to tackle full games have high computational costs, usually requiring the use of thousands of GPUs and CPUs for weeks. This paper has two main contributions to address this issue: 1) We introduce Gym-$μ$RTS (pronounced \"gym-micro-RTS\") as a fast-to-run RL environment for full-game RTS research and 2) we present a collection of techniques to scale DRL to play full-game $μ$RTS as well as ablation studies to demonstrate their empirical importance. Our best-trained bot can defeat every $μ$RTS bot we tested from the past $μ$RTS competitions when working in a single-map setting, resulting in a state-of-the-art DRL agent while only taking about 60 hours of training using a single machine (one GPU, three vCPU, 16GB RAM). See the blog post at https://wandb.ai/vwxyzjn/gym-microrts-paper/reports/Gym-RTS-Toward-Affordable-Deep-Reinforcement-Learning-Research-in-Real-Time-Strategy-Games--Vmlldzo2MDIzMTg and the source code at https://github.com/vwxyzjn/gym-microrts-paper", "published": "2021-05-21T20:13:35Z", "authors": ["Shengyi Huang", "Santiago Ontañón", "Chris Bamford", "Lukasz Grela"], "categories": ["cs.LG"], "doi": "", "_query": "real estate valuation deep learning", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/1510.09202v1", "title": "Generating Text with Deep Reinforcement Learning", "abstract": "We introduce a novel schema for sequence to sequence learning with a Deep Q-Network (DQN), which decodes the output sequence iteratively. The aim here is to enable the decoder to first tackle easier portions of the sequences, and then turn to cope with difficult parts. Specifically, in each iteration, an encoder-decoder Long Short-Term Memory (LSTM) network is employed to, from the input sequence, automatically create features to represent the internal states of and formulate a list of potential actions for the DQN. Take rephrasing a natural sentence as an example. This list can contain ranked potential words. Next, the DQN learns to make decision on which action (e.g., word) will be selected from the list to modify the current decoded sequence. The newly modified output sequence is subsequently used as the input to the DQN for the next decoding iteration. In each iteration, we also bias the reinforcement learning's attention to explore sequence portions which are previously difficult to be decoded. For evaluation, the proposed strategy was trained to decode ten thousands natural sentences. Our experiments indicate that, when compared to a left-to-right greedy beam search LSTM decoder, the proposed method performed competitively well when decoding sentences from the training set, but significantly outperformed the baseline when decoding unseen sentences, in terms of BLEU score obtained.", "published": "2015-10-30T19:02:53Z", "authors": ["Hongyu Guo"], "categories": ["cs.CL", "cs.LG", "cs.NE"], "doi": "", "_query": "real estate valuation deep learning", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2311.16109v1", "title": "Transfer Learning between Motor Imagery Datasets using Deep Learning -- Validation of Framework and Comparison of Datasets", "abstract": "We present a simple deep learning-based framework commonly used in computer vision and demonstrate its effectiveness for cross-dataset transfer learning in mental imagery decoding tasks that are common in the field of Brain-Computer Interfaces (BCI). We investigate, on a large selection of 12 motor-imagery datasets, which ones are well suited for transfer, both as donors and as receivers. Challenges. Deep learning models typically require long training times and are data-hungry, which impedes their use for BCI systems that have to minimize the recording time for (training) examples and are subject to constraints induced by experiments involving human subjects. A solution to both issues is transfer learning, but it comes with its own challenge, i.e., substantial data distribution shifts between datasets, subjects and even between subsequent sessions of the same subject. Approach. For every pair of pre-training (donor) and test (receiver) dataset, we first train a model on the donor before training merely an additional new linear classification layer based on a few receiver trials. Performance of this transfer approach is then tested on other trials of the receiver dataset. Significance. First, we lower the threshold to use transfer learning between motor imagery datasets: the overall framework is extremely simple and nevertheless obtains decent classification scores. Second, we demonstrate that deep learning models are a good option for motor imagery cross-dataset transfer both for the reasons outlined in the first point and because the framework presented is viable in online scenarios. Finally, analysing which datasets are best suited for transfer learning can be used as a reference for future researchers to determine which to use for pre-training or benchmarking.", "published": "2023-09-04T20:58:57Z", "authors": ["Pierre Guetschel", "Michael Tangermann"], "categories": ["cs.CV", "cs.AI", "cs.HC", "cs.LG"], "doi": "", "_query": "real estate valuation deep learning", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/1704.03732v4", "title": "Deep Q-learning from Demonstrations", "abstract": "Deep reinforcement learning (RL) has achieved several high profile successes in difficult decision-making problems. However, these algorithms typically require a huge amount of data before they reach reasonable performance. In fact, their performance during learning can be extremely poor. This may be acceptable for a simulator, but it severely limits the applicability of deep RL to many real-world tasks, where the agent must learn in the real environment. In this paper we study a setting where the agent may access data from previous control of the system. We present an algorithm, Deep Q-learning from Demonstrations (DQfD), that leverages small sets of demonstration data to massively accelerate the learning process even from relatively small amounts of demonstration data and is able to automatically assess the necessary ratio of demonstration data while learning thanks to a prioritized replay mechanism. DQfD works by combining temporal difference updates with supervised classification of the demonstrator's actions. We show that DQfD has better initial performance than Prioritized Dueling Double Deep Q-Networks (PDD DQN) as it starts with better scores on the first million steps on 41 of 42 games and on average it takes PDD DQN 83 million steps to catch up to DQfD's performance. DQfD learns to out-perform the best demonstration given in 14 of 42 games. In addition, DQfD leverages human demonstrations to achieve state-of-the-art results for 11 games. Finally, we show that DQfD performs better than three related algorithms for incorporating demonstration data into DQN.", "published": "2017-04-12T12:44:37Z", "authors": ["Todd Hester", "Matej Vecerik", "Olivier Pietquin", "Marc Lanctot", "Tom Schaul", "Bilal Piot", "Dan Horgan", "John Quan", "Andrew Sendonaris", "Gabriel Dulac-Arnold", "Ian Osband", "John Agapiou", "Joel Z. Leibo", "Audrunas Gruslys"], "categories": ["cs.AI", "cs.LG"], "doi": "", "_query": "real estate valuation deep learning", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2512.09960v1", "title": "Numerical Behavior of the Riemann Zeta Function Using Real-to-Complex Conversion Without Gram Points or Bracketing", "abstract": "The Riemann zeta function and the distribution of its nontrivial zeros on the critical line remain central topics in analytic number theory and large-scale computation. This work develops a numerical framework that replaces classical Gram-point bracketing with a real-to-complex parametrization of the critical line. Combined with high-precision evaluation of the Hardy Z function using the Riemann-Siegel formula with Gabcke type remainder control, this parametrization induces a Valley Scanner algorithm that identifies local minima of the absolute value of Z and refines them using safeguarded Newton iterations. The method exploits the mountain-valley geometry of the Z function rather than sign changes, and is implemented in a cloud based C language and MPFR pipeline with parallel execution on multi-core CPU instances. The paper reports Zero computations from the classical low lying range up to heights near 1e20, compares the observed spacing of zeros with Riemann - von Mangoldt predictions across several test windows, and summarizes consistency checks based on independent datasets and a local ball test. The work also introduces a fully real staircase formulation of the explicit-formula correction term for the Chebyshev function, expressed directly in terms of the zero ordinates and derived quantities. All numerical datasets, Docker images, and reproducibility materials are publicly archived via Zenodo and GitHub.", "published": "2025-12-09T22:14:45Z", "authors": ["Jacob Orellana Real"], "categories": ["math.GM"], "doi": "", "_query": "real estate valuation deep learning", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/1805.09921v4", "title": "Meta-Learning Probabilistic Inference For Prediction", "abstract": "This paper introduces a new framework for data efficient and versatile learning. Specifically: 1) We develop ML-PIP, a general framework for Meta-Learning approximate Probabilistic Inference for Prediction. ML-PIP extends existing probabilistic interpretations of meta-learning to cover a broad class of methods. 2) We introduce VERSA, an instance of the framework employing a flexible and versatile amortization network that takes few-shot learning datasets as inputs, with arbitrary numbers of shots, and outputs a distribution over task-specific parameters in a single forward pass. VERSA substitutes optimization at test time with forward passes through inference networks, amortizing the cost of inference and relieving the need for second derivatives during training. 3) We evaluate VERSA on benchmark datasets where the method sets new state-of-the-art results, handles arbitrary numbers of shots, and for classification, arbitrary numbers of classes at train and test time. The power of the approach is then demonstrated through a challenging few-shot ShapeNet view reconstruction task.", "published": "2018-05-24T22:08:27Z", "authors": ["Jonathan Gordon", "John Bronskill", "Matthias Bauer", "Sebastian Nowozin", "Richard E. Turner"], "categories": ["stat.ML", "cs.LG"], "doi": "", "_query": "real estate valuation deep learning", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2004.03722v1", "title": "Challenges in Vessel Behavior and Anomaly Detection: From Classical Machine Learning to Deep Learning", "abstract": "The global expansion of maritime activities and the development of the Automatic Identification System (AIS) have driven the advances in maritime monitoring systems in the last decade. Monitoring vessel behavior is fundamental to safeguard maritime operations, protecting other vessels sailing the ocean and the marine fauna and flora. Given the enormous volume of vessel data continually being generated, real-time analysis of vessel behaviors is only possible because of decision support systems provided with event and anomaly detection methods. However, current works on vessel event detection are ad-hoc methods able to handle only a single or a few predefined types of vessel behavior. Most of the existing approaches do not learn from the data and require the definition of queries and rules for describing each behavior. In this paper, we discuss challenges and opportunities in classical machine learning and deep learning for vessel event and anomaly detection. We hope to motivate the research of novel methods and tools, since addressing these challenges is an essential step towards actual intelligent maritime monitoring systems.", "published": "2020-04-07T21:25:12Z", "authors": ["Lucas May Petry", "Amilcar Soares", "Vania Bogorny", "Bruno Brandoli", "Stan Matwin"], "categories": ["cs.LG", "stat.ML"], "doi": "", "_query": "real estate valuation deep learning", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/1705.05172v1", "title": "Emotion in Reinforcement Learning Agents and Robots: A Survey", "abstract": "This article provides the first survey of computational models of emotion in reinforcement learning (RL) agents. The survey focuses on agent/robot emotions, and mostly ignores human user emotions. Emotions are recognized as functional in decision-making by influencing motivation and action selection. Therefore, computational emotion models are usually grounded in the agent's decision making architecture, of which RL is an important subclass. Studying emotions in RL-based agents is useful for three research fields. For machine learning (ML) researchers, emotion models may improve learning efficiency. For the interactive ML and human-robot interaction (HRI) community, emotions can communicate state and enhance user investment. Lastly, it allows affective modelling (AM) researchers to investigate their emotion theories in a successful AI agent class. This survey provides background on emotion theory and RL. It systematically addresses 1) from what underlying dimensions (e.g., homeostasis, appraisal) emotions can be derived and how these can be modelled in RL-agents, 2) what types of emotions have been derived from these dimensions, and 3) how these emotions may either influence the learning efficiency of the agent or be useful as social signals. We also systematically compare evaluation criteria, and draw connections to important RL sub-domains like (intrinsic) motivation and model-based RL. In short, this survey provides both a practical overview for engineers wanting to implement emotions in their RL agents, and identifies challenges and directions for future emotion-RL research.", "published": "2017-05-15T11:49:56Z", "authors": ["Thomas M. Moerland", "Joost Broekens", "Catholijn M. Jonker"], "categories": ["cs.LG", "cs.AI", "cs.HC", "cs.RO", "stat.ML"], "doi": "10.1007/s10994-017-5666-0", "_query": "real estate valuation deep learning", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2211.09018v1", "title": "Real Estate Attribute Prediction from Multiple Visual Modalities with Missing Data", "abstract": "The assessment and valuation of real estate requires large datasets with real estate information. Unfortunately, real estate databases are usually sparse in practice, i.e., not for each property every important attribute is available. In this paper, we study the potential of predicting high-level real estate attributes from visual data, specifically from two visual modalities, namely indoor (interior) and outdoor (facade) photos. We design three models using different multimodal fusion strategies and evaluate them for three different use cases. Thereby, a particular challenge is to handle missing modalities. We evaluate different fusion strategies, present baselines for the different prediction tasks, and find that enriching the training data with additional incomplete samples can lead to an improvement in prediction accuracy. Furthermore, the fusion of information from indoor and outdoor photos results in a performance boost of up to 5% in Macro F1-score.", "published": "2022-11-16T16:24:17Z", "authors": ["Eric Stumpe", "Miroslav Despotovic", "Zedong Zhang", "Matthias Zeppelzauer"], "categories": ["cs.CV", "cs.AI", "cs.LG"], "doi": "10.3217/978-3-85125-869-1-06", "_query": "real estate valuation deep learning", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/1904.05328v1", "title": "PropTech for Proactive Pricing of Houses in Classified Advertisements in the Indian Real Estate Market", "abstract": "Property Technology (PropTech) is the next big thing that is going to disrupt the real estate market. Nowadays, we see applications of Machine Learning (ML) and Artificial Intelligence (AI) in almost all the domains but for a long time the real estate industry was quite slow in adopting data science and machine learning for problem solving and improving their processes. However, things are changing quite fast as we see a lot of adoption of AI and ML in the US and European real estate markets. But the Indian real estate market has to catch-up a lot. This paper proposes a machine learning approach for solving the house price prediction problem in the classified advertisements. This study focuses on the Indian real estate market. We apply advanced machine learning algorithms such as Random forest, Gradient boosting and Artificial neural networks on a real world dataset and compare the performance of these methods. We find that the Random forest method is the best performer in terms of prediction accuracy.", "published": "2019-03-28T03:38:05Z", "authors": ["Sayan Putatunda"], "categories": ["cs.SI"], "doi": "", "_query": "real estate valuation deep learning", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2005.11082v2", "title": "Tractometry-based Anomaly Detection for Single-subject White Matter Analysis", "abstract": "There is an urgent need for a paradigm shift from group-wise comparisons to individual diagnosis in diffusion MRI (dMRI) to enable the analysis of rare cases and clinically-heterogeneous groups. Deep autoencoders have shown great potential to detect anomalies in neuroimaging data. We present a framework that operates on the manifold of white matter (WM) pathways to learn normative microstructural features, and discriminate those at genetic risk from controls in a paediatric population.", "published": "2020-05-22T09:50:22Z", "authors": ["Maxime Chamberland", "Sila Genc", "Erika P. Raven", "Greg D. Parker", "Adam Cunningham", "Joanne Doherty", "Marianne van den Bree", "Chantal M. W. Tax", "Derek K. Jones"], "categories": ["q-bio.QM", "cs.LG"], "doi": "", "_query": "real estate valuation deep learning", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/1910.11094v1", "title": "An application of a deep learning algorithm for automatic detection of unexpected accidents under bad CCTV monitoring conditions in tunnels", "abstract": "In this paper, Object Detection and Tracking System (ODTS) in combination with a well-known deep learning network, Faster Regional Convolution Neural Network (Faster R-CNN), for Object Detection and Conventional Object Tracking algorithm will be introduced and applied for automatic detection and monitoring of unexpected events on CCTVs in tunnels, which are likely to (1) Wrong-Way Driving (WWD), (2) Stop, (3) Person out of vehicle in tunnel (4) Fire. ODTS accepts a video frame in time as an input to obtain Bounding Box (BBox) results by Object Detection and compares the BBoxs of the current and previous video frames to assign a unique ID number to each moving and detected object. This system makes it possible to track a moving object in time, which is not usual to be achieved in conventional object detection frameworks. A deep learning model in ODTS was trained with a dataset of event images in tunnels to Average Precision (AP) values of 0.8479, 0.7161 and 0.9085 for target objects: Car, Person, and Fire, respectively. Then, based on trained deep learning model, the ODTS based Tunnel CCTV Accident Detection System was tested using four accident videos which including each accident. As a result, the system can detect all accidents within 10 seconds. The more important point is that the detection capacity of ODTS could be enhanced automatically without any changes in the program codes as the training dataset becomes rich.", "published": "2019-10-11T04:28:44Z", "authors": ["Kyu-Beom Lee", "Hyu-Soung Shin"], "categories": ["cs.CV", "cs.LG"], "doi": "10.1109/Deep-ML.2019.00010", "_query": "real estate valuation deep learning", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2405.20715v1", "title": "Transforming Japan Real Estate", "abstract": "The Japanese real estate market, valued over 35 trillion USD, offers significant investment opportunities. Accurate rent and price forecasting could provide a substantial competitive edge. This paper explores using alternative data variables to predict real estate performance in 1100 Japanese municipalities. A comprehensive house price index was created, covering all municipalities from 2005 to the present, using a dataset of over 5 million transactions. This core dataset was enriched with economic factors spanning decades, allowing for price trajectory predictions. The findings show that alternative data variables can indeed forecast real estate performance effectively. Investment signals based on these variables yielded notable returns with low volatility. For example, the net migration ratio delivered an annualized return of 4.6% with a Sharpe ratio of 1.5. Taxable income growth and new dwellings ratio also performed well, with annualized returns of 4.1% (Sharpe ratio of 1.3) and 3.3% (Sharpe ratio of 0.9), respectively. When combined with transformer models to predict risk-adjusted returns 4 years in advance, the model achieved an R-squared score of 0.28, explaining nearly 30% of the variation in future municipality prices. These results highlight the potential of alternative data variables in real estate investment. They underscore the need for further research to identify more predictive factors. Nonetheless, the evidence suggests that such data can provide valuable insights into real estate price drivers, enabling more informed investment decisions in the Japanese market.", "published": "2024-05-31T09:12:28Z", "authors": ["Diabul Haque"], "categories": ["cs.CE", "econ.EM", "q-fin.ST"], "doi": "", "_query": "real estate valuation deep learning", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2205.01180v1", "title": "Using Machine Learning to Evaluate Real Estate Prices Using Location Big Data", "abstract": "With everyone trying to enter the real estate market nowadays, knowing the proper valuations for residential and commercial properties has become crucial. Past researchers have been known to utilize static real estate data (e.g. number of beds, baths, square footage) or even a combination of real estate and demographic information to predict property prices. In this investigation, we attempted to improve upon past research. So we decided to explore a unique approach: we wanted to determine if mobile location data could be used to improve the predictive power of popular regression and tree-based models. To prepare our data for our models, we processed the mobility data by attaching it to individual properties from the real estate data that aggregated users within 500 meters of the property for each day of the week. We removed people that lived within 500 meters of each property, so each property's aggregated mobility data only contained non-resident census features. On top of these dynamic census features, we also included static census features, including the number of people in the area, the average proportion of people commuting, and the number of residents in the area. Finally, we tested multiple models to predict real estate prices. Our proposed model is two stacked random forest modules combined using a ridge regression that uses the random forest outputs as predictors. The first random forest model used static features only and the second random forest model used dynamic features only. Comparing our models with and without the dynamic mobile location features concludes the model with dynamic mobile location features achieves 3/% percent lower mean squared error than the same model but without dynamic mobile location features.", "published": "2022-05-02T19:58:18Z", "authors": ["Walter Coleman", "Ben Johann", "Nicholas Pasternak", "Jaya Vellayan", "Natasha Foutz", "Heman Shakeri"], "categories": ["cs.LG"], "doi": "", "_query": "real estate valuation deep learning", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2012.02775v1", "title": "Representation Based Complexity Measures for Predicting Generalization in Deep Learning", "abstract": "Deep Neural Networks can generalize despite being significantly overparametrized. Recent research has tried to examine this phenomenon from various view points and to provide bounds on the generalization error or measures predictive of the generalization gap based on these viewpoints, such as norm-based, PAC-Bayes based, and margin-based analysis. In this work, we provide an interpretation of generalization from the perspective of quality of internal representations of deep neural networks, based on neuroscientific theories of how the human visual system creates invariant and untangled object representations. Instead of providing theoretical bounds, we demonstrate practical complexity measures which can be computed ad-hoc to uncover generalization behaviour in deep models. We also provide a detailed description of our solution that won the NeurIPS competition on Predicting Generalization in Deep Learning held at NeurIPS 2020. An implementation of our solution is available at https://github.com/parthnatekar/pgdl.", "published": "2020-12-04T18:53:44Z", "authors": ["Parth Natekar", "Manik Sharma"], "categories": ["cs.LG", "cs.CV"], "doi": "", "_query": "real estate valuation deep learning", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/1102.2015v4", "title": "Real estate appraisal of land lots using GAMLSS models", "abstract": "The valuation of real estates (e.g., house, land, among others) is of extreme importance for decision making. Their singular characteristics make valuation through hedonic pricing methods dificult since the theory does not specify the correct regression functional form nor which explanatory variables should be included in the hedonic equation. In this article we perform real estate appraisal using a class of regression models proposed by Rigby & Stasinopoulos (2005): generalized additive models for location, scale and shape (GAMLSS). Our empirical analysis shows that these models seem to be more appropriate for estimation of the hedonic prices function than the regression models currently used to that end.", "published": "2011-02-10T00:24:21Z", "authors": ["Lutemberg Florencio", "Francisco Cribari-Neto", "Raydonal Ospina"], "categories": ["stat.AP"], "doi": "", "_query": "real estate valuation deep learning", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2512.23078v1", "title": "Deep Learning for Art Market Valuation", "abstract": "We study how deep learning can improve valuation in the art market by incorporating the visual content of artworks into predictive models. Using a large repeated-sales dataset from major auction houses, we benchmark classical hedonic regressions and tree-based methods against modern deep architectures, including multi-modal models that fuse tabular and image data. We find that while artist identity and prior transaction history dominate overall predictive power, visual embeddings provide a distinct and economically meaningful contribution for fresh-to-market works where historical anchors are absent. Interpretability analyses using Grad-CAM and embedding visualizations show that models attend to compositional and stylistic cues. Our findings demonstrate that multi-modal deep learning delivers significant value precisely when valuation is hardest, namely first-time sales, and thus offers new insights for both academic research and practice in art market valuation.", "published": "2025-12-28T21:04:09Z", "authors": ["Jianping Mei", "Michael Moses", "Jan Waelty", "Yucheng Yang"], "categories": ["q-fin.GN", "cs.AI", "cs.CV", "cs.LG", "econ.GN"], "doi": "", "_query": "real estate valuation deep learning", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/1609.03759v2", "title": "3D Simulation for Robot Arm Control with Deep Q-Learning", "abstract": "Recent trends in robot arm control have seen a shift towards end-to-end solutions, using deep reinforcement learning to learn a controller directly from raw sensor data, rather than relying on a hand-crafted, modular pipeline. However, the high dimensionality of the state space often means that it is impractical to generate sufficient training data with real-world experiments. As an alternative solution, we propose to learn a robot controller in simulation, with the potential of then transferring this to a real robot. Building upon the recent success of deep Q-networks, we present an approach which uses 3D simulations to train a 7-DOF robotic arm in a control task without any prior knowledge. The controller accepts images of the environment as its only input, and outputs motor actions for the task of locating and grasping a cube, over a range of initial configurations. To encourage efficient learning, a structured reward function is designed with intermediate rewards. We also present preliminary results in direct transfer of policies over to a real robot, without any further training.", "published": "2016-09-13T10:40:24Z", "authors": ["Stephen James", "Edward Johns"], "categories": ["cs.RO", "cs.CV", "cs.LG"], "doi": "", "_query": "real estate valuation deep learning", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/1907.04543v4", "title": "An Optimistic Perspective on Offline Reinforcement Learning", "abstract": "Off-policy reinforcement learning (RL) using a fixed offline dataset of logged interactions is an important consideration in real world applications. This paper studies offline RL using the DQN replay dataset comprising the entire replay experience of a DQN agent on 60 Atari 2600 games. We demonstrate that recent off-policy deep RL algorithms, even when trained solely on this fixed dataset, outperform the fully trained DQN agent. To enhance generalization in the offline setting, we present Random Ensemble Mixture (REM), a robust Q-learning algorithm that enforces optimal Bellman consistency on random convex combinations of multiple Q-value estimates. Offline REM trained on the DQN replay dataset surpasses strong RL baselines. Ablation studies highlight the role of offline dataset size and diversity as well as the algorithm choice in our positive results. Overall, the results here present an optimistic view that robust RL algorithms trained on sufficiently large and diverse offline datasets can lead to high quality policies. The DQN replay dataset can serve as an offline RL benchmark and is open-sourced.", "published": "2019-07-10T07:23:27Z", "authors": ["Rishabh Agarwal", "Dale Schuurmans", "Mohammad Norouzi"], "categories": ["cs.LG", "cs.AI", "stat.ML"], "doi": "", "_query": "real estate valuation deep learning", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2302.10473v6", "title": "Oriented object detection in optical remote sensing images using deep learning: a survey", "abstract": "Oriented object detection is a fundamental yet challenging task in remote sensing (RS), aiming to locate and classify objects with arbitrary orientations. Recent advancements in deep learning have significantly enhanced the capabilities of oriented object detection methods. Given the rapid development of this field, a comprehensive survey of the recent advances in oriented object detection is presented in this paper. Specifically, we begin by tracing the technical evolution from horizontal object detection to oriented object detection and highlighting the specific related challenges, including feature misalignment, spatial misalignment, oriented bounding box (OBB) regression problems, and common issues encountered in RS. Subsequently, we further categorize the existing methods into detection frameworks, OBB regression techniques, feature representation approaches, and solutions to common issues and provide an in-depth discussion of how these methods address the above challenges. In addition, we cover several publicly available datasets and evaluation protocols. Furthermore, we provide a comprehensive comparison and analysis involving the state-of-the-art methods. Toward the end of this paper, we identify several future directions for oriented object detection research.", "published": "2023-02-21T06:31:53Z", "authors": ["Kun Wang", "Zi Wang", "Zhang Li", "Ang Su", "Xichao Teng", "Erting Pan", "Minhao Liu", "Qifeng Yu"], "categories": ["cs.CV"], "doi": "10.1007/s10462-025-11256-0", "_query": "real estate valuation deep learning", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/1402.4735v2", "title": "Searching for Exotic Particles in High-Energy Physics with Deep Learning", "abstract": "Collisions at high-energy particle colliders are a traditionally fruitful source of exotic particle discoveries. Finding these rare particles requires solving difficult signal-versus-background classification problems, hence machine learning approaches are often used. Standard approaches have relied on `shallow' machine learning models that have a limited capacity to learn complex non-linear functions of the inputs, and rely on a pain-staking search through manually constructed non-linear features. Progress on this problem has slowed, as a variety of techniques have shown equivalent performance. Recent advances in the field of deep learning make it possible to learn more complex functions and better discriminate between signal and background classes. Using benchmark datasets, we show that deep learning methods need no manually constructed inputs and yet improve the classification metric by as much as 8\\% over the best current approaches. This demonstrates that deep learning approaches can improve the power of collider searches for exotic particles.", "published": "2014-02-19T17:20:08Z", "authors": ["Pierre Baldi", "Peter Sadowski", "Daniel Whiteson"], "categories": ["hep-ph", "hep-ex"], "doi": "10.1038/ncomms5308", "_query": "real estate valuation deep learning", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/1711.07966v2", "title": "Deep Learning for Real-time Gravitational Wave Detection and Parameter Estimation with LIGO Data", "abstract": "The recent Nobel-prize-winning detections of gravitational waves from merging black holes and the subsequent detection of the collision of two neutron stars in coincidence with electromagnetic observations have inaugurated a new era of multimessenger astrophysics. To enhance the scope of this emergent science, we proposed the use of deep convolutional neural networks for the detection and characterization of gravitational wave signals in real-time. This method, Deep Filtering, was initially demonstrated using simulated LIGO noise. In this article, we present the extension of Deep Filtering using real data from the first observing run of LIGO, for both detection and parameter estimation of gravitational waves from binary black hole mergers with continuous data streams from multiple LIGO detectors. We show for the first time that machine learning can detect and estimate the true parameters of a real GW event observed by LIGO. Our comparisons show that Deep Filtering is far more computationally efficient than matched-filtering, while retaining similar sensitivity and lower errors, allowing real-time processing of weak time-series signals in non-stationary non-Gaussian noise, with minimal resources, and also enables the detection of new classes of gravitational wave sources that may go unnoticed with existing detection algorithms. This approach is uniquely suited to enable coincident detection campaigns of gravitational waves and their multimessenger counterparts in real-time.", "published": "2017-11-21T18:45:01Z", "authors": ["Daniel George", "E. A. Huerta"], "categories": ["gr-qc", "astro-ph.HE", "astro-ph.IM", "cs.LG", "cs.NE"], "doi": "", "_query": "real estate valuation deep learning", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2405.01852v1", "title": "Tokenization of Real Estate Assets Using Blockchain", "abstract": "Blockchain technology is one of the key technologies that have revolutionized various facets of society, such as the banking, healthcare, and other critical ecosystems. One area that can harness the usage of blockchain is the real estate sector. The most lucrative long-term investment is real estate, followed by gold, equities, mutual funds, and savings accounts. Nevertheless, it has administrative overheads such as lack of transparency, fraud, several intermediaries, title issues, paperwork, an increasing number of arbitrations, and the lack of liquidity. This paper proposes a framework that uses blockchain as an underlying technology. With the aid of blockchain and the suite of tools, it supports many of these problems that can be alleviated in the real estate investment ecosystem. These include smart contracts, immutable record management, tokenization, record tracking, and time-stamped storage. Tokenization of real estate lowers the entry barrier by fixing liquidity and interoperability and improving the interaction between various stakeholders.", "published": "2024-05-03T04:50:17Z", "authors": ["Shashank Joshi", "Arhan Choudhury"], "categories": ["cs.DC", "cs.CR", "cs.ET"], "doi": "10.4018/IJIIT.309588", "_query": "real estate valuation deep learning", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/1705.03071v1", "title": "Geometry of Optimization and Implicit Regularization in Deep Learning", "abstract": "We argue that the optimization plays a crucial role in generalization of deep learning models through implicit regularization. We do this by demonstrating that generalization ability is not controlled by network size but rather by some other implicit control. We then demonstrate how changing the empirical optimization procedure can improve generalization, even if actual optimization quality is not affected. We do so by studying the geometry of the parameter space of deep networks, and devising an optimization algorithm attuned to this geometry.", "published": "2017-05-08T20:12:08Z", "authors": ["Behnam Neyshabur", "Ryota Tomioka", "Ruslan Salakhutdinov", "Nathan Srebro"], "categories": ["cs.LG"], "doi": "", "_query": "real estate valuation deep learning", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2406.18007v1", "title": "Deep Mamba Multi-modal Learning", "abstract": "Inspired by the excellent performance of Mamba networks, we propose a novel Deep Mamba Multi-modal Learning (DMML). It can be used to achieve the fusion of multi-modal features. We apply DMML to the field of multimedia retrieval and propose an innovative Deep Mamba Multi-modal Hashing (DMMH) method. It combines the advantages of algorithm accuracy and inference speed. We validated the effectiveness of DMMH on three public datasets and achieved state-of-the-art results.", "published": "2024-04-09T07:17:08Z", "authors": ["Jian Zhu", "Xin Zou", "Yu Cui", "Zhangmin Huang", "Chenshu Hu", "Bo Lyu"], "categories": ["cs.MM"], "doi": "", "_query": "real estate valuation deep learning", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2304.06907v2", "title": "Toward Real-Time Image Annotation Using Marginalized Coupled Dictionary Learning", "abstract": "In most image retrieval systems, images include various high-level semantics, called tags or annotations. Virtually all the state-of-the-art image annotation methods that handle imbalanced labeling are search-based techniques which are time-consuming. In this paper, a novel coupled dictionary learning approach is proposed to learn a limited number of visual prototypes and their corresponding semantics simultaneously. This approach leads to a real-time image annotation procedure. Another contribution of this paper is that utilizes a marginalized loss function instead of the squared loss function that is inappropriate for image annotation with imbalanced labels. We have employed a marginalized loss function in our method to leverage a simple and effective method of prototype updating. Meanwhile, we have introduced ${\\ell}_1$ regularization on semantic prototypes to preserve the sparse and imbalanced nature of labels in learned semantic prototypes. Finally, comprehensive experimental results on various datasets demonstrate the efficiency of the proposed method for image annotation tasks in terms of accuracy and time. The reference implementation is publicly available on https://github.com/hamid-amiri/MCDL-Image-Annotation.", "published": "2023-04-14T03:01:44Z", "authors": ["Seyed Mahdi Roostaiyan", "Mohammad Mehdi Hosseini", "Mahya Mohammadi Kashani", "S. Hamid Amiri"], "categories": ["cs.CV", "cs.LG"], "doi": "10.1007/s11554-022-01210-6", "_query": "real estate valuation deep learning", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2112.09429v2", "title": "Federated Learning with Superquantile Aggregation for Heterogeneous Data", "abstract": "We present a federated learning framework that is designed to robustly deliver good predictive performance across individual clients with heterogeneous data. The proposed approach hinges upon a superquantile-based learning objective that captures the tail statistics of the error distribution over heterogeneous clients. We present a stochastic training algorithm that interleaves differentially private client filtering with federated averaging steps. We prove finite time convergence guarantees for the algorithm: $O(1/\\sqrt{T})$ in the nonconvex case in $T$ communication rounds and $O(\\exp(-T/κ^{3/2}) + κ/T)$ in the strongly convex case with local condition number $κ$. Experimental results on benchmark datasets for federated learning demonstrate that our approach is competitive with classical ones in terms of average error and outperforms them in terms of tail statistics of the error.", "published": "2021-12-17T11:00:23Z", "authors": ["Krishna Pillutla", "Yassine Laguel", "Jérôme Malick", "Zaid Harchaoui"], "categories": ["cs.LG", "math.OC", "stat.ML"], "doi": "10.1007/s10994-023-06332-x", "_query": "real estate valuation deep learning", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2412.20269v2", "title": "TeLU Activation Function for Fast and Stable Deep Learning", "abstract": "We propose the Hyperbolic Tangent Exponential Linear Unit (TeLU), a neural network hidden activation function defined as TeLU(x)=xtanh(exp(x)). TeLU's design is grounded in the core principles of key activation functions, achieving strong convergence by closely approximating the identity function in its active region while effectively mitigating the vanishing gradient problem in its saturating region. Its simple formulation enhances computational efficiency, leading to improvements in scalability and convergence speed. Unlike many modern activation functions, TeLU seamlessly combines the simplicity and effectiveness of ReLU with the smoothness and analytic properties essential for learning stability in deep neural networks. TeLU's ability to mimic the behavior and optimal hyperparameter settings of ReLU, while introducing the benefits of smoothness and curvature, makes it an ideal drop-in replacement. Its analytic nature positions TeLU as a powerful universal approximator, enhancing both robustness and generalization across a multitude of experiments. We rigorously validate these claims through theoretical analysis and experimental validation, demonstrating TeLU's performance across challenging benchmarks; including ResNet18 on ImageNet, Dynamic-Pooling Transformers on Text8, and Recurrent Neural Networks (RNNs) on the Penn TreeBank dataset. These results highlight TeLU's potential to set a new standard in activation functions, driving more efficient and stable learning in deep neural networks, thereby accelerating scientific discoveries across various fields.", "published": "2024-12-28T20:50:08Z", "authors": ["Alfredo Fernandez", "Ankur Mali"], "categories": ["cs.LG"], "doi": "", "_query": "real estate valuation deep learning", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2212.00966v1", "title": "A Hybrid Deep Learning Anomaly Detection Framework for Intrusion Detection", "abstract": "Cyber intrusion attacks that compromise the users' critical and sensitive data are escalating in volume and intensity, especially with the growing connections between our daily life and the Internet. The large volume and high complexity of such intrusion attacks have impeded the effectiveness of most traditional defence techniques. While at the same time, the remarkable performance of the machine learning methods, especially deep learning, in computer vision, had garnered research interests from the cyber security community to further enhance and automate intrusion detections. However, the expensive data labeling and limitation of anomalous data make it challenging to train an intrusion detector in a fully supervised manner. Therefore, intrusion detection based on unsupervised anomaly detection is an important feature too. In this paper, we propose a three-stage deep learning anomaly detection based network intrusion attack detection framework. The framework comprises an integration of unsupervised (K-means clustering), semi-supervised (GANomaly) and supervised learning (CNN) algorithms. We then evaluated and showed the performance of our implemented framework on three benchmark datasets: NSL-KDD, CIC-IDS2018, and TON_IoT.", "published": "2022-12-02T04:40:54Z", "authors": ["Rahul Kale", "Zhi Lu", "Kar Wai Fok", "Vrizlynn L. L. Thing"], "categories": ["cs.CR", "cs.AI", "cs.LG"], "doi": "10.1109/BigDataSecurityHPSCIDS54978.2022.00034", "_query": "real estate valuation deep learning", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2210.12385v2", "title": "Deep Learning in Single-Cell Analysis", "abstract": "Single-cell technologies are revolutionizing the entire field of biology. The large volumes of data generated by single-cell technologies are high-dimensional, sparse, heterogeneous, and have complicated dependency structures, making analyses using conventional machine learning approaches challenging and impractical. In tackling these challenges, deep learning often demonstrates superior performance compared to traditional machine learning methods. In this work, we give a comprehensive survey on deep learning in single-cell analysis. We first introduce background on single-cell technologies and their development, as well as fundamental concepts of deep learning including the most popular deep architectures. We present an overview of the single-cell analytic pipeline pursued in research applications while noting divergences due to data sources or specific applications. We then review seven popular tasks spanning through different stages of the single-cell analysis pipeline, including multimodal integration, imputation, clustering, spatial domain identification, cell-type deconvolution, cell segmentation, and cell-type annotation. Under each task, we describe the most recent developments in classical and deep learning methods and discuss their advantages and disadvantages. Deep learning tools and benchmark datasets are also summarized for each task. Finally, we discuss the future directions and the most recent challenges. This survey will serve as a reference for biologists and computer scientists, encouraging collaborations.", "published": "2022-10-22T08:26:41Z", "authors": ["Dylan Molho", "Jiayuan Ding", "Zhaoheng Li", "Hongzhi Wen", "Wenzhuo Tang", "Yixin Wang", "Julian Venegas", "Wei Jin", "Renming Liu", "Runze Su", "Patrick Danaher", "Robert Yang", "Yu Leo Lei", "Yuying Xie", "Jiliang Tang"], "categories": ["q-bio.QM", "cs.AI"], "doi": "", "_query": "real estate valuation deep learning", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2112.05923v2", "title": "ElegantRL-Podracer: Scalable and Elastic Library for Cloud-Native Deep Reinforcement Learning", "abstract": "Deep reinforcement learning (DRL) has revolutionized learning and actuation in applications such as game playing and robotic control. The cost of data collection, i.e., generating transitions from agent-environment interactions, remains a major challenge for wider DRL adoption in complex real-world problems. Following a cloud-native paradigm to train DRL agents on a GPU cloud platform is a promising solution. In this paper, we present a scalable and elastic library ElegantRL-podracer for cloud-native deep reinforcement learning, which efficiently supports millions of GPU cores to carry out massively parallel training at multiple levels. At a high-level, ElegantRL-podracer employs a tournament-based ensemble scheme to orchestrate the training process on hundreds or even thousands of GPUs, scheduling the interactions between a leaderboard and a training pool with hundreds of pods. At a low-level, each pod simulates agent-environment interactions in parallel by fully utilizing nearly 7,000 GPU CUDA cores in a single GPU. Our ElegantRL-podracer library features high scalability, elasticity and accessibility by following the development principles of containerization, microservices and MLOps. Using an NVIDIA DGX SuperPOD cloud, we conduct extensive experiments on various tasks in locomotion and stock trading and show that ElegantRL-podracer substantially outperforms RLlib. Our codes are available on GitHub.", "published": "2021-12-11T06:31:21Z", "authors": ["Xiao-Yang Liu", "Zechu Li", "Zhuoran Yang", "Jiahao Zheng", "Zhaoran Wang", "Anwar Walid", "Jian Guo", "Michael I. Jordan"], "categories": ["cs.LG", "cs.AI", "cs.DC"], "doi": "", "_query": "real estate valuation deep learning", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2009.10149v2", "title": "Crafting Adversarial Examples for Deep Learning Based Prognostics (Extended Version)", "abstract": "In manufacturing, unexpected failures are considered a primary operational risk, as they can hinder productivity and can incur huge losses. State-of-the-art Prognostics and Health Management (PHM) systems incorporate Deep Learning (DL) algorithms and Internet of Things (IoT) devices to ascertain the health status of equipment, and thus reduce the downtime, maintenance cost and increase the productivity. Unfortunately, IoT sensors and DL algorithms, both are vulnerable to cyber attacks, and hence pose a significant threat to PHM systems. In this paper, we adopt the adversarial example crafting techniques from the computer vision domain and apply them to the PHM domain. Specifically, we craft adversarial examples using the Fast Gradient Sign Method (FGSM) and Basic Iterative Method (BIM) and apply them on the Long Short-Term Memory (LSTM), Gated Recurrent Unit (GRU), and Convolutional Neural Network (CNN) based PHM models. We evaluate the impact of adversarial attacks using NASA's turbofan engine dataset. The obtained results show that all the evaluated PHM models are vulnerable to adversarial attacks and can cause a serious defect in the remaining useful life estimation. The obtained results also show that the crafted adversarial examples are highly transferable and may cause significant damages to PHM systems.", "published": "2020-09-21T19:43:38Z", "authors": ["Gautam Raj Mode", "Khaza Anuarul Hoque"], "categories": ["cs.LG", "cs.AI", "cs.CR", "stat.ML"], "doi": "", "_query": "real estate valuation deep learning", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2002.04881v3", "title": "Learning Flat Latent Manifolds with VAEs", "abstract": "Measuring the similarity between data points often requires domain knowledge, which can in parts be compensated by relying on unsupervised methods such as latent-variable models, where similarity/distance is estimated in a more compact latent space. Prevalent is the use of the Euclidean metric, which has the drawback of ignoring information about similarity of data stored in the decoder, as captured by the framework of Riemannian geometry. We propose an extension to the framework of variational auto-encoders allows learning flat latent manifolds, where the Euclidean metric is a proxy for the similarity between data points. This is achieved by defining the latent space as a Riemannian manifold and by regularising the metric tensor to be a scaled identity matrix. Additionally, we replace the compact prior typically used in variational auto-encoders with a recently presented, more expressive hierarchical one---and formulate the learning problem as a constrained optimisation problem. We evaluate our method on a range of data-sets, including a video-tracking benchmark, where the performance of our unsupervised approach nears that of state-of-the-art supervised approaches, while retaining the computational efficiency of straight-line-based approaches.", "published": "2020-02-12T09:54:52Z", "authors": ["Nutan Chen", "Alexej Klushyn", "Francesco Ferroni", "Justin Bayer", "Patrick van der Smagt"], "categories": ["stat.ML", "cs.LG"], "doi": "", "_query": "real estate valuation deep learning", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2002.05810v1", "title": "RNA Secondary Structure Prediction By Learning Unrolled Algorithms", "abstract": "In this paper, we propose an end-to-end deep learning model, called E2Efold, for RNA secondary structure prediction which can effectively take into account the inherent constraints in the problem. The key idea of E2Efold is to directly predict the RNA base-pairing matrix, and use an unrolled algorithm for constrained programming as the template for deep architectures to enforce constraints. With comprehensive experiments on benchmark datasets, we demonstrate the superior performance of E2Efold: it predicts significantly better structures compared to previous SOTA (especially for pseudoknotted structures), while being as efficient as the fastest algorithms in terms of inference time.", "published": "2020-02-13T23:21:25Z", "authors": ["Xinshi Chen", "Yu Li", "Ramzan Umarov", "Xin Gao", "Le Song"], "categories": ["cs.LG", "stat.ML"], "doi": "", "_query": "real estate valuation deep learning", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2112.04893v1", "title": "Real-World Dexterous Object Manipulation based Deep Reinforcement Learning", "abstract": "Deep reinforcement learning has shown its advantages in real-time decision-making based on the state of the agent. In this stage, we solved the task of using a real robot to manipulate the cube to a given trajectory. The task is broken down into different procedures and we propose a hierarchical structure, the high-level deep reinforcement learning model selects appropriate contact positions and the low-level control module performs the position control under the corresponding trajectory. Our framework reduces the disadvantage of low sample efficiency of deep reinforcement learning and lacking adaptability of traditional robot control methods. Our algorithm is trained in simulation and migrated to reality without fine-tuning. The experimental results show the effectiveness of our method both simulation and reality. Our code and video can be found at https://github.com/42jaylonw/RRC2021ThreeWolves and https://youtu.be/Jr176xsn9wg.", "published": "2021-11-22T02:48:05Z", "authors": ["Qingfeng Yao", "Jilong Wang", "Shuyu Yang"], "categories": ["cs.RO", "cs.LG"], "doi": "", "_query": "real estate valuation deep learning", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2108.03952v2", "title": "Safe Deep Reinforcement Learning for Multi-Agent Systems with Continuous Action Spaces", "abstract": "Multi-agent control problems constitute an interesting area of application for deep reinforcement learning models with continuous action spaces. Such real-world applications, however, typically come with critical safety constraints that must not be violated. In order to ensure safety, we enhance the well-known multi-agent deep deterministic policy gradient (MADDPG) framework by adding a safety layer to the deep policy network. In particular, we extend the idea of linearizing the single-step transition dynamics, as was done for single-agent systems in Safe DDPG (Dalal et al., 2018), to multi-agent settings. We additionally propose to circumvent infeasibility problems in the action correction step using soft constraints (Kerrigan & Maciejowski, 2000). Results from the theory of exact penalty functions can be used to guarantee constraint satisfaction of the soft constraints under mild assumptions. We empirically find that the soft formulation achieves a dramatic decrease in constraint violations, making safety available even during the learning procedure.", "published": "2021-08-09T11:33:46Z", "authors": ["Ziyad Sheebaelhamd", "Konstantinos Zisis", "Athina Nisioti", "Dimitris Gkouletsos", "Dario Pavllo", "Jonas Kohler"], "categories": ["cs.LG", "cs.RO"], "doi": "", "_query": "real estate valuation deep learning", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2308.10609v1", "title": "ST-RAP: A Spatio-Temporal Framework for Real Estate Appraisal", "abstract": "In this paper, we introduce ST-RAP, a novel Spatio-Temporal framework for Real estate APpraisal. ST-RAP employs a hierarchical architecture with a heterogeneous graph neural network to encapsulate temporal dynamics and spatial relationships simultaneously. Through comprehensive experiments on a large-scale real estate dataset, ST-RAP outperforms previous methods, demonstrating the significant benefits of integrating spatial and temporal aspects in real estate appraisal. Our code and dataset are available at https://github.com/dojeon-ai/STRAP.", "published": "2023-08-21T10:18:26Z", "authors": ["Hojoon Lee", "Hawon Jeong", "Byungkun Lee", "Kyungyup Lee", "Jaegul Choo"], "categories": ["cs.LG"], "doi": "", "_query": "real estate valuation deep learning", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/1510.08231v3", "title": "Operator-valued Kernels for Learning from Functional Response Data", "abstract": "In this paper we consider the problems of supervised classification and regression in the case where attributes and labels are functions: a data is represented by a set of functions, and the label is also a function. We focus on the use of reproducing kernel Hilbert space theory to learn from such functional data. Basic concepts and properties of kernel-based learning are extended to include the estimation of function-valued functions. In this setting, the representer theorem is restated, a set of rigorously defined infinite-dimensional operator-valued kernels that can be valuably applied when the data are functions is described, and a learning algorithm for nonlinear functional data analysis is introduced. The methodology is illustrated through speech and audio signal processing experiments.", "published": "2015-10-28T09:18:50Z", "authors": ["Hachem Kadri", "Emmanuel Duflos", "Philippe Preux", "Stéphane Canu", "Alain Rakotomamonjy", "Julien Audiffren"], "categories": ["cs.LG", "stat.ML"], "doi": "", "_query": "real estate valuation deep learning", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/1508.04186v2", "title": "Distributed Deep Q-Learning", "abstract": "We propose a distributed deep learning model to successfully learn control policies directly from high-dimensional sensory input using reinforcement learning. The model is based on the deep Q-network, a convolutional neural network trained with a variant of Q-learning. Its input is raw pixels and its output is a value function estimating future rewards from taking an action given a system state. To distribute the deep Q-network training, we adapt the DistBelief software framework to the context of efficiently training reinforcement learning agents. As a result, the method is completely asynchronous and scales well with the number of machines. We demonstrate that the deep Q-network agent, receiving only the pixels and the game score as inputs, was able to achieve reasonable success on a simple game with minimal parameter tuning.", "published": "2015-08-18T01:00:32Z", "authors": ["Hao Yi Ong", "Kevin Chavez", "Augustus Hong"], "categories": ["cs.LG", "cs.AI", "cs.DC", "cs.NE"], "doi": "", "_query": "real estate valuation deep learning", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2606.06624v2", "title": "Principles and Practice of Deep Representation Learning: or a Mathematical Theory of Memory", "abstract": "In the current era of deep learning and especially generative models, there is significant investment in training very large deep neural networks. Thus far, such models have been \"black boxes\" that are difficult to understand in the sense that they have opaque internal mechanisms, leading to difficulties in interpretability, reliability, and control. Naturally, this lack of understanding has led to both hype and fear. This book is an attempt to \"open the black box\" and understand the mechanisms of large deep networks, through the perspective of representation learning, which is a major factor - arguably the single most important one - in the empirical power of deep learning models. A brief outline of this book is as follows. Chapter 1 will summarize the threads that underlie the whole text. Chapters 2, 3, 4, 5, and 6 will explain the design principles of modern neural network architectures through optimization and information theory, reducing the process of architecture development (long having been described as a sort of \"alchemy\") to undergraduate-level linear algebra and calculus exercises once the underlying principles are introduced. Chapters 7 and 8 will discuss applications of these principles to solve problems in more paradigmatic ways, obtaining new methods and models which are efficient, interpretable, and controllable by design, and yet no less - sometimes even more - powerful than the black-box models they resemble. Chapter 9 will discuss potential future directions for deep learning, the role of representation learning, as well as some open problems.", "published": "2026-06-04T18:21:03Z", "authors": ["Sam Buchanan", "Druv Pai", "Peng Wang", "Yi Ma"], "categories": ["cs.LG"], "doi": "", "_query": "real estate valuation deep learning", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/1811.11400v2", "title": "FADL:Federated-Autonomous Deep Learning for Distributed Electronic Health Record", "abstract": "Electronic health record (EHR) data is collected by individual institutions and often stored across locations in silos. Getting access to these data is difficult and slow due to security, privacy, regulatory, and operational issues. We show, using ICU data from 58 different hospitals, that machine learning models to predict patient mortality can be trained efficiently without moving health data out of their silos using a distributed machine learning strategy. We propose a new method, called Federated-Autonomous Deep Learning (FADL) that trains part of the model using all data sources in a distributed manner and other parts using data from specific data sources. We observed that FADL outperforms traditional federated learning strategy and conclude that balance between global and local training is an important factor to consider when design distributed machine learning methods , especially in healthcare.", "published": "2018-11-28T06:06:38Z", "authors": ["Dianbo Liu", "Timothy Miller", "Raheel Sayeed", "Kenneth D. Mandl"], "categories": ["cs.CY", "cs.LG"], "doi": "", "_query": "real estate valuation deep learning", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/1711.09482v2", "title": "An Introduction to Deep Visual Explanation", "abstract": "The practical impact of deep learning on complex supervised learning problems has been significant, so much so that almost every Artificial Intelligence problem, or at least a portion thereof, has been somehow recast as a deep learning problem. The applications appeal is significant, but this appeal is increasingly challenged by what some call the challenge of explainability, or more generally the more traditional challenge of debuggability: if the outcomes of a deep learning process produce unexpected results (e.g., less than expected performance of a classifier), then there is little available in the way of theories or tools to help investigate the potential causes of such unexpected behavior, especially when this behavior could impact people's lives. We describe a preliminary framework to help address this issue, which we call \"deep visual explanation\" (DVE). \"Deep,\" because it is the development and performance of deep neural network models that we want to understand. \"Visual,\" because we believe that the most rapid insight into a complex multi-dimensional model is provided by appropriate visualization techniques, and \"Explanation,\" because in the spectrum from instrumentation by inserting print statements to the abductive inference of explanatory hypotheses, we believe that the key to understanding deep learning relies on the identification and exposure of hypotheses about the performance behavior of a learned deep model. In the exposition of our preliminary framework, we use relatively straightforward image classification examples and a variety of choices on initial configuration of a deep model building scenario. By careful but not complicated instrumentation, we expose classification outcomes of deep models using visualization, and also show initial results for one potential application of interpretability.", "published": "2017-11-26T22:54:18Z", "authors": ["Housam Khalifa Bashier Babiker", "Randy Goebel"], "categories": ["stat.ML", "cs.CV", "cs.LG"], "doi": "", "_query": "real estate valuation deep learning", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/1908.02548v1", "title": "Automated Corrosion Detection Using Crowd Sourced Training for Deep Learning", "abstract": "The automated detection of corrosion from images (i.e., photographs) or video (i.e., drone footage) presents significant advantages in terms of corrosion monitoring. Such advantages include access to remote locations, mitigation of risk to inspectors, cost savings and monitoring speed. The automated detection of corrosion requires deep learning to approach human level artificial intelligence (A.I.). The training of a deep learning model requires intensive image labelling, and in order to generate a large database of labelled images, crowd sourced labelling via a dedicated website was sought. The website (corrosiondetector.com) permits any user to label images, with such labelling then contributing to the training of a cloud based A.I. model - with such a cloud-based model then capable of assessing any fresh (or uploaded) image for the presence of corrosion. In other words, the website includes both the crowd sourced training process, but also the end use of the evolving model. Herein, the results and findings from the website (corrosiondetector.com) over the period of approximately one month, are reported.", "published": "2019-08-04T00:44:15Z", "authors": ["W. T. Nash", "C. J. Powell", "T. Drummond", "N. Birbilis"], "categories": ["cs.HC", "eess.IV"], "doi": "", "_query": "real estate valuation deep learning", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2411.11863v1", "title": "Longitudinal Wrist PPG Analysis for Reliable Hypertension Risk Screening Using Deep Learning", "abstract": "Hypertension is a leading risk factor for cardiovascular diseases. Traditional blood pressure monitoring methods are cumbersome and inadequate for continuous tracking, prompting the development of PPG-based cuffless blood pressure monitoring wearables. This study leverages deep learning models, including ResNet and Transformer, to analyze wrist PPG data collected with a smartwatch for efficient hypertension risk screening, eliminating the need for handcrafted PPG features. Using the Home Blood Pressure Monitoring (HBPM) longitudinal dataset of 448 subjects and five-fold cross-validation, our model was trained on over 68k spot-check instances from 358 subjects and tested on real-world continuous recordings of 90 subjects. The compact ResNet model with 0.124M parameters performed significantly better than traditional machine learning methods, demonstrating its effectiveness in distinguishing between healthy and abnormal cases in real-world scenarios.", "published": "2024-11-02T20:42:20Z", "authors": ["Hui Lin", "Jiyang Li", "Ramy Hussein", "Xin Sui", "Xiaoyu Li", "Guangpu Zhu", "Aggelos K. Katsaggelos", "Zijing Zeng", "Yelei Li"], "categories": ["eess.SP", "cs.LG"], "doi": "", "_query": "real estate valuation deep learning", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2210.06261v1", "title": "Predicting housing prices and analyzing real estate market in the Chicago suburbs using Machine Learning", "abstract": "The pricing of housing properties is determined by a variety of factors. However, post-pandemic markets have experienced volatility in the Chicago suburb area, which have affected house prices greatly. In this study, analysis was done on the Naperville/Bolingbrook real estate market to predict property prices based on these housing attributes through machine learning models, and to evaluate the effectiveness of such models in a volatile market space. Gathering data from Redfin, a real estate website, sales data from 2018 up until the summer season of 2022 were collected for research. By analyzing these sales in this range of time, we can also look at the state of the housing market and identify trends in price. For modeling the data, the models used were linear regression, support vector regression, decision tree regression, random forest regression, and XGBoost regression. To analyze results, comparison was made on the MAE, RMSE, and R-squared values for each model. It was found that the XGBoost model performs the best in predicting house prices despite the additional volatility sponsored by post-pandemic conditions. After modeling, Shapley Values (SHAP) were used to evaluate the weights of the variables in constructing models.", "published": "2022-10-12T14:41:53Z", "authors": ["Kevin Xu", "Hieu Nguyen"], "categories": ["cs.LG"], "doi": "", "_query": "real estate valuation deep learning", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/1804.10113v1", "title": "Visual Estimation of Building Condition with Patch-level ConvNets", "abstract": "The condition of a building is an important factor for real estate valuation. Currently, the estimation of condition is determined by real estate appraisers which makes it subjective to a certain degree. We propose a novel vision-based approach for the assessment of the building condition from exterior views of the building. To this end, we develop a multi-scale patch-based pattern extraction approach and combine it with convolutional neural networks to estimate building condition from visual clues. Our evaluation shows that visually estimated building condition can serve as a proxy for condition estimates by appraisers.", "published": "2018-04-26T15:33:57Z", "authors": ["David Koch", "Miroslav Despotovic", "Muntaha Sakeena", "Mario Döller", "Matthias Zeppelzauer"], "categories": ["cs.CV"], "doi": "10.1145/3210499.3210526", "_query": "real estate valuation deep learning", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2403.14848v2", "title": "Learning WENO for entropy stable schemes to solve conservation laws", "abstract": "Entropy conditions play a crucial role in the extraction of a physically relevant solution for systems of conservation laws, thus motivating the construction of entropy stable schemes that satisfy a discrete analogue of such conditions. TeCNO schemes (Fjordholm et al. 2012) form a class of arbitrary high-order entropy stable finite difference solvers, which require specialized reconstruction algorithms satisfying the sign property at each cell interface. Third-order weighted essentially non-oscillatory (WENO) schemes called SP-WENO (Fjordholm and Ray, 2016) and SP-WENOc (Ray, 2018) have been designed to satisfy the sign property. However, these WENO algorithms can perform poorly near shocks, with the numerical solutions exhibiting large spurious oscillations. In the present work, we propose a variant of the SP-WENO, termed as Deep Sign-Preserving WENO (DSP-WENO), where a neural network is trained to learn the WENO weighting strategy. The sign property and third-order accuracy are strongly imposed in the algorithm, which constrains the WENO weight selection region to a convex polygon. Thereafter, a neural network is trained to select the WENO weights from this convex region with the goal of improving the shock-capturing capabilities without sacrificing the rate of convergence in smooth regions. The proposed synergistic approach retains the mathematical framework of the TeCNO scheme while integrating deep learning to remedy the computational issues of the WENO-based reconstruction. We present several numerical experiments to demonstrate the significant improvement with DSP-WENO over the existing variants of WENO satisfying the sign property.", "published": "2024-03-21T21:39:05Z", "authors": ["Philip Charles", "Deep Ray"], "categories": ["math.NA", "cs.LG"], "doi": "", "_query": "real estate valuation deep learning", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/1004.3299v4", "title": "Valuation equations for stochastic volatility models", "abstract": "We analyze the valuation partial differential equation for European contingent claims in a general framework of stochastic volatility models where the diffusion coefficients may grow faster than linearly and degenerate on the boundaries of the state space. We allow for various types of model behavior: the volatility process in our model can potentially reach zero and either stay there or instantaneously reflect, and the asset-price process may be a strict local martingale. Our main result is a necessary and sufficient condition on the uniqueness of classical solutions to the valuation equation: the value function is the unique nonnegative classical solution to the valuation equation among functions with at most linear growth if and only if the asset-price is a martingale.", "published": "2010-04-19T20:23:36Z", "authors": ["Erhan Bayraktar", "Constantinos Kardaras", "Hao Xing"], "categories": ["math.PR", "q-fin.PR"], "doi": "", "_query": "real estate valuation deep learning", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/1810.05424v2", "title": "Real-time self-adaptive deep stereo", "abstract": "Deep convolutional neural networks trained end-to-end are the state-of-the-art methods to regress dense disparity maps from stereo pairs. These models, however, suffer from a notable decrease in accuracy when exposed to scenarios significantly different from the training set, e.g., real vs synthetic images, etc.). We argue that it is extremely unlikely to gather enough samples to achieve effective training/tuning in any target domain, thus making this setup impractical for many applications. Instead, we propose to perform unsupervised and continuous online adaptation of a deep stereo network, which allows for preserving its accuracy in any environment. However, this strategy is extremely computationally demanding and thus prevents real-time inference. We address this issue introducing a new lightweight, yet effective, deep stereo architecture, Modularly ADaptive Network (MADNet) and developing a Modular ADaptation (MAD) algorithm, which independently trains sub-portions of the network. By deploying MADNet together with MAD we introduce the first real-time self-adaptive deep stereo system enabling competitive performance on heterogeneous datasets.", "published": "2018-10-12T09:17:53Z", "authors": ["Alessio Tonioni", "Fabio Tosi", "Matteo Poggi", "Stefano Mattoccia", "Luigi Di Stefano"], "categories": ["cs.CV"], "doi": "", "_query": "real estate valuation deep learning", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/1812.09225v4", "title": "Learning Representations from Dendrograms", "abstract": "We propose unsupervised representation learning and feature extraction from dendrograms. The commonly used Minimax distance measures correspond to building a dendrogram with single linkage criterion, with defining specific forms of a level function and a distance function over that. Therefore, we extend this method to arbitrary dendrograms. We develop a generalized framework wherein different distance measures and representations can be inferred from different types of dendrograms, level functions and distance functions. Via an appropriate embedding, we compute a vector-based representation of the inferred distances, in order to enable many numerical machine learning algorithms to employ such distances. Then, to address the model selection problem, we study the aggregation of different dendrogram-based distances respectively in solution space and in representation space in the spirit of deep representations. In the first approach, for example for the clustering problem, we build a graph with positive and negative edge weights according to the consistency of the clustering labels of different objects among different solutions, in the context of ensemble methods. Then, we use an efficient variant of correlation clustering to produce the final clusters. In the second approach, we investigate the combination of different distances and features sequentially in the spirit of multi-layered architectures to obtain the final features. Finally, we demonstrate the effectiveness of our approach via several numerical studies.", "published": "2018-12-21T16:11:00Z", "authors": ["Morteza Haghir Chehreghani", "Mostafa Haghir Chehreghani"], "categories": ["cs.LG", "stat.ML"], "doi": "10.1007/s10994-020-05895-3", "_query": "real estate valuation deep learning", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/1804.00432v1", "title": "Deep Residual Learning for Accelerated MRI using Magnitude and Phase Networks", "abstract": "Accelerated magnetic resonance (MR) scan acquisition with compressed sensing (CS) and parallel imaging is a powerful method to reduce MR imaging scan time. However, many reconstruction algorithms have high computational costs. To address this, we investigate deep residual learning networks to remove aliasing artifacts from artifact corrupted images. The proposed deep residual learning networks are composed of magnitude and phase networks that are separately trained. If both phase and magnitude information are available, the proposed algorithm can work as an iterative k-space interpolation algorithm using framelet representation. When only magnitude data is available, the proposed approach works as an image domain post-processing algorithm. Even with strong coherent aliasing artifacts, the proposed network successfully learned and removed the aliasing artifacts, whereas current parallel and CS reconstruction methods were unable to remove these artifacts. Comparisons using single and multiple coil show that the proposed residual network provides good reconstruction results with orders of magnitude faster computational time than existing compressed sensing methods. The proposed deep learning framework may have a great potential for accelerated MR reconstruction by generating accurate results immediately.", "published": "2018-04-02T09:08:02Z", "authors": ["Dongwook Lee", "Jaejun Yoo", "Sungho Tak", "Jong Chul Ye"], "categories": ["cs.CV", "cs.AI", "cs.LG", "stat.ML"], "doi": "", "_query": "real estate valuation deep learning", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/1812.01662v1", "title": "Feed-Forward Neural Networks Need Inductive Bias to Learn Equality Relations", "abstract": "Basic binary relations such as equality and inequality are fundamental to relational data structures. Neural networks should learn such relations and generalise to new unseen data. We show in this study, however, that this generalisation fails with standard feed-forward networks on binary vectors. Even when trained with maximal training data, standard networks do not reliably detect equality.We introduce differential rectifier (DR) units that we add to the network in different configurations. The DR units create an inductive bias in the networks, so that they do learn to generalise, even from small numbers of examples and we have not found any negative effect of their inclusion in the network. Given the fundamental nature of these relations, we hypothesize that feed-forward neural network learning benefits from inductive bias in other relations as well. Consequently, the further development of suitable inductive biases will be beneficial to many tasks in relational learning with neural networks.", "published": "2018-12-04T20:02:38Z", "authors": ["Tillman Weyde", "Radha Manisha Kopparti"], "categories": ["cs.LG", "stat.ML"], "doi": "", "_query": "real estate valuation deep learning", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2406.14525v1", "title": "Towards evolution of Deep Neural Networks through contrastive Self-Supervised learning", "abstract": "Deep Neural Networks (DNNs) have been successfully applied to a wide range of problems. However, two main limitations are commonly pointed out. The first one is that they require long time to design. The other is that they heavily rely on labelled data, which can sometimes be costly and hard to obtain. In order to address the first problem, neuroevolution has been proved to be a plausible option to automate the design of DNNs. As for the second problem, self-supervised learning has been used to leverage unlabelled data to learn representations. Our goal is to study how neuroevolution can help self-supervised learning to bridge the gap to supervised learning in terms of performance. In this work, we propose a framework that is able to evolve deep neural networks using self-supervised learning. Our results on the CIFAR-10 dataset show that it is possible to evolve adequate neural networks while reducing the reliance on labelled data. Moreover, an analysis to the structure of the evolved networks suggests that the amount of labelled data fed to them has less effect on the structure of networks that learned via self-supervised learning, when compared to individuals that relied on supervised learning.", "published": "2024-06-20T17:38:16Z", "authors": ["Adriano Vinhas", "João Correia", "Penousal Machado"], "categories": ["cs.NE", "cs.AI", "cs.LG"], "doi": "", "_query": "real estate valuation deep learning", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2011.13093v1", "title": "Predictive PER: Balancing Priority and Diversity towards Stable Deep Reinforcement Learning", "abstract": "Prioritized experience replay (PER) samples important transitions, rather than uniformly, to improve the performance of a deep reinforcement learning agent. We claim that such prioritization has to be balanced with sample diversity for making the DQN stabilized and preventing forgetting. Our proposed improvement over PER, called Predictive PER (PPER), takes three countermeasures (TDInit, TDClip, TDPred) to (i) eliminate priority outliers and explosions and (ii) improve the sample diversity and distributions, weighted by priorities, both leading to stabilizing the DQN. The most notable among the three is the introduction of the second DNN called TDPred to generalize the in-distribution priorities. Ablation study and full experiments with Atari games show that each countermeasure by its own way and PPER contribute to successfully enhancing stability and thus performance over PER.", "published": "2020-11-26T02:12:31Z", "authors": ["Sanghwa Lee", "Jaeyoung Lee", "Ichiro Hasuo"], "categories": ["cs.LG", "cs.AI"], "doi": "", "_query": "real estate valuation deep learning", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/1712.04371v2", "title": "Music Generation by Deep Learning - Challenges and Directions", "abstract": "In addition to traditional tasks such as prediction, classification and translation, deep learning is receiving growing attention as an approach for music generation, as witnessed by recent research groups such as Magenta at Google and CTRL (Creator Technology Research Lab) at Spotify. The motivation is in using the capacity of deep learning architectures and training techniques to automatically learn musical styles from arbitrary musical corpora and then to generate samples from the estimated distribution. However, a direct application of deep learning to generate content rapidly reaches limits as the generated content tends to mimic the training set without exhibiting true creativity. Moreover, deep learning architectures do not offer direct ways for controlling generation (e.g., imposing some tonality or other arbitrary constraints). Furthermore, deep learning architectures alone are autistic automata which generate music autonomously without human user interaction, far from the objective of interactively assisting musicians to compose and refine music. Issues such as: control, structure, creativity and interactivity are the focus of our analysis. In this paper, we select some limitations of a direct application of deep learning to music generation, analyze why the issues are not fulfilled and how to address them by possible approaches. Various examples of recent systems are cited as examples of promising directions.", "published": "2017-12-09T14:57:47Z", "authors": ["Jean-Pierre Briot", "François Pachet"], "categories": ["cs.SD", "cs.LG", "eess.AS"], "doi": "10.1007/s00521-018-3813-6", "_query": "real estate valuation deep learning", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2409.12034v2", "title": "Multi-Sensor Deep Learning for Glacier Mapping", "abstract": "The more than 200,000 glaciers outside the ice sheets play a crucial role in our society by influencing sea-level rise, water resource management, natural hazards, biodiversity, and tourism. However, only a fraction of these glaciers benefit from consistent and detailed in-situ observations that allow for assessing their status and changes over time. This limitation can, in part, be overcome by relying on satellite-based Earth Observation techniques. Satellite-based glacier mapping applications have historically mainly relied on manual and semi-automatic detection methods, while recently, a fast and notable transition to deep learning techniques has started. This chapter reviews how combining multi-sensor remote sensing data and deep learning allows us to better delineate (i.e. map) glaciers and detect their temporal changes. We explain how relying on deep learning multi-sensor frameworks to map glaciers benefits from the extensive availability of regional and global glacier inventories. We also analyse the rationale behind glacier mapping, the benefits of deep learning methodologies, and the inherent challenges in integrating multi-sensor earth observation data with deep learning algorithms. While our review aims to provide a broad overview of glacier mapping efforts, we highlight a few setups where deep learning multi-sensor remote sensing applications have a considerable potential added value. This includes applications for debris-covered and rock glaciers that are visually difficult to distinguish from surroundings and for calving glaciers that are in contact with the ocean. These specific cases are illustrated through a series of visual imageries, highlighting some significant advantages and challenges when detecting glacier changes, including dealing with seasonal snow cover, changing debris coverage, and distinguishing glacier fronts from the surrounding sea ice.", "published": "2024-09-18T14:51:36Z", "authors": ["Codruţ-Andrei Diaconu", "Konrad Heidler", "Jonathan L. Bamber", "Harry Zekollari"], "categories": ["cs.CV", "eess.IV"], "doi": "", "_query": "real estate valuation deep learning", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/1910.14215v2", "title": "Multivariate Uncertainty in Deep Learning", "abstract": "Deep learning has the potential to dramatically impact navigation and tracking state estimation problems critical to autonomous vehicles and robotics. Measurement uncertainties in state estimation systems based on Kalman and other Bayes filters are typically assumed to be a fixed covariance matrix. This assumption is risky, particularly for \"black box\" deep learning models, in which uncertainty can vary dramatically and unexpectedly. Accurate quantification of multivariate uncertainty will allow for the full potential of deep learning to be used more safely and reliably in these applications. We show how to model multivariate uncertainty for regression problems with neural networks, incorporating both aleatoric and epistemic sources of heteroscedastic uncertainty. We train a deep uncertainty covariance matrix model in two ways: directly using a multivariate Gaussian density loss function, and indirectly using end-to-end training through a Kalman filter. We experimentally show in a visual tracking problem the large impact that accurate multivariate uncertainty quantification can have on Kalman filter performance for both in-domain and out-of-domain evaluation data. We additionally show in a challenging visual odometry problem how end-to-end filter training can allow uncertainty predictions to compensate for filter weaknesses.", "published": "2019-10-31T02:25:16Z", "authors": ["Rebecca L. Russell", "Christopher Reale"], "categories": ["cs.LG", "cs.NE", "cs.RO", "stat.ML"], "doi": "", "_query": "real estate valuation deep learning", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2312.05933v1", "title": "Temporal Supervised Contrastive Learning for Modeling Patient Risk Progression", "abstract": "We consider the problem of predicting how the likelihood of an outcome of interest for a patient changes over time as we observe more of the patient data. To solve this problem, we propose a supervised contrastive learning framework that learns an embedding representation for each time step of a patient time series. Our framework learns the embedding space to have the following properties: (1) nearby points in the embedding space have similar predicted class probabilities, (2) adjacent time steps of the same time series map to nearby points in the embedding space, and (3) time steps with very different raw feature vectors map to far apart regions of the embedding space. To achieve property (3), we employ a nearest neighbor pairing mechanism in the raw feature space. This mechanism also serves as an alternative to data augmentation, a key ingredient of contrastive learning, which lacks a standard procedure that is adequately realistic for clinical tabular data, to our knowledge. We demonstrate that our approach outperforms state-of-the-art baselines in predicting mortality of septic patients (MIMIC-III dataset) and tracking progression of cognitive impairment (ADNI dataset). Our method also consistently recovers the correct synthetic dataset embedding structure across experiments, a feat not achieved by baselines. Our ablation experiments show the pivotal role of our nearest neighbor pairing.", "published": "2023-12-10T16:43:15Z", "authors": ["Shahriar Noroozizadeh", "Jeremy C. Weiss", "George H. Chen"], "categories": ["cs.LG", "cs.AI", "stat.ML"], "doi": "", "_query": "real estate valuation deep learning", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2403.00273v1", "title": "ARED: Argentina Real Estate Dataset", "abstract": "The Argentinian real estate market presents a unique case study characterized by its unstable and rapidly shifting macroeconomic circumstances over the past decades. Despite the existence of a few datasets for price prediction, there is a lack of mixed modality datasets specifically focused on Argentina. In this paper, the first edition of ARED is introduced. A comprehensive real estate price prediction dataset series, designed for the Argentinian market. This edition contains information solely for Jan-Feb 2024. It was found that despite the short time range captured by this zeroth edition (44 days), time dependent phenomena has been occurring mostly on a market level (market as a whole). Nevertheless future editions of this dataset, will most likely contain historical data. Each listing in ARED comprises descriptive features, and variable-length sets of images.", "published": "2024-03-01T04:25:39Z", "authors": ["Iván Belenky"], "categories": ["cs.LG", "cs.DL", "q-fin.ST"], "doi": "", "_query": "real estate valuation deep learning", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/1905.13613v2", "title": "Regression Networks for Meta-Learning Few-Shot Classification", "abstract": "We propose regression networks for the problem of few-shot classification, where a classifier must generalize to new classes not seen in the training set, given only a small number of examples of each class. In high dimensional embedding spaces the direction of data generally contains richer information than magnitude. Next to this, state-of-the-art few-shot metric methods that compare distances with aggregated class representations, have shown superior performance. Combining these two insights, we propose to meta-learn classification of embedded points by regressing the closest approximation in every class subspace while using the regression error as a distance metric. Similarly to recent approaches for few-shot learning, regression networks reflect a simple inductive bias that is beneficial in this limited-data regime and they achieve excellent results, especially when more aggregate class representations can be formed with multiple shots.", "published": "2019-05-31T13:35:41Z", "authors": ["Arnout Devos", "Matthias Grossglauser"], "categories": ["cs.LG", "cs.CV", "stat.ML"], "doi": "", "_query": "real estate valuation deep learning", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2503.18670v1", "title": "Deep learning-based identification of precipitation clouds from all-sky camera data for observatory safety", "abstract": "For monitoring the night sky conditions, wide-angle all-sky cameras are used in most astronomical observatories to monitor the sky cloudiness. In this manuscript, we apply a deep-learning approach for automating the identification of precipitation clouds in all-sky camera data as a cloud warning system. We construct our original training and test sets using the all-sky camera image archive of the Iranian National Observatory (INO). The training and test set images are labeled manually based on their potential rainfall and their distribution in the sky. We train our model on a set of roughly 2445 images taken by the INO all-sky camera through the deep learning method based on the EfficientNet network. Our model reaches an average accuracy of 99\\% in determining the cloud rainfall's potential and an accuracy of 96\\% for cloud coverage. To enable a comprehensive comparison and evaluate the performance of alternative architectures for the task, we additionally trained three models LeNet, DeiT, and AlexNet. This approach can be used for early warning of incoming dangerous clouds toward telescopes and harnesses the power of deep learning to automatically analyze vast amounts of all-sky camera data and accurately identify precipitation clouds formations. Our trained model can be deployed for real-time analysis, enabling the rapid identification of potential threats, and offering a scaleable solution that can improve our ability to safeguard telescopes and instruments in observatories. This is important now that numerous small and medium-sized telescopes are increasingly integrated with smart control systems to reduce manual operation.", "published": "2025-03-24T13:40:51Z", "authors": ["Mohammad H. Zhoolideh Haghighi", "Alireza Ghasrimanesh", "Habib Khosroshahi"], "categories": ["astro-ph.IM", "physics.data-an", "stat.ML"], "doi": "10.1016/j.mlwa.2025.100640", "_query": "real estate valuation deep learning", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2107.13233v1", "title": "C^3Net: End-to-End deep learning for efficient real-time visual active camera control", "abstract": "The need for automated real-time visual systems in applications such as smart camera surveillance, smart environments, and drones necessitates the improvement of methods for visual active monitoring and control. Traditionally, the active monitoring task has been handled through a pipeline of modules such as detection, filtering, and control. However, such methods are difficult to jointly optimize and tune their various parameters for real-time processing in resource constraint systems. In this paper a deep Convolutional Camera Controller Neural Network is proposed to go directly from visual information to camera movement to provide an efficient solution to the active vision problem. It is trained end-to-end without bounding box annotations to control a camera and follow multiple targets from raw pixel values. Evaluation through both a simulation framework and real experimental setup, indicate that the proposed solution is robust to varying conditions and able to achieve better monitoring performance than traditional approaches both in terms of number of targets monitored as well as in effective monitoring time. The advantage of the proposed approach is that it is computationally less demanding and can run at over 10 FPS (~4x speedup) on an embedded smart camera providing a practical and affordable solution to real-time active monitoring.", "published": "2021-07-28T09:31:46Z", "authors": ["Christos Kyrkou"], "categories": ["cs.CV", "cs.RO"], "doi": "10.1007/s11554-021-01077-z", "_query": "real estate valuation deep learning", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2009.10679v1", "title": "An embedded deep learning system for augmented reality in firefighting applications", "abstract": "Firefighting is a dynamic activity, in which numerous operations occur simultaneously. Maintaining situational awareness (i.e., knowledge of current conditions and activities at the scene) is critical to the accurate decision-making necessary for the safe and successful navigation of a fire environment by firefighters. Conversely, the disorientation caused by hazards such as smoke and extreme heat can lead to injury or even fatality. This research implements recent advancements in technology such as deep learning, point cloud and thermal imaging, and augmented reality platforms to improve a firefighter's situational awareness and scene navigation through improved interpretation of that scene. We have designed and built a prototype embedded system that can leverage data streamed from cameras built into a firefighter's personal protective equipment (PPE) to capture thermal, RGB color, and depth imagery and then deploy already developed deep learning models to analyze the input data in real time. The embedded system analyzes and returns the processed images via wireless streaming, where they can be viewed remotely and relayed back to the firefighter using an augmented reality platform that visualizes the results of the analyzed inputs and draws the firefighter's attention to objects of interest, such as doors and windows otherwise invisible through smoke and flames.", "published": "2020-09-22T16:55:44Z", "authors": ["Manish Bhattarai", "Aura Rose Jensen-Curtis", "Manel MartíNez-Ramón"], "categories": ["cs.CV", "cs.AI"], "doi": "", "_query": "real estate valuation deep learning", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/1904.11797v1", "title": "Development of an Entropy-Based Feature Selection Method and Analysis of Online Reviews on Real Estate", "abstract": "In recent years, data posted about real estate on the Internet is currently increasing. In this study, in order to analyze user needs for real estate, we focus on \"Mansion Community\" which is a Japanese bulletin board system (hereinafter referred to as BBS) about Japanese real estate. In our study, extraction of keywords is performed based on the calculation of the entropy value of each word, and we used them as features in a machine learning classifier to analyze 6 million posts at \"Mansion Community\". As a result, we achieved a 0.69 F-measure and found that the customers are particularly concerned about the facility of apartment, access, and price of an apartment.", "published": "2019-04-23T15:48:10Z", "authors": ["Hiroki Horino", "Hirofumi Nonaka", "Elisa Claire Alemán Carreón", "Toru Hiraoka"], "categories": ["cs.IR"], "doi": "10.1109/IEEM.2017.8290312", "_query": "real estate valuation deep learning", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2301.00681v2", "title": "Historical Patterns and Recent Impacts of Chinese Investors in United States Real Estate", "abstract": "Since supplanting Canada in 2014, Chinese investors have been the lead foreign buyers of U.S. real estate, concentrating their purchases in urban areas with higher Chinese populations like California. The reasons for investment include prestige, freedom from capital confiscation, and safe, diversified opportunities from abroad simply being more lucrative and available than in their home country, where the market is eroding. Interestingly, since 2019, Chinese investors have sold a net 23.6 billion dollars of U.S. commercial real estate, a stark contrast to past acquisitions between 2013 to 2018 where they were net buyers of almost 52 billion dollars worth of properties. A similar trend appears in the residential real estate segment too. In both 2017 and 2018, Chinese buyers purchased over 40,000 U.S. residential properties which were halved in 2019 and steadily declined to only 6,700 in the past year. This turnaround in Chinese investment can be attributed to a deteriorating relationship between the U.S. and China during the Trump Presidency, financial distress in China, and new Chinese government regulations prohibiting outbound investments. Additionally, while Chinese investment is a small share of U.S. real estate (~1.5% at its peak), it has outsized impacts on market valuations of home prices in U.S. zip codes with higher populations of foreign-born Chinese, increasing property prices and exacerbating the issue of housing affordability in these areas. This paper investigates the rapid growth and decline of Chinese investment in U.S. real estate and its effect on U.S. home prices in certain demographics.", "published": "2022-12-28T07:57:38Z", "authors": ["Kevin Sun"], "categories": ["econ.GN"], "doi": "", "_query": "real estate valuation deep learning", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2511.20591v3", "title": "Attention Trajectories as a Diagnostic Axis for Deep Reinforcement Learning", "abstract": "The emergence and evolution of feature reliance in deep reinforcement learning agents remain poorly understood. Here, we introduce a methodological framework for analyzing the learning process through quantitative analysis of saliency maps. This approach aggregates saliency information at the object and modality level into hierarchical attention profiles, quantifying how agents allocate attention over time, thereby forming attention trajectories throughout training. These profiles are then compared across controlled conditions, connected to behavioral measurements and reproduced with different saliency methods to assess the robustness of the findings. Applied to Atari 2600 benchmarks, custom Pong environments, and biomechanical user simulations in visuomotor tasks, this framework uncovers algorithm-specific attention biases, diagnosed unintended reward-driven strategies, and overfitting to redundant sensory channels. These patterns correspond to measurable behavioral differences, demonstrating empirical links between attention profiles, learning dynamics, and agent behavior. The results establish attention trajectories as a promising diagnostic axis for tracing how feature reliance develops during training and for identifying biases and vulnerabilities invisible to performance metrics alone.", "published": "2025-11-25T18:20:42Z", "authors": ["Charlotte Beylier", "Hannah Selder", "Arthur Fleig", "Simon M. Hofmann", "Nico Scherf"], "categories": ["cs.LG"], "doi": "", "_query": "real estate valuation deep learning", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/1711.07468v2", "title": "Glitch Classification and Clustering for LIGO with Deep Transfer Learning", "abstract": "The detection of gravitational waves with LIGO and Virgo requires a detailed understanding of the response of these instruments in the presence of environmental and instrumental noise. Of particular interest is the study of anomalous non-Gaussian noise transients known as glitches, since their high occurrence rate in LIGO/Virgo data can obscure or even mimic true gravitational wave signals. Therefore, successfully identifying and excising glitches is of utmost importance to detect and characterize gravitational waves. In this article, we present the first application of Deep Learning combined with Transfer Learning for glitch classification, using real data from LIGO's first discovery campaign labeled by Gravity Spy, showing that knowledge from pre-trained models for real-world object recognition can be transferred for classifying spectrograms of glitches. We demonstrate that this method enables the optimal use of very deep convolutional neural networks for glitch classification given small unbalanced training datasets, significantly reduces the training time, and achieves state-of-the-art accuracy above 98.8%. Once trained via transfer learning, we show that the networks can be truncated and used as feature extractors for unsupervised clustering to automatically group together new classes of glitches and anomalies. This novel capability is of critical importance to identify and remove new types of glitches which will occur as the LIGO/Virgo detectors gradually attain design sensitivity.", "published": "2017-11-20T18:58:28Z", "authors": ["Daniel George", "Hongyu Shen", "E. A. Huerta"], "categories": ["astro-ph.IM", "cs.LG", "gr-qc", "stat.ML"], "doi": "10.1103/PhysRevD.97.101501", "_query": "real estate valuation deep learning", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/1511.04674v1", "title": "Using Text Mining To Analyze Real Estate Classifieds", "abstract": "Many brokers have adapted their operation to exploit the potential of the web. Despite the importance of the real estate classifieds, there has been little work in analyzing such data. In this paper we propose a two-stage regression model that exploits the textual data in real estate classifieds. We show how our model can be used to predict the price of a real estate classified. We also show how our model can be used to highlight keywords that affect the price positively or negatively. To assess our contributions, we analyze four real world data sets, which we gathered from three different property websites. The analysis shows that our model (which exploits textual features) achieves significantly lower root mean squared error across the different data sets and against variety of regression models.", "published": "2015-11-15T09:00:56Z", "authors": ["Sherief Abdallah"], "categories": ["cs.IR"], "doi": "", "_query": "real estate valuation deep learning", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2111.03577v1", "title": "Mixtures of Laplace Approximations for Improved Post-Hoc Uncertainty in Deep Learning", "abstract": "Deep neural networks are prone to overconfident predictions on outliers. Bayesian neural networks and deep ensembles have both been shown to mitigate this problem to some extent. In this work, we aim to combine the benefits of the two approaches by proposing to predict with a Gaussian mixture model posterior that consists of a weighted sum of Laplace approximations of independently trained deep neural networks. The method can be used post hoc with any set of pre-trained networks and only requires a small computational and memory overhead compared to regular ensembles. We theoretically validate that our approach mitigates overconfidence \"far away\" from the training data and empirically compare against state-of-the-art baselines on standard uncertainty quantification benchmarks.", "published": "2021-11-05T15:52:48Z", "authors": ["Runa Eschenhagen", "Erik Daxberger", "Philipp Hennig", "Agustinus Kristiadi"], "categories": ["cs.LG", "stat.ML"], "doi": "", "_query": "real estate valuation deep learning", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2006.12567v2", "title": "A Survey on Deep Learning for Localization and Mapping: Towards the Age of Spatial Machine Intelligence", "abstract": "Deep learning based localization and mapping has recently attracted significant attention. Instead of creating hand-designed algorithms through exploitation of physical models or geometric theories, deep learning based solutions provide an alternative to solve the problem in a data-driven way. Benefiting from ever-increasing volumes of data and computational power, these methods are fast evolving into a new area that offers accurate and robust systems to track motion and estimate scenes and their structure for real-world applications. In this work, we provide a comprehensive survey, and propose a new taxonomy for localization and mapping using deep learning. We also discuss the limitations of current models, and indicate possible future directions. A wide range of topics are covered, from learning odometry estimation, mapping, to global localization and simultaneous localization and mapping (SLAM). We revisit the problem of perceiving self-motion and scene understanding with on-board sensors, and show how to solve it by integrating these modules into a prospective spatial machine intelligence system (SMIS). It is our hope that this work can connect emerging works from robotics, computer vision and machine learning communities, and serve as a guide for future researchers to apply deep learning to tackle localization and mapping problems.", "published": "2020-06-22T19:01:21Z", "authors": ["Changhao Chen", "Bing Wang", "Chris Xiaoxuan Lu", "Niki Trigoni", "Andrew Markham"], "categories": ["cs.CV", "cs.LG", "cs.RO", "eess.IV"], "doi": "", "_query": "real estate valuation deep learning", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2410.23450v2", "title": "Return Augmented Decision Transformer for Off-Dynamics Reinforcement Learning", "abstract": "We study offline off-dynamics reinforcement learning (RL) to utilize data from an easily accessible source domain to enhance policy learning in a target domain with limited data. Our approach centers on return-conditioned supervised learning (RCSL), particularly focusing on Decision Transformer (DT) type frameworks, which can predict actions conditioned on desired return guidance and complete trajectory history. Previous works address the dynamics shift problem by augmenting the reward in the trajectory from the source domain to match the optimal trajectory in the target domain. However, this strategy can not be directly applicable in RCSL owing to (1) the unique form of the RCSL policy class, which explicitly depends on the return, and (2) the absence of a straightforward representation of the optimal trajectory distribution. We propose the Return Augmented (REAG) method for DT type frameworks, where we augment the return in the source domain by aligning its distribution with that in the target domain. We provide the theoretical analysis demonstrating that the RCSL policy learned from REAG achieves the same level of suboptimality as would be obtained without a dynamics shift. We introduce two practical implementations REAG$_\\text{Dara}^{*}$ and REAG$_\\text{MV}^{*}$ respectively. Thorough experiments on D4RL datasets and various DT-type baselines demonstrate that our methods consistently enhance the performance of DT type frameworks in off-dynamics RL.", "published": "2024-10-30T20:46:26Z", "authors": ["Ruhan Wang", "Yu Yang", "Zhishuai Liu", "Dongruo Zhou", "Pan Xu"], "categories": ["cs.LG", "cs.AI", "cs.RO", "stat.ML"], "doi": "", "_query": "real estate valuation deep learning", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2310.10250v1", "title": "Leveraging Topological Maps in Deep Reinforcement Learning for Multi-Object Navigation", "abstract": "This work addresses the challenge of navigating expansive spaces with sparse rewards through Reinforcement Learning (RL). Using topological maps, we elevate elementary actions to object-oriented macro actions, enabling a simple Deep Q-Network (DQN) agent to solve otherwise practically impossible environments.", "published": "2023-10-16T10:19:45Z", "authors": ["Simon Hakenes", "Tobias Glasmachers"], "categories": ["cs.LG"], "doi": "", "_query": "real estate valuation deep learning", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2308.16215v6", "title": "Deep Video Codec Control for Vision Models", "abstract": "Standardized lossy video coding is at the core of almost all real-world video processing pipelines. Rate control is used to enable standard codecs to adapt to different network bandwidth conditions or storage constraints. However, standard video codecs (e.g., H.264) and their rate control modules aim to minimize video distortion w.r.t. human quality assessment. We demonstrate empirically that standard-coded videos vastly deteriorate the performance of deep vision models. To overcome the deterioration of vision performance, this paper presents the first end-to-end learnable deep video codec control that considers both bandwidth constraints and downstream deep vision performance, while adhering to existing standardization. We demonstrate that our approach better preserves downstream deep vision performance than traditional standard video coding.", "published": "2023-08-30T16:44:38Z", "authors": ["Christoph Reich", "Biplob Debnath", "Deep Patel", "Tim Prangemeier", "Daniel Cremers", "Srimat Chakradhar"], "categories": ["eess.IV", "cs.CV", "cs.LG", "cs.MM"], "doi": "10.1109/CVPRW63382.2024.00582", "_query": "real estate valuation deep learning", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2306.04338v1", "title": "Changing Data Sources in the Age of Machine Learning for Official Statistics", "abstract": "Data science has become increasingly essential for the production of official statistics, as it enables the automated collection, processing, and analysis of large amounts of data. With such data science practices in place, it enables more timely, more insightful and more flexible reporting. However, the quality and integrity of data-science-driven statistics rely on the accuracy and reliability of the data sources and the machine learning techniques that support them. In particular, changes in data sources are inevitable to occur and pose significant risks that are crucial to address in the context of machine learning for official statistics. This paper gives an overview of the main risks, liabilities, and uncertainties associated with changing data sources in the context of machine learning for official statistics. We provide a checklist of the most prevalent origins and causes of changing data sources; not only on a technical level but also regarding ownership, ethics, regulation, and public perception. Next, we highlight the repercussions of changing data sources on statistical reporting. These include technical effects such as concept drift, bias, availability, validity, accuracy and completeness, but also the neutrality and potential discontinuation of the statistical offering. We offer a few important precautionary measures, such as enhancing robustness in both data sourcing and statistical techniques, and thorough monitoring. In doing so, machine learning-based official statistics can maintain integrity, reliability, consistency, and relevance in policy-making, decision-making, and public discourse.", "published": "2023-06-07T11:08:12Z", "authors": ["Cedric De Boom", "Michael Reusens"], "categories": ["stat.ML", "cs.LG"], "doi": "", "_query": "real estate valuation deep learning", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2302.05326v3", "title": "Scalable Real-Time Recurrent Learning Using Columnar-Constructive Networks", "abstract": "Constructing states from sequences of observations is an important component of reinforcement learning agents. One solution for state construction is to use recurrent neural networks. Back-propagation through time (BPTT), and real-time recurrent learning (RTRL) are two popular gradient-based methods for recurrent learning. BPTT requires complete trajectories of observations before it can compute the gradients and is unsuitable for online updates. RTRL can do online updates but scales poorly to large networks. In this paper, we propose two constraints that make RTRL scalable. We show that by either decomposing the network into independent modules or learning the network in stages, we can make RTRL scale linearly with the number of parameters. Unlike prior scalable gradient estimation algorithms, such as UORO and Truncated-BPTT, our algorithms do not add noise or bias to the gradient estimate. Instead, they trade off the functional capacity of the network for computationally efficient learning. We demonstrate the effectiveness of our approach over Truncated-BPTT on a prediction benchmark inspired by animal learning and by doing policy evaluation of pre-trained policies for Atari 2600 games.", "published": "2023-01-20T23:17:48Z", "authors": ["Khurram Javed", "Haseeb Shah", "Rich Sutton", "Martha White"], "categories": ["cs.LG", "cs.AI"], "doi": "", "_query": "real estate valuation deep learning", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2501.10848v1", "title": "Fake Advertisements Detection Using Automated Multimodal Learning: A Case Study for Vietnamese Real Estate Data", "abstract": "The popularity of e-commerce has given rise to fake advertisements that can expose users to financial and data risks while damaging the reputation of these e-commerce platforms. For these reasons, detecting and removing such fake advertisements are important for the success of e-commerce websites. In this paper, we propose FADAML, a novel end-to-end machine learning system to detect and filter out fake online advertisements. Our system combines techniques in multimodal machine learning and automated machine learning to achieve a high detection rate. As a case study, we apply FADAML to detect fake advertisements on popular Vietnamese real estate websites. Our experiments show that we can achieve 91.5% detection accuracy, which significantly outperforms three different state-of-the-art fake news detection systems.", "published": "2025-01-18T18:48:06Z", "authors": ["Duy Nguyen", "Trung T. Nguyen", "Cuong V. Nguyen"], "categories": ["cs.LG", "cs.AI"], "doi": "", "_query": "real estate valuation deep learning", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2011.12216v3", "title": "Energy-Based Models for Continual Learning", "abstract": "We motivate Energy-Based Models (EBMs) as a promising model class for continual learning problems. Instead of tackling continual learning via the use of external memory, growing models, or regularization, EBMs change the underlying training objective to cause less interference with previously learned information. Our proposed version of EBMs for continual learning is simple, efficient, and outperforms baseline methods by a large margin on several benchmarks. Moreover, our proposed contrastive divergence-based training objective can be combined with other continual learning methods, resulting in substantial boosts in their performance. We further show that EBMs are adaptable to a more general continual learning setting where the data distribution changes without the notion of explicitly delineated tasks. These observations point towards EBMs as a useful building block for future continual learning methods.", "published": "2020-11-24T17:08:13Z", "authors": ["Shuang Li", "Yilun Du", "Gido M. van de Ven", "Igor Mordatch"], "categories": ["cs.LG", "cs.AI", "stat.ML"], "doi": "", "_query": "real estate valuation deep learning", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2302.09566v2", "title": "Optimization Methods in Deep Learning: A Comprehensive Overview", "abstract": "In recent years, deep learning has achieved remarkable success in various fields such as image recognition, natural language processing, and speech recognition. The effectiveness of deep learning largely depends on the optimization methods used to train deep neural networks. In this paper, we provide an overview of first-order optimization methods such as Stochastic Gradient Descent, Adagrad, Adadelta, and RMSprop, as well as recent momentum-based and adaptive gradient methods such as Nesterov accelerated gradient, Adam, Nadam, AdaMax, and AMSGrad. We also discuss the challenges associated with optimization in deep learning and explore techniques for addressing these challenges, including weight initialization, batch normalization, and layer normalization. Finally, we provide recommendations for selecting optimization methods for different deep learning tasks and datasets. This paper serves as a comprehensive guide to optimization methods in deep learning and can be used as a reference for researchers and practitioners in the field.", "published": "2023-02-19T13:01:53Z", "authors": ["David Shulman"], "categories": ["cs.LG", "math.OC"], "doi": "", "_query": "real estate valuation deep learning", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2004.01608v3", "title": "Learning 2-opt Heuristics for the Traveling Salesman Problem via Deep Reinforcement Learning", "abstract": "Recent works using deep learning to solve the Traveling Salesman Problem (TSP) have focused on learning construction heuristics. Such approaches find TSP solutions of good quality but require additional procedures such as beam search and sampling to improve solutions and achieve state-of-the-art performance. However, few studies have focused on improvement heuristics, where a given solution is improved until reaching a near-optimal one. In this work, we propose to learn a local search heuristic based on 2-opt operators via deep reinforcement learning. We propose a policy gradient algorithm to learn a stochastic policy that selects 2-opt operations given a current solution. Moreover, we introduce a policy neural network that leverages a pointing attention mechanism, which unlike previous works, can be easily extended to more general k-opt moves. Our results show that the learned policies can improve even over random initial solutions and approach near-optimal solutions at a faster rate than previous state-of-the-art deep learning methods.", "published": "2020-04-03T14:51:54Z", "authors": ["Paulo R. de O. da Costa", "Jason Rhuggenaath", "Yingqian Zhang", "Alp Akcay"], "categories": ["cs.LG", "cs.AI", "stat.ML"], "doi": "", "_query": "real estate valuation deep learning", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/1810.06339v1", "title": "Deep Reinforcement Learning", "abstract": "We discuss deep reinforcement learning in an overview style. We draw a big picture, filled with details. We discuss six core elements, six important mechanisms, and twelve applications, focusing on contemporary work, and in historical contexts. We start with background of artificial intelligence, machine learning, deep learning, and reinforcement learning (RL), with resources. Next we discuss RL core elements, including value function, policy, reward, model, exploration vs. exploitation, and representation. Then we discuss important mechanisms for RL, including attention and memory, unsupervised learning, hierarchical RL, multi-agent RL, relational RL, and learning to learn. After that, we discuss RL applications, including games, robotics, natural language processing (NLP), computer vision, finance, business management, healthcare, education, energy, transportation, computer systems, and, science, engineering, and art. Finally we summarize briefly, discuss challenges and opportunities, and close with an epilogue.", "published": "2018-10-15T13:20:56Z", "authors": ["Yuxi Li"], "categories": ["cs.LG", "stat.ML"], "doi": "", "_query": "real estate valuation deep learning", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/1805.04829v2", "title": "Spatial Uncertainty Sampling for End-to-End Control", "abstract": "End-to-end trained neural networks (NNs) are a compelling approach to autonomous vehicle control because of their ability to learn complex tasks without manual engineering of rule-based decisions. However, challenging road conditions, ambiguous navigation situations, and safety considerations require reliable uncertainty estimation for the eventual adoption of full-scale autonomous vehicles. Bayesian deep learning approaches provide a way to estimate uncertainty by approximating the posterior distribution of weights given a set of training data. Dropout training in deep NNs approximates Bayesian inference in a deep Gaussian process and can thus be used to estimate model uncertainty. In this paper, we propose a Bayesian NN for end-to-end control that estimates uncertainty by exploiting feature map correlation during training. This approach achieves improved model fits, as well as tighter uncertainty estimates, than traditional element-wise dropout. We evaluate our algorithms on a challenging dataset collected over many different road types, times of day, and weather conditions, and demonstrate how uncertainties can be used in conjunction with a human controller in a parallel autonomous setting.", "published": "2018-05-13T06:19:14Z", "authors": ["Alexander Amini", "Ava Soleimany", "Sertac Karaman", "Daniela Rus"], "categories": ["cs.AI", "cs.LG", "cs.RO"], "doi": "", "_query": "real estate valuation deep learning", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2410.09673v1", "title": "Incorporating Asymmetric Loss for Real Estate Prediction with Area-level Spatial Data", "abstract": "We investigate two asymmetric loss functions, namely LINEX loss and power divergence loss for optimal spatial prediction with area-level data. With our motivation arising from the real estate industry, namely in real estate valuation, we use the Zillow Home Value Index (ZHVI) for county-level values to show the change in prediction when the loss is different (asymmetric) from a traditional squared error loss (symmetric) function. Additionally, we discuss the importance of choosing the asymmetry parameter, and propose a solution to this choice for a general asymmetric loss function. Since the focus is on area-level data predictions, we propose the methodology in the context of conditionally autoregressive (CAR) models. We conclude that choice of the loss functions for spatial area-level predictions can play a crucial role, and is heavily driven by the choice of parameters in the respective loss.", "published": "2024-10-12T23:52:37Z", "authors": ["Vaidehi Dixit", "Scott H. Holan", "Christopher K. Wikle"], "categories": ["stat.AP"], "doi": "", "_query": "real estate valuation deep learning", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/physics/0605133v1", "title": "Real estate price peaks: a comparative overview", "abstract": "First, we emphasize that the real estate price peaks which are currently under way in many industrialized countries (one important exception is Japan) share many of the characteristics of previous historical price peaks. In particular, we show that: (i) In the present episode real price increases are, at least for now, of the same order of magnitude as in previous episodes, typically of the order of 80 percent to 100 percent. (ii) Historically, price peaks turned out to be symmetrical with respect to the peak; soft landing, i.e. an upgoing phase followed by a plateau, has rarely (if ever) been observed. (iii) The inflated demand is mainly boosted by investors and high-income buyers. (iv) In the present as well as in previous episodes, the main engines in the upgoing phase have been the hot markets which developed in major cities such as London, Los Angeles, New York, Paris, San Francisco or Sydney. In our conclusion, we propose a prediction for real estate prices in the West of the United States over the period 2005-2011. We also point out that investment funds, which already play a key role in stock markets, have in recent times began to heavily invest in real estate. In the future, one can expect them to become major players in property markets worldwide. The outcome of the present episode will tell us how quickly this transformation evolves. Thus, if the height of the present peak substantially surpasses the magnitude of previous ones, one may infer that investment funds have been able to establish strong communication channels between real estate assets on the one hand and financial assets (e.g. bonds, stocks, options) on the other hand.", "published": "2006-05-16T11:10:42Z", "authors": ["Bertrand M. Roehner"], "categories": ["physics.soc-ph"], "doi": "", "_query": "real estate valuation deep learning", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2003.01916v2", "title": "Optimal Deep Learning for Robot Touch", "abstract": "This article illustrates the application of deep learning to robot touch by considering a basic yet fundamental capability: estimating the relative pose of part of an object in contact with a tactile sensor. We begin by surveying deep learning applied to tactile robotics, focussing on optical tactile sensors, which help bridge from deep learning for vision to touch. We then show how deep learning can be used to train accurate pose models of 3D surfaces and edges that are insensitive to nuisance variables such as motion-dependent shear. This involves including representative motions as unlabelled perturbations of the training data and using Bayesian optimization of the network and training hyperparameters to find the most accurate models. Accurate estimation of pose from touch will enable robots to safely and precisely control their physical interactions, underlying a wide range of object exploration and manipulation tasks.", "published": "2020-03-04T06:55:19Z", "authors": ["Nathan F. Lepora", "John Lloyd"], "categories": ["cs.RO"], "doi": "", "_query": "real estate valuation deep learning", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/1811.02640v2", "title": "Deep Probabilistic Ensembles: Approximate Variational Inference through KL Regularization", "abstract": "In this paper, we introduce Deep Probabilistic Ensembles (DPEs), a scalable technique that uses a regularized ensemble to approximate a deep Bayesian Neural Network (BNN). We do so by incorporating a KL divergence penalty term into the training objective of an ensemble, derived from the evidence lower bound used in variational inference. We evaluate the uncertainty estimates obtained from our models for active learning on visual classification. Our approach steadily improves upon active learning baselines as the annotation budget is increased.", "published": "2018-11-06T20:59:51Z", "authors": ["Kashyap Chitta", "Jose M. Alvarez", "Adam Lesnikowski"], "categories": ["stat.ML", "cs.LG"], "doi": "", "_query": "real estate valuation deep learning", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2103.01006v4", "title": "GaNDLF: A Generally Nuanced Deep Learning Framework for Scalable End-to-End Clinical Workflows in Medical Imaging", "abstract": "Deep Learning (DL) has the potential to optimize machine learning in both the scientific and clinical communities. However, greater expertise is required to develop DL algorithms, and the variability of implementations hinders their reproducibility, translation, and deployment. Here we present the community-driven Generally Nuanced Deep Learning Framework (GaNDLF), with the goal of lowering these barriers. GaNDLF makes the mechanism of DL development, training, and inference more stable, reproducible, interpretable, and scalable, without requiring an extensive technical background. GaNDLF aims to provide an end-to-end solution for all DL-related tasks in computational precision medicine. We demonstrate the ability of GaNDLF to analyze both radiology and histology images, with built-in support for k-fold cross-validation, data augmentation, multiple modalities and output classes. Our quantitative performance evaluation on numerous use cases, anatomies, and computational tasks supports GaNDLF as a robust application framework for deployment in clinical workflows.", "published": "2021-02-26T02:24:52Z", "authors": ["Sarthak Pati", "Siddhesh P. Thakur", "İbrahim Ethem Hamamcı", "Ujjwal Baid", "Bhakti Baheti", "Megh Bhalerao", "Orhun Güley", "Sofia Mouchtaris", "David Lang", "Spyridon Thermos", "Karol Gotkowski", "Camila González", "Caleb Grenko", "Alexander Getka", "Brandon Edwards", "Micah Sheller", "Junwen Wu", "Deepthi Karkada", "Ravi Panchumarthy", "Vinayak Ahluwalia", "Chunrui Zou", "Vishnu Bashyam", "Yuemeng Li", "Babak Haghighi", "Rhea Chitalia", "Shahira Abousamra", "Tahsin M. Kurc", "Aimilia Gastounioti", "Sezgin Er", "Mark Bergman", "Joel H. Saltz", "Yong Fan", "Prashant Shah", "Anirban Mukhopadhyay", "Sotirios A. Tsaftaris", "Bjoern Menze", "Christos Davatzikos", "Despina Kontos", "Alexandros Karargyris", "Renato Umeton", "Peter Mattson", "Spyridon Bakas"], "categories": ["cs.LG"], "doi": "10.1038/s44172-023-00066-3", "_query": "real estate valuation deep learning", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/1612.03653v2", "title": "Learning to Drive using Inverse Reinforcement Learning and Deep Q-Networks", "abstract": "We propose an inverse reinforcement learning (IRL) approach using Deep Q-Networks to extract the rewards in problems with large state spaces. We evaluate the performance of this approach in a simulation-based autonomous driving scenario. Our results resemble the intuitive relation between the reward function and readings of distance sensors mounted at different poses on the car. We also show that, after a few learning rounds, our simulated agent generates collision-free motions and performs human-like lane change behaviour.", "published": "2016-12-12T12:57:10Z", "authors": ["Sahand Sharifzadeh", "Ioannis Chiotellis", "Rudolph Triebel", "Daniel Cremers"], "categories": ["cs.AI", "cs.RO"], "doi": "", "_query": "real estate valuation deep learning", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2002.02826v3", "title": "Conditional Deep Gaussian Processes: multi-fidelity kernel learning", "abstract": "Deep Gaussian Processes (DGPs) were proposed as an expressive Bayesian model capable of a mathematically grounded estimation of uncertainty. The expressivity of DPGs results from not only the compositional character but the distribution propagation within the hierarchy. Recently, [1] pointed out that the hierarchical structure of DGP well suited modeling the multi-fidelity regression, in which one is provided sparse observations with high precision and plenty of low fidelity observations. We propose the conditional DGP model in which the latent GPs are directly supported by the fixed lower fidelity data. Then the moment matching method in [2] is applied to approximate the marginal prior of conditional DGP with a GP. The obtained effective kernels are implicit functions of the lower-fidelity data, manifesting the expressivity contributed by distribution propagation within the hierarchy. The hyperparameters are learned via optimizing the approximate marginal likelihood. Experiments with synthetic and high dimensional data show comparable performance against other multi-fidelity regression methods, variational inference, and multi-output GP. We conclude that, with the low fidelity data and the hierarchical DGP structure, the effective kernel encodes the inductive bias for true function allowing the compositional freedom discussed in [3,4].", "published": "2020-02-07T14:56:11Z", "authors": ["Chi-Ken Lu", "Patrick Shafto"], "categories": ["cs.LG", "cond-mat.dis-nn", "stat.ML"], "doi": "10.3390/e23111545", "_query": "real estate valuation deep learning", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/1910.12908v3", "title": "Certified Adversarial Robustness for Deep Reinforcement Learning", "abstract": "Deep Neural Network-based systems are now the state-of-the-art in many robotics tasks, but their application in safety-critical domains remains dangerous without formal guarantees on network robustness. Small perturbations to sensor inputs (from noise or adversarial examples) are often enough to change network-based decisions, which was already shown to cause an autonomous vehicle to swerve into oncoming traffic. In light of these dangers, numerous algorithms have been developed as defensive mechanisms from these adversarial inputs, some of which provide formal robustness guarantees or certificates. This work leverages research on certified adversarial robustness to develop an online certified defense for deep reinforcement learning algorithms. The proposed defense computes guaranteed lower bounds on state-action values during execution to identify and choose the optimal action under a worst-case deviation in input space due to possible adversaries or noise. The approach is demonstrated on a Deep Q-Network policy and is shown to increase robustness to noise and adversaries in pedestrian collision avoidance scenarios and a classic control task.", "published": "2019-10-28T18:45:38Z", "authors": ["Björn Lütjens", "Michael Everett", "Jonathan P. How"], "categories": ["cs.RO", "cs.AI", "cs.LG"], "doi": "", "_query": "real estate valuation deep learning", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/1904.09489v1", "title": "Compression and Localization in Reinforcement Learning for ATARI Games", "abstract": "Deep neural networks have become commonplace in the domain of reinforcement learning, but are often expensive in terms of the number of parameters needed. While compressing deep neural networks has of late assumed great importance to overcome this drawback, little work has been done to address this problem in the context of reinforcement learning agents. This work aims at making first steps towards model compression in an RL agent. In particular, we compress networks to drastically reduce the number of parameters in them (to sizes less than 3% of their original size), further facilitated by applying a global max pool after the final convolution layer, and propose using Actor-Mimic in the context of compression. Finally, we show that this global max-pool allows for weakly supervised object localization, improving the ability to identify the agent's points of focus.", "published": "2019-04-20T19:42:50Z", "authors": ["Joel Ruben Antony Moniz", "Barun Patra", "Sarthak Garg"], "categories": ["cs.LG", "cs.AI", "stat.ML"], "doi": "", "_query": "real estate valuation deep learning", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2605.08529v1", "title": "The Propagation Field: A Geometric Substrate Theory of Deep Learning", "abstract": "Modern deep learning treats neural networks primarily as endpoint functions from inputs to outputs. Inspired by the shift from force to geometry in physics, we ask whether a network should instead be understood through the geometry of its internal propagation. We define a neural propagation field as the collection of hidden-state trajectories and local Jacobian operators across depth. Endpoint losses constrain only the boundary behavior of this field, leaving its interior geometry underdetermined. We show that endpoint-equivalent models can differ by orders of magnitude in trajectory and Jacobian structure, and introduce observable field metrics such as path sensitivity, solver consistency, and trajectory/Jacobian retention. In controlled teacher-flow and PDE systems, endpoint fitting fails to recover the underlying propagation law. In real multi-path tasks, field-aware objectives improve unseen-path generalization, OOD robustness, and calibration when aligned with the observation structure, but can collapse when over-constrained. In continual learning, field-preservation regularization complements replay and distillation: on Split CIFAR-100, DER++ with field preservation improves average accuracy, backward transfer, and field-retention metrics. These results identify propagation-field quality as a measurable and trainable property of neural networks beyond endpoint performance.", "published": "2026-05-08T22:26:50Z", "authors": ["Xingrui Gu"], "categories": ["cs.LG"], "doi": "", "_query": "real estate valuation deep learning", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/1707.01649v3", "title": "Frobenius splitting of valuation rings and $F$-singularities of centers", "abstract": "Using a local monomialization result of Knaf and Kuhlmann, we prove that the valuation ring of an Abhyankar valuation of a function field over a perfect ground field of prime characteristic is Frobenius split. We show that a Frobenius splitting of a sufficiently well-behaved center lifts to a Frobenius splitting of the valuation ring. We also investigate properties of valuations centered on arbitrary Noetherian domains of prime characteristic. In contrast to [arXiv:1507.06009], this paper emphasizes the role of centers in controlling Frobenius properties of valuations rings in prime characteristic.", "published": "2017-07-06T06:14:21Z", "authors": ["Rankeya Datta"], "categories": ["math.AC", "math.AG"], "doi": "10.2140/ant.2021.15.2485", "_query": "real estate valuation deep learning", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2007.01135v1", "title": "Student-Teacher Curriculum Learning via Reinforcement Learning: Predicting Hospital Inpatient Admission Location", "abstract": "Accurate and reliable prediction of hospital admission location is important due to resource-constraints and space availability in a clinical setting, particularly when dealing with patients who come from the emergency department. In this work we propose a student-teacher network via reinforcement learning to deal with this specific problem. A representation of the weights of the student network is treated as the state and is fed as an input to the teacher network. The teacher network's action is to select the most appropriate batch of data to train the student network on from a training set sorted according to entropy. By validating on three datasets, not only do we show that our approach outperforms state-of-the-art methods on tabular data and performs competitively on image recognition, but also that novel curricula are learned by the teacher network. We demonstrate experimentally that the teacher network can actively learn about the student network and guide it to achieve better performance than if trained alone.", "published": "2020-07-01T15:00:43Z", "authors": ["Rasheed el-Bouri", "David Eyre", "Peter Watkinson", "Tingting Zhu", "David Clifton"], "categories": ["cs.LG", "cs.CV", "stat.ML"], "doi": "", "_query": "real estate valuation deep learning", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2006.16189v4", "title": "DOME: Recommendations for supervised machine learning validation in biology", "abstract": "Modern biology frequently relies on machine learning to provide predictions and improve decision processes. There have been recent calls for more scrutiny on machine learning performance and possible limitations. Here we present a set of community-wide recommendations aiming to help establish standards of supervised machine learning validation in biology. Adopting a structured methods description for machine learning based on data, optimization, model, evaluation (DOME) will aim to help both reviewers and readers to better understand and assess the performance and limitations of a method or outcome. The recommendations are formulated as questions to anyone wishing to pursue implementation of a machine learning algorithm. Answers to these questions can be easily included in the supplementary material of published papers.", "published": "2020-06-25T12:01:39Z", "authors": ["Ian Walsh", "Dmytro Fishman", "Dario Garcia-Gasulla", "Tiina Titma", "Gianluca Pollastri", "The ELIXIR Machine Learning focus group", "Jen Harrow", "Fotis E. Psomopoulos", "Silvio C. E. Tosatto"], "categories": ["q-bio.OT", "cs.LG"], "doi": "", "_query": "real estate valuation deep learning", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/1611.09894v1", "title": "Exploration for Multi-task Reinforcement Learning with Deep Generative Models", "abstract": "Exploration in multi-task reinforcement learning is critical in training agents to deduce the underlying MDP. Many of the existing exploration frameworks such as $E^3$, $R_{max}$, Thompson sampling assume a single stationary MDP and are not suitable for system identification in the multi-task setting. We present a novel method to facilitate exploration in multi-task reinforcement learning using deep generative models. We supplement our method with a low dimensional energy model to learn the underlying MDP distribution and provide a resilient and adaptive exploration signal to the agent. We evaluate our method on a new set of environments and provide intuitive interpretation of our results.", "published": "2016-11-29T21:32:25Z", "authors": ["Sai Praveen Bangaru", "JS Suhas", "Balaraman Ravindran"], "categories": ["cs.AI", "cs.LG", "stat.ML"], "doi": "", "_query": "real estate valuation deep learning", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/1811.03390v2", "title": "Bayesian Deep Learning for Exoplanet Atmospheric Retrieval", "abstract": "Over the past decade, the study of extrasolar planets has evolved rapidly from plain detection and identification to comprehensive categorization and characterization of exoplanet systems and their atmospheres. Atmospheric retrieval, the inverse modeling technique used to determine an exoplanetary atmosphere's temperature structure and composition from an observed spectrum, is both time-consuming and compute-intensive, requiring complex algorithms that compare thousands to millions of atmospheric models to the observational data to find the most probable values and associated uncertainties for each model parameter. For rocky, terrestrial planets, the retrieved atmospheric composition can give insight into the surface fluxes of gaseous species necessary to maintain the stability of that atmosphere, which may in turn provide insight into the geological and/or biological processes active on the planet. These atmospheres contain many molecules, some of them biosignatures, spectral fingerprints indicative of biological activity, which will become observable with the next generation of telescopes. Runtimes of traditional retrieval models scale with the number of model parameters, so as more molecular species are considered, runtimes can become prohibitively long. Recent advances in machine learning (ML) and computer vision offer new ways to reduce the time to perform a retrieval by orders of magnitude, given a sufficient data set to train with. Here we present an ML-based retrieval framework called Intelligent exoplaNet Atmospheric RetrievAl (INARA) that consists of a Bayesian deep learning model for retrieval and a data set of 3,000,000 synthetic rocky exoplanetary spectra generated using the NASA Planetary Spectrum Generator. Our work represents the first ML retrieval model for rocky, terrestrial exoplanets and the first synthetic data set of terrestrial spectra generated at this scale.", "published": "2018-11-08T13:03:08Z", "authors": ["Frank Soboczenski", "Michael D. Himes", "Molly D. O'Beirne", "Simone Zorzan", "Atilim Gunes Baydin", "Adam D. Cobb", "Yarin Gal", "Daniel Angerhausen", "Massimo Mascaro", "Giada N. Arney", "Shawn D. Domagal-Goldman"], "categories": ["astro-ph.EP", "cs.LG"], "doi": "", "_query": "real estate valuation deep learning", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/1611.08655v1", "title": "A Deep Neural Network to identify foreshocks in real time", "abstract": "Foreshock events provide valuable insight to predict imminent major earthquakes. However, it is difficult to identify them in real time. In this paper, I propose an algorithm based on deep learning to instantaneously classify a seismic waveform as a foreshock, mainshock or an aftershock event achieving a high accuracy of 99% in classification. As a result, this is by far the most reliable method to predict major earthquakes that are preceded by foreshocks. In addition, I discuss methods to create an earthquake dataset that is compatible with deep networks.", "published": "2016-11-26T04:19:54Z", "authors": ["K. Vikraman"], "categories": ["physics.geo-ph", "cs.LG"], "doi": "", "_query": "real estate valuation deep learning", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2311.11812v1", "title": "Improving Real Estate Appraisal with POI Integration and Areal Embedding", "abstract": "Despite advancements in real estate appraisal methods, this study primarily focuses on two pivotal challenges. Firstly, we explore the often-underestimated impact of Points of Interest (POI) on property values, emphasizing the necessity for a comprehensive, data-driven approach to feature selection. Secondly, we integrate road-network-based Areal Embedding to enhance spatial understanding for real estate appraisal. We first propose a revised method for POI feature extraction, and discuss the impact of each POI for house price appraisal. Then we present the Areal embedding-enabled Masked Multihead Attention-based Spatial Interpolation for House Price Prediction (AMMASI) model, an improvement upon the existing ASI model, which leverages masked multi-head attention on geographic neighbor houses and similar-featured houses. Our model outperforms current baselines and also offers promising avenues for future optimization in real estate appraisal methodologies.", "published": "2023-11-20T14:48:09Z", "authors": ["Sumin Han", "Youngjun Park", "Sonia Sabir", "Jisun An", "Dongman Lee"], "categories": ["cs.AI"], "doi": "", "_query": "real estate valuation deep learning", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2512.14020v1", "title": "Deep Learning Perspective of Scene Understanding in Autonomous Robots", "abstract": "This paper provides a review of deep learning applications in scene understanding in autonomous robots, including innovations in object detection, semantic and instance segmentation, depth estimation, 3D reconstruction, and visual SLAM. It emphasizes how these techniques address limitations of traditional geometric models, improve depth perception in real time despite occlusions and textureless surfaces, and enhance semantic reasoning to understand the environment better. When these perception modules are integrated into dynamic and unstructured environments, they become more effective in decisionmaking, navigation and interaction. Lastly, the review outlines the existing problems and research directions to advance learning-based scene understanding of autonomous robots.", "published": "2025-12-16T02:31:54Z", "authors": ["Afia Maham", "Dur E Nayab Tashfa"], "categories": ["cs.CV"], "doi": "", "_query": "real estate valuation deep learning", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/1610.01076v1", "title": "Tutorial on Answering Questions about Images with Deep Learning", "abstract": "Together with the development of more accurate methods in Computer Vision and Natural Language Understanding, holistic architectures that answer on questions about the content of real-world images have emerged. In this tutorial, we build a neural-based approach to answer questions about images. We base our tutorial on two datasets: (mostly on) DAQUAR, and (a bit on) VQA. With small tweaks the models that we present here can achieve a competitive performance on both datasets, in fact, they are among the best methods that use a combination of LSTM with a global, full frame CNN representation of an image. We hope that after reading this tutorial, the reader will be able to use Deep Learning frameworks, such as Keras and introduced Kraino, to build various architectures that will lead to a further performance improvement on this challenging task.", "published": "2016-10-04T16:29:28Z", "authors": ["Mateusz Malinowski", "Mario Fritz"], "categories": ["cs.CV", "cs.AI", "cs.CL", "cs.LG", "cs.NE"], "doi": "", "_query": "real estate valuation deep learning", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/1706.09829v1", "title": "Towards Monocular Vision based Obstacle Avoidance through Deep Reinforcement Learning", "abstract": "Obstacle avoidance is a fundamental requirement for autonomous robots which operate in, and interact with, the real world. When perception is limited to monocular vision avoiding collision becomes significantly more challenging due to the lack of 3D information. Conventional path planners for obstacle avoidance require tuning a number of parameters and do not have the ability to directly benefit from large datasets and continuous use. In this paper, a dueling architecture based deep double-Q network (D3QN) is proposed for obstacle avoidance, using only monocular RGB vision. Based on the dueling and double-Q mechanisms, D3QN can efficiently learn how to avoid obstacles in a simulator even with very noisy depth information predicted from RGB image. Extensive experiments show that D3QN enables twofold acceleration on learning compared with a normal deep Q network and the models trained solely in virtual environments can be directly transferred to real robots, generalizing well to various new environments with previously unseen dynamic objects.", "published": "2017-06-29T16:16:47Z", "authors": ["Linhai Xie", "Sen Wang", "Andrew Markham", "Niki Trigoni"], "categories": ["cs.RO"], "doi": "", "_query": "real estate valuation deep learning", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/1611.07078v2", "title": "A Deep Learning Approach for Joint Video Frame and Reward Prediction in Atari Games", "abstract": "Reinforcement learning is concerned with identifying reward-maximizing behaviour policies in environments that are initially unknown. State-of-the-art reinforcement learning approaches, such as deep Q-networks, are model-free and learn to act effectively across a wide range of environments such as Atari games, but require huge amounts of data. Model-based techniques are more data-efficient, but need to acquire explicit knowledge about the environment. In this paper, we take a step towards using model-based techniques in environments with a high-dimensional visual state space by demonstrating that it is possible to learn system dynamics and the reward structure jointly. Our contribution is to extend a recently developed deep neural network for video frame prediction in Atari games to enable reward prediction as well. To this end, we phrase a joint optimization problem for minimizing both video frame and reward reconstruction loss, and adapt network parameters accordingly. Empirical evaluations on five Atari games demonstrate accurate cumulative reward prediction of up to 200 frames. We consider these results as opening up important directions for model-based reinforcement learning in complex, initially unknown environments.", "published": "2016-11-21T22:06:23Z", "authors": ["Felix Leibfried", "Nate Kushman", "Katja Hofmann"], "categories": ["cs.AI", "cs.LG", "stat.ML"], "doi": "", "_query": "real estate valuation deep learning", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/1712.05016v2", "title": "Deep Prior", "abstract": "The recent literature on deep learning offers new tools to learn a rich probability distribution over high dimensional data such as images or sounds. In this work we investigate the possibility of learning the prior distribution over neural network parameters using such tools. Our resulting variational Bayes algorithm generalizes well to new tasks, even when very few training examples are provided. Furthermore, this learned prior allows the model to extrapolate correctly far from a given task's training data on a meta-dataset of periodic signals.", "published": "2017-12-13T21:41:56Z", "authors": ["Alexandre Lacoste", "Thomas Boquet", "Negar Rostamzadeh", "Boris Oreshkin", "Wonchang Chung", "David Krueger"], "categories": ["stat.ML", "cs.LG"], "doi": "", "_query": "real estate valuation deep learning", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/1812.06175v3", "title": "Can Deep Learning Predict Risky Retail Investors? A Case Study in Financial Risk Behavior Forecasting", "abstract": "The paper examines the potential of deep learning to support decisions in financial risk management. We develop a deep learning model for predicting whether individual spread traders secure profits from future trades. This task embodies typical modeling challenges faced in risk and behavior forecasting. Conventional machine learning requires data that is representative of the feature-target relationship and relies on the often costly development, maintenance, and revision of handcrafted features. Consequently, modeling highly variable, heterogeneous patterns such as trader behavior is challenging. Deep learning promises a remedy. Learning hierarchical distributed representations of the data in an automatic manner (e.g. risk taking behavior), it uncovers generative features that determine the target (e.g., trader's profitability), avoids manual feature engineering, and is more robust toward change (e.g. dynamic market conditions). The results of employing a deep network for operational risk forecasting confirm the feature learning capability of deep learning, provide guidance on designing a suitable network architecture and demonstrate the superiority of deep learning over machine learning and rule-based benchmarks.", "published": "2018-12-14T21:31:41Z", "authors": ["Yaodong Yang", "Alisa Kolesnikova", "Stefan Lessmann", "Tiejun Ma", "Ming-Chien Sung", "Johnnie E. V. Johnson"], "categories": ["q-fin.RM", "cs.LG", "stat.AP"], "doi": "", "_query": "real estate valuation deep learning", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2003.00559v1", "title": "The Sloop System for Individual Animal Identification with Deep Learning", "abstract": "The MIT Sloop system indexes and retrieves photographs from databases of non-stationary animal population distributions. To do this, it adaptively represents and matches generic visual feature representations using sparse relevance feedback from experts and crowds. Here, we describe the Sloop system and its application, then compare its approach to a standard deep learning formulation. We then show that priming with amplitude and deformation features requires very shallow networks to produce superior recognition results. Results suggest that relevance feedback, which enables Sloop's high-recall performance may also be essential for deep learning approaches to individual identification to deliver comparable results.", "published": "2020-03-01T19:08:06Z", "authors": ["Kshitij Bakliwal", "Sai Ravela"], "categories": ["cs.CV", "cs.IR"], "doi": "", "_query": "real estate valuation deep learning", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2011.06220v3", "title": "Artificial Neural Variability for Deep Learning: On Overfitting, Noise Memorization, and Catastrophic Forgetting", "abstract": "Deep learning is often criticized by two serious issues which rarely exist in natural nervous systems: overfitting and catastrophic forgetting. It can even memorize randomly labelled data, which has little knowledge behind the instance-label pairs. When a deep network continually learns over time by accommodating new tasks, it usually quickly overwrites the knowledge learned from previous tasks. Referred to as the {\\it neural variability}, it is well-known in neuroscience that human brain reactions exhibit substantial variability even in response to the same stimulus. This mechanism balances accuracy and plasticity/flexibility in the motor learning of natural nervous systems. Thus it motivates us to design a similar mechanism named {\\it artificial neural variability} (ANV), which helps artificial neural networks learn some advantages from ``natural'' neural networks. We rigorously prove that ANV plays as an implicit regularizer of the mutual information between the training data and the learned model. This result theoretically guarantees ANV a strictly improved generalizability, robustness to label noise, and robustness to catastrophic forgetting. We then devise a {\\it neural variable risk minimization} (NVRM) framework and {\\it neural variable optimizers} to achieve ANV for conventional network architectures in practice. The empirical studies demonstrate that NVRM can effectively relieve overfitting, label noise memorization, and catastrophic forgetting at negligible costs. \\footnote{Code: \\url{https://github.com/zeke-xie/artificial-neural-variability-for-deep-learning}.", "published": "2020-11-12T06:06:33Z", "authors": ["Zeke Xie", "Fengxiang He", "Shaopeng Fu", "Issei Sato", "Dacheng Tao", "Masashi Sugiyama"], "categories": ["cs.LG"], "doi": "", "_query": "real estate valuation deep learning", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2205.10745v1", "title": "Classification of Quasars, Galaxies, and Stars in the Mapping of the Universe Multi-modal Deep Learning", "abstract": "In this paper, the fourth version the Sloan Digital Sky Survey (SDSS-4), Data Release 16 dataset was used to classify the SDSS dataset into galaxies, stars, and quasars using machine learning and deep learning architectures. We efficiently utilize both image and metadata in tabular format to build a novel multi-modal architecture and achieve state-of-the-art results. In addition, our experiments on transfer learning using Imagenet weights on five different architectures (Resnet-50, DenseNet-121 VGG-16, Xception, and EfficientNet) reveal that freezing all layers and adding a final trainable layer may not be an optimal solution for transfer learning. It is hypothesized that higher the number of trainable layers, higher will be the training time and accuracy of predictions. It is also hypothesized that any subsequent increase in the number of training layers towards the base layers will not increase in accuracy as the pre trained lower layers only help in low level feature extraction which would be quite similar in all the datasets. Hence the ideal level of trainable layers needs to be identified for each model in respect to the number of parameters. For the tabular data, we compared classical machine learning algorithms (Logistic Regression, Random Forest, Decision Trees, Adaboost, LightGBM etc.,) with artificial neural networks. Our works shed new light on transfer learning and multi-modal deep learning architectures. The multi-modal architecture not only resulted in higher metrics (accuracy, precision, recall, F1 score) than models using only image data or tabular data. Furthermore, multi-modal architecture achieved the best metrics in lesser training epochs and improved the metrics on all classes.", "published": "2022-05-22T05:17:31Z", "authors": ["Sabeesh Ethiraj", "Bharath Kumar Bolla"], "categories": ["cs.CV", "cs.AI"], "doi": "", "_query": "real estate valuation deep learning", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2507.02910v1", "title": "Causal-Paced Deep Reinforcement Learning", "abstract": "Designing effective task sequences is crucial for curriculum reinforcement learning (CRL), where agents must gradually acquire skills by training on intermediate tasks. A key challenge in CRL is to identify tasks that promote exploration, yet are similar enough to support effective transfer. While recent approach suggests comparing tasks via their Structural Causal Models (SCMs), the method requires access to ground-truth causal structures, an unrealistic assumption in most RL settings. In this work, we propose Causal-Paced Deep Reinforcement Learning (CP-DRL), a curriculum learning framework aware of SCM differences between tasks based on interaction data approximation. This signal captures task novelty, which we combine with the agent's learnability, measured by reward gain, to form a unified objective. Empirically, CP-DRL outperforms existing curriculum methods on the Point Mass benchmark, achieving faster convergence and higher returns. CP-DRL demonstrates reduced variance with comparable final returns in the Bipedal Walker-Trivial setting, and achieves the highest average performance in the Infeasible variant. These results indicate that leveraging causal relationships between tasks can improve the structure-awareness and sample efficiency of curriculum reinforcement learning. We provide the full implementation of CP-DRL to facilitate the reproduction of our main results at https://github.com/Cho-Geonwoo/CP-DRL.", "published": "2025-06-24T20:15:01Z", "authors": ["Geonwoo Cho", "Jaegyun Im", "Doyoon Kim", "Sundong Kim"], "categories": ["cs.LG", "cs.AI", "stat.ML"], "doi": "", "_query": "real estate valuation deep learning", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2107.12808v2", "title": "Open-Ended Learning Leads to Generally Capable Agents", "abstract": "In this work we create agents that can perform well beyond a single, individual task, that exhibit much wider generalisation of behaviour to a massive, rich space of challenges. We define a universe of tasks within an environment domain and demonstrate the ability to train agents that are generally capable across this vast space and beyond. The environment is natively multi-agent, spanning the continuum of competitive, cooperative, and independent games, which are situated within procedurally generated physical 3D worlds. The resulting space is exceptionally diverse in terms of the challenges posed to agents, and as such, even measuring the learning progress of an agent is an open research problem. We propose an iterative notion of improvement between successive generations of agents, rather than seeking to maximise a singular objective, allowing us to quantify progress despite tasks being incomparable in terms of achievable rewards. We show that through constructing an open-ended learning process, which dynamically changes the training task distributions and training objectives such that the agent never stops learning, we achieve consistent learning of new behaviours. The resulting agent is able to score reward in every one of our humanly solvable evaluation levels, with behaviour generalising to many held-out points in the universe of tasks. Examples of this zero-shot generalisation include good performance on Hide and Seek, Capture the Flag, and Tag. Through analysis and hand-authored probe tasks we characterise the behaviour of our agent, and find interesting emergent heuristic behaviours such as trial-and-error experimentation, simple tool use, option switching, and cooperation. Finally, we demonstrate that the general capabilities of this agent could unlock larger scale transfer of behaviour through cheap finetuning.", "published": "2021-07-27T13:30:07Z", "authors": [" Open Ended Learning Team", "Adam Stooke", "Anuj Mahajan", "Catarina Barros", "Charlie Deck", "Jakob Bauer", "Jakub Sygnowski", "Maja Trebacz", "Max Jaderberg", "Michael Mathieu", "Nat McAleese", "Nathalie Bradley-Schmieg", "Nathaniel Wong", "Nicolas Porcel", "Roberta Raileanu", "Steph Hughes-Fitt", "Valentin Dalibard", "Wojciech Marian Czarnecki"], "categories": ["cs.LG", "cs.AI", "cs.MA"], "doi": "", "_query": "real estate valuation deep learning", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2205.15821v2", "title": "Unsupervised Image Representation Learning with Deep Latent Particles", "abstract": "We propose a new representation of visual data that disentangles object position from appearance. Our method, termed Deep Latent Particles (DLP), decomposes the visual input into low-dimensional latent ``particles'', where each particle is described by its spatial location and features of its surrounding region. To drive learning of such representations, we follow a VAE-based approach and introduce a prior for particle positions based on a spatial-softmax architecture, and a modification of the evidence lower bound loss inspired by the Chamfer distance between particles. We demonstrate that our DLP representations are useful for downstream tasks such as unsupervised keypoint (KP) detection, image manipulation, and video prediction for scenes composed of multiple dynamic objects. In addition, we show that our probabilistic interpretation of the problem naturally provides uncertainty estimates for particle locations, which can be used for model selection, among other tasks. Videos and code are available: https://taldatech.github.io/deep-latent-particles-web/", "published": "2022-05-31T14:23:37Z", "authors": ["Tal Daniel", "Aviv Tamar"], "categories": ["cs.CV", "cs.AI", "cs.LG"], "doi": "", "_query": "real estate valuation deep learning", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/1810.05587v3", "title": "A Survey and Critique of Multiagent Deep Reinforcement Learning", "abstract": "Deep reinforcement learning (RL) has achieved outstanding results in recent years. This has led to a dramatic increase in the number of applications and methods. Recent works have explored learning beyond single-agent scenarios and have considered multiagent learning (MAL) scenarios. Initial results report successes in complex multiagent domains, although there are several challenges to be addressed. The primary goal of this article is to provide a clear overview of current multiagent deep reinforcement learning (MDRL) literature. Additionally, we complement the overview with a broader analysis: (i) we revisit previous key components, originally presented in MAL and RL, and highlight how they have been adapted to multiagent deep reinforcement learning settings. (ii) We provide general guidelines to new practitioners in the area: describing lessons learned from MDRL works, pointing to recent benchmarks, and outlining open avenues of research. (iii) We take a more critical tone raising practical challenges of MDRL (e.g., implementation and computational demands). We expect this article will help unify and motivate future research to take advantage of the abundant literature that exists (e.g., RL and MAL) in a joint effort to promote fruitful research in the multiagent community.", "published": "2018-10-12T15:54:05Z", "authors": ["Pablo Hernandez-Leal", "Bilal Kartal", "Matthew E. Taylor"], "categories": ["cs.MA", "cs.AI", "cs.LG"], "doi": "10.1007/s10458-019-09421-1", "_query": "real estate valuation deep learning", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2010.07842v1", "title": "Deep Learning on Real Geophysical Data: A Case Study for Distributed Acoustic Sensing Research", "abstract": "Deep Learning approaches for real, large, and complex scientific data sets can be very challenging to design. In this work, we present a complete search for a finely-tuned and efficiently scaled deep learning classifier to identify usable energy from seismic data acquired using Distributed Acoustic Sensing (DAS). While using only a subset of labeled images during training, we were able to identify suitable models that can be accurately generalized to unknown signal patterns. We show that by using 16 times more GPUs, we can increase the training speed by more than two orders of magnitude on a 50,000-image data set.", "published": "2020-10-15T15:59:52Z", "authors": ["Vincent Dumont", "Verónica Rodríguez Tribaldos", "Jonathan Ajo-Franklin", "Kesheng Wu"], "categories": ["cs.LG", "physics.geo-ph"], "doi": "", "_query": "real estate valuation deep learning", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2007.15840v3", "title": "A Survey on Concept Factorization: From Shallow to Deep Representation Learning", "abstract": "The quality of learned features by representation learning determines the performance of learning algorithms and the related application tasks (such as high-dimensional data clustering). As a relatively new paradigm for representation learning, Concept Factorization (CF) has attracted a great deal of interests in the areas of machine learning and data mining for over a decade. Lots of effective CF based methods have been proposed based on different perspectives and properties, but note that it still remains not easy to grasp the essential connections and figure out the underlying explanatory factors from exiting studies. In this paper, we therefore survey the recent advances on CF methodologies and the potential benchmarks by categorizing and summarizing the current methods. Specifically, we first re-view the root CF method, and then explore the advancement of CF-based representation learning ranging from shallow to deep/multilayer cases. We also introduce the potential application areas of CF-based methods. Finally, we point out some future directions for studying the CF-based representation learning. Overall, this survey provides an insightful overview of both theoretical basis and current developments in the field of CF, which can also help the interested researchers to understand the current trends of CF and find the most appropriate CF techniques to deal with particular applications.", "published": "2020-07-31T04:19:14Z", "authors": ["Zhao Zhang", "Yan Zhang", "Mingliang Xu", "Li Zhang", "Yi Yang", "Shuicheng Yan"], "categories": ["cs.LG", "cs.CV", "stat.ML"], "doi": "", "_query": "real estate valuation deep learning", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2210.15142v1", "title": "Taxonomic Recommendations of Real Estate Properties with Textual Attribute Information", "abstract": "In this extended abstract, we present an end to end approach for building a taxonomy of home attribute terms that enables hierarchical recommendations of real estate properties. We cover the methodology for building a real-estate taxonomy, metrics for measuring this structure's quality, and then conclude with a production use-case of making recommendations from search keywords at different levels of topical similarity.", "published": "2022-10-27T03:03:12Z", "authors": ["Zachary Harrison", "Anish Khazane"], "categories": ["cs.IR"], "doi": "", "_query": "real estate valuation deep learning", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2009.08525v1", "title": "Deep Learning & Software Engineering: State of Research and Future Directions", "abstract": "Given the current transformative potential of research that sits at the intersection of Deep Learning (DL) and Software Engineering (SE), an NSF-sponsored community workshop was conducted in co-location with the 34th IEEE/ACM International Conference on Automated Software Engineering (ASE'19) in San Diego, California. The goal of this workshop was to outline high priority areas for cross-cutting research. While a multitude of exciting directions for future work were identified, this report provides a general summary of the research areas representing the areas of highest priority which were discussed at the workshop. The intent of this report is to serve as a potential roadmap to guide future work that sits at the intersection of SE & DL.", "published": "2020-09-17T20:46:08Z", "authors": ["Prem Devanbu", "Matthew Dwyer", "Sebastian Elbaum", "Michael Lowry", "Kevin Moran", "Denys Poshyvanyk", "Baishakhi Ray", "Rishabh Singh", "Xiangyu Zhang"], "categories": ["cs.SE", "cs.AI", "cs.LG"], "doi": "", "_query": "real estate valuation deep learning", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2301.13816v4", "title": "Execution-based Code Generation using Deep Reinforcement Learning", "abstract": "The utilization of programming language (PL) models, pre-trained on large-scale code corpora, as a means of automating software engineering processes has demonstrated considerable potential in streamlining various code generation tasks such as code completion, code translation, and program synthesis. However, current approaches mainly rely on supervised fine-tuning objectives borrowed from text generation, neglecting unique sequence-level characteristics of code, including but not limited to compilability as well as syntactic and functional correctness. To address this limitation, we propose PPOCoder, a new framework for code generation that synergistically combines pre-trained PL models with Proximal Policy Optimization (PPO) which is a widely used deep reinforcement learning technique. By utilizing non-differentiable feedback from code execution and structure alignment, PPOCoder seamlessly integrates external code-specific knowledge into the model optimization process. It's important to note that PPOCoder is a task-agnostic and model-agnostic framework that can be used across different code generation tasks and PLs. Extensive experiments on three code generation tasks demonstrate the effectiveness of our proposed approach compared to SOTA methods, achieving significant improvements in compilation success rates and functional correctness across different PLs.", "published": "2023-01-31T18:02:26Z", "authors": ["Parshin Shojaee", "Aneesh Jain", "Sindhu Tipirneni", "Chandan K. Reddy"], "categories": ["cs.LG", "cs.AI", "cs.CL", "cs.PL"], "doi": "", "_query": "real estate valuation deep learning", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/1612.07640v1", "title": "Deep Learning and Its Applications to Machine Health Monitoring: A Survey", "abstract": "Since 2006, deep learning (DL) has become a rapidly growing research direction, redefining state-of-the-art performances in a wide range of areas such as object recognition, image segmentation, speech recognition and machine translation. In modern manufacturing systems, data-driven machine health monitoring is gaining in popularity due to the widespread deployment of low-cost sensors and their connection to the Internet. Meanwhile, deep learning provides useful tools for processing and analyzing these big machinery data. The main purpose of this paper is to review and summarize the emerging research work of deep learning on machine health monitoring. After the brief introduction of deep learning techniques, the applications of deep learning in machine health monitoring systems are reviewed mainly from the following aspects: Auto-encoder (AE) and its variants, Restricted Boltzmann Machines and its variants including Deep Belief Network (DBN) and Deep Boltzmann Machines (DBM), Convolutional Neural Networks (CNN) and Recurrent Neural Networks (RNN). Finally, some new trends of DL-based machine health monitoring methods are discussed.", "published": "2016-12-16T04:56:30Z", "authors": ["Rui Zhao", "Ruqiang Yan", "Zhenghua Chen", "Kezhi Mao", "Peng Wang", "Robert X. Gao"], "categories": ["cs.LG", "stat.ML"], "doi": "", "_query": "real estate valuation deep learning", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2301.08028v4", "title": "A Tutorial on Meta-Reinforcement Learning", "abstract": "While deep reinforcement learning (RL) has fueled multiple high-profile successes in machine learning, it is held back from more widespread adoption by its often poor data efficiency and the limited generality of the policies it produces. A promising approach for alleviating these limitations is to cast the development of better RL algorithms as a machine learning problem itself in a process called meta-RL. Meta-RL is most commonly studied in a problem setting where, given a distribution of tasks, the goal is to learn a policy that is capable of adapting to any new task from the task distribution with as little data as possible. In this survey, we describe the meta-RL problem setting in detail as well as its major variations. We discuss how, at a high level, meta-RL research can be clustered based on the presence of a task distribution and the learning budget available for each individual task. Using these clusters, we then survey meta-RL algorithms and applications. We conclude by presenting the open problems on the path to making meta-RL part of the standard toolbox for a deep RL practitioner.", "published": "2023-01-19T12:01:41Z", "authors": ["Jacob Beck", "Risto Vuorio", "Evan Zheran Liu", "Zheng Xiong", "Luisa Zintgraf", "Chelsea Finn", "Shimon Whiteson"], "categories": ["cs.LG"], "doi": "10.1561/2200000080", "_query": "real estate valuation deep learning", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2501.06564v1", "title": "Natural Language Processing and Deep Learning Models to Classify Phase of Flight in Aviation Safety Occurrences", "abstract": "The air transport system recognizes the criticality of safety, as even minor anomalies can have severe consequences. Reporting accidents and incidents play a vital role in identifying their causes and proposing safety recommendations. However, the narratives describing pre-accident events are presented in unstructured text that is not easily understood by computer systems. Classifying and categorizing safety occurrences based on these narratives can support informed decision-making by aviation industry stakeholders. In this study, researchers applied natural language processing (NLP) and artificial intelligence (AI) models to process text narratives to classify the flight phases of safety occurrences. The classification performance of two deep learning models, ResNet and sRNN was evaluated, using an initial dataset of 27,000 safety occurrence reports from the NTSB. The results demonstrated good performance, with both models achieving an accuracy exceeding 68%, well above the random guess rate of 14% for a seven-class classification problem. The models also exhibited high precision, recall, and F1 scores. The sRNN model greatly outperformed the simplified ResNet model architecture used in this study. These findings indicate that NLP and deep learning models can infer the flight phase from raw text narratives, enabling effective analysis of safety occurrences.", "published": "2025-01-11T15:02:49Z", "authors": ["Aziida Nanyonga", "Hassan Wasswa", "Oleksandra Molloy", "Ugur Turhan", "Graham Wild"], "categories": ["cs.CL", "cs.LG"], "doi": "", "_query": "real estate valuation deep learning", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/1808.06670v5", "title": "Learning deep representations by mutual information estimation and maximization", "abstract": "In this work, we perform unsupervised learning of representations by maximizing mutual information between an input and the output of a deep neural network encoder. Importantly, we show that structure matters: incorporating knowledge about locality of the input to the objective can greatly influence a representation's suitability for downstream tasks. We further control characteristics of the representation by matching to a prior distribution adversarially. Our method, which we call Deep InfoMax (DIM), outperforms a number of popular unsupervised learning methods and competes with fully-supervised learning on several classification tasks. DIM opens new avenues for unsupervised learning of representations and is an important step towards flexible formulations of representation-learning objectives for specific end-goals.", "published": "2018-08-20T19:52:51Z", "authors": ["R Devon Hjelm", "Alex Fedorov", "Samuel Lavoie-Marchildon", "Karan Grewal", "Phil Bachman", "Adam Trischler", "Yoshua Bengio"], "categories": ["stat.ML", "cs.LG"], "doi": "", "_query": "real estate valuation deep learning", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2602.19740v1", "title": "Volatility Spillovers in China's Real Estate Crisis: A Network Approach", "abstract": "Sentiment towards the Chinese real estate sector has deteriorated following the introduction of financing constraints in 2020 with the ''three red lines.\" Forcing developers to restructure their debt, the policy triggered a cascade of financing troubles, defaults, and reduced housing demand, ultimately culminating in a prolonged real estate crisis. This paper utilizes a network approach in line with Demirer et al. (2018) and Diebold and Yilmaz (2014) to measure daily time-varying connectedness in the stock return volatilities of major Chinese real estate developers throughout the crisis. Focusing on spillover between companies as reflected by market perception, this paper examines how connectedness evolves over time across firms with different regional exposures and state-ownership statuses, filling a gap in the literature to elucidate where property demand and real estate firm trustworthiness have deteriorated most. An event-study analysis of four key moments of the crisis outlines distinct phases of market sentiment: with the introduction of the three red lines, connectedness primarily reflects shared exposure and a uniform shock to the market. Then, the early unrest surrounding Evergrande exposes strong regional differentiation, with firms concentrated in less developed regions receiving significant spillover. By one year into the crisis, previously stable regions receive higher levels of spillover, and there is evidence of a substitution effect towards private developers. Two years into the crisis, the market has much less homogeneity in effects across regions and state-ownership status: major shocks induce minimal network changes, reflecting how investors have already priced in their beliefs. This paper also offers one of the most extensive timelines of the Chinese real estate crisis to date, and a new R package, GephiForR, was created for the network visualization in this paper.", "published": "2026-02-23T11:36:35Z", "authors": ["Julia Manso"], "categories": ["econ.EM", "stat.AP"], "doi": "", "_query": "real estate valuation deep learning", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2011.01706v1", "title": "Amortized Variational Deep Q Network", "abstract": "Efficient exploration is one of the most important issues in deep reinforcement learning. To address this issue, recent methods consider the value function parameters as random variables, and resort variational inference to approximate the posterior of the parameters. In this paper, we propose an amortized variational inference framework to approximate the posterior distribution of the action value function in Deep Q Network. We establish the equivalence between the loss of the new model and the amortized variational inference loss. We realize the balance of exploration and exploitation by assuming the posterior as Cauchy and Gaussian, respectively in a two-stage training process. We show that the amortized framework can results in significant less learning parameters than existing state-of-the-art method. Experimental results on classical control tasks in OpenAI Gym and chain Markov Decision Process tasks show that the proposed method performs significantly better than state-of-art methods and requires much less training time.", "published": "2020-11-03T13:48:18Z", "authors": ["Haotian Zhang", "Yuhao Wang", "Jianyong Sun", "Zongben Xu"], "categories": ["cs.LG", "cs.AI", "stat.ML"], "doi": "", "_query": "real estate valuation deep learning", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/1806.05009v3", "title": "Tree Edit Distance Learning via Adaptive Symbol Embeddings", "abstract": "Metric learning has the aim to improve classification accuracy by learning a distance measure which brings data points from the same class closer together and pushes data points from different classes further apart. Recent research has demonstrated that metric learning approaches can also be applied to trees, such as molecular structures, abstract syntax trees of computer programs, or syntax trees of natural language, by learning the cost function of an edit distance, i.e. the costs of replacing, deleting, or inserting nodes in a tree. However, learning such costs directly may yield an edit distance which violates metric axioms, is challenging to interpret, and may not generalize well. In this contribution, we propose a novel metric learning approach for trees which we call embedding edit distance learning (BEDL) and which learns an edit distance indirectly by embedding the tree nodes as vectors, such that the Euclidean distance between those vectors supports class discrimination. We learn such embeddings by reducing the distance to prototypical trees from the same class and increasing the distance to prototypical trees from different classes. In our experiments, we show that BEDL improves upon the state-of-the-art in metric learning for trees on six benchmark data sets, ranging from computer science over biomedical data to a natural-language processing data set containing over 300,000 nodes.", "published": "2018-06-13T13:08:16Z", "authors": ["Benjamin Paaßen", "Claudio Gallicchio", "Alessio Micheli", "Barbara Hammer"], "categories": ["cs.LG", "stat.ML"], "doi": "", "_query": "real estate valuation deep learning", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2404.18043v3", "title": "Utilizing Large Language Models for Information Extraction from Real Estate Transactions", "abstract": "Real estate sales contracts contain crucial information for property transactions, but manual data extraction can be time-consuming and error-prone. This paper explores the application of large language models, specifically transformer-based architectures, for automated information extraction from real estate contracts. We discuss challenges, techniques, and future directions in leveraging these models to improve efficiency and accuracy in real estate contract analysis. We generated synthetic contracts using the real-world transaction dataset, thereby fine-tuning the large-language model and achieving significant metrics improvements and qualitative improvements in information retrieval and reasoning tasks.", "published": "2024-04-28T01:38:38Z", "authors": ["Yu Zhao", "Haoxiang Gao", "Jinghan Cao", "Shiqi Yang"], "categories": ["cs.CL", "cs.IR", "cs.LG"], "doi": "", "_query": "real estate valuation deep learning", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2301.00641v1", "title": "Federated Multi-Agent Deep Reinforcement Learning Approach via Physics-Informed Reward for Multi-Microgrid Energy Management", "abstract": "The utilization of large-scale distributed renewable energy promotes the development of the multi-microgrid (MMG), which raises the need of developing an effective energy management method to minimize economic costs and keep self energy-sufficiency. The multi-agent deep reinforcement learning (MADRL) has been widely used for the energy management problem because of its real-time scheduling ability. However, its training requires massive energy operation data of microgrids (MGs), while gathering these data from different MGs would threaten their privacy and data security. Therefore, this paper tackles this practical yet challenging issue by proposing a federated multi-agent deep reinforcement learning (F-MADRL) algorithm via the physics-informed reward. In this algorithm, the federated learning (FL) mechanism is introduced to train the F-MADRL algorithm thus ensures the privacy and the security of data. In addition, a decentralized MMG model is built, and the energy of each participated MG is managed by an agent, which aims to minimize economic costs and keep self energy-sufficiency according to the physics-informed reward. At first, MGs individually execute the self-training based on local energy operation data to train their local agent models. Then, these local models are periodically uploaded to a server and their parameters are aggregated to build a global agent, which will be broadcasted to MGs and replace their local agents. In this way, the experience of each MG agent can be shared and the energy operation data is not explicitly transmitted, thus protecting the privacy and ensuring data security. Finally, experiments are conducted on Oak Ridge national laboratory distributed energy control communication lab microgrid (ORNL-MG) test system, and the comparisons are carried out to verify the effectiveness of introducing the FL mechanism and the outperformance of our proposed F-MADRL.", "published": "2022-12-29T08:35:11Z", "authors": ["Yuanzheng Li", "Shangyang He", "Yang Li", "Yang Shi", "Zhigang Zeng"], "categories": ["eess.SY", "cs.LG"], "doi": "10.1109/TNNLS.2022.3232630", "_query": "real estate valuation deep learning", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/1103.5972v1", "title": "A Comparative Anatomy of REITs and Residential Real Estate Indexes: Returns, Risks and Distributional Characteristics", "abstract": "Real Estate Investment Trusts (REITs) are the only truly liquid assets related to real estate investments. We study the behavior of U.S. REITs over the past three decades and document their return characteristics. REITs have somewhat less market risk than equity; their betas against a broad market index average about .65. Decomposing their covariances into principal components reveals several strong factors. REIT characteristics differ to some extent from those of the S&P/Case-Shiller (SCS) residential real estate indexes. This is partly attributable to methods of index construction. Our examination of REITs suggests that investment in real estate is far more risky than what might be inferred from the widely-followed SCS series. REITs, unlike SCS series are forward looking, and this helps them in the prediction of SCS returns. REIT forecasts of SCS returns are reasonably precise over a number of periods.", "published": "2011-03-30T15:58:00Z", "authors": ["John Cotter", "Richard Roll"], "categories": ["q-fin.PM", "q-fin.PR", "q-fin.ST"], "doi": "", "_query": "real estate valuation deep learning", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2201.09196v2", "title": "Learning to Predict Gradients for Semi-Supervised Continual Learning", "abstract": "A key challenge for machine intelligence is to learn new visual concepts without forgetting the previously acquired knowledge. Continual learning is aimed towards addressing this challenge. However, there is a gap between existing supervised continual learning and human-like intelligence, where human is able to learn from both labeled and unlabeled data. How unlabeled data affects learning and catastrophic forgetting in the continual learning process remains unknown. To explore these issues, we formulate a new semi-supervised continual learning method, which can be generically applied to existing continual learning models. Specifically, a novel gradient learner learns from labeled data to predict gradients on unlabeled data. Hence, the unlabeled data could fit into the supervised continual learning method. Different from conventional semi-supervised settings, we do not hypothesize that the underlying classes, which are associated to the unlabeled data, are known to the learning process. In other words, the unlabeled data could be very distinct from the labeled data. We evaluate the proposed method on mainstream continual learning, adversarial continual learning, and semi-supervised learning tasks. The proposed method achieves state-of-the-art performance on classification accuracy and backward transfer in the continual learning setting while achieving desired performance on classification accuracy in the semi-supervised learning setting. This implies that the unlabeled images can enhance the generalizability of continual learning models on the predictive ability on unseen data and significantly alleviate catastrophic forgetting. The code is available at \\url{https://github.com/luoyan407/grad_prediction.git}.", "published": "2022-01-23T06:45:47Z", "authors": ["Yan Luo", "Yongkang Wong", "Mohan Kankanhalli", "Qi Zhao"], "categories": ["cs.LG", "cs.CV"], "doi": "", "_query": "real estate valuation deep learning", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/1812.05551v3", "title": "Exploration Conscious Reinforcement Learning Revisited", "abstract": "The Exploration-Exploitation tradeoff arises in Reinforcement Learning when one cannot tell if a policy is optimal. Then, there is a constant need to explore new actions instead of exploiting past experience. In practice, it is common to resolve the tradeoff by using a fixed exploration mechanism, such as $ε$-greedy exploration or by adding Gaussian noise, while still trying to learn an optimal policy. In this work, we take a different approach and study exploration-conscious criteria, that result in optimal policies with respect to the exploration mechanism. Solving these criteria, as we establish, amounts to solving a surrogate Markov Decision Process. We continue and analyze properties of exploration-conscious optimal policies and characterize two general approaches to solve such criteria. Building on the approaches, we apply simple changes in existing tabular and deep Reinforcement Learning algorithms and empirically demonstrate superior performance relatively to their non-exploration-conscious counterparts, both for discrete and continuous action spaces.", "published": "2018-12-13T18:08:04Z", "authors": ["Lior Shani", "Yonathan Efroni", "Shie Mannor"], "categories": ["cs.LG", "stat.ML"], "doi": "", "_query": "real estate valuation deep learning", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2312.05346v1", "title": "Deep Learning for Dynamic NFT Valuation", "abstract": "I study the price dynamics of non-fungible tokens (NFTs) and propose a deep learning framework for dynamic valuation of NFTs. I use data from the Ethereum blockchain and OpenSea to train a deep learning model on historical trades, market trends, and traits/rarity features of Bored Ape Yacht Club NFTs. After hyperparameter tuning, the model is able to predict the price of NFTs with high accuracy. I propose an application framework for this model using zero-knowledge machine learning (zkML) and discuss its potential use cases in the context of decentralized finance (DeFi) applications.", "published": "2023-12-08T20:10:56Z", "authors": ["Mingxuan He"], "categories": ["q-fin.CP"], "doi": "", "_query": "real estate valuation deep learning", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2011.03705v1", "title": "Blind Motion Deblurring through SinGAN Architecture", "abstract": "Blind motion deblurring involves reconstructing a sharp image from an observation that is blurry. It is a problem that is ill-posed and lies in the categories of image restoration problems. The training data-based methods for image deblurring mostly involve training models that take a lot of time. These models are data-hungry i.e., they require a lot of training data to generate satisfactory results. Recently, there are various image feature learning methods developed which relieve us of the need for training data and perform image restoration and image synthesis, e.g., DIP, InGAN, and SinGAN. SinGAN is a generative model that is unconditional and could be learned from a single natural image. This model primarily captures the internal distribution of the patches which are present in the image and is capable of generating samples of varied diversity while preserving the visual content of the image. Images generated from the model are very much like real natural images. In this paper, we focus on blind motion deblurring through SinGAN architecture.", "published": "2020-11-07T06:09:16Z", "authors": ["Harshil Jain", "Rohit Patil", "Indra Deep Mastan", "Shanmuganathan Raman"], "categories": ["cs.CV"], "doi": "", "_query": "real estate valuation deep learning", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/1802.08200v1", "title": "Multiparametric Deep Learning Tissue Signatures for a Radiological Biomarker of Breast Cancer: Preliminary Results", "abstract": "A new paradigm is beginning to emerge in Radiology with the advent of increased computational capabilities and algorithms. This has led to the ability of real time learning by computer systems of different lesion types to help the radiologist in defining disease. For example, using a deep learning network, we developed and tested a multiparametric deep learning (MPDL) network for segmentation and classification using multiparametric magnetic resonance imaging (mpMRI) radiological images. The MPDL network was constructed from stacked sparse autoencoders with inputs from mpMRI. Evaluation of MPDL consisted of cross-validation, sensitivity, and specificity. Dice similarity between MPDL and post-DCE lesions were evaluated. We demonstrate high sensitivity and specificity for differentiation of malignant from benign lesions of 90% and 85% respectively with an AUC of 0.93. The Integrated MPDL method accurately segmented and classified different breast tissue from multiparametric breast MRI using deep leaning tissue signatures.", "published": "2018-02-10T02:51:59Z", "authors": ["Vishwa S. Parekh", "Katarzyna J. Macura", "Susan Harvey", "Ihab Kamel", "Riham EI-Khouli", "David A. Bluemke", "Michael A. Jacobs"], "categories": ["physics.med-ph", "cs.AI", "cs.CV", "q-bio.QM"], "doi": "10.1002/mp.13849", "_query": "real estate valuation deep learning", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2012.04377v1", "title": "Learning Structured Declarative Rule Sets -- A Challenge for Deep Discrete Learning", "abstract": "Arguably the key reason for the success of deep neural networks is their ability to autonomously form non-linear combinations of the input features, which can be used in subsequent layers of the network. The analogon to this capability in inductive rule learning is to learn a structured rule base, where the inputs are combined to learn new auxiliary concepts, which can then be used as inputs by subsequent rules. Yet, research on rule learning algorithms that have such capabilities is still in their infancy, which is - we would argue - one of the key impediments to substantial progress in this field. In this position paper, we want to draw attention to this unsolved problem, with a particular focus on previous work in predicate invention and multi-label rule learning", "published": "2020-12-08T11:43:16Z", "authors": ["Johannes Fürnkranz", "Eyke Hüllermeier", "Eneldo Loza Mencía", "Michael Rapp"], "categories": ["cs.LG"], "doi": "", "_query": "real estate valuation deep learning", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2311.13471v1", "title": "Comparative Analysis of Linear Regression, Gaussian Elimination, and LU Decomposition for CT Real Estate Purchase Decisions", "abstract": "This paper presents a comprehensive evaluation of three distinct computational algorithms applied to the decision-making process of real estate purchases. Specifically, we analyze the efficacy of Linear Regression from Scikit-learn library, Gaussian Elimination with partial pivoting, and LU Decomposition in predicting the advisability of buying a house in the State of Connecticut based on a set of financial and market-related parameters. The algorithms' performances were compared using a dataset encompassing town-specific details, yearly data, interest rates, and median sale ratios. Our results demonstrate significant differences in predictive accuracy, with Linear Regression and LU Decomposition providing the most reliable recommendations and Gaussian Elimination showing limitations in stability and performance. The study's findings emphasize the importance of algorithm selection in predictive analytic and offer insights into the practical applications of computational methods in real estate investment strategies. By evaluating model efficacy through metrics such as R-squared scores and Mean Squared Error, we provide a nuanced understanding of each method's strengths and weaknesses, contributing valuable knowledge to the fields of real estate analysis and predictive modeling.", "published": "2023-11-22T15:35:56Z", "authors": ["Xilin Cheng"], "categories": ["cs.LG", "cs.CC", "cs.CE", "math.NA"], "doi": "", "_query": "real estate valuation deep learning", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/1806.09998v1", "title": "Real time state monitoring and fault diagnosis system for motor based on LabVIEW", "abstract": "Motor is the most widely used production equipment in industrial field. In order to realize the real-time state monitoring and multi-fault pre-diagnosis of three-phase motor, this paper presents a design of three-phase motor state monitoring and fault diagnosis system based on LabVIEW. The multi-dimensional vibration acceleration, rotational speed, temperature, current and voltage signals of the motor are collected with NI cDAQ acquisition equipment in real time and high speed. At the same time, the model of motor health state and fault state is established. The order analysis algorithm is used to analyze the data at an advanced level, and the diagnosis and classification of different fault types are realized. The system is equipped with multi-channel acquisition, display, analysis and storage. Combined with the current cloud transmission technology, we will back up the data to the cloud to be used by other terminals.", "published": "2018-06-25T02:37:56Z", "authors": ["S. Q. Liu", "Z. S. Ji", "Y Wang", "Z. C. Zhang"], "categories": ["eess.SP", "cs.SE", "eess.SY"], "doi": "", "_query": "real estate valuation deep learning", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/1802.04051v4", "title": "One Deep Music Representation to Rule Them All? : A comparative analysis of different representation learning strategies", "abstract": "Inspired by the success of deploying deep learning in the fields of Computer Vision and Natural Language Processing, this learning paradigm has also found its way into the field of Music Information Retrieval. In order to benefit from deep learning in an effective, but also efficient manner, deep transfer learning has become a common approach. In this approach, it is possible to reuse the output of a pre-trained neural network as the basis for a new learning task. The underlying hypothesis is that if the initial and new learning tasks show commonalities and are applied to the same type of input data (e.g. music audio), the generated deep representation of the data is also informative for the new task. Since, however, most of the networks used to generate deep representations are trained using a single initial learning source, their representation is unlikely to be informative for all possible future tasks. In this paper, we present the results of our investigation of what are the most important factors to generate deep representations for the data and learning tasks in the music domain. We conducted this investigation via an extensive empirical study that involves multiple learning sources, as well as multiple deep learning architectures with varying levels of information sharing between sources, in order to learn music representations. We then validate these representations considering multiple target datasets for evaluation. The results of our experiments yield several insights on how to approach the design of methods for learning widely deployable deep data representations in the music domain.", "published": "2018-02-12T14:08:54Z", "authors": ["Jaehun Kim", "Julián Urbano", "Cynthia C. S. Liem", "Alan Hanjalic"], "categories": ["cs.NE", "cs.SD", "eess.AS"], "doi": "", "_query": "real estate valuation deep learning", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/1904.13223v3", "title": "Unsupervised Representation Learning with Minimax Distance Measures", "abstract": "We investigate the use of Minimax distances to extract in a nonparametric way the features that capture the unknown underlying patterns and structures in the data. We develop a general-purpose and computationally efficient framework to employ Minimax distances with many machine learning methods that perform on numerical data. We study both computing the pairwise Minimax distances for all pairs of objects and as well as computing the Minimax distances of all the objects to/from a fixed (test) object. We first efficiently compute the pairwise Minimax distances between the objects, using the equivalence of Minimax distances over a graph and over a minimum spanning tree constructed on that. Then, we perform an embedding of the pairwise Minimax distances into a new vector space, such that their squared Euclidean distances in the new space equal to the pairwise Minimax distances in the original space. We also study the case of having multiple pairwise Minimax matrices, instead of a single one. Thereby, we propose an embedding via first summing up the centered matrices and then performing an eigenvalue decomposition to obtain the relevant features. In the following, we study computing Minimax distances from a fixed (test) object which can be used for instance in K-nearest neighbor search. Similar to the case of all-pair pairwise Minimax distances, we develop an efficient and general-purpose algorithm that is applicable with any arbitrary base distance measure. Moreover, we investigate in detail the edges selected by the Minimax distances and thereby explore the ability of Minimax distances in detecting outlier objects. Finally, for each setting, we perform several experiments to demonstrate the effectiveness of our framework.", "published": "2019-04-27T16:13:08Z", "authors": ["Morteza Haghir Chehreghani"], "categories": ["cs.LG", "cs.AI", "stat.ML"], "doi": "10.1007/s10994-020-05886-4", "_query": "real estate valuation deep learning", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/1908.06040v2", "title": "Performing Deep Recurrent Double Q-Learning for Atari Games", "abstract": "Currently, many applications in Machine Learning are based on define new models to extract more information about data, In this case Deep Reinforcement Learning with the most common application in video games like Atari, Mario, and others causes an impact in how to computers can learning by himself with only information called rewards obtained from any action. There is a lot of algorithms modeled and implemented based on Deep Recurrent Q-Learning proposed by DeepMind used in AlphaZero and Go. In this document, We proposed Deep Recurrent Double Q-Learning that is an implementation of Deep Reinforcement Learning using Double Q-Learning algorithms and Recurrent Networks like LSTM and DRQN.", "published": "2019-08-16T15:56:16Z", "authors": ["Felipe Moreno-Vera"], "categories": ["cs.LG", "cs.AI", "cs.NE", "stat.ML"], "doi": "", "_query": "real estate valuation deep learning", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2412.10104v2", "title": "RETQA: A Large-Scale Open-Domain Tabular Question Answering Dataset for Real Estate Sector", "abstract": "The real estate market relies heavily on structured data, such as property details, market trends, and price fluctuations. However, the lack of specialized Tabular Question Answering datasets in this domain limits the development of automated question-answering systems. To fill this gap, we introduce RETQA, the first large-scale open-domain Chinese Tabular Question Answering dataset for Real Estate. RETQA comprises 4,932 tables and 20,762 question-answer pairs across 16 sub-fields within three major domains: property information, real estate company finance information and land auction information. Compared with existing tabular question answering datasets, RETQA poses greater challenges due to three key factors: long-table structures, open-domain retrieval, and multi-domain queries. To tackle these challenges, we propose the SLUTQA framework, which integrates large language models with spoken language understanding tasks to enhance retrieval and answering accuracy. Extensive experiments demonstrate that SLUTQA significantly improves the performance of large language models on RETQA by in-context learning. RETQA and SLUTQA provide essential resources for advancing tabular question answering research in the real estate domain, addressing critical challenges in open-domain and long-table question-answering. The dataset and code are publicly available at \\url{https://github.com/jensen-w/RETQA}.", "published": "2024-12-13T12:45:14Z", "authors": ["Zhensheng Wang", "Wenmian Yang", "Kun Zhou", "Yiquan Zhang", "Weijia Jia"], "categories": ["cs.CL", "cs.AI"], "doi": "10.1609/AAAI.V39I24.34734", "_query": "real estate valuation deep learning", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/1912.01206v1", "title": "Deep Probabilistic Models to Detect Data Poisoning Attacks", "abstract": "Data poisoning attacks compromise the integrity of machine-learning models by introducing malicious training samples to influence the results during test time. In this work, we investigate backdoor data poisoning attack on deep neural networks (DNNs) by inserting a backdoor pattern in the training images. The resulting attack will misclassify poisoned test samples while maintaining high accuracies for the clean test-set. We present two approaches for detection of such poisoned samples by quantifying the uncertainty estimates associated with the trained models. In the first approach, we model the outputs of the various layers (deep features) with parametric probability distributions learnt from the clean held-out dataset. At inference, the likelihoods of deep features w.r.t these distributions are calculated to derive uncertainty estimates. In the second approach, we use Bayesian deep neural networks trained with mean-field variational inference to estimate model uncertainty associated with the predictions. The uncertainty estimates from these methods are used to discriminate clean from the poisoned samples.", "published": "2019-12-03T05:58:51Z", "authors": ["Mahesh Subedar", "Nilesh Ahuja", "Ranganath Krishnan", "Ibrahima J. Ndiour", "Omesh Tickoo"], "categories": ["cs.LG", "stat.ML"], "doi": "", "_query": "real estate valuation deep learning", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2108.01867v2", "title": "A Pragmatic Look at Deep Imitation Learning", "abstract": "The introduction of the generative adversarial imitation learning (GAIL) algorithm has spurred the development of scalable imitation learning approaches using deep neural networks. Many of the algorithms that followed used a similar procedure, combining on-policy actor-critic algorithms with inverse reinforcement learning. More recently there have been an even larger breadth of approaches, most of which use off-policy algorithms. However, with the breadth of algorithms, everything from datasets to base reinforcement learning algorithms to evaluation settings can vary, making it difficult to fairly compare them. In this work we re-implement 6 different IL algorithms, updating 3 of them to be off-policy, base them on a common off-policy algorithm (SAC), and evaluate them on a widely-used expert trajectory dataset (D4RL) for the most common benchmark (MuJoCo). After giving all algorithms the same hyperparameter optimisation budget, we compare their results for a range of expert trajectories. In summary, GAIL, with all of its improvements, consistently performs well across a range of sample sizes, AdRIL is a simple contender that performs well with one important hyperparameter to tune, and behavioural cloning remains a strong baseline when data is more plentiful.", "published": "2021-08-04T06:33:10Z", "authors": ["Kai Arulkumaran", "Dan Ogawa Lillrank"], "categories": ["cs.LG", "cs.NE", "stat.ML"], "doi": "", "_query": "real estate valuation deep learning", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2109.08618v3", "title": "A review and experimental evaluation of deep learning methods for MRI reconstruction", "abstract": "Following the success of deep learning in a wide range of applications, neural network-based machine-learning techniques have received significant interest for accelerating magnetic resonance imaging (MRI) acquisition and reconstruction strategies. A number of ideas inspired by deep learning techniques for computer vision and image processing have been successfully applied to nonlinear image reconstruction in the spirit of compressed sensing for accelerated MRI. Given the rapidly growing nature of the field, it is imperative to consolidate and summarize the large number of deep learning methods that have been reported in the literature, to obtain a better understanding of the field in general. This article provides an overview of the recent developments in neural-network based approaches that have been proposed specifically for improving parallel imaging. A general background and introduction to parallel MRI is also given from a classical view of k-space based reconstruction methods. Image domain based techniques that introduce improved regularizers are covered along with k-space based methods which focus on better interpolation strategies using neural networks. While the field is rapidly evolving with plenty of papers published each year, in this review, we attempt to cover broad categories of methods that have shown good performance on publicly available data sets. Limitations and open problems are also discussed and recent efforts for producing open data sets and benchmarks for the community are examined.", "published": "2021-09-17T15:50:51Z", "authors": ["Arghya Pal", "Yogesh Rathi"], "categories": ["eess.IV", "cs.CV", "cs.LG"], "doi": "", "_query": "real estate valuation deep learning", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/1910.01708v1", "title": "Benchmarking Batch Deep Reinforcement Learning Algorithms", "abstract": "Widely-used deep reinforcement learning algorithms have been shown to fail in the batch setting--learning from a fixed data set without interaction with the environment. Following this result, there have been several papers showing reasonable performances under a variety of environments and batch settings. In this paper, we benchmark the performance of recent off-policy and batch reinforcement learning algorithms under unified settings on the Atari domain, with data generated by a single partially-trained behavioral policy. We find that under these conditions, many of these algorithms underperform DQN trained online with the same amount of data, as well as the partially-trained behavioral policy. To introduce a strong baseline, we adapt the Batch-Constrained Q-learning algorithm to a discrete-action setting, and show it outperforms all existing algorithms at this task.", "published": "2019-10-03T20:15:55Z", "authors": ["Scott Fujimoto", "Edoardo Conti", "Mohammad Ghavamzadeh", "Joelle Pineau"], "categories": ["cs.LG", "cs.AI", "stat.ML"], "doi": "", "_query": "real estate valuation deep learning", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/1402.1349v1", "title": "Dissimilarity-based Ensembles for Multiple Instance Learning", "abstract": "In multiple instance learning, objects are sets (bags) of feature vectors (instances) rather than individual feature vectors. In this paper we address the problem of how these bags can best be represented. Two standard approaches are to use (dis)similarities between bags and prototype bags, or between bags and prototype instances. The first approach results in a relatively low-dimensional representation determined by the number of training bags, while the second approach results in a relatively high-dimensional representation, determined by the total number of instances in the training set. In this paper a third, intermediate approach is proposed, which links the two approaches and combines their strengths. Our classifier is inspired by a random subspace ensemble, and considers subspaces of the dissimilarity space, defined by subsets of instances, as prototypes. We provide guidelines for using such an ensemble, and show state-of-the-art performances on a range of multiple instance learning problems.", "published": "2014-02-06T13:35:01Z", "authors": ["Veronika Cheplygina", "David M. J. Tax", "Marco Loog"], "categories": ["stat.ML", "cs.LG"], "doi": "10.1109/TNNLS.2015.2424254", "_query": "real estate valuation deep learning", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/1310.8499v2", "title": "Deep AutoRegressive Networks", "abstract": "We introduce a deep, generative autoencoder capable of learning hierarchies of distributed representations from data. Successive deep stochastic hidden layers are equipped with autoregressive connections, which enable the model to be sampled from quickly and exactly via ancestral sampling. We derive an efficient approximate parameter estimation method based on the minimum description length (MDL) principle, which can be seen as maximising a variational lower bound on the log-likelihood, with a feedforward neural network implementing approximate inference. We demonstrate state-of-the-art generative performance on a number of classic data sets: several UCI data sets, MNIST and Atari 2600 games.", "published": "2013-10-31T13:47:30Z", "authors": ["Karol Gregor", "Ivo Danihelka", "Andriy Mnih", "Charles Blundell", "Daan Wierstra"], "categories": ["cs.LG", "stat.ML"], "doi": "", "_query": "real estate valuation deep learning", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/1904.06950v1", "title": "Human-Guided Learning of Column Networks: Augmenting Deep Learning with Advice", "abstract": "Recently, deep models have been successfully applied in several applications, especially with low-level representations. However, sparse, noisy samples and structured domains (with multiple objects and interactions) are some of the open challenges in most deep models. Column Networks, a deep architecture, can succinctly capture such domain structure and interactions, but may still be prone to sub-optimal learning from sparse and noisy samples. Inspired by the success of human-advice guided learning in AI, especially in data-scarce domains, we propose Knowledge-augmented Column Networks that leverage human advice/knowledge for better learning with noisy/sparse samples. Our experiments demonstrate that our approach leads to either superior overall performance or faster convergence (i.e., both effective and efficient).", "published": "2019-04-15T10:23:10Z", "authors": ["Mayukh Das", "Yang Yu", "Devendra Singh Dhami", "Gautam Kunapuli", "Sriraam Natarajan"], "categories": ["cs.LG", "cs.AI", "stat.ML"], "doi": "", "_query": "real estate valuation deep learning", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2410.10860v1", "title": "A Recipe For Building a Compliant Real Estate Chatbot", "abstract": "In recent years, there has been significant effort to align large language models with human preferences. This work focuses on developing a chatbot specialized in the real estate domain, with an emphasis on incorporating compliant behavior to ensure it can be used without perpetuating discriminatory practices like steering and redlining, which have historically plagued the real estate industry in the United States. Building on prior work, we present a method for generating a synthetic general instruction-following dataset, along with safety data. Through extensive evaluations and benchmarks, we fine-tuned a llama-3-8B-instruct model and demonstrated that we can enhance it's performance significantly to match huge closed-source models like GPT-4o while making it safer and more compliant. We open-source the model, data and code to support further development and research in the community.", "published": "2024-10-07T16:03:47Z", "authors": ["Navid Madani", "Anusha Bagalkotkar", "Supriya Anand", "Gabriel Arnson", "Rohini Srihari", "Kenneth Joseph"], "categories": ["cs.CL", "cs.AI"], "doi": "", "_query": "real estate valuation deep learning", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2207.05696v1", "title": "RE-Tagger: A light-weight Real-Estate Image Classifier", "abstract": "Real-estate image tagging is one of the essential use-cases to save efforts involved in manual annotation and enhance the user experience. This paper proposes an end-to-end pipeline (referred to as RE-Tagger) for the real-estate image classification problem. We present a two-stage transfer learning approach using custom InceptionV3 architecture to classify images into different categories (i.e., bedroom, bathroom, kitchen, balcony, hall, and others). Finally, we released the application as REST API hosted as a web application running on 2 cores machine with 2 GB RAM. The demo video is available here.", "published": "2022-07-12T17:16:06Z", "authors": ["Prateek Chhikara", "Anil Goyal", "Chirag Sharma"], "categories": ["cs.CV", "cs.LG"], "doi": "10.1007/978-3-031-26422-1_44", "_query": "real estate valuation deep learning", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/1910.01716v4", "title": "False Data Injection Attacks in Internet of Things and Deep Learning enabled Predictive Analytics", "abstract": "Industry 4.0 is the latest industrial revolution primarily merging automation with advanced manufacturing to reduce direct human effort and resources. Predictive maintenance (PdM) is an industry 4.0 solution, which facilitates predicting faults in a component or a system powered by state-of-the-art machine learning (ML) algorithms and the Internet-of-Things (IoT) sensors. However, IoT sensors and deep learning (DL) algorithms, both are known for their vulnerabilities to cyber-attacks. In the context of PdM systems, such attacks can have catastrophic consequences as they are hard to detect due to the nature of the attack. To date, the majority of the published literature focuses on the accuracy of DL enabled PdM systems and often ignores the effect of such attacks. In this paper, we demonstrate the effect of IoT sensor attacks on a PdM system. At first, we use three state-of-the-art DL algorithms, specifically, Long Short-Term Memory (LSTM), Gated Recurrent Unit (GRU), and Convolutional Neural Network (CNN) for predicting the Remaining Useful Life (RUL) of a turbofan engine using NASA's C-MAPSS dataset. The obtained results show that the GRU-based PdM model outperforms some of the recent literature on RUL prediction using the C-MAPSS dataset. Afterward, we model two different types of false data injection attacks (FDIA) on turbofan engine sensor data and evaluate their impact on CNN, LSTM, and GRU-based PdM systems. The obtained results demonstrate that FDI attacks on even a few IoT sensors can strongly defect the RUL prediction. However, the GRU-based PdM model performs better in terms of accuracy and resiliency. Lastly, we perform a study on the GRU-based PdM model using four different GRU networks with different sequence lengths. Our experiments reveal an interesting relationship between the accuracy, resiliency and sequence length for the GRU-based PdM models.", "published": "2019-10-03T20:50:08Z", "authors": ["Gautam Raj Mode", "Prasad Calyam", "Khaza Anuarul Hoque"], "categories": ["cs.LG", "cs.PF", "eess.SP", "stat.ML"], "doi": "", "_query": "real estate valuation deep learning", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2303.02060v2", "title": "Spectral learning of Bernoulli linear dynamical systems models", "abstract": "Latent linear dynamical systems with Bernoulli observations provide a powerful modeling framework for identifying the temporal dynamics underlying binary time series data, which arise in a variety of contexts such as binary decision-making and discrete stochastic processes (e.g., binned neural spike trains). Here we develop a spectral learning method for fast, efficient fitting of probit-Bernoulli latent linear dynamical system (LDS) models. Our approach extends traditional subspace identification methods to the Bernoulli setting via a transformation of the first and second sample moments. This results in a robust, fixed-cost estimator that avoids the hazards of local optima and the long computation time of iterative fitting procedures like the expectation-maximization (EM) algorithm. In regimes where data is limited or assumptions about the statistical structure of the data are not met, we demonstrate that the spectral estimate provides a good initialization for Laplace-EM fitting. Finally, we show that the estimator provides substantial benefits to real world settings by analyzing data from mice performing a sensory decision-making task.", "published": "2023-03-03T16:29:12Z", "authors": ["Iris R. Stone", "Yotam Sagiv", "Il Memming Park", "Jonathan W. Pillow"], "categories": ["stat.ML", "cs.LG"], "doi": "", "_query": "real estate valuation deep learning", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2201.02093v1", "title": "Deep Learning Based Classification System For Recognizing Local Spinach", "abstract": "A deep learning model gives an incredible result for image processing by studying from the trained dataset. Spinach is a leaf vegetable that contains vitamins and nutrients. In our research, a Deep learning method has been used that can automatically identify spinach and this method has a dataset of a total of five species of spinach that contains 3785 images. Four Convolutional Neural Network (CNN) models were used to classify our spinach. These models give more accurate results for image classification. Before applying these models there is some preprocessing of the image data. For the preprocessing of data, some methods need to happen. Those are RGB conversion, filtering, resize & rescaling, and categorization. After applying these methods image data are pre-processed and ready to be used in the classifier algorithms. The accuracy of these classifiers is in between 98.68% - 99.79%. Among those models, VGG16 achieved the highest accuracy of 99.79%.", "published": "2022-01-06T15:10:41Z", "authors": ["Mirajul Islam", "Nushrat Jahan Ria", "Jannatul Ferdous Ani", "Abu Kaisar Mohammad Masum", "Sheikh Abujar", "Syed Akhter Hossain"], "categories": ["cs.CV", "cs.LG", "eess.IV"], "doi": "10.1007/978-3-030-85365-5_1", "_query": "real estate valuation deep learning", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2209.05471v2", "title": "PATE: Property, Amenities, Traffic and Emotions Coming Together for Real Estate Price Prediction", "abstract": "Real estate prices have a significant impact on individuals, families, businesses, and governments. The general objective of real estate price prediction is to identify and exploit socioeconomic patterns arising from real estate transactions over multiple aspects, ranging from the property itself to other contributing factors. However, price prediction is a challenging multidimensional problem that involves estimating many characteristics beyond the property itself. In this paper, we use multiple sources of data to evaluate the economic contribution of different socioeconomic characteristics such as surrounding amenities, traffic conditions and social emotions. Our experiments were conducted on 28,550 houses in Beijing, China and we rank each characteristic by its importance. Since the use of multi-source information improves the accuracy of predictions, the aforementioned characteristics can be an invaluable resource to assess the economic and social value of real estate. Code and data are available at: https://github.com/IndigoPurple/PATE", "published": "2022-08-29T12:31:10Z", "authors": ["Yaping Zhao", "Ramgopal Ravi", "Shuhui Shi", "Zhongrui Wang", "Edmund Y. Lam", "Jichang Zhao"], "categories": ["cs.CY"], "doi": "", "_query": "real estate valuation deep learning", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/1907.03423v7", "title": "On-Policy Robot Imitation Learning from a Converging Supervisor", "abstract": "Existing on-policy imitation learning algorithms, such as DAgger, assume access to a fixed supervisor. However, there are many settings where the supervisor may evolve during policy learning, such as a human performing a novel task or an improving algorithmic controller. We formalize imitation learning from a \"converging supervisor\" and provide sublinear static and dynamic regret guarantees against the best policy in hindsight with labels from the converged supervisor, even when labels during learning are only from intermediate supervisors. We then show that this framework is closely connected to a class of reinforcement learning (RL) algorithms known as dual policy iteration (DPI), which alternate between training a reactive learner with imitation learning and a model-based supervisor with data from the learner. Experiments suggest that when this framework is applied with the state-of-the-art deep model-based RL algorithm PETS as an improving supervisor, it outperforms deep RL baselines on continuous control tasks and provides up to an 80-fold speedup in policy evaluation.", "published": "2019-07-08T07:02:57Z", "authors": ["Ashwin Balakrishna", "Brijen Thananjeyan", "Jonathan Lee", "Felix Li", "Arsh Zahed", "Joseph E. Gonzalez", "Ken Goldberg"], "categories": ["cs.LG", "cs.AI", "cs.RO"], "doi": "", "_query": "real estate valuation deep learning", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2006.06753v1", "title": "PRGFlow: Benchmarking SWAP-Aware Unified Deep Visual Inertial Odometry", "abstract": "Odometry on aerial robots has to be of low latency and high robustness whilst also respecting the Size, Weight, Area and Power (SWAP) constraints as demanded by the size of the robot. A combination of visual sensors coupled with Inertial Measurement Units (IMUs) has proven to be the best combination to obtain robust and low latency odometry on resource-constrained aerial robots. Recently, deep learning approaches for Visual Inertial fusion have gained momentum due to their high accuracy and robustness. However, the remarkable advantages of these techniques are their inherent scalability (adaptation to different sized aerial robots) and unification (same method works on different sized aerial robots) by utilizing compression methods and hardware acceleration, which have been lacking from previous approaches. To this end, we present a deep learning approach for visual translation estimation and loosely fuse it with an Inertial sensor for full 6DoF odometry estimation. We also present a detailed benchmark comparing different architectures, loss functions and compression methods to enable scalability. We evaluate our network on the MSCOCO dataset and evaluate the VI fusion on multiple real-flight trajectories.", "published": "2020-06-11T19:12:54Z", "authors": ["Nitin J. Sanket", "Chahat Deep Singh", "Cornelia Fermüller", "Yiannis Aloimonos"], "categories": ["cs.CV", "cs.RO"], "doi": "", "_query": "real estate valuation deep learning", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2312.06994v1", "title": "The behavioral intention to adopt Proptech services in Vietnam real estate market", "abstract": "One of the main stages for achieving success is the adoption of new technology by its users. Several studies show that Property technology is advantageous for real estate stakeholders. Hence, the purpose of this paper is to investigate the users' engagement behavior to adopt Property technology in the Vietnamese real estate market. To that end, a purposive sample of 142 participants was recruited to complete an online quantitative approach based survey. The survey consisted of a modified and previously validated measure of acceptance based on the extended demographic version of the unified theory of acceptance and use of technology), as well as usage scale items. The empirical findings confirm that participants were generally accepting of Property technology in the Vietnamese real estate market. The highest mean values were associated with the subscales of effort expectancy and performance expectancy, while we can easily identify the lowest mean value in the social influence subscale. The usage of Property technology was slightly more concerned with the gathering of information on properties and markets than transactions or portfolio management. This study provides an in depth understanding of Property technology for firms' managers and marketers. Online social interactions might be either harmful or fruitful for firms depending on the type of interaction and engagement behavior. This is especially true of property portals and social media forums that would help investors to connect, communicate and learn. Findings can help users to improve their strategies for digital marketing. By providing robust findings by addressing issues like omitted variables and endogeneity, the findings of this study are promising for developing new hypotheses and theoretical models in the context of the Vietnamese real estate market.", "published": "2023-12-12T05:48:08Z", "authors": ["Le Tung Bach"], "categories": ["econ.GN"], "doi": "10.5281/zenodo.10360520", "_query": "real estate valuation deep learning", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2508.18331v1", "title": "Metrics, KPIs, and Taxonomy for Data Valuation and Monetisation -- A Systematic Literature Review", "abstract": "Data valuation and data monetisation are complex subjects but essential to most organisations today. Unfortunately, they still lack standard procedures and frameworks for organisations to follow. In this survey, we introduce the reader to the concepts by providing the definitions and the background required to better understand data, monetisation strategies, and finally metrics and KPIs used in these strategies. We have conducted a systematic literature review on metrics and KPIs used in data valuation and monetisation, in every aspect of an organisation's business, and by a variety of stakeholders. We provide an expansive list of such metrics and KPIs with 162 references. We then categorise all the metrics and KPIs found into a large taxonomy, following the Balanced Scorecard (BSC) approach with further subclustering to cover every aspect of an organisation's business. This taxonomy will help every level of data management understand the complex landscape of the domain. We also discuss the difficulty in creating a standard framework for data valuation and data monetisation and the major challenges the domain is currently facing.", "published": "2025-08-25T09:43:11Z", "authors": ["Eduardo Vyhmeister", "Bastien Pietropaoli", "Alejando Martinez Molina", "Montserrat Gonzalez-Ferreiro", "Gabriel Gonzalez-Castane", "Jordi Arjona Aroca", "Andrea Visentin"], "categories": ["cs.DB"], "doi": "", "_query": "real estate valuation deep learning", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2209.05424v1", "title": "Towards More Efficient Data Valuation in Healthcare Federated Learning using Ensembling", "abstract": "Federated Learning (FL) wherein multiple institutions collaboratively train a machine learning model without sharing data is becoming popular. Participating institutions might not contribute equally, some contribute more data, some better quality data or some more diverse data. To fairly rank the contribution of different institutions, Shapley value (SV) has emerged as the method of choice. Exact SV computation is impossibly expensive, especially when there are hundreds of contributors. Existing SV computation techniques use approximations. However, in healthcare where the number of contributing institutions are likely not of a colossal scale, computing exact SVs is still exorbitantly expensive, but not impossible. For such settings, we propose an efficient SV computation technique called SaFE (Shapley Value for Federated Learning using Ensembling). We empirically show that SaFE computes values that are close to exact SVs, and that it performs better than current SV approximations. This is particularly relevant in medical imaging setting where widespread heterogeneity across institutions is rampant and fast accurate data valuation is required to determine the contribution of each participant in multi-institutional collaborative learning.", "published": "2022-09-12T17:30:10Z", "authors": ["Sourav Kumar", "A. Lakshminarayanan", "Ken Chang", "Feri Guretno", "Ivan Ho Mien", "Jayashree Kalpathy-Cramer", "Pavitra Krishnaswamy", "Praveer Singh"], "categories": ["cs.LG", "cs.AI"], "doi": "", "_query": "real estate valuation deep learning", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/1707.01692v1", "title": "Ramification theory for degree $p$ extensions of arbitrary valuation rings in mixed characteristic $(0,p)$", "abstract": "We previously obtained a generalization and refinement of results about the ramification theory of Artin-Schreier extensions of discretely valued fields in characteristic $p$ with perfect residue fields to the case of fields with more general valuations and residue fields. As seen in VT16, the \"defect\" case gives rise to many interesting complications. In this paper, we present analogous results for degree $p$ extensions of arbitrary valuation rings in mixed characteristic $(0,p)$ in a more general setting. More specifically, the only assumption here is that the base field $K$ is henselian. In particular, these results are true for defect extensions even if the rank of the valuation is greater than $1$. A similar method also works in equal characteristic, generalizing the results of VT16.", "published": "2017-07-06T08:56:41Z", "authors": ["Vaidehee Thatte"], "categories": ["math.NT"], "doi": "", "_query": "real estate valuation deep learning", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2103.07585v1", "title": "Quantum circuit optimization with deep reinforcement learning", "abstract": "A central aspect for operating future quantum computers is quantum circuit optimization, i.e., the search for efficient realizations of quantum algorithms given the device capabilities. In recent years, powerful approaches have been developed which focus on optimizing the high-level circuit structure. However, these approaches do not consider and thus cannot optimize for the hardware details of the quantum architecture, which is especially important for near-term devices. To address this point, we present an approach to quantum circuit optimization based on reinforcement learning. We demonstrate how an agent, realized by a deep convolutional neural network, can autonomously learn generic strategies to optimize arbitrary circuits on a specific architecture, where the optimization target can be chosen freely by the user. We demonstrate the feasibility of this approach by training agents on 12-qubit random circuits, where we find on average a depth reduction by 27% and a gate count reduction by 15%. We examine the extrapolation to larger circuits than used for training, and envision how this approach can be utilized for near-term quantum devices.", "published": "2021-03-13T00:49:51Z", "authors": ["Thomas Fösel", "Murphy Yuezhen Niu", "Florian Marquardt", "Li Li"], "categories": ["quant-ph"], "doi": "", "_query": "real estate valuation deep learning", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2007.04466v1", "title": "URSABench: Comprehensive Benchmarking of Approximate Bayesian Inference Methods for Deep Neural Networks", "abstract": "While deep learning methods continue to improve in predictive accuracy on a wide range of application domains, significant issues remain with other aspects of their performance including their ability to quantify uncertainty and their robustness. Recent advances in approximate Bayesian inference hold significant promise for addressing these concerns, but the computational scalability of these methods can be problematic when applied to large-scale models. In this paper, we describe initial work on the development ofURSABench(the Uncertainty, Robustness, Scalability, and Accu-racy Benchmark), an open-source suite of bench-marking tools for comprehensive assessment of approximate Bayesian inference methods with a focus on deep learning-based classification tasks", "published": "2020-07-08T22:51:28Z", "authors": ["Meet P. Vadera", "Adam D. Cobb", "Brian Jalaian", "Benjamin M. Marlin"], "categories": ["cs.LG", "stat.ML"], "doi": "", "_query": "real estate valuation deep learning", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/1901.08551v1", "title": "A Universal Logic Operator for Interpretable Deep Convolution Networks", "abstract": "Explaining neural network computation in terms of probabilistic/fuzzy logical operations has attracted much attention due to its simplicity and high interpretability. Different choices of logical operators such as AND, OR and XOR give rise to another dimension for network optimization, and in this paper, we study the open problem of learning a universal logical operator without prescribing to any logical operations manually. Insightful observations along this exploration furnish deep convolution networks with a novel logical interpretation.", "published": "2019-01-20T18:50:24Z", "authors": ["KamWoh Ng", "Lixin Fan", "Chee Seng Chan"], "categories": ["cs.LG", "stat.ML"], "doi": "", "_query": "real estate valuation deep learning", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2405.02547v1", "title": "Crypto Market Analysis & Real-Estate Business Protocol Proposal | Application of Ethereum Blockchain", "abstract": "This paper examines the dynamics of the cryptocurrency market and proposes a novel blockchain-based protocol for real estate transactions. Our analysis includes a detailed review of price trends, volatility, and correlations within the cryptocurrency market, focusing on major assets like Bitcoin, Ethereum, and Tether. We provide a critical assessment of the impact of significant market events, such as the FTX bankruptcy, highlighting the vulnerabilities and resilience of the crypto market. The study also explores the potential of blockchain technology to innovate real estate transactions by enabling the secure and transparent handling of property deeds without traditional intermediaries. We introduce a blockchain protocol that reduces transaction costs, enhances security, and increases transparency, making real estate transactions more accessible and efficient. Our proposal aims to leverage the inherent benefits of blockchain to address real-world challenges in real estate transactions, providing a scalable and secure platform for property sales in a global market.", "published": "2024-05-04T03:07:10Z", "authors": ["Sid Bhatia", "Samuel Gedal", "Himaya Jeyakumar Grace Lee", "Ravinder Chopra", "Daniel Roman", "Shrijani Chakroborty"], "categories": ["econ.GN"], "doi": "", "_query": "real estate valuation deep learning", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/1905.04149v5", "title": "A Survey on Deep Learning-based Non-Invasive Brain Signals:Recent Advances and New Frontiers", "abstract": "Brain-Computer Interface (BCI) bridges the human's neural world and the outer physical world by decoding individuals' brain signals into commands recognizable by computer devices. Deep learning has lifted the performance of brain-computer interface systems significantly in recent years. In this article, we systematically investigate brain signal types for BCI and related deep learning concepts for brain signal analysis. We then present a comprehensive survey of deep learning techniques used for BCI, by summarizing over 230 contributions most published in the past five years. Finally, we discuss the applied areas, opening challenges, and future directions for deep learning-based BCI.", "published": "2019-05-10T13:04:00Z", "authors": ["Xiang Zhang", "Lina Yao", "Xianzhi Wang", "Jessica Monaghan", "David Mcalpine", "Yu Zhang"], "categories": ["cs.HC", "cs.LG", "eess.SP", "q-bio.NC"], "doi": "", "_query": "real estate valuation deep learning", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2006.00684v1", "title": "Symbol Spotting on Digital Architectural Floor Plans Using a Deep Learning-based Framework", "abstract": "This papers focuses on symbol spotting on real-world digital architectural floor plans with a deep learning (DL)-based framework. Traditional on-the-fly symbol spotting methods are unable to address the semantic challenge of graphical notation variability, i.e. low intra-class symbol similarity, an issue that is particularly important in architectural floor plan analysis. The presence of occlusion and clutter, characteristic of real-world plans, along with a varying graphical symbol complexity from almost trivial to highly complex, also pose challenges to existing spotting methods. In this paper, we address all of the above issues by leveraging recent advances in DL and adapting an object detection framework based on the You-Only-Look-Once (YOLO) architecture. We propose a training strategy based on tiles, avoiding many issues particular to DL-based object detection networks related to the relative small size of symbols compared to entire floor plans, aspect ratios, and data augmentation. Experiments on real-world floor plans demonstrate that our method successfully detects architectural symbols with low intra-class similarity and of variable graphical complexity, even in the presence of heavy occlusion and clutter. Additional experiments on the public SESYD dataset confirm that our proposed approach can deal with various degradation and noise levels and outperforms other symbol spotting methods.", "published": "2020-06-01T03:16:05Z", "authors": ["Alireza Rezvanifar", "Melissa Cote", "Alexandra Branzan Albu"], "categories": ["cs.CV", "cs.LG"], "doi": "", "_query": "real estate valuation deep learning", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2408.00386v1", "title": "What comes after transformers? -- A selective survey connecting ideas in deep learning", "abstract": "Transformers have become the de-facto standard model in artificial intelligence since 2017 despite numerous shortcomings ranging from energy inefficiency to hallucinations. Research has made a lot of progress in improving elements of transformers, and, more generally, deep learning manifesting in many proposals for architectures, layers, optimization objectives, and optimization techniques. For researchers it is difficult to keep track of such developments on a broader level. We provide a comprehensive overview of the many important, recent works in these areas to those who already have a basic understanding of deep learning. Our focus differs from other works, as we target specifically novel, alternative potentially disruptive approaches to transformers as well as successful ideas of recent deep learning. We hope that such a holistic and unified treatment of influential, recent works and novel ideas helps researchers to form new connections between diverse areas of deep learning. We identify and discuss multiple patterns that summarize the key strategies for successful innovations over the last decade as well as works that can be seen as rising stars. Especially, we discuss attempts on how to improve on transformers covering (partially) proven methods such as state space models but also including far-out ideas in deep learning that seem promising despite not achieving state-of-the-art results. We also cover a discussion on recent state-of-the-art models such as OpenAI's GPT series and Meta's LLama models and, Google's Gemini model family.", "published": "2024-08-01T08:50:25Z", "authors": ["Johannes Schneider"], "categories": ["cs.LG"], "doi": "", "_query": "real estate valuation deep learning", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2109.09307v4", "title": "Assisted Learning for Organizations with Limited Imbalanced Data", "abstract": "In the era of big data, many big organizations are integrating machine learning into their work pipelines to facilitate data analysis. However, the performance of their trained models is often restricted by limited and imbalanced data available to them. In this work, we develop an assisted learning framework for assisting organizations to improve their learning performance. The organizations have sufficient computation resources but are subject to stringent data-sharing and collaboration policies. Their limited imbalanced data often cause biased inference and sub-optimal decision-making. In assisted learning, an organizational learner purchases assistance service from an external service provider and aims to enhance its model performance within only a few assistance rounds. We develop effective stochastic training algorithms for both assisted deep learning and assisted reinforcement learning. Different from existing distributed algorithms that need to frequently transmit gradients or models, our framework allows the learner to only occasionally share information with the service provider, but still obtain a model that achieves near-oracle performance as if all the data were centralized.", "published": "2021-09-20T05:57:52Z", "authors": ["Cheng Chen", "Jiaying Zhou", "Jie Ding", "Yi Zhou"], "categories": ["cs.LG"], "doi": "", "_query": "real estate valuation deep learning", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/1706.03235v3", "title": "ACCNet: Actor-Coordinator-Critic Net for \"Learning-to-Communicate\" with Deep Multi-agent Reinforcement Learning", "abstract": "Communication is a critical factor for the big multi-agent world to stay organized and productive. Typically, most previous multi-agent \"learning-to-communicate\" studies try to predefine the communication protocols or use technologies such as tabular reinforcement learning and evolutionary algorithm, which can not generalize to changing environment or large collection of agents. In this paper, we propose an Actor-Coordinator-Critic Net (ACCNet) framework for solving \"learning-to-communicate\" problem. The ACCNet naturally combines the powerful actor-critic reinforcement learning technology with deep learning technology. It can efficiently learn the communication protocols even from scratch under partially observable environment. We demonstrate that the ACCNet can achieve better results than several baselines under both continuous and discrete action space environments. We also analyse the learned protocols and discuss some design considerations.", "published": "2017-06-10T13:50:23Z", "authors": ["Hangyu Mao", "Zhibo Gong", "Yan Ni", "Zhen Xiao"], "categories": ["cs.AI", "cs.LG"], "doi": "", "_query": "real estate valuation deep learning", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/1302.2493v1", "title": "Evaluation on the Financial Competitiveness of Chinese Listed Real Estate Companies Based on Entropy Method", "abstract": "The real estate is a pillar industry of China's national economy. Due to changes in policy and market conditions, the real estate companies are facing greater pressures to survive in a competitive environment. They must improve their financial competitiveness. Based on the conceptual framework of financial competitiveness, this paper presented a financial competitiveness evaluation index system, covering four aspects, including profitability, solvency, sustainable development and operational capacity. Entropy value method is applied to determine the index weight. 105 listed real estate company's financial competitiveness are evaluated, the results show that: high-scoring company has strong profitability, sustainable development and operational capacity; low-scoring company has weak profitability and poor ability of sustainable development; solvency doesn't affect the company's financial competitiveness obviously.", "published": "2013-02-11T15:01:48Z", "authors": ["Wei Lin", "Linbo Shao"], "categories": ["q-fin.GN", "q-fin.CP"], "doi": "", "_query": "real estate valuation deep learning", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2305.09539v1", "title": "Learning Higher-order Object Interactions for Keypoint-based Video Understanding", "abstract": "Action recognition is an important problem that requires identifying actions in video by learning complex interactions across scene actors and objects. However, modern deep-learning based networks often require significant computation, and may capture scene context using various modalities that further increases compute costs. Efficient methods such as those used for AR/VR often only use human-keypoint information but suffer from a loss of scene context that hurts accuracy. In this paper, we describe an action-localization method, KeyNet, that uses only the keypoint data for tracking and action recognition. Specifically, KeyNet introduces the use of object based keypoint information to capture context in the scene. Our method illustrates how to build a structured intermediate representation that allows modeling higher-order interactions in the scene from object and human keypoints without using any RGB information. We find that KeyNet is able to track and classify human actions at just 5 FPS. More importantly, we demonstrate that object keypoints can be modeled to recover any loss in context from using keypoint information over AVA action and Kinetics datasets.", "published": "2023-05-16T15:30:33Z", "authors": ["Yi Huang", "Asim Kadav", "Farley Lai", "Deep Patel", "Hans Peter Graf"], "categories": ["cs.CV"], "doi": "", "_query": "real estate valuation deep learning", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/1802.00385v2", "title": "A Unified Deep Learning Architecture for Abuse Detection", "abstract": "Hate speech, offensive language, sexism, racism and other types of abusive behavior have become a common phenomenon in many online social media platforms. In recent years, such diverse abusive behaviors have been manifesting with increased frequency and levels of intensity. This is due to the openness and willingness of popular media platforms, such as Twitter and Facebook, to host content of sensitive or controversial topics. However, these platforms have not adequately addressed the problem of online abusive behavior, and their responsiveness to the effective detection and blocking of such inappropriate behavior remains limited. In the present paper, we study this complex problem by following a more holistic approach, which considers the various aspects of abusive behavior. To make the approach tangible, we focus on Twitter data and analyze user and textual properties from different angles of abusive posting behavior. We propose a deep learning architecture, which utilizes a wide variety of available metadata, and combines it with automatically-extracted hidden patterns within the text of the tweets, to detect multiple abusive behavioral norms which are highly inter-related. We apply this unified architecture in a seamless, transparent fashion to detect different types of abusive behavior (hate speech, sexism vs. racism, bullying, sarcasm, etc.) without the need for any tuning of the model architecture for each task. We test the proposed approach with multiple datasets addressing different and multiple abusive behaviors on Twitter. Our results demonstrate that it largely outperforms the state-of-art methods (between 21 and 45\\% improvement in AUC, depending on the dataset).", "published": "2018-02-01T16:48:39Z", "authors": ["Antigoni-Maria Founta", "Despoina Chatzakou", "Nicolas Kourtellis", "Jeremy Blackburn", "Athena Vakali", "Ilias Leontiadis"], "categories": ["cs.CL", "cs.SI"], "doi": "", "_query": "real estate valuation deep learning", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/1803.09050v3", "title": "Learning to Reweight Examples for Robust Deep Learning", "abstract": "Deep neural networks have been shown to be very powerful modeling tools for many supervised learning tasks involving complex input patterns. However, they can also easily overfit to training set biases and label noises. In addition to various regularizers, example reweighting algorithms are popular solutions to these problems, but they require careful tuning of additional hyperparameters, such as example mining schedules and regularization hyperparameters. In contrast to past reweighting methods, which typically consist of functions of the cost value of each example, in this work we propose a novel meta-learning algorithm that learns to assign weights to training examples based on their gradient directions. To determine the example weights, our method performs a meta gradient descent step on the current mini-batch example weights (which are initialized from zero) to minimize the loss on a clean unbiased validation set. Our proposed method can be easily implemented on any type of deep network, does not require any additional hyperparameter tuning, and achieves impressive performance on class imbalance and corrupted label problems where only a small amount of clean validation data is available.", "published": "2018-03-24T03:41:59Z", "authors": ["Mengye Ren", "Wenyuan Zeng", "Bin Yang", "Raquel Urtasun"], "categories": ["cs.LG", "stat.ML"], "doi": "", "_query": "real estate valuation deep learning", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/1008.0287v3", "title": "New structures on valuations and applications", "abstract": "An overview of some of the recent developments in the theory of valuations on convex sets and its generalizations to manifolds is given. The exposition is focused towards applications to integral geometry; several of such applications are discussed.", "published": "2010-08-02T12:32:11Z", "authors": ["Semyon Alesker"], "categories": ["math.MG", "math.DG"], "doi": "", "_query": "real estate valuation deep learning", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2511.06465v1", "title": "Sim-to-Real Transfer in Deep Reinforcement Learning for Bipedal Locomotion", "abstract": "This chapter addresses the critical challenge of simulation-to-reality (sim-to-real) transfer for deep reinforcement learning (DRL) in bipedal locomotion. After contextualizing the problem within various control architectures, we dissect the ``curse of simulation'' by analyzing the primary sources of sim-to-real gap: robot dynamics, contact modeling, state estimation, and numerical solvers. Building on this diagnosis, we structure the solutions around two complementary philosophies. The first is to shrink the gap through model-centric strategies that systematically improve the simulator's physical fidelity. The second is to harden the policy, a complementary approach that uses in-simulation robustness training and post-deployment adaptation to make the policy inherently resilient to model inaccuracies. The chapter concludes by synthesizing these philosophies into a strategic framework, providing a clear roadmap for developing and evaluating robust sim-to-real solutions.", "published": "2025-11-09T17:20:04Z", "authors": ["Lingfan Bao", "Tianhu Peng", "Chengxu Zhou"], "categories": ["cs.RO"], "doi": "", "_query": "real estate valuation deep learning", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2607.24057v1", "title": "Constrained Reinforcement Learning Using Successor Representations", "abstract": "Real-world Reinforcement Learning depends on the ability to formulate safety constraints into a policy. A common way to model such constraints is to introduce an additional cost signal in the Markov Decision Process, which notifies the agent of unwanted behavior independently of the reward signal. Unfortunately, current methods are hard to adapt to changes in the cost function introduced by, e.g., domain shift or obstacles moving over time. The lack of adaptability means that policies are too unflexible to deal with complex real-world conditions. We propose the Safe Deep Successor Representation (SafeDSR), a novel method that allows quick retraining of policies towards new cost structures. SafeDSR extends the Deep Successor Representation (Kulkarni et al., 2016) to Constrained Reinforcement Learning by introducing a single learnable weight matrix to decouple the learned value function across dynamics, rewards, and costs. This matrix can be updated in a supervised manner instead of having to adapt the whole network if the cost structure of the environment changes. We demonstrate this ability in a freely configurable two-dimensional navigation environment and show that our method is competitive on a simple navigation task while being considerably more flexible", "published": "2026-07-27T06:57:01Z", "authors": ["Michael Girstl", "Alexander Mattick", "Christopher Mutschler"], "categories": ["cs.LG"], "doi": "", "_query": "real estate valuation deep learning", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2201.09267v1", "title": "Spectral, Probabilistic, and Deep Metric Learning: Tutorial and Survey", "abstract": "This is a tutorial and survey paper on metric learning. Algorithms are divided into spectral, probabilistic, and deep metric learning. We first start with the definition of distance metric, Mahalanobis distance, and generalized Mahalanobis distance. In spectral methods, we start with methods using scatters of data, including the first spectral metric learning, relevant methods to Fisher discriminant analysis, Relevant Component Analysis (RCA), Discriminant Component Analysis (DCA), and the Fisher-HSIC method. Then, large-margin metric learning, imbalanced metric learning, locally linear metric adaptation, and adversarial metric learning are covered. We also explain several kernel spectral methods for metric learning in the feature space. We also introduce geometric metric learning methods on the Riemannian manifolds. In probabilistic methods, we start with collapsing classes in both input and feature spaces and then explain the neighborhood component analysis methods, Bayesian metric learning, information theoretic methods, and empirical risk minimization in metric learning. In deep learning methods, we first introduce reconstruction autoencoders and supervised loss functions for metric learning. Then, Siamese networks and its various loss functions, triplet mining, and triplet sampling are explained. Deep discriminant analysis methods, based on Fisher discriminant analysis, are also reviewed. Finally, we introduce multi-modal deep metric learning, geometric metric learning by neural networks, and few-shot metric learning.", "published": "2022-01-23T13:53:23Z", "authors": ["Benyamin Ghojogh", "Ali Ghodsi", "Fakhri Karray", "Mark Crowley"], "categories": ["stat.ML", "cs.CV", "cs.LG"], "doi": "", "_query": "real estate valuation deep learning", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2201.12150v2", "title": "Learning Curves for Decision Making in Supervised Machine Learning: A Survey", "abstract": "Learning curves are a concept from social sciences that has been adopted in the context of machine learning to assess the performance of a learning algorithm with respect to a certain resource, e.g., the number of training examples or the number of training iterations. Learning curves have important applications in several machine learning contexts, most notably in data acquisition, early stopping of model training, and model selection. For instance, learning curves can be used to model the performance of the combination of an algorithm and its hyperparameter configuration, providing insights into their potential suitability at an early stage and often expediting the algorithm selection process. Various learning curve models have been proposed to use learning curves for decision making. Some of these models answer the binary decision question of whether a given algorithm at a certain budget will outperform a certain reference performance, whereas more complex models predict the entire learning curve of an algorithm. We contribute a framework that categorises learning curve approaches using three criteria: the decision-making situation they address, the intrinsic learning curve question they answer and the type of resources they use. We survey papers from the literature and classify them into this framework.", "published": "2022-01-28T14:34:32Z", "authors": ["Felix Mohr", "Jan N. van Rijn"], "categories": ["cs.LG"], "doi": "10.1007/s10994-024-06619-7", "_query": "real estate valuation deep learning", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2509.02391v3", "title": "Gaming and Cooperation in Federated Learning: What Can Happen and How to Monitor It", "abstract": "The success of federated learning (FL) ultimately depends on how strategic participants behave under partial observability, yet most formulations still treat FL as a static optimization problem. We instead view FL deployments as governed strategic systems and develop an analytical framework that separates welfare-improving behavior from metric gaming. Within this framework, we introduce indices that quantify manipulability, the price of gaming, and the price of cooperation, and we use them to study how rules, information disclosure, evaluation metrics, and aggregator-switching policies reshape incentives and cooperation patterns. We derive threshold conditions for deterring harmful gaming while preserving benign cooperation, and for triggering auto-switch rules when early-warning indicators become critical. Building on these results, we construct a design toolkit including a governance checklist and a simple audit-budget allocation algorithm with a provable performance guarantee. Simulations across diverse stylized environments and a federated learning case study consistently match the qualitative and quantitative patterns predicted by our framework. Taken together, our results provide design principles and operational guidelines for reducing metric gaming while sustaining stable, high-welfare cooperation in FL platforms.", "published": "2025-09-02T14:55:01Z", "authors": ["Dongseok Kim", "Hyoungsun Choi", "Mohamed Jismy Aashik Rasool", "Gisung Oh"], "categories": ["cs.LG", "cs.GT", "stat.ML"], "doi": "", "_query": "real estate valuation deep learning", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/1305.2505v1", "title": "On the Generalization Ability of Online Learning Algorithms for Pairwise Loss Functions", "abstract": "In this paper, we study the generalization properties of online learning based stochastic methods for supervised learning problems where the loss function is dependent on more than one training sample (e.g., metric learning, ranking). We present a generic decoupling technique that enables us to provide Rademacher complexity-based generalization error bounds. Our bounds are in general tighter than those obtained by Wang et al (COLT 2012) for the same problem. Using our decoupling technique, we are further able to obtain fast convergence rates for strongly convex pairwise loss functions. We are also able to analyze a class of memory efficient online learning algorithms for pairwise learning problems that use only a bounded subset of past training samples to update the hypothesis at each step. Finally, in order to complement our generalization bounds, we propose a novel memory efficient online learning algorithm for higher order learning problems with bounded regret guarantees.", "published": "2013-05-11T13:52:37Z", "authors": ["Purushottam Kar", "Bharath K Sriperumbudur", "Prateek Jain", "Harish C Karnick"], "categories": ["cs.LG", "stat.ML"], "doi": "", "_query": "real estate valuation deep learning", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/1906.02919v3", "title": "EVDodgeNet: Deep Dynamic Obstacle Dodging with Event Cameras", "abstract": "Dynamic obstacle avoidance on quadrotors requires low latency. A class of sensors that are particularly suitable for such scenarios are event cameras. In this paper, we present a deep learning -- based solution for dodging multiple dynamic obstacles on a quadrotor with a single event camera and on-board computation. Our approach uses a series of shallow neural networks for estimating both the ego-motion and the motion of independently moving objects. The networks are trained in simulation and directly transfer to the real world without any fine-tuning or retraining. We successfully evaluate and demonstrate the proposed approach in many real-world experiments with obstacles of different shapes and sizes, achieving an overall success rate of 70% including objects of unknown shape and a low light testing scenario. To our knowledge, this is the first deep learning -- based solution to the problem of dynamic obstacle avoidance using event cameras on a quadrotor. Finally, we also extend our work to the pursuit task by merely reversing the control policy, proving that our navigation stack can cater to different scenarios.", "published": "2019-06-07T06:27:46Z", "authors": ["Nitin J. Sanket", "Chethan M. Parameshwara", "Chahat Deep Singh", "Ashwin V. Kuruttukulam", "Cornelia Fermüller", "Davide Scaramuzza", "Yiannis Aloimonos"], "categories": ["cs.RO", "cs.CV"], "doi": "", "_query": "real estate valuation deep learning", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2512.19184v2", "title": "Operator-Based Generalization Bound for Deep Learning: Insights on Multi-Task Learning", "abstract": "This paper presents novel generalization bounds for vector-valued neural networks and deep kernel methods, focusing on multi-task learning through an operator-theoretic framework. Our key development lies in strategically combining a Koopman based approach with existing techniques, achieving tighter generalization guarantees compared to traditional norm-based bounds. To mitigate computational challenges associated with Koopman-based methods, we introduce sketching techniques applicable to vector valued neural networks. These techniques yield excess risk bounds under generic Lipschitz losses, providing performance guarantees for applications including robust and multiple quantile regression. Furthermore, we propose a novel deep learning framework, deep vector-valued reproducing kernel Hilbert spaces (vvRKHS), leveraging Perron Frobenius (PF) operators to enhance deep kernel methods. We derive a new Rademacher generalization bound for this framework, explicitly addressing underfitting and overfitting through kernel refinement strategies. This work offers novel insights into the generalization properties of multitask learning with deep learning architectures, an area that has been relatively unexplored until recent developments.", "published": "2025-12-22T09:18:30Z", "authors": ["Mahdi Mohammadigohari", "Giuseppe Di Fatta", "Giuseppe Nicosia", "Panos M. Pardalos"], "categories": ["cs.LG", "cs.AI"], "doi": "10.1007/978-3-032-21480-5_9", "_query": "real estate valuation deep learning", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/1903.09233v3", "title": "SkelNetOn 2019: Dataset and Challenge on Deep Learning for Geometric Shape Understanding", "abstract": "We present SkelNetOn 2019 Challenge and Deep Learning for Geometric Shape Understanding workshop to utilize existing and develop novel deep learning architectures for shape understanding. We observed that unlike traditional segmentation and detection tasks, geometry understanding is still a new area for deep learning techniques. SkelNetOn aims to bring together researchers from different domains to foster learning methods on global shape understanding tasks. We aim to improve and evaluate the state-of-the-art shape understanding approaches, and to serve as reference benchmarks for future research. Similar to other challenges in computer vision, SkelNetOn proposes three datasets and corresponding evaluation methodologies; all coherently bundled in three competitions with a dedicated workshop co-located with CVPR 2019 conference. In this paper, we describe and analyze characteristics of datasets, define the evaluation criteria of the public competitions, and provide baselines for each task.", "published": "2019-03-21T20:43:19Z", "authors": ["Ilke Demir", "Camilla Hahn", "Kathryn Leonard", "Geraldine Morin", "Dana Rahbani", "Athina Panotopoulou", "Amelie Fondevilla", "Elena Balashova", "Bastien Durix", "Adam Kortylewski"], "categories": ["cs.CV"], "doi": "", "_query": "real estate valuation deep learning", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/1901.08557v2", "title": "On Network Science and Mutual Information for Explaining Deep Neural Networks", "abstract": "In this paper, we present a new approach to interpret deep learning models. By coupling mutual information with network science, we explore how information flows through feedforward networks. We show that efficiently approximating mutual information allows us to create an information measure that quantifies how much information flows between any two neurons of a deep learning model. To that end, we propose NIF, Neural Information Flow, a technique for codifying information flow that exposes deep learning model internals and provides feature attributions.", "published": "2019-01-20T18:47:00Z", "authors": ["Brian Davis", "Umang Bhatt", "Kartikeya Bhardwaj", "Radu Marculescu", "José M. F. Moura"], "categories": ["cs.LG", "stat.ML"], "doi": "10.1109/ICASSP40776.2020.9053078", "_query": "real estate valuation deep learning", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/1808.08121v1", "title": "An Improvement of Data Classification Using Random Multimodel Deep Learning (RMDL)", "abstract": "The exponential growth in the number of complex datasets every year requires more enhancement in machine learning methods to provide robust and accurate data classification. Lately, deep learning approaches have achieved surpassing results in comparison to previous machine learning algorithms. However, finding the suitable structure for these models has been a challenge for researchers. This paper introduces Random Multimodel Deep Learning (RMDL): a new ensemble, deep learning approach for classification. RMDL solves the problem of finding the best deep learning structure and architecture while simultaneously improving robustness and accuracy through ensembles of deep learning architectures. In short, RMDL trains multiple randomly generated models of Deep Neural Network (DNN), Convolutional Neural Network (CNN) and Recurrent Neural Network (RNN) in parallel and combines their results to produce better result of any of those models individually. In this paper, we describe RMDL model and compare the results for image and text classification as well as face recognition. We used MNIST and CIFAR-10 datasets as ground truth datasets for image classification and WOS, Reuters, IMDB, and 20newsgroup datasets for text classification. Lastly, we used ORL dataset to compare the model performance on face recognition task.", "published": "2018-08-23T00:38:14Z", "authors": ["Mojtaba Heidarysafa", "Kamran Kowsari", "Donald E. Brown", "Kiana Jafari Meimandi", "Laura E. Barnes"], "categories": ["cs.LG", "cs.CV", "cs.IR", "cs.NE", "stat.ML"], "doi": "10.18178/ijmlc.2018.8.4.703", "_query": "real estate valuation deep learning", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2110.15829v5", "title": "Holistic Deep Learning", "abstract": "This paper presents a novel holistic deep learning framework that simultaneously addresses the challenges of vulnerability to input perturbations, overparametrization, and performance instability from different train-validation splits. The proposed framework holistically improves accuracy, robustness, sparsity, and stability over standard deep learning models, as demonstrated by extensive experiments on both tabular and image data sets. The results are further validated by ablation experiments and SHAP value analysis, which reveal the interactions and trade-offs between the different evaluation metrics. To support practitioners applying our framework, we provide a prescriptive approach that offers recommendations for selecting an appropriate training loss function based on their specific objectives. All the code to reproduce the results can be found at https://github.com/kimvc7/HDL.", "published": "2021-10-29T14:46:32Z", "authors": ["Dimitris Bertsimas", "Kimberly Villalobos Carballo", "Léonard Boussioux", "Michael Lingzhi Li", "Alex Paskov", "Ivan Paskov"], "categories": ["cs.LG", "cs.AI"], "doi": "", "_query": "real estate valuation deep learning", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2201.05867v1", "title": "Transferability in Deep Learning: A Survey", "abstract": "The success of deep learning algorithms generally depends on large-scale data, while humans appear to have inherent ability of knowledge transfer, by recognizing and applying relevant knowledge from previous learning experiences when encountering and solving unseen tasks. Such an ability to acquire and reuse knowledge is known as transferability in deep learning. It has formed the long-term quest towards making deep learning as data-efficient as human learning, and has been motivating fruitful design of more powerful deep learning algorithms. We present this survey to connect different isolated areas in deep learning with their relation to transferability, and to provide a unified and complete view to investigating transferability through the whole lifecycle of deep learning. The survey elaborates the fundamental goals and challenges in parallel with the core principles and methods, covering recent cornerstones in deep architectures, pre-training, task adaptation and domain adaptation. This highlights unanswered questions on the appropriate objectives for learning transferable knowledge and for adapting the knowledge to new tasks and domains, avoiding catastrophic forgetting and negative transfer. Finally, we implement a benchmark and an open-source library, enabling a fair evaluation of deep learning methods in terms of transferability.", "published": "2022-01-15T15:03:17Z", "authors": ["Junguang Jiang", "Yang Shu", "Jianmin Wang", "Mingsheng Long"], "categories": ["cs.LG"], "doi": "", "_query": "real estate valuation deep learning", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2402.08871v3", "title": "Position: Topological Deep Learning is the New Frontier for Relational Learning", "abstract": "Topological deep learning (TDL) is a rapidly evolving field that uses topological features to understand and design deep learning models. This paper posits that TDL is the new frontier for relational learning. TDL may complement graph representation learning and geometric deep learning by incorporating topological concepts, and can thus provide a natural choice for various machine learning settings. To this end, this paper discusses open problems in TDL, ranging from practical benefits to theoretical foundations. For each problem, it outlines potential solutions and future research opportunities. At the same time, this paper serves as an invitation to the scientific community to actively participate in TDL research to unlock the potential of this emerging field.", "published": "2024-02-14T00:35:10Z", "authors": ["Theodore Papamarkou", "Tolga Birdal", "Michael Bronstein", "Gunnar Carlsson", "Justin Curry", "Yue Gao", "Mustafa Hajij", "Roland Kwitt", "Pietro Liò", "Paolo Di Lorenzo", "Vasileios Maroulas", "Nina Miolane", "Farzana Nasrin", "Karthikeyan Natesan Ramamurthy", "Bastian Rieck", "Simone Scardapane", "Michael T. Schaub", "Petar Veličković", "Bei Wang", "Yusu Wang", "Guo-Wei Wei", "Ghada Zamzmi"], "categories": ["cs.LG", "stat.ML"], "doi": "", "_query": "real estate valuation deep learning", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2608.20255v1", "title": "Transfer Learning in Nonparametric Regression with Deep ReLU Networks", "abstract": "This paper develops a general transfer learning framework for nonparametric regression with data consisting of multiple groups. Under the assumption that groups share a common structure along with group-specific deviations in additive form, the proposed method employs a two-stage offset learning procedure: the first stage pools data from all groups to estimate an overall mean function, and the second stage estimates offsets for each group, yielding final group-level estimators through additive combination. Upper bounds on the $\\mathcal L_2$ error are established for the proposed framework, covering a broad class of nonparametric estimators under mild complexity and noise conditions. When instantiated with deep ReLU networks, explicit convergence rates are derived under hierarchical composition models, demonstrating the ability to overcome the curse of dimensionality. Conditions that enable positive transfer with faster rates are considered, including learning with simpler functions and data augmentation through pooling samples across groups. Various simulations and real-data experiments further validate the effectiveness of the proposed method.", "published": "2026-08-20T16:53:00Z", "authors": ["Junpeng Ren", "Carlos Misael Madrid Padilla", "Yanzhen Chen", "Oscar Hernan Madrid Padilla"], "categories": ["stat.ML", "cs.LG", "stat.ME"], "doi": "", "_query": "real estate valuation deep learning", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/1303.4514v1", "title": "Is There A Real Estate Bubble in Switzerland?", "abstract": "We have analyzed the risks of possible development of bubbles in the Swiss residential real estate market. The data employed in this work has been collected by comparis.ch, and carefully cleaned from duplicate records through a procedure based on supervised machine learning methods. The study uses the log periodic power law (LPPL) bubble model to analyze the development of asking prices of residential properties in all Swiss districts between 2005 and 2013. The results suggest that there are 11 critical districts that exhibit signatures of bubbles, and seven districts where bubbles have already burst. Despite these strong signatures, it is argued that, based on the current economic environment, a soft landing rather than a severe crash is expected.", "published": "2013-03-19T08:52:17Z", "authors": ["Diego Ardila", "Peter Cauwels", "Dorsa Sanadgol", "Didier Sornette"], "categories": ["q-fin.GN"], "doi": "", "_query": "real estate valuation deep learning", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/1801.04354v5", "title": "Black-box Generation of Adversarial Text Sequences to Evade Deep Learning Classifiers", "abstract": "Although various techniques have been proposed to generate adversarial samples for white-box attacks on text, little attention has been paid to black-box attacks, which are more realistic scenarios. In this paper, we present a novel algorithm, DeepWordBug, to effectively generate small text perturbations in a black-box setting that forces a deep-learning classifier to misclassify a text input. We employ novel scoring strategies to identify the critical tokens that, if modified, cause the classifier to make an incorrect prediction. Simple character-level transformations are applied to the highest-ranked tokens in order to minimize the edit distance of the perturbation, yet change the original classification. We evaluated DeepWordBug on eight real-world text datasets, including text classification, sentiment analysis, and spam detection. We compare the result of DeepWordBug with two baselines: Random (Black-box) and Gradient (White-box). Our experimental results indicate that DeepWordBug reduces the prediction accuracy of current state-of-the-art deep-learning models, including a decrease of 68\\% on average for a Word-LSTM model and 48\\% on average for a Char-CNN model.", "published": "2018-01-13T00:42:30Z", "authors": ["Ji Gao", "Jack Lanchantin", "Mary Lou Soffa", "Yanjun Qi"], "categories": ["cs.CL", "cs.CR", "cs.IR", "cs.LG"], "doi": "", "_query": "real estate valuation deep learning", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2108.02892v1", "title": "Deep Reinforcement Learning for Intelligent Reflecting Surface-assisted D2D Communications", "abstract": "In this paper, we propose a deep reinforcement learning (DRL) approach for solving the optimisation problem of the network's sum-rate in device-to-device (D2D) communications supported by an intelligent reflecting surface (IRS). The IRS is deployed to mitigate the interference and enhance the signal between the D2D transmitter and the associated D2D receiver. Our objective is to jointly optimise the transmit power at the D2D transmitter and the phase shift matrix at the IRS to maximise the network sum-rate. We formulate a Markov decision process and then propose the proximal policy optimisation for solving the maximisation game. Simulation results show impressive performance in terms of the achievable rate and processing time.", "published": "2021-08-06T00:02:37Z", "authors": ["Khoi Khac Nguyen", "Antonino Masaracchia", "Cheng Yin", "Long D. Nguyen", "Octavia A. Dobre", "Trung Q. Duong"], "categories": ["eess.SP", "cs.AI", "cs.GT", "cs.LG"], "doi": "", "_query": "real estate valuation deep learning", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2504.02221v1", "title": "Learning and Improving Backgammon Strategy", "abstract": "A novel approach to learning is presented, combining features of on-line and off-line methods to achieve considerable performance in the task of learning a backgammon value function in a process that exploits the processing power of parallel supercomputers. The off-line methods comprise a set of techniques for parallelizing neural network training and $TD(λ)$ reinforcement learning; here Monte-Carlo ``Rollouts'' are introduced as a massively parallel on-line policy improvement technique which applies resources to the decision points encountered during the search of the game tree to further augment the learned value function estimate. A level of play roughly as good as, or possibly better than, the current champion human and computer backgammon players has been achieved in a short period of learning.", "published": "2025-04-03T02:27:22Z", "authors": ["Gregory R. Galperin"], "categories": ["cs.LG", "cs.AI", "cs.NE"], "doi": "", "_query": "real estate valuation deep learning", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2607.16329v2", "title": "Lipschitz Continuity in Deep Learning: A Systematic Review of Theoretical Foundations, Estimation Methods, Regularization Approaches, and Certifiable Robustness", "abstract": "Lipschitz continuity is a fundamental property of neural networks that characterizes their sensitivity to input perturbations. It plays a pivotal role in deep learning, governing \\textbf{robustness}, \\textbf{generalization} and \\textbf{optimization dynamics}. Despite its importance, research on Lipschitz continuity is scattered across various domains, lacking a unified perspective. This paper addresses this gap by providing a systematic review of Lipschitz continuity in deep learning. We explore its \\textbf{theoretical foundations}, \\textbf{estimation methods}, \\textbf{regularization approaches}, and \\textbf{certifiable robustness}. By reviewing existing research through the lens of Lipschitz continuity, this survey serves as a comprehensive reference for researchers and practitioners seeking a deeper understanding of Lipschitz continuity and its implications in deep learning.", "published": "2026-07-15T16:04:37Z", "authors": ["Róisín Luo", "James McDermott", "Colm O'Riordan"], "categories": ["stat.ML", "cs.LG"], "doi": "", "_query": "real estate valuation deep learning", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/1811.12560v2", "title": "An Introduction to Deep Reinforcement Learning", "abstract": "Deep reinforcement learning is the combination of reinforcement learning (RL) and deep learning. This field of research has been able to solve a wide range of complex decision-making tasks that were previously out of reach for a machine. Thus, deep RL opens up many new applications in domains such as healthcare, robotics, smart grids, finance, and many more. This manuscript provides an introduction to deep reinforcement learning models, algorithms and techniques. Particular focus is on the aspects related to generalization and how deep RL can be used for practical applications. We assume the reader is familiar with basic machine learning concepts.", "published": "2018-11-30T00:57:30Z", "authors": ["Vincent Francois-Lavet", "Peter Henderson", "Riashat Islam", "Marc G. Bellemare", "Joelle Pineau"], "categories": ["cs.LG", "cs.AI", "stat.ML"], "doi": "10.1561/2200000071", "_query": "real estate valuation deep learning", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/1905.09638v5", "title": "Estimating Risk and Uncertainty in Deep Reinforcement Learning", "abstract": "Reinforcement learning agents are faced with two types of uncertainty. Epistemic uncertainty stems from limited data and is useful for exploration, whereas aleatoric uncertainty arises from stochastic environments and must be accounted for in risk-sensitive applications. We highlight the challenges involved in simultaneously estimating both of them, and propose a framework for disentangling and estimating these uncertainties on learned Q-values. We derive unbiased estimators of these uncertainties and introduce an uncertainty-aware DQN algorithm, which we show exhibits safe learning behavior and outperforms other DQN variants on the MinAtar testbed.", "published": "2019-05-23T13:13:56Z", "authors": ["William R. Clements", "Bastien Van Delft", "Benoît-Marie Robaglia", "Reda Bahi Slaoui", "Sébastien Toth"], "categories": ["cs.LG", "cs.AI", "stat.ML"], "doi": "", "_query": "real estate valuation deep learning", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2404.11754v4", "title": "Differentiated Aggregation to Improve Generalization in Federated Learning", "abstract": "This paper focuses on reducing the communication cost of federated learning by exploring generalization bounds and representation learning. We first characterize a tighter generalization bound for one-round federated learning based on local clients' generalizations and heterogeneity of data distribution (non-iid scenario). We also characterize a generalization bound in R-round federated learning and its relation to the number of local updates (local stochastic gradient descents (SGDs)). Then, based on our generalization bound analysis and its interpretation through representation learning, we infer that less frequent aggregations for the representation extractor (typically corresponds to initial layers) compared to the head (usually the final layers) leads to the creation of more generalizable models, particularly in non-iid scenarios. We design a novel Federated Learning with Adaptive Local Steps (FedALS) algorithm based on our generalization bound and representation learning analysis. FedALS employs varying aggregation frequencies for different parts of the model, so reduces the communication cost. The paper is followed with experimental results showing the effectiveness of FedALS. Our codes are available at for reproducibility.", "published": "2024-04-17T21:17:48Z", "authors": ["Peyman Gholami", "Hulya Seferoglu"], "categories": ["cs.LG", "cs.AI"], "doi": "", "_query": "real estate valuation deep learning", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/1709.01620v4", "title": "Deep Learning Techniques for Music Generation -- A Survey", "abstract": "This paper is a survey and an analysis of different ways of using deep learning (deep artificial neural networks) to generate musical content. We propose a methodology based on five dimensions for our analysis: Objective - What musical content is to be generated? Examples are: melody, polyphony, accompaniment or counterpoint. - For what destination and for what use? To be performed by a human(s) (in the case of a musical score), or by a machine (in the case of an audio file). Representation - What are the concepts to be manipulated? Examples are: waveform, spectrogram, note, chord, meter and beat. - What format is to be used? Examples are: MIDI, piano roll or text. - How will the representation be encoded? Examples are: scalar, one-hot or many-hot. Architecture - What type(s) of deep neural network is (are) to be used? Examples are: feedforward network, recurrent network, autoencoder or generative adversarial networks. Challenge - What are the limitations and open challenges? Examples are: variability, interactivity and creativity. Strategy - How do we model and control the process of generation? Examples are: single-step feedforward, iterative feedforward, sampling or input manipulation. For each dimension, we conduct a comparative analysis of various models and techniques and we propose some tentative multidimensional typology. This typology is bottom-up, based on the analysis of many existing deep-learning based systems for music generation selected from the relevant literature. These systems are described and are used to exemplify the various choices of objective, representation, architecture, challenge and strategy. The last section includes some discussion and some prospects.", "published": "2017-09-05T23:12:01Z", "authors": ["Jean-Pierre Briot", "Gaëtan Hadjeres", "François-David Pachet"], "categories": ["cs.SD", "cs.LG"], "doi": "", "_query": "real estate valuation deep learning", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/1911.06832v1", "title": "Data-efficient Co-Adaptation of Morphology and Behaviour with Deep Reinforcement Learning", "abstract": "Humans and animals are capable of quickly learning new behaviours to solve new tasks. Yet, we often forget that they also rely on a highly specialized morphology that co-adapted with motor control throughout thousands of years. Although compelling, the idea of co-adapting morphology and behaviours in robots is often unfeasible because of the long manufacturing times, and the need to re-design an appropriate controller for each morphology. In this paper, we propose a novel approach to automatically and efficiently co-adapt a robot morphology and its controller. Our approach is based on recent advances in deep reinforcement learning, and specifically the soft actor critic algorithm. Key to our approach is the possibility of leveraging previously tested morphologies and behaviors to estimate the performance of new candidate morphologies. As such, we can make full use of the information available for making more informed decisions, with the ultimate goal of achieving a more data-efficient co-adaptation (i.e., reducing the number of morphologies and behaviors tested). Simulated experiments show that our approach requires drastically less design prototypes to find good morphology-behaviour combinations, making this method particularly suitable for future co-adaptation of robot designs in the real world.", "published": "2019-11-15T19:01:21Z", "authors": ["Kevin Sebastian Luck", "Heni Ben Amor", "Roberto Calandra"], "categories": ["cs.LG", "cs.AI", "cs.NE", "cs.RO", "stat.ML"], "doi": "", "_query": "real estate valuation deep learning", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/1908.06472v1", "title": "Training Deep Learning Models via Synthetic Data: Application in Unmanned Aerial Vehicles", "abstract": "This paper describes preliminary work in the recent promising approach of generating synthetic training data for facilitating the learning procedure of deep learning (DL) models, with a focus on aerial photos produced by unmanned aerial vehicles (UAV). The general concept and methodology are described, and preliminary results are presented, based on a classification problem of fire identification in forests as well as a counting problem of estimating number of houses in urban areas. The proposed technique constitutes a new possibility for the DL community, especially related to UAV-based imagery analysis, with much potential, promising results, and unexplored ground for further research.", "published": "2019-08-18T16:25:14Z", "authors": ["Andreas Kamilaris", "Corjan van den Brink", "Savvas Karatsiolis"], "categories": ["cs.CV", "cs.LG", "eess.IV"], "doi": "", "_query": "real estate valuation deep learning", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2609.12684v1", "title": "Generative AI Use Cases In Real Estate Marketing: Adoption and Constraints in Germany", "abstract": "Generative artificial intelligence (GenAI) is changing how work is organized and performed. Real estate marketing is a prime example of this, yet evidence of GenAI in real estate agents' day-to-day practice remains scarce. In this work, we report on our insights from a German-based empirical study with eleven semi-structured interviews. GenAI is already utilized across different activities, with marketing communication being the most prominent. Concrete use cases are emergent and unevenly adopted, with writing exposé texts being the only widely established one. Interaction is predominantly human-in-the-loop: GenAI drafts, structures, and retrieves, while real estate agents curate, verify, and decide. Constraints stem less from model capability than from integration with listings and documents, data availability, and compliance in sensitive tasks. The study contributes a grounded map of existing and potential use cases and identifies tentative practical implications for adoption.", "published": "2026-09-11T10:30:19Z", "authors": ["Victor Kolominsky-Rabas", "Leopold Müller", "Felicia Perpina", "Niklas Kühl"], "categories": ["cs.AI", "cs.HC"], "doi": "", "_query": "real estate valuation deep learning", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2410.06772v1", "title": "Assessment of the Financial Competitiveness of Publicly Listed Indian Real Estate Companies Using the Entropy Method", "abstract": "The real estate sector is one of the key drivers of India's national economy, contributing about 7.3\\% to the GDP. As the market evolves, more players enter, and government policies become more stringent, Indian real estate companies face increasing competition. Improving financial competitiveness is crucial for the survival and growth of these companies. This paper presents a financial competitiveness evaluation index system for Indian-listed real estate companies, covering profitability, solvency, and operational capacity. Using key financial ratios and a scoring system, the financial competitiveness of various companies was evaluated, revealing that companies with high scores have strong profitability and operational capacity. In contrast, those with lower scores struggle with solvency and working capital.", "published": "2024-10-09T11:05:12Z", "authors": ["Ritij Saini", "Aditya Deora", "Kirtesh Gadiya"], "categories": ["q-fin.GN"], "doi": "", "_query": "real estate valuation deep learning", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2501.07923v1", "title": "Aviation Safety Enhancement via NLP & Deep Learning: Classifying Flight Phases in ATSB Safety Reports", "abstract": "Aviation safety is paramount, demanding precise analysis of safety occurrences during different flight phases. This study employs Natural Language Processing (NLP) and Deep Learning models, including LSTM, CNN, Bidirectional LSTM (BLSTM), and simple Recurrent Neural Networks (sRNN), to classify flight phases in safety reports from the Australian Transport Safety Bureau (ATSB). The models exhibited high accuracy, precision, recall, and F1 scores, with LSTM achieving the highest performance of 87%, 88%, 87%, and 88%, respectively. This performance highlights their effectiveness in automating safety occurrence analysis. The integration of NLP and Deep Learning technologies promises transformative enhancements in aviation safety analysis, enabling targeted safety measures and streamlined report handling.", "published": "2025-01-14T08:18:41Z", "authors": ["Aziida Nanyonga", "Hassan Wasswa", "Graham Wild"], "categories": ["cs.LG", "cs.CL"], "doi": "", "_query": "real estate valuation deep learning", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2107.10390v1", "title": "Reinforcement Learning Agent Training with Goals for Real World Tasks", "abstract": "Reinforcement Learning (RL) is a promising approach for solving various control, optimization, and sequential decision making tasks. However, designing reward functions for complex tasks (e.g., with multiple objectives and safety constraints) can be challenging for most users and usually requires multiple expensive trials (reward function hacking). In this paper we propose a specification language (Inkling Goal Specification) for complex control and optimization tasks, which is very close to natural language and allows a practitioner to focus on problem specification instead of reward function hacking. The core elements of our framework are: (i) mapping the high level language to a predicate temporal logic tailored to control and optimization tasks, (ii) a novel automaton-guided dense reward generation that can be used to drive RL algorithms, and (iii) a set of performance metrics to assess the behavior of the system. We include a set of experiments showing that the proposed method provides great ease of use to specify a wide range of real world tasks; and that the reward generated is able to drive the policy training to achieve the specified goal.", "published": "2021-07-21T23:21:16Z", "authors": ["Xuan Zhao", "Marcos Campos"], "categories": ["cs.AI"], "doi": "", "_query": "real estate valuation deep learning", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2105.07636v2", "title": "DOC3-Deep One Class Classification using Contradictions", "abstract": "This paper introduces the notion of learning from contradictions (a.k.a Universum learning) for deep one class classification problems. We formalize this notion for the widely adopted one class large-margin loss, and propose the Deep One Class Classification using Contradictions (DOC3) algorithm. We show that learning from contradictions incurs lower generalization error by comparing the Empirical Rademacher Complexity (ERC) of DOC3 against its traditional inductive learning counterpart. Our empirical results demonstrate the efficacy of DOC3 compared to popular baseline algorithms on several real-life data sets.", "published": "2021-05-17T06:48:25Z", "authors": ["Sauptik Dhar", "Bernardo Gonzalez Torres"], "categories": ["cs.LG", "cs.AI", "cs.CV", "stat.ML"], "doi": "", "_query": "real estate valuation deep learning", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2510.18054v1", "title": "HouseTour: A Virtual Real Estate A(I)gent", "abstract": "We introduce HouseTour, a method for spatially-aware 3D camera trajectory and natural language summary generation from a collection of images depicting an existing 3D space. Unlike existing vision-language models (VLMs), which struggle with geometric reasoning, our approach generates smooth video trajectories via a diffusion process constrained by known camera poses and integrates this information into the VLM for 3D-grounded descriptions. We synthesize the final video using 3D Gaussian splatting to render novel views along the trajectory. To support this task, we present the HouseTour dataset, which includes over 1,200 house-tour videos with camera poses, 3D reconstructions, and real estate descriptions. Experiments demonstrate that incorporating 3D camera trajectories into the text generation process improves performance over methods handling each task independently. We evaluate both individual and end-to-end performance, introducing a new joint metric. Our work enables automated, professional-quality video creation for real estate and touristic applications without requiring specialized expertise or equipment.", "published": "2025-10-20T19:47:35Z", "authors": ["Ata Çelen", "Marc Pollefeys", "Daniel Barath", "Iro Armeni"], "categories": ["cs.CV", "cs.CL"], "doi": "", "_query": "real estate valuation deep learning", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2401.09886v2", "title": "Cooperative Edge Caching Based on Elastic Federated and Multi-Agent Deep Reinforcement Learning in Next-Generation Network", "abstract": "Edge caching is a promising solution for next-generation networks by empowering caching units in small-cell base stations (SBSs), which allows user equipments (UEs) to fetch users' requested contents that have been pre-cached in SBSs. It is crucial for SBSs to predict accurate popular contents through learning while protecting users' personal information. Traditional federated learning (FL) can protect users' privacy but the data discrepancies among UEs can lead to a degradation in model quality. Therefore, it is necessary to train personalized local models for each UE to predict popular contents accurately. In addition, the cached contents can be shared among adjacent SBSs in next-generation networks, thus caching predicted popular contents in different SBSs may affect the cost to fetch contents. Hence, it is critical to determine where the popular contents are cached cooperatively. To address these issues, we propose a cooperative edge caching scheme based on elastic federated and multi-agent deep reinforcement learning (CEFMR) to optimize the cost in the network. We first propose an elastic FL algorithm to train the personalized model for each UE, where adversarial autoencoder (AAE) model is adopted for training to improve the prediction accuracy, then {a popular} content prediction algorithm is proposed to predict the popular contents for each SBS based on the trained AAE model. Finally, we propose a multi-agent deep reinforcement learning (MADRL) based algorithm to decide where the predicted popular contents are collaboratively cached among SBSs. Our experimental results demonstrate the superiority of our proposed scheme to existing baseline caching schemes.", "published": "2024-01-18T10:59:18Z", "authors": ["Qiong Wu", "Wenhua Wang", "Pingyi Fan", "Qiang Fan", "Huiling Zhu", "Khaled B. Letaief"], "categories": ["cs.LG", "cs.AI"], "doi": "", "_query": "real estate valuation deep learning", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2306.14300v2", "title": "Screening Autism Spectrum Disorder in children using Deep Learning Approach : Evaluating the classification model of YOLOv26s by comparing with other models", "abstract": "Autism spectrum disorder (ASD) is a developmental condition that presents significant challenges in social interac- tion, communication, and behavior. Early intervention plays a pivotal role in enhancing cognitive abilities and reducing autistic symptoms in children with ASD. Numerous clinical studies have highlighted distinctive facial characteristics that distinguish ASD children from typically developing (TD) children. In this study, we propose a practical solution for ASD screening using facial images using YOLOv26s model. By employing YOLOv26s, a deep learning technique, we achieved exceptional results. Our model achieved a remarkable 92.86% accuracy in classification and an F1-score of 0.9291. Our findings provide support for the clini- cal observations regarding facial feature discrepancies between children with ASD. The high F1-score obtained demonstrates the potential of deep learning models in screening children with ASD. We conclude that the newest version of YOLOv26s which is usually used for object detection can be used for classification problem of Austistic and Non-autistic images.", "published": "2023-06-25T18:02:01Z", "authors": ["Subash Gautam", "Sagar Pathak", "Prabin Sharma", "Bidhya Shrestha", "Kisan Thapa", "Shubham Joshi", "Mala Deep Upadhaya", "Dikshya Thapa", "Chandiprasad Chintalapati", "Sagar Duwal", "Angela Upreti", "Salik Ram Khanal"], "categories": ["cs.CV", "cs.AI"], "doi": "", "_query": "real estate valuation deep learning", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2005.05541v2", "title": "Modularizing Deep Learning via Pairwise Learning With Kernels", "abstract": "By redefining the conventional notions of layers, we present an alternative view on finitely wide, fully trainable deep neural networks as stacked linear models in feature spaces, leading to a kernel machine interpretation. Based on this construction, we then propose a provably optimal modular learning framework for classification that does not require between-module backpropagation. This modular approach brings new insights into the label requirement of deep learning: It leverages only implicit pairwise labels (weak supervision) when learning the hidden modules. When training the output module, on the other hand, it requires full supervision but achieves high label efficiency, needing as few as 10 randomly selected labeled examples (one from each class) to achieve 94.88% accuracy on CIFAR-10 using a ResNet-18 backbone. Moreover, modular training enables fully modularized deep learning workflows, which then simplify the design and implementation of pipelines and improve the maintainability and reusability of models. To showcase the advantages of such a modularized workflow, we describe a simple yet reliable method for estimating reusability of pre-trained modules as well as task transferability in a transfer learning setting. At practically no computation overhead, it precisely described the task space structure of 15 binary classification tasks from CIFAR-10.", "published": "2020-05-12T04:19:37Z", "authors": ["Shiyu Duan", "Shujian Yu", "Jose Principe"], "categories": ["stat.ML", "cs.LG"], "doi": "", "_query": "real estate valuation deep learning", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/1902.00407v2", "title": "Understanding Impacts of High-Order Loss Approximations and Features in Deep Learning Interpretation", "abstract": "Current methods to interpret deep learning models by generating saliency maps generally rely on two key assumptions. First, they use first-order approximations of the loss function neglecting higher-order terms such as the loss curvatures. Second, they evaluate each feature's importance in isolation, ignoring their inter-dependencies. In this work, we study the effect of relaxing these two assumptions. First, by characterizing a closed-form formula for the Hessian matrix of a deep ReLU network, we prove that, for a classification problem with a large number of classes, if an input has a high confidence classification score, the inclusion of the Hessian term has small impacts in the final solution. We prove this result by showing that in this case the Hessian matrix is approximately of rank one and its leading eigenvector is almost parallel to the gradient of the loss function. Our empirical experiments on ImageNet samples are consistent with our theory. This result can have implications in other related problems such as adversarial examples as well. Second, we compute the importance of group-features in deep learning interpretation by introducing a sparsity regularization term. We use the $L_0-L_1$ relaxation technique along with the proximal gradient descent to have an efficient computation of group feature importance scores. Our empirical results indicate that considering group features can improve deep learning interpretation significantly.", "published": "2019-02-01T15:42:46Z", "authors": ["Sahil Singla", "Eric Wallace", "Shi Feng", "Soheil Feizi"], "categories": ["cs.LG", "stat.ML"], "doi": "", "_query": "real estate valuation deep learning", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2007.05721v1", "title": "Towards Robust Classification with Deep Generative Forests", "abstract": "Decision Trees and Random Forests are among the most widely used machine learning models, and often achieve state-of-the-art performance in tabular, domain-agnostic datasets. Nonetheless, being primarily discriminative models they lack principled methods to manipulate the uncertainty of predictions. In this paper, we exploit Generative Forests (GeFs), a recent class of deep probabilistic models that addresses these issues by extending Random Forests to generative models representing the full joint distribution over the feature space. We demonstrate that GeFs are uncertainty-aware classifiers, capable of measuring the robustness of each prediction as well as detecting out-of-distribution samples.", "published": "2020-07-11T08:57:52Z", "authors": ["Alvaro H. C. Correia", "Robert Peharz", "Cassio de Campos"], "categories": ["stat.ML", "cs.LG"], "doi": "", "_query": "real estate valuation deep learning", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2004.02396v1", "title": "A Learning Framework for n-bit Quantized Neural Networks toward FPGAs", "abstract": "The quantized neural network (QNN) is an efficient approach for network compression and can be widely used in the implementation of FPGAs. This paper proposes a novel learning framework for n-bit QNNs, whose weights are constrained to the power of two. To solve the gradient vanishing problem, we propose a reconstructed gradient function for QNNs in back-propagation algorithm that can directly get the real gradient rather than estimating an approximate gradient of the expected loss. We also propose a novel QNN structure named n-BQ-NN, which uses shift operation to replace the multiply operation and is more suitable for the inference on FPGAs. Furthermore, we also design a shift vector processing element (SVPE) array to replace all 16-bit multiplications with SHIFT operations in convolution operation on FPGAs. We also carry out comparable experiments to evaluate our framework. The experimental results show that the quantized models of ResNet, DenseNet and AlexNet through our learning framework can achieve almost the same accuracies with the original full-precision models. Moreover, when using our learning framework to train our n-BQ-NN from scratch, it can achieve state-of-the-art results compared with typical low-precision QNNs. Experiments on Xilinx ZCU102 platform show that our n-BQ-NN with our SVPE can execute 2.9 times faster than with the vector processing element (VPE) in inference. As the SHIFT operation in our SVPE array will not consume Digital Signal Processings (DSPs) resources on FPGAs, the experiments have shown that the use of SVPE array also reduces average energy consumption to 68.7% of the VPE array with 16-bit.", "published": "2020-04-06T04:21:24Z", "authors": ["Jun Chen", "Liang Liu", "Yong Liu", "Xianfang Zeng"], "categories": ["cs.LG", "eess.SP", "stat.ML"], "doi": "10.1109/TNNLS.2020.2980041", "_query": "real estate valuation deep learning", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2308.12039v1", "title": "Hybrid Retrieval and Multi-stage Text Ranking Solution at TREC 2022 Deep Learning Track", "abstract": "Large-scale text retrieval technology has been widely used in various practical business scenarios. This paper presents our systems for the TREC 2022 Deep Learning Track. We explain the hybrid text retrieval and multi-stage text ranking method adopted in our solution. The retrieval stage combined the two structures of traditional sparse retrieval and neural dense retrieval. In the ranking stage, in addition to the full interaction-based ranking model built on large pre-trained language model, we also proposes a lightweight sub-ranking module to further enhance the final text ranking performance. Evaluation results demonstrate the effectiveness of our proposed approach. Our models achieve the 1st and 4th rank on the test set of passage ranking and document ranking respectively.", "published": "2023-08-23T09:56:59Z", "authors": ["Guangwei Xu", "Yangzhao Zhang", "Longhui Zhang", "Dingkun Long", "Pengjun Xie", "Ruijie Guo"], "categories": ["cs.IR", "cs.CL"], "doi": "", "_query": "real estate valuation deep learning", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/1910.10386v1", "title": "Stabilising priors for robust Bayesian deep learning", "abstract": "Bayesian neural networks (BNNs) have developed into useful tools for probabilistic modelling due to recent advances in variational inference enabling large scale BNNs. However, BNNs remain brittle and hard to train, especially: (1) when using deep architectures consisting of many hidden layers and (2) in situations with large weight variances. We use signal propagation theory to quantify these challenges and propose self-stabilising priors. This is achieved by a reformulation of the ELBO to allow the prior to influence network signal propagation. Then, we develop a stabilising prior, where the distributional parameters of the prior are adjusted before each forward pass to ensure stability of the propagating signal. This stabilised signal propagation leads to improved convergence and robustness making it possible to train deeper networks and in more noisy settings.", "published": "2019-10-23T07:01:17Z", "authors": ["Felix McGregor", "Arnu Pretorius", "Johan du Preez", "Steve Kroon"], "categories": ["cs.LG", "stat.ML"], "doi": "", "_query": "real estate valuation deep learning", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2504.06180v1", "title": "Blockchain Oracles for Real Estate Rental", "abstract": "Blockchain technology has seen adoption across various industries and the real estate sector is no exception. The traditional property leasing process guarantees no trust between parties, uses insecure communication channels, and forces participants who are not familiar with the process to perform contracts. Blockchain technology emerges as a solution to simplify the traditional property leasing process. This work proposes the use of two blockchain oracles to handle, respectively, maintenance issues and automate rent payments in the context of property rental. These two components are introduced in a blockchain-based property rental platform.", "published": "2025-04-08T16:21:30Z", "authors": ["Nuno Braz", "João Santos", "Tiago Dias", "Miguel Correia"], "categories": ["cs.CR"], "doi": "", "_query": "real estate valuation deep learning", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/1807.10466v1", "title": "A Deep Learning Framework for Automatic Diagnosis in Lung Cancer", "abstract": "We developed a deep learning framework that helps to automatically identify and segment lung cancer areas in patients' tissue specimens. The study was based on a cohort of lung cancer patients operated at the Uppsala University Hospital. The tissues were reviewed by lung pathologists and then the cores were compiled to tissue micro-arrays (TMAs). For experiments, hematoxylin-eosin stained slides from 712 patients were scanned and then manually annotated. Then these scans and annotations were used to train segmentation models of the developed framework. The performance of the developed deep learning framework was evaluated on fully annotated TMA cores from 178 patients reaching pixel-wise precision of 0.80 and recall of 0.86. Finally, publicly available Stanford TMA cores were used to demonstrate high performance of the framework qualitatively.", "published": "2018-07-27T07:32:46Z", "authors": ["Nikolay Burlutskiy", "Feng Gu", "Lena Kajland Wilen", "Max Backman", "Patrick Micke"], "categories": ["cs.CV"], "doi": "", "_query": "real estate valuation deep learning", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/1912.08905v1", "title": "The Spectral Bias of the Deep Image Prior", "abstract": "The \"deep image prior\" proposed by Ulyanov et al. is an intriguing property of neural nets: a convolutional encoder-decoder network can be used as a prior for natural images. The network architecture implicitly introduces a bias; If we train the model to map white noise to a corrupted image, this bias guides the model to fit the true image before fitting the corrupted regions. This paper explores why the deep image prior helps in denoising natural images. We present a novel method to analyze trajectories generated by the deep image prior optimization and demonstrate: (i) convolution layers of the an encoder-decoder decouple the frequency components of the image, learning each at different rates (ii) the model fits lower frequencies first, making early stopping behave as a low pass filter. The experiments study an extension of Cheng et al which showed that at initialization, the deep image prior is equivalent to a stationary Gaussian process.", "published": "2019-12-18T21:51:58Z", "authors": ["Prithvijit Chakrabarty", "Subhransu Maji"], "categories": ["cs.CV", "cs.LG"], "doi": "", "_query": "real estate valuation deep learning", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2507.13458v1", "title": "Domain-randomized deep learning for neuroimage analysis", "abstract": "Deep learning has revolutionized neuroimage analysis by delivering unprecedented speed and accuracy. However, the narrow scope of many training datasets constrains model robustness and generalizability. This challenge is particularly acute in magnetic resonance imaging (MRI), where image appearance varies widely across pulse sequences and scanner hardware. A recent domain-randomization strategy addresses the generalization problem by training deep neural networks on synthetic images with randomized intensities and anatomical content. By generating diverse data from anatomical segmentation maps, the approach enables models to accurately process image types unseen during training, without retraining or fine-tuning. It has demonstrated effectiveness across modalities including MRI, computed tomography, positron emission tomography, and optical coherence tomography, as well as beyond neuroimaging in ultrasound, electron and fluorescence microscopy, and X-ray microtomography. This tutorial paper reviews the principles, implementation, and potential of the synthesis-driven training paradigm. It highlights key benefits, such as improved generalization and resistance to overfitting, while discussing trade-offs such as increased computational demands. Finally, the article explores practical considerations for adopting the technique, aiming to accelerate the development of generalizable tools that make deep learning more accessible to domain experts without extensive computational resources or machine learning knowledge.", "published": "2025-07-17T18:07:42Z", "authors": ["Malte Hoffmann"], "categories": ["eess.IV", "cs.CV", "cs.LG"], "doi": "10.1109/MSP.2025.3590806", "_query": "real estate valuation deep learning", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2107.04333v2", "title": "Attend2Pack: Bin Packing through Deep Reinforcement Learning with Attention", "abstract": "This paper seeks to tackle the bin packing problem (BPP) through a learning perspective. Building on self-attention-based encoding and deep reinforcement learning algorithms, we propose a new end-to-end learning model for this task of interest. By decomposing the combinatorial action space, as well as utilizing a new training technique denoted as prioritized oversampling, which is a general scheme to speed up on-policy learning, we achieve state-of-the-art performance in a range of experimental settings. Moreover, although the proposed approach attend2pack targets offline-BPP, we strip our method down to the strict online-BPP setting where it is also able to achieve state-of-the-art performance. With a set of ablation studies as well as comparisons against a range of previous works, we hope to offer as a valid baseline approach to this field of study.", "published": "2021-07-09T10:00:30Z", "authors": ["Jingwei Zhang", "Bin Zi", "Xiaoyu Ge"], "categories": ["cs.LG", "cs.AI"], "doi": "", "_query": "real estate valuation deep learning", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2002.05229v1", "title": "Data Efficient Training for Reinforcement Learning with Adaptive Behavior Policy Sharing", "abstract": "Deep Reinforcement Learning (RL) is proven powerful for decision making in simulated environments. However, training deep RL model is challenging in real world applications such as production-scale health-care or recommender systems because of the expensiveness of interaction and limitation of budget at deployment. One aspect of the data inefficiency comes from the expensive hyper-parameter tuning when optimizing deep neural networks. We propose Adaptive Behavior Policy Sharing (ABPS), a data-efficient training algorithm that allows sharing of experience collected by behavior policy that is adaptively selected from a pool of agents trained with an ensemble of hyper-parameters. We further extend ABPS to evolve hyper-parameters during training by hybridizing ABPS with an adapted version of Population Based Training (ABPS-PBT). We conduct experiments with multiple Atari games with up to 16 hyper-parameter/architecture setups. ABPS achieves superior overall performance, reduced variance on top 25% agents, and equivalent performance on the best agent compared to conventional hyper-parameter tuning with independent training, even though ABPS only requires the same number of environmental interactions as training a single agent. We also show that ABPS-PBT further improves the convergence speed and reduces the variance.", "published": "2020-02-12T20:35:31Z", "authors": ["Ge Liu", "Rui Wu", "Heng-Tze Cheng", "Jing Wang", "Jayden Ooi", "Lihong Li", "Ang Li", "Wai Lok Sibon Li", "Craig Boutilier", "Ed Chi"], "categories": ["cs.LG", "cs.AI", "stat.ML"], "doi": "", "_query": "real estate valuation deep learning", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/1704.06040v1", "title": "Understanding the Mechanisms of Deep Transfer Learning for Medical Images", "abstract": "The ability to automatically learn task specific feature representations has led to a huge success of deep learning methods. When large training data is scarce, such as in medical imaging problems, transfer learning has been very effective. In this paper, we systematically investigate the process of transferring a Convolutional Neural Network, trained on ImageNet images to perform image classification, to kidney detection problem in ultrasound images. We study how the detection performance depends on the extent of transfer. We show that a transferred and tuned CNN can outperform a state-of-the-art feature engineered pipeline and a hybridization of these two techniques achieves 20\\% higher performance. We also investigate how the evolution of intermediate response images from our network. Finally, we compare these responses to state-of-the-art image processing filters in order to gain greater insight into how transfer learning is able to effectively manage widely varying imaging regimes.", "published": "2017-04-20T08:04:52Z", "authors": ["Hariharan Ravishankar", "Prasad Sudhakar", "Rahul Venkataramani", "Sheshadri Thiruvenkadam", "Pavan Annangi", "Narayanan Babu", "Vivek Vaidya"], "categories": ["cs.CV"], "doi": "", "_query": "real estate valuation deep learning", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2309.15097v1", "title": "Case Study: Ensemble Decision-Based Annotation of Unconstrained Real Estate Images", "abstract": "We describe a proof-of-concept for annotating real estate images using simple iterative rule-based semi-supervised learning. In this study, we have gained important insights into the content characteristics and uniqueness of individual image classes as well as essential requirements for a practical implementation.", "published": "2023-09-26T17:43:58Z", "authors": ["Miroslav Despotovic", "Zedong Zhang", "Eric Stumpe", "Matthias Zeppelzauer"], "categories": ["cs.CV"], "doi": "", "_query": "real estate valuation deep learning", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2302.12168v2", "title": "A comparative assessment of deep learning models for day-ahead load forecasting: Investigating key accuracy drivers", "abstract": "Short-term load forecasting (STLF) is vital for the effective and economic operation of power grids and energy markets. However, the non-linearity and non-stationarity of electricity demand as well as its dependency on various external factors renders STLF a challenging task. To that end, several deep learning models have been proposed in the literature for STLF, reporting promising results. In order to evaluate the accuracy of said models in day-ahead forecasting settings, in this paper we focus on the national net aggregated STLF of Portugal and conduct a comparative study considering a set of indicative, well-established deep autoregressive models, namely multi-layer perceptrons (MLP), long short-term memory networks (LSTM), neural basis expansion coefficient analysis (N-BEATS), temporal convolutional networks (TCN), and temporal fusion transformers (TFT). Moreover, we identify factors that significantly affect the demand and investigate their impact on the accuracy of each model. Our results suggest that N-BEATS consistently outperforms the rest of the examined models. MLP follows, providing further evidence towards the use of feed-forward networks over relatively more sophisticated architectures. Finally, certain calendar and weather features like the hour of the day and the temperature are identified as key accuracy drivers, providing insights regarding the forecasting approach that should be used per case.", "published": "2023-02-23T17:11:04Z", "authors": ["Sotiris Pelekis", "Ioannis-Konstantinos Seisopoulos", "Evangelos Spiliotis", "Theodosios Pountridis", "Evangelos Karakolis", "Spiros Mouzakitis", "Dimitris Askounis"], "categories": ["cs.LG", "cs.AI"], "doi": "10.1016/j.segan.2023.101171", "_query": "real estate valuation deep learning", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2206.15306v2", "title": "Transfer Learning with Deep Tabular Models", "abstract": "Recent work on deep learning for tabular data demonstrates the strong performance of deep tabular models, often bridging the gap between gradient boosted decision trees and neural networks. Accuracy aside, a major advantage of neural models is that they learn reusable features and are easily fine-tuned in new domains. This property is often exploited in computer vision and natural language applications, where transfer learning is indispensable when task-specific training data is scarce. In this work, we demonstrate that upstream data gives tabular neural networks a decisive advantage over widely used GBDT models. We propose a realistic medical diagnosis benchmark for tabular transfer learning, and we present a how-to guide for using upstream data to boost performance with a variety of tabular neural network architectures. Finally, we propose a pseudo-feature method for cases where the upstream and downstream feature sets differ, a tabular-specific problem widespread in real-world applications. Our code is available at https://github.com/LevinRoman/tabular-transfer-learning .", "published": "2022-06-30T14:24:32Z", "authors": ["Roman Levin", "Valeriia Cherepanova", "Avi Schwarzschild", "Arpit Bansal", "C. Bayan Bruss", "Tom Goldstein", "Andrew Gordon Wilson", "Micah Goldblum"], "categories": ["cs.LG", "stat.ML"], "doi": "", "_query": "real estate valuation deep learning", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2011.12737v1", "title": "Ranking Deep Learning Generalization using Label Variation in Latent Geometry Graphs", "abstract": "Measuring the generalization performance of a Deep Neural Network (DNN) without relying on a validation set is a difficult task. In this work, we propose exploiting Latent Geometry Graphs (LGGs) to represent the latent spaces of trained DNN architectures. Such graphs are obtained by connecting samples that yield similar latent representations at a given layer of the considered DNN. We then obtain a generalization score by looking at how strongly connected are samples of distinct classes in LGGs. This score allowed us to rank 3rd on the NeurIPS 2020 Predicting Generalization in Deep Learning (PGDL) competition.", "published": "2020-11-25T13:49:21Z", "authors": ["Carlos Lassance", "Louis Béthune", "Myriam Bontonou", "Mounia Hamidouche", "Vincent Gripon"], "categories": ["cs.LG"], "doi": "", "_query": "real estate valuation deep learning", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2211.00024v2", "title": "A robust estimator of mutual information for deep learning interpretability", "abstract": "We develop the use of mutual information (MI), a well-established metric in information theory, to interpret the inner workings of deep learning models. To accurately estimate MI from a finite number of samples, we present GMM-MI (pronounced $``$Jimmie$\"$), an algorithm based on Gaussian mixture models that can be applied to both discrete and continuous settings. GMM-MI is computationally efficient, robust to the choice of hyperparameters and provides the uncertainty on the MI estimate due to the finite sample size. We extensively validate GMM-MI on toy data for which the ground truth MI is known, comparing its performance against established mutual information estimators. We then demonstrate the use of our MI estimator in the context of representation learning, working with synthetic data and physical datasets describing highly non-linear processes. We train deep learning models to encode high-dimensional data within a meaningful compressed (latent) representation, and use GMM-MI to quantify both the level of disentanglement between the latent variables, and their association with relevant physical quantities, thus unlocking the interpretability of the latent representation. We make GMM-MI publicly available.", "published": "2022-10-31T18:00:02Z", "authors": ["Davide Piras", "Hiranya V. Peiris", "Andrew Pontzen", "Luisa Lucie-Smith", "Ningyuan Guo", "Brian Nord"], "categories": ["physics.data-an", "astro-ph.IM", "cs.LG"], "doi": "10.1088/2632-2153/acc444", "_query": "real estate valuation deep learning", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/1605.06636v2", "title": "Deep Transfer Learning with Joint Adaptation Networks", "abstract": "Deep networks have been successfully applied to learn transferable features for adapting models from a source domain to a different target domain. In this paper, we present joint adaptation networks (JAN), which learn a transfer network by aligning the joint distributions of multiple domain-specific layers across domains based on a joint maximum mean discrepancy (JMMD) criterion. Adversarial training strategy is adopted to maximize JMMD such that the distributions of the source and target domains are made more distinguishable. Learning can be performed by stochastic gradient descent with the gradients computed by back-propagation in linear-time. Experiments testify that our model yields state of the art results on standard datasets.", "published": "2016-05-21T12:56:14Z", "authors": ["Mingsheng Long", "Han Zhu", "Jianmin Wang", "Michael I. Jordan"], "categories": ["cs.LG", "stat.ML"], "doi": "", "_query": "real estate valuation deep learning", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2507.21364v1", "title": "Evaluating Deep Learning Models for African Wildlife Image Classification: From DenseNet to Vision Transformers", "abstract": "Wildlife populations in Africa face severe threats, with vertebrate numbers declining by over 65% in the past five decades. In response, image classification using deep learning has emerged as a promising tool for biodiversity monitoring and conservation. This paper presents a comparative study of deep learning models for automatically classifying African wildlife images, focusing on transfer learning with frozen feature extractors. Using a public dataset of four species: buffalo, elephant, rhinoceros, and zebra; we evaluate the performance of DenseNet-201, ResNet-152, EfficientNet-B4, and Vision Transformer ViT-H/14. DenseNet-201 achieved the best performance among convolutional networks (67% accuracy), while ViT-H/14 achieved the highest overall accuracy (99%), but with significantly higher computational cost, raising deployment concerns. Our experiments highlight the trade-offs between accuracy, resource requirements, and deployability. The best-performing CNN (DenseNet-201) was integrated into a Hugging Face Gradio Space for real-time field use, demonstrating the feasibility of deploying lightweight models in conservation settings. This work contributes to African-grounded AI research by offering practical insights into model selection, dataset preparation, and responsible deployment of deep learning tools for wildlife conservation.", "published": "2025-07-28T22:18:13Z", "authors": ["Lukman Jibril Aliyu", "Umar Sani Muhammad", "Bilqisu Ismail", "Nasiru Muhammad", "Almustapha A Wakili", "Seid Muhie Yimam", "Shamsuddeen Hassan Muhammad", "Mustapha Abdullahi"], "categories": ["cs.CV", "cs.AI"], "doi": "", "_query": "real estate valuation deep learning", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/1907.11711v1", "title": "Deep MRI Reconstruction: Unrolled Optimization Algorithms Meet Neural Networks", "abstract": "Image reconstruction from undersampled k-space data has been playing an important role for fast MRI. Recently, deep learning has demonstrated tremendous success in various fields and also shown potential to significantly speed up MR reconstruction with reduced measurements. This article gives an overview of deep learning-based image reconstruction methods for MRI. Three types of deep learning-based approaches are reviewed, the data-driven, model-driven and integrated approaches. The main structure of each network in three approaches is explained and the analysis of common parts of reviewed networks and differences in-between are highlighted. Based on the review, a number of signal processing issues are discussed for maximizing the potential of deep reconstruction for fast MRI. the discussion may facilitate further development of \"optimal\" network and performance analysis from a theoretical point of view.", "published": "2019-07-26T08:22:53Z", "authors": ["Dong Liang", "Jing Cheng", "Ziwen Ke", "Leslie Ying"], "categories": ["eess.IV", "cs.CV", "cs.LG", "eess.SP", "physics.med-ph", "stat.ML"], "doi": "", "_query": "real estate valuation deep learning", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/1905.02005v2", "title": "Deep Ordinal Reinforcement Learning", "abstract": "Reinforcement learning usually makes use of numerical rewards, which have nice properties but also come with drawbacks and difficulties. Using rewards on an ordinal scale (ordinal rewards) is an alternative to numerical rewards that has received more attention in recent years. In this paper, a general approach to adapting reinforcement learning problems to the use of ordinal rewards is presented and motivated. We show how to convert common reinforcement learning algorithms to an ordinal variation by the example of Q-learning and introduce Ordinal Deep Q-Networks, which adapt deep reinforcement learning to ordinal rewards. Additionally, we run evaluations on problems provided by the OpenAI Gym framework, showing that our ordinal variants exhibit a performance that is comparable to the numerical variations for a number of problems. We also give first evidence that our ordinal variant is able to produce better results for problems with less engineered and simpler-to-design reward signals.", "published": "2019-05-06T12:54:22Z", "authors": ["Alexander Zap", "Tobias Joppen", "Johannes Fürnkranz"], "categories": ["cs.LG", "cs.AI", "stat.ML"], "doi": "10.1007/978-3-030-46133-1_1", "_query": "real estate valuation deep learning", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/math/0605011v2", "title": "A valuation criterion for normal bases in elementary abelian extensions", "abstract": "Let $p$ be a prime number and let $K$ be a finite extension of the field $\\mathbb{Q}_p$ of $p$-adic numbers. Let $N$ be a fully ramified, elementary abelian extension of $K$. Under a mild hypothesis on the extension $N/K$, we show that every element of $N$ with valuation congruent mod $[N:K]$ to the largest lower ramification number of $N/K$ generates a normal basis for $N$ over $K$.", "published": "2006-04-29T17:42:35Z", "authors": ["Nigel P. Byott", "G. Griffith Elder"], "categories": ["math.NT"], "doi": "", "_query": "real estate valuation deep learning", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2512.24457v1", "title": "Document Data Matching for Blockchain-Supported Real Estate", "abstract": "The real estate sector remains highly dependent on manual document handling and verification, making processes inefficient and prone to fraud. This work presents a system that integrates optical character recognition (OCR), natural language processing (NLP), and verifiable credentials (VCs) to automate document extraction, verification, and management. The approach standardizes heterogeneous document formats into VCs and applies automated data matching to detect inconsistencies, while the blockchain provides a decentralized trust layer that reinforces transparency and integrity. A prototype was developed that comprises (i) an OCR-NLP extraction pipeline trained on synthetic datasets, (ii) a backend for credential issuance and management, and (iii) a frontend supporting issuer, holder, and verifier interactions. Experimental results show that the models achieve competitive accuracy across multiple document types and that the end-to-end pipeline reduces verification time while preserving reliability. The proposed framework demonstrates the potential to streamline real estate transactions, strengthen stakeholder trust, and enable scalable, secure digital processes.", "published": "2025-12-30T20:30:48Z", "authors": ["Henrique Lin", "Tiago Dias", "Miguel Correia"], "categories": ["cs.CR", "cs.DC"], "doi": "", "_query": "real estate valuation deep learning", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2205.10250v2", "title": "Explanatory machine learning for sequential human teaching", "abstract": "The topic of comprehensibility of machine-learned theories has recently drawn increasing attention. Inductive Logic Programming (ILP) uses logic programming to derive logic theories from small data based on abduction and induction techniques. Learned theories are represented in the form of rules as declarative descriptions of obtained knowledge. In earlier work, the authors provided the first evidence of a measurable increase in human comprehension based on machine-learned logic rules for simple classification tasks. In a later study, it was found that the presentation of machine-learned explanations to humans can produce both beneficial and harmful effects in the context of game learning. We continue our investigation of comprehensibility by examining the effects of the ordering of concept presentations on human comprehension. In this work, we examine the explanatory effects of curriculum order and the presence of machine-learned explanations for sequential problem-solving. We show that 1) there exist tasks A and B such that learning A before B has a better human comprehension with respect to learning B before A and 2) there exist tasks A and B such that the presence of explanations when learning A contributes to improved human comprehension when subsequently learning B. We propose a framework for the effects of sequential teaching on comprehension based on an existing definition of comprehensibility and provide evidence for support from data collected in human trials. Empirical results show that sequential teaching of concepts with increasing complexity a) has a beneficial effect on human comprehension and b) leads to human re-discovery of divide-and-conquer problem-solving strategies, and c) studying machine-learned explanations allows adaptations of human problem-solving strategy with better performance.", "published": "2022-05-20T15:23:46Z", "authors": ["Lun Ai", "Johannes Langer", "Stephen H. Muggleton", "Ute Schmid"], "categories": ["cs.AI", "cs.LG"], "doi": "10.1007/s10994-023-06351-8", "_query": "real estate valuation deep learning", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2303.13568v1", "title": "Extracting real estate values of rental apartment floor plans using graph convolutional networks", "abstract": "Access graphs that indicate adjacency relationships from the perspective of flow lines of rooms are extracted automatically from a large number of floor plan images of a family-oriented rental apartment complex in Osaka Prefecture, Japan, based on a recently proposed access graph extraction method with slight modifications. We define and implement a graph convolutional network (GCN) for access graphs and propose a model to estimate the real estate value of access graphs as the floor plan value. The model, which includes the floor plan value and hedonic method using other general explanatory variables, is used to estimate rents and their estimation accuracies are compared. In addition, the features of the floor plan that explain the rent are analyzed from the learned convolution network. Therefore, a new model for comprehensively estimating the value of real estate floor plans is proposed and validated. The results show that the proposed method significantly improves the accuracy of rent estimation compared to that of conventional models, and it is possible to understand the specific spatial configuration rules that influence the value of a floor plan by analyzing the learned GCN.", "published": "2023-03-23T14:38:34Z", "authors": ["Atsushi Takizawa"], "categories": ["cs.LG", "cs.AI", "cs.CV"], "doi": "10.1177/23998083231213894", "_query": "real estate valuation deep learning", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/1603.07704v2", "title": "Probabilistic Reasoning via Deep Learning: Neural Association Models", "abstract": "In this paper, we propose a new deep learning approach, called neural association model (NAM), for probabilistic reasoning in artificial intelligence. We propose to use neural networks to model association between any two events in a domain. Neural networks take one event as input and compute a conditional probability of the other event to model how likely these two events are to be associated. The actual meaning of the conditional probabilities varies between applications and depends on how the models are trained. In this work, as two case studies, we have investigated two NAM structures, namely deep neural networks (DNN) and relation-modulated neural nets (RMNN), on several probabilistic reasoning tasks in AI, including recognizing textual entailment, triple classification in multi-relational knowledge bases and commonsense reasoning. Experimental results on several popular datasets derived from WordNet, FreeBase and ConceptNet have all demonstrated that both DNNs and RMNNs perform equally well and they can significantly outperform the conventional methods available for these reasoning tasks. Moreover, compared with DNNs, RMNNs are superior in knowledge transfer, where a pre-trained model can be quickly extended to an unseen relation after observing only a few training samples. To further prove the effectiveness of the proposed models, in this work, we have applied NAMs to solving challenging Winograd Schema (WS) problems. Experiments conducted on a set of WS problems prove that the proposed models have the potential for commonsense reasoning.", "published": "2016-03-24T18:54:18Z", "authors": ["Quan Liu", "Hui Jiang", "Andrew Evdokimov", "Zhen-Hua Ling", "Xiaodan Zhu", "Si Wei", "Yu Hu"], "categories": ["cs.AI", "cs.LG", "cs.NE"], "doi": "", "_query": "real estate valuation deep learning", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2411.07732v1", "title": "Implementing Dynamic Pricing Across Multiple Pricing Groups in Real Estate", "abstract": "This article presents a mathematical model of dynamic pricing for real estate (RE) that incorporates multiple pricing groups, thereby expanding the capabilities of existing models. The developed model solves the problem of maximizing aggregate cumulative revenue at the end of the sales period while meeting the revenue and sales goals. A method is proposed for distributing aggregate cumulative revenue goals across different RE pricing groups. The model is further modified to account for the time value of money and the real estate value increase as construction progresses. The algorithm for constructing a pricing policy for multiple pricing groups is described, and numerical simulations are performed to demonstrate how the algorithm operates.", "published": "2024-11-12T11:45:17Z", "authors": ["Lev Razumovskiy", "Mariya Gerasimova", "Nikolay Karenin", "Mikhail Safro"], "categories": ["q-fin.MF", "econ.TH", "q-fin.CP", "q-fin.TR"], "doi": "", "_query": "real estate valuation deep learning", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2402.00849v5", "title": "Score-based Causal Representation Learning: Linear and General Transformations", "abstract": "This paper addresses intervention-based causal representation learning (CRL) under a general nonparametric latent causal model and an unknown transformation that maps the latent variables to the observed variables. Linear and general transformations are investigated. The paper addresses both the identifiability and achievability aspects. Identifiability refers to determining algorithm-agnostic conditions that ensure the recovery of the true latent causal variables and the underlying latent causal graph. Achievability refers to the algorithmic aspects and addresses designing algorithms that achieve identifiability guarantees. By drawing novel connections between score functions (i.e., the gradients of the logarithm of density functions) and CRL, this paper designs a score-based class of algorithms that ensures both identifiability and achievability. First, the paper focuses on linear transformations and shows that one stochastic hard intervention per node suffices to guarantee identifiability. It also provides partial identifiability guarantees for soft interventions, including identifiability up to mixing with parents for general causal models and perfect recovery of the latent graph for sufficiently nonlinear causal models. Secondly, it focuses on general transformations and demonstrates that two stochastic hard interventions per node are sufficient for identifiability. This is achieved by defining a differentiable loss function whose global optima ensure identifiability for general CRL. Notably, one does not need to know which pair of interventional environments has the same node intervened. Finally, the theoretical results are empirically validated via experiments on structured synthetic data and image data.", "published": "2024-02-01T18:40:03Z", "authors": ["Burak Varıcı", "Emre Acartürk", "Karthikeyan Shanmugam", "Abhishek Kumar", "Ali Tajer"], "categories": ["cs.LG", "stat.ML"], "doi": "", "_query": "real estate valuation deep learning", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2503.15532v1", "title": "Koedds: A National Real Estate Investment Analysis", "abstract": "With costs and risks increasing for investors and home buyers alike, additional analysis of the housing market is required to help individuals make the right choice. In addition to traditional market analysis, other aspects such as the economic vulnerabilities of the local community must be taken into account to further ensure real estate buyers receive a positive return on investment from their purchases as well as ensuring that traditional home owners get the best price for their future home.", "published": "2025-02-28T06:03:51Z", "authors": ["Sean Kouma", "William Edds"], "categories": ["q-fin.GN", "cs.CE"], "doi": "", "_query": "real estate valuation deep learning", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2007.03212v1", "title": "Soft Labeling Affects Out-of-Distribution Detection of Deep Neural Networks", "abstract": "Soft labeling becomes a common output regularization for generalization and model compression of deep neural networks. However, the effect of soft labeling on out-of-distribution (OOD) detection, which is an important topic of machine learning safety, is not explored. In this study, we show that soft labeling can determine OOD detection performance. Specifically, how to regularize outputs of incorrect classes by soft labeling can deteriorate or improve OOD detection. Based on the empirical results, we postulate a future work for OOD-robust DNNs: a proper output regularization by soft labeling can construct OOD-robust DNNs without additional training of OOD samples or modifying the models, while improving classification accuracy.", "published": "2020-07-07T05:50:52Z", "authors": ["Doyup Lee", "Yeongjae Cheon"], "categories": ["cs.LG", "stat.ML"], "doi": "", "_query": "real estate valuation deep learning", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/1804.09859v1", "title": "Competitive Learning Enriches Learning Representation and Accelerates the Fine-tuning of CNNs", "abstract": "In this study, we propose the integration of competitive learning into convolutional neural networks (CNNs) to improve the representation learning and efficiency of fine-tuning. Conventional CNNs use back propagation learning, and it enables powerful representation learning by a discrimination task. However, it requires huge amount of labeled data, and acquisition of labeled data is much harder than that of unlabeled data. Thus, efficient use of unlabeled data is getting crucial for DNNs. To address the problem, we introduce unsupervised competitive learning into the convolutional layer, and utilize unlabeled data for effective representation learning. The results of validation experiments using a toy model demonstrated that strong representation learning effectively extracted bases of images into convolutional filters using unlabeled data, and accelerated the speed of the fine-tuning of subsequent supervised back propagation learning. The leverage was more apparent when the number of filters was sufficiently large, and, in such a case, the error rate steeply decreased in the initial phase of fine-tuning. Thus, the proposed method enlarged the number of filters in CNNs, and enabled a more detailed and generalized representation. It could provide a possibility of not only deep but broad neural networks.", "published": "2018-04-26T02:28:48Z", "authors": ["Takashi Shinozaki"], "categories": ["cs.LG", "cs.CV", "cs.NE", "stat.ML"], "doi": "", "_query": "real estate valuation deep learning", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/1605.01133v2", "title": "Deep Motif: Visualizing Genomic Sequence Classifications", "abstract": "This paper applies a deep convolutional/highway MLP framework to classify genomic sequences on the transcription factor binding site task. To make the model understandable, we propose an optimization driven strategy to extract \"motifs\", or symbolic patterns which visualize the positive class learned by the network. We show that our system, Deep Motif (DeMo), extracts motifs that are similar to, and in some cases outperform the current well known motifs. In addition, we find that a deeper model consisting of multiple convolutional and highway layers can outperform a single convolutional and fully connected layer in the previous state-of-the-art.", "published": "2016-05-04T03:33:48Z", "authors": ["Jack Lanchantin", "Ritambhara Singh", "Zeming Lin", "Yanjun Qi"], "categories": ["cs.LG"], "doi": "", "_query": "real estate valuation deep learning", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2011.02511v3", "title": "Offline Reinforcement Learning from Human Feedback in Real-World Sequence-to-Sequence Tasks", "abstract": "Large volumes of interaction logs can be collected from NLP systems that are deployed in the real world. How can this wealth of information be leveraged? Using such interaction logs in an offline reinforcement learning (RL) setting is a promising approach. However, due to the nature of NLP tasks and the constraints of production systems, a series of challenges arise. We present a concise overview of these challenges and discuss possible solutions.", "published": "2020-11-04T19:30:46Z", "authors": ["Julia Kreutzer", "Stefan Riezler", "Carolin Lawrence"], "categories": ["cs.CL", "cs.LG"], "doi": "", "_query": "real estate valuation deep learning", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2301.02033v1", "title": "Physics-informed self-supervised deep learning reconstruction for accelerated first-pass perfusion cardiac MRI", "abstract": "First-pass perfusion cardiac magnetic resonance (FPP-CMR) is becoming an essential non-invasive imaging method for detecting deficits of myocardial blood flow, allowing the assessment of coronary heart disease. Nevertheless, acquisitions suffer from relatively low spatial resolution and limited heart coverage. Compressed sensing (CS) methods have been proposed to accelerate FPP-CMR and achieve higher spatial resolution. However, the long reconstruction times have limited the widespread clinical use of CS in FPP-CMR. Deep learning techniques based on supervised learning have emerged as alternatives for speeding up reconstructions. However, these approaches require fully sampled data for training, which is not possible to obtain, particularly high-resolution FPP-CMR images. Here, we propose a physics-informed self-supervised deep learning FPP-CMR reconstruction approach for accelerating FPP-CMR scans and hence facilitate high spatial resolution imaging. The proposed method provides high-quality FPP-CMR images from 10x undersampled data without using fully sampled reference data.", "published": "2023-01-05T12:11:17Z", "authors": ["Elena Martín-González", "Ebraham Alskaf", "Amedeo Chiribiri", "Pablo Casaseca-de-la-Higuera", "Carlos Alberola-López", "Rita G Nunes", "Teresa M Correia"], "categories": ["eess.IV", "cs.LG", "physics.med-ph"], "doi": "10.1007/978-3-030-88552-6_9", "_query": "real estate valuation deep learning", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2010.04143v1", "title": "Deep SVBRDF Estimation on Real Materials", "abstract": "Recent work has demonstrated that deep learning approaches can successfully be used to recover accurate estimates of the spatially-varying BRDF (SVBRDF) of a surface from as little as a single image. Closer inspection reveals, however, that most approaches in the literature are trained purely on synthetic data, which, while diverse and realistic, is often not representative of the richness of the real world. In this paper, we show that training such networks exclusively on synthetic data is insufficient to achieve adequate results when tested on real data. Our analysis leverages a new dataset of real materials obtained with a novel portable multi-light capture apparatus. Through an extensive series of experiments and with the use of a novel deep learning architecture, we explore two strategies for improving results on real data: finetuning, and a per-material optimization procedure. We show that adapting network weights to real data is of critical importance, resulting in an approach which significantly outperforms previous methods for SVBRDF estimation on real materials. Dataset and code are available at https://lvsn.github.io/real-svbrdf", "published": "2020-10-08T17:41:26Z", "authors": ["Louis-Philippe Asselin", "Denis Laurendeau", "Jean-François Lalonde"], "categories": ["cs.CV"], "doi": "10.1109/3DV50981.2020.00126", "_query": "real estate valuation deep learning", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/1811.00972v2", "title": "Clustering and Learning from Imbalanced Data", "abstract": "A learning classifier must outperform a trivial solution, in case of imbalanced data, this condition usually does not hold true. To overcome this problem, we propose a novel data level resampling method - Clustering Based Oversampling for improved learning from class imbalanced datasets. The essential idea behind the proposed method is to use the distance between a minority class sample and its respective cluster centroid to infer the number of new sample points to be generated for that minority class sample. The proposed algorithm has very less dependence on the technique used for finding cluster centroids and does not effect the majority class learning in any way. It also improves learning from imbalanced data by incorporating the distribution structure of minority class samples in generation of new data samples. The newly generated minority class data is handled in a way as to prevent outlier production and overfitting. Implementation analysis on different datasets using deep neural networks as the learning classifier shows the effectiveness of this method as compared to other synthetic data resampling techniques across several evaluation metrics.", "published": "2018-11-02T16:37:09Z", "authors": ["Naman D. Singh", "Abhinav Dhall"], "categories": ["cs.LG", "stat.ML"], "doi": "", "_query": "real estate valuation deep learning", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2502.07977v3", "title": "RESIST: Resilient Decentralized Learning Using Consensus Gradient Descent", "abstract": "Empirical risk minimization (ERM) is a cornerstone of modern machine learning. This paper focuses on the man-in-the-middle (MITM) attack, wherein adversaries exploit communication vulnerabilities between devices to inject malicious updates during training. To address this challenge, we propose RESIST (Resilient dEcentralized learning using conSensus gradIent deScenT), an optimization algorithm designed to be robust against adversarially compromised communication links. RESIST uses a multistep consensus gradient descent framework with robust-statistics-based screening of neighbor messages. It has a design parameter $J$ that controls the frequency of local gradient computation: each local gradient update is preceded by $J-1$ communication and robust aggregation rounds. Compared with methods that perform both communication and a local gradient update at every iteration, RESIST with $J>2$ uses fewer local gradient updates at the same communication budget. We establish geometric algorithmic convergence guarantees for strongly convex and Polyak-Lojasiewicz ERM problems, and sublinear and finite-horizon guarantees for smooth nonconvex ERM problems. For heterogeneous local objectives, these guarantees quantify neighborhoods for the relevant iterate, objective-value, and stationarity errors. In the strongly convex homogeneous case, convergence becomes exact. We also establish statistical learning-rate guarantees under common-population independent and identically distributed sampling assumptions, including regimes in which the statistical error vanishes as the sample size grows. Experimental results demonstrate the robustness of RESIST across diverse attack strategies, screening methods, and loss functions. The $J$-ablation experiments further show that larger $J$ can improve convergence and reduce the number of local gradient updates at a fixed communication budget.", "published": "2025-02-11T21:48:10Z", "authors": ["Cheng Fang", "Rishabh Dixit", "Waheed U. Bajwa", "Mert Gürbüzbalaban"], "categories": ["cs.LG", "math.OC", "stat.ML"], "doi": "", "_query": "real estate valuation deep learning", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/1905.06549v2", "title": "TapNet: Neural Network Augmented with Task-Adaptive Projection for Few-Shot Learning", "abstract": "Handling previously unseen tasks after given only a few training examples continues to be a tough challenge in machine learning. We propose TapNets, neural networks augmented with task-adaptive projection for improved few-shot learning. Here, employing a meta-learning strategy with episode-based training, a network and a set of per-class reference vectors are learned across widely varying tasks. At the same time, for every episode, features in the embedding space are linearly projected into a new space as a form of quick task-specific conditioning. The training loss is obtained based on a distance metric between the query and the reference vectors in the projection space. Excellent generalization results in this way. When tested on the Omniglot, miniImageNet and tieredImageNet datasets, we obtain state of the art classification accuracies under various few-shot scenarios.", "published": "2019-05-16T06:21:28Z", "authors": ["Sung Whan Yoon", "Jun Seo", "Jaekyun Moon"], "categories": ["cs.LG", "stat.ML"], "doi": "", "_query": "real estate valuation deep learning", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2512.05736v1", "title": "The Mathematics of Income Property Valuation", "abstract": "This paper surveys and generalizes the main valuation formulas used in real estate valuation and presents unified proofs. The results are otherwise scattered in obscure journals and books while the proofs are rarely available to researchers in the field. The material was originally developed so that it could be used by mathematically-trained appraisers and researchers in the former Soviet Union and in other transition economies that were starting their real estate valuation profession. Keywords: real estate valuations; six functions of one; Ellwood, Akerson, and Hoskold formulas; capitalization rate methods; amortization tables JEL: G12, R3", "published": "2025-12-05T14:15:50Z", "authors": ["David Ellerman"], "categories": ["econ.TH"], "doi": "", "_query": "real estate valuation deep learning", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2512.13018v1", "title": "Comprehensive Deployment-Oriented Assessment for Cross-Environment Generalization in Deep Learning-Based mmWave Radar Sensing", "abstract": "This study presents the first comprehensive evaluation of spatial generalization techniques, which are essential for the practical deployment of deep learning-based radio-frequency (RF) sensing. Focusing on people counting in indoor environments using frequency-modulated continuous-wave (FMCW) multiple-input multiple-output (MIMO) radar, we systematically investigate a broad set of approaches, including amplitude-based statistical preprocessing (sigmoid weighting and threshold zeroing), frequency-domain filtering, autoencoder-based background suppression, data augmentation strategies, and transfer learning. Experimental results collected across two environments with different layouts demonstrate that sigmoid-based amplitude weighting consistently achieves superior cross-environment performance, yielding 50.1% and 55.2% reductions in root-mean-square error (RMSE) and mean absolute error (MAE), respectively, compared with baseline methods. Data augmentation provides additional though modest benefits, with improvements up to 8.8% in MAE. By contrast, transfer learning proves indispensable for large spatial shifts, achieving 82.1% and 91.3% reductions in RMSE and MAE, respectively, with 540 target-domain samples. Taken together, these findings establish a highly practical direction for developing radar sensing systems capable of maintaining robust accuracy under spatial variations by integrating deep learning models with amplitude-based preprocessing and efficient transfer learning.", "published": "2025-12-15T06:29:51Z", "authors": ["Tomoya Tanaka", "Tomonori Ikeda", "Ryo Yonemoto"], "categories": ["cs.CV", "cs.LG"], "doi": "", "_query": "real estate valuation deep learning", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2405.18765v1", "title": "Large Brain Model for Learning Generic Representations with Tremendous EEG Data in BCI", "abstract": "The current electroencephalogram (EEG) based deep learning models are typically designed for specific datasets and applications in brain-computer interaction (BCI), limiting the scale of the models and thus diminishing their perceptual capabilities and generalizability. Recently, Large Language Models (LLMs) have achieved unprecedented success in text processing, prompting us to explore the capabilities of Large EEG Models (LEMs). We hope that LEMs can break through the limitations of different task types of EEG datasets, and obtain universal perceptual capabilities of EEG signals through unsupervised pre-training. Then the models can be fine-tuned for different downstream tasks. However, compared to text data, the volume of EEG datasets is generally small and the format varies widely. For example, there can be mismatched numbers of electrodes, unequal length data samples, varied task designs, and low signal-to-noise ratio. To overcome these challenges, we propose a unified foundation model for EEG called Large Brain Model (LaBraM). LaBraM enables cross-dataset learning by segmenting the EEG signals into EEG channel patches. Vector-quantized neural spectrum prediction is used to train a semantically rich neural tokenizer that encodes continuous raw EEG channel patches into compact neural codes. We then pre-train neural Transformers by predicting the original neural codes for the masked EEG channel patches. The LaBraMs were pre-trained on about 2,500 hours of various types of EEG signals from around 20 datasets and validated on multiple different types of downstream tasks. Experiments on abnormal detection, event type classification, emotion recognition, and gait prediction show that our LaBraM outperforms all compared SOTA methods in their respective fields. Our code is available at https://github.com/935963004/LaBraM.", "published": "2024-05-29T05:08:16Z", "authors": ["Wei-Bang Jiang", "Li-Ming Zhao", "Bao-Liang Lu"], "categories": ["cs.LG"], "doi": "", "_query": "real estate valuation deep learning", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2112.01565v2", "title": "A Generic Graph Sparsification Framework using Deep Reinforcement Learning", "abstract": "The interconnectedness and interdependence of modern graphs are growing ever more complex, causing enormous resources for processing, storage, communication, and decision-making of these graphs. In this work, we focus on the task graph sparsification: an edge-reduced graph of a similar structure to the original graph is produced while various user-defined graph metrics are largely preserved. Existing graph sparsification methods are mostly sampling-based, which introduce high computation complexity in general and lack of flexibility for a different reduction objective. We present SparRL, the first generic and effective graph sparsification framework enabled by deep reinforcement learning. SparRL can easily adapt to different reduction goals and promise graph-size-independent complexity. Extensive experiments show that SparRL outperforms all prevailing sparsification methods in producing high-quality sparsified graphs concerning a variety of objectives.", "published": "2021-12-02T19:20:06Z", "authors": ["Ryan Wickman", "Xiaofei Zhang", "Weizi Li"], "categories": ["cs.LG"], "doi": "", "_query": "real estate valuation deep learning", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2510.01658v1", "title": "Learning Time-Series Representations by Hierarchical Uniformity-Tolerance Latent Balancing", "abstract": "We propose TimeHUT, a novel method for learning time-series representations by hierarchical uniformity-tolerance balancing of contrastive representations. Our method uses two distinct losses to learn strong representations with the aim of striking an effective balance between uniformity and tolerance in the embedding space. First, TimeHUT uses a hierarchical setup to learn both instance-wise and temporal information from input time-series. Next, we integrate a temperature scheduler within the vanilla contrastive loss to balance the uniformity and tolerance characteristics of the embeddings. Additionally, a hierarchical angular margin loss enforces instance-wise and temporal contrast losses, creating geometric margins between positive and negative pairs of temporal sequences. This approach improves the coherence of positive pairs and their separation from the negatives, enhancing the capture of temporal dependencies within a time-series sample. We evaluate our approach on a wide range of tasks, namely 128 UCR and 30 UAE datasets for univariate and multivariate classification, as well as Yahoo and KPI datasets for anomaly detection. The results demonstrate that TimeHUT outperforms prior methods by considerable margins on classification, while obtaining competitive results for anomaly detection. Finally, detailed sensitivity and ablation studies are performed to evaluate different components and hyperparameters of our method.", "published": "2025-10-02T04:30:13Z", "authors": ["Amin Jalali", "Milad Soltany", "Michael Greenspan", "Ali Etemad"], "categories": ["cs.LG", "cs.AI"], "doi": "", "_query": "real estate valuation deep learning", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2106.03129v1", "title": "3D UAV Trajectory and Data Collection Optimisation via Deep Reinforcement Learning", "abstract": "Unmanned aerial vehicles (UAVs) are now beginning to be deployed for enhancing the network performance and coverage in wireless communication. However, due to the limitation of their on-board power and flight time, it is challenging to obtain an optimal resource allocation scheme for the UAV-assisted Internet of Things (IoT). In this paper, we design a new UAV-assisted IoT systems relying on the shortest flight path of the UAVs while maximising the amount of data collected from IoT devices. Then, a deep reinforcement learning-based technique is conceived for finding the optimal trajectory and throughput in a specific coverage area. After training, the UAV has the ability to autonomously collect all the data from user nodes at a significant total sum-rate improvement while minimising the associated resources used. Numerical results are provided to highlight how our techniques strike a balance between the throughput attained, trajectory, and the time spent. More explicitly, we characterise the attainable performance in terms of the UAV trajectory, the expected reward and the total sum-rate.", "published": "2021-06-06T14:08:41Z", "authors": ["Khoi Khac Nguyen", "Trung Q. Duong", "Tan Do-Duy", "Holger Claussen", "and Lajos Hanzo"], "categories": ["eess.SP", "cs.AI", "cs.LG"], "doi": "", "_query": "real estate valuation deep learning", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2101.05390v4", "title": "Universal Approximation Theorems for Differentiable Geometric Deep Learning", "abstract": "This paper addresses the growing need to process non-Euclidean data, by introducing a geometric deep learning (GDL) framework for building universal feedforward-type models compatible with differentiable manifold geometries. We show that our GDL models can approximate any continuous target function uniformly on compact sets of a controlled maximum diameter. We obtain curvature-dependent lower-bounds on this maximum diameter and upper-bounds on the depth of our approximating GDL models. Conversely, we find that there is always a continuous function between any two non-degenerate compact manifolds that any \"locally-defined\" GDL model cannot uniformly approximate. Our last main result identifies data-dependent conditions guaranteeing that the GDL model implementing our approximation breaks \"the curse of dimensionality.\" We find that any \"real-world\" (i.e. finite) dataset always satisfies our condition and, conversely, any dataset satisfies our requirement if the target function is smooth. As applications, we confirm the universal approximation capabilities of the following GDL models: Ganea et al. (2018)'s hyperbolic feedforward networks, the architecture implementing Krishnan et al. (2015)'s deep Kalman-Filter, and deep softmax classifiers. We build universal extensions/variants of: the SPD-matrix regressor of Meyer et al. (2011), and Fletcher (2003)'s Procrustean regressor. In the Euclidean setting, our results imply a quantitative version of Kidger and Lyons (2020)'s approximation theorem and a data-dependent version of Yarotsky and Zhevnerchuk (2019)'s uncursed approximation rates.", "published": "2021-01-13T23:29:40Z", "authors": ["Anastasis Kratsios", "Leonie Papon"], "categories": ["cs.LG", "math.FA", "math.GN", "math.GT"], "doi": "", "_query": "real estate valuation deep learning", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/1812.11780v2", "title": "Weakly Supervised Active Learning with Cluster Annotation", "abstract": "In this work, we introduce a novel framework that employs cluster annotation to boost active learning by reducing the number of human interactions required to train deep neural networks. Instead of annotating single samples individually, humans can also label clusters, producing a higher number of annotated samples with the cost of a small label error. Our experiments show that the proposed framework requires 82% and 87% less human interactions for CIFAR-10 and EuroSAT datasets respectively when compared with the fully-supervised training while maintaining similar performance on the test set.", "published": "2018-12-31T13:06:09Z", "authors": ["Fábio Perez", "Rémi Lebret", "Karl Aberer"], "categories": ["cs.LG", "cs.CV", "stat.ML"], "doi": "", "_query": "real estate valuation deep learning", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/1306.0239v4", "title": "Deep Learning using Linear Support Vector Machines", "abstract": "Recently, fully-connected and convolutional neural networks have been trained to achieve state-of-the-art performance on a wide variety of tasks such as speech recognition, image classification, natural language processing, and bioinformatics. For classification tasks, most of these \"deep learning\" models employ the softmax activation function for prediction and minimize cross-entropy loss. In this paper, we demonstrate a small but consistent advantage of replacing the softmax layer with a linear support vector machine. Learning minimizes a margin-based loss instead of the cross-entropy loss. While there have been various combinations of neural nets and SVMs in prior art, our results using L2-SVMs show that by simply replacing softmax with linear SVMs gives significant gains on popular deep learning datasets MNIST, CIFAR-10, and the ICML 2013 Representation Learning Workshop's face expression recognition challenge.", "published": "2013-06-02T18:46:58Z", "authors": ["Yichuan Tang"], "categories": ["cs.LG", "stat.ML"], "doi": "", "_query": "real estate valuation deep learning", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/1611.03718v2", "title": "Hierarchical Object Detection with Deep Reinforcement Learning", "abstract": "We present a method for performing hierarchical object detection in images guided by a deep reinforcement learning agent. The key idea is to focus on those parts of the image that contain richer information and zoom on them. We train an intelligent agent that, given an image window, is capable of deciding where to focus the attention among five different predefined region candidates (smaller windows). This procedure is iterated providing a hierarchical image analysis.We compare two different candidate proposal strategies to guide the object search: with and without overlap. Moreover, our work compares two different strategies to extract features from a convolutional neural network for each region proposal: a first one that computes new feature maps for each region proposal, and a second one that computes the feature maps for the whole image to later generate crops for each region proposal. Experiments indicate better results for the overlapping candidate proposal strategy and a loss of performance for the cropped image features due to the loss of spatial resolution. We argue that, while this loss seems unavoidable when working with large amounts of object candidates, the much more reduced amount of region proposals generated by our reinforcement learning agent allows considering to extract features for each location without sharing convolutional computation among regions.", "published": "2016-11-11T14:25:54Z", "authors": ["Miriam Bellver", "Xavier Giro-i-Nieto", "Ferran Marques", "Jordi Torres"], "categories": ["cs.CV", "cs.LG"], "doi": "", "_query": "real estate valuation deep learning", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/1612.04340v1", "title": "End-to-End Deep Reinforcement Learning for Lane Keeping Assist", "abstract": "Reinforcement learning is considered to be a strong AI paradigm which can be used to teach machines through interaction with the environment and learning from their mistakes, but it has not yet been successfully used for automotive applications. There has recently been a revival of interest in the topic, however, driven by the ability of deep learning algorithms to learn good representations of the environment. Motivated by Google DeepMind's successful demonstrations of learning for games from Breakout to Go, we will propose different methods for autonomous driving using deep reinforcement learning. This is of particular interest as it is difficult to pose autonomous driving as a supervised learning problem as it has a strong interaction with the environment including other vehicles, pedestrians and roadworks. As this is a relatively new area of research for autonomous driving, we will formulate two main categories of algorithms: 1) Discrete actions category, and 2) Continuous actions category. For the discrete actions category, we will deal with Deep Q-Network Algorithm (DQN) while for the continuous actions category, we will deal with Deep Deterministic Actor Critic Algorithm (DDAC). In addition to that, We will also discover the performance of these two categories on an open source car simulator for Racing called (TORCS) which stands for The Open Racing car Simulator. Our simulation results demonstrate learning of autonomous maneuvering in a scenario of complex road curvatures and simple interaction with other vehicles. Finally, we explain the effect of some restricted conditions, put on the car during the learning phase, on the convergence time for finishing its learning phase.", "published": "2016-12-13T20:19:42Z", "authors": ["Ahmad El Sallab", "Mohammed Abdou", "Etienne Perot", "Senthil Yogamani"], "categories": ["stat.ML", "cs.LG", "cs.RO"], "doi": "", "_query": "real estate valuation deep learning", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2208.04221v1", "title": "Uncertain Bayesian Networks: Learning from Incomplete Data", "abstract": "When the historical data are limited, the conditional probabilities associated with the nodes of Bayesian networks are uncertain and can be empirically estimated. Second order estimation methods provide a framework for both estimating the probabilities and quantifying the uncertainty in these estimates. We refer to these cases as uncer tain or second-order Bayesian networks. When such data are complete, i.e., all variable values are observed for each instantiation, the conditional probabilities are known to be Dirichlet-distributed. This paper improves the current state-of-the-art approaches for handling uncertain Bayesian networks by enabling them to learn distributions for their parameters, i.e., conditional probabilities, with incomplete data. We extensively evaluate various methods to learn the posterior of the parameters through the desired and empirically derived strength of confidence bounds for various queries.", "published": "2022-08-08T15:46:44Z", "authors": ["Conrad D. Hougen", "Lance M. Kaplan", "Federico Cerutti", "Alfred O. Hero"], "categories": ["stat.ML", "cs.AI", "cs.LG"], "doi": "10.1109/MLSP52302.2021.9596205", "_query": "real estate valuation deep learning", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2302.01952v3", "title": "On a continuous time model of gradient descent dynamics and instability in deep learning", "abstract": "The recipe behind the success of deep learning has been the combination of neural networks and gradient-based optimization. Understanding the behavior of gradient descent however, and particularly its instability, has lagged behind its empirical success. To add to the theoretical tools available to study gradient descent we propose the principal flow (PF), a continuous time flow that approximates gradient descent dynamics. To our knowledge, the PF is the only continuous flow that captures the divergent and oscillatory behaviors of gradient descent, including escaping local minima and saddle points. Through its dependence on the eigendecomposition of the Hessian the PF sheds light on the recently observed edge of stability phenomena in deep learning. Using our new understanding of instability we propose a learning rate adaptation method which enables us to control the trade-off between training stability and test set evaluation performance.", "published": "2023-02-03T19:03:10Z", "authors": ["Mihaela Rosca", "Yan Wu", "Chongli Qin", "Benoit Dherin"], "categories": ["stat.ML", "cs.LG"], "doi": "", "_query": "real estate valuation deep learning", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/1802.00003v3", "title": "Deep Learning of Nonnegativity-Constrained Autoencoders for Enhanced Understanding of Data", "abstract": "Unsupervised feature extractors are known to perform an efficient and discriminative representation of data. Insight into the mappings they perform and human ability to understand them, however, remain very limited. This is especially prominent when multilayer deep learning architectures are used. This paper demonstrates how to remove these bottlenecks within the architecture of Nonnegativity Constrained Autoencoder (NCSAE). It is shown that by using both L1 and L2 regularization that induce nonnegativity of weights, most of the weights in the network become constrained to be nonnegative thereby resulting into a more understandable structure with minute deterioration in classification accuracy. Also, this proposed approach extracts features that are more sparse and produces additional output layer sparsification. The method is analyzed for accuracy and feature interpretation on the MNIST data, the NORB normalized uniform object data, and the Reuters text categorization dataset.", "published": "2018-01-31T02:32:40Z", "authors": ["Babajide O. Ayinde", "Jacek M. Zurada"], "categories": ["cs.LG"], "doi": "10.1109/TNNLS.2017.2747861", "_query": "real estate valuation deep learning", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/1206.3713v4", "title": "Learning the Structure and Parameters of Large-Population Graphical Games from Behavioral Data", "abstract": "We consider learning, from strictly behavioral data, the structure and parameters of linear influence games (LIGs), a class of parametric graphical games introduced by Irfan and Ortiz (2014). LIGs facilitate causal strategic inference (CSI): Making inferences from causal interventions on stable behavior in strategic settings. Applications include the identification of the most influential individuals in large (social) networks. Such tasks can also support policy-making analysis. Motivated by the computational work on LIGs, we cast the learning problem as maximum-likelihood estimation (MLE) of a generative model defined by pure-strategy Nash equilibria (PSNE). Our simple formulation uncovers the fundamental interplay between goodness-of-fit and model complexity: good models capture equilibrium behavior within the data while controlling the true number of equilibria, including those unobserved. We provide a generalization bound establishing the sample complexity for MLE in our framework. We propose several algorithms including convex loss minimization (CLM) and sigmoidal approximations. We prove that the number of exact PSNE in LIGs is small, with high probability; thus, CLM is sound. We illustrate our approach on synthetic data and real-world U.S. congressional voting records. We briefly discuss our learning framework's generality and potential applicability to general graphical games.", "published": "2012-06-16T23:20:09Z", "authors": ["Jean Honorio", "Luis Ortiz"], "categories": ["cs.LG", "cs.GT", "stat.ML"], "doi": "", "_query": "real estate valuation deep learning", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/1512.05616v1", "title": "Deep-Spying: Spying using Smartwatch and Deep Learning", "abstract": "Wearable technologies are today on the rise, becoming more common and broadly available to mainstream users. In fact, wristband and armband devices such as smartwatches and fitness trackers already took an important place in the consumer electronics market and are becoming ubiquitous. By their very nature of being wearable, these devices, however, provide a new pervasive attack surface threatening users privacy, among others. In the meantime, advances in machine learning are providing unprecedented possibilities to process complex data efficiently. Allowing patterns to emerge from high dimensional unavoidably noisy data. The goal of this work is to raise awareness about the potential risks related to motion sensors built-in wearable devices and to demonstrate abuse opportunities leveraged by advanced neural network architectures. The LSTM-based implementation presented in this research can perform touchlogging and keylogging on 12-keys keypads with above-average accuracy even when confronted with raw unprocessed data. Thus demonstrating that deep neural networks are capable of making keystroke inference attacks based on motion sensors easier to achieve by removing the need for non-trivial pre-processing pipelines and carefully engineered feature extraction strategies. Our results suggest that the complete technological ecosystem of a user can be compromised when a wearable wristband device is worn.", "published": "2015-12-17T14:58:26Z", "authors": ["Tony Beltramelli", "Sebastian Risi"], "categories": ["cs.CR", "cs.CY", "cs.LG"], "doi": "", "_query": "real estate valuation deep learning", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/1806.00746v1", "title": "Eye in the Sky: Real-time Drone Surveillance System (DSS) for Violent Individuals Identification using ScatterNet Hybrid Deep Learning Network", "abstract": "Drone systems have been deployed by various law enforcement agencies to monitor hostiles, spy on foreign drug cartels, conduct border control operations, etc. This paper introduces a real-time drone surveillance system to identify violent individuals in public areas. The system first uses the Feature Pyramid Network to detect humans from aerial images. The image region with the human is used by the proposed ScatterNet Hybrid Deep Learning (SHDL) network for human pose estimation. The orientations between the limbs of the estimated pose are next used to identify the violent individuals. The proposed deep network can learn meaningful representations quickly using ScatterNet and structural priors with relatively fewer labeled examples. The system detects the violent individuals in real-time by processing the drone images in the cloud. This research also introduces the aerial violent individual dataset used for training the deep network which hopefully may encourage researchers interested in using deep learning for aerial surveillance. The pose estimation and violent individuals identification performance is compared with the state-of-the-art techniques.", "published": "2018-06-03T07:44:11Z", "authors": ["Amarjot Singh", "Devendra Patil", "SN Omkar"], "categories": ["cs.CV"], "doi": "", "_query": "real estate valuation deep learning", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2109.07321v1", "title": "PoWareMatch: a Quality-aware Deep Learning Approach to Improve Human Schema Matching", "abstract": "Schema matching is a core task of any data integration process. Being investigated in the fields of databases, AI, Semantic Web and data mining for many years, the main challenge remains the ability to generate quality matches among data concepts (e.g., database attributes). In this work, we examine a novel angle on the behavior of humans as matchers, studying match creation as a process. We analyze the dynamics of common evaluation measures (precision, recall, and f-measure), with respect to this angle and highlight the need for unbiased matching to support this analysis. Unbiased matching, a newly defined concept that describes the common assumption that human decisions represent reliable assessments of schemata correspondences, is, however, not an inherent property of human matchers. In what follows, we design PoWareMatch that makes use of a deep learning mechanism to calibrate and filter human matching decisions adhering the quality of a match, which are then combined with algorithmic matching to generate better match results. We provide an empirical evidence, established based on an experiment with more than 200 human matchers over common benchmarks, that PoWareMatch predicts well the benefit of extending the match with an additional correspondence and generates high quality matches. In addition, PoWareMatch outperforms state-of-the-art matching algorithms.", "published": "2021-09-15T14:24:56Z", "authors": ["Roee Shraga", "Avigdor Gal"], "categories": ["cs.DB", "cs.HC", "cs.LG"], "doi": "", "_query": "real estate valuation deep learning", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2008.02066v2", "title": "Follow the Object: Curriculum Learning for Manipulation Tasks with Imagined Goals", "abstract": "Learning robot manipulation through deep reinforcement learning in environments with sparse rewards is a challenging task. In this paper we address this problem by introducing a notion of imaginary object goals. For a given manipulation task, the object of interest is first trained to reach a desired target position on its own, without being manipulated, through physically realistic simulations. The object policy is then leveraged to build a predictive model of plausible object trajectories providing the robot with a curriculum of incrementally more difficult object goals to reach during training. The proposed algorithm, Follow the Object (FO), has been evaluated on 7 MuJoCo environments requiring increasing degree of exploration, and has achieved higher success rates compared to alternative algorithms. In particularly challenging learning scenarios, e.g. where the object's initial and target positions are far apart, our approach can still learn a policy whereas competing methods currently fail.", "published": "2020-08-05T12:19:14Z", "authors": ["Ozsel Kilinc", "Giovanni Montana"], "categories": ["cs.LG", "cs.AI", "cs.RO", "stat.ML"], "doi": "", "_query": "real estate valuation deep learning", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2402.00809v5", "title": "Position: Bayesian Deep Learning is Needed in the Age of Large-Scale AI", "abstract": "In the current landscape of deep learning research, there is a predominant emphasis on achieving high predictive accuracy in supervised tasks involving large image and language datasets. However, a broader perspective reveals a multitude of overlooked metrics, tasks, and data types, such as uncertainty, active and continual learning, and scientific data, that demand attention. Bayesian deep learning (BDL) constitutes a promising avenue, offering advantages across these diverse settings. This paper posits that BDL can elevate the capabilities of deep learning. It revisits the strengths of BDL, acknowledges existing challenges, and highlights some exciting research avenues aimed at addressing these obstacles. Looking ahead, the discussion focuses on possible ways to combine large-scale foundation models with BDL to unlock their full potential.", "published": "2024-02-01T17:45:26Z", "authors": ["Theodore Papamarkou", "Maria Skoularidou", "Konstantina Palla", "Laurence Aitchison", "Julyan Arbel", "David Dunson", "Maurizio Filippone", "Vincent Fortuin", "Philipp Hennig", "José Miguel Hernández-Lobato", "Aliaksandr Hubin", "Alexander Immer", "Theofanis Karaletsos", "Mohammad Emtiyaz Khan", "Agustinus Kristiadi", "Yingzhen Li", "Stephan Mandt", "Christopher Nemeth", "Michael A. Osborne", "Tim G. J. Rudner", "David Rügamer", "Yee Whye Teh", "Max Welling", "Andrew Gordon Wilson", "Ruqi Zhang"], "categories": ["cs.LG", "stat.ML"], "doi": "", "_query": "real estate valuation deep learning", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2106.07329v1", "title": "On-Policy Deep Reinforcement Learning for the Average-Reward Criterion", "abstract": "We develop theory and algorithms for average-reward on-policy Reinforcement Learning (RL). We first consider bounding the difference of the long-term average reward for two policies. We show that previous work based on the discounted return (Schulman et al., 2015; Achiam et al., 2017) results in a non-meaningful bound in the average-reward setting. By addressing the average-reward criterion directly, we then derive a novel bound which depends on the average divergence between the two policies and Kemeny's constant. Based on this bound, we develop an iterative procedure which produces a sequence of monotonically improved policies for the average reward criterion. This iterative procedure can then be combined with classic DRL (Deep Reinforcement Learning) methods, resulting in practical DRL algorithms that target the long-run average reward criterion. In particular, we demonstrate that Average-Reward TRPO (ATRPO), which adapts the on-policy TRPO algorithm to the average-reward criterion, significantly outperforms TRPO in the most challenging MuJuCo environments.", "published": "2021-06-14T12:12:09Z", "authors": ["Yiming Zhang", "Keith W. Ross"], "categories": ["cs.LG", "cs.AI", "stat.ML"], "doi": "", "_query": "real estate valuation deep learning", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/1901.08553v1", "title": "Data Interpolations in Deep Generative Models under Non-Simply-Connected Manifold Topology", "abstract": "Exploiting the deep generative model's remarkable ability of learning the data-manifold structure, some recent researches proposed a geometric data interpolation method based on the geodesic curves on the learned data-manifold. However, this interpolation method often gives poor results due to a topological difference between the model and the dataset. The model defines a family of simply-connected manifolds, whereas the dataset generally contains disconnected regions or holes that make them non-simply-connected. To compensate this difference, we propose a novel density regularizer that make the interpolation path circumvent the holes denoted by low probability density. We confirm that our method gives consistently better interpolation results from the experiments with real-world image datasets.", "published": "2019-01-20T18:48:14Z", "authors": ["Jiseob Kim", "Byoung-Tak Zhang"], "categories": ["cs.LG", "stat.ML"], "doi": "", "_query": "real estate valuation deep learning", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2009.01555v3", "title": "Sample-Efficient Automated Deep Reinforcement Learning", "abstract": "Despite significant progress in challenging problems across various domains, applying state-of-the-art deep reinforcement learning (RL) algorithms remains challenging due to their sensitivity to the choice of hyperparameters. This sensitivity can partly be attributed to the non-stationarity of the RL problem, potentially requiring different hyperparameter settings at various stages of the learning process. Additionally, in the RL setting, hyperparameter optimization (HPO) requires a large number of environment interactions, hindering the transfer of the successes in RL to real-world applications. In this work, we tackle the issues of sample-efficient and dynamic HPO in RL. We propose a population-based automated RL (AutoRL) framework to meta-optimize arbitrary off-policy RL algorithms. In this framework, we optimize the hyperparameters and also the neural architecture while simultaneously training the agent. By sharing the collected experience across the population, we substantially increase the sample efficiency of the meta-optimization. We demonstrate the capabilities of our sample-efficient AutoRL approach in a case study with the popular TD3 algorithm in the MuJoCo benchmark suite, where we reduce the number of environment interactions needed for meta-optimization by up to an order of magnitude compared to population-based training.", "published": "2020-09-03T10:04:06Z", "authors": ["Jörg K. H. Franke", "Gregor Köhler", "André Biedenkapp", "Frank Hutter"], "categories": ["cs.LG", "stat.ML"], "doi": "", "_query": "real estate valuation deep learning", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/1801.02805v2", "title": "DeepTraffic: Crowdsourced Hyperparameter Tuning of Deep Reinforcement Learning Systems for Multi-Agent Dense Traffic Navigation", "abstract": "We present a traffic simulation named DeepTraffic where the planning systems for a subset of the vehicles are handled by a neural network as part of a model-free, off-policy reinforcement learning process. The primary goal of DeepTraffic is to make the hands-on study of deep reinforcement learning accessible to thousands of students, educators, and researchers in order to inspire and fuel the exploration and evaluation of deep Q-learning network variants and hyperparameter configurations through large-scale, open competition. This paper investigates the crowd-sourced hyperparameter tuning of the policy network that resulted from the first iteration of the DeepTraffic competition where thousands of participants actively searched through the hyperparameter space.", "published": "2018-01-09T05:56:15Z", "authors": ["Lex Fridman", "Jack Terwilliger", "Benedikt Jenik"], "categories": ["cs.NE", "cs.AI", "cs.RO"], "doi": "", "_query": "real estate valuation deep learning", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2404.06526v1", "title": "Onboard Processing of Hyperspectral Imagery: Deep Learning Advancements, Methodologies, Challenges, and Emerging Trends", "abstract": "Recent advancements in deep learning techniques have spurred considerable interest in their application to hyperspectral imagery processing. This paper provides a comprehensive review of the latest developments in this field, focusing on methodologies, challenges, and emerging trends. Deep learning architectures such as Convolutional Neural Networks (CNNs), Autoencoders, Deep Belief Networks (DBNs), Generative Adversarial Networks (GANs), and Recurrent Neural Networks (RNNs) are examined for their suitability in processing hyperspectral data. Key challenges, including limited training data and computational constraints, are identified, along with strategies such as data augmentation and noise reduction using GANs. The paper discusses the efficacy of different network architectures, highlighting the advantages of lightweight CNN models and 1D CNNs for onboard processing. Moreover, the potential of hardware accelerators, particularly Field Programmable Gate Arrays (FPGAs), for enhancing processing efficiency is explored. The review concludes with insights into ongoing research trends, including the integration of deep learning techniques into Earth observation missions such as the CHIME mission, and emphasizes the need for further exploration and refinement of deep learning methodologies to address the evolving demands of hyperspectral image processing.", "published": "2024-04-09T14:32:39Z", "authors": ["Nafiseh Ghasemi", "Jon Alvarez Justo", "Marco Celesti", "Laurent Despoisse", "Jens Nieke"], "categories": ["eess.IV"], "doi": "", "_query": "real estate valuation deep learning", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2304.01203v7", "title": "Optimal Goal-Reaching Reinforcement Learning via Quasimetric Learning", "abstract": "In goal-reaching reinforcement learning (RL), the optimal value function has a particular geometry, called quasimetric structure. This paper introduces Quasimetric Reinforcement Learning (QRL), a new RL method that utilizes quasimetric models to learn optimal value functions. Distinct from prior approaches, the QRL objective is specifically designed for quasimetrics, and provides strong theoretical recovery guarantees. Empirically, we conduct thorough analyses on a discretized MountainCar environment, identifying properties of QRL and its advantages over alternatives. On offline and online goal-reaching benchmarks, QRL also demonstrates improved sample efficiency and performance, across both state-based and image-based observations.", "published": "2023-04-03T17:59:58Z", "authors": ["Tongzhou Wang", "Antonio Torralba", "Phillip Isola", "Amy Zhang"], "categories": ["cs.LG"], "doi": "", "_query": "real estate valuation deep learning", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2209.05158v4", "title": "Singular valuations and the Hadwiger theorem on convex functions", "abstract": "We give a characterization of smooth, rotation and dually epi-translation invariant valuations and use this result to obtain a new proof of the Hadwiger theorem on convex functions. We also give a description of the construction of the functional intrinsic volumes using integration over the differential cycle and provide a new representation of these functionals as principal value integrals with respect to the Hessian measures.", "published": "2022-09-12T11:33:31Z", "authors": ["Jonas Knoerr"], "categories": ["math.MG"], "doi": "", "_query": "real estate valuation deep learning", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/1909.11671v1", "title": "Data Valuation using Reinforcement Learning", "abstract": "Quantifying the value of data is a fundamental problem in machine learning. Data valuation has multiple important use cases: (1) building insights about the learning task, (2) domain adaptation, (3) corrupted sample discovery, and (4) robust learning. To adaptively learn data values jointly with the target task predictor model, we propose a meta learning framework which we name Data Valuation using Reinforcement Learning (DVRL). We employ a data value estimator (modeled by a deep neural network) to learn how likely each datum is used in training of the predictor model. We train the data value estimator using a reinforcement signal of the reward obtained on a small validation set that reflects performance on the target task. We demonstrate that DVRL yields superior data value estimates compared to alternative methods across different types of datasets and in a diverse set of application scenarios. The corrupted sample discovery performance of DVRL is close to optimal in many regimes (i.e. as if the noisy samples were known apriori), and for domain adaptation and robust learning DVRL significantly outperforms state-of-the-art by 14.6% and 10.8%, respectively.", "published": "2019-09-25T18:00:02Z", "authors": ["Jinsung Yoon", "Sercan O. Arik", "Tomas Pfister"], "categories": ["cs.LG", "stat.ML"], "doi": "", "_query": "real estate valuation deep learning", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/1802.08238v1", "title": "What are the most important factors that influence the changes in London Real Estate Prices? How to quantify them?", "abstract": "In recent years, real estate industry has captured government and public attention around the world. The factors influencing the prices of real estate are diversified and complex. However, due to the limitations and one-sidedness of their respective views, they did not provide enough theoretical basis for the fluctuation of house price and its influential factors. The purpose of this paper is to build a housing price model to make the scientific and objective analysis of London's real estate market trends from the year 1996 to 2016 and proposes some countermeasures to reasonably control house prices. Specifically, the paper analyzes eight factors which affect the house prices from two aspects: housing supply and demand and find out the factor which is of vital importance to the increase of housing price per square meter. The problem of a high level of multicollinearity between them is solved by using principal components analysis.", "published": "2018-02-22T18:50:51Z", "authors": ["Yiyang Gu"], "categories": ["stat.AP", "q-fin.GN"], "doi": "", "_query": "real estate valuation deep learning", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2109.05439v3", "title": "Concave Utility Reinforcement Learning with Zero-Constraint Violations", "abstract": "We consider the problem of tabular infinite horizon concave utility reinforcement learning (CURL) with convex constraints. For this, we propose a model-based learning algorithm that also achieves zero constraint violations. Assuming that the concave objective and the convex constraints have a solution interior to the set of feasible occupation measures, we solve a tighter optimization problem to ensure that the constraints are never violated despite the imprecise model knowledge and model stochasticity. We use Bellman error-based analysis for tabular infinite-horizon setups which allows analyzing stochastic policies. Combining the Bellman error-based analysis and tighter optimization equation, for $T$ interactions with the environment, we obtain a high-probability regret guarantee for objective which grows as $\\Tilde{O}(1/\\sqrt{T})$, excluding other factors. The proposed method can be applied for optimistic algorithms to obtain high-probability regret bounds and also be used for posterior sampling algorithms to obtain a loose Bayesian regret bounds but with significant improvement in computational complexity.", "published": "2021-09-12T06:13:33Z", "authors": ["Mridul Agarwal", "Qinbo Bai", "Vaneet Aggarwal"], "categories": ["cs.LG", "cs.AI"], "doi": "", "_query": "real estate valuation deep learning", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/1906.00421v5", "title": "Air Learning: A Deep Reinforcement Learning Gym for Autonomous Aerial Robot Visual Navigation", "abstract": "We introduce Air Learning, an open-source simulator, and a gym environment for deep reinforcement learning research on resource-constrained aerial robots. Equipped with domain randomization, Air Learning exposes a UAV agent to a diverse set of challenging scenarios. We seed the toolset with point-to-point obstacle avoidance tasks in three different environments and Deep Q Networks (DQN) and Proximal Policy Optimization (PPO) trainers. Air Learning assesses the policies' performance under various quality-of-flight (QoF) metrics, such as the energy consumed, endurance, and the average trajectory length, on resource-constrained embedded platforms like a Raspberry Pi. We find that the trajectories on an embedded Ras-Pi are vastly different from those predicted on a high-end desktop system, resulting in up to 40% longer trajectories in one of the environments. To understand the source of such discrepancies, we use Air Learning to artificially degrade high-end desktop performance to mimic what happens on a low-end embedded system. We then propose a mitigation technique that uses the hardware-in-the-loop to determine the latency distribution of running the policy on the target platform (onboard compute on the aerial robot). A randomly sampled latency from the latency distribution is then added as an artificial delay within the training loop. Training the policy with artificial delays allows us to minimize the hardware gap (discrepancy in the flight time metric reduced from 37.73% to 0.5%). Thus, Air Learning with hardware-in-the-loop characterizes those differences and exposes how the onboard compute's choice affects the aerial robot's performance. We also conduct reliability studies to assess the effect of sensor failures on the learned policies. All put together, Air Learning enables a broad class of deep RL research on UAVs. The source code is available at:http://bit.ly/2JNAVb6.", "published": "2019-06-02T15:20:11Z", "authors": ["Srivatsan Krishnan", "Behzad Boroujerdian", "William Fu", "Aleksandra Faust", "Vijay Janapa Reddi"], "categories": ["cs.RO", "cs.LG"], "doi": "", "_query": "real estate valuation deep learning", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2501.01166v2", "title": "Deep Learning in Palmprint Recognition-A Comprehensive Survey", "abstract": "Palmprint recognition has emerged as a prominent biometric technology, widely applied in diverse scenarios. Traditional handcrafted methods for palmprint recognition often fall short in representation capability, as they heavily depend on researchers' prior knowledge. Deep learning (DL) has been introduced to address this limitation, leveraging its remarkable successes across various domains. While existing surveys focus narrowly on specific tasks within palmprint recognition-often grounded in traditional methodologies-there remains a significant gap in comprehensive research exploring DL-based approaches across all facets of palmprint recognition. This paper bridges that gap by thoroughly reviewing recent advancements in DL-powered palmprint recognition. The paper systematically examines progress across key tasks, including region-of-interest segmentation, feature extraction, and security/privacy-oriented challenges. Beyond highlighting these advancements, the paper identifies current challenges and uncovers promising opportunities for future research. By consolidating state-of-the-art progress, this review serves as a valuable resource for researchers, enabling them to stay abreast of cutting-edge technologies and drive innovation in palmprint recognition.", "published": "2025-01-02T09:38:44Z", "authors": ["Chengrui Gao", "Ziyuan Yang", "Wei Jia", "Lu Leng", "Bob Zhang", "Andrew Beng Jin Teoh"], "categories": ["cs.CV", "cs.AI"], "doi": "", "_query": "real estate valuation deep learning", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2004.11238v1", "title": "Learning Constrained Dynamics with Gauss Principle adhering Gaussian Processes", "abstract": "The identification of the constrained dynamics of mechanical systems is often challenging. Learning methods promise to ease an analytical analysis, but require considerable amounts of data for training. We propose to combine insights from analytical mechanics with Gaussian process regression to improve the model's data efficiency and constraint integrity. The result is a Gaussian process model that incorporates a priori constraint knowledge such that its predictions adhere to Gauss' principle of least constraint. In return, predictions of the system's acceleration naturally respect potentially non-ideal (non-)holonomic equality constraints. As corollary results, our model enables to infer the acceleration of the unconstrained system from data of the constrained system and enables knowledge transfer between differing constraint configurations.", "published": "2020-04-23T15:26:51Z", "authors": ["A. Rene Geist", "Sebastian Trimpe"], "categories": ["cs.LG", "cs.RO", "eess.SY", "stat.ML"], "doi": "", "_query": "real estate valuation deep learning", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2406.08267v2", "title": "A deep cut into Split Federated Self-supervised Learning", "abstract": "Collaborative self-supervised learning has recently become feasible in highly distributed environments by dividing the network layers between client devices and a central server. However, state-of-the-art methods, such as MocoSFL, are optimized for network division at the initial layers, which decreases the protection of the client data and increases communication overhead. In this paper, we demonstrate that splitting depth is crucial for maintaining privacy and communication efficiency in distributed training. We also show that MocoSFL suffers from a catastrophic quality deterioration for the minimal communication overhead. As a remedy, we introduce Momentum-Aligned contrastive Split Federated Learning (MonAcoSFL), which aligns online and momentum client models during training procedure. Consequently, we achieve state-of-the-art accuracy while significantly reducing the communication overhead, making MonAcoSFL more practical in real-world scenarios.", "published": "2024-06-12T14:35:13Z", "authors": ["Marcin Przewięźlikowski", "Marcin Osial", "Bartosz Zieliński", "Marek Śmieja"], "categories": ["cs.LG", "cs.AI", "cs.DC"], "doi": "10.1007/978-3-031-70344-7_26", "_query": "real estate valuation deep learning", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2109.06180v1", "title": "Deep Generative Models to Extend Active Directory Graphs with Honeypot Users", "abstract": "Active Directory (AD) is a crucial element of large organizations, given its central role in managing access to resources. Since AD is used by all users in the organization, it is hard to detect attackers. We propose to generate and place fake users (honeyusers) in AD structures to help detect attacks. However, not any honeyuser will attract attackers. Our method generates honeyusers with a Variational Autoencoder that enriches the AD structure with well-positioned honeyusers. It first learns the embeddings of the original nodes and edges in the AD, then it uses a modified Bidirectional DAG-RNN to encode the parameters of the probability distribution of the latent space of node representations. Finally, it samples nodes from this distribution and uses an MLP to decide where the nodes are connected. The model was evaluated by the similarity of the generated AD with the original, by the positions of the new nodes, by the similarity with GraphRNN and finally by making real intruders attack the generated AD structure to see if they select the honeyusers. Results show that our machine learning model is good enough to generate well-placed honeyusers for existing AD structures so that intruders are lured into them.", "published": "2021-09-13T15:07:35Z", "authors": ["Ondrej Lukas", "Sebastian Garcia"], "categories": ["cs.CR", "cs.LG"], "doi": "10.5220/0010556601400147", "_query": "real estate valuation deep learning", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2507.04187v1", "title": "Where to Intervene: Action Selection in Deep Reinforcement Learning", "abstract": "Deep reinforcement learning (RL) has gained widespread adoption in recent years but faces significant challenges, particularly in unknown and complex environments. Among these, high-dimensional action selection stands out as a critical problem. Existing works often require a sophisticated prior design to eliminate redundancy in the action space, relying heavily on domain expert experience or involving high computational complexity, which limits their generalizability across different RL tasks. In this paper, we address these challenges by proposing a general data-driven action selection approach with model-free and computationally friendly properties. Our method not only selects minimal sufficient actions but also controls the false discovery rate via knockoff sampling. More importantly, we seamlessly integrate the action selection into deep RL methods during online training. Empirical experiments validate the established theoretical guarantees, demonstrating that our method surpasses various alternative techniques in terms of both performance in variable selection and overall achieved rewards.", "published": "2025-07-05T23:40:55Z", "authors": ["Wenbo Zhang", "Hengrui Cai"], "categories": ["stat.ML", "cs.LG"], "doi": "", "_query": "real estate valuation deep learning", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/1906.01432v1", "title": "Knowledge-augmented Column Networks: Guiding Deep Learning with Advice", "abstract": "Recently, deep models have had considerable success in several tasks, especially with low-level representations. However, effective learning from sparse noisy samples is a major challenge in most deep models, especially in domains with structured representations. Inspired by the proven success of human guided machine learning, we propose Knowledge-augmented Column Networks, a relational deep learning framework that leverages human advice/knowledge to learn better models in presence of sparsity and systematic noise.", "published": "2019-05-31T21:09:21Z", "authors": ["Mayukh Das", "Devendra Singh Dhami", "Yang Yu", "Gautam Kunapuli", "Sriraam Natarajan"], "categories": ["cs.LG", "cs.AI", "stat.ML"], "doi": "", "_query": "real estate valuation deep learning", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2603.14483v2", "title": "Disentangling Dynamical Systems: Causal Representation Learning Meets Local Sparse Attention", "abstract": "Parametric system identification methods estimate the parameters of explicitly defined physical systems from data. Yet, they remain constrained by the need to provide an explicit function space, typically through a predefined library of candidate functions chosen via available domain knowledge. In contrast, deep learning can demonstrably model systems of broad complexity with high fidelity, but black-box function approximation typically fails to yield explicit descriptive or disentangled representations revealing the structure of a system. We develop a novel identifiability theorem, leveraging causal representation learning, to uncover disentangled representations of system parameters without structural assumptions. We derive a graphical criterion specifying when system parameters can be uniquely disentangled from raw trajectory data, up to permutation and diffeomorphism. Crucially, our analysis demonstrates that global causal structures provide a lower bound on the disentanglement guarantees achievable when considering local state-dependent causal structures. We instantiate system parameter identification as a variational inference problem, leveraging a sparsity-regularised transformer to uncover state-dependent causal structures. We empirically validate our approach across four synthetic domains, demonstrating its ability to recover highly disentangled representations that baselines fail to recover. Corroborating our theoretical analysis, our results confirm that enforcing local causal structure is often necessary for full identifiability.", "published": "2026-03-15T17:03:34Z", "authors": ["Markus W. Baumgartner", "Anson Lei", "Joe Watson", "Ingmar Posner"], "categories": ["cs.LG"], "doi": "", "_query": "real estate valuation deep learning", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2609.02984v1", "title": "From Euclidean to Graph-Structured Data: A Survey of Collaborative Learning", "abstract": "The conventional approach to machine learning, that is, collecting data, training models, and performing inference in a single location, faces fundamental limitations, including scalability and privacy, that restrict its applicability. To address these challenges, recent research has explored collaborative learning approaches, including federated learning and decentralized learning, where individual agents perform training and inference locally, with limited collaboration. Most collaborative learning research focuses on Euclidean data with regular, grid-like structure (e.g., images, text). However, these approaches fail to capture the relational patterns in many real-world applications, best represented by graphs. Learning on graphs relies on message-passing mechanisms to propagate information between connected nodes, making it conceptually well-suited for collaborative environments where agents must exchange information. Yet, the opportunities and challenges of learning on graph-structured data in collaborative settings remain largely underexplored. This survey provides a comprehensive investigation of collaborative learning from Euclidean to graph-structured data, aiming to consolidate this emerging field. We begin by reviewing its foundational principles for Euclidean data, organizing them along three core dimensions: learning effectiveness, efficiency, and privacy preservation. We then extend the discussion to graph-structured data, introducing a taxonomy of graph distribution scenarios, characterizing associated statistical heterogeneities, and developing standardized problem formulations and algorithmic frameworks. Finally, we systematically identify open challenges and promising research directions.", "published": "2026-09-02T13:49:46Z", "authors": ["Rémi Bourgerie", "Šarūnas Girdzijauskas", "Viktoria Fodor"], "categories": ["cs.LG", "cs.MA", "cs.SI"], "doi": "", "_query": "real estate valuation deep learning", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2406.06714v2", "title": "Coprocessor Actor Critic: A Model-Based Reinforcement Learning Approach For Adaptive Brain Stimulation", "abstract": "Adaptive brain stimulation can treat neurological conditions such as Parkinson's disease and post-stroke motor deficits by influencing abnormal neural activity. Because of patient heterogeneity, each patient requires a unique stimulation policy to achieve optimal neural responses. Model-free reinforcement learning (MFRL) holds promise in learning effective policies for a variety of similar control tasks, but is limited in domains like brain stimulation by a need for numerous costly environment interactions. In this work we introduce Coprocessor Actor Critic, a novel, model-based reinforcement learning (MBRL) approach for learning neural coprocessor policies for brain stimulation. Our key insight is that coprocessor policy learning is a combination of learning how to act optimally in the world and learning how to induce optimal actions in the world through stimulation of an injured brain. We show that our approach overcomes the limitations of traditional MFRL methods in terms of sample efficiency and task success and outperforms baseline MBRL approaches in a neurologically realistic model of an injured brain.", "published": "2024-06-10T18:23:03Z", "authors": ["Michelle Pan", "Mariah Schrum", "Vivek Myers", "Erdem Bıyık", "Anca Dragan"], "categories": ["cs.LG", "cs.AI", "cs.HC"], "doi": "", "_query": "real estate valuation deep learning", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/1811.09317v4", "title": "Feature Selection for Survival Analysis with Competing Risks using Deep Learning", "abstract": "Deep learning models for survival analysis have gained significant attention in the literature, but they suffer from severe performance deficits when the dataset contains many irrelevant features. We give empirical evidence for this problem in real-world medical settings using the state-of-the-art model DeepHit. Furthermore, we develop methods to improve the deep learning model through novel approaches to feature selection in survival analysis. We propose filter methods for hard feature selection and a neural network architecture that weights features for soft feature selection. Our experiments on two real-world medical datasets demonstrate that substantial performance improvements against the original models are achievable.", "published": "2018-11-22T22:25:46Z", "authors": ["Carl Rietschel", "Jinsung Yoon", "Mihaela van der Schaar"], "categories": ["cs.LG", "stat.ML"], "doi": "", "_query": "real estate valuation deep learning", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/1810.08217v3", "title": "Deep Learning Methods for Reynolds-Averaged Navier-Stokes Simulations of Airfoil Flows", "abstract": "With this study we investigate the accuracy of deep learning models for the inference of Reynolds-Averaged Navier-Stokes solutions. We focus on a modernized U-net architecture, and evaluate a large number of trained neural networks with respect to their accuracy for the calculation of pressure and velocity distributions. In particular, we illustrate how training data size and the number of weights influence the accuracy of the solutions. With our best models we arrive at a mean relative pressure and velocity error of less than 3% across a range of previously unseen airfoil shapes. In addition all source code is publicly available in order to ensure reproducibility and to provide a starting point for researchers interested in deep learning methods for physics problems. While this work focuses on RANS solutions, the neural network architecture and learning setup are very generic, and applicable to a wide range of PDE boundary value problems on Cartesian grids.", "published": "2018-10-18T18:01:01Z", "authors": ["Nils Thuerey", "Konstantin Weissenow", "Lukas Prantl", "Xiangyu Hu"], "categories": ["cs.LG", "physics.flu-dyn", "stat.ML"], "doi": "10.2514/1.j058291", "_query": "real estate valuation deep learning", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/1411.7783v2", "title": "From neural PCA to deep unsupervised learning", "abstract": "A network supporting deep unsupervised learning is presented. The network is an autoencoder with lateral shortcut connections from the encoder to decoder at each level of the hierarchy. The lateral shortcut connections allow the higher levels of the hierarchy to focus on abstract invariant features. While standard autoencoders are analogous to latent variable models with a single layer of stochastic variables, the proposed network is analogous to hierarchical latent variables models. Learning combines denoising autoencoder and denoising sources separation frameworks. Each layer of the network contributes to the cost function a term which measures the distance of the representations produced by the encoder and the decoder. Since training signals originate from all levels of the network, all layers can learn efficiently even in deep networks. The speedup offered by cost terms from higher levels of the hierarchy and the ability to learn invariant features are demonstrated in experiments.", "published": "2014-11-28T09:03:24Z", "authors": ["Harri Valpola"], "categories": ["stat.ML", "cs.LG", "cs.NE"], "doi": "", "_query": "real estate valuation deep learning", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2602.12375v1", "title": "Value Bonuses using Ensemble Errors for Exploration in Reinforcement Learning", "abstract": "Optimistic value estimates provide one mechanism for directed exploration in reinforcement learning (RL). The agent acts greedily with respect to an estimate of the value plus what can be seen as a value bonus. The value bonus can be learned by estimating a value function on reward bonuses, propagating local uncertainties around rewards. However, this approach only increases the value bonus for an action retroactively, after seeing a higher reward bonus from that state and action. Such an approach does not encourage the agent to visit a state and action for the first time. In this work, we introduce an algorithm for exploration called Value Bonuses with Ensemble errors (VBE), that maintains an ensemble of random action-value functions (RQFs). VBE uses the errors in the estimation of these RQFs to design value bonuses that provide first-visit optimism and deep exploration. The key idea is to design the rewards for these RQFs in such a way that the value bonus can decrease to zero. We show that VBE outperforms Bootstrap DQN and two reward bonus approaches (RND and ACB) on several classic environments used to test exploration and provide demonstrative experiments that it can scale easily to more complex environments like Atari.", "published": "2026-02-12T20:12:17Z", "authors": ["Abdul Wahab", "Raksha Kumaraswamy", "Martha White"], "categories": ["cs.LG", "cs.AI"], "doi": "", "_query": "real estate valuation deep learning", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/1906.02280v1", "title": "Deep Q-Learning for Directed Acyclic Graph Generation", "abstract": "We present a method to generate directed acyclic graphs (DAGs) using deep reinforcement learning, specifically deep Q-learning. Generating graphs with specified structures is an important and challenging task in various application fields, however most current graph generation methods produce graphs with undirected edges. We demonstrate that this method is capable of generating DAGs with topology and node types satisfying specified criteria in highly sparse reward environments.", "published": "2019-06-05T19:56:44Z", "authors": ["Laura D'Arcy", "Padraig Corcoran", "Alun Preece"], "categories": ["cs.LG", "stat.ML"], "doi": "", "_query": "real estate valuation deep learning", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2512.19527v1", "title": "Deep Learning for Unrelated-Machines Scheduling: Handling Variable Dimensions", "abstract": "Deep learning has been effectively applied to many discrete optimization problems. However, learning-based scheduling on unrelated parallel machines remains particularly difficult to design. Not only do the numbers of jobs and machines vary, but each job-machine pair has a unique processing time, dynamically altering feature dimensions. We propose a novel approach with a neural network tailored for offline deterministic scheduling of arbitrary sizes on unrelated machines. The goal is to minimize a complex objective function that includes the makespan and the weighted tardiness of jobs and machines. Unlike existing online approaches, which process jobs sequentially, our method generates a complete schedule considering the entire input at once. The key contribution of this work lies in the sophisticated architecture of our model. By leveraging various NLP-inspired architectures, it effectively processes any number of jobs and machines with varying feature dimensions imposed by unrelated processing times. Our approach enables supervised training on small problem instances while demonstrating strong generalization to much larger scheduling environments. Trained and tested on instances with 8 jobs and 4 machines, costs were only 2.51% above optimal. Across all tested configurations of up to 100 jobs and 10 machines, our network consistently outperformed an advanced dispatching rule, which incurred 22.22% higher costs on average. As our method allows fast retraining with simulated data and adaptation to various scheduling conditions, we believe it has the potential to become a standard approach for learning-based scheduling on unrelated machines and similar problem environments.", "published": "2025-12-22T16:18:29Z", "authors": ["Diego Hitzges", "Guillaume Sagnol"], "categories": ["cs.LG", "cs.DM"], "doi": "", "_query": "real estate valuation deep learning", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2007.11344v2", "title": "DEAL: Deep Evidential Active Learning for Image Classification", "abstract": "Convolutional Neural Networks (CNNs) have proven to be state-of-the-art models for supervised computer vision tasks, such as image classification. However, large labeled data sets are generally needed for the training and validation of such models. In many domains, unlabeled data is available but labeling is expensive, for instance when specific expert knowledge is required. Active Learning (AL) is one approach to mitigate the problem of limited labeled data. Through selecting the most informative and representative data instances for labeling, AL can contribute to more efficient learning of the model. Recent AL methods for CNNs propose different solutions for the selection of instances to be labeled. However, they do not perform consistently well and are often computationally expensive. In this paper, we propose a novel AL algorithm that efficiently learns from unlabeled data by capturing high prediction uncertainty. By replacing the softmax standard output of a CNN with the parameters of a Dirichlet density, the model learns to identify data instances that contribute efficiently to improving model performance during training. We demonstrate in several experiments with publicly available data that our method consistently outperforms other state-of-the-art AL approaches. It can be easily implemented and does not require extensive computational resources for training. Additionally, we are able to show the benefits of the approach on a real-world medical use case in the field of automated detection of visual signals for pneumonia on chest radiographs.", "published": "2020-07-22T11:14:23Z", "authors": ["Patrick Hemmer", "Niklas Kühl", "Jakob Schöffer"], "categories": ["cs.LG", "cs.CV", "stat.ML"], "doi": "", "_query": "real estate valuation deep learning", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/1910.05396v3", "title": "Network Randomization: A Simple Technique for Generalization in Deep Reinforcement Learning", "abstract": "Deep reinforcement learning (RL) agents often fail to generalize to unseen environments (yet semantically similar to trained agents), particularly when they are trained on high-dimensional state spaces, such as images. In this paper, we propose a simple technique to improve a generalization ability of deep RL agents by introducing a randomized (convolutional) neural network that randomly perturbs input observations. It enables trained agents to adapt to new domains by learning robust features invariant across varied and randomized environments. Furthermore, we consider an inference method based on the Monte Carlo approximation to reduce the variance induced by this randomization. We demonstrate the superiority of our method across 2D CoinRun, 3D DeepMind Lab exploration and 3D robotics control tasks: it significantly outperforms various regularization and data augmentation methods for the same purpose.", "published": "2019-10-11T20:12:52Z", "authors": ["Kimin Lee", "Kibok Lee", "Jinwoo Shin", "Honglak Lee"], "categories": ["cs.LG", "stat.ML"], "doi": "", "_query": "real estate valuation deep learning", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2008.05880v1", "title": "Lifelong Property Price Prediction: A Case Study for the Toronto Real Estate Market", "abstract": "We present Luce, the first life-long predictive model for automated property valuation. Luce addresses two critical issues of property valuation: the lack of recent sold prices and the sparsity of house data. It is designed to operate on a limited volume of recent house transaction data. As a departure from prior work, Luce organizes the house data in a heterogeneous information network (HIN) where graph nodes are house entities and attributes that are important for house price valuation. We employ a Graph Convolutional Network (GCN) to extract the spatial information from the HIN for house-related data like geographical locations, and then use a Long Short Term Memory (LSTM) network to model the temporal dependencies for house transaction data over time. Unlike prior work, Luce can make effective use of the limited house transactions data in the past few months to update valuation information for all house entities within the HIN. By providing a complete and up-to-date house valuation dataset, Luce thus massively simplifies the downstream valuation task for the targeting properties. We demonstrate the benefit of Luce by applying it to large, real-life datasets obtained from the Toronto real estate market. Extensive experimental results show that Luce not only significantly outperforms prior property valuation methods but also often reaches and sometimes exceeds the valuation accuracy given by independent experts when using the actual realization price as the ground truth.", "published": "2020-08-12T07:32:16Z", "authors": ["Hao Peng", "Jianxin Li", "Zheng Wang", "Renyu Yang", "Mingzhe Liu", "Mingming Zhang", "Philip S. Yu", "Lifang He"], "categories": ["cs.LG"], "doi": "", "_query": "real estate valuation deep learning", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/1610.09903v1", "title": "Learning Runtime Parameters in Computer Systems with Delayed Experience Injection", "abstract": "Learning effective configurations in computer systems without hand-crafting models for every parameter is a long-standing problem. This paper investigates the use of deep reinforcement learning for runtime parameters of cloud databases under latency constraints. Cloud services serve up to thousands of concurrent requests per second and can adjust critical parameters by leveraging performance metrics. In this work, we use continuous deep reinforcement learning to learn optimal cache expirations for HTTP caching in content delivery networks. To this end, we introduce a technique for asynchronous experience management called delayed experience injection, which facilitates delayed reward and next-state computation in concurrent environments where measurements are not immediately available. Evaluation results show that our approach based on normalized advantage functions and asynchronous CPU-only training outperforms a statistical estimator.", "published": "2016-10-31T12:57:25Z", "authors": ["Michael Schaarschmidt", "Felix Gessert", "Valentin Dalibard", "Eiko Yoneki"], "categories": ["cs.LG"], "doi": "", "_query": "real estate valuation deep learning", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/1706.08675v1", "title": "Proceedings of the First International Workshop on Deep Learning and Music", "abstract": "Proceedings of the First International Workshop on Deep Learning and Music, joint with IJCNN, Anchorage, US, May 17-18, 2017", "published": "2017-06-27T05:28:06Z", "authors": ["Dorien Herremans", "Ching-Hua Chuan"], "categories": ["cs.NE", "cs.LG", "cs.MM", "cs.SD"], "doi": "10.13140/RG.2.2.22227.99364/1", "_query": "real estate valuation deep learning", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/physics/0608167v1", "title": "Electricity Real Options Valuation", "abstract": "In this paper a real option approach for the valuation of real assets is presented. Two continuous time models used for valuation are described: geometric Brownian motion model and interest rate model. The valuation for electricity spread option under Vasicek interest model is placed and the formulas for parameter estimators are calculated. The theoretical part is confronted with real data from electricity market.", "published": "2006-08-16T13:18:44Z", "authors": ["Ewa Broszkiewicz-Suwaj"], "categories": ["physics.ao-ph", "math-ph", "physics.data-an"], "doi": "", "_query": "real estate valuation deep learning", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/1810.00619v3", "title": "SmartChoices: Hybridizing Programming and Machine Learning", "abstract": "We present SmartChoices, an approach to making machine learning (ML) a first class citizen in programming languages which we see as one way to lower the entrance cost to applying ML to problems in new domains. There is a growing divide in approaches to building systems: on the one hand, programming leverages human experts to define a system while on the other hand behavior is learned from data in machine learning. We propose to hybridize these two by providing a 3-call API which we expose through an object called SmartChoice. We describe the SmartChoices-interface, how it can be used in programming with minimal code changes, and demonstrate that it is an easy to use but still powerful tool by demonstrating improvements over not using ML at all on three algorithmic problems: binary search, QuickSort, and caches. In these three examples, we replace the commonly used heuristics with an ML model entirely encapsulated within a SmartChoice and thus requiring minimal code changes. As opposed to previous work applying ML to algorithmic problems, our proposed approach does not require to drop existing implementations but seamlessly integrates into the standard software development workflow and gives full control to the software developer over how ML methods are applied. Our implementation relies on standard Reinforcement Learning (RL) methods. To learn faster, we use the heuristic function, which they are replacing, as an initial function. We show how this initial function can be used to speed up and stabilize learning while providing a safety net that prevents performance to become substantially worse -- allowing for a safe deployment in critical applications in real life.", "published": "2018-10-01T11:14:22Z", "authors": ["Victor Carbune", "Thierry Coppey", "Alexander Daryin", "Thomas Deselaers", "Nikhil Sarda", "Jay Yagnik"], "categories": ["cs.LG", "cs.PL", "stat.ML"], "doi": "", "_query": "real estate valuation deep learning", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/1907.02163v1", "title": "A Quantum Field Theory of Representation Learning", "abstract": "Continuous symmetries and their breaking play a prominent role in contemporary physics. Effective low-energy field theories around symmetry breaking states explain diverse phenomena such as superconductivity, magnetism, and the mass of nucleons. We show that such field theories can also be a useful tool in machine learning, in particular for loss functions with continuous symmetries that are spontaneously broken by random initializations. In this paper, we illuminate our earlier published work (Bamler & Mandt, 2018) on this topic more from the perspective of theoretical physics. We show that the analogies between superconductivity and symmetry breaking in temporal representation learning are rather deep, allowing us to formulate a gauge theory of `charged' embedding vectors in time series models. We show that making the loss function gauge invariant speeds up convergence in such models.", "published": "2019-07-04T00:01:34Z", "authors": ["Robert Bamler", "Stephan Mandt"], "categories": ["stat.ML", "cond-mat.stat-mech", "cs.LG"], "doi": "", "_query": "real estate valuation deep learning", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2009.10588v2", "title": "Anomalous diffusion dynamics of learning in deep neural networks", "abstract": "Learning in deep neural networks (DNNs) is implemented through minimizing a highly non-convex loss function, typically by a stochastic gradient descent (SGD) method. This learning process can effectively find good wide minima without being trapped in poor local ones. We present a novel account of how such effective deep learning emerges through the interactions of the SGD and the geometrical structure of the loss landscape. Rather than being a normal diffusion process (i.e. Brownian motion) as often assumed, we find that the SGD exhibits rich, complex dynamics when navigating through the loss landscape; initially, the SGD exhibits anomalous superdiffusion, which attenuates gradually and changes to subdiffusion at long times when the solution is reached. Such learning dynamics happen ubiquitously in different DNNs such as ResNet and VGG-like networks and are insensitive to batch size and learning rate. The anomalous superdiffusion process during the initial learning phase indicates that the motion of SGD along the loss landscape possesses intermittent, big jumps; this non-equilibrium property enables the SGD to escape from sharp local minima. By adapting the methods developed for studying energy landscapes in complex physical systems, we find that such superdiffusive learning dynamics are due to the interactions of the SGD and the fractal-like structure of the loss landscape. We further develop a simple model to demonstrate the mechanistic role of the fractal loss landscape in enabling the SGD to effectively find global minima. Our results thus reveal the effectiveness of deep learning from a novel perspective and have implications for designing efficient deep neural networks.", "published": "2020-09-22T14:57:59Z", "authors": ["Guozhang Chen", "Cheng Kevin Qu", "Pulin Gong"], "categories": ["cs.LG", "stat.ML"], "doi": "", "_query": "real estate valuation deep learning", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2411.17570v1", "title": "Learning Explainable Treatment Policies with Clinician-Informed Representations: A Practical Approach", "abstract": "Digital health interventions (DHIs) and remote patient monitoring (RPM) have shown great potential in improving chronic disease management through personalized care. However, barriers like limited efficacy and workload concerns hinder adoption of existing DHIs; while limited sample sizes and lack of interpretability limit the effectiveness and adoption of purely black-box algorithmic DHIs. In this paper, we address these challenges by developing a pipeline for learning explainable treatment policies for RPM-enabled DHIs. We apply our approach in the real-world setting of RPM using a DHI to improve glycemic control of youth with type 1 diabetes. Our main contribution is to reveal the importance of clinical domain knowledge in developing state and action representations for effective, efficient, and interpretable targeting policies. We observe that policies learned from clinician-informed representations are significantly more efficacious and efficient than policies learned from black-box representations. This work emphasizes the importance of collaboration between ML researchers and clinicians for developing effective DHIs in the real world.", "published": "2024-11-26T16:32:08Z", "authors": ["Johannes O. Ferstad", "Emily B. Fox", "David Scheinker", "Ramesh Johari"], "categories": ["cs.LG", "cs.AI", "stat.AP", "stat.ML"], "doi": "", "_query": "real estate valuation deep learning", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2410.01990v3", "title": "Deep Learning Alternatives of the Kolmogorov Superposition Theorem", "abstract": "This paper explores alternative formulations of the Kolmogorov Superposition Theorem (KST) as a foundation for neural network design. The original KST formulation, while mathematically elegant, presents practical challenges due to its limited insight into the structure of inner and outer functions and the large number of unknown variables it introduces. Kolmogorov-Arnold Networks (KANs) leverage KST for function approximation, but they have faced scrutiny due to mixed results compared to traditional multilayer perceptrons (MLPs) and practical limitations imposed by the original KST formulation. To address these issues, we introduce ActNet, a scalable deep learning model that builds on the KST and overcomes many of the drawbacks of Kolmogorov's original formulation. We evaluate ActNet in the context of Physics-Informed Neural Networks (PINNs), a framework well-suited for leveraging KST's strengths in low-dimensional function approximation, particularly for simulating partial differential equations (PDEs). In this challenging setting, where models must learn latent functions without direct measurements, ActNet consistently outperforms KANs across multiple benchmarks and is competitive against the current best MLP-based approaches. These results present ActNet as a promising new direction for KST-based deep learning applications, particularly in scientific computing and PDE simulation tasks.", "published": "2024-10-02T19:53:14Z", "authors": ["Leonardo Ferreira Guilhoto", "Paris Perdikaris"], "categories": ["cs.LG", "cs.CE"], "doi": "", "_query": "real estate valuation deep learning", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/1806.09351v3", "title": "Multi-objective Model-based Policy Search for Data-efficient Learning with Sparse Rewards", "abstract": "The most data-efficient algorithms for reinforcement learning in robotics are model-based policy search algorithms, which alternate between learning a dynamical model of the robot and optimizing a policy to maximize the expected return given the model and its uncertainties. However, the current algorithms lack an effective exploration strategy to deal with sparse or misleading reward scenarios: if they do not experience any state with a positive reward during the initial random exploration, it is very unlikely to solve the problem. Here, we propose a novel model-based policy search algorithm, Multi-DEX, that leverages a learned dynamical model to efficiently explore the task space and solve tasks with sparse rewards in a few episodes. To achieve this, we frame the policy search problem as a multi-objective, model-based policy optimization problem with three objectives: (1) generate maximally novel state trajectories, (2) maximize the expected return and (3) keep the system in state-space regions for which the model is as accurate as possible. We then optimize these objectives using a Pareto-based multi-objective optimization algorithm. The experiments show that Multi-DEX is able to solve sparse reward scenarios (with a simulated robotic arm) in much lower interaction time than VIME, TRPO, GEP-PG, CMA-ES and Black-DROPS.", "published": "2018-06-25T09:46:47Z", "authors": ["Rituraj Kaushik", "Konstantinos Chatzilygeroudis", "Jean-Baptiste Mouret"], "categories": ["cs.LG", "cs.AI", "cs.NE", "cs.RO", "stat.ML"], "doi": "", "_query": "real estate valuation deep learning", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/1311.2987v1", "title": "Learning Input and Recurrent Weight Matrices in Echo State Networks", "abstract": "Echo State Networks (ESNs) are a special type of the temporally deep network model, the Recurrent Neural Network (RNN), where the recurrent matrix is carefully designed and both the recurrent and input matrices are fixed. An ESN uses the linearity of the activation function of the output units to simplify the learning of the output matrix. In this paper, we devise a special technique that take advantage of this linearity in the output units of an ESN, to learn the input and recurrent matrices. This has not been done in earlier ESNs due to their well known difficulty in learning those matrices. Compared to the technique of BackPropagation Through Time (BPTT) in learning general RNNs, our proposed method exploits linearity of activation function in the output units to formulate the relationships amongst the various matrices in an RNN. These relationships results in the gradient of the cost function having an analytical form and being more accurate. This would enable us to compute the gradients instead of obtaining them by recursion as in BPTT. Experimental results on phone state classification show that learning one or both the input and recurrent matrices in an ESN yields superior results compared to traditional ESNs that do not learn these matrices, especially when longer time steps are used.", "published": "2013-11-13T00:11:09Z", "authors": ["Hamid Palangi", "Li Deng", "Rabab K Ward"], "categories": ["cs.LG"], "doi": "", "_query": "real estate valuation deep learning", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2103.12608v3", "title": "DIG: A Turnkey Library for Diving into Graph Deep Learning Research", "abstract": "Although there exist several libraries for deep learning on graphs, they are aiming at implementing basic operations for graph deep learning. In the research community, implementing and benchmarking various advanced tasks are still painful and time-consuming with existing libraries. To facilitate graph deep learning research, we introduce DIG: Dive into Graphs, a turnkey library that provides a unified testbed for higher level, research-oriented graph deep learning tasks. Currently, we consider graph generation, self-supervised learning on graphs, explainability of graph neural networks, and deep learning on 3D graphs. For each direction, we provide unified implementations of data interfaces, common algorithms, and evaluation metrics. Altogether, DIG is an extensible, open-source, and turnkey library for researchers to develop new methods and effortlessly compare with common baselines using widely used datasets and evaluation metrics. Source code is available at https://github.com/divelab/DIG.", "published": "2021-03-23T15:05:10Z", "authors": ["Meng Liu", "Youzhi Luo", "Limei Wang", "Yaochen Xie", "Hao Yuan", "Shurui Gui", "Haiyang Yu", "Zhao Xu", "Jingtun Zhang", "Yi Liu", "Keqiang Yan", "Haoran Liu", "Cong Fu", "Bora Oztekin", "Xuan Zhang", "Shuiwang Ji"], "categories": ["cs.LG"], "doi": "", "_query": "real estate valuation deep learning", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2103.10492v2", "title": "Recent Advances in Deep Learning Techniques for Face Recognition", "abstract": "In recent years, researchers have proposed many deep learning (DL) methods for various tasks, and particularly face recognition (FR) made an enormous leap using these techniques. Deep FR systems benefit from the hierarchical architecture of the DL methods to learn discriminative face representation. Therefore, DL techniques significantly improve state-of-the-art performance on FR systems and encourage diverse and efficient real-world applications. In this paper, we present a comprehensive analysis of various FR systems that leverage the different types of DL techniques, and for the study, we summarize 168 recent contributions from this area. We discuss the papers related to different algorithms, architectures, loss functions, activation functions, datasets, challenges, improvement ideas, current and future trends of DL-based FR systems. We provide a detailed discussion of various DL methods to understand the current state-of-the-art, and then we discuss various activation and loss functions for the methods. Additionally, we summarize different datasets used widely for FR tasks and discuss challenges related to illumination, expression, pose variations, and occlusion. Finally, we discuss improvement ideas, current and future trends of FR tasks.", "published": "2021-03-18T19:39:12Z", "authors": ["Md. Tahmid Hasan Fuad", "Awal Ahmed Fime", "Delowar Sikder", "Md. Akil Raihan Iftee", "Jakaria Rabbi", "Mabrook S. Al-rakhami", "Abdu Gumae", "Ovishake Sen", "Mohtasim Fuad", "Md. Nazrul Islam"], "categories": ["cs.CV", "cs.AI", "cs.LG"], "doi": "10.1109/ACCESS.2021.3096136", "_query": "real estate valuation deep learning", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2212.00017v1", "title": "Knowledge-augmented Deep Learning and Its Applications: A Survey", "abstract": "Deep learning models, though having achieved great success in many different fields over the past years, are usually data hungry, fail to perform well on unseen samples, and lack of interpretability. Various prior knowledge often exists in the target domain and their use can alleviate the deficiencies with deep learning. To better mimic the behavior of human brains, different advanced methods have been proposed to identify domain knowledge and integrate it into deep models for data-efficient, generalizable, and interpretable deep learning, which we refer to as knowledge-augmented deep learning (KADL). In this survey, we define the concept of KADL, and introduce its three major tasks, i.e., knowledge identification, knowledge representation, and knowledge integration. Different from existing surveys that are focused on a specific type of knowledge, we provide a broad and complete taxonomy of domain knowledge and its representations. Based on our taxonomy, we provide a systematic review of existing techniques, different from existing works that survey integration approaches agnostic to taxonomy of knowledge. This survey subsumes existing works and offers a bird's-eye view of research in the general area of knowledge-augmented deep learning. The thorough and critical reviews of numerous papers help not only understand current progresses but also identify future directions for the research on knowledge-augmented deep learning.", "published": "2022-11-30T03:44:15Z", "authors": ["Zijun Cui", "Tian Gao", "Kartik Talamadupula", "Qiang Ji"], "categories": ["cs.LG", "cs.AI"], "doi": "", "_query": "real estate valuation deep learning", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2107.02363v4", "title": "Asymptotics of Network Embeddings Learned via Subsampling", "abstract": "Network data are ubiquitous in modern machine learning, with tasks of interest including node classification, node clustering and link prediction. A frequent approach begins by learning an Euclidean embedding of the network, to which algorithms developed for vector-valued data are applied. For large networks, embeddings are learned using stochastic gradient methods where the sub-sampling scheme can be freely chosen. Despite the strong empirical performance of such methods, they are not well understood theoretically. Our work encapsulates representation methods using a subsampling approach, such as node2vec, into a single unifying framework. We prove, under the assumption that the graph is exchangeable, that the distribution of the learned embedding vectors asymptotically decouples. Moreover, we characterize the asymptotic distribution and provided rates of convergence, in terms of the latent parameters, which includes the choice of loss function and the embedding dimension. This provides a theoretical foundation to understand what the embedding vectors represent and how well these methods perform on downstream tasks. Notably, we observe that typically used loss functions may lead to shortcomings, such as a lack of Fisher consistency.", "published": "2021-07-06T02:54:53Z", "authors": ["Andrew Davison", "Morgane Austern"], "categories": ["stat.ML", "cs.LG", "math.ST"], "doi": "", "_query": "real estate valuation deep learning", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2110.11290v1", "title": "Physical Side-Channel Attacks on Embedded Neural Networks: A Survey", "abstract": "During the last decade, Deep Neural Networks (DNN) have progressively been integrated on all types of platforms, from data centers to embedded systems including low-power processors and, recently, FPGAs. Neural Networks (NN) are expected to become ubiquitous in IoT systems by transforming all sorts of real-world applications, including applications in the safety-critical and security-sensitive domains. However, the underlying hardware security vulnerabilities of embedded NN implementations remain unaddressed. In particular, embedded DNN implementations are vulnerable to Side-Channel Analysis (SCA) attacks, which are especially important in the IoT and edge computing contexts where an attacker can usually gain physical access to the targeted device. A research field has therefore emerged and is rapidly growing in terms of the use of SCA including timing, electromagnetic attacks and power attacks to target NN embedded implementations. Since 2018, research papers have shown that SCA enables an attacker to recover inference models architectures and parameters, to expose industrial IP and endangers data confidentiality and privacy. Without a complete review of this emerging field in the literature so far, this paper surveys state-of-the-art physical SCA attacks relative to the implementation of embedded DNNs on micro-controllers and FPGAs in order to provide a thorough analysis on the current landscape. It provides a taxonomy and a detailed classification of current attacks. It first discusses mitigation techniques and then provides insights for future research leads.", "published": "2021-10-21T17:18:52Z", "authors": ["Maria Méndez Real", "Rubén Salvador"], "categories": ["cs.CR", "cs.LG", "cs.NE"], "doi": "10.3390/app11156790", "_query": "real estate valuation deep learning", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2506.14929v2", "title": "FedOne: Query-Efficient Federated Learning for Black-box Discrete Prompt Learning", "abstract": "Black-Box Discrete Prompt Learning is a prompt-tuning method that optimizes discrete prompts without accessing model parameters or gradients, making the prompt tuning on a cloud-based Large Language Model (LLM) feasible. Adapting federated learning to BDPL could further enhance prompt tuning performance by leveraging data from diverse sources. However, all previous research on federated black-box prompt tuning had neglected the substantial query cost associated with the cloud-based LLM service. To address this gap, we conducted a theoretical analysis of query efficiency within the context of federated black-box prompt tuning. Our findings revealed that degrading FedAvg to activate only one client per round, a strategy we called \\textit{FedOne}, enabled optimal query efficiency in federated black-box prompt learning. Building on this insight, we proposed the FedOne framework, a federated black-box discrete prompt learning method designed to maximize query efficiency when interacting with cloud-based LLMs. We conducted numerical experiments on various aspects of our framework, demonstrating a significant improvement in query efficiency, which aligns with our theoretical results.", "published": "2025-06-17T19:21:22Z", "authors": ["Ganyu Wang", "Jinjie Fang", "Maxwell J. Yin", "Bin Gu", "Xi Chen", "Boyu Wang", "Yi Chang", "Charles Ling"], "categories": ["cs.LG"], "doi": "", "_query": "real estate valuation deep learning", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/astro-ph/0303381v1", "title": "Shear-selected clusters from the Deep Lens Survey", "abstract": "Weak gravitational lensing has the potential to select clusters independently of their baryon content, dynamical state, and star formation history. We present steps toward the first shear-selected sample of clusters, from the Deep Lens Survey (DLS), a deep BVRz' imaging survey of 28 square degrees. Cluster redshifts are determined from photometric redshifts of members and from lensing tomography, and in some cases have been confirmed spectroscopically. DLS imaging data are also used to derive mass-to-light ratios, and upcoming Chandra and XMM time will yield X-ray luminosities and temperatures for a subsample of 12 clusters. Thus we can begin to address any baryon or luminous-matter bias which may be present in current optical and X-ray samples. When the DLS is complete, we expect to have a sample of ~ 100 shear-selected clusters from z ~ 0.2-1.", "published": "2003-03-17T18:31:11Z", "authors": ["V. E. Margoniner", "the Deep Lens Survey Team"], "categories": ["astro-ph"], "doi": "", "_query": "real estate valuation deep learning", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2203.09410v4", "title": "A Framework and Benchmark for Deep Batch Active Learning for Regression", "abstract": "The acquisition of labels for supervised learning can be expensive. To improve the sample efficiency of neural network regression, we study active learning methods that adaptively select batches of unlabeled data for labeling. We present a framework for constructing such methods out of (network-dependent) base kernels, kernel transformations, and selection methods. Our framework encompasses many existing Bayesian methods based on Gaussian process approximations of neural networks as well as non-Bayesian methods. Additionally, we propose to replace the commonly used last-layer features with sketched finite-width neural tangent kernels and to combine them with a novel clustering method. To evaluate different methods, we introduce an open-source benchmark consisting of 15 large tabular regression data sets. Our proposed method outperforms the state-of-the-art on our benchmark, scales to large data sets, and works out-of-the-box without adjusting the network architecture or training code. We provide open-source code that includes efficient implementations of all kernels, kernel transformations, and selection methods, and can be used for reproducing our results.", "published": "2022-03-17T16:11:36Z", "authors": ["David Holzmüller", "Viktor Zaverkin", "Johannes Kästner", "Ingo Steinwart"], "categories": ["stat.ML", "cs.LG", "cs.NE"], "doi": "", "_query": "real estate valuation deep learning", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/1910.08215v1", "title": "A Deep Learning-based Framework for the Detection of Schools of Herring in Echograms", "abstract": "Tracking the abundance of underwater species is crucial for understanding the effects of climate change on marine ecosystems. Biologists typically monitor underwater sites with echosounders and visualize data as 2D images (echograms); they interpret these data manually or semi-automatically, which is time-consuming and prone to inconsistencies. This paper proposes a deep learning framework for the automatic detection of schools of herring from echograms. Experiments demonstrated that our approach outperforms a traditional machine learning algorithm using hand-crafted features. Our framework could easily be expanded to detect more species of interest to sustainable fisheries.", "published": "2019-10-18T01:12:46Z", "authors": ["Alireza Rezvanifar", "Tunai Porto Marques", "Melissa Cote", "Alexandra Branzan Albu", "Alex Slonimer", "Thomas Tolhurst", "Kaan Ersahin", "Todd Mudge", "Stephane Gauthier"], "categories": ["cs.LG", "cs.CV", "eess.IV", "stat.ML"], "doi": "", "_query": "real estate valuation deep learning", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2004.14942v1", "title": "Memristors -- from In-memory computing, Deep Learning Acceleration, Spiking Neural Networks, to the Future of Neuromorphic and Bio-inspired Computing", "abstract": "Machine learning, particularly in the form of deep learning, has driven most of the recent fundamental developments in artificial intelligence. Deep learning is based on computational models that are, to a certain extent, bio-inspired, as they rely on networks of connected simple computing units operating in parallel. Deep learning has been successfully applied in areas such as object/pattern recognition, speech and natural language processing, self-driving vehicles, intelligent self-diagnostics tools, autonomous robots, knowledgeable personal assistants, and monitoring. These successes have been mostly supported by three factors: availability of vast amounts of data, continuous growth in computing power, and algorithmic innovations. The approaching demise of Moore's law, and the consequent expected modest improvements in computing power that can be achieved by scaling, raise the question of whether the described progress will be slowed or halted due to hardware limitations. This paper reviews the case for a novel beyond CMOS hardware technology, memristors, as a potential solution for the implementation of power-efficient in-memory computing, deep learning accelerators, and spiking neural networks. Central themes are the reliance on non-von-Neumann computing architectures and the need for developing tailored learning and inference algorithms. To argue that lessons from biology can be useful in providing directions for further progress in artificial intelligence, we briefly discuss an example based reservoir computing. We conclude the review by speculating on the big picture view of future neuromorphic and brain-inspired computing systems.", "published": "2020-04-30T16:49:03Z", "authors": ["Adnan Mehonic", "Abu Sebastian", "Bipin Rajendran", "Osvaldo Simeone", "Eleni Vasilaki", "Anthony J. Kenyon"], "categories": ["cs.ET", "cs.NE"], "doi": "", "_query": "real estate valuation deep learning", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2001.10169v1", "title": "A Deep Neural Framework for Contextual Affect Detection", "abstract": "A short and simple text carrying no emotion can represent some strong emotions when reading along with its context, i.e., the same sentence can express extreme anger as well as happiness depending on its context. In this paper, we propose a Contextual Affect Detection (CAD) framework which learns the inter-dependence of words in a sentence, and at the same time the inter-dependence of sentences in a dialogue. Our proposed CAD framework is based on a Gated Recurrent Unit (GRU), which is further assisted by contextual word embeddings and other diverse hand-crafted feature sets. Evaluation and analysis suggest that our model outperforms the state-of-the-art methods by 5.49% and 9.14% on Friends and EmotionPush dataset, respectively.", "published": "2020-01-28T05:03:15Z", "authors": ["Kumar Shikhar Deep", "Asif Ekbal", "Pushpak Bhattacharyya"], "categories": ["cs.CL"], "doi": "10.1007/978-3-030-36718-3_34", "_query": "real estate valuation deep learning", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2411.02466v1", "title": "Weakly supervised deep learning model with size constraint for prostate cancer detection in multiparametric MRI and generalization to unseen domains", "abstract": "Fully supervised deep models have shown promising performance for many medical segmentation tasks. Still, the deployment of these tools in clinics is limited by the very timeconsuming collection of manually expert-annotated data. Moreover, most of the state-ofthe-art models have been trained and validated on moderately homogeneous datasets. It is known that deep learning methods are often greatly degraded by domain or label shifts and are yet to be built in such a way as to be robust to unseen data or label distributions. In the clinical setting, this problematic is particularly relevant as the deployment institutions may have different scanners or acquisition protocols than those from which the data has been collected to train the model. In this work, we propose to address these two challenges on the detection of clinically significant prostate cancer (csPCa) from bi-parametric MRI. We evaluate the method proposed by (Kervadec et al., 2018), which introduces a size constaint loss to produce fine semantic cancer lesions segmentations from weak circle scribbles annotations. Performance of the model is based on two public (PI-CAI and Prostate158) and one private databases. First, we show that the model achieves on-par performance with strong fully supervised baseline models, both on in-distribution validation data and unseen test images. Second, we observe a performance decrease for both fully supervised and weakly supervised models when tested on unseen data domains. This confirms the crucial need for efficient domain adaptation methods if deep learning models are aimed to be deployed in a clinical environment. Finally, we show that ensemble predictions from multiple trainings increase generalization performance.", "published": "2024-11-04T12:24:33Z", "authors": ["Robin Trombetta", "Olivier Rouvière", "Carole Lartizien"], "categories": ["eess.IV", "cs.AI", "cs.LG"], "doi": "", "_query": "real estate valuation deep learning", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2004.09691v2", "title": "A Data and Compute Efficient Design for Limited-Resources Deep Learning", "abstract": "Thanks to their improved data efficiency, equivariant neural networks have gained increased interest in the deep learning community. They have been successfully applied in the medical domain where symmetries in the data can be effectively exploited to build more accurate and robust models. To be able to reach a much larger body of patients, mobile, on-device implementations of deep learning solutions have been developed for medical applications. However, equivariant models are commonly implemented using large and computationally expensive architectures, not suitable to run on mobile devices. In this work, we design and test an equivariant version of MobileNetV2 and further optimize it with model quantization to enable more efficient inference. We achieve close-to state of the art performance on the Patch Camelyon (PCam) medical dataset while being more computationally efficient.", "published": "2020-04-21T00:49:11Z", "authors": ["Mirgahney Mohamed", "Gabriele Cesa", "Taco S. Cohen", "Max Welling"], "categories": ["cs.LG", "eess.IV", "stat.ML"], "doi": "", "_query": "real estate valuation deep learning", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/1902.00522v1", "title": "Deep Learning for Multi-Messenger Astrophysics: A Gateway for Discovery in the Big Data Era", "abstract": "This report provides an overview of recent work that harnesses the Big Data Revolution and Large Scale Computing to address grand computational challenges in Multi-Messenger Astrophysics, with a particular emphasis on real-time discovery campaigns. Acknowledging the transdisciplinary nature of Multi-Messenger Astrophysics, this document has been prepared by members of the physics, astronomy, computer science, data science, software and cyberinfrastructure communities who attended the NSF-, DOE- and NVIDIA-funded \"Deep Learning for Multi-Messenger Astrophysics: Real-time Discovery at Scale\" workshop, hosted at the National Center for Supercomputing Applications, October 17-19, 2018. Highlights of this report include unanimous agreement that it is critical to accelerate the development and deployment of novel, signal-processing algorithms that use the synergy between artificial intelligence (AI) and high performance computing to maximize the potential for scientific discovery with Multi-Messenger Astrophysics. We discuss key aspects to realize this endeavor, namely (i) the design and exploitation of scalable and computationally efficient AI algorithms for Multi-Messenger Astrophysics; (ii) cyberinfrastructure requirements to numerically simulate astrophysical sources, and to process and interpret Multi-Messenger Astrophysics data; (iii) management of gravitational wave detections and triggers to enable electromagnetic and astro-particle follow-ups; (iv) a vision to harness future developments of machine and deep learning and cyberinfrastructure resources to cope with the scale of discovery in the Big Data Era; (v) and the need to build a community that brings domain experts together with data scientists on equal footing to maximize and accelerate discovery in the nascent field of Multi-Messenger Astrophysics.", "published": "2019-02-01T19:02:18Z", "authors": ["Gabrielle Allen", "Igor Andreoni", "Etienne Bachelet", "G. Bruce Berriman", "Federica B. Bianco", "Rahul Biswas", "Matias Carrasco Kind", "Kyle Chard", "Minsik Cho", "Philip S. Cowperthwaite", "Zachariah B. Etienne", "Daniel George", "Tom Gibbs", "Matthew Graham", "William Gropp", "Anushri Gupta", "Roland Haas", "E. A. Huerta", "Elise Jennings", "Daniel S. Katz", "Asad Khan", "Volodymyr Kindratenko", "William T. C. Kramer", "Xin Liu", "Ashish Mahabal", "Kenton McHenry", "J. M. Miller", "M. S. Neubauer", "Steve Oberlin", "Alexander R. Olivas", "Shawn Rosofsky", "Milton Ruiz", "Aaron Saxton", "Bernard Schutz", "Alex Schwing", "Ed Seidel", "Stuart L. Shapiro", "Hongyu Shen", "Yue Shen", "Brigitta M. Sipőcz", "Lunan Sun", "John Towns", "Antonios Tsokaros", "Wei Wei", "Jack Wells", "Timothy J. Williams", "Jinjun Xiong", "Zhizhen Zhao"], "categories": ["astro-ph.IM", "astro-ph.HE", "cs.LG", "gr-qc"], "doi": "", "_query": "real estate valuation deep learning", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/1806.10729v5", "title": "Illuminating Generalization in Deep Reinforcement Learning through Procedural Level Generation", "abstract": "Deep reinforcement learning (RL) has shown impressive results in a variety of domains, learning directly from high-dimensional sensory streams. However, when neural networks are trained in a fixed environment, such as a single level in a video game, they will usually overfit and fail to generalize to new levels. When RL models overfit, even slight modifications to the environment can result in poor agent performance. This paper explores how procedurally generated levels during training can increase generality. We show that for some games procedural level generation enables generalization to new levels within the same distribution. Additionally, it is possible to achieve better performance with less data by manipulating the difficulty of the levels in response to the performance of the agent. The generality of the learned behaviors is also evaluated on a set of human-designed levels. The results suggest that the ability to generalize to human-designed levels highly depends on the design of the level generators. We apply dimensionality reduction and clustering techniques to visualize the generators' distributions of levels and analyze to what degree they can produce levels similar to those designed by a human.", "published": "2018-06-28T01:16:11Z", "authors": ["Niels Justesen", "Ruben Rodriguez Torrado", "Philip Bontrager", "Ahmed Khalifa", "Julian Togelius", "Sebastian Risi"], "categories": ["cs.LG", "cs.AI", "stat.ML"], "doi": "", "_query": "real estate valuation deep learning", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2503.18001v2", "title": "Z-REx: Human-Interpretable GNN Explanations for Real Estate Recommendations", "abstract": "Transparency and interpretability are crucial for enhancing customer confidence and user engagement, especially when dealing with black-box Machine Learning (ML)-based recommendation systems. Modern recommendation systems leverage Graph Neural Network (GNN) due to their ability to produce high-quality recommendations in terms of both relevance and diversity. Therefore, the explainability of GNN is especially important for Link Prediction (LP) tasks since recommending relevant items can be viewed as predicting links between users and items. GNN explainability has been a well-studied field, but existing methods primarily focus on node or graph-level tasks, leaving a gap in LP explanation techniques. This work introduces Z-REx, a GNN explanation framework designed explicitly for heterogeneous link prediction tasks. Z-REx utilizes structural and attribute perturbation to identify critical substructures and important features while reducing the search space by leveraging domain-specific knowledge. In our experimentation, we show the efficacy of Z-REx in generating contextually relevant and human-interpretable explanations for ZiGNN, a GNN-based recommendation engine, using a real-world real-estate dataset from Zillow Group, Inc. We compare against State-of-The-Art (SOTA) GNN explainers to show Z-REx outperforms them by 61% in the Fidelity metric by producing superior human-interpretable explanations.", "published": "2025-02-12T02:42:25Z", "authors": ["Kunal Mukherjee", "Zachary Harrison", "Saeid Balaneshin"], "categories": ["cs.IR", "cs.LG", "cs.SI"], "doi": "", "_query": "real estate valuation deep learning", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/1906.07315v3", "title": "Evolutionary Reinforcement Learning for Sample-Efficient Multiagent Coordination", "abstract": "Many cooperative multiagent reinforcement learning environments provide agents with a sparse team-based reward, as well as a dense agent-specific reward that incentivizes learning basic skills. Training policies solely on the team-based reward is often difficult due to its sparsity. Furthermore, relying solely on the agent-specific reward is sub-optimal because it usually does not capture the team coordination objective. A common approach is to use reward shaping to construct a proxy reward by combining the individual rewards. However, this requires manual tuning for each environment. We introduce Multiagent Evolutionary Reinforcement Learning (MERL), a split-level training platform that handles the two objectives separately through two optimization processes. An evolutionary algorithm maximizes the sparse team-based objective through neuroevolution on a population of teams. Concurrently, a gradient-based optimizer trains policies to only maximize the dense agent-specific rewards. The gradient-based policies are periodically added to the evolutionary population as a way of information transfer between the two optimization processes. This enables the evolutionary algorithm to use skills learned via the agent-specific rewards toward optimizing the global objective. Results demonstrate that MERL significantly outperforms state-of-the-art methods, such as MADDPG, on a number of difficult coordination benchmarks.", "published": "2019-06-18T00:25:27Z", "authors": ["Shauharda Khadka", "Somdeb Majumdar", "Santiago Miret", "Stephen McAleer", "Kagan Tumer"], "categories": ["cs.LG", "cs.AI", "cs.MA", "stat.ML"], "doi": "", "_query": "real estate valuation deep learning", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2407.08462v2", "title": "Distributed Deep Reinforcement Learning Based Gradient Quantization for Federated Learning Enabled Vehicle Edge Computing", "abstract": "Federated Learning (FL) can protect the privacy of the vehicles in vehicle edge computing (VEC) to a certain extent through sharing the gradients of vehicles' local models instead of local data. The gradients of vehicles' local models are usually large for the vehicular artificial intelligence (AI) applications, thus transmitting such large gradients would cause large per-round latency. Gradient quantization has been proposed as one effective approach to reduce the per-round latency in FL enabled VEC through compressing gradients and reducing the number of bits, i.e., the quantization level, to transmit gradients. The selection of quantization level and thresholds determines the quantization error, which further affects the model accuracy and training time. To do so, the total training time and quantization error (QE) become two key metrics for the FL enabled VEC. It is critical to jointly optimize the total training time and QE for the FL enabled VEC. However, the time-varying channel condition causes more challenges to solve this problem. In this paper, we propose a distributed deep reinforcement learning (DRL)-based quantization level allocation scheme to optimize the long-term reward in terms of the total training time and QE. Extensive simulations identify the optimal weighted factors between the total training time and QE, and demonstrate the feasibility and effectiveness of the proposed scheme.", "published": "2024-07-11T12:58:47Z", "authors": ["Cui Zhang", "Wenjun Zhang", "Qiong Wu", "Pingyi Fan", "Qiang Fan", "Jiangzhou Wang", "Khaled B. Letaief"], "categories": ["cs.LG", "cs.NI"], "doi": "", "_query": "real estate valuation deep learning", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2105.10448v1", "title": "Distinguishing artefacts: evaluating the saturation point of convolutional neural networks", "abstract": "Prior work has shown Convolutional Neural Networks (CNNs) trained on surrogate Computer Aided Design (CAD) models are able to detect and classify real-world artefacts from photographs. The applications of which support twinning of digital and physical assets in design, including rapid extraction of part geometry from model repositories, information search \\& retrieval and identifying components in the field for maintenance, repair, and recording. The performance of CNNs in classification tasks have been shown dependent on training data set size and number of classes. Where prior works have used relatively small surrogate model data sets ($<100$ models), the question remains as to the ability of a CNN to differentiate between models in increasingly large model repositories. This paper presents a method for generating synthetic image data sets from online CAD model repositories, and further investigates the capacity of an off-the-shelf CNN architecture trained on synthetic data to classify models as class size increases. 1,000 CAD models were curated and processed to generate large scale surrogate data sets, featuring model coverage at steps of 10$^{\\circ}$, 30$^{\\circ}$, 60$^{\\circ}$, and 120$^{\\circ}$ degrees. The findings demonstrate the capability of computer vision algorithms to classify artefacts in model repositories of up to 200, beyond this point the CNN's performance is observed to deteriorate significantly, limiting its present ability for automated twinning of physical to digital artefacts. Although, a match is more often found in the top-5 results showing potential for information search and retrieval on large repositories of surrogate models.", "published": "2021-05-21T16:33:20Z", "authors": ["Ric Real", "James Gopsill", "David Jones", "Chris Snider", "Ben Hicks"], "categories": ["cs.LG", "cs.AI", "cs.CV"], "doi": "10.1016/j.procir.2021.05.089", "_query": "real estate valuation deep learning", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2006.11695v4", "title": "Uncertainty-Aware (UNA) Bases for Deep Bayesian Regression Using Multi-Headed Auxiliary Networks", "abstract": "Neural Linear Models (NLM) are deep Bayesian models that produce predictive uncertainties by learning features from the data and then performing Bayesian linear regression over these features. Despite their popularity, few works have focused on methodically evaluating the predictive uncertainties of these models. In this work, we demonstrate that traditional training procedures for NLMs drastically underestimate uncertainty on out-of-distribution inputs, and that they therefore cannot be naively deployed in risk-sensitive applications. We identify the underlying reasons for this behavior and propose a novel training framework that captures useful predictive uncertainties for downstream tasks.", "published": "2020-06-21T02:46:05Z", "authors": ["Sujay Thakur", "Cooper Lorsung", "Yaniv Yacoby", "Finale Doshi-Velez", "Weiwei Pan"], "categories": ["stat.ML", "cs.LG"], "doi": "", "_query": "real estate valuation deep learning", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/1807.10707v1", "title": "End-to-end Deep Learning from Raw Sensor Data: Atrial Fibrillation Detection using Wearables", "abstract": "We present a convolutional-recurrent neural network architecture with long short-term memory for real-time processing and classification of digital sensor data. The network implicitly performs typical signal processing tasks such as filtering and peak detection, and learns time-resolved embeddings of the input signal. We use a prototype multi-sensor wearable device to collect over 180h of photoplethysmography (PPG) data sampled at 20Hz, of which 36h are during atrial fibrillation (AFib). We use end-to-end learning to achieve state-of-the-art results in detecting AFib from raw PPG data. For classification labels output every 0.8s, we demonstrate an area under ROC curve of 0.9999, with false positive and false negative rates both below $2\\times 10^{-3}$. This constitutes a significant improvement on previous results utilising domain-specific feature engineering, such as heart rate extraction, and brings large-scale atrial fibrillation screenings within imminent reach.", "published": "2018-07-27T16:17:31Z", "authors": ["Igor Gotlibovych", "Stuart Crawford", "Dileep Goyal", "Jiaqi Liu", "Yaniv Kerem", "David Benaron", "Defne Yilmaz", "Gregory Marcus", "Yihan Li"], "categories": ["stat.ML", "cs.LG"], "doi": "", "_query": "real estate valuation deep learning", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/1802.10215v2", "title": "Var-CNN: A Data-Efficient Website Fingerprinting Attack Based on Deep Learning", "abstract": "In recent years, there have been several works that use website fingerprinting techniques to enable a local adversary to determine which website a Tor user visits. While the current state-of-the-art attack, which uses deep learning, outperforms prior art with medium to large amounts of data, it attains marginal to no accuracy improvements when both use small amounts of training data. In this work, we propose Var-CNN, a website fingerprinting attack that leverages deep learning techniques along with novel insights specific to packet sequence classification. In open-world settings with large amounts of data, Var-CNN attains over $1\\%$ higher true positive rate (TPR) than state-of-the-art attacks while achieving $4\\times$ lower false positive rate (FPR). Var-CNN's improvements are especially notable in low-data scenarios, where it reduces the FPR of prior art by $3.12\\%$ while increasing the TPR by $13\\%$. Overall, insights used to develop Var-CNN can be applied to future deep learning based attacks, and substantially reduce the amount of training data needed to perform a successful website fingerprinting attack. This shortens the time needed for data collection and lowers the likelihood of having data staleness issues.", "published": "2018-02-28T00:21:09Z", "authors": ["Sanjit Bhat", "David Lu", "Albert Kwon", "Srinivas Devadas"], "categories": ["cs.CR", "cs.LG"], "doi": "10.2478/popets-2019-0070", "_query": "real estate valuation deep learning", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2512.06785v1", "title": "Angular Regularization for Positive-Unlabeled Learning on the Hypersphere", "abstract": "Positive-Unlabeled (PU) learning addresses classification problems where only a subset of positive examples is labeled and the remaining data is unlabeled, making explicit negative supervision unavailable. Existing PU methods often rely on negative-risk estimation or pseudo-labeling, which either require strong distributional assumptions or can collapse in high-dimensional settings. We propose AngularPU, a novel PU framework that operates on the unit hypersphere using cosine similarity and angular margin. In our formulation, the positive class is represented by a learnable prototype vector, and classification reduces to thresholding the cosine similarity between an embedding and this prototype-eliminating the need for explicit negative modeling. To counteract the tendency of unlabeled embeddings to cluster near the positive prototype, we introduce an angular regularizer that encourages dispersion of the unlabeled set over the hypersphere, improving separation. We provide theoretical guarantees on the Bayes-optimality of the angular decision rule, consistency of the learned prototype, and the effect of the regularizer on the unlabeled distribution. Experiments on benchmark datasets demonstrate that AngularPU achieves competitive or superior performance compared to state-of-the-art PU methods, particularly in settings with scarce positives and high-dimensional embeddings, while offering geometric interpretability and scalability.", "published": "2025-12-07T10:59:35Z", "authors": ["Vasileios Sevetlidis", "George Pavlidis", "Antonios Gasteratos"], "categories": ["cs.LG", "cs.AI"], "doi": "", "_query": "real estate valuation deep learning", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/1811.04380v1", "title": "ReSet: Learning Recurrent Dynamic Routing in ResNet-like Neural Networks", "abstract": "Neural Network is a powerful Machine Learning tool that shows outstanding performance in Computer Vision, Natural Language Processing, and Artificial Intelligence. In particular, recently proposed ResNet architecture and its modifications produce state-of-the-art results in image classification problems. ResNet and most of the previously proposed architectures have a fixed structure and apply the same transformation to all input images. In this work, we develop a ResNet-based model that dynamically selects Computational Units (CU) for each input object from a learned set of transformations. Dynamic selection allows the network to learn a sequence of useful transformations and apply only required units to predict the image label. We compare our model to ResNet-38 architecture and achieve better results than the original ResNet on CIFAR-10.1 test set. While examining the produced paths, we discovered that the network learned different routes for images from different classes and similar routes for similar images.", "published": "2018-11-11T09:45:41Z", "authors": ["Iurii Kemaev", "Daniil Polykovskiy", "Dmitry Vetrov"], "categories": ["stat.ML", "cs.LG"], "doi": "", "_query": "real estate valuation deep learning", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2501.09687v1", "title": "U-Fair: Uncertainty-based Multimodal Multitask Learning for Fairer Depression Detection", "abstract": "Machine learning bias in mental health is becoming an increasingly pertinent challenge. Despite promising efforts indicating that multitask approaches often work better than unitask approaches, there is minimal work investigating the impact of multitask learning on performance and fairness in depression detection nor leveraged it to achieve fairer prediction outcomes. In this work, we undertake a systematic investigation of using a multitask approach to improve performance and fairness for depression detection. We propose a novel gender-based task-reweighting method using uncertainty grounded in how the PHQ-8 questionnaire is structured. Our results indicate that, although a multitask approach improves performance and fairness compared to a unitask approach, the results are not always consistent and we see evidence of negative transfer and a reduction in the Pareto frontier, which is concerning given the high-stake healthcare setting. Our proposed approach of gender-based reweighting with uncertainty improves performance and fairness and alleviates both challenges to a certain extent. Our findings on each PHQ-8 subitem task difficulty are also in agreement with the largest study conducted on the PHQ-8 subitem discrimination capacity, thus providing the very first tangible evidence linking ML findings with large-scale empirical population studies conducted on the PHQ-8.", "published": "2025-01-16T17:39:25Z", "authors": ["Jiaee Cheong", "Aditya Bangar", "Sinan Kalkan", "Hatice Gunes"], "categories": ["cs.LG"], "doi": "", "_query": "real estate valuation deep learning", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/1904.07154v3", "title": "Are Nearby Neighbors Relatives?: Testing Deep Music Embeddings", "abstract": "Deep neural networks have frequently been used to directly learn representations useful for a given task from raw input data. In terms of overall performance metrics, machine learning solutions employing deep representations frequently have been reported to greatly outperform those using hand-crafted feature representations. At the same time, they may pick up on aspects that are predominant in the data, yet not actually meaningful or interpretable. In this paper, we therefore propose a systematic way to test the trustworthiness of deep music representations, considering musical semantics. The underlying assumption is that in case a deep representation is to be trusted, distance consistency between known related points should be maintained both in the input audio space and corresponding latent deep space. We generate known related points through semantically meaningful transformations, both considering imperceptible and graver transformations. Then, we examine within- and between-space distance consistencies, both considering audio space and latent embedded space, the latter either being a result of a conventional feature extractor or a deep encoder. We illustrate how our method, as a complement to task-specific performance, provides interpretable insight into what a network may have captured from training data signals.", "published": "2019-04-15T16:08:41Z", "authors": ["Jaehun Kim", "Julián Urbano", "Cynthia C. S. Liem", "Alan Hanjalic"], "categories": ["cs.LG", "cs.SD", "eess.AS", "stat.ML"], "doi": "", "_query": "real estate valuation deep learning", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2401.09787v2", "title": "Querying Easily Flip-flopped Samples for Deep Active Learning", "abstract": "Active learning is a machine learning paradigm that aims to improve the performance of a model by strategically selecting and querying unlabeled data. One effective selection strategy is to base it on the model's predictive uncertainty, which can be interpreted as a measure of how informative a sample is. The sample's distance to the decision boundary is a natural measure of predictive uncertainty, but it is often intractable to compute, especially for complex decision boundaries formed in multiclass classification tasks. To address this issue, this paper proposes the {\\it least disagree metric} (LDM), defined as the smallest probability of disagreement of the predicted label, and an estimator for LDM proven to be asymptotically consistent under mild assumptions. The estimator is computationally efficient and can be easily implemented for deep learning models using parameter perturbation. The LDM-based active learning is performed by querying unlabeled data with the smallest LDM. Experimental results show that our LDM-based active learning algorithm obtains state-of-the-art overall performance on all considered datasets and deep architectures.", "published": "2024-01-18T08:12:23Z", "authors": ["Seong Jin Cho", "Gwangsu Kim", "Junghyun Lee", "Jinwoo Shin", "Chang D. Yoo"], "categories": ["cs.LG", "cs.AI", "stat.ML"], "doi": "", "_query": "real estate valuation deep learning", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2502.05776v4", "title": "Dynamic Pricing in the Linear Valuation Model using Shape Constraints", "abstract": "We propose a shape-constrained approach to dynamic pricing for censored data in the linear valuation model eliminating the need for tuning parameters commonly required by existing methods. Previous works have addressed the challenge of unknown market noise distribution $F_0$ using strategies ranging from kernel methods to reinforcement learning algorithms, such as bandit techniques and upper confidence bounds (UCB), under the assumption that $F_0$ satisfies Lipschitz (or stronger) conditions. In contrast, our method relies on isotonic regression under the weaker assumption that $F_0$ is $α$-Hölder continuous for some $α\\in (0,1]$, for which we derive a regret upper bound. Simulations and experiments with real-world data obtained by Welltower Inc (a major healthcare Real Estate Investment Trust) consistently demonstrate that our method attains lower empirical regret in comparison to several existing methods in the literature while offering the advantage of being tuning-parameter free.", "published": "2025-02-09T04:58:33Z", "authors": ["Daniele Bracale", "Moulinath Banerjee", "Yuekai Sun", "Kevin Stoll", "Salam Turki"], "categories": ["stat.ML", "cs.LG"], "doi": "", "_query": "real estate valuation deep learning", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2012.07994v1", "title": "Binary Black-box Evasion Attacks Against Deep Learning-based Static Malware Detectors with Adversarial Byte-Level Language Model", "abstract": "Anti-malware engines are the first line of defense against malicious software. While widely used, feature engineering-based anti-malware engines are vulnerable to unseen (zero-day) attacks. Recently, deep learning-based static anti-malware detectors have achieved success in identifying unseen attacks without requiring feature engineering and dynamic analysis. However, these detectors are susceptible to malware variants with slight perturbations, known as adversarial examples. Generating effective adversarial examples is useful to reveal the vulnerabilities of such systems. Current methods for launching such attacks require accessing either the specifications of the targeted anti-malware model, the confidence score of the anti-malware response, or dynamic malware analysis, which are either unrealistic or expensive. We propose MalRNN, a novel deep learning-based approach to automatically generate evasive malware variants without any of these restrictions. Our approach features an adversarial example generation process, which learns a language model via a generative sequence-to-sequence recurrent neural network to augment malware binaries. MalRNN effectively evades three recent deep learning-based malware detectors and outperforms current benchmark methods. Findings from applying our MalRNN on a real dataset with eight malware categories are discussed.", "published": "2020-12-14T22:54:53Z", "authors": ["Mohammadreza Ebrahimi", "Ning Zhang", "James Hu", "Muhammad Taqi Raza", "Hsinchun Chen"], "categories": ["cs.CR", "cs.LG"], "doi": "", "_query": "real estate valuation deep learning", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2310.20025v3", "title": "GOPlan: Goal-conditioned Offline Reinforcement Learning by Planning with Learned Models", "abstract": "Offline Goal-Conditioned RL (GCRL) offers a feasible paradigm for learning general-purpose policies from diverse and multi-task offline datasets. Despite notable recent progress, the predominant offline GCRL methods, mainly model-free, face constraints in handling limited data and generalizing to unseen goals. In this work, we propose Goal-conditioned Offline Planning (GOPlan), a novel model-based framework that contains two key phases: (1) pretraining a prior policy capable of capturing multi-modal action distribution within the multi-goal dataset; (2) employing the reanalysis method with planning to generate imagined trajectories for funetuning policies. Specifically, we base the prior policy on an advantage-weighted conditioned generative adversarial network, which facilitates distinct mode separation, mitigating the pitfalls of out-of-distribution (OOD) actions. For further policy optimization, the reanalysis method generates high-quality imaginary data by planning with learned models for both intra-trajectory and inter-trajectory goals. With thorough experimental evaluations, we demonstrate that GOPlan achieves state-of-the-art performance on various offline multi-goal navigation and manipulation tasks. Moreover, our results highlight the superior ability of GOPlan to handle small data budgets and generalize to OOD goals.", "published": "2023-10-30T21:19:52Z", "authors": ["Mianchu Wang", "Rui Yang", "Xi Chen", "Hao Sun", "Meng Fang", "Giovanni Montana"], "categories": ["cs.LG", "cs.AI"], "doi": "", "_query": "real estate valuation deep learning", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2601.22166v1", "title": "A Real-Options-Aware Multi-Criteria Framework for Ex-Ante Real Estate Redevelopment Use Selection", "abstract": "A growing share of the existing real estate stock exhibits persistent underperformance that can no longer be explained by cyclical market phases or inadequate maintenance alone. In many cases, technically recoverable assets located in non-marginal contexts fail to generate economic value consistent with the capital immobilized. This condition reflects a structural misalignment between intended use and effective demand rather than episodic market weakness, and calls for a decision framework capable of integrating value, risk, complexity, and irreversibility in strategic use selection. This study proposes a decision-analytic framework for the ex-ante selection of intended use in real estate redevelopment processes. The framework integrates real-options logic on irreversibility and managerial flexibility with a multi-criteria decision-analysis structure, enabling comparative evaluation of expected economic value, market and operational risk, technical and managerial complexity, and time-to-income. By treating redevelopment primarily as a problem of strategic option selection rather than design or financial optimization, the framework operationalizes option value preservation through disciplined ex-ante screening. Illustrative cases demonstrate how this integration of real options reasoning and MCDA reduces over-complexification and misalignment across different asset types and urban contexts.", "published": "2026-01-15T22:56:09Z", "authors": ["Roberto Garrone"], "categories": ["q-fin.GN", "econ.GN"], "doi": "", "_query": "real estate valuation deep learning", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2107.13648v1", "title": "Spot What Matters: Learning Context Using Graph Convolutional Networks for Weakly-Supervised Action Detection", "abstract": "The dominant paradigm in spatiotemporal action detection is to classify actions using spatiotemporal features learned by 2D or 3D Convolutional Networks. We argue that several actions are characterized by their context, such as relevant objects and actors present in the video. To this end, we introduce an architecture based on self-attention and Graph Convolutional Networks in order to model contextual cues, such as actor-actor and actor-object interactions, to improve human action detection in video. We are interested in achieving this in a weakly-supervised setting, i.e. using as less annotations as possible in terms of action bounding boxes. Our model aids explainability by visualizing the learned context as an attention map, even for actions and objects unseen during training. We evaluate how well our model highlights the relevant context by introducing a quantitative metric based on recall of objects retrieved by attention maps. Our model relies on a 3D convolutional RGB stream, and does not require expensive optical flow computation. We evaluate our models on the DALY dataset, which consists of human-object interaction actions. Experimental results show that our contextualized approach outperforms a baseline action detection approach by more than 2 points in Video-mAP. Code is available at \\url{https://github.com/micts/acgcn}", "published": "2021-07-28T21:37:18Z", "authors": ["Michail Tsiaousis", "Gertjan Burghouts", "Fieke Hillerström", "Peter van der Putten"], "categories": ["cs.LG", "cs.CV"], "doi": "10.1007/978-3-030-68799-1_9", "_query": "real estate valuation deep learning", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2604.11507v2", "title": "Deep Learning for Sequential Decision Making under Uncertainty: Foundations, Frameworks, and Frontiers", "abstract": "Artificial intelligence (AI) is moving increasingly beyond prediction to support decisions in complex, uncertain, and dynamic environments. This shift creates a natural intersection with operations research and management science (OR/MS), which has long provided methodological foundations for sequential decision making under uncertainty. At the same time, deep learning advances, including feedforward neural networks, recurrent architectures, transformers, large language models (LLMs), and deep reinforcement learning, have expanded data-driven modeling for large-scale decisions. This tutorial presents an OR/MS-centered perspective on deep learning for sequential decision making under uncertainty, bridging neural architectures and OR/MS approaches to decision making. Its premise: deep learning complements optimization rather than replacing it. Deep learning brings adaptability and scalable approximation, whereas OR/MS provides the mathematical rigor to represent constraints, recourse, uncertainty, and decision quality. The tutorial reviews key decision making foundations, connects them to the major neural architectures in modern AI, and organizes the field around three central themes: predict-then-optimize and decision-aware learning, learning-based decision generation under constraints for continuous and discrete problems with temporal coupling, and deep reinforcement learning for sequential and combinatorial decision making. Impact spans supply chains, service systems, healthcare and epidemic response, agriculture, energy, environmental sustainability, and autonomous operations. This tutorial frames these developments as part of a shift from predictive AI toward decision-capable AI, highlighting OR/MS's role in shaping the next generation of integrated learning--optimization systems.", "published": "2026-04-13T14:11:06Z", "authors": ["I. Esra Buyuktahtakin"], "categories": ["math.OC", "cs.AI", "cs.LG", "eess.SY", "stat.ML"], "doi": "", "_query": "real estate valuation deep learning", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/1904.02206v1", "title": "Jointly Pre-training with Supervised, Autoencoder, and Value Losses for Deep Reinforcement Learning", "abstract": "Deep Reinforcement Learning (DRL) algorithms are known to be data inefficient. One reason is that a DRL agent learns both the feature and the policy tabula rasa. Integrating prior knowledge into DRL algorithms is one way to improve learning efficiency since it helps to build helpful representations. In this work, we consider incorporating human knowledge to accelerate the asynchronous advantage actor-critic (A3C) algorithm by pre-training a small amount of non-expert human demonstrations. We leverage the supervised autoencoder framework and propose a novel pre-training strategy that jointly trains a weighted supervised classification loss, an unsupervised reconstruction loss, and an expected return loss. The resulting pre-trained model learns more useful features compared to independently training in supervised or unsupervised fashion. Our pre-training method drastically improved the learning performance of the A3C agent in Atari games of Pong and MsPacman, exceeding the performance of the state-of-the-art algorithms at a much smaller number of game interactions. Our method is light-weight and easy to implement in a single machine. For reproducibility, our code is available at github.com/gabrieledcjr/DeepRL/tree/A3C-ALA2019", "published": "2019-04-03T19:14:15Z", "authors": ["Gabriel V. de la Cruz", "Yunshu Du", "Matthew E. Taylor"], "categories": ["cs.LG", "stat.ML"], "doi": "", "_query": "real estate valuation deep learning", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2304.01567v1", "title": "A real-time algorithm for human action recognition in RGB and thermal video", "abstract": "Monitoring the movement and actions of humans in video in real-time is an important task. We present a deep learning based algorithm for human action recognition for both RGB and thermal cameras. It is able to detect and track humans and recognize four basic actions (standing, walking, running, lying) in real-time on a notebook with a NVIDIA GPU. For this, it combines state of the art components for object detection (Scaled YoloV4), optical flow (RAFT) and pose estimation (EvoSkeleton). Qualitative experiments on a set of tunnel videos show that the proposed algorithm works robustly for both RGB and thermal video.", "published": "2023-04-04T06:44:13Z", "authors": ["Hannes Fassold", "Karlheinz Gutjahr", "Anna Weber", "Roland Perko"], "categories": ["cs.CV"], "doi": "", "_query": "real estate valuation deep learning", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2405.09037v2", "title": "SSFL: Discovering Sparse Unified Subnetworks at Initialization for Efficient Federated Learning", "abstract": "In this work, we propose Salient Sparse Federated Learning (SSFL), a streamlined approach for sparse federated learning with efficient communication. SSFL identifies a sparse subnetwork prior to training, leveraging parameter saliency scores computed separately on local client data in non-IID scenarios, and then aggregated, to determine a global mask. Only the sparse model weights are trained and communicated each round between the clients and the server. On standard benchmarks including CIFAR-10, CIFAR-100, and Tiny-ImageNet, SSFL consistently improves the accuracy sparsity trade off, achieving more than 20\\% relative error reduction on CIFAR-10 compared to the strongest sparse baseline, while reducing communication costs by $2 \\times$ relative to dense FL. Finally, in a real-world federated learning deployment, SSFL delivers over $2.3 \\times$ faster communication time, underscoring its practical efficiency.", "published": "2024-05-15T02:13:51Z", "authors": ["Riyasat Ohib", "Bishal Thapaliya", "Gintare Karolina Dziugaite", "Jingyu Liu", "Vince Calhoun", "Sergey Plis"], "categories": ["cs.LG", "cs.AI", "cs.DC"], "doi": "", "_query": "real estate valuation deep learning", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2408.07155v1", "title": "Integration of Genetic Algorithms and Deep Learning for the Generation and Bioactivity Prediction of Novel Tyrosine Kinase Inhibitors", "abstract": "The intersection of artificial intelligence and bioinformatics has enabled significant advancements in drug discovery, particularly through the application of machine learning models. In this study, we present a combined approach using genetic algorithms and deep learning models to address two critical aspects of drug discovery: the generation of novel tyrosine kinase inhibitors and the prediction of their bioactivity. The generative model leverages genetic algorithms to create new small molecules with optimized ADMET (absorption, distribution, metabolism, excretion, and toxicity) and drug-likeness properties. Concurrently, a deep learning model is employed to predict the bioactivity of these generated molecules against tyrosine kinases, a key enzyme family involved in various cellular processes and cancer progression. By integrating these advanced computational methods, we demonstrate a powerful framework for accelerating the generation and identification of potential tyrosine kinase inhibitors, contributing to more efficient and effective early-stage drug discovery processes.", "published": "2024-08-13T18:50:47Z", "authors": ["Ricardo Romero"], "categories": ["q-bio.BM", "cs.LG"], "doi": "", "_query": "real estate valuation deep learning", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2406.04896v2", "title": "Stabilizing Extreme Q-learning by Maclaurin Expansion", "abstract": "In offline reinforcement learning, in-sample learning methods have been widely used to prevent performance degradation caused by evaluating out-of-distribution actions from the dataset. Extreme Q-learning (XQL) employs a loss function based on the assumption that Bellman error follows a Gumbel distribution, enabling it to model the soft optimal value function in an in-sample manner. It has demonstrated strong performance in both offline and online reinforcement learning settings. However, issues remain, such as the instability caused by the exponential term in the loss function and the risk of the error distribution deviating from the Gumbel distribution. Therefore, we propose Maclaurin Expanded Extreme Q-learning to enhance stability. In this method, applying Maclaurin expansion to the loss function in XQL enhances stability against large errors. This approach involves adjusting the modeled value function between the value function under the behavior policy and the soft optimal value function, thus achieving a trade-off between stability and optimality depending on the order of expansion. It also enables adjustment of the error distribution assumption from a normal distribution to a Gumbel distribution. Our method significantly stabilizes learning in online RL tasks from DM Control, where XQL was previously unstable. Additionally, it improves performance in several offline RL tasks from D4RL.", "published": "2024-06-07T12:43:17Z", "authors": ["Motoki Omura", "Takayuki Osa", "Yusuke Mukuta", "Tatsuya Harada"], "categories": ["cs.LG", "cs.AI"], "doi": "10.5281/zenodo.13899776", "_query": "real estate valuation deep learning", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2006.05082v1", "title": "Learning to Stop While Learning to Predict", "abstract": "There is a recent surge of interest in designing deep architectures based on the update steps in traditional algorithms, or learning neural networks to improve and replace traditional algorithms. While traditional algorithms have certain stopping criteria for outputting results at different iterations, many algorithm-inspired deep models are restricted to a ``fixed-depth'' for all inputs. Similar to algorithms, the optimal depth of a deep architecture may be different for different input instances, either to avoid ``over-thinking'', or because we want to compute less for operations converged already. In this paper, we tackle this varying depth problem using a steerable architecture, where a feed-forward deep model and a variational stopping policy are learned together to sequentially determine the optimal number of layers for each input instance. Training such architecture is very challenging. We provide a variational Bayes perspective and design a novel and effective training procedure which decomposes the task into an oracle model learning stage and an imitation stage. Experimentally, we show that the learned deep model along with the stopping policy improves the performances on a diverse set of tasks, including learning sparse recovery, few-shot meta learning, and computer vision tasks.", "published": "2020-06-09T07:22:01Z", "authors": ["Xinshi Chen", "Hanjun Dai", "Yu Li", "Xin Gao", "Le Song"], "categories": ["cs.LG", "stat.ML"], "doi": "", "_query": "real estate valuation deep learning", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2306.13030v2", "title": "Online Self-Supervised Deep Learning for Intrusion Detection Systems", "abstract": "This paper proposes a novel Self-Supervised Intrusion Detection (SSID) framework, which enables a fully online Deep Learning (DL) based Intrusion Detection System (IDS) that requires no human intervention or prior off-line learning. The proposed framework analyzes and labels incoming traffic packets based only on the decisions of the IDS itself using an Auto-Associative Deep Random Neural Network, and on an online estimate of its statistically measured trustworthiness. The SSID framework enables IDS to adapt rapidly to time-varying characteristics of the network traffic, and eliminates the need for offline data collection. This approach avoids human errors in data labeling, and human labor and computational costs of model training and data collection. The approach is experimentally evaluated on public datasets and compared with well-known {machine learning and deep learning} models, showing that this SSID framework is very useful and advantageous as an accurate and online learning DL-based IDS for IoT systems.", "published": "2023-06-22T16:46:35Z", "authors": ["Mert Nakıp", "Erol Gelenbe"], "categories": ["cs.CR", "cs.LG", "cs.NI"], "doi": "10.1109/TIFS.2024.3402148", "_query": "real estate valuation deep learning", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/1906.04904v3", "title": "Learning Deep Generative Models with Annealed Importance Sampling", "abstract": "Variational inference (VI) and Markov chain Monte Carlo (MCMC) are two main approximate approaches for learning deep generative models by maximizing marginal likelihood. In this paper, we propose using annealed importance sampling for learning deep generative models. Our proposed approach bridges VI with MCMC. It generalizes VI methods such as variational auto-encoders and importance weighted auto-encoders (IWAE) and the MCMC method proposed in (Hoffman, 2017). It also provides insights into why running multiple short MCMC chains can help learning deep generative models. Through experiments, we show that our approach yields better density models than IWAE and can effectively trade computation for model accuracy without increasing memory cost.", "published": "2019-06-12T02:54:34Z", "authors": ["Xinqiang Ding", "David J. Freedman"], "categories": ["stat.ML", "cs.LG"], "doi": "", "_query": "real estate valuation deep learning", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/1908.09381v5", "title": "Tutorial and Survey on Probabilistic Graphical Model and Variational Inference in Deep Reinforcement Learning", "abstract": "Aiming at a comprehensive and concise tutorial survey, recap of variational inference and reinforcement learning with Probabilistic Graphical Models are given with detailed derivations. Reviews and comparisons on recent advances in deep reinforcement learning are made from various aspects. We offer detailed derivations to a taxonomy of Probabilistic Graphical Model and Variational Inference methods in deep reinforcement learning, which serves as a complementary material on top of the original contributions.", "published": "2019-08-25T19:36:36Z", "authors": ["Xudong Sun", "Bernd Bischl"], "categories": ["cs.LG", "cs.AI", "stat.ML"], "doi": "", "_query": "real estate valuation deep learning", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2601.22531v2", "title": "Learn from A Rationalist: Distilling Intermediate Interpretable Rationales", "abstract": "Because of the pervasive use of deep neural networks (DNNs), especially in high-stakes domains, the interpretability of DNNs has received increased attention. The general idea of rationale extraction (RE) is to provide an interpretable-by-design framework for DNNs via a select-predict architecture where two neural networks learn jointly to perform feature selection and prediction, respectively. Given only the remote supervision from the final task prediction, the process of learning to select subsets of features (or rationales) requires searching in the space of all possible feature combinations, which is computationally challenging and even harder when the base neural networks are not sufficiently capable. To improve the predictive performance of RE models that are based on less capable or smaller neural networks (i.e., the students), we propose REKD (Rationale Extraction with Knowledge Distillation) where a student RE model learns from the rationales and predictions of a teacher (i.e., a rationalist) in addition to the student's own RE optimization. This structural adjustment to RE aligns well with how humans could learn effectively from interpretable and verifiable knowledge. Because of the neural-model agnostic nature of the method, any black-box neural network could be integrated as a backbone model. To demonstrate the viability of REKD, we conduct experiments with multiple variants of BERT and vision transformer (ViT) models. Our experiments across language and vision classification datasets (i.e., IMDB movie reviews, CIFAR 10 and CIFAR 100) show that REKD significantly improves the predictive performance of the student RE models.", "published": "2026-01-30T04:07:47Z", "authors": ["Jiayi Dai", "Randy Goebel"], "categories": ["cs.LG", "cs.AI"], "doi": "", "_query": "real estate valuation deep learning", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/1601.02733v1", "title": "Deep Learning of Part-based Representation of Data Using Sparse Autoencoders with Nonnegativity Constraints", "abstract": "We demonstrate a new deep learning autoencoder network, trained by a nonnegativity constraint algorithm (NCAE), that learns features which show part-based representation of data. The learning algorithm is based on constraining negative weights. The performance of the algorithm is assessed based on decomposing data into parts and its prediction performance is tested on three standard image data sets and one text dataset. The results indicate that the nonnegativity constraint forces the autoencoder to learn features that amount to a part-based representation of data, while improving sparsity and reconstruction quality in comparison with the traditional sparse autoencoder and Nonnegative Matrix Factorization. It is also shown that this newly acquired representation improves the prediction performance of a deep neural network.", "published": "2016-01-12T05:33:03Z", "authors": ["Ehsan Hosseini-Asl", "Jacek M. Zurada", "Olfa Nasraoui"], "categories": ["cs.LG", "stat.ML"], "doi": "10.1109/TNNLS.2015.2479223", "_query": "real estate valuation deep learning", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/1911.02743v1", "title": "Accounting for Physics Uncertainty in Ultrasonic Wave Propagation using Deep Learning", "abstract": "Ultrasonic guided waves are commonly used to localize structural damage in infrastructures such as buildings, airplanes, bridges. Damage localization can be viewed as an inverse problem. Physical model based techniques are popular for guided wave based damage localization. The performance of these techniques depend on the degree of faithfulness with which the physical model describes wave propagation. External factors such as environmental variations and random noise are a source of uncertainty in wave propagation. The physical modeling of uncertainty in an inverse problem is still a challenging problem. In this work, we propose a deep learning based model for robust damage localization in presence of uncertainty. Wave data with uncertainty is simulated to reflect variations due to external factors and Gaussian noise is added to reflect random noise in the environment. After evaluating the localization error on test data with uncertainty, we observe that the deep learning model trained with uncertainty can learn robust representations. The approach shows potential for dealing with uncertainty in physical science problems using deep learning models.", "published": "2019-11-07T03:44:18Z", "authors": ["Ishan D. Khurjekar", "Joel B. Harley"], "categories": ["cs.LG", "eess.AS", "eess.SP", "stat.ML"], "doi": "", "_query": "real estate valuation deep learning", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/1905.04749v2", "title": "A Benchmark Study of Machine Learning Models for Online Fake News Detection", "abstract": "The proliferation of fake news and its propagation on social media has become a major concern due to its ability to create devastating impacts. Different machine learning approaches have been suggested to detect fake news. However, most of those focused on a specific type of news (such as political) which leads us to the question of dataset-bias of the models used. In this research, we conducted a benchmark study to assess the performance of different applicable machine learning approaches on three different datasets where we accumulated the largest and most diversified one. We explored a number of advanced pre-trained language models for fake news detection along with the traditional and deep learning ones and compared their performances from different aspects for the first time to the best of our knowledge. We find that BERT and similar pre-trained models perform the best for fake news detection, especially with very small dataset. Hence, these models are significantly better option for languages with limited electronic contents, i.e., training data. We also carried out several analysis based on the models' performance, article's topic, article's length, and discussed different lessons learned from them. We believe that this benchmark study will help the research community to explore further and news sites/blogs to select the most appropriate fake news detection method.", "published": "2019-05-12T17:15:11Z", "authors": ["Junaed Younus Khan", "Md. Tawkat Islam Khondaker", "Sadia Afroz", "Gias Uddin", "Anindya Iqbal"], "categories": ["cs.CL", "cs.IR", "cs.LG", "stat.ML"], "doi": "10.1016/j.mlwa.2021.100032", "_query": "real estate valuation deep learning", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2209.04259v1", "title": "Knowledge-based Deep Learning for Modeling Chaotic Systems", "abstract": "Deep Learning has received increased attention due to its unbeatable success in many fields, such as computer vision, natural language processing, recommendation systems, and most recently in simulating multiphysics problems and predicting nonlinear dynamical systems. However, modeling and forecasting the dynamics of chaotic systems remains an open research problem since training deep learning models requires big data, which is not always available in many cases. Such deep learners can be trained from additional information obtained from simulated results and by enforcing the physical laws of the chaotic systems. This paper considers extreme events and their dynamics and proposes elegant models based on deep neural networks, called knowledge-based deep learning (KDL). Our proposed KDL can learn the complex patterns governing chaotic systems by jointly training on real and simulated data directly from the dynamics and their differential equations. This knowledge is transferred to model and forecast real-world chaotic events exhibiting extreme behavior. We validate the efficiency of our model by assessing it on three real-world benchmark datasets: El Nino sea surface temperature, San Juan Dengue viral infection, and Bjørnøya daily precipitation, all governed by extreme events' dynamics. Using prior knowledge of extreme events and physics-based loss functions to lead the neural network learning, we ensure physically consistent, generalizable, and accurate forecasting, even in a small data regime.", "published": "2022-09-09T11:46:25Z", "authors": ["Zakaria Elabid", "Tanujit Chakraborty", "Abdenour Hadid"], "categories": ["cs.LG", "physics.comp-ph", "stat.ML"], "doi": "10.1109/ICMLA55696.2022.00111", "_query": "real estate valuation deep learning", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/1307.7038v1", "title": "The Solar Neighborhood XXIX: The Habitable Real Estate of our Nearest Stellar Neighbors", "abstract": "We use the sample of known stars and brown dwarfs within 5 pc of the Sun, supplemented with AFGK stars within 10 pc, to determine which stellar spectral types provide the most habitable real estate --- defined to be locations where liquid water could be present on Earth-like planets. Stellar temperatures and radii are determined by fitting model spectra to spatially resolved broad-band photometric energy distributions for stars in the sample. The locations of the HZ's are calculated using an empirical formula for planetary surface temperature and assuming the condition of liquid water, called here the empirical habitable zone, or EHZ. Systems that have dynamically disruptive companions, assuming a 5:1 separation ratios for primary/secondary pairs and either object and a planet, are considered not habitable. We then derive a simple formula to predict the location of the EHZ for main sequence stars based on V-K color. We consider EHZ widths as more useful measures of the habitable real estate around stars than areas because multiple planets are not expected to orbit stars at identical stellar distances. This EHZ provides a qualitative guide on where to expect the largest population of planets in the HZ of main sequence stars. M stars provide more EHZ real estate than other spectral types, possessing 36.5% of the habitable real estate en masse. K stars are second with 21.5%, while A, F, and G stars offer 18.5%, 6.9% and 16.6%, respectively. Our calculations show that three M dwarfs within 10 pc harbor planets in their EHZs. GJ 581 may have two planets (d with msini = 6.1 M_E; g with msini = 3.1 M_E), GJ 667 C has one (c with msini = 4.5 M_E), and GJ 876 has two (b with msini = 1.89 M_J and c with msini = 0.56 M_J). If Earth-like planets are as common around low mass stars as recent Kepler results suggest, M stars are the most likely place to find Earth-like planets in HZ's.", "published": "2013-07-26T14:07:47Z", "authors": ["Justin R. Cantrell", "Todd J. Henry", "Russel J. White"], "categories": ["astro-ph.SR"], "doi": "10.1088/0004-6256/146/4/99", "_query": "real estate valuation deep learning", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2509.17971v2", "title": "Intra-Cluster Mixup: An Effective Data Augmentation Technique for Complementary-Label Learning", "abstract": "In this paper, we investigate the challenges of complementary-label learning (CLL), a specialized form of weakly-supervised learning (WSL) where models are trained with labels indicating classes to which instances do not belong, rather than standard ordinary labels. This alternative supervision is appealing because collecting complementary labels is generally cheaper and less labor-intensive. Although most existing research in CLL emphasizes the development of novel loss functions, the potential of data augmentation in this domain remains largely underexplored. In this work, we uncover that the widely-used Mixup data augmentation technique is ineffective when directly applied to CLL. Through in-depth analysis, we identify that the complementary-label noise generated by Mixup negatively impacts the performance of CLL models. We then propose an improved technique called Intra-Cluster Mixup (ICM), which only synthesizes augmented data from nearby examples, to mitigate the noise effect. ICM carries the benefits of encouraging complementary label sharing of nearby examples, and leads to substantial performance improvements across synthetic and real-world labeled datasets. In particular, our wide spectrum of experimental results on both balanced and imbalanced CLL settings justifies the potential of ICM in allying with state-of-the-art CLL algorithms, achieving significant accuracy increases of 30% and 10% on MNIST and CIFAR datasets, respectively.", "published": "2025-09-22T16:20:41Z", "authors": ["Tan-Ha Mai", "Hsuan-Tien Lin"], "categories": ["cs.LG", "cs.AI", "cs.CV"], "doi": "", "_query": "real estate valuation deep learning", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/1404.1559v1", "title": "Sparse Coding: A Deep Learning using Unlabeled Data for High - Level Representation", "abstract": "Sparse coding algorithm is an learning algorithm mainly for unsupervised feature for finding succinct, a little above high - level Representation of inputs, and it has successfully given a way for Deep learning. Our objective is to use High - Level Representation data in form of unlabeled category to help unsupervised learning task. when compared with labeled data, unlabeled data is easier to acquire because, unlike labeled data it does not follow some particular class labels. This really makes the Deep learning wider and applicable to practical problems and learning. The main problem with sparse coding is it uses Quadratic loss function and Gaussian noise mode. So, its performs is very poor when binary or integer value or other Non- Gaussian type data is applied. Thus first we propose an algorithm for solving the L1 - regularized convex optimization algorithm for the problem to allow High - Level Representation of unlabeled data. Through this we derive a optimal solution for describing an approach to Deep learning algorithm by using sparse code.", "published": "2014-04-06T09:50:45Z", "authors": ["R. Vidya", "Dr. G. M. Nasira", "R. P. Jaia Priyankka"], "categories": ["cs.LG", "cs.NE"], "doi": "10.1109/WCCCT.2014.69", "_query": "real estate valuation deep learning", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/1903.07424v1", "title": "Communication-Efficient Federated Deep Learning with Asynchronous Model Update and Temporally Weighted Aggregation", "abstract": "Federated learning obtains a central model on the server by aggregating models trained locally on clients. As a result, federated learning does not require clients to upload their data to the server, thereby preserving the data privacy of the clients. One challenge in federated learning is to reduce the client-server communication since the end devices typically have very limited communication bandwidth. This paper presents an enhanced federated learning technique by proposing a synchronous learning strategy on the clients and a temporally weighted aggregation of the local models on the server. In the asynchronous learning strategy, different layers of the deep neural networks are categorized into shallow and deeps layers and the parameters of the deep layers are updated less frequently than those of the shallow layers. Furthermore, a temporally weighted aggregation strategy is introduced on the server to make use of the previously trained local models, thereby enhancing the accuracy and convergence of the central model. The proposed algorithm is empirically on two datasets with different deep neural networks. Our results demonstrate that the proposed asynchronous federated deep learning outperforms the baseline algorithm both in terms of communication cost and model accuracy.", "published": "2019-03-18T13:27:42Z", "authors": ["Yang Chen", "Xiaoyan Sun", "Yaochu Jin"], "categories": ["cs.LG", "cs.AI", "cs.DC", "stat.ML"], "doi": "10.1109/TNNLS.2019.2953131", "_query": "real estate valuation deep learning", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2004.06277v1", "title": "A Demonstration of Issues with Value-Based Multiobjective Reinforcement Learning Under Stochastic State Transitions", "abstract": "We report a previously unidentified issue with model-free, value-based approaches to multiobjective reinforcement learning in the context of environments with stochastic state transitions. An example multiobjective Markov Decision Process (MOMDP) is used to demonstrate that under such conditions these approaches may be unable to discover the policy which maximises the Scalarised Expected Return, and in fact may converge to a Pareto-dominated solution. We discuss several alternative methods which may be more suitable for maximising SER in MOMDPs with stochastic transitions.", "published": "2020-04-14T02:55:12Z", "authors": ["Peter Vamplew", "Cameron Foale", "Richard Dazeley"], "categories": ["cs.LG", "cs.MA", "stat.ML"], "doi": "10.1007/s00521-021-05859-1", "_query": "real estate valuation deep learning", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2301.06870v1", "title": "Learning to solve arithmetic problems with a virtual abacus", "abstract": "Acquiring mathematical skills is considered a key challenge for modern Artificial Intelligence systems. Inspired by the way humans discover numerical knowledge, here we introduce a deep reinforcement learning framework that allows to simulate how cognitive agents could gradually learn to solve arithmetic problems by interacting with a virtual abacus. The proposed model successfully learn to perform multi-digit additions and subtractions, achieving an error rate below 1% even when operands are much longer than those observed during training. We also compare the performance of learning agents receiving a different amount of explicit supervision, and we analyze the most common error patterns to better understand the limitations and biases resulting from our design choices.", "published": "2023-01-17T13:25:52Z", "authors": ["Flavio Petruzzellis", "Ling Xuan Chen", "Alberto Testolin"], "categories": ["cs.LG", "cs.AI"], "doi": "10.7557/18.6805", "_query": "real estate valuation deep learning", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2304.10466v1", "title": "Efficient Deep Reinforcement Learning Requires Regulating Overfitting", "abstract": "Deep reinforcement learning algorithms that learn policies by trial-and-error must learn from limited amounts of data collected by actively interacting with the environment. While many prior works have shown that proper regularization techniques are crucial for enabling data-efficient RL, a general understanding of the bottlenecks in data-efficient RL has remained unclear. Consequently, it has been difficult to devise a universal technique that works well across all domains. In this paper, we attempt to understand the primary bottleneck in sample-efficient deep RL by examining several potential hypotheses such as non-stationarity, excessive action distribution shift, and overfitting. We perform thorough empirical analysis on state-based DeepMind control suite (DMC) tasks in a controlled and systematic way to show that high temporal-difference (TD) error on the validation set of transitions is the main culprit that severely affects the performance of deep RL algorithms, and prior methods that lead to good performance do in fact, control the validation TD error to be low. This observation gives us a robust principle for making deep RL efficient: we can hill-climb on the validation TD error by utilizing any form of regularization techniques from supervised learning. We show that a simple online model selection method that targets the validation TD error is effective across state-based DMC and Gym tasks.", "published": "2023-04-20T17:11:05Z", "authors": ["Qiyang Li", "Aviral Kumar", "Ilya Kostrikov", "Sergey Levine"], "categories": ["cs.LG", "cs.AI", "stat.ML"], "doi": "", "_query": "real estate valuation deep learning", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/1911.04175v1", "title": "Multi-Agent Connected Autonomous Driving using Deep Reinforcement Learning", "abstract": "The capability to learn and adapt to changes in the driving environment is crucial for developing autonomous driving systems that are scalable beyond geo-fenced operational design domains. Deep Reinforcement Learning (RL) provides a promising and scalable framework for developing adaptive learning based solutions. Deep RL methods usually model the problem as a (Partially Observable) Markov Decision Process in which an agent acts in a stationary environment to learn an optimal behavior policy. However, driving involves complex interaction between multiple, intelligent (artificial or human) agents in a highly non-stationary environment. In this paper, we propose the use of Partially Observable Markov Games(POSG) for formulating the connected autonomous driving problems with realistic assumptions. We provide a taxonomy of multi-agent learning environments based on the nature of tasks, nature of agents and the nature of the environment to help in categorizing various autonomous driving problems that can be addressed under the proposed formulation. As our main contributions, we provide MACAD-Gym, a Multi-Agent Connected, Autonomous Driving agent learning platform for furthering research in this direction. Our MACAD-Gym platform provides an extensible set of Connected Autonomous Driving (CAD) simulation environments that enable the research and development of Deep RL- based integrated sensing, perception, planning and control algorithms for CAD systems with unlimited operational design domain under realistic, multi-agent settings. We also share the MACAD-Agents that were trained successfully using the MACAD-Gym platform to learn control policies for multiple vehicle agents in a partially observable, stop-sign controlled, 3-way urban intersection environment with raw (camera) sensor observations.", "published": "2019-11-11T10:55:25Z", "authors": ["Praveen Palanisamy"], "categories": ["cs.LG", "cs.AI", "cs.MA", "cs.RO", "stat.ML"], "doi": "", "_query": "real estate valuation deep learning", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2601.21297v2", "title": "Deep QP Safety Filter: Model-free Learning for Reachability-based Safety Filter", "abstract": "We introduce Deep QP Safety Filter, a fully data-driven safety layer for black-box dynamical systems. Our method learns a Quadratic-Program (QP) safety filter without model knowledge by combining Hamilton-Jacobi (HJ) reachability with model-free learning. We construct contraction-based losses for both the safety value and its derivatives, and train two neural networks accordingly. In the exact setting, the learned critic converges to the viscosity solution (and its derivative), even for non-smooth values. Across diverse dynamical systems -- even including a hybrid system -- and multiple RL tasks, Deep QP Safety Filter substantially reduces pre-convergence failures while accelerating learning toward higher returns than strong baselines, offering a principled and practical route to safe, model-free control.", "published": "2026-01-29T05:49:48Z", "authors": ["Byeongjun Kim", "H. Jin Kim"], "categories": ["cs.RO", "eess.SY"], "doi": "", "_query": "real estate valuation deep learning", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/1710.09513v4", "title": "Maximum Principle Based Algorithms for Deep Learning", "abstract": "The continuous dynamical system approach to deep learning is explored in order to devise alternative frameworks for training algorithms. Training is recast as a control problem and this allows us to formulate necessary optimality conditions in continuous time using the Pontryagin's maximum principle (PMP). A modification of the method of successive approximations is then used to solve the PMP, giving rise to an alternative training algorithm for deep learning. This approach has the advantage that rigorous error estimates and convergence results can be established. We also show that it may avoid some pitfalls of gradient-based methods, such as slow convergence on flat landscapes near saddle points. Furthermore, we demonstrate that it obtains favorable initial convergence rate per-iteration, provided Hamiltonian maximization can be efficiently carried out - a step which is still in need of improvement. Overall, the approach opens up new avenues to attack problems associated with deep learning, such as trapping in slow manifolds and inapplicability of gradient-based methods for discrete trainable variables.", "published": "2017-10-26T02:04:33Z", "authors": ["Qianxiao Li", "Long Chen", "Cheng Tai", "Weinan E"], "categories": ["cs.LG", "stat.ML"], "doi": "", "_query": "real estate valuation deep learning", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/1809.07731v1", "title": "Benchmarking Reinforcement Learning Algorithms on Real-World Robots", "abstract": "Through many recent successes in simulation, model-free reinforcement learning has emerged as a promising approach to solving continuous control robotic tasks. The research community is now able to reproduce, analyze and build quickly on these results due to open source implementations of learning algorithms and simulated benchmark tasks. To carry forward these successes to real-world applications, it is crucial to withhold utilizing the unique advantages of simulations that do not transfer to the real world and experiment directly with physical robots. However, reinforcement learning research with physical robots faces substantial resistance due to the lack of benchmark tasks and supporting source code. In this work, we introduce several reinforcement learning tasks with multiple commercially available robots that present varying levels of learning difficulty, setup, and repeatability. On these tasks, we test the learning performance of off-the-shelf implementations of four reinforcement learning algorithms and analyze sensitivity to their hyper-parameters to determine their readiness for applications in various real-world tasks. Our results show that with a careful setup of the task interface and computations, some of these implementations can be readily applicable to physical robots. We find that state-of-the-art learning algorithms are highly sensitive to their hyper-parameters and their relative ordering does not transfer across tasks, indicating the necessity of re-tuning them for each task for best performance. On the other hand, the best hyper-parameter configuration from one task may often result in effective learning on held-out tasks even with different robots, providing a reasonable default. We make the benchmark tasks publicly available to enhance reproducibility in real-world reinforcement learning.", "published": "2018-09-20T16:46:04Z", "authors": ["A. Rupam Mahmood", "Dmytro Korenkevych", "Gautham Vasan", "William Ma", "James Bergstra"], "categories": ["cs.LG", "cs.AI", "cs.RO", "stat.ML"], "doi": "", "_query": "real estate valuation deep learning", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2210.14396v4", "title": "FeDXL: Provable Federated Learning for Deep X-Risk Optimization", "abstract": "In this paper, we tackle a novel federated learning (FL) problem for optimizing a family of X-risks, to which no existing FL algorithms are applicable. In particular, the objective has the form of $\\mathbb E_{z\\sim S_1} f(\\mathbb E_{z'\\sim S_2} \\ell(w; z, z'))$, where two sets of data $S_1, S_2$ are distributed over multiple machines, $\\ell(\\cdot)$ is a pairwise loss that only depends on the prediction outputs of the input data pairs $(z, z')$, and $f(\\cdot)$ is possibly a non-linear non-convex function. This problem has important applications in machine learning, e.g., AUROC maximization with a pairwise loss, and partial AUROC maximization with a compositional loss. The challenges for designing an FL algorithm for X-risks lie in the non-decomposability of the objective over multiple machines and the interdependency between different machines. To this end, we propose an active-passive decomposition framework that decouples the gradient's components with two types, namely active parts and passive parts, where the active parts depend on local data that are computed with the local model and the passive parts depend on other machines that are communicated/computed based on historical models and samples. Under this framework, we develop two provable FL algorithms (FeDXL) for handling linear and nonlinear $f$, respectively, based on federated averaging and merging. We develop a novel theoretical analysis to combat the latency of the passive parts and the interdependency between the local model parameters and the involved data for computing local gradient estimators. We establish both iteration and communication complexities and show that using the historical samples and models for computing the passive parts do not degrade the complexities. We conduct empirical studies of FeDXL for deep AUROC and partial AUROC maximization, and demonstrate their performance compared with several baselines.", "published": "2022-10-26T00:23:36Z", "authors": ["Zhishuai Guo", "Rong Jin", "Jiebo Luo", "Tianbao Yang"], "categories": ["cs.LG", "cs.DC", "math.OC", "stat.ML"], "doi": "", "_query": "real estate valuation deep learning", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/1703.03121v2", "title": "Coordinated Multi-Agent Imitation Learning", "abstract": "We study the problem of imitation learning from demonstrations of multiple coordinating agents. One key challenge in this setting is that learning a good model of coordination can be difficult, since coordination is often implicit in the demonstrations and must be inferred as a latent variable. We propose a joint approach that simultaneously learns a latent coordination model along with the individual policies. In particular, our method integrates unsupervised structure learning with conventional imitation learning. We illustrate the power of our approach on a difficult problem of learning multiple policies for fine-grained behavior modeling in team sports, where different players occupy different roles in the coordinated team strategy. We show that having a coordination model to infer the roles of players yields substantially improved imitation loss compared to conventional baselines.", "published": "2017-03-09T03:45:42Z", "authors": ["Hoang M. Le", "Yisong Yue", "Peter Carr", "Patrick Lucey"], "categories": ["cs.LG"], "doi": "", "_query": "real estate valuation deep learning", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2001.03612v1", "title": "A Deep Learning Approach Towards Prediction of Faults in Wind Turbines", "abstract": "With the rising costs of conventional sources of energy, the world is moving towards sustainable energy sources including wind energy. Wind turbines consist of several electrical and mechanical components and experience an enormous amount of irregular loads, making their operational behaviour at times inconsistent. Operations and Maintenance (O&M) is a key factor in monitoring such inconsistent behaviour of the turbines in order to predict and prevent any incipient faults which may occur in the near future. Machine learning has been applied to the domain of wind energy over the last decade for analysing, diagnosing and predicting wind turbine faults. In particular, we follow the idea of modelling a turbine's performance as a power curve where any power outputs that fall off the curve can be seen as performance errors. Existing work using this idea has used data from a turbine's Supervisory Control & Acquisition (SCADA) system to filter and analyse fault & alarm data using regression techniques. In contrast to previous work, we explore how deep learning can be applied to fault prediction from open access meteorological data only.", "published": "2019-12-12T00:15:33Z", "authors": ["Joyjit Chatterjee", "Nina Dethlefs"], "categories": ["cs.LG", "eess.SP"], "doi": "", "_query": "real estate valuation deep learning", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/1905.12019v5", "title": "Unified Probabilistic Deep Continual Learning through Generative Replay and Open Set Recognition", "abstract": "Modern deep neural networks are well known to be brittle in the face of unknown data instances and recognition of the latter remains a challenge. Although it is inevitable for continual-learning systems to encounter such unseen concepts, the corresponding literature appears to nonetheless focus primarily on alleviating catastrophic interference with learned representations. In this work, we introduce a probabilistic approach that connects these perspectives based on variational inference in a single deep autoencoder model. Specifically, we propose to bound the approximate posterior by fitting regions of high density on the basis of correctly classified data points. These bounds are shown to serve a dual purpose: unseen unknown out-of-distribution data can be distinguished from already trained known tasks towards robust application. Simultaneously, to retain already acquired knowledge, a generative replay process can be narrowed to strictly in-distribution samples, in order to significantly alleviate catastrophic interference.", "published": "2019-05-28T18:26:04Z", "authors": ["Martin Mundt", "Iuliia Pliushch", "Sagnik Majumder", "Yongwon Hong", "Visvanathan Ramesh"], "categories": ["cs.LG", "cs.CV", "cs.NE", "stat.ML"], "doi": "10.3390/jimaging8040093", "_query": "real estate valuation deep learning", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/1904.09274v1", "title": "Deep Learning on Mobile Devices - A Review", "abstract": "Recent breakthroughs in deep learning and artificial intelligence technologies have enabled numerous mobile applications. While traditional computation paradigms rely on mobile sensing and cloud computing, deep learning implemented on mobile devices provides several advantages. These advantages include low communication bandwidth, small cloud computing resource cost, quick response time, and improved data privacy. Research and development of deep learning on mobile and embedded devices has recently attracted much attention. This paper provides a timely review of this fast-paced field to give the researcher, engineer, practitioner, and graduate student a quick grasp on the recent advancements of deep learning on mobile devices. In this paper, we discuss hardware architectures for mobile deep learning, including Field Programmable Gate Arrays, Application Specific Integrated Circuit, and recent mobile Graphic Processing Units. We present Size, Weight, Area and Power considerations and their relation to algorithm optimizations, such as quantization, pruning, compression, and approximations that simplify computation while retaining performance accuracy. We cover existing systems and give a state-of-the-industry review of TensorFlow, MXNet, Mobile AI Compute Engine, and Paddle-mobile deep learning platform. We discuss resources for mobile deep learning practitioners, including tools, libraries, models, and performance benchmarks. We present applications of various mobile sensing modalities to industries, ranging from robotics, healthcare and multi-media, biometrics to autonomous drive and defense. We address the key deep learning challenges to overcome, including low quality data, and small training/adaptation data sets. In addition, the review provides numerous citations and links to existing code bases implementing various technologies.", "published": "2019-03-21T00:41:20Z", "authors": ["Yunbin Deng"], "categories": ["cs.LG"], "doi": "10.13140/RG.2.2.15012.12167", "_query": "real estate valuation deep learning", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2303.05155v2", "title": "Aux-Drop: Handling Haphazard Inputs in Online Learning Using Auxiliary Dropouts", "abstract": "Many real-world applications based on online learning produce streaming data that is haphazard in nature, i.e., contains missing features, features becoming obsolete in time, the appearance of new features at later points in time and a lack of clarity on the total number of input features. These challenges make it hard to build a learnable system for such applications, and almost no work exists in deep learning that addresses this issue. In this paper, we present Aux-Drop, an auxiliary dropout regularization strategy for online learning that handles the haphazard input features in an effective manner. Aux-Drop adapts the conventional dropout regularization scheme for the haphazard input feature space ensuring that the final output is minimally impacted by the chaotic appearance of such features. It helps to prevent the co-adaptation of especially the auxiliary and base features, as well as reduces the strong dependence of the output on any of the auxiliary inputs of the model. This helps in better learning for scenarios where certain features disappear in time or when new features are to be modelled. The efficacy of Aux-Drop has been demonstrated through extensive numerical experiments on SOTA benchmarking datasets that include Italy Power Demand, HIGGS, SUSY and multiple UCI datasets. The code is available at https://github.com/Rohit102497/Aux-Drop.", "published": "2023-03-09T10:15:03Z", "authors": ["Rohit Agarwal", "Deepak Gupta", "Alexander Horsch", "Dilip K. Prasad"], "categories": ["cs.LG", "cs.AI"], "doi": "", "_query": "real estate valuation deep learning", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/astro-ph/0208209v1", "title": "Ground-Based Optical Deep Pencil Beam Surveys", "abstract": "The initial results of the Deep Lens Survey (http://dls.bell-labs.com) to identify possible brown dwarfs and extremely metal poor red halo subdwarfs near the hydrogen burning limit are presented. Individual deep CCD high galactic latitude survey fields appear to offer a low probability of discovering field BD's, but taken collectively offer an opportunity to begin addressing questions regarding the scale height and distribution of these objects. In all likelihood, the very depth of such surveys will greatly increase our knowledge of the coolest extreme halo objects, which currently are known in far fewer numbers than T dwarfs. Ultimately, the large volume surveyed by the Large Synoptic Survey Telescope will identify vast numbers of such objects, providing a more complete picture of their spatial distribution.", "published": "2002-08-09T19:57:04Z", "authors": ["P. C. Boeshaar", "V. E. Margoniner", "the Deep Lens Survey Team"], "categories": ["astro-ph"], "doi": "", "_query": "real estate valuation deep learning", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2502.10786v2", "title": "Epidemic-guided deep learning for spatiotemporal forecasting of Tuberculosis outbreak", "abstract": "Tuberculosis (TB) remains a formidable global health challenge, driven by complex spatiotemporal transmission dynamics and influenced by factors such as population mobility and behavioral changes. We propose an Epidemic-Guided Deep Learning (EGDL) approach that fuses mechanistic epidemiological principles with advanced deep learning techniques to enhance early warning systems and intervention strategies for TB outbreaks. Our framework is built upon a modified networked Susceptible-Infectious-Recovered (MN-SIR) model augmented with a saturated incidence rate and graph Laplacian diffusion, capturing both long-term transmission dynamics and region-specific population mobility patterns. Compartmental model parameters are rigorously estimated using Bayesian inference via the Markov Chain Monte Carlo approach. Theoretical analysis leveraging the comparison principle and Green's formula establishes global stability properties of the disease-free and endemic equilibria. Building on these epidemiological insights, we design two forecasting architectures, EGDL-Parallel and EGDL-Series, that integrate the mechanistic outputs of the MN-SIR model within deep neural networks. This integration mitigates the overfitting risks commonly encountered in data-driven methods and filters out noise inherent in surveillance data, resulting in reliable forecasts of real-world epidemic trends. Experiments conducted on TB incidence data from 47 prefectures in Japan and 31 provinces in mainland China demonstrate that our approach delivers robust and accurate predictions across multiple time horizons (short to medium-term forecasts), supporting its generalizability across regions with different population dynamics.", "published": "2025-02-15T12:39:42Z", "authors": ["Madhab Barman", "Madhurima Panja", "Nachiketa Mishra", "Tanujit Chakraborty"], "categories": ["cs.LG", "q-bio.QM", "stat.ML"], "doi": "10.1007/s10994-025-06848-4", "_query": "real estate valuation deep learning", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2603.13597v1", "title": "DQ-Ladder: A Deep Reinforcement Learning-based Bitrate Ladder for Adaptive Video Streaming", "abstract": "Adaptive streaming of segmented video over HTTP typically relies on a predefined set of bitrate-resolution pairs, known as a bitrate ladder. However, fixed ladders often overlook variations in content and decoding complexities, leading to suboptimal trade-offs between encoding time, decoding efficiency, and video quality. This article introduces DQ-Ladder, a deep reinforcement learning (DRL)-based scheme for constructing time- and quality-aware bitrate ladders for adaptive video streaming applications. DQ-Ladder employs predicted decoding time, quality scores, and bitrate levels per segment as inputs to a Deep Q-Network (DQN) agent, guided by a weighted reward function of decoding time, video quality, and resolution smoothness. We leverage machine learning models to predict decoding time, bitrate level, and objective quality metrics (VMAF, XPSNR), eliminating the need for exhaustive encoding or quality metric computation. We evaluate DQ-Ladder using the Versatile Video Coding (VVC) toolchain (VVenC/VVdeC) on 750 video sequences across six Apple HLS-compliant resolutions and 41 quantization parameters. Experimental results against four baselines show that DQ-Ladder achieves BD-rate reductions of at least 10.3% for XPSNR compared to the HLS ladder, while reducing decoding time by 22%. DQ-Ladder shows significantly lower sensitivity to prediction errors than competing methods, remaining robust even with up to 20% noise.", "published": "2026-03-13T21:13:00Z", "authors": ["Reza Farahani", "Zoha Azimi", "Vignesh V Menon", "Hermann Hellwagner", "Radu Prodan", "Schahram Dustdar", "Christian Timmerer"], "categories": ["eess.IV", "cs.MM"], "doi": "", "_query": "real estate valuation deep learning", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2609.24117v1", "title": "PAC-Bayesian Meta-Learning for Few-Shot Identification of Linear Dynamical Systems", "abstract": "Identifying linear time-invariant (LTI) dynamical systems is challenging when trajectories are short, noisy, or high-dimensional. Traditional system identification typically treats each system independently and cannot exploit shared structure across related systems. We propose PBML-LTI, a PAC-Bayesian meta-learning framework for few-shot LTI system identification that learns a transferable prior over task-specific dynamics while preserving task heterogeneity. Each task corresponds to an unknown LTI system, and the meta-learner uses training trajectories to learn a data-dependent prior over transition matrices. For a new system with limited data, PBML-LTI performs Bayesian adaptation under this prior to obtain a task-specific posterior, providing accurate estimates and principled uncertainty quantification. A key challenge is temporal dependence, since LTI trajectories violate the i.i.d. assumptions underlying most PAC-Bayes meta-learning analyses. We address this with a martingale PAC-Bayes analysis for dependent trajectory losses and derive a support-query predictive-risk bound that motivates a fit-KL meta-training objective. The bound clarifies the roles of empirical fit, posterior complexity, and prior quality in few-shot adaptation under sequential dependence. We further derive corollaries for transition-matrix recovery and multi-step trajectory prediction, connecting uncertainty-aware meta-identification with finite-sample guarantees for dependent dynamical data.", "published": "2026-09-21T05:12:58Z", "authors": ["Chenfeng Huang", "George Michailidis"], "categories": ["cs.LG"], "doi": "", "_query": "real estate valuation deep learning", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2106.11779v1", "title": "Emphatic Algorithms for Deep Reinforcement Learning", "abstract": "Off-policy learning allows us to learn about possible policies of behavior from experience generated by a different behavior policy. Temporal difference (TD) learning algorithms can become unstable when combined with function approximation and off-policy sampling - this is known as the ''deadly triad''. Emphatic temporal difference (ETD($λ$)) algorithm ensures convergence in the linear case by appropriately weighting the TD($λ$) updates. In this paper, we extend the use of emphatic methods to deep reinforcement learning agents. We show that naively adapting ETD($λ$) to popular deep reinforcement learning algorithms, which use forward view multi-step returns, results in poor performance. We then derive new emphatic algorithms for use in the context of such algorithms, and we demonstrate that they provide noticeable benefits in small problems designed to highlight the instability of TD methods. Finally, we observed improved performance when applying these algorithms at scale on classic Atari games from the Arcade Learning Environment.", "published": "2021-06-21T12:11:39Z", "authors": ["Ray Jiang", "Tom Zahavy", "Zhongwen Xu", "Adam White", "Matteo Hessel", "Charles Blundell", "Hado van Hasselt"], "categories": ["cs.LG", "stat.ML"], "doi": "", "_query": "real estate valuation deep learning", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2001.03350v2", "title": "Two Applications of Deep Learning in the Physical Layer of Communication Systems", "abstract": "Deep learning has proved itself to be a powerful tool to develop data-driven signal processing algorithms for challenging engineering problems. By learning the key features and characteristics of the input signals, instead of requiring a human to first identify and model them, learned algorithms can beat many man-made algorithms. In particular, deep neural networks are capable of learning the complicated features in nature-made signals, such as photos and audio recordings, and use them for classification and decision making. The situation is rather different in communication systems, where the information signals are man-made, the propagation channels are relatively easy to model, and we know how to operate close to the Shannon capacity limits. Does this mean that there is no role for deep learning in the development of future communication systems?", "published": "2020-01-10T08:52:34Z", "authors": ["Emil Björnson", "Pontus Giselsson"], "categories": ["cs.IT", "cs.LG"], "doi": "10.1109/MSP.2020.2996545", "_query": "real estate valuation deep learning", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2001.04782v1", "title": "Towards detection and classification of microscopic foraminifera using transfer learning", "abstract": "Foraminifera are single-celled marine organisms, which may have a planktic or benthic lifestyle. During their life cycle they construct shells consisting of one or more chambers, and these shells remain as fossils in marine sediments. Classifying and counting these fossils have become an important tool in e.g. oceanography and climatology. Currently the process of identifying and counting microfossils is performed manually using a microscope and is very time consuming. Developing methods to automate this process is therefore considered important across a range of research fields. The first steps towards developing a deep learning model that can detect and classify microscopic foraminifera are proposed. The proposed model is based on a VGG16 model that has been pretrained on the ImageNet dataset, and adapted to the foraminifera task using transfer learning. Additionally, a novel image dataset consisting of microscopic foraminifera and sediments from the Barents Sea region is introduced.", "published": "2020-01-14T13:57:08Z", "authors": ["Thomas Haugland Johansen", "Steffen Aagaard Sørensen"], "categories": ["cs.LG", "cs.CV", "stat.ML"], "doi": "", "_query": "real estate valuation deep learning", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/1806.07851v2", "title": "Sim-to-Real Reinforcement Learning for Deformable Object Manipulation", "abstract": "We have seen much recent progress in rigid object manipulation, but interaction with deformable objects has notably lagged behind. Due to the large configuration space of deformable objects, solutions using traditional modelling approaches require significant engineering work. Perhaps then, bypassing the need for explicit modelling and instead learning the control in an end-to-end manner serves as a better approach? Despite the growing interest in the use of end-to-end robot learning approaches, only a small amount of work has focused on their applicability to deformable object manipulation. Moreover, due to the large amount of data needed to learn these end-to-end solutions, an emerging trend is to learn control policies in simulation and then transfer them over to the real world. To-date, no work has explored whether it is possible to learn and transfer deformable object policies. We believe that if sim-to-real methods are to be employed further, then it should be possible to learn to interact with a wide variety of objects, and not only rigid objects. In this work, we use a combination of state-of-the-art deep reinforcement learning algorithms to solve the problem of manipulating deformable objects (specifically cloth). We evaluate our approach on three tasks --- folding a towel up to a mark, folding a face towel diagonally, and draping a piece of cloth over a hanger. Our agents are fully trained in simulation with domain randomisation, and then successfully deployed in the real world without having seen any real deformable objects.", "published": "2018-06-20T17:22:12Z", "authors": ["Jan Matas", "Stephen James", "Andrew J. Davison"], "categories": ["cs.RO", "cs.AI", "cs.LG"], "doi": "", "_query": "real estate valuation deep learning", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/1409.5165v1", "title": "A Method for Stopping Active Learning Based on Stabilizing Predictions and the Need for User-Adjustable Stopping", "abstract": "A survey of existing methods for stopping active learning (AL) reveals the needs for methods that are: more widely applicable; more aggressive in saving annotations; and more stable across changing datasets. A new method for stopping AL based on stabilizing predictions is presented that addresses these needs. Furthermore, stopping methods are required to handle a broad range of different annotation/performance tradeoff valuations. Despite this, the existing body of work is dominated by conservative methods with little (if any) attention paid to providing users with control over the behavior of stopping methods. The proposed method is shown to fill a gap in the level of aggressiveness available for stopping AL and supports providing users with control over stopping behavior.", "published": "2014-09-17T23:28:59Z", "authors": ["Michael Bloodgood", "K. Vijay-Shanker"], "categories": ["cs.LG", "cs.CL", "stat.ML"], "doi": "", "_query": "real estate valuation deep learning", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2004.01613v2", "title": "Deep Learning for Image Search and Retrieval in Large Remote Sensing Archives", "abstract": "This chapter presents recent advances in content based image search and retrieval (CBIR) systems in remote sensing (RS) for fast and accurate information discovery from massive data archives. Initially, we analyze the limitations of the traditional CBIR systems that rely on the hand-crafted RS image descriptors. Then, we focus our attention on the advances in RS CBIR systems for which deep learning (DL) models are at the forefront. In particular, we present the theoretical properties of the most recent DL based CBIR systems for the characterization of the complex semantic content of RS images. After discussing their strengths and limitations, we present the deep hashing based CBIR systems that have high time-efficient search capability within huge data archives. Finally, the most promising research directions in RS CBIR are discussed.", "published": "2020-04-03T15:03:41Z", "authors": ["Gencer Sumbul", "Jian Kang", "Begüm Demir"], "categories": ["cs.CV"], "doi": "", "_query": "real estate valuation deep learning", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/1806.07377v6", "title": "Transfer Learning for Related Reinforcement Learning Tasks via Image-to-Image Translation", "abstract": "Despite the remarkable success of Deep RL in learning control policies from raw pixels, the resulting models do not generalize. We demonstrate that a trained agent fails completely when facing small visual changes, and that fine-tuning---the common transfer learning paradigm---fails to adapt to these changes, to the extent that it is faster to re-train the model from scratch. We show that by separating the visual transfer task from the control policy we achieve substantially better sample efficiency and transfer behavior, allowing an agent trained on the source task to transfer well to the target tasks. The visual mapping from the target to the source domain is performed using unaligned GANs, resulting in a control policy that can be further improved using imitation learning from imperfect demonstrations. We demonstrate the approach on synthetic visual variants of the Breakout game, as well as on transfer between subsequent levels of Road Fighter, a Nintendo car-driving game. A visualization of our approach can be seen in https://youtu.be/4mnkzYyXMn4 and https://youtu.be/KCGTrQi6Ogo .", "published": "2018-05-31T09:25:29Z", "authors": ["Shani Gamrian", "Yoav Goldberg"], "categories": ["cs.CV", "cs.AI", "cs.LG"], "doi": "", "_query": "real estate valuation deep learning", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2604.27562v1", "title": "Online semi-supervised perception: Real-time learning without explicit feedback", "abstract": "This paper proposes an algorithm for real-time learning without explicit feedback. The algorithm combines the ideas of semi-supervised learning on graphs and online learning. In particular, it iteratively builds a graphical representation of its world and updates it with observed examples. Labeled examples constitute the initial bias of the algorithm and are provided offline, and a stream of unlabeled examples is collected online to update this bias. We motivate the algorithm, discuss how to implement it efficiently, prove a regret bound on the quality of its solutions, and apply it to the problem of real-time face recognition. Our recognizer runs in real time, and achieves superior precision and recall on 3 challenging video datasets.", "published": "2026-04-30T08:14:13Z", "authors": ["Branislav Kveton", "Michal Valko", "Matthai Phillipose", "Ling Huang"], "categories": ["cs.LG", "stat.ML"], "doi": "10.1109/CVPRW.2010.5543877", "_query": "real estate valuation deep learning", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2304.02381v2", "title": "Physics-Inspired Interpretability Of Machine Learning Models", "abstract": "The ability to explain decisions made by machine learning models remains one of the most significant hurdles towards widespread adoption of AI in highly sensitive areas such as medicine, cybersecurity or autonomous driving. Great interest exists in understanding which features of the input data prompt model decision making. In this contribution, we propose a novel approach to identify relevant features of the input data, inspired by methods from the energy landscapes field, developed in the physical sciences. By identifying conserved weights within groups of minima of the loss landscapes, we can identify the drivers of model decision making. Analogues to this idea exist in the molecular sciences, where coordinate invariants or order parameters are employed to identify critical features of a molecule. However, no such approach exists for machine learning loss landscapes. We will demonstrate the applicability of energy landscape methods to machine learning models and give examples, both synthetic and from the real world, for how these methods can help to make models more interpretable.", "published": "2023-04-05T11:35:17Z", "authors": ["Maximilian P Niroomand", "David J Wales"], "categories": ["cs.LG", "cs.AI"], "doi": "", "_query": "real estate valuation deep learning", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2011.08413v2", "title": "Quantifying Sources of Uncertainty in Deep Learning-Based Image Reconstruction", "abstract": "Image reconstruction methods based on deep neural networks have shown outstanding performance, equalling or exceeding the state-of-the-art results of conventional approaches, but often do not provide uncertainty information about the reconstruction. In this work we propose a scalable and efficient framework to simultaneously quantify aleatoric and epistemic uncertainties in learned iterative image reconstruction. We build on a Bayesian deep gradient descent method for quantifying epistemic uncertainty, and incorporate the heteroscedastic variance of the noise to account for the aleatoric uncertainty. We show that our method exhibits competitive performance against conventional benchmarks for computed tomography with both sparse view and limited angle data. The estimated uncertainty captures the variability in the reconstructions, caused by the restricted measurement model, and by missing information, due to the limited angle geometry.", "published": "2020-11-17T04:12:52Z", "authors": ["Riccardo Barbano", "Željko Kereta", "Chen Zhang", "Andreas Hauptmann", "Simon Arridge", "Bangti Jin"], "categories": ["cs.CV"], "doi": "", "_query": "real estate valuation deep learning", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2603.00579v1", "title": "DeepAFL: Deep Analytic Federated Learning", "abstract": "Federated Learning (FL) is a popular distributed learning paradigm to break down data silo. Traditional FL approaches largely rely on gradient-based updates, facing significant issues about heterogeneity, scalability, convergence, and overhead, etc. Recently, some analytic-learning-based work has attempted to handle these issues by eliminating gradient-based updates via analytical (i.e., closed-form) solutions. Despite achieving superior invariance to data heterogeneity, these approaches are fundamentally limited by their single-layer linear model with a frozen pre-trained backbone. As a result, they can only achieve suboptimal performance due to their lack of representation learning capabilities. In this paper, to enable representable analytic models while preserving the ideal invariance to data heterogeneity for FL, we propose our Deep Analytic Federated Learning approach, named DeepAFL. Drawing inspiration from the great success of ResNet in gradient-based learning, we design gradient-free residual blocks in our DeepAFL with analytical solutions. We introduce an efficient layer-wise protocol for training our deep analytic models layer by layer in FL through least squares. Both theoretical analyses and empirical evaluations validate our DeepAFL's superior performance with its dual advantages in heterogeneity invariance and representation learning, outperforming state-of-the-art baselines by up to 5.68%-8.42% across three benchmark datasets.", "published": "2026-02-28T09:58:18Z", "authors": ["Jianheng Tang", "Yajiang Huang", "Kejia Fan", "Feijiang Han", "Jiaxu Li", "Jinfeng Xu", "Run He", "Anfeng Liu", "Houbing Herbert Song", "Huiping Zhuang", "Yunhuai Liu"], "categories": ["cs.LG", "cs.AI"], "doi": "", "_query": "real estate valuation deep learning", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/1810.12176v2", "title": "Semi-unsupervised Learning of Human Activity using Deep Generative Models", "abstract": "We introduce 'semi-unsupervised learning', a problem regime related to transfer learning and zero-shot learning where, in the training data, some classes are sparsely labelled and others entirely unlabelled. Models able to learn from training data of this type are potentially of great use as many real-world datasets are like this. Here we demonstrate a new deep generative model for classification in this regime. Our model, a Gaussian mixture deep generative model, demonstrates superior semi-unsupervised classification performance on MNIST to model M2 from Kingma and Welling (2014). We apply the model to human accelerometer data, performing activity classification and structure discovery on windows of time series data.", "published": "2018-10-29T15:06:43Z", "authors": ["Matthew Willetts", "Aiden Doherty", "Stephen Roberts", "Chris Holmes"], "categories": ["stat.ML", "cs.LG"], "doi": "", "_query": "real estate valuation deep learning", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/1909.01520v3", "title": "Lifelong Machine Learning with Deep Streaming Linear Discriminant Analysis", "abstract": "When an agent acquires new information, ideally it would immediately be capable of using that information to understand its environment. This is not possible using conventional deep neural networks, which suffer from catastrophic forgetting when they are incrementally updated, with new knowledge overwriting established representations. A variety of approaches have been developed that attempt to mitigate catastrophic forgetting in the incremental batch learning scenario, where a model learns from a series of large collections of labeled samples. However, in this setting, inference is only possible after a batch has been accumulated, which prohibits many applications. An alternative paradigm is online learning in a single pass through the training dataset on a resource constrained budget, which is known as streaming learning. Streaming learning has been much less studied in the deep learning community. In streaming learning, an agent learns instances one-by-one and can be tested at any time, rather than only after learning a large batch. Here, we revisit streaming linear discriminant analysis, which has been widely used in the data mining research community. By combining streaming linear discriminant analysis with deep learning, we are able to outperform both incremental batch learning and streaming learning algorithms on both ImageNet ILSVRC-2012 and CORe50, a dataset that involves learning to classify from temporally ordered samples.", "published": "2019-09-04T02:13:22Z", "authors": ["Tyler L. Hayes", "Christopher Kanan"], "categories": ["cs.LG", "cs.CV", "stat.ML"], "doi": "", "_query": "real estate valuation deep learning", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/1512.01693v1", "title": "Deep Attention Recurrent Q-Network", "abstract": "A deep learning approach to reinforcement learning led to a general learner able to train on visual input to play a variety of arcade games at the human and superhuman levels. Its creators at the Google DeepMind's team called the approach: Deep Q-Network (DQN). We present an extension of DQN by \"soft\" and \"hard\" attention mechanisms. Tests of the proposed Deep Attention Recurrent Q-Network (DARQN) algorithm on multiple Atari 2600 games show level of performance superior to that of DQN. Moreover, built-in attention mechanisms allow a direct online monitoring of the training process by highlighting the regions of the game screen the agent is focusing on when making decisions.", "published": "2015-12-05T18:35:40Z", "authors": ["Ivan Sorokin", "Alexey Seleznev", "Mikhail Pavlov", "Aleksandr Fedorov", "Anastasiia Ignateva"], "categories": ["cs.LG"], "doi": "", "_query": "real estate valuation deep learning", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2007.06229v1", "title": "Deep Claim: Payer Response Prediction from Claims Data with Deep Learning", "abstract": "Each year, almost 10% of claims are denied by payers (i.e., health insurance plans). With the cost to recover these denials and underpayments, predicting payer response (likelihood of payment) from claims data with a high degree of accuracy and precision is anticipated to improve healthcare staffs' performance productivity and drive better patient financial experience and satisfaction in the revenue cycle (Barkholz, 2017). However, constructing advanced predictive analytics models has been considered challenging in the last twenty years. That said, we propose a (low-level) context-dependent compact representation of patients' historical claim records by effectively learning complicated dependencies in the (high-level) claim inputs. Built on this new latent representation, we demonstrate that a deep learning-based framework, Deep Claim, can accurately predict various responses from multiple payers using 2,905,026 de-identified claims data from two US health systems. Deep Claim's improvements over carefully chosen baselines in predicting claim denials are most pronounced as 22.21% relative recall gain (at 95% precision) on Health System A, which implies Deep Claim can find 22.21% more denials than the best baseline system.", "published": "2020-07-13T08:05:17Z", "authors": ["Byung-Hak Kim", "Seshadri Sridharan", "Andy Atwal", "Varun Ganapathi"], "categories": ["cs.LG", "cs.CY", "stat.ML"], "doi": "", "_query": "real estate valuation deep learning", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2111.04736v1", "title": "Multi-Modality Cardiac Image Analysis with Deep Learning", "abstract": "Accurate cardiac computing, analysis and modeling from multi-modality images are important for the diagnosis and treatment of cardiac disease. Late gadolinium enhancement magnetic resonance imaging (LGE MRI) is a promising technique to visualize and quantify myocardial infarction (MI) and atrial scars. Automating quantification of MI and atrial scars can be challenging due to the low image quality and complex enhancement patterns of LGE MRI. Moreover, compared with the other sequences LGE MRIs with gold standard labels are particularly limited, which represents another obstacle for developing novel algorithms for automatic segmentation and quantification of LGE MRIs. This chapter aims to summarize the state-of-the-art and our recent advanced contributions on deep learning based multi-modality cardiac image analysis. Firstly, we introduce two benchmark works for multi-sequence cardiac MRI based myocardial and pathology segmentation. Secondly, two novel frameworks for left atrial scar segmentation and quantification from LGE MRI were presented. Thirdly, we present three unsupervised domain adaptation techniques for cross-modality cardiac image segmentation.", "published": "2021-11-08T12:54:11Z", "authors": ["Lei Li", "Fuping Wu", "Sihang Wang", "Xiahai Zhuang"], "categories": ["eess.IV", "cs.CV"], "doi": "", "_query": "real estate valuation deep learning", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/1810.13243v1", "title": "A Closer Look at Deep Learning Heuristics: Learning rate restarts, Warmup and Distillation", "abstract": "The convergence rate and final performance of common deep learning models have significantly benefited from heuristics such as learning rate schedules, knowledge distillation, skip connections, and normalization layers. In the absence of theoretical underpinnings, controlled experiments aimed at explaining these strategies can aid our understanding of deep learning landscapes and the training dynamics. Existing approaches for empirical analysis rely on tools of linear interpolation and visualizations with dimensionality reduction, each with their limitations. Instead, we revisit such analysis of heuristics through the lens of recently proposed methods for loss surface and representation analysis, viz., mode connectivity and canonical correlation analysis (CCA), and hypothesize reasons for the success of the heuristics. In particular, we explore knowledge distillation and learning rate heuristics of (cosine) restarts and warmup using mode connectivity and CCA. Our empirical analysis suggests that: (a) the reasons often quoted for the success of cosine annealing are not evidenced in practice; (b) that the effect of learning rate warmup is to prevent the deeper layers from creating training instability; and (c) that the latent knowledge shared by the teacher is primarily disbursed to the deeper layers.", "published": "2018-10-29T19:36:07Z", "authors": ["Akhilesh Gotmare", "Nitish Shirish Keskar", "Caiming Xiong", "Richard Socher"], "categories": ["cs.LG", "stat.ML"], "doi": "", "_query": "real estate valuation deep learning", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/1811.10111v2", "title": "Real-Time Sleep Staging using Deep Learning on a Smartphone for a Wearable EEG", "abstract": "We present the first real-time sleep staging system that uses deep learning without the need for servers in a smartphone application for a wearable EEG. We employ real-time adaptation of a single channel Electroencephalography (EEG) to infer from a Time-Distributed 1-D Deep Convolutional Neural Network. Polysomnography (PSG)-the gold standard for sleep staging, requires a human scorer and is both complex and resource-intensive. Our work demonstrates an end-to-end on-smartphone pipeline that can infer sleep stages in just single 30-second epochs, with an overall accuracy of 83.5% on 20-fold cross validation for five-class classification of sleep stages using the open Sleep-EDF dataset.", "published": "2018-11-25T22:25:31Z", "authors": ["Abhay Koushik", "Judith Amores", "Pattie Maes"], "categories": ["cs.HC", "cs.LG", "eess.SP", "q-bio.NC"], "doi": "", "_query": "real estate valuation deep learning", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/1108.5669v2", "title": "Learning Valuation Functions", "abstract": "In this paper we study the approximate learnability of valuations commonly used throughout economics and game theory for the quantitative encoding of agent preferences. We provide upper and lower bounds regarding the learnability of important subclasses of valuation functions that express no-complementarities. Our main results concern their approximate learnability in the distributional learning (PAC-style) setting. We provide nearly tight lower and upper bounds of $\\tildeΘ(n^{1/2})$ on the approximation factor for learning XOS and subadditive valuations, both widely studied superclasses of submodular valuations. Interestingly, we show that the $\\tildeΩ(n^{1/2})$ lower bound can be circumvented for XOS functions of polynomial complexity; we provide an algorithm for learning the class of XOS valuations with a representation of polynomial size achieving an $O(n^{\\eps})$ approximation factor in time $O(n^{1/\\eps})$ for any $\\eps > 0$. This highlights the importance of considering the complexity of the target function for polynomial time learning. We also provide new learning results for interesting subclasses of submodular functions. Our upper bounds for distributional learning leverage novel structural results for all these valuation classes. We show that many of these results provide new learnability results in the Goemans et al. model (SODA 2009) of approximate learning everywhere via value queries. We also introduce a new model that is more realistic in economic settings, in which the learner can set prices and observe purchase decisions at these prices rather than observing the valuation function directly. In this model, most of our upper bounds continue to hold despite the fact that the learner receives less information (both for learning in the distributional setting and with value queries), while our lower bounds naturally extend.", "published": "2011-08-29T17:48:28Z", "authors": ["Maria Florina Balcan", "Florin Constantin", "Satoru Iwata", "Lei Wang"], "categories": ["cs.GT", "cs.DS", "stat.ML"], "doi": "", "_query": "real estate valuation deep learning", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2202.05146v4", "title": "EquiBind: Geometric Deep Learning for Drug Binding Structure Prediction", "abstract": "Predicting how a drug-like molecule binds to a specific protein target is a core problem in drug discovery. An extremely fast computational binding method would enable key applications such as fast virtual screening or drug engineering. Existing methods are computationally expensive as they rely on heavy candidate sampling coupled with scoring, ranking, and fine-tuning steps. We challenge this paradigm with EquiBind, an SE(3)-equivariant geometric deep learning model performing direct-shot prediction of both i) the receptor binding location (blind docking) and ii) the ligand's bound pose and orientation. EquiBind achieves significant speed-ups and better quality compared to traditional and recent baselines. Further, we show extra improvements when coupling it with existing fine-tuning techniques at the cost of increased running time. Finally, we propose a novel and fast fine-tuning model that adjusts torsion angles of a ligand's rotatable bonds based on closed-form global minima of the von Mises angular distance to a given input atomic point cloud, avoiding previous expensive differential evolution strategies for energy minimization.", "published": "2022-02-07T16:26:05Z", "authors": ["Hannes Stärk", "Octavian-Eugen Ganea", "Lagnajit Pattanaik", "Regina Barzilay", "Tommi Jaakkola"], "categories": ["q-bio.BM", "cs.LG"], "doi": "", "_query": "real estate valuation deep learning", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2412.20724v2", "title": "Soft Diamond Regularizers for Deep Learning", "abstract": "This chapter presents the new family of soft diamond synaptic regularizers based on thick-tailed symmetric alpha stable $SαS$ probability bell curves. These new parametrized weight priors improved deep-learning performance on image and language-translation test sets and increased the sparsity of the trained weights. They outperformed the state-of-the-art hard-diamond Laplacian regularizer of sparse lasso regression and classification. The $SαS$ synaptic weight priors have power-law bell-curve tails that are thicker than the thin exponential tails of Gaussian bell curves that underly ridge regularizers. Their tails get thicker as the $α$ parameter decreases. These thicker tails model more impulsive behavior and allow for occasional distant search in synaptic weight spaces of extremely high dimension. The geometry of their constraint sets has a diamond shape. The shape varies from a circle to a star or diamond that depends on the $α$ tail thickness and dispersion of the $SαS$ weight prior. These $SαS$ bell curves lack a closed form in general and this makes direct training computationally intensive. We removed this computational bottleneck by using a precomputed look-up table. We tested the soft diamond regularizers with deep neural classifiers on both image test sets and German-to-English language translation. The image simulations used the three datasets CIFAR-10, CIFAR-100, and Caltech-256. The regularizers improved the accuracy and sparsity of the classifiers. We also tested with deep neural machine-translation models on the IWSLT-2016 Evaluation dataset for German-to-English text translation. They also outperformed ridge regularizers and lasso regularizers. These findings recommend the sub-Cauchy $α= 0.5$ soft diamond regularizer as a competitive and sparse regularizer for large-scale machine learning.", "published": "2024-12-30T05:53:17Z", "authors": ["Olaoluwa Adigun", "Bart Kosko"], "categories": ["stat.ML", "cs.LG"], "doi": "", "_query": "real estate valuation deep learning", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2306.02216v3", "title": "Forgettable Federated Linear Learning with Certified Data Unlearning", "abstract": "Federated Learning (FL) enables collaborative model training across distributed clients while preserving user privacy. Recently, Federated Unlearning (FU) has emerged to address the \"right to be forgotten\" and to remove the influence of poisoned or target clients without retraining the entire FL system. However, many FU methods require communication with retained or target clients, introduce additional security risks, or store historical models, limiting their efficiency and practicality. Moreover, most FU methods for deep neural networks (DNNs) lack theoretical certification due to the complexity of nonlinear models and their training dynamics. In this work, we introduce Forgettable Federated Linear Learning, a training and unlearning framework for DNNs. Our approach uses pre-trained models to linearly approximate DNNs and achieve performance comparable to the original networks through Federated Linear Training. We further present a certified, efficient, and secure unlearning strategy that enables the server to remove a target client's influence without additional client communication or storage. Extensive experiments on small- to large-scale datasets, using both convolutional neural networks and modern foundation models, show that our method balances model accuracy with effective target-client unlearning. This work provides a practical pipeline for efficient and trustworthy FU. Code: https://github.com/Nanboy-Ronan/2F2L-Federated-Unlearning", "published": "2023-06-03T23:53:57Z", "authors": ["Ruinan Jin", "Minghui Chen", "Qiong Zhang", "Xiaoxiao Li"], "categories": ["cs.LG", "cs.CV"], "doi": "10.1109/TNNLS.2026.3683398", "_query": "real estate valuation deep learning", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2007.06081v1", "title": "VAFL: a Method of Vertical Asynchronous Federated Learning", "abstract": "Horizontal Federated learning (FL) handles multi-client data that share the same set of features, and vertical FL trains a better predictor that combine all the features from different clients. This paper targets solving vertical FL in an asynchronous fashion, and develops a simple FL method. The new method allows each client to run stochastic gradient algorithms without coordination with other clients, so it is suitable for intermittent connectivity of clients. This method further uses a new technique of perturbed local embedding to ensure data privacy and improve communication efficiency. Theoretically, we present the convergence rate and privacy level of our method for strongly convex, nonconvex and even nonsmooth objectives separately. Empirically, we apply our method to FL on various image and healthcare datasets. The results compare favorably to centralized and synchronous FL methods.", "published": "2020-07-12T20:09:25Z", "authors": ["Tianyi Chen", "Xiao Jin", "Yuejiao Sun", "Wotao Yin"], "categories": ["cs.LG", "cs.DC", "math.OC", "stat.ML"], "doi": "", "_query": "real estate valuation deep learning", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2411.09722v1", "title": "Iterative Batch Reinforcement Learning via Safe Diversified Model-based Policy Search", "abstract": "Batch reinforcement learning enables policy learning without direct interaction with the environment during training, relying exclusively on previously collected sets of interactions. This approach is, therefore, well-suited for high-risk and cost-intensive applications, such as industrial control. Learned policies are commonly restricted to act in a similar fashion as observed in the batch. In a real-world scenario, learned policies are deployed in the industrial system, inevitably leading to the collection of new data that can subsequently be added to the existing recording. The process of learning and deployment can thus take place multiple times throughout the lifespan of a system. In this work, we propose to exploit this iterative nature of applying offline reinforcement learning to guide learned policies towards efficient and informative data collection during deployment, leading to continuous improvement of learned policies while remaining within the support of collected data. We present an algorithmic methodology for iterative batch reinforcement learning based on ensemble-based model-based policy search, augmented with safety and, importantly, a diversity criterion.", "published": "2024-11-14T11:10:36Z", "authors": ["Amna Najib", "Stefan Depeweg", "Phillip Swazinna"], "categories": ["cs.LG", "cs.AI", "stat.ML"], "doi": "", "_query": "real estate valuation deep learning", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/1112.1937v1", "title": "Bootstrapping Intrinsically Motivated Learning with Human Demonstrations", "abstract": "This paper studies the coupling of internally guided learning and social interaction, and more specifically the improvement owing to demonstrations of the learning by intrinsic motivation. We present Socially Guided Intrinsic Motivation by Demonstration (SGIM-D), an algorithm for learning in continuous, unbounded and non-preset environments. After introducing social learning and intrinsic motivation, we describe the design of our algorithm, before showing through a fishing experiment that SGIM-D efficiently combines the advantages of social learning and intrinsic motivation to gain a wide repertoire while being specialised in specific subspaces.", "published": "2011-12-08T20:27:31Z", "authors": ["Sao Mai Nguyen", "Adrien Baranes", "Pierre-Yves Oudeyer"], "categories": ["cs.LG", "cs.AI", "cs.RO"], "doi": "10.1109/DEVLRN.2011.6037329", "_query": "real estate valuation deep learning", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2110.04830v2", "title": "MARVEL: Raster Manga Vectorization via Primitive-wise Deep Reinforcement Learning", "abstract": "Manga is a fashionable Japanese-style comic form that is composed of black-and-white strokes and is generally displayed as raster images on digital devices. Typical mangas have simple textures, wide lines, and few color gradients, which are vectorizable natures to enjoy the merits of vector graphics, e.g., adaptive resolutions and small file sizes. In this paper, we propose MARVEL (MAnga's Raster to VEctor Learning), a primitive-wise approach for vectorizing raster mangas by Deep Reinforcement Learning (DRL). Unlike previous learning-based methods which predict vector parameters for an entire image, MARVEL introduces a new perspective that regards an entire manga as a collection of basic primitives\\textemdash stroke lines, and designs a DRL model to decompose the target image into a primitive sequence for achieving accurate vectorization. To improve vectorization accuracies and decrease file sizes, we further propose a stroke accuracy reward to predict accurate stroke lines, and a pruning mechanism to avoid generating erroneous and repeated strokes. Extensive subjective and objective experiments show that our MARVEL can generate impressive results and reaches the state-of-the-art level. Our code is open-source at: https://github.com/SwordHolderSH/Mang2Vec.", "published": "2021-10-10T15:52:38Z", "authors": ["Hao Su", "Jianwei Niu", "Xuefeng Liu", "Jiahe Cui", "Ji Wan"], "categories": ["cs.CV"], "doi": "", "_query": "real estate valuation deep learning", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/1901.03887v3", "title": "Improving Coordination in Small-Scale Multi-Agent Deep Reinforcement Learning through Memory-driven Communication", "abstract": "Deep reinforcement learning algorithms have recently been used to train multiple interacting agents in a centralised manner whilst keeping their execution decentralised. When the agents can only acquire partial observations and are faced with tasks requiring coordination and synchronisation skills, inter-agent communication plays an essential role. In this work, we propose a framework for multi-agent training using deep deterministic policy gradients that enables concurrent, end-to-end learning of an explicit communication protocol through a memory device. During training, the agents learn to perform read and write operations enabling them to infer a shared representation of the world. We empirically demonstrate that concurrent learning of the communication device and individual policies can improve inter-agent coordination and performance in small-scale systems. Our experimental results show that the proposed method achieves superior performance in scenarios with up to six agents. We illustrate how different communication patterns can emerge on six different tasks of increasing complexity. Furthermore, we study the effects of corrupting the communication channel, provide a visualisation of the time-varying memory content as the underlying task is being solved and validate the building blocks of the proposed memory device through ablation studies.", "published": "2019-01-12T18:12:15Z", "authors": ["Emanuele Pesce", "Giovanni Montana"], "categories": ["cs.LG", "cs.AI", "cs.MA", "stat.ML"], "doi": "10.1007/s10994-019-05864-5", "_query": "real estate valuation deep learning", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2406.14779v1", "title": "Learning to Select Goals in Automated Planning with Deep-Q Learning", "abstract": "In this work we propose a planning and acting architecture endowed with a module which learns to select subgoals with Deep Q-Learning. This allows us to decrease the load of a planner when faced with scenarios with real-time restrictions. We have trained this architecture on a video game environment used as a standard test-bed for intelligent systems applications, testing it on different levels of the same game to evaluate its generalization abilities. We have measured the performance of our approach as more training data is made available, as well as compared it with both a state-of-the-art, classical planner and the standard Deep Q-Learning algorithm. The results obtained show our model performs better than the alternative methods considered, when both plan quality (plan length) and time requirements are taken into account. On the one hand, it is more sample-efficient than standard Deep Q-Learning, and it is able to generalize better across levels. On the other hand, it reduces problem-solving time when compared with a state-of-the-art automated planner, at the expense of obtaining plans with only 9% more actions.", "published": "2024-06-20T23:02:44Z", "authors": ["Carlos Núñez-Molina", "Juan Fernández-Olivares", "Raúl Pérez"], "categories": ["cs.AI"], "doi": "10.1016/j.eswa.2022.117265", "_query": "real estate valuation deep learning", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2604.14519v1", "title": "CI-CBM: Class-Incremental Concept Bottleneck Model for Interpretable Continual Learning", "abstract": "Catastrophic forgetting remains a fundamental challenge in continual learning, in which models often forget previous knowledge when fine-tuned on a new task. This issue is especially pronounced in class incremental learning (CIL), which is the most challenging setting in continual learning. Existing methods to address catastrophic forgetting often sacrifice either model interpretability or accuracy. To address this challenge, we introduce ClassIncremental Concept Bottleneck Model (CI-CBM), which leverage effective techniques, including concept regularization and pseudo-concept generation to maintain interpretable decision processes throughout incremental learning phases. Through extensive evaluation on seven datasets, CI-CBM achieves comparable performance to black-box models and outperforms previous interpretable approaches in CIL, with an average 36% accuracy gain. CICBM provides interpretable decisions on individual inputs and understandable global decision rules, as shown in our experiments, thereby demonstrating that human understandable concepts can be maintained during incremental learning without compromising model performance. Our approach is effective in both pretrained and non-pretrained scenarios; in the latter, the backbone is trained from scratch during the first learning phase. Code is publicly available at github.com/importAmir/CI-CBM.", "published": "2026-04-16T01:20:33Z", "authors": ["Amirhosein Javadi", "Tuomas Oikarinen", "Tara Javidi", "Tsui-Wei Weng"], "categories": ["cs.LG", "cs.CV"], "doi": "", "_query": "real estate valuation deep learning", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2610.16275v1", "title": "A Survey of Flow Matching in Reinforcement Learning", "abstract": "Flow Matching (FM) has recently emerged as a principled and efficient generative modeling framework for reinforcement learning (RL), enabling expressive, multimodal policy parameterizations via deterministic probability transport. Compared to diffusion-based policies that rely on stochastic denoising chains, FM uses sampling based on ordinary differential equations (ODEs), with learned velocity fields, which can substantially reduce inference latency and simplify the incorporation of RL objectives. As research in flow-based RL rapidly accelerates across offline continuous control, online fine-tuning, and foundation model alignment, the literature has become highly fragmented. In this survey, we provide a comprehensive taxonomy of flow-matching approaches in reinforcement learning. We organize the literature along two axes: the target distribution being modeled (e.g., action policies, value critics, transition dynamics) and the mechanism of RL signal integration (e.g., energy-weighted regression, flow-based policy gradients, and group relative policy optimization). Furthermore, we survey emerging frontiers such as discrete and non-Euclidean action spaces, provide a systematic comparative analysis against Gaussian and diffusion baselines, and outline critical open problems. Ultimately, this survey serves as a foundational roadmap for the next generation of generative reinforcement learning and alignment.", "published": "2026-10-12T06:55:17Z", "authors": ["Nabuat Zaman Nahim", "Fairoz Nower Khan", "Peizhong Ju"], "categories": ["cs.LG", "cs.AI"], "doi": "", "_query": "real estate valuation deep learning", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2109.13498v2", "title": "Learning to Superoptimize Real-world Programs", "abstract": "Program optimization is the process of modifying software to execute more efficiently. Superoptimizers attempt to find the optimal program by employing significantly more expensive search and constraint solving techniques. Generally, these methods do not scale well to programs in real development scenarios, and as a result, superoptimization has largely been confined to small-scale, domain-specific, and/or synthetic program benchmarks. In this paper, we propose a framework to learn to superoptimize real-world programs by using neural sequence-to-sequence models. We created a dataset consisting of over 25K real-world x86-64 assembly functions mined from open-source projects and propose an approach, Self Imitation Learning for Optimization (SILO) that is easy to implement and outperforms a standard policy gradient learning approach on our dataset. Our method, SILO, superoptimizes 5.9% of our test set when compared with the gcc version 10.3 compiler's aggressive optimization level -O3. We also report that SILO's rate of superoptimization on our test set is over five times that of a standard policy gradient approach and a model pre-trained on compiler optimization demonstration.", "published": "2021-09-28T05:33:21Z", "authors": ["Alex Shypula", "Pengcheng Yin", "Jeremy Lacomis", "Claire Le Goues", "Edward Schwartz", "Graham Neubig"], "categories": ["cs.LG", "cs.AI", "cs.PL", "cs.SE"], "doi": "", "_query": "real estate valuation deep learning", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/1606.03798v1", "title": "Deep Image Homography Estimation", "abstract": "We present a deep convolutional neural network for estimating the relative homography between a pair of images. Our feed-forward network has 10 layers, takes two stacked grayscale images as input, and produces an 8 degree of freedom homography which can be used to map the pixels from the first image to the second. We present two convolutional neural network architectures for HomographyNet: a regression network which directly estimates the real-valued homography parameters, and a classification network which produces a distribution over quantized homographies. We use a 4-point homography parameterization which maps the four corners from one image into the second image. Our networks are trained in an end-to-end fashion using warped MS-COCO images. Our approach works without the need for separate local feature detection and transformation estimation stages. Our deep models are compared to a traditional homography estimator based on ORB features and we highlight the scenarios where HomographyNet outperforms the traditional technique. We also describe a variety of applications powered by deep homography estimation, thus showcasing the flexibility of a deep learning approach.", "published": "2016-06-13T02:46:38Z", "authors": ["Daniel DeTone", "Tomasz Malisiewicz", "Andrew Rabinovich"], "categories": ["cs.CV"], "doi": "", "_query": "real estate valuation deep learning", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2303.08273v1", "title": "Towards a Deep Learning Pain-Level Detection Deployment at UAE for Patient-Centric-Pain Management and Diagnosis Support: Framework and Performance Evaluation", "abstract": "The outbreak of the COVID-19 pandemic revealed the criticality of timely intervention in a situation exacerbated by a shortage in medical staff and equipment. Pain-level screening is the initial step toward identifying the severity of patient conditions. Automatic recognition of state and feelings help in identifying patient symptoms to take immediate adequate action and providing a patient-centric medical plan tailored to a patient's state. In this paper, we propose a framework for pain-level detection for deployment in the United Arab Emirates and assess its performance using the most used approaches in the literature. Our results show that a deployment of a pain-level deep learning detection framework is promising in identifying the pain level accurately.", "published": "2023-03-14T23:29:20Z", "authors": ["Leila Ismail", "Muhammad Danish Waseem"], "categories": ["cs.HC", "cs.CV", "cs.LG", "q-bio.QM"], "doi": "", "_query": "real estate valuation deep learning", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/1703.01732v1", "title": "Surprise-Based Intrinsic Motivation for Deep Reinforcement Learning", "abstract": "Exploration in complex domains is a key challenge in reinforcement learning, especially for tasks with very sparse rewards. Recent successes in deep reinforcement learning have been achieved mostly using simple heuristic exploration strategies such as $ε$-greedy action selection or Gaussian control noise, but there are many tasks where these methods are insufficient to make any learning progress. Here, we consider more complex heuristics: efficient and scalable exploration strategies that maximize a notion of an agent's surprise about its experiences via intrinsic motivation. We propose to learn a model of the MDP transition probabilities concurrently with the policy, and to form intrinsic rewards that approximate the KL-divergence of the true transition probabilities from the learned model. One of our approximations results in using surprisal as intrinsic motivation, while the other gives the $k$-step learning progress. We show that our incentives enable agents to succeed in a wide range of environments with high-dimensional state spaces and very sparse rewards, including continuous control tasks and games in the Atari RAM domain, outperforming several other heuristic exploration techniques.", "published": "2017-03-06T05:51:42Z", "authors": ["Joshua Achiam", "Shankar Sastry"], "categories": ["cs.LG"], "doi": "", "_query": "real estate valuation deep learning", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/1803.06773v1", "title": "Composable Deep Reinforcement Learning for Robotic Manipulation", "abstract": "Model-free deep reinforcement learning has been shown to exhibit good performance in domains ranging from video games to simulated robotic manipulation and locomotion. However, model-free methods are known to perform poorly when the interaction time with the environment is limited, as is the case for most real-world robotic tasks. In this paper, we study how maximum entropy policies trained using soft Q-learning can be applied to real-world robotic manipulation. The application of this method to real-world manipulation is facilitated by two important features of soft Q-learning. First, soft Q-learning can learn multimodal exploration strategies by learning policies represented by expressive energy-based models. Second, we show that policies learned with soft Q-learning can be composed to create new policies, and that the optimality of the resulting policy can be bounded in terms of the divergence between the composed policies. This compositionality provides an especially valuable tool for real-world manipulation, where constructing new policies by composing existing skills can provide a large gain in efficiency over training from scratch. Our experimental evaluation demonstrates that soft Q-learning is substantially more sample efficient than prior model-free deep reinforcement learning methods, and that compositionality can be performed for both simulated and real-world tasks.", "published": "2018-03-19T01:17:16Z", "authors": ["Tuomas Haarnoja", "Vitchyr Pong", "Aurick Zhou", "Murtaza Dalal", "Pieter Abbeel", "Sergey Levine"], "categories": ["cs.LG", "cs.AI", "cs.RO", "stat.ML"], "doi": "", "_query": "real estate valuation deep learning", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2206.01088v2", "title": "Machine Learning-based Lung and Colon Cancer Detection using Deep Feature Extraction and Ensemble Learning", "abstract": "Cancer is a fatal disease caused by a combination of genetic diseases and a variety of biochemical abnormalities. Lung and colon cancer have emerged as two of the leading causes of death and disability in humans. The histopathological detection of such malignancies is usually the most important component in determining the best course of action. Early detection of the ailment on either front considerably decreases the likelihood of mortality. Machine learning and deep learning techniques can be utilized to speed up such cancer detection, allowing researchers to study a large number of patients in a much shorter amount of time and at a lower cost. In this research work, we introduced a hybrid ensemble feature extraction model to efficiently identify lung and colon cancer. It integrates deep feature extraction and ensemble learning with high-performance filtering for cancer image datasets. The model is evaluated on histopathological (LC25000) lung and colon datasets. According to the study findings, our hybrid model can detect lung, colon, and (lung and colon) cancer with accuracy rates of 99.05%, 100%, and 99.30%, respectively. The study's findings show that our proposed strategy outperforms existing models significantly. Thus, these models could be applicable in clinics to support the doctor in the diagnosis of cancers.", "published": "2022-06-02T15:14:41Z", "authors": ["Md. Alamin Talukder", "Md. Manowarul Islam", "Md Ashraf Uddin", "Arnisha Akhter", "Khondokar Fida Hasan", "Mohammad Ali Moni"], "categories": ["eess.IV", "cs.CV", "cs.LG"], "doi": "", "_query": "real estate valuation deep learning", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2101.06536v6", "title": "Deep Cox Mixtures for Survival Regression", "abstract": "Survival analysis is a challenging variation of regression modeling because of the presence of censoring, where the outcome measurement is only partially known, due to, for example, loss to follow up. Such problems come up frequently in medical applications, making survival analysis a key endeavor in biostatistics and machine learning for healthcare, with Cox regression models being amongst the most commonly employed models. We describe a new approach for survival analysis regression models, based on learning mixtures of Cox regressions to model individual survival distributions. We propose an approximation to the Expectation Maximization algorithm for this model that does hard assignments to mixture groups to make optimization efficient. In each group assignment, we fit the hazard ratios within each group using deep neural networks, and the baseline hazard for each mixture component non-parametrically. We perform experiments on multiple real world datasets, and look at the mortality rates of patients across ethnicity and gender. We emphasize the importance of calibration in healthcare settings and demonstrate that our approach outperforms classical and modern survival analysis baselines, both in terms of discriminative performance and calibration, with large gains in performance on the minority demographics.", "published": "2021-01-16T22:41:22Z", "authors": ["Chirag Nagpal", "Steve Yadlowsky", "Negar Rostamzadeh", "Katherine Heller"], "categories": ["cs.LG", "stat.ME", "stat.ML"], "doi": "", "_query": "real estate valuation deep learning", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2507.06415v3", "title": "PERK: Long-Context Reasoning as Test-Time Learning", "abstract": "Long-context reasoning requires accurately identifying relevant information in extensive, noisy input contexts. In this work, we propose PERK (Parameter Efficient Reasoning over Knowledge), a scalable approach for learning to encode long contexts using gradient updates at test time. Specifically, PERK employs two nested optimization loops in a meta-training phase. The inner loop rapidly encodes contexts into a low-rank adapter (LoRA) that serves as a parameter-efficient memory module for the base model. Concurrently, the outer loop learns to use the updated adapter to accurately recall and reason over relevant information from the encoded long context. Our evaluations on several long-context reasoning tasks show that PERK significantly outperforms the standard long-context finetuning, achieving average absolute performance gains of up to 20% for Qwen-2.5 (0.5B & 7B) on synthetic and real-world long-context reasoning. PERK also maintains its advantages across model scales and families. Compared to specialized long-context LLMs, PERK matches or surpasses their performance. Finally, our analyses show PERK is more robust to reasoning complexity, length extrapolation, and the positions of relevant information in contexts. https://perk-long-context.web.app", "published": "2025-07-08T21:38:45Z", "authors": ["Zeming Chen", "Angelika Romanou", "Gail Weiss", "Antoine Bosselut"], "categories": ["cs.CL", "cs.LG"], "doi": "", "_query": "real estate valuation deep learning", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2501.11836v1", "title": "Data-driven Detection and Evaluation of Damages in Concrete Structures: Using Deep Learning and Computer Vision", "abstract": "Structural integrity is vital for maintaining the safety and longevity of concrete infrastructures such as bridges, tunnels, and walls. Traditional methods for detecting damages like cracks and spalls are labor-intensive, time-consuming, and prone to human error. To address these challenges, this study explores advanced data-driven techniques using deep learning for automated damage detection and analysis. Two state-of-the-art instance segmentation models, YOLO-v7 instance segmentation and Mask R-CNN, were evaluated using a dataset comprising 400 images, augmented to 10,995 images through geometric and color-based transformations to enhance robustness. The models were trained and validated using a dataset split into 90% training set, validation and test set 10%. Performance metrics such as precision, recall, mean average precision (mAP@0.5), and frames per second (FPS) were used for evaluation. YOLO-v7 achieved a superior mAP@0.5 of 96.1% and processed 40 FPS, outperforming Mask R-CNN, which achieved a mAP@0.5 of 92.1% with a slower processing speed of 18 FPS. The findings recommend YOLO-v7 instance segmentation model for real-time, high-speed structural health monitoring, while Mask R-CNN is better suited for detailed offline assessments. This study demonstrates the potential of deep learning to revolutionize infrastructure maintenance, offering a scalable and efficient solution for automated damage detection.", "published": "2025-01-21T02:44:05Z", "authors": ["Saeid Ataei", "Saeed Adibnazari", "Seyyed Taghi Ataei"], "categories": ["cs.CV", "cs.AI", "cs.LG"], "doi": "", "_query": "real estate valuation deep learning", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2106.14708v1", "title": "Weighted multi-level deep learning analysis and framework for processing breast cancer WSIs", "abstract": "Prevention and early diagnosis of breast cancer (BC) is an essential prerequisite for the selection of proper treatment. The substantial pressure due to the increase of demand for faster and more precise diagnostic results drives for automatic solutions. In the past decade, deep learning techniques have demonstrated their power over several domains, and Computer-Aided (CAD) diagnostic became one of them. However, when it comes to the analysis of Whole Slide Images (WSI), most of the existing works compute predictions from levels independently. This is, however, in contrast to the histopathologist expert approach who requires to see a global architecture of tissue structures important in BC classification. We present a deep learning-based solution and framework for processing WSI based on a novel approach utilizing the advantages of image levels. We apply the weighing of information extracted from several levels into the final classification of the malignancy. Our results demonstrate the profitability of global information with an increase of accuracy from 72.2% to 84.8%.", "published": "2021-06-28T13:38:11Z", "authors": ["Peter Bokor", "Lukas Hudec", "Ondrej Fabian", "Wanda Benesova"], "categories": ["eess.IV", "cs.CV", "cs.LG"], "doi": "", "_query": "real estate valuation deep learning", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2011.09607v2", "title": "FinRL: A Deep Reinforcement Learning Library for Automated Stock Trading in Quantitative Finance", "abstract": "As deep reinforcement learning (DRL) has been recognized as an effective approach in quantitative finance, getting hands-on experiences is attractive to beginners. However, to train a practical DRL trading agent that decides where to trade, at what price, and what quantity involves error-prone and arduous development and debugging. In this paper, we introduce a DRL library FinRL that facilitates beginners to expose themselves to quantitative finance and to develop their own stock trading strategies. Along with easily-reproducible tutorials, FinRL library allows users to streamline their own developments and to compare with existing schemes easily. Within FinRL, virtual environments are configured with stock market datasets, trading agents are trained with neural networks, and extensive backtesting is analyzed via trading performance. Moreover, it incorporates important trading constraints such as transaction cost, market liquidity and the investor's degree of risk-aversion. FinRL is featured with completeness, hands-on tutorial and reproducibility that favors beginners: (i) at multiple levels of time granularity, FinRL simulates trading environments across various stock markets, including NASDAQ-100, DJIA, S&P 500, HSI, SSE 50, and CSI 300; (ii) organized in a layered architecture with modular structure, FinRL provides fine-tuned state-of-the-art DRL algorithms (DQN, DDPG, PPO, SAC, A2C, TD3, etc.), commonly-used reward functions and standard evaluation baselines to alleviate the debugging workloads and promote the reproducibility, and (iii) being highly extendable, FinRL reserves a complete set of user-import interfaces. Furthermore, we incorporated three application demonstrations, namely single stock trading, multiple stock trading, and portfolio allocation. The FinRL library will be available on Github at link https://github.com/AI4Finance-LLC/FinRL-Library.", "published": "2020-11-19T01:35:05Z", "authors": ["Xiao-Yang Liu", "Hongyang Yang", "Qian Chen", "Runjia Zhang", "Liuqing Yang", "Bowen Xiao", "Christina Dan Wang"], "categories": ["q-fin.TR", "cs.LG"], "doi": "", "_query": "real estate valuation deep learning", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2606.18729v3", "title": "TimeLAVA: Learning-Agnostic Valuation for Time Series Data", "abstract": "Data valuation quantifies the intrinsic quality of individual samples to enable principled data curation, quality control, and robust learning. For time series in critical domains such as healthcare, finance, and industrial monitoring, effective valuation methods are essential yet fundamentally lacking. Existing approaches are either model-dependent, limiting their generalizability, or designed for i.i.d. data and thus fail to capture temporal dependencies, multi-scale patterns, and non-stationary dynamics inherent to sequential data. We introduce TimeLAVA, a learning-agnostic framework that values temporal segments by their marginal contribution to minimizing distributional discrepancy between evaluated and reference data. At its core is a novel Selective Wavelet-based Wasserstein discrepancy combining multi-scale wavelet transforms for temporal localization with unbalanced optimal transport for robustness to distributional shifts. Segment values are efficiently computed via sensitivity analysis without requiring model training and aggregated into point-wise scores. We provide theoretical guarantees linking valuation to model-agnostic generalization and prove bounded sensitivity to outlier contamination. Extensive experiments across anomaly detection, data pruning, and label noise detection demonstrate that TimeLAVA produces significantly more informative value scores than existing methods on diverse real-world datasets.", "published": "2026-06-17T06:16:16Z", "authors": ["Wenqin Liu", "Weizhi Quan", "Aoqi Zuo", "Erdun Gao", "Vu Nguyen", "Dino Sejdinovic", "Howard Bondell", "Mingming Gong"], "categories": ["stat.ML", "cs.LG"], "doi": "", "_query": "real estate valuation deep learning", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/1906.03671v2", "title": "Deep Batch Active Learning by Diverse, Uncertain Gradient Lower Bounds", "abstract": "We design a new algorithm for batch active learning with deep neural network models. Our algorithm, Batch Active learning by Diverse Gradient Embeddings (BADGE), samples groups of points that are disparate and high-magnitude when represented in a hallucinated gradient space, a strategy designed to incorporate both predictive uncertainty and sample diversity into every selected batch. Crucially, BADGE trades off between diversity and uncertainty without requiring any hand-tuned hyperparameters. We show that while other approaches sometimes succeed for particular batch sizes or architectures, BADGE consistently performs as well or better, making it a versatile option for practical active learning problems.", "published": "2019-06-09T16:52:09Z", "authors": ["Jordan T. Ash", "Chicheng Zhang", "Akshay Krishnamurthy", "John Langford", "Alekh Agarwal"], "categories": ["cs.LG", "stat.ML"], "doi": "", "_query": "real estate valuation deep learning", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/1805.07646v1", "title": "Long-term face tracking in the wild using deep learning", "abstract": "This paper investigates long-term face tracking of a specific person given his/her face image in a single frame as a query in a video stream. Through taking advantage of pre-trained deep learning models on big data, a novel system is developed for accurate video face tracking in the unconstrained environments depicting various people and objects moving in and out of the frame. In the proposed system, we present a detection-verification-tracking method (dubbed as 'DVT') which accomplishes the long-term face tracking task through the collaboration of face detection, face verification, and (short-term) face tracking. An offline trained detector based on cascaded convolutional neural networks localizes all faces appeared in the frames, and an offline trained face verifier based on deep convolutional neural networks and similarity metric learning decides if any face or which face corresponds to the queried person. An online trained tracker follows the face from frame to frame. When validated on a sitcom episode and a TV show, the DVT method outperforms tracking-learning-detection (TLD) and face-TLD in terms of recall and precision. The proposed system is also tested on many other types of videos and shows very promising results.", "published": "2018-05-19T20:00:26Z", "authors": ["Kunlei Zhang", "Elaheh Rashedi", "Elaheh Barati", "Xue-wen Chen"], "categories": ["cs.CV"], "doi": "", "_query": "real estate valuation deep learning", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2512.19199v2", "title": "On the Koopman-Based Generalization Bounds for Multi-Task Deep Learning", "abstract": "The paper establishes generalization bounds for multitask deep neural networks using operator-theoretic techniques. The authors propose a tighter bound than those derived from conventional norm based methods by leveraging small condition numbers in the weight matrices and introducing a tailored Sobolev space as an expanded hypothesis space. This enhanced bound remains valid even in single output settings, outperforming existing Koopman based bounds. The resulting framework maintains key advantages such as flexibility and independence from network width, offering a more precise theoretical understanding of multitask deep learning in the context of kernel methods.", "published": "2025-12-22T09:36:24Z", "authors": ["Mahdi Mohammadigohari", "Giuseppe Di Fatta", "Giuseppe Nicosia", "Panos M. Pardalos"], "categories": ["cs.LG", "cs.AI"], "doi": "10.1007/978-3-032-21480-5_25", "_query": "real estate valuation deep learning", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2305.03483v1", "title": "Flat dimension for power series over valuation rings", "abstract": "We examine the power series ring $R[[X]]$ over a valuation ring $R$ of rank 1, with proper, dense value group. We give a counterexample to Hilbert's syzygy theorem for $R[[X]]$, i.e. an $R[[X]]$-module $C$ that is flat over $R$ and has flat dimension at least 2 over $R[[X]]$, contradicting a previously published result. The key ingredient in our construction is an exploration of the valuation theory of $R[[X]]$. We also use this theory to give a new proof that $R[[X]]$ is not a coherent ring, a fact which is essential in our construction of the module $C$.", "published": "2023-05-05T12:51:01Z", "authors": ["Adam Jones"], "categories": ["math.AC", "math.RA"], "doi": "10.1016/j.jpaa.2024.107778", "_query": "real estate valuation deep learning", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2002.06684v2", "title": "R-MADDPG for Partially Observable Environments and Limited Communication", "abstract": "There are several real-world tasks that would benefit from applying multiagent reinforcement learning (MARL) algorithms, including the coordination among self-driving cars. The real world has challenging conditions for multiagent learning systems, such as its partial observable and nonstationary nature. Moreover, if agents must share a limited resource (e.g. network bandwidth) they must all learn how to coordinate resource use. This paper introduces a deep recurrent multiagent actor-critic framework (R-MADDPG) for handling multiagent coordination under partial observable set-tings and limited communication. We investigate recurrency effects on performance and communication use of a team of agents. We demonstrate that the resulting framework learns time dependencies for sharing missing observations, handling resource limitations, and developing different communication patterns among agents.", "published": "2020-02-16T21:25:44Z", "authors": ["Rose E. Wang", "Michael Everett", "Jonathan P. How"], "categories": ["cs.MA", "cs.AI"], "doi": "", "_query": "real estate valuation deep learning", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/1901.11493v1", "title": "Deep Recurrent Factor Model: Interpretable Non-Linear and Time-Varying Multi-Factor Model", "abstract": "A linear multi-factor model is one of the most important tools in equity portfolio management. The linear multi-factor models are widely used because they can be easily interpreted. However, financial markets are not linear and their accuracy is limited. Recently, deep learning methods were proposed to predict stock return in terms of the multi-factor model. Although these methods perform quite well, they have significant disadvantages such as a lack of transparency and limitations in the interpretability of the prediction. It is thus difficult for institutional investors to use black-box-type machine learning techniques in actual investment practice because they should show accountability to their customers. Consequently, the solution we propose is based on LSTM with LRP. Specifically, we extend the linear multi-factor model to be non-linear and time-varying with LSTM. Then, we approximate and linearize the learned LSTM models by LRP. We call this LSTM+LRP model a deep recurrent factor model. Finally, we perform an empirical analysis of the Japanese stock market and show that our recurrent model has better predictive capability than the traditional linear model and fully-connected deep learning methods.", "published": "2019-01-20T18:47:56Z", "authors": ["Kei Nakagawa", "Tomoki Ito", "Masaya Abe", "Kiyoshi Izumi"], "categories": ["cs.LG", "q-fin.ST"], "doi": "", "_query": "real estate valuation deep learning", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/1911.07418v1", "title": "Grassmannian Packings in Neural Networks: Learning with Maximal Subspace Packings for Diversity and Anti-Sparsity", "abstract": "Kernel sparsity (\"dying ReLUs\") and lack of diversity are commonly observed in CNN kernels, which decreases model capacity. Drawing inspiration from information theory and wireless communications, we demonstrate the intersection of coding theory and deep learning through the Grassmannian subspace packing problem in CNNs. We propose Grassmannian packings for initial kernel layers to be initialized maximally far apart based on chordal or Fubini-Study distance. Convolutional kernels initialized with Grassmannian packings exhibit diverse features and obtain diverse representations. We show that Grassmannian packings, especially in the initial layers, address kernel sparsity and encourage diversity, while improving classification accuracy across shallow and deep CNNs with better convergence rates.", "published": "2019-11-18T04:17:06Z", "authors": ["Dian Ang Yap", "Nicholas Roberts", "Vinay Uday Prabhu"], "categories": ["cs.LG", "cs.IT", "stat.ML"], "doi": "", "_query": "real estate valuation deep learning", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/1704.03723v1", "title": "Beliefs in Markov Trees - From Local Computations to Local Valuation", "abstract": "This paper is devoted to expressiveness of hypergraphs for which uncertainty propagation by local computations via Shenoy/Shafer method applies. It is demonstrated that for this propagation method for a given joint belief distribution no valuation of hyperedges of a hypergraph may provide with simpler hypergraph structure than valuation of hyperedges by conditional distributions. This has vital implication that methods recovering belief networks from data have no better alternative for finding the simplest hypergraph structure for belief propagation. A method for recovery tree-structured belief networks has been developed and specialized for Dempster-Shafer belief functions", "published": "2017-04-12T12:30:17Z", "authors": ["Mieczysław A. Kłopotek"], "categories": ["cs.AI"], "doi": "", "_query": "real estate valuation deep learning", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/1905.02544v1", "title": "Ocular Diseases Diagnosis in Fundus Images using a Deep Learning: Approaches, tools and Performance evaluation", "abstract": "Ocular pathology detection from fundus images presents an important challenge on health care. In fact, each pathology has different severity stages that may be deduced by verifying the existence of specific lesions. Each lesion is characterized by morphological features. Moreover, several lesions of different pathologies have similar features. We note that patient may be affected simultaneously by several pathologies. Consequently, the ocular pathology detection presents a multi-class classification with a complex resolution principle. Several detection methods of ocular pathologies from fundus images have been proposed. The methods based on deep learning are distinguished by higher performance detection, due to their capability to configure the network with respect to the detection objective. This work proposes a survey of ocular pathology detection methods based on deep learning. First, we study the existing methods either for lesion segmentation or pathology classification. Afterwards, we extract the principle steps of processing and we analyze the proposed neural network structures. Subsequently, we identify the hardware and software environment required to employ the deep learning architecture. Thereafter, we investigate about the experimentation principles involved to evaluate the methods and the databases used either for training and testing phases. The detection performance ratios and execution times are also reported and discussed.", "published": "2019-05-07T13:21:01Z", "authors": ["Yaroub Elloumi", "Mohamed Akil", "Henda Boudegga"], "categories": ["eess.IV", "cs.CV", "cs.LG", "cs.NE"], "doi": "", "_query": "real estate valuation deep learning", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2411.18612v2", "title": "Robust Offline Reinforcement Learning with Linearly Structured f-Divergence Regularization", "abstract": "The Robust Regularized Markov Decision Process (RRMDP) is proposed to learn policies robust to dynamics shifts by adding regularization to the transition dynamics in the value function. Existing methods mostly use unstructured regularization, potentially leading to conservative policies under unrealistic transitions. To address this limitation, we propose a novel framework, the $d$-rectangular linear RRMDP ($d$-RRMDP), which introduces latent structures into both transition kernels and regularization. We focus on offline reinforcement learning, where an agent learns policies from a precollected dataset in the nominal environment. We develop the Robust Regularized Pessimistic Value Iteration (R2PVI) algorithm that employs linear function approximation for robust policy learning in $d$-RRMDPs with $f$-divergence based regularization terms on transition kernels. We provide instance-dependent upper bounds on the suboptimality gap of R2PVI policies, demonstrating that these bounds are influenced by how well the dataset covers state-action spaces visited by the optimal robust policy under robustly admissible transitions. We establish information-theoretic lower bounds to verify that our algorithm is near-optimal. Finally, numerical experiments validate that R2PVI learns robust policies and exhibits superior computational efficiency compared to baseline methods.", "published": "2024-11-27T18:57:03Z", "authors": ["Cheng Tang", "Zhishuai Liu", "Pan Xu"], "categories": ["cs.LG", "cs.AI", "cs.RO", "stat.ML"], "doi": "", "_query": "real estate valuation deep learning", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2502.19356v1", "title": "Recurrent Auto-Encoders for Enhanced Deep Reinforcement Learning in Wilderness Search and Rescue Planning", "abstract": "Wilderness search and rescue operations are often carried out over vast landscapes. The search efforts, however, must be undertaken in minimum time to maximize the chance of survival of the victim. Whilst the advent of cheap multicopters in recent years has changed the way search operations are handled, it has not solved the challenges of the massive areas at hand. The problem therefore is not one of complete coverage, but one of maximizing the information gathered in the limited time available. In this work we propose that a combination of a recurrent autoencoder and deep reinforcement learning is a more efficient solution to the search problem than previous pure deep reinforcement learning or optimisation approaches. The autoencoder training paradigm efficiently maximizes the information throughput of the encoder into its latent space representation which deep reinforcement learning is primed to leverage. Without the overhead of independently solving the problem that the recurrent autoencoder is designed for, it is more efficient in learning the control task. We further implement three additional architectures for a comprehensive comparison of the main proposed architecture. Similarly, we apply both soft actor-critic and proximal policy optimisation to provide an insight into the performance of both in a highly non-linear and complex application with a large observation Results show that the proposed architecture is vastly superior to the benchmarks, with soft actor-critic achieving the best performance. This model further outperformed work from the literature whilst having below a fifth of the total learnable parameters and training in a quarter of the time.", "published": "2025-02-26T17:54:47Z", "authors": ["Jan-Hendrik Ewers", "David Anderson", "Douglas Thomson"], "categories": ["cs.LG", "eess.SY"], "doi": "", "_query": "real estate valuation deep learning", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/cs/0201021v1", "title": "Learning to Play Games in Extensive Form by Valuation", "abstract": "A valuation for a player in a game in extensive form is an assignment of numeric values to the players moves. The valuation reflects the desirability moves. We assume a myopic player, who chooses a move with the highest valuation. Valuations can also be revised, and hopefully improved, after each play of the game. Here, a very simple valuation revision is considered, in which the moves made in a play are assigned the payoff obtained in the play. We show that by adopting such a learning process a player who has a winning strategy in a win-lose game can almost surely guarantee a win in a repeated game. When a player has more than two payoffs, a more elaborate learning procedure is required. We consider one that associates with each move the average payoff in the rounds in which this move was made. When all players adopt this learning procedure, with some perturbations, then, with probability 1, strategies that are close to subgame perfect equilibrium are played after some time. A single player who adopts this procedure can guarantee only her individually rational payoff.", "published": "2002-01-23T11:58:17Z", "authors": ["Philippe Jehiel", "Dov Samet"], "categories": ["cs.LG", "cs.GT"], "doi": "", "_query": "real estate valuation deep learning", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/1310.3101v1", "title": "Deep Multiple Kernel Learning", "abstract": "Deep learning methods have predominantly been applied to large artificial neural networks. Despite their state-of-the-art performance, these large networks typically do not generalize well to datasets with limited sample sizes. In this paper, we take a different approach by learning multiple layers of kernels. We combine kernels at each layer and then optimize over an estimate of the support vector machine leave-one-out error rather than the dual objective function. Our experiments on a variety of datasets show that each layer successively increases performance with only a few base kernels.", "published": "2013-10-11T12:14:00Z", "authors": ["Eric Strobl", "Shyam Visweswaran"], "categories": ["stat.ML", "cs.LG"], "doi": "10.1109/ICMLA.2013.84", "_query": "real estate valuation deep learning", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/1903.08333v1", "title": "On the Robustness of Deep K-Nearest Neighbors", "abstract": "Despite a large amount of attention on adversarial examples, very few works have demonstrated an effective defense against this threat. We examine Deep k-Nearest Neighbor (DkNN), a proposed defense that combines k-Nearest Neighbor (kNN) and deep learning to improve the model's robustness to adversarial examples. It is challenging to evaluate the robustness of this scheme due to a lack of efficient algorithm for attacking kNN classifiers with large k and high-dimensional data. We propose a heuristic attack that allows us to use gradient descent to find adversarial examples for kNN classifiers, and then apply it to attack the DkNN defense as well. Results suggest that our attack is moderately stronger than any naive attack on kNN and significantly outperforms other attacks on DkNN.", "published": "2019-03-20T03:50:34Z", "authors": ["Chawin Sitawarin", "David Wagner"], "categories": ["cs.CR", "stat.ML"], "doi": "", "_query": "real estate valuation deep learning", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/1105.1749v2", "title": "A Real-Time Model-Based Reinforcement Learning Architecture for Robot Control", "abstract": "Reinforcement Learning (RL) is a method for learning decision-making tasks that could enable robots to learn and adapt to their situation on-line. For an RL algorithm to be practical for robotic control tasks, it must learn in very few actions, while continually taking those actions in real-time. Existing model-based RL methods learn in relatively few actions, but typically take too much time between each action for practical on-line learning. In this paper, we present a novel parallel architecture for model-based RL that runs in real-time by 1) taking advantage of sample-based approximate planning methods and 2) parallelizing the acting, model learning, and planning processes such that the acting process is sufficiently fast for typical robot control cycles. We demonstrate that algorithms using this architecture perform nearly as well as methods using the typical sequential architecture when both are given unlimited time, and greatly out-perform these methods on tasks that require real-time actions such as controlling an autonomous vehicle.", "published": "2011-05-09T18:17:20Z", "authors": ["Todd Hester", "Michael Quinlan", "Peter Stone"], "categories": ["cs.AI", "cs.RO", "cs.SE"], "doi": "", "_query": "real estate valuation deep learning", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2211.16881v2", "title": "Generalized Deep Learning-based Proximal Gradient Descent for MR Reconstruction", "abstract": "The data consistency for the physical forward model is crucial in inverse problems, especially in MR imaging reconstruction. The standard way is to unroll an iterative algorithm into a neural network with a forward model embedded. The forward model always changes in clinical practice, so the learning component's entanglement with the forward model makes the reconstruction hard to generalize. The deep learning-based proximal gradient descent was proposed and use a network as regularization term that is independent of the forward model, which makes it more generalizable for different MR acquisition settings. This one-time pre-trained regularization is applied to different MR acquisition settings and was compared to conventional L1 regularization showing ~3 dB improvement in the peak signal-to-noise ratio. We also demonstrated the flexibility of the proposed method in choosing different undersampling patterns.", "published": "2022-11-30T10:31:06Z", "authors": ["Guanxiong Luo", "Mengmeng Kuang", "Peng Cao"], "categories": ["eess.IV", "cs.CV"], "doi": "10.1007/978-3-031-34344-5_28", "_query": "real estate valuation deep learning", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2210.06479v1", "title": "Real World Offline Reinforcement Learning with Realistic Data Source", "abstract": "Offline reinforcement learning (ORL) holds great promise for robot learning due to its ability to learn from arbitrary pre-generated experience. However, current ORL benchmarks are almost entirely in simulation and utilize contrived datasets like replay buffers of online RL agents or sub-optimal trajectories, and thus hold limited relevance for real-world robotics. In this work (Real-ORL), we posit that data collected from safe operations of closely related tasks are more practical data sources for real-world robot learning. Under these settings, we perform an extensive (6500+ trajectories collected over 800+ robot hours and 270+ human labor hour) empirical study evaluating generalization and transfer capabilities of representative ORL methods on four real-world tabletop manipulation tasks. Our study finds that ORL and imitation learning prefer different action spaces, and that ORL algorithms can generalize from leveraging offline heterogeneous data sources and outperform imitation learning. We release our dataset and implementations at URL: https://sites.google.com/view/real-orl", "published": "2022-10-12T17:57:05Z", "authors": ["Gaoyue Zhou", "Liyiming Ke", "Siddhartha Srinivasa", "Abhinav Gupta", "Aravind Rajeswaran", "Vikash Kumar"], "categories": ["cs.RO", "cs.LG"], "doi": "", "_query": "real estate valuation deep learning", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2310.11896v1", "title": "A New Multimodal Medical Image Fusion based on Laplacian Autoencoder with Channel Attention", "abstract": "Medical image fusion combines the complementary information of multimodal medical images to assist medical professionals in the clinical diagnosis of patients' disorders and provide guidance during preoperative and intra-operative procedures. Deep learning (DL) models have achieved end-to-end image fusion with highly robust and accurate fusion performance. However, most DL-based fusion models perform down-sampling on the input images to minimize the number of learnable parameters and computations. During this process, salient features of the source images become irretrievable leading to the loss of crucial diagnostic edge details and contrast of various brain tissues. In this paper, we propose a new multimodal medical image fusion model is proposed that is based on integrated Laplacian-Gaussian concatenation with attention pooling (LGCA). We prove that our model preserves effectively complementary information and important tissue structures.", "published": "2023-10-18T11:29:53Z", "authors": ["Payal Wankhede", "Manisha Das", "Deep Gupta", "Petia Radeva", "Ashwini M Bakde"], "categories": ["eess.IV", "cs.CV", "cs.LG"], "doi": "", "_query": "real estate valuation deep learning", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2303.12095v2", "title": "Interpretable histopathology-based prediction of disease relevant features in Inflammatory Bowel Disease biopsies using weakly-supervised deep learning", "abstract": "Crohn's Disease (CD) and Ulcerative Colitis (UC) are the two main Inflammatory Bowel Disease (IBD) types. We developed deep learning models to identify histological disease features for both CD and UC using only endoscopic labels. We explored fine-tuning and end-to-end training of two state-of-the-art self-supervised models for predicting three different endoscopic categories (i) CD vs UC (AUC=0.87), (ii) normal vs lesional (AUC=0.81), (iii) low vs high disease severity score (AUC=0.80). We produced visual attention maps to interpret what the models learned and validated them with the support of a pathologist, where we observed a strong association between the models' predictions and histopathological inflammatory features of the disease. Additionally, we identified several cases where the model incorrectly predicted normal samples as lesional but were correct on the microscopic level when reviewed by the pathologist. This tendency of histological presentation to be more severe than endoscopic presentation was previously published in the literature. In parallel, we utilised a model trained on the Colon Nuclei Identification and Counting (CoNIC) dataset to predict and explore 6 cell populations. We observed correlation between areas enriched with the predicted immune cells in biopsies and the pathologist's feedback on the attention maps. Finally, we identified several cell level features indicative of disease severity in CD and UC. These models can enhance our understanding about the pathology behind IBD and can shape our strategies for patient stratification in clinical trials.", "published": "2023-03-20T15:59:29Z", "authors": ["Ricardo Mokhtari", "Azam Hamidinekoo", "Daniel Sutton", "Arthur Lewis", "Bastian Angermann", "Ulf Gehrmann", "Pal Lundin", "Hibret Adissu", "Junmei Cairns", "Jessica Neisen", "Emon Khan", "Daniel Marks", "Nia Khachapuridze", "Talha Qaiser", "Nikolay Burlutskiy"], "categories": ["eess.IV", "cs.CV"], "doi": "", "_query": "real estate valuation deep learning", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2510.02717v1", "title": "CST-AFNet: A dual attention-based deep learning framework for intrusion detection in IoT networks", "abstract": "The rapid expansion of the Internet of Things (IoT) has revolutionized modern industries by enabling smart automation and real time connectivity. However, this evolution has also introduced complex cybersecurity challenges due to the heterogeneous, resource constrained, and distributed nature of these environments. To address these challenges, this research presents CST AFNet, a novel dual attention based deep learning framework specifically designed for robust intrusion detection in IoT networks. The model integrates multi scale Convolutional Neural Networks (CNNs) for spatial feature extraction, Bidirectional Gated Recurrent Units (BiGRUs) for capturing temporal dependencies, and a dual attention mechanism, channel and temporal attention, to enhance focus on critical patterns in the data. The proposed method was trained and evaluated on the Edge IIoTset dataset, a comprehensive and realistic benchmark containing more than 2.2 million labeled instances spanning 15 attack types and benign traffic, collected from a seven layer industrial testbed. Our proposed model achieves outstanding accuracy for both 15 attack types and benign traffic. CST AFNet achieves 99.97 percent accuracy. Moreover, this model demonstrates exceptional performance with macro averaged precision, recall, and F1 score all above 99.3 percent. Experimental results show that CST AFNet achieves superior detection accuracy, significantly outperforming traditional deep learning models. The findings confirm that CST AFNet is a powerful and scalable solution for real time cyber threat detection in complex IoT and IIoT environments, paving the way for more secure, intelligent, and adaptive cyber physical systems.", "published": "2025-10-03T04:36:19Z", "authors": ["Waqas Ishtiaq", "Ashrafun Zannat", "A. H. M. Shahariar Parvez", "Md. Alamgir Hossain", "Muntasir Hasan Kanchan", "Muhammad Masud Tarek"], "categories": ["cs.LG", "cs.AI", "cs.CR"], "doi": "10.1016/j.array.2025.100501", "_query": "real estate valuation deep learning", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2405.06553v1", "title": "Scalable Property Valuation Models via Graph-based Deep Learning", "abstract": "This paper aims to enrich the capabilities of existing deep learning-based automated valuation models through an efficient graph representation of peer dependencies, thus capturing intricate spatial relationships. In particular, we develop two novel graph neural network models that effectively identify sequences of neighboring houses with similar features, employing different message passing algorithms. The first strategy consider standard spatial graph convolutions, while the second one utilizes transformer graph convolutions. This approach confers scalability to the modeling process. The experimental evaluation is conducted using a proprietary dataset comprising approximately 200,000 houses located in Santiago, Chile. We show that employing tailored graph neural networks significantly improves the accuracy of house price prediction, especially when utilizing transformer convolutional message passing layers.", "published": "2024-05-10T15:54:55Z", "authors": ["Enrique Riveros", "Carla Vairetti", "Christian Wegmann", "Santiago Truffa", "Sebastián Maldonado"], "categories": ["cs.LG", "cs.AI"], "doi": "", "_query": "real estate valuation deep learning", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2308.08611v1", "title": "Integrating Renewable Energy in Agriculture: A Deep Reinforcement Learning-based Approach", "abstract": "This article investigates the use of Deep Q-Networks (DQNs) to optimize decision-making for photovoltaic (PV) systems installations in the agriculture sector. The study develops a DQN framework to assist agricultural investors in making informed decisions considering factors such as installation budget, government incentives, energy requirements, system cost, and long-term benefits. By implementing a reward mechanism, the DQN learns to make data-driven decisions on PV integration. The analysis provides a comprehensive understanding of how DQNs can support investors in making decisions about PV installations in agriculture. This research has significant implications for promoting sustainable and efficient farming practices while also paving the way for future advancements in this field. By leveraging DQNs, agricultural investors can make optimized decisions that improve energy efficiency, reduce environmental impact, and enhance profitability. This study contributes to the advancement of PV integration in agriculture and encourages further innovation in this promising area.", "published": "2023-08-16T18:03:33Z", "authors": ["A. Wahid", "I faiud", "K. Mason"], "categories": ["cs.AI", "cs.LG"], "doi": "", "_query": "real estate valuation deep learning", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2602.07496v2", "title": "A Behavior-first approach to Multi-Intention Inverse Reinforcement Learning", "abstract": "Inverse Reinforcement Learning (IRL) seeks to infer reward functions from expert demonstrations. When demonstrations originate from multiple experts with different intentions, the problem is known as Multi-Intention IRL (MI-IRL). Recent deep generative MI-IRL approaches couple behavior clustering and reward learning, but typically require prior knowledge of the number of true behavioral modes $K^*$. This reliance on expert knowledge limits their adaptability to new behaviors, and only enables analysis related to the learned rewards, and not across the behavior modes used to train them. We propose Contrastive Multi-Intention IRL (CoMI-IRL), a transformer-based unsupervised framework that decouples behavior representation and clustering from downstream reward learning. Our experiments show that CoMI-IRL outperforms existing approaches without a priori knowledge of $K^*$ or labels, while allowing for visual interpretation of behavior relationships and adaptation to unseen behavior without full retraining.", "published": "2026-02-07T11:14:34Z", "authors": ["Antonio Mone", "Frans A. Oliehoek", "Luciano Cavalcante Siebert"], "categories": ["cs.LG"], "doi": "", "_query": "real estate valuation deep learning", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/1911.00623v2", "title": "On-Device Machine Learning: An Algorithms and Learning Theory Perspective", "abstract": "The predominant paradigm for using machine learning models on a device is to train a model in the cloud and perform inference using the trained model on the device. However, with increasing number of smart devices and improved hardware, there is interest in performing model training on the device. Given this surge in interest, a comprehensive survey of the field from a device-agnostic perspective sets the stage for both understanding the state-of-the-art and for identifying open challenges and future avenues of research. However, on-device learning is an expansive field with connections to a large number of related topics in AI and machine learning (including online learning, model adaptation, one/few-shot learning, etc.). Hence, covering such a large number of topics in a single survey is impractical. This survey finds a middle ground by reformulating the problem of on-device learning as resource constrained learning where the resources are compute and memory. This reformulation allows tools, techniques, and algorithms from a wide variety of research areas to be compared equitably. In addition to summarizing the state-of-the-art, the survey also identifies a number of challenges and next steps for both the algorithmic and theoretical aspects of on-device learning.", "published": "2019-11-02T01:16:02Z", "authors": ["Sauptik Dhar", "Junyao Guo", "Jiayi Liu", "Samarth Tripathi", "Unmesh Kurup", "Mohak Shah"], "categories": ["cs.LG", "cs.DC", "stat.ML"], "doi": "", "_query": "real estate valuation deep learning", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2509.20913v1", "title": "Deep Learning for Crime Forecasting: The Role of Mobility at Fine-grained Spatiotemporal Scales", "abstract": "Objectives: To develop a deep learning framework to evaluate if and how incorporating micro-level mobility features, alongside historical crime and sociodemographic data, enhances predictive performance in crime forecasting at fine-grained spatial and temporal resolutions. Methods: We advance the literature on computational methods and crime forecasting by focusing on four U.S. cities (i.e., Baltimore, Chicago, Los Angeles, and Philadelphia). We employ crime incident data obtained from each city's police department, combined with sociodemographic data from the American Community Survey and human mobility data from Advan, collected from 2019 to 2023. This data is aggregated into grids with equally sized cells of 0.077 sq. miles (0.2 sq. kms) and used to train our deep learning forecasting model, a Convolutional Long Short-Term Memory (ConvLSTM) network, which predicts crime occurrences 12 hours ahead using 14-day and 2-day input sequences. We also compare its performance against three baseline models: logistic regression, random forest, and standard LSTM. Results: Incorporating mobility features improves predictive performance, especially when using shorter input sequences. Noteworthy, however, the best results are obtained when both mobility and sociodemographic features are used together, with our deep learning model achieving the highest recall, precision, and F1 score in all four cities, outperforming alternative methods. With this configuration, longer input sequences enhance predictions for violent crimes, while shorter sequences are more effective for property crimes. Conclusion: These findings underscore the importance of integrating diverse data sources for spatiotemporal crime forecasting, mobility included. They also highlight the advantages (and limits) of deep learning when dealing with fine-grained spatial and temporal scales.", "published": "2025-09-25T08:58:56Z", "authors": ["Ariadna Albors Zumel", "Michele Tizzoni", "Gian Maria Campedelli"], "categories": ["cs.LG", "cs.AI"], "doi": "10.1007/s10940-025-09629-3", "_query": "real estate valuation deep learning", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2409.14289v3", "title": "Deep Learning Technology for Face Forgery Detection: A Survey", "abstract": "Currently, the rapid development of computer vision and deep learning has enabled the creation or manipulation of high-fidelity facial images and videos via deep generative approaches. This technology, also known as deepfake, has achieved dramatic progress and become increasingly popular in social media. However, the technology can generate threats to personal privacy and national security by spreading misinformation. To diminish the risks of deepfake, it is desirable to develop powerful forgery detection methods to distinguish fake faces from real faces. This paper presents a comprehensive survey of recent deep learning-based approaches for facial forgery detection. We attempt to provide the reader with a deeper understanding of the current advances as well as the major challenges for deepfake detection based on deep learning. We present an overview of deepfake techniques and analyse the characteristics of various deepfake datasets. We then provide a systematic review of different categories of deepfake detection and state-of-the-art deepfake detection methods. The drawbacks of existing detection methods are analyzed, and future research directions are discussed to address the challenges in improving both the performance and generalization of deepfake detection.", "published": "2024-09-22T01:42:01Z", "authors": ["Lixia Ma", "Puning Yang", "Yuting Xu", "Ziming Yang", "Peipei Li", "Huaibo Huang"], "categories": ["cs.CV"], "doi": "", "_query": "real estate valuation deep learning", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/1504.06329v1", "title": "Analysis of Stopping Active Learning based on Stabilizing Predictions", "abstract": "Within the natural language processing (NLP) community, active learning has been widely investigated and applied in order to alleviate the annotation bottleneck faced by developers of new NLP systems and technologies. This paper presents the first theoretical analysis of stopping active learning based on stabilizing predictions (SP). The analysis has revealed three elements that are central to the success of the SP method: (1) bounds on Cohen's Kappa agreement between successively trained models impose bounds on differences in F-measure performance of the models; (2) since the stop set does not have to be labeled, it can be made large in practice, helping to guarantee that the results transfer to previously unseen streams of examples at test/application time; and (3) good (low variance) sample estimates of Kappa between successive models can be obtained. Proofs of relationships between the level of Kappa agreement and the difference in performance between consecutive models are presented. Specifically, if the Kappa agreement between two models exceeds a threshold T (where $T>0$), then the difference in F-measure performance between those models is bounded above by $\\frac{4(1-T)}{T}$ in all cases. If precision of the positive conjunction of the models is assumed to be $p$, then the bound can be tightened to $\\frac{4(1-T)}{(p+1)T}$.", "published": "2015-04-23T20:07:01Z", "authors": ["Michael Bloodgood", "John Grothendieck"], "categories": ["cs.LG", "cs.CL", "stat.ML"], "doi": "", "_query": "real estate valuation deep learning", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/1810.07132v1", "title": "Improving Data Quality through Deep Learning and Statistical Models", "abstract": "Traditional data quality control methods are based on users experience or previously established business rules, and this limits performance in addition to being a very time consuming process with lower than desirable accuracy. Utilizing deep learning, we can leverage computing resources and advanced techniques to overcome these challenges and provide greater value to users. In this paper, we, the authors, first review relevant works and discuss machine learning techniques, tools, and statistical quality models. Second, we offer a creative data quality framework based on deep learning and statistical model algorithm for identifying data quality. Third, we use data involving salary levels from an open dataset published by the state of Arkansas to demonstrate how to identify outlier data and how to improve data quality via deep learning. Finally, we discuss future work.", "published": "2018-10-16T16:57:07Z", "authors": ["Wei Dai", "Kenji Yoshigoe", "William Parsley"], "categories": ["cs.AI", "cs.LG"], "doi": "10.1007/978-3-319-54978-1_66", "_query": "real estate valuation deep learning", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/1705.03670v1", "title": "Deep Speaker Feature Learning for Text-independent Speaker Verification", "abstract": "Recently deep neural networks (DNNs) have been used to learn speaker features. However, the quality of the learned features is not sufficiently good, so a complex back-end model, either neural or probabilistic, has to be used to address the residual uncertainty when applied to speaker verification, just as with raw features. This paper presents a convolutional time-delay deep neural network structure (CT-DNN) for speaker feature learning. Our experimental results on the Fisher database demonstrated that this CT-DNN can produce high-quality speaker features: even with a single feature (0.3 seconds including the context), the EER can be as low as 7.68%. This effectively confirmed that the speaker trait is largely a deterministic short-time property rather than a long-time distributional pattern, and therefore can be extracted from just dozens of frames.", "published": "2017-05-10T09:30:42Z", "authors": ["Lantian Li", "Yixiang Chen", "Ying Shi", "Zhiyuan Tang", "Dong Wang"], "categories": ["cs.SD", "cs.CL", "cs.LG"], "doi": "", "_query": "real estate valuation deep learning", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2402.19163v3", "title": "Decoupled Subgraph Federated Learning", "abstract": "We address the challenge of federated learning on graph-structured data distributed across multiple clients. Specifically, we focus on the prevalent scenario of interconnected subgraphs, where interconnections between different clients play a critical role. We present a novel framework for this scenario, named FedStruct, that harnesses deep structural dependencies. To uphold privacy, unlike existing methods, FedStruct eliminates the necessity of sharing or generating sensitive node features or embeddings among clients. Instead, it leverages explicit global graph structure information to capture inter-node dependencies. We validate the effectiveness of FedStruct through experimental results conducted on six datasets for semi-supervised node classification, showcasing performance close to the centralized approach across various scenarios, including different data partitioning methods, varying levels of label availability, and number of clients.", "published": "2024-02-29T13:47:23Z", "authors": ["Javad Aliakbari", "Johan Östman", "Alexandre Graell i Amat"], "categories": ["cs.LG", "cs.IT"], "doi": "", "_query": "real estate valuation deep learning", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2002.08860v3", "title": "Dissipative SymODEN: Encoding Hamiltonian Dynamics with Dissipation and Control into Deep Learning", "abstract": "In this work, we introduce Dissipative SymODEN, a deep learning architecture which can infer the dynamics of a physical system with dissipation from observed state trajectories. To improve prediction accuracy while reducing network size, Dissipative SymODEN encodes the port-Hamiltonian dynamics with energy dissipation and external input into the design of its computation graph and learns the dynamics in a structured way. The learned model, by revealing key aspects of the system, such as the inertia, dissipation, and potential energy, paves the way for energy-based controllers.", "published": "2020-02-20T16:44:10Z", "authors": ["Yaofeng Desmond Zhong", "Biswadip Dey", "Amit Chakraborty"], "categories": ["cs.LG", "eess.SY", "stat.ML"], "doi": "", "_query": "real estate valuation deep learning", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2507.13271v1", "title": "Natural Hyperbolicity of Hexagonal Boron Nitride in the Deep Ultraviolet", "abstract": "Hyperbolic media enable unique optical phenomena including hyperlensing, negative refraction, enhanced photonic density of states (PDOS), and highly confined polaritons. While most hyperbolic media are artificially engineered metamaterials, certain natural materials with extreme anisotropy can exhibit hyperbolic dispersion. Here, we report the first observation of natural hyperbolic dispersion in hexagonal boron nitride (hBN) in the deep-ultraviolet (DUV) regime, induced by strong, anisotropic exciton resonances. Using imaging spectroscopic ellipsometry (ISE), we characterize the complex dielectric function along in-plane and out-of-plane directions down to 190 nm (6.53 eV), revealing a type-II hyperbolic window in the DUV regime. This hyperbolicity supports hyperbolic exciton polaritons (HEP) with high directionality and slow group velocity. Our findings establish hBN as a promising platform for nanophotonic applications in the technologically significant DUV spectral range.", "published": "2025-07-17T16:26:02Z", "authors": ["Bongjun Choi", "Jason Lynch", "Wangleong Chen", "Seong-Joon Jeon", "Hyungseob Cho", "Kyungmin Yang", "Jonghwan Kim", "Nader Engheta", "Deep Jariwala"], "categories": ["physics.optics", "physics.app-ph"], "doi": "", "_query": "real estate valuation deep learning", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2010.09163v2", "title": "D2RL: Deep Dense Architectures in Reinforcement Learning", "abstract": "While improvements in deep learning architectures have played a crucial role in improving the state of supervised and unsupervised learning in computer vision and natural language processing, neural network architecture choices for reinforcement learning remain relatively under-explored. We take inspiration from successful architectural choices in computer vision and generative modelling, and investigate the use of deeper networks and dense connections for reinforcement learning on a variety of simulated robotic learning benchmark environments. Our findings reveal that current methods benefit significantly from dense connections and deeper networks, across a suite of manipulation and locomotion tasks, for both proprioceptive and image-based observations. We hope that our results can serve as a strong baseline and further motivate future research into neural network architectures for reinforcement learning. The project website with code is at this link https://sites.google.com/view/d2rl/home.", "published": "2020-10-19T01:27:07Z", "authors": ["Samarth Sinha", "Homanga Bharadhwaj", "Aravind Srinivas", "Animesh Garg"], "categories": ["cs.LG"], "doi": "", "_query": "real estate valuation deep learning", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2011.07200v1", "title": "Deep Spatial Learning with Molecular Vibration", "abstract": "Machine learning over-fitting caused by data scarcity greatly limits the application of machine learning for molecules. Due to manufacturing processes difference, big data is not always rendered available through computational chemistry methods for some tasks, causing data scarcity problem for machine learning algorithms. Here we propose to extract the natural features of molecular structures and rationally distort them to augment the data availability. This method allows a machine learning project to leverage the powerful fit of physics-informed augmentation for providing significant boost to predictive accuracy. Successfully verified by the prediction of rejection rate and flux of thin film polyamide nanofiltration membranes, with the relative error dropping from 16.34% to 6.71% and the coefficient of determination rising from 0.16 to 0.75, the proposed deep spatial learning with molecular vibration is widely instructive for molecular science. Experimental comparison unequivocally demonstrates its superiority over common learning algorithms.", "published": "2020-11-14T02:46:43Z", "authors": ["Ziyang Zhang", "Yingtao Luo"], "categories": ["cs.LG", "physics.chem-ph"], "doi": "", "_query": "real estate valuation deep learning", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2207.02337v1", "title": "Federated and Transfer Learning: A Survey on Adversaries and Defense Mechanisms", "abstract": "The advent of federated learning has facilitated large-scale data exchange amongst machine learning models while maintaining privacy. Despite its brief history, federated learning is rapidly evolving to make wider use more practical. One of the most significant advancements in this domain is the incorporation of transfer learning into federated learning, which overcomes fundamental constraints of primary federated learning, particularly in terms of security. This chapter performs a comprehensive survey on the intersection of federated and transfer learning from a security point of view. The main goal of this study is to uncover potential vulnerabilities and defense mechanisms that might compromise the privacy and performance of systems that use federated and transfer learning.", "published": "2022-07-05T22:07:26Z", "authors": ["Ehsan Hallaji", "Roozbeh Razavi-Far", "Mehrdad Saif"], "categories": ["cs.LG", "cs.AI", "cs.CR", "cs.CV", "cs.DC"], "doi": "10.1007/978-3-031-11748-0_3", "_query": "real estate valuation deep learning", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2307.07881v2", "title": "Graph Embedded Intuitionistic Fuzzy Random Vector Functional Link Neural Network for Class Imbalance Learning", "abstract": "The domain of machine learning is confronted with a crucial research area known as class imbalance learning, which presents considerable hurdles in precise classification of minority classes. This issue can result in biased models where the majority class takes precedence in the training process, leading to the underrepresentation of the minority class. The random vector functional link (RVFL) network is a widely used and effective learning model for classification due to its good generalization performance and efficiency. However, it suffers when dealing with imbalanced datasets. To overcome this limitation, we propose a novel graph embedded intuitionistic fuzzy RVFL for class imbalance learning (GE-IFRVFL-CIL) model incorporating a weighting mechanism to handle imbalanced datasets. The proposed GE-IFRVFL-CIL model offers plethora of benefits: $(i)$ leveraging graph embedding to preserve the inherent topological structure of the datasets, $(ii)$ employing intuitionistic fuzzy theory to handle uncertainty and imprecision in the data, $(iii)$ and the most important, it tackles class imbalance learning. The amalgamation of a weighting scheme, graph embedding, and intuitionistic fuzzy sets leads to the superior performance of the proposed models on KEEL benchmark imbalanced datasets with and without Gaussian noise. Furthermore, we implemented the proposed GE-IFRVFL-CIL on the ADNI dataset and achieved promising results, demonstrating the model's effectiveness in real-world applications. The proposed GE-IFRVFL-CIL model offers a promising solution to address the class imbalance issue, mitigates the detrimental effect of noise and outliers, and preserves the inherent geometrical structures of the dataset.", "published": "2023-07-15T20:45:45Z", "authors": ["M. A. Ganaie", "M. Sajid", "A. K. Malik", "M. Tanveer"], "categories": ["cs.LG"], "doi": "10.1109/TNNLS.2024.3353531", "_query": "real estate valuation deep learning", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2311.07343v1", "title": "Fine-Tuning the Retrieval Mechanism for Tabular Deep Learning", "abstract": "While interests in tabular deep learning has significantly grown, conventional tree-based models still outperform deep learning methods. To narrow this performance gap, we explore the innovative retrieval mechanism, a methodology that allows neural networks to refer to other data points while making predictions. Our experiments reveal that retrieval-based training, especially when fine-tuning the pretrained TabPFN model, notably surpasses existing methods. Moreover, the extensive pretraining plays a crucial role to enhance the performance of the model. These insights imply that blending the retrieval mechanism with pretraining and transfer learning schemes offers considerable potential for advancing the field of tabular deep learning.", "published": "2023-11-13T13:55:52Z", "authors": ["Felix den Breejen", "Sangmin Bae", "Stephen Cha", "Tae-Young Kim", "Seoung Hyun Koh", "Se-Young Yun"], "categories": ["cs.LG"], "doi": "", "_query": "real estate valuation deep learning", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2303.15563v1", "title": "Privacy-preserving machine learning for healthcare: open challenges and future perspectives", "abstract": "Machine Learning (ML) has recently shown tremendous success in modeling various healthcare prediction tasks, ranging from disease diagnosis and prognosis to patient treatment. Due to the sensitive nature of medical data, privacy must be considered along the entire ML pipeline, from model training to inference. In this paper, we conduct a review of recent literature concerning Privacy-Preserving Machine Learning (PPML) for healthcare. We primarily focus on privacy-preserving training and inference-as-a-service, and perform a comprehensive review of existing trends, identify challenges, and discuss opportunities for future research directions. The aim of this review is to guide the development of private and efficient ML models in healthcare, with the prospects of translating research efforts into real-world settings.", "published": "2023-03-27T19:20:51Z", "authors": ["Alejandro Guerra-Manzanares", "L. Julian Lechuga Lopez", "Michail Maniatakos", "Farah E. Shamout"], "categories": ["cs.LG", "cs.CR"], "doi": "10.1007/978-3-031-39539-0_3", "_query": "real estate valuation deep learning", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2209.12871v3", "title": "Variationally Mimetic Operator Networks", "abstract": "In recent years operator networks have emerged as promising deep learning tools for approximating the solution to partial differential equations (PDEs). These networks map input functions that describe material properties, forcing functions and boundary data to the solution of a PDE. This work describes a new architecture for operator networks that mimics the form of the numerical solution obtained from an approximate variational or weak formulation of the problem. The application of these ideas to a generic elliptic PDE leads to a variationally mimetic operator network (VarMiON). Like the conventional Deep Operator Network (DeepONet) the VarMiON is also composed of a sub-network that constructs the basis functions for the output and another that constructs the coefficients for these basis functions. However, in contrast to the DeepONet, the architecture of these sub-networks in the VarMiON is precisely determined. An analysis of the error in the VarMiON solution reveals that it contains contributions from the error in the training data, the training error, the quadrature error in sampling input and output functions, and a \"covering error\" that measures the distance between the test input functions and the nearest functions in the training dataset. It also depends on the stability constants for the exact solution operator and its VarMiON approximation. The application of the VarMiON to a canonical elliptic PDE and a nonlinear PDE reveals that for approximately the same number of network parameters, on average the VarMiON incurs smaller errors than a standard DeepONet and a recently proposed multiple-input operator network (MIONet). Further, its performance is more robust to variations in input functions, the techniques used to sample the input and output functions, the techniques used to construct the basis functions, and the number of input functions.", "published": "2022-09-26T17:39:53Z", "authors": ["Dhruv Patel", "Deep Ray", "Michael R. A. Abdelmalik", "Thomas J. R. Hughes", "Assad A. Oberai"], "categories": ["math.NA", "cs.LG"], "doi": "", "_query": "real estate valuation deep learning", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2305.03963v2", "title": "Beyond the Model: Data Pre-processing Attack to Deep Learning Models in Android Apps", "abstract": "The increasing popularity of deep learning (DL) models and the advantages of computing, including low latency and bandwidth savings on smartphones, have led to the emergence of intelligent mobile applications, also known as DL apps, in recent years. However, this technological development has also given rise to several security concerns, including adversarial examples, model stealing, and data poisoning issues. Existing works on attacks and countermeasures for on-device DL models have primarily focused on the models themselves. However, scant attention has been paid to the impact of data processing disturbance on the model inference. This knowledge disparity highlights the need for additional research to fully comprehend and address security issues related to data processing for on-device models. In this paper, we introduce a data processing-based attacks against real-world DL apps. In particular, our attack could influence the performance and latency of the model without affecting the operation of a DL app. To demonstrate the effectiveness of our attack, we carry out an empirical study on 517 real-world DL apps collected from Google Play. Among 320 apps utilizing MLkit, we find that 81.56\\% of them can be successfully attacked. The results emphasize the importance of DL app developers being aware of and taking actions to secure on-device models from the perspective of data processing.", "published": "2023-05-06T07:35:39Z", "authors": ["Ye Sang", "Yujin Huang", "Shuo Huang", "Helei Cui"], "categories": ["cs.CR", "cs.AI", "cs.CV"], "doi": "", "_query": "real estate valuation deep learning", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/1812.04998v2", "title": "Neural Processes Mixed-Effect Models for Deep Normative Modeling of Clinical Neuroimaging Data", "abstract": "Normative modeling has recently been introduced as a promising approach for modeling variation of neuroimaging measures across individuals in order to derive biomarkers of psychiatric disorders. Current implementations rely on Gaussian process regression, which provides coherent estimates of uncertainty needed for the method but also suffers from drawbacks including poor scaling to large datasets and a reliance on fixed parametric kernels. In this paper, we propose a deep normative modeling framework based on neural processes (NPs) to solve these problems. To achieve this, we define a stochastic process formulation for mixed-effect models and show how NPs can be adopted for spatially structured mixed-effect modeling of neuroimaging data. This enables us to learn optimal feature representations and covariance structure for the random-effect and noise via global latent variables. In this scheme, predictive uncertainty can be approximated by sampling from the distribution of these global latent variables. On a publicly available clinical fMRI dataset, we compare the novelty detection performance of multivariate normative models estimated by the proposed NP approach to a baseline multi-task Gaussian process regression approach and show substantial improvements for certain diagnostic problems.", "published": "2018-12-12T16:02:06Z", "authors": ["Seyed Mostafa Kia", "Andre F. Marquand"], "categories": ["stat.ML", "cs.LG", "cs.NE"], "doi": "", "_query": "real estate valuation deep learning", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2006.13309v4", "title": "Fast Deep Mixtures of Gaussian Process Experts", "abstract": "Mixtures of experts have become an indispensable tool for flexible modelling in a supervised learning context, allowing not only the mean function but the entire density of the output to change with the inputs. Sparse Gaussian processes (GP) have shown promise as a leading candidate for the experts in such models, and in this article, we propose to design the gating network for selecting the experts from such mixtures of sparse GPs using a deep neural network (DNN). Furthermore, a fast one pass algorithm called Cluster-Classify-Regress (CCR) is leveraged to approximate the maximum a posteriori (MAP) estimator extremely quickly. This powerful combination of model and algorithm together delivers a novel method which is flexible, robust, and extremely efficient. In particular, the method is able to outperform competing methods in terms of accuracy and uncertainty quantification. The cost is competitive on low-dimensional and small data sets, but is significantly lower for higher-dimensional and big data sets. Iteratively maximizing the distribution of experts given allocations and allocations given experts does not provide significant improvement, which indicates that the algorithm achieves a good approximation to the local MAP estimator very fast. This insight can be useful also in the context of other mixture of experts models.", "published": "2020-06-11T18:52:34Z", "authors": ["Clement Etienam", "Kody Law", "Sara Wade", "Vitaly Zankin"], "categories": ["cs.LG", "stat.ML"], "doi": "10.1007/s10994-023-06491-x", "_query": "real estate valuation deep learning", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2011.05791v1", "title": "Interpretable and synergistic deep learning for visual explanation and statistical estimations of segmentation of disease features from medical images", "abstract": "Deep learning (DL) models for disease classification or segmentation from medical images are increasingly trained using transfer learning (TL) from unrelated natural world images. However, shortcomings and utility of TL for specialized tasks in the medical imaging domain remain unknown and are based on assumptions that increasing training data will improve performance. We report detailed comparisons, rigorous statistical analysis and comparisons of widely used DL architecture for binary segmentation after TL with ImageNet initialization (TII-models) with supervised learning with only medical images(LMI-models) of macroscopic optical skin cancer, microscopic prostate core biopsy and Computed Tomography (CT) DICOM images. Through visual inspection of TII and LMI model outputs and their Grad-CAM counterparts, our results identify several counter intuitive scenarios where automated segmentation of one tumor by both models or the use of individual segmentation output masks in various combinations from individual models leads to 10% increase in performance. We also report sophisticated ensemble DL strategies for achieving clinical grade medical image segmentation and model explanations under low data regimes. For example; estimating performance, explanations and replicability of LMI and TII models described by us can be used for situations in which sparsity promotes better learning. A free GitHub repository of TII and LMI models, code and more than 10,000 medical images and their Grad-CAM output from this study can be used as starting points for advanced computational medicine and DL research for biomedical discovery and applications.", "published": "2020-11-11T14:08:17Z", "authors": ["Sambuddha Ghosal", "Pratik Shah"], "categories": ["stat.ML", "cs.LG"], "doi": "10.1016/j.crmeth.2021.100107", "_query": "real estate valuation deep learning", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2303.10455v1", "title": "Learn, Unlearn and Relearn: An Online Learning Paradigm for Deep Neural Networks", "abstract": "Deep neural networks (DNNs) are often trained on the premise that the complete training data set is provided ahead of time. However, in real-world scenarios, data often arrive in chunks over time. This leads to important considerations about the optimal strategy for training DNNs, such as whether to fine-tune them with each chunk of incoming data (warm-start) or to retrain them from scratch with the entire corpus of data whenever a new chunk is available. While employing the latter for training can be resource-intensive, recent work has pointed out the lack of generalization in warm-start models. Therefore, to strike a balance between efficiency and generalization, we introduce Learn, Unlearn, and Relearn (LURE) an online learning paradigm for DNNs. LURE interchanges between the unlearning phase, which selectively forgets the undesirable information in the model through weight reinitialization in a data-dependent manner, and the relearning phase, which emphasizes learning on generalizable features. We show that our training paradigm provides consistent performance gains across datasets in both classification and few-shot settings. We further show that it leads to more robust and well-calibrated models.", "published": "2023-03-18T16:45:54Z", "authors": ["Vijaya Raghavan T. Ramkumar", "Elahe Arani", "Bahram Zonooz"], "categories": ["cs.LG", "cs.AI", "cs.CV"], "doi": "", "_query": "real estate valuation deep learning", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2402.09146v6", "title": "ResQuNNs: Towards Enabling Deep Learning in Quantum Convolution Neural Networks", "abstract": "In this paper, we present a novel framework for enhancing the performance of Quanvolutional Neural Networks (QuNNs) by introducing trainable quanvolutional layers and addressing the critical challenges associated with them. Traditional quanvolutional layers, although beneficial for feature extraction, have largely been static, offering limited adaptability. Unlike state-of-the-art, our research overcomes this limitation by enabling training within these layers, significantly increasing the flexibility and potential of QuNNs. However, the introduction of multiple trainable quanvolutional layers induces complexities in gradient-based optimization, primarily due to the difficulty in accessing gradients across these layers. To resolve this, we propose a novel architecture, Residual Quanvolutional Neural Networks (ResQuNNs), leveraging the concept of residual learning, which facilitates the flow of gradients by adding skip connections between layers. By inserting residual blocks between quanvolutional layers, we ensure enhanced gradient access throughout the network, leading to improved training performance. Moreover, we provide empirical evidence on the strategic placement of these residual blocks within QuNNs. Through extensive experimentation, we identify an efficient configuration of residual blocks, which enables gradients across all the layers in the network that eventually results in efficient training. Our findings suggest that the precise location of residual blocks plays a crucial role in maximizing the performance gains in QuNNs. Our results mark a substantial step forward in the evolution of quantum deep learning, offering new avenues for both theoretical development and practical quantum computing applications.", "published": "2024-02-14T12:55:28Z", "authors": ["Muhammad Kashif", "Muhammad Shafique"], "categories": ["cs.LG", "cs.AI", "quant-ph"], "doi": "10.1038/s41598-025-06035-4", "_query": "real estate valuation deep learning", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/1706.09557v1", "title": "Machine listening intelligence", "abstract": "This manifesto paper will introduce machine listening intelligence, an integrated research framework for acoustic and musical signals modelling, based on signal processing, deep learning and computational musicology.", "published": "2017-06-29T02:52:25Z", "authors": ["C. E. Cella"], "categories": ["cs.SD", "cs.LG"], "doi": "", "_query": "real estate valuation deep learning", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2408.12308v3", "title": "Deep Learning with CNNs: A Compact Holistic Tutorial with Focus on Supervised Regression (Preprint)", "abstract": "In this tutorial, we present a compact and holistic discussion of Deep Learning with a focus on Convolutional Neural Networks (CNNs) and supervised regression. While there are numerous books and articles on the individual topics we cover, comprehensive and detailed tutorials that address Deep Learning from a foundational yet rigorous and accessible perspective are rare. Most resources on CNNs are either too advanced, focusing on cutting-edge architectures, or too narrow, addressing only specific applications like image classification.This tutorial not only summarizes the most relevant concepts but also provides an in-depth exploration of each, offering a complete yet agile set of ideas. Moreover, we highlight the powerful synergy between learning theory, statistic, and machine learning, which together underpin the Deep Learning and CNN frameworks. We aim for this tutorial to serve as an optimal resource for students, professors, and anyone interested in understanding the foundations of Deep Learning. Upon acceptance we will provide an accompanying repository under \\href{https://github.com/neoglez/deep-learning-tutorial}{https://github.com/neoglez/deep-learning-tutorial} Keywords: Tutorial, Deep Learning, Convolutional Neural Networks, Machine Learning.", "published": "2024-08-22T11:34:34Z", "authors": ["Yansel Gonzalez Tejeda", "Helmut A. Mayer"], "categories": ["cs.AI", "cs.LG"], "doi": "10.3390/make6040132", "_query": "real estate valuation deep learning", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/1905.11027v9", "title": "A Geometric Modeling of Occam's Razor in Deep Learning", "abstract": "Why do deep neural networks (DNNs) benefit from very high dimensional parameter spaces? Their huge parameter complexities vs stunning performance in practice is all the more intriguing and not explainable using the standard theory of model selection for regular models. In this work, we propose a geometrically flavored information-theoretic approach to study this phenomenon. With the belief that simplicity is linked to better generalization, as grounded in the theory of minimum description length, the objective of our analysis is to examine and bound the complexity of DNNs. We introduce the locally varying dimensionality of the parameter space of neural network models by considering the number of significant dimensions of the Fisher information matrix, and model the parameter space as a manifold using the framework of singular semi-Riemannian geometry. We derive model complexity measures which yield short description lengths for deep neural network models based on their singularity analysis thus explaining the good performance of DNNs despite their large number of parameters.", "published": "2019-05-27T07:57:26Z", "authors": ["Ke Sun", "Frank Nielsen"], "categories": ["cs.LG", "stat.ML"], "doi": "10.1007/s41884-025-00167-2", "_query": "real estate valuation deep learning", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2510.19579v1", "title": "Multi-modal Co-learning for Earth Observation: Enhancing single-modality models via modality collaboration", "abstract": "Multi-modal co-learning is emerging as an effective paradigm in machine learning, enabling models to collaboratively learn from different modalities to enhance single-modality predictions. Earth Observation (EO) represents a quintessential domain for multi-modal data analysis, wherein diverse remote sensors collect data to sense our planet. This unprecedented volume of data introduces novel challenges. Specifically, the access to the same sensor modalities at both training and inference stages becomes increasingly complex based on real-world constraints affecting remote sensing platforms. In this context, multi-modal co-learning presents a promising strategy to leverage the vast amount of sensor-derived data available at the training stage to improve single-modality models for inference-time deployment. Most current research efforts focus on designing customized solutions for either particular downstream tasks or specific modalities available at the inference stage. To address this, we propose a novel multi-modal co-learning framework capable of generalizing across various tasks without targeting a specific modality for inference. Our approach combines contrastive and modality discriminative learning together to guide single-modality models to structure the internal model manifold into modality-shared and modality-specific information. We evaluate our framework on four EO benchmarks spanning classification and regression tasks across different sensor modalities, where only one of the modalities available during training is accessible at inference time. Our results demonstrate consistent predictive improvements over state-of-the-art approaches from the recent machine learning and computer vision literature, as well as EO-specific methods. The obtained findings validate our framework in the single-modality inference scenarios across a diverse range of EO applications.", "published": "2025-10-22T13:29:32Z", "authors": ["Francisco Mena", "Dino Ienco", "Cassio F. Dantas", "Roberto Interdonato", "Andreas Dengel"], "categories": ["cs.CV", "cs.AI", "cs.LG"], "doi": "10.1007/s10994-025-06903-0", "_query": "real estate valuation deep learning", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2103.00113v2", "title": "Anomaly Detection on Attributed Networks via Contrastive Self-Supervised Learning", "abstract": "Anomaly detection on attributed networks attracts considerable research interests due to wide applications of attributed networks in modeling a wide range of complex systems. Recently, the deep learning-based anomaly detection methods have shown promising results over shallow approaches, especially on networks with high-dimensional attributes and complex structures. However, existing approaches, which employ graph autoencoder as their backbone, do not fully exploit the rich information of the network, resulting in suboptimal performance. Furthermore, these methods do not directly target anomaly detection in their learning objective and fail to scale to large networks due to the full graph training mechanism. To overcome these limitations, in this paper, we present a novel contrastive self-supervised learning framework for anomaly detection on attributed networks. Our framework fully exploits the local information from network data by sampling a novel type of contrastive instance pair, which can capture the relationship between each node and its neighboring substructure in an unsupervised way. Meanwhile, a well-designed graph neural network-based contrastive learning model is proposed to learn informative embedding from high-dimensional attributes and local structure and measure the agreement of each instance pairs with its outputted scores. The multi-round predicted scores by the contrastive learning model are further used to evaluate the abnormality of each node with statistical estimation. In this way, the learning model is trained by a specific anomaly detection-aware target. Furthermore, since the input of the graph neural network module is batches of instance pairs instead of the full network, our framework can adapt to large networks flexibly. Experimental results show that our proposed framework outperforms the state-of-the-art baseline methods on all seven benchmark datasets.", "published": "2021-02-27T03:17:20Z", "authors": ["Yixin Liu", "Zhao Li", "Shirui Pan", "Chen Gong", "Chuan Zhou", "George Karypis"], "categories": ["cs.LG"], "doi": "10.1109/TNNLS.2021.3068344", "_query": "real estate valuation deep learning", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/1912.10798v1", "title": "Deep learning predictions of sand dune migration", "abstract": "A dry decade in the Navajo Nation has killed vegetation, dessicated soils, and released once-stable sand into the wind. This sand now covers one-third of the Nation's land, threatening roads, gardens and hundreds of homes. Many arid regions have similar problems: global warming has increased dune movement across farmland in Namibia and Angola, and the southwestern US. Current dune models, unfortunately, do not scale well enough to provide useful forecasts for the $\\sim$5\\% of land surfaces covered by mobile sand. We test the ability of two deep learning algorithms, a GAN and a CNN, to model the motion of sand dunes. The models are trained on simulated data from community-standard cellular automaton model of sand dunes. Preliminary results show the GAN producing reasonable forward predictions of dune migration at ten million times the speed of the existing model.", "published": "2019-12-13T01:17:42Z", "authors": ["Kelly Kochanski", "Divya Mohan", "Jenna Horrall", "Barry Rountree", "Ghaleb Abdulla"], "categories": ["cs.LG", "stat.ML"], "doi": "", "_query": "real estate valuation deep learning", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2106.02938v4", "title": "Energy-Based Learning for Cooperative Games, with Applications to Valuation Problems in Machine Learning", "abstract": "Valuation problems, such as feature interpretation, data valuation and model valuation for ensembles, become increasingly more important in many machine learning applications. Such problems are commonly solved by well-known game-theoretic criteria, such as Shapley value or Banzhaf value. In this work, we present a novel energy-based treatment for cooperative games, with a theoretical justification by the maximum entropy framework. Surprisingly, by conducting variational inference of the energy-based model, we recover various game-theoretic valuation criteria through conducting one-step fixed point iteration for maximizing the mean-field ELBO objective. This observation also verifies the rationality of existing criteria, as they are all attempting to decouple the correlations among the players through the mean-field approach. By running fixed point iteration for multiple steps, we achieve a trajectory of the valuations, among which we define the valuation with the best conceivable decoupling error as the Variational Index. We prove that under uniform initializations, these variational valuations all satisfy a set of game-theoretic axioms. We experimentally demonstrate that the proposed Variational Index enjoys lower decoupling error and better valuation performance on certain synthetic and real-world valuation problems.", "published": "2021-06-05T17:39:04Z", "authors": ["Yatao Bian", "Yu Rong", "Tingyang Xu", "Jiaxiang Wu", "Andreas Krause", "Junzhou Huang"], "categories": ["cs.LG"], "doi": "", "_query": "real estate valuation deep learning", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2504.08713v5", "title": "ProtoECGNet: Case-Based Interpretable Deep Learning for Multi-Label ECG Classification with Contrastive Learning", "abstract": "Deep learning-based electrocardiogram (ECG) classification has shown impressive performance but clinical adoption has been slowed by the lack of transparent and faithful explanations. Post hoc methods such as saliency maps may fail to reflect a model's true decision process. Prototype-based reasoning offers a more transparent alternative by grounding decisions in similarity to learned representations of real ECG segments, enabling faithful, case-based explanations. We introduce ProtoECGNet, a prototype-based deep learning model for interpretable, multi-label ECG classification. ProtoECGNet employs a structured, multi-branch architecture that reflects clinical interpretation workflows: it integrates a 1D CNN with global prototypes for rhythm classification, a 2D CNN with time-localized prototypes for morphology-based reasoning, and a 2D CNN with global prototypes for diffuse abnormalities. Each branch is trained with a prototype loss designed for multi-label learning, combining clustering, separation, diversity, and a novel contrastive loss that encourages appropriate separation between prototypes of unrelated classes while allowing clustering for frequently co-occurring diagnoses. We evaluate ProtoECGNet on all 71 diagnostic labels from the PTB-XL dataset, demonstrating competitive performance relative to state-of-the-art black-box models while providing structured, case-based explanations. To assess prototype quality, we conduct a structured clinician review of the final model's projected prototypes, finding that they are rated as representative and clear. ProtoECGNet shows that prototype learning can be effectively scaled to complex, multi-label time-series classification, offering a practical path toward transparent and trustworthy deep learning models for clinical decision support.", "published": "2025-04-11T17:23:37Z", "authors": ["Sahil Sethi", "David Chen", "Thomas Statchen", "Michael C. Burkhart", "Nipun Bhandari", "Bashar Ramadan", "Brett Beaulieu-Jones"], "categories": ["cs.LG", "cs.AI"], "doi": "", "_query": "real estate valuation deep learning", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2512.15521v1", "title": "Autonomous Pressure Control in MuVacAS via Deep Reinforcement Learning and Deep Learning Surrogate Models", "abstract": "The development of nuclear fusion requires materials that can withstand extreme conditions. The IFMIF-DONES facility, a high-power particle accelerator, is being designed to qualify these materials. A critical testbed for its development is the MuVacAS prototype, which replicates the final segment of the accelerator beamline. Precise regulation of argon gas pressure within its ultra-high vacuum chamber is vital for this task. This work presents a fully data-driven approach for autonomous pressure control. A Deep Learning Surrogate Model, trained on real operational data, emulates the dynamics of the argon injection system. This high-fidelity digital twin then serves as a fast-simulation environment to train a Deep Reinforcement Learning agent. The results demonstrate that the agent successfully learns a control policy that maintains gas pressure within strict operational limits despite dynamic disturbances. This approach marks a significant step toward the intelligent, autonomous control systems required for the demanding next-generation particle accelerator facilities.", "published": "2025-12-17T15:19:55Z", "authors": ["Guillermo Rodriguez-Llorente", "Galo Gallardo", "Rodrigo Morant Navascués", "Nikita Khvatkin Petrovsky", "Anderson Sabogal", "Roberto Gómez-Espinosa Martín"], "categories": ["physics.acc-ph", "cs.LG"], "doi": "", "_query": "real estate valuation deep learning", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2504.08713v5", "title": "ProtoECGNet: Case-Based Interpretable Deep Learning for Multi-Label ECG Classification with Contrastive Learning", "abstract": "Deep learning-based electrocardiogram (ECG) classification has shown impressive performance but clinical adoption has been slowed by the lack of transparent and faithful explanations. Post hoc methods such as saliency maps may fail to reflect a model's true decision process. Prototype-based reasoning offers a more transparent alternative by grounding decisions in similarity to learned representations of real ECG segments, enabling faithful, case-based explanations. We introduce ProtoECGNet, a prototype-based deep learning model for interpretable, multi-label ECG classification. ProtoECGNet employs a structured, multi-branch architecture that reflects clinical interpretation workflows: it integrates a 1D CNN with global prototypes for rhythm classification, a 2D CNN with time-localized prototypes for morphology-based reasoning, and a 2D CNN with global prototypes for diffuse abnormalities. Each branch is trained with a prototype loss designed for multi-label learning, combining clustering, separation, diversity, and a novel contrastive loss that encourages appropriate separation between prototypes of unrelated classes while allowing clustering for frequently co-occurring diagnoses. We evaluate ProtoECGNet on all 71 diagnostic labels from the PTB-XL dataset, demonstrating competitive performance relative to state-of-the-art black-box models while providing structured, case-based explanations. To assess prototype quality, we conduct a structured clinician review of the final model's projected prototypes, finding that they are rated as representative and clear. ProtoECGNet shows that prototype learning can be effectively scaled to complex, multi-label time-series classification, offering a practical path toward transparent and trustworthy deep learning models for clinical decision support.", "published": "2025-04-11T17:23:37Z", "authors": ["Sahil Sethi", "David Chen", "Thomas Statchen", "Michael C. Burkhart", "Nipun Bhandari", "Bashar Ramadan", "Brett Beaulieu-Jones"], "categories": ["cs.LG", "cs.AI"], "doi": "", "_query": "real estate valuation deep learning", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/1711.09223v1", "title": "Malaria Likelihood Prediction By Effectively Surveying Households Using Deep Reinforcement Learning", "abstract": "We build a deep reinforcement learning (RL) agent that can predict the likelihood of an individual testing positive for malaria by asking questions about their household. The RL agent learns to determine which survey question to ask next and when to stop to make a prediction about their likelihood of malaria based on their responses hitherto. The agent incurs a small penalty for each question asked, and a large reward/penalty for making the correct/wrong prediction; it thus has to learn to balance the length of the survey with the accuracy of its final predictions. Our RL agent is a Deep Q-network that learns a policy directly from the responses to the questions, with an action defined for each possible survey question and for each possible prediction class. We focus on Kenya, where malaria is a massive health burden, and train the RL agent on a dataset of 6481 households from the Kenya Malaria Indicator Survey 2015. To investigate the importance of having survey questions be adaptive to responses, we compare our RL agent to a supervised learning (SL) baseline that fixes its set of survey questions a priori. We evaluate on prediction accuracy and on the number of survey questions asked on a holdout set and find that the RL agent is able to predict with 80% accuracy, using only 2.5 questions on average. In addition, the RL agent learns to survey adaptively to responses and is able to match the SL baseline in prediction accuracy while significantly reducing survey length.", "published": "2017-11-25T09:33:05Z", "authors": ["Pranav Rajpurkar", "Vinaya Polamreddi", "Anusha Balakrishnan"], "categories": ["cs.LG", "stat.ML"], "doi": "", "_query": "real estate valuation deep learning", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2311.05304v3", "title": "Data Valuation and Detections in Federated Learning", "abstract": "Federated Learning (FL) enables collaborative model training while preserving the privacy of raw data. A challenge in this framework is the fair and efficient valuation of data, which is crucial for incentivizing clients to contribute high-quality data in the FL task. In scenarios involving numerous data clients within FL, it is often the case that only a subset of clients and datasets are pertinent to a specific learning task, while others might have either a negative or negligible impact on the model training process. This paper introduces a novel privacy-preserving method for evaluating client contributions and selecting relevant datasets without a pre-specified training algorithm in an FL task. Our proposed approach FedBary, utilizes Wasserstein distance within the federated context, offering a new solution for data valuation in the FL framework. This method ensures transparent data valuation and efficient computation of the Wasserstein barycenter and reduces the dependence on validation datasets. Through extensive empirical experiments and theoretical analyses, we demonstrate the potential of this data valuation method as a promising avenue for FL research.", "published": "2023-11-09T12:01:32Z", "authors": ["Wenqian Li", "Shuran Fu", "Fengrui Zhang", "Yan Pang"], "categories": ["cs.LG", "cs.AI", "cs.CR"], "doi": "", "_query": "real estate valuation deep learning", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2112.09668v1", "title": "Deep Learning for Spatiotemporal Modeling of Urbanization", "abstract": "Urbanization has a strong impact on the health and wellbeing of populations across the world. Predictive spatial modeling of urbanization therefore can be a useful tool for effective public health planning. Many spatial urbanization models have been developed using classic machine learning and numerical modeling techniques. However, deep learning with its proven capacity to capture complex spatiotemporal phenomena has not been applied to urbanization modeling. Here we explore the capacity of deep spatial learning for the predictive modeling of urbanization. We treat numerical geospatial data as images with pixels and channels, and enrich the dataset by augmentation, in order to leverage the high capacity of deep learning. Our resulting model can generate end-to-end multi-variable urbanization predictions, and outperforms a state-of-the-art classic machine learning urbanization model in preliminary comparisons.", "published": "2021-12-17T18:27:52Z", "authors": ["Tang Li", "Jing Gao", "Xi Peng"], "categories": ["cs.LG", "cs.AI", "cs.CV"], "doi": "", "_query": "real estate valuation deep learning", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/1605.05365v3", "title": "Dynamic Frame skip Deep Q Network", "abstract": "Deep Reinforcement Learning methods have achieved state of the art performance in learning control policies for the games in the Atari 2600 domain. One of the important parameters in the Arcade Learning Environment (ALE) is the frame skip rate. It decides the granularity at which agents can control game play. A frame skip value of $k$ allows the agent to repeat a selected action $k$ number of times. The current state of the art architectures like Deep Q-Network (DQN) and Dueling Network Architectures (DuDQN) consist of a framework with a static frame skip rate, where the action output from the network is repeated for a fixed number of frames regardless of the current state. In this paper, we propose a new architecture, Dynamic Frame skip Deep Q-Network (DFDQN) which makes the frame skip rate a dynamic learnable parameter. This allows us to choose the number of times an action is to be repeated based on the current state. We show empirically that such a setting improves the performance on relatively harder games like Seaquest.", "published": "2016-05-17T20:58:41Z", "authors": ["Aravind Srinivas", "Sahil Sharma", "Balaraman Ravindran"], "categories": ["cs.LG", "cs.AI", "cs.NE"], "doi": "", "_query": "real estate valuation deep learning", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2406.05064v3", "title": "Pretraining Decision Transformers with Reward Prediction for In-Context Multi-task Structured Bandit Learning", "abstract": "We study learning to learn for the multi-task structured bandit problem where the goal is to learn a near-optimal algorithm that minimizes cumulative regret. The tasks share a common structure and an algorithm should exploit the shared structure to minimize the cumulative regret for an unseen but related test task. We use a transformer as a decision-making algorithm to learn this shared structure from data collected by a demonstrator on a set of training task instances. Our objective is to devise a training procedure such that the transformer will learn to outperform the demonstrator's learning algorithm on unseen test task instances. Prior work on pretraining decision transformers either requires privileged information like access to optimal arms or cannot outperform the demonstrator. Going beyond these approaches, we introduce a pre-training approach that trains a transformer network to learn a near-optimal policy in-context. This approach leverages the shared structure across tasks, does not require access to optimal actions, and can outperform the demonstrator. We validate these claims over a wide variety of structured bandit problems to show that our proposed solution is general and can quickly identify expected rewards on unseen test tasks to support effective exploration.", "published": "2024-06-07T16:34:31Z", "authors": ["Subhojyoti Mukherjee", "Josiah P. Hanna", "Qiaomin Xie", "Robert Nowak"], "categories": ["cs.LG"], "doi": "", "_query": "real estate valuation deep learning", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2411.04569v1", "title": "Impact of Label Noise on Learning Complex Features", "abstract": "Neural networks trained with stochastic gradient descent exhibit an inductive bias towards simpler decision boundaries, typically converging to a narrow family of functions, and often fail to capture more complex features. This phenomenon raises concerns about the capacity of deep models to adequately learn and represent real-world datasets. Traditional approaches such as explicit regularization, data augmentation, architectural modifications, etc., have largely proven ineffective in encouraging the models to learn diverse features. In this work, we investigate the impact of pre-training models with noisy labels on the dynamics of SGD across various architectures and datasets. We show that pretraining promotes learning complex functions and diverse features in the presence of noise. Our experiments demonstrate that pre-training with noisy labels encourages gradient descent to find alternate minima that do not solely depend upon simple features, rather learns more complex and broader set of features, without hurting performance.", "published": "2024-11-07T09:47:18Z", "authors": ["Rahul Vashisht", "P. Krishna Kumar", "Harsha Vardhan Govind", "Harish G. Ramaswamy"], "categories": ["cs.LG", "cs.AI"], "doi": "", "_query": "real estate valuation deep learning", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/1311.1761v1", "title": "Exploring Deep and Recurrent Architectures for Optimal Control", "abstract": "Sophisticated multilayer neural networks have achieved state of the art results on multiple supervised tasks. However, successful applications of such multilayer networks to control have so far been limited largely to the perception portion of the control pipeline. In this paper, we explore the application of deep and recurrent neural networks to a continuous, high-dimensional locomotion task, where the network is used to represent a control policy that maps the state of the system (represented by joint angles) directly to the torques at each joint. By using a recent reinforcement learning algorithm called guided policy search, we can successfully train neural network controllers with thousands of parameters, allowing us to compare a variety of architectures. We discuss the differences between the locomotion control task and previous supervised perception tasks, present experimental results comparing various architectures, and discuss future directions in the application of techniques from deep learning to the problem of optimal control.", "published": "2013-11-07T17:39:31Z", "authors": ["Sergey Levine"], "categories": ["cs.LG", "cs.AI", "cs.NE", "cs.RO", "eess.SY"], "doi": "", "_query": "real estate valuation deep learning", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2212.13067v3", "title": "Online Active Learning for Soft Sensor Development using Semi-Supervised Autoencoders", "abstract": "Data-driven soft sensors are extensively used in industrial and chemical processes to predict hard-to-measure process variables whose real value is difficult to track during routine operations. The regression models used by these sensors often require a large number of labeled examples, yet obtaining the label information can be very expensive given the high time and cost required by quality inspections. In this context, active learning methods can be highly beneficial as they can suggest the most informative labels to query. However, most of the active learning strategies proposed for regression focus on the offline setting. In this work, we adapt some of these approaches to the stream-based scenario and show how they can be used to select the most informative data points. We also demonstrate how to use a semi-supervised architecture based on orthogonal autoencoders to learn salient features in a lower dimensional space. The Tennessee Eastman Process is used to compare the predictive performance of the proposed approaches.", "published": "2022-12-26T09:45:41Z", "authors": ["Davide Cacciarelli", "Murat Kulahci", "John Tyssedal"], "categories": ["cs.LG", "stat.ML"], "doi": "", "_query": "real estate valuation deep learning", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2208.14376v1", "title": "Associative Learning for Network Embedding", "abstract": "The network embedding task is to represent the node in the network as a low-dimensional vector while incorporating the topological and structural information. Most existing approaches solve this problem by factorizing a proximity matrix, either directly or implicitly. In this work, we introduce a network embedding method from a new perspective, which leverages Modern Hopfield Networks (MHN) for associative learning. Our network learns associations between the content of each node and that node's neighbors. These associations serve as memories in the MHN. The recurrent dynamics of the network make it possible to recover the masked node, given that node's neighbors. Our proposed method is evaluated on different downstream tasks such as node classification and linkage prediction. The results show competitive performance compared to the common matrix factorization techniques and deep learning based methods.", "published": "2022-08-30T16:35:45Z", "authors": ["Yuchen Liang", "Dmitry Krotov", "Mohammed J. Zaki"], "categories": ["cs.LG", "cs.NE", "cs.SI", "q-bio.NC", "stat.ML"], "doi": "", "_query": "real estate valuation deep learning", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2307.02694v5", "title": "Loss Functions and Metrics in Deep Learning", "abstract": "This paper presents a comprehensive review of loss functions and performance metrics in deep learning, highlighting key developments and practical insights across diverse application areas. We begin by outlining fundamental considerations in classic tasks such as regression and classification, then extend our analysis to specialized domains like computer vision and natural language processing including retrieval-augmented generation. In each setting, we systematically examine how different loss functions and evaluation metrics can be paired to address task-specific challenges such as class imbalance, outliers, and sequence-level optimization. Key contributions of this work include: (1) a unified framework for understanding how losses and metrics align with different learning objectives, (2) an in-depth discussion of multi-loss setups that balance competing goals, and (3) new insights into specialized metrics used to evaluate modern applications like retrieval-augmented generation, where faithfulness and context relevance are pivotal. Along the way, we highlight best practices for selecting or combining losses and metrics based on empirical behaviors and domain constraints. Finally, we identify open problems and promising directions, including the automation of loss-function search and the development of robust, interpretable evaluation measures for increasingly complex deep learning tasks. Our review aims to equip researchers and practitioners with clearer guidance in designing effective training pipelines and reliable model assessments for a wide spectrum of real-world applications.", "published": "2023-07-05T23:53:55Z", "authors": ["Juan Terven", "Diana M. Cordova-Esparza", "Alfonso Ramirez-Pedraza", "Edgar A. Chavez-Urbiola", "Julio A. Romero-Gonzalez"], "categories": ["cs.LG", "cs.AI", "cs.CV"], "doi": "10.1007/s10462-025-11198-7", "_query": "real estate valuation deep learning", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/1812.00596v1", "title": "Deep Learning Approach for Predicting 30 Day Readmissions after Coronary Artery Bypass Graft Surgery", "abstract": "Hospital Readmissions within 30 days after discharge following Coronary Artery Bypass Graft (CABG) Surgery are substantial contributors to healthcare costs. Many predictive models were developed to identify risk factors for readmissions. However, majority of the existing models use statistical analysis techniques with data available at discharge. We propose an ensembled model to predict CABG readmissions using pre-discharge perioperative data and machine learning survival analysis techniques. Firstly, we applied fifty one potential readmission risk variables to Cox Proportional Hazard (CPH) survival regression univariate analysis. Fourteen of them turned out to be significant (with p value < 0.05), contributing to readmissions. Subsequently, we applied these 14 predictors to multivariate CPH model and Deep Learning Neural Network (NN) representation of the CPH model, DeepSurv. We validated this new ensembled model with 453 isolated adult CABG cases. Nine of the fourteen perioperative risk variables were identified as the most significant with Hazard Ratios (HR) of greater than 1.0. The concordance index metrics for CPH, DeepSurv, and ensembled models were then evaluated with training and validation datasets. Our ensembled model yielded promising results in terms of c-statistics, as we raised the the number of iterations and data set sizes. 30 day all-cause readmissions among isolated CABG patients can be predicted more effectively with perioperative pre-discharge data, using machine learning survival analysis techniques. Prediction accuracy levels could be improved further with deep learning algorithms.", "published": "2018-12-03T08:20:36Z", "authors": ["Ramesh B. Manyam", "Yanqing Zhang", "William B. Keeling", "Jose Binongo", "Michael Kayatta", "Seth Carter"], "categories": ["cs.LG", "q-bio.QM", "stat.ML"], "doi": "", "_query": "real estate valuation deep learning", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/1610.07940v2", "title": "End-to-end Learning of Deep Visual Representations for Image Retrieval", "abstract": "While deep learning has become a key ingredient in the top performing methods for many computer vision tasks, it has failed so far to bring similar improvements to instance-level image retrieval. In this article, we argue that reasons for the underwhelming results of deep methods on image retrieval are threefold: i) noisy training data, ii) inappropriate deep architecture, and iii) suboptimal training procedure. We address all three issues. First, we leverage a large-scale but noisy landmark dataset and develop an automatic cleaning method that produces a suitable training set for deep retrieval. Second, we build on the recent R-MAC descriptor, show that it can be interpreted as a deep and differentiable architecture, and present improvements to enhance it. Last, we train this network with a siamese architecture that combines three streams with a triplet loss. At the end of the training process, the proposed architecture produces a global image representation in a single forward pass that is well suited for image retrieval. Extensive experiments show that our approach significantly outperforms previous retrieval approaches, including state-of-the-art methods based on costly local descriptor indexing and spatial verification. On Oxford 5k, Paris 6k and Holidays, we respectively report 94.7, 96.6, and 94.8 mean average precision. Our representations can also be heavily compressed using product quantization with little loss in accuracy. For additional material, please see www.xrce.xerox.com/Deep-Image-Retrieval.", "published": "2016-10-25T16:02:42Z", "authors": ["Albert Gordo", "Jon Almazan", "Jerome Revaud", "Diane Larlus"], "categories": ["cs.CV"], "doi": "", "_query": "real estate valuation deep learning", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2410.08355v3", "title": "Metalic: Meta-Learning In-Context with Protein Language Models", "abstract": "Predicting the biophysical and functional properties of proteins is essential for in silico protein design. Machine learning has emerged as a promising technique for such prediction tasks. However, the relative scarcity of in vitro annotations means that these models often have little, or no, specific data on the desired fitness prediction task. As a result of limited data, protein language models (PLMs) are typically trained on general protein sequence modeling tasks, and then fine-tuned, or applied zero-shot, to protein fitness prediction. When no task data is available, the models make strong assumptions about the correlation between the protein sequence likelihood and fitness scores. In contrast, we propose meta-learning over a distribution of standard fitness prediction tasks, and demonstrate positive transfer to unseen fitness prediction tasks. Our method, called Metalic (Meta-Learning In-Context), uses in-context learning and fine-tuning, when data is available, to adapt to new tasks. Crucially, fine-tuning enables considerable generalization, even though it is not accounted for during meta-training. Our fine-tuned models achieve strong results with 18 times fewer parameters than state-of-the-art models. Moreover, our method sets a new state-of-the-art in low-data settings on ProteinGym, an established fitness-prediction benchmark. Due to data scarcity, we believe meta-learning will play a pivotal role in advancing protein engineering.", "published": "2024-10-10T20:19:35Z", "authors": ["Jacob Beck", "Shikha Surana", "Manus McAuliffe", "Oliver Bent", "Thomas D. Barrett", "Juan Jose Garau Luis", "Paul Duckworth"], "categories": ["cs.LG"], "doi": "", "_query": "real estate valuation deep learning", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2002.06470v4", "title": "Pitfalls of In-Domain Uncertainty Estimation and Ensembling in Deep Learning", "abstract": "Uncertainty estimation and ensembling methods go hand-in-hand. Uncertainty estimation is one of the main benchmarks for assessment of ensembling performance. At the same time, deep learning ensembles have provided state-of-the-art results in uncertainty estimation. In this work, we focus on in-domain uncertainty for image classification. We explore the standards for its quantification and point out pitfalls of existing metrics. Avoiding these pitfalls, we perform a broad study of different ensembling techniques. To provide more insight in this study, we introduce the deep ensemble equivalent score (DEE) and show that many sophisticated ensembling techniques are equivalent to an ensemble of only few independently trained networks in terms of test performance.", "published": "2020-02-15T23:28:19Z", "authors": ["Arsenii Ashukha", "Alexander Lyzhov", "Dmitry Molchanov", "Dmitry Vetrov"], "categories": ["stat.ML", "cs.LG"], "doi": "", "_query": "real estate valuation deep learning", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/1705.00470v2", "title": "Learning Multimodal Transition Dynamics for Model-Based Reinforcement Learning", "abstract": "In this paper we study how to learn stochastic, multimodal transition dynamics in reinforcement learning (RL) tasks. We focus on evaluating transition function estimation, while we defer planning over this model to future work. Stochasticity is a fundamental property of many task environments. However, discriminative function approximators have difficulty estimating multimodal stochasticity. In contrast, deep generative models do capture complex high-dimensional outcome distributions. First we discuss why, amongst such models, conditional variational inference (VI) is theoretically most appealing for model-based RL. Subsequently, we compare different VI models on their ability to learn complex stochasticity on simulated functions, as well as on a typical RL gridworld with multimodal dynamics. Results show VI successfully predicts multimodal outcomes, but also robustly ignores these for deterministic parts of the transition dynamics. In summary, we show a robust method to learn multimodal transitions using function approximation, which is a key preliminary for model-based RL in stochastic domains.", "published": "2017-05-01T11:06:04Z", "authors": ["Thomas M. Moerland", "Joost Broekens", "Catholijn M. Jonker"], "categories": ["stat.ML", "cs.LG"], "doi": "", "_query": "real estate valuation deep learning", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/1904.02868v2", "title": "Data Shapley: Equitable Valuation of Data for Machine Learning", "abstract": "As data becomes the fuel driving technological and economic growth, a fundamental challenge is how to quantify the value of data in algorithmic predictions and decisions. For example, in healthcare and consumer markets, it has been suggested that individuals should be compensated for the data that they generate, but it is not clear what is an equitable valuation for individual data. In this work, we develop a principled framework to address data valuation in the context of supervised machine learning. Given a learning algorithm trained on $n$ data points to produce a predictor, we propose data Shapley as a metric to quantify the value of each training datum to the predictor performance. Data Shapley value uniquely satisfies several natural properties of equitable data valuation. We develop Monte Carlo and gradient-based methods to efficiently estimate data Shapley values in practical settings where complex learning algorithms, including neural networks, are trained on large datasets. In addition to being equitable, extensive experiments across biomedical, image and synthetic data demonstrate that data Shapley has several other benefits: 1) it is more powerful than the popular leave-one-out or leverage score in providing insight on what data is more valuable for a given learning task; 2) low Shapley value data effectively capture outliers and corruptions; 3) high Shapley value data inform what type of new data to acquire to improve the predictor.", "published": "2019-04-05T04:54:10Z", "authors": ["Amirata Ghorbani", "James Zou"], "categories": ["stat.ML", "cs.AI", "cs.LG"], "doi": "", "_query": "real estate valuation deep learning", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2204.07254v1", "title": "Methodical Advice Collection and Reuse in Deep Reinforcement Learning", "abstract": "Reinforcement learning (RL) has shown great success in solving many challenging tasks via use of deep neural networks. Although using deep learning for RL brings immense representational power, it also causes a well-known sample-inefficiency problem. This means that the algorithms are data-hungry and require millions of training samples to converge to an adequate policy. One way to combat this issue is to use action advising in a teacher-student framework, where a knowledgeable teacher provides action advice to help the student. This work considers how to better leverage uncertainties about when a student should ask for advice and if the student can model the teacher to ask for less advice. The student could decide to ask for advice when it is uncertain or when both it and its model of the teacher are uncertain. In addition to this investigation, this paper introduces a new method to compute uncertainty for a deep RL agent using a secondary neural network. Our empirical results show that using dual uncertainties to drive advice collection and reuse may improve learning performance across several Atari games.", "published": "2022-04-14T22:24:55Z", "authors": [" Sahir", "Ercüment İlhan", "Srijita Das", "Matthew E. Taylor"], "categories": ["cs.LG", "cs.AI", "cs.MA"], "doi": "", "_query": "real estate valuation deep learning", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2001.09745v3", "title": "Constraints and Benefits of the Blockchain Use for Real Estate and Property Rights", "abstract": "Many recent social media posts and news may create a perception of big success in the use of blockchain for the real estate industry, land registration and protection of titles and property rights. A sobering outlook is crucial because misleading concepts may bury the whole idea of blockchain use. The paper aims to research the possibilities of blockchain and other distributed ledger technologies (DLT) and applicability of these technologies for different purposes in real estate, property rights and public registries. Blockchain, which is distinguished from permissioned systems as the technology of the immutable ledger that does not require authorities, is a new word in governance. However, this technology has some principal features that can restrain its implementation at the state level, and thus require further research and development. The application of blockchain requires a proper architecture of overlaid technologies to support changes of outdated and mistaken data, address issues of digital identity and privacy, legal compliance and enforceability of smart contracts and scalability of the ledger. This paper shows the constraints of the technology's properties which were not explained before in the context of title rights and land registration even though technological limits are known in more specific technical sources. Along with the known benefits this meant to help to avoid misinterpretation of some DLT features by non-technical people. A multidisciplinary approach in analysing the technology and laws helped to better understand what can and cannot be beneficial for public registries and the protection of property rights. The presented outcomes can be laid down as requirements for the technical protocols aimed at addressing the issues of DLT and public policies to put blockchain at the service of society.", "published": "2020-01-04T04:11:01Z", "authors": ["Oleksii Konashevych"], "categories": ["cs.CY"], "doi": "", "_query": "real estate valuation deep learning", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/1401.7615v1", "title": "Testing for rational speculative bubbles in the Brazilian residential real-estate market", "abstract": "Speculative bubbles have been occurring periodically in local or global real estate markets and are considered a potential cause of economic crises. In this context, the detection of explosive behaviors in the financial market and the implementation of early warning diagnosis tests are of critical importance. The recent increase in Brazilian housing prices has risen concerns that the Brazilian economy may have a speculative housing bubble. In the present paper, we employ a recently proposed recursive unit root test in order to identify possible speculative bubbles in data from the Brazilian residential real-estate market. The empirical results show evidence for speculative price bubbles both in Rio de Janeiro and Sao Paulo, the two main Brazilian cities.", "published": "2014-01-29T18:25:57Z", "authors": ["Marcelo M. de Oliveira", "Alexandre C. L. Almeida"], "categories": ["q-fin.GN"], "doi": "", "_query": "real estate valuation deep learning", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/1012.3476v1", "title": "Adaptive Parallel Tempering for Stochastic Maximum Likelihood Learning of RBMs", "abstract": "Restricted Boltzmann Machines (RBM) have attracted a lot of attention of late, as one the principle building blocks of deep networks. Training RBMs remains problematic however, because of the intractibility of their partition function. The maximum likelihood gradient requires a very robust sampler which can accurately sample from the model despite the loss of ergodicity often incurred during learning. While using Parallel Tempering in the negative phase of Stochastic Maximum Likelihood (SML-PT) helps address the issue, it imposes a trade-off between computational complexity and high ergodicity, and requires careful hand-tuning of the temperatures. In this paper, we show that this trade-off is unnecessary. The choice of optimal temperatures can be automated by minimizing average return time (a concept first proposed by [Katzgraber et al., 2006]) while chains can be spawned dynamically, as needed, thus minimizing the computational overhead. We show on a synthetic dataset, that this results in better likelihood scores.", "published": "2010-12-15T21:23:09Z", "authors": ["Guillaume Desjardins", "Aaron Courville", "Yoshua Bengio"], "categories": ["stat.ML", "cs.NE"], "doi": "", "_query": "real estate valuation deep learning", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2512.14002v1", "title": "Real-Time Service Subscription and Adaptive Offloading Control in Vehicular Edge Computing", "abstract": "Vehicular Edge Computing (VEC) has emerged as a promising paradigm for enhancing the computational efficiency and service quality in intelligent transportation systems by enabling vehicles to wirelessly offload computation-intensive tasks to nearby Roadside Units. However, efficient task offloading and resource allocation for time-critical applications in VEC remain challenging due to constrained network bandwidth and computational resources, stringent task deadlines, and rapidly changing network conditions. To address these challenges, we formulate a Deadline-Constrained Task Offloading and Resource Allocation Problem (DOAP), denoted as $\\mathbf{P}$, in VEC with both bandwidth and computational resource constraints, aiming to maximize the total vehicle utility. To solve $\\mathbf{P}$, we propose $\\mathtt{SARound}$, an approximation algorithm based on Linear Program rounding and local-ratio techniques, that improves the best-known approximation ratio for DOAP from $\\frac{1}{6}$ to $\\frac{1}{4}$. Additionally, we design an online service subscription and offloading control framework to address the challenges of short task deadlines and rapidly changing wireless network conditions. To validate our approach, we develop a comprehensive VEC simulator, VecSim, using the open-source simulation libraries OMNeT++ and Simu5G. VecSim integrates our designed framework to manage the full life-cycle of real-time vehicular tasks. Experimental results, based on profiled object detection applications and real-world taxi trace data, show that $\\mathtt{SARound}$ consistently outperforms state-of-the-art baselines under varying network conditions while maintaining runtime efficiency.", "published": "2025-12-16T01:49:52Z", "authors": ["Chuanchao Gao", "Arvind Easwaran"], "categories": ["cs.DC", "cs.DM"], "doi": "10.1109/RTSS66672.2025.00023", "_query": "real estate valuation deep learning", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2312.13277v2", "title": "Deep Learning on Object-centric 3D Neural Fields", "abstract": "In recent years, Neural Fields (NFs) have emerged as an effective tool for encoding diverse continuous signals such as images, videos, audio, and 3D shapes. When applied to 3D data, NFs offer a solution to the fragmentation and limitations associated with prevalent discrete representations. However, given that NFs are essentially neural networks, it remains unclear whether and how they can be seamlessly integrated into deep learning pipelines for solving downstream tasks. This paper addresses this research problem and introduces nf2vec, a framework capable of generating a compact latent representation for an input NF in a single inference pass. We demonstrate that nf2vec effectively embeds 3D objects represented by the input NFs and showcase how the resulting embeddings can be employed in deep learning pipelines to successfully address various tasks, all while processing exclusively NFs. We test this framework on several NFs used to represent 3D surfaces, such as unsigned/signed distance and occupancy fields. Moreover, we demonstrate the effectiveness of our approach with more complex NFs that encompass both geometry and appearance of 3D objects such as neural radiance fields.", "published": "2023-12-20T18:56:45Z", "authors": ["Pierluigi Zama Ramirez", "Luca De Luigi", "Daniele Sirocchi", "Adriano Cardace", "Riccardo Spezialetti", "Francesco Ballerini", "Samuele Salti", "Luigi Di Stefano"], "categories": ["cs.CV"], "doi": "", "_query": "real estate valuation deep learning", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/1912.12630v1", "title": "Real-time Policy Distillation in Deep Reinforcement Learning", "abstract": "Policy distillation in deep reinforcement learning provides an effective way to transfer control policies from a larger network to a smaller untrained network without a significant degradation in performance. However, policy distillation is underexplored in deep reinforcement learning, and existing approaches are computationally inefficient, resulting in a long distillation time. In addition, the effectiveness of the distillation process is still limited to the model capacity. We propose a new distillation mechanism, called real-time policy distillation, in which training the teacher model and distilling the policy to the student model occur simultaneously. Accordingly, the teacher's latest policy is transferred to the student model in real time. This reduces the distillation time to half the original time or even less and also makes it possible for extremely small student models to learn skills at the expert level. We evaluated the proposed algorithm in the Atari 2600 domain. The results show that our approach can achieve full distillation in most games, even with compression ratios up to 1.7%.", "published": "2019-12-29T11:10:37Z", "authors": ["Yuxiang Sun", "Pooyan Fazli"], "categories": ["cs.LG", "cs.AI", "stat.ML"], "doi": "", "_query": "real estate valuation deep learning", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/1711.03156v2", "title": "Learning Deep Mean Field Games for Modeling Large Population Behavior", "abstract": "We consider the problem of representing collective behavior of large populations and predicting the evolution of a population distribution over a discrete state space. A discrete time mean field game (MFG) is motivated as an interpretable model founded on game theory for understanding the aggregate effect of individual actions and predicting the temporal evolution of population distributions. We achieve a synthesis of MFG and Markov decision processes (MDP) by showing that a special MFG is reducible to an MDP. This enables us to broaden the scope of mean field game theory and infer MFG models of large real-world systems via deep inverse reinforcement learning. Our method learns both the reward function and forward dynamics of an MFG from real data, and we report the first empirical test of a mean field game model of a real-world social media population.", "published": "2017-11-08T20:43:39Z", "authors": ["Jiachen Yang", "Xiaojing Ye", "Rakshit Trivedi", "Huan Xu", "Hongyuan Zha"], "categories": ["cs.LG", "cs.CE"], "doi": "", "_query": "real estate valuation deep learning", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/1911.08856v1", "title": "Learning Generalized Quasi-Geostrophic Models Using Deep Neural Numerical Models", "abstract": "We introduce a new strategy designed to help physicists discover hidden laws governing dynamical systems. We propose to use machine learning automatic differentiation libraries to develop hybrid numerical models that combine components based on prior physical knowledge with components based on neural networks. In these architectures, named Deep Neural Numerical Models (DNNMs), the neural network components are used as building-blocks then deployed for learning hidden variables of underlying physical laws governing dynamical systems. In this paper, we illustrate an application of DNNMs to upper ocean dynamics, more precisely the dynamics of a sea surface tracer, the Sea Surface Height (SSH). We develop an advection-based fully differentiable numerical scheme, where parts of the computations can be replaced with learnable ConvNets, and make connections with the single-layer Quasi-Geostrophic (QG) model, a baseline theory in physical oceanography developed decades ago.", "published": "2019-11-20T12:30:58Z", "authors": ["Redouane Lguensat", "Julien Le Sommer", "Sammy Metref", "Emmanuel Cosme", "Ronan Fablet"], "categories": ["cs.LG", "physics.ao-ph", "stat.ML"], "doi": "", "_query": "real estate valuation deep learning", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2309.01592v3", "title": "Les Houches Lectures on Deep Learning at Large & Infinite Width", "abstract": "These lectures, presented at the 2022 Les Houches Summer School on Statistical Physics and Machine Learning, focus on the infinite-width limit and large-width regime of deep neural networks. Topics covered include various statistical and dynamical properties of these networks. In particular, the lecturers discuss properties of random deep neural networks; connections between trained deep neural networks, linear models, kernels, and Gaussian processes that arise in the infinite-width limit; and perturbative and non-perturbative treatments of large but finite-width networks, at initialization and after training.", "published": "2023-09-04T13:21:18Z", "authors": ["Yasaman Bahri", "Boris Hanin", "Antonin Brossollet", "Vittorio Erba", "Christian Keup", "Rosalba Pacelli", "James B. Simon"], "categories": ["stat.ML", "cs.AI", "cs.LG", "hep-th", "math.PR"], "doi": "", "_query": "real estate valuation deep learning", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/1905.12588v2", "title": "Meta-Learning Representations for Continual Learning", "abstract": "A continual learning agent should be able to build on top of existing knowledge to learn on new data quickly while minimizing forgetting. Current intelligent systems based on neural network function approximators arguably do the opposite---they are highly prone to forgetting and rarely trained to facilitate future learning. One reason for this poor behavior is that they learn from a representation that is not explicitly trained for these two goals. In this paper, we propose OML, an objective that directly minimizes catastrophic interference by learning representations that accelerate future learning and are robust to forgetting under online updates in continual learning. We show that it is possible to learn naturally sparse representations that are more effective for online updating. Moreover, our algorithm is complementary to existing continual learning strategies, such as MER and GEM. Finally, we demonstrate that a basic online updating strategy on representations learned by OML is competitive with rehearsal based methods for continual learning. We release an implementation of our method at https://github.com/khurramjaved96/mrcl .", "published": "2019-05-29T17:09:31Z", "authors": ["Khurram Javed", "Martha White"], "categories": ["cs.LG", "cs.AI", "stat.ML"], "doi": "", "_query": "real estate valuation deep learning", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2309.05221v1", "title": "Exploring the Law of Numbers: Evidence from China's Real Estate", "abstract": "The renowned proverb, Numbers do not lie, underscores the reliability and insight that lie beneath numbers, a concept of undisputed importance, especially in economics and finance etc. Despite the prosperity of Benford's Law in the first digit analysis, its scope fails to remain comprehensiveness when it comes to deciphering the laws of number. This paper delves into number laws by taking the financial statements of China real estate as a representative, quantitatively study not only the first digit, but also depict the other two dimensions of numbers: frequency and length. The research outcomes transcend mere reservations about data manipulation and open the door to discussions surrounding number diversity and the delineation of the usage insights. This study wields both economic significance and the capacity to foster a deeper comprehension of numerical phenomena.", "published": "2023-09-11T03:54:38Z", "authors": ["Fuqian Zhang", "Zhenhua Wang"], "categories": ["cs.CL"], "doi": "", "_query": "real estate valuation deep learning", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2310.11910v1", "title": "Multi-modal Medical Neurological Image Fusion using Wavelet Pooled Edge Preserving Autoencoder", "abstract": "Medical image fusion integrates the complementary diagnostic information of the source image modalities for improved visualization and analysis of underlying anomalies. Recently, deep learning-based models have excelled the conventional fusion methods by executing feature extraction, feature selection, and feature fusion tasks, simultaneously. However, most of the existing convolutional neural network (CNN) architectures use conventional pooling or strided convolutional strategies to downsample the feature maps. It causes the blurring or loss of important diagnostic information and edge details available in the source images and dilutes the efficacy of the feature extraction process. Therefore, this paper presents an end-to-end unsupervised fusion model for multimodal medical images based on an edge-preserving dense autoencoder network. In the proposed model, feature extraction is improved by using wavelet decomposition-based attention pooling of feature maps. This helps in preserving the fine edge detail information present in both the source images and enhances the visual perception of fused images. Further, the proposed model is trained on a variety of medical image pairs which helps in capturing the intensity distributions of the source images and preserves the diagnostic information effectively. Substantial experiments are conducted which demonstrate that the proposed method provides improved visual and quantitative results as compared to the other state-of-the-art fusion methods.", "published": "2023-10-18T11:59:35Z", "authors": ["Manisha Das", "Deep Gupta", "Petia Radeva", "Ashwini M Bakde"], "categories": ["eess.IV", "cs.CV", "cs.LG"], "doi": "", "_query": "real estate valuation deep learning", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2511.05396v1", "title": "Sample Complexity of Distributionally Robust Off-Dynamics Reinforcement Learning with Online Interaction", "abstract": "Off-dynamics reinforcement learning (RL), where training and deployment transition dynamics are different, can be formulated as learning in a robust Markov decision process (RMDP) where uncertainties in transition dynamics are imposed. Existing literature mostly assumes access to generative models allowing arbitrary state-action queries or pre-collected datasets with a good state coverage of the deployment environment, bypassing the challenge of exploration. In this work, we study a more realistic and challenging setting where the agent is limited to online interaction with the training environment. To capture the intrinsic difficulty of exploration in online RMDPs, we introduce the supremal visitation ratio, a novel quantity that measures the mismatch between the training dynamics and the deployment dynamics. We show that if this ratio is unbounded, online learning becomes exponentially hard. We propose the first computationally efficient algorithm that achieves sublinear regret in online RMDPs with $f$-divergence based transition uncertainties. We also establish matching regret lower bounds, demonstrating that our algorithm achieves optimal dependence on both the supremal visitation ratio and the number of interaction episodes. Finally, we validate our theoretical results through comprehensive numerical experiments.", "published": "2025-11-07T16:24:22Z", "authors": ["Yiting He", "Zhishuai Liu", "Weixin Wang", "Pan Xu"], "categories": ["cs.LG", "cs.AI", "cs.RO", "stat.ML"], "doi": "", "_query": "real estate valuation deep learning", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2506.13672v1", "title": "The Courage to Stop: Overcoming Sunk Cost Fallacy in Deep Reinforcement Learning", "abstract": "Off-policy deep reinforcement learning (RL) typically leverages replay buffers for reusing past experiences during learning. This can help improve sample efficiency when the collected data is informative and aligned with the learning objectives; when that is not the case, it can have the effect of \"polluting\" the replay buffer with data which can exacerbate optimization challenges in addition to wasting environment interactions due to wasteful sampling. We argue that sampling these uninformative and wasteful transitions can be avoided by addressing the sunk cost fallacy, which, in the context of deep RL, is the tendency towards continuing an episode until termination. To address this, we propose learn to stop (LEAST), a lightweight mechanism that enables strategic early episode termination based on Q-value and gradient statistics, which helps agents recognize when to terminate unproductive episodes early. We demonstrate that our method improves learning efficiency on a variety of RL algorithms, evaluated on both the MuJoCo and DeepMind Control Suite benchmarks.", "published": "2025-06-16T16:30:00Z", "authors": ["Jiashun Liu", "Johan Obando-Ceron", "Pablo Samuel Castro", "Aaron Courville", "Ling Pan"], "categories": ["cs.LG"], "doi": "", "_query": "real estate valuation deep learning", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/1911.04947v2", "title": "Accelerating Training in Pommerman with Imitation and Reinforcement Learning", "abstract": "The Pommerman simulation was recently developed to mimic the classic Japanese game Bomberman, and focuses on competitive gameplay in a multi-agent setting. We focus on the 2$\\times$2 team version of Pommerman, developed for a competition at NeurIPS 2018. Our methodology involves training an agent initially through imitation learning on a noisy expert policy, followed by a proximal-policy optimization (PPO) reinforcement learning algorithm. The basic PPO approach is modified for stable transition from the imitation learning phase through reward shaping, action filters based on heuristics, and curriculum learning. The proposed methodology is able to beat heuristic and pure reinforcement learning baselines with a combined 100,000 training games, significantly faster than other non-tree-search methods in literature. We present results against multiple agents provided by the developers of the simulation, including some that we have enhanced. We include a sensitivity analysis over different parameters, and highlight undesirable effects of some strategies that initially appear promising. Since Pommerman is a complex multi-agent competitive environment, the strategies developed here provide insights into several real-world problems with characteristics such as partial observability, decentralized execution (without communication), and very sparse and delayed rewards.", "published": "2019-11-12T15:50:18Z", "authors": ["Hardik Meisheri", "Omkar Shelke", "Richa Verma", "Harshad Khadilkar"], "categories": ["cs.LG", "stat.ML"], "doi": "", "_query": "real estate valuation deep learning", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2407.08250v3", "title": "Gradient Boosting Reinforcement Learning", "abstract": "We present Gradient Boosting Reinforcement Learning (GBRL), a framework that adapts the strengths of gradient boosting trees (GBT) to reinforcement learning (RL) tasks. While neural networks (NNs) have become the de facto choice for RL, they face significant challenges with structured and categorical features and tend to generalize poorly to out-of-distribution samples. These are challenges for which GBTs have traditionally excelled in supervised learning. However, GBT's application in RL has been limited. The design of traditional GBT libraries is optimized for static datasets with fixed labels, making them incompatible with RL's dynamic nature, where both state distributions and reward signals evolve during training. GBRL overcomes this limitation by continuously interleaving tree construction with environment interaction. Through extensive experiments, we demonstrate that GBRL outperforms NNs in domains with structured observations and categorical features while maintaining competitive performance on standard continuous control benchmarks. Like its supervised learning counterpart, GBRL demonstrates superior robustness to out-of-distribution samples and better handles irregular state-action relationships.", "published": "2024-07-11T07:52:33Z", "authors": ["Benjamin Fuhrer", "Chen Tessler", "Gal Dalal"], "categories": ["cs.LG", "cs.AI"], "doi": "", "_query": "real estate valuation deep learning", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/1807.11459v1", "title": "Improving Transferability of Deep Neural Networks", "abstract": "Learning from small amounts of labeled data is a challenge in the area of deep learning. This is currently addressed by Transfer Learning where one learns the small data set as a transfer task from a larger source dataset. Transfer Learning can deliver higher accuracy if the hyperparameters and source dataset are chosen well. One of the important parameters is the learning rate for the layers of the neural network. We show through experiments on the ImageNet22k and Oxford Flowers datasets that improvements in accuracy in range of 127% can be obtained by proper choice of learning rates. We also show that the images/label parameter for a dataset can potentially be used to determine optimal learning rates for the layers to get the best overall accuracy. We additionally validate this method on a sample of real-world image classification tasks from a public visual recognition API.", "published": "2018-07-30T17:34:24Z", "authors": ["Parijat Dube", "Bishwaranjan Bhattacharjee", "Elisabeth Petit-Bois", "Matthew Hill"], "categories": ["cs.CV", "cs.LG", "stat.ML"], "doi": "", "_query": "real estate valuation deep learning", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2210.13599v1", "title": "Noise Injection as a Probe of Deep Learning Dynamics", "abstract": "We propose a new method to probe the learning mechanism of Deep Neural Networks (DNN) by perturbing the system using Noise Injection Nodes (NINs). These nodes inject uncorrelated noise via additional optimizable weights to existing feed-forward network architectures, without changing the optimization algorithm. We find that the system displays distinct phases during training, dictated by the scale of injected noise. We first derive expressions for the dynamics of the network and utilize a simple linear model as a test case. We find that in some cases, the evolution of the noise nodes is similar to that of the unperturbed loss, thus indicating the possibility of using NINs to learn more about the full system in the future.", "published": "2022-10-24T20:51:59Z", "authors": ["Noam Levi", "Itay Bloch", "Marat Freytsis", "Tomer Volansky"], "categories": ["cs.LG", "cond-mat.stat-mech", "cs.AI", "stat.ML"], "doi": "", "_query": "real estate valuation deep learning", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2510.01345v2", "title": "Self-Supervised Representation Learning as Mutual Information Maximization", "abstract": "Self-supervised representation learning (SSRL) has demonstrated remarkable empirical success, yet its underlying principles remain insufficiently understood. While recent works attempt to unify SSRL methods by examining their information-theoretic objectives or summarizing their heuristics for preventing representation collapse, architectural elements like predictor networks, stop-gradient operations, and statistical regularizers are often viewed as empirically motivated additions. In this paper, we adopt a first-principles approach and investigate whether the learning objective of an SSRL algorithm dictates its possible optimization strategies and model design choices. In particular, by starting from a variational mutual information (MI) lower bound, we derive two training paradigms, namely Self-Distillation MI (SDMI) and Joint MI (JMI), each imposing distinct structural constraints and covering a set of existing SSRL algorithms. SDMI relies on alternating optimization, in which stop-gradient operations serve as a principled mechanism for realizing the alternating updates. In contrast, JMI admits joint optimization through symmetric architectures without such components. Under the proposed formulation, predictor networks in SDMI and statistical regularizers in JMI emerge as tractable surrogates for the MI objective. We show that many existing SSRL methods are specific instances or approximations of these two paradigms. This paper provides a theoretical explanation for the choices of different architectural components of existing SSRL methods, going beyond heuristic conveniences.", "published": "2025-10-01T18:18:14Z", "authors": ["Akhlaqur Rahman Sabby", "Yi Sui", "Tongzi Wu", "Jesse C. Cresswell", "Ga Wu"], "categories": ["cs.LG"], "doi": "", "_query": "real estate valuation deep learning", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2006.07021v2", "title": "A benchmark study on reliable molecular supervised learning via Bayesian learning", "abstract": "Virtual screening aims to find desirable compounds from chemical library by using computational methods. For this purpose with machine learning, model outputs that can be interpreted as predictive probability will be beneficial, in that a high prediction score corresponds to high probability of correctness. In this work, we present a study on the prediction performance and reliability of graph neural networks trained with the recently proposed Bayesian learning algorithms. Our work shows that Bayesian learning algorithms allow well-calibrated predictions for various GNN architectures and classification tasks. Also, we show the implications of reliable predictions on virtual screening, where Bayesian learning may lead to higher success in finding hit compounds.", "published": "2020-06-12T09:06:16Z", "authors": ["Doyeong Hwang", "Grace Lee", "Hanseok Jo", "Seyoul Yoon", "Seongok Ryu"], "categories": ["cs.LG", "stat.ML"], "doi": "", "_query": "real estate valuation deep learning", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2108.09065v1", "title": "Regulating Ownership Verification for Deep Neural Networks: Scenarios, Protocols, and Prospects", "abstract": "With the broad application of deep neural networks, the necessity of protecting them as intellectual properties has become evident. Numerous watermarking schemes have been proposed to identify the owner of a deep neural network and verify the ownership. However, most of them focused on the watermark embedding rather than the protocol for provable verification. To bridge the gap between those proposals and real-world demands, we study the deep learning model intellectual property protection in three scenarios: the ownership proof, the federated learning, and the intellectual property transfer. We present three protocols respectively. These protocols raise several new requirements for the bottom-level watermarking schemes.", "published": "2021-08-20T08:47:40Z", "authors": ["Fang-Qi Li", "Shi-Lin Wang", "Alan Wee-Chung Liew"], "categories": ["cs.CR"], "doi": "", "_query": "real estate valuation deep learning", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2601.02311v1", "title": "Placement Semantics for Distributed Deep Learning: A Systematic Framework for Analyzing Parallelism Strategies", "abstract": "Training large language models requires distributing computation across many accelerators, yet practitioners select parallelism strategies (data, tensor, pipeline, ZeRO) through trial and error because no unified systematic framework predicts their behavior. We introduce placement semantics: each strategy is specified by how it places four training states (parameters, optimizer, gradients, activations) across devices using five modes (replicated, sharded, sharded-with-gather, materialized, offloaded). From placement alone, without implementation details, we derive memory consumption and communication volume. Our predictions match published results exactly: ZeRO-3 uses 8x less memory than data parallelism at 1.5x communication cost, as reported in the original paper. We prove two conditions (gradient integrity, state consistency) are necessary and sufficient for distributed training to match single-device results, and provide composition rules for combining strategies safely. The framework unifies ZeRO Stages 1-3, Fully Sharded Data Parallel (FSDP), tensor parallelism, and pipeline parallelism as instances with different placement choices.", "published": "2026-01-05T18:01:38Z", "authors": ["Deep Pankajbhai Mehta"], "categories": ["cs.DC", "cs.AI"], "doi": "", "_query": "real estate valuation deep learning", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2410.02759v1", "title": "Forecasting Smog Clouds With Deep Learning", "abstract": "In this proof-of-concept study, we conduct multivariate timeseries forecasting for the concentrations of nitrogen dioxide (NO2), ozone (O3), and (fine) particulate matter (PM10 & PM2.5) with meteorological covariates between two locations using various deep learning models, with a focus on long short-term memory (LSTM) and gated recurrent unit (GRU) architectures. In particular, we propose an integrated, hierarchical model architecture inspired by air pollution dynamics and atmospheric science that employs multi-task learning and is benchmarked by unidirectional and fully-connected models. Results demonstrate that, above all, the hierarchical GRU proves itself as a competitive and efficient method for forecasting the concentration of smog-related pollutants.", "published": "2024-10-03T17:59:13Z", "authors": ["Valentijn Oldenburg", "Juan Cardenas-Cartagena", "Matias Valdenegro-Toro"], "categories": ["cs.LG"], "doi": "", "_query": "real estate valuation deep learning", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2404.06106v3", "title": "Unifying Low Dimensional Spectra in Deep Learning", "abstract": "Low dimensional structures appear ubiquitously in the eigenspectra of deep learning matrices in classification networks trained in the overparameterized regime. While theoretical advances have aimed to explain this phenomenology, they typically succeed only in capturing subsets of the full behavior or rely on assumptions that cannot hold in practice. In this work, we provide an analytic explanation for the bulk plus outlier structure of several canonical deep learning matrices, including the Hessian, gradients, and weights. We achieve this using unconstrained feature models (UFMs), a now-common tool for studying the emergence of deep neural collapse (DNC). We show that DNC is the source of these low dimensional eigenspectra, in each case, the eigenvalues and eigenvectors can be constructed from feature means, the characterizing objects of DNC. This provides a unifying analytic explanation for a wide range of spectral phenomena in deep learning and goes beyond empirical characterizations, which typically focus on eigenvalues, by providing a detailed analysis of eigenvectors. We prove that our results hold for both linear and ReLU networks and provide numerical validation in both the modeling context and standard deep-network architectures on canonical datasets.", "published": "2024-04-09T08:17:32Z", "authors": ["Connall Garrod", "Jonathan P. Keating"], "categories": ["cs.LG"], "doi": "", "_query": "real estate valuation deep learning", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2306.11144v1", "title": "Evaluating Loss Functions and Learning Data Pre-Processing for Climate Downscaling Deep Learning Models", "abstract": "Deep learning models have gained popularity in climate science, following their success in computer vision and other domains. For instance, researchers are increasingly employing deep learning techniques for downscaling climate data, drawing inspiration from image super-resolution models. However, there are notable differences between image data and climate data. While image data typically falls within a specific range (e.g., [0, 255]) and exhibits a relatively uniform or normal distribution, climate data can possess arbitrary value ranges and highly uneven distributions, such as precipitation data. This non-uniform distribution presents challenges when attempting to directly apply existing computer vision models to climate science tasks. Few studies have addressed this issue thus far. In this study, we explore the effects of loss functions and non-linear data pre-processing methods for deep learning models in the context of climate downscaling. We employ a climate downscaling experiment as an example to evaluate these factors. Our findings reveal that L1 loss and L2 loss perform similarly on some more balanced data like temperature data while for some imbalanced data like precipitation data, L2 loss performs significantly better than L1 loss. Additionally, we propose an approach to automatically learn the non-linear pre-processing function, which further enhances model accuracy and achieves the best results.", "published": "2023-06-19T19:58:42Z", "authors": ["Xingying Huang"], "categories": ["cs.LG", "physics.ao-ph"], "doi": "", "_query": "real estate valuation deep learning", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2412.04426v4", "title": "Towards Fast Safe Online Reinforcement Learning via Policy Finetuning", "abstract": "The high costs and risks involved in extensive environment interactions hinder the practical application of current online safe reinforcement learning (RL) methods. While offline safe RL addresses this by learning policies from static datasets, the performance therein is usually limited due to reliance on data quality and challenges with out-of-distribution (OOD) actions. Inspired by recent successes in offline-to-online (O2O) RL, it is crucial to explore whether offline safe RL can be leveraged to facilitate faster and safer online policy learning, a direction that has yet to be fully investigated. To fill this gap, we first demonstrate that naively applying existing O2O algorithms from standard RL would not work well in the safe RL setting due to two unique challenges: \\emph{erroneous Q-estimations}, resulted from offline-online objective mismatch and offline cost sparsity, and \\emph{Lagrangian mismatch}, resulted from difficulties in aligning Lagrange multipliers between offline and online policies. To address these challenges, we introduce \\textbf{Marvel}, a novel framework for O2O safe RL, comprising two key components that work in concert: \\emph{Value Pre-Alignment} to align the Q-functions with the underlying truth before online learning, and \\emph{Adaptive PID Control} to effectively adjust the Lagrange multipliers during online finetuning. Extensive experiments demonstrate that Marvel significantly outperforms existing baselines in both reward maximization and safety constraint satisfaction. By introducing the first policy-finetuning based framework for O2O safe RL, which is compatible with many offline and online safe RL methods, our work has the great potential to advance the field towards more efficient and practical safe RL solutions.", "published": "2024-12-05T18:51:18Z", "authors": ["Keru Chen", "Honghao Wei", "Zhigang Deng", "Sen Lin"], "categories": ["cs.LG", "cs.AI"], "doi": "", "_query": "real estate valuation deep learning", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2407.13455v1", "title": "Real Poincaré series of a plane divisorial valuation", "abstract": "Earlier, there was computed the Poincaré series of a valuation or of a collection of valuations on the ring of germs of holomorphic functions in two variables. For a collection of several plane curve valuations it appeared to coincide with the Alexander polynomial of the corresponding algebraic link. Recently, the authors defined Poincaré series of a valuation or of a collection of valuations in the real setting. (Actually, there were defined three versions of them, however, one of them was found to be ``not computable''.) These two Poincar'e series were computed for one plane curve valuation. Here we compute them for a plane divisorial valuation.", "published": "2024-07-18T12:33:28Z", "authors": ["Antonio Campillo", "Félix Delgado", "Sabir M. Gusein-Zade"], "categories": ["math.AG"], "doi": "", "_query": "real estate valuation deep learning", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2402.09288v5", "title": "EcoVal: An Efficient Data Valuation Framework for Machine Learning", "abstract": "Quantifying the value of data within a machine learning workflow can play a pivotal role in making more strategic decisions in machine learning initiatives. The existing Shapley value based frameworks for data valuation in machine learning are computationally expensive as they require considerable amount of repeated training of the model to obtain the Shapley value. In this paper, we introduce an efficient data valuation framework EcoVal, to estimate the value of data for machine learning models in a fast and practical manner. Instead of directly working with individual data sample, we determine the value of a cluster of similar data points. This value is further propagated amongst all the member cluster points. We show that the overall value of the data can be determined by estimating the intrinsic and extrinsic value of each data. This is enabled by formulating the performance of a model as a \\textit{production function}, a concept which is popularly used to estimate the amount of output based on factors like labor and capital in a traditional free economic market. We provide a formal proof of our valuation technique and elucidate the principles and mechanisms that enable its accelerated performance. We demonstrate the real-world applicability of our method by showcasing its effectiveness for both in-distribution and out-of-sample data. This work addresses one of the core challenges of efficient data valuation at scale in machine learning models. The code is available at \\underline{https://github.com/respai-lab/ecoval}.", "published": "2024-02-14T16:21:47Z", "authors": ["Ayush K Tarun", "Vikram S Chundawat", "Murari Mandal", "Hong Ming Tan", "Bowei Chen", "Mohan Kankanhalli"], "categories": ["cs.LG"], "doi": "", "_query": "real estate valuation deep learning", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2510.14190v3", "title": "Contrastive Diffusion Alignment: Learning Structured Latents for Controllable Generation", "abstract": "Diffusion models excel at generation, but their latent spaces are high dimensional and not explicitly organized for interpretation or control. We introduce ConDA (Contrastive Diffusion Alignment), a plug-and-play geometry layer that applies contrastive learning to pretrained diffusion latents using auxiliary variables (e.g., time, stimulation parameters, facial action units). ConDA learns a low-dimensional embedding whose directions align with underlying dynamical factors, consistent with recent contrastive learning results on structured and disentangled representations. In this embedding, simple nonlinear trajectories support smooth interpolation, extrapolation, and counterfactual editing while rendering remains in the original diffusion space. ConDA separates editing and rendering by lifting embedding trajectories back to diffusion latents with a neighborhood-preserving kNN decoder and is robust across inversion solvers. Across fluid dynamics, neural calcium imaging, therapeutic neurostimulation, facial expression dynamics, and monkey motor cortex activity, ConDA yields more interpretable and controllable latent structure than linear traversals and conditioning-based baselines, indicating that diffusion latents encode dynamics-relevant structure that can be exploited by an explicit contrastive geometry layer.", "published": "2025-10-16T00:48:05Z", "authors": ["Ruchi Sandilya", "Sumaira Perez", "Charles Lynch", "Lindsay Victoria", "Benjamin Zebley", "Derrick Matthew Buchanan", "Mahendra T. Bhati", "Nolan Williams", "Timothy J. Spellman", "Faith M. Gunning", "Conor Liston", "Logan Grosenick"], "categories": ["cs.LG"], "doi": "", "_query": "real estate valuation deep learning", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2506.14638v1", "title": "A Machine Learning Framework for Climate-Resilient Insurance and Real Estate Decisions", "abstract": "Extreme weather events increasingly threaten the insurance and real estate industries, creating conflicts between profitability and homeowner burdens. To address this, we propose the SSC-Insurance Model, which integrates SMOTE, SVM, and C-D-C algorithms to evaluate weather impacts on policies and investments. Our model achieves 88.3% accuracy in Zhejiang and 79.6% in Ireland, identifying a critical threshold (43% weather increase) for insurance viability. Additionally, we develop the TOA-Preservation Model using TOPSIS-ORM and AHP to prioritize building protection, with cultural value scoring highest (weight: 0.3383). Case studies on Nanxun Ancient Town show a 65.32% insurability probability and a protection score of 0.512. This work provides actionable tools for insurers, developers, and policymakers to manage climate risks sustainably.", "published": "2025-06-17T15:32:13Z", "authors": ["Lang Qin", "Yuejin Xie", "Daili Hua", "Xuhui Meng"], "categories": ["cs.CE"], "doi": "", "_query": "real estate valuation deep learning", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2507.09087v2", "title": "Deep Reinforcement Learning with Gradient Eligibility Traces", "abstract": "Achieving fast and stable off-policy learning in deep reinforcement learning (RL) is challenging. Most existing methods rely on semi-gradient temporal-difference (TD) methods for their simplicity and efficiency, but are consequently susceptible to divergence. While more principled approaches like Gradient TD (GTD) methods have strong convergence guarantees, they have rarely been used in deep RL. Recent work introduced the generalized Projected Bellman Error ($\\overline{\\text{PBE}}$), enabling GTD methods to work efficiently with nonlinear function approximation. However, this work is limited to one-step methods, which are slow at credit assignment and require a large number of samples. In this paper, we extend the generalized $\\overline{\\text{PBE}}$ objective to support multistep credit assignment based on the $λ$-return and derive three gradient-based methods that optimize this new objective. We provide both a forward-view formulation compatible with experience replay and a backward-view formulation compatible with streaming algorithms. Finally, we evaluate the proposed algorithms and show that they outperform both PPO and StreamQ in MuJoCo and MinAtar environments, respectively. Code available at https://github.com/esraaelelimy/gtd\\_algos", "published": "2025-07-12T00:12:05Z", "authors": ["Esraa Elelimy", "Brett Daley", "Andrew Patterson", "Marlos C. Machado", "Adam White", "Martha White"], "categories": ["cs.LG", "cs.AI", "stat.ML"], "doi": "", "_query": "real estate valuation deep learning", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/1906.11626v1", "title": "On improving deep learning generalization with adaptive sparse connectivity", "abstract": "Large neural networks are very successful in various tasks. However, with limited data, the generalization capabilities of deep neural networks are also very limited. In this paper, we empirically start showing that intrinsically sparse neural networks with adaptive sparse connectivity, which by design have a strict parameter budget during the training phase, have better generalization capabilities than their fully-connected counterparts. Besides this, we propose a new technique to train these sparse models by combining the Sparse Evolutionary Training (SET) procedure with neurons pruning. Operated on MultiLayer Perceptron (MLP) and tested on 15 datasets, our proposed technique zeros out around 50% of the hidden neurons during training, while having a linear number of parameters to optimize with respect to the number of neurons. The results show a competitive classification and generalization performance.", "published": "2019-06-27T13:31:30Z", "authors": ["Shiwei Liu", "Decebal Constantin Mocanu", "Mykola Pechenizkiy"], "categories": ["cs.NE", "cs.LG"], "doi": "", "_query": "real estate valuation deep learning", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2509.20008v2", "title": "Learning Robust Penetration Testing Policies under Partial Observability: A systematic evaluation", "abstract": "Penetration testing, the simulation of cyberattacks to identify security vulnerabilities, presents a sequential decision-making problem well-suited for reinforcement learning (RL) automation. Like many applications of RL to real-world problems, partial observability presents a major challenge, as it invalidates the Markov property present in Markov Decision Processes (MDPs). Partially Observable MDPs require history aggregation or belief state estimation to learn successful policies. We investigate stochastic, partially observable penetration testing scenarios over host networks of varying size, aiming to better reflect real-world complexity through more challenging and representative benchmarks. This approach leads to the development of more robust and transferable policies, which are crucial for ensuring reliable performance across diverse and unpredictable real-world environments. Using vanilla Proximal Policy Optimization (PPO) as a baseline, we compare a selection of PPO-based variants designed to mitigate partial observability, including frame-stacking, augmenting observations with historical information, and employing LSTM or TrXL architectures. We conduct a systematic empirical analysis of these algorithms across different host network sizes. We find that this task greatly benefits from history aggregation. Converging up to four times faster than other approaches. Manual inspection of the learned policies by the algorithms reveals clear distinctions and provides insights that go beyond quantitative results.", "published": "2025-09-24T11:27:54Z", "authors": ["Raphael Simon", "Pieter Libin", "Wim Mees"], "categories": ["cs.LG", "cs.CR"], "doi": "", "_query": "real estate valuation deep learning", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2105.02866v2", "title": "Membership Inference Attacks on Deep Regression Models for Neuroimaging", "abstract": "Ensuring the privacy of research participants is vital, even more so in healthcare environments. Deep learning approaches to neuroimaging require large datasets, and this often necessitates sharing data between multiple sites, which is antithetical to the privacy objectives. Federated learning is a commonly proposed solution to this problem. It circumvents the need for data sharing by sharing parameters during the training process. However, we demonstrate that allowing access to parameters may leak private information even if data is never directly shared. In particular, we show that it is possible to infer if a sample was used to train the model given only access to the model prediction (black-box) or access to the model itself (white-box) and some leaked samples from the training data distribution. Such attacks are commonly referred to as Membership Inference attacks. We show realistic Membership Inference attacks on deep learning models trained for 3D neuroimaging tasks in a centralized as well as decentralized setup. We demonstrate feasible attacks on brain age prediction models (deep learning models that predict a person's age from their brain MRI scan). We correctly identified whether an MRI scan was used in model training with a 60% to over 80% success rate depending on model complexity and security assumptions.", "published": "2021-05-06T17:51:06Z", "authors": ["Umang Gupta", "Dimitris Stripelis", "Pradeep K. Lam", "Paul M. Thompson", "José Luis Ambite", "Greg Ver Steeg"], "categories": ["q-bio.QM", "cs.CR", "cs.LG", "eess.IV"], "doi": "", "_query": "real estate valuation deep learning", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2407.17844v4", "title": "Innovative Speech-Based Deep Learning Approaches for Parkinson's Disease Classification: A Systematic Review", "abstract": "Parkinson's disease (PD), the second most prevalent neurodegenerative disorder worldwide, frequently presents with early-stage speech impairments. Recent advancements in Artificial Intelligence (AI), particularly deep learning (DL), have significantly enhanced PD diagnosis through the analysis of speech data. Nevertheless, the progress of research is restricted by the limited availability of publicly accessible speech-based PD datasets, primarily due to privacy concerns. The goal of this systematic review is to explore the current landscape of speech-based DL approaches for PD classification, based on 33 scientific works published between January 2020 and March 2024. We discuss their available resources, capabilities, and potential limitations, and issues related to bias, explainability, and privacy. Furthermore, this review provides an overview of publicly accessible speech-based datasets and open-source material for PD. The DL approaches identified are categorized into end-to-end (E2E) learning, transfer learning (TL), and deep acoustic feature extraction (DAFE). Among E2E approaches, Convolutional Neural Networks (CNNs) are prevalent, though Transformers are increasingly popular. E2E approaches face challenges such as limited data and computational resources, especially with Transformers. TL addresses these issues by providing more robust PD diagnosis and better generalizability across languages. DAFE aims to improve the explainability and interpretability of results by examining the specific effects of deep features on both other DL approaches and more traditional machine learning (ML) methods. However, it often underperforms compared to E2E and TL approaches.", "published": "2024-07-25T07:58:19Z", "authors": ["Lisanne van Gelderen", "Cristian Tejedor-García"], "categories": ["cs.SD", "cs.AI", "cs.CL", "cs.LG", "eess.AS"], "doi": "10.3390/app14177873", "_query": "real estate valuation deep learning", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2607.25098v1", "title": "An Artificial Market for Brazilian Real Estate Investment Funds: An Agent-Based Proposal", "abstract": "This article presents the development and validation of an artificial market for Brazilian Real Estate Investment Trusts (REITs), known as Fundos de Investimento Imobiliario (FIIs), using agent-based modeling methodology. The central contribution of this work is the integration, within a single multi-agent system, of the FII value chain, from the generation of real estate revenues subject to vacancy and operational costs, through dividend distribution, to the trading of shares by heterogeneous investors mediated by a double auction mechanism with an order book. The model incorporates endogenous macroeconomic variables, such as the Selic, the Brazilian benchmark interest rate, and inflation, and represents agent heterogeneity through a behavioral decomposition into fundamentalist, speculator, and noise trader components, modulated by individual financial literacy levels. The model was calibrated using the Method of Simulated Moments applied to the historical series of the IFIX index, the Brazilian REIT market index, between 2021 and 2025. The validation results, obtained using two distinct methods, demonstrate that the model reproduces the main stylized facts observed in the real market: (i) the coverage rate of calibrated moments exceeds 75 percent; (ii) 96 percent of simulated trajectories are structurally indistinguishable from real IFIX periods according to the nearest-neighbor criterion; and (iii) stylized facts such as the power law of autocorrelations of absolute returns and aggregational Gaussianity emerge spontaneously, without being incorporated into the calibration objective function. The results of the validation process indicate that the artificial market captures structural dynamics of the FII market, opening perspectives for its use as a computational laboratory for the analysis of regulatory policies and pricing mechanisms.", "published": "2026-07-27T21:54:55Z", "authors": ["Gilberto Gil F. G. Passos", "Eber Assis Schmitz", "Sildenir Alves Ribeiro"], "categories": ["cs.CC"], "doi": "", "_query": "real estate valuation deep learning", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2006.01641v2", "title": "Unsupervised Deep Learning for Optimizing Wireless Systems with Instantaneous and Statistic Constraints", "abstract": "Deep neural networks (DNNs) have been introduced for designing wireless policies by approximating the mappings from environmental parameters to solutions of optimization problems. Considering that labeled training samples are hard to obtain, unsupervised deep learning has been proposed to solve functional optimization problems with statistical constraints recently. However, most existing problems in wireless communications are variable optimizations, and many problems are with instantaneous constraints. In this paper, we establish a unified framework of using unsupervised deep learning to solve both kinds of problems with both instantaneous and statistic constraints. For a constrained variable optimization, we first convert it into an equivalent functional optimization problem with instantaneous constraints. Then, to ensure the instantaneous constraints in the functional optimization problems, we use DNN to approximate the Lagrange multiplier functions, which is trained together with a DNN to approximate the policy. We take two resource allocation problems in ultra-reliable and low-latency communications as examples to illustrate how to guarantee the complex and stringent quality-of-service (QoS) constraints with the framework. Simulation results show that unsupervised learning outperforms supervised learning in terms of QoS violation probability and approximation accuracy of the optimal policy, and can converge rapidly with pre-training.", "published": "2020-05-30T13:37:14Z", "authors": ["Chengjian Sun", "Changyang She", "Chenyang Yang"], "categories": ["cs.IT", "cs.LG", "eess.SP"], "doi": "", "_query": "real estate valuation deep learning", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2609.18952v1", "title": "Automated Dental Caries Segmentation in Panoramic Radiographs Using Dual-Stage Deep Learning", "abstract": "Early detection of dental caries remains challenging due to limitations in traditional diagnostic methods, particularly for proximal lesions in posterior teeth. Deep learning models show promise for automated caries detection but face scalability constraints due to requirements for large volumes of expertly annotated training data. This study presents a dual-stage deep learning framework combining Faster R-CNN for tooth localization with U-Net for pixel-wise caries segmentation in panoramic radiographs. We developed a systematic transformation pipeline to convert large-scale polygon-annotated datasets into high-resolution binary segmentation masks, enabling pixel-wise supervised learning. The framework was trained using both expert-verified datasets and algorithmically processed labels from 3,000 panoramic images. Our approach achieved robust performance with an IoU of 0.9013, Dice coefficient of 0.9482, Recall of 0.9433, and Precision of 0.9774, demonstrating superior accuracy compared to existing methods while significantly reducing false-positive rates. The dual-stage framework effectively addresses data annotation bottlenecks in dental AI applications and demonstrates potential for scalable, automated caries detection systems that can improve diagnostic consistency and support clinical decision-making.", "published": "2026-07-15T07:46:32Z", "authors": ["Jihun Kim", "Kyeonghun Kim", "Jong-yeol Lee", "Yeongseok Seo", "Dohyun Chun"], "categories": ["cs.CV", "cs.AI"], "doi": "10.1007/978-3-032-09513-8_53", "_query": "real estate valuation deep learning", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2103.06671v6", "title": "Sample Complexity of Offline Reinforcement Learning with Deep ReLU Networks", "abstract": "Offline reinforcement learning (RL) leverages previously collected data for policy optimization without any further active exploration. Despite the recent interest in this problem, its theoretical results in neural network function approximation settings remain elusive. In this paper, we study the statistical theory of offline RL with deep ReLU network function approximation. In particular, we establish the sample complexity of $n = \\tilde{\\mathcal{O}}( H^{4 + 4 \\frac{d}α} κ_μ^{1 + \\frac{d}α} ε^{-2 - 2\\frac{d}α} )$ for offline RL with deep ReLU networks, where $κ_μ$ is a measure of distributional shift, {$H = (1-γ)^{-1}$ is the effective horizon length}, $d$ is the dimension of the state-action space, $α$ is a (possibly fractional) smoothness parameter of the underlying Markov decision process (MDP), and $ε$ is a user-specified error. Notably, our sample complexity holds under two novel considerations: the Besov dynamic closure and the correlated structure. While the Besov dynamic closure subsumes the dynamic conditions for offline RL in the prior works, the correlated structure renders the prior works of offline RL with general/neural network function approximation improper or inefficient {in long (effective) horizon problems}. To the best of our knowledge, this is the first theoretical characterization of the sample complexity of offline RL with deep neural network function approximation under the general Besov regularity condition that goes beyond {the linearity regime} in the traditional Reproducing Hilbert kernel spaces and Neural Tangent Kernels.", "published": "2021-03-11T14:01:14Z", "authors": ["Thanh Nguyen-Tang", "Sunil Gupta", "Hung Tran-The", "Svetha Venkatesh"], "categories": ["stat.ML", "cs.LG"], "doi": "", "_query": "real estate valuation deep learning", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2408.02339v2", "title": "Modeling the impact of Climate transition on real estate prices", "abstract": "In this work, we propose a model to quantify the impact of the climate transition on a property in housing market. We begin by noting that property is an asset in an economy. That economy is organized in sectors, driven by its productivity which is a multidimensional Ornstein-Uhlenbeck process, while the climate transition is declined thanks to the carbon price, a continuous deterministic process. We then extend the sales comparison approach and the income approach to valuate an energy inefficient real estate asset. We obtain its value as the difference between the price of an equivalent efficient building following an exponential Ornstein-Uhlenbeck as well as the actualized renovation costs and the actualized sum of the future additional energy costs (before and after the renovation date). These costs are due to the inefficiency of the building, before an optimal renovation date which depends on the carbon price process. Moreover, since the renovation increases the efficiency of the building, which is random, the future additional energy costs become smaller and even zero if the optimal energy efficiency is reached. Finally, we carry out simulations based on the French economy and the house price index of France. The findings support the conclusion that the order of magnitude of the depreciation obtained by our model is the same as the empirical observations.", "published": "2024-08-05T09:34:47Z", "authors": ["Lionel Sopgoui"], "categories": ["q-fin.RM", "econ.GN", "q-fin.GN"], "doi": "", "_query": "real estate valuation deep learning", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2108.02889v1", "title": "RIS-assisted UAV Communications for IoT with Wireless Power Transfer Using Deep Reinforcement Learning", "abstract": "Many of the devices used in Internet-of-Things (IoT) applications are energy-limited, and thus supplying energy while maintaining seamless connectivity for IoT devices is of considerable importance. In this context, we propose a simultaneous wireless power transfer and information transmission scheme for IoT devices with support from reconfigurable intelligent surface (RIS)-aided unmanned aerial vehicle (UAV) communications. In particular, in a first phase, IoT devices harvest energy from the UAV through wireless power transfer; and then in a second phase, the UAV collects data from the IoT devices through information transmission. To characterise the agility of the UAV, we consider two scenarios: a hovering UAV and a mobile UAV. Aiming at maximizing the total network sum-rate, we jointly optimize the trajectory of the UAV, the energy harvesting scheduling of IoT devices, and the phaseshift matrix of the RIS. We formulate a Markov decision process and propose two deep reinforcement learning algorithms to solve the optimization problem of maximizing the total network sum-rate. Numerical results illustrate the effectiveness of the UAV's flying path optimization and the network's throughput of our proposed techniques compared with other benchmark schemes. Given the strict requirements of the RIS and UAV, the significant improvement in processing time and throughput performance demonstrates that our proposed scheme is well applicable for practical IoT applications.", "published": "2021-08-05T23:55:44Z", "authors": ["Khoi Khac Nguyen", "Antonino Masaracchia", "Tan Do-Duy", "H. Vincent Poor", "Trung Q. Duong"], "categories": ["eess.SP", "cs.GT", "cs.LG"], "doi": "", "_query": "real estate valuation deep learning", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2010.10070v1", "title": "Real-Time Optimisation for Online Learning in Auctions", "abstract": "In display advertising, a small group of sellers and bidders face each other in up to 10 12 auctions a day. In this context, revenue maximisation via monopoly price learning is a high-value problem for sellers. By nature, these auctions are online and produce a very high frequency stream of data. This results in a computational strain that requires algorithms be real-time. Unfortunately, existing methods inherited from the batch setting suffer O($\\sqrt t$) time/memory complexity at each update, prohibiting their use. In this paper, we provide the first algorithm for online learning of monopoly prices in online auctions whose update is constant in time and memory.", "published": "2020-10-20T06:58:46Z", "authors": ["Lorenzo Croissant", "Marc Abeille", "Clément Calauzènes"], "categories": ["cs.LG", "cs.GT", "stat.ML"], "doi": "", "_query": "real estate valuation deep learning", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2112.14707v2", "title": "Controlling Chaos in Van Der Pol Dynamics Using Signal-Encoded Deep Learning", "abstract": "Controlling nonlinear dynamics is a long-standing problem in engineering. Harnessing known physical information to accelerate or constrain stochastic learning pursues a new paradigm of scientific machine learning. By linearizing nonlinear systems, traditional control methods cannot learn nonlinear features from chaotic data for use in control. Here, we introduce Physics-Informed Deep Operator Control (PIDOC), and by encoding the control signal and initial position into the losses of a physics-informed neural network (PINN), the nonlinear system is forced to exhibit the desired trajectory given the control signal. PIDOC receives signals as physics commands and learns from the chaotic data output from the nonlinear van der Pol system, where the output of the PINN is the control. Applied to a benchmark problem, PIDOC successfully implements control with higher stochasticity for higher-order terms. PIDOC has also been proven to be capable of converging to different desired trajectories based on case studies. Initial positions slightly affect the control accuracy at the beginning stage yet do not change the overall control quality. For highly nonlinear systems, PIDOC is not able to execute control with high accuracy compared with the benchmark problem. The depth and width of the neural network structure do not greatly change the convergence of PIDOC based on case studies of van der Pol systems with low and high nonlinearities. Surprisingly, enlarging the control signal does not help to improve the control quality. The proposed framework can potentially be applied to many nonlinear systems for nonlinear controls.", "published": "2021-12-25T01:26:14Z", "authors": ["Hanfeng Zhai", "Timothy Sands"], "categories": ["eess.SY"], "doi": "10.3390/math10030453", "_query": "real estate valuation deep learning", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2107.08083v1", "title": "Robust Risk-Sensitive Reinforcement Learning Agents for Trading Markets", "abstract": "Trading markets represent a real-world financial application to deploy reinforcement learning agents, however, they carry hard fundamental challenges such as high variance and costly exploration. Moreover, markets are inherently a multiagent domain composed of many actors taking actions and changing the environment. To tackle these type of scenarios agents need to exhibit certain characteristics such as risk-awareness, robustness to perturbations and low learning variance. We take those as building blocks and propose a family of four algorithms. First, we contribute with two algorithms that use risk-averse objective functions and variance reduction techniques. Then, we augment the framework to multi-agent learning and assume an adversary which can take over and perturb the learning process. Our third and fourth algorithms perform well under this setting and balance theoretical guarantees with practical use. Additionally, we consider the multi-agent nature of the environment and our work is the first one extending empirical game theory analysis for multi-agent learning by considering risk-sensitive payoffs.", "published": "2021-07-16T19:15:13Z", "authors": ["Yue Gao", "Kry Yik Chau Lui", "Pablo Hernandez-Leal"], "categories": ["cs.LG", "cs.MA"], "doi": "", "_query": "real estate valuation deep learning", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2402.13256v1", "title": "Deep Learning Based Event Reconstruction for Cyclotron Radiation Emission Spectroscopy", "abstract": "The objective of the Cyclotron Radiation Emission Spectroscopy (CRES) technology is to build precise particle energy spectra. This is achieved by identifying the start frequencies of charged particle trajectories which, when exposed to an external magnetic field, leave semi-linear profiles (called tracks) in the time-frequency plane. Due to the need for excellent instrumental energy resolution in application, highly efficient and accurate track reconstruction methods are desired. Deep learning convolutional neural networks (CNNs) - particularly suited to deal with information-sparse data and which offer precise foreground localization - may be utilized to extract track properties from measured CRES signals (called events) with relative computational ease. In this work, we develop a novel machine learning based model which operates a CNN and a support vector machine in tandem to perform this reconstruction. A primary application of our method is shown on simulated CRES signals which mimic those of the Project 8 experiment - a novel effort to extract the unknown absolute neutrino mass value from a precise measurement of tritium $β^-$-decay energy spectrum. When compared to a point-clustering based technique used as a baseline, we show a relative gain of 24.1% in event reconstruction efficiency and comparable performance in accuracy of track parameter reconstruction.", "published": "2024-01-05T15:55:27Z", "authors": ["A. Ashtari Esfahani", "S. Böser", "N. Buzinsky", "M. C. Carmona-Benitez", "R. Cervantes", "C. Claessens", "L. de Viveiros", "M. Fertl", "J. A. Formaggio", "J. K. Gaison", "L. Gladstone", "M. Grando", "M. Guigue", "J. Hartse", "K. M. Heeger", "X. Huyan", "A. M. Jones", "K. Kazkaz", "M. Li", "A. Lindman", "A. Marsteller", "C. Matthé", "R. Mohiuddin", "B. Monreal", "E. C. Morrison", "R. Mueller", "J. A. Nikkel", "E. Novitski", "N. S. Oblath", "J. I. Peña", "W. Pettus", "R. Reimann", "R. G. H. Robertson", "L. Saldaña", "M. Schram", "P. L. Slocum", "J. Stachurska", "Y. -H. Sun", "P. T. Surukuchi", "A. B. Telles", "F. Thomas", "M. Thomas", "L. A. Thorne", "T. Thümmler", "L. Tvrznikova", "W. Van De Pontseele", "B. A. VanDevender", "T. E. Weiss", "T. Wendler", "E. Zayas", "A. Ziegler"], "categories": ["physics.ins-det", "nucl-ex"], "doi": "10.1088/2632-2153/ad3ee3", "_query": "real estate valuation deep learning", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2311.02960v6", "title": "Understanding Deep Representation Learning via Layerwise Feature Compression and Discrimination", "abstract": "Over the past decade, deep learning has proven to be a highly effective tool for learning meaningful features from raw data. However, it remains an open question how deep networks perform hierarchical feature learning across layers. In this work, we attempt to unveil this mystery by investigating the structures of intermediate features. Motivated by our empirical findings that linear layers mimic the roles of deep layers in nonlinear networks for feature learning, we explore how deep linear networks transform input data into output by investigating the output (i.e., features) of each layer after training in the context of multi-class classification problems. Toward this goal, we first define metrics to measure within-class compression and between-class discrimination of intermediate features, respectively. Through theoretical analysis of these two metrics, we show that the evolution of features follows a simple and quantitative pattern from shallow to deep layers when the input data is nearly orthogonal and the network weights are minimum-norm, balanced, and approximate low-rank: Each layer of the linear network progressively compresses within-class features at a geometric rate and discriminates between-class features at a linear rate with respect to the number of layers that data have passed through. To the best of our knowledge, this is the first quantitative characterization of feature evolution in hierarchical representations of deep linear networks. Empirically, our extensive experiments not only validate our theoretical results numerically but also reveal a similar pattern in deep nonlinear networks which aligns well with recent empirical studies. Moreover, we demonstrate the practical implications of our results in transfer learning. Our code is available at https://github.com/Heimine/PNC_DLN.", "published": "2023-11-06T09:00:38Z", "authors": ["Peng Wang", "Xiao Li", "Can Yaras", "Zhihui Zhu", "Laura Balzano", "Wei Hu", "Qing Qu"], "categories": ["cs.LG", "cs.CV", "math.OC"], "doi": "", "_query": "real estate valuation deep learning", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2605.11134v2", "title": "Spurious Correlation Learning in Preference Optimization: Mechanisms, Consequences, and Mitigation via Tie Training", "abstract": "Preference learning methods like Direct Preference Optimization (DPO) are known to induce reliance on spurious correlations, leading to sycophancy and length bias in today's language models and potentially severe goal misgeneralization in future systems. In this work, we provide a unified theoretical analysis of this phenomenon, characterizing the mechanisms of spurious learning, its consequences on deployment, and a provable mitigation strategy. Focusing on log-linear policies, we show that standard preference-learning objectives induce reliance on spurious features at the population level through two channels: mean spurious bias and causal-spurious correlation leakage. We then show that this reliance creates an irreducible vulnerability to distribution shift: more data from the same training distribution fails to reduce the model's dependence on spurious features. To address this, we propose tie training, a data augmentation strategy using ties (equal-utility preference pairs) to introduce data-driven regularization. We demonstrate that this approach selectively reduces spurious learning without degrading causal learning. Finally, we validate our theory on log-linear models and provide empirical evidence that both the spurious learning mechanisms and the benefits of tie training persist for neural networks and large language models.", "published": "2026-05-11T18:41:12Z", "authors": ["Christian Moya", "Alex Semendinger", "Guang Lin", "Elliott Thornley"], "categories": ["cs.LG", "cs.AI"], "doi": "", "_query": "real estate valuation deep learning", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2211.10828v1", "title": "Instability in clinical risk stratification models using deep learning", "abstract": "While it has been well known in the ML community that deep learning models suffer from instability, the consequences for healthcare deployments are under characterised. We study the stability of different model architectures trained on electronic health records, using a set of outpatient prediction tasks as a case study. We show that repeated training runs of the same deep learning model on the same training data can result in significantly different outcomes at a patient level even though global performance metrics remain stable. We propose two stability metrics for measuring the effect of randomness of model training, as well as mitigation strategies for improving model stability.", "published": "2022-11-20T00:16:04Z", "authors": ["Daniel Lopez-Martinez", "Alex Yakubovich", "Martin Seneviratne", "Adam D. Lelkes", "Akshit Tyagi", "Jonas Kemp", "Ethan Steinberg", "N. Lance Downing", "Ron C. Li", "Keith E. Morse", "Nigam H. Shah", "Ming-Jun Chen"], "categories": ["cs.LG", "cs.AI"], "doi": "", "_query": "real estate valuation deep learning", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2003.08561v2", "title": "XtarNet: Learning to Extract Task-Adaptive Representation for Incremental Few-Shot Learning", "abstract": "Learning novel concepts while preserving prior knowledge is a long-standing challenge in machine learning. The challenge gets greater when a novel task is given with only a few labeled examples, a problem known as incremental few-shot learning. We propose XtarNet, which learns to extract task-adaptive representation (TAR) for facilitating incremental few-shot learning. The method utilizes a backbone network pretrained on a set of base categories while also employing additional modules that are meta-trained across episodes. Given a new task, the novel feature extracted from the meta-trained modules is mixed with the base feature obtained from the pretrained model. The process of combining two different features provides TAR and is also controlled by meta-trained modules. The TAR contains effective information for classifying both novel and base categories. The base and novel classifiers quickly adapt to a given task by utilizing the TAR. Experiments on standard image datasets indicate that XtarNet achieves state-of-the-art incremental few-shot learning performance. The concept of TAR can also be used in conjunction with existing incremental few-shot learning methods; extensive simulation results in fact show that applying TAR enhances the known methods significantly.", "published": "2020-03-19T04:02:44Z", "authors": ["Sung Whan Yoon", "Do-Yeon Kim", "Jun Seo", "Jaekyun Moon"], "categories": ["cs.LG", "cs.AI", "cs.NE"], "doi": "", "_query": "real estate valuation deep learning", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/1610.04046v2", "title": "Is state-dependent valuation more adaptive than simpler rules?", "abstract": "McNamara, Trimmer, and Houston (2012) claim to provide an explanation of certain systematic deviations from rational behavior using a mechanism that could arise through natural selection. We provide an arguably much simpler mechanism in terms of computational limitations, that performs better in the environment described by McNamara, Trimmer, and Houston (2012). To argue convincingly that animals' use of state-dependent valuation is adaptive and is likely to be selected for by natural selection, one must argue that, in some sense, it is a better approach than the simple strategies that we propose.", "published": "2016-10-13T12:21:11Z", "authors": ["Joseph Y. Halpern", "Lior Seeman"], "categories": ["q-bio.PE", "cs.GT"], "doi": "", "_query": "real estate valuation deep learning", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2105.14142v2", "title": "Reconfigurable Intelligent Surface-assisted Multi-UAV Networks: Efficient Resource Allocation with Deep Reinforcement Learning", "abstract": "In this paper, we propose reconfigurable intelligent surface (RIS)-assisted unmanned aerial vehicles (UAVs) networks that can utilise both advantages of UAV's agility and RIS's reflection for enhancing the network's performance. To aim at maximising the energy efficiency (EE) of the considered networks, we jointly optimise the power allocation of the UAVs and the phase-shift matrix of the RIS. A deep reinforcement learning (DRL) approach is proposed for solving the continuous optimisation problem with time-varying channels in a centralised fashion. Moreover, a parallel learning approach is also proposed for reducing the information transmission requirement of the centralised approach. Numerical results show a significant improvement of our proposed schemes compared with the conventional approaches in terms of EE, flexibility, and processing time. Our proposed DRL methods for RIS-assisted UAV networks can be used for real-time applications due to their capability of instant decision-making and handling the time-varying channel with the dynamic environmental setting.", "published": "2021-05-28T23:23:46Z", "authors": ["Khoi Khac Nguyen", "Saeed Khosravirad", "Daniel Benevides da Costa", "Long D. Nguyen", "Trung Q. Duong"], "categories": ["eess.SP"], "doi": "", "_query": "real estate valuation deep learning", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2305.00054v3", "title": "LAVA: Data Valuation without Pre-Specified Learning Algorithms", "abstract": "Traditionally, data valuation (DV) is posed as a problem of equitably splitting the validation performance of a learning algorithm among the training data. As a result, the calculated data values depend on many design choices of the underlying learning algorithm. However, this dependence is undesirable for many DV use cases, such as setting priorities over different data sources in a data acquisition process and informing pricing mechanisms in a data marketplace. In these scenarios, data needs to be valued before the actual analysis and the choice of the learning algorithm is still undetermined then. Another side-effect of the dependence is that to assess the value of individual points, one needs to re-run the learning algorithm with and without a point, which incurs a large computation burden. This work leapfrogs over the current limits of data valuation methods by introducing a new framework that can value training data in a way that is oblivious to the downstream learning algorithm. Our main results are as follows. (1) We develop a proxy for the validation performance associated with a training set based on a non-conventional class-wise Wasserstein distance between training and validation sets. We show that the distance characterizes the upper bound of the validation performance for any given model under certain Lipschitz conditions. (2) We develop a novel method to value individual data based on the sensitivity analysis of the class-wise Wasserstein distance. Importantly, these values can be directly obtained for free from the output of off-the-shelf optimization solvers when computing the distance. (3) We evaluate our new data valuation framework over various use cases related to detecting low-quality data and show that, surprisingly, the learning-agnostic feature of our framework enables a significant improvement over SOTA performance while being orders of magnitude faster.", "published": "2023-04-28T19:05:16Z", "authors": ["Hoang Anh Just", "Feiyang Kang", "Jiachen T. Wang", "Yi Zeng", "Myeongseob Ko", "Ming Jin", "Ruoxi Jia"], "categories": ["cs.LG", "cs.AI", "stat.ML"], "doi": "", "_query": "real estate valuation deep learning", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/1910.06789v1", "title": "Deep learning for Aerosol Forecasting", "abstract": "Reanalysis datasets combining numerical physics models and limited observations to generate a synthesised estimate of variables in an Earth system, are prone to biases against ground truth. Biases identified with the NASA Modern-Era Retrospective Analysis for Research and Applications, Version 2 (MERRA-2) aerosol optical depth (AOD) dataset, against the Aerosol Robotic Network (AERONET) ground measurements in previous studies, motivated the development of a deep learning based AOD prediction model globally. This study combines a convolutional neural network (CNN) with MERRA-2, tested against all AERONET sites. The new hybrid CNN-based model provides better estimates validated versus AERONET ground truth, than only using MERRA-2 reanalysis.", "published": "2019-10-14T17:35:08Z", "authors": ["Caleb Hoyne", "S. Karthik Mukkavilli", "David Meger"], "categories": ["cs.LG", "cs.CV", "physics.ao-ph", "physics.data-an", "stat.ML"], "doi": "", "_query": "real estate valuation deep learning", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/1804.04888v2", "title": "Scalable and Interpretable One-class SVMs with Deep Learning and Random Fourier features", "abstract": "One-class support vector machine (OC-SVM) for a long time has been one of the most effective anomaly detection methods and extensively adopted in both research as well as industrial applications. The biggest issue for OC-SVM is yet the capability to operate with large and high-dimensional datasets due to optimization complexity. Those problems might be mitigated via dimensionality reduction techniques such as manifold learning or autoencoder. However, previous work often treats representation learning and anomaly prediction separately. In this paper, we propose autoencoder based one-class support vector machine (AE-1SVM) that brings OC-SVM, with the aid of random Fourier features to approximate the radial basis kernel, into deep learning context by combining it with a representation learning architecture and jointly exploit stochastic gradient descent to obtain end-to-end training. Interestingly, this also opens up the possible use of gradient-based attribution methods to explain the decision making for anomaly detection, which has ever been challenging as a result of the implicit mappings between the input space and the kernel space. To the best of our knowledge, this is the first work to study the interpretability of deep learning in anomaly detection. We evaluate our method on a wide range of unsupervised anomaly detection tasks in which our end-to-end training architecture achieves a performance significantly better than the previous work using separate training.", "published": "2018-04-13T11:24:33Z", "authors": ["Minh-Nghia Nguyen", "Ngo Anh Vien"], "categories": ["cs.LG", "stat.ML"], "doi": "", "_query": "real estate valuation deep learning", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2606.25989v1", "title": "Taxonomy-aware deep learning for hierarchical marine species classification in underwater imagery", "abstract": "Automated classification of marine species from underwater imagery is essential for scalable ocean biodiversity monitoring and conservation policy. Existing approaches struggle with severe domain shift across collection platforms, fine-grained visual similarity between closely related species, and uneven annotation granularity, where many specimens can only be identified to genus or a coarser taxonomic rank. We present a taxonomy-aware deep learning framework that aligns both the training loss and the inference rule with the hierarchical structure of biological classification, combining a taxonomy-weighted loss, minimum-risk Bayesian inference, multi-scale feature encoding, and independent per-rank classification heads. Evaluated on the FathomNet 2025 dataset1 (79 marine classes across seven taxonomic ranks), the system achieves a mean taxonomic distance of 1.581, within 3% of the 1st-place solution (1.535), with the largest gains from metric-aligned inference and simple, decoupled components that generalize better than learned dependencies under distribution shift.", "published": "2026-06-24T15:59:48Z", "authors": ["Dan Zimmerman", "Dimitris A. Pados", "George Sklivanitis"], "categories": ["cs.CV", "cs.LG"], "doi": "10.1117/12.3098267", "_query": "real estate valuation deep learning", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2102.09812v1", "title": "Deep Latent Competition: Learning to Race Using Visual Control Policies in Latent Space", "abstract": "Learning competitive behaviors in multi-agent settings such as racing requires long-term reasoning about potential adversarial interactions. This paper presents Deep Latent Competition (DLC), a novel reinforcement learning algorithm that learns competitive visual control policies through self-play in imagination. The DLC agent imagines multi-agent interaction sequences in the compact latent space of a learned world model that combines a joint transition function with opponent viewpoint prediction. Imagined self-play reduces costly sample generation in the real world, while the latent representation enables planning to scale gracefully with observation dimensionality. We demonstrate the effectiveness of our algorithm in learning competitive behaviors on a novel multi-agent racing benchmark that requires planning from image observations. Code and videos available at https://sites.google.com/view/deep-latent-competition.", "published": "2021-02-19T09:00:29Z", "authors": ["Wilko Schwarting", "Tim Seyde", "Igor Gilitschenski", "Lucas Liebenwein", "Ryan Sander", "Sertac Karaman", "Daniela Rus"], "categories": ["cs.LG", "cs.AI", "cs.RO"], "doi": "", "_query": "real estate valuation deep learning", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2110.12520v1", "title": "Learning convex regularizers satisfying the variational source condition for inverse problems", "abstract": "Variational regularization has remained one of the most successful approaches for reconstruction in imaging inverse problems for several decades. With the emergence and astonishing success of deep learning in recent years, a considerable amount of research has gone into data-driven modeling of the regularizer in the variational setting. Our work extends a recently proposed method, referred to as adversarial convex regularization (ACR), that seeks to learn data-driven convex regularizers via adversarial training in an attempt to combine the power of data with the classical convex regularization theory. Specifically, we leverage the variational source condition (SC) during training to enforce that the ground-truth images minimize the variational loss corresponding to the learned convex regularizer. This is achieved by adding an appropriate penalty term to the ACR training objective. The resulting regularizer (abbreviated as ACR-SC) performs on par with the ACR, but unlike ACR, comes with a quantitative convergence rate estimate.", "published": "2021-10-24T20:05:59Z", "authors": ["Subhadip Mukherjee", "Carola-Bibiane Schönlieb", "Martin Burger"], "categories": ["cs.LG", "eess.IV"], "doi": "", "_query": "real estate valuation deep learning", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/1710.10628v3", "title": "Variational Continual Learning", "abstract": "This paper develops variational continual learning (VCL), a simple but general framework for continual learning that fuses online variational inference (VI) and recent advances in Monte Carlo VI for neural networks. The framework can successfully train both deep discriminative models and deep generative models in complex continual learning settings where existing tasks evolve over time and entirely new tasks emerge. Experimental results show that VCL outperforms state-of-the-art continual learning methods on a variety of tasks, avoiding catastrophic forgetting in a fully automatic way.", "published": "2017-10-29T15:30:58Z", "authors": ["Cuong V. Nguyen", "Yingzhen Li", "Thang D. Bui", "Richard E. Turner"], "categories": ["stat.ML", "cs.LG"], "doi": "", "_query": "real estate valuation deep learning", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2208.00250v1", "title": "A Bayesian Approach to Learning Bandit Structure in Markov Decision Processes", "abstract": "In the reinforcement learning literature, there are many algorithms developed for either Contextual Bandit (CB) or Markov Decision Processes (MDP) environments. However, when deploying reinforcement learning algorithms in the real world, even with domain expertise, it is often difficult to know whether it is appropriate to treat a sequential decision making problem as a CB or an MDP. In other words, do actions affect future states, or only the immediate rewards? Making the wrong assumption regarding the nature of the environment can lead to inefficient learning, or even prevent the algorithm from ever learning an optimal policy, even with infinite data. In this work we develop an online algorithm that uses a Bayesian hypothesis testing approach to learn the nature of the environment. Our algorithm allows practitioners to incorporate prior knowledge about whether the environment is that of a CB or an MDP, and effectively interpolate between classical CB and MDP-based algorithms to mitigate against the effects of misspecifying the environment. We perform simulations and demonstrate that in CB settings our algorithm achieves lower regret than MDP-based algorithms, while in non-bandit MDP settings our algorithm is able to learn the optimal policy, often achieving comparable regret to MDP-based algorithms.", "published": "2022-07-30T15:18:11Z", "authors": ["Kelly W. Zhang", "Omer Gottesman", "Finale Doshi-Velez"], "categories": ["cs.LG", "cs.AI"], "doi": "", "_query": "real estate valuation deep learning", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2110.14049v2", "title": "Beta Shapley: a Unified and Noise-reduced Data Valuation Framework for Machine Learning", "abstract": "Data Shapley has recently been proposed as a principled framework to quantify the contribution of individual datum in machine learning. It can effectively identify helpful or harmful data points for a learning algorithm. In this paper, we propose Beta Shapley, which is a substantial generalization of Data Shapley. Beta Shapley arises naturally by relaxing the efficiency axiom of the Shapley value, which is not critical for machine learning settings. Beta Shapley unifies several popular data valuation methods and includes data Shapley as a special case. Moreover, we prove that Beta Shapley has several desirable statistical properties and propose efficient algorithms to estimate it. We demonstrate that Beta Shapley outperforms state-of-the-art data valuation methods on several downstream ML tasks such as: 1) detecting mislabeled training data; 2) learning with subsamples; and 3) identifying points whose addition or removal have the largest positive or negative impact on the model.", "published": "2021-10-26T22:03:55Z", "authors": ["Yongchan Kwon", "James Zou"], "categories": ["cs.LG", "stat.ML"], "doi": "", "_query": "real estate valuation deep learning", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2409.07825v4", "title": "Deep Multimodal Learning with Missing Modality: A Survey", "abstract": "During multimodal model training and testing, certain data modalities may be absent due to sensor limitations, cost constraints, privacy concerns, or data loss, negatively affecting performance. Multimodal learning techniques designed to handle missing modalities can mitigate this by ensuring model robustness even when some modalities are unavailable. This survey reviews recent progress in Multimodal Learning with Missing Modality (MLMM), focusing on deep learning methods. It provides the first comprehensive survey that covers the motivation and distinctions between MLMM and standard multimodal learning setups, followed by a detailed analysis of current methods, applications, and datasets, concluding with challenges and future directions.", "published": "2024-09-12T08:15:39Z", "authors": ["Renjie Wu", "Hu Wang", "Hsiang-Ting Chen", "Gustavo Carneiro"], "categories": ["cs.CV", "cs.AI", "cs.LG"], "doi": "", "_query": "real estate valuation deep learning", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2608.30442v1", "title": "Confounding Masquerading as Improvement: A Systematic Evaluation of Offline Reinforcement Learning for Stroke Antithrombotic Treatment in a 129,000-Patient Registry", "abstract": "Recent offline reinforcement learning (RL) studies report policies that outperform physician decisions on clinical outcomes. We conduct a systematic, partially crossed evaluation of five offline RL algorithm families and 14 reward designs in 44,894 post-2018 acute ischemic stroke patients from a nationwide registry (N = 129,033). Standard Fitted Q-Evaluation (FQE) yields an apparent policy-improvement estimate of +0.0069; adding an Early Neurological Deterioration penalty increases it to +0.0101. We identify reward-embedded confounding, in which a proxy terminal reward encodes baseline severity and prognosis as well as treatment efficacy. A 2 x 2 factorial analysis finds that terminal reward confounding accounts for 218.6% of the observed signal change, so its removal overshoots the null. After DML-inspired GBM reward residualization, the FQE estimate attenuates to +0.0033 (p = 0.132), and full deconfounding yields +0.0025 (p = 0.291). FQE-based diagnostics, T-learner analyses, and direct recurrence analyses converge away from a clinically meaningful aggregate improvement. A 1-year mRS factorial analysis replicates the attenuation. We provide an empirically motivated six-step evaluation checklist. NIHSS-stratified heterogeneity is hypothesis-generating for prospective trial design; hospital-level disagreement does not persist after full reward deconfounding.", "published": "2026-08-31T08:32:27Z", "authors": ["Kihun Rhee"], "categories": ["cs.LG", "stat.ML"], "doi": "", "_query": "real estate valuation deep learning", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/1809.07048v1", "title": "New approach for solar tracking systems based on computer vision, low cost hardware and deep learning", "abstract": "In this work, a new approach for Sun tracking systems is presented. Due to the current system limitations regarding costs and operational problems, a new approach based on low cost, computer vision open hardware and deep learning has been developed. The preliminary tests carried out successfully in Plataforma solar de Almeria (PSA), reveal the great potential and show the new approach as a good alternative to traditional systems. The proposed approach can provide key variables for the Sun tracking system control like cloud movements prediction, block and shadow detection, atmospheric attenuation or measures of concentrated solar radiation, which can improve the control strategies of the system and therefore the system performance.", "published": "2018-09-19T08:09:04Z", "authors": ["Jose A. Carballo", "Javier Bonilla", "Manuel Berenguel", "Jesús Fernández-Reche", "Ginés García"], "categories": ["cs.LG", "cs.CV", "stat.ML"], "doi": "10.1016/j.renene.2018.08.101", "_query": "real estate valuation deep learning", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2207.03576v2", "title": "Robustness Evaluation of Deep Unsupervised Learning Algorithms for Intrusion Detection Systems", "abstract": "Recently, advances in deep learning have been observed in various fields, including computer vision, natural language processing, and cybersecurity. Machine learning (ML) has demonstrated its ability as a potential tool for anomaly detection-based intrusion detection systems to build secure computer networks. Increasingly, ML approaches are widely adopted than heuristic approaches for cybersecurity because they learn directly from data. Data is critical for the development of ML systems, and becomes potential targets for attackers. Basically, data poisoning or contamination is one of the most common techniques used to fool ML models through data. This paper evaluates the robustness of six recent deep learning algorithms for intrusion detection on contaminated data. Our experiments suggest that the state-of-the-art algorithms used in this study are sensitive to data contamination and reveal the importance of self-defense against data perturbation when developing novel models, especially for intrusion detection systems.", "published": "2022-06-25T02:28:39Z", "authors": ["D'Jeff Kanda Nkashama", "Arian Soltani", "Jean-Charles Verdier", "Marc Frappier", "Pierre-Martin Tardif", "Froduald Kabanza"], "categories": ["cs.CR", "cs.LG", "cs.NI"], "doi": "", "_query": "real estate valuation deep learning", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/1112.4078v3", "title": "A note on $\\aleph_α$-saturated o-minimal expansions of real closed fields", "abstract": "We give necessary and sufficient conditions for a polynomially bounded o-minimal expansion of a real closed field (in a language of arbitrary cardinality) to be $\\aleph_α$-saturated. The conditions are in terms of the value group, residue field, and pseudo- Cauchy sequences of the natural valuation on the real closed field. This is achieved by an analysis of types, leading to the trichotomy. Our characterization provides a construction method for saturated models, using fields of generalized power series.", "published": "2011-12-17T19:10:17Z", "authors": ["Paola D'Aquino", "Salma Kuhlmann"], "categories": ["math.LO"], "doi": "", "_query": "real estate valuation deep learning", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2509.23462v2", "title": "Generative Evolutionary Meta-Solver (GEMS): Scalable Surrogate-Free Multi-Agent Reinforcement Learning", "abstract": "Scalable multi-agent reinforcement learning (MARL) remains a central challenge for AI. Existing population-based methods, like Policy-Space Response Oracles, PSRO, require storing explicit policy populations and constructing full payoff matrices, incurring quadratic computation and linear memory costs. We present Generative Evolutionary Meta-Solver (GEMS), a surrogate-free framework that replaces explicit populations with a compact set of latent anchors and a single amortized generator. Instead of exhaustively constructing the payoff matrix, GEMS relies on unbiased Monte Carlo rollouts, multiplicative-weights meta-dynamics, and a model-free empirical-Bernstein UCB oracle to adaptively expand the policy set. Best responses are trained within the generator using an advantage-based trust-region objective, eliminating the need to store and train separate actors. We evaluated GEMS in a variety of Two-player and Multi-Player games such as the Deceptive Messages Game, Kuhn Poker and Multi-Particle environment. We find that GEMS is up to ~$\\mathbf{6\\times}$ faster, has $\\mathbf{1.3\\times}$ less memory usage than PSRO, while also reaps higher rewards simultaneously. These results demonstrate that GEMS retains the game theoretic guarantees of PSRO, while overcoming its fundamental inefficiencies, hence enabling scalable multi-agent learning in multiple domains.", "published": "2025-09-27T19:23:38Z", "authors": ["Alakh Sharma", "Gaurish Trivedi", "Kartikey Singh Bhandari", "Yash Sinha", "Dhruv Kumar", "Pratik Narang", "Jagat Sesh Challa"], "categories": ["cs.LG", "cs.AI"], "doi": "", "_query": "real estate valuation deep learning", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2103.03450v2", "title": "DeepFreight: Integrating Deep Reinforcement Learning and Mixed Integer Programming for Multi-transfer Truck Freight Delivery", "abstract": "With the freight delivery demands and shipping costs increasing rapidly, intelligent control of fleets to enable efficient and cost-conscious solutions becomes an important problem. In this paper, we propose DeepFreight, a model-free deep-reinforcement-learning-based algorithm for multi-transfer freight delivery, which includes two closely-collaborative components: truck-dispatch and package-matching. Specifically, a deep multi-agent reinforcement learning framework called QMIX is leveraged to learn a dispatch policy, with which we can obtain the multi-step joint vehicle dispatch decisions for the fleet with respect to the delivery requests. Then an efficient multi-transfer matching algorithm is executed to assign the delivery requests to the trucks. Also, DeepFreight is integrated with a Mixed-Integer Linear Programming optimizer for further optimization. The evaluation results show that the proposed system is highly scalable and ensures a 100\\% delivery success while maintaining low delivery-time and fuel consumption. The codes are available at https://github.com/LucasCJYSDL/DeepFreight.", "published": "2021-03-05T03:06:48Z", "authors": ["Jiayu Chen", "Abhishek K. Umrawal", "Tian Lan", "Vaneet Aggarwal"], "categories": ["cs.LG", "cs.MA"], "doi": "", "_query": "real estate valuation deep learning", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2512.13031v1", "title": "Comprehensive Evaluation of Rule-Based, Machine Learning, and Deep Learning in Human Estimation Using Radio Wave Sensing: Accuracy, Spatial Generalization, and Output Granularity Trade-offs", "abstract": "This study presents the first comprehensive comparison of rule-based methods, traditional machine learning models, and deep learning models in radio wave sensing with frequency modulated continuous wave multiple input multiple output radar. We systematically evaluated five approaches in two indoor environments with distinct layouts: a rule-based connected component method; three traditional machine learning models, namely k-nearest neighbors, random forest, and support vector machine; and a deep learning model combining a convolutional neural network and long short term memory. In the training environment, the convolutional neural network long short term memory model achieved the highest accuracy, while traditional machine learning models provided moderate performance. In a new layout, however, all learning based methods showed significant degradation, whereas the rule-based method remained stable. Notably, for binary detection of presence versus absence of people, all models consistently achieved high accuracy across layouts. These results demonstrate that high capacity models can produce fine grained outputs with high accuracy in the same environment, but they are vulnerable to domain shift. In contrast, rule-based methods cannot provide fine grained outputs but exhibit robustness against domain shift. Moreover, regardless of the model type, a clear trade off was revealed between spatial generalization performance and output granularity.", "published": "2025-12-15T07:02:06Z", "authors": ["Tomoya Tanaka", "Tomonori Ikeda", "Ryo Yonemoto"], "categories": ["cs.CV"], "doi": "", "_query": "real estate valuation deep learning", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/1906.08312v1", "title": "Calibrated Model-Based Deep Reinforcement Learning", "abstract": "Estimates of predictive uncertainty are important for accurate model-based planning and reinforcement learning. However, predictive uncertainties---especially ones derived from modern deep learning systems---can be inaccurate and impose a bottleneck on performance. This paper explores which uncertainties are needed for model-based reinforcement learning and argues that good uncertainties must be calibrated, i.e. their probabilities should match empirical frequencies of predicted events. We describe a simple way to augment any model-based reinforcement learning agent with a calibrated model and show that doing so consistently improves planning, sample complexity, and exploration. On the \\textsc{HalfCheetah} MuJoCo task, our system achieves state-of-the-art performance using 50\\% fewer samples than the current leading approach. Our findings suggest that calibration can improve the performance of model-based reinforcement learning with minimal computational and implementation overhead.", "published": "2019-06-19T19:10:26Z", "authors": ["Ali Malik", "Volodymyr Kuleshov", "Jiaming Song", "Danny Nemer", "Harlan Seymour", "Stefano Ermon"], "categories": ["cs.LG", "stat.ML"], "doi": "", "_query": "real estate valuation deep learning", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2010.11560v1", "title": "Deep Learning is Singular, and That's Good", "abstract": "In singular models, the optimal set of parameters forms an analytic set with singularities and classical statistical inference cannot be applied to such models. This is significant for deep learning as neural networks are singular and thus \"dividing\" by the determinant of the Hessian or employing the Laplace approximation are not appropriate. Despite its potential for addressing fundamental issues in deep learning, singular learning theory appears to have made little inroads into the developing canon of deep learning theory. Via a mix of theory and experiment, we present an invitation to singular learning theory as a vehicle for understanding deep learning and suggest important future work to make singular learning theory directly applicable to how deep learning is performed in practice.", "published": "2020-10-22T09:33:59Z", "authors": ["Daniel Murfet", "Susan Wei", "Mingming Gong", "Hui Li", "Jesse Gell-Redman", "Thomas Quella"], "categories": ["cs.LG"], "doi": "10.1109/TNNLS.2022.3167409", "_query": "real estate valuation deep learning", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2503.05546v2", "title": "Impoola: The Power of Average Pooling for Image-Based Deep Reinforcement Learning", "abstract": "As image-based deep reinforcement learning tackles more challenging tasks, increasing model size has become an important factor in improving performance. Recent studies achieved this by focusing on the parameter efficiency of scaled networks, typically using Impala-CNN, a 15-layer ResNet-inspired network, as the image encoder. However, while Impala-CNN evidently outperforms older CNN architectures, potential advancements in network design for deep reinforcement learning-specific image encoders remain largely unexplored. We find that replacing the flattening of output feature maps in Impala-CNN with global average pooling leads to a notable performance improvement. This approach outperforms larger and more complex models in the Procgen Benchmark, particularly in terms of generalization. We call our proposed encoder model Impoola-CNN. A decrease in the network's translation sensitivity may be central to this improvement, as we observe the most significant gains in games without agent-centered observations. Our results demonstrate that network scaling is not just about increasing model size - efficient network design is also an essential factor. We make our code available at https://github.com/raphajaner/impoola.", "published": "2025-03-07T16:19:19Z", "authors": ["Raphael Trumpp", "Ansgar Schäfftlein", "Mirco Theile", "Marco Caccamo"], "categories": ["cs.LG", "cs.AI"], "doi": "", "_query": "real estate valuation deep learning", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2312.04174v1", "title": "Coherent energy and force uncertainty in deep learning force fields", "abstract": "In machine learning energy potentials for atomic systems, forces are commonly obtained as the negative derivative of the energy function with respect to atomic positions. To quantify aleatoric uncertainty in the predicted energies, a widely used modeling approach involves predicting both a mean and variance for each energy value. However, this model is not differentiable under the usual white noise assumption, so energy uncertainty does not naturally translate to force uncertainty. In this work we propose a machine learning potential energy model in which energy and force aleatoric uncertainty are linked through a spatially correlated noise process. We demonstrate our approach on an equivariant messages passing neural network potential trained on energies and forces on two out-of-equilibrium molecular datasets. Furthermore, we also show how to obtain epistemic uncertainties in this setting based on a Bayesian interpretation of deep ensemble models.", "published": "2023-12-07T09:49:05Z", "authors": ["Peter Bjørn Jørgensen", "Jonas Busk", "Ole Winther", "Mikkel N. Schmidt"], "categories": ["stat.ML", "cs.LG", "physics.comp-ph"], "doi": "", "_query": "real estate valuation deep learning", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/1803.08456v1", "title": "Deep Reinforcement Learning with Model Learning and Monte Carlo Tree Search in Minecraft", "abstract": "Deep reinforcement learning has been successfully applied to several visual-input tasks using model-free methods. In this paper, we propose a model-based approach that combines learning a DNN-based transition model with Monte Carlo tree search to solve a block-placing task in Minecraft. Our learned transition model predicts the next frame and the rewards one step ahead given the last four frames of the agent's first-person-view image and the current action. Then a Monte Carlo tree search algorithm uses this model to plan the best sequence of actions for the agent to perform. On the proposed task in Minecraft, our model-based approach reaches the performance comparable to the Deep Q-Network's, but learns faster and, thus, is more training sample efficient.", "published": "2018-03-22T16:53:34Z", "authors": ["Stephan Alaniz"], "categories": ["cs.AI", "cs.LG", "stat.ML"], "doi": "", "_query": "real estate valuation deep learning", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2210.14266v1", "title": "Hedonic Models of Real Estate Prices: GAM and Environmental Factors", "abstract": "We consider the use of P-spline generalized additive hedonic models for real estate prices in large U.S. cities, contrasting their predictive efficiency against linear and polynomial based generalized linear models. Using intrinsic and extrinsic factors available from Redfin, we show that GAM models are capable of describing 84% to 92% of the variance in the expected ln(sales price), based upon 2021 data. As climate change is becoming increasingly important, we utilized the GAM model to examine the significance of environmental factors in two urban centers on the northwest coast. The results indicate city dependent differences in the significance of environmental factors. We find that inclusion of the environmental factors increases the adjusted R-squared of the GAM model by less than one percent.", "published": "2022-10-25T18:38:52Z", "authors": ["Jason R. Bailey", "Davide Lauria", "W. Brent Lindquist", "Stefan Mittnik", "Svetlozar T. Rachev"], "categories": ["q-fin.CP"], "doi": "", "_query": "real estate valuation deep learning", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2610.21554v1", "title": "Uncertainty Quantification in Pairwise Difference Learning for Classification", "abstract": "Instead of learning a mapping from instances to outcomes in the standard way, the key idea of pairwise difference learning (PDL) is to learn a function that takes two instances as input and predicts the difference between the respective outcomes. Given a function of this kind, predictions for a query instance are derived from every training example and then averaged. We propose a version of PDL for the task of classification and propose a suitable meta-learning technique for this purpose. Moreover, we address the problem of uncertainty quantification, i.e., of representing and quantifying the uncertainty of a PDL classifier's predictions in a faithful manner. Building on recent work on the decomposition of total uncertainty into aleatoric and epistemic components, we propose different ways of extending corresponding measures to the setting of PDL, depending on how and to what extent uncertainty is represented by the underlying base learner. In large-scale empirical studies, we analyze both the predictive accuracy and the uncertainty awareness of our PDL classifier.", "published": "2026-10-15T09:15:08Z", "authors": ["Mohamed Karim Belaid", "Maximilian Rabus", "Eyke Hüllermeier"], "categories": ["cs.LG", "cs.AI"], "doi": "10.1007/s10994-025-06949-0", "_query": "real estate valuation deep learning", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/1905.07366v1", "title": "Stratospheric Aerosol Injection as a Deep Reinforcement Learning Problem", "abstract": "As global greenhouse gas emissions continue to rise, the use of stratospheric aerosol injection (SAI), a form of solar geoengineering, is increasingly considered in order to artificially mitigate climate change effects. However, initial research in simulation suggests that naive SAI can have catastrophic regional consequences, which may induce serious geostrategic conflicts. Current geo-engineering research treats SAI control in low-dimensional approximation only. We suggest treating SAI as a high-dimensional control problem, with policies trained according to a context-sensitive reward function within the Deep Reinforcement Learning (DRL) paradigm. In order to facilitate training in simulation, we suggest to emulate HadCM3, a widely used General Circulation Model, using deep learning techniques. We believe this is the first application of DRL to the climate sciences.", "published": "2019-05-17T16:36:00Z", "authors": ["Christian Schroeder de Witt", "Thomas Hornigold"], "categories": ["cs.LG", "physics.ao-ph", "stat.ML"], "doi": "", "_query": "real estate valuation deep learning", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2003.01176v3", "title": "Deep Survival Machines: Fully Parametric Survival Regression and Representation Learning for Censored Data with Competing Risks", "abstract": "We describe a new approach to estimating relative risks in time-to-event prediction problems with censored data in a fully parametric manner. Our approach does not require making strong assumptions of constant proportional hazard of the underlying survival distribution, as required by the Cox-proportional hazard model. By jointly learning deep nonlinear representations of the input covariates, we demonstrate the benefits of our approach when used to estimate survival risks through extensive experimentation on multiple real world datasets with different levels of censoring. We further demonstrate advantages of our model in the competing risks scenario. To the best of our knowledge, this is the first work involving fully parametric estimation of survival times with competing risks in the presence of censoring.", "published": "2020-03-02T20:21:59Z", "authors": ["Chirag Nagpal", "Xinyu Rachel Li", "Artur Dubrawski"], "categories": ["cs.LG", "stat.AP", "stat.ML"], "doi": "", "_query": "real estate valuation deep learning", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2509.08759v3", "title": "Fourier Learning Machines: Nonharmonic Fourier-Based Neural Networks for Scientific Machine Learning", "abstract": "We introduce the Fourier Learning Machine (FLM), a neural network (NN) architecture designed to represent a multidimensional nonharmonic Fourier series. The FLM uses a simple feedforward structure with cosine activation functions to learn the frequencies, amplitudes, and phase shifts of the series as trainable parameters. This design allows the model to create a problem-specific spectral basis adaptable to both periodic and nonperiodic functions. Unlike previous Fourier-inspired NN models, the FLM is the first architecture able to represent a multidimensional Fourier series with a complete set of basis functions in separable form, doing so by using a standard Multilayer Perceptron-like architecture. A one-to-one correspondence between the Fourier coefficients and amplitudes and phase-shifts is demonstrated, allowing for the translation between a full, separable basis form and the cosine phase-shifted one. Additionally, we evaluate the performance of FLMs on several scientific computing problems, including benchmark Partial Differential Equations (PDEs) and a family of Optimal Control Problems (OCPs). Computational experiments show that the performance of FLMs is comparable, and often superior, to that of established architectures like SIREN and vanilla feedforward NNs.", "published": "2025-09-10T16:49:20Z", "authors": ["Mominul Rubel", "Adam Meyers", "Gabriel Nicolosi"], "categories": ["cs.LG", "math.OC"], "doi": "", "_query": "real estate valuation deep learning", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2211.10902v2", "title": "Noisy Symbolic Abstractions for Deep RL: A case study with Reward Machines", "abstract": "Natural and formal languages provide an effective mechanism for humans to specify instructions and reward functions. We investigate how to generate policies via RL when reward functions are specified in a symbolic language captured by Reward Machines, an increasingly popular automaton-inspired structure. We are interested in the case where the mapping of environment state to a symbolic (here, Reward Machine) vocabulary -- commonly known as the labelling function -- is uncertain from the perspective of the agent. We formulate the problem of policy learning in Reward Machines with noisy symbolic abstractions as a special class of POMDP optimization problem, and investigate several methods to address the problem, building on existing and new techniques, the latter focused on predicting Reward Machine state, rather than on grounding of individual symbols. We analyze these methods and evaluate them experimentally under varying degrees of uncertainty in the correct interpretation of the symbolic vocabulary. We verify the strength of our approach and the limitation of existing methods via an empirical investigation on both illustrative, toy domains and partially observable, deep RL domains.", "published": "2022-11-20T08:13:48Z", "authors": ["Andrew C. Li", "Zizhao Chen", "Pashootan Vaezipoor", "Toryn Q. Klassen", "Rodrigo Toro Icarte", "Sheila A. McIlraith"], "categories": ["cs.LG", "cs.AI", "cs.FL"], "doi": "", "_query": "real estate valuation deep learning", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/1209.0738v3", "title": "Sparse coding for multitask and transfer learning", "abstract": "We investigate the use of sparse coding and dictionary learning in the context of multitask and transfer learning. The central assumption of our learning method is that the tasks parameters are well approximated by sparse linear combinations of the atoms of a dictionary on a high or infinite dimensional space. This assumption, together with the large quantity of available data in the multitask and transfer learning settings, allows a principled choice of the dictionary. We provide bounds on the generalization error of this approach, for both settings. Numerical experiments on one synthetic and two real datasets show the advantage of our method over single task learning, a previous method based on orthogonal and dense representation of the tasks and a related method learning task grouping.", "published": "2012-09-04T19:06:51Z", "authors": ["Andreas Maurer", "Massimiliano Pontil", "Bernardino Romera-Paredes"], "categories": ["cs.LG", "stat.ML"], "doi": "", "_query": "real estate valuation deep learning", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/1905.09449v5", "title": "Exploring Structural Sparsity of Deep Networks via Inverse Scale Spaces", "abstract": "The great success of deep neural networks is built upon their over-parameterization, which smooths the optimization landscape without degrading the generalization ability. Despite the benefits of over-parameterization, a huge amount of parameters makes deep networks cumbersome in daily life applications. Though techniques such as pruning and distillation are developed, they are expensive in fully training a dense network as backward selection methods, and there is still a void on systematically exploring forward selection methods for learning structural sparsity in deep networks. To fill in this gap, this paper proposes a new approach based on differential inclusions of inverse scale spaces, which generate a family of models from simple to complex ones along the dynamics via coupling a pair of parameters, such that over-parameterized deep models and their structural sparsity can be explored simultaneously. This kind of differential inclusion scheme has a simple discretization, dubbed Deep structure splitting Linearized Bregman Iteration (DessiLBI), whose global convergence in learning deep networks could be established under the Kurdyka-Lojasiewicz framework. Experimental evidence shows that our method achieves comparable and even better performance than the competitive optimizers in exploring the sparse structure of several widely used backbones on the benchmark datasets. Remarkably, with early stopping, our method unveils `winning tickets' in early epochs: the effective sparse network structures with comparable test accuracy to fully trained over-parameterized models, that are further transferable to similar alternative tasks. Furthermore, our method is able to grow networks efficiently with adaptive filter configurations, demonstrating a good performance with much less computational cost. Codes and models can be downloaded at {https://github.com/DessiLBI2020/DessiLBI}.", "published": "2019-05-23T03:27:03Z", "authors": ["Yanwei Fu", "Chen Liu", "Donghao Li", "Zuyuan Zhong", "Xinwei Sun", "Jinshan Zeng", "Yuan Yao"], "categories": ["cs.LG", "math.DS", "math.OC", "stat.ML"], "doi": "", "_query": "real estate valuation deep learning", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/1808.05535v1", "title": "Combining time-series and textual data for taxi demand prediction in event areas: a deep learning approach", "abstract": "Accurate time-series forecasting is vital for numerous areas of application such as transportation, energy, finance, economics, etc. However, while modern techniques are able to explore large sets of temporal data to build forecasting models, they typically neglect valuable information that is often available under the form of unstructured text. Although this data is in a radically different format, it often contains contextual explanations for many of the patterns that are observed in the temporal data. In this paper, we propose two deep learning architectures that leverage word embeddings, convolutional layers and attention mechanisms for combining text information with time-series data. We apply these approaches for the problem of taxi demand forecasting in event areas. Using publicly available taxi data from New York, we empirically show that by fusing these two complementary cross-modal sources of information, the proposed models are able to significantly reduce the error in the forecasts.", "published": "2018-08-16T15:19:34Z", "authors": ["Filipe Rodrigues", "Ioulia Markou", "Francisco Pereira"], "categories": ["stat.ML", "cs.CL", "cs.LG"], "doi": "10.1016/j.inffus.2018.07.007", "_query": "real estate valuation deep learning", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/1912.04871v4", "title": "Deep symbolic regression: Recovering mathematical expressions from data via risk-seeking policy gradients", "abstract": "Discovering the underlying mathematical expressions describing a dataset is a core challenge for artificial intelligence. This is the problem of $\\textit{symbolic regression}$. Despite recent advances in training neural networks to solve complex tasks, deep learning approaches to symbolic regression are underexplored. We propose a framework that leverages deep learning for symbolic regression via a simple idea: use a large model to search the space of small models. Specifically, we use a recurrent neural network to emit a distribution over tractable mathematical expressions and employ a novel risk-seeking policy gradient to train the network to generate better-fitting expressions. Our algorithm outperforms several baseline methods (including Eureqa, the gold standard for symbolic regression) in its ability to exactly recover symbolic expressions on a series of benchmark problems, both with and without added noise. More broadly, our contributions include a framework that can be applied to optimize hierarchical, variable-length objects under a black-box performance metric, with the ability to incorporate constraints in situ, and a risk-seeking policy gradient formulation that optimizes for best-case performance instead of expected performance.", "published": "2019-12-10T18:25:48Z", "authors": ["Brenden K. Petersen", "Mikel Landajuela", "T. Nathan Mundhenk", "Claudio P. Santiago", "Soo K. Kim", "Joanne T. Kim"], "categories": ["cs.LG", "stat.ML"], "doi": "", "_query": "real estate valuation deep learning", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/1806.04854v3", "title": "Fast and Scalable Bayesian Deep Learning by Weight-Perturbation in Adam", "abstract": "Uncertainty computation in deep learning is essential to design robust and reliable systems. Variational inference (VI) is a promising approach for such computation, but requires more effort to implement and execute compared to maximum-likelihood methods. In this paper, we propose new natural-gradient algorithms to reduce such efforts for Gaussian mean-field VI. Our algorithms can be implemented within the Adam optimizer by perturbing the network weights during gradient evaluations, and uncertainty estimates can be cheaply obtained by using the vector that adapts the learning rate. This requires lower memory, computation, and implementation effort than existing VI methods, while obtaining uncertainty estimates of comparable quality. Our empirical results confirm this and further suggest that the weight-perturbation in our algorithm could be useful for exploration in reinforcement learning and stochastic optimization.", "published": "2018-06-13T05:45:22Z", "authors": ["Mohammad Emtiyaz Khan", "Didrik Nielsen", "Voot Tangkaratt", "Wu Lin", "Yarin Gal", "Akash Srivastava"], "categories": ["stat.ML", "cs.AI", "cs.LG", "stat.CO"], "doi": "", "_query": "real estate valuation deep learning", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2305.01657v1", "title": "Scalable Data Point Valuation in Decentralized Learning", "abstract": "Existing research on data valuation in federated and swarm learning focuses on valuing client contributions and works best when data across clients is independent and identically distributed (IID). In practice, data is rarely distributed IID. We develop an approach called DDVal for decentralized data valuation, capable of valuing individual data points in federated and swarm learning. DDVal is based on sharing deep features and approximating Shapley values through a k-nearest neighbor approximation method. This allows for novel applications, for example, to simultaneously reward institutions and individuals for providing data to a decentralized machine learning task. The valuation of data points through DDVal allows to also draw hierarchical conclusions on the contribution of institutions, and we empirically show that the accuracy of DDVal in estimating institutional contributions is higher than existing Shapley value approximation methods for federated learning. Specifically, it reaches a cosine similarity in approximating Shapley values of 99.969 % in both, IID and non-IID data distributions across institutions, compared with 99.301 % and 97.250 % for the best state of the art methods. DDVal scales with the number of data points instead of the number of clients, and has a loglinear complexity. This scales more favorably than existing approaches with an exponential complexity. We show that DDVal is especially efficient in data distribution scenarios with many clients that have few data points - for example, more than 16 clients with 8,000 data points each. By integrating DDVal into a decentralized system, we show that it is not only suitable for centralized federated learning, but also decentralized swarm learning, which aligns well with the research on emerging internet technologies such as web3 to reward users for providing data to algorithms.", "published": "2023-05-01T22:15:50Z", "authors": ["Konstantin D. Pandl", "Chun-Yin Huang", "Ivan Beschastnikh", "Xiaoxiao Li", "Scott Thiebes", "Ali Sunyaev"], "categories": ["cs.LG", "cs.AI", "cs.DC"], "doi": "", "_query": "real estate valuation deep learning", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/1812.01484v1", "title": "Privacy-Preserving Distributed Deep Learning for Clinical Data", "abstract": "Deep learning with medical data often requires larger samples sizes than are available at single providers. While data sharing among institutions is desirable to train more accurate and sophisticated models, it can lead to severe privacy concerns due the sensitive nature of the data. This problem has motivated a number of studies on distributed training of neural networks that do not require direct sharing of the training data. However, simple distributed training does not offer provable privacy guarantees to satisfy technical safe standards and may reveal information about the underlying patients. We present a method to train neural networks for clinical data in a distributed fashion under differential privacy. We demonstrate these methods on two datasets that include information from multiple independent sites, the eICU collaborative Research Database and The Cancer Genome Atlas.", "published": "2018-12-04T15:25:29Z", "authors": ["Brett K. Beaulieu-Jones", "William Yuan", "Samuel G. Finlayson", "Zhiwei Steven Wu"], "categories": ["cs.LG", "stat.ML"], "doi": "", "_query": "real estate valuation deep learning", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2602.11092v2", "title": "MerLin: A Discovery Engine for Photonic and Hybrid Quantum Machine Learning", "abstract": "Identifying where quantum models may offer practical benefits in near term quantum machine learning (QML) requires moving beyond isolated algorithmic proposals toward systematic and empirical exploration across models, datasets, and hardware constraints. We introduce MerLin, an open-source framework designed as a discovery engine for photonic and hybrid quantum machine learning. MerLin integrates optimized strong simulation of linear optical circuits into standard PyTorch and scikit learn workflows, enabling end-to-end differentiable training of quantum layers. MerLin is designed around systematic benchmarking and reproducibility. As an initial contribution, we reproduce eighteen state-of-the-art photonic and hybrid QML works spanning kernel methods, reservoir computing, convolutional and recurrent architectures, generative models, and modern training paradigms. These reproductions are released as reusable, modular experiments that can be directly extended and adapted, establishing a shared experimental baseline consistent with empirical benchmarking methodologies widely adopted in modern artificial intelligence. By embedding photonic quantum models within established machine learning ecosystems, MerLin allows practitioners to leverage existing tooling for ablation studies, cross-modality comparisons, and hybrid classical-quantum workflows. The framework already implements hardware-aware features, allowing tests on available quantum hardware while enabling exploration beyond its current capabilities, positioning MerLin as a forward-looking co-design tool linking algorithms, benchmarks, and hardware.", "published": "2026-02-11T18:00:01Z", "authors": ["Cassandre Notton", "Benjamin Stott", "Philippe Schoeb", "Anthony Walsh", "Grégoire Leboucher", "Vincent Espitalier", "Vassilis Apostolou", "Louis-Félix Vigneux", "Alexia Salavrakos", "Jean Senellart"], "categories": ["cs.LG", "cs.PL", "quant-ph"], "doi": "", "_query": "real estate valuation deep learning", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2507.05134v2", "title": "Deep Learning to Automate Parameter Extraction and Model Fitting of Two-Dimensional Transistors", "abstract": "We present a deep learning approach to extract physical parameters (e.g., mobility, Schottky contact barrier height, defect profiles) of two-dimensional (2D) transistors from electrical measurements, enabling automated parameter extraction and technology computer-aided design (TCAD) fitting. To facilitate this task, we implement a simple data augmentation and pre-training approach by training a secondary neural network to approximate a physics-based device simulator. This method enables high-quality fits after training the neural network on electrical data generated from physics-based simulations of ~500 devices, a factor >40$\\times$ fewer than other recent efforts. Consequently, fitting can be achieved by training on physically rigorous TCAD models, including complex geometry, self-consistent transport, and electrostatic effects, and is not limited to computationally inexpensive compact models. We apply our approach to reverse-engineer key parameters from experimental monolayer WS$_2$ transistors, achieving a median coefficient of determination ($R^2$) = 0.99 when fitting measured electrical data. We also demonstrate that this approach generalizes and scales well by reverse-engineering electrical data on high-electron-mobility transistors while fitting 35 parameters simultaneously. To facilitate future research on deep learning approaches for inverse transistor design, we have published our code and sample data sets online.", "published": "2025-07-07T15:46:25Z", "authors": ["Robert K. A. Bennett", "Jan-Lucas Uslu", "Harmon F. Gault", "Asir Intisar Khan", "Lauren Hoang", "Tara Peña", "Kathryn Neilson", "Young Suh Song", "Zhepeng Zhang", "Andrew J. Mannix", "Eric Pop"], "categories": ["cs.LG", "cond-mat.mtrl-sci", "physics.app-ph"], "doi": "10.34133/research.1103", "_query": "real estate valuation deep learning", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2502.14833v2", "title": "Probabilistic Robustness in Deep Learning: A Concise yet Comprehensive Guide", "abstract": "Deep learning (DL) has demonstrated significant potential across various safety-critical applications, yet ensuring its robustness remains a key challenge. While adversarial robustness has been extensively studied in worst-case scenarios, probabilistic robustness (PR) offers a more practical perspective by quantifying the likelihood of failures under stochastic perturbations. This paper provides a concise yet comprehensive overview of PR, covering its formal definitions, evaluation and enhancement methods. We introduce a reformulated ''min-max'' optimisation framework for adversarial training specifically designed to improve PR. Furthermore, we explore the integration of PR verification evidence into system-level safety assurance, addressing challenges in translating DL model-level robustness to system-level claims. Finally, we highlight open research questions, including benchmarking PR evaluation methods, extending PR to generative AI tasks, and developing rigorous methodologies and case studies for system-level integration.", "published": "2025-02-20T18:47:17Z", "authors": ["Xingyu Zhao"], "categories": ["cs.LG"], "doi": "", "_query": "real estate valuation deep learning", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2210.00973v2", "title": "NCVX: A General-Purpose Optimization Solver for Constrained Machine and Deep Learning", "abstract": "Imposing explicit constraints is relatively new but increasingly pressing in deep learning, stimulated by, e.g., trustworthy AI that performs robust optimization over complicated perturbation sets and scientific applications that need to respect physical laws and constraints. However, it can be hard to reliably solve constrained deep learning problems without optimization expertise. The existing deep learning frameworks do not admit constraints. General-purpose optimization packages can handle constraints but do not perform auto-differentiation and have trouble dealing with nonsmoothness. In this paper, we introduce a new software package called NCVX, whose initial release contains the solver PyGRANSO, a PyTorch-enabled general-purpose optimization package for constrained machine/deep learning problems, the first of its kind. NCVX inherits auto-differentiation, GPU acceleration, and tensor variables from PyTorch, and is built on freely available and widely used open-source frameworks. NCVX is available at https://ncvx.org, with detailed documentation and numerous examples from machine/deep learning and other fields.", "published": "2022-10-03T14:41:26Z", "authors": ["Buyun Liang", "Tim Mitchell", "Ju Sun"], "categories": ["cs.LG", "cs.CV", "cs.MS", "eess.SP", "math.OC"], "doi": "", "_query": "real estate valuation deep learning", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/1903.12253v4", "title": "Baryon density extraction and isotropy analysis of Cosmic Microwave Background using Deep Learning", "abstract": "The discovery of cosmic microwave background (CMB) was a paradigm shift in the study and fundamental understanding of the early universe and also the Big Bang phenomenon. Cosmic microwave background is one of the richest and intriguing sources of information available to cosmologists and one parameter of special interest is baryon density of the universe. Baryon density can be primarily estimated by analyzing CMB data or through the study of big bang nucleosynthesis(BBN). Hence, it is necessary that both of the results found though the two methods are in agreement with each other. Although there are some well established statistical methods for the analysis of CMB to estimate baryon density, here we explore the use of deep learning in this respect. We correlate the baryon density obtained from the power spectrum of simulated CMB temperature maps with the corresponding map image and form the dataset for training the neural network model. We analyze the accuracy with which the model is able to predict the results from a relatively abstract dataset considering the fact that CMB is a Gaussian random field. CMB is anisotropic due to temperature fluctuations at small scales but on a larger scale CMB is considered isotropic, here we analyze the isotropy of CMB by training the model with CMB maps centered at different galactic coordinates and compare the predictions of neural network models.", "published": "2019-03-28T20:35:35Z", "authors": ["Amit Mishra", "Pranath Reddy", "Rahul Nigam"], "categories": ["astro-ph.CO"], "doi": "10.1088/2632-2153/abab63", "_query": "real estate valuation deep learning", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2307.15690v1", "title": "Benchmarking Offline Reinforcement Learning on Real-Robot Hardware", "abstract": "Learning policies from previously recorded data is a promising direction for real-world robotics tasks, as online learning is often infeasible. Dexterous manipulation in particular remains an open problem in its general form. The combination of offline reinforcement learning with large diverse datasets, however, has the potential to lead to a breakthrough in this challenging domain analogously to the rapid progress made in supervised learning in recent years. To coordinate the efforts of the research community toward tackling this problem, we propose a benchmark including: i) a large collection of data for offline learning from a dexterous manipulation platform on two tasks, obtained with capable RL agents trained in simulation; ii) the option to execute learned policies on a real-world robotic system and a simulation for efficient debugging. We evaluate prominent open-sourced offline reinforcement learning algorithms on the datasets and provide a reproducible experimental setup for offline reinforcement learning on real systems.", "published": "2023-07-28T17:29:49Z", "authors": ["Nico Gürtler", "Sebastian Blaes", "Pavel Kolev", "Felix Widmaier", "Manuel Wüthrich", "Stefan Bauer", "Bernhard Schölkopf", "Georg Martius"], "categories": ["cs.LG", "cs.RO"], "doi": "", "_query": "real estate valuation deep learning", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/1811.03179v4", "title": "How Well Generative Adversarial Networks Learn Distributions", "abstract": "This paper studies the rates of convergence for learning distributions implicitly with the adversarial framework and Generative Adversarial Networks (GANs), which subsume Wasserstein, Sobolev, MMD GAN, and Generalized/Simulated Method of Moments (GMM/SMM) as special cases. We study a wide range of parametric and nonparametric target distributions under a host of objective evaluation metrics. We investigate how to obtain valid statistical guarantees for GANs through the lens of regularization. On the nonparametric end, we derive the optimal minimax rates for distribution estimation under the adversarial framework. On the parametric end, we establish a theory for general neural network classes (including deep leaky ReLU networks) that characterizes the interplay on the choice of generator and discriminator pair. We discover and isolate a new notion of regularization, called the generator-discriminator-pair regularization, that sheds light on the advantage of GANs compared to classical parametric and nonparametric approaches for explicit distribution estimation. We develop novel oracle inequalities as the main technical tools for analyzing GANs, which are of independent interest.", "published": "2018-11-07T23:14:45Z", "authors": ["Tengyuan Liang"], "categories": ["math.ST", "cs.LG", "stat.ML"], "doi": "", "_query": "real estate valuation deep learning", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2406.07213v4", "title": "Semantic-Aware Spectrum Sharing in Internet of Vehicles Based on Deep Reinforcement Learning", "abstract": "This work aims to investigate semantic communication in high-speed mobile Internet of vehicles (IoV) environments, with a focus on the spectrum sharing between vehicle-to-vehicle (V2V) and vehicle-to-infrastructure (V2I) communications. We specifically address spectrum scarcity and network traffic and then propose a semantic-aware spectrum sharing algorithm (SSS) based on the deep reinforcement learning (DRL) soft actor-critic (SAC) approach. Firstly, we delve into the extraction of semantic information. Secondly, we redefine metrics for semantic information in V2V and V2I spectrum sharing in IoV environments, introducing high-speed semantic spectrum efficiency (HSSE) and semantic transmission rate (HSR). Finally, we employ the SAC algorithm for decision optimization in V2V and V2I spectrum sharing based on semantic information. This optimization encompasses the optimal link of V2V and V2I sharing strategies, the transmission power for vehicles sending semantic information and the length of transmitted semantic symbols, aiming at maximizing HSSE of V2I and enhancing success rate of effective semantic information transmission (SRS) of V2V. Experimental results demonstrate that the SSS algorithm outperforms other baseline algorithms, including other traditional-communication-based spectrum sharing algorithms and spectrum sharing algorithm using other reinforcement learning approaches. The SSS algorithm exhibits a 15% increase in HSSE and approximately a 7% increase in SRS.", "published": "2024-06-11T12:42:41Z", "authors": ["Zhiyu Shao", "Qiong Wu", "Pingyi Fan", "Nan Cheng", "Wen Chen", "Jiangzhou Wang", "Khaled B. Letaief"], "categories": ["cs.LG"], "doi": "", "_query": "real estate valuation deep learning", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2601.05082v1", "title": "Exploring Student Expectations and Confidence in Learning Analytics", "abstract": "Learning Analytics (LA) is nowadays ubiquitous in many educational systems, providing the ability to collect and analyze student data in order to understand and optimize learning and the environments in which it occurs. On the other hand, the collection of data requires to comply with the growing demand regarding privacy legislation. In this paper, we use the Student Expectation of Learning Analytics Questionnaire (SELAQ) to analyze the expectations and confidence of students from different faculties regarding the processing of their data for Learning Analytics purposes. This allows us to identify four clusters of students through clustering algorithms: Enthusiasts, Realists, Cautious and Indifferents. This structured analysis provides valuable insights into the acceptance and criticism of Learning Analytics among students.", "published": "2026-01-08T16:27:09Z", "authors": ["Hayk Asatryan", "Basile Tousside", "Janis Mohr", "Malte Neugebauer", "Hildo Bijl", "Paul Spiegelberg", "Claudia Frohn-Schauf", "Jörg Frochte"], "categories": ["cs.LG", "cs.CY", "cs.HC"], "doi": "10.1145/3636555.3636923", "_query": "real estate valuation deep learning", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/1903.03825v5", "title": "Interpolation Consistency Training for Semi-Supervised Learning", "abstract": "We introduce Interpolation Consistency Training (ICT), a simple and computation efficient algorithm for training Deep Neural Networks in the semi-supervised learning paradigm. ICT encourages the prediction at an interpolation of unlabeled points to be consistent with the interpolation of the predictions at those points. In classification problems, ICT moves the decision boundary to low-density regions of the data distribution. Our experiments show that ICT achieves state-of-the-art performance when applied to standard neural network architectures on the CIFAR-10 and SVHN benchmark datasets. Our theoretical analysis shows that ICT corresponds to a certain type of data-adaptive regularization with unlabeled points which reduces overfitting to labeled points under high confidence values.", "published": "2019-03-09T16:39:22Z", "authors": ["Vikas Verma", "Kenji Kawaguchi", "Alex Lamb", "Juho Kannala", "Arno Solin", "Yoshua Bengio", "David Lopez-Paz"], "categories": ["stat.ML", "cs.AI", "cs.LG"], "doi": "10.1016/j.neunet.2021.10.008", "_query": "real estate valuation deep learning", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2301.12636v2", "title": "Exploring Image Augmentations for Siamese Representation Learning with Chest X-Rays", "abstract": "Image augmentations are quintessential for effective visual representation learning across self-supervised learning techniques. While augmentation strategies for natural imaging have been studied extensively, medical images are vastly different from their natural counterparts. Thus, it is unknown whether common augmentation strategies employed in Siamese representation learning generalize to medical images and to what extent. To address this challenge, in this study, we systematically assess the effect of various augmentations on the quality and robustness of the learned representations. We train and evaluate Siamese Networks for abnormality detection on chest X-Rays across three large datasets (MIMIC-CXR, CheXpert and VinDR-CXR). We investigate the efficacy of the learned representations through experiments involving linear probing, fine-tuning, zero-shot transfer, and data efficiency. Finally, we identify a set of augmentations that yield robust representations that generalize well to both out-of-distribution data and diseases, while outperforming supervised baselines using just zero-shot transfer and linear probes by up to 20%. Our code is available at https://github.com/StanfordMIMI/siaug.", "published": "2023-01-30T03:42:02Z", "authors": ["Rogier van der Sluijs", "Nandita Bhaskhar", "Daniel Rubin", "Curtis Langlotz", "Akshay Chaudhari"], "categories": ["eess.IV", "cs.AI", "cs.CV", "cs.LG"], "doi": "", "_query": "real estate valuation deep learning", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2008.00524v2", "title": "Interactive Imitation Learning in State-Space", "abstract": "Imitation Learning techniques enable programming the behavior of agents through demonstrations rather than manual engineering. However, they are limited by the quality of available demonstration data. Interactive Imitation Learning techniques can improve the efficacy of learning since they involve teachers providing feedback while the agent executes its task. In this work, we propose a novel Interactive Learning technique that uses human feedback in state-space to train and improve agent behavior (as opposed to alternative methods that use feedback in action-space). Our method titled Teaching Imitative Policies in State-space~(TIPS) enables providing guidance to the agent in terms of `changing its state' which is often more intuitive for a human demonstrator. Through continuous improvement via corrective feedback, agents trained by non-expert demonstrators using TIPS outperformed the demonstrator and conventional Imitation Learning agents.", "published": "2020-08-02T17:23:54Z", "authors": ["Snehal Jauhri", "Carlos Celemin", "Jens Kober"], "categories": ["cs.RO", "cs.LG"], "doi": "", "_query": "real estate valuation deep learning", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2401.02576v2", "title": "t-DGR: A Trajectory-Based Deep Generative Replay Method for Continual Learning in Decision Making", "abstract": "Deep generative replay has emerged as a promising approach for continual learning in decision-making tasks. This approach addresses the problem of catastrophic forgetting by leveraging the generation of trajectories from previously encountered tasks to augment the current dataset. However, existing deep generative replay methods for continual learning rely on autoregressive models, which suffer from compounding errors in the generated trajectories. In this paper, we propose a simple, scalable, and non-autoregressive method for continual learning in decision-making tasks using a generative model that generates task samples conditioned on the trajectory timestep. We evaluate our method on Continual World benchmarks and find that our approach achieves state-of-the-art performance on the average success rate metric among continual learning methods. Code is available at https://github.com/WilliamYue37/t-DGR.", "published": "2024-01-04T23:44:35Z", "authors": ["William Yue", "Bo Liu", "Peter Stone"], "categories": ["cs.LG", "cs.AI", "cs.NE"], "doi": "", "_query": "real estate valuation deep learning", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/1811.01504v3", "title": "Deep Multiple Description Coding by Learning Scalar Quantization", "abstract": "In this paper, we propose a deep multiple description coding framework, whose quantizers are adaptively learned via the minimization of multiple description compressive loss. Firstly, our framework is built upon auto-encoder networks, which have multiple description multi-scale dilated encoder network and multiple description decoder networks. Secondly, two entropy estimation networks are learned to estimate the informative amounts of the quantized tensors, which can further supervise the learning of multiple description encoder network to represent the input image delicately. Thirdly, a pair of scalar quantizers accompanied by two importance-indicator maps is automatically learned in an end-to-end self-supervised way. Finally, multiple description structural dissimilarity distance loss is imposed on multiple description decoded images in pixel domain for diversified multiple description generations rather than on feature tensors in feature domain, in addition to multiple description reconstruction loss. Through testing on two commonly used datasets, it is verified that our method is beyond several state-of-the-art multiple description coding approaches in terms of coding efficiency.", "published": "2018-11-05T03:49:23Z", "authors": ["Lijun Zhao", "Huihui Bai", "Anhong Wang", "Yao Zhao"], "categories": ["cs.MM", "cs.CV"], "doi": "", "_query": "real estate valuation deep learning", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2007.13114v1", "title": "Deep CHORES: Estimating Hallmark Measures of Physical Activity Using Deep Learning", "abstract": "Wrist accelerometers for assessing hallmark measures of physical activity (PA) are rapidly growing with the advent of smartwatch technology. Given the growing popularity of wrist-worn accelerometers, there needs to be a rigorous evaluation for recognizing (PA) type and estimating energy expenditure (EE) across the lifespan. Participants (66% women, aged 20-89 yrs) performed a battery of 33 daily activities in a standardized laboratory setting while a tri-axial accelerometer collected data from the right wrist. A portable metabolic unit was worn to measure metabolic intensity. We built deep learning networks to extract spatial and temporal representations from the time-series data, and used them to recognize PA type and estimate EE. The deep learning models resulted in high performance; the F1 score was: 0.82, 0.81, and 95 for recognizing sedentary, locomotor, and lifestyle activities, respectively. The root mean square error was 1.1 (+/-0.13) for the estimation of EE.", "published": "2020-07-26T12:15:50Z", "authors": ["Mamoun T. Mardini", "Subhash Nerella Amal A. Wanigatunga", "Santiago Saldana", "Ramon Casanova", "Todd M. Manini"], "categories": ["eess.SP", "cs.LG", "stat.ML"], "doi": "", "_query": "real estate valuation deep learning", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2402.01393v3", "title": "ALERT-Transformer: Bridging Asynchronous and Synchronous Machine Learning for Real-Time Event-based Spatio-Temporal Data", "abstract": "We seek to enable classic processing of continuous ultra-sparse spatiotemporal data generated by event-based sensors with dense machine learning models. We propose a novel hybrid pipeline composed of asynchronous sensing and synchronous processing that combines several ideas: (1) an embedding based on PointNet models -- the ALERT module -- that can continuously integrate new and dismiss old events thanks to a leakage mechanism, (2) a flexible readout of the embedded data that allows to feed any downstream model with always up-to-date features at any sampling rate, (3) exploiting the input sparsity in a patch-based approach inspired by Vision Transformer to optimize the efficiency of the method. These embeddings are then processed by a transformer model trained for object and gesture recognition. Using this approach, we achieve performances at the state-of-the-art with a lower latency than competitors. We also demonstrate that our asynchronous model can operate at any desired sampling rate.", "published": "2024-02-02T13:17:19Z", "authors": ["Carmen Martin-Turrero", "Maxence Bouvier", "Manuel Breitenstein", "Pietro Zanuttigh", "Vincent Parret"], "categories": ["cs.CV", "cs.LG", "cs.NE"], "doi": "", "_query": "real estate valuation deep learning", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2107.07095v1", "title": "Applying the Case Difference Heuristic to Learn Adaptations from Deep Network Features", "abstract": "The case difference heuristic (CDH) approach is a knowledge-light method for learning case adaptation knowledge from the case base of a case-based reasoning system. Given a pair of cases, the CDH approach attributes the difference in their solutions to the difference in the problems they solve, and generates adaptation rules to adjust solutions accordingly when a retrieved case and new query have similar problem differences. As an alternative to learning adaptation rules, several researchers have applied neural networks to learn to predict solution differences from problem differences. Previous work on such approaches has assumed that the feature set describing problems is predefined. This paper investigates a two-phase process combining deep learning for feature extraction and neural network based adaptation learning from extracted features. Its performance is demonstrated in a regression task on an image data: predicting age given the image of a face. Results show that the combined process can successfully learn adaptation knowledge applicable to nonsymbolic differences in cases. The CBR system achieves slightly lower performance overall than a baseline deep network regressor, but better performance than the baseline on novel queries.", "published": "2021-07-15T03:11:56Z", "authors": ["Xiaomeng Ye", "Ziwei Zhao", "David Leake", "Xizi Wang", "David Crandall"], "categories": ["cs.AI", "cs.CV"], "doi": "", "_query": "real estate valuation deep learning", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2306.11715v2", "title": "Multi-Fidelity Active Learning with GFlowNets", "abstract": "In the last decades, the capacity to generate large amounts of data in science and engineering applications has been growing steadily. Meanwhile, machine learning has progressed to become a suitable tool to process and utilise the available data. Nonetheless, many relevant scientific and engineering problems present challenges where current machine learning methods cannot yet efficiently leverage the available data and resources. For example, in scientific discovery, we are often faced with the problem of exploring very large, structured and high-dimensional spaces. Moreover, the high fidelity, black-box objective function is often very expensive to evaluate. Progress in machine learning methods that can efficiently tackle such challenges would help accelerate currently crucial areas such as drug and materials discovery. In this paper, we propose a multi-fidelity active learning algorithm with GFlowNets as a sampler, to efficiently discover diverse, high-scoring candidates where multiple approximations of the black-box function are available at lower fidelity and cost. Our evaluation on molecular discovery tasks shows that multi-fidelity active learning with GFlowNets can discover high-scoring candidates at a fraction of the budget of its single-fidelity counterpart while maintaining diversity, unlike RL-based alternatives. These results open new avenues for multi-fidelity active learning to accelerate scientific discovery and engineering design.", "published": "2023-06-20T17:43:42Z", "authors": ["Alex Hernandez-Garcia", "Nikita Saxena", "Moksh Jain", "Cheng-Hao Liu", "Yoshua Bengio"], "categories": ["cs.LG", "q-bio.BM"], "doi": "", "_query": "real estate valuation deep learning", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2005.02057v1", "title": "Discrete-to-Deep Supervised Policy Learning", "abstract": "Neural networks are effective function approximators, but hard to train in the reinforcement learning (RL) context mainly because samples are correlated. For years, scholars have got around this by employing experience replay or an asynchronous parallel-agent system. This paper proposes Discrete-to-Deep Supervised Policy Learning (D2D-SPL) for training neural networks in RL. D2D-SPL discretises the continuous state space into discrete states and uses actor-critic to learn a policy. It then selects from each discrete state an input value and the action with the highest numerical preference as an input/target pair. Finally it uses input/target pairs from all discrete states to train a classifier. D2D-SPL uses a single agent, needs no experience replay and learns much faster than state-of-the-art methods. We test our method with two RL environments, the Cartpole and an aircraft manoeuvring simulator.", "published": "2020-05-05T10:49:00Z", "authors": ["Budi Kurniawan", "Peter Vamplew", "Michael Papasimeon", "Richard Dazeley", "Cameron Foale"], "categories": ["cs.LG", "stat.ML"], "doi": "", "_query": "real estate valuation deep learning", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2406.14009v1", "title": "Confidence Intervals and Simultaneous Confidence Bands Based on Deep Learning", "abstract": "Deep learning models have significantly improved prediction accuracy in various fields, gaining recognition across numerous disciplines. Yet, an aspect of deep learning that remains insufficiently addressed is the assessment of prediction uncertainty. Producing reliable uncertainty estimators could be crucial in practical terms. For instance, predictions associated with a high degree of uncertainty could be sent for further evaluation. Recent works in uncertainty quantification of deep learning predictions, including Bayesian posterior credible intervals and a frequentist confidence-interval estimation, have proven to yield either invalid or overly conservative intervals. Furthermore, there is currently no method for quantifying uncertainty that can accommodate deep neural networks for survival (time-to-event) data that involves right-censored outcomes. In this work, we provide a valid non-parametric bootstrap method that correctly disentangles data uncertainty from the noise inherent in the adopted optimization algorithm, ensuring that the resulting point-wise confidence intervals or the simultaneous confidence bands are accurate (i.e., valid and not overly conservative). The proposed ad-hoc method can be easily integrated into any deep neural network without interfering with the training process. The utility of the proposed approach is illustrated by constructing simultaneous confidence bands for survival curves derived from deep neural networks for survival data with right censoring.", "published": "2024-06-20T05:51:37Z", "authors": ["Asaf Ben Arie", "Malka Gorfine"], "categories": ["stat.ML", "cs.LG"], "doi": "", "_query": "real estate valuation deep learning", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2012.03002v2", "title": "Fixed Priority Global Scheduling from a Deep Learning Perspective", "abstract": "Deep Learning has been recently recognized as one of the feasible solutions to effectively address combinatorial optimization problems, which are often considered important yet challenging in various research domains. In this work, we first present how to adopt Deep Learning for real-time task scheduling through our preliminary work upon fixed priority global scheduling (FPGS) problems. We then briefly discuss possible generalizations of Deep Learning adoption for several realistic and complicated FPGS scenarios, e.g., scheduling tasks with dependency, mixed-criticality task scheduling. We believe that there are many opportunities for leveraging advanced Deep Learning technologies to improve the quality of scheduling in various system configurations and problem scenarios.", "published": "2020-12-05T10:52:33Z", "authors": ["Hyunsung Lee", "Michael Wang", "Honguk Woo"], "categories": ["cs.AI", "cs.LG"], "doi": "", "_query": "real estate valuation deep learning", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2605.21475v1", "title": "Is Fixing Schema Graphs Necessary? Full-Resolution Graph Structure Learning for Relational Deep Learning", "abstract": "Relational prediction tasks are fundamental in many real-world applications, where data are naturally stored in relational databases (RDBs). Relational Deep Learning (RDL) addresses this problem by modeling RDBs as graphs and applying graph neural networks (GNNs) for end-to-end learning. However, the full-resolution property is commonly adopted as a design principle in graph construction for RDBs to preserve relational semantics, which leads most existing methods to rely on fixed graph structures. In this paper, we propose FROG, a Full-Resolution and Optimizable Graph Structure Learning} framework for RDL that formulates relational structure learning as a learnable table role modeling problem, allowing tables to contribute as nodes and edges in message passing. We further design role-driven message passing mechanisms to capture relational semantics, enabling joint optimization of graph structure and GNN representations. To ensure semantic consistency, we introduce functional dependency constraints that regularize representations across table and entity levels. Extensive experiments demonstrate that our method outperforms existing approaches and reveal how table roles impact downstream tasks, offering new insights into graph construction for RDL", "published": "2026-05-20T17:56:09Z", "authors": ["Yi Huang", "Qingyun Sun", "Jia Li", "Xingcheng Fu", "Jianxin Li"], "categories": ["cs.LG"], "doi": "", "_query": "real estate valuation deep learning", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2405.04494v2", "title": "Representation Learning of Daily Movement Data Using Text Encoders", "abstract": "Time-series representation learning is a key area of research for remote healthcare monitoring applications. In this work, we focus on a dataset of recordings of in-home activity from people living with Dementia. We design a representation learning method based on converting activity to text strings that can be encoded using a language model fine-tuned to transform data from the same participants within a $30$-day window to similar embeddings in the vector space. This allows for clustering and vector searching over participants and days, and the identification of activity deviations to aid with personalised delivery of care.", "published": "2024-05-07T17:04:21Z", "authors": ["Alexander Capstick", "Tianyu Cui", "Yu Chen", "Payam Barnaghi"], "categories": ["cs.LG"], "doi": "", "_query": "real estate valuation deep learning", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2007.05233v3", "title": "Continual Adaptation for Deep Stereo", "abstract": "Depth estimation from stereo images is carried out with unmatched results by convolutional neural networks trained end-to-end to regress dense disparities. Like for most tasks, this is possible if large amounts of labelled samples are available for training, possibly covering the whole data distribution encountered at deployment time. Being such an assumption systematically unmet in real applications, the capacity of adapting to any unseen setting becomes of paramount importance. Purposely, we propose a continual adaptation paradigm for deep stereo networks designed to deal with challenging and ever-changing environments. We design a lightweight and modular architecture, Modularly ADaptive Network (MADNet), and formulate Modular ADaptation algorithms (MAD, MAD++) which permit efficient optimization of independent sub-portions of the entire network. In our paradigm, the learning signals needed to continuously adapt models online can be sourced from self-supervision via right-to-left image warping or from traditional stereo algorithms. With both sources, no other data than the input images being gathered at deployment time are needed. Thus, our network architecture and adaptation algorithms realize the first real-time self-adaptive deep stereo system and pave the way for a new paradigm that can facilitate practical deployment of end-to-end architectures for dense disparity regression.", "published": "2020-07-10T08:15:58Z", "authors": ["Matteo Poggi", "Alessio Tonioni", "Fabio Tosi", "Stefano Mattoccia", "Luigi Di Stefano"], "categories": ["cs.CV", "cs.LG", "eess.IV"], "doi": "", "_query": "real estate valuation deep learning", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/1912.04174v1", "title": "Deep Bayesian Recurrent Neural Networks for Somatic Variant Calling in Cancer", "abstract": "The emerging field of precision oncology relies on the accurate pinpointing of alterations in the molecular profile of a tumor to provide personalized targeted treatments. Current methodologies in the field commonly include the application of next generation sequencing technologies to a tumor sample, followed by the identification of mutations in the DNA known as somatic variants. The differentiation of these variants from sequencing error poses a classic classification problem, which has traditionally been approached with Bayesian statistics, and more recently with supervised machine learning methods such as neural networks. Although these methods provide greater accuracy, classic neural networks lack the ability to indicate the confidence of a variant call. In this paper, we explore the performance of deep Bayesian neural networks on next generation sequencing data, and their ability to give probability estimates for somatic variant calls. In addition to demonstrating similar performance in comparison to standard neural networks, we show that the resultant output probabilities make these better suited to the disparate and highly-variable sequencing data-sets these models are likely to encounter in the real world. We aim to deliver algorithms to oncologists for which model certainty better reflects accuracy, for improved clinical application. By moving away from point estimates to reliable confidence intervals, we expect the resultant clinical and treatment decisions to be more robust and more informed by the underlying reality of the tumor molecular profile.", "published": "2019-12-06T16:01:15Z", "authors": ["Geoffroy Dubourg-Felonneau", "Omar Darwish", "Christopher Parsons", "Dami Rebergen", "John W Cassidy", "Nirmesh Patel", "Harry W Clifford"], "categories": ["cs.LG", "q-bio.GN", "stat.ML"], "doi": "", "_query": "real estate valuation deep learning", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2008.10293v1", "title": "Bosch Deep Learning Hardware Benchmark", "abstract": "The widespread use of Deep Learning (DL) applications in science and industry has created a large demand for efficient inference systems. This has resulted in a rapid increase of available Hardware Accelerators (HWAs) making comparison challenging and laborious. To address this, several DL hardware benchmarks have been proposed aiming at a comprehensive comparison for many models, tasks, and hardware platforms. Here, we present our DL hardware benchmark which has been specifically developed for inference on embedded HWAs and tasks required for autonomous driving. In addition to previous benchmarks, we propose a new granularity level to evaluate common submodules of DL models, a twofold benchmark procedure that accounts for hardware and model optimizations done by HWA manufacturers, and an extended set of performance indicators that can help to identify a mismatch between a HWA and the DL models used in our benchmark.", "published": "2020-08-24T09:50:24Z", "authors": ["Armin Runge", "Thomas Wenzel", "Dimitrios Bariamis", "Benedikt Sebastian Staffler", "Lucas Rego Drumond", "Michael Pfeiffer"], "categories": ["cs.LG", "cs.AI", "cs.CV", "stat.ML"], "doi": "", "_query": "real estate valuation deep learning", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/1611.01639v7", "title": "Robustly representing uncertainty in deep neural networks through sampling", "abstract": "As deep neural networks (DNNs) are applied to increasingly challenging problems, they will need to be able to represent their own uncertainty. Modeling uncertainty is one of the key features of Bayesian methods. Using Bernoulli dropout with sampling at prediction time has recently been proposed as an efficient and well performing variational inference method for DNNs. However, sampling from other multiplicative noise based variational distributions has not been investigated in depth. We evaluated Bayesian DNNs trained with Bernoulli or Gaussian multiplicative masking of either the units (dropout) or the weights (dropconnect). We tested the calibration of the probabilistic predictions of Bayesian convolutional neural networks (CNNs) on MNIST and CIFAR-10. Sampling at prediction time increased the calibration of the DNNs' probabalistic predictions. Sampling weights, whether Gaussian or Bernoulli, led to more robust representation of uncertainty compared to sampling of units. However, using either Gaussian or Bernoulli dropout led to increased test set classification accuracy. Based on these findings we used both Bernoulli dropout and Gaussian dropconnect concurrently, which we show approximates the use of a spike-and-slab variational distribution without increasing the number of learned parameters. We found that spike-and-slab sampling had higher test set performance than Gaussian dropconnect and more robustly represented its uncertainty compared to Bernoulli dropout.", "published": "2016-11-05T12:32:16Z", "authors": ["Patrick McClure", "Nikolaus Kriegeskorte"], "categories": ["cs.LG", "cs.CV", "cs.NE", "q-bio.NC"], "doi": "", "_query": "real estate valuation deep learning", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/1712.00563v1", "title": "Anesthesiologist-level forecasting of hypoxemia with only SpO2 data using deep learning", "abstract": "We use a deep learning model trained only on a patient's blood oxygenation data (measurable with an inexpensive fingertip sensor) to predict impending hypoxemia (low blood oxygen) more accurately than trained anesthesiologists with access to all the data recorded in a modern operating room. We also provide a simple way to visualize the reason why a patient's risk is low or high by assigning weight to the patient's past blood oxygen values. This work has the potential to provide cutting-edge clinical decision support in low-resource settings, where rates of surgical complication and death are substantially greater than in high-resource areas.", "published": "2017-12-02T07:27:28Z", "authors": ["Gabriel Erion", "Hugh Chen", "Scott M. Lundberg", "Su-In Lee"], "categories": ["cs.LG", "stat.AP", "stat.ML"], "doi": "", "_query": "real estate valuation deep learning", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2008.10805v1", "title": "New Directions in Distributed Deep Learning: Bringing the Network at Forefront of IoT Design", "abstract": "In this paper, we first highlight three major challenges to large-scale adoption of deep learning at the edge: (i) Hardware-constrained IoT devices, (ii) Data security and privacy in the IoT era, and (iii) Lack of network-aware deep learning algorithms for distributed inference across multiple IoT devices. We then provide a unified view targeting three research directions that naturally emerge from the above challenges: (1) Federated learning for training deep networks, (2) Data-independent deployment of learning algorithms, and (3) Communication-aware distributed inference. We believe that the above research directions need a network-centric approach to enable the edge intelligence and, therefore, fully exploit the true potential of IoT.", "published": "2020-08-25T04:08:10Z", "authors": ["Kartikeya Bhardwaj", "Wei Chen", "Radu Marculescu"], "categories": ["stat.ML", "cs.CV", "cs.LG", "eess.SP"], "doi": "", "_query": "real estate valuation deep learning", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/1901.02144v1", "title": "Guidelines and Benchmarks for Deployment of Deep Learning Models on Smartphones as Real-Time Apps", "abstract": "Deep learning solutions are being increasingly used in mobile applications. Although there are many open-source software tools for the development of deep learning solutions, there are no guidelines in one place in a unified manner for using these tools towards real-time deployment of these solutions on smartphones. From the variety of available deep learning tools, the most suited ones are used in this paper to enable real-time deployment of deep learning inference networks on smartphones. A uniform flow of implementation is devised for both Android and iOS smartphones. The advantage of using multi-threading to achieve or improve real-time throughputs is also showcased. A benchmarking framework consisting of accuracy, CPU/GPU consumption and real-time throughput is considered for validation purposes. The developed deployment approach allows deep learning models to be turned into real-time smartphone apps with ease based on publicly available deep learning and smartphone software tools. This approach is applied to six popular or representative convolutional neural network models and the validation results based on the benchmarking metrics are reported.", "published": "2019-01-08T03:32:31Z", "authors": ["Abhishek Sehgal", "Nasser Kehtarnavaz"], "categories": ["cs.LG", "stat.ML"], "doi": "", "_query": "real estate valuation deep learning", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2204.12802v1", "title": "GTNet: A Tree-Based Deep Graph Learning Architecture", "abstract": "We propose Graph Tree Networks (GTNets), a deep graph learning architecture with a new general message passing scheme that originates from the tree representation of graphs. In the tree representation, messages propagate upward from the leaf nodes to the root node, and each node preserves its initial information prior to receiving information from its child nodes (neighbors). We formulate a general propagation rule following the nature of message passing in the tree to update a node's feature by aggregating its initial feature and its neighbor nodes' updated features. Two graph representation learning models are proposed within this GTNet architecture - Graph Tree Attention Network (GTAN) and Graph Tree Convolution Network (GTCN), with experimentally demonstrated state-of-the-art performance on several popular benchmark datasets. Unlike the vanilla Graph Attention Network (GAT) and Graph Convolution Network (GCN) which have the \"over-smoothing\" issue, the proposed GTAN and GTCN models can go deep as demonstrated by comprehensive experiments and rigorous theoretical analysis.", "published": "2022-04-27T09:43:14Z", "authors": ["Nan Wu", "Chaofan Wang"], "categories": ["cs.LG", "cs.AI"], "doi": "", "_query": "real estate valuation deep learning", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2311.14028v2", "title": "Continual Learning of Diffusion Models with Generative Distillation", "abstract": "Diffusion models are powerful generative models that achieve state-of-the-art performance in image synthesis. However, training them demands substantial amounts of data and computational resources. Continual learning would allow for incrementally learning new tasks and accumulating knowledge, thus enabling the reuse of trained models for further learning. One potentially suitable continual learning approach is generative replay, where a copy of a generative model trained on previous tasks produces synthetic data that are interleaved with data from the current task. However, standard generative replay applied to diffusion models results in a catastrophic loss in denoising capabilities. In this paper, we propose generative distillation, an approach that distils the entire reverse process of a diffusion model. We demonstrate that our approach substantially improves the continual learning performance of generative replay with only a modest increase in the computational costs.", "published": "2023-11-23T14:33:03Z", "authors": ["Sergi Masip", "Pau Rodriguez", "Tinne Tuytelaars", "Gido M. van de Ven"], "categories": ["cs.LG", "cs.AI", "cs.CV"], "doi": "", "_query": "real estate valuation deep learning", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/1812.00050v2", "title": "Learning Interpretable Rules for Multi-label Classification", "abstract": "Multi-label classification (MLC) is a supervised learning problem in which, contrary to standard multiclass classification, an instance can be associated with several class labels simultaneously. In this chapter, we advocate a rule-based approach to multi-label classification. Rule learning algorithms are often employed when one is not only interested in accurate predictions, but also requires an interpretable theory that can be understood, analyzed, and qualitatively evaluated by domain experts. Ideally, by revealing patterns and regularities contained in the data, a rule-based theory yields new insights in the application domain. Recently, several authors have started to investigate how rule-based models can be used for modeling multi-label data. Discussing this task in detail, we highlight some of the problems that make rule learning considerably more challenging for MLC than for conventional classification. While mainly focusing on our own previous work, we also provide a short overview of related work in this area.", "published": "2018-11-30T20:48:12Z", "authors": ["Eneldo Loza Mencía", "Johannes Fürnkranz", "Eyke Hüllermeier", "Michael Rapp"], "categories": ["cs.LG", "stat.ML"], "doi": "10.1007/978-3-319-98131-4_4", "_query": "real estate valuation deep learning", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2402.04836v4", "title": "On the Completeness of Invariant Geometric Deep Learning Models", "abstract": "Invariant models, one important class of geometric deep learning models, are capable of generating meaningful geometric representations by leveraging informative geometric features in point clouds. These models are characterized by their simplicity, good experimental results and computational efficiency. However, their theoretical expressive power still remains unclear, restricting a deeper understanding of the potential of such models. In this work, we concentrate on characterizing the theoretical expressiveness of a wide range of invariant models under fully-connected conditions. We first rigorously characterize the expressiveness of the most classic invariant model, message-passing neural networks incorporating distance (DisGNN), restricting its unidentifiable cases to be only highly symmetric point clouds. We then prove that GeoNGNN, the geometric counterpart of one of the simplest subgraph graph neural networks, can effectively break these corner cases' symmetry and thus achieve E(3)-completeness. By leveraging GeoNGNN as a theoretical tool, we further prove that: 1) most subgraph GNNs developed in traditional graph learning can be seamlessly extended to geometric scenarios with E(3)-completeness; 2) DimeNet, GemNet and SphereNet, three well-established invariant models, are also all capable of achieving E(3)-completeness. Our theoretical results fill the gap in the expressive power of invariant models, contributing to a rigorous and comprehensive understanding of their capabilities.", "published": "2024-02-07T13:32:53Z", "authors": ["Zian Li", "Xiyuan Wang", "Shijia Kang", "Muhan Zhang"], "categories": ["cs.LG", "cs.AI"], "doi": "", "_query": "real estate valuation deep learning", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2412.12024v1", "title": "Learning to Navigate in Mazes with Novel Layouts using Abstract Top-down Maps", "abstract": "Learning navigation capabilities in different environments has long been one of the major challenges in decision-making. In this work, we focus on zero-shot navigation ability using given abstract $2$-D top-down maps. Like human navigation by reading a paper map, the agent reads the map as an image when navigating in a novel layout, after learning to navigate on a set of training maps. We propose a model-based reinforcement learning approach for this multi-task learning problem, where it jointly learns a hypermodel that takes top-down maps as input and predicts the weights of the transition network. We use the DeepMind Lab environment and customize layouts using generated maps. Our method can adapt better to novel environments in zero-shot and is more robust to noise.", "published": "2024-12-16T17:51:09Z", "authors": ["Linfeng Zhao", "Lawson L. S. Wong"], "categories": ["cs.LG", "cs.AI", "cs.RO"], "doi": "", "_query": "real estate valuation deep learning", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2106.07798v1", "title": "Poisoning Deep Reinforcement Learning Agents with In-Distribution Triggers", "abstract": "In this paper, we propose a new data poisoning attack and apply it to deep reinforcement learning agents. Our attack centers on what we call in-distribution triggers, which are triggers native to the data distributions the model will be trained on and deployed in. We outline a simple procedure for embedding these, and other, triggers in deep reinforcement learning agents following a multi-task learning paradigm, and demonstrate in three common reinforcement learning environments. We believe that this work has important implications for the security of deep learning models.", "published": "2021-06-14T23:16:41Z", "authors": ["Chace Ashcraft", "Kiran Karra"], "categories": ["cs.LG", "cs.CR"], "doi": "", "_query": "real estate valuation deep learning", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/1905.02219v2", "title": "Lessons from Contextual Bandit Learning in a Customer Support Bot", "abstract": "In this work, we describe practical lessons we have learned from successfully using contextual bandits (CBs) to improve key business metrics of the Microsoft Virtual Agent for customer support. While our current use cases focus on single step einforcement learning (RL) and mostly in the domain of natural language processing and information retrieval we believe many of our findings are generally applicable. Through this article, we highlight certain issues that RL practitioners may encounter in similar types of applications as well as offer practical solutions to these challenges.", "published": "2019-05-06T18:00:46Z", "authors": ["Nikos Karampatziakis", "Sebastian Kochman", "Jade Huang", "Paul Mineiro", "Kathy Osborne", "Weizhu Chen"], "categories": ["cs.LG", "stat.ML"], "doi": "", "_query": "real estate valuation deep learning", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/1905.00322v3", "title": "Multi-level Encoder-Decoder Architectures for Image Restoration", "abstract": "Many real-world solutions for image restoration are learning-free and based on handcrafted image priors such as self-similarity. Recently, deep-learning methods that use training data have achieved state-of-the-art results in various image restoration tasks (e.g., super-resolution and inpainting). Ulyanov et al. bridge the gap between these two families of methods (CVPR 18). They have shown that learning-free methods perform close to the state-of-the-art learning-based methods (approximately 1 PSNR). Their approach benefits from the encoder-decoder network. In this paper, we propose a framework based on the multi-level extensions of the encoder-decoder network, to investigate interesting aspects of the relationship between image restoration and network construction independent of learning. Our framework allows various network structures by modifying the following network components: skip links, cascading of the network input into intermediate layers, a composition of the encoder-decoder subnetworks, and network depth. These handcrafted network structures illustrate how the construction of untrained networks influence the following image restoration tasks: denoising, super-resolution, and inpainting. We also demonstrate image reconstruction using flash and no-flash image pairs. We provide performance comparisons with the state-of-the-art methods for all the restoration tasks above.", "published": "2019-05-01T14:13:51Z", "authors": ["Indra Deep Mastan", "Shanmuganathan Raman"], "categories": ["eess.IV", "cs.CV"], "doi": "", "_query": "real estate valuation deep learning", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/1911.08967v1", "title": "Transfer Learning Toolkit: Primers and Benchmarks", "abstract": "The transfer learning toolkit wraps the codes of 17 transfer learning models and provides integrated interfaces, allowing users to use those models by calling a simple function. It is easy for primary researchers to use this toolkit and to choose proper models for real-world applications. The toolkit is written in Python and distributed under MIT open source license. In this paper, the current state of this toolkit is described and the necessary environment setting and usage are introduced.", "published": "2019-11-20T15:30:36Z", "authors": ["Fuzhen Zhuang", "Keyu Duan", "Tongjia Guo", "Yongchun Zhu", "Dongbo Xi", "Zhiyuan Qi", "Qing He"], "categories": ["cs.LG", "stat.ML"], "doi": "", "_query": "real estate valuation deep learning", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2003.00541v1", "title": "Deep Learning for Musculoskeletal Image Analysis", "abstract": "The diagnosis, prognosis, and treatment of patients with musculoskeletal (MSK) disorders require radiology imaging (using computed tomography, magnetic resonance imaging(MRI), and ultrasound) and their precise analysis by expert radiologists. Radiology scans can also help assessment of metabolic health, aging, and diabetes. This study presents how machinelearning, specifically deep learning methods, can be used for rapidand accurate image analysis of MRI scans, an unmet clinicalneed in MSK radiology. As a challenging example, we focus on automatic analysis of knee images from MRI scans and study machine learning classification of various abnormalities including meniscus and anterior cruciate ligament tears. Using widely used convolutional neural network (CNN) based architectures, we comparatively evaluated the knee abnormality classification performances of different neural network architectures under limited imaging data regime and compared single and multi-view imaging when classifying the abnormalities. Promising results indicated the potential use of multi-view deep learning based classification of MSK abnormalities in routine clinical assessment.", "published": "2020-03-01T18:13:59Z", "authors": ["Ismail Irmakci", "Syed Muhammad Anwar", "Drew A. Torigian", "Ulas Bagci"], "categories": ["eess.IV", "cs.LG", "stat.ML"], "doi": "", "_query": "real estate valuation deep learning", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2406.17098v2", "title": "Learning Temporal Distances: Contrastive Successor Features Can Provide a Metric Structure for Decision-Making", "abstract": "Temporal distances lie at the heart of many algorithms for planning, control, and reinforcement learning that involve reaching goals, allowing one to estimate the transit time between two states. However, prior attempts to define such temporal distances in stochastic settings have been stymied by an important limitation: these prior approaches do not satisfy the triangle inequality. This is not merely a definitional concern, but translates to an inability to generalize and find shortest paths. In this paper, we build on prior work in contrastive learning and quasimetrics to show how successor features learned by contrastive learning (after a change of variables) form a temporal distance that does satisfy the triangle inequality, even in stochastic settings. Importantly, this temporal distance is computationally efficient to estimate, even in high-dimensional and stochastic settings. Experiments in controlled settings and benchmark suites demonstrate that an RL algorithm based on these new temporal distances exhibits combinatorial generalization (i.e., \"stitching\") and can sometimes learn more quickly than prior methods, including those based on quasimetrics.", "published": "2024-06-24T19:36:45Z", "authors": ["Vivek Myers", "Chongyi Zheng", "Anca Dragan", "Sergey Levine", "Benjamin Eysenbach"], "categories": ["cs.LG", "cs.AI"], "doi": "", "_query": "real estate valuation deep learning", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2502.00494v3", "title": "Data Overvaluation Attack and Truthful Data Valuation in Federated Learning", "abstract": "In collaborative machine learning (CML), data valuation, i.e., evaluating the contribution of each client's data to the machine learning model, has become a critical task for incentivizing and selecting positive data contributions. However, existing studies often assume that clients engage in data valuation truthfully, overlooking the practical motivation for clients to exaggerate their contributions. To unlock this threat, this paper introduces the data overvaluation attack, enabling strategic clients to have their data significantly overvalued in federated learning, a widely adopted paradigm for decentralized CML. Furthermore, we propose a Bayesian truthful data valuation metric, named Truth-Shapley. Truth-Shapley is the unique metric that guarantees some promising axioms for data valuation while ensuring that clients' optimal strategy is to perform truthful data valuation under certain conditions. Our experiments demonstrate the vulnerability of existing data valuation metrics to the proposed attack and validate the robustness and effectiveness of Truth-Shapley.", "published": "2025-02-01T17:05:37Z", "authors": ["Shuyuan Zheng", "Sudong Cai", "Chuan Xiao", "Yang Cao", "Jianbin Qin", "Masatoshi Yoshikawa", "Makoto Onizuka"], "categories": ["cs.CR", "cs.AI", "cs.LG"], "doi": "", "_query": "real estate valuation deep learning", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2110.10381v1", "title": "Medical Knowledge-Guided Deep Curriculum Learning for Elbow Fracture Diagnosis from X-Ray Images", "abstract": "Elbow fractures are one of the most common fracture types. Diagnoses on elbow fractures often need the help of radiographic imaging to be read and analyzed by a specialized radiologist with years of training. Thanks to the recent advances of deep learning, a model that can classify and detect different types of bone fractures needs only hours of training and has shown promising results. However, most existing deep learning models are purely data-driven, lacking incorporation of known domain knowledge from human experts. In this work, we propose a novel deep learning method to diagnose elbow fracture from elbow X-ray images by integrating domain-specific medical knowledge into a curriculum learning framework. In our method, the training data are permutated by sampling without replacement at the beginning of each training epoch. The sampling probability of each training sample is guided by a scoring criterion constructed based on clinically known knowledge from human experts, where the scoring indicates the diagnosis difficultness of different elbow fracture subtypes. We also propose an algorithm that updates the sampling probabilities at each epoch, which is applicable to other sampling-based curriculum learning frameworks. We design an experiment with 1865 elbow X-ray images for a fracture/normal binary classification task and compare our proposed method to a baseline method and a previous method using multiple metrics. Our results show that the proposed method achieves the highest classification performance. Also, our proposed probability update algorithm boosts the performance of the previous method.", "published": "2021-10-20T05:24:35Z", "authors": ["Jun Luo", "Gene Kitamura", "Emine Doganay", "Dooman Arefan", "Shandong Wu"], "categories": ["eess.IV", "cs.CV", "cs.LG"], "doi": "10.1117/12.2582184", "_query": "real estate valuation deep learning", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2010.09635v1", "title": "Deep Reinforcement Learning with Population-Coded Spiking Neural Network for Continuous Control", "abstract": "The energy-efficient control of mobile robots is crucial as the complexity of their real-world applications increasingly involves high-dimensional observation and action spaces, which cannot be offset by limited on-board resources. An emerging non-Von Neumann model of intelligence, where spiking neural networks (SNNs) are run on neuromorphic processors, is regarded as an energy-efficient and robust alternative to the state-of-the-art real-time robotic controllers for low dimensional control tasks. The challenge now for this new computing paradigm is to scale so that it can keep up with real-world tasks. To do so, SNNs need to overcome the inherent limitations of their training, namely the limited ability of their spiking neurons to represent information and the lack of effective learning algorithms. Here, we propose a population-coded spiking actor network (PopSAN) trained in conjunction with a deep critic network using deep reinforcement learning (DRL). The population coding scheme dramatically increased the representation capacity of the network and the hybrid learning combined the training advantages of deep networks with the energy-efficient inference of spiking networks. To show the general applicability of our approach, we integrated it with a spectrum of both on-policy and off-policy DRL algorithms. We deployed the trained PopSAN on Intel's Loihi neuromorphic chip and benchmarked our method against the mainstream DRL algorithms for continuous control. To allow for a fair comparison among all methods, we validated them on OpenAI gym tasks. Our Loihi-run PopSAN consumed 140 times less energy per inference when compared against the deep actor network on Jetson TX2, and had the same level of performance. Our results support the efficiency of neuromorphic controllers and suggest our hybrid RL as an alternative to deep learning, when both energy-efficiency and robustness are important.", "published": "2020-10-19T16:20:45Z", "authors": ["Guangzhi Tang", "Neelesh Kumar", "Raymond Yoo", "Konstantinos P. Michmizos"], "categories": ["cs.NE", "cs.LG", "cs.RO"], "doi": "", "_query": "real estate valuation deep learning", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2203.16887v1", "title": "Mutual information estimation for graph convolutional neural networks", "abstract": "Measuring model performance is a key issue for deep learning practitioners. However, we often lack the ability to explain why a specific architecture attains superior predictive accuracy for a given data set. Often, validation accuracy is used as a performance heuristic quantifying how well a network generalizes to unseen data, but it does not capture anything about the information flow in the model. Mutual information can be used as a measure of the quality of internal representations in deep learning models, and the information plane may provide insights into whether the model exploits the available information in the data. The information plane has previously been explored for fully connected neural networks and convolutional architectures. We present an architecture-agnostic method for tracking a network's internal representations during training, which are then used to create the mutual information plane. The method is exemplified for graph-based neural networks fitted on citation data. We compare how the inductive bias introduced in graph-based architectures changes the mutual information plane relative to a fully connected neural network.", "published": "2022-03-31T08:30:04Z", "authors": ["Marius C. Landverk", "Signe Riemer-Sørensen"], "categories": ["cs.LG"], "doi": "10.7557/18.6257", "_query": "real estate valuation deep learning", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2110.03032v3", "title": "Learning Multi-Objective Curricula for Robotic Policy Learning", "abstract": "Various automatic curriculum learning (ACL) methods have been proposed to improve the sample efficiency and final performance of deep reinforcement learning (DRL). They are designed to control how a DRL agent collects data, which is inspired by how humans gradually adapt their learning processes to their capabilities. For example, ACL can be used for subgoal generation, reward shaping, environment generation, or initial state generation. However, prior work only considers curriculum learning following one of the aforementioned predefined paradigms. It is unclear which of these paradigms are complementary, and how the combination of them can be learned from interactions with the environment. Therefore, in this paper, we propose a unified automatic curriculum learning framework to create multi-objective but coherent curricula that are generated by a set of parametric curriculum modules. Each curriculum module is instantiated as a neural network and is responsible for generating a particular curriculum. In order to coordinate those potentially conflicting modules in unified parameter space, we propose a multi-task hyper-net learning framework that uses a single hyper-net to parameterize all those curriculum modules. In addition to existing hand-designed curricula paradigms, we further design a flexible memory mechanism to learn an abstract curriculum, which may otherwise be difficult to design manually. We evaluate our method on a series of robotic manipulation tasks and demonstrate its superiority over other state-of-the-art ACL methods in terms of sample efficiency and final performance.", "published": "2021-10-06T19:30:25Z", "authors": ["Jikun Kang", "Miao Liu", "Abhinav Gupta", "Chris Pal", "Xue Liu", "Jie Fu"], "categories": ["cs.LG", "cs.AI", "cs.RO", "eess.SY", "stat.ML"], "doi": "", "_query": "real estate valuation deep learning", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2609.35072v1", "title": "Not All Rollouts Are Worth Learning: On Trajectory Valuation for Post-Training Reinforcement Learning", "abstract": "We consider the problem of trajectory valuation in reinforcement learning: how to identify and mitigate detrimental trajectories during online training. Unlike classification, where data valuation relies on fixed training and validation sets, reinforcement learning involves dynamically generated trajectories without explicit validation signals, making conventional influence-based methods inapplicable. We propose Dynamic Trajectory Valuation (DTV), a simple and efficient framework that estimates trajectory utility at the mini-batch level and filters detrimental trajectories based solely on gradient information. By operating at the optimization level, DTV integrates seamlessly with existing reinforcement learning pipelines with minimal overhead. Extensive experiments across diverse settings, including PPO, GRPO, and DPO, demonstrate that DTV consistently improves performance, enhances data efficiency, and stabilizes optimization.", "published": "2026-09-28T12:57:33Z", "authors": ["Xuesong Jia", "Ziao Yang", "Zhanhe Huang", "Hongfu Liu"], "categories": ["cs.LG"], "doi": "", "_query": "real estate valuation deep learning", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/1211.0932v1", "title": "Kernels and Submodels of Deep Belief Networks", "abstract": "We study the mixtures of factorizing probability distributions represented as visible marginal distributions in stochastic layered networks. We take the perspective of kernel transitions of distributions, which gives a unified picture of distributed representations arising from Deep Belief Networks (DBN) and other networks without lateral connections. We describe combinatorial and geometric properties of the set of kernels and products of kernels realizable by DBNs as the network parameters vary. We describe explicit classes of probability distributions, including exponential families, that can be learned by DBNs. We use these submodels to bound the maximal and the expected Kullback-Leibler approximation errors of DBNs from above depending on the number of hidden layers and units that they contain.", "published": "2012-11-05T17:13:51Z", "authors": ["Guido F. Montufar", "Jason Morton"], "categories": ["stat.ML"], "doi": "", "_query": "real estate valuation deep learning", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/1802.00625v1", "title": "Real-Time-Data Analytics in Raw Materials Handling", "abstract": "This paper proposes a system for the ingestion and analysis of real-time sensor and actor data of bulk materials handling plants and machinery. It references issues that concern mining sensor data in cyber physical systems (CPS). The advance of cyber physical systems has created a significant change in the architecture of sensor and actor data. It affects the complexity of the observed systems in general, the number of signals being processed, the spatial distribution of the signal sources on a machine or plant and the global availability of the data. There are different definitions for what constitutes cyber physical systems: the most succinct and pertinent to the work shown in this paper is the definition given by the IEEE: A CPS is a system with a coupling of the cyber aspects of computing and communications with the physical aspects of dynamics and engineering that must abide by the laws of physics. This includes sensor networks, real-time and hybrid systems. Results computed from sensor and actor data must obey the equations used for modelling the physics of the observed system - this fundamentally poses an inverse problem. Such problems are not covered sufficiently by literature addressing mining of sensor data. Even available standard books on mining sensor data do not discuss the special nature of sensor data. Typically, present approaches of mining data rely on correlation as being a sole, reliable measure for significance. It is not taken into account that the inverse solutions to the model-describing equations are required to establish a semantic link between a sensor observation and its precedent cause. Without this link - without causality - there can be no physics based knowledge discovery.", "published": "2018-02-02T10:27:38Z", "authors": ["Christopher Josef Rothschedl", "Roland Ritt", "Paul O'Leary", "Matthew Harker", "Michael Habacher", "Michael Brandner"], "categories": ["eess.SP"], "doi": "", "_query": "real estate valuation deep learning", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/1902.03657v1", "title": "A Bandit Framework for Optimal Selection of Reinforcement Learning Agents", "abstract": "Deep Reinforcement Learning has been shown to be very successful in complex games, e.g. Atari or Go. These games have clearly defined rules, and hence allow simulation. In many practical applications, however, interactions with the environment are costly and a good simulator of the environment is not available. Further, as environments differ by application, the optimal inductive bias (architecture, hyperparameters, etc.) of a reinforcement agent depends on the application. In this work, we propose a multi-arm bandit framework that selects from a set of different reinforcement learning agents to choose the one with the best inductive bias. To alleviate the problem of sparse rewards, the reinforcement learning agents are augmented with surrogate rewards. This helps the bandit framework to select the best agents early, since these rewards are smoother and less sparse than the environment reward. The bandit has the double objective of maximizing the reward while the agents are learning and selecting the best agent after a finite number of learning steps. Our experimental results on standard environments show that the proposed framework is able to consistently select the optimal agent after a finite number of steps, while collecting more cumulative reward compared to selecting a sub-optimal architecture or uniformly alternating between different agents.", "published": "2019-02-10T19:39:28Z", "authors": ["Andreas Merentitis", "Kashif Rasul", "Roland Vollgraf", "Abdul-Saboor Sheikh", "Urs Bergmann"], "categories": ["cs.LG", "stat.ML"], "doi": "", "_query": "real estate valuation deep learning", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2409.08754v1", "title": "Uncertainty Estimation by Density Aware Evidential Deep Learning", "abstract": "Evidential deep learning (EDL) has shown remarkable success in uncertainty estimation. However, there is still room for improvement, particularly in out-of-distribution (OOD) detection and classification tasks. The limited OOD detection performance of EDL arises from its inability to reflect the distance between the testing example and training data when quantifying uncertainty, while its limited classification performance stems from its parameterization of the concentration parameters. To address these limitations, we propose a novel method called Density Aware Evidential Deep Learning (DAEDL). DAEDL integrates the feature space density of the testing example with the output of EDL during the prediction stage, while using a novel parameterization that resolves the issues in the conventional parameterization. We prove that DAEDL enjoys a number of favorable theoretical properties. DAEDL demonstrates state-of-the-art performance across diverse downstream tasks related to uncertainty estimation and classification", "published": "2024-09-13T12:04:45Z", "authors": ["Taeseong Yoon", "Heeyoung Kim"], "categories": ["cs.LG", "stat.ML"], "doi": "", "_query": "real estate valuation deep learning", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2310.00527v3", "title": "Self-supervised Learning of Contextualized Local Visual Embeddings", "abstract": "We present Contextualized Local Visual Embeddings (CLoVE), a self-supervised convolutional-based method that learns representations suited for dense prediction tasks. CLoVE deviates from current methods and optimizes a single loss function that operates at the level of contextualized local embeddings learned from output feature maps of convolution neural network (CNN) encoders. To learn contextualized embeddings, CLoVE proposes a normalized mult-head self-attention layer that combines local features from different parts of an image based on similarity. We extensively benchmark CLoVE's pre-trained representations on multiple datasets. CLoVE reaches state-of-the-art performance for CNN-based architectures in 4 dense prediction downstream tasks, including object detection, instance segmentation, keypoint detection, and dense pose estimation.", "published": "2023-10-01T00:13:06Z", "authors": ["Thalles Santos Silva", "Helio Pedrini", "Adín Ramírez Rivera"], "categories": ["cs.CV"], "doi": "", "_query": "real estate valuation deep learning", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2312.01294v4", "title": "Deep sub-ensembles meets quantile regression: uncertainty-aware imputation for time series", "abstract": "Real-world time series data often exhibits substantial missing values, posing challenges for advanced analysis. A common approach to addressing this issue is imputation, where the primary challenge lies in determining the appropriate values to fill in. While previous deep learning methods have proven effective for time series imputation, they often produce overconfident imputations, which poses a potentially overlooked risk to the reliability of the intelligent system. Diffusion methods are proficient in estimating probability distributions but face challenges under a high missing rate and are, moreover, computationally expensive due to the nature of the generative model framework. In this paper, we propose Quantile Sub-Ensembles, a novel method that estimates uncertainty with ensembles of quantile-regression-based task networks and incorporate Quantile Sub-Ensembles into a non-generative time series imputation method. Our method not only produces accurate and reliable imputations, but also remains computationally efficient due to its non-generative framework. We conduct extensive experiments on five real-world datasets, and the results demonstrates superior performance in both deterministic and probabilistic imputation compared to baselines across most experimental settings. The code is available at https://github.com/yingliu-coder/QSE.", "published": "2023-12-03T05:52:30Z", "authors": ["Ying Liu", "Peng Cui", "Wenbo Hu", "Richang Hong"], "categories": ["cs.LG", "stat.ML"], "doi": "10.1007/s10994-025-06922-x", "_query": "real estate valuation deep learning", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2608.00252v1", "title": "Shrinking the Haystack: One-Class Machine Learning Detection of Magnetosheath Current Sheets in MMS Burst Data", "abstract": "We present a morphology-first framework for narrowing the search for magnetic-reconnection candidates in Magnetospheric Multiscale (MMS) burst-mode data. The target is the small, short, and frequently electron-only reconnecting current sheets that occur in turbulent magnetosheath plasma. The pipeline operates in two stages. A local frame-quality gate based on minimum-variance analysis first retains only windows whose current-sheet coordinates are well defined. A one-class Deep Support Vector Data Description neural network then scores those windows against a library of 3,000 physically calibrated synthetic current sheets generated by Monte Carlo from a single published reference event. Acceptance into the surrogate library is governed by the second-order structure function $S_2(τ)$: a candidate is admitted only if its multi-scale fingerprint tracks that of the seed event inside a tolerance band, together with a small number of shape-based checks. This $S_2(τ)$-anchored construction defines the in-class distribution directly from a well-understood reference event and sidesteps the absence of a curated negative class in turbulent magnetosheath data. Applied to 15 magnetosheath turbulence intervals from the literature (1.58 h of burst-mode coverage), the framework compresses 22,775 sliding windows to 270 candidate detections (a 98.8% reduction). Manual visual screening identifies 93 of these as candidate reconnection events and a further 118 as sheet-like, retaining 78% of the queue for follow-up; the candidate-reconnection pool extends well beyond the 22 detections that overlap the published reconnection-event catalog used here as a sanity check. The framework is intended as the data-reduction stage of a broader reconnection-search workflow, offered here as an initial proof of concept before extending the one-class design to additional feature channels.", "published": "2026-07-31T19:47:41Z", "authors": ["Deep Ghuge", "Daniel J. Gershman", "Vadim Uritsky", "Julia E. Stawarz"], "categories": ["physics.space-ph"], "doi": "", "_query": "real estate valuation deep learning", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/1905.07346v1", "title": "EmBench: Quantifying Performance Variations of Deep Neural Networks across Modern Commodity Devices", "abstract": "In recent years, advances in deep learning have resulted in unprecedented leaps in diverse tasks spanning from speech and object recognition to context awareness and health monitoring. As a result, an increasing number of AI-enabled applications are being developed targeting ubiquitous and mobile devices. While deep neural networks (DNNs) are getting bigger and more complex, they also impose a heavy computational and energy burden on the host devices, which has led to the integration of various specialized processors in commodity devices. Given the broad range of competing DNN architectures and the heterogeneity of the target hardware, there is an emerging need to understand the compatibility between DNN-platform pairs and the expected performance benefits on each platform. This work attempts to demystify this landscape by systematically evaluating a collection of state-of-the-art DNNs on a wide variety of commodity devices. In this respect, we identify potential bottlenecks in each architecture and provide important guidelines that can assist the community in the co-design of more efficient DNNs and accelerators.", "published": "2019-05-17T16:03:29Z", "authors": ["Mario Almeida", "Stefanos Laskaridis", "Ilias Leontiadis", "Stylianos I. Venieris", "Nicholas D. Lane"], "categories": ["cs.LG", "cs.PF", "stat.ML"], "doi": "10.1145/3325413.3329793", "_query": "real estate valuation deep learning", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/1807.08133v1", "title": "What is not where: the challenge of integrating spatial representations into deep learning architectures", "abstract": "This paper examines to what degree current deep learning architectures for image caption generation capture spatial language. On the basis of the evaluation of examples of generated captions from the literature we argue that systems capture what objects are in the image data but not where these objects are located: the captions generated by these systems are the output of a language model conditioned on the output of an object detector that cannot capture fine-grained location information. Although language models provide useful knowledge for image captions, we argue that deep learning image captioning architectures should also model geometric relations between objects.", "published": "2018-07-21T11:55:17Z", "authors": ["John D. Kelleher", "Simon Dobnik"], "categories": ["cs.LG", "cs.AI", "cs.CL", "cs.NE", "stat.ML"], "doi": "", "_query": "real estate valuation deep learning", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/1909.11150v1", "title": "Exascale Deep Learning for Scientific Inverse Problems", "abstract": "We introduce novel communication strategies in synchronous distributed Deep Learning consisting of decentralized gradient reduction orchestration and computational graph-aware grouping of gradient tensors. These new techniques produce an optimal overlap between computation and communication and result in near-linear scaling (0.93) of distributed training up to 27,600 NVIDIA V100 GPUs on the Summit Supercomputer. We demonstrate our gradient reduction techniques in the context of training a Fully Convolutional Neural Network to approximate the solution of a longstanding scientific inverse problem in materials imaging. The efficient distributed training on a dataset size of 0.5 PB, produces a model capable of an atomically-accurate reconstruction of materials, and in the process reaching a peak performance of 2.15(4) EFLOPS$_{16}$.", "published": "2019-09-24T19:40:59Z", "authors": ["Nouamane Laanait", "Joshua Romero", "Junqi Yin", "M. Todd Young", "Sean Treichler", "Vitalii Starchenko", "Albina Borisevich", "Alex Sergeev", "Michael Matheson"], "categories": ["cs.LG", "cond-mat.mtrl-sci", "cs.DC", "physics.comp-ph", "stat.ML"], "doi": "", "_query": "real estate valuation deep learning", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2508.14311v2", "title": "Online Learning with Multiple Fairness Regularizers via Graph-Structured Feedback", "abstract": "There is an increasing need to enforce multiple, often competing, measures of fairness within automated decision systems. The appropriate weighting of these fairness objectives is typically unknown a priori, may change over time and, in our setting, must be learned adaptively through sequential interactions. In this work, we address this challenge in a bandit setting, where decisions are made with graph-structured feedback.", "published": "2025-08-19T23:32:26Z", "authors": ["Quan Zhou", "Jakub Marecek", "Robert Shorten"], "categories": ["cs.LG", "cs.AI"], "doi": "", "_query": "real estate valuation deep learning", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2007.11984v1", "title": "Unsupervised Deep Representation Learning for Real-Time Tracking", "abstract": "The advancement of visual tracking has continuously been brought by deep learning models. Typically, supervised learning is employed to train these models with expensive labeled data. In order to reduce the workload of manual annotations and learn to track arbitrary objects, we propose an unsupervised learning method for visual tracking. The motivation of our unsupervised learning is that a robust tracker should be effective in bidirectional tracking. Specifically, the tracker is able to forward localize a target object in successive frames and backtrace to its initial position in the first frame. Based on such a motivation, in the training process, we measure the consistency between forward and backward trajectories to learn a robust tracker from scratch merely using unlabeled videos. We build our framework on a Siamese correlation filter network, and propose a multi-frame validation scheme and a cost-sensitive loss to facilitate unsupervised learning. Without bells and whistles, the proposed unsupervised tracker achieves the baseline accuracy as classic fully supervised trackers while achieving a real-time speed. Furthermore, our unsupervised framework exhibits a potential in leveraging more unlabeled or weakly labeled data to further improve the tracking accuracy.", "published": "2020-07-22T08:23:12Z", "authors": ["Ning Wang", "Wengang Zhou", "Yibing Song", "Chao Ma", "Wei Liu", "Houqiang Li"], "categories": ["cs.CV"], "doi": "", "_query": "real estate valuation deep learning", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2010.09034v2", "title": "Model-Based Inverse Reinforcement Learning from Visual Demonstrations", "abstract": "Scaling model-based inverse reinforcement learning (IRL) to real robotic manipulation tasks with unknown dynamics remains an open problem. The key challenges lie in learning good dynamics models, developing algorithms that scale to high-dimensional state-spaces and being able to learn from both visual and proprioceptive demonstrations. In this work, we present a gradient-based inverse reinforcement learning framework that utilizes a pre-trained visual dynamics model to learn cost functions when given only visual human demonstrations. The learned cost functions are then used to reproduce the demonstrated behavior via visual model predictive control. We evaluate our framework on hardware on two basic object manipulation tasks.", "published": "2020-10-18T17:07:53Z", "authors": ["Neha Das", "Sarah Bechtle", "Todor Davchev", "Dinesh Jayaraman", "Akshara Rai", "Franziska Meier"], "categories": ["cs.RO", "cs.LG"], "doi": "", "_query": "real estate valuation deep learning", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/1911.08090v1", "title": "Deep Detector Health Management under Adversarial Campaigns", "abstract": "Machine learning models are vulnerable to adversarial inputs that induce seemingly unjustifiable errors. As automated classifiers are increasingly used in industrial control systems and machinery, these adversarial errors could grow to be a serious problem. Despite numerous studies over the past few years, the field of adversarial ML is still considered alchemy, with no practical unbroken defenses demonstrated to date, leaving PHM practitioners with few meaningful ways of addressing the problem. We introduce turbidity detection as a practical superset of the adversarial input detection problem, coping with adversarial campaigns rather than statistically invisible one-offs. This perspective is coupled with ROC-theoretic design guidance that prescribes an inexpensive domain adaptation layer at the output of a deep learning model during an attack campaign. The result aims to approximate the Bayes optimal mitigation that ameliorates the detection model's degraded health. A proactively reactive type of prognostics is achieved via Monte Carlo simulation of various adversarial campaign scenarios, by sampling from the model's own turbidity distribution to quickly deploy the correct mitigation during a real-world campaign.", "published": "2019-11-19T04:33:05Z", "authors": ["Javier Echauz", "Keith Kenemer", "Sarfaraz Hussein", "Jay Dhaliwal", "Saurabh Shintre", "Slawomir Grzonkowski", "Andrew Gardner"], "categories": ["cs.LG", "cs.CR", "cs.CV", "stat.ML"], "doi": "", "_query": "real estate valuation deep learning", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/1912.01277v2", "title": "Make Thunderbolts Less Frightening -- Predicting Extreme Weather Using Deep Learning", "abstract": "Forecasting severe weather conditions is still a very challenging and computationally expensive task due to the enormous amount of data and the complexity of the underlying physics. Machine learning approaches and especially deep learning have however shown huge improvements in many research areas dealing with large datasets in recent years. In this work, we tackle one specific sub-problem of weather forecasting, namely the prediction of thunderstorms and lightning. We propose the use of a convolutional neural network architecture inspired by UNet++ and ResNet to predict thunderstorms as a binary classification problem based on satellite images and lightnings recorded in the past. We achieve a probability of detection of more than 94% for lightnings within the next 15 minutes while at the same time minimizing the false alarm ratio compared to previous approaches.", "published": "2019-12-03T10:17:48Z", "authors": ["Christian Schön", "Jens Dittrich"], "categories": ["cs.LG", "stat.ML"], "doi": "", "_query": "real estate valuation deep learning", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2502.06963v3", "title": "Intelligent Offloading in Vehicular Edge Computing: A Comprehensive Review of Deep Reinforcement Learning Approaches and Architectures", "abstract": "The increasing complexity of Intelligent Transportation Systems (ITS) has led to significant interest in computational offloading to external infrastructures such as edge servers, vehicular nodes, and UAVs. These dynamic and heterogeneous environments pose challenges for traditional offloading strategies, prompting the exploration of Reinforcement Learning (RL) and Deep Reinforcement Learning (DRL) as adaptive decision-making frameworks. This survey presents a comprehensive review of recent advances in DRL-based offloading for vehicular edge computing (VEC). We classify and compare existing works based on learning paradigms (e.g., single-agent, multi-agent), system architectures (e.g., centralized, distributed, hierarchical), and optimization objectives (e.g., latency, energy, fairness). Furthermore, we analyze how Markov Decision Process (MDP) formulations are applied and highlight emerging trends in reward design, coordination mechanisms, and scalability. Finally, we identify open challenges and outline future research directions to guide the development of robust and intelligent offloading strategies for next-generation ITS.", "published": "2025-02-10T19:02:20Z", "authors": ["Ashab Uddin", "Ahmed Hamdi Sakr", "Ning Zhang"], "categories": ["cs.LG", "cs.AI", "cs.DC", "cs.MA"], "doi": "", "_query": "real estate valuation deep learning", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2502.20089v3", "title": "RIZE: Adaptive Regularization for Imitation Learning", "abstract": "We propose a novel Inverse Reinforcement Learning (IRL) method that mitigates the rigidity of fixed reward structures and the limited flexibility of implicit reward regularization. Building on the Maximum Entropy IRL framework, our approach incorporates a squared temporal-difference (TD) regularizer with adaptive targets that evolve dynamically during training, thereby imposing adaptive bounds on recovered rewards and promoting robust decision-making. To capture richer return information, we integrate distributional RL into the learning process. Empirically, our method achieves expert-level performance on complex MuJoCo and Adroit environments, surpassing baseline methods on the Humanoid-v2 task with limited expert demonstrations. Extensive experiments and ablation studies further validate the effectiveness of the approach and provide insights into reward dynamics in imitation learning. Our source code is available at https://github.com/adibka/RIZE.", "published": "2025-02-27T13:47:29Z", "authors": ["Adib Karimi", "Mohammad Mehdi Ebadzadeh"], "categories": ["cs.LG", "cs.AI", "cs.RO"], "doi": "", "_query": "real estate valuation deep learning", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2203.00176v5", "title": "When AUC meets DRO: Optimizing Partial AUC for Deep Learning with Non-Convex Convergence Guarantee", "abstract": "In this paper, we propose systematic and efficient gradient-based methods for both one-way and two-way partial AUC (pAUC) maximization that are applicable to deep learning. We propose new formulations of pAUC surrogate objectives by using the distributionally robust optimization (DRO) to define the loss for each individual positive data. We consider two formulations of DRO, one of which is based on conditional-value-at-risk (CVaR) that yields a non-smooth but exact estimator for pAUC, and another one is based on a KL divergence regularized DRO that yields an inexact but smooth (soft) estimator for pAUC. For both one-way and two-way pAUC maximization, we propose two algorithms and prove their convergence for optimizing their two formulations, respectively. Experiments demonstrate the effectiveness of the proposed algorithms for pAUC maximization for deep learning on various datasets.", "published": "2022-03-01T01:59:53Z", "authors": ["Dixian Zhu", "Gang Li", "Bokun Wang", "Xiaodong Wu", "Tianbao Yang"], "categories": ["cs.LG", "math.OC", "stat.ML"], "doi": "", "_query": "real estate valuation deep learning", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/1703.07115v1", "title": "Layer-wise training of deep networks using kernel similarity", "abstract": "Deep learning has shown promising results in many machine learning applications. The hierarchical feature representation built by deep networks enable compact and precise encoding of the data. A kernel analysis of the trained deep networks demonstrated that with deeper layers, more simple and more accurate data representations are obtained. In this paper, we propose an approach for layer-wise training of a deep network for the supervised classification task. A transformation matrix of each layer is obtained by solving an optimization aimed at a better representation where a subsequent layer builds its representation on the top of the features produced by a previous layer. We compared the performance of our approach with a DNN trained using back-propagation which has same architecture as ours. Experimental results on the real image datasets demonstrate efficacy of our approach. We also performed kernel analysis of layer representations to validate the claim of better feature encoding.", "published": "2017-03-21T09:53:51Z", "authors": ["Mandar Kulkarni", "Shirish Karande"], "categories": ["cs.LG"], "doi": "", "_query": "real estate valuation deep learning", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2211.13956v2", "title": "Learning General Audio Representations with Large-Scale Training of Patchout Audio Transformers", "abstract": "The success of supervised deep learning methods is largely due to their ability to learn relevant features from raw data. Deep Neural Networks (DNNs) trained on large-scale datasets are capable of capturing a diverse set of features, and learning a representation that can generalize onto unseen tasks and datasets that are from the same domain. Hence, these models can be used as powerful feature extractors, in combination with shallower models as classifiers, for smaller tasks and datasets where the amount of training data is insufficient for learning an end-to-end model from scratch. During the past years, Convolutional Neural Networks (CNNs) have largely been the method of choice for audio processing. However, recently attention-based transformer models have demonstrated great potential in supervised settings, outperforming CNNs. In this work, we investigate the use of audio transformers trained on large-scale datasets to learn general-purpose representations. We study how the different setups in these audio transformers affect the quality of their embeddings. We experiment with the models' time resolution, extracted embedding level, and receptive fields in order to see how they affect performance on a variety of tasks and datasets, following the HEAR 2021 NeurIPS challenge evaluation setup. Our results show that representations extracted by audio transformers outperform CNN representations. Furthermore, we will show that transformers trained on Audioset can be extremely effective representation extractors for a wide range of downstream tasks.", "published": "2022-11-25T08:39:12Z", "authors": ["Khaled Koutini", "Shahed Masoudian", "Florian Schmid", "Hamid Eghbal-zadeh", "Jan Schlüter", "Gerhard Widmer"], "categories": ["cs.SD", "cs.LG", "eess.AS"], "doi": "", "_query": "real estate valuation deep learning", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2012.06498v1", "title": "DeepObjStyle: Deep Object-based Photo Style Transfer", "abstract": "One of the major challenges of style transfer is the appropriate image features supervision between the output image and the input (style and content) images. An efficient strategy would be to define an object map between the objects of the style and the content images. However, such a mapping is not well established when there are semantic objects of different types and numbers in the style and the content images. It also leads to content mismatch in the style transfer output, which could reduce the visual quality of the results. We propose an object-based style transfer approach, called DeepObjStyle, for the style supervision in the training data-independent framework. DeepObjStyle preserves the semantics of the objects and achieves better style transfer in the challenging scenario when the style and the content images have a mismatch of image features. We also perform style transfer of images containing a word cloud to demonstrate that DeepObjStyle enables an appropriate image features supervision. We validate the results using quantitative comparisons and user studies.", "published": "2020-12-11T17:02:01Z", "authors": ["Indra Deep Mastan", "Shanmuganathan Raman"], "categories": ["cs.CV"], "doi": "", "_query": "real estate valuation deep learning", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2112.02694v1", "title": "Benchmark for Out-of-Distribution Detection in Deep Reinforcement Learning", "abstract": "Reinforcement Learning (RL) based solutions are being adopted in a variety of domains including robotics, health care and industrial automation. Most focus is given to when these solutions work well, but they fail when presented with out of distribution inputs. RL policies share the same faults as most machine learning models. Out of distribution detection for RL is generally not well covered in the literature, and there is a lack of benchmarks for this task. In this work we propose a benchmark to evaluate OOD detection methods in a Reinforcement Learning setting, by modifying the physical parameters of non-visual standard environments or corrupting the state observation for visual environments. We discuss ways to generate custom RL environments that can produce OOD data, and evaluate three uncertainty methods for the OOD detection task. Our results show that ensemble methods have the best OOD detection performance with a lower standard deviation across multiple environments.", "published": "2021-12-05T22:21:11Z", "authors": ["Aaqib Parvez Mohammed", "Matias Valdenegro-Toro"], "categories": ["cs.LG"], "doi": "", "_query": "real estate valuation deep learning", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2307.15555v1", "title": "All-for-One and One-For-All: Deep learning-based feature fusion for Synthetic Speech Detection", "abstract": "Recent advances in deep learning and computer vision have made the synthesis and counterfeiting of multimedia content more accessible than ever, leading to possible threats and dangers from malicious users. In the audio field, we are witnessing the growth of speech deepfake generation techniques, which solicit the development of synthetic speech detection algorithms to counter possible mischievous uses such as frauds or identity thefts. In this paper, we consider three different feature sets proposed in the literature for the synthetic speech detection task and present a model that fuses them, achieving overall better performances with respect to the state-of-the-art solutions. The system was tested on different scenarios and datasets to prove its robustness to anti-forensic attacks and its generalization capabilities.", "published": "2023-07-28T13:50:25Z", "authors": ["Daniele Mari", "Davide Salvi", "Paolo Bestagini", "Simone Milani"], "categories": ["cs.SD", "cs.CL", "cs.CR", "eess.AS"], "doi": "", "_query": "real estate valuation deep learning", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/1711.02837v1", "title": "Revealing structure components of the retina by deep learning networks", "abstract": "Deep convolutional neural networks (CNNs) have demonstrated impressive performance on visual object classification tasks. In addition, it is a useful model for predication of neuronal responses recorded in visual system. However, there is still no clear understanding of what CNNs learn in terms of visual neuronal circuits. Visualizing CNN's features to obtain possible connections to neuronscience underpinnings is not easy due to highly complex circuits from the retina to higher visual cortex. Here we address this issue by focusing on single retinal ganglion cells with a simple model and electrophysiological recordings from salamanders. By training CNNs with white noise images to predicate neural responses, we found that convolutional filters learned in the end are resembling to biological components of the retinal circuit. Features represented by these filters tile the space of conventional receptive field of retinal ganglion cells. These results suggest that CNN could be used to reveal structure components of neuronal circuits.", "published": "2017-11-08T05:35:27Z", "authors": ["Qi Yan", "Zhaofei Yu", "Feng Chen", "Jian K. Liu"], "categories": ["stat.ML", "cs.CV", "q-bio.NC"], "doi": "", "_query": "real estate valuation deep learning", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2405.00334v2", "title": "A Survey on Deep Active Learning: Recent Advances and New Frontiers", "abstract": "Active learning seeks to achieve strong performance with fewer training samples. It does this by iteratively asking an oracle to label new selected samples in a human-in-the-loop manner. This technique has gained increasing popularity due to its broad applicability, yet its survey papers, especially for deep learning-based active learning (DAL), remain scarce. Therefore, we conduct an advanced and comprehensive survey on DAL. We first introduce reviewed paper collection and filtering. Second, we formally define the DAL task and summarize the most influential baselines and widely used datasets. Third, we systematically provide a taxonomy of DAL methods from five perspectives, including annotation types, query strategies, deep model architectures, learning paradigms, and training processes, and objectively analyze their strengths and weaknesses. Then, we comprehensively summarize main applications of DAL in Natural Language Processing (NLP), Computer Vision (CV), and Data Mining (DM), etc. Finally, we discuss challenges and perspectives after a detailed analysis of current studies. This work aims to serve as a useful and quick guide for researchers in overcoming difficulties in DAL. We hope that this survey will spur further progress in this burgeoning field.", "published": "2024-05-01T05:54:33Z", "authors": ["Dongyuan Li", "Zhen Wang", "Yankai Chen", "Renhe Jiang", "Weiping Ding", "Manabu Okumura"], "categories": ["cs.LG"], "doi": "", "_query": "real estate valuation deep learning", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2608.01544v1", "title": "Measuring Product Quality Using Images: The CLIP Q-Score and an Application to Real Estate", "abstract": "The CLIP Q-score is a novel, safe, fully reproducible, and computationally efficient method for extracting objective product quality metrics from visual data using contrastive language-image pre-training. We introduce the technique and provide an extensive application to real estate data from an online platform ($\\sim500,000$ images). Our open-source metric aligns with LLM assessments and proves to be a powerful predictor of housing market prices for both sales and rentals. We also show that a higher CLIP Q-store is associated with better liquidity (reduced time on the market), especially for properties on sale.", "published": "2026-08-02T23:54:20Z", "authors": ["Fabian Slonimczyk", "Danila Karapsin"], "categories": ["econ.EM", "cs.CV"], "doi": "", "_query": "real estate valuation deep learning", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/1505.06279v2", "title": "The Benefit of Multitask Representation Learning", "abstract": "We discuss a general method to learn data representations from multiple tasks. We provide a justification for this method in both settings of multitask learning and learning-to-learn. The method is illustrated in detail in the special case of linear feature learning. Conditions on the theoretical advantage offered by multitask representation learning over independent task learning are established. In particular, focusing on the important example of half-space learning, we derive the regime in which multitask representation learning is beneficial over independent task learning, as a function of the sample size, the number of tasks and the intrinsic data dimensionality. Other potential applications of our results include multitask feature learning in reproducing kernel Hilbert spaces and multilayer, deep networks.", "published": "2015-05-23T06:37:17Z", "authors": ["Andreas Maurer", "Massimiliano Pontil", "Bernardino Romera-Paredes"], "categories": ["stat.ML", "cs.LG"], "doi": "", "_query": "real estate valuation deep learning", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2104.13488v1", "title": "Text Generation with Deep Variational GAN", "abstract": "Generating realistic sequences is a central task in many machine learning applications. There has been considerable recent progress on building deep generative models for sequence generation tasks. However, the issue of mode-collapsing remains a main issue for the current models. In this paper we propose a GAN-based generic framework to address the problem of mode-collapse in a principled approach. We change the standard GAN objective to maximize a variational lower-bound of the log-likelihood while minimizing the Jensen-Shanon divergence between data and model distributions. We experiment our model with text generation task and show that it can generate realistic text with high diversity.", "published": "2021-04-27T21:42:13Z", "authors": ["Mahmoud Hossam", "Trung Le", "Michael Papasimeon", "Viet Huynh", "Dinh Phung"], "categories": ["cs.LG", "cs.AI", "cs.CL"], "doi": "", "_query": "real estate valuation deep learning", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2003.08839v2", "title": "Monotonic Value Function Factorisation for Deep Multi-Agent Reinforcement Learning", "abstract": "In many real-world settings, a team of agents must coordinate its behaviour while acting in a decentralised fashion. At the same time, it is often possible to train the agents in a centralised fashion where global state information is available and communication constraints are lifted. Learning joint action-values conditioned on extra state information is an attractive way to exploit centralised learning, but the best strategy for then extracting decentralised policies is unclear. Our solution is QMIX, a novel value-based method that can train decentralised policies in a centralised end-to-end fashion. QMIX employs a mixing network that estimates joint action-values as a monotonic combination of per-agent values. We structurally enforce that the joint-action value is monotonic in the per-agent values, through the use of non-negative weights in the mixing network, which guarantees consistency between the centralised and decentralised policies. To evaluate the performance of QMIX, we propose the StarCraft Multi-Agent Challenge (SMAC) as a new benchmark for deep multi-agent reinforcement learning. We evaluate QMIX on a challenging set of SMAC scenarios and show that it significantly outperforms existing multi-agent reinforcement learning methods.", "published": "2020-03-19T16:51:51Z", "authors": ["Tabish Rashid", "Mikayel Samvelyan", "Christian Schroeder de Witt", "Gregory Farquhar", "Jakob Foerster", "Shimon Whiteson"], "categories": ["cs.LG", "cs.MA", "stat.ML"], "doi": "", "_query": "real estate valuation deep learning", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/1602.04511v2", "title": "Learning Granger Causality for Hawkes Processes", "abstract": "Learning Granger causality for general point processes is a very challenging task. In this paper, we propose an effective method, learning Granger causality, for a special but significant type of point processes --- Hawkes process. We reveal the relationship between Hawkes process's impact function and its Granger causality graph. Specifically, our model represents impact functions using a series of basis functions and recovers the Granger causality graph via group sparsity of the impact functions' coefficients. We propose an effective learning algorithm combining a maximum likelihood estimator (MLE) with a sparse-group-lasso (SGL) regularizer. Additionally, the flexibility of our model allows to incorporate the clustering structure event types into learning framework. We analyze our learning algorithm and propose an adaptive procedure to select basis functions. Experiments on both synthetic and real-world data show that our method can learn the Granger causality graph and the triggering patterns of the Hawkes processes simultaneously.", "published": "2016-02-14T21:14:07Z", "authors": ["Hongteng Xu", "Mehrdad Farajtabar", "Hongyuan Zha"], "categories": ["cs.LG", "stat.ML"], "doi": "", "_query": "real estate valuation deep learning", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2501.08850v1", "title": "Graph Counterfactual Explainable AI via Latent Space Traversal", "abstract": "Explaining the predictions of a deep neural network is a nontrivial task, yet high-quality explanations for predictions are often a prerequisite for practitioners to trust these models. Counterfactual explanations aim to explain predictions by finding the ''nearest'' in-distribution alternative input whose prediction changes in a pre-specified way. However, it remains an open question how to define this nearest alternative input, whose solution depends on both the domain (e.g. images, graphs, tabular data, etc.) and the specific application considered. For graphs, this problem is complicated i) by their discrete nature, as opposed to the continuous nature of state-of-the-art graph classifiers; and ii) by the node permutation group acting on the graphs. We propose a method to generate counterfactual explanations for any differentiable black-box graph classifier, utilizing a case-specific permutation equivariant graph variational autoencoder. We generate counterfactual explanations in a continuous fashion by traversing the latent space of the autoencoder across the classification boundary of the classifier, allowing for seamless integration of discrete graph structure and continuous graph attributes. We empirically validate the approach on three graph datasets, showing that our model is consistently high-performing and more robust than the baselines.", "published": "2025-01-15T15:04:10Z", "authors": ["Andreas Abildtrup Hansen", "Paraskevas Pegios", "Anna Calissano", "Aasa Feragen"], "categories": ["cs.LG", "cs.AI", "stat.ML"], "doi": "", "_query": "real estate valuation deep learning", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2610.03620v1", "title": "UniIntervene++: An Adaptive Intervention Agent for Efficient Real-World Reinforcement Learning", "abstract": "Online reinforcement learning (RL) enables robot policies to improve through physical interaction, but the assistance they require changes as their competence evolves. Existing intervention strategies based on offline estimates or fixed decision rules can therefore become mismatched to the current policy. To address this, we propose UniIntervene++, an adaptive intervention agent that learns to allocate control between autonomous execution and heterogeneous assisted behaviors during online RL. Specifically, UniIntervene++ first formulates the evolving RL policy, trajectory correction, and a task-structured CodePolicy as Options in a unified semi-Markov decision process and learns their relative values online. Building on this, competence-adaptive intervention periodically probes the RL policy through unassisted execution, keeping control allocation responsive to its evolving capability. Finally, coupled experience learning allows assisted behaviors to improve the RL policy, whose evolving outcomes in turn reshape future intervention decisions. In this way, UniIntervene++ jointly determines when to intervene, how to intervene, and when to return control as the RL policy improves. Across five real-world manipulation tasks, UniIntervene++ achieves an average success rate of 89.67%, outperforming all baselines by at least 6 percentage points, while reducing human intervention to 0.77%, a relative reduction of at least 94.6% from the best baseline. Code is available in our \\href{https://github.com/dannyyudong/An-Adaptive-Intervention-Agent-for-Efficient-Real-World-Reinforcement-Learning}{GitHub repository}.", "published": "2026-10-02T17:18:12Z", "authors": ["Yudong Lin", "Haoyuan Deng", "Zhuoxuan Yuan", "Zaijia Yang", "Yuanjiang Xue", "Ziwei Wang"], "categories": ["cs.LG", "cs.RO"], "doi": "", "_query": "real estate valuation deep learning", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2308.10309v1", "title": "Digital Real Estate in the Metaverse: An Empirical Analysis of Retail Investor Motivations", "abstract": "This paper investigates retail investor motivations for digital real estate ownership in the crypto-metaverse. Utilizing a detailed financial behavior survey of metaverse landowners' intrinsic and extrinsic motivations, we apply principal components analysis to uncover four distinct motivational groups: (1) Aesthetics and Identity, (2) Social and Community, (3) Speculation and Investment, and (4) Innovation and Technology. Our findings reveal that age, education, investment knowledge, risk-taking, and impulsivity significantly influence investor group membership. This research provides valuable insights to investors and developers, underscoring the potential of a platform to attract retail investors with speculative intentions, engagement longevity, and passive or active trading characteristics, contingent on unique crypto-metaverse attributes.", "published": "2023-08-20T16:08:06Z", "authors": ["Lennart Ante", "Friedrich-Philipp Wazinski", "Aman Saggu"], "categories": ["q-fin.GN"], "doi": "10.1016/j.frl.2023.104299", "_query": "real estate valuation deep learning", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/1905.08369v1", "title": "A Bi-Directional Co-Design Approach to Enable Deep Learning on IoT Devices", "abstract": "Developing deep learning models for resource-constrained Internet-of-Things (IoT) devices is challenging, as it is difficult to achieve both good quality of results (QoR), such as DNN model inference accuracy, and quality of service (QoS), such as inference latency, throughput, and power consumption. Existing approaches typically separate the DNN model development step from its deployment on IoT devices, resulting in suboptimal solutions. In this paper, we first introduce a few interesting but counterintuitive observations about such a separate design approach, and empirically show why it may lead to suboptimal designs. Motivated by these observations, we then propose a novel and practical bi-directional co-design approach: a bottom-up DNN model design strategy together with a top-down flow for DNN accelerator design. It enables a joint optimization of both DNN models and their deployment configurations on IoT devices as represented as FPGAs. We demonstrate the effectiveness of the proposed co-design approach on a real-life object detection application using Pynq-Z1 embedded FPGA. Our method obtains the state-of-the-art results on both QoR with high accuracy (IoU) and QoS with high throughput (FPS) and high energy efficiency.", "published": "2019-05-20T22:36:38Z", "authors": ["Xiaofan Zhang", "Cong Hao", "Yuhong Li", "Yao Chen", "Jinjun Xiong", "Wen-mei Hwu", "Deming Chen"], "categories": ["cs.CV"], "doi": "", "_query": "real estate valuation deep learning", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/1208.3561v3", "title": "Efficient Active Learning of Halfspaces: an Aggressive Approach", "abstract": "We study pool-based active learning of half-spaces. We revisit the aggressive approach for active learning in the realizable case, and show that it can be made efficient and practical, while also having theoretical guarantees under reasonable assumptions. We further show, both theoretically and experimentally, that it can be preferable to mellow approaches. Our efficient aggressive active learner of half-spaces has formal approximation guarantees that hold when the pool is separable with a margin. While our analysis is focused on the realizable setting, we show that a simple heuristic allows using the same algorithm successfully for pools with low error as well. We further compare the aggressive approach to the mellow approach, and prove that there are cases in which the aggressive approach results in significantly better label complexity compared to the mellow approach. We demonstrate experimentally that substantial improvements in label complexity can be achieved using the aggressive approach, for both realizable and low-error settings.", "published": "2012-08-17T09:49:31Z", "authors": ["Alon Gonen", "Sivan Sabato", "Shai Shalev-Shwartz"], "categories": ["cs.LG"], "doi": "", "_query": "real estate valuation deep learning", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2404.07754v1", "title": "Generating Synthetic Satellite Imagery With Deep-Learning Text-to-Image Models -- Technical Challenges and Implications for Monitoring and Verification", "abstract": "Novel deep-learning (DL) architectures have reached a level where they can generate digital media, including photorealistic images, that are difficult to distinguish from real data. These technologies have already been used to generate training data for Machine Learning (ML) models, and large text-to-image models like DALL-E 2, Imagen, and Stable Diffusion are achieving remarkable results in realistic high-resolution image generation. Given these developments, issues of data authentication in monitoring and verification deserve a careful and systematic analysis: How realistic are synthetic images? How easily can they be generated? How useful are they for ML researchers, and what is their potential for Open Science? In this work, we use novel DL models to explore how synthetic satellite images can be created using conditioning mechanisms. We investigate the challenges of synthetic satellite image generation and evaluate the results based on authenticity and state-of-the-art metrics. Furthermore, we investigate how synthetic data can alleviate the lack of data in the context of ML methods for remote-sensing. Finally we discuss implications of synthetic satellite imagery in the context of monitoring and verification.", "published": "2024-04-11T14:00:20Z", "authors": ["Tuong Vy Nguyen", "Alexander Glaser", "Felix Biessmann"], "categories": ["cs.CV", "cs.AI", "cs.HC", "cs.LG"], "doi": "", "_query": "real estate valuation deep learning", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/1609.03675v4", "title": "Deep Coevolutionary Network: Embedding User and Item Features for Recommendation", "abstract": "Recommender systems often use latent features to explain the behaviors of users and capture the properties of items. As users interact with different items over time, user and item features can influence each other, evolve and co-evolve over time. The compatibility of user and item's feature further influence the future interaction between users and items. Recently, point process based models have been proposed in the literature aiming to capture the temporally evolving nature of these latent features. However, these models often make strong parametric assumptions about the evolution process of the user and item latent features, which may not reflect the reality, and has limited power in expressing the complex and nonlinear dynamics underlying these processes. To address these limitations, we propose a novel deep coevolutionary network model (DeepCoevolve), for learning user and item features based on their interaction graph. DeepCoevolve use recurrent neural network (RNN) over evolving networks to define the intensity function in point processes, which allows the model to capture complex mutual influence between users and items, and the feature evolution over time. We also develop an efficient procedure for training the model parameters, and show that the learned models lead to significant improvements in recommendation and activity prediction compared to previous state-of-the-arts parametric models.", "published": "2016-09-13T04:39:33Z", "authors": ["Hanjun Dai", "Yichen Wang", "Rakshit Trivedi", "Le Song"], "categories": ["cs.LG", "cs.IR"], "doi": "", "_query": "real estate valuation deep learning", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2310.16210v4", "title": "Semantic Segmentation in Satellite Hyperspectral Imagery by Deep Learning", "abstract": "Satellites are increasingly adopting on-board AI to optimize operations and increase autonomy through in-orbit inference. The use of Deep Learning (DL) models for segmentation in hyperspectral imagery offers advantages for remote sensing applications. In this work, we train and test 20 models for multi-class segmentation in hyperspectral imagery, selected for their potential in future space deployment. These models include 1D and 2D Convolutional Neural Networks (CNNs) and the latest vision transformers (ViTs). We propose a lightweight 1D-CNN model, 1D-Justo-LiuNet, which outperforms state-of-the-art models in the hypespectral domain. 1D-Justo-LiuNet exceeds the performance of 2D-CNN UNets and outperforms Apple's lightweight vision transformers designed for mobile inference. 1D-Justo-LiuNet achieves the highest accuracy (0.93) with the smallest model size (4,563 parameters) among all tested models, while maintaining fast inference. Unlike 2D-CNNs and ViTs, which encode both spectral and spatial information, 1D-Justo-LiuNet focuses solely on the rich spectral features in hyperspectral data, benefitting from the high-dimensional feature space. Our findings are validated across various satellite datasets, with the HYPSO-1 mission serving as the primary case study for sea, land, and cloud segmentation. We further confirm our conclusions through generalization tests on other hyperspectral missions, such as NASA's EO-1. Based on its superior performance and compact size, we conclude that 1D-Justo-LiuNet is highly suitable for in-orbit deployment, providing an effective solution for optimizing and automating satellite operations at edge.", "published": "2023-10-24T21:57:59Z", "authors": ["Jon Alvarez Justo", "Alexandru Ghita", "Daniel Kovac", "Joseph L. Garrett", "Mariana-Iuliana Georgescu", "Jesus Gonzalez-Llorente", "Radu Tudor Ionescu", "Tor Arne Johansen"], "categories": ["cs.CV", "eess.IV"], "doi": "", "_query": "real estate valuation deep learning", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/1905.07822v2", "title": "Minimal Achievable Sufficient Statistic Learning", "abstract": "We introduce Minimal Achievable Sufficient Statistic (MASS) Learning, a training method for machine learning models that attempts to produce minimal sufficient statistics with respect to a class of functions (e.g. deep networks) being optimized over. In deriving MASS Learning, we also introduce Conserved Differential Information (CDI), an information-theoretic quantity that - unlike standard mutual information - can be usefully applied to deterministically-dependent continuous random variables like the input and output of a deep network. In a series of experiments, we show that deep networks trained with MASS Learning achieve competitive performance on supervised learning and uncertainty quantification benchmarks.", "published": "2019-05-19T22:46:23Z", "authors": ["Milan Cvitkovic", "Günther Koliander"], "categories": ["cs.LG", "stat.ML"], "doi": "", "_query": "real estate valuation deep learning", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2605.11989v1", "title": "A Transfer Learning Evaluation of Deep Neural Networks for Image Classification", "abstract": "Transfer learning is a machine learning technique that uses previously acquired knowledge from a source domain to enhance learning in a target domain by reusing learned weights. This technique is ubiquitous because of its great advantages in achieving high performance while saving training time, memory, and effort in network design. In this paper, we investigate how to select the best pre-trained model that meets the target domain requirements for image classification tasks. In our study, we refined the output layers and general network parameters to apply the knowledge of eleven image processing models, pre-trained on ImageNet, to five different target domain datasets. We measured the accuracy, accuracy density, training time, and model size to evaluate the pre-trained models both in training sessions in one episode and with ten episodes.", "published": "2026-05-12T11:40:49Z", "authors": ["Nermeen Abou Baker", "Nico Zengeler", "Uwe Handmann"], "categories": ["cs.CV", "cs.AI"], "doi": "10.3390/make4010002", "_query": "real estate valuation deep learning", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/1909.06414v1", "title": "Learning Household Task Knowledge from WikiHow Descriptions", "abstract": "Commonsense procedural knowledge is important for AI agents and robots that operate in a human environment. While previous attempts at constructing procedural knowledge are mostly rule- and template-based, recent advances in deep learning provide the possibility of acquiring such knowledge directly from natural language sources. As a first step in this direction, we propose a model to learn embeddings for tasks, as well as the individual steps that need to be taken to solve them, based on WikiHow articles. We learn these embeddings such that they are predictive of both step relevance and step ordering. We also experiment with the use of integer programming for inferring consistent global step orderings from noisy pairwise predictions.", "published": "2019-09-13T19:16:53Z", "authors": ["Yilun Zhou", "Julie A. Shah", "Steven Schockaert"], "categories": ["cs.CL", "cs.AI", "cs.LG"], "doi": "", "_query": "real estate valuation deep learning", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/1707.07465v2", "title": "Building Graph Representations of Deep Vector Embeddings", "abstract": "Patterns stored within pre-trained deep neural networks compose large and powerful descriptive languages that can be used for many different purposes. Typically, deep network representations are implemented within vector embedding spaces, which enables the use of traditional machine learning algorithms on top of them. In this short paper we propose the construction of a graph embedding space instead, introducing a methodology to transform the knowledge coded within a deep convolutional network into a topological space (i.e. a network). We outline how such graph can hold data instances, data features, relations between instances and features, and relations among features. Finally, we introduce some preliminary experiments to illustrate how the resultant graph embedding space can be exploited through graph analytics algorithms.", "published": "2017-07-24T10:20:12Z", "authors": ["Dario Garcia-Gasulla", "Armand Vilalta", "Ferran Parés", "Jonatan Moreno", "Eduard Ayguadé", "Jesus Labarta", "Ulises Cortés", "Toyotaro Suzumura"], "categories": ["cs.NE"], "doi": "", "_query": "real estate valuation deep learning", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2011.04999v1", "title": "Untangling Dense Knots by Learning Task-Relevant Keypoints", "abstract": "Untangling ropes, wires, and cables is a challenging task for robots due to the high-dimensional configuration space, visual homogeneity, self-occlusions, and complex dynamics. We consider dense (tight) knots that lack space between self-intersections and present an iterative approach that uses learned geometric structure in configurations. We instantiate this into an algorithm, HULK: Hierarchical Untangling from Learned Keypoints, which combines learning-based perception with a geometric planner into a policy that guides a bilateral robot to untangle knots. To evaluate the policy, we perform experiments both in a novel simulation environment modelling cables with varied knot types and textures and in a physical system using the da Vinci surgical robot. We find that HULK is able to untangle cables with dense figure-eight and overhand knots and generalize to varied textures and appearances. We compare two variants of HULK to three baselines and observe that HULK achieves 43.3% higher success rates on a physical system compared to the next best baseline. HULK successfully untangles a cable from a dense initial configuration containing up to two overhand and figure-eight knots in 97.9% of 378 simulation experiments with an average of 12.1 actions per trial. In physical experiments, HULK achieves 61.7% untangling success, averaging 8.48 actions per trial. Supplementary material, code, and videos can be found at https://tinyurl.com/y3a88ycu.", "published": "2020-11-10T09:29:01Z", "authors": ["Jennifer Grannen", "Priya Sundaresan", "Brijen Thananjeyan", "Jeffrey Ichnowski", "Ashwin Balakrishna", "Minho Hwang", "Vainavi Viswanath", "Michael Laskey", "Joseph E. Gonzalez", "Ken Goldberg"], "categories": ["cs.RO", "cs.AI", "cs.LG"], "doi": "", "_query": "real estate valuation deep learning", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/physics/0303028v2", "title": "2000-2003 Real Estate Bubble in the UK but not in the USA", "abstract": "In the aftermath of the burst of the ``new economy'' bubble in 2000, the Federal Reserve aggressively reduced short-term rates yields in less than two years from 6.5% to 1.25% in an attempt to coax forth a stronger recovery of the US economy. But, there is growing apprehension that this is creating a new bubble in real estate, as strong housing demand is fuelled by historically low mortgage rates. Are we going from Charybdis to Scylla? This question is all the more excruciating at a time when many other indicators suggest a significant deflationary risk. Using economic data, Federal Reserve Chairman A. Greenspan and Governor D.L. Kohn dismissed recently this possibility. Using the theory of critical phenomena resulting from positive feedbacks in markets, we confirm this view point for the US but find that mayhem may be in store for the UK: we unearth the unmistakable signatures (log-periodicity and power law super-exponential acceleration) of a strong unsustainable bubble there, which could burst before the end of the year 2003.", "published": "2003-03-07T09:21:38Z", "authors": ["W. -X. Zhou", "D. Sornette"], "categories": ["physics.soc-ph", "cond-mat.soft", "cond-mat.stat-mech", "physics.gen-ph", "q-fin.GN"], "doi": "10.1016/S0378-4371(03)00600-9", "_query": "real estate valuation deep learning", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/1810.09005v1", "title": "Theoretical and Practical Aspects of the Linear Tape Scheduling Problem", "abstract": "Magnetic tapes have been playing a key role as means for storage of digital data for decades, and their unsurpassed cost-effectiveness still make them the technology of choice in several industries, such as media and entertainment. Tapes are mostly used for cold storage nowadays, and therefore the study of scheduling algorithms for read requests tailored for these devices has been largely neglected in the literature. In this article, we investigate the Linear Tape Scheduling Problem (LTSP), in which read requests associated with files stored on a single-tracked magnetic tape should be scheduled in a way that the sum of all response times are minimized. LTSP has many similarities with classical combinatorial optimization problems such as the Traveling Repairmen Problem and the Dial-a-Ride Problem restricted to the real line; nevertheless, significant differences on structural properties and strict time-limit constraints of real-world scenarios make LTSP challenging and interesting on its own. In this work, we investigate several properties and algorithms for LTSP and some of its extensions. The results allowed for the identification of 3-approximation algorithms for LTSP and efficient exact algorithms for some of its special cases. We also show that LTSPR, the version of the problem with heterogeneous release times for requests, is NP-complete. OLTSP, the online extension of LTSPR, does not admit c-competitive algorithms for any constant factor c, but we nevertheless introduce an algorithm for the problem and show through extensive computational experiments on synthetic and real-world datasets that different embodiments of the proposed strategy are computationally efficient and over-perform by orders of magnitude an algorithm being currently used by real-world tape file systems.", "published": "2018-10-21T18:42:32Z", "authors": ["Carlos Cardonha", "Lucas C. Villa Real"], "categories": ["cs.DS", "math.OC"], "doi": "", "_query": "real estate valuation deep learning", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/1904.08827v3", "title": "Deep Residual Autoencoders for Expectation Maximization-inspired Dictionary Learning", "abstract": "We introduce a neural-network architecture, termed the constrained recurrent sparse autoencoder (CRsAE), that solves convolutional dictionary learning problems, thus establishing a link between dictionary learning and neural networks. Specifically, we leverage the interpretation of the alternating-minimization algorithm for dictionary learning as an approximate Expectation-Maximization algorithm to develop autoencoders that enable the simultaneous training of the dictionary and regularization parameter (ReLU bias). The forward pass of the encoder approximates the sufficient statistics of the E-step as the solution to a sparse coding problem, using an iterative proximal gradient algorithm called FISTA. The encoder can be interpreted either as a recurrent neural network or as a deep residual network, with two-sided ReLU non-linearities in both cases. The M-step is implemented via a two-stage back-propagation. The first stage relies on a linear decoder applied to the encoder and a norm-squared loss. It parallels the dictionary update step in dictionary learning. The second stage updates the regularization parameter by applying a loss function to the encoder that includes a prior on the parameter motivated by Bayesian statistics. We demonstrate in an image-denoising task that CRsAE learns Gabor-like filters, and that the EM-inspired approach for learning biases is superior to the conventional approach. In an application to recordings of electrical activity from the brain, we demonstrate that CRsAE learns realistic spike templates and speeds up the process of identifying spike times by 900x compared to algorithms based on convex optimization.", "published": "2019-04-18T15:08:10Z", "authors": ["Bahareh Tolooshams", "Sourav Dey", "Demba Ba"], "categories": ["cs.LG", "stat.ML"], "doi": "10.1109/TNNLS.2020.3005348", "_query": "real estate valuation deep learning", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2505.16946v3", "title": "NY Real Estate Racial Equity Analysis via Applied Machine Learning", "abstract": "This study analyzes tract-level real estate ownership patterns in New York State (NYS) and New York City (NYC) to uncover racial disparities. We use an advanced race/ethnicity imputation model (LSTM+Geo with XGBoost filtering, validated at 89.2% accuracy) to compare the predicted racial composition of property owners to the resident population from census data. We examine both a Full Model (statewide) and a Name-Only LSTM Model (NYC) to assess how incorporating geospatial context affects our predictions and disparity estimates. The results reveal significant inequities: White individuals hold a disproportionate share of properties and property value relative to their population, while Black, Hispanic, and Asian communities are underrepresented as property owners. These disparities are most pronounced in minority-majority neighborhoods, where ownership is predominantly White despite a predominantly non-White population. Corporate ownership (LLCs, trusts, etc.) exacerbates these gaps by reducing owner-occupied opportunities in urban minority communities. We provide a breakdown of ownership vs. population by race for majority-White, -Black, -Hispanic, and -Asian tracts, identify those with extreme ownership disparities, and compare patterns in urban, suburban, and rural contexts. The findings underscore persistent racial inequity in property ownership, reflecting broader historical and socio-economic forces, and highlight the importance of data-driven approaches to address these issues.", "published": "2025-05-22T17:32:28Z", "authors": ["Sanjana Chalavadi", "Andrei Pastor", "Terry Leitch"], "categories": ["cs.CY", "cs.LG"], "doi": "", "_query": "real estate valuation deep learning", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2310.16485v2", "title": "A Comprehensive Python Library for Deep Learning-Based Event Detection in Multivariate Time Series Data and Information Retrieval in NLP", "abstract": "Event detection in time series data is crucial in various domains, including finance, healthcare, cybersecurity, and science. Accurately identifying events in time series data is vital for making informed decisions, detecting anomalies, and predicting future trends. Despite extensive research exploring diverse methods for event detection in time series, with deep learning approaches being among the most advanced, there is still room for improvement and innovation in this field. In this paper, we present a new deep learning supervised method for detecting events in multivariate time series data. Our method combines four distinct novelties compared to existing deep-learning supervised methods. Firstly, it is based on regression instead of binary classification. Secondly, it does not require labeled datasets where each point is labeled; instead, it only requires reference events defined as time points or intervals of time. Thirdly, it is designed to be robust by using a stacked ensemble learning meta-model that combines deep learning models, ranging from classic feed-forward neural networks (FFNs) to state-of-the-art architectures like transformers. This ensemble approach can mitigate individual model weaknesses and biases, resulting in more robust predictions. Finally, to facilitate practical implementation, we have developed a Python package to accompany our proposed method. The package, called eventdetector-ts, can be installed through the Python Package Index (PyPI). In this paper, we present our method and provide a comprehensive guide on the usage of the package. We showcase its versatility and effectiveness through different real-world use cases from natural language processing (NLP) to financial security domains.", "published": "2023-10-25T09:13:19Z", "authors": ["Menouar Azib", "Benjamin Renard", "Philippe Garnier", "Vincent Génot", "Nicolas André"], "categories": ["cs.LG"], "doi": "", "_query": "real estate valuation deep learning", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2007.03514v1", "title": "Imitation Learning Approach for AI Driving Olympics Trained on Real-world and Simulation Data Simultaneously", "abstract": "In this paper, we describe our winning approach to solving the Lane Following Challenge at the AI Driving Olympics Competition through imitation learning on a mixed set of simulation and real-world data. AI Driving Olympics is a two-stage competition: at stage one, algorithms compete in a simulated environment with the best ones advancing to a real-world final. One of the main problems that participants encounter during the competition is that algorithms trained for the best performance in simulated environments do not hold up in a real-world environment and vice versa. Classic control algorithms also do not translate well between tasks since most of them have to be tuned to specific driving conditions such as lighting, road type, camera position, etc. To overcome this problem, we employed the imitation learning algorithm and trained it on a dataset collected from sources both from simulation and real-world, forcing our model to perform equally well in all environments.", "published": "2020-07-07T14:48:11Z", "authors": ["Mikita Sazanovich", "Konstantin Chaika", "Kirill Krinkin", "Aleksei Shpilman"], "categories": ["cs.LG", "cs.CV", "cs.RO", "stat.ML"], "doi": "", "_query": "real estate valuation deep learning", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/1906.05803v1", "title": "Modeling and Interpreting Real-world Human Risk Decision Making with Inverse Reinforcement Learning", "abstract": "We model human decision-making behaviors in a risk-taking task using inverse reinforcement learning (IRL) for the purposes of understanding real human decision making under risk. To the best of our knowledge, this is the first work applying IRL to reveal the implicit reward function in human risk-taking decision making and to interpret risk-prone and risk-averse decision-making policies. We hypothesize that the state history (e.g. rewards and decisions in previous trials) are related to the human reward function, which leads to risk-averse and risk-prone decisions. We design features that reflect these factors in the reward function of IRL and learn the corresponding weight that is interpretable as the importance of features. The results confirm the sub-optimal risk-related decisions of human-driven by the personalized reward function. In particular, the risk-prone person tends to decide based on the current pump number, while the risk-averse person relies on burst information from the previous trial and the average end status. Our results demonstrate that IRL is an effective tool to model human decision-making behavior, as well as to help interpret the human psychological process in risk decision-making.", "published": "2019-06-13T16:35:32Z", "authors": ["Quanying Liu", "Haiyan Wu", "Anqi Liu"], "categories": ["cs.LG", "q-bio.NC", "stat.ML"], "doi": "", "_query": "real estate valuation deep learning", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2306.01276v4", "title": "Symmetric Replay Training: Enhancing Sample Efficiency in Deep Reinforcement Learning for Combinatorial Optimization", "abstract": "Deep reinforcement learning (DRL) has significantly advanced the field of combinatorial optimization (CO). However, its practicality is hindered by the necessity for a large number of reward evaluations, especially in scenarios involving computationally intensive function assessments. To enhance the sample efficiency, we propose a simple but effective method, called symmetric replay training (SRT), which can be easily integrated into various DRL methods. Our method leverages high-reward samples to encourage exploration of the under-explored symmetric regions without additional online interactions - free. Through replay training, the policy is trained to maximize the likelihood of the symmetric trajectories of discovered high-rewarded samples. Experimental results demonstrate the consistent improvement of our method in sample efficiency across diverse DRL methods applied to real-world tasks, such as molecular optimization and hardware design.", "published": "2023-06-02T05:34:01Z", "authors": ["Hyeonah Kim", "Minsu Kim", "Sungsoo Ahn", "Jinkyoo Park"], "categories": ["cs.LG"], "doi": "", "_query": "real estate valuation deep learning", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2106.06508v2", "title": "Preferential Temporal Difference Learning", "abstract": "Temporal-Difference (TD) learning is a general and very useful tool for estimating the value function of a given policy, which in turn is required to find good policies. Generally speaking, TD learning updates states whenever they are visited. When the agent lands in a state, its value can be used to compute the TD-error, which is then propagated to other states. However, it may be interesting, when computing updates, to take into account other information than whether a state is visited or not. For example, some states might be more important than others (such as states which are frequently seen in a successful trajectory). Or, some states might have unreliable value estimates (for example, due to partial observability or lack of data), making their values less desirable as targets. We propose an approach to re-weighting states used in TD updates, both when they are the input and when they provide the target for the update. We prove that our approach converges with linear function approximation and illustrate its desirable empirical behaviour compared to other TD-style methods.", "published": "2021-06-11T17:05:15Z", "authors": ["Nishanth Anand", "Doina Precup"], "categories": ["cs.LG", "cs.AI"], "doi": "", "_query": "real estate valuation deep learning", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/1412.7659v3", "title": "Transformation Properties of Learned Visual Representations", "abstract": "When a three-dimensional object moves relative to an observer, a change occurs on the observer's image plane and in the visual representation computed by a learned model. Starting with the idea that a good visual representation is one that transforms linearly under scene motions, we show, using the theory of group representations, that any such representation is equivalent to a combination of the elementary irreducible representations. We derive a striking relationship between irreducibility and the statistical dependency structure of the representation, by showing that under restricted conditions, irreducible representations are decorrelated. Under partial observability, as induced by the perspective projection of a scene onto the image plane, the motion group does not have a linear action on the space of images, so that it becomes necessary to perform inference over a latent representation that does transform linearly. This idea is demonstrated in a model of rotating NORB objects that employs a latent representation of the non-commutative 3D rotation group SO(3).", "published": "2014-12-24T13:19:20Z", "authors": ["Taco S. Cohen", "Max Welling"], "categories": ["cs.LG", "cs.CV", "cs.NE"], "doi": "", "_query": "real estate valuation deep learning", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2004.08646v2", "title": "Macro-Action-Based Deep Multi-Agent Reinforcement Learning", "abstract": "In real-world multi-robot systems, performing high-quality, collaborative behaviors requires robots to asynchronously reason about high-level action selection at varying time durations. Macro-Action Decentralized Partially Observable Markov Decision Processes (MacDec-POMDPs) provide a general framework for asynchronous decision making under uncertainty in fully cooperative multi-agent tasks. However, multi-agent deep reinforcement learning methods have only been developed for (synchronous) primitive-action problems. This paper proposes two Deep Q-Network (DQN) based methods for learning decentralized and centralized macro-action-value functions with novel macro-action trajectory replay buffers introduced for each case. Evaluations on benchmark problems and a larger domain demonstrate the advantage of learning with macro-actions over primitive-actions and the scalability of our approaches.", "published": "2020-04-18T15:46:38Z", "authors": ["Yuchen Xiao", "Joshua Hoffman", "Christopher Amato"], "categories": ["cs.LG", "cs.AI", "cs.RO"], "doi": "", "_query": "real estate valuation deep learning", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2003.11059v2", "title": "Integrating Physiological Time Series and Clinical Notes with Deep Learning for Improved ICU Mortality Prediction", "abstract": "Intensive Care Unit Electronic Health Records (ICU EHRs) store multimodal data about patients including clinical notes, sparse and irregularly sampled physiological time series, lab results, and more. To date, most methods designed to learn predictive models from ICU EHR data have focused on a single modality. In this paper, we leverage the recently proposed interpolation-prediction deep learning architecture(Shukla and Marlin 2019) as a basis for exploring how physiological time series data and clinical notes can be integrated into a unified mortality prediction model. We study both early and late fusion approaches and demonstrate how the relative predictive value of clinical text and physiological data change over time. Our results show that a late fusion approach can provide a statistically significant improvement in mortality prediction performance over using individual modalities in isolation.", "published": "2020-03-24T18:25:49Z", "authors": ["Satya Narayan Shukla", "Benjamin M. Marlin"], "categories": ["cs.LG", "cs.CY", "stat.ML"], "doi": "", "_query": "real estate valuation deep learning", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/1901.08547v1", "title": "Human-centric Transfer Learning Explanation via Knowledge Graph [Extended Abstract]", "abstract": "Transfer learning which aims at utilizing knowledge learned from one problem (source domain) to solve another different but related problem (target domain) has attracted wide research attentions. However, the current transfer learning methods are mostly uninterpretable, especially to people without ML expertise. In this extended abstract, we brief introduce two knowledge graph (KG) based frameworks towards human understandable transfer learning explanation. The first one explains the transferability of features learned by Convolutional Neural Network (CNN) from one domain to another through pre-training and fine-tuning, while the second justifies the model of a target domain predicted by models from multiple source domains in zero-shot learning (ZSL). Both methods utilize KG and its reasoning capability to provide rich and human understandable explanations to the transfer procedure.", "published": "2019-01-20T18:51:15Z", "authors": ["Yuxia Geng", "Jiaoyan Chen", "Ernesto Jimenez-Ruiz", "Huajun Chen"], "categories": ["cs.LG", "cs.AI", "stat.ML"], "doi": "", "_query": "real estate valuation deep learning", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/1811.08357v4", "title": "Learning deep kernels for exponential family densities", "abstract": "The kernel exponential family is a rich class of distributions, which can be fit efficiently and with statistical guarantees by score matching. Being required to choose a priori a simple kernel such as the Gaussian, however, limits its practical applicability. We provide a scheme for learning a kernel parameterized by a deep network, which can find complex location-dependent local features of the data geometry. This gives a very rich class of density models, capable of fitting complex structures on moderate-dimensional problems. Compared to deep density models fit via maximum likelihood, our approach provides a complementary set of strengths and tradeoffs: in empirical studies, the former can yield higher likelihoods, whereas the latter gives better estimates of the gradient of the log density, the score, which describes the distribution's shape.", "published": "2018-11-20T16:40:45Z", "authors": ["Li Wenliang", "Danica J. Sutherland", "Heiko Strathmann", "Arthur Gretton"], "categories": ["stat.ML", "cs.LG", "stat.ME"], "doi": "", "_query": "real estate valuation deep learning", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/1811.08067v2", "title": "Reinforcement Learning of Active Vision for Manipulating Objects under Occlusions", "abstract": "We consider artificial agents that learn to jointly control their gripperand camera in order to reinforcement learn manipulation policies in the presenceof occlusions from distractor objects. Distractors often occlude the object of in-terest and cause it to disappear from the field of view. We propose hand/eye con-trollers that learn to move the camera to keep the object within the field of viewand visible, in coordination to manipulating it to achieve the desired goal, e.g.,pushing it to a target location. We incorporate structural biases of object-centricattention within our actor-critic architectures, which our experiments suggest tobe a key for good performance. Our results further highlight the importance ofcurriculum with regards to environment difficulty. The resulting active vision /manipulation policies outperform static camera setups for a variety of clutteredenvironments.", "published": "2018-11-20T04:24:38Z", "authors": ["Ricson Cheng", "Arpit Agarwal", "Katerina Fragkiadaki"], "categories": ["cs.RO", "cs.CV", "cs.LG"], "doi": "", "_query": "real estate valuation deep learning", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2211.07052v1", "title": "Treatment-RSPN: Recurrent Sum-Product Networks for Sequential Treatment Regimes", "abstract": "Sum-product networks (SPNs) have recently emerged as a novel deep learning architecture enabling highly efficient probabilistic inference. Since their introduction, SPNs have been applied to a wide range of data modalities and extended to time-sequence data. In this paper, we propose a general framework for modelling sequential treatment decision-making behaviour and treatment response using recurrent sum-product networks (RSPNs). Models developed using our framework benefit from the full range of RSPN capabilities, including the abilities to model the full distribution of the data, to seamlessly handle latent variables, missing values and categorical data, and to efficiently perform marginal and conditional inference. Our methodology is complemented by a novel variant of the expectation-maximization algorithm for RSPNs, enabling efficient training of our models. We evaluate our approach on a synthetic dataset as well as real-world data from the MIMIC-IV intensive care unit medical database. Our evaluation demonstrates that our approach can closely match the ground-truth data generation process on synthetic data and achieve results close to neural and probabilistic baselines while using a tractable and interpretable model.", "published": "2022-11-14T00:18:44Z", "authors": ["Adam Dejl", "Harsh Deep", "Jonathan Fei", "Ardavan Saeedi", "Li-wei H. Lehman"], "categories": ["cs.LG"], "doi": "", "_query": "real estate valuation deep learning", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2501.17634v2", "title": "Federated Learning With Individualized Privacy Through Client Sampling", "abstract": "With growing concerns about user data collection, individualized privacy has emerged as a promising solution to balance protection and utility by accounting for diverse user privacy preferences. Instead of enforcing a uniform level of anonymization for all users, this approach allows individuals to choose privacy settings that align with their comfort levels. Building on this idea, we propose an adapted method for enabling Individualized Differential Privacy (IDP) in Federated Learning (FL) by handling clients according to their personal privacy preferences. By extending the SAMPLE algorithm from centralized settings to FL, we calculate client-specific sampling rates based on their heterogeneous privacy budgets and integrate them into a modified IDP-FedAvg algorithm. We test this method under realistic privacy distributions and multiple datasets. The experimental results demonstrate that our approach achieves clear improvements over uniform DP baselines, reducing the trade-off between privacy and utility. Compared to the alternative SCALE method in related work, which assigns differing noise scales to clients, our method performs notably better. However, challenges remain for complex tasks with non-i.i.d. data, primarily stemming from the constraints of the decentralized setting.", "published": "2025-01-29T13:11:21Z", "authors": ["Lucas Lange", "Ole Borchardt", "Erhard Rahm"], "categories": ["cs.LG", "cs.AI", "cs.CR", "cs.CV"], "doi": "10.1109/ICMLT65785.2025.11193238", "_query": "real estate valuation deep learning", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2605.08253v2", "title": "Path-Coupled Bellman Flows for Distributional Reinforcement Learning", "abstract": "Distributional reinforcement learning (DRL) models the full return distribution, but existing finite-support or quantile-based methods rely on projections, while recent flow-based approaches can suffer from \\emph{boundary mismatch} at the flow source or from \\emph{high-variance} bootstrapping when current and successor noises are independent. We propose Path-Coupled Bellman Flows (PCBF), a continuous-time DRL method that learns return distributions with flow matching using \\textbf{source-consistent Bellman-coupled paths}: the current path starts from the required base prior at $t{=}0$, reaches the Bellman target at $t{=}1$, and maintains a pathwise affine relation to the successor flow at intermediate times (without requiring time-$t$ marginals to satisfy a distributional Bellman fixed point for all $t$). PCBF couples current and successor return flows through shared base noise and uses a $λ$-parameterized control-variate target: $λ{=}0$ recovers an unbiased sample Bellman target, while $λ{>}0$ trades controlled bias for variance reduction. Experiments on analytically tractable MRPs, OGBench, and D4RL show improved distributional fidelity and training stability, and competitive offline RL performance.", "published": "2026-05-07T19:05:01Z", "authors": ["Boyang Xu", "Qing Zou", "Siqin Yang", "Hao Yan"], "categories": ["cs.LG", "cs.AI"], "doi": "", "_query": "real estate valuation deep learning", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2101.04904v4", "title": "EEC: Learning to Encode and Regenerate Images for Continual Learning", "abstract": "The two main impediments to continual learning are catastrophic forgetting and memory limitations on the storage of data. To cope with these challenges, we propose a novel, cognitively-inspired approach which trains autoencoders with Neural Style Transfer to encode and store images. During training on a new task, reconstructed images from encoded episodes are replayed in order to avoid catastrophic forgetting. The loss function for the reconstructed images is weighted to reduce its effect during classifier training to cope with image degradation. When the system runs out of memory the encoded episodes are converted into centroids and covariance matrices, which are used to generate pseudo-images during classifier training, keeping classifier performance stable while using less memory. Our approach increases classification accuracy by 13-17% over state-of-the-art methods on benchmark datasets, while requiring 78% less storage space.", "published": "2021-01-13T06:43:10Z", "authors": ["Ali Ayub", "Alan R. Wagner"], "categories": ["cs.CV", "cs.AI", "cs.LG"], "doi": "", "_query": "real estate valuation deep learning", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/1909.01606v1", "title": "Model Asset eXchange: Path to Ubiquitous Deep Learning Deployment", "abstract": "A recent trend observed in traditionally challenging fields such as computer vision and natural language processing has been the significant performance gains shown by deep learning (DL). In many different research fields, DL models have been evolving rapidly and become ubiquitous. Despite researchers' excitement, unfortunately, most software developers are not DL experts and oftentimes have a difficult time following the booming DL research outputs. As a result, it usually takes a significant amount of time for the latest superior DL models to prevail in industry. This issue is further exacerbated by the common use of sundry incompatible DL programming frameworks, such as Tensorflow, PyTorch, Theano, etc. To address this issue, we propose a system, called Model Asset Exchange (MAX), that avails developers of easy access to state-of-the-art DL models. Regardless of the underlying DL programming frameworks, it provides an open source Python library (called the MAX framework) that wraps DL models and unifies programming interfaces with our standardized RESTful APIs. These RESTful APIs enable developers to exploit the wrapped DL models for inference tasks without the need to fully understand different DL programming frameworks. Using MAX, we have wrapped and open-sourced more than 30 state-of-the-art DL models from various research fields, including computer vision, natural language processing and signal processing, etc. In the end, we selectively demonstrate two web applications that are built on top of MAX, as well as the process of adding a DL model to MAX.", "published": "2019-09-04T08:05:03Z", "authors": ["Alex Bozarth", "Brendan Dwyer", "Fei Hu", "Daniel Jalova", "Karthik Muthuraman", "Nick Pentreath", "Simon Plovyt", "Gabriela de Queiroz", "Saishruthi Swaminathan", "Patrick Titzler", "Xin Wu", "Hong Xu", "Frederick R Reiss", "Vijay Bommireddipalli"], "categories": ["cs.LG", "stat.ML"], "doi": "10.1145/3357384.3357860", "_query": "real estate valuation deep learning", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/1105.1033v2", "title": "Adaptively Learning the Crowd Kernel", "abstract": "We introduce an algorithm that, given n objects, learns a similarity matrix over all n^2 pairs, from crowdsourced data alone. The algorithm samples responses to adaptively chosen triplet-based relative-similarity queries. Each query has the form \"is object 'a' more similar to 'b' or to 'c'?\" and is chosen to be maximally informative given the preceding responses. The output is an embedding of the objects into Euclidean space (like MDS); we refer to this as the \"crowd kernel.\" SVMs reveal that the crowd kernel captures prominent and subtle features across a number of domains, such as \"is striped\" among neckties and \"vowel vs. consonant\" among letters.", "published": "2011-05-05T11:03:03Z", "authors": ["Omer Tamuz", "Ce Liu", "Serge Belongie", "Ohad Shamir", "Adam Tauman Kalai"], "categories": ["cs.LG"], "doi": "", "_query": "real estate valuation deep learning", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/1807.00780v1", "title": "Ambient Hidden Space of Generative Adversarial Networks", "abstract": "Generative adversarial models are powerful tools to model structure in complex distributions for a variety of tasks. Current techniques for learning generative models require an access to samples which have high quality, and advanced generative models are applied to generate samples from noisy training data through ambient modules. However, the modules are only practical for the output space of the generator, and their application in the hidden space is not well studied. In this paper, we extend the ambient module to the hidden space of the generator, and provide the uniqueness condition and the corresponding strategy for the ambient hidden generator in the adversarial training process. We report the practicality of the proposed method on the benchmark dataset.", "published": "2018-07-02T16:51:27Z", "authors": ["Xinhan Di", "Pengqian Yu", "Meng Tian"], "categories": ["cs.LG", "cs.AI", "cs.CV", "stat.ML"], "doi": "", "_query": "real estate valuation deep learning", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2310.09924v1", "title": "Deep Reinforcement Learning with Explicit Context Representation", "abstract": "Reinforcement learning (RL) has shown an outstanding capability for solving complex computational problems. However, most RL algorithms lack an explicit method that would allow learning from contextual information. Humans use context to identify patterns and relations among elements in the environment, along with how to avoid making wrong actions. On the other hand, what may seem like an obviously wrong decision from a human perspective could take hundreds of steps for an RL agent to learn to avoid. This paper proposes a framework for discrete environments called Iota explicit context representation (IECR). The framework involves representing each state using contextual key frames (CKFs), which can then be used to extract a function that represents the affordances of the state; in addition, two loss functions are introduced with respect to the affordances of the state. The novelty of the IECR framework lies in its capacity to extract contextual information from the environment and learn from the CKFs' representation. We validate the framework by developing four new algorithms that learn using context: Iota deep Q-network (IDQN), Iota double deep Q-network (IDDQN), Iota dueling deep Q-network (IDuDQN), and Iota dueling double deep Q-network (IDDDQN). Furthermore, we evaluate the framework and the new algorithms in five discrete environments. We show that all the algorithms, which use contextual information, converge in around 40,000 training steps of the neural networks, significantly outperforming their state-of-the-art equivalents.", "published": "2023-10-15T19:23:05Z", "authors": ["Francisco Munguia-Galeano", "Ah-Hwee Tan", "Ze Ji"], "categories": ["cs.LG"], "doi": "", "_query": "real estate valuation deep learning", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2011.07215v2", "title": "SoftGym: Benchmarking Deep Reinforcement Learning for Deformable Object Manipulation", "abstract": "Manipulating deformable objects has long been a challenge in robotics due to its high dimensional state representation and complex dynamics. Recent success in deep reinforcement learning provides a promising direction for learning to manipulate deformable objects with data driven methods. However, existing reinforcement learning benchmarks only cover tasks with direct state observability and simple low-dimensional dynamics or with relatively simple image-based environments, such as those with rigid objects. In this paper, we present SoftGym, a set of open-source simulated benchmarks for manipulating deformable objects, with a standard OpenAI Gym API and a Python interface for creating new environments. Our benchmark will enable reproducible research in this important area. Further, we evaluate a variety of algorithms on these tasks and highlight challenges for reinforcement learning algorithms, including dealing with a state representation that has a high intrinsic dimensionality and is partially observable. The experiments and analysis indicate the strengths and limitations of existing methods in the context of deformable object manipulation that can help point the way forward for future methods development. Code and videos of the learned policies can be found on our project website.", "published": "2020-11-14T03:46:59Z", "authors": ["Xingyu Lin", "Yufei Wang", "Jake Olkin", "David Held"], "categories": ["cs.RO", "cs.LG"], "doi": "", "_query": "real estate valuation deep learning", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2312.01624v1", "title": "GVFs in the Real World: Making Predictions Online for Water Treatment", "abstract": "In this paper we investigate the use of reinforcement-learning based prediction approaches for a real drinking-water treatment plant. Developing such a prediction system is a critical step on the path to optimizing and automating water treatment. Before that, there are many questions to answer about the predictability of the data, suitable neural network architectures, how to overcome partial observability and more. We first describe this dataset, and highlight challenges with seasonality, nonstationarity, partial observability, and heterogeneity across sensors and operation modes of the plant. We then describe General Value Function (GVF) predictions -- discounted cumulative sums of observations -- and highlight why they might be preferable to classical n-step predictions common in time series prediction. We discuss how to use offline data to appropriately pre-train our temporal difference learning (TD) agents that learn these GVF predictions, including how to select hyperparameters for online fine-tuning in deployment. We find that the TD-prediction agent obtains an overall lower normalized mean-squared error than the n-step prediction agent. Finally, we show the importance of learning in deployment, by comparing a TD agent trained purely offline with no online updating to a TD agent that learns online. This final result is one of the first to motivate the importance of adapting predictions in real-time, for non-stationary high-volume systems in the real world.", "published": "2023-12-04T04:49:10Z", "authors": ["Muhammad Kamran Janjua", "Haseeb Shah", "Martha White", "Erfan Miahi", "Marlos C. Machado", "Adam White"], "categories": ["cs.LG", "cs.AI"], "doi": "", "_query": "real estate valuation deep learning", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2109.09463v2", "title": "Predicting Visual Improvement after Macular Hole Surgery: a Cautionary Tale on Deep Learning with Very Limited Data", "abstract": "We investigate the potential of machine learning models for the prediction of visual improvement after macular hole surgery from preoperative data (retinal images and clinical features). Collecting our own data for the task, we end up with only 121 total samples, putting our work in the very limited data regime. We explore a variety of deep learning methods for limited data to train deep computer vision models, finding that all tested deep vision models are outperformed by a simple regression model on the clinical features. We believe this is compelling evidence of the extreme difficulty of using deep learning on very limited data.", "published": "2021-09-20T12:23:04Z", "authors": ["M. Godbout", "A. Lachance", "F. Antaki", "A. Dirani", "A. Durand"], "categories": ["eess.IV", "cs.CV", "cs.LG"], "doi": "", "_query": "real estate valuation deep learning", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/1806.08616v1", "title": "Deploying Deep Neural Networks in the Embedded Space", "abstract": "Recently, Deep Neural Networks (DNNs) have emerged as the dominant model across various AI applications. In the era of IoT and mobile systems, the efficient deployment of DNNs on embedded platforms is vital to enable the development of intelligent applications. This paper summarises our recent work on the optimised mapping of DNNs on embedded settings. By covering such diverse topics as DNN-to-accelerator toolflows, high-throughput cascaded classifiers and domain-specific model design, the presented set of works aim to enable the deployment of sophisticated deep learning models on cutting-edge mobile and embedded systems.", "published": "2018-06-22T12:01:11Z", "authors": ["Stylianos I. Venieris", "Alexandros Kouris", "Christos-Savvas Bouganis"], "categories": ["cs.CV", "cs.AI", "cs.LG"], "doi": "", "_query": "real estate valuation deep learning", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2401.03947v1", "title": "Guiding drones by information gain", "abstract": "The accurate estimation of locations and emission rates of gas sources is crucial across various domains, including environmental monitoring and greenhouse gas emission analysis. This study investigates two drone sampling strategies for inferring source term parameters of gas plumes from atmospheric measurements. Both strategies are guided by the goal of maximizing information gain attained from observations at sequential locations. Our research compares the myopic approach of infotaxis to a far-sighted navigation strategy trained through deep reinforcement learning. We demonstrate the superior performance of deep reinforcement learning over infotaxis in environments with non-isotropic gas plumes.", "published": "2024-01-08T15:13:23Z", "authors": ["Alouette van Hove", "Kristoffer Aalstad", "Norbert Pirk"], "categories": ["cs.LG"], "doi": "", "_query": "real estate valuation deep learning", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2201.02135v5", "title": "Deep Reinforcement Learning, a textbook", "abstract": "Deep reinforcement learning has gathered much attention recently. Impressive results were achieved in activities as diverse as autonomous driving, game playing, molecular recombination, and robotics. In all these fields, computer programs have taught themselves to solve difficult problems. They have learned to fly model helicopters and perform aerobatic manoeuvers such as loops and rolls. In some applications they have even become better than the best humans, such as in Atari, Go, poker and StarCraft. The way in which deep reinforcement learning explores complex environments reminds us of how children learn, by playfully trying out things, getting feedback, and trying again. The computer seems to truly possess aspects of human learning; this goes to the heart of the dream of artificial intelligence. The successes in research have not gone unnoticed by educators, and universities have started to offer courses on the subject. The aim of this book is to provide a comprehensive overview of the field of deep reinforcement learning. The book is written for graduate students of artificial intelligence, and for researchers and practitioners who wish to better understand deep reinforcement learning methods and their challenges. We assume an undergraduate-level of understanding of computer science and artificial intelligence; the programming language of this book is Python. We describe the foundations, the algorithms and the applications of deep reinforcement learning. We cover the established model-free and model-based methods that form the basis of the field. Developments go quickly, and we also cover advanced topics: deep multi-agent reinforcement learning, deep hierarchical reinforcement learning, and deep meta learning.", "published": "2022-01-04T11:47:21Z", "authors": ["Aske Plaat"], "categories": ["cs.AI", "cs.LG"], "doi": "10.1007/978-981-19-0638-1", "_query": "real estate valuation deep learning", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2308.02537v1", "title": "ALE: A Simulation-Based Active Learning Evaluation Framework for the Parameter-Driven Comparison of Query Strategies for NLP", "abstract": "Supervised machine learning and deep learning require a large amount of labeled data, which data scientists obtain in a manual, and time-consuming annotation process. To mitigate this challenge, Active Learning (AL) proposes promising data points to annotators they annotate next instead of a subsequent or random sample. This method is supposed to save annotation effort while maintaining model performance. However, practitioners face many AL strategies for different tasks and need an empirical basis to choose between them. Surveys categorize AL strategies into taxonomies without performance indications. Presentations of novel AL strategies compare the performance to a small subset of strategies. Our contribution addresses the empirical basis by introducing a reproducible active learning evaluation (ALE) framework for the comparative evaluation of AL strategies in NLP. The framework allows the implementation of AL strategies with low effort and a fair data-driven comparison through defining and tracking experiment parameters (e.g., initial dataset size, number of data points per query step, and the budget). ALE helps practitioners to make more informed decisions, and researchers can focus on developing new, effective AL strategies and deriving best practices for specific use cases. With best practices, practitioners can lower their annotation costs. We present a case study to illustrate how to use the framework.", "published": "2023-08-01T10:42:11Z", "authors": ["Philipp Kohl", "Nils Freyer", "Yoka Krämer", "Henri Werth", "Steffen Wolf", "Bodo Kraft", "Matthias Meinecke", "Albert Zündorf"], "categories": ["cs.CL", "cs.AI"], "doi": "10.1007/978-3-031-39059-3_16", "_query": "real estate valuation deep learning", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/1811.11441v2", "title": "Trajectory-based Learning for Ball-in-Maze Games", "abstract": "Deep Reinforcement Learning has shown tremendous success in solving several games and tasks in robotics. However, unlike humans, it generally requires a lot of training instances. Trajectories imitating to solve the task at hand can help to increase sample-efficiency of deep RL methods. In this paper, we present a simple approach to use such trajectories, applied to the challenging Ball-in-Maze Games, recently introduced in the literature. We show that in spite of not using human-generated trajectories and just using the simulator as a model to generate a limited number of trajectories, we can get a speed-up of about 2-3x in the learning process. We also discuss some challenges we observed while using trajectory-based learning for very sparse reward functions.", "published": "2018-11-28T08:38:40Z", "authors": ["Sujoy Paul", "Jeroen van Baar"], "categories": ["cs.LG", "stat.ML"], "doi": "", "_query": "real estate valuation deep learning", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2510.21994v1", "title": "Deep Learning on Real-World Graphs", "abstract": "Graph Neural Networks (GNNs) have become a central tool for learning on graph-structured data, yet their applicability to real-world systems remains limited by key challenges such as scalability, temporality, directionality, data incompleteness, and structural uncertainty. This thesis introduces a series of models addressing these limitations: SIGN for scalable graph learning, TGN for temporal graphs, Dir-GNN for directed and heterophilic networks, Feature Propagation (FP) for learning with missing node features, and NuGget for game-theoretic structural inference. Together, these contributions bridge the gap between academic benchmarks and industrial-scale graphs, enabling the use of GNNs in domains such as social and recommender systems.", "published": "2025-10-24T19:58:13Z", "authors": ["Emanuele Rossi"], "categories": ["cs.LG"], "doi": "10.25560/112863", "_query": "real estate valuation deep learning", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2009.01672v1", "title": "Yet Meta Learning Can Adapt Fast, It Can Also Break Easily", "abstract": "Meta learning algorithms have been widely applied in many tasks for efficient learning, such as few-shot image classification and fast reinforcement learning. During meta training, the meta learner develops a common learning strategy, or experience, from a variety of learning tasks. Therefore, during meta test, the meta learner can use the learned strategy to quickly adapt to new tasks even with a few training samples. However, there is still a dark side about meta learning in terms of reliability and robustness. In particular, is meta learning vulnerable to adversarial attacks? In other words, would a well-trained meta learner utilize its learned experience to build wrong or likely useless knowledge, if an adversary unnoticeably manipulates the given training set? Without the understanding of this problem, it is extremely risky to apply meta learning in safety-critical applications. Thus, in this paper, we perform the initial study about adversarial attacks on meta learning under the few-shot classification problem. In particular, we formally define key elements of adversarial attacks unique to meta learning and propose the first attacking algorithm against meta learning under various settings. We evaluate the effectiveness of the proposed attacking strategy as well as the robustness of several representative meta learning algorithms. Experimental results demonstrate that the proposed attacking strategy can easily break the meta learner and meta learning is vulnerable to adversarial attacks. The implementation of the proposed framework will be released upon the acceptance of this paper.", "published": "2020-09-02T15:03:14Z", "authors": ["Han Xu", "Yaxin Li", "Xiaorui Liu", "Hui Liu", "Jiliang Tang"], "categories": ["cs.LG", "stat.ML"], "doi": "", "_query": "real estate valuation deep learning", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/1704.04463v2", "title": "On Generalized Bellman Equations and Temporal-Difference Learning", "abstract": "We consider off-policy temporal-difference (TD) learning in discounted Markov decision processes, where the goal is to evaluate a policy in a model-free way by using observations of a state process generated without executing the policy. To curb the high variance issue in off-policy TD learning, we propose a new scheme of setting the $λ$-parameters of TD, based on generalized Bellman equations. Our scheme is to set $λ$ according to the eligibility trace iterates calculated in TD, thereby easily keeping these traces in a desired bounded range. Compared with prior work, this scheme is more direct and flexible, and allows much larger $λ$ values for off-policy TD learning with bounded traces. As to its soundness, using Markov chain theory, we prove the ergodicity of the joint state-trace process under nonrestrictive conditions, and we show that associated with our scheme is a generalized Bellman equation (for the policy to be evaluated) that depends on both the evolution of $λ$ and the unique invariant probability measure of the state-trace process. These results not only lead immediately to a characterization of the convergence behavior of least-squares based implementation of our scheme, but also prepare the ground for further analysis of gradient-based implementations.", "published": "2017-04-14T16:01:18Z", "authors": ["Huizhen Yu", "A. Rupam Mahmood", "Richard S. Sutton"], "categories": ["cs.LG", "math.OC"], "doi": "", "_query": "real estate valuation deep learning", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2312.10557v1", "title": "Improving Environment Robustness of Deep Reinforcement Learning Approaches for Autonomous Racing Using Bayesian Optimization-based Curriculum Learning", "abstract": "Deep reinforcement learning (RL) approaches have been broadly applied to a large number of robotics tasks, such as robot manipulation and autonomous driving. However, an open problem in deep RL is learning policies that are robust to variations in the environment, which is an important condition for such systems to be deployed into real-world, unstructured settings. Curriculum learning is one approach that has been applied to improve generalization performance in both supervised and reinforcement learning domains, but selecting the appropriate curriculum to achieve robustness can be a user-intensive process. In our work, we show that performing probabilistic inference of the underlying curriculum-reward function using Bayesian Optimization can be a promising technique for finding a robust curriculum. We demonstrate that a curriculum found with Bayesian optimization can outperform a vanilla deep RL agent and a hand-engineered curriculum in the domain of autonomous racing with obstacle avoidance. Our code is available at https://github.com/PRISHIta123/Curriculum_RL_for_Driving.", "published": "2023-12-16T23:11:52Z", "authors": ["Rohan Banerjee", "Prishita Ray", "Mark Campbell"], "categories": ["cs.RO", "cs.LG"], "doi": "", "_query": "real estate valuation deep learning", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/1907.04433v2", "title": "GluonCV and GluonNLP: Deep Learning in Computer Vision and Natural Language Processing", "abstract": "We present GluonCV and GluonNLP, the deep learning toolkits for computer vision and natural language processing based on Apache MXNet (incubating). These toolkits provide state-of-the-art pre-trained models, training scripts, and training logs, to facilitate rapid prototyping and promote reproducible research. We also provide modular APIs with flexible building blocks to enable efficient customization. Leveraging the MXNet ecosystem, the deep learning models in GluonCV and GluonNLP can be deployed onto a variety of platforms with different programming languages. The Apache 2.0 license has been adopted by GluonCV and GluonNLP to allow for software distribution, modification, and usage.", "published": "2019-07-09T21:59:44Z", "authors": ["Jian Guo", "He He", "Tong He", "Leonard Lausen", "Mu Li", "Haibin Lin", "Xingjian Shi", "Chenguang Wang", "Junyuan Xie", "Sheng Zha", "Aston Zhang", "Hang Zhang", "Zhi Zhang", "Zhongyue Zhang", "Shuai Zheng", "Yi Zhu"], "categories": ["cs.LG", "cs.CL", "cs.CV", "stat.ML"], "doi": "", "_query": "real estate valuation deep learning", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/1909.11939v6", "title": "MERL: Multi-Head Reinforcement Learning", "abstract": "A common challenge in reinforcement learning is how to convert the agent's interactions with an environment into fast and robust learning. For instance, earlier work makes use of domain knowledge to improve existing reinforcement learning algorithms in complex tasks. While promising, previously acquired knowledge is often costly and challenging to scale up. Instead, we decide to consider problem knowledge with signals from quantities relevant to solve any task, e.g., self-performance assessment and accurate expectations. $\\mathcal{V}^{ex}$ is such a quantity. It is the fraction of variance explained by the value function $V$ and measures the discrepancy between $V$ and the returns. Taking advantage of $\\mathcal{V}^{ex}$, we propose MERL, a general framework for structuring reinforcement learning by injecting problem knowledge into policy gradient updates. As a result, the agent is not only optimized for a reward but learns using problem-focused quantities provided by MERL, applicable out-of-the-box to any task. In this paper: (a) We introduce and define MERL, the multi-head reinforcement learning framework we use throughout this work. (b) We conduct experiments across a variety of standard benchmark environments, including 9 continuous control tasks, where results show improved performance. (c) We demonstrate that MERL also improves transfer learning on a set of challenging pixel-based tasks. (d) We ponder how MERL tackles the problem of reward sparsity and better conditions the feature space of reinforcement learning agents.", "published": "2019-09-26T06:57:51Z", "authors": ["Yannis Flet-Berliac", "Philippe Preux"], "categories": ["cs.LG", "cs.AI", "stat.ML"], "doi": "", "_query": "real estate valuation deep learning", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2110.08265v4", "title": "Knowledge-driven Active Learning", "abstract": "The deployment of Deep Learning (DL) models is still precluded in those contexts where the amount of supervised data is limited. To answer this issue, active learning strategies aim at minimizing the amount of labelled data required to train a DL model. Most active strategies are based on uncertain sample selection, and even often restricted to samples lying close to the decision boundary. These techniques are theoretically sound, but an understanding of the selected samples based on their content is not straightforward, further driving non-experts to consider DL as a black-box. For the first time, here we propose to take into consideration common domain-knowledge and enable non-expert users to train a model with fewer samples. In our Knowledge-driven Active Learning (KAL) framework, rule-based knowledge is converted into logic constraints and their violation is checked as a natural guide for sample selection. We show that even simple relationships among data and output classes offer a way to spot predictions for which the model need supervision. We empirically show that KAL (i) outperforms many active learning strategies, particularly in those contexts where domain knowledge is rich, (ii) it discovers data distribution lying far from the initial training data, (iii) it ensures domain experts that the provided knowledge is acquired by the model, (iv) it is suitable for regression and object recognition tasks unlike uncertainty-based strategies, and (v) its computational demand is low.", "published": "2021-10-15T06:11:53Z", "authors": ["Gabriele Ciravegna", "Frédéric Precioso", "Alessandro Betti", "Kevin Mottin", "Marco Gori"], "categories": ["cs.LG", "cs.AI"], "doi": "10.1007/978-3-031-43412-9_3", "_query": "real estate valuation deep learning", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2109.06668v6", "title": "Exploration in Deep Reinforcement Learning: From Single-Agent to Multiagent Domain", "abstract": "Deep Reinforcement Learning (DRL) and Deep Multi-agent Reinforcement Learning (MARL) have achieved significant successes across a wide range of domains, including game AI, autonomous vehicles, robotics, and so on. However, DRL and deep MARL agents are widely known to be sample inefficient that millions of interactions are usually needed even for relatively simple problem settings, thus preventing the wide application and deployment in real-industry scenarios. One bottleneck challenge behind is the well-known exploration problem, i.e., how efficiently exploring the environment and collecting informative experiences that could benefit policy learning towards the optimal ones. This problem becomes more challenging in complex environments with sparse rewards, noisy distractions, long horizons, and non-stationary co-learners. In this paper, we conduct a comprehensive survey on existing exploration methods for both single-agent and multi-agent RL. We start the survey by identifying several key challenges to efficient exploration. Beyond the above two main branches, we also include other notable exploration methods with different ideas and techniques. In addition to algorithmic analysis, we provide a comprehensive and unified empirical comparison of different exploration methods for DRL on a set of commonly used benchmarks. According to our algorithmic and empirical investigation, we finally summarize the open problems of exploration in DRL and deep MARL and point out a few future directions.", "published": "2021-09-14T13:16:33Z", "authors": ["Jianye Hao", "Tianpei Yang", "Hongyao Tang", "Chenjia Bai", "Jinyi Liu", "Zhaopeng Meng", "Peng Liu", "Zhen Wang"], "categories": ["cs.AI", "cs.LG", "cs.MA"], "doi": "10.1109/TNNLS.2023.3236361", "_query": "real estate valuation deep learning", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2407.07664v2", "title": "A Coding-Theoretic Analysis of Hyperspherical Prototypical Learning Geometry", "abstract": "Hyperspherical Prototypical Learning (HPL) is a supervised approach to representation learning that designs class prototypes on the unit hypersphere. The prototypes bias the representations to class separation in a scale invariant and known geometry. Previous approaches to HPL have either of the following shortcomings: (i) they follow an unprincipled optimisation procedure; or (ii) they are theoretically sound, but are constrained to only one possible latent dimension. In this paper, we address both shortcomings. To address (i), we present a principled optimisation procedure whose solution we show is optimal. To address (ii), we construct well-separated prototypes in a wide range of dimensions using linear block codes. Additionally, we give a full characterisation of the optimal prototype placement in terms of achievable and converse bounds, showing that our proposed methods are near-optimal.", "published": "2024-07-10T13:44:19Z", "authors": ["Martin Lindström", "Borja Rodríguez-Gálvez", "Ragnar Thobaben", "Mikael Skoglund"], "categories": ["cs.LG", "cs.AI", "cs.CV", "eess.SP", "stat.ML"], "doi": "", "_query": "real estate valuation deep learning", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/1811.11818v1", "title": "Disease phenotyping using deep learning: A diabetes case study", "abstract": "Characterization of a patient clinical phenotype is central to biomedical informatics. ICD codes, assigned to inpatient encounters by coders, is important for population health and cohort discovery when clinical information is limited. While ICD codes are assigned to patients by professionals trained and certified in coding there is substantial variability in coding. We present a methodology that uses deep learning methods to model coder decision making and that predicts ICD codes. Our approach predicts codes based on demographics, lab results, and medications, as well as codes from previous encounters. We are able to predict existing codes with high accuracy for all three of the test cases we investigated: diabetes, acute renal failure, and chronic kidney disease. We employed a panel of clinicians, in a blinded manner, to assess ground truth and compared the predictions of coders, model and clinicians. When disparities between the model prediction and coder assigned codes were reviewed, our model outperformed coder assigned ICD codes.", "published": "2018-11-28T20:37:04Z", "authors": ["Sina Rashidian", "Janos Hajagos", "Richard Moffitt", "Fusheng Wang", "Xinyu Dong", "Kayley Abell-Hart", "Kimberly Noel", "Rajarsi Gupta", "Mathew Tharakan", "Veena Lingam", "Joel Saltz", "Mary Saltz"], "categories": ["cs.LG", "stat.ML"], "doi": "", "_query": "real estate valuation deep learning", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/1612.04318v1", "title": "Incorporating Human Domain Knowledge into Large Scale Cost Function Learning", "abstract": "Recent advances have shown the capability of Fully Convolutional Neural Networks (FCN) to model cost functions for motion planning in the context of learning driving preferences purely based on demonstration data from human drivers. While pure learning from demonstrations in the framework of Inverse Reinforcement Learning (IRL) is a promising approach, we can benefit from well informed human priors and incorporate them into the learning process. Our work achieves this by pretraining a model to regress to a manual cost function and refining it based on Maximum Entropy Deep Inverse Reinforcement Learning. When injecting prior knowledge as pretraining for the network, we achieve higher robustness, more visually distinct obstacle boundaries, and the ability to capture instances of obstacles that elude models that purely learn from demonstration data. Furthermore, by exploiting these human priors, the resulting model can more accurately handle corner cases that are scarcely seen in the demonstration data, such as stairs, slopes, and underpasses.", "published": "2016-12-13T18:56:03Z", "authors": ["Markus Wulfmeier", "Dushyant Rao", "Ingmar Posner"], "categories": ["cs.RO", "cs.AI", "cs.LG"], "doi": "", "_query": "real estate valuation deep learning", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2510.09898v2", "title": "Learning Bug Context for PyTorch-to-JAX Translation with LLMs", "abstract": "Large language models (LLMs) have shown strong performance on code translation between widely used programming languages. However, translation becomes much less reliable for domain-specific code, where correctness depends on framework-specific APIs and execution semantics. One example is translating deep-learning code from PyTorch to JAX, where LLM outputs often contain subtle bugs or non-idiomatic usage that prevents execution or changes behavior. Prior work suggests that curated bug-fix data from LLM-generated code can help improve code generation quality, but such resources are still limited for PyTorch-to-JAX translation. In this work, we introduce T2J, a benchmark of LLM translation bugs paired with developer-written fixes for PyTorch-to-JAX code. We start from 20 kernels in the TorchLeet dataset, translate them to JAX using the weak LLM gpt-4o-mini, and hire software developers to debug and repair the generated JAX implementations. We then use T2J to improve PyTorch-to-JAX translation for the weak LLM gpt-4o-mini via in-context learning. Our evaluation shows that using T2J yields up to 20% improvement of our proposed metric T2J-CodeTrans-Score.", "published": "2025-10-10T22:22:36Z", "authors": ["Hung Phan", "Son Vu", "Tuan Dinh", "Nesreen Ahmed", "Ali Payani", "Ali Jannesari"], "categories": ["cs.LG", "cs.AI"], "doi": "", "_query": "real estate valuation deep learning", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2211.15557v2", "title": "Beyond CAGE: Investigating Generalization of Learned Autonomous Network Defense Policies", "abstract": "Advancements in reinforcement learning (RL) have inspired new directions in intelligent automation of network defense. However, many of these advancements have either outpaced their application to network security or have not considered the challenges associated with implementing them in the real-world. To understand these problems, this work evaluates several RL approaches implemented in the second edition of the CAGE Challenge, a public competition to build an autonomous network defender agent in a high-fidelity network simulator. Our approaches all build on the Proximal Policy Optimization (PPO) family of algorithms, and include hierarchical RL, action masking, custom training, and ensemble RL. We find that the ensemble RL technique performs strongest, outperforming our other models and taking second place in the competition. To understand applicability to real environments we evaluate each method's ability to generalize to unseen networks and against an unknown attack strategy. In unseen environments, all of our approaches perform worse, with degradation varied based on the type of environmental change. Against an unknown attacker strategy, we found that our models had reduced overall performance even though the new strategy was less efficient than the ones our models trained on. Together, these results highlight promising research directions for autonomous network defense in the real world.", "published": "2022-11-28T17:01:24Z", "authors": ["Melody Wolk", "Andy Applebaum", "Camron Dennler", "Patrick Dwyer", "Marina Moskowitz", "Harold Nguyen", "Nicole Nichols", "Nicole Park", "Paul Rachwalski", "Frank Rau", "Adrian Webster"], "categories": ["cs.LG", "cs.CR"], "doi": "", "_query": "real estate valuation deep learning", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2311.14653v2", "title": "Data-driven Prior Learning for Bayesian Optimisation", "abstract": "Transfer learning for Bayesian optimisation has generally assumed a strong similarity between optimisation tasks, with at least a subset having similar optimal inputs. This assumption can reduce computational costs, but it is violated in a wide range of optimisation problems where transfer learning may nonetheless be useful. We replace this assumption with a weaker one only requiring the shape of the optimisation landscape to be similar, and analyse the recent method Prior Learning for Bayesian Optimisation - PLeBO - in this setting. By learning priors for the hyperparameters of the Gaussian process surrogate model we can better approximate the underlying function, especially for few function evaluations. We validate the learned priors and compare to a breadth of transfer learning approaches, using synthetic data and a recent air pollution optimisation problem as benchmarks. We show that PLeBO and prior transfer find good inputs in fewer evaluations.", "published": "2023-11-24T18:37:52Z", "authors": ["Sigrid Passano Hellan", "Christopher G. Lucas", "Nigel H. Goddard"], "categories": ["cs.LG", "stat.ML"], "doi": "", "_query": "real estate valuation deep learning", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2412.01103v1", "title": "FRIDAY: Real-time Learning DNN-based Stable LQR controller for Nonlinear Systems under Uncertain Disturbances", "abstract": "Linear Quadratic Regulator (LQR) is often combined with feedback linearization (FBL) for nonlinear systems that have the nonlinearity additive to the input. Conventional approaches estimate and cancel the nonlinearity based on the first principle or data-driven methods such as Gaussian Processes (GPs). However, the former needs an elaborate modeling process, and the latter provides a fixed learned model, which may be suffering when the model dynamics are changing. In this letter, we take a Deep Neural Network (DNN) using a real-time-updated dataset to approximate the unknown nonlinearity while the controller is running. Spectrally normalizing the weights in each time-step, we stably incorporate the DNN prediction to an LQR controller and compensate for the nonlinear term. Leveraging the property of the bounded Lipschitz constant of the DNN, we provide theoretical analysis and locally exponential stability of the proposed controller. Simulation results show that our controller significantly outperforms Baseline controllers in trajectory tracking cases.", "published": "2024-12-02T04:22:18Z", "authors": ["Takahito Fujimori"], "categories": ["eess.SY"], "doi": "", "_query": "real estate valuation deep learning", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2012.10902v1", "title": "Learning to Localize Using a LiDAR Intensity Map", "abstract": "In this paper we propose a real-time, calibration-agnostic and effective localization system for self-driving cars. Our method learns to embed the online LiDAR sweeps and intensity map into a joint deep embedding space. Localization is then conducted through an efficient convolutional matching between the embeddings. Our full system can operate in real-time at 15Hz while achieving centimeter level accuracy across different LiDAR sensors and environments. Our experiments illustrate the performance of the proposed approach over a large-scale dataset consisting of over 4000km of driving.", "published": "2020-12-20T11:56:23Z", "authors": ["Ioan Andrei Bârsan", "Shenlong Wang", "Andrei Pokrovsky", "Raquel Urtasun"], "categories": ["cs.CV", "cs.LG", "cs.RO"], "doi": "", "_query": "real estate valuation deep learning", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2308.14144v1", "title": "Learning end-to-end inversion of circular Radon transforms in the partial radial setup", "abstract": "We present a deep learning-based computational algorithm for inversion of circular Radon transforms in the partial radial setup, arising in photoacoustic tomography. We first demonstrate that the truncated singular value decomposition-based method, which is the only traditional algorithm available to solve this problem, leads to severe artifacts which renders the reconstructed field as unusable. With the objective of overcoming this computational bottleneck, we train a ResBlock based U-Net to recover the inferred field that directly operates on the measured data. Numerical results with augmented Shepp-Logan phantoms, in the presence of noisy full and limited view data, demonstrate the superiority of the proposed algorithm.", "published": "2023-08-27T15:57:08Z", "authors": ["Deep Ray", "Souvik Roy"], "categories": ["cs.LG", "eess.IV", "math-ph"], "doi": "", "_query": "real estate valuation deep learning", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2405.01304v1", "title": "Misclassification bounds for PAC-Bayesian sparse deep learning", "abstract": "Recently, there has been a significant focus on exploring the theoretical aspects of deep learning, especially regarding its performance in classification tasks. Bayesian deep learning has emerged as a unified probabilistic framework, seeking to integrate deep learning with Bayesian methodologies seamlessly. However, there exists a gap in the theoretical understanding of Bayesian approaches in deep learning for classification. This study presents an attempt to bridge that gap. By leveraging PAC-Bayes bounds techniques, we present theoretical results on the prediction or misclassification error of a probabilistic approach utilizing Spike-and-Slab priors for sparse deep learning in classification. We establish non-asymptotic results for the prediction error. Additionally, we demonstrate that, by considering different architectures, our results can achieve minimax optimal rates in both low and high-dimensional settings, up to a logarithmic factor. Moreover, our additional logarithmic term yields slight improvements over previous works. Additionally, we propose and analyze an automated model selection approach aimed at optimally choosing a network architecture with guaranteed optimality.", "published": "2024-05-02T14:11:48Z", "authors": ["The Tien Mai"], "categories": ["math.ST", "stat.ML"], "doi": "10.1007/s10994-024-06690-0", "_query": "real estate valuation deep learning", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2503.16128v1", "title": "Coupling deep and handcrafted features to assess smile genuineness", "abstract": "Assessing smile genuineness from video sequences is a vital topic concerned with recognizing facial expression and linking them with the underlying emotional states. There have been a number of techniques proposed underpinned with handcrafted features, as well as those that rely on deep learning to elaborate the useful features. As both of these approaches have certain benefits and limitations, in this work we propose to combine the features learned by a long short-term memory network with the features handcrafted to capture the dynamics of facial action units. The results of our experiments indicate that the proposed solution is more effective than the baseline techniques and it allows for assessing the smile genuineness from video sequences in real-time.", "published": "2025-03-20T13:24:02Z", "authors": ["Benedykt Pawlus", "Bogdan Smolka", "Jolanta Kawulok", "Michal Kawulok"], "categories": ["cs.CV"], "doi": "10.1117/12.3013815", "_query": "real estate valuation deep learning", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2106.07214v4", "title": "Backdoor Learning Curves: Explaining Backdoor Poisoning Beyond Influence Functions", "abstract": "Backdoor attacks inject poisoning samples during training, with the goal of forcing a machine learning model to output an attacker-chosen class when presented a specific trigger at test time. Although backdoor attacks have been demonstrated in a variety of settings and against different models, the factors affecting their effectiveness are still not well understood. In this work, we provide a unifying framework to study the process of backdoor learning under the lens of incremental learning and influence functions. We show that the effectiveness of backdoor attacks depends on: (i) the complexity of the learning algorithm, controlled by its hyperparameters; (ii) the fraction of backdoor samples injected into the training set; and (iii) the size and visibility of the backdoor trigger. These factors affect how fast a model learns to correlate the presence of the backdoor trigger with the target class. Our analysis unveils the intriguing existence of a region in the hyperparameter space in which the accuracy on clean test samples is still high while backdoor attacks are ineffective, thereby suggesting novel criteria to improve existing defenses.", "published": "2021-06-14T08:00:48Z", "authors": ["Antonio Emanuele Cinà", "Kathrin Grosse", "Sebastiano Vascon", "Ambra Demontis", "Battista Biggio", "Fabio Roli", "Marcello Pelillo"], "categories": ["cs.LG", "cs.CR"], "doi": "10.1007/s13042-024-02363-5", "_query": "real estate valuation deep learning", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2011.14696v2", "title": "On Initial Pools for Deep Active Learning", "abstract": "Active Learning (AL) techniques aim to minimize the training data required to train a model for a given task. Pool-based AL techniques start with a small initial labeled pool and then iteratively pick batches of the most informative samples for labeling. Generally, the initial pool is sampled randomly and labeled to seed the AL iterations. While recent studies have focused on evaluating the robustness of various query functions in AL, little to no attention has been given to the design of the initial labeled pool for deep active learning. Given the recent successes of learning representations in self-supervised/unsupervised ways, we study if an intelligently sampled initial labeled pool can improve deep AL performance. We investigate the effect of intelligently sampled initial labeled pools, including the use of self-supervised and unsupervised strategies, on deep AL methods. The setup, hypotheses, methodology, and implementation details were evaluated by peer review before experiments were conducted. Experimental results could not conclusively prove that intelligently sampled initial pools are better for AL than random initial pools in the long run, although a Variational Autoencoder-based initial pool sampling strategy showed interesting trends that merit deeper investigation.", "published": "2020-11-30T11:22:31Z", "authors": ["Akshay L Chandra", "Sai Vikas Desai", "Chaitanya Devaguptapu", "Vineeth N Balasubramanian"], "categories": ["cs.LG", "cs.CV"], "doi": "", "_query": "real estate valuation deep learning", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2607.25123v1", "title": "Endpoint Replay: Compressing the Recency Buffer in Deep Reinforcement Learning", "abstract": "Experience replay remains one of the most practical and useful algorithmic tools in the deep reinforcement learning (DRL) toolbox. Aside from the limited success of prioritized replay and specialized approaches for large asynchronous systems, most DRL algorithms make use of a large, uniformly sampled recency buffer---even the size, one million, remains unchanged. Could we store less data, reduce redundancy, or more effectively chain experience together to speed up value propagation and still retain the performance of large buffers? In this paper, we investigate a simple compression approach that stores representative transitions derived from the end-points of a chain of connected $n$-step sequences. By curating these end-points in a smaller recency buffer, our method maintains an effective memory horizon comparable to a standard large buffer while requiring an order of magnitude less storage. Through empirical evaluation, we demonstrate that this approach prevents the systematic bias inherent in naive compression strategies and matches the performance of traditional large buffers in the Pinball environment and the Atari 2600 benchmark.", "published": "2026-07-27T22:33:09Z", "authors": ["Parham Mohammad Panahi", "Armin Ashrafi", "Haoyu Du", "Andrew Patterson", "Martha White", "Adam White"], "categories": ["cs.LG"], "doi": "", "_query": "real estate valuation deep learning", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2209.07921v2", "title": "ImDrug: A Benchmark for Deep Imbalanced Learning in AI-aided Drug Discovery", "abstract": "The last decade has witnessed a prosperous development of computational methods and dataset curation for AI-aided drug discovery (AIDD). However, real-world pharmaceutical datasets often exhibit highly imbalanced distribution, which is overlooked by the current literature but may severely compromise the fairness and generalization of machine learning applications. Motivated by this observation, we introduce ImDrug, a comprehensive benchmark with an open-source Python library which consists of 4 imbalance settings, 11 AI-ready datasets, 54 learning tasks and 16 baseline algorithms tailored for imbalanced learning. It provides an accessible and customizable testbed for problems and solutions spanning a broad spectrum of the drug discovery pipeline such as molecular modeling, drug-target interaction and retrosynthesis. We conduct extensive empirical studies with novel evaluation metrics, to demonstrate that the existing algorithms fall short of solving medicinal and pharmaceutical challenges in the data imbalance scenario. We believe that ImDrug opens up avenues for future research and development, on real-world challenges at the intersection of AIDD and deep imbalanced learning.", "published": "2022-09-16T13:35:57Z", "authors": ["Lanqing Li", "Liang Zeng", "Ziqi Gao", "Shen Yuan", "Yatao Bian", "Bingzhe Wu", "Hengtong Zhang", "Yang Yu", "Chan Lu", "Zhipeng Zhou", "Hongteng Xu", "Jia Li", "Peilin Zhao", "Pheng-Ann Heng"], "categories": ["cs.LG", "cs.AI", "q-bio.QM"], "doi": "", "_query": "real estate valuation deep learning", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2102.07838v2", "title": "A Comparison of Deep-Learning Methods for Analysing and Predicting Business Processes", "abstract": "Deep-learning models such as Convolutional Neural Networks (CNN) and Long Short-Term Memory (LSTM) have been successfully used for process-mining tasks. They have achieved better performance for different predictive tasks than traditional approaches. We extend the existing body of research by testing four different variants of Graph Neural Networks (GNN) and a fully connected Multi-layer Perceptron (MLP) with dropout for the tasks of predicting the nature and timestamp of the next process activity. In contrast to existing studies, we evaluate our models' performance at different stages of a process, determined by quartiles of the number of events and normalized quarters of the case duration. This provides new insights into the performance of a prediction model, as they behave differently at different stages of a business-process. Interestingly, our experiments show that the simple MLP often outperforms more sophisticated deep-learning models in both prediction tasks. We argue that care needs to be taken when applying automated process-prediction techniques at different stages of a process. We further argue that researchers should reflect their results with strong baselines methods like simple MLPs.", "published": "2021-02-11T21:10:30Z", "authors": ["Ishwar Venugopal", "Jessica Töllich", "Michael Fairbank", "Ansgar Scherp"], "categories": ["cs.LG", "cs.AI"], "doi": "", "_query": "real estate valuation deep learning", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/1910.05437v1", "title": "Roweis Discriminant Analysis: A Generalized Subspace Learning Method", "abstract": "We present a new method which generalizes subspace learning based on eigenvalue and generalized eigenvalue problems. This method, Roweis Discriminant Analysis (RDA), is named after Sam Roweis to whom the field of subspace learning owes significantly. RDA is a family of infinite number of algorithms where Principal Component Analysis (PCA), Supervised PCA (SPCA), and Fisher Discriminant Analysis (FDA) are special cases. One of the extreme special cases, which we name Double Supervised Discriminant Analysis (DSDA), uses the labels twice; it is novel and has not appeared elsewhere. We propose a dual for RDA for some special cases. We also propose kernel RDA, generalizing kernel PCA, kernel SPCA, and kernel FDA, using both dual RDA and representation theory. Our theoretical analysis explains previously known facts such as why SPCA can use regression but FDA cannot, why PCA and SPCA have duals but FDA does not, why kernel PCA and kernel SPCA use kernel trick but kernel FDA does not, and why PCA is the best linear method for reconstruction. Roweisfaces and kernel Roweisfaces are also proposed generalizing eigenfaces, Fisherfaces, supervised eigenfaces, and their kernel variants. We also report experiments showing the effectiveness of RDA and kernel RDA on some benchmark datasets.", "published": "2019-10-11T23:14:09Z", "authors": ["Benyamin Ghojogh", "Fakhri Karray", "Mark Crowley"], "categories": ["stat.ML", "cs.CV", "cs.LG"], "doi": "10.1007/978-3-030-50347-5_29", "_query": "real estate valuation deep learning", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/1706.01350v3", "title": "Emergence of Invariance and Disentanglement in Deep Representations", "abstract": "Using established principles from Statistics and Information Theory, we show that invariance to nuisance factors in a deep neural network is equivalent to information minimality of the learned representation, and that stacking layers and injecting noise during training naturally bias the network towards learning invariant representations. We then decompose the cross-entropy loss used during training and highlight the presence of an inherent overfitting term. We propose regularizing the loss by bounding such a term in two equivalent ways: One with a Kullbach-Leibler term, which relates to a PAC-Bayes perspective; the other using the information in the weights as a measure of complexity of a learned model, yielding a novel Information Bottleneck for the weights. Finally, we show that invariance and independence of the components of the representation learned by the network are bounded above and below by the information in the weights, and therefore are implicitly optimized during training. The theory enables us to quantify and predict sharp phase transitions between underfitting and overfitting of random labels when using our regularized loss, which we verify in experiments, and sheds light on the relation between the geometry of the loss function, invariance properties of the learned representation, and generalization error.", "published": "2017-06-05T14:31:03Z", "authors": ["Alessandro Achille", "Stefano Soatto"], "categories": ["cs.LG", "cs.AI", "stat.ML"], "doi": "", "_query": "real estate valuation deep learning", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2408.02045v1", "title": "DNA-SE: Towards Deep Neural-Nets Assisted Semiparametric Estimation", "abstract": "Semiparametric statistics play a pivotal role in a wide range of domains, including but not limited to missing data, causal inference, and transfer learning, to name a few. In many settings, semiparametric theory leads to (nearly) statistically optimal procedures that yet involve numerically solving Fredholm integral equations of the second kind. Traditional numerical methods, such as polynomial or spline approximations, are difficult to scale to multi-dimensional problems. Alternatively, statisticians may choose to approximate the original integral equations by ones with closed-form solutions, resulting in computationally more efficient, but statistically suboptimal or even incorrect procedures. To bridge this gap, we propose a novel framework by formulating the semiparametric estimation problem as a bi-level optimization problem; and then we develop a scalable algorithm called Deep Neural-Nets Assisted Semiparametric Estimation (DNA-SE) by leveraging the universal approximation property of Deep Neural-Nets (DNN) to streamline semiparametric procedures. Through extensive numerical experiments and a real data analysis, we demonstrate the numerical and statistical advantages of $\\dnase$ over traditional methods. To the best of our knowledge, we are the first to bring DNN into semiparametric statistics as a numerical solver of integral equations in our proposed general framework.", "published": "2024-08-04T14:45:26Z", "authors": ["Qinshuo Liu", "Zixin Wang", "Xi-An Li", "Xinyao Ji", "Lei Zhang", "Lin Liu", "Zhonghua Liu"], "categories": ["stat.ML", "cs.LG"], "doi": "", "_query": "real estate valuation deep learning", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/1905.10988v3", "title": "Natural Compression for Distributed Deep Learning", "abstract": "Modern deep learning models are often trained in parallel over a collection of distributed machines to reduce training time. In such settings, communication of model updates among machines becomes a significant performance bottleneck and various lossy update compression techniques have been proposed to alleviate this problem. In this work, we introduce a new, simple yet theoretically and practically effective compression technique: natural compression (NC). Our technique is applied individually to all entries of the to-be-compressed update vector and works by randomized rounding to the nearest (negative or positive) power of two, which can be computed in a \"natural\" way by ignoring the mantissa. We show that compared to no compression, NC increases the second moment of the compressed vector by not more than the tiny factor $\\frac{9}{8}$, which means that the effect of NC on the convergence speed of popular training algorithms, such as distributed SGD, is negligible. However, the communications savings enabled by NC are substantial, leading to $3$-$4\\times$ improvement in overall theoretical running time. For applications requiring more aggressive compression, we generalize NC to natural dithering, which we prove is exponentially better than the common random dithering technique. Our compression operators can be used on their own or in combination with existing operators for a more aggressive combined effect and offer new state-of-the-art both in theory and practice.", "published": "2019-05-27T06:10:59Z", "authors": ["Samuel Horvath", "Chen-Yu Ho", "Ludovit Horvath", "Atal Narayan Sahu", "Marco Canini", "Peter Richtarik"], "categories": ["cs.LG", "math.OC", "stat.ML"], "doi": "", "_query": "real estate valuation deep learning", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/1909.01500v2", "title": "rlpyt: A Research Code Base for Deep Reinforcement Learning in PyTorch", "abstract": "Since the recent advent of deep reinforcement learning for game play and simulated robotic control, a multitude of new algorithms have flourished. Most are model-free algorithms which can be categorized into three families: deep Q-learning, policy gradients, and Q-value policy gradients. These have developed along separate lines of research, such that few, if any, code bases incorporate all three kinds. Yet these algorithms share a great depth of common deep reinforcement learning machinery. We are pleased to share rlpyt, which implements all three algorithm families on top of a shared, optimized infrastructure, in a single repository. It contains modular implementations of many common deep RL algorithms in Python using PyTorch, a leading deep learning library. rlpyt is designed as a high-throughput code base for small- to medium-scale research in deep RL. This white paper summarizes its features, algorithms implemented, and relation to prior work, and concludes with detailed implementation and usage notes. rlpyt is available at https://github.com/astooke/rlpyt.", "published": "2019-09-03T23:57:13Z", "authors": ["Adam Stooke", "Pieter Abbeel"], "categories": ["cs.LG", "cs.AI"], "doi": "", "_query": "real estate valuation deep learning", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/1810.05157v4", "title": "Learning under Misspecified Objective Spaces", "abstract": "Learning robot objective functions from human input has become increasingly important, but state-of-the-art techniques assume that the human's desired objective lies within the robot's hypothesis space. When this is not true, even methods that keep track of uncertainty over the objective fail because they reason about which hypothesis might be correct, and not whether any of the hypotheses are correct. We focus specifically on learning from physical human corrections during the robot's task execution, where not having a rich enough hypothesis space leads to the robot updating its objective in ways that the person did not actually intend. We observe that such corrections appear irrelevant to the robot, because they are not the best way of achieving any of the candidate objectives. Instead of naively trusting and learning from every human interaction, we propose robots learn conservatively by reasoning in real time about how relevant the human's correction is for the robot's hypothesis space. We test our inference method in an experiment with human interaction data, and demonstrate that this alleviates unintended learning in an in-person user study with a 7DoF robot manipulator.", "published": "2018-10-11T17:58:27Z", "authors": ["Andreea Bobu", "Andrea Bajcsy", "Jaime F. Fisac", "Anca D. Dragan"], "categories": ["cs.LG", "cs.AI", "cs.HC", "cs.RO", "stat.ML"], "doi": "", "_query": "real estate valuation deep learning", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2207.03820v2", "title": "Deep Learning for Anomaly Detection in Log Data: A Survey", "abstract": "Automatic log file analysis enables early detection of relevant incidents such as system failures. In particular, self-learning anomaly detection techniques capture patterns in log data and subsequently report unexpected log event occurrences to system operators without the need to provide or manually model anomalous scenarios in advance. Recently, an increasing number of approaches leveraging deep learning neural networks for this purpose have been presented. These approaches have demonstrated superior detection performance in comparison to conventional machine learning techniques and simultaneously resolve issues with unstable data formats. However, there exist many different architectures for deep learning and it is non-trivial to encode raw and unstructured log data to be analyzed by neural networks. We therefore carry out a systematic literature review that provides an overview of deployed models, data pre-processing mechanisms, anomaly detection techniques, and evaluations. The survey does not quantitatively compare existing approaches but instead aims to help readers understand relevant aspects of different model architectures and emphasizes open issues for future work.", "published": "2022-07-08T10:58:28Z", "authors": ["Max Landauer", "Sebastian Onder", "Florian Skopik", "Markus Wurzenberger"], "categories": ["cs.LG"], "doi": "10.1016/j.mlwa.2023.100470", "_query": "real estate valuation deep learning", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/1710.10006v1", "title": "Deep Learning for Accelerated Ultrasound Imaging", "abstract": "In portable, 3-D, or ultra-fast ultrasound (US) imaging systems, there is an increasing demand to reconstruct high quality images from limited number of data. However, the existing solutions require either hardware changes or computationally expansive algorithms. To overcome these limitations, here we propose a novel deep learning approach that interpolates the missing RF data by utilizing the sparsity of the RF data in the Fourier domain. Extensive experimental results from sub-sampled RF data from a real US system confirmed that the proposed method can effectively reduce the data rate without sacrificing the image quality.", "published": "2017-10-27T06:49:37Z", "authors": ["Yeo Hun Yoon", "Jong Chul Ye"], "categories": ["cs.CV", "cs.LG", "stat.ML"], "doi": "", "_query": "real estate valuation deep learning", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2602.19770v2", "title": "The Confusion is Real: GRAPHIC -- A Network Science Approach to Confusion Matrices in Deep Learning", "abstract": "Explainable artificial intelligence has emerged as a promising field of research to address reliability concerns in artificial intelligence. Despite significant progress in explainable artificial intelligence, few methods provide a systematic way to visualize and understand how classes are confused and how their relationships evolve as training progresses. In this work, we present GRAPHIC, an architecture-agnostic approach that analyzes neural networks on a class level. It leverages confusion matrices derived from intermediate layers using linear classifiers. We interpret these as adjacency matrices of directed graphs, allowing tools from network science to visualize and quantify learning dynamics across training epochs and intermediate layers. GRAPHIC provides insights into linear class separability, dataset issues, and architectural behavior, revealing, for example, similarities between flatfish and man and labeling ambiguities validated in a human study. In summary, by uncovering real confusions, GRAPHIC offers new perspectives on how neural networks learn. The code is available at https://github.com/Johanna-S-Froehlich/GRAPHIC.", "published": "2026-02-23T12:20:37Z", "authors": ["Johanna S. Fröhlich", "Bastian Heinlein", "Jan U. Claar", "Hans Rosenberger", "Vasileios Belagiannis", "Ralf R. Müller"], "categories": ["cs.LG", "cs.AI"], "doi": "", "_query": "real estate valuation deep learning", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/1906.01101v1", "title": "MEMe: An Accurate Maximum Entropy Method for Efficient Approximations in Large-Scale Machine Learning", "abstract": "Efficient approximation lies at the heart of large-scale machine learning problems. In this paper, we propose a novel, robust maximum entropy algorithm, which is capable of dealing with hundreds of moments and allows for computationally efficient approximations. We showcase the usefulness of the proposed method, its equivalence to constrained Bayesian variational inference and demonstrate its superiority over existing approaches in two applications, namely, fast log determinant estimation and information-theoretic Bayesian optimisation.", "published": "2019-06-03T22:10:52Z", "authors": ["Diego Granziol", "Binxin Ru", "Stefan Zohren", "Xiaowen Doing", "Michael Osborne", "Stephen Roberts"], "categories": ["stat.ML", "cs.LG"], "doi": "10.3390/e21060551", "_query": "real estate valuation deep learning", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2112.08364v3", "title": "Data Valuation for Vertical Federated Learning: A Model-free and Privacy-preserving Method", "abstract": "Vertical Federated learning (VFL) is a promising paradigm for predictive analytics, empowering an organization (i.e., task party) to enhance its predictive models through collaborations with multiple data suppliers (i.e., data parties) in a decentralized and privacy-preserving way. Despite the fast-growing interest in VFL, the lack of effective and secure tools for assessing the value of data owned by data parties hinders the application of VFL in business contexts. In response, we propose FedValue, a privacy-preserving, task-specific but model-free data valuation method for VFL, which consists of a data valuation metric and a federated computation method. Specifically, we first introduce a novel data valuation metric, namely MShapley-CMI. The metric evaluates a data party's contribution to a predictive analytics task without the need of executing a machine learning model, making it well-suited for real-world applications of VFL. Next, we develop an innovative federated computation method that calculates the MShapley-CMI value for each data party in a privacy-preserving manner. Extensive experiments conducted on six public datasets validate the efficacy of FedValue for data valuation in the context of VFL. In addition, we illustrate the practical utility of FedValue with a case study involving federated movie recommendations.", "published": "2021-12-15T02:42:28Z", "authors": ["Xiao Han", "Leye Wang", "Junjie Wu", "Xiao Fang"], "categories": ["cs.LG", "cs.AI"], "doi": "", "_query": "real estate valuation deep learning", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2202.02674v4", "title": "Beurling's Theorem for Valuation Hilbert Modules and Several Complex Variables", "abstract": "We develop a theory of Valuation Hilbert Modules and prove a version of Beurling's theorem for these. Then we apply our version of Beurling's theorem to obtain complete descriptions of the closed invariant subspaces of a number of Hilbert spaces of analytic functions in several complex variables, including $H^2$ of the polydisk the ball, and bounded symmetric domains, and weighted $A^2$ spaces on complex analytic manifolds.", "published": "2022-02-06T01:36:56Z", "authors": ["Charles W. Neville"], "categories": ["math.CV"], "doi": "", "_query": "real estate valuation deep learning", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2502.07409v5", "title": "MGPATH: Vision-Language Model with Multi-Granular Prompt Learning for Few-Shot WSI Classification", "abstract": "Whole slide pathology image classification presents challenges due to gigapixel image sizes and limited annotation labels, hindering model generalization. This paper introduces a prompt learning method to adapt large vision-language models for few-shot pathology classification. We first extend the Prov-GigaPath vision foundation model, pre-trained on 1.3 billion pathology image tiles, into a vision-language model by adding adaptors and aligning it with medical text encoders via contrastive learning on 923K image-text pairs. The model is then used to extract visual features and text embeddings from few-shot annotations and fine-tunes with learnable prompt embeddings. Unlike prior methods that combine prompts with frozen features using prefix embeddings or self-attention, we propose multi-granular attention that compares interactions between learnable prompts with individual image patches and groups of them. This approach improves the model's ability to capture both fine-grained details and broader context, enhancing its recognition of complex patterns across sub-regions. To further improve accuracy, we leverage (unbalanced) optimal transport-based visual-text distance to secure model robustness by mitigating perturbations that might occur during the data augmentation process. Empirical experiments on lung, kidney, and breast pathology modalities validate the effectiveness of our approach; thereby, we surpass several of the latest competitors and consistently improve performance across diverse architectures, including CLIP, PLIP, and Prov-GigaPath integrated PLIP.", "published": "2025-02-11T09:42:13Z", "authors": ["Anh-Tien Nguyen", "Duy Minh Ho Nguyen", "Nghiem Tuong Diep", "Trung Quoc Nguyen", "Nhat Ho", "Jacqueline Michelle Metsch", "Miriam Cindy Maurer", "Daniel Sonntag", "Hanibal Bohnenberger", "Anne-Christin Hauschild"], "categories": ["cs.CV", "cs.LG"], "doi": "", "_query": "real estate valuation deep learning", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2605.10546v2", "title": "Higher Resolution, Better Generalization: Unlocking Visual Scaling in Deep Reinforcement Learning", "abstract": "Pixel-based deep reinforcement learning agents are typically trained on heavily downsampled visual observations, a convention inherited from early benchmarks rather than grounded in principled design. In this work, we show that observation resolution is a critical yet overlooked variable for policy learning: higher-resolution inputs can substantially improve both performance and generalization, provided the network architecture can process them effectively. We find that the widely used Impala encoder, which flattens spatial features into a vector, suffers from quadratic parameter growth as resolution increases and fails to leverage the additional visual detail. We test different modifications to the Impala architecture and conclude that, in particular, introducing a global average pooling layer, as in the Impoola architecture, yields consistent improvements across resolutions and network widths while decoupling parameter count from resolution - at their respective best conditions, visual scaling unlocks a 28 % performance gain for Impoola over Impala. These gains are strongest in environments that require precise perception of small or distant objects, and gradient saliency analysis suggests that the underlying mechanism is a more spatially localized visual attention of the policy at higher resolutions. Our results challenge the prevailing practice of aggressive input downsampling and position resolution-independent architectures as a simple, effective path toward scalable visual deep reinforcement learning.", "published": "2026-05-11T13:23:58Z", "authors": ["Raphael Trumpp", "Ömer Veysel Çağatan", "Barış Akgün", "Marco Caccamo"], "categories": ["cs.LG"], "doi": "", "_query": "real estate valuation deep learning", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2410.10344v1", "title": "Definable henselian valuations on dp-minimal real fields", "abstract": "We give an explicit algebraic characterisation of all definable henselian valuations on a dp-minimal real field. Additionally we characterise all dp-minimal real fields that admit a definable henselian valuation with real closed residue field. We do so by first proving this for the more general setting of almost real closed fields.", "published": "2024-10-14T09:58:39Z", "authors": ["Lothar Sebastian Krapp", "Salma Kuhlmann", "Lasse Vogel"], "categories": ["math.LO", "math.AC"], "doi": "", "_query": "real estate valuation deep learning", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2111.04153v2", "title": "Data-Efficient Deep Reinforcement Learning for Attitude Control of Fixed-Wing UAVs: Field Experiments", "abstract": "Attitude control of fixed-wing unmanned aerial vehicles (UAVs) is a difficult control problem in part due to uncertain nonlinear dynamics, actuator constraints, and coupled longitudinal and lateral motions. Current state-of-the-art autopilots are based on linear control and are thus limited in their effectiveness and performance. Deep reinforcement learning (DRL) is a machine learning method to automatically discover optimal control laws through interaction with the controlled system, which can handle complex nonlinear dynamics. We show in this paper that DRL can successfully learn to perform attitude control of a fixed-wing UAV operating directly on the original nonlinear dynamics, requiring as little as three minutes of flight data. We initially train our model in a simulation environment and then deploy the learned controller on the UAV in flight tests, demonstrating comparable performance to the state-of-the-art ArduPlane proportional-integral-derivative (PID) attitude controller with no further online learning required. Learning with significant actuation delay and diversified simulated dynamics were found to be crucial for successful transfer to control of the real UAV. In addition to a qualitative comparison with the ArduPlane autopilot, we present a quantitative assessment based on linear analysis to better understand the learning controller's behavior.", "published": "2021-11-07T19:07:46Z", "authors": ["Eivind Bøhn", "Erlend M. Coates", "Dirk Reinhardt", "Tor Arne Johansen"], "categories": ["eess.SY", "cs.LG", "cs.RO"], "doi": "10.1109/TNNLS.2023.3263430", "_query": "real estate valuation deep learning", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2006.07262v3", "title": "A Brief Look at Generalization in Visual Meta-Reinforcement Learning", "abstract": "Due to the realization that deep reinforcement learning algorithms trained on high-dimensional tasks can strongly overfit to their training environments, there have been several studies that investigated the generalization performance of these algorithms. However, there has been no similar study that evaluated the generalization performance of algorithms that were specifically designed for generalization, i.e. meta-reinforcement learning algorithms. In this paper, we assess the generalization performance of these algorithms by leveraging high-dimensional, procedurally generated environments. We find that these algorithms can display strong overfitting when they are evaluated on challenging tasks. We also observe that scalability to high-dimensional tasks with sparse rewards remains a significant problem among many of the current meta-reinforcement learning algorithms. With these results, we highlight the need for developing meta-reinforcement learning algorithms that can both generalize and scale.", "published": "2020-06-12T15:17:17Z", "authors": ["Safa Alver", "Doina Precup"], "categories": ["cs.LG", "cs.AI", "stat.ML"], "doi": "", "_query": "real estate valuation deep learning", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2409.12642v1", "title": "Deep generative models as an adversarial attack strategy for tabular machine learning", "abstract": "Deep Generative Models (DGMs) have found application in computer vision for generating adversarial examples to test the robustness of machine learning (ML) systems. Extending these adversarial techniques to tabular ML presents unique challenges due to the distinct nature of tabular data and the necessity to preserve domain constraints in adversarial examples. In this paper, we adapt four popular tabular DGMs into adversarial DGMs (AdvDGMs) and evaluate their effectiveness in generating realistic adversarial examples that conform to domain constraints.", "published": "2024-09-19T10:41:23Z", "authors": ["Salijona Dyrmishi", "Mihaela Cătălina Stoian", "Eleonora Giunchiglia", "Maxime Cordy"], "categories": ["cs.LG", "cs.AI"], "doi": "", "_query": "real estate valuation deep learning", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2010.08141v2", "title": "Autonomous Control of a Particle Accelerator using Deep Reinforcement Learning", "abstract": "We describe an approach to learning optimal control policies for a large, linear particle accelerator using deep reinforcement learning coupled with a high-fidelity physics engine. The framework consists of an AI controller that uses deep neural nets for state and action-space representation and learns optimal policies using reward signals that are provided by the physics simulator. For this work, we only focus on controlling a small section of the entire accelerator. Nevertheless, initial results indicate that we can achieve better-than-human level performance in terms of particle beam current and distribution. The ultimate goal of this line of work is to substantially reduce the tuning time for such facilities by orders of magnitude, and achieve near-autonomous control.", "published": "2020-10-16T04:02:01Z", "authors": ["Xiaoying Pang", "Sunil Thulasidasan", "Larry Rybarcyk"], "categories": ["cs.AI", "cs.LG", "physics.acc-ph"], "doi": "", "_query": "real estate valuation deep learning", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2307.01316v3", "title": "Towards Safe Autonomous Driving Policies using a Neuro-Symbolic Deep Reinforcement Learning Approach", "abstract": "The dynamic nature of driving environments and the presence of diverse road users pose significant challenges for decision-making in autonomous driving. Deep reinforcement learning (DRL) has emerged as a popular approach to tackle this problem. However, the application of existing DRL solutions is mainly confined to simulated environments due to safety concerns, impeding their deployment in real-world. To overcome this limitation, this paper introduces a novel neuro-symbolic model-free DRL approach, called DRL with Symbolic Logic (DRLSL) that combines the strengths of DRL (learning from experience) and symbolic first-order logic (knowledge-driven reasoning) to enable safe learning in real-time interactions of autonomous driving within real environments. This innovative approach provides a means to learn autonomous driving policies by actively engaging with the physical environment while ensuring safety. We have implemented the DRLSL framework in a highway driving scenario using the HighD dataset and demonstrated that our method successfully avoids unsafe actions during both the training and testing phases. Furthermore, our results indicate that DRLSL achieves faster convergence during training and exhibits better generalizability to new highway driving scenarios compared to traditional DRL methods.", "published": "2023-07-03T19:43:21Z", "authors": ["Iman Sharifi", "Mustafa Yildirim", "Saber Fallah"], "categories": ["cs.RO", "cs.AI", "cs.LG", "cs.LO", "eess.SY"], "doi": "10.1177/03611981251357006", "_query": "real estate valuation deep learning", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2306.08638v2", "title": "Your Email Address Holds the Key: Understanding the Connection Between Email and Password Security with Deep Learning", "abstract": "In this work, we investigate the effectiveness of deep-learning-based password guessing models for targeted attacks on human-chosen passwords. In recent years, service providers have increased the level of security of users'passwords. This is done by requiring more complex password generation patterns and by using computationally expensive hash functions. For the attackers this means a reduced number of available guessing attempts, which introduces the necessity to target their guess by exploiting a victim's publicly available information. In this work, we introduce a context-aware password guessing model that better capture attackers'behavior. We demonstrate that knowing a victim's email address is already critical in compromising the associated password and provide an in-depth analysis of the relationship between them. We also show the potential of such models to identify clusters of users based on their password generation behaviour, which can spot fake profiles and populations more vulnerable to context-aware guesses. The code is publicly available at https://github.com/spring-epfl/DCM_sp", "published": "2023-06-14T17:10:56Z", "authors": ["Etienne Salimbeni", "Nina Mainusch", "Dario Pasquini"], "categories": ["cs.CR"], "doi": "", "_query": "real estate valuation deep learning", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/1801.00235v3", "title": "Early detection of Crossfire attacks using deep learning", "abstract": "Crossfire attack is a recently proposed threat designed to disconnect whole geographical areas, such as cities or states, from the Internet. Orchestrated in multiple phases, the attack uses a massively distributed botnet to generate low-rate benign traffic aiming to congest selected network links, so-called target links. The adoption of benign traffic, while simultaneously targeting multiple network links, makes the detection of the Crossfire attack a serious challenge. In this paper, we propose a framework for early detection of Crossfire attack, i.e., detection in the warm-up period of the attack. We propose to monitor traffic at the potential decoy servers and discuss the advantages comparing with other monitoring approaches. Since the low-rate attack traffic is very difficult to distinguish from the background traffic, we investigate several deep learning methods to mine the spatiotemporal features for attack detection. We investigate Autoencoder, Convolutional Neural Network (CNN) and Long Short-Term Memory (LSTM) Network to detect the Crossfire attack during its warm-up period. We report encouraging experiment results.", "published": "2017-12-31T04:29:21Z", "authors": ["Saurabh Misra", "Mengxuan Tan", "Mostafa Rezazad", "Matthias R. Brust", "Ngai-Man Cheung"], "categories": ["cs.CR"], "doi": "", "_query": "real estate valuation deep learning", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2111.05953v1", "title": "Robust Learning via Ensemble Density Propagation in Deep Neural Networks", "abstract": "Learning in uncertain, noisy, or adversarial environments is a challenging task for deep neural networks (DNNs). We propose a new theoretically grounded and efficient approach for robust learning that builds upon Bayesian estimation and Variational Inference. We formulate the problem of density propagation through layers of a DNN and solve it using an Ensemble Density Propagation (EnDP) scheme. The EnDP approach allows us to propagate moments of the variational probability distribution across the layers of a Bayesian DNN, enabling the estimation of the mean and covariance of the predictive distribution at the output of the model. Our experiments using MNIST and CIFAR-10 datasets show a significant improvement in the robustness of the trained models to random noise and adversarial attacks.", "published": "2021-11-10T21:26:08Z", "authors": ["Giuseppina Carannante", "Dimah Dera", "Ghulam Rasool", "Nidhal C. Bouaynaya", "Lyudmila Mihaylova"], "categories": ["cs.LG", "cs.AI", "cs.CV", "math.PR"], "doi": "", "_query": "real estate valuation deep learning", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2001.02658v4", "title": "Distributionally Robust Deep Learning using Hardness Weighted Sampling", "abstract": "Limiting failures of machine learning systems is of paramount importance for safety-critical applications. In order to improve the robustness of machine learning systems, Distributionally Robust Optimization (DRO) has been proposed as a generalization of Empirical Risk Minimization (ERM). However, its use in deep learning has been severely restricted due to the relative inefficiency of the optimizers available for DRO in comparison to the wide-spread variants of Stochastic Gradient Descent (SGD) optimizers for ERM. We propose SGD with hardness weighted sampling, a principled and efficient optimization method for DRO in machine learning that is particularly suited in the context of deep learning. Similar to a hard example mining strategy in practice, the proposed algorithm is straightforward to implement and computationally as efficient as SGD-based optimizers used for deep learning, requiring minimal overhead computation. In contrast to typical ad hoc hard mining approaches, we prove the convergence of our DRO algorithm for over-parameterized deep learning networks with ReLU activation and a finite number of layers and parameters. Our experiments on fetal brain 3D MRI segmentation and brain tumor segmentation in MRI demonstrate the feasibility and the usefulness of our approach. Using our hardness weighted sampling for training a state-of-the-art deep learning pipeline leads to improved robustness to anatomical variabilities in automatic fetal brain 3D MRI segmentation using deep learning and to improved robustness to the image protocol variations in brain tumor segmentation. Our code is available at https://github.com/LucasFidon/HardnessWeightedSampler.", "published": "2020-01-08T18:02:56Z", "authors": ["Lucas Fidon", "Michael Aertsen", "Thomas Deprest", "Doaa Emam", "Frédéric Guffens", "Nada Mufti", "Esther Van Elslander", "Ernst Schwartz", "Michael Ebner", "Daniela Prayer", "Gregor Kasprian", "Anna L. David", "Andrew Melbourne", "Sébastien Ourselin", "Jan Deprest", "Georg Langs", "Tom Vercauteren"], "categories": ["cs.LG", "cs.CV"], "doi": "10.59275/j.melba.2022-8b6a", "_query": "real estate valuation deep learning", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2406.01782v1", "title": "Multi-agent assignment via state augmented reinforcement learning", "abstract": "We address the conflicting requirements of a multi-agent assignment problem through constrained reinforcement learning, emphasizing the inadequacy of standard regularization techniques for this purpose. Instead, we recur to a state augmentation approach in which the oscillation of dual variables is exploited by agents to alternate between tasks. In addition, we coordinate the actions of the multiple agents acting on their local states through these multipliers, which are gossiped through a communication network, eliminating the need to access other agent states. By these means, we propose a distributed multi-agent assignment protocol with theoretical feasibility guarantees that we corroborate in a monitoring numerical experiment.", "published": "2024-06-03T20:56:12Z", "authors": ["Leopoldo Agorio", "Sean Van Alen", "Miguel Calvo-Fullana", "Santiago Paternain", "Juan Andres Bazerque"], "categories": ["eess.SY", "cs.AI", "cs.LG", "cs.MA"], "doi": "", "_query": "real estate valuation deep learning", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2409.18574v2", "title": "Climate Adaptation with Reinforcement Learning: Experiments with Flooding and Transportation in Copenhagen", "abstract": "Due to climate change the frequency and intensity of extreme rainfall events, which contribute to urban flooding, are expected to increase in many places. These floods can damage transport infrastructure and disrupt mobility, highlighting the need for cities to adapt to escalating risks. Reinforcement learning (RL) serves as a powerful tool for uncovering optimal adaptation strategies, determining how and where to deploy adaptation measures effectively, even under significant uncertainty. In this study, we leverage RL to identify the most effective timing and locations for implementing measures, aiming to reduce both direct and indirect impacts of flooding. Our framework integrates climate change projections of future rainfall events and floods, models city-wide motorized trips, and quantifies direct and indirect impacts on infrastructure and mobility. Preliminary results suggest that our RL-based approach can significantly enhance decision-making by prioritizing interventions in specific urban areas and identifying the optimal periods for their implementation. Our framework is publicly available: \\url{https://github.com/MLSM-at-DTU/floods_transport_rl}.", "published": "2024-09-27T09:18:57Z", "authors": ["Miguel Costa", "Morten W. Petersen", "Arthur Vandervoort", "Martin Drews", "Karyn Morrissey", "Francisco C. Pereira"], "categories": ["cs.LG"], "doi": "", "_query": "real estate valuation deep learning", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/1902.03701v1", "title": "Generalization through Simulation: Integrating Simulated and Real Data into Deep Reinforcement Learning for Vision-Based Autonomous Flight", "abstract": "Deep reinforcement learning provides a promising approach for vision-based control of real-world robots. However, the generalization of such models depends critically on the quantity and variety of data available for training. This data can be difficult to obtain for some types of robotic systems, such as fragile, small-scale quadrotors. Simulated rendering and physics can provide for much larger datasets, but such data is inherently of lower quality: many of the phenomena that make the real-world autonomous flight problem challenging, such as complex physics and air currents, are modeled poorly or not at all, and the systematic differences between simulation and the real world are typically impossible to eliminate. In this work, we investigate how data from both simulation and the real world can be combined in a hybrid deep reinforcement learning algorithm. Our method uses real-world data to learn about the dynamics of the system, and simulated data to learn a generalizable perception system that can enable the robot to avoid collisions using only a monocular camera. We demonstrate our approach on a real-world nano aerial vehicle collision avoidance task, showing that with only an hour of real-world data, the quadrotor can avoid collisions in new environments with various lighting conditions and geometry. Code, instructions for building the aerial vehicles, and videos of the experiments can be found at github.com/gkahn13/GtS", "published": "2019-02-11T01:45:53Z", "authors": ["Katie Kang", "Suneel Belkhale", "Gregory Kahn", "Pieter Abbeel", "Sergey Levine"], "categories": ["cs.LG", "cs.RO", "stat.ML"], "doi": "", "_query": "real estate valuation deep learning", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/1902.08153v3", "title": "Learned Step Size Quantization", "abstract": "Deep networks run with low precision operations at inference time offer power and space advantages over high precision alternatives, but need to overcome the challenge of maintaining high accuracy as precision decreases. Here, we present a method for training such networks, Learned Step Size Quantization, that achieves the highest accuracy to date on the ImageNet dataset when using models, from a variety of architectures, with weights and activations quantized to 2-, 3- or 4-bits of precision, and that can train 3-bit models that reach full precision baseline accuracy. Our approach builds upon existing methods for learning weights in quantized networks by improving how the quantizer itself is configured. Specifically, we introduce a novel means to estimate and scale the task loss gradient at each weight and activation layer's quantizer step size, such that it can be learned in conjunction with other network parameters. This approach works using different levels of precision as needed for a given system and requires only a simple modification of existing training code.", "published": "2019-02-21T17:31:32Z", "authors": ["Steven K. Esser", "Jeffrey L. McKinstry", "Deepika Bablani", "Rathinakumar Appuswamy", "Dharmendra S. Modha"], "categories": ["cs.LG", "stat.ML"], "doi": "", "_query": "real estate valuation deep learning", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2201.08281v4", "title": "Symplectic Momentum Neural Networks -- Using Discrete Variational Mechanics as a prior in Deep Learning", "abstract": "With deep learning gaining attention from the research community for prediction and control of real physical systems, learning important representations is becoming now more than ever mandatory. It is of extreme importance that deep learning representations are coherent with physics. When learning from discrete data this can be guaranteed by including some sort of prior into the learning, however, not all discretization priors preserve important structures from the physics. In this paper, we introduce Symplectic Momentum Neural Networks (SyMo) as models from a discrete formulation of mechanics for non-separable mechanical systems. The combination of such formulation leads SyMos to be constrained towards preserving important geometric structures such as momentum and a symplectic form and learn from limited data. Furthermore, it allows to learn dynamics only from the poses as training data. We extend SyMos to include variational integrators within the learning framework by developing an implicit root-find layer which leads to End-to-End Symplectic Momentum Neural Networks (E2E-SyMo). Through experimental results, using the pendulum and cartpole, we show that such combination not only allows these models to learn from limited data but also provides the models with the capability of preserving the symplectic form and show better long-term behaviour.", "published": "2022-01-20T16:33:19Z", "authors": ["Saul Santos", "Monica Ekal", "Rodrigo Ventura"], "categories": ["cs.LG"], "doi": "", "_query": "real estate valuation deep learning", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/1903.01969v1", "title": "PDP: A General Neural Framework for Learning Constraint Satisfaction Solvers", "abstract": "There have been recent efforts for incorporating Graph Neural Network models for learning full-stack solvers for constraint satisfaction problems (CSP) and particularly Boolean satisfiability (SAT). Despite the unique representational power of these neural embedding models, it is not clear how the search strategy in the learned models actually works. On the other hand, by fixing the search strategy (e.g. greedy search), we would effectively deprive the neural models of learning better strategies than those given. In this paper, we propose a generic neural framework for learning CSP solvers that can be described in terms of probabilistic inference and yet learn search strategies beyond greedy search. Our framework is based on the idea of propagation, decimation and prediction (and hence the name PDP) in graphical models, and can be trained directly toward solving CSP in a fully unsupervised manner via energy minimization, as shown in the paper. Our experimental results demonstrate the effectiveness of our framework for SAT solving compared to both neural and the state-of-the-art baselines.", "published": "2019-03-05T18:26:33Z", "authors": ["Saeed Amizadeh", "Sergiy Matusevych", "Markus Weimer"], "categories": ["cs.LG", "cs.LO", "cs.NE", "stat.ML"], "doi": "", "_query": "real estate valuation deep learning", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2506.07459v5", "title": "ProteinZero: Self-Improving Protein Generation via Online Reinforcement Learning", "abstract": "Protein generative models have shown remarkable promise in protein design, yet their success rates remain constrained by reliance on curated sequence-structure datasets and by misalignment between supervised objectives and real design goals. We present ProteinZero, an online reinforcement learning framework for inverse folding models that enables scalable, automated, and continuous self-improvement with computationally efficient feedback. ProteinZero employs a reward pipeline that combines structural guidance from ESMFold with a novel self-derived ddG predictor, providing stable multi-objective signals while avoiding the prohibitive cost of physics-based methods. To ensure robustness in online RL, we further introduce a novel embedding-level diversity regularizer that mitigates mode collapse and promotes sequence-level diversity among generated designs. Within a general RL formulation balancing multi-reward optimization, KL-divergence from a reference model, and diversity regularization, ProteinZero achieves robust improvements across designability, predicted stability, recovery, and diversity. On the CATH-4.3 benchmark, it consistently outperforms state-of-the-art baselines including ProteinMPNN, ESM-IF, and InstructPLM, reducing design failure rates by 36-48% and achieving success rates above 90% across diverse folds. Importantly, a complete RL run can be executed on a single 8xGPU node within three days, including reward computation and data generation. These results indicate that efficient online RL fine-tuning can complement supervised pretraining by allowing protein generative models to evolve continuously from their own outputs and optimize multiple design objectives without labeled data, opening new possibilities for exploring the vast protein design space. Code and model checkpoints are available at https://github.com/ziwenwang28/ProteinZero.", "published": "2025-06-09T06:08:59Z", "authors": ["Ziwen Wang", "Jiajun Fan", "Ruihan Guo", "Thao Nguyen", "Heng Ji", "Ge Liu"], "categories": ["cs.LG", "q-bio.QM"], "doi": "", "_query": "real estate valuation deep learning", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2410.02605v5", "title": "Policy Gradients for Cumulative Prospect Theory in Reinforcement Learning", "abstract": "We derive a policy gradient theorem for Cumulative Prospect Theory (CPT) objectives in finite-horizon Reinforcement Learning (RL), generalizing the standard policy gradient theorem and encompassing distortion-based risk objectives as special cases. Motivated by behavioral economics, CPT combines an asymmetric utility transformation around a reference point with probability distortion. Building on our theorem, we design a first-order policy gradient algorithm for CPT-RL using a Monte Carlo gradient estimator based on order statistics. We establish statistical guarantees for the estimator and prove asymptotic convergence of the resulting algorithm to first-order stationary points of the (generally nonconvex) CPT objective. We complement our asymptotic analysis with a non-asymptotic total sample complexity analysis to reach an approximate first-order stationary policy. Simulations illustrate qualitative behaviors induced by CPT and compare our first-order approach to existing zeroth-order methods.", "published": "2024-10-03T15:45:39Z", "authors": ["Olivier Lepel", "Anas Barakat"], "categories": ["cs.LG", "cs.AI"], "doi": "", "_query": "real estate valuation deep learning", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2303.16963v1", "title": "Fairness-Aware Data Valuation for Supervised Learning", "abstract": "Data valuation is a ML field that studies the value of training instances towards a given predictive task. Although data bias is one of the main sources of downstream model unfairness, previous work in data valuation does not consider how training instances may influence both performance and fairness of ML models. Thus, we propose Fairness-Aware Data vauatiOn (FADO), a data valuation framework that can be used to incorporate fairness concerns into a series of ML-related tasks (e.g., data pre-processing, exploratory data analysis, active learning). We propose an entropy-based data valuation metric suited to address our two-pronged goal of maximizing both performance and fairness, which is more computationally efficient than existing metrics. We then show how FADO can be applied as the basis for unfairness mitigation pre-processing techniques. Our methods achieve promising results -- up to a 40 p.p. improvement in fairness at a less than 1 p.p. loss in performance compared to a baseline -- and promote fairness in a data-centric way, where a deeper understanding of data quality takes center stage.", "published": "2023-03-29T18:51:13Z", "authors": ["José Pombal", "Pedro Saleiro", "Mário A. T. Figueiredo", "Pedro Bizarro"], "categories": ["cs.LG", "cs.CY"], "doi": "", "_query": "real estate valuation deep learning", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2412.13847v2", "title": "A Concept-Centric Approach to Multi-Modality Learning", "abstract": "Humans possess a remarkable ability to acquire knowledge efficiently and apply it across diverse modalities through a coherent and shared understanding of the world. Inspired by this cognitive capability, we introduce a concept-centric multi-modality learning framework built around a modality-agnostic concept space that captures structured, abstract knowledge, alongside a set of modality-specific projection models that map raw inputs onto this shared space. The concept space is decoupled from any specific modality and serves as a repository of universally applicable knowledge. Once learned, the knowledge embedded in the concept space enables more efficient adaptation to new modalities, as projection models can align with existing conceptual representations rather than learning from scratch. This efficiency is empirically validated in our experiments, where the proposed framework exhibits faster convergence compared to baseline models. In addition, the framework's modular design supports seamless integration of new modalities, since projection models are trained independently yet produce unified outputs within the shared concept space. We evaluate the framework on two representative downstream tasks. While the focus is not on task-specific optimization, the framework attains comparable results with a smaller training footprint, no task-specific fine-tuning, and inference performed entirely within a shared space of learned concepts that offers interpretability. These findings point toward a promising direction for developing learning systems that operate in a manner more consistent with human cognitive processes.", "published": "2024-12-18T13:40:21Z", "authors": ["Yuchong Geng", "Ao Tang"], "categories": ["cs.AI", "cs.LG"], "doi": "", "_query": "real estate valuation deep learning", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2209.09658v2", "title": "Lazy vs hasty: linearization in deep networks impacts learning schedule based on example difficulty", "abstract": "Among attempts at giving a theoretical account of the success of deep neural networks, a recent line of work has identified a so-called lazy training regime in which the network can be well approximated by its linearization around initialization. Here we investigate the comparative effect of the lazy (linear) and feature learning (non-linear) regimes on subgroups of examples based on their difficulty. Specifically, we show that easier examples are given more weight in feature learning mode, resulting in faster training compared to more difficult ones. In other words, the non-linear dynamics tends to sequentialize the learning of examples of increasing difficulty. We illustrate this phenomenon across different ways to quantify example difficulty, including c-score, label noise, and in the presence of easy-to-learn spurious correlations. Our results reveal a new understanding of how deep networks prioritize resources across example difficulty.", "published": "2022-09-19T16:10:47Z", "authors": ["Thomas George", "Guillaume Lajoie", "Aristide Baratin"], "categories": ["cs.LG", "stat.ML"], "doi": "", "_query": "real estate valuation deep learning", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2101.05960v1", "title": "DeepWaste: Applying Deep Learning to Waste Classification for a Sustainable Planet", "abstract": "Accurate waste disposal, at the point of disposal, is crucial to fighting climate change. When materials that could be recycled or composted get diverted into landfills, they cause the emission of potent greenhouse gases such as methane. Current attempts to reduce erroneous waste disposal are expensive, inaccurate, and confusing. In this work, we propose DeepWaste, an easy-to-use mobile app, that utilizes highly optimized deep learning techniques to provide users instantaneous waste classification into trash, recycling, and compost. We experiment with several convolution neural network architectures to detect and classify waste items. Our best model, a deep learning residual neural network with 50 layers, achieves an average precision of 0.881 on the test set. We demonstrate the performance and efficiency of our app on a set of real-world images.", "published": "2021-01-15T04:06:25Z", "authors": ["Yash Narayan"], "categories": ["cs.LG"], "doi": "", "_query": "real estate valuation deep learning", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/1605.06391v2", "title": "Deep Multi-task Representation Learning: A Tensor Factorisation Approach", "abstract": "Most contemporary multi-task learning methods assume linear models. This setting is considered shallow in the era of deep learning. In this paper, we present a new deep multi-task representation learning framework that learns cross-task sharing structure at every layer in a deep network. Our approach is based on generalising the matrix factorisation techniques explicitly or implicitly used by many conventional MTL algorithms to tensor factorisation, to realise automatic learning of end-to-end knowledge sharing in deep networks. This is in contrast to existing deep learning approaches that need a user-defined multi-task sharing strategy. Our approach applies to both homogeneous and heterogeneous MTL. Experiments demonstrate the efficacy of our deep multi-task representation learning in terms of both higher accuracy and fewer design choices.", "published": "2016-05-20T15:05:58Z", "authors": ["Yongxin Yang", "Timothy Hospedales"], "categories": ["cs.LG"], "doi": "", "_query": "real estate valuation deep learning", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2108.12916v2", "title": "A Policy Efficient Reduction Approach to Convex Constrained Deep Reinforcement Learning", "abstract": "Although well-established in general reinforcement learning (RL), value-based methods are rarely explored in constrained RL (CRL) for their incapability of finding policies that can randomize among multiple actions. To apply value-based methods to CRL, a recent groundbreaking line of game-theoretic approaches uses the mixed policy that randomizes among a set of carefully generated policies to converge to the desired constraint-satisfying policy. However, these approaches require storing a large set of policies, which is not policy efficient, and may incur prohibitive memory costs in constrained deep RL. To address this problem, we propose an alternative approach. Our approach first reformulates the CRL to an equivalent distance optimization problem. With a specially designed linear optimization oracle, we derive a meta-algorithm that solves it using any off-the-shelf RL algorithm and any conditional gradient (CG) type algorithm as subroutines. We then propose a new variant of the CG-type algorithm, which generalizes the minimum norm point (MNP) method. The proposed method matches the convergence rate of the existing game-theoretic approaches and achieves the worst-case optimal policy efficiency. The experiments on a navigation task show that our method reduces the memory costs by an order of magnitude, and meanwhile achieves better performance, demonstrating both its effectiveness and efficiency.", "published": "2021-08-29T20:51:32Z", "authors": ["Tianchi Cai", "Wenpeng Zhang", "Lihong Gu", "Xiaodong Zeng", "Jinjie Gu"], "categories": ["cs.LG"], "doi": "", "_query": "real estate valuation deep learning", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2511.03618v2", "title": "Towards Formalizing Reinforcement Learning Theory: A Robbins-Siegmund Approach", "abstract": "In this paper, we formalize the almost sure convergence of $Q$-learning and linear temporal difference (TD) learning with Markovian samples using the Lean 4 theorem prover based on the Mathlib library. $Q$-learning and linear TD are among the earliest and most influential reinforcement learning (RL) algorithms. The investigation of their convergence properties is not only a major research topic during the early development of the RL field but also receives significant attention nowadays. This paper formally verifies their almost sure convergence in a unified framework based on the Robbins-Siegmund theorem. The framework developed in this work can potentially be extended to convergence rates and other modes of convergence. This work thus makes an important step towards fully formalizing convergent RL results. The code is available at https://github.com/ShangtongZhang/rl-theory-in-lean.", "published": "2025-11-05T16:35:47Z", "authors": ["Shangtong Zhang"], "categories": ["cs.LG", "stat.ML"], "doi": "", "_query": "real estate valuation deep learning", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/astro-ph/0403628v1", "title": "The VIMOS VLT Deep Survey: Public release of 1599 redshifts to IAB<=24 across the Chandra Deep Field South", "abstract": "{This paper presents the VIMOS VLT Deep Survey around the Chandra Deep Field South (CDFS). We have measured 1599 new redshifts with VIMOS on the European Observatory Very Large Telescope - UT3, in an area 21x21.6 arcmin^2, including 784 redshifts in the Hubble Space Telescope - Advanced Camera for Surveys GOODS area. 30% of all objects with I_AB=24 have been observed independently of magnitude, indicating that the sample is purely magnitude limited. We have reached an unprecedented completeness level of 88% in terms of the ratio of secure measurements vs. observed objects, while 95% of all objects have a redshift measurement. A total of 1452 galaxies, 139 stars, 8 QSOs have a redshift identification, 141 of these being unsecure measurements. The redshift distribution down to I_AB=24 is peaked at a median redshift z=0.73, with a significant high redshift tail extending up to ~4. Several high density peaks in the distribution of galaxies are identified. In particular, the strong peak at z=0.735 contains more than 130 galaxies in a velocity range +/-2000 km/s distributed all across the transverse ~20 h^-1 Mpc of the survey. We are releasing all redshifts to the community, along with the cross identification with HST-ACS GOODS sources on the CENCOS database environment http://cencosw.oamp.fr.", "published": "2004-03-26T18:12:19Z", "authors": ["O. Le Fevre", "G. Vettolani", "S. Paltani", "L. Tresse", "G. Zamorani", "V. Le Brun", "C. Moreau", "VIMOS VLT Deep Survey team"], "categories": ["astro-ph"], "doi": "10.1051/0004-6361:20048072", "_query": "real estate valuation deep learning", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2205.09550v1", "title": "Data Valuation for Offline Reinforcement Learning", "abstract": "The success of deep reinforcement learning (DRL) hinges on the availability of training data, which is typically obtained via a large number of environment interactions. In many real-world scenarios, costs and risks are associated with gathering these data. The field of offline reinforcement learning addresses these issues through outsourcing the collection of data to a domain expert or a carefully monitored program and subsequently searching for a batch-constrained optimal policy. With the emergence of data markets, an alternative to constructing a dataset in-house is to purchase external data. However, while state-of-the-art offline reinforcement learning approaches have shown a lot of promise, they currently rely on carefully constructed datasets that are well aligned with the intended target domains. This raises questions regarding the transferability and robustness of an offline reinforcement learning agent trained on externally acquired data. In this paper, we empirically evaluate the ability of the current state-of-the-art offline reinforcement learning approaches to coping with the source-target domain mismatch within two MuJoCo environments, finding that current state-of-the-art offline reinforcement learning algorithms underperform in the target domain. To address this, we propose data valuation for offline reinforcement learning (DVORL), which allows us to identify relevant and high-quality transitions, improving the performance and transferability of policies learned by offline reinforcement learning algorithms. The results show that our method outperforms offline reinforcement learning baselines on two MuJoCo environments.", "published": "2022-05-19T13:21:40Z", "authors": ["Amir Abolfazli", "Gregory Palmer", "Daniel Kudenko"], "categories": ["cs.LG"], "doi": "", "_query": "real estate valuation deep learning", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2506.06204v2", "title": "How to craft a deep reinforcement learning policy for wind farm flow control", "abstract": "Within wind farms, wake effects between turbines can significantly reduce overall energy production. Wind farm flow control encompasses methods designed to mitigate these effects through coordinated turbine control. Wake steering, for example, consists in intentionally misaligning certain turbines with the wind to optimize airflow and increase power output. However, designing a robust wake steering controller remains challenging, and existing machine learning approaches are limited to quasi-static wind conditions or small wind farms. This work presents a new deep reinforcement learning methodology to develop a wake steering policy that overcomes these limitations. Our approach introduces a novel architecture that combines graph attention networks and multi-head self-attention blocks, alongside a novel reward function and training strategy. The resulting model computes the yaw angles of each turbine, optimizing energy production in time-varying wind conditions. An empirical study conducted on steady-state, low-fidelity simulation, shows that our model requires approximately 10 times fewer training steps than a fully connected neural network and achieves more robust performance compared to a strong optimization baseline, increasing energy production by up to 14 %. To the best of our knowledge, this is the first deep reinforcement learning-based wake steering controller to generalize effectively across any time-varying wind conditions in a low-fidelity, steady-state numerical simulation setting.", "published": "2025-06-06T16:07:05Z", "authors": ["Elie Kadoche", "Pascal Bianchi", "Florence Carton", "Philippe Ciblat", "Damien Ernst"], "categories": ["cs.LG"], "doi": "", "_query": "real estate valuation deep learning", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2004.08546v4", "title": "Towards Non-I.I.D. and Invisible Data with FedNAS: Federated Deep Learning via Neural Architecture Search", "abstract": "Federated Learning (FL) has been proved to be an effective learning framework when data cannot be centralized due to privacy, communication costs, and regulatory restrictions. When training deep learning models under an FL setting, people employ the predefined model architecture discovered in the centralized environment. However, this predefined architecture may not be the optimal choice because it may not fit data with non-identical and independent distribution (non-IID). Thus, we advocate automating federated learning (AutoFL) to improve model accuracy and reduce the manual design effort. We specifically study AutoFL via Neural Architecture Search (NAS), which can automate the design process. We propose a Federated NAS (FedNAS) algorithm to help scattered workers collaboratively searching for a better architecture with higher accuracy. We also build a system based on FedNAS. Our experiments on non-IID dataset show that the architecture searched by FedNAS can outperform the manually predefined architecture.", "published": "2020-04-18T08:04:44Z", "authors": ["Chaoyang He", "Murali Annavaram", "Salman Avestimehr"], "categories": ["cs.LG", "cs.CV", "cs.DC", "cs.MA", "stat.ML"], "doi": "", "_query": "real estate valuation deep learning", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2409.06356v2", "title": "Double Successive Over-Relaxation Q-Learning with an Extension to Deep Reinforcement Learning", "abstract": "Q-learning is a widely used algorithm in reinforcement learning (RL), but its convergence can be slow, especially when the discount factor is close to one. Successive Over-Relaxation (SOR) Q-learning, which introduces a relaxation factor to speed up convergence, addresses this issue but has two major limitations: In the tabular setting, the relaxation parameter depends on transition probability, making it not entirely model-free, and it suffers from overestimation bias. To overcome these limitations, we propose a sample-based, model-free double SOR Q-learning algorithm. Theoretically and empirically, this algorithm is shown to be less biased than SOR Q-learning. Further, in the tabular setting, the convergence analysis under boundedness assumptions on iterates is discussed. The proposed algorithm is extended to large-scale problems using deep RL. Finally, the tabular version of the proposed algorithm is compared using roulette and grid world environments, while the deep RL version is tested on a maximization bias example and OpenAI Gym environments.", "published": "2024-09-10T09:23:03Z", "authors": ["Shreyas S R"], "categories": ["cs.LG", "cs.AI"], "doi": "10.1109/TNNLS.2025.3576581", "_query": "real estate valuation deep learning", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2306.15744v1", "title": "Ticketed Learning-Unlearning Schemes", "abstract": "We consider the learning--unlearning paradigm defined as follows. First given a dataset, the goal is to learn a good predictor, such as one minimizing a certain loss. Subsequently, given any subset of examples that wish to be unlearnt, the goal is to learn, without the knowledge of the original training dataset, a good predictor that is identical to the predictor that would have been produced when learning from scratch on the surviving examples. We propose a new ticketed model for learning--unlearning wherein the learning algorithm can send back additional information in the form of a small-sized (encrypted) ``ticket'' to each participating training example, in addition to retaining a small amount of ``central'' information for later. Subsequently, the examples that wish to be unlearnt present their tickets to the unlearning algorithm, which additionally uses the central information to return a new predictor. We provide space-efficient ticketed learning--unlearning schemes for a broad family of concept classes, including thresholds, parities, intersection-closed classes, among others. En route, we introduce the count-to-zero problem, where during unlearning, the goal is to simply know if there are any examples that survived. We give a ticketed learning--unlearning scheme for this problem that relies on the construction of Sperner families with certain properties, which might be of independent interest.", "published": "2023-06-27T18:54:40Z", "authors": ["Badih Ghazi", "Pritish Kamath", "Ravi Kumar", "Pasin Manurangsi", "Ayush Sekhari", "Chiyuan Zhang"], "categories": ["cs.LG", "cs.DS", "stat.ML"], "doi": "", "_query": "real estate valuation deep learning", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2009.10282v1", "title": "Design of Efficient Deep Learning models for Determining Road Surface Condition from Roadside Camera Images and Weather Data", "abstract": "Road maintenance during the Winter season is a safety critical and resource demanding operation. One of its key activities is determining road surface condition (RSC) in order to prioritize roads and allocate cleaning efforts such as plowing or salting. Two conventional approaches for determining RSC are: visual examination of roadside camera images by trained personnel and patrolling the roads to perform on-site inspections. However, with more than 500 cameras collecting images across Ontario, visual examination becomes a resource-intensive activity, difficult to scale especially during periods of snowstorms. This paper presents the results of a study focused on improving the efficiency of road maintenance operations. We use multiple Deep Learning models to automatically determine RSC from roadside camera images and weather variables, extending previous research where similar methods have been used to deal with the problem. The dataset we use was collected during the 2017-2018 Winter season from 40 stations connected to the Ontario Road Weather Information System (RWIS), it includes 14.000 labeled images and 70.000 weather measurements. We train and evaluate the performance of seven state-of-the-art models from the Computer Vision literature, including the recent DenseNet, NASNet, and MobileNet. Moreover, by following systematic ablation experiments we adapt previously published Deep Learning models and reduce their number of parameters to about ~1.3% compared to their original parameter count, and by integrating observations from weather variables the models are able to better ascertain RSC under poor visibility conditions.", "published": "2020-09-22T02:30:32Z", "authors": ["Juan Carrillo", "Mark Crowley", "Guangyuan Pan", "Liping Fu"], "categories": ["cs.CV", "cs.AI", "eess.IV"], "doi": "", "_query": "real estate valuation deep learning", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2309.16519v4", "title": "AtomSurf : Surface Representation for Learning on Protein Structures", "abstract": "While there has been significant progress in evaluating and comparing different representations for learning on protein data, the role of surface-based learning approaches remains not well-understood. In particular, there is a lack of direct and fair benchmark comparison between the best available surface-based learning methods against alternative representations such as graphs. Moreover, the few existing surface-based approaches either use surface information in isolation or, at best, perform global pooling between surface and graph-based architectures. In this work, we fill this gap by first adapting a state-of-the-art surface encoder for protein learning tasks. We then perform a direct and fair comparison of the resulting method against alternative approaches within the Atom3D benchmark, highlighting the limitations of pure surface-based learning. Finally, we propose an integrated approach, which allows learned feature sharing between graphs and surface representations on the level of nodes and vertices across all layers. We demonstrate that the resulting architecture achieves state-of-the-art results on all tasks in the Atom3D benchmark, while adhering to the strict benchmark protocol, as well as more broadly on binding site identification and binding pocket classification. Furthermore, we use coarsened surfaces and optimize our approach for efficiency, making our tool competitive in training and inference time with existing techniques. Code can be found online: https://github.com/Vincentx15/atomsurf", "published": "2023-09-28T15:25:17Z", "authors": ["Vincent Mallet", "Souhaib Attaiki", "Yangyang Miao", "Bruno Correia", "Maks Ovsjanikov"], "categories": ["cs.LG", "q-bio.BM"], "doi": "", "_query": "real estate valuation deep learning", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2504.20096v1", "title": "Towards Practical Second-Order Optimizers in Deep Learning: Insights from Fisher Information Analysis", "abstract": "First-order optimization methods remain the standard for training deep neural networks (DNNs). Optimizers like Adam incorporate limited curvature information by preconditioning the stochastic gradient with a diagonal matrix. Despite the widespread adoption of first-order methods, second-order optimization algorithms often exhibit superior convergence compared to methods like Adam and SGD. However, their practicality in training DNNs is still limited by a significantly higher per-iteration computational cost compared to first-order methods. In this thesis, we present AdaFisher, a novel adaptive second-order optimizer that leverages a diagonal block-Kronecker approximation of the Fisher information matrix to adaptively precondition gradients. AdaFisher aims to bridge the gap between the improved convergence and generalization of second-order methods and the computational efficiency needed for training DNNs. Despite the traditionally slower speed of second-order optimizers, AdaFisher is effective for tasks such as image classification and language modeling, exhibiting remarkable stability and robustness during hyperparameter tuning. We demonstrate that AdaFisher outperforms state-of-the-art optimizers in both accuracy and convergence speed. The code is available from https://github.com/AtlasAnalyticsLab/AdaFisher.", "published": "2025-04-26T05:02:21Z", "authors": ["Damien Martins Gomes"], "categories": ["cs.LG", "math.OC"], "doi": "", "_query": "real estate valuation deep learning", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/1712.06283v3", "title": "A Bridge Between Hyperparameter Optimization and Learning-to-learn", "abstract": "We consider a class of a nested optimization problems involving inner and outer objectives. We observe that by taking into explicit account the optimization dynamics for the inner objective it is possible to derive a general framework that unifies gradient-based hyperparameter optimization and meta-learning (or learning-to-learn). Depending on the specific setting, the variables of the outer objective take either the meaning of hyperparameters in a supervised learning problem or parameters of a meta-learner. We show that some recently proposed methods in the latter setting can be instantiated in our framework and tackled with the same gradient-based algorithms. Finally, we discuss possible design patterns for learning-to-learn and present encouraging preliminary experiments for few-shot learning.", "published": "2017-12-18T08:02:50Z", "authors": ["Luca Franceschi", "Michele Donini", "Paolo Frasconi", "Massimiliano Pontil"], "categories": ["stat.ML", "cs.LG"], "doi": "", "_query": "real estate valuation deep learning", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2606.17889v1", "title": "Dimensionality Controls When Modularity Helps in Continual Learning", "abstract": "Compositional learning systems must balance plasticity, the ability to acquire new knowledge, with stability, the preservation of previously learned components, especially when tasks share structure and risk interference. We study how modular architecture, task similarity, and representational dimensionality jointly shape compositional continual learning in a sequential A-B-A paradigm, comparing a task-partitioned recurrent network to a single-network baseline while inducing high- and low-dimensional regimes via weight-scale manipulations. In a high-dimensional \"lazy\" regime, both architectures achieve similar performance and internal geometry, suggesting that explicit modular structure has little impact when representations are weakly constrained. In a lower-dimensional \"rich\" regime, modularity becomes decisive: the modular network develops graded task-specific subspaces that overlap for similar tasks, partially align for moderately dissimilar tasks, and separate for dissimilar tasks, yielding a more compositional and interpretable organization than the single network. These findings identify the representational regime induced by initialization scale, which co-varies with representational dimensionality, as a key factor governing when compositional, modular structure is functionally beneficial in continual learning, and support viewing safety and robustness as problems of adaptive allocation of representational subspaces rather than fixed separation versus sharing.", "published": "2026-06-16T13:10:21Z", "authors": ["Kathrin Korte", "Christian Medeiros Adriano", "Joachim Winther Pedersen", "Eleni Nisioti", "Sebastian Risi"], "categories": ["cs.LG", "cs.AI", "cs.NE"], "doi": "", "_query": "real estate valuation deep learning", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2308.08886v2", "title": "Dual Gauss-Newton Directions for Deep Learning", "abstract": "Inspired by Gauss-Newton-like methods, we study the benefit of leveraging the structure of deep learning objectives, namely, the composition of a convex loss function and of a nonlinear network, in order to derive better direction oracles than stochastic gradients, based on the idea of partial linearization. In a departure from previous works, we propose to compute such direction oracles via their dual formulation, leading to both computational benefits and new insights. We demonstrate that the resulting oracles define descent directions that can be used as a drop-in replacement for stochastic gradients, in existing optimization algorithms. We empirically study the advantage of using the dual formulation as well as the computational trade-offs involved in the computation of such oracles.", "published": "2023-08-17T09:44:05Z", "authors": ["Vincent Roulet", "Mathieu Blondel"], "categories": ["cs.LG", "math.OC"], "doi": "", "_query": "real estate valuation deep learning", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2301.02873v1", "title": "\"It's a Match!\" -- A Benchmark of Task Affinity Scores for Joint Learning", "abstract": "While the promises of Multi-Task Learning (MTL) are attractive, characterizing the conditions of its success is still an open problem in Deep Learning. Some tasks may benefit from being learned together while others may be detrimental to one another. From a task perspective, grouping cooperative tasks while separating competing tasks is paramount to reap the benefits of MTL, i.e., reducing training and inference costs. Therefore, estimating task affinity for joint learning is a key endeavor. Recent work suggests that the training conditions themselves have a significant impact on the outcomes of MTL. Yet, the literature is lacking of a benchmark to assess the effectiveness of tasks affinity estimation techniques and their relation with actual MTL performance. In this paper, we take a first step in recovering this gap by (i) defining a set of affinity scores by both revisiting contributions from previous literature as well presenting new ones and (ii) benchmarking them on the Taskonomy dataset. Our empirical campaign reveals how, even in a small-scale scenario, task affinity scoring does not correlate well with actual MTL performance. Yet, some metrics can be more indicative than others.", "published": "2023-01-07T15:16:35Z", "authors": ["Raphael Azorin", "Massimo Gallo", "Alessandro Finamore", "Dario Rossi", "Pietro Michiardi"], "categories": ["cs.LG"], "doi": "", "_query": "real estate valuation deep learning", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/1911.11251v8", "title": "Hexagonal Image Processing in the Context of Machine Learning: Conception of a Biologically Inspired Hexagonal Deep Learning Framework", "abstract": "Inspired by the human visual perception system, hexagonal image processing in the context of machine learning deals with the development of image processing systems that combine the advantages of evolutionary motivated structures based on biological models. While conventional state-of-the-art image processing systems of recording and output devices almost exclusively utilize square arranged methods, their hexagonal counterparts offer a number of key advantages that can benefit both researchers and users. This contribution serves as a general application-oriented approach the synthesis of the therefore designed hexagonal image processing framework, called Hexnet, the processing steps of hexagonal image transformation, and dependent methods. The results of our created test environment show that the realized framework surpasses current approaches of hexagonal image processing systems, while hexagonal artificial neural networks can benefit from the implemented hexagonal architecture. As hexagonal lattice format based deep neural networks, also called H-DNN, can be compared to their square counterparts by transforming classical square lattice based data sets into their hexagonal representation, they can also result in a reduction of trainable parameters as well as result in increased training and test rates.", "published": "2019-11-25T21:58:31Z", "authors": ["Tobias Schlosser", "Michael Friedrich", "Danny Kowerko"], "categories": ["cs.LG", "cs.CV", "eess.IV", "stat.ML"], "doi": "10.1109/ICMLA.2019.00300", "_query": "real estate valuation deep learning", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/1811.01027v2", "title": "AiDroid: When Heterogeneous Information Network Marries Deep Neural Network for Real-time Android Malware Detection", "abstract": "The explosive growth and increasing sophistication of Android malware call for new defensive techniques that are capable of protecting mobile users against novel threats. In this paper, we first extract the runtime Application Programming Interface (API) call sequences from Android apps, and then analyze higher-level semantic relations within the ecosystem to comprehensively characterize the apps. To model different types of entities (i.e., app, API, IMEI, signature, affiliation) and the rich semantic relations among them, we then construct a structural heterogeneous information network (HIN) and present meta-path based approach to depict the relatedness over apps. To efficiently classify nodes (e.g., apps) in the constructed HIN, we propose the HinLearning method to first obtain in-sample node embeddings and then learn representations of out-of-sample nodes without rerunning/adjusting HIN embeddings at the first attempt. Afterwards, we design a deep neural network (DNN) classifier taking the learned HIN representations as inputs for Android malware detection. A comprehensive experimental study on the large-scale real sample collections from Tencent Security Lab is performed to compare various baselines. Promising experimental results demonstrate that our developed system AiDroid which integrates our proposed method outperforms others in real-time Android malware detection. AiDroid has already been incorporated into Tencent Mobile Security product that serves millions of users worldwide.", "published": "2018-11-02T18:11:55Z", "authors": ["Yanfang Ye", "Shifu Hou", "Lingwei Chen", "Jingwei Lei", "Wenqiang Wan", "Jiabin Wang", "Qi Xiong", "Fudong Shao"], "categories": ["cs.CR", "cs.LG"], "doi": "", "_query": "real estate valuation deep learning", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/1411.7200v1", "title": "Localized Complexities for Transductive Learning", "abstract": "We show two novel concentration inequalities for suprema of empirical processes when sampling without replacement, which both take the variance of the functions into account. While these inequalities may potentially have broad applications in learning theory in general, we exemplify their significance by studying the transductive setting of learning theory. For which we provide the first excess risk bounds based on the localized complexity of the hypothesis class, which can yield fast rates of convergence also in the transductive learning setting. We give a preliminary analysis of the localized complexities for the prominent case of kernel classes.", "published": "2014-11-26T12:14:22Z", "authors": ["Ilya Tolstikhin", "Gilles Blanchard", "Marius Kloft"], "categories": ["stat.ML", "cs.LG"], "doi": "", "_query": "real estate valuation deep learning", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2105.12584v2", "title": "A Comprehensive Survey on Community Detection with Deep Learning", "abstract": "A community reveals the features and connections of its members that are different from those in other communities in a network. Detecting communities is of great significance in network analysis. Despite the classical spectral clustering and statistical inference methods, we notice a significant development of deep learning techniques for community detection in recent years with their advantages in handling high dimensional network data. Hence, a comprehensive overview of community detection's latest progress through deep learning is timely to academics and practitioners. This survey devises and proposes a new taxonomy covering different state-of-the-art methods, including deep learning-based models upon deep neural networks, deep nonnegative matrix factorization and deep sparse filtering. The main category, i.e., deep neural networks, is further divided into convolutional networks, graph attention networks, generative adversarial networks and autoencoders. The survey also summarizes the popular benchmark data sets, evaluation metrics, and open-source implementations to address experimentation settings. We then discuss the practical applications of community detection in various domains and point to implementation scenarios. Finally, we outline future directions by suggesting challenging topics in this fast-growing deep learning field.", "published": "2021-05-26T14:37:07Z", "authors": ["Xing Su", "Shan Xue", "Fanzhen Liu", "Jia Wu", "Jian Yang", "Chuan Zhou", "Wenbin Hu", "Cecile Paris", "Surya Nepal", "Di Jin", "Quan Z. Sheng", "Philip S. Yu"], "categories": ["cs.SI", "cs.AI", "cs.LG"], "doi": "10.1109/TNNLS.2021.3137396", "_query": "real estate valuation deep learning", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2412.04121v1", "title": "DeepFEA: Deep Learning for Prediction of Transient Finite Element Analysis Solutions", "abstract": "Finite Element Analysis (FEA) is a powerful but computationally intensive method for simulating physical phenomena. Recent advancements in machine learning have led to surrogate models capable of accelerating FEA. Yet there are still limitations in developing surrogates of transient FEA models that can simultaneously predict the solutions for both nodes and elements with applicability on both the 2D and 3D domains. Motivated by this research gap, this study proposes DeepFEA, a deep learning-based framework that leverages a multilayer Convolutional Long Short-Term Memory (ConvLSTM) network branching into two parallel convolutional neural networks to predict the solutions for both nodes and elements of FEA models. The proposed network is optimized using a novel adaptive learning algorithm, called Node-Element Loss Optimization (NELO). NELO minimizes the error occurring at both branches of the network enabling the prediction of solutions for transient FEA simulations. The experimental evaluation of DeepFEA is performed on three datasets in the context of structural mechanics, generated to serve as publicly available reference datasets. The results show that DeepFEA can achieve less than 3% normalized mean and root mean squared error for 2D and 3D simulation scenarios, and inference times that are two orders of magnitude faster than FEA. In contrast, relevant state-of-the-art methods face challenges with multi-dimensional output and dynamic input prediction. Furthermore, DeepFEA's robustness was demonstrated in a real-life biomedical scenario, confirming its suitability for accurate and efficient predictions of FEA simulations.", "published": "2024-12-05T12:46:18Z", "authors": ["Georgios Triantafyllou", "Panagiotis G. Kalozoumis", "George Dimas", "Dimitris K. Iakovidis"], "categories": ["cs.LG", "cs.AI", "cs.CE"], "doi": "10.1016/j.eswa.2024.126343", "_query": "real estate valuation deep learning", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2002.09116v3", "title": "Learning Deep Kernels for Non-Parametric Two-Sample Tests", "abstract": "We propose a class of kernel-based two-sample tests, which aim to determine whether two sets of samples are drawn from the same distribution. Our tests are constructed from kernels parameterized by deep neural nets, trained to maximize test power. These tests adapt to variations in distribution smoothness and shape over space, and are especially suited to high dimensions and complex data. By contrast, the simpler kernels used in prior kernel testing work are spatially homogeneous, and adaptive only in lengthscale. We explain how this scheme includes popular classifier-based two-sample tests as a special case, but improves on them in general. We provide the first proof of consistency for the proposed adaptation method, which applies both to kernels on deep features and to simpler radial basis kernels or multiple kernel learning. In experiments, we establish the superior performance of our deep kernels in hypothesis testing on benchmark and real-world data. The code of our deep-kernel-based two sample tests is available at https://github.com/fengliu90/DK-for-TST.", "published": "2020-02-21T03:54:23Z", "authors": ["Feng Liu", "Wenkai Xu", "Jie Lu", "Guangquan Zhang", "Arthur Gretton", "Danica J. Sutherland"], "categories": ["stat.ML", "cs.LG", "stat.ME"], "doi": "", "_query": "real estate valuation deep learning", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2212.00557v1", "title": "Deep Kernel Learning for Mortality Prediction in the Face of Temporal Shift", "abstract": "Neural models, with their ability to provide novel representations, have shown promising results in prediction tasks in healthcare. However, patient demographics, medical technology, and quality of care change over time. This often leads to drop in the performance of neural models for prospective patients, especially in terms of their calibration. The deep kernel learning (DKL) framework may be robust to such changes as it combines neural models with Gaussian processes, which are aware of prediction uncertainty. Our hypothesis is that out-of-distribution test points will result in probabilities closer to the global mean and hence prevent overconfident predictions. This in turn, we hypothesise, will result in better calibration on prospective data. This paper investigates DKL's behaviour when facing a temporal shift, which was naturally introduced when an information system that feeds a cohort database was changed. We compare DKL's performance to that of a neural baseline based on recurrent neural networks. We show that DKL indeed produced superior calibrated predictions. We also confirm that the DKL's predictions were indeed less sharp. In addition, DKL's discrimination ability was even improved: its AUC was 0.746 (+- 0.014 std), compared to 0.739 (+- 0.028 std) for the baseline. The paper demonstrated the importance of including uncertainty in neural computing, especially for their prospective use.", "published": "2022-12-01T15:04:05Z", "authors": ["Miguel Rios", "Ameen Abu-Hanna"], "categories": ["cs.LG"], "doi": "10.1007/978-3-030-77211-6_22", "_query": "real estate valuation deep learning", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/1803.02811v2", "title": "Accelerated Methods for Deep Reinforcement Learning", "abstract": "Deep reinforcement learning (RL) has achieved many recent successes, yet experiment turn-around time remains a key bottleneck in research and in practice. We investigate how to optimize existing deep RL algorithms for modern computers, specifically for a combination of CPUs and GPUs. We confirm that both policy gradient and Q-value learning algorithms can be adapted to learn using many parallel simulator instances. We further find it possible to train using batch sizes considerably larger than are standard, without negatively affecting sample complexity or final performance. We leverage these facts to build a unified framework for parallelization that dramatically hastens experiments in both classes of algorithm. All neural network computations use GPUs, accelerating both data collection and training. Our results include using an entire DGX-1 to learn successful strategies in Atari games in mere minutes, using both synchronous and asynchronous algorithms.", "published": "2018-03-07T18:39:12Z", "authors": ["Adam Stooke", "Pieter Abbeel"], "categories": ["cs.LG", "cs.AI", "cs.DC"], "doi": "", "_query": "real estate valuation deep learning", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2007.05724v2", "title": "Learning Randomly Perturbed Structured Predictors for Direct Loss Minimization", "abstract": "Direct loss minimization is a popular approach for learning predictors over structured label spaces. This approach is computationally appealing as it replaces integration with optimization and allows to propagate gradients in a deep net using loss-perturbed prediction. Recently, this technique was extended to generative models, while introducing a randomized predictor that samples a structure from a randomly perturbed score function. In this work, we learn the variance of these randomized structured predictors and show that it balances better between the learned score function and the randomized noise in structured prediction. We demonstrate empirically the effectiveness of learning the balance between the signal and the random noise in structured discrete spaces.", "published": "2020-07-11T08:59:11Z", "authors": ["Hedda Cohen Indelman", "Tamir Hazan"], "categories": ["stat.ML", "cs.LG"], "doi": "", "_query": "real estate valuation deep learning", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2202.00821v3", "title": "Optimizing Sequential Experimental Design with Deep Reinforcement Learning", "abstract": "Bayesian approaches developed to solve the optimal design of sequential experiments are mathematically elegant but computationally challenging. Recently, techniques using amortization have been proposed to make these Bayesian approaches practical, by training a parameterized policy that proposes designs efficiently at deployment time. However, these methods may not sufficiently explore the design space, require access to a differentiable probabilistic model and can only optimize over continuous design spaces. Here, we address these limitations by showing that the problem of optimizing policies can be reduced to solving a Markov decision process (MDP). We solve the equivalent MDP with modern deep reinforcement learning techniques. Our experiments show that our approach is also computationally efficient at deployment time and exhibits state-of-the-art performance on both continuous and discrete design spaces, even when the probabilistic model is a black box.", "published": "2022-02-02T00:23:05Z", "authors": ["Tom Blau", "Edwin V. Bonilla", "Iadine Chades", "Amir Dezfouli"], "categories": ["cs.LG", "stat.ML"], "doi": "", "_query": "real estate valuation deep learning", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/1511.05497v2", "title": "Learning Neural Network Architectures using Backpropagation", "abstract": "Deep neural networks with millions of parameters are at the heart of many state of the art machine learning models today. However, recent works have shown that models with much smaller number of parameters can also perform just as well. In this work, we introduce the problem of architecture-learning, i.e; learning the architecture of a neural network along with weights. We introduce a new trainable parameter called tri-state ReLU, which helps in eliminating unnecessary neurons. We also propose a smooth regularizer which encourages the total number of neurons after elimination to be small. The resulting objective is differentiable and simple to optimize. We experimentally validate our method on both small and large networks, and show that it can learn models with a considerably small number of parameters without affecting prediction accuracy.", "published": "2015-11-17T18:26:11Z", "authors": ["Suraj Srinivas", "R. Venkatesh Babu"], "categories": ["cs.LG", "cs.CV", "cs.NE"], "doi": "", "_query": "real estate valuation deep learning", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/1702.08887v3", "title": "Stabilising Experience Replay for Deep Multi-Agent Reinforcement Learning", "abstract": "Many real-world problems, such as network packet routing and urban traffic control, are naturally modeled as multi-agent reinforcement learning (RL) problems. However, existing multi-agent RL methods typically scale poorly in the problem size. Therefore, a key challenge is to translate the success of deep learning on single-agent RL to the multi-agent setting. A major stumbling block is that independent Q-learning, the most popular multi-agent RL method, introduces nonstationarity that makes it incompatible with the experience replay memory on which deep Q-learning relies. This paper proposes two methods that address this problem: 1) using a multi-agent variant of importance sampling to naturally decay obsolete data and 2) conditioning each agent's value function on a fingerprint that disambiguates the age of the data sampled from the replay memory. Results on a challenging decentralised variant of StarCraft unit micromanagement confirm that these methods enable the successful combination of experience replay with multi-agent RL.", "published": "2017-02-28T17:56:41Z", "authors": ["Jakob Foerster", "Nantas Nardelli", "Gregory Farquhar", "Triantafyllos Afouras", "Philip H. S. Torr", "Pushmeet Kohli", "Shimon Whiteson"], "categories": ["cs.AI", "cs.LG", "cs.MA"], "doi": "", "_query": "real estate valuation deep learning", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/1611.03218v4", "title": "Learning to Play Guess Who? and Inventing a Grounded Language as a Consequence", "abstract": "Acquiring your first language is an incredible feat and not easily duplicated. Learning to communicate using nothing but a few pictureless books, a corpus, would likely be impossible even for humans. Nevertheless, this is the dominating approach in most natural language processing today. As an alternative, we propose the use of situated interactions between agents as a driving force for communication, and the framework of Deep Recurrent Q-Networks for evolving a shared language grounded in the provided environment. We task the agents with interactive image search in the form of the game Guess Who?. The images from the game provide a non trivial environment for the agents to discuss and a natural grounding for the concepts they decide to encode in their communication. Our experiments show that the agents learn not only to encode physical concepts in their words, i.e. grounding, but also that the agents learn to hold a multi-step dialogue remembering the state of the dialogue from step to step.", "published": "2016-11-10T08:44:52Z", "authors": ["Emilio Jorge", "Mikael Kågebäck", "Fredrik D. Johansson", "Emil Gustavsson"], "categories": ["cs.AI", "cs.CL", "cs.LG", "cs.MA"], "doi": "", "_query": "real estate valuation deep learning", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/1806.07822v2", "title": "Learning Neural Parsers with Deterministic Differentiable Imitation Learning", "abstract": "We explore the problem of learning to decompose spatial tasks into segments, as exemplified by the problem of a painting robot covering a large object. Inspired by the ability of classical decision tree algorithms to construct structured partitions of their input spaces, we formulate the problem of decomposing objects into segments as a parsing approach. We make the insight that the derivation of a parse-tree that decomposes the object into segments closely resembles a decision tree constructed by ID3, which can be done when the ground-truth available. We learn to imitate an expert parsing oracle, such that our neural parser can generalize to parse natural images without ground truth. We introduce a novel deterministic policy gradient update, DRAG (i.e., DeteRministically AGgrevate) in the form of a deterministic actor-critic variant of AggreVaTeD, to train our neural parser. From another perspective, our approach is a variant of the Deterministic Policy Gradient suitable for the imitation learning setting. The deterministic policy representation offered by training our neural parser with DRAG allows it to outperform state of the art imitation and reinforcement learning approaches.", "published": "2018-06-20T16:15:54Z", "authors": ["Tanmay Shankar", "Nicholas Rhinehart", "Katharina Muelling", "Kris M. Kitani"], "categories": ["cs.LG", "cs.AI", "cs.CV", "cs.RO", "stat.ML"], "doi": "", "_query": "real estate valuation deep learning", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2305.14109v3", "title": "Combining Multi-Objective Bayesian Optimization with Reinforcement Learning for TinyML", "abstract": "Deploying deep neural networks (DNNs) on microcontrollers (TinyML) is a common trend to process the increasing amount of sensor data generated at the edge, but in practice, resource and latency constraints make it difficult to find optimal DNN candidates. Neural architecture search (NAS) is an excellent approach to automate this search and can easily be combined with DNN compression techniques commonly used in TinyML. However, many NAS techniques are not only computationally expensive, especially hyperparameter optimization (HPO), but also often focus on optimizing only a single objective, e.g., maximizing accuracy, without considering additional objectives such as memory requirements or computational complexity of a DNN, which are key to making deployment at the edge feasible. In this paper, we propose a novel NAS strategy for TinyML based on multi-objective Bayesian optimization (MOBOpt) and an ensemble of competing parametric policies trained using Augmented Random Search (ARS) reinforcement learning (RL) agents. Our methodology aims at efficiently finding tradeoffs between a DNN's predictive accuracy, memory requirements on a given target system, and computational complexity. Our experiments show that we consistently outperform existing MOBOpt approaches on different datasets and architectures such as ResNet-18 and MobileNetV3.", "published": "2023-05-23T14:31:52Z", "authors": ["Mark Deutel", "Georgios Kontes", "Christopher Mutschler", "Jürgen Teich"], "categories": ["cs.LG", "cs.AI"], "doi": "10.1145/3715012", "_query": "real estate valuation deep learning", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2006.03361v1", "title": "Learning to Rank Learning Curves", "abstract": "Many automated machine learning methods, such as those for hyperparameter and neural architecture optimization, are computationally expensive because they involve training many different model configurations. In this work, we present a new method that saves computational budget by terminating poor configurations early on in the training. In contrast to existing methods, we consider this task as a ranking and transfer learning problem. We qualitatively show that by optimizing a pairwise ranking loss and leveraging learning curves from other datasets, our model is able to effectively rank learning curves without having to observe many or very long learning curves. We further demonstrate that our method can be used to accelerate a neural architecture search by a factor of up to 100 without a significant performance degradation of the discovered architecture. In further experiments we analyze the quality of ranking, the influence of different model components as well as the predictive behavior of the model.", "published": "2020-06-05T10:49:52Z", "authors": ["Martin Wistuba", "Tejaswini Pedapati"], "categories": ["cs.LG", "cs.CV", "stat.ML"], "doi": "", "_query": "real estate valuation deep learning", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2205.02708v5", "title": "Meta-learning Adaptive Deep Kernel Gaussian Processes for Molecular Property Prediction", "abstract": "We propose Adaptive Deep Kernel Fitting with Implicit Function Theorem (ADKF-IFT), a novel framework for learning deep kernel Gaussian processes (GPs) by interpolating between meta-learning and conventional deep kernel learning. Our approach employs a bilevel optimization objective where we meta-learn generally useful feature representations across tasks, in the sense that task-specific GP models estimated on top of such features achieve the lowest possible predictive loss on average. We solve the resulting nested optimization problem using the implicit function theorem (IFT). We show that our ADKF-IFT framework contains previously proposed Deep Kernel Learning (DKL) and Deep Kernel Transfer (DKT) as special cases. Although ADKF-IFT is a completely general method, we argue that it is especially well-suited for drug discovery problems and demonstrate that it significantly outperforms previous state-of-the-art methods on a variety of real-world few-shot molecular property prediction tasks and out-of-domain molecular property prediction and optimization tasks.", "published": "2022-05-05T15:26:53Z", "authors": ["Wenlin Chen", "Austin Tripp", "José Miguel Hernández-Lobato"], "categories": ["cs.LG"], "doi": "", "_query": "real estate valuation deep learning", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2409.12575v1", "title": "Deep Transfer Hashing for Adaptive Learning on Federated Streaming Data", "abstract": "This extended abstract explores the integration of federated learning with deep transfer hashing for distributed prediction tasks, emphasizing resource-efficient client training from evolving data streams. Federated learning allows multiple clients to collaboratively train a shared model while maintaining data privacy - by incorporating deep transfer hashing, high-dimensional data can be converted into compact hash codes, reducing data transmission size and network loads. The proposed framework utilizes transfer learning, pre-training deep neural networks on a central server, and fine-tuning on clients to enhance model accuracy and adaptability. A selective hash code sharing mechanism using a privacy-preserving global memory bank further supports client fine-tuning. This approach addresses challenges in previous research by improving computational efficiency and scalability. Practical applications include Car2X event predictions, where a shared model is collectively trained to recognize traffic patterns, aiding in tasks such as traffic density assessment and accident detection. The research aims to develop a robust framework that combines federated learning, deep transfer hashing and transfer learning for efficient and secure downstream task execution.", "published": "2024-09-19T08:52:49Z", "authors": ["Manuel Röder", "Frank-Michael Schleif"], "categories": ["cs.LG", "cs.CR", "cs.DC"], "doi": "", "_query": "real estate valuation deep learning", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/1911.01872v1", "title": "Interpretability Study on Deep Learning for Jet Physics at the Large Hadron Collider", "abstract": "Using deep neural networks for identifying physics objects at the Large Hadron Collider (LHC) has become a powerful alternative approach in recent years. After successful training of deep neural networks, examining the trained networks not only helps us understand the behaviour of neural networks, but also helps improve the performance of deep learning models through proper interpretation. We take jet tagging problem at the LHC as an example, using recursive neural networks as a starting point, aim at a thorough understanding of the behaviour of the physics-oriented DNNs and the information encoded in the embedding space. We make a comparative study on a series of different jet tagging tasks dominated by different underlying physics. Interesting observations on the latent space are obtained.", "published": "2019-11-05T15:27:30Z", "authors": ["Taoli Cheng"], "categories": ["hep-ph", "cs.LG", "hep-ex", "stat.ML"], "doi": "", "_query": "real estate valuation deep learning", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2205.08671v2", "title": "K-textures, a self-supervised hard clustering deep learning algorithm for satellite image segmentation", "abstract": "Deep learning self-supervised algorithms that can segment an image in a fixed number of hard labels such as the k-means algorithm and relying only on deep learning techniques are still lacking. Here, we introduce the k-textures algorithm which provides self-supervised segmentation of a 4-band image (RGB-NIR) for a $k$ number of classes. An example of its application on high resolution Planet satellite imagery is given. Our algorithm shows that discrete search is feasible using convolutional neural networks (CNN) and gradient descent. The model detects $k$ hard clustering classes represented in the model as $k$ discrete binary masks and their associated $k$ independently generated textures, that combined are a simulation of the original image. The similarity loss is the mean squared error between the features of the original and the simulated image, both extracted from the penultimate convolutional block of Keras 'imagenet' pretrained VGG-16 model and a custom feature extractor made with Planet data. The main advances of the k-textures model are: first, the $k$ discrete binary masks are obtained inside the model using gradient descent. The model allows for the generation of discrete binary masks using a novel method using a hard sigmoid activation function. Second, it provides hard clustering classes -- each pixels has only one class. Finally, in comparison to k-means, where each pixel is considered independently, here, contextual information is also considered and each class is not associated only to similar values in the color channels but also to a texture. Our approach is designed to ease the production of training samples for satellite image segmentation and the k-textures architecture could be adapted to support different number of bands and for more complex tasks, such as object self-segmentation. The model codes and weights are available at https://doi.org/10.5281/zenodo.6359859", "published": "2022-05-18T00:51:53Z", "authors": ["Fabien H. Wagner", "Ricardo Dalagnol", "Alber H. Sánchez", "Mayumi C. M. Hirye", "Samuel Favrichon", "Jake H. Lee", "Steffen Mauceri", "Yan Yang", "Sassan Saatchi"], "categories": ["cs.CV"], "doi": "", "_query": "real estate valuation deep learning", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/1712.03303v2", "title": "Sim-to-Real Transfer of Accurate Grasping with Eye-In-Hand Observations and Continuous Control", "abstract": "In the context of deep learning for robotics, we show effective method of training a real robot to grasp a tiny sphere (1.37cm of diameter), with an original combination of system design choices. We decompose the end-to-end system into a vision module and a closed-loop controller module. The two modules use target object segmentation as their common interface. The vision module extracts information from the robot end-effector camera, in the form of a binary segmentation mask of the target. We train it to achieve effective domain transfer by composing real background images with simulated images of the target. The controller module takes as input the binary segmentation mask, and thus is agnostic to visual discrepancies between simulated and real environments. We train our closed-loop controller in simulation using imitation learning and show it is robust with respect to discrepancies between the dynamic model of the simulated and real robot: when combined with eye-in-hand observations, we achieve a 90% success rate in grasping a tiny sphere with a real robot. The controller can generalize to unseen scenarios where the target is moving and even learns to recover from failures.", "published": "2017-12-08T22:46:43Z", "authors": ["Mengyuan Yan", "Iuri Frosio", "Stephen Tyree", "Jan Kautz"], "categories": ["cs.RO"], "doi": "", "_query": "real estate valuation deep learning", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2002.12394v2", "title": "Affinity guided Geometric Semi-Supervised Metric Learning", "abstract": "In this paper, we revamp the forgotten classical Semi-Supervised Distance Metric Learning (SSDML) problem from a Riemannian geometric lens, to leverage stochastic optimization within a end-to-end deep framework. The motivation comes from the fact that apart from a few classical SSDML approaches learning a linear Mahalanobis metric, deep SSDML has not been studied. We first extend existing SSDML methods to their deep counterparts and then propose a new method to overcome their limitations. Due to the nature of constraints on our metric parameters, we leverage Riemannian optimization. Our deep SSDML method with a novel affinity propagation based triplet mining strategy outperforms its competitors.", "published": "2020-02-27T19:10:21Z", "authors": ["Ujjal Kr Dutta", "Mehrtash Harandi", "Chellu Chandra Sekhar"], "categories": ["cs.CV", "cs.LG"], "doi": "", "_query": "real estate valuation deep learning", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2501.17603v1", "title": "Characterization of Fractal Basins Using Deep Convolutional Neural Networks", "abstract": "Neural network models have recently demonstrated impressive prediction performance in complex systems where chaos and unpredictability appear. In spite of the research efforts carried out on predicting future trajectories or improving their accuracy compared to numerical methods, not sufficient work has been done by using deep learning techniques in which they characterize the unpredictability of chaotic systems or give a general view of the global unpredictability of a system. In this work we propose a novel approach based on deep learning techniques to measure the fractal dimension of the basins of attraction of the Duffing oscillator for a variety of parameters. As a consequence, we provide an algorithm capable of predicting fractal dimension measures as accurately as the conventional algorithm, but with a computation speed about ten times faster.", "published": "2025-01-29T12:17:33Z", "authors": ["David Valle", "Alexandre Wagemakers", "Alvar Daza", "Miguel A. F. Sanjuán"], "categories": ["nlin.CD"], "doi": "10.1142/S0218127422502005", "_query": "real estate valuation deep learning", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2009.11942v1", "title": "Online Learning With Adaptive Rebalancing in Nonstationary Environments", "abstract": "An enormous and ever-growing volume of data is nowadays becoming available in a sequential fashion in various real-world applications. Learning in nonstationary environments constitutes a major challenge, and this problem becomes orders of magnitude more complex in the presence of class imbalance. We provide new insights into learning from nonstationary and imbalanced data in online learning, a largely unexplored area. We propose the novel Adaptive REBAlancing (AREBA) algorithm that selectively includes in the training set a subset of the majority and minority examples that appeared so far, while at its heart lies an adaptive mechanism to continually maintain the class balance between the selected examples. We compare AREBA with strong baselines and other state-of-the-art algorithms and perform extensive experimental work in scenarios with various class imbalance rates and different concept drift types on both synthetic and real-world data. AREBA significantly outperforms the rest with respect to both learning speed and learning quality. Our code is made publicly available to the scientific community.", "published": "2020-09-24T20:40:04Z", "authors": ["Kleanthis Malialis", "Christos G. Panayiotou", "Marios M. Polycarpou"], "categories": ["cs.LG", "stat.ML"], "doi": "10.1109/TNNLS.2020.3017863", "_query": "real estate valuation deep learning", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/1809.09501v1", "title": "Anderson Acceleration for Reinforcement Learning", "abstract": "Anderson acceleration is an old and simple method for accelerating the computation of a fixed point. However, as far as we know and quite surprisingly, it has never been applied to dynamic programming or reinforcement learning. In this paper, we explain briefly what Anderson acceleration is and how it can be applied to value iteration, this being supported by preliminary experiments showing a significant speed up of convergence, that we critically discuss. We also discuss how this idea could be applied more generally to (deep) reinforcement learning.", "published": "2018-09-25T14:04:25Z", "authors": ["Matthieu Geist", "Bruno Scherrer"], "categories": ["cs.LG", "stat.ML"], "doi": "", "_query": "real estate valuation deep learning", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2012.10394v1", "title": "Deep Learning for Climate Model Output Statistics", "abstract": "Climate models are an important tool for the assessment of prospective climate change effects but they suffer from systematic and representation errors, especially for precipitation. Model output statistics (MOS) reduce these errors by fitting the model output to observational data with machine learning. In this work, we explore the feasibility and potential of deep learning with convolutional neural networks (CNNs) for MOS. We propose the CNN architecture ConvMOS specifically designed for reducing errors in climate model outputs and apply it to the climate model REMO. Our results show a considerable reduction of errors and mostly improved performance compared to three commonly used MOS approaches.", "published": "2020-12-09T11:38:37Z", "authors": ["Michael Steininger", "Daniel Abel", "Katrin Ziegler", "Anna Krause", "Heiko Paeth", "Andreas Hotho"], "categories": ["physics.ao-ph", "cs.LG"], "doi": "", "_query": "real estate valuation deep learning", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/1908.04682v3", "title": "CMB-GAN: Fast Simulations of Cosmic Microwave background anisotropy maps using Deep Learning", "abstract": "Cosmic Microwave Background (CMB) has been a cornerstone in many cosmology experiments and studies since it was discovered back in 1964. Traditional computational models like CAMB that are used for generating CMB temperature anisotropy maps are extremely resource intensive and act as a bottleneck in cosmology experiments that require a large amount of CMB data for analysis. In this paper, we present a new approach to the generation of CMB temperature maps using a specific class of neural networks called Generative Adversarial Network (GAN). We train our deep generative model to learn the complex distribution of CMB maps and efficiently generate new sets of CMB data in the form of 2D patches of anisotropy maps without losing much accuracy. We limit our experiment to the generation of 56$^{\\circ}$ and 112$^{\\circ}$ square patches of CMB maps. We have also trained a Multilayer perceptron model for estimation of baryon density from a CMB map, we will be using this model for the performance evaluation of our generative model using diagnostic measures like Histogram of pixel intensities, the standard deviation of pixel intensity distribution, Power Spectrum, Cross power spectrum, Correlation matrix of the power spectrum and Peak count. We show that the GAN model is able to efficiently generate CMB samples of multiple sizes and is sensitive to the cosmological parameters corresponding to the underlying distribution of the data. The primiary advantage of this method is the exponential reduction in the computational time needed to generate the CMB data, the GAN model is able to generate the samples within seconds as opposed to hours required by the CAMB package with an acceptable value to error and loss of information. We hope that future iterations of this methodology will replace traditional statistical methods of CMB data generation and help in large scale cosmological experiments.", "published": "2019-08-11T07:23:41Z", "authors": ["Amit Mishra", "Pranath Reddy", "Rahul Nigam"], "categories": ["astro-ph.CO", "cs.LG", "eess.IV"], "doi": "", "_query": "real estate valuation deep learning", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/1804.09298v2", "title": "Recent Progresses in Deep Learning based Acoustic Models (Updated)", "abstract": "In this paper, we summarize recent progresses made in deep learning based acoustic models and the motivation and insights behind the surveyed techniques. We first discuss acoustic models that can effectively exploit variable-length contextual information, such as recurrent neural networks (RNNs), convolutional neural networks (CNNs), and their various combination with other models. We then describe acoustic models that are optimized end-to-end with emphasis on feature representations learned jointly with rest of the system, the connectionist temporal classification (CTC) criterion, and the attention-based sequence-to-sequence model. We further illustrate robustness issues in speech recognition systems, and discuss acoustic model adaptation, speech enhancement and separation, and robust training strategies. We also cover modeling techniques that lead to more efficient decoding and discuss possible future directions in acoustic model research.", "published": "2018-04-25T00:24:39Z", "authors": ["Dong Yu", "Jinyu Li"], "categories": ["eess.AS", "cs.CL", "cs.SD"], "doi": "", "_query": "real estate valuation deep learning", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/1607.05258v3", "title": "Deep learning trends for focal brain pathology segmentation in MRI", "abstract": "Segmentation of focal (localized) brain pathologies such as brain tumors and brain lesions caused by multiple sclerosis and ischemic strokes are necessary for medical diagnosis, surgical planning and disease development as well as other applications such as tractography. Over the years, attempts have been made to automate this process for both clinical and research reasons. In this regard, machine learning methods have long been a focus of attention. Over the past two years, the medical imaging field has seen a rise in the use of a particular branch of machine learning commonly known as deep learning. In the non-medical computer vision world, deep learning based methods have obtained state-of-the-art results on many datasets. Recent studies in computer aided diagnostics have shown deep learning methods (and especially convolutional neural networks - CNN) to yield promising results. In this chapter, we provide a survey of CNN methods applied to medical imaging with a focus on brain pathology segmentation. In particular, we discuss their characteristic peculiarities and their specific configuration and adjustments that are best suited to segment medical images. We also underline the intrinsic differences deep learning methods have with other machine learning methods.", "published": "2016-07-18T19:52:00Z", "authors": ["Mohammad Havaei", "Nicolas Guizard", "Hugo Larochelle", "Pierre-Marc Jodoin"], "categories": ["cs.CV"], "doi": "10.1007/978-3-319-50478-0_6", "_query": "real estate valuation deep learning", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2404.12511v1", "title": "Generalizing Machine Learning Evaluation through the Integration of Shannon Entropy and Rough Set Theory", "abstract": "This research paper delves into the innovative integration of Shannon entropy and rough set theory, presenting a novel approach to generalize the evaluation approach in machine learning. The conventional application of entropy, primarily focused on information uncertainty, is extended through its combination with rough set theory to offer a deeper insight into data's intrinsic structure and the interpretability of machine learning models. We introduce a comprehensive framework that synergizes the granularity of rough set theory with the uncertainty quantification of Shannon entropy, applied across a spectrum of machine learning algorithms. Our methodology is rigorously tested on various datasets, showcasing its capability to not only assess predictive performance but also to illuminate the underlying data complexity and model robustness. The results underscore the utility of this integrated approach in enhancing the evaluation landscape of machine learning, offering a multi-faceted perspective that balances accuracy with a profound understanding of data attributes and model dynamics. This paper contributes a groundbreaking perspective to machine learning evaluation, proposing a method that encapsulates a holistic view of model performance, thereby facilitating more informed decision-making in model selection and application.", "published": "2024-04-18T21:22:42Z", "authors": ["Olga Cherednichenko", "Dmytro Chernyshov", "Dmytro Sytnikov", "Polina Sytnikova"], "categories": ["cs.LG"], "doi": "", "_query": "real estate valuation deep learning", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/1902.00183v2", "title": "Learning Action Representations for Reinforcement Learning", "abstract": "Most model-free reinforcement learning methods leverage state representations (embeddings) for generalization, but either ignore structure in the space of actions or assume the structure is provided a priori. We show how a policy can be decomposed into a component that acts in a low-dimensional space of action representations and a component that transforms these representations into actual actions. These representations improve generalization over large, finite action sets by allowing the agent to infer the outcomes of actions similar to actions already taken. We provide an algorithm to both learn and use action representations and provide conditions for its convergence. The efficacy of the proposed method is demonstrated on large-scale real-world problems.", "published": "2019-02-01T04:58:40Z", "authors": ["Yash Chandak", "Georgios Theocharous", "James Kostas", "Scott Jordan", "Philip S. Thomas"], "categories": ["cs.LG", "stat.ML"], "doi": "", "_query": "real estate valuation deep learning", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2106.00058v5", "title": "Stable and Interpretable Unrolled Dictionary Learning", "abstract": "The dictionary learning problem, representing data as a combination of a few atoms, has long stood as a popular method for learning representations in statistics and signal processing. The most popular dictionary learning algorithm alternates between sparse coding and dictionary update steps, and a rich literature has studied its theoretical convergence. The success of dictionary learning relies on access to a \"good\" initial estimate of the dictionary and the ability of the sparse coding step to provide an unbiased estimate of the code. The growing popularity of unrolled sparse coding networks has led to the empirical finding that backpropagation through such networks performs dictionary learning. We offer the theoretical analysis of these empirical results through PUDLE, a Provable Unrolled Dictionary LEarning method. We provide conditions on the network initialization and data distribution sufficient to recover and preserve the support of the latent code. Additionally, we address two challenges; first, the vanilla unrolled sparse coding computes a biased code estimate, and second, gradients during backpropagated learning can become unstable. We show approaches to reduce the bias of the code estimate in the forward pass, and that of the dictionary estimate in the backward pass. We propose strategies to resolve the learning instability by tuning network parameters and modifying the loss function. Overall, we highlight the impact of loss, unrolling, and backpropagation on convergence. We complement our findings through synthetic and image denoising experiments. Finally, we demonstrate PUDLE's interpretability, a driving factor in designing deep networks based on iterative optimizations, by building a mathematical relation between network weights, its output, and the training set.", "published": "2021-05-31T18:49:58Z", "authors": ["Bahareh Tolooshams", "Demba Ba"], "categories": ["cs.LG", "eess.SP", "stat.ML"], "doi": "", "_query": "real estate valuation deep learning", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2211.07076v1", "title": "Learning predictive checklists from continuous medical data", "abstract": "Checklists, while being only recently introduced in the medical domain, have become highly popular in daily clinical practice due to their combined effectiveness and great interpretability. Checklists are usually designed by expert clinicians that manually collect and analyze available evidence. However, the increasing quantity of available medical data is calling for a partially automated checklist design. Recent works have taken a step in that direction by learning predictive checklists from categorical data. In this work, we propose to extend this approach to accomodate learning checklists from continuous medical data using mixed-integer programming approach. We show that this extension outperforms a range of explainable machine learning baselines on the prediction of sepsis from intensive care clinical trajectories.", "published": "2022-11-14T02:51:04Z", "authors": ["Yukti Makhija", "Edward De Brouwer", "Rahul G. Krishnan"], "categories": ["cs.LG"], "doi": "", "_query": "real estate valuation deep learning", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2607.18488v1", "title": "The Open Ant: A Robot Platform for Reinforcement Learning Research", "abstract": "Reinforcement learning (RL) research has demonstrated success in both physical and simulated domains; however, the predominant methodology remains rooted in simulations. The predominance of simulations makes translating research to physical reality uncertain for both algorithms and researchers. We propose a physical platform that is designed to simplify the transition. In this paper, we present the Open Ant: a physical variant of the commonly used Gymnasium Ant environment, along with a simulation. We demonstrate that competent walking policies can be learned from scratch in approximately one hour directly from the physical robot's experience for two substantially different RL algorithms: SARSA($λ$) and Soft Actor-Critic (SAC). Separately, we show policies that were learned in simulation transfer to reality. We also examine how well the platform supports a nimble experimental ecosystem. Specifically, we observe the speed with which new users from diverse backgrounds achieve their first success with the platform, and how easily the platform can be repaired and updated when hardware issues arise. Both the hardware design and software are available as open-source on GitHub for ease of customization. In summary, we advocate for the use of the Open Ant for RL researchers who frequently use simulated environments, so they can more easily include robot experiments in their evaluations.", "published": "2026-07-20T20:18:29Z", "authors": ["Elena Sorina Lupu", "Patrick Spieler", "Khurram Javed", "Kris De Asis", "John D. Martin", "Martha Steenstrup", "Joseph Modayil"], "categories": ["cs.RO", "cs.AI", "eess.SY"], "doi": "", "_query": "real estate valuation deep learning", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/1912.00512v2", "title": "Knowledge Infused Learning (K-IL): Towards Deep Incorporation of Knowledge in Deep Learning", "abstract": "Learning the underlying patterns in data goes beyond instance-based generalization to external knowledge represented in structured graphs or networks. Deep learning that primarily constitutes neural computing stream in AI has shown significant advances in probabilistically learning latent patterns using a multi-layered network of computational nodes (i.e., neurons/hidden units). Structured knowledge that underlies symbolic computing approaches and often supports reasoning, has also seen significant growth in recent years, in the form of broad-based (e.g., DBPedia, Yago) and domain, industry or application specific knowledge graphs. A common substrate with careful integration of the two will raise opportunities to develop neuro-symbolic learning approaches for AI, where conceptual and probabilistic representations are combined. As the incorporation of external knowledge will aid in supervising the learning of features for the model, deep infusion of representational knowledge from knowledge graphs within hidden layers will further enhance the learning process. Although much work remains, we believe that knowledge graphs will play an increasing role in developing hybrid neuro-symbolic intelligent systems (bottom-up deep learning with top-down symbolic computing) as well as in building explainable AI systems for which knowledge graphs will provide scaffolding for punctuating neural computing. In this position paper, we describe our motivation for such a neuro-symbolic approach and framework that combines knowledge graph and neural networks.", "published": "2019-12-01T22:36:14Z", "authors": ["Ugur Kursuncu", "Manas Gaur", "Amit Sheth"], "categories": ["cs.AI", "cs.CL", "cs.LG"], "doi": "", "_query": "real estate valuation deep learning", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2205.05588v1", "title": "Characterizing the Action-Generalization Gap in Deep Q-Learning", "abstract": "We study the action generalization ability of deep Q-learning in discrete action spaces. Generalization is crucial for efficient reinforcement learning (RL) because it allows agents to use knowledge learned from past experiences on new tasks. But while function approximation provides deep RL agents with a natural way to generalize over state inputs, the same generalization mechanism does not apply to discrete action outputs. And yet, surprisingly, our experiments indicate that Deep Q-Networks (DQN), which use exactly this type of function approximator, are still able to achieve modest action generalization. Our main contribution is twofold: first, we propose a method of evaluating action generalization using expert knowledge of action similarity, and empirically confirm that action generalization leads to faster learning; second, we characterize the action-generalization gap (the difference in learning performance between DQN and the expert) in different domains. We find that DQN can indeed generalize over actions in several simple domains, but that its ability to do so decreases as the action space grows larger.", "published": "2022-05-11T16:00:46Z", "authors": ["Zhiyuan Zhou", "Cameron Allen", "Kavosh Asadi", "George Konidaris"], "categories": ["cs.AI", "cs.LG"], "doi": "", "_query": "real estate valuation deep learning", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/1906.00336v1", "title": "The Principle of Unchanged Optimality in Reinforcement Learning Generalization", "abstract": "Several recent papers have examined generalization in reinforcement learning (RL), by proposing new environments or ways to add noise to existing environments, then benchmarking algorithms and model architectures on those environments. We discuss subtle conceptual properties of RL benchmarks that are not required in supervised learning (SL), and also properties that an RL benchmark should possess. Chief among them is one we call the principle of unchanged optimality: there should exist a single $π$ that is optimal across all train and test tasks. In this work, we argue why this principle is important, and ways it can be broken or satisfied due to subtle choices in state representation or model architecture. We conclude by discussing challenges and future lines of research in theoretically analyzing generalization benchmarks.", "published": "2019-06-02T03:52:28Z", "authors": ["Alex Irpan", "Xingyou Song"], "categories": ["cs.LG", "cs.AI", "stat.ML"], "doi": "", "_query": "real estate valuation deep learning", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/1812.00098v1", "title": "Deep Factors with Gaussian Processes for Forecasting", "abstract": "A large collection of time series poses significant challenges for classical and neural forecasting approaches. Classical time series models fail to fit data well and to scale to large problems, but succeed at providing uncertainty estimates. The converse is true for deep neural networks. In this paper, we propose a hybrid model that incorporates the benefits of both approaches. Our new method is data-driven and scalable via a latent, global, deep component. It also handles uncertainty through a local classical Gaussian Process model. Our experiments demonstrate that our method obtains higher accuracy than state-of-the-art methods.", "published": "2018-11-30T23:42:53Z", "authors": ["Danielle C. Maddix", "Yuyang Wang", "Alex Smola"], "categories": ["stat.ML", "cs.LG"], "doi": "", "_query": "real estate valuation deep learning", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/1809.02441v3", "title": "StackNet: Stacking Parameters for Continual learning", "abstract": "Training a neural network for a classification task typically assumes that the data to train are given from the beginning. However, in the real world, additional data accumulate gradually and the model requires additional training without accessing the old training data. This usually leads to the catastrophic forgetting problem which is inevitable for the traditional training methodology of neural networks. In this paper, we propose a continual learning method that is able to learn additional tasks while retaining the performance of previously learned tasks by stacking parameters. Composed of two complementary components, the index module and the StackNet, our method estimates the index of the corresponding task for an input sample with the index module and utilizes a particular portion of StackNet with this index. The StackNet guarantees no degradation in the performance of the previously learned tasks and the index module shows high confidence in finding the origin of an input sample. Compared to the previous work of PackNet, our method is competitive and highly intuitive.", "published": "2018-09-07T12:39:13Z", "authors": ["Jangho Kim", "Jeesoo Kim", "Nojun Kwak"], "categories": ["cs.LG", "cs.AI", "stat.ML"], "doi": "", "_query": "real estate valuation deep learning", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2012.02684v2", "title": "Model-Agnostic Learning to Meta-Learn", "abstract": "In this paper, we propose a learning algorithm that enables a model to quickly exploit commonalities among related tasks from an unseen task distribution, before quickly adapting to specific tasks from that same distribution. We investigate how learning with different task distributions can first improve adaptability by meta-finetuning on related tasks before improving goal task generalization with finetuning. Synthetic regression experiments validate the intuition that learning to meta-learn improves adaptability and consecutively generalization. Experiments on more complex image classification, continual regression, and reinforcement learning tasks demonstrate that learning to meta-learn generally improves task-specific adaptation. The methodology, setup, and hypotheses in this proposal were positively evaluated by peer review before conclusive experiments were carried out.", "published": "2020-12-04T15:55:08Z", "authors": ["Arnout Devos", "Yatin Dandi"], "categories": ["cs.LG", "cs.AI", "stat.ML"], "doi": "", "_query": "real estate valuation deep learning", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/1810.02513v2", "title": "Learning To Simulate", "abstract": "Simulation is a useful tool in situations where training data for machine learning models is costly to annotate or even hard to acquire. In this work, we propose a reinforcement learning-based method for automatically adjusting the parameters of any (non-differentiable) simulator, thereby controlling the distribution of synthesized data in order to maximize the accuracy of a model trained on that data. In contrast to prior art that hand-crafts these simulation parameters or adjusts only parts of the available parameters, our approach fully controls the simulator with the actual underlying goal of maximizing accuracy, rather than mimicking the real data distribution or randomly generating a large volume of data. We find that our approach (i) quickly converges to the optimal simulation parameters in controlled experiments and (ii) can indeed discover good sets of parameters for an image rendering simulator in actual computer vision applications.", "published": "2018-10-05T04:11:25Z", "authors": ["Nataniel Ruiz", "Samuel Schulter", "Manmohan Chandraker"], "categories": ["cs.LG", "cs.CV", "stat.ML"], "doi": "", "_query": "real estate valuation deep learning", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2112.03555v3", "title": "FedDAG: Federated DAG Structure Learning", "abstract": "To date, most directed acyclic graphs (DAGs) structure learning approaches require data to be stored in a central server. However, due to the consideration of privacy protection, data owners gradually refuse to share their personalized raw data to avoid private information leakage, making this task more troublesome by cutting off the first step. Thus, a puzzle arises: \\textit{how do we discover the underlying DAG structure from decentralized data?} In this paper, focusing on the additive noise models (ANMs) assumption of data generation, we take the first step in developing a gradient-based learning framework named FedDAG, which can learn the DAG structure without directly touching the local data and also can naturally handle the data heterogeneity. Our method benefits from a two-level structure of each local model. The first level structure learns the edges and directions of the graph and communicates with the server to get the model information from other clients during the learning procedure, while the second level structure approximates the mechanisms among variables and personally updates on its own data to accommodate the data heterogeneity. Moreover, FedDAG formulates the overall learning task as a continuous optimization problem by taking advantage of an equality acyclicity constraint, which can be solved by gradient descent methods to boost the searching efficiency. Extensive experiments on both synthetic and real-world datasets verify the efficacy of the proposed method.", "published": "2021-12-07T08:04:12Z", "authors": ["Erdun Gao", "Junjia Chen", "Li Shen", "Tongliang Liu", "Mingming Gong", "Howard Bondell"], "categories": ["cs.LG", "stat.ML"], "doi": "", "_query": "real estate valuation deep learning", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/1806.04139v2", "title": "Deep speckle correlation: a deep learning approach towards scalable imaging through scattering media", "abstract": "Imaging through scattering is an important, yet challenging problem. Tremendous progress has been made by exploiting the deterministic input-output \"transmission matrix\" for a fixed medium. However, this \"one-to-one\" mapping is highly susceptible to speckle decorrelations - small perturbations to the scattering medium lead to model errors and severe degradation of the imaging performance. Our goal here is to develop a new framework that is highly scalable to both medium perturbations and measurement requirement. To do so, we propose a statistical \"one-to-all\" deep learning technique that encapsulates a wide range of statistical variations for the model to be resilient to speckle decorrelations. Specifically, we develop a convolutional neural network (CNN) that is able to learn the statistical information contained in the speckle intensity patterns captured on a set of diffusers having the same macroscopic parameter. We then show for the first time, to the best of our knowledge, that the trained CNN is able to generalize and make high-quality object predictions through an entirely different set of diffusers of the same class. Our work paves the way to a highly scalable deep learning approach for imaging through scattering media.", "published": "2018-06-11T16:27:19Z", "authors": ["Yunzhe Li", "Yujia Xue", "Lei Tian"], "categories": ["eess.IV", "physics.optics"], "doi": "10.1364/OPTICA.5.001181", "_query": "real estate valuation deep learning", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/1912.00186v1", "title": "Quantized deep learning models on low-power edge devices for robotic systems", "abstract": "In this work, we present a quantized deep neural network deployed on a low-power edge device, inferring learned motor-movements of a suspended robot in a defined space. This serves as the fundamental building block for the original setup, a robotic system for farms or greenhouses aimed at a wide range of agricultural tasks. Deep learning on edge devices and its implications could have a substantial impact on farming systems in the developing world, leading not only to sustainable food production and income, but also increased data privacy and autonomy.", "published": "2019-11-30T11:27:12Z", "authors": ["Anugraha Sinha", "Naveen Kumar", "Murukesh Mohanan", "MD Muhaimin Rahman", "Yves Quemener", "Amina Mim", "Suzana Ilić"], "categories": ["eess.SP", "cs.LG"], "doi": "", "_query": "real estate valuation deep learning", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2105.10148v2", "title": "On Instrumental Variable Regression for Deep Offline Policy Evaluation", "abstract": "We show that the popular reinforcement learning (RL) strategy of estimating the state-action value (Q-function) by minimizing the mean squared Bellman error leads to a regression problem with confounding, the inputs and output noise being correlated. Hence, direct minimization of the Bellman error can result in significantly biased Q-function estimates. We explain why fixing the target Q-network in Deep Q-Networks and Fitted Q Evaluation provides a way of overcoming this confounding, thus shedding new light on this popular but not well understood trick in the deep RL literature. An alternative approach to address confounding is to leverage techniques developed in the causality literature, notably instrumental variables (IV). We bring together here the literature on IV and RL by investigating whether IV approaches can lead to improved Q-function estimates. This paper analyzes and compares a wide range of recent IV methods in the context of offline policy evaluation (OPE), where the goal is to estimate the value of a policy using logged data only. By applying different IV techniques to OPE, we are not only able to recover previously proposed OPE methods such as model-based techniques but also to obtain competitive new techniques. We find empirically that state-of-the-art OPE methods are closely matched in performance by some IV methods such as AGMM, which were not developed for OPE. We open-source all our code and datasets at https://github.com/liyuan9988/IVOPEwithACME.", "published": "2021-05-21T06:22:34Z", "authors": ["Yutian Chen", "Liyuan Xu", "Caglar Gulcehre", "Tom Le Paine", "Arthur Gretton", "Nando de Freitas", "Arnaud Doucet"], "categories": ["cs.LG", "stat.ML"], "doi": "", "_query": "real estate valuation deep learning", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2309.16521v2", "title": "Generating Personalized Insulin Treatments Strategies with Deep Conditional Generative Time Series Models", "abstract": "We propose a novel framework that combines deep generative time series models with decision theory for generating personalized treatment strategies. It leverages historical patient trajectory data to jointly learn the generation of realistic personalized treatment and future outcome trajectories through deep generative time series models. In particular, our framework enables the generation of novel multivariate treatment strategies tailored to the personalized patient history and trained for optimal expected future outcomes based on conditional expected utility maximization. We demonstrate our framework by generating personalized insulin treatment strategies and blood glucose predictions for hospitalized diabetes patients, showcasing the potential of our approach for generating improved personalized treatment strategies. Keywords: deep generative model, probabilistic decision support, personalized treatment generation, insulin and blood glucose prediction", "published": "2023-09-28T15:27:28Z", "authors": ["Manuel Schürch", "Xiang Li", "Ahmed Allam", "Giulia Rathmes", "Amina Mollaysa", "Claudia Cavelti-Weder", "Michael Krauthammer"], "categories": ["stat.ML", "cs.LG"], "doi": "", "_query": "real estate valuation deep learning", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/1611.01652v2", "title": "A Differentiable Physics Engine for Deep Learning in Robotics", "abstract": "An important field in robotics is the optimization of controllers. Currently, robots are often treated as a black box in this optimization process, which is the reason why derivative-free optimization methods such as evolutionary algorithms or reinforcement learning are omnipresent. When gradient-based methods are used, models are kept small or rely on finite difference approximations for the Jacobian. This method quickly grows expensive with increasing numbers of parameters, such as found in deep learning. We propose the implementation of a modern physics engine, which can differentiate control parameters. This engine is implemented for both CPU and GPU. Firstly, this paper shows how such an engine speeds up the optimization process, even for small problems. Furthermore, it explains why this is an alternative approach to deep Q-learning, for using deep learning in robotics. Finally, we argue that this is a big step for deep learning in robotics, as it opens up new possibilities to optimize robots, both in hardware and software.", "published": "2016-11-05T13:34:58Z", "authors": ["Jonas Degrave", "Michiel Hermans", "Joni Dambre", "Francis wyffels"], "categories": ["cs.NE", "cs.AI", "cs.RO"], "doi": "", "_query": "real estate valuation deep learning", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2411.04285v1", "title": "Robust Real-Time Mortality Prediction in the Intensive Care Unit using Temporal Difference Learning", "abstract": "The task of predicting long-term patient outcomes using supervised machine learning is a challenging one, in part because of the high variance of each patient's trajectory, which can result in the model over-fitting to the training data. Temporal difference (TD) learning, a common reinforcement learning technique, may reduce variance by generalising learning to the pattern of state transitions rather than terminal outcomes. However, in healthcare this method requires several strong assumptions about patient states, and there appears to be limited literature evaluating the performance of TD learning against traditional supervised learning methods for long-term health outcome prediction tasks. In this study, we define a framework for applying TD learning to real-time irregularly sampled time series data using a Semi-Markov Reward Process. We evaluate the model framework in predicting intensive care mortality and show that TD learning under this framework can result in improved model robustness compared to standard supervised learning methods. and that this robustness is maintained even when validated on external datasets. This approach may offer a more reliable method when learning to predict patient outcomes using high-variance irregular time series data.", "published": "2024-11-06T22:11:20Z", "authors": ["Thomas Frost", "Kezhi Li", "Steve Harris"], "categories": ["cs.LG", "cs.AI"], "doi": "", "_query": "real estate valuation deep learning", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2310.06148v1", "title": "Understanding Transfer Learning and Gradient-Based Meta-Learning Techniques", "abstract": "Deep neural networks can yield good performance on various tasks but often require large amounts of data to train them. Meta-learning received considerable attention as one approach to improve the generalization of these networks from a limited amount of data. Whilst meta-learning techniques have been observed to be successful at this in various scenarios, recent results suggest that when evaluated on tasks from a different data distribution than the one used for training, a baseline that simply finetunes a pre-trained network may be more effective than more complicated meta-learning techniques such as MAML, which is one of the most popular meta-learning techniques. This is surprising as the learning behaviour of MAML mimics that of finetuning: both rely on re-using learned features. We investigate the observed performance differences between finetuning, MAML, and another meta-learning technique called Reptile, and show that MAML and Reptile specialize for fast adaptation in low-data regimes of similar data distribution as the one used for training. Our findings show that both the output layer and the noisy training conditions induced by data scarcity play important roles in facilitating this specialization for MAML. Lastly, we show that the pre-trained features as obtained by the finetuning baseline are more diverse and discriminative than those learned by MAML and Reptile. Due to this lack of diversity and distribution specialization, MAML and Reptile may fail to generalize to out-of-distribution tasks whereas finetuning can fall back on the diversity of the learned features.", "published": "2023-10-09T20:51:49Z", "authors": ["Mike Huisman", "Aske Plaat", "Jan N. van Rijn"], "categories": ["cs.LG", "cs.AI", "stat.ML"], "doi": "", "_query": "real estate valuation deep learning", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2206.10588v2", "title": "Robust SDE-Based Variational Formulations for Solving Linear PDEs via Deep Learning", "abstract": "The combination of Monte Carlo methods and deep learning has recently led to efficient algorithms for solving partial differential equations (PDEs) in high dimensions. Related learning problems are often stated as variational formulations based on associated stochastic differential equations (SDEs), which allow the minimization of corresponding losses using gradient-based optimization methods. In respective numerical implementations it is therefore crucial to rely on adequate gradient estimators that exhibit low variance in order to reach convergence accurately and swiftly. In this article, we rigorously investigate corresponding numerical aspects that appear in the context of linear Kolmogorov PDEs. In particular, we systematically compare existing deep learning approaches and provide theoretical explanations for their performances. Subsequently, we suggest novel methods that can be shown to be more robust both theoretically and numerically, leading to substantial performance improvements.", "published": "2022-06-21T17:59:39Z", "authors": ["Lorenz Richter", "Julius Berner"], "categories": ["cs.LG", "math.NA", "stat.ML"], "doi": "", "_query": "real estate valuation deep learning", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/astro-ph/0011479v1", "title": "SCUBA deep fields and source confusion", "abstract": "Deep submillimetre(submm)-wave surveys made over the last three years using the SCUBA camera at the 15-m James Clerk Maxwell Telescope (JCMT) have revealed a new population of very luminous high-redshift galaxies. The properties of this population, and their contribution to the intensity of the extragalactic background radiation field are described briefly, especially in the context of the SCUBA lens survey (carried out since 1997 by Ian Smail, Rob Ivison, Jean-Paul Kneib and the author: a full description of the survey and references to supporting work can be found in the recent catalogue paper). The potential problems caused by source confusion in the large 15-arcsec SCUBA observing beam for the identification and follow up of the results are discussed. The effects of confusion are not important for the study of 850-micron SCUBA sources brighter than about 2 mJy.", "published": "2000-11-26T16:33:14Z", "authors": ["Andrew W. Blain"], "categories": ["astro-ph"], "doi": "10.1007/10854354_24", "_query": "real estate valuation deep learning", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2304.05366v3", "title": "The No Free Lunch Theorem, Kolmogorov Complexity, and the Role of Inductive Biases in Machine Learning", "abstract": "No free lunch theorems for supervised learning state that no learner can solve all problems or that all learners achieve exactly the same accuracy on average over a uniform distribution on learning problems. Accordingly, these theorems are often referenced in support of the notion that individual problems require specially tailored inductive biases. While virtually all uniformly sampled datasets have high complexity, real-world problems disproportionately generate low-complexity data, and we argue that neural network models share this same preference, formalized using Kolmogorov complexity. Notably, we show that architectures designed for a particular domain, such as computer vision, can compress datasets on a variety of seemingly unrelated domains. Our experiments show that pre-trained and even randomly initialized language models prefer to generate low-complexity sequences. Whereas no free lunch theorems seemingly indicate that individual problems require specialized learners, we explain how tasks that often require human intervention such as picking an appropriately sized model when labeled data is scarce or plentiful can be automated into a single learning algorithm. These observations justify the trend in deep learning of unifying seemingly disparate problems with an increasingly small set of machine learning models.", "published": "2023-04-11T17:22:22Z", "authors": ["Micah Goldblum", "Marc Finzi", "Keefer Rowan", "Andrew Gordon Wilson"], "categories": ["cs.LG", "stat.ML"], "doi": "", "_query": "real estate valuation deep learning", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/1807.04439v1", "title": "Will it Blend? Composing Value Functions in Reinforcement Learning", "abstract": "An important property for lifelong-learning agents is the ability to combine existing skills to solve unseen tasks. In general, however, it is unclear how to compose skills in a principled way. We provide a \"recipe\" for optimal value function composition in entropy-regularised reinforcement learning (RL) and then extend this to the standard RL setting. Composition is demonstrated in a video game environment, where an agent with an existing library of policies is able to solve new tasks without the need for further learning.", "published": "2018-07-12T06:43:12Z", "authors": ["Benjamin van Niekerk", "Steven James", "Adam Earle", "Benjamin Rosman"], "categories": ["cs.LG", "stat.ML"], "doi": "", "_query": "real estate valuation deep learning", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/1907.10418v1", "title": "Improving Malaria Parasite Detection from Red Blood Cell using Deep Convolutional Neural Networks", "abstract": "Malaria is a female anopheles mosquito-bite inflicted life-threatening disease which is considered endemic in many parts of the world. This article focuses on improving malaria detection from patches segmented from microscopic images of red blood cell smears by introducing a deep convolutional neural network. Compared to the traditional methods that use tedious hand engineering feature extraction, the proposed method uses deep learning in an end-to-end arrangement that performs both feature extraction and classification directly from the raw segmented patches of the red blood smears. The dataset used in this study was taken from National Institute of Health named NIH Malaria Dataset. The evaluation metric accuracy and loss along with 5-fold cross validation was used to compare and select the best performing architecture. To maximize the performance, existing standard pre-processing techniques from the literature has also been experimented. In addition, several other complex architectures have been implemented and tested to pick the best performing model. A holdout test has also been conducted to verify how well the proposed model generalizes on unseen data. Our best model achieves an accuracy of almost 97.77%.", "published": "2019-07-23T04:30:12Z", "authors": ["Aimon Rahman", "Hasib Zunair", "M Sohel Rahman", "Jesia Quader Yuki", "Sabyasachi Biswas", "Md Ashraful Alam", "Nabila Binte Alam", "M. R. C. Mahdy"], "categories": ["eess.IV", "cs.LG", "stat.ML"], "doi": "", "_query": "real estate valuation deep learning", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2211.00534v3", "title": "Deep Learning for Global Wildfire Forecasting", "abstract": "Climate change is expected to aggravate wildfire activity through the exacerbation of fire weather. Improving our capabilities to anticipate wildfires on a global scale is of uttermost importance for mitigating their negative effects. In this work, we create a global fire dataset and demonstrate a prototype for predicting the presence of global burned areas on a sub-seasonal scale with the use of segmentation deep learning models. Particularly, we present an open-access global analysis-ready datacube, which contains a variety of variables related to the seasonal and sub-seasonal fire drivers (climate, vegetation, oceanic indices, human-related variables), as well as the historical burned areas and wildfire emissions for 2001-2021. We train a deep learning model, which treats global wildfire forecasting as an image segmentation task and skillfully predicts the presence of burned areas 8, 16, 32 and 64 days ahead of time. Our work motivates the use of deep learning for global burned area forecasting and paves the way towards improved anticipation of global wildfire patterns.", "published": "2022-11-01T15:39:01Z", "authors": ["Ioannis Prapas", "Akanksha Ahuja", "Spyros Kondylatos", "Ilektra Karasante", "Eleanna Panagiotou", "Lazaro Alonso", "Charalampos Davalas", "Dimitrios Michail", "Nuno Carvalhais", "Ioannis Papoutsis"], "categories": ["cs.LG", "cs.AI", "cs.CV"], "doi": "", "_query": "real estate valuation deep learning", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/1609.05317v1", "title": "Deep Kinematic Pose Regression", "abstract": "Learning articulated object pose is inherently difficult because the pose is high dimensional but has many structural constraints. Most existing work do not model such constraints and does not guarantee the geometric validity of their pose estimation, therefore requiring a post-processing to recover the correct geometry if desired, which is cumbersome and sub-optimal. In this work, we propose to directly embed a kinematic object model into the deep neutral network learning for general articulated object pose estimation. The kinematic function is defined on the appropriately parameterized object motion variables. It is differentiable and can be used in the gradient descent based optimization in network training. The prior knowledge on the object geometric model is fully exploited and the structure is guaranteed to be valid. We show convincing experiment results on a toy example and the 3D human pose estimation problem. For the latter we achieve state-of-the-art result on Human3.6M dataset.", "published": "2016-09-17T11:22:11Z", "authors": ["Xingyi Zhou", "Xiao Sun", "Wei Zhang", "Shuang Liang", "Yichen Wei"], "categories": ["cs.CV"], "doi": "", "_query": "real estate valuation deep learning", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/1911.02875v3", "title": "$H_\\infty$ Model-free Reinforcement Learning with Robust Stability Guarantee", "abstract": "Reinforcement learning is showing great potentials in robotics applications, including autonomous driving, robot manipulation and locomotion. However, with complex uncertainties in the real-world environment, it is difficult to guarantee the successful generalization and sim-to-real transfer of learned policies theoretically. In this paper, we introduce and extend the idea of robust stability and $H_\\infty$ control to design policies with both stability and robustness guarantee. Specifically, a sample-based approach for analyzing the Lyapunov stability and performance robustness of a learning-based control system is proposed. Based on the theoretical results, a maximum entropy algorithm is developed for searching Lyapunov function and designing a policy with provable robust stability guarantee. Without any specific domain knowledge, our method can find a policy that is robust to various uncertainties and generalizes well to different test environments. In our experiments, we show that our method achieves better robustness to both large impulsive disturbances and parametric variations in the environment than the state-of-art results in both robust and generic RL, as well as classic control. Anonymous code is available to reproduce the experimental results at https://github.com/RobustStabilityGuaranteeRL/RobustStabilityGuaranteeRL.", "published": "2019-11-07T12:32:27Z", "authors": ["Minghao Han", "Yuan Tian", "Lixian Zhang", "Jun Wang", "Wei Pan"], "categories": ["cs.LG", "cs.RO", "eess.SY"], "doi": "", "_query": "real estate valuation deep learning", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2302.04357v1", "title": "On Computable Online Learning", "abstract": "We initiate a study of computable online (c-online) learning, which we analyze under varying requirements for \"optimality\" in terms of the mistake bound. Our main contribution is to give a necessary and sufficient condition for optimal c-online learning and show that the Littlestone dimension no longer characterizes the optimal mistake bound of c-online learning. Furthermore, we introduce anytime optimal (a-optimal) online learning, a more natural conceptualization of \"optimality\" and a generalization of Littlestone's Standard Optimal Algorithm. We show the existence of a computational separation between a-optimal and optimal online learning, proving that a-optimal online learning is computationally more difficult. Finally, we consider online learning with no requirements for optimality, and show, under a weaker notion of computability, that the finiteness of the Littlestone dimension no longer characterizes whether a class is c-online learnable with finite mistake bound. A potential avenue for strengthening this result is suggested by exploring the relationship between c-online and CPAC learning, where we show that c-online learning is as difficult as improper CPAC learning.", "published": "2023-02-08T22:09:06Z", "authors": ["Niki Hasrati", "Shai Ben-David"], "categories": ["cs.LG", "stat.ML"], "doi": "", "_query": "real estate valuation deep learning", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2406.03260v3", "title": "Feature learning in finite-width Bayesian deep linear networks with multiple outputs and convolutional layers", "abstract": "Deep linear networks have been extensively studied, as they provide simplified models of deep learning. However, little is known in the case of finite-width architectures with multiple outputs and convolutional layers. In this manuscript, we provide rigorous results for the statistics of functions implemented by the aforementioned class of networks, thus moving closer to a complete characterization of feature learning in the Bayesian setting. Our results include: (i) an exact and elementary non-asymptotic integral representation for the joint prior distribution over the outputs, given in terms of a mixture of Gaussians; (ii) an analytical formula for the posterior distribution in the case of squared error loss function (Gaussian likelihood); (iii) a quantitative description of the feature learning infinite-width regime, using large deviation theory. From a physical perspective, deep architectures with multiple outputs or convolutional layers represent different manifestations of kernel shape renormalization, and our work provides a dictionary that translates this physics intuition and terminology into rigorous Bayesian statistics.", "published": "2024-06-05T13:37:42Z", "authors": ["Federico Bassetti", "Marco Gherardi", "Alessandro Ingrosso", "Mauro Pastore", "Pietro Rotondo"], "categories": ["stat.ML", "cond-mat.dis-nn", "cs.LG", "math.ST"], "doi": "", "_query": "real estate valuation deep learning", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/1610.07187v3", "title": "Learning Deep Architectures for Interaction Prediction in Structure-based Virtual Screening", "abstract": "We introduce a deep learning architecture for structure-based virtual screening that generates fixed-sized fingerprints of proteins and small molecules by applying learnable atom convolution and softmax operations to each compound separately. These fingerprints are further transformed non-linearly, their inner-product is calculated and used to predict the binding potential. Moreover, we show that widely used benchmark datasets may be insufficient for testing structure-based virtual screening methods that utilize machine learning. Therefore, we introduce a new benchmark dataset, which we constructed based on DUD-E and PDBBind databases.", "published": "2016-10-23T15:51:46Z", "authors": ["Adam Gonczarek", "Jakub M. Tomczak", "Szymon Zaręba", "Joanna Kaczmar", "Piotr Dąbrowski", "Michał J. Walczak"], "categories": ["stat.ML", "cs.LG"], "doi": "10.1016/j.compbiomed.2017.09.007", "_query": "real estate valuation deep learning", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/1608.00220v1", "title": "Learning Robust Features using Deep Learning for Automatic Seizure Detection", "abstract": "We present and evaluate the capacity of a deep neural network to learn robust features from EEG to automatically detect seizures. This is a challenging problem because seizure manifestations on EEG are extremely variable both inter- and intra-patient. By simultaneously capturing spectral, temporal and spatial information our recurrent convolutional neural network learns a general spatially invariant representation of a seizure. The proposed approach exceeds significantly previous results obtained on cross-patient classifiers both in terms of sensitivity and false positive rate. Furthermore, our model proves to be robust to missing channel and variable electrode montage.", "published": "2016-07-31T14:28:15Z", "authors": ["Pierre Thodoroff", "Joelle Pineau", "Andrew Lim"], "categories": ["cs.LG", "cs.CV"], "doi": "", "_query": "real estate valuation deep learning", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2509.23544v2", "title": "End-to-End Deep Learning for Predicting Metric Space-Valued Outputs", "abstract": "Many modern applications involve predicting structured, non-Euclidean outputs such as probability distributions, networks, and symmetric positive-definite matrices. These outputs are naturally modeled as elements of general metric spaces, where classical regression techniques that rely on vector space structure no longer apply. We introduce E2M (End-to-End Metric regression), a deep learning framework for predicting metric space-valued outputs. E2M performs prediction via weighted Fréchet means over training outputs, where the weights are learned by a neural network conditioned on the input. This construction provides a principled mechanism for geometry-aware prediction that avoids surrogate embeddings and restrictive parametric assumptions, while fully preserving the intrinsic geometry of the output space. We establish theoretical guarantees, including a universal approximation theorem that characterizes the expressive capacity of the model and a convergence analysis of the entropy-regularized training objective. Through extensive simulations involving probability distributions, networks, and symmetric positive-definite matrices, we show that E2M consistently achieves state-of-the-art performance, with its advantages becoming more pronounced at larger sample sizes. Applications to human mortality distributions and New York City taxi networks further demonstrate the flexibility and practical utility of this framework.", "published": "2025-09-28T00:46:12Z", "authors": ["Yidong Zhou", "Su I Iao", "Hans-Georg Müller"], "categories": ["stat.ML", "cs.AI", "cs.LG", "stat.ME"], "doi": "", "_query": "real estate valuation deep learning", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2011.09004v1", "title": "Explaining Conditions for Reinforcement Learning Behaviors from Real and Imagined Data", "abstract": "The deployment of reinforcement learning (RL) in the real world comes with challenges in calibrating user trust and expectations. As a step toward developing RL systems that are able to communicate their competencies, we present a method of generating human-interpretable abstract behavior models that identify the experiential conditions leading to different task execution strategies and outcomes. Our approach consists of extracting experiential features from state representations, abstracting strategy descriptors from trajectories, and training an interpretable decision tree that identifies the conditions most predictive of different RL behaviors. We demonstrate our method on trajectory data generated from interactions with the environment and on imagined trajectory data that comes from a trained probabilistic world model in a model-based RL setting.", "published": "2020-11-17T23:40:47Z", "authors": ["Aastha Acharya", "Rebecca Russell", "Nisar R. Ahmed"], "categories": ["cs.LG", "cs.AI", "cs.HC", "cs.NE", "cs.RO"], "doi": "", "_query": "real estate valuation deep learning", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/1711.08946v2", "title": "Action Branching Architectures for Deep Reinforcement Learning", "abstract": "Discrete-action algorithms have been central to numerous recent successes of deep reinforcement learning. However, applying these algorithms to high-dimensional action tasks requires tackling the combinatorial increase of the number of possible actions with the number of action dimensions. This problem is further exacerbated for continuous-action tasks that require fine control of actions via discretization. In this paper, we propose a novel neural architecture featuring a shared decision module followed by several network branches, one for each action dimension. This approach achieves a linear increase of the number of network outputs with the number of degrees of freedom by allowing a level of independence for each individual action dimension. To illustrate the approach, we present a novel agent, called Branching Dueling Q-Network (BDQ), as a branching variant of the Dueling Double Deep Q-Network (Dueling DDQN). We evaluate the performance of our agent on a set of challenging continuous control tasks. The empirical results show that the proposed agent scales gracefully to environments with increasing action dimensionality and indicate the significance of the shared decision module in coordination of the distributed action branches. Furthermore, we show that the proposed agent performs competitively against a state-of-the-art continuous control algorithm, Deep Deterministic Policy Gradient (DDPG).", "published": "2017-11-24T12:45:30Z", "authors": ["Arash Tavakoli", "Fabio Pardo", "Petar Kormushev"], "categories": ["cs.LG", "cs.AI"], "doi": "", "_query": "real estate valuation deep learning", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2311.00235v1", "title": "Implicit biases in multitask and continual learning from a backward error analysis perspective", "abstract": "Using backward error analysis, we compute implicit training biases in multitask and continual learning settings for neural networks trained with stochastic gradient descent. In particular, we derive modified losses that are implicitly minimized during training. They have three terms: the original loss, accounting for convergence, an implicit flatness regularization term proportional to the learning rate, and a last term, the conflict term, which can theoretically be detrimental to both convergence and implicit regularization. In multitask, the conflict term is a well-known quantity, measuring the gradient alignment between the tasks, while in continual learning the conflict term is a new quantity in deep learning optimization, although a basic tool in differential geometry: The Lie bracket between the task gradients.", "published": "2023-11-01T02:37:32Z", "authors": ["Benoit Dherin"], "categories": ["stat.ML", "cs.AI", "cs.LG"], "doi": "", "_query": "real estate valuation deep learning", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/1811.11233v1", "title": "Distributed traffic light control at uncoupled intersections with real-world topology by deep reinforcement learning", "abstract": "This work examines the implications of uncoupled intersections with local real-world topology and sensor setup on traffic light control approaches. Control approaches are evaluated with respect to: Traffic flow, fuel consumption and noise emission at intersections. The real-world road network of Friedrichshafen is depicted, preprocessed and the present traffic light controlled intersections are modeled with respect to state space and action space. Different strategies, containing fixed-time, gap-based and time-based control approaches as well as our deep reinforcement learning based control approach, are implemented and assessed. Our novel DRL approach allows for modeling the TLC action space, with respect to phase selection as well as selection of transition timings. It was found that real-world topologies, and thus irregularly arranged intersections have an influence on the performance of traffic light control approaches. This is even to be observed within the same intersection types (n-arm, m-phases). Moreover we could show, that these influences can be efficiently dealt with by our deep reinforcement learning based control approach.", "published": "2018-11-27T20:08:28Z", "authors": ["Mark Schutera", "Niklas Goby", "Stefan Smolarek", "Markus Reischl"], "categories": ["cs.AI"], "doi": "", "_query": "real estate valuation deep learning", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2201.02658v2", "title": "Fair and efficient contribution valuation for vertical federated learning", "abstract": "Federated learning is an emerging technology for training machine learning models across decentralized data sources without sharing data. Vertical federated learning, also known as feature-based federated learning, applies to scenarios where data sources have the same sample IDs but different feature sets. To ensure fairness among data owners, it is critical to objectively assess the contributions from different data sources and compensate the corresponding data owners accordingly. The Shapley value is a provably fair contribution valuation metric originating from cooperative game theory. However, its straight-forward computation requires extensively retraining a model on each potential combination of data sources, leading to prohibitively high communication and computation overheads due to multiple rounds of federated learning. To tackle this challenge, we propose a contribution valuation metric called vertical federated Shapley value (VerFedSV) based on the classic Shapley value. We show that VerFedSV not only satisfies many desirable properties of fairness but is also efficient to compute. Moreover, VerFedSV can be adapted to both synchronous and asynchronous vertical federated learning algorithms. Both theoretical analysis and extensive experimental results demonstrate the fairness, efficiency, adaptability, and effectiveness of VerFedSV.", "published": "2022-01-07T19:57:15Z", "authors": ["Zhenan Fan", "Huang Fang", "Xinglu Wang", "Zirui Zhou", "Jian Pei", "Michael P. Friedlander", "Yong Zhang"], "categories": ["cs.LG"], "doi": "", "_query": "real estate valuation deep learning", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2605.30389v1", "title": "The Inclusion Depth of Pattern Languages: An Open Problem in Algorithmic Learning Theory", "abstract": "Pattern languages are a classical model in formal language theory and algorithmic learning theory. This note formulates the problem of computing the inclusion depth of a pattern language: the length of the longest strict inclusion chain from the universal pattern language to the language generated by a given pattern. Inclusion depth captures the mind-change complexity of pattern identification from positive data. The central open question is whether the inclusion depth ID_Sigma(p) is computable for every pattern p over every finite alphabet Sigma with at least two symbols, and whether it is computable in polynomial time. A simple conjectured formula, ID_Sigma(p) = 2|p| - #var(p) - 1, would imply a linear-time algorithm. The problem connects pattern language inclusion, combinatorics on words, language identification in the limit, and mind-change-bounded learning.", "published": "2026-05-28T11:19:25Z", "authors": ["Wei Luo"], "categories": ["cs.FL", "cs.LG"], "doi": "10.1007/11503415_48", "_query": "real estate valuation deep learning", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/1911.09560v1", "title": "Memory-Efficient Episodic Control Reinforcement Learning with Dynamic Online k-means", "abstract": "Recently, neuro-inspired episodic control (EC) methods have been developed to overcome the data-inefficiency of standard deep reinforcement learning approaches. Using non-/semi-parametric models to estimate the value function, they learn rapidly, retrieving cached values from similar past states. In realistic scenarios, with limited resources and noisy data, maintaining meaningful representations in memory is essential to speed up the learning and avoid catastrophic forgetting. Unfortunately, EC methods have a large space and time complexity. We investigate different solutions to these problems based on prioritising and ranking stored states, as well as online clustering techniques. We also propose a new dynamic online k-means algorithm that is both computationally-efficient and yields significantly better performance at smaller memory sizes; we validate this approach on classic reinforcement learning environments and Atari games.", "published": "2019-11-21T15:54:49Z", "authors": ["Andrea Agostinelli", "Kai Arulkumaran", "Marta Sarrico", "Pierre Richemond", "Anil Anthony Bharath"], "categories": ["cs.LG", "cs.NE", "stat.ML"], "doi": "", "_query": "real estate valuation deep learning", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2411.18704v1", "title": "Exponential Moving Average of Weights in Deep Learning: Dynamics and Benefits", "abstract": "Weight averaging of Stochastic Gradient Descent (SGD) iterates is a popular method for training deep learning models. While it is often used as part of complex training pipelines to improve generalization or serve as a `teacher' model, weight averaging lacks proper evaluation on its own. In this work, we present a systematic study of the Exponential Moving Average (EMA) of weights. We first explore the training dynamics of EMA, give guidelines for hyperparameter tuning, and highlight its good early performance, partly explaining its success as a teacher. We also observe that EMA requires less learning rate decay compared to SGD since averaging naturally reduces noise, introducing a form of implicit regularization. Through extensive experiments, we show that EMA solutions differ from last-iterate solutions. EMA models not only generalize better but also exhibit improved i) robustness to noisy labels, ii) prediction consistency, iii) calibration and iv) transfer learning. Therefore, we suggest that an EMA of weights is a simple yet effective plug-in to improve the performance of deep learning models.", "published": "2024-11-27T19:14:27Z", "authors": ["Daniel Morales-Brotons", "Thijs Vogels", "Hadrien Hendrikx"], "categories": ["cs.LG"], "doi": "", "_query": "real estate valuation deep learning", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/1710.10363v6", "title": "Diff-DAC: Distributed Actor-Critic for Average Multitask Deep Reinforcement Learning", "abstract": "We propose a fully distributed actor-critic algorithm approximated by deep neural networks, named \\textit{Diff-DAC}, with application to single-task and to average multitask reinforcement learning (MRL). Each agent has access to data from its local task only, but it aims to learn a policy that performs well on average for the whole set of tasks. During the learning process, agents communicate their value-policy parameters to their neighbors, diffusing the information across the network, so that they converge to a common policy, with no need for a central node. The method is scalable, since the computational and communication costs per agent grow with its number of neighbors. We derive Diff-DAC's from duality theory and provide novel insights into the standard actor-critic framework, showing that it is actually an instance of the dual ascent method that approximates the solution of a linear program. Experiments suggest that Diff-DAC can outperform the single previous distributed MRL approach (i.e., Dist-MTLPS) and even the centralized architecture.", "published": "2017-10-28T00:55:01Z", "authors": ["Sergio Valcarcel Macua", "Aleksi Tukiainen", "Daniel García-Ocaña Hernández", "David Baldazo", "Enrique Munoz de Cote", "Santiago Zazo"], "categories": ["cs.LG", "cs.MA", "math.OC", "stat.ML"], "doi": "", "_query": "real estate valuation deep learning", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2404.19675v1", "title": "Deep Learning for Educational Data Science", "abstract": "With the ever-growing presence of deep artificial neural networks in every facet of modern life, a growing body of researchers in educational data science -- a field consisting of various interrelated research communities -- have turned their attention to leveraging these powerful algorithms within the domain of education. Use cases range from advanced knowledge tracing models that can leverage open-ended student essays or snippets of code to automatic affect and behavior detectors that can identify when a student is frustrated or aimlessly trying to solve problems unproductively -- and much more. This chapter provides a brief introduction to deep learning, describes some of its advantages and limitations, presents a survey of its many uses in education, and discusses how it may further come to shape the field of educational data science.", "published": "2024-04-12T19:17:14Z", "authors": ["Juan D. Pinto", "Luc Paquette"], "categories": ["cs.CY", "cs.AI", "cs.LG"], "doi": "", "_query": "real estate valuation deep learning", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/1708.04133v1", "title": "Reproducibility of Benchmarked Deep Reinforcement Learning Tasks for Continuous Control", "abstract": "Policy gradient methods in reinforcement learning have become increasingly prevalent for state-of-the-art performance in continuous control tasks. Novel methods typically benchmark against a few key algorithms such as deep deterministic policy gradients and trust region policy optimization. As such, it is important to present and use consistent baselines experiments. However, this can be difficult due to general variance in the algorithms, hyper-parameter tuning, and environment stochasticity. We investigate and discuss: the significance of hyper-parameters in policy gradients for continuous control, general variance in the algorithms, and reproducibility of reported results. We provide guidelines on reporting novel results as comparisons against baseline methods such that future researchers can make informed decisions when investigating novel methods.", "published": "2017-08-10T19:20:15Z", "authors": ["Riashat Islam", "Peter Henderson", "Maziar Gomrokchi", "Doina Precup"], "categories": ["cs.LG"], "doi": "", "_query": "real estate valuation deep learning", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2406.00907v2", "title": "DDA: Dimensionality Driven Augmentation Search for Contrastive Learning in Laparoscopic Surgery", "abstract": "Self-supervised learning (SSL) has potential for effective representation learning in medical imaging, but the choice of data augmentation is critical and domain-specific. It remains uncertain if general augmentation policies suit surgical applications. In this work, we automate the search for suitable augmentation policies through a new method called Dimensionality Driven Augmentation Search (DDA). DDA leverages the local dimensionality of deep representations as a proxy target, and differentiably searches for suitable data augmentation policies in contrastive learning. We demonstrate the effectiveness and efficiency of DDA in navigating a large search space and successfully identifying an appropriate data augmentation policy for laparoscopic surgery. We systematically evaluate DDA across three laparoscopic image classification and segmentation tasks, where it significantly improves over existing baselines. Furthermore, DDA's optimised set of augmentations provides insight into domain-specific dependencies when applying contrastive learning in medical applications. For example, while hue is an effective augmentation for natural images, it is not advantageous for laparoscopic images.", "published": "2024-06-03T00:30:23Z", "authors": ["Yuning Zhou", "Henry Badgery", "Matthew Read", "James Bailey", "Catherine E. Davey"], "categories": ["cs.CV", "cs.LG"], "doi": "", "_query": "real estate valuation deep learning", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2512.14767v1", "title": "Privacy-Preserving Feature Valuation in Vertical Federated Learning Using Shapley-CMI and PSI Permutation", "abstract": "Federated Learning (FL) is an emerging machine learning paradigm that enables multiple parties to collaboratively train models without sharing raw data, ensuring data privacy. In Vertical FL (VFL), where each party holds different features for the same users, a key challenge is to evaluate the feature contribution of each party before any model is trained, particularly in the early stages when no model exists. To address this, the Shapley-CMI method was recently proposed as a model-free, information-theoretic approach to feature valuation using Conditional Mutual Information (CMI). However, its original formulation did not provide a practical implementation capable of computing the required permutations and intersections securely. This paper presents a novel privacy-preserving implementation of Shapley-CMI for VFL. Our system introduces a private set intersection (PSI) server that performs all necessary feature permutations and computes encrypted intersection sizes across discretized and encrypted ID groups, without the need for raw data exchange. Each party then uses these intersection results to compute Shapley-CMI values, computing the marginal utility of their features. Initial experiments confirm the correctness and privacy of the proposed system, demonstrating its viability for secure and efficient feature contribution estimation in VFL. This approach ensures data confidentiality, scales across multiple parties, and enables fair data valuation without requiring the sharing of raw data or training models.", "published": "2025-12-16T08:01:39Z", "authors": ["Unai Laskurain", "Aitor Aguirre-Ortuzar", "Urko Zurutuza"], "categories": ["cs.CR", "cs.AI", "cs.DC"], "doi": "", "_query": "real estate valuation deep learning", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2511.02100v1", "title": "Geometric Data Valuation via Leverage Scores", "abstract": "Shapley data valuation provides a principled, axiomatic framework for assigning importance to individual datapoints, and has gained traction in dataset curation, pruning, and pricing. However, it is a combinatorial measure that requires evaluating marginal utility across all subsets of the data, making it computationally infeasible at scale. We propose a geometric alternative based on statistical leverage scores, which quantify each datapoint's structural influence in the representation space by measuring how much it extends the span of the dataset and contributes to the effective dimensionality of the training problem. We show that our scores satisfy the dummy, efficiency, and symmetry axioms of Shapley valuation and that extending them to \\emph{ridge leverage scores} yields strictly positive marginal gains that connect naturally to classical A- and D-optimal design criteria. We further show that training on a leverage-sampled subset produces a model whose parameters and predictive risk are within $O(\\varepsilon)$ of the full-data optimum, thereby providing a rigorous link between data valuation and downstream decision quality. Finally, we conduct an active learning experiment in which we empirically demonstrate that ridge-leverage sampling outperforms standard baselines without requiring access gradients or backward passes.", "published": "2025-11-03T22:20:50Z", "authors": ["Rodrigo Mendoza-Smith"], "categories": ["cs.LG", "cs.AI", "math.NA", "math.OC"], "doi": "", "_query": "real estate valuation deep learning", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/1708.08551v1", "title": "Deep Learning for Accelerated Reliability Analysis of Infrastructure Networks", "abstract": "Natural disasters can have catastrophic impacts on the functionality of infrastructure systems and cause severe physical and socio-economic losses. Given budget constraints, it is crucial to optimize decisions regarding mitigation, preparedness, response, and recovery practices for these systems. This requires accurate and efficient means to evaluate the infrastructure system reliability. While numerous research efforts have addressed and quantified the impact of natural disasters on infrastructure systems, typically using the Monte Carlo approach, they still suffer from high computational cost and, thus, are of limited applicability to large systems. This paper presents a deep learning framework for accelerating infrastructure system reliability analysis. In particular, two distinct deep neural network surrogates are constructed and studied: (1) A classifier surrogate which speeds up the connectivity determination of networks, and (2) An end-to-end surrogate that replaces a number of components such as roadway status realization, connectivity determination, and connectivity averaging. The proposed approach is applied to a simulation-based study of the two-terminal connectivity of a California transportation network subject to extreme probabilistic earthquake events. Numerical results highlight the effectiveness of the proposed approach in accelerating the transportation system two-terminal reliability analysis with extremely high prediction accuracy.", "published": "2017-08-28T22:41:11Z", "authors": ["Mohammad Amin Nabian", "Hadi Meidani"], "categories": ["cs.CE", "cs.AI", "stat.ML"], "doi": "10.1111/mice.12359", "_query": "real estate valuation deep learning", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2202.11985v1", "title": "Can deep neural networks learn process model structure? An assessment framework and analysis", "abstract": "Predictive process monitoring concerns itself with the prediction of ongoing cases in (business) processes. Prediction tasks typically focus on remaining time, outcome, next event or full case suffix prediction. Various methods using machine and deep learning havebeen proposed for these tasks in recent years. Especially recurrent neural networks (RNNs) such as long short-term memory nets (LSTMs) have gained in popularity. However, no research focuses on whether such neural network-based models can truly learn the structure of underlying process models. For instance, can such neural networks effectively learn parallel behaviour or loops? Therefore, in this work, we propose an evaluation scheme complemented with new fitness, precision, and generalisation metrics, specifically tailored towards measuring the capacity of deep learning models to learn process model structure. We apply this framework to several process models with simple control-flow behaviour, on the task of next-event prediction. Our results show that, even for such simplistic models, careful tuning of overfitting countermeasures is required to allow these models to learn process model structure.", "published": "2022-02-24T09:44:13Z", "authors": ["Jari Peeperkorn", "Seppe vanden Broucke", "Jochen De Weerdt"], "categories": ["cs.LG"], "doi": "10.1007/978-3-030-98581-3_10", "_query": "real estate valuation deep learning", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2010.11832v1", "title": "Ordered fields dense in their real closure and definable convex valuations", "abstract": "In this paper, we undertake a systematic model and valuation theoretic study of the class of ordered fields which are dense in their real closure. We apply this study to determine definable henselian valuations on ordered fields, in the language of ordered rings. In light of our results, we re-examine the Shelah-Hasson Conjecture (specialised to ordered fields) and provide an example limiting its valuation theoretic conclusions.", "published": "2020-10-22T16:09:05Z", "authors": ["Lothar Sebastian Krapp", "Salma Kuhlmann", "Gabriel Lehéricy"], "categories": ["math.LO"], "doi": "10.1515/forum-2020-0030", "_query": "real estate valuation deep learning", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/1901.09085v1", "title": "Generalisation dynamics of online learning in over-parameterised neural networks", "abstract": "Deep neural networks achieve stellar generalisation on a variety of problems, despite often being large enough to easily fit all their training data. Here we study the generalisation dynamics of two-layer neural networks in a teacher-student setup, where one network, the student, is trained using stochastic gradient descent (SGD) on data generated by another network, called the teacher. We show how for this problem, the dynamics of SGD are captured by a set of differential equations. In particular, we demonstrate analytically that the generalisation error of the student increases linearly with the network size, with other relevant parameters held constant. Our results indicate that achieving good generalisation in neural networks depends on the interplay of at least the algorithm, its learning rate, the model architecture, and the data set.", "published": "2019-01-25T21:13:15Z", "authors": ["Sebastian Goldt", "Madhu S. Advani", "Andrew M. Saxe", "Florent Krzakala", "Lenka Zdeborová"], "categories": ["stat.ML", "cond-mat.dis-nn", "cond-mat.stat-mech", "cs.LG"], "doi": "", "_query": "real estate valuation deep learning", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2002.07920v1", "title": "Block Switching: A Stochastic Approach for Deep Learning Security", "abstract": "Recent study of adversarial attacks has revealed the vulnerability of modern deep learning models. That is, subtly crafted perturbations of the input can make a trained network with high accuracy produce arbitrary incorrect predictions, while maintain imperceptible to human vision system. In this paper, we introduce Block Switching (BS), a defense strategy against adversarial attacks based on stochasticity. BS replaces a block of model layers with multiple parallel channels, and the active channel is randomly assigned in the run time hence unpredictable to the adversary. We show empirically that BS leads to a more dispersed input gradient distribution and superior defense effectiveness compared with other stochastic defenses such as stochastic activation pruning (SAP). Compared to other defenses, BS is also characterized by the following features: (i) BS causes less test accuracy drop; (ii) BS is attack-independent and (iii) BS is compatible with other defenses and can be used jointly with others.", "published": "2020-02-18T23:14:25Z", "authors": ["Xiao Wang", "Siyue Wang", "Pin-Yu Chen", "Xue Lin", "Peter Chin"], "categories": ["cs.LG", "cs.CV"], "doi": "10.47852/bonviewJCCE2202320", "_query": "real estate valuation deep learning", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2206.10369v1", "title": "The State of Sparse Training in Deep Reinforcement Learning", "abstract": "The use of sparse neural networks has seen rapid growth in recent years, particularly in computer vision. Their appeal stems largely from the reduced number of parameters required to train and store, as well as in an increase in learning efficiency. Somewhat surprisingly, there have been very few efforts exploring their use in Deep Reinforcement Learning (DRL). In this work we perform a systematic investigation into applying a number of existing sparse training techniques on a variety of DRL agents and environments. Our results corroborate the findings from sparse training in the computer vision domain - sparse networks perform better than dense networks for the same parameter count - in the DRL domain. We provide detailed analyses on how the various components in DRL are affected by the use of sparse networks and conclude by suggesting promising avenues for improving the effectiveness of sparse training methods, as well as for advancing their use in DRL.", "published": "2022-06-17T14:08:00Z", "authors": ["Laura Graesser", "Utku Evci", "Erich Elsen", "Pablo Samuel Castro"], "categories": ["cs.LG", "cs.AI"], "doi": "", "_query": "real estate valuation deep learning", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2405.06582v3", "title": "The Role of Learning Algorithms in Collective Action", "abstract": "Collective action in machine learning is the study of the control that a coordinated group can have over machine learning algorithms. While previous research has concentrated on assessing the impact of collectives against Bayes (sub-)optimal classifiers, this perspective is limited in that it does not account for the choice of learning algorithm. Since classifiers seldom behave like Bayes classifiers and are influenced by the choice of learning algorithms along with their inherent biases, in this work we initiate the study of how the choice of the learning algorithm plays a role in the success of a collective in practical settings. Specifically, we focus on distributionally robust optimization (DRO), popular for improving a worst group error, and on the ubiquitous stochastic gradient descent (SGD), due to its inductive bias for \"simpler\" functions. Our empirical results, supported by a theoretical foundation, show that the effective size and success of the collective are highly dependent on properties of the learning algorithm. This highlights the necessity of taking the learning algorithm into account when studying the impact of collective action in machine learning.", "published": "2024-05-10T16:36:59Z", "authors": ["Omri Ben-Dov", "Jake Fawkes", "Samira Samadi", "Amartya Sanyal"], "categories": ["cs.LG", "cs.CY", "stat.ML"], "doi": "", "_query": "real estate valuation deep learning", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2407.01704v1", "title": "Weight Clipping for Deep Continual and Reinforcement Learning", "abstract": "Many failures in deep continual and reinforcement learning are associated with increasing magnitudes of the weights, making them hard to change and potentially causing overfitting. While many methods address these learning failures, they often change the optimizer or the architecture, a complexity that hinders widespread adoption in various systems. In this paper, we focus on learning failures that are associated with increasing weight norm and we propose a simple technique that can be easily added on top of existing learning systems: clipping neural network weights to limit them to a specific range. We study the effectiveness of weight clipping in a series of supervised and reinforcement learning experiments. Our empirical results highlight the benefits of weight clipping for generalization, addressing loss of plasticity and policy collapse, and facilitating learning with a large replay ratio.", "published": "2024-07-01T18:29:29Z", "authors": ["Mohamed Elsayed", "Qingfeng Lan", "Clare Lyle", "A. Rupam Mahmood"], "categories": ["cs.LG", "cs.AI"], "doi": "", "_query": "real estate valuation deep learning", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2302.09656v5", "title": "Credal Bayesian Deep Learning", "abstract": "Uncertainty quantification and robustness to distribution shifts are important goals in machine learning and artificial intelligence. Although Bayesian Neural Networks (BNNs) allow for uncertainty in the predictions to be assessed, different sources of predictive uncertainty cannot be distinguished properly. We present Credal Bayesian Deep Learning (CBDL). Heuristically, CBDL allows to train an (uncountably) infinite ensemble of BNNs, using only finitely many elements. This is possible thanks to prior and likelihood finitely generated credal sets (FGCSs), a concept from the imprecise probability literature. Intuitively, convex combinations of a finite collection of prior-likelihood pairs are able to represent infinitely many such pairs. After training, CBDL outputs a set of posteriors on the parameters of the neural network. At inference time, such posterior set is used to derive a set of predictive distributions that is in turn utilized to distinguish between (predictive) aleatoric and epistemic uncertainties, and to quantify them. The predictive set also produces either (i) a collection of outputs enjoying desirable probabilistic guarantees, or (ii) the single output that is deemed the best, that is, the one having the highest predictive lower probability -- another imprecise-probabilistic concept. CBDL is more robust than single BNNs to prior and likelihood misspecification, and to distribution shift. We show that CBDL is better at quantifying and disentangling different types of (predictive) uncertainties than single BNNs and ensemble of BNNs. In addition, we apply CBDL to two case studies to demonstrate its downstream tasks capabilities: one, for motion prediction in autonomous driving scenarios, and two, to model blood glucose and insulin dynamics for artificial pancreas control. We show that CBDL performs better when compared to an ensemble of BNNs baseline.", "published": "2023-02-19T19:03:26Z", "authors": ["Michele Caprio", "Souradeep Dutta", "Kuk Jin Jang", "Vivian Lin", "Radoslav Ivanov", "Oleg Sokolsky", "Insup Lee"], "categories": ["cs.LG", "stat.ML"], "doi": "", "_query": "real estate valuation deep learning", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/1806.02199v7", "title": "SOM-VAE: Interpretable Discrete Representation Learning on Time Series", "abstract": "High-dimensional time series are common in many domains. Since human cognition is not optimized to work well in high-dimensional spaces, these areas could benefit from interpretable low-dimensional representations. However, most representation learning algorithms for time series data are difficult to interpret. This is due to non-intuitive mappings from data features to salient properties of the representation and non-smoothness over time. To address this problem, we propose a new representation learning framework building on ideas from interpretable discrete dimensionality reduction and deep generative modeling. This framework allows us to learn discrete representations of time series, which give rise to smooth and interpretable embeddings with superior clustering performance. We introduce a new way to overcome the non-differentiability in discrete representation learning and present a gradient-based version of the traditional self-organizing map algorithm that is more performant than the original. Furthermore, to allow for a probabilistic interpretation of our method, we integrate a Markov model in the representation space. This model uncovers the temporal transition structure, improves clustering performance even further and provides additional explanatory insights as well as a natural representation of uncertainty. We evaluate our model in terms of clustering performance and interpretability on static (Fashion-)MNIST data, a time series of linearly interpolated (Fashion-)MNIST images, a chaotic Lorenz attractor system with two macro states, as well as on a challenging real world medical time series application on the eICU data set. Our learned representations compare favorably with competitor methods and facilitate downstream tasks on the real world data.", "published": "2018-06-06T14:11:30Z", "authors": ["Vincent Fortuin", "Matthias Hüser", "Francesco Locatello", "Heiko Strathmann", "Gunnar Rätsch"], "categories": ["cs.LG", "stat.ML"], "doi": "", "_query": "real estate valuation deep learning", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2004.01077v2", "title": "Learning Sparse & Ternary Neural Networks with Entropy-Constrained Trained Ternarization (EC2T)", "abstract": "Deep neural networks (DNN) have shown remarkable success in a variety of machine learning applications. The capacity of these models (i.e., number of parameters), endows them with expressive power and allows them to reach the desired performance. In recent years, there is an increasing interest in deploying DNNs to resource-constrained devices (i.e., mobile devices) with limited energy, memory, and computational budget. To address this problem, we propose Entropy-Constrained Trained Ternarization (EC2T), a general framework to create sparse and ternary neural networks which are efficient in terms of storage (e.g., at most two binary-masks and two full-precision values are required to save a weight matrix) and computation (e.g., MAC operations are reduced to a few accumulations plus two multiplications). This approach consists of two steps. First, a super-network is created by scaling the dimensions of a pre-trained model (i.e., its width and depth). Subsequently, this super-network is simultaneously pruned (using an entropy constraint) and quantized (that is, ternary values are assigned layer-wise) in a training process, resulting in a sparse and ternary network representation. We validate the proposed approach in CIFAR-10, CIFAR-100, and ImageNet datasets, showing its effectiveness in image classification tasks.", "published": "2020-04-02T15:38:00Z", "authors": ["Arturo Marban", "Daniel Becking", "Simon Wiedemann", "Wojciech Samek"], "categories": ["cs.LG", "cs.IT", "stat.ML"], "doi": "10.1109/CVPRW50498.2020.00369", "_query": "real estate valuation deep learning", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2107.02847v2", "title": "Transfer Learning in Information Criteria-based Feature Selection", "abstract": "This paper investigates the effectiveness of transfer learning based on Mallows' Cp. We propose a procedure that combines transfer learning with Mallows' Cp (TLCp) and prove that it outperforms the conventional Mallows' Cp criterion in terms of accuracy and stability. Our theoretical results indicate that, for any sample size in the target domain, the proposed TLCp estimator performs better than the Cp estimator by the mean squared error (MSE) metric in the case of orthogonal predictors, provided that i) the dissimilarity between the tasks from source domain and target domain is small, and ii) the procedure parameters (complexity penalties) are tuned according to certain explicit rules. Moreover, we show that our transfer learning framework can be extended to other feature selection criteria, such as the Bayesian information criterion. By analyzing the solution of the orthogonalized Cp, we identify an estimator that asymptotically approximates the solution of the Cp criterion in the case of non-orthogonal predictors. Similar results are obtained for the non-orthogonal TLCp. Finally, simulation studies and applications with real data demonstrate the usefulness of the TLCp scheme.", "published": "2021-07-06T19:12:15Z", "authors": ["Shaohan Chen", "Nikolaos V. Sahinidis", "Chuanhou Gao"], "categories": ["stat.ML", "cs.LG"], "doi": "", "_query": "real estate valuation deep learning", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/1905.10345v1", "title": "Automatic Machine Learning by Pipeline Synthesis using Model-Based Reinforcement Learning and a Grammar", "abstract": "Automatic machine learning is an important problem in the forefront of machine learning. The strongest AutoML systems are based on neural networks, evolutionary algorithms, and Bayesian optimization. Recently AlphaD3M reached state-of-the-art results with an order of magnitude speedup using reinforcement learning with self-play. In this work we extend AlphaD3M by using a pipeline grammar and a pre-trained model which generalizes from many different datasets and similar tasks. Our results demonstrate improved performance compared with our earlier work and existing methods on AutoML benchmark datasets for classification and regression tasks. In the spirit of reproducible research we make our data, models, and code publicly available.", "published": "2019-05-24T17:27:10Z", "authors": ["Iddo Drori", "Yamuna Krishnamurthy", "Raoni Lourenco", "Remi Rampin", "Kyunghyun Cho", "Claudio Silva", "Juliana Freire"], "categories": ["cs.LG", "stat.ML"], "doi": "", "_query": "real estate valuation deep learning", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2202.13733v2", "title": "On the Benefits of Large Learning Rates for Kernel Methods", "abstract": "This paper studies an intriguing phenomenon related to the good generalization performance of estimators obtained by using large learning rates within gradient descent algorithms. First observed in the deep learning literature, we show that a phenomenon can be precisely characterized in the context of kernel methods, even though the resulting optimization problem is convex. Specifically, we consider the minimization of a quadratic objective in a separable Hilbert space, and show that with early stopping, the choice of learning rate influences the spectral decomposition of the obtained solution on the Hessian's eigenvectors. This extends an intuition described by Nakkiran (2020) on a two-dimensional toy problem to realistic learning scenarios such as kernel ridge regression. While large learning rates may be proven beneficial as soon as there is a mismatch between the train and test objectives, we further explain why it already occurs in classification tasks without assuming any particular mismatch between train and test data distributions.", "published": "2022-02-28T13:01:04Z", "authors": ["Gaspard Beugnot", "Julien Mairal", "Alessandro Rudi"], "categories": ["stat.ML", "cs.LG", "math.OC"], "doi": "", "_query": "real estate valuation deep learning", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2305.00654v2", "title": "Representations and Exploration for Deep Reinforcement Learning using Singular Value Decomposition", "abstract": "Representation learning and exploration are among the key challenges for any deep reinforcement learning agent. In this work, we provide a singular value decomposition based method that can be used to obtain representations that preserve the underlying transition structure in the domain. Perhaps interestingly, we show that these representations also capture the relative frequency of state visitations, thereby providing an estimate for pseudo-counts for free. To scale this decomposition method to large-scale domains, we provide an algorithm that never requires building the transition matrix, can make use of deep networks, and also permits mini-batch training. Further, we draw inspiration from predictive state representations and extend our decomposition method to partially observable environments. With experiments on multi-task settings with partially observable domains, we show that the proposed method can not only learn useful representation on DM-Lab-30 environments (that have inputs involving language instructions, pixel images, and rewards, among others) but it can also be effective at hard exploration tasks in DM-Hard-8 environments.", "published": "2023-05-01T04:26:03Z", "authors": ["Yash Chandak", "Shantanu Thakoor", "Zhaohan Daniel Guo", "Yunhao Tang", "Remi Munos", "Will Dabney", "Diana L Borsa"], "categories": ["cs.LG", "cs.AI"], "doi": "", "_query": "real estate valuation deep learning", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2205.15466v7", "title": "Data Banzhaf: A Robust Data Valuation Framework for Machine Learning", "abstract": "Data valuation has wide use cases in machine learning, including improving data quality and creating economic incentives for data sharing. This paper studies the robustness of data valuation to noisy model performance scores. Particularly, we find that the inherent randomness of the widely used stochastic gradient descent can cause existing data value notions (e.g., the Shapley value and the Leave-one-out error) to produce inconsistent data value rankings across different runs. To address this challenge, we introduce the concept of safety margin, which measures the robustness of a data value notion. We show that the Banzhaf value, a famous value notion that originated from cooperative game theory literature, achieves the largest safety margin among all semivalues (a class of value notions that satisfy crucial properties entailed by ML applications and include the famous Shapley value and Leave-one-out error). We propose an algorithm to efficiently estimate the Banzhaf value based on the Maximum Sample Reuse (MSR) principle. Our evaluation demonstrates that the Banzhaf value outperforms the existing semivalue-based data value notions on several ML tasks such as learning with weighted samples and noisy label detection. Overall, our study suggests that when the underlying ML algorithm is stochastic, the Banzhaf value is a promising alternative to the other semivalue-based data value schemes given its computational advantage and ability to robustly differentiate data quality.", "published": "2022-05-30T23:44:09Z", "authors": ["Jiachen T. Wang", "Ruoxi Jia"], "categories": ["cs.LG", "cs.GT", "stat.ML"], "doi": "", "_query": "real estate valuation deep learning", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/1809.00817v2", "title": "Model Risk in Real Option Valuation", "abstract": "We introduce a general decision tree framework to value an option to invest/divest in a project, focusing on the model risk inherent in the assumptions made by standard real option valuation methods. We examine how real option values depend on the dynamics of project value and investment costs, the frequency of exercise opportunities, the size of the project relative to initial wealth, the investor's risk tolerance (and how it changes with wealth) and several other choices about model structure. For instance, contrary to stylized facts from previous literature, real option values can actually decrease with the volatility of the underlying project value and increase with investment costs.", "published": "2018-09-04T07:40:06Z", "authors": ["Carol Alexander", "Xi Chen"], "categories": ["q-fin.GN"], "doi": "", "_query": "real estate valuation deep learning", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2212.07295v2", "title": "Maximal Initial Learning Rates in Deep ReLU Networks", "abstract": "Training a neural network requires choosing a suitable learning rate, which involves a trade-off between speed and effectiveness of convergence. While there has been considerable theoretical and empirical analysis of how large the learning rate can be, most prior work focuses only on late-stage training. In this work, we introduce the maximal initial learning rate $η^{\\ast}$ - the largest learning rate at which a randomly initialized neural network can successfully begin training and achieve (at least) a given threshold accuracy. Using a simple approach to estimate $η^{\\ast}$, we observe that in constant-width fully-connected ReLU networks, $η^{\\ast}$ behaves differently from the maximum learning rate later in training. Specifically, we find that $η^{\\ast}$ is well predicted as a power of depth $\\times$ width, provided that (i) the width of the network is sufficiently large compared to the depth, and (ii) the input layer is trained at a relatively small learning rate. We further analyze the relationship between $η^{\\ast}$ and the sharpness $λ_{1}$ of the network at initialization, indicating they are closely though not inversely related. We formally prove bounds for $λ_{1}$ in terms of depth $\\times$ width that align with our empirical results.", "published": "2022-12-14T15:58:37Z", "authors": ["Gaurav Iyer", "Boris Hanin", "David Rolnick"], "categories": ["stat.ML", "cs.LG"], "doi": "", "_query": "real estate valuation deep learning", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2406.01130v2", "title": "SAVA: Scalable Learning-Agnostic Data Valuation", "abstract": "Selecting data for training machine learning models is crucial since large, web-scraped, real datasets contain noisy artifacts that affect the quality and relevance of individual data points. These noisy artifacts will impact model performance. We formulate this problem as a data valuation task, assigning a value to data points in the training set according to how similar or dissimilar they are to a clean and curated validation set. Recently, LAVA demonstrated the use of optimal transport (OT) between a large noisy training dataset and a clean validation set, to value training data efficiently, without the dependency on model performance. However, the LAVA algorithm requires the entire dataset as an input, this limits its application to larger datasets. Inspired by the scalability of stochastic (gradient) approaches which carry out computations on batches of data points instead of the entire dataset, we analogously propose SAVA, a scalable variant of LAVA with its computation on batches of data points. Intuitively, SAVA follows the same scheme as LAVA which leverages the hierarchically defined OT for data valuation. However, while LAVA processes the whole dataset, SAVA divides the dataset into batches of data points, and carries out the OT problem computation on those batches. Moreover, our theoretical derivations on the trade-off of using entropic regularization for OT problems include refinements of prior work. We perform extensive experiments, to demonstrate that SAVA can scale to large datasets with millions of data points and does not trade off data valuation performance.", "published": "2024-06-03T09:17:35Z", "authors": ["Samuel Kessler", "Tam Le", "Vu Nguyen"], "categories": ["cs.LG"], "doi": "", "_query": "real estate valuation deep learning", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/1611.08666v1", "title": "Training an Interactive Humanoid Robot Using Multimodal Deep Reinforcement Learning", "abstract": "Training robots to perceive, act and communicate using multiple modalities still represents a challenging problem, particularly if robots are expected to learn efficiently from small sets of example interactions. We describe a learning approach as a step in this direction, where we teach a humanoid robot how to play the game of noughts and crosses. Given that multiple multimodal skills can be trained to play this game, we focus our attention to training the robot to perceive the game, and to interact in this game. Our multimodal deep reinforcement learning agent perceives multimodal features and exhibits verbal and non-verbal actions while playing. Experimental results using simulations show that the robot can learn to win or draw up to 98% of the games. A pilot test of the proposed multimodal system for the targeted game---integrating speech, vision and gestures---reports that reasonable and fluent interactions can be achieved using the proposed approach.", "published": "2016-11-26T06:25:08Z", "authors": ["Heriberto Cuayáhuitl", "Guillaume Couly", "Clément Olalainty"], "categories": ["cs.LG", "cs.AI", "cs.RO"], "doi": "", "_query": "real estate valuation deep learning", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/1912.03896v4", "title": "Explicit Group Sparse Projection with Applications to Deep Learning and NMF", "abstract": "We design a new sparse projection method for a set of vectors that guarantees a desired average sparsity level measured leveraging the popular Hoyer measure (an affine function of the ratio of the $\\ell_1$ and $\\ell_2$ norms). Existing approaches either project each vector individually or require the use of a regularization parameter which implicitly maps to the average $\\ell_0$-measure of sparsity. Instead, in our approach we set the sparsity level for the whole set explicitly and simultaneously project a group of vectors with the sparsity level of each vector tuned automatically. We show that the computational complexity of our projection operator is linear in the size of the problem. Additionally, we propose a generalization of this projection by replacing the $\\ell_1$ norm by its weighted version. We showcase the efficacy of our approach in both supervised and unsupervised learning tasks on image datasets including CIFAR10 and ImageNet. In deep neural network pruning, the sparse models produced by our method on ResNet50 have significantly higher accuracies at corresponding sparsity values compared to existing competitors. In nonnegative matrix factorization, our approach yields competitive reconstruction errors against state-of-the-art algorithms.", "published": "2019-12-09T08:24:29Z", "authors": ["Riyasat Ohib", "Nicolas Gillis", "Niccolò Dalmasso", "Sameena Shah", "Vamsi K. Potluru", "Sergey Plis"], "categories": ["cs.LG", "eess.SP", "stat.ML"], "doi": "", "_query": "real estate valuation deep learning", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2109.04793v1", "title": "Risk-Adjusted Valuation for Real Option Decisions", "abstract": "We model investor heterogeneity using different required returns on an investment and evaluate the impact on the valuation of an investment. By assuming no disagreement on the cash flows, we emphasize how risk preferences in particular, but also the costs of capital, influence a subjective evaluation of the decision to invest now or retain the option to invest in future. We propose a risk-adjusted valuation model to facilitate investors' subjective decision making, in response to the market valuation of an investment opportunity. The investor's subjective assessment arises from their perceived misvaluation of the investment by the market, so projected cash flows are discounted using two different rates representing the investor's and the market's view. This liberates our model from perfect or imperfect hedging assumptions and instead, we are able to illustrate the hedging effect on the real option value when perceptions of risk premia diverge. During crises periods, delaying an investment becomes more valuable as the idiosyncratic risk of future cash flows increases, but the decision-maker may rush to invest too quickly when the risk level is exceptionally high. Our model verifies features established by classical real-option valuation models and provides many new insights about the importance of modelling divergences in decision-makers risk premia, especially during crisis periods. It also has many practical advantages because it requires no more parameter inputs than basic discounted cash flow approaches, such as the marketed asset disclaimer method, but the outputs are much richer. They allow for complex interactions between cost and revenue uncertainties as well as an easy exploration of the effects of hedgeable and un-hedgeable risks on the real option value. Furthermore, we provide fully-adjustable Python code in which all parameter values can be chosen by the user.", "published": "2021-09-10T11:18:27Z", "authors": ["Carol Alexander", "Xi Chen", "Charles Ward"], "categories": ["q-fin.GN"], "doi": "", "_query": "real estate valuation deep learning", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/1810.03779v3", "title": "Reinforcement Learning for Improving Agent Design", "abstract": "In many reinforcement learning tasks, the goal is to learn a policy to manipulate an agent, whose design is fixed, to maximize some notion of cumulative reward. The design of the agent's physical structure is rarely optimized for the task at hand. In this work, we explore the possibility of learning a version of the agent's design that is better suited for its task, jointly with the policy. We propose an alteration to the popular OpenAI Gym framework, where we parameterize parts of an environment, and allow an agent to jointly learn to modify these environment parameters along with its policy. We demonstrate that an agent can learn a better structure of its body that is not only better suited for the task, but also facilitates policy learning. Joint learning of policy and structure may even uncover design principles that are useful for assisted-design applications. Videos of results at https://designrl.github.io/", "published": "2018-10-09T02:32:37Z", "authors": ["David Ha"], "categories": ["cs.LG", "stat.ML"], "doi": "10.1162/artl_a_00301", "_query": "real estate valuation deep learning", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/1711.11157v2", "title": "A Semantic Loss Function for Deep Learning with Symbolic Knowledge", "abstract": "This paper develops a novel methodology for using symbolic knowledge in deep learning. From first principles, we derive a semantic loss function that bridges between neural output vectors and logical constraints. This loss function captures how close the neural network is to satisfying the constraints on its output. An experimental evaluation shows that it effectively guides the learner to achieve (near-)state-of-the-art results on semi-supervised multi-class classification. Moreover, it significantly increases the ability of the neural network to predict structured objects, such as rankings and paths. These discrete concepts are tremendously difficult to learn, and benefit from a tight integration of deep learning and symbolic reasoning methods.", "published": "2017-11-29T23:49:55Z", "authors": ["Jingyi Xu", "Zilu Zhang", "Tal Friedman", "Yitao Liang", "Guy Van den Broeck"], "categories": ["cs.AI", "cs.LG", "cs.LO", "stat.ML"], "doi": "", "_query": "real estate valuation deep learning", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2004.05442v1", "title": "Discriminative Learning via Adaptive Questioning", "abstract": "We consider the problem of designing an adaptive sequence of questions that optimally classify a candidate's ability into one of several categories or discriminative grades. A candidate's ability is modeled as an unknown parameter, which, together with the difficulty of the question asked, determines the likelihood with which s/he is able to answer a question correctly. The learning algorithm is only able to observe these noisy responses to its queries. We consider this problem from a fixed confidence-based $δ$-correct framework, that in our setting seeks to arrive at the correct ability discrimination at the fastest possible rate while guaranteeing that the probability of error is less than a pre-specified and small $δ$. In this setting we develop lower bounds on any sequential questioning strategy and develop geometrical insights into the problem structure both from primal and dual formulation. In addition, we arrive at algorithms that essentially match these lower bounds. Our key conclusions are that, asymptotically, any candidate needs to be asked questions at most at two (candidate ability-specific) levels, although, in a reasonably general framework, questions need to be asked only at a single level. Further, and interestingly, the problem structure facilitates endogenous exploration, so there is no need for a separately designed exploration stage in the algorithm.", "published": "2020-04-11T16:50:00Z", "authors": ["Achal Bassamboo", "Vikas Deep", "Sandeep Juneja", "Assaf Zeevi"], "categories": ["cs.LG", "math.PR", "stat.ML"], "doi": "", "_query": "real estate valuation deep learning", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2604.20078v1", "title": "Improved large-scale graph learning through ridge spectral sparsification", "abstract": "Graph-based techniques and spectral graph theory have enriched the field of machine learning with a variety of critical advances. A central object in the analysis is the graph Laplacian L, which encodes the structure of the graph. We consider the problem of learning over this Laplacian in a distributed streaming setting, where new edges of the graph are observed in real time by a network of workers. In this setting, it is hard to learn quickly or approximately while keeping a distributed representation of L. To address this challenge, we present a novel algorithm, GSQUEAK, which efficiently sparsifies the Laplacian by maintaining a small subset of effective resistances. We show that our algorithm produces sparsifiers with strong spectral approximation guarantees, all while processing edges in a single pass and in a distributed fashion.", "published": "2026-04-22T00:49:31Z", "authors": ["Daniele Calandriello", "Ioannis Koutis", "Alessandro Lazaric", "Michal Valko"], "categories": ["cs.LG", "stat.ML"], "doi": "", "_query": "real estate valuation deep learning", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2005.00872v1", "title": "How deep the machine learning can be", "abstract": "Today we live in the age of artificial intelligence and machine learning; from small startups to HW or SW giants, everyone wants to build machine intelligence chips, applications. The task, however, is hard: not only because of the size of the problem: the technology one can utilize (and the paradigm it is based upon) strongly degrades the chances to succeed efficiently. Today the single-processor performance practically reached the limits the laws of nature enable. The only feasible way to achieve the needed high computing performance seems to be parallelizing many sequentially working units. The laws of the (massively) parallelized computing, however, are different from those experienced in connection with assembling and utilizing systems comprising just-a-few single processors. As machine learning is mostly based on the conventional computing (processors), we scrutinize the (known, but somewhat faded) laws of the parallel computing, concerning AI. This paper attempts to review some of the caveats, especially concerning scaling the computing performance of the AI solutions.", "published": "2020-05-02T16:06:31Z", "authors": ["János Végh"], "categories": ["cs.DC", "cs.LG"], "doi": "", "_query": "real estate valuation deep learning", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/1611.03824v6", "title": "Learning to Learn without Gradient Descent by Gradient Descent", "abstract": "We learn recurrent neural network optimizers trained on simple synthetic functions by gradient descent. We show that these learned optimizers exhibit a remarkable degree of transfer in that they can be used to efficiently optimize a broad range of derivative-free black-box functions, including Gaussian process bandits, simple control objectives, global optimization benchmarks and hyper-parameter tuning tasks. Up to the training horizon, the learned optimizers learn to trade-off exploration and exploitation, and compare favourably with heavily engineered Bayesian optimization packages for hyper-parameter tuning.", "published": "2016-11-11T19:33:01Z", "authors": ["Yutian Chen", "Matthew W. Hoffman", "Sergio Gomez Colmenarejo", "Misha Denil", "Timothy P. Lillicrap", "Matt Botvinick", "Nando de Freitas"], "categories": ["stat.ML", "cs.LG"], "doi": "", "_query": "real estate valuation deep learning", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2106.03300v2", "title": "Sum of Ranked Range Loss for Supervised Learning", "abstract": "In forming learning objectives, one oftentimes needs to aggregate a set of individual values to a single output. Such cases occur in the aggregate loss, which combines individual losses of a learning model over each training sample, and in the individual loss for multi-label learning, which combines prediction scores over all class labels. In this work, we introduce the sum of ranked range (SoRR) as a general approach to form learning objectives. A ranked range is a consecutive sequence of sorted values of a set of real numbers. The minimization of SoRR is solved with the difference of convex algorithm (DCA). We explore two applications in machine learning of the minimization of the SoRR framework, namely the AoRR aggregate loss for binary/multi-class classification at the sample level and the TKML individual loss for multi-label/multi-class classification at the label level. A combination loss of AoRR and TKML is proposed as a new learning objective for improving the robustness of multi-label learning in the face of outliers in sample and labels alike. Our empirical results highlight the effectiveness of the proposed optimization frameworks and demonstrate the applicability of proposed losses using synthetic and real data sets.", "published": "2021-06-07T02:11:27Z", "authors": ["Shu Hu", "Yiming Ying", "Xin Wang", "Siwei Lyu"], "categories": ["cs.LG", "stat.ML"], "doi": "", "_query": "real estate valuation deep learning", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2007.13531v3", "title": "Learning \"What-if\" Explanations for Sequential Decision-Making", "abstract": "Building interpretable parameterizations of real-world decision-making on the basis of demonstrated behavior -- i.e. trajectories of observations and actions made by an expert maximizing some unknown reward function -- is essential for introspecting and auditing policies in different institutions. In this paper, we propose learning explanations of expert decisions by modeling their reward function in terms of preferences with respect to \"what if\" outcomes: Given the current history of observations, what would happen if we took a particular action? To learn these cost-benefit tradeoffs associated with the expert's actions, we integrate counterfactual reasoning into batch inverse reinforcement learning. This offers a principled way of defining reward functions and explaining expert behavior, and also satisfies the constraints of real-world decision-making -- where active experimentation is often impossible (e.g. in healthcare). Additionally, by estimating the effects of different actions, counterfactuals readily tackle the off-policy nature of policy evaluation in the batch setting, and can naturally accommodate settings where the expert policies depend on histories of observations rather than just current states. Through illustrative experiments in both real and simulated medical environments, we highlight the effectiveness of our batch, counterfactual inverse reinforcement learning approach in recovering accurate and interpretable descriptions of behavior.", "published": "2020-07-02T14:24:17Z", "authors": ["Ioana Bica", "Daniel Jarrett", "Alihan Hüyük", "Mihaela van der Schaar"], "categories": ["cs.LG", "cs.AI", "stat.ML"], "doi": "", "_query": "real estate valuation deep learning", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2206.03787v3", "title": "Action Noise in Off-Policy Deep Reinforcement Learning: Impact on Exploration and Performance", "abstract": "Many Deep Reinforcement Learning (D-RL) algorithms rely on simple forms of exploration such as the additive action noise often used in continuous control domains. Typically, the scaling factor of this action noise is chosen as a hyper-parameter and is kept constant during training. In this paper, we focus on action noise in off-policy deep reinforcement learning for continuous control. We analyze how the learned policy is impacted by the noise type, noise scale, and impact scaling factor reduction schedule. We consider the two most prominent types of action noise, Gaussian and Ornstein-Uhlenbeck noise, and perform a vast experimental campaign by systematically varying the noise type and scale parameter, and by measuring variables of interest like the expected return of the policy and the state-space coverage during exploration. For the latter, we propose a novel state-space coverage measure $\\operatorname{X}_{\\mathcal{U}\\text{rel}}$ that is more robust to estimation artifacts caused by points close to the state-space boundary than previously-proposed measures. Larger noise scales generally increase state-space coverage. However, we found that increasing the space coverage using a larger noise scale is often not beneficial. On the contrary, reducing the noise scale over the training process reduces the variance and generally improves the learning performance. We conclude that the best noise type and scale are environment dependent, and based on our observations derive heuristic rules for guiding the choice of the action noise as a starting point for further optimization.", "published": "2022-06-08T10:06:24Z", "authors": ["Jakob Hollenstein", "Sayantan Auddy", "Matteo Saveriano", "Erwan Renaudo", "Justus Piater"], "categories": ["cs.LG", "cs.AI"], "doi": "", "_query": "real estate valuation deep learning", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/1805.12254v2", "title": "Multi-level 3D CNN for Learning Multi-scale Spatial Features", "abstract": "3D object recognition accuracy can be improved by learning the multi-scale spatial features from 3D spatial geometric representations of objects such as point clouds, 3D models, surfaces, and RGB-D data. Current deep learning approaches learn such features either using structured data representations (voxel grids and octrees) or from unstructured representations (graphs and point clouds). Learning features from such structured representations is limited by the restriction on resolution and tree depth while unstructured representations creates a challenge due to non-uniformity among data samples. In this paper, we propose an end-to-end multi-level learning approach on a multi-level voxel grid to overcome these drawbacks. To demonstrate the utility of the proposed multi-level learning, we use a multi-level voxel representation of 3D objects to perform object recognition. The multi-level voxel representation consists of a coarse voxel grid that contains volumetric information of the 3D object. In addition, each voxel in the coarse grid that contains a portion of the object boundary is subdivided into multiple fine-level voxel grids. The performance of our multi-level learning algorithm for object recognition is comparable to dense voxel representations while using significantly lower memory.", "published": "2018-05-30T22:53:28Z", "authors": ["Sambit Ghadai", "Xian Lee", "Aditya Balu", "Soumik Sarkar", "Adarsh Krishnamurthy"], "categories": ["cs.CV", "stat.ML"], "doi": "", "_query": "real estate valuation deep learning", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2309.16143v1", "title": "Generative Semi-supervised Learning with Meta-Optimized Synthetic Samples", "abstract": "Semi-supervised learning (SSL) is a promising approach for training deep classification models using labeled and unlabeled datasets. However, existing SSL methods rely on a large unlabeled dataset, which may not always be available in many real-world applications due to legal constraints (e.g., GDPR). In this paper, we investigate the research question: Can we train SSL models without real unlabeled datasets? Instead of using real unlabeled datasets, we propose an SSL method using synthetic datasets generated from generative foundation models trained on datasets containing millions of samples in diverse domains (e.g., ImageNet). Our main concepts are identifying synthetic samples that emulate unlabeled samples from generative foundation models and training classifiers using these synthetic samples. To achieve this, our method is formulated as an alternating optimization problem: (i) meta-learning of generative foundation models and (ii) SSL of classifiers using real labeled and synthetic unlabeled samples. For (i), we propose a meta-learning objective that optimizes latent variables to generate samples that resemble real labeled samples and minimize the validation loss. For (ii), we propose a simple unsupervised loss function that regularizes the feature extractors of classifiers to maximize the performance improvement obtained from synthetic samples. We confirm that our method outperforms baselines using generative foundation models on SSL. We also demonstrate that our methods outperform SSL using real unlabeled datasets in scenarios with extremely small amounts of labeled datasets. This suggests that synthetic samples have the potential to provide improvement gains more efficiently than real unlabeled data.", "published": "2023-09-28T03:47:26Z", "authors": ["Shin'ya Yamaguchi"], "categories": ["cs.LG", "cs.AI", "cs.CV", "stat.ML"], "doi": "", "_query": "real estate valuation deep learning", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/1805.02716v1", "title": "Real-time regression analysis with deep convolutional neural networks", "abstract": "We discuss the development of novel deep learning algorithms to enable real-time regression analysis for time series data. We showcase the application of this new method with a timely case study, and then discuss the applicability of this approach to tackle similar challenges across science domains.", "published": "2018-05-07T19:43:26Z", "authors": ["E. A. Huerta", "Daniel George", "Zhizhen Zhao", "Gabrielle Allen"], "categories": ["cs.LG", "astro-ph.IM", "cs.AI", "stat.ML"], "doi": "", "_query": "real estate valuation deep learning", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2010.15156v2", "title": "Diagnostic data integration using deep neural networks for real-time plasma analysis", "abstract": "Recent advances in acquisition equipment is providing experiments with growing amounts of precise yet affordable sensors. At the same time an improved computational power, coming from new hardware resources (GPU, FPGA, ACAP), has been made available at relatively low costs. This led us to explore the possibility of completely renewing the chain of acquisition for a fusion experiment, where many high-rate sources of data, coming from different diagnostics, can be combined in a wide framework of algorithms. If on one hand adding new data sources with different diagnostics enriches our knowledge about physical aspects, on the other hand the dimensions of the overall model grow, making relations among variables more and more opaque. A new approach for the integration of such heterogeneous diagnostics, based on composition of deep variational autoencoders, could ease this problem, acting as a structural sparse regularizer. This has been applied to RFX-mod experiment data, integrating the soft X-ray linear images of plasma temperature with the magnetic state. However to ensure a real-time signal analysis, those algorithmic techniques must be adapted to run in well suited hardware. In particular it is shown that, attempting a quantization of neurons transfer functions, such models can be modified to create an embedded firmware. This firmware, approximating the deep inference model to a set of simple operations, fits well with the simple logic units that are largely abundant in FPGAs. This is the key factor that permits the use of affordable hardware with complex deep neural topology and operates them in real-time.", "published": "2020-10-28T18:09:44Z", "authors": ["A. Rigoni Garola", "R. Cavazzana", "M. Gobbin", "R. S. Delogu", "G. Manduchi", "C. Taliercio", "A. Luchetta"], "categories": ["physics.comp-ph", "cs.LG", "nlin.AO"], "doi": "10.1109/TNS.2021.3096837", "_query": "real estate valuation deep learning", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/1812.09195v1", "title": "Learning to Navigate the Web", "abstract": "Learning in environments with large state and action spaces, and sparse rewards, can hinder a Reinforcement Learning (RL) agent's learning through trial-and-error. For instance, following natural language instructions on the Web (such as booking a flight ticket) leads to RL settings where input vocabulary and number of actionable elements on a page can grow very large. Even though recent approaches improve the success rate on relatively simple environments with the help of human demonstrations to guide the exploration, they still fail in environments where the set of possible instructions can reach millions. We approach the aforementioned problems from a different perspective and propose guided RL approaches that can generate unbounded amount of experience for an agent to learn from. Instead of learning from a complicated instruction with a large vocabulary, we decompose it into multiple sub-instructions and schedule a curriculum in which an agent is tasked with a gradually increasing subset of these relatively easier sub-instructions. In addition, when the expert demonstrations are not available, we propose a novel meta-learning framework that generates new instruction following tasks and trains the agent more effectively. We train DQN, deep reinforcement learning agent, with Q-value function approximated with a novel QWeb neural network architecture on these smaller, synthetic instructions. We evaluate the ability of our agent to generalize to new instructions on World of Bits benchmark, on forms with up to 100 elements, supporting 14 million possible instructions. The QWeb agent outperforms the baseline without using any human demonstration achieving 100% success rate on several difficult environments.", "published": "2018-12-21T15:32:59Z", "authors": ["Izzeddin Gur", "Ulrich Rueckert", "Aleksandra Faust", "Dilek Hakkani-Tur"], "categories": ["cs.LG", "cs.CL", "stat.ML"], "doi": "", "_query": "real estate valuation deep learning", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/1702.05747v2", "title": "A Survey on Deep Learning in Medical Image Analysis", "abstract": "Deep learning algorithms, in particular convolutional networks, have rapidly become a methodology of choice for analyzing medical images. This paper reviews the major deep learning concepts pertinent to medical image analysis and summarizes over 300 contributions to the field, most of which appeared in the last year. We survey the use of deep learning for image classification, object detection, segmentation, registration, and other tasks and provide concise overviews of studies per application area. Open challenges and directions for future research are discussed.", "published": "2017-02-19T13:02:28Z", "authors": ["Geert Litjens", "Thijs Kooi", "Babak Ehteshami Bejnordi", "Arnaud Arindra Adiyoso Setio", "Francesco Ciompi", "Mohsen Ghafoorian", "Jeroen A. W. M. van der Laak", "Bram van Ginneken", "Clara I. Sánchez"], "categories": ["cs.CV"], "doi": "10.1016/j.media.2017.07.005", "_query": "real estate valuation deep learning", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/1611.06996v1", "title": "Spatial contrasting for deep unsupervised learning", "abstract": "Convolutional networks have marked their place over the last few years as the best performing model for various visual tasks. They are, however, most suited for supervised learning from large amounts of labeled data. Previous attempts have been made to use unlabeled data to improve model performance by applying unsupervised techniques. These attempts require different architectures and training methods. In this work we present a novel approach for unsupervised training of Convolutional networks that is based on contrasting between spatial regions within images. This criterion can be employed within conventional neural networks and trained using standard techniques such as SGD and back-propagation, thus complementing supervised methods.", "published": "2016-11-21T20:24:58Z", "authors": ["Elad Hoffer", "Itay Hubara", "Nir Ailon"], "categories": ["stat.ML", "cs.LG"], "doi": "", "_query": "real estate valuation deep learning", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2409.05211v1", "title": "ICML Topological Deep Learning Challenge 2024: Beyond the Graph Domain", "abstract": "This paper describes the 2nd edition of the ICML Topological Deep Learning Challenge that was hosted within the ICML 2024 ELLIS Workshop on Geometry-grounded Representation Learning and Generative Modeling (GRaM). The challenge focused on the problem of representing data in different discrete topological domains in order to bridge the gap between Topological Deep Learning (TDL) and other types of structured datasets (e.g. point clouds, graphs). Specifically, participants were asked to design and implement topological liftings, i.e. mappings between different data structures and topological domains --like hypergraphs, or simplicial/cell/combinatorial complexes. The challenge received 52 submissions satisfying all the requirements. This paper introduces the main scope of the challenge, and summarizes the main results and findings.", "published": "2024-09-08T19:59:53Z", "authors": ["Guillermo Bernárdez", "Lev Telyatnikov", "Marco Montagna", "Federica Baccini", "Mathilde Papillon", "Miquel Ferriol-Galmés", "Mustafa Hajij", "Theodore Papamarkou", "Maria Sofia Bucarelli", "Olga Zaghen", "Johan Mathe", "Audun Myers", "Scott Mahan", "Hansen Lillemark", "Sharvaree Vadgama", "Erik Bekkers", "Tim Doster", "Tegan Emerson", "Henry Kvinge", "Katrina Agate", "Nesreen K Ahmed", "Pengfei Bai", "Michael Banf", "Claudio Battiloro", "Maxim Beketov", "Paul Bogdan", "Martin Carrasco", "Andrea Cavallo", "Yun Young Choi", "George Dasoulas", "Matouš Elphick", "Giordan Escalona", "Dominik Filipiak", "Halley Fritze", "Thomas Gebhart", "Manel Gil-Sorribes", "Salvish Goomanee", "Victor Guallar", "Liliya Imasheva", "Andrei Irimia", "Hongwei Jin", "Graham Johnson", "Nikos Kanakaris", "Boshko Koloski", "Veljko Kovač", "Manuel Lecha", "Minho Lee", "Pierrick Leroy", "Theodore Long", "German Magai", "Alvaro Martinez", "Marissa Masden", "Sebastian Mežnar", "Bertran Miquel-Oliver", "Alexis Molina", "Alexander Nikitin", "Marco Nurisso", "Matt Piekenbrock", "Yu Qin", "Patryk Rygiel", "Alessandro Salatiello", "Max Schattauer", "Pavel Snopov", "Julian Suk", "Valentina Sánchez", "Mauricio Tec", "Francesco Vaccarino", "Jonas Verhellen", "Frederic Wantiez", "Alexander Weers", "Patrik Zajec", "Blaž Škrlj", "Nina Miolane"], "categories": ["cs.LG", "cs.AI"], "doi": "", "_query": "real estate valuation deep learning", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/1910.04956v2", "title": "Central Server Free Federated Learning over Single-sided Trust Social Networks", "abstract": "Federated learning has become increasingly important for modern machine learning, especially for data privacy-sensitive scenarios. Existing federated learning mostly adopts the central server-based architecture or centralized architecture. However, in many social network scenarios, centralized federated learning is not applicable (e.g., a central agent or server connecting all users may not exist, or the communication cost to the central server is not affordable). In this paper, we consider a generic setting: 1) the central server may not exist, and 2) the social network is unidirectional or of single-sided trust (i.e., user A trusts user B but user B may not trust user A). We propose a central server free federated learning algorithm, named Online Push-Sum (OPS) method, to handle this challenging but generic scenario. A rigorous regret analysis is also provided, which shows very interesting results on how users can benefit from communication with trusted users in the federated learning scenario. This work builds upon the fundamental algorithm framework and theoretical guarantees for federated learning in the generic social network scenario.", "published": "2019-10-11T03:36:53Z", "authors": ["Chaoyang He", "Conghui Tan", "Hanlin Tang", "Shuang Qiu", "Ji Liu"], "categories": ["cs.LG", "cs.SI", "stat.ML"], "doi": "", "_query": "real estate valuation deep learning", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2207.06883v2", "title": "RF-Photonic Deep Learning Processor with Shannon-Limited Data Movement", "abstract": "Edholm's Law predicts exponential growth in data rate and spectrum bandwidth for communications and is forecasted to remain true for the upcoming deployment of 6G. Compounding this issue is the exponentially increasing demand for deep neural network (DNN) compute, including DNNs for signal processing. However, the slowing of Moore's Law due to the limitations of transistor-based electronics means that completely new paradigms for computing will be required to meet these increasing demands for advanced communications. Optical neural networks (ONNs) are promising DNN accelerators with ultra-low latency and energy consumption. Yet state-of-the-art ONNs struggle with scalability and implementing linear with in-line nonlinear operations. Here we introduce our multiplicative analog frequency transform ONN (MAFT-ONN) that encodes the data in the frequency domain, achieves matrix-vector products in a single shot using photoelectric multiplication, and uses a single electro-optic modulator for the nonlinear activation of all neurons in each layer. We experimentally demonstrate the first hardware accelerator that computes fully-analog deep learning on raw RF signals, performing single-shot modulation classification with 85% accuracy, where a 'majority vote' multi-measurement scheme can boost the accuracy to 95% within 5 consecutive measurements. In addition, we demonstrate frequency-domain finite impulse response (FIR) linear-time-invariant (LTI) operations, enabling a powerful combination of traditional and AI signal processing. We also demonstrate the scalability of our architecture by computing nearly 4 million fully-analog multiplies-and-accumulates for MNIST digit classification. Our latency estimation model shows that due to the Shannon capacity-limited analog data movement, MAFT-ONN is hundreds of times faster than traditional RF receivers operating at their theoretical peak performance.", "published": "2022-07-08T16:37:13Z", "authors": ["Ronald Davis", "Zaijun Chen", "Ryan Hamerly", "Dirk Englund"], "categories": ["cs.ET", "cs.LG", "eess.SP", "physics.app-ph", "physics.optics"], "doi": "", "_query": "real estate valuation deep learning", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2011.14826v2", "title": "Revisiting Rainbow: Promoting more Insightful and Inclusive Deep Reinforcement Learning Research", "abstract": "Since the introduction of DQN, a vast majority of reinforcement learning research has focused on reinforcement learning with deep neural networks as function approximators. New methods are typically evaluated on a set of environments that have now become standard, such as Atari 2600 games. While these benchmarks help standardize evaluation, their computational cost has the unfortunate side effect of widening the gap between those with ample access to computational resources, and those without. In this work we argue that, despite the community's emphasis on large-scale environments, the traditional small-scale environments can still yield valuable scientific insights and can help reduce the barriers to entry for underprivileged communities. To substantiate our claims, we empirically revisit the paper which introduced the Rainbow algorithm [Hessel et al., 2018] and present some new insights into the algorithms used by Rainbow.", "published": "2020-11-20T15:23:40Z", "authors": ["Johan S. Obando-Ceron", "Pablo Samuel Castro"], "categories": ["cs.LG", "cs.AI"], "doi": "", "_query": "real estate valuation deep learning", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2410.04259v2", "title": "Is deeper always better? Replacing linear mappings with deep learning networks in the Discriminative Lexicon Model", "abstract": "Recently, deep learning models have increasingly been used in cognitive modelling of language. This study asks whether deep learning can help us to better understand the learning problem that needs to be solved by speakers, above and beyond linear methods. We utilise the Discriminative Lexicon Model introduced by Baayen and colleagues, which models comprehension and production with mappings between numeric form and meaning vectors. While so far, these mappings have been linear (Linear Discriminative Learning, LDL), in the present study we replace them with deep dense neural networks (Deep Discriminative Learning, DDL). We find that DDL affords more accurate mappings for large and diverse datasets from English and Dutch, but not necessarily for Estonian and Taiwan Mandarin. DDL outperforms LDL in particular for words with pseudo-morphological structure such as chol+er. Applied to average reaction times, we find that DDL is outperformed by frequency-informed linear mappings (FIL). However, DDL trained in a frequency-informed way ('frequency-informed' deep learning, FIDDL) substantially outperforms FIL. Finally, while linear mappings can very effectively be updated from trial-to-trial to model incremental lexical learning, deep mappings cannot do so as effectively. At present, both linear and deep mappings are informative for understanding language.", "published": "2024-10-05T18:41:49Z", "authors": ["Maria Heitmeier", "Valeria Schmidt", "Hendrik P. A. Lensch", "R. Harald Baayen"], "categories": ["cs.CL"], "doi": "10.1515/lingvan-2024-0192", "_query": "real estate valuation deep learning", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/1710.11573v3", "title": "Deep Learning as a Mixed Convex-Combinatorial Optimization Problem", "abstract": "As neural networks grow deeper and wider, learning networks with hard-threshold activations is becoming increasingly important, both for network quantization, which can drastically reduce time and energy requirements, and for creating large integrated systems of deep networks, which may have non-differentiable components and must avoid vanishing and exploding gradients for effective learning. However, since gradient descent is not applicable to hard-threshold functions, it is not clear how to learn networks of them in a principled way. We address this problem by observing that setting targets for hard-threshold hidden units in order to minimize loss is a discrete optimization problem, and can be solved as such. The discrete optimization goal is to find a set of targets such that each unit, including the output, has a linearly separable problem to solve. Given these targets, the network decomposes into individual perceptrons, which can then be learned with standard convex approaches. Based on this, we develop a recursive mini-batch algorithm for learning deep hard-threshold networks that includes the popular but poorly justified straight-through estimator as a special case. Empirically, we show that our algorithm improves classification accuracy in a number of settings, including for AlexNet and ResNet-18 on ImageNet, when compared to the straight-through estimator.", "published": "2017-10-31T16:42:44Z", "authors": ["Abram L. Friesen", "Pedro Domingos"], "categories": ["cs.LG", "cs.CV", "cs.NE"], "doi": "", "_query": "real estate valuation deep learning", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2406.14220v2", "title": "Evaluation of Deep Learning Semantic Segmentation for Land Cover Mapping on Multispectral, Hyperspectral and High Spatial Aerial Imagery", "abstract": "In the rise of climate change, land cover mapping has become such an urgent need in environmental monitoring. The accuracy of land cover classification has gotten increasingly based on the improvement of remote sensing data. Land cover classification using satellite imageries has been explored and become more prevalent in recent years, but the methodologies remain some drawbacks of subjective and time-consuming. Some deep learning techniques have been utilized to overcome these limitations. However, most studies implemented just one image type to evaluate algorithms for land cover mapping. Therefore, our study conducted deep learning semantic segmentation in multispectral, hyperspectral, and high spatial aerial image datasets for landcover mapping. This research implemented a semantic segmentation method such as Unet, Linknet, FPN, and PSPnet for categorizing vegetation, water, and others (i.e., soil and impervious surface). The LinkNet model obtained high accuracy in IoU (Intersection Over Union) at 0.92 in all datasets, which is comparable with other mentioned techniques. In evaluation with different image types, the multispectral images showed higher performance with the IoU, and F1-score are 0.993 and 0.997, respectively. Our outcome highlighted the efficiency and broad applicability of LinkNet and multispectral image on land cover classification. This research contributes to establishing an approach on landcover segmentation via open source for long-term future application.", "published": "2024-06-20T11:40:12Z", "authors": ["Ilham Adi Panuntun", "Ying-Nong Chen", "Ilham Jamaluddin", "Thi Linh Chi Tran"], "categories": ["cs.CV", "cs.LG"], "doi": "", "_query": "real estate valuation deep learning", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2007.14390v5", "title": "Flower: A Friendly Federated Learning Research Framework", "abstract": "Federated Learning (FL) has emerged as a promising technique for edge devices to collaboratively learn a shared prediction model, while keeping their training data on the device, thereby decoupling the ability to do machine learning from the need to store the data in the cloud. However, FL is difficult to implement realistically, both in terms of scale and systems heterogeneity. Although there are a number of research frameworks available to simulate FL algorithms, they do not support the study of scalable FL workloads on heterogeneous edge devices. In this paper, we present Flower -- a comprehensive FL framework that distinguishes itself from existing platforms by offering new facilities to execute large-scale FL experiments and consider richly heterogeneous FL device scenarios. Our experiments show Flower can perform FL experiments up to 15M in client size using only a pair of high-end GPUs. Researchers can then seamlessly migrate experiments to real devices to examine other parts of the design space. We believe Flower provides the community with a critical new tool for FL study and development.", "published": "2020-07-28T17:59:07Z", "authors": ["Daniel J. Beutel", "Taner Topal", "Akhil Mathur", "Xinchi Qiu", "Javier Fernandez-Marques", "Yan Gao", "Lorenzo Sani", "Kwing Hei Li", "Titouan Parcollet", "Pedro Porto Buarque de Gusmão", "Nicholas D. Lane"], "categories": ["cs.LG", "cs.CV", "stat.ML"], "doi": "", "_query": "real estate valuation deep learning", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2103.04590v2", "title": "CheXseen: Unseen Disease Detection for Deep Learning Interpretation of Chest X-rays", "abstract": "We systematically evaluate the performance of deep learning models in the presence of diseases not labeled for or present during training. First, we evaluate whether deep learning models trained on a subset of diseases (seen diseases) can detect the presence of any one of a larger set of diseases. We find that models tend to falsely classify diseases outside of the subset (unseen diseases) as \"no disease\". Second, we evaluate whether models trained on seen diseases can detect seen diseases when co-occurring with diseases outside the subset (unseen diseases). We find that models are still able to detect seen diseases even when co-occurring with unseen diseases. Third, we evaluate whether feature representations learned by models may be used to detect the presence of unseen diseases given a small labeled set of unseen diseases. We find that the penultimate layer of the deep neural network provides useful features for unseen disease detection. Our results can inform the safe clinical deployment of deep learning models trained on a non-exhaustive set of disease classes.", "published": "2021-03-08T08:13:21Z", "authors": ["Siyu Shi", "Ishaan Malhi", "Kevin Tran", "Andrew Y. Ng", "Pranav Rajpurkar"], "categories": ["cs.CV", "cs.AI", "cs.LG"], "doi": "", "_query": "real estate valuation deep learning", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/1909.04121v3", "title": "AC-Teach: A Bayesian Actor-Critic Method for Policy Learning with an Ensemble of Suboptimal Teachers", "abstract": "The exploration mechanism used by a Deep Reinforcement Learning (RL) agent plays a key role in determining its sample efficiency. Thus, improving over random exploration is crucial to solve long-horizon tasks with sparse rewards. We propose to leverage an ensemble of partial solutions as teachers that guide the agent's exploration with action suggestions throughout training. While the setup of learning with teachers has been previously studied, our proposed approach - Actor-Critic with Teacher Ensembles (AC-Teach) - is the first to work with an ensemble of suboptimal teachers that may solve only part of the problem or contradict other each other, forming a unified algorithmic solution that is compatible with a broad range of teacher ensembles. AC-Teach leverages a probabilistic representation of the expected outcome of the teachers' and student's actions to direct exploration, reduce dithering, and adapt to the dynamically changing quality of the learner. We evaluate a variant of AC-Teach that guides the learning of a Bayesian DDPG agent on three tasks - path following, robotic pick and place, and robotic cube sweeping using a hook - and show that it improves largely on sampling efficiency over a set of baselines, both for our target scenario of unconstrained suboptimal teachers and for easier setups with optimal or single teachers. Additional results and videos at https://sites.google.com/view/acteach/home.", "published": "2019-09-09T19:38:25Z", "authors": ["Andrey Kurenkov", "Ajay Mandlekar", "Roberto Martin-Martin", "Silvio Savarese", "Animesh Garg"], "categories": ["cs.LG", "cs.AI", "stat.ML"], "doi": "", "_query": "real estate valuation deep learning", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/1910.07210v2", "title": "On Learning Paradigms for the Travelling Salesman Problem", "abstract": "We explore the impact of learning paradigms on training deep neural networks for the Travelling Salesman Problem. We design controlled experiments to train supervised learning (SL) and reinforcement learning (RL) models on fixed graph sizes up to 100 nodes, and evaluate them on variable sized graphs up to 500 nodes. Beyond not needing labelled data, our results reveal favorable properties of RL over SL: RL training leads to better emergent generalization to variable graph sizes and is a key component for learning scale-invariant solvers for novel combinatorial problems.", "published": "2019-10-16T08:17:37Z", "authors": ["Chaitanya K. Joshi", "Thomas Laurent", "Xavier Bresson"], "categories": ["cs.LG", "stat.ML"], "doi": "", "_query": "real estate valuation deep learning", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/1901.10042v1", "title": "Deep Features Analysis with Attention Networks", "abstract": "Deep neural network models have recently draw lots of attention, as it consistently produce impressive results in many computer vision tasks such as image classification, object detection, etc. However, interpreting such model and show the reason why it performs quite well becomes a challenging question. In this paper, we propose a novel method to interpret the neural network models with attention mechanism. Inspired by the heatmap visualization, we analyze the relation between classification accuracy with the attention based heatmap. An improved attention based method is also included and illustrate that a better classifier can be interpreted by the attention based heatmap.", "published": "2019-01-20T18:44:43Z", "authors": ["Shipeng Xie", "Da Chen", "Rong Zhang", "Hui Xue"], "categories": ["cs.LG", "cs.CV", "stat.ML"], "doi": "", "_query": "real estate valuation deep learning", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2603.25779v2", "title": "Pure and physics-guided deep learning approaches for spatio-temporal groundwater level prediction", "abstract": "Groundwater represents a key element of the water cycle, yet it exhibits complex and context-dependent relationships that make its modeling challenging. Theory-based models have been the cornerstone of scientific understanding. However, their computational cost, simplifying assumptions, and calibration requirements limit their use. In recent years, data-driven models have emerged as powerful alternatives. In particular, deep learning has proven to be a promising approach for its design flexibility and ability to learn complex relationships directly from the data without requiring extensive domain information. We proposed an attention-based pure deep learning model, named STAINet, to predict weekly groundwater levels in Piedmont (Italy), leveraging both irregular groundwater time series and weather image sequences. To enhance the model's trustworthiness and generalization ability, we merged the theory and data-driven approaches by considering physics-guided strategies to inject the groundwater flow equation into the model. Firstly, we restructured the tail of the architecture to predict the three terms of the governing equation, named the autoregressive, diffusion, and residual components - we thus obtained the PSTAINet-IB. Then, we further injected physics priors by adding loss terms related to the estimated equation components, obtaining the PSTAINet-ILB model. Lastly, we developed the PSTAINet-ILRB by imposing a loss term specific to the residual component, which forces the groundwater recharge to occur within the groundwater body recharge zone, which is identified by domain experts. The models were evaluated both by feeding true lagged values as input and by iterating their own predictions (rollouts) over the whole test set. The PSTAINet-ILB model performed the best, achieving remarkable test performance, and generating equation components in line with domain experts' expectations.", "published": "2026-03-26T15:57:08Z", "authors": ["Matteo Salis", "Gabriele Sartor", "Rosa Meo", "Stefano Ferraris", "Abdourrahmane M. Atto"], "categories": ["cs.LG", "cs.AI"], "doi": "10.1088/2632-2153/ae9af0", "_query": "real estate valuation deep learning", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2006.15815v11", "title": "Adaptive Inertia: Disentangling the Effects of Adaptive Learning Rate and Momentum", "abstract": "Adaptive Moment Estimation (Adam), which combines Adaptive Learning Rate and Momentum, would be the most popular stochastic optimizer for accelerating the training of deep neural networks. However, it is empirically known that Adam often generalizes worse than Stochastic Gradient Descent (SGD). The purpose of this paper is to unveil the mystery of this behavior in the diffusion theoretical framework. Specifically, we disentangle the effects of Adaptive Learning Rate and Momentum of the Adam dynamics on saddle-point escaping and flat minima selection. We prove that Adaptive Learning Rate can escape saddle points efficiently, but cannot select flat minima as SGD does. In contrast, Momentum provides a drift effect to help the training process pass through saddle points, and almost does not affect flat minima selection. This partly explains why SGD (with Momentum) generalizes better, while Adam generalizes worse but converges faster. Furthermore, motivated by the analysis, we design a novel adaptive optimization framework named Adaptive Inertia, which uses parameter-wise adaptive inertia to accelerate the training and provably favors flat minima as well as SGD. Our extensive experiments demonstrate that the proposed adaptive inertia method can generalize significantly better than SGD and conventional adaptive gradient methods.", "published": "2020-06-29T05:21:02Z", "authors": ["Zeke Xie", "Xinrui Wang", "Huishuai Zhang", "Issei Sato", "Masashi Sugiyama"], "categories": ["cs.LG", "stat.ML"], "doi": "", "_query": "real estate valuation deep learning", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2206.03809v3", "title": "Control with Patterns: A D-learning Method", "abstract": "Learning-based control policies are widely used in various tasks in the field of robotics and control. However, formal (Lyapunov) stability guarantees for learning-based controllers with nonlinear dynamical systems are difficult to obtain. We propose a novel control approach, namely Control with Patterns (CWP), to address the stability issue over data sets corresponding to nonlinear dynamical systems. For such data sets, we introduce a new definition, namely exponential attraction on data sets, to describe the nonlinear dynamical systems under consideration. The problem of exponential attraction on data sets is transformed into a problem of pattern classification one based on the data sets and parameterized Lyapunov functions. Furthermore, D-learning is proposed as a method to perform CWP without knowledge of the system dynamics. Finally, the effectiveness of CWP based on D-learning is demonstrated through simulations and real flight experiments. In these experiments, the position of the multicopter is stabilized using real-time images as feedback, which can be considered as an Image-Based Visual Servoing (IBVS) problem.", "published": "2022-06-08T11:14:13Z", "authors": ["Quan Quan", "Kai-Yuan Cai", "Chenyu Wang"], "categories": ["cs.RO"], "doi": "", "_query": "real estate valuation deep learning", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2001.01215v2", "title": "A System for Real-Time Interactive Analysis of Deep Learning Training", "abstract": "Performing diagnosis or exploratory analysis during the training of deep learning models is challenging but often necessary for making a sequence of decisions guided by the incremental observations. Currently available systems for this purpose are limited to monitoring only the logged data that must be specified before the training process starts. Each time a new information is desired, a cycle of stop-change-restart is required in the training process. These limitations make interactive exploration and diagnosis tasks difficult, imposing long tedious iterations during the model development. We present a new system that enables users to perform interactive queries on live processes generating real-time information that can be rendered in multiple formats on multiple surfaces in the form of several desired visualizations simultaneously. To achieve this, we model various exploratory inspection and diagnostic tasks for deep learning training processes as specifications for streams using a map-reduce paradigm with which many data scientists are already familiar. Our design achieves generality and extensibility by defining composable primitives which is a fundamentally different approach than is used by currently available systems. The open source implementation of our system is available as TensorWatch project at https://github.com/microsoft/tensorwatch.", "published": "2020-01-05T11:33:31Z", "authors": ["Shital Shah", "Roland Fernandez", "Steven Drucker"], "categories": ["cs.LG", "cs.HC", "stat.ML"], "doi": "10.1145/3319499.3328231", "_query": "real estate valuation deep learning", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2012.09092v1", "title": "Sample-Efficient Reinforcement Learning via Counterfactual-Based Data Augmentation", "abstract": "Reinforcement learning (RL) algorithms usually require a substantial amount of interaction data and perform well only for specific tasks in a fixed environment. In some scenarios such as healthcare, however, usually only few records are available for each patient, and patients may show different responses to the same treatment, impeding the application of current RL algorithms to learn optimal policies. To address the issues of mechanism heterogeneity and related data scarcity, we propose a data-efficient RL algorithm that exploits structural causal models (SCMs) to model the state dynamics, which are estimated by leveraging both commonalities and differences across subjects. The learned SCM enables us to counterfactually reason what would have happened had another treatment been taken. It helps avoid real (possibly risky) exploration and mitigates the issue that limited experiences lead to biased policies. We propose counterfactual RL algorithms to learn both population-level and individual-level policies. We show that counterfactual outcomes are identifiable under mild conditions and that Q- learning on the counterfactual-based augmented data set converges to the optimal value function. Experimental results on synthetic and real-world data demonstrate the efficacy of the proposed approach.", "published": "2020-12-16T17:21:13Z", "authors": ["Chaochao Lu", "Biwei Huang", "Ke Wang", "José Miguel Hernández-Lobato", "Kun Zhang", "Bernhard Schölkopf"], "categories": ["cs.LG", "stat.ML"], "doi": "", "_query": "real estate valuation deep learning", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/1902.06841v2", "title": "An Adaptive Deep Learning Algorithm Based Autoencoder for Interference Channels", "abstract": "Deep learning (DL) based autoencoder has shown great potential to significantly enhance the physical layer performance. In this paper, we present a DL based autoencoder for interference channel. Based on a characterization of a k-user Gaussian interference channel, where the interferences are classified as different levels from weak to very strong interferences based on a coupling parameter α, a DL neural network (NN) based autoencoder is designed to train the data set and decode the received signals. The performance such a DL autoencoder for different interference scenarios are studied, with α known or partially known, where we assume that α is predictable but with a varying up to 10\\% at the training stage. The results demonstrate that DL based approach has a significant capability to mitigate the effect induced by a poor signal-to-noise ratio (SNR) and a high interference-to-noise ratio (INR). However, the enhancement depends on the knowledge of α as well as the interference levels. The proposed DL approach performs well with α up to 10\\% offset for weak interference level. For strong and very strong interference channel, the offset of α needs to be constrained to less than 5\\% and 2\\%, respectively, to maintain similar performance as α is known.", "published": "2019-02-18T23:59:17Z", "authors": ["Dehao Wu", "Maziar Nekovee", "Yue Wang"], "categories": ["cs.LG", "stat.ML"], "doi": "", "_query": "real estate valuation deep learning", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2202.00091v2", "title": "Query Efficient Decision Based Sparse Attacks Against Black-Box Deep Learning Models", "abstract": "Despite our best efforts, deep learning models remain highly vulnerable to even tiny adversarial perturbations applied to the inputs. The ability to extract information from solely the output of a machine learning model to craft adversarial perturbations to black-box models is a practical threat against real-world systems, such as autonomous cars or machine learning models exposed as a service (MLaaS). Of particular interest are sparse attacks. The realization of sparse attacks in black-box models demonstrates that machine learning models are more vulnerable than we believe. Because these attacks aim to minimize the number of perturbed pixels measured by l_0 norm-required to mislead a model by solely observing the decision (the predicted label) returned to a model query; the so-called decision-based attack setting. But, such an attack leads to an NP-hard optimization problem. We develop an evolution-based algorithm-SparseEvo-for the problem and evaluate against both convolutional deep neural networks and vision transformers. Notably, vision transformers are yet to be investigated under a decision-based attack setting. SparseEvo requires significantly fewer model queries than the state-of-the-art sparse attack Pointwise for both untargeted and targeted attacks. The attack algorithm, although conceptually simple, is also competitive with only a limited query budget against the state-of-the-art gradient-based whitebox attacks in standard computer vision tasks such as ImageNet. Importantly, the query efficient SparseEvo, along with decision-based attacks, in general, raise new questions regarding the safety of deployed systems and poses new directions to study and understand the robustness of machine learning models.", "published": "2022-01-31T21:10:47Z", "authors": ["Viet Quoc Vo", "Ehsan Abbasnejad", "Damith C. Ranasinghe"], "categories": ["cs.LG", "cs.AI", "cs.CR", "cs.CV"], "doi": "", "_query": "real estate valuation deep learning", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2405.10315v3", "title": "TRANSIC: Sim-to-Real Policy Transfer by Learning from Online Correction", "abstract": "Learning in simulation and transferring the learned policy to the real world has the potential to enable generalist robots. The key challenge of this approach is to address simulation-to-reality (sim-to-real) gaps. Previous methods often require domain-specific knowledge a priori. We argue that a straightforward way to obtain such knowledge is by asking humans to observe and assist robot policy execution in the real world. The robots can then learn from humans to close various sim-to-real gaps. We propose TRANSIC, a data-driven approach to enable successful sim-to-real transfer based on a human-in-the-loop framework. TRANSIC allows humans to augment simulation policies to overcome various unmodeled sim-to-real gaps holistically through intervention and online correction. Residual policies can be learned from human corrections and integrated with simulation policies for autonomous execution. We show that our approach can achieve successful sim-to-real transfer in complex and contact-rich manipulation tasks such as furniture assembly. Through synergistic integration of policies learned in simulation and from humans, TRANSIC is effective as a holistic approach to addressing various, often coexisting sim-to-real gaps. It displays attractive properties such as scaling with human effort. Videos and code are available at https://transic-robot.github.io/", "published": "2024-05-16T17:59:07Z", "authors": ["Yunfan Jiang", "Chen Wang", "Ruohan Zhang", "Jiajun Wu", "Li Fei-Fei"], "categories": ["cs.RO", "cs.AI", "cs.LG"], "doi": "", "_query": "real estate valuation deep learning", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2007.04395v4", "title": "Multilevel Graph Matching Networks for Deep Graph Similarity Learning", "abstract": "While the celebrated graph neural networks yield effective representations for individual nodes of a graph, there has been relatively less success in extending to the task of graph similarity learning. Recent work on graph similarity learning has considered either global-level graph-graph interactions or low-level node-node interactions, however ignoring the rich cross-level interactions (e.g., between each node of one graph and the other whole graph). In this paper, we propose a multi-level graph matching network (MGMN) framework for computing the graph similarity between any pair of graph-structured objects in an end-to-end fashion. In particular, the proposed MGMN consists of a node-graph matching network for effectively learning cross-level interactions between each node of one graph and the other whole graph, and a siamese graph neural network to learn global-level interactions between two input graphs. Furthermore, to compensate for the lack of standard benchmark datasets, we have created and collected a set of datasets for both the graph-graph classification and graph-graph regression tasks with different sizes in order to evaluate the effectiveness and robustness of our models. Comprehensive experiments demonstrate that MGMN consistently outperforms state-of-the-art baseline models on both the graph-graph classification and graph-graph regression tasks. Compared with previous work, MGMN also exhibits stronger robustness as the sizes of the two input graphs increase.", "published": "2020-07-08T19:48:19Z", "authors": ["Xiang Ling", "Lingfei Wu", "Saizhuo Wang", "Tengfei Ma", "Fangli Xu", "Alex X. Liu", "Chunming Wu", "Shouling Ji"], "categories": ["cs.LG", "cs.AI", "stat.ML"], "doi": "10.1109/TNNLS.2021.3102234", "_query": "real estate valuation deep learning", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2206.03483v1", "title": "Few-Shot Learning by Dimensionality Reduction in Gradient Space", "abstract": "We introduce SubGD, a novel few-shot learning method which is based on the recent finding that stochastic gradient descent updates tend to live in a low-dimensional parameter subspace. In experimental and theoretical analyses, we show that models confined to a suitable predefined subspace generalize well for few-shot learning. A suitable subspace fulfills three criteria across the given tasks: it (a) allows to reduce the training error by gradient flow, (b) leads to models that generalize well, and (c) can be identified by stochastic gradient descent. SubGD identifies these subspaces from an eigendecomposition of the auto-correlation matrix of update directions across different tasks. Demonstrably, we can identify low-dimensional suitable subspaces for few-shot learning of dynamical systems, which have varying properties described by one or few parameters of the analytical system description. Such systems are ubiquitous among real-world applications in science and engineering. We experimentally corroborate the advantages of SubGD on three distinct dynamical systems problem settings, significantly outperforming popular few-shot learning methods both in terms of sample efficiency and performance.", "published": "2022-06-07T17:58:35Z", "authors": ["Martin Gauch", "Maximilian Beck", "Thomas Adler", "Dmytro Kotsur", "Stefan Fiel", "Hamid Eghbal-zadeh", "Johannes Brandstetter", "Johannes Kofler", "Markus Holzleitner", "Werner Zellinger", "Daniel Klotz", "Sepp Hochreiter", "Sebastian Lehner"], "categories": ["cs.LG"], "doi": "", "_query": "real estate valuation deep learning", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2402.04054v2", "title": "More Flexible PAC-Bayesian Meta-Learning by Learning Learning Algorithms", "abstract": "We introduce a new framework for studying meta-learning methods using PAC-Bayesian theory. Its main advantage over previous work is that it allows for more flexibility in how the transfer of knowledge between tasks is realized. For previous approaches, this could only happen indirectly, by means of learning prior distributions over models. In contrast, the new generalization bounds that we prove express the process of meta-learning much more directly as learning the learning algorithm that should be used for future tasks. The flexibility of our framework makes it suitable to analyze a wide range of meta-learning mechanisms and even design new mechanisms. Other than our theoretical contributions we also show empirically that our framework improves the prediction quality in practical meta-learning mechanisms.", "published": "2024-02-06T15:00:08Z", "authors": ["Hossein Zakerinia", "Amin Behjati", "Christoph H. Lampert"], "categories": ["cs.LG", "stat.ML"], "doi": "", "_query": "real estate valuation deep learning", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2505.13230v3", "title": "Implicit bias produces neural scaling laws in learning curves, from perceptrons to deep networks", "abstract": "Scaling laws in deep learning -- empirical power-law relationships linking model performance to resource growth -- have emerged as simple yet striking regularities across architectures, datasets, and tasks. These laws are particularly impactful in guiding the design of state-of-the-art models, since they quantify the benefits of increasing data or model size, and hint at the foundations of interpretability in machine learning. However, most studies focus on asymptotic behavior at the end of training. In this work, we describe a richer picture by analyzing the entire training dynamics: we identify two novel \\textit{dynamical} scaling laws that govern how performance evolves as function of different norm-based complexity measures. Combined, our new laws recover the well-known scaling for test error at convergence. Our findings are consistent across CNNs, ResNets, and Vision Transformers trained on MNIST, CIFAR-10 and CIFAR-100. Furthermore, we provide analytical support using a single-layer perceptron trained with logistic loss, where we derive the new dynamical scaling laws, and we explain them through the implicit bias induced by gradient-based training.", "published": "2025-05-19T15:13:36Z", "authors": ["Francesco D'Amico", "Dario Bocchi", "Matteo Negri"], "categories": ["cs.LG", "cond-mat.dis-nn", "stat.ML"], "doi": "", "_query": "real estate valuation deep learning", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2402.01720v4", "title": "Deep Learning Based Amharic Chatbot for FAQs in Universities", "abstract": "University students often spend a considerable amount of time seeking answers to common questions from administrators or teachers. This can become tedious for both parties, leading to a need for a solution. In response, this paper proposes a chatbot model that utilizes natural language processing and deep learning techniques to answer frequently asked questions (FAQs) in the Amharic language. Chatbots are computer programs that simulate human conversation through the use of artificial intelligence (AI), acting as a virtual assistant to handle questions and other tasks. The proposed chatbot program employs tokenization, normalization, stop word removal, and stemming to analyze and categorize Amharic input sentences. Three machine learning model algorithms were used to classify tokens and retrieve appropriate responses: Support Vector Machine (SVM), Multinomial Naïve Bayes, and deep neural networks implemented through TensorFlow, Keras, and NLTK. The deep learning model achieved the best results with 91.55% accuracy and a validation loss of 0.3548 using an Adam optimizer and SoftMax activation function. The chatbot model was integrated with Facebook Messenger and deployed on a Heroku server for 24-hour accessibility. The experimental results demonstrate that the chatbot framework achieved its objectives and effectively addressed challenges such as Amharic Fidel variation, morphological variation, and lexical gaps. Future research could explore the integration of Amharic WordNet to narrow the lexical gap and support more complex questions.", "published": "2024-01-26T18:37:21Z", "authors": ["Goitom Ybrah Hailu", "Hadush Hailu", "Shishay Welay"], "categories": ["cs.CY", "cs.AI", "cs.CL", "cs.LG"], "doi": "10.48550/arXiv.2402.01720", "_query": "real estate valuation deep learning", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2306.08106v1", "title": "Applications of Deep Learning to physics workflows", "abstract": "Modern large-scale physics experiments create datasets with sizes and streaming rates that can exceed those from industry leaders such as Google Cloud and Netflix. Fully processing these datasets requires both sufficient compute power and efficient workflows. Recent advances in Machine Learning (ML) and Artificial Intelligence (AI) can either improve or replace existing domain-specific algorithms to increase workflow efficiency. Not only can these algorithms improve the physics performance of current algorithms, but they can often be executed more quickly, especially when run on coprocessors such as GPUs or FPGAs. In the winter of 2023, MIT hosted the Accelerating Physics with ML at MIT workshop, which brought together researchers from gravitational-wave physics, multi-messenger astrophysics, and particle physics to discuss and share current efforts to integrate ML tools into their workflows. The following white paper highlights examples of algorithms and computing frameworks discussed during this workshop and summarizes the expected computing needs for the immediate future of the involved fields.", "published": "2023-06-13T19:50:36Z", "authors": ["Manan Agarwal", "Jay Alameda", "Jeroen Audenaert", "Will Benoit", "Damon Beveridge", "Meghna Bhattacharya", "Chayan Chatterjee", "Deep Chatterjee", "Andy Chen", "Muhammed Saleem Cholayil", "Chia-Jui Chou", "Sunil Choudhary", "Michael Coughlin", "Maximilian Dax", "Aman Desai", "Andrea Di Luca", "Javier Mauricio Duarte", "Steven Farrell", "Yongbin Feng", "Pooyan Goodarzi", "Ekaterina Govorkova", "Matthew Graham", "Jonathan Guiang", "Alec Gunny", "Weichangfeng Guo", "Janina Hakenmueller", "Ben Hawks", "Shih-Chieh Hsu", "Pratik Jawahar", "Xiangyang Ju", "Erik Katsavounidis", "Manolis Kellis", "Elham E Khoda", "Fatima Zahra Lahbabi", "Van Tha Bik Lian", "Mia Liu", "Konstantin Malanchev", "Ethan Marx", "William Patrick McCormack", "Alistair McLeod", "Geoffrey Mo", "Eric Anton Moreno", "Daniel Muthukrishna", "Gautham Narayan", "Andrew Naylor", "Mark Neubauer", "Michael Norman", "Rafia Omer", "Kevin Pedro", "Joshua Peterson", "Michael Pürrer", "Ryan Raikman", "Shivam Raj", "George Ricker", "Jared Robbins", "Batool Safarzadeh Samani", "Kate Scholberg", "Alex Schuy", "Vasileios Skliris", "Siddharth Soni", "Niharika Sravan", "Patrick Sutton", "Victoria Ashley Villar", "Xiwei Wang", "Linqing Wen", "Frank Wuerthwein", "Tingjun Yang", "Shu-Wei Yeh"], "categories": ["hep-ex", "astro-ph.HE", "gr-qc"], "doi": "", "_query": "real estate valuation deep learning", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/1602.03146v2", "title": "DCM Bandits: Learning to Rank with Multiple Clicks", "abstract": "A search engine recommends to the user a list of web pages. The user examines this list, from the first page to the last, and clicks on all attractive pages until the user is satisfied. This behavior of the user can be described by the dependent click model (DCM). We propose DCM bandits, an online learning variant of the DCM where the goal is to maximize the probability of recommending satisfactory items, such as web pages. The main challenge of our learning problem is that we do not observe which attractive item is satisfactory. We propose a computationally-efficient learning algorithm for solving our problem, dcmKL-UCB; derive gap-dependent upper bounds on its regret under reasonable assumptions; and also prove a matching lower bound up to logarithmic factors. We evaluate our algorithm on synthetic and real-world problems, and show that it performs well even when our model is misspecified. This work presents the first practical and regret-optimal online algorithm for learning to rank with multiple clicks in a cascade-like click model.", "published": "2016-02-09T20:03:30Z", "authors": ["Sumeet Katariya", "Branislav Kveton", "Csaba Szepesvári", "Zheng Wen"], "categories": ["cs.LG", "stat.ML"], "doi": "", "_query": "real estate valuation deep learning", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2506.06454v2", "title": "LETS Forecast: Learning Embedology for Time Series Forecasting", "abstract": "Real-world time series are often governed by complex nonlinear dynamics. Understanding these underlying dynamics is crucial for precise future prediction. While deep learning has achieved major success in time series forecasting, many existing approaches do not explicitly model the dynamics. To bridge this gap, we introduce DeepEDM, a framework that integrates nonlinear dynamical systems modeling with deep neural networks. Inspired by empirical dynamic modeling (EDM) and rooted in Takens' theorem, DeepEDM presents a novel deep model that learns a latent space from time-delayed embeddings, and employs kernel regression to approximate the underlying dynamics, while leveraging efficient implementation of softmax attention and allowing for accurate prediction of future time steps. To evaluate our method, we conduct comprehensive experiments on synthetic data of nonlinear dynamical systems as well as real-world time series across domains. Our results show that DeepEDM is robust to input noise, and outperforms state-of-the-art methods in forecasting accuracy. Our code is available at: https://abrarmajeedi.github.io/deep_edm.", "published": "2025-06-06T18:24:12Z", "authors": ["Abrar Majeedi", "Viswanatha Reddy Gajjala", "Satya Sai Srinath Namburi GNVV", "Nada Magdi Elkordi", "Yin Li"], "categories": ["cs.LG", "cs.AI", "stat.ML"], "doi": "", "_query": "real estate valuation deep learning", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2403.18936v2", "title": "Neural post-Einsteinian framework for efficient theory-agnostic tests of general relativity with gravitational waves", "abstract": "The parametrized post-Einsteinian (ppE) framework and its variants are widely used to probe gravity through gravitational-wave tests that apply to a large class of theories beyond general relativity. However, the ppE framework is not truly theory-agnostic as it only captures certain types of deviations from general relativity: those that admit a post-Newtonian series representation in the inspiral of coalescing compact objects. Moreover, each type of deviation in the ppE framework has to be tested separately, making the whole process computationally inefficient and expensive, possibly obscuring the theoretical interpretation of potential deviations that could be detected in the future. We here present the neural post-Einsteinian (npE) framework, an extension of the ppE formalism that overcomes the above weaknesses using deep-learning neural networks. The core of the npE framework is a variational autoencoder that maps the discrete ppE theories into a continuous latent space in a well-organized manner. This design enables the npE framework to test many theories simultaneously and to select the theory that best describes the observation in a single parameter estimation run. The smooth extension of the ppE parametrization also allows for more general types of deviations to be searched for with the npE model. We showcase the application of the new npE framework to future tests of general relativity with the fifth observing run of the LIGO-Virgo-KAGRA collaboration. In particular, the npE framework is demonstrated to efficiently explore modifications to general relativity beyond what can be mapped by the ppE framework, including modifications coming from higher-order curvature corrections to the Einstein-Hilbert action at high post-Newtonian order, and dark-photon interactions in possibly hidden sectors of matter that do not admit a post-Newtonian representation.", "published": "2024-03-27T18:35:26Z", "authors": ["Yiqi Xie", "Deep Chatterjee", "Gautham Narayan", "Nicolás Yunes"], "categories": ["gr-qc", "astro-ph.HE"], "doi": "10.1103/PhysRevD.110.024036", "_query": "real estate valuation deep learning", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/1901.05850v1", "title": "Fast Deep Learning for Automatic Modulation Classification", "abstract": "In this work, we investigate the feasibility and effectiveness of employing deep learning algorithms for automatic recognition of the modulation type of received wireless communication signals from subsampled data. Recent work considered a GNU radio-based data set that mimics the imperfections in a real wireless channel and uses 10 different modulation types. A Convolutional Neural Network (CNN) architecture was then developed and shown to achieve performance that exceeds that of expert-based approaches. Here, we continue this line of work and investigate deep neural network architectures that deliver high classification accuracy. We identify three architectures - namely, a Convolutional Long Short-term Deep Neural Network (CLDNN), a Long Short-Term Memory neural network (LSTM), and a deep Residual Network (ResNet) - that lead to typical classification accuracy values around 90% at high SNR. We then study algorithms to reduce the training time by minimizing the size of the training data set, while incurring a minimal loss in classification accuracy. To this end, we demonstrate the performance of Principal Component Analysis in significantly reducing the training time, while maintaining good performance at low SNR. We also investigate subsampling techniques that further reduce the training time, and pave the way for online classification at high SNR. Finally, we identify representative SNR values for training each of the candidate architectures, and consequently, realize drastic reductions of the training time, with negligible loss in classification accuracy.", "published": "2019-01-16T01:15:50Z", "authors": ["Sharan Ramjee", "Shengtai Ju", "Diyu Yang", "Xiaoyu Liu", "Aly El Gamal", "Yonina C. Eldar"], "categories": ["eess.SP", "cs.AI", "cs.LG", "stat.ML"], "doi": "", "_query": "real estate valuation deep learning", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2103.04046v6", "title": "Simplicial Complex Representation Learning", "abstract": "Simplicial complexes form an important class of topological spaces that are frequently used in many application areas such as computer-aided design, computer graphics, and simulation. Representation learning on graphs, which are just 1-d simplicial complexes, has witnessed a great attention in recent years. However, there has not been enough effort to extend representation learning to higher dimensional simplicial objects due to the additional complexity these objects hold, especially when it comes to entire-simplicial complex representation learning. In this work, we propose a method for simplicial complex-level representation learning that embeds a simplicial complex to a universal embedding space in a way that complex-to-complex proximity is preserved. Our method uses our novel geometric message passing schemes to learn an entire simplicial complex representation in an end-to-end fashion. We demonstrate the proposed model on publicly available mesh dataset. To the best of our knowledge, this work presents the first method for learning simplicial complex-level representation.", "published": "2021-03-06T06:33:04Z", "authors": ["Mustafa Hajij", "Ghada Zamzmi", "Theodore Papamarkou", "Vasileios Maroulas", "Xuanting Cai"], "categories": ["cs.LG", "cs.CG", "cs.CV", "math.AT", "stat.ML"], "doi": "", "_query": "real estate valuation deep learning", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/1905.12131v2", "title": "Adaptive Deep Kernel Learning", "abstract": "Deep kernel learning provides an elegant and principled framework for combining the structural properties of deep learning algorithms with the flexibility of kernel methods. By means of a deep neural network, we learn a parametrized kernel operator that can be combined with a differentiable kernel algorithm during inference. While previous work within this framework has focused on learning a single kernel for large datasets, we learn a kernel family for a variety of few-shot regression tasks. Compared to single deep kernel learning, our algorithm enables the identification of the appropriate kernel for each task during inference. As such, it is well adapted for complex task distributions in a few-shot learning setting, which we demonstrate by comparing against existing state-of-the-art algorithms using real-world, few-shot regression tasks related to the field of drug discovery.", "published": "2019-05-28T23:20:05Z", "authors": ["Prudencio Tossou", "Basile Dura", "Francois Laviolette", "Mario Marchand", "Alexandre Lacoste"], "categories": ["cs.LG", "stat.ML"], "doi": "", "_query": "real estate valuation deep learning", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/1901.09837v1", "title": "Designing a Multi-Objective Reward Function for Creating Teams of Robotic Bodyguards Using Deep Reinforcement Learning", "abstract": "We are considering a scenario where a team of bodyguard robots provides physical protection to a VIP in a crowded public space. We use deep reinforcement learning to learn the policy to be followed by the robots. As the robot bodyguards need to follow several difficult-to-reconcile goals, we study several primitive and composite reward functions and their impact on the overall behavior of the robotic bodyguards.", "published": "2019-01-28T17:33:45Z", "authors": ["Hassam Ullah Sheikh", "Ladislau Bölöni"], "categories": ["cs.MA", "cs.LG"], "doi": "", "_query": "real estate valuation deep learning", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/1808.10724v3", "title": "Learning Data-adaptive Nonparametric Kernels", "abstract": "In this paper, we propose a data-adaptive non-parametric kernel learning framework in margin based kernel methods. In model formulation, given an initial kernel matrix, a data-adaptive matrix with two constraints is imposed in an entry-wise scheme. Learning this data-adaptive matrix in a formulation-free strategy enlarges the margin between classes and thus improves the model flexibility. The introduced two constraints are imposed either exactly (on small data sets) or approximately (on large data sets) in our model, which provides a controllable trade-off between model flexibility and complexity with theoretical demonstration. In algorithm optimization, the objective function of our learning framework is proven to be gradient-Lipschitz continuous. Thereby, kernel and classifier/regressor learning can be efficiently optimized in a unified framework via Nesterov's acceleration. For the scalability issue, we study a decomposition-based approach to our model in the large sample case. The effectiveness of this approximation is illustrated by both empirical studies and theoretical guarantees. Experimental results on various classification and regression benchmark data sets demonstrate that our non-parametric kernel learning framework achieves good performance when compared with other representative kernel learning based algorithms.", "published": "2018-08-31T13:16:31Z", "authors": ["Fanghui Liu", "Xiaolin Huang", "Chen Gong", "Jie Yang", "Li Li"], "categories": ["cs.LG", "stat.ML"], "doi": "", "_query": "real estate valuation deep learning", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2006.15690v1", "title": "Lookahead-Bounded Q-Learning", "abstract": "We introduce the lookahead-bounded Q-learning (LBQL) algorithm, a new, provably convergent variant of Q-learning that seeks to improve the performance of standard Q-learning in stochastic environments through the use of ``lookahead'' upper and lower bounds. To do this, LBQL employs previously collected experience and each iteration's state-action values as dual feasible penalties to construct a sequence of sampled information relaxation problems. The solutions to these problems provide estimated upper and lower bounds on the optimal value, which we track via stochastic approximation. These quantities are then used to constrain the iterates to stay within the bounds at every iteration. Numerical experiments on benchmark problems show that LBQL exhibits faster convergence and more robustness to hyperparameters when compared to standard Q-learning and several related techniques. Our approach is particularly appealing in problems that require expensive simulations or real-world interactions.", "published": "2020-06-28T19:50:55Z", "authors": ["Ibrahim El Shar", "Daniel R. Jiang"], "categories": ["cs.LG", "math.OC", "stat.ML"], "doi": "", "_query": "real estate valuation deep learning", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/1909.04787v2", "title": "MAT: Multi-Fingered Adaptive Tactile Grasping via Deep Reinforcement Learning", "abstract": "Vision-based grasping systems typically adopt an open-loop execution of a planned grasp. This policy can fail due to many reasons, including ubiquitous calibration error. Recovery from a failed grasp is further complicated by visual occlusion, as the hand is usually occluding the vision sensor as it attempts another open-loop regrasp. This work presents MAT, a tactile closed-loop method capable of realizing grasps provided by a coarse initial positioning of the hand above an object. Our algorithm is a deep reinforcement learning (RL) policy optimized through the clipped surrogate objective within a maximum entropy RL framework to balance exploitation and exploration. The method utilizes tactile and proprioceptive information to act through both fine finger motions and larger regrasp movements to execute stable grasps. A novel curriculum of action motion magnitude makes learning more tractable and helps turn common failure cases into successes. Careful selection of features that exhibit small sim-to-real gaps enables this tactile grasping policy, trained purely in simulation, to transfer well to real world environments without the need for additional learning. Experimentally, this methodology improves over a vision-only grasp success rate substantially on a multi-fingered robot hand. When this methodology is used to realize grasps from coarse initial positions provided by a vision-only planner, the system is made dramatically more robust to calibration errors in the camera-robot transform.", "published": "2019-09-10T23:02:04Z", "authors": ["Bohan Wu", "Iretiayo Akinola", "Jacob Varley", "Peter Allen"], "categories": ["cs.RO", "cs.AI", "cs.LG"], "doi": "", "_query": "real estate valuation deep learning", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/1904.06859v1", "title": "Pedestrian Detection in Thermal Images using Saliency Maps", "abstract": "Thermal images are mainly used to detect the presence of people at night or in bad lighting conditions, but perform poorly at daytime. To solve this problem, most state-of-the-art techniques employ a fusion network that uses features from paired thermal and color images. Instead, we propose to augment thermal images with their saliency maps, to serve as an attention mechanism for the pedestrian detector especially during daytime. We investigate how such an approach results in improved performance for pedestrian detection using only thermal images, eliminating the need for paired color images. For our experiments, we train the Faster R-CNN for pedestrian detection and report the added effect of saliency maps generated using static and deep methods (PiCA-Net and R3-Net). Our best performing model results in an absolute reduction of miss rate by 13.4% and 19.4% over the baseline in day and night images respectively. We also annotate and release pixel level masks of pedestrians on a subset of the KAIST Multispectral Pedestrian Detection dataset, which is a first publicly available dataset for salient pedestrian detection.", "published": "2019-04-15T05:42:44Z", "authors": ["Debasmita Ghose", "Shasvat Mukeshkumar Desai", "Sneha Bhattacharya", "Deep Chakraborty", "Madalina Fiterau", "Tauhidur Rahman"], "categories": ["cs.CV"], "doi": "", "_query": "real estate valuation deep learning", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2309.07982v1", "title": "Uncertainty quantification for learned ISTA", "abstract": "Model-based deep learning solutions to inverse problems have attracted increasing attention in recent years as they bridge state-of-the-art numerical performance with interpretability. In addition, the incorporated prior domain knowledge can make the training more efficient as the smaller number of parameters allows the training step to be executed with smaller datasets. Algorithm unrolling schemes stand out among these model-based learning techniques. Despite their rapid advancement and their close connection to traditional high-dimensional statistical methods, they lack certainty estimates and a theory for uncertainty quantification is still elusive. This work provides a step towards closing this gap proposing a rigorous way to obtain confidence intervals for the LISTA estimator.", "published": "2023-09-14T18:39:07Z", "authors": ["Frederik Hoppe", "Claudio Mayrink Verdun", "Felix Krahmer", "Hannah Laus", "Holger Rauhut"], "categories": ["stat.ML", "cs.IT", "cs.LG", "eess.IV", "eess.SP"], "doi": "", "_query": "real estate valuation deep learning", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/1911.09615v1", "title": "Sample-Efficient Reinforcement Learning with Maximum Entropy Mellowmax Episodic Control", "abstract": "Deep networks have enabled reinforcement learning to scale to more complex and challenging domains, but these methods typically require large quantities of training data. An alternative is to use sample-efficient episodic control methods: neuro-inspired algorithms which use non-/semi-parametric models that predict values based on storing and retrieving previously experienced transitions. One way to further improve the sample efficiency of these approaches is to use more principled exploration strategies. In this work, we therefore propose maximum entropy mellowmax episodic control (MEMEC), which samples actions according to a Boltzmann policy with a state-dependent temperature. We demonstrate that MEMEC outperforms other uncertainty- and softmax-based exploration methods on classic reinforcement learning environments and Atari games, achieving both more rapid learning and higher final rewards.", "published": "2019-11-21T17:19:36Z", "authors": ["Marta Sarrico", "Kai Arulkumaran", "Andrea Agostinelli", "Pierre Richemond", "Anil Anthony Bharath"], "categories": ["cs.LG", "cs.NE", "stat.ML"], "doi": "", "_query": "real estate valuation deep learning", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2012.08950v5", "title": "Revocable Deep Reinforcement Learning with Affinity Regularization for Outlier-Robust Graph Matching", "abstract": "Graph matching (GM) has been a building block in various areas including computer vision and pattern recognition. Despite recent impressive progress, existing deep GM methods often have obvious difficulty in handling outliers, which are ubiquitous in practice. We propose a deep reinforcement learning based approach RGM, whose sequential node matching scheme naturally fits the strategy for selective inlier matching against outliers. A revocable action framework is devised to improve the agent's flexibility against the complex constrained GM. Moreover, we propose a quadratic approximation technique to regularize the affinity score, in the presence of outliers. As such, the agent can finish inlier matching timely when the affinity score stops growing, for which otherwise an additional parameter i.e. the number of inliers is needed to avoid matching outliers. In this paper, we focus on learning the back-end solver under the most general form of GM: the Lawler's QAP, whose input is the affinity matrix. Especially, our approach can also boost existing GM methods that use such input. Experiments on multiple real-world datasets demonstrate its performance regarding both accuracy and robustness.", "published": "2020-12-16T13:48:48Z", "authors": ["Chang Liu", "Zetian Jiang", "Runzhong Wang", "Junchi Yan", "Lingxiao Huang", "Pinyan Lu"], "categories": ["cs.CV", "cs.LG"], "doi": "", "_query": "real estate valuation deep learning", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2503.04024v1", "title": "An optimal Petrov-Galerkin framework for operator networks", "abstract": "The optimal Petrov-Galerkin formulation to solve partial differential equations (PDEs) recovers the best approximation in a specified finite-dimensional (trial) space with respect to a suitable norm. However, the recovery of this optimal solution is contingent on being able to construct the optimal weighting functions associated with the trial basis. While explicit constructions are available for simple one- and two-dimensional problems, such constructions for a general multidimensional problem remain elusive. In the present work, we revisit the optimal Petrov-Galerkin formulation through the lens of deep learning. We propose an operator network framework called Petrov-Galerkin Variationally Mimetic Operator Network (PG-VarMiON), which emulates the optimal Petrov-Galerkin weak form of the underlying PDE. The PG-VarMiON is trained in a supervised manner using a labeled dataset comprising the PDE data and the corresponding PDE solution, with the training loss depending on the choice of the optimal norm. The special architecture of the PG-VarMiON allows it to implicitly learn the optimal weighting functions, thus endowing the proposed operator network with the ability to generalize well beyond the training set. We derive approximation error estimates for PG-VarMiON, highlighting the contributions of various error sources, particularly the error in learning the true weighting functions. Several numerical results are presented for the advection-diffusion equation to demonstrate the efficacy of the proposed method. By embedding the Petrov-Galerkin structure into the network architecture, PG-VarMiON exhibits greater robustness and improved generalization compared to other popular deep operator frameworks, particularly when the training data is limited.", "published": "2025-03-06T02:21:32Z", "authors": ["Philip Charles", "Deep Ray", "Yue Yu", "Joost Prins", "Hugo Melchers", "Michael R. A. Abdelmalik", "Jeffrey Cochran", "Assad A. Oberai", "Thomas J. R. Hughes", "Mats G. Larson"], "categories": ["math.NA", "cs.LG"], "doi": "", "_query": "real estate valuation deep learning", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/1509.05009v3", "title": "On the Expressive Power of Deep Learning: A Tensor Analysis", "abstract": "It has long been conjectured that hypotheses spaces suitable for data that is compositional in nature, such as text or images, may be more efficiently represented with deep hierarchical networks than with shallow ones. Despite the vast empirical evidence supporting this belief, theoretical justifications to date are limited. In particular, they do not account for the locality, sharing and pooling constructs of convolutional networks, the most successful deep learning architecture to date. In this work we derive a deep network architecture based on arithmetic circuits that inherently employs locality, sharing and pooling. An equivalence between the networks and hierarchical tensor factorizations is established. We show that a shallow network corresponds to CP (rank-1) decomposition, whereas a deep network corresponds to Hierarchical Tucker decomposition. Using tools from measure theory and matrix algebra, we prove that besides a negligible set, all functions that can be implemented by a deep network of polynomial size, require exponential size in order to be realized (or even approximated) by a shallow network. Since log-space computation transforms our networks into SimNets, the result applies directly to a deep learning architecture demonstrating promising empirical performance. The construction and theory developed in this paper shed new light on various practices and ideas employed by the deep learning community.", "published": "2015-09-16T19:32:54Z", "authors": ["Nadav Cohen", "Or Sharir", "Amnon Shashua"], "categories": ["cs.NE", "cs.LG", "math.NA", "stat.ML"], "doi": "", "_query": "real estate valuation deep learning", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/1612.06129v1", "title": "Active and Continuous Exploration with Deep Neural Networks and Expected Model Output Changes", "abstract": "The demands on visual recognition systems do not end with the complexity offered by current large-scale image datasets, such as ImageNet. In consequence, we need curious and continuously learning algorithms that actively acquire knowledge about semantic concepts which are present in available unlabeled data. As a step towards this goal, we show how to perform continuous active learning and exploration, where an algorithm actively selects relevant batches of unlabeled examples for annotation. These examples could either belong to already known or to yet undiscovered classes. Our algorithm is based on a new generalization of the Expected Model Output Change principle for deep architectures and is especially tailored to deep neural networks. Furthermore, we show easy-to-implement approximations that yield efficient techniques for active selection. Empirical experiments show that our method outperforms currently used heuristics.", "published": "2016-12-19T11:27:33Z", "authors": ["Christoph Käding", "Erik Rodner", "Alexander Freytag", "Joachim Denzler"], "categories": ["cs.CV"], "doi": "", "_query": "real estate valuation deep learning", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/1604.00647v1", "title": "Multi-Relational Learning at Scale with ADMM", "abstract": "Learning from multiple-relational data which contains noise, ambiguities, or duplicate entities is essential to a wide range of applications such as statistical inference based on Web Linked Data, recommender systems, computational biology, and natural language processing. These tasks usually require working with very large and complex datasets - e.g., the Web graph - however, current approaches to multi-relational learning are not practical for such scenarios due to their high computational complexity and poor scalability on large data. In this paper, we propose a novel and scalable approach for multi-relational factorization based on consensus optimization. Our model, called ConsMRF, is based on the Alternating Direction Method of Multipliers (ADMM) framework, which enables us to optimize each target relation using a smaller set of parameters than the state-of-the-art competitors in this task. Due to ADMM's nature, ConsMRF can be easily parallelized which makes it suitable for large multi-relational data. Experiments on large Web datasets - derived from DBpedia, Wikipedia and YAGO - show the efficiency and performance improvement of ConsMRF over strong competitors. In addition, ConsMRF near-linear scalability indicates great potential to tackle Web-scale problem sizes.", "published": "2016-04-03T15:42:36Z", "authors": ["Lucas Drumond", "Ernesto Diaz-Aviles", "Lars Schmidt-Thieme"], "categories": ["stat.ML", "cs.AI", "cs.LG"], "doi": "", "_query": "real estate valuation deep learning", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2502.00601v2", "title": "Enhancing Offline Reinforcement Learning with Curriculum Learning-Based Trajectory Valuation", "abstract": "The success of deep reinforcement learning (DRL) relies on the availability and quality of training data, often requiring extensive interactions with specific environments. In many real-world scenarios, where data collection is costly and risky, offline reinforcement learning (RL) offers a solution by utilizing data collected by domain experts and searching for a batch-constrained optimal policy. This approach is further augmented by incorporating external data sources, expanding the range and diversity of data collection possibilities. However, existing offline RL methods often struggle with challenges posed by non-matching data from these external sources. In this work, we specifically address the problem of source-target domain mismatch in scenarios involving mixed datasets, characterized by a predominance of source data generated from random or suboptimal policies and a limited amount of target data generated from higher-quality policies. To tackle this problem, we introduce Transition Scoring (TS), a novel method that assigns scores to transitions based on their similarity to the target domain, and propose Curriculum Learning-Based Trajectory Valuation (CLTV), which effectively leverages these transition scores to identify and prioritize high-quality trajectories through a curriculum learning approach. Our extensive experiments across various offline RL methods and MuJoCo environments, complemented by rigorous theoretical analysis, demonstrate that CLTV enhances the overall performance and transferability of policies learned by offline RL algorithms.", "published": "2025-02-02T00:03:53Z", "authors": ["Amir Abolfazli", "Zekun Song", "Avishek Anand", "Wolfgang Nejdl"], "categories": ["cs.LG"], "doi": "", "_query": "real estate valuation deep learning", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2203.13534v2", "title": "Generalization bounds for learning under graph-dependence: A survey", "abstract": "Traditional statistical learning theory relies on the assumption that data are identically and independently distributed (i.i.d.). However, this assumption often does not hold in many real-life applications. In this survey, we explore learning scenarios where examples are dependent and their dependence relationship is described by a dependency graph, a commonly utilized model in probability and combinatorics. We collect various graph-dependent concentration bounds, which are then used to derive Rademacher complexity and stability generalization bounds for learning from graph-dependent data. We illustrate this paradigm through practical learning tasks and provide some research directions for future work. To our knowledge, this survey is the first of this kind on this subject.", "published": "2022-03-25T09:33:49Z", "authors": ["Rui-Ray Zhang", "Massih-Reza Amini"], "categories": ["cs.LG", "stat.ML"], "doi": "10.1007/s10994-024-06536-9", "_query": "real estate valuation deep learning", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2001.08116v1", "title": "Q-Learning in enormous action spaces via amortized approximate maximization", "abstract": "Applying Q-learning to high-dimensional or continuous action spaces can be difficult due to the required maximization over the set of possible actions. Motivated by techniques from amortized inference, we replace the expensive maximization over all actions with a maximization over a small subset of possible actions sampled from a learned proposal distribution. The resulting approach, which we dub Amortized Q-learning (AQL), is able to handle discrete, continuous, or hybrid action spaces while maintaining the benefits of Q-learning. Our experiments on continuous control tasks with up to 21 dimensional actions show that AQL outperforms D3PG (Barth-Maron et al, 2018) and QT-Opt (Kalashnikov et al, 2018). Experiments on structured discrete action spaces demonstrate that AQL can efficiently learn good policies in spaces with thousands of discrete actions.", "published": "2020-01-22T16:14:38Z", "authors": ["Tom Van de Wiele", "David Warde-Farley", "Andriy Mnih", "Volodymyr Mnih"], "categories": ["cs.LG", "cs.AI", "stat.ML"], "doi": "", "_query": "real estate valuation deep learning", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/1807.04950v1", "title": "Deep Learning in the Wild", "abstract": "Deep learning with neural networks is applied by an increasing number of people outside of classic research environments, due to the vast success of the methodology on a wide range of machine perception tasks. While this interest is fueled by beautiful success stories, practical work in deep learning on novel tasks without existing baselines remains challenging. This paper explores the specific challenges arising in the realm of real world tasks, based on case studies from research \\& development in conjunction with industry, and extracts lessons learned from them. It thus fills a gap between the publication of latest algorithmic and methodical developments, and the usually omitted nitty-gritty of how to make them work. Specifically, we give insight into deep learning projects on face matching, print media monitoring, industrial quality control, music scanning, strategy game playing, and automated machine learning, thereby providing best practices for deep learning in practice.", "published": "2018-07-13T07:22:45Z", "authors": ["Thilo Stadelmann", "Mohammadreza Amirian", "Ismail Arabaci", "Marek Arnold", "Gilbert François Duivesteijn", "Ismail Elezi", "Melanie Geiger", "Stefan Lörwald", "Benjamin Bruno Meier", "Katharina Rombach", "Lukas Tuggener"], "categories": ["cs.LG", "cs.AI", "cs.CV", "stat.ML"], "doi": "", "_query": "real estate valuation deep learning", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/1803.00590v2", "title": "Hierarchical Imitation and Reinforcement Learning", "abstract": "We study how to effectively leverage expert feedback to learn sequential decision-making policies. We focus on problems with sparse rewards and long time horizons, which typically pose significant challenges in reinforcement learning. We propose an algorithmic framework, called hierarchical guidance, that leverages the hierarchical structure of the underlying problem to integrate different modes of expert interaction. Our framework can incorporate different combinations of imitation learning (IL) and reinforcement learning (RL) at different levels, leading to dramatic reductions in both expert effort and cost of exploration. Using long-horizon benchmarks, including Montezuma's Revenge, we demonstrate that our approach can learn significantly faster than hierarchical RL, and be significantly more label-efficient than standard IL. We also theoretically analyze labeling cost for certain instantiations of our framework.", "published": "2018-03-01T19:12:27Z", "authors": ["Hoang M. Le", "Nan Jiang", "Alekh Agarwal", "Miroslav Dudík", "Yisong Yue", "Hal Daumé"], "categories": ["cs.LG", "cs.AI", "stat.ML"], "doi": "", "_query": "real estate valuation deep learning", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/1510.03608v5", "title": "Deep convolutional neural networks for pedestrian detection", "abstract": "Pedestrian detection is a popular research topic due to its paramount importance for a number of applications, especially in the fields of automotive, surveillance and robotics. Despite the significant improvements, pedestrian detection is still an open challenge that calls for more and more accurate algorithms. In the last few years, deep learning and in particular convolutional neural networks emerged as the state of the art in terms of accuracy for a number of computer vision tasks such as image classification, object detection and segmentation, often outperforming the previous gold standards by a large margin. In this paper, we propose a pedestrian detection system based on deep learning, adapting a general-purpose convolutional network to the task at hand. By thoroughly analyzing and optimizing each step of the detection pipeline we propose an architecture that outperforms traditional methods, achieving a task accuracy close to that of state-of-the-art approaches, while requiring a low computational time. Finally, we tested the system on an NVIDIA Jetson TK1, a 192-core platform that is envisioned to be a forerunner computational brain of future self-driving cars.", "published": "2015-10-13T09:57:46Z", "authors": ["Denis Tomè", "Federico Monti", "Luca Baroffio", "Luca Bondi", "Marco Tagliasacchi", "Stefano Tubaro"], "categories": ["cs.CV"], "doi": "10.1016/j.image.2016.05.007", "_query": "real estate valuation deep learning", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/1910.04729v1", "title": "Efficient Intrinsically Motivated Robotic Grasping with Learning-Adaptive Imagination in Latent Space", "abstract": "Combining model-based and model-free deep reinforcement learning has shown great promise for improving sample efficiency on complex control tasks while still retaining high performance. Incorporating imagination is a recent effort in this direction inspired by human mental simulation of motor behavior. We propose a learning-adaptive imagination approach which, unlike previous approaches, takes into account the reliability of the learned dynamics model used for imagining the future. Our approach learns an ensemble of disjoint local dynamics models in latent space and derives an intrinsic reward based on learning progress, motivating the controller to take actions leading to data that improves the models. The learned models are used to generate imagined experiences, augmenting the training set of real experiences. We evaluate our approach on learning vision-based robotic grasping and show that it significantly improves sample efficiency and achieves near-optimal performance in a sparse reward environment.", "published": "2019-10-10T17:43:05Z", "authors": ["Muhammad Burhan Hafez", "Cornelius Weber", "Matthias Kerzel", "Stefan Wermter"], "categories": ["cs.LG", "cs.AI", "cs.RO", "stat.ML"], "doi": "10.1109/DEVLRN.2019.8850723", "_query": "real estate valuation deep learning", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2206.05703v2", "title": "PAC-Net: A Model Pruning Approach to Inductive Transfer Learning", "abstract": "Inductive transfer learning aims to learn from a small amount of training data for the target task by utilizing a pre-trained model from the source task. Most strategies that involve large-scale deep learning models adopt initialization with the pre-trained model and fine-tuning for the target task. However, when using over-parameterized models, we can often prune the model without sacrificing the accuracy of the source task. This motivates us to adopt model pruning for transfer learning with deep learning models. In this paper, we propose PAC-Net, a simple yet effective approach for transfer learning based on pruning. PAC-Net consists of three steps: Prune, Allocate, and Calibrate (PAC). The main idea behind these steps is to identify essential weights for the source task, fine-tune on the source task by updating the essential weights, and then calibrate on the target task by updating the remaining redundant weights. Under the various and extensive set of inductive transfer learning experiments, we show that our method achieves state-of-the-art performance by a large margin.", "published": "2022-06-12T09:45:16Z", "authors": ["Sanghoon Myung", "In Huh", "Wonik Jang", "Jae Myung Choe", "Jisu Ryu", "Dae Sin Kim", "Kee-Eung Kim", "Changwook Jeong"], "categories": ["cs.LG", "cs.AI", "physics.comp-ph", "stat.AP", "stat.ML"], "doi": "", "_query": "real estate valuation deep learning", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2301.13375v2", "title": "Optimal Transport Perturbations for Safe Reinforcement Learning with Robustness Guarantees", "abstract": "Robustness and safety are critical for the trustworthy deployment of deep reinforcement learning. Real-world decision making applications require algorithms that can guarantee robust performance and safety in the presence of general environment disturbances, while making limited assumptions on the data collection process during training. In order to accomplish this goal, we introduce a safe reinforcement learning framework that incorporates robustness through the use of an optimal transport cost uncertainty set. We provide an efficient implementation based on applying Optimal Transport Perturbations to construct worst-case virtual state transitions, which does not impact data collection during training and does not require detailed simulator access. In experiments on continuous control tasks with safety constraints, our approach demonstrates robust performance while significantly improving safety at deployment time compared to standard safe reinforcement learning.", "published": "2023-01-31T02:39:52Z", "authors": ["James Queeney", "Erhan Can Ozcan", "Ioannis Ch. Paschalidis", "Christos G. Cassandras"], "categories": ["cs.LG", "cs.AI", "stat.ML"], "doi": "", "_query": "real estate valuation deep learning", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2502.19921v1", "title": "Shifting the Paradigm: A Diffeomorphism Between Time Series Data Manifolds for Achieving Shift-Invariancy in Deep Learning", "abstract": "Deep learning models lack shift invariance, making them sensitive to input shifts that cause changes in output. While recent techniques seek to address this for images, our findings show that these approaches fail to provide shift-invariance in time series, where the data generation mechanism is more challenging due to the interaction of low and high frequencies. Worse, they also decrease performance across several tasks. In this paper, we propose a novel differentiable bijective function that maps samples from their high-dimensional data manifold to another manifold of the same dimension, without any dimensional reduction. Our approach guarantees that samples -- when subjected to random shifts -- are mapped to a unique point in the manifold while preserving all task-relevant information without loss. We theoretically and empirically demonstrate that the proposed transformation guarantees shift-invariance in deep learning models without imposing any limits to the shift. Our experiments on six time series tasks with state-of-the-art methods show that our approach consistently improves the performance while enabling models to achieve complete shift-invariance without modifying or imposing restrictions on the model's topology. The source code is available on \\href{https://github.com/eth-siplab/Shifting-the-Paradigm}{GitHub}.", "published": "2025-02-27T09:42:52Z", "authors": ["Berken Utku Demirel", "Christian Holz"], "categories": ["cs.LG"], "doi": "", "_query": "real estate valuation deep learning", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2306.03566v1", "title": "Memory-Based Dual Gaussian Processes for Sequential Learning", "abstract": "Sequential learning with Gaussian processes (GPs) is challenging when access to past data is limited, for example, in continual and active learning. In such cases, errors can accumulate over time due to inaccuracies in the posterior, hyperparameters, and inducing points, making accurate learning challenging. Here, we present a method to keep all such errors in check using the recently proposed dual sparse variational GP. Our method enables accurate inference for generic likelihoods and improves learning by actively building and updating a memory of past data. We demonstrate its effectiveness in several applications involving Bayesian optimization, active learning, and continual learning.", "published": "2023-06-06T10:34:03Z", "authors": ["Paul E. Chang", "Prakhar Verma", "S. T. John", "Arno Solin", "Mohammad Emtiyaz Khan"], "categories": ["cs.LG", "stat.ML"], "doi": "", "_query": "real estate valuation deep learning", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2110.03861v3", "title": "QTN-VQC: An End-to-End Learning framework for Quantum Neural Networks", "abstract": "The advent of noisy intermediate-scale quantum (NISQ) computers raises a crucial challenge to design quantum neural networks for fully quantum learning tasks. To bridge the gap, this work proposes an end-to-end learning framework named QTN-VQC, by introducing a trainable quantum tensor network (QTN) for quantum embedding on a variational quantum circuit (VQC). The architecture of QTN is composed of a parametric tensor-train network for feature extraction and a tensor product encoding for quantum embedding. We highlight the QTN for quantum embedding in terms of two perspectives: (1) we theoretically characterize QTN by analyzing its representation power of input features; (2) QTN enables an end-to-end parametric model pipeline, namely QTN-VQC, from the generation of quantum embedding to the output measurement. Our experiments on the MNIST dataset demonstrate the advantages of QTN for quantum embedding over other quantum embedding approaches.", "published": "2021-10-06T14:44:51Z", "authors": ["Jun Qi", "Chao-Han Huck Yang", "Pin-Yu Chen"], "categories": ["quant-ph", "cs.AI", "cs.CL", "cs.CV", "cs.LG", "cs.NE"], "doi": "", "_query": "real estate valuation deep learning", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2009.13303v2", "title": "Sim-to-Real Transfer in Deep Reinforcement Learning for Robotics: a Survey", "abstract": "Deep reinforcement learning has recently seen huge success across multiple areas in the robotics domain. Owing to the limitations of gathering real-world data, i.e., sample inefficiency and the cost of collecting it, simulation environments are utilized for training the different agents. This not only aids in providing a potentially infinite data source, but also alleviates safety concerns with real robots. Nonetheless, the gap between the simulated and real worlds degrades the performance of the policies once the models are transferred into real robots. Multiple research efforts are therefore now being directed towards closing this sim-to-real gap and accomplish more efficient policy transfer. Recent years have seen the emergence of multiple methods applicable to different domains, but there is a lack, to the best of our knowledge, of a comprehensive review summarizing and putting into context the different methods. In this survey paper, we cover the fundamental background behind sim-to-real transfer in deep reinforcement learning and overview the main methods being utilized at the moment: domain randomization, domain adaptation, imitation learning, meta-learning and knowledge distillation. We categorize some of the most relevant recent works, and outline the main application scenarios. Finally, we discuss the main opportunities and challenges of the different approaches and point to the most promising directions.", "published": "2020-09-24T21:05:46Z", "authors": ["Wenshuai Zhao", "Jorge Peña Queralta", "Tomi Westerlund"], "categories": ["cs.LG", "cs.RO"], "doi": "10.1109/SSCI47803.2020.9308468.", "_query": "real estate valuation deep learning", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2005.12968v1", "title": "Towards intervention-centric causal reasoning in learning agents", "abstract": "Interventions are central to causal learning and reasoning. Yet ultimately an intervention is an abstraction: an agent embedded in a physical environment (perhaps modeled as a Markov decision process) does not typically come equipped with the notion of an intervention -- its action space is typically ego-centric, without actions of the form `intervene on X'. Such a correspondence between ego-centric actions and interventions would be challenging to hard-code. It would instead be better if an agent learnt which sequence of actions allow it to make targeted manipulations of the environment, and learnt corresponding representations that permitted learning from observation. Here we show how a meta-learning approach can be used to perform causal learning in this challenging setting, where the action-space is not a set of interventions and the observation space is a high-dimensional space with a latent causal structure. A meta-reinforcement learning algorithm is used to learn relationships that transfer on observational causal learning tasks. This work shows how advances in deep reinforcement learning and meta-learning can provide intervention-centric causal learning in high-dimensional environments with a latent causal structure.", "published": "2020-05-26T18:53:04Z", "authors": ["Benjamin Lansdell"], "categories": ["cs.LG", "stat.ML"], "doi": "", "_query": "real estate valuation deep learning", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2202.00280v1", "title": "Recycling Model Updates in Federated Learning: Are Gradient Subspaces Low-Rank?", "abstract": "In this paper, we question the rationale behind propagating large numbers of parameters through a distributed system during federated learning. We start by examining the rank characteristics of the subspace spanned by gradients across epochs (i.e., the gradient-space) in centralized model training, and observe that this gradient-space often consists of a few leading principal components accounting for an overwhelming majority (95-99%) of the explained variance. Motivated by this, we propose the \"Look-back Gradient Multiplier\" (LBGM) algorithm, which exploits this low-rank property to enable gradient recycling between model update rounds of federated learning, reducing transmissions of large parameters to single scalars for aggregation. We analytically characterize the convergence behavior of LBGM, revealing the nature of the trade-off between communication savings and model performance. Our subsequent experimental results demonstrate the improvement LBGM obtains in communication overhead compared to conventional federated learning on several datasets and deep learning models. Additionally, we show that LBGM is a general plug-and-play algorithm that can be used standalone or stacked on top of existing sparsification techniques for distributed model training.", "published": "2022-02-01T09:05:32Z", "authors": ["Sheikh Shams Azam", "Seyyedali Hosseinalipour", "Qiang Qiu", "Christopher Brinton"], "categories": ["cs.LG", "stat.ML"], "doi": "", "_query": "real estate valuation deep learning", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2305.13689v1", "title": "Know Your Self-supervised Learning: A Survey on Image-based Generative and Discriminative Training", "abstract": "Although supervised learning has been highly successful in improving the state-of-the-art in the domain of image-based computer vision in the past, the margin of improvement has diminished significantly in recent years, indicating that a plateau is in sight. Meanwhile, the use of self-supervised learning (SSL) for the purpose of natural language processing (NLP) has seen tremendous successes during the past couple of years, with this new learning paradigm yielding powerful language models. Inspired by the excellent results obtained in the field of NLP, self-supervised methods that rely on clustering, contrastive learning, distillation, and information-maximization, which all fall under the banner of discriminative SSL, have experienced a swift uptake in the area of computer vision. Shortly afterwards, generative SSL frameworks that are mostly based on masked image modeling, complemented and surpassed the results obtained with discriminative SSL. Consequently, within a span of three years, over $100$ unique general-purpose frameworks for generative and discriminative SSL, with a focus on imaging, were proposed. In this survey, we review a plethora of research efforts conducted on image-oriented SSL, providing a historic view and paying attention to best practices as well as useful software packages. While doing so, we discuss pretext tasks for image-based SSL, as well as techniques that are commonly used in image-based SSL. Lastly, to aid researchers who aim at contributing to image-focused SSL, we outline a number of promising research directions.", "published": "2023-05-23T04:54:09Z", "authors": ["Utku Ozbulak", "Hyun Jung Lee", "Beril Boga", "Esla Timothy Anzaku", "Homin Park", "Arnout Van Messem", "Wesley De Neve", "Joris Vankerschaver"], "categories": ["cs.CV", "cs.AI"], "doi": "", "_query": "real estate valuation deep learning", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/1901.01536v3", "title": "Exploring applications of deep reinforcement learning for real-world autonomous driving systems", "abstract": "Deep Reinforcement Learning (DRL) has become increasingly powerful in recent years, with notable achievements such as Deepmind's AlphaGo. It has been successfully deployed in commercial vehicles like Mobileye's path planning system. However, a vast majority of work on DRL is focused on toy examples in controlled synthetic car simulator environments such as TORCS and CARLA. In general, DRL is still at its infancy in terms of usability in real-world applications. Our goal in this paper is to encourage real-world deployment of DRL in various autonomous driving (AD) applications. We first provide an overview of the tasks in autonomous driving systems, reinforcement learning algorithms and applications of DRL to AD systems. We then discuss the challenges which must be addressed to enable further progress towards real-world deployment.", "published": "2019-01-06T13:02:46Z", "authors": ["Victor Talpaert", "Ibrahim Sobh", "B Ravi Kiran", "Patrick Mannion", "Senthil Yogamani", "Ahmad El-Sallab", "Patrick Perez"], "categories": ["cs.LG", "cs.RO", "stat.ML"], "doi": "", "_query": "real estate valuation deep learning", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2403.03949v3", "title": "Reconciling Reality through Simulation: A Real-to-Sim-to-Real Approach for Robust Manipulation", "abstract": "Imitation learning methods need significant human supervision to learn policies robust to changes in object poses, physical disturbances, and visual distractors. Reinforcement learning, on the other hand, can explore the environment autonomously to learn robust behaviors but may require impractical amounts of unsafe real-world data collection. To learn performant, robust policies without the burden of unsafe real-world data collection or extensive human supervision, we propose RialTo, a system for robustifying real-world imitation learning policies via reinforcement learning in \"digital twin\" simulation environments constructed on the fly from small amounts of real-world data. To enable this real-to-sim-to-real pipeline, RialTo proposes an easy-to-use interface for quickly scanning and constructing digital twins of real-world environments. We also introduce a novel \"inverse distillation\" procedure for bringing real-world demonstrations into simulated environments for efficient fine-tuning, with minimal human intervention and engineering required. We evaluate RialTo across a variety of robotic manipulation problems in the real world, such as robustly stacking dishes on a rack, placing books on a shelf, and six other tasks. RialTo increases (over 67%) in policy robustness without requiring extensive human data collection. Project website and videos at https://real-to-sim-to-real.github.io/RialTo/", "published": "2024-03-06T18:55:36Z", "authors": ["Marcel Torne", "Anthony Simeonov", "Zechu Li", "April Chan", "Tao Chen", "Abhishek Gupta", "Pulkit Agrawal"], "categories": ["cs.RO", "cs.AI", "cs.LG"], "doi": "", "_query": "real estate valuation deep learning", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2211.09423v2", "title": "DexPoint: Generalizable Point Cloud Reinforcement Learning for Sim-to-Real Dexterous Manipulation", "abstract": "We propose a sim-to-real framework for dexterous manipulation which can generalize to new objects of the same category in the real world. The key of our framework is to train the manipulation policy with point cloud inputs and dexterous hands. We propose two new techniques to enable joint learning on multiple objects and sim-to-real generalization: (i) using imagined hand point clouds as augmented inputs; and (ii) designing novel contact-based rewards. We empirically evaluate our method using an Allegro Hand to grasp novel objects in both simulation and real world. To the best of our knowledge, this is the first policy learning-based framework that achieves such generalization results with dexterous hands. Our project page is available at https://yzqin.github.io/dexpoint", "published": "2022-11-17T09:17:42Z", "authors": ["Yuzhe Qin", "Binghao Huang", "Zhao-Heng Yin", "Hao Su", "Xiaolong Wang"], "categories": ["cs.RO", "cs.CV", "cs.LG"], "doi": "", "_query": "real estate valuation deep learning", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/1906.05449v1", "title": "Factors for the Generalisation of Identity Relations by Neural Networks", "abstract": "Many researchers implicitly assume that neural networks learn relations and generalise them to new unseen data. It has been shown recently, however, that the generalisation of feed-forward networks fails for identity relations.The proposed solution for this problem is to create an inductive bias with Differential Rectifier (DR) units. In this work we explore various factors in the neural network architecture and learning process whether they make a difference to the generalisation on equality detection of Neural Networks without and and with DR units in early and mid fusion architectures. We find in experiments with synthetic data effects of the number of hidden layers, the activation function and the data representation. The training set size in relation to the total possible set of vectors also makes a difference. However, the accuracy never exceeds 61% without DR units at 50% chance level. DR units improve generalisation in all tasks and lead to almost perfect test accuracy in the Mid Fusion setting. Thus, DR units seem to be a promising approach for creating generalisation abilities that standard networks lack.", "published": "2019-06-13T01:36:44Z", "authors": ["Radha Kopparti", "Tillman Weyde"], "categories": ["cs.LG", "stat.ML"], "doi": "", "_query": "real estate valuation deep learning", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2010.04404v1", "title": "Deep Reinforcement Learning for Asset Allocation in US Equities", "abstract": "Reinforcement learning is a machine learning approach concerned with solving dynamic optimization problems in an almost model-free way by maximizing a reward function in state and action spaces. This property makes it an exciting area of research for financial problems. Asset allocation, where the goal is to obtain the weights of the assets that maximize the rewards in a given state of the market considering risk and transaction costs, is a problem easily framed using a reinforcement learning framework. It is first a prediction problem for expected returns and covariance matrix and then an optimization problem for returns, risk, and market impact. Investors and financial researchers have been working with approaches like mean-variance optimization, minimum variance, risk parity, and equally weighted and several methods to make expected returns and covariance matrices' predictions more robust. This paper demonstrates the application of reinforcement learning to create a financial model-free solution to the asset allocation problem, learning to solve the problem using time series and deep neural networks. We demonstrate this on daily data for the top 24 stocks in the US equities universe with daily rebalancing. We use a deep reinforcement model on US stocks using different architectures. We use Long Short Term Memory networks, Convolutional Neural Networks, and Recurrent Neural Networks and compare them with more traditional portfolio management. The Deep Reinforcement Learning approach shows better results than traditional approaches using a simple reward function and only being given the time series of stocks. In Finance, no training to test error generalization results come guaranteed. We can say that the modeling framework can deal with time series prediction and asset allocation, including transaction costs.", "published": "2020-10-09T07:25:55Z", "authors": ["Miquel Noguer i Alonso", "Sonam Srivastava"], "categories": ["q-fin.PM", "q-fin.CP"], "doi": "", "_query": "real estate valuation deep learning", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2604.17944v1", "title": "ReCoQA: A Benchmark for Tool-Augmented and Multi-Step Reasoning in Real Estate Question and Answering", "abstract": "Developing agents capable of navigating fragmented, multi-source information remains challenging, primarily due to the scarcity of benchmarks reflecting hybrid workflows combining database querying with external APIs. To bridge this gap, we introduce ReCoQA, a large-scale benchmark of 29,270 real-estate instances featuring machine-verifiable supervision for intermediate steps, including structured intent labels, SQL queries, and API calls. Complementarily, we propose HIRE-Agent, a hierarchical framework instantiating an understand-plan-execute architecture as a strong baseline. By orchestrating a Front-end parser, a planning Supervisor, and execution Specialists, HIRE-Agent effectively integrates heterogeneous evidence. Extensive experiments demonstrate that HIRE-Agent constitutes a strong baseline and substantiates the necessity of hierarchical collaboration for complex, real-world reasoning tasks.", "published": "2026-04-20T08:22:18Z", "authors": ["Yindong Zhang", "Wenmian Yang", "Yiquan Zhang", "Weijia Jia"], "categories": ["cs.CL"], "doi": "", "_query": "real estate valuation deep learning", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2002.08599v4", "title": "On Learning Sets of Symmetric Elements", "abstract": "Learning from unordered sets is a fundamental learning setup, recently attracting increasing attention. Research in this area has focused on the case where elements of the set are represented by feature vectors, and far less emphasis has been given to the common case where set elements themselves adhere to their own symmetries. That case is relevant to numerous applications, from deblurring image bursts to multi-view 3D shape recognition and reconstruction. In this paper, we present a principled approach to learning sets of general symmetric elements. We first characterize the space of linear layers that are equivariant both to element reordering and to the inherent symmetries of elements, like translation in the case of images. We further show that networks that are composed of these layers, called Deep Sets for Symmetric Elements (DSS) layers, are universal approximators of both invariant and equivariant functions, and that these networks are strictly more expressive than Siamese networks. DSS layers are also straightforward to implement. Finally, we show that they improve over existing set-learning architectures in a series of experiments with images, graphs, and point-clouds.", "published": "2020-02-20T07:29:20Z", "authors": ["Haggai Maron", "Or Litany", "Gal Chechik", "Ethan Fetaya"], "categories": ["cs.LG", "stat.ML"], "doi": "", "_query": "real estate valuation deep learning", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2506.17607v1", "title": "Towards Fundamental Limits for Active Multi-distribution Learning", "abstract": "Multi-distribution learning extends agnostic Probably Approximately Correct (PAC) learning to the setting in which a family of $k$ distributions, $\\{D_i\\}_{i\\in[k]}$, is considered and a classifier's performance is measured by its error under the worst distribution. This problem has attracted a lot of recent interests due to its applications in collaborative learning, fairness, and robustness. Despite a rather complete picture of sample complexity of passive multi-distribution learning, research on active multi-distribution learning remains scarce, with algorithms whose optimality remaining unknown. In this paper, we develop new algorithms for active multi-distribution learning and establish improved label complexity upper and lower bounds, in distribution-dependent and distribution-free settings. Specifically, in the near-realizable setting we prove an upper bound of $\\widetilde{O}\\Bigl(θ_{\\max}(d+k)\\ln\\frac{1}{\\varepsilon}\\Bigr)$ and $\\widetilde{O}\\Bigl(θ_{\\max}(d+k)\\Bigl(\\ln\\frac{1}{\\varepsilon}+\\frac{ν^2}{\\varepsilon^2}\\Bigr)+\\frac{kν}{\\varepsilon^2}\\Bigr)$ in the realizable and agnostic settings respectively, where $θ_{\\max}$ is the maximum disagreement coefficient among the $k$ distributions, $d$ is the VC dimension of the hypothesis class, $ν$ is the multi-distribution error of the best hypothesis, and $\\varepsilon$ is the target excess error. Moreover, we show that the bound in the realizable setting is information-theoretically optimal and that the $kν/\\varepsilon^2$ term in the agnostic setting is fundamental for proper learners. We also establish instance-dependent sample complexity bound for passive multidistribution learning that smoothly interpolates between realizable and agnostic regimes~\\citep{blum2017collaborative,zhang2024optimal}, which may be of independent interest.", "published": "2025-06-21T06:08:58Z", "authors": ["Chicheng Zhang", "Yihan Zhou"], "categories": ["cs.LG", "stat.ML"], "doi": "", "_query": "real estate valuation deep learning", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2603.06894v1", "title": "Learning From Design Procedure To Generate CAD Programs for Data Augmentation", "abstract": "Large Language Models (LLMs) have demonstrated impressive capabilities in a wide range of code generation tasks. However, generating code for certain domains remains challenging. One such domain is Computer-Aided Design (CAD) program, where the goal is to produce scripted parametric models that define object geometry for precise design and manufacturing applications. A key challenge in LLM-based CAD program generation is the limited geometric complexity of generated shapes compared to those found in real-world industrial designs. This shortfall is in part due to the lack of diversity in the available CAD program training data. To address this, we propose a novel data augmentation paradigm that prompts an LLM to generate CAD programs conditioned on a reference surface program and a modeling procedure - an idea inspired by practices in industrial design. By varying the reference surface using a collection of organic shapes, our method enriches the geometric distribution of generated CAD models. In particular, it introduces edges and faces defined by spline-based curvature, which are typically missing or underrepresented in existing open-source CAD program datasets. Experiments show that our method produces CAD samples with significantly greater geometric diversity and a higher resemblance to industry-grade CAD designs in terms of the proportion of organic shape primitives. This enhancement makes our CAD data augmentation approach a useful tool for training LLMs and other deep learning models in CAD generation.", "published": "2026-03-06T21:32:15Z", "authors": ["Yan-Ying Chen", "Dule Shu", "Matthew Hong", "Andrew Taber", "Jonathan Li", "Matthew Klenk"], "categories": ["cs.LG", "cs.CV"], "doi": "", "_query": "real estate valuation deep learning", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/1803.07519v4", "title": "DeepGauge: Multi-Granularity Testing Criteria for Deep Learning Systems", "abstract": "Deep learning (DL) defines a new data-driven programming paradigm that constructs the internal system logic of a crafted neuron network through a set of training data. We have seen wide adoption of DL in many safety-critical scenarios. However, a plethora of studies have shown that the state-of-the-art DL systems suffer from various vulnerabilities which can lead to severe consequences when applied to real-world applications. Currently, the testing adequacy of a DL system is usually measured by the accuracy of test data. Considering the limitation of accessible high quality test data, good accuracy performance on test data can hardly provide confidence to the testing adequacy and generality of DL systems. Unlike traditional software systems that have clear and controllable logic and functionality, the lack of interpretability in a DL system makes system analysis and defect detection difficult, which could potentially hinder its real-world deployment. In this paper, we propose DeepGauge, a set of multi-granularity testing criteria for DL systems, which aims at rendering a multi-faceted portrayal of the testbed. The in-depth evaluation of our proposed testing criteria is demonstrated on two well-known datasets, five DL systems, and with four state-of-the-art adversarial attack techniques against DL. The potential usefulness of DeepGauge sheds light on the construction of more generic and robust DL systems.", "published": "2018-03-20T16:52:12Z", "authors": ["Lei Ma", "Felix Juefei-Xu", "Fuyuan Zhang", "Jiyuan Sun", "Minhui Xue", "Bo Li", "Chunyang Chen", "Ting Su", "Li Li", "Yang Liu", "Jianjun Zhao", "Yadong Wang"], "categories": ["cs.SE", "cs.CR", "cs.LG", "stat.ML"], "doi": "10.1145/3238147.3238202", "_query": "real estate valuation deep learning", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2303.03381v2", "title": "Real-World Humanoid Locomotion with Reinforcement Learning", "abstract": "Humanoid robots that can autonomously operate in diverse environments have the potential to help address labour shortages in factories, assist elderly at homes, and colonize new planets. While classical controllers for humanoid robots have shown impressive results in a number of settings, they are challenging to generalize and adapt to new environments. Here, we present a fully learning-based approach for real-world humanoid locomotion. Our controller is a causal transformer that takes the history of proprioceptive observations and actions as input and predicts the next action. We hypothesize that the observation-action history contains useful information about the world that a powerful transformer model can use to adapt its behavior in-context, without updating its weights. We train our model with large-scale model-free reinforcement learning on an ensemble of randomized environments in simulation and deploy it to the real world zero-shot. Our controller can walk over various outdoor terrains, is robust to external disturbances, and can adapt in context.", "published": "2023-03-06T18:59:09Z", "authors": ["Ilija Radosavovic", "Tete Xiao", "Bike Zhang", "Trevor Darrell", "Jitendra Malik", "Koushil Sreenath"], "categories": ["cs.RO", "cs.LG"], "doi": "", "_query": "real estate valuation deep learning", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/1706.09739v2", "title": "A Deep Multimodal Approach for Cold-start Music Recommendation", "abstract": "An increasing amount of digital music is being published daily. Music streaming services often ingest all available music, but this poses a challenge: how to recommend new artists for which prior knowledge is scarce? In this work we aim to address this so-called cold-start problem by combining text and audio information with user feedback data using deep network architectures. Our method is divided into three steps. First, artist embeddings are learned from biographies by combining semantics, text features, and aggregated usage data. Second, track embeddings are learned from the audio signal and available feedback data. Finally, artist and track embeddings are combined in a multimodal network. Results suggest that both splitting the recommendation problem between feature levels (i.e., artist metadata and audio track), and merging feature embeddings in a multimodal approach improve the accuracy of the recommendations.", "published": "2017-06-29T13:13:26Z", "authors": ["Sergio Oramas", "Oriol Nieto", "Mohamed Sordo", "Xavier Serra"], "categories": ["cs.IR", "cs.LG"], "doi": "10.1145/3125486.3125492", "_query": "real estate valuation deep learning", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/1909.02119v2", "title": "Inductive-bias-driven Reinforcement Learning For Efficient Schedules in Heterogeneous Clusters", "abstract": "The problem of scheduling of workloads onto heterogeneous processors (e.g., CPUs, GPUs, FPGAs) is of fundamental importance in modern data centers. Current system schedulers rely on application/system-specific heuristics that have to be built on a case-by-case basis. Recent work has demonstrated ML techniques for automating the heuristic search by using black-box approaches which require significant training data and time, which make them challenging to use in practice. This paper presents Symphony, a scheduling framework that addresses the challenge in two ways: (i) a domain-driven Bayesian reinforcement learning (RL) model for scheduling, which inherently models the resource dependencies identified from the system architecture; and (ii) a sampling-based technique to compute the gradients of a Bayesian model without performing full probabilistic inference. Together, these techniques reduce both the amount of training data and the time required to produce scheduling policies that significantly outperform black-box approaches by up to 2.2x.", "published": "2019-09-04T21:19:14Z", "authors": ["Subho S Banerjee", "Saurabh Jha", "Zbigniew T. Kalbarczyk", "Ravishankar K. Iyer"], "categories": ["cs.DC", "cs.LG"], "doi": "", "_query": "real estate valuation deep learning", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2406.02612v1", "title": "Is Data Valuation Learnable and Interpretable?", "abstract": "Measuring the value of individual samples is critical for many data-driven tasks, e.g., the training of a deep learning model. Recent literature witnesses the substantial efforts in developing data valuation methods. The primary data valuation methodology is based on the Shapley value from game theory, and various methods are proposed along this path. {Even though Shapley value-based valuation has solid theoretical basis, it is entirely an experiment-based approach and no valuation model has been constructed so far.} In addition, current data valuation methods ignore the interpretability of the output values, despite an interptable data valuation method is of great helpful for applications such as data pricing. This study aims to answer an important question: is data valuation learnable and interpretable? A learned valuation model have several desirable merits such as fixed number of parameters and knowledge reusability. An intrepretable data valuation model can explain why a sample is valuable or invaluable. To this end, two new data value modeling frameworks are proposed, in which a multi-layer perception~(MLP) and a new regression tree are utilized as specific base models for model training and interpretability, respectively. Extensive experiments are conducted on benchmark datasets. {The experimental results provide a positive answer for the question.} Our study opens up a new technical path for the assessing of data values. Large data valuation models can be built across many different data-driven tasks, which can promote the widespread application of data valuation.", "published": "2024-06-03T08:13:47Z", "authors": ["Ou Wu", "Weiyao Zhu", "Mengyang Li"], "categories": ["cs.LG", "cs.AI"], "doi": "", "_query": "real estate valuation deep learning", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2305.06295v3", "title": "Extracting Diagnosis Pathways from Electronic Health Records Using Deep Reinforcement Learning", "abstract": "Clinical diagnosis guidelines aim at specifying the steps that may lead to a diagnosis. Inspired by guidelines, we aim to learn the optimal sequence of actions to perform in order to obtain a correct diagnosis from electronic health records. We apply various deep reinforcement learning algorithms to this task and experiment on a synthetic but realistic dataset to differentially diagnose anemia and its subtypes and particularly evaluate the robustness of various approaches to noise and missing data. Experimental results show that the deep reinforcement learning algorithms show competitive performance compared to the state-of-the-art methods with the added advantage that they enable the progressive generation of a pathway to the suggested diagnosis, which can both guide and explain the decision process.", "published": "2023-05-10T16:36:54Z", "authors": ["Lillian Muyama", "Antoine Neuraz", "Adrien Coulet"], "categories": ["cs.LG"], "doi": "", "_query": "real estate valuation deep learning", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2109.07180v2", "title": "Back to Basics: Deep Reinforcement Learning in Traffic Signal Control", "abstract": "In this paper we revisit some of the fundamental premises for a reinforcement learning (RL) approach to self-learning traffic lights. We propose RLight, a combination of choices that offers robust performance and good generalization to unseen traffic flows. In particular, our main contributions are threefold: our lightweight and cluster-aware state representation leads to improved performance; we reformulate the Markov Decision Process (MDP) such that it skips redundant timesteps of yellow light, speeding up learning by 30%; and we investigate the action space and provide insight into the difference in performance between acyclic and cyclic phase transitions. Additionally, we provide insights into the generalisation of the methods to unseen traffic. Evaluations using the real-world Hangzhou traffic dataset show that RLight outperforms state-of-the-art rule-based and deep reinforcement learning algorithms, demonstrating the potential of RL-based methods to improve urban traffic flows.", "published": "2021-09-15T09:36:23Z", "authors": ["Sierk Kanis", "Laurens Samson", "Daan Bloembergen", "Tim Bakker"], "categories": ["cs.LG"], "doi": "", "_query": "real estate valuation deep learning", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2006.11184v2", "title": "Poisson Learning: Graph Based Semi-Supervised Learning At Very Low Label Rates", "abstract": "We propose a new framework, called Poisson learning, for graph based semi-supervised learning at very low label rates. Poisson learning is motivated by the need to address the degeneracy of Laplacian semi-supervised learning in this regime. The method replaces the assignment of label values at training points with the placement of sources and sinks, and solves the resulting Poisson equation on the graph. The outcomes are provably more stable and informative than those of Laplacian learning. Poisson learning is efficient and simple to implement, and we present numerical experiments showing the method is superior to other recent approaches to semi-supervised learning at low label rates on MNIST, FashionMNIST, and Cifar-10. We also propose a graph-cut enhancement of Poisson learning, called Poisson MBO, that gives higher accuracy and can incorporate prior knowledge of relative class sizes.", "published": "2020-06-19T15:21:04Z", "authors": ["Jeff Calder", "Brendan Cook", "Matthew Thorpe", "Dejan Slepcev"], "categories": ["cs.LG", "cs.CV", "math.AP", "math.NA", "stat.ML"], "doi": "", "_query": "real estate valuation deep learning", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2409.05335v1", "title": "A Multi-Modal Deep Learning Based Approach for House Price Prediction", "abstract": "Accurate prediction of house price, a vital aspect of the residential real estate sector, is of substantial interest for a wide range of stakeholders. However, predicting house prices is a complex task due to the significant variability influenced by factors such as house features, location, neighborhood, and many others. Despite numerous attempts utilizing a wide array of algorithms, including recent deep learning techniques, to predict house prices accurately, existing approaches have fallen short of considering a wide range of factors such as textual and visual features. This paper addresses this gap by comprehensively incorporating attributes, such as features, textual descriptions, geo-spatial neighborhood, and house images, typically showcased in real estate listings in a house price prediction system. Specifically, we propose a multi-modal deep learning approach that leverages different types of data to learn more accurate representation of the house. In particular, we learn a joint embedding of raw house attributes, geo-spatial neighborhood, and most importantly from textual description and images representing the house; and finally use a downstream regression model to predict the house price from this jointly learned embedding vector. Our experimental results with a real-world dataset show that the text embedding of the house advertisement description and image embedding of the house pictures in addition to raw attributes and geo-spatial embedding, can significantly improve the house price prediction accuracy. The relevant source code and dataset are publicly accessible at the following URL: https://github.com/4P0N/mhpp", "published": "2024-09-09T05:26:33Z", "authors": ["Md Hasebul Hasan", "Md Abid Jahan", "Mohammed Eunus Ali", "Yuan-Fang Li", "Timos Sellis"], "categories": ["cs.LG", "cs.AI", "cs.CV"], "doi": "", "_query": "real estate valuation deep learning", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2212.14538v2", "title": "Transformer in Transformer as Backbone for Deep Reinforcement Learning", "abstract": "Designing better deep networks and better reinforcement learning (RL) algorithms are both important for deep RL. This work focuses on the former. Previous methods build the network with several modules like CNN, LSTM and Attention. Recent methods combine the Transformer with these modules for better performance. However, it requires tedious optimization skills to train a network composed of mixed modules, making these methods inconvenient to be used in practice. In this paper, we propose to design \\emph{pure Transformer-based networks} for deep RL, aiming at providing off-the-shelf backbones for both the online and offline settings. Specifically, the Transformer in Transformer (TIT) backbone is proposed, which cascades two Transformers in a very natural way: the inner one is used to process a single observation, while the outer one is responsible for processing the observation history; combining both is expected to extract spatial-temporal representations for good decision-making. Experiments show that TIT can achieve satisfactory performance in different settings consistently.", "published": "2022-12-30T03:50:38Z", "authors": ["Hangyu Mao", "Rui Zhao", "Hao Chen", "Jianye Hao", "Yiqun Chen", "Dong Li", "Junge Zhang", "Zhen Xiao"], "categories": ["cs.LG", "cs.AI", "cs.RO"], "doi": "", "_query": "real estate valuation deep learning", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2407.19724v1", "title": "Constructing artificial life and materials scientists with accelerated AI using Deep AndersoNN", "abstract": "Deep AndersoNN accelerates AI by exploiting the continuum limit as the number of explicit layers in a neural network approaches infinity and can be taken as a single implicit layer, known as a deep equilibrium model. Solving for deep equilibrium model parameters reduces to a nonlinear fixed point iteration problem, enabling the use of vector-to-vector iterative solvers and windowing techniques, such as Anderson extrapolation, for accelerating convergence to the fixed point deep equilibrium. Here we show that Deep AndersoNN achieves up to an order of magnitude of speed-up in training and inference. The method is demonstrated on density functional theory results for industrial applications by constructing artificial life and materials `scientists' capable of classifying drugs as strongly or weakly polar, metal-organic frameworks by pore size, and crystalline materials as metals, semiconductors, and insulators, using graph images of node-neighbor representations transformed from atom-bond networks. Results exhibit accuracy up to 98\\% and showcase synergy between Deep AndersoNN and machine learning capabilities of modern computing architectures, such as GPUs, for accelerated computational life and materials science by quickly identifying structure-property relationships. This paves the way for saving up to 90\\% of compute required for AI, reducing its carbon footprint by up to 60 gigatons per year by 2030, and scaling above memory limits of explicit neural networks in life and materials science, and beyond.", "published": "2024-07-29T06:12:47Z", "authors": ["Saleem Abdul Fattah Ahmed Al Dajani", "David Keyes"], "categories": ["cs.LG", "physics.app-ph"], "doi": "", "_query": "real estate valuation deep learning", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/1907.00038v1", "title": "The Practical Challenges of Active Learning: Lessons Learned from Live Experimentation", "abstract": "We tested in a live setting the use of active learning for selecting text sentences for human annotations used in training a Thai segmentation machine learning model. In our study, two concurrent annotated samples were constructed, one through random sampling of sentences from a text corpus, and the other through model-based scoring and ranking of sentences from the same corpus. In the course of the experiment, we observed the effect of significant changes to the learning environment which are likely to occur in real-world learning tasks. We describe how our active learning strategy interacted with these events and discuss other practical challenges encountered in using active learning in the live setting.", "published": "2019-06-28T18:56:20Z", "authors": ["Jean-François Kagy", "Tolga Kayadelen", "Ji Ma", "Afshin Rostamizadeh", "Jana Strnadova"], "categories": ["cs.LG", "stat.ML"], "doi": "", "_query": "real estate valuation deep learning", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2606.20858v2", "title": "Evolutionary Discovery of Developmental Reward Schedules in Deep Reinforcement Learning", "abstract": "The temporal structure of reward composition in reinforcement learning (RL) is typically hand-designed and held fixed throughout training, leaving the progression of motivational priorities largely unexplored. In this work, we propose an evolutionary framework for discovering developmental reward schedules, in which three distinct biologically inspired motivational components -- agency, novelty, and reactivity -- are combined through time-varying weights that dynamically shift over the course of training. Evaluated on two sparse-reward MiniGrid tasks: DoorKey-6x6 and KeyCorridorS3R1, our framework compares the generalizability of four evolutionary algorithms: CMA-ES, xNES, DE, and L-SHADE against an extrinsically motivated baseline (our main comparison point), and three additional hand-designed methods. On DoorKey-6x6, all evolved methods outperform the non-evolved baselines, with L-SHADE achieving the best performance -- an approximate relative mean improvement of 11.4% over the extrinsic only baseline. On KeyCorridorS3R1, CMA-ES achieves the best overall performance, with the remaining evolved methods showing weaker and less reliable generalization capability compared to the extrinsic only baseline. Interestingly, the discovered schedules diverge from our defined developmental ordering, with novelty consistently emerging as the dominant early signal during training, across both tasks. Collectively, our results position evolutionary optimization as a promising approach for developmental reward schedule discovery in deep reinforcement learning, and suggest that what evolution finds to be optimal in computational settings may differ from what it finds to be optimal in biology. The code for this project can be found at: https://github.com/alannadels/Evolutionary_RL.git.", "published": "2026-06-18T18:47:26Z", "authors": ["Alan Nadelsticher Ruvalcaba"], "categories": ["cs.LG", "cs.NE"], "doi": "", "_query": "real estate valuation deep learning", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/cs/0609093v1", "title": "PAC Learning Mixtures of Axis-Aligned Gaussians with No Separation Assumption", "abstract": "We propose and analyze a new vantage point for the learning of mixtures of Gaussians: namely, the PAC-style model of learning probability distributions introduced by Kearns et al. Here the task is to construct a hypothesis mixture of Gaussians that is statistically indistinguishable from the actual mixture generating the data; specifically, the KL-divergence should be at most epsilon. In this scenario, we give a poly(n/epsilon)-time algorithm that learns the class of mixtures of any constant number of axis-aligned Gaussians in n-dimensional Euclidean space. Our algorithm makes no assumptions about the separation between the means of the Gaussians, nor does it have any dependence on the minimum mixing weight. This is in contrast to learning results known in the ``clustering'' model, where such assumptions are unavoidable. Our algorithm relies on the method of moments, and a subalgorithm developed in previous work by the authors (FOCS 2005) for a discrete mixture-learning problem.", "published": "2006-09-16T14:43:27Z", "authors": ["Jon Feldman", "Ryan O'Donnell", "Rocco A. Servedio"], "categories": ["cs.LG"], "doi": "", "_query": "real estate valuation deep learning", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/1910.03081v1", "title": "On the Interpretability and Evaluation of Graph Representation Learning", "abstract": "With the rising interest in graph representation learning, a variety of approaches have been proposed to effectively capture a graph's properties. While these approaches have improved performance in graph machine learning tasks compared to traditional graph techniques, they are still perceived as techniques with limited insight into the information encoded in these representations. In this work, we explore methods to interpret node embeddings and propose the creation of a robust evaluation framework for comparing graph representation learning algorithms and hyperparameters. We test our methods on graphs with different properties and investigate the relationship between embedding training parameters and the ability of the produced embedding to recover the structure of the original graph in a downstream task.", "published": "2019-10-07T21:02:13Z", "authors": ["Antonia Gogoglou", "C. Bayan Bruss", "Keegan E. Hines"], "categories": ["cs.LG", "stat.ML"], "doi": "", "_query": "real estate valuation deep learning", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2307.05281v1", "title": "Smart Environment for Adaptive Learning of Cybersecurity Skills", "abstract": "Hands-on computing education requires a realistic learning environment that enables students to gain and deepen their skills. Available learning environments, including virtual and physical labs, provide students with real-world computer systems but rarely adapt the learning environment to individual students of various proficiency and background. We designed a unique and novel smart environment for adaptive training of cybersecurity skills. The environment collects a variety of student data to assign a suitable learning path through the training. To enable such adaptiveness, we proposed, developed, and deployed a new tutor model and a training format. We evaluated the learning environment using two different adaptive trainings attended by 114 students of various proficiency. The results show students were assigned tasks with a more appropriate difficulty, which enabled them to successfully complete the training. Students reported that they enjoyed the training, felt the training difficulty was appropriately designed, and would attend more training sessions like these. Instructors can use the environment for teaching any topic involving real-world computer networks and systems because it is not tailored to particular training. We freely released the software along with exemplary training so that other instructors can adopt the innovations in their teaching practice.", "published": "2023-07-11T14:20:29Z", "authors": ["Jan Vykopal", "Pavel Seda", "Valdemar Švábenský", "Pavel Čeleda"], "categories": ["cs.CR", "cs.CY"], "doi": "10.1109/TLT.2022.3216345", "_query": "real estate valuation deep learning", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/1801.05062v2", "title": "Multi-Label Learning from Medical Plain Text with Convolutional Residual Models", "abstract": "Predicting diagnoses from Electronic Health Records (EHRs) is an important medical application of multi-label learning. We propose a convolutional residual model for multi-label classification from doctor notes in EHR data. A given patient may have multiple diagnoses, and therefore multi-label learning is required. We employ a Convolutional Neural Network (CNN) to encode plain text into a fixed-length sentence embedding vector. Since diagnoses are typically correlated, a deep residual network is employed on top of the CNN encoder, to capture label (diagnosis) dependencies and incorporate information directly from the encoded sentence vector. A real EHR dataset is considered, and we compare the proposed model with several well-known baselines, to predict diagnoses based on doctor notes. Experimental results demonstrate the superiority of the proposed convolutional residual model.", "published": "2018-01-15T22:59:17Z", "authors": ["Xinyuan Zhang", "Ricardo Henao", "Zhe Gan", "Yitong Li", "Lawrence Carin"], "categories": ["stat.ML", "cs.LG", "stat.AP"], "doi": "", "_query": "real estate valuation deep learning", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2401.16197v1", "title": "Geospatial Disparities: A Case Study on Real Estate Prices in Paris", "abstract": "Driven by an increasing prevalence of trackers, ever more IoT sensors, and the declining cost of computing power, geospatial information has come to play a pivotal role in contemporary predictive models. While enhancing prognostic performance, geospatial data also has the potential to perpetuate many historical socio-economic patterns, raising concerns about a resurgence of biases and exclusionary practices, with their disproportionate impacts on society. Addressing this, our paper emphasizes the crucial need to identify and rectify such biases and calibration errors in predictive models, particularly as algorithms become more intricate and less interpretable. The increasing granularity of geospatial information further introduces ethical concerns, as choosing different geographical scales may exacerbate disparities akin to redlining and exclusionary zoning. To address these issues, we propose a toolkit for identifying and mitigating biases arising from geospatial data. Extending classical fairness definitions, we incorporate an ordinal regression case with spatial attributes, deviating from the binary classification focus. This extension allows us to gauge disparities stemming from data aggregation levels and advocates for a less interfering correction approach. Illustrating our methodology using a Parisian real estate dataset, we showcase practical applications and scrutinize the implications of choosing geographical aggregation levels for fairness and calibration measures.", "published": "2024-01-29T14:53:14Z", "authors": ["Agathe Fernandes Machado", "François Hu", "Philipp Ratz", "Ewen Gallic", "Arthur Charpentier"], "categories": ["cs.LG", "cs.CY"], "doi": "", "_query": "real estate valuation deep learning", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/1206.6419v1", "title": "Cross-Domain Multitask Learning with Latent Probit Models", "abstract": "Learning multiple tasks across heterogeneous domains is a challenging problem since the feature space may not be the same for different tasks. We assume the data in multiple tasks are generated from a latent common domain via sparse domain transforms and propose a latent probit model (LPM) to jointly learn the domain transforms, and the shared probit classifier in the common domain. To learn meaningful task relatedness and avoid over-fitting in classification, we introduce sparsity in the domain transforms matrices, as well as in the common classifier. We derive theoretical bounds for the estimation error of the classifier in terms of the sparsity of domain transforms. An expectation-maximization algorithm is derived for learning the LPM. The effectiveness of the approach is demonstrated on several real datasets.", "published": "2012-06-27T19:59:59Z", "authors": ["Shaobo Han", "Xuejun Liao", "Lawrence Carin"], "categories": ["cs.LG", "stat.ML"], "doi": "", "_query": "real estate valuation deep learning", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2504.06591v2", "title": "NAPER: Fault Protection for Real-Time Resource-Constrained Deep Neural Networks", "abstract": "Fault tolerance in Deep Neural Networks (DNNs) deployed on resource-constrained systems presents unique challenges for high-accuracy applications with strict timing requirements. Memory bit-flips can severely degrade DNN accuracy, while traditional protection approaches like Triple Modular Redundancy (TMR) often sacrifice accuracy to maintain reliability, creating a three-way dilemma between reliability, accuracy, and timeliness. We introduce NAPER, a novel protection approach that addresses this challenge through ensemble learning. Unlike conventional redundancy methods, NAPER employs heterogeneous model redundancy, where diverse models collectively achieve higher accuracy than any individual model. This is complemented by an efficient fault detection mechanism and a real-time scheduler that prioritizes meeting deadlines by intelligently scheduling recovery operations without interrupting inference. Our evaluations demonstrate NAPER's superiority: 40% faster inference in both normal and fault conditions, maintained accuracy 4.2% higher than TMR-based strategies, and guaranteed uninterrupted operation even during fault recovery. NAPER effectively balances the competing demands of accuracy, reliability, and timeliness in real-time DNN applications", "published": "2025-04-09T05:37:54Z", "authors": ["Rian Adam Rajagede", "Muhammad Husni Santriaji", "Muhammad Arya Fikriansyah", "Hilal Hudan Nuha", "Yanjie Fu", "Yan Solihin"], "categories": ["cs.LG", "eess.SY"], "doi": "10.1109/IOLTS65288.2025.11116827", "_query": "real estate valuation deep learning", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2602.15926v1", "title": "A Study on Real-time Object Detection using Deep Learning", "abstract": "Object detection has compelling applications over a range of domains, including human-computer interfaces, security and video surveillance, navigation and road traffic monitoring, transportation systems, industrial automation healthcare, the world of Augmented Reality (AR) and Virtual Reality (VR), environment monitoring and activity identification. Applications of real time object detection in all these areas provide dynamic analysis of the visual information that helps in immediate decision making. Furthermore, advanced deep learning algorithms leverage the progress in the field of object detection providing more accurate and efficient solutions. There are some outstanding deep learning algorithms for object detection which includes, Faster R CNN(Region-based Convolutional Neural Network),Mask R-CNN, Cascade R-CNN, YOLO (You Only Look Once), SSD (Single Shot Multibox Detector), RetinaNet etc. This article goes into great detail on how deep learning algorithms are used to enhance real time object recognition. It provides information on the different object detection models available, open benchmark datasets, and studies on the use of object detection models in a range of applications. Additionally, controlled studies are provided to compare various strategies and produce some illuminating findings. Last but not least, a number of encouraging challenges and approaches are offered as suggestions for further investigation in both relevant deep learning approaches and object recognition.", "published": "2026-02-17T18:12:42Z", "authors": ["Ankita Bose", "Jayasravani Bhumireddy", "Naveen N"], "categories": ["cs.CV", "cs.LG"], "doi": "", "_query": "real estate valuation deep learning", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2211.00246v2", "title": "Batch Active Learning from the Perspective of Sparse Approximation", "abstract": "Active learning enables efficient model training by leveraging interactions between machine learning agents and human annotators. We study and propose a novel framework that formulates batch active learning from the sparse approximation's perspective. Our active learning method aims to find an informative subset from the unlabeled data pool such that the corresponding training loss function approximates its full data pool counterpart. We realize the framework as sparsity-constrained discontinuous optimization problems, which explicitly balance uncertainty and representation for large-scale applications and could be solved by greedy or proximal iterative hard thresholding algorithms. The proposed method can adapt to various settings, including both Bayesian and non-Bayesian neural networks. Numerical experiments show that our work achieves competitive performance across different settings with lower computational complexity.", "published": "2022-11-01T03:20:28Z", "authors": ["Maohao Shen", "Bowen Jiang", "Jacky Yibo Zhang", "Oluwasanmi Koyejo"], "categories": ["cs.LG", "stat.ML"], "doi": "", "_query": "real estate valuation deep learning", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2006.04678v2", "title": "Primal Wasserstein Imitation Learning", "abstract": "Imitation Learning (IL) methods seek to match the behavior of an agent with that of an expert. In the present work, we propose a new IL method based on a conceptually simple algorithm: Primal Wasserstein Imitation Learning (PWIL), which ties to the primal form of the Wasserstein distance between the expert and the agent state-action distributions. We present a reward function which is derived offline, as opposed to recent adversarial IL algorithms that learn a reward function through interactions with the environment, and which requires little fine-tuning. We show that we can recover expert behavior on a variety of continuous control tasks of the MuJoCo domain in a sample efficient manner in terms of agent interactions and of expert interactions with the environment. Finally, we show that the behavior of the agent we train matches the behavior of the expert with the Wasserstein distance, rather than the commonly used proxy of performance.", "published": "2020-06-08T15:30:11Z", "authors": ["Robert Dadashi", "Léonard Hussenot", "Matthieu Geist", "Olivier Pietquin"], "categories": ["cs.LG", "stat.ML"], "doi": "", "_query": "real estate valuation deep learning", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2305.15613v7", "title": "O$n$ Learning Deep O($n$)-Equivariant Hyperspheres", "abstract": "In this paper, we utilize hyperspheres and regular $n$-simplexes and propose an approach to learning deep features equivariant under the transformations of $n$D reflections and rotations, encompassed by the powerful group of O$(n)$. Namely, we propose O$(n)$-equivariant neurons with spherical decision surfaces that generalize to any dimension $n$, which we call Deep Equivariant Hyperspheres. We demonstrate how to combine them in a network that directly operates on the basis of the input points and propose an invariant operator based on the relation between two points and a sphere, which as we show, turns out to be a Gram matrix. Using synthetic and real-world data in $n$D, we experimentally verify our theoretical contributions and find that our approach is superior to the competing methods for O$(n)$-equivariant benchmark datasets (classification and regression), demonstrating a favorable speed/performance trade-off. The code is available at https://github.com/pavlo-melnyk/equivariant-hyperspheres.", "published": "2023-05-24T23:04:34Z", "authors": ["Pavlo Melnyk", "Michael Felsberg", "Mårten Wadenbäck", "Andreas Robinson", "Cuong Le"], "categories": ["cs.LG"], "doi": "", "_query": "real estate valuation deep learning", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2411.05044v1", "title": "Deep Heuristic Learning for Real-Time Urban Pathfinding", "abstract": "This paper introduces a novel approach to urban pathfinding by transforming traditional heuristic-based algorithms into deep learning models that leverage real-time contextual data, such as traffic and weather conditions. We propose two methods: an enhanced A* algorithm that dynamically adjusts routes based on current environmental conditions, and a neural network model that predicts the next optimal path segment using historical and live data. An extensive benchmark was conducted to compare the performance of different deep learning models, including MLP, GRU, LSTM, Autoencoders, and Transformers. Both methods were evaluated in a simulated urban environment in Berlin, with the neural network model outperforming traditional methods, reducing travel times by up to 40%, while the enhanced A* algorithm achieved a 34% improvement. These results demonstrate the potential of deep learning to optimize urban navigation in real time, providing more adaptable and efficient routing solutions.", "published": "2024-11-07T00:22:04Z", "authors": ["Mohamed Hussein Abo El-Ela", "Ali Hamdi Fergany"], "categories": ["cs.AI", "cs.LG", "stat.ML"], "doi": "", "_query": "real estate valuation deep learning", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/1904.10922v1", "title": "The Scientific Method in the Science of Machine Learning", "abstract": "In the quest to align deep learning with the sciences to address calls for rigor, safety, and interpretability in machine learning systems, this contribution identifies key missing pieces: the stages of hypothesis formulation and testing, as well as statistical and systematic uncertainty estimation -- core tenets of the scientific method. This position paper discusses the ways in which contemporary science is conducted in other domains and identifies potentially useful practices. We present a case study from physics and describe how this field has promoted rigor through specific methodological practices, and provide recommendations on how machine learning researchers can adopt these practices into the research ecosystem. We argue that both domain-driven experiments and application-agnostic questions of the inner workings of fundamental building blocks of machine learning models ought to be examined with the tools of the scientific method, to ensure we not only understand effect, but also begin to understand cause, which is the raison d'être of science.", "published": "2019-04-24T17:01:43Z", "authors": ["Jessica Zosa Forde", "Michela Paganini"], "categories": ["cs.LG", "stat.ML"], "doi": "", "_query": "real estate valuation deep learning", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2111.07508v1", "title": "Public Policymaking for International Agricultural Trade using Association Rules and Ensemble Machine Learning", "abstract": "International economics has a long history of improving our understanding of factors causing trade, and the consequences of free flow of goods and services across countries. The recent shocks to the free trade regime, especially trade disputes among major economies, as well as black swan events, such as trade wars and pandemics, raise the need for improved predictions to inform policy decisions. AI methods are allowing economists to solve such prediction problems in new ways. In this manuscript, we present novel methods that predict and associate food and agricultural commodities traded internationally. Association Rules (AR) analysis has been deployed successfully for economic scenarios at the consumer or store level, such as for market basket analysis. In our work however, we present analysis of imports and exports associations and their effects on commodity trade flows. Moreover, Ensemble Machine Learning methods are developed to provide improved agricultural trade predictions, outlier events' implications, and quantitative pointers to policy makers.", "published": "2021-11-15T02:58:03Z", "authors": ["Feras A. Batarseh", "Munisamy Gopinath", "Anderson Monken", "Zhengrong Gu"], "categories": ["cs.LG", "cs.AI", "econ.GN"], "doi": "10.1016/j.mlwa.2021.100046", "_query": "real estate valuation deep learning", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/1910.13110v3", "title": "Unsupervised Deep Basis Pursuit: Learning inverse problems without ground-truth data", "abstract": "Basis pursuit is a compressed sensing optimization in which the l1-norm is minimized subject to model error constraints. Here we use a deep neural network prior instead of l1-regularization. Using known noise statistics, we jointly learn the prior and reconstruct images without access to ground-truth data. During training, we use alternating minimization across an unrolled iterative network and jointly solve for the neural network weights and training set image reconstructions. At inference, we fix the weights and pass the measurements through the network. We compare reconstruction performance between unsupervised and supervised (i.e. with ground-truth) methods. We hypothesize this technique could be used to learn reconstruction when ground-truth data are unavailable, such as in high-resolution dynamic MRI.", "published": "2019-10-29T06:46:49Z", "authors": ["Jonathan I. Tamir", "Stella X. Yu", "Michael Lustig"], "categories": ["eess.SP", "eess.IV"], "doi": "", "_query": "real estate valuation deep learning", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/1605.03004v1", "title": "MUST-CNN: A Multilayer Shift-and-Stitch Deep Convolutional Architecture for Sequence-based Protein Structure Prediction", "abstract": "Predicting protein properties such as solvent accessibility and secondary structure from its primary amino acid sequence is an important task in bioinformatics. Recently, a few deep learning models have surpassed the traditional window based multilayer perceptron. Taking inspiration from the image classification domain we propose a deep convolutional neural network architecture, MUST-CNN, to predict protein properties. This architecture uses a novel multilayer shift-and-stitch (MUST) technique to generate fully dense per-position predictions on protein sequences. Our model is significantly simpler than the state-of-the-art, yet achieves better results. By combining MUST and the efficient convolution operation, we can consider far more parameters while retaining very fast prediction speeds. We beat the state-of-the-art performance on two large protein property prediction datasets.", "published": "2016-05-10T13:31:52Z", "authors": ["Zeming Lin", "Jack Lanchantin", "Yanjun Qi"], "categories": ["cs.LG"], "doi": "", "_query": "real estate valuation deep learning", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2112.12086v1", "title": "Improved skin lesion recognition by a Self-Supervised Curricular Deep Learning approach", "abstract": "State-of-the-art deep learning approaches for skin lesion recognition often require pretraining on larger and more varied datasets, to overcome the generalization limitations derived from the reduced size of the skin lesion imaging datasets. ImageNet is often used as the pretraining dataset, but its transferring potential is hindered by the domain gap between the source dataset and the target dermatoscopic scenario. In this work, we introduce a novel pretraining approach that sequentially trains a series of Self-Supervised Learning pretext tasks and only requires the unlabeled skin lesion imaging data. We present a simple methodology to establish an ordering that defines a pretext task curriculum. For the multi-class skin lesion classification problem, and ISIC-2019 dataset, we provide experimental evidence showing that: i) a model pretrained by a curriculum of pretext tasks outperforms models pretrained by individual pretext tasks, and ii) a model pretrained by the optimal pretext task curriculum outperforms a model pretrained on ImageNet. We demonstrate that this performance gain is related to the fact that the curriculum of pretext tasks better focuses the attention of the final model on the skin lesion. Beyond performance improvement, this strategy allows for a large reduction in the training time with respect to ImageNet pretraining, which is especially advantageous for network architectures tailored for a specific problem.", "published": "2021-12-22T17:45:47Z", "authors": ["Kirill Sirotkin", "Marcos Escudero-Viñolo", "Pablo Carballeira", "Juan Carlos SanMiguel"], "categories": ["cs.CV"], "doi": "", "_query": "real estate valuation deep learning", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2302.00375v2", "title": "Bayes-optimal Learning of Deep Random Networks of Extensive-width", "abstract": "We consider the problem of learning a target function corresponding to a deep, extensive-width, non-linear neural network with random Gaussian weights. We consider the asymptotic limit where the number of samples, the input dimension and the network width are proportionally large. We propose a closed-form expression for the Bayes-optimal test error, for regression and classification tasks. We further compute closed-form expressions for the test errors of ridge regression, kernel and random features regression. We find, in particular, that optimally regularized ridge regression, as well as kernel regression, achieve Bayes-optimal performances, while the logistic loss yields a near-optimal test error for classification. We further show numerically that when the number of samples grows faster than the dimension, ridge and kernel methods become suboptimal, while neural networks achieve test error close to zero from quadratically many samples.", "published": "2023-02-01T11:14:08Z", "authors": ["Hugo Cui", "Florent Krzakala", "Lenka Zdeborová"], "categories": ["stat.ML", "cond-mat.dis-nn", "cs.LG"], "doi": "", "_query": "real estate valuation deep learning", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2005.13695v1", "title": "An ENAS Based Approach for Constructing Deep Learning Models for Breast Cancer Recognition from Ultrasound Images", "abstract": "Deep Convolutional Neural Networks (CNN) provides an \"end-to-end\" solution for image pattern recognition with impressive performance in many areas of application including medical imaging. Most CNN models of high performance use hand-crafted network architectures that require expertise in CNNs to utilise their potentials. In this paper, we applied the Efficient Neural Architecture Search (ENAS) method to find optimal CNN architectures for classifying breast lesions from ultrasound (US) images. Our empirical study with a dataset of 524 US images shows that the optimal models generated by using ENAS achieve an average accuracy of 89.3%, surpassing other hand-crafted alternatives. Furthermore, the models are simpler in complexity and more efficient. Our study demonstrates that the ENAS approach to CNN model design is a promising direction for classifying ultrasound images of breast lesions.", "published": "2020-05-27T22:49:45Z", "authors": ["Mohammed Ahmed", "Hongbo Du", "Alaa AlZoubi"], "categories": ["eess.IV", "cs.CV", "cs.LG"], "doi": "", "_query": "real estate valuation deep learning", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/1909.12051v2", "title": "The Implicit Bias of Depth: How Incremental Learning Drives Generalization", "abstract": "A leading hypothesis for the surprising generalization of neural networks is that the dynamics of gradient descent bias the model towards simple solutions, by searching through the solution space in an incremental order of complexity. We formally define the notion of incremental learning dynamics and derive the conditions on depth and initialization for which this phenomenon arises in deep linear models. Our main theoretical contribution is a dynamical depth separation result, proving that while shallow models can exhibit incremental learning dynamics, they require the initialization to be exponentially small for these dynamics to present themselves. However, once the model becomes deeper, the dependence becomes polynomial and incremental learning can arise in more natural settings. We complement our theoretical findings by experimenting with deep matrix sensing, quadratic neural networks and with binary classification using diagonal and convolutional linear networks, showing all of these models exhibit incremental learning.", "published": "2019-09-26T12:38:41Z", "authors": ["Daniel Gissin", "Shai Shalev-Shwartz", "Amit Daniely"], "categories": ["cs.LG", "stat.ML"], "doi": "", "_query": "real estate valuation deep learning", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/1802.06293v2", "title": "Black-Box Reductions for Parameter-free Online Learning in Banach Spaces", "abstract": "We introduce several new black-box reductions that significantly improve the design of adaptive and parameter-free online learning algorithms by simplifying analysis, improving regret guarantees, and sometimes even improving runtime. We reduce parameter-free online learning to online exp-concave optimization, we reduce optimization in a Banach space to one-dimensional optimization, and we reduce optimization over a constrained domain to unconstrained optimization. All of our reductions run as fast as online gradient descent. We use our new techniques to improve upon the previously best regret bounds for parameter-free learning, and do so for arbitrary norms.", "published": "2018-02-17T20:59:02Z", "authors": ["Ashok Cutkosky", "Francesco Orabona"], "categories": ["cs.LG", "math.OC", "stat.ML"], "doi": "", "_query": "real estate valuation deep learning", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/1812.05069v1", "title": "Recent Advances in Autoencoder-Based Representation Learning", "abstract": "Learning useful representations with little or no supervision is a key challenge in artificial intelligence. We provide an in-depth review of recent advances in representation learning with a focus on autoencoder-based models. To organize these results we make use of meta-priors believed useful for downstream tasks, such as disentanglement and hierarchical organization of features. In particular, we uncover three main mechanisms to enforce such properties, namely (i) regularizing the (approximate or aggregate) posterior distribution, (ii) factorizing the encoding and decoding distribution, or (iii) introducing a structured prior distribution. While there are some promising results, implicit or explicit supervision remains a key enabler and all current methods use strong inductive biases and modeling assumptions. Finally, we provide an analysis of autoencoder-based representation learning through the lens of rate-distortion theory and identify a clear tradeoff between the amount of prior knowledge available about the downstream tasks, and how useful the representation is for this task.", "published": "2018-12-12T18:13:41Z", "authors": ["Michael Tschannen", "Olivier Bachem", "Mario Lucic"], "categories": ["cs.LG", "cs.CV", "stat.ML"], "doi": "", "_query": "real estate valuation deep learning", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/1904.06387v5", "title": "Extrapolating Beyond Suboptimal Demonstrations via Inverse Reinforcement Learning from Observations", "abstract": "A critical flaw of existing inverse reinforcement learning (IRL) methods is their inability to significantly outperform the demonstrator. This is because IRL typically seeks a reward function that makes the demonstrator appear near-optimal, rather than inferring the underlying intentions of the demonstrator that may have been poorly executed in practice. In this paper, we introduce a novel reward-learning-from-observation algorithm, Trajectory-ranked Reward EXtrapolation (T-REX), that extrapolates beyond a set of (approximately) ranked demonstrations in order to infer high-quality reward functions from a set of potentially poor demonstrations. When combined with deep reinforcement learning, T-REX outperforms state-of-the-art imitation learning and IRL methods on multiple Atari and MuJoCo benchmark tasks and achieves performance that is often more than twice the performance of the best demonstration. We also demonstrate that T-REX is robust to ranking noise and can accurately extrapolate intention by simply watching a learner noisily improve at a task over time.", "published": "2019-04-12T19:34:43Z", "authors": ["Daniel S. Brown", "Wonjoon Goo", "Prabhat Nagarajan", "Scott Niekum"], "categories": ["cs.LG", "stat.ML"], "doi": "", "_query": "real estate valuation deep learning", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2511.13745v1", "title": "A Deep Learning Density Shaping Model Predictive Gust Load Alleviation Control of a Compliant Wing Subjected to Atmospheric Turbulence", "abstract": "This study presents a novel deep learning approach aimed at enhancing stochastic Gust Load Alleviation (GLA) specifically for compliant wings. The approach incorporates the concept of smooth wing camber variation, where the camber of the wing's chord is actively adjusted during flight using a control signal to achieve the desired aerodynamic loading. The proposed method employs a deep learning-based model predictive controller designed for probability density shaping. This controller effectively solves the probability density evolution equation through a custom Physics-Informed Neural Network (PINN) and utilizes Automatic Differentiation for Model Predictive Control (MPC) optimization. Comprehensive numerical simulations were conducted on a compliant wing (CW) model, evaluating performance of the proposed approach against stochastic gust profiles. The evaluation involved stochastic aerodynamic loads generated from Band-Limited White Noise (BLWN) and Dryden gust models. The evaluation were conducted for two distinct Compliant Chord Fractions (CCF). The results demonstrate the effectiveness of the proposed probability density shaping model predictive control in alleviating stochastic gust load and reducing wing tip deflection.", "published": "2025-11-13T12:00:35Z", "authors": ["Seid H. Pourtakdoust", "Amir H. Khodabakhsh"], "categories": ["eess.SY", "cs.LG", "math.PR", "stat.AP"], "doi": "10.1016/j.ast.2025.111214", "_query": "real estate valuation deep learning", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/1809.00175v3", "title": "Hyperparameter Learning for Conditional Kernel Mean Embeddings with Rademacher Complexity Bounds", "abstract": "Conditional kernel mean embeddings are nonparametric models that encode conditional expectations in a reproducing kernel Hilbert space. While they provide a flexible and powerful framework for probabilistic inference, their performance is highly dependent on the choice of kernel and regularization hyperparameters. Nevertheless, current hyperparameter tuning methods predominantly rely on expensive cross validation or heuristics that is not optimized for the inference task. For conditional kernel mean embeddings with categorical targets and arbitrary inputs, we propose a hyperparameter learning framework based on Rademacher complexity bounds to prevent overfitting by balancing data fit against model complexity. Our approach only requires batch updates, allowing scalable kernel hyperparameter tuning without invoking kernel approximations. Experiments demonstrate that our learning framework outperforms competing methods, and can be further extended to incorporate and learn deep neural network weights to improve generalization.", "published": "2018-09-01T13:33:31Z", "authors": ["Kelvin Hsu", "Richard Nock", "Fabio Ramos"], "categories": ["stat.ML", "cs.LG"], "doi": "", "_query": "real estate valuation deep learning", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/1812.11951v2", "title": "Learning to Design RNA", "abstract": "Designing RNA molecules has garnered recent interest in medicine, synthetic biology, biotechnology and bioinformatics since many functional RNA molecules were shown to be involved in regulatory processes for transcription, epigenetics and translation. Since an RNA's function depends on its structural properties, the RNA Design problem is to find an RNA sequence which satisfies given structural constraints. Here, we propose a new algorithm for the RNA Design problem, dubbed LEARNA. LEARNA uses deep reinforcement learning to train a policy network to sequentially design an entire RNA sequence given a specified target structure. By meta-learning across 65000 different RNA Design tasks for one hour on 20 CPU cores, our extension Meta-LEARNA constructs an RNA Design policy that can be applied out of the box to solve novel RNA Design tasks. Methodologically, for what we believe to be the first time, we jointly optimize over a rich space of architectures for the policy network, the hyperparameters of the training procedure and the formulation of the decision process. Comprehensive empirical results on two widely-used RNA Design benchmarks, as well as a third one that we introduce, show that our approach achieves new state-of-the-art performance on the former while also being orders of magnitudes faster in reaching the previous state-of-the-art performance. In an ablation study, we analyze the importance of our method's different components.", "published": "2018-12-31T18:45:51Z", "authors": ["Frederic Runge", "Danny Stoll", "Stefan Falkner", "Frank Hutter"], "categories": ["cs.LG", "q-bio.QM", "stat.ML"], "doi": "", "_query": "real estate valuation deep learning", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/1912.05098v2", "title": "Memory-efficient Learning for Large-scale Computational Imaging -- NeurIPS deep inverse workshop", "abstract": "Computational imaging systems jointly design computation and hardware to retrieve information which is not traditionally accessible with standard imaging systems. Recently, critical aspects such as experimental design and image priors are optimized through deep neural networks formed by the unrolled iterations of classical physics-based reconstructions (termed physics-based networks). However, for real-world large-scale systems, computing gradients via backpropagation restricts learning due to memory limitations of graphical processing units. In this work, we propose a memory-efficient learning procedure that exploits the reversibility of the network's layers to enable data-driven design for large-scale computational imaging. We demonstrate our methods practicality on two large-scale systems: super-resolution optical microscopy and multi-channel magnetic resonance imaging.", "published": "2019-12-11T03:19:48Z", "authors": ["Michael Kellman", "Jon Tamir", "Emrah Boston", "Michael Lustig", "Laura Waller"], "categories": ["eess.IV", "cs.LG", "eess.SP"], "doi": "", "_query": "real estate valuation deep learning", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2008.02790v4", "title": "Decoupling Exploration and Exploitation for Meta-Reinforcement Learning without Sacrifices", "abstract": "The goal of meta-reinforcement learning (meta-RL) is to build agents that can quickly learn new tasks by leveraging prior experience on related tasks. Learning a new task often requires both exploring to gather task-relevant information and exploiting this information to solve the task. In principle, optimal exploration and exploitation can be learned end-to-end by simply maximizing task performance. However, such meta-RL approaches struggle with local optima due to a chicken-and-egg problem: learning to explore requires good exploitation to gauge the exploration's utility, but learning to exploit requires information gathered via exploration. Optimizing separate objectives for exploration and exploitation can avoid this problem, but prior meta-RL exploration objectives yield suboptimal policies that gather information irrelevant to the task. We alleviate both concerns by constructing an exploitation objective that automatically identifies task-relevant information and an exploration objective to recover only this information. This avoids local optima in end-to-end training, without sacrificing optimal exploration. Empirically, DREAM substantially outperforms existing approaches on complex meta-RL problems, such as sparse-reward 3D visual navigation. Videos of DREAM: https://ezliu.github.io/dream/", "published": "2020-08-06T17:57:36Z", "authors": ["Evan Zheran Liu", "Aditi Raghunathan", "Percy Liang", "Chelsea Finn"], "categories": ["cs.LG", "cs.AI", "stat.ML"], "doi": "", "_query": "real estate valuation deep learning", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/1710.10784v1", "title": "How deep learning works --The geometry of deep learning", "abstract": "Why and how that deep learning works well on different tasks remains a mystery from a theoretical perspective. In this paper we draw a geometric picture of the deep learning system by finding its analogies with two existing geometric structures, the geometry of quantum computations and the geometry of the diffeomorphic template matching. In this framework, we give the geometric structures of different deep learning systems including convolutional neural networks, residual networks, recursive neural networks, recurrent neural networks and the equilibrium prapagation framework. We can also analysis the relationship between the geometrical structures and their performance of different networks in an algorithmic level so that the geometric framework may guide the design of the structures and algorithms of deep learning systems.", "published": "2017-10-30T06:42:23Z", "authors": ["Xiao Dong", "Jiasong Wu", "Ling Zhou"], "categories": ["cs.LG", "stat.ML"], "doi": "", "_query": "real estate valuation deep learning", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/1807.05527v2", "title": "Learning Probabilistic Logic Programs in Continuous Domains", "abstract": "The field of statistical relational learning aims at unifying logic and probability to reason and learn from data. Perhaps the most successful paradigm in the field is probabilistic logic programming: the enabling of stochastic primitives in logic programming, which is now increasingly seen to provide a declarative background to complex machine learning applications. While many systems offer inference capabilities, the more significant challenge is that of learning meaningful and interpretable symbolic representations from data. In that regard, inductive logic programming and related techniques have paved much of the way for the last few decades. Unfortunately, a major limitation of this exciting landscape is that much of the work is limited to finite-domain discrete probability distributions. Recently, a handful of systems have been extended to represent and perform inference with continuous distributions. The problem, of course, is that classical solutions for inference are either restricted to well-known parametric families (e.g., Gaussians) or resort to sampling strategies that provide correct answers only in the limit. When it comes to learning, moreover, inducing representations remains entirely open, other than \"data-fitting\" solutions that force-fit points to aforementioned parametric families. In this paper, we take the first steps towards inducing probabilistic logic programs for continuous and mixed discrete-continuous data, without being pigeon-holed to a fixed set of distribution families. Our key insight is to leverage techniques from piecewise polynomial function approximation theory, yielding a principled way to learn and compositionally construct density functions. We test the framework and discuss the learned representations.", "published": "2018-07-15T11:00:00Z", "authors": ["Stefanie Speichert", "Vaishak Belle"], "categories": ["cs.LG", "cs.AI", "stat.ML"], "doi": "", "_query": "real estate valuation deep learning", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/1610.05775v1", "title": "Modeling the Dynamics of Online Learning Activity", "abstract": "People are increasingly relying on the Web and social media to find solutions to their problems in a wide range of domains. In this online setting, closely related problems often lead to the same characteristic learning pattern, in which people sharing these problems visit related pieces of information, perform almost identical queries or, more generally, take a series of similar actions. In this paper, we introduce a novel modeling framework for clustering continuous-time grouped streaming data, the hierarchical Dirichlet Hawkes process (HDHP), which allows us to automatically uncover a wide variety of learning patterns from detailed traces of learning activity. Our model allows for efficient inference, scaling to millions of actions taken by thousands of users. Experiments on real data gathered from Stack Overflow reveal that our framework can recover meaningful learning patterns in terms of both content and temporal dynamics, as well as accurately track users' interests and goals over time.", "published": "2016-10-18T20:00:09Z", "authors": ["Charalampos Mavroforakis", "Isabel Valera", "Manuel Gomez Rodriguez"], "categories": ["stat.ML", "cs.LG", "cs.SI"], "doi": "", "_query": "real estate valuation deep learning", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/1907.03976v3", "title": "Better-than-Demonstrator Imitation Learning via Automatically-Ranked Demonstrations", "abstract": "The performance of imitation learning is typically upper-bounded by the performance of the demonstrator. While recent empirical results demonstrate that ranked demonstrations allow for better-than-demonstrator performance, preferences over demonstrations may be difficult to obtain, and little is known theoretically about when such methods can be expected to successfully extrapolate beyond the performance of the demonstrator. To address these issues, we first contribute a sufficient condition for better-than-demonstrator imitation learning and provide theoretical results showing why preferences over demonstrations can better reduce reward function ambiguity when performing inverse reinforcement learning. Building on this theory, we introduce Disturbance-based Reward Extrapolation (D-REX), a ranking-based imitation learning method that injects noise into a policy learned through behavioral cloning to automatically generate ranked demonstrations. These ranked demonstrations are used to efficiently learn a reward function that can then be optimized using reinforcement learning. We empirically validate our approach on simulated robot and Atari imitation learning benchmarks and show that D-REX outperforms standard imitation learning approaches and can significantly surpass the performance of the demonstrator. D-REX is the first imitation learning approach to achieve significant extrapolation beyond the demonstrator's performance without additional side-information or supervision, such as rewards or human preferences. By generating rankings automatically, we show that preference-based inverse reinforcement learning can be applied in traditional imitation learning settings where only unlabeled demonstrations are available.", "published": "2019-07-09T04:11:53Z", "authors": ["Daniel S. Brown", "Wonjoon Goo", "Scott Niekum"], "categories": ["cs.LG", "stat.ML"], "doi": "", "_query": "real estate valuation deep learning", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/1807.06962v1", "title": "Active Learning for Segmentation by Optimizing Content Information for Maximal Entropy", "abstract": "Segmentation is essential for medical image analysis tasks such as intervention planning, therapy guidance, diagnosis, treatment decisions. Deep learning is becoming increasingly prominent for segmentation, where the lack of annotations, however, often becomes the main limitation. Due to privacy concerns and ethical considerations, most medical datasets are created, curated, and allow access only locally. Furthermore, current deep learning methods are often suboptimal in translating anatomical knowledge between different medical imaging modalities. Active learning can be used to select an informed set of image samples to request for manual annotation, in order to best utilize the limited annotation time of clinical experts for optimal outcomes, which we focus on in this work. Our contributions herein are two fold: (1) we enforce domain-representativeness of selected samples using a proposed penalization scheme to maximize information at the network abstraction layer, and (2) we propose a Borda-count based sample querying scheme for selecting samples for segmentation. Comparative experiments with baseline approaches show that the samples queried with our proposed method, where both above contributions are combined, result in significantly improved segmentation performance for this active learning task.", "published": "2018-07-18T14:24:39Z", "authors": ["Firat Ozdemir", "Zixuan Peng", "Christine Tanner", "Philipp Fuernstahl", "Orcun Goksel"], "categories": ["cs.CV", "stat.ML"], "doi": "10.1007/978-3-030-00889-5_21", "_query": "real estate valuation deep learning", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/1907.04490v1", "title": "Deep Lagrangian Networks: Using Physics as Model Prior for Deep Learning", "abstract": "Deep learning has achieved astonishing results on many tasks with large amounts of data and generalization within the proximity of training data. For many important real-world applications, these requirements are unfeasible and additional prior knowledge on the task domain is required to overcome the resulting problems. In particular, learning physics models for model-based control requires robust extrapolation from fewer samples - often collected online in real-time - and model errors may lead to drastic damages of the system. Directly incorporating physical insight has enabled us to obtain a novel deep model learning approach that extrapolates well while requiring fewer samples. As a first example, we propose Deep Lagrangian Networks (DeLaN) as a deep network structure upon which Lagrangian Mechanics have been imposed. DeLaN can learn the equations of motion of a mechanical system (i.e., system dynamics) with a deep network efficiently while ensuring physical plausibility. The resulting DeLaN network performs very well at robot tracking control. The proposed method did not only outperform previous model learning approaches at learning speed but exhibits substantially improved and more robust extrapolation to novel trajectories and learns online in real-time", "published": "2019-07-10T02:31:51Z", "authors": ["Michael Lutter", "Christian Ritter", "Jan Peters"], "categories": ["cs.LG", "cs.RO", "eess.SY", "stat.ML"], "doi": "", "_query": "real estate valuation deep learning", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2404.05051v1", "title": "Skill Transfer and Discovery for Sim-to-Real Learning: A Representation-Based Viewpoint", "abstract": "We study sim-to-real skill transfer and discovery in the context of robotics control using representation learning. We draw inspiration from spectral decomposition of Markov decision processes. The spectral decomposition brings about representation that can linearly represent the state-action value function induced by any policies, thus can be regarded as skills. The skill representations are transferable across arbitrary tasks with the same transition dynamics. Moreover, to handle the sim-to-real gap in the dynamics, we propose a skill discovery algorithm that learns new skills caused by the sim-to-real gap from real-world data. We promote the discovery of new skills by enforcing orthogonal constraints between the skills to learn and the skills from simulators, and then synthesize the policy using the enlarged skill sets. We demonstrate our methodology by transferring quadrotor controllers from simulators to Crazyflie 2.1 quadrotors. We show that we can learn the skill representations from a single simulator task and transfer these to multiple different real-world tasks including hovering, taking off, landing and trajectory tracking. Our skill discovery approach helps narrow the sim-to-real gap and improve the real-world controller performance by up to 30.2%.", "published": "2024-04-07T19:22:51Z", "authors": ["Haitong Ma", "Zhaolin Ren", "Bo Dai", "Na Li"], "categories": ["cs.LG", "cs.RO"], "doi": "", "_query": "real estate valuation deep learning", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/1910.00270v4", "title": "Robust Learning with the Hilbert-Schmidt Independence Criterion", "abstract": "We investigate the use of a non-parametric independence measure, the Hilbert-Schmidt Independence Criterion (HSIC), as a loss-function for learning robust regression and classification models. This loss-function encourages learning models where the distribution of the residuals between the label and the model prediction is statistically independent of the distribution of the instances themselves. This loss-function was first proposed by Mooij et al. (2009) in the context of learning causal graphs. We adapt it to the task of learning for unsupervised covariate shift: learning on a source domain without access to any instances or labels from the unknown target domain, but with the assumption that $p(y|x)$ (the conditional probability of labels given instances) remains the same in the target domain. We show that the proposed loss is expected to give rise to models that generalize well on a class of target domains characterised by the complexity of their description within a reproducing kernel Hilbert space. Experiments on unsupervised covariate shift tasks demonstrate that models learned with the proposed loss-function outperform models learned with standard loss functions, achieving state-of-the-art results on a challenging cell-microscopy unsupervised covariate shift task.", "published": "2019-10-01T09:27:33Z", "authors": ["Daniel Greenfeld", "Uri Shalit"], "categories": ["cs.LG", "stat.ML"], "doi": "", "_query": "real estate valuation deep learning", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2011.04475v3", "title": "Deep Transfer Learning for Automated Diagnosis of Skin Lesions from Photographs", "abstract": "Melanoma is not the most common form of skin cancer, but it is the most deadly. Currently, the disease is diagnosed by expert dermatologists, which is costly and requires timely access to medical treatment. Recent advances in deep learning have the potential to improve diagnostic performance, expedite urgent referrals and reduce burden on clinicians. Through smart phones, the technology could reach people who would not normally have access to such healthcare services, e.g. in remote parts of the world, due to financial constraints or in 2020, COVID-19 cancellations. To this end, we have investigated various transfer learning approaches by leveraging model parameters pre-trained on ImageNet with finetuning on melanoma detection. We compare EfficientNet, MnasNet, MobileNet, DenseNet, SqueezeNet, ShuffleNet, GoogleNet, ResNet, ResNeXt, VGG and a simple CNN with and without transfer learning. We find the mobile network, EfficientNet (with transfer learning) achieves the best mean performance with an area under the receiver operating characteristic curve (AUROC) of 0.931$\\pm$0.005 and an area under the precision recall curve (AUPRC) of 0.840$\\pm$0.010. This is significantly better than general practitioners (0.83$\\pm$0.03 AUROC) and dermatologists (0.91$\\pm$0.02 AUROC).", "published": "2020-11-06T16:49:40Z", "authors": ["Emma Rocheteau", "Doyoon Kim"], "categories": ["cs.CV", "cs.LG", "eess.IV"], "doi": "", "_query": "real estate valuation deep learning", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/1905.10817v1", "title": "Deep Online Learning with Stochastic Constraints", "abstract": "Deep learning models are considered to be state-of-the-art in many offline machine learning tasks. However, many of the techniques developed are not suitable for online learning tasks. The problem of using deep learning models with sequential data becomes even harder when several loss functions need to be considered simultaneously, as in many real-world applications. In this paper, we, therefore, propose a novel online deep learning training procedure which can be used regardless of the neural network's architecture, aiming to deal with the multiple objectives case. We demonstrate and show the effectiveness of our algorithm on the Neyman-Pearson classification problem on several benchmark datasets.", "published": "2019-05-26T15:36:31Z", "authors": ["Guy Uziel"], "categories": ["cs.LG", "stat.ML"], "doi": "", "_query": "real estate valuation deep learning", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/1812.00475v4", "title": "Multiple Instance Learning for ECG Risk Stratification", "abstract": "Patients who suffer an acute coronary syndrome are at elevated risk for adverse cardiovascular events such as myocardial infarction and cardiovascular death. Accurate assessment of this risk is crucial to their course of care. We focus on estimating a patient's risk of cardiovascular death after an acute coronary syndrome based on a patient's raw electrocardiogram (ECG) signal. Learning from this signal is challenging for two reasons: 1) positive examples signifying a downstream cardiovascular event are scarce, causing drastic class imbalance, and 2) each patient's ECG signal consists of thousands of heartbeats, accompanied by a single label for the downstream outcome. Machine learning has been previously applied to this task, but most approaches rely on hand-crafted features and domain knowledge. We propose a method that learns a representation from the raw ECG signal by using a multiple instance learning framework. We present a learned risk score for cardiovascular death that outperforms existing risk metrics in predicting cardiovascular death within 30, 60, 90, and 365 days on a dataset of 5000 patients.", "published": "2018-12-02T21:55:53Z", "authors": ["Divya Shanmugam", "Davis Blalock", "John Guttag"], "categories": ["cs.LG", "stat.ML"], "doi": "", "_query": "real estate valuation deep learning", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/1703.02527v2", "title": "Online Learning to Rank in Stochastic Click Models", "abstract": "Online learning to rank is a core problem in information retrieval and machine learning. Many provably efficient algorithms have been recently proposed for this problem in specific click models. The click model is a model of how the user interacts with a list of documents. Though these results are significant, their impact on practice is limited, because all proposed algorithms are designed for specific click models and lack convergence guarantees in other models. In this work, we propose BatchRank, the first online learning to rank algorithm for a broad class of click models. The class encompasses two most fundamental click models, the cascade and position-based models. We derive a gap-dependent upper bound on the $T$-step regret of BatchRank and evaluate it on a range of web search queries. We observe that BatchRank outperforms ranked bandits and is more robust than CascadeKL-UCB, an existing algorithm for the cascade model.", "published": "2017-03-07T18:53:58Z", "authors": ["Masrour Zoghi", "Tomas Tunys", "Mohammad Ghavamzadeh", "Branislav Kveton", "Csaba Szepesvari", "Zheng Wen"], "categories": ["cs.LG", "stat.ML"], "doi": "", "_query": "real estate valuation deep learning", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2005.06041v1", "title": "Guaranteeing Reproducibility in Deep Learning Competitions", "abstract": "To encourage the development of methods with reproducible and robust training behavior, we propose a challenge paradigm where competitors are evaluated directly on the performance of their learning procedures rather than pre-trained agents. Since competition organizers re-train proposed methods in a controlled setting they can guarantee reproducibility, and -- by retraining submissions using a held-out test set -- help ensure generalization past the environments on which they were trained.", "published": "2020-05-12T20:43:05Z", "authors": ["Brandon Houghton", "Stephanie Milani", "Nicholay Topin", "William Guss", "Katja Hofmann", "Diego Perez-Liebana", "Manuela Veloso", "Ruslan Salakhutdinov"], "categories": ["cs.LG", "stat.ML"], "doi": "", "_query": "real estate valuation deep learning", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2409.04326v2", "title": "Digital Platform Consolidation and Offline Expansion: Strategic Convergence and Market Welfare in China's Second-hand Real Estate Market", "abstract": "This study analyzes the impact of offline expansion and online platform consolidation in China's second-hand real estate market. Using micro-level transaction data and difference-in-differences estimations, we find offline store entry significantly boosts transaction volumes (9-10\\%) and reduces price concessions (1\\%) initially, though effects diminish over time. Platform consolidation via Lianjia's Agent Cooperation Network yields delayed yet persistent transaction volume increases (5-6\\%), particularly in less concentrated markets, and consistently lowers price concessions. These strategies sustainably enhance brokerage competitiveness, bargaining power, and market welfare, despite increased market concentration, ultimately benefiting sellers and improving overall efficiency.", "published": "2024-09-06T14:55:26Z", "authors": ["Guoying Deng", "Xuyuan Zhang"], "categories": ["econ.GN"], "doi": "", "_query": "real estate valuation deep learning", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/1901.02354v2", "title": "Geometrization of deep networks for the interpretability of deep learning systems", "abstract": "How to understand deep learning systems remains an open problem. In this paper we propose that the answer may lie in the geometrization of deep networks. Geometrization is a bridge to connect physics, geometry, deep network and quantum computation and this may result in a new scheme to reveal the rule of the physical world. By comparing the geometry of image matching and deep networks, we show that geometrization of deep networks can be used to understand existing deep learning systems and it may also help to solve the interpretability problem of deep learning systems.", "published": "2019-01-06T14:32:45Z", "authors": ["Xiao Dong", "Ling Zhou"], "categories": ["cs.LG", "cs.AI", "stat.ML"], "doi": "", "_query": "real estate valuation deep learning", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2112.04236v1", "title": "Application of Deep Reinforcement Learning to Payment Fraud", "abstract": "The large variety of digital payment choices available to consumers today has been a key driver of e-commerce transactions in the past decade. Unfortunately, this has also given rise to cybercriminals and fraudsters who are constantly looking for vulnerabilities in these systems by deploying increasingly sophisticated fraud attacks. A typical fraud detection system employs standard supervised learning methods where the focus is on maximizing the fraud recall rate. However, we argue that such a formulation can lead to sub-optimal solutions. The design requirements for these fraud models requires that they are robust to the high-class imbalance in the data, adaptive to changes in fraud patterns, maintain a balance between the fraud rate and the decline rate to maximize revenue, and be amenable to asynchronous feedback since usually there is a significant lag between the transaction and the fraud realization. To achieve this, we formulate fraud detection as a sequential decision-making problem by including the utility maximization within the model in the form of the reward function. The historical decline rate and fraud rate define the state of the system with a binary action space composed of approving or declining the transaction. In this study, we primarily focus on utility maximization and explore different reward functions to this end. The performance of the proposed Reinforcement Learning system has been evaluated for two publicly available fraud datasets using Deep Q-learning and compared with different classifiers. We aim to address the rest of the issues in future work.", "published": "2021-12-08T11:30:53Z", "authors": ["Siddharth Vimal", "Kanishka Kayathwal", "Hardik Wadhwa", "Gaurav Dhama"], "categories": ["cs.LG", "cs.AI"], "doi": "", "_query": "real estate valuation deep learning", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2205.11722v2", "title": "Improving Shape Awareness and Interpretability in Deep Networks Using Geometric Moments", "abstract": "Deep networks for image classification often rely more on texture information than object shape. While efforts have been made to make deep-models shape-aware, it is often difficult to make such models simple, interpretable, or rooted in known mathematical definitions of shape. This paper presents a deep-learning model inspired by geometric moments, a classically well understood approach to measure shape-related properties. The proposed method consists of a trainable network for generating coordinate bases and affine parameters for making the features geometrically invariant yet in a task-specific manner. The proposed model improves the final feature's interpretation. We demonstrate the effectiveness of our method on standard image classification datasets. The proposed model achieves higher classification performance compared to the baseline and standard ResNet models while substantially improving interpretability.", "published": "2022-05-24T02:08:05Z", "authors": ["Rajhans Singh", "Ankita Shukla", "Pavan Turaga"], "categories": ["cs.CV"], "doi": "", "_query": "real estate valuation deep learning", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2007.08483v2", "title": "On Power Laws in Deep Ensembles", "abstract": "Ensembles of deep neural networks are known to achieve state-of-the-art performance in uncertainty estimation and lead to accuracy improvement. In this work, we focus on a classification problem and investigate the behavior of both non-calibrated and calibrated negative log-likelihood (CNLL) of a deep ensemble as a function of the ensemble size and the member network size. We indicate the conditions under which CNLL follows a power law w.r.t. ensemble size or member network size, and analyze the dynamics of the parameters of the discovered power laws. Our important practical finding is that one large network may perform worse than an ensemble of several medium-size networks with the same total number of parameters (we call this ensemble a memory split). Using the detected power law-like dependencies, we can predict (1) the possible gain from the ensembling of networks with given structure, (2) the optimal memory split given a memory budget, based on a relatively small number of trained networks. We describe the memory split advantage effect in more details in arXiv:2005.07292", "published": "2020-07-16T17:35:32Z", "authors": ["Ekaterina Lobacheva", "Nadezhda Chirkova", "Maxim Kodryan", "Dmitry Vetrov"], "categories": ["cs.LG", "stat.ML"], "doi": "", "_query": "real estate valuation deep learning", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2511.11603v1", "title": "Machine learning-based cloud resource allocation algorithms: a comprehensive comparative review", "abstract": "Cloud resource allocation has emerged as a major challenge in modern computing environments, with organizations struggling to manage complex, dynamic workloads while optimizing performance and cost efficiency. Traditional heuristic approaches prove inadequate for handling the multi-objective optimization demands of existing cloud infrastructures. This paper presents a comparative analysis of state-of-the-art artificial intelligence and machine learning algorithms for resource allocation. We systematically evaluate 10 algorithms across four categories: Deep Reinforcement Learning approaches, Neural Network architectures, Traditional Machine Learning enhanced methods, and Multi-Agent systems. Analysis of published results demonstrates significant performance improvements across multiple metrics including makespan reduction, cost optimization, and energy efficiency gains compared to traditional methods. The findings reveal that hybrid architectures combining multiple artificial intelligence and machine learning techniques consistently outperform single-method approaches, with edge computing environments showing the highest deployment readiness. Our analysis provides critical insights for both academic researchers and industry practitioners seeking to implement next-generation cloud resource allocation strategies in increasingly complex and dynamic computing environments.", "published": "2025-10-31T20:30:21Z", "authors": ["Deep Bodra", "Sushil Khairnar"], "categories": ["cs.DC", "cs.AI"], "doi": "10.3389/fcomp.2025.1678976", "_query": "real estate valuation deep learning", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2006.15078v1", "title": "Continual Learning from the Perspective of Compression", "abstract": "Connectionist models such as neural networks suffer from catastrophic forgetting. In this work, we study this problem from the perspective of information theory and define forgetting as the increase of description lengths of previous data when they are compressed with a sequentially learned model. In addition, we show that continual learning approaches based on variational posterior approximation and generative replay can be considered as approximations to two prequential coding methods in compression, namely, the Bayesian mixture code and maximum likelihood (ML) plug-in code. We compare these approaches in terms of both compression and forgetting and empirically study the reasons that limit the performance of continual learning methods based on variational posterior approximation. To address these limitations, we propose a new continual learning method that combines ML plug-in and Bayesian mixture codes.", "published": "2020-06-26T16:15:49Z", "authors": ["Xu He", "Min Lin"], "categories": ["cs.LG", "cs.NE", "stat.ML"], "doi": "", "_query": "real estate valuation deep learning", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/1206.6398v2", "title": "Learning Parameterized Skills", "abstract": "We introduce a method for constructing skills capable of solving tasks drawn from a distribution of parameterized reinforcement learning problems. The method draws example tasks from a distribution of interest and uses the corresponding learned policies to estimate the topology of the lower-dimensional piecewise-smooth manifold on which the skill policies lie. This manifold models how policy parameters change as task parameters vary. The method identifies the number of charts that compose the manifold and then applies non-linear regression in each chart to construct a parameterized skill by predicting policy parameters from task parameters. We evaluate our method on an underactuated simulated robotic arm tasked with learning to accurately throw darts at a parameterized target location.", "published": "2012-06-27T19:59:59Z", "authors": ["Bruno Da Silva", "George Konidaris", "Andrew Barto"], "categories": ["cs.LG", "stat.ML"], "doi": "", "_query": "real estate valuation deep learning", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2002.00573v1", "title": "Revisiting Meta-Learning as Supervised Learning", "abstract": "Recent years have witnessed an abundance of new publications and approaches on meta-learning. This community-wide enthusiasm has sparked great insights but has also created a plethora of seemingly different frameworks, which can be hard to compare and evaluate. In this paper, we aim to provide a principled, unifying framework by revisiting and strengthening the connection between meta-learning and traditional supervised learning. By treating pairs of task-specific data sets and target models as (feature, label) samples, we can reduce many meta-learning algorithms to instances of supervised learning. This view not only unifies meta-learning into an intuitive and practical framework but also allows us to transfer insights from supervised learning directly to improve meta-learning. For example, we obtain a better understanding of generalization properties, and we can readily transfer well-understood techniques, such as model ensemble, pre-training, joint training, data augmentation, and even nearest neighbor based methods. We provide an intuitive analogy of these methods in the context of meta-learning and show that they give rise to significant improvements in model performance on few-shot learning.", "published": "2020-02-03T06:13:01Z", "authors": ["Wei-Lun Chao", "Han-Jia Ye", "De-Chuan Zhan", "Mark Campbell", "Kilian Q. Weinberger"], "categories": ["cs.LG", "cs.CV", "stat.ML"], "doi": "", "_query": "real estate valuation deep learning", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2011.01861v1", "title": "DeL-haTE: A Deep Learning Tunable Ensemble for Hate Speech Detection", "abstract": "Online hate speech on social media has become a fast-growing problem in recent times. Nefarious groups have developed large content delivery networks across several main-stream (Twitter and Facebook) and fringe (Gab, 4chan, 8chan, etc.) outlets to deliver cascades of hate messages directed both at individuals and communities. Thus addressing these issues has become a top priority for large-scale social media outlets. Three key challenges in automated detection and classification of hateful content are the lack of clearly labeled data, evolving vocabulary and lexicon - hashtags, emojis, etc. - and the lack of baseline models for fringe outlets such as Gab. In this work, we propose a novel framework with three major contributions. (a) We engineer an ensemble of deep learning models that combines the strengths of state-of-the-art approaches, (b) we incorporate a tuning factor into this framework that leverages transfer learning to conduct automated hate speech classification on unlabeled datasets, like Gab, and (c) we develop a weak supervised learning methodology that allows our framework to train on unlabeled data. Our ensemble models achieve an 83% hate recall on the HON dataset, surpassing the performance of the state-of-the-art deep models. We demonstrate that weak supervised training in combination with classifier tuning significantly increases model performance on unlabeled data from Gab, achieving a hate recall of 67%.", "published": "2020-11-03T17:32:50Z", "authors": ["Joshua Melton", "Arunkumar Bagavathi", "Siddharth Krishnan"], "categories": ["cs.CL", "cs.LG"], "doi": "", "_query": "real estate valuation deep learning", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2505.08995v1", "title": "Enhancing Aerial Combat Tactics through Hierarchical Multi-Agent Reinforcement Learning", "abstract": "This work presents a Hierarchical Multi-Agent Reinforcement Learning framework for analyzing simulated air combat scenarios involving heterogeneous agents. The objective is to identify effective Courses of Action that lead to mission success within preset simulations, thereby enabling the exploration of real-world defense scenarios at low cost and in a safe-to-fail setting. Applying deep Reinforcement Learning in this context poses specific challenges, such as complex flight dynamics, the exponential size of the state and action spaces in multi-agent systems, and the capability to integrate real-time control of individual units with look-ahead planning. To address these challenges, the decision-making process is split into two levels of abstraction: low-level policies control individual units, while a high-level commander policy issues macro commands aligned with the overall mission targets. This hierarchical structure facilitates the training process by exploiting policy symmetries of individual agents and by separating control from command tasks. The low-level policies are trained for individual combat control in a curriculum of increasing complexity. The high-level commander is then trained on mission targets given pre-trained control policies. The empirical validation confirms the advantages of the proposed framework.", "published": "2025-05-13T22:13:48Z", "authors": ["Ardian Selmonaj", "Oleg Szehr", "Giacomo Del Rio", "Alessandro Antonucci", "Adrian Schneider", "Michael Rüegsegger"], "categories": ["cs.AI", "cs.LG", "cs.MA", "cs.RO"], "doi": "", "_query": "real estate valuation deep learning", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/1802.07051v2", "title": "On Learning Causal Structures from Non-Experimental Data without Any Faithfulness Assumption", "abstract": "Consider the problem of learning, from non-experimental data, the causal (Markov equivalence) structure of the true, unknown causal Bayesian network (CBN) on a given, fixed set of (categorical) variables. This learning problem is known to be so hard that there is no learning algorithm that converges to the truth for all possible CBNs (on the given set of variables). So the convergence property has to be sacrificed for some CBNs---but for which? In response, the standard practice has been to design and employ learning algorithms that secure the convergence property for at least all the CBNs that satisfy the famous faithfulness condition, which implies sacrificing the convergence property for some CBNs that violate the faithfulness condition (Spirtes et al. 2000). This standard design practice can be justified by assuming---that is, accepting on faith---that the true, unknown CBN satisfies the faithfulness condition. But the real question is this: Is it possible to explain, without assuming the faithfulness condition or any of its weaker variants, why it is mandatory rather than optional to follow the standard design practice? This paper aims to answer the above question in the affirmative. We first define an array of modes of convergence to the truth as desiderata that might or might not be achieved by a causal learning algorithm. Those modes of convergence concern (i) how pervasive the domain of convergence is on the space of all possible CBNs and (ii) how uniformly the convergence happens. Then we prove a result to the following effect: for any learning algorithm that tackles the causal learning problem in question, if it achieves the best achievable mode of convergence (considered in this paper), then it must follow the standard design practice of converging to the truth for at least all CBNs that satisfy the faithfulness condition---it is a requirement, not an option.", "published": "2018-02-20T10:46:12Z", "authors": ["Hanti Lin", "Jiji Zhang"], "categories": ["stat.ML", "cs.LG", "stat.ME"], "doi": "", "_query": "real estate valuation deep learning", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/1906.03685v1", "title": "Novelty Detection via Network Saliency in Visual-based Deep Learning", "abstract": "Machine-learning driven safety-critical autonomous systems, such as self-driving cars, must be able to detect situations where its trained model is not able to make a trustworthy prediction. Often viewed as a black-box, it is non-obvious to determine when a model will make a safe decision and when it will make an erroneous, perhaps life-threatening one. Prior work on novelty detection deal with highly structured data and do not translate well to dynamic, real-world situations. This paper proposes a multi-step framework for the detection of novel scenarios in vision-based autonomous systems by leveraging information learned by the trained prediction model and a new image similarity metric. We demonstrate the efficacy of this method through experiments on a real-world driving dataset as well as on our in-house indoor racing environment.", "published": "2019-06-09T18:23:57Z", "authors": ["Valerie Chen", "Man-Ki Yoon", "Zhong Shao"], "categories": ["cs.LG", "cs.CV", "stat.ML"], "doi": "", "_query": "real estate valuation deep learning", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2111.14346v1", "title": "Pessimistic Model Selection for Offline Deep Reinforcement Learning", "abstract": "Deep Reinforcement Learning (DRL) has demonstrated great potentials in solving sequential decision making problems in many applications. Despite its promising performance, practical gaps exist when deploying DRL in real-world scenarios. One main barrier is the over-fitting issue that leads to poor generalizability of the policy learned by DRL. In particular, for offline DRL with observational data, model selection is a challenging task as there is no ground truth available for performance demonstration, in contrast with the online setting with simulated environments. In this work, we propose a pessimistic model selection (PMS) approach for offline DRL with a theoretical guarantee, which features a provably effective framework for finding the best policy among a set of candidate models. Two refined approaches are also proposed to address the potential bias of DRL model in identifying the optimal policy. Numerical studies demonstrated the superior performance of our approach over existing methods.", "published": "2021-11-29T06:29:49Z", "authors": ["Chao-Han Huck Yang", "Zhengling Qi", "Yifan Cui", "Pin-Yu Chen"], "categories": ["cs.LG", "cs.AI", "cs.CE", "cs.NE", "eess.SY"], "doi": "", "_query": "real estate valuation deep learning", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2403.18337v1", "title": "Macroscale fracture surface segmentation via semi-supervised learning considering the structural similarity", "abstract": "To this date the safety assessment of materials, used for example in the nuclear power sector, commonly relies on a fracture mechanical analysis utilizing macroscopic concepts, where a global load quantity K or J is compared to the materials fracture toughness curve. Part of the experimental effort involved in these concepts is dedicated to the quantitative analysis of fracture surfaces. Within the scope of this study a methodology for the semi-supervised training of deep learning models for fracture surface segmentation on a macroscopic level was established. Therefore, three distinct and unique datasets were created to analyze the influence of structural similarity on the segmentation capability. The structural similarity differs due to the assessed materials and specimen, as well as imaging-induced variance due to fluctuations in image acquisition in different laboratories. The datasets correspond to typical isolated laboratory conditions, complex real-world circumstances, and a curated subset of the two. We implemented a weak-to-strong consistency regularization for semi-supervised learning. On the heterogeneous dataset we were able to train robust and well-generalizing models that learned feature representations from images across different domains without observing a significant drop in prediction quality. Furthermore, our approach reduced the number of labeled images required for training by a factor of 6. To demonstrate the success of our method and the benefit of our approach for the fracture mechanics assessment, we utilized the models for initial crack size measurements with the area average method. For the laboratory setting, the deep learning assisted measurements proved to have the same quality as manual measurements. For models trained on the heterogeneous dataset, very good measurement accuracies with mean deviations smaller than 1 % could be achieved...", "published": "2024-03-27T08:21:41Z", "authors": ["Johannes Rosenberger", "Johannes Tlatlik", "Sebastian Münstermann"], "categories": ["cs.LG"], "doi": "10.1016/j.engfracmech.2023.109686", "_query": "real estate valuation deep learning", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2010.05125v2", "title": "Learning Task-aware Robust Deep Learning Systems", "abstract": "Many works demonstrate that deep learning system is vulnerable to adversarial attack. A deep learning system consists of two parts: the deep learning task and the deep model. Nowadays, most existing works investigate the impact of the deep model on robustness of deep learning systems, ignoring the impact of the learning task. In this paper, we adopt the binary and interval label encoding strategy to redefine the classification task and design corresponding loss to improve robustness of the deep learning system. Our method can be viewed as improving the robustness of deep learning systems from both the learning task and deep model. Experimental results demonstrate that our learning task-aware method is much more robust than traditional classification while retaining the accuracy.", "published": "2020-10-11T01:06:49Z", "authors": ["Keji Han", "Yun Li", "Xianzhong Long", "Yao Ge"], "categories": ["cs.LG", "cs.AI", "stat.ML"], "doi": "", "_query": "real estate valuation deep learning", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2506.10073v2", "title": "Patient-Specific Deep Reinforcement Learning for Automatic Replanning in Head-and-Neck Cancer Proton Therapy", "abstract": "Anatomical changes during intensity-modulated proton therapy (IMPT) for head-and-neck cancer (HNC) can shift Bragg peaks, risking tumor underdosing and organ-at-risk overdosing. Treatment replanning is often required to maintain clinically acceptable treatment quality. However, current manual replanning processes are resource-intensive and time-consuming. We propose a patient-specific deep reinforcement learning (DRL) framework for automated IMPT replanning, with a reward-shaping mechanism based on a $150$-point plan quality score addressing competing clinical objectives. We formulate the planning process as a reinforcement learning problem where agents learn control policies to adjust optimization priorities, maximizing plan quality. Unlike population-based approaches, our framework trains agents for each patient using their planning Computed Tomography (CT) and augmented anatomies simulating anatomical changes (tumor progression and regression). This patient-specific approach leverages anatomical similarities along the treatment course, enabling effective plan adaptation. We implemented two DRL algorithms, Deep Q-Network and Proximal Policy Optimization, using dose-volume histograms (DVHs) as state representations and a $22$-dimensional action space of priority adjustments. Evaluation on eight HNC patients using actual replanning CT data showed that both agents improved initial plan scores from $120.78 \\pm 17.18$ to $139.59 \\pm 5.50$ (DQN) and $141.50 \\pm 4.69$ (PPO), surpassing the replans manually generated by a human planner ($136.32 \\pm 4.79$). Clinical validation confirms that improvements translate to better tumor coverage and OAR sparing across diverse anatomical changes. This work highlights DRL's potential in addressing geometric and dosimetric complexities of adaptive proton therapy, offering efficient offline adaptation solutions and advancing online adaptive proton therapy.", "published": "2025-06-11T18:00:06Z", "authors": ["Malvern Madondo", "Yuan Shao", "Yingzi Liu", "Jun Zhou", "Xiaofeng Yang", "Zhen Tian"], "categories": ["physics.med-ph", "cs.LG"], "doi": "", "_query": "real estate valuation deep learning", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/1505.08082v1", "title": "Learning to count with deep object features", "abstract": "Learning to count is a learning strategy that has been recently proposed in the literature for dealing with problems where estimating the number of object instances in a scene is the final objective. In this framework, the task of learning to detect and localize individual object instances is seen as a harder task that can be evaded by casting the problem as that of computing a regression value from hand-crafted image features. In this paper we explore the features that are learned when training a counting convolutional neural network in order to understand their underlying representation. To this end we define a counting problem for MNIST data and show that the internal representation of the network is able to classify digits in spite of the fact that no direct supervision was provided for them during training. We also present preliminary results about a deep network that is able to count the number of pedestrians in a scene.", "published": "2015-05-29T15:06:15Z", "authors": ["Santi Seguí", "Oriol Pujol", "Jordi Vitrià"], "categories": ["cs.CV"], "doi": "", "_query": "real estate valuation deep learning", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2501.19064v1", "title": "Machine Learning in Gamma Astronomy", "abstract": "The purpose of this paper is to review the most popular deep learning methods used to analyze astroparticle data obtained with Imaging Atmospheric Cherenkov Telescopes and provide references to the original papers.", "published": "2025-01-31T11:52:26Z", "authors": ["A. P. Kryukov", "A. P. Demichev", "V. A. Ilyin"], "categories": ["astro-ph.IM", "astro-ph.HE", "cs.LG"], "doi": "10.3103/S002713492470214X", "_query": "real estate valuation deep learning", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2310.14196v1", "title": "Learning to Discern: Imitating Heterogeneous Human Demonstrations with Preference and Representation Learning", "abstract": "Practical Imitation Learning (IL) systems rely on large human demonstration datasets for successful policy learning. However, challenges lie in maintaining the quality of collected data and addressing the suboptimal nature of some demonstrations, which can compromise the overall dataset quality and hence the learning outcome. Furthermore, the intrinsic heterogeneity in human behavior can produce equally successful but disparate demonstrations, further exacerbating the challenge of discerning demonstration quality. To address these challenges, this paper introduces Learning to Discern (L2D), an offline imitation learning framework for learning from demonstrations with diverse quality and style. Given a small batch of demonstrations with sparse quality labels, we learn a latent representation for temporally embedded trajectory segments. Preference learning in this latent space trains a quality evaluator that generalizes to new demonstrators exhibiting different styles. Empirically, we show that L2D can effectively assess and learn from varying demonstrations, thereby leading to improved policy performance across a range of tasks in both simulations and on a physical robot.", "published": "2023-10-22T06:08:55Z", "authors": ["Sachit Kuhar", "Shuo Cheng", "Shivang Chopra", "Matthew Bronars", "Danfei Xu"], "categories": ["cs.RO", "cs.AI"], "doi": "", "_query": "real estate valuation deep learning", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/1901.09890v1", "title": "Few-shot Learning with Meta Metric Learners", "abstract": "Few-shot Learning aims to learn classifiers for new classes with only a few training examples per class. Existing meta-learning or metric-learning based few-shot learning approaches are limited in handling diverse domains with various number of labels. The meta-learning approaches train a meta learner to predict weights of homogeneous-structured task-specific networks, requiring a uniform number of classes across tasks. The metric-learning approaches learn one task-invariant metric for all the tasks, and they fail if the tasks diverge. We propose to deal with these limitations with meta metric learning. Our meta metric learning approach consists of task-specific learners, that exploit metric learning to handle flexible labels, and a meta learner, that discovers good parameters and gradient decent to specify the metrics in task-specific learners. Thus the proposed model is able to handle unbalanced classes as well as to generate task-specific metrics. We test our approach in the `$k$-shot $N$-way' few-shot learning setting used in previous work and new realistic few-shot setting with diverse multi-domain tasks and flexible label numbers. Experiments show that our approach attains superior performances in both settings.", "published": "2019-01-26T01:55:33Z", "authors": ["Yu Cheng", "Mo Yu", "Xiaoxiao Guo", "Bowen Zhou"], "categories": ["cs.LG", "cs.AI", "stat.ML"], "doi": "", "_query": "real estate valuation deep learning", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2302.07930v2", "title": "Interpretable Deep Learning Methods for Multiview Learning", "abstract": "Technological advances have enabled the generation of unique and complementary types of data or views (e.g. genomics, proteomics, metabolomics) and opened up a new era in multiview learning research with the potential to lead to new biomedical discoveries. We propose iDeepViewLearn (Interpretable Deep Learning Method for Multiview Learning) for learning nonlinear relationships in data from multiple views while achieving feature selection. iDeepViewLearn combines deep learning flexibility with the statistical benefits of data and knowledge-driven feature selection, giving interpretable results. Deep neural networks are used to learn view-independent low-dimensional embedding through an optimization problem that minimizes the difference between observed and reconstructed data, while imposing a regularization penalty on the reconstructed data. The normalized Laplacian of a graph is used to model bilateral relationships between variables in each view, therefore, encouraging selection of related variables. iDeepViewLearn is tested on simulated and two real-world data, including breast cancer-related gene expression and methylation data. iDeepViewLearn had competitive classification results and identified genes and CpG sites that differentiated between individuals who died from breast cancer and those who did not. The results of our real data application and simulations with small to moderate sample sizes suggest that iDeepViewLearn may be a useful method for small-sample-size problems compared to other deep learning methods for multiview learning.", "published": "2023-02-15T20:11:25Z", "authors": ["Hengkang Wang", "Han Lu", "Ju Sun", "Sandra E Safo"], "categories": ["cs.LG", "stat.ME", "stat.ML"], "doi": "10.1186/s12859-024-05679-9", "_query": "real estate valuation deep learning", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2412.07027v1", "title": "Deep Learning for Cross-Border Transaction Anomaly Detection in Anti-Money Laundering Systems", "abstract": "In the context of globalization and the rapid expansion of the digital economy, anti-money laundering (AML) has become a crucial aspect of financial oversight, particularly in cross-border transactions. The rising complexity and scale of international financial flows necessitate more intelligent and adaptive AML systems to combat increasingly sophisticated money laundering techniques. This paper explores the application of unsupervised learning models in cross-border AML systems, focusing on rule optimization through contrastive learning techniques. Five deep learning models, ranging from basic convolutional neural networks (CNNs) to hybrid CNNGRU architectures, were designed and tested to assess their performance in detecting abnormal transactions. The results demonstrate that as model complexity increases, so does the system's detection accuracy and responsiveness. In particular, the self-developed hybrid Convolutional-Recurrent Neural Integration Model (CRNIM) model showed superior performance in terms of accuracy and area under the receiver operating characteristic curve (AUROC). These findings highlight the potential of unsupervised learning models to significantly improve the intelligence, flexibility, and real-time capabilities of AML systems. By optimizing detection rules and enhancing adaptability to emerging money laundering schemes, this research provides both theoretical and practical contributions to the advancement of AML technologies, which are essential for safeguarding the global financial system against illicit activities.", "published": "2024-11-21T03:55:41Z", "authors": ["Qian Yu", "Zhen Xu", "Zong Ke"], "categories": ["cs.LG", "cs.CY", "cs.SI", "q-fin.RM"], "doi": "", "_query": "real estate valuation deep learning", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2306.00956v1", "title": "The ObjectFolder Benchmark: Multisensory Learning with Neural and Real Objects", "abstract": "We introduce the ObjectFolder Benchmark, a benchmark suite of 10 tasks for multisensory object-centric learning, centered around object recognition, reconstruction, and manipulation with sight, sound, and touch. We also introduce the ObjectFolder Real dataset, including the multisensory measurements for 100 real-world household objects, building upon a newly designed pipeline for collecting the 3D meshes, videos, impact sounds, and tactile readings of real-world objects. We conduct systematic benchmarking on both the 1,000 multisensory neural objects from ObjectFolder, and the real multisensory data from ObjectFolder Real. Our results demonstrate the importance of multisensory perception and reveal the respective roles of vision, audio, and touch for different object-centric learning tasks. By publicly releasing our dataset and benchmark suite, we hope to catalyze and enable new research in multisensory object-centric learning in computer vision, robotics, and beyond. Project page: https://objectfolder.stanford.edu", "published": "2023-06-01T17:51:22Z", "authors": ["Ruohan Gao", "Yiming Dou", "Hao Li", "Tanmay Agarwal", "Jeannette Bohg", "Yunzhu Li", "Li Fei-Fei", "Jiajun Wu"], "categories": ["cs.CV", "cs.AI", "cs.GR", "cs.HC", "cs.RO"], "doi": "", "_query": "real estate valuation deep learning", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2006.06183v1", "title": "G5: A Universal GRAPH-BERT for Graph-to-Graph Transfer and Apocalypse Learning", "abstract": "The recent GRAPH-BERT model introduces a new approach to learning graph representations merely based on the attention mechanism. GRAPH-BERT provides an opportunity for transferring pre-trained models and learned graph representations across different tasks within the same graph dataset. In this paper, we will further investigate the graph-to-graph transfer of a universal GRAPH-BERT for graph representation learning across different graph datasets, and our proposed model is also referred to as the G5 for simplicity. Many challenges exist in learning G5 to adapt the distinct input and output configurations for each graph data source, as well as the information distributions differences. G5 introduces a pluggable model architecture: (a) each data source will be pre-processed with a unique input representation learning component; (b) each output application task will also have a specific functional component; and (c) all such diverse input and output components will all be conjuncted with a universal GRAPH-BERT core component via an input size unification layer and an output representation fusion layer, respectively. The G5 model removes the last obstacle for cross-graph representation learning and transfer. For the graph sources with very sparse training data, the G5 model pre-trained on other graphs can still be utilized for representation learning with necessary fine-tuning. What's more, the architecture of G5 also allows us to learn a supervised functional classifier for data sources without any training data at all. Such a problem is also named as the Apocalypse Learning task in this paper. Two different label reasoning strategies, i.e., Cross-Source Classification Consistency Maximization (CCCM) and Cross-Source Dynamic Routing (CDR), are introduced in this paper to address the problem.", "published": "2020-06-11T04:19:18Z", "authors": ["Jiawei Zhang"], "categories": ["cs.LG", "cs.NE", "cs.SI", "stat.ML"], "doi": "", "_query": "real estate valuation deep learning", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/1206.6486v1", "title": "Flexible Modeling of Latent Task Structures in Multitask Learning", "abstract": "Multitask learning algorithms are typically designed assuming some fixed, a priori known latent structure shared by all the tasks. However, it is usually unclear what type of latent task structure is the most appropriate for a given multitask learning problem. Ideally, the \"right\" latent task structure should be learned in a data-driven manner. We present a flexible, nonparametric Bayesian model that posits a mixture of factor analyzers structure on the tasks. The nonparametric aspect makes the model expressive enough to subsume many existing models of latent task structures (e.g, mean-regularized tasks, clustered tasks, low-rank or linear/non-linear subspace assumption on tasks, etc.). Moreover, it can also learn more general task structures, addressing the shortcomings of such models. We present a variational inference algorithm for our model. Experimental results on synthetic and real-world datasets, on both regression and classification problems, demonstrate the effectiveness of the proposed method.", "published": "2012-06-27T19:59:59Z", "authors": ["Alexandre Passos", "Piyush Rai", "Jacques Wainer", "Hal Daume"], "categories": ["cs.LG", "stat.ML"], "doi": "", "_query": "real estate valuation deep learning", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2009.06681v1", "title": "Deep Actor-Critic Learning for Distributed Power Control in Wireless Mobile Networks", "abstract": "Deep reinforcement learning offers a model-free alternative to supervised deep learning and classical optimization for solving the transmit power control problem in wireless networks. The multi-agent deep reinforcement learning approach considers each transmitter as an individual learning agent that determines its transmit power level by observing the local wireless environment. Following a certain policy, these agents learn to collaboratively maximize a global objective, e.g., a sum-rate utility function. This multi-agent scheme is easily scalable and practically applicable to large-scale cellular networks. In this work, we present a distributively executed continuous power control algorithm with the help of deep actor-critic learning, and more specifically, by adapting deep deterministic policy gradient. Furthermore, we integrate the proposed power control algorithm to a time-slotted system where devices are mobile and channel conditions change rapidly. We demonstrate the functionality of the proposed algorithm using simulation results.", "published": "2020-09-14T18:29:12Z", "authors": ["Yasar Sinan Nasir", "Dongning Guo"], "categories": ["eess.SP", "cs.IT", "stat.ML"], "doi": "", "_query": "real estate valuation deep learning", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/1901.02324v2", "title": "Learning with Fenchel-Young Losses", "abstract": "Over the past decades, numerous loss functions have been been proposed for a variety of supervised learning tasks, including regression, classification, ranking, and more generally structured prediction. Understanding the core principles and theoretical properties underpinning these losses is key to choose the right loss for the right problem, as well as to create new losses which combine their strengths. In this paper, we introduce Fenchel-Young losses, a generic way to construct a convex loss function for a regularized prediction function. We provide an in-depth study of their properties in a very broad setting, covering all the aforementioned supervised learning tasks, and revealing new connections between sparsity, generalized entropies, and separation margins. We show that Fenchel-Young losses unify many well-known loss functions and allow to create useful new ones easily. Finally, we derive efficient predictive and training algorithms, making Fenchel-Young losses appealing both in theory and practice.", "published": "2019-01-08T14:37:56Z", "authors": ["Mathieu Blondel", "André F. T. Martins", "Vlad Niculae"], "categories": ["stat.ML", "cs.LG"], "doi": "", "_query": "real estate valuation deep learning", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/1902.06494v1", "title": "A Unifying Bayesian View of Continual Learning", "abstract": "Some machine learning applications require continual learning - where data comes in a sequence of datasets, each is used for training and then permanently discarded. From a Bayesian perspective, continual learning seems straightforward: Given the model posterior one would simply use this as the prior for the next task. However, exact posterior evaluation is intractable with many models, especially with Bayesian neural networks (BNNs). Instead, posterior approximations are often sought. Unfortunately, when posterior approximations are used, prior-focused approaches do not succeed in evaluations designed to capture properties of realistic continual learning use cases. As an alternative to prior-focused methods, we introduce a new approximate Bayesian derivation of the continual learning loss. Our loss does not rely on the posterior from earlier tasks, and instead adapts the model itself by changing the likelihood term. We call these approaches likelihood-focused. We then combine prior- and likelihood-focused methods into one objective, tying the two views together under a single unifying framework of approximate Bayesian continual learning.", "published": "2019-02-18T10:22:13Z", "authors": ["Sebastian Farquhar", "Yarin Gal"], "categories": ["stat.ML", "cs.LG"], "doi": "", "_query": "real estate valuation deep learning", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2506.03207v1", "title": "Fingerprinting Deep Learning Models via Network Traffic Patterns in Federated Learning", "abstract": "Federated Learning (FL) is increasingly adopted as a decentralized machine learning paradigm due to its capability to preserve data privacy by training models without centralizing user data. However, FL is susceptible to indirect privacy breaches via network traffic analysis-an area not explored in existing research. The primary objective of this research is to study the feasibility of fingerprinting deep learning models deployed within FL environments by analyzing their network-layer traffic information. In this paper, we conduct an experimental evaluation using various deep learning architectures (i.e., CNN, RNN) within a federated learning testbed. We utilize machine learning algorithms, including Support Vector Machines (SVM), Random Forest, and Gradient-Boosting, to fingerprint unique patterns within the traffic data. Our experiments show high fingerprinting accuracy, achieving 100% accuracy using Random Forest and around 95.7% accuracy using SVM and Gradient Boosting classifiers. This analysis suggests that we can identify specific architectures running within the subsection of the network traffic. Hence, if an adversary knows about the underlying DL architecture, they can exploit that information and conduct targeted attacks. These findings suggest a notable security vulnerability in FL systems and the necessity of strengthening it at the network level.", "published": "2025-06-02T21:37:20Z", "authors": ["Md Nahid Hasan Shuvo", "Moinul Hossain"], "categories": ["cs.LG", "cs.AI", "cs.CR"], "doi": "", "_query": "real estate valuation deep learning", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/1906.07480v3", "title": "Deep Multicameral Decoding for Localizing Unoccluded Object Instances from a Single RGB Image", "abstract": "Occlusion-aware instance-sensitive segmentation is a complex task generally split into region-based segmentations, by approximating instances as their bounding box. We address the showcase scenario of dense homogeneous layouts in which this approximation does not hold. In this scenario, outlining unoccluded instances by decoding a deep encoder becomes difficult, due to the translation invariance of convolutional layers and the lack of complexity in the decoder. We therefore propose a multicameral design composed of subtask-specific lightweight decoder and encoder-decoder units, coupled in cascade to encourage subtask-specific feature reuse and enforce a learning path within the decoding process. Furthermore, the state-of-the-art datasets for occlusion-aware instance segmentation contain real images with few instances and occlusions mostly due to objects occluding the background, unlike dense object layouts. We thus also introduce a synthetic dataset of dense homogeneous object layouts, namely Mikado, which extensibly contains more instances and inter-instance occlusions per image than these public datasets. Our extensive experiments on Mikado and public datasets show that ordinal multiscale units within the decoding process prove more effective than state-of-the-art design patterns for capturing position-sensitive representations. We also show that Mikado is plausible with respect to real-world problems, in the sense that it enables the learning of performance-enhancing representations transferable to real images, while drastically reducing the need of hand-made annotations for finetuning. The proposed dataset will be made publicly available.", "published": "2019-06-18T10:20:17Z", "authors": ["Matthieu Grard", "Emmanuel Dellandréa", "Liming Chen"], "categories": ["cs.CV", "cs.LG", "eess.IV"], "doi": "10.1007/s11263-020-01323-0", "_query": "real estate valuation deep learning", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/1612.05970v2", "title": "Adversarial Deep Structural Networks for Mammographic Mass Segmentation", "abstract": "Mass segmentation is an important task in mammogram analysis, providing effective morphological features and regions of interest (ROI) for mass detection and classification. Inspired by the success of using deep convolutional features for natural image analysis and conditional random fields (CRF) for structural learning, we propose an end-to-end network for mammographic mass segmentation. The network employs a fully convolutional network (FCN) to model potential function, followed by a CRF to perform structural learning. Because the mass distribution varies greatly with pixel position, the FCN is combined with position priori for the task. Due to the small size of mammogram datasets, we use adversarial training to control over-fitting. Four models with different convolutional kernels are further fused to improve the segmentation results. Experimental results on two public datasets, INbreast and DDSM-BCRP, show that our end-to-end network combined with adversarial training achieves the-state-of-the-art results.", "published": "2016-12-18T18:40:21Z", "authors": ["Wentao Zhu", "Xiang Xiang", "Trac D. Tran", "Xiaohui Xie"], "categories": ["cs.CV", "cs.LG"], "doi": "", "_query": "real estate valuation deep learning", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2504.19145v2", "title": "Global Climate Model Bias Correction Using Deep Learning", "abstract": "Climate change affects ocean temperature, salinity and sea level, impacting monsoons and ocean productivity. Future projections by Global Climate Models based on shared socioeconomic pathways from the Coupled Model Intercomparison Project (CMIP) are widely used to understand the effects of climate change. However, CMIP models have significant bias compared to reanalysis in the Bay of Bengal for the time period when both projections and reanalysis are available. For example, there is a 1.5C root mean square error (RMSE) in the sea surface temperature (SST) projections of the climate model CNRM-CM6 compared to the Ocean Reanalysis System (ORAS5). We develop a suite of data-driven deep learning models for bias correction of climate model projections and apply it to correct SST projections of the Bay of Bengal. We propose the use of three different deep neural network architectures: convolutional encoder-decoder UNet, Bidirectional LSTM and ConvLSTM. We also use a baseline linear regression model and the Equi-Distant Cumulative Density Function (EDCDF) bias correction method for comparison and evaluating the impact of the new deep learning models. All bias correction models are trained using pairs of monthly CMIP6 projections and the corresponding month's ORAS5 as input and output. Historical data (1950-2014) and future projection data (2015-2020) of CNRM-CM6 are used for training and validation, including hyperparameter tuning. Testing is performed on future projection data from 2021 to 2024. Detailed analysis of the three deep neural models has been completed. We found that the UNet architecture trained using a climatology-removed CNRM-CM6 projection as input and climatology-removed ORAS5 as output gives the best bias-corrected projections. Our novel deep learning-based method for correcting CNRM-CM6 data has a 15% reduction in RMSE compared EDCDF.", "published": "2025-04-27T07:56:57Z", "authors": ["Abhishek Pasula", "Deepak N. Subramani"], "categories": ["physics.ao-ph", "cs.LG", "stat.AP"], "doi": "10.1088/3049-4753/ade9c3", "_query": "real estate valuation deep learning", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2008.09301v1", "title": "Amortized learning of neural causal representations", "abstract": "Causal models can compactly and efficiently encode the data-generating process under all interventions and hence may generalize better under changes in distribution. These models are often represented as Bayesian networks and learning them scales poorly with the number of variables. Moreover, these approaches cannot leverage previously learned knowledge to help with learning new causal models. In order to tackle these challenges, we represent a novel algorithm called \\textit{causal relational networks} (CRN) for learning causal models using neural networks. The CRN represent causal models using continuous representations and hence could scale much better with the number of variables. These models also take in previously learned information to facilitate learning of new causal models. Finally, we propose a decoding-based metric to evaluate causal models with continuous representations. We test our method on synthetic data achieving high accuracy and quick adaptation to previously unseen causal models.", "published": "2020-08-21T04:35:06Z", "authors": ["Nan Rosemary Ke", "Jane. X. Wang", "Jovana Mitrovic", "Martin Szummer", "Danilo J. Rezende"], "categories": ["stat.ML", "cs.LG"], "doi": "", "_query": "real estate valuation deep learning", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2101.02842v1", "title": "Grasp and Motion Planning for Dexterous Manipulation for the Real Robot Challenge", "abstract": "This report describes our winning submission to the Real Robot Challenge (https://real-robot-challenge.com/). The Real Robot Challenge is a three-phase dexterous manipulation competition that involves manipulating various rectangular objects with the TriFinger Platform. Our approach combines motion planning with several motion primitives to manipulate the object. For Phases 1 and 2, we additionally learn a residual policy in simulation that applies corrective actions on top of our controller. Our approach won first place in Phase 2 and Phase 3 of the competition. We were anonymously known as `ardentstork' on the competition leaderboard (https://real-robot-challenge.com/leader-board). Videos and our code can be found at https://github.com/ripl-ttic/real-robot-challenge.", "published": "2021-01-08T04:13:39Z", "authors": ["Takuma Yoneda", "Charles Schaff", "Takahiro Maeda", "Matthew Walter"], "categories": ["cs.RO", "cs.AI"], "doi": "", "_query": "real estate valuation deep learning", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/1906.10343v2", "title": "Exploring Self-Supervised Regularization for Supervised and Semi-Supervised Learning", "abstract": "Recent advances in semi-supervised learning have shown tremendous potential in overcoming a major barrier to the success of modern machine learning algorithms: access to vast amounts of human-labeled training data. Previous algorithms based on consistency regularization can harness the abundance of unlabeled data to produce impressive results on a number of semi-supervised benchmarks, approaching the performance of strong supervised baselines using only a fraction of the available labeled data. In this work, we challenge the long-standing success of consistency regularization by introducing self-supervised regularization as the basis for combining semantic feature representations from unlabeled data. We perform extensive comparative experiments to demonstrate the effectiveness of self-supervised regularization for supervised and semi-supervised image classification on SVHN, CIFAR-10, and CIFAR-100 benchmark datasets. We present two main results: (1) models augmented with self-supervised regularization significantly improve upon traditional supervised classifiers without the need for unlabeled data; (2) together with unlabeled data, our models yield semi-supervised performance competitive with, and in many cases exceeding, prior state-of-the-art consistency baselines. Lastly, our models have the practical utility of being efficiently trained end-to-end and require no additional hyper-parameters to tune for optimal performance beyond the standard set for training neural networks. Reference code and data are available at https://github.com/vuptran/sesemi", "published": "2019-06-25T06:42:05Z", "authors": ["Phi Vu Tran"], "categories": ["cs.LG", "cs.CV", "stat.ML"], "doi": "", "_query": "real estate valuation deep learning", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2012.11293v1", "title": "Curiosity in exploring chemical space: Intrinsic rewards for deep molecular reinforcement learning", "abstract": "Computer-aided design of molecules has the potential to disrupt the field of drug and material discovery. Machine learning, and deep learning, in particular, have been topics where the field has been developing at a rapid pace. Reinforcement learning is a particularly promising approach since it allows for molecular design without prior knowledge. However, the search space is vast and efficient exploration is desirable when using reinforcement learning agents. In this study, we propose an algorithm to aid efficient exploration. The algorithm is inspired by a concept known in the literature as curiosity. We show on three benchmarks that a curious agent finds better performing molecules. This indicates an exciting new research direction for reinforcement learning agents that can explore the chemical space out of their own motivation. This has the potential to eventually lead to unexpected new molecules that no human has thought about so far.", "published": "2020-12-17T17:21:59Z", "authors": ["Luca A. Thiede", "Mario Krenn", "AkshatKumar Nigam", "Alan Aspuru-Guzik"], "categories": ["cs.LG", "cs.AI", "physics.chem-ph"], "doi": "10.1088/2632-2153/ac7ddc", "_query": "real estate valuation deep learning", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/1906.04452v1", "title": "Continual Reinforcement Learning deployed in Real-life using Policy Distillation and Sim2Real Transfer", "abstract": "We focus on the problem of teaching a robot to solve tasks presented sequentially, i.e., in a continual learning scenario. The robot should be able to solve all tasks it has encountered, without forgetting past tasks. We provide preliminary work on applying Reinforcement Learning to such setting, on 2D navigation tasks for a 3 wheel omni-directional robot. Our approach takes advantage of state representation learning and policy distillation. Policies are trained using learned features as input, rather than raw observations, allowing better sample efficiency. Policy distillation is used to combine multiple policies into a single one that solves all encountered tasks.", "published": "2019-06-11T09:06:19Z", "authors": ["René Traoré", "Hugo Caselles-Dupré", "Timothée Lesort", "Te Sun", "Natalia Díaz-Rodríguez", "David Filliat"], "categories": ["cs.LG", "cs.RO", "stat.ML"], "doi": "", "_query": "real estate valuation deep learning", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2202.13890v2", "title": "Pessimistic Q-Learning for Offline Reinforcement Learning: Towards Optimal Sample Complexity", "abstract": "Offline or batch reinforcement learning seeks to learn a near-optimal policy using history data without active exploration of the environment. To counter the insufficient coverage and sample scarcity of many offline datasets, the principle of pessimism has been recently introduced to mitigate high bias of the estimated values. While pessimistic variants of model-based algorithms (e.g., value iteration with lower confidence bounds) have been theoretically investigated, their model-free counterparts -- which do not require explicit model estimation -- have not been adequately studied, especially in terms of sample efficiency. To address this inadequacy, we study a pessimistic variant of Q-learning in the context of finite-horizon Markov decision processes, and characterize its sample complexity under the single-policy concentrability assumption which does not require the full coverage of the state-action space. In addition, a variance-reduced pessimistic Q-learning algorithm is proposed to achieve near-optimal sample complexity. Altogether, this work highlights the efficiency of model-free algorithms in offline RL when used in conjunction with pessimism and variance reduction.", "published": "2022-02-28T15:39:36Z", "authors": ["Laixi Shi", "Gen Li", "Yuting Wei", "Yuxin Chen", "Yuejie Chi"], "categories": ["cs.LG", "stat.ML"], "doi": "", "_query": "real estate valuation deep learning", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2212.11110v3", "title": "Lifelong Reinforcement Learning with Modulating Masks", "abstract": "Lifelong learning aims to create AI systems that continuously and incrementally learn during a lifetime, similar to biological learning. Attempts so far have met problems, including catastrophic forgetting, interference among tasks, and the inability to exploit previous knowledge. While considerable research has focused on learning multiple supervised classification tasks that involve changes in the input distribution, lifelong reinforcement learning (LRL) must deal with variations in the state and transition distributions, and in the reward functions. Modulating masks with a fixed backbone network, recently developed for classification, are particularly suitable to deal with such a large spectrum of task variations. In this paper, we adapted modulating masks to work with deep LRL, specifically PPO and IMPALA agents. The comparison with LRL baselines in both discrete and continuous RL tasks shows superior performance. We further investigated the use of a linear combination of previously learned masks to exploit previous knowledge when learning new tasks: not only is learning faster, the algorithm solves tasks that we could not otherwise solve from scratch due to extremely sparse rewards. The results suggest that RL with modulating masks is a promising approach to lifelong learning, to the composition of knowledge to learn increasingly complex tasks, and to knowledge reuse for efficient and faster learning.", "published": "2022-12-21T15:49:20Z", "authors": ["Eseoghene Ben-Iwhiwhu", "Saptarshi Nath", "Praveen K. Pilly", "Soheil Kolouri", "Andrea Soltoggio"], "categories": ["cs.LG", "cs.AI", "stat.ML"], "doi": "", "_query": "real estate valuation deep learning", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2010.11944v1", "title": "Accelerating Reinforcement Learning with Learned Skill Priors", "abstract": "Intelligent agents rely heavily on prior experience when learning a new task, yet most modern reinforcement learning (RL) approaches learn every task from scratch. One approach for leveraging prior knowledge is to transfer skills learned on prior tasks to the new task. However, as the amount of prior experience increases, the number of transferable skills grows too, making it challenging to explore the full set of available skills during downstream learning. Yet, intuitively, not all skills should be explored with equal probability; for example information about the current state can hint which skills are promising to explore. In this work, we propose to implement this intuition by learning a prior over skills. We propose a deep latent variable model that jointly learns an embedding space of skills and the skill prior from offline agent experience. We then extend common maximum-entropy RL approaches to use skill priors to guide downstream learning. We validate our approach, SPiRL (Skill-Prior RL), on complex navigation and robotic manipulation tasks and show that learned skill priors are essential for effective skill transfer from rich datasets. Videos and code are available at https://clvrai.com/spirl.", "published": "2020-10-22T17:59:51Z", "authors": ["Karl Pertsch", "Youngwoon Lee", "Joseph J. Lim"], "categories": ["cs.LG", "cs.AI", "cs.RO"], "doi": "", "_query": "real estate valuation deep learning", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/1910.12024v1", "title": "SUPER Learning: A Supervised-Unsupervised Framework for Low-Dose CT Image Reconstruction", "abstract": "Recent years have witnessed growing interest in machine learning-based models and techniques for low-dose X-ray CT (LDCT) imaging tasks. The methods can typically be categorized into supervised learning methods and unsupervised or model-based learning methods. Supervised learning methods have recently shown success in image restoration tasks. However, they often rely on large training sets. Model-based learning methods such as dictionary or transform learning do not require large or paired training sets and often have good generalization properties, since they learn general properties of CT image sets. Recent works have shown the promising reconstruction performance of methods such as PWLS-ULTRA that rely on clustering the underlying (reconstructed) image patches into a learned union of transforms. In this paper, we propose a new Supervised-UnsuPERvised (SUPER) reconstruction framework for LDCT image reconstruction that combines the benefits of supervised learning methods and (unsupervised) transform learning-based methods such as PWLS-ULTRA that involve highly image-adaptive clustering. The SUPER model consists of several layers, each of which includes a deep network learned in a supervised manner and an unsupervised iterative method that involves image-adaptive components. The SUPER reconstruction algorithms are learned in a greedy manner from training data. The proposed SUPER learning methods dramatically outperform both the constituent supervised learning-based networks and iterative algorithms for LDCT, and use much fewer iterations in the iterative reconstruction modules.", "published": "2019-10-26T09:04:45Z", "authors": ["Zhipeng Li", "Siqi Ye", "Yong Long", "Saiprasad Ravishankar"], "categories": ["cs.LG", "cs.CV", "eess.IV", "eess.SP", "stat.ML"], "doi": "", "_query": "real estate valuation deep learning", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2007.15543v2", "title": "PixL2R: Guiding Reinforcement Learning Using Natural Language by Mapping Pixels to Rewards", "abstract": "Reinforcement learning (RL), particularly in sparse reward settings, often requires prohibitively large numbers of interactions with the environment, thereby limiting its applicability to complex problems. To address this, several prior approaches have used natural language to guide the agent's exploration. However, these approaches typically operate on structured representations of the environment, and/or assume some structure in the natural language commands. In this work, we propose a model that directly maps pixels to rewards, given a free-form natural language description of the task, which can then be used for policy learning. Our experiments on the Meta-World robot manipulation domain show that language-based rewards significantly improves the sample efficiency of policy learning, both in sparse and dense reward settings.", "published": "2020-07-30T15:50:38Z", "authors": ["Prasoon Goyal", "Scott Niekum", "Raymond J. Mooney"], "categories": ["cs.LG", "cs.AI", "stat.ML"], "doi": "", "_query": "real estate valuation deep learning", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/1506.08760v1", "title": "S2: An Efficient Graph Based Active Learning Algorithm with Application to Nonparametric Classification", "abstract": "This paper investigates the problem of active learning for binary label prediction on a graph. We introduce a simple and label-efficient algorithm called S2 for this task. At each step, S2 selects the vertex to be labeled based on the structure of the graph and all previously gathered labels. Specifically, S2 queries for the label of the vertex that bisects the *shortest shortest* path between any pair of oppositely labeled vertices. We present a theoretical estimate of the number of queries S2 needs in terms of a novel parametrization of the complexity of binary functions on graphs. We also present experimental results demonstrating the performance of S2 on both real and synthetic data. While other graph-based active learning algorithms have shown promise in practice, our algorithm is the first with both good performance and theoretical guarantees. Finally, we demonstrate the implications of the S2 algorithm to the theory of nonparametric active learning. In particular, we show that S2 achieves near minimax optimal excess risk for an important class of nonparametric classification problems.", "published": "2015-06-29T18:03:25Z", "authors": ["Gautam Dasarathy", "Robert Nowak", "Xiaojin Zhu"], "categories": ["cs.LG", "stat.ML"], "doi": "", "_query": "real estate valuation deep learning", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2305.06820v1", "title": "DeepSTEP -- Deep Learning-Based Spatio-Temporal End-To-End Perception for Autonomous Vehicles", "abstract": "Autonomous vehicles demand high accuracy and robustness of perception algorithms. To develop efficient and scalable perception algorithms, the maximum information should be extracted from the available sensor data. In this work, we present our concept for an end-to-end perception architecture, named DeepSTEP. The deep learning-based architecture processes raw sensor data from the camera, LiDAR, and RaDAR, and combines the extracted data in a deep fusion network. The output of this deep fusion network is a shared feature space, which is used by perception head networks to fulfill several perception tasks, such as object detection or local mapping. DeepSTEP incorporates multiple ideas to advance state of the art: First, combining detection and localization into a single pipeline allows for efficient processing to reduce computational overhead and further improves overall performance. Second, the architecture leverages the temporal domain by using a self-attention mechanism that focuses on the most important features. We believe that our concept of DeepSTEP will advance the development of end-to-end perception systems. The network will be deployed on our research vehicle, which will be used as a platform for data collection, real-world testing, and validation. In conclusion, DeepSTEP represents a significant advancement in the field of perception for autonomous vehicles. The architecture's end-to-end design, time-aware attention mechanism, and integration of multiple perception tasks make it a promising solution for real-world deployment. This research is a work in progress and presents the first concept of establishing a novel perception pipeline.", "published": "2023-05-11T14:13:37Z", "authors": ["Sebastian Huch", "Florian Sauerbeck", "Johannes Betz"], "categories": ["cs.CV"], "doi": "", "_query": "real estate valuation deep learning", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2012.12062v1", "title": "QVMix and QVMix-Max: Extending the Deep Quality-Value Family of Algorithms to Cooperative Multi-Agent Reinforcement Learning", "abstract": "This paper introduces four new algorithms that can be used for tackling multi-agent reinforcement learning (MARL) problems occurring in cooperative settings. All algorithms are based on the Deep Quality-Value (DQV) family of algorithms, a set of techniques that have proven to be successful when dealing with single-agent reinforcement learning problems (SARL). The key idea of DQV algorithms is to jointly learn an approximation of the state-value function $V$, alongside an approximation of the state-action value function $Q$. We follow this principle and generalise these algorithms by introducing two fully decentralised MARL algorithms (IQV and IQV-Max) and two algorithms that are based on the centralised training with decentralised execution training paradigm (QVMix and QVMix-Max). We compare our algorithms with state-of-the-art MARL techniques on the popular StarCraft Multi-Agent Challenge (SMAC) environment. We show competitive results when QVMix and QVMix-Max are compared to well-known MARL techniques such as QMIX and MAVEN and show that QVMix can even outperform them on some of the tested environments, being the algorithm which performs best overall. We hypothesise that this is due to the fact that QVMix suffers less from the overestimation bias of the $Q$ function.", "published": "2020-12-22T14:53:42Z", "authors": ["Pascal Leroy", "Damien Ernst", "Pierre Geurts", "Gilles Louppe", "Jonathan Pisane", "Matthia Sabatelli"], "categories": ["cs.LG", "cs.AI", "cs.MA"], "doi": "", "_query": "real estate valuation deep learning", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/1906.05799v4", "title": "Deep Reinforcement Learning for Cyber Security", "abstract": "The scale of Internet-connected systems has increased considerably, and these systems are being exposed to cyber attacks more than ever. The complexity and dynamics of cyber attacks require protecting mechanisms to be responsive, adaptive, and scalable. Machine learning, or more specifically deep reinforcement learning (DRL), methods have been proposed widely to address these issues. By incorporating deep learning into traditional RL, DRL is highly capable of solving complex, dynamic, and especially high-dimensional cyber defense problems. This paper presents a survey of DRL approaches developed for cyber security. We touch on different vital aspects, including DRL-based security methods for cyber-physical systems, autonomous intrusion detection techniques, and multiagent DRL-based game theory simulations for defense strategies against cyber attacks. Extensive discussions and future research directions on DRL-based cyber security are also given. We expect that this comprehensive review provides the foundations for and facilitates future studies on exploring the potential of emerging DRL to cope with increasingly complex cyber security problems.", "published": "2019-06-13T16:34:12Z", "authors": ["Thanh Thi Nguyen", "Vijay Janapa Reddi"], "categories": ["cs.CR", "cs.AI", "cs.LG", "stat.ML"], "doi": "10.1109/TNNLS.2021.3121870", "_query": "real estate valuation deep learning", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/1806.10792v1", "title": "Hierarchical Reinforcement Learning with Abductive Planning", "abstract": "One of the key challenges in applying reinforcement learning to real-life problems is that the amount of train-and-error required to learn a good policy increases drastically as the task becomes complex. One potential solution to this problem is to combine reinforcement learning with automated symbol planning and utilize prior knowledge on the domain. However, existing methods have limitations in their applicability and expressiveness. In this paper we propose a hierarchical reinforcement learning method based on abductive symbolic planning. The planner can deal with user-defined evaluation functions and is not based on the Herbrand theorem. Therefore it can utilize prior knowledge of the rewards and can work in a domain where the state space is unknown. We demonstrate empirically that our architecture significantly improves learning efficiency with respect to the amount of training examples on the evaluation domain, in which the state space is unknown and there exist multiple goals.", "published": "2018-06-28T06:56:19Z", "authors": ["Kazeto Yamamoto", "Takashi Onishi", "Yoshimasa Tsuruoka"], "categories": ["cs.LG", "cs.AI", "stat.ML"], "doi": "", "_query": "real estate valuation deep learning", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2007.06252v2", "title": "Intrinsic-Extrinsic Convolution and Pooling for Learning on 3D Protein Structures", "abstract": "Proteins perform a large variety of functions in living organisms, thus playing a key role in biology. As of now, available learning algorithms to process protein data do not consider several particularities of such data and/or do not scale well for large protein conformations. To fill this gap, we propose two new learning operations enabling deep 3D analysis of large-scale protein data. First, we introduce a novel convolution operator which considers both, the intrinsic (invariant under protein folding) as well as extrinsic (invariant under bonding) structure, by using $n$-D convolutions defined on both the Euclidean distance, as well as multiple geodesic distances between atoms in a multi-graph. Second, we enable a multi-scale protein analysis by introducing hierarchical pooling operators, exploiting the fact that proteins are a recombination of a finite set of amino acids, which can be pooled using shared pooling matrices. Lastly, we evaluate the accuracy of our algorithms on several large-scale data sets for common protein analysis tasks, where we outperform state-of-the-art methods.", "published": "2020-07-13T09:02:40Z", "authors": ["Pedro Hermosilla", "Marco Schäfer", "Matěj Lang", "Gloria Fackelmann", "Pere Pau Vázquez", "Barbora Kozlíková", "Michael Krone", "Tobias Ritschel", "Timo Ropinski"], "categories": ["cs.LG", "q-bio.BM", "stat.ML"], "doi": "", "_query": "real estate valuation deep learning", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2403.10929v1", "title": "Function-space Parameterization of Neural Networks for Sequential Learning", "abstract": "Sequential learning paradigms pose challenges for gradient-based deep learning due to difficulties incorporating new data and retaining prior knowledge. While Gaussian processes elegantly tackle these problems, they struggle with scalability and handling rich inputs, such as images. To address these issues, we introduce a technique that converts neural networks from weight space to function space, through a dual parameterization. Our parameterization offers: (i) a way to scale function-space methods to large data sets via sparsification, (ii) retention of prior knowledge when access to past data is limited, and (iii) a mechanism to incorporate new data without retraining. Our experiments demonstrate that we can retain knowledge in continual learning and incorporate new data efficiently. We further show its strengths in uncertainty quantification and guiding exploration in model-based RL. Further information and code is available on the project website.", "published": "2024-03-16T14:00:04Z", "authors": ["Aidan Scannell", "Riccardo Mereu", "Paul Chang", "Ella Tamir", "Joni Pajarinen", "Arno Solin"], "categories": ["stat.ML", "cs.LG"], "doi": "", "_query": "real estate valuation deep learning", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/1807.07987v2", "title": "Deep Learning", "abstract": "Deep learning (DL) is a high dimensional data reduction technique for constructing high-dimensional predictors in input-output models. DL is a form of machine learning that uses hierarchical layers of latent features. In this article, we review the state-of-the-art of deep learning from a modeling and algorithmic perspective. We provide a list of successful areas of applications in Artificial Intelligence (AI), Image Processing, Robotics and Automation. Deep learning is predictive in its nature rather then inferential and can be viewed as a black-box methodology for high-dimensional function estimation.", "published": "2018-07-20T18:20:34Z", "authors": ["Nicholas G. Polson", "Vadim O. Sokolov"], "categories": ["stat.ML", "cs.LG"], "doi": "", "_query": "real estate valuation deep learning", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/1811.12557v1", "title": "Deep Multi-Agent Reinforcement Learning with Relevance Graphs", "abstract": "Over recent years, deep reinforcement learning has shown strong successes in complex single-agent tasks, and more recently this approach has also been applied to multi-agent domains. In this paper, we propose a novel approach, called MAGnet, to multi-agent reinforcement learning (MARL) that utilizes a relevance graph representation of the environment obtained by a self-attention mechanism, and a message-generation technique inspired by the NerveNet architecture. We applied our MAGnet approach to the Pommerman game and the results show that it significantly outperforms state-of-the-art MARL solutions, including DQN, MADDPG, and MCTS.", "published": "2018-11-30T00:38:18Z", "authors": ["Aleksandra Malysheva", "Tegg Taekyong Sung", "Chae-Bong Sohn", "Daniel Kudenko", "Aleksei Shpilman"], "categories": ["cs.MA", "cs.LG"], "doi": "", "_query": "real estate valuation deep learning", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2006.13823v3", "title": "Preventing Value Function Collapse in Ensemble {Q}-Learning by Maximizing Representation Diversity", "abstract": "The classic DQN algorithm is limited by the overestimation bias of the learned Q-function. Subsequent algorithms have proposed techniques to reduce this problem, without fully eliminating it. Recently, the Maxmin and Ensemble Q-learning algorithms have used different estimates provided by the ensembles of learners to reduce the overestimation bias. Unfortunately, these learners can converge to the same point in the parametric or representation space, falling back to the classic single neural network DQN. In this paper, we describe a regularization technique to maximize ensemble diversity in these algorithms. We propose and compare five regularization functions inspired from economics theory and consensus optimization. We show that the regularized approach significantly outperforms the Maxmin and Ensemble Q-learning algorithms as well as non-ensemble baselines.", "published": "2020-06-24T15:53:20Z", "authors": ["Hassam Ullah Sheikh", "Ladislau Bölöni"], "categories": ["cs.LG", "cs.AI", "stat.ML"], "doi": "", "_query": "real estate valuation deep learning", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2107.10804v2", "title": "Active Learning in Incomplete Label Multiple Instance Multiple Label Learning", "abstract": "In multiple instance multiple label learning, each sample, a bag, consists of multiple instances. To alleviate labeling complexity, each sample is associated with a set of bag-level labels leaving instances within the bag unlabeled. This setting is more convenient and natural for representing complicated objects, which have multiple semantic meanings. Compared to single instance labeling, this approach allows for labeling larger datasets at an equivalent labeling cost. However, for sufficiently large datasets, labeling all bags may become prohibitively costly. Active learning uses an iterative labeling and retraining approach aiming to provide reasonable classification performance using a small number of labeled samples. To our knowledge, only a few works in the area of active learning in the MIML setting are available. These approaches can provide practical solutions to reduce labeling cost but their efficacy remains unclear. In this paper, we propose a novel bag-class pair based approach for active learning in the MIML setting. Due to the partial availability of bag-level labels, we focus on the incomplete-label MIML setting for the proposed active learning approach. Our approach is based on a discriminative graphical model with efficient and exact inference. For the query process, we adapt active learning criteria to the novel bag-class pair selection strategy. Additionally, we introduce an online stochastic gradient descent algorithm to provide an efficient model update after each query. Numerical experiments on benchmark datasets illustrate the robustness of the proposed approach.", "published": "2021-07-22T17:01:28Z", "authors": ["Tam Nguyen", "Raviv Raich"], "categories": ["cs.LG"], "doi": "", "_query": "real estate valuation deep learning", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2206.14987v2", "title": "Lookback for Learning to Branch", "abstract": "The expressive and computationally inexpensive bipartite Graph Neural Networks (GNN) have been shown to be an important component of deep learning based Mixed-Integer Linear Program (MILP) solvers. Recent works have demonstrated the effectiveness of such GNNs in replacing the branching (variable selection) heuristic in branch-and-bound (B&B) solvers. These GNNs are trained, offline and on a collection of MILPs, to imitate a very good but computationally expensive branching heuristic, strong branching. Given that B&B results in a tree of sub-MILPs, we ask (a) whether there are strong dependencies exhibited by the target heuristic among the neighboring nodes of the B&B tree, and (b) if so, whether we can incorporate them in our training procedure. Specifically, we find that with the strong branching heuristic, a child node's best choice was often the parent's second-best choice. We call this the \"lookback\" phenomenon. Surprisingly, the typical branching GNN of Gasse et al. (2019) often misses this simple \"answer\". To imitate the target behavior more closely by incorporating the lookback phenomenon in GNNs, we propose two methods: (a) target smoothing for the standard cross-entropy loss function, and (b) adding a Parent-as-Target (PAT) Lookback regularizer term. Finally, we propose a model selection framework to incorporate harder-to-formulate objectives such as solving time in the final models. Through extensive experimentation on standard benchmark instances, we show that our proposal results in up to 22% decrease in the size of the B&B tree and up to 15% improvement in the solving times.", "published": "2022-06-30T02:33:32Z", "authors": ["Prateek Gupta", "Elias B. Khalil", "Didier Chetélat", "Maxime Gasse", "Yoshua Bengio", "Andrea Lodi", "M. Pawan Kumar"], "categories": ["cs.LG", "math.OC", "stat.ML"], "doi": "", "_query": "real estate valuation deep learning", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2011.07589v3", "title": "DIRL: Domain-Invariant Representation Learning for Sim-to-Real Transfer", "abstract": "Generating large-scale synthetic data in simulation is a feasible alternative to collecting/labelling real data for training vision-based deep learning models, albeit the modelling inaccuracies do not generalize to the physical world. In this paper, we present a domain-invariant representation learning (DIRL) algorithm to adapt deep models to the physical environment with a small amount of real data. Existing approaches that only mitigate the covariate shift by aligning the marginal distributions across the domains and assume the conditional distributions to be domain-invariant can lead to ambiguous transfer in real scenarios. We propose to jointly align the marginal (input domains) and the conditional (output labels) distributions to mitigate the covariate and the conditional shift across the domains with adversarial learning, and combine it with a triplet distribution loss to make the conditional distributions disjoint in the shared feature space. Experiments on digit domains yield state-of-the-art performance on challenging benchmarks, while sim-to-real transfer of object recognition for vision-based decluttering with a mobile robot improves from 26.8 % to 91.0 %, resulting in 86.5 % grasping accuracy of a wide variety of objects. Code and supplementary details are available at https://sites.google.com/view/dirl", "published": "2020-11-15T17:39:01Z", "authors": ["Ajay Kumar Tanwani"], "categories": ["cs.CV", "cs.RO"], "doi": "", "_query": "real estate valuation deep learning", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/1909.12732v4", "title": "Alleviating Privacy Attacks via Causal Learning", "abstract": "Machine learning models, especially deep neural networks have been shown to be susceptible to privacy attacks such as membership inference where an adversary can detect whether a data point was used for training a black-box model. Such privacy risks are exacerbated when a model's predictions are used on an unseen data distribution. To alleviate privacy attacks, we demonstrate the benefit of predictive models that are based on the causal relationships between input features and the outcome. We first show that models learnt using causal structure generalize better to unseen data, especially on data from different distributions than the train distribution. Based on this generalization property, we establish a theoretical link between causality and privacy: compared to associational models, causal models provide stronger differential privacy guarantees and are more robust to membership inference attacks. Experiments on simulated Bayesian networks and the colored-MNIST dataset show that associational models exhibit upto 80% attack accuracy under different test distributions and sample sizes whereas causal models exhibit attack accuracy close to a random guess.", "published": "2019-09-27T15:06:42Z", "authors": ["Shruti Tople", "Amit Sharma", "Aditya Nori"], "categories": ["cs.LG", "stat.ML"], "doi": "", "_query": "real estate valuation deep learning", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/1212.6842v1", "title": "A valuation theoretic characterization of recursively saturated real closed fields", "abstract": "We give a valuation theoretic characterization for a real closed field to be recursively saturated. Our result extends the characterization of Harnik and Ressayre \\cite{hr} for a divisible ordered abelian group to be recursively saturated.", "published": "2012-12-31T08:51:09Z", "authors": ["Paola D'Aquino", "Salma Kuhlmann", "Karen Lange"], "categories": ["math.LO", "math.AC"], "doi": "", "_query": "real estate valuation deep learning", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2002.05551v5", "title": "PACOH: Bayes-Optimal Meta-Learning with PAC-Guarantees", "abstract": "Meta-learning can successfully acquire useful inductive biases from data. Yet, its generalization properties to unseen learning tasks are poorly understood. Particularly if the number of meta-training tasks is small, this raises concerns about overfitting. We provide a theoretical analysis using the PAC-Bayesian framework and derive novel generalization bounds for meta-learning. Using these bounds, we develop a class of PAC-optimal meta-learning algorithms with performance guarantees and a principled meta-level regularization. Unlike previous PAC-Bayesian meta-learners, our method results in a standard stochastic optimization problem which can be solved efficiently and scales well. When instantiating our PAC-optimal hyper-posterior (PACOH) with Gaussian processes and Bayesian Neural Networks as base learners, the resulting methods yield state-of-the-art performance, both in terms of predictive accuracy and the quality of uncertainty estimates. Thanks to their principled treatment of uncertainty, our meta-learners can also be successfully employed for sequential decision problems.", "published": "2020-02-13T15:01:38Z", "authors": ["Jonas Rothfuss", "Vincent Fortuin", "Martin Josifoski", "Andreas Krause"], "categories": ["stat.ML", "cs.LG"], "doi": "", "_query": "real estate valuation deep learning", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2609.37740v1", "title": "HyDI: A hybrid Deep Learning-Inductive Logic Programming ensemble for multi-label classification", "abstract": "While attaining remarkable results for many applications, Deep Learning models are notoriously difficult to explain. This work introduces HyDI, a hybrid ensemble architecture for hierarchical multi-label classification. It combines a Deep Learning (DL) model with rule-based classifiers generated by Inductive Logic Programming (ILP). For leaf classes of the label hierarchy, the rule-based classifiers replace the DL model, leading to more transparent classification results. HyDI is applied to the Chemical Entities of Biological Interest (ChEBI) ontology, providing ILP-generated rules for 314 classes. For these classes, HyDI can generate global explanations as well as local explanations that combine visual and text-based descriptions.", "published": "2026-09-29T14:56:03Z", "authors": ["Simon Flügel", "Till Mossakowski"], "categories": ["cs.LG", "cs.LO"], "doi": "", "_query": "real estate valuation deep learning", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2311.18356v2", "title": "Towards Comparable Active Learning", "abstract": "Active Learning has received significant attention in the field of machine learning for its potential in selecting the most informative samples for labeling, thereby reducing data annotation costs. However, we show that the reported lifts in recent literature generalize poorly to other domains leading to an inconclusive landscape in Active Learning research. Furthermore, we highlight overlooked problems for reproducing AL experiments that can lead to unfair comparisons and increased variance in the results. This paper addresses these issues by providing an Active Learning framework for a fair comparison of algorithms across different tasks and domains, as well as a fast and performant oracle algorithm for evaluation. To the best of our knowledge, we propose the first AL benchmark that tests algorithms in 3 major domains: Tabular, Image, and Text. We report empirical results for 6 widely used algorithms on 7 real-world and 2 synthetic datasets and aggregate them into a domain-specific ranking of AL algorithms.", "published": "2023-11-30T08:54:32Z", "authors": ["Thorben Werner", "Johannes Burchert", "Lars Schmidt-Thieme"], "categories": ["cs.LG", "stat.ML"], "doi": "", "_query": "real estate valuation deep learning", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/1705.08997v1", "title": "State Space Decomposition and Subgoal Creation for Transfer in Deep Reinforcement Learning", "abstract": "Typical reinforcement learning (RL) agents learn to complete tasks specified by reward functions tailored to their domain. As such, the policies they learn do not generalize even to similar domains. To address this issue, we develop a framework through which a deep RL agent learns to generalize policies from smaller, simpler domains to more complex ones using a recurrent attention mechanism. The task is presented to the agent as an image and an instruction specifying the goal. This meta-controller guides the agent towards its goal by designing a sequence of smaller subtasks on the part of the state space within the attention, effectively decomposing it. As a baseline, we consider a setup without attention as well. Our experiments show that the meta-controller learns to create subgoals within the attention.", "published": "2017-05-24T23:19:44Z", "authors": ["Himanshu Sahni", "Saurabh Kumar", "Farhan Tejani", "Yannick Schroecker", "Charles Isbell"], "categories": ["cs.AI", "cs.LG", "stat.ML"], "doi": "", "_query": "real estate valuation deep learning", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2402.07586v4", "title": "Unveiling Group-Specific Distributed Concept Drift: A Fairness Imperative in Federated Learning", "abstract": "In the evolving field of machine learning, ensuring group fairness has become a critical concern, prompting the development of algorithms designed to mitigate bias in decision-making processes. Group fairness refers to the principle that a model's decisions should be equitable across different groups defined by sensitive attributes such as gender or race, ensuring that individuals from privileged groups and unprivileged groups are treated fairly and receive similar outcomes. However, achieving fairness in the presence of group-specific concept drift remains an unexplored frontier, and our research represents pioneering efforts in this regard. Group-specific concept drift refers to situations where one group experiences concept drift over time while another does not, leading to a decrease in fairness even if accuracy remains fairly stable. Within the framework of Federated Learning, where clients collaboratively train models, its distributed nature further amplifies these challenges since each client can experience group-specific concept drift independently while still sharing the same underlying concept, creating a complex and dynamic environment for maintaining fairness. The most significant contribution of our research is the formalization and introduction of the problem of group-specific concept drift and its distributed counterpart, shedding light on its critical importance in the field of fairness. Additionally, leveraging insights from prior research, we adapt an existing distributed concept drift adaptation algorithm to tackle group-specific distributed concept drift which uses a multi-model approach, a local group-specific drift detection mechanism, and continuous clustering of models over time. The findings from our experiments highlight the importance of addressing group-specific concept drift and its distributed counterpart to advance fairness in machine learning.", "published": "2024-02-12T11:35:25Z", "authors": ["Teresa Salazar", "João Gama", "Helder Araújo", "Pedro Henriques Abreu"], "categories": ["cs.LG"], "doi": "10.1109/TNNLS.2025.3601834", "_query": "real estate valuation deep learning", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2311.18356v2", "title": "Towards Comparable Active Learning", "abstract": "Active Learning has received significant attention in the field of machine learning for its potential in selecting the most informative samples for labeling, thereby reducing data annotation costs. However, we show that the reported lifts in recent literature generalize poorly to other domains leading to an inconclusive landscape in Active Learning research. Furthermore, we highlight overlooked problems for reproducing AL experiments that can lead to unfair comparisons and increased variance in the results. This paper addresses these issues by providing an Active Learning framework for a fair comparison of algorithms across different tasks and domains, as well as a fast and performant oracle algorithm for evaluation. To the best of our knowledge, we propose the first AL benchmark that tests algorithms in 3 major domains: Tabular, Image, and Text. We report empirical results for 6 widely used algorithms on 7 real-world and 2 synthetic datasets and aggregate them into a domain-specific ranking of AL algorithms.", "published": "2023-11-30T08:54:32Z", "authors": ["Thorben Werner", "Johannes Burchert", "Lars Schmidt-Thieme"], "categories": ["cs.LG", "stat.ML"], "doi": "", "_query": "real estate valuation deep learning", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2011.08992v1", "title": "Towards Spatial Variability Aware Deep Neural Networks (SVANN): A Summary of Results", "abstract": "Spatial variability has been observed in many geo-phenomena including climatic zones, USDA plant hardiness zones, and terrestrial habitat types (e.g., forest, grasslands, wetlands, and deserts). However, current deep learning methods follow a spatial-one-size-fits-all(OSFA) approach to train single deep neural network models that do not account for spatial variability. In this work, we propose and investigate a spatial-variability aware deep neural network(SVANN) approach, where distinct deep neural network models are built for each geographic area. We evaluate this approach using aerial imagery from two geographic areas for the task of mapping urban gardens. The experimental results show that SVANN provides better performance than OSFA in terms of precision, recall,and F1-score to identify urban gardens.", "published": "2020-11-17T22:52:40Z", "authors": ["Jayant Gupta", "Yiqun Xie", "Shashi Shekhar"], "categories": ["cs.CV", "cs.LG"], "doi": "", "_query": "real estate valuation deep learning", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2406.04897v3", "title": "From Link Prediction to Forecasting: Addressing Challenges in Batch-based Temporal Graph Learning", "abstract": "Dynamic link prediction is an important problem considered in many recent works that propose approaches for learning temporal edge patterns. To assess their efficacy, models are evaluated on continuous-time and discrete-time temporal graph datasets, typically using a traditional batch-oriented evaluation setup. However, as we show in this work, a batch-oriented evaluation is often unsuitable and can cause several issues. Grouping edges into fixed-sized batches regardless of their occurrence time leads to information loss or leakage, depending on the temporal granularity of the data. Furthermore, fixed-size batches create time windows with different durations, resulting in an inconsistent dynamic link prediction task. In this work, we empirically show how traditional batch-based evaluation leads to skewed model performance and hinders the fair comparison of methods. We mitigate this problem by reformulating dynamic link prediction as a link forecasting task that better accounts for temporal information present in the data.", "published": "2024-06-07T12:45:12Z", "authors": ["Moritz Lampert", "Christopher Blöcker", "Ingo Scholtes"], "categories": ["cs.LG"], "doi": "", "_query": "real estate valuation deep learning", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2506.14110v2", "title": "Universal rates of ERM for agnostic learning", "abstract": "The universal learning framework has been developed to obtain guarantees on the learning rates that hold for any fixed distribution, which can be much faster than the ones uniformly hold over all the distributions. Given that the Empirical Risk Minimization (ERM) principle being fundamental in the PAC theory and ubiquitous in practical machine learning, the recent work of arXiv:2412.02810 studied the universal rates of ERM for binary classification under the realizable setting. However, the assumption of realizability is too restrictive to hold in practice. Indeed, the majority of the literature on universal learning has focused on the realizable case, leaving the non-realizable case barely explored. In this paper, we consider the problem of universal learning by ERM for binary classification under the agnostic setting, where the ''learning curve\" reflects the decay of the excess risk as the sample size increases. We explore the possibilities of agnostic universal rates and reveal a compact trichotomy: there are three possible agnostic universal rates of ERM, being either $e^{-n}$, $o(n^{-1/2})$, or arbitrarily slow. We provide a complete characterization of which concept classes fall into each of these categories. Moreover, we also establish complete characterizations for the target-dependent universal rates as well as the Bayes-dependent universal rates.", "published": "2025-06-17T02:02:11Z", "authors": ["Steve Hanneke", "Mingyue Xu"], "categories": ["stat.ML", "cs.LG"], "doi": "", "_query": "real estate valuation deep learning", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2203.10494v1", "title": "MicroRacer: a didactic environment for Deep Reinforcement Learning", "abstract": "MicroRacer is a simple, open source environment inspired by car racing especially meant for the didactics of Deep Reinforcement Learning. The complexity of the environment has been explicitly calibrated to allow users to experiment with many different methods, networks and hyperparameters settings without requiring sophisticated software or the need of exceedingly long training times. Baseline agents for major learning algorithms such as DDPG, PPO, SAC, TD2 and DSAC are provided too, along with a preliminary comparison in terms of training time and performance.", "published": "2022-03-20T08:55:12Z", "authors": ["Andrea Asperti", "Marco Del Brutto"], "categories": ["cs.LG"], "doi": "", "_query": "real estate valuation deep learning", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2002.04803v2", "title": "Machine Learning in Python: Main developments and technology trends in data science, machine learning, and artificial intelligence", "abstract": "Smarter applications are making better use of the insights gleaned from data, having an impact on every industry and research discipline. At the core of this revolution lies the tools and the methods that are driving it, from processing the massive piles of data generated each day to learning from and taking useful action. Deep neural networks, along with advancements in classical ML and scalable general-purpose GPU computing, have become critical components of artificial intelligence, enabling many of these astounding breakthroughs and lowering the barrier to adoption. Python continues to be the most preferred language for scientific computing, data science, and machine learning, boosting both performance and productivity by enabling the use of low-level libraries and clean high-level APIs. This survey offers insight into the field of machine learning with Python, taking a tour through important topics to identify some of the core hardware and software paradigms that have enabled it. We cover widely-used libraries and concepts, collected together for holistic comparison, with the goal of educating the reader and driving the field of Python machine learning forward.", "published": "2020-02-12T05:20:59Z", "authors": ["Sebastian Raschka", "Joshua Patterson", "Corey Nolet"], "categories": ["cs.LG", "stat.ML"], "doi": "", "_query": "real estate valuation deep learning", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/1412.6830v3", "title": "Learning Activation Functions to Improve Deep Neural Networks", "abstract": "Artificial neural networks typically have a fixed, non-linear activation function at each neuron. We have designed a novel form of piecewise linear activation function that is learned independently for each neuron using gradient descent. With this adaptive activation function, we are able to improve upon deep neural network architectures composed of static rectified linear units, achieving state-of-the-art performance on CIFAR-10 (7.51%), CIFAR-100 (30.83%), and a benchmark from high-energy physics involving Higgs boson decay modes.", "published": "2014-12-21T20:20:21Z", "authors": ["Forest Agostinelli", "Matthew Hoffman", "Peter Sadowski", "Pierre Baldi"], "categories": ["cs.NE", "cs.CV", "cs.LG", "stat.ML"], "doi": "", "_query": "real estate valuation deep learning", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2005.09830v1", "title": "Deep Learning for LiDAR Point Clouds in Autonomous Driving: A Review", "abstract": "Recently, the advancement of deep learning in discriminative feature learning from 3D LiDAR data has led to rapid development in the field of autonomous driving. However, automated processing uneven, unstructured, noisy, and massive 3D point clouds is a challenging and tedious task. In this paper, we provide a systematic review of existing compelling deep learning architectures applied in LiDAR point clouds, detailing for specific tasks in autonomous driving such as segmentation, detection, and classification. Although several published research papers focus on specific topics in computer vision for autonomous vehicles, to date, no general survey on deep learning applied in LiDAR point clouds for autonomous vehicles exists. Thus, the goal of this paper is to narrow the gap in this topic. More than 140 key contributions in the recent five years are summarized in this survey, including the milestone 3D deep architectures, the remarkable deep learning applications in 3D semantic segmentation, object detection, and classification; specific datasets, evaluation metrics, and the state of the art performance. Finally, we conclude the remaining challenges and future researches.", "published": "2020-05-20T03:01:40Z", "authors": ["Ying Li", "Lingfei Ma", "Zilong Zhong", "Fei Liu", "Dongpu Cao", "Jonathan Li", "Michael A. Chapman"], "categories": ["cs.CV"], "doi": "", "_query": "real estate valuation deep learning", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2408.14518v3", "title": "A Survey on Reinforcement Learning Applications in SLAM", "abstract": "Simultaneous localization and mapping (SLAM) allows a mobile robot or autonomous vehicle to build a map of an unknown environment while estimating its own pose within that map. Reinforcement learning (RL), in which an agent learns a decision policy from interaction and reward, has been applied to decide how such systems move, explore, and recognize places they have visited before. This survey reviews the applications of RL in SLAM. We first distinguish passive SLAM, in which the robot's motion is not chosen by the SLAM system, from active SLAM, in which it is, and summarize the sensors that provide the input to SLAM. We then introduce the RL methods used in this literature, from value-based and policy-based methods to actor-critic and deep RL. Next, we classify RL applications in SLAM into three categories: path planning, including environment exploration and obstacle avoidance; loop closure detection; and active SLAM. Thirteen representative studies are compared in terms of their simulation environment, deep learning method, SLAM method, and RL algorithm. Most of these studies are evaluated mainly in simulation, and value-based methods from the deep Q-network family are the most common. Finally, we discuss the challenges of applying RL to SLAM, namely computational demands, safety, generalization, high-dimensional state and action spaces, sample efficiency, and sensor and actuator delays, and we outline directions for future research.", "published": "2024-08-26T00:13:14Z", "authors": ["Mohammad Dehghani Tezerjani", "Mohammad Khoshnazar", "Mohammadhamed Tangestanizadeh", "Arman Kiani", "Qing Yang"], "categories": ["cs.RO", "cs.LG"], "doi": "10.64820/AEPJMLDL.11.20.31.122024", "_query": "real estate valuation deep learning", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/1807.09341v1", "title": "Learning Plannable Representations with Causal InfoGAN", "abstract": "In recent years, deep generative models have been shown to 'imagine' convincing high-dimensional observations such as images, audio, and even video, learning directly from raw data. In this work, we ask how to imagine goal-directed visual plans -- a plausible sequence of observations that transition a dynamical system from its current configuration to a desired goal state, which can later be used as a reference trajectory for control. We focus on systems with high-dimensional observations, such as images, and propose an approach that naturally combines representation learning and planning. Our framework learns a generative model of sequential observations, where the generative process is induced by a transition in a low-dimensional planning model, and an additional noise. By maximizing the mutual information between the generated observations and the transition in the planning model, we obtain a low-dimensional representation that best explains the causal nature of the data. We structure the planning model to be compatible with efficient planning algorithms, and we propose several such models based on either discrete or continuous states. Finally, to generate a visual plan, we project the current and goal observations onto their respective states in the planning model, plan a trajectory, and then use the generative model to transform the trajectory to a sequence of observations. We demonstrate our method on imagining plausible visual plans of rope manipulation.", "published": "2018-07-24T20:46:05Z", "authors": ["Thanard Kurutach", "Aviv Tamar", "Ge Yang", "Stuart Russell", "Pieter Abbeel"], "categories": ["cs.LG", "cs.AI", "cs.CV", "cs.NE", "cs.RO", "stat.ML"], "doi": "", "_query": "real estate valuation deep learning", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2602.07370v1", "title": "Privately Learning Decision Lists and a Differentially Private Winnow", "abstract": "We give new differentially private algorithms for the classic problems of learning decision lists and large-margin halfspaces in the PAC and online models. In the PAC model, we give a computationally efficient algorithm for learning decision lists with minimal sample overhead over the best non-private algorithms. In the online model, we give a private analog of the influential Winnow algorithm for learning halfspaces with mistake bound polylogarithmic in the dimension and inverse polynomial in the margin. As an application, we describe how to privately learn decision lists in the online model, qualitatively matching state-of-the art non-private guarantees.", "published": "2026-02-07T05:30:09Z", "authors": ["Mark Bun", "William Fang"], "categories": ["cs.LG", "cs.CR", "stat.ML"], "doi": "", "_query": "real estate valuation deep learning", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/1803.11485v2", "title": "QMIX: Monotonic Value Function Factorisation for Deep Multi-Agent Reinforcement Learning", "abstract": "In many real-world settings, a team of agents must coordinate their behaviour while acting in a decentralised way. At the same time, it is often possible to train the agents in a centralised fashion in a simulated or laboratory setting, where global state information is available and communication constraints are lifted. Learning joint action-values conditioned on extra state information is an attractive way to exploit centralised learning, but the best strategy for then extracting decentralised policies is unclear. Our solution is QMIX, a novel value-based method that can train decentralised policies in a centralised end-to-end fashion. QMIX employs a network that estimates joint action-values as a complex non-linear combination of per-agent values that condition only on local observations. We structurally enforce that the joint-action value is monotonic in the per-agent values, which allows tractable maximisation of the joint action-value in off-policy learning, and guarantees consistency between the centralised and decentralised policies. We evaluate QMIX on a challenging set of StarCraft II micromanagement tasks, and show that QMIX significantly outperforms existing value-based multi-agent reinforcement learning methods.", "published": "2018-03-30T14:23:39Z", "authors": ["Tabish Rashid", "Mikayel Samvelyan", "Christian Schroeder de Witt", "Gregory Farquhar", "Jakob Foerster", "Shimon Whiteson"], "categories": ["cs.LG", "cs.MA", "stat.ML"], "doi": "", "_query": "real estate valuation deep learning", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/1610.03518v1", "title": "Transfer from Simulation to Real World through Learning Deep Inverse Dynamics Model", "abstract": "Developing control policies in simulation is often more practical and safer than directly running experiments in the real world. This applies to policies obtained from planning and optimization, and even more so to policies obtained from reinforcement learning, which is often very data demanding. However, a policy that succeeds in simulation often doesn't work when deployed on a real robot. Nevertheless, often the overall gist of what the policy does in simulation remains valid in the real world. In this paper we investigate such settings, where the sequence of states traversed in simulation remains reasonable for the real world, even if the details of the controls are not, as could be the case when the key differences lie in detailed friction, contact, mass and geometry properties. During execution, at each time step our approach computes what the simulation-based control policy would do, but then, rather than executing these controls on the real robot, our approach computes what the simulation expects the resulting next state(s) will be, and then relies on a learned deep inverse dynamics model to decide which real-world action is most suitable to achieve those next states. Deep models are only as good as their training data, and we also propose an approach for data collection to (incrementally) learn the deep inverse dynamics model. Our experiments shows our approach compares favorably with various baselines that have been developed for dealing with simulation to real world model discrepancy, including output error control and Gaussian dynamics adaptation.", "published": "2016-10-11T20:24:31Z", "authors": ["Paul Christiano", "Zain Shah", "Igor Mordatch", "Jonas Schneider", "Trevor Blackwell", "Joshua Tobin", "Pieter Abbeel", "Wojciech Zaremba"], "categories": ["cs.RO", "cs.AI", "cs.LG", "eess.SY"], "doi": "", "_query": "real estate valuation deep learning", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/1906.07248v1", "title": "Iterative Model-Based Reinforcement Learning Using Simulations in the Differentiable Neural Computer", "abstract": "We propose a lifelong learning architecture, the Neural Computer Agent (NCA), where a Reinforcement Learning agent is paired with a predictive model of the environment learned by a Differentiable Neural Computer (DNC). The agent and DNC model are trained in conjunction iteratively. The agent improves its policy in simulations generated by the DNC model and rolls out the policy to the live environment, collecting experiences in new portions or tasks of the environment for further learning. Experiments in two synthetic environments show that DNC models can continually learn from pixels alone to simulate new tasks as they are encountered by the agent, while the agents can be successfully trained to solve the tasks using Proximal Policy Optimization entirely in simulations.", "published": "2019-06-17T20:15:02Z", "authors": ["Adeel Mufti", "Svetlin Penkov", "Subramanian Ramamoorthy"], "categories": ["cs.LG", "stat.ML"], "doi": "", "_query": "real estate valuation deep learning", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2502.15962v2", "title": "Towards Efficient Contrastive PAC Learning", "abstract": "We study contrastive learning under the PAC learning framework. While a series of recent works have shown statistical results for learning under contrastive loss, based either on the VC-dimension or Rademacher complexity, their algorithms are inherently inefficient or not implying PAC guarantees. In this paper, we consider contrastive learning of the fundamental concept of linear representations. Surprisingly, even under such basic setting, the existence of efficient PAC learners is largely open. We first show that the problem of contrastive PAC learning of linear representations is intractable to solve in general. We then show that it can be relaxed to a semi-definite program when the distance between contrastive samples is measured by the $\\ell_2$-norm. We then establish generalization guarantees based on Rademacher complexity, and connect it to PAC guarantees under certain contrastive large-margin conditions. To the best of our knowledge, this is the first efficient PAC learning algorithm for contrastive learning.", "published": "2025-02-21T21:51:01Z", "authors": ["Jie Shen"], "categories": ["cs.LG", "cs.DS", "stat.ML"], "doi": "", "_query": "real estate valuation deep learning", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/1902.09432v3", "title": "Scalable and Order-robust Continual Learning with Additive Parameter Decomposition", "abstract": "While recent continual learning methods largely alleviate the catastrophic problem on toy-sized datasets, some issues remain to be tackled to apply them to real-world problem domains. First, a continual learning model should effectively handle catastrophic forgetting and be efficient to train even with a large number of tasks. Secondly, it needs to tackle the problem of order-sensitivity, where the performance of the tasks largely varies based on the order of the task arrival sequence, as it may cause serious problems where fairness plays a critical role (e.g. medical diagnosis). To tackle these practical challenges, we propose a novel continual learning method that is scalable as well as order-robust, which instead of learning a completely shared set of weights, represents the parameters for each task as a sum of task-shared and sparse task-adaptive parameters. With our Additive Parameter Decomposition (APD), the task-adaptive parameters for earlier tasks remain mostly unaffected, where we update them only to reflect the changes made to the task-shared parameters. This decomposition of parameters effectively prevents catastrophic forgetting and order-sensitivity, while being computation- and memory-efficient. Further, we can achieve even better scalability with APD using hierarchical knowledge consolidation, which clusters the task-adaptive parameters to obtain hierarchically shared parameters. We validate our network with APD, APD-Net, on multiple benchmark datasets against state-of-the-art continual learning methods, which it largely outperforms in accuracy, scalability, and order-robustness.", "published": "2019-02-25T16:49:52Z", "authors": ["Jaehong Yoon", "Saehoon Kim", "Eunho Yang", "Sung Ju Hwang"], "categories": ["cs.LG", "stat.ML"], "doi": "", "_query": "real estate valuation deep learning", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/1406.2721v1", "title": "Learning Latent Variable Gaussian Graphical Models", "abstract": "Gaussian graphical models (GGM) have been widely used in many high-dimensional applications ranging from biological and financial data to recommender systems. Sparsity in GGM plays a central role both statistically and computationally. Unfortunately, real-world data often does not fit well to sparse graphical models. In this paper, we focus on a family of latent variable Gaussian graphical models (LVGGM), where the model is conditionally sparse given latent variables, but marginally non-sparse. In LVGGM, the inverse covariance matrix has a low-rank plus sparse structure, and can be learned in a regularized maximum likelihood framework. We derive novel parameter estimation error bounds for LVGGM under mild conditions in the high-dimensional setting. These results complement the existing theory on the structural learning, and open up new possibilities of using LVGGM for statistical inference.", "published": "2014-06-10T21:03:22Z", "authors": ["Zhaoshi Meng", "Brian Eriksson", "Alfred O. Hero"], "categories": ["stat.ML", "cs.LG", "math.ST"], "doi": "", "_query": "real estate valuation deep learning", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/1906.03574v1", "title": "Transfer Learning by Modeling a Distribution over Policies", "abstract": "Exploration and adaptation to new tasks in a transfer learning setup is a central challenge in reinforcement learning. In this work, we build on the idea of modeling a distribution over policies in a Bayesian deep reinforcement learning setup to propose a transfer strategy. Recent works have shown to induce diversity in the learned policies by maximizing the entropy of a distribution of policies (Bachman et al., 2018; Garnelo et al., 2018) and thus, we postulate that our proposed approach leads to faster exploration resulting in improved transfer learning. We support our hypothesis by demonstrating favorable experimental results on a variety of settings on fully-observable GridWorld and partially observable MiniGrid (Chevalier-Boisvert et al., 2018) environments.", "published": "2019-06-09T06:00:50Z", "authors": ["Disha Shrivastava", "Eeshan Gunesh Dhekane", "Riashat Islam"], "categories": ["cs.LG", "cs.AI", "stat.ML"], "doi": "", "_query": "real estate valuation deep learning", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2105.07346v1", "title": "Understanding the Effect of Bias in Deep Anomaly Detection", "abstract": "Anomaly detection presents a unique challenge in machine learning, due to the scarcity of labeled anomaly data. Recent work attempts to mitigate such problems by augmenting training of deep anomaly detection models with additional labeled anomaly samples. However, the labeled data often does not align with the target distribution and introduces harmful bias to the trained model. In this paper, we aim to understand the effect of a biased anomaly set on anomaly detection. Concretely, we view anomaly detection as a supervised learning task where the objective is to optimize the recall at a given false positive rate. We formally study the relative scoring bias of an anomaly detector, defined as the difference in performance with respect to a baseline anomaly detector. We establish the first finite sample rates for estimating the relative scoring bias for deep anomaly detection, and empirically validate our theoretical results on both synthetic and real-world datasets. We also provide an extensive empirical study on how a biased training anomaly set affects the anomaly score function and therefore the detection performance on different anomaly classes. Our study demonstrates scenarios in which the biased anomaly set can be useful or problematic, and provides a solid benchmark for future research.", "published": "2021-05-16T03:55:02Z", "authors": ["Ziyu Ye", "Yuxin Chen", "Haitao Zheng"], "categories": ["cs.LG", "cs.AI"], "doi": "", "_query": "real estate valuation deep learning", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/1807.05464v3", "title": "Tractable Querying and Learning in Hybrid Domains via Sum-Product Networks", "abstract": "Probabilistic representations, such as Bayesian and Markov networks, are fundamental to much of statistical machine learning. Thus, learning probabilistic representations directly from data is a deep challenge, the main computational bottleneck being inference that is intractable. Tractable learning is a powerful new paradigm that attempts to learn distributions that support efficient probabilistic querying. By leveraging local structure, representations such as sum-product networks (SPNs) can capture high tree-width models with many hidden layers, essentially a deep architecture, while still admitting a range of probabilistic queries to be computable in time polynomial in the network size. The leaf nodes in SPNs, from which more intricate mixtures are formed, are tractable univariate distributions, and so the literature has focused on Bernoulli and Gaussian random variables. This is clearly a restriction for handling mixed discrete-continuous data, especially if the continuous features are generated from non-parametric and non-Gaussian distribution families. In this work, we present a framework that systematically integrates SPN structure learning with weighted model integration, a recently introduced computational abstraction for performing inference in hybrid domains, by means of piecewise polynomial approximations of density functions of arbitrary shape. Our framework is instantiated by exploiting the notion of propositional abstractions, thus minimally interfering with the SPN structure learning module, and supports a powerful query interface for conditioning on interval constraints. Our empirical results show that our approach is effective, and allows a study of the trade off between the granularity of the learned model and its predictive power.", "published": "2018-07-14T23:25:16Z", "authors": ["Andreas Bueff", "Stefanie Speichert", "Vaishak Belle"], "categories": ["cs.LG", "cs.AI", "stat.ML"], "doi": "", "_query": "real estate valuation deep learning", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/1906.06706v7", "title": "Interpretations of Deep Learning by Forests and Haar Wavelets", "abstract": "This paper presents a basic property of region dividing of ReLU (rectified linear unit) deep learning when new layers are successively added, by which two new perspectives of interpreting deep learning are given. The first is related to decision trees and forests; we construct a deep learning structure equivalent to a forest in classification abilities, which means that certain kinds of ReLU deep learning can be considered as forests. The second perspective is that Haar wavelet represented functions can be approximated by ReLU deep learning with arbitrary precision; and then a general conclusion of function approximation abilities of ReLU deep learning is given. Finally, generalize some of the conclusions of ReLU deep learning to the case of sigmoid-unit deep learning.", "published": "2019-06-16T14:38:41Z", "authors": ["Changcun Huang"], "categories": ["cs.LG", "math.NA", "stat.ML"], "doi": "", "_query": "real estate valuation deep learning", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/1709.00845v1", "title": "Semi-supervised Learning with Deep Generative Models for Asset Failure Prediction", "abstract": "This work presents a novel semi-supervised learning approach for data-driven modeling of asset failures when health status is only partially known in historical data. We combine a generative model parameterized by deep neural networks with non-linear embedding technique. It allows us to build prognostic models with the limited amount of health status information for the precise prediction of future asset reliability. The proposed method is evaluated on a publicly available dataset for remaining useful life (RUL) estimation, which shows significant improvement even when a fraction of the data with known health status is as sparse as 1% of the total. Our study suggests that the non-linear embedding based on a deep generative model can efficiently regularize a complex model with deep architectures while achieving high prediction accuracy that is far less sensitive to the availability of health status information.", "published": "2017-09-04T07:42:57Z", "authors": ["Andre S. Yoon", "Taehoon Lee", "Yongsub Lim", "Deokwoo Jung", "Philgyun Kang", "Dongwon Kim", "Keuntae Park", "Yongjin Choi"], "categories": ["cs.LG"], "doi": "", "_query": "real estate valuation deep learning", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2206.10034v2", "title": "Deep Learning Models on CPUs: A Methodology for Efficient Training", "abstract": "GPUs have been favored for training deep learning models due to their highly parallelized architecture. As a result, most studies on training optimization focus on GPUs. There is often a trade-off, however, between cost and efficiency when deciding on how to choose the proper hardware for training. In particular, CPU servers can be beneficial if training on CPUs was more efficient, as they incur fewer hardware update costs and better utilizing existing infrastructure. This paper makes several contributions to research on training deep learning models using CPUs. First, it presents a method for optimizing the training of deep learning models on Intel CPUs and a toolkit called ProfileDNN, which we developed to improve performance profiling. Second, we describe a generic training optimization method that guides our workflow and explores several case studies where we identified performance issues and then optimized the Intel Extension for PyTorch, resulting in an overall 2x training performance increase for the RetinaNet-ResNext50 model. Third, we show how to leverage the visualization capabilities of ProfileDNN, which enabled us to pinpoint bottlenecks and create a custom focal loss kernel that was two times faster than the official reference PyTorch implementation.", "published": "2022-06-20T22:42:14Z", "authors": ["Quchen Fu", "Ramesh Chukka", "Keith Achorn", "Thomas Atta-fosu", "Deepak R. Canchi", "Zhongwei Teng", "Jules White", "Douglas C. Schmidt"], "categories": ["cs.LG", "cs.AI"], "doi": "10.13052/jmltapissn.2022.003", "_query": "real estate valuation deep learning", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/1711.10658v4", "title": "Deep-Person: Learning Discriminative Deep Features for Person Re-Identification", "abstract": "Recently, many methods of person re-identification (Re-ID) rely on part-based feature representation to learn a discriminative pedestrian descriptor. However, the spatial context between these parts is ignored for the independent extractor to each separate part. In this paper, we propose to apply Long Short-Term Memory (LSTM) in an end-to-end way to model the pedestrian, seen as a sequence of body parts from head to foot. Integrating the contextual information strengthens the discriminative ability of local representation. We also leverage the complementary information between local and global feature. Furthermore, we integrate both identification task and ranking task in one network, where a discriminative embedding and a similarity measurement are learned concurrently. This results in a novel three-branch framework named Deep-Person, which learns highly discriminative features for person Re-ID. Experimental results demonstrate that Deep-Person outperforms the state-of-the-art methods by a large margin on three challenging datasets including Market-1501, CUHK03, and DukeMTMC-reID. Specifically, combining with a re-ranking approach, we achieve a 90.84% mAP on Market-1501 under single query setting.", "published": "2017-11-29T03:15:07Z", "authors": ["Xiang Bai", "Mingkun Yang", "Tengteng Huang", "Zhiyong Dou", "Rui Yu", "Yongchao Xu"], "categories": ["cs.CV"], "doi": "", "_query": "real estate valuation deep learning", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/1805.08355v1", "title": "Opening the black box of deep learning", "abstract": "The great success of deep learning shows that its technology contains profound truth, and understanding its internal mechanism not only has important implications for the development of its technology and effective application in various fields, but also provides meaningful insights into the understanding of human brain mechanism. At present, most of the theoretical research on deep learning is based on mathematics. This dissertation proposes that the neural network of deep learning is a physical system, examines deep learning from three different perspectives: microscopic, macroscopic, and physical world views, answers multiple theoretical puzzles in deep learning by using physics principles. For example, from the perspective of quantum mechanics and statistical physics, this dissertation presents the calculation methods for convolution calculation, pooling, normalization, and Restricted Boltzmann Machine, as well as the selection of cost functions, explains why deep learning must be deep, what characteristics are learned in deep learning, why Convolutional Neural Networks do not have to be trained layer by layer, and the limitations of deep learning, etc., and proposes the theoretical direction and basis for the further development of deep learning now and in the future. The brilliance of physics flashes in deep learning, we try to establish the deep learning technology based on the scientific theory of physics.", "published": "2018-05-22T02:12:33Z", "authors": ["Dian Lei", "Xiaoxiao Chen", "Jianfei Zhao"], "categories": ["cs.LG", "stat.ML"], "doi": "", "_query": "real estate valuation deep learning", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2005.08126v1", "title": "Inferring astrophysical X-ray polarization with deep learning", "abstract": "We investigate the use of deep learning in the context of X-ray polarization detection from astrophysical sources as will be observed by the Imaging X-ray Polarimetry Explorer (IXPE), a future NASA selected space-based mission expected to be operative in 2021. In particular, we propose two models that can be used to estimate the impact point as well as the polarization direction of the incoming radiation. The results obtained show that data-driven approaches depict a promising alternative to the existing analytical approaches. We also discuss problems and challenges to be addressed in the near future.", "published": "2020-05-16T23:19:07Z", "authors": ["Nikita Moriakov", "Ashwin Samudre", "Michela Negro", "Fabian Gieseke", "Sydney Otten", "Luc Hendriks"], "categories": ["astro-ph.IM", "astro-ph.HE", "cs.LG"], "doi": "", "_query": "real estate valuation deep learning", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2006.15408v1", "title": "Learning Optimal Tree Models Under Beam Search", "abstract": "Retrieving relevant targets from an extremely large target set under computational limits is a common challenge for information retrieval and recommendation systems. Tree models, which formulate targets as leaves of a tree with trainable node-wise scorers, have attracted a lot of interests in tackling this challenge due to their logarithmic computational complexity in both training and testing. Tree-based deep models (TDMs) and probabilistic label trees (PLTs) are two representative kinds of them. Though achieving many practical successes, existing tree models suffer from the training-testing discrepancy, where the retrieval performance deterioration caused by beam search in testing is not considered in training. This leads to an intrinsic gap between the most relevant targets and those retrieved by beam search with even the optimally trained node-wise scorers. We take a first step towards understanding and analyzing this problem theoretically, and develop the concept of Bayes optimality under beam search and calibration under beam search as general analyzing tools for this purpose. Moreover, to eliminate the discrepancy, we propose a novel algorithm for learning optimal tree models under beam search. Experiments on both synthetic and real data verify the rationality of our theoretical analysis and demonstrate the superiority of our algorithm compared to state-of-the-art methods.", "published": "2020-06-27T17:20:04Z", "authors": ["Jingwei Zhuo", "Ziru Xu", "Wei Dai", "Han Zhu", "Han Li", "Jian Xu", "Kun Gai"], "categories": ["stat.ML", "cs.LG"], "doi": "", "_query": "real estate valuation deep learning", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2212.04458v2", "title": "General-Purpose In-Context Learning by Meta-Learning Transformers", "abstract": "Modern machine learning requires system designers to specify aspects of the learning pipeline, such as losses, architectures, and optimizers. Meta-learning, or learning-to-learn, instead aims to learn those aspects, and promises to unlock greater capabilities with less manual effort. One particularly ambitious goal of meta-learning is to train general-purpose in-context learning algorithms from scratch, using only black-box models with minimal inductive bias. Such a model takes in training data, and produces test-set predictions across a wide range of problems, without any explicit definition of an inference model, training loss, or optimization algorithm. In this paper we show that Transformers and other black-box models can be meta-trained to act as general-purpose in-context learners. We characterize transitions between algorithms that generalize, algorithms that memorize, and algorithms that fail to meta-train at all, induced by changes in model size, number of tasks, and meta-optimization. We further show that the capabilities of meta-trained algorithms are bottlenecked by the accessible state size (memory) determining the next prediction, unlike standard models which are thought to be bottlenecked by parameter count. Finally, we propose practical interventions such as biasing the training distribution that improve the meta-training and meta-generalization of general-purpose in-context learning algorithms.", "published": "2022-12-08T18:30:22Z", "authors": ["Louis Kirsch", "James Harrison", "Jascha Sohl-Dickstein", "Luke Metz"], "categories": ["cs.LG", "cs.AI", "cs.NE", "stat.ML"], "doi": "", "_query": "real estate valuation deep learning", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/1206.6418v1", "title": "Learning Invariant Representations with Local Transformations", "abstract": "Learning invariant representations is an important problem in machine learning and pattern recognition. In this paper, we present a novel framework of transformation-invariant feature learning by incorporating linear transformations into the feature learning algorithms. For example, we present the transformation-invariant restricted Boltzmann machine that compactly represents data by its weights and their transformations, which achieves invariance of the feature representation via probabilistic max pooling. In addition, we show that our transformation-invariant feature learning framework can also be extended to other unsupervised learning methods, such as autoencoders or sparse coding. We evaluate our method on several image classification benchmark datasets, such as MNIST variations, CIFAR-10, and STL-10, and show competitive or superior classification performance when compared to the state-of-the-art. Furthermore, our method achieves state-of-the-art performance on phone classification tasks with the TIMIT dataset, which demonstrates wide applicability of our proposed algorithms to other domains.", "published": "2012-06-27T19:59:59Z", "authors": ["Kihyuk Sohn", "Honglak Lee"], "categories": ["cs.LG", "cs.CV", "stat.ML"], "doi": "", "_query": "real estate valuation deep learning", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/1802.03358v1", "title": "Deep Learning for Malicious Flow Detection", "abstract": "Cyber security has grown up to be a hot issue in recent years. How to identify potential malware becomes a challenging task. To tackle this challenge, we adopt deep learning approaches and perform flow detection on real data. However, real data often encounters an issue of imbalanced data distribution which will lead to a gradient dilution issue. When training a neural network, this problem will not only result in a bias toward the majority class but show the inability to learn from the minority classes. In this paper, we propose an end-to-end trainable Tree-Shaped Deep Neural Network (TSDNN) which classifies the data in a layer-wise manner. To better learn from the minority classes, we propose a Quantity Dependent Backpropagation (QDBP) algorithm which incorporates the knowledge of the disparity between classes. We evaluate our method on an imbalanced data set. Experimental result demonstrates that our approach outperforms the state-of-the-art methods and justifies that the proposed method is able to overcome the difficulty of imbalanced learning. We also conduct a partial flow experiment which shows the feasibility of real-time detection and a zero-shot learning experiment which justifies the generalization capability of deep learning in cyber security.", "published": "2018-02-09T17:16:02Z", "authors": ["Yun-Chun Chen", "Yu-Jhe Li", "Aragorn Tseng", "Tsungnan Lin"], "categories": ["cs.LG", "cs.CR", "stat.ML"], "doi": "", "_query": "real estate valuation deep learning", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/cond-mat/9801297v1", "title": "Statistical Mechanics of Online Learning of Drifting Concepts : A Variational Approach", "abstract": "We review the application of Statistical Mechanics methods to the study of online learning of a drifting concept in the limit of large systems. The model where a feed-forward network learns from examples generated by a time dependent teacher of the same architecture is analyzed. The best possible generalization ability is determined exactly, through the use of a variational method. The constructive variational method also suggests a learning algorithm. It depends, however, on some unavailable quantities, such as the present performance of the student. The construction of estimators for these quantities permits the implementation of a very effective, highly adaptive algorithm. Several other algorithms are also studied for comparison with the optimal bound and the adaptive algorithm, for different types of time evolution of the rule.", "published": "1998-01-28T16:30:40Z", "authors": ["Renato Vicente", "Osame Kinouchi", "Nestor Caticha"], "categories": ["cond-mat.dis-nn", "cond-mat.stat-mech"], "doi": "", "_query": "real estate valuation deep learning", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2008.02852v1", "title": "Learning Insulin-Glucose Dynamics in the Wild", "abstract": "We develop a new model of insulin-glucose dynamics for forecasting blood glucose in type 1 diabetics. We augment an existing biomedical model by introducing time-varying dynamics driven by a machine learning sequence model. Our model maintains a physiologically plausible inductive bias and clinically interpretable parameters -- e.g., insulin sensitivity -- while inheriting the flexibility of modern pattern recognition algorithms. Critical to modeling success are the flexible, but structured representations of subject variability with a sequence model. In contrast, less constrained models like the LSTM fail to provide reliable or physiologically plausible forecasts. We conduct an extensive empirical study. We show that allowing biomedical model dynamics to vary in time improves forecasting at long time horizons, up to six hours, and produces forecasts consistent with the physiological effects of insulin and carbohydrates.", "published": "2020-08-06T19:47:00Z", "authors": ["Andrew C. Miller", "Nicholas J. Foti", "Emily Fox"], "categories": ["stat.ML", "cs.LG", "stat.AP"], "doi": "", "_query": "real estate valuation deep learning", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2308.01649v1", "title": "MARLIM: Multi-Agent Reinforcement Learning for Inventory Management", "abstract": "Maintaining a balance between the supply and demand of products by optimizing replenishment decisions is one of the most important challenges in the supply chain industry. This paper presents a novel reinforcement learning framework called MARLIM, to address the inventory management problem for a single-echelon multi-products supply chain with stochastic demands and lead-times. Within this context, controllers are developed through single or multiple agents in a cooperative setting. Numerical experiments on real data demonstrate the benefits of reinforcement learning methods over traditional baselines.", "published": "2023-08-03T09:31:45Z", "authors": ["Rémi Leluc", "Elie Kadoche", "Antoine Bertoncello", "Sébastien Gourvénec"], "categories": ["cs.LG", "cs.AI", "cs.MA"], "doi": "", "_query": "real estate valuation deep learning", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2006.14078v2", "title": "Machine learning the real discriminant locus", "abstract": "Parameterized systems of polynomial equations arise in many applications in science and engineering with the real solutions describing, for example, equilibria of a dynamical system, linkages satisfying design constraints, and scene reconstruction in computer vision. Since different parameter values can have a different number of real solutions, the parameter space is decomposed into regions whose boundary forms the real discriminant locus. This article views locating the real discriminant locus as a supervised classification problem in machine learning where the goal is to determine classification boundaries over the parameter space, with the classes being the number of real solutions. For multidimensional parameter spaces, this article presents a novel sampling method which carefully samples the parameter space. At each sample point, homotopy continuation is used to obtain the number of real solutions to the corresponding polynomial system. Machine learning techniques including nearest neighbor and deep learning are used to efficiently approximate the real discriminant locus. One application of having learned the real discriminant locus is to develop a real homotopy method that only tracks the real solution paths unlike traditional methods which track all~complex~solution~paths. Examples show that the proposed approach can efficiently approximate complicated solution boundaries such as those arising from the equilibria of the Kuramoto model.", "published": "2020-06-24T22:18:08Z", "authors": ["Edgar A. Bernal", "Jonathan D. Hauenstein", "Dhagash Mehta", "Margaret H. Regan", "Tingting Tang"], "categories": ["stat.ML", "cs.LG", "cs.SC", "math.AG", "stat.AP"], "doi": "", "_query": "real estate valuation deep learning", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2310.03848v1", "title": "OpenIncrement: A Unified Framework for Open Set Recognition and Deep Class-Incremental Learning", "abstract": "In most works on deep incremental learning research, it is assumed that novel samples are pre-identified for neural network retraining. However, practical deep classifiers often misidentify these samples, leading to erroneous predictions. Such misclassifications can degrade model performance. Techniques like open set recognition offer a means to detect these novel samples, representing a significant area in the machine learning domain. In this paper, we introduce a deep class-incremental learning framework integrated with open set recognition. Our approach refines class-incrementally learned features to adapt them for distance-based open set recognition. Experimental results validate that our method outperforms state-of-the-art incremental learning techniques and exhibits superior performance in open set recognition compared to baseline methods.", "published": "2023-10-05T19:08:08Z", "authors": ["Jiawen Xu", "Claas Grohnfeldt", "Odej Kao"], "categories": ["cs.CV", "cs.LG"], "doi": "", "_query": "real estate valuation deep learning", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2007.06918v1", "title": "Lifelong Learning using Eigentasks: Task Separation, Skill Acquisition, and Selective Transfer", "abstract": "We introduce the eigentask framework for lifelong learning. An eigentask is a pairing of a skill that solves a set of related tasks, paired with a generative model that can sample from the skill's input space. The framework extends generative replay approaches, which have mainly been used to avoid catastrophic forgetting, to also address other lifelong learning goals such as forward knowledge transfer. We propose a wake-sleep cycle of alternating task learning and knowledge consolidation for learning in our framework, and instantiate it for lifelong supervised learning and lifelong RL. We achieve improved performance over the state-of-the-art in supervised continual learning, and show evidence of forward knowledge transfer in a lifelong RL application in the game Starcraft2.", "published": "2020-07-14T09:06:13Z", "authors": ["Aswin Raghavan", "Jesse Hostetler", "Indranil Sur", "Abrar Rahman", "Ajay Divakaran"], "categories": ["cs.LG", "cs.AI", "cs.CV", "stat.ML"], "doi": "", "_query": "real estate valuation deep learning", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/1909.06434v1", "title": "Adaptive Scheduling for Multi-Task Learning", "abstract": "To train neural machine translation models simultaneously on multiple tasks (languages), it is common to sample each task uniformly or in proportion to dataset sizes. As these methods offer little control over performance trade-offs, we explore different task scheduling approaches. We first consider existing non-adaptive techniques, then move on to adaptive schedules that over-sample tasks with poorer results compared to their respective baseline. As explicit schedules can be inefficient, especially if one task is highly over-sampled, we also consider implicit schedules, learning to scale learning rates or gradients of individual tasks instead. These techniques allow training multilingual models that perform better for low-resource language pairs (tasks with small amount of data), while minimizing negative effects on high-resource tasks.", "published": "2019-09-13T20:23:40Z", "authors": ["Sébastien Jean", "Orhan Firat", "Melvin Johnson"], "categories": ["cs.LG", "cs.CL", "stat.ML"], "doi": "", "_query": "real estate valuation deep learning", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2003.05996v2", "title": "Meta-Learning GNN Initializations for Low-Resource Molecular Property Prediction", "abstract": "Building in silico models to predict chemical properties and activities is a crucial step in drug discovery. However, limited labeled data often hinders the application of deep learning in this setting. Meanwhile advances in meta-learning have enabled state-of-the-art performances in few-shot learning benchmarks, naturally prompting the question: Can meta-learning improve deep learning performance in low-resource drug discovery projects? In this work, we assess the transferability of graph neural networks initializations learned by the Model-Agnostic Meta-Learning (MAML) algorithm - and its variants FO-MAML and ANIL - for chemical properties and activities tasks. Using the ChEMBL20 dataset to emulate low-resource settings, our benchmark shows that meta-initializations perform comparably to or outperform multi-task pre-training baselines on 16 out of 20 in-distribution tasks and on all out-of-distribution tasks, providing an average improvement in AUPRC of 11.2% and 26.9% respectively. Finally, we observe that meta-initializations consistently result in the best performing models across fine-tuning sets with $k \\in \\{16, 32, 64, 128, 256\\}$ instances.", "published": "2020-03-12T19:49:57Z", "authors": ["Cuong Q. Nguyen", "Constantine Kreatsoulas", "Kim M. Branson"], "categories": ["cs.LG", "physics.chem-ph", "stat.ML"], "doi": "", "_query": "real estate valuation deep learning", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2605.26509v1", "title": "SIKA-GP: Accelerating Gaussian Process Inference with Sparse Inducing Kernel Approximations for Bayesian Deep Learning", "abstract": "Gaussian processes (GPs) provide a principled Bayesian framework for uncertainty estimation, but their computational complexity severely limits scalability to large datasets. We propose SIKA-GP, which accelerates GP inference using sparse inducing kernel approximations based on a dyadic ordered template basis, incurring only ${O}(\\log M)$ complexity dependence on the number of inducing points. Our approach constructs compact and expressive kernel representations from sparsely activated bases, enabling efficient tensorized GPU computation and seamless integration with modern large-scale models. SIKA-GP can be naturally embedded into Bayesian neural networks (BNNs) with sparse activations, yielding significant speedups in both training and inference without sacrificing predictive performance. The method naturally extends to deep feature learning, addressing the scalability challenges introduced by deep architectures and high-dimensional feature representations. Empirical results on vision and transformer-based language benchmarks demonstrate that our approach consistently delivers fast and accurate GP models, providing a principled path toward scalable kernel learning.", "published": "2026-05-26T03:49:13Z", "authors": ["Wenyuan Zhao", "Rui Tuo", "Chao Tian"], "categories": ["cs.LG", "math.PR", "stat.CO"], "doi": "", "_query": "real estate valuation deep learning", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2104.10527v1", "title": "Stateless Neural Meta-Learning using Second-Order Gradients", "abstract": "Deep learning typically requires large data sets and much compute power for each new problem that is learned. Meta-learning can be used to learn a good prior that facilitates quick learning, thereby relaxing these requirements so that new tasks can be learned quicker; two popular approaches are MAML and the meta-learner LSTM. In this work, we compare the two and formally show that the meta-learner LSTM subsumes MAML. Combining this insight with recent empirical findings, we construct a new algorithm (dubbed TURTLE) which is simpler than the meta-learner LSTM yet more expressive than MAML. TURTLE outperforms both techniques at few-shot sine wave regression and image classification on miniImageNet and CUB without any additional hyperparameter tuning, at a computational cost that is comparable with second-order MAML. The key to TURTLE's success lies in the use of second-order gradients, which also significantly increases the performance of the meta-learner LSTM by 1-6% accuracy.", "published": "2021-04-21T13:34:31Z", "authors": ["Mike Huisman", "Aske Plaat", "Jan N. van Rijn"], "categories": ["cs.LG", "cs.AI", "stat.ML"], "doi": "10.1007/s10994-022-06210-y", "_query": "real estate valuation deep learning", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/1808.08618v2", "title": "Deep Learning: Computational Aspects", "abstract": "In this article we review computational aspects of Deep Learning (DL). Deep learning uses network architectures consisting of hierarchical layers of latent variables to construct predictors for high-dimensional input-output models. Training a deep learning architecture is computationally intensive, and efficient linear algebra libraries is the key for training and inference. Stochastic gradient descent (SGD) optimization and batch sampling are used to learn from massive data sets.", "published": "2018-08-26T20:26:11Z", "authors": ["Nicholas Polson", "Vadim Sokolov"], "categories": ["cs.LG", "stat.CO", "stat.ML"], "doi": "", "_query": "real estate valuation deep learning", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2108.12828v4", "title": "MEDIC: A Multi-Task Learning Dataset for Disaster Image Classification", "abstract": "Recent research in disaster informatics demonstrates a practical and important use case of artificial intelligence to save human lives and suffering during natural disasters based on social media contents (text and images). While notable progress has been made using texts, research on exploiting the images remains relatively under-explored. To advance image-based approaches, we propose MEDIC (Available at: https://crisisnlp.qcri.org/medic/index.html), which is the largest social media image classification dataset for humanitarian response consisting of 71,198 images to address four different tasks in a multi-task learning setup. This is the first dataset of its kind: social media images, disaster response, and multi-task learning research. An important property of this dataset is its high potential to facilitate research on multi-task learning, which recently receives much interest from the machine learning community and has shown remarkable results in terms of memory, inference speed, performance, and generalization capability. Therefore, the proposed dataset is an important resource for advancing image-based disaster management and multi-task machine learning research. We experiment with different deep learning architectures and report promising results, which are above the majority baselines for all tasks. Along with the dataset, we also release all relevant scripts (https://github.com/firojalam/medic).", "published": "2021-08-29T11:55:50Z", "authors": ["Firoj Alam", "Tanvirul Alam", "Md. Arid Hasan", "Abul Hasnat", "Muhammad Imran", "Ferda Ofli"], "categories": ["cs.CV", "cs.CY", "cs.LG", "cs.SI"], "doi": "10.1007/s00521-022-07717-0", "_query": "real estate valuation deep learning", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2104.12000v2", "title": "A Deep Reinforcement Learning Approach for the Meal Delivery Problem", "abstract": "We consider a meal delivery service fulfilling dynamic customer requests given a set of couriers over the course of a day. A courier's duty is to pick-up an order from a restaurant and deliver it to a customer. We model this service as a Markov decision process and use deep reinforcement learning as the solution approach. We experiment with the resulting policies on synthetic and real-world datasets and compare those with the baseline policies. We also examine the courier utilization for different numbers of couriers. In our analysis, we specifically focus on the impact of the limited available resources in the meal delivery problem. Furthermore, we investigate the effect of intelligent order rejection and re-positioning of the couriers. Our numerical experiments show that, by incorporating the geographical locations of the restaurants, customers, and the depot, our model significantly improves the overall service quality as characterized by the expected total reward and the delivery times. Our results present valuable insights on both the courier assignment process and the optimal number of couriers for different order frequencies on a given day. The proposed model also shows a robust performance under a variety of scenarios for real-world implementation.", "published": "2021-04-24T19:01:59Z", "authors": ["Hadi Jahanshahi", "Aysun Bozanta", "Mucahit Cevik", "Eray Mert Kavuk", "Ayşe Tosun", "Sibel B. Sonuc", "Bilgin Kosucu", "Ayşe Başar"], "categories": ["cs.LG", "cs.AI", "math.OC"], "doi": "10.1016/j.knosys.2022.108489", "_query": "real estate valuation deep learning", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/1901.01672v2", "title": "Generalization in Deep Networks: The Role of Distance from Initialization", "abstract": "Why does training deep neural networks using stochastic gradient descent (SGD) result in a generalization error that does not worsen with the number of parameters in the network? To answer this question, we advocate a notion of effective model capacity that is dependent on {\\em a given random initialization of the network} and not just the training algorithm and the data distribution. We provide empirical evidences that demonstrate that the model capacity of SGD-trained deep networks is in fact restricted through implicit regularization of {\\em the $\\ell_2$ distance from the initialization}. We also provide theoretical arguments that further highlight the need for initialization-dependent notions of model capacity. We leave as open questions how and why distance from initialization is regularized, and whether it is sufficient to explain generalization.", "published": "2019-01-07T05:59:11Z", "authors": ["Vaishnavh Nagarajan", "J. Zico Kolter"], "categories": ["cs.LG", "cs.AI", "stat.ML"], "doi": "", "_query": "real estate valuation deep learning", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2301.11898v2", "title": "DAG Learning on the Permutahedron", "abstract": "We propose a continuous optimization framework for discovering a latent directed acyclic graph (DAG) from observational data. Our approach optimizes over the polytope of permutation vectors, the so-called Permutahedron, to learn a topological ordering. Edges can be optimized jointly, or learned conditional on the ordering via a non-differentiable subroutine. Compared to existing continuous optimization approaches our formulation has a number of advantages including: 1. validity: optimizes over exact DAGs as opposed to other relaxations optimizing approximate DAGs; 2. modularity: accommodates any edge-optimization procedure, edge structural parameterization, and optimization loss; 3. end-to-end: either alternately iterates between node-ordering and edge-optimization, or optimizes them jointly. We demonstrate, on real-world data problems in protein-signaling and transcriptional network discovery, that our approach lies on the Pareto frontier of two key metrics, the SID and SHD.", "published": "2023-01-27T18:22:25Z", "authors": ["Valentina Zantedeschi", "Luca Franceschi", "Jean Kaddour", "Matt J. Kusner", "Vlad Niculae"], "categories": ["cs.LG", "cs.AI", "stat.ML"], "doi": "", "_query": "real estate valuation deep learning", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/1701.06452v1", "title": "Learning what to look in chest X-rays with a recurrent visual attention model", "abstract": "X-rays are commonly performed imaging tests that use small amounts of radiation to produce pictures of the organs, tissues, and bones of the body. X-rays of the chest are used to detect abnormalities or diseases of the airways, blood vessels, bones, heart, and lungs. In this work we present a stochastic attention-based model that is capable of learning what regions within a chest X-ray scan should be visually explored in order to conclude that the scan contains a specific radiological abnormality. The proposed model is a recurrent neural network (RNN) that learns to sequentially sample the entire X-ray and focus only on informative areas that are likely to contain the relevant information. We report on experiments carried out with more than $100,000$ X-rays containing enlarged hearts or medical devices. The model has been trained using reinforcement learning methods to learn task-specific policies.", "published": "2017-01-23T15:29:47Z", "authors": ["Petros-Pavlos Ypsilantis", "Giovanni Montana"], "categories": ["stat.ML", "cs.CV", "cs.LG"], "doi": "", "_query": "real estate valuation deep learning", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2005.08785v1", "title": "Optical Fiber Communication Systems Based on End-to-End Deep Learning", "abstract": "We investigate end-to-end optimized optical transmission systems based on feedforward or bidirectional recurrent neural networks (BRNN) and deep learning. In particular, we report the first experimental demonstration of a BRNN auto-encoder, highlighting the performance improvement achieved with recurrent processing for communication over dispersive nonlinear channels.", "published": "2020-05-18T15:02:42Z", "authors": ["Boris Karanov", "Mathieu Chagnon", "Vahid Aref", "Domanic Lavery", "Polina Bayvel", "Laurent Schmalen"], "categories": ["eess.SP", "cs.IT", "cs.LG"], "doi": "", "_query": "real estate valuation deep learning", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2106.14568v6", "title": "Deep Ensembling with No Overhead for either Training or Testing: The All-Round Blessings of Dynamic Sparsity", "abstract": "The success of deep ensembles on improving predictive performance, uncertainty estimation, and out-of-distribution robustness has been extensively studied in the machine learning literature. Albeit the promising results, naively training multiple deep neural networks and combining their predictions at inference leads to prohibitive computational costs and memory requirements. Recently proposed efficient ensemble approaches reach the performance of the traditional deep ensembles with significantly lower costs. However, the training resources required by these approaches are still at least the same as training a single dense model. In this work, we draw a unique connection between sparse neural network training and deep ensembles, yielding a novel efficient ensemble learning framework called FreeTickets. Instead of training multiple dense networks and averaging them, we directly train sparse subnetworks from scratch and extract diverse yet accurate subnetworks during this efficient, sparse-to-sparse training. Our framework, FreeTickets, is defined as the ensemble of these relatively cheap sparse subnetworks. Despite being an ensemble method, FreeTickets has even fewer parameters and training FLOPs than a single dense model. This seemingly counter-intuitive outcome is due to the ultra training/inference efficiency of dynamic sparse training. FreeTickets surpasses the dense baseline in all the following criteria: prediction accuracy, uncertainty estimation, out-of-distribution (OoD) robustness, as well as efficiency for both training and inference. Impressively, FreeTickets outperforms the naive deep ensemble with ResNet50 on ImageNet using around only 1/5 of the training FLOPs required by the latter. We have released our source code at https://github.com/VITA-Group/FreeTickets.", "published": "2021-06-28T10:48:20Z", "authors": ["Shiwei Liu", "Tianlong Chen", "Zahra Atashgahi", "Xiaohan Chen", "Ghada Sokar", "Elena Mocanu", "Mykola Pechenizkiy", "Zhangyang Wang", "Decebal Constantin Mocanu"], "categories": ["cs.LG", "cs.CV"], "doi": "", "_query": "real estate valuation deep learning", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/1911.12976v2", "title": "Learning and Planning for Time-Varying MDPs Using Maximum Likelihood Estimation", "abstract": "This paper proposes a formal approach to online learning and planning for agents operating in a priori unknown, time-varying environments. The proposed method computes the maximally likely model of the environment, given the observations about the environment made by an agent earlier in the system run and assuming knowledge of a bound on the maximal rate of change of system dynamics. Such an approach generalizes the estimation method commonly used in learning algorithms for unknown Markov decision processes with time-invariant transition probabilities, but is also able to quickly and correctly identify the system dynamics following a change. Based on the proposed method, we generalize the exploration bonuses used in learning for time-invariant Markov decision processes by introducing a notion of uncertainty in a learned time-varying model, and develop a control policy for time-varying Markov decision processes based on the exploitation and exploration trade-off. We demonstrate the proposed methods on four numerical examples: a patrolling task with a change in system dynamics, a two-state MDP with periodically changing outcomes of actions, a wind flow estimation task, and a multi-armed bandit problem with periodically changing probabilities of different rewards.", "published": "2019-11-29T07:02:14Z", "authors": ["Melkior Ornik", "Ufuk Topcu"], "categories": ["cs.LG", "eess.SY", "math.OC", "stat.ML"], "doi": "", "_query": "real estate valuation deep learning", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2104.06788v1", "title": "Neural Architecture Search of Deep Priors: Towards Continual Learning without Catastrophic Interference", "abstract": "In this paper we analyze the classification performance of neural network structures without parametric inference. Making use of neural architecture search, we empirically demonstrate that it is possible to find random weight architectures, a deep prior, that enables a linear classification to perform on par with fully trained deep counterparts. Through ablation experiments, we exclude the possibility of winning a weight initialization lottery and confirm that suitable deep priors do not require additional inference. In an extension to continual learning, we investigate the possibility of catastrophic interference free incremental learning. Under the assumption of classes originating from the same data distribution, a deep prior found on only a subset of classes is shown to allow discrimination of further classes through training of a simple linear classifier.", "published": "2021-04-14T11:25:30Z", "authors": ["Martin Mundt", "Iuliia Pliushch", "Visvanathan Ramesh"], "categories": ["cs.LG"], "doi": "", "_query": "real estate valuation deep learning", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2210.13319v3", "title": "MARS: Meta-Learning as Score Matching in the Function Space", "abstract": "Meta-learning aims to extract useful inductive biases from a set of related datasets. In Bayesian meta-learning, this is typically achieved by constructing a prior distribution over neural network parameters. However, specifying families of computationally viable prior distributions over the high-dimensional neural network parameters is difficult. As a result, existing approaches resort to meta-learning restrictive diagonal Gaussian priors, severely limiting their expressiveness and performance. To circumvent these issues, we approach meta-learning through the lens of functional Bayesian neural network inference, which views the prior as a stochastic process and performs inference in the function space. Specifically, we view the meta-training tasks as samples from the data-generating process and formalize meta-learning as empirically estimating the law of this stochastic process. Our approach can seamlessly acquire and represent complex prior knowledge by meta-learning the score function of the data-generating process marginals instead of parameter space priors. In a comprehensive benchmark, we demonstrate that our method achieves state-of-the-art performance in terms of predictive accuracy and substantial improvements in the quality of uncertainty estimates.", "published": "2022-10-24T15:14:26Z", "authors": ["Krunoslav Lehman Pavasovic", "Jonas Rothfuss", "Andreas Krause"], "categories": ["cs.LG", "stat.ML"], "doi": "", "_query": "real estate valuation deep learning", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2504.08000v1", "title": "Neuron-level Balance between Stability and Plasticity in Deep Reinforcement Learning", "abstract": "In contrast to the human ability to continuously acquire knowledge, agents struggle with the stability-plasticity dilemma in deep reinforcement learning (DRL), which refers to the trade-off between retaining existing skills (stability) and learning new knowledge (plasticity). Current methods focus on balancing these two aspects at the network level, lacking sufficient differentiation and fine-grained control of individual neurons. To overcome this limitation, we propose Neuron-level Balance between Stability and Plasticity (NBSP) method, by taking inspiration from the observation that specific neurons are strongly relevant to task-relevant skills. Specifically, NBSP first (1) defines and identifies RL skill neurons that are crucial for knowledge retention through a goal-oriented method, and then (2) introduces a framework by employing gradient masking and experience replay techniques targeting these neurons to preserve the encoded existing skills while enabling adaptation to new tasks. Numerous experimental results on the Meta-World and Atari benchmarks demonstrate that NBSP significantly outperforms existing approaches in balancing stability and plasticity.", "published": "2025-04-09T05:43:30Z", "authors": ["Jiahua Lan", "Sen Zhang", "Haixia Pan", "Ruijun Liu", "Li Shen", "Dacheng Tao"], "categories": ["cs.AI", "cs.LG"], "doi": "", "_query": "real estate valuation deep learning", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/1909.00240v1", "title": "Integrating Data and Image Domain Deep Learning for Limited Angle Tomography using Consensus Equilibrium", "abstract": "Computed Tomography (CT) is a non-invasive imaging modality with applications ranging from healthcare to security. It reconstructs cross-sectional images of an object using a collection of projection data collected at different angles. Conventional methods, such as FBP, require that the projection data be uniformly acquired over the complete angular range. In some applications, it is not possible to acquire such data. Security is one such domain where non-rotational scanning configurations are being developed which violate the complete data assumption. Conventional methods produce images from such data that are filled with artifacts. The recent success of deep learning (DL) methods has inspired researchers to post-process these artifact laden images using deep neural networks (DNNs). This approach has seen limited success on real CT problems. Another approach has been to pre-process the incomplete data using DNNs aiming to avoid the creation of artifacts altogether. Due to imperfections in the learning process, this approach can still leave perceptible residual artifacts. In this work, we aim to combine the power of deep learning in both the data and image domains through a two-step process based on the consensus equilibrium (CE) framework. Specifically, we use conditional generative adversarial networks (cGANs) in both the data and the image domain for enhanced performance and efficient computation and combine them through a consensus process. We demonstrate the effectiveness of our approach on a real security CT dataset for a challenging 90 degree limited-angle problem. The same framework can be applied to other limited data problems arising in applications such as electron microscopy, non-destructive evaluation, and medical imaging.", "published": "2019-08-31T16:46:12Z", "authors": ["Muhammad Usman Ghani", "W. Clem Karl"], "categories": ["eess.IV", "cs.CV", "cs.LG"], "doi": "", "_query": "real estate valuation deep learning", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2011.02574v1", "title": "Learning Trajectories for Visual-Inertial System Calibration via Model-based Heuristic Deep Reinforcement Learning", "abstract": "Visual-inertial systems rely on precise calibrations of both camera intrinsics and inter-sensor extrinsics, which typically require manually performing complex motions in front of a calibration target. In this work we present a novel approach to obtain favorable trajectories for visual-inertial system calibration, using model-based deep reinforcement learning. Our key contribution is to model the calibration process as a Markov decision process and then use model-based deep reinforcement learning with particle swarm optimization to establish a sequence of calibration trajectories to be performed by a robot arm. Our experiments show that while maintaining similar or shorter path lengths, the trajectories generated by our learned policy result in lower calibration errors compared to random or handcrafted trajectories.", "published": "2020-11-04T23:20:15Z", "authors": ["Le Chen", "Yunke Ao", "Florian Tschopp", "Andrei Cramariuc", "Michel Breyer", "Jen Jen Chung", "Roland Siegwart", "Cesar Cadena"], "categories": ["cs.RO", "cs.LG"], "doi": "", "_query": "real estate valuation deep learning", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/1908.07782v1", "title": "Decentralized Federated Learning: A Segmented Gossip Approach", "abstract": "The emerging concern about data privacy and security has motivated the proposal of federated learning, which allows nodes to only synchronize the locally-trained models instead their own original data. Conventional federated learning architecture, inherited from the parameter server design, relies on highly centralized topologies and the assumption of large nodes-to-server bandwidths. However, in real-world federated learning scenarios the network capacities between nodes are highly uniformly distributed and smaller than that in a datacenter. It is of great challenges for conventional federated learning approaches to efficiently utilize network capacities between nodes. In this paper, we propose a model segment level decentralized federated learning to tackle this problem. In particular, we propose a segmented gossip approach, which not only makes full utilization of node-to-node bandwidth, but also has good training convergence. The experimental results show that even the training time can be highly reduced as compared to centralized federated learning.", "published": "2019-08-21T10:21:43Z", "authors": ["Chenghao Hu", "Jingyan Jiang", "Zhi Wang"], "categories": ["cs.LG", "cs.DC", "cs.NI", "stat.ML"], "doi": "", "_query": "real estate valuation deep learning", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/1809.00510v2", "title": "Flatland: a Lightweight First-Person 2-D Environment for Reinforcement Learning", "abstract": "Flatland is a simple, lightweight environment for fast prototyping and testing of reinforcement learning agents. It is of lower complexity compared to similar 3D platforms (e.g. DeepMind Lab or VizDoom), but emulates physical properties of the real world, such as continuity, multi-modal partially-observable states with first-person view and coherent physics. We propose to use it as an intermediary benchmark for problems related to Lifelong Learning. Flatland is highly customizable and offers a wide range of task difficulty to extensively evaluate the properties of artificial agents. We experiment with three reinforcement learning baseline agents and show that they can rapidly solve a navigation task in Flatland. A video of an agent acting in Flatland is available here: https://youtu.be/I5y6Y2ZypdA.", "published": "2018-09-03T09:07:30Z", "authors": ["Hugo Caselles-Dupré", "Louis Annabi", "Oksana Hagen", "Michael Garcia-Ortiz", "David Filliat"], "categories": ["cs.LG", "stat.ML"], "doi": "", "_query": "real estate valuation deep learning", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2402.11362v1", "title": "Exploiting T-norms for Deep Learning in Autonomous Driving", "abstract": "Deep learning has been at the core of the autonomous driving field development, due to the neural networks' success in finding patterns in raw data and turning them into accurate predictions. Moreover, recent neuro-symbolic works have shown that incorporating the available background knowledge about the problem at hand in the loss function via t-norms can further improve the deep learning models' performance. However, t-norm-based losses may have very high memory requirements and, thus, they may be impossible to apply in complex application domains like autonomous driving. In this paper, we show how it is possible to define memory-efficient t-norm-based losses, allowing for exploiting t-norms for the task of event detection in autonomous driving. We conduct an extensive experimental analysis on the ROAD-R dataset and show (i) that our proposal can be implemented and run on GPUs with less than 25 GiB of available memory, while standard t-norm-based losses are estimated to require more than 100 GiB, far exceeding the amount of memory normally available, (ii) that t-norm-based losses improve performance, especially when limited labelled data are available, and (iii) that t-norm-based losses can further improve performance when exploited on both labelled and unlabelled data.", "published": "2024-02-17T18:51:21Z", "authors": ["Mihaela Cătălina Stoian", "Eleonora Giunchiglia", "Thomas Lukasiewicz"], "categories": ["cs.LG", "cs.CV"], "doi": "", "_query": "real estate valuation deep learning", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/1905.10468v1", "title": "A Deep Learning Wireless Transceiver with Fully Learned Modulation and Synchronization", "abstract": "In this paper, we present a deep learning based wireless transceiver. We describe in detail the corresponding artificial neural network architecture, the training process, and report on excessive over-the-air measurement results. We employ the end-to-end training approach with an autoencoder model that includes a channel model in the middle layers as previously proposed in the literature. In contrast to other state-of-the-art results, our architecture supports learning time synchronization without any manually designed signal processing operations. Moreover, the neural transceiver has been tested over the air with an implementation in software defined radio. Our experimental results for the implemented single antenna system demonstrate a raw bit-rate of 0.5 million bits per second. This exceeds results from comparable systems presented in the literature and suggests the feasibility of high throughput deep learning transceivers.", "published": "2019-05-24T22:44:00Z", "authors": ["Johannes Schmitz", "Caspar von Lengerke", "Nikita Airee", "Arash Behboodi", "Rudolf Mathar"], "categories": ["eess.SP", "cs.IT"], "doi": "", "_query": "real estate valuation deep learning", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2103.09777v5", "title": "Robust recognition and exploratory analysis of crystal structures via Bayesian deep learning", "abstract": "Due to their ability to recognize complex patterns, neural networks can drive a paradigm shift in the analysis of materials science data. Here, we introduce ARISE, a crystal-structure identification method based on Bayesian deep learning. As a major step forward, ARISE is robust to structural noise and can treat more than 100 crystal structures, a number that can be extended on demand. While being trained on ideal structures only, ARISE correctly characterizes strongly perturbed single- and polycrystalline systems, from both synthetic and experimental resources. The probabilistic nature of the Bayesian-deep-learning model allows to obtain principled uncertainty estimates, which are found to be correlated with crystalline order of metallic nanoparticles in electron tomography experiments. Applying unsupervised learning to the internal neural-network representations reveals grain boundaries and (unapparent) structural regions sharing easily interpretable geometrical properties. This work enables the hitherto hindered analysis of noisy atomic structural data from computations or experiments.", "published": "2021-03-17T17:04:13Z", "authors": ["Andreas Leitherer", "Angelo Ziletti", "Luca M. Ghiringhelli"], "categories": ["cond-mat.mtrl-sci"], "doi": "10.1038/s41467-021-26511-5", "_query": "real estate valuation deep learning", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/1808.04096v1", "title": "Directed Policy Gradient for Safe Reinforcement Learning with Human Advice", "abstract": "Many currently deployed Reinforcement Learning agents work in an environment shared with humans, be them co-workers, users or clients. It is desirable that these agents adjust to people's preferences, learn faster thanks to their help, and act safely around them. We argue that most current approaches that learn from human feedback are unsafe: rewarding or punishing the agent a-posteriori cannot immediately prevent it from wrong-doing. In this paper, we extend Policy Gradient to make it robust to external directives, that would otherwise break the fundamentally on-policy nature of Policy Gradient. Our technique, Directed Policy Gradient (DPG), allows a teacher or backup policy to override the agent before it acts undesirably, while allowing the agent to leverage human advice or directives to learn faster. Our experiments demonstrate that DPG makes the agent learn much faster than reward-based approaches, while requiring an order of magnitude less advice.", "published": "2018-08-13T08:12:22Z", "authors": ["Hélène Plisnier", "Denis Steckelmacher", "Tim Brys", "Diederik M. Roijers", "Ann Nowé"], "categories": ["cs.LG", "cs.AI", "stat.ML"], "doi": "", "_query": "real estate valuation deep learning", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2207.05250v1", "title": "Efficient Real-world Testing of Causal Decision Making via Bayesian Experimental Design for Contextual Optimisation", "abstract": "The real-world testing of decisions made using causal machine learning models is an essential prerequisite for their successful application. We focus on evaluating and improving contextual treatment assignment decisions: these are personalised treatments applied to e.g. customers, each with their own contextual information, with the aim of maximising a reward. In this paper we introduce a model-agnostic framework for gathering data to evaluate and improve contextual decision making through Bayesian Experimental Design. Specifically, our method is used for the data-efficient evaluation of the regret of past treatment assignments. Unlike approaches such as A/B testing, our method avoids assigning treatments that are known to be highly sub-optimal, whilst engaging in some exploration to gather pertinent information. We achieve this by introducing an information-based design objective, which we optimise end-to-end. Our method applies to discrete and continuous treatments. Comparing our information-theoretic approach to baselines in several simulation studies demonstrates the superior performance of our proposed approach.", "published": "2022-07-12T01:20:11Z", "authors": ["Desi R. Ivanova", "Joel Jennings", "Cheng Zhang", "Adam Foster"], "categories": ["stat.ML", "cs.AI", "cs.LG", "stat.CO", "stat.ME"], "doi": "", "_query": "real estate valuation deep learning", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2401.12708v2", "title": "Deep Neural Network Benchmarks for Selective Classification", "abstract": "With the increasing deployment of machine learning models in many socially sensitive tasks, there is a growing demand for reliable and trustworthy predictions. One way to accomplish these requirements is to allow a model to abstain from making a prediction when there is a high risk of making an error. This requires adding a selection mechanism to the model, which selects those examples for which the model will provide a prediction. The selective classification framework aims to design a mechanism that balances the fraction of rejected predictions (i.e., the proportion of examples for which the model does not make a prediction) versus the improvement in predictive performance on the selected predictions. Multiple selective classification frameworks exist, most of which rely on deep neural network architectures. However, the empirical evaluation of the existing approaches is still limited to partial comparisons among methods and settings, providing practitioners with little insight into their relative merits. We fill this gap by benchmarking 18 baselines on a diverse set of 44 datasets that includes both image and tabular data. Moreover, there is a mix of binary and multiclass tasks. We evaluate these approaches using several criteria, including selective error rate, empirical coverage, distribution of rejected instance's classes, and performance on out-of-distribution instances. The results indicate that there is not a single clear winner among the surveyed baselines, and the best method depends on the users' objectives.", "published": "2024-01-23T12:15:47Z", "authors": ["Andrea Pugnana", "Lorenzo Perini", "Jesse Davis", "Salvatore Ruggieri"], "categories": ["cs.LG", "cs.AI", "stat.ML"], "doi": "", "_query": "real estate valuation deep learning", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2206.07795v1", "title": "On Calibrated Model Uncertainty in Deep Learning", "abstract": "Estimated uncertainty by approximate posteriors in Bayesian neural networks are prone to miscalibration, which leads to overconfident predictions in critical tasks that have a clear asymmetric cost or significant losses. Here, we extend the approximate inference for the loss-calibrated Bayesian framework to dropweights based Bayesian neural networks by maximising expected utility over a model posterior to calibrate uncertainty in deep learning. Furthermore, we show that decisions informed by loss-calibrated uncertainty can improve diagnostic performance to a greater extent than straightforward alternatives. We propose Maximum Uncertainty Calibration Error (MUCE) as a metric to measure calibrated confidence, in addition to its prediction especially for high-risk applications, where the goal is to minimise the worst-case deviation between error and estimated uncertainty. In experiments, we show the correlation between error in prediction and estimated uncertainty by interpreting Wasserstein distance as the accuracy of prediction. We evaluated the effectiveness of our approach to detecting Covid-19 from X-Ray images. Experimental results show that our method reduces miscalibration considerably, without impacting the models accuracy and improves reliability of computer-based diagnostics.", "published": "2022-06-15T20:16:32Z", "authors": ["Biraja Ghoshal", "Allan Tucker"], "categories": ["cs.LG", "cs.CV", "eess.IV"], "doi": "", "_query": "real estate valuation deep learning", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2102.03622v2", "title": "Deep Semi-Supervised Learning for Time Series Classification", "abstract": "While Semi-supervised learning has gained much attention in computer vision on image data, yet limited research exists on its applicability in the time series domain. In this work, we investigate the transferability of state-of-the-art deep semi-supervised models from image to time series classification. We discuss the necessary model adaptations, in particular an appropriate model backbone architecture and the use of tailored data augmentation strategies. Based on these adaptations, we explore the potential of deep semi-supervised learning in the context of time series classification by evaluating our methods on large public time series classification problems with varying amounts of labelled samples. We perform extensive comparisons under a decidedly realistic and appropriate evaluation scheme with a unified reimplementation of all algorithms considered, which is yet lacking in the field. We find that these transferred semi-supervised models show significant performance gains over strong supervised, semi-supervised and self-supervised alternatives, especially for scenarios with very few labelled samples.", "published": "2021-02-06T17:40:56Z", "authors": ["Jann Goschenhofer", "Rasmus Hvingelby", "David Rügamer", "Janek Thomas", "Moritz Wagner", "Bernd Bischl"], "categories": ["cs.LG"], "doi": "10.1109/ICMLA52953.2021.00072", "_query": "real estate valuation deep learning", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2210.06640v2", "title": "Compute-Efficient Deep Learning: Algorithmic Trends and Opportunities", "abstract": "Although deep learning has made great progress in recent years, the exploding economic and environmental costs of training neural networks are becoming unsustainable. To address this problem, there has been a great deal of research on *algorithmically-efficient deep learning*, which seeks to reduce training costs not at the hardware or implementation level, but through changes in the semantics of the training program. In this paper, we present a structured and comprehensive overview of the research in this field. First, we formalize the *algorithmic speedup* problem, then we use fundamental building blocks of algorithmically efficient training to develop a taxonomy. Our taxonomy highlights commonalities of seemingly disparate methods and reveals current research gaps. Next, we present evaluation best practices to enable comprehensive, fair, and reliable comparisons of speedup techniques. To further aid research and applications, we discuss common bottlenecks in the training pipeline (illustrated via experiments) and offer taxonomic mitigation strategies for them. Finally, we highlight some unsolved research challenges and present promising future directions.", "published": "2022-10-13T00:40:04Z", "authors": ["Brian R. Bartoldson", "Bhavya Kailkhura", "Davis Blalock"], "categories": ["cs.LG"], "doi": "", "_query": "real estate valuation deep learning", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2403.17637v3", "title": "PeersimGym: An Environment for Solving the Task Offloading Problem with Reinforcement Learning", "abstract": "Task offloading, crucial for balancing computational loads across devices in networks such as the Internet of Things, poses significant optimization challenges, including minimizing latency and energy usage under strict communication and storage constraints. While traditional optimization falls short in scalability; and heuristic approaches lack in achieving optimal outcomes, Reinforcement Learning (RL) offers a promising avenue by enabling the learning of optimal offloading strategies through iterative interactions. However, the efficacy of RL hinges on access to rich datasets and custom-tailored, realistic training environments. To address this, we introduce PeersimGym, an open-source, customizable simulation environment tailored for developing and optimizing task offloading strategies within computational networks. PeersimGym supports a wide range of network topologies and computational constraints and integrates a \\textit{PettingZoo}-based interface for RL agent deployment in both solo and multi-agent setups. Furthermore, we demonstrate the utility of the environment through experiments with Deep Reinforcement Learning agents, showcasing the potential of RL-based approaches to significantly enhance offloading strategies in distributed computing settings. PeersimGym thus bridges the gap between theoretical RL models and their practical applications, paving the way for advancements in efficient task offloading methodologies.", "published": "2024-03-26T12:12:44Z", "authors": ["Frederico Metelo", "Stevo Racković", "Pedro Ákos Costa", "Cláudia Soares"], "categories": ["cs.LG", "cs.AI"], "doi": "10.1007/978-3-031-70378-2_3", "_query": "real estate valuation deep learning", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2011.13885v1", "title": "Offline Learning from Demonstrations and Unlabeled Experience", "abstract": "Behavior cloning (BC) is often practical for robot learning because it allows a policy to be trained offline without rewards, by supervised learning on expert demonstrations. However, BC does not effectively leverage what we will refer to as unlabeled experience: data of mixed and unknown quality without reward annotations. This unlabeled data can be generated by a variety of sources such as human teleoperation, scripted policies and other agents on the same robot. Towards data-driven offline robot learning that can use this unlabeled experience, we introduce Offline Reinforced Imitation Learning (ORIL). ORIL first learns a reward function by contrasting observations from demonstrator and unlabeled trajectories, then annotates all data with the learned reward, and finally trains an agent via offline reinforcement learning. Across a diverse set of continuous control and simulated robotic manipulation tasks, we show that ORIL consistently outperforms comparable BC agents by effectively leveraging unlabeled experience.", "published": "2020-11-27T18:20:04Z", "authors": ["Konrad Zolna", "Alexander Novikov", "Ksenia Konyushkova", "Caglar Gulcehre", "Ziyu Wang", "Yusuf Aytar", "Misha Denil", "Nando de Freitas", "Scott Reed"], "categories": ["cs.LG", "cs.AI", "cs.RO", "stat.ML"], "doi": "", "_query": "real estate valuation deep learning", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2404.09226v2", "title": "Breast Cancer Image Classification Method Based on Deep Transfer Learning", "abstract": "To address the issues of limited samples, time-consuming feature design, and low accuracy in detection and classification of breast cancer pathological images, a breast cancer image classification model algorithm combining deep learning and transfer learning is proposed. This algorithm is based on the DenseNet structure of deep neural networks, and constructs a network model by introducing attention mechanisms, and trains the enhanced dataset using multi-level transfer learning. Experimental results demonstrate that the algorithm achieves an efficiency of over 84.0\\% in the test set, with a significantly improved classification accuracy compared to previous models, making it applicable to medical breast cancer detection tasks.", "published": "2024-04-14T12:09:47Z", "authors": ["Weimin Wang", "Yufeng Li", "Xu Yan", "Mingxuan Xiao", "Min Gao"], "categories": ["eess.IV", "cs.CV", "cs.LG"], "doi": "", "_query": "real estate valuation deep learning", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2204.07729v3", "title": "Efficient Bayesian Policy Reuse with a Scalable Observation Model in Deep Reinforcement Learning", "abstract": "Bayesian policy reuse (BPR) is a general policy transfer framework for selecting a source policy from an offline library by inferring the task belief based on some observation signals and a trained observation model. In this paper, we propose an improved BPR method to achieve more efficient policy transfer in deep reinforcement learning (DRL). First, most BPR algorithms use the episodic return as the observation signal that contains limited information and cannot be obtained until the end of an episode. Instead, we employ the state transition sample, which is informative and instantaneous, as the observation signal for faster and more accurate task inference. Second, BPR algorithms usually require numerous samples to estimate the probability distribution of the tabular-based observation model, which may be expensive and even infeasible to learn and maintain, especially when using the state transition sample as the signal. Hence, we propose a scalable observation model based on fitting state transition functions of source tasks from only a small number of samples, which can generalize to any signals observed in the target task. Moreover, we extend the offline-mode BPR to the continual learning setting by expanding the scalable observation model in a plug-and-play fashion, which can avoid negative transfer when faced with new unknown tasks. Experimental results show that our method can consistently facilitate faster and more efficient policy transfer.", "published": "2022-04-16T05:55:43Z", "authors": ["Jinmei Liu", "Zhi Wang", "Chunlin Chen", "Daoyi Dong"], "categories": ["cs.LG"], "doi": "10.1109/TNNLS.2023.3281604", "_query": "real estate valuation deep learning", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2511.15004v1", "title": "IonCast: A Deep Learning Framework for Forecasting Ionospheric Dynamics", "abstract": "The ionosphere is a critical component of near-Earth space, shaping GNSS accuracy, high-frequency communications, and aviation operations. For these reasons, accurate forecasting and modeling of ionospheric variability has become increasingly relevant. To address this gap, we present IonCast, a suite of deep learning models that include a GraphCast-inspired model tailored for ionospheric dynamics. IonCast leverages spatiotemporal learning to forecast global Total Electron Content (TEC), integrating diverse physical drivers and observational datasets. Validating on held-out storm-time and quiet conditions highlights improved skill compared to persistence. By unifying heterogeneous data with scalable graph-based spatiotemporal learning, IonCast demonstrates how machine learning can augment physical understanding of ionospheric variability and advance operational space weather resilience.", "published": "2025-11-19T00:58:17Z", "authors": ["Halil S. Kelebek", "Linnea M. Wolniewicz", "Michael D. Vergalla", "Simone Mestici", "Giacomo Acciarini", "Bala Poduval", "Olga Verkhoglyadova", "Madhulika Guhathakurta", "Thomas E. Berger", "Frank Soboczenski", "Atılım Güneş Baydin"], "categories": ["cs.LG", "astro-ph.EP"], "doi": "", "_query": "real estate valuation deep learning", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2203.02230v2", "title": "Cloud-Edge Training Architecture for Sim-to-Real Deep Reinforcement Learning", "abstract": "Deep reinforcement learning (DRL) is a promising approach to solve complex control tasks by learning policies through interactions with the environment. However, the training of DRL policies requires large amounts of training experiences, making it impractical to learn the policy directly on physical systems. Sim-to-real approaches leverage simulations to pretrain DRL policies and then deploy them in the real world. Unfortunately, the direct real-world deployment of pretrained policies usually suffers from performance deterioration due to the different dynamics, known as the reality gap. Recent sim-to-real methods, such as domain randomization and domain adaptation, focus on improving the robustness of the pretrained agents. Nevertheless, the simulation-trained policies often need to be tuned with real-world data to reach optimal performance, which is challenging due to the high cost of real-world samples. This work proposes a distributed cloud-edge architecture to train DRL agents in the real world in real-time. In the architecture, the inference and training are assigned to the edge and cloud, separating the real-time control loop from the computationally expensive training loop. To overcome the reality gap, our architecture exploits sim-to-real transfer strategies to continue the training of simulation-pretrained agents on a physical system. We demonstrate its applicability on a physical inverted-pendulum control system, analyzing critical parameters. The real-world experiments show that our architecture can adapt the pretrained DRL agents to unseen dynamics consistently and efficiently.", "published": "2022-03-04T10:27:01Z", "authors": ["Hongpeng Cao", "Mirco Theile", "Federico G. Wyrwal", "Marco Caccamo"], "categories": ["cs.LG", "cs.RO"], "doi": "10.1109/IROS47612.2022.9981565", "_query": "real estate valuation deep learning", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2010.03268v2", "title": "Low-Rank Robust Online Distance/Similarity Learning based on the Rescaled Hinge Loss", "abstract": "An important challenge in metric learning is scalability to both size and dimension of input data. Online metric learning algorithms are proposed to address this challenge. Existing methods are commonly based on (Passive Aggressive) PA approach. Hence, they can rapidly process large volumes of data with an adaptive learning rate. However, these algorithms are based on the Hinge loss and so are not robust against outliers and label noise. Also, existing online methods usually assume training triplets or pairwise constraints are exist in advance. However, many datasets in real-world applications are in the form of input data and their associated labels. We address these challenges by formulating the online Distance-Similarity learning problem with the robust Rescaled hinge loss function. The proposed model is rather general and can be applied to any PA-based online Distance-Similarity algorithm. Also, we develop an efficient robust one-pass triplet construction algorithm. Finally, to provide scalability in high dimensional DML environments, the low-rank version of the proposed methods is presented that not only reduces the computational cost significantly but also keeps the predictive performance of the learned metrics. Also, it provides a straightforward extension of our methods for deep Distance-Similarity learning. We conduct several experiments on datasets from various applications. The results confirm that the proposed methods significantly outperform state-of-the-art online DML methods in the presence of label noise and outliers by a large margin.", "published": "2020-10-07T08:38:34Z", "authors": ["Davood Zabihzadeh", "Amar Tuama", "Ali Karami-Mollaee"], "categories": ["cs.LG", "cs.CV", "stat.ML"], "doi": "", "_query": "real estate valuation deep learning", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/1804.01882v3", "title": "Hyperbolic Entailment Cones for Learning Hierarchical Embeddings", "abstract": "Learning graph representations via low-dimensional embeddings that preserve relevant network properties is an important class of problems in machine learning. We here present a novel method to embed directed acyclic graphs. Following prior work, we first advocate for using hyperbolic spaces which provably model tree-like structures better than Euclidean geometry. Second, we view hierarchical relations as partial orders defined using a family of nested geodesically convex cones. We prove that these entailment cones admit an optimal shape with a closed form expression both in the Euclidean and hyperbolic spaces, and they canonically define the embedding learning process. Experiments show significant improvements of our method over strong recent baselines both in terms of representational capacity and generalization.", "published": "2018-04-03T19:25:10Z", "authors": ["Octavian-Eugen Ganea", "Gary Bécigneul", "Thomas Hofmann"], "categories": ["cs.LG", "stat.ML"], "doi": "", "_query": "real estate valuation deep learning", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2006.03364v1", "title": "Structure preserving deep learning", "abstract": "Over the past few years, deep learning has risen to the foreground as a topic of massive interest, mainly as a result of successes obtained in solving large-scale image processing tasks. There are multiple challenging mathematical problems involved in applying deep learning: most deep learning methods require the solution of hard optimisation problems, and a good understanding of the tradeoff between computational effort, amount of data and model complexity is required to successfully design a deep learning approach for a given problem. A large amount of progress made in deep learning has been based on heuristic explorations, but there is a growing effort to mathematically understand the structure in existing deep learning methods and to systematically design new deep learning methods to preserve certain types of structure in deep learning. In this article, we review a number of these directions: some deep neural networks can be understood as discretisations of dynamical systems, neural networks can be designed to have desirable properties such as invertibility or group equivariance, and new algorithmic frameworks based on conformal Hamiltonian systems and Riemannian manifolds to solve the optimisation problems have been proposed. We conclude our review of each of these topics by discussing some open problems that we consider to be interesting directions for future research.", "published": "2020-06-05T10:59:09Z", "authors": ["Elena Celledoni", "Matthias J. Ehrhardt", "Christian Etmann", "Robert I McLachlan", "Brynjulf Owren", "Carola-Bibiane Schönlieb", "Ferdia Sherry"], "categories": ["cs.LG", "math.NA", "stat.ML"], "doi": "", "_query": "real estate valuation deep learning", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/1904.11567v3", "title": "Unsupervised Deep Learning by Neighbourhood Discovery", "abstract": "Deep convolutional neural networks (CNNs) have demonstrated remarkable success in computer vision by supervisedly learning strong visual feature representations. However, training CNNs relies heavily on the availability of exhaustive training data annotations, limiting significantly their deployment and scalability in many application scenarios. In this work, we introduce a generic unsupervised deep learning approach to training deep models without the need for any manual label supervision. Specifically, we progressively discover sample anchored/centred neighbourhoods to reason and learn the underlying class decision boundaries iteratively and accumulatively. Every single neighbourhood is specially formulated so that all the member samples can share the same unseen class labels at high probability for facilitating the extraction of class discriminative feature representations during training. Experiments on image classification show the performance advantages of the proposed method over the state-of-the-art unsupervised learning models on six benchmarks including both coarse-grained and fine-grained object image categorisation.", "published": "2019-04-25T20:09:00Z", "authors": ["Jiabo Huang", "Qi Dong", "Shaogang Gong", "Xiatian Zhu"], "categories": ["cs.CV"], "doi": "", "_query": "real estate valuation deep learning", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/1901.00248v2", "title": "A Survey on Multi-output Learning", "abstract": "Multi-output learning aims to simultaneously predict multiple outputs given an input. It is an important learning problem due to the pressing need for sophisticated decision making in real-world applications. Inspired by big data, the 4Vs characteristics of multi-output imposes a set of challenges to multi-output learning, in terms of the volume, velocity, variety and veracity of the outputs. Increasing number of works in the literature have been devoted to the study of multi-output learning and the development of novel approaches for addressing the challenges encountered. However, it lacks a comprehensive overview on different types of challenges of multi-output learning brought by the characteristics of the multiple outputs and the techniques proposed to overcome the challenges. This paper thus attempts to fill in this gap to provide a comprehensive review on this area. We first introduce different stages of the life cycle of the output labels. Then we present the paradigm on multi-output learning, including its myriads of output structures, definitions of its different sub-problems, model evaluation metrics and popular data repositories used in the study. Subsequently, we review a number of state-of-the-art multi-output learning methods, which are categorized based on the challenges.", "published": "2019-01-02T03:10:24Z", "authors": ["Donna Xu", "Yaxin Shi", "Ivor W. Tsang", "Yew-Soon Ong", "Chen Gong", "Xiaobo Shen"], "categories": ["cs.LG", "cs.CV", "stat.ML"], "doi": "", "_query": "real estate valuation deep learning", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/1909.03204v1", "title": "Multi Pseudo Q-learning Based Deterministic Policy Gradient for Tracking Control of Autonomous Underwater Vehicles", "abstract": "This paper investigates trajectory tracking problem for a class of underactuated autonomous underwater vehicles (AUVs) with unknown dynamics and constrained inputs. Different from existing policy gradient methods which employ single actor-critic but cannot realize satisfactory tracking control accuracy and stable learning, our proposed algorithm can achieve high-level tracking control accuracy of AUVs and stable learning by applying a hybrid actors-critics architecture, where multiple actors and critics are trained to learn a deterministic policy and action-value function, respectively. Specifically, for the critics, the expected absolute Bellman error based updating rule is used to choose the worst critic to be updated in each time step. Subsequently, to calculate the loss function with more accurate target value for the chosen critic, Pseudo Q-learning, which uses sub-greedy policy to replace the greedy policy in Q-learning, is developed for continuous action spaces, and Multi Pseudo Q-learning (MPQ) is proposed to reduce the overestimation of action-value function and to stabilize the learning. As for the actors, deterministic policy gradient is applied to update the weights, and the final learned policy is defined as the average of all actors to avoid large but bad updates. Moreover, the stability analysis of the learning is given qualitatively. The effectiveness and generality of the proposed MPQ-based Deterministic Policy Gradient (MPQ-DPG) algorithm are verified by the application on AUV with two different reference trajectories. And the results demonstrate high-level tracking control accuracy and stable learning of MPQ-DPG. Besides, the results also validate that increasing the number of the actors and critics will further improve the performance.", "published": "2019-09-07T07:18:41Z", "authors": ["Wenjie Shi", "Shiji Song", "Cheng Wu", "C. L. Philip Chen"], "categories": ["cs.LG", "cs.AI", "stat.ML"], "doi": "", "_query": "real estate valuation deep learning", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2006.12694v2", "title": "Limits of Transfer Learning", "abstract": "Transfer learning involves taking information and insight from one problem domain and applying it to a new problem domain. Although widely used in practice, theory for transfer learning remains less well-developed. To address this, we prove several novel results related to transfer learning, showing the need to carefully select which sets of information to transfer and the need for dependence between transferred information and target problems. Furthermore, we prove how the degree of probabilistic change in an algorithm using transfer learning places an upper bound on the amount of improvement possible. These results build on the algorithmic search framework for machine learning, allowing the results to apply to a wide range of learning problems using transfer.", "published": "2020-06-23T01:48:23Z", "authors": ["Jake Williams", "Abel Tadesse", "Tyler Sam", "Huey Sun", "George D. Montanez"], "categories": ["stat.ML", "cs.AI", "cs.LG"], "doi": "", "_query": "real estate valuation deep learning", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2112.00950v1", "title": "Quantile Filtered Imitation Learning", "abstract": "We introduce quantile filtered imitation learning (QFIL), a novel policy improvement operator designed for offline reinforcement learning. QFIL performs policy improvement by running imitation learning on a filtered version of the offline dataset. The filtering process removes $ s,a $ pairs whose estimated Q values fall below a given quantile of the pushforward distribution over values induced by sampling actions from the behavior policy. The definitions of both the pushforward Q distribution and resulting value function quantile are key contributions of our method. We prove that QFIL gives us a safe policy improvement step with function approximation and that the choice of quantile provides a natural hyperparameter to trade off bias and variance of the improvement step. Empirically, we perform a synthetic experiment illustrating how QFIL effectively makes a bias-variance tradeoff and we see that QFIL performs well on the D4RL benchmark.", "published": "2021-12-02T03:08:23Z", "authors": ["David Brandfonbrener", "William F. Whitney", "Rajesh Ranganath", "Joan Bruna"], "categories": ["cs.LG", "stat.ML"], "doi": "", "_query": "real estate valuation deep learning", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2605.25991v1", "title": "Fuzzy PyTorch: Rapid Numerical Variability Evaluation for Deep Learning Models", "abstract": "We introduce Fuzzy PyTorch, a framework for rapid evaluation of numerical variability in deep learning (DL) models. As DL is increasingly applied to diverse tasks, understanding variability from floating-point arithmetic is essential to ensure robust and reliable performance. Tools assessing such variability must be scalable, efficient, and integrate seamlessly with existing frameworks while minimizing code modifications. Fuzzy PyTorch enables this by integrating stochastic arithmetic into PyTorch through Probabilistic Rounding with Instruction Set Management, a novel library interfacing with Verificarlo, a numerical analysis compiler. The library offers stochastic rounding mode and a novel mode; up-down rounding. Comparative evaluations show Fuzzy PyTorch maintains model performance and achieves runtime reductions of 5x to 60x versus Verrou, a state-of-the-art tool. We further demonstrate scalability by running models from 1 to 341 million parameters, confirming applicability across small and large DL architectures. Overall, Fuzzy PyTorch provides an efficient, scalable, and practical solution for assessing numerical variability in deep learning, enabling researchers and practitioners to quantify and manage floating-point uncertainty without compromising performance or computational efficiency.", "published": "2026-05-25T16:10:21Z", "authors": ["Inés Gonzalez-Pepe", "Hiba Akhaddar", "Tristan Glatard", "Yohan Chatelain"], "categories": ["cs.LG", "math.NA"], "doi": "", "_query": "real estate valuation deep learning", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/1811.01335v2", "title": "Bi-Real Net: Binarizing Deep Network Towards Real-Network Performance", "abstract": "In this paper, we study 1-bit convolutional neural networks (CNNs), of which both the weights and activations are binary. While efficient, the lacking of representational capability and the training difficulty impede 1-bit CNNs from performing as well as real-valued networks. We propose Bi-Real net with a novel training algorithm to tackle these two challenges. To enhance the representational capability, we propagate the real-valued activations generated by each 1-bit convolution via a parameter-free shortcut. To address the training difficulty, we propose a training algorithm using a tighter approximation to the derivative of the sign function, a magnitude-aware gradient for weight updating, a better initialization method, and a two-step scheme for training a deep network. Experiments on ImageNet show that an 18-layer Bi-Real net with the proposed training algorithm achieves 56.4% top-1 classification accuracy, which is 10% higher than the state-of-the-arts (e.g., XNOR-Net) with greater memory saving and lower computational cost. Bi-Real net is also the first to scale up 1-bit CNNs to an ultra-deep network with 152 layers, and achieves 64.5% top-1 accuracy on ImageNet. A 50-layer Bi-Real net shows comparable performance to a real-valued network on the depth estimation task with only a 0.3% accuracy gap.", "published": "2018-11-04T08:15:26Z", "authors": ["Zechun Liu", "Wenhan Luo", "Baoyuan Wu", "Xin Yang", "Wei Liu", "Kwang-Ting Cheng"], "categories": ["cs.CV"], "doi": "", "_query": "real estate valuation deep learning", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/1604.01662v4", "title": "A Survey on Bayesian Deep Learning", "abstract": "A comprehensive artificial intelligence system needs to not only perceive the environment with different `senses' (e.g., seeing and hearing) but also infer the world's conditional (or even causal) relations and corresponding uncertainty. The past decade has seen major advances in many perception tasks such as visual object recognition and speech recognition using deep learning models. For higher-level inference, however, probabilistic graphical models with their Bayesian nature are still more powerful and flexible. In recent years, Bayesian deep learning has emerged as a unified probabilistic framework to tightly integrate deep learning and Bayesian models. In this general framework, the perception of text or images using deep learning can boost the performance of higher-level inference and in turn, the feedback from the inference process is able to enhance the perception of text or images. This survey provides a comprehensive introduction to Bayesian deep learning and reviews its recent applications on recommender systems, topic models, control, etc. Besides, we also discuss the relationship and differences between Bayesian deep learning and other related topics such as Bayesian treatment of neural networks. For a constantly updating project page, please refer to https://github.com/js05212/BayesianDeepLearning-Survey.", "published": "2016-04-06T15:35:08Z", "authors": ["Hao Wang", "Dit-Yan Yeung"], "categories": ["stat.ML", "cs.AI", "cs.CV", "cs.LG", "cs.NE"], "doi": "", "_query": "real estate valuation deep learning", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/1903.07822v2", "title": "A semi-supervised deep learning algorithm for abnormal EEG identification", "abstract": "Systems that can automatically analyze EEG signals can aid neurologists by reducing heavy workload and delays. However, such systems need to be first trained using a labeled dataset. While large corpuses of EEG data exist, a fraction of them are labeled. Hand-labeling data increases workload for the very neurologists we try to aid. This paper proposes a semi-supervised learning workflow that can not only extract meaningful information from large unlabeled EEG datasets but also make predictions with minimal supervision, using labeled datasets as small as 5 examples.", "published": "2019-03-19T04:23:04Z", "authors": ["Subhrajit Roy", "Kiran Kate", "Martin Hirzel"], "categories": ["cs.LG", "eess.SP", "stat.ML"], "doi": "", "_query": "real estate valuation deep learning", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2406.14324v2", "title": "Revealing the Learning Process in Reinforcement Learning Agents Through Attention-Oriented Metrics", "abstract": "The learning process of a reinforcement learning (RL) agent remains poorly understood beyond the mathematical formulation of its learning algorithm. To address this gap, we introduce attention-oriented metrics (ATOMs) to investigate the development of an RL agent's attention during training. In a controlled experiment, we tested ATOMs on three variations of a Pong game, each designed to teach the agent distinct behaviours, complemented by a behavioural assessment. ATOMs successfully delineate the attention patterns of an agent trained on each game variation, and that these differences in attention patterns translate into differences in the agent's behaviour. Through continuous monitoring of ATOMs during training, we observed that the agent's attention developed in phases, and that these phases were consistent across game variations. Overall, we believe that ATOM could help improve our understanding of the learning processes of RL agents and better understand the relationship between attention and learning.", "published": "2024-06-20T13:56:05Z", "authors": ["Charlotte Beylier", "Simon M. Hofmann", "Nico Scherf"], "categories": ["cs.LG"], "doi": "", "_query": "real estate valuation deep learning", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/1904.02838v1", "title": "Transfer Learning for Performance Modeling of Deep Neural Network Systems", "abstract": "Modern deep neural network (DNN) systems are highly configurable with large a number of options that significantly affect their non-functional behavior, for example inference time and energy consumption. Performance models allow to understand and predict the effects of such configuration options on system behavior, but are costly to build because of large configuration spaces. Performance models from one environment cannot be transferred directly to another; usually models are rebuilt from scratch for different environments, for example different hardware. Recently, transfer learning methods have been applied to reuse knowledge from performance models trained in one environment in another. In this paper, we perform an empirical study to understand the effectiveness of different transfer learning strategies for building performance models of DNN systems. Our results show that transferring information on the most influential configuration options and their interactions is an effective way of reducing the cost to build performance models in new environments.", "published": "2019-04-04T14:16:37Z", "authors": ["Md Shahriar Iqbal", "Lars Kotthoff", "Pooyan Jamshidi"], "categories": ["cs.LG", "cs.DC"], "doi": "", "_query": "real estate valuation deep learning", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2306.04201v1", "title": "Improving Hyperparameter Learning under Approximate Inference in Gaussian Process Models", "abstract": "Approximate inference in Gaussian process (GP) models with non-conjugate likelihoods gets entangled with the learning of the model hyperparameters. We improve hyperparameter learning in GP models and focus on the interplay between variational inference (VI) and the learning target. While VI's lower bound to the marginal likelihood is a suitable objective for inferring the approximate posterior, we show that a direct approximation of the marginal likelihood as in Expectation Propagation (EP) is a better learning objective for hyperparameter optimization. We design a hybrid training procedure to bring the best of both worlds: it leverages conjugate-computation VI for inference and uses an EP-like marginal likelihood approximation for hyperparameter learning. We compare VI, EP, Laplace approximation, and our proposed training procedure and empirically demonstrate the effectiveness of our proposal across a wide range of data sets.", "published": "2023-06-07T07:15:08Z", "authors": ["Rui Li", "ST John", "Arno Solin"], "categories": ["cs.LG", "stat.ML"], "doi": "", "_query": "real estate valuation deep learning", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2405.16674v3", "title": "Limits of Deep Learning: Sequence Modeling through the Lens of Complexity Theory", "abstract": "Despite their successes, deep learning models struggle with tasks requiring complex reasoning and function composition. We present a theoretical and empirical investigation into the limitations of Structured State Space Models (SSMs) and Transformers in such tasks. We prove that one-layer SSMs cannot efficiently perform function composition over large domains without impractically large state sizes, and even with Chain-of-Thought prompting, they require a number of steps that scale unfavorably with the complexity of the function composition. Also, the language of a finite-precision SSM is within the class of regular languages. Our experiments corroborate these theoretical findings. Evaluating models on tasks including various function composition settings, multi-digit multiplication, dynamic programming, and Einstein's puzzle, we find significant performance degradation even with advanced prompting techniques. Models often resort to shortcuts, leading to compounding errors. These findings highlight fundamental barriers within current deep learning architectures rooted in their computational capacities. We underscore the need for innovative solutions to transcend these constraints and achieve reliable multi-step reasoning and compositional task-solving, which is critical for advancing toward general artificial intelligence.", "published": "2024-05-26T19:33:23Z", "authors": ["Nikola Zubić", "Federico Soldá", "Aurelio Sulser", "Davide Scaramuzza"], "categories": ["cs.LG", "cs.CC", "cs.LO"], "doi": "", "_query": "real estate valuation deep learning", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2206.05573v1", "title": "Learning Model Preconditions for Planning with Multiple Models", "abstract": "Different models can provide differing levels of fidelity when a robot is planning. Analytical models are often fast to evaluate but only work in limited ranges of conditions. Meanwhile, physics simulators are effective at modeling complex interactions between objects but are typically more computationally expensive. Learning when to switch between the various models can greatly improve the speed of planning and task success reliability. In this work, we learn model deviation estimators (MDEs) to predict the error between real-world states and the states outputted by transition models. MDEs can be used to define a model precondition that describes which transitions are accurately modeled. We then propose a planner that uses the learned model preconditions to switch between various models in order to use models in conditions where they are accurate, prioritizing faster models when possible. We evaluate our method on two real-world tasks: placing a rod into a box and placing a rod into a closed drawer.", "published": "2022-06-11T17:31:07Z", "authors": ["Alex LaGrassa", "Oliver Kroemer"], "categories": ["cs.RO"], "doi": "", "_query": "real estate valuation deep learning", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/1405.5488v1", "title": "On Learning Where To Look", "abstract": "Current automatic vision systems face two major challenges: scalability and extreme variability of appearance. First, the computational time required to process an image typically scales linearly with the number of pixels in the image, therefore limiting the resolution of input images to thumbnail size. Second, variability in appearance and pose of the objects constitute a major hurdle for robust recognition and detection. In this work, we propose a model that makes baby steps towards addressing these challenges. We describe a learning based method that recognizes objects through a series of glimpses. This system performs an amount of computation that scales with the complexity of the input rather than its number of pixels. Moreover, the proposed method is potentially more robust to changes in appearance since its parameters are learned in a data driven manner. Preliminary experiments on a handwritten dataset of digits demonstrate the computational advantages of this approach.", "published": "2014-04-24T02:29:19Z", "authors": ["Marc'Aurelio Ranzato"], "categories": ["cs.CV", "cs.LG"], "doi": "", "_query": "real estate valuation deep learning", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/1910.07796v1", "title": "Overcoming Forgetting in Federated Learning on Non-IID Data", "abstract": "We tackle the problem of Federated Learning in the non i.i.d. case, in which local models drift apart, inhibiting learning. Building on an analogy with Lifelong Learning, we adapt a solution for catastrophic forgetting to Federated Learning. We add a penalty term to the loss function, compelling all local models to converge to a shared optimum. We show that this can be done efficiently for communication (adding no further privacy risks), scaling with the number of nodes in the distributed setting. Our experiments show that this method is superior to competing ones for image recognition on the MNIST dataset.", "published": "2019-10-17T09:53:16Z", "authors": ["Neta Shoham", "Tomer Avidor", "Aviv Keren", "Nadav Israel", "Daniel Benditkis", "Liron Mor-Yosef", "Itai Zeitak"], "categories": ["cs.LG", "cs.CR", "stat.ML"], "doi": "", "_query": "real estate valuation deep learning", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2510.12843v2", "title": "Local Timescale Gates for Timescale-Robust Continual Spiking Neural Networks", "abstract": "Spiking neural networks (SNNs) promise energy-efficient artificial intelligence on neuromorphic hardware but struggle with tasks requiring both fast adaptation and long-term memory, especially in continual learning. We propose Local Timescale Gating (LT-Gate), a neuron model that combines dual time-constant dynamics with an adaptive gating mechanism. Each spiking neuron tracks information on a fast and a slow timescale in parallel, and a learned gate locally adjusts their influence. This design enables individual neurons to preserve slow contextual information while responding to fast signals, addressing the stability-plasticity dilemma. We further introduce a variance-tracking regularization that stabilizes firing activity, inspired by biological homeostasis. Empirically, LT-Gate yields significantly improved accuracy and retention in sequential learning tasks: on a challenging temporal classification benchmark it achieves about 51 percent final accuracy, compared to about 46 percent for a recent Hebbian continual-learning baseline and lower for prior SNN methods. Unlike approaches that require external replay or expensive orthogonalizations, LT-Gate operates with local updates and is fully compatible with neuromorphic hardware. In particular, it leverages features of Intel's Loihi chip (multiple synaptic traces with different decay rates) for on-chip learning. Our results demonstrate that multi-timescale gating can substantially enhance continual learning in SNNs, narrowing the gap between spiking and conventional deep networks on lifelong-learning tasks.", "published": "2025-10-13T23:31:07Z", "authors": ["Devansh Deep", "Ansh Tiwari", "Ayush Chauhan"], "categories": ["cs.LG"], "doi": "", "_query": "real estate valuation deep learning", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2505.15201v5", "title": "Pass@K Policy Optimization: Solving Harder Reinforcement Learning Problems", "abstract": "Reinforcement Learning (RL) algorithms sample multiple n>1 solution attempts for each problem and reward them independently. This optimizes for pass@1 performance and prioritizes the strength of isolated samples at the expense of the diversity and collective utility of sets of samples. This under-utilizes the sampling capacity, limiting exploration and eventual improvement on harder examples. As a fix, we propose Pass-at-k Policy Optimization (PKPO), a transformation on the final rewards which leads to direct optimization of pass@k performance, thus optimizing for sets of samples that maximize reward when considered jointly. Our contribution is to derive novel low variance unbiased estimators for pass@k and its gradient, in both the binary and continuous reward settings. We show optimization with our estimators reduces to standard RL with rewards that have been jointly transformed by a stable and efficient transformation function. While previous efforts are restricted to k=n, ours is the first to enable robust optimization of pass@k for any arbitrary k <= n. Moreover, instead of trading off pass@1 performance for pass@k gains, our method allows annealing k during training, optimizing both metrics and often achieving strong pass@1 numbers alongside significant pass@k gains. We validate our reward transformations on toy experiments, which reveal the variance reducing properties of our formulations. We also include real-world examples using the open-source LLM, GEMMA-2. We find that our transformation effectively optimizes for the target k. Furthermore, higher k values enable solving more and harder problems, while annealing k boosts both the pass@1 and pass@k . Crucially, for challenging task sets where conventional pass@1 optimization stalls, our pass@k approach unblocks learning, likely due to better exploration by prioritizing joint utility over the utility of individual samples.", "published": "2025-05-21T07:26:36Z", "authors": ["Christian Walder", "Deep Karkhanis"], "categories": ["cs.LG", "cs.AI", "cs.CL", "stat.ML"], "doi": "", "_query": "real estate valuation deep learning", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2310.09028v1", "title": "Subspace Adaptation Prior for Few-Shot Learning", "abstract": "Gradient-based meta-learning techniques aim to distill useful prior knowledge from a set of training tasks such that new tasks can be learned more efficiently with gradient descent. While these methods have achieved successes in various scenarios, they commonly adapt all parameters of trainable layers when learning new tasks. This neglects potentially more efficient learning strategies for a given task distribution and may be susceptible to overfitting, especially in few-shot learning where tasks must be learned from a limited number of examples. To address these issues, we propose Subspace Adaptation Prior (SAP), a novel gradient-based meta-learning algorithm that jointly learns good initialization parameters (prior knowledge) and layer-wise parameter subspaces in the form of operation subsets that should be adaptable. In this way, SAP can learn which operation subsets to adjust with gradient descent based on the underlying task distribution, simultaneously decreasing the risk of overfitting when learning new tasks. We demonstrate that this ability is helpful as SAP yields superior or competitive performance in few-shot image classification settings (gains between 0.1% and 3.9% in accuracy). Analysis of the learned subspaces demonstrates that low-dimensional operations often yield high activation strengths, indicating that they may be important for achieving good few-shot learning performance. For reproducibility purposes, we publish all our research code publicly.", "published": "2023-10-13T11:40:18Z", "authors": ["Mike Huisman", "Aske Plaat", "Jan N. van Rijn"], "categories": ["cs.LG", "cs.AI", "stat.ML"], "doi": "", "_query": "real estate valuation deep learning", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/1706.02999v1", "title": "Symmetry Learning for Function Approximation in Reinforcement Learning", "abstract": "In this paper we explore methods to exploit symmetries for ensuring sample efficiency in reinforcement learning (RL), this problem deserves ever increasing attention with the recent advances in the use of deep networks for complex RL tasks which require large amount of training data. We introduce a novel method to detect symmetries using reward trails observed during episodic experience and prove its completeness. We also provide a framework to incorporate the discovered symmetries for functional approximation. Finally we show that the use of potential based reward shaping is especially effective for our symmetry exploitation mechanism. Experiments on various classical problems show that our method improves the learning performance significantly by utilizing symmetry information.", "published": "2017-06-09T15:30:32Z", "authors": ["Anuj Mahajan", "Theja Tulabandhula"], "categories": ["stat.ML", "cs.AI", "cs.LG"], "doi": "", "_query": "real estate valuation deep learning", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2608.11381v1", "title": "From Numbers to Judgment: Specialist LLM Agents and Reinforcement Learning for European Listed Real Estate", "abstract": "We study whether the localized numerical operations and integrative judgments of financial analysis benefit from the same form of LLM specialization. Larix maps a 16-lens European listed-real-estate analysis framework to eight lens-aligned specialists; we compare a frontier LLM under monolithic versus specialist-decomposed prompting while holding the model, source evidence, task instructions, output schema, and scoring fixed. Across 19 firms spanning seven regulatory wrappers, decomposition improves the numerical-task aggregate by 15.8 percentage points but does not reliably improve, and can reduce, performance on judgment tasks, a pattern stable across four frozen-template dispatches; a single-agent control given the complete framework does not reproduce the numerical gain. Post-training Qwen3.5-9B with GRPO using task-aligned structured rewards then raises the development-split score by 12.0 points and the judgment aggregate by 14.2 points, with gains on all four sub-ceiling tasks; the gains transfer to unseen firms (+15.2 points overall; +40.4 on covenant stress) and to unseen regulatory wrappers (+4.3), with positive transfer on all three anti-memorization splits. Prompt-level decomposition thus improves modular numerical execution, whereas targeted parameter adaptation improves integrative financial judgment.", "published": "2026-08-11T19:42:15Z", "authors": ["Pardis Taghavi", "Santosh Bhavani"], "categories": ["cs.AI", "cs.LG"], "doi": "", "_query": "real estate valuation deep learning", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/1805.03359v2", "title": "Reward Estimation for Variance Reduction in Deep Reinforcement Learning", "abstract": "Reinforcement Learning (RL) agents require the specification of a reward signal for learning behaviours. However, introduction of corrupt or stochastic rewards can yield high variance in learning. Such corruption may be a direct result of goal misspecification, randomness in the reward signal, or correlation of the reward with external factors that are not known to the agent. Corruption or stochasticity of the reward signal can be especially problematic in robotics, where goal specification can be particularly difficult for complex tasks. While many variance reduction techniques have been studied to improve the robustness of the RL process, handling such stochastic or corrupted reward structures remains difficult. As an alternative for handling this scenario in model-free RL methods, we suggest using an estimator for both rewards and value functions. We demonstrate that this improves performance under corrupted stochastic rewards in both the tabular and non-linear function approximation settings for a variety of noise types and environments. The use of reward estimation is a robust and easy-to-implement improvement for handling corrupted reward signals in model-free RL.", "published": "2018-05-09T03:11:29Z", "authors": ["Joshua Romoff", "Peter Henderson", "Alexandre Piché", "Vincent Francois-Lavet", "Joelle Pineau"], "categories": ["cs.LG", "cs.AI", "stat.ML"], "doi": "", "_query": "real estate valuation deep learning", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/1809.09810v3", "title": "Deep Transfer Learning of Pick Points on Fabric for Robot Bed-Making", "abstract": "A fundamental challenge in manipulating fabric for clothes folding and textiles manufacturing is computing \"pick points\" to effectively modify the state of an uncertain manifold. We present a supervised deep transfer learning approach to locate pick points using depth images for invariance to color and texture. We consider the task of bed-making, where a robot sequentially grasps and pulls at pick points to increase blanket coverage. We perform physical experiments with two mobile manipulator robots, the Toyota HSR and the Fetch, and three blankets of different colors and textures. We compare coverage results from (1) human supervision, (2) a baseline of picking at the uppermost blanket point, and (3) learned pick points. On a quarter-scale twin bed, a model trained with combined data from the two robots achieves 92% blanket coverage compared with 83% for the baseline and 95% for human supervisors. The model transfers to two novel blankets and achieves 93% coverage. Average coverage results of 92% for 193 beds suggest that transfer-invariant robot pick points on fabric can be effectively learned.", "published": "2018-09-26T05:09:55Z", "authors": ["Daniel Seita", "Nawid Jamali", "Michael Laskey", "Ajay Kumar Tanwani", "Ron Berenstein", "Prakash Baskaran", "Soshi Iba", "John Canny", "Ken Goldberg"], "categories": ["cs.RO", "cs.AI"], "doi": "", "_query": "real estate valuation deep learning", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/1911.07679v1", "title": "Towards Quantification of Bias in Machine Learning for Healthcare: A Case Study of Renal Failure Prediction", "abstract": "As machine learning (ML) models, trained on real-world datasets, become common practice, it is critical to measure and quantify their potential biases. In this paper, we focus on renal failure and compare a commonly used traditional risk score, Tangri, with a more powerful machine learning model, which has access to a larger variable set and trained on 1.6 million patients' EHR data. We will compare and discuss the generalization and applicability of these two models, in an attempt to quantify biases of status quo clinical practice, compared to ML-driven models.", "published": "2019-11-18T15:04:31Z", "authors": ["Josie Williams", "Narges Razavian"], "categories": ["cs.LG", "stat.AP", "stat.ML"], "doi": "", "_query": "real estate valuation deep learning", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/1810.03880v3", "title": "Continual State Representation Learning for Reinforcement Learning using Generative Replay", "abstract": "We consider the problem of building a state representation model in a continual fashion. As the environment changes, the aim is to efficiently compress the sensory state's information without losing past knowledge. The learned features are then fed to a Reinforcement Learning algorithm to learn a policy. We propose to use Variational Auto-Encoders for state representation, and Generative Replay, i.e. the use of generated samples, to maintain past knowledge. We also provide a general and statistically sound method for automatic environment change detection. Our method provides efficient state representation as well as forward transfer, and avoids catastrophic forgetting. The resulting model is capable of incrementally learning information without using past data and with a bounded system size.", "published": "2018-10-09T09:42:53Z", "authors": ["Hugo Caselles-Dupré", "Michael Garcia-Ortiz", "David Filliat"], "categories": ["cs.LG", "stat.ML"], "doi": "", "_query": "real estate valuation deep learning", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2411.15806v2", "title": "Broad Critic Deep Actor Reinforcement Learning for Continuous Control", "abstract": "In the domain of continuous control, deep reinforcement learning (DRL) demonstrates promising results. However, the dependence of DRL on deep neural networks (DNNs) results in the demand for extensive data and increased computational cost. To address this issue, a novel hybrid actor-critic reinforcement learning (RL) framework is introduced. The proposed framework integrates the broad learning system (BLS) with DNN, aiming to merge the strengths of both distinct architectural paradigms. Specifically, the critic network employs BLS for rapid value estimation via ridge regression, while the actor network retains the DNN structure to optimize policy gradients. This hybrid design is generalizable and can enhance existing actor-critic algorithms. To demonstrate its versatility, the proposed framework is integrated into three widely used actor-critic algorithms -- deep deterministic policy gradient (DDPG), soft actor-critic (SAC), and twin delayed DDPG (TD3), resulting in BLS-augmented variants. Experimental results reveal that all BLS-enhanced versions surpass their original counterparts in terms of training efficiency and accuracy. These improvements highlight the suitability of the proposed framework for real-time control scenarios, where computational efficiency and rapid adaptation are critical.", "published": "2024-11-24T12:24:46Z", "authors": ["Shiron Thalagala", "Pak Kin Wong", "Xiaozheng Wang", "Tianang Sun"], "categories": ["cs.LG", "cs.AI"], "doi": "10.1109/TNNLS.2025.3554082", "_query": "real estate valuation deep learning", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/1310.6536v1", "title": "Randomized co-training: from cortical neurons to machine learning and back again", "abstract": "Despite its size and complexity, the human cortex exhibits striking anatomical regularities, suggesting there may simple meta-algorithms underlying cortical learning and computation. We expect such meta-algorithms to be of interest since they need to operate quickly, scalably and effectively with little-to-no specialized assumptions. This note focuses on a specific question: How can neurons use vast quantities of unlabeled data to speed up learning from the comparatively rare labels provided by reward systems? As a partial answer, we propose randomized co-training as a biologically plausible meta-algorithm satisfying the above requirements. As evidence, we describe a biologically-inspired algorithm, Correlated Nystrom Views (XNV) that achieves state-of-the-art performance in semi-supervised learning, and sketch work in progress on a neuronal implementation.", "published": "2013-10-24T09:33:17Z", "authors": ["David Balduzzi"], "categories": ["cs.LG", "q-bio.NC", "stat.ML"], "doi": "", "_query": "real estate valuation deep learning", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2311.11847v1", "title": "Deep learning complete intersection Calabi-Yau manifolds", "abstract": "We review advancements in deep learning techniques for complete intersection Calabi-Yau (CICY) 3- and 4-folds, with the aim of understanding better how to handle algebraic topological data with machine learning. We first discuss methodological aspects and data analysis, before describing neural networks architectures. Then, we describe the state-of-the art accuracy in predicting Hodge numbers. We include new results on extrapolating predictions from low to high Hodge numbers, and conversely.", "published": "2023-11-20T15:37:39Z", "authors": ["Harold Erbin", "Riccardo Finotello"], "categories": ["hep-th", "cs.LG", "math.AG"], "doi": "10.1142/9781800613706_0005", "_query": "real estate valuation deep learning", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2007.02786v1", "title": "TDprop: Does Jacobi Preconditioning Help Temporal Difference Learning?", "abstract": "We investigate whether Jacobi preconditioning, accounting for the bootstrap term in temporal difference (TD) learning, can help boost performance of adaptive optimizers. Our method, TDprop, computes a per parameter learning rate based on the diagonal preconditioning of the TD update rule. We show how this can be used in both $n$-step returns and TD($λ$). Our theoretical findings demonstrate that including this additional preconditioning information is, surprisingly, comparable to normal semi-gradient TD if the optimal learning rate is found for both via a hyperparameter search. In Deep RL experiments using Expected SARSA, TDprop meets or exceeds the performance of Adam in all tested games under near-optimal learning rates, but a well-tuned SGD can yield similar improvements -- matching our theory. Our findings suggest that Jacobi preconditioning may improve upon typical adaptive optimization methods in Deep RL, but despite incorporating additional information from the TD bootstrap term, may not always be better than SGD.", "published": "2020-07-06T14:40:34Z", "authors": ["Joshua Romoff", "Peter Henderson", "David Kanaa", "Emmanuel Bengio", "Ahmed Touati", "Pierre-Luc Bacon", "Joelle Pineau"], "categories": ["cs.LG", "stat.ML"], "doi": "", "_query": "real estate valuation deep learning", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2608.28672v1", "title": "FrameScope: Temporal Data Valuation for Stream Active Learning in Autonomous Vehicle Systems", "abstract": "Autonomous vehicles operate in dynamic, ever-changing environments where new scenarios and edge cases constantly emerge. As a result, static learning models are inadequate for ensuring safe and reliable operation. Continuous learning is essential for adapting to these evolving conditions and maintaining robust performance across diverse real-world settings. However, autonomous vehicles generate massive streams of visual data during operation, and existing continuous learning approaches typically rely on heuristic sampling methods that fail to capture temporal dynamics, often overlooking critical learning opportunities or selecting redundant frames. In this paper, we introduce FrameScope, a temporal data valuation framework for continuous learning in autonomous vehicles. FrameScope extends neural tangent kernel theory to temporal domains, enabling principled valuation of streaming visual data. Unlike cloud-centric methods that transmit all video data for processing, our approach performs principled, local frame selection on the vehicle and queries a cloud-based oracle model only for labels of those high-value frames. Extensive experiments across multiple domain shifts show that FrameScope consistently outperforms existing methods, achieving higher sample efficiency and significantly reducing catastrophic forgetting in autonomous vehicle perception. By valuing data on the vehicle and querying only labels for selected frames, FrameScope reduces bandwidth requirements, enabling scalable operation with a lightweight cloud labeling service.", "published": "2026-08-24T21:26:23Z", "authors": ["Yuheng Zhu", "Man-Ki Yoon"], "categories": ["cs.CV", "cs.LG"], "doi": "10.1145/3769102.3770611", "_query": "real estate valuation deep learning", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/1711.09602v1", "title": "Deep Reinforcement Learning for Sepsis Treatment", "abstract": "Sepsis is a leading cause of mortality in intensive care units and costs hospitals billions annually. Treating a septic patient is highly challenging, because individual patients respond very differently to medical interventions and there is no universally agreed-upon treatment for sepsis. In this work, we propose an approach to deduce treatment policies for septic patients by using continuous state-space models and deep reinforcement learning. Our model learns clinically interpretable treatment policies, similar in important aspects to the treatment policies of physicians. The learned policies could be used to aid intensive care clinicians in medical decision making and improve the likelihood of patient survival.", "published": "2017-11-27T09:49:57Z", "authors": ["Aniruddh Raghu", "Matthieu Komorowski", "Imran Ahmed", "Leo Celi", "Peter Szolovits", "Marzyeh Ghassemi"], "categories": ["cs.AI", "cs.LG"], "doi": "", "_query": "real estate valuation deep learning", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/1706.01513v2", "title": "Beyond Volume: The Impact of Complex Healthcare Data on the Machine Learning Pipeline", "abstract": "From medical charts to national census, healthcare has traditionally operated under a paper-based paradigm. However, the past decade has marked a long and arduous transformation bringing healthcare into the digital age. Ranging from electronic health records, to digitized imaging and laboratory reports, to public health datasets, today, healthcare now generates an incredible amount of digital information. Such a wealth of data presents an exciting opportunity for integrated machine learning solutions to address problems across multiple facets of healthcare practice and administration. Unfortunately, the ability to derive accurate and informative insights requires more than the ability to execute machine learning models. Rather, a deeper understanding of the data on which the models are run is imperative for their success. While a significant effort has been undertaken to develop models able to process the volume of data obtained during the analysis of millions of digitalized patient records, it is important to remember that volume represents only one aspect of the data. In fact, drawing on data from an increasingly diverse set of sources, healthcare data presents an incredibly complex set of attributes that must be accounted for throughout the machine learning pipeline. This chapter focuses on highlighting such challenges, and is broken down into three distinct components, each representing a phase of the pipeline. We begin with attributes of the data accounted for during preprocessing, then move to considerations during model building, and end with challenges to the interpretation of model output. For each component, we present a discussion around data as it relates to the healthcare domain and offer insight into the challenges each may impose on the efficiency of machine learning techniques.", "published": "2017-06-01T20:34:41Z", "authors": ["Keith Feldman", "Louis Faust", "Xian Wu", "Chao Huang", "Nitesh V. Chawla"], "categories": ["cs.CY", "cs.LG", "stat.ML"], "doi": "10.1007/978-3-319-69775-8_9", "_query": "real estate valuation deep learning", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/1910.05366v2", "title": "Learning Nearly Decomposable Value Functions Via Communication Minimization", "abstract": "Reinforcement learning encounters major challenges in multi-agent settings, such as scalability and non-stationarity. Recently, value function factorization learning emerges as a promising way to address these challenges in collaborative multi-agent systems. However, existing methods have been focusing on learning fully decentralized value functions, which are not efficient for tasks requiring communication. To address this limitation, this paper presents a novel framework for learning nearly decomposable Q-functions (NDQ) via communication minimization, with which agents act on their own most of the time but occasionally send messages to other agents in order for effective coordination. This framework hybridizes value function factorization learning and communication learning by introducing two information-theoretic regularizers. These regularizers are maximizing mutual information between agents' action selection and communication messages while minimizing the entropy of messages between agents. We show how to optimize these regularizers in a way that is easily integrated with existing value function factorization methods such as QMIX. Finally, we demonstrate that, on the StarCraft unit micromanagement benchmark, our framework significantly outperforms baseline methods and allows us to cut off more than $80\\%$ of communication without sacrificing the performance. The videos of our experiments are available at https://sites.google.com/view/ndq.", "published": "2019-10-11T18:31:15Z", "authors": ["Tonghan Wang", "Jianhao Wang", "Chongyi Zheng", "Chongjie Zhang"], "categories": ["cs.LG", "stat.ML"], "doi": "", "_query": "real estate valuation deep learning", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2004.11794v2", "title": "A Review of Privacy-preserving Federated Learning for the Internet-of-Things", "abstract": "The Internet-of-Things (IoT) generates vast quantities of data, much of it attributable to individuals' activity and behaviour. Gathering personal data and performing machine learning tasks on this data in a central location presents a significant privacy risk to individuals as well as challenges with communicating this data to the cloud. However, analytics based on machine learning and in particular deep learning benefit greatly from large amounts of data to develop high-performance predictive models. This work reviews federated learning as an approach for performing machine learning on distributed data with the goal of protecting the privacy of user-generated data as well as reducing communication costs associated with data transfer. We survey a wide variety of papers covering communication-efficiency, client heterogeneity and privacy preserving methods that are crucial for federated learning in the context of the IoT. Throughout this review, we identify the strengths and weaknesses of different methods applied to federated learning and finally, we outline future directions for privacy preserving federated learning research, particularly focusing on IoT applications.", "published": "2020-04-24T15:27:23Z", "authors": ["Christopher Briggs", "Zhong Fan", "Peter Andras"], "categories": ["cs.LG", "cs.CR", "stat.ML"], "doi": "", "_query": "real estate valuation deep learning", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2210.00762v3", "title": "Meta-Learning Priors for Safe Bayesian Optimization", "abstract": "In robotics, optimizing controller parameters under safety constraints is an important challenge. Safe Bayesian optimization (BO) quantifies uncertainty in the objective and constraints to safely guide exploration in such settings. Hand-designing a suitable probabilistic model can be challenging, however. In the presence of unknown safety constraints, it is crucial to choose reliable model hyper-parameters to avoid safety violations. Here, we propose a data-driven approach to this problem by meta-learning priors for safe BO from offline data. We build on a meta-learning algorithm, F-PACOH, capable of providing reliable uncertainty quantification in settings of data scarcity. As core contribution, we develop a novel framework for choosing safety-compliant priors in a data-riven manner via empirical uncertainty metrics and a frontier search algorithm. On benchmark functions and a high-precision motion system, we demonstrate that our meta-learned priors accelerate the convergence of safe BO approaches while maintaining safety.", "published": "2022-10-03T08:38:38Z", "authors": ["Jonas Rothfuss", "Christopher Koenig", "Alisa Rupenyan", "Andreas Krause"], "categories": ["cs.LG", "cs.AI", "cs.RO", "stat.ML"], "doi": "", "_query": "real estate valuation deep learning", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/1809.06064v1", "title": "Object-sensitive Deep Reinforcement Learning", "abstract": "Deep reinforcement learning has become popular over recent years, showing superiority on different visual-input tasks such as playing Atari games and robot navigation. Although objects are important image elements, few work considers enhancing deep reinforcement learning with object characteristics. In this paper, we propose a novel method that can incorporate object recognition processing to deep reinforcement learning models. This approach can be adapted to any existing deep reinforcement learning frameworks. State-of-the-art results are shown in experiments on Atari games. We also propose a new approach called \"object saliency maps\" to visually explain the actions made by deep reinforcement learning agents.", "published": "2018-09-17T07:59:36Z", "authors": ["Yuezhang Li", "Katia Sycara", "Rahul Iyer"], "categories": ["cs.LG", "cs.CV", "stat.ML"], "doi": "", "_query": "real estate valuation deep learning", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/1111.6925v1", "title": "Structure Learning of Probabilistic Graphical Models: A Comprehensive Survey", "abstract": "Probabilistic graphical models combine the graph theory and probability theory to give a multivariate statistical modeling. They provide a unified description of uncertainty using probability and complexity using the graphical model. Especially, graphical models provide the following several useful properties: - Graphical models provide a simple and intuitive interpretation of the structures of probabilistic models. On the other hand, they can be used to design and motivate new models. - Graphical models provide additional insights into the properties of the model, including the conditional independence properties. - Complex computations which are required to perform inference and learning in sophisticated models can be expressed in terms of graphical manipulations, in which the underlying mathematical expressions are carried along implicitly. The graphical models have been applied to a large number of fields, including bioinformatics, social science, control theory, image processing, marketing analysis, among others. However, structure learning for graphical models remains an open challenge, since one must cope with a combinatorial search over the space of all possible structures. In this paper, we present a comprehensive survey of the existing structure learning algorithms.", "published": "2011-11-29T18:33:01Z", "authors": ["Yang Zhou"], "categories": ["stat.ML", "cs.LG"], "doi": "", "_query": "real estate valuation deep learning", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2007.07805v1", "title": "Data-Efficient Deep Learning Method for Image Classification Using Data Augmentation, Focal Cosine Loss, and Ensemble", "abstract": "In general, sufficient data is essential for the better performance and generalization of deep-learning models. However, lots of limitations(cost, resources, etc.) of data collection leads to lack of enough data in most of the areas. In addition, various domains of each data sources and licenses also lead to difficulties in collection of sufficient data. This situation makes us hard to utilize not only the pre-trained model, but also the external knowledge. Therefore, it is important to leverage small dataset effectively for achieving the better performance. We applied some techniques in three aspects: data, loss function, and prediction to enable training from scratch with less data. With these methods, we obtain high accuracy by leveraging ImageNet data which consist of only 50 images per class. Furthermore, our model is ranked 4th in Visual Inductive Printers for Data-Effective Computer Vision Challenge.", "published": "2020-07-15T16:30:57Z", "authors": ["Byeongjo Kim", "Chanran Kim", "Jaehoon Lee", "Jein Song", "Gyoungsoo Park"], "categories": ["cs.CV"], "doi": "", "_query": "real estate valuation deep learning", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2007.11126v1", "title": "Efficient Graph-Based Active Learning with Probit Likelihood via Gaussian Approximations", "abstract": "We present a novel adaptation of active learning to graph-based semi-supervised learning (SSL) under non-Gaussian Bayesian models. We present an approximation of non-Gaussian distributions to adapt previously Gaussian-based acquisition functions to these more general cases. We develop an efficient rank-one update for applying \"look-ahead\" based methods as well as model retraining. We also introduce a novel \"model change\" acquisition function based on these approximations that further expands the available collection of active learning acquisition functions for such methods.", "published": "2020-07-21T23:03:11Z", "authors": ["Kevin Miller", "Hao Li", "Andrea L. Bertozzi"], "categories": ["stat.ML", "cs.LG"], "doi": "", "_query": "real estate valuation deep learning", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2402.07314v3", "title": "Online Iterative Reinforcement Learning from Human Feedback with General Preference Model", "abstract": "We investigate Reinforcement Learning from Human Feedback (RLHF) in the context of a general preference oracle. In particular, we do not assume the existence of a reward function and an oracle preference signal drawn from the Bradley-Terry model as most of the prior works do. We consider a standard mathematical formulation, the reverse-KL regularized minimax game between two LLMs for RLHF under general preference oracle. The learning objective of this formulation is to find a policy so that it is consistently preferred by the KL-regularized preference oracle over any competing LLMs. We show that this framework is strictly more general than the reward-based one, and propose sample-efficient algorithms for both the offline learning from a pre-collected preference dataset and online learning where we can query the preference oracle along the way of training. Empirical studies verify the effectiveness of the proposed framework.", "published": "2024-02-11T21:44:21Z", "authors": ["Chenlu Ye", "Wei Xiong", "Yuheng Zhang", "Hanze Dong", "Nan Jiang", "Tong Zhang"], "categories": ["cs.LG", "stat.ML"], "doi": "", "_query": "real estate valuation deep learning", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/1910.04153v1", "title": "High Mutual Information in Representation Learning with Symmetric Variational Inference", "abstract": "We introduce the Mutual Information Machine (MIM), a novel formulation of representation learning, using a joint distribution over the observations and latent state in an encoder/decoder framework. Our key principles are symmetry and mutual information, where symmetry encourages the encoder and decoder to learn different factorizations of the same underlying distribution, and mutual information, to encourage the learning of useful representations for downstream tasks. Our starting point is the symmetric Jensen-Shannon divergence between the encoding and decoding joint distributions, plus a mutual information encouraging regularizer. We show that this can be bounded by a tractable cross entropy loss function between the true model and a parameterized approximation, and relate this to the maximum likelihood framework. We also relate MIM to variational autoencoders (VAEs) and demonstrate that MIM is capable of learning symmetric factorizations, with high mutual information that avoids posterior collapse.", "published": "2019-10-04T01:37:50Z", "authors": ["Micha Livne", "Kevin Swersky", "David J. Fleet"], "categories": ["stat.ML", "cs.IT", "cs.LG"], "doi": "", "_query": "real estate valuation deep learning", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2312.02871v1", "title": "Attention-enhanced neural differential equations for physics-informed deep learning of ion transport", "abstract": "Species transport models typically combine partial differential equations (PDEs) with relations from hindered transport theory to quantify electromigrative, convective, and diffusive transport through complex nanoporous systems; however, these formulations are frequently substantial simplifications of the governing dynamics, leading to the poor generalization performance of PDE-based models. Given the growing interest in deep learning methods for the physical sciences, we develop a machine learning-based approach to characterize ion transport across nanoporous membranes. Our proposed framework centers around attention-enhanced neural differential equations that incorporate electroneutrality-based inductive biases to improve generalization performance relative to conventional PDE-based methods. In addition, we study the role of the attention mechanism in illuminating physically-meaningful ion-pairing relationships across diverse mixture compositions. Further, we investigate the importance of pre-training on simulated data from PDE-based models, as well as the performance benefits from hard vs. soft inductive biases. Our results indicate that physics-informed deep learning solutions can outperform their classical PDE-based counterparts and provide promising avenues for modelling complex transport phenomena across diverse applications.", "published": "2023-12-05T16:39:24Z", "authors": ["Danyal Rehman", "John H. Lienhard"], "categories": ["cs.LG", "math-ph", "physics.comp-ph"], "doi": "", "_query": "real estate valuation deep learning", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2410.06074v3", "title": "Scalable Mechanistic Neural Networks for Differential Equations and Machine Learning", "abstract": "We propose Scalable Mechanistic Neural Network (S-MNN), an enhanced neural network framework designed for scientific machine learning applications involving long temporal sequences. By reformulating the original Mechanistic Neural Network (MNN) (Pervez et al., 2024), we reduce the computational time and space complexities from cubic and quadratic with respect to the sequence length, respectively, to linear. This significant improvement enables efficient modeling of long-term dynamics without sacrificing accuracy or interpretability. Extensive experiments demonstrate that S-MNN matches the original MNN in precision while substantially reducing computational resources. Consequently, S-MNN can drop-in replace the original MNN in applications, providing a practical and efficient tool for integrating mechanistic bottlenecks into neural network models of complex dynamical systems. Source code is available at https://github.com/IST-DASLab/ScalableMNN.", "published": "2024-10-08T14:27:28Z", "authors": ["Jiale Chen", "Dingling Yao", "Adeel Pervez", "Dan Alistarh", "Francesco Locatello"], "categories": ["cs.LG", "math.NA"], "doi": "", "_query": "real estate valuation deep learning", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/1912.10364v3", "title": "Learning to Impute: A General Framework for Semi-supervised Learning", "abstract": "Recent semi-supervised learning methods have shown to achieve comparable results to their supervised counterparts while using only a small portion of labels in image classification tasks thanks to their regularization strategies. In this paper, we take a more direct approach for semi-supervised learning and propose learning to impute the labels of unlabeled samples such that a network achieves better generalization when it is trained on these labels. We pose the problem in a learning-to-learn formulation which can easily be incorporated to the state-of-the-art semi-supervised techniques and boost their performance especially when the labels are limited. We demonstrate that our method is applicable to both classification and regression problems including image classification and facial landmark detection tasks.", "published": "2019-12-22T00:27:21Z", "authors": ["Wei-Hong Li", "Chuan-Sheng Foo", "Hakan Bilen"], "categories": ["cs.LG", "cs.CV"], "doi": "", "_query": "real estate valuation deep learning", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/1901.04195v1", "title": "Integrating Learning and Reasoning with Deep Logic Models", "abstract": "Deep learning is very effective at jointly learning feature representations and classification models, especially when dealing with high dimensional input patterns. Probabilistic logic reasoning, on the other hand, is capable to take consistent and robust decisions in complex environments. The integration of deep learning and logic reasoning is still an open-research problem and it is considered to be the key for the development of real intelligent agents. This paper presents Deep Logic Models, which are deep graphical models integrating deep learning and logic reasoning both for learning and inference. Deep Logic Models create an end-to-end differentiable architecture, where deep learners are embedded into a network implementing a continuous relaxation of the logic knowledge. The learning process allows to jointly learn the weights of the deep learners and the meta-parameters controlling the high-level reasoning. The experimental results show that the proposed methodology overtakes the limitations of the other approaches that have been proposed to bridge deep learning and reasoning.", "published": "2019-01-14T09:06:28Z", "authors": ["Giuseppe Marra", "Francesco Giannini", "Michelangelo Diligenti", "Marco Gori"], "categories": ["cs.LG", "stat.ML"], "doi": "", "_query": "real estate valuation deep learning", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2402.15498v1", "title": "Using CPI in Loss Given Default Forecasting Models for Commercial Real Estate Portfolio", "abstract": "Forecasting the loss given default (LGD) for defaulted Commercial Real Estate (CRE) loans poses a significant challenge due to the extended resolution and workout time associated with such defaults, particularly in CCAR and CECL framework where the utilization of post-default information, including macroeconomic variables (MEVs) such as unemployment (UER) and various rates, is restricted. The current environment of persistent inflation and resultant elevated rates further compounds the uncertainty surrounding predictive LGD models. In this paper, we leverage both internal and public data sources, including observations from the COVID-19 period, to present a list of evidence indicating that the growth rates of the Consumer Price, such as Year-over-Year (YoY) growth and logarithmic growth, are good leading indicators for various CRE related rates and indices. These include the Federal Funds Effective Rate and CRE market sales price indices in key locations such as Los Angeles, New York, and nationwide, encompassing both apartment and office segments. Furthermore, with CRE LGD data we demonstrate how incorporating CPI at the time of default can improve the accuracy of predicting CRE workout LGD. This is particularly helpful in addressing the common issue of early downturn underestimation encountered in CRE LGD models.", "published": "2024-02-23T18:48:42Z", "authors": ["Ying Wu", "Garvit Arora", "Xuan Mei"], "categories": ["q-fin.RM"], "doi": "", "_query": "real estate valuation deep learning", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2408.07579v1", "title": "TabularBench: Benchmarking Adversarial Robustness for Tabular Deep Learning in Real-world Use-cases", "abstract": "While adversarial robustness in computer vision is a mature research field, fewer researchers have tackled the evasion attacks against tabular deep learning, and even fewer investigated robustification mechanisms and reliable defenses. We hypothesize that this lag in the research on tabular adversarial attacks is in part due to the lack of standardized benchmarks. To fill this gap, we propose TabularBench, the first comprehensive benchmark of robustness of tabular deep learning classification models. We evaluated adversarial robustness with CAA, an ensemble of gradient and search attacks which was recently demonstrated as the most effective attack against a tabular model. In addition to our open benchmark (https://github.com/serval-uni-lu/tabularbench) where we welcome submissions of new models and defenses, we implement 7 robustification mechanisms inspired by state-of-the-art defenses in computer vision and propose the largest benchmark of robust tabular deep learning over 200 models across five critical scenarios in finance, healthcare and security. We curated real datasets for each use case, augmented with hundreds of thousands of realistic synthetic inputs, and trained and assessed our models with and without data augmentations. We open-source our library that provides API access to all our pre-trained robust tabular models, and the largest datasets of real and synthetic tabular inputs. Finally, we analyze the impact of various defenses on the robustness and provide actionable insights to design new defenses and robustification mechanisms.", "published": "2024-08-14T14:23:12Z", "authors": ["Thibault Simonetto", "Salah Ghamizi", "Maxime Cordy"], "categories": ["cs.LG"], "doi": "", "_query": "real estate valuation deep learning", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2107.04562v4", "title": "The Bayesian Learning Rule", "abstract": "We show that many machine-learning algorithms are specific instances of a single algorithm called the \\emph{Bayesian learning rule}. The rule, derived from Bayesian principles, yields a wide-range of algorithms from fields such as optimization, deep learning, and graphical models. This includes classical algorithms such as ridge regression, Newton's method, and Kalman filter, as well as modern deep-learning algorithms such as stochastic-gradient descent, RMSprop, and Dropout. The key idea in deriving such algorithms is to approximate the posterior using candidate distributions estimated by using natural gradients. Different candidate distributions result in different algorithms and further approximations to natural gradients give rise to variants of those algorithms. Our work not only unifies, generalizes, and improves existing algorithms, but also helps us design new ones.", "published": "2021-07-09T17:28:55Z", "authors": ["Mohammad Emtiyaz Khan", "Håvard Rue"], "categories": ["stat.ML", "cs.LG"], "doi": "", "_query": "real estate valuation deep learning", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2006.05278v2", "title": "An Overview of Deep Semi-Supervised Learning", "abstract": "Deep neural networks demonstrated their ability to provide remarkable performances on a wide range of supervised learning tasks (e.g., image classification) when trained on extensive collections of labeled data (e.g., ImageNet). However, creating such large datasets requires a considerable amount of resources, time, and effort. Such resources may not be available in many practical cases, limiting the adoption and the application of many deep learning methods. In a search for more data-efficient deep learning methods to overcome the need for large annotated datasets, there is a rising research interest in semi-supervised learning and its applications to deep neural networks to reduce the amount of labeled data required, by either developing novel methods or adopting existing semi-supervised learning frameworks for a deep learning setting. In this paper, we provide a comprehensive overview of deep semi-supervised learning, starting with an introduction to the field, followed by a summarization of the dominant semi-supervised approaches in deep learning.", "published": "2020-06-09T14:08:03Z", "authors": ["Yassine Ouali", "Céline Hudelot", "Myriam Tami"], "categories": ["cs.LG", "stat.ML"], "doi": "", "_query": "real estate valuation deep learning", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2202.08566v1", "title": "Efficient and Reliable Probabilistic Interactive Learning with Structured Outputs", "abstract": "In this position paper, we study interactive learning for structured output spaces, with a focus on active learning, in which labels are unknown and must be acquired, and on skeptical learning, in which the labels are noisy and may need relabeling. These scenarios require expressive models that guarantee reliable and efficient computation of probabilistic quantities to measure uncertainty. We identify conditions under which a class of probabilistic models -- which we denote CRISPs -- meet all of these conditions, thus delivering tractable computation of the above quantities while preserving expressiveness. Building on prior work on tractable probabilistic circuits, we illustrate how CRISPs enable robust and efficient active and skeptical learning in large structured output spaces.", "published": "2022-02-17T10:29:32Z", "authors": ["Stefano Teso", "Antonio Vergari"], "categories": ["cs.LG", "stat.ML"], "doi": "", "_query": "real estate valuation deep learning", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2610.02615v1", "title": "Quantifying the Value of Constructive Induction, Knowledge, and Noise Filtering on Inductive Learning", "abstract": "Learning research, as one of its central goals, tries to measure, model, and understand how learning-problem properties affect average-case learning performance. For example, we would like to quantify the value of constructive induction, noise filtering, and background knowledge. This paper describes the effective dimension, a new learning measure that helps link problem properties to learning performance. Like the Vapnik-Chervonenkis (VC) dimension, the effective dimension is often in a simple linear relation with problem properties. Unlike the VC dimension, the effective dimension can be estimated empirically and makes average-case predictions. It is therefore more widely applicable to machine and human learning research. The measure is demonstrated on several learning systems including Backpropagation. Finally, the measure is used to precisely predict the benefit of using FRINGE, a feature construction system. The benefit is found to decrease as the complexity of the target concept increases.", "published": "2026-10-02T00:14:56Z", "authors": ["Carl M. Kadie"], "categories": ["cs.LG"], "doi": "10.1016/B978-1-55860-200-7.50034-9", "_query": "real estate valuation deep learning", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2106.09352v4", "title": "Large Scale Private Learning via Low-rank Reparametrization", "abstract": "We propose a reparametrization scheme to address the challenges of applying differentially private SGD on large neural networks, which are 1) the huge memory cost of storing individual gradients, 2) the added noise suffering notorious dimensional dependence. Specifically, we reparametrize each weight matrix with two \\emph{gradient-carrier} matrices of small dimension and a \\emph{residual weight} matrix. We argue that such reparametrization keeps the forward/backward process unchanged while enabling us to compute the projected gradient without computing the gradient itself. To learn with differential privacy, we design \\emph{reparametrized gradient perturbation (RGP)} that perturbs the gradients on gradient-carrier matrices and reconstructs an update for the original weight from the noisy gradients. Importantly, we use historical updates to find the gradient-carrier matrices, whose optimality is rigorously justified under linear regression and empirically verified with deep learning tasks. RGP significantly reduces the memory cost and improves the utility. For example, we are the first able to apply differential privacy on the BERT model and achieve an average accuracy of $83.9\\%$ on four downstream tasks with $ε=8$, which is within $5\\%$ loss compared to the non-private baseline but enjoys much lower privacy leakage risk.", "published": "2021-06-17T10:14:43Z", "authors": ["Da Yu", "Huishuai Zhang", "Wei Chen", "Jian Yin", "Tie-Yan Liu"], "categories": ["cs.LG", "cs.CR"], "doi": "", "_query": "real estate valuation deep learning", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/1812.02497v1", "title": "Active Learning Methods based on Statistical Leverage Scores", "abstract": "In many real-world machine learning applications, unlabeled data are abundant whereas class labels are expensive and scarce. An active learner aims to obtain a model of high accuracy with as few labeled instances as possible by effectively selecting useful examples for labeling. We propose a new selection criterion that is based on statistical leverage scores and present two novel active learning methods based on this criterion: ALEVS for querying single example at each iteration and DBALEVS for querying a batch of examples. To assess the representativeness of the examples in the pool, ALEVS and DBALEVS use the statistical leverage scores of the kernel matrices computed on the examples of each class. Additionally, DBALEVS selects a diverse a set of examples that are highly representative but are dissimilar to already labeled examples through maximizing a submodular set function defined with the statistical leverage scores and the kernel matrix computed on the pool of the examples. The submodularity property of the set scoring function let us identify batches with a constant factor approximate to the optimal batch in an efficient manner. Our experiments on diverse datasets show that querying based on leverage scores is a powerful strategy for active learning.", "published": "2018-12-06T12:38:22Z", "authors": ["Cem Orhan", "Oznur Tastan"], "categories": ["cs.LG", "stat.ML"], "doi": "", "_query": "real estate valuation deep learning", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2310.07831v2", "title": "Optimal Linear Decay Learning Rate Schedules and Further Refinements", "abstract": "Learning rate schedules used in practice bear little resemblance to those recommended by theory. We close much of this theory/practice gap, and as a consequence are able to derive new problem-adaptive learning rate schedules. Our main technical contribution is a refined analysis of learning rate schedules for a wide class of optimization algorithms (including SGD). When considering only worst-case analysis, our theory predicts that the optimal choice is the linear decay schedule where the step-size is set proportional to 1 - t/T, where t is the current iteration and T is the total number of steps. To go beyond this worst-case analysis, we use the observed gradient norms to derive schedules refined for any particular task. These refined schedules exhibit learning rate warm-up and rapid learning rate annealing near the end of training. Ours is the first systematic approach to automatically yield both of these properties. We perform the most comprehensive evaluation of learning rate schedules to date, evaluating across 10 diverse deep learning problems, a series of LLMs, and a suite of logistic regression problems. We validate that overall, the linear-decay schedule outperforms all commonly used default schedules including cosine annealing. Our adaptive schedule refinement method gives further improvements.", "published": "2023-10-11T19:16:35Z", "authors": ["Aaron Defazio", "Ashok Cutkosky", "Harsh Mehta", "Konstantin Mishchenko"], "categories": ["cs.LG", "cs.AI", "stat.ML"], "doi": "", "_query": "real estate valuation deep learning", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/1910.05907v1", "title": "Coordination of PV Smart Inverters Using Deep Reinforcement Learning for Grid Voltage Regulation", "abstract": "Increasing adoption of solar photovoltaic (PV) presents new challenges to modern power grid due to its variable and intermittent nature. Fluctuating outputs from PV generation can cause the grid violating voltage operation limits. PV smart inverters (SIs) provide a fast-response method to regulate voltage by modulating real and/or reactive power at the connection point. Yet existing local autonomous control scheme of SIs is based on local information without coordination, which can lead to suboptimal performance. In this paper, a deep reinforcement learning (DRL) based algorithm is developed and implemented for coordinating multiple SIs. The reward scheme of the DRL is carefully designed to ensure voltage operation limits of the grid are met with more effective utilization of SI reactive power. The proposed DRL agent for voltage control can learn its policy through interaction with massive offline simulations, and adapts to load and solar variations. The performance of the DRL agent is compared against the local autonomous control on the IEEE 37 node system with thousands of scenarios. The results show a properly trained DRL agent can intelligently coordinate different SIs for maintaining grid voltage within allowable ranges, achieving reduction of PV production curtailment, and decreasing system losses.", "published": "2019-10-14T03:50:58Z", "authors": ["Changfu Li", "Chenrui Jin", "Ratnesh Sharma"], "categories": ["eess.SY", "cs.LG"], "doi": "", "_query": "real estate valuation deep learning", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2104.14847v1", "title": "Active WeaSuL: Improving Weak Supervision with Active Learning", "abstract": "The availability of labelled data is one of the main limitations in machine learning. We can alleviate this using weak supervision: a framework that uses expert-defined rules $\\boldsymbolλ$ to estimate probabilistic labels $p(y|\\boldsymbolλ)$ for the entire data set. These rules, however, are dependent on what experts know about the problem, and hence may be inaccurate or may fail to capture important parts of the problem-space. To mitigate this, we propose Active WeaSuL: an approach that incorporates active learning into weak supervision. In Active WeaSuL, experts do not only define rules, but they also iteratively provide the true label for a small set of points where the weak supervision model is most likely to be mistaken, which are then used to better estimate the probabilistic labels. In this way, the weak labels provide a warm start, which active learning then improves upon. We make two contributions: 1) a modification of the weak supervision loss function, such that the expert-labelled data inform and improve the combination of weak labels; and 2) the maxKL divergence sampling strategy, which determines for which data points expert labelling is most beneficial. Our experiments show that when the budget for labelling data is limited (e.g. $\\leq 60$ data points), Active WeaSuL outperforms weak supervision, active learning, and competing strategies, with only a handful of labelled data points. This makes Active WeaSuL ideal for situations where obtaining labelled data is difficult.", "published": "2021-04-30T08:58:26Z", "authors": ["Samantha Biegel", "Rafah El-Khatib", "Luiz Otavio Vilas Boas Oliveira", "Max Baak", "Nanne Aben"], "categories": ["cs.LG", "stat.ML"], "doi": "", "_query": "real estate valuation deep learning", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/1810.10777v2", "title": "Efficient Learning of Restricted Boltzmann Machines Using Covariance Estimates", "abstract": "Learning RBMs using standard algorithms such as CD(k) involves gradient descent on the negative log-likelihood. One of the terms in the gradient, which involves expectation w.r.t. the model distribution, is intractable and is obtained through an MCMC estimate. In this work we show that the Hessian of the log-likelihood can be written in terms of covariances of hidden and visible units and hence, all elements of the Hessian can also be estimated using the same MCMC samples with small extra computational costs. Since inverting the Hessian may be computationally expensive, we propose an algorithm that uses inverse of the diagonal approximation of the Hessian, instead. This essentially results in parameter-specific adaptive learning rates for the gradient descent process and improves the efficiency of learning RBMs compared to the standard methods. Specifically we show that using the inverse of diagonal approximation of Hessian in the stochastic DC (difference of convex functions) program approach results in very efficient learning of RBMs.", "published": "2018-10-25T08:51:19Z", "authors": ["Vidyadhar Upadhya", "P. S. Sastry"], "categories": ["cs.LG", "stat.ML"], "doi": "", "_query": "real estate valuation deep learning", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/1707.03815v4", "title": "Deep Gaussian Embedding of Graphs: Unsupervised Inductive Learning via Ranking", "abstract": "Methods that learn representations of nodes in a graph play a critical role in network analysis since they enable many downstream learning tasks. We propose Graph2Gauss - an approach that can efficiently learn versatile node embeddings on large scale (attributed) graphs that show strong performance on tasks such as link prediction and node classification. Unlike most approaches that represent nodes as point vectors in a low-dimensional continuous space, we embed each node as a Gaussian distribution, allowing us to capture uncertainty about the representation. Furthermore, we propose an unsupervised method that handles inductive learning scenarios and is applicable to different types of graphs: plain/attributed, directed/undirected. By leveraging both the network structure and the associated node attributes, we are able to generalize to unseen nodes without additional training. To learn the embeddings we adopt a personalized ranking formulation w.r.t. the node distances that exploits the natural ordering of the nodes imposed by the network structure. Experiments on real world networks demonstrate the high performance of our approach, outperforming state-of-the-art network embedding methods on several different tasks. Additionally, we demonstrate the benefits of modeling uncertainty - by analyzing it we can estimate neighborhood diversity and detect the intrinsic latent dimensionality of a graph.", "published": "2017-07-12T17:54:04Z", "authors": ["Aleksandar Bojchevski", "Stephan Günnemann"], "categories": ["stat.ML", "cs.LG", "cs.SI"], "doi": "", "_query": "real estate valuation deep learning", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/1905.03418v2", "title": "Deep Learning Acceleration Techniques for Real Time Mobile Vision Applications", "abstract": "Deep Learning (DL) has become a crucial technology for Artificial Intelligence (AI). It is a powerful technique to automatically extract high-level features from complex data which can be exploited for applications such as computer vision, natural language processing, cybersecurity, communications, and so on. For the particular case of computer vision, several algorithms like object detection in real time videos have been proposed and they work well on Desktop GPUs and distributed computing platforms. However these algorithms are still heavy for mobile and embedded visual applications. The rapid spreading of smart portable devices and the emerging 5G network are introducing new smart multimedia applications in mobile environments. As a consequence, the possibility of implementing deep neural networks to mobile environments has attracted a lot of researchers. This paper presents emerging deep learning acceleration techniques that can enable the delivery of real time visual recognition into the hands of end users, anytime and anywhere.", "published": "2019-05-09T02:39:37Z", "authors": ["Gael Kamdem De Teyou"], "categories": ["cs.CV", "cs.LG", "cs.NE"], "doi": "", "_query": "real estate valuation deep learning", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/1910.09308v1", "title": "MIScnn: A Framework for Medical Image Segmentation with Convolutional Neural Networks and Deep Learning", "abstract": "The increased availability and usage of modern medical imaging induced a strong need for automatic medical image segmentation. Still, current image segmentation platforms do not provide the required functionalities for plain setup of medical image segmentation pipelines. Already implemented pipelines are commonly standalone software, optimized on a specific public data set. Therefore, this paper introduces the open-source Python library MIScnn. The aim of MIScnn is to provide an intuitive API allowing fast building of medical image segmentation pipelines including data I/O, preprocessing, data augmentation, patch-wise analysis, metrics, a library with state-of-the-art deep learning models and model utilization like training, prediction, as well as fully automatic evaluation (e.g. cross-validation). Similarly, high configurability and multiple open interfaces allow full pipeline customization. Running a cross-validation with MIScnn on the Kidney Tumor Segmentation Challenge 2019 data set (multi-class semantic segmentation with 300 CT scans) resulted into a powerful predictor based on the standard 3D U-Net model. With this experiment, we could show that the MIScnn framework enables researchers to rapidly set up a complete medical image segmentation pipeline by using just a few lines of code. The source code for MIScnn is available in the Git repository: https://github.com/frankkramer-lab/MIScnn.", "published": "2019-10-21T12:43:25Z", "authors": ["Dominik Müller", "Frank Kramer"], "categories": ["eess.IV", "cs.CV", "cs.LG", "cs.NE"], "doi": "10.1186/s12880-020-00543-7", "_query": "real estate valuation deep learning", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2507.20993v4", "title": "Annotation-Assisted Learning of Treatment Policies From Multimodal Electronic Health Records", "abstract": "We study how to learn treatment policies from multimodal electronic health records (EHRs) that consist of tabular data and clinical text. These policies can help physicians make better treatment decisions and allocate healthcare resources more efficiently. Causal policy learning methods prioritize patients with the largest expected treatment benefit. Yet, existing estimators are designed for tabular covariates under causal assumptions that may be hard to justify in the multimodal setting. A pragmatic alternative is to apply causal estimators directly to multimodal representations, but this can produce biased treatment effect estimates when the representations do not preserve the relevant confounding information. As a result, predictive models of baseline risk are commonly used in practice to guide treatment decisions, although they are not designed to identify which patients benefit most from treatment. We propose AACE (Annotation-Assisted Coarsened Effects), an annotation-assisted approach to causal policy learning for multimodal EHRs. The method uses expert-provided annotations during training to support confounding adjustment, and then predicts treatment benefit from only multimodal representations at inference. We show that the proposed method achieves strong empirical performance across synthetic, semi-synthetic, and real-world EHR datasets, outperforming risk-based and representation-based causal baselines, and offering practical insights for applying causal machine learning in clinical practice.", "published": "2025-07-28T16:52:31Z", "authors": ["Henri Arno", "Thomas Demeester"], "categories": ["cs.LG", "cs.AI", "stat.ML"], "doi": "", "_query": "real estate valuation deep learning", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2108.04452v1", "title": "High Quality Related Search Query Suggestions using Deep Reinforcement Learning", "abstract": "\"High Quality Related Search Query Suggestions\" task aims at recommending search queries which are real, accurate, diverse, relevant and engaging. Obtaining large amounts of query-quality human annotations is expensive. Prior work on supervised query suggestion models suffered from selection and exposure bias, and relied on sparse and noisy immediate user-feedback (e.g., clicks), leading to low quality suggestions. Reinforcement Learning techniques employed to reformulate a query using terms from search results, have limited scalability to large-scale industry applications. To recommend high quality related search queries, we train a Deep Reinforcement Learning model to predict the query a user would enter next. The reward signal is composed of long-term session-based user feedback, syntactic relatedness and estimated naturalness of generated query. Over the baseline supervised model, our proposed approach achieves a significant relative improvement in terms of recommendation diversity (3%), down-stream user-engagement (4.2%) and per-sentence word repetitions (82%).", "published": "2021-08-10T05:22:32Z", "authors": ["Praveen Kumar Bodigutla"], "categories": ["cs.IR", "cs.AI", "cs.LG"], "doi": "", "_query": "real estate valuation deep learning", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2205.09745v3", "title": "Understanding Gradient Descent on Edge of Stability in Deep Learning", "abstract": "Deep learning experiments by Cohen et al. [2021] using deterministic Gradient Descent (GD) revealed an Edge of Stability (EoS) phase when learning rate (LR) and sharpness (i.e., the largest eigenvalue of Hessian) no longer behave as in traditional optimization. Sharpness stabilizes around $2/$LR and loss goes up and down across iterations, yet still with an overall downward trend. The current paper mathematically analyzes a new mechanism of implicit regularization in the EoS phase, whereby GD updates due to non-smooth loss landscape turn out to evolve along some deterministic flow on the manifold of minimum loss. This is in contrast to many previous results about implicit bias either relying on infinitesimal updates or noise in gradient. Formally, for any smooth function $L$ with certain regularity condition, this effect is demonstrated for (1) Normalized GD, i.e., GD with a varying LR $η_t =\\fracη{\\| \\nabla L(x(t)) \\|}$ and loss $L$; (2) GD with constant LR and loss $\\sqrt{L- \\min_x L(x)}$. Both provably enter the Edge of Stability, with the associated flow on the manifold minimizing $λ_{1}(\\nabla^2 L)$. The above theoretical results have been corroborated by an experimental study.", "published": "2022-05-19T17:57:01Z", "authors": ["Sanjeev Arora", "Zhiyuan Li", "Abhishek Panigrahi"], "categories": ["cs.LG", "math.OC"], "doi": "", "_query": "real estate valuation deep learning", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2007.05196v1", "title": "Pre-trained Word Embeddings for Goal-conditional Transfer Learning in Reinforcement Learning", "abstract": "Reinforcement learning (RL) algorithms typically start tabula rasa, without any prior knowledge of the environment, and without any prior skills. This however often leads to low sample efficiency, requiring a large amount of interaction with the environment. This is especially true in a lifelong learning setting, in which the agent needs to continually extend its capabilities. In this paper, we examine how a pre-trained task-independent language model can make a goal-conditional RL agent more sample efficient. We do this by facilitating transfer learning between different related tasks. We experimentally demonstrate our approach on a set of object navigation tasks.", "published": "2020-07-10T06:42:00Z", "authors": ["Matthias Hutsebaut-Buysse", "Kevin Mets", "Steven Latré"], "categories": ["cs.LG", "cs.AI", "stat.ML"], "doi": "", "_query": "real estate valuation deep learning", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/1810.03642v4", "title": "Fast Context Adaptation via Meta-Learning", "abstract": "We propose CAVIA for meta-learning, a simple extension to MAML that is less prone to meta-overfitting, easier to parallelise, and more interpretable. CAVIA partitions the model parameters into two parts: context parameters that serve as additional input to the model and are adapted on individual tasks, and shared parameters that are meta-trained and shared across tasks. At test time, only the context parameters are updated, leading to a low-dimensional task representation. We show empirically that CAVIA outperforms MAML for regression, classification, and reinforcement learning. Our experiments also highlight weaknesses in current benchmarks, in that the amount of adaptation needed in some cases is small.", "published": "2018-10-08T18:11:01Z", "authors": ["Luisa M Zintgraf", "Kyriacos Shiarlis", "Vitaly Kurin", "Katja Hofmann", "Shimon Whiteson"], "categories": ["cs.LG", "stat.ML"], "doi": "", "_query": "real estate valuation deep learning", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2206.13025v1", "title": "Towards Harnessing Feature Embedding for Robust Learning with Noisy Labels", "abstract": "The memorization effect of deep neural networks (DNNs) plays a pivotal role in recent label noise learning methods. To exploit this effect, the model prediction-based methods have been widely adopted, which aim to exploit the outputs of DNNs in the early stage of learning to correct noisy labels. However, we observe that the model will make mistakes during label prediction, resulting in unsatisfactory performance. By contrast, the produced features in the early stage of learning show better robustness. Inspired by this observation, in this paper, we propose a novel feature embedding-based method for deep learning with label noise, termed LabEl NoiseDilution (LEND). To be specific, we first compute a similarity matrix based on current embedded features to capture the local structure of training data. Then, the noisy supervision signals carried by mislabeled data are overwhelmed by nearby correctly labeled ones (\\textit{i.e.}, label noise dilution), of which the effectiveness is guaranteed by the inherent robustness of feature embedding. Finally, the training data with diluted labels are further used to train a robust classifier. Empirically, we conduct extensive experiments on both synthetic and real-world noisy datasets by comparing our LEND with several representative robust learning approaches. The results verify the effectiveness of our LEND.", "published": "2022-06-27T02:45:09Z", "authors": ["Chuang Zhang", "Li Shen", "Jian Yang", "Chen Gong"], "categories": ["cs.LG", "cs.AI"], "doi": "", "_query": "real estate valuation deep learning", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2505.02506v1", "title": "Exploring Design Choices for Autoregressive Deep Learning Climate Models", "abstract": "Deep Learning models have achieved state-of-the-art performance in medium-range weather prediction but often fail to maintain physically consistent rollouts beyond 14 days. In contrast, a few atmospheric models demonstrate stability over decades, though the key design choices enabling this remain unclear. This study quantitatively compares the long-term stability of three prominent DL-MWP architectures - FourCastNet, SFNO, and ClimaX - trained on ERA5 reanalysis data at 5.625° resolution. We systematically assess the impact of autoregressive training steps, model capacity, and choice of prognostic variables, identifying configurations that enable stable 10-year rollouts while preserving the statistical properties of the reference dataset. Notably, rollouts with SFNO exhibit the greatest robustness to hyperparameter choices, yet all models can experience instability depending on the random seed and the set of prognostic variables", "published": "2025-05-05T09:37:58Z", "authors": ["Florian Gallusser", "Simon Hentschel", "Anna Krause", "Andreas Hotho"], "categories": ["cs.LG"], "doi": "", "_query": "real estate valuation deep learning", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/1611.01886v4", "title": "An Information-Theoretic Framework for Fast and Robust Unsupervised Learning via Neural Population Infomax", "abstract": "A framework is presented for unsupervised learning of representations based on infomax principle for large-scale neural populations. We use an asymptotic approximation to the Shannon's mutual information for a large neural population to demonstrate that a good initial approximation to the global information-theoretic optimum can be obtained by a hierarchical infomax method. Starting from the initial solution, an efficient algorithm based on gradient descent of the final objective function is proposed to learn representations from the input datasets, and the method works for complete, overcomplete, and undercomplete bases. As confirmed by numerical experiments, our method is robust and highly efficient for extracting salient features from input datasets. Compared with the main existing methods, our algorithm has a distinct advantage in both the training speed and the robustness of unsupervised representation learning. Furthermore, the proposed method is easily extended to the supervised or unsupervised model for training deep structure networks.", "published": "2016-11-07T04:17:28Z", "authors": ["Wentao Huang", "Kechen Zhang"], "categories": ["cs.LG", "cs.AI", "cs.IT", "q-bio.NC", "stat.ML"], "doi": "", "_query": "real estate valuation deep learning", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/1902.04132v1", "title": "Automatic Inspection of Utility Scale Solar Power Plants using Deep Learning", "abstract": "Solar energy has the potential to become the backbone energy source for the world. Utility scale solar power plants (more than 50 MW) could have more than 100K individual solar modules and be spread over more than 200 acres of land. Traditionally methods of monitoring each module become too costly in the utility scale. We demonstrate an alternative using the recent advances in deep learning to automatically analyze drone footage. We show that this can be a quick and reliable alternative. We show that it can save huge amounts of power and the impact the developing world hugely.", "published": "2018-12-20T14:26:36Z", "authors": ["Alekh Karkada Ashok", "Chandan G", "Adithya Bhat", "Kausthubh Karnataki", "Ganesh Shankar"], "categories": ["cs.LG"], "doi": "", "_query": "real estate valuation deep learning", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/1809.01859v1", "title": "Deep Learning-Based Decoding for Constrained Sequence Codes", "abstract": "Constrained sequence codes have been widely used in modern communication and data storage systems. Sequences encoded with constrained sequence codes satisfy constraints imposed by the physical channel, hence enabling efficient and reliable transmission of coded symbols. Traditional encoding and decoding of constrained sequence codes rely on table look-up, which is prone to errors that occur during transmission. In this paper, we introduce constrained sequence decoding based on deep learning. With multiple layer perception (MLP) networks and convolutional neural networks (CNNs), we are able to achieve low bit error rates that are close to maximum a posteriori probability (MAP) decoding as well as improve the system throughput. Moreover, implementation of capacity-achieving fixed-length codes, where the complexity is prohibitively high with table look-up decoding, becomes practical with deep learning-based decoding.", "published": "2018-09-06T07:44:33Z", "authors": ["Congzhe Cao", "Duanshun Li", "Ivan Fair"], "categories": ["cs.IT", "cs.LG", "eess.SP", "stat.ML"], "doi": "", "_query": "real estate valuation deep learning", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2211.02100v1", "title": "Contrastive Value Learning: Implicit Models for Simple Offline RL", "abstract": "Model-based reinforcement learning (RL) methods are appealing in the offline setting because they allow an agent to reason about the consequences of actions without interacting with the environment. Prior methods learn a 1-step dynamics model, which predicts the next state given the current state and action. These models do not immediately tell the agent which actions to take, but must be integrated into a larger RL framework. Can we model the environment dynamics in a different way, such that the learned model does directly indicate the value of each action? In this paper, we propose Contrastive Value Learning (CVL), which learns an implicit, multi-step model of the environment dynamics. This model can be learned without access to reward functions, but nonetheless can be used to directly estimate the value of each action, without requiring any TD learning. Because this model represents the multi-step transitions implicitly, it avoids having to predict high-dimensional observations and thus scales to high-dimensional tasks. Our experiments demonstrate that CVL outperforms prior offline RL methods on complex continuous control benchmarks.", "published": "2022-11-03T19:10:05Z", "authors": ["Bogdan Mazoure", "Benjamin Eysenbach", "Ofir Nachum", "Jonathan Tompson"], "categories": ["cs.LG", "cs.AI"], "doi": "", "_query": "real estate valuation deep learning", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2203.01387v3", "title": "A Survey on Offline Reinforcement Learning: Taxonomy, Review, and Open Problems", "abstract": "With the widespread adoption of deep learning, reinforcement learning (RL) has experienced a dramatic increase in popularity, scaling to previously intractable problems, such as playing complex games from pixel observations, sustaining conversations with humans, and controlling robotic agents. However, there is still a wide range of domains inaccessible to RL due to the high cost and danger of interacting with the environment. Offline RL is a paradigm that learns exclusively from static datasets of previously collected interactions, making it feasible to extract policies from large and diverse training datasets. Effective offline RL algorithms have a much wider range of applications than online RL, being particularly appealing for real-world applications, such as education, healthcare, and robotics. In this work, we contribute with a unifying taxonomy to classify offline RL methods. Furthermore, we provide a comprehensive review of the latest algorithmic breakthroughs in the field using a unified notation as well as a review of existing benchmarks' properties and shortcomings. Additionally, we provide a figure that summarizes the performance of each method and class of methods on different dataset properties, equipping researchers with the tools to decide which type of algorithm is best suited for the problem at hand and identify which classes of algorithms look the most promising. Finally, we provide our perspective on open problems and propose future research directions for this rapidly growing field.", "published": "2022-03-02T20:05:11Z", "authors": ["Rafael Figueiredo Prudencio", "Marcos R. O. A. Maximo", "Esther Luna Colombini"], "categories": ["cs.LG", "cs.AI", "stat.ML"], "doi": "10.1109/TNNLS.2023.3250269", "_query": "real estate valuation deep learning", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2606.14416v1", "title": "Federated Learning for Feature Generalization with Convex Constraints", "abstract": "Federated learning (FL) often struggles with generalization due to heterogeneous client data. Local models are prone to overfitting their local data distributions, and even transferable features can be distorted during aggregation. To address these challenges, we propose FedCONST, an approach that adaptively modulates update magnitudes based on the parameter strength of the global model. This prevents over-emphasizing well-learned parameters while reinforcing underdeveloped ones. Specifically, FedCONST employs linear convex constraints to ensure training stability and preserve locally learned generalization capabilities during aggregation. A Gradient Signal to Noise Ratio (GSNR) analysis further validates the effectiveness of FedCONST in enhancing feature transferability and robustness. As a result, FedCONST effectively aligns local and global objectives, mitigating overfitting and promoting stronger generalization across diverse FL environments, achieving state-of-the-art performance.", "published": "2026-06-12T12:50:33Z", "authors": ["Dongwon Kim", "Donghee Kim", "Sung Kuk Shyn", "Kwangsu Kim"], "categories": ["cs.LG", "stat.ML"], "doi": "", "_query": "real estate valuation deep learning", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2512.20562v2", "title": "Shallow Neural Networks Learn Low-Degree Spherical Polynomials with Feature Learning by Learnable Channel Attention", "abstract": "We study the problem of learning a low-degree spherical polynomial of degree $\\ell_0 = Θ(1) \\ge 1$ defined on the unit sphere in $\\RR^d$ by training an over-parameterized two-layer neural network (NN) with channel attention in this paper. Our main result is the significantly improved sample complexity for learning such low-degree polynomials. We show that, for any regression risk $\\eps \\in (0,1)$, a carefully designed two-layer NN with channel attention and finite width trained by the vanilla gradient descent (GD) requires the lowest sample complexity of $n \\asymp Θ(d^{\\ell_0}/\\eps)$ with high probability, in contrast with the representative sample complexity $Θ\\pth{d^{\\ell_0} \\max\\set{\\eps^{-2},\\log d}}$, where $n$ is the training data size. Moreover, such sample complexity is not improvable since the trained network renders a sharp rate of the nonparametric regression risk of the order $Θ(d^{\\ell_0}/{n})$ with high probability. On the other hand, the minimax optimal rate for the regression risk with a kernel of rank $Θ(d^{\\ell_0})$ is $Θ(d^{\\ell_0}/{n})$, so that the rate of the nonparametric regression risk of the network trained by GD is minimax optimal. Training the two-layer NN with channel attention proceeds in two stages: (1) a provable learnable channel selection algorithm, as a learnable harmonic-degree selection process, identifies the ground truth channel number in the target function, $\\ell_0$, from $L \\ge \\ell_0$ channels in the first-layer activation; (2) the second layer is trained by standard GD using the selected channels. To the best of our knowledge, this is the first time a minimax optimal risk bound is obtained by training an over-parameterized but finite-width neural network with feature learning capability to learn low-degree spherical polynomials.", "published": "2025-12-23T18:05:55Z", "authors": ["Yingzhen Yang"], "categories": ["stat.ML", "cs.LG", "math.OC"], "doi": "", "_query": "real estate valuation deep learning", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2009.07988v1", "title": "Deep Collective Learning: Learning Optimal Inputs and Weights Jointly in Deep Neural Networks", "abstract": "It is well observed that in deep learning and computer vision literature, visual data are always represented in a manually designed coding scheme (eg., RGB images are represented as integers ranging from 0 to 255 for each channel) when they are input to an end-to-end deep neural network (DNN) for any learning task. We boldly question whether the manually designed inputs are good for DNN training for different tasks and study whether the input to a DNN can be optimally learned end-to-end together with learning the weights of the DNN. In this paper, we propose the paradigm of {\\em deep collective learning} which aims to learn the weights of DNNs and the inputs to DNNs simultaneously for given tasks. We note that collective learning has been implicitly but widely used in natural language processing while it has almost never been studied in computer vision. Consequently, we propose the lookup vision networks (Lookup-VNets) as a solution to deep collective learning in computer vision. This is achieved by associating each color in each channel with a vector in lookup tables. As learning inputs in computer vision has almost never been studied in the existing literature, we explore several aspects of this question through varieties of experiments on image classification tasks. Experimental results on four benchmark datasets, i.e., CIFAR-10, CIFAR-100, Tiny ImageNet, and ImageNet (ILSVRC2012) have shown several surprising characteristics of Lookup-VNets and have demonstrated the advantages and promise of Lookup-VNets and deep collective learning.", "published": "2020-09-17T00:33:04Z", "authors": ["Xiang Deng", " Zhongfei", " Zhang"], "categories": ["cs.LG", "cs.CV", "stat.ML"], "doi": "", "_query": "real estate valuation deep learning", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/1812.11103v3", "title": "Learning to Walk via Deep Reinforcement Learning", "abstract": "Deep reinforcement learning (deep RL) holds the promise of automating the acquisition of complex controllers that can map sensory inputs directly to low-level actions. In the domain of robotic locomotion, deep RL could enable learning locomotion skills with minimal engineering and without an explicit model of the robot dynamics. Unfortunately, applying deep RL to real-world robotic tasks is exceptionally difficult, primarily due to poor sample complexity and sensitivity to hyperparameters. While hyperparameters can be easily tuned in simulated domains, tuning may be prohibitively expensive on physical systems, such as legged robots, that can be damaged through extensive trial-and-error learning. In this paper, we propose a sample-efficient deep RL algorithm based on maximum entropy RL that requires minimal per-task tuning and only a modest number of trials to learn neural network policies. We apply this method to learning walking gaits on a real-world Minitaur robot. Our method can acquire a stable gait from scratch directly in the real world in about two hours, without relying on any model or simulation, and the resulting policy is robust to moderate variations in the environment. We further show that our algorithm achieves state-of-the-art performance on simulated benchmarks with a single set of hyperparameters. Videos of training and the learned policy can be found on the project website.", "published": "2018-12-26T10:07:13Z", "authors": ["Tuomas Haarnoja", "Sehoon Ha", "Aurick Zhou", "Jie Tan", "George Tucker", "Sergey Levine"], "categories": ["cs.LG", "cs.AI", "cs.RO", "stat.ML"], "doi": "", "_query": "real estate valuation deep learning", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2312.09433v2", "title": "Point-of-Care Real-Time Signal Quality for Fetal Doppler Ultrasound Using a Deep Learning Approach", "abstract": "In this study, we present a deep learning framework designed to integrate with our previously developed system that facilitates large-scale 1D fetal Doppler data collection, aiming to enhance data quality. This system, tailored for traditional Indigenous midwives in low-resource communities, leverages a cost-effective Android phone to improve the quality of recorded signals. We have shown that the Doppler data can be used to identify fetal growth restriction, hypertension, and other concerning issues during pregnancy. However, the quality of the signal is dependent on many factors, including radio frequency interference, position of the fetus, maternal body habitus, and usage of the Doppler by the birth attendants. In order to provide instant feedback to allow correction of the data at source, a signal quality metric is required that can run in real-time on the mobile phone. In this study, 191 DUS signals with durations mainly in the range between 5 to 10 minutes were evaluated for quality and classified into five categories: Good, Poor, (Radiofrequency) Interference, Talking, and Silent, at a resolution of 3.75 seconds. A deep neural network was trained on each 3.75-second segment from these recordings and validated using five-fold cross-validation. An average micro F1 = 97.4\\% and macro F1 = 94.2\\% were achieved, with F1 = 99.2\\% for `Good' quality data. These results indicate that the algorithm, which will now be implemented in the midwives' app, should allow a significant increase in the quality of data at the time of capture.", "published": "2023-11-15T03:33:19Z", "authors": ["Mohsen Motie-Shirazi", "Reza Sameni", "Peter Rohloff", "Nasim Katebi", "Gari D. Clifford"], "categories": ["eess.SP", "cs.LG"], "doi": "", "_query": "real estate valuation deep learning", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/1806.05454v1", "title": "Low-rank geometric mean metric learning", "abstract": "We propose a low-rank approach to learning a Mahalanobis metric from data. Inspired by the recent geometric mean metric learning (GMML) algorithm, we propose a low-rank variant of the algorithm. This allows to jointly learn a low-dimensional subspace where the data reside and the Mahalanobis metric that appropriately fits the data. Our results show that we compete effectively with GMML at lower ranks.", "published": "2018-06-14T10:35:21Z", "authors": ["Mukul Bhutani", "Pratik Jawanpuria", "Hiroyuki Kasai", "Bamdev Mishra"], "categories": ["cs.LG", "stat.ML"], "doi": "", "_query": "real estate valuation deep learning", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2308.14039v1", "title": "Deep Learning for Visual Localization and Mapping: A Survey", "abstract": "Deep learning based localization and mapping approaches have recently emerged as a new research direction and receive significant attentions from both industry and academia. Instead of creating hand-designed algorithms based on physical models or geometric theories, deep learning solutions provide an alternative to solve the problem in a data-driven way. Benefiting from the ever-increasing volumes of data and computational power on devices, these learning methods are fast evolving into a new area that shows potentials to track self-motion and estimate environmental model accurately and robustly for mobile agents. In this work, we provide a comprehensive survey, and propose a taxonomy for the localization and mapping methods using deep learning. This survey aims to discuss two basic questions: whether deep learning is promising to localization and mapping; how deep learning should be applied to solve this problem. To this end, a series of localization and mapping topics are investigated, from the learning based visual odometry, global relocalization, to mapping, and simultaneous localization and mapping (SLAM). It is our hope that this survey organically weaves together the recent works in this vein from robotics, computer vision and machine learning communities, and serves as a guideline for future researchers to apply deep learning to tackle the problem of visual localization and mapping.", "published": "2023-08-27T08:25:00Z", "authors": ["Changhao Chen", "Bing Wang", "Chris Xiaoxuan Lu", "Niki Trigoni", "Andrew Markham"], "categories": ["cs.CV"], "doi": "", "_query": "real estate valuation deep learning", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2111.13485v2", "title": "Learning Long-Term Reward Redistribution via Randomized Return Decomposition", "abstract": "Many practical applications of reinforcement learning require agents to learn from sparse and delayed rewards. It challenges the ability of agents to attribute their actions to future outcomes. In this paper, we consider the problem formulation of episodic reinforcement learning with trajectory feedback. It refers to an extreme delay of reward signals, in which the agent can only obtain one reward signal at the end of each trajectory. A popular paradigm for this problem setting is learning with a designed auxiliary dense reward function, namely proxy reward, instead of sparse environmental signals. Based on this framework, this paper proposes a novel reward redistribution algorithm, randomized return decomposition (RRD), to learn a proxy reward function for episodic reinforcement learning. We establish a surrogate problem by Monte-Carlo sampling that scales up least-squares-based reward redistribution to long-horizon problems. We analyze our surrogate loss function by connection with existing methods in the literature, which illustrates the algorithmic properties of our approach. In experiments, we extensively evaluate our proposed method on a variety of benchmark tasks with episodic rewards and demonstrate substantial improvement over baseline algorithms.", "published": "2021-11-26T13:23:36Z", "authors": ["Zhizhou Ren", "Ruihan Guo", "Yuan Zhou", "Jian Peng"], "categories": ["cs.LG", "cs.AI", "stat.ML"], "doi": "", "_query": "real estate valuation deep learning", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2302.02406v1", "title": "Pre-screening breast cancer with machine learning and deep learning", "abstract": "We suggest that deep learning can be used for pre-screening cancer by analyzing demographic and anthropometric information of patients, as well as biological markers obtained from routine blood samples and relative risks obtained from meta-analysis and international databases. We applied feature selection algorithms to a database of 116 women, including 52 healthy women and 64 women diagnosed with breast cancer, to identify the best pre-screening predictors of cancer. We utilized the best predictors to perform k-fold Monte Carlo cross-validation experiments that compare deep learning against traditional machine learning algorithms. Our results indicate that a deep learning model with an input-layer architecture that is fine-tuned using feature selection can effectively distinguish between patients with and without cancer. Additionally, compared to machine learning, deep learning has the lowest uncertainty in its predictions. These findings suggest that deep learning algorithms applied to cancer pre-screening offer a radiation-free, non-invasive, and affordable complement to screening methods based on imagery. The implementation of deep learning algorithms in cancer pre-screening offer opportunities to identify individuals who may require imaging-based screening, can encourage self-examination, and decrease the psychological externalities associated with false positives in cancer screening. The integration of deep learning algorithms for both screening and pre-screening will ultimately lead to earlier detection of malignancy, reducing the healthcare and societal burden associated to cancer treatment.", "published": "2023-02-05T15:27:50Z", "authors": ["Rolando Gonzales Martinez", "Daan-Max van Dongen"], "categories": ["stat.ML", "cs.LG"], "doi": "", "_query": "real estate valuation deep learning", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2311.17553v1", "title": "Deep Learning 21cm Lightcones in 3D", "abstract": "Interferometric measurements of the 21cm signal are a prime example of the data-driven era in astrophysics we are entering with current and upcoming experiments. We showcase the use of deep networks that are tailored for the structure of 3D tomographic 21cm light-cones to firstly detect and characterise HI sources and to secondly directly infer global astrophysical and cosmological model parameters. We compare different architectures and highlight how 3D CNN architectures that mirror the data structure are the best-performing model.", "published": "2023-11-29T11:36:15Z", "authors": ["Caroline Heneka"], "categories": ["astro-ph.CO", "astro-ph.IM"], "doi": "10.1007/978-3-031-34167-0_34", "_query": "real estate valuation deep learning", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/1809.05214v1", "title": "Model-Based Reinforcement Learning via Meta-Policy Optimization", "abstract": "Model-based reinforcement learning approaches carry the promise of being data efficient. However, due to challenges in learning dynamics models that sufficiently match the real-world dynamics, they struggle to achieve the same asymptotic performance as model-free methods. We propose Model-Based Meta-Policy-Optimization (MB-MPO), an approach that foregoes the strong reliance on accurate learned dynamics models. Using an ensemble of learned dynamic models, MB-MPO meta-learns a policy that can quickly adapt to any model in the ensemble with one policy gradient step. This steers the meta-policy towards internalizing consistent dynamics predictions among the ensemble while shifting the burden of behaving optimally w.r.t. the model discrepancies towards the adaptation step. Our experiments show that MB-MPO is more robust to model imperfections than previous model-based approaches. Finally, we demonstrate that our approach is able to match the asymptotic performance of model-free methods while requiring significantly less experience.", "published": "2018-09-14T01:15:28Z", "authors": ["Ignasi Clavera", "Jonas Rothfuss", "John Schulman", "Yasuhiro Fujita", "Tamim Asfour", "Pieter Abbeel"], "categories": ["cs.LG", "cs.AI", "stat.ML"], "doi": "", "_query": "real estate valuation deep learning", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2402.05424v1", "title": "Neural Circuit Diagrams: Robust Diagrams for the Communication, Implementation, and Analysis of Deep Learning Architectures", "abstract": "Diagrams matter. Unfortunately, the deep learning community has no standard method for diagramming architectures. The current combination of linear algebra notation and ad-hoc diagrams fails to offer the necessary precision to understand architectures in all their detail. However, this detail is critical for faithful implementation, mathematical analysis, further innovation, and ethical assurances. I present neural circuit diagrams, a graphical language tailored to the needs of communicating deep learning architectures. Neural circuit diagrams naturally keep track of the changing arrangement of data, precisely show how operations are broadcast over axes, and display the critical parallel behavior of linear operations. A lingering issue with existing diagramming methods is the inability to simultaneously express the detail of axes and the free arrangement of data, which neural circuit diagrams solve. Their compositional structure is analogous to code, creating a close correspondence between diagrams and implementation. In this work, I introduce neural circuit diagrams for an audience of machine learning researchers. After introducing neural circuit diagrams, I cover a host of architectures to show their utility and breed familiarity. This includes the transformer architecture, convolution (and its difficult-to-explain extensions), residual networks, the U-Net, and the vision transformer. I include a Jupyter notebook that provides evidence for the close correspondence between diagrams and code. Finally, I examine backpropagation using neural circuit diagrams. I show their utility in providing mathematical insight and analyzing algorithms' time and space complexities.", "published": "2024-02-08T05:42:13Z", "authors": ["Vincent Abbott"], "categories": ["cs.LG"], "doi": "", "_query": "real estate valuation deep learning", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2203.01471v2", "title": "Structure Learning of Latent Factors via Clique Search on Correlation Thresholded Graphs", "abstract": "Despite the widespread application of latent factor analysis, existing methods suffer from the following weaknesses: requiring the number of factors to be known, lack of theoretical guarantees for learning the model structure, and nonidentifiability of the parameters due to rotation invariance properties of the likelihood. We address these concerns by proposing a fast correlation thresholding (CT) algorithm that simultaneously learns the number of latent factors and a rotationally identifiable model structure. Our novel approach translates this structure learning problem into the search for so-called independent maximal cliques in a thresholded correlation graph that can be easily constructed from the observed data. Our clique analysis technique scales well up to thousands of variables, while competing methods are not applicable in a reasonable amount of running time. We establish a finite-sample error bound and high-dimensional consistency for the structure learning of our method. Through a series of simulation studies and a real data example, we show that the CT algorithm is an accurate method for learning the structure of factor analysis models and is robust to violations of its assumptions.", "published": "2022-03-03T00:56:03Z", "authors": ["Dale S. Kim", "Qing Zhou"], "categories": ["stat.ME"], "doi": "", "_query": "real estate valuation deep learning", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/1905.12226v1", "title": "Address Instance-level Label Prediction in Multiple Instance Learning", "abstract": "\\textit{Multiple Instance Learning} (MIL) is concerned with learning from bags of instances, where only bag labels are given and instance labels are unknown. Existent approaches in this field were mainly designed for the bag-level label prediction (predict labels for bags) but not the instance-level (predict labels for instances), with the task loss being only defined at the bag level. This restricts their application in many tasks, where the instance-level labels are more interested. In this paper, we propose a novel algorithm, whose loss is specifically defined at the instance level, to address instance-level label prediction in MIL. We prove that the loss of this algorithm can be unbiasedly and consistently estimated without using instance labels, under the i.i.d assumption. Empirical study validates the above statements and shows that the proposed algorithm can achieve superior instance-level and comparative bag-level performance, compared to state-of-the-art MIL methods. In addition, it shows that the proposed method can achieve similar results as the fully supervised model (trained with instance labels) for label prediction at the instance level.", "published": "2019-05-29T05:36:06Z", "authors": ["Minlong Peng", "Qi Zhang"], "categories": ["cs.LG", "stat.ML"], "doi": "", "_query": "real estate valuation deep learning", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2111.10919v2", "title": "Offline Reinforcement Learning: Fundamental Barriers for Value Function Approximation", "abstract": "We consider the offline reinforcement learning problem, where the aim is to learn a decision making policy from logged data. Offline RL -- particularly when coupled with (value) function approximation to allow for generalization in large or continuous state spaces -- is becoming increasingly relevant in practice, because it avoids costly and time-consuming online data collection and is well suited to safety-critical domains. Existing sample complexity guarantees for offline value function approximation methods typically require both (1) distributional assumptions (i.e., good coverage) and (2) representational assumptions (i.e., ability to represent some or all $Q$-value functions) stronger than what is required for supervised learning. However, the necessity of these conditions and the fundamental limits of offline RL are not well understood in spite of decades of research. This led Chen and Jiang (2019) to conjecture that concentrability (the most standard notion of coverage) and realizability (the weakest representation condition) alone are not sufficient for sample-efficient offline RL. We resolve this conjecture in the positive by proving that in general, even if both concentrability and realizability are satisfied, any algorithm requires sample complexity polynomial in the size of the state space to learn a non-trivial policy. Our results show that sample-efficient offline reinforcement learning requires either restrictive coverage conditions or representation conditions that go beyond supervised learning, and highlight a phenomenon called over-coverage which serves as a fundamental barrier for offline value function approximation methods. A consequence of our results for reinforcement learning with linear function approximation is that the separation between online and offline RL can be arbitrarily large, even in constant dimension.", "published": "2021-11-21T23:22:37Z", "authors": ["Dylan J. Foster", "Akshay Krishnamurthy", "David Simchi-Levi", "Yunzong Xu"], "categories": ["cs.LG", "stat.ML"], "doi": "", "_query": "real estate valuation deep learning", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/1812.00524v1", "title": "Generalization in anti-causal learning", "abstract": "The ability to learn and act in novel situations is still a prerogative of animate intelligence, as current machine learning methods mostly fail when moving beyond the standard i.i.d. setting. What is the reason for this discrepancy? Most machine learning tasks are anti-causal, i.e., we infer causes (labels) from effects (observations). Typically, in supervised learning we build systems that try to directly invert causal mechanisms. Instead, in this paper we argue that strong generalization capabilities crucially hinge on searching and validating meaningful hypotheses, requiring access to a causal model. In such a framework, we want to find a cause that leads to the observed effect. Anti-causal models are used to drive this search, but a causal model is required for validation. We investigate the fundamental differences between causal and anti-causal tasks, discuss implications for topics ranging from adversarial attacks to disentangling factors of variation, and provide extensive evidence from the literature to substantiate our view. We advocate for incorporating causal models in supervised learning to shift the paradigm from inference only, to search and validation.", "published": "2018-12-03T02:26:35Z", "authors": ["Niki Kilbertus", "Giambattista Parascandolo", "Bernhard Schölkopf"], "categories": ["cs.LG", "stat.ML"], "doi": "", "_query": "real estate valuation deep learning", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/1812.07627v1", "title": "Clustering-Oriented Representation Learning with Attractive-Repulsive Loss", "abstract": "The standard loss function used to train neural network classifiers, categorical cross-entropy (CCE), seeks to maximize accuracy on the training data; building useful representations is not a necessary byproduct of this objective. In this work, we propose clustering-oriented representation learning (COREL) as an alternative to CCE in the context of a generalized attractive-repulsive loss framework. COREL has the consequence of building latent representations that collectively exhibit the quality of natural clustering within the latent space of the final hidden layer, according to a predefined similarity function. Despite being simple to implement, COREL variants outperform or perform equivalently to CCE in a variety of scenarios, including image and news article classification using both feed-forward and convolutional neural networks. Analysis of the latent spaces created with different similarity functions facilitates insights on the different use cases COREL variants can satisfy, where the Cosine-COREL variant makes a consistently clusterable latent space, while Gaussian-COREL consistently obtains better classification accuracy than CCE.", "published": "2018-12-18T20:07:56Z", "authors": ["Kian Kenyon-Dean", "Andre Cianflone", "Lucas Page-Caccia", "Guillaume Rabusseau", "Jackie Chi Kit Cheung", "Doina Precup"], "categories": ["cs.LG", "cs.AI", "stat.ML"], "doi": "", "_query": "real estate valuation deep learning", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2007.07740v1", "title": "Deep Representation Learning and Clustering of Traffic Scenarios", "abstract": "Determining the traffic scenario space is a major challenge for the homologation and coverage assessment of automated driving functions. In contrast to current approaches that are mainly scenario-based and rely on expert knowledge, we introduce two data driven autoencoding models that learn a latent representation of traffic scenes. First is a CNN based spatio-temporal model that autoencodes a grid of traffic participants' positions. Secondly, we develop a pure temporal RNN based model that auto-encodes a sequence of sets. To handle the unordered set data, we had to incorporate the permutation invariance property. Finally, we show how the latent scenario embeddings can be used for clustering traffic scenarios and similarity retrieval.", "published": "2020-07-15T15:12:23Z", "authors": ["Nick Harmening", "Marin Biloš", "Stephan Günnemann"], "categories": ["cs.LG", "stat.ML"], "doi": "", "_query": "real estate valuation deep learning", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2005.05719v2", "title": "Smooth Exploration for Robotic Reinforcement Learning", "abstract": "Reinforcement learning (RL) enables robots to learn skills from interactions with the real world. In practice, the unstructured step-based exploration used in Deep RL -- often very successful in simulation -- leads to jerky motion patterns on real robots. Consequences of the resulting shaky behavior are poor exploration, or even damage to the robot. We address these issues by adapting state-dependent exploration (SDE) to current Deep RL algorithms. To enable this adaptation, we propose two extensions to the original SDE, using more general features and re-sampling the noise periodically, which leads to a new exploration method generalized state-dependent exploration (gSDE). We evaluate gSDE both in simulation, on PyBullet continuous control tasks, and directly on three different real robots: a tendon-driven elastic robot, a quadruped and an RC car. The noise sampling interval of gSDE permits to have a compromise between performance and smoothness, which allows training directly on the real robots without loss of performance. The code is available at https://github.com/DLR-RM/stable-baselines3.", "published": "2020-05-12T12:28:25Z", "authors": ["Antonin Raffin", "Jens Kober", "Freek Stulp"], "categories": ["cs.LG", "cs.RO", "stat.ML"], "doi": "", "_query": "real estate valuation deep learning", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2510.23237v1", "title": "Robust Iterative Learning Hidden Quantum Markov Models", "abstract": "Hidden Quantum Markov Models (HQMMs) extend classical Hidden Markov Models to the quantum domain, offering a powerful probabilistic framework for modeling sequential data with quantum coherence. However, existing HQMM learning algorithms are highly sensitive to data corruption and lack mechanisms to ensure robustness under adversarial perturbations. In this work, we introduce the Adversarially Corrupted HQMM (AC-HQMM), which formalizes robustness analysis by allowing a controlled fraction of observation sequences to be adversarially corrupted. To learn AC-HQMMs, we propose the Robust Iterative Learning Algorithm (RILA), a derivative-free method that integrates a Remove Corrupted Rows by Entropy Filtering (RCR-EF) module with an iterative stochastic resampling procedure for physically valid Kraus operator updates. RILA incorporates L1-penalized likelihood objectives to enhance stability, resist overfitting, and remain effective under non-differentiable conditions. Across multiple HQMM and HMM benchmarks, RILA demonstrates superior convergence stability, corruption resilience, and preservation of physical validity compared to existing algorithms, establishing a principled and efficient approach for robust quantum sequential learning.", "published": "2025-10-27T11:48:44Z", "authors": ["Ning Ning"], "categories": ["cs.LG", "quant-ph", "stat.CO", "stat.ME", "stat.ML"], "doi": "", "_query": "real estate valuation deep learning", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/1203.3783v1", "title": "Learning Feature Hierarchies with Centered Deep Boltzmann Machines", "abstract": "Deep Boltzmann machines are in principle powerful models for extracting the hierarchical structure of data. Unfortunately, attempts to train layers jointly (without greedy layer-wise pretraining) have been largely unsuccessful. We propose a modification of the learning algorithm that initially recenters the output of the activation functions to zero. This modification leads to a better conditioned Hessian and thus makes learning easier. We test the algorithm on real data and demonstrate that our suggestion, the centered deep Boltzmann machine, learns a hierarchy of increasingly abstract representations and a better generative model of data.", "published": "2012-03-16T19:01:10Z", "authors": ["Grégoire Montavon", "Klaus-Robert Müller"], "categories": ["stat.ML", "cs.AI", "cs.LG"], "doi": "10.1007/978-3-642-35289-8_33", "_query": "real estate valuation deep learning", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2306.12754v1", "title": "Highly accurate and efficient deep learning paradigm for full-atom protein loop modeling with KarmaLoop", "abstract": "Protein loop modeling is the most challenging yet highly non-trivial task in protein structure prediction. Despite recent progress, existing methods including knowledge-based, ab initio, hybrid and deep learning (DL) methods fall significantly short of either atomic accuracy or computational efficiency. Moreover, an overarching focus on backbone atoms has resulted in a dearth of attention given to side-chain conformation, a critical aspect in a host of downstream applications including ligand docking, molecular dynamics simulation and drug design. To overcome these limitations, we present KarmaLoop, a novel paradigm that distinguishes itself as the first DL method centered on full-atom (encompassing both backbone and side-chain heavy atoms) protein loop modeling. Our results demonstrate that KarmaLoop considerably outperforms conventional and DL-based methods of loop modeling in terms of both accuracy and efficiency, with the average RMSD improved by over two-fold compared to the second-best baseline method across different tasks, and manifests at least two orders of magnitude speedup in general. Consequently, our comprehensive evaluations indicate that KarmaLoop provides a state-of-the-art DL solution for protein loop modeling, with the potential to hasten the advancement of protein engineering, antibody-antigen recognition, and drug design.", "published": "2023-06-22T09:16:20Z", "authors": ["Tianyue Wang", "Xujun Zhang", "Odin Zhang", "Peichen Pan", "Guangyong Chen", "Yu Kang", "Chang-Yu Hsieh", "Tingjun Hou"], "categories": ["q-bio.BM"], "doi": "", "_query": "real estate valuation deep learning", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/1412.6039v3", "title": "Generative Deep Deconvolutional Learning", "abstract": "A generative Bayesian model is developed for deep (multi-layer) convolutional dictionary learning. A novel probabilistic pooling operation is integrated into the deep model, yielding efficient bottom-up and top-down probabilistic learning. After learning the deep convolutional dictionary, testing is implemented via deconvolutional inference. To speed up this inference, a new statistical approach is proposed to project the top-layer dictionary elements to the data level. Following this, only one layer of deconvolution is required during testing. Experimental results demonstrate powerful capabilities of the model to learn multi-layer features from images. Excellent classification results are obtained on both the MNIST and Caltech 101 datasets.", "published": "2014-12-18T20:01:38Z", "authors": ["Yunchen Pu", "Xin Yuan", "Lawrence Carin"], "categories": ["stat.ML", "cs.LG"], "doi": "", "_query": "real estate valuation deep learning", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/1910.06474v1", "title": "End-to-End Adversarial Shape Learning for Abdomen Organ Deep Segmentation", "abstract": "Automatic segmentation of abdomen organs using medical imaging has many potential applications in clinical workflows. Recently, the state-of-the-art performance for organ segmentation has been achieved by deep learning models, i.e., convolutional neural network (CNN). However, it is challenging to train the conventional CNN-based segmentation models that aware of the shape and topology of organs. In this work, we tackle this problem by introducing a novel end-to-end shape learning architecture -- organ point-network. It takes deep learning features as inputs and generates organ shape representations as points that located on organ surface. We later present a novel adversarial shape learning objective function to optimize the point-network to capture shape information better. We train the point-network together with a CNN-based segmentation model in a multi-task fashion so that the shared network parameters can benefit from both shape learning and segmentation tasks. We demonstrate our method with three challenging abdomen organs including liver, spleen, and pancreas. The point-network generates surface points with fine-grained details and it is found critical for improving organ segmentation. Consequently, the deep segmentation model is improved by the introduced shape learning as significantly better Dice scores are observed for spleen and pancreas segmentation.", "published": "2019-10-15T01:48:29Z", "authors": ["Jinzheng Cai", "Yingda Xia", "Dong Yang", "Daguang Xu", "Lin Yang", "Holger Roth"], "categories": ["cs.CV"], "doi": "10.1007/978-3-030-32692-0_15", "_query": "real estate valuation deep learning", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/1805.10451v2", "title": "Geometric Understanding of Deep Learning", "abstract": "Deep learning is the mainstream technique for many machine learning tasks, including image recognition, machine translation, speech recognition, and so on. It has outperformed conventional methods in various fields and achieved great successes. Unfortunately, the understanding on how it works remains unclear. It has the central importance to lay down the theoretic foundation for deep learning. In this work, we give a geometric view to understand deep learning: we show that the fundamental principle attributing to the success is the manifold structure in data, namely natural high dimensional data concentrates close to a low-dimensional manifold, deep learning learns the manifold and the probability distribution on it. We further introduce the concepts of rectified linear complexity for deep neural network measuring its learning capability, rectified linear complexity of an embedding manifold describing the difficulty to be learned. Then we show for any deep neural network with fixed architecture, there exists a manifold that cannot be learned by the network. Finally, we propose to apply optimal mass transportation theory to control the probability distribution in the latent space.", "published": "2018-05-26T09:15:53Z", "authors": ["Na Lei", "Zhongxuan Luo", "Shing-Tung Yau", "David Xianfeng Gu"], "categories": ["cs.LG", "stat.ML"], "doi": "", "_query": "real estate valuation deep learning", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2007.14313v2", "title": "Deep frequency principle towards understanding why deeper learning is faster", "abstract": "Understanding the effect of depth in deep learning is a critical problem. In this work, we utilize the Fourier analysis to empirically provide a promising mechanism to understand why feedforward deeper learning is faster. To this end, we separate a deep neural network, trained by normal stochastic gradient descent, into two parts during analysis, i.e., a pre-condition component and a learning component, in which the output of the pre-condition one is the input of the learning one. We use a filtering method to characterize the frequency distribution of a high-dimensional function. Based on experiments of deep networks and real dataset, we propose a deep frequency principle, that is, the effective target function for a deeper hidden layer biases towards lower frequency during the training. Therefore, the learning component effectively learns a lower frequency function if the pre-condition component has more layers. Due to the well-studied frequency principle, i.e., deep neural networks learn lower frequency functions faster, the deep frequency principle provides a reasonable explanation to why deeper learning is faster. We believe these empirical studies would be valuable for future theoretical studies of the effect of depth in deep learning.", "published": "2020-07-28T15:35:49Z", "authors": ["Zhi-Qin John Xu", "Hanxu Zhou"], "categories": ["cs.LG", "stat.ML"], "doi": "", "_query": "real estate valuation deep learning", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2008.10870v2", "title": "Deep Q-Learning: Theoretical Insights from an Asymptotic Analysis", "abstract": "Deep Q-Learning is an important reinforcement learning algorithm, which involves training a deep neural network, called Deep Q-Network (DQN), to approximate the well-known Q-function. Although wildly successful under laboratory conditions, serious gaps between theory and practice as well as a lack of formal guarantees prevent its use in the real world. Adopting a dynamical systems perspective, we provide a theoretical analysis of a popular version of Deep Q-Learning under realistic and verifiable assumptions. More specifically, we prove an important result on the convergence of the algorithm, characterizing the asymptotic behavior of the learning process. Our result sheds light on hitherto unexplained properties of the algorithm and helps understand empirical observations, such as performance inconsistencies even after training. Unlike previous theories, our analysis accommodates state Markov processes with multiple stationary distributions. In spite of the focus on Deep Q-Learning, we believe that our theory may be applied to understand other deep learning algorithms", "published": "2020-08-25T07:59:20Z", "authors": ["Arunselvan Ramaswamy", "Eyke Hüllermeier"], "categories": ["cs.LG", "cs.AI", "stat.ML"], "doi": "", "_query": "real estate valuation deep learning", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/1812.03057v1", "title": "Open Problems in Engineering and Quality Assurance of Safety Critical Machine Learning Systems", "abstract": "Fatal accidents are a major issue hindering the wide acceptance of safety-critical systems using machine-learning and deep-learning models, such as automated-driving vehicles. Quality assurance frameworks are required for such machine learning systems, but there are no widely accepted and established quality-assurance concepts and techniques. At the same time, open problems and the relevant technical fields are not organized. To establish standard quality assurance frameworks, it is necessary to visualize and organize these open problems in an interdisciplinary way, so that the experts from many different technical fields may discuss these problems in depth and develop solutions. In the present study, we identify, classify, and explore the open problems in quality assurance of safety-critical machine-learning systems, and their relevant corresponding industry and technological trends, using automated-driving vehicles as an example. Our results show that addressing these open problems requires incorporating knowledge from several different technological and industrial fields, including the automobile industry, statistics, software engineering, and machine learning.", "published": "2018-12-07T15:02:40Z", "authors": ["Hiroshi Kuwajima", "Hirotoshi Yasuoka", "Toshihiro Nakae"], "categories": ["cs.CY", "cs.LG", "stat.ML"], "doi": "", "_query": "real estate valuation deep learning", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2201.12347v1", "title": "Adversarial Robustness in Deep Learning: Attacks on Fragile Neurons", "abstract": "We identify fragile and robust neurons of deep learning architectures using nodal dropouts of the first convolutional layer. Using an adversarial targeting algorithm, we correlate these neurons with the distribution of adversarial attacks on the network. Adversarial robustness of neural networks has gained significant attention in recent times and highlights intrinsic weaknesses of deep learning networks against carefully constructed distortion applied to input images. In this paper, we evaluate the robustness of state-of-the-art image classification models trained on the MNIST and CIFAR10 datasets against the fast gradient sign method attack, a simple yet effective method of deceiving neural networks. Our method identifies the specific neurons of a network that are most affected by the adversarial attack being applied. We, therefore, propose to make fragile neurons more robust against these attacks by compressing features within robust neurons and amplifying the fragile neurons proportionally.", "published": "2022-01-31T14:34:07Z", "authors": ["Chandresh Pravin", "Ivan Martino", "Giuseppe Nicosia", "Varun Ojha"], "categories": ["cs.LG", "cs.CR"], "doi": "10.1007/978-3-030-86362-3_2", "_query": "real estate valuation deep learning", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2105.14275v4", "title": "Greedy Bayesian Posterior Approximation with Deep Ensembles", "abstract": "Ensembles of independently trained neural networks are a state-of-the-art approach to estimate predictive uncertainty in Deep Learning, and can be interpreted as an approximation of the posterior distribution via a mixture of delta functions. The training of ensembles relies on non-convexity of the loss landscape and random initialization of their individual members, making the resulting posterior approximation uncontrolled. This paper proposes a novel and principled method to tackle this limitation, minimizing an $f$-divergence between the true posterior and a kernel density estimator (KDE) in a function space. We analyze this objective from a combinatorial point of view, and show that it is submodular with respect to mixture components for any $f$. Subsequently, we consider the problem of greedy ensemble construction. From the marginal gain on the negative $f$-divergence, which quantifies an improvement in posterior approximation yielded by adding a new component into the KDE, we derive a novel diversity term for ensemble methods. The performance of our approach is demonstrated on computer vision out-of-distribution detection benchmarks in a range of architectures trained on multiple datasets. The source code of our method is made publicly available at https://github.com/Oulu-IMEDS/greedy_ensembles_training.", "published": "2021-05-29T11:35:27Z", "authors": ["Aleksei Tiulpin", "Matthew B. Blaschko"], "categories": ["cs.LG", "cs.CV"], "doi": "", "_query": "real estate valuation deep learning", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2007.03051v1", "title": "Carbontracker: Tracking and Predicting the Carbon Footprint of Training Deep Learning Models", "abstract": "Deep learning (DL) can achieve impressive results across a wide variety of tasks, but this often comes at the cost of training models for extensive periods on specialized hardware accelerators. This energy-intensive workload has seen immense growth in recent years. Machine learning (ML) may become a significant contributor to climate change if this exponential trend continues. If practitioners are aware of their energy and carbon footprint, then they may actively take steps to reduce it whenever possible. In this work, we present Carbontracker, a tool for tracking and predicting the energy and carbon footprint of training DL models. We propose that energy and carbon footprint of model development and training is reported alongside performance metrics using tools like Carbontracker. We hope this will promote responsible computing in ML and encourage research into energy-efficient deep neural networks.", "published": "2020-07-06T20:24:31Z", "authors": ["Lasse F. Wolff Anthony", "Benjamin Kanding", "Raghavendra Selvan"], "categories": ["cs.CY", "cs.LG", "eess.SP", "stat.ML"], "doi": "", "_query": "real estate valuation deep learning", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2110.04442v2", "title": "A Primer on Deep Learning for Causal Inference", "abstract": "This review systematizes the emerging literature for causal inference using deep neural networks under the potential outcomes framework. It provides an intuitive introduction on how deep learning can be used to estimate/predict heterogeneous treatment effects and extend causal inference to settings where confounding is non-linear, time varying, or encoded in text, networks, and images. To maximize accessibility, we also introduce prerequisite concepts from causal inference and deep learning. The survey differs from other treatments of deep learning and causal inference in its sharp focus on observational causal estimation, its extended exposition of key algorithms, and its detailed tutorials for implementing, training, and selecting among deep estimators in Tensorflow 2 available at github.com/kochbj/Deep-Learning-for-Causal-Inference.", "published": "2021-10-09T03:25:01Z", "authors": ["Bernard Koch", "Tim Sainburg", "Pablo Geraldo", "Song Jiang", "Yizhou Sun", "Jacob Gates Foster"], "categories": ["cs.LG", "econ.EM", "stat.ME", "stat.ML"], "doi": "", "_query": "real estate valuation deep learning", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2407.18439v1", "title": "Impact of Recurrent Neural Networks and Deep Learning Frameworks on Real-time Lightweight Time Series Anomaly Detection", "abstract": "Real-time lightweight time series anomaly detection has become increasingly crucial in cybersecurity and many other domains. Its ability to adapt to unforeseen pattern changes and swiftly identify anomalies enables prompt responses and critical decision-making. While several such anomaly detection approaches have been introduced in recent years, they primarily utilize a single type of recurrent neural networks (RNNs) and have been implemented in only one deep learning framework. It is unclear how the use of different types of RNNs available in various deep learning frameworks affects the performance of these anomaly detection approaches due to the absence of comprehensive evaluations. Arbitrarily choosing a RNN variant and a deep learning framework to implement an anomaly detection approach may not reflect its true performance and could potentially mislead users into favoring one approach over another. In this paper, we aim to study the influence of various types of RNNs available in popular deep learning frameworks on real-time lightweight time series anomaly detection. We reviewed several state-of-the-art approaches and implemented a representative anomaly detection approach using well-known RNN variants supported by three widely recognized deep learning frameworks. A comprehensive evaluation is then conducted to analyze the performance of each implementation across real-world, open-source time series datasets. The evaluation results provide valuable guidance for selecting the appropriate RNN variant and deep learning framework for real-time, lightweight time series anomaly detection.", "published": "2024-07-26T00:38:51Z", "authors": ["Ming-Chang Lee", "Jia-Chun Lin", "Sokratis Katsikas"], "categories": ["cs.LG"], "doi": "", "_query": "real estate valuation deep learning", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2410.19241v2", "title": "Enhancing Exchange Rate Forecasting with Explainable Deep Learning Models", "abstract": "Accurate exchange rate prediction is fundamental to financial stability and international trade, positioning it as a critical focus in economic and financial research. Traditional forecasting models often falter when addressing the inherent complexities and non-linearities of exchange rate data. This study explores the application of advanced deep learning models, including LSTM, CNN, and transformer-based architectures, to enhance the predictive accuracy of the RMB/USD exchange rate. Utilizing 40 features across 6 categories, the analysis identifies TSMixer as the most effective model for this task. A rigorous feature selection process emphasizes the inclusion of key economic indicators, such as China-U.S. trade volumes and exchange rates of other major currencies like the euro-RMB and yen-dollar pairs. The integration of grad-CAM visualization techniques further enhances model interpretability, allowing for clearer identification of the most influential features and bolstering the credibility of the predictions. These findings underscore the pivotal role of fundamental economic data in exchange rate forecasting and highlight the substantial potential of machine learning models to deliver more accurate and reliable predictions, thereby serving as a valuable tool for financial analysis and decision-making.", "published": "2024-10-25T01:29:54Z", "authors": ["Shuchen Meng", "Andi Chen", "Chihang Wang", "Mengyao Zheng", "Fangyu Wu", "Xupeng Chen", "Haowei Ni", "Panfeng Li"], "categories": ["cs.LG"], "doi": "10.1109/EIECS63941.2024.10800545", "_query": "real estate valuation deep learning", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2405.17464v2", "title": "Data Valuation by Fusing Global and Local Statistical Information", "abstract": "Data valuation has garnered increasing attention in recent years, given the critical role of high-quality data in various applications. Among diverse data valuation approaches, Shapley value-based methods are predominant due to their strong theoretical grounding. However, the exact computation of Shapley values is often computationally prohibitive, prompting the development of numerous approximation techniques. Despite notable advancements, existing methods generally neglect the incorporation of value distribution information and fail to account for dynamic data conditions, thereby compromising their performance and application potential. In this paper, we highlight the crucial role of both global and local statistical properties of value distributions in the context of data valuation for machine learning. First, we conduct a comprehensive analysis of these distributions across various simulated and real-world datasets, uncovering valuable insights and key patterns. Second, we propose an enhanced data valuation method that fuses the explored distribution characteristics into two regularization terms to refine Shapley value estimation. The proposed regularizers can be seamlessly incorporated into various existing data valuation methods. Third, we introduce a novel approach for dynamic data valuation that infers updated data values without recomputing Shapley values, thereby significantly improving computational efficiency. Extensive experiments have been conducted across a range of tasks, including Shapley value estimation, value-based data addition and removal, mislabeled data detection, and dynamic data valuation. The results showcase the consistent effectiveness and efficiency of our proposed methodologies, affirming the significant potential of global and local value distributions in data valuation.", "published": "2024-05-23T08:58:08Z", "authors": ["Xiaoling Zhou", "Ou Wu", "Michael K. Ng", "Hao Jiang"], "categories": ["cs.LG", "cs.AI", "stat.ML"], "doi": "", "_query": "real estate valuation deep learning", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/1511.02222v1", "title": "Deep Kernel Learning", "abstract": "We introduce scalable deep kernels, which combine the structural properties of deep learning architectures with the non-parametric flexibility of kernel methods. Specifically, we transform the inputs of a spectral mixture base kernel with a deep architecture, using local kernel interpolation, inducing points, and structure exploiting (Kronecker and Toeplitz) algebra for a scalable kernel representation. These closed-form kernels can be used as drop-in replacements for standard kernels, with benefits in expressive power and scalability. We jointly learn the properties of these kernels through the marginal likelihood of a Gaussian process. Inference and learning cost $O(n)$ for $n$ training points, and predictions cost $O(1)$ per test point. On a large and diverse collection of applications, including a dataset with 2 million examples, we show improved performance over scalable Gaussian processes with flexible kernel learning models, and stand-alone deep architectures.", "published": "2015-11-06T20:38:08Z", "authors": ["Andrew Gordon Wilson", "Zhiting Hu", "Ruslan Salakhutdinov", "Eric P. Xing"], "categories": ["cs.LG", "cs.AI", "stat.ME", "stat.ML"], "doi": "", "_query": "real estate valuation deep learning", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2111.09115v1", "title": "Using Deep Learning to Identify Patients with Cognitive Impairment in Electronic Health Records", "abstract": "Dementia is a neurodegenerative disorder that causes cognitive decline and affects more than 50 million people worldwide. Dementia is under-diagnosed by healthcare professionals - only one in four people who suffer from dementia are diagnosed. Even when a diagnosis is made, it may not be entered as a structured International Classification of Diseases (ICD) diagnosis code in a patient's charts. Information relevant to cognitive impairment (CI) is often found within electronic health records (EHR), but manual review of clinician notes by experts is both time consuming and often prone to errors. Automated mining of these notes presents an opportunity to label patients with cognitive impairment in EHR data. We developed natural language processing (NLP) tools to identify patients with cognitive impairment and demonstrate that linguistic context enhances performance for the cognitive impairment classification task. We fine-tuned our attention based deep learning model, which can learn from complex language structures, and substantially improved accuracy (0.93) relative to a baseline NLP model (0.84). Further, we show that deep learning NLP can successfully identify dementia patients without dementia-related ICD codes or medications.", "published": "2021-11-13T01:44:10Z", "authors": ["Tanish Tyagi", "Colin G. Magdamo", "Ayush Noori", "Zhaozhi Li", "Xiao Liu", "Mayuresh Deodhar", "Zhuoqiao Hong", "Wendong Ge", "Elissa M. Ye", "Yi-han Sheu", "Haitham Alabsi", "Laura Brenner", "Gregory K. Robbins", "Sahar Zafar", "Nicole Benson", "Lidia Moura", "John Hsu", "Alberto Serrano-Pozo", "Dimitry Prokopenko", "Rudolph E. Tanzi", "Bradley T. Hyman", "Deborah Blacker", "Shibani S. Mukerji", "M. Brandon Westover", "Sudeshna Das"], "categories": ["cs.CL", "cs.LG"], "doi": "", "_query": "real estate valuation deep learning", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2206.05255v1", "title": "Interactively Learning Preference Constraints in Linear Bandits", "abstract": "We study sequential decision-making with known rewards and unknown constraints, motivated by situations where the constraints represent expensive-to-evaluate human preferences, such as safe and comfortable driving behavior. We formalize the challenge of interactively learning about these constraints as a novel linear bandit problem which we call constrained linear best-arm identification. To solve this problem, we propose the Adaptive Constraint Learning (ACOL) algorithm. We provide an instance-dependent lower bound for constrained linear best-arm identification and show that ACOL's sample complexity matches the lower bound in the worst-case. In the average case, ACOL's sample complexity bound is still significantly tighter than bounds of simpler approaches. In synthetic experiments, ACOL performs on par with an oracle solution and outperforms a range of baselines. As an application, we consider learning constraints to represent human preferences in a driving simulation. ACOL is significantly more sample efficient than alternatives for this application. Further, we find that learning preferences as constraints is more robust to changes in the driving scenario than encoding the preferences directly in the reward function.", "published": "2022-06-10T17:52:58Z", "authors": ["David Lindner", "Sebastian Tschiatschek", "Katja Hofmann", "Andreas Krause"], "categories": ["cs.LG", "cs.AI", "stat.ML"], "doi": "", "_query": "real estate valuation deep learning", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2103.13192v3", "title": "On Preference Learning Based on Sequential Bayesian Optimization with Pairwise Comparison", "abstract": "User preference learning is generally a hard problem. Individual preferences are typically unknown even to users themselves, while the space of choices is infinite. Here we study user preference learning from information-theoretic perspective. We model preference learning as a system with two interacting sub-systems, one representing a user with his/her preferences and another one representing an agent that has to learn these preferences. The user with his/her behaviour is modeled by a parametric preference function. To efficiently learn the preferences and reduce search space quickly, we propose the agent that interacts with the user to collect the most informative data for learning. The agent presents two proposals to the user for evaluation, and the user rates them based on his/her preference function. We show that the optimum agent strategy for data collection and preference learning is a result of maximin optimization of the normalized weighted Kullback-Leibler (KL) divergence between true and agent-assigned predictive user response distributions. The resulting value of KL-divergence, which we also call remaining system uncertainty (RSU), provides an efficient performance metric in the absence of the ground truth. This metric characterises how well the agent can predict user and, thus, the quality of the underlying learned user (preference) model. Our proposed agent comprises sequential mechanisms for user model inference and proposal generation. To infer the user model (preference function), Bayesian approximate inference is used in the agent. The data collection strategy is to generate proposals, responses to which help resolving uncertainty associated with prediction of the user responses the most. The efficiency of our approach is validated by numerical simulations. Also a real-life example of preference learning application is provided.", "published": "2021-03-24T13:46:27Z", "authors": ["Tanya Ignatenko", "Kirill Kondrashov", "Marco Cox", "Bert de Vries"], "categories": ["cs.LG", "cs.AI", "cs.IT", "stat.ML"], "doi": "", "_query": "real estate valuation deep learning", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2311.06066v1", "title": "Lidar-based Norwegian tree species detection using deep learning", "abstract": "Background: The mapping of tree species within Norwegian forests is a time-consuming process, involving forest associations relying on manual labeling by experts. The process can involve both aerial imagery, personal familiarity, or on-scene references, and remote sensing data. The state-of-the-art methods usually use high resolution aerial imagery with semantic segmentation methods. Methods: We present a deep learning based tree species classification model utilizing only lidar (Light Detection And Ranging) data. The lidar images are segmented into four classes (Norway Spruce, Scots Pine, Birch, background) with a U-Net based network. The model is trained with focal loss over partial weak labels. A major benefit of the approach is that both the lidar imagery and the base map for the labels have free and open access. Results: Our tree species classification model achieves a macro-averaged F1 score of 0.70 on an independent validation with National Forest Inventory (NFI) in-situ sample plots. That is close to, but below the performance of aerial, or aerial and lidar combined models.", "published": "2023-11-10T14:01:05Z", "authors": ["Martijn Vermeer", "Jacob Alexander Hay", "David Völgyes", "Zsófia Koma", "Johannes Breidenbach", "Daniele Stefano Maria Fantin"], "categories": ["cs.CV", "eess.IV"], "doi": "", "_query": "real estate valuation deep learning", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/1911.05121v1", "title": "Detecting Patterns of Physiological Response to Hemodynamic Stress via Unsupervised Deep Learning", "abstract": "Monitoring physiological responses to hemodynamic stress can help in determining appropriate treatment and ensuring good patient outcomes. Physicians' intuition suggests that the human body has a number of physiological response patterns to hemorrhage which escalate as blood loss continues, however the exact etiology and phenotypes of such responses are not well known or understood only at a coarse level. Although previous research has shown that machine learning models can perform well in hemorrhage detection and survival prediction, it is unclear whether machine learning could help to identify and characterize the underlying physiological responses in raw vital sign data. We approach this problem by first transforming the high-dimensional vital sign time series into a tractable, lower-dimensional latent space using a dilated, causal convolutional encoder model trained purely unsupervised. Second, we identify informative clusters in the embeddings. By analyzing the clusters of latent embeddings and visualizing them over time, we hypothesize that the clusters correspond to the physiological response patterns that match physicians' intuition. Furthermore, we attempt to evaluate the latent embeddings using a variety of methods, such as predicting the cluster labels using explainable features.", "published": "2019-11-12T19:55:16Z", "authors": ["Chufan Gao", "Fabian Falck", "Mononito Goswami", "Anthony Wertz", "Michael R. Pinsky", "Artur Dubrawski"], "categories": ["cs.LG", "stat.ML"], "doi": "", "_query": "real estate valuation deep learning", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2211.07941v1", "title": "Automatic Evaluation of Excavator Operators using Learned Reward Functions", "abstract": "Training novice users to operate an excavator for learning different skills requires the presence of expert teachers. Considering the complexity of the problem, it is comparatively expensive to find skilled experts as the process is time-consuming and requires precise focus. Moreover, since humans tend to be biased, the evaluation process is noisy and will lead to high variance in the final score of different operators with similar skills. In this work, we address these issues and propose a novel strategy for the automatic evaluation of excavator operators. We take into account the internal dynamics of the excavator and the safety criterion at every time step to evaluate the performance. To further validate our approach, we use this score prediction model as a source of reward for a reinforcement learning agent to learn the task of maneuvering an excavator in a simulated environment that closely replicates the real-world dynamics. For a policy learned using these external reward prediction models, our results demonstrate safer solutions following the required dynamic constraints when compared to policy trained with task-based reward functions only, making it one step closer to real-life adoption. For future research, we release our codebase at https://github.com/pranavAL/InvRL_Auto-Evaluate and video results https://drive.google.com/file/d/1jR1otOAu8zrY8mkhUOUZW9jkBOAKK71Z/view?usp=share_link .", "published": "2022-11-15T06:58:00Z", "authors": ["Pranav Agarwal", "Marek Teichmann", "Sheldon Andrews", "Samira Ebrahimi Kahou"], "categories": ["cs.RO", "cs.AI", "cs.LG"], "doi": "", "_query": "real estate valuation deep learning", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2105.08382v2", "title": "Transfer learning approach to Classify the X-ray image that corresponds to corona disease Using ResNet50 pretrained by ChexNet", "abstract": "Coronavirus adversely has affected people worldwide. There are common symptoms between the Covid19 virus disease and other respiratory diseases like pneumonia or Influenza. Therefore, diagnosing it fast is crucial not only to save patients but also to prevent it from spreading. One of the most reliant methods of diagnosis is through X-ray images of a lung. With the help of deep learning approaches, we can teach the deep model to learn the condition of an affected lung. Therefore, it can classify the new sample as if it is a Covid19 infected patient or not. In this project, we train a deep model based on ResNet50 pretrained by ImageNet dataset and CheXNet dataset. Based on the imbalanced CoronaHack Chest X-Ray dataset introducing by Kaggle we applied both binary and multi-class classification. Also, we compare the results when using Focal loss and Cross entropy loss.", "published": "2021-05-18T09:12:23Z", "authors": ["Mahyar Bolhassani"], "categories": ["eess.IV", "cs.CV"], "doi": "10.4236/jilsa.2024.162006", "_query": "real estate valuation deep learning", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2410.08934v4", "title": "Understanding the Statistical Accuracy-Communication Trade-off in Personalized Federated Learning with Minimax Guarantees", "abstract": "Personalized federated learning (PFL) offers a flexible framework for aggregating information across distributed clients with heterogeneous data. This work considers a personalized federated learning setting that simultaneously learns global and local models. While purely local training has no communication cost, collaborative learning among the clients can leverage shared knowledge to improve statistical accuracy, presenting an accuracy-communication trade-off in personalized federated learning. However, the theoretical analysis of how personalization quantitatively influences sample and algorithmic efficiency and their inherent trade-off is largely unexplored. This paper makes a contribution towards filling this gap, by providing a quantitative characterization of the personalization degree on the tradeoff. The results further offers theoretical insights for choosing the personalization degree. As a side contribution, we establish the minimax optimality in terms of statistical accuracy for a widely studied PFL formulation. The theoretical result is validated on both synthetic and real-world datasets and its generalizability is verified in a non-convex setting.", "published": "2024-10-11T16:00:21Z", "authors": ["Xin Yu", "Zelin He", "Ying Sun", "Lingzhou Xue", "Runze Li"], "categories": ["stat.ML", "cs.DC", "cs.LG", "math.ST", "stat.CO"], "doi": "", "_query": "real estate valuation deep learning", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2107.00218v1", "title": "Comparing Example-Based Collaborative Reflection to Problem Solving Practice for Learning during Team-Based Software Engineering Projects", "abstract": "Contributing to the literature on aptitude-treatment interactions between worked examples and problem-solving, this paper addresses differential learning from the two approaches when students are positioned as domain experts learning new concepts. Our evaluation is situated in a team project that is part of an advanced software engineering course. In this course, students who possess foundational domain knowledge but are learning new concepts engage alternatively in programming followed by worked example-based reflection. They are either allowed to finish programming or are curtailed after a pre-specified time to participate in a longer worked example-based reflection. We find significant pre- to post-test learning gains in both conditions. Then, we not only find significantly more learning when students participated in longer worked example-based reflections but also a significant performance improvement on a problem-solving transfer task. These findings suggest that domain experts learning new concepts benefit more from worked example-based reflections than from problem-solving.", "published": "2021-07-01T05:04:24Z", "authors": ["Sreecharan Sankaranarayanan", "Siddharth Reddy Kandimalla", "Christopher Bogart", "R. Charles Murray", "Haokang An", "Michael Hilton", "Majd Sakr", "Carolyn Rosé"], "categories": ["cs.SE"], "doi": "", "_query": "real estate valuation deep learning", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2009.01871v3", "title": "Federated Learning for Breast Density Classification: A Real-World Implementation", "abstract": "Building robust deep learning-based models requires large quantities of diverse training data. In this study, we investigate the use of federated learning (FL) to build medical imaging classification models in a real-world collaborative setting. Seven clinical institutions from across the world joined this FL effort to train a model for breast density classification based on Breast Imaging, Reporting & Data System (BI-RADS). We show that despite substantial differences among the datasets from all sites (mammography system, class distribution, and data set size) and without centralizing data, we can successfully train AI models in federation. The results show that models trained using FL perform 6.3% on average better than their counterparts trained on an institute's local data alone. Furthermore, we show a 45.8% relative improvement in the models' generalizability when evaluated on the other participating sites' testing data.", "published": "2020-09-03T18:34:59Z", "authors": ["Holger R. Roth", "Ken Chang", "Praveer Singh", "Nir Neumark", "Wenqi Li", "Vikash Gupta", "Sharut Gupta", "Liangqiong Qu", "Alvin Ihsani", "Bernardo C. Bizzo", "Yuhong Wen", "Varun Buch", "Meesam Shah", "Felipe Kitamura", "Matheus Mendonça", "Vitor Lavor", "Ahmed Harouni", "Colin Compas", "Jesse Tetreault", "Prerna Dogra", "Yan Cheng", "Selnur Erdal", "Richard White", "Behrooz Hashemian", "Thomas Schultz", "Miao Zhang", "Adam McCarthy", "B. Min Yun", "Elshaimaa Sharaf", "Katharina V. Hoebel", "Jay B. Patel", "Bryan Chen", "Sean Ko", "Evan Leibovitz", "Etta D. Pisano", "Laura Coombs", "Daguang Xu", "Keith J. Dreyer", "Ittai Dayan", "Ram C. Naidu", "Mona Flores", "Daniel Rubin", "Jayashree Kalpathy-Cramer"], "categories": ["eess.IV", "cs.CV"], "doi": "10.1007/978-3-030-60548-3_18", "_query": "real estate valuation deep learning", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/1812.01478v1", "title": "Matrix Factorization via Deep Learning", "abstract": "Matrix completion is one of the key problems in signal processing and machine learning. In recent years, deep-learning-based models have achieved state-of-the-art results in matrix completion. Nevertheless, they suffer from two drawbacks: (i) they can not be extended easily to rows or columns unseen during training; and (ii) their results are often degraded in case discrete predictions are required. This paper addresses these two drawbacks by presenting a deep matrix factorization model and a generic method to allow joint training of the factorization model and the discretization operator. Experiments on a real movie rating dataset show the efficacy of the proposed models.", "published": "2018-12-04T15:15:23Z", "authors": ["Duc Minh Nguyen", "Evaggelia Tsiligianni", "Nikos Deligiannis"], "categories": ["cs.LG", "stat.ML"], "doi": "", "_query": "real estate valuation deep learning", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2203.11656v1", "title": "Is Vanilla Policy Gradient Overlooked? Analyzing Deep Reinforcement Learning for Hanabi", "abstract": "In pursuit of enhanced multi-agent collaboration, we analyze several on-policy deep reinforcement learning algorithms in the recently published Hanabi benchmark. Our research suggests a perhaps counter-intuitive finding, where Proximal Policy Optimization (PPO) is outperformed by Vanilla Policy Gradient over multiple random seeds in a simplified environment of the multi-agent cooperative card game. In our analysis of this behavior we look into Hanabi-specific metrics and hypothesize a reason for PPO's plateau. In addition, we provide proofs for the maximum length of a perfect game (71 turns) and any game (89 turns). Our code can be found at: https://github.com/bramgrooten/DeepRL-for-Hanabi", "published": "2022-03-22T12:28:06Z", "authors": ["Bram Grooten", "Jelle Wemmenhove", "Maurice Poot", "Jim Portegies"], "categories": ["cs.LG", "cs.AI", "cs.MA"], "doi": "", "_query": "real estate valuation deep learning", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/1806.06207v1", "title": "Meta-learning: searching in the model space", "abstract": "There is no free lunch, no single learning algorithm that will outperform other algorithms on all data. In practice different approaches are tried and the best algorithm selected. An alternative solution is to build new algorithms on demand by creating a framework that accommodates many algorithms. The best combination of parameters and procedures is searched here in the space of all possible models belonging to the framework of Similarity-Based Methods (SBMs). Such meta-learning approach gives a chance to find the best method in all cases. Issues related to the meta-learning and first tests of this approach are presented.", "published": "2018-06-16T08:24:35Z", "authors": ["Włodzisław Duch", "Karol Grudzińsk"], "categories": ["cs.LG", "cs.AI", "stat.ML"], "doi": "", "_query": "real estate valuation deep learning", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/1805.05206v2", "title": "DeepMutation: Mutation Testing of Deep Learning Systems", "abstract": "Deep learning (DL) defines a new data-driven programming paradigm where the internal system logic is largely shaped by the training data. The standard way of evaluating DL models is to examine their performance on a test dataset. The quality of the test dataset is of great importance to gain confidence of the trained models. Using an inadequate test dataset, DL models that have achieved high test accuracy may still lack generality and robustness. In traditional software testing, mutation testing is a well-established technique for quality evaluation of test suites, which analyzes to what extent a test suite detects the injected faults. However, due to the fundamental difference between traditional software and deep learning-based software, traditional mutation testing techniques cannot be directly applied to DL systems. In this paper, we propose a mutation testing framework specialized for DL systems to measure the quality of test data. To do this, by sharing the same spirit of mutation testing in traditional software, we first define a set of source-level mutation operators to inject faults to the source of DL (i.e., training data and training programs). Then we design a set of model-level mutation operators that directly inject faults into DL models without a training process. Eventually, the quality of test data could be evaluated from the analysis on to what extent the injected faults could be detected. The usefulness of the proposed mutation testing techniques is demonstrated on two public datasets, namely MNIST and CIFAR-10, with three DL models.", "published": "2018-05-14T14:57:44Z", "authors": ["Lei Ma", "Fuyuan Zhang", "Jiyuan Sun", "Minhui Xue", "Bo Li", "Felix Juefei-Xu", "Chao Xie", "Li Li", "Yang Liu", "Jianjun Zhao", "Yadong Wang"], "categories": ["cs.SE"], "doi": "", "_query": "real estate valuation deep learning", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2304.02146v2", "title": "Structure Learning with Continuous Optimization: A Sober Look and Beyond", "abstract": "This paper investigates in which cases continuous optimization for directed acyclic graph (DAG) structure learning can and cannot perform well and why this happens, and suggests possible directions to make the search procedure more reliable. Reisach et al. (2021) suggested that the remarkable performance of several continuous structure learning approaches is primarily driven by a high agreement between the order of increasing marginal variances and the topological order, and demonstrated that these approaches do not perform well after data standardization. We analyze this phenomenon for continuous approaches assuming equal and non-equal noise variances, and show that the statement may not hold in either case by providing counterexamples, justifications, and possible alternative explanations. We further demonstrate that nonconvexity may be a main concern especially for the non-equal noise variances formulation, while recent advances in continuous structure learning fail to achieve improvement in this case. Our findings suggest that future works should take into account the non-equal noise variances formulation to handle more general settings and for a more comprehensive empirical evaluation. Lastly, we provide insights into other aspects of the search procedure, including thresholding and sparsity, and show that they play an important role in the final solutions.", "published": "2023-04-04T22:10:40Z", "authors": ["Ignavier Ng", "Biwei Huang", "Kun Zhang"], "categories": ["cs.LG", "stat.ML"], "doi": "", "_query": "real estate valuation deep learning", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/1502.06309v3", "title": "Learning with Differential Privacy: Stability, Learnability and the Sufficiency and Necessity of ERM Principle", "abstract": "While machine learning has proven to be a powerful data-driven solution to many real-life problems, its use in sensitive domains has been limited due to privacy concerns. A popular approach known as **differential privacy** offers provable privacy guarantees, but it is often observed in practice that it could substantially hamper learning accuracy. In this paper we study the learnability (whether a problem can be learned by any algorithm) under Vapnik's general learning setting with differential privacy constraint, and reveal some intricate relationships between privacy, stability and learnability. In particular, we show that a problem is privately learnable **if an only if** there is a private algorithm that asymptotically minimizes the empirical risk (AERM). In contrast, for non-private learning AERM alone is not sufficient for learnability. This result suggests that when searching for private learning algorithms, we can restrict the search to algorithms that are AERM. In light of this, we propose a conceptual procedure that always finds a universally consistent algorithm whenever the problem is learnable under privacy constraint. We also propose a generic and practical algorithm and show that under very general conditions it privately learns a wide class of learning problems. Lastly, we extend some of the results to the more practical $(ε,δ)$-differential privacy and establish the existence of a phase-transition on the class of problems that are approximately privately learnable with respect to how small $δ$ needs to be.", "published": "2015-02-23T03:52:08Z", "authors": ["Yu-Xiang Wang", "Jing Lei", "Stephen E. Fienberg"], "categories": ["stat.ML", "cs.CR", "cs.LG"], "doi": "", "_query": "real estate valuation deep learning", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2005.01797v1", "title": "3D Printed Brain-Controlled Robot-Arm Prosthetic via Embedded Deep Learning from sEMG Sensors", "abstract": "In this paper, we present our work on developing robot arm prosthetic via deep learning. Our work proposes to use transfer learning techniques applied to the Google Inception model to retrain the final layer for surface electromyography (sEMG) classification. Data have been collected using the Thalmic Labs Myo Armband and used to generate graph images comprised of 8 subplots per image containing sEMG data captured from 40 data points per sensor, corresponding to the array of 8 sEMG sensors in the armband. Data captured were then classified into four categories (Fist, Thumbs Up, Open Hand, Rest) via using a deep learning model, Inception-v3, with transfer learning to train the model for accurate prediction of each on real-time input of new data. This trained model was then downloaded to the ARM processor based embedding system to enable the brain-controlled robot-arm prosthetic manufactured from our 3D printer. Testing of the functionality of the method, a robotic arm was produced using a 3D printer and off-the-shelf hardware to control it. SSH communication protocols are employed to execute python files hosted on an embedded Raspberry Pi with ARM processors to trigger movement on the robot arm of the predicted gesture.", "published": "2020-05-04T19:14:44Z", "authors": ["David Lonsdale", "Li Zhang", "Richard Jiang"], "categories": ["eess.SP", "cs.LG", "cs.RO"], "doi": "", "_query": "real estate valuation deep learning", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/1912.00690v1", "title": "EduBERT: Pretrained Deep Language Models for Learning Analytics", "abstract": "The use of large pretrained neural networks to create contextualized word embeddings has drastically improved performance on several natural language processing (NLP) tasks. These computationally expensive models have begun to be applied to domain-specific NLP tasks such as re-hospitalization prediction from clinical notes. This paper demonstrates that using large pretrained models produces excellent results on common learning analytics tasks. Pre-training deep language models using student forum data from a wide array of online courses improves performance beyond the state of the art on three text classification tasks. We also show that a smaller, distilled version of our model produces the best results on two of the three tasks while limiting computational cost. We make both models available to the research community at large.", "published": "2019-12-02T11:32:53Z", "authors": ["Benjamin Clavié", "Kobi Gal"], "categories": ["cs.CY", "cs.AI", "cs.CL", "cs.LG"], "doi": "", "_query": "real estate valuation deep learning", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2412.16336v2", "title": "Real Faults in Deep Learning Fault Benchmarks: How Real Are They?", "abstract": "As the adoption of Deep Learning (DL) systems continues to rise, an increasing number of approaches are being proposed to test these systems, localise faults within them, and repair those faults. The best attestation of effectiveness for such techniques is an evaluation that showcases their capability to detect, localise and fix real faults. To facilitate these evaluations, the research community has collected multiple benchmarks of real faults in DL systems. In this work, we perform a manual analysis of 490 faults from five different benchmarks and identify that 305 of them are eligible for our study. Our investigation focuses specifically on how well the bugs correspond to the sources they were extracted from, which fault types are represented, and whether the bugs are reproducible. Our findings indicate that only 15.4% of the faults satisfy our realism conditions. Our attempts to reproduce these faults were successful only in 54% of cases.", "published": "2024-12-20T20:52:10Z", "authors": ["Gunel Jahangirova", "Nargiz Humbatova", "Jinhan Kim", "Shin Yoo", "Paolo Tonella"], "categories": ["cs.SE", "cs.AI", "cs.LG"], "doi": "", "_query": "real estate valuation deep learning", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/1612.05730v2", "title": "Towards Wide Learning: Experiments in Healthcare", "abstract": "In this paper, a Wide Learning architecture is proposed that attempts to automate the feature engineering portion of the machine learning (ML) pipeline. Feature engineering is widely considered as the most time consuming and expert knowledge demanding portion of any ML task. The proposed feature recommendation approach is tested on 3 healthcare datasets: a) PhysioNet Challenge 2016 dataset of phonocardiogram (PCG) signals, b) MIMIC II blood pressure classification dataset of photoplethysmogram (PPG) signals and c) an emotion classification dataset of PPG signals. While the proposed method beats the state of the art techniques for 2nd and 3rd dataset, it reaches 94.38% of the accuracy level of the winner of PhysioNet Challenge 2016. In all cases, the effort to reach a satisfactory performance was drastically less (a few days) than manual feature engineering.", "published": "2016-12-17T11:00:49Z", "authors": ["Snehasis Banerjee", "Tanushyam Chattopadhyay", "Swagata Biswas", "Rohan Banerjee", "Anirban Dutta Choudhury", "Arpan Pal", "Utpal Garain"], "categories": ["stat.ML", "cs.LG"], "doi": "", "_query": "real estate valuation deep learning", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2104.12225v1", "title": "DC3: A learning method for optimization with hard constraints", "abstract": "Large optimization problems with hard constraints arise in many settings, yet classical solvers are often prohibitively slow, motivating the use of deep networks as cheap \"approximate solvers.\" Unfortunately, naive deep learning approaches typically cannot enforce the hard constraints of such problems, leading to infeasible solutions. In this work, we present Deep Constraint Completion and Correction (DC3), an algorithm to address this challenge. Specifically, this method enforces feasibility via a differentiable procedure, which implicitly completes partial solutions to satisfy equality constraints and unrolls gradient-based corrections to satisfy inequality constraints. We demonstrate the effectiveness of DC3 in both synthetic optimization tasks and the real-world setting of AC optimal power flow, where hard constraints encode the physics of the electrical grid. In both cases, DC3 achieves near-optimal objective values while preserving feasibility.", "published": "2021-04-25T18:21:59Z", "authors": ["Priya L. Donti", "David Rolnick", "J. Zico Kolter"], "categories": ["cs.LG", "math.OC", "stat.ML"], "doi": "", "_query": "real estate valuation deep learning", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2209.06119v6", "title": "APTx: better activation function than MISH, SWISH, and ReLU's variants used in deep learning", "abstract": "Activation Functions introduce non-linearity in the deep neural networks. This nonlinearity helps the neural networks learn faster and efficiently from the dataset. In deep learning, many activation functions are developed and used based on the type of problem statement. ReLU's variants, SWISH, and MISH are goto activation functions. MISH function is considered having similar or even better performance than SWISH, and much better than ReLU. In this paper, we propose an activation function named APTx which behaves similar to MISH, but requires lesser mathematical operations to compute. The lesser computational requirements of APTx does speed up the model training, and thus also reduces the hardware requirement for the deep learning model. Source code: https://github.com/mr-ravin/aptx_activation", "published": "2022-09-10T14:26:04Z", "authors": ["Ravin Kumar"], "categories": ["cs.LG", "cs.AI", "cs.CV", "cs.NE"], "doi": "10.51483/IJAIML.2.2.2022.56-61", "_query": "real estate valuation deep learning", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2103.03703v5", "title": "Semi-Supervised Federated Peer Learning for Skin Lesion Classification", "abstract": "Globally, Skin carcinoma is among the most lethal diseases. Millions of people are diagnosed with this cancer every year. Sill, early detection can decrease the medication cost and mortality rate substantially. The recent improvement in automated cancer classification using deep learning methods has reached a human-level performance requiring a large amount of annotated data assembled in one location, yet, finding such conditions usually is not feasible. Recently, federated learning (FL) has been proposed to train decentralized models in a privacy-preserved fashion depending on labeled data at the client-side, which is usually not available and costly. To address this, we propose \\verb!FedPerl!, a semi-supervised federated learning method. Our method is inspired by peer learning from educational psychology and ensemble averaging from committee machines. FedPerl builds communities based on clients' similarities. Then it encourages communities members to learn from each other to generate more accurate pseudo labels for the unlabeled data. We also proposed the peer anonymization (PA) technique to anonymize clients. As a core component of our method, PA is orthogonal to other methods without additional complexity and reduces the communication cost while enhancing performance. Finally, we propose a dynamic peer-learning policy that controls the learning stream to avoid any degradation in the performance, especially for individual clients. Our experimental setup consists of 71,000 skin lesion images collected from 5 publicly available datasets. We test our method in four different scenarios in SSFL. With few annotated data, FedPerl is on par with a state-of-the-art method in skin lesion classification in the standard setup while outperforming SSFLs and the baselines by 1.8% and 15.8%, respectively. Also, it generalizes better to unseen clients while being less sensitive to noisy ones.", "published": "2021-03-05T14:26:15Z", "authors": ["Tariq Bdair", "Nassir Navab", "Shadi Albarqouni"], "categories": ["cs.CV"], "doi": "", "_query": "real estate valuation deep learning", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/1903.05895v2", "title": "Learning Fast Algorithms for Linear Transforms Using Butterfly Factorizations", "abstract": "Fast linear transforms are ubiquitous in machine learning, including the discrete Fourier transform, discrete cosine transform, and other structured transformations such as convolutions. All of these transforms can be represented by dense matrix-vector multiplication, yet each has a specialized and highly efficient (subquadratic) algorithm. We ask to what extent hand-crafting these algorithms and implementations is necessary, what structural priors they encode, and how much knowledge is required to automatically learn a fast algorithm for a provided structured transform. Motivated by a characterization of fast matrix-vector multiplication as products of sparse matrices, we introduce a parameterization of divide-and-conquer methods that is capable of representing a large class of transforms. This generic formulation can automatically learn an efficient algorithm for many important transforms; for example, it recovers the $O(N \\log N)$ Cooley-Tukey FFT algorithm to machine precision, for dimensions $N$ up to $1024$. Furthermore, our method can be incorporated as a lightweight replacement of generic matrices in machine learning pipelines to learn efficient and compressible transformations. On a standard task of compressing a single hidden-layer network, our method exceeds the classification accuracy of unconstrained matrices on CIFAR-10 by 3.9 points -- the first time a structured approach has done so -- with 4X faster inference speed and 40X fewer parameters.", "published": "2019-03-14T10:20:38Z", "authors": ["Tri Dao", "Albert Gu", "Matthew Eichhorn", "Atri Rudra", "Christopher Ré"], "categories": ["cs.LG", "stat.ML"], "doi": "", "_query": "real estate valuation deep learning", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/1910.04420v1", "title": "Learning beyond Predefined Label Space via Bayesian Nonparametric Topic Modelling", "abstract": "In real world machine learning applications, testing data may contain some meaningful new categories that have not been seen in labeled training data. To simultaneously recognize new data categories and assign most appropriate category labels to the data actually from known categories, existing models assume the number of unknown new categories is pre-specified, though it is difficult to determine in advance. In this paper, we propose a Bayesian nonparametric topic model to automatically infer this number, based on the hierarchical Dirichlet process and the notion of latent Dirichlet allocation. Exact inference in our model is intractable, so we provide an efficient collapsed Gibbs sampling algorithm for approximate posterior inference. Extensive experiments on various text data sets show that: (a) compared with parametric approaches that use pre-specified true number of new categories, the proposed nonparametric approach can yield comparable performance; and (b) when the exact number of new categories is unavailable, i.e. the parametric approaches only have a rough idea about the new categories, our approach has evident performance advantages.", "published": "2019-10-10T08:15:08Z", "authors": ["Changying Du", "Fuzhen Zhuang", "Jia He", "Qing He", "Guoping Long"], "categories": ["cs.LG", "stat.ML"], "doi": "", "_query": "real estate valuation deep learning", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2105.05757v1", "title": "Exploring the Similarity of Representations in Model-Agnostic Meta-Learning", "abstract": "In past years model-agnostic meta-learning (MAML) has been one of the most promising approaches in meta-learning. It can be applied to different kinds of problems, e.g., reinforcement learning, but also shows good results on few-shot learning tasks. Besides their tremendous success in these tasks, it has still not been fully revealed yet, why it works so well. Recent work proposes that MAML rather reuses features than rapidly learns. In this paper, we want to inspire a deeper understanding of this question by analyzing MAML's representation. We apply representation similarity analysis (RSA), a well-established method in neuroscience, to the few-shot learning instantiation of MAML. Although some part of our analysis supports their general results that feature reuse is predominant, we also reveal arguments against their conclusion. The similarity-increase of layers closer to the input layers arises from the learning task itself and not from the model. In addition, the representations after inner gradient steps make a broader change to the representation than the changes during meta-training.", "published": "2021-05-12T16:20:40Z", "authors": ["Thomas Goerttler", "Klaus Obermayer"], "categories": ["cs.LG", "cs.AI", "cs.NE", "stat.ML"], "doi": "", "_query": "real estate valuation deep learning", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2010.13483v3", "title": "High Acceleration Reinforcement Learning for Real-World Juggling with Binary Rewards", "abstract": "Robots that can learn in the physical world will be important to en-able robots to escape their stiff and pre-programmed movements. For dynamic high-acceleration tasks, such as juggling, learning in the real-world is particularly challenging as one must push the limits of the robot and its actuation without harming the system, amplifying the necessity of sample efficiency and safety for robot learning algorithms. In contrast to prior work which mainly focuses on the learning algorithm, we propose a learning system, that directly incorporates these requirements in the design of the policy representation, initialization, and optimization. We demonstrate that this system enables the high-speed Barrett WAM manipulator to learn juggling two balls from 56 minutes of experience with a binary reward signal. The final policy juggles continuously for up to 33 minutes or about 4500 repeated catches. The videos documenting the learning process and the evaluation can be found at https://sites.google.com/view/jugglingbot", "published": "2020-10-26T11:13:47Z", "authors": ["Kai Ploeger", "Michael Lutter", "Jan Peters"], "categories": ["cs.RO", "cs.LG", "stat.ML"], "doi": "", "_query": "real estate valuation deep learning", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/1206.6476v1", "title": "Similarity Learning for Provably Accurate Sparse Linear Classification", "abstract": "In recent years, the crucial importance of metrics in machine learning algorithms has led to an increasing interest for optimizing distance and similarity functions. Most of the state of the art focus on learning Mahalanobis distances (requiring to fulfill a constraint of positive semi-definiteness) for use in a local k-NN algorithm. However, no theoretical link is established between the learned metrics and their performance in classification. In this paper, we make use of the formal framework of good similarities introduced by Balcan et al. to design an algorithm for learning a non PSD linear similarity optimized in a nonlinear feature space, which is then used to build a global linear classifier. We show that our approach has uniform stability and derive a generalization bound on the classification error. Experiments performed on various datasets confirm the effectiveness of our approach compared to state-of-the-art methods and provide evidence that (i) it is fast, (ii) robust to overfitting and (iii) produces very sparse classifiers.", "published": "2012-06-27T19:59:59Z", "authors": ["Aurelien Bellet", "Amaury Habrard", "Marc Sebban"], "categories": ["cs.LG", "cs.AI", "stat.ML"], "doi": "", "_query": "real estate valuation deep learning", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2405.14374v2", "title": "State-Constrained Offline Reinforcement Learning", "abstract": "Traditional offline reinforcement learning (RL) methods predominantly operate in a batch-constrained setting. This confines the algorithms to a specific state-action distribution present in the dataset, reducing the effects of distributional shift but restricting the policy to seen actions. In this paper, we alleviate this limitation by introducing state-constrained offline RL, a novel framework that focuses solely on the dataset's state distribution. This approach allows the policy to take high-quality out-of-distribution actions that lead to in-distribution states, significantly enhancing learning potential. The proposed setting not only broadens the learning horizon but also improves the ability to combine different trajectories from the dataset effectively, a desirable property inherent in offline RL. Our research is underpinned by theoretical findings that pave the way for subsequent advancements in this area. Additionally, we introduce StaCQ, a deep learning algorithm that achieves state-of-the-art performance on the D4RL benchmark datasets and aligns with our theoretical propositions. StaCQ establishes a strong baseline for forthcoming explorations in this domain.", "published": "2024-05-23T09:50:04Z", "authors": ["Charles A. Hepburn", "Yue Jin", "Giovanni Montana"], "categories": ["stat.ML", "cs.AI", "cs.LG"], "doi": "", "_query": "real estate valuation deep learning", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/1912.03980v1", "title": "Hybrid Physical-Deep Learning Model for Astronomical Inverse Problems", "abstract": "We present a Bayesian machine learning architecture that combines a physically motivated parametrization and an analytic error model for the likelihood with a deep generative model providing a powerful data-driven prior for complex signals. This combination yields an interpretable and differentiable generative model, allows the incorporation of prior knowledge, and can be utilized for observations with different data quality without having to retrain the deep network. We demonstrate our approach with an example of astronomical source separation in current imaging data, yielding a physical and interpretable model of astronomical scenes.", "published": "2019-12-09T11:46:56Z", "authors": ["Francois Lanusse", "Peter Melchior", "Fred Moolekamp"], "categories": ["astro-ph.IM", "cs.LG"], "doi": "", "_query": "real estate valuation deep learning", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2006.12122v2", "title": "Learning with AMIGo: Adversarially Motivated Intrinsic Goals", "abstract": "A key challenge for reinforcement learning (RL) consists of learning in environments with sparse extrinsic rewards. In contrast to current RL methods, humans are able to learn new skills with little or no reward by using various forms of intrinsic motivation. We propose AMIGo, a novel agent incorporating -- as form of meta-learning -- a goal-generating teacher that proposes Adversarially Motivated Intrinsic Goals to train a goal-conditioned \"student\" policy in the absence of (or alongside) environment reward. Specifically, through a simple but effective \"constructively adversarial\" objective, the teacher learns to propose increasingly challenging -- yet achievable -- goals that allow the student to learn general skills for acting in a new environment, independent of the task to be solved. We show that our method generates a natural curriculum of self-proposed goals which ultimately allows the agent to solve challenging procedurally-generated tasks where other forms of intrinsic motivation and state-of-the-art RL methods fail.", "published": "2020-06-22T10:22:08Z", "authors": ["Andres Campero", "Roberta Raileanu", "Heinrich Küttler", "Joshua B. Tenenbaum", "Tim Rocktäschel", "Edward Grefenstette"], "categories": ["cs.LG", "cs.AI", "stat.ML"], "doi": "", "_query": "real estate valuation deep learning", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2406.14585v1", "title": "Deep-learning-assisted reconfigurable metasurface antenna for real-time holographic beam steering", "abstract": "We propose a metasurface antenna capable of real time holographic beam steering. An array of reconfigurable dipoeles can generate on demand far field patterns of radiation through the specific encoding of meta atomic states. i.e., the configuration of each dipole. Suitable states for the generation of the desired patterns can be identified using iteartion, but this is very slow and needs to be done for each far field pattern. Here, we present a deep learning based method for the control of a metasurface antenna with point dipole elements that vary in their state using dipole polarizability. Instead of iteration, we adopt a deep learning algorithm that combines an autoencoder with an electromagnetic scattering equation to determin the states required for a target far field pattern in real time. The scattering equation from Born approximation is used as the decoder in training the neural network, and analytic Green's function calculation is used to check the validity of Born approximation. Our learning based algorithm requires a computing time of within in 200 microseconds to determine the meta atomic states, thus enabling the real time opeartion of a holographic antenna.", "published": "2024-06-19T10:12:17Z", "authors": ["Hyunjun Ma", "Jin-soo Kim", "Jong-Ho Choe", "Q-Han Park"], "categories": ["physics.comp-ph", "cs.LG", "physics.data-an", "physics.optics"], "doi": "10.1515/nanoph-2022-0789", "_query": "real estate valuation deep learning", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2208.05100v2", "title": "KL-divergence Based Deep Learning for Discrete Time Model", "abstract": "Neural Network (Deep Learning) is a modern model in Artificial Intelligence and it has been exploited in Survival Analysis. Although several improvements have been shown by previous works, training an excellent deep learning model requires a huge amount of data, which may not hold in practice. To address this challenge, we develop a Kullback-Leibler-based (KL) deep learning procedure to integrate external survival prediction models with newly collected time-to-event data. Time-dependent KL discrimination information is utilized to measure the discrepancy between the external and internal data. This is the first work considering using prior information to deal with short data problem in Survival Analysis for deep learning. Simulation and real data results show that the proposed model achieves better performance and higher robustness compared with previous works.", "published": "2022-08-10T01:46:26Z", "authors": ["Li Liu", "Xiangeng Fang", "Di Wang", "Weijing Tang", "Kevin He"], "categories": ["stat.ML", "cs.LG"], "doi": "", "_query": "real estate valuation deep learning", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2006.08572v3", "title": "Flexible Dataset Distillation: Learn Labels Instead of Images", "abstract": "We study the problem of dataset distillation - creating a small set of synthetic examples capable of training a good model. In particular, we study the problem of label distillation - creating synthetic labels for a small set of real images, and show it to be more effective than the prior image-based approach to dataset distillation. Methodologically, we introduce a more robust and flexible meta-learning algorithm for distillation, as well as an effective first-order strategy based on convex optimization layers. Distilling labels with our new algorithm leads to improved results over prior image-based distillation. More importantly, it leads to clear improvements in flexibility of the distilled dataset in terms of compatibility with off-the-shelf optimizers and diverse neural architectures. Interestingly, label distillation can also be applied across datasets, for example enabling learning Japanese character recognition by training only on synthetically labeled English letters.", "published": "2020-06-15T17:37:23Z", "authors": ["Ondrej Bohdal", "Yongxin Yang", "Timothy Hospedales"], "categories": ["cs.LG", "stat.ML"], "doi": "", "_query": "real estate valuation deep learning", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2610.17998v1", "title": "Saliency-Guided Replay for Continual Learning in Deep Spiking Neural Networks", "abstract": "Spiking Neural Networks (SNNs) are promising for energy-efficient neuromorphic computing, but they still suffer from catastrophic forgetting when learning tasks sequentially. This work investigates the integration of Experience Packing and Replay (EPR) as a memory-efficient continual learning strategy for deep SNNs under low-latency constraints. The proposed framework combines Batch Normalization Through Time stabilized VGG9 SNNs with Spike Activation Maps (SAM), which provide forward-only explanations based on spike activity. These explanations are temporally aggregated into spatial attention maps and used to select the most informative image patches for replay, instead of storing full images. Experiments on Split CIFAR10 and Split CIFAR100 show that SAM-guided EPR improves the accuracy-memory trade-off under tight memory budgets, and remains competitive while requiring substantially less storage.", "published": "2026-10-13T08:58:28Z", "authors": ["Francesco Panetta", "Federico Di Valerio", "Michela Proietti"], "categories": ["cs.LG", "cs.AI", "cs.CV", "cs.NE"], "doi": "", "_query": "real estate valuation deep learning", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2006.12285v3", "title": "Human-Expert-Level Brain Tumor Detection Using Deep Learning with Data Distillation and Augmentation", "abstract": "The application of Deep Learning (DL) for medical diagnosis is often hampered by two problems. First, the amount of training data may be scarce, as it is limited by the number of patients who have acquired the condition to be diagnosed. Second, the training data may be corrupted by various types of noise. Here, we study the problem of brain tumor detection from magnetic resonance spectroscopy (MRS) data, where both types of problems are prominent. To overcome these challenges, we propose a new method for training a deep neural network that distills particularly representative training examples and augments the training data by mixing these samples from one class with those from the same and other classes to create additional training samples. We demonstrate that this technique substantially improves performance, allowing our method to reach human-expert-level accuracy with just a few thousand training examples. Interestingly, the network learns to rely on features of the data that are usually ignored by human experts, suggesting new directions for future research.", "published": "2020-06-17T15:52:28Z", "authors": ["Diyuan Lu", "Nenad Polomac", "Iskra Gacheva", "Elke Hattingen", "Jochen Triesch"], "categories": ["cs.CV", "cs.LG", "stat.ML"], "doi": "", "_query": "real estate valuation deep learning", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2008.06738v1", "title": "Reducing Sampling Error in Batch Temporal Difference Learning", "abstract": "Temporal difference (TD) learning is one of the main foundations of modern reinforcement learning. This paper studies the use of TD(0), a canonical TD algorithm, to estimate the value function of a given policy from a batch of data. In this batch setting, we show that TD(0) may converge to an inaccurate value function because the update following an action is weighted according to the number of times that action occurred in the batch -- not the true probability of the action under the given policy. To address this limitation, we introduce \\textit{policy sampling error corrected}-TD(0) (PSEC-TD(0)). PSEC-TD(0) first estimates the empirical distribution of actions in each state in the batch and then uses importance sampling to correct for the mismatch between the empirical weighting and the correct weighting for updates following each action. We refine the concept of a certainty-equivalence estimate and argue that PSEC-TD(0) is a more data efficient estimator than TD(0) for a fixed batch of data. Finally, we conduct an empirical evaluation of PSEC-TD(0) on three batch value function learning tasks, with a hyperparameter sensitivity analysis, and show that PSEC-TD(0) produces value function estimates with lower mean squared error than TD(0).", "published": "2020-08-15T15:30:06Z", "authors": ["Brahma Pavse", "Ishan Durugkar", "Josiah Hanna", "Peter Stone"], "categories": ["cs.LG", "cs.AI", "stat.ML"], "doi": "", "_query": "real estate valuation deep learning", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2309.02995v1", "title": "Continual Evidential Deep Learning for Out-of-Distribution Detection", "abstract": "Uncertainty-based deep learning models have attracted a great deal of interest for their ability to provide accurate and reliable predictions. Evidential deep learning stands out achieving remarkable performance in detecting out-of-distribution (OOD) data with a single deterministic neural network. Motivated by this fact, in this paper we propose the integration of an evidential deep learning method into a continual learning framework in order to perform simultaneously incremental object classification and OOD detection. Moreover, we analyze the ability of vacuity and dissonance to differentiate between in-distribution data belonging to old classes and OOD data. The proposed method, called CEDL, is evaluated on CIFAR-100 considering two settings consisting of 5 and 10 tasks, respectively. From the obtained results, we could appreciate that the proposed method, in addition to provide comparable results in object classification with respect to the baseline, largely outperforms OOD detection compared to several posthoc methods on three evaluation metrics: AUROC, AUPR and FPR95.", "published": "2023-09-06T13:36:59Z", "authors": ["Eduardo Aguilar", "Bogdan Raducanu", "Petia Radeva", "Joost Van de Weijer"], "categories": ["cs.CV"], "doi": "", "_query": "real estate valuation deep learning", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2211.08992v2", "title": "DLKoopman: A deep learning software package for Koopman theory", "abstract": "We present DLKoopman -- a software package for Koopman theory that uses deep learning to learn an encoding of a nonlinear dynamical system into a linear space, while simultaneously learning the linear dynamics. While several previous efforts have either restricted the ability to learn encodings, or been bespoke efforts designed for specific systems, DLKoopman is a generalized tool that can be applied to data-driven learning and optimization of any dynamical system. It can either be trained on data from individual states (snapshots) of a system and used to predict its unknown states, or trained on data from trajectories of a system and used to predict unknown trajectories for new initial states. DLKoopman is available on the Python Package Index (PyPI) as 'dlkoopman', and includes extensive documentation and tutorials. Additional contributions of the package include a novel metric called Average Normalized Absolute Error for evaluating performance, and a ready-to-use hyperparameter search module for improving performance.", "published": "2022-11-15T18:45:51Z", "authors": ["Sourya Dey", "Eric Davis"], "categories": ["cs.LG", "eess.SY"], "doi": "", "_query": "real estate valuation deep learning", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/1708.09212v1", "title": "ScatterNet Hybrid Deep Learning (SHDL) Network For Object Classification", "abstract": "The paper proposes the ScatterNet Hybrid Deep Learning (SHDL) network that extracts invariant and discriminative image representations for object recognition. SHDL framework is constructed with a multi-layer ScatterNet front-end, an unsupervised learning middle, and a supervised learning back-end module. Each layer of the SHDL network is automatically designed as an explicit optimization problem leading to an optimal deep learning architecture with improved computational performance as compared to the more usual deep network architectures. SHDL network produces the state-of-the-art classification performance against unsupervised and semi-supervised learning (GANs) on two image datasets. Advantages of the SHDL network over supervised methods (NIN, VGG) are also demonstrated with experiments performed on training datasets of reduced size.", "published": "2017-08-30T11:02:20Z", "authors": ["Amarjot Singh", "Nick Kingsbury"], "categories": ["cs.CV"], "doi": "", "_query": "real estate valuation deep learning", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2502.18300v4", "title": "Bayesian Computation in Deep Learning", "abstract": "Bayesian methods have shown success in deep learning applications. For example, in predictive tasks, Bayesian neural networks leverage Bayesian reasoning of model uncertainty to improve the reliability and uncertainty awareness of deep neural networks. In generative modeling domain, many widely used deep generative models, such as deep latent variable models, require approximate Bayesian inference to infer their latent variables for the training. In this chapter, we provide an introduction to approximate inference techniques as Bayesian computation methods applied to deep learning models, with a focus on Bayesian neural networks and deep generative models. We review two arguably most popular approximate Bayesian computational methods, stochastic gradient Markov chain Monte Carlo (SG-MCMC) and variational inference (VI), and explain their unique challenges in posterior inference as well as the solutions when applied to deep learning models.", "published": "2025-02-25T15:39:33Z", "authors": ["Wenlong Chen", "Bolian Li", "Ruqi Zhang", "Yingzhen Li"], "categories": ["cs.LG", "stat.ML"], "doi": "", "_query": "real estate valuation deep learning", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2406.11730v3", "title": "CHG Shapley: Efficient Data Valuation and Selection towards Trustworthy Machine Learning", "abstract": "Understanding the decision-making process of machine learning models is crucial for ensuring trustworthy machine learning. Data Shapley, a landmark study on data valuation, advances this understanding by assessing the contribution of each datum to model performance. However, the resource-intensive and time-consuming nature of multiple model retraining poses challenges for applying Data Shapley to large datasets. To address this, we propose the CHG (compound of Hardness and Gradient) utility function, which approximates the utility of each data subset on model performance in every training epoch. By deriving the closed-form Shapley value for each data point using the CHG utility function, we reduce the computational complexity to that of a single model retraining, achieving a quadratic improvement over existing marginal contribution-based methods. We further leverage CHG Shapley for real-time data selection, conducting experiments across three settings: standard datasets, label noise datasets, and class imbalance datasets. These experiments demonstrate its effectiveness in identifying high-value and noisy data. By enabling efficient data valuation, CHG Shapley promotes trustworthy model training through a novel data-centric perspective. Our codes are available at https://github.com/caihuaiguang/CHG-Shapley-for-Data-Valuation and https://github.com/caihuaiguang/CHG-Shapley-for-Data-Selection.", "published": "2024-06-17T16:48:31Z", "authors": ["Huaiguang Cai"], "categories": ["cs.GT", "cs.LG"], "doi": "", "_query": "real estate valuation deep learning", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/1504.00641v1", "title": "A Probabilistic Theory of Deep Learning", "abstract": "A grand challenge in machine learning is the development of computational algorithms that match or outperform humans in perceptual inference tasks that are complicated by nuisance variation. For instance, visual object recognition involves the unknown object position, orientation, and scale in object recognition while speech recognition involves the unknown voice pronunciation, pitch, and speed. Recently, a new breed of deep learning algorithms have emerged for high-nuisance inference tasks that routinely yield pattern recognition systems with near- or super-human capabilities. But a fundamental question remains: Why do they work? Intuitions abound, but a coherent framework for understanding, analyzing, and synthesizing deep learning architectures has remained elusive. We answer this question by developing a new probabilistic framework for deep learning based on the Deep Rendering Model: a generative probabilistic model that explicitly captures latent nuisance variation. By relaxing the generative model to a discriminative one, we can recover two of the current leading deep learning systems, deep convolutional neural networks and random decision forests, providing insights into their successes and shortcomings, as well as a principled route to their improvement.", "published": "2015-04-02T18:38:38Z", "authors": ["Ankit B. Patel", "Tan Nguyen", "Richard G. Baraniuk"], "categories": ["stat.ML", "cs.CV", "cs.LG", "cs.NE"], "doi": "", "_query": "real estate valuation deep learning", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2306.10577v3", "title": "OpenDataVal: a Unified Benchmark for Data Valuation", "abstract": "Assessing the quality and impact of individual data points is critical for improving model performance and mitigating undesirable biases within the training dataset. Several data valuation algorithms have been proposed to quantify data quality, however, there lacks a systemic and standardized benchmarking system for data valuation. In this paper, we introduce OpenDataVal, an easy-to-use and unified benchmark framework that empowers researchers and practitioners to apply and compare various data valuation algorithms. OpenDataVal provides an integrated environment that includes (i) a diverse collection of image, natural language, and tabular datasets, (ii) implementations of eleven different state-of-the-art data valuation algorithms, and (iii) a prediction model API that can import any models in scikit-learn. Furthermore, we propose four downstream machine learning tasks for evaluating the quality of data values. We perform benchmarking analysis using OpenDataVal, quantifying and comparing the efficacy of state-of-the-art data valuation approaches. We find that no single algorithm performs uniformly best across all tasks, and an appropriate algorithm should be employed for a user's downstream task. OpenDataVal is publicly available at https://opendataval.github.io with comprehensive documentation. Furthermore, we provide a leaderboard where researchers can evaluate the effectiveness of their own data valuation algorithms.", "published": "2023-06-18T14:38:29Z", "authors": ["Kevin Fu Jiang", "Weixin Liang", "James Zou", "Yongchan Kwon"], "categories": ["cs.LG", "stat.ML"], "doi": "", "_query": "real estate valuation deep learning", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/1810.12836v4", "title": "Learning Cross-Lingual Sentence Representations via a Multi-task Dual-Encoder Model", "abstract": "A significant roadblock in multilingual neural language modeling is the lack of labeled non-English data. One potential method for overcoming this issue is learning cross-lingual text representations that can be used to transfer the performance from training on English tasks to non-English tasks, despite little to no task-specific non-English data. In this paper, we explore a natural setup for learning cross-lingual sentence representations: the dual-encoder. We provide a comprehensive evaluation of our cross-lingual representations on a number of monolingual, cross-lingual, and zero-shot/few-shot learning tasks, and also give an analysis of different learned cross-lingual embedding spaces.", "published": "2018-10-30T16:18:05Z", "authors": ["Muthuraman Chidambaram", "Yinfei Yang", "Daniel Cer", "Steve Yuan", "Yun-Hsuan Sung", "Brian Strope", "Ray Kurzweil"], "categories": ["cs.CL"], "doi": "", "_query": "real estate valuation deep learning", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/1910.04371v1", "title": "Active Learning with Importance Sampling", "abstract": "We consider an active learning setting where the algorithm has access to a large pool of unlabeled data and a small pool of labeled data. In each iteration, the algorithm chooses few unlabeled data points and obtains their labels from an oracle. In this paper, we consider a probabilistic querying procedure to choose the points to be labeled. We propose an algorithm for Active Learning with Importance Sampling (ALIS), and derive upper bounds on the true loss incurred by the algorithm for any arbitrary probabilistic sampling procedure. Further, we propose an optimal sampling distribution that minimizes the upper bound on the true loss.", "published": "2019-10-10T05:11:08Z", "authors": ["Muni Sreenivas Pydi", "Vishnu Suresh Lokhande"], "categories": ["cs.LG", "stat.ML"], "doi": "", "_query": "real estate valuation deep learning", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2506.14577v1", "title": "Object-Centric Neuro-Argumentative Learning", "abstract": "Over the last decade, as we rely more on deep learning technologies to make critical decisions, concerns regarding their safety, reliability and interpretability have emerged. We introduce a novel Neural Argumentative Learning (NAL) architecture that integrates Assumption-Based Argumentation (ABA) with deep learning for image analysis. Our architecture consists of neural and symbolic components. The former segments and encodes images into facts using object-centric learning, while the latter applies ABA learning to develop ABA frameworks enabling predictions with images. Experiments on synthetic data show that the NAL architecture can be competitive with a state-of-the-art alternative.", "published": "2025-06-17T14:35:01Z", "authors": ["Abdul Rahman Jacob", "Avinash Kori", "Emanuele De Angelis", "Ben Glocker", "Maurizio Proietti", "Francesca Toni"], "categories": ["cs.LG", "cs.AI"], "doi": "", "_query": "real estate valuation deep learning", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2103.00286v1", "title": "A Novel Adaptive Deep Network for Building Footprint Segmentation", "abstract": "Building footprint segmentations for high resolution images are increasingly demanded for many remote sensing applications. By the emerging deep learning approaches, segmentation networks have made significant advances in the semantic segmentation of objects. However, these advances and the increased access to satellite images require the generation of accurate object boundaries in satellite images. In the current paper, we propose a novel network-based on Pix2Pix methodology to solve the problem of inaccurate boundaries obtained by converting satellite images into maps using segmentation networks in order to segment building footprints. To define the new network named G2G, our framework includes two generators where the first generator extracts localization features in order to merge them with the boundary features extracted from the second generator to segment all detailed building edges. Moreover, different strategies are implemented to enhance the quality of the proposed networks' results, implying that the proposed network outperforms state-of-the-art networks in segmentation accuracy with a large margin for all evaluation metrics. The implementation is available at https://github.com/A2Amir/A-Novel-Adaptive-Deep-Network-for-Building-Footprint-Segmentation.", "published": "2021-02-27T18:13:48Z", "authors": ["A. Ziaee", "R. Dehbozorgi", "M. Döller"], "categories": ["cs.CV", "cs.LG", "eess.IV", "math.NA"], "doi": "", "_query": "real estate valuation deep learning", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2408.04590v1", "title": "Learn To Learn More Precisely", "abstract": "Meta-learning has been extensively applied in the domains of few-shot learning and fast adaptation, achieving remarkable performance. While Meta-learning methods like Model-Agnostic Meta-Learning (MAML) and its variants provide a good set of initial parameters for the model, the model still tends to learn shortcut features, which leads to poor generalization. In this paper, we propose the formal conception of \"learn to learn more precisely\", which aims to make the model learn precise target knowledge from data and reduce the effect of noisy knowledge, such as background and noise. To achieve this target, we proposed a simple and effective meta-learning framework named Meta Self-Distillation(MSD) to maximize the consistency of learned knowledge, enhancing the models' ability to learn precise target knowledge. In the inner loop, MSD uses different augmented views of the same support data to update the model respectively. Then in the outer loop, MSD utilizes the same query data to optimize the consistency of learned knowledge, enhancing the model's ability to learn more precisely. Our experiment demonstrates that MSD exhibits remarkable performance in few-shot classification tasks in both standard and augmented scenarios, effectively boosting the accuracy and consistency of knowledge learned by the model.", "published": "2024-08-08T17:01:26Z", "authors": ["Runxi Cheng", "Yongxian Wei", "Xianglong He", "Wanyun Zhu", "Songsong Huang", "Fei Richard Yu", "Fei Ma", "Chun Yuan"], "categories": ["cs.LG"], "doi": "", "_query": "real estate valuation deep learning", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/1908.06315v4", "title": "Implicit Deep Learning", "abstract": "Implicit deep learning prediction rules generalize the recursive rules of feedforward neural networks. Such rules are based on the solution of a fixed-point equation involving a single vector of hidden features, which is thus only implicitly defined. The implicit framework greatly simplifies the notation of deep learning, and opens up many new possibilities, in terms of novel architectures and algorithms, robustness analysis and design, interpretability, sparsity, and network architecture optimization.", "published": "2019-08-17T15:36:37Z", "authors": ["Laurent El Ghaoui", "Fangda Gu", "Bertrand Travacca", "Armin Askari", "Alicia Y. Tsai"], "categories": ["cs.LG", "math.OC", "stat.ML"], "doi": "", "_query": "real estate valuation deep learning", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/1806.03019v1", "title": "3D FCN Feature Driven Regression Forest-Based Pancreas Localization and Segmentation", "abstract": "This paper presents a fully automated atlas-based pancreas segmentation method from CT volumes utilizing 3D fully convolutional network (FCN) feature-based pancreas localization. Segmentation of the pancreas is difficult because it has larger inter-patient spatial variations than other organs. Previous pancreas segmentation methods failed to deal with such variations. We propose a fully automated pancreas segmentation method that contains novel localization and segmentation. Since the pancreas neighbors many other organs, its position and size are strongly related to the positions of the surrounding organs. We estimate the position and the size of the pancreas (localized) from global features by regression forests. As global features, we use intensity differences and 3D FCN deep learned features, which include automatically extracted essential features for segmentation. We chose 3D FCN features from a trained 3D U-Net, which is trained to perform multi-organ segmentation. The global features include both the pancreas and surrounding organ information. After localization, a patient-specific probabilistic atlas-based pancreas segmentation is performed. In evaluation results with 146 CT volumes, we achieved 60.6% of the Jaccard index and 73.9% of the Dice overlap.", "published": "2018-06-08T08:34:30Z", "authors": ["Masahiro Oda", "Natsuki Shimizu", "Holger R. Roth", "Ken'ichi Karasawa", "Takayuki Kitasaka", "Kazunari Misawa", "Michitaka Fujiwara", "Daniel Rueckert", "Kensaku Mori"], "categories": ["cs.CV"], "doi": "10.1007/978-3-319-67558-9_26", "_query": "real estate valuation deep learning", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2305.02927v1", "title": "Forward-Forward Contrastive Learning", "abstract": "Medical image classification is one of the most important tasks for computer-aided diagnosis. Deep learning models, particularly convolutional neural networks, have been successfully used for disease classification from medical images, facilitated by automated feature learning. However, the diverse imaging modalities and clinical pathology make it challenging to construct generalized and robust classifications. Towards improving the model performance, we propose a novel pretraining approach, namely Forward Forward Contrastive Learning (FFCL), which leverages the Forward-Forward Algorithm in a contrastive learning framework--both locally and globally. Our experimental results on the chest X-ray dataset indicate that the proposed FFCL achieves superior performance (3.69% accuracy over ImageNet pretrained ResNet-18) over existing pretraining models in the pneumonia classification task. Moreover, extensive ablation experiments support the particular local and global contrastive pretraining design in FFCL.", "published": "2023-05-04T15:29:06Z", "authors": ["Md. Atik Ahamed", "Jin Chen", "Abdullah-Al-Zubaer Imran"], "categories": ["cs.CV"], "doi": "", "_query": "real estate valuation deep learning", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2303.00399v1", "title": "D4FT: A Deep Learning Approach to Kohn-Sham Density Functional Theory", "abstract": "Kohn-Sham Density Functional Theory (KS-DFT) has been traditionally solved by the Self-Consistent Field (SCF) method. Behind the SCF loop is the physics intuition of solving a system of non-interactive single-electron wave functions under an effective potential. In this work, we propose a deep learning approach to KS-DFT. First, in contrast to the conventional SCF loop, we propose to directly minimize the total energy by reparameterizing the orthogonal constraint as a feed-forward computation. We prove that such an approach has the same expressivity as the SCF method, yet reduces the computational complexity from O(N^4) to O(N^3). Second, the numerical integration which involves a summation over the quadrature grids can be amortized to the optimization steps. At each step, stochastic gradient descent (SGD) is performed with a sampled minibatch of the grids. Extensive experiments are carried out to demonstrate the advantage of our approach in terms of efficiency and stability. In addition, we show that our approach enables us to explore more complex neural-based wave functions.", "published": "2023-03-01T10:38:10Z", "authors": ["Tianbo Li", "Min Lin", "Zheyuan Hu", "Kunhao Zheng", "Giovanni Vignale", "Kenji Kawaguchi", "A. H. Castro Neto", "Kostya S. Novoselov", "Shuicheng Yan"], "categories": ["cs.LG"], "doi": "", "_query": "real estate valuation deep learning", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2211.14939v2", "title": "Applying Deep Reinforcement Learning to the HP Model for Protein Structure Prediction", "abstract": "A central problem in computational biophysics is protein structure prediction, i.e., finding the optimal folding of a given amino acid sequence. This problem has been studied in a classical abstract model, the HP model, where the protein is modeled as a sequence of H (hydrophobic) and P (polar) amino acids on a lattice. The objective is to find conformations maximizing H-H contacts. It is known that even in this reduced setting, the problem is intractable (NP-hard). In this work, we apply deep reinforcement learning (DRL) to the two-dimensional HP model. We can obtain the conformations of best known energies for benchmark HP sequences with lengths from 20 to 50. Our DRL is based on a deep Q-network (DQN). We find that a DQN based on long short-term memory (LSTM) architecture greatly enhances the RL learning ability and significantly improves the search process. DRL can sample the state space efficiently, without the need of manual heuristics. Experimentally we show that it can find multiple distinct best-known solutions per trial. This study demonstrates the effectiveness of deep reinforcement learning in the HP model for protein folding.", "published": "2022-11-27T21:17:48Z", "authors": ["Kaiyuan Yang", "Houjing Huang", "Olafs Vandans", "Adithya Murali", "Fujia Tian", "Roland H. C. Yap", "Liang Dai"], "categories": ["cs.LG", "q-bio.BM"], "doi": "10.1016/j.physa.2022.128395", "_query": "real estate valuation deep learning", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/1709.03768v3", "title": "Learning with Bounded Instance- and Label-dependent Label Noise", "abstract": "Instance- and Label-dependent label Noise (ILN) widely exists in real-world datasets but has been rarely studied. In this paper, we focus on Bounded Instance- and Label-dependent label Noise (BILN), a particular case of ILN where the label noise rates -- the probabilities that the true labels of examples flip into the corrupted ones -- have upper bound less than $1$. Specifically, we introduce the concept of distilled examples, i.e. examples whose labels are identical with the labels assigned for them by the Bayes optimal classifier, and prove that under certain conditions classifiers learnt on distilled examples will converge to the Bayes optimal classifier. Inspired by the idea of learning with distilled examples, we then propose a learning algorithm with theoretical guarantees for its robustness to BILN. At last, empirical evaluations on both synthetic and real-world datasets show effectiveness of our algorithm in learning with BILN.", "published": "2017-09-12T09:59:59Z", "authors": ["Jiacheng Cheng", "Tongliang Liu", "Kotagiri Ramamohanarao", "Dacheng Tao"], "categories": ["stat.ML", "cs.LG"], "doi": "", "_query": "real estate valuation deep learning", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2602.18572v1", "title": "Sub-City Real Estate Price Index Forecasting at Weekly Horizons Using Satellite Radar and News Sentiment", "abstract": "Reliable real estate price indicators are typically published at city level and low frequency, limiting their use for neighborhood-scale monitoring and long-horizon planning. We study whether sub-city price indices can be forecasted at weekly frequency by combining physical development signals from satellite radar with market narratives from news text. Using over 350,000 transactions from Dubai Land Department (2015-2025), we construct weekly price indices for 19 sub-city regions and evaluate forecasts from 2 to 34 weeks ahead. Our framework fuses regional transaction history with Sentinel-1 SAR backscatter, news sentiment combining lexical tone and semantic embeddings, and macroeconomic context. Results are strongly horizon dependent: at horizons up to 10 weeks, price history alone matches multimodal configurations, but beyond 14 weeks sentiment and SAR become critical. At long horizons (26-34 weeks), the full multimodal model reduces mean absolute error from 4.48 to 2.93 (35% reduction), with gains statistically significant across regions. Nonparametric learners consistently outperform deep architectures in this data regime. These findings establish benchmarks for weekly sub-city index forecasting and demonstrate that remote sensing and news sentiment materially improve predictability at strategically relevant horizons.", "published": "2026-02-20T19:25:48Z", "authors": ["Baris Arat", "Hasan Fehmi Ates", "Emre Sefer"], "categories": ["cs.LG", "q-fin.ST"], "doi": "", "_query": "real estate valuation deep learning", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/1906.04859v3", "title": "Reinforcement Learning for Integer Programming: Learning to Cut", "abstract": "Integer programming (IP) is a general optimization framework widely applicable to a variety of unstructured and structured problems arising in, e.g., scheduling, production planning, and graph optimization. As IP models many provably hard to solve problems, modern IP solvers rely on many heuristics. These heuristics are usually human-designed, and naturally prone to suboptimality. The goal of this work is to show that the performance of those solvers can be greatly enhanced using reinforcement learning (RL). In particular, we investigate a specific methodology for solving IPs, known as the Cutting Plane Method. This method is employed as a subroutine by all modern IP solvers. We present a deep RL formulation, network architecture, and algorithms for intelligent adaptive selection of cutting planes (aka cuts). Across a wide range of IP tasks, we show that the trained RL agent significantly outperforms human-designed heuristics, and effectively generalizes to 10X larger instances and across IP problem classes. The trained agent is also demonstrated to benefit the popular downstream application of cutting plane methods in Branch-and-Cut algorithm, which is the backbone of state-of-the-art commercial IP solvers.", "published": "2019-06-11T23:14:46Z", "authors": ["Yunhao Tang", "Shipra Agrawal", "Yuri Faenza"], "categories": ["cs.LG", "math.OC", "stat.ML"], "doi": "", "_query": "real estate valuation deep learning", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2102.05107v1", "title": "Sub-seasonal forecasting with a large ensemble of deep-learning weather prediction models", "abstract": "We present an ensemble prediction system using a Deep Learning Weather Prediction (DLWP) model that recursively predicts key atmospheric variables with six-hour time resolution. This model uses convolutional neural networks (CNNs) on a cubed sphere grid to produce global forecasts. The approach is computationally efficient, requiring just three minutes on a single GPU to produce a 320-member set of six-week forecasts at 1.4° resolution. Ensemble spread is primarily produced by randomizing the CNN training process to create a set of 32 DLWP models with slightly different learned weights. Although our DLWP model does not forecast precipitation, it does forecast total column water vapor, and it gives a reasonable 4.5-day deterministic forecast of Hurricane Irma. In addition to simulating mid-latitude weather systems, it spontaneously generates tropical cyclones in a one-year free-running simulation. Averaged globally and over a two-year test set, the ensemble mean RMSE retains skill relative to climatology beyond two-weeks, with anomaly correlation coefficients remaining above 0.6 through six days. Our primary application is to subseasonal-to-seasonal (S2S) forecasting at lead times from two to six weeks. Current forecast systems have low skill in predicting one- or 2-week-average weather patterns at S2S time scales. The continuous ranked probability score (CRPS) and the ranked probability skill score (RPSS) show that the DLWP ensemble is only modestly inferior in performance to the European Centre for Medium Range Weather Forecasts (ECMWF) S2S ensemble over land at lead times of 4 and 5-6 weeks. At shorter lead times, the ECMWF ensemble performs better than DLWP.", "published": "2021-02-09T20:14:43Z", "authors": ["Jonathan A. Weyn", "Dale R. Durran", "Rich Caruana", "Nathaniel Cresswell-Clay"], "categories": ["physics.ao-ph", "cs.LG"], "doi": "10.1029/2021MS002502", "_query": "real estate valuation deep learning", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2302.05433v1", "title": "Unified Functional Hashing in Automatic Machine Learning", "abstract": "The field of Automatic Machine Learning (AutoML) has recently attained impressive results, including the discovery of state-of-the-art machine learning solutions, such as neural image classifiers. This is often done by applying an evolutionary search method, which samples multiple candidate solutions from a large space and evaluates the quality of each candidate through a long training process. As a result, the search tends to be slow. In this paper, we show that large efficiency gains can be obtained by employing a fast unified functional hash, especially through the functional equivalence caching technique, which we also present. The central idea is to detect by hashing when the search method produces equivalent candidates, which occurs very frequently, and this way avoid their costly re-evaluation. Our hash is \"functional\" in that it identifies equivalent candidates even if they were represented or coded differently, and it is \"unified\" in that the same algorithm can hash arbitrary representations; e.g. compute graphs, imperative code, or lambda functions. As evidence, we show dramatic improvements on multiple AutoML domains, including neural architecture search and algorithm discovery. Finally, we consider the effect of hash collisions, evaluation noise, and search distribution through empirical analysis. Altogether, we hope this paper may serve as a guide to hashing techniques in AutoML.", "published": "2023-02-10T18:50:37Z", "authors": ["Ryan Gillard", "Stephen Jonany", "Yingjie Miao", "Michael Munn", "Connal de Souza", "Jonathan Dungay", "Chen Liang", "David R. So", "Quoc V. Le", "Esteban Real"], "categories": ["cs.LG", "cs.NE"], "doi": "", "_query": "real estate valuation deep learning", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2006.07200v4", "title": "Learning to Communicate Using Counterfactual Reasoning", "abstract": "Learning to communicate in order to share state information is an active problem in the area of multi-agent reinforcement learning (MARL). The credit assignment problem, the non-stationarity of the communication environment and the creation of influenceable agents are major challenges within this research field which need to be overcome in order to learn a valid communication protocol. This paper introduces the novel multi-agent counterfactual communication learning (MACC) method which adapts counterfactual reasoning in order to overcome the credit assignment problem for communicating agents. Secondly, the non-stationarity of the communication environment while learning the communication Q-function is overcome by creating the communication Q-function using the action policy of the other agents and the Q-function of the action environment. Additionally, a social loss function is introduced in order to create influenceable agents which is required to learn a valid communication protocol. Our experiments show that MACC is able to outperform the state-of-the-art baselines in four different scenarios in the Particle environment.", "published": "2020-06-12T14:02:04Z", "authors": ["Simon Vanneste", "Astrid Vanneste", "Kevin Mets", "Tom De Schepper", "Ali Anwar", "Siegfried Mercelis", "Steven Latré", "Peter Hellinckx"], "categories": ["cs.LG", "cs.MA", "stat.ML"], "doi": "", "_query": "real estate valuation deep learning", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/1911.13159v1", "title": "VIABLE: Fast Adaptation via Backpropagating Learned Loss", "abstract": "In few-shot learning, typically, the loss function which is applied at test time is the one we are ultimately interested in minimising, such as the mean-squared-error loss for a regression problem. However, given that we have few samples at test time, we argue that the loss function that we are interested in minimising is not necessarily the loss function most suitable for computing gradients in a few-shot setting. We propose VIABLE, a generic meta-learning extension that builds on existing meta-gradient-based methods by learning a differentiable loss function, replacing the pre-defined inner-loop loss function in performing task-specific updates. We show that learning a loss function capable of leveraging relational information between samples reduces underfitting, and significantly improves performance and sample efficiency on a simple regression task. Furthermore, we show VIABLE is scalable by evaluating on the Mini-Imagenet dataset.", "published": "2019-11-29T15:47:09Z", "authors": ["Leo Feng", "Luisa Zintgraf", "Bei Peng", "Shimon Whiteson"], "categories": ["cs.LG", "stat.ML"], "doi": "", "_query": "real estate valuation deep learning", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2401.02349v2", "title": "A Survey Analyzing Generalization in Deep Reinforcement Learning", "abstract": "Reinforcement learning research obtained significant success and attention with the utilization of deep neural networks to solve problems in high dimensional state or action spaces. While deep reinforcement learning policies are currently being deployed in many different fields from medical applications to large language models, there are still ongoing questions the field is trying to answer on the generalization capabilities of deep reinforcement learning policies. In this paper, we will formalize and analyze generalization in deep reinforcement learning. We will explain the fundamental reasons why deep reinforcement learning policies encounter overfitting problems that limit their generalization capabilities. Furthermore, we will categorize and explain the manifold solution approaches to increase generalization, and overcome overfitting in deep reinforcement learning policies. From exploration to adversarial analysis and from regularization to robustness our paper provides an analysis on a wide range of subfields within deep reinforcement learning with a broad scope and in-depth view. We believe our study can provide a compact guideline for the current advancements in deep reinforcement learning, and help to construct robust deep neural policies with higher generalization skills.", "published": "2024-01-04T16:45:01Z", "authors": ["Ezgi Korkmaz"], "categories": ["cs.LG", "cs.AI", "stat.ML"], "doi": "", "_query": "real estate valuation deep learning", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2307.15878v1", "title": "Explaining Full-disk Deep Learning Model for Solar Flare Prediction using Attribution Methods", "abstract": "This paper contributes to the growing body of research on deep learning methods for solar flare prediction, primarily focusing on highly overlooked near-limb flares and utilizing the attribution methods to provide a post hoc qualitative explanation of the model's predictions. We present a solar flare prediction model, which is trained using hourly full-disk line-of-sight magnetogram images and employs a binary prediction mode to forecast $\\geq$M-class flares that may occur within the following 24-hour period. To address the class imbalance, we employ a fusion of data augmentation and class weighting techniques; and evaluate the overall performance of our model using the true skill statistic (TSS) and Heidke skill score (HSS). Moreover, we applied three attribution methods, namely Guided Gradient-weighted Class Activation Mapping, Integrated Gradients, and Deep Shapley Additive Explanations, to interpret and cross-validate our model's predictions with the explanations. Our analysis revealed that full-disk prediction of solar flares aligns with characteristics related to active regions (ARs). In particular, the key findings of this study are: (1) our deep learning models achieved an average TSS=0.51 and HSS=0.35, and the results further demonstrate a competent capability to predict near-limb solar flares and (2) the qualitative analysis of the model explanation indicates that our model identifies and uses features associated with ARs in central and near-limb locations from full-disk magnetograms to make corresponding predictions. In other words, our models learn the shape and texture-based characteristics of flaring ARs even at near-limb areas, which is a novel and critical capability with significant implications for operational forecasting.", "published": "2023-07-29T03:18:56Z", "authors": ["Chetraj Pandey", "Rafal A. Angryk", "Berkay Aydin"], "categories": ["cs.LG", "astro-ph.SR"], "doi": "10.1007/978-3-031-43430-3_5", "_query": "real estate valuation deep learning", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/1812.03511v1", "title": "Physics-informed deep generative models", "abstract": "We consider the application of deep generative models in propagating uncertainty through complex physical systems. Specifically, we put forth an implicit variational inference formulation that constrains the generative model output to satisfy given physical laws expressed by partial differential equations. Such physics-informed constraints provide a regularization mechanism for effectively training deep probabilistic models for modeling physical systems in which the cost of data acquisition is high and training data-sets are typically small. This provides a scalable framework for characterizing uncertainty in the outputs of physical systems due to randomness in their inputs or noise in their observations. We demonstrate the effectiveness of our approach through a canonical example in transport dynamics.", "published": "2018-12-09T16:12:59Z", "authors": ["Yibo Yang", "Paris Perdikaris"], "categories": ["stat.ML", "cs.LG"], "doi": "", "_query": "real estate valuation deep learning", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/1312.0286v2", "title": "Efficient Learning and Planning with Compressed Predictive States", "abstract": "Predictive state representations (PSRs) offer an expressive framework for modelling partially observable systems. By compactly representing systems as functions of observable quantities, the PSR learning approach avoids using local-minima prone expectation-maximization and instead employs a globally optimal moment-based algorithm. Moreover, since PSRs do not require a predetermined latent state structure as an input, they offer an attractive framework for model-based reinforcement learning when agents must plan without a priori access to a system model. Unfortunately, the expressiveness of PSRs comes with significant computational cost, and this cost is a major factor inhibiting the use of PSRs in applications. In order to alleviate this shortcoming, we introduce the notion of compressed PSRs (CPSRs). The CPSR learning approach combines recent advancements in dimensionality reduction, incremental matrix decomposition, and compressed sensing. We show how this approach provides a principled avenue for learning accurate approximations of PSRs, drastically reducing the computational costs associated with learning while also providing effective regularization. Going further, we propose a planning framework which exploits these learned models. And we show that this approach facilitates model-learning and planning in large complex partially observable domains, a task that is infeasible without the principled use of compression.", "published": "2013-12-01T23:17:06Z", "authors": ["William L. Hamilton", "Mahdi Milani Fard", "Joelle Pineau"], "categories": ["cs.LG", "stat.ML"], "doi": "", "_query": "real estate valuation deep learning", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2004.08600v1", "title": "Time Adaptive Reinforcement Learning", "abstract": "Reinforcement learning (RL) allows to solve complex tasks such as Go often with a stronger performance than humans. However, the learned behaviors are usually fixed to specific tasks and unable to adapt to different contexts. Here we consider the case of adapting RL agents to different time restrictions, such as finishing a task with a given time limit that might change from one task execution to the next. We define such problems as Time Adaptive Markov Decision Processes and introduce two model-free, value-based algorithms: the Independent Gamma-Ensemble and the n-Step Ensemble. In difference to classical approaches, they allow a zero-shot adaptation between different time restrictions. The proposed approaches represent general mechanisms to handle time adaptive tasks making them compatible with many existing RL methods, algorithms, and scenarios.", "published": "2020-04-18T11:52:07Z", "authors": ["Chris Reinke"], "categories": ["cs.LG", "cs.AI", "stat.ML"], "doi": "", "_query": "real estate valuation deep learning", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2302.09826v4", "title": "On the Expressivity of Persistent Homology in Graph Learning", "abstract": "Persistent homology, a technique from computational topology, has recently shown strong empirical performance in the context of graph classification. Being able to capture long range graph properties via higher-order topological features, such as cycles of arbitrary length, in combination with multi-scale topological descriptors, has improved predictive performance for data sets with prominent topological structures, such as molecules. At the same time, the theoretical properties of persistent homology have not been formally assessed in this context. This paper intends to bridge the gap between computational topology and graph machine learning by providing a brief introduction to persistent homology in the context of graphs, as well as a theoretical discussion and empirical analysis of its expressivity for graph learning tasks.", "published": "2023-02-20T08:19:19Z", "authors": ["Rubén Ballester", "Bastian Rieck"], "categories": ["cs.LG", "math.AT", "stat.ML"], "doi": "", "_query": "real estate valuation deep learning", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2006.07886v3", "title": "On Disentangled Representations Learned From Correlated Data", "abstract": "The focus of disentanglement approaches has been on identifying independent factors of variation in data. However, the causal variables underlying real-world observations are often not statistically independent. In this work, we bridge the gap to real-world scenarios by analyzing the behavior of the most prominent disentanglement approaches on correlated data in a large-scale empirical study (including 4260 models). We show and quantify that systematically induced correlations in the dataset are being learned and reflected in the latent representations, which has implications for downstream applications of disentanglement such as fairness. We also demonstrate how to resolve these latent correlations, either using weak supervision during training or by post-hoc correcting a pre-trained model with a small number of labels.", "published": "2020-06-14T12:47:34Z", "authors": ["Frederik Träuble", "Elliot Creager", "Niki Kilbertus", "Francesco Locatello", "Andrea Dittadi", "Anirudh Goyal", "Bernhard Schölkopf", "Stefan Bauer"], "categories": ["cs.LG", "stat.ML"], "doi": "", "_query": "real estate valuation deep learning", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2211.05239v4", "title": "RecD: Deduplication for End-to-End Deep Learning Recommendation Model Training Infrastructure", "abstract": "We present RecD (Recommendation Deduplication), a suite of end-to-end infrastructure optimizations across the Deep Learning Recommendation Model (DLRM) training pipeline. RecD addresses immense storage, preprocessing, and training overheads caused by feature duplication inherent in industry-scale DLRM training datasets. Feature duplication arises because DLRM datasets are generated from interactions. While each user session can generate multiple training samples, many features' values do not change across these samples. We demonstrate how RecD exploits this property, end-to-end, across a deployed training pipeline. RecD optimizes data generation pipelines to decrease dataset storage and preprocessing resource demands and to maximize duplication within a training batch. RecD introduces a new tensor format, InverseKeyedJaggedTensors (IKJTs), to deduplicate feature values in each batch. We show how DLRM model architectures can leverage IKJTs to drastically increase training throughput. RecD improves the training and preprocessing throughput and storage efficiency by up to 2.48x, 1.79x, and 3.71x, respectively, in an industry-scale DLRM training system.", "published": "2022-11-09T22:21:19Z", "authors": ["Mark Zhao", "Dhruv Choudhary", "Devashish Tyagi", "Ajay Somani", "Max Kaplan", "Sung-Han Lin", "Sarunya Pumma", "Jongsoo Park", "Aarti Basant", "Niket Agarwal", "Carole-Jean Wu", "Christos Kozyrakis"], "categories": ["cs.LG", "cs.DC", "cs.IR", "cs.PF"], "doi": "", "_query": "real estate valuation deep learning", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2012.10931v2", "title": "Recent advances in deep learning theory", "abstract": "Deep learning is usually described as an experiment-driven field under continuous criticizes of lacking theoretical foundations. This problem has been partially fixed by a large volume of literature which has so far not been well organized. This paper reviews and organizes the recent advances in deep learning theory. The literature is categorized in six groups: (1) complexity and capacity-based approaches for analyzing the generalizability of deep learning; (2) stochastic differential equations and their dynamic systems for modelling stochastic gradient descent and its variants, which characterize the optimization and generalization of deep learning, partially inspired by Bayesian inference; (3) the geometrical structures of the loss landscape that drives the trajectories of the dynamic systems; (4) the roles of over-parameterization of deep neural networks from both positive and negative perspectives; (5) theoretical foundations of several special structures in network architectures; and (6) the increasingly intensive concerns in ethics and security and their relationships with generalizability.", "published": "2020-12-20T14:16:41Z", "authors": ["Fengxiang He", "Dacheng Tao"], "categories": ["cs.LG", "stat.ML"], "doi": "", "_query": "real estate valuation deep learning", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2411.02138v3", "title": "Generalizable and Robust Spectral Method for Multi-view Representation Learning", "abstract": "Multi-view representation learning (MvRL) has garnered substantial attention in recent years, driven by the increasing demand for applications that can effectively process and analyze data from multiple sources. In this context, graph Laplacian-based MvRL methods have demonstrated remarkable success in representing multi-view data. However, these methods often struggle with generalization to new data and face challenges with scalability. Moreover, in many practical scenarios, multi-view data is contaminated by noise or outliers. In such cases, modern deep-learning-based MvRL approaches that rely on alignment or contrastive objectives present degraded performance in downstream tasks, as they may impose incorrect consistency between clear and corrupted data sources. We introduce $\\textit{SpecRaGE}$, a novel fusion-based framework that integrates the strengths of graph Laplacian methods with the power of deep learning to overcome these challenges. SpecRage uses neural networks to learn parametric mapping that approximates a joint diagonalization of graph Laplacians. This solution bypasses the need for alignment while enabling generalizable and scalable learning of informative and meaningful representations. Moreover, it incorporates a meta-learning fusion module that dynamically adapts to data quality, ensuring robustness against outliers and noisy views. Our extensive experiments demonstrate that SpecRaGE outperforms state-of-the-art methods, particularly in scenarios with data contamination, paving the way for more reliable and efficient multi-view learning.", "published": "2024-11-04T14:51:35Z", "authors": ["Amitai Yacobi", "Ofir Lindenbaum", "Uri Shaham"], "categories": ["stat.ML", "cs.LG"], "doi": "", "_query": "real estate valuation deep learning", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2605.09842v1", "title": "Yield Curve Forecasting using Machine Learning and Econometrics: A Comparative Analysis", "abstract": "While machine learning has revolutionized many fields such as natural language processing (NLP) and computer vision, its impact on time-series forecasting is still widely disputed, especially in the finance domain. This paper compares forecasting performance on U.S. Treasury yield curve data across econometrics/time-series analysis, classical machine learning, and deep learning methods, using daily data over 47 years. The Treasury yield curve is important because it is widely used by every participant in the bond markets, which are larger than equity markets. We examine a variety of methods that have not been tested on yield curve forecasting, especially deep learning algorithms. The algorithms include the Autoregressive Integrated Moving Average (ARIMA) model and its extensions, naive benchmarks, ensemble methods, Recurrent Neural Networks (RNNs), and multiple transformers built for forecasting. ARIMA and naive econometric models outperform other models overall, except in one time block. Of the machine learning methods, TimeGPT, LGBM and RNNs perform the best. Furthermore, the paper explores whether stationary or nonstationary data are more appropriate as input to deep learning models.", "published": "2026-05-11T00:53:26Z", "authors": ["Aman Singh", "Tokunbo Ogunfunmi", "Sanjiv Das"], "categories": ["cs.AI"], "doi": "", "_query": "real estate valuation deep learning", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2004.04617v2", "title": "Cortical surface registration using unsupervised learning", "abstract": "Non-rigid cortical registration is an important and challenging task due to the geometric complexity of the human cortex and the high degree of inter-subject variability. A conventional solution is to use a spherical representation of surface properties and perform registration by aligning cortical folding patterns in that space. This strategy produces accurate spatial alignment but often requires a high computational cost. Recently, convolutional neural networks (CNNs) have demonstrated the potential to dramatically speed up volumetric registration. However, due to distortions introduced by projecting a sphere to a 2D plane, a direct application of recent learning-based methods to surfaces yields poor results. In this study, we present SphereMorph, a diffeomorphic registration framework for cortical surfaces using deep networks that addresses these issues. SphereMorph uses a UNet-style network associated with a spherical kernel to learn the displacement field and warps the sphere using a modified spatial transformer layer. We propose a resampling weight in computing the data fitting loss to account for distortions introduced by polar projection, and demonstrate the performance of our proposed method on two tasks, including cortical parcellation and group-wise functional area alignment. The experiments show that the proposed SphereMorph is capable of modeling the geometric registration problem in a CNN framework and demonstrate superior registration accuracy and computational efficiency. The source code of SphereMorph will be released to the public upon acceptance of this manuscript at https://github.com/voxelmorph/spheremorph.", "published": "2020-04-09T15:59:13Z", "authors": ["Jieyu Cheng", "Adrian V. Dalca", "Bruce Fischl", "Lilla Zollei"], "categories": ["eess.IV", "cs.CV", "q-bio.NC"], "doi": "10.1016/j.neuroimage.2020.117161", "_query": "real estate valuation deep learning", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/1912.11615v2", "title": "Deep Graph Similarity Learning: A Survey", "abstract": "In many domains where data are represented as graphs, learning a similarity metric among graphs is considered a key problem, which can further facilitate various learning tasks, such as classification, clustering, and similarity search. Recently, there has been an increasing interest in deep graph similarity learning, where the key idea is to learn a deep learning model that maps input graphs to a target space such that the distance in the target space approximates the structural distance in the input space. Here, we provide a comprehensive review of the existing literature of deep graph similarity learning. We propose a systematic taxonomy for the methods and applications. Finally, we discuss the challenges and future directions for this problem.", "published": "2019-12-25T08:04:52Z", "authors": ["Guixiang Ma", "Nesreen K. Ahmed", "Theodore L. Willke", "Philip S. Yu"], "categories": ["cs.LG", "stat.ML"], "doi": "", "_query": "real estate valuation deep learning", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2005.02191v3", "title": "Localized active learning of Gaussian process state space models", "abstract": "The performance of learning-based control techniques crucially depends on how effectively the system is explored. While most exploration techniques aim to achieve a globally accurate model, such approaches are generally unsuited for systems with unbounded state spaces. Furthermore, a globally accurate model is not required to achieve good performance in many common control applications, e.g., local stabilization tasks. In this paper, we propose an active learning strategy for Gaussian process state space models that aims to obtain an accurate model on a bounded subset of the state-action space. Our approach aims to maximize the mutual information of the exploration trajectories with respect to a discretization of the region of interest. By employing model predictive control, the proposed technique integrates information collected during exploration and adaptively improves its exploration strategy. To enable computational tractability, we decouple the choice of most informative data points from the model predictive control optimization step. This yields two optimization problems that can be solved in parallel. We apply the proposed method to explore the state space of various dynamical systems and compare our approach to a commonly used entropy-based exploration strategy. In all experiments, our method yields a better model within the region of interest than the entropy-based method.", "published": "2020-05-04T05:35:02Z", "authors": ["Alexandre Capone", "Jonas Umlauft", "Thomas Beckers", "Armin Lederer", "Sandra Hirche"], "categories": ["cs.LG", "eess.SY", "stat.ML"], "doi": "", "_query": "real estate valuation deep learning", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2107.06317v3", "title": "Inverse Contextual Bandits: Learning How Behavior Evolves over Time", "abstract": "Understanding a decision-maker's priorities by observing their behavior is critical for transparency and accountability in decision processes, such as in healthcare. Though conventional approaches to policy learning almost invariably assume stationarity in behavior, this is hardly true in practice: Medical practice is constantly evolving as clinical professionals fine-tune their knowledge over time. For instance, as the medical community's understanding of organ transplantations has progressed over the years, a pertinent question is: How have actual organ allocation policies been evolving? To give an answer, we desire a policy learning method that provides interpretable representations of decision-making, in particular capturing an agent's non-stationary knowledge of the world, as well as operating in an offline manner. First, we model the evolving behavior of decision-makers in terms of contextual bandits, and formalize the problem of Inverse Contextual Bandits (ICB). Second, we propose two concrete algorithms as solutions, learning parametric and nonparametric representations of an agent's behavior. Finally, using both real and simulated data for liver transplantations, we illustrate the applicability and explainability of our method, as well as benchmarking and validating its accuracy.", "published": "2021-07-13T18:24:18Z", "authors": ["Alihan Hüyük", "Daniel Jarrett", "Mihaela van der Schaar"], "categories": ["cs.LG", "stat.ML"], "doi": "", "_query": "real estate valuation deep learning", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/1906.05212v2", "title": "Is Deep Learning a Renormalization Group Flow?", "abstract": "Although there has been a rapid development of practical applications, theoretical explanations of deep learning are in their infancy. Deep learning performs a sophisticated coarse graining. Since coarse graining is a key ingredient of the renormalization group (RG), RG may provide a useful theoretical framework directly relevant to deep learning. In this study we pursue this possibility. A statistical mechanics model for a magnet, the Ising model, is used to train an unsupervised restricted Boltzmann machine (RBM). The patterns generated by the trained RBM are compared to the configurations generated through an RG treatment of the Ising model. Although we are motivated by the connection between deep learning and RG flow, in this study we focus mainly on comparing a single layer of a deep network to a single step in the RG flow. We argue that correlation functions between hidden and visible neurons are capable of diagnosing RG-like coarse graining. Numerical experiments show the presence of RG-like patterns in correlators computed using the trained RBMs. The observables we consider are also able to exhibit important differences between RG and deep learning.", "published": "2019-06-12T15:33:43Z", "authors": ["Ellen de Mello Koch", "Robert de Mello Koch", "Ling Cheng"], "categories": ["cs.LG", "cond-mat.stat-mech", "physics.comp-ph", "stat.ML"], "doi": "10.1109/ACCESS.2020.3000901", "_query": "real estate valuation deep learning", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2011.10134v2", "title": "Provable Multi-Objective Reinforcement Learning with Generative Models", "abstract": "Multi-objective reinforcement learning (MORL) is an extension of ordinary, single-objective reinforcement learning (RL) that is applicable to many real-world tasks where multiple objectives exist without known relative costs. We study the problem of single policy MORL, which learns an optimal policy given the preference of objectives. Existing methods require strong assumptions such as exact knowledge of the multi-objective Markov decision process, and are analyzed in the limit of infinite data and time. We propose a new algorithm called model-based envelop value iteration (EVI), which generalizes the enveloped multi-objective $Q$-learning algorithm in Yang et al., 2019. Our method can learn a near-optimal value function with polynomial sample complexity and linear convergence speed. To the best of our knowledge, this is the first finite-sample analysis of MORL algorithms.", "published": "2020-11-19T22:35:31Z", "authors": ["Dongruo Zhou", "Jiahao Chen", "Quanquan Gu"], "categories": ["cs.LG", "cs.AI", "math.OC"], "doi": "", "_query": "real estate valuation deep learning", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/1711.11225v1", "title": "Variational Deep Q Network", "abstract": "We propose a framework that directly tackles the probability distribution of the value function parameters in Deep Q Network (DQN), with powerful variational inference subroutines to approximate the posterior of the parameters. We will establish the equivalence between our proposed surrogate objective and variational inference loss. Our new algorithm achieves efficient exploration and performs well on large scale chain Markov Decision Process (MDP).", "published": "2017-11-30T04:52:09Z", "authors": ["Yunhao Tang", "Alp Kucukelbir"], "categories": ["cs.LG", "cs.AI", "stat.ML"], "doi": "", "_query": "real estate valuation deep learning", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/1612.04600v2", "title": "Predicting Process Behaviour using Deep Learning", "abstract": "Predicting business process behaviour is an important aspect of business process management. Motivated by research in natural language processing, this paper describes an application of deep learning with recurrent neural networks to the problem of predicting the next event in a business process. This is both a novel method in process prediction, which has largely relied on explicit process models, and also a novel application of deep learning methods. The approach is evaluated on two real datasets and our results surpass the state-of-the-art in prediction precision.", "published": "2016-12-14T12:33:28Z", "authors": ["Joerg Evermann", "Jana-Rebecca Rehse", "Peter Fettke"], "categories": ["cs.LG", "stat.ML"], "doi": "10.1016/j.dss.2017.04.003", "_query": "real estate valuation deep learning", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2107.13944v1", "title": "Lyapunov-based uncertainty-aware safe reinforcement learning", "abstract": "Reinforcement learning (RL) has shown a promising performance in learning optimal policies for a variety of sequential decision-making tasks. However, in many real-world RL problems, besides optimizing the main objectives, the agent is expected to satisfy a certain level of safety (e.g., avoiding collisions in autonomous driving). While RL problems are commonly formalized as Markov decision processes (MDPs), safety constraints are incorporated via constrained Markov decision processes (CMDPs). Although recent advances in safe RL have enabled learning safe policies in CMDPs, these safety requirements should be satisfied during both training and in the deployment process. Furthermore, it is shown that in memory-based and partially observable environments, these methods fail to maintain safety over unseen out-of-distribution observations. To address these limitations, we propose a Lyapunov-based uncertainty-aware safe RL model. The introduced model adopts a Lyapunov function that converts trajectory-based constraints to a set of local linear constraints. Furthermore, to ensure the safety of the agent in highly uncertain environments, an uncertainty quantification method is developed that enables identifying risk-averse actions through estimating the probability of constraint violations. Moreover, a Transformers model is integrated to provide the agent with memory to process long time horizons of information via the self-attention mechanism. The proposed model is evaluated in grid-world navigation tasks where safety is defined as avoiding static and dynamic obstacles in fully and partially observable environments. The results of these experiments show a significant improvement in the performance of the agent both in achieving optimality and satisfying safety constraints.", "published": "2021-07-29T13:08:15Z", "authors": ["Ashkan B. Jeddi", "Nariman L. Dehghani", "Abdollah Shafieezadeh"], "categories": ["cs.LG", "cs.AI", "stat.ML"], "doi": "", "_query": "real estate valuation deep learning", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/1206.6485v1", "title": "Greedy Algorithms for Sparse Reinforcement Learning", "abstract": "Feature selection and regularization are becoming increasingly prominent tools in the efforts of the reinforcement learning (RL) community to expand the reach and applicability of RL. One approach to the problem of feature selection is to impose a sparsity-inducing form of regularization on the learning method. Recent work on $L_1$ regularization has adapted techniques from the supervised learning literature for use with RL. Another approach that has received renewed attention in the supervised learning community is that of using a simple algorithm that greedily adds new features. Such algorithms have many of the good properties of the $L_1$ regularization methods, while also being extremely efficient and, in some cases, allowing theoretical guarantees on recovery of the true form of a sparse target function from sampled data. This paper considers variants of orthogonal matching pursuit (OMP) applied to reinforcement learning. The resulting algorithms are analyzed and compared experimentally with existing $L_1$ regularized approaches. We demonstrate that perhaps the most natural scenario in which one might hope to achieve sparse recovery fails; however, one variant, OMP-BRM, provides promising theoretical guarantees under certain assumptions on the feature dictionary. Another variant, OMP-TD, empirically outperforms prior methods both in approximation accuracy and efficiency on several benchmark problems.", "published": "2012-06-27T19:59:59Z", "authors": ["Christopher Painter-Wakefield", "Ronald Parr"], "categories": ["cs.LG", "stat.ML"], "doi": "", "_query": "real estate valuation deep learning", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/1808.01974v1", "title": "A Survey on Deep Transfer Learning", "abstract": "As a new classification platform, deep learning has recently received increasing attention from researchers and has been successfully applied to many domains. In some domains, like bioinformatics and robotics, it is very difficult to construct a large-scale well-annotated dataset due to the expense of data acquisition and costly annotation, which limits its development. Transfer learning relaxes the hypothesis that the training data must be independent and identically distributed (i.i.d.) with the test data, which motivates us to use transfer learning to solve the problem of insufficient training data. This survey focuses on reviewing the current researches of transfer learning by using deep neural network and its applications. We defined deep transfer learning, category and review the recent research works based on the techniques used in deep transfer learning.", "published": "2018-08-06T16:06:43Z", "authors": ["Chuanqi Tan", "Fuchun Sun", "Tao Kong", "Wenchang Zhang", "Chao Yang", "Chunfang Liu"], "categories": ["cs.LG", "stat.ML"], "doi": "", "_query": "real estate valuation deep learning", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2208.12863v1", "title": "Ammunition Component Classification Using Deep Learning", "abstract": "Ammunition scrap inspection is an essential step in the process of recycling ammunition metal scrap. Most ammunition is composed of a number of components, including case, primer, powder, and projectile. Ammo scrap containing energetics is considered to be potentially dangerous and should be separated before the recycling process. Manually inspecting each piece of scrap is tedious and time-consuming. We have gathered a dataset of ammunition components with the goal of applying artificial intelligence for classifying safe and unsafe scrap pieces automatically. First, two training datasets are manually created from visual and x-ray images of ammo. Second, the x-ray dataset is augmented using the spatial transforms of histogram equalization, averaging, sharpening, power law, and Gaussian blurring in order to compensate for the lack of sufficient training data. Lastly, the representative YOLOv4 object detection method is applied to detect the ammo components and classify the scrap pieces into safe and unsafe classes, respectively. The trained models are tested against unseen data in order to evaluate the performance of the applied method. The experiments demonstrate the feasibility of ammo component detection and classification using deep learning. The datasets and the pre-trained models are available at https://github.com/hadi-ghnd/Scrap-Classification.", "published": "2022-08-26T20:42:39Z", "authors": ["Hadi Ghahremannezhad", "Chengjun Liu", "Hang Shi"], "categories": ["cs.CV"], "doi": "", "_query": "real estate valuation deep learning", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2403.16369v3", "title": "Learning Action-based Representations Using Invariance", "abstract": "Robust reinforcement learning agents using high-dimensional observations must be able to identify relevant state features amidst many exogeneous distractors. A representation that captures controllability identifies these state elements by determining what affects agent control. While methods such as inverse dynamics and mutual information capture controllability for a limited number of timesteps, capturing long-horizon elements remains a challenging problem. Myopic controllability can capture the moment right before an agent crashes into a wall, but not the control-relevance of the wall while the agent is still some distance away. To address this we introduce action-bisimulation encoding, a method inspired by the bisimulation invariance pseudometric, that extends single-step controllability with a recursive invariance constraint. By doing this, action-bisimulation learns a multi-step controllability metric that smoothly discounts distant state features that are relevant for control. We demonstrate that action-bisimulation pretraining on reward-free, uniformly random data improves sample efficiency in several environments, including a photorealistic 3D simulation domain, Habitat. Additionally, we provide theoretical analysis and qualitative results demonstrating the information captured by action-bisimulation.", "published": "2024-03-25T02:17:54Z", "authors": ["Max Rudolph", "Caleb Chuck", "Kevin Black", "Misha Lvovsky", "Scott Niekum", "Amy Zhang"], "categories": ["cs.LG", "cs.AI", "stat.ML"], "doi": "", "_query": "real estate valuation deep learning", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2204.11827v1", "title": "Task-Induced Representation Learning", "abstract": "In this work, we evaluate the effectiveness of representation learning approaches for decision making in visually complex environments. Representation learning is essential for effective reinforcement learning (RL) from high-dimensional inputs. Unsupervised representation learning approaches based on reconstruction, prediction or contrastive learning have shown substantial learning efficiency gains. Yet, they have mostly been evaluated in clean laboratory or simulated settings. In contrast, real environments are visually complex and contain substantial amounts of clutter and distractors. Unsupervised representations will learn to model such distractors, potentially impairing the agent's learning efficiency. In contrast, an alternative class of approaches, which we call task-induced representation learning, leverages task information such as rewards or demonstrations from prior tasks to focus on task-relevant parts of the scene and ignore distractors. We investigate the effectiveness of unsupervised and task-induced representation learning approaches on four visually complex environments, from Distracting DMControl to the CARLA driving simulator. For both, RL and imitation learning, we find that representation learning generally improves sample efficiency on unseen tasks even in visually complex scenes and that task-induced representations can double learning efficiency compared to unsupervised alternatives. Code is available at https://clvrai.com/tarp.", "published": "2022-04-25T17:57:10Z", "authors": ["Jun Yamada", "Karl Pertsch", "Anisha Gunjal", "Joseph J. Lim"], "categories": ["cs.LG", "cs.AI", "cs.RO"], "doi": "", "_query": "real estate valuation deep learning", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/1906.06635v1", "title": "Conditional Computation for Continual Learning", "abstract": "Catastrophic forgetting of connectionist neural networks is caused by the global sharing of parameters among all training examples. In this study, we analyze parameter sharing under the conditional computation framework where the parameters of a neural network are conditioned on each input example. At one extreme, if each input example uses a disjoint set of parameters, there is no sharing of parameters thus no catastrophic forgetting. At the other extreme, if the parameters are the same for every example, it reduces to the conventional neural network. We then introduce a clipped version of maxout networks which lies in the middle, i.e. parameters are shared partially among examples. Based on the parameter sharing analysis, we can locate a limited set of examples that are interfered when learning a new example. We propose to perform rehearsal on this set to prevent forgetting, which is termed as conditional rehearsal. Finally, we demonstrate the effectiveness of the proposed method in an online non-stationary setup, where updates are made after each new example and the distribution of the received example shifts over time.", "published": "2019-06-16T02:11:39Z", "authors": ["Min Lin", "Jie Fu", "Yoshua Bengio"], "categories": ["cs.LG", "cs.NE", "stat.ML"], "doi": "", "_query": "real estate valuation deep learning", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2206.10797v1", "title": "Imitation Learning for Generalizable Self-driving Policy with Sim-to-real Transfer", "abstract": "Imitation Learning uses the demonstrations of an expert to uncover the optimal policy and it is suitable for real-world robotics tasks as well. In this case, however, the training of the agent is carried out in a simulation environment due to safety, economic and time constraints. Later, the agent is applied in the real-life domain using sim-to-real methods. In this paper, we apply Imitation Learning methods that solve a robotics task in a simulated environment and use transfer learning to apply these solutions in the real-world environment. Our task is set in the Duckietown environment, where the robotic agent has to follow the right lane based on the input images of a single forward-facing camera. We present three Imitation Learning and two sim-to-real methods capable of achieving this task. A detailed comparison is provided on these techniques to highlight their advantages and disadvantages.", "published": "2022-06-22T01:36:14Z", "authors": ["Zoltán Lőrincz", "Márton Szemenyei", "Róbert Moni"], "categories": ["cs.LG", "cs.CV", "cs.RO"], "doi": "", "_query": "real estate valuation deep learning", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2607.27626v2", "title": "Real-Time Hard Peak Age-of-Information Safety with No-Regret Learning", "abstract": "Safety-critical IoT systems such as industrial closed-loop control, V2X coordination, and remote teleoperation require every sensor's peak Age of Information (peak AoI, also abbreviated PAoI) to stay below a hard per-slot deadline, not merely an average bound. Existing approaches meet this requirement only under restrictive assumptions: stochastic channels for Whittle-index AoI, simulator rollouts for deep reinforcement learning, or sublinear cumulative violation for long-term constrained online convex optimization. Under adversarial coefficients, OCO-PAoI-Hard guarantees zero per-slot violation of the modeled AoI state under one-step viability and O(sqrt(T)) regret against any static safe comparator; packet-level safety requires stronger service assumptions. Our key observation is that the fractional peak-AoI deadline collapses exactly to an affine half-space constraint on the resource-allocation vector, turning hard real-time scheduling into time-varying constrained online convex optimization over a polyhedral safe set. A strictly causal proposal-shield-update loop enforces feasibility through one Euclidean projection per slot, the gradient step preserves no-regret behavior, and the classical virtual queue is reduced to an a-posteriori certificate. We establish closed-form static and dynamic regret bounds, a matching Omega(sqrt(T)) minimax lower bound, a margin-safe variant against execution noise, and a deadline-induced competitive ratio. On a four-sensor adversarial fluid-model trap channel, OCO-PAoI-Hard attains zero modeled-state deadline violations across all ten seeds, while four representative baselines miss between 1.65 percent and 64.0 percent of slots, and the empirical normalized regret stays below the theoretical envelope across two orders of magnitude in T.", "published": "2026-07-30T03:30:18Z", "authors": ["Wentao Zhang", "Wentao Mo"], "categories": ["cs.LG"], "doi": "", "_query": "real estate valuation deep learning", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2310.10509v1", "title": "Efficient Sim-to-real Transfer of Contact-Rich Manipulation Skills with Online Admittance Residual Learning", "abstract": "Learning contact-rich manipulation skills is essential. Such skills require the robots to interact with the environment with feasible manipulation trajectories and suitable compliance control parameters to enable safe and stable contact. However, learning these skills is challenging due to data inefficiency in the real world and the sim-to-real gap in simulation. In this paper, we introduce a hybrid offline-online framework to learn robust manipulation skills. We employ model-free reinforcement learning for the offline phase to obtain the robot motion and compliance control parameters in simulation \\RV{with domain randomization}. Subsequently, in the online phase, we learn the residual of the compliance control parameters to maximize robot performance-related criteria with force sensor measurements in real time. To demonstrate the effectiveness and robustness of our approach, we provide comparative results against existing methods for assembly, pivoting, and screwing tasks.", "published": "2023-10-16T15:28:10Z", "authors": ["Xiang Zhang", "Changhao Wang", "Lingfeng Sun", "Zheng Wu", "Xinghao Zhu", "Masayoshi Tomizuka"], "categories": ["cs.RO"], "doi": "", "_query": "real estate valuation deep learning", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/1910.08168v2", "title": "Deep Sub-Ensembles for Fast Uncertainty Estimation in Image Classification", "abstract": "Fast estimates of model uncertainty are required for many robust robotics applications. Deep Ensembles provides state of the art uncertainty without requiring Bayesian methods, but still it is computationally expensive. In this paper we propose deep sub-ensembles, an approximation to deep ensembles where the core idea is to ensemble only the layers close to the output, and not the whole model. With ResNet-20 on the CIFAR10 dataset, we obtain 1.5-2.5 speedup over a Deep Ensemble, with a small increase in error and NLL, and similarly up to 5-15 speedup with a VGG-like network on the SVHN dataset. Our results show that this idea enables a trade-off between error and uncertainty quality versus computational performance.", "published": "2019-10-17T21:07:40Z", "authors": ["Matias Valdenegro-Toro"], "categories": ["cs.LG", "cs.CV", "stat.ML"], "doi": "", "_query": "real estate valuation deep learning", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2410.09491v1", "title": "Dying Clusters Is All You Need -- Deep Clustering With an Unknown Number of Clusters", "abstract": "Finding meaningful groups, i.e., clusters, in high-dimensional data such as images or texts without labeled data at hand is an important challenge in data mining. In recent years, deep clustering methods have achieved remarkable results in these tasks. However, most of these methods require the user to specify the number of clusters in advance. This is a major limitation since the number of clusters is typically unknown if labeled data is unavailable. Thus, an area of research has emerged that addresses this problem. Most of these approaches estimate the number of clusters separated from the clustering process. This results in a strong dependency of the clustering result on the quality of the initial embedding. Other approaches are tailored to specific clustering processes, making them hard to adapt to other scenarios. In this paper, we propose UNSEEN, a general framework that, starting from a given upper bound, is able to estimate the number of clusters. To the best of our knowledge, it is the first method that can be easily combined with various deep clustering algorithms. We demonstrate the applicability of our approach by combining UNSEEN with the popular deep clustering algorithms DCN, DEC, and DKM and verify its effectiveness through an extensive experimental evaluation on several image and tabular datasets. Moreover, we perform numerous ablations to analyze our approach and show the importance of its components. The code is available at: https://github.com/collinleiber/UNSEEN", "published": "2024-10-12T11:04:10Z", "authors": ["Collin Leiber", "Niklas Strauß", "Matthias Schubert", "Thomas Seidl"], "categories": ["cs.LG", "cs.AI"], "doi": "", "_query": "real estate valuation deep learning", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/1409.2944v2", "title": "Collaborative Deep Learning for Recommender Systems", "abstract": "Collaborative filtering (CF) is a successful approach commonly used by many recommender systems. Conventional CF-based methods use the ratings given to items by users as the sole source of information for learning to make recommendation. However, the ratings are often very sparse in many applications, causing CF-based methods to degrade significantly in their recommendation performance. To address this sparsity problem, auxiliary information such as item content information may be utilized. Collaborative topic regression (CTR) is an appealing recent method taking this approach which tightly couples the two components that learn from two different sources of information. Nevertheless, the latent representation learned by CTR may not be very effective when the auxiliary information is very sparse. To address this problem, we generalize recent advances in deep learning from i.i.d. input to non-i.i.d. (CF-based) input and propose in this paper a hierarchical Bayesian model called collaborative deep learning (CDL), which jointly performs deep representation learning for the content information and collaborative filtering for the ratings (feedback) matrix. Extensive experiments on three real-world datasets from different domains show that CDL can significantly advance the state of the art.", "published": "2014-09-10T03:05:22Z", "authors": ["Hao Wang", "Naiyan Wang", "Dit-Yan Yeung"], "categories": ["cs.LG", "cs.CL", "cs.IR", "cs.NE", "stat.ML"], "doi": "", "_query": "real estate valuation deep learning", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/1807.00051v3", "title": "Adversarial Examples in Deep Learning: Characterization and Divergence", "abstract": "The burgeoning success of deep learning has raised the security and privacy concerns as more and more tasks are accompanied with sensitive data. Adversarial attacks in deep learning have emerged as one of the dominating security threat to a range of mission-critical deep learning systems and applications. This paper takes a holistic and principled approach to perform statistical characterization of adversarial examples in deep learning. We provide a general formulation of adversarial examples and elaborate on the basic principle for adversarial attack algorithm design. We introduce easy and hard categorization of adversarial attacks to analyze the effectiveness of adversarial examples in terms of attack success rate, degree of change in adversarial perturbation, average entropy of prediction qualities, and fraction of adversarial examples that lead to successful attacks. We conduct extensive experimental study on adversarial behavior in easy and hard attacks under deep learning models with different hyperparameters and different deep learning frameworks. We show that the same adversarial attack behaves differently under different hyperparameters and across different frameworks due to the different features learned under different deep learning model training process. Our statistical characterization with strong empirical evidence provides a transformative enlightenment on mitigation strategies towards effective countermeasures against present and future adversarial attacks.", "published": "2018-06-29T19:50:25Z", "authors": ["Wenqi Wei", "Ling Liu", "Margaret Loper", "Stacey Truex", "Lei Yu", "Mehmet Emre Gursoy", "Yanzhao Wu"], "categories": ["cs.LG", "stat.ML"], "doi": "", "_query": "real estate valuation deep learning", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2511.09790v2", "title": "A Robust Task-Level Control Architecture for Learned Dynamical Systems", "abstract": "Dynamical system (DS)-based learning from demonstration (LfD) is a powerful tool for generating motion plans in the operation ('task') space of robotic systems. However, realizing generated motion plans is often compromised by a \"task-execution mismatch\", where unmodeled dynamics, persistent disturbances, and system latency cause the robot's task-space state to diverge from the desired state. We propose a novel task-level robust control architecture, L1-augmented Dynamical Systems (L1-DS), that explicitly handles the task-execution mismatch in tracking a nominal motion plan generated by any DS-based LfD scheme. Our framework augments any DS-based LfD model with a nominal stabilizing controller and an L1 adaptive controller. Furthermore, we introduce a windowed Dynamic Time Warping (DTW)-based target selector, which enables the nominal stabilizing controller to handle temporal misalignment for improved phase-consistent tracking. We demonstrate the efficacy of our architecture on the LASA and IROS handwriting datasets.", "published": "2025-11-12T22:45:32Z", "authors": ["Eshika Pathak", "Ahmed Aboudonia", "Sandeep Banik", "Naira Hovakimyan"], "categories": ["cs.RO", "cs.LG", "eess.SY"], "doi": "", "_query": "real estate valuation deep learning", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/1909.11201v4", "title": "Matrix Sketching for Secure Collaborative Machine Learning", "abstract": "Collaborative learning allows participants to jointly train a model without data sharing. To update the model parameters, the central server broadcasts model parameters to the clients, and the clients send updating directions such as gradients to the server. While data do not leave a client device, the communicated gradients and parameters will leak a client's privacy. Attacks that infer clients' privacy from gradients and parameters have been developed by prior work. Simple defenses such as dropout and differential privacy either fail to defend the attacks or seriously hurt test accuracy. We propose a practical defense which we call Double-Blind Collaborative Learning (DBCL). The high-level idea is to apply random matrix sketching to the parameters (aka weights) and re-generate random sketching after each iteration. DBCL prevents clients from conducting gradient-based privacy inferences which are the most effective attacks. DBCL works because from the attacker's perspective, sketching is effectively random noise that outweighs the signal. Notably, DBCL does not much increase computation and communication costs and does not hurt test accuracy at all.", "published": "2019-09-24T21:55:26Z", "authors": ["Mengjiao Zhang", "Shusen Wang"], "categories": ["cs.LG", "cs.CR", "stat.ML"], "doi": "", "_query": "real estate valuation deep learning", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/1811.12273v1", "title": "On the Transferability of Representations in Neural Networks Between Datasets and Tasks", "abstract": "Deep networks, composed of multiple layers of hierarchical distributed representations, tend to learn low-level features in initial layers and transition to high-level features towards final layers. Paradigms such as transfer learning, multi-task learning, and continual learning leverage this notion of generic hierarchical distributed representations to share knowledge across datasets and tasks. Herein, we study the layer-wise transferability of representations in deep networks across a few datasets and tasks and note some interesting empirical observations.", "published": "2018-11-29T16:06:57Z", "authors": ["Haytham M. Fayek", "Lawrence Cavedon", "Hong Ren Wu"], "categories": ["cs.LG", "stat.ML"], "doi": "", "_query": "real estate valuation deep learning", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/1912.06733v1", "title": "Private Federated Learning with Domain Adaptation", "abstract": "Federated Learning (FL) is a distributed machine learning (ML) paradigm that enables multiple parties to jointly re-train a shared model without sharing their data with any other parties, offering advantages in both scale and privacy. We propose a framework to augment this collaborative model-building with per-user domain adaptation. We show that this technique improves model accuracy for all users, using both real and synthetic data, and that this improvement is much more pronounced when differential privacy bounds are imposed on the FL model.", "published": "2019-12-13T22:48:43Z", "authors": ["Daniel Peterson", "Pallika Kanani", "Virendra J. Marathe"], "categories": ["cs.LG", "cs.CR", "stat.ML"], "doi": "", "_query": "real estate valuation deep learning", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/1810.10090v1", "title": "NestDNN: Resource-Aware Multi-Tenant On-Device Deep Learning for Continuous Mobile Vision", "abstract": "Mobile vision systems such as smartphones, drones, and augmented-reality headsets are revolutionizing our lives. These systems usually run multiple applications concurrently and their available resources at runtime are dynamic due to events such as starting new applications, closing existing applications, and application priority changes. In this paper, we present NestDNN, a framework that takes the dynamics of runtime resources into account to enable resource-aware multi-tenant on-device deep learning for mobile vision systems. NestDNN enables each deep learning model to offer flexible resource-accuracy trade-offs. At runtime, it dynamically selects the optimal resource-accuracy trade-off for each deep learning model to fit the model's resource demand to the system's available runtime resources. In doing so, NestDNN efficiently utilizes the limited resources in mobile vision systems to jointly maximize the performance of all the concurrently running applications. Our experiments show that compared to the resource-agnostic status quo approach, NestDNN achieves as much as 4.2% increase in inference accuracy, 2.0x increase in video frame processing rate and 1.7x reduction on energy consumption.", "published": "2018-10-23T21:07:42Z", "authors": ["Biyi Fang", "Xiao Zeng", "Mi Zhang"], "categories": ["cs.CV"], "doi": "10.1145/3241539.3241559", "_query": "real estate valuation deep learning", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2006.14897v2", "title": "Hop Sampling: A Simple Regularized Graph Learning for Non-Stationary Environments", "abstract": "Graph representation learning is gaining popularity in a wide range of applications, such as social networks analysis, computational biology, and recommender systems. However, different with positive results from many academic studies, applying graph neural networks (GNNs) in a real-world application is still challenging due to non-stationary environments. The underlying distribution of streaming data changes unexpectedly, resulting in different graph structures (a.k.a., concept drift). Therefore, it is essential to devise a robust graph learning technique so that the model does not overfit to the training graphs. In this work, we present Hop Sampling, a straightforward regularization method that can effectively prevent GNNs from overfishing. The hop sampling randomly selects the number of propagation steps rather than fixing it, and by doing so, it encourages the model to learn meaningful node representation for all intermediate propagation layers and to experience a variety of plausible graphs that are not in the training set. Particularly, we describe the use case of our method in recommender systems, a representative example of the real-world non-stationary case. We evaluated hop sampling on a large-scale real-world LINE dataset and conducted an online A/B/n test in LINE Coupon recommender systems of LINE Wallet Tab. Experimental results demonstrate that the proposed scheme improves the prediction accuracy of GNNs. We observed hop sampling provides 7.97% and 16.93% improvements for NDCG and MAP compared to non-regularized GNN models in our online service. Furthermore, models using hop sampling alleviate the oversmoothing issue in GNNs enabling a deeper model as well as more diversified representation.", "published": "2020-06-26T10:22:57Z", "authors": ["Young-Jin Park", "Kyuyong Shin", "Kyung-Min Kim"], "categories": ["stat.ML", "cs.LG"], "doi": "", "_query": "real estate valuation deep learning", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2510.15839v2", "title": "Learning Correlated Reward Models: Statistical Barriers and Opportunities", "abstract": "Random Utility Models (RUMs) are a classical framework for modeling user preferences and play a key role in reward modeling for Reinforcement Learning from Human Feedback (RLHF). However, a crucial shortcoming of many of these techniques is the Independence of Irrelevant Alternatives (IIA) assumption, which collapses \\emph{all} human preferences to a universal underlying utility function, yielding a coarse approximation of the range of human preferences. On the other hand, statistical and computational guarantees for models avoiding this assumption are scarce. In this paper, we investigate the statistical and computational challenges of learning a \\emph{correlated} probit model, a fundamental RUM that avoids the IIA assumption. First, we establish that the classical data collection paradigm of pairwise preference data is \\emph{fundamentally insufficient} to learn correlational information, explaining the lack of statistical and computational guarantees in this setting. Next, we demonstrate that \\emph{best-of-three} preference data provably overcomes these shortcomings, and devise a statistically and computationally efficient estimator with near-optimal performance. These results highlight the benefits of higher-order preference data in learning correlated utilities, allowing for more fine-grained modeling of human preferences. Finally, we validate these theoretical guarantees on several real-world datasets, demonstrating improved personalization of human preferences.", "published": "2025-10-17T17:31:17Z", "authors": ["Yeshwanth Cherapanamjeri", "Constantinos Daskalakis", "Gabriele Farina", "Sobhan Mohammadpour"], "categories": ["cs.LG", "econ.EM", "stat.ML"], "doi": "", "_query": "real estate valuation deep learning", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2012.07976v1", "title": "NeurIPS 2020 Competition: Predicting Generalization in Deep Learning", "abstract": "Understanding generalization in deep learning is arguably one of the most important questions in deep learning. Deep learning has been successfully adopted to a large number of problems ranging from pattern recognition to complex decision making, but many recent researchers have raised many concerns about deep learning, among which the most important is generalization. Despite numerous attempts, conventional statistical learning approaches have yet been able to provide a satisfactory explanation on why deep learning works. A recent line of works aims to address the problem by trying to predict the generalization performance through complexity measures. In this competition, we invite the community to propose complexity measures that can accurately predict generalization of models. A robust and general complexity measure would potentially lead to a better understanding of deep learning's underlying mechanism and behavior of deep models on unseen data, or shed light on better generalization bounds. All these outcomes will be important for making deep learning more robust and reliable.", "published": "2020-12-14T22:21:37Z", "authors": ["Yiding Jiang", "Pierre Foret", "Scott Yak", "Daniel M. Roy", "Hossein Mobahi", "Gintare Karolina Dziugaite", "Samy Bengio", "Suriya Gunasekar", "Isabelle Guyon", "Behnam Neyshabur"], "categories": ["cs.LG", "stat.ML"], "doi": "", "_query": "real estate valuation deep learning", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2411.11088v1", "title": "An Investigation of Offline Reinforcement Learning in Factorisable Action Spaces", "abstract": "Expanding reinforcement learning (RL) to offline domains generates promising prospects, particularly in sectors where data collection poses substantial challenges or risks. Pivotal to the success of transferring RL offline is mitigating overestimation bias in value estimates for state-action pairs absent from data. Whilst numerous approaches have been proposed in recent years, these tend to focus primarily on continuous or small-scale discrete action spaces. Factorised discrete action spaces, on the other hand, have received relatively little attention, despite many real-world problems naturally having factorisable actions. In this work, we undertake a formative investigation into offline reinforcement learning in factorisable action spaces. Using value-decomposition as formulated in DecQN as a foundation, we present the case for a factorised approach and conduct an extensive empirical evaluation of several offline techniques adapted to the factorised setting. In the absence of established benchmarks, we introduce a suite of our own comprising datasets of varying quality and task complexity. Advocating for reproducible research and innovation, we make all datasets available for public use alongside our code base.", "published": "2024-11-17T14:31:14Z", "authors": ["Alex Beeson", "David Ireland", "Giovanni Montana"], "categories": ["stat.ML", "cs.LG"], "doi": "", "_query": "real estate valuation deep learning", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2508.12252v2", "title": "Robot Trains Robot: Automatic Real-World Policy Adaptation and Learning for Humanoids", "abstract": "Simulation-based reinforcement learning (RL) has significantly advanced humanoid locomotion tasks, yet direct real-world RL from scratch or adapting from pretrained policies remains rare, limiting the full potential of humanoid robots. Real-world learning, despite being crucial for overcoming the sim-to-real gap, faces substantial challenges related to safety, reward design, and learning efficiency. To address these limitations, we propose Robot-Trains-Robot (RTR), a novel framework where a robotic arm teacher actively supports and guides a humanoid robot student. The RTR system provides protection, learning schedule, reward, perturbation, failure detection, and automatic resets. It enables efficient long-term real-world humanoid training with minimal human intervention. Furthermore, we propose a novel RL pipeline that facilitates and stabilizes sim-to-real transfer by optimizing a single dynamics-encoded latent variable in the real world. We validate our method through two challenging real-world humanoid tasks: fine-tuning a walking policy for precise speed tracking and learning a humanoid swing-up task from scratch, illustrating the promising capabilities of real-world humanoid learning realized by RTR-style systems. See https://robot-trains-robot.github.io/ for more info.", "published": "2025-08-17T06:17:06Z", "authors": ["Kaizhe Hu", "Haochen Shi", "Yao He", "Weizhuo Wang", "C. Karen Liu", "Shuran Song"], "categories": ["cs.RO"], "doi": "", "_query": "real estate valuation deep learning", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2301.08897v2", "title": "ScaDLES: Scalable Deep Learning over Streaming data at the Edge", "abstract": "Distributed deep learning (DDL) training systems are designed for cloud and data-center environments that assumes homogeneous compute resources, high network bandwidth, sufficient memory and storage, as well as independent and identically distributed (IID) data across all nodes. However, these assumptions don't necessarily apply on the edge, especially when training neural networks on streaming data in an online manner. Computing on the edge suffers from both systems and statistical heterogeneity. Systems heterogeneity is attributed to differences in compute resources and bandwidth specific to each device, while statistical heterogeneity comes from unbalanced and skewed data on the edge. Different streaming-rates among devices can be another source of heterogeneity when dealing with streaming data. If the streaming rate is lower than training batch-size, device needs to wait until enough samples have streamed in before performing a single iteration of stochastic gradient descent (SGD). Thus, low-volume streams act like stragglers slowing down devices with high-volume streams in synchronous training. On the other hand, data can accumulate quickly in the buffer if the streaming rate is too high and the devices can't train at line-rate. In this paper, we introduce ScaDLES to efficiently train on streaming data at the edge in an online fashion, while also addressing the challenges of limited bandwidth and training with non-IID data. We empirically show that ScaDLES converges up to 3.29 times faster compared to conventional distributed SGD.", "published": "2023-01-21T05:29:17Z", "authors": ["Sahil Tyagi", "Martin Swany"], "categories": ["cs.DC", "cs.LG", "cs.NI"], "doi": "10.1109/BigData55660.2022.10020597", "_query": "real estate valuation deep learning", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2402.16131v1", "title": "A VAE-based Framework for Learning Multi-Level Neural Granger-Causal Connectivity", "abstract": "Granger causality has been widely used in various application domains to capture lead-lag relationships amongst the components of complex dynamical systems, and the focus in extant literature has been on a single dynamical system. In certain applications in macroeconomics and neuroscience, one has access to data from a collection of related such systems, wherein the modeling task of interest is to extract the shared common structure that is embedded across them, as well as to identify the idiosyncrasies within individual ones. This paper introduces a Variational Autoencoder (VAE) based framework that jointly learns Granger-causal relationships amongst components in a collection of related-yet-heterogeneous dynamical systems, and handles the aforementioned task in a principled way. The performance of the proposed framework is evaluated on several synthetic data settings and benchmarked against existing approaches designed for individual system learning. The method is further illustrated on a real dataset involving time series data from a neurophysiological experiment and produces interpretable results.", "published": "2024-02-25T16:11:32Z", "authors": ["Jiahe Lin", "Huitian Lei", "George Michailidis"], "categories": ["cs.LG", "stat.ME", "stat.ML"], "doi": "", "_query": "real estate valuation deep learning", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2111.00961v2", "title": "Robustness of deep learning algorithms in astronomy -- galaxy morphology studies", "abstract": "Deep learning models are being increasingly adopted in wide array of scientific domains, especially to handle high-dimensionality and volume of the scientific data. However, these models tend to be brittle due to their complexity and overparametrization, especially to the inadvertent adversarial perturbations that can appear due to common image processing such as compression or blurring that are often seen with real scientific data. It is crucial to understand this brittleness and develop models robust to these adversarial perturbations. To this end, we study the effect of observational noise from the exposure time, as well as the worst case scenario of a one-pixel attack as a proxy for compression or telescope errors on performance of ResNet18 trained to distinguish between galaxies of different morphologies in LSST mock data. We also explore how domain adaptation techniques can help improve model robustness in case of this type of naturally occurring attacks and help scientists build more trustworthy and stable models.", "published": "2021-11-01T14:12:15Z", "authors": ["A. Ćiprijanović", "D. Kafkes", "G. N. Perdue", "K. Pedro", "G. Snyder", "F. J. Sánchez", "S. Madireddy", "S. M. Wild", "B. Nord"], "categories": ["astro-ph.GA", "cs.CV", "cs.LG"], "doi": "", "_query": "real estate valuation deep learning", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2607.06772v5", "title": "Efficient Long-Horizon Learning for Learned Optimization", "abstract": "Learned optimization aims to improve upon hand-designed optimizers (e.g., Adam and Muon) by meta-learning small neural network optimizers over a distribution of tasks. While recent work has greatly advanced the architectural design and inductive biases of learned optimizers (LOs), their meta-training remains biased toward short-unroll learning on particular tasks, resulting in redundant computation and leaving LOs often unable to compete with hand-designed optimizers. We introduce Efficient Long-hOrizon (ELO) learning, an efficient meta-training algorithm that (1) reallocates wasted meta-training compute to longer failure regimes, achieving efficient long-horizon learning, and (2) enforces decoupled progressive expert supervision, providing stable meta-learning signals that additionally improve the generalization of LOs. Our empirical study evaluates ELO for meta-training both element-wise and matrix-based LOs. Across downstream language modeling (GPT-2 and GPT-3 on FineWeb) and image classification (ViT-B/16, ResNet-50 on ImageNet-1K) tasks, ELO substantially improves the long-unroll performance and out-of-distribution generalization of the base LOs. In particular, ELO-Celo2 outperforms well-tuned AdamW and Muon on GPT-2 (30M and 124M) language pretraining. \\textit{Notably, all ELO baselines require less than 7 H100 GPU-hours for meta-training.}", "published": "2026-07-07T20:05:22Z", "authors": ["Xiaolong Huang", "Benjamin Thérien", "James Harrison", "Eugene Belilovsky"], "categories": ["cs.LG"], "doi": "", "_query": "real estate valuation deep learning", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2107.10455v1", "title": "Time Varying Risk in U.S. Housing Sector and Real Estate Investment Trusts Equity Return", "abstract": "This study examines how housing sector volatilities affect real estate investment trust (REIT) equity return in the United States. I argue that unexpected changes in housing variables can be a source of aggregate housing risk, and the first principal component extracted from the volatilities of U.S. housing variables can predict the expected REIT equity returns. I propose and construct a factor-based housing risk index as an additional factor in asset price models that uses the time-varying conditional volatility of housing variables within the U.S. housing sector. The findings show that the proposed housing risk index is economically and theoretically consistent with the risk-return relationship of the conditional Intertemporal Capital Asset Pricing Model (ICAPM) of Merton (1973), which predicts an average maximum of 5.6 percent of risk premium in REIT equity return. In subsample analyses, the positive relationship is not affected by sample periods' choice but shows higher housing risk beta values for the 2009-18 sample period. The relationship remains significant after controlling for VIX, Fama-French three factors, and a broad set of macroeconomic and financial variables. Moreover, the proposed housing beta also accurately forecasts U.S. macroeconomic and financial conditions.", "published": "2021-07-22T04:57:39Z", "authors": ["Masud Alam"], "categories": ["econ.GN"], "doi": "", "_query": "real estate valuation deep learning", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/1905.12665v3", "title": "Graph Learning Network: A Structure Learning Algorithm", "abstract": "Recently, graph neural networks (GNNs) have proved to be suitable in tasks on unstructured data. Particularly in tasks as community detection, node classification, and link prediction. However, most GNN models still operate with static relationships. We propose the Graph Learning Network (GLN), a simple yet effective process to learn node embeddings and structure prediction functions. Our model uses graph convolutions to propose expected node features, and predict the best structure based on them. We repeat these steps recursively to enhance the prediction and the embeddings.", "published": "2019-05-29T18:20:47Z", "authors": ["Darwin Saire Pilco", "Adín Ramírez Rivera"], "categories": ["cs.LG", "stat.ML"], "doi": "", "_query": "real estate valuation deep learning", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/1803.11333v1", "title": "Learning View-Specific Deep Networks for Person Re-Identification", "abstract": "In recent years, a growing body of research has focused on the problem of person re-identification (re-id). The re-id techniques attempt to match the images of pedestrians from disjoint non-overlapping camera views. A major challenge of re-id is the serious intra-class variations caused by changing viewpoints. To overcome this challenge, we propose a deep neural network-based framework which utilizes the view information in the feature extraction stage. The proposed framework learns a view-specific network for each camera view with a cross-view Euclidean constraint (CV-EC) and a cross-view center loss (CV-CL). We utilize CV-EC to decrease the margin of the features between diverse views and extend the center loss metric to a view-specific version to better adapt the re-id problem. Moreover, we propose an iterative algorithm to optimize the parameters of the view-specific networks from coarse to fine. The experiments demonstrate that our approach significantly improves the performance of the existing deep networks and outperforms the state-of-the-art methods on the VIPeR, CUHK01, CUHK03, SYSU-mReId, and Market-1501 benchmarks.", "published": "2018-03-30T04:10:06Z", "authors": ["Zhanxiang Feng", "Jianhuang Lai", "Xiaohua Xie"], "categories": ["cs.CV"], "doi": "10.1109/TIP.2018.2818438", "_query": "real estate valuation deep learning", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/1909.03772v2", "title": "A Survey on Reproducibility by Evaluating Deep Reinforcement Learning Algorithms on Real-World Robots", "abstract": "As reinforcement learning (RL) achieves more success in solving complex tasks, more care is needed to ensure that RL research is reproducible and that algorithms herein can be compared easily and fairly with minimal bias. RL results are, however, notoriously hard to reproduce due to the algorithms' intrinsic variance, the environments' stochasticity, and numerous (potentially unreported) hyper-parameters. In this work we investigate the many issues leading to irreproducible research and how to manage those. We further show how to utilise a rigorous and standardised evaluation approach for easing the process of documentation, evaluation and fair comparison of different algorithms, where we emphasise the importance of choosing the right measurement metrics and conducting proper statistics on the results, for unbiased reporting of the results.", "published": "2019-09-09T11:33:09Z", "authors": ["Nicolai A. Lynnerup", "Laura Nolling", "Rasmus Hasle", "John Hallam"], "categories": ["cs.LG", "cs.AI", "cs.RO", "stat.ML"], "doi": "", "_query": "real estate valuation deep learning", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2501.05656v1", "title": "Evidential Deep Learning for Uncertainty Quantification and Out-of-Distribution Detection in Jet Identification using Deep Neural Networks", "abstract": "Current methods commonly used for uncertainty quantification (UQ) in deep learning (DL) models utilize Bayesian methods which are computationally expensive and time-consuming. In this paper, we provide a detailed study of UQ based on evidential deep learning (EDL) for deep neural network models designed to identify jets in high energy proton-proton collisions at the Large Hadron Collider and explore its utility in anomaly detection. EDL is a DL approach that treats learning as an evidence acquisition process designed to provide confidence (or epistemic uncertainty) about test data. Using publicly available datasets for jet classification benchmarking, we explore hyperparameter optimizations for EDL applied to the challenge of UQ for jet identification. We also investigate how the uncertainty is distributed for each jet class, how this method can be implemented for the detection of anomalies, how the uncertainty compares with Bayesian ensemble methods, and how the uncertainty maps onto latent spaces for the models. Our studies uncover some pitfalls of EDL applied to anomaly detection and a more effective way to quantify uncertainty from EDL as compared with the foundational EDL setup. These studies illustrate a methodological approach to interpreting EDL in jet classification models, providing new insights on how EDL quantifies uncertainty and detects out-of-distribution data which may lead to improved EDL methods for DL models applied to classification tasks.", "published": "2025-01-10T02:14:29Z", "authors": ["Ayush Khot", "Xiwei Wang", "Avik Roy", "Volodymyr Kindratenko", "Mark S. Neubauer"], "categories": ["hep-ex", "cs.LG"], "doi": "", "_query": "real estate valuation deep learning", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2004.05002v2", "title": "Self Punishment and Reward Backfill for Deep Q-Learning", "abstract": "Reinforcement learning agents learn by encouraging behaviours which maximize their total reward, usually provided by the environment. In many environments, however, the reward is provided after a series of actions rather than each single action, leading the agent to experience ambiguity in terms of whether those actions are effective, an issue known as the credit assignment problem. In this paper, we propose two strategies inspired by behavioural psychology to enable the agent to intrinsically estimate more informative reward values for actions with no reward. The first strategy, called self-punishment (SP), discourages the agent from making mistakes that lead to undesirable terminal states. The second strategy, called the rewards backfill (RB), backpropagates the rewards between two rewarded actions. We prove that, under certain assumptions and regardless of the reinforcement learning algorithm used, these two strategies maintain the order of policies in the space of all possible policies in terms of their total reward, and, by extension, maintain the optimal policy. Hence, our proposed strategies integrate with any reinforcement learning algorithm that learns a value or action-value function through experience. We incorporated these two strategies into three popular deep reinforcement learning approaches and evaluated the results on thirty Atari games. After parameter tuning, our results indicate that the proposed strategies improve the tested methods in over 65 percent of tested games by up to over 25 times performance improvement.", "published": "2020-04-10T11:53:11Z", "authors": ["Mohammad Reza Bonyadi", "Rui Wang", "Maryam Ziaei"], "categories": ["cs.AI", "cs.LG"], "doi": "10.1109/TNNLS.2021.3140042", "_query": "real estate valuation deep learning", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2208.02048v2", "title": "Centroids Matching: an efficient Continual Learning approach operating in the embedding space", "abstract": "Catastrophic forgetting (CF) occurs when a neural network loses the information previously learned while training on a set of samples from a different distribution, i.e., a new task. Existing approaches have achieved remarkable results in mitigating CF, especially in a scenario called task incremental learning. However, this scenario is not realistic, and limited work has been done to achieve good results on more realistic scenarios. In this paper, we propose a novel regularization method called Centroids Matching, that, inspired by meta-learning approaches, fights CF by operating in the feature space produced by the neural network, achieving good results while requiring a small memory footprint. Specifically, the approach classifies the samples directly using the feature vectors produced by the neural network, by matching those vectors with the centroids representing the classes from the current task, or all the tasks up to that point. Centroids Matching is faster than competing baselines, and it can be exploited to efficiently mitigate CF, by preserving the distances between the embedding space produced by the model when past tasks were over, and the one currently produced, leading to a method that achieves high accuracy on all the tasks, without using an external memory when operating on easy scenarios, or using a small one for more realistic ones. Extensive experiments demonstrate that Centroids Matching achieves accuracy gains on multiple datasets and scenarios.", "published": "2022-08-03T13:17:16Z", "authors": ["Jary Pomponi", "Simone Scardapane", "Aurelio Uncini"], "categories": ["cs.LG", "stat.ML"], "doi": "", "_query": "real estate valuation deep learning", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2202.12967v3", "title": "Exploring with Sticky Mittens: Reinforcement Learning with Expert Interventions via Option Templates", "abstract": "Long horizon robot learning tasks with sparse rewards pose a significant challenge for current reinforcement learning algorithms. A key feature enabling humans to learn challenging control tasks is that they often receive expert intervention that enables them to understand the high-level structure of the task before mastering low-level control actions. We propose a framework for leveraging expert intervention to solve long-horizon reinforcement learning tasks. We consider \\emph{option templates}, which are specifications encoding a potential option that can be trained using reinforcement learning. We formulate expert intervention as allowing the agent to execute option templates before learning an implementation. This enables them to use an option, before committing costly resources to learning it. We evaluate our approach on three challenging reinforcement learning problems, showing that it outperforms state-of-the-art approaches by two orders of magnitude. Videos of trained agents and our code can be found at: https://sites.google.com/view/stickymittens", "published": "2022-02-25T20:55:34Z", "authors": ["Souradeep Dutta", "Kaustubh Sridhar", "Osbert Bastani", "Edgar Dobriban", "James Weimer", "Insup Lee", "Julia Parish-Morris"], "categories": ["cs.LG", "cs.AI", "stat.ML"], "doi": "", "_query": "real estate valuation deep learning", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2208.11887v1", "title": "A deep learning approach to predict the number of k-barriers for intrusion detection over a circular region using wireless sensor networks", "abstract": "Wireless Sensor Networks (WSNs) is a promising technology with enormous applications in almost every walk of life. One of the crucial applications of WSNs is intrusion detection and surveillance at the border areas and in the defense establishments. The border areas are stretched in hundreds to thousands of miles, hence, it is not possible to patrol the entire border region. As a result, an enemy may enter from any point absence of surveillance and cause the loss of lives or destroy the military establishments. WSNs can be a feasible solution for the problem of intrusion detection and surveillance at the border areas. Detection of an enemy at the border areas and nearby critical areas such as military cantonments is a time-sensitive task as a delay of few seconds may have disastrous consequences. Therefore, it becomes imperative to design systems that are able to identify and detect the enemy as soon as it comes in the range of the deployed system. In this paper, we have proposed a deep learning architecture based on a fully connected feed-forward Artificial Neural Network (ANN) for the accurate prediction of the number of k-barriers for fast intrusion detection and prevention. We have trained and evaluated the feed-forward ANN model using four potential features, namely area of the circular region, sensing range of sensors, the transmission range of sensors, and the number of sensor for Gaussian and uniform sensor distribution. These features are extracted through Monte Carlo simulation. In doing so, we found that the model accurately predicts the number of k-barriers for both Gaussian and uniform sensor distribution with correlation coefficient (R = 0.78) and Root Mean Square Error (RMSE = 41.15) for the former and R = 0.79 and RMSE = 48.36 for the latter. Further, the proposed approach outperforms the other benchmark algorithms in terms of accuracy and computational time complexity.", "published": "2022-08-25T06:39:29Z", "authors": ["Abhilash Singh", "J. Amutha", "Jaiprakash Nagar", "Sandeep Sharma"], "categories": ["cs.LG", "cs.NI"], "doi": "10.1016/j.eswa.2022.118588", "_query": "real estate valuation deep learning", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2202.01666v5", "title": "Proportional Fairness in Federated Learning", "abstract": "With the increasingly broad deployment of federated learning (FL) systems in the real world, it is critical but challenging to ensure fairness in FL, i.e. reasonably satisfactory performances for each of the numerous diverse clients. In this work, we introduce and study a new fairness notion in FL, called proportional fairness (PF), which is based on the relative change of each client's performance. From its connection with the bargaining games, we propose PropFair, a novel and easy-to-implement algorithm for finding proportionally fair solutions in FL and study its convergence properties. Through extensive experiments on vision and language datasets, we demonstrate that PropFair can approximately find PF solutions, and it achieves a good balance between the average performances of all clients and of the worst 10% clients. Our code is available at \\url{https://github.com/huawei-noah/Federated-Learning/tree/main/FairFL}.", "published": "2022-02-03T16:28:04Z", "authors": ["Guojun Zhang", "Saber Malekmohammadi", "Xi Chen", "Yaoliang Yu"], "categories": ["cs.LG", "cs.AI", "math.OC", "stat.ML"], "doi": "", "_query": "real estate valuation deep learning", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/1810.01403v4", "title": "GLAD: GLocalized Anomaly Detection via Human-in-the-Loop Learning", "abstract": "Human analysts that use anomaly detection systems in practice want to retain the use of simple and explainable global anomaly detectors. In this paper, we propose a novel human-in-the-loop learning algorithm called GLAD (GLocalized Anomaly Detection) that supports global anomaly detectors. GLAD automatically learns their local relevance to specific data instances using label feedback from human analysts. The key idea is to place a uniform prior on the relevance of each member of the anomaly detection ensemble over the input feature space via a neural network trained on unlabeled instances. Subsequently, weights of the neural network are tuned to adjust the local relevance of each ensemble member using all labeled instances. GLAD also provides explanations which can improve the understanding of end-users about anomalies. Our experiments on synthetic and real-world data show the effectiveness of GLAD in learning the local relevance of ensemble members and discovering anomalies via label feedback.", "published": "2018-10-02T17:54:15Z", "authors": ["Md Rakibul Islam", "Shubhomoy Das", "Janardhan Rao Doppa", "Sriraam Natarajan"], "categories": ["cs.LG", "stat.ML"], "doi": "", "_query": "real estate valuation deep learning", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/1906.03455v2", "title": "Sensitivity of Deep Convolutional Networks to Gabor Noise", "abstract": "Deep Convolutional Networks (DCNs) have been shown to be sensitive to Universal Adversarial Perturbations (UAPs): input-agnostic perturbations that fool a model on large portions of a dataset. These UAPs exhibit interesting visual patterns, but this phenomena is, as yet, poorly understood. Our work shows that visually similar procedural noise patterns also act as UAPs. In particular, we demonstrate that different DCN architectures are sensitive to Gabor noise patterns. This behaviour, its causes, and implications deserve further in-depth study.", "published": "2019-06-08T13:41:38Z", "authors": ["Kenneth T. Co", "Luis Muñoz-González", "Emil C. Lupu"], "categories": ["cs.LG", "cs.CR", "stat.ML"], "doi": "", "_query": "real estate valuation deep learning", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2210.03109v1", "title": "Real-World Robot Learning with Masked Visual Pre-training", "abstract": "In this work, we explore self-supervised visual pre-training on images from diverse, in-the-wild videos for real-world robotic tasks. Like prior work, our visual representations are pre-trained via a masked autoencoder (MAE), frozen, and then passed into a learnable control module. Unlike prior work, we show that the pre-trained representations are effective across a range of real-world robotic tasks and embodiments. We find that our encoder consistently outperforms CLIP (up to 75%), supervised ImageNet pre-training (up to 81%), and training from scratch (up to 81%). Finally, we train a 307M parameter vision transformer on a massive collection of 4.5M images from the Internet and egocentric videos, and demonstrate clearly the benefits of scaling visual pre-training for robot learning.", "published": "2022-10-06T17:59:01Z", "authors": ["Ilija Radosavovic", "Tete Xiao", "Stephen James", "Pieter Abbeel", "Jitendra Malik", "Trevor Darrell"], "categories": ["cs.RO", "cs.CV", "cs.LG"], "doi": "", "_query": "real estate valuation deep learning", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2007.04756v1", "title": "Learning to Prune Deep Neural Networks via Reinforcement Learning", "abstract": "This paper proposes PuRL - a deep reinforcement learning (RL) based algorithm for pruning neural networks. Unlike current RL based model compression approaches where feedback is given only at the end of each episode to the agent, PuRL provides rewards at every pruning step. This enables PuRL to achieve sparsity and accuracy comparable to current state-of-the-art methods, while having a much shorter training cycle. PuRL achieves more than 80% sparsity on the ResNet-50 model while retaining a Top-1 accuracy of 75.37% on the ImageNet dataset. Through our experiments we show that PuRL is also able to sparsify already efficient architectures like MobileNet-V2. In addition to performance characterisation experiments, we also provide a discussion and analysis of the various RL design choices that went into the tuning of the Markov Decision Process underlying PuRL. Lastly, we point out that PuRL is simple to use and can be easily adapted for various architectures.", "published": "2020-07-09T13:06:07Z", "authors": ["Manas Gupta", "Siddharth Aravindan", "Aleksandra Kalisz", "Vijay Chandrasekhar", "Lin Jie"], "categories": ["cs.AI", "cs.CV", "cs.LG", "cs.NE"], "doi": "", "_query": "real estate valuation deep learning", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2608.23182v1", "title": "A Comparative Study of Label-free Representation Quality Metrics in Deep Learning", "abstract": "We present a comparative study of label-free metrics for assessing the quality of representations in deep neural networks to understand their reliability under a wide variety of configurations. We group existing label-free metrics into three families based on their construction and analytically establish connections between metrics within the same family. We then characterise the sensitivity of spectral metrics through controlled synthetic experiments. Finally, all label-free metrics are evaluated against downstream task accuracy across a diverse set of 260 vision models on six datasets spanning generic object classification, fine-grained object classification, scene recognition and geospatial task, stratifying results by architecture class and training objective. We find that intrinsic dimensionality (ID) is the most reliable predictor among the metrics considered. However, the reliability of all metrics, including ID, is moderated by architecture class and training objective. Our results provide a clearer understanding of what label-free representation quality metrics measure, when they are reliable, and how to interpret them in practice.", "published": "2026-08-24T12:31:37Z", "authors": ["Daniel Richards Arputharaj", "Daniel Jönsson", "Gabriel Eilertsen"], "categories": ["cs.LG", "cs.CV"], "doi": "", "_query": "real estate valuation deep learning", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/1811.07769v1", "title": "Addressing the Invisible: Street Address Generation for Developing Countries with Deep Learning", "abstract": "More than half of the world's roads lack adequate street addressing systems. Lack of addresses is even more visible in daily lives of people in developing countries. We would like to object to the assumption that having an address is a luxury, by proposing a generative address design that maps the world in accordance with streets. The addressing scheme is designed considering several traditional street addressing methodologies employed in the urban development scenarios around the world. Our algorithm applies deep learning to extract roads from satellite images, converts the road pixel confidences into a road network, partitions the road network to find neighborhoods, and labels the regions, roads, and address units using graph- and proximity-based algorithms. We present our results on a sample US city, and several developing cities, compare travel times of users using current ad hoc and new complete addresses, and contrast our addressing solution to current industrial and open geocoding alternatives.", "published": "2018-11-10T07:34:04Z", "authors": ["Ilke Demir", "Ramesh Raskar"], "categories": ["cs.CV", "cs.CY", "cs.LG", "stat.ML"], "doi": "", "_query": "real estate valuation deep learning", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2007.15745v3", "title": "On Hyperparameter Optimization of Machine Learning Algorithms: Theory and Practice", "abstract": "Machine learning algorithms have been used widely in various applications and areas. To fit a machine learning model into different problems, its hyper-parameters must be tuned. Selecting the best hyper-parameter configuration for machine learning models has a direct impact on the model's performance. It often requires deep knowledge of machine learning algorithms and appropriate hyper-parameter optimization techniques. Although several automatic optimization techniques exist, they have different strengths and drawbacks when applied to different types of problems. In this paper, optimizing the hyper-parameters of common machine learning models is studied. We introduce several state-of-the-art optimization techniques and discuss how to apply them to machine learning algorithms. Many available libraries and frameworks developed for hyper-parameter optimization problems are provided, and some open challenges of hyper-parameter optimization research are also discussed in this paper. Moreover, experiments are conducted on benchmark datasets to compare the performance of different optimization methods and provide practical examples of hyper-parameter optimization. This survey paper will help industrial users, data analysts, and researchers to better develop machine learning models by identifying the proper hyper-parameter configurations effectively.", "published": "2020-07-30T21:11:01Z", "authors": ["Li Yang", "Abdallah Shami"], "categories": ["cs.LG", "stat.ML"], "doi": "10.1016/j.neucom.2020.07.061", "_query": "real estate valuation deep learning", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/1911.02455v1", "title": "Unfairness towards subjective opinions in Machine Learning", "abstract": "Despite the high interest for Machine Learning (ML) in academia and industry, many issues related to the application of ML to real-life problems are yet to be addressed. Here we put forward one limitation which arises from a lack of adaptation of ML models and datasets to specific applications. We formalise a new notion of unfairness as exclusion of opinions. We propose ways to quantify this unfairness, and aid understanding its causes through visualisation. These insights into the functioning of ML-based systems hint at methods to mitigate unfairness.", "published": "2019-11-06T16:11:41Z", "authors": ["Agathe Balayn", "Alessandro Bozzon", "Zoltan Szlavik"], "categories": ["cs.LG", "cs.CY", "cs.HC", "stat.ML"], "doi": "", "_query": "real estate valuation deep learning", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/1901.11478v3", "title": "An Optimization Framework for Task Sequencing in Curriculum Learning", "abstract": "Curriculum learning in reinforcement learning is used to shape exploration by presenting the agent with increasingly complex tasks. The idea of curriculum learning has been largely applied in both animal training and pedagogy. In reinforcement learning, all previous task sequencing methods have shaped exploration with the objective of reducing the time to reach a given performance level. We propose novel uses of curriculum learning, which arise from choosing different objective functions. Furthermore, we define a general optimization framework for task sequencing and evaluate the performance of popular metaheuristic search methods on several tasks. We show that curriculum learning can be successfully used to: improve the initial performance, take fewer suboptimal actions during exploration, and discover better policies.", "published": "2019-01-31T17:08:27Z", "authors": ["Francesco Foglino", "Christiano Coletto Christakou", "Matteo Leonetti"], "categories": ["cs.LG", "cs.AI", "stat.ML"], "doi": "", "_query": "real estate valuation deep learning", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2406.00793v1", "title": "Is In-Context Learning in Large Language Models Bayesian? A Martingale Perspective", "abstract": "In-context learning (ICL) has emerged as a particularly remarkable characteristic of Large Language Models (LLM): given a pretrained LLM and an observed dataset, LLMs can make predictions for new data points from the same distribution without fine-tuning. Numerous works have postulated ICL as approximately Bayesian inference, rendering this a natural hypothesis. In this work, we analyse this hypothesis from a new angle through the martingale property, a fundamental requirement of a Bayesian learning system for exchangeable data. We show that the martingale property is a necessary condition for unambiguous predictions in such scenarios, and enables a principled, decomposed notion of uncertainty vital in trustworthy, safety-critical systems. We derive actionable checks with corresponding theory and test statistics which must hold if the martingale property is satisfied. We also examine if uncertainty in LLMs decreases as expected in Bayesian learning when more data is observed. In three experiments, we provide evidence for violations of the martingale property, and deviations from a Bayesian scaling behaviour of uncertainty, falsifying the hypothesis that ICL is Bayesian.", "published": "2024-06-02T16:20:30Z", "authors": ["Fabian Falck", "Ziyu Wang", "Chris Holmes"], "categories": ["stat.ML", "cs.LG"], "doi": "", "_query": "real estate valuation deep learning", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2210.10431v1", "title": "Hierarchical Reinforcement Learning for Furniture Layout in Virtual Indoor Scenes", "abstract": "In real life, the decoration of 3D indoor scenes through designing furniture layout provides a rich experience for people. In this paper, we explore the furniture layout task as a Markov decision process (MDP) in virtual reality, which is solved by hierarchical reinforcement learning (HRL). The goal is to produce a proper two-furniture layout in the virtual reality of the indoor scenes. In particular, we first design a simulation environment and introduce the HRL formulation for a two-furniture layout. We then apply a hierarchical actor-critic algorithm with curriculum learning to solve the MDP. We conduct our experiments on a large-scale real-world interior layout dataset that contains industrial designs from professional designers. Our numerical results demonstrate that the proposed model yields higher-quality layouts as compared with the state-of-art models.", "published": "2022-10-19T09:58:10Z", "authors": ["Xinhan Di", "Pengqian Yu"], "categories": ["cs.CV", "cs.AI"], "doi": "", "_query": "real estate valuation deep learning", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/1905.12091v1", "title": "Approximate Guarantees for Dictionary Learning", "abstract": "In the dictionary learning (or sparse coding) problem, we are given a collection of signals (vectors in $\\mathbb{R}^d$), and the goal is to find a \"basis\" in which the signals have a sparse (approximate) representation. The problem has received a lot of attention in signal processing, learning, and theoretical computer science. The problem is formalized as factorizing a matrix $X (d \\times n)$ (whose columns are the signals) as $X = AY$, where $A$ has a prescribed number $m$ of columns (typically $m \\ll n$), and $Y$ has columns that are $k$-sparse (typically $k \\ll d$). Most of the known theoretical results involve assuming that the columns of the unknown $A$ have certain incoherence properties, and that the coefficient matrix $Y$ has random (or partly random) structure. The goal of our work is to understand what can be said in the absence of such assumptions. Can we still find $A$ and $Y$ such that $X \\approx AY$? We show that this is possible, if we allow violating the bounds on $m$ and $k$ by appropriate factors that depend on $k$ and the desired approximation. Our results rely on an algorithm for what we call the threshold correlation problem, which turns out to be related to hypercontractive norms of matrices. We also show that our algorithmic ideas apply to a setting in which some of the columns of $X$ are outliers, thus giving similar guarantees even in this challenging setting.", "published": "2019-05-28T21:11:27Z", "authors": ["Aditya Bhaskara", "Wai Ming Tai"], "categories": ["cs.LG", "stat.ML"], "doi": "", "_query": "real estate valuation deep learning", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/1908.09219v1", "title": "Heterogeneous Relational Kernel Learning", "abstract": "Recent work has developed Bayesian methods for the automatic statistical analysis and description of single time series as well as of homogeneous sets of time series data. We extend prior work to create an interpretable kernel embedding for heterogeneous time series. Our method adds practically no computational cost compared to prior results by leveraging previously discarded intermediate results. We show the practical utility of our method by leveraging the learned embeddings for clustering, pattern discovery, and anomaly detection. These applications are beyond the ability of prior relational kernel learning approaches.", "published": "2019-08-24T22:00:39Z", "authors": ["Andre T. Nguyen", "Edward Raff"], "categories": ["cs.LG", "stat.ML"], "doi": "", "_query": "real estate valuation deep learning", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2011.05574v1", "title": "Deep Transfer Learning-Assisted Signal Detection for Ambient Backscatter Communications", "abstract": "Existing tag signal detection algorithms inevitably suffer from a high bit error rate (BER) due to the difficulties in estimating the channel state information (CSI). To eliminate the requirement of channel estimation and to improve the system performance, in this paper, we adopt a deep transfer learning (DTL) approach to implicitly extract the features of communication channel and directly recover tag symbols. Inspired by the powerful capability of convolutional neural networks (CNN) in exploring the features of data in a matrix form, we design a novel covariance matrix aware neural network (CMNet)-based detection scheme to facilitate DTL for tag signal detection, which consists of offline learning, transfer learning, and online detection. Specifically, a CMNet-based likelihood ratio test (CMNet-LRT) is derived based on the minimum error probability (MEP) criterion. Taking advantage of the outstanding performance of DTL in transferring knowledge with only a few training data, the proposed scheme can adaptively fine-tune the detector for different channel environments to further improve the detection performance. Finally, extensive simulation results demonstrate that the BER performance of the proposed method is comparable to that of the optimal detection method with perfect CSI.", "published": "2020-11-10T04:43:28Z", "authors": ["Chang Liu", "Xuemeng Liu", "Zhiqiang Wei", "Derrick Wing Kwan Ng", "Jinhong Yuan", "Ying-Chang Liang"], "categories": ["eess.SP", "cs.IT"], "doi": "", "_query": "real estate valuation deep learning", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/1803.00225v4", "title": "Global Convergence of Block Coordinate Descent in Deep Learning", "abstract": "Deep learning has aroused extensive attention due to its great empirical success. The efficiency of the block coordinate descent (BCD) methods has been recently demonstrated in deep neural network (DNN) training. However, theoretical studies on their convergence properties are limited due to the highly nonconvex nature of DNN training. In this paper, we aim at providing a general methodology for provable convergence guarantees for this type of methods. In particular, for most of the commonly used DNN training models involving both two- and three-splitting schemes, we establish the global convergence to a critical point at a rate of ${\\cal O}(1/k)$, where $k$ is the number of iterations. The results extend to general loss functions which have Lipschitz continuous gradients and deep residual networks (ResNets). Our key development adds several new elements to the Kurdyka-Łojasiewicz inequality framework that enables us to carry out the global convergence analysis of BCD in the general scenario of deep learning.", "published": "2018-03-01T06:11:53Z", "authors": ["Jinshan Zeng", "Tim Tsz-Kit Lau", "Shaobo Lin", "Yuan Yao"], "categories": ["math.OC", "cs.LG", "stat.ML"], "doi": "", "_query": "real estate valuation deep learning", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2610.19274v1", "title": "Deep Learning for Atmospheric Turbulence Mitigation: A Comparison Between GANs and Transformers", "abstract": "Atmospheric turbulence creates complex spatial and temporal distortions that significantly reduce image quality. This work compares two deep learning methods for turbulence reduction: Generative Adversarial Networks (GANs) and Transformer-based models. Both methods are trained and tested on the SOTIS dataset, which includes multi-frame sequences under different turbulence conditions with ground truth references. Our results show an important finding: GAN-based models perform better in severely degraded, low-quality situations, while Transformer-based models achieve better results in moderate to high-quality settings, especially in correcting geometric distortions. We also enhance the Turbulence Mitigation Transformer (TMT) by introducing a refined training strategy, an augmented input design that includes a temporally averaged frame as prior structural information, and a modified loss function that incorporates the Structural Similarity Index (SSIM). These improvements lead to consistent performance gains across various turbulence conditions. Overall, this study provides an in-depth evaluation of deep learning methods for turbulence reduction and offers practical guidance for selecting models in different imaging environments.", "published": "2026-10-13T22:50:29Z", "authors": ["Juan S. Carrillo", "Jérôme Gilles"], "categories": ["eess.IV", "cs.CV"], "doi": "10.1117/1.JEI.35.5.053014", "_query": "real estate valuation deep learning", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2006.04976v2", "title": "Physics Informed Deep Kernel Learning", "abstract": "Deep kernel learning is a promising combination of deep neural networks and nonparametric function learning. However, as a data driven approach, the performance of deep kernel learning can still be restricted by scarce or insufficient data, especially in extrapolation tasks. To address these limitations, we propose Physics Informed Deep Kernel Learning (PI-DKL) that exploits physics knowledge represented by differential equations with latent sources. Specifically, we use the posterior function sample of the Gaussian process as the surrogate for the solution of the differential equation, and construct a generative component to integrate the equation in a principled Bayesian hybrid framework. For efficient and effective inference, we marginalize out the latent variables in the joint probability and derive a collapsed model evidence lower bound (ELBO), based on which we develop a stochastic model estimation algorithm. Our ELBO can be viewed as a nice, interpretable posterior regularization objective. On synthetic datasets and real-world applications, we show the advantage of our approach in both prediction accuracy and uncertainty quantification.", "published": "2020-06-08T22:43:31Z", "authors": ["Zheng Wang", "Wei Xing", "Robert Kirby", "Shandian Zhe"], "categories": ["stat.ML", "cs.LG"], "doi": "", "_query": "real estate valuation deep learning", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/1906.08834v2", "title": "Deep Learning in the Automotive Industry: Recent Advances and Application Examples", "abstract": "One of the most exciting technology breakthroughs in the last few years has been the rise of deep learning. State-of-the-art deep learning models are being widely deployed in academia and industry, across a variety of areas, from image analysis to natural language processing. These models have grown from fledgling research subjects to mature techniques in real-world use. The increasing scale of data, computational power and the associated algorithmic innovations are the main drivers for the progress we see in this field. These developments also have a huge potential for the automotive industry and therefore the interest in deep learning-based technology is growing. A lot of the product innovations, such as self-driving cars, parking and lane-change assist or safety functions, such as autonomous emergency braking, are powered by deep learning algorithms. Deep learning is poised to offer gains in performance and functionality for most ADAS (Advanced Driver Assistance System) solutions. Virtual sensing for vehicle dynamics application, vehicle inspection/heath monitoring, automated driving and data-driven product development are key areas that are expected to get the most attention. This article provides an overview of the recent advances and some associated challenges in deep learning techniques in the context of automotive applications.", "published": "2019-06-20T20:30:39Z", "authors": ["Kanwar Bharat Singh", "Mustafa Ali Arat"], "categories": ["cs.LG", "cs.RO", "eess.SP", "stat.ML"], "doi": "", "_query": "real estate valuation deep learning", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/1808.05443v1", "title": "Transfer Learning and Organic Computing for Autonomous Vehicles", "abstract": "Autonomous Vehicles(AV) are one of the brightest promises of the future which would help cut down fatalities and improve travel time while working in harmony. Autonomous vehicles will face with challenging situations and experiences not seen before. These experiences should be converted to knowledge and help the vehicle prepare better in the future. Online Transfer Learning will help transferring prior knowledge to a new task and also keep the knowledge updated as the task evolves. This paper presents the different methods of transfer learning, online transfer learning and organic computing that could be adapted to the domain of autonomous vehicles.", "published": "2018-08-16T12:28:11Z", "authors": ["Christofer Fellicious"], "categories": ["cs.LG", "cs.RO", "stat.ML"], "doi": "", "_query": "real estate valuation deep learning", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2010.01062v3", "title": "Exploration in Approximate Hyper-State Space for Meta Reinforcement Learning", "abstract": "To rapidly learn a new task, it is often essential for agents to explore efficiently -- especially when performance matters from the first timestep. One way to learn such behaviour is via meta-learning. Many existing methods however rely on dense rewards for meta-training, and can fail catastrophically if the rewards are sparse. Without a suitable reward signal, the need for exploration during meta-training is exacerbated. To address this, we propose HyperX, which uses novel reward bonuses for meta-training to explore in approximate hyper-state space (where hyper-states represent the environment state and the agent's task belief). We show empirically that HyperX meta-learns better task-exploration and adapts more successfully to new tasks than existing methods.", "published": "2020-10-02T15:43:31Z", "authors": ["Luisa Zintgraf", "Leo Feng", "Cong Lu", "Maximilian Igl", "Kristian Hartikainen", "Katja Hofmann", "Shimon Whiteson"], "categories": ["cs.LG", "cs.AI", "stat.ML"], "doi": "", "_query": "real estate valuation deep learning", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2607.08595v1", "title": "Federated Deep Learning for Privacy-Preserving Cardiovascular Disease Risk Prediction", "abstract": "Cardiovascular disease risk prediction models often rely on data from a single institution or centrally pooled datasets. Extending these models across institutions could be limited by privacy regulations and constraints on sharing patient-level data. Federated learning enables collaborative model development without transferring sensitive patient data, but its application in healthcare remains challenging because datasets often differ in size, population characteristics, and outcome definitions. In this study, we present a federated deep learning approach for privacy-preserving cardiovascular disease risk prediction that integrates two population-based cohorts with different characteristics: Lifelines, including 148,230 participants meeting the study inclusion criteria with self-reported outcomes, and the Rotterdam Study, including a smaller cohort of 10,155 participants with digitally linked clinical outcomes. Model performance was primarily evaluated on the Rotterdam Study because of its complete follow-up. Deep survival models trained using federated learning achieved higher predictive performance than models trained locally without federation. For the Rotterdam Study, the C-statistic increased from 0.728 (95% CI: 0.717-0.739) to 0.739 (95% CI: 0.728-0.749). For Lifelines, the C-statistic increased from 0.783 (95% CI: 0.775-0.791) to 0.787 (95% CI: 0.780-0.792). These findings suggest that federated deep learning across heterogeneous cohorts can improve cardiovascular disease risk prediction while preserving the privacy of individual-level patient data.", "published": "2026-07-09T15:29:04Z", "authors": ["Hyunho Mo", "Djura Smits", "Mahlet A. Birhanu", "Maarten J. G. Leening", "Daniel Bos", "Pim van der Harst", "Esther E. Bron"], "categories": ["cs.LG", "cs.HC"], "doi": "", "_query": "real estate valuation deep learning", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2005.02612v1", "title": "Deep Divergence Learning", "abstract": "Classical linear metric learning methods have recently been extended along two distinct lines: deep metric learning methods for learning embeddings of the data using neural networks, and Bregman divergence learning approaches for extending learning Euclidean distances to more general divergence measures such as divergences over distributions. In this paper, we introduce deep Bregman divergences, which are based on learning and parameterizing functional Bregman divergences using neural networks, and which unify and extend these existing lines of work. We show in particular how deep metric learning formulations, kernel metric learning, Mahalanobis metric learning, and moment-matching functions for comparing distributions arise as special cases of these divergences in the symmetric setting. We then describe a deep learning framework for learning general functional Bregman divergences, and show in experiments that this method yields superior performance on benchmark datasets as compared to existing deep metric learning approaches. We also discuss novel applications, including a semi-supervised distributional clustering problem, and a new loss function for unsupervised data generation.", "published": "2020-05-06T06:43:25Z", "authors": ["Kubra Cilingir", "Rachel Manzelli", "Brian Kulis"], "categories": ["cs.LG", "stat.ML"], "doi": "", "_query": "real estate valuation deep learning", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2005.01584v3", "title": "MARS: Malleable Actor-Critic Reinforcement Learning Scheduler", "abstract": "In this paper, we introduce MARS, a new scheduling system for HPC-cloud infrastructures based on a cost-aware, flexible reinforcement learning approach, which serves as an intermediate layer for next generation HPC-cloud resource manager. MARS ensembles the pre-trained models from heuristic workloads and decides on the most cost-effective strategy for optimization. A whole workflow application would be split into several optimizable dependent sub-tasks, then based on the pre-defined resource management plan, a reward will be generated after executing a scheduled task. Lastly, MARS updates the Deep Neural Network (DNN) model based on the reward. MARS is designed to optimize the existing models through reinforcement mechanisms. MARS adapts to the dynamics of workflow applications, selects the most cost-effective scheduling solution among pre-built scheduling strategies (backfilling, SJF, etc.) and self-learning deep neural network model at run-time. We evaluate MARS with different real-world workflow traces. MARS can achieve 5%-60% increased performance compared to the state-of-the-art approaches.", "published": "2020-05-04T15:51:41Z", "authors": ["Betis Baheri", "Jacob Tronge", "Bo Fang", "Ang Li", "Vipin Chaudhary", "Qiang Guan"], "categories": ["cs.DC"], "doi": "10.1109/IPCCC55026.2022.9894315", "_query": "real estate valuation deep learning", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2311.16135v2", "title": "Use of Deep Neural Networks for Uncertain Stress Functions with Extensions to Impact Mechanics", "abstract": "Stress-strain curves, or more generally, stress functions, are an extremely important characterization of a material's mechanical properties. However, stress functions are often difficult to derive and are narrowly tailored to a specific material. Further, large deformations, high strain-rates, temperature sensitivity, and effect of material parameters compound modeling challenges. We propose a generalized deep neural network approach to model stress as a state function with quantile regression to capture uncertainty. We extend these models to uniaxial impact mechanics using stochastic differential equations to demonstrate a use case and provide a framework for implementing this uncertainty-aware stress function. We provide experiments benchmarking our approach against leading constitutive, machine learning, and transfer learning approaches to stress and impact mechanics modeling on publicly available and newly presented data sets. We also provide a framework to optimize material parameters given multiple competing impact scenarios.", "published": "2023-11-03T00:12:24Z", "authors": ["Garrett Blum", "Ryan Doris", "Diego Klabjan", "Horacio Espinosa", "Ron Szalkowski"], "categories": ["cond-mat.mtrl-sci", "cs.LG"], "doi": "", "_query": "real estate valuation deep learning", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2011.13880v2", "title": "REAL-X -- Robot open-Ended Autonomous Learning Architectures: Achieving Truly End-to-End Sensorimotor Autonomous Learning Systems", "abstract": "Open-ended learning is a core research field of developmental robotics and AI aiming to build learning machines and robots that can autonomously acquire knowledge and skills incrementally as infants and children. The first contribution of this work is to study the challenges posed by the previously proposed benchmark `REAL competition' aiming to foster the development of truly open-ended learning robot architectures. The competition involves a simulated camera-arm robot that: (a) in a first `intrinsic phase' acquires sensorimotor competence by autonomously interacting with objects; (b) in a second `extrinsic phase' is tested with tasks unknown in the intrinsic phase to measure the quality of knowledge previously acquired. This benchmark requires the solution of multiple challenges usually tackled in isolation, in particular exploration, sparse-rewards, object learning, generalisation, task/goal self-generation, and autonomous skill learning. As a second contribution, we present a set of `REAL-X' robot architectures that are able to solve different versions of the benchmark, where we progressively release initial simplifications. The architectures are based on a planning approach that dynamically increases abstraction, and intrinsic motivations to foster exploration. REAL-X achieves a good performance level in very demanding conditions. We argue that the REAL benchmark represents a valuable tool for studying open-ended learning in its hardest form.", "published": "2020-11-27T18:12:06Z", "authors": ["Emilio Cartoni", "Davide Montella", "Jochen Triesch", "Gianluca Baldassarre"], "categories": ["cs.RO", "cs.AI"], "doi": "", "_query": "real estate valuation deep learning", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/1812.04202v3", "title": "Deep Learning on Graphs: A Survey", "abstract": "Deep learning has been shown to be successful in a number of domains, ranging from acoustics, images, to natural language processing. However, applying deep learning to the ubiquitous graph data is non-trivial because of the unique characteristics of graphs. Recently, substantial research efforts have been devoted to applying deep learning methods to graphs, resulting in beneficial advances in graph analysis techniques. In this survey, we comprehensively review the different types of deep learning methods on graphs. We divide the existing methods into five categories based on their model architectures and training strategies: graph recurrent neural networks, graph convolutional networks, graph autoencoders, graph reinforcement learning, and graph adversarial methods. We then provide a comprehensive overview of these methods in a systematic manner mainly by following their development history. We also analyze the differences and compositions of different methods. Finally, we briefly outline the applications in which they have been used and discuss potential future research directions.", "published": "2018-12-11T03:16:57Z", "authors": ["Ziwei Zhang", "Peng Cui", "Wenwu Zhu"], "categories": ["cs.LG", "cs.SI", "stat.ML"], "doi": "", "_query": "real estate valuation deep learning", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2211.08559v2", "title": "Robust Alzheimer's Progression Modeling using Cross-Domain Self-Supervised Deep Learning", "abstract": "Developing successful artificial intelligence systems in practice depends on both robust deep learning models and large, high-quality data. However, acquiring and labeling data can be prohibitively expensive and time-consuming in many real-world applications, such as clinical disease models. Self-supervised learning has demonstrated great potential in increasing model accuracy and robustness in small data regimes. In addition, many clinical imaging and disease modeling applications rely heavily on regression of continuous quantities. However, the applicability of self-supervised learning for these medical-imaging regression tasks has not been extensively studied. In this study, we develop a cross-domain self-supervised learning approach for disease prognostic modeling as a regression problem using medical images as input. We demonstrate that self-supervised pretraining can improve the prediction of Alzheimer's Disease progression from brain MRI. We also show that pretraining on extended (but not labeled) brain MRI data outperforms pretraining on natural images. We further observe that the highest performance is achieved when both natural images and extended brain-MRI data are used for pretraining.", "published": "2022-11-15T23:04:15Z", "authors": ["Saba Dadsetan", "Mohsen Hejrati", "Shandong Wu", "Somaye Hashemifar"], "categories": ["cs.CV", "cs.AI", "cs.LG"], "doi": "", "_query": "real estate valuation deep learning", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2105.00619v2", "title": "OpTorch: Optimized deep learning architectures for resource limited environments", "abstract": "Deep learning algorithms have made many breakthroughs and have various applications in real life. Computational resources become a bottleneck as the data and complexity of the deep learning pipeline increases. In this paper, we propose optimized deep learning pipelines in multiple aspects of training including time and memory. OpTorch is a machine learning library designed to overcome weaknesses in existing implementations of neural network training. OpTorch provides features to train complex neural networks with limited computational resources. OpTorch achieved the same accuracy as existing libraries on Cifar-10 and Cifar-100 datasets while reducing memory usage to approximately 50%. We also explore the effect of weights on total memory usage in deep learning pipelines. In our experiments, parallel encoding-decoding along with sequential checkpoints results in much improved memory and time usage while keeping the accuracy similar to existing pipelines. OpTorch python package is available at available at https://github.com/cbrl-nuces/optorch", "published": "2021-05-03T03:58:57Z", "authors": ["Salman Ahmed", "Hammad Naveed"], "categories": ["cs.LG", "cs.AR", "stat.ML"], "doi": "", "_query": "real estate valuation deep learning", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2101.11714v1", "title": "TT-Rec: Tensor Train Compression for Deep Learning Recommendation Models", "abstract": "The memory capacity of embedding tables in deep learning recommendation models (DLRMs) is increasing dramatically from tens of GBs to TBs across the industry. Given the fast growth in DLRMs, novel solutions are urgently needed, in order to enable fast and efficient DLRM innovations. At the same time, this must be done without having to exponentially increase infrastructure capacity demands. In this paper, we demonstrate the promising potential of Tensor Train decomposition for DLRMs (TT-Rec), an important yet under-investigated context. We design and implement optimized kernels (TT-EmbeddingBag) to evaluate the proposed TT-Rec design. TT-EmbeddingBag is 3 times faster than the SOTA TT implementation. The performance of TT-Rec is further optimized with the batched matrix multiplication and caching strategies for embedding vector lookup operations. In addition, we present mathematically and empirically the effect of weight initialization distribution on DLRM accuracy and propose to initialize the tensor cores of TT-Rec following the sampled Gaussian distribution. We evaluate TT-Rec across three important design space dimensions -- memory capacity, accuracy, and timing performance -- by training MLPerf-DLRM with Criteo's Kaggle and Terabyte data sets. TT-Rec achieves 117 times and 112 times model size compression, for Kaggle and Terabyte, respectively. This impressive model size reduction can come with no accuracy nor training time overhead as compared to the uncompressed baseline.", "published": "2021-01-25T23:19:03Z", "authors": ["Chunxing Yin", "Bilge Acun", "Xing Liu", "Carole-Jean Wu"], "categories": ["cs.LG"], "doi": "", "_query": "real estate valuation deep learning", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2301.11695v3", "title": "LegendreTron: Uprising Proper Multiclass Loss Learning", "abstract": "Loss functions serve as the foundation of supervised learning and are often chosen prior to model development. To avoid potentially ad hoc choices of losses, statistical decision theory describes a desirable property for losses known as \\emph{properness}, which asserts that Bayes' rule is optimal. Recent works have sought to \\emph{learn losses} and models jointly. Existing methods do this by fitting an inverse canonical link function which monotonically maps $\\mathbb{R}$ to $[0,1]$ to estimate probabilities for binary problems. In this paper, we extend monotonicity to maps between $\\mathbb{R}^{C-1}$ and the projected probability simplex $\\tildeΔ^{C-1}$ by using monotonicity of gradients of convex functions. We present {\\sc LegendreTron} as a novel and practical method that jointly learns \\emph{proper canonical losses} and probabilities for multiclass problems. Tested on a benchmark of domains with up to 1,000 classes, our experimental results show that our method consistently outperforms the natural multiclass baseline under a $t$-test at 99% significance on all datasets with greater than 10 classes.", "published": "2023-01-27T13:10:45Z", "authors": ["Kevin Lam", "Christian Walder", "Spiridon Penev", "Richard Nock"], "categories": ["stat.ML", "cs.LG"], "doi": "", "_query": "real estate valuation deep learning", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2204.12885v1", "title": "Narrowing the Gap between Combinatorial and Hyperbolic Knot Invariants via Deep Learning", "abstract": "We present a statistical approach for the discovery of relationships between mathematical entities that is based on linear regression and deep learning with fully connected artificial neural networks. The strategy is applied to computational knot data and empirical connections between combinatorial and hyperbolic knot invariants are revealed.", "published": "2022-04-27T12:41:51Z", "authors": ["Daniel Grünbaum"], "categories": ["math.GT"], "doi": "10.1142/S0218216522500031", "_query": "real estate valuation deep learning", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/1806.03256v1", "title": "Incorporating Features Learned by an Enhanced Deep Knowledge Tracing Model for STEM/Non-STEM Job Prediction", "abstract": "The 2017 ASSISTments Data Mining competition aims to use data from a longitudinal study for predicting a brand-new outcome of students which had never been studied before by the educational data mining research community. Specifically, it facilitates research in developing predictive models that predict whether the first job of a student out of college belongs to a STEM (the acronym for science, technology, engineering, and mathematics) field. This is based on the student's learning history on the ASSISTments blended learning platform in the form of extensive clickstream data gathered during the middle school years. To tackle this challenge, we first estimate the expected knowledge state of students with respect to different mathematical skills using a deep knowledge tracing (DKT) model and an enhanced DKT (DKT+) model. We then combine the features corresponding to the DKT/DKT+ expected knowledge state with other features extracted directly from the student profile in the dataset to train several machine learning models for the STEM/non-STEM job prediction. Our experiments show that models trained with the combined features generally perform better than the models trained with the student profile alone. Detailed analysis of the student's knowledge state reveals that, when compared with non-STEM students, STEM students generally show a higher mastery level and a higher learning gain in mathematics.", "published": "2018-06-06T13:57:47Z", "authors": ["Chun-kit Yeung", "Zizheng Lin", "Kai Yang", "Dit-yan Yeung"], "categories": ["cs.CY", "cs.AI", "cs.LG"], "doi": "", "_query": "real estate valuation deep learning", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2307.10780v2", "title": "Learned Thresholds Token Merging and Pruning for Vision Transformers", "abstract": "Vision transformers have demonstrated remarkable success in a wide range of computer vision tasks over the last years. However, their high computational costs remain a significant barrier to their practical deployment. In particular, the complexity of transformer models is quadratic with respect to the number of input tokens. Therefore techniques that reduce the number of input tokens that need to be processed have been proposed. This paper introduces Learned Thresholds token Merging and Pruning (LTMP), a novel approach that leverages the strengths of both token merging and token pruning. LTMP uses learned threshold masking modules that dynamically determine which tokens to merge and which to prune. We demonstrate our approach with extensive experiments on vision transformers on the ImageNet classification task. Our results demonstrate that LTMP achieves state-of-the-art accuracy across reduction rates while requiring only a single fine-tuning epoch, which is an order of magnitude faster than previous methods. Code is available at https://github.com/Mxbonn/ltmp .", "published": "2023-07-20T11:30:12Z", "authors": ["Maxim Bonnaerens", "Joni Dambre"], "categories": ["cs.CV"], "doi": "", "_query": "real estate valuation deep learning", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2107.06336v2", "title": "Improving Cooperative Game Theory-based Data Valuation via Data Utility Learning", "abstract": "The Shapley value (SV) and Least core (LC) are classic methods in cooperative game theory for cost/profit sharing problems. Both methods have recently been proposed as a principled solution for data valuation tasks, i.e., quantifying the contribution of individual datum in machine learning. However, both SV and LC suffer computational challenges due to the need for retraining models on combinatorially many data subsets. In this work, we propose to boost the efficiency in computing Shapley value or Least core by learning to estimate the performance of a learning algorithm on unseen data combinations. Theoretically, we derive bounds relating the error in the predicted learning performance to the approximation error in SV and LC. Empirically, we show that the proposed method can significantly improve the accuracy of SV and LC estimation.", "published": "2021-07-13T18:56:04Z", "authors": ["Tianhao Wang", "Yu Yang", "Ruoxi Jia"], "categories": ["cs.LG"], "doi": "", "_query": "real estate valuation deep learning", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2203.09607v1", "title": "Learning Distributionally Robust Models at Scale via Composite Optimization", "abstract": "To train machine learning models that are robust to distribution shifts in the data, distributionally robust optimization (DRO) has been proven very effective. However, the existing approaches to learning a distributionally robust model either require solving complex optimization problems such as semidefinite programming or a first-order method whose convergence scales linearly with the number of data samples -- which hinders their scalability to large datasets. In this paper, we show how different variants of DRO are simply instances of a finite-sum composite optimization for which we provide scalable methods. We also provide empirical results that demonstrate the effectiveness of our proposed algorithm with respect to the prior art in order to learn robust models from very large datasets.", "published": "2022-03-17T20:47:42Z", "authors": ["Farzin Haddadpour", "Mohammad Mahdi Kamani", "Mehrdad Mahdavi", "Amin Karbasi"], "categories": ["cs.LG", "stat.ML"], "doi": "", "_query": "real estate valuation deep learning", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2206.05516v2", "title": "Deep Learning-Based MR Image Re-parameterization", "abstract": "Magnetic resonance (MR) image re-parameterization refers to the process of generating via simulations of an MR image with a new set of MRI scanning parameters. Different parameter values generate distinct contrast between different tissues, helping identify pathologic tissue. Typically, more than one scan is required for diagnosis; however, acquiring repeated scans can be costly, time-consuming, and difficult for patients. Thus, using MR image re-parameterization to predict and estimate the contrast in these imaging scans can be an effective alternative. In this work, we propose a novel deep learning (DL) based convolutional model for MRI re-parameterization. Based on our preliminary results, DL-based techniques hold the potential to learn the non-linearities that govern the re-parameterization.", "published": "2022-06-11T12:39:37Z", "authors": ["Abhijeet Narang", "Abhigyan Raj", "Mihaela Pop", "Mehran Ebrahimi"], "categories": ["eess.IV", "cs.CV"], "doi": "10.1109/CSCE60160.2023.00094", "_query": "real estate valuation deep learning", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/1805.01890v2", "title": "RMDL: Random Multimodel Deep Learning for Classification", "abstract": "The continually increasing number of complex datasets each year necessitates ever improving machine learning methods for robust and accurate categorization of these data. This paper introduces Random Multimodel Deep Learning (RMDL): a new ensemble, deep learning approach for classification. Deep learning models have achieved state-of-the-art results across many domains. RMDL solves the problem of finding the best deep learning structure and architecture while simultaneously improving robustness and accuracy through ensembles of deep learning architectures. RDML can accept as input a variety data to include text, video, images, and symbolic. This paper describes RMDL and shows test results for image and text data including MNIST, CIFAR-10, WOS, Reuters, IMDB, and 20newsgroup. These test results show that RDML produces consistently better performance than standard methods over a broad range of data types and classification problems.", "published": "2018-05-03T19:36:43Z", "authors": ["Kamran Kowsari", "Mojtaba Heidarysafa", "Donald E. Brown", "Kiana Jafari Meimandi", "Laura E. Barnes"], "categories": ["cs.LG", "cs.AI", "cs.CV", "cs.NE", "stat.ML"], "doi": "10.1145/3206098.3206111", "_query": "real estate valuation deep learning", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2508.20207v2", "title": "Operator learning meets inverse problems: A probabilistic perspective", "abstract": "Operator learning offers a robust framework for approximating mappings between infinite-dimensional function spaces. It has also become a powerful tool for solving inverse problems in the computational sciences. This chapter surveys methodological and theoretical developments at the intersection of operator learning and inverse problems. It begins by summarizing the probabilistic and deterministic approaches to inverse problems, and pays special attention to emerging measure-centric formulations that treat observed data or unknown parameters as probability distributions. The discussion then turns to operator learning by covering essential components such as data generation, loss functions, and widely used architectures for representing function-to-function maps. The core of the chapter centers on the end-to-end inverse operator learning paradigm, which aims to directly map observed data to the solution of the inverse problem without requiring explicit knowledge of the forward map. It highlights the unique challenge that noise plays in this data-driven inversion setting, presents structure-aware architectures for both point predictions and posterior estimates, and surveys relevant theory for linear and nonlinear inverse problems. The chapter also discusses the estimation of priors and regularizers, where operator learning is used more selectively within classical inversion algorithms.", "published": "2025-08-27T18:34:40Z", "authors": ["Nicholas H. Nelsen", "Yunan Yang"], "categories": ["math.NA", "cs.LG", "math.ST"], "doi": "10.1016/bs.hna.2026.03.010", "_query": "real estate valuation deep learning", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/1710.07110v1", "title": "Meta-Learning via Feature-Label Memory Network", "abstract": "Deep learning typically requires training a very capable architecture using large datasets. However, many important learning problems demand an ability to draw valid inferences from small size datasets, and such problems pose a particular challenge for deep learning. In this regard, various researches on \"meta-learning\" are being actively conducted. Recent work has suggested a Memory Augmented Neural Network (MANN) for meta-learning. MANN is an implementation of a Neural Turing Machine (NTM) with the ability to rapidly assimilate new data in its memory, and use this data to make accurate predictions. In models such as MANN, the input data samples and their appropriate labels from previous step are bound together in the same memory locations. This often leads to memory interference when performing a task as these models have to retrieve a feature of an input from a certain memory location and read only the label information bound to that location. In this paper, we tried to address this issue by presenting a more robust MANN. We revisited the idea of meta-learning and proposed a new memory augmented neural network by explicitly splitting the external memory into feature and label memories. The feature memory is used to store the features of input data samples and the label memory stores their labels. Hence, when predicting the label of a given input, our model uses its feature memory unit as a reference to extract the stored feature of the input, and based on that feature, it retrieves the label information of the input from the label memory unit. In order for the network to function in this framework, a new memory-writingmodule to encode label information into the label memory in accordance with the meta-learning task structure is designed. Here, we demonstrate that our model outperforms MANN by a large margin in supervised one-shot classification tasks using Omniglot and MNIST datasets.", "published": "2017-10-19T12:08:59Z", "authors": ["Dawit Mureja", "Hyunsin Park", "Chang D. Yoo"], "categories": ["cs.LG", "stat.ML"], "doi": "", "_query": "real estate valuation deep learning", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/1909.05862v2", "title": "Learning Symbolic Physics with Graph Networks", "abstract": "We introduce an approach for imposing physically motivated inductive biases on graph networks to learn interpretable representations and improved zero-shot generalization. Our experiments show that our graph network models, which implement this inductive bias, can learn message representations equivalent to the true force vector when trained on n-body gravitational and spring-like simulations. We use symbolic regression to fit explicit algebraic equations to our trained model's message function and recover the symbolic form of Newton's law of gravitation without prior knowledge. We also show that our model generalizes better at inference time to systems with more bodies than had been experienced during training. Our approach is extensible, in principle, to any unknown interaction law learned by a graph network, and offers a valuable technique for interpreting and inferring explicit causal theories about the world from implicit knowledge captured by deep learning.", "published": "2019-09-12T18:00:00Z", "authors": ["Miles D. Cranmer", "Rui Xu", "Peter Battaglia", "Shirley Ho"], "categories": ["cs.LG", "astro-ph.IM", "physics.comp-ph", "stat.ML"], "doi": "", "_query": "real estate valuation deep learning", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2209.11883v2", "title": "Hebbian Deep Learning Without Feedback", "abstract": "Recent approximations to backpropagation (BP) have mitigated many of BP's computational inefficiencies and incompatibilities with biology, but important limitations still remain. Moreover, the approximations significantly decrease accuracy in benchmarks, suggesting that an entirely different approach may be more fruitful. Here, grounded on recent theory for Hebbian learning in soft winner-take-all networks, we present multilayer SoftHebb, i.e. an algorithm that trains deep neural networks, without any feedback, target, or error signals. As a result, it achieves efficiency by avoiding weight transport, non-local plasticity, time-locking of layer updates, iterative equilibria, and (self-) supervisory or other feedback signals -- which were necessary in other approaches. Its increased efficiency and biological compatibility do not trade off accuracy compared to state-of-the-art bio-plausible learning, but rather improve it. With up to five hidden layers and an added linear classifier, accuracies on MNIST, CIFAR-10, STL-10, and ImageNet, respectively reach 99.4%, 80.3%, 76.2%, and 27.3%. In conclusion, SoftHebb shows with a radically different approach from BP that Deep Learning over few layers may be plausible in the brain and increases the accuracy of bio-plausible machine learning. Code is available at https://github.com/NeuromorphicComputing/SoftHebb.", "published": "2022-09-23T23:12:59Z", "authors": ["Adrien Journé", "Hector Garcia Rodriguez", "Qinghai Guo", "Timoleon Moraitis"], "categories": ["cs.NE", "cs.LG", "q-bio.NC"], "doi": "", "_query": "real estate valuation deep learning", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2405.02171v1", "title": "Self-Supervised Learning for Real-World Super-Resolution from Dual and Multiple Zoomed Observations", "abstract": "In this paper, we consider two challenging issues in reference-based super-resolution (RefSR) for smartphone, (i) how to choose a proper reference image, and (ii) how to learn RefSR in a self-supervised manner. Particularly, we propose a novel self-supervised learning approach for real-world RefSR from observations at dual and multiple camera zooms. Firstly, considering the popularity of multiple cameras in modern smartphones, the more zoomed (telephoto) image can be naturally leveraged as the reference to guide the super-resolution (SR) of the lesser zoomed (ultra-wide) image, which gives us a chance to learn a deep network that performs SR from the dual zoomed observations (DZSR). Secondly, for self-supervised learning of DZSR, we take the telephoto image instead of an additional high-resolution image as the supervision information, and select a center patch from it as the reference to super-resolve the corresponding ultra-wide image patch. To mitigate the effect of the misalignment between ultra-wide low-resolution (LR) patch and telephoto ground-truth (GT) image during training, we first adopt patch-based optical flow alignment and then design an auxiliary-LR to guide the deforming of the warped LR features. To generate visually pleasing results, we present local overlapped sliced Wasserstein loss to better represent the perceptual difference between GT and output in the feature space. During testing, DZSR can be directly deployed to super-solve the whole ultra-wide image with the reference of the telephoto image. In addition, we further take multiple zoomed observations to explore self-supervised RefSR, and present a progressive fusion scheme for the effective utilization of reference images. Experiments show that our methods achieve better quantitative and qualitative performance against state-of-the-arts. Codes are available at https://github.com/cszhilu1998/SelfDZSR_PlusPlus.", "published": "2024-05-03T15:20:30Z", "authors": ["Zhilu Zhang", "Ruohao Wang", "Hongzhi Zhang", "Wangmeng Zuo"], "categories": ["cs.CV"], "doi": "", "_query": "real estate valuation deep learning", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/1807.05351v1", "title": "ML-Schema: Exposing the Semantics of Machine Learning with Schemas and Ontologies", "abstract": "The ML-Schema, proposed by the W3C Machine Learning Schema Community Group, is a top-level ontology that provides a set of classes, properties, and restrictions for representing and interchanging information on machine learning algorithms, datasets, and experiments. It can be easily extended and specialized and it is also mapped to other more domain-specific ontologies developed in the area of machine learning and data mining. In this paper we overview existing state-of-the-art machine learning interchange formats and present the first release of ML-Schema, a canonical format resulted of more than seven years of experience among different research institutions. We argue that exposing semantics of machine learning algorithms, models, and experiments through a canonical format may pave the way to better interpretability and to realistically achieve the full interoperability of experiments regardless of platform or adopted workflow solution.", "published": "2018-07-14T08:07:31Z", "authors": ["Gustavo Correa Publio", "Diego Esteves", "Agnieszka Ławrynowicz", "Panče Panov", "Larisa Soldatova", "Tommaso Soru", "Joaquin Vanschoren", "Hamid Zafar"], "categories": ["cs.LG", "cs.DB", "cs.IR", "stat.ML"], "doi": "", "_query": "real estate valuation deep learning", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2002.08053v3", "title": "Progressive Identification of True Labels for Partial-Label Learning", "abstract": "Partial-label learning (PLL) is a typical weakly supervised learning problem, where each training instance is equipped with a set of candidate labels among which only one is the true label. Most existing methods elaborately designed learning objectives as constrained optimizations that must be solved in specific manners, making their computational complexity a bottleneck for scaling up to big data. The goal of this paper is to propose a novel framework of PLL with flexibility on the model and optimization algorithm. More specifically, we propose a novel estimator of the classification risk, theoretically analyze the classifier-consistency, and establish an estimation error bound. Then we propose a progressive identification algorithm for approximately minimizing the proposed risk estimator, where the update of the model and identification of true labels are conducted in a seamless manner. The resulting algorithm is model-independent and loss-independent, and compatible with stochastic optimization. Thorough experiments demonstrate it sets the new state of the art.", "published": "2020-02-19T08:35:15Z", "authors": ["Jiaqi Lv", "Miao Xu", "Lei Feng", "Gang Niu", "Xin Geng", "Masashi Sugiyama"], "categories": ["cs.LG", "stat.ML"], "doi": "", "_query": "real estate valuation deep learning", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/1911.04102v1", "title": "Activity Monitoring of Islamic Prayer (Salat) Postures using Deep Learning", "abstract": "In the Muslim community, the prayer (i.e. Salat) is the second pillar of Islam, and it is the most essential and fundamental worshiping activity that believers have to perform five times a day. From a gestures' perspective, there are predefined human postures that must be performed in a precise manner. However, for several people, these postures are not correctly performed, due to being new to Salat or even having learned prayers in an incorrect manner. Furthermore, the time spent in each posture has to be balanced. To address these issues, we propose to develop an artificial intelligence assistive framework that guides worshippers to evaluate the correctness of the postures of their prayers. This paper represents the first step to achieve this objective and addresses the problem of the recognition of the basic gestures of Islamic prayer using Convolutional Neural Networks (CNN). The contribution of this paper lies in building a dataset for the basic Salat positions, and train a YOLOv3 neural network for the recognition of the gestures. Experimental results demonstrate that the mean average precision attains 85% for a training dataset of 764 images of the different postures. To the best of our knowledge, this is the first work that addresses human activity recognition of Salat using deep learning.", "published": "2019-11-11T06:31:40Z", "authors": ["Anis Koubaa", "Adel Ammar", "Bilel Benjdira", "Abdullatif Al-Hadid", "Belal Kawaf", "Saleh Ali Al-Yahri", "Abdelrahman Babiker", "Koutaiba Assaf", "Mohannad Ba Ras"], "categories": ["cs.CV", "cs.CY", "cs.LG"], "doi": "", "_query": "real estate valuation deep learning", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2303.10559v3", "title": "Deep Learning for Camera Calibration and Beyond: A Survey", "abstract": "Camera calibration involves estimating camera parameters to infer geometric features from captured sequences, which is crucial for computer vision and robotics. However, conventional calibration is laborious and requires dedicated collection. Recent efforts show that learning-based solutions have the potential to be used in place of the repeatability works of manual calibrations. Among these solutions, various learning strategies, networks, geometric priors, and datasets have been investigated. In this paper, we provide a comprehensive survey of learning-based camera calibration techniques, by analyzing their strengths and limitations. Our main calibration categories include the standard pinhole camera model, distortion camera model, cross-view model, and cross-sensor model, following the research trend and extended applications. As there is no unified benchmark in this community, we collect a holistic calibration dataset that can serve as a public platform to evaluate the generalization of existing methods. It comprises both synthetic and real-world data, with images and videos captured by different cameras in diverse scenes. Toward the end of this paper, we discuss the challenges and provide further research directions. To our knowledge, this is the first survey for the learning-based camera calibration (spanned 10 years). The summarized methods, datasets, and benchmarks are available and will be regularly updated at https://github.com/KangLiao929/Awesome-Deep-Camera-Calibration.", "published": "2023-03-19T04:00:05Z", "authors": ["Kang Liao", "Lang Nie", "Shujuan Huang", "Chunyu Lin", "Jing Zhang", "Yao Zhao", "Moncef Gabbouj", "Dacheng Tao"], "categories": ["cs.CV"], "doi": "", "_query": "real estate valuation deep learning", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2106.00120v3", "title": "Probabilistic Deep Learning with Probabilistic Neural Networks and Deep Probabilistic Models", "abstract": "Probabilistic deep learning is deep learning that accounts for uncertainty, both model uncertainty and data uncertainty. It is based on the use of probabilistic models and deep neural networks. We distinguish two approaches to probabilistic deep learning: probabilistic neural networks and deep probabilistic models. The former employs deep neural networks that utilize probabilistic layers which can represent and process uncertainty; the latter uses probabilistic models that incorporate deep neural network components which capture complex non-linear stochastic relationships between the random variables. We discuss some major examples of each approach including Bayesian neural networks and mixture density networks (for probabilistic neural networks), and variational autoencoders, deep Gaussian processes and deep mixed effects models (for deep probabilistic models). TensorFlow Probability is a library for probabilistic modeling and inference which can be used for both approaches of probabilistic deep learning. We include its code examples for illustration.", "published": "2021-05-31T22:13:21Z", "authors": ["Daniel T. Chang"], "categories": ["cs.LG", "stat.ML"], "doi": "", "_query": "real estate valuation deep learning", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/1805.02686v2", "title": "Holarchic Structures for Decentralized Deep Learning - A Performance Analysis", "abstract": "Structure plays a key role in learning performance. In centralized computational systems, hyperparameter optimization and regularization techniques such as dropout are computational means to enhance learning performance by adjusting the deep hierarchical structure. However, in decentralized deep learning by the Internet of Things, the structure is an actual network of autonomous interconnected devices such as smart phones that interact via complex network protocols. Self-adaptation of the learning structure is a challenge. Uncertainties such as network latency, node and link failures or even bottlenecks by limited processing capacity and energy availability can signif- icantly downgrade learning performance. Network self-organization and self-management is complex, while it requires additional computational and network resources that hinder the feasibility of decentralized deep learning. In contrast, this paper introduces a self-adaptive learning approach based on holarchic learning structures for exploring, mitigating and boosting learning performance in distributed environments with uncertainties. A large-scale performance analysis with 864000 experiments fed with synthetic and real-world data from smart grid and smart city pilot projects confirm the cost-effectiveness of holarchic structures for decentralized deep learning.", "published": "2018-05-07T18:33:43Z", "authors": ["Evangelos Pournaras", "Srivatsan Yadhunathan", "Ada Diaconescu"], "categories": ["cs.LG", "cs.AI", "stat.ML"], "doi": "10.1007/s10586-019-02906-4", "_query": "real estate valuation deep learning", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2211.01520v2", "title": "Deep Residual Networks for Gravitational Wave Detection", "abstract": "Traditionally, gravitational waves are detected with techniques such as matched filtering or unmodeled searches based on wavelets. However, in the case of generic black hole binaries with non-aligned spins, if one wants to explore the whole parameter space, matched filtering can become impractical, which sets severe restrictions on the sensitivity and computational efficiency of gravitational-wave searches. Here, we use a novel combination of machine-learning algorithms and arrive at sensitive distances that surpass traditional techniques in a specific setting. Moreover, the computational cost is only a small fraction of the computational cost of matched filtering. The main ingredients are a 54-layer deep residual network (ResNet), a Deep Adaptive Input Normalization (DAIN), a dynamic dataset augmentation, and curriculum learning, based on an empirical relation for the signal-to-noise ratio. We compare the algorithm's sensitivity with two traditional algorithms on a dataset consisting of a large number of injected waveforms of non-aligned binary black hole mergers in real LIGO O3a noise samples. Our machine-learning algorithm can be used in upcoming rapid online searches of gravitational-wave events in a sizeable portion of the astrophysically interesting parameter space. We make our code, AResGW, and detailed results publicly available at https://github.com/vivinousi/gw-detection-deep-learning .", "published": "2022-11-02T23:45:50Z", "authors": ["Paraskevi Nousi", "Alexandra E. Koloniari", "Nikolaos Passalis", "Panagiotis Iosif", "Nikolaos Stergioulas", "Anastasios Tefas"], "categories": ["gr-qc", "astro-ph.HE"], "doi": "10.1103/PhysRevD.108.024022", "_query": "real estate valuation deep learning", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/1909.07218v4", "title": "Learning to Tune XGBoost with XGBoost", "abstract": "In this short paper we investigate whether meta-learning techniques can be used to more effectively tune the hyperparameters of machine learning models using successive halving (SH). We propose a novel variant of the SH algorithm (MeSH), that uses meta-regressors to determine which candidate configurations should be eliminated at each round. We apply MeSH to the problem of tuning the hyperparameters of a gradient-boosted decision tree model. By training and tuning our meta-regressors using existing tuning jobs from 95 datasets, we demonstrate that MeSH can often find a superior solution to both SH and random search.", "published": "2019-09-16T14:09:55Z", "authors": ["Johanna Sommer", "Dimitrios Sarigiannis", "Thomas Parnell"], "categories": ["cs.LG", "stat.ML"], "doi": "", "_query": "real estate valuation deep learning", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/1812.08904v1", "title": "Pre-training with Non-expert Human Demonstration for Deep Reinforcement Learning", "abstract": "Deep reinforcement learning (deep RL) has achieved superior performance in complex sequential tasks by using deep neural networks as function approximators to learn directly from raw input images. However, learning directly from raw images is data inefficient. The agent must learn feature representation of complex states in addition to learning a policy. As a result, deep RL typically suffers from slow learning speeds and often requires a prohibitively large amount of training time and data to reach reasonable performance, making it inapplicable to real-world settings where data is expensive. In this work, we improve data efficiency in deep RL by addressing one of the two learning goals, feature learning. We leverage supervised learning to pre-train on a small set of non-expert human demonstrations and empirically evaluate our approach using the asynchronous advantage actor-critic algorithms (A3C) in the Atari domain. Our results show significant improvements in learning speed, even when the provided demonstration is noisy and of low quality.", "published": "2018-12-21T01:10:06Z", "authors": ["Gabriel V. de la Cruz", "Yunshu Du", "Matthew E. Taylor"], "categories": ["cs.LG", "cs.AI", "stat.ML"], "doi": "10.1017/S0269888919000055", "_query": "real estate valuation deep learning", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/1507.04155v1", "title": "ALEVS: Active Learning by Statistical Leverage Sampling", "abstract": "Active learning aims to obtain a classifier of high accuracy by using fewer label requests in comparison to passive learning by selecting effective queries. Many active learning methods have been developed in the past two decades, which sample queries based on informativeness or representativeness of unlabeled data points. In this work, we explore a novel querying criterion based on statistical leverage scores. The statistical leverage scores of a row in a matrix are the squared row-norms of the matrix containing its (top) left singular vectors and is a measure of influence of the row on the matrix. Leverage scores have been used for detecting high influential points in regression diagnostics and have been recently shown to be useful for data analysis and randomized low-rank matrix approximation algorithms. We explore how sampling data instances with high statistical leverage scores perform in active learning. Our empirical comparison on several binary classification datasets indicate that querying high leverage points is an effective strategy.", "published": "2015-07-15T10:31:00Z", "authors": ["Cem Orhan", "Öznur Taştan"], "categories": ["cs.LG", "stat.ML"], "doi": "", "_query": "real estate valuation deep learning", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/1806.04360v1", "title": "MSplit LBI: Realizing Feature Selection and Dense Estimation Simultaneously in Few-shot and Zero-shot Learning", "abstract": "It is one typical and general topic of learning a good embedding model to efficiently learn the representation coefficients between two spaces/subspaces. To solve this task, $L_{1}$ regularization is widely used for the pursuit of feature selection and avoiding overfitting, and yet the sparse estimation of features in $L_{1}$ regularization may cause the underfitting of training data. $L_{2}$ regularization is also frequently used, but it is a biased estimator. In this paper, we propose the idea that the features consist of three orthogonal parts, \\emph{namely} sparse strong signals, dense weak signals and random noise, in which both strong and weak signals contribute to the fitting of data. To facilitate such novel decomposition, \\emph{MSplit} LBI is for the first time proposed to realize feature selection and dense estimation simultaneously. We provide theoretical and simulational verification that our method exceeds $L_{1}$ and $L_{2}$ regularization, and extensive experimental results show that our method achieves state-of-the-art performance in the few-shot and zero-shot learning.", "published": "2018-06-12T07:07:44Z", "authors": ["Bo Zhao", "Xinwei Sun", "Yanwei Fu", "Yuan Yao", "Yizhou Wang"], "categories": ["cs.CV"], "doi": "", "_query": "real estate valuation deep learning", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2310.14743v1", "title": "The Safety Challenges of Deep Learning in Real-World Type 1 Diabetes Management", "abstract": "Blood glucose simulation allows the effectiveness of type 1 diabetes (T1D) management strategies to be evaluated without patient harm. Deep learning algorithms provide a promising avenue for extending simulator capabilities; however, these algorithms are limited in that they do not necessarily learn physiologically correct glucose dynamics and can learn incorrect and potentially dangerous relationships from confounders in training data. This is likely to be more important in real-world scenarios, as data is not collected under strict research protocol. This work explores the implications of using deep learning algorithms trained on real-world data to model glucose dynamics. Free-living data was processed from the OpenAPS Data Commons and supplemented with patient-reported tags of challenging diabetes events, constituting one of the most detailed real-world T1D datasets. This dataset was used to train and evaluate state-of-the-art glucose simulators, comparing their prediction error across safety critical scenarios and assessing the physiological appropriateness of the learned dynamics using Shapley Additive Explanations (SHAP). While deep learning prediction accuracy surpassed the widely-used mathematical simulator approach, the model deteriorated in safety critical scenarios and struggled to leverage self-reported meal and exercise information. SHAP value analysis also indicated the model had fundamentally confused the roles of insulin and carbohydrates, which is one of the most basic T1D management principles. This work highlights the importance of considering physiological appropriateness when using deep learning to model real-world systems in T1D and healthcare more broadly, and provides recommendations for building models that are robust to real-world data constraints.", "published": "2023-10-23T09:25:50Z", "authors": ["Harry Emerson", "Ryan McConville", "Matthew Guy"], "categories": ["cs.LG", "cs.AI"], "doi": "", "_query": "real estate valuation deep learning", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/1910.11089v3", "title": "Real-World Image Datasets for Federated Learning", "abstract": "Federated learning is a new machine learning paradigm which allows data parties to build machine learning models collaboratively while keeping their data secure and private. While research efforts on federated learning have been growing tremendously in the past two years, most existing works still depend on pre-existing public datasets and artificial partitions to simulate data federations due to the lack of high-quality labeled data generated from real-world edge applications. Consequently, advances on benchmark and model evaluations for federated learning have been lagging behind. In this paper, we introduce a real-world image dataset. The dataset contains more than 900 images generated from 26 street cameras and 7 object categories annotated with detailed bounding box. The data distribution is non-IID and unbalanced, reflecting the characteristic real-world federated learning scenarios. Based on this dataset, we implemented two mainstream object detection algorithms (YOLO and Faster R-CNN) and provided an extensive benchmark on model performance, efficiency, and communication in a federated learning setting. Both the dataset and algorithms are made publicly available.", "published": "2019-10-14T09:33:26Z", "authors": ["Jiahuan Luo", "Xueyang Wu", "Yun Luo", "Anbu Huang", "Yunfeng Huang", "Yang Liu", "Qiang Yang"], "categories": ["cs.CV", "cs.LG", "stat.ML"], "doi": "", "_query": "real estate valuation deep learning", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2412.08016v3", "title": "GLL: A Differentiable Graph Learning Layer for Neural Networks", "abstract": "Standard deep learning architectures used for classification generate label predictions with a projection head and softmax activation function. Although successful, these methods fail to leverage the relational information between samples for generating label predictions. In recent works, graph-based learning techniques, namely Laplace learning, have been heuristically combined with neural networks for both supervised and semi-supervised learning (SSL) tasks. However, prior works approximate the gradient of the loss function with respect to the graph learning algorithm or decouple the processes; end-to-end integration with neural networks is not achieved. In this work, we derive backpropagation equations, via the adjoint method, for inclusion of a general family of graph learning layers into a neural network. The resulting method, distinct from graph neural networks, allows us to precisely integrate similarity graph construction and graph Laplacian-based label propagation into a neural network layer, replacing a projection head and softmax activation function for general classification task. Our experimental results demonstrate smooth label transitions across data, improved generalization and robustness to adversarial attacks, and improved training dynamics compared to a standard softmax-based approach.", "published": "2024-12-11T01:54:29Z", "authors": ["Jason Brown", "Bohan Chen", "Harris Hardiman-Mostow", "Jeff Calder", "Andrea L. Bertozzi"], "categories": ["cs.LG", "stat.ML"], "doi": "", "_query": "real estate valuation deep learning", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2308.03704v1", "title": "DeRisk: An Effective Deep Learning Framework for Credit Risk Prediction over Real-World Financial Data", "abstract": "Despite the tremendous advances achieved over the past years by deep learning techniques, the latest risk prediction models for industrial applications still rely on highly handtuned stage-wised statistical learning tools, such as gradient boosting and random forest methods. Different from images or languages, real-world financial data are high-dimensional, sparse, noisy and extremely imbalanced, which makes deep neural network models particularly challenging to train and fragile in practice. In this work, we propose DeRisk, an effective deep learning risk prediction framework for credit risk prediction on real-world financial data. DeRisk is the first deep risk prediction model that outperforms statistical learning approaches deployed in our company's production system. We also perform extensive ablation studies on our method to present the most critical factors for the empirical success of DeRisk.", "published": "2023-08-07T16:22:59Z", "authors": ["Yancheng Liang", "Jiajie Zhang", "Hui Li", "Xiaochen Liu", "Yi Hu", "Yong Wu", "Jinyao Zhang", "Yongyan Liu", "Yi Wu"], "categories": ["cs.LG", "q-fin.ST"], "doi": "", "_query": "real estate valuation deep learning", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/1811.09724v1", "title": "3D Deep Learning with voxelized atomic configurations for modeling atomistic potentials in complex solid-solution alloys", "abstract": "The need for advanced materials has led to the development of complex, multi-component alloys or solid-solution alloys. These materials have shown exceptional properties like strength, toughness, ductility, electrical and electronic properties. Current development of such material systems are hindered by expensive experiments and computationally demanding first-principles simulations. Atomistic simulations can provide reasonable insights on properties in such material systems. However, the issue of designing robust potentials still exists. In this paper, we explore a deep convolutional neural-network based approach to develop the atomistic potential for such complex alloys to investigate materials for insights into controlling properties. In the present work, we propose a voxel representation of the atomic configuration of a cell and design a 3D convolutional neural network to learn the interaction of the atoms. Our results highlight the performance of the 3D convolutional neural network and its efficacy in machine-learning the atomistic potential. We also explore the role of voxel resolution and provide insights into the two bounding box methodologies implemented for voxelization.", "published": "2018-11-23T23:12:22Z", "authors": ["Rahul Singh", "Aayush Sharma", "Onur Rauf Bingol", "Aditya Balu", "Ganesh Balasubramanian", "Duane D. Johnson", "Soumik Sarkar"], "categories": ["cond-mat.mtrl-sci", "cs.LG", "physics.comp-ph", "stat.ML"], "doi": "", "_query": "real estate valuation deep learning", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/1901.08558v1", "title": "Quantifying Interpretability and Trust in Machine Learning Systems", "abstract": "Decisions by Machine Learning (ML) models have become ubiquitous. Trusting these decisions requires understanding how algorithms take them. Hence interpretability methods for ML are an active focus of research. A central problem in this context is that both the quality of interpretability methods as well as trust in ML predictions are difficult to measure. Yet evaluations, comparisons and improvements of trust and interpretability require quantifiable measures. Here we propose a quantitative measure for the quality of interpretability methods. Based on that we derive a quantitative measure of trust in ML decisions. Building on previous work we propose to measure intuitive understanding of algorithmic decisions using the information transfer rate at which humans replicate ML model predictions. We provide empirical evidence from crowdsourcing experiments that the proposed metric robustly differentiates interpretability methods. The proposed metric also demonstrates the value of interpretability for ML assisted human decision making: in our experiments providing explanations more than doubled productivity in annotation tasks. However unbiased human judgement is critical for doctors, judges, policy makers and others. Here we derive a trust metric that identifies when human decisions are overly biased towards ML predictions. Our results complement existing qualitative work on trust and interpretability by quantifiable measures that can serve as objectives for further improving methods in this field of research.", "published": "2019-01-20T18:46:39Z", "authors": ["Philipp Schmidt", "Felix Biessmann"], "categories": ["cs.LG", "stat.ML"], "doi": "", "_query": "real estate valuation deep learning", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2004.08410v1", "title": "Deep Reinforcement Learning for Adaptive Learning Systems", "abstract": "In this paper, we formulate the adaptive learning problem---the problem of how to find an individualized learning plan (called policy) that chooses the most appropriate learning materials based on learner's latent traits---faced in adaptive learning systems as a Markov decision process (MDP). We assume latent traits to be continuous with an unknown transition model. We apply a model-free deep reinforcement learning algorithm---the deep Q-learning algorithm---that can effectively find the optimal learning policy from data on learners' learning process without knowing the actual transition model of the learners' continuous latent traits. To efficiently utilize available data, we also develop a transition model estimator that emulates the learner's learning process using neural networks. The transition model estimator can be used in the deep Q-learning algorithm so that it can more efficiently discover the optimal learning policy for a learner. Numerical simulation studies verify that the proposed algorithm is very efficient in finding a good learning policy, especially with the aid of a transition model estimator, it can find the optimal learning policy after training using a small number of learners.", "published": "2020-04-17T18:04:03Z", "authors": ["Xiao Li", "Hanchen Xu", "Jinming Zhang", "Hua-hua Chang"], "categories": ["cs.LG", "stat.ML"], "doi": "", "_query": "real estate valuation deep learning", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2501.15893v2", "title": "Benchmarking Quantum Reinforcement Learning", "abstract": "Benchmarking and establishing proper statistical validation metrics for reinforcement learning (RL) remain ongoing challenges, where no consensus has been established yet. The emergence of quantum computing and its potential applications in quantum reinforcement learning (QRL) further complicate benchmarking efforts. To enable valid performance comparisons and to streamline current research in this area, we propose a novel benchmarking methodology, which is based on a statistical estimator for sample complexity and a definition of statistical outperformance. Furthermore, considering QRL, our methodology casts doubt on some previous claims regarding its superiority. We conducted experiments on a novel benchmarking environment with flexible levels of complexity. While we still identify possible advantages, our findings are more nuanced overall. We discuss the potential limitations of these results and explore their implications for empirical research on quantum advantage in QRL.", "published": "2025-01-27T09:40:18Z", "authors": ["Nico Meyer", "Christian Ufrecht", "George Yammine", "Georgios Kontes", "Christopher Mutschler", "Daniel D. Scherer"], "categories": ["quant-ph", "cs.LG"], "doi": "", "_query": "real estate valuation deep learning", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2212.11726v2", "title": "Reusable Options through Gradient-based Meta Learning", "abstract": "Hierarchical methods in reinforcement learning have the potential to reduce the amount of decisions that the agent needs to perform when learning new tasks. However, finding reusable useful temporal abstractions that facilitate fast learning remains a challenging problem. Recently, several deep learning approaches were proposed to learn such temporal abstractions in the form of options in an end-to-end manner. In this work, we point out several shortcomings of these methods and discuss their potential negative consequences. Subsequently, we formulate the desiderata for reusable options and use these to frame the problem of learning options as a gradient-based meta-learning problem. This allows us to formulate an objective that explicitly incentivizes options which allow a higher-level decision maker to adjust in few steps to different tasks. Experimentally, we show that our method is able to learn transferable components which accelerate learning and performs better than existing prior methods developed for this setting. Additionally, we perform ablations to quantify the impact of using gradient-based meta-learning as well as other proposed changes.", "published": "2022-12-22T14:19:35Z", "authors": ["David Kuric", "Herke van Hoof"], "categories": ["cs.LG"], "doi": "", "_query": "real estate valuation deep learning", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2001.00449v1", "title": "Continuous-Discrete Reinforcement Learning for Hybrid Control in Robotics", "abstract": "Many real-world control problems involve both discrete decision variables - such as the choice of control modes, gear switching or digital outputs - as well as continuous decision variables - such as velocity setpoints, control gains or analogue outputs. However, when defining the corresponding optimal control or reinforcement learning problem, it is commonly approximated with fully continuous or fully discrete action spaces. These simplifications aim at tailoring the problem to a particular algorithm or solver which may only support one type of action space. Alternatively, expert heuristics are used to remove discrete actions from an otherwise continuous space. In contrast, we propose to treat hybrid problems in their 'native' form by solving them with hybrid reinforcement learning, which optimizes for discrete and continuous actions simultaneously. In our experiments, we first demonstrate that the proposed approach efficiently solves such natively hybrid reinforcement learning problems. We then show, both in simulation and on robotic hardware, the benefits of removing possibly imperfect expert-designed heuristics. Lastly, hybrid reinforcement learning encourages us to rethink problem definitions. We propose reformulating control problems, e.g. by adding meta actions, to improve exploration or reduce mechanical wear and tear.", "published": "2020-01-02T14:19:33Z", "authors": ["Michael Neunert", "Abbas Abdolmaleki", "Markus Wulfmeier", "Thomas Lampe", "Jost Tobias Springenberg", "Roland Hafner", "Francesco Romano", "Jonas Buchli", "Nicolas Heess", "Martin Riedmiller"], "categories": ["cs.LG", "cs.RO", "stat.ML"], "doi": "", "_query": "real estate valuation deep learning", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2001.02992v1", "title": "Poly-time universality and limitations of deep learning", "abstract": "The goal of this paper is to characterize function distributions that deep learning can or cannot learn in poly-time. A universality result is proved for SGD-based deep learning and a non-universality result is proved for GD-based deep learning; this also gives a separation between SGD-based deep learning and statistical query algorithms: (1) {\\it Deep learning with SGD is efficiently universal.} Any function distribution that can be learned from samples in poly-time can also be learned by a poly-size neural net trained with SGD on a poly-time initialization with poly-steps, poly-rate and possibly poly-noise. Therefore deep learning provides a universal learning paradigm: it was known that the approximation and estimation errors could be controlled with poly-size neural nets, using ERM that is NP-hard; this new result shows that the optimization error can also be controlled with SGD in poly-time. The picture changes for GD with large enough batches: (2) {\\it Result (1) does not hold for GD:} Neural nets of poly-size trained with GD (full gradients or large enough batches) on any initialization with poly-steps, poly-range and at least poly-noise cannot learn any function distribution that has super-polynomial {\\it cross-predictability,} where the cross-predictability gives a measure of ``average'' function correlation -- relations and distinctions to the statistical dimension are discussed. In particular, GD with these constraints can learn efficiently monomials of degree $k$ if and only if $k$ is constant. Thus (1) and (2) point to an interesting contrast: SGD is universal even with some poly-noise while full GD or SQ algorithms are not (e.g., parities).", "published": "2020-01-07T08:31:50Z", "authors": ["Emmanuel Abbe", "Colin Sandon"], "categories": ["cs.LG", "cs.CC", "cs.IT", "stat.ML"], "doi": "", "_query": "real estate valuation deep learning", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2012.04336v1", "title": "Interpretable deep learning regression for breast density estimation on MRI", "abstract": "Breast density, which is the ratio between fibroglandular tissue (FGT) and total breast volume, can be assessed qualitatively by radiologists and quantitatively by computer algorithms. These algorithms often rely on segmentation of breast and FGT volume. In this study, we propose a method to directly assess breast density on MRI, and provide interpretations of these assessments. We assessed breast density in 506 patients with breast cancer using a regression convolutional neural network (CNN). The input for the CNN were slices of breast MRI of 128 x 128 voxels, and the output was a continuous density value between 0 (fatty breast) and 1 (dense breast). We used 350 patients to train the CNN, 75 for validation, and 81 for independent testing. We investigated why the CNN came to its predicted density using Deep SHapley Additive exPlanations (SHAP). The density predicted by the CNN on the testing set was significantly correlated with the ground truth densities (N = 81 patients, Spearman's rho = 0.86, P < 0.001). When inspecting what the CNN based its predictions on, we found that voxels in FGT commonly had positive SHAP-values, voxels in fatty tissue commonly had negative SHAP-values, and voxels in non-breast tissue commonly had SHAP-values near zero. This means that the prediction of density is based on the structures we expect it to be based on, namely FGT and fatty tissue. To conclude, we presented an interpretable deep learning regression method for breast density estimation on MRI with promising results.", "published": "2020-12-08T10:23:49Z", "authors": ["Bas H. M. van der Velden", "Max A. A. Ragusi", "Markus H. A. Janse", "Claudette E. Loo", "Kenneth G. A. Gilhuijs"], "categories": ["eess.IV", "cs.CV"], "doi": "10.1117/12.2549003", "_query": "real estate valuation deep learning", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2207.03605v2", "title": "Learning-based Autonomous Channel Access in the Presence of Hidden Terminals", "abstract": "We consider the problem of autonomous channel access (AutoCA), where a group of terminals tries to discover a communication strategy with an access point (AP) via a common wireless channel in a distributed fashion. Due to the irregular topology and the limited communication range of terminals, a practical challenge for AutoCA is the hidden terminal problem, which is notorious in wireless networks for deteriorating the throughput and delay performances. To meet the challenge, this paper presents a new multi-agent deep reinforcement learning paradigm, dubbed MADRL-HT, tailored for AutoCA in the presence of hidden terminals. MADRL-HT exploits topological insights and transforms the observation space of each terminal into a scalable form independent of the number of terminals. To compensate for the partial observability, we put forth a look-back mechanism such that the terminals can infer behaviors of their hidden terminals from the carrier sensed channel states as well as feedback from the AP. A window-based global reward function is proposed, whereby the terminals are instructed to maximize the system throughput while balancing the terminals' transmission opportunities over the course of learning. Extensive numerical experiments verified the superior performance of our solution benchmarked against the legacy carrier-sense multiple access with collision avoidance (CSMA/CA) protocol.", "published": "2022-07-07T22:30:31Z", "authors": ["Yulin Shao", "Yucheng Cai", "Taotao Wang", "Ziyang Guo", "Peng Liu", "Jiajun Luo", "Deniz Gunduz"], "categories": ["cs.LG", "cs.IT", "cs.MA", "eess.SY"], "doi": "", "_query": "real estate valuation deep learning", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2502.19167v2", "title": "Generalizable deep learning for photoplethysmography-based blood pressure estimation -- A Benchmarking Study", "abstract": "Photoplethysmography (PPG)-based blood pressure (BP) estimation represents a promising alternative to cuff-based BP measurements. Recently, an increasing number of deep learning models have been proposed to infer BP from the raw PPG waveform. However, these models have been predominantly evaluated on in-distribution test sets, which immediately raises the question of the generalizability of these models to external datasets. To investigate this question, we trained five deep learning models on the recently released PulseDB dataset, provided in-distribution benchmarking results on this dataset, and then assessed out-of-distribution performance on several external datasets. The best model (XResNet1d101) achieved in-distribution MAEs of 9.4 and 6.0 mmHg for systolic and diastolic BP respectively on PulseDB (with subject-specific calibration), and 14.0 and 8.5 mmHg respectively without calibration. Equivalent MAEs on external test datasets without calibration ranged from 15.0 to 25.1 mmHg (SBP) and 7.0 to 10.4 mmHg (DBP). Our results indicate that the performance is strongly influenced by the differences in BP distributions between datasets. We investigated a simple way of improving performance through sample-based domain adaptation and put forward recommendations for training models with good generalization properties. With this work, we hope to educate more researchers for the importance and challenges of out-of-distribution generalization.", "published": "2025-02-26T14:22:59Z", "authors": ["Mohammad Moulaeifard", "Peter H. Charlton", "Nils Strodthoff"], "categories": ["cs.LG", "eess.SP"], "doi": "10.1088/3049-477X/ae01a8", "_query": "real estate valuation deep learning", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/1901.02161v2", "title": "Risk-Aware Active Inverse Reinforcement Learning", "abstract": "Active learning from demonstration allows a robot to query a human for specific types of input to achieve efficient learning. Existing work has explored a variety of active query strategies; however, to our knowledge, none of these strategies directly minimize the performance risk of the policy the robot is learning. Utilizing recent advances in performance bounds for inverse reinforcement learning, we propose a risk-aware active inverse reinforcement learning algorithm that focuses active queries on areas of the state space with the potential for large generalization error. We show that risk-aware active learning outperforms standard active IRL approaches on gridworld, simulated driving, and table setting tasks, while also providing a performance-based stopping criterion that allows a robot to know when it has received enough demonstrations to safely perform a task.", "published": "2019-01-08T05:23:03Z", "authors": ["Daniel S. Brown", "Yuchen Cui", "Scott Niekum"], "categories": ["cs.LG", "stat.ML"], "doi": "", "_query": "real estate valuation deep learning", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/1711.11023v2", "title": "A Benchmarking Environment for Reinforcement Learning Based Task Oriented Dialogue Management", "abstract": "Dialogue assistants are rapidly becoming an indispensable daily aid. To avoid the significant effort needed to hand-craft the required dialogue flow, the Dialogue Management (DM) module can be cast as a continuous Markov Decision Process (MDP) and trained through Reinforcement Learning (RL). Several RL models have been investigated over recent years. However, the lack of a common benchmarking framework makes it difficult to perform a fair comparison between different models and their capability to generalise to different environments. Therefore, this paper proposes a set of challenging simulated environments for dialogue model development and evaluation. To provide some baselines, we investigate a number of representative parametric algorithms, namely deep reinforcement learning algorithms - DQN, A2C and Natural Actor-Critic and compare them to a non-parametric model, GP-SARSA. Both the environments and policy models are implemented using the publicly available PyDial toolkit and released on-line, in order to establish a testbed framework for further experiments and to facilitate experimental reproducibility.", "published": "2017-11-29T18:51:14Z", "authors": ["Iñigo Casanueva", "Paweł Budzianowski", "Pei-Hao Su", "Nikola Mrkšić", "Tsung-Hsien Wen", "Stefan Ultes", "Lina Rojas-Barahona", "Steve Young", "Milica Gašić"], "categories": ["stat.ML", "cs.CL", "cs.NE"], "doi": "", "_query": "real estate valuation deep learning", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2004.09043v1", "title": "Learning as Reinforcement: Applying Principles of Neuroscience for More General Reinforcement Learning Agents", "abstract": "A significant challenge in developing AI that can generalize well is designing agents that learn about their world without being told what to learn, and apply that learning to challenges with sparse rewards. Moreover, most traditional reinforcement learning approaches explicitly separate learning and decision making in a way that does not correspond to biological learning. We implement an architecture founded in principles of experimental neuroscience, by combining computationally efficient abstractions of biological algorithms. Our approach is inspired by research on spike-timing dependent plasticity, the transition between short and long term memory, and the role of various neurotransmitters in rewarding curiosity. The Neurons-in-a-Box architecture can learn in a wholly generalizable manner, and demonstrates an efficient way to build and apply representations without explicitly optimizing over a set of criteria or actions. We find it performs well in many environments including OpenAI Gym's Mountain Car, which has no reward besides touching a hard-to-reach flag on a hill, Inverted Pendulum, where it learns simple strategies to improve the time it holds a pendulum up, a video stream, where it spontaneously learns to distinguish an open and closed hand, as well as other environments like Google Chrome's Dinosaur Game.", "published": "2020-04-20T04:06:21Z", "authors": ["Eric Zelikman", "William Yin", "Kenneth Wang"], "categories": ["cs.LG", "cs.NE", "stat.ML"], "doi": "", "_query": "real estate valuation deep learning", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2402.09236v2", "title": "Learning Interpretable Concepts: Unifying Causal Representation Learning and Foundation Models", "abstract": "To build intelligent machine learning systems, there are two broad approaches. One approach is to build inherently interpretable models, as endeavored by the growing field of causal representation learning. The other approach is to build highly-performant foundation models and then invest efforts into understanding how they work. In this work, we relate these two approaches and study how to learn human-interpretable concepts from data. Weaving together ideas from both fields, we formally define a notion of concepts and show that they can be provably recovered from diverse data. Experiments on synthetic data and large language models show the utility of our unified approach.", "published": "2024-02-14T15:23:59Z", "authors": ["Goutham Rajendran", "Simon Buchholz", "Bryon Aragam", "Bernhard Schölkopf", "Pradeep Ravikumar"], "categories": ["cs.LG", "cs.AI", "math.ST", "stat.ML"], "doi": "", "_query": "real estate valuation deep learning", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/1206.6452v1", "title": "Smoothness and Structure Learning by Proxy", "abstract": "As data sets grow in size, the ability of learning methods to find structure in them is increasingly hampered by the time needed to search the large spaces of possibilities and generate a score for each that takes all of the observed data into account. For instance, Bayesian networks, the model chosen in this paper, have a super-exponentially large search space for a fixed number of variables. One possible method to alleviate this problem is to use a proxy, such as a Gaussian Process regressor, in place of the true scoring function, training it on a selection of sampled networks. We prove here that the use of such a proxy is well-founded, as we can bound the smoothness of a commonly-used scoring function for Bayesian network structure learning. We show here that, compared to an identical search strategy using the network?s exact scores, our proxy-based search is able to get equivalent or better scores on a number of data sets in a fraction of the time.", "published": "2012-06-27T19:59:59Z", "authors": ["Benjamin Yackley", "Terran Lane"], "categories": ["cs.LG", "math.OC", "stat.ML"], "doi": "", "_query": "real estate valuation deep learning", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2009.04381v1", "title": "Routing Networks with Co-training for Continual Learning", "abstract": "The core challenge with continual learning is catastrophic forgetting, the phenomenon that when neural networks are trained on a sequence of tasks they rapidly forget previously learned tasks. It has been observed that catastrophic forgetting is most severe when tasks are dissimilar to each other. We propose the use of sparse routing networks for continual learning. For each input, these network architectures activate a different path through a network of experts. Routing networks have been shown to learn to route similar tasks to overlapping sets of experts and dissimilar tasks to disjoint sets of experts. In the continual learning context this behaviour is desirable as it minimizes interference between dissimilar tasks while allowing positive transfer between related tasks. In practice, we find it is necessary to develop a new training method for routing networks, which we call co-training which avoids poorly initialized experts when new tasks are presented. When combined with a small episodic memory replay buffer, sparse routing networks with co-training outperform densely connected networks on the MNIST-Permutations and MNIST-Rotations benchmarks.", "published": "2020-09-09T15:58:51Z", "authors": ["Mark Collier", "Efi Kokiopoulou", "Andrea Gesmundo", "Jesse Berent"], "categories": ["cs.LG", "stat.ML"], "doi": "", "_query": "real estate valuation deep learning", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/1412.1632v1", "title": "Deep Learning for Answer Sentence Selection", "abstract": "Answer sentence selection is the task of identifying sentences that contain the answer to a given question. This is an important problem in its own right as well as in the larger context of open domain question answering. We propose a novel approach to solving this task via means of distributed representations, and learn to match questions with answers by considering their semantic encoding. This contrasts prior work on this task, which typically relies on classifiers with large numbers of hand-crafted syntactic and semantic features and various external resources. Our approach does not require any feature engineering nor does it involve specialist linguistic data, making this model easily applicable to a wide range of domains and languages. Experimental results on a standard benchmark dataset from TREC demonstrate that---despite its simplicity---our model matches state of the art performance on the answer sentence selection task.", "published": "2014-12-04T11:53:02Z", "authors": ["Lei Yu", "Karl Moritz Hermann", "Phil Blunsom", "Stephen Pulman"], "categories": ["cs.CL"], "doi": "", "_query": "real estate valuation deep learning", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/1902.11175v2", "title": "One-Shot Federated Learning", "abstract": "We present one-shot federated learning, where a central server learns a global model over a network of federated devices in a single round of communication. Our approach - drawing on ensemble learning and knowledge aggregation - achieves an average relative gain of 51.5% in AUC over local baselines and comes within 90.1% of the (unattainable) global ideal. We discuss these methods and identify several promising directions of future work.", "published": "2019-02-28T15:55:18Z", "authors": ["Neel Guha", "Ameet Talwalkar", "Virginia Smith"], "categories": ["cs.LG", "stat.ML"], "doi": "", "_query": "real estate valuation deep learning", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/1206.6428v1", "title": "A Binary Classification Framework for Two-Stage Multiple Kernel Learning", "abstract": "With the advent of kernel methods, automating the task of specifying a suitable kernel has become increasingly important. In this context, the Multiple Kernel Learning (MKL) problem of finding a combination of pre-specified base kernels that is suitable for the task at hand has received significant attention from researchers. In this paper we show that Multiple Kernel Learning can be framed as a standard binary classification problem with additional constraints that ensure the positive definiteness of the learned kernel. Framing MKL in this way has the distinct advantage that it makes it easy to leverage the extensive research in binary classification to develop better performing and more scalable MKL algorithms that are conceptually simpler, and, arguably, more accessible to practitioners. Experiments on nine data sets from different domains show that, despite its simplicity, the proposed technique compares favorably with current leading MKL approaches.", "published": "2012-06-27T19:59:59Z", "authors": ["Abhishek Kumar", "Alexandru Niculescu-Mizil", "Koray Kavukcuoglu", "Hal Daume"], "categories": ["cs.LG", "stat.ML"], "doi": "", "_query": "real estate valuation deep learning", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2211.13829v2", "title": "Learning-enhanced Nonlinear Model Predictive Control using Knowledge-based Neural Ordinary Differential Equations and Deep Ensembles", "abstract": "Nonlinear model predictive control (MPC) is a flexible and increasingly popular framework used to synthesize feedback control strategies that can satisfy both state and control input constraints. In this framework, an optimization problem, subjected to a set of dynamics constraints characterized by a nonlinear dynamics model, is solved at each time step. Despite its versatility, the performance of nonlinear MPC often depends on the accuracy of the dynamics model. In this work, we leverage deep learning tools, namely knowledge-based neural ordinary differential equations (KNODE) and deep ensembles, to improve the prediction accuracy of this model. In particular, we learn an ensemble of KNODE models, which we refer to as the KNODE ensemble, to obtain an accurate prediction of the true system dynamics. This learned model is then integrated into a novel learning-enhanced nonlinear MPC framework. We provide sufficient conditions that guarantees asymptotic stability of the closed-loop system and show that these conditions can be implemented in practice. We show that the KNODE ensemble provides more accurate predictions and illustrate the efficacy and closed-loop performance of the proposed nonlinear MPC framework using two case studies.", "published": "2022-11-24T23:51:18Z", "authors": ["Kong Yao Chee", "M. Ani Hsieh", "Nikolai Matni"], "categories": ["eess.SY", "cs.LG", "cs.RO"], "doi": "", "_query": "real estate valuation deep learning", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/1811.08840v1", "title": "Integrating Reinforcement Learning to Self Training for Pulmonary Nodule Segmentation in Chest X-rays", "abstract": "Machine learning applications in medical imaging are frequently limited by the lack of quality labeled data. In this paper, we explore the self training method, a form of semi-supervised learning, to address the labeling burden. By integrating reinforcement learning, we were able to expand the application of self training to complex segmentation networks without any further human annotation. The proposed approach, reinforced self training (ReST), fine tunes a semantic segmentation networks by introducing a policy network that learns to generate pseudolabels. We incorporate an expert demonstration network, based on inverse reinforcement learning, to enhance clinical validity and convergence of the policy network. The model was tested on a pulmonary nodule segmentation task in chest X-rays and achieved the performance of a standard U-Net while using only 50% of the labeled data, by exploiting unlabeled data. When the same number of labeled data was used, a moderate to significant cross validation accuracy improvement was achieved depending on the absolute number of labels used.", "published": "2018-11-21T17:37:22Z", "authors": ["Sejin Park", "Woochan Hwang", "Kyu-Hwan Jung"], "categories": ["cs.LG", "stat.ML"], "doi": "", "_query": "real estate valuation deep learning", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2108.00819v1", "title": "Active Learning in Gaussian Process State Space Model", "abstract": "We investigate active learning in Gaussian Process state-space models (GPSSM). Our problem is to actively steer the system through latent states by determining its inputs such that the underlying dynamics can be optimally learned by a GPSSM. In order that the most informative inputs are selected, we employ mutual information as our active learning criterion. In particular, we present two approaches for the approximation of mutual information for the GPSSM given latent states. The proposed approaches are evaluated in several physical systems where we actively learn the underlying non-linear dynamics represented by the state-space model.", "published": "2021-07-30T16:52:38Z", "authors": ["Hon Sum Alec Yu", "Dingling Yao", "Christoph Zimmer", "Marc Toussaint", "Duy Nguyen-Tuong"], "categories": ["cs.LG", "cs.AI", "stat.ML"], "doi": "", "_query": "real estate valuation deep learning", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2005.10242v10", "title": "Understanding Contrastive Representation Learning through Alignment and Uniformity on the Hypersphere", "abstract": "Contrastive representation learning has been outstandingly successful in practice. In this work, we identify two key properties related to the contrastive loss: (1) alignment (closeness) of features from positive pairs, and (2) uniformity of the induced distribution of the (normalized) features on the hypersphere. We prove that, asymptotically, the contrastive loss optimizes these properties, and analyze their positive effects on downstream tasks. Empirically, we introduce an optimizable metric to quantify each property. Extensive experiments on standard vision and language datasets confirm the strong agreement between both metrics and downstream task performance. Remarkably, directly optimizing for these two metrics leads to representations with comparable or better performance at downstream tasks than contrastive learning. Project Page: https://tongzhouwang.info/hypersphere Code: https://github.com/SsnL/align_uniform , https://github.com/SsnL/moco_align_uniform", "published": "2020-05-20T17:59:57Z", "authors": ["Tongzhou Wang", "Phillip Isola"], "categories": ["cs.LG", "cs.CV", "stat.ML"], "doi": "", "_query": "real estate valuation deep learning", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/1811.10455v1", "title": "A Framework for Implementing Machine Learning on Omics Data", "abstract": "The potential benefits of applying machine learning methods to -omics data are becoming increasingly apparent, especially in clinical settings. However, the unique characteristics of these data are not always well suited to machine learning techniques. These data are often generated across different technologies in different labs, and frequently with high dimensionality. In this paper we present a framework for combining -omics data sets, and for handling high dimensional data, making -omics research more accessible to machine learning applications. We demonstrate the success of this framework through integration and analysis of multi-analyte data for a set of 3,533 breast cancers. We then use this data-set to predict breast cancer patient survival for individuals at risk of an impending event, with higher accuracy and lower variance than methods trained on individual data-sets. We hope that our pipelines for data-set generation and transformation will open up -omics data to machine learning researchers. We have made these freely available for noncommercial use at www.ccg.ai.", "published": "2018-11-26T15:35:57Z", "authors": ["Geoffroy Dubourg-Felonneau", "Timothy Cannings", "Fergal Cotter", "Hannah Thompson", "Nirmesh Patel", "John W Cassidy", "Harry W Clifford"], "categories": ["cs.LG", "cs.AI", "q-bio.GN", "stat.ML"], "doi": "", "_query": "real estate valuation deep learning", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/1812.04783v3", "title": "Deep Air Quality Forecasting Using Hybrid Deep Learning Framework", "abstract": "Air quality forecasting has been regarded as the key problem of air pollution early warning and control management. In this paper, we propose a novel deep learning model for air quality (mainly PM2.5) forecasting, which learns the spatial-temporal correlation features and interdependence of multivariate air quality related time series data by hybrid deep learning architecture. Due to the nonlinear and dynamic characteristics of multivariate air quality time series data, the base modules of our model include one-dimensional Convolutional Neural Networks (1D-CNNs) and Bi-directional Long Short-term Memory networks (Bi-LSTM). The former is to extract the local trend features and spatial correlation features, and the latter is to learn spatial-temporal dependencies. Then we design a jointly hybrid deep learning framework based on one-dimensional CNNs and Bi-LSTM for shared representation features learning of multivariate air quality related time series data. We conduct extensive experimental evaluations using two real-world datasets, and the results show that our model is capable of dealing with PM2.5 air pollution forecasting with satisfied accuracy.", "published": "2018-12-12T02:38:29Z", "authors": ["Shengdong Du", "Tianrui Li", "Yan Yang", "Shi-Jinn Horng"], "categories": ["cs.LG", "stat.ML"], "doi": "", "_query": "real estate valuation deep learning", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2102.04828v2", "title": "Consensus Control for Decentralized Deep Learning", "abstract": "Decentralized training of deep learning models enables on-device learning over networks, as well as efficient scaling to large compute clusters. Experiments in earlier works reveal that, even in a data-center setup, decentralized training often suffers from the degradation in the quality of the model: the training and test performance of models trained in a decentralized fashion is in general worse than that of models trained in a centralized fashion, and this performance drop is impacted by parameters such as network size, communication topology and data partitioning. We identify the changing consensus distance between devices as a key parameter to explain the gap between centralized and decentralized training. We show in theory that when the training consensus distance is lower than a critical quantity, decentralized training converges as fast as the centralized counterpart. We empirically validate that the relation between generalization performance and consensus distance is consistent with this theoretical observation. Our empirical insights allow the principled design of better decentralized training schemes that mitigate the performance drop. To this end, we provide practical training guidelines and exemplify its effectiveness on the data-center setup as the important first step.", "published": "2021-02-09T13:58:33Z", "authors": ["Lingjing Kong", "Tao Lin", "Anastasia Koloskova", "Martin Jaggi", "Sebastian U. Stich"], "categories": ["cs.LG"], "doi": "", "_query": "real estate valuation deep learning", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2001.02153v2", "title": "Information Theoretic Model Predictive Q-Learning", "abstract": "Model-free Reinforcement Learning (RL) works well when experience can be collected cheaply and model-based RL is effective when system dynamics can be modeled accurately. However, both assumptions can be violated in real world problems such as robotics, where querying the system can be expensive and real-world dynamics can be difficult to model. In contrast to RL, Model Predictive Control (MPC) algorithms use a simulator to optimize a simple policy class online, constructing a closed-loop controller that can effectively contend with real-world dynamics. MPC performance is usually limited by factors such as model bias and the limited horizon of optimization. In this work, we present a novel theoretical connection between information theoretic MPC and entropy regularized RL and develop a Q-learning algorithm that can leverage biased models. We validate the proposed algorithm on sim-to-sim control tasks to demonstrate the improvements over optimal control and reinforcement learning from scratch. Our approach paves the way for deploying reinforcement learning algorithms on real systems in a systematic manner.", "published": "2019-12-31T00:29:22Z", "authors": ["Mohak Bhardwaj", "Ankur Handa", "Dieter Fox", "Byron Boots"], "categories": ["cs.LG", "cs.RO", "stat.ML"], "doi": "", "_query": "real estate valuation deep learning", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/1806.10897v3", "title": "Deep learning in business analytics and operations research: Models, applications and managerial implications", "abstract": "Business analytics refers to methods and practices that create value through data for individuals, firms, and organizations. This field is currently experiencing a radical shift due to the advent of deep learning: deep neural networks promise improvements in prediction performance as compared to models from traditional machine learning. However, our research into the existing body of literature reveals a scarcity of research works utilizing deep learning in our discipline. Accordingly, the objectives of this overview article are as follows: (1) we review research on deep learning for business analytics from an operational point of view. (2) We motivate why researchers and practitioners from business analytics should utilize deep neural networks and review potential use cases, necessary requirements, and benefits. (3) We investigate the added value to operations research in different case studies with real data from entrepreneurial undertakings. All such cases demonstrate improvements in operational performance over traditional machine learning and thus direct value gains. (4) We provide guidelines and implications for researchers, managers and practitioners in operations research who want to advance their capabilities for business analytics with regard to deep learning. (5) Our computational experiments find that default, out-of-the-box architectures are often suboptimal and thus highlight the value of customized architectures by proposing a novel deep-embedded network.", "published": "2018-06-28T11:48:36Z", "authors": ["Mathias Kraus", "Stefan Feuerriegel", "Asil Oztekin"], "categories": ["cs.LG", "stat.ML"], "doi": "", "_query": "real estate valuation deep learning", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2512.18071v1", "title": "Deep Learning Surrogate for Fast CIR Prediction in Reactive Molecular Diffusion Advection Channels", "abstract": "Accurate channel impulse response (CIR) modeling in molecular communication (MC) often requires solving coupled reactive diffusion-advection equations, which is computationally expensive for large parameter sweeps or design loops. We develop a deep-learning surrogate for a three-dimensional duct MC channel with reactive diffusion-advection transport and reversible ligand-receptor binding on a finite ring receiver. Using a physics-based partial differential equation (PDE)-ordinary differential equation (ODE) model, we generate a large CIR dataset across broad transport, reaction, and geometric ranges and train a neural network that maps these parameters directly to the CIR. On an independent test set, the surrogate closely matches reference CIRs both qualitatively and quantitatively: the empirical cumulative distribution function (CDF) of the normalized root mean square error (NRMSE) shows that 90% of test channels are predicted with error below 0.15, with only weak dependence on individual parameters. The surrogate therefore offers an accurate and computationally efficient replacement for repeated PDE-based CIR evaluations in MC system analysis and design.", "published": "2025-12-19T21:20:04Z", "authors": ["Meysam Ghanbari", "Mohammad Taghi Dabiri", "Mazen Hasna", "Tanvir Alam", "Khalid Qaraqe"], "categories": ["eess.SP"], "doi": "", "_query": "real estate valuation deep learning", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2004.14227v1", "title": "Metric learning by Similarity Network for Deep Semi-Supervised Learning", "abstract": "Deep semi-supervised learning has been widely implemented in the real-world due to the rapid development of deep learning. Recently, attention has shifted to the approaches such as Mean-Teacher to penalize the inconsistency between two perturbed input sets. Although these methods may achieve positive results, they ignore the relationship information between data instances. To solve this problem, we propose a novel method named Metric Learning by Similarity Network (MLSN), which aims to learn a distance metric adaptively on different domains. By co-training with the classification network, similarity network can learn more information about pairwise relationships and performs better on some empirical tasks than state-of-art methods.", "published": "2020-04-29T14:25:13Z", "authors": ["Sanyou Wu", "Xingdong Feng", "Fan Zhou"], "categories": ["stat.ML", "cs.LG"], "doi": "", "_query": "real estate valuation deep learning", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2203.12114v2", "title": "An Optical Control Environment for Benchmarking Reinforcement Learning Algorithms", "abstract": "Deep reinforcement learning has the potential to address various scientific problems. In this paper, we implement an optics simulation environment for reinforcement learning based controllers. The environment captures the essence of nonconvexity, nonlinearity, and time-dependent noise inherent in optical systems, offering a more realistic setting. Subsequently, we provide the benchmark results of several reinforcement learning algorithms on the proposed simulation environment. The experimental findings demonstrate the superiority of off-policy reinforcement learning approaches over traditional control algorithms in navigating the intricacies of complex optical control environments. The code of the paper is available at https://github.com/Walleclipse/Reinforcement-Learning-Pulse-Stacking.", "published": "2022-03-23T00:59:35Z", "authors": ["Abulikemu Abuduweili", "Changliu Liu"], "categories": ["cs.LG", "cs.RO", "eess.SY"], "doi": "", "_query": "real estate valuation deep learning", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2404.10980v1", "title": "Hyper Evidential Deep Learning to Quantify Composite Classification Uncertainty", "abstract": "Deep neural networks (DNNs) have been shown to perform well on exclusive, multi-class classification tasks. However, when different classes have similar visual features, it becomes challenging for human annotators to differentiate them. This scenario necessitates the use of composite class labels. In this paper, we propose a novel framework called Hyper-Evidential Neural Network (HENN) that explicitly models predictive uncertainty due to composite class labels in training data in the context of the belief theory called Subjective Logic (SL). By placing a grouped Dirichlet distribution on the class probabilities, we treat predictions of a neural network as parameters of hyper-subjective opinions and learn the network that collects both single and composite evidence leading to these hyper-opinions by a deterministic DNN from data. We introduce a new uncertainty type called vagueness originally designed for hyper-opinions in SL to quantify composite classification uncertainty for DNNs. Our results demonstrate that HENN outperforms its state-of-the-art counterparts based on four image datasets. The code and datasets are available at: https://github.com/Hugo101/HyperEvidentialNN.", "published": "2024-04-17T01:26:15Z", "authors": ["Changbin Li", "Kangshuo Li", "Yuzhe Ou", "Lance M. Kaplan", "Audun Jøsang", "Jin-Hee Cho", "Dong Hyun Jeong", "Feng Chen"], "categories": ["cs.CV", "cs.LG"], "doi": "", "_query": "real estate valuation deep learning", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2006.09430v2", "title": "Wasserstein Embedding for Graph Learning", "abstract": "We present Wasserstein Embedding for Graph Learning (WEGL), a novel and fast framework for embedding entire graphs in a vector space, in which various machine learning models are applicable for graph-level prediction tasks. We leverage new insights on defining similarity between graphs as a function of the similarity between their node embedding distributions. Specifically, we use the Wasserstein distance to measure the dissimilarity between node embeddings of different graphs. Unlike prior work, we avoid pairwise calculation of distances between graphs and reduce the computational complexity from quadratic to linear in the number of graphs. WEGL calculates Monge maps from a reference distribution to each node embedding and, based on these maps, creates a fixed-sized vector representation of the graph. We evaluate our new graph embedding approach on various benchmark graph-property prediction tasks, showing state-of-the-art classification performance while having superior computational efficiency. The code is available at https://github.com/navid-naderi/WEGL.", "published": "2020-06-16T18:23:00Z", "authors": ["Soheil Kolouri", "Navid Naderializadeh", "Gustavo K. Rohde", "Heiko Hoffmann"], "categories": ["cs.LG", "stat.ML"], "doi": "", "_query": "real estate valuation deep learning", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2106.09017v1", "title": "Bridging Multi-Task Learning and Meta-Learning: Towards Efficient Training and Effective Adaptation", "abstract": "Multi-task learning (MTL) aims to improve the generalization of several related tasks by learning them jointly. As a comparison, in addition to the joint training scheme, modern meta-learning allows unseen tasks with limited labels during the test phase, in the hope of fast adaptation over them. Despite the subtle difference between MTL and meta-learning in the problem formulation, both learning paradigms share the same insight that the shared structure between existing training tasks could lead to better generalization and adaptation. In this paper, we take one important step further to understand the close connection between these two learning paradigms, through both theoretical analysis and empirical investigation. Theoretically, we first demonstrate that MTL shares the same optimization formulation with a class of gradient-based meta-learning (GBML) algorithms. We then prove that for over-parameterized neural networks with sufficient depth, the learned predictive functions of MTL and GBML are close. In particular, this result implies that the predictions given by these two models are similar over the same unseen task. Empirically, we corroborate our theoretical findings by showing that, with proper implementation, MTL is competitive against state-of-the-art GBML algorithms on a set of few-shot image classification benchmarks. Since existing GBML algorithms often involve costly second-order bi-level optimization, our first-order MTL method is an order of magnitude faster on large-scale datasets such as mini-ImageNet. We believe this work could help bridge the gap between these two learning paradigms, and provide a computationally efficient alternative to GBML that also supports fast task adaptation.", "published": "2021-06-16T17:58:23Z", "authors": ["Haoxiang Wang", "Han Zhao", "Bo Li"], "categories": ["cs.LG", "cs.CV", "stat.ML"], "doi": "", "_query": "real estate valuation deep learning", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/1909.04999v2", "title": "Domain-Agnostic Few-Shot Classification by Learning Disparate Modulators", "abstract": "Although few-shot learning research has advanced rapidly with the help of meta-learning, its practical usefulness is still limited because most of them assumed that all meta-training and meta-testing examples came from a single domain. We propose a simple but effective way for few-shot classification in which a task distribution spans multiple domains including ones never seen during meta-training. The key idea is to build a pool of models to cover this wide task distribution and learn to select the best one for a particular task through cross-domain meta-learning. All models in the pool share a base network while each model has a separate modulator to refine the base network in its own way. This framework allows the pool to have representational diversity without losing beneficial domain-invariant features. We verify the effectiveness of the proposed algorithm through experiments on various datasets across diverse domains.", "published": "2019-09-11T12:18:15Z", "authors": ["Yongseok Choi", "Junyoung Park", "Subin Yi", "Dong-Yeon Cho"], "categories": ["cs.LG", "cs.CV", "stat.ML"], "doi": "", "_query": "real estate valuation deep learning", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/1808.04572v3", "title": "Small Sample Learning in Big Data Era", "abstract": "As a promising area in artificial intelligence, a new learning paradigm, called Small Sample Learning (SSL), has been attracting prominent research attention in the recent years. In this paper, we aim to present a survey to comprehensively introduce the current techniques proposed on this topic. Specifically, current SSL techniques can be mainly divided into two categories. The first category of SSL approaches can be called \"concept learning\", which emphasizes learning new concepts from only few related observations. The purpose is mainly to simulate human learning behaviors like recognition, generation, imagination, synthesis and analysis. The second category is called \"experience learning\", which usually co-exists with the large sample learning manner of conventional machine learning. This category mainly focuses on learning with insufficient samples, and can also be called small data learning in some literatures. More extensive surveys on both categories of SSL techniques are introduced and some neuroscience evidences are provided to clarify the rationality of the entire SSL regime, and the relationship with human learning process. Some discussions on the main challenges and possible future research directions along this line are also presented.", "published": "2018-08-14T08:01:07Z", "authors": ["Jun Shu", "Zongben Xu", "Deyu Meng"], "categories": ["cs.LG", "stat.ML"], "doi": "", "_query": "real estate valuation deep learning", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/1812.02159v1", "title": "The effects of negative adaptation in Model-Agnostic Meta-Learning", "abstract": "The capacity of meta-learning algorithms to quickly adapt to a variety of tasks, including ones they did not experience during meta-training, has been a key factor in the recent success of these methods on few-shot learning problems. This particular advantage of using meta-learning over standard supervised or reinforcement learning is only well founded under the assumption that the adaptation phase does improve the performance of our model on the task of interest. However, in the classical framework of meta-learning, this constraint is only mildly enforced, if not at all, and we only see an improvement on average over a distribution of tasks. In this paper, we show that the adaptation in an algorithm like MAML can significantly decrease the performance of an agent in a meta-reinforcement learning setting, even on a range of meta-training tasks.", "published": "2018-12-05T18:53:02Z", "authors": ["Tristan Deleu", "Yoshua Bengio"], "categories": ["cs.LG", "stat.ML"], "doi": "", "_query": "real estate valuation deep learning", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/1905.12827v1", "title": "Deep ensemble learning for Alzheimers disease classification", "abstract": "Ensemble learning use multiple algorithms to obtain better predictive performance than any single one of its constituent algorithms could. With growing popularity of deep learning, researchers have started to ensemble them for various purposes. Few if any, however, has used the deep learning approach as a means to ensemble algorithms. This paper presents a deep ensemble learning framework which aims to harness deep learning algorithms to integrate multisource data and tap the wisdom of experts. At the voting layer, a sparse autoencoder is trained for feature learning to reduce the correlation of attributes and diversify the base classifiers ultimately. At the stacking layer, a nonlinear feature-weighted method based on deep belief networks is proposed to rank the base classifiers which may violate the conditional independence. Neural network is used as meta classifier. At the optimizing layer, under-sampling and threshold-moving are used to cope with cost-sensitive problem. Optimized predictions are obtained based on ensemble of probabilistic predictions by similarity calculation. The proposed deep ensemble learning framework is used for Alzheimers disease classification. Experiments with the clinical dataset from national Alzheimers coordinating center demonstrate that the classification accuracy of our proposed framework is 4% better than 6 well-known ensemble approaches as well as the standard stacking algorithm. Adequate coverage of more accurate diagnostic services can be provided by utilizing the wisdom of averaged physicians. This paper points out a new way to boost the primary care of Alzheimers disease from the view of machine learning.", "published": "2019-05-30T02:04:57Z", "authors": ["Ning An", "Huitong Ding", "Jiaoyun Yang", "Rhoda Au", "Ting Fang Alvin Ang"], "categories": ["cs.LG", "stat.ML"], "doi": "", "_query": "real estate valuation deep learning", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2409.14748v1", "title": "Economic effects on households of an augmentation of the cash back duration of real estate loan", "abstract": "This article examines the economic effects of an increase in the duration of home loans on households, focusing on the French real estate market. It highlights trends in the property market, existing loan systems in other countries (such as bullet loans in Sweden and Japanese home loans), the current state of the property market in France, the potential effects of an increase in the amortization period of home loans, and the financial implications for households.The article points out that increasing the repayment period on home loans could reduce the amount of monthly instalments to be repaid, thereby facilitating access to credit for the most modest households. However, this measure also raises concerns about overall credit costs, financial stability and the impact on property prices. In addition, it highlights the differences between existing lending systems in other countries, such as the bullet loan in Sweden and Japanese home loans, and the current characteristics of home loans in France, notably interest rates and house price trends. The article proposes a model of the potential effects of an increase in the amortization period of home loans on housing demand, housing supply, property prices and the associated financial risks.In conclusion, the article highlights the crucial importance of household debt for individual and economic financial stability. It highlights the distortion between supply and demand for home loans as amortization periods increase, and the significant rise in overall loan costs for households. It also underlines the need to address structural issues such as the sustainable reduction in interest rates, the stabilization of banks' equity capital and the development of a regulatory framework for intergenerational lending to ensure a properly functioning market.", "published": "2024-09-23T06:52:03Z", "authors": ["Hugo Spring-Ragain"], "categories": ["q-fin.CP"], "doi": "", "_query": "real estate valuation deep learning", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2607.14835v2", "title": "LLM-Based Re-Ranking for Real Estate Search", "abstract": "QuintoAndar Group operates the leading housing marketplace in Latin America for both rentals and sales. The platform replaces traditionally paper-heavy workflows with a fully digital experience, making housing transactions faster and more accessible to tenants, buyers, and landlords in the region. Finding the ideal home in such a vast catalog is inherently difficult. At the same time, the widespread adoption of conversational assistants is reshaping user expectations: people increasingly want to express their needs through open, multi-turn dialog rather than rigid filter menus and faceted search. This shift is particularly pronounced in housing, where intent is multi-dimensional, context-dependent, and rarely reducible to a small set of structured constraints. To meet these expectations, we propose a Large Language Model (LLM) based re-ranker that augments a conversational recommendation system by reordering retrieved candidates according to the nuanced, context-rich intent expressed across the user's conversation. We additionally construct a large-scale offline evaluation dataset for conversational real-estate search, containing 960,000 query-item pairs constructed from both synthetic and production queries and annotated using an LLM-as-a-Judge framework with human validation. We validate our approach both offline, on this proprietary dataset, and online, through a production A/B test. Both evaluations show consistent improvements in ranking quality, including a statistically significant increase in production of +5.3% in click-through rate and +4.8% in scheduled visits, demonstrating the value of integrating conversational context into housing recommendations.", "published": "2026-07-16T10:58:32Z", "authors": ["Nkateko Ntimane", "Rafael Guedes", "Tiago Cunha", "Pedro Nogueira"], "categories": ["cs.IR"], "doi": "", "_query": "real estate valuation deep learning", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/1805.04784v3", "title": "Nonlinear Metric Learning through Geodesic Interpolation within Lie Groups", "abstract": "In this paper, we propose a nonlinear distance metric learning scheme based on the fusion of component linear metrics. Instead of merging displacements at each data point, our model calculates the velocities induced by the component transformations, via a geodesic interpolation on a Lie transfor- mation group. Such velocities are later summed up to produce a global transformation that is guaranteed to be diffeomorphic. Consequently, pair-wise distances computed this way conform to a smooth and spatially varying metric, which can greatly benefit k-NN classification. Experiments on synthetic and real datasets demonstrate the effectiveness of our model.", "published": "2018-05-12T21:12:20Z", "authors": ["Zhewei Wang", "Bibo Shi", "Charles D. Smith", "Jundong Liu"], "categories": ["cs.LG", "stat.ML"], "doi": "", "_query": "real estate valuation deep learning", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/1805.04784v3", "title": "Nonlinear Metric Learning through Geodesic Interpolation within Lie Groups", "abstract": "In this paper, we propose a nonlinear distance metric learning scheme based on the fusion of component linear metrics. Instead of merging displacements at each data point, our model calculates the velocities induced by the component transformations, via a geodesic interpolation on a Lie transfor- mation group. Such velocities are later summed up to produce a global transformation that is guaranteed to be diffeomorphic. Consequently, pair-wise distances computed this way conform to a smooth and spatially varying metric, which can greatly benefit k-NN classification. Experiments on synthetic and real datasets demonstrate the effectiveness of our model.", "published": "2018-05-12T21:12:20Z", "authors": ["Zhewei Wang", "Bibo Shi", "Charles D. Smith", "Jundong Liu"], "categories": ["cs.LG", "stat.ML"], "doi": "", "_query": "real estate valuation deep learning", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/1705.09780v3", "title": "Deep Metric Learning and Image Classification with Nearest Neighbour Gaussian Kernels", "abstract": "We present a Gaussian kernel loss function and training algorithm for convolutional neural networks that can be directly applied to both distance metric learning and image classification problems. Our method treats all training features from a deep neural network as Gaussian kernel centres and computes loss by summing the influence of a feature's nearby centres in the feature embedding space. Our approach is made scalable by treating it as an approximate nearest neighbour search problem. We show how to make end-to-end learning feasible, resulting in a well formed embedding space, in which semantically related instances are likely to be located near one another, regardless of whether or not the network was trained on those classes. Our approach outperforms state-of-the-art deep metric learning approaches on embedding learning challenges, as well as conventional softmax classification on several datasets.", "published": "2017-05-27T07:34:48Z", "authors": ["Benjamin J. Meyer", "Ben Harwood", "Tom Drummond"], "categories": ["cs.CV"], "doi": "", "_query": "real estate valuation deep learning", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/1804.01653v2", "title": "Review of Deep Learning", "abstract": "In recent years, China, the United States and other countries, Google and other high-tech companies have increased investment in artificial intelligence. Deep learning is one of the current artificial intelligence research's key areas. This paper analyzes and summarizes the latest progress and future research directions of deep learning. Firstly, three basic models of deep learning are outlined, including multilayer perceptrons, convolutional neural networks, and recurrent neural networks. On this basis, we further analyze the emerging new models of convolution neural networks and recurrent neural networks. This paper then summarizes deep learning's applications in many areas of artificial intelligence, including speech processing, computer vision, natural language processing and so on. Finally, this paper discusses the existing problems of deep learning and gives the corresponding possible solutions.", "published": "2018-04-05T02:23:59Z", "authors": ["Rong Zhang", "Weiping Li", "Tong Mo"], "categories": ["cs.LG", "cs.CV", "cs.NE", "stat.ML"], "doi": "", "_query": "real estate valuation deep learning", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2007.07206v4", "title": "Learning Robust State Abstractions for Hidden-Parameter Block MDPs", "abstract": "Many control tasks exhibit similar dynamics that can be modeled as having common latent structure. Hidden-Parameter Markov Decision Processes (HiP-MDPs) explicitly model this structure to improve sample efficiency in multi-task settings. However, this setting makes strong assumptions on the observability of the state that limit its application in real-world scenarios with rich observation spaces. In this work, we leverage ideas of common structure from the HiP-MDP setting, and extend it to enable robust state abstractions inspired by Block MDPs. We derive instantiations of this new framework for both multi-task reinforcement learning (MTRL) and meta-reinforcement learning (Meta-RL) settings. Further, we provide transfer and generalization bounds based on task and state similarity, along with sample complexity bounds that depend on the aggregate number of samples across tasks, rather than the number of tasks, a significant improvement over prior work that use the same environment assumptions. To further demonstrate the efficacy of the proposed method, we empirically compare and show improvement over multi-task and meta-reinforcement learning baselines.", "published": "2020-07-14T17:25:27Z", "authors": ["Amy Zhang", "Shagun Sodhani", "Khimya Khetarpal", "Joelle Pineau"], "categories": ["cs.LG", "cs.AI", "stat.ML"], "doi": "", "_query": "real estate valuation deep learning", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2004.05512v1", "title": "Reinforcement Learning via Reasoning from Demonstration", "abstract": "Demonstration is an appealing way for humans to provide assistance to reinforcement-learning agents. Most approaches in this area view demonstrations primarily as sources of behavioral bias. But in sparse-reward tasks, humans seem to treat demonstrations more as sources of causal knowledge. This paper proposes a framework for agents that benefit from demonstration in this human-inspired way. In this framework, agents develop causal models through observation, and reason from this knowledge to decompose tasks for effective reinforcement learning. Experimental results show that a basic implementation of Reasoning from Demonstration (RfD) is effective in a range of sparse-reward tasks.", "published": "2020-04-12T00:41:28Z", "authors": ["Lisa Torrey"], "categories": ["cs.LG", "cs.AI", "stat.ML"], "doi": "", "_query": "real estate valuation deep learning", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2601.21855v3", "title": "Adaptive Probabilistic Skyline Analytics for Mobile Edge-Cloud Systems via State-Aware Deep Reinforcement Learning", "abstract": "The proliferation of Mobile Edge Computing (MEC) and distributed sensing necessitates efficient Probabilistic Skyline (PSKY) query analytics at the network edge. However, this process is severely constrained by the computation-communication trade-off under constrained wireless capacity and dynamic traffic conditions. Furthermore, mobility-induced workload fluctuations and spatio-temporal data shifts render static filtering policies inefficient, often triggering network congestion or compromising query fidelity. To address these systemic inefficiencies in dynamic mobile environments, this paper introduces SA-PSKY, a self-adaptive framework integrating deep reinforcement learning into a distributed query optimization architecture. We formulate the probabilistic skyline filtering as an adaptive control problem and develop a State-Aware Adaptive Weighting (SAAW) mechanism to dynamically regulate computation, communication, and fidelity. By incorporating Prioritized Experience Replay (PER) to stabilize learning around abrupt network transitions, our framework reliably navigates the Pareto-efficient operating point. Empirical evaluations confirm that SA-PSKY achieves substantial latency reduction while preserving query fidelity under constrained communication resources. Furthermore, zero-shot robustness analyses reveal superior adaptation to unseen mobility-induced uncertainty shifts, where rigid methods exhibit severe policy degradation.", "published": "2026-01-29T15:27:53Z", "authors": ["Chuan-Chi Lai"], "categories": ["cs.DC", "cs.DB", "cs.NI"], "doi": "", "_query": "real estate valuation deep learning", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2107.11059v1", "title": "LocalGLMnet: interpretable deep learning for tabular data", "abstract": "Deep learning models have gained great popularity in statistical modeling because they lead to very competitive regression models, often outperforming classical statistical models such as generalized linear models. The disadvantage of deep learning models is that their solutions are difficult to interpret and explain, and variable selection is not easily possible because deep learning models solve feature engineering and variable selection internally in a nontransparent way. Inspired by the appealing structure of generalized linear models, we propose a new network architecture that shares similar features as generalized linear models, but provides superior predictive power benefiting from the art of representation learning. This new architecture allows for variable selection of tabular data and for interpretation of the calibrated deep learning model, in fact, our approach provides an additive decomposition in the spirit of Shapley values and integrated gradients.", "published": "2021-07-23T07:38:33Z", "authors": ["Ronald Richman", "Mario V. Wüthrich"], "categories": ["cs.LG", "cs.AI", "q-fin.ST", "stat.AP", "stat.ML"], "doi": "", "_query": "real estate valuation deep learning", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/1911.05861v1", "title": "Federated and Differentially Private Learning for Electronic Health Records", "abstract": "The use of collaborative and decentralized machine learning techniques such as federated learning have the potential to enable the development and deployment of clinical risk predictions models in low-resource settings without requiring sensitive data be shared or stored in a central repository. This process necessitates communication of model weights or updates between collaborating entities, but it is unclear to what extent patient privacy is compromised as a result. To gain insight into this question, we study the efficacy of centralized versus federated learning in both private and non-private settings. The clinical prediction tasks we consider are the prediction of prolonged length of stay and in-hospital mortality across thirty one hospitals in the eICU Collaborative Research Database. We find that while it is straightforward to apply differentially private stochastic gradient descent to achieve strong privacy bounds when training in a centralized setting, it is considerably more difficult to do so in the federated setting.", "published": "2019-11-13T23:42:06Z", "authors": ["Stephen R. Pfohl", "Andrew M. Dai", "Katherine Heller"], "categories": ["cs.LG", "stat.ML"], "doi": "", "_query": "real estate valuation deep learning", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2210.09925v1", "title": "Generalizing in the Real World with Representation Learning", "abstract": "Machine learning (ML) formalizes the problem of getting computers to learn from experience as optimization of performance according to some metric(s) on a set of data examples. This is in contrast to requiring behaviour specified in advance (e.g. by hard-coded rules). Formalization of this problem has enabled great progress in many applications with large real-world impact, including translation, speech recognition, self-driving cars, and drug discovery. But practical instantiations of this formalism make many assumptions - for example, that data are i.i.d.: independent and identically distributed - whose soundness is seldom investigated. And in making great progress in such a short time, the field has developed many norms and ad-hoc standards, focused on a relatively small range of problem settings. As applications of ML, particularly in artificial intelligence (AI) systems, become more pervasive in the real world, we need to critically examine these assumptions, norms, and problem settings, as well as the methods that have become de-facto standards. There is much we still do not understand about how and why deep networks trained with stochastic gradient descent are able to generalize as well as they do, why they fail when they do, and how they will perform on out-of-distribution data. In this thesis I cover some of my work towards better understanding deep net generalization, identify several ways assumptions and problem settings fail to generalize to the real world, and propose ways to address those failures in practice.", "published": "2022-10-18T15:11:09Z", "authors": ["Tegan Maharaj"], "categories": ["cs.LG", "stat.ML"], "doi": "", "_query": "real estate valuation deep learning", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/1708.00367v1", "title": "Self-Supervised Learning for Spinal MRIs", "abstract": "A significant proportion of patients scanned in a clinical setting have follow-up scans. We show in this work that such longitudinal scans alone can be used as a form of 'free' self-supervision for training a deep network. We demonstrate this self-supervised learning for the case of T2-weighted sagittal lumbar Magnetic Resonance Images (MRIs). A Siamese convolutional neural network (CNN) is trained using two losses: (i) a contrastive loss on whether the scan is of the same person (i.e. longitudinal) or not, together with (ii) a classification loss on predicting the level of vertebral bodies. The performance of this pre-trained network is then assessed on a grading classification task. We experiment on a dataset of 1016 subjects, 423 possessing follow-up scans, with the end goal of learning the disc degeneration radiological gradings attached to the intervertebral discs. We show that the performance of the pre-trained CNN on the supervised classification task is (i) superior to that of a network trained from scratch; and (ii) requires far fewer annotated training samples to reach an equivalent performance to that of the network trained from scratch.", "published": "2017-08-01T14:44:48Z", "authors": ["Amir Jamaludin", "Timor Kadir", "Andrew Zisserman"], "categories": ["cs.CV"], "doi": "", "_query": "real estate valuation deep learning", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/1807.02816v1", "title": "Improving Deep Learning through Automatic Programming", "abstract": "Deep learning and deep architectures are emerging as the best machine learning methods so far in many practical applications such as reducing the dimensionality of data, image classification, speech recognition or object segmentation. In fact, many leading technology companies such as Google, Microsoft or IBM are researching and using deep architectures in their systems to replace other traditional models. Therefore, improving the performance of these models could make a strong impact in the area of machine learning. However, deep learning is a very fast-growing research domain with many core methodologies and paradigms just discovered over the last few years. This thesis will first serve as a short summary of deep learning, which tries to include all of the most important ideas in this research area. Based on this knowledge, we suggested, and conducted some experiments to investigate the possibility of improving the deep learning based on automatic programming (ADATE). Although our experiments did produce good results, there are still many more possibilities that we could not try due to limited time as well as some limitations of the current ADATE version. I hope that this thesis can promote future work on this topic, especially when the next version of ADATE comes out. This thesis also includes a short analysis of the power of ADATE system, which could be useful for other researchers who want to know what it is capable of.", "published": "2018-07-08T13:38:21Z", "authors": ["The-Hien Dang-Ha"], "categories": ["cs.LG", "stat.ML"], "doi": "", "_query": "real estate valuation deep learning", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2211.11565v1", "title": "IEEE Big Data Cup 2022: Privacy Preserving Matching of Encrypted Images with Deep Learning", "abstract": "Smart sensors, devices and systems deployed in smart cities have brought improved physical protections to their citizens. Enhanced crime prevention, and fire and life safety protection are achieved through these technologies that perform motion detection, threat and actors profiling, and real-time alerts. However, an important requirement in these increasingly prevalent deployments is the preservation of privacy and enforcement of protection of personal identifiable information. Thus, strong encryption and anonymization techniques should be applied to the collected data. In this IEEE Big Data Cup 2022 challenge, different masking, encoding and homomorphic encryption techniques were applied to the images to protect the privacy of their contents. Participants are required to develop detection solutions to perform privacy preserving matching of these images. In this paper, we describe our solution which is based on state-of-the-art deep convolutional neural networks and various data augmentation techniques. Our solution achieved 1st place at the IEEE Big Data Cup 2022: Privacy Preserving Matching of Encrypted Images Challenge.", "published": "2022-11-18T12:20:40Z", "authors": ["Vrizlynn L. L. Thing"], "categories": ["cs.CR", "cs.AI", "cs.CV", "cs.LG", "cs.MM"], "doi": "", "_query": "real estate valuation deep learning", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2301.04268v1", "title": "Adversarial Online Multi-Task Reinforcement Learning", "abstract": "We consider the adversarial online multi-task reinforcement learning setting, where in each of $K$ episodes the learner is given an unknown task taken from a finite set of $M$ unknown finite-horizon MDP models. The learner's objective is to minimize its regret with respect to the optimal policy for each task. We assume the MDPs in $\\mathcal{M}$ are well-separated under a notion of $λ$-separability, and show that this notion generalizes many task-separability notions from previous works. We prove a minimax lower bound of $Ω(K\\sqrt{DSAH})$ on the regret of any learning algorithm and an instance-specific lower bound of $Ω(\\frac{K}{λ^2})$ in sample complexity for a class of uniformly-good cluster-then-learn algorithms. We use a novel construction called 2-JAO MDP for proving the instance-specific lower bound. The lower bounds are complemented with a polynomial time algorithm that obtains $\\tilde{O}(\\frac{K}{λ^2})$ sample complexity guarantee for the clustering phase and $\\tilde{O}(\\sqrt{MK})$ regret guarantee for the learning phase, indicating that the dependency on $K$ and $\\frac{1}{λ^2}$ is tight.", "published": "2023-01-11T02:18:26Z", "authors": ["Quan Nguyen", "Nishant A. Mehta"], "categories": ["cs.LG", "cs.AI", "stat.ML"], "doi": "", "_query": "real estate valuation deep learning", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2210.11392v1", "title": "Deep reinforcement learning oriented for real world dynamic scenarios", "abstract": "Autonomous navigation in dynamic environments is a complex but essential task for autonomous robots. Recent deep reinforcement learning approaches show promising results to solve the problem, but it is not solved yet, as they typically assume no robot kinodynamic restrictions, holonomic movement or perfect environment knowledge. Moreover, most algorithms fail in the real world due to the inability to generate real-world training data for the huge variability of possible scenarios. In this work, we present a novel planner, DQN-DOVS, that uses deep reinforcement learning on a descriptive robocentric velocity space model to navigate in highly dynamic environments. It is trained using a smart curriculum learning approach on a simulator that faithfully reproduces the real world, reducing the gap between the reality and simulation. We test the resulting algorithm in scenarios with different number of obstacles and compare it with many state-of-the-art approaches, obtaining a better performance. Finally, we try the algorithm in a ground robot, using the same setup as in the simulation experiments.", "published": "2022-10-20T16:37:37Z", "authors": ["Diego Martinez", "Luis Riazuelo", "Luis Montano"], "categories": ["cs.RO"], "doi": "", "_query": "real estate valuation deep learning", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2003.02218v1", "title": "The large learning rate phase of deep learning: the catapult mechanism", "abstract": "The choice of initial learning rate can have a profound effect on the performance of deep networks. We present a class of neural networks with solvable training dynamics, and confirm their predictions empirically in practical deep learning settings. The networks exhibit sharply distinct behaviors at small and large learning rates. The two regimes are separated by a phase transition. In the small learning rate phase, training can be understood using the existing theory of infinitely wide neural networks. At large learning rates the model captures qualitatively distinct phenomena, including the convergence of gradient descent dynamics to flatter minima. One key prediction of our model is a narrow range of large, stable learning rates. We find good agreement between our model's predictions and training dynamics in realistic deep learning settings. Furthermore, we find that the optimal performance in such settings is often found in the large learning rate phase. We believe our results shed light on characteristics of models trained at different learning rates. In particular, they fill a gap between existing wide neural network theory, and the nonlinear, large learning rate, training dynamics relevant to practice.", "published": "2020-03-04T17:52:48Z", "authors": ["Aitor Lewkowycz", "Yasaman Bahri", "Ethan Dyer", "Jascha Sohl-Dickstein", "Guy Gur-Ari"], "categories": ["stat.ML", "cs.LG"], "doi": "", "_query": "real estate valuation deep learning", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2007.04759v1", "title": "Expressivity of Deep Neural Networks", "abstract": "In this review paper, we give a comprehensive overview of the large variety of approximation results for neural networks. Approximation rates for classical function spaces as well as benefits of deep neural networks over shallow ones for specifically structured function classes are discussed. While the mainbody of existing results is for general feedforward architectures, we also depict approximation results for convolutional, residual and recurrent neural networks.", "published": "2020-07-09T13:08:01Z", "authors": ["Ingo Gühring", "Mones Raslan", "Gitta Kutyniok"], "categories": ["cs.LG", "math.FA", "stat.ML"], "doi": "", "_query": "real estate valuation deep learning", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2404.18909v3", "title": "Sample-Efficient Robust Multi-Agent Reinforcement Learning in the Face of Environmental Uncertainty", "abstract": "To overcome the sim-to-real gap in reinforcement learning (RL), learned policies must maintain robustness against environmental uncertainties. While robust RL has been widely studied in single-agent regimes, in multi-agent environments, the problem remains understudied -- despite the fact that the problems posed by environmental uncertainties are often exacerbated by strategic interactions. This work focuses on learning in distributionally robust Markov games (RMGs), a robust variant of standard Markov games, wherein each agent aims to learn a policy that maximizes its own worst-case performance when the deployed environment deviates within its own prescribed uncertainty set. This results in a set of robust equilibrium strategies for all agents that align with classic notions of game-theoretic equilibria. Assuming a non-adaptive sampling mechanism from a generative model, we propose a sample-efficient model-based algorithm (DRNVI) with finite-sample complexity guarantees for learning robust variants of various notions of game-theoretic equilibria. We also establish an information-theoretic lower bound for solving RMGs, which confirms the near-optimal sample complexity of DRNVI with respect to problem-dependent factors such as the size of the state space, the target accuracy, and the horizon length.", "published": "2024-04-29T17:51:47Z", "authors": ["Laixi Shi", "Eric Mazumdar", "Yuejie Chi", "Adam Wierman"], "categories": ["cs.LG", "cs.MA", "stat.ML"], "doi": "", "_query": "real estate valuation deep learning", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2410.16449v2", "title": "Robust Feature Learning for Multi-Index Models in High Dimensions", "abstract": "Recently, there have been numerous studies on feature learning with neural networks, specifically on learning single- and multi-index models where the target is a function of a low-dimensional projection of the input. Prior works have shown that in high dimensions, the majority of the compute and data resources are spent on recovering the low-dimensional projection; once this subspace is recovered, the remainder of the target can be learned independently of the ambient dimension. However, implications of feature learning in adversarial settings remain unexplored. In this work, we take the first steps towards understanding adversarially robust feature learning with neural networks. Specifically, we prove that the hidden directions of a multi-index model offer a Bayes optimal low-dimensional projection for robustness against $\\ell_2$-bounded adversarial perturbations under the squared loss, assuming that the multi-index coordinates are statistically independent from the rest of the coordinates. Therefore, robust learning can be achieved by first performing standard feature learning, then robustly tuning a linear readout layer on top of the standard representations. In particular, we show that adversarially robust learning is just as easy as standard learning. Specifically, the additional number of samples needed to robustly learn multi-index models when compared to standard learning does not depend on dimensionality.", "published": "2024-10-21T19:20:34Z", "authors": ["Alireza Mousavi-Hosseini", "Adel Javanmard", "Murat A. Erdogdu"], "categories": ["stat.ML", "cs.LG"], "doi": "", "_query": "real estate valuation deep learning", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2304.12877v1", "title": "Proximal Curriculum for Reinforcement Learning Agents", "abstract": "We consider the problem of curriculum design for reinforcement learning (RL) agents in contextual multi-task settings. Existing techniques on automatic curriculum design typically require domain-specific hyperparameter tuning or have limited theoretical underpinnings. To tackle these limitations, we design our curriculum strategy, ProCuRL, inspired by the pedagogical concept of Zone of Proximal Development (ZPD). ProCuRL captures the intuition that learning progress is maximized when picking tasks that are neither too hard nor too easy for the learner. We mathematically derive ProCuRL by analyzing two simple learning settings. We also present a practical variant of ProCuRL that can be directly integrated with deep RL frameworks with minimal hyperparameter tuning. Experimental results on a variety of domains demonstrate the effectiveness of our curriculum strategy over state-of-the-art baselines in accelerating the training process of deep RL agents.", "published": "2023-04-25T14:49:34Z", "authors": ["Georgios Tzannetos", "Bárbara Gomes Ribeiro", "Parameswaran Kamalaruban", "Adish Singla"], "categories": ["cs.LG"], "doi": "", "_query": "real estate valuation deep learning", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/1806.05413v2", "title": "Learning Dynamics of Linear Denoising Autoencoders", "abstract": "Denoising autoencoders (DAEs) have proven useful for unsupervised representation learning, but a thorough theoretical understanding is still lacking of how the input noise influences learning. Here we develop theory for how noise influences learning in DAEs. By focusing on linear DAEs, we are able to derive analytic expressions that exactly describe their learning dynamics. We verify our theoretical predictions with simulations as well as experiments on MNIST and CIFAR-10. The theory illustrates how, when tuned correctly, noise allows DAEs to ignore low variance directions in the inputs while learning to reconstruct them. Furthermore, in a comparison of the learning dynamics of DAEs to standard regularised autoencoders, we show that noise has a similar regularisation effect to weight decay, but with faster training dynamics. We also show that our theoretical predictions approximate learning dynamics on real-world data and qualitatively match observed dynamics in nonlinear DAEs.", "published": "2018-06-14T08:46:36Z", "authors": ["Arnu Pretorius", "Steve Kroon", "Herman Kamper"], "categories": ["stat.ML", "cs.LG"], "doi": "", "_query": "real estate valuation deep learning", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2012.09508v2", "title": "Towards Optimal District Heating Temperature Control in China with Deep Reinforcement Learning", "abstract": "Achieving efficiency gains in Chinese district heating networks, thereby reducing their carbon footprint, requires new optimal control methods going beyond current industry tools. Focusing on the secondary network, we propose a data-driven deep reinforcement learning (DRL) approach to address this task. We build a recurrent neural network, trained on simulated data, to predict the indoor temperatures. This model is then used to train two DRL agents, with or without expert guidance, for the optimal control of the supply water temperature. Our tests in a multi-apartment setting show that both agents can ensure a higher thermal comfort and at the same time a smaller energy cost, compared to an optimized baseline strategy.", "published": "2020-12-17T11:16:08Z", "authors": ["Adrien Le-Coz", "Tahar Nabil", "Francois Courtot"], "categories": ["eess.SY", "cs.AI", "cs.LG"], "doi": "", "_query": "real estate valuation deep learning", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2108.08712v1", "title": "Teaching Uncertainty Quantification in Machine Learning through Use Cases", "abstract": "Uncertainty in machine learning is not generally taught as general knowledge in Machine Learning course curricula. In this paper we propose a short curriculum for a course about uncertainty in machine learning, and complement the course with a selection of use cases, aimed to trigger discussion and let students play with the concepts of uncertainty in a programming setting. Our use cases cover the concept of output uncertainty, Bayesian neural networks and weight distributions, sources of uncertainty, and out of distribution detection. We expect that this curriculum and set of use cases motivates the community to adopt these important concepts into courses for safety in AI.", "published": "2021-08-19T14:22:17Z", "authors": ["Matias Valdenegro-Toro"], "categories": ["cs.LG", "stat.ML"], "doi": "", "_query": "real estate valuation deep learning", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2108.07380v2", "title": "InfoGram and Admissible Machine Learning", "abstract": "We have entered a new era of machine learning (ML), where the most accurate algorithm with superior predictive power may not even be deployable, unless it is admissible under the regulatory constraints. This has led to great interest in developing fair, transparent and trustworthy ML methods. The purpose of this article is to introduce a new information-theoretic learning framework (admissible machine learning) and algorithmic risk-management tools (InfoGram, L-features, ALFA-testing) that can guide an analyst to redesign off-the-shelf ML methods to be regulatory compliant, while maintaining good prediction accuracy. We have illustrated our approach using several real-data examples from financial sectors, biomedical research, marketing campaigns, and the criminal justice system.", "published": "2021-08-17T00:04:38Z", "authors": ["Subhadeep Mukhopadhyay"], "categories": ["stat.ML", "cs.AI", "cs.LG", "econ.EM"], "doi": "", "_query": "real estate valuation deep learning", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/1910.02600v2", "title": "Deep Evidential Regression", "abstract": "Deterministic neural networks (NNs) are increasingly being deployed in safety critical domains, where calibrated, robust, and efficient measures of uncertainty are crucial. In this paper, we propose a novel method for training non-Bayesian NNs to estimate a continuous target as well as its associated evidence in order to learn both aleatoric and epistemic uncertainty. We accomplish this by placing evidential priors over the original Gaussian likelihood function and training the NN to infer the hyperparameters of the evidential distribution. We additionally impose priors during training such that the model is regularized when its predicted evidence is not aligned with the correct output. Our method does not rely on sampling during inference or on out-of-distribution (OOD) examples for training, thus enabling efficient and scalable uncertainty learning. We demonstrate learning well-calibrated measures of uncertainty on various benchmarks, scaling to complex computer vision tasks, as well as robustness to adversarial and OOD test samples.", "published": "2019-10-07T04:11:34Z", "authors": ["Alexander Amini", "Wilko Schwarting", "Ava Soleimany", "Daniela Rus"], "categories": ["cs.LG", "cs.NE", "stat.ML"], "doi": "", "_query": "real estate valuation deep learning", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/1811.02756v4", "title": "Bayesian State Estimation for Unobservable Distribution Systems via Deep Learning", "abstract": "The problem of state estimation for unobservable distribution systems is considered. A deep learning approach to Bayesian state estimation is proposed for real-time applications. The proposed technique consists of distribution learning of stochastic power injection, a Monte Carlo technique for the training of a deep neural network for state estimation, and a Bayesian bad-data detection and filtering algorithm. Structural characteristics of the deep neural networks are investigated. Simulations illustrate the accuracy of Bayesian state estimation for unobservable systems and demonstrate the benefit of employing a deep neural network. Numerical results show the robustness of Bayesian state estimation against modeling and estimation errors and the presence of bad and missing data. Comparing with pseudo-measurement techniques, direct Bayesian state estimation via deep learning neural network outperforms existing benchmarks.", "published": "2018-11-07T04:37:33Z", "authors": ["Kursat Rasim Mestav", "Jaime Luengo-Rozas", "Lang Tong"], "categories": ["stat.ML", "cs.LG", "stat.AP"], "doi": "", "_query": "real estate valuation deep learning", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/1905.07785v2", "title": "Sparse Transfer Learning via Winning Lottery Tickets", "abstract": "The recently proposed Lottery Ticket Hypothesis of Frankle and Carbin (2019) suggests that the performance of over-parameterized deep networks is due to the random initialization seeding the network with a small fraction of favorable weights. These weights retain their dominant status throughout training -- in a very real sense, this sub-network \"won the lottery\" during initialization. The authors find sub-networks via unstructured magnitude pruning with 85-95% of parameters removed that train to the same accuracy as the original network at a similar speed, which they call winning tickets. In this paper, we extend the Lottery Ticket Hypothesis to a variety of transfer learning tasks. We show that sparse sub-networks with approximately 90-95% of weights removed achieve (and often exceed) the accuracy of the original dense network in several realistic settings. We experimentally validate this by transferring the sparse representation found via pruning on CIFAR-10 to SmallNORB and FashionMNIST for object recognition tasks.", "published": "2019-05-19T17:48:29Z", "authors": ["Rahul Mehta"], "categories": ["cs.LG", "cs.NE", "stat.ML"], "doi": "", "_query": "real estate valuation deep learning", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2307.12135v1", "title": "The Sample Complexity of Multi-Distribution Learning for VC Classes", "abstract": "Multi-distribution learning is a natural generalization of PAC learning to settings with multiple data distributions. There remains a significant gap between the known upper and lower bounds for PAC-learnable classes. In particular, though we understand the sample complexity of learning a VC dimension d class on $k$ distributions to be $O(ε^{-2} \\ln(k)(d + k) + \\min\\{ε^{-1} dk, ε^{-4} \\ln(k) d\\})$, the best lower bound is $Ω(ε^{-2}(d + k \\ln(k)))$. We discuss recent progress on this problem and some hurdles that are fundamental to the use of game dynamics in statistical learning.", "published": "2023-07-22T18:02:53Z", "authors": ["Pranjal Awasthi", "Nika Haghtalab", "Eric Zhao"], "categories": ["cs.LG", "stat.ML"], "doi": "", "_query": "real estate valuation deep learning", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2309.02160v1", "title": "Bias Propagation in Federated Learning", "abstract": "We show that participating in federated learning can be detrimental to group fairness. In fact, the bias of a few parties against under-represented groups (identified by sensitive attributes such as gender or race) can propagate through the network to all the parties in the network. We analyze and explain bias propagation in federated learning on naturally partitioned real-world datasets. Our analysis reveals that biased parties unintentionally yet stealthily encode their bias in a small number of model parameters, and throughout the training, they steadily increase the dependence of the global model on sensitive attributes. What is important to highlight is that the experienced bias in federated learning is higher than what parties would otherwise encounter in centralized training with a model trained on the union of all their data. This indicates that the bias is due to the algorithm. Our work calls for auditing group fairness in federated learning and designing learning algorithms that are robust to bias propagation.", "published": "2023-09-05T11:55:03Z", "authors": ["Hongyan Chang", "Reza Shokri"], "categories": ["cs.LG", "cs.CY", "stat.ML"], "doi": "", "_query": "real estate valuation deep learning", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/1707.03340v1", "title": "Deep Learning for Real Time Crime Forecasting", "abstract": "Accurate real time crime prediction is a fundamental issue for public safety, but remains a challenging problem for the scientific community. Crime occurrences depend on many complex factors. Compared to many predictable events, crime is sparse. At different spatio-temporal scales, crime distributions display dramatically different patterns. These distributions are of very low regularity in both space and time. In this work, we adapt the state-of-the-art deep learning spatio-temporal predictor, ST-ResNet [Zhang et al, AAAI, 2017], to collectively predict crime distribution over the Los Angeles area. Our models are two staged. First, we preprocess the raw crime data. This includes regularization in both space and time to enhance predictable signals. Second, we adapt hierarchical structures of residual convolutional units to train multi-factor crime prediction models. Experiments over a half year period in Los Angeles reveal highly accurate predictive power of our models.", "published": "2017-07-09T17:36:53Z", "authors": ["Bao Wang", "Duo Zhang", "Duanhao Zhang", "P. Jeffery Brantingham", "Andrea L. Bertozzi"], "categories": ["math.NA", "cs.LG", "stat.ML"], "doi": "", "_query": "real estate valuation deep learning", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2008.07875v1", "title": "Towards Closing the Sim-to-Real Gap in Collaborative Multi-Robot Deep Reinforcement Learning", "abstract": "Current research directions in deep reinforcement learning include bridging the simulation-reality gap, improving sample efficiency of experiences in distributed multi-agent reinforcement learning, together with the development of robust methods against adversarial agents in distributed learning, among many others. In this work, we are particularly interested in analyzing how multi-agent reinforcement learning can bridge the gap to reality in distributed multi-robot systems where the operation of the different robots is not necessarily homogeneous. These variations can happen due to sensing mismatches, inherent errors in terms of calibration of the mechanical joints, or simple differences in accuracy. While our results are simulation-based, we introduce the effect of sensing, calibration, and accuracy mismatches in distributed reinforcement learning with proximal policy optimization (PPO). We discuss on how both the different types of perturbances and how the number of agents experiencing those perturbances affect the collaborative learning effort. The simulations are carried out using a Kuka arm model in the Bullet physics engine. This is, to the best of our knowledge, the first work exploring the limitations of PPO in multi-robot systems when considering that different robots might be exposed to different environments where their sensors or actuators have induced errors. With the conclusions of this work, we set the initial point for future work on designing and developing methods to achieve robust reinforcement learning on the presence of real-world perturbances that might differ within a multi-robot system.", "published": "2020-08-18T11:57:33Z", "authors": ["Wenshuai Zhao", "Jorge Peña Queralta", "Li Qingqing", "Tomi Westerlund"], "categories": ["cs.LG", "cs.DC", "stat.ML"], "doi": "", "_query": "real estate valuation deep learning", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2006.05142v1", "title": "Smooth Proxy-Anchor Loss for Noisy Metric Learning", "abstract": "Many industrial applications use Metric Learning as a way to circumvent scalability issues when designing systems with a high number of classes. Because of this, this field of research is attracting a lot of interest from the academic and non-academic communities. Such industrial applications require large-scale datasets, which are usually generated with web data and, as a result, often contain a high number of noisy labels. While Metric Learning systems are sensitive to noisy labels, this is usually not tackled in the literature, that relies on manually annotated datasets. In this work, we propose a Metric Learning method that is able to overcome the presence of noisy labels using our novel Smooth Proxy-Anchor Loss. We also present an architecture that uses the aforementioned loss with a two-phase learning procedure. First, we train a confidence module that computes sample class confidences. Second, these confidences are used to weight the influence of each sample for the training of the embeddings. This results in a system that is able to provide robust sample embeddings. We compare the performance of the described method with current state-of-the-art Metric Learning losses (proxy-based and pair-based), when trained with a dataset containing noisy labels. The results showcase an improvement of 2.63 and 3.29 in Recall@1 with respect to MultiSimilarity and Proxy-Anchor Loss respectively, proving that our method outperforms the state-of-the-art of Metric Learning in noisy labeling conditions.", "published": "2020-06-09T09:33:04Z", "authors": ["Carlos Roig", "David Varas", "Issey Masuda", "Juan Carlos Riveiro", "Elisenda Bou-Balust"], "categories": ["cs.CV"], "doi": "", "_query": "real estate valuation deep learning", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2603.26982v1", "title": "Online Statistical Inference of Constant Sample-averaged Q-Learning", "abstract": "Reinforcement learning algorithms have been widely used for decision-making tasks in various domains. However, the performance of these algorithms can be impacted by high variance and instability, particularly in environments with noise or sparse rewards. In this paper, we propose a framework to perform statistical online inference for a sample-averaged Q-learning approach. We adapt the functional central limit theorem (FCLT) for the modified algorithm under some general conditions and then construct confidence intervals for the Q-values via random scaling. We conduct experiments to perform inference on both the modified approach and its traditional counterpart, Q-learning using random scaling and report their coverage rates and confidence interval widths on two problems: a grid world problem as a simple toy example and a dynamic resource-matching problem as a real-world example for comparison between the two solution approaches.", "published": "2026-03-27T20:49:15Z", "authors": ["Saunak Kumar Panda", "Tong Li", "Ruiqi Liu", "Yisha Xiang"], "categories": ["stat.ML", "cs.AI", "cs.LG"], "doi": "", "_query": "real estate valuation deep learning", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/1206.6417v1", "title": "Learning Task Grouping and Overlap in Multi-task Learning", "abstract": "In the paradigm of multi-task learning, mul- tiple related prediction tasks are learned jointly, sharing information across the tasks. We propose a framework for multi-task learn- ing that enables one to selectively share the information across the tasks. We assume that each task parameter vector is a linear combi- nation of a finite number of underlying basis tasks. The coefficients of the linear combina- tion are sparse in nature and the overlap in the sparsity patterns of two tasks controls the amount of sharing across these. Our model is based on on the assumption that task pa- rameters within a group lie in a low dimen- sional subspace but allows the tasks in differ- ent groups to overlap with each other in one or more bases. Experimental results on four datasets show that our approach outperforms competing methods.", "published": "2012-06-27T19:59:59Z", "authors": ["Abhishek Kumar", "Hal Daume"], "categories": ["cs.LG", "stat.ML"], "doi": "", "_query": "real estate valuation deep learning", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/1907.01463v1", "title": "Reproducibility in Machine Learning for Health", "abstract": "Machine learning algorithms designed to characterize, monitor, and intervene on human health (ML4H) are expected to perform safely and reliably when operating at scale, potentially outside strict human supervision. This requirement warrants a stricter attention to issues of reproducibility than other fields of machine learning. In this work, we conduct a systematic evaluation of over 100 recently published ML4H research papers along several dimensions related to reproducibility. We find that the field of ML4H compares poorly to more established machine learning fields, particularly concerning data and code accessibility. Finally, drawing from success in other fields of science, we propose recommendations to data providers, academic publishers, and the ML4H research community in order to promote reproducible research moving forward.", "published": "2019-07-02T15:46:46Z", "authors": ["Matthew B. A. McDermott", "Shirly Wang", "Nikki Marinsek", "Rajesh Ranganath", "Marzyeh Ghassemi", "Luca Foschini"], "categories": ["cs.LG", "cs.CY", "stat.ML"], "doi": "", "_query": "real estate valuation deep learning", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2005.00478v3", "title": "DriveML: An R Package for Driverless Machine Learning", "abstract": "In recent years, the concept of automated machine learning has become very popular. Automated Machine Learning (AutoML) mainly refers to the automated methods for model selection and hyper-parameter optimization of various algorithms such as random forests, gradient boosting, neural networks, etc. In this paper, we introduce a new package i.e. DriveML for automated machine learning. DriveML helps in implementing some of the pillars of an automated machine learning pipeline such as automated data preparation, feature engineering, model building and model explanation by running the function instead of writing lengthy R codes. The DriveML package is available in CRAN. We compare the DriveML package with other relevant packages in CRAN/Github and find that DriveML performs the best across different parameters. We also provide an illustration by applying the DriveML package with default configuration on a real world dataset. Overall, the main benefits of DriveML are in development time savings, reduce developer's errors, optimal tuning of machine learning models and reproducibility.", "published": "2020-05-01T16:40:25Z", "authors": ["Sayan Putatunda", "Dayananda Ubrangala", "Kiran Rama", "Ravi Kondapalli"], "categories": ["cs.LG", "stat.ML"], "doi": "", "_query": "real estate valuation deep learning", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/1811.08227v1", "title": "Analytic Network Learning", "abstract": "Based on the property that solving the system of linear matrix equations via the column space and the row space projections boils down to an approximation in the least squares error sense, a formulation for learning the weight matrices of the multilayer network can be derived. By exploiting into the vast number of feasible solutions of these interdependent weight matrices, the learning can be performed analytically layer by layer without needing of gradient computation after an initialization. Possible initialization schemes include utilizing the data matrix as initial weights and random initialization. The study is followed by an investigation into the representation capability and the output variance of the learning scheme. An extensive experimentation on synthetic and real-world data sets validates its numerical feasibility.", "published": "2018-11-20T13:03:07Z", "authors": ["Kar-Ann Toh"], "categories": ["cs.LG", "stat.ML"], "doi": "", "_query": "real estate valuation deep learning", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/1709.05870v1", "title": "ZhuSuan: A Library for Bayesian Deep Learning", "abstract": "In this paper we introduce ZhuSuan, a python probabilistic programming library for Bayesian deep learning, which conjoins the complimentary advantages of Bayesian methods and deep learning. ZhuSuan is built upon Tensorflow. Unlike existing deep learning libraries, which are mainly designed for deterministic neural networks and supervised tasks, ZhuSuan is featured for its deep root into Bayesian inference, thus supporting various kinds of probabilistic models, including both the traditional hierarchical Bayesian models and recent deep generative models. We use running examples to illustrate the probabilistic programming on ZhuSuan, including Bayesian logistic regression, variational auto-encoders, deep sigmoid belief networks and Bayesian recurrent neural networks.", "published": "2017-09-18T11:30:08Z", "authors": ["Jiaxin Shi", "Jianfei Chen", "Jun Zhu", "Shengyang Sun", "Yucen Luo", "Yihong Gu", "Yuhao Zhou"], "categories": ["stat.ML", "cs.AI", "cs.LG", "cs.NE", "stat.CO"], "doi": "", "_query": "real estate valuation deep learning", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/1906.10025v2", "title": "Modern Deep Reinforcement Learning Algorithms", "abstract": "Recent advances in Reinforcement Learning, grounded on combining classical theoretical results with Deep Learning paradigm, led to breakthroughs in many artificial intelligence tasks and gave birth to Deep Reinforcement Learning (DRL) as a field of research. In this work latest DRL algorithms are reviewed with a focus on their theoretical justification, practical limitations and observed empirical properties.", "published": "2019-06-24T15:27:51Z", "authors": ["Sergey Ivanov", "Alexander D'yakonov"], "categories": ["cs.LG", "cs.AI", "stat.ML"], "doi": "", "_query": "real estate valuation deep learning", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2302.08606v1", "title": "Intrinsic and extrinsic deep learning on manifolds", "abstract": "We propose extrinsic and intrinsic deep neural network architectures as general frameworks for deep learning on manifolds. Specifically, extrinsic deep neural networks (eDNNs) preserve geometric features on manifolds by utilizing an equivariant embedding from the manifold to its image in the Euclidean space. Moreover, intrinsic deep neural networks (iDNNs) incorporate the underlying intrinsic geometry of manifolds via exponential and log maps with respect to a Riemannian structure. Consequently, we prove that the empirical risk of the empirical risk minimizers (ERM) of eDNNs and iDNNs converge in optimal rates. Overall, The eDNNs framework is simple and easy to compute, while the iDNNs framework is accurate and fast converging. To demonstrate the utilities of our framework, various simulation studies, and real data analyses are presented with eDNNs and iDNNs.", "published": "2023-02-16T22:10:38Z", "authors": ["Yihao Fang", "Ilsang Ohn", "Vijay Gupta", "Lizhen Lin"], "categories": ["stat.ML", "cs.LG", "stat.ME"], "doi": "", "_query": "real estate valuation deep learning", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2403.07091v2", "title": "Sim-to-Real gap in RL: Use Case with TIAGo and Isaac Sim/Gym", "abstract": "This paper explores policy-learning approaches in the context of sim-to-real transfer for robotic manipulation using a TIAGo mobile manipulator, focusing on two state-of-art simulators, Isaac Gym and Isaac Sim, both developed by Nvidia. Control architectures are discussed, with a particular emphasis on achieving collision-less movement in both simulation and the real environment. Presented results demonstrate successful sim-to-real transfer, showcasing similar movements executed by an RL-trained model in both simulated and real setups.", "published": "2024-03-11T18:35:32Z", "authors": ["Jaume Albardaner", "Alberto San Miguel", "Néstor García", "Magí Dalmau-Moreno"], "categories": ["cs.RO"], "doi": "", "_query": "real estate valuation deep learning", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/1602.00203v1", "title": "Greedy Deep Dictionary Learning", "abstract": "In this work we propose a new deep learning tool called deep dictionary learning. Multi-level dictionaries are learnt in a greedy fashion, one layer at a time. This requires solving a simple (shallow) dictionary learning problem, the solution to this is well known. We apply the proposed technique on some benchmark deep learning datasets. We compare our results with other deep learning tools like stacked autoencoder and deep belief network; and state of the art supervised dictionary learning tools like discriminative KSVD and label consistent KSVD. Our method yields better results than all.", "published": "2016-01-31T06:12:58Z", "authors": ["Snigdha Tariyal", "Angshul Majumdar", "Richa Singh", "Mayank Vatsa"], "categories": ["cs.LG", "cs.AI", "stat.ML"], "doi": "", "_query": "real estate valuation deep learning", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2002.00815v1", "title": "Learning Extremal Representations with Deep Archetypal Analysis", "abstract": "Archetypes are typical population representatives in an extremal sense, where typicality is understood as the most extreme manifestation of a trait or feature. In linear feature space, archetypes approximate the data convex hull allowing all data points to be expressed as convex mixtures of archetypes. However, it might not always be possible to identify meaningful archetypes in a given feature space. Learning an appropriate feature space and identifying suitable archetypes simultaneously addresses this problem. This paper introduces a generative formulation of the linear archetype model, parameterized by neural networks. By introducing the distance-dependent archetype loss, the linear archetype model can be integrated into the latent space of a variational autoencoder, and an optimal representation with respect to the unknown archetypes can be learned end-to-end. The reformulation of linear Archetypal Analysis as deep variational information bottleneck, allows the incorporation of arbitrarily complex side information during training. Furthermore, an alternative prior, based on a modified Dirichlet distribution, is proposed. The real-world applicability of the proposed method is demonstrated by exploring archetypes of female facial expressions while using multi-rater based emotion scores of these expressions as side information. A second application illustrates the exploration of the chemical space of small organic molecules. In this experiment, it is demonstrated that exchanging the side information but keeping the same set of molecules, e. g. using as side information the heat capacity of each molecule instead of the band gap energy, will result in the identification of different archetypes. As an application, these learned representations of chemical space might reveal distinct starting points for de novo molecular design.", "published": "2020-02-03T15:13:49Z", "authors": ["Sebastian Mathias Keller", "Maxim Samarin", "Fabricio Arend Torres", "Mario Wieser", "Volker Roth"], "categories": ["cs.CV", "cs.LG"], "doi": "", "_query": "real estate valuation deep learning", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/1707.08551v3", "title": "TensorLayer: A Versatile Library for Efficient Deep Learning Development", "abstract": "Deep learning has enabled major advances in the fields of computer vision, natural language processing, and multimedia among many others. Developing a deep learning system is arduous and complex, as it involves constructing neural network architectures, managing training/trained models, tuning optimization process, preprocessing and organizing data, etc. TensorLayer is a versatile Python library that aims at helping researchers and engineers efficiently develop deep learning systems. It offers rich abstractions for neural networks, model and data management, and parallel workflow mechanism. While boosting efficiency, TensorLayer maintains both performance and scalability. TensorLayer was released in September 2016 on GitHub, and has helped people from academia and industry develop real-world applications of deep learning.", "published": "2017-07-26T17:29:49Z", "authors": ["Hao Dong", "Akara Supratak", "Luo Mai", "Fangde Liu", "Axel Oehmichen", "Simiao Yu", "Yike Guo"], "categories": ["cs.LG", "cs.DC", "stat.ML"], "doi": "10.1145/3123266.3129391", "_query": "real estate valuation deep learning", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2411.00230v3", "title": "Reinforcement learning with learned gadgets to tackle hard quantum problems on real hardware", "abstract": "Quantum computing offers exciting opportunities for simulating complex quantum systems and optimizing large scale combinatorial problems, but its practical use is limited by device noise and constrained connectivity. Designing quantum circuits, which are fundamental to quantum algorithms, is therefore a central challenge in current quantum hardware. Existing reinforcement learning based methods for circuit design lose accuracy when restricted to hardware native gates and device level compilation. Here, we introduce gadget reinforcement learning (GRL), which combines learning with program synthesis to automatically construct composite gates that expand the action space while respecting hardware constraints. We show that this approach improves accuracy, hardware compatibility, and scalability for transverse-field Ising and quantum chemistry problems, reaching systems of up to ten qubits within realistic computational budgets. This framework demonstrates how learned, reusable circuit building blocks can guide the co-design of algorithms and hardware for quantum processors.", "published": "2024-10-31T22:02:32Z", "authors": ["Akash Kundu", "Leopoldo Sarra"], "categories": ["quant-ph", "cs.AI", "cs.LG"], "doi": "10.1038/s42005-025-02475-6", "_query": "real estate valuation deep learning", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2008.03937v1", "title": "Feature Ranking for Semi-supervised Learning", "abstract": "The data made available for analysis are becoming more and more complex along several directions: high dimensionality, number of examples and the amount of labels per example. This poses a variety of challenges for the existing machine learning methods: coping with dataset with a large number of examples that are described in a high-dimensional space and not all examples have labels provided. For example, when investigating the toxicity of chemical compounds there are a lot of compounds available, that can be described with information rich high-dimensional representations, but not all of the compounds have information on their toxicity. To address these challenges, we propose semi-supervised learning of feature ranking. The feature rankings are learned in the context of classification and regression as well as in the context of structured output prediction (multi-label classification, hierarchical multi-label classification and multi-target regression). To the best of our knowledge, this is the first work that treats the task of feature ranking within the semi-supervised structured output prediction context. More specifically, we propose two approaches that are based on tree ensembles and the Relief family of algorithms. The extensive evaluation across 38 benchmark datasets reveals the following: Random Forests perform the best for the classification-like tasks, while for the regression-like tasks Extra-PCTs perform the best, Random Forests are the most efficient method considering induction times across all tasks, and semi-supervised feature rankings outperform their supervised counterpart across a majority of the datasets from the different tasks.", "published": "2020-08-10T07:50:50Z", "authors": ["Matej Petković", "Sašo Džeroski", "Dragi Kocev"], "categories": ["cs.LG", "stat.ML"], "doi": "", "_query": "real estate valuation deep learning", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/1812.07768v1", "title": "Modular meta-learning in abstract graph networks for combinatorial generalization", "abstract": "Modular meta-learning is a new framework that generalizes to unseen datasets by combining a small set of neural modules in different ways. In this work we propose abstract graph networks: using graphs as abstractions of a system's subparts without a fixed assignment of nodes to system subparts, for which we would need supervision. We combine this idea with modular meta-learning to get a flexible framework with combinatorial generalization to new tasks built in. We then use it to model the pushing of arbitrarily shaped objects from little or no training data.", "published": "2018-12-19T06:07:51Z", "authors": ["Ferran Alet", "Maria Bauza", "Alberto Rodriguez", "Tomas Lozano-Perez", "Leslie P. Kaelbling"], "categories": ["cs.LG", "stat.ML"], "doi": "", "_query": "real estate valuation deep learning", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/1908.10206v1", "title": "The many faces of deep learning", "abstract": "Deep learning has sparked a network of mutual interactions between different disciplines and AI. Naturally, each discipline focuses and interprets the workings of deep learning in different ways. This diversity of perspectives on deep learning, from neuroscience to statistical physics, is a rich source of inspiration that fuels novel developments in the theory and applications of machine learning. In this perspective, we collect and synthesize different intuitions scattered across several communities as for how deep learning works. In particular, we will briefly discuss the different perspectives that disciplines across mathematics, physics, computation, and neuroscience take on how deep learning does its tricks. Our discussion on each perspective is necessarily shallow due to the multiple views that had to be covered. The deepness in this case should come from putting all these faces of deep learning together in the reader's mind, so that one can look at the same problem from different angles.", "published": "2019-08-25T12:04:49Z", "authors": ["Raul Vicente"], "categories": ["cs.LG", "physics.data-an", "q-bio.NC", "stat.ML"], "doi": "", "_query": "real estate valuation deep learning", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2002.08095v2", "title": "Logarithmic Regret for Learning Linear Quadratic Regulators Efficiently", "abstract": "We consider the problem of learning in Linear Quadratic Control systems whose transition parameters are initially unknown. Recent results in this setting have demonstrated efficient learning algorithms with regret growing with the square root of the number of decision steps. We present new efficient algorithms that achieve, perhaps surprisingly, regret that scales only (poly)logarithmically with the number of steps in two scenarios: when only the state transition matrix $A$ is unknown, and when only the state-action transition matrix $B$ is unknown and the optimal policy satisfies a certain non-degeneracy condition. On the other hand, we give a lower bound that shows that when the latter condition is violated, square root regret is unavoidable.", "published": "2020-02-19T10:09:26Z", "authors": ["Asaf Cassel", "Alon Cohen", "Tomer Koren"], "categories": ["cs.LG", "stat.ML"], "doi": "", "_query": "real estate valuation deep learning", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2401.07639v1", "title": "Compute-Efficient Active Learning", "abstract": "Active learning, a powerful paradigm in machine learning, aims at reducing labeling costs by selecting the most informative samples from an unlabeled dataset. However, the traditional active learning process often demands extensive computational resources, hindering scalability and efficiency. In this paper, we address this critical issue by presenting a novel method designed to alleviate the computational burden associated with active learning on massive datasets. To achieve this goal, we introduce a simple, yet effective method-agnostic framework that outlines how to strategically choose and annotate data points, optimizing the process for efficiency while maintaining model performance. Through case studies, we demonstrate the effectiveness of our proposed method in reducing computational costs while maintaining or, in some cases, even surpassing baseline model outcomes. Code is available at https://github.com/aimotive/Compute-Efficient-Active-Learning.", "published": "2024-01-15T12:32:07Z", "authors": ["Gábor Németh", "Tamás Matuszka"], "categories": ["cs.LG"], "doi": "", "_query": "real estate valuation deep learning", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2309.14483v1", "title": "Unveiling the Potential of Deep Learning Models for Solar Flare Prediction in Near-Limb Regions", "abstract": "This study aims to evaluate the performance of deep learning models in predicting $\\geq$M-class solar flares with a prediction window of 24 hours, using hourly sampled full-disk line-of-sight (LoS) magnetogram images, particularly focusing on the often overlooked flare events corresponding to the near-limb regions (beyond $\\pm$70$^{\\circ}$ of the solar disk). We trained three well-known deep learning architectures--AlexNet, VGG16, and ResNet34 using transfer learning and compared and evaluated the overall performance of our models using true skill statistics (TSS) and Heidke skill score (HSS) and computed recall scores to understand the prediction sensitivity in central and near-limb regions for both X- and M-class flares. The following points summarize the key findings of our study: (1) The highest overall performance was observed with the AlexNet-based model, which achieved an average TSS$\\sim$0.53 and HSS$\\sim$0.37; (2) Further, a spatial analysis of recall scores disclosed that for the near-limb events, the VGG16- and ResNet34-based models exhibited superior prediction sensitivity. The best results, however, were seen with the ResNet34-based model for the near-limb flares, where the average recall was approximately 0.59 (the recall for X- and M-class was 0.81 and 0.56 respectively) and (3) Our research findings demonstrate that our models are capable of discerning complex spatial patterns from full-disk magnetograms and exhibit skill in predicting solar flares, even in the vicinity of near-limb regions. This ability holds substantial importance for operational flare forecasting systems.", "published": "2023-09-25T19:30:02Z", "authors": ["Chetraj Pandey", "Rafal A. Angryk", "Berkay Aydin"], "categories": ["astro-ph.SR", "astro-ph.IM", "cs.CV", "cs.LG"], "doi": "", "_query": "real estate valuation deep learning", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2107.04205v3", "title": "On the Variance of the Fisher Information for Deep Learning", "abstract": "In the realm of deep learning, the Fisher information matrix (FIM) gives novel insights and useful tools to characterize the loss landscape, perform second-order optimization, and build geometric learning theories. The exact FIM is either unavailable in closed form or too expensive to compute. In practice, it is almost always estimated based on empirical samples. We investigate two such estimators based on two equivalent representations of the FIM -- both unbiased and consistent. Their estimation quality is naturally gauged by their variance given in closed form. We analyze how the parametric structure of a deep neural network can affect the variance. The meaning of this variance measure and its upper bounds are then discussed in the context of deep learning.", "published": "2021-07-09T04:46:50Z", "authors": ["Alexander Soen", "Ke Sun"], "categories": ["cs.LG", "stat.ML"], "doi": "", "_query": "real estate valuation deep learning", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2406.06507v2", "title": "Verification-Guided Shielding for Deep Reinforcement Learning", "abstract": "In recent years, Deep Reinforcement Learning (DRL) has emerged as an effective approach to solving real-world tasks. However, despite their successes, DRL-based policies suffer from poor reliability, which limits their deployment in safety-critical domains. Various methods have been put forth to address this issue by providing formal safety guarantees. Two main approaches include shielding and verification. While shielding ensures the safe behavior of the policy by employing an external online component (i.e., a ``shield'') that overrides potentially dangerous actions, this approach has a significant computational cost as the shield must be invoked at runtime to validate every decision. On the other hand, verification is an offline process that can identify policies that are unsafe, prior to their deployment, yet, without providing alternative actions when such a policy is deemed unsafe. In this work, we present verification-guided shielding -- a novel approach that bridges the DRL reliability gap by integrating these two methods. Our approach combines both formal and probabilistic verification tools to partition the input domain into safe and unsafe regions. In addition, we employ clustering and symbolic representation procedures that compress the unsafe regions into a compact representation. This, in turn, allows to temporarily activate the shield solely in (potentially) unsafe regions, in an efficient manner. Our novel approach allows to significantly reduce runtime overhead while still preserving formal safety guarantees. We extensively evaluate our approach on two benchmarks from the robotic navigation domain, as well as provide an in-depth analysis of its scalability and completeness.", "published": "2024-06-10T17:44:59Z", "authors": ["Davide Corsi", "Guy Amir", "Andoni Rodriguez", "Cesar Sanchez", "Guy Katz", "Roy Fox"], "categories": ["cs.LG"], "doi": "", "_query": "real estate valuation deep learning", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/1812.10426v3", "title": "Stochastic Trust Region Inexact Newton Method for Large-scale Machine Learning", "abstract": "Nowadays stochastic approximation methods are one of the major research direction to deal with the large-scale machine learning problems. From stochastic first order methods, now the focus is shifting to stochastic second order methods due to their faster convergence and availability of computing resources. In this paper, we have proposed a novel Stochastic Trust RegiOn Inexact Newton method, called as STRON, to solve large-scale learning problems which uses conjugate gradient (CG) to inexactly solve trust region subproblem. The method uses progressive subsampling in the calculation of gradient and Hessian values to take the advantage of both, stochastic and full-batch regimes. We have extended STRON using existing variance reduction techniques to deal with the noisy gradients and using preconditioned conjugate gradient (PCG) as subproblem solver, and empirically proved that they do not work as expected, for the large-scale learning problems. Finally, our empirical results prove efficacy of the proposed method against existing methods with bench marked datasets.", "published": "2018-12-26T17:33:43Z", "authors": ["Vinod Kumar Chauhan", "Anuj Sharma", "Kalpana Dahiya"], "categories": ["cs.LG", "stat.ML"], "doi": "10.1007/s13042-019-01055-9", "_query": "real estate valuation deep learning", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/1907.00397v3", "title": "Variational Quantum Circuits for Deep Reinforcement Learning", "abstract": "The state-of-the-art machine learning approaches are based on classical von Neumann computing architectures and have been widely used in many industrial and academic domains. With the recent development of quantum computing, researchers and tech-giants have attempted new quantum circuits for machine learning tasks. However, the existing quantum computing platforms are hard to simulate classical deep learning models or problems because of the intractability of deep quantum circuits. Thus, it is necessary to design feasible quantum algorithms for quantum machine learning for noisy intermediate scale quantum (NISQ) devices. This work explores variational quantum circuits for deep reinforcement learning. Specifically, we reshape classical deep reinforcement learning algorithms like experience replay and target network into a representation of variational quantum circuits. Moreover, we use a quantum information encoding scheme to reduce the number of model parameters compared to classical neural networks. To the best of our knowledge, this work is the first proof-of-principle demonstration of variational quantum circuits to approximate the deep $Q$-value function for decision-making and policy-selection reinforcement learning with experience replay and target network. Besides, our variational quantum circuits can be deployed in many near-term NISQ machines.", "published": "2019-06-30T15:35:07Z", "authors": ["Samuel Yen-Chi Chen", "Chao-Han Huck Yang", "Jun Qi", "Pin-Yu Chen", "Xiaoli Ma", "Hsi-Sheng Goan"], "categories": ["cs.LG", "cs.AI", "quant-ph", "stat.ML"], "doi": "", "_query": "real estate valuation deep learning", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2105.06868v3", "title": "Priors in Bayesian Deep Learning: A Review", "abstract": "While the choice of prior is one of the most critical parts of the Bayesian inference workflow, recent Bayesian deep learning models have often fallen back on vague priors, such as standard Gaussians. In this review, we highlight the importance of prior choices for Bayesian deep learning and present an overview of different priors that have been proposed for (deep) Gaussian processes, variational autoencoders, and Bayesian neural networks. We also outline different methods of learning priors for these models from data. We hope to motivate practitioners in Bayesian deep learning to think more carefully about the prior specification for their models and to provide them with some inspiration in this regard.", "published": "2021-05-14T14:53:30Z", "authors": ["Vincent Fortuin"], "categories": ["stat.ML", "cs.LG"], "doi": "", "_query": "real estate valuation deep learning", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2106.03775v1", "title": "Explainable Artificial Intelligence (XAI) for Increasing User Trust in Deep Reinforcement Learning Driven Autonomous Systems", "abstract": "We consider the problem of providing users of deep Reinforcement Learning (RL) based systems with a better understanding of when their output can be trusted. We offer an explainable artificial intelligence (XAI) framework that provides a three-fold explanation: a graphical depiction of the systems generalization and performance in the current game state, how well the agent would play in semantically similar environments, and a narrative explanation of what the graphical information implies. We created a user-interface for our XAI framework and evaluated its efficacy via a human-user experiment. The results demonstrate a statistically significant increase in user trust and acceptance of the AI system with explanation, versus the AI system without explanation.", "published": "2021-06-07T16:38:43Z", "authors": ["Jeff Druce", "Michael Harradon", "James Tittle"], "categories": ["cs.AI", "cs.LG"], "doi": "", "_query": "real estate valuation deep learning", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2310.06251v1", "title": "Deep Learning: A Tutorial", "abstract": "Our goal is to provide a review of deep learning methods which provide insight into structured high-dimensional data. Rather than using shallow additive architectures common to most statistical models, deep learning uses layers of semi-affine input transformations to provide a predictive rule. Applying these layers of transformations leads to a set of attributes (or, features) to which probabilistic statistical methods can be applied. Thus, the best of both worlds can be achieved: scalable prediction rules fortified with uncertainty quantification, where sparse regularization finds the features.", "published": "2023-10-10T01:55:22Z", "authors": ["Nick Polson", "Vadim Sokolov"], "categories": ["stat.ML", "cs.LG"], "doi": "", "_query": "real estate valuation deep learning", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2505.18526v2", "title": "Scalable Deep Basis Kernel Gaussian Processes", "abstract": "Learning expressive kernels while retaining tractable inference remains a central challenge in scaling Gaussian processes (GPs) to large and complex datasets. We propose a scalable GP regressor based on deep basis kernels (DBKs). Our DBK is constructed from a small set of neural-network-parameterized basis functions with an explicit low-rank structure. This formulation immediately enables linear-complexity inference with respect to the number of samples, possibly without inducing points. DBKs provide a unifying perspective that recovers sparse deep kernel learning and Gaussian Bayesian last-layer methods as special cases. We further identify that naively maximizing the marginal likelihood can lead to oversimplified uncertainty and rank-deficient solutions. To address this, we introduce a mini-batch stochastic objective that directly targets the predictive distribution with decoupled regularization. Empirically, DBKs show advantages in predictive accuracy, uncertainty quantification, and computational efficiency across a range of large-scale regression benchmarks.", "published": "2025-05-24T05:42:11Z", "authors": ["Yunqin Zhu", "Henry Shaowu Yuchi", "Yao Xie"], "categories": ["stat.ML", "cs.LG"], "doi": "", "_query": "real estate valuation deep learning", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/1602.08194v2", "title": "Scalable and Sustainable Deep Learning via Randomized Hashing", "abstract": "Current deep learning architectures are growing larger in order to learn from complex datasets. These architectures require giant matrix multiplication operations to train millions of parameters. Conversely, there is another growing trend to bring deep learning to low-power, embedded devices. The matrix operations, associated with both training and testing of deep networks, are very expensive from a computational and energy standpoint. We present a novel hashing based technique to drastically reduce the amount of computation needed to train and test deep networks. Our approach combines recent ideas from adaptive dropouts and randomized hashing for maximum inner product search to select the nodes with the highest activation efficiently. Our new algorithm for deep learning reduces the overall computational cost of forward and back-propagation by operating on significantly fewer (sparse) nodes. As a consequence, our algorithm uses only 5% of the total multiplications, while keeping on average within 1% of the accuracy of the original model. A unique property of the proposed hashing based back-propagation is that the updates are always sparse. Due to the sparse gradient updates, our algorithm is ideally suited for asynchronous and parallel training leading to near linear speedup with increasing number of cores. We demonstrate the scalability and sustainability (energy efficiency) of our proposed algorithm via rigorous experimental evaluations on several real datasets.", "published": "2016-02-26T05:07:23Z", "authors": ["Ryan Spring", "Anshumali Shrivastava"], "categories": ["stat.ML", "cs.LG", "cs.NE"], "doi": "", "_query": "real estate valuation deep learning", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2304.10701v9", "title": "GMValuator: Similarity-based Data Valuation for Generative Models", "abstract": "Data valuation plays a crucial role in machine learning. Existing data valuation methods, mainly focused on discriminative models, overlook generative models that have gained attention recently. In generative models, data valuation measures the impact of training data on generated datasets. Very few existing attempts at data valuation methods designed for deep generative models either concentrate on specific models or lack robustness in their outcomes. Moreover, efficiency still reveals vulnerable shortcomings. We formulate the data valuation problem in generative models from a similarity matching perspective to bridge the gaps. Specifically, we introduce Generative Model Valuator (GMValuator), the first training-free and model-agnostic approach to providing data valuation for image generation tasks. It empowers efficient data valuation through our innovative similarity matching module, calibrates biased contributions by incorporating image quality assessment, and attributes credits to all training samples based on their contributions to the generated samples. Additionally, we introduce four evaluation criteria for assessing data valuation methods in generative models. GMValuator is extensively evaluated on benchmark and high-resolution datasets and various mainstream generative architectures to demonstrate its effectiveness.", "published": "2023-04-21T02:02:02Z", "authors": ["Jiaxi Yang", "Wenglong Deng", "Benlin Liu", "Yangsibo Huang", "James Zou", "Xiaoxiao Li"], "categories": ["cs.CV", "cs.LG"], "doi": "", "_query": "real estate valuation deep learning", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2209.05544v4", "title": "The Mori-Zwanzig formulation of deep learning", "abstract": "We develop a new formulation of deep learning based on the Mori-Zwanzig (MZ) formalism of irreversible statistical mechanics. The new formulation is built upon the well-known duality between deep neural networks and discrete dynamical systems, and it allows us to directly propagate quantities of interest (conditional expectations and probability density functions) forward and backward through the network by means of exact linear operator equations. Such new equations can be used as a starting point to develop new effective parameterizations of deep neural networks, and provide a new framework to study deep-learning via operator theoretic methods. The proposed MZ formulation of deep learning naturally introduces a new concept, i.e., the memory of the neural network, which plays a fundamental role in low-dimensional modeling and parameterization. By using the theory of contraction mappings, we develop sufficient conditions for the memory of the neural network to decay with the number of layers. This allows us to rigorously transform deep networks into shallow ones, e.g., by reducing the number of neurons per layer (using projection operators), or by reducing the total number of layers (using the decay property of the memory operator).", "published": "2022-09-12T18:44:50Z", "authors": ["Daniele Venturi", "Xiantao Li"], "categories": ["cs.LG", "cond-mat.stat-mech", "stat.ML"], "doi": "", "_query": "real estate valuation deep learning", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2002.12462v2", "title": "LEEP: A New Measure to Evaluate Transferability of Learned Representations", "abstract": "We introduce a new measure to evaluate the transferability of representations learned by classifiers. Our measure, the Log Expected Empirical Prediction (LEEP), is simple and easy to compute: when given a classifier trained on a source data set, it only requires running the target data set through this classifier once. We analyze the properties of LEEP theoretically and demonstrate its effectiveness empirically. Our analysis shows that LEEP can predict the performance and convergence speed of both transfer and meta-transfer learning methods, even for small or imbalanced data. Moreover, LEEP outperforms recently proposed transferability measures such as negative conditional entropy and H scores. Notably, when transferring from ImageNet to CIFAR100, LEEP can achieve up to 30% improvement compared to the best competing method in terms of the correlations with actual transfer accuracy.", "published": "2020-02-27T22:02:20Z", "authors": ["Cuong V. Nguyen", "Tal Hassner", "Matthias Seeger", "Cedric Archambeau"], "categories": ["cs.LG", "cs.CV", "stat.ML"], "doi": "", "_query": "real estate valuation deep learning", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2502.18558v2", "title": "Transfer Learning for Transient Classification: From Simulations to Real Data and ZTF to LSST", "abstract": "Machine learning has become essential for automated classification of astronomical transients, but current approaches face significant limitations: classifiers trained on simulations struggle with real data, models developed for one survey cannot be easily applied to another, and new surveys require prohibitively large amounts of labelled training data. These challenges are particularly pressing as we approach the era of the Vera C. Rubin Observatory's Legacy Survey of Space and Time (LSST), where existing classification models will need to be retrained using LSST observations. We demonstrate that transfer learning can overcome these challenges by repurposing existing models trained on either simulations or data from other surveys. Starting with a model trained on simulated Zwicky Transient Facility (ZTF) light curves, we show that transfer learning reduces the amount of labelled real ZTF transients needed by 95% while maintaining equivalent performance to models trained from scratch. Similarly, when adapting ZTF models for LSST simulations, transfer learning achieves 94% of the baseline performance while requiring only 30% of the training data. These findings have significant implications for the early operations of LSST, suggesting that reliable automated classification will be possible soon after the survey begins, rather than waiting months or years to accumulate sufficient training data.", "published": "2025-02-25T19:00:00Z", "authors": ["Rithwik Gupta", "Daniel Muthukrishna", "Nabeel Rehemtulla", "Ved Shah"], "categories": ["astro-ph.IM", "astro-ph.HE", "cs.LG"], "doi": "10.1093/mnrasl/slaf074", "_query": "real estate valuation deep learning", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/1906.04304v1", "title": "Meta-Learning Neural Bloom Filters", "abstract": "There has been a recent trend in training neural networks to replace data structures that have been crafted by hand, with an aim for faster execution, better accuracy, or greater compression. In this setting, a neural data structure is instantiated by training a network over many epochs of its inputs until convergence. In applications where inputs arrive at high throughput, or are ephemeral, training a network from scratch is not practical. This motivates the need for few-shot neural data structures. In this paper we explore the learning of approximate set membership over a set of data in one-shot via meta-learning. We propose a novel memory architecture, the Neural Bloom Filter, which is able to achieve significant compression gains over classical Bloom Filters and existing memory-augmented neural networks.", "published": "2019-06-10T22:23:14Z", "authors": ["Jack W Rae", "Sergey Bartunov", "Timothy P Lillicrap"], "categories": ["cs.LG", "cs.DB", "cs.DS", "stat.ML"], "doi": "", "_query": "real estate valuation deep learning", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/1206.6458v1", "title": "Batch Active Learning via Coordinated Matching", "abstract": "Most prior work on active learning of classifiers has focused on sequentially selecting one unlabeled example at a time to be labeled in order to reduce the overall labeling effort. In many scenarios, however, it is desirable to label an entire batch of examples at once, for example, when labels can be acquired in parallel. This motivates us to study batch active learning, which iteratively selects batches of $k>1$ examples to be labeled. We propose a novel batch active learning method that leverages the availability of high-quality and efficient sequential active-learning policies by attempting to approximate their behavior when applied for $k$ steps. Specifically, our algorithm first uses Monte-Carlo simulation to estimate the distribution of unlabeled examples selected by a sequential policy over $k$ step executions. The algorithm then attempts to select a set of $k$ examples that best matches this distribution, leading to a combinatorial optimization problem that we term \"bounded coordinated matching\". While we show this problem is NP-hard in general, we give an efficient greedy solution, which inherits approximation bounds from supermodular minimization theory. Our experimental results on eight benchmark datasets show that the proposed approach is highly effective", "published": "2012-06-27T19:59:59Z", "authors": ["Javad Azimi", "Alan Fern", "Xiaoli Zhang-Fern", "Glencora Borradaile", "Brent Heeringa"], "categories": ["cs.LG", "stat.ML"], "doi": "", "_query": "real estate valuation deep learning", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2003.02455v3", "title": "PAC-Bayes meta-learning with implicit task-specific posteriors", "abstract": "We introduce a new and rigorously-formulated PAC-Bayes meta-learning algorithm that solves few-shot learning. Our proposed method extends the PAC-Bayes framework from a single task setting to the meta-learning multiple task setting to upper-bound the error evaluated on any, even unseen, tasks and samples. We also propose a generative-based approach to estimate the posterior of task-specific model parameters more expressively compared to the usual assumption based on a multivariate normal distribution with a diagonal covariance matrix. We show that the models trained with our proposed meta-learning algorithm are well calibrated and accurate, with state-of-the-art calibration and classification results on few-shot classification (mini-ImageNet and tiered-ImageNet) and regression (multi-modal task-distribution regression) benchmarks.", "published": "2020-03-05T06:56:19Z", "authors": ["Cuong Nguyen", "Thanh-Toan Do", "Gustavo Carneiro"], "categories": ["cs.LG", "stat.ML"], "doi": "", "_query": "real estate valuation deep learning", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/1809.08092v2", "title": "Power Market Price Forecasting via Deep Learning", "abstract": "A study on power market price forecasting by deep learning is presented. As one of the most successful deep learning frameworks, the LSTM (Long short-term memory) neural network is utilized. The hourly prices data from the New England and PJM day-ahead markets are used in this study. First, a LSTM network is formulated and trained. Then the raw input and output data are preprocessed by unit scaling, and the trained network is tested on the real price data under different input lengths, forecasting horizons and data sizes. Its performance is also compared with other existing methods. The forecasted results demonstrate that, the LSTM deep neural network can outperform the others under different application settings in this problem.", "published": "2018-09-18T19:00:18Z", "authors": ["Yongli Zhu", "Songtao Lu", "Renchang Dai", "Guangyi Liu", "Zhiwei Wang"], "categories": ["cs.LG", "stat.ML"], "doi": "", "_query": "real estate valuation deep learning", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2008.12571v1", "title": "Hierarchical Deep Learning Ensemble to Automate the Classification of Breast Cancer Pathology Reports by ICD-O Topography", "abstract": "Like most global cancer registries, the National Cancer Registry in South Africa employs expert human coders to label pathology reports using appropriate International Classification of Disease for Oncology (ICD-O) codes spanning 42 different cancer types. The annotation is extensive for the large volume of cancer pathology reports the registry receives annually from public and private sector institutions. This manual process, coupled with other challenges results in a significant 4-year lag in reporting of annual cancer statistics in South Africa. We present a hierarchical deep learning ensemble method incorporating state of the art convolutional neural network models for the automatic labelling of 2201 de-identified, free text pathology reports, with appropriate ICD-O breast cancer topography codes across 8 classes. Our results show an improvement in primary site classification over the state of the art CNN model by greater than 14% for F1 micro and 55% for F1 macro scores. We demonstrate that the hierarchical deep learning ensemble improves on state-of-the-art models for ICD-O topography classification in comparison to a flat multiclass model for predicting ICD-O topography codes for pathology reports.", "published": "2020-08-28T10:29:56Z", "authors": ["Waheeda Saib", "David Sengeh", "Gcininwe Dlamini", "Elvira Singh"], "categories": ["cs.LG", "cs.CV", "eess.IV"], "doi": "", "_query": "real estate valuation deep learning", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2010.04816v1", "title": "Characterizing Policy Divergence for Personalized Meta-Reinforcement Learning", "abstract": "Despite ample motivation from costly exploration and limited trajectory data, rapidly adapting to new environments with few-shot reinforcement learning (RL) can remain a challenging task, especially with respect to personalized settings. Here, we consider the problem of recommending optimal policies to a set of multiple entities each with potentially different characteristics, such that individual entities may parameterize distinct environments with unique transition dynamics. Inspired by existing literature in meta-learning, we extend previous work by focusing on the notion that certain environments are more similar to each other than others in personalized settings, and propose a model-free meta-learning algorithm that prioritizes past experiences by relevance during gradient-based adaptation. Our algorithm involves characterizing past policy divergence through methods in inverse reinforcement learning, and we illustrate how such metrics are able to effectively distinguish past policy parameters by the environment they were deployed in, leading to more effective fast adaptation during test time. To study personalization more effectively we introduce a navigation testbed to specifically incorporate environment diversity across training episodes, and demonstrate that our approach outperforms meta-learning alternatives with respect to few-shot reinforcement learning in personalized settings.", "published": "2020-10-09T21:31:53Z", "authors": ["Michael Zhang"], "categories": ["cs.LG", "cs.AI"], "doi": "", "_query": "real estate valuation deep learning", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/1812.09582v5", "title": "Online learning with stability guarantees: A memory-based real-time model predictive controller", "abstract": "We propose and analyze a real-time model predictive control (MPC) scheme that utilizes stored data to improve its performance by learning the value function online with stability guarantees. For linear and nonlinear systems, a learning method is presented that makes use of basic analytic properties of the cost function and is proven to learn the MPC control law and the value function on the limit set of the closed-loop state trajectory. The main idea is to generate a smart warm start based on historical data that improves future data points and thus future warm starts. We show that these warm starts are asymptotically exact and converge to the solution of the MPC optimization problem. Thereby, the suboptimality of the applied control input resulting from the real-time requirements vanishes over time. Simulative examples show that existing real-time MPC schemes can be improved by storing data and the proposed learning scheme.", "published": "2018-12-22T18:58:46Z", "authors": ["Lukas Schwenkel", "Meriem Gharbi", "Sebastian Trimpe", "Christian Ebenbauer"], "categories": ["math.OC"], "doi": "10.1016/j.automatica.2020.109247", "_query": "real estate valuation deep learning", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2510.03247v2", "title": "Towards Multimodal Active Learning: Efficient Learning with Limited Paired Data", "abstract": "Active learning (AL) is a principled strategy to reduce annotation cost in data-hungry deep learning. However, existing AL algorithms focus almost exclusively on unimodal data, overlooking the substantial annotation burden in multimodal learning. We introduce the first framework for multimodal active learning with unaligned data, where the learner must actively acquire cross-modal alignments rather than labels on pre-aligned pairs. This setting captures the practical bottleneck in modern multimodal pipelines, where unimodal features are easy to obtain but high-quality alignment is costly. We develop a new algorithm that combines uncertainty and diversity principles in a modality-aware design, achieves linear-time acquisition, and applies seamlessly to both pool-based and streaming-based settings. Extensive experiments on benchmark datasets demonstrate that our approach consistently reduces multimodal annotation cost while preserving performance; for instance, on the ColorSwap dataset it cuts annotation requirements by up to 40% without loss in accuracy.", "published": "2025-09-25T23:08:03Z", "authors": ["Jiancheng Zhang", "Yinglun Zhu"], "categories": ["cs.LG", "cs.AI"], "doi": "", "_query": "real estate valuation deep learning", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/1811.07216v2", "title": "Machine Learning for Health (ML4H) Workshop at NeurIPS 2018", "abstract": "This volume represents the accepted submissions from the Machine Learning for Health (ML4H) workshop at the conference on Neural Information Processing Systems (NeurIPS) 2018, held on December 8, 2018 in Montreal, Canada.", "published": "2018-11-17T20:14:43Z", "authors": ["Natalia Antropova", "Andrew L. Beam", "Brett K. Beaulieu-Jones", "Irene Chen", "Corey Chivers", "Adrian Dalca", "Sam Finlayson", "Madalina Fiterau", "Jason Alan Fries", "Marzyeh Ghassemi", "Mike Hughes", "Bruno Jedynak", "Jasvinder S. Kandola", "Matthew McDermott", "Tristan Naumann", "Peter Schulam", "Farah Shamout", "Alexandre Yahi"], "categories": ["cs.LG", "stat.ML"], "doi": "", "_query": "real estate valuation deep learning", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/1904.05526v2", "title": "A Selective Overview of Deep Learning", "abstract": "Deep learning has arguably achieved tremendous success in recent years. In simple words, deep learning uses the composition of many nonlinear functions to model the complex dependency between input features and labels. While neural networks have a long history, recent advances have greatly improved their performance in computer vision, natural language processing, etc. From the statistical and scientific perspective, it is natural to ask: What is deep learning? What are the new characteristics of deep learning, compared with classical methods? What are the theoretical foundations of deep learning? To answer these questions, we introduce common neural network models (e.g., convolutional neural nets, recurrent neural nets, generative adversarial nets) and training techniques (e.g., stochastic gradient descent, dropout, batch normalization) from a statistical point of view. Along the way, we highlight new characteristics of deep learning (including depth and over-parametrization) and explain their practical and theoretical benefits. We also sample recent results on theories of deep learning, many of which are only suggestive. While a complete understanding of deep learning remains elusive, we hope that our perspectives and discussions serve as a stimulus for new statistical research.", "published": "2019-04-10T17:53:15Z", "authors": ["Jianqing Fan", "Cong Ma", "Yiqiao Zhong"], "categories": ["stat.ML", "cs.LG", "math.ST", "stat.ME"], "doi": "", "_query": "real estate valuation deep learning", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2002.04258v3", "title": "Learning to Switch Among Agents in a Team via 2-Layer Markov Decision Processes", "abstract": "Reinforcement learning agents have been mostly developed and evaluated under the assumption that they will operate in a fully autonomous manner -- they will take all actions. In this work, our goal is to develop algorithms that, by learning to switch control between agents, allow existing reinforcement learning agents to operate under different automation levels. To this end, we first formally define the problem of learning to switch control among agents in a team via a 2-layer Markov decision process. Then, we develop an online learning algorithm that uses upper confidence bounds on the agents' policies and the environment's transition probabilities to find a sequence of switching policies. The total regret of our algorithm with respect to the optimal switching policy is sublinear in the number of learning steps and, whenever multiple teams of agents operate in a similar environment, our algorithm greatly benefits from maintaining shared confidence bounds for the environments' transition probabilities and it enjoys a better regret bound than problem-agnostic algorithms. Simulation experiments in an obstacle avoidance task illustrate our theoretical findings and demonstrate that, by exploiting the specific structure of the problem, our proposed algorithm is superior to problem-agnostic algorithms.", "published": "2020-02-11T08:50:52Z", "authors": ["Vahid Balazadeh", "Abir De", "Adish Singla", "Manuel Gomez-Rodriguez"], "categories": ["cs.LG", "cs.CY", "cs.HC", "eess.SY", "stat.ML"], "doi": "", "_query": "real estate valuation deep learning", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/1906.02226v2", "title": "Gradient-Based Neural DAG Learning", "abstract": "We propose a novel score-based approach to learning a directed acyclic graph (DAG) from observational data. We adapt a recently proposed continuous constrained optimization formulation to allow for nonlinear relationships between variables using neural networks. This extension allows to model complex interactions while avoiding the combinatorial nature of the problem. In addition to comparing our method to existing continuous optimization methods, we provide missing empirical comparisons to nonlinear greedy search methods. On both synthetic and real-world data sets, this new method outperforms current continuous methods on most tasks, while being competitive with existing greedy search methods on important metrics for causal inference.", "published": "2019-06-05T18:09:55Z", "authors": ["Sébastien Lachapelle", "Philippe Brouillard", "Tristan Deleu", "Simon Lacoste-Julien"], "categories": ["cs.LG", "stat.ML"], "doi": "", "_query": "real estate valuation deep learning", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2311.01061v1", "title": "Deep Learning for real-time neural decoding of grasp", "abstract": "Neural decoding involves correlating signals acquired from the brain to variables in the physical world like limb movement or robot control in Brain Machine Interfaces. In this context, this work starts from a specific pre-existing dataset of neural recordings from monkey motor cortex and presents a Deep Learning-based approach to the decoding of neural signals for grasp type classification. Specifically, we propose here an approach that exploits LSTM networks to classify time series containing neural data (i.e., spike trains) into classes representing the object being grasped. The main goal of the presented approach is to improve over state-of-the-art decoding accuracy without relying on any prior neuroscience knowledge, and leveraging only the capability of deep learning models to extract correlations from data. The paper presents the results achieved for the considered dataset and compares them with previous works on the same dataset, showing a significant improvement in classification accuracy, even if considering simulated real-time decoding.", "published": "2023-11-02T08:26:29Z", "authors": ["Paolo Viviani", "Ilaria Gesmundo", "Elios Ghinato", "Andres Agudelo-Toro", "Chiara Vercellino", "Giacomo Vitali", "Letizia Bergamasco", "Alberto Scionti", "Marco Ghislieri", "Valentina Agostini", "Olivier Terzo", "Hansjörg Scherberger"], "categories": ["cs.LG", "q-bio.NC"], "doi": "10.1007/978-3-031-43427-3_23", "_query": "real estate valuation deep learning", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2009.08107v3", "title": "Few-Shot Unsupervised Continual Learning through Meta-Examples", "abstract": "In real-world applications, data do not reflect the ones commonly used for neural networks training, since they are usually few, unlabeled and can be available as a stream. Hence many existing deep learning solutions suffer from a limited range of applications, in particular in the case of online streaming data that evolve over time. To narrow this gap, in this work we introduce a novel and complex setting involving unsupervised meta-continual learning with unbalanced tasks. These tasks are built through a clustering procedure applied to a fitted embedding space. We exploit a meta-learning scheme that simultaneously alleviates catastrophic forgetting and favors the generalization to new tasks. Moreover, to encourage feature reuse during the meta-optimization, we exploit a single inner loop taking advantage of an aggregated representation achieved through the use of a self-attention mechanism. Experimental results on few-shot learning benchmarks show competitive performance even compared to the supervised case. Additionally, we empirically observe that in an unsupervised scenario, the small tasks and the variability in the clusters pooling play a crucial role in the generalization capability of the network. Further, on complex datasets, the exploitation of more clusters than the true number of classes leads to higher results, even compared to the ones obtained with full supervision, suggesting that a predefined partitioning into classes can miss relevant structural information.", "published": "2020-09-17T07:02:07Z", "authors": ["Alessia Bertugli", "Stefano Vincenzi", "Simone Calderara", "Andrea Passerini"], "categories": ["cs.LG", "cs.CV", "stat.ML"], "doi": "", "_query": "real estate valuation deep learning", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2304.13178v2", "title": "Robust Non-Linear Feedback Coding via Power-Constrained Deep Learning", "abstract": "The design of codes for feedback-enabled communications has been a long-standing open problem. Recent research on non-linear, deep learning-based coding schemes have demonstrated significant improvements in communication reliability over linear codes, but are still vulnerable to the presence of forward and feedback noise over the channel. In this paper, we develop a new family of non-linear feedback codes that greatly enhance robustness to channel noise. Our autoencoder-based architecture is designed to learn codes based on consecutive blocks of bits, which obtains de-noising advantages over bit-by-bit processing to help overcome the physical separation between the encoder and decoder over a noisy channel. Moreover, we develop a power control layer at the encoder to explicitly incorporate hardware constraints into the learning optimization, and prove that the resulting average power constraint is satisfied asymptotically. Numerical experiments demonstrate that our scheme outperforms state-of-the-art feedback codes by wide margins over practical forward and feedback noise regimes, and provide information-theoretic insights on the behavior of our non-linear codes. Moreover, we observe that, in a long blocklength regime, canonical error correction codes are still preferable to feedback codes when the feedback noise becomes high.", "published": "2023-04-25T22:21:26Z", "authors": ["Junghoon Kim", "Taejoon Kim", "David Love", "Christopher Brinton"], "categories": ["cs.IT", "cs.LG"], "doi": "", "_query": "real estate valuation deep learning", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2604.02756v1", "title": "STDDN: A Physics-Guided Deep Learning Framework for Crowd Simulation", "abstract": "Accurate crowd simulation is crucial for public safety management, emergency evacuation planning, and intelligent transportation systems. However, existing methods, which typically model crowds as a collection of independent individual trajectories, are limited in their ability to capture macroscopic physical laws. This microscopic approach often leads to error accumulation and compromises simulation stability. Furthermore, deep learning-driven methods tend to suffer from low inference efficiency and high computational overhead, making them impractical for large-scale, efficient simulations. To address these challenges, we propose the Spatio-Temporal Decoupled Differential Equation Network (STDDN), a novel framework that guides microscopic trajectory prediction with macroscopic physics. We innovatively introduce the continuity equation from fluid dynamics as a strong physical constraint. A Neural Ordinary Differential Equation (Neural ODE) is employed to model the macroscopic density evolution driven by individual movements, thereby physically regularizing the microscopic trajectory prediction model. We design a density-velocity coupled dynamic graph learning module to formulate the derivative of the density field within the Neural ODE, effectively mitigating error accumulation. We also propose a differentiable density mapping module to eliminate discontinuous gradients caused by discretization and introduce a cross-grid detection module to accurately model the impact of individual cross-grid movements on local density changes. The proposed STDDN method has demonstrated significantly superior simulation performance compared to state-of-the-art methods on long-term tasks across four real-world datasets, as well as a major reduction in inference latency.", "published": "2026-04-03T06:06:04Z", "authors": ["Zijin Liu", "Xu Geng", "Wenshuai Xu", "Xiang Zhao", "Yan Xia", "You Song"], "categories": ["cs.LG"], "doi": "", "_query": "real estate valuation deep learning", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2312.08884v2", "title": "Global Rewards in Multi-Agent Deep Reinforcement Learning for Autonomous Mobility on Demand Systems", "abstract": "We study vehicle dispatching in autonomous mobility on demand (AMoD) systems, where a central operator assigns vehicles to customer requests or rejects these with the aim of maximizing its total profit. Recent approaches use multi-agent deep reinforcement learning (MADRL) to realize scalable yet performant algorithms, but train agents based on local rewards, which distorts the reward signal with respect to the system-wide profit, leading to lower performance. We therefore propose a novel global-rewards-based MADRL algorithm for vehicle dispatching in AMoD systems, which resolves so far existing goal conflicts between the trained agents and the operator by assigning rewards to agents leveraging a counterfactual baseline. Our algorithm shows statistically significant improvements across various settings on real-world data compared to state-of-the-art MADRL algorithms with local rewards. We further provide a structural analysis which shows that the utilization of global rewards can improve implicit vehicle balancing and demand forecasting abilities. Our code is available at https://github.com/tumBAIS/GR-MADRL-AMoD.", "published": "2023-12-14T12:47:33Z", "authors": ["Heiko Hoppe", "Tobias Enders", "Quentin Cappart", "Maximilian Schiffer"], "categories": ["cs.LG", "cs.MA", "eess.SY"], "doi": "", "_query": "real estate valuation deep learning", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/1810.13348v4", "title": "Multimodal Machine Learning for Automated ICD Coding", "abstract": "This study presents a multimodal machine learning model to predict ICD-10 diagnostic codes. We developed separate machine learning models that can handle data from different modalities, including unstructured text, semi-structured text and structured tabular data. We further employed an ensemble method to integrate all modality-specific models to generate ICD-10 codes. Key evidence was also extracted to make our prediction more convincing and explainable. We used the Medical Information Mart for Intensive Care III (MIMIC -III) dataset to validate our approach. For ICD code prediction, our best-performing model (micro-F1 = 0.7633, micro-AUC = 0.9541) significantly outperforms other baseline models including TF-IDF (micro-F1 = 0.6721, micro-AUC = 0.7879) and Text-CNN model (micro-F1 = 0.6569, micro-AUC = 0.9235). For interpretability, our approach achieves a Jaccard Similarity Coefficient (JSC) of 0.1806 on text data and 0.3105 on tabular data, where well-trained physicians achieve 0.2780 and 0.5002 respectively.", "published": "2018-10-31T15:39:32Z", "authors": ["Keyang Xu", "Mike Lam", "Jingzhi Pang", "Xin Gao", "Charlotte Band", "Piyush Mathur", "Frank Papay", "Ashish K. Khanna", "Jacek B. Cywinski", "Kamal Maheshwari", "Pengtao Xie", "Eric Xing"], "categories": ["cs.LG", "stat.ML"], "doi": "", "_query": "real estate valuation deep learning", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2211.12717v1", "title": "Benchmarking Bayesian Deep Learning on Diabetic Retinopathy Detection Tasks", "abstract": "Bayesian deep learning seeks to equip deep neural networks with the ability to precisely quantify their predictive uncertainty, and has promised to make deep learning more reliable for safety-critical real-world applications. Yet, existing Bayesian deep learning methods fall short of this promise; new methods continue to be evaluated on unrealistic test beds that do not reflect the complexities of downstream real-world tasks that would benefit most from reliable uncertainty quantification. We propose the RETINA Benchmark, a set of real-world tasks that accurately reflect such complexities and are designed to assess the reliability of predictive models in safety-critical scenarios. Specifically, we curate two publicly available datasets of high-resolution human retina images exhibiting varying degrees of diabetic retinopathy, a medical condition that can lead to blindness, and use them to design a suite of automated diagnosis tasks that require reliable predictive uncertainty quantification. We use these tasks to benchmark well-established and state-of-the-art Bayesian deep learning methods on task-specific evaluation metrics. We provide an easy-to-use codebase for fast and easy benchmarking following reproducibility and software design principles. We provide implementations of all methods included in the benchmark as well as results computed over 100 TPU days, 20 GPU days, 400 hyperparameter configurations, and evaluation on at least 6 random seeds each.", "published": "2022-11-23T05:44:42Z", "authors": ["Neil Band", "Tim G. J. Rudner", "Qixuan Feng", "Angelos Filos", "Zachary Nado", "Michael W. Dusenberry", "Ghassen Jerfel", "Dustin Tran", "Yarin Gal"], "categories": ["stat.ML", "cs.AI", "cs.CV", "cs.LG"], "doi": "", "_query": "real estate valuation deep learning", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/1904.13195v1", "title": "Test Selection for Deep Learning Systems", "abstract": "Testing of deep learning models is challenging due to the excessive number and complexity of computations involved. As a result, test data selection is performed manually and in an ad hoc way. This raises the question of how we can automatically select candidate test data to test deep learning models. Recent research has focused on adapting test selection metrics from code-based software testing (such as coverage) to deep learning. However, deep learning models have different attributes from code such as spread of computations across the entire network reflecting training data properties, balance of neuron weights and redundancy (use of many more neurons than needed). Such differences make code-based metrics inappropriate to select data that can challenge the models (can trigger misclassification). We thus propose a set of test selection metrics based on the notion of model uncertainty (model confidence on specific inputs). Intuitively, the more uncertain we are about a candidate sample, the more likely it is that this sample triggers a misclassification. Similarly, the samples for which we are the most uncertain, are the most informative and should be used to improve the model by retraining. We evaluate these metrics on two widely-used image classification problems involving real and artificial (adversarial) data. We show that uncertainty-based metrics have a strong ability to select data that are misclassified and lead to major improvement in classification accuracy during retraining: up to 80% more gain than random selection and other state-of-the-art metrics on one dataset and up to 29% on the other.", "published": "2019-04-30T12:44:10Z", "authors": ["Wei Ma", "Mike Papadakis", "Anestis Tsakmalis", "Maxime Cordy", "Yves Le Traon"], "categories": ["cs.LG", "cs.SE", "stat.ML"], "doi": "", "_query": "real estate valuation deep learning", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/1812.00490v1", "title": "Improving Clinical Predictions through Unsupervised Time Series Representation Learning", "abstract": "In this work, we investigate unsupervised representation learning on medical time series, which bears the promise of leveraging copious amounts of existing unlabeled data in order to eventually assist clinical decision making. By evaluating on the prediction of clinically relevant outcomes, we show that in a practical setting, unsupervised representation learning can offer clear performance benefits over end-to-end supervised architectures. We experiment with using sequence-to-sequence (Seq2Seq) models in two different ways, as an autoencoder and as a forecaster, and show that the best performance is achieved by a forecasting Seq2Seq model with an integrated attention mechanism, proposed here for the first time in the setting of unsupervised learning for medical time series.", "published": "2018-12-02T23:59:36Z", "authors": ["Xinrui Lyu", "Matthias Hueser", "Stephanie L. Hyland", "George Zerveas", "Gunnar Raetsch"], "categories": ["cs.LG", "stat.ML"], "doi": "", "_query": "real estate valuation deep learning", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/1912.02532v1", "title": "Iterative Policy-Space Expansion in Reinforcement Learning", "abstract": "Humans and animals solve a difficult problem much more easily when they are presented with a sequence of problems that starts simple and slowly increases in difficulty. We explore this idea in the context of reinforcement learning. Rather than providing the agent with an externally provided curriculum of progressively more difficult tasks, the agent solves a single task utilizing a decreasingly constrained policy space. The algorithm we propose first learns to categorize features into positive and negative before gradually learning a more refined policy. Experimental results in Tetris demonstrate superior learning rate of our approach when compared to existing algorithms.", "published": "2019-12-05T12:32:15Z", "authors": ["Jan Malte Lichtenberg", "Özgür Şimşek"], "categories": ["cs.LG", "cs.AI", "stat.ML"], "doi": "", "_query": "real estate valuation deep learning", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2411.08587v1", "title": "DeepUQ: Assessing the Aleatoric Uncertainties from two Deep Learning Methods", "abstract": "Assessing the quality of aleatoric uncertainty estimates from uncertainty quantification (UQ) deep learning methods is important in scientific contexts, where uncertainty is physically meaningful and important to characterize and interpret exactly. We systematically compare aleatoric uncertainty measured by two UQ techniques, Deep Ensembles (DE) and Deep Evidential Regression (DER). Our method focuses on both zero-dimensional (0D) and two-dimensional (2D) data, to explore how the UQ methods function for different data dimensionalities. We investigate uncertainty injected on the input and output variables and include a method to propagate uncertainty in the case of input uncertainty so that we can compare the predicted aleatoric uncertainty to the known values. We experiment with three levels of noise. The aleatoric uncertainty predicted across all models and experiments scales with the injected noise level. However, the predicted uncertainty is miscalibrated to $\\rm{std}(σ_{\\rm al})$ with the true uncertainty for half of the DE experiments and almost all of the DER experiments. The predicted uncertainty is the least accurate for both UQ methods for the 2D input uncertainty experiment and the high-noise level. While these results do not apply to more complex data, they highlight that further research on post-facto calibration for these methods would be beneficial, particularly for high-noise and high-dimensional settings.", "published": "2024-11-13T13:11:49Z", "authors": ["Rebecca Nevin", "Aleksandra Ćiprijanović", "Brian D. Nord"], "categories": ["cs.LG", "cs.AI"], "doi": "", "_query": "real estate valuation deep learning", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/1810.07382v3", "title": "Analysis of Railway Accidents' Narratives Using Deep Learning", "abstract": "Automatic understanding of domain specific texts in order to extract useful relationships for later use is a non-trivial task. One such relationship would be between railroad accidents' causes and their correspondent descriptions in reports. From 2001 to 2016 rail accidents in the U.S. cost more than $4.6B. Railroads involved in accidents are required to submit an accident report to the Federal Railroad Administration (FRA). These reports contain a variety of fixed field entries including primary cause of the accidents (a coded variable with 389 values) as well as a narrative field which is a short text description of the accident. Although these narratives provide more information than a fixed field entry, the terminologies used in these reports are not easy to understand by a non-expert reader. Therefore, providing an assisting method to fill in the primary cause from such domain specific texts(narratives) would help to label the accidents with more accuracy. Another important question for transportation safety is whether the reported accident cause is consistent with narrative description. To address these questions, we applied deep learning methods together with powerful word embeddings such as Word2Vec and GloVe to classify accident cause values for the primary cause field using the text in the narratives. The results show that such approaches can both accurately classify accident causes based on report narratives and find important inconsistencies in accident reporting.", "published": "2018-10-17T04:30:02Z", "authors": ["Mojtaba Heidarysafa", "Kamran Kowsari", "Laura E. Barnes", "Donald E. Brown"], "categories": ["cs.CL", "cs.IR", "cs.LG", "cs.NE", "stat.ML"], "doi": "10.1109/ICMLA.2018.00235", "_query": "real estate valuation deep learning", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/1905.12105v3", "title": "Certifiably Robust Interpretation in Deep Learning", "abstract": "Deep learning interpretation is essential to explain the reasoning behind model predictions. Understanding the robustness of interpretation methods is important especially in sensitive domains such as medical applications since interpretation results are often used in downstream tasks. Although gradient-based saliency maps are popular methods for deep learning interpretation, recent works show that they can be vulnerable to adversarial attacks. In this paper, we address this problem and provide a certifiable defense method for deep learning interpretation. We show that a sparsified version of the popular SmoothGrad method, which computes the average saliency maps over random perturbations of the input, is certifiably robust against adversarial perturbations. We obtain this result by extending recent bounds for certifiably robust smooth classifiers to the interpretation setting. Experiments on ImageNet samples validate our theory.", "published": "2019-05-28T21:49:40Z", "authors": ["Alexander Levine", "Sahil Singla", "Soheil Feizi"], "categories": ["cs.LG", "stat.ML"], "doi": "", "_query": "real estate valuation deep learning", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2009.07888v7", "title": "Transfer Learning in Deep Reinforcement Learning: A Survey", "abstract": "Reinforcement learning is a learning paradigm for solving sequential decision-making problems. Recent years have witnessed remarkable progress in reinforcement learning upon the fast development of deep neural networks. Along with the promising prospects of reinforcement learning in numerous domains such as robotics and game-playing, transfer learning has arisen to tackle various challenges faced by reinforcement learning, by transferring knowledge from external expertise to facilitate the efficiency and effectiveness of the learning process. In this survey, we systematically investigate the recent progress of transfer learning approaches in the context of deep reinforcement learning. Specifically, we provide a framework for categorizing the state-of-the-art transfer learning approaches, under which we analyze their goals, methodologies, compatible reinforcement learning backbones, and practical applications. We also draw connections between transfer learning and other relevant topics from the reinforcement learning perspective and explore their potential challenges that await future research progress.", "published": "2020-09-16T18:38:54Z", "authors": ["Zhuangdi Zhu", "Kaixiang Lin", "Anil K. Jain", "Jiayu Zhou"], "categories": ["cs.LG", "cs.AI", "stat.ML"], "doi": "", "_query": "real estate valuation deep learning", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2004.05366v2", "title": "In-Machine-Learning Database: Reimagining Deep Learning with Old-School SQL", "abstract": "In-database machine learning has been very popular, almost being a cliche. However, can we do it the other way around? In this work, we say \"yes\" by applying plain old SQL to deep learning, in a sense implementing deep learning algorithms with SQL. Most deep learning frameworks, as well as generic machine learning ones, share a de facto standard of multidimensional array operations, underneath fancier infrastructure such as automatic differentiation. As SQL tables can be regarded as generalisations of (multi-dimensional) arrays, we have found a way to express common deep learning operations in SQL, encouraging a different way of thinking and thus potentially novel models. In particular, one of the latest trend in deep learning was the introduction of sparsity in the name of graph convolutional networks, whereas we take sparsity almost for granted in the database world. As both databases and machine learning involve transformation of datasets, we hope this work can inspire further works utilizing the large body of existing wisdom, algorithms and technologies in the database field to advance the state of the art in machine learning, rather than merely integerating machine learning into databases.", "published": "2020-04-11T11:00:26Z", "authors": ["Len Du"], "categories": ["cs.LG", "cs.DB", "stat.ML"], "doi": "", "_query": "real estate valuation deep learning", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/1912.11090v3", "title": "Deep learning architectures for nonlinear operator functions and nonlinear inverse problems", "abstract": "We develop a theoretical analysis for special neural network architectures, termed operator recurrent neural networks, for approximating nonlinear functions whose inputs are linear operators. Such functions commonly arise in solution algorithms for inverse boundary value problems. Traditional neural networks treat input data as vectors, and thus they do not effectively capture the multiplicative structure associated with the linear operators that correspond to the data in such inverse problems. We therefore introduce a new family that resembles a standard neural network architecture, but where the input data acts multiplicatively on vectors. Motivated by compact operators appearing in boundary control and the analysis of inverse boundary value problems for the wave equation, we promote structure and sparsity in selected weight matrices in the network. After describing this architecture, we study its representation properties as well as its approximation properties. We furthermore show that an explicit regularization can be introduced that can be derived from the mathematical analysis of the mentioned inverse problems, and which leads to certain guarantees on the generalization properties. We observe that the sparsity of the weight matrices improves the generalization estimates. Lastly, we discuss how operator recurrent networks can be viewed as a deep learning analogue to deterministic algorithms such as boundary control for reconstructing the unknown wavespeed in the acoustic wave equation from boundary measurements.", "published": "2019-12-23T20:05:47Z", "authors": ["Maarten V. de Hoop", "Matti Lassas", "Christopher A. Wong"], "categories": ["math.OC", "cs.LG"], "doi": "", "_query": "real estate valuation deep learning", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/1904.07854v2", "title": "End-to-End Robotic Reinforcement Learning without Reward Engineering", "abstract": "The combination of deep neural network models and reinforcement learning algorithms can make it possible to learn policies for robotic behaviors that directly read in raw sensory inputs, such as camera images, effectively subsuming both estimation and control into one model. However, real-world applications of reinforcement learning must specify the goal of the task by means of a manually programmed reward function, which in practice requires either designing the very same perception pipeline that end-to-end reinforcement learning promises to avoid, or else instrumenting the environment with additional sensors to determine if the task has been performed successfully. In this paper, we propose an approach for removing the need for manual engineering of reward specifications by enabling a robot to learn from a modest number of examples of successful outcomes, followed by actively solicited queries, where the robot shows the user a state and asks for a label to determine whether that state represents successful completion of the task. While requesting labels for every single state would amount to asking the user to manually provide the reward signal, our method requires labels for only a tiny fraction of the states seen during training, making it an efficient and practical approach for learning skills without manually engineered rewards. We evaluate our method on real-world robotic manipulation tasks where the observations consist of images viewed by the robot's camera. In our experiments, our method effectively learns to arrange objects, place books, and drape cloth, directly from images and without any manually specified reward functions, and with only 1-4 hours of interaction with the real world.", "published": "2019-04-16T17:59:23Z", "authors": ["Avi Singh", "Larry Yang", "Kristian Hartikainen", "Chelsea Finn", "Sergey Levine"], "categories": ["cs.LG", "cs.CV", "cs.RO", "stat.ML"], "doi": "", "_query": "real estate valuation deep learning", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/1910.02574v1", "title": "Representation Learning of EHR Data via Graph-Based Medical Entity Embedding", "abstract": "Automatic representation learning of key entities in electronic health record (EHR) data is a critical step for healthcare informatics that turns heterogeneous medical records into structured and actionable information. Here we propose ME2Vec, an algorithmic framework for learning low-dimensional vectors of the most common entities in EHR: medical services, doctors, and patients. ME2Vec leverages diverse graph embedding techniques to cater for the unique characteristic of each medical entity. Using real-world clinical data, we demonstrate the efficacy of ME2Vec over competitive baselines on disease diagnosis prediction.", "published": "2019-10-07T02:01:32Z", "authors": ["Tong Wu", "Yunlong Wang", "Yue Wang", "Emily Zhao", "Yilian Yuan", "Zhi Yang"], "categories": ["cs.LG", "cs.IR", "stat.ML"], "doi": "", "_query": "real estate valuation deep learning", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/1805.08882v2", "title": "Multi-task Maximum Entropy Inverse Reinforcement Learning", "abstract": "Multi-task Inverse Reinforcement Learning (IRL) is the problem of inferring multiple reward functions from expert demonstrations. Prior work, built on Bayesian IRL, is unable to scale to complex environments due to computational constraints. This paper contributes a formulation of multi-task IRL in the more computationally efficient Maximum Causal Entropy (MCE) IRL framework. Experiments show our approach can perform one-shot imitation learning in a gridworld environment that single-task IRL algorithms need hundreds of demonstrations to solve. We outline preliminary work using meta-learning to extend our method to the function approximator setting of modern MCE IRL algorithms. Evaluating on multi-task variants of common simulated robotics benchmarks, we discover serious limitations of these IRL algorithms, and conclude with suggestions for further work.", "published": "2018-05-22T21:57:34Z", "authors": ["Adam Gleave", "Oliver Habryka"], "categories": ["cs.LG", "cs.AI", "stat.ML"], "doi": "", "_query": "real estate valuation deep learning", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/1907.04061v1", "title": "A Light weight and Hybrid Deep Learning Model based Online Signature Verification", "abstract": "The augmented usage of deep learning-based models for various AI related problems are as a result of modern architectures of deeper length and the availability of voluminous interpreted datasets. The models based on these architectures require huge training and storage cost, which makes them inefficient to use in critical applications like online signature verification (OSV) and to deploy in resource constraint devices. As a solution, in this work, our contribution is two-fold. 1) An efficient dimensionality reduction technique, to reduce the number of features to be considered and 2) a state-of-the-art model CNN-LSTM based hybrid architecture for online signature verification. Thorough experiments on the publicly available datasets MCYT, SUSIG, SVC confirms that the proposed model achieves better accuracy even with as low as one training sample. The proposed models yield state-of-the-art performance in various categories of all the three datasets.", "published": "2019-07-09T09:55:38Z", "authors": ["Chandra Sekhar V.", "Anoushka Doctor", "Prerana Mukherjee", "Viswanath Pulabaigiri"], "categories": ["cs.CV", "cs.LG"], "doi": "", "_query": "real estate valuation deep learning", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/1810.04303v1", "title": "Batch Active Preference-Based Learning of Reward Functions", "abstract": "Data generation and labeling are usually an expensive part of learning for robotics. While active learning methods are commonly used to tackle the former problem, preference-based learning is a concept that attempts to solve the latter by querying users with preference questions. In this paper, we will develop a new algorithm, batch active preference-based learning, that enables efficient learning of reward functions using as few data samples as possible while still having short query generation times. We introduce several approximations to the batch active learning problem, and provide theoretical guarantees for the convergence of our algorithms. Finally, we present our experimental results for a variety of robotics tasks in simulation. Our results suggest that our batch active learning algorithm requires only a few queries that are computed in a short amount of time. We then showcase our algorithm in a study to learn human users' preferences.", "published": "2018-10-10T00:02:55Z", "authors": ["Erdem Bıyık", "Dorsa Sadigh"], "categories": ["cs.LG", "cs.AI", "cs.RO", "stat.ML"], "doi": "", "_query": "real estate valuation deep learning", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/1909.05024v2", "title": "Learning to Propagate for Graph Meta-Learning", "abstract": "Meta-learning extracts common knowledge from learning different tasks and uses it for unseen tasks. It can significantly improve tasks that suffer from insufficient training data, e.g., few shot learning. In most meta-learning methods, tasks are implicitly related by sharing parameters or optimizer. In this paper, we show that a meta-learner that explicitly relates tasks on a graph describing the relations of their output dimensions (e.g., classes) can significantly improve few shot learning. The graph's structure is usually free or cheap to obtain but has rarely been explored in previous works. We develop a novel meta-learner of this type for prototype-based classification, in which a prototype is generated for each class, such that the nearest neighbor search among the prototypes produces an accurate classification. The meta-learner, called \"Gated Propagation Network (GPN)\", learns to propagate messages between prototypes of different classes on the graph, so that learning the prototype of each class benefits from the data of other related classes. In GPN, an attention mechanism aggregates messages from neighboring classes of each class, with a gate choosing between the aggregated message and the message from the class itself. We train GPN on a sequence of tasks from many-shot to few shot generated by subgraph sampling. During training, it is able to reuse and update previously achieved prototypes from the memory in a life-long learning cycle. In experiments, under different training-test discrepancy and test task generation settings, GPN outperforms recent meta-learning methods on two benchmark datasets. The code of GPN and dataset generation is available at https://github.com/liulu112601/Gated-Propagation-Net.", "published": "2019-09-11T13:00:24Z", "authors": ["Lu Liu", "Tianyi Zhou", "Guodong Long", "Jing Jiang", "Chengqi Zhang"], "categories": ["cs.LG", "cs.CV", "stat.ML"], "doi": "", "_query": "real estate valuation deep learning", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/1910.10368v1", "title": "MLAT: Metric Learning for kNN in Streaming Time Series", "abstract": "Learning a good distance measure for distance-based classification in time series leads to significant performance improvement in many tasks. Specifically, it is critical to effectively deal with variations and temporal dependencies in time series. However, existing metric learning approaches focus on tackling variations mainly using a strict alignment of two sequences, thereby being not able to capture temporal dependencies. To overcome this limitation, we propose MLAT, which covers both alignment and temporal dependencies at the same time. MLAT achieves the alignment effect as well as preserves temporal dependencies by augmenting a given time series using a sliding window. Furthermore, MLAT employs time-invariant metric learning to derive the most appropriate distance measure from the augmented samples which can also capture the temporal dependencies among them well. We show that MLAT outperforms other existing algorithms in the extensive experiments on various real-world data sets.", "published": "2019-10-23T05:48:33Z", "authors": ["Dongmin Park", "Susik Yoon", "Hwanjun Song", "Jae-Gil Lee"], "categories": ["cs.LG", "stat.ML"], "doi": "", "_query": "real estate valuation deep learning", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2405.08217v1", "title": "Data Valuation with Gradient Similarity", "abstract": "High-quality data is crucial for accurate machine learning and actionable analytics, however, mislabeled or noisy data is a common problem in many domains. Distinguishing low- from high-quality data can be challenging, often requiring expert knowledge and considerable manual intervention. Data Valuation algorithms are a class of methods that seek to quantify the value of each sample in a dataset based on its contribution or importance to a given predictive task. These data values have shown an impressive ability to identify mislabeled observations, and filtering low-value data can boost machine learning performance. In this work, we present a simple alternative to existing methods, termed Data Valuation with Gradient Similarity (DVGS). This approach can be easily applied to any gradient descent learning algorithm, scales well to large datasets, and performs comparably or better than baseline valuation methods for tasks such as corrupted label discovery and noise quantification. We evaluate the DVGS method on tabular, image and RNA expression datasets to show the effectiveness of the method across domains. Our approach has the ability to rapidly and accurately identify low-quality data, which can reduce the need for expert knowledge and manual intervention in data cleaning tasks.", "published": "2024-05-13T22:10:00Z", "authors": ["Nathaniel J. Evans", "Gordon B. Mills", "Guanming Wu", "Xubo Song", "Shannon McWeeney"], "categories": ["cs.LG", "q-bio.GN", "q-bio.QM", "stat.ML"], "doi": "", "_query": "real estate valuation deep learning", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2001.10109v3", "title": "Supervised Learning for Non-Sequential Data: A Canonical Polyadic Decomposition Approach", "abstract": "Efficient modelling of feature interactions underpins supervised learning for non-sequential tasks, characterized by a lack of inherent ordering of features (variables). The brute force approach of learning a parameter for each interaction of every order comes at an exponential computational and memory cost (Curse of Dimensionality). To alleviate this issue, it has been proposed to implicitly represent the model parameters as a tensor, the order of which is equal to the number of features; for efficiency, it can be further factorized into a compact Tensor Train (TT) format. However, both TT and other Tensor Networks (TNs), such as Tensor Ring and Hierarchical Tucker, are sensitive to the ordering of their indices (and hence to the features). To establish the desired invariance to feature ordering, we propose to represent the weight tensor through the Canonical Polyadic (CP) Decomposition (CPD), and introduce the associated inference and learning algorithms, including suitable regularization and initialization schemes. It is demonstrated that the proposed CP-based predictor significantly outperforms other TN-based predictors on sparse data while exhibiting comparable performance on dense non-sequential tasks. Furthermore, for enhanced expressiveness, we generalize the framework to allow feature mapping to arbitrarily high-dimensional feature vectors. In conjunction with feature vector normalization, this is shown to yield dramatic improvements in performance for dense non-sequential tasks, matching models such as fully-connected neural networks.", "published": "2020-01-27T22:38:40Z", "authors": ["Alexandros Haliassos", "Kriton Konstantinidis", "Danilo P. Mandic"], "categories": ["cs.LG", "stat.ML"], "doi": "", "_query": "real estate valuation deep learning", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/1907.06312v1", "title": "Exploring Deep Anomaly Detection Methods Based on Capsule Net", "abstract": "In this paper, we develop and explore deep anomaly detection techniques based on the capsule network (CapsNet) for image data. Being able to encoding intrinsic spatial relationship between parts and a whole, CapsNet has been applied as both a classifier and deep autoencoder. This inspires us to design a prediction-probability-based and a reconstruction-error-based normality score functions for evaluating the \"outlierness\" of unseen images. Our results on three datasets demonstrate that the prediction-probability-based method performs consistently well, while the reconstruction-error-based approach is relatively sensitive to the similarity between labeled and unlabeled images. Furthermore, both of the CapsNet-based methods outperform the principled benchmark methods in many cases.", "published": "2019-07-15T02:15:58Z", "authors": ["Xiaoyan Li", "Iluju Kiringa", "Tet Yeap", "Xiaodan Zhu", "Yifeng Li"], "categories": ["cs.LG", "cs.CV", "stat.ML"], "doi": "", "_query": "real estate valuation deep learning", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/1710.05090v1", "title": "Burn-In Demonstrations for Multi-Modal Imitation Learning", "abstract": "Recent work on imitation learning has generated policies that reproduce expert behavior from multi-modal data. However, past approaches have focused only on recreating a small number of distinct, expert maneuvers, or have relied on supervised learning techniques that produce unstable policies. This work extends InfoGAIL, an algorithm for multi-modal imitation learning, to reproduce behavior over an extended period of time. Our approach involves reformulating the typical imitation learning setting to include \"burn-in demonstrations\" upon which policies are conditioned at test time. We demonstrate that our approach outperforms standard InfoGAIL in maximizing the mutual information between predicted and unseen style labels in road scene simulations, and we show that our method leads to policies that imitate expert autonomous driving systems over long time horizons.", "published": "2017-10-13T22:29:51Z", "authors": ["Alex Kuefler", "Mykel J. Kochenderfer"], "categories": ["cs.LG", "stat.ML"], "doi": "", "_query": "real estate valuation deep learning", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/1806.06940v1", "title": "Pressure Predictions of Turbine Blades with Deep Learning", "abstract": "Deep learning has been used in many areas, such as feature detections in images and the game of go. This paper presents a study that attempts to use the deep learning method to predict turbomachinery performance. Three different deep neural networks are built and trained to predict the pressure distributions of turbine airfoils. The performance of a library of turbine airfoils were firstly predicted using methods based on Euler equations, which were then used to train and validate the deep learning neural networks. The results show that network with four layers of convolutional neural network and two layers of fully connected neural network provides the best predictions. For the best neural network architecture, the pressure prediction on more than 99% locations are better than 3% and 90% locations are better than 1%.", "published": "2018-06-12T03:17:08Z", "authors": ["Cheng'an Bai", "Chao Zhou"], "categories": ["cs.LG", "stat.ML"], "doi": "", "_query": "real estate valuation deep learning", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/1910.07719v3", "title": "Single Episode Policy Transfer in Reinforcement Learning", "abstract": "Transfer and adaptation to new unknown environmental dynamics is a key challenge for reinforcement learning (RL). An even greater challenge is performing near-optimally in a single attempt at test time, possibly without access to dense rewards, which is not addressed by current methods that require multiple experience rollouts for adaptation. To achieve single episode transfer in a family of environments with related dynamics, we propose a general algorithm that optimizes a probe and an inference model to rapidly estimate underlying latent variables of test dynamics, which are then immediately used as input to a universal control policy. This modular approach enables integration of state-of-the-art algorithms for variational inference or RL. Moreover, our approach does not require access to rewards at test time, allowing it to perform in settings where existing adaptive approaches cannot. In diverse experimental domains with a single episode test constraint, our method significantly outperforms existing adaptive approaches and shows favorable performance against baselines for robust transfer.", "published": "2019-10-17T05:37:50Z", "authors": ["Jiachen Yang", "Brenden Petersen", "Hongyuan Zha", "Daniel Faissol"], "categories": ["cs.LG", "stat.ML"], "doi": "", "_query": "real estate valuation deep learning", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2502.05498v2", "title": "Riemannian Manifold Learning for Stackelberg Games with Neural Flow Representations", "abstract": "We present a novel framework for online learning in Stackelberg general-sum games, where two agents, the leader and follower, engage in sequential turn-based interactions. At the core of this approach is a learned diffeomorphism that maps the joint action space to a smooth spherical Riemannian manifold, referred to as the Stackelberg manifold. This mapping, facilitated by neural normalizing flows, ensures the formation of tractable isoplanar subspaces, enabling efficient techniques for online learning. Leveraging the linearity of the agents' reward functions on the Stackelberg manifold, our construct allows the application of linear bandit algorithms. We then provide a rigorous theoretical basis for regret minimization on the learned manifold and establish bounds on the simple regret for learning Stackelberg equilibrium. This integration of manifold learning into game theory uncovers a previously unrecognized potential for neural normalizing flows as an effective tool for multi-agent learning. We present empirical results demonstrating the effectiveness of our approach compared to standard baselines, with applications spanning domains such as cybersecurity and economic supply chain optimization.", "published": "2025-02-08T09:10:44Z", "authors": ["Larkin Liu", "Kashif Rasul", "Yutong Chao", "Jalal Etesami"], "categories": ["cs.LG", "cs.AI", "cs.GT", "cs.MA"], "doi": "", "_query": "real estate valuation deep learning", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2312.02074v1", "title": "Federated Learning is Better with Non-Homomorphic Encryption", "abstract": "Traditional AI methodologies necessitate centralized data collection, which becomes impractical when facing problems with network communication, data privacy, or storage capacity. Federated Learning (FL) offers a paradigm that empowers distributed AI model training without collecting raw data. There are different choices for providing privacy during FL training. One of the popular methodologies is employing Homomorphic Encryption (HE) - a breakthrough in privacy-preserving computation from Cryptography. However, these methods have a price in the form of extra computation and memory footprint. To resolve these issues, we propose an innovative framework that synergizes permutation-based compressors with Classical Cryptography, even though employing Classical Cryptography was assumed to be impossible in the past in the context of FL. Our framework offers a way to replace HE with cheaper Classical Cryptography primitives which provides security for the training process. It fosters asynchronous communication and provides flexible deployment options in various communication topologies.", "published": "2023-12-04T17:37:41Z", "authors": ["Konstantin Burlachenko", "Abdulmajeed Alrowithi", "Fahad Ali Albalawi", "Peter Richtarik"], "categories": ["cs.CR", "cs.LG", "math.OC"], "doi": "10.1145/3630048.3630182", "_query": "real estate valuation deep learning", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/1907.10701v4", "title": "Benchmarking TPU, GPU, and CPU Platforms for Deep Learning", "abstract": "Training deep learning models is compute-intensive and there is an industry-wide trend towards hardware specialization to improve performance. To systematically benchmark deep learning platforms, we introduce ParaDnn, a parameterized benchmark suite for deep learning that generates end-to-end models for fully connected (FC), convolutional (CNN), and recurrent (RNN) neural networks. Along with six real-world models, we benchmark Google's Cloud TPU v2/v3, NVIDIA's V100 GPU, and an Intel Skylake CPU platform. We take a deep dive into TPU architecture, reveal its bottlenecks, and highlight valuable lessons learned for future specialized system design. We also provide a thorough comparison of the platforms and find that each has unique strengths for some types of models. Finally, we quantify the rapid performance improvements that specialized software stacks provide for the TPU and GPU platforms.", "published": "2019-07-24T20:18:28Z", "authors": ["Yu Emma Wang", "Gu-Yeon Wei", "David Brooks"], "categories": ["cs.LG", "cs.PF", "stat.ML"], "doi": "", "_query": "real estate valuation deep learning", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/1909.08167v1", "title": "Weighed Domain-Invariant Representation Learning for Cross-domain Sentiment Analysis", "abstract": "Cross-domain sentiment analysis is currently a hot topic in the research and engineering areas. One of the most popular frameworks in this field is the domain-invariant representation learning (DIRL) paradigm, which aims to learn a distribution-invariant feature representation across domains. However, in this work, we find out that applying DIRL may harm domain adaptation when the label distribution $\\rm{P}(\\rm{Y})$ changes across domains. To address this problem, we propose a modification to DIRL, obtaining a novel weighted domain-invariant representation learning (WDIRL) framework. We show that it is easy to transfer existing SOTA DIRL models to WDIRL. Empirical studies on extensive cross-domain sentiment analysis tasks verified our statements and showed the effectiveness of our proposed solution.", "published": "2019-09-18T02:03:03Z", "authors": ["Minlong Peng", "Qi Zhang", "Xuanjing Huang"], "categories": ["cs.LG", "cs.CL", "stat.ML"], "doi": "", "_query": "real estate valuation deep learning", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2610.01903v1", "title": "Higher-Order Positional Encodings for Graph Representation Learning", "abstract": "Many real-world systems exhibit higher-order interactions among groups of entities that cannot be captured by pairwise relationships alone. Graph Transformers and Graph Neural Networks increasingly rely on positional encodings to enrich graph representations, yet existing positional encodings are computed solely from the original graph and therefore cannot directly capture observed higher-order interactions. Topological Deep Learning addresses this limitation by lifting graphs to simplicial complexes, but typically requires performing message passing or attention on higher-order neural network representations. We introduce a representation learning paradigm that enriches graph representations with higher-order topology through positional encodings, enabling standard graph learning models to exploit lifted incidence structure without modifying the backbone. We derive a theoretical characterization of the expressivity of higher-order positional encodings, proving that node-level operators induced by higher-order lifts can mix graph Laplacian frequencies in ways that scalar graph spectral filters cannot. Guided by this theory, we instantiate higher-order positional encodings using Hodge Laplacians derived from clique complexes. Experiments with Graph Transformers on ZINC and controlled synthetic benchmarks demonstrate improvements in predictive performance, while a fixed-1-skeleton experiment shows that the pipeline can transmit higher-order information when cells are supplied independently of the graph. Together, our results establish higher-order positional encodings as a principled bridge between graph positional encodings and topological deep learning.", "published": "2026-10-01T15:49:18Z", "authors": ["Caleb Stam", "Aagrim Hoysal", "Sanjukta Krishnagopal"], "categories": ["cs.LG"], "doi": "", "_query": "real estate valuation deep learning", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/1602.02285v1", "title": "A Deep Learning Approach to Unsupervised Ensemble Learning", "abstract": "We show how deep learning methods can be applied in the context of crowdsourcing and unsupervised ensemble learning. First, we prove that the popular model of Dawid and Skene, which assumes that all classifiers are conditionally independent, is {\\em equivalent} to a Restricted Boltzmann Machine (RBM) with a single hidden node. Hence, under this model, the posterior probabilities of the true labels can be instead estimated via a trained RBM. Next, to address the more general case, where classifiers may strongly violate the conditional independence assumption, we propose to apply RBM-based Deep Neural Net (DNN). Experimental results on various simulated and real-world datasets demonstrate that our proposed DNN approach outperforms other state-of-the-art methods, in particular when the data violates the conditional independence assumption.", "published": "2016-02-06T17:56:59Z", "authors": ["Uri Shaham", "Xiuyuan Cheng", "Omer Dror", "Ariel Jaffe", "Boaz Nadler", "Joseph Chang", "Yuval Kluger"], "categories": ["stat.ML", "cs.LG"], "doi": "", "_query": "real estate valuation deep learning", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/1709.04905v1", "title": "One-Shot Visual Imitation Learning via Meta-Learning", "abstract": "In order for a robot to be a generalist that can perform a wide range of jobs, it must be able to acquire a wide variety of skills quickly and efficiently in complex unstructured environments. High-capacity models such as deep neural networks can enable a robot to represent complex skills, but learning each skill from scratch then becomes infeasible. In this work, we present a meta-imitation learning method that enables a robot to learn how to learn more efficiently, allowing it to acquire new skills from just a single demonstration. Unlike prior methods for one-shot imitation, our method can scale to raw pixel inputs and requires data from significantly fewer prior tasks for effective learning of new skills. Our experiments on both simulated and real robot platforms demonstrate the ability to learn new tasks, end-to-end, from a single visual demonstration.", "published": "2017-09-14T17:50:18Z", "authors": ["Chelsea Finn", "Tianhe Yu", "Tianhao Zhang", "Pieter Abbeel", "Sergey Levine"], "categories": ["cs.LG", "cs.AI", "cs.CV", "cs.RO"], "doi": "", "_query": "real estate valuation deep learning", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2008.12858v1", "title": "Real-world Video Adaptation with Reinforcement Learning", "abstract": "Client-side video players employ adaptive bitrate (ABR) algorithms to optimize user quality of experience (QoE). We evaluate recently proposed RL-based ABR methods in Facebook's web-based video streaming platform. Real-world ABR contains several challenges that requires customized designs beyond off-the-shelf RL algorithms -- we implement a scalable neural network architecture that supports videos with arbitrary bitrate encodings; we design a training method to cope with the variance resulting from the stochasticity in network conditions; and we leverage constrained Bayesian optimization for reward shaping in order to optimize the conflicting QoE objectives. In a week-long worldwide deployment with more than 30 million video streaming sessions, our RL approach outperforms the existing human-engineered ABR algorithms.", "published": "2020-08-28T21:44:24Z", "authors": ["Hongzi Mao", "Shannon Chen", "Drew Dimmery", "Shaun Singh", "Drew Blaisdell", "Yuandong Tian", "Mohammad Alizadeh", "Eytan Bakshy"], "categories": ["cs.NI", "cs.AI"], "doi": "", "_query": "real estate valuation deep learning", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2208.02377v1", "title": "Improving Meta-Learning Generalization with Activation-Based Early-Stopping", "abstract": "Meta-Learning algorithms for few-shot learning aim to train neural networks capable of generalizing to novel tasks using only a few examples. Early-stopping is critical for performance, halting model training when it reaches optimal generalization to the new task distribution. Early-stopping mechanisms in Meta-Learning typically rely on measuring the model performance on labeled examples from a meta-validation set drawn from the training (source) dataset. This is problematic in few-shot transfer learning settings, where the meta-test set comes from a different target dataset (OOD) and can potentially have a large distributional shift with the meta-validation set. In this work, we propose Activation Based Early-stopping (ABE), an alternative to using validation-based early-stopping for meta-learning. Specifically, we analyze the evolution, during meta-training, of the neural activations at each hidden layer, on a small set of unlabelled support examples from a single task of the target tasks distribution, as this constitutes a minimal and justifiably accessible information from the target problem. Our experiments show that simple, label agnostic statistics on the activations offer an effective way to estimate how the target generalization evolves over time. At each hidden layer, we characterize the activation distributions, from their first and second order moments, then further summarized along the feature dimensions, resulting in a compact yet intuitive characterization in a four-dimensional space. Detecting when, throughout training time, and at which layer, the target activation trajectory diverges from the activation trajectory of the source data, allows us to perform early-stopping and improve generalization in a large array of few-shot transfer learning settings, across different algorithms, source and target datasets.", "published": "2022-08-03T22:55:45Z", "authors": ["Simon Guiroy", "Christopher Pal", "Gonçalo Mordido", "Sarath Chandar"], "categories": ["cs.LG", "cs.AI", "stat.ML"], "doi": "", "_query": "real estate valuation deep learning", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/1504.04054v1", "title": "A Generative Model for Deep Convolutional Learning", "abstract": "A generative model is developed for deep (multi-layered) convolutional dictionary learning. A novel probabilistic pooling operation is integrated into the deep model, yielding efficient bottom-up (pretraining) and top-down (refinement) probabilistic learning. Experimental results demonstrate powerful capabilities of the model to learn multi-layer features from images, and excellent classification results are obtained on the MNIST and Caltech 101 datasets.", "published": "2015-04-15T21:31:58Z", "authors": ["Yunchen Pu", "Xin Yuan", "Lawrence Carin"], "categories": ["stat.ML", "cs.LG", "cs.NE"], "doi": "", "_query": "real estate valuation deep learning", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2307.04887v1", "title": "Measuring and Mitigating Interference in Reinforcement Learning", "abstract": "Catastrophic interference is common in many network-based learning systems, and many proposals exist for mitigating it. Before overcoming interference we must understand it better. In this work, we provide a definition and novel measure of interference for value-based reinforcement learning methods such as Fitted Q-Iteration and DQN. We systematically evaluate our measure of interference, showing that it correlates with instability in control performance, across a variety of network architectures. Our new interference measure allows us to ask novel scientific questions about commonly used deep learning architectures and study learning algorithms which mitigate interference. Lastly, we outline a class of algorithms which we call online-aware that are designed to mitigate interference, and show they do reduce interference according to our measure and that they improve stability and performance in several classic control environments.", "published": "2023-07-10T20:20:20Z", "authors": ["Vincent Liu", "Han Wang", "Ruo Yu Tao", "Khurram Javed", "Adam White", "Martha White"], "categories": ["cs.LG", "cs.AI"], "doi": "", "_query": "real estate valuation deep learning", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/1905.13159v2", "title": "Distribution-dependent and Time-uniform Bounds for Piecewise i.i.d Bandits", "abstract": "We consider the setup of stochastic multi-armed bandits in the case when reward distributions are piecewise i.i.d. and bounded with unknown changepoints. We focus on the case when changes happen simultaneously on all arms, and in stark contrast with the existing literature, we target gap-dependent (as opposed to only gap-independent) regret bounds involving the magnitude of changes $(Δ^{chg}_{i,g})$ and optimality-gaps ($Δ^{opt}_{i,g}$). Diverging from previous works, we assume the more realistic scenario that there can be undetectable changepoint gaps and under a different set of assumptions, we show that as long as the compounded delayed detection for each changepoint is bounded there is no need for forced exploration to actively detect changepoints. We introduce two adaptations of UCB-strategies that employ scan-statistics in order to actively detect the changepoints, without knowing in advance the changepoints and also the mean before and after any change. Our first method \\UCBLCPD does not know the number of changepoints $G$ or time horizon $T$ and achieves the first time-uniform concentration bound for this setting using the Laplace method of integration. The second strategy \\ImpCPD makes use of the knowledge of $T$ to achieve the order optimal regret bound of $\\min\\big\\lbrace O(\\sum\\limits_{i=1}^{K} \\sum\\limits_{g=1}^{G}\\frac{\\log(T/H_{1,g})}{Δ^{opt}_{i,g}}), O(\\sqrt{GT})\\big\\rbrace$, (where $H_{1,g}$ is the problem complexity) thereby closing an important gap with respect to the lower bound in a specific challenging setting. Our theoretical findings are supported by numerical experiments on synthetic and real-life datasets.", "published": "2019-05-30T16:30:19Z", "authors": ["Subhojyoti Mukherjee", "Odalric-Ambrym Maillard"], "categories": ["cs.LG", "stat.ML"], "doi": "", "_query": "real estate valuation deep learning", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/1811.07211v2", "title": "Classifiers Based on Deep Sparse Coding Architectures are Robust to Deep Learning Transferable Examples", "abstract": "Although deep learning has shown great success in recent years, researchers have discovered a critical flaw where small, imperceptible changes in the input to the system can drastically change the output classification. These attacks are exploitable in nearly all of the existing deep learning classification frameworks. However, the susceptibility of deep sparse coding models to adversarial examples has not been examined. Here, we show that classifiers based on a deep sparse coding model whose classification accuracy is competitive with a variety of deep neural network models are robust to adversarial examples that effectively fool those same deep learning models. We demonstrate both quantitatively and qualitatively that the robustness of deep sparse coding models to adversarial examples arises from two key properties. First, because deep sparse coding models learn general features corresponding to generators of the dataset as a whole, rather than highly discriminative features for distinguishing specific classes, the resulting classifiers are less dependent on idiosyncratic features that might be more easily exploited. Second, because deep sparse coding models utilize fixed point attractor dynamics with top-down feedback, it is more difficult to find small changes to the input that drive the resulting representations out of the correct attractor basin.", "published": "2018-11-17T19:39:54Z", "authors": ["Jacob M. Springer", "Charles S. Strauss", "Austin M. Thresher", "Edward Kim", "Garrett T. Kenyon"], "categories": ["cs.LG", "cs.CR", "cs.CV", "cs.NE", "stat.ML"], "doi": "", "_query": "real estate valuation deep learning", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2412.20992v1", "title": "Verified Lifting of Deep learning Operators", "abstract": "Deep learning operators are fundamental components of modern deep learning frameworks. With the growing demand for customized operators, it has become increasingly common for developers to create their own. However, designing and implementing operators is complex and error-prone, due to hardware-specific optimizations and the need for numerical stability. There is a pressing need for tools that can summarize the functionality of both existing and user-defined operators. To address this gap, this work introduces a novel framework for the verified lifting of deep learning operators, which synthesizes high-level mathematical formulas from low-level implementations. Our approach combines symbolic execution, syntax-guided synthesis, and SMT-based verification to produce readable and formally verified mathematical formulas. In synthesis, we employ a combination of top-down and bottom-up strategies to explore the vast search space efficiently; In verification, we design invariant synthesis patterns and leverage SMT solvers to validate the correctness of the derived summaries; In simplification, we use egraph-based techniques with custom rules to restore complex formulas to their natural, intuitive forms. Evaluated on a dataset of deep learning operators implemented in Triton from the real world, our method demonstrates the effectiveness of synthesis and verification compared to existing techniques. This framework bridges the gap between low-level implementations and high-level abstractions, improving understanding and reliability in deep learning operator development.", "published": "2024-12-30T14:57:32Z", "authors": ["Qi Zhan", "Xing Hu", "Xin Xia", "Shanping Li"], "categories": ["cs.LG", "cs.PL", "stat.ML"], "doi": "", "_query": "real estate valuation deep learning", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2605.29326v1", "title": "NeuroEdge: Real-Time Hand Gesture Recognition with High-Density EMG Using Deep Learning at the Edge", "abstract": "High-density electromyography (HD-EMG) has emerged as a powerful modality for decoding fine-grained neuromuscular activity, enabling real-time neural-machine interfaces (NMIs) for applications such as prosthetic control, rehabilitation, and augmented interaction. While deep learning approaches such as convolutional neural networks (CNNs)have demonstrated high classification accuracy for EMG-based gesture recognition, their deployment on embedded hardware remains a major challenge due to computational and memory constraints. This paper presents NeuroEdge, a real-time HD EMG-based NMI system that performs gesture recognition entirely on resource-constrained microcontrollers. The system features two custom-designed modules: the HD-EMG StreamBridge, a wireless communication interface that streams raw HD-EMG data from a Quattrocento amplifier to an ESP32 microcontroller; and the EdgeDL Inference Engine, a lightweight deep learning framework executing on a Sony Spresense microcontroller. A compact 1-dimensional CNN optimized for embedded inference processes, sliding windows of EMG data in real time. Data streaming and inference are pipelined and synchronized through an architecture that utilizes Direct Memory Access (DMA) for data transfer and Serial Peripheral Interface (SPI) burst communication between the ESP32 and Spresense, ensuring low-latency performance. Experimental results show that NeuroEdge achieves a real-time classification accuracy of 90% across seven hand gestures, with a total average latency of 83 ms using 192 channels of HD-EMG recorded from the forearm. Our system demonstrates the feasibility of deploying complex HD-EMG-based gesture recognition on microcontroller-based edge devices, bridging the gap between high-resolution biosignal acquisition and deep learning-based embedded inference for next-generation NMIs.", "published": "2026-05-28T04:01:06Z", "authors": ["Peter Chudinov", "Zhenyu Lin", "Jay Motamarry", "Srihita Panati", "Xiaorong Zhang", "Zhuwei Qin"], "categories": ["cs.LG"], "doi": "", "_query": "real estate valuation deep learning", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/1906.02506v2", "title": "Practical Deep Learning with Bayesian Principles", "abstract": "Bayesian methods promise to fix many shortcomings of deep learning, but they are impractical and rarely match the performance of standard methods, let alone improve them. In this paper, we demonstrate practical training of deep networks with natural-gradient variational inference. By applying techniques such as batch normalisation, data augmentation, and distributed training, we achieve similar performance in about the same number of epochs as the Adam optimiser, even on large datasets such as ImageNet. Importantly, the benefits of Bayesian principles are preserved: predictive probabilities are well-calibrated, uncertainties on out-of-distribution data are improved, and continual-learning performance is boosted. This work enables practical deep learning while preserving benefits of Bayesian principles. A PyTorch implementation is available as a plug-and-play optimiser.", "published": "2019-06-06T10:22:45Z", "authors": ["Kazuki Osawa", "Siddharth Swaroop", "Anirudh Jain", "Runa Eschenhagen", "Richard E. Turner", "Rio Yokota", "Mohammad Emtiyaz Khan"], "categories": ["stat.ML", "cs.LG"], "doi": "", "_query": "real estate valuation deep learning", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/1802.01396v3", "title": "To understand deep learning we need to understand kernel learning", "abstract": "Generalization performance of classifiers in deep learning has recently become a subject of intense study. Deep models, typically over-parametrized, tend to fit the training data exactly. Despite this \"overfitting\", they perform well on test data, a phenomenon not yet fully understood. The first point of our paper is that strong performance of overfitted classifiers is not a unique feature of deep learning. Using six real-world and two synthetic datasets, we establish experimentally that kernel machines trained to have zero classification or near zero regression error perform very well on test data, even when the labels are corrupted with a high level of noise. We proceed to give a lower bound on the norm of zero loss solutions for smooth kernels, showing that they increase nearly exponentially with data size. We point out that this is difficult to reconcile with the existing generalization bounds. Moreover, none of the bounds produce non-trivial results for interpolating solutions. Second, we show experimentally that (non-smooth) Laplacian kernels easily fit random labels, a finding that parallels results for ReLU neural networks. In contrast, fitting noisy data requires many more epochs for smooth Gaussian kernels. Similar performance of overfitted Laplacian and Gaussian classifiers on test, suggests that generalization is tied to the properties of the kernel function rather than the optimization process. Certain key phenomena of deep learning are manifested similarly in kernel methods in the modern \"overfitted\" regime. The combination of the experimental and theoretical results presented in this paper indicates a need for new theoretical ideas for understanding properties of classical kernel methods. We argue that progress on understanding deep learning will be difficult until more tractable \"shallow\" kernel methods are better understood.", "published": "2018-02-05T14:14:28Z", "authors": ["Mikhail Belkin", "Siyuan Ma", "Soumik Mandal"], "categories": ["stat.ML", "cs.LG"], "doi": "", "_query": "real estate valuation deep learning", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/1902.11261v5", "title": "NOODL: Provable Online Dictionary Learning and Sparse Coding", "abstract": "We consider the dictionary learning problem, where the aim is to model the given data as a linear combination of a few columns of a matrix known as a dictionary, where the sparse weights forming the linear combination are known as coefficients. Since the dictionary and coefficients, parameterizing the linear model are unknown, the corresponding optimization is inherently non-convex. This was a major challenge until recently, when provable algorithms for dictionary learning were proposed. Yet, these provide guarantees only on the recovery of the dictionary, without explicit recovery guarantees on the coefficients. Moreover, any estimation error in the dictionary adversely impacts the ability to successfully localize and estimate the coefficients. This potentially limits the utility of existing provable dictionary learning methods in applications where coefficient recovery is of interest. To this end, we develop NOODL: a simple Neurally plausible alternating Optimization-based Online Dictionary Learning algorithm, which recovers both the dictionary and coefficients exactly at a geometric rate, when initialized appropriately. Our algorithm, NOODL, is also scalable and amenable for large scale distributed implementations in neural architectures, by which we mean that it only involves simple linear and non-linear operations. Finally, we corroborate these theoretical results via experimental evaluation of the proposed algorithm with the current state-of-the-art techniques. Keywords: dictionary learning, provable dictionary learning, online dictionary learning, non-convex, sparse coding, support recovery, iterative hard thresholding, matrix factorization, neural architectures, neural networks, noodl, sparse representations, sparse signal processing.", "published": "2019-02-28T18:08:01Z", "authors": ["Sirisha Rambhatla", "Xingguo Li", "Jarvis Haupt"], "categories": ["cs.LG", "cs.NE", "stat.ML"], "doi": "", "_query": "real estate valuation deep learning", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2201.08520v2", "title": "Learning Two-Step Hybrid Policy for Graph-Based Interpretable Reinforcement Learning", "abstract": "We present a two-step hybrid reinforcement learning (RL) policy that is designed to generate interpretable and robust hierarchical policies on the RL problem with graph-based input. Unlike prior deep reinforcement learning policies parameterized by an end-to-end black-box graph neural network, our approach disentangles the decision-making process into two steps. The first step is a simplified classification problem that maps the graph input to an action group where all actions share a similar semantic meaning. The second step implements a sophisticated rule-miner that conducts explicit one-hop reasoning over the graph and identifies decisive edges in the graph input without the necessity of heavy domain knowledge. This two-step hybrid policy presents human-friendly interpretations and achieves better performance in terms of generalization and robustness. Extensive experimental studies on four levels of complex text-based games have demonstrated the superiority of the proposed method compared to the state-of-the-art.", "published": "2022-01-21T03:06:24Z", "authors": ["Tongzhou Mu", "Kaixiang Lin", "Feiyang Niu", "Govind Thattai"], "categories": ["cs.LG", "cs.AI", "cs.CL"], "doi": "", "_query": "real estate valuation deep learning", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2006.05468v3", "title": "Variational Auto-Regressive Gaussian Processes for Continual Learning", "abstract": "Through sequential construction of posteriors on observing data online, Bayes' theorem provides a natural framework for continual learning. We develop Variational Auto-Regressive Gaussian Processes (VAR-GPs), a principled posterior updating mechanism to solve sequential tasks in continual learning. By relying on sparse inducing point approximations for scalable posteriors, we propose a novel auto-regressive variational distribution which reveals two fruitful connections to existing results in Bayesian inference, expectation propagation and orthogonal inducing points. Mean predictive entropy estimates show VAR-GPs prevent catastrophic forgetting, which is empirically supported by strong performance on modern continual learning benchmarks against competitive baselines. A thorough ablation study demonstrates the efficacy of our modeling choices.", "published": "2020-06-09T19:23:57Z", "authors": ["Sanyam Kapoor", "Theofanis Karaletsos", "Thang D. Bui"], "categories": ["stat.ML", "cs.LG"], "doi": "", "_query": "real estate valuation deep learning", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/1812.07102v1", "title": "Deep Learning with Attention to Predict Gestational Age of the Fetal Brain", "abstract": "Fetal brain imaging is a cornerstone of prenatal screening and early diagnosis of congenital anomalies. Knowledge of fetal gestational age is the key to the accurate assessment of brain development. This study develops an attention-based deep learning model to predict gestational age of the fetal brain. The proposed model is an end-to-end framework that combines key insights from multi-view MRI including axial, coronal, and sagittal views. The model also uses age-activated weakly-supervised attention maps to enable rotation-invariant localization of the fetal brain among background noise. We evaluate our methods on the collected fetal brain MRI cohort with a large age distribution from 125 to 273 days. Our extensive experiments show age prediction performance with R2 = 0.94 using multi-view MRI and attention.", "published": "2018-12-09T09:30:11Z", "authors": ["Liyue Shen", "Katie Shpanskaya", "Edward Lee", "Emily McKenna", "Maryam Maleki", "Quin Lu", "Safwan Halabi", "John Pauly", "Kristen Yeom"], "categories": ["cs.CV", "cs.LG", "stat.ML"], "doi": "", "_query": "real estate valuation deep learning", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/1901.09066v1", "title": "Visualizing Semantic Structures of Sequential Data by Learning Temporal Dependencies", "abstract": "While conventional methods for sequential learning focus on interaction between consecutive inputs, we suggest a new method which captures composite semantic flows with variable-length dependencies. In addition, the semantic structures within given sequential data can be interpreted by visualizing temporal dependencies learned from the method. The proposed method, called Temporal Dependency Network (TDN), represents a video as a temporal graph whose node represents a frame of the video and whose edge represents the temporal dependency between two frames of a variable distance. The temporal dependency structure of semantic is discovered by learning parameterized kernels of graph convolutional methods. We evaluate the proposed method on the large-scale video dataset, Youtube-8M. By visualizing the temporal dependency structures as experimental results, we show that the suggested method can find the temporal dependency structures of video semantic.", "published": "2019-01-20T18:46:21Z", "authors": ["Kyoung-Woon On", "Eun-Sol Kim", "Yu-Jung Heo", "Byoung-Tak Zhang"], "categories": ["cs.LG", "cs.CL"], "doi": "", "_query": "real estate valuation deep learning", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/1712.02831v1", "title": "RelNN: A Deep Neural Model for Relational Learning", "abstract": "Statistical relational AI (StarAI) aims at reasoning and learning in noisy domains described in terms of objects and relationships by combining probability with first-order logic. With huge advances in deep learning in the current years, combining deep networks with first-order logic has been the focus of several recent studies. Many of the existing attempts, however, only focus on relations and ignore object properties. The attempts that do consider object properties are limited in terms of modelling power or scalability. In this paper, we develop relational neural networks (RelNNs) by adding hidden layers to relational logistic regression (the relational counterpart of logistic regression). We learn latent properties for objects both directly and through general rules. Back-propagation is used for training these models. A modular, layer-wise architecture facilitates utilizing the techniques developed within deep learning community to our architecture. Initial experiments on eight tasks over three real-world datasets show that RelNNs are promising models for relational learning.", "published": "2017-12-07T19:40:01Z", "authors": ["Seyed Mehran Kazemi", "David Poole"], "categories": ["stat.ML", "cs.LG"], "doi": "", "_query": "real estate valuation deep learning", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2210.15353v1", "title": "Learning Discrete Directed Acyclic Graphs via Backpropagation", "abstract": "Recently continuous relaxations have been proposed in order to learn Directed Acyclic Graphs (DAGs) from data by backpropagation, instead of using combinatorial optimization. However, a number of techniques for fully discrete backpropagation could instead be applied. In this paper, we explore that direction and propose DAG-DB, a framework for learning DAGs by Discrete Backpropagation. Based on the architecture of Implicit Maximum Likelihood Estimation [I-MLE, arXiv:2106.01798], DAG-DB adopts a probabilistic approach to the problem, sampling binary adjacency matrices from an implicit probability distribution. DAG-DB learns a parameter for the distribution from the loss incurred by each sample, performing competitively using either of two fully discrete backpropagation techniques, namely I-MLE and Straight-Through Estimation.", "published": "2022-10-27T12:03:55Z", "authors": ["Andrew J. Wren", "Pasquale Minervini", "Luca Franceschi", "Valentina Zantedeschi"], "categories": ["cs.LG", "cs.AI", "stat.ML"], "doi": "", "_query": "real estate valuation deep learning", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2107.14759v1", "title": "Tiny Machine Learning for Concept Drift", "abstract": "Tiny Machine Learning (TML) is a new research area whose goal is to design machine and deep learning techniques able to operate in Embedded Systems and IoT units, hence satisfying the severe technological constraints on memory, computation, and energy characterizing these pervasive devices. Interestingly, the related literature mainly focused on reducing the computational and memory demand of the inference phase of machine and deep learning models. At the same time, the training is typically assumed to be carried out in Cloud or edge computing systems (due to the larger memory and computational requirements). This assumption results in TML solutions that might become obsolete when the process generating the data is affected by concept drift (e.g., due to periodicity or seasonality effect, faults or malfunctioning affecting sensors or actuators, or changes in the users' behavior), a common situation in real-world application scenarios. For the first time in the literature, this paper introduces a Tiny Machine Learning for Concept Drift (TML-CD) solution based on deep learning feature extractors and a k-nearest neighbors classifier integrating a hybrid adaptation module able to deal with concept drift affecting the data-generating process. This adaptation module continuously updates (in a passive way) the knowledge base of TML-CD and, at the same time, employs a Change Detection Test to inspect for changes (in an active way) to quickly adapt to concept drift by removing the obsolete knowledge. Experimental results on both image and audio benchmarks show the effectiveness of the proposed solution, whilst the porting of TML-CD on three off-the-shelf micro-controller units shows the feasibility of what is proposed in real-world pervasive systems.", "published": "2021-07-30T17:02:04Z", "authors": ["Simone Disabato", "Manuel Roveri"], "categories": ["cs.LG"], "doi": "", "_query": "real estate valuation deep learning", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2302.14567v1", "title": "Active Learning with Combinatorial Coverage", "abstract": "Active learning is a practical field of machine learning that automates the process of selecting which data to label. Current methods are effective in reducing the burden of data labeling but are heavily model-reliant. This has led to the inability of sampled data to be transferred to new models as well as issues with sampling bias. Both issues are of crucial concern in machine learning deployment. We propose active learning methods utilizing combinatorial coverage to overcome these issues. The proposed methods are data-centric, as opposed to model-centric, and through our experiments we show that the inclusion of coverage in active learning leads to sampling data that tends to be the best in transferring to better performing models and has a competitive sampling bias compared to benchmark methods.", "published": "2023-02-28T13:43:23Z", "authors": ["Sai Prathyush Katragadda", "Tyler Cody", "Peter Beling", "Laura Freeman"], "categories": ["cs.LG", "stat.ML"], "doi": "", "_query": "real estate valuation deep learning", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/1905.08786v3", "title": "Maximum Entropy-Regularized Multi-Goal Reinforcement Learning", "abstract": "In Multi-Goal Reinforcement Learning, an agent learns to achieve multiple goals with a goal-conditioned policy. During learning, the agent first collects the trajectories into a replay buffer, and later these trajectories are selected randomly for replay. However, the achieved goals in the replay buffer are often biased towards the behavior policies. From a Bayesian perspective, when there is no prior knowledge about the target goal distribution, the agent should learn uniformly from diverse achieved goals. Therefore, we first propose a novel multi-goal RL objective based on weighted entropy. This objective encourages the agent to maximize the expected return, as well as to achieve more diverse goals. Secondly, we developed a maximum entropy-based prioritization framework to optimize the proposed objective. For evaluation of this framework, we combine it with Deep Deterministic Policy Gradient, both with or without Hindsight Experience Replay. On a set of multi-goal robotic tasks of OpenAI Gym, we compare our method with other baselines and show promising improvements in both performance and sample-efficiency.", "published": "2019-05-21T11:38:39Z", "authors": ["Rui Zhao", "Xudong Sun", "Volker Tresp"], "categories": ["cs.LG", "stat.ML"], "doi": "", "_query": "real estate valuation deep learning", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2307.12083v3", "title": "Active Control of Flow over Rotating Cylinder by Multiple Jets using Deep Reinforcement Learning", "abstract": "The real power of artificial intelligence appears in reinforcement learning, which is computationally and physically more sophisticated due to its dynamic nature. Rotation and injection are some of the proven ways in active flow control for drag reduction on blunt bodies. In this paper, rotation will be added to the cylinder alongside the deep reinforcement learning (DRL) algorithm, which uses multiple controlled jets to reach the maximum possible drag suppression. Characteristics of the DRL code, including controlling parameters, their limitations, and optimization of the DRL network for use with rotation will be presented. This work will focus on optimizing the number and positions of the jets, the sensors location, and the maximum allowed flow rate to jets in the form of the maximum allowed flow rate of each actuation and the total number of them per episode. It is found that combining the rotation and DRL is promising since it suppresses the vortex shedding, stabilizes the Karman vortex street, and reduces the drag coefficient by up to 49.75%. Also, it will be shown that having more sensors at more locations is not always a good choice and the sensor number and location should be determined based on the need of the user and corresponding configuration. Also, allowing the agent to have access to higher flow rates, mostly reduces the performance, except when the cylinder rotates. In all cases, the agent can keep the lift coefficient at a value near zero, or stabilize it at a smaller number.", "published": "2023-07-22T14:15:29Z", "authors": ["Kamyar Dobakhti", "Jafar Ghazanfarian"], "categories": ["physics.flu-dyn", "cs.LG"], "doi": "", "_query": "real estate valuation deep learning", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/1806.08894v1", "title": "Deep Reinforcement Learning: An Overview", "abstract": "In recent years, a specific machine learning method called deep learning has gained huge attraction, as it has obtained astonishing results in broad applications such as pattern recognition, speech recognition, computer vision, and natural language processing. Recent research has also been shown that deep learning techniques can be combined with reinforcement learning methods to learn useful representations for the problems with high dimensional raw data input. This chapter reviews the recent advances in deep reinforcement learning with a focus on the most used deep architectures such as autoencoders, convolutional neural networks and recurrent neural networks which have successfully been come together with the reinforcement learning framework.", "published": "2018-06-23T02:18:26Z", "authors": ["Seyed Sajad Mousavi", "Michael Schukat", "Enda Howley"], "categories": ["cs.LG", "cs.AI", "stat.ML"], "doi": "10.1007/978-3-319-56991-8_32", "_query": "real estate valuation deep learning", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/1807.08169v1", "title": "Recent Advances in Deep Learning: An Overview", "abstract": "Deep Learning is one of the newest trends in Machine Learning and Artificial Intelligence research. It is also one of the most popular scientific research trends now-a-days. Deep learning methods have brought revolutionary advances in computer vision and machine learning. Every now and then, new and new deep learning techniques are being born, outperforming state-of-the-art machine learning and even existing deep learning techniques. In recent years, the world has seen many major breakthroughs in this field. Since deep learning is evolving at a huge speed, its kind of hard to keep track of the regular advances especially for new researchers. In this paper, we are going to briefly discuss about recent advances in Deep Learning for past few years.", "published": "2018-07-21T15:40:10Z", "authors": ["Matiur Rahman Minar", "Jibon Naher"], "categories": ["cs.LG", "cs.AI", "stat.ML"], "doi": "10.13140/RG.2.2.24831.10403", "_query": "real estate valuation deep learning", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/1206.6462v1", "title": "Learning Object Arrangements in 3D Scenes using Human Context", "abstract": "We consider the problem of learning object arrangements in a 3D scene. The key idea here is to learn how objects relate to human poses based on their affordances, ease of use and reachability. In contrast to modeling object-object relationships, modeling human-object relationships scales linearly in the number of objects. We design appropriate density functions based on 3D spatial features to capture this. We learn the distribution of human poses in a scene using a variant of the Dirichlet process mixture model that allows sharing of the density function parameters across the same object types. Then we can reason about arrangements of the objects in the room based on these meaningful human poses. In our extensive experiments on 20 different rooms with a total of 47 objects, our algorithm predicted correct placements with an average error of 1.6 meters from ground truth. In arranging five real scenes, it received a score of 4.3/5 compared to 3.7 for the best baseline method.", "published": "2012-06-27T19:59:59Z", "authors": ["Yun Jiang", "Marcus Lim", "Ashutosh Saxena"], "categories": ["cs.LG", "cs.CV", "cs.RO", "stat.ML"], "doi": "", "_query": "real estate valuation deep learning", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2306.10189v4", "title": "MOCK: an Algorithm for Learning Nonparametric Differential Equations via Multivariate Occupation Kernel Functions", "abstract": "Learning a nonparametric system of ordinary differential equations from trajectories in a $d$-dimensional state space requires learning $d$ functions of $d$ variables. Explicit formulations often scale quadratically in $d$ unless additional knowledge about system properties, such as sparsity and symmetries, is available. In this work, we propose a linear approach, the multivariate occupation kernel method (MOCK), using the implicit formulation provided by vector-valued reproducing kernel Hilbert spaces. The solution for the vector field relies on multivariate occupation kernel functions associated with the trajectories and scales linearly with the dimension of the state space. We validate through experiments on a variety of simulated and real datasets ranging from 2 to 1024 dimensions. MOCK outperforms all other comparators on 3 of the 9 datasets on full trajectory prediction and 4 out of the 9 datasets on next-point prediction.", "published": "2023-06-16T21:49:36Z", "authors": ["Victor Rielly", "Kamel Lahouel", "Ethan Lew", "Nicholas Fisher", "Vicky Haney", "Michael Wells", "Bruno Jedynak"], "categories": ["stat.ML", "cs.LG"], "doi": "", "_query": "real estate valuation deep learning", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/1206.6443v2", "title": "Isoelastic Agents and Wealth Updates in Machine Learning Markets", "abstract": "Recently, prediction markets have shown considerable promise for developing flexible mechanisms for machine learning. In this paper, agents with isoelastic utilities are considered. It is shown that the costs associated with homogeneous markets of agents with isoelastic utilities produce equilibrium prices corresponding to alpha-mixtures, with a particular form of mixing component relating to each agent's wealth. We also demonstrate that wealth accumulation for logarithmic and other isoelastic agents (through payoffs on prediction of training targets) can implement both Bayesian model updates and mixture weight updates by imposing different market payoff structures. An iterative algorithm is given for market equilibrium computation. We demonstrate that inhomogeneous markets of agents with isoelastic utilities outperform state of the art aggregate classifiers such as random forests, as well as single classifiers (neural networks, decision trees) on a number of machine learning benchmarks, and show that isoelastic combination methods are generally better than their logarithmic counterparts.", "published": "2012-06-27T19:59:59Z", "authors": ["Amos Storkey", "Jono Millin", "Krzysztof Geras"], "categories": ["cs.LG", "cs.GT", "stat.ML"], "doi": "", "_query": "real estate valuation deep learning", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2502.18553v3", "title": "Applications of Statistical Field Theory in Deep Learning", "abstract": "Deep learning algorithms have made incredible strides in the past decade, yet due to their complexity, the science of deep learning remains in its early stages. Being an experimentally driven field, it is natural to seek a theory of deep learning within the physics paradigm. As deep learning is largely about learning functions and distributions over functions, statistical field theory, a rich and versatile toolbox for tackling complex distributions over functions (fields) is an obvious choice of formalism. Research efforts carried out in the past few years have demonstrated the ability of field theory to provide useful insights on generalization, implicit bias, and feature learning effects. Here we provide a pedagogical review of this emerging line of research.", "published": "2025-02-25T18:19:06Z", "authors": ["Zohar Ringel", "Noa Rubin", "Edo Mor", "Moritz Helias", "Inbar Seroussi"], "categories": ["stat.ML", "cond-mat.dis-nn", "cs.AI", "cs.LG"], "doi": "", "_query": "real estate valuation deep learning", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/1901.09697v5", "title": "Bayesian Differential Privacy for Machine Learning", "abstract": "Traditional differential privacy is independent of the data distribution. However, this is not well-matched with the modern machine learning context, where models are trained on specific data. As a result, achieving meaningful privacy guarantees in ML often excessively reduces accuracy. We propose Bayesian differential privacy (BDP), which takes into account the data distribution to provide more practical privacy guarantees. We also derive a general privacy accounting method under BDP, building upon the well-known moments accountant. Our experiments demonstrate that in-distribution samples in classic machine learning datasets, such as MNIST and CIFAR-10, enjoy significantly stronger privacy guarantees than postulated by DP, while models maintain high classification accuracy.", "published": "2019-01-28T14:37:17Z", "authors": ["Aleksei Triastcyn", "Boi Faltings"], "categories": ["cs.LG", "cs.CR", "stat.ML"], "doi": "", "_query": "real estate valuation deep learning", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/1904.05391v5", "title": "Deep Learning without Weight Transport", "abstract": "Current algorithms for deep learning probably cannot run in the brain because they rely on weight transport, where forward-path neurons transmit their synaptic weights to a feedback path, in a way that is likely impossible biologically. An algorithm called feedback alignment achieves deep learning without weight transport by using random feedback weights, but it performs poorly on hard visual-recognition tasks. Here we describe two mechanisms - a neural circuit called a weight mirror and a modification of an algorithm proposed by Kolen and Pollack in 1994 - both of which let the feedback path learn appropriate synaptic weights quickly and accurately even in large networks, without weight transport or complex wiring.Tested on the ImageNet visual-recognition task, these mechanisms outperform both feedback alignment and the newer sign-symmetry method, and nearly match backprop, the standard algorithm of deep learning, which uses weight transport.", "published": "2019-04-10T18:55:59Z", "authors": ["Mohamed Akrout", "Collin Wilson", "Peter C. Humphreys", "Timothy Lillicrap", "Douglas Tweed"], "categories": ["cs.LG", "stat.ML"], "doi": "", "_query": "real estate valuation deep learning", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2609.13272v1", "title": "Survey of Novel Deep Learning Architectures for Denoising Gravitational-wave Signals", "abstract": "Gravitational-wave denoising must handle the full diversity of spinning, precessing binaries, since the recovered waveform underpins parameter estimation, tests of general relativity, and population studies. Matched filtering achieves this at a cost that becomes prohibitive as next-generation detectors push event rates higher; deep learning offers real-time reconstruction, but current methods are developed on narrow parameter spaces, precluding principled comparison and reliable deployment. We present the first controlled comparison of five neural-network architectures for gravitational-wave denoising, trained identically across the full astrophysically-motivated spinning binary-black-hole parameter space. A unifying principle emerges: matching network structure to the spectral anatomy of a coalescence -- inspiral, merger, ringdown -- outperforms brute-force model scaling. Our Multi-Scale Frequency-Aware architecture embodies this via dedicated parallel branches per frequency regime, achieving the best fidelity while using fewer parameters than larger models. It generalizes from simulated training to real LIGO-Virgo-KAGRA data without retraining, recovering merger morphology across confirmed events spanning three observing runs and both detectors, despite training on a single detector's simulated noise. We construct population-level uncertainty bands from denoising residuals, validate their calibration, and stress-test the framework on extended mass ratios and a spin population resembling hierarchical-merger remnants. Applied to real noise with no known signal, the network suppresses its output almost everywhere, with rare exceptions traced to noise transients rather than a general weakness -- indicating the statistic discriminates signal from noise and motivating a future detection study. Released weights give a deployable, reproducible benchmark for future extensions.", "published": "2026-09-07T18:00:10Z", "authors": ["Rohan Raha", "Prayush Kumar"], "categories": ["gr-qc", "astro-ph.HE", "astro-ph.IM", "cs.LG", "cs.NE"], "doi": "", "_query": "real estate valuation deep learning", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2006.06791v1", "title": "Deep Transfer Learning with Ridge Regression", "abstract": "The large amount of online data and vast array of computing resources enable current researchers in both industry and academia to employ the power of deep learning with neural networks. While deep models trained with massive amounts of data demonstrate promising generalisation ability on unseen data from relevant domains, the computational cost of finetuning gradually becomes a bottleneck in transfering the learning to new domains. We address this issue by leveraging the low-rank property of learnt feature vectors produced from deep neural networks (DNNs) with the closed-form solution provided in kernel ridge regression (KRR). This frees transfer learning from finetuning and replaces it with an ensemble of linear systems with many fewer hyperparameters. Our method is successful on supervised and semi-supervised transfer learning tasks.", "published": "2020-06-11T20:21:35Z", "authors": ["Shuai Tang", "Virginia R. de Sa"], "categories": ["cs.LG", "stat.ML"], "doi": "", "_query": "real estate valuation deep learning", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2607.03998v5", "title": "Directional Curvature from Armijo Backtracking: A Low-Cost Sharpness Probe and a Calibration-Free Learning-Rate Safeguard for Adam", "abstract": "Local sharpness, defined by the largest Hessian eigenvalue $λ_1$, sets the maximum stable gradient update size, but its computation would usually require running Lanczos or Hessian-vector products. However, we notice that even a single Armijo backtracking line search already contains this information with just a few forward passes, as the accepted step $α$ determines the directional curvature along the search direction up to the multiplicative band set by the backtracking factor. The correlation between $\\logα$ and $\\logλ_1$ on CIFAR-10, Fashion-MNIST and Imagenette reaches $-0.91$ to $-0.95$ in Pearson correlation, and even after removing the trend per run the correlation remains at $-0.60$ to $-0.70$. This allows for a cheap online Edge-of-Stability estimate of the slow sharpness component. The employed probing mechanism searches along Adam's first-step update direction, at initialisation and nine times over the course of the first 50 optimiser steps. The learning-rate cap is set as twice the smallest observed step size, and in the studied learning-rate ranges ($10^{-3}$ to $3.0$) and GPT-2 pretraining experiments all capped runs avoid divergence; in the general architecture analysis, one MLP architecture is still sensitive to the initial batch order. This probing protocol incurs an approximately one percent overhead, and using a non-binding cap means that the optimiser's state and first update are bit-identical. There is no fine-tuning of any of the protocol parameters to any specific architecture; this is the sense in which this is a calibration-free safeguard. It is meant as a way of avoiding divergence, not achieving accuracy. At GPT-2 scale, multiple measurements also illustrate why an initialisation-only cap is insufficient: directional curvature rises substantially in the first five optimiser steps, which motivates the short probationary window.", "published": "2026-07-04T19:49:01Z", "authors": ["Ashmitha R", "Jörg Frochte"], "categories": ["cs.LG"], "doi": "", "_query": "real estate valuation deep learning", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/1805.07504v2", "title": "Deep Loopy Neural Network Model for Graph Structured Data Representation Learning", "abstract": "Existing deep learning models may encounter great challenges in handling graph structured data. In this paper, we introduce a new deep learning model for graph data specifically, namely the deep loopy neural network. Significantly different from the previous deep models, inside the deep loopy neural network, there exist a large number of loops created by the extensive connections among nodes in the input graph data, which makes model learning an infeasible task. To resolve such a problem, in this paper, we will introduce a new learning algorithm for the deep loopy neural network specifically. Instead of learning the model variables based on the original model, in the proposed learning algorithm, errors will be back-propagated through the edges in a group of extracted spanning trees. Extensive numerical experiments have been done on several real-world graph datasets, and the experimental results demonstrate the effectiveness of both the proposed model and the learning algorithm in handling graph data.", "published": "2018-05-19T03:33:20Z", "authors": ["Jiawei Zhang"], "categories": ["cs.LG", "cs.AI", "cs.NE", "stat.ML"], "doi": "", "_query": "real estate valuation deep learning", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2011.02578v2", "title": "Learning and Evaluating Representations for Deep One-class Classification", "abstract": "We present a two-stage framework for deep one-class classification. We first learn self-supervised representations from one-class data, and then build one-class classifiers on learned representations. The framework not only allows to learn better representations, but also permits building one-class classifiers that are faithful to the target task. We argue that classifiers inspired by the statistical perspective in generative or discriminative models are more effective than existing approaches, such as a normality score from a surrogate classifier. We thoroughly evaluate different self-supervised representation learning algorithms under the proposed framework for one-class classification. Moreover, we present a novel distribution-augmented contrastive learning that extends training distributions via data augmentation to obstruct the uniformity of contrastive representations. In experiments, we demonstrate state-of-the-art performance on visual domain one-class classification benchmarks, including novelty and anomaly detection. Finally, we present visual explanations, confirming that the decision-making process of deep one-class classifiers is intuitive to humans. The code is available at https://github.com/google-research/deep_representation_one_class.", "published": "2020-11-04T23:33:41Z", "authors": ["Kihyuk Sohn", "Chun-Liang Li", "Jinsung Yoon", "Minho Jin", "Tomas Pfister"], "categories": ["cs.CV"], "doi": "", "_query": "real estate valuation deep learning", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2406.01098v1", "title": "Learning Decision Trees and Forests with Algorithmic Recourse", "abstract": "This paper proposes a new algorithm for learning accurate tree-based models while ensuring the existence of recourse actions. Algorithmic Recourse (AR) aims to provide a recourse action for altering the undesired prediction result given by a model. Typical AR methods provide a reasonable action by solving an optimization task of minimizing the required effort among executable actions. In practice, however, such actions do not always exist for models optimized only for predictive performance. To alleviate this issue, we formulate the task of learning an accurate classification tree under the constraint of ensuring the existence of reasonable actions for as many instances as possible. Then, we propose an efficient top-down greedy algorithm by leveraging the adversarial training techniques. We also show that our proposed algorithm can be applied to the random forest, which is known as a popular framework for learning tree ensembles. Experimental results demonstrated that our method successfully provided reasonable actions to more instances than the baselines without significantly degrading accuracy and computational efficiency.", "published": "2024-06-03T08:33:42Z", "authors": ["Kentaro Kanamori", "Takuya Takagi", "Ken Kobayashi", "Yuichi Ike"], "categories": ["cs.LG", "stat.ML"], "doi": "", "_query": "real estate valuation deep learning", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2006.16239v2", "title": "An Imitation Learning Approach for Cache Replacement", "abstract": "Program execution speed critically depends on increasing cache hits, as cache hits are orders of magnitude faster than misses. To increase cache hits, we focus on the problem of cache replacement: choosing which cache line to evict upon inserting a new line. This is challenging because it requires planning far ahead and currently there is no known practical solution. As a result, current replacement policies typically resort to heuristics designed for specific common access patterns, which fail on more diverse and complex access patterns. In contrast, we propose an imitation learning approach to automatically learn cache access patterns by leveraging Belady's, an oracle policy that computes the optimal eviction decision given the future cache accesses. While directly applying Belady's is infeasible since the future is unknown, we train a policy conditioned only on past accesses that accurately approximates Belady's even on diverse and complex access patterns, and call this approach Parrot. When evaluated on 13 of the most memory-intensive SPEC applications, Parrot increases cache miss rates by 20% over the current state of the art. In addition, on a large-scale web search benchmark, Parrot increases cache hit rates by 61% over a conventional LRU policy. We release a Gym environment to facilitate research in this area, as data is plentiful, and further advancements can have significant real-world impact.", "published": "2020-06-29T17:58:40Z", "authors": ["Evan Zheran Liu", "Milad Hashemi", "Kevin Swersky", "Parthasarathy Ranganathan", "Junwhan Ahn"], "categories": ["cs.LG", "cs.AR", "stat.ML"], "doi": "", "_query": "real estate valuation deep learning", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2207.07958v1", "title": "FastML Science Benchmarks: Accelerating Real-Time Scientific Edge Machine Learning", "abstract": "Applications of machine learning (ML) are growing by the day for many unique and challenging scientific applications. However, a crucial challenge facing these applications is their need for ultra low-latency and on-detector ML capabilities. Given the slowdown in Moore's law and Dennard scaling, coupled with the rapid advances in scientific instrumentation that is resulting in growing data rates, there is a need for ultra-fast ML at the extreme edge. Fast ML at the edge is essential for reducing and filtering scientific data in real-time to accelerate science experimentation and enable more profound insights. To accelerate real-time scientific edge ML hardware and software solutions, we need well-constrained benchmark tasks with enough specifications to be generically applicable and accessible. These benchmarks can guide the design of future edge ML hardware for scientific applications capable of meeting the nanosecond and microsecond level latency requirements. To this end, we present an initial set of scientific ML benchmarks, covering a variety of ML and embedded system techniques.", "published": "2022-07-16T14:30:15Z", "authors": ["Javier Duarte", "Nhan Tran", "Ben Hawks", "Christian Herwig", "Jules Muhizi", "Shvetank Prakash", "Vijay Janapa Reddi"], "categories": ["cs.LG", "physics.comp-ph", "physics.ins-det"], "doi": "", "_query": "real estate valuation deep learning", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/1912.12270v4", "title": "Combining Deep Learning and Verification for Precise Object Instance Detection", "abstract": "Deep learning object detectors often return false positives with very high confidence. Although they optimize generic detection performance, such as mean average precision (mAP), they are not designed for reliability. For a reliable detection system, if a high confidence detection is made, we would want high certainty that the object has indeed been detected. To achieve this, we have developed a set of verification tests which a proposed detection must pass to be accepted. We develop a theoretical framework which proves that, under certain assumptions, our verification tests will not accept any false positives. Based on an approximation to this framework, we present a practical detection system that can verify, with high precision, whether each detection of a machine-learning based object detector is correct. We show that these tests can improve the overall accuracy of a base detector and that accepted examples are highly likely to be correct. This allows the detector to operate in a high precision regime and can thus be used for robotic perception systems as a reliable instance detection method. Code is available at https://github.com/siddancha/FlowVerify.", "published": "2019-12-27T18:11:20Z", "authors": ["Siddharth Ancha", "Junyu Nan", "David Held"], "categories": ["cs.CV", "cs.LG", "eess.IV"], "doi": "", "_query": "real estate valuation deep learning", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/1807.01521v3", "title": "Curiosity Driven Exploration of Learned Disentangled Goal Spaces", "abstract": "Intrinsically motivated goal exploration processes enable agents to autonomously sample goals to explore efficiently complex environments with high-dimensional continuous actions. They have been applied successfully to real world robots to discover repertoires of policies producing a wide diversity of effects. Often these algorithms relied on engineered goal spaces but it was recently shown that one can use deep representation learning algorithms to learn an adequate goal space in simple environments. However, in the case of more complex environments containing multiple objects or distractors, an efficient exploration requires that the structure of the goal space reflects the one of the environment. In this paper we show that using a disentangled goal space leads to better exploration performances than an entangled goal space. We further show that when the representation is disentangled, one can leverage it by sampling goals that maximize learning progress in a modular manner. Finally, we show that the measure of learning progress, used to drive curiosity-driven exploration, can be used simultaneously to discover abstract independently controllable features of the environment.", "published": "2018-07-04T11:23:57Z", "authors": ["Adrien Laversanne-Finot", "Alexandre Péré", "Pierre-Yves Oudeyer"], "categories": ["cs.LG", "cs.AI", "cs.NE", "cs.RO", "stat.ML"], "doi": "", "_query": "real estate valuation deep learning", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/1901.09732v2", "title": "Making Deep Q-learning methods robust to time discretization", "abstract": "Despite remarkable successes, Deep Reinforcement Learning (DRL) is not robust to hyperparameterization, implementation details, or small environment changes (Henderson et al. 2017, Zhang et al. 2018). Overcoming such sensitivity is key to making DRL applicable to real world problems. In this paper, we identify sensitivity to time discretization in near continuous-time environments as a critical factor; this covers, e.g., changing the number of frames per second, or the action frequency of the controller. Empirically, we find that Q-learning-based approaches such as Deep Q- learning (Mnih et al., 2015) and Deep Deterministic Policy Gradient (Lillicrap et al., 2015) collapse with small time steps. Formally, we prove that Q-learning does not exist in continuous time. We detail a principled way to build an off-policy RL algorithm that yields similar performances over a wide range of time discretizations, and confirm this robustness empirically.", "published": "2019-01-28T15:25:10Z", "authors": ["Corentin Tallec", "Léonard Blier", "Yann Ollivier"], "categories": ["cs.LG", "stat.ML"], "doi": "", "_query": "real estate valuation deep learning", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2605.22597v1", "title": "MoSA: Motion-constrained Stress Adaptation for Mitigating Real-to-Sim Gap in Continuum Dynamics via Learning Residual Anisotropy", "abstract": "Learning real-world dynamics from visual observations is crucial for various domains. A common strategy is to calibrate simulators by estimating physical parameters, yet accuracy is ultimately bounded by the underlying physical models, which often assume materials are homogeneous and isotropic. Even if reasonable, real-world objects typically exhibit mild anisotropy and heterogeneity. After the near-isotropic backbone is well calibrated, these residual effects become the key bottleneck for further closing the real-to-sim gap. Although neural networks can fit dynamics end-to-end, such black-box modeling discards strong physical priors, leading to poor data efficiency and overfitting. Therefore, we propose MoSA, a motion-constrained stress adaptation framework that targets these residual effects to further improve real-to-sim dynamics learning. MoSA uses an isotropic model as a physics prior and learns residual stress operators to capture mild anisotropy and heterogeneity. It progressively adapts stresses via microplane-constrained redistribution in a physics-informed cascaded network. We further impose motion constraints by supervising temporal and spatial derivatives of the deformation field. Experimentally, our learned dynamics achieves superior accuracy, generalization, and robustness, while learning physically meaningful residual anisotropy. Finally, we validate MoSA in a robot manipulation setting, showing that better real-to-sim dynamics modeling translates into more reliable sim-to-real transfer. Project Page is available at https://mercerai.github.io/MoSA/.", "published": "2026-05-21T15:13:49Z", "authors": ["Jiaxu Wang", "Junhao He", "Jingkai Sun", "Yi Gu", "Yunyang Mo", "Jiahang Cao", "Qiang Zhang", "Renjing Xu"], "categories": ["cs.LG", "cs.AI", "cs.GR", "cs.RO"], "doi": "", "_query": "real estate valuation deep learning", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2102.09024v1", "title": "Deep Learning Approaches for Forecasting Strawberry Yields and Prices Using Satellite Images and Station-Based Soil Parameters", "abstract": "Computational tools for forecasting yields and prices for fresh produce have been based on traditional machine learning approaches or time series modelling. We propose here an alternate approach based on deep learning algorithms for forecasting strawberry yields and prices in Santa Barbara county, California. Building the proposed forecasting model comprises three stages: first, the station-based ensemble model (ATT-CNN-LSTM-SeriesNet_Ens) with its compound deep learning components, SeriesNet with Gated Recurrent Unit (GRU) and Convolutional Neural Network LSTM with Attention layer (Att-CNN-LSTM), are trained and tested using the station-based soil temperature and moisture data of SantaBarbara as input and the corresponding strawberry yields or prices as output. Secondly, the remote sensing ensemble model (SIM_CNN-LSTM_Ens), which is an ensemble model of Convolutional NeuralNetwork LSTM (CNN-LSTM) models, is trained and tested using satellite images of the same county as input mapped to the same yields and prices as output. These two ensembles forecast strawberry yields and prices with minimal forecasting errors and highest model correlation for five weeks ahead forecasts.Finally, the forecasts of these two models are ensembled to have a final forecasted value for yields and prices by introducing a voting ensemble. Based on an aggregated performance measure (AGM), it is found that this voting ensemble not only enhances the forecasting performance by 5% compared to its best performing component model but also outperforms the Deep Learning (DL) ensemble model found in literature by 33% for forecasting yields and 21% for forecasting prices", "published": "2021-02-17T20:54:34Z", "authors": ["Mohita Chaudhary", "Mohamed Sadok Gastli", "Lobna Nassar", "Fakhri Karray"], "categories": ["cs.LG", "cs.AI"], "doi": "", "_query": "real estate valuation deep learning", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2204.09603v3", "title": "Comparing Deep Reinforcement Learning Algorithms in Two-Echelon Supply Chains", "abstract": "In this study, we analyze and compare the performance of state-of-the-art deep reinforcement learning algorithms for solving the supply chain inventory management problem. This complex sequential decision-making problem consists of determining the optimal quantity of products to be produced and shipped across different warehouses over a given time horizon. In particular, we present a mathematical formulation of a two-echelon supply chain environment with stochastic and seasonal demand, which allows managing an arbitrary number of warehouses and product types. Through a rich set of numerical experiments, we compare the performance of different deep reinforcement learning algorithms under various supply chain structures, topologies, demands, capacities, and costs. The results of the experimental plan indicate that deep reinforcement learning algorithms outperform traditional inventory management strategies, such as the static (s, Q)-policy. Furthermore, this study provides detailed insight into the design and development of an open-source software library that provides a customizable environment for solving the supply chain inventory management problem using a wide range of data-driven approaches.", "published": "2022-04-20T16:33:01Z", "authors": ["Francesco Stranieri", "Fabio Stella"], "categories": ["cs.LG", "cs.AI", "math.OC"], "doi": "10.1007/978-3-031-74640-6_37", "_query": "real estate valuation deep learning", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/1804.04503v2", "title": "Unleashing Linear Optimizers for Group-Fair Learning and Optimization", "abstract": "Most systems and learning algorithms optimize average performance or average loss -- one reason being computational complexity. However, many objectives of practical interest are more complex than simply average loss. This arises, for example, when balancing performance or loss with fairness across people. We prove that, from a computational perspective, optimizing arbitrary objectives that take into account performance over a small number of groups is not significantly harder to optimize than average performance. Our main result is a polynomial-time reduction that uses a linear optimizer to optimize an arbitrary (Lipschitz continuous) function of performance over a (constant) number of possibly-overlapping groups. This includes fairness objectives over small numbers of groups, and we further point out that other existing notions of fairness such as individual fairness can be cast as convex optimization and hence more standard convex techniques can be used. Beyond learning, our approach applies to multi-objective optimization, more generally.", "published": "2018-04-11T02:51:07Z", "authors": ["Daniel Alabi", "Nicole Immorlica", "Adam Tauman Kalai"], "categories": ["cs.LG", "cs.DS", "stat.ML"], "doi": "", "_query": "real estate valuation deep learning", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2104.10093v2", "title": "Class-Incremental Learning with Generative Classifiers", "abstract": "Incrementally training deep neural networks to recognize new classes is a challenging problem. Most existing class-incremental learning methods store data or use generative replay, both of which have drawbacks, while 'rehearsal-free' alternatives such as parameter regularization or bias-correction methods do not consistently achieve high performance. Here, we put forward a new strategy for class-incremental learning: generative classification. Rather than directly learning the conditional distribution p(y|x), our proposal is to learn the joint distribution p(x,y), factorized as p(x|y)p(y), and to perform classification using Bayes' rule. As a proof-of-principle, here we implement this strategy by training a variational autoencoder for each class to be learned and by using importance sampling to estimate the likelihoods p(x|y). This simple approach performs very well on a diverse set of continual learning benchmarks, outperforming generative replay and other existing baselines that do not store data.", "published": "2021-04-20T16:26:14Z", "authors": ["Gido M. van de Ven", "Zhe Li", "Andreas S. Tolias"], "categories": ["cs.LG", "cs.AI", "cs.CV", "stat.ML"], "doi": "", "_query": "real estate valuation deep learning", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2401.00282v1", "title": "Deep Generative Symbolic Regression", "abstract": "Symbolic regression (SR) aims to discover concise closed-form mathematical equations from data, a task fundamental to scientific discovery. However, the problem is highly challenging because closed-form equations lie in a complex combinatorial search space. Existing methods, ranging from heuristic search to reinforcement learning, fail to scale with the number of input variables. We make the observation that closed-form equations often have structural characteristics and invariances (e.g., the commutative law) that could be further exploited to build more effective symbolic regression solutions. Motivated by this observation, our key contribution is to leverage pre-trained deep generative models to capture the intrinsic regularities of equations, thereby providing a solid foundation for subsequent optimization steps. We show that our novel formalism unifies several prominent approaches of symbolic regression and offers a new perspective to justify and improve on the previous ad hoc designs, such as the usage of cross-entropy loss during pre-training. Specifically, we propose an instantiation of our framework, Deep Generative Symbolic Regression (DGSR). In our experiments, we show that DGSR achieves a higher recovery rate of true equations in the setting of a larger number of input variables, and it is more computationally efficient at inference time than state-of-the-art RL symbolic regression solutions.", "published": "2023-12-30T17:05:31Z", "authors": ["Samuel Holt", "Zhaozhi Qian", "Mihaela van der Schaar"], "categories": ["cs.LG"], "doi": "", "_query": "real estate valuation deep learning", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2008.06767v2", "title": "Heterogeneous Federated Learning", "abstract": "Federated learning learns from scattered data by fusing collaborative models from local nodes. However, due to chaotic information distribution, the model fusion may suffer from structural misalignment with regard to unmatched parameters. In this work, we propose a novel federated learning framework to resolve this issue by establishing a firm structure-information alignment across collaborative models. Specifically, we design a feature-oriented regulation method ({$Ψ$-Net}) to ensure explicit feature information allocation in different neural network structures. Applying this regulating method to collaborative models, matchable structures with similar feature information can be initialized at the very early training stage. During the federated learning process under either IID or non-IID scenarios, dedicated collaboration schemes further guarantee ordered information distribution with definite structure matching, so as the comprehensive model alignment. Eventually, this framework effectively enhances the federated learning applicability to extensive heterogeneous settings, while providing excellent convergence speed, accuracy, and computation/communication efficiency.", "published": "2020-08-15T19:06:59Z", "authors": ["Fuxun Yu", "Weishan Zhang", "Zhuwei Qin", "Zirui Xu", "Di Wang", "Chenchen Liu", "Zhi Tian", "Xiang Chen"], "categories": ["cs.LG", "stat.ML"], "doi": "", "_query": "real estate valuation deep learning", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/1911.09781v3", "title": "Beyond Synthetic Noise: Deep Learning on Controlled Noisy Labels", "abstract": "Performing controlled experiments on noisy data is essential in understanding deep learning across noise levels. Due to the lack of suitable datasets, previous research has only examined deep learning on controlled synthetic label noise, and real-world label noise has never been studied in a controlled setting. This paper makes three contributions. First, we establish the first benchmark of controlled real-world label noise from the web. This new benchmark enables us to study the web label noise in a controlled setting for the first time. The second contribution is a simple but effective method to overcome both synthetic and real noisy labels. We show that our method achieves the best result on our dataset as well as on two public benchmarks (CIFAR and WebVision). Third, we conduct the largest study by far into understanding deep neural networks trained on noisy labels across different noise levels, noise types, network architectures, and training settings. The data and code are released at the following link: http://www.lujiang.info/cnlw.html", "published": "2019-11-21T23:05:28Z", "authors": ["Lu Jiang", "Di Huang", "Mason Liu", "Weilong Yang"], "categories": ["cs.LG", "cs.CV", "stat.ML"], "doi": "", "_query": "real estate valuation deep learning", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/1909.02803v3", "title": "Personalization of Deep Learning", "abstract": "We discuss training techniques, objectives and metrics toward personalization of deep learning models. In machine learning, personalization addresses the goal of a trained model to target a particular individual by optimizing one or more performance metrics, while conforming to certain constraints. To personalize, we investigate three methods of ``curriculum learning`` and two approaches for data grouping, i.e., augmenting the data of an individual by adding similar data identified with an auto-encoder. We show that both ``curriculuum learning'' and ``personalized'' data augmentation lead to improved performance on data of an individual. Mostly, this comes at the cost of reduced performance on a more general, broader dataset.", "published": "2019-09-06T10:17:25Z", "authors": ["Johannes Schneider", "Michail Vlachos"], "categories": ["cs.LG", "stat.ML"], "doi": "", "_query": "real estate valuation deep learning", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2508.11210v1", "title": "Borrowing From the Future: Enhancing Early Risk Assessment through Contrastive Learning", "abstract": "Risk assessments for a pediatric population are often conducted across multiple stages. For example, clinicians may evaluate risks prenatally, at birth, and during Well-Child visits. Although predictions made at later stages typically achieve higher precision, it is clinically desirable to make reliable risk assessments as early as possible. Therefore, this study focuses on improving prediction performance in early-stage risk assessments. Our solution, \\textbf{Borrowing From the Future (BFF)}, is a contrastive multi-modal framework that treats each time window as a distinct modality. In BFF, a model is trained on all available data throughout the time while performing a risk assessment using up-to-date information. This contrastive framework allows the model to ``borrow'' informative signals from later stages (e.g., Well-Child visits) to implicitly supervise the learning at earlier stages (e.g., prenatal/birth stages). We validate BFF on two real-world pediatric outcome prediction tasks, demonstrating consistent improvements in early risk assessments. The code is available at https://github.com/scotsun/bff.", "published": "2025-08-15T04:40:21Z", "authors": ["Minghui Sun", "Matthew M. Engelhard", "Benjamin A. Goldstein"], "categories": ["cs.LG", "stat.ML"], "doi": "", "_query": "real estate valuation deep learning", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2307.02732v1", "title": "Evaluating the Evaluators: Are Current Few-Shot Learning Benchmarks Fit for Purpose?", "abstract": "Numerous benchmarks for Few-Shot Learning have been proposed in the last decade. However all of these benchmarks focus on performance averaged over many tasks, and the question of how to reliably evaluate and tune models trained for individual tasks in this regime has not been addressed. This paper presents the first investigation into task-level evaluation -- a fundamental step when deploying a model. We measure the accuracy of performance estimators in the few-shot setting, consider strategies for model selection, and examine the reasons for the failure of evaluators usually thought of as being robust. We conclude that cross-validation with a low number of folds is the best choice for directly estimating the performance of a model, whereas using bootstrapping or cross validation with a large number of folds is better for model selection purposes. Overall, we find that existing benchmarks for few-shot learning are not designed in such a way that one can get a reliable picture of how effectively methods can be used on individual tasks.", "published": "2023-07-06T02:31:38Z", "authors": ["Luísa Shimabucoro", "Timothy Hospedales", "Henry Gouk"], "categories": ["cs.LG", "stat.ML"], "doi": "", "_query": "real estate valuation deep learning", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/1906.01930v3", "title": "Approximate Inference Turns Deep Networks into Gaussian Processes", "abstract": "Deep neural networks (DNN) and Gaussian processes (GP) are two powerful models with several theoretical connections relating them, but the relationship between their training methods is not well understood. In this paper, we show that certain Gaussian posterior approximations for Bayesian DNNs are equivalent to GP posteriors. This enables us to relate solutions and iterations of a deep-learning algorithm to GP inference. As a result, we can obtain a GP kernel and a nonlinear feature map while training a DNN. Surprisingly, the resulting kernel is the neural tangent kernel. We show kernels obtained on real datasets and demonstrate the use of the GP marginal likelihood to tune hyperparameters of DNNs. Our work aims to facilitate further research on combining DNNs and GPs in practical settings.", "published": "2019-06-05T10:43:20Z", "authors": ["Mohammad Emtiyaz Khan", "Alexander Immer", "Ehsan Abedi", "Maciej Korzepa"], "categories": ["stat.ML", "cs.AI", "cs.LG"], "doi": "", "_query": "real estate valuation deep learning", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/1912.04002v1", "title": "Learning Sparse Representations Incrementally in Deep Reinforcement Learning", "abstract": "Sparse representations have been shown to be useful in deep reinforcement learning for mitigating catastrophic interference and improving the performance of agents in terms of cumulative reward. Previous results were based on a two step process were the representation was learned offline and the action-value function was learned online afterwards. In this paper, we investigate if it is possible to learn a sparse representation and the action-value function simultaneously and incrementally. We investigate this question by employing several regularization techniques and observing how they affect sparsity of the representation learned by a DQN agent in two different benchmark domains. Our results show that with appropriate regularization it is possible to increase the sparsity of the representations learned by DQN agents. Moreover, we found that learning sparse representations also resulted in improved performance in terms of cumulative reward. Finally, we found that the performance of the agents that learned a sparse representation was more robust to the size of the experience replay buffer. This last finding supports the long standing hypothesis that the overlap in representations learned by deep neural networks is the leading cause of catastrophic interference.", "published": "2019-12-09T12:41:17Z", "authors": ["J. Fernando Hernandez-Garcia", "Richard S. Sutton"], "categories": ["cs.LG", "stat.ML"], "doi": "", "_query": "real estate valuation deep learning", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/1206.6400v1", "title": "Online Bandit Learning against an Adaptive Adversary: from Regret to Policy Regret", "abstract": "Online learning algorithms are designed to learn even when their input is generated by an adversary. The widely-accepted formal definition of an online algorithm's ability to learn is the game-theoretic notion of regret. We argue that the standard definition of regret becomes inadequate if the adversary is allowed to adapt to the online algorithm's actions. We define the alternative notion of policy regret, which attempts to provide a more meaningful way to measure an online algorithm's performance against adaptive adversaries. Focusing on the online bandit setting, we show that no bandit algorithm can guarantee a sublinear policy regret against an adaptive adversary with unbounded memory. On the other hand, if the adversary's memory is bounded, we present a general technique that converts any bandit algorithm with a sublinear regret bound into an algorithm with a sublinear policy regret bound. We extend this result to other variants of regret, such as switching regret, internal regret, and swap regret.", "published": "2012-06-27T19:59:59Z", "authors": ["Raman Arora", "Ofer Dekel", "Ambuj Tewari"], "categories": ["cs.LG", "stat.ML"], "doi": "", "_query": "real estate valuation deep learning", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/1901.08554v1", "title": "Explainable Failure Predictions with RNN Classifiers based on Time Series Data", "abstract": "Given key performance indicators collected with fine granularity as time series, our aim is to predict and explain failures in storage environments. Although explainable predictive modeling based on spiky telemetry data is key in many domains, current approaches cannot tackle this problem. Deep learning methods suitable for sequence modeling and learning temporal dependencies, such as RNNs, are effective, but opaque from an explainability perspective. Our approach first extracts the anomalous spikes from time series as events and then builds an RNN classifier with attention mechanisms to embed the irregularity and frequency of these events. A preliminary evaluation on real world storage environments shows that our approach can predict failures within a 3-day prediction window with comparable accuracy as traditional RNN-based classifiers. At the same time it can explain the predictions by returning the key anomalous events which led to those failure predictions.", "published": "2019-01-20T18:48:48Z", "authors": ["Ioana Giurgiu", "Anika Schumann"], "categories": ["cs.LG", "stat.ML"], "doi": "", "_query": "real estate valuation deep learning", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2001.06776v1", "title": "Algebraic and Analytic Approaches for Parameter Learning in Mixture Models", "abstract": "We present two different approaches for parameter learning in several mixture models in one dimension. Our first approach uses complex-analytic methods and applies to Gaussian mixtures with shared variance, binomial mixtures with shared success probability, and Poisson mixtures, among others. An example result is that $\\exp(O(N^{1/3}))$ samples suffice to exactly learn a mixture of $k<N$ Poisson distributions, each with integral rate parameters bounded by $N$. Our second approach uses algebraic and combinatorial tools and applies to binomial mixtures with shared trial parameter $N$ and differing success parameters, as well as to mixtures of geometric distributions. Again, as an example, for binomial mixtures with $k$ components and success parameters discretized to resolution $ε$, $O(k^2(N/ε)^{8/\\sqrtε})$ samples suffice to exactly recover the parameters. For some of these distributions, our results represent the first guarantees for parameter estimation.", "published": "2020-01-19T05:10:56Z", "authors": ["Akshay Krishnamurthy", "Arya Mazumdar", "Andrew McGregor", "Soumyabrata Pal"], "categories": ["cs.LG", "stat.ML"], "doi": "", "_query": "real estate valuation deep learning", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2205.05587v3", "title": "Choice of training label matters: how to best use deep learning for quantitative MRI parameter estimation", "abstract": "Deep learning (DL) is gaining popularity as a parameter estimation method for quantitative MRI. A range of competing implementations have been proposed, relying on either supervised or self-supervised learning. Self-supervised approaches, sometimes referred to as unsupervised, have been loosely based on auto-encoders, whereas supervised methods have, to date, been trained on groundtruth labels. These two learning paradigms have been shown to have distinct strengths. Notably, self-supervised approaches have offered lower-bias parameter estimates than their supervised alternatives. This result is counterintuitive - incorporating prior knowledge with supervised labels should, in theory, lead to improved accuracy. In this work, we show that this apparent limitation of supervised approaches stems from the naive choice of groundtruth training labels. By training on labels which are deliberately not groundtruth, we show that the low-bias parameter estimation previously associated with self-supervised methods can be replicated - and improved on - within a supervised learning framework. This approach sets the stage for a single, unifying, deep learning parameter estimation framework, based on supervised learning, where trade-offs between bias and variance are made by careful adjustment of training label.", "published": "2022-05-11T16:00:14Z", "authors": ["Sean C. Epstein", "Timothy J. P. Bray", "Margaret Hall-Craggs", "Hui Zhang"], "categories": ["physics.med-ph", "cs.LG", "eess.IV"], "doi": "10.59275/j.melba.2024-geb5", "_query": "real estate valuation deep learning", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2402.00258v3", "title": "Multi-group Learning for Hierarchical Groups", "abstract": "The multi-group learning model formalizes the learning scenario in which a single predictor must generalize well on multiple, possibly overlapping subgroups of interest. We extend the study of multi-group learning to the natural case where the groups are hierarchically structured. We design an algorithm for this setting that outputs an interpretable and deterministic decision tree predictor with near-optimal sample complexity. We then conduct an empirical evaluation of our algorithm and find that it achieves attractive generalization properties on real datasets with hierarchical group structure.", "published": "2024-02-01T01:06:32Z", "authors": ["Samuel Deng", "Daniel Hsu"], "categories": ["cs.LG"], "doi": "", "_query": "real estate valuation deep learning", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/1902.06497v1", "title": "Differentially Private Continual Learning", "abstract": "Catastrophic forgetting can be a significant problem for institutions that must delete historic data for privacy reasons. For example, hospitals might not be able to retain patient data permanently. But neural networks trained on recent data alone will tend to forget lessons learned on old data. We present a differentially private continual learning framework based on variational inference. We estimate the likelihood of past data given the current model using differentially private generative models of old datasets.", "published": "2019-02-18T10:31:28Z", "authors": ["Sebastian Farquhar", "Yarin Gal"], "categories": ["stat.ML", "cs.LG"], "doi": "", "_query": "real estate valuation deep learning", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2007.10791v3", "title": "Learning to Match Distributions for Domain Adaptation", "abstract": "When the training and test data are from different distributions, domain adaptation is needed to reduce dataset bias to improve the model's generalization ability. Since it is difficult to directly match the cross-domain joint distributions, existing methods tend to reduce the marginal or conditional distribution divergence using predefined distances such as MMD and adversarial-based discrepancies. However, it remains challenging to determine which method is suitable for a given application since they are built with certain priors or bias. Thus they may fail to uncover the underlying relationship between transferable features and joint distributions. This paper proposes Learning to Match (L2M) to automatically learn the cross-domain distribution matching without relying on hand-crafted priors on the matching loss. Instead, L2M reduces the inductive bias by using a meta-network to learn the distribution matching loss in a data-driven way. L2M is a general framework that unifies task-independent and human-designed matching features. We design a novel optimization algorithm for this challenging objective with self-supervised label propagation. Experiments on public datasets substantiate the superiority of L2M over SOTA methods. Moreover, we apply L2M to transfer from pneumonia to COVID-19 chest X-ray images with remarkable performance. L2M can also be extended in other distribution matching applications where we show in a trial experiment that L2M generates more realistic and sharper MNIST samples.", "published": "2020-07-17T03:26:13Z", "authors": ["Chaohui Yu", "Jindong Wang", "Chang Liu", "Tao Qin", "Renjun Xu", "Wenjie Feng", "Yiqiang Chen", "Tie-Yan Liu"], "categories": ["cs.LG", "cs.CV", "stat.ML"], "doi": "", "_query": "real estate valuation deep learning", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2410.07523v3", "title": "DemoShapley: Valuation of Demonstrations for In-Context Learning", "abstract": "Large language models (LLMs) using in-context learning (ICL) excel in many tasks without task-specific fine-tuning. However, demonstration selection and ordering greatly impact ICL effectiveness. Focus on this issue, we propose DemoShapley, a Shapley-value based method that evaluates each demonstration's contribution by measuring its marginal effect across different prompt permutations. To further account for ICL's limited context windows and frequent low-shot settings, we introduce Beta-DemoShapley, a weighted extension that emphasizes the influence of smaller prompt sizes. Experiments on multiple benchmarks show that DemoShapley consistently outperforms existing influence-based selection strategies, while Beta-DemoShapley further improves performance in low-shot scenarios. Both methods also detect mislabeled data, enhance generalization to out-of-distribution tasks, and reduce demographic bias. Together, they provide a unified and robust framework for demonstration valuation in ICL.", "published": "2024-10-10T01:35:03Z", "authors": ["Shan Xie", "Man Luo", "Chadly Daniel Stern", "Mengnan Du", "Lu Cheng"], "categories": ["cs.CL", "cs.AI", "cs.LG"], "doi": "", "_query": "real estate valuation deep learning", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2409.12293v3", "title": "In-Context Learning of Linear Systems: Generalization Theory and Applications to Operator Learning", "abstract": "We study theoretical guarantees for solving linear systems in-context using a linear transformer architecture. For in-domain generalization, we provide neural scaling laws that bound the generalization error in terms of the number of tasks and sizes of samples used in training and inference. For out-of-domain generalization, we find that the behavior of trained transformers under task distribution shifts depends crucially on the distribution of the tasks seen during training. We introduce a novel notion of task diversity and show that it defines a necessary and sufficient condition for pre-trained transformers generalize under task distribution shifts. We also explore applications of learning linear systems in-context, such as to in-context operator learning for PDEs. Finally, we provide some numerical experiments to validate the established theory.", "published": "2024-09-18T19:59:50Z", "authors": ["Frank Cole", "Yulong Lu", "Wuzhe Xu", "Tianhao Zhang"], "categories": ["cs.LG", "math.NA", "stat.ML"], "doi": "", "_query": "real estate valuation deep learning", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2410.12096v1", "title": "Bridging Large Language Models and Graph Structure Learning Models for Robust Representation Learning", "abstract": "Graph representation learning, involving both node features and graph structures, is crucial for real-world applications but often encounters pervasive noise. State-of-the-art methods typically address noise by focusing separately on node features with large language models (LLMs) and on graph structures with graph structure learning models (GSLMs). In this paper, we introduce LangGSL, a robust framework that integrates the complementary strengths of pre-trained language models and GSLMs to jointly enhance both node feature and graph structure learning. In LangGSL, we first leverage LLMs to filter noise in the raw data and extract valuable cleaned information as features, enhancing the synergy of downstream models. During the mutual learning phase in LangGSL, the core idea is to leverage the relatively small language model (LM) to process local attributes and generate reliable pseudo-labels and informative node embeddings, which are then integrated into the GSLM's prediction phase. This approach enriches the global context and enhances overall performance. Meanwhile, GSLM refines the evolving graph structure constructed from the LM's output, offering updated labels back to the LM as additional guidance, thus facilitating a more effective mutual learning process. The LM and GSLM work synergistically, complementing each other's strengths and offsetting weaknesses within a variational information-maximizing framework, resulting in enhanced node features and a more robust graph structure. Extensive experiments on diverse graph datasets of varying scales and across different task scenarios demonstrate the scalability and effectiveness of the proposed approach.", "published": "2024-10-15T22:43:32Z", "authors": ["Guangxin Su", "Yifan Zhu", "Wenjie Zhang", "Hanchen Wang", "Ying Zhang"], "categories": ["cs.LG", "cs.AI"], "doi": "", "_query": "real estate valuation deep learning", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2001.08089v5", "title": "Maximum Likelihood Estimation of Spatially Varying Coefficient Models for Large Data with an Application to Real Estate Price Prediction", "abstract": "In regression models for spatial data, it is often assumed that the marginal effects of covariates on the response are constant over space. In practice, this assumption might often be questionable. In this article, we show how a Gaussian process-based spatially varying coefficient (SVC) model can be estimated using maximum likelihood estimation (MLE). In addition, we present an approach that scales to large data by applying covariance tapering. We compare our methodology to existing methods such as a Bayesian approach using the stochastic partial differential equation (SPDE) link, geographically weighted regression (GWR), and eigenvector spatial filtering (ESF) in both a simulation study and an application where the goal is to predict prices of real estate apartments in Switzerland. The results from both the simulation study and application show that the MLE approach results in increased predictive accuracy and more precise estimates. Since we use a model-based approach, we can also provide predictive variances. In contrast to existing model-based approaches, our method scales better to data where both the number of spatial points is large and the number of spatially varying covariates is moderately-sized, e.g., above ten.", "published": "2020-01-22T15:46:29Z", "authors": ["Jakob A. Dambon", "Fabio Sigrist", "Reinhard Furrer"], "categories": ["stat.ME", "stat.AP"], "doi": "10.1016/j.spasta.2020.100470", "_query": "real estate valuation deep learning", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/1810.01217v1", "title": "Sparse Gaussian Process Temporal Difference Learning for Marine Robot Navigation", "abstract": "We present a method for Temporal Difference (TD) learning that addresses several challenges faced by robots learning to navigate in a marine environment. For improved data efficiency, our method reduces TD updates to Gaussian Process regression. To make predictions amenable to online settings, we introduce a sparse approximation with improved quality over current rejection-based sparse methods. We derive the predictive value function posterior and use the moments to obtain a new algorithm for model-free policy evaluation, SPGP-SARSA. With simple changes, we show SPGP-SARSA can be reduced to a model-based equivalent, SPGP-TD. We perform comprehensive simulation studies and also conduct physical learning trials with an underwater robot. Our results show SPGP-SARSA can outperform the state-of-the-art sparse method, replicate the prediction quality of its exact counterpart, and be applied to solve underwater navigation tasks.", "published": "2018-10-02T13:04:47Z", "authors": ["John Martin", "Jinkun Wang", "Brendan Englot"], "categories": ["cs.LG", "stat.ML"], "doi": "", "_query": "real estate valuation deep learning", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/1805.10407v4", "title": "Semi-supervised Deep Kernel Learning: Regression with Unlabeled Data by Minimizing Predictive Variance", "abstract": "Large amounts of labeled data are typically required to train deep learning models. For many real-world problems, however, acquiring additional data can be expensive or even impossible. We present semi-supervised deep kernel learning (SSDKL), a semi-supervised regression model based on minimizing predictive variance in the posterior regularization framework. SSDKL combines the hierarchical representation learning of neural networks with the probabilistic modeling capabilities of Gaussian processes. By leveraging unlabeled data, we show improvements on a diverse set of real-world regression tasks over supervised deep kernel learning and semi-supervised methods such as VAT and mean teacher adapted for regression.", "published": "2018-05-26T00:47:14Z", "authors": ["Neal Jean", "Sang Michael Xie", "Stefano Ermon"], "categories": ["cs.LG", "cs.AI", "stat.ML"], "doi": "", "_query": "real estate valuation deep learning", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2012.02702v1", "title": "Bayesian Active Learning for Wearable Stress and Affect Detection", "abstract": "In the recent past, psychological stress has been increasingly observed in humans, and early detection is crucial to prevent health risks. Stress detection using on-device deep learning algorithms has been on the rise owing to advancements in pervasive computing. However, an important challenge that needs to be addressed is handling unlabeled data in real-time via suitable ground truthing techniques (like Active Learning), which should help establish affective states (labels) while also selecting only the most informative data points to query from an oracle. In this paper, we propose a framework with capabilities to represent model uncertainties through approximations in Bayesian Neural Networks using Monte-Carlo (MC) Dropout. This is combined with suitable acquisition functions for active learning. Empirical results on a popular stress and affect detection dataset experimented on a Raspberry Pi 2 indicate that our proposed framework achieves a considerable efficiency boost during inference, with a substantially low number of acquired pool points during active learning across various acquisition functions. Variation Ratios achieves an accuracy of 90.38% which is comparable to the maximum test accuracy achieved while training on about 40% lesser data.", "published": "2020-12-04T16:19:37Z", "authors": ["Abhijith Ragav", "Gautham Krishna Gudur"], "categories": ["cs.LG", "stat.ML"], "doi": "", "_query": "real estate valuation deep learning", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/1907.01332v1", "title": "Applying Transfer Learning To Deep Learned Models For EEG Analysis", "abstract": "The introduction of deep learning and transfer learning techniques in fields such as computer vision allowed a leap forward in the accuracy of image classification tasks. Currently there is only limited use of such techniques in neuroscience. The challenge of using deep learning methods to successfully train models in neuroscience, lies in the complexity of the information that is processed, the availability of data and the cost of producing sufficient high quality annotations. Inspired by its application in computer vision, we introduce transfer learning on electrophysiological data to enable training a model with limited amounts of data. Our method was tested on the dataset of the BCI competition IV 2a and compared to the top results that were obtained using traditional machine learning techniques. Using our DL model we outperform the top result of the competition by 33%. We also explore transferability of knowledge between trained models over different experiments, called inter-experimental transfer learning. This reduces the amount of required data even further and is especially useful when few subjects are available. This method is able to outperform the standard deep learning methods used in the BCI competition IV 2b approaches by 18%. In this project we propose a method that can produce reliable electroencephalography (EEG) signal classification, based on modest amounts of training data through the use of transfer learning.", "published": "2019-07-02T12:51:26Z", "authors": ["Axel Uran", "Coert van Gemeren", "Rosanne van Diepen", "Ricardo Chavarriaga", "José del R. Millán"], "categories": ["cs.LG", "eess.SP", "stat.ML"], "doi": "", "_query": "real estate valuation deep learning", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2111.10656v2", "title": "Simple End-to-end Deep Learning Model for CDR-H3 Loop Structure Prediction", "abstract": "Predicting a structure of an antibody from its sequence is important since it allows for a better design process of synthetic antibodies that play a vital role in the health industry. Most of the structure of an antibody is conservative. The most variable and hard-to-predict part is the third complementarity-determining region of the antibody heavy chain (CDR H3). Lately, deep learning has been employed to solve the task of CDR H3 prediction. However, current state-of-the-art methods are not end-to-end, but rather they output inter-residue distances and orientations to the RosettaAntibody package that uses this additional information alongside statistical and physics-based methods to predict the 3D structure. This does not allow a fast screening process and, therefore, inhibits the development of targeted synthetic antibodies. In this work, we present an end-to-end model to predict CDR H3 loop structure, that performs on par with state-of-the-art methods in terms of accuracy but an order of magnitude faster. We also raise an issue with a commonly used RosettaAntibody benchmark that leads to data leaks, i.e., the presence of identical sequences in the train and test datasets.", "published": "2021-11-20T18:55:09Z", "authors": ["Natalia Zenkova", "Ekaterina Sedykh", "Tatiana Shugaeva", "Vladislav Strashko", "Timofei Ermak", "Aleksei Shpilman"], "categories": ["q-bio.BM", "cs.AI", "cs.LG"], "doi": "", "_query": "real estate valuation deep learning", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2506.23349v2", "title": "A case for data valuation transparency via DValCards", "abstract": "Following the rise in popularity of data-centric machine learning (ML), various data valuation methods have been proposed to quantify the contribution of each datapoint to desired ML model performance metrics (e.g., accuracy). Beyond the technical applications of data valuation methods (e.g., data cleaning, data acquisition, etc.), it has been suggested that within the context of data markets, data buyers might utilize such methods to fairly compensate data owners. Here we demonstrate that data valuation metrics are inherently biased and unstable under simple algorithmic design choices, resulting in both technical and ethical implications. By analyzing 9 tabular classification datasets and 6 data valuation methods, we illustrate how (1) common and inexpensive data pre-processing techniques can drastically alter estimated data values; (2) subsampling via data valuation metrics may increase class imbalance; and (3) data valuation metrics may undervalue underrepresented group data. Consequently, we argue in favor of increased transparency associated with data valuation in-the-wild and introduce the novel Data Valuation Cards (DValCards) framework towards this aim. The proliferation of DValCards will reduce misuse of data valuation metrics, including in data pricing, and build trust in responsible ML systems.", "published": "2025-06-29T17:53:00Z", "authors": ["Keziah Naggita", "Julienne LaChance"], "categories": ["cs.LG"], "doi": "", "_query": "real estate valuation deep learning", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2111.07407v1", "title": "A Machine Learning Approach for Recruitment Prediction in Clinical Trial Design", "abstract": "Significant advancements have been made in recent years to optimize patient recruitment for clinical trials, however, improved methods for patient recruitment prediction are needed to support trial site selection and to estimate appropriate enrollment timelines in the trial design stage. In this paper, using data from thousands of historical clinical trials, we explore machine learning methods to predict the number of patients enrolled per month at a clinical trial site over the course of a trial's enrollment duration. We show that these methods can reduce the error that is observed with current industry standards and propose opportunities for further improvement.", "published": "2021-11-14T18:24:11Z", "authors": ["Jingshu Liu", "Patricia J Allen", "Luke Benz", "Daniel Blickstein", "Evon Okidi", "Xiao Shi"], "categories": ["cs.LG", "stat.AP", "stat.ML"], "doi": "", "_query": "real estate valuation deep learning", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2009.00540v1", "title": "Training Deep Neural Networks with Constrained Learning Parameters", "abstract": "Today's deep learning models are primarily trained on CPUs and GPUs. Although these models tend to have low error, they consume high power and utilize large amount of memory owing to double precision floating point learning parameters. Beyond the Moore's law, a significant portion of deep learning tasks would run on edge computing systems, which will form an indispensable part of the entire computation fabric. Subsequently, training deep learning models for such systems will have to be tailored and adopted to generate models that have the following desirable characteristics: low error, low memory, and low power. We believe that deep neural networks (DNNs), where learning parameters are constrained to have a set of finite discrete values, running on neuromorphic computing systems would be instrumental for intelligent edge computing systems having these desirable characteristics. To this extent, we propose the Combinatorial Neural Network Training Algorithm (CoNNTrA), that leverages a coordinate gradient descent-based approach for training deep learning models with finite discrete learning parameters. Next, we elaborate on the theoretical underpinnings and evaluate the computational complexity of CoNNTrA. As a proof of concept, we use CoNNTrA to train deep learning models with ternary learning parameters on the MNIST, Iris and ImageNet data sets and compare their performance to the same models trained using Backpropagation. We use following performance metrics for the comparison: (i) Training error; (ii) Validation error; (iii) Memory usage; and (iv) Training time. Our results indicate that CoNNTrA models use 32x less memory and have errors at par with the Backpropagation models.", "published": "2020-09-01T16:20:11Z", "authors": ["Prasanna Date", "Christopher D. Carothers", "John E. Mitchell", "James A. Hendler", "Malik Magdon-Ismail"], "categories": ["cs.LG", "cs.CV", "stat.ML"], "doi": "10.1109/ICRC2020.2020.00018", "_query": "real estate valuation deep learning", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/1908.08843v2", "title": "Fairness in Deep Learning: A Computational Perspective", "abstract": "Deep learning is increasingly being used in high-stake decision making applications that affect individual lives. However, deep learning models might exhibit algorithmic discrimination behaviors with respect to protected groups, potentially posing negative impacts on individuals and society. Therefore, fairness in deep learning has attracted tremendous attention recently. We provide a review covering recent progresses to tackle algorithmic fairness problems of deep learning from the computational perspective. Specifically, we show that interpretability can serve as a useful ingredient to diagnose the reasons that lead to algorithmic discrimination. We also discuss fairness mitigation approaches categorized according to three stages of deep learning life-cycle, aiming to push forward the area of fairness in deep learning and build genuinely fair and reliable deep learning systems.", "published": "2019-08-23T14:38:07Z", "authors": ["Mengnan Du", "Fan Yang", "Na Zou", "Xia Hu"], "categories": ["cs.LG", "cs.AI", "cs.CY", "stat.ML"], "doi": "", "_query": "real estate valuation deep learning", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2409.13940v1", "title": "Learning Recourse Costs from Pairwise Feature Comparisons", "abstract": "This paper presents a novel technique for incorporating user input when learning and inferring user preferences. When trying to provide users of black-box machine learning models with actionable recourse, we often wish to incorporate their personal preferences about the ease of modifying each individual feature. These recourse finding algorithms usually require an exhaustive set of tuples associating each feature to its cost of modification. Since it is hard to obtain such costs by directly surveying humans, in this paper, we propose the use of the Bradley-Terry model to automatically infer feature-wise costs using non-exhaustive human comparison surveys. We propose that users only provide inputs comparing entire recourses, with all candidate feature modifications, determining which recourses are easier to implement relative to others, without explicit quantification of their costs. We demonstrate the efficient learning of individual feature costs using MAP estimates, and show that these non-exhaustive human surveys, which do not necessarily contain data for each feature pair comparison, are sufficient to learn an exhaustive set of feature costs, where each feature is associated with a modification cost.", "published": "2024-09-20T23:04:08Z", "authors": ["Kaivalya Rawal", "Himabindu Lakkaraju"], "categories": ["cs.LG", "cs.AI", "cs.CY", "stat.ML"], "doi": "", "_query": "real estate valuation deep learning", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/1903.05202v2", "title": "Continual Learning in Practice", "abstract": "This paper describes a reference architecture for self-maintaining systems that can learn continually, as data arrives. In environments where data evolves, we need architectures that manage Machine Learning (ML) models in production, adapt to shifting data distributions, cope with outliers, retrain when necessary, and adapt to new tasks. This represents continual AutoML or Automatically Adaptive Machine Learning. We describe the challenges and proposes a reference architecture.", "published": "2019-03-12T20:41:36Z", "authors": ["Tom Diethe", "Tom Borchert", "Eno Thereska", "Borja Balle", "Neil Lawrence"], "categories": ["stat.ML", "cs.LG"], "doi": "", "_query": "real estate valuation deep learning", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/1706.00473v4", "title": "Deep Learning: A Bayesian Perspective", "abstract": "Deep learning is a form of machine learning for nonlinear high dimensional pattern matching and prediction. By taking a Bayesian probabilistic perspective, we provide a number of insights into more efficient algorithms for optimisation and hyper-parameter tuning. Traditional high-dimensional data reduction techniques, such as principal component analysis (PCA), partial least squares (PLS), reduced rank regression (RRR), projection pursuit regression (PPR) are all shown to be shallow learners. Their deep learning counterparts exploit multiple deep layers of data reduction which provide predictive performance gains. Stochastic gradient descent (SGD) training optimisation and Dropout (DO) regularization provide estimation and variable selection. Bayesian regularization is central to finding weights and connections in networks to optimize the predictive bias-variance trade-off. To illustrate our methodology, we provide an analysis of international bookings on Airbnb. Finally, we conclude with directions for future research.", "published": "2017-06-01T19:50:37Z", "authors": ["Nicholas Polson", "Vadim Sokolov"], "categories": ["stat.ML", "cs.LG", "stat.ME"], "doi": "10.1214/17-BA1082", "_query": "real estate valuation deep learning", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/1812.00602v1", "title": "Examining Deep Learning Architectures for Crime Classification and Prediction", "abstract": "In this paper, a detailed study on crime classification and prediction using deep learning architectures is presented. We examine the effectiveness of deep learning algorithms on this domain and provide recommendations for designing and training deep learning systems for predicting crime areas, using open data from police reports. Having as training data time-series of crime types per location, a comparative study of 10 state-of-the-art methods against 3 different deep learning configurations is conducted. In our experiments with five publicly available datasets, we demonstrate that the deep learning-based methods consistently outperform the existing best-performing methods. Moreover, we evaluate the effectiveness of different parameters in the deep learning architectures and give insights for configuring them in order to achieve improved performance in crime classification and finally crime prediction.", "published": "2018-12-03T08:40:50Z", "authors": ["Panagiotis Stalidis", "Theodoros Semertzidis", "Petros Daras"], "categories": ["cs.LG", "cs.CY", "stat.ML"], "doi": "", "_query": "real estate valuation deep learning", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2006.09590v2", "title": "Deep Learning with Functional Inputs", "abstract": "We present a methodology for integrating functional data into deep densely connected feed-forward neural networks. The model is defined for scalar responses with multiple functional and scalar covariates. A by-product of the method is a set of dynamic functional weights that can be visualized during the optimization process. This visualization leads to greater interpretability of the relationship between the covariates and the response relative to conventional neural networks. The model is shown to perform well in a number of contexts including prediction of new data and recovery of the true underlying functional weights; these results were confirmed through real applications and simulation studies. A forthcoming R package is developed on top of a popular deep learning library (Keras) allowing for general use of the approach.", "published": "2020-06-17T01:23:00Z", "authors": ["Barinder Thind", "Kevin Multani", "Jiguo Cao"], "categories": ["stat.ML", "cs.LG", "stat.ME"], "doi": "10.1080/10618600.2022.2097914", "_query": "real estate valuation deep learning", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2103.09957v3", "title": "CheXbreak: Misclassification Identification for Deep Learning Models Interpreting Chest X-rays", "abstract": "A major obstacle to the integration of deep learning models for chest x-ray interpretation into clinical settings is the lack of understanding of their failure modes. In this work, we first investigate whether there are patient subgroups that chest x-ray models are likely to misclassify. We find that patient age and the radiographic finding of lung lesion, pneumothorax or support devices are statistically relevant features for predicting misclassification for some chest x-ray models. Second, we develop misclassification predictors on chest x-ray models using their outputs and clinical features. We find that our best performing misclassification identifier achieves an AUROC close to 0.9 for most diseases. Third, employing our misclassification identifiers, we develop a corrective algorithm to selectively flip model predictions that have high likelihood of misclassification at inference time. We observe F1 improvement on the prediction of Consolidation (0.008 [95% CI 0.005, 0.010]) and Edema (0.003, [95% CI 0.001, 0.006]). By carrying out our investigation on ten distinct and high-performing chest x-ray models, we are able to derive insights across model architectures and offer a generalizable framework applicable to other medical imaging tasks.", "published": "2021-03-18T00:30:19Z", "authors": ["Emma Chen", "Andy Kim", "Rayan Krishnan", "Jin Long", "Andrew Y. Ng", "Pranav Rajpurkar"], "categories": ["cs.CV", "cs.AI", "cs.LG"], "doi": "", "_query": "real estate valuation deep learning", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/1906.04463v1", "title": "TW-SMNet: Deep Multitask Learning of Tele-Wide Stereo Matching", "abstract": "In this paper, we introduce the problem of estimating the real world depth of elements in a scene captured by two cameras with different field of views, where the first field of view (FOV) is a Wide FOV (WFOV) captured by a wide angle lens, and the second FOV is contained in the first FOV and is captured by a tele zoom lens. We refer to the problem of estimating the inverse depth for the union of FOVs, while leveraging the stereo information in the overlapping FOV, as Tele-Wide Stereo Matching (TW-SM). We propose different deep learning solutions to the TW-SM problem. Since the disparity is proportional to the inverse depth, we train stereo matching disparity estimation (SMDE) networks to estimate the disparity for the union WFOV. We further propose an end-to-end deep multitask tele-wide stereo matching neural network (MT-TW-SMNet), which simultaneously learns the SMDE task for the overlapped Tele FOV and the single image inverse depth estimation (SIDE) task for the WFOV. Moreover, we design multiple methods for the fusion of the SMDE and SIDE networks. We evaluate the performance of TW-SM on the popular KITTI and SceneFlow stereo datasets, and demonstrate its practicality by synthesizing the Bokeh effect on the WFOV from a tele-wide stereo image pair.", "published": "2019-06-11T09:46:26Z", "authors": ["Mostafa El-Khamy", "Haoyu Ren", "Xianzhi Du", "Jungwon Lee"], "categories": ["cs.CV"], "doi": "10.1109/ACCESS.2020.3029085", "_query": "real estate valuation deep learning", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2311.16381v1", "title": "Deep Learning for Time Series Classification of Parkinson's Disease Eye Tracking Data", "abstract": "Eye-tracking is an accessible and non-invasive technology that provides information about a subject's motor and cognitive abilities. As such, it has proven to be a valuable resource in the study of neurodegenerative diseases such as Parkinson's disease. Saccade experiments, in particular, have proven useful in the diagnosis and staging of Parkinson's disease. However, to date, no single eye-movement biomarker has been found to conclusively differentiate patients from healthy controls. In the present work, we investigate the use of state-of-the-art deep learning algorithms to perform Parkinson's disease classification using eye-tracking data from saccade experiments. In contrast to previous work, instead of using hand-crafted features from the saccades, we use raw $\\sim1.5\\,s$ long fixation intervals recorded during the preparatory phase before each trial. Using these short time series as input we implement two different classification models, InceptionTime and ROCKET. We find that the models are able to learn the classification task and generalize to unseen subjects. InceptionTime achieves $78\\%$ accuracy, while ROCKET achieves $88\\%$ accuracy. We also employ a novel method for pruning the ROCKET model to improve interpretability and generalizability, achieving an accuracy of $96\\%$. Our results suggest that fixation data has low inter-subject variability and potentially carries useful information about brain cognitive and motor conditions, making it suitable for use with machine learning in the discovery of disease-relevant biomarkers.", "published": "2023-11-28T00:03:18Z", "authors": ["Gonzalo Uribarri", "Simon Ekman von Huth", "Josefine Waldthaler", "Per Svenningsson", "Erik Fransén"], "categories": ["cs.LG", "q-bio.QM"], "doi": "10.1016/j.ijmedinf.2026.106626", "_query": "real estate valuation deep learning", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2109.09046v3", "title": "Improving Fairness for Data Valuation in Horizontal Federated Learning", "abstract": "Federated learning is an emerging decentralized machine learning scheme that allows multiple data owners to work collaboratively while ensuring data privacy. The success of federated learning depends largely on the participation of data owners. To sustain and encourage data owners' participation, it is crucial to fairly evaluate the quality of the data provided by the data owners and reward them correspondingly. Federated Shapley value, recently proposed by Wang et al. [Federated Learning, 2020], is a measure for data value under the framework of federated learning that satisfies many desired properties for data valuation. However, there are still factors of potential unfairness in the design of federated Shapley value because two data owners with the same local data may not receive the same evaluation. We propose a new measure called completed federated Shapley value to improve the fairness of federated Shapley value. The design depends on completing a matrix consisting of all the possible contributions by different subsets of the data owners. It is shown under mild conditions that this matrix is approximately low-rank by leveraging concepts and tools from optimization. Both theoretical analysis and empirical evaluation verify that the proposed measure does improve fairness in many circumstances.", "published": "2021-09-19T02:39:59Z", "authors": ["Zhenan Fan", "Huang Fang", "Zirui Zhou", "Jian Pei", "Michael P. Friedlander", "Changxin Liu", "Yong Zhang"], "categories": ["cs.LG"], "doi": "10.1109/ICDE53745.2022.00228", "_query": "real estate valuation deep learning", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2001.04413v6", "title": "Backward Feature Correction: How Deep Learning Performs Deep (Hierarchical) Learning", "abstract": "Deep learning is also known as hierarchical learning, where the learner _learns_ to represent a complicated target function by decomposing it into a sequence of simpler functions to reduce sample and time complexity. This paper formally analyzes how multi-layer neural networks can perform such hierarchical learning _efficiently_ and _automatically_ by SGD on the training objective. On the conceptual side, we present a theoretical characterizations of how certain types of deep (i.e. super-constant layer) neural networks can still be sample and time efficiently trained on some hierarchical tasks, when no existing algorithm (including layerwise training, kernel method, etc) is known to be efficient. We establish a new principle called \"backward feature correction\", where the errors in the lower-level features can be automatically corrected when training together with the higher-level layers. We believe this is a key behind how deep learning is performing deep (hierarchical) learning, as opposed to layerwise learning or simulating some non-hierarchical method. On the technical side, we show for every input dimension $d > 0$, there is a concept class of degree $ω(1)$ multi-variate polynomials so that, using $ω(1)$-layer neural networks as learners, SGD can learn any function from this class in $\\mathsf{poly}(d)$ time to any $\\frac{1}{\\mathsf{poly}(d)}$ error, through learning to represent it as a composition of $ω(1)$ layers of quadratic functions using \"backward feature correction.\" In contrast, we do not know any other simpler algorithm (including layerwise training, applying kernel method sequentially, training a two-layer network, etc) that can learn this concept class in $\\mathsf{poly}(d)$ time even to any $d^{-0.01}$ error. As a side result, we prove $d^{ω(1)}$ lower bounds for several non-hierarchical learners, including any kernel methods.", "published": "2020-01-13T17:28:29Z", "authors": ["Zeyuan Allen-Zhu", "Yuanzhi Li"], "categories": ["cs.LG", "cs.DS", "cs.NE", "math.OC", "stat.ML"], "doi": "", "_query": "real estate valuation deep learning", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2610.15862v1", "title": "A Hybrid Gaussian Process-Extreme Gradient Boosting Machine Learning Framework for Robust Knockdown Factor Prediction and Reliability-Based Design of Thin Spherical Shell Caps", "abstract": "Elastic buckling remains a critical concern in the design of deep spherical shell caps, given that experiments reveal a significant reduction of critical loads from those predicted by linearized eigen-buckling analysis. This discrepancy has been attributed to initial geometric imperfections and is often reconciled through a dimensionless parameter, namely the knockdown factor (KDF). However, conventional KDFs (NASA SP-8007; Eurocode 3) are highly conservative, as they represent the lower bound of an extensive body of experimental data. This study aims to narrow the conservatism in the KDFs for spherical shell caps through machine learning (ML) regression models trained on experimental data on spherical shell buckling. A hybrid model is proposed by combining Gaussian Process Regression (GPR) with Extreme Gradient Boosting (XGBoost), and its improved performance is demonstrated over conventional ML algorithms, i.e., the Artificial Neural Network (ANN), Support Vector Regressor (SVR), Random Forest Regressor (RFR), and Histogram-based Gradient Boosting (HGBoost). Each of these models uses six input features and a single output, i.e., the KDF. The most critical feature affecting the KDF is ascertained through a sensitivity analysis. The study concludes with a reliability-based design framework that relies on the predictive ML model to obtain safety-consistent, yet less conservative, KDFs.", "published": "2026-10-11T19:30:20Z", "authors": ["Budhaditya De", "Rohan Majumder", "Devasmit Dutta", "Aman Deep Gupta", "Sudib Kumar Mishra"], "categories": ["cs.LG"], "doi": "", "_query": "real estate valuation deep learning", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/math/0110314v1", "title": "Computing Cocycles on Simplicial Complexes", "abstract": "In this note, working in the context of simplicial sets, we give a detailed study of the complexity for computing chain level Steenrod squares, in terms of the number of face operators required. This analysis is based on the combinatorial formulation given in [R. Gonzalez-Diaz, P. Real. A Combinatorial Method for Computing Steenrod Squares. J. of Pure and Applied Algebra, 139 (1999) 89-108]. As an application, we give here an algorithm for computing cup-$i$ products over integers on a simplicial complex at chain level.", "published": "2001-10-30T09:51:12Z", "authors": ["Rocio Gonzalez-Diaz", "Pedro Real"], "categories": ["math.AT", "math.CO"], "doi": "", "_query": "real estate valuation deep learning", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/1305.2532v1", "title": "Learning Policies for Contextual Submodular Prediction", "abstract": "Many prediction domains, such as ad placement, recommendation, trajectory prediction, and document summarization, require predicting a set or list of options. Such lists are often evaluated using submodular reward functions that measure both quality and diversity. We propose a simple, efficient, and provably near-optimal approach to optimizing such prediction problems based on no-regret learning. Our method leverages a surprising result from online submodular optimization: a single no-regret online learner can compete with an optimal sequence of predictions. Compared to previous work, which either learn a sequence of classifiers or rely on stronger assumptions such as realizability, we ensure both data-efficiency as well as performance guarantees in the fully agnostic setting. Experiments validate the efficiency and applicability of the approach on a wide range of problems including manipulator trajectory optimization, news recommendation and document summarization.", "published": "2013-05-11T18:09:52Z", "authors": ["Stephane Ross", "Jiaji Zhou", "Yisong Yue", "Debadeepta Dey", "J. Andrew Bagnell"], "categories": ["cs.LG", "stat.ML"], "doi": "", "_query": "real estate valuation deep learning", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2301.06680v1", "title": "DIGITOUR: Automatic Digital Tours for Real-Estate Properties", "abstract": "A virtual or digital tour is a form of virtual reality technology which allows a user to experience a specific location remotely. Currently, these virtual tours are created by following a 2-step strategy. First, a photographer clicks a 360 degree equirectangular image; then, a team of annotators manually links these images for the \"walkthrough\" user experience. The major challenge in the mass adoption of virtual tours is the time and cost involved in manual annotation/linking of images. Therefore, this paper presents an end-to-end pipeline to automate the generation of 3D virtual tours using equirectangular images for real-estate properties. We propose a novel HSV-based coloring scheme for paper tags that need to be placed at different locations before clicking the equirectangular images using 360 degree cameras. These tags have two characteristics: i) they are numbered to help the photographer for placement of tags in sequence and; ii) bi-colored, which allows better learning of tag detection (using YOLOv5 architecture) in an image and digit recognition (using custom MobileNet architecture) tasks. Finally, we link/connect all the equirectangular images based on detected tags. We show the efficiency of the proposed pipeline on a real-world equirectangular image dataset collected from the Housing.com database.", "published": "2023-01-17T03:43:34Z", "authors": ["Prateek Chhikara", "Harshul Kuhar", "Anil Goyal", "Chirag Sharma"], "categories": ["cs.CV", "cs.GR", "cs.LG"], "doi": "10.1145/3570991.3571060", "_query": "real estate valuation deep learning", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2311.15404v2", "title": "Applying statistical learning theory to deep learning", "abstract": "Although statistical learning theory provides a robust framework to understand supervised learning, many theoretical aspects of deep learning remain unclear, in particular how different architectures may lead to inductive bias when trained using gradient based methods. The goal of these lectures is to provide an overview of some of the main questions that arise when attempting to understand deep learning from a learning theory perspective. After a brief reminder on statistical learning theory and stochastic optimization, we discuss implicit bias in the context of benign overfitting. We then move to a general description of the mirror descent algorithm, showing how we may go back and forth between a parameter space and the corresponding function space for a given learning problem, as well as how the geometry of the learning problem may be represented by a metric tensor. Building on this framework, we provide a detailed study of the implicit bias of gradient descent on linear diagonal networks for various regression tasks, showing how the loss function, scale of parameters at initialization and depth of the network may lead to various forms of implicit bias, in particular transitioning between kernel or feature learning.", "published": "2023-11-26T20:00:53Z", "authors": ["Cédric Gerbelot", "Avetik Karagulyan", "Stefani Karp", "Kavya Ravichandran", "Menachem Stern", "Nathan Srebro"], "categories": ["cs.LG", "cond-mat.dis-nn", "stat.ML"], "doi": "", "_query": "real estate valuation deep learning", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/1912.02877v2", "title": "Training Agents using Upside-Down Reinforcement Learning", "abstract": "We develop Upside-Down Reinforcement Learning (UDRL), a method for learning to act using only supervised learning techniques. Unlike traditional algorithms, UDRL does not use reward prediction or search for an optimal policy. Instead, it trains agents to follow commands such as \"obtain so much total reward in so much time.\" Many of its general principles are outlined in a companion report; the goal of this paper is to develop a practical learning algorithm and show that this conceptually simple perspective on agent training can produce a range of rewarding behaviors for multiple episodic environments. Experiments show that on some tasks UDRL's performance can be surprisingly competitive with, and even exceed that of some traditional baseline algorithms developed over decades of research. Based on these results, we suggest that alternative approaches to expected reward maximization have an important role to play in training useful autonomous agents.", "published": "2019-12-05T21:13:36Z", "authors": ["Rupesh Kumar Srivastava", "Pranav Shyam", "Filipe Mutz", "Wojciech Jaśkowski", "Jürgen Schmidhuber"], "categories": ["cs.LG", "cs.AI", "cs.RO"], "doi": "", "_query": "real estate valuation deep learning", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/1811.09956v1", "title": "Glottal Closure Instants Detection From Pathological Acoustic Speech Signal Using Deep Learning", "abstract": "In this paper, we propose a classification based glottal closure instants (GCI) detection from pathological acoustic speech signal, which finds many applications in vocal disorder analysis. Till date, GCI for pathological disorder is extracted from laryngeal (glottal source) signal recorded from Electroglottograph, a dedicated device designed to measure the vocal folds vibration around the larynx. We have created a pathological dataset which consists of simultaneous recordings of glottal source and acoustic speech signal of six different disorders from vocal disordered patients. The GCI locations are manually annotated for disorder analysis and supervised learning. We have proposed convolutional neural network based GCI detection method by fusing deep acoustic speech and linear prediction residual features for robust GCI detection. The experimental results showed that the proposed method is significantly better than the state-of-the-art GCI detection methods.", "published": "2018-11-25T06:18:24Z", "authors": ["Gurunath Reddy M", "Tanumay Mandal", "Krothapalli Sreenivasa Rao"], "categories": ["cs.SD", "cs.LG", "eess.AS", "stat.ML"], "doi": "", "_query": "real estate valuation deep learning", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/1707.00372v5", "title": "Deep Convolutional Framelets: A General Deep Learning Framework for Inverse Problems", "abstract": "Recently, deep learning approaches with various network architectures have achieved significant performance improvement over existing iterative reconstruction methods in various imaging problems. However, it is still unclear why these deep learning architectures work for specific inverse problems. To address these issues, here we show that the long-searched-for missing link is the convolution framelets for representing a signal by convolving local and non-local bases. The convolution framelets was originally developed to generalize the theory of low-rank Hankel matrix approaches for inverse problems, and this paper further extends the idea so that we can obtain a deep neural network using multilayer convolution framelets with perfect reconstruction (PR) under rectilinear linear unit nonlinearity (ReLU). Our analysis also shows that the popular deep network components such as residual block, redundant filter channels, and concatenated ReLU (CReLU) do indeed help to achieve the PR, while the pooling and unpooling layers should be augmented with high-pass branches to meet the PR condition. Moreover, by changing the number of filter channels and bias, we can control the shrinkage behaviors of the neural network. This discovery leads us to propose a novel theory for deep convolutional framelets neural network. Using numerical experiments with various inverse problems, we demonstrated that our deep convolution framelets network shows consistent improvement over existing deep architectures.This discovery suggests that the success of deep learning is not from a magical power of a black-box, but rather comes from the power of a novel signal representation using non-local basis combined with data-driven local basis, which is indeed a natural extension of classical signal processing theory.", "published": "2017-07-03T00:16:04Z", "authors": ["Jong Chul Ye", "Yoseob Han", "Eunju Cha"], "categories": ["stat.ML", "cs.CV", "cs.IT", "cs.LG"], "doi": "", "_query": "real estate valuation deep learning", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2006.04001v2", "title": "Real-Time Model Calibration with Deep Reinforcement Learning", "abstract": "The dynamic, real-time, and accurate inference of model parameters from empirical data is of great importance in many scientific and engineering disciplines that use computational models (such as a digital twin) for the analysis and prediction of complex physical processes. However, fast and accurate inference for processes with large and high dimensional datasets cannot easily be achieved with state-of-the-art methods under noisy real-world conditions. The primary reason is that the inference of model parameters with traditional techniques based on optimisation or sampling often suffers from computational and statistical challenges, resulting in a trade-off between accuracy and deployment time. In this paper, we propose a novel framework for inference of model parameters based on reinforcement learning. The contribution of the paper is twofold: 1) We reformulate the inference problem as a tracking problem with the objective of learning a policy that forces the response of the physics-based model to follow the observations; 2) We propose the constrained Lyapunov-based actor-critic (CLAC) algorithm to enable the robust and accurate inference of physics-based model parameters in real time under noisy real-world conditions. The proposed methodology is demonstrated and evaluated on two model-based diagnostics test cases utilizing two different physics-based models of turbofan engines. The performance of the methodology is compared to that of two alternative approaches: a state update method (unscented Kalman filter) and a supervised end-to-end mapping with deep neural networks. The experimental results demonstrate that the proposed methodology outperforms all other tested methods in terms of speed and robustness, with high inference accuracy.", "published": "2020-06-07T00:11:42Z", "authors": ["Yuan Tian", "Manuel Arias Chao", "Chetan Kulkarni", "Kai Goebel", "Olga Fink"], "categories": ["eess.SP", "cs.AI", "cs.LG"], "doi": "", "_query": "real estate valuation deep learning", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/1909.07587v2", "title": "A Hybrid Deep Learning Approach for Diagnosis of the Erythemato-Squamous Disease", "abstract": "The diagnosis of the Erythemato-squamous disease (ESD) is accepted as a difficult problem in dermatology. ESD is a form of skin disease. It generally causes redness of the skin and also may cause loss of skin. They are generally due to genetic or environmental factors. ESD comprises six classes of skin conditions namely, pityriasis rubra pilaris, lichen planus, chronic dermatitis, psoriasis, seboreic dermatitis and pityriasis rosea. The automated diagnosis of ESD can help doctors and dermatologists in reducing the efforts from their end and in taking faster decisions for treatment. The literature is replete with works that used conventional machine learning methods for the diagnosis of ESD. However, there isn't much instances of application of Deep learning for the diagnosis of ESD. In this paper, we propose a novel hybrid deep learning approach i.e. Derm2Vec for the diagnosis of the ESD. Derm2Vec is a hybrid deep learning model that consists of both Autoencoders and Deep Neural Networks. We also apply a conventional Deep Neural Network (DNN) for the classification of ESD. We apply both Derm2Vec and DNN along with other traditional machine learning methods on a real world dermatology dataset. The Derm2Vec method is found to be the best performer (when taking the prediction accuracy into account) followed by DNN and Extreme Gradient Boosting.The mean CV score of Derm2Vec, DNN and Extreme Gradient Boosting are 96.92 percent, 96.65 percent and 95.80 percent respectively.", "published": "2019-09-17T04:50:28Z", "authors": ["Sayan Putatunda"], "categories": ["cs.LG", "stat.ML"], "doi": "10.1109/CONECCT50063.2020.9198447", "_query": "real estate valuation deep learning", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/1805.08180v2", "title": "Hierarchical Reinforcement Learning with Hindsight", "abstract": "Reinforcement Learning (RL) algorithms can suffer from poor sample efficiency when rewards are delayed and sparse. We introduce a solution that enables agents to learn temporally extended actions at multiple levels of abstraction in a sample efficient and automated fashion. Our approach combines universal value functions and hindsight learning, allowing agents to learn policies belonging to different time scales in parallel. We show that our method significantly accelerates learning in a variety of discrete and continuous tasks.", "published": "2018-05-21T17:02:53Z", "authors": ["Andrew Levy", "Robert Platt", "Kate Saenko"], "categories": ["cs.LG", "cs.AI", "cs.NE", "cs.RO", "stat.ML"], "doi": "", "_query": "real estate valuation deep learning", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2204.04507v1", "title": "MR-iNet Gym: Framework for Edge Deployment of Deep Reinforcement Learning on Embedded Software Defined Radio", "abstract": "Dynamic resource allocation plays a critical role in the next generation of intelligent wireless communication systems. Machine learning has been leveraged as a powerful tool to make strides in this domain. In most cases, the progress has been limited to simulations due to the challenging nature of hardware deployment of these solutions. In this paper, for the first time, we design and deploy deep reinforcement learning (DRL)-based power control agents on the GPU embedded software defined radios (SDRs). To this end, we propose an end-to-end framework (MR-iNet Gym) where the simulation suite and the embedded SDR development work cohesively to overcome real-world implementation hurdles. To prove feasibility, we consider the problem of distributed power control for code-division multiple access (DS-CDMA)-based LPI/D transceivers. We first build a DS-CDMA ns3 module that interacts with the OpenAI Gym environment. Next, we train the power control DRL agents in this ns3-gym simulation environment in a scenario that replicates our hardware testbed. Next, for edge (embedded on-device) deployment, the trained models are optimized for real-time operation without loss of performance. Hardware-based evaluation verifies the efficiency of DRL agents over traditional distributed constrained power control (DCPC) algorithm. More significantly, as the primary goal, this is the first work that has established the feasibility of deploying DRL to provide optimized distributed resource allocation for next-generation of GPU-embedded radios.", "published": "2022-04-09T16:28:43Z", "authors": ["Jithin Jagannath", "Kian Hamedani", "Collin Farquhar", "Keyvan Ramezanpour", "Anu Jagannath"], "categories": ["cs.LG", "cs.NI"], "doi": "", "_query": "real estate valuation deep learning", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2009.00542v1", "title": "Hierarchical Deep Learning Classification of Unstructured Pathology Reports to Automate ICD-O Morphology Grading", "abstract": "Timely cancer reporting data are required in order to understand the impact of cancer, inform public health resource planning and implement cancer policy especially in Sub Saharan Africa where the reporting lag is behind world averages. Unstructured pathology reports, which contain tumor specific data, are the main source of information collected by cancer registries. Due to manual processing and labelling of pathology reports using the International Classification of Disease for oncology (ICD-O) codes, by human coders employed by cancer registries, has led to a considerable lag in cancer reporting. We present a hierarchical deep learning classification method that employs convolutional neural network models to automate the classification of 1813 anonymized breast cancer pathology reports with applicable ICD-O morphology codes across 9 classes. We demonstrate that the hierarchical deep learning classification method improves on performance in comparison to a flat multiclass CNN model for ICD-O morphology classification of the same reports.", "published": "2020-08-28T12:36:58Z", "authors": ["Waheeda Saib", "Tapiwa Chiwewe", "Elvira Singh"], "categories": ["cs.LG"], "doi": "", "_query": "real estate valuation deep learning", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2206.08671v2", "title": "FiT: Parameter Efficient Few-shot Transfer Learning for Personalized and Federated Image Classification", "abstract": "Modern deep learning systems are increasingly deployed in situations such as personalization and federated learning where it is necessary to support i) learning on small amounts of data, and ii) communication efficient distributed training protocols. In this work, we develop FiLM Transfer (FiT) which fulfills these requirements in the image classification setting by combining ideas from transfer learning (fixed pretrained backbones and fine-tuned FiLM adapter layers) and meta-learning (automatically configured Naive Bayes classifiers and episodic training) to yield parameter efficient models with superior classification accuracy at low-shot. The resulting parameter efficiency is key for enabling few-shot learning, inexpensive model updates for personalization, and communication efficient federated learning. We experiment with FiT on a wide range of downstream datasets and show that it achieves better classification accuracy than the leading Big Transfer (BiT) algorithm at low-shot and achieves state-of-the art accuracy on the challenging VTAB-1k benchmark, with fewer than 1% of the updateable parameters. Finally, we demonstrate the parameter efficiency and superior accuracy of FiT in distributed low-shot applications including model personalization and federated learning where model update size is an important performance metric.", "published": "2022-06-17T10:17:20Z", "authors": ["Aliaksandra Shysheya", "John Bronskill", "Massimiliano Patacchiola", "Sebastian Nowozin", "Richard E Turner"], "categories": ["stat.ML", "cs.CV", "cs.LG"], "doi": "", "_query": "real estate valuation deep learning", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/1911.00348v7", "title": "Hierarchical Expert Networks for Meta-Learning", "abstract": "The goal of meta-learning is to train a model on a variety of learning tasks, such that it can adapt to new problems within only a few iterations. Here we propose a principled information-theoretic model that optimally partitions the underlying problem space such that specialized expert decision-makers solve the resulting sub-problems. To drive this specialization we impose the same kind of information processing constraints both on the partitioning and the expert decision-makers. We argue that this specialization leads to efficient adaptation to new tasks. To demonstrate the generality of our approach we evaluate three meta-learning domains: image classification, regression, and reinforcement learning.", "published": "2019-10-31T06:23:52Z", "authors": ["Heinke Hihn", "Daniel A. Braun"], "categories": ["stat.ML", "cs.LG"], "doi": "", "_query": "real estate valuation deep learning", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2208.01220v2", "title": "GeoECG: Data Augmentation via Wasserstein Geodesic Perturbation for Robust Electrocardiogram Prediction", "abstract": "There has been an increased interest in applying deep neural networks to automatically interpret and analyze the 12-lead electrocardiogram (ECG). The current paradigms with machine learning methods are often limited by the amount of labeled data. This phenomenon is particularly problematic for clinically-relevant data, where labeling at scale can be time-consuming and costly in terms of the specialized expertise and human effort required. Moreover, deep learning classifiers may be vulnerable to adversarial examples and perturbations, which could have catastrophic consequences, for example, when applied in the context of medical treatment, clinical trials, or insurance claims. In this paper, we propose a physiologically-inspired data augmentation method to improve performance and increase the robustness of heart disease detection based on ECG signals. We obtain augmented samples by perturbing the data distribution towards other classes along the geodesic in Wasserstein space. To better utilize domain-specific knowledge, we design a ground metric that recognizes the difference between ECG signals based on physiologically determined features. Learning from 12-lead ECG signals, our model is able to distinguish five categories of cardiac conditions. Our results demonstrate improvements in accuracy and robustness, reflecting the effectiveness of our data augmentation method.", "published": "2022-08-02T03:14:13Z", "authors": ["Jiacheng Zhu", "Jielin Qiu", "Zhuolin Yang", "Douglas Weber", "Michael A. Rosenberg", "Emerson Liu", "Bo Li", "Ding Zhao"], "categories": ["stat.ML", "cs.LG", "eess.SP"], "doi": "", "_query": "real estate valuation deep learning", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/1907.06162v1", "title": "Modeling the Uncertainty in Electronic Health Records: a Bayesian Deep Learning Approach", "abstract": "Deep learning models have exhibited superior performance in predictive tasks with the explosively increasing Electronic Health Records (EHR). However, due to the lack of transparency, behaviors of deep learning models are difficult to interpret. Without trustworthiness, deep learning models will not be able to assist in the real-world decision-making process of healthcare issues. We propose a deep learning model based on Bayesian Neural Networks (BNN) to predict uncertainty induced by data noise. The uncertainty is introduced to provide model predictions with an extra level of confidence. Our experiments verify that instances with high uncertainty are harmful to model performance. Moreover, by investigating the distributions of model prediction and uncertainty, we show that it is possible to identify a group of patients for timely intervention, such that decreasing data noise will benefit more on the prediction accuracy for these patients.", "published": "2019-07-14T03:50:38Z", "authors": ["Riyi Qiu", "Yugang Jia", "Mirsad Hadzikadic", "Michael Dulin", "Xi Niu", "Xin Wang"], "categories": ["cs.LG", "stat.ML"], "doi": "", "_query": "real estate valuation deep learning", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2411.10153v3", "title": "A unifying framework for generalised Bayesian online learning in non-stationary environments", "abstract": "We propose a unifying framework for methods that perform probabilistic online learning in non-stationary environments. We call the framework BONE, which stands for generalised (B)ayesian (O)nline learning in (N)on-stationary (E)nvironments. BONE provides a common structure to tackle a variety of problems, including online continual learning, prequential forecasting, and contextual bandits. The framework requires specifying three modelling choices: (i) a model for measurements (e.g., a neural network), (ii) an auxiliary process to model non-stationarity (e.g., the time since the last changepoint), and (iii) a conditional prior over model parameters (e.g., a multivariate Gaussian). The framework also requires two algorithmic choices, which we use to carry out approximate inference under this framework: (i) an algorithm to estimate beliefs (posterior distribution) about the model parameters given the auxiliary variable, and (ii) an algorithm to estimate beliefs about the auxiliary variable. We show how the modularity of our framework allows for many existing methods to be reinterpreted as instances of BONE, and it allows us to propose new methods. We compare experimentally existing methods with our proposed new method on several datasets, providing insights into the situations that make each method more suitable for a specific task. We provide a Jax open source library to facilitate the adoption of this framework.", "published": "2024-11-15T12:52:02Z", "authors": ["Gerardo Duran-Martin", "Leandro Sánchez-Betancourt", "Alexander Y. Shestopaloff", "Kevin Murphy"], "categories": ["stat.ML", "cs.LG"], "doi": "", "_query": "real estate valuation deep learning", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2202.12348v1", "title": "Bayesian Deep Learning for Graphs", "abstract": "The adaptive processing of structured data is a long-standing research topic in machine learning that investigates how to automatically learn a mapping from a structured input to outputs of various nature. Recently, there has been an increasing interest in the adaptive processing of graphs, which led to the development of different neural network-based methodologies. In this thesis, we take a different route and develop a Bayesian Deep Learning framework for graph learning. The dissertation begins with a review of the principles over which most of the methods in the field are built, followed by a study on graph classification reproducibility issues. We then proceed to bridge the basic ideas of deep learning for graphs with the Bayesian world, by building our deep architectures in an incremental fashion. This framework allows us to consider graphs with discrete and continuous edge features, producing unsupervised embeddings rich enough to reach the state of the art on several classification tasks. Our approach is also amenable to a Bayesian nonparametric extension that automatizes the choice of almost all model's hyper-parameters. Two real-world applications demonstrate the efficacy of deep learning for graphs. The first concerns the prediction of information-theoretic quantities for molecular simulations with supervised neural models. After that, we exploit our Bayesian models to solve a malware-classification task while being robust to intra-procedural code obfuscation techniques. We conclude the dissertation with an attempt to blend the best of the neural and Bayesian worlds together. The resulting hybrid model is able to predict multimodal distributions conditioned on input graphs, with the consequent ability to model stochasticity and uncertainty better than most works. Overall, we aim to provide a Bayesian perspective into the articulated research field of deep learning for graphs.", "published": "2022-02-24T20:18:41Z", "authors": ["Federico Errica"], "categories": ["cs.LG", "cs.AI", "stat.ML"], "doi": "", "_query": "real estate valuation deep learning", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2111.02071v1", "title": "The Impact of Batch Learning in Stochastic Bandits", "abstract": "We consider a special case of bandit problems, namely batched bandits. Motivated by natural restrictions of recommender systems and e-commerce platforms, we assume that a learning agent observes responses batched in groups over a certain time period. Unlike previous work, we consider a more practically relevant batch-centric scenario of batch learning. We provide a policy-agnostic regret analysis and demonstrate upper and lower bounds for the regret of a candidate policy. Our main theoretical results show that the impact of batch learning can be measured in terms of online behavior. Finally, we demonstrate the consistency of theoretical results by conducting empirical experiments and reflect on the optimal batch size choice.", "published": "2021-11-03T08:38:10Z", "authors": ["Danil Provodin", "Pratik Gajane", "Mykola Pechenizkiy", "Maurits Kaptein"], "categories": ["cs.LG", "stat.ML"], "doi": "", "_query": "real estate valuation deep learning", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2102.12178v1", "title": "Learning to Generate Wasserstein Barycenters", "abstract": "Optimal transport is a notoriously difficult problem to solve numerically, with current approaches often remaining intractable for very large scale applications such as those encountered in machine learning. Wasserstein barycenters -- the problem of finding measures in-between given input measures in the optimal transport sense -- is even more computationally demanding as it requires to solve an optimization problem involving optimal transport distances. By training a deep convolutional neural network, we improve by a factor of 60 the computational speed of Wasserstein barycenters over the fastest state-of-the-art approach on the GPU, resulting in milliseconds computational times on $512\\times512$ regular grids. We show that our network, trained on Wasserstein barycenters of pairs of measures, generalizes well to the problem of finding Wasserstein barycenters of more than two measures. We demonstrate the efficiency of our approach for computing barycenters of sketches and transferring colors between multiple images.", "published": "2021-02-24T10:13:48Z", "authors": ["Julien Lacombe", "Julie Digne", "Nicolas Courty", "Nicolas Bonneel"], "categories": ["cs.LG", "stat.ML"], "doi": "", "_query": "real estate valuation deep learning", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2011.06709v2", "title": "Active Reinforcement Learning: Observing Rewards at a Cost", "abstract": "Active reinforcement learning (ARL) is a variant on reinforcement learning where the agent does not observe the reward unless it chooses to pay a query cost c > 0. The central question of ARL is how to quantify the long-term value of reward information. Even in multi-armed bandits, computing the value of this information is intractable and we have to rely on heuristics. We propose and evaluate several heuristic approaches for ARL in multi-armed bandits and (tabular) Markov decision processes, and discuss and illustrate some challenging aspects of the ARL problem.", "published": "2020-11-13T01:01:13Z", "authors": ["David Krueger", "Jan Leike", "Owain Evans", "John Salvatier"], "categories": ["cs.LG", "cs.AI", "stat.ML"], "doi": "", "_query": "real estate valuation deep learning", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2102.07657v1", "title": "Real-Time Topology Optimization in 3D via Deep Transfer Learning", "abstract": "The published literature on topology optimization has exploded over the last two decades to include methods that use shape and topological derivatives or evolutionary algorithms formulated on various geometric representations and parametrizations. One of the key challenges of all these methods is the massive computational cost associated with 3D topology optimization problems. We introduce a transfer learning method based on a convolutional neural network that (1) can handle high-resolution 3D design domains of various shapes and topologies; (2) supports real-time design space explorations as the domain and boundary conditions change; (3) requires a much smaller set of high-resolution examples for the improvement of learning in a new task compared to traditional deep learning networks; (4) is multiple orders of magnitude more efficient than the established gradient-based methods, such as SIMP. We provide numerous 2D and 3D examples to showcase the effectiveness and accuracy of our proposed approach, including for design domains that are unseen to our source network, as well as the generalization capabilities of the transfer learning-based approach. Our experiments achieved an average binary accuracy of around 95% at real-time prediction rates. These properties, in turn, suggest that the proposed transfer-learning method may serve as the first practical underlying framework for real-time 3D design exploration based on topology optimization", "published": "2021-02-11T21:09:58Z", "authors": ["MohammadMahdi Behzadi", "Horea T. Ilies"], "categories": ["cs.LG", "cs.AI"], "doi": "", "_query": "real estate valuation deep learning", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2407.14379v1", "title": "Deep learning density functional theory Hamiltonian in real space", "abstract": "Deep learning electronic structures from ab initio calculations holds great potential to revolutionize computational materials studies. While existing methods proved success in deep-learning density functional theory (DFT) Hamiltonian matrices, they are limited to DFT programs using localized atomic-like bases and heavily depend on the form of the bases. Here, we propose the DeepH-r method for deep-learning DFT Hamiltonians in real space, facilitating the prediction of DFT Hamiltonian in a basis-independent manner. An equivariant neural network architecture for modeling the real-space DFT potential is developed, targeting a more fundamental quantity in DFT. The real-space potential exhibits simplified principles of equivariance and enhanced nearsightedness, further boosting the performance of deep learning. When applied to evaluate the Hamiltonian matrix, this method significantly improved in accuracy, as exemplified in multiple case studies. Given the abundance of data in the real-space potential, this work may pave a novel pathway for establishing a ``large materials model\" with increased accuracy.", "published": "2024-07-19T15:07:22Z", "authors": ["Zilong Yuan", "Zechen Tang", "Honggeng Tao", "Xiaoxun Gong", "Zezhou Chen", "Yuxiang Wang", "He Li", "Yang Li", "Zhiming Xu", "Minghui Sun", "Boheng Zhao", "Chong Wang", "Wenhui Duan", "Yong Xu"], "categories": ["physics.comp-ph", "cond-mat.mtrl-sci"], "doi": "", "_query": "real estate valuation deep learning", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2401.08318v2", "title": "OpenDPD: An Open-Source End-to-End Learning & Benchmarking Framework for Wideband Power Amplifier Modeling and Digital Pre-Distortion", "abstract": "With the rise in communication capacity, deep neural networks (DNN) for digital pre-distortion (DPD) to correct non-linearity in wideband power amplifiers (PAs) have become prominent. Yet, there is a void in open-source and measurement-setup-independent platforms for fast DPD exploration and objective DPD model comparison. This paper presents an open-source framework, OpenDPD, crafted in PyTorch, with an associated dataset for PA modeling and DPD learning. We introduce a Dense Gated Recurrent Unit (DGRU)-DPD, trained via a novel end-to-end learning architecture, outperforming previous DPD models on a digital PA (DPA) in the new digital transmitter (DTX) architecture with unconventional transfer characteristics compared to analog PAs. Measurements show our DGRU-DPD achieves an ACPR of -44.69/-44.47 dBc and an EVM of -35.22 dB for 200 MHz OFDM signals. OpenDPD code, datasets, and documentation are publicly available at https://github.com/lab-emi/OpenDPD.", "published": "2024-01-16T12:36:17Z", "authors": ["Yizhuo Wu", "Gagan Deep Singh", "Mohammadreza Beikmirza", "Leo C. N. de Vreede", "Morteza Alavi", "Chang Gao"], "categories": ["cs.LG", "eess.SP"], "doi": "10.1109/ISCAS58744.2024.10558162", "_query": "real estate valuation deep learning", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/1804.10318v3", "title": "Efficiently Learning Nonstationary Gaussian Processes for Real World Impact", "abstract": "Most real world phenomena such as sunlight distribution under a forest canopy, minerals concentration, stock valuation, exhibit nonstationary dynamics i.e. phenomenon variation changes depending on the locality. Nonstationary dynamics pose both theoretical and practical challenges to statistical machine learning algorithms that aim to accurately capture the complexities governing the evolution of such processes. Typically the nonstationary dynamics are modeled using nonstationary Gaussian Process models (NGPS) that employ local latent dynamics parameterization to correspondingly model the nonstationary real observable dynamics. Recently, an approach based on most likely induced latent dynamics representation attracted research community's attention for a while. The approach could not be employed for large scale real world applications because learning a most likely latent dynamics representation involves maximization of marginal likelihood of the observed real dynamics that becomes intractable as the number of induced latent points grows with problem size. We have established a direct relationship between informativeness of the induced latent dynamics and the marginal likelihood of the observed real dynamics. This opens up the possibility of maximizing marginal likelihood of observed real dynamics indirectly by near optimally maximizing entropy or mutual information gain on the induced latent dynamics using greedy algorithms. Therefore, for an efficient yet accurate inference, we propose to build an induced latent dynamics representation using a novel algorithm LISAL that adaptively maximizes entropy or mutual information on the induced latent dynamics and marginal likelihood of observed real dynamics in an iterative manner. The relevance of LISAL is validated using real world datasets.", "published": "2018-04-27T01:25:41Z", "authors": ["Sahil Garg"], "categories": ["cs.LG", "stat.ML"], "doi": "", "_query": "real estate valuation deep learning", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/1911.07420v1", "title": "A Graph Autoencoder Approach to Causal Structure Learning", "abstract": "Causal structure learning has been a challenging task in the past decades and several mainstream approaches such as constraint- and score-based methods have been studied with theoretical guarantees. Recently, a new approach has transformed the combinatorial structure learning problem into a continuous one and then solved it using gradient-based optimization methods. Following the recent state-of-the-arts, we propose a new gradient-based method to learn causal structures from observational data. The proposed method generalizes the recent gradient-based methods to a graph autoencoder framework that allows nonlinear structural equation models and is easily applicable to vector-valued variables. We demonstrate that on synthetic datasets, our proposed method outperforms other gradient-based methods significantly, especially on large causal graphs. We further investigate the scalability and efficiency of our method, and observe a near linear training time when scaling up the graph size.", "published": "2019-11-18T04:22:00Z", "authors": ["Ignavier Ng", "Shengyu Zhu", "Zhitang Chen", "Zhuangyan Fang"], "categories": ["cs.LG", "stat.ML"], "doi": "", "_query": "real estate valuation deep learning", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2006.05865v3", "title": "Deep Dimension Reduction for Supervised Representation Learning", "abstract": "The goal of supervised representation learning is to construct effective data representations for prediction. Among all the characteristics of an ideal nonparametric representation of high-dimensional complex data, sufficiency, low dimensionality and disentanglement are some of the most essential ones. We propose a deep dimension reduction approach to learning representations with these characteristics. The proposed approach is a nonparametric generalization of the sufficient dimension reduction method. We formulate the ideal representation learning task as that of finding a nonparametric representation that minimizes an objective function characterizing conditional independence and promoting disentanglement at the population level. We then estimate the target representation at the sample level nonparametrically using deep neural networks. We show that the estimated deep nonparametric representation is consistent in the sense that its excess risk converges to zero. Our extensive numerical experiments using simulated and real benchmark data demonstrate that the proposed methods have better performance than several existing dimension reduction methods and the standard deep learning models in the context of classification and regression.", "published": "2020-06-10T14:47:43Z", "authors": ["Jian Huang", "Yuling Jiao", "Xu Liao", "Jin Liu", "Zhou Yu"], "categories": ["cs.LG", "stat.ML"], "doi": "", "_query": "real estate valuation deep learning", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/1807.11809v1", "title": "Deep learning in agriculture: A survey", "abstract": "Deep learning constitutes a recent, modern technique for image processing and data analysis, with promising results and large potential. As deep learning has been successfully applied in various domains, it has recently entered also the domain of agriculture. In this paper, we perform a survey of 40 research efforts that employ deep learning techniques, applied to various agricultural and food production challenges. We examine the particular agricultural problems under study, the specific models and frameworks employed, the sources, nature and pre-processing of data used, and the overall performance achieved according to the metrics used at each work under study. Moreover, we study comparisons of deep learning with other existing popular techniques, in respect to differences in classification or regression performance. Our findings indicate that deep learning provides high accuracy, outperforming existing commonly used image processing techniques.", "published": "2018-07-31T13:30:03Z", "authors": ["Andreas Kamilaris", "Francesc X. Prenafeta-Boldu"], "categories": ["cs.LG", "cs.CV", "stat.ML"], "doi": "10.1016/j.compag.2018.02.016", "_query": "real estate valuation deep learning", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2201.08780v2", "title": "Real-Time Seizure Detection using EEG: A Comprehensive Comparison of Recent Approaches under a Realistic Setting", "abstract": "Electroencephalogram (EEG) is an important diagnostic test that physicians use to record brain activity and detect seizures by monitoring the signals. There have been several attempts to detect seizures and abnormalities in EEG signals with modern deep learning models to reduce the clinical burden. However, they cannot be fairly compared against each other as they were tested in distinct experimental settings. Also, some of them are not trained in real-time seizure detection tasks, making it hard for on-device applications. Therefore in this work, for the first time, we extensively compare multiple state-of-the-art models and signal feature extractors in a real-time seizure detection framework suitable for real-world application, using various evaluation metrics including a new one we propose to evaluate more practical aspects of seizure detection models. Our code is available at https://github.com/AITRICS/EEG_real_time_seizure_detection.", "published": "2022-01-21T16:53:32Z", "authors": ["Kwanhyung Lee", "Hyewon Jeong", "Seyun Kim", "Donghwa Yang", "Hoon-Chul Kang", "Edward Choi"], "categories": ["cs.LG"], "doi": "", "_query": "real estate valuation deep learning", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2502.17993v1", "title": "A Perspective on Symbolic Machine Learning in Physical Sciences", "abstract": "Machine learning is rapidly making its pathway across all of the natural sciences, including physical sciences. The rate at which ML is impacting non-scientific disciplines is incomparable to that in the physical sciences. This is partly due to the uninterpretable nature of deep neural networks. Symbolic machine learning stands as an equal and complementary partner to numerical machine learning in speeding up scientific discovery in physics. This perspective discusses the main differences between the ML and scientific approaches. It stresses the need to develop and apply symbolic machine learning to physics problems equally, in parallel to numerical machine learning, because of the dual nature of physics research.", "published": "2025-02-25T09:02:02Z", "authors": ["Nour Makke", "Sanjay Chawla"], "categories": ["cs.LG", "hep-ph", "hep-th"], "doi": "", "_query": "real estate valuation deep learning", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2211.14666v2", "title": "Synergies between Disentanglement and Sparsity: Generalization and Identifiability in Multi-Task Learning", "abstract": "Although disentangled representations are often said to be beneficial for downstream tasks, current empirical and theoretical understanding is limited. In this work, we provide evidence that disentangled representations coupled with sparse base-predictors improve generalization. In the context of multi-task learning, we prove a new identifiability result that provides conditions under which maximally sparse base-predictors yield disentangled representations. Motivated by this theoretical result, we propose a practical approach to learn disentangled representations based on a sparsity-promoting bi-level optimization problem. Finally, we explore a meta-learning version of this algorithm based on group Lasso multiclass SVM base-predictors, for which we derive a tractable dual formulation. It obtains competitive results on standard few-shot classification benchmarks, while each task is using only a fraction of the learned representations.", "published": "2022-11-26T21:02:09Z", "authors": ["Sébastien Lachapelle", "Tristan Deleu", "Divyat Mahajan", "Ioannis Mitliagkas", "Yoshua Bengio", "Simon Lacoste-Julien", "Quentin Bertrand"], "categories": ["cs.LG", "stat.ML"], "doi": "", "_query": "real estate valuation deep learning", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/1701.04503v1", "title": "Deep Learning for Computational Chemistry", "abstract": "The rise and fall of artificial neural networks is well documented in the scientific literature of both computer science and computational chemistry. Yet almost two decades later, we are now seeing a resurgence of interest in deep learning, a machine learning algorithm based on multilayer neural networks. Within the last few years, we have seen the transformative impact of deep learning in many domains, particularly in speech recognition and computer vision, to the extent that the majority of expert practitioners in those field are now regularly eschewing prior established models in favor of deep learning models. In this review, we provide an introductory overview into the theory of deep neural networks and their unique properties that distinguish them from traditional machine learning algorithms used in cheminformatics. By providing an overview of the variety of emerging applications of deep neural networks, we highlight its ubiquity and broad applicability to a wide range of challenges in the field, including QSAR, virtual screening, protein structure prediction, quantum chemistry, materials design and property prediction. In reviewing the performance of deep neural networks, we observed a consistent outperformance against non-neural networks state-of-the-art models across disparate research topics, and deep neural network based models often exceeded the \"glass ceiling\" expectations of their respective tasks. Coupled with the maturity of GPU-accelerated computing for training deep neural networks and the exponential growth of chemical data on which to train these networks on, we anticipate that deep learning algorithms will be a valuable tool for computational chemistry.", "published": "2017-01-17T01:15:14Z", "authors": ["Garrett B. Goh", "Nathan O. Hodas", "Abhinav Vishnu"], "categories": ["stat.ML", "cs.AI", "cs.CE", "cs.LG", "physics.chem-ph"], "doi": "", "_query": "real estate valuation deep learning", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2307.04957v2", "title": "Reinforcement Learning with Non-Cumulative Objective", "abstract": "In reinforcement learning, the objective is almost always defined as a \\emph{cumulative} function over the rewards along the process. However, there are many optimal control and reinforcement learning problems in various application fields, especially in communications and networking, where the objectives are not naturally expressed as summations of the rewards. In this paper, we recognize the prevalence of non-cumulative objectives in various problems, and propose a modification to existing algorithms for optimizing such objectives. Specifically, we dive into the fundamental building block for many optimal control and reinforcement learning algorithms: the Bellman optimality equation. To optimize a non-cumulative objective, we replace the original summation operation in the Bellman update rule with a generalized operation corresponding to the objective. Furthermore, we provide sufficient conditions on the form of the generalized operation as well as assumptions on the Markov decision process under which the globally optimal convergence of the generalized Bellman updates can be guaranteed. We demonstrate the idea experimentally with the bottleneck objective, i.e., the objectives determined by the minimum reward along the process, on classical optimal control and reinforcement learning tasks, as well as on two network routing problems on maximizing the flow rates.", "published": "2023-07-11T01:20:09Z", "authors": ["Wei Cui", "Wei Yu"], "categories": ["cs.LG", "cs.AI", "cs.NI", "math.OC", "stat.ML"], "doi": "10.1109/TMLCN.2023.3285543", "_query": "real estate valuation deep learning", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2510.22370v1", "title": "BLIP-FusePPO: A Vision-Language Deep Reinforcement Learning Framework for Lane Keeping in Autonomous Vehicles", "abstract": "In this paper, we propose Bootstrapped Language-Image Pretraining-driven Fused State Representation in Proximal Policy Optimization (BLIP-FusePPO), a novel multimodal reinforcement learning (RL) framework for autonomous lane-keeping (LK), in which semantic embeddings generated by a vision-language model (VLM) are directly fused with geometric states, LiDAR observations, and Proportional-Integral-Derivative-based (PID) control feedback within the agent observation space. The proposed method lets the agent learn driving rules that are aware of their surroundings and easy to understand by combining high-level scene understanding from the VLM with low-level control and spatial signals. Our architecture brings together semantic, geometric, and control-aware representations to make policy learning more robust. A hybrid reward function that includes semantic alignment, LK accuracy, obstacle avoidance, and speed regulation helps learning to be more efficient and generalizable. Our method is different from the approaches that only use semantic models to shape rewards. Instead, it directly embeds semantic features into the state representation. This cuts down on expensive runtime inference and makes sure that semantic guidance is always available. The simulation results show that the proposed model is better at LK stability and adaptability than the best vision-based and multimodal RL baselines in a wide range of difficult driving situations. We make our code publicly available.", "published": "2025-10-25T17:27:08Z", "authors": ["Seyed Ahmad Hosseini Miangoleh", "Amin Jalal Aghdasian", "Farzaneh Abdollahi"], "categories": ["cs.RO", "cs.AI", "cs.CV", "cs.LG", "cs.SE"], "doi": "", "_query": "real estate valuation deep learning", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2312.17210v1", "title": "Continual Learning via Sequential Function-Space Variational Inference", "abstract": "Sequential Bayesian inference over predictive functions is a natural framework for continual learning from streams of data. However, applying it to neural networks has proved challenging in practice. Addressing the drawbacks of existing techniques, we propose an optimization objective derived by formulating continual learning as sequential function-space variational inference. In contrast to existing methods that regularize neural network parameters directly, this objective allows parameters to vary widely during training, enabling better adaptation to new tasks. Compared to objectives that directly regularize neural network predictions, the proposed objective allows for more flexible variational distributions and more effective regularization. We demonstrate that, across a range of task sequences, neural networks trained via sequential function-space variational inference achieve better predictive accuracy than networks trained with related methods while depending less on maintaining a set of representative points from previous tasks.", "published": "2023-12-28T18:44:32Z", "authors": ["Tim G. J. Rudner", "Freddie Bickford Smith", "Qixuan Feng", "Yee Whye Teh", "Yarin Gal"], "categories": ["stat.ML", "cs.AI", "cs.LG"], "doi": "", "_query": "real estate valuation deep learning", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/1410.8803v2", "title": "The Lie group of real analytic diffeomorphisms is not real analytic", "abstract": "We construct an infinite dimensional real analytic manifold structure for the space of real analytic mappings from a compact manifold to a locally convex manifold. Here a map is real analytic if it extends to a holomorphic map on some neighbourhood of the complexification of its domain. As is well known the construction turns the group of real analytic diffeomorphisms into a smooth locally convex Lie group. We prove then that the diffeomorphism group is regular in the sense of Milnor. In the inequivalent \"convenient setting of calculus\" the real analytic diffeomorphisms even form a real analytic Lie group. However, we prove that the Lie group structure on the group of real analytic diffeomorphisms is in general not real analytic in our sense.", "published": "2014-10-24T07:02:37Z", "authors": ["Rafael Dahmen", "Alexander Schmeding"], "categories": ["math.DG", "math.GR"], "doi": "10.4064/sm8130-12-2015", "_query": "real estate valuation deep learning", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2011.14229v3", "title": "Deep Learning for Regularization Prediction in Diffeomorphic Image Registration", "abstract": "This paper presents a predictive model for estimating regularization parameters of diffeomorphic image registration. We introduce a novel framework that automatically determines the parameters controlling the smoothness of diffeomorphic transformations. Our method significantly reduces the effort of parameter tuning, which is time and labor-consuming. To achieve the goal, we develop a predictive model based on deep convolutional neural networks (CNN) that learns the mapping between pairwise images and the regularization parameter of image registration. In contrast to previous methods that estimate such parameters in a high-dimensional image space, our model is built in an efficient bandlimited space with much lower dimensions. We demonstrate the effectiveness of our model on both 2D synthetic data and 3D real brain images. Experimental results show that our model not only predicts appropriate regularization parameters for image registration, but also improving the network training in terms of time and memory efficiency.", "published": "2020-11-28T22:56:44Z", "authors": ["Jian Wang", "Miaomiao Zhang"], "categories": ["eess.IV", "cs.CV", "cs.LG"], "doi": "", "_query": "real estate valuation deep learning", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/1708.04099v1", "title": "Context-based Normalization of Histological Stains using Deep Convolutional Features", "abstract": "While human observers are able to cope with variations in color and appearance of histological stains, digital pathology algorithms commonly require a well-normalized setting to achieve peak performance, especially when a limited amount of labeled data is available. This work provides a fully automated, end-to-end learning-based setup for normalizing histological stains, which considers the texture context of the tissue. We introduce Feature Aware Normalization, which extends the framework of batch normalization in combination with gating elements from Long Short-Term Memory units for normalization among different spatial regions of interest. By incorporating a pretrained deep neural network as a feature extractor steering a pixelwise processing pipeline, we achieve excellent normalization results and ensure a consistent representation of color and texture. The evaluation comprises a comparison of color histogram deviations, structural similarity and measures the color volume obtained by the different methods.", "published": "2017-08-14T12:40:23Z", "authors": ["Daniel Bug", "Steffen Schneider", "Anne Grote", "Eva Oswald", "Friedrich Feuerhake", "Julia Schüler", "Dorit Merhof"], "categories": ["cs.CV"], "doi": "", "_query": "real estate valuation deep learning", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/1702.07800v4", "title": "On the Origin of Deep Learning", "abstract": "This paper is a review of the evolutionary history of deep learning models. It covers from the genesis of neural networks when associationism modeling of the brain is studied, to the models that dominate the last decade of research in deep learning like convolutional neural networks, deep belief networks, and recurrent neural networks. In addition to a review of these models, this paper primarily focuses on the precedents of the models above, examining how the initial ideas are assembled to construct the early models and how these preliminary models are developed into their current forms. Many of these evolutionary paths last more than half a century and have a diversity of directions. For example, CNN is built on prior knowledge of biological vision system; DBN is evolved from a trade-off of modeling power and computation complexity of graphical models and many nowadays models are neural counterparts of ancient linear models. This paper reviews these evolutionary paths and offers a concise thought flow of how these models are developed, and aims to provide a thorough background for deep learning. More importantly, along with the path, this paper summarizes the gist behind these milestones and proposes many directions to guide the future research of deep learning.", "published": "2017-02-24T23:30:08Z", "authors": ["Haohan Wang", "Bhiksha Raj"], "categories": ["cs.LG", "cs.NE", "stat.ML"], "doi": "", "_query": "real estate valuation deep learning", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/1901.08560v3", "title": "Semi-Unsupervised Learning: Clustering and Classifying using Ultra-Sparse Labels", "abstract": "In semi-supervised learning for classification, it is assumed that every ground truth class of data is present in the small labelled dataset. Many real-world sparsely-labelled datasets are plausibly not of this type. It could easily be the case that some classes of data are found only in the unlabelled dataset -- perhaps the labelling process was biased -- so we do not have any labelled examples to train on for some classes. We call this learning regime $\\textit{semi-unsupervised learning}$, an extreme case of semi-supervised learning, where some classes have no labelled exemplars in the training set. First, we outline the pitfalls associated with trying to apply deep generative model (DGM)-based semi-supervised learning algorithms to datasets of this type. We then show how a combination of clustering and semi-supervised learning, using DGMs, can be brought to bear on this problem. We study several different datasets, showing how one can still learn effectively when half of the ground truth classes are entirely unlabelled and the other half are sparsely labelled.", "published": "2019-01-24T18:24:39Z", "authors": ["Matthew Willetts", "Stephen J Roberts", "Christopher C Holmes"], "categories": ["stat.ML", "cs.LG"], "doi": "", "_query": "real estate valuation deep learning", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2410.18918v2", "title": "MissNODAG: Differentiable Cyclic Causal Graph Learning from Incomplete Data", "abstract": "Causal discovery in real-world systems, such as biological networks, is often complicated by feedback loops and incomplete data. Standard algorithms, which assume acyclic structures or fully observed data, struggle with these challenges. To address this gap, we propose MissNODAG, a differentiable framework for learning both the underlying cyclic causal graph and the missingness mechanism from partially observed data, including data missing not at random. Our framework integrates an additive noise model with an expectation-maximization procedure, alternating between imputing missing values and optimizing the observed data likelihood, to uncover both the cyclic structures and the missingness mechanism. We establish consistency guarantees under exact maximization of the score function in the large sample setting. Finally, we demonstrate the effectiveness of MissNODAG through synthetic experiments and an application to real-world gene perturbation data.", "published": "2024-10-24T17:09:10Z", "authors": ["Muralikrishnna G. Sethuraman", "Razieh Nabi", "Faramarz Fekri"], "categories": ["stat.ML", "cs.LG"], "doi": "", "_query": "real estate valuation deep learning", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/1909.03354v7", "title": "Deep Weakly-Supervised Learning Methods for Classification and Localization in Histology Images: A Survey", "abstract": "Using deep learning models to diagnose cancer from histology data presents several challenges. Cancer grading and localization of regions of interest (ROIs) in these images normally relies on both image- and pixel-level labels, the latter requiring a costly annotation process. Deep weakly-supervised object localization (WSOL) methods provide different strategies for low-cost training of deep learning models. Using only image-class annotations, these methods can be trained to classify an image, and yield class activation maps (CAMs) for ROI localization. This paper provides a review of state-of-art DL methods for WSOL. We propose a taxonomy where these methods are divided into bottom-up and top-down methods according to the information flow in models. Although the latter have seen limited progress, recent bottom-up methods are currently driving much progress with deep WSOL methods. Early works focused on designing different spatial pooling functions. However, these methods reached limited localization accuracy, and unveiled a major limitation -- the under-activation of CAMs which leads to high false negative localization. Subsequent works aimed to alleviate this issue and recover complete object. Representative methods from our taxonomy are evaluated and compared in terms of classification and localization accuracy on two challenging histology datasets. Overall, the results indicate poor localization performance, particularly for generic methods that were initially designed to process natural images. Methods designed to address the challenges of histology data yielded good results. However, all methods suffer from high false positive/negative localization. Four key challenges are identified for the application of deep WSOL methods in histology -- under/over activation of CAMs, sensitivity to thresholding, and model selection.", "published": "2019-09-08T00:01:37Z", "authors": ["Jérôme Rony", "Soufiane Belharbi", "Jose Dolz", "Ismail Ben Ayed", "Luke McCaffrey", "Eric Granger"], "categories": ["cs.CV", "cs.LG", "eess.IV"], "doi": "10.59275/j.melba.2023-5g54", "_query": "real estate valuation deep learning", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/1911.06557v1", "title": "Multi-Label Learning with Deep Forest", "abstract": "In multi-label learning, each instance is associated with multiple labels and the crucial task is how to leverage label correlations in building models. Deep neural network methods usually jointly embed the feature and label information into a latent space to exploit label correlations. However, the success of these methods highly depends on the precise choice of model depth. Deep forest is a recent deep learning framework based on tree model ensembles, which does not rely on backpropagation. We consider the advantages of deep forest models are very appropriate for solving multi-label problems. Therefore we design the Multi-Label Deep Forest (MLDF) method with two mechanisms: measure-aware feature reuse and measure-aware layer growth. The measure-aware feature reuse mechanism reuses the good representation in the previous layer guided by confidence. The measure-aware layer growth mechanism ensures MLDF gradually increase the model complexity by performance measure. MLDF handles two challenging problems at the same time: one is restricting the model complexity to ease the overfitting issue; another is optimizing the performance measure on user's demand since there are many different measures in the multi-label evaluation. Experiments show that our proposal not only beats the compared methods over six measures on benchmark datasets but also enjoys label correlation discovery and other desired properties in multi-label learning.", "published": "2019-11-15T10:40:12Z", "authors": ["Liang Yang", "Xi-Zhu Wu", "Yuan Jiang", "Zhi-Hua Zhou"], "categories": ["cs.LG", "stat.ML"], "doi": "", "_query": "real estate valuation deep learning", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/1912.12115v1", "title": "Split Learning for collaborative deep learning in healthcare", "abstract": "Shortage of labeled data has been holding the surge of deep learning in healthcare back, as sample sizes are often small, patient information cannot be shared openly, and multi-center collaborative studies are a burden to set up. Distributed machine learning methods promise to mitigate these problems. We argue for a split learning based approach and apply this distributed learning method for the first time in the medical field to compare performance against (1) centrally hosted and (2) non collaborative configurations for a range of participants. Two medical deep learning tasks are used to compare split learning to conventional single and multi center approaches: a binary classification problem of a data set of 9000 fundus photos, and multi-label classification problem of a data set of 156,535 chest X-rays. The several distributed learning setups are compared for a range of 1-50 distributed participants. Performance of the split learning configuration remained constant for any number of clients compared to a single center study, showing a marked difference compared to the non collaborative configuration after 2 clients (p < 0.001) for both sets. Our results affirm the benefits of collaborative training of deep neural networks in health care. Our work proves the significant benefit of distributed learning in healthcare, and paves the way for future real-world implementations.", "published": "2019-12-27T14:39:58Z", "authors": ["Maarten G. Poirot", "Praneeth Vepakomma", "Ken Chang", "Jayashree Kalpathy-Cramer", "Rajiv Gupta", "Ramesh Raskar"], "categories": ["cs.LG", "cs.DC", "stat.ML"], "doi": "", "_query": "real estate valuation deep learning", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2501.13890v4", "title": "Federated Granger Causality Learning for Interdependent Clients with State Space Representation", "abstract": "Advanced sensors and IoT devices have improved the monitoring and control of complex industrial enterprises. They have also created an interdependent fabric of geographically distributed process operations (clients) across these enterprises. Granger causality is an effective approach to detect and quantify interdependencies by examining how one client's state affects others over time. Understanding these interdependencies captures how localized events, such as faults and disruptions, can propagate throughout the system, possibly causing widespread operational impacts. However, the large volume and complexity of industrial data pose challenges in modeling these interdependencies. This paper develops a federated approach to learning Granger causality. We utilize a linear state space system framework that leverages low-dimensional state estimates to analyze interdependencies. This addresses bandwidth limitations and the computational burden commonly associated with centralized data processing. We propose augmenting the client models with the Granger causality information learned by the server through a Machine Learning (ML) function. We examine the co-dependence between the augmented client and server models and reformulate the framework as a standalone ML algorithm providing conditions for its sublinear and linear convergence rates. We also study the convergence of the framework to a centralized oracle model. Moreover, we include a differential privacy analysis to ensure data security while preserving causal insights. Using synthetic data, we conduct comprehensive experiments to demonstrate the robustness of our approach to perturbations in causality, the scalability to the size of communication, number of clients, and the dimensions of raw data. We also evaluate the performance on two real-world industrial control system datasets by reporting the volume of data saved by decentralization.", "published": "2025-01-23T18:04:21Z", "authors": ["Ayush Mohanty", "Nazal Mohamed", "Paritosh Ramanan", "Nagi Gebraeel"], "categories": ["cs.LG", "stat.ML"], "doi": "", "_query": "real estate valuation deep learning", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/1802.06416v3", "title": "Sim-to-Real Optimization of Complex Real World Mobile Network with Imperfect Information via Deep Reinforcement Learning from Self-play", "abstract": "Mobile network that millions of people use every day is one of the most complex systems in the world. Optimization of mobile network to meet exploding customer demand and reduce capital/operation expenditures poses great challenges. Despite recent progress, application of deep reinforcement learning (DRL) to complex real world problem still remains unsolved, given data scarcity, partial observability, risk and complex rules/dynamics in real world, as well as the huge reality gap between simulation and real world. To bridge the reality gap, we introduce a Sim-to-Real framework to directly transfer learning from simulation to real world via graph convolutional neural network (CNN) - by abstracting partially observable mobile network into graph, then distilling domain-variant irregular graph into domain-invariant tensor in locally Euclidean space as input to CNN -, domain randomization and multi-task learning. We use a novel self-play mechanism to encourage competition among DRL agents for best record on multiple tasks via simulated annealing, just like athletes compete for world record in decathlon. We also propose a decentralized multi-agent, competitive and cooperative DRL method to coordinate the actions of multi-cells to maximize global reward and minimize negative impact to neighbor cells. Using 6 field trials on commercial mobile networks, we demonstrate for the first time that a DRL agent can successfully transfer learning from simulation to complex real world problem with imperfect information, complex rules/dynamics, huge state/action space, and multi-agent interactions, without any training in the real world.", "published": "2018-02-18T18:03:39Z", "authors": ["Yongxi Tan", "Jin Yang", "Xin Chen", "Qitao Song", "Yunjun Chen", "Zhangxiang Ye", "Zhenqiang Su"], "categories": ["cs.AI", "cs.LG", "stat.ML"], "doi": "", "_query": "real estate valuation deep learning", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/1808.01174v3", "title": "Generalization Error in Deep Learning", "abstract": "Deep learning models have lately shown great performance in various fields such as computer vision, speech recognition, speech translation, and natural language processing. However, alongside their state-of-the-art performance, it is still generally unclear what is the source of their generalization ability. Thus, an important question is what makes deep neural networks able to generalize well from the training set to new data. In this article, we provide an overview of the existing theory and bounds for the characterization of the generalization error of deep neural networks, combining both classical and more recent theoretical and empirical results.", "published": "2018-08-03T12:57:12Z", "authors": ["Daniel Jakubovitz", "Raja Giryes", "Miguel R. D. Rodrigues"], "categories": ["cs.LG", "cs.AI", "stat.ML"], "doi": "", "_query": "real estate valuation deep learning", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2506.14597v1", "title": "Deep Learning Surrogates for Real-Time Gas Emission Inversion", "abstract": "Real-time identification and quantification of greenhouse-gas emissions under transient atmospheric conditions is a critical challenge in environmental monitoring. We introduce a spatio-temporal inversion framework that embeds a deep-learning surrogate of computational fluid dynamics (CFD) within a sequential Monte Carlo algorithm to perform Bayesian inference of both emission rate and source location in dynamic flow fields. By substituting costly numerical solvers with a multilayer perceptron trained on high-fidelity CFD outputs, our surrogate captures spatial heterogeneity and temporal evolution of gas dispersion, while delivering near-real-time predictions. Validation on the Chilbolton methane release dataset demonstrates comparable accuracy to full CFD solvers and Gaussian plume models, yet achieves orders-of-magnitude faster runtimes. Further experiments under simulated obstructed-flow scenarios confirm robustness in complex environments. This work reconciles physical fidelity with computational feasibility, offering a scalable solution for industrial emissions monitoring and other time-sensitive spatio-temporal inversion tasks in environmental and scientific modeling.", "published": "2025-06-17T15:03:21Z", "authors": ["Thomas Newman", "Christopher Nemeth", "Matthew Jones", "Philip Jonathan"], "categories": ["cs.LG", "stat.AP", "stat.ML"], "doi": "", "_query": "real estate valuation deep learning", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2506.12408v1", "title": "PROTOCOL: Partial Optimal Transport-enhanced Contrastive Learning for Imbalanced Multi-view Clustering", "abstract": "While contrastive multi-view clustering has achieved remarkable success, it implicitly assumes balanced class distribution. However, real-world multi-view data primarily exhibits class imbalance distribution. Consequently, existing methods suffer performance degradation due to their inability to perceive and model such imbalance. To address this challenge, we present the first systematic study of imbalanced multi-view clustering, focusing on two fundamental problems: i. perceiving class imbalance distribution, and ii. mitigating representation degradation of minority samples. We propose PROTOCOL, a novel PaRtial Optimal TranspOrt-enhanced COntrastive Learning framework for imbalanced multi-view clustering. First, for class imbalance perception, we map multi-view features into a consensus space and reformulate the imbalanced clustering as a partial optimal transport (POT) problem, augmented with progressive mass constraints and weighted KL divergence for class distributions. Second, we develop a POT-enhanced class-rebalanced contrastive learning at both feature and class levels, incorporating logit adjustment and class-sensitive learning to enhance minority sample representations. Extensive experiments demonstrate that PROTOCOL significantly improves clustering performance on imbalanced multi-view data, filling a critical research gap in this field.", "published": "2025-06-14T08:58:14Z", "authors": ["Xuqian Xue", "Yiming Lei", "Qi Cai", "Hongming Shan", "Junping Zhang"], "categories": ["cs.LG", "stat.ML"], "doi": "", "_query": "real estate valuation deep learning", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2004.04116v1", "title": "DeepStreamCE: A Streaming Approach to Concept Evolution Detection in Deep Neural Networks", "abstract": "Deep neural networks have experimentally demonstrated superior performance over other machine learning approaches in decision-making predictions. However, one major concern is the closed set nature of the classification decision on the trained classes, which can have serious consequences in safety critical systems. When the deep neural network is in a streaming environment, fast interpretation of this classification is required to determine if the classification result is trusted. Un-trusted classifications can occur when the input data to the deep neural network changes over time. One type of change that can occur is concept evolution, where a new class is introduced that the deep neural network was not trained on. In the majority of deep neural network architectures, the only option is to assign this instance to one of the classes it was trained on, which would be incorrect. The aim of this research is to detect the arrival of a new class in the stream. Existing work on interpreting deep neural networks often focuses on neuron activations to provide visual interpretation and feature extraction. Our novel approach, coined DeepStreamCE, uses streaming approaches for real-time concept evolution detection in deep neural networks. DeepStreamCE applies neuron activation reduction using an autoencoder and MCOD stream-based clustering in the offline phase. Both outputs are used in the online phase to analyse the neuron activations in the evolving stream in order to detect concept evolution occurrence in real time. We evaluate DeepStreamCE by training VGG16 convolutional neural networks on combinations of data from the CIFAR-10 dataset, holding out some classes to be used as concept evolution. For comparison, we apply the data and VGG16 networks to an open-set deep network solution - OpenMax. DeepStreamCE outperforms OpenMax when identifying concept evolution for our datasets.", "published": "2020-04-08T16:53:26Z", "authors": ["Lorraine Chambers", "Mohamed Medhat Gaber", "Zahraa S. Abdallah"], "categories": ["cs.LG", "stat.ML"], "doi": "", "_query": "real estate valuation deep learning", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/1910.12191v1", "title": "Federated Uncertainty-Aware Learning for Distributed Hospital EHR Data", "abstract": "Recent works have shown that applying Machine Learning to Electronic Health Records (EHR) can strongly accelerate precision medicine. This requires developing models based on diverse EHR sources. Federated Learning (FL) has enabled predictive modeling using distributed training which lifted the need of sharing data and compromising privacy. Since models are distributed in FL, it is attractive to devise ensembles of Deep Neural Networks that also assess model uncertainty. We propose a new FL model called Federated Uncertainty-Aware Learning Algorithm (FUALA) that improves on Federated Averaging (FedAvg) in the context of EHR. FUALA embeds uncertainty information in two ways: It reduces the contribution of models with high uncertainty in the aggregated model. It also introduces model ensembling at prediction time by keeping the last layers of each hospital from the final round. In FUALA, the Federator (central node) sends at each round the average model to all hospitals as well as a randomly assigned hospital model update to estimate its generalization on that hospital own data. Each hospital sends back its model update as well a generalization estimation of the assigned model. At prediction time, the model outputs C predictions for each sample where C is the number of hospital models. The experimental analysis conducted on a cohort of 87K deliveries for the task of preterm-birth prediction showed that the proposed approach outperforms FedAvg when evaluated on out-of-distribution data. We illustrated how uncertainty could be measured using the proposed approach.", "published": "2019-10-27T06:33:34Z", "authors": ["Sabri Boughorbel", "Fethi Jarray", "Neethu Venugopal", "Shabir Moosa", "Haithum Elhadi", "Michel Makhlouf"], "categories": ["cs.LG", "cs.AI", "stat.ML"], "doi": "", "_query": "real estate valuation deep learning", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2109.08134v1", "title": "Comparison and Unification of Three Regularization Methods in Batch Reinforcement Learning", "abstract": "In batch reinforcement learning, there can be poorly explored state-action pairs resulting in poorly learned, inaccurate models and poorly performing associated policies. Various regularization methods can mitigate the problem of learning overly-complex models in Markov decision processes (MDPs), however they operate in technically and intuitively distinct ways and lack a common form in which to compare them. This paper unifies three regularization methods in a common framework -- a weighted average transition matrix. Considering regularization methods in this common form illuminates how the MDP structure and the state-action pair distribution of the batch data set influence the relative performance of regularization methods. We confirm intuitions generated from the common framework by empirical evaluation across a range of MDPs and data collection policies.", "published": "2021-09-16T17:43:09Z", "authors": ["Sarah Rathnam", "Susan A. Murphy", "Finale Doshi-Velez"], "categories": ["cs.LG", "stat.ML"], "doi": "", "_query": "real estate valuation deep learning", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2011.14048v2", "title": "Is Support Set Diversity Necessary for Meta-Learning?", "abstract": "Meta-learning is a popular framework for learning with limited data in which an algorithm is produced by training over multiple few-shot learning tasks. For classification problems, these tasks are typically constructed by sampling a small number of support and query examples from a subset of the classes. While conventional wisdom is that task diversity should improve the performance of meta-learning, in this work we find evidence to the contrary: we propose a modification to traditional meta-learning approaches in which we keep the support sets fixed across tasks, thus reducing task diversity. Surprisingly, we find that not only does this modification not result in adverse effects, it almost always improves the performance for a variety of datasets and meta-learning methods. We also provide several initial analyses to understand this phenomenon. Our work serves to: (i) more closely investigate the effect of support set construction for the problem of meta-learning, and (ii) suggest a simple, general, and competitive baseline for few-shot learning.", "published": "2020-11-28T02:28:42Z", "authors": ["Amrith Setlur", "Oscar Li", "Virginia Smith"], "categories": ["cs.LG", "stat.ML"], "doi": "", "_query": "real estate valuation deep learning", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2205.07428v2", "title": "On the Convergence of the Shapley Value in Parametric Bayesian Learning Games", "abstract": "Measuring contributions is a classical problem in cooperative game theory where the Shapley value is the most well-known solution concept. In this paper, we establish the convergence property of the Shapley value in parametric Bayesian learning games where players perform a Bayesian inference using their combined data, and the posterior-prior KL divergence is used as the characteristic function. We show that for any two players, under some regularity conditions, their difference in Shapley value converges in probability to the difference in Shapley value of a limiting game whose characteristic function is proportional to the log-determinant of the joint Fisher information. As an application, we present an online collaborative learning framework that is asymptotically Shapley-fair. Our result enables this to be achieved without any costly computations of posterior-prior KL divergences. Only a consistent estimator of the Fisher information is needed. The effectiveness of our framework is demonstrated with experiments using real-world data.", "published": "2022-05-16T02:29:14Z", "authors": ["Lucas Agussurja", "Xinyi Xu", "Bryan Kian Hsiang Low"], "categories": ["cs.LG", "cs.GT", "stat.ML"], "doi": "", "_query": "real estate valuation deep learning", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2203.02474v2", "title": "Rate-Distortion Theoretic Generalization Bounds for Stochastic Learning Algorithms", "abstract": "Understanding generalization in modern machine learning settings has been one of the major challenges in statistical learning theory. In this context, recent years have witnessed the development of various generalization bounds suggesting different complexity notions such as the mutual information between the data sample and the algorithm output, compressibility of the hypothesis space, and the fractal dimension of the hypothesis space. While these bounds have illuminated the problem at hand from different angles, their suggested complexity notions might appear seemingly unrelated, thereby restricting their high-level impact. In this study, we prove novel generalization bounds through the lens of rate-distortion theory, and explicitly relate the concepts of mutual information, compressibility, and fractal dimensions in a single mathematical framework. Our approach consists of (i) defining a generalized notion of compressibility by using source coding concepts, and (ii) showing that the `compression error rate' can be linked to the generalization error both in expectation and with high probability. We show that in the `lossless compression' setting, we recover and improve existing mutual information-based bounds, whereas a `lossy compression' scheme allows us to link generalization to the rate-distortion dimension -- a particular notion of fractal dimension. Our results bring a more unified perspective on generalization and open up several future research directions.", "published": "2022-03-04T18:12:31Z", "authors": ["Milad Sefidgaran", "Amin Gohari", "Gaël Richard", "Umut Şimşekli"], "categories": ["stat.ML", "cs.IT", "cs.LG"], "doi": "", "_query": "real estate valuation deep learning", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2003.03284v2", "title": "TaskNorm: Rethinking Batch Normalization for Meta-Learning", "abstract": "Modern meta-learning approaches for image classification rely on increasingly deep networks to achieve state-of-the-art performance, making batch normalization an essential component of meta-learning pipelines. However, the hierarchical nature of the meta-learning setting presents several challenges that can render conventional batch normalization ineffective, giving rise to the need to rethink normalization in this setting. We evaluate a range of approaches to batch normalization for meta-learning scenarios, and develop a novel approach that we call TaskNorm. Experiments on fourteen datasets demonstrate that the choice of batch normalization has a dramatic effect on both classification accuracy and training time for both gradient based and gradient-free meta-learning approaches. Importantly, TaskNorm is found to consistently improve performance. Finally, we provide a set of best practices for normalization that will allow fair comparison of meta-learning algorithms.", "published": "2020-03-06T15:43:27Z", "authors": ["John Bronskill", "Jonathan Gordon", "James Requeima", "Sebastian Nowozin", "Richard E. Turner"], "categories": ["stat.ML", "cs.LG"], "doi": "", "_query": "real estate valuation deep learning", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2208.11512v2", "title": "FedOS: using open-set learning to stabilize training in federated learning", "abstract": "Federated Learning is a recent approach to train statistical models on distributed datasets without violating privacy constraints. The data locality principle is preserved by sharing the model instead of the data between clients and the server. This brings many advantages but also poses new challenges. In this report, we explore this new research area and perform several experiments to deepen our understanding of what these challenges are and how different problem settings affect the performance of the final model. Finally, we present a novel approach to one of these challenges and compare it to other methods found in literature.", "published": "2022-08-22T19:53:39Z", "authors": ["Mohamad Mohamad", "Julian Neubert", "Juan Segundo Argayo"], "categories": ["stat.ML", "cs.LG"], "doi": "", "_query": "real estate valuation deep learning", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/1907.04708v1", "title": "Learning a Behavior Model of Hybrid Systems Through Combining Model-Based Testing and Machine Learning (Full Version)", "abstract": "Models play an essential role in the design process of cyber-physical systems. They form the basis for simulation and analysis and help in identifying design problems as early as possible. However, the construction of models that comprise physical and digital behavior is challenging. Therefore, there is considerable interest in learning such hybrid behavior by means of machine learning which requires sufficient and representative training data covering the behavior of the physical system adequately. In this work, we exploit a combination of automata learning and model-based testing to generate sufficient training data fully automatically. Experimental results on a platooning scenario show that recurrent neural networks learned with this data achieved significantly better results compared to models learned from randomly generated data. In particular, the classification error for crash detection is reduced by a factor of five and a similar F1-score is obtained with up to three orders of magnitude fewer training samples.", "published": "2019-07-10T13:22:32Z", "authors": ["Bernhard K. Aichernig", "Roderick Bloem", "Masoud Ebrahimi", "Martin Horn", "Franz Pernkopf", "Wolfgang Roth", "Astrid Rupp", "Martin Tappler", "Markus Tranninger"], "categories": ["cs.LG", "stat.ML"], "doi": "", "_query": "real estate valuation deep learning", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2408.13448v4", "title": "Reinforcement Learning for Causal Discovery without Acyclicity Constraints", "abstract": "Recently, reinforcement learning (RL) has proved a promising alternative for conventional local heuristics in score-based approaches to learning directed acyclic causal graphs (DAGs) from observational data. However, the intricate acyclicity constraint still challenges the efficient exploration of the vast space of DAGs in existing methods. In this study, we introduce ALIAS (reinforced dAg Learning wIthout Acyclicity conStraints), a novel approach to causal discovery powered by the RL machinery. Our method features an efficient policy for generating DAGs in just a single step with an optimal quadratic complexity, fueled by a novel parametrization of DAGs that directly translates a continuous space to the space of all DAGs, bypassing the need for explicitly enforcing acyclicity constraints. This approach enables us to navigate the search space more effectively by utilizing policy gradient methods and established scoring functions. In addition, we provide compelling empirical evidence for the strong performance of ALIAS in comparison with state-of-the-arts in causal discovery over increasingly difficult experiment conditions on both synthetic and real datasets.", "published": "2024-08-24T03:12:21Z", "authors": ["Bao Duong", "Hung Le", "Biwei Huang", "Thin Nguyen"], "categories": ["cs.LG", "stat.ME", "stat.ML"], "doi": "", "_query": "real estate valuation deep learning", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2106.11721v1", "title": "A Deep Latent Space Model for Graph Representation Learning", "abstract": "Graph representation learning is a fundamental problem for modeling relational data and benefits a number of downstream applications. Traditional Bayesian-based graph models and recent deep learning based GNN either suffer from impracticability or lack interpretability, thus combined models for undirected graphs have been proposed to overcome the weaknesses. As a large portion of real-world graphs are directed graphs (of which undirected graphs are special cases), in this paper, we propose a Deep Latent Space Model (DLSM) for directed graphs to incorporate the traditional latent variable based generative model into deep learning frameworks. Our proposed model consists of a graph convolutional network (GCN) encoder and a stochastic decoder, which are layer-wise connected by a hierarchical variational auto-encoder architecture. By specifically modeling the degree heterogeneity using node random factors, our model possesses better interpretability in both community structure and degree heterogeneity. For fast inference, the stochastic gradient variational Bayes (SGVB) is adopted using a non-iterative recognition model, which is much more scalable than traditional MCMC-based methods. The experiments on real-world datasets show that the proposed model achieves the state-of-the-art performances on both link prediction and community detection tasks while learning interpretable node embeddings. The source code is available at https://github.com/upperr/DLSM.", "published": "2021-06-22T12:41:19Z", "authors": ["Hanxuan Yang", "Qingchao Kong", "Wenji Mao"], "categories": ["cs.LG", "stat.ML"], "doi": "10.1016/j.neucom.2024.127342", "_query": "real estate valuation deep learning", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2007.08663v1", "title": "TUDataset: A collection of benchmark datasets for learning with graphs", "abstract": "Recently, there has been an increasing interest in (supervised) learning with graph data, especially using graph neural networks. However, the development of meaningful benchmark datasets and standardized evaluation procedures is lagging, consequently hindering advancements in this area. To address this, we introduce the TUDataset for graph classification and regression. The collection consists of over 120 datasets of varying sizes from a wide range of applications. We provide Python-based data loaders, kernel and graph neural network baseline implementations, and evaluation tools. Here, we give an overview of the datasets, standardized evaluation procedures, and provide baseline experiments. All datasets are available at www.graphlearning.io. The experiments are fully reproducible from the code available at www.github.com/chrsmrrs/tudataset.", "published": "2020-07-16T21:46:33Z", "authors": ["Christopher Morris", "Nils M. Kriege", "Franka Bause", "Kristian Kersting", "Petra Mutzel", "Marion Neumann"], "categories": ["cs.LG", "cs.NE", "stat.ML"], "doi": "", "_query": "real estate valuation deep learning", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2310.13966v3", "title": "Transfer Learning for Kernel-based Regression", "abstract": "In recent years, transfer learning has garnered significant attention. Its ability to leverage knowledge from related studies to improve generalization performance in a target study has made it highly appealing. This paper focuses on investigating the transfer learning problem within the context of nonparametric regression over a reproducing kernel Hilbert space. The aim is to bridge the gap between practical effectiveness and theoretical guarantees. We specifically consider two scenarios: one where the transferable sources are known and another where they are unknown. For the known transferable source case, we propose a two-step kernel-based estimator by solely using kernel ridge regression. For the unknown case, we develop a novel method based on an efficient aggregation algorithm, which can automatically detect and alleviate the effects of negative sources. This paper provides the statistical properties of the desired estimators and establishes the minimax rate. Through extensive numerical experiments on synthetic data and real examples, we validate our theoretical findings and demonstrate the effectiveness of our proposed method.", "published": "2023-10-21T10:55:31Z", "authors": ["Chao Wang", "Caixing Wang", "Xin He", "Xingdong Feng"], "categories": ["stat.ML", "cs.LG", "stat.ME"], "doi": "", "_query": "real estate valuation deep learning", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2505.22957v1", "title": "Fast Derivative Valuation from Volatility Surfaces using Machine Learning", "abstract": "We introduce a fast and flexible Machine Learning (ML) framework for pricing derivative products whose valuation depends on volatility surfaces. By parameterizing volatility surfaces with the 5-parameter stochastic volatility inspired (SVI) model augmented by a one-factor term structure adjustment, we first generate numerous volatility surfaces over realistic ranges for these parameters. From these synthetic market scenarios, we then compute high-accuracy valuations using conventional methodologies for two representative products: the fair strike of a variance swap and the price and Greeks of an American put. We then train the Gaussian Process Regressor (GPR) to learn the nonlinear mapping from the input risk factors, which are the volatility surface parameters, strike and interest rate, to the valuation outputs. Once trained, We use the GPR to perform out-of-sample valuations and compare the results against valuations using conventional methodologies. Our ML model achieves very accurate results of $0.5\\%$ relative error for the fair strike of variance swap and $1.7\\% \\sim 3.5\\%$ relative error for American put prices and first-order Greeks. More importantly, after training, the model computes valuations almost instantly, yielding a three to four orders of magnitude speedup over Crank-Nicolson finite-difference method for American puts, enabling real-time risk analytics, dynamic hedging and large-scale scenario analysis. Our approach is general and can be extended to other path-dependent derivative products with early-exercise features, paving the way for hybrid quantitative engines for modern financial systems.", "published": "2025-05-29T00:42:56Z", "authors": ["Lijie Ding", "Egang Lu", "Kin Cheung"], "categories": ["q-fin.PR"], "doi": "", "_query": "real estate valuation deep learning", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2006.11645v3", "title": "Accelerating Safe Reinforcement Learning with Constraint-mismatched Policies", "abstract": "We consider the problem of reinforcement learning when provided with (1) a baseline control policy and (2) a set of constraints that the learner must satisfy. The baseline policy can arise from demonstration data or a teacher agent and may provide useful cues for learning, but it might also be sub-optimal for the task at hand, and is not guaranteed to satisfy the specified constraints, which might encode safety, fairness or other application-specific requirements. In order to safely learn from baseline policies, we propose an iterative policy optimization algorithm that alternates between maximizing expected return on the task, minimizing distance to the baseline policy, and projecting the policy onto the constraint-satisfying set. We analyze our algorithm theoretically and provide a finite-time convergence guarantee. In our experiments on five different control tasks, our algorithm consistently outperforms several state-of-the-art baselines, achieving 10 times fewer constraint violations and 40% higher reward on average.", "published": "2020-06-20T20:20:47Z", "authors": ["Tsung-Yen Yang", "Justinian Rosca", "Karthik Narasimhan", "Peter J. Ramadge"], "categories": ["cs.LG", "cs.AI", "cs.RO", "stat.ML"], "doi": "", "_query": "real estate valuation deep learning", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/1811.09602v1", "title": "Model-Based Reinforcement Learning for Sepsis Treatment", "abstract": "Sepsis is a dangerous condition that is a leading cause of patient mortality. Treating sepsis is highly challenging, because individual patients respond very differently to medical interventions and there is no universally agreed-upon treatment for sepsis. In this work, we explore the use of continuous state-space model-based reinforcement learning (RL) to discover high-quality treatment policies for sepsis patients. Our quantitative evaluation reveals that by blending the treatment strategy discovered with RL with what clinicians follow, we can obtain improved policies, potentially allowing for better medical treatment for sepsis.", "published": "2018-11-23T18:57:30Z", "authors": ["Aniruddh Raghu", "Matthieu Komorowski", "Sumeetpal Singh"], "categories": ["cs.LG", "cs.AI", "stat.ML"], "doi": "", "_query": "real estate valuation deep learning", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2102.11107v1", "title": "Towards Causal Representation Learning", "abstract": "The two fields of machine learning and graphical causality arose and developed separately. However, there is now cross-pollination and increasing interest in both fields to benefit from the advances of the other. In the present paper, we review fundamental concepts of causal inference and relate them to crucial open problems of machine learning, including transfer and generalization, thereby assaying how causality can contribute to modern machine learning research. This also applies in the opposite direction: we note that most work in causality starts from the premise that the causal variables are given. A central problem for AI and causality is, thus, causal representation learning, the discovery of high-level causal variables from low-level observations. Finally, we delineate some implications of causality for machine learning and propose key research areas at the intersection of both communities.", "published": "2021-02-22T15:26:57Z", "authors": ["Bernhard Schölkopf", "Francesco Locatello", "Stefan Bauer", "Nan Rosemary Ke", "Nal Kalchbrenner", "Anirudh Goyal", "Yoshua Bengio"], "categories": ["cs.LG", "cs.AI"], "doi": "", "_query": "real estate valuation deep learning", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2003.00641v1", "title": "VAE/WGAN-Based Image Representation Learning For Pose-Preserving Seamless Identity Replacement In Facial Images", "abstract": "We present a novel variational generative adversarial network (VGAN) based on Wasserstein loss to learn a latent representation from a face image that is invariant to identity but preserves head-pose information. This facilitates synthesis of a realistic face image with the same head pose as a given input image, but with a different identity. One application of this network is in privacy-sensitive scenarios; after identity replacement in an image, utility, such as head pose, can still be recovered. Extensive experimental validation on synthetic and real human-face image datasets performed under 3 threat scenarios confirms the ability of the proposed network to preserve head pose of the input image, mask the input identity, and synthesize a good-quality realistic face image of a desired identity. We also show that our network can be used to perform pose-preserving identity morphing and identity-preserving pose morphing. The proposed method improves over a recent state-of-the-art method in terms of quantitative metrics as well as synthesized image quality.", "published": "2020-03-02T03:35:59Z", "authors": ["Hiroki Kawai", "Jiawei Chen", "Prakash Ishwar", "Janusz Konrad"], "categories": ["cs.CV", "cs.LG"], "doi": "10.1109/MLSP.2019.8918926", "_query": "real estate valuation deep learning", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2502.01654v1", "title": "Predicting concentration levels of air pollutants by transfer learning and recurrent neural network", "abstract": "Air pollution (AP) poses a great threat to human health, and people are paying more attention than ever to its prediction. Accurate prediction of AP helps people to plan for their outdoor activities and aids protecting human health. In this paper, long-short term memory (LSTM) recurrent neural networks (RNNs) have been used to predict the future concentration of air pollutants (APS) in Macau. Additionally, meteorological data and data on the concentration of APS have been utilized. Moreover, in Macau, some air quality monitoring stations (AQMSs) have less observed data in quantity, and, at the same time, some AQMSs recorded less observed data of certain types of APS. Therefore, the transfer learning and pre-trained neural networks have been employed to assist AQMSs with less observed data to build a neural network with high prediction accuracy. The experimental sample covers a period longer than 12-year and includes daily measurements from several APS as well as other more classical meteorological values. Records from five stations, four out of them are AQMSs and the remaining one is an automatic weather station, have been prepared from the aforesaid period and eventually underwent to computational intelligence techniques to build and extract a prediction knowledge-based system. As shown by experimentation, LSTM RNNs initialized with transfer learning methods have higher prediction accuracy; it incurred shorter training time than randomly initialized recurrent neural networks.", "published": "2025-01-30T23:39:19Z", "authors": ["Iat Hang Fong", "Tengyue Li", "Simon Fong", "Raymond K. Wong", "Antonio J. Tallón-Ballesteros"], "categories": ["cs.LG", "cs.NE", "physics.ao-ph"], "doi": "", "_query": "real estate valuation deep learning", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/1906.01374v1", "title": "Autonomous Reinforcement Learning of Multiple Interrelated Tasks", "abstract": "Autonomous multiple tasks learning is a fundamental capability to develop versatile artificial agents that can act in complex environments. In real-world scenarios, tasks may be interrelated (or \"hierarchical\") so that a robot has to first learn to achieve some of them to set the preconditions for learning other ones. Even though different strategies have been used in robotics to tackle the acquisition of interrelated tasks, in particular within the developmental robotics framework, autonomous learning in this kind of scenarios is still an open question. Building on previous research in the framework of intrinsically motivated open-ended learning, in this work we describe how this question can be addressed working on the level of task selection, in particular considering the multiple interrelated tasks scenario as an MDP where the system is trying to maximise its competence over all the tasks.", "published": "2019-06-04T12:30:04Z", "authors": ["Vieri Giuliano Santucci", "Gianluca Baldassarre", "Emilio Cartoni"], "categories": ["cs.LG", "cs.AI", "stat.ML"], "doi": "", "_query": "real estate valuation deep learning", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/1909.12127v2", "title": "Intensity-Free Learning of Temporal Point Processes", "abstract": "Temporal point processes are the dominant paradigm for modeling sequences of events happening at irregular intervals. The standard way of learning in such models is by estimating the conditional intensity function. However, parameterizing the intensity function usually incurs several trade-offs. We show how to overcome the limitations of intensity-based approaches by directly modeling the conditional distribution of inter-event times. We draw on the literature on normalizing flows to design models that are flexible and efficient. We additionally propose a simple mixture model that matches the flexibility of flow-based models, but also permits sampling and computing moments in closed form. The proposed models achieve state-of-the-art performance in standard prediction tasks and are suitable for novel applications, such as learning sequence embeddings and imputing missing data.", "published": "2019-09-26T14:11:55Z", "authors": ["Oleksandr Shchur", "Marin Biloš", "Stephan Günnemann"], "categories": ["cs.LG", "stat.ML"], "doi": "", "_query": "real estate valuation deep learning", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/1712.01145v2", "title": "Learning Fast and Slow: PROPEDEUTICA for Real-time Malware Detection", "abstract": "Existing malware detectors on safety-critical devices have difficulties in runtime detection due to the performance overhead. In this paper, we introduce PROPEDEUTICA, a framework for efficient and effective real-time malware detection, leveraging the best of conventional machine learning (ML) and deep learning (DL) techniques. In PROPEDEUTICA, all software start execution are considered as benign and monitored by a conventional ML classifier for fast detection. If the software receives a borderline classification from the ML detector (e.g. the software is 50% likely to be benign and 50% likely to be malicious), the software will be transferred to a more accurate, yet performance demanding DL detector. To address spatial-temporal dynamics and software execution heterogeneity, we introduce a novel DL architecture (DEEPMALWARE) for PROPEDEUTICA with multi-stream inputs. We evaluated PROPEDEUTICA with 9,115 malware samples and 1,338 benign software from various categories for the Windows OS. With a borderline interval of [30%-70%], PROPEDEUTICA achieves an accuracy of 94.34% and a false-positive rate of 8.75%, with 41.45% of the samples moved for DEEPMALWARE analysis. Even using only CPU, PROPEDEUTICA can detect malware within less than 0.1 seconds.", "published": "2017-12-04T15:30:03Z", "authors": ["Ruimin Sun", "Xiaoyong Yuan", "Pan He", "Qile Zhu", "Aokun Chen", "Andre Gregio", "Daniela Oliveira", "Xiaolin Li"], "categories": ["cs.CR", "cs.LG", "stat.ML"], "doi": "", "_query": "real estate valuation deep learning", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2509.10074v1", "title": "Prototypical Contrastive Learning For Improved Few-Shot Audio Classification", "abstract": "Few-shot learning has emerged as a powerful paradigm for training models with limited labeled data, addressing challenges in scenarios where large-scale annotation is impractical. While extensive research has been conducted in the image domain, few-shot learning in audio classification remains relatively underexplored. In this work, we investigate the effect of integrating supervised contrastive loss into prototypical few shot training for audio classification. In detail, we demonstrate that angular loss further improves the performance compared to the standard contrastive loss. Our method leverages SpecAugment followed by a self-attention mechanism to encapsulate diverse information of augmented input versions into one unified embedding. We evaluate our approach on MetaAudio, a benchmark including five datasets with predefined splits, standardized preprocessing, and a comprehensive set of few-shot learning models for comparison. The proposed approach achieves state-of-the-art performance in a 5-way, 5-shot setting.", "published": "2025-09-12T09:10:55Z", "authors": ["Christos Sgouropoulos", "Christos Nikou", "Stefanos Vlachos", "Vasileios Theiou", "Christos Foukanelis", "Theodoros Giannakopoulos"], "categories": ["cs.SD", "cs.LG"], "doi": "10.1109/MLSP62443.2025.11204215", "_query": "real estate valuation deep learning", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2306.09995v2", "title": "Fairness in Preference-based Reinforcement Learning", "abstract": "In this paper, we address the issue of fairness in preference-based reinforcement learning (PbRL) in the presence of multiple objectives. The main objective is to design control policies that can optimize multiple objectives while treating each objective fairly. Toward this objective, we design a new fairness-induced preference-based reinforcement learning or FPbRL. The main idea of FPbRL is to learn vector reward functions associated with multiple objectives via new welfare-based preferences rather than reward-based preference in PbRL, coupled with policy learning via maximizing a generalized Gini welfare function. Finally, we provide experiment studies on three different environments to show that the proposed FPbRL approach can achieve both efficiency and equity for learning effective and fair policies.", "published": "2023-06-16T17:47:36Z", "authors": ["Umer Siddique", "Abhinav Sinha", "Yongcan Cao"], "categories": ["cs.LG", "cs.AI", "cs.CY", "eess.SY"], "doi": "", "_query": "real estate valuation deep learning", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/1806.05310v1", "title": "Deep Reinforcement Learning for Dynamic Urban Transportation Problems", "abstract": "We explore the use of deep learning and deep reinforcement learning for optimization problems in transportation. Many transportation system analysis tasks are formulated as an optimization problem - such as optimal control problems in intelligent transportation systems and long term urban planning. Often transportation models used to represent dynamics of a transportation system involve large data sets with complex input-output interactions and are difficult to use in the context of optimization. Use of deep learning metamodels can produce a lower dimensional representation of those relations and allow to implement optimization and reinforcement learning algorithms in an efficient manner. In particular, we develop deep learning models for calibrating transportation simulators and for reinforcement learning to solve the problem of optimal scheduling of travelers on the network.", "published": "2018-06-14T00:24:49Z", "authors": ["Laura Schultz", "Vadim Sokolov"], "categories": ["stat.ML", "cs.LG"], "doi": "", "_query": "real estate valuation deep learning", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2006.00339v3", "title": "Rethinking Assumptions in Deep Anomaly Detection", "abstract": "Though anomaly detection (AD) can be viewed as a classification problem (nominal vs. anomalous) it is usually treated in an unsupervised manner since one typically does not have access to, or it is infeasible to utilize, a dataset that sufficiently characterizes what it means to be \"anomalous.\" In this paper we present results demonstrating that this intuition surprisingly seems not to extend to deep AD on images. For a recent AD benchmark on ImageNet, classifiers trained to discern between normal samples and just a few (64) random natural images are able to outperform the current state of the art in deep AD. Experimentally we discover that the multiscale structure of image data makes example anomalies exceptionally informative.", "published": "2020-05-30T19:30:38Z", "authors": ["Lukas Ruff", "Robert A. Vandermeulen", "Billy Joe Franks", "Klaus-Robert Müller", "Marius Kloft"], "categories": ["cs.LG", "stat.ML"], "doi": "", "_query": "real estate valuation deep learning", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2507.04754v2", "title": "Intervening to Learn and Compose Causally Disentangled Representations", "abstract": "In designing generative models, it is commonly believed that in order to learn useful latent structure, we face a fundamental tension between expressivity and structure. In this paper we challenge this view by proposing a new approach to training arbitrarily expressive generative models that simultaneously learn causally disentangled concepts. This is accomplished by adding a simple context module to an arbitrarily complex black-box model, which learns to process concept information by implicitly inverting linear representations from the model's encoder. Inspired by the notion of intervention in a causal model, our module selectively modifies its architecture during training, allowing it to learn a compact joint model over different contexts. We show how adding this module leads to causally disentangled representations that can be composed for out-of-distribution generation on both real and simulated data. The resulting models can be trained end-to-end or fine-tuned from pre-trained models. To further validate our proposed approach, we prove a new identifiability result that extends existing work on identifying structured representations.", "published": "2025-07-07T08:30:27Z", "authors": ["Alex Markham", "Isaac Hirsch", "Jeri A. Chang", "Liam Solus", "Bryon Aragam"], "categories": ["stat.ML", "cs.LG"], "doi": "", "_query": "real estate valuation deep learning", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2202.08549v3", "title": "Oracle-Efficient Online Learning for Beyond Worst-Case Adversaries", "abstract": "In this paper, we study oracle-efficient algorithms for beyond worst-case analysis of online learning. We focus on two settings. First, the smoothed analysis setting of [RST11,HRS22] where an adversary is constrained to generating samples from distributions whose density is upper bounded by $1/σ$ times the uniform density. Second, the setting of $K$-hint transductive learning, where the learner is given access to $K$ hints per time step that are guaranteed to include the true instance. We give the first known oracle-efficient algorithms for both settings that depend only on the pseudo (or VC) dimension of the class and parameters $σ$ and $K$ that capture the power of the adversary. In particular, we achieve oracle-efficient regret bounds of $ \\widetilde{O} ( \\sqrt{T dσ^{-1}} ) $ and $ \\widetilde{O} ( \\sqrt{T dK} ) $ for learning real-valued functions and $ O ( \\sqrt{T dσ^{-\\frac{1}{2}} } )$ for learning binary-valued functions. For the smoothed analysis setting, our results give the first oracle-efficient algorithm for online learning with smoothed adversaries [HRS22]. This contrasts the computational separation between online learning with worst-case adversaries and offline learning established by [HK16]. Our algorithms also achieve improved bounds for worst-case setting with small domains. In particular, we give an oracle-efficient algorithm with regret of $O ( \\sqrt{T(d |\\mathcal{X}|)^{1/2} })$, which is a refinement of the earlier $O ( \\sqrt{T|\\mathcal{X}|})$ bound by [DS16].", "published": "2022-02-17T09:49:40Z", "authors": ["Nika Haghtalab", "Yanjun Han", "Abhishek Shetty", "Kunhe Yang"], "categories": ["cs.LG", "cs.DS", "stat.ML"], "doi": "", "_query": "real estate valuation deep learning", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/1902.00908v3", "title": "Stochastic Gradient Descent for Nonconvex Learning without Bounded Gradient Assumptions", "abstract": "Stochastic gradient descent (SGD) is a popular and efficient method with wide applications in training deep neural nets and other nonconvex models. While the behavior of SGD is well understood in the convex learning setting, the existing theoretical results for SGD applied to nonconvex objective functions are far from mature. For example, existing results require to impose a nontrivial assumption on the uniform boundedness of gradients for all iterates encountered in the learning process, which is hard to verify in practical implementations. In this paper, we establish a rigorous theoretical foundation for SGD in nonconvex learning by showing that this boundedness assumption can be removed without affecting convergence rates. In particular, we establish sufficient conditions for almost sure convergence as well as optimal convergence rates for SGD applied to both general nonconvex objective functions and gradient-dominated objective functions. A linear convergence is further derived in the case with zero variances.", "published": "2019-02-03T15:18:11Z", "authors": ["Yunwen Lei", "Ting Hu", "Guiying Li", "Ke Tang"], "categories": ["cs.LG", "math.OC", "stat.ML"], "doi": "", "_query": "real estate valuation deep learning", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2002.02913v4", "title": "Learning Autoencoders with Relational Regularization", "abstract": "A new algorithmic framework is proposed for learning autoencoders of data distributions. We minimize the discrepancy between the model and target distributions, with a \\emph{relational regularization} on the learnable latent prior. This regularization penalizes the fused Gromov-Wasserstein (FGW) distance between the latent prior and its corresponding posterior, allowing one to flexibly learn a structured prior distribution associated with the generative model. Moreover, it helps co-training of multiple autoencoders even if they have heterogeneous architectures and incomparable latent spaces. We implement the framework with two scalable algorithms, making it applicable for both probabilistic and deterministic autoencoders. Our relational regularized autoencoder (RAE) outperforms existing methods, $e.g.$, the variational autoencoder, Wasserstein autoencoder, and their variants, on generating images. Additionally, our relational co-training strategy for autoencoders achieves encouraging results in both synthesis and real-world multi-view learning tasks. The code is at https://github.com/HongtengXu/ Relational-AutoEncoders.", "published": "2020-02-07T17:27:30Z", "authors": ["Hongteng Xu", "Dixin Luo", "Ricardo Henao", "Svati Shah", "Lawrence Carin"], "categories": ["cs.LG", "stat.ML"], "doi": "", "_query": "real estate valuation deep learning", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2404.16553v1", "title": "RE-RecSys: An End-to-End system for recommending properties in Real-Estate domain", "abstract": "We propose an end-to-end real-estate recommendation system, RE-RecSys, which has been productionized in real-world industry setting. We categorize any user into 4 categories based on available historical data: i) cold-start users; ii) short-term users; iii) long-term users; and iv) short-long term users. For cold-start users, we propose a novel rule-based engine that is based on the popularity of locality and user preferences. For short-term users, we propose to use content-filtering model which recommends properties based on recent interactions of users. For long-term and short-long term users, we propose a novel combination of content and collaborative filtering based approach which can be easily productionized in the real-world scenario. Moreover, based on the conversion rate, we have designed a novel weighing scheme for different impressions done by users on the platform for the training of content and collaborative models. Finally, we show the efficiency of the proposed pipeline, RE-RecSys, on a real-world property and clickstream dataset collected from leading real-estate platform in India. We show that the proposed pipeline is deployable in real-world scenario with an average latency of <40 ms serving 1000 rpm.", "published": "2024-04-25T12:09:17Z", "authors": ["Venkatesh C", "Harshit Oberoi", "Anil Goyal", "Nikhil Sikka"], "categories": ["cs.IR", "cs.LG"], "doi": "10.1145/3632410.3632487", "_query": "real estate valuation deep learning", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2204.04292v3", "title": "Evolving Pareto-Optimal Actor-Critic Algorithms for Generalizability and Stability", "abstract": "Generalizability and stability are two key objectives for operating reinforcement learning (RL) agents in the real world. Designing RL algorithms that optimize these objectives can be a costly and painstaking process. This paper presents MetaPG, an evolutionary method for automated design of actor-critic loss functions. MetaPG explicitly optimizes for generalizability and performance, and implicitly optimizes the stability of both metrics. We initialize our loss function population with Soft Actor-Critic (SAC) and perform multi-objective optimization using fitness metrics encoding single-task performance, zero-shot generalizability to unseen environment configurations, and stability across independent runs with different random seeds. On a set of continuous control tasks from the Real-World RL Benchmark Suite, we find that our method, using a single environment during evolution, evolves algorithms that improve upon SAC's performance and generalizability by 4% and 20%, respectively, and reduce instability up to 67%. Then, we scale up to more complex environments from the Brax physics simulator and replicate generalizability tests encountered in practical settings, such as different friction coefficients. MetaPG evolves algorithms that can obtain 10% better generalizability without loss of performance within the same meta-training environment and obtain similar results to SAC when doing cross-domain evaluations in other Brax environments. The evolution results are interpretable; by analyzing the structure of the best algorithms we identify elements that help optimizing certain objectives, such as regularization terms for the critic loss.", "published": "2022-04-08T20:46:16Z", "authors": ["Juan Jose Garau-Luis", "Yingjie Miao", "John D. Co-Reyes", "Aaron Parisi", "Jie Tan", "Esteban Real", "Aleksandra Faust"], "categories": ["cs.LG"], "doi": "", "_query": "real estate valuation deep learning", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2409.10589v4", "title": "Offline Reinforcement Learning for Learning to Dispatch for Job Shop Scheduling", "abstract": "The Job Shop Scheduling Problem (JSSP) is a complex combinatorial optimization problem. While online Reinforcement Learning (RL) has shown promise by quickly finding acceptable solutions for JSSP, it faces key limitations: it requires extensive training interactions from scratch leading to sample inefficiency, cannot leverage existing high-quality solutions from traditional methods like Constraint Programming (CP), and require simulated environments to train in, which are impracticable to build for complex scheduling environments. We introduce Offline Learned Dispatching (Offline-LD), an offline reinforcement learning approach for JSSP, which addresses these limitations by learning from historical scheduling data. Our approach is motivated by scenarios where historical scheduling data and expert solutions are available or scenarios where online training of RL approaches with simulated environments is impracticable. Offline-LD introduces maskable variants of two Q-learning methods, namely, Maskable Quantile Regression DQN (mQRDQN) and discrete maskable Soft Actor-Critic (d-mSAC), that are able to learn from historical data, through Conservative Q-Learning (CQL). Moreover, we present a novel entropy bonus modification for d-mSAC, for maskable action spaces. Moreover, we introduce a novel reward normalization method for JSSP in an offline RL setting. Our experiments demonstrate that Offline-LD outperforms online RL on both generated and benchmark instances when trained on only 100 solutions generated by CP. Notably, introducing noise to the expert dataset yields comparable or superior results to using the expert dataset, with the same amount of instances, a promising finding for real-world applications, where data is inherently noisy and imperfect.", "published": "2024-09-16T15:18:10Z", "authors": ["Jesse van Remmerden", "Zaharah Bukhsh", "Yingqian Zhang"], "categories": ["cs.LG", "cs.AI"], "doi": "", "_query": "real estate valuation deep learning", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2204.10782v1", "title": "On Feature Learning in Neural Networks with Global Convergence Guarantees", "abstract": "We study the optimization of wide neural networks (NNs) via gradient flow (GF) in setups that allow feature learning while admitting non-asymptotic global convergence guarantees. First, for wide shallow NNs under the mean-field scaling and with a general class of activation functions, we prove that when the input dimension is no less than the size of the training set, the training loss converges to zero at a linear rate under GF. Building upon this analysis, we study a model of wide multi-layer NNs whose second-to-last layer is trained via GF, for which we also prove a linear-rate convergence of the training loss to zero, but regardless of the input dimension. We also show empirically that, unlike in the Neural Tangent Kernel (NTK) regime, our multi-layer model exhibits feature learning and can achieve better generalization performance than its NTK counterpart.", "published": "2022-04-22T15:56:43Z", "authors": ["Zhengdao Chen", "Eric Vanden-Eijnden", "Joan Bruna"], "categories": ["cs.LG", "math.OC", "math.PR", "stat.ML"], "doi": "", "_query": "real estate valuation deep learning", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2109.06321v1", "title": "Mitigating Sampling Bias and Improving Robustness in Active Learning", "abstract": "This paper presents simple and efficient methods to mitigate sampling bias in active learning while achieving state-of-the-art accuracy and model robustness. We introduce supervised contrastive active learning by leveraging the contrastive loss for active learning under a supervised setting. We propose an unbiased query strategy that selects informative data samples of diverse feature representations with our methods: supervised contrastive active learning (SCAL) and deep feature modeling (DFM). We empirically demonstrate our proposed methods reduce sampling bias, achieve state-of-the-art accuracy and model calibration in an active learning setup with the query computation 26x faster than Bayesian active learning by disagreement and 11x faster than CoreSet. The proposed SCAL method outperforms by a big margin in robustness to dataset shift and out-of-distribution.", "published": "2021-09-13T20:58:40Z", "authors": ["Ranganath Krishnan", "Alok Sinha", "Nilesh Ahuja", "Mahesh Subedar", "Omesh Tickoo", "Ravi Iyer"], "categories": ["cs.LG"], "doi": "", "_query": "real estate valuation deep learning", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2008.11416v3", "title": "Learning Node Representations against Perturbations", "abstract": "Recent graph neural networks (GNN) has achieved remarkable performance in node representation learning. One key factor of GNN's success is the \\emph{smoothness} property on node representations. Despite this, most GNN models are fragile to the perturbations on graph inputs and could learn unreliable node representations. In this paper, we study how to learn node representations against perturbations in GNN. Specifically, we consider that a node representation should remain stable under slight perturbations on the input, and node representations from different structures should be identifiable, which two are termed as the \\emph{stability} and \\emph{identifiability} on node representations, respectively. To this end, we propose a novel model called Stability-Identifiability GNN Against Perturbations (SIGNNAP) that learns reliable node representations in an unsupervised manner. SIGNNAP formalizes the \\emph{stability} and \\emph{identifiability} by a contrastive objective and preserves the \\emph{smoothness} with existing GNN backbones. The proposed method is a generic framework that can be equipped with many other backbone models (e.g. GCN, GraphSage and GAT). Extensive experiments on six benchmarks under both transductive and inductive learning setups of node classification demonstrate the effectiveness of our method. Codes and data are available online:~\\url{https://github.com/xuChenSJTU/SIGNNAP-master-online}", "published": "2020-08-26T07:11:01Z", "authors": ["Xu Chen", "Yuangang Pan", "Ivor Tsang", "Ya Zhang"], "categories": ["cs.LG", "stat.ML"], "doi": "", "_query": "real estate valuation deep learning", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/1808.05032v1", "title": "Deep RTS: A Game Environment for Deep Reinforcement Learning in Real-Time Strategy Games", "abstract": "Reinforcement learning (RL) is an area of research that has blossomed tremendously in recent years and has shown remarkable potential for artificial intelligence based opponents in computer games. This success is primarily due to the vast capabilities of convolutional neural networks, that can extract useful features from noisy and complex data. Games are excellent tools to test and push the boundaries of novel RL algorithms because they give valuable insight into how well an algorithm can perform in isolated environments without the real-life consequences. Real-time strategy games (RTS) is a genre that has tremendous complexity and challenges the player in short and long-term planning. There is much research that focuses on applied RL in RTS games, and novel advances are therefore anticipated in the not too distant future. However, there are to date few environments for testing RTS AIs. Environments in the literature are often either overly simplistic, such as microRTS, or complex and without the possibility for accelerated learning on consumer hardware like StarCraft II. This paper introduces the Deep RTS game environment for testing cutting-edge artificial intelligence algorithms for RTS games. Deep RTS is a high-performance RTS game made specifically for artificial intelligence research. It supports accelerated learning, meaning that it can learn at a magnitude of 50 000 times faster compared to existing RTS games. Deep RTS has a flexible configuration, enabling research in several different RTS scenarios, including partially observable state-spaces and map complexity. We show that Deep RTS lives up to our promises by comparing its performance with microRTS, ELF, and StarCraft II on high-end consumer hardware. Using Deep RTS, we show that a Deep Q-Network agent beats random-play agents over 70% of the time. Deep RTS is publicly available at https://github.com/cair/DeepRTS.", "published": "2018-08-15T10:30:41Z", "authors": ["Per-Arne Andersen", "Morten Goodwin", "Ole-Christoffer Granmo"], "categories": ["cs.AI", "cs.LG"], "doi": "", "_query": "real estate valuation deep learning", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2109.03693v1", "title": "PialNN: A Fast Deep Learning Framework for Cortical Pial Surface Reconstruction", "abstract": "Traditional cortical surface reconstruction is time consuming and limited by the resolution of brain Magnetic Resonance Imaging (MRI). In this work, we introduce Pial Neural Network (PialNN), a 3D deep learning framework for pial surface reconstruction. PialNN is trained end-to-end to deform an initial white matter surface to a target pial surface by a sequence of learned deformation blocks. A local convolutional operation is incorporated in each block to capture the multi-scale MRI information of each vertex and its neighborhood. This is fast and memory-efficient, which allows reconstructing a pial surface mesh with 150k vertices in one second. The performance is evaluated on the Human Connectome Project (HCP) dataset including T1-weighted MRI scans of 300 subjects. The experimental results demonstrate that PialNN reduces the geometric error of the predicted pial surface by 30% compared to state-of-the-art deep learning approaches.", "published": "2021-09-06T22:40:50Z", "authors": ["Qiang Ma", "Emma C. Robinson", "Bernhard Kainz", "Daniel Rueckert", "Amir Alansary"], "categories": ["eess.IV"], "doi": "", "_query": "real estate valuation deep learning", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2010.03522v2", "title": "A Survey of Deep Meta-Learning", "abstract": "Deep neural networks can achieve great successes when presented with large data sets and sufficient computational resources. However, their ability to learn new concepts quickly is limited. Meta-learning is one approach to address this issue, by enabling the network to learn how to learn. The field of Deep Meta-Learning advances at great speed, but lacks a unified, in-depth overview of current techniques. With this work, we aim to bridge this gap. After providing the reader with a theoretical foundation, we investigate and summarize key methods, which are categorized into i)~metric-, ii)~model-, and iii)~optimization-based techniques. In addition, we identify the main open challenges, such as performance evaluations on heterogeneous benchmarks, and reduction of the computational costs of meta-learning.", "published": "2020-10-07T17:09:02Z", "authors": ["Mike Huisman", "Jan N. van Rijn", "Aske Plaat"], "categories": ["cs.LG", "cs.AI", "stat.ML"], "doi": "10.1007/s10462-021-10004-4", "_query": "real estate valuation deep learning", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/1908.02388v3", "title": "Benchmarking Bonus-Based Exploration Methods on the Arcade Learning Environment", "abstract": "This paper provides an empirical evaluation of recently developed exploration algorithms within the Arcade Learning Environment (ALE). We study the use of different reward bonuses that incentives exploration in reinforcement learning. We do so by fixing the learning algorithm used and focusing only on the impact of the different exploration bonuses in the agent's performance. We use Rainbow, the state-of-the-art algorithm for value-based agents, and focus on some of the bonuses proposed in the last few years. We consider the impact these algorithms have on performance within the popular game Montezuma's Revenge which has gathered a lot of interest from the exploration community, across the the set of seven games identified by Bellemare et al. (2016) as challenging for exploration, and easier games where exploration is not an issue. We find that, in our setting, recently developed bonuses do not provide significantly improved performance on Montezuma's Revenge or hard exploration games. We also find that existing bonus-based methods may negatively impact performance on games in which exploration is not an issue and may even perform worse than $ε$-greedy exploration.", "published": "2019-08-06T22:36:35Z", "authors": ["Adrien Ali Taïga", "William Fedus", "Marlos C. Machado", "Aaron Courville", "Marc G. Bellemare"], "categories": ["cs.LG", "stat.ML"], "doi": "", "_query": "real estate valuation deep learning", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2406.05666v12", "title": "Probability Distribution Learning and Its Application in Deep Learning", "abstract": "Despite its empirical success, deep learning still lacks a comprehensive theoretical understanding of model fitting and generalization. This paper proposes the probability distribution (PD) learning framework to analyze the optimization and generalization mechanisms of deep learning. Within this framework, the conditional distribution of labels given features is the primary learning target, with the loss function, prior knowledge, and model properties explicitly characterized. Under these formulations, we establish theoretical guarantees on optimizability, even in non-convex settings, and derive generalization error bounds that provide meaningful explanations for practical performance. Specifically, we first prove theoretically that the Fenchel-Young loss is the natural and necessary choice for solving PD learning problems, thereby justifying the generality of conclusions based on this loss. Second, to capture the characteristics of deep neural networks (DNNs), we introduce the notions of $\\mathcal{H}(ψ)$-convexity and $\\mathcal{H}(Ψ)$-smoothness, which generalize the classical concepts of strong convexity and Lipschitz smoothness. Based on them, we provide a theoretical explanation for the effectiveness of SGD in training DNNs. Finally, we derive model-independent bounds on the expected risk and generalization error for trained models, revealing the influence of the training set size, regularization term, the mutual information between labels and features, and the information loss caused by model irreversibility on risk and generalization. Based on our theoretical analysis and experimental validation, we believe that the PD learning framework facilitates a deeper and more unified theoretical understanding of deep learning.", "published": "2024-06-09T06:49:22Z", "authors": ["Binchuan Qi", "Wei Gong", "Li Li"], "categories": ["cs.LG", "cs.IR", "stat.ML"], "doi": "", "_query": "real estate valuation deep learning", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/1807.04587v2", "title": "Assessing the Scalability of Biologically-Motivated Deep Learning Algorithms and Architectures", "abstract": "The backpropagation of error algorithm (BP) is impossible to implement in a real brain. The recent success of deep networks in machine learning and AI, however, has inspired proposals for understanding how the brain might learn across multiple layers, and hence how it might approximate BP. As of yet, none of these proposals have been rigorously evaluated on tasks where BP-guided deep learning has proved critical, or in architectures more structured than simple fully-connected networks. Here we present results on scaling up biologically motivated models of deep learning on datasets which need deep networks with appropriate architectures to achieve good performance. We present results on the MNIST, CIFAR-10, and ImageNet datasets and explore variants of target-propagation (TP) and feedback alignment (FA) algorithms, and explore performance in both fully- and locally-connected architectures. We also introduce weight-transport-free variants of difference target propagation (DTP) modified to remove backpropagation from the penultimate layer. Many of these algorithms perform well for MNIST, but for CIFAR and ImageNet we find that TP and FA variants perform significantly worse than BP, especially for networks composed of locally connected units, opening questions about whether new architectures and algorithms are required to scale these approaches. Our results and implementation details help establish baselines for biologically motivated deep learning schemes going forward.", "published": "2018-07-12T12:53:50Z", "authors": ["Sergey Bartunov", "Adam Santoro", "Blake A. Richards", "Luke Marris", "Geoffrey E. Hinton", "Timothy Lillicrap"], "categories": ["cs.LG", "cs.AI", "cs.NE", "stat.ML"], "doi": "", "_query": "real estate valuation deep learning", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2411.15931v2", "title": "Improving Pre-trained Self-Supervised Embeddings Through Effective Entropy Maximization", "abstract": "A number of different architectures and loss functions have been applied to the problem of self-supervised learning (SSL), with the goal of developing embeddings that provide the best possible pre-training for as-yet-unknown, lightly supervised downstream tasks. One of these SSL criteria is to maximize the entropy of a set of embeddings in some compact space. But the goal of maximizing the embedding entropy often depends -- whether explicitly or implicitly -- upon high dimensional entropy estimates, which typically perform poorly in more than a few dimensions. In this paper, we motivate an effective entropy maximization criterion (E2MC), defined in terms of easy-to-estimate, low-dimensional constraints. We demonstrate that using it to continue training an already-trained SSL model for only a handful of epochs leads to a consistent and, in some cases, significant improvement in downstream performance. We perform careful ablation studies to show that the improved performance is due to the proposed add-on criterion. We also show that continued pre-training with alternative criteria does not lead to notable improvements, and in some cases, even degrades performance.", "published": "2024-11-24T17:38:23Z", "authors": ["Deep Chakraborty", "Yann LeCun", "Tim G. J. Rudner", "Erik Learned-Miller"], "categories": ["cs.LG", "cs.CV", "cs.IT", "stat.AP", "stat.ML"], "doi": "", "_query": "real estate valuation deep learning", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2405.11907v5", "title": "Ensemble and Mixture-of-Experts DeepONets For Operator Learning", "abstract": "We present a novel deep operator network (DeepONet) architecture for operator learning, the ensemble DeepONet, that allows for enriching the trunk network of a single DeepONet with multiple distinct trunk networks. This trunk enrichment allows for greater expressivity and generalization capabilities over a range of operator learning problems. We also present a spatial mixture-of-experts (MoE) DeepONet trunk network architecture that utilizes a partition-of-unity (PoU) approximation to promote spatial locality and model sparsity in the operator learning problem. We first prove that both the ensemble and PoU-MoE DeepONets are universal approximators. We then demonstrate that ensemble DeepONets containing a trunk ensemble of a standard trunk, the PoU-MoE trunk, and/or a proper orthogonal decomposition (POD) trunk can achieve 2-4x lower relative $\\ell_2$ errors than standard DeepONets and POD-DeepONets on both standard and challenging new operator learning problems involving partial differential equations (PDEs) in two and three dimensions. Our new PoU-MoE formulation provides a natural way to incorporate spatial locality and model sparsity into any neural network architecture, while our new ensemble DeepONet provides a powerful and general framework for incorporating basis enrichment in scientific machine learning architectures for operator learning.", "published": "2024-05-20T09:42:44Z", "authors": ["Ramansh Sharma", "Varun Shankar"], "categories": ["cs.LG"], "doi": "", "_query": "real estate valuation deep learning", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/1811.04820v3", "title": "Learning from positive and unlabeled data: a survey", "abstract": "Learning from positive and unlabeled data or PU learning is the setting where a learner only has access to positive examples and unlabeled data. The assumption is that the unlabeled data can contain both positive and negative examples. This setting has attracted increasing interest within the machine learning literature as this type of data naturally arises in applications such as medical diagnosis and knowledge base completion. This article provides a survey of the current state of the art in PU learning. It proposes seven key research questions that commonly arise in this field and provides a broad overview of how the field has tried to address them.", "published": "2018-11-12T16:00:36Z", "authors": ["Jessa Bekker", "Jesse Davis"], "categories": ["cs.LG", "stat.ML"], "doi": "10.1007/s10994-020-05877-5", "_query": "real estate valuation deep learning", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2205.07802v1", "title": "The Primacy Bias in Deep Reinforcement Learning", "abstract": "This work identifies a common flaw of deep reinforcement learning (RL) algorithms: a tendency to rely on early interactions and ignore useful evidence encountered later. Because of training on progressively growing datasets, deep RL agents incur a risk of overfitting to earlier experiences, negatively affecting the rest of the learning process. Inspired by cognitive science, we refer to this effect as the primacy bias. Through a series of experiments, we dissect the algorithmic aspects of deep RL that exacerbate this bias. We then propose a simple yet generally-applicable mechanism that tackles the primacy bias by periodically resetting a part of the agent. We apply this mechanism to algorithms in both discrete (Atari 100k) and continuous action (DeepMind Control Suite) domains, consistently improving their performance.", "published": "2022-05-16T16:48:36Z", "authors": ["Evgenii Nikishin", "Max Schwarzer", "Pierluca D'Oro", "Pierre-Luc Bacon", "Aaron Courville"], "categories": ["cs.LG", "cs.AI", "stat.ML"], "doi": "", "_query": "real estate valuation deep learning", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/1807.06613v3", "title": "Deep Reinforcement Learning for Swarm Systems", "abstract": "Recently, deep reinforcement learning (RL) methods have been applied successfully to multi-agent scenarios. Typically, these methods rely on a concatenation of agent states to represent the information content required for decentralized decision making. However, concatenation scales poorly to swarm systems with a large number of homogeneous agents as it does not exploit the fundamental properties inherent to these systems: (i) the agents in the swarm are interchangeable and (ii) the exact number of agents in the swarm is irrelevant. Therefore, we propose a new state representation for deep multi-agent RL based on mean embeddings of distributions. We treat the agents as samples of a distribution and use the empirical mean embedding as input for a decentralized policy. We define different feature spaces of the mean embedding using histograms, radial basis functions and a neural network learned end-to-end. We evaluate the representation on two well known problems from the swarm literature (rendezvous and pursuit evasion), in a globally and locally observable setup. For the local setup we furthermore introduce simple communication protocols. Of all approaches, the mean embedding representation using neural network features enables the richest information exchange between neighboring agents facilitating the development of more complex collective strategies.", "published": "2018-07-17T18:27:03Z", "authors": ["Maximilian Hüttenrauch", "Adrian Šošić", "Gerhard Neumann"], "categories": ["cs.MA", "cs.AI", "cs.LG", "eess.SY", "stat.ML"], "doi": "", "_query": "real estate valuation deep learning", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/1806.07908v1", "title": "Como funciona o Deep Learning", "abstract": "Deep Learning methods are currently the state-of-the-art in many problems which can be tackled via machine learning, in particular classification problems. However there is still lack of understanding on how those methods work, why they work and what are the limitations involved in using them. In this chapter we will describe in detail the transition from shallow to deep networks, include examples of code on how to implement them, as well as the main issues one faces when training a deep network. Afterwards, we introduce some theoretical background behind the use of deep models, and discuss their limitations.", "published": "2018-06-20T18:04:09Z", "authors": ["Moacir Antonelli Ponti", "Gabriel B. Paranhos da Costa"], "categories": ["cs.LG", "cs.CV", "stat.ML"], "doi": "", "_query": "real estate valuation deep learning", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2305.09126v3", "title": "Transfer Learning for Causal Effect Estimation", "abstract": "We present a Transfer Causal Learning (TCL) framework when target and source domains share the same covariate/feature spaces, aiming to improve causal effect estimation accuracy in limited data. Limited data is very common in medical applications, where some rare medical conditions, such as sepsis, are of interest. Our proposed method, named \\texttt{$\\ell_1$-TCL}, incorporates $\\ell_1$ regularized TL for nuisance models (e.g., propensity score model); the TL estimator of the nuisance parameters is plugged into downstream average causal/treatment effect estimators (e.g., inverse probability weighted estimator). We establish non-asymptotic recovery guarantees for the \\texttt{$\\ell_1$-TCL} with generalized linear model (GLM) under the sparsity assumption in the high-dimensional setting, and demonstrate the empirical benefits of \\texttt{$\\ell_1$-TCL} through extensive numerical simulation for GLM and recent neural network nuisance models. Our method is subsequently extended to real data and generates meaningful insights consistent with medical literature, a case where all baseline methods fail.", "published": "2023-05-16T03:13:55Z", "authors": ["Song Wei", "Hanyu Zhang", "Ronald Moore", "Rishikesan Kamaleswaran", "Yao Xie"], "categories": ["cs.LG", "math.ST", "stat.ME", "stat.ML"], "doi": "", "_query": "real estate valuation deep learning", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/1910.05983v2", "title": "On the Reduction of Variance and Overestimation of Deep Q-Learning", "abstract": "The breakthrough of deep Q-Learning on different types of environments revolutionized the algorithmic design of Reinforcement Learning to introduce more stable and robust algorithms, to that end many extensions to deep Q-Learning algorithm have been proposed to reduce the variance of the target values and the overestimation phenomena. In this paper, we examine new methodology to solve these issues, we propose using Dropout techniques on deep Q-Learning algorithm as a way to reduce variance and overestimation. We also present experiments conducted on benchmark environments, demonstrating the effectiveness of our methodology in enhancing stability and reducing both variance and overestimation in model performance.", "published": "2019-10-14T08:43:06Z", "authors": ["Mohammed Sabry", "Amr M. A. Khalifa"], "categories": ["cs.LG", "stat.ML"], "doi": "", "_query": "real estate valuation deep learning", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/1807.00905v2", "title": "Learning under selective labels in the presence of expert consistency", "abstract": "We explore the problem of learning under selective labels in the context of algorithm-assisted decision making. Selective labels is a pervasive selection bias problem that arises when historical decision making blinds us to the true outcome for certain instances. Examples of this are common in many applications, ranging from predicting recidivism using pre-trial release data to diagnosing patients. In this paper we discuss why selective labels often cannot be effectively tackled by standard methods for adjusting for sample selection bias, even if there are no unobservables. We propose a data augmentation approach that can be used to either leverage expert consistency to mitigate the partial blindness that results from selective labels, or to empirically validate whether learning under such framework may lead to unreliable models prone to systemic discrimination.", "published": "2018-07-02T21:48:59Z", "authors": ["Maria De-Arteaga", "Artur Dubrawski", "Alexandra Chouldechova"], "categories": ["cs.LG", "stat.ML"], "doi": "", "_query": "real estate valuation deep learning", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2508.02414v1", "title": "ASMR: Angular Support for Malfunctioning Client Resilience in Federated Learning", "abstract": "Federated Learning (FL) allows the training of deep neural networks in a distributed and privacy-preserving manner. However, this concept suffers from malfunctioning updates sent by the attending clients that cause global model performance degradation. Reasons for this malfunctioning might be technical issues, disadvantageous training data, or malicious attacks. Most of the current defense mechanisms are meant to require impractical prerequisites like knowledge about the number of malfunctioning updates, which makes them unsuitable for real-world applications. To counteract these problems, we introduce a novel method called Angular Support for Malfunctioning Client Resilience (ASMR), that dynamically excludes malfunctioning clients based on their angular distance. Our novel method does not require any hyperparameters or knowledge about the number of malfunctioning clients. Our experiments showcase the detection capabilities of ASMR in an image classification task on a histopathological dataset, while also presenting findings on the significance of dynamically adapting decision boundaries.", "published": "2025-08-04T13:36:41Z", "authors": ["Mirko Konstantin", "Moritz Fuchs", "Anirban Mukhopadhyay"], "categories": ["cs.LG"], "doi": "", "_query": "real estate valuation deep learning", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2006.05026v2", "title": "Learning for Dose Allocation in Adaptive Clinical Trials with Safety Constraints", "abstract": "Phase I dose-finding trials are increasingly challenging as the relationship between efficacy and toxicity of new compounds (or combination of them) becomes more complex. Despite this, most commonly used methods in practice focus on identifying a Maximum Tolerated Dose (MTD) by learning only from toxicity events. We present a novel adaptive clinical trial methodology, called Safe Efficacy Exploration Dose Allocation (SEEDA), that aims at maximizing the cumulative efficacies while satisfying the toxicity safety constraint with high probability. We evaluate performance objectives that have operational meanings in practical clinical trials, including cumulative efficacy, recommendation/allocation success probabilities, toxicity violation probability, and sample efficiency. An extended SEEDA-Plateau algorithm that is tailored for the increase-then-plateau efficacy behavior of molecularly targeted agents (MTA) is also presented. Through numerical experiments using both synthetic and real-world datasets, we show that SEEDA outperforms state-of-the-art clinical trial designs by finding the optimal dose with higher success rate and fewer patients.", "published": "2020-06-09T03:06:45Z", "authors": ["Cong Shen", "Zhiyang Wang", "Sofia S. Villar", "Mihaela van der Schaar"], "categories": ["cs.LG", "stat.AP", "stat.ME", "stat.ML"], "doi": "", "_query": "real estate valuation deep learning", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/1810.10731v3", "title": "Law and Adversarial Machine Learning", "abstract": "When machine learning systems fail because of adversarial manipulation, how should society expect the law to respond? Through scenarios grounded in adversarial ML literature, we explore how some aspects of computer crime, copyright, and tort law interface with perturbation, poisoning, model stealing and model inversion attacks to show how some attacks are more likely to result in liability than others. We end with a call for action to ML researchers to invest in transparent benchmarks of attacks and defenses; architect ML systems with forensics in mind and finally, think more about adversarial machine learning in the context of civil liberties. The paper is targeted towards ML researchers who have no legal background.", "published": "2018-10-25T06:17:34Z", "authors": ["Ram Shankar Siva Kumar", "David R. O'Brien", "Kendra Albert", "Salome Vilojen"], "categories": ["cs.LG", "cs.CR", "cs.CY", "stat.ML"], "doi": "", "_query": "real estate valuation deep learning", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/1407.2855v1", "title": "Learning Valuation Distributions from Partial Observation", "abstract": "Auction theory traditionally assumes that bidders' valuation distributions are known to the auctioneer, such as in the celebrated, revenue-optimal Myerson auction. However, this theory does not describe how the auctioneer comes to possess this information. Recently, Cole and Roughgarden [2014] showed that an approximation based on a finite sample of independent draws from each bidder's distribution is sufficient to produce a near-optimal auction. In this work, we consider the problem of learning bidders' valuation distributions from much weaker forms of observations. Specifically, we consider a setting where there is a repeated, sealed-bid auction with $n$ bidders, but all we observe for each round is who won, but not how much they bid or paid. We can also participate (i.e., submit a bid) ourselves, and observe when we win. From this information, our goal is to (approximately) recover the inherently recoverable part of the underlying bid distributions. We also consider extensions where different subsets of bidders participate in each round, and where bidders' valuations have a common-value component added to their independent private values.", "published": "2014-07-10T16:35:09Z", "authors": ["Avrim Blum", "Yishay Mansour", "Jamie Morgenstern"], "categories": ["cs.GT"], "doi": "", "_query": "real estate valuation deep learning", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2401.01789v1", "title": "Deep learning the Hurst parameter of linear fractional processes and assessing its reliability", "abstract": "This research explores the reliability of deep learning, specifically Long Short-Term Memory (LSTM) networks, for estimating the Hurst parameter in fractional stochastic processes. The study focuses on three types of processes: fractional Brownian motion (fBm), fractional Ornstein-Uhlenbeck (fOU) process, and linear fractional stable motions (lfsm). The work involves a fast generation of extensive datasets for fBm and fOU to train the LSTM network on a large volume of data in a feasible time. The study analyses the accuracy of the LSTM network's Hurst parameter estimation regarding various performance measures like RMSE, MAE, MRE, and quantiles of the absolute and relative errors. It finds that LSTM outperforms the traditional statistical methods in the case of fBm and fOU processes; however, it has limited accuracy on lfsm processes. The research also delves into the implications of training length and valuation sequence length on the LSTM's performance. The methodology is applied by estimating the Hurst parameter in Li-ion battery degradation data and obtaining confidence bounds for the estimation. The study concludes that while deep learning methods show promise in parameter estimation of fractional processes, their effectiveness is contingent on the process type and the quality of training data.", "published": "2024-01-03T15:42:45Z", "authors": ["Dániel Boros", "Bálint Csanády", "Iván Ivkovic", "Lóránt Nagy", "András Lukács", "László Márkus"], "categories": ["stat.ML", "cs.AI", "cs.LG"], "doi": "", "_query": "real estate valuation deep learning", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2002.04189v3", "title": "Optimal Transfer Learning Model for Binary Classification of Funduscopic Images through Simple Heuristics", "abstract": "Deep learning models have the capacity to fundamentally revolutionize medical imaging analysis, and they have particularly interesting applications in computer-aided diagnosis. We attempt to use deep learning neural networks to diagnose funduscopic images, visual representations of the interior of the eye. Recently, a few robust deep learning approaches have performed binary classification to infer the presence of a specific ocular disease, such as glaucoma or diabetic retinopathy. In an effort to broaden the applications of computer-aided ocular disease diagnosis, we propose a unifying model for disease classification: low-cost inference of a fundus image to determine whether it is healthy or diseased. To achieve this, we use transfer learning techniques, which retain the more overarching capabilities of a pre-trained base architecture but can adapt to another dataset. For comparisons, we then develop a custom heuristic equation and evaluation metric ranking system to determine the optimal base architecture and hyperparameters. The Xception base architecture, Adam optimizer, and mean squared error loss function perform best, achieving 90% accuracy, 94% sensitivity, and 86% specificity. For additional ease of use, we contain the model in a web interface whose file chooser can access the local filesystem, allowing for use on any internet-connected device: mobile, PC, or otherwise.", "published": "2020-02-11T03:49:14Z", "authors": ["Rohit Jammula", "Vishnu Rajan Tejus", "Shreya Shankar"], "categories": ["cs.LG", "cs.CV", "eess.IV", "stat.ML"], "doi": "", "_query": "real estate valuation deep learning", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2309.09478v1", "title": "The DECam Ecliptic Exploration Project (DEEP): V. The Absolute Magnitude Distribution of the Cold Classical Kuiper Belt", "abstract": "The DECam Ecliptic Exploration Project (DEEP) is a deep survey of the trans-Neptunian solar system being carried out on the 4-meter Blanco telescope at Cerro Tololo Inter-American Observatory in Chile using the Dark Energy Camera (DECam). By using a shift-and-stack technique to achieve a mean limiting magnitude of $r \\sim 26.2$, DEEP achieves an unprecedented combination of survey area and depth, enabling quantitative leaps forward in our understanding of the Kuiper Belt populations. This work reports results from an analysis of twenty 3 sq.\\ deg.\\ DECam fields along the invariable plane. We characterize the efficiency and false-positive rates for our moving-object detection pipeline, and use this information to construct a Bayesian signal probability for each detected source. This procedure allows us to treat all of our Kuiper Belt Object (KBO) detections statistically, simultaneously accounting for efficiency and false positives. We detect approximately 2300 candidate sources with KBO-like motion at S/N $>6.5$. We use a subset of these objects to compute the luminosity function of the Kuiper Belt as a whole, as well as the Cold Classical (CC) population. We also investigate the absolute magnitude ($H$) distribution of the CCs, and find consistency with both an exponentially tapered power-law, which is predicted by streaming instability models of planetesimal formation, and a rolling power law. Finally, we provide an updated mass estimate for the Cold Classical Kuiper Belt of $M_{CC}(H_r < 12) = 0.0017^{+0.0010}_{-0.0004} M_{\\oplus}$, assuming albedo $p = 0.15$ and density $ρ= 1$ g cm$^{-3}$.", "published": "2023-09-18T04:21:59Z", "authors": ["Kevin J. Napier", "Hsing-Wen Lin", "David W. Gerdes", "Fred C. Adams", "Anna M. Simpson", "Matthew W. Porter", "Katherine G. Weber", "Larissa Markwardt", "Gabriel Gowman", "Hayden Smotherman", "Pedro H. Bernardinelli", "Mario Jurić", "Andrew J. Connolly", "J. Bryce Kalmbach", "Stephen K. N. Portillo", "David E. Trilling", "Ryder Strauss", "William J. Oldroyd", "Chadwick A. Trujillo", "Colin Orion Chandler", "Matthew J. Holman", "Hilke E. Schlichting", "Andrew McNeill", "the DEEP Collaboration"], "categories": ["astro-ph.EP"], "doi": "", "_query": "real estate valuation deep learning", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2310.20360v3", "title": "Mathematical Introduction to Deep Learning: Methods, Implementations, and Theory", "abstract": "This book aims to provide an introduction to the topic of deep learning algorithms. We review essential components of deep learning algorithms in full mathematical detail including different artificial neural network (ANN) architectures (such as fully-connected feedforward ANNs, convolutional ANNs, recurrent ANNs, residual ANNs, and ANNs with batch normalization) and different optimization algorithms (such as the basic stochastic gradient descent (SGD) method, accelerated methods, and adaptive methods). We also cover several theoretical aspects of deep learning algorithms such as approximation capacities of ANNs (including a calculus for ANNs), optimization theory (including Kurdyka-Łojasiewicz inequalities), and generalization errors. In the last part of the book some deep learning approximation methods for PDEs are reviewed including physics-informed neural networks (PINNs) and deep Galerkin methods. We hope that this book will be useful for students and scientists who do not yet have any background in deep learning at all and would like to gain a solid foundation as well as for practitioners who would like to obtain a firmer mathematical understanding of the objects and methods considered in deep learning.", "published": "2023-10-31T11:01:23Z", "authors": ["Arnulf Jentzen", "Benno Kuckuck", "Philippe von Wurstemberger"], "categories": ["cs.LG", "cs.AI", "math.NA", "math.PR", "stat.ML"], "doi": "", "_query": "real estate valuation deep learning", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/1611.00336v2", "title": "Stochastic Variational Deep Kernel Learning", "abstract": "Deep kernel learning combines the non-parametric flexibility of kernel methods with the inductive biases of deep learning architectures. We propose a novel deep kernel learning model and stochastic variational inference procedure which generalizes deep kernel learning approaches to enable classification, multi-task learning, additive covariance structures, and stochastic gradient training. Specifically, we apply additive base kernels to subsets of output features from deep neural architectures, and jointly learn the parameters of the base kernels and deep network through a Gaussian process marginal likelihood objective. Within this framework, we derive an efficient form of stochastic variational inference which leverages local kernel interpolation, inducing points, and structure exploiting algebra. We show improved performance over stand alone deep networks, SVMs, and state of the art scalable Gaussian processes on several classification benchmarks, including an airline delay dataset containing 6 million training points, CIFAR, and ImageNet.", "published": "2016-11-01T19:04:47Z", "authors": ["Andrew Gordon Wilson", "Zhiting Hu", "Ruslan Salakhutdinov", "Eric P. Xing"], "categories": ["stat.ML", "cs.LG", "stat.ME"], "doi": "", "_query": "real estate valuation deep learning", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2210.01090v1", "title": "Nonstationary data stream classification with online active learning and siamese neural networks", "abstract": "We have witnessed in recent years an ever-growing volume of information becoming available in a streaming manner in various application areas. As a result, there is an emerging need for online learning methods that train predictive models on-the-fly. A series of open challenges, however, hinder their deployment in practice. These are, learning as data arrive in real-time one-by-one, learning from data with limited ground truth information, learning from nonstationary data, and learning from severely imbalanced data, while occupying a limited amount of memory for data storage. We propose the ActiSiamese algorithm, which addresses these challenges by combining online active learning, siamese networks, and a multi-queue memory. It develops a new density-based active learning strategy which considers similarity in the latent (rather than the input) space. We conduct an extensive study that compares the role of different active learning budgets and strategies, the performance with/without memory, the performance with/without ensembling, in both synthetic and real-world datasets, under different data nonstationarity characteristics and class imbalance levels. ActiSiamese outperforms baseline and state-of-the-art algorithms, and is effective under severe imbalance, even only when a fraction of the arriving instances' labels is available. We publicly release our code to the community.", "published": "2022-10-03T17:16:03Z", "authors": ["Kleanthis Malialis", "Christos G. Panayiotou", "Marios M. Polycarpou"], "categories": ["cs.LG"], "doi": "10.1016/j.neucom.2022.09.065", "_query": "real estate valuation deep learning", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/1904.11711v2", "title": "Robust Metric Learning based on the Rescaled Hinge Loss", "abstract": "Distance/Similarity learning is a fundamental problem in machine learning. For example, kNN classifier or clustering methods are based on a distance/similarity measure. Metric learning algorithms enhance the efficiency of these methods by learning an optimal distance function from data. Most metric learning methods need training information in the form of pair or triplet sets. Nowadays, this training information often is obtained from the Internet via crowdsourcing methods. Therefore, this information may contain label noise or outliers leading to the poor performance of the learned metric. It is even possible that the learned metric functions perform worse than the general metrics such as Euclidean distance. To address this challenge, this paper presents a new robust metric learning method based on the Rescaled Hinge loss. This loss function is a general case of the popular Hinge loss and initially introduced in (Xu et al. 2017) to develop a new robust SVM algorithm. In this paper, we formulate the metric learning problem using the Rescaled Hinge loss function and then develop an efficient algorithm based on HQ (Half-Quadratic) to solve the problem. Experimental results on a variety of both real and synthetic datasets confirm that our new robust algorithm considerably outperforms state-of-the-art metric learning methods in the presence of label noise and outliers.", "published": "2019-04-26T08:20:59Z", "authors": ["Sumia Abdulhussien Razooqi Al-Obaidi", "Davood Zabihzadeh", "Hamideh Hajiabadi"], "categories": ["cs.LG", "stat.ML"], "doi": "10.1007/s13042-020-01137-z", "_query": "real estate valuation deep learning", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/1611.07422v1", "title": "Deep Learning Approximation for Stochastic Control Problems", "abstract": "Many real world stochastic control problems suffer from the \"curse of dimensionality\". To overcome this difficulty, we develop a deep learning approach that directly solves high-dimensional stochastic control problems based on Monte-Carlo sampling. We approximate the time-dependent controls as feedforward neural networks and stack these networks together through model dynamics. The objective function for the control problem plays the role of the loss function for the deep neural network. We test this approach using examples from the areas of optimal trading and energy storage. Our results suggest that the algorithm presented here achieves satisfactory accuracy and at the same time, can handle rather high dimensional problems.", "published": "2016-11-02T02:47:26Z", "authors": ["Jiequn Han", "Weinan E"], "categories": ["cs.LG", "cs.AI", "cs.NE", "math.OC", "stat.ML"], "doi": "", "_query": "real estate valuation deep learning", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2406.01838v2", "title": "Learning the Target Network in Function Space", "abstract": "We focus on the task of learning the value function in the reinforcement learning (RL) setting. This task is often solved by updating a pair of online and target networks while ensuring that the parameters of these two networks are equivalent. We propose Lookahead-Replicate (LR), a new value-function approximation algorithm that is agnostic to this parameter-space equivalence. Instead, the LR algorithm is designed to maintain an equivalence between the two networks in the function space. This value-based equivalence is obtained by employing a new target-network update. We show that LR leads to a convergent behavior in learning the value function. We also present empirical results demonstrating that LR-based target-network updates significantly improve deep RL on the Atari benchmark.", "published": "2024-06-03T23:10:35Z", "authors": ["Kavosh Asadi", "Yao Liu", "Shoham Sabach", "Ming Yin", "Rasool Fakoor"], "categories": ["cs.LG", "cs.AI"], "doi": "", "_query": "real estate valuation deep learning", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2401.01242v1", "title": "Encoding Binary Events from Continuous Time Series in Rooted Trees using Contrastive Learning", "abstract": "Broadband infrastructure owners do not always know how their customers are connected in the local networks, which are structured as rooted trees. A recent study is able to infer the topology of a local network using discrete time series data from the leaves of the tree (customers). In this study we propose a contrastive approach for learning a binary event encoder from continuous time series data. As a preliminary result, we show that our approach has some potential in learning a valuable encoder.", "published": "2024-01-02T15:18:23Z", "authors": ["Tobias Engelhardt Rasmussen", "Siv Sørensen"], "categories": ["cs.LG", "cs.AI", "cs.SI", "stat.ML"], "doi": "", "_query": "real estate valuation deep learning", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2201.07372v2", "title": "Prospective Learning: Principled Extrapolation to the Future", "abstract": "Learning is a process which can update decision rules, based on past experience, such that future performance improves. Traditionally, machine learning is often evaluated under the assumption that the future will be identical to the past in distribution or change adversarially. But these assumptions can be either too optimistic or pessimistic for many problems in the real world. Real world scenarios evolve over multiple spatiotemporal scales with partially predictable dynamics. Here we reformulate the learning problem to one that centers around this idea of dynamic futures that are partially learnable. We conjecture that certain sequences of tasks are not retrospectively learnable (in which the data distribution is fixed), but are prospectively learnable (in which distributions may be dynamic), suggesting that prospective learning is more difficult in kind than retrospective learning. We argue that prospective learning more accurately characterizes many real world problems that (1) currently stymie existing artificial intelligence solutions and/or (2) lack adequate explanations for how natural intelligences solve them. Thus, studying prospective learning will lead to deeper insights and solutions to currently vexing challenges in both natural and artificial intelligences.", "published": "2022-01-19T01:19:57Z", "authors": ["Ashwin De Silva", "Rahul Ramesh", "Lyle Ungar", "Marshall Hussain Shuler", "Noah J. Cowan", "Michael Platt", "Chen Li", "Leyla Isik", "Seung-Eon Roh", "Adam Charles", "Archana Venkataraman", "Brian Caffo", "Javier J. How", "Justus M Kebschull", "John W. Krakauer", "Maxim Bichuch", "Kaleab Alemayehu Kinfu", "Eva Yezerets", "Dinesh Jayaraman", "Jong M. Shin", "Soledad Villar", "Ian Phillips", "Carey E. Priebe", "Thomas Hartung", "Michael I. Miller", "Jayanta Dey", " Ningyuan", " Huang", "Eric Eaton", "Ralph Etienne-Cummings", "Elizabeth L. Ogburn", "Randal Burns", "Onyema Osuagwu", "Brett Mensh", "Alysson R. Muotri", "Julia Brown", "Chris White", "Weiwei Yang", "Andrei A. Rusu", "Timothy Verstynen", "Konrad P. Kording", "Pratik Chaudhari", "Joshua T. Vogelstein"], "categories": ["cs.LG", "cs.AI"], "doi": "", "_query": "real estate valuation deep learning", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/1906.04928v2", "title": "Deep Learning for Spatio-Temporal Data Mining: A Survey", "abstract": "With the fast development of various positioning techniques such as Global Position System (GPS), mobile devices and remote sensing, spatio-temporal data has become increasingly available nowadays. Mining valuable knowledge from spatio-temporal data is critically important to many real world applications including human mobility understanding, smart transportation, urban planning, public safety, health care and environmental management. As the number, volume and resolution of spatio-temporal datasets increase rapidly, traditional data mining methods, especially statistics based methods for dealing with such data are becoming overwhelmed. Recently, with the advances of deep learning techniques, deep leaning models such as convolutional neural network (CNN) and recurrent neural network (RNN) have enjoyed considerable success in various machine learning tasks due to their powerful hierarchical feature learning ability in both spatial and temporal domains, and have been widely applied in various spatio-temporal data mining (STDM) tasks such as predictive learning, representation learning, anomaly detection and classification. In this paper, we provide a comprehensive survey on recent progress in applying deep learning techniques for STDM. We first categorize the types of spatio-temporal data and briefly introduce the popular deep learning models that are used in STDM. Then a framework is introduced to show a general pipeline of the utilization of deep learning models for STDM. Next we classify existing literatures based on the types of ST data, the data mining tasks, and the deep learning models, followed by the applications of deep learning for STDM in different domains including transportation, climate science, human mobility, location based social network, crime analysis, and neuroscience. Finally, we conclude the limitations of current research and point out future research directions.", "published": "2019-06-11T13:43:54Z", "authors": ["Senzhang Wang", "Jiannong Cao", "Philip S. Yu"], "categories": ["cs.LG", "stat.ML"], "doi": "", "_query": "real estate valuation deep learning", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/1911.09249v1", "title": "Semantic Segmentation of Thigh Muscle using 2.5D Deep Learning Network Trained with Limited Datasets", "abstract": "Purpose: We propose a 2.5D deep learning neural network (DLNN) to automatically classify thigh muscle into 11 classes and evaluate its classification accuracy over 2D and 3D DLNN when trained with limited datasets. Enables operator invariant quantitative assessment of the thigh muscle volume change with respect to the disease progression. Materials and methods: Retrospective datasets consist of 48 thigh volume (TV) cropped from CT DICOM images. Cropped volumes were aligned with femur axis and resample in 2 mm voxel-spacing. Proposed 2.5D DLNN consists of three 2D U-Net trained with axial, coronal and sagittal muscle slices respectively. A voting algorithm was used to combine the output of U-Nets to create final segmentation. 2.5D U-Net was trained on PC with 38 TV and the remaining 10 TV were used to evaluate segmentation accuracy of 10 classes within Thigh. The result segmentation of both left and right thigh were de-cropped to original CT volume space. Finally, segmentation accuracies were compared between proposed DLNN and 2D/3D U-Net. Results: Average segmentation DSC score accuracy of all classes with 2.5D U-Net as 91.18% and Average Surface distance (ASD) accuracy as 0.84 mm. We found, mean DSC score for 2D U-Net was 3.3% lower than the that of 2.5D U-Net and mean DSC score of 3D U-Net was 5.7% lower than that of 2.5D U-Net when trained with same datasets. Conclusion: We achieved a faster computationally efficient and automatic segmentation of thigh muscle into 11 classes with reasonable accuracy. Enables quantitative evaluation of muscle atrophy with disease progression.", "published": "2019-11-21T02:30:31Z", "authors": ["Hasnine Haque", "Masahiro Hashimoto", "Nozomu Uetake", "Masahiro Jinzaki"], "categories": ["eess.IV", "cs.CV", "cs.LG"], "doi": "", "_query": "real estate valuation deep learning", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/1910.05765v1", "title": "Real-Time and Embedded Deep Learning on FPGA for RF Signal Classification", "abstract": "We designed and implemented a deep learning based RF signal classifier on the Field Programmable Gate Array (FPGA) of an embedded software-defined radio platform, DeepRadio, that classifies the signals received through the RF front end to different modulation types in real time and with low power. This classifier implementation successfully captures complex characteristics of wireless signals to serve critical applications in wireless security and communications systems such as identifying spoofing signals in signal authentication systems, detecting target emitters and jammers in electronic warfare (EW) applications, discriminating primary and secondary users in cognitive radio networks, interference hunting, and adaptive modulation. Empowered by low-power and low-latency embedded computing, the deep neural network runs directly on the FPGA fabric of DeepRadio, while maintaining classifier accuracy close to the software performance. We evaluated the performance when another SDR (USRP) transmits signals with different modulation types at different power levels and DeepRadio receives the signals and classifies them in real time on its FPGA. A smartphone with a mobile app is connected to DeepRadio to initiate the experiment and visualize the classification results. With real radio transmissions over the air, we show that the classifier implemented on DeepRadio achieves high accuracy with low latency (microsecond per sample) and low energy consumption (microJoule per sample), and this performance is not matched by other embedded platforms such as embedded graphics processing unit (GPU).", "published": "2019-10-13T15:01:56Z", "authors": ["Sohraab Soltani", "Yalin E. Sagduyu", "Raqibul Hasan", "Kemal Davaslioglu", "Hongmei Deng", "Tugba Erpek"], "categories": ["cs.NI", "cs.LG", "eess.SP"], "doi": "", "_query": "real estate valuation deep learning", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2509.06953v1", "title": "Deep Reactive Policy: Learning Reactive Manipulator Motion Planning for Dynamic Environments", "abstract": "Generating collision-free motion in dynamic, partially observable environments is a fundamental challenge for robotic manipulators. Classical motion planners can compute globally optimal trajectories but require full environment knowledge and are typically too slow for dynamic scenes. Neural motion policies offer a promising alternative by operating in closed-loop directly on raw sensory inputs but often struggle to generalize in complex or dynamic settings. We propose Deep Reactive Policy (DRP), a visuo-motor neural motion policy designed for reactive motion generation in diverse dynamic environments, operating directly on point cloud sensory input. At its core is IMPACT, a transformer-based neural motion policy pretrained on 10 million generated expert trajectories across diverse simulation scenarios. We further improve IMPACT's static obstacle avoidance through iterative student-teacher finetuning. We additionally enhance the policy's dynamic obstacle avoidance at inference time using DCP-RMP, a locally reactive goal-proposal module. We evaluate DRP on challenging tasks featuring cluttered scenes, dynamic moving obstacles, and goal obstructions. DRP achieves strong generalization, outperforming prior classical and neural methods in success rate across both simulated and real-world settings. Video results and code available at https://deep-reactive-policy.com", "published": "2025-09-08T17:59:35Z", "authors": ["Jiahui Yang", "Jason Jingzhou Liu", "Yulong Li", "Youssef Khaky", "Kenneth Shaw", "Deepak Pathak"], "categories": ["cs.RO", "cs.AI", "cs.CV", "cs.LG", "eess.SY"], "doi": "", "_query": "real estate valuation deep learning", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2001.06116v1", "title": "Learning Stable Deep Dynamics Models", "abstract": "Deep networks are commonly used to model dynamical systems, predicting how the state of a system will evolve over time (either autonomously or in response to control inputs). Despite the predictive power of these systems, it has been difficult to make formal claims about the basic properties of the learned systems. In this paper, we propose an approach for learning dynamical systems that are guaranteed to be stable over the entire state space. The approach works by jointly learning a dynamics model and Lyapunov function that guarantees non-expansiveness of the dynamics under the learned Lyapunov function. We show that such learning systems are able to model simple dynamical systems and can be combined with additional deep generative models to learn complex dynamics, such as video textures, in a fully end-to-end fashion.", "published": "2020-01-17T00:04:45Z", "authors": ["Gaurav Manek", "J. Zico Kolter"], "categories": ["cs.LG", "math.DS", "stat.ML"], "doi": "", "_query": "real estate valuation deep learning", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2011.05479v1", "title": "ForestNet: Classifying Drivers of Deforestation in Indonesia using Deep Learning on Satellite Imagery", "abstract": "Characterizing the processes leading to deforestation is critical to the development and implementation of targeted forest conservation and management policies. In this work, we develop a deep learning model called ForestNet to classify the drivers of primary forest loss in Indonesia, a country with one of the highest deforestation rates in the world. Using satellite imagery, ForestNet identifies the direct drivers of deforestation in forest loss patches of any size. We curate a dataset of Landsat 8 satellite images of known forest loss events paired with driver annotations from expert interpreters. We use the dataset to train and validate the models and demonstrate that ForestNet substantially outperforms other standard driver classification approaches. In order to support future research on automated approaches to deforestation driver classification, the dataset curated in this study is publicly available at https://stanfordmlgroup.github.io/projects/forestnet .", "published": "2020-11-11T00:28:40Z", "authors": ["Jeremy Irvin", "Hao Sheng", "Neel Ramachandran", "Sonja Johnson-Yu", "Sharon Zhou", "Kyle Story", "Rose Rustowicz", "Cooper Elsworth", "Kemen Austin", "Andrew Y. Ng"], "categories": ["cs.CV", "cs.LG", "eess.IV"], "doi": "", "_query": "real estate valuation deep learning", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/1207.4255v2", "title": "On the Statistical Efficiency of $\\ell_{1,p}$ Multi-Task Learning of Gaussian Graphical Models", "abstract": "In this paper, we present $\\ell_{1,p}$ multi-task structure learning for Gaussian graphical models. We analyze the sufficient number of samples for the correct recovery of the support union and edge signs. We also analyze the necessary number of samples for any conceivable method by providing information-theoretic lower bounds. We compare the statistical efficiency of multi-task learning versus that of single-task learning. For experiments, we use a block coordinate descent method that is provably convergent and generates a sequence of positive definite solutions. We provide experimental validation on synthetic data as well as on two publicly available real-world data sets, including functional magnetic resonance imaging and gene expression data.", "published": "2012-07-18T02:53:02Z", "authors": ["Jean Honorio", "Tommi Jaakkola", "Dimitris Samaras"], "categories": ["cs.LG", "stat.ML"], "doi": "", "_query": "real estate valuation deep learning", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2411.11099v1", "title": "Mitigating Relative Over-Generalization in Multi-Agent Reinforcement Learning", "abstract": "In decentralized multi-agent reinforcement learning, agents learning in isolation can lead to relative over-generalization (RO), where optimal joint actions are undervalued in favor of suboptimal ones. This hinders effective coordination in cooperative tasks, as agents tend to choose actions that are individually rational but collectively suboptimal. To address this issue, we introduce MaxMax Q-Learning (MMQ), which employs an iterative process of sampling and evaluating potential next states, selecting those with maximal Q-values for learning. This approach refines approximations of ideal state transitions, aligning more closely with the optimal joint policy of collaborating agents. We provide theoretical analysis supporting MMQ's potential and present empirical evaluations across various environments susceptible to RO. Our results demonstrate that MMQ frequently outperforms existing baselines, exhibiting enhanced convergence and sample efficiency.", "published": "2024-11-17T15:00:39Z", "authors": ["Ting Zhu", "Yue Jin", "Jeremie Houssineau", "Giovanni Montana"], "categories": ["cs.LG", "cs.AI", "stat.ML"], "doi": "", "_query": "real estate valuation deep learning", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/1905.09523v2", "title": "Hierarchical Annotation of Images with Two-Alternative-Forced-Choice Metric Learning", "abstract": "Many tasks such as retrieval and recommendations can significantly benefit from structuring the data, commonly in a hierarchical way. To achieve this through annotations of high dimensional data such as images or natural text can be significantly labor intensive. We propose an approach for uncovering the hierarchical structure of data based on efficient discriminative testing rather than annotations of individual datapoints. Using two-alternative-forced-choice (2AFC) testing and deep metric learning we achieve embedding of the data in semantic space where we are able to successfully hierarchically cluster. We actively select triplets for the 2AFC test such that the modeling process is highly efficient with respect to the number of tests presented to the annotator. We empirically demonstrate the feasibility of the method by confirming the shape bias on synthetic data and extract hierarchical structure on the Fashion-MNIST dataset to a finer granularity than the original labels.", "published": "2019-05-23T08:11:15Z", "authors": ["Niels Hellinga", "Vlado Menkovski"], "categories": ["cs.LG", "cs.CV", "stat.ML"], "doi": "", "_query": "real estate valuation deep learning", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/1812.04145v1", "title": "Learning Sharing Behaviors with Arbitrary Numbers of Agents", "abstract": "We propose a method for modeling and learning turn-taking behaviors for accessing a shared resource. We model the individual behavior for each agent in an interaction and then use a multi-agent fusion model to generate a summary over the expected actions of the group to render the model independent of the number of agents. The individual behavior models are weighted finite state transducers (WFSTs) with weights dynamically updated during interactions, and the multi-agent fusion model is a logistic regression classifier. We test our models in a multi-agent tower-building environment, where a Q-learning agent learns to interact with rule-based agents. Our approach accurately models the underlying behavior patterns of the rule-based agents with accuracy ranging between 0.63 and 1.0 depending on the stochasticity of the other agent behaviors. In addition we show using KL-divergence that the model accurately captures the distribution of next actions when interacting with both a single agent (KL-divergence < 0.1) and with multiple agents (KL-divergence < 0.37). Finally, we demonstrate that our behavior model can be used by a Q-learning agent to take turns in an interactive turn-taking environment.", "published": "2018-12-10T23:20:45Z", "authors": ["Katherine Metcalf", "Barry-John Theobald", "Nicholas Apostoloff"], "categories": ["cs.LG", "cs.MA", "stat.ML"], "doi": "", "_query": "real estate valuation deep learning", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/1806.04245v1", "title": "Learning to Speed Up Structured Output Prediction", "abstract": "Predicting structured outputs can be computationally onerous due to the combinatorially large output spaces. In this paper, we focus on reducing the prediction time of a trained black-box structured classifier without losing accuracy. To do so, we train a speedup classifier that learns to mimic a black-box classifier under the learning-to-search approach. As the structured classifier predicts more examples, the speedup classifier will operate as a learned heuristic to guide search to favorable regions of the output space. We present a mistake bound for the speedup classifier and identify inference situations where it can independently make correct judgments without input features. We evaluate our method on the task of entity and relation extraction and show that the speedup classifier outperforms even greedy search in terms of speed without loss of accuracy.", "published": "2018-06-11T21:06:51Z", "authors": ["Xingyuan Pan", "Vivek Srikumar"], "categories": ["cs.LG", "stat.ML"], "doi": "", "_query": "real estate valuation deep learning", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2201.07999v1", "title": "Sentiment Analysis: Predicting Yelp Scores", "abstract": "In this work, we predict the sentiment of restaurant reviews based on a subset of the Yelp Open Dataset. We utilize the meta features and text available in the dataset and evaluate several machine learning and state-of-the-art deep learning approaches for the prediction task. Through several qualitative experiments, we show the success of the deep models with attention mechanism in learning a balanced model for reviews across different restaurants. Finally, we propose a novel Multi-tasked joint BERT model that improves the overall classification performance.", "published": "2022-01-20T04:47:12Z", "authors": ["Bhanu Prakash Reddy Guda", "Mashrin Srivastava", "Deep Karkhanis"], "categories": ["cs.LG", "cs.CL"], "doi": "", "_query": "real estate valuation deep learning", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/1911.10653v1", "title": "A Unified Deep Learning Approach for Prediction of Parkinson's Disease", "abstract": "The paper presents a novel approach, based on deep learning, for diagnosis of Parkinson's disease through medical imaging. The approach includes analysis and use of the knowledge extracted by Deep Convolutional and Recurrent Neural Networks (DNNs) when trained with medical images, such as Magnetic Resonance Images and DaTscans. Internal representations of the trained DNNs constitute the extracted knowledge which is used in a transfer learning and domain adaptation manner, so as to create a unified framework for prediction of Parkinson's across different medical environments. A large experimental study is presented illustrating the ability of the proposed approach to effectively predict Parkinson's, using different medical image sets from real environments.", "published": "2019-11-25T01:20:38Z", "authors": ["James Wingate", "Ilianna Kollia", "Luc Bidaut", "Stefanos Kollias"], "categories": ["cs.LG", "eess.SP", "stat.ML"], "doi": "", "_query": "real estate valuation deep learning", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2409.16391v1", "title": "Patch-Based Contrastive Learning and Memory Consolidation for Online Unsupervised Continual Learning", "abstract": "We focus on a relatively unexplored learning paradigm known as {\\em Online Unsupervised Continual Learning} (O-UCL), where an agent receives a non-stationary, unlabeled data stream and progressively learns to identify an increasing number of classes. This paradigm is designed to model real-world applications where encountering novelty is the norm, such as exploring a terrain with several unknown and time-varying entities. Unlike prior work in unsupervised, continual, or online learning, O-UCL combines all three areas into a single challenging and realistic learning paradigm. In this setting, agents are frequently evaluated and must aim to maintain the best possible representation at any point of the data stream, rather than at the end of pre-specified offline tasks. The proposed approach, called \\textbf{P}atch-based \\textbf{C}ontrastive learning and \\textbf{M}emory \\textbf{C}onsolidation (PCMC), builds a compositional understanding of data by identifying and clustering patch-level features. Embeddings for these patch-level features are extracted with an encoder trained via patch-based contrastive learning. PCMC incorporates new data into its distribution while avoiding catastrophic forgetting, and it consolidates memory examples during ``sleep\" periods. We evaluate PCMC's performance on streams created from the ImageNet and Places365 datasets. Additionally, we explore various versions of the PCMC algorithm and compare its performance against several existing methods and simple baselines.", "published": "2024-09-24T18:46:32Z", "authors": ["Cameron Taylor", "Vassilis Vassiliades", "Constantine Dovrolis"], "categories": ["cs.LG", "cs.CV"], "doi": "", "_query": "real estate valuation deep learning", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2110.10804v2", "title": "Identifiable Deep Generative Models via Sparse Decoding", "abstract": "We develop the sparse VAE for unsupervised representation learning on high-dimensional data. The sparse VAE learns a set of latent factors (representations) which summarize the associations in the observed data features. The underlying model is sparse in that each observed feature (i.e. each dimension of the data) depends on a small subset of the latent factors. As examples, in ratings data each movie is only described by a few genres; in text data each word is only applicable to a few topics; in genomics, each gene is active in only a few biological processes. We prove such sparse deep generative models are identifiable: with infinite data, the true model parameters can be learned. (In contrast, most deep generative models are not identifiable.) We empirically study the sparse VAE with both simulated and real data. We find that it recovers meaningful latent factors and has smaller heldout reconstruction error than related methods.", "published": "2021-10-20T22:11:33Z", "authors": ["Gemma E. Moran", "Dhanya Sridhar", "Yixin Wang", "David M. Blei"], "categories": ["stat.ML", "cs.LG", "stat.ME"], "doi": "", "_query": "real estate valuation deep learning", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2006.08762v3", "title": "Learning Incompressible Fluid Dynamics from Scratch -- Towards Fast, Differentiable Fluid Models that Generalize", "abstract": "Fast and stable fluid simulations are an essential prerequisite for applications ranging from computer-generated imagery to computer-aided design in research and development. However, solving the partial differential equations of incompressible fluids is a challenging task and traditional numerical approximation schemes come at high computational costs. Recent deep learning based approaches promise vast speed-ups but do not generalize to new fluid domains, require fluid simulation data for training, or rely on complex pipelines that outsource major parts of the fluid simulation to traditional methods. In this work, we propose a novel physics-constrained training approach that generalizes to new fluid domains, requires no fluid simulation data, and allows convolutional neural networks to map a fluid state from time-point t to a subsequent state at time t + dt in a single forward pass. This simplifies the pipeline to train and evaluate neural fluid models. After training, the framework yields models that are capable of fast fluid simulations and can handle various fluid phenomena including the Magnus effect and Karman vortex streets. We present an interactive real-time demo to show the speed and generalization capabilities of our trained models. Moreover, the trained neural networks are efficient differentiable fluid solvers as they offer a differentiable update step to advance the fluid simulation in time. We exploit this fact in a proof-of-concept optimal control experiment. Our models significantly outperform a recent differentiable fluid solver in terms of computational speed and accuracy.", "published": "2020-06-15T20:59:28Z", "authors": ["Nils Wandel", "Michael Weinmann", "Reinhard Klein"], "categories": ["cs.LG", "stat.ML"], "doi": "", "_query": "real estate valuation deep learning", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/1810.00368v2", "title": "Deep Quality-Value (DQV) Learning", "abstract": "We introduce a novel Deep Reinforcement Learning (DRL) algorithm called Deep Quality-Value (DQV) Learning. DQV uses temporal-difference learning to train a Value neural network and uses this network for training a second Quality-value network that learns to estimate state-action values. We first test DQV's update rules with Multilayer Perceptrons as function approximators on two classic RL problems, and then extend DQV with the use of Deep Convolutional Neural Networks, `Experience Replay' and `Target Neural Networks' for tackling four games of the Atari Arcade Learning environment. Our results show that DQV learns significantly faster and better than Deep Q-Learning and Double Deep Q-Learning, suggesting that our algorithm can potentially be a better performing synchronous temporal difference algorithm than what is currently present in DRL.", "published": "2018-09-30T12:52:31Z", "authors": ["Matthia Sabatelli", "Gilles Louppe", "Pierre Geurts", "Marco A. Wiering"], "categories": ["stat.ML", "cs.LG"], "doi": "", "_query": "real estate valuation deep learning", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2004.06353v1", "title": "Knowledge Elicitation using Deep Metric Learning and Psychometric Testing", "abstract": "Knowledge present in a domain is well expressed as relationships between corresponding concepts. For example, in zoology, animal species form complex hierarchies; in genomics, the different (parts of) molecules are organized in groups and subgroups based on their functions; plants, molecules, and astronomical objects all form complex taxonomies. Nevertheless, when applying supervised machine learning (ML) in such domains, we commonly reduce the complex and rich knowledge to a fixed set of labels, and induce a model shows good generalization performance with respect to these labels. The main reason for such a reductionist approach is the difficulty in eliciting the domain knowledge from the experts. Developing a label structure with sufficient fidelity and providing comprehensive multi-label annotation can be exceedingly labor-intensive in many real-world applications. In this paper, we provide a method for efficient hierarchical knowledge elicitation (HKE) from experts working with high-dimensional data such as images or videos. Our method is based on psychometric testing and active deep metric learning. The developed models embed the high-dimensional data in a metric space where distances are semantically meaningful, and the data can be organized in a hierarchical structure. We provide empirical evidence with a series of experiments on a synthetically generated dataset of simple shapes, and Cifar 10 and Fashion-MNIST benchmarks that our method is indeed successful in uncovering hierarchical structures.", "published": "2020-04-14T08:33:42Z", "authors": ["Lu Yin", "Vlado Menkovski", "Mykola Pechenizkiy"], "categories": ["cs.LG", "cs.AI"], "doi": "10.1007/978-3-030-67661-2_10", "_query": "real estate valuation deep learning", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/1801.01528v1", "title": "A deep learning approach for detecting traffic accidents from social media data", "abstract": "This paper employs deep learning in detecting the traffic accident from social media data. First, we thoroughly investigate the 1-year over 3 million tweet contents in two metropolitan areas: Northern Virginia and New York City. Our results show that paired tokens can capture the association rules inherent in the accident-related tweets and further increase the accuracy of the traffic accident detection. Second, two deep learning methods: Deep Belief Network (DBN) and Long Short-Term Memory (LSTM) are investigated and implemented on the extracted token. Results show that DBN can obtain an overall accuracy of 85% with about 44 individual token features and 17 paired token features. The classification results from DBN outperform those of Support Vector Machines (SVMs) and supervised Latent Dirichlet allocation (sLDA). Finally, to validate this study, we compare the accident-related tweets with both the traffic accident log on freeways and traffic data on local roads from 15,000 loop detectors. It is found that nearly 66% of the accident-related tweets can be located by the accident log and more than 80% of them can be tied to nearby abnormal traffic data. Several important issues of using Twitter to detect traffic accidents have been brought up by the comparison including the location and time bias, as well as the characteristics of influential users and hashtags.", "published": "2018-01-04T19:54:35Z", "authors": ["Zhenhua Zhang", "Qing Heb", "Jing Gao", "Ming Ni"], "categories": ["cs.SI", "stat.OT"], "doi": "", "_query": "real estate valuation deep learning", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2109.00426v1", "title": "Separating minimal valuations, point-continuous valuations and continuous valuations", "abstract": "We give two concrete examples of continuous valuations on dcpo's to separate minimal valuations, point-continuous valuations and continuous valuations: (1) Let $\\mathcal J$ be the Johnstone's non-sober dcpo, and $μ$ be the continuous valuation on $\\mathcal J$ with $μ(U) =1$ for nonempty Scott opens $U$ and $μ(U) = 0$ for $U=\\emptyset$. Then $μ$ is a point-continuous valuation on $\\mathcal J$ that is not minimal. (2) Lebesgue measure extends to a measure on the Sorgenfrey line $\\mathbb R_{l}$. Its restriction to the open subsets of $\\mathbb R_{l}$ is a continuous valuation $λ$. Then its image valuation $\\overlineλ$ through the embedding of $\\mathbb R_{l}$ into its Smyth powerdomain $\\mathcal Q\\mathbb R_{l}$ in the Scott topology is a continuous valuation that is not point-continuous. We believe that our construction $\\overlineλ$ might be useful in giving counterexamples displaying the failure of the general Fubini-type equations on dcpo's.", "published": "2021-09-01T15:19:53Z", "authors": ["Jean Goubault-Larrecq", "Xiaodong Jia"], "categories": ["cs.LO", "math.GN"], "doi": "", "_query": "real estate valuation deep learning", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/1905.08152v1", "title": "Stochastic Variance Reduction for Deep Q-learning", "abstract": "Recent advances in deep reinforcement learning have achieved human-level performance on a variety of real-world applications. However, the current algorithms still suffer from poor gradient estimation with excessive variance, resulting in unstable training and poor sample efficiency. In our paper, we proposed an innovative optimization strategy by utilizing stochastic variance reduced gradient (SVRG) techniques. With extensive experiments on Atari domain, our method outperforms the deep q-learning baselines on 18 out of 20 games.", "published": "2019-05-20T14:56:22Z", "authors": ["Wei-Ye Zhao", "Xi-Ya Guan", "Yang Liu", "Xiaoming Zhao", "Jian Peng"], "categories": ["cs.LG", "stat.ML"], "doi": "", "_query": "real estate valuation deep learning", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/1501.04325v1", "title": "Deep Belief Nets for Topic Modeling", "abstract": "Applying traditional collaborative filtering to digital publishing is challenging because user data is very sparse due to the high volume of documents relative to the number of users. Content based approaches, on the other hand, is attractive because textual content is often very informative. In this paper we describe large-scale content based collaborative filtering for digital publishing. To solve the digital publishing recommender problem we compare two approaches: latent Dirichlet allocation (LDA) and deep belief nets (DBN) that both find low-dimensional latent representations for documents. Efficient retrieval can be carried out in the latent representation. We work both on public benchmarks and digital media content provided by Issuu, an online publishing platform. This article also comes with a newly developed deep belief nets toolbox for topic modeling tailored towards performance evaluation of the DBN model and comparisons to the LDA model.", "published": "2015-01-18T17:12:59Z", "authors": ["Lars Maaloe", "Morten Arngren", "Ole Winther"], "categories": ["cs.CL", "cs.LG", "stat.ML"], "doi": "", "_query": "real estate valuation deep learning", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2010.13083v2", "title": "How to Make Deep RL Work in Practice", "abstract": "In recent years, challenging control problems became solvable with deep reinforcement learning (RL). To be able to use RL for large-scale real-world applications, a certain degree of reliability in their performance is necessary. Reported results of state-of-the-art algorithms are often difficult to reproduce. One reason for this is that certain implementation details influence the performance significantly. Commonly, these details are not highlighted as important techniques to achieve state-of-the-art performance. Additionally, techniques from supervised learning are often used by default but influence the algorithms in a reinforcement learning setting in different and not well-understood ways. In this paper, we investigate the influence of certain initialization, input normalization, and adaptive learning techniques on the performance of state-of-the-art RL algorithms. We make suggestions which of those techniques to use by default and highlight areas that could benefit from a solution specifically tailored to RL.", "published": "2020-10-25T10:37:54Z", "authors": ["Nirnai Rao", "Elie Aljalbout", "Axel Sauer", "Sami Haddadin"], "categories": ["cs.LG", "cs.RO"], "doi": "", "_query": "real estate valuation deep learning", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/1206.6421v1", "title": "Structured Learning from Partial Annotations", "abstract": "Structured learning is appropriate when predicting structured outputs such as trees, graphs, or sequences. Most prior work requires the training set to consist of complete trees, graphs or sequences. Specifying such detailed ground truth can be tedious or infeasible for large outputs. Our main contribution is a large margin formulation that makes structured learning from only partially annotated data possible. The resulting optimization problem is non-convex, yet can be efficiently solve by concave-convex procedure (CCCP) with novel speedup strategies. We apply our method to a challenging tracking-by-assignment problem of a variable number of divisible objects. On this benchmark, using only 25% of a full annotation we achieve a performance comparable to a model learned with a full annotation. Finally, we offer a unifying perspective of previous work using the hinge, ramp, or max loss for structured learning, followed by an empirical comparison on their practical performance.", "published": "2012-06-27T19:59:59Z", "authors": ["Xinghua Lou", "Fred Hamprecht"], "categories": ["cs.LG", "stat.ML"], "doi": "", "_query": "real estate valuation deep learning", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2112.01195v1", "title": "Maximum Entropy Model-based Reinforcement Learning", "abstract": "Recent advances in reinforcement learning have demonstrated its ability to solve hard agent-environment interaction tasks on a super-human level. However, the application of reinforcement learning methods to practical and real-world tasks is currently limited due to most RL state-of-art algorithms' sample inefficiency, i.e., the need for a vast number of training episodes. For example, OpenAI Five algorithm that has beaten human players in Dota 2 has trained for thousands of years of game time. Several approaches exist that tackle the issue of sample inefficiency, that either offers a more efficient usage of already gathered experience or aim to gain a more relevant and diverse experience via a better exploration of an environment. However, to our knowledge, no such approach exists for model-based algorithms, that showed their high sample efficiency in solving hard control tasks with high-dimensional state space. This work connects exploration techniques and model-based reinforcement learning. We have designed a novel exploration method that takes into account features of the model-based approach. We also demonstrate through experiments that our method significantly improves the performance of the model-based algorithm Dreamer.", "published": "2021-12-02T13:07:29Z", "authors": ["Oleg Svidchenko", "Aleksei Shpilman"], "categories": ["cs.AI", "cs.LG"], "doi": "", "_query": "real estate valuation deep learning", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/1812.07832v1", "title": "Semi-Supervised Deep Learning for Abnormality Classification in Retinal Images", "abstract": "Supervised deep learning algorithms have enabled significant performance gains in medical image classification tasks. But these methods rely on large labeled datasets that require resource-intensive expert annotation. Semi-supervised generative adversarial network (GAN) approaches offer a means to learn from limited labeled data alongside larger unlabeled datasets, but have not been applied to discern fine-scale, sparse or localized features that define medical abnormalities. To overcome these limitations, we propose a patch-based semi-supervised learning approach and evaluate performance on classification of diabetic retinopathy from funduscopic images. Our semi-supervised approach achieves high AUC with just 10-20 labeled training images, and outperforms the supervised baselines by upto 15% when less than 30% of the training dataset is labeled. Further, our method implicitly enables interpretation of the SSL predictions. As this approach enables good accuracy, resolution and interpretability with lower annotation burden, it sets the pathway for scalable applications of deep learning in clinical imaging.", "published": "2018-12-19T09:18:56Z", "authors": ["Bruno Lecouat", "Ken Chang", "Chuan-Sheng Foo", "Balagopal Unnikrishnan", "James M. Brown", "Houssam Zenati", "Andrew Beers", "Vijay Chandrasekhar", "Jayashree Kalpathy-Cramer", "Pavitra Krishnaswamy"], "categories": ["cs.CV"], "doi": "", "_query": "real estate valuation deep learning", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/1805.09411v2", "title": "Deep Active Learning for Anomaly Detection", "abstract": "Anomalies are intuitively easy for human experts to understand, but they are hard to define mathematically. Therefore, in order to have performance guarantees in unsupervised anomaly detection, priors need to be assumed on what the anomalies are. By contrast, active learning provides the necessary priors through appropriate expert feedback. Thus, in this work we present an active learning method that can be built upon existing deep learning solutions for unsupervised anomaly detection, so that outliers can be separated from normal data effectively. We introduce a new layer that can be easily attached to any deep learning model designed for unsupervised anomaly detection to transform it into an active method. We report results on both synthetic and real anomaly detection datasets, using multi-layer perceptrons and autoencoder architectures empowered with the proposed active layer, and we discuss their performance on finding clustered and low density anomalies.", "published": "2018-05-23T20:09:07Z", "authors": ["Tiago Pimentel", "Marianne Monteiro", "Adriano Veloso", "Nivio Ziviani"], "categories": ["stat.ML", "cs.LG"], "doi": "", "_query": "real estate valuation deep learning", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2210.08085v2", "title": "Adaptive patch foraging in deep reinforcement learning agents", "abstract": "Patch foraging is one of the most heavily studied behavioral optimization challenges in biology. However, despite its importance to biological intelligence, this behavioral optimization problem is understudied in artificial intelligence research. Patch foraging is especially amenable to study given that it has a known optimal solution, which may be difficult to discover given current techniques in deep reinforcement learning. Here, we investigate deep reinforcement learning agents in an ecological patch foraging task. For the first time, we show that machine learning agents can learn to patch forage adaptively in patterns similar to biological foragers, and approach optimal patch foraging behavior when accounting for temporal discounting. Finally, we show emergent internal dynamics in these agents that resemble single-cell recordings from foraging non-human primates, which complements experimental and theoretical work on the neural mechanisms of biological foraging. This work suggests that agents interacting in complex environments with ecologically valid pressures arrive at common solutions, suggesting the emergence of foundational computations behind adaptive, intelligent behavior in both biological and artificial agents.", "published": "2022-10-14T20:16:02Z", "authors": ["Nathan J. Wispinski", "Andrew Butcher", "Kory W. Mathewson", "Craig S. Chapman", "Matthew M. Botvinick", "Patrick M. Pilarski"], "categories": ["cs.AI", "q-bio.NC"], "doi": "", "_query": "real estate valuation deep learning", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2303.02273v1", "title": "Learning Label Encodings for Deep Regression", "abstract": "Deep regression networks are widely used to tackle the problem of predicting a continuous value for a given input. Task-specialized approaches for training regression networks have shown significant improvement over generic approaches, such as direct regression. More recently, a generic approach based on regression by binary classification using binary-encoded labels has shown significant improvement over direct regression. The space of label encodings for regression is large. Lacking heretofore have been automated approaches to find a good label encoding for a given application. This paper introduces Regularized Label Encoding Learning (RLEL) for end-to-end training of an entire network and its label encoding. RLEL provides a generic approach for tackling regression. Underlying RLEL is our observation that the search space of label encodings can be constrained and efficiently explored by using a continuous search space of real-valued label encodings combined with a regularization function designed to encourage encodings with certain properties. These properties balance the probability of classification error in individual bits against error correction capability. Label encodings found by RLEL result in lower or comparable errors to manually designed label encodings. Applying RLEL results in 10.9% and 12.4% improvement in Mean Absolute Error (MAE) over direct regression and multiclass classification, respectively. Our evaluation demonstrates that RLEL can be combined with off-the-shelf feature extractors and is suitable across different architectures, datasets, and tasks. Code is available at https://github.com/ubc-aamodt-group/RLEL_regression.", "published": "2023-03-04T00:11:34Z", "authors": ["Deval Shah", "Tor M. Aamodt"], "categories": ["cs.LG", "cs.CV"], "doi": "", "_query": "real estate valuation deep learning", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2010.05783v3", "title": "Structural Forecasting for Tropical Cyclone Intensity Prediction: Providing Insight with Deep Learning", "abstract": "Tropical cyclone (TC) intensity forecasts are ultimately issued by human forecasters. The human in-the-loop pipeline requires that any forecasting guidance must be easily digestible by TC experts if it is to be adopted at operational centers like the National Hurricane Center. Our proposed framework leverages deep learning to provide forecasters with something neither end-to-end prediction models nor traditional intensity guidance does: a powerful tool for monitoring high-dimensional time series of key physically relevant predictors and the means to understand how the predictors relate to one another and to short-term intensity changes.", "published": "2020-10-07T21:01:06Z", "authors": ["Trey McNeely", "Niccolò Dalmasso", "Kimberly M. Wood", "Ann B. Lee"], "categories": ["cs.LG", "stat.AP"], "doi": "", "_query": "real estate valuation deep learning", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/1910.07178v1", "title": "Generative Learning of Counterfactual for Synthetic Control Applications in Econometrics", "abstract": "A common statistical problem in econometrics is to estimate the impact of a treatment on a treated unit given a control sample with untreated outcomes. Here we develop a generative learning approach to this problem, learning the probability distribution of the data, which can be used for downstream tasks such as post-treatment counterfactual prediction and hypothesis testing. We use control samples to transform the data to a Gaussian and homoschedastic form and then perform Gaussian process analysis in Fourier space, evaluating the optimal Gaussian kernel via non-parametric power spectrum estimation. We combine this Gaussian prior with the data likelihood given by the pre-treatment data of the single unit, to obtain the synthetic prediction of the unit post-treatment, which minimizes the error variance of synthetic prediction. Given the generative model the minimum variance counterfactual is unique, and comes with an associated error covariance matrix. We extend this basic formalism to include correlations of primary variable with other covariates of interest. Given the probabilistic description of generative model we can compare synthetic data prediction with real data to address the question of whether the treatment had a statistically significant impact. For this purpose we develop a hypothesis testing approach and evaluate the Bayes factor. We apply the method to the well studied example of California (CA) tobacco sales tax of 1988. We also perform a placebo analysis using control states to validate our methodology. Our hypothesis testing method suggests 5.8:1 odds in favor of CA tobacco sales tax having an impact on the tobacco sales, a value that is at least three times higher than any of the 38 control states.", "published": "2019-10-16T05:50:14Z", "authors": ["Chirag Modi", "Uros Seljak"], "categories": ["stat.ML", "cs.LG"], "doi": "", "_query": "real estate valuation deep learning", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2401.01996v1", "title": "Mean-Field Assisted Deep Boltzmann Learning with Probabilistic Computers", "abstract": "Despite their appeal as physics-inspired, energy-based and generative nature, general Boltzmann Machines (BM) are considered intractable to train. This belief led to simplified models of BMs with restricted intralayer connections or layer-by-layer training of deep BMs. Recent developments in domain-specific hardware -- specifically probabilistic computers (p-computer) with probabilistic bits (p-bit) -- may change established wisdom on the tractability of deep BMs. In this paper, we show that deep and unrestricted BMs can be trained using p-computers generating hundreds of billions of Markov Chain Monte Carlo (MCMC) samples per second, on sparse networks developed originally for use in D-Wave's annealers. To maximize the efficiency of learning the p-computer, we introduce two families of Mean-Field Theory assisted learning algorithms, or xMFTs (x = Naive and Hierarchical). The xMFTs are used to estimate the averages and correlations during the positive phase of the contrastive divergence (CD) algorithm and our custom-designed p-computer is used to estimate the averages and correlations in the negative phase. A custom Field-Programmable-Gate Array (FPGA) emulation of the p-computer architecture takes up to 45 billion flips per second, allowing the implementation of CD-$n$ where $n$ can be of the order of millions, unlike RBMs where $n$ is typically 1 or 2. Experiments on the full MNIST dataset with the combined algorithm show that the positive phase can be efficiently computed by xMFTs without much degradation when the negative phase is computed by the p-computer. Our algorithm can be used in other scalable Ising machines and its variants can be used to train BMs, previously thought to be intractable.", "published": "2024-01-03T22:19:57Z", "authors": ["Shuvro Chowdhury", "Shaila Niazi", "Kerem Y. Camsari"], "categories": ["cs.ET", "cs.AR", "cs.LG", "cs.NE"], "doi": "", "_query": "real estate valuation deep learning", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/1706.02237v1", "title": "Efficient Reinforcement Learning via Initial Pure Exploration", "abstract": "In several realistic situations, an interactive learning agent can practice and refine its strategy before going on to be evaluated. For instance, consider a student preparing for a series of tests. She would typically take a few practice tests to know which areas she needs to improve upon. Based of the scores she obtains in these practice tests, she would formulate a strategy for maximizing her scores in the actual tests. We treat this scenario in the context of an agent exploring a fixed-horizon episodic Markov Decision Process (MDP), where the agent can practice on the MDP for some number of episodes (not necessarily known in advance) before starting to incur regret for its actions. During practice, the agent's goal must be to maximize the probability of following an optimal policy. This is akin to the problem of Pure Exploration (PE). We extend the PE problem of Multi Armed Bandits (MAB) to MDPs and propose a Bayesian algorithm called Posterior Sampling for Pure Exploration (PSPE), which is similar to its bandit counterpart. We show that the Bayesian simple regret converges at an optimal exponential rate when using PSPE. When the agent starts being evaluated, its goal would be to minimize the cumulative regret incurred. This is akin to the problem of Reinforcement Learning (RL). The agent uses the Posterior Sampling for Reinforcement Learning algorithm (PSRL) initialized with the posteriors of the practice phase. We hypothesize that this PSPE + PSRL combination is an optimal strategy for minimizing regret in RL problems with an initial practice phase. We show empirical results which prove that having a lower simple regret at the end of the practice phase results in having lower cumulative regret during evaluation.", "published": "2017-06-07T16:18:44Z", "authors": ["Sudeep Raja Putta", "Theja Tulabandhula"], "categories": ["cs.LG", "stat.ML"], "doi": "", "_query": "real estate valuation deep learning", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/1907.11703v1", "title": "Action Guidance with MCTS for Deep Reinforcement Learning", "abstract": "Deep reinforcement learning has achieved great successes in recent years, however, one main challenge is the sample inefficiency. In this paper, we focus on how to use action guidance by means of a non-expert demonstrator to improve sample efficiency in a domain with sparse, delayed, and possibly deceptive rewards: the recently-proposed multi-agent benchmark of Pommerman. We propose a new framework where even a non-expert simulated demonstrator, e.g., planning algorithms such as Monte Carlo tree search with a small number rollouts, can be integrated within asynchronous distributed deep reinforcement learning methods. Compared to a vanilla deep RL algorithm, our proposed methods both learn faster and converge to better policies on a two-player mini version of the Pommerman game.", "published": "2019-07-25T19:19:42Z", "authors": ["Bilal Kartal", "Pablo Hernandez-Leal", "Matthew E. Taylor"], "categories": ["cs.LG", "cs.MA", "stat.ML"], "doi": "", "_query": "real estate valuation deep learning", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2203.08951v1", "title": "Meta-Learning of NAS for Few-shot Learning in Medical Image Applications", "abstract": "Deep learning methods have been successful in solving tasks in machine learning and have made breakthroughs in many sectors owing to their ability to automatically extract features from unstructured data. However, their performance relies on manual trial-and-error processes for selecting an appropriate network architecture, hyperparameters for training, and pre-/post-procedures. Even though it has been shown that network architecture plays a critical role in learning feature representation feature from data and the final performance, searching for the best network architecture is computationally intensive and heavily relies on researchers' experience. Automated machine learning (AutoML) and its advanced techniques i.e. Neural Architecture Search (NAS) have been promoted to address those limitations. Not only in general computer vision tasks, but NAS has also motivated various applications in multiple areas including medical imaging. In medical imaging, NAS has significant progress in improving the accuracy of image classification, segmentation, reconstruction, and more. However, NAS requires the availability of large annotated data, considerable computation resources, and pre-defined tasks. To address such limitations, meta-learning has been adopted in the scenarios of few-shot learning and multiple tasks. In this book chapter, we first present a brief review of NAS by discussing well-known approaches in search space, search strategy, and evaluation strategy. We then introduce various NAS approaches in medical imaging with different applications such as classification, segmentation, detection, reconstruction, etc. Meta-learning in NAS for few-shot learning and multiple tasks is then explained. Finally, we describe several open problems in NAS.", "published": "2022-03-16T21:21:51Z", "authors": ["Viet-Khoa Vo-Ho", "Kashu Yamazaki", "Hieu Hoang", "Minh-Triet Tran", "Ngan Le"], "categories": ["cs.LG", "cs.CV", "eess.IV"], "doi": "", "_query": "real estate valuation deep learning", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/1812.03399v1", "title": "Efficient transfer learning and online adaptation with latent variable models for continuous control", "abstract": "Traditional model-based RL relies on hand-specified or learned models of transition dynamics of the environment. These methods are sample efficient and facilitate learning in the real world but fail to generalize to subtle variations in the underlying dynamics, e.g., due to differences in mass, friction, or actuators across robotic agents or across time. We propose using variational inference to learn an explicit latent representation of unknown environment properties that accelerates learning and facilitates generalization on novel environments at test time. We use Online Bayesian Inference of these learned latents to rapidly adapt online to changes in environments without retaining large replay buffers of recent data. Combined with a neural network ensemble that models dynamics and captures uncertainty over dynamics, our approach demonstrates positive transfer during training and online adaptation on the continuous control task HalfCheetah.", "published": "2018-12-08T22:46:37Z", "authors": ["Christian F. Perez", "Felipe Petroski Such", "Theofanis Karaletsos"], "categories": ["cs.LG", "stat.ML"], "doi": "", "_query": "real estate valuation deep learning", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2006.08386v2", "title": "COALA: Co-Aligned Autoencoders for Learning Semantically Enriched Audio Representations", "abstract": "Audio representation learning based on deep neural networks (DNNs) emerged as an alternative approach to hand-crafted features. For achieving high performance, DNNs often need a large amount of annotated data which can be difficult and costly to obtain. In this paper, we propose a method for learning audio representations, aligning the learned latent representations of audio and associated tags. Aligning is done by maximizing the agreement of the latent representations of audio and tags, using a contrastive loss. The result is an audio embedding model which reflects acoustic and semantic characteristics of sounds. We evaluate the quality of our embedding model, measuring its performance as a feature extractor on three different tasks (namely, sound event recognition, and music genre and musical instrument classification), and investigate what type of characteristics the model captures. Our results are promising, sometimes in par with the state-of-the-art in the considered tasks and the embeddings produced with our method are well correlated with some acoustic descriptors.", "published": "2020-06-15T13:17:18Z", "authors": ["Xavier Favory", "Konstantinos Drossos", "Tuomas Virtanen", "Xavier Serra"], "categories": ["cs.LG", "cs.IR", "eess.AS", "stat.ML"], "doi": "", "_query": "real estate valuation deep learning", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2202.04005v2", "title": "Improved Convergence Rates for Sparse Approximation Methods in Kernel-Based Learning", "abstract": "Kernel-based models such as kernel ridge regression and Gaussian processes are ubiquitous in machine learning applications for regression and optimization. It is well known that a major downside for kernel-based models is the high computational cost; given a dataset of $n$ samples, the cost grows as $\\mathcal{O}(n^3)$. Existing sparse approximation methods can yield a significant reduction in the computational cost, effectively reducing the actual cost down to as low as $\\mathcal{O}(n)$ in certain cases. Despite this remarkable empirical success, significant gaps remain in the existing results for the analytical bounds on the error due to approximation. In this work, we provide novel confidence intervals for the Nyström method and the sparse variational Gaussian process approximation method, which we establish using novel interpretations of the approximate (surrogate) posterior variance of the models. Our confidence intervals lead to improved performance bounds in both regression and optimization problems.", "published": "2022-02-08T17:22:09Z", "authors": ["Sattar Vakili", "Jonathan Scarlett", "Da-shan Shiu", "Alberto Bernacchia"], "categories": ["cs.LG", "stat.ML"], "doi": "", "_query": "real estate valuation deep learning", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/1907.10994v1", "title": "Dynamic Input for Deep Reinforcement Learning in Autonomous Driving", "abstract": "In many real-world decision making problems, reaching an optimal decision requires taking into account a variable number of objects around the agent. Autonomous driving is a domain in which this is especially relevant, since the number of cars surrounding the agent varies considerably over time and affects the optimal action to be taken. Classical methods that process object lists can deal with this requirement. However, to take advantage of recent high-performing methods based on deep reinforcement learning in modular pipelines, special architectures are necessary. For these, a number of options exist, but a thorough comparison of the different possibilities is missing. In this paper, we elaborate limitations of fully-connected neural networks and other established approaches like convolutional and recurrent neural networks in the context of reinforcement learning problems that have to deal with variable sized inputs. We employ the structure of Deep Sets in off-policy reinforcement learning for high-level decision making, highlight their capabilities to alleviate these limitations, and show that Deep Sets not only yield the best overall performance but also offer better generalization to unseen situations than the other approaches.", "published": "2019-07-25T12:08:28Z", "authors": ["Maria Hügle", "Gabriel Kalweit", "Branka Mirchevska", "Moritz Werling", "Joschka Boedecker"], "categories": ["cs.LG", "cs.RO", "stat.ML"], "doi": "10.1109/IROS40897.2019.8968560", "_query": "real estate valuation deep learning", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/1901.01379v1", "title": "Deep Reinforcement Learning for Imbalanced Classification", "abstract": "Data in real-world application often exhibit skewed class distribution which poses an intense challenge for machine learning. Conventional classification algorithms are not effective in the case of imbalanced data distribution, and may fail when the data distribution is highly imbalanced. To address this issue, we propose a general imbalanced classification model based on deep reinforcement learning. We formulate the classification problem as a sequential decision-making process and solve it by deep Q-learning network. The agent performs a classification action on one sample at each time step, and the environment evaluates the classification action and returns a reward to the agent. The reward from minority class sample is larger so the agent is more sensitive to the minority class. The agent finally finds an optimal classification policy in imbalanced data under the guidance of specific reward function and beneficial learning environment. Experiments show that our proposed model outperforms the other imbalanced classification algorithms, and it can identify more minority samples and has great classification performance.", "published": "2019-01-05T07:59:47Z", "authors": ["Enlu Lin", "Qiong Chen", "Xiaoming Qi"], "categories": ["cs.LG", "cs.AI", "stat.ML"], "doi": "", "_query": "real estate valuation deep learning", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2011.02081v1", "title": "A deep learning classifier for local ancestry inference", "abstract": "Local ancestry inference (LAI) identifies the ancestry of each segment of an individual's genome and is an important step in medical and population genetic studies of diverse cohorts. Several techniques have been used for LAI, including Hidden Markov Models and Random Forests. Here, we formulate the LAI task as an image segmentation problem and develop a new LAI tool using a deep convolutional neural network with an encoder-decoder architecture. We train our model using complete genome sequences from 982 unadmixed individuals from each of five continental ancestry groups, and we evaluate it using simulated admixed data derived from an additional 279 individuals selected from the same populations. We show that our model is able to learn admixture as a zero-shot task, yielding ancestry assignments that are nearly as accurate as those from the existing gold standard tool, RFMix.", "published": "2020-11-04T00:42:01Z", "authors": ["Matthew Aguirre", "Jan Sokol", "Guhan Venkataraman", "Alexander Ioannidis"], "categories": ["q-bio.GN", "cs.LG", "cs.NE"], "doi": "", "_query": "real estate valuation deep learning", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2205.05784v1", "title": "Learning to Guide Multiple Heterogeneous Actors from a Single Human Demonstration via Automatic Curriculum Learning in StarCraft II", "abstract": "Traditionally, learning from human demonstrations via direct behavior cloning can lead to high-performance policies given that the algorithm has access to large amounts of high-quality data covering the most likely scenarios to be encountered when the agent is operating. However, in real-world scenarios, expert data is limited and it is desired to train an agent that learns a behavior policy general enough to handle situations that were not demonstrated by the human expert. Another alternative is to learn these policies with no supervision via deep reinforcement learning, however, these algorithms require a large amount of computing time to perform well on complex tasks with high-dimensional state and action spaces, such as those found in StarCraft II. Automatic curriculum learning is a recent mechanism comprised of techniques designed to speed up deep reinforcement learning by adjusting the difficulty of the current task to be solved according to the agent's current capabilities. Designing a proper curriculum, however, can be challenging for sufficiently complex tasks, and thus we leverage human demonstrations as a way to guide agent exploration during training. In this work, we aim to train deep reinforcement learning agents that can command multiple heterogeneous actors where starting positions and overall difficulty of the task are controlled by an automatically-generated curriculum from a single human demonstration. Our results show that an agent trained via automated curriculum learning can outperform state-of-the-art deep reinforcement learning baselines and match the performance of the human expert in a simulated command and control task in StarCraft II modeled over a real military scenario.", "published": "2022-05-11T21:53:11Z", "authors": ["Nicholas Waytowich", "James Hare", "Vinicius G. Goecks", "Mark Mittrick", "John Richardson", "Anjon Basak", "Derrik E. Asher"], "categories": ["cs.LG", "cs.MA"], "doi": "", "_query": "real estate valuation deep learning", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/1810.08305v2", "title": "Open Vocabulary Learning on Source Code with a Graph-Structured Cache", "abstract": "Machine learning models that take computer program source code as input typically use Natural Language Processing (NLP) techniques. However, a major challenge is that code is written using an open, rapidly changing vocabulary due to, e.g., the coinage of new variable and method names. Reasoning over such a vocabulary is not something for which most NLP methods are designed. We introduce a Graph-Structured Cache to address this problem; this cache contains a node for each new word the model encounters with edges connecting each word to its occurrences in the code. We find that combining this graph-structured cache strategy with recent Graph-Neural-Network-based models for supervised learning on code improves the models' performance on a code completion task and a variable naming task --- with over $100\\%$ relative improvement on the latter --- at the cost of a moderate increase in computation time.", "published": "2018-10-18T23:33:11Z", "authors": ["Milan Cvitkovic", "Badal Singh", "Anima Anandkumar"], "categories": ["cs.LG", "stat.ML"], "doi": "", "_query": "real estate valuation deep learning", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2107.11320v2", "title": "Tackling the Overestimation of Forest Carbon with Deep Learning and Aerial Imagery", "abstract": "Forest carbon offsets are increasingly popular and can play a significant role in financing climate mitigation, forest conservation, and reforestation. Measuring how much carbon is stored in forests is, however, still largely done via expensive, time-consuming, and sometimes unaccountable field measurements. To overcome these limitations, many verification bodies are leveraging machine learning (ML) algorithms to estimate forest carbon from satellite or aerial imagery. Aerial imagery allows for tree species or family classification, which improves the satellite imagery-based forest type classification. However, aerial imagery is significantly more expensive to collect and it is unclear by how much the higher resolution improves the forest carbon estimation. This proposal paper describes the first systematic comparison of forest carbon estimation from aerial imagery, satellite imagery, and ground-truth field measurements via deep learning-based algorithms for a tropical reforestation project. Our initial results show that forest carbon estimates from satellite imagery can overestimate above-ground biomass by up to 10-times for tropical reforestation projects. The significant difference between aerial and satellite-derived forest carbon measurements shows the potential for aerial imagery-based ML algorithms and raises the importance to extend this study to a global benchmark between options for carbon measurements.", "published": "2021-07-23T15:59:52Z", "authors": ["Gyri Reiersen", "David Dao", "Björn Lütjens", "Konstantin Klemmer", "Xiaoxiang Zhu", "Ce Zhang"], "categories": ["cs.CV", "cs.LG"], "doi": "", "_query": "real estate valuation deep learning", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/1909.05950v1", "title": "Mutual-Information Regularization in Markov Decision Processes and Actor-Critic Learning", "abstract": "Cumulative entropy regularization introduces a regulatory signal to the reinforcement learning (RL) problem that encourages policies with high-entropy actions, which is equivalent to enforcing small deviations from a uniform reference marginal policy. This has been shown to improve exploration and robustness, and it tackles the value overestimation problem. It also leads to a significant performance increase in tabular and high-dimensional settings, as demonstrated via algorithms such as soft Q-learning (SQL) and soft actor-critic (SAC). Cumulative entropy regularization has been extended to optimize over the reference marginal policy instead of keeping it fixed, yielding a regularization that minimizes the mutual information between states and actions. While this has been initially proposed for Markov Decision Processes (MDPs) in tabular settings, it was recently shown that a similar principle leads to significant improvements over vanilla SQL in RL for high-dimensional domains with discrete actions and function approximators. Here, we follow the motivation of mutual-information regularization from an inference perspective and theoretically analyze the corresponding Bellman operator. Inspired by this Bellman operator, we devise a novel mutual-information regularized actor-critic learning (MIRACLE) algorithm for continuous action spaces that optimizes over the reference marginal policy. We empirically validate MIRACLE in the Mujoco robotics simulator, where we demonstrate that it can compete with contemporary RL methods. Most notably, it can improve over the model-free state-of-the-art SAC algorithm which implicitly assumes a fixed reference policy.", "published": "2019-09-11T16:43:25Z", "authors": ["Felix Leibfried", "Jordi Grau-Moya"], "categories": ["cs.LG", "cs.AI", "stat.ML"], "doi": "", "_query": "real estate valuation deep learning", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/1904.06316v1", "title": "Spatio-Temporal Deep Graph Infomax", "abstract": "Spatio-temporal graphs such as traffic networks or gene regulatory systems present challenges for the existing deep learning methods due to the complexity of structural changes over time. To address these issues, we introduce Spatio-Temporal Deep Graph Infomax (STDGI)---a fully unsupervised node representation learning approach based on mutual information maximization that exploits both the temporal and spatial dynamics of the graph. Our model tackles the challenging task of node-level regression by training embeddings to maximize the mutual information between patches of the graph, at any given time step, and between features of the central nodes of patches, in the future. We demonstrate through experiments and qualitative studies that the learned representations can successfully encode relevant information about the input graph and improve the predictive performance of spatio-temporal auto-regressive forecasting models.", "published": "2019-04-12T16:47:30Z", "authors": ["Felix L. Opolka", "Aaron Solomon", "Cătălina Cangea", "Petar Veličković", "Pietro Liò", "R Devon Hjelm"], "categories": ["cs.LG", "stat.ML"], "doi": "", "_query": "real estate valuation deep learning", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2005.01097v2", "title": "Adaptive Learning of the Optimal Batch Size of SGD", "abstract": "Recent advances in the theoretical understanding of SGD led to a formula for the optimal batch size minimizing the number of effective data passes, i.e., the number of iterations times the batch size. However, this formula is of no practical value as it depends on the knowledge of the variance of the stochastic gradients evaluated at the optimum. In this paper we design a practical SGD method capable of learning the optimal batch size adaptively throughout its iterations for strongly convex and smooth functions. Our method does this provably, and in our experiments with synthetic and real data robustly exhibits nearly optimal behaviour; that is, it works as if the optimal batch size was known a-priori. Further, we generalize our method to several new batch strategies not considered in the literature before, including a sampling suitable for distributed implementations.", "published": "2020-05-03T14:28:32Z", "authors": ["Motasem Alfarra", "Slavomir Hanzely", "Alyazeed Albasyoni", "Bernard Ghanem", "Peter Richtarik"], "categories": ["cs.LG", "math.OC", "stat.ML"], "doi": "", "_query": "real estate valuation deep learning", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2003.05012v4", "title": "Retrospective Analysis of the 2019 MineRL Competition on Sample Efficient Reinforcement Learning", "abstract": "To facilitate research in the direction of sample efficient reinforcement learning, we held the MineRL Competition on Sample Efficient Reinforcement Learning Using Human Priors at the Thirty-third Conference on Neural Information Processing Systems (NeurIPS 2019). The primary goal of this competition was to promote the development of algorithms that use human demonstrations alongside reinforcement learning to reduce the number of samples needed to solve complex, hierarchical, and sparse environments. We describe the competition, outlining the primary challenge, the competition design, and the resources that we provided to the participants. We provide an overview of the top solutions, each of which use deep reinforcement learning and/or imitation learning. We also discuss the impact of our organizational decisions on the competition and future directions for improvement.", "published": "2020-03-10T21:39:52Z", "authors": ["Stephanie Milani", "Nicholay Topin", "Brandon Houghton", "William H. Guss", "Sharada P. Mohanty", "Keisuke Nakata", "Oriol Vinyals", "Noboru Sean Kuno"], "categories": ["cs.LG", "cs.AI", "stat.ML"], "doi": "", "_query": "real estate valuation deep learning", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2208.01587v4", "title": "Learning to Incorporate Texture Saliency Adaptive Attention to Image Cartoonization", "abstract": "Image cartoonization is recently dominated by generative adversarial networks (GANs) from the perspective of unsupervised image-to-image translation, in which an inherent challenge is to precisely capture and sufficiently transfer characteristic cartoon styles (e.g., clear edges, smooth color shading, abstract fine structures, etc.). Existing advanced models try to enhance cartoonization effect by learning to promote edges adversarially, introducing style transfer loss, or learning to align style from multiple representation space. This paper demonstrates that more distinct and vivid cartoonization effect could be easily achieved with only basic adversarial loss. Observing that cartoon style is more evident in cartoon-texture-salient local image regions, we build a region-level adversarial learning branch in parallel with the normal image-level one, which constrains adversarial learning on cartoon-texture-salient local patches for better perceiving and transferring cartoon texture features. To this end, a novel cartoon-texture-saliency-sampler (CTSS) module is proposed to dynamically sample cartoon-texture-salient patches from training data. With extensive experiments, we demonstrate that texture saliency adaptive attention in adversarial learning, as a missing ingredient of related methods in image cartoonization, is of significant importance in facilitating and enhancing image cartoon stylization, especially for high-resolution input pictures.", "published": "2022-08-02T16:45:55Z", "authors": ["Xiang Gao", "Yuqi Zhang", "Yingjie Tian"], "categories": ["cs.CV"], "doi": "", "_query": "real estate valuation deep learning", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/1904.09425v3", "title": "Deep Learning for Large-Scale Real-World ACARS and ADS-B Radio Signal Classification", "abstract": "Radio signal classification has a very wide range of applications in the field of wireless communications and electromagnetic spectrum management. In recent years, deep learning has been used to solve the problem of radio signal classification and has achieved good results. However, the radio signal data currently used is very limited in scale. In order to verify the performance of the deep learning-based radio signal classification on real-world radio signal data, in this paper we conduct experiments on large-scale real-world ACARS and ADS-B signal data with sample sizes of 900,000 and 13,000,000, respectively, and with categories of 3,143 and 5,157 respectively. We use the same Inception-Residual neural network model structure for ACARS signal classification and ADS-B signal classification to verify the ability of a single basic deep neural network model structure to process different types of radio signals, i.e., communication bursts in ACARS and pulse bursts in ADS-B. We build an experimental system for radio signal deep learning experiments. Experimental results show that the signal classification accuracy of ACARS and ADS-B is 98.1% and 96.3%, respectively. When the signal-to-noise ratio (with injected additive white Gaussian noise) is greater than 9 dB, the classification accuracy is greater than 92%. These experimental results validate the ability of deep learning to classify large-scale real-world radio signals. The results of the transfer learning experiment show that the model trained on large-scale ADS-B datasets is more conducive to the learning and training of new tasks than the model trained on small-scale datasets.", "published": "2019-04-20T10:04:34Z", "authors": ["Shichuan Chen", "Shilian Zheng", "Lifeng Yang", "Xiaoniu Yang"], "categories": ["cs.LG", "eess.SP"], "doi": "10.1109/ACCESS.2019.2925569", "_query": "real estate valuation deep learning", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/1709.09590v2", "title": "An attentive neural architecture for joint segmentation and parsing and its application to real estate ads", "abstract": "In processing human produced text using natural language processing (NLP) techniques, two fundamental subtasks that arise are (i) segmentation of the plain text into meaningful subunits (e.g., entities), and (ii) dependency parsing, to establish relations between subunits. In this paper, we develop a relatively simple and effective neural joint model that performs both segmentation and dependency parsing together, instead of one after the other as in most state-of-the-art works. We will focus in particular on the real estate ad setting, aiming to convert an ad to a structured description, which we name property tree, comprising the tasks of (1) identifying important entities of a property (e.g., rooms) from classifieds and (2) structuring them into a tree format. In this work, we propose a new joint model that is able to tackle the two tasks simultaneously and construct the property tree by (i) avoiding the error propagation that would arise from the subtasks one after the other in a pipelined fashion, and (ii) exploiting the interactions between the subtasks. For this purpose, we perform an extensive comparative study of the pipeline methods and the new proposed joint model, reporting an improvement of over three percentage points in the overall edge F1 score of the property tree. Also, we propose attention methods, to encourage our model to focus on salient tokens during the construction of the property tree. Thus we experimentally demonstrate the usefulness of attentive neural architectures for the proposed joint model, showcasing a further improvement of two percentage points in edge F1 score for our application.", "published": "2017-09-27T15:50:53Z", "authors": ["Giannis Bekoulis", "Johannes Deleu", "Thomas Demeester", "Chris Develder"], "categories": ["cs.CL"], "doi": "10.1016/j.eswa.2018.02.031", "_query": "real estate valuation deep learning", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2601.09693v3", "title": "Contrastive Geometric Learning Unlocks Unified Structure- and Ligand-Based Drug Design", "abstract": "Structure-based and ligand-based computational drug design have traditionally relied on disjoint data sources and modeling assumptions, limiting their joint use at scale. In this work, we introduce Contrastive Geometric Learning for Unified Computational Drug Design (ConGLUDe), a single contrastive geometric model that unifies structure- and ligand-based training. ConGLUDe couples a geometric protein encoder that produces whole-protein representations and implicit embeddings of predicted binding sites with a fast ligand encoder, removing the need for predefined pockets. By aligning ligands with both global protein representations and multiple candidate binding sites through contrastive learning, ConGLUDe supports ligand-conditioned pocket prediction in addition to virtual screening and target fishing, while being trained jointly on protein-ligand complexes and large-scale bioactivity data. Across diverse benchmarks, ConGLUDe achieves competitive zero-shot virtual screening performance, substantially outperforms existing methods on a challenging target fishing task, and demonstrates state-of-the-art ligand-conditioned pocket selection. These results highlight the advantages of unified structure-ligand training and position ConGLUDe as a step toward general-purpose foundation models for drug discovery.", "published": "2026-01-14T18:45:08Z", "authors": ["Lisa Schneckenreiter", "Sohvi Luukkonen", "Lukas Friedrich", "Daniel Kuhn", "Günter Klambauer"], "categories": ["cs.LG", "stat.ML"], "doi": "", "_query": "real estate valuation deep learning", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2008.04460v2", "title": "Hardware as Policy: Mechanical and Computational Co-Optimization using Deep Reinforcement Learning", "abstract": "Deep Reinforcement Learning (RL) has shown great success in learning complex control policies for a variety of applications in robotics. However, in most such cases, the hardware of the robot has been considered immutable, modeled as part of the environment. In this study, we explore the problem of learning hardware and control parameters together in a unified RL framework. To achieve this, we propose to model the robot body as a \"hardware policy\", analogous to and optimized jointly with its computational counterpart. We show that, by modeling such hardware policies as auto-differentiable computational graphs, the ensuing optimization problem can be solved efficiently by gradient-based algorithms from the Policy Optimization family. We present two such design examples: a toy mass-spring problem, and a real-world problem of designing an underactuated hand. We compare our method against traditional co-optimization approaches, and also demonstrate its effectiveness by building a physical prototype based on the learned hardware parameters. Videos and more details are available at https://roamlab.github.io/hwasp/ .", "published": "2020-08-11T00:10:44Z", "authors": ["Tianjian Chen", "Zhanpeng He", "Matei Ciocarlie"], "categories": ["cs.RO"], "doi": "", "_query": "real estate valuation deep learning", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2411.06720v1", "title": "Real-time Monitoring and Analysis of Track and Field Athletes Based on Edge Computing and Deep Reinforcement Learning Algorithm", "abstract": "This research focuses on real-time monitoring and analysis of track and field athletes, addressing the limitations of traditional monitoring systems in terms of real-time performance and accuracy. We propose an IoT-optimized system that integrates edge computing and deep learning algorithms. Traditional systems often experience delays and reduced accuracy when handling complex motion data, whereas our method, by incorporating a SAC-optimized deep learning model within the IoT architecture, achieves efficient motion recognition and real-time feedback. Experimental results show that this system significantly outperforms traditional methods in response time, data processing accuracy, and energy efficiency, particularly excelling in complex track and field events. This research not only enhances the precision and efficiency of athlete monitoring but also provides new technical support and application prospects for sports science research.", "published": "2024-11-11T05:12:15Z", "authors": ["Xiaowei Tang", "Bin Long", "Li Zhou"], "categories": ["cs.LG", "eess.SP"], "doi": "", "_query": "real estate valuation deep learning", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2304.11706v1", "title": "Deep Convolutional Tables: Deep Learning without Convolutions", "abstract": "We propose a novel formulation of deep networks that do not use dot-product neurons and rely on a hierarchy of voting tables instead, denoted as Convolutional Tables (CT), to enable accelerated CPU-based inference. Convolutional layers are the most time-consuming bottleneck in contemporary deep learning techniques, severely limiting their use in Internet of Things and CPU-based devices. The proposed CT performs a fern operation at each image location: it encodes the location environment into a binary index and uses the index to retrieve the desired local output from a table. The results of multiple tables are combined to derive the final output. The computational complexity of a CT transformation is independent of the patch (filter) size and grows gracefully with the number of channels, outperforming comparable convolutional layers. It is shown to have a better capacity:compute ratio than dot-product neurons, and that deep CT networks exhibit a universal approximation property similar to neural networks. As the transformation involves computing discrete indices, we derive a soft relaxation and gradient-based approach for training the CT hierarchy. Deep CT networks have been experimentally shown to have accuracy comparable to that of CNNs of similar architectures. In the low compute regime, they enable an error:speed trade-off superior to alternative efficient CNN architectures.", "published": "2023-04-23T17:49:21Z", "authors": ["Shay Dekel", "Yosi Keller", "Aharon Bar-Hillel"], "categories": ["cs.CV"], "doi": "", "_query": "real estate valuation deep learning", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2009.08449v1", "title": "'Less Than One'-Shot Learning: Learning N Classes From M<N Samples", "abstract": "Deep neural networks require large training sets but suffer from high computational cost and long training times. Training on much smaller training sets while maintaining nearly the same accuracy would be very beneficial. In the few-shot learning setting, a model must learn a new class given only a small number of samples from that class. One-shot learning is an extreme form of few-shot learning where the model must learn a new class from a single example. We propose the `less than one'-shot learning task where models must learn $N$ new classes given only $M<N$ examples and we show that this is achievable with the help of soft labels. We use a soft-label generalization of the k-Nearest Neighbors classifier to explore the intricate decision landscapes that can be created in the `less than one'-shot learning setting. We analyze these decision landscapes to derive theoretical lower bounds for separating $N$ classes using $M<N$ soft-label samples and investigate the robustness of the resulting systems.", "published": "2020-09-17T17:55:29Z", "authors": ["Ilia Sucholutsky", "Matthias Schonlau"], "categories": ["cs.LG", "stat.ML"], "doi": "", "_query": "real estate valuation deep learning", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/1912.05796v1", "title": "Automatic Layout Generation with Applications in Machine Learning Engine Evaluation", "abstract": "Machine learning-based lithography hotspot detection has been deeply studied recently, from varies feature extraction techniques to efficient learning models. It has been observed that such machine learning-based frameworks are providing satisfactory metal layer hotspot prediction results on known public metal layer benchmarks. In this work, we seek to evaluate how these machine learning-based hotspot detectors generalize to complicated patterns. We first introduce a automatic layout generation tool that can synthesize varies layout patterns given a set of design rules. The tool currently supports both metal layer and via layer generation. As a case study, we conduct hotspot detection on the generated via layer layouts with representative machine learning-based hotspot detectors, which shows that continuous study on model robustness and generality is necessary to prototype and integrate the learning engines in DFM flows. The source code of the layout generation tool will be available at https://github. com/phdyang007/layout-generation.", "published": "2019-12-12T06:52:12Z", "authors": ["Haoyu Yang", "Wen Chen", "Piyush Pathak", "Frank Gennari", "Ya-Chieh Lai", "Bei Yu"], "categories": ["cs.LG", "cs.AI", "stat.ML"], "doi": "", "_query": "real estate valuation deep learning", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/1812.01063v1", "title": "A Hybrid Instance-based Transfer Learning Method", "abstract": "In recent years, supervised machine learning models have demonstrated tremendous success in a variety of application domains. Despite the promising results, these successful models are data hungry and their performance relies heavily on the size of training data. However, in many healthcare applications it is difficult to collect sufficiently large training datasets. Transfer learning can help overcome this issue by transferring the knowledge from readily available datasets (source) to a new dataset (target). In this work, we propose a hybrid instance-based transfer learning method that outperforms a set of baselines including state-of-the-art instance-based transfer learning approaches. Our method uses a probabilistic weighting strategy to fuse information from the source domain to the model learned in the target domain. Our method is generic, applicable to multiple source domains, and robust with respect to negative transfer. We demonstrate the effectiveness of our approach through extensive experiments for two different applications.", "published": "2018-12-03T20:15:05Z", "authors": ["Azin Asgarian", "Parinaz Sobhani", "Ji Chao Zhang", "Madalin Mihailescu", "Ariel Sibilia", "Ahmed Bilal Ashraf", "Babak Taati"], "categories": ["cs.LG", "cs.AI", "cs.CV", "stat.ML"], "doi": "", "_query": "real estate valuation deep learning", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2103.16223v3", "title": "Towards Real-World Deployment of Reinforcement Learning for Traffic Signal Control", "abstract": "Sub-optimal control policies in intersection traffic signal controllers (TSC) contribute to congestion and lead to negative effects on human health and the environment. Reinforcement learning (RL) for traffic signal control is a promising approach to design better control policies and has attracted considerable research interest in recent years. However, most work done in this area used simplified simulation environments of traffic scenarios to train RL-based TSC. To deploy RL in real-world traffic systems, the gap between simplified simulation environments and real-world applications has to be closed. Therefore, we propose LemgoRL, a benchmark tool to train RL agents as TSC in a realistic simulation environment of Lemgo, a medium-sized town in Germany. In addition to the realistic simulation model, LemgoRL encompasses a traffic signal logic unit that ensures compliance with all regulatory and safety requirements. LemgoRL offers the same interface as the wellknown OpenAI gym toolkit to enable easy deployment in existing research work. To demonstrate the functionality and applicability of LemgoRL, we train a state-of-the-art Deep RL algorithm on a CPU cluster utilizing a framework for distributed and parallel RL and compare its performance with other methods. Our benchmark tool drives the development of RL algorithms towards real-world applications.", "published": "2021-03-30T10:11:09Z", "authors": ["Arthur Müller", "Vishal Rangras", "Georg Schnittker", "Michael Waldmann", "Maxim Friesen", "Tobias Ferfers", "Lukas Schreckenberg", "Florian Hufen", "Jürgen Jasperneite", "Marco Wiering"], "categories": ["cs.LG", "eess.SY"], "doi": "10.1109/ICMLA52953.2021.00085", "_query": "real estate valuation deep learning", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/1711.04851v3", "title": "Learning and Visualizing Localized Geometric Features Using 3D-CNN: An Application to Manufacturability Analysis of Drilled Holes", "abstract": "3D Convolutional Neural Networks (3D-CNN) have been used for object recognition based on the voxelized shape of an object. However, interpreting the decision making process of these 3D-CNNs is still an infeasible task. In this paper, we present a unique 3D-CNN based Gradient-weighted Class Activation Mapping method (3D-GradCAM) for visual explanations of the distinct local geometric features of interest within an object. To enable efficient learning of 3D geometries, we augment the voxel data with surface normals of the object boundary. We then train a 3D-CNN with this augmented data and identify the local features critical for decision-making using 3D GradCAM. An application of this feature identification framework is to recognize difficult-to-manufacture drilled hole features in a complex CAD geometry. The framework can be extended to identify difficult-to-manufacture features at multiple spatial scales leading to a real-time design for manufacturability decision support system.", "published": "2017-11-13T21:05:39Z", "authors": ["Sambit Ghadai", "Aditya Balu", "Adarsh Krishnamurthy", "Soumik Sarkar"], "categories": ["stat.ML", "cs.CV", "cs.LG"], "doi": "", "_query": "real estate valuation deep learning", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/1801.07756v5", "title": "Deep Learning for Electromyographic Hand Gesture Signal Classification Using Transfer Learning", "abstract": "In recent years, deep learning algorithms have become increasingly more prominent for their unparalleled ability to automatically learn discriminant features from large amounts of data. However, within the field of electromyography-based gesture recognition, deep learning algorithms are seldom employed as they require an unreasonable amount of effort from a single person, to generate tens of thousands of examples. This work's hypothesis is that general, informative features can be learned from the large amounts of data generated by aggregating the signals of multiple users, thus reducing the recording burden while enhancing gesture recognition. Consequently, this paper proposes applying transfer learning on aggregated data from multiple users, while leveraging the capacity of deep learning algorithms to learn discriminant features from large datasets. Two datasets comprised of 19 and 17 able-bodied participants respectively (the first one is employed for pre-training) were recorded for this work, using the Myo Armband. A third Myo Armband dataset was taken from the NinaPro database and is comprised of 10 able-bodied participants. Three different deep learning networks employing three different modalities as input (raw EMG, Spectrograms and Continuous Wavelet Transform (CWT)) are tested on the second and third dataset. The proposed transfer learning scheme is shown to systematically and significantly enhance the performance for all three networks on the two datasets, achieving an offline accuracy of 98.31% for 7 gestures over 17 participants for the CWT-based ConvNet and 68.98% for 18 gestures over 10 participants for the raw EMG-based ConvNet. Finally, a use-case study employing eight able-bodied participants suggests that real-time feedback allows users to adapt their muscle activation strategy which reduces the degradation in accuracy normally experienced over time.", "published": "2018-01-10T11:42:30Z", "authors": ["Ulysse Côté-Allard", "Cheikh Latyr Fall", "Alexandre Drouin", "Alexandre Campeau-Lecours", "Clément Gosselin", "Kyrre Glette", "François Laviolette", "Benoit Gosselin"], "categories": ["cs.LG", "stat.ML"], "doi": "", "_query": "real estate valuation deep learning", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2007.05558v2", "title": "The Computational Limits of Deep Learning", "abstract": "Deep learning's recent history has been one of achievement: from triumphing over humans in the game of Go to world-leading performance in image classification, voice recognition, translation, and other tasks. But this progress has come with a voracious appetite for computing power. This article catalogs the extent of this dependency, showing that progress across a wide variety of applications is strongly reliant on increases in computing power. Extrapolating forward this reliance reveals that progress along current lines is rapidly becoming economically, technically, and environmentally unsustainable. Thus, continued progress in these applications will require dramatically more computationally-efficient methods, which will either have to come from changes to deep learning or from moving to other machine learning methods.", "published": "2020-07-10T18:26:17Z", "authors": ["Neil C. Thompson", "Kristjan Greenewald", "Keeheon Lee", "Gabriel F. Manso"], "categories": ["cs.LG", "stat.ML"], "doi": "", "_query": "real estate valuation deep learning", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2008.01171v1", "title": "Deep Reinforcement Learning using Cyclical Learning Rates", "abstract": "Deep Reinforcement Learning (DRL) methods often rely on the meticulous tuning of hyperparameters to successfully resolve problems. One of the most influential parameters in optimization procedures based on stochastic gradient descent (SGD) is the learning rate. We investigate cyclical learning and propose a method for defining a general cyclical learning rate for various DRL problems. In this paper we present a method for cyclical learning applied to complex DRL problems. Our experiments show that, utilizing cyclical learning achieves similar or even better results than highly tuned fixed learning rates. This paper presents the first application of cyclical learning rates in DRL settings and is a step towards overcoming manual hyperparameter tuning.", "published": "2020-07-31T10:06:02Z", "authors": ["Ralf Gulde", "Marc Tuscher", "Akos Csiszar", "Oliver Riedel", "Alexander Verl"], "categories": ["cs.LG", "stat.ML"], "doi": "", "_query": "real estate valuation deep learning", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/1509.00151v3", "title": "Learning A Task-Specific Deep Architecture For Clustering", "abstract": "While sparse coding-based clustering methods have shown to be successful, their bottlenecks in both efficiency and scalability limit the practical usage. In recent years, deep learning has been proved to be a highly effective, efficient and scalable feature learning tool. In this paper, we propose to emulate the sparse coding-based clustering pipeline in the context of deep learning, leading to a carefully crafted deep model benefiting from both. A feed-forward network structure, named TAGnet, is constructed based on a graph-regularized sparse coding algorithm. It is then trained with task-specific loss functions from end to end. We discover that connecting deep learning to sparse coding benefits not only the model performance, but also its initialization and interpretation. Moreover, by introducing auxiliary clustering tasks to the intermediate feature hierarchy, we formulate DTAGnet and obtain a further performance boost. Extensive experiments demonstrate that the proposed model gains remarkable margins over several state-of-the-art methods.", "published": "2015-09-01T06:12:29Z", "authors": ["Zhangyang Wang", "Shiyu Chang", "Jiayu Zhou", "Meng Wang", "Thomas S. Huang"], "categories": ["cs.LG", "cs.CV", "stat.ML"], "doi": "", "_query": "real estate valuation deep learning", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2107.04497v1", "title": "Batch Inverse-Variance Weighting: Deep Heteroscedastic Regression", "abstract": "Heteroscedastic regression is the task of supervised learning where each label is subject to noise from a different distribution. This noise can be caused by the labelling process, and impacts negatively the performance of the learning algorithm as it violates the i.i.d. assumptions. In many situations however, the labelling process is able to estimate the variance of such distribution for each label, which can be used as an additional information to mitigate this impact. We adapt an inverse-variance weighted mean square error, based on the Gauss-Markov theorem, for parameter optimization on neural networks. We introduce Batch Inverse-Variance, a loss function which is robust to near-ground truth samples, and allows to control the effective learning rate. Our experimental results show that BIV improves significantly the performance of the networks on two noisy datasets, compared to L2 loss, inverse-variance weighting, as well as a filtering-based baseline.", "published": "2021-07-09T15:39:31Z", "authors": ["Vincent Mai", "Waleed Khamies", "Liam Paull"], "categories": ["cs.LG", "cs.AI", "stat.ML"], "doi": "", "_query": "real estate valuation deep learning", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2606.26497v2", "title": "Learning Probabilistic Filters with Strictly Proper Scoring Rules", "abstract": "Bayesian filtering of partially and noisily observed dynamical systems seeks to infer the evolving conditional distribution of the state of a dynamical system given observations, in an online fashion. This Bayesian filtering distribution is rarely available as a supervised learning target. However, one can often use the forecast model to generate synthetic trajectories, with corresponding synthetic observations. We introduce the proper scoring ensemble filter (PSEF), an ensemble data assimilation method trained using only synthetic trajectories. The analysis step is represented as a permutation-equivariant, transformer-based map. Training is based on strictly proper scoring rules---with the energy score used in our implementation---so that probabilistic accuracy is rewarded over the whole probability distribution. Under a realizability assumption, the population mean-field objective is minimized by the true Bayesian filtering distribution. Our methodology allows the same learned parameters to be shared between different ensemble sizes, subject to an ensemble-dependent fine-tuning. Numerical experiments show that the learned filter accurately approximates challenging filtering distributions, including highly non-Gaussian and multi-modal posteriors, and achieves stronger performance in data assimilation tasks than classical methods or learning-based methods with mean-squared-error objectives.", "published": "2026-06-25T01:04:21Z", "authors": ["Eviatar Bach", "Ricardo Baptista", "Jochen Bröcker", "Bohan Chen", "Andrew Stuart"], "categories": ["cs.LG", "math.DS", "stat.ML"], "doi": "", "_query": "real estate valuation deep learning", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/1908.01244v1", "title": "Real-time Deep Learning at the Edge for Scalable Reliability Modeling of Si-MOSFET Power Electronics Converters", "abstract": "With the significant growth of advanced high-frequency power converters, on-line monitoring and active reliability assessment of power electronic devices are extremely crucial. This article presents a transformative approach, named Deep Learning Reliability Awareness of Converters at the Edge (Deep RACE), for real-time reliability modeling and prediction of high-frequency MOSFET power electronic converters. Deep RACE offers a holistic solution which comprises algorithm advances, and full system integration (from the cloud down to the edge node) to create a near real-time reliability awareness. On the algorithm side, this paper proposes a deep learning algorithmic solution based on stacked LSTM for collective reliability training and inference across collective MOSFET converters based on device resistance changes. Deep RACE also proposes an integrative edge-to-cloud solution to offer a scalable decentralized devices-specific reliability monitoring, awareness, and modeling. The MOSFET convertors are IoT devices which have been empowered with edge real-time deep learning processing capabilities. The proposed Deep RACE solution has been prototyped and implemented through learning from MOSFET data set provided by NASA. Our experimental results show an average miss prediction of $8.9\\%$ over five different devices which is a much higher accuracy compared to well-known classical approaches (Kalman Filter, and Particle Filter). Deep RACE only requires $26ms$ processing time and $1.87W$ computing power on Edge IoT device.", "published": "2019-08-03T22:52:03Z", "authors": ["Mohammadreza Baharani", "Mehrdad Biglarbegian", "Babak Parkhideh", "Hamed Tabkhi"], "categories": ["cs.LG", "eess.SP"], "doi": "10.1109/JIOT.2019.2896174", "_query": "real estate valuation deep learning", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/1805.07123v1", "title": "Tree Edit Distance Learning via Adaptive Symbol Embeddings: Supplementary Materials and Results", "abstract": "Metric learning has the aim to improve classification accuracy by learning a distance measure which brings data points from the same class closer together and pushes data points from different classes further apart. Recent research has demonstrated that metric learning approaches can also be applied to trees, such as molecular structures, abstract syntax trees of computer programs, or syntax trees of natural language, by learning the cost function of an edit distance, i.e. the costs of replacing, deleting, or inserting nodes in a tree. However, learning such costs directly may yield an edit distance which violates metric axioms, is challenging to interpret, and may not generalize well. In this contribution, we propose a novel metric learning approach for trees which learns an edit distance indirectly by embedding the tree nodes as vectors, such that the Euclidean distance between those vectors supports class discrimination. We learn such embeddings by reducing the distance to prototypical trees from the same class and increasing the distance to prototypical trees from different classes. In our experiments, we show that our proposed metric learning approach improves upon the state-of-the-art in metric learning for trees on six benchmark data sets, ranging from computer science over biomedical data to a natural-language processing data set containing over 300,000 nodes.", "published": "2018-05-18T10:09:41Z", "authors": ["Benjamin Paaßen"], "categories": ["cs.LG", "stat.ML"], "doi": "", "_query": "real estate valuation deep learning", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2402.00592v4", "title": "Partial-Label Learning with a Reject Option", "abstract": "In real-world applications, one often encounters ambiguously labeled data, where different annotators assign conflicting class labels. Partial-label learning allows training classifiers in this weakly supervised setting, where state-of-the-art methods already show good predictive performance. However, even the best algorithms give incorrect predictions, which can have severe consequences when they impact actions or decisions. We propose a novel risk-consistent nearest-neighbor-based partial-label learning algorithm with a reject option, that is, the algorithm can reject unsure predictions. Extensive experiments on artificial and real-world datasets show that our method provides the best trade-off between the number and accuracy of non-rejected predictions when compared to our competitors, which use confidence thresholds for rejecting unsure predictions. When evaluated without the reject option, our nearest-neighbor-based approach also achieves competitive prediction performance.", "published": "2024-02-01T13:41:44Z", "authors": ["Tobias Fuchs", "Florian Kalinke", "Klemens Böhm"], "categories": ["cs.LG", "stat.ML"], "doi": "", "_query": "real estate valuation deep learning", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2308.06821v1", "title": "Optimizing Brain Tumor Classification: A Comprehensive Study on Transfer Learning and Imbalance Handling in Deep Learning Models", "abstract": "Deep learning has emerged as a prominent field in recent literature, showcasing the introduction of models that utilize transfer learning to achieve remarkable accuracies in the classification of brain tumor MRI images. However, the majority of these proposals primarily focus on balanced datasets, neglecting the inherent data imbalance present in real-world scenarios. Consequently, there is a pressing need for approaches that not only address the data imbalance but also prioritize precise classification of brain cancer. In this work, we present a novel deep learning-based approach, called Transfer Learning-CNN, for brain tumor classification using MRI data. The proposed model leverages the predictive capabilities of existing publicly available models by utilizing their pre-trained weights and transferring those weights to the CNN. By leveraging a publicly available Brain MRI dataset, the experiment evaluated various transfer learning models for classifying different tumor types, including meningioma, glioma, and pituitary tumors. We investigate the impact of different loss functions, including focal loss, and oversampling methods, such as SMOTE and ADASYN, in addressing the data imbalance issue. Notably, the proposed strategy, which combines VGG-16 and CNN, achieved an impressive accuracy rate of 96%, surpassing alternative approaches significantly.", "published": "2023-08-13T17:30:32Z", "authors": ["Raza Imam", "Mohammed Talha Alam"], "categories": ["cs.CV"], "doi": "", "_query": "real estate valuation deep learning", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2404.00505v2", "title": "Transfer Learning with Reconstruction Loss", "abstract": "In most applications of utilizing neural networks for mathematical optimization, a dedicated model is trained for each specific optimization objective. However, in many scenarios, several distinct yet correlated objectives or tasks often need to be optimized on the same set of problem inputs. Instead of independently training a different neural network for each problem separately, it would be more efficient to exploit the correlations between these objectives and to train multiple neural network models with shared model parameters and feature representations. To achieve this, this paper first establishes the concept of common information: the shared knowledge required for solving the correlated tasks, then proposes a novel approach for model training by adding into the model an additional reconstruction stage associated with a new reconstruction loss. This loss is for reconstructing the common information starting from a selected hidden layer in the model. The proposed approach encourages the learned features to be general and transferable, and therefore can be readily used for efficient transfer learning. For numerical simulations, three applications are studied: transfer learning on classifying MNIST handwritten digits, the device-to-device wireless network power allocation, and the multiple-input-single-output network downlink beamforming and localization. Simulation results suggest that the proposed approach is highly efficient in data and model complexity, is resilient to over-fitting, and has competitive performances.", "published": "2024-03-31T00:22:36Z", "authors": ["Wei Cui", "Wei Yu"], "categories": ["cs.LG", "cs.AI", "cs.NI", "stat.ML"], "doi": "10.1109/TMLCN.2024.3384329", "_query": "real estate valuation deep learning", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2002.03425v3", "title": "Cyclic Boosting -- an explainable supervised machine learning algorithm", "abstract": "Supervised machine learning algorithms have seen spectacular advances and surpassed human level performance in a wide range of specific applications. However, using complex ensemble or deep learning algorithms typically results in black box models, where the path leading to individual predictions cannot be followed in detail. In order to address this issue, we propose the novel \"Cyclic Boosting\" machine learning algorithm, which allows to efficiently perform accurate regression and classification tasks while at the same time allowing a detailed understanding of how each individual prediction was made.", "published": "2020-02-09T18:52:42Z", "authors": ["Felix Wick", "Ulrich Kerzel", "Michael Feindt"], "categories": ["cs.LG", "stat.ML"], "doi": "10.1109/ICMLA.2019.00067", "_query": "real estate valuation deep learning", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2607.25258v1", "title": "Robust Hedging Valuation Adjustment for Deep Hedging Policies under Market Frictions", "abstract": "Hedging a derivative position under transaction costs and market frictions requires a trading rule that adapts to changing conditions. Deep hedging trains a neural policy for this task but policy training does not determine whether a trading desk can afford to run the policy. We apply robust hedging valuation adjustment (HVA) as a post-training valuation-adjustment layer that evaluates tracking-loss CVaR together with explicit funding and margin add-ons. The funding and margin add-ons share the same KL uncertainty set as HVA. For each policy, a single common-stress tilt computes HVA, funding and margin jointly and a trading desk can get one internally consistent reserve instead of the three separately. We compare classical hedge policies with learned hedge specifications across three market environments with different liquidity. No single specification dominates in every market. Under the strict tracking-risk budget, gamma-wide classical bands are selected in High and Middle Liquidity while sparse learned execution is selected in Low Liquidity. At looser validation budgets wider classical bands are generally selected.", "published": "2026-07-28T03:59:19Z", "authors": ["Takayuki Sakuma"], "categories": ["q-fin.RM", "q-fin.CP", "q-fin.PR"], "doi": "", "_query": "real estate valuation deep learning", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/1808.05355v1", "title": "Conceptual Domain Adaptation Using Deep Learning", "abstract": "Deep learning has recently been shown to be instrumental in the problem of domain adaptation, where the goal is to learn a model on a target domain using a similar --but not identical-- source domain. The rationale for coupling both techniques is the possibility of extracting common concepts across domains. Considering (strictly) local representations, traditional deep learning assumes common concepts must be captured in the same hidden units. We contend that jointly training a model with source and target data using a single deep network is prone to failure when there is inherently lower-level representational discrepancy between the two domains; such discrepancy leads to a misalignment of corresponding concepts in separate hidden units. We introduce a search framework to correctly align high-level representations when training deep networks; such framework leads to the notion of conceptual --as opposed to representational-- domain adaptation.", "published": "2018-08-16T06:25:24Z", "authors": ["Behrang Mehrparvar", "Ricardo Vilalta"], "categories": ["cs.LG", "stat.ML"], "doi": "", "_query": "real estate valuation deep learning", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2106.09563v5", "title": "On Anytime Learning at Macroscale", "abstract": "In many practical applications of machine learning data arrives sequentially over time in large chunks. Practitioners have then to decide how to allocate their computational budget in order to obtain the best performance at any point in time. Online learning theory for convex optimization suggests that the best strategy is to use data as soon as it arrives. However, this might not be the best strategy when using deep non-linear networks, particularly when these perform multiple passes over each chunk of data rendering the overall distribution non i.i.d.. In this paper, we formalize this learning setting in the simplest scenario in which each data chunk is drawn from the same underlying distribution, and make a first attempt at empirically answering the following questions: How long should the learner wait before training on the newly arrived chunks? What architecture should the learner adopt? Should the learner increase capacity over time as more data is observed? We probe this learning setting using convolutional neural networks trained on classic computer vision benchmarks as well as a large transformer model trained on a large-scale language modeling task. Code is available at \\url{www.github.com/facebookresearch/ALMA}.", "published": "2021-06-17T14:45:22Z", "authors": ["Lucas Caccia", "Jing Xu", "Myle Ott", "Marc'Aurelio Ranzato", "Ludovic Denoyer"], "categories": ["cs.LG", "cs.CV"], "doi": "", "_query": "real estate valuation deep learning", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2406.17813v2", "title": "Unsupervised Concept Drift Detection from Deep Learning Representations in Real-time", "abstract": "Concept drift is the phenomenon in which the underlying data distributions and statistical properties of a target domain change over time, leading to a degradation in model performance. Consequently, production models require continuous drift detection monitoring. Most drift detection methods to date are supervised, relying on ground-truth labels. However, they are inapplicable in many real-world scenarios, as true labels are often unavailable. Although recent efforts have proposed unsupervised drift detectors, many lack the accuracy required for reliable detection or are too computationally intensive for real-time use in high-dimensional, large-scale production environments. Moreover, they often fail to characterize or explain drift effectively. To address these limitations, we propose \\textsc{DriftLens}, an unsupervised framework for real-time concept drift detection and characterization. Designed for deep learning classifiers handling unstructured data, \\textsc{DriftLens} leverages distribution distances in deep learning representations to enable efficient and accurate detection. Additionally, it characterizes drift by analyzing and explaining its impact on each label. Our evaluation across classifiers and data-types demonstrates that \\textsc{DriftLens} (i) outperforms previous methods in detecting drift in 15/17 use cases; (ii) runs at least 5 times faster; (iii) produces drift curves that align closely with actual drift (correlation $\\geq\\!0.85$); (iv) effectively identifies representative drift samples as explanations.", "published": "2024-06-24T23:41:46Z", "authors": ["Salvatore Greco", "Bartolomeo Vacchetti", "Daniele Apiletti", "Tania Cerquitelli"], "categories": ["cs.LG", "cs.AI"], "doi": "10.1109/TKDE.2025.3593123", "_query": "real estate valuation deep learning", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/1905.11017v1", "title": "Learning to Optimize with Unsupervised Learning: Training Deep Neural Networks for URLLC", "abstract": "Learning the optimized solution as a function of environmental parameters is effective in solving numerical optimization in real time for time-sensitive applications. Existing works of learning to optimize train deep neural networks (DNN) with labels, and the learnt solution are inaccurate, which cannot be employed to ensure the stringent quality of service. In this paper, we propose a framework to learn the latent function with unsupervised deep learning, where the property that the optimal solution should satisfy is used as the \"supervision signal\" implicitly. The framework is applicable to both functional and variable optimization problems with constraints. We take a variable optimization problem in ultra-reliable and low-latency communications as an example, which demonstrates that the ultra-high reliability can be supported by the DNN without supervision labels.", "published": "2019-05-27T07:42:06Z", "authors": ["Chengjian Sun", "Chenyang Yang"], "categories": ["cs.LG", "eess.SP", "stat.ML"], "doi": "", "_query": "real estate valuation deep learning", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2005.04536v1", "title": "Accelerating Deep Neuroevolution on Distributed FPGAs for Reinforcement Learning Problems", "abstract": "Reinforcement learning augmented by the representational power of deep neural networks, has shown promising results on high-dimensional problems, such as game playing and robotic control. However, the sequential nature of these problems poses a fundamental challenge for computational efficiency. Recently, alternative approaches such as evolutionary strategies and deep neuroevolution demonstrated competitive results with faster training time on distributed CPU cores. Here, we report record training times (running at about 1 million frames per second) for Atari 2600 games using deep neuroevolution implemented on distributed FPGAs. Combined hardware implementation of the game console, image pre-processing and the neural network in an optimized pipeline, multiplied with the system level parallelism enabled the acceleration. These results are the first application demonstration on the IBM Neural Computer, which is a custom designed system that consists of 432 Xilinx FPGAs interconnected in a 3D mesh network topology. In addition to high performance, experiments also showed improvement in accuracy for all games compared to the CPU-implementation of the same algorithm.", "published": "2020-05-10T00:41:39Z", "authors": ["Alexis Asseman", "Nicolas Antoine", "Ahmet S. Ozcan"], "categories": ["cs.NE", "cs.AI", "cs.AR", "cs.DC", "cs.LG"], "doi": "10.1145/3425500", "_query": "real estate valuation deep learning", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/1611.01606v1", "title": "Learning to Play in a Day: Faster Deep Reinforcement Learning by Optimality Tightening", "abstract": "We propose a novel training algorithm for reinforcement learning which combines the strength of deep Q-learning with a constrained optimization approach to tighten optimality and encourage faster reward propagation. Our novel technique makes deep reinforcement learning more practical by drastically reducing the training time. We evaluate the performance of our approach on the 49 games of the challenging Arcade Learning Environment, and report significant improvements in both training time and accuracy.", "published": "2016-11-05T05:42:40Z", "authors": ["Frank S. He", "Yang Liu", "Alexander G. Schwing", "Jian Peng"], "categories": ["cs.LG", "stat.ML"], "doi": "", "_query": "real estate valuation deep learning", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/1712.04350v1", "title": "Predicting Yelp Star Reviews Based on Network Structure with Deep Learning", "abstract": "In this paper, we tackle the real-world problem of predicting Yelp star-review rating based on business features (such as images, descriptions), user features (average previous ratings), and, of particular interest, network properties (which businesses has a user rated before). We compare multiple models on different sets of features -- from simple linear regression on network features only to deep learning models on network and item features. In recent years, breakthroughs in deep learning have led to increased accuracy in common supervised learning tasks, such as image classification, captioning, and language understanding. However, the idea of combining deep learning with network feature and structure appears to be novel. While the problem of predicting future interactions in a network has been studied at length, these approaches have often ignored either node-specific data or global structure. We demonstrate that taking a mixed approach combining both node-level features and network information can effectively be used to predict Yelp-review star ratings. We evaluate on the Yelp dataset by splitting our data along the time dimension (as would naturally occur in the real-world) and comparing our model against others which do no take advantage of the network structure and/or deep learning.", "published": "2017-12-11T18:54:23Z", "authors": ["Luis Perez"], "categories": ["cs.LG", "stat.ML"], "doi": "", "_query": "real estate valuation deep learning", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2009.10819v1", "title": "Stock Price Prediction Using Machine Learning and LSTM-Based Deep Learning Models", "abstract": "Prediction of stock prices has been an important area of research for a long time. While supporters of the efficient market hypothesis believe that it is impossible to predict stock prices accurately, there are formal propositions demonstrating that accurate modeling and designing of appropriate variables may lead to models using which stock prices and stock price movement patterns can be very accurately predicted. In this work, we propose an approach of hybrid modeling for stock price prediction building different machine learning and deep learning-based models. For the purpose of our study, we have used NIFTY 50 index values of the National Stock Exchange (NSE) of India, during the period December 29, 2014 till July 31, 2020. We have built eight regression models using the training data that consisted of NIFTY 50 index records during December 29, 2014 till December 28, 2018. Using these regression models, we predicted the open values of NIFTY 50 for the period December 31, 2018 till July 31, 2020. We, then, augment the predictive power of our forecasting framework by building four deep learning-based regression models using long-and short-term memory (LSTM) networks with a novel approach of walk-forward validation. We exploit the power of LSTM regression models in forecasting the future NIFTY 50 open values using four different models that differ in their architecture and in the structure of their input data. Extensive results are presented on various metrics for the all the regression models. The results clearly indicate that the LSTM-based univariate model that uses one-week prior data as input for predicting the next week open value of the NIFTY 50 time series is the most accurate model.", "published": "2020-09-20T20:32:33Z", "authors": ["Sidra Mehtab", "Jaydip Sen", "Abhishek Dutta"], "categories": ["q-fin.ST", "cs.LG"], "doi": "10.1007/978-981-16-0419-5_8", "_query": "real estate valuation deep learning", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/1908.07005v1", "title": "On Regularization Properties of Artificial Datasets for Deep Learning", "abstract": "The paper discusses regularization properties of artificial data for deep learning. Artificial datasets allow to train neural networks in the case of a real data shortage. It is demonstrated that the artificial data generation process, described as injecting noise to high-level features, bears several similarities to existing regularization methods for deep neural networks. One can treat this property of artificial data as a kind of \"deep\" regularization. It is thus possible to regularize hidden layers of the network by generating the training data in a certain way.", "published": "2019-08-19T18:09:07Z", "authors": ["Karol Antczak"], "categories": ["cs.LG", "stat.ML"], "doi": "", "_query": "real estate valuation deep learning", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/cond-mat/0001253v1", "title": "Learning short-option valuation in the presence of rare events", "abstract": "We present a neural-network valuation of financial derivatives in the case of fat-tailed underlying asset returns. A two-layer perceptron is trained on simulated prices taking into account the well-known effect of volatility smile. The prices of the underlier are generated using fractional calculus algorithms, and option prices are computed by means of the Bouchaud-Potters formula. This learning scheme is tested on market data; the results show a very good agreement between perceptron option prices and real market ones.", "published": "2000-01-18T23:51:58Z", "authors": ["M. Raberto", "G. Cuniberti", "E. Scalas", "M. Riani", "F. Mainardi", "G. Servizi"], "categories": ["cond-mat.stat-mech", "cond-mat.dis-nn", "q-fin.PR"], "doi": "10.1142/S0219024900000590", "_query": "real estate valuation deep learning", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/1902.07958v1", "title": "Deep Learning Multidimensional Projections", "abstract": "Dimensionality reduction methods, also known as projections, are frequently used for exploring multidimensional data in machine learning, data science, and information visualization. Among these, t-SNE and its variants have become very popular for their ability to visually separate distinct data clusters. However, such methods are computationally expensive for large datasets, suffer from stability problems, and cannot directly handle out-of-sample data. We propose a learning approach to construct such projections. We train a deep neural network based on a collection of samples from a given data universe, and their corresponding projections, and next use the network to infer projections of data from the same, or similar, universes. Our approach generates projections with similar characteristics as the learned ones, is computationally two to three orders of magnitude faster than SNE-class methods, has no complex-to-set user parameters, handles out-of-sample data in a stable manner, and can be used to learn any projection technique. We demonstrate our proposal on several real-world high dimensional datasets from machine learning.", "published": "2019-02-21T10:50:44Z", "authors": ["Mateus Espadoto", "Nina S. T. Hirata", "Alexandru C. Telea"], "categories": ["cs.LG", "stat.ML"], "doi": "", "_query": "real estate valuation deep learning", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2009.04549v1", "title": "Multimodal Deep Learning for Flaw Detection in Software Programs", "abstract": "We explore the use of multiple deep learning models for detecting flaws in software programs. Current, standard approaches for flaw detection rely on a single representation of a software program (e.g., source code or a program binary). We illustrate that, by using techniques from multimodal deep learning, we can simultaneously leverage multiple representations of software programs to improve flaw detection over single representation analyses. Specifically, we adapt three deep learning models from the multimodal learning literature for use in flaw detection and demonstrate how these models outperform traditional deep learning models. We present results on detecting software flaws using the Juliet Test Suite and Linux Kernel.", "published": "2020-09-09T20:15:11Z", "authors": ["Scott Heidbrink", "Kathryn N. Rodhouse", "Daniel M. Dunlavy"], "categories": ["cs.LG", "cs.AI", "cs.CR", "cs.SE", "stat.ML"], "doi": "", "_query": "real estate valuation deep learning", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2203.10681v3", "title": "Online Continual Learning for Embedded Devices", "abstract": "Real-time on-device continual learning is needed for new applications such as home robots, user personalization on smartphones, and augmented/virtual reality headsets. However, this setting poses unique challenges: embedded devices have limited memory and compute capacity and conventional machine learning models suffer from catastrophic forgetting when updated on non-stationary data streams. While several online continual learning models have been developed, their effectiveness for embedded applications has not been rigorously studied. In this paper, we first identify criteria that online continual learners must meet to effectively perform real-time, on-device learning. We then study the efficacy of several online continual learning methods when used with mobile neural networks. We measure their performance, memory usage, compute requirements, and ability to generalize to out-of-domain inputs.", "published": "2022-03-21T00:23:09Z", "authors": ["Tyler L. Hayes", "Christopher Kanan"], "categories": ["cs.LG", "cs.AI"], "doi": "", "_query": "real estate valuation deep learning", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/1509.03354v3", "title": "Valuation Semirings", "abstract": "The main scope of this paper is to introduce valuation semirings in general and discrete valuation semirings in particular. In order to do that, first we define valuation maps and investigate them. Then we define valuation semirings with the help of valuation maps and prove that a multiplicatively cancellative semiring is a valuation semiring if and only if its ideals are totally ordered by inclusion. We also prove that if the unique maximal ideal of a valuation semiring is subtractive, then it is integrally closed. We end this paper by introducing discrete valuation semirings and show that a semiring is a discrete valuation semiring if and only if it is a multiplicatively cancellative principal ideal semiring possessing a nonzero unique maximal ideal. We also prove that a discrete valuation semiring is a Gaussian semiring if and only if its unique maximal ideal is subtractive.", "published": "2015-09-10T22:32:26Z", "authors": ["Peyman Nasehpour"], "categories": ["math.AC", "math.GR", "math.RA"], "doi": "10.1142/S0219498818500731", "_query": "real estate valuation deep learning", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/1805.12176v1", "title": "Deep Segment Hash Learning for Music Generation", "abstract": "Music generation research has grown in popularity over the past decade, thanks to the deep learning revolution that has redefined the landscape of artificial intelligence. In this paper, we propose a novel approach to music generation inspired by musical segment concatenation methods and hash learning algorithms. Given a segment of music, we use a deep recurrent neural network and ranking-based hash learning to assign a forward hash code to the segment to retrieve candidate segments for continuation with matching backward hash codes. The proposed method is thus called Deep Segment Hash Learning (DSHL). To the best of our knowledge, DSHL is the first end-to-end segment hash learning method for music generation, and the first to use pair-wise training with segments of music. We demonstrate that this method is capable of generating music which is both original and enjoyable, and that DSHL offers a promising new direction for music generation research.", "published": "2018-05-30T18:49:35Z", "authors": ["Kevin Joslyn", "Naifan Zhuang", "Kien A. Hua"], "categories": ["cs.LG", "cs.MM", "stat.ML"], "doi": "", "_query": "real estate valuation deep learning", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2607.06012v1", "title": "Structured Data Extraction from Real Estate Documents using Clustering, Classification, and Large Language Models", "abstract": "Real estate property listings expose structured metadata through the API. Still, the richest property-level information (i.e., legal status, structural condition, utility supplies, heating systems) sits in attached questionnaire documents that no automated system currently processes at scale. These documents are heterogeneous. Some are digitally generated with selectable text, others are scanned physical forms. There are even more complex layouts that contain checkbox annotations that defeat conventional text extraction. In this paper, we present an end-to-end pipeline for acquiring, classifying, and extracting structured data from selectable text documents. The pipeline was applied to 3965 questionnaire documents collected from a live property platform via reverse-engineered REST APIs. First, we classified each document into one of three structural categories (text_only, scanned, and special_char), then extracted 35 predefined property attributes from eligible documents using DeepSeek R1 as the Large Language Model, prompted to return a structured JSON object. All 2781 submitted documents were processed successfully, producing a final dataset of 2766 unique property records. Downstream validation confirmed the data quality. Cosine similarity matching achieves a Jaccard consistency score of 0.82, and K-Means clustering produces interpretable market segments with a silhouette score of 0.2088. Results show that the proposed extraction from each property document is both feasible and reliable at this scale.", "published": "2026-07-07T08:54:11Z", "authors": ["Muhammad Assad Shehbaz", "Carlos Francisco Moreno-García"], "categories": ["cs.CV"], "doi": "", "_query": "real estate valuation deep learning", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2505.19809v3", "title": "Representation Learning for Equivariant Inference with Guarantees", "abstract": "In many real-world applications of regression, conditional probability estimation, and uncertainty quantification, exploiting symmetries rooted in physics or geometry can dramatically improve generalization and sample efficiency. While geometric deep learning has made empirical advances by incorporating symmetry and geometry priors, less attention has been given to statistical learning guarantees. In this paper, we introduce an equivariant representation learning framework that simultaneously addresses regression, conditional probability estimation, and uncertainty quantification while providing first-of-its-kind non-asymptotic statistical learning guarantees. Grounded in operator and group representation theory, our framework approximates the spectral decomposition of the conditional expectation operator, building representations that are both equivariant and disentangled along independent symmetry quotient groups. Empirical evaluations on synthetic datasets and real-world robotics applications confirm the potential of our approach, matching or outperforming existing equivariant baselines in regression while providing well-calibrated uncertainty estimates.", "published": "2025-05-26T10:47:23Z", "authors": ["Daniel Ordoñez-Apraez", "Vladimir Kostić", "Alek Fröhlich", "Vivien Brandt", "Karim Lounici", "Massimiliano Pontil"], "categories": ["cs.LG", "cs.AI", "cs.RO"], "doi": "", "_query": "real estate valuation deep learning", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/1909.10225v1", "title": "WiCV 2019: The Sixth Women In Computer Vision Workshop", "abstract": "In this paper we present the Women in Computer Vision Workshop - WiCV 2019, organized in conjunction with CVPR 2019. This event is meant for increasing the visibility and inclusion of women researchers in the computer vision field. Computer vision and machine learning have made incredible progress over the past years, but the number of female researchers is still low both in academia and in industry. WiCV is organized especially for the following reason: to raise visibility of female researchers, to increase collaborations between them, and to provide mentorship to female junior researchers in the field. In this paper, we present a report of trends over the past years, along with a summary of statistics regarding presenters, attendees, and sponsorship for the current workshop.", "published": "2019-09-23T08:52:33Z", "authors": ["Irene Amerini", "Elena Balashova", "Sayna Ebrahimi", "Kathryn Leonard", "Arsha Nagrani", "Amaia Salvador"], "categories": ["cs.CV"], "doi": "", "_query": "property valuation computer vision", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2205.04675v2", "title": "Spatial Monitoring and Insect Behavioural Analysis Using Computer Vision for Precision Pollination", "abstract": "Insects are the most important global pollinator of crops and play a key role in maintaining the sustainability of natural ecosystems. Insect pollination monitoring and management are therefore essential for improving crop production and food security. Computer vision facilitated pollinator monitoring can intensify data collection over what is feasible using manual approaches. The new data it generates may provide a detailed understanding of insect distributions and facilitate fine-grained analysis sufficient to predict their pollination efficacy and underpin precision pollination. Current computer vision facilitated insect tracking in complex outdoor environments is restricted in spatial coverage and often constrained to a single insect species. This limits its relevance to agriculture. Therefore, in this article we introduce a novel system to facilitate markerless data capture for insect counting, insect motion tracking, behaviour analysis and pollination prediction across large agricultural areas. Our system is comprised of edge computing multi-point video recording, offline automated multispecies insect counting, tracking and behavioural analysis. We implement and test our system on a commercial berry farm to demonstrate its capabilities. Our system successfully tracked four insect varieties, at nine monitoring stations within polytunnels, obtaining an F-score above 0.8 for each variety. The system enabled calculation of key metrics to assess the relative pollination impact of each insect variety. With this technological advancement, detailed, ongoing data collection for precision pollination becomes achievable. This is important to inform growers and apiarists managing crop pollination, as it allows data-driven decisions to be made to improve food production and food security.", "published": "2022-05-10T05:11:28Z", "authors": ["Malika Nisal Ratnayake", "Don Chathurika Amarathunga", "Asaduz Zaman", "Adrian G. Dyer", "Alan Dorin"], "categories": ["cs.CV", "q-bio.QM"], "doi": "10.1007/s11263-022-01715-4", "_query": "property valuation computer vision", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2010.01177v4", "title": "Global Adaptive Filtering Layer for Computer Vision", "abstract": "We devise a universal adaptive neural layer to \"learn\" optimal frequency filter for each image together with the weights of the base neural network that performs some computer vision task. The proposed approach takes the source image in the spatial domain, automatically selects the best frequencies from the frequency domain, and transmits the inverse-transform image to the main neural network. Remarkably, such a simple add-on layer dramatically improves the performance of the main network regardless of its design. We observe that the light networks gain a noticeable boost in the performance metrics; whereas, the training of the heavy ones converges faster when our adaptive layer is allowed to \"learn\" alongside the main architecture. We validate the idea in four classical computer vision tasks: classification, segmentation, denoising, and erasing, considering popular natural and medical data benchmarks.", "published": "2020-10-02T19:43:49Z", "authors": ["Viktor Shipitsin", "Iaroslav Bespalov", "Dmitry V. Dylov"], "categories": ["eess.IV", "cs.CV"], "doi": "10.1016/j.cviu.2022.103519", "_query": "property valuation computer vision", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/1211.0398v1", "title": "Extending valuations to formal completions", "abstract": "This paper is an extended version of the talk given by Miguel Olalla at the International Conference on Valuation Theory in El Escorial in July 2011. Its purpose is to provide an introduction to our joint paper \"Extending a valuation centered in a local domain to the formal completion\" (Proc. London Math. Soc. (2012) 105 (3), 571-621) without grinding through all of its technical details.", "published": "2012-11-02T09:09:38Z", "authors": ["F. J. Herrera Govantes", "M. A. Olalla Acosta", "M. Spivakovsky", "B. Teissier"], "categories": ["math.AG", "math.RA"], "doi": "10.4171/149-1/12", "_query": "property valuation computer vision", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2511.20644v2", "title": "Vision-Language Memory for Spatial Reasoning", "abstract": "Spatial reasoning is a critical capability for intelligent robots, yet current vision-language models (VLMs) still fall short of human-level performance in video-based spatial reasoning. This gap mainly stems from two challenges: a semantic-geometric misalignment that prevents consistent 3D understanding, and the absence of persistent memory to retain 3D representation and understanding across frames. To address these limitations, we present VLM$^2$, a Vision-Language Model with persistent Memory for spatial reasoning with a view-consistent, 3D-aware representation purely from 2D videos. Specifically, we incorporate a dual-memory module consisting of a working memory that operates as a sliding window to focus on immediate context, and an episodic memory that consolidates and stores critical information across frames. This design enables bounded and efficient spatial reasoning under a fixed computational cost. Extensive experiments on multiple benchmarks show that VLM$^2$ achieves state-of-the-art performance among video-based models, significantly advancing the frontier of visual-spatial intelligence.", "published": "2025-11-25T18:59:02Z", "authors": ["Zuntao Liu", "Yi Du", "Taimeng Fu", "Shaoshu Su", "Cherie Ho", "Chen Wang"], "categories": ["cs.CV"], "doi": "10.1007/978-3-032-37356-4_17", "_query": "property valuation computer vision", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2107.14072v2", "title": "What Does TERRA-REF's High Resolution, Multi Sensor Plant Sensing Public Domain Data Offer the Computer Vision Community?", "abstract": "A core objective of the TERRA-REF project was to generate an open-access reference dataset for the evaluation of sensing technologies to study plants under field conditions. The TERRA-REF program deployed a suite of high-resolution, cutting edge technology sensors on a gantry system with the aim of scanning 1 hectare (10$^4$) at around 1 mm$^2$ spatial resolution multiple times per week. The system contains co-located sensors including a stereo-pair RGB camera, a thermal imager, a laser scanner to capture 3D structure, and two hyperspectral cameras covering wavelengths of 300-2500nm. This sensor data is provided alongside over sixty types of traditional plant phenotype measurements that can be used to train new machine learning models. Associated weather and environmental measurements, information about agronomic management and experimental design, and the genomic sequences of hundreds of plant varieties have been collected and are available alongside the sensor and plant phenotype data. Over the course of four years and ten growing seasons, the TERRA-REF system generated over 1 PB of sensor data and almost 45 million files. The subset that has been released to the public domain accounts for two seasons and about half of the total data volume. This provides an unprecedented opportunity for investigations far beyond the core biological scope of the project. The focus of this paper is to provide the Computer Vision and Machine Learning communities an overview of the available data and some potential applications of this one of a kind data.", "published": "2021-07-29T15:01:29Z", "authors": ["David LeBauer", "Max Burnette", "Noah Fahlgren", "Rob Kooper", "Kenton McHenry", "Abby Stylianou"], "categories": ["cs.CV", "cs.LG"], "doi": "", "_query": "property valuation computer vision", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/1402.0859v3", "title": "The Informed Sampler: A Discriminative Approach to Bayesian Inference in Generative Computer Vision Models", "abstract": "Computer vision is hard because of a large variability in lighting, shape, and texture; in addition the image signal is non-additive due to occlusion. Generative models promised to account for this variability by accurately modelling the image formation process as a function of latent variables with prior beliefs. Bayesian posterior inference could then, in principle, explain the observation. While intuitively appealing, generative models for computer vision have largely failed to deliver on that promise due to the difficulty of posterior inference. As a result the community has favoured efficient discriminative approaches. We still believe in the usefulness of generative models in computer vision, but argue that we need to leverage existing discriminative or even heuristic computer vision methods. We implement this idea in a principled way with an \"informed sampler\" and in careful experiments demonstrate it on challenging generative models which contain renderer programs as their components. We concentrate on the problem of inverting an existing graphics rendering engine, an approach that can be understood as \"Inverse Graphics\". The informed sampler, using simple discriminative proposals based on existing computer vision technology, achieves significant improvements of inference.", "published": "2014-02-04T20:52:26Z", "authors": ["Varun Jampani", "Sebastian Nowozin", "Matthew Loper", "Peter V. Gehler"], "categories": ["cs.CV", "cs.LG", "stat.ML"], "doi": "10.1016/j.cviu.2015.03.002", "_query": "property valuation computer vision", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/1806.07996v1", "title": "Novel Convolution Kernels for Computer Vision and Shape Analysis based on Electromagnetism", "abstract": "Computer vision is a growing field with a lot of new applications in automation and robotics, since it allows the analysis of images and shapes for the generation of numerical or analytical information. One of the most used method of information extraction is image filtering through convolution kernels, with each kernel specialized for specific applications. The objective of this paper is to present a novel convolution kernels, based on principles of electromagnetic potentials and fields, for a general use in computer vision and to demonstrate its usage for shape and stroke analysis. Such filtering possesses unique geometrical properties that can be interpreted using well understood physics theorems. Therefore, this paper focuses on the development of the electromagnetic kernels and on their application on images for shape and stroke analysis. It also presents several interesting features of electromagnetic kernels, such as resolution, size and orientation independence, robustness to noise and deformation, long distance stroke interaction and ability to work with 3D images", "published": "2018-06-20T21:31:00Z", "authors": ["Dominique Beaini", "Sofiane Achiche", "Yann-Seing Law-Kam Cio", "Maxime Raison"], "categories": ["cs.CV"], "doi": "", "_query": "property valuation computer vision", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/1002.2191v1", "title": "Vision Based Game Development Using Human Computer Interaction", "abstract": "A Human Computer Interface (HCI) System for playing games is designed here for more natural communication with the machines. The system presented here is a vision-based system for detection of long voluntary eye blinks and interpretation of blink patterns for communication between man and machine. This system replaces the mouse with the human face as a new way to interact with the computer. Facial features (nose tip and eyes) are detected and tracked in realtime to use their actions as mouse events. The coordinates and movement of the nose tip in the live video feed are translated to become the coordinates and movement of the mouse pointer on the application. The left or right eye blinks fire left or right mouse click events. The system works with inexpensive USB cameras and runs at a frame rate of 30 frames per second.", "published": "2010-02-10T19:46:07Z", "authors": ["S. Sumathi", "S. K. Srivatsa", "M. Uma Maheswari"], "categories": ["cs.HC", "cs.CV", "cs.MM"], "doi": "", "_query": "property valuation computer vision", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2604.00086v2", "title": "Hierarchical Pre-Training of Vision Encoders with Large Language Model", "abstract": "The field of computer vision has experienced significant advancements through scalable vision encoders and multimodal pre-training frameworks. However, existing approaches often treat vision encoders and large language models (LLMs) as independent modules, limiting the integration of hierarchical visual features. In this work, we propose HIVE (Hierarchical Pre-Training of Vision Encoders), a novel framework that enhances vision-language alignment by introducing hierarchical cross-attention between the vision encoder and LLM. Unlike conventional methods that flatten image embeddings, HIVE enables structured feature fusion across multiple layers, improving gradient flow and representation learning. To optimize this interaction, we introduce a three-stage training strategy that progressively aligns the vision encoder with the LLM, ensuring stable optimization and effective multimodal fusion. Empirical evaluations demonstrate that HIVE achieves superior performance not only in image classification but also on various vision-language tasks, outperforming self-attention-based methods in benchmarks such as MME, GQA, OK-VQA, and ScienceQA. Our results highlight the benefits of hierarchical feature integration, paving the way for more efficient and expressive vision-language models.", "published": "2026-03-31T18:00:39Z", "authors": ["Eugene Lee", "Ting-Yu Chang", "Jui-Huang Tsai", "Jiajie Diao", "Chen-Yi Lee"], "categories": ["cs.CV", "cs.AI", "cs.CL", "cs.LG"], "doi": "", "_query": "property valuation computer vision", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/1611.02145v1", "title": "Crowdsourcing in Computer Vision", "abstract": "Computer vision systems require large amounts of manually annotated data to properly learn challenging visual concepts. Crowdsourcing platforms offer an inexpensive method to capture human knowledge and understanding, for a vast number of visual perception tasks. In this survey, we describe the types of annotations computer vision researchers have collected using crowdsourcing, and how they have ensured that this data is of high quality while annotation effort is minimized. We begin by discussing data collection on both classic (e.g., object recognition) and recent (e.g., visual story-telling) vision tasks. We then summarize key design decisions for creating effective data collection interfaces and workflows, and present strategies for intelligently selecting the most important data instances to annotate. Finally, we conclude with some thoughts on the future of crowdsourcing in computer vision.", "published": "2016-11-07T16:11:19Z", "authors": ["Adriana Kovashka", "Olga Russakovsky", "Li Fei-Fei", "Kristen Grauman"], "categories": ["cs.CV", "cs.HC"], "doi": "10.1561/0600000073", "_query": "property valuation computer vision", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2111.11066v1", "title": "FedCV: A Federated Learning Framework for Diverse Computer Vision Tasks", "abstract": "Federated Learning (FL) is a distributed learning paradigm that can learn a global or personalized model from decentralized datasets on edge devices. However, in the computer vision domain, model performance in FL is far behind centralized training due to the lack of exploration in diverse tasks with a unified FL framework. FL has rarely been demonstrated effectively in advanced computer vision tasks such as object detection and image segmentation. To bridge the gap and facilitate the development of FL for computer vision tasks, in this work, we propose a federated learning library and benchmarking framework, named FedCV, to evaluate FL on the three most representative computer vision tasks: image classification, image segmentation, and object detection. We provide non-I.I.D. benchmarking datasets, models, and various reference FL algorithms. Our benchmark study suggests that there are multiple challenges that deserve future exploration: centralized training tricks may not be directly applied to FL; the non-I.I.D. dataset actually downgrades the model accuracy to some degree in different tasks; improving the system efficiency of federated training is challenging given the huge number of parameters and the per-client memory cost. We believe that such a library and benchmark, along with comparable evaluation settings, is necessary to make meaningful progress in FL on computer vision tasks. FedCV is publicly available: https://github.com/FedML-AI/FedCV.", "published": "2021-11-22T09:26:08Z", "authors": ["Chaoyang He", "Alay Dilipbhai Shah", "Zhenheng Tang", "Di Fan1Adarshan Naiynar Sivashunmugam", "Keerti Bhogaraju", "Mita Shimpi", "Li Shen", "Xiaowen Chu", "Mahdi Soltanolkotabi", "Salman Avestimehr"], "categories": ["cs.CV", "cs.AI", "cs.LG"], "doi": "", "_query": "property valuation computer vision", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/1310.0319v3", "title": "Second Croatian Computer Vision Workshop (CCVW 2013)", "abstract": "Proceedings of the Second Croatian Computer Vision Workshop (CCVW 2013, http://www.fer.unizg.hr/crv/ccvw2013) held September 19, 2013, in Zagreb, Croatia. Workshop was organized by the Center of Excellence for Computer Vision of the University of Zagreb.", "published": "2013-10-01T14:26:29Z", "authors": ["Sven Lončarić", "Siniša Šegvić"], "categories": ["cs.CV"], "doi": "", "_query": "property valuation computer vision", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/1409.1484v3", "title": "The Evolution of First Person Vision Methods: A Survey", "abstract": "The emergence of new wearable technologies such as action cameras and smart-glasses has increased the interest of computer vision scientists in the First Person perspective. Nowadays, this field is attracting attention and investments of companies aiming to develop commercial devices with First Person Vision recording capabilities. Due to this interest, an increasing demand of methods to process these videos, possibly in real-time, is expected. Current approaches present a particular combinations of different image features and quantitative methods to accomplish specific objectives like object detection, activity recognition, user machine interaction and so on. This paper summarizes the evolution of the state of the art in First Person Vision video analysis between 1997 and 2014, highlighting, among others, most commonly used features, methods, challenges and opportunities within the field.", "published": "2014-09-04T16:38:43Z", "authors": ["Alejandro Betancourt", "Pietro Morerio", "Carlo S. Regazzoni", "Matthias Rauterberg"], "categories": ["cs.CV"], "doi": "10.1109/TCSVT.2015.2409731", "_query": "property valuation computer vision", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2207.00291v2", "title": "A Comparative Study of Graph Matching Algorithms in Computer Vision", "abstract": "The graph matching optimization problem is an essential component for many tasks in computer vision, such as bringing two deformable objects in correspondence. Naturally, a wide range of applicable algorithms have been proposed in the last decades. Since a common standard benchmark has not been developed, their performance claims are often hard to verify as evaluation on differing problem instances and criteria make the results incomparable. To address these shortcomings, we present a comparative study of graph matching algorithms. We create a uniform benchmark where we collect and categorize a large set of existing and publicly available computer vision graph matching problems in a common format. At the same time we collect and categorize the most popular open-source implementations of graph matching algorithms. Their performance is evaluated in a way that is in line with the best practices for comparing optimization algorithms. The study is designed to be reproducible and extensible to serve as a valuable resource in the future. Our study provides three notable insights: 1.) popular problem instances are exactly solvable in substantially less than 1 second and, therefore, are insufficient for future empirical evaluations; 2.) the most popular baseline methods are highly inferior to the best available methods; 3.) despite the NP-hardness of the problem, instances coming from vision applications are often solvable in a few seconds even for graphs with more than 500 vertices.", "published": "2022-07-01T09:37:34Z", "authors": ["Stefan Haller", "Lorenz Feineis", "Lisa Hutschenreiter", "Florian Bernard", "Carsten Rother", "Dagmar Kainmüller", "Paul Swoboda", "Bogdan Savchynskyy"], "categories": ["cs.CV", "math.OC"], "doi": "", "_query": "property valuation computer vision", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/1910.13796v1", "title": "Deep Learning vs. Traditional Computer Vision", "abstract": "Deep Learning has pushed the limits of what was possible in the domain of Digital Image Processing. However, that is not to say that the traditional computer vision techniques which had been undergoing progressive development in years prior to the rise of DL have become obsolete. This paper will analyse the benefits and drawbacks of each approach. The aim of this paper is to promote a discussion on whether knowledge of classical computer vision techniques should be maintained. The paper will also explore how the two sides of computer vision can be combined. Several recent hybrid methodologies are reviewed which have demonstrated the ability to improve computer vision performance and to tackle problems not suited to Deep Learning. For example, combining traditional computer vision techniques with Deep Learning has been popular in emerging domains such as Panoramic Vision and 3D vision for which Deep Learning models have not yet been fully optimised", "published": "2019-10-30T12:25:10Z", "authors": ["Niall O' Mahony", "Sean Campbell", "Anderson Carvalho", "Suman Harapanahalli", "Gustavo Velasco-Hernandez", "Lenka Krpalkova", "Daniel Riordan", "Joseph Walsh"], "categories": ["cs.CV", "cs.LG"], "doi": "10.1007/978-3-030-17795-9", "_query": "property valuation computer vision", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/1905.12487v1", "title": "Food for thought: Ethical considerations of user trust in computer vision", "abstract": "In computer vision research, especially when novel applications of tools are developed, ethical implications around user perceptions of trust in the underlying technology should be considered and supported. Here, we describe an example of the incorporation of such considerations within the long-term care sector for tracking resident food and fluid intake. We highlight our recent user study conducted to develop a Goldilocks quality horizontal prototype designed to support trust cues in which perceived trust in our horizontal prototype was higher than the existing system in place. We discuss the importance and need for user engagement as part of ongoing computer vision-driven technology development and describe several important factors related to trust that are relevant to developing decision-making tools.", "published": "2019-05-29T14:25:43Z", "authors": ["Kaylen J. Pfisterer", "Jennifer Boger", "Alexander Wong"], "categories": ["cs.CY", "cs.CV", "cs.HC"], "doi": "", "_query": "property valuation computer vision", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/1310.0315v1", "title": "Computer Vision Systems in Road Vehicles: A Review", "abstract": "The number of road vehicles significantly increased in recent decades. This trend accompanied a build-up of road infrastructure and development of various control systems to increase road traffic safety, road capacity and travel comfort. In traffic safety significant development has been made and today's systems more and more include cameras and computer vision methods. Cameras are used as part of the road infrastructure or in vehicles. In this paper a review on computer vision systems in vehicles from the stand point of traffic engineering is given. Safety problems of road vehicles are presented, current state of the art in-vehicle vision systems is described and open problems with future research directions are discussed.", "published": "2013-10-01T14:19:11Z", "authors": ["Kristian Kovačić", "Edouard Ivanjko", "Hrvoje Gold"], "categories": ["cs.CV"], "doi": "", "_query": "property valuation computer vision", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2406.00239v1", "title": "A Review of Pulse-Coupled Neural Network Applications in Computer Vision and Image Processing", "abstract": "Research in neural models inspired by mammal's visual cortex has led to many spiking neural networks such as pulse-coupled neural networks (PCNNs). These models are oscillating, spatio-temporal models stimulated with images to produce several time-based responses. This paper reviews PCNN's state of the art, covering its mathematical formulation, variants, and other simplifications found in the literature. We present several applications in which PCNN architectures have successfully addressed some fundamental image processing and computer vision challenges, including image segmentation, edge detection, medical imaging, image fusion, image compression, object recognition, and remote sensing. Results achieved in these applications suggest that the PCNN architecture generates useful perceptual information relevant to a wide variety of computer vision tasks.", "published": "2024-06-01T00:10:05Z", "authors": ["Nurul Rafi", "Pablo Rivas"], "categories": ["cs.CV", "cs.LG", "cs.NE"], "doi": "", "_query": "property valuation computer vision", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2604.13244v1", "title": "4th Workshop on Maritime Computer Vision (MaCVi): Challenge Overview", "abstract": "The 4th Workshop on Maritime Computer Vision (MaCVi) is organized as part of CVPR 2026. This edition features five benchmark challenges with emphasis on both predictive accuracy and embedded real-time feasibility. This report summarizes the MaCVi 2026 challenge setup, evaluation protocols, datasets, and benchmark tracks, and presents quantitative results, qualitative comparisons, and cross-challenge analyses of emerging method trends. We also include technical reports from top-performing teams to highlight practical design choices and lessons learned across the benchmark suite. Datasets, leaderboards, and challenge resources are available at https://macvi.org/workshop/cvpr26.", "published": "2026-04-14T19:21:41Z", "authors": ["Benjamin Kiefer", "Jan Lukas Augustin", "Jon Muhovič", "Mingi Jeong", "Arnold Wiliem", "Janez Pers", "Matej Kristan", "Alberto Quattrini Li", "Matija Teršek", "Josip Šarić", "Arpita Vats", "Dominik Hildebrand", "Rafia Rahim", "Mahmut Karaaslan", "Arpit Vaishya", "Steve Xie", "Ersin Kaya", "Akib Mashrur", "Tze-Hsiang Tang", "Chun-Ming Tsai", "Jun-Wei Hsieh", "Ming-Ching Chang", "Wonwoo Jo", "Doyeon Lee", "Yusi Cao", "Lingling Li", "Vinayak Nageli", "Arshad Jamal", "Gorthi Rama Krishna Sai Subrahmanyam", "Jemo Maeng", "Seongju Lee", "Kyoobin Lee", "Xu Liu", "LiCheng Jiao", "Jannik Sheikh", "Martin Weinmann", "Ivan Martinović", "Jose Mateus Raitz Persch", "Rahul Harsha Cheppally", "Mehmet E. Belviranli", "Dimitris Gahtidis", "Hyewon Chun", "Sangmun Lee", "Philipp Gorczak", "Hansol Kim", "Jeeyeon Jeon", "Borja Carrillo Perez", "Jiahui Wang", "Sangmin Park", "Andreas Michel", "Jannick Kuester", "Bettina Felten", "Wolfgang Gross", "Yuan Feng", "Justin Davis"], "categories": ["cs.CV", "cs.AI", "cs.RO"], "doi": "", "_query": "property valuation computer vision", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2104.04430v1", "title": "Ice Core Science Meets Computer Vision: Challenges and Perspectives", "abstract": "Polar ice cores play a central role in studies of the earth's climate system through natural archives. A pressing issue is the analysis of the oldest, highly thinned ice core sections, where the identification of paleoclimate signals is particularly challenging. For this, state-of-the-art imaging by laser-ablation inductively-coupled plasma mass spectrometry (LA-ICP-MS) has the potential to be revolutionary due to its combination of micron-scale 2D chemical information with visual features. However, the quantitative study of record preservation in chemical images raises new questions that call for the expertise of the computer vision community. To illustrate this new inter-disciplinary frontier, we describe a selected set of key questions. One critical task is to assess the paleoclimate significance of single line profiles along the main core axis, which we show is a scale-dependent problem for which advanced image analysis methods are critical. Another important issue is the evaluation of post-depositional layer changes, for which the chemical images provide rich information. Accordingly, the time is ripe to begin an intensified exchange among the two scientific communities of computer vision and ice core science. The collaborative building of a new framework for investigating high-resolution chemical images with automated image analysis techniques will also benefit the already wide-spread application of LA-ICP-MS chemical imaging in the geosciences.", "published": "2021-04-09T15:27:44Z", "authors": ["P. Bohleber", "M. Roman", "C. Barbante", "S. Vascon", "K. Siddiqi", "M. Pelillo"], "categories": ["cs.CV", "physics.geo-ph"], "doi": "", "_query": "property valuation computer vision", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/1802.06464v3", "title": "Robust Fitting in Computer Vision: Easy or Hard?", "abstract": "Robust model fitting plays a vital role in computer vision, and research into algorithms for robust fitting continues to be active. Arguably the most popular paradigm for robust fitting in computer vision is consensus maximisation, which strives to find the model parameters that maximise the number of inliers. Despite the significant developments in algorithms for consensus maximisation, there has been a lack of fundamental analysis of the problem in the computer vision literature. In particular, whether consensus maximisation is \"tractable\" remains a question that has not been rigorously dealt with, thus making it difficult to assess and compare the performance of proposed algorithms, relative to what is theoretically achievable. To shed light on these issues, we present several computational hardness results for consensus maximisation. Our results underline the fundamental intractability of the problem, and resolve several ambiguities existing in the literature.", "published": "2018-02-18T22:54:50Z", "authors": ["Tat-Jun Chin", "Zhipeng Cai", "Frank Neumann"], "categories": ["cs.CV", "cs.CC"], "doi": "", "_query": "property valuation computer vision", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/1708.05869v2", "title": "Sim4CV: A Photo-Realistic Simulator for Computer Vision Applications", "abstract": "We present a photo-realistic training and evaluation simulator (Sim4CV) with extensive applications across various fields of computer vision. Built on top of the Unreal Engine, the simulator integrates full featured physics based cars, unmanned aerial vehicles (UAVs), and animated human actors in diverse urban and suburban 3D environments. We demonstrate the versatility of the simulator with two case studies: autonomous UAV-based tracking of moving objects and autonomous driving using supervised learning. The simulator fully integrates both several state-of-the-art tracking algorithms with a benchmark evaluation tool and a deep neural network (DNN) architecture for training vehicles to drive autonomously. It generates synthetic photo-realistic datasets with automatic ground truth annotations to easily extend existing real-world datasets and provides extensive synthetic data variety through its ability to reconfigure synthetic worlds on the fly using an automatic world generation tool. The supplementary video can be viewed a https://youtu.be/SqAxzsQ7qUU", "published": "2017-08-19T16:09:06Z", "authors": ["Matthias Müller", "Vincent Casser", "Jean Lahoud", "Neil Smith", "Bernard Ghanem"], "categories": ["cs.CV"], "doi": "10.1007/s11263-018-1073-7", "_query": "property valuation computer vision", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2311.14762v1", "title": "The 2nd Workshop on Maritime Computer Vision (MaCVi) 2024", "abstract": "The 2nd Workshop on Maritime Computer Vision (MaCVi) 2024 addresses maritime computer vision for Unmanned Aerial Vehicles (UAV) and Unmanned Surface Vehicles (USV). Three challenges categories are considered: (i) UAV-based Maritime Object Tracking with Re-identification, (ii) USV-based Maritime Obstacle Segmentation and Detection, (iii) USV-based Maritime Boat Tracking. The USV-based Maritime Obstacle Segmentation and Detection features three sub-challenges, including a new embedded challenge addressing efficicent inference on real-world embedded devices. This report offers a comprehensive overview of the findings from the challenges. We provide both statistical and qualitative analyses, evaluating trends from over 195 submissions. All datasets, evaluation code, and the leaderboard are available to the public at https://macvi.org/workshop/macvi24.", "published": "2023-11-23T21:01:14Z", "authors": ["Benjamin Kiefer", "Lojze Žust", "Matej Kristan", "Janez Perš", "Matija Teršek", "Arnold Wiliem", "Martin Messmer", "Cheng-Yen Yang", "Hsiang-Wei Huang", "Zhongyu Jiang", "Heng-Cheng Kuo", "Jie Mei", "Jenq-Neng Hwang", "Daniel Stadler", "Lars Sommer", "Kaer Huang", "Aiguo Zheng", "Weitu Chong", "Kanokphan Lertniphonphan", "Jun Xie", "Feng Chen", "Jian Li", "Zhepeng Wang", "Luca Zedda", "Andrea Loddo", "Cecilia Di Ruberto", "Tuan-Anh Vu", "Hai Nguyen-Truong", "Tan-Sang Ha", "Quan-Dung Pham", "Sai-Kit Yeung", "Yuan Feng", "Nguyen Thanh Thien", "Lixin Tian", "Sheng-Yao Kuan", "Yuan-Hao Ho", "Angel Bueno Rodriguez", "Borja Carrillo-Perez", "Alexander Klein", "Antje Alex", "Yannik Steiniger", "Felix Sattler", "Edgardo Solano-Carrillo", "Matej Fabijanić", "Magdalena Šumunec", "Nadir Kapetanović", "Andreas Michel", "Wolfgang Gross", "Martin Weinmann"], "categories": ["cs.CV", "cs.AI"], "doi": "", "_query": "property valuation computer vision", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/1701.06859v1", "title": "Sparse models for Computer Vision", "abstract": "The representation of images in the brain is known to be sparse. That is, as neural activity is recorded in a visual area ---for instance the primary visual cortex of primates--- only a few neurons are active at a given time with respect to the whole population. It is believed that such a property reflects the efficient match of the representation with the statistics of natural scenes. Applying such a paradigm to computer vision therefore seems a promising approach towards more biomimetic algorithms. Herein, we will describe a biologically-inspired approach to this problem. First, we will describe an unsupervised learning paradigm which is particularly adapted to the efficient coding of image patches. Then, we will outline a complete multi-scale framework ---SparseLets--- implementing a biologically inspired sparse representation of natural images. Finally, we will propose novel methods for integrating prior information into these algorithms and provide some preliminary experimental results. We will conclude by giving some perspective on applying such algorithms to computer vision. More specifically, we will propose that bio-inspired approaches may be applied to computer vision using predictive coding schemes, sparse models being one simple and efficient instance of such schemes.", "published": "2017-01-24T13:20:11Z", "authors": ["Laurent Perrinet"], "categories": ["cs.CV", "q-bio.NC"], "doi": "10.1002/9783527680863.ch14", "_query": "property valuation computer vision", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2310.09692v2", "title": "Spike-based Neuromorphic Computing for Next-Generation Computer Vision", "abstract": "Neuromorphic Computing promises orders of magnitude improvement in energy efficiency compared to traditional von Neumann computing paradigm. The goal is to develop an adaptive, fault-tolerant, low-footprint, fast, low-energy intelligent system by learning and emulating brain functionality which can be realized through innovation in different abstraction layers including material, device, circuit, architecture and algorithm. As the energy consumption in complex vision tasks keep increasing exponentially due to larger data set and resource-constrained edge devices become increasingly ubiquitous, spike-based neuromorphic computing approaches can be viable alternative to deep convolutional neural network that is dominating the vision field today. In this book chapter, we introduce neuromorphic computing, outline a few representative examples from different layers of the design stack (devices, circuits and algorithms) and conclude with a few exciting applications and future research directions that seem promising for computer vision in the near future.", "published": "2023-10-15T01:05:35Z", "authors": ["Md Sakib Hasan", "Catherine D. Schuman", "Zhongyang Zhang", "Tauhidur Rahman", "Garrett S. Rose"], "categories": ["cs.NE", "cs.AI", "cs.ET", "cs.LG", "eess.IV"], "doi": "", "_query": "property valuation computer vision", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2501.00142v1", "title": "Minimalist Vision with Freeform Pixels", "abstract": "A minimalist vision system uses the smallest number of pixels needed to solve a vision task. While traditional cameras use a large grid of square pixels, a minimalist camera uses freeform pixels that can take on arbitrary shapes to increase their information content. We show that the hardware of a minimalist camera can be modeled as the first layer of a neural network, where the subsequent layers are used for inference. Training the network for any given task yields the shapes of the camera's freeform pixels, each of which is implemented using a photodetector and an optical mask. We have designed minimalist cameras for monitoring indoor spaces (with 8 pixels), measuring room lighting (with 8 pixels), and estimating traffic flow (with 8 pixels). The performance demonstrated by these systems is on par with a traditional camera with orders of magnitude more pixels. Minimalist vision has two major advantages. First, it naturally tends to preserve the privacy of individuals in the scene since the captured information is inadequate for extracting visual details. Second, since the number of measurements made by a minimalist camera is very small, we show that it can be fully self-powered, i.e., function without an external power supply or a battery.", "published": "2024-12-30T21:27:07Z", "authors": ["Jeremy Klotz", "Shree K. Nayar"], "categories": ["cs.CV", "eess.IV"], "doi": "10.1007/978-3-031-73039-9_19", "_query": "property valuation computer vision", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2305.06773v4", "title": "Towards a Better Understanding of the Computer Vision Research Community in Africa", "abstract": "Computer vision is a broad field of study that encompasses different tasks (e.g., object detection). Although computer vision is relevant to the African communities in various applications, yet computer vision research is under-explored in the continent and constructs only 0.06% of top-tier publications in the last ten years. In this paper, our goal is to have a better understanding of the computer vision research conducted in Africa and provide pointers on whether there is equity in research or not. We do this through an empirical analysis of the African computer vision publications that are Scopus indexed, where we collect around 63,000 publications over the period 2012-2022. We first study the opportunities available for African institutions to publish in top-tier computer vision venues. We show that African publishing trends in top-tier venues over the years do not exhibit consistent growth, unlike other continents such as North America or Asia. Moreover, we study all computer vision publications beyond top-tier venues in different African regions to find that mainly Northern and Southern Africa are publishing in computer vision with 68.5% and 15.9% of publications, resp. Nonetheless, we highlight that both Eastern and Western Africa are exhibiting a promising increase with the last two years closing the gap with Southern Africa. Additionally, we study the collaboration patterns in these publications to find that most of these exhibit international collaborations rather than African ones. We also show that most of these publications include an African author that is a key contributor as the first or last author. Finally, we present the most recurring keywords in computer vision publications per African region.", "published": "2023-05-11T12:54:10Z", "authors": ["Abdul-Hakeem Omotayo", "Mai Gamal", "Eman Ehab", "Gbetondji Dovonon", "Zainab Akinjobi", "Ismaila Lukman", "Houcemeddine Turki", "Mahmod Abdien", "Idriss Tondji", "Abigail Oppong", "Yvan Pimi", "Karim Gamal", " Ro'ya-CV4Africa", "Mennatullah Siam"], "categories": ["cs.CV"], "doi": "", "_query": "property valuation computer vision", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2602.20930v1", "title": "Computing a Characteristic Orientation for Rotation-Independent Image Analysis", "abstract": "Handling geometric transformations, particularly rotations, remains a challenge in deep learning for computer vision. Standard neural networks lack inherent rotation invariance and typically rely on data augmentation or architectural modifications to improve robustness. Although effective, these approaches increase computational demands, require specialised implementations, or alter network structures, limiting their applicability. This paper introduces General Intensity Direction (GID), a preprocessing method that improves rotation robustness without modifying the network architecture. The method estimates a global orientation for each image and aligns it to a canonical reference frame, allowing standard models to process inputs more consistently across different rotations. Unlike moment-based approaches that extract invariant descriptors, this method directly transforms the image while preserving spatial structure, making it compatible with convolutional networks. Experimental evaluation on the rotated MNIST dataset shows that the proposed method achieves higher accuracy than state-of-the-art rotation-invariant architectures. Additional experiments on the CIFAR-10 dataset, confirm that the method remains effective under more complex conditions.", "published": "2026-02-24T14:08:12Z", "authors": ["Cristian Valero-Abundio", "Emilio Sansano-Sansano", "Raúl Montoliu", "Marina Martínez García"], "categories": ["cs.CV"], "doi": "10.5220/0014299900004084", "_query": "property valuation computer vision", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2007.15857v2", "title": "Real-Time Uncertainty Estimation in Computer Vision via Uncertainty-Aware Distribution Distillation", "abstract": "Calibrated estimates of uncertainty are critical for many real-world computer vision applications of deep learning. While there are several widely-used uncertainty estimation methods, dropout inference stands out for its simplicity and efficacy. This technique, however, requires multiple forward passes through the network during inference and therefore can be too resource-intensive to be deployed in real-time applications. We propose a simple, easy-to-optimize distillation method for learning the conditional predictive distribution of a pre-trained dropout model for fast, sample-free uncertainty estimation in computer vision tasks. We empirically test the effectiveness of the proposed method on both semantic segmentation and depth estimation tasks and demonstrate our method can significantly reduce the inference time, enabling real-time uncertainty quantification, while achieving improved quality of both the uncertainty estimates and predictive performance over the regular dropout model.", "published": "2020-07-31T05:40:39Z", "authors": ["Yichen Shen", "Zhilu Zhang", "Mert R. Sabuncu", "Lin Sun"], "categories": ["cs.CV", "cs.LG", "stat.ML"], "doi": "", "_query": "property valuation computer vision", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2108.05779v2", "title": "DiagViB-6: A Diagnostic Benchmark Suite for Vision Models in the Presence of Shortcut and Generalization Opportunities", "abstract": "Common deep neural networks (DNNs) for image classification have been shown to rely on shortcut opportunities (SO) in the form of predictive and easy-to-represent visual factors. This is known as shortcut learning and leads to impaired generalization. In this work, we show that common DNNs also suffer from shortcut learning when predicting only basic visual object factors of variation (FoV) such as shape, color, or texture. We argue that besides shortcut opportunities, generalization opportunities (GO) are also an inherent part of real-world vision data and arise from partial independence between predicted classes and FoVs. We also argue that it is necessary for DNNs to exploit GO to overcome shortcut learning. Our core contribution is to introduce the Diagnostic Vision Benchmark suite DiagViB-6, which includes datasets and metrics to study a network's shortcut vulnerability and generalization capability for six independent FoV. In particular, DiagViB-6 allows controlling the type and degree of SO and GO in a dataset. We benchmark a wide range of popular vision architectures and show that they can exploit GO only to a limited extent.", "published": "2021-08-12T14:43:24Z", "authors": ["Elias Eulig", "Piyapat Saranrittichai", "Chaithanya Kumar Mummadi", "Kilian Rambach", "William Beluch", "Xiahan Shi", "Volker Fischer"], "categories": ["cs.CV"], "doi": "", "_query": "property valuation computer vision", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/1811.02234v1", "title": "Semantic bottleneck for computer vision tasks", "abstract": "This paper introduces a novel method for the representation of images that is semantic by nature, addressing the question of computation intelligibility in computer vision tasks. More specifically, our proposition is to introduce what we call a semantic bottleneck in the processing pipeline, which is a crossing point in which the representation of the image is entirely expressed with natural language , while retaining the efficiency of numerical representations. We show that our approach is able to generate semantic representations that give state-of-the-art results on semantic content-based image retrieval and also perform very well on image classification tasks. Intelligibility is evaluated through user centered experiments for failure detection.", "published": "2018-11-06T09:01:02Z", "authors": ["Maxime Bucher", "Stéphane Herbin", "Frédéric Jurie"], "categories": ["cs.CV", "cs.AI", "cs.LG", "cs.NE"], "doi": "", "_query": "property valuation computer vision", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/1806.01339v2", "title": "Computing the Spatial Probability of Inclusion inside Partial Contours for Computer Vision Applications", "abstract": "In Computer Vision, edge detection is one of the favored approaches for feature and object detection in images since it provides information about their objects boundaries. Other region-based approaches use probabilistic analysis such as clustering and Markov random fields, but those methods cannot be used to analyze edges and their interaction. In fact, only image segmentation can produce regions based on edges, but it requires thresholding by simply separating the regions into binary in-out information. Hence, there is currently a gap between edge-based and region-based algorithms, since edges cannot be used to study the properties of a region and vice versa. The objective of this paper is to present a novel spatial probability analysis that allows determining the probability of inclusion inside a set of partial contours (strokes). To answer this objective, we developed a new approach that uses electromagnetic convolutions and repulsion optimization to compute the required probabilities. Hence, it becomes possible to generate a continuous space of probability based only on the edge information, thus bridging the gap between the edge-based methods and the region-based methods. The developed method is consistent with the fundamental properties of inclusion probabilities and its results are validated by comparing an image with the probability-based estimation given by our algorithm. The method can also be generalized to take into consideration the intensity of the edges or to be used for 3D shapes. This is the first documented method that allows computing a space of probability based on interacting edges, which opens the path to broader applications such as image segmentation and contour completion.", "published": "2018-06-04T19:26:51Z", "authors": ["Dominique Beaini", "Sofiane Achiche", "Fabrice Nonez", "Maxime Raison"], "categories": ["cs.CV", "math.NA"], "doi": "", "_query": "property valuation computer vision", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2105.10990v1", "title": "Wisdom for the Crowd: Discoursive Power in Annotation Instructions for Computer Vision", "abstract": "Developers of computer vision algorithms outsource some of the labor involved in annotating training data through business process outsourcing companies and crowdsourcing platforms. Many data annotators are situated in the Global South and are considered independent contractors. This paper focuses on the experiences of Argentinian and Venezuelan annotation workers. Through qualitative methods, we explore the discourses encoded in the task instructions that these workers follow to annotate computer vision datasets. Our preliminary findings indicate that annotation instructions reflect worldviews imposed on workers and, through their labor, on datasets. Moreover, we observe that for-profit goals drive task instructions and that managers and algorithms make sure annotations are done according to requesters' commands. This configuration presents a form of commodified labor that perpetuates power asymmetries while reinforcing social inequalities and is compelled to reproduce them into datasets and, subsequently, in computer vision systems.", "published": "2021-05-23T18:20:39Z", "authors": ["Milagros Miceli", "Julian Posada"], "categories": ["cs.CV", "cs.CY"], "doi": "", "_query": "property valuation computer vision", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2410.00982v2", "title": "ScVLM: Enhancing Vision-Language Model for Safety-Critical Event Understanding", "abstract": "Accurately identifying, understanding and describing traffic safety-critical events (SCEs), including crashes, tire strikes, and near-crashes, is crucial for advanced driver assistance systems, automated driving systems, and traffic safety. As SCEs are rare events, most general vision-language models (VLMs) have not been trained sufficiently to link SCE videos and narratives, which could lead to hallucinations and missing key safety characteristics. Here, we introduce ScVLM, a novel hybrid methodology that integrates supervised and contrastive learning techniques to classify the severity and types of SCEs, as well as to generate narrative descriptions of SCEs. This approach utilizes classification to enhance VLMs' comprehension of driving videos and improve the rationality of event descriptions. The proposed approach is trained on and evaluated by more than 8,600 SCEs from the Second Strategic Highway Research Program Naturalistic Driving Study dataset, the largest publicly accessible driving dataset with videos and SCE annotations. The results demonstrate the superiority of the proposed approach in generating contextually accurate event descriptions and mitigating VLM hallucinations. The code will be available at https://github.com/datadrivenwheels/ScVLM.", "published": "2024-10-01T18:10:23Z", "authors": ["Liang Shi", "Boyu Jiang", "Tong Zeng", "Feng Guo"], "categories": ["cs.CV"], "doi": "", "_query": "property valuation computer vision", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/1801.08388v3", "title": "A Benchmark and Evaluation of Non-Rigid Structure from Motion", "abstract": "Non-Rigid structure from motion (NRSfM), is a long standing and central problem in computer vision and its solution is necessary for obtaining 3D information from multiple images when the scene is dynamic. A main issue regarding the further development of this important computer vision topic, is the lack of high quality data sets. We here address this issue by presenting a data set created for this purpose, which is made publicly available, and considerably larger than the previous state of the art. To validate the applicability of this data set, and provide an investigation into the state of the art of NRSfM, including potential directions forward, we here present a benchmark and a scrupulous evaluation using this data set. This benchmark evaluates 18 different methods with available code that reasonably spans the state of the art in sparse NRSfM. This new public data set and evaluation protocol will provide benchmark tools for further development in this challenging field.", "published": "2018-01-25T12:59:09Z", "authors": ["Sebastian Hoppe Nesgaard Jensen", "Mads Emil Brix Doest", "Henrik Aanaes", "Alessio Del Bue"], "categories": ["cs.CV"], "doi": "", "_query": "property valuation computer vision", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/1405.4802v2", "title": "Use of Computer Vision to Detect Tangles in Tangled Objects", "abstract": "Untangling of structures like ropes and wires by autonomous robots can be useful in areas such as personal robotics, industries and electrical wiring & repairing by robots. This problem can be tackled by using computer vision system in robot. This paper proposes a computer vision based method for analyzing visual data acquired from camera for perceiving the overlap of wires, ropes, hoses i.e. detecting tangles. Information obtained after processing image according to the proposed method comprises of position of tangles in tangled object and which wire passes over which wire. This information can then be used to guide robot to untangle wire/s. Given an image, preprocessing is done to remove noise. Then edges of wire are detected. After that, the image is divided into smaller blocks and each block is checked for wire overlap/s and finding other relevant information. TANGLED-100 dataset was introduced, which consists of images of tangled linear deformable objects. Method discussed in here was tested on the TANGLED-100 dataset. Accuracy achieved during experiments was found to be 74.9%. Robotic simulations were carried out to demonstrate the use of the proposed method in applications of robot. Proposed method is a general method that can be used by robots working in different situations.", "published": "2014-05-19T16:51:11Z", "authors": ["Paritosh Parmar"], "categories": ["cs.CV"], "doi": "10.1109/ICIIP.2013.6707551", "_query": "property valuation computer vision", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2302.00117v1", "title": "Real Estate Property Valuation using Self-Supervised Vision Transformers", "abstract": "The use of Artificial Intelligence (AI) in the real estate market has been growing in recent years. In this paper, we propose a new method for property valuation that utilizes self-supervised vision transformers, a recent breakthrough in computer vision and deep learning. Our proposed algorithm uses a combination of machine learning, computer vision and hedonic pricing models trained on real estate data to estimate the value of a given property. We collected and pre-processed a data set of real estate properties in the city of Boulder, Colorado and used it to train, validate and test our algorithm. Our data set consisted of qualitative images (including house interiors, exteriors, and street views) as well as quantitative features such as the number of bedrooms, bathrooms, square footage, lot square footage, property age, crime rates, and proximity to amenities. We evaluated the performance of our model using metrics such as Root Mean Squared Error (RMSE). Our findings indicate that these techniques are able to accurately predict the value of properties, with a low RMSE. The proposed algorithm outperforms traditional appraisal methods that do not leverage property images and has the potential to be used in real-world applications.", "published": "2023-01-31T21:54:15Z", "authors": ["Mahdieh Yazdani", "Maziar Raissi"], "categories": ["cs.CV", "cs.AI", "cs.LG", "econ.EM"], "doi": "", "_query": "property valuation computer vision", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/astro-ph/0606411v1", "title": "The Stellar Imager (SI) Vision Mission", "abstract": "The Stellar Imager (SI) is a UV-Optical, Space-Based Interferometer designed to enable 0.1 milli-arcsecond (mas) spectral imaging of stellar surfaces and of the Universe in general and asteroseismic imaging of stellar interiors. SI is identified as a \"Flagship and Landmark Discovery Mission\" in the 2005 Sun Solar System Connection (SSSC) Roadmap and as a candidate for a \"Pathways to Life Observatory\" in the Exploration of the Universe Division (EUD) Roadmap (May, 2005). SI will revolutionize our view of many dynamic astrophysical processes: its resolution will transform point sources into extended sources, and snapshots into evolving views. SI's science focuses on the role of magnetism in the Universe, particularly on magnetic activity on the surfaces of stars like the Sun. SI's prime goal is to enable long-term forecasting of solar activity and the space weather that it drives. SI will also revolutionize our understanding of the formation of planetary systems, of the habitability and climatology of distant planets, and of many magneto-hydrodynamically controlled processes in the Universe. The results of the SI \"Vision Mission\" Study are presented in this paper. Additional information on the SI mission concept and related technology development can be found at URL: http://hires.gsfc.nasa.gov/si/.", "published": "2006-06-16T19:05:51Z", "authors": ["Kenneth G. Carpenter", "Carolus J. Schrijver", "Margarita Karovska", "SI Vision Mission Team"], "categories": ["astro-ph"], "doi": "10.1117/12.669713", "_query": "property valuation computer vision", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2608.14047v3", "title": "Evolve Vision-Language-Action Model into an Agent with On-the-fly Tool-use", "abstract": "This paper integrates end-to-end Visual-Language-Action (VLA) models with agentic tool-use to propose Agentic Robot with Tool-use (ART). ART is a tool-injection framework that tunes any VLA model to leverage off-the-shelf tool modules for low-level vision, high-level affordance, and embodiment enhancement. Compared to vanilla VLA models with a whole continuous action solution space, ART reduces the complexity of the action solution space through tool-use, which not only improves generalizability across different tasks but also reduces data dependency. To demonstrate the advantages (high generalizability and low data dependency) of this framework, we first built a dataset of 30K tool-use trajectories and action demonstrations, which is much smaller than those used by baseline methods. We then designed a training regimen for long-trajectory tool-use reasoning in challenging environments. Experiments show that ART achieves a 20% higher success rate than mainstream baselines on simulation and real-world tasks, such as pick-and-place in the dark at novel viewpoints. Empirical results highlight the benefits of an agent-based approach: modular tool utilization enables more efficient training, lightweight deployment, and scalable integration of new tools. This design fosters robustness, adaptability, and extensibility, paving the way for the practical deployment of VLA systems in complex real-world scenarios.", "published": "2026-08-14T07:53:18Z", "authors": ["Ding Yi", "Yanzhao Yu", "Xili Dai", "Xianbiao Qi", "Peiwen Sun", "Xueqian Wang", "Xiangyu Yue", "Jianan Wang"], "categories": ["cs.RO", "cs.AI", "cs.CV"], "doi": "", "_query": "property valuation computer vision", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2506.09954v1", "title": "Vision Generalist Model: A Survey", "abstract": "Recently, we have witnessed the great success of the generalist model in natural language processing. The generalist model is a general framework trained with massive data and is able to process various downstream tasks simultaneously. Encouraged by their impressive performance, an increasing number of researchers are venturing into the realm of applying these models to computer vision tasks. However, the inputs and outputs of vision tasks are more diverse, and it is difficult to summarize them as a unified representation. In this paper, we provide a comprehensive overview of the vision generalist models, delving into their characteristics and capabilities within the field. First, we review the background, including the datasets, tasks, and benchmarks. Then, we dig into the design of frameworks that have been proposed in existing research, while also introducing the techniques employed to enhance their performance. To better help the researchers comprehend the area, we take a brief excursion into related domains, shedding light on their interconnections and potential synergies. To conclude, we provide some real-world application scenarios, undertake a thorough examination of the persistent challenges, and offer insights into possible directions for future research endeavors.", "published": "2025-06-11T17:23:41Z", "authors": ["Ziyi Wang", "Yongming Rao", "Shuofeng Sun", "Xinrun Liu", "Yi Wei", "Xumin Yu", "Zuyan Liu", "Yanbo Wang", "Hongmin Liu", "Jie Zhou", "Jiwen Lu"], "categories": ["cs.CV", "cs.AI"], "doi": "", "_query": "property valuation computer vision", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2305.09293v1", "title": "Out-of-Distribution Detection for Adaptive Computer Vision", "abstract": "It is well known that computer vision can be unreliable when faced with previously unseen imaging conditions. This paper proposes a method to adapt camera parameters according to a normalizing flow-based out-of-distibution detector. A small-scale study is conducted which shows that adapting camera parameters according to this out-of-distibution detector leads to an average increase of 3 to 4 percentage points in mAP, mAR and F1 performance metrics of a YOLOv4 object detector. As a secondary result, this paper also shows that it is possible to train a normalizing flow model for out-of-distribution detection on the COCO dataset, which is larger and more diverse than most benchmarks for out-of-distibution detectors.", "published": "2023-05-16T09:01:42Z", "authors": ["Simon Kristoffersson Lind", "Rudolph Triebel", "Luigi Nardi", "Volker Krueger"], "categories": ["cs.CV", "cs.LG"], "doi": "10.1007/978-3-031-31438-4_21", "_query": "property valuation computer vision", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2508.17595v1", "title": "TinyGiantVLM: A Lightweight Vision-Language Architecture for Spatial Reasoning under Resource Constraints", "abstract": "Reasoning about fine-grained spatial relationships in warehouse-scale environments poses a significant challenge for existing vision-language models (VLMs), which often struggle to comprehend 3D layouts, object arrangements, and multimodal cues in real-world industrial settings. In this paper, we present TinyGiantVLM, a lightweight and modular two-stage framework designed for physical spatial reasoning, distinguishing itself from traditional geographic reasoning in complex logistics scenes. Our approach encodes both global and region-level features from RGB and depth modalities using pretrained visual backbones. To effectively handle the complexity of high-modality inputs and diverse question types, we incorporate a Mixture-of-Experts (MoE) fusion module, which dynamically combines spatial representations to support downstream reasoning tasks and improve convergence. Training is conducted in a two-phase strategy: the first phase focuses on generating free-form answers to enhance spatial reasoning ability, while the second phase uses normalized answers for evaluation. Evaluated on Track 3 of the AI City Challenge 2025, our 64M-parameter base model achieved 5th place on the leaderboard with a score of 66.8861, demonstrating strong performance in bridging visual perception and spatial understanding in industrial environments. We further present an 80M-parameter variant with expanded MoE capacity, which demonstrates improved performance on spatial reasoning tasks.", "published": "2025-08-25T01:36:22Z", "authors": ["Vinh-Thuan Ly", "Hoang M. Truong", "Xuan-Huong Nguyen"], "categories": ["cs.CV"], "doi": "", "_query": "property valuation computer vision", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2212.09408v3", "title": "Universal Object Detection with Large Vision Model", "abstract": "Over the past few years, there has been growing interest in developing a broad, universal, and general-purpose computer vision system. Such systems have the potential to address a wide range of vision tasks simultaneously, without being limited to specific problems or data domains. This universality is crucial for practical, real-world computer vision applications. In this study, our focus is on a specific challenge: the large-scale, multi-domain universal object detection problem, which contributes to the broader goal of achieving a universal vision system. This problem presents several intricate challenges, including cross-dataset category label duplication, label conflicts, and the necessity to handle hierarchical taxonomies. To address these challenges, we introduce our approach to label handling, hierarchy-aware loss design, and resource-efficient model training utilizing a pre-trained large vision model. Our method has demonstrated remarkable performance, securing a prestigious second-place ranking in the object detection track of the Robust Vision Challenge 2022 (RVC 2022) on a million-scale cross-dataset object detection benchmark. We believe that our comprehensive study will serve as a valuable reference and offer an alternative approach for addressing similar challenges within the computer vision community. The source code for our work is openly available at https://github.com/linfeng93/Large-UniDet.", "published": "2022-12-19T12:40:13Z", "authors": ["Feng Lin", "Wenze Hu", "Yaowei Wang", "Yonghong Tian", "Guangming Lu", "Fanglin Chen", "Yong Xu", "Xiaoyu Wang"], "categories": ["cs.CV"], "doi": "", "_query": "property valuation computer vision", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2212.04132v1", "title": "Combining Photogrammetric Computer Vision and Semantic Segmentation for Fine-grained Understanding of Coral Reef Growth under Climate Change", "abstract": "Corals are the primary habitat-building life-form on reefs that support a quarter of the species in the ocean. A coral reef ecosystem usually consists of reefs, each of which is like a tall building in any city. These reef-building corals secrete hard calcareous exoskeletons that give them structural rigidity, and are also a prerequisite for our accurate 3D modeling and semantic mapping using advanced photogrammetric computer vision and machine learning. Underwater videography as a modern underwater remote sensing tool is a high-resolution coral habitat survey and mapping technique. In this paper, detailed 3D mesh models, digital surface models and orthophotos of the coral habitat are generated from the collected coral images and underwater control points. Meanwhile, a novel pixel-wise semantic segmentation approach of orthophotos is performed by advanced deep learning. Finally, the semantic map is mapped into 3D space. For the first time, 3D fine-grained semantic modeling and rugosity evaluation of coral reefs have been completed at millimeter (mm) accuracy. This provides a new and powerful method for understanding the processes and characteristics of coral reef change at high spatial and temporal resolution under climate change.", "published": "2022-12-08T08:09:57Z", "authors": ["Jiageng Zhong", "Ming Li", "Hanqi Zhang", "Jiangying Qin"], "categories": ["cs.CV"], "doi": "", "_query": "property valuation computer vision", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2410.21361v2", "title": "Domain Adaptation with a Single Vision-Language Embedding", "abstract": "Domain adaptation has been extensively investigated in computer vision but still requires access to target data at the training time, which might be difficult to obtain in real-world autonomous driving scenarios, especially under rare or adverse conditions. In this paper, we present a new framework for domain adaptation relying on a single Vision-Language (VL) latent embedding instead of full target data. First, leveraging a contrastive language-image pre-training model (CLIP), we propose prompt/photo-driven instance normalization (PIN). PIN is a feature augmentation method that mines multiple visual styles using a single target VL latent embedding, by optimizing affine transformations of low-level source features. The VL embedding can come from a language prompt describing the target domain, a partially optimized language prompt, or a single unlabeled target image. Second, we show that these mined styles (i.e., augmentations) can be used for zero-shot (i.e., target-free) and one-shot unsupervised domain adaptation. Experiments on semantic segmentation in real-world driving datasets, including Cityscapes and ACDC (adverse conditions), demonstrate the effectiveness of the proposed method, which outperforms relevant baselines in the practical zero-shot and one-shot settings.", "published": "2024-10-28T17:59:53Z", "authors": ["Mohammad Fahes", "Tuan-Hung Vu", "Andrei Bursuc", "Patrick Pérez", "Raoul de Charette"], "categories": ["cs.CV", "cs.LG"], "doi": "", "_query": "property valuation computer vision", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/1603.09599v1", "title": "Total Variation Applications in Computer Vision", "abstract": "The objectives of this chapter are: (i) to introduce a concise overview of regularization; (ii) to define and to explain the role of a particular type of regularization called total variation norm (TV-norm) in computer vision tasks; (iii) to set up a brief discussion on the mathematical background of TV methods; and (iv) to establish a relationship between models and a few existing methods to solve problems cast as TV-norm. For the most part, image-processing algorithms blur the edges of the estimated images, however TV regularization preserves the edges with no prior information on the observed and the original images. The regularization scalar parameter λ controls the amount of regularization allowed and it is an essential to obtain a high-quality regularized output. A wide-ranging review of several ways to put into practice TV regularization as well as its advantages and limitations are discussed.", "published": "2016-03-31T14:08:53Z", "authors": ["Vania V. Estrela", "Hermes Aguiar Magalhaes", "Osamu Saotome"], "categories": ["cs.CV"], "doi": "10.4018/978-1-4666-8654-0.ch002", "_query": "property valuation computer vision", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2208.07220v2", "title": "PatchDropout: Economizing Vision Transformers Using Patch Dropout", "abstract": "Vision transformers have demonstrated the potential to outperform CNNs in a variety of vision tasks. But the computational and memory requirements of these models prohibit their use in many applications, especially those that depend on high-resolution images, such as medical image classification. Efforts to train ViTs more efficiently are overly complicated, necessitating architectural changes or intricate training schemes. In this work, we show that standard ViT models can be efficiently trained at high resolution by randomly dropping input image patches. This simple approach, PatchDropout, reduces FLOPs and memory by at least 50% in standard natural image datasets such as ImageNet, and those savings only increase with image size. On CSAW, a high-resolution medical dataset, we observe a 5 times savings in computation and memory using PatchDropout, along with a boost in performance. For practitioners with a fixed computational or memory budget, PatchDropout makes it possible to choose image resolution, hyperparameters, or model size to get the most performance out of their model.", "published": "2022-08-10T14:08:55Z", "authors": ["Yue Liu", "Christos Matsoukas", "Fredrik Strand", "Hossein Azizpour", "Kevin Smith"], "categories": ["cs.CV", "cs.LG"], "doi": "", "_query": "property valuation computer vision", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2206.11461v3", "title": "Towards Better User Studies in Computer Graphics and Vision", "abstract": "Online crowdsourcing platforms have made it increasingly easy to perform evaluations of algorithm outputs with survey questions like \"which image is better, A or B?\", leading to their proliferation in vision and graphics research papers. Results of these studies are often used as quantitative evidence in support of a paper's contributions. On the one hand we argue that, when conducted hastily as an afterthought, such studies lead to an increase of uninformative, and, potentially, misleading conclusions. On the other hand, in these same communities, user research is underutilized in driving project direction and forecasting user needs and reception. We call for increased attention to both the design and reporting of user studies in computer vision and graphics papers towards (1) improved replicability and (2) improved project direction. Together with this call, we offer an overview of methodologies from user experience research (UXR), human-computer interaction (HCI), and applied perception to increase exposure to the available methodologies and best practices. We discuss foundational user research methods (e.g., needfinding) that are presently underutilized in computer vision and graphics research, but can provide valuable project direction. We provide further pointers to the literature for readers interested in exploring other UXR methodologies. Finally, we describe broader open issues and recommendations for the research community.", "published": "2022-06-23T02:44:22Z", "authors": ["Zoya Bylinskii", "Laura Herman", "Aaron Hertzmann", "Stefanie Hutka", "Yile Zhang"], "categories": ["cs.GR", "cs.CV", "cs.HC"], "doi": "10.1561/0600000106", "_query": "property valuation computer vision", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2310.05572v1", "title": "A Simple and Robust Framework for Cross-Modality Medical Image Segmentation applied to Vision Transformers", "abstract": "When it comes to clinical images, automatic segmentation has a wide variety of applications and a considerable diversity of input domains, such as different types of Magnetic Resonance Images (MRIs) and Computerized Tomography (CT) scans. This heterogeneity is a challenge for cross-modality algorithms that should equally perform independently of the input image type fed to them. Often, segmentation models are trained using a single modality, preventing generalization to other types of input data without resorting to transfer learning techniques. Furthermore, the multi-modal or cross-modality architectures proposed in the literature frequently require registered images, which are not easy to collect in clinical environments, or need additional processing steps, such as synthetic image generation. In this work, we propose a simple framework to achieve fair image segmentation of multiple modalities using a single conditional model that adapts its normalization layers based on the input type, trained with non-registered interleaved mixed data. We show that our framework outperforms other cross-modality segmentation methods, when applied to the same 3D UNet baseline model, on the Multi-Modality Whole Heart Segmentation Challenge. Furthermore, we define the Conditional Vision Transformer (C-ViT) encoder, based on the proposed cross-modality framework, and we show that it brings significant improvements to the resulting segmentation, up to 6.87\\% of Dice accuracy, with respect to its baseline reference. The code to reproduce our experiments and the trained model weights are available at https://github.com/matteo-bastico/MI-Seg.", "published": "2023-10-09T09:51:44Z", "authors": ["Matteo Bastico", "David Ryckelynck", "Laurent Corté", "Yannick Tillier", "Etienne Decencière"], "categories": ["eess.IV", "cs.CV", "cs.LG"], "doi": "10.1109/ICCVW60793.2023.00446", "_query": "property valuation computer vision", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2603.20694v1", "title": "FALQON-MST: A Fully Quantum Framework for Graph Optimization in Vision Systems", "abstract": "Finding the minimum spanning tree (MST) of a graph is an important task in computer vision, as it enables a sparse and low-cost representation of connectivity among elements (such as superpixels, points, or regions), which is useful for tasks such as segmentation, reconstruction, and clustering. In this work, we propose and evaluate a fully quantum pipeline for computing MSTs using the FALQON algorithm, a feedback-based quantum optimization method that does not require classical optimizers. We construct a Hamiltonian formulation whose ground-state energy encodes the MST of a graph and compare different FALQON strategies: (i) time rescaling (TR-FALQON) and (ii) multi-driver configurations. To avoid domain-specific biases, we adopt graphs with random weights and show that the FALQON variants exhibit significant differences in ground-state fidelity. We discuss the relevance of this approach for computer vision problems that naturally yield graph representations, and experimental results on synthetic instances together with a small demonstrative study on image segmentation illustrate both the potential and the current limitations of the method. Our numerical simulations on randomly weighted graphs show that standard one drive FALQON, although it reduces the expected energy, fails to concentrate amplitude in the MST solution. The multi drive variant succeeds in redistributing probability mass toward the ground state so that the MST appears among the most probable outcomes, and TR FALQON applied over multi drive produces the best results with faster convergence, lower final energy, and the highest solution state probability or fidelity in our tested instances. These improvements were observed on small synthetic graphs, underscoring both the promise of multi drive controls with temporal rescaling and the need for further scaling and hardware validation.", "published": "2026-03-21T07:34:07Z", "authors": ["Guilherme E. L. Pexe", "Lucas A. M. Rattighieri", "Leandro A. Passos", "Douglas Rodrigues", "Danilo S. Jodas", "João P. Papa", "Kelton A. P. da Costa"], "categories": ["quant-ph"], "doi": "10.5220/0014312400004084", "_query": "property valuation computer vision", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2609.13198v1", "title": "SPICE: Simple Polysemantic Feature Interpretation via Clustering-based Explanation", "abstract": "One of the pivotal recent challenges in neural network interpretability is polysemanticity, where a single neuron is activated by multiple, often unrelated concepts, hindering clear functional understanding. Although prior work has explored this phenomenon, existing approaches remain architecture-specific and depend on manual heuristics such as a fixed number of concept clusters ($K$), limiting their generality and scalability--especially for modern Transformer-based models. To address these limitations, we introduce SPICE (\\textbf{S}imple \\textbf{P}olysemantic Feature \\textbf{I}nterpretation via \\textbf{C}lustering-based \\textbf{E}xplanation), a generalizable framework for analyzing polysemanticity in deep vision architectures. SPICE avoids architecture-dependent propagation rules, enabling the first systematic comparison of polysemanticity across both CNNs and Transformers, and automatically determines the number of concept clusters per neuron, eliminating reliance on a preset $K$ and supporting scalable analysis for large models. Using SPICE, we conduct a comprehensive investigation into how polysemanticity emerges, varies across depth and architecture, and forms through distinct computational pathways.", "published": "2026-08-14T01:04:22Z", "authors": ["Sehyun Lee", "Dahee Kwon", "Damin Lee", "Jaesik Choi"], "categories": ["cs.LG", "cs.CV"], "doi": "", "_query": "property valuation computer vision", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2407.14799v1", "title": "FairViT: Fair Vision Transformer via Adaptive Masking", "abstract": "Vision Transformer (ViT) has achieved excellent performance and demonstrated its promising potential in various computer vision tasks. The wide deployment of ViT in real-world tasks requires a thorough understanding of the societal impact of the model. However, most ViT-based works do not take fairness into account and it is unclear whether directly applying CNN-oriented debiased algorithm to ViT is feasible. Moreover, previous works typically sacrifice accuracy for fairness. Therefore, we aim to develop an algorithm that improves accuracy without sacrificing fairness. In this paper, we propose FairViT, a novel accurate and fair ViT framework. To this end, we introduce a novel distance loss and deploy adaptive fairness-aware masks on attention layers updating with model parameters. Experimental results show \\sys can achieve accuracy better than other alternatives, even with competitive computational efficiency. Furthermore, \\sys achieves appreciable fairness results.", "published": "2024-07-20T08:10:37Z", "authors": ["Bowei Tian", "Ruijie Du", "Yanning Shen"], "categories": ["cs.CV", "cs.CY"], "doi": "", "_query": "property valuation computer vision", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/1704.03723v1", "title": "Beliefs in Markov Trees - From Local Computations to Local Valuation", "abstract": "This paper is devoted to expressiveness of hypergraphs for which uncertainty propagation by local computations via Shenoy/Shafer method applies. It is demonstrated that for this propagation method for a given joint belief distribution no valuation of hyperedges of a hypergraph may provide with simpler hypergraph structure than valuation of hyperedges by conditional distributions. This has vital implication that methods recovering belief networks from data have no better alternative for finding the simplest hypergraph structure for belief propagation. A method for recovery tree-structured belief networks has been developed and specialized for Dempster-Shafer belief functions", "published": "2017-04-12T12:30:17Z", "authors": ["Mieczysław A. Kłopotek"], "categories": ["cs.AI"], "doi": "", "_query": "property valuation computer vision", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2412.09706v2", "title": "Diffusion-Enhanced Test-time Adaptation with Text and Image Augmentation", "abstract": "Existing test-time prompt tuning (TPT) methods focus on single-modality data, primarily enhancing images and using confidence ratings to filter out inaccurate images. However, while image generation models can produce visually diverse images, single-modality data enhancement techniques still fail to capture the comprehensive knowledge provided by different modalities. Additionally, we note that the performance of TPT-based methods drops significantly when the number of augmented images is limited, which is not unusual given the computational expense of generative augmentation. To address these issues, we introduce IT3A, a novel test-time adaptation method that utilizes a pre-trained generative model for multi-modal augmentation of each test sample from unknown new domains. By combining augmented data from pre-trained vision and language models, we enhance the ability of the model to adapt to unknown new test data. Additionally, to ensure that key semantics are accurately retained when generating various visual and text enhancements, we employ cosine similarity filtering between the logits of the enhanced images and text with the original test data. This process allows us to filter out some spurious augmentation and inadequate combinations. To leverage the diverse enhancements provided by the generation model across different modals, we have replaced prompt tuning with an adapter for greater flexibility in utilizing text templates. Our experiments on the test datasets with distribution shifts and domain gaps show that in a zero-shot setting, IT3A outperforms state-of-the-art test-time prompt tuning methods with a 5.50% increase in accuracy.", "published": "2024-12-12T20:01:24Z", "authors": ["Chun-Mei Feng", "Yuanyang He", "Jian Zou", "Salman Khan", "Huan Xiong", "Zhen Li", "Wangmeng Zuo", "Rick Siow Mong Goh", "Yong Liu"], "categories": ["cs.CV"], "doi": "", "_query": "property valuation computer vision", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2308.12127v1", "title": "Masking Strategies for Background Bias Removal in Computer Vision Models", "abstract": "Models for fine-grained image classification tasks, where the difference between some classes can be extremely subtle and the number of samples per class tends to be low, are particularly prone to picking up background-related biases and demand robust methods to handle potential examples with out-of-distribution (OOD) backgrounds. To gain deeper insights into this critical problem, our research investigates the impact of background-induced bias on fine-grained image classification, evaluating standard backbone models such as Convolutional Neural Network (CNN) and Vision Transformers (ViT). We explore two masking strategies to mitigate background-induced bias: Early masking, which removes background information at the (input) image level, and late masking, which selectively masks high-level spatial features corresponding to the background. Extensive experiments assess the behavior of CNN and ViT models under different masking strategies, with a focus on their generalization to OOD backgrounds. The obtained findings demonstrate that both proposed strategies enhance OOD performance compared to the baseline models, with early masking consistently exhibiting the best OOD performance. Notably, a ViT variant employing GAP-Pooled Patch token-based classification combined with early masking achieves the highest OOD robustness.", "published": "2023-08-23T13:33:39Z", "authors": ["Ananthu Aniraj", "Cassio F. Dantas", "Dino Ienco", "Diego Marcos"], "categories": ["cs.CV", "cs.AI", "cs.LG"], "doi": "", "_query": "property valuation computer vision", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2504.06925v1", "title": "Are Vision-Language Models Ready for Dietary Assessment? Exploring the Next Frontier in AI-Powered Food Image Recognition", "abstract": "Automatic dietary assessment based on food images remains a challenge, requiring precise food detection, segmentation, and classification. Vision-Language Models (VLMs) offer new possibilities by integrating visual and textual reasoning. In this study, we evaluate six state-of-the-art VLMs (ChatGPT, Gemini, Claude, Moondream, DeepSeek, and LLaVA), analyzing their capabilities in food recognition at different levels. For the experimental framework, we introduce the FoodNExTDB, a unique food image database that contains 9,263 expert-labeled images across 10 categories (e.g., \"protein source\"), 62 subcategories (e.g., \"poultry\"), and 9 cooking styles (e.g., \"grilled\"). In total, FoodNExTDB includes 50k nutritional labels generated by seven experts who manually annotated all images in the database. Also, we propose a novel evaluation metric, Expert-Weighted Recall (EWR), that accounts for the inter-annotator variability. Results show that closed-source models outperform open-source ones, achieving over 90% EWR in recognizing food products in images containing a single product. Despite their potential, current VLMs face challenges in fine-grained food recognition, particularly in distinguishing subtle differences in cooking styles and visually similar food items, which limits their reliability for automatic dietary assessment. The FoodNExTDB database is publicly available at https://github.com/AI4Food/FoodNExtDB.", "published": "2025-04-09T14:33:59Z", "authors": ["Sergio Romero-Tapiador", "Ruben Tolosana", "Blanca Lacruz-Pleguezuelos", "Laura Judith Marcos Zambrano", "Guadalupe X. Bazán", "Isabel Espinosa-Salinas", "Julian Fierrez", "Javier Ortega-Garcia", "Enrique Carrillo de Santa Pau", "Aythami Morales"], "categories": ["cs.CV", "cs.AI"], "doi": "10.1109/CVPRW67362.2025.00047", "_query": "property valuation computer vision", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2608.17095v1", "title": "Inference-Time Attention Steering for Vision-Language-Action Driving Models", "abstract": "Vision-language-action (VLA) driving models couple a reasoning stage with a diffusion-based trajectory decoder, but do not give a direct way to redirect attention toward safety-critical actors at inference time without retraining. We studied a bounded additive pre-softmax attention bias on the visual tokens of detector localized traffic actors on Alpamayo-R1's Qwen3-VL backbone. It is applied as a fail open forward pre-hook with no weight changes. On 50 lane-change scenarios from the Physical AI World Model Synthetic dataset. The trajectory decoder shows a monotonic dose response in the bias magnitude, separate from a paired zero bias control at every tested magnitude. It reaches $\\approx 17$\\,cm mean displacement with lateral shifts up to $\\sim 140$\\ cm at the clamp. A layer ablation places the action-relevant signal in late layers, where the effect increases with the number of hooked layers (2.0cm for the first 8 layers; 67.6cm for all 36). A per call injection audit explains why the Chain-of-Causation text never changes. The mask based bias never reaches the reasoning pathway in this serving stack, so the invariance is verified exposure, not robustness. Steered trajectories tend to shift toward the attended actor, suggesting the bias governs where the model looks rather than encoding a target behavior.", "published": "2026-08-17T20:03:38Z", "authors": ["Darshan Nagendra Prasad", "Lars Ullrich", "Knut Graichen"], "categories": ["cs.CV"], "doi": "", "_query": "property valuation computer vision", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2103.04037v2", "title": "Perspectives and Prospects on Transformer Architecture for Cross-Modal Tasks with Language and Vision", "abstract": "Transformer architectures have brought about fundamental changes to computational linguistic field, which had been dominated by recurrent neural networks for many years. Its success also implies drastic changes in cross-modal tasks with language and vision, and many researchers have already tackled the issue. In this paper, we review some of the most critical milestones in the field, as well as overall trends on how transformer architecture has been incorporated into visuolinguistic cross-modal tasks. Furthermore, we discuss its current limitations and speculate upon some of the prospects that we find imminent.", "published": "2021-03-06T05:44:27Z", "authors": ["Andrew Shin", "Masato Ishii", "Takuya Narihira"], "categories": ["cs.CV", "cs.CL"], "doi": "", "_query": "property valuation computer vision", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2205.11397v5", "title": "Super Vision Transformer", "abstract": "We attempt to reduce the computational costs in vision transformers (ViTs), which increase quadratically in the token number. We present a novel training paradigm that trains only one ViT model at a time, but is capable of providing improved image recognition performance with various computational costs. Here, the trained ViT model, termed super vision transformer (SuperViT), is empowered with the versatile ability to solve incoming patches of multiple sizes as well as preserve informative tokens with multiple keeping rates (the ratio of keeping tokens) to achieve good hardware efficiency for inference, given that the available hardware resources often change from time to time. Experimental results on ImageNet demonstrate that our SuperViT can considerably reduce the computational costs of ViT models with even performance increase. For example, we reduce 2x FLOPs of DeiT-S while increasing the Top-1 accuracy by 0.2% and 0.7% for 1.5x reduction. Also, our SuperViT significantly outperforms existing studies on efficient vision transformers. For example, when consuming the same amount of FLOPs, our SuperViT surpasses the recent state-of-the-art (SOTA) EViT by 1.1% when using DeiT-S as their backbones. The project of this work is made publicly available at https://github.com/lmbxmu/SuperViT.", "published": "2022-05-23T15:42:12Z", "authors": ["Mingbao Lin", "Mengzhao Chen", "Yuxin Zhang", "Chunhua Shen", "Rongrong Ji", "Liujuan Cao"], "categories": ["cs.CV"], "doi": "", "_query": "property valuation computer vision", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2608.07984v1", "title": "AgriField-40K: Adapting Vision Models to Agriculture With Efficient Continual Pretraining", "abstract": "Field-based agricultural computer vision is important for precision agriculture, yet it largely depends on expensive annotations and costly adaptation of large pretrained models. We introduce AgriField-40K, a field-centric dataset curated from 17 public resources and covering diverse crops, weeds, and field conditions. Building on this, we present AgriMAE, a parameter-efficient continual pretraining baseline that adapts a masked autoencoder pretrained on natural images by training only lightweight adapters. We further explore semantic feature reconstruction as an alternative pretraining objective and evaluate transfer across multiple tasks. AgriMAE consistently improves downstream performance and can match or even outperform full fine-tuning while using up to $9\\times$ fewer trainable parameters, showing that AgriField-40K is a practical resource for continual pretraining in agricultural vision. Project page: https://dtu-pas.github.io/agrifield40k/", "published": "2026-08-08T07:38:01Z", "authors": ["Vasileios Tzouras", "Paraskevas Pegios", "Lazaros Nalpantidis"], "categories": ["cs.CV"], "doi": "", "_query": "property valuation computer vision", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2405.06849v1", "title": "GreedyViG: Dynamic Axial Graph Construction for Efficient Vision GNNs", "abstract": "Vision graph neural networks (ViG) offer a new avenue for exploration in computer vision. A major bottleneck in ViGs is the inefficient k-nearest neighbor (KNN) operation used for graph construction. To solve this issue, we propose a new method for designing ViGs, Dynamic Axial Graph Construction (DAGC), which is more efficient than KNN as it limits the number of considered graph connections made within an image. Additionally, we propose a novel CNN-GNN architecture, GreedyViG, which uses DAGC. Extensive experiments show that GreedyViG beats existing ViG, CNN, and ViT architectures in terms of accuracy, GMACs, and parameters on image classification, object detection, instance segmentation, and semantic segmentation tasks. Our smallest model, GreedyViG-S, achieves 81.1% top-1 accuracy on ImageNet-1K, 2.9% higher than Vision GNN and 2.2% higher than Vision HyperGraph Neural Network (ViHGNN), with less GMACs and a similar number of parameters. Our largest model, GreedyViG-B obtains 83.9% top-1 accuracy, 0.2% higher than Vision GNN, with a 66.6% decrease in parameters and a 69% decrease in GMACs. GreedyViG-B also obtains the same accuracy as ViHGNN with a 67.3% decrease in parameters and a 71.3% decrease in GMACs. Our work shows that hybrid CNN-GNN architectures not only provide a new avenue for designing efficient models, but that they can also exceed the performance of current state-of-the-art models.", "published": "2024-05-10T23:21:16Z", "authors": ["Mustafa Munir", "William Avery", "Md Mostafijur Rahman", "Radu Marculescu"], "categories": ["cs.CV", "cs.AI", "cs.LG"], "doi": "", "_query": "property valuation computer vision", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2112.09898v1", "title": "Does Explainable Machine Learning Uncover the Black Box in Vision Applications?", "abstract": "Machine learning (ML) in general and deep learning (DL) in particular has become an extremely popular tool in several vision applications (like object detection, super resolution, segmentation, object tracking etc.). Almost in parallel, the issue of explainability in ML (i.e. the ability to explain/elaborate the way a trained ML model arrived at its decision) in vision has also received fairly significant attention from various quarters. However, we argue that the current philosophy behind explainable ML suffers from certain limitations, and the resulting explanations may not meaningfully uncover black box ML models. To elaborate our assertion, we first raise a few fundamental questions which have not been adequately discussed in the corresponding literature. We also provide perspectives on how explainablity in ML can benefit by relying on more rigorous principles in the related areas.", "published": "2021-12-18T10:37:52Z", "authors": ["Manish Narwaria"], "categories": ["cs.CV", "cs.LG"], "doi": "10.1016/j.imavis.2021.104353", "_query": "property valuation computer vision", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2407.04538v3", "title": "PDiscoFormer: Relaxing Part Discovery Constraints with Vision Transformers", "abstract": "Computer vision methods that explicitly detect object parts and reason on them are a step towards inherently interpretable models. Existing approaches that perform part discovery driven by a fine-grained classification task make very restrictive assumptions on the geometric properties of the discovered parts; they should be small and compact. Although this prior is useful in some cases, in this paper we show that pre-trained transformer-based vision models, such as self-supervised DINOv2 ViT, enable the relaxation of these constraints. In particular, we find that a total variation (TV) prior, which allows for multiple connected components of any size, substantially outperforms previous work. We test our approach on three fine-grained classification benchmarks: CUB, PartImageNet and Oxford Flowers, and compare our results to previously published methods as well as a re-implementation of the state-of-the-art method PDiscoNet with a transformer-based backbone. We consistently obtain substantial improvements across the board, both on part discovery metrics and the downstream classification task, showing that the strong inductive biases in self-supervised ViT models require to rethink the geometric priors that can be used for unsupervised part discovery.", "published": "2024-07-05T14:24:37Z", "authors": ["Ananthu Aniraj", "Cassio F. Dantas", "Dino Ienco", "Diego Marcos"], "categories": ["cs.CV", "cs.AI", "cs.LG"], "doi": "", "_query": "property valuation computer vision", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2603.26551v2", "title": "Beyond MACs: Hardware Efficient Architecture Design for Vision Backbones", "abstract": "Vision backbone networks play a central role in modern computer vision. Enhancing their efficiency directly benefits a wide range of downstream applications. To measure efficiency, many publications rely on MACs (Multiply Accumulate operations) as a predictor of execution time. In this paper, we experimentally demonstrate the shortcomings of such a metric, especially in the context of edge devices. By contrasting the MAC count and execution time of common architectural design elements, we identify key factors for efficient execution and provide insights to optimize backbone design. Based on these insights, we present LowFormer, a novel vision backbone family. LowFormer features a streamlined macro and micro design that includes Lowtention, a lightweight alternative to Multi-Head Self-Attention. Lowtention not only proves more efficient, but also enables superior results on ImageNet. Additionally, we present an edge GPU version of LowFormer, that can further improve upon its baseline's speed on edge GPU and desktop GPU. We demonstrate LowFormer's wide applicability by evaluating it on smaller image classification datasets, as well as adapting it to several downstream tasks, such as object detection, semantic segmentation, image retrieval, and visual object tracking. LowFormer models consistently achieve remarkable speed-ups across various hardware platforms compared to recent state-of-the-art backbones. Code and models are available at https://github.com/altair199797/LowFormer/blob/main/Beyond_MACs.md.", "published": "2026-03-27T16:05:20Z", "authors": ["Moritz Nottebaum", "Matteo Dunnhofer", "Christian Micheloni"], "categories": ["cs.CV", "cs.AI"], "doi": "10.1007/s11263-026-02873-5", "_query": "property valuation computer vision", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2006.01423v1", "title": "Monocular Human Pose Estimation: A Survey of Deep Learning-based Methods", "abstract": "Vision-based monocular human pose estimation, as one of the most fundamental and challenging problems in computer vision, aims to obtain posture of the human body from input images or video sequences. The recent developments of deep learning techniques have been brought significant progress and remarkable breakthroughs in the field of human pose estimation. This survey extensively reviews the recent deep learning-based 2D and 3D human pose estimation methods published since 2014. This paper summarizes the challenges, main frameworks, benchmark datasets, evaluation metrics, performance comparison, and discusses some promising future research directions.", "published": "2020-06-02T07:07:45Z", "authors": ["Yucheng Chen", "Yingli Tian", "Mingyi He"], "categories": ["cs.CV"], "doi": "10.1016/j.cviu.2019.102897", "_query": "property valuation computer vision", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2211.03705v1", "title": "A Survey on Computer Vision based Human Analysis in the COVID-19 Era", "abstract": "The emergence of COVID-19 has had a global and profound impact, not only on society as a whole, but also on the lives of individuals. Various prevention measures were introduced around the world to limit the transmission of the disease, including face masks, mandates for social distancing and regular disinfection in public spaces, and the use of screening applications. These developments also triggered the need for novel and improved computer vision techniques capable of (i) providing support to the prevention measures through an automated analysis of visual data, on the one hand, and (ii) facilitating normal operation of existing vision-based services, such as biometric authentication schemes, on the other. Especially important here, are computer vision techniques that focus on the analysis of people and faces in visual data and have been affected the most by the partial occlusions introduced by the mandates for facial masks. Such computer vision based human analysis techniques include face and face-mask detection approaches, face recognition techniques, crowd counting solutions, age and expression estimation procedures, models for detecting face-hand interactions and many others, and have seen considerable attention over recent years. The goal of this survey is to provide an introduction to the problems induced by COVID-19 into such research and to present a comprehensive review of the work done in the computer vision based human analysis field. Particular attention is paid to the impact of facial masks on the performance of various methods and recent solutions to mitigate this problem. Additionally, a detailed review of existing datasets useful for the development and evaluation of methods for COVID-19 related applications is also provided. Finally, to help advance the field further, a discussion on the main open challenges and future research direction is given.", "published": "2022-11-07T17:20:39Z", "authors": ["Fevziye Irem Eyiokur", "Alperen Kantarcı", "Mustafa Ekrem Erakın", "Naser Damer", "Ferda Ofli", "Muhammad Imran", "Janez Križaj", "Albert Ali Salah", "Alexander Waibel", "Vitomir Štruc", "Hazım Kemal Ekenel"], "categories": ["cs.CV", "eess.IV"], "doi": "", "_query": "property valuation computer vision", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2311.04505v1", "title": "NITEC: Versatile Hand-Annotated Eye Contact Dataset for Ego-Vision Interaction", "abstract": "Eye contact is a crucial non-verbal interaction modality and plays an important role in our everyday social life. While humans are very sensitive to eye contact, the capabilities of machines to capture a person's gaze are still mediocre. We tackle this challenge and present NITEC, a hand-annotated eye contact dataset for ego-vision interaction. NITEC exceeds existing datasets for ego-vision eye contact in size and variety of demographics, social contexts, and lighting conditions, making it a valuable resource for advancing ego-vision-based eye contact research. Our extensive evaluations on NITEC demonstrate strong cross-dataset performance, emphasizing its effectiveness and adaptability in various scenarios, that allows seamless utilization to the fields of computer vision, human-computer interaction, and social robotics. We make our NITEC dataset publicly available to foster reproducibility and further exploration in the field of ego-vision interaction. https://github.com/thohemp/nitec", "published": "2023-11-08T07:42:31Z", "authors": ["Thorsten Hempel", "Magnus Jung", "Ahmed A. Abdelrahman", "Ayoub Al-Hamadi"], "categories": ["cs.CV"], "doi": "", "_query": "property valuation computer vision", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2309.10878v1", "title": "DeepliteRT: Computer Vision at the Edge", "abstract": "The proliferation of edge devices has unlocked unprecedented opportunities for deep learning model deployment in computer vision applications. However, these complex models require considerable power, memory and compute resources that are typically not available on edge platforms. Ultra low-bit quantization presents an attractive solution to this problem by scaling down the model weights and activations from 32-bit to less than 8-bit. We implement highly optimized ultra low-bit convolution operators for ARM-based targets that outperform existing methods by up to 4.34x. Our operator is implemented within Deeplite Runtime (DeepliteRT), an end-to-end solution for the compilation, tuning, and inference of ultra low-bit models on ARM devices. Compiler passes in DeepliteRT automatically convert a fake-quantized model in full precision to a compact ultra low-bit representation, easing the process of quantized model deployment on commodity hardware. We analyze the performance of DeepliteRT on classification and detection models against optimized 32-bit floating-point, 8-bit integer, and 2-bit baselines, achieving significant speedups of up to 2.20x, 2.33x and 2.17x, respectively.", "published": "2023-09-19T18:58:38Z", "authors": ["Saad Ashfaq", "Alexander Hoffman", "Saptarshi Mitra", "Sudhakar Sah", "MohammadHossein AskariHemmat", "Ehsan Saboori"], "categories": ["cs.LG", "cs.CV"], "doi": "", "_query": "property valuation computer vision", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/1904.08487v1", "title": "Machine Vision Guided 3D Medical Image Compression for Efficient Transmission and Accurate Segmentation in the Clouds", "abstract": "Cloud based medical image analysis has become popular recently due to the high computation complexities of various deep neural network (DNN) based frameworks and the increasingly large volume of medical images that need to be processed. It has been demonstrated that for medical images the transmission from local to clouds is much more expensive than the computation in the clouds itself. Towards this, 3D image compression techniques have been widely applied to reduce the data traffic. However, most of the existing image compression techniques are developed around human vision, i.e., they are designed to minimize distortions that can be perceived by human eyes. In this paper we will use deep learning based medical image segmentation as a vehicle and demonstrate that interestingly, machine and human view the compression quality differently. Medical images compressed with good quality w.r.t. human vision may result in inferior segmentation accuracy. We then design a machine vision oriented 3D image compression framework tailored for segmentation using DNNs. Our method automatically extracts and retains image features that are most important to the segmentation. Comprehensive experiments on widely adopted segmentation frameworks with HVSMR 2016 challenge dataset show that our method can achieve significantly higher segmentation accuracy at the same compression rate, or much better compression rate under the same segmentation accuracy, when compared with the existing JPEG 2000 method. To the best of the authors' knowledge, this is the first machine vision guided medical image compression framework for segmentation in the clouds.", "published": "2019-04-09T13:34:25Z", "authors": ["Zihao Liu", "Xiaowei Xu", "Tao Liu", "Qi Liu", "Yanzhi Wang", "Yiyu Shi", "Wujie Wen", "Meiping Huang", "Haiyun Yuan", "Jian Zhuang"], "categories": ["cs.CV", "cs.LG", "stat.ML"], "doi": "", "_query": "property valuation computer vision", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2107.13411v1", "title": "Predicting the Future from First Person (Egocentric) Vision: A Survey", "abstract": "Egocentric videos can bring a lot of information about how humans perceive the world and interact with the environment, which can be beneficial for the analysis of human behaviour. The research in egocentric video analysis is developing rapidly thanks to the increasing availability of wearable devices and the opportunities offered by new large-scale egocentric datasets. As computer vision techniques continue to develop at an increasing pace, the tasks related to the prediction of future are starting to evolve from the need of understanding the present. Predicting future human activities, trajectories and interactions with objects is crucial in applications such as human-robot interaction, assistive wearable technologies for both industrial and daily living scenarios, entertainment and virtual or augmented reality. This survey summarises the evolution of studies in the context of future prediction from egocentric vision making an overview of applications, devices, existing problems, commonly used datasets, models and input modalities. Our analysis highlights that methods for future prediction from egocentric vision can have a significant impact in a range of applications and that further research efforts should be devoted to the standardisation of tasks and the proposal of datasets considering real-world scenarios such as the ones with an industrial vocation.", "published": "2021-07-28T14:58:13Z", "authors": ["Ivan Rodin", "Antonino Furnari", "Dimitrios Mavroedis", "Giovanni Maria Farinella"], "categories": ["cs.CV"], "doi": "", "_query": "property valuation computer vision", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/1004.3299v4", "title": "Valuation equations for stochastic volatility models", "abstract": "We analyze the valuation partial differential equation for European contingent claims in a general framework of stochastic volatility models where the diffusion coefficients may grow faster than linearly and degenerate on the boundaries of the state space. We allow for various types of model behavior: the volatility process in our model can potentially reach zero and either stay there or instantaneously reflect, and the asset-price process may be a strict local martingale. Our main result is a necessary and sufficient condition on the uniqueness of classical solutions to the valuation equation: the value function is the unique nonnegative classical solution to the valuation equation among functions with at most linear growth if and only if the asset-price is a martingale.", "published": "2010-04-19T20:23:36Z", "authors": ["Erhan Bayraktar", "Constantinos Kardaras", "Hao Xing"], "categories": ["math.PR", "q-fin.PR"], "doi": "", "_query": "property valuation computer vision", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/1707.01649v3", "title": "Frobenius splitting of valuation rings and $F$-singularities of centers", "abstract": "Using a local monomialization result of Knaf and Kuhlmann, we prove that the valuation ring of an Abhyankar valuation of a function field over a perfect ground field of prime characteristic is Frobenius split. We show that a Frobenius splitting of a sufficiently well-behaved center lifts to a Frobenius splitting of the valuation ring. We also investigate properties of valuations centered on arbitrary Noetherian domains of prime characteristic. In contrast to [arXiv:1507.06009], this paper emphasizes the role of centers in controlling Frobenius properties of valuations rings in prime characteristic.", "published": "2017-07-06T06:14:21Z", "authors": ["Rankeya Datta"], "categories": ["math.AC", "math.AG"], "doi": "10.2140/ant.2021.15.2485", "_query": "property valuation computer vision", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/1803.10081v3", "title": "DeepJDOT: Deep Joint Distribution Optimal Transport for Unsupervised Domain Adaptation", "abstract": "In computer vision, one is often confronted with problems of domain shifts, which occur when one applies a classifier trained on a source dataset to target data sharing similar characteristics (e.g. same classes), but also different latent data structures (e.g. different acquisition conditions). In such a situation, the model will perform poorly on the new data, since the classifier is specialized to recognize visual cues specific to the source domain. In this work we explore a solution, named DeepJDOT, to tackle this problem: through a measure of discrepancy on joint deep representations/labels based on optimal transport, we not only learn new data representations aligned between the source and target domain, but also simultaneously preserve the discriminative information used by the classifier. We applied DeepJDOT to a series of visual recognition tasks, where it compares favorably against state-of-the-art deep domain adaptation methods.", "published": "2018-03-27T13:54:05Z", "authors": ["Bharath Bhushan Damodaran", "Benjamin Kellenberger", "Rémi Flamary", "Devis Tuia", "Nicolas Courty"], "categories": ["cs.CV", "cs.AI"], "doi": "", "_query": "property valuation computer vision", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2405.16091v2", "title": "Near OOD Detection for Vision-Language Prompt Learning with Contrastive Logit Score", "abstract": "Prompt learning has emerged as an efficient and effective method for fine-tuning vision-language models such as CLIP. While many studies have explored generalisation abilities of these models in few-shot classification tasks and a few studies have addressed far out-of-distribution (OOD) of the models, their potential for addressing near OOD detection remains underexplored. Existing methods either require training from scratch, need fine-tuning, or are not designed for vision-language prompt learning. To address this, we introduce the Contrastive Logit Score (CLS), a novel post-hoc, plug-and-play scoring function. CLS significantly improves near OOD detection of pre-trained vision-language prompt learning methods without modifying their model architectures or requiring retraining. Our method achieves up to an 11.67% improvement in AUROC for near OOD detection with minimal computational overhead. Extensive evaluations validate the effectiveness, efficiency, and generalisability of our approach. Our code is available at https://github.com/davidmcjung/near-OOD-prompt-learning.", "published": "2024-05-25T06:46:16Z", "authors": ["Myong Chol Jung", "Joanna Dipnall", "Belinda Gabbe", "He Zhao"], "categories": ["cs.CV"], "doi": "", "_query": "property valuation computer vision", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2511.09942v1", "title": "AdaptViG: Adaptive Vision GNN with Exponential Decay Gating", "abstract": "Vision Graph Neural Networks (ViGs) offer a new direction for advancements in vision architectures. While powerful, ViGs often face substantial computational challenges stemming from their graph construction phase, which can hinder their efficiency. To address this issue we propose AdaptViG, an efficient and powerful hybrid Vision GNN that introduces a novel graph construction mechanism called Adaptive Graph Convolution. This mechanism builds upon a highly efficient static axial scaffold and a dynamic, content-aware gating strategy called Exponential Decay Gating. This gating mechanism selectively weighs long-range connections based on feature similarity. Furthermore, AdaptViG employs a hybrid strategy, utilizing our efficient gating mechanism in the early stages and a full Global Attention block in the final stage for maximum feature aggregation. Our method achieves a new state-of-the-art trade-off between accuracy and efficiency among Vision GNNs. For instance, our AdaptViG-M achieves 82.6% top-1 accuracy, outperforming ViG-B by 0.3% while using 80% fewer parameters and 84% fewer GMACs. On downstream tasks, AdaptViG-M obtains 45.8 mIoU, 44.8 APbox, and 41.1 APmask, surpassing the much larger EfficientFormer-L7 by 0.7 mIoU, 2.2 APbox, and 2.1 APmask, respectively, with 78% fewer parameters.", "published": "2025-11-13T04:16:20Z", "authors": ["Mustafa Munir", "Md Mostafijur Rahman", "Radu Marculescu"], "categories": ["cs.CV", "cs.AI", "cs.LG"], "doi": "", "_query": "property valuation computer vision", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2312.02141v3", "title": "iMatching: Imperative Correspondence Learning", "abstract": "Learning feature correspondence is a foundational task in computer vision, holding immense importance for downstream applications such as visual odometry and 3D reconstruction. Despite recent progress in data-driven models, feature correspondence learning is still limited by the lack of accurate per-pixel correspondence labels. To overcome this difficulty, we introduce a new self-supervised scheme, imperative learning (IL), for training feature correspondence. It enables correspondence learning on arbitrary uninterrupted videos without any camera pose or depth labels, heralding a new era for self-supervised correspondence learning. Specifically, we formulated the problem of correspondence learning as a bilevel optimization, which takes the reprojection error from bundle adjustment as a supervisory signal for the model. To avoid large memory and computation overhead, we leverage the stationary point to effectively back-propagate the implicit gradients through bundle adjustment. Through extensive experiments, we demonstrate superior performance on tasks including feature matching and pose estimation, in which we obtained an average of 30% accuracy gain over the state-of-the-art matching models.", "published": "2023-12-04T18:58:20Z", "authors": ["Zitong Zhan", "Dasong Gao", "Yun-Jou Lin", "Youjie Xia", "Chen Wang"], "categories": ["cs.CV"], "doi": "10.1007/978-3-031-72933-1_11", "_query": "property valuation computer vision", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2101.11002v1", "title": "New Algorithms for Computing Field of Vision over 2D Grids", "abstract": "The aim of this paper is to propose new algorithms for Field of Vision (FOV) computation which improve on existing work at high resolutions. FOV refers to the set of locations that are visible from a specific position in a scene of a computer game. We summarize existing algorithms for FOV computation, describe their limitations, and present new algorithms which aim to address these limitations. We first present an algorithm which makes use of spatial data structures in a way which is new for FOV calculation. We then present a novel technique which updates a previously calculated FOV, rather than re-calculating an FOV from scratch. We compare our algorithms to existing FOV algorithms and show they provide substantial improvements to running time. Our algorithms provide the largest improvement over existing FOV algorithms at large grid sizes, thus allowing the possibility of the design of high resolution FOV-based video games.", "published": "2021-01-26T20:38:35Z", "authors": ["Evan R. M. Debenham", "Roberto Solis-Oba"], "categories": ["cs.CV"], "doi": "", "_query": "property valuation computer vision", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2604.20329v3", "title": "Image Generators are Generalist Vision Learners", "abstract": "Recent works show that image and video generators exhibit zero-shot visual understanding behaviors, in a way reminiscent of how LLMs develop emergent capabilities of language understanding and reasoning from generative pretraining. While it has long been conjectured that the ability to create visual content implies an ability to understand it, there has been limited evidence that generative vision models have developed strong understanding capabilities. In this work, we demonstrate that image generation training serves a role similar to LLM pretraining, and lets models learn powerful and general visual representations that enable SOTA performance on various vision tasks. We introduce Vision Banana, a generalist model built by instruction-tuning Nano Banana Pro (NBP) on a mixture of its original training data alongside a small amount of vision task data. By parameterizing the output space of vision tasks as RGB images, we seamlessly reframe perception as image generation. Our generalist model, Vision Banana, achieves SOTA results on a variety of vision tasks involving both 2D and 3D understanding, beating or rivaling zero-shot domain-specialists, including Segment Anything Model 3 on segmentation tasks, and the Depth Anything series on metric depth estimation. We show that these results can be achieved with lightweight instruction-tuning without sacrificing the base model's image generation capabilities. The superior results suggest that image generation pretraining is a generalist vision learner. It also shows that image generation serves as a unified and universal interface for vision tasks, similar to text generation's role in language understanding and reasoning. We could be witnessing a major paradigm shift for computer vision, where generative vision pretraining takes a central role in building Foundational Vision Models for both generation and understanding.", "published": "2026-04-22T08:23:48Z", "authors": ["Valentin Gabeur", "Shangbang Long", "Songyou Peng", "Paul Voigtlaender", "Shuyang Sun", "Yanan Bao", "Karen Truong", "Zhicheng Wang", "Wenlei Zhou", "Jonathan T. Barron", "Kyle Genova", "Nithish Kannen", "Sherry Ben", "Yandong Li", "Mandy Guo", "Suhas Yogin", "Yiming Gu", "Huizhong Chen", "Oliver Wang", "Saining Xie", "Howard Zhou", "Kaiming He", "Thomas Funkhouser", "Jean-Baptiste Alayrac", "Radu Soricut"], "categories": ["cs.CV", "cs.AI"], "doi": "", "_query": "property valuation computer vision", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2603.14604v2", "title": "Tactile Modality Fusion for Vision-Language-Action Models", "abstract": "We propose TacFiLM, a lightweight modality-fusion approach that integrates visual-tactile signals into vision-language-action (VLA) models. While advances in VLAs have introduced robot policies that are both generalizable and semantically grounded, these models mainly rely on vision-based perception. Vision alone, however, cannot capture the complex interaction dynamics that occur during contact-rich manipulation, including contact forces, surface friction, compliance, and shear. While recent attempts to integrate tactile signals into VLA models often increase complexity through token concatenation or large-scale pretraining, the heavy computational demands of behaviour models necessitate lightweight fusion strategies. To address these challenges, TacFiLM outlines a post-training finetuning approach that conditions intermediate visual features on pretrained tactile representations using feature-wise linear modulation (FiLM). Experimental results on insertion and drawer opening tasks demonstrate consistent improvements in success rate, direct task performance, completion time, and force stability across both in-distribution and out-of-distribution tasks. Together, these results support our method as an effective approach to integrating tactile signals into VLA models, improving contact-rich manipulation behaviours. Project page: https://charliem7.github.io/projects/TacFilm/", "published": "2026-03-15T20:57:51Z", "authors": ["Charlotte Morissette", "Amin Abyaneh", "Wei-Di Chang", "Anas Houssaini", "David Meger", "Hsiu-Chin Lin", "Jonathan Tremblay", "Gregory Dudek"], "categories": ["cs.RO", "cs.CV", "cs.LG"], "doi": "", "_query": "property valuation computer vision", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2502.09927v1", "title": "Granite Vision: a lightweight, open-source multimodal model for enterprise Intelligence", "abstract": "We introduce Granite Vision, a lightweight large language model with vision capabilities, specifically designed to excel in enterprise use cases, particularly in visual document understanding. Our model is trained on a comprehensive instruction-following dataset, including document-related tasks, such as content extraction from tables, charts, diagrams, sketches, and infographics, as well as general image tasks. The architecture of Granite Vision is centered around visual modality alignment with a decoder-only, 2 billion parameter Granite large language model. Additionally, we introduce a dedicated safety classification approach in test-time that leverages a sparse set of attention vectors to identify potential harmful inputs. Despite its lightweight architecture, Granite Vision achieves strong results in standard benchmarks related to visual document understanding, as well as on the LiveXiv benchmark, which is designed to avoid test set contamination by using a constantly updated corpus of recently published Arxiv papers. We are releasing the model under the Apache-2 license, allowing for both research and commercial use, while offering complete visibility into the training data and other relevant details. See https://huggingface.co/ibm-granite/ for model weights.", "published": "2025-02-14T05:36:32Z", "authors": [" Granite Vision Team", "Leonid Karlinsky", "Assaf Arbelle", "Abraham Daniels", "Ahmed Nassar", "Amit Alfassi", "Bo Wu", "Eli Schwartz", "Dhiraj Joshi", "Jovana Kondic", "Nimrod Shabtay", "Pengyuan Li", "Roei Herzig", "Shafiq Abedin", "Shaked Perek", "Sivan Harary", "Udi Barzelay", "Adi Raz Goldfarb", "Aude Oliva", "Ben Wieles", "Bishwaranjan Bhattacharjee", "Brandon Huang", "Christoph Auer", "Dan Gutfreund", "David Beymer", "David Wood", "Hilde Kuehne", "Jacob Hansen", "Joseph Shtok", "Ken Wong", "Luis Angel Bathen", "Mayank Mishra", "Maksym Lysak", "Michele Dolfi", "Mikhail Yurochkin", "Nikolaos Livathinos", "Nimrod Harel", "Ophir Azulai", "Oshri Naparstek", "Rafael Teixeira de Lima", "Rameswar Panda", "Sivan Doveh", "Shubham Gupta", "Subhro Das", "Syed Zawad", "Yusik Kim", "Zexue He", "Alexander Brooks", "Gabe Goodhart", "Anita Govindjee", "Derek Leist", "Ibrahim Ibrahim", "Aya Soffer", "David Cox", "Kate Soule", "Luis Lastras", "Nirmit Desai", "Shila Ofek-koifman", "Sriram Raghavan", "Tanveer Syeda-Mahmood", "Peter Staar", "Tal Drory", "Rogerio Feris"], "categories": ["cs.CV", "cs.AI"], "doi": "", "_query": "property valuation computer vision", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/1904.08298v1", "title": "Events-to-Video: Bringing Modern Computer Vision to Event Cameras", "abstract": "Event cameras are novel sensors that report brightness changes in the form of asynchronous \"events\" instead of intensity frames. They have significant advantages over conventional cameras: high temporal resolution, high dynamic range, and no motion blur. Since the output of event cameras is fundamentally different from conventional cameras, it is commonly accepted that they require the development of specialized algorithms to accommodate the particular nature of events. In this work, we take a different view and propose to apply existing, mature computer vision techniques to videos reconstructed from event data. We propose a novel recurrent network to reconstruct videos from a stream of events, and train it on a large amount of simulated event data. Our experiments show that our approach surpasses state-of-the-art reconstruction methods by a large margin (> 20%) in terms of image quality. We further apply off-the-shelf computer vision algorithms to videos reconstructed from event data on tasks such as object classification and visual-inertial odometry, and show that this strategy consistently outperforms algorithms that were specifically designed for event data. We believe that our approach opens the door to bringing the outstanding properties of event cameras to an entirely new range of tasks. A video of the experiments is available at https://youtu.be/IdYrC4cUO0I", "published": "2019-04-17T14:54:49Z", "authors": ["Henri Rebecq", "René Ranftl", "Vladlen Koltun", "Davide Scaramuzza"], "categories": ["cs.CV"], "doi": "", "_query": "property valuation computer vision", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2503.10191v1", "title": "Robustness Tokens: Towards Adversarial Robustness of Transformers", "abstract": "Recently, large pre-trained foundation models have become widely adopted by machine learning practitioners for a multitude of tasks. Given that such models are publicly available, relying on their use as backbone models for downstream tasks might result in high vulnerability to adversarial attacks crafted with the same public model. In this work, we propose Robustness Tokens, a novel approach specific to the transformer architecture that fine-tunes a few additional private tokens with low computational requirements instead of tuning model parameters as done in traditional adversarial training. We show that Robustness Tokens make Vision Transformer models significantly more robust to white-box adversarial attacks while also retaining the original downstream performances.", "published": "2025-03-13T09:26:19Z", "authors": ["Brian Pulfer", "Yury Belousov", "Slava Voloshynovskiy"], "categories": ["cs.LG", "cs.AI", "cs.CV"], "doi": "10.1007/978-3-031-73202-7_7", "_query": "property valuation computer vision", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2407.03268v2", "title": "For a semiotic AI: Bridging computer vision and visual semiotics for computational observation of large scale facial image archives", "abstract": "Social networks are creating a digital world in which the cognitive, emotional, and pragmatic value of the imagery of human faces and bodies is arguably changing. However, researchers in the digital humanities are often ill-equipped to study these phenomena at scale. This work presents FRESCO (Face Representation in E-Societies through Computational Observation), a framework designed to explore the socio-cultural implications of images on social media platforms at scale. FRESCO deconstructs images into numerical and categorical variables using state-of-the-art computer vision techniques, aligning with the principles of visual semiotics. The framework analyzes images across three levels: the plastic level, encompassing fundamental visual features like lines and colors; the figurative level, representing specific entities or concepts; and the enunciation level, which focuses particularly on constructing the point of view of the spectator and observer. These levels are analyzed to discern deeper narrative layers within the imagery. Experimental validation confirms the reliability and utility of FRESCO, and we assess its consistency and precision across two public datasets. Subsequently, we introduce the FRESCO score, a metric derived from the framework's output that serves as a reliable measure of similarity in image content.", "published": "2024-07-03T16:57:38Z", "authors": ["Lia Morra", "Antonio Santangelo", "Pietro Basci", "Luca Piano", "Fabio Garcea", "Fabrizio Lamberti", "Massimo Leone"], "categories": ["cs.CV"], "doi": "10.1016/j.cviu.2024.104187", "_query": "property valuation computer vision", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/0708.0927v1", "title": "Modeling Visual Information Processing in Brain: A Computer Vision Point of View and Approach", "abstract": "We live in the Information Age, and information has become a critically important component of our life. The success of the Internet made huge amounts of it easily available and accessible to everyone. To keep the flow of this information manageable, means for its faultless circulation and effective handling have become urgently required. Considerable research efforts are dedicated today to address this necessity, but they are seriously hampered by the lack of a common agreement about \"What is information?\" In particular, what is \"visual information\" - human's primary input from the surrounding world. The problem is further aggravated by a long-lasting stance borrowed from the biological vision research that assumes human-like information processing as an enigmatic mix of perceptual and cognitive vision faculties. I am trying to find a remedy for this bizarre situation. Relying on a new definition of \"information\", which can be derived from Kolmogorov's compexity theory and Chaitin's notion of algorithmic information, I propose a unifying framework for visual information processing, which explicitly accounts for the perceptual and cognitive image processing peculiarities. I believe that this framework will be useful to overcome the difficulties that are impeding our attempts to develop the right model of human-like intelligent image processing.", "published": "2007-08-07T11:16:15Z", "authors": ["Emanuel Diamant"], "categories": ["cs.AI", "cs.CV"], "doi": "", "_query": "property valuation computer vision", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2305.11927v2", "title": "Evaluating how interactive visualizations can assist in finding samples where and how computer vision models make mistakes", "abstract": "Creating Computer Vision (CV) models remains a complex practice, despite their ubiquity. Access to data, the requirement for ML expertise, and model opacity are just a few points of complexity that limit the ability of end-users to build, inspect, and improve these models. Interactive ML perspectives have helped address some of these issues by considering a teacher in the loop where planning, teaching, and evaluating tasks take place. We present and evaluate two interactive visualizations in the context of Sprite, a system for creating CV classification and detection models for images originating from videos. We study how these visualizations help Sprite's users identify (evaluate) and select (plan) images where a model is struggling and can lead to improved performance, compared to a baseline condition where users used a query language. We found that users who had used the visualizations found more images across a wider set of potential types of model errors.", "published": "2023-05-19T14:43:00Z", "authors": ["Hayeong Song", "Gonzalo Ramos", "Peter Bodik"], "categories": ["cs.HC", "cs.CV", "cs.LG"], "doi": "", "_query": "property valuation computer vision", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2606.21216v1", "title": "A scalar per patch from pre-trained ViTs enables fast moving navigation in the real world", "abstract": "Trained policies for real-world robotics rely on computer vision components, typically in the form of pre-trained visual encoders. These encoders are an essential component and it has been shown that their power does not emerge from training on robotics downstream losses alone. Pre-training with auxiliary losses in the form of computer-vision pre-text tasks is a defining factor and heavily conditions agent performance in robotics tasks. In this unprecedented large-scale study, we ran 966 navigation episodes of static point goal navigation in a real-world building for 24km and asked which components really matter for the computer vision aspects of robotics: we evaluate state-of-the art visual encoders in realistic conditions. We explore the usefulness of heterogeneous multi-teacher distillation leading to encoders with multiple different and complementary skills. We investigate how much information from these encoders is necessary for robotics by bottlenecking them in a principled and spatially useful way and we show that this leads to the emergence of interpretable features linked to affordances. We also argue that training policies on RGB data alone does not lead to an optimal usage of visual features and show this by finetuning policies pre-trained on privileged information. All in all, we paint a more complete picture of what aspects of computer vision are relevant for real-world navigation.", "published": "2026-06-19T08:32:24Z", "authors": ["Steeven Janny", "Leonid Antsfeld", "Christian Wolf"], "categories": ["cs.RO"], "doi": "", "_query": "property valuation computer vision", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/1411.3230v2", "title": "Sparse Modeling for Image and Vision Processing", "abstract": "In recent years, a large amount of multi-disciplinary research has been conducted on sparse models and their applications. In statistics and machine learning, the sparsity principle is used to perform model selection---that is, automatically selecting a simple model among a large collection of them. In signal processing, sparse coding consists of representing data with linear combinations of a few dictionary elements. Subsequently, the corresponding tools have been widely adopted by several scientific communities such as neuroscience, bioinformatics, or computer vision. The goal of this monograph is to offer a self-contained view of sparse modeling for visual recognition and image processing. More specifically, we focus on applications where the dictionary is learned and adapted to data, yielding a compact representation that has been successful in various contexts.", "published": "2014-11-12T16:33:37Z", "authors": ["Julien Mairal", "Francis Bach", "Jean Ponce"], "categories": ["cs.CV"], "doi": "", "_query": "property valuation computer vision", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2507.01955v3", "title": "How Well Does GPT-4o Understand Vision? Evaluating Multimodal Foundation Models on Standard Computer Vision Tasks", "abstract": "Multimodal foundation models (MFMs), such as GPT-4o, have recently made remarkable progress. However, their detailed visual understanding beyond question answering remains unclear. In this paper, we benchmark popular MFMs (GPT-4o, o4-mini, Gemini 1.5 Pro and Gemini 2.0 Flash, Claude 3.5 Sonnet, Qwen2-VL, Llama 3.2) on standard computer vision tasks (semantic segmentation, object detection, image classification, depth and surface normal prediction) using established datasets (e.g., COCO, ImageNet, etc). The main challenges in performing this analysis are: 1) most models are trained to output text and cannot natively express versatile domains, such as segments or 3D geometry, and 2) many leading models are proprietary and accessible only at an API level, i.e., there is no weight access to adapt them. We address these by translating vision tasks into text-promptable, API-compatible formats via prompt chaining, creating a standardized benchmarking framework. We observe that: 1) The MFMs are not close to the state-of-the-art specialist models at any task. 2) They are respectable generalists; this is remarkable, as they are presumably trained on image-text-based tasks. 3) They perform semantic tasks notably better than geometric ones. 4) GPT-4o performs the best among non-reasoning models, securing the top position in 4 out of 6 tasks. 5) Reasoning models, e.g., o3, show improvements in geometric tasks. 6) While prompt chaining techniques affect performance, better models are less sensitive to prompt variations. 7) An analysis of models with native image generation, such as the latest GPT-4o, shows they exhibit failure modes, such as hallucinated objects or misalignment between input and output.", "published": "2025-07-02T17:59:07Z", "authors": ["Rahul Ramachandran", "Ali Garjani", "Roman Bachmann", "Andrei Atanov", "Oğuzhan Fatih Kar", "Amir Zamir"], "categories": ["cs.CV", "cs.AI", "cs.LG"], "doi": "", "_query": "property valuation computer vision", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2506.05429v1", "title": "Coordinated Robustness Evaluation Framework for Vision-Language Models", "abstract": "Vision-language models, which integrate computer vision and natural language processing capabilities, have demonstrated significant advancements in tasks such as image captioning and visual question and answering. However, similar to traditional models, they are susceptible to small perturbations, posing a challenge to their robustness, particularly in deployment scenarios. Evaluating the robustness of these models requires perturbations in both the vision and language modalities to learn their inter-modal dependencies. In this work, we train a generic surrogate model that can take both image and text as input and generate joint representation which is further used to generate adversarial perturbations for both the text and image modalities. This coordinated attack strategy is evaluated on the visual question and answering and visual reasoning datasets using various state-of-the-art vision-language models. Our results indicate that the proposed strategy outperforms other multi-modal attacks and single-modality attacks from the recent literature. Our results demonstrate their effectiveness in compromising the robustness of several state-of-the-art pre-trained multi-modal models such as instruct-BLIP, ViLT and others.", "published": "2025-06-05T08:09:05Z", "authors": ["Ashwin Ramesh Babu", "Sajad Mousavi", "Vineet Gundecha", "Sahand Ghorbanpour", "Avisek Naug", "Antonio Guillen", "Ricardo Luna Gutierrez", "Soumyendu Sarkar"], "categories": ["cs.CV", "cs.AI", "cs.CL", "cs.LG"], "doi": "", "_query": "property valuation computer vision", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/cs/0407054v2", "title": "From truth to computability I", "abstract": "The recently initiated approach called computability logic is a formal theory of interactive computation. See a comprehensive online source on the subject at http://www.cis.upenn.edu/~giorgi/cl.html . The present paper contains a soundness and completeness proof for the deductive system CL3 which axiomatizes the most basic first-order fragment of computability logic called the finite-depth, elementary-base fragment. Among the potential application areas for this result are the theory of interactive computation, constructive applied theories, knowledgebase systems, systems for resource-bound planning and action. This paper is self-contained as it reintroduces all relevant definitions as well as main motivations.", "published": "2004-07-21T03:58:22Z", "authors": ["Giorgi Japaridze"], "categories": ["cs.LO", "cs.AI", "cs.GT", "math.LO"], "doi": "10.1016/j.tcs.2006.03.014", "_query": "property valuation computer vision", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2501.07451v4", "title": "A Survey on Dynamic Neural Networks: from Computer Vision to Multi-modal Sensor Fusion", "abstract": "Model compression is essential in the deployment of large Computer Vision models on embedded devices. However, static optimization techniques (e.g. pruning, quantization, etc.) neglect the fact that different inputs have different complexities, thus requiring different amount of computations. Dynamic Neural Networks allow to condition the number of computations to the specific input. The current literature on the topic is very extensive and fragmented. We present a comprehensive survey that synthesizes and unifies existing Dynamic Neural Networks research in the context of Computer Vision. Additionally, we provide a logical taxonomy based on which component of the network is adaptive: the output, the computation graph or the input. Furthermore, we argue that Dynamic Neural Networks are particularly beneficial in the context of Sensor Fusion for better adaptivity, noise reduction and information prioritization. We present preliminary works in this direction. We complement this survey with a curated repository listing all the surveyed papers, each with a brief summary of the solution and the code base when available: https://github.com/DTU-PAS/awesome-dynn-for-cv .", "published": "2025-01-13T16:24:49Z", "authors": ["Fabio Montello", "Ronja Güldenring", "Simone Scardapane", "Lazaros Nalpantidis"], "categories": ["cs.CV"], "doi": "10.1016/j.imavis.2026.105980", "_query": "property valuation computer vision", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/1002.2409v1", "title": "Changing Neighbors k Secure Sum Protocol for Secure Multi Party Computation", "abstract": "Secure sum computation of private data inputs is an important component of Secure Multi party Computation (SMC).In this paper we provide a protocol to compute the sum of individual data inputs with zero probability of data leakage. In our proposed protocol we break input of each party into number of segments and change the arrangement of the parties such that in each round of the computation the neighbors are changed. In this protocol it becomes impossible for semi honest parties to know the private data of some other party.", "published": "2010-02-11T19:58:10Z", "authors": ["Rashid Sheikh", "Beerendra Kumar", "Durgesh Kumar Mishra"], "categories": ["cs.CR"], "doi": "", "_query": "property valuation computer vision", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2404.02227v1", "title": "OOSTraj: Out-of-Sight Trajectory Prediction With Vision-Positioning Denoising", "abstract": "Trajectory prediction is fundamental in computer vision and autonomous driving, particularly for understanding pedestrian behavior and enabling proactive decision-making. Existing approaches in this field often assume precise and complete observational data, neglecting the challenges associated with out-of-view objects and the noise inherent in sensor data due to limited camera range, physical obstructions, and the absence of ground truth for denoised sensor data. Such oversights are critical safety concerns, as they can result in missing essential, non-visible objects. To bridge this gap, we present a novel method for out-of-sight trajectory prediction that leverages a vision-positioning technique. Our approach denoises noisy sensor observations in an unsupervised manner and precisely maps sensor-based trajectories of out-of-sight objects into visual trajectories. This method has demonstrated state-of-the-art performance in out-of-sight noisy sensor trajectory denoising and prediction on the Vi-Fi and JRDB datasets. By enhancing trajectory prediction accuracy and addressing the challenges of out-of-sight objects, our work significantly contributes to improving the safety and reliability of autonomous driving in complex environments. Our work represents the first initiative towards Out-Of-Sight Trajectory prediction (OOSTraj), setting a new benchmark for future research. The code is available at \\url{https://github.com/Hai-chao-Zhang/OOSTraj}.", "published": "2024-04-02T18:30:29Z", "authors": ["Haichao Zhang", "Yi Xu", "Hongsheng Lu", "Takayuki Shimizu", "Yun Fu"], "categories": ["cs.CV", "cs.AI", "cs.LG", "cs.RO"], "doi": "", "_query": "property valuation computer vision", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/1810.01069v2", "title": "Cloud Chaser: Real Time Deep Learning Computer Vision on Low Computing Power Devices", "abstract": "Internet of Things(IoT) devices, mobile phones, and robotic systems are often denied the power of deep learning algorithms due to their limited computing power. However, to provide time-critical services such as emergency response, home assistance, surveillance, etc, these devices often need real-time analysis of their camera data. This paper strives to offer a viable approach to integrate high-performance deep learning-based computer vision algorithms with low-resource and low-power devices by leveraging the computing power of the cloud. By offloading the computation work to the cloud, no dedicated hardware is needed to enable deep neural networks on existing low computing power devices. A Raspberry Pi based robot, Cloud Chaser, is built to demonstrate the power of using cloud computing to perform real-time vision tasks. Furthermore, to reduce latency and improve real-time performance, compression algorithms are proposed and evaluated for streaming real-time video frames to the cloud.", "published": "2018-10-02T05:08:12Z", "authors": ["Zhengyi Luo", "Austin Small", "Liam Dugan", "Stephen Lane"], "categories": ["cs.CV"], "doi": "10.1117/12.2523087", "_query": "property valuation computer vision", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2307.00395v1", "title": "MobileViG: Graph-Based Sparse Attention for Mobile Vision Applications", "abstract": "Traditionally, convolutional neural networks (CNN) and vision transformers (ViT) have dominated computer vision. However, recently proposed vision graph neural networks (ViG) provide a new avenue for exploration. Unfortunately, for mobile applications, ViGs are computationally expensive due to the overhead of representing images as graph structures. In this work, we propose a new graph-based sparse attention mechanism, Sparse Vision Graph Attention (SVGA), that is designed for ViGs running on mobile devices. Additionally, we propose the first hybrid CNN-GNN architecture for vision tasks on mobile devices, MobileViG, which uses SVGA. Extensive experiments show that MobileViG beats existing ViG models and existing mobile CNN and ViT architectures in terms of accuracy and/or speed on image classification, object detection, and instance segmentation tasks. Our fastest model, MobileViG-Ti, achieves 75.7% top-1 accuracy on ImageNet-1K with 0.78 ms inference latency on iPhone 13 Mini NPU (compiled with CoreML), which is faster than MobileNetV2x1.4 (1.02 ms, 74.7% top-1) and MobileNetV2x1.0 (0.81 ms, 71.8% top-1). Our largest model, MobileViG-B obtains 82.6% top-1 accuracy with only 2.30 ms latency, which is faster and more accurate than the similarly sized EfficientFormer-L3 model (2.77 ms, 82.4%). Our work proves that well designed hybrid CNN-GNN architectures can be a new avenue of exploration for designing models that are extremely fast and accurate on mobile devices. Our code is publicly available at https://github.com/SLDGroup/MobileViG.", "published": "2023-07-01T17:49:12Z", "authors": ["Mustafa Munir", "William Avery", "Radu Marculescu"], "categories": ["cs.CV", "cs.LG"], "doi": "", "_query": "property valuation computer vision", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2307.03135v3", "title": "Distilling Large Vision-Language Model with Out-of-Distribution Generalizability", "abstract": "Large vision-language models have achieved outstanding performance, but their size and computational requirements make their deployment on resource-constrained devices and time-sensitive tasks impractical. Model distillation, the process of creating smaller, faster models that maintain the performance of larger models, is a promising direction towards the solution. This paper investigates the distillation of visual representations in large teacher vision-language models into lightweight student models using a small- or mid-scale dataset. Notably, this study focuses on open-vocabulary out-of-distribution (OOD) generalization, a challenging problem that has been overlooked in previous model distillation literature. We propose two principles from vision and language modality perspectives to enhance student's OOD generalization: (1) by better imitating teacher's visual representation space, and carefully promoting better coherence in vision-language alignment with the teacher; (2) by enriching the teacher's language representations with informative and finegrained semantic attributes to effectively distinguish between different labels. We propose several metrics and conduct extensive experiments to investigate their techniques. The results demonstrate significant improvements in zero-shot and few-shot student performance on open-vocabulary out-of-distribution classification, highlighting the effectiveness of our proposed approaches. Poster: https://xuanlinli17.github.io/pdfs/iccv23_large_vlm_distillation_poster.pdf Code: https://github.com/xuanlinli17/large_vlm_distillation_ood", "published": "2023-07-06T17:05:26Z", "authors": ["Xuanlin Li", "Yunhao Fang", "Minghua Liu", "Zhan Ling", "Zhuowen Tu", "Hao Su"], "categories": ["cs.CV", "cs.AI", "cs.CL", "cs.LG"], "doi": "", "_query": "property valuation computer vision", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/1003.1455v1", "title": "A Computational Algorithm based on Empirical Analysis, that Composes Sanskrit Poetry", "abstract": "Poetry-writing in Sanskrit is riddled with problems for even those who know the language well. This is so because the rules that govern Sanskrit prosody are numerous and stringent. We propose a computational algorithm that converts prose given as E-text into poetry in accordance with the metrical rules of Sanskrit prosody, simultaneously taking care to ensure that sandhi or euphonic conjunction, which is compulsory in verse, is handled. The algorithm is considerably speeded up by a novel method of reducing the target search database. The algorithm further gives suggestions to the poet in case what he/she has given as the input prose is impossible to fit into any allowed metrical format. There is also an interactive component of the algorithm by which the algorithm interacts with the poet to resolve ambiguities. In addition, this unique work, which provides a solution to a problem that has never been addressed before, provides a simple yet effective speech recognition interface that would help the visually impaired dictate words in E-text, which is in turn versified by our Poetry Composer Engine.", "published": "2010-03-07T11:28:08Z", "authors": ["Rama N.", "Meenakshi Lakshmanan"], "categories": ["cs.CL"], "doi": "", "_query": "property valuation computer vision", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2604.19196v2", "title": "Benchmarking Vision Foundation Models for Domain-Generalizable Face Anti-Spoofing", "abstract": "Face Anti-Spoofing (FAS) remains challenging due to the requirement for robust domain generalization across unseen environments. While recent trends leverage Vision-Language Models (VLMs) for semantic supervision, these multimodal approaches often demand prohibitive computational resources and exhibit high inference latency. Furthermore, their efficacy is inherently limited by the quality of the underlying visual features. This paper revisits the potential of vision-only foundation models to establish a highly efficient and robust baseline for FAS. We conduct a systematic benchmarking of 15 pre-trained models, such as supervised CNNs, supervised ViTs, and self-supervised ViTs, under severe cross-domain scenarios including the MICO and Limited Source Domains (LSD) protocols. Our comprehensive analysis reveals that self-supervised vision models, particularly DINOv2 with Registers, significantly suppress attention artifacts and capture critical, fine-grained spoofing cues. Combined with Face Anti-Spoofing Data Augmentation (FAS-Aug), Patch-wise Data Augmentation (PDA) and Attention-weighted Patch Loss (APL), our proposed vision-only baseline achieves state-of-the-art performance in the MICO protocol. This baseline outperforms existing methods under the data-constrained LSD protocol while maintaining superior computational efficiency. This work provides a definitive vision-only baseline for FAS, demonstrating that optimized self-supervised vision transformers can serve as a backbone for both vision-only and future multimodal FAS systems. The project page is available at: https://gsisaoki.github.io/FAS-VFMbenchmark-CVPRW2026/ .", "published": "2026-04-21T08:05:21Z", "authors": ["Mika Feng", "Pierre Gallin-Martel", "Koichi Ito", "Takafumi Aoki"], "categories": ["cs.CV"], "doi": "", "_query": "property valuation computer vision", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2003.01456v2", "title": "Implicit Functions in Feature Space for 3D Shape Reconstruction and Completion", "abstract": "While many works focus on 3D reconstruction from images, in this paper, we focus on 3D shape reconstruction and completion from a variety of 3D inputs, which are deficient in some respect: low and high resolution voxels, sparse and dense point clouds, complete or incomplete. Processing of such 3D inputs is an increasingly important problem as they are the output of 3D scanners, which are becoming more accessible, and are the intermediate output of 3D computer vision algorithms. Recently, learned implicit functions have shown great promise as they produce continuous reconstructions. However, we identified two limitations in reconstruction from 3D inputs: 1) details present in the input data are not retained, and 2) poor reconstruction of articulated humans. To solve this, we propose Implicit Feature Networks (IF-Nets), which deliver continuous outputs, can handle multiple topologies, and complete shapes for missing or sparse input data retaining the nice properties of recent learned implicit functions, but critically they can also retain detail when it is present in the input data, and can reconstruct articulated humans. Our work differs from prior work in two crucial aspects. First, instead of using a single vector to encode a 3D shape, we extract a learnable 3-dimensional multi-scale tensor of deep features, which is aligned with the original Euclidean space embedding the shape. Second, instead of classifying x-y-z point coordinates directly, we classify deep features extracted from the tensor at a continuous query point. We show that this forces our model to make decisions based on global and local shape structure, as opposed to point coordinates, which are arbitrary under Euclidean transformations. Experiments demonstrate that IF-Nets clearly outperform prior work in 3D object reconstruction in ShapeNet, and obtain significantly more accurate 3D human reconstructions.", "published": "2020-03-03T11:14:29Z", "authors": ["Julian Chibane", "Thiemo Alldieck", "Gerard Pons-Moll"], "categories": ["cs.CV", "cs.LG"], "doi": "", "_query": "property valuation computer vision", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/1902.06806v2", "title": "FreeLabel: A Publicly Available Annotation Tool based on Freehand Traces", "abstract": "Large-scale annotation of image segmentation datasets is often prohibitively expensive, as it usually requires a huge number of worker hours to obtain high-quality results. Abundant and reliable data has been, however, crucial for the advances on image understanding tasks achieved by deep learning models. In this paper, we introduce FreeLabel, an intuitive open-source web interface that allows users to obtain high-quality segmentation masks with just a few freehand scribbles, in a matter of seconds. The efficacy of FreeLabel is quantitatively demonstrated by experimental results on the PASCAL dataset as well as on a dataset from the agricultural domain. Designed to benefit the computer vision community, FreeLabel can be used for both crowdsourced or private annotation and has a modular structure that can be easily adapted for any image dataset.", "published": "2019-02-18T21:47:39Z", "authors": ["Philipe A. Dias", "Zhou Shen", "Amy Tabb", "Henry Medeiros"], "categories": ["cs.CV"], "doi": "10.1109/WACV.2019.00010", "_query": "property valuation computer vision", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2206.10552v2", "title": "Vicinity Vision Transformer", "abstract": "Vision transformers have shown great success on numerous computer vision tasks. However, its central component, softmax attention, prohibits vision transformers from scaling up to high-resolution images, due to both the computational complexity and memory footprint being quadratic. Although linear attention was introduced in natural language processing (NLP) tasks to mitigate a similar issue, directly applying existing linear attention to vision transformers may not lead to satisfactory results. We investigate this problem and find that computer vision tasks focus more on local information compared with NLP tasks. Based on this observation, we present a Vicinity Attention that introduces a locality bias to vision transformers with linear complexity. Specifically, for each image patch, we adjust its attention weight based on its 2D Manhattan distance measured by its neighbouring patches. In this case, the neighbouring patches will receive stronger attention than far-away patches. Moreover, since our Vicinity Attention requires the token length to be much larger than the feature dimension to show its efficiency advantages, we further propose a new Vicinity Vision Transformer (VVT) structure to reduce the feature dimension without degenerating the accuracy. We perform extensive experiments on the CIFAR100, ImageNet1K, and ADE20K datasets to validate the effectiveness of our method. Our method has a slower growth rate of GFlops than previous transformer-based and convolution-based networks when the input resolution increases. In particular, our approach achieves state-of-the-art image classification accuracy with 50% fewer parameters than previous methods.", "published": "2022-06-21T17:33:53Z", "authors": ["Weixuan Sun", "Zhen Qin", "Hui Deng", "Jianyuan Wang", "Yi Zhang", "Kaihao Zhang", "Nick Barnes", "Stan Birchfield", "Lingpeng Kong", "Yiran Zhong"], "categories": ["cs.CV"], "doi": "", "_query": "property valuation computer vision", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2609.01743v1", "title": "SCULPT: Training Edge Vision Models for Post-Training Quantization Readiness", "abstract": "Edge vision models are difficult to deploy on resource-constrained hardware, making low-bit post-training quantization (PTQ) attractive. In practice, standard FP32 training often produces heavy-tailed activation distributions whose outliers destabilize activation quantization: preserving the full range wastes quantization bins on rare extremes, while aggressive clipping causes information loss. Existing solutions typically rely on quantization-aware training (QAT), which adds training complexity and bit-width coupling, or advanced PTQ procedures that repair the model after training. We present SCULPT (Statistical Clipping and Uniform Loss for Post-Training), a training-time method that improves PTQ readiness during ordinary FP32 fine-tuning. SCULPT combines a topology-aware activation regularizer that suppresses quantization-hostile skewness and kurtosis with a stable percentile-based clipping mechanism that learns deployment-ready activation bounds. Unlike QAT, SCULPT does not simulate quantization during optimization; unlike post hoc outlier-repair PTQ methods, it does not require runtime activation transformations. The learned clipping bounds can be exported directly into a standard PTQ workflow for low-bit deployment, including INT8 and lower-bit settings such as W4A8.", "published": "2026-09-01T18:10:19Z", "authors": ["Bharadwaj Kavuri", "Sourav Babu-PK", "Varadhraj Ellapan", "Pullarao Maddu", "Prasad Deshpande"], "categories": ["cs.CV"], "doi": "", "_query": "property valuation computer vision", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2409.02006v1", "title": "Robust Fitting on a Gate Quantum Computer", "abstract": "Gate quantum computers generate significant interest due to their potential to solve certain difficult problems such as prime factorization in polynomial time. Computer vision researchers have long been attracted to the power of quantum computers. Robust fitting, which is fundamentally important to many computer vision pipelines, has recently been shown to be amenable to gate quantum computing. The previous proposed solution was to compute Boolean influence as a measure of outlyingness using the Bernstein-Vazirani quantum circuit. However, the method assumed a quantum implementation of an $\\ell_\\infty$ feasibility test, which has not been demonstrated. In this paper, we take a big stride towards quantum robust fitting: we propose a quantum circuit to solve the $\\ell_\\infty$ feasibility test in the 1D case, which allows to demonstrate for the first time quantum robust fitting on a real gate quantum computer, the IonQ Aria. We also show how 1D Boolean influences can be accumulated to compute Boolean influences for higher-dimensional non-linear models, which we experimentally validate on real benchmark datasets.", "published": "2024-09-03T15:54:20Z", "authors": ["Frances Fengyi Yang", "Michele Sasdelli", "Tat-Jun Chin"], "categories": ["cs.CV"], "doi": "", "_query": "property valuation computer vision", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2603.27773v1", "title": "RINO: Rotation-Invariant Non-Rigid Correspondences", "abstract": "Dense 3D shape correspondence remains a central challenge in computer vision and graphics as many deep learning approaches still rely on intermediate geometric features or handcrafted descriptors, limiting their effectiveness under non-isometric deformations, partial data, and non-manifold inputs. To overcome these issues, we introduce RINO, an unsupervised, rotation-invariant dense correspondence framework that effectively unifies rigid and non-rigid shape matching. The core of our method is the novel RINONet, a feature extractor that integrates vector-based SO(3)-invariant learning with orientation-aware complex functional maps to extract robust features directly from raw geometry. This allows for a fully end-to-end, data-driven approach that bypasses the need for shape pre-alignment or handcrafted features. Extensive experiments show unprecedented performance of RINO across challenging non-rigid matching tasks, including arbitrary poses, non-isometry, partiality, non-manifoldness, and noise.", "published": "2026-03-29T17:11:57Z", "authors": ["Maolin Gao", "Shao Jie Hu-Chen", "Congyue Deng", "Riccardo Marin", "Leonidas Guibas", "Daniel Cremers"], "categories": ["cs.CV"], "doi": "", "_query": "property valuation computer vision", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2308.01058v2", "title": "Improving Generalization of Synthetically Trained Sonar Image Descriptors for Underwater Place Recognition", "abstract": "Autonomous navigation in underwater environments presents challenges due to factors such as light absorption and water turbidity, limiting the effectiveness of optical sensors. Sonar systems are commonly used for perception in underwater operations as they are unaffected by these limitations. Traditional computer vision algorithms are less effective when applied to sonar-generated acoustic images, while convolutional neural networks (CNNs) typically require large amounts of labeled training data that are often unavailable or difficult to acquire. To this end, we propose a novel compact deep sonar descriptor pipeline that can generalize to real scenarios while being trained exclusively on synthetic data. Our architecture is based on a ResNet18 back-end and a properly parameterized random Gaussian projection layer, whereas input sonar data is enhanced with standard ad-hoc normalization/prefiltering techniques. A customized synthetic data generation procedure is also presented. The proposed method has been evaluated extensively using both synthetic and publicly available real data, demonstrating its effectiveness compared to state-of-the-art methods.", "published": "2023-08-02T10:10:25Z", "authors": ["Ivano Donadi", "Emilio Olivastri", "Daniel Fusaro", "Wanmeng Li", "Daniele Evangelista", "Alberto Pretto"], "categories": ["cs.CV", "cs.RO"], "doi": "10.1007/978-3-031-44137-0_28", "_query": "property valuation computer vision", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2403.10030v3", "title": "Multi-criteria Token Fusion with One-step-ahead Attention for Efficient Vision Transformers", "abstract": "Vision Transformer (ViT) has emerged as a prominent backbone for computer vision. For more efficient ViTs, recent works lessen the quadratic cost of the self-attention layer by pruning or fusing the redundant tokens. However, these works faced the speed-accuracy trade-off caused by the loss of information. Here, we argue that token fusion needs to consider diverse relations between tokens to minimize information loss. In this paper, we propose a Multi-criteria Token Fusion (MCTF), that gradually fuses the tokens based on multi-criteria (e.g., similarity, informativeness, and size of fused tokens). Further, we utilize the one-step-ahead attention, which is the improved approach to capture the informativeness of the tokens. By training the model equipped with MCTF using a token reduction consistency, we achieve the best speed-accuracy trade-off in the image classification (ImageNet1K). Experimental results prove that MCTF consistently surpasses the previous reduction methods with and without training. Specifically, DeiT-T and DeiT-S with MCTF reduce FLOPs by about 44% while improving the performance (+0.5%, and +0.3%) over the base model, respectively. We also demonstrate the applicability of MCTF in various Vision Transformers (e.g., T2T-ViT, LV-ViT), achieving at least 31% speedup without performance degradation. Code is available at https://github.com/mlvlab/MCTF.", "published": "2024-03-15T05:30:29Z", "authors": ["Sanghyeok Lee", "Joonmyung Choi", "Hyunwoo J. Kim"], "categories": ["cs.CV"], "doi": "", "_query": "property valuation computer vision", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2201.05761v2", "title": "A Survey on RGB-D Datasets", "abstract": "RGB-D data is essential for solving many problems in computer vision. Hundreds of public RGB-D datasets containing various scenes, such as indoor, outdoor, aerial, driving, and medical, have been proposed. These datasets are useful for different applications and are fundamental for addressing classic computer vision tasks, such as monocular depth estimation. This paper reviewed and categorized image datasets that include depth information. We gathered 203 datasets that contain accessible data and grouped them into three categories: scene/objects, body, and medical. We also provided an overview of the different types of sensors, depth applications, and we examined trends and future directions of the usage and creation of datasets containing depth data, and how they can be applied to investigate the development of generalizable machine learning models in the monocular depth estimation field.", "published": "2022-01-15T05:35:19Z", "authors": ["Alexandre Lopes", "Roberto Souza", "Helio Pedrini"], "categories": ["cs.CV"], "doi": "10.1016/j.cviu.2022.103489", "_query": "property valuation computer vision", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2508.18331v1", "title": "Metrics, KPIs, and Taxonomy for Data Valuation and Monetisation -- A Systematic Literature Review", "abstract": "Data valuation and data monetisation are complex subjects but essential to most organisations today. Unfortunately, they still lack standard procedures and frameworks for organisations to follow. In this survey, we introduce the reader to the concepts by providing the definitions and the background required to better understand data, monetisation strategies, and finally metrics and KPIs used in these strategies. We have conducted a systematic literature review on metrics and KPIs used in data valuation and monetisation, in every aspect of an organisation's business, and by a variety of stakeholders. We provide an expansive list of such metrics and KPIs with 162 references. We then categorise all the metrics and KPIs found into a large taxonomy, following the Balanced Scorecard (BSC) approach with further subclustering to cover every aspect of an organisation's business. This taxonomy will help every level of data management understand the complex landscape of the domain. We also discuss the difficulty in creating a standard framework for data valuation and data monetisation and the major challenges the domain is currently facing.", "published": "2025-08-25T09:43:11Z", "authors": ["Eduardo Vyhmeister", "Bastien Pietropaoli", "Alejando Martinez Molina", "Montserrat Gonzalez-Ferreiro", "Gabriel Gonzalez-Castane", "Jordi Arjona Aroca", "Andrea Visentin"], "categories": ["cs.DB"], "doi": "", "_query": "property valuation computer vision", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2407.08507v2", "title": "Bootstrapping Vision-language Models for Self-supervised Remote Physiological Measurement", "abstract": "Facial video-based remote physiological measurement is a promising research area for detecting human vital signs (e.g., heart rate, respiration frequency) in a non-contact way. Conventional approaches are mostly supervised learning, requiring extensive collections of facial videos and synchronously recorded photoplethysmography (PPG) signals. To tackle it, self-supervised learning has recently gained attentions; due to the lack of ground truth PPG signals, its performance is however limited. In this paper, we propose a novel self-supervised framework that successfully integrates the popular vision-language models (VLMs) into the remote physiological measurement task. Given a facial video, we first augment its positive and negative video samples with varying rPPG signal frequencies. Next, we introduce a frequency-oriented vision-text pair generation method by carefully creating contrastive spatio-temporal maps from positive and negative samples and designing proper text prompts to describe their relative ratios of signal frequencies. A pre-trained VLM is employed to extract features for these formed vision-text pairs and estimate rPPG signals thereafter. We develop a series of generative and contrastive learning mechanisms to optimize the VLM, including the text-guided visual map reconstruction task, the vision-text contrastive learning task, and the frequency contrastive and ranking task. Overall, our method for the first time adapts VLMs to digest and align the frequency-related knowledge in vision and text modalities. Extensive experiments on four benchmark datasets demonstrate that it significantly outperforms state of the art self-supervised methods.", "published": "2024-07-11T13:45:50Z", "authors": ["Zijie Yue", "Miaojing Shi", "Hanli Wang", "Shuai Ding", "Qijun Chen", "Shanlin Yang"], "categories": ["cs.CV"], "doi": "", "_query": "property valuation computer vision", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2306.16678v1", "title": "BinaryViT: Pushing Binary Vision Transformers Towards Convolutional Models", "abstract": "With the increasing popularity and the increasing size of vision transformers (ViTs), there has been an increasing interest in making them more efficient and less computationally costly for deployment on edge devices with limited computing resources. Binarization can be used to help reduce the size of ViT models and their computational cost significantly, using popcount operations when the weights and the activations are in binary. However, ViTs suffer a larger performance drop when directly applying convolutional neural network (CNN) binarization methods or existing binarization methods to binarize ViTs compared to CNNs on datasets with a large number of classes such as ImageNet-1k. With extensive analysis, we find that binary vanilla ViTs such as DeiT miss out on a lot of key architectural properties that CNNs have that allow binary CNNs to have much higher representational capability than binary vanilla ViT. Therefore, we propose BinaryViT, in which inspired by the CNN architecture, we include operations from the CNN architecture into a pure ViT architecture to enrich the representational capability of a binary ViT without introducing convolutions. These include an average pooling layer instead of a token pooling layer, a block that contains multiple average pooling branches, an affine transformation right before the addition of each main residual connection, and a pyramid structure. Experimental results on the ImageNet-1k dataset show the effectiveness of these operations that allow a binary pure ViT model to be competitive with previous state-of-the-art (SOTA) binary CNN models.", "published": "2023-06-29T04:48:02Z", "authors": ["Phuoc-Hoan Charles Le", "Xinlin Li"], "categories": ["cs.CV", "cs.LG"], "doi": "", "_query": "property valuation computer vision", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/1707.01692v1", "title": "Ramification theory for degree $p$ extensions of arbitrary valuation rings in mixed characteristic $(0,p)$", "abstract": "We previously obtained a generalization and refinement of results about the ramification theory of Artin-Schreier extensions of discretely valued fields in characteristic $p$ with perfect residue fields to the case of fields with more general valuations and residue fields. As seen in VT16, the \"defect\" case gives rise to many interesting complications. In this paper, we present analogous results for degree $p$ extensions of arbitrary valuation rings in mixed characteristic $(0,p)$ in a more general setting. More specifically, the only assumption here is that the base field $K$ is henselian. In particular, these results are true for defect extensions even if the rank of the valuation is greater than $1$. A similar method also works in equal characteristic, generalizing the results of VT16.", "published": "2017-07-06T08:56:41Z", "authors": ["Vaidehee Thatte"], "categories": ["math.NT"], "doi": "", "_query": "property valuation computer vision", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/1705.07884v1", "title": "Facial Affect Estimation in the Wild Using Deep Residual and Convolutional Networks", "abstract": "Automated affective computing in the wild is a challenging task in the field of computer vision. This paper presents three neural network-based methods proposed for the task of facial affect estimation submitted to the First Affect-in-the-Wild challenge. These methods are based on Inception-ResNet modules redesigned specifically for the task of facial affect estimation. These methods are: Shallow Inception-ResNet, Deep Inception-ResNet, and Inception-ResNet with LSTMs. These networks extract facial features in different scales and simultaneously estimate both the valence and arousal in each frame. Root Mean Square Error (RMSE) rates of 0.4 and 0.3 are achieved for the valence and arousal respectively with corresponding Concordance Correlation Coefficient (CCC) rates of 0.04 and 0.29 using Deep Inception-ResNet method.", "published": "2017-05-22T17:59:10Z", "authors": ["Behzad Hasani", "Mohammad H. Mahoor"], "categories": ["cs.CV"], "doi": "10.1109/CVPRW.2017.245", "_query": "property valuation computer vision", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/1008.0287v3", "title": "New structures on valuations and applications", "abstract": "An overview of some of the recent developments in the theory of valuations on convex sets and its generalizations to manifolds is given. The exposition is focused towards applications to integral geometry; several of such applications are discussed.", "published": "2010-08-02T12:32:11Z", "authors": ["Semyon Alesker"], "categories": ["math.MG", "math.DG"], "doi": "", "_query": "property valuation computer vision", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2506.06719v2", "title": "Improving Wildlife Out-of-Distribution Detection: Africas Big Five", "abstract": "Mitigating human-wildlife conflict seeks to resolve unwanted encounters between these parties. Computer Vision provides a solution to identifying individuals that might escalate into conflict, such as members of the Big Five African animals. However, environments often contain several varied species. The current state-of-the-art animal classification models are trained under a closed-world assumption. They almost always remain overconfident in their predictions even when presented with unknown classes. This study investigates out-of-distribution (OOD) detection of wildlife, specifically the Big Five. To this end, we select a parametric Nearest Class Mean (NCM) and a non-parametric contrastive learning approach as baselines to take advantage of pretrained and projected features from popular classification encoders. Moreover, we compare our baselines to various common OOD methods in the literature. The results show feature-based methods reflect stronger generalisation capability across varying classification thresholds. Specifically, NCM with ImageNet pre-trained features achieves a 2%, 4% and 22% improvement on AUPR-IN, AUPR-OUT and AUTC over the best OOD methods, respectively. The code can be found here https://github.com/pxpana/BIG5OOD", "published": "2025-06-07T09:02:48Z", "authors": ["Mufhumudzi Muthivhi", "Jiahao Huo", "Fredrik Gustafsson", "Terence L. van Zyl"], "categories": ["cs.CV", "cs.AI"], "doi": "", "_query": "property valuation computer vision", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/1608.01745v2", "title": "Play and Learn: Using Video Games to Train Computer Vision Models", "abstract": "Video games are a compelling source of annotated data as they can readily provide fine-grained groundtruth for diverse tasks. However, it is not clear whether the synthetically generated data has enough resemblance to the real-world images to improve the performance of computer vision models in practice. We present experiments assessing the effectiveness on real-world data of systems trained on synthetic RGB images that are extracted from a video game. We collected over 60000 synthetic samples from a modern video game with similar conditions to the real-world CamVid and Cityscapes datasets. We provide several experiments to demonstrate that the synthetically generated RGB images can be used to improve the performance of deep neural networks on both image segmentation and depth estimation. These results show that a convolutional network trained on synthetic data achieves a similar test error to a network that is trained on real-world data for dense image classification. Furthermore, the synthetically generated RGB images can provide similar or better results compared to the real-world datasets if a simple domain adaptation technique is applied. Our results suggest that collaboration with game developers for an accessible interface to gather data is potentially a fruitful direction for future work in computer vision.", "published": "2016-08-05T03:16:07Z", "authors": ["Alireza Shafaei", "James J. Little", "Mark Schmidt"], "categories": ["cs.CV"], "doi": "", "_query": "property valuation computer vision", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/1710.11374v1", "title": "A Computer Vision System to Localize and Classify Wastes on the Streets", "abstract": "Littering quantification is an important step for improving cleanliness of cities. When human interpretation is too cumbersome or in some cases impossible, an objective index of cleanliness could reduce the littering by awareness actions. In this paper, we present a fully automated computer vision application for littering quantification based on images taken from the streets and sidewalks. We have employed a deep learning based framework to localize and classify different types of wastes. Since there was no waste dataset available, we built our acquisition system mounted on a vehicle. Collected images containing different types of wastes. These images are then annotated for training and benchmarking the developed system. Our results on real case scenarios show accurate detection of littering on variant backgrounds.", "published": "2017-10-31T08:57:23Z", "authors": ["Mohammad Saeed Rad", "Andreas von Kaenel", "Andre Droux", "Francois Tieche", "Nabil Ouerhani", "Hazim Kemal Ekenel", "Jean-Philippe Thiran"], "categories": ["cs.CV"], "doi": "10.1007/978-3-319-68345-4_18", "_query": "property valuation computer vision", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2503.01052v2", "title": "ALinFiK: Learning to Approximate Linearized Future Influence Kernel for Scalable Third-Party LLM Data Valuation", "abstract": "Large Language Models (LLMs) heavily rely on high-quality training data, making data valuation crucial for optimizing model performance, especially when working within a limited budget. In this work, we aim to offer a third-party data valuation approach that benefits both data providers and model developers. We introduce a linearized future influence kernel (LinFiK), which assesses the value of individual data samples in improving LLM performance during training. We further propose ALinFiK, a learning strategy to approximate LinFiK, enabling scalable data valuation. Our comprehensive evaluations demonstrate that this approach surpasses existing baselines in effectiveness and efficiency, demonstrating significant scalability advantages as LLM parameters increase.", "published": "2025-03-02T22:51:12Z", "authors": ["Yanzhou Pan", "Huawei Lin", "Yide Ran", "Jiamin Chen", "Xiaodong Yu", "Weijie Zhao", "Denghui Zhang", "Zhaozhuo Xu"], "categories": ["cs.LG"], "doi": "", "_query": "property valuation computer vision", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2404.13222v2", "title": "Vim4Path: Self-Supervised Vision Mamba for Histopathology Images", "abstract": "Representation learning from Gigapixel Whole Slide Images (WSI) poses a significant challenge in computational pathology due to the complicated nature of tissue structures and the scarcity of labeled data. Multi-instance learning methods have addressed this challenge, leveraging image patches to classify slides utilizing pretrained models using Self-Supervised Learning (SSL) approaches. The performance of both SSL and MIL methods relies on the architecture of the feature encoder. This paper proposes leveraging the Vision Mamba (Vim) architecture, inspired by state space models, within the DINO framework for representation learning in computational pathology. We evaluate the performance of Vim against Vision Transformers (ViT) on the Camelyon16 dataset for both patch-level and slide-level classification. Our findings highlight Vim's enhanced performance compared to ViT, particularly at smaller scales, where Vim achieves an 8.21 increase in ROC AUC for models of similar size. An explainability analysis further highlights Vim's capabilities, which reveals that Vim uniquely emulates the pathologist workflow-unlike ViT. This alignment with human expert analysis highlights Vim's potential in practical diagnostic settings and contributes significantly to developing effective representation-learning algorithms in computational pathology. We release the codes and pretrained weights at \\url{https://github.com/AtlasAnalyticsLab/Vim4Path}.", "published": "2024-04-20T00:44:40Z", "authors": ["Ali Nasiri-Sarvi", "Vincent Quoc-Huy Trinh", "Hassan Rivaz", "Mahdi S. Hosseini"], "categories": ["eess.IV", "cs.CV"], "doi": "", "_query": "property valuation computer vision", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2406.04303v3", "title": "Vision-LSTM: xLSTM as Generic Vision Backbone", "abstract": "Transformers are widely used as generic backbones in computer vision, despite initially introduced for natural language processing. Recently, the Long Short-Term Memory (LSTM) has been extended to a scalable and performant architecture - the xLSTM - which overcomes long-standing LSTM limitations via exponential gating and parallelizable matrix memory structure. In this report, we introduce Vision-LSTM (ViL), an adaption of the xLSTM building blocks to computer vision. ViL comprises a stack of xLSTM blocks where odd blocks process the sequence of patch tokens from top to bottom while even blocks go from bottom to top. Experiments show that ViL holds promise to be further deployed as new generic backbone for computer vision architectures.", "published": "2024-06-06T17:49:21Z", "authors": ["Benedikt Alkin", "Maximilian Beck", "Korbinian Pöppel", "Sepp Hochreiter", "Johannes Brandstetter"], "categories": ["cs.CV", "cs.AI", "cs.LG"], "doi": "", "_query": "property valuation computer vision", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2508.05838v1", "title": "Integrating Vision Foundation Models with Reinforcement Learning for Enhanced Object Interaction", "abstract": "This paper presents a novel approach that integrates vision foundation models with reinforcement learning to enhance object interaction capabilities in simulated environments. By combining the Segment Anything Model (SAM) and YOLOv5 with a Proximal Policy Optimization (PPO) agent operating in the AI2-THOR simulation environment, we enable the agent to perceive and interact with objects more effectively. Our comprehensive experiments, conducted across four diverse indoor kitchen settings, demonstrate significant improvements in object interaction success rates and navigation efficiency compared to a baseline agent without advanced perception. The results show a 68% increase in average cumulative reward, a 52.5% improvement in object interaction success rate, and a 33% increase in navigation efficiency. These findings highlight the potential of integrating foundation models with reinforcement learning for complex robotic tasks, paving the way for more sophisticated and capable autonomous agents.", "published": "2025-08-07T20:29:01Z", "authors": ["Ahmad Farooq", "Kamran Iqbal"], "categories": ["cs.RO", "cs.AI", "cs.CV", "cs.LG", "eess.SY"], "doi": "10.1145/3747393.3747399", "_query": "property valuation computer vision", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2407.17339v2", "title": "Preliminary study on artificial intelligence methods for cybersecurity threat detection in computer networks based on raw data packets", "abstract": "Most of the intrusion detection methods in computer networks are based on traffic flow characteristics. However, this approach may not fully exploit the potential of deep learning algorithms to directly extract features and patterns from raw packets. Moreover, it impedes real-time monitoring due to the necessity of waiting for the processing pipeline to complete and introduces dependencies on additional software components. In this paper, we investigate deep learning methodologies capable of detecting attacks in real-time directly from raw packet data within network traffic. We propose a novel approach where packets are stacked into windows and separately recognised, with a 2D image representation suitable for processing with computer vision models. Our investigation utilizes the CIC IDS-2017 dataset, which includes both benign traffic and prevalent real-world attacks, providing a comprehensive foundation for our research.", "published": "2024-07-24T15:04:00Z", "authors": ["Aleksander Ogonowski", "Michał Żebrowski", "Arkadiusz Ćwiek", "Tobiasz Jarosiewicz", "Konrad Klimaszewski", "Adam Padee", "Piotr Wasiuk", "Michał Wójcik"], "categories": ["cs.CV", "cs.AI", "cs.CR"], "doi": "10.7494/csci.2025.26.SI.7079", "_query": "property valuation computer vision", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2506.11595v1", "title": "EasyARC: Evaluating Vision Language Models on True Visual Reasoning", "abstract": "Building on recent advances in language-based reasoning models, we explore multimodal reasoning that integrates vision and text. Existing multimodal benchmarks primarily test visual extraction combined with text-based reasoning, lacking true visual reasoning with more complex interactions between vision and language. Inspired by the ARC challenge, we introduce EasyARC, a vision-language benchmark requiring multi-image, multi-step reasoning, and self-correction. EasyARC is procedurally generated, fully verifiable, and scalable, making it ideal for reinforcement learning (RL) pipelines. The generators incorporate progressive difficulty levels, enabling structured evaluation across task types and complexities. We benchmark state-of-the-art vision-language models and analyze their failure modes. We argue that EasyARC sets a new standard for evaluating true reasoning and test-time scaling capabilities in vision-language models. We open-source our benchmark dataset and evaluation code.", "published": "2025-06-13T09:03:33Z", "authors": ["Mert Unsal", "Aylin Akkus"], "categories": ["cs.CV", "cs.LG"], "doi": "", "_query": "property valuation computer vision", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2405.09546v1", "title": "BEHAVIOR Vision Suite: Customizable Dataset Generation via Simulation", "abstract": "The systematic evaluation and understanding of computer vision models under varying conditions require large amounts of data with comprehensive and customized labels, which real-world vision datasets rarely satisfy. While current synthetic data generators offer a promising alternative, particularly for embodied AI tasks, they often fall short for computer vision tasks due to low asset and rendering quality, limited diversity, and unrealistic physical properties. We introduce the BEHAVIOR Vision Suite (BVS), a set of tools and assets to generate fully customized synthetic data for systematic evaluation of computer vision models, based on the newly developed embodied AI benchmark, BEHAVIOR-1K. BVS supports a large number of adjustable parameters at the scene level (e.g., lighting, object placement), the object level (e.g., joint configuration, attributes such as \"filled\" and \"folded\"), and the camera level (e.g., field of view, focal length). Researchers can arbitrarily vary these parameters during data generation to perform controlled experiments. We showcase three example application scenarios: systematically evaluating the robustness of models across different continuous axes of domain shift, evaluating scene understanding models on the same set of images, and training and evaluating simulation-to-real transfer for a novel vision task: unary and binary state prediction. Project website: https://behavior-vision-suite.github.io/", "published": "2024-05-15T17:57:56Z", "authors": ["Yunhao Ge", "Yihe Tang", "Jiashu Xu", "Cem Gokmen", "Chengshu Li", "Wensi Ai", "Benjamin Jose Martinez", "Arman Aydin", "Mona Anvari", "Ayush K Chakravarthy", "Hong-Xing Yu", "Josiah Wong", "Sanjana Srivastava", "Sharon Lee", "Shengxin Zha", "Laurent Itti", "Yunzhu Li", "Roberto Martín-Martín", "Miao Liu", "Pengchuan Zhang", "Ruohan Zhang", "Li Fei-Fei", "Jiajun Wu"], "categories": ["cs.CV"], "doi": "", "_query": "property valuation computer vision", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/math/0605011v2", "title": "A valuation criterion for normal bases in elementary abelian extensions", "abstract": "Let $p$ be a prime number and let $K$ be a finite extension of the field $\\mathbb{Q}_p$ of $p$-adic numbers. Let $N$ be a fully ramified, elementary abelian extension of $K$. Under a mild hypothesis on the extension $N/K$, we show that every element of $N$ with valuation congruent mod $[N:K]$ to the largest lower ramification number of $N/K$ generates a normal basis for $N$ over $K$.", "published": "2006-04-29T17:42:35Z", "authors": ["Nigel P. Byott", "G. Griffith Elder"], "categories": ["math.NT"], "doi": "", "_query": "property valuation computer vision", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/1907.06796v1", "title": "Instant Motion Tracking and Its Applications to Augmented Reality", "abstract": "Augmented Reality (AR) brings immersive experiences to users. With recent advances in computer vision and mobile computing, AR has scaled across platforms, and has increased adoption in major products. One of the key challenges in enabling AR features is proper anchoring of the virtual content to the real world, a process referred to as tracking. In this paper, we present a system for motion tracking, which is capable of robustly tracking planar targets and performing relative-scale 6DoF tracking without calibration. Our system runs in real-time on mobile phones and has been deployed in multiple major products on hundreds of millions of devices.", "published": "2019-07-16T00:13:09Z", "authors": ["Jianing Wei", "Genzhi Ye", "Tyler Mullen", "Matthias Grundmann", "Adel Ahmadyan", "Tingbo Hou"], "categories": ["cs.CV"], "doi": "", "_query": "property valuation computer vision", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2406.07738v1", "title": "On the Application of Egocentric Computer Vision to Industrial Scenarios", "abstract": "Egocentric vision aims to capture and analyse the world from the first-person perspective. We explore the possibilities for egocentric wearable devices to improve and enhance industrial use cases w.r.t. data collection, annotation, labelling and downstream applications. This would contribute to easier data collection and allow users to provide additional context. We envision that this approach could serve as a supplement to the traditional industrial Machine Vision workflow. Code, Dataset and related resources will be available at: https://github.com/Vivek9Chavan/EgoVis24", "published": "2024-06-11T21:48:20Z", "authors": ["Vivek Chavan", "Oliver Heimann", "Jörg Krüger"], "categories": ["cs.CV"], "doi": "", "_query": "property valuation computer vision", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2411.15241v2", "title": "EfficientViM: Efficient Vision Mamba with Hidden State Mixer based State Space Duality", "abstract": "For the deployment of neural networks in resource-constrained environments, prior works have built lightweight architectures with convolution and attention for capturing local and global dependencies, respectively. Recently, the state space model (SSM) has emerged as an effective operation for global interaction with its favorable linear computational cost in the number of tokens. To harness the efficacy of SSM, we introduce Efficient Vision Mamba (EfficientViM), a novel architecture built on hidden state mixer-based state space duality (HSM-SSD) that efficiently captures global dependencies with further reduced computational cost. With the observation that the runtime of the SSD layer is driven by the linear projections on the input sequences, we redesign the original SSD layer to perform the channel mixing operation within compressed hidden states in the HSM-SSD layer. Additionally, we propose multi-stage hidden state fusion to reinforce the representation power of hidden states and provide the design to alleviate the bottleneck caused by the memory-bound operations. As a result, the EfficientViM family achieves a new state-of-the-art speed-accuracy trade-off on ImageNet-1k, offering up to a 0.7% performance improvement over the second-best model SHViT with faster speed. Further, we observe significant improvements in throughput and accuracy compared to prior works, when scaling images or employing distillation training. Code is available at https://github.com/mlvlab/EfficientViM.", "published": "2024-11-22T02:02:06Z", "authors": ["Sanghyeok Lee", "Joonmyung Choi", "Hyunwoo J. Kim"], "categories": ["cs.CV"], "doi": "", "_query": "property valuation computer vision", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2002.12557v1", "title": "Hand-Priming in Object Localization for Assistive Egocentric Vision", "abstract": "Egocentric vision holds great promises for increasing access to visual information and improving the quality of life for people with visual impairments, with object recognition being one of the daily challenges for this population. While we strive to improve recognition performance, it remains difficult to identify which object is of interest to the user; the object may not even be included in the frame due to challenges in camera aiming without visual feedback. Also, gaze information, commonly used to infer the area of interest in egocentric vision, is often not dependable. However, blind users often tend to include their hand either interacting with the object that they wish to recognize or simply placing it in proximity for better camera aiming. We propose localization models that leverage the presence of the hand as the contextual information for priming the center area of the object of interest. In our approach, hand segmentation is fed to either the entire localization network or its last convolutional layers. Using egocentric datasets from sighted and blind individuals, we show that the hand-priming achieves higher precision than other approaches, such as fine-tuning, multi-class, and multi-task learning, which also encode hand-object interactions in localization.", "published": "2020-02-28T05:32:36Z", "authors": ["Kyungjun Lee", "Abhinav Shrivastava", "Hernisa Kacorri"], "categories": ["cs.CV", "cs.HC"], "doi": "", "_query": "property valuation computer vision", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/1808.01976v2", "title": "Adversarial Vision Challenge", "abstract": "The NIPS 2018 Adversarial Vision Challenge is a competition to facilitate measurable progress towards robust machine vision models and more generally applicable adversarial attacks. This document is an updated version of our competition proposal that was accepted in the competition track of 32nd Conference on Neural Information Processing Systems (NIPS 2018).", "published": "2018-08-06T16:13:43Z", "authors": ["Wieland Brendel", "Jonas Rauber", "Alexey Kurakin", "Nicolas Papernot", "Behar Veliqi", "Marcel Salathé", "Sharada P. Mohanty", "Matthias Bethge"], "categories": ["cs.LG", "cs.CV", "stat.ML"], "doi": "", "_query": "property valuation computer vision", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/1404.7174v7", "title": "Computer vision-based recognition of liquid surfaces and phase boundaries in transparent vessels, with emphasis on chemistry applications", "abstract": "The ability to recognize the liquid surface and the liquid level in transparent containers is perhaps the most commonly used evaluation method when dealing with fluids. Such recognition is essential in determining the liquid volume, fill level, phase boundaries and phase separation in various fluid systems. The recognition of liquid surfaces is particularly important in solution chemistry, where it is essential to many laboratory techniques (e.g., extraction, distillation, titration). A general method for the recognition of interfaces between liquid and air or between phase-separating liquids could have a wide range of applications and contribute to the understanding of the visual properties of such interfaces. This work examines a computer vision method for the recognition of liquid surfaces and liquid levels in various transparent containers. The method can be applied to recognition of both liquid-air and liquid-liquid surfaces. No prior knowledge of the number of phases is required. The method receives the image of the liquid container and the boundaries of the container in the image and scans all possible curves that could correspond to the outlines of liquid surfaces in the image. The method then compares each curve to the image to rate its correspondence with the outline of the real liquid surface by examining various image properties in the area surrounding each point of the curve. The image properties that were found to give the best indication of the liquid surface are the relative intensity change, the edge density change and the gradient direction relative to the curve normal.", "published": "2014-04-28T21:41:30Z", "authors": ["Sagi Eppel", "Tal Kachman"], "categories": ["cs.CV"], "doi": "", "_query": "property valuation computer vision", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2212.11468v1", "title": "IPProtect: protecting the intellectual property of visual datasets during data valuation", "abstract": "Data trading is essential to accelerate the development of data-driven machine learning pipelines. The central problem in data trading is to estimate the utility of a seller's dataset with respect to a given buyer's machine learning task, also known as data valuation. Typically, data valuation requires one or more participants to share their raw dataset with others, leading to potential risks of intellectual property (IP) violations. In this paper, we tackle the novel task of preemptively protecting the IP of datasets that need to be shared during data valuation. First, we identify and formalize two kinds of novel IP risks in visual datasets: data-item (image) IP and statistical (dataset) IP. Then, we propose a novel algorithm to convert the raw dataset into a sanitized version, that provides resistance to IP violations, while at the same time allowing accurate data valuation. The key idea is to limit the transfer of information from the raw dataset to the sanitized dataset, thereby protecting against potential intellectual property violations. Next, we analyze our method for the likely existence of a solution and immunity against reconstruction attacks. Finally, we conduct extensive experiments on three computer vision datasets demonstrating the advantages of our method in comparison to other baselines.", "published": "2022-12-22T03:36:19Z", "authors": ["Gursimran Singh", "Chendi Wang", "Ahnaf Tazwar", "Lanjun Wang", "Yong Zhang"], "categories": ["cs.CV", "cs.CR"], "doi": "", "_query": "property valuation computer vision", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2605.15484v1", "title": "When Does Sparse MoE Help in Vision? The Role of Backbone Compute Leverage in Sparse Routing", "abstract": "Mixture-of-Experts (MoE) networks promise favorable accuracy-compute trade-offs, yet practical vision deployments are hindered by expert collapse and limited end-to-end efficiency gains. We study when sparse top-$k$ routing with hard capacity constraints helps in vision classification, evaluated under multi-seed protocols on four benchmarks (CIFAR-10/100, Tiny-ImageNet, ImageNet-1K). We observe a \\emph{compute-leverage pattern}: positive accuracy gaps require a substantial fraction $ρ$ of total FLOPs to be routed; at ImageNet scale this is necessary but not sufficient, as multi-expert routing ($k \\geq 2$) is additionally required. Two controlled experiments isolate these factors. A hidden-size sweep on CIFAR-10 yields both predicted sign reversals across standard and depthwise backbones, ruling out backbone family as the active variable. An ImageNet-1K ablation that varies only top-$k$ -- holding architecture, initialization, and $ρ$ fixed -- reverses the gap from positive to negative across all five seeds. A per-sample variant of Soft MoE that softmaxes over experts rather than the batch rescues CIFAR-100 above the dense baseline, identifying batch-axis dispatch as the dominant failure mode in per-sample CNN settings. Code and aggregate results: https://github.com/libophd/sparse-moe-vision-rho.", "published": "2026-05-15T00:01:11Z", "authors": ["Libo Sun", "Po-wei Harn", "Peixiong He", "Xiao Qin"], "categories": ["cs.CV", "cs.LG"], "doi": "", "_query": "property valuation computer vision", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2209.05158v4", "title": "Singular valuations and the Hadwiger theorem on convex functions", "abstract": "We give a characterization of smooth, rotation and dually epi-translation invariant valuations and use this result to obtain a new proof of the Hadwiger theorem on convex functions. We also give a description of the construction of the functional intrinsic volumes using integration over the differential cycle and provide a new representation of these functionals as principal value integrals with respect to the Hessian measures.", "published": "2022-09-12T11:33:31Z", "authors": ["Jonas Knoerr"], "categories": ["math.MG"], "doi": "", "_query": "property valuation computer vision", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2106.15183v3", "title": "Multi-Exit Vision Transformer for Dynamic Inference", "abstract": "Deep neural networks can be converted to multi-exit architectures by inserting early exit branches after some of their intermediate layers. This allows their inference process to become dynamic, which is useful for time critical IoT applications with stringent latency requirements, but with time-variant communication and computation resources. In particular, in edge computing systems and IoT networks where the exact computation time budget is variable and not known beforehand. Vision Transformer is a recently proposed architecture which has since found many applications across various domains of computer vision. In this work, we propose seven different architectures for early exit branches that can be used for dynamic inference in Vision Transformer backbones. Through extensive experiments involving both classification and regression problems, we show that each one of our proposed architectures could prove useful in the trade-off between accuracy and speed.", "published": "2021-06-29T09:01:13Z", "authors": ["Arian Bakhtiarnia", "Qi Zhang", "Alexandros Iosifidis"], "categories": ["cs.CV"], "doi": "", "_query": "property valuation computer vision", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2510.25051v1", "title": "Breast Cancer VLMs: Clinically Practical Vision-Language Train-Inference Models", "abstract": "Breast cancer remains the most commonly diagnosed malignancy among women in the developed world. Early detection through mammography screening plays a pivotal role in reducing mortality rates. While computer-aided diagnosis (CAD) systems have shown promise in assisting radiologists, existing approaches face critical limitations in clinical deployment - particularly in handling the nuanced interpretation of multi-modal data and feasibility due to the requirement of prior clinical history. This study introduces a novel framework that synergistically combines visual features from 2D mammograms with structured textual descriptors derived from easily accessible clinical metadata and synthesized radiological reports through innovative tokenization modules. Our proposed methods in this study demonstrate that strategic integration of convolutional neural networks (ConvNets) with language representations achieves superior performance to vision transformer-based models while handling high-resolution images and enabling practical deployment across diverse populations. By evaluating it on multi-national cohort screening mammograms, our multi-modal approach achieves superior performance in cancer detection and calcification identification compared to unimodal baselines, with particular improvements. The proposed method establishes a new paradigm for developing clinically viable VLM-based CAD systems that effectively leverage imaging data and contextual patient information through effective fusion mechanisms.", "published": "2025-10-29T00:37:18Z", "authors": ["Shunjie-Fabian Zheng", "Hyeonjun Lee", "Thijs Kooi", "Ali Diba"], "categories": ["cs.CV", "cs.LG"], "doi": "", "_query": "property valuation computer vision", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2512.13855v2", "title": "Improvise, Adapt, Overcome -- Telescopic Adapters for Efficient Fine-tuning of Vision Language Models in Medical Imaging", "abstract": "Adapting Vision Language Segmentation Models (VLSMs) to medical imaging domains requires significant computational overhead when using conventional fine-tuning approaches. Existing Parameter-Efficient Fine-Tuning (PEFT) methods apply uniform adapter dimensions across all transformer layers, leading to suboptimal parameter allocation and reduced adaptation efficiency. We introduce Telescopic Adapters, a novel PEFT framework that employs depth-aware scaling to progressively increase adapter capacity from shallow to deep transformer layers. Our method integrates lightweight bottleneck modules within CLIPSeg's vision and text encoders, with adapter dimensions dynamically scaled based on layer depth and semantic relevance. Using only 613k trainable parameters--244x fewer than end-to-end fine-tuning, Telescopic Adapters achieve superior performance across five diverse medical datasets spanning polyp segmentation, skin lesion detection, and breast ultrasound imaging. Comprehensive ablation studies demonstrate that deeper layers require substantially more adaptation capacity than shallow layers, validating our telescopic scaling hypothesis. Our approach establishes a new paradigm for efficient medical VLSM fine-tuning, enabling deployment in resource-constrained clinical environments while maintaining competitive segmentation accuracy. Our source code is publicly available at https://github.com/Ujjwal238/Telescopic_adapters", "published": "2025-12-15T19:40:15Z", "authors": ["Ujjwal Mishra", "Vinita Shukla", "Praful Hambarde", "Amit Shukla"], "categories": ["cs.CV", "cs.AI"], "doi": "", "_query": "property valuation computer vision", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2209.13502v1", "title": "Visual Object Tracking in First Person Vision", "abstract": "The understanding of human-object interactions is fundamental in First Person Vision (FPV). Visual tracking algorithms which follow the objects manipulated by the camera wearer can provide useful information to effectively model such interactions. In the last years, the computer vision community has significantly improved the performance of tracking algorithms for a large variety of target objects and scenarios. Despite a few previous attempts to exploit trackers in the FPV domain, a methodical analysis of the performance of state-of-the-art trackers is still missing. This research gap raises the question of whether current solutions can be used ``off-the-shelf'' or more domain-specific investigations should be carried out. This paper aims to provide answers to such questions. We present the first systematic investigation of single object tracking in FPV. Our study extensively analyses the performance of 42 algorithms including generic object trackers and baseline FPV-specific trackers. The analysis is carried out by focusing on different aspects of the FPV setting, introducing new performance measures, and in relation to FPV-specific tasks. The study is made possible through the introduction of TREK-150, a novel benchmark dataset composed of 150 densely annotated video sequences. Our results show that object tracking in FPV poses new challenges to current visual trackers. We highlight the factors causing such behavior and point out possible research directions. Despite their difficulties, we prove that trackers bring benefits to FPV downstream tasks requiring short-term object tracking. We expect that generic object tracking will gain popularity in FPV as new and FPV-specific methodologies are investigated.", "published": "2022-09-27T16:18:47Z", "authors": ["Matteo Dunnhofer", "Antonino Furnari", "Giovanni Maria Farinella", "Christian Micheloni"], "categories": ["cs.CV"], "doi": "10.1007/s11263-022-01694-6", "_query": "property valuation computer vision", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2411.18199v3", "title": "Semantic Edge Computing and Semantic Communications in 6G Networks: A Unifying Survey and Research Challenges", "abstract": "Semantic Edge Computing (SEC) and Semantic Communications (SemComs) have been proposed as viable approaches to achieve real-time edge-enabled intelligence in sixth-generation (6G) wireless networks. On one hand, SemCom leverages the strength of Deep Neural Networks (DNNs) to encode and communicate the semantic information only, while making it robust to channel distortions by compensating for wireless effects. Ultimately, this leads to an improvement in the communication efficiency. On the other hand, SEC has leveraged distributed DNNs to divide the computation of a DNN across different devices based on their computational and networking constraints. Although significant progress has been made in both fields, the literature lacks a systematic view to connect both fields. In this work, we fulfill the current gap by unifying the SEC and SemCom fields. We summarize the research problems in these two fields and provide a comprehensive review of the state of the art with a focus on their technical strengths and challenges.", "published": "2024-11-27T10:21:10Z", "authors": ["Milin Zhang", "Mohammad Abdi", "Venkat R. Dasari", "Francesco Restuccia"], "categories": ["cs.LG", "cs.NI", "eess.SP"], "doi": "10.1016/j.comnet.2025.111531", "_query": "property valuation computer vision", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2606.31811v1", "title": "MuSViT: A Foundation Vision Model for Sheet Music Representation", "abstract": "Foundation models have transformed vision and language processing by providing rich, reusable representations that transfer across diverse tasks. Sheet music, as a visual encoding of musical language, lacks such a strong domain-specific backbone. We introduce MuSViT (Music Score Vision Transformer): the first foundation vision model for sheet music representation -- a ViT encoder pre-trained via Masked Autoencoders on 9.7 million pages from the IMSLP. To handle the complexity of real-world scores, we adopt a two-stage curriculum: a synthetic warm-up on typeset scores followed by large-scale training on the full IMSLP corpus. We evaluate MuSViT on four downstream tasks -- full-page and staff-level music score recognition, music symbol detection, and score difficulty classification -- under two scenarios: linear probing (frozen encoder) and fine-tuning. Under linear probing, MuSViT consistently outperforms modern vision encoders, revealing that general-purpose representations, regardless of scale, fall systematically short on the structured symbolic properties of musical notation. Under fine-tuning, MuSViT generally improves upon task-specific state-of-the-art methods. An additional embedding-transcription consistency analysis reveals that MuSViT encodes symbolic musical structure directly in its representation space -- unlike other encoders, whose embeddings do not correlate with music notation content. These results establish MuSViT as a foundation backbone for sheet music understanding.", "published": "2026-06-30T15:27:12Z", "authors": ["Carlos Penarrubia", "Antonio Rios-Vila", "Eliseo Fuentes-Martinez", "Juan C. Martinez-Sevilla", "Francisco J. Castellanos", "María Alfaro-Contreras", "Jorge Calvo-Zaragoza"], "categories": ["cs.CV"], "doi": "", "_query": "property valuation computer vision", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2411.18711v4", "title": "Evaluating Vision-Language Models as Evaluators in Path Planning", "abstract": "Despite their promise to perform complex reasoning, large language models (LLMs) have been shown to have limited effectiveness in end-to-end planning. This has inspired an intriguing question: if these models cannot plan well, can they still contribute to the planning framework as a helpful plan evaluator? In this work, we generalize this question to consider LLMs augmented with visual understanding, i.e., Vision-Language Models (VLMs). We introduce PathEval, a novel benchmark evaluating VLMs as plan evaluators in complex path-planning scenarios. Succeeding in the benchmark requires a VLM to be able to abstract traits of optimal paths from the scenario description, demonstrate precise low-level perception on each path, and integrate this information to decide the better path. Our analysis of state-of-the-art VLMs reveals that these models face significant challenges on the benchmark. We observe that the VLMs can precisely abstract given scenarios to identify the desired traits and exhibit mixed performance in integrating the provided information. Yet, their vision component presents a critical bottleneck, with models struggling to perceive low-level details about a path. Our experimental results show that this issue cannot be trivially addressed via end-to-end fine-tuning; rather, task-specific discriminative adaptation of these vision encoders is needed for these VLMs to become effective path evaluators.", "published": "2024-11-27T19:32:03Z", "authors": ["Mohamed Aghzal", "Xiang Yue", "Erion Plaku", "Ziyu Yao"], "categories": ["cs.CV", "cs.CL"], "doi": "", "_query": "property valuation computer vision", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2107.01824v1", "title": "Exploring Data Pipelines through the Process Lens: a Reference Model forComputer Vision", "abstract": "Researchers have identified datasets used for training computer vision (CV) models as an important source of hazardous outcomes, and continue to examine popular CV datasets to expose their harms. These works tend to treat datasets as objects, or focus on particular steps in data production pipelines. We argue here that we could further systematize our analysis of harms by examining CV data pipelines through a process-oriented lens that captures the creation, the evolution and use of these datasets. As a step towards cultivating a process-oriented lens, we embarked on an empirical study of CV data pipelines informed by the field of method engineering. We present here a preliminary result: a reference model of CV data pipelines. Besides exploring the questions that this endeavor raises, we discuss how the process lens could support researchers in discovering understudied issues, and could help practitioners in making their processes more transparent.", "published": "2021-07-05T07:15:57Z", "authors": ["Agathe Balayn", "Bogdan Kulynych", "Seda Guerses"], "categories": ["cs.CV"], "doi": "", "_query": "property valuation computer vision", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2606.02672v1", "title": "Heterogeneous Mapping for Analog In-Memory Computing Accelerators: A Unified Workflow", "abstract": "Analog In-Memory Computing (AIMC) accelerators execute matrix-vector multiplications directly within memory arrays, reducing data movement and improving DNN inference efficiency. Their limited effective precision motivates heterogeneous architectures that combine analog compute tiles with digital processing units. This letter classifies existing methods for partitioning DNN workloads across these resources by mapping granularity, optimization strategy, and model support, and distills them into a unified four-stage workflow. To demonstrate the workflow on a model class not yet addressed by existing methods, we apply its first two stages to GPT-2, producing the first AIMC-specific precision sensitivity profile for a decoder-only transformer. Sensitivity is dominated by 4 of 49 projections, with the first decoder block's attention output dominating by an order of magnitude. This suggests that projection-level mapping and selective digital execution of early-block and output-facing projections are important for reliable decoder-transformer deployment on AIMC hardware.", "published": "2026-06-01T12:17:51Z", "authors": ["Corey Lammie"], "categories": ["cs.AR", "cs.ET"], "doi": "10.1109/LCA.2026.3699929", "_query": "property valuation computer vision", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2605.25127v1", "title": "PQDT: Pseudo-Query Dual Transformer for Robust Point Cloud Restoration", "abstract": "Point clouds are a fundamental 3D representation in computer vision, enabling a wide range of perception tasks. However, real-world point clouds often suffer from degradations such as incompleteness, noise, outliers, and irregular density, caused by sensor limitations or occlusions. Recovering clean and detailed shapes from such degraded data is crucial for downstream applications. While existing learning-based methods achieve progress on individual tasks like completion or denoising, they typically rely on global bottleneck features, which lose fine-grained geometry and remain sensitive to varying input quality. We propose a unified 3D restoration network that directly takes point clouds as input and adaptively reconstructs high-quality geometry under diverse degradation scenarios. At the core of our approach is a Pseudo-Query module, implemented within a Transformer backbone, which reformulates geometric translation into two cooperative stages to enhance structural clarity, robustness, and local detail preservation. Extensive experiments on curated benchmarks demonstrate that our approach surpasses state-of-the-art performance in general 3D restoration. It effectively handles complex combinations of completion, deformation, and denoising degradations. With this work, we provide a novel unified, point-only backbone for robust 3D restoration, enabling more versatile 3D perception.", "published": "2026-05-24T15:15:13Z", "authors": ["Haoqing Wu", "Alexa Nawotki", "Jochen Garcke"], "categories": ["cs.CV", "cs.LG"], "doi": "", "_query": "property valuation computer vision", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2110.03860v2", "title": "Token Pooling in Vision Transformers", "abstract": "Despite the recent success in many applications, the high computational requirements of vision transformers limit their use in resource-constrained settings. While many existing methods improve the quadratic complexity of attention, in most vision transformers, self-attention is not the major computation bottleneck, e.g., more than 80% of the computation is spent on fully-connected layers. To improve the computational complexity of all layers, we propose a novel token downsampling method, called Token Pooling, efficiently exploiting redundancies in the images and intermediate token representations. We show that, under mild assumptions, softmax-attention acts as a high-dimensional low-pass (smoothing) filter. Thus, its output contains redundancy that can be pruned to achieve a better trade-off between the computational cost and accuracy. Our new technique accurately approximates a set of tokens by minimizing the reconstruction error caused by downsampling. We solve this optimization problem via cost-efficient clustering. We rigorously analyze and compare to prior downsampling methods. Our experiments show that Token Pooling significantly improves the cost-accuracy trade-off over the state-of-the-art downsampling. Token Pooling is a simple and effective operator that can benefit many architectures. Applied to DeiT, it achieves the same ImageNet top-1 accuracy using 42% fewer computations.", "published": "2021-10-08T02:22:50Z", "authors": ["Dmitrii Marin", "Jen-Hao Rick Chang", "Anurag Ranjan", "Anish Prabhu", "Mohammad Rastegari", "Oncel Tuzel"], "categories": ["cs.CV", "cs.LG"], "doi": "", "_query": "property valuation computer vision", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2106.10587v2", "title": "Exploring Vision Transformers for Fine-grained Classification", "abstract": "Existing computer vision research in categorization struggles with fine-grained attributes recognition due to the inherently high intra-class variances and low inter-class variances. SOTA methods tackle this challenge by locating the most informative image regions and rely on them to classify the complete image. The most recent work, Vision Transformer (ViT), shows its strong performance in both traditional and fine-grained classification tasks. In this work, we propose a multi-stage ViT framework for fine-grained image classification tasks, which localizes the informative image regions without requiring architectural changes using the inherent multi-head self-attention mechanism. We also introduce attention-guided augmentations for improving the model's capabilities. We demonstrate the value of our approach by experimenting with four popular fine-grained benchmarks: CUB-200-2011, Stanford Cars, Stanford Dogs, and FGVC7 Plant Pathology. We also prove our model's interpretability via qualitative results.", "published": "2021-06-19T23:57:31Z", "authors": ["Marcos V. Conde", "Kerem Turgutlu"], "categories": ["cs.CV", "cs.LG"], "doi": "", "_query": "property valuation computer vision", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2403.14302v2", "title": "SpikingResformer: Bridging ResNet and Vision Transformer in Spiking Neural Networks", "abstract": "The remarkable success of Vision Transformers in Artificial Neural Networks (ANNs) has led to a growing interest in incorporating the self-attention mechanism and transformer-based architecture into Spiking Neural Networks (SNNs). While existing methods propose spiking self-attention mechanisms that are compatible with SNNs, they lack reasonable scaling methods, and the overall architectures proposed by these methods suffer from a bottleneck in effectively extracting local features. To address these challenges, we propose a novel spiking self-attention mechanism named Dual Spike Self-Attention (DSSA) with a reasonable scaling method. Based on DSSA, we propose a novel spiking Vision Transformer architecture called SpikingResformer, which combines the ResNet-based multi-stage architecture with our proposed DSSA to improve both performance and energy efficiency while reducing parameters. Experimental results show that SpikingResformer achieves higher accuracy with fewer parameters and lower energy consumption than other spiking Vision Transformer counterparts. Notably, our SpikingResformer-L achieves 79.40% top-1 accuracy on ImageNet with 4 time-steps, which is the state-of-the-art result in the SNN field.", "published": "2024-03-21T11:16:42Z", "authors": ["Xinyu Shi", "Zecheng Hao", "Zhaofei Yu"], "categories": ["cs.NE", "cs.CV", "cs.LG"], "doi": "", "_query": "property valuation computer vision", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2109.14979v3", "title": "Moving Object Detection for Event-based vision using Graph Spectral Clustering", "abstract": "Moving object detection has been a central topic of discussion in computer vision for its wide range of applications like in self-driving cars, video surveillance, security, and enforcement. Neuromorphic Vision Sensors (NVS) are bio-inspired sensors that mimic the working of the human eye. Unlike conventional frame-based cameras, these sensors capture a stream of asynchronous 'events' that pose multiple advantages over the former, like high dynamic range, low latency, low power consumption, and reduced motion blur. However, these advantages come at a high cost, as the event camera data typically contains more noise and has low resolution. Moreover, as event-based cameras can only capture the relative changes in brightness of a scene, event data do not contain usual visual information (like texture and color) as available in video data from normal cameras. So, moving object detection in event-based cameras becomes an extremely challenging task. In this paper, we present an unsupervised Graph Spectral Clustering technique for Moving Object Detection in Event-based data (GSCEventMOD). We additionally show how the optimum number of moving objects can be automatically determined. Experimental comparisons on publicly available datasets show that the proposed GSCEventMOD algorithm outperforms a number of state-of-the-art techniques by a maximum margin of 30%.", "published": "2021-09-30T10:19:22Z", "authors": ["Anindya Mondal", "Shashant R", "Jhony H. Giraldo", "Thierry Bouwmans", "Ananda S. Chowdhury"], "categories": ["cs.CV", "cs.AI", "eess.IV"], "doi": "10.1109/ICCVW54120.2021.00103", "_query": "property valuation computer vision", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2402.17372v2", "title": "Coupled Laplacian Eigenmaps for Locally-Aware 3D Rigid Point Cloud Matching", "abstract": "Point cloud matching, a crucial technique in computer vision, medical and robotics fields, is primarily concerned with finding correspondences between pairs of point clouds or voxels. In some practical scenarios, emphasizing local differences is crucial for accurately identifying a correct match, thereby enhancing the overall robustness and reliability of the matching process. Commonly used shape descriptors have several limitations and often fail to provide meaningful local insights about the paired geometries. In this work, we propose a new technique, based on graph Laplacian eigenmaps, to match point clouds by taking into account fine local structures. To deal with the order and sign ambiguity of Laplacian eigenmaps, we introduce a new operator, called Coupled Laplacian (https://github.com/matteo-bastico/CoupLap), that allows to easily generate aligned eigenspaces for multiple registered geometries. We show that the similarity between those aligned high-dimensional spaces provides a locally meaningful score to match shapes. We firstly evaluate the performance of the proposed technique in a point-wise manner, focusing on the task of object anomaly localization on the MVTec 3D-AD dataset. Additionally, we define a new medical task, called automatic Bone Side Estimation (BSE), which we address through a global similarity score derived from coupled eigenspaces. In order to test it, we propose a benchmark collecting bone surface structures from various public datasets. Our matching technique, based on Coupled Laplacian, outperforms other methods by reaching an impressive accuracy on both tasks.", "published": "2024-02-27T10:10:12Z", "authors": ["Matteo Bastico", "Etienne Decencière", "Laurent Corté", "Yannick Tillier", "David Ryckelynck"], "categories": ["cs.CV"], "doi": "10.1109/CVPR52733.2024.00331", "_query": "property valuation computer vision", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2303.02724v1", "title": "TACHYON: Efficient Shared Memory Parallel Computation of Extremum Graphs", "abstract": "The extremum graph is a succinct representation of the Morse decomposition of a scalar field. It has increasingly become a useful data structure that supports topological feature directed visualization of 2D / 3D scalar fields, and enables dimensionality reduction together with exploratory analysis of high dimensional scalar fields. Current methods that employ the extremum graph compute it either using a simple sequential algorithm for computing the Morse decomposition or by computing the more detailed Morse-Smale complex. Both approaches are typically limited to two and three dimensional scalar fields. We describe a GPU-CPU hybrid parallel algorithm for computing the extremum graph of scalar fields in all dimensions. The proposed shared memory algorithm utilizes both fine grained parallelism and task parallelism to achieve efficiency. An open source software library, TACHYON, that implements the algorithm exhibits superior performance and good scaling behavior.", "published": "2023-03-05T17:44:05Z", "authors": ["Abhijath Ande", "Varshini Subhash", "Vijay Natarajan"], "categories": ["cs.GR", "cs.CG"], "doi": "10.1111/cgf.14784", "_query": "property valuation computer vision", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2403.10075v2", "title": "A survey of synthetic data augmentation methods in computer vision", "abstract": "The standard approach to tackling computer vision problems is to train deep convolutional neural network (CNN) models using large-scale image datasets which are representative of the target task. However, in many scenarios, it is often challenging to obtain sufficient image data for the target task. Data augmentation is a way to mitigate this challenge. A common practice is to explicitly transform existing images in desired ways so as to create the required volume and variability of training data necessary to achieve good generalization performance. In situations where data for the target domain is not accessible, a viable workaround is to synthesize training data from scratch--i.e., synthetic data augmentation. This paper presents an extensive review of synthetic data augmentation techniques. It covers data synthesis approaches based on realistic 3D graphics modeling, neural style transfer (NST), differential neural rendering, and generative artificial intelligence (AI) techniques such as generative adversarial networks (GANs) and variational autoencoders (VAEs). For each of these classes of methods, we focus on the important data generation and augmentation techniques, general scope of application and specific use-cases, as well as existing limitations and possible workarounds. Additionally, we provide a summary of common synthetic datasets for training computer vision models, highlighting the main features, application domains and supported tasks. Finally, we discuss the effectiveness of synthetic data augmentation methods. Since this is the first paper to explore synthetic data augmentation methods in great detail, we are hoping to equip readers with the necessary background information and in-depth knowledge of existing methods and their attendant issues.", "published": "2024-03-15T07:34:08Z", "authors": ["Alhassan Mumuni", "Fuseini Mumuni", "Nana Kobina Gerrar"], "categories": ["cs.CV", "cs.GR", "cs.LG"], "doi": "10.1007/s11633-022-1411-7", "_query": "property valuation computer vision", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2603.27070v1", "title": "Structural Graph Probing of Vision-Language Models", "abstract": "Vision-language models (VLMs) achieve strong multimodal performance, yet how computation is organized across populations of neurons remains poorly understood. In this work, we study VLMs through the lens of neural topology, representing each layer as a within-layer correlation graph derived from neuron-neuron co-activations. This view allows us to ask whether population-level structure is behaviorally meaningful, how it changes across modalities and depth, and whether it identifies causally influential internal components under intervention. We show that correlation topology carries recoverable behavioral signal; moreover, cross-modal structure progressively consolidates with depth around a compact set of recurrent hub neurons, whose targeted perturbation substantially alters model output. Neural topology thus emerges as a meaningful intermediate scale for VLM interpretability: richer than local attribution, more tractable than full circuit recovery, and empirically tied to multimodal behavior. Code is publicly available at https://github.com/he-h/vlm-graph-probing.", "published": "2026-03-28T01:14:40Z", "authors": ["Haoyu He", "Yue Zhuo", "Yu Zheng", "Qi R. Wang"], "categories": ["cs.CV"], "doi": "", "_query": "property valuation computer vision", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2609.37870v1", "title": "Learning from synthetic photorealistic raindrop for single image raindrop removal", "abstract": "Raindrops adhered to camera lens or windshield are inevitable in rainy scenes and can become an issue for many computer vision systems such as autonomous driving. Because raindrop appearance is affected by too many parameters, therefore it is unlikely to find an effective model based solution. Learning based methods are also problematic, because traditional learning method cannot properly model the complex appearance. Whereas deep learning method lacks sufficiently large and realistic training data. To solve it, in our work, we propose the first photo-realistic dataset of synthetic adherent raindrops for training. The rendering is physics based with consideration of the water dynamic, geometric and photometry. The dataset contains various types of rainy scenes and particularly the rainy driving scenes. Based on the modeling of raindrop imagery, we introduce a detection network which has the awareness of the raindrop refraction as well as its blurring. Based on that, we propose the removal network that can well recover the image structure. Rigorous experiments demonstrate the state-of-the-art performance of our proposed framework.", "published": "2026-09-29T15:48:00Z", "authors": ["Zhixiang Hao", "Shaodi You", "Yu Li", "Kunming Li", "Feng Lu"], "categories": ["cs.CV"], "doi": "10.1109/ICCVW.2019.00534", "_query": "property valuation computer vision", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/1809.07048v1", "title": "New approach for solar tracking systems based on computer vision, low cost hardware and deep learning", "abstract": "In this work, a new approach for Sun tracking systems is presented. Due to the current system limitations regarding costs and operational problems, a new approach based on low cost, computer vision open hardware and deep learning has been developed. The preliminary tests carried out successfully in Plataforma solar de Almeria (PSA), reveal the great potential and show the new approach as a good alternative to traditional systems. The proposed approach can provide key variables for the Sun tracking system control like cloud movements prediction, block and shadow detection, atmospheric attenuation or measures of concentrated solar radiation, which can improve the control strategies of the system and therefore the system performance.", "published": "2018-09-19T08:09:04Z", "authors": ["Jose A. Carballo", "Javier Bonilla", "Manuel Berenguel", "Jesús Fernández-Reche", "Ginés García"], "categories": ["cs.LG", "cs.CV", "stat.ML"], "doi": "10.1016/j.renene.2018.08.101", "_query": "property valuation computer vision", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/1610.04046v2", "title": "Is state-dependent valuation more adaptive than simpler rules?", "abstract": "McNamara, Trimmer, and Houston (2012) claim to provide an explanation of certain systematic deviations from rational behavior using a mechanism that could arise through natural selection. We provide an arguably much simpler mechanism in terms of computational limitations, that performs better in the environment described by McNamara, Trimmer, and Houston (2012). To argue convincingly that animals' use of state-dependent valuation is adaptive and is likely to be selected for by natural selection, one must argue that, in some sense, it is a better approach than the simple strategies that we propose.", "published": "2016-10-13T12:21:11Z", "authors": ["Joseph Y. Halpern", "Lior Seeman"], "categories": ["q-bio.PE", "cs.GT"], "doi": "", "_query": "property valuation computer vision", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2403.18922v1", "title": "Lift3D: Zero-Shot Lifting of Any 2D Vision Model to 3D", "abstract": "In recent years, there has been an explosion of 2D vision models for numerous tasks such as semantic segmentation, style transfer or scene editing, enabled by large-scale 2D image datasets. At the same time, there has been renewed interest in 3D scene representations such as neural radiance fields from multi-view images. However, the availability of 3D or multiview data is still substantially limited compared to 2D image datasets, making extending 2D vision models to 3D data highly desirable but also very challenging. Indeed, extending a single 2D vision operator like scene editing to 3D typically requires a highly creative method specialized to that task and often requires per-scene optimization. In this paper, we ask the question of whether any 2D vision model can be lifted to make 3D consistent predictions. We answer this question in the affirmative; our new Lift3D method trains to predict unseen views on feature spaces generated by a few visual models (i.e. DINO and CLIP), but then generalizes to novel vision operators and tasks, such as style transfer, super-resolution, open vocabulary segmentation and image colorization; for some of these tasks, there is no comparable previous 3D method. In many cases, we even outperform state-of-the-art methods specialized for the task in question. Moreover, Lift3D is a zero-shot method, in the sense that it requires no task-specific training, nor scene-specific optimization.", "published": "2024-03-27T18:13:16Z", "authors": ["Mukund Varma T", "Peihao Wang", "Zhiwen Fan", "Zhangyang Wang", "Hao Su", "Ravi Ramamoorthi"], "categories": ["cs.CV"], "doi": "", "_query": "property valuation computer vision", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2108.13665v1", "title": "Is First Person Vision Challenging for Object Tracking?", "abstract": "Understanding human-object interactions is fundamental in First Person Vision (FPV). Tracking algorithms which follow the objects manipulated by the camera wearer can provide useful cues to effectively model such interactions. Visual tracking solutions available in the computer vision literature have significantly improved their performance in the last years for a large variety of target objects and tracking scenarios. However, despite a few previous attempts to exploit trackers in FPV applications, a methodical analysis of the performance of state-of-the-art trackers in this domain is still missing. In this paper, we fill the gap by presenting the first systematic study of object tracking in FPV. Our study extensively analyses the performance of recent visual trackers and baseline FPV trackers with respect to different aspects and considering a new performance measure. This is achieved through TREK-150, a novel benchmark dataset composed of 150 densely annotated video sequences. Our results show that object tracking in FPV is challenging, which suggests that more research efforts should be devoted to this problem so that tracking could benefit FPV tasks.", "published": "2021-08-31T08:06:01Z", "authors": ["Matteo Dunnhofer", "Antonino Furnari", "Giovanni Maria Farinella", "Christian Micheloni"], "categories": ["cs.CV"], "doi": "10.1109/ICCVW54120.2021.00304", "_query": "property valuation computer vision", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2507.07104v2", "title": "Vision-Language-Vision Auto-Encoder: Scalable Knowledge Distillation from Diffusion Models", "abstract": "Building state-of-the-art Vision-Language Models (VLMs) with strong captioning capabilities typically necessitates training on billions of high-quality image-text pairs, requiring millions of GPU hours. This paper introduces the Vision-Language-Vision (VLV) auto-encoder framework, which strategically leverages key pretrained components: a vision encoder, the decoder of a Text-to-Image (T2I) diffusion model, and subsequently, a Large Language Model (LLM). Specifically, we establish an information bottleneck by regularizing the language representation space, achieved through freezing the pretrained T2I diffusion decoder. Our VLV pipeline effectively distills knowledge from the text-conditioned diffusion model using continuous embeddings, demonstrating comprehensive semantic understanding via high-quality reconstructions. Furthermore, by fine-tuning a pretrained LLM to decode the intermediate language representations into detailed descriptions, we construct a state-of-the-art (SoTA) captioner comparable to leading models like GPT-4o and Gemini 2.0 Flash. Our method demonstrates exceptional cost-efficiency and significantly reduces data requirements; by primarily utilizing single-modal images for training and maximizing the utility of existing pretrained models (image encoder, T2I diffusion model, and LLM), it circumvents the need for massive paired image-text datasets, keeping the total training expenditure under $1,000 USD.", "published": "2025-07-09T17:59:04Z", "authors": ["Tiezheng Zhang", "Yitong Li", "Yu-cheng Chou", "Jieneng Chen", "Alan Yuille", "Chen Wei", "Junfei Xiao"], "categories": ["cs.CV"], "doi": "", "_query": "property valuation computer vision", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2104.09248v2", "title": "LSPnet: A 2D Localization-oriented Spacecraft Pose Estimation Neural Network", "abstract": "Being capable of estimating the pose of uncooperative objects in space has been proposed as a key asset for enabling safe close-proximity operations such as space rendezvous, in-orbit servicing and active debris removal. Usual approaches for pose estimation involve classical computer vision-based solutions or the application of Deep Learning (DL) techniques. This work explores a novel DL-based methodology, using Convolutional Neural Networks (CNNs), for estimating the pose of uncooperative spacecrafts. Contrary to other approaches, the proposed CNN directly regresses poses without needing any prior 3D information. Moreover, bounding boxes of the spacecraft in the image are predicted in a simple, yet efficient manner. The performed experiments show how this work competes with the state-of-the-art in uncooperative spacecraft pose estimation, including works which require 3D information as well as works which predict bounding boxes through sophisticated CNNs.", "published": "2021-04-19T12:46:05Z", "authors": ["Albert Garcia", "Mohamed Adel Musallam", "Vincent Gaudilliere", "Enjie Ghorbel", "Kassem Al Ismaeil", "Marcos Perez", "Djamila Aouada"], "categories": ["cs.CV", "cs.LG"], "doi": "", "_query": "property valuation computer vision", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2608.18734v2", "title": "CL4D: Contrastive Language-4D Pretraining for Vision-Language Reasoning in Dynamic Scenes", "abstract": "4D understanding and reasoning is a fundamental capability for embodied AI agents operating in dynamic physical environments. However, existing vision encoders are largely limited to static 2D images or 3D point clouds without temporal modeling, or to 2D videos that lack accurate geometric depth reasoning. Consequently, current approaches fail to jointly capture spatial structure and motion evolution in dynamic scenes. We present CL4D, the first foundational 4D vision encoder that directly operates on dynamic point clouds, trained with a contrastive learning objective to align spatio-temporal geometric representations with natural language descriptions. By learning a shared embedding space between text and 4D scene dynamics, CL4D enables zero-shot motion-to-text and text-to-motion retrieval in dynamic environments and serves as a foundational 4D vision encoder for downstream 4D vision-language tasks. Building on this encoder, we introduce 4DVLM, a 4D vision-language model that conditions language generation on dynamic geometric representations. 4DVLM is the first VLM designed to operate directly on 4D point clouds without relying on 2D images, 2D videos, or static 3D point clouds. We train CL4D and subsequently 4DVLM on a newly constructed dataset termed DynAction4D capturing diverse human motions across varying object interactions and scene environments. Extensive experiments across multiple 4D human action benchmarks demonstrate that CL4D achieves state-of-the-art performance, with improvements of approximately ~16.75% over prior methods. Furthermore, 4DVLM outperforms frontier video VLMs such as Gemini and GPT-5 even when these models are provided with RGB video sequences corresponding to the same scenes represented as 4D point clouds for 4DVLM.", "published": "2026-08-19T09:37:15Z", "authors": ["Kumal Hewagamage", "Isuranga Senavirathne", "Sasika Amarasinghe", "Hasitha Gallella", "Dulanga Weerakoon", "Vigneshwaran Subbaraju", "Ranga Rodrigo"], "categories": ["cs.CV"], "doi": "", "_query": "property valuation computer vision", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/1709.03399v1", "title": "Automated Identification of Trampoline Skills Using Computer Vision Extracted Pose Estimation", "abstract": "A novel method to identify trampoline skills using a single video camera is proposed herein. Conventional computer vision techniques are used for identification, estimation, and tracking of the gymnast's body in a video recording of the routine. For each frame, an open source convolutional neural network is used to estimate the pose of the athlete's body. Body orientation and joint angle estimates are extracted from these pose estimates. The trajectories of these angle estimates over time are compared with those of labelled reference skills. A nearest neighbour classifier utilising a mean squared error distance metric is used to identify the skill performed. A dataset containing 714 skill examples with 20 distinct skills performed by adult male and female gymnasts was recorded and used for evaluation of the system. The system was found to achieve a skill identification accuracy of 80.7% for the dataset.", "published": "2017-09-11T14:23:51Z", "authors": ["Paul W. Connolly", "Guenole C. Silvestre", "Chris J. Bleakley"], "categories": ["cs.CV"], "doi": "", "_query": "property valuation computer vision", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2507.16015v1", "title": "Is Tracking really more challenging in First Person Egocentric Vision?", "abstract": "Visual object tracking and segmentation are becoming fundamental tasks for understanding human activities in egocentric vision. Recent research has benchmarked state-of-the-art methods and concluded that first person egocentric vision presents challenges compared to previously studied domains. However, these claims are based on evaluations conducted across significantly different scenarios. Many of the challenging characteristics attributed to egocentric vision are also present in third person videos of human-object activities. This raises a critical question: how much of the observed performance drop stems from the unique first person viewpoint inherent to egocentric vision versus the domain of human-object activities? To address this question, we introduce a new benchmark study designed to disentangle such factors. Our evaluation strategy enables a more precise separation of challenges related to the first person perspective from those linked to the broader domain of human-object activity understanding. By doing so, we provide deeper insights into the true sources of difficulty in egocentric tracking and segmentation, facilitating more targeted advancements on this task.", "published": "2025-07-21T19:25:50Z", "authors": ["Matteo Dunnhofer", "Zaira Manigrasso", "Christian Micheloni"], "categories": ["cs.CV"], "doi": "10.1109/ICCV51701.2025.00556", "_query": "property valuation computer vision", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/cs/0506061v5", "title": "Security Policies as Membranes in Systems for Global Computing", "abstract": "We propose a simple global computing framework, whose main concern is code migration. Systems are structured in sites, and each site is divided into two parts: a computing body, and a membrane, which regulates the interactions between the computing body and the external environment. More precisely, membranes are filters which control access to the associated site, and they also rely on the well-established notion of trust between sites. We develop a basic theory to express and enforce security policies via membranes. Initially, these only control the actions incoming agents intend to perform locally. We then adapt the basic theory to encompass more sophisticated policies, where the number of actions an agent wants to perform, and also their order, are considered.", "published": "2005-06-14T18:08:10Z", "authors": ["Daniele Gorla", "Matthew Hennessy", "Vladimiro Sassone"], "categories": ["cs.PL", "cs.LO"], "doi": "10.2168/LMCS-1(3:2)2005", "_query": "property valuation computer vision", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2303.15105v1", "title": "Vision Transformer with Quadrangle Attention", "abstract": "Window-based attention has become a popular choice in vision transformers due to its superior performance, lower computational complexity, and less memory footprint. However, the design of hand-crafted windows, which is data-agnostic, constrains the flexibility of transformers to adapt to objects of varying sizes, shapes, and orientations. To address this issue, we propose a novel quadrangle attention (QA) method that extends the window-based attention to a general quadrangle formulation. Our method employs an end-to-end learnable quadrangle regression module that predicts a transformation matrix to transform default windows into target quadrangles for token sampling and attention calculation, enabling the network to model various targets with different shapes and orientations and capture rich context information. We integrate QA into plain and hierarchical vision transformers to create a new architecture named QFormer, which offers minor code modifications and negligible extra computational cost. Extensive experiments on public benchmarks demonstrate that QFormer outperforms existing representative vision transformers on various vision tasks, including classification, object detection, semantic segmentation, and pose estimation. The code will be made publicly available at \\href{https://github.com/ViTAE-Transformer/QFormer}{QFormer}.", "published": "2023-03-27T11:13:50Z", "authors": ["Qiming Zhang", "Jing Zhang", "Yufei Xu", "Dacheng Tao"], "categories": ["cs.CV"], "doi": "", "_query": "property valuation computer vision", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2604.15280v2", "title": "Why Do Vision Language Models Struggle To Recognize Human Emotions?", "abstract": "Understanding emotions is a fundamental ability for intelligent systems to be able to interact with humans. Vision-language models (VLMs) have made tremendous progress in the last few years for many visual tasks, potentially offering a promising solution for understanding emotions. However, it is surprising that even the most sophisticated contemporary VLMs struggle to recognize human emotions or to outperform even specialized vision-only classifiers. In this paper we ask the question 'Why do VLMs struggle to recognize human emotions?', and observe that the inherently continuous and dynamic task of facial expression recognition (DFER) exposes two critical VLM vulnerabilities. First, emotion datasets are naturally long-tailed, and the web-scale data used to pre-train VLMs exacerbates this head-class bias, causing them to systematically collapse rare, under-represented emotions into common categories. We propose alternative sampling strategies that prevent favoring common concepts. Second, temporal information is critical for understanding emotions. However, VLMs are unable to represent temporal information over dense frame sequences, as they are limited by context size and the number of tokens that can fit in memory, which poses a clear challenge for emotion recognition. We demonstrate that the sparse temporal sampling strategy used in VLMs is inherently misaligned with the fleeting nature of micro-expressions (0.25-0.5 seconds), which are often the most critical affective signal. As a diagnostic probe, we propose a multi-stage context enrichment strategy that utilizes the information from 'in-between' frames by first converting them into natural language summaries. This enriched textual context is provided as input to the VLM alongside sparse keyframes, preventing attentional dilution from excessive visual data while preserving the emotional trajectory.", "published": "2026-04-16T17:49:58Z", "authors": ["Madhav Agarwal", "Sotirios A. Tsaftaris", "Laura Sevilla-Lara", "Steven McDonagh"], "categories": ["cs.CV", "cs.AI"], "doi": "", "_query": "property valuation computer vision", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2310.19731v2", "title": "ViR: Towards Efficient Vision Retention Backbones", "abstract": "Vision Transformers (ViTs) have attracted a lot of popularity in recent years, due to their exceptional capabilities in modeling long-range spatial dependencies and scalability for large scale training. Although the training parallelism of self-attention mechanism plays an important role in retaining great performance, its quadratic complexity baffles the application of ViTs in many scenarios which demand fast inference. This effect is even more pronounced in applications in which autoregressive modeling of input features is required. In Natural Language Processing (NLP), a new stream of efforts has proposed parallelizable models with recurrent formulation that allows for efficient inference in generative applications. Inspired by this trend, we propose a new class of computer vision models, dubbed Vision Retention Networks (ViR), with dual parallel and recurrent formulations, which strike an optimal balance between fast inference and parallel training with competitive performance. In particular, ViR scales favorably for image throughput and memory consumption in tasks that require higher-resolution images due to its flexible formulation in processing large sequence lengths. The ViR is the first attempt to realize dual parallel and recurrent equivalency in a general vision backbone for recognition tasks. We have validated the effectiveness of ViR through extensive experiments with different dataset sizes and various image resolutions and achieved competitive performance. Code: https://github.com/NVlabs/ViR", "published": "2023-10-30T16:55:50Z", "authors": ["Ali Hatamizadeh", "Michael Ranzinger", "Shiyi Lan", "Jose M. Alvarez", "Sanja Fidler", "Jan Kautz"], "categories": ["cs.CV", "cs.AI", "cs.LG"], "doi": "", "_query": "property valuation computer vision", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2605.18177v2", "title": "Token-Space Mask Prediction for Efficient Vision Transformer Segmentation", "abstract": "Query-based Vision Transformer segmentation models typically reconstruct dense spatial feature maps to predict masks, inheriting design patterns from convolutional architectures. We show that this explicit image-space reconstruction is not required. We introduce TokenMask, a token-space mask head that computes mask logits directly from query-token affinities and performs interpolation in logit space rather than feature space. This reformulation preserves the original linear scoring mechanism while simplifying the computational structure. Across diverse ViT backbones, datasets and segmentation tasks, TokenMask consistently improves efficiency over prior approaches by reducing computational and memory requirements while maintaining competitive accuracy, leading to tangible speedups on NVIDIA Jetson AGX Orin using TensorRT FP16 inference. Overall, TokenMask yields a simpler and more deployment-friendly design for embedded vision systems.", "published": "2026-05-18T10:20:23Z", "authors": ["Calvin Galagain", "Martyna Poreba", "François Goulette"], "categories": ["cs.CV"], "doi": "", "_query": "property valuation computer vision", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/1102.1443v3", "title": "On Communication Protocols that Compute Almost Privately", "abstract": "A traditionally desired goal when designing auction mechanisms is incentive compatibility, i.e., ensuring that bidders fare best by truthfully reporting their preferences. A complementary goal, which has, thus far, received significantly less attention, is to preserve privacy, i.e., to ensure that bidders reveal no more information than necessary. We further investigate and generalize the approximate privacy model for two-party communication recently introduced by Feigenbaum et al.[8]. We explore the privacy properties of a natural class of communication protocols that we refer to as \"dissection protocols\". Dissection protocols include, among others, the bisection auction in [9,10] and the bisection protocol for the millionaires problem in [8]. Informally, in a dissection protocol the communicating parties are restricted to answering simple questions of the form \"Is your input between the values αand β(under a predefined order over the possible inputs)?\". We prove that for a large class of functions, called tiling functions, which include the 2nd-price Vickrey auction, there always exists a dissection protocol that provides a constant average-case privacy approximation ratio for uniform or \"almost uniform\" probability distributions over inputs. To establish this result we present an interesting connection between the approximate privacy framework and basic concepts in computational geometry. We show that such a good privacy approximation ratio for tiling functions does not, in general, exist in the worst case. We also discuss extensions of the basic setup to more than two parties and to non-tiling functions, and provide calculations of privacy approximation ratios for two functions of interest.", "published": "2011-02-07T21:38:58Z", "authors": ["Marco Comi", "Bhaskar DasGupta", "Michael Schapira", "Venkatakumar Srinivasan"], "categories": ["cs.CR", "cs.GT"], "doi": "10.1016/j.tcs.2012.07.008", "_query": "property valuation computer vision", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2510.16118v1", "title": "ObjectTransforms for Uncertainty Quantification and Reduction in Vision-Based Perception for Autonomous Vehicles", "abstract": "Reliable perception is fundamental for safety critical decision making in autonomous driving. Yet, vision based object detector neural networks remain vulnerable to uncertainty arising from issues such as data bias and distributional shifts. In this paper, we introduce ObjectTransforms, a technique for quantifying and reducing uncertainty in vision based object detection through object specific transformations at both training and inference times. At training time, ObjectTransforms perform color space perturbations on individual objects, improving robustness to lighting and color variations. ObjectTransforms also uses diffusion models to generate realistic, diverse pedestrian instances. At inference time, object perturbations are applied to detected objects and the variance of detection scores are used to quantify predictive uncertainty in real time. This uncertainty signal is then used to filter out false positives and also recover false negatives, improving the overall precision recall curve. Experiments with YOLOv8 on the NuImages 10K dataset demonstrate that our method yields notable accuracy improvements and uncertainty reduction across all object classes during training, while predicting desirably higher uncertainty values for false positives as compared to true positives during inference. Our results highlight the potential of ObjectTransforms as a lightweight yet effective mechanism for reducing and quantifying uncertainty in vision-based perception during training and inference respectively.", "published": "2025-10-17T18:04:31Z", "authors": ["Nishad Sahu", "Shounak Sural", "Aditya Satish Patil", " Ragunathan", " Rajkumar"], "categories": ["cs.CV"], "doi": "", "_query": "property valuation computer vision", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2505.21513v1", "title": "Enhancing Vision Transformer Explainability Using Artificial Astrocytes", "abstract": "Machine learning models achieve high precision, but their decision-making processes often lack explainability. Furthermore, as model complexity increases, explainability typically decreases. Existing efforts to improve explainability primarily involve developing new eXplainable artificial intelligence (XAI) techniques or incorporating explainability constraints during training. While these approaches yield specific improvements, their applicability remains limited. In this work, we propose the Vision Transformer with artificial Astrocytes (ViTA). This training-free approach is inspired by neuroscience and enhances the reasoning of a pretrained deep neural network to generate more human-aligned explanations. We evaluated our approach employing two well-known XAI techniques, Grad-CAM and Grad-CAM++, and compared it to a standard Vision Transformer (ViT). Using the ClickMe dataset, we quantified the similarity between the heatmaps produced by the XAI techniques and a (human-aligned) ground truth. Our results consistently demonstrate that incorporating artificial astrocytes enhances the alignment of model explanations with human perception, leading to statistically significant improvements across all XAI techniques and metrics utilized.", "published": "2025-05-20T23:16:10Z", "authors": ["Nicolas Echevarrieta-Catalan", "Ana Ribas-Rodriguez", "Francisco Cedron", "Odelia Schwartz", "Vanessa Aguiar-Pulido"], "categories": ["cs.CV", "cs.AI", "cs.LG"], "doi": "", "_query": "property valuation computer vision", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2210.09263v1", "title": "Vision-Language Pre-training: Basics, Recent Advances, and Future Trends", "abstract": "This paper surveys vision-language pre-training (VLP) methods for multimodal intelligence that have been developed in the last few years. We group these approaches into three categories: ($i$) VLP for image-text tasks, such as image captioning, image-text retrieval, visual question answering, and visual grounding; ($ii$) VLP for core computer vision tasks, such as (open-set) image classification, object detection, and segmentation; and ($iii$) VLP for video-text tasks, such as video captioning, video-text retrieval, and video question answering. For each category, we present a comprehensive review of state-of-the-art methods, and discuss the progress that has been made and challenges still being faced, using specific systems and models as case studies. In addition, for each category, we discuss advanced topics being actively explored in the research community, such as big foundation models, unified modeling, in-context few-shot learning, knowledge, robustness, and computer vision in the wild, to name a few.", "published": "2022-10-17T17:11:36Z", "authors": ["Zhe Gan", "Linjie Li", "Chunyuan Li", "Lijuan Wang", "Zicheng Liu", "Jianfeng Gao"], "categories": ["cs.CV", "cs.CL"], "doi": "", "_query": "property valuation computer vision", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2305.03483v1", "title": "Flat dimension for power series over valuation rings", "abstract": "We examine the power series ring $R[[X]]$ over a valuation ring $R$ of rank 1, with proper, dense value group. We give a counterexample to Hilbert's syzygy theorem for $R[[X]]$, i.e. an $R[[X]]$-module $C$ that is flat over $R$ and has flat dimension at least 2 over $R[[X]]$, contradicting a previously published result. The key ingredient in our construction is an exploration of the valuation theory of $R[[X]]$. We also use this theory to give a new proof that $R[[X]]$ is not a coherent ring, a fact which is essential in our construction of the module $C$.", "published": "2023-05-05T12:51:01Z", "authors": ["Adam Jones"], "categories": ["math.AC", "math.RA"], "doi": "10.1016/j.jpaa.2024.107778", "_query": "property valuation computer vision", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2210.16900v1", "title": "High Resolution Multi-Scale RAFT (Robust Vision Challenge 2022)", "abstract": "In this report, we present our optical flow approach, MS-RAFT+, that won the Robust Vision Challenge 2022. It is based on the MS-RAFT method, which successfully integrates several multi-scale concepts into single-scale RAFT. Our approach extends this method by exploiting an additional finer scale for estimating the flow, which is made feasible by on-demand cost computation. This way, it can not only operate at half the original resolution, but also use MS-RAFT's shared convex upsampler to obtain full resolution flow. Moreover, our approach relies on an adjusted fine-tuning scheme during training. This in turn aims at improving the generalization across benchmarks. Among all participating methods in the Robust Vision Challenge, our approach ranks first on VIPER and second on KITTI, Sintel, and Middlebury, resulting in the first place of the overall ranking.", "published": "2022-10-30T17:48:11Z", "authors": ["Azin Jahedi", "Maximilian Luz", "Lukas Mehl", "Marc Rivinius", "Andrés Bruhn"], "categories": ["cs.CV"], "doi": "", "_query": "property valuation computer vision", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2201.08683v1", "title": "A Comprehensive Study of Vision Transformers on Dense Prediction Tasks", "abstract": "Convolutional Neural Networks (CNNs), architectures consisting of convolutional layers, have been the standard choice in vision tasks. Recent studies have shown that Vision Transformers (VTs), architectures based on self-attention modules, achieve comparable performance in challenging tasks such as object detection and semantic segmentation. However, the image processing mechanism of VTs is different from that of conventional CNNs. This poses several questions about their generalizability, robustness, reliability, and texture bias when used to extract features for complex tasks. To address these questions, we study and compare VT and CNN architectures as feature extractors in object detection and semantic segmentation. Our extensive empirical results show that the features generated by VTs are more robust to distribution shifts, natural corruptions, and adversarial attacks in both tasks, whereas CNNs perform better at higher image resolutions in object detection. Furthermore, our results demonstrate that VTs in dense prediction tasks produce more reliable and less texture-biased predictions.", "published": "2022-01-21T13:18:16Z", "authors": ["Kishaan Jeeveswaran", "Senthilkumar Kathiresan", "Arnav Varma", "Omar Magdy", "Bahram Zonooz", "Elahe Arani"], "categories": ["cs.CV"], "doi": "", "_query": "property valuation computer vision", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2603.09625v2", "title": "Grounding Synthetic Data Generation With Vision and Language Models", "abstract": "Deep learning models benefit from increasing data diversity and volume, motivating synthetic data augmentation to improve existing datasets. However, existing evaluation metrics for synthetic data typically calculate latent feature similarity, which is difficult to interpret and does not always correlate with the contribution to downstream tasks. We propose a vision-language grounded framework for interpretable synthetic data augmentation and evaluation in remote sensing. Our approach combines generative models, semantic segmentation and image captioning with vision and language models. Based on this framework, we introduce ARAS400k: A large-scale Remote sensing dataset Augmented with Synthetic data for segmentation and captioning, containing 100k real images and 300k synthetic images, each paired with segmentation maps and descriptions. ARAS400k enables the automated evaluation of synthetic data by analyzing semantic composition, minimizing caption redundancy, and verifying cross-modal consistency between visual structures and language descriptions. Experimental results indicate that while models trained exclusively on synthetic data reach competitive performance levels, those trained with augmented data (a combination of real and synthetic images) consistently outperform real-data baselines. Consequently, this work establishes a scalable benchmark for remote sensing tasks, specifically in semantic segmentation and image captioning. The dataset is available at zenodo.org/records/18890661 and the code base at github.com/caglarmert/ARAS400k.", "published": "2026-03-10T13:03:53Z", "authors": ["Ümit Mert Çağlar", "Alptekin Temizel"], "categories": ["cs.CV", "cs.AI"], "doi": "", "_query": "property valuation computer vision", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2309.16662v1", "title": "Geodesic Regression Characterizes 3D Shape Changes in the Female Brain During Menstruation", "abstract": "Women are at higher risk of Alzheimer's and other neurological diseases after menopause, and yet research connecting female brain health to sex hormone fluctuations is limited. We seek to investigate this connection by developing tools that quantify 3D shape changes that occur in the brain during sex hormone fluctuations. Geodesic regression on the space of 3D discrete surfaces offers a principled way to characterize the evolution of a brain's shape. However, in its current form, this approach is too computationally expensive for practical use. In this paper, we propose approximation schemes that accelerate geodesic regression on shape spaces of 3D discrete surfaces. We also provide rules of thumb for when each approximation can be used. We test our approach on synthetic data to quantify the speed-accuracy trade-off of these approximations and show that practitioners can expect very significant speed-up while only sacrificing little accuracy. Finally, we apply the method to real brain shape data and produce the first characterization of how the female hippocampus changes shape during the menstrual cycle as a function of progesterone: a characterization made (practically) possible by our approximation schemes. Our work paves the way for comprehensive, practical shape analyses in the fields of bio-medicine and computer vision. Our implementation is publicly available on GitHub: https://github.com/bioshape-lab/my28brains.", "published": "2023-09-28T17:58:19Z", "authors": ["Adele Myers", "Caitlin Taylor", "Emily Jacobs", "Nina Miolane"], "categories": ["cs.CV", "cs.LG"], "doi": "", "_query": "property valuation computer vision", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2108.01548v3", "title": "Inference via Sparse Coding in a Hierarchical Vision Model", "abstract": "Sparse coding has been incorporated in models of the visual cortex for its computational advantages and connection to biology. But how the level of sparsity contributes to performance on visual tasks is not well understood. In this work, sparse coding has been integrated into an existing hierarchical V2 model (Hosoya and Hyvärinen, 2015), but replacing its independent component analysis (ICA) with an explicit sparse coding in which the degree of sparsity can be controlled. After training, the sparse coding basis functions with a higher degree of sparsity resembled qualitatively different structures, such as curves and corners. The contributions of the models were assessed with image classification tasks, specifically tasks associated with mid-level vision including figure-ground classification, texture classification, and angle prediction between two line stimuli. In addition, the models were assessed in comparison to a texture sensitivity measure that has been reported in V2 (Freeman et al., 2013), and a deleted-region inference task. The results from the experiments show that while sparse coding performed worse than ICA at classifying images, only sparse coding was able to better match the texture sensitivity level of V2 and infer deleted image regions, both by increasing the degree of sparsity in sparse coding. Higher degrees of sparsity allowed for inference over larger deleted image regions. The mechanism that allows for this inference capability in sparse coding is described here.", "published": "2021-08-03T14:55:33Z", "authors": ["Joshua Bowren", "Luis Sanchez-Giraldo", "Odelia Schwartz"], "categories": ["cs.CV", "cs.LG"], "doi": "", "_query": "property valuation computer vision", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2202.09061v4", "title": "VLP: A Survey on Vision-Language Pre-training", "abstract": "In the past few years, the emergence of pre-training models has brought uni-modal fields such as computer vision (CV) and natural language processing (NLP) to a new era. Substantial works have shown they are beneficial for downstream uni-modal tasks and avoid training a new model from scratch. So can such pre-trained models be applied to multi-modal tasks? Researchers have explored this problem and made significant progress. This paper surveys recent advances and new frontiers in vision-language pre-training (VLP), including image-text and video-text pre-training. To give readers a better overall grasp of VLP, we first review its recent advances from five aspects: feature extraction, model architecture, pre-training objectives, pre-training datasets, and downstream tasks. Then, we summarize the specific VLP models in detail. Finally, we discuss the new frontiers in VLP. To the best of our knowledge, this is the first survey focused on VLP. We hope that this survey can shed light on future research in the VLP field.", "published": "2022-02-18T07:54:02Z", "authors": ["Feilong Chen", "Duzhen Zhang", "Minglun Han", "Xiuyi Chen", "Jing Shi", "Shuang Xu", "Bo Xu"], "categories": ["cs.CV", "cs.CL"], "doi": "10.1007/s11633-022-1369-5", "_query": "property valuation computer vision", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2504.09480v1", "title": "Vision-Language Model for Object Detection and Segmentation: A Review and Evaluation", "abstract": "Vision-Language Model (VLM) have gained widespread adoption in Open-Vocabulary (OV) object detection and segmentation tasks. Despite they have shown promise on OV-related tasks, their effectiveness in conventional vision tasks has thus far been unevaluated. In this work, we present the systematic review of VLM-based detection and segmentation, view VLM as the foundational model and conduct comprehensive evaluations across multiple downstream tasks for the first time: 1) The evaluation spans eight detection scenarios (closed-set detection, domain adaptation, crowded objects, etc.) and eight segmentation scenarios (few-shot, open-world, small object, etc.), revealing distinct performance advantages and limitations of various VLM architectures across tasks. 2) As for detection tasks, we evaluate VLMs under three finetuning granularities: \\textit{zero prediction}, \\textit{visual fine-tuning}, and \\textit{text prompt}, and further analyze how different finetuning strategies impact performance under varied task. 3) Based on empirical findings, we provide in-depth analysis of the correlations between task characteristics, model architectures, and training methodologies, offering insights for future VLM design. 4) We believe that this work shall be valuable to the pattern recognition experts working in the fields of computer vision, multimodal learning, and vision foundation models by introducing them to the problem, and familiarizing them with the current status of the progress while providing promising directions for future research. A project associated with this review and evaluation has been created at https://github.com/better-chao/perceptual_abilities_evaluation.", "published": "2025-04-13T08:28:13Z", "authors": ["Yongchao Feng", "Yajie Liu", "Shuai Yang", "Wenrui Cai", "Jinqing Zhang", "Qiqi Zhan", "Ziyue Huang", "Hongxi Yan", "Qiao Wan", "Chenguang Liu", "Junzhe Wang", "Jiahui Lv", "Ziqi Liu", "Tengyuan Shi", "Qingjie Liu", "Yunhong Wang"], "categories": ["cs.CV", "cs.AI"], "doi": "", "_query": "property valuation computer vision", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2205.15836v1", "title": "Surface Analysis with Vision Transformers", "abstract": "The extension of convolutional neural networks (CNNs) to non-Euclidean geometries has led to multiple frameworks for studying manifolds. Many of those methods have shown design limitations resulting in poor modelling of long-range associations, as the generalisation of convolutions to irregular surfaces is non-trivial. Recent state-of-the-art performance of Vision Transformers (ViTs) demonstrates that a general-purpose architecture, which implements self-attention, could replace the local feature learning operations of CNNs. Motivated by the success of attention-modelling in computer vision, we extend ViTs to surfaces by reformulating the task of surface learning as a sequence-to-sequence problem and propose a patching mechanism for surface meshes. We validate the performance of the proposed Surface Vision Transformer (SiT) on two brain age prediction tasks in the developing Human Connectome Project (dHCP) dataset and investigate the impact of pre-training on model performance. Experiments show that the SiT outperforms many surface CNNs, while indicating some evidence of general transformation invariance. Code available at https://github.com/metrics-lab/surface-vision-transformers", "published": "2022-05-31T14:41:01Z", "authors": ["Simon Dahan", "Logan Z. J. Williams", "Abdulah Fawaz", "Daniel Rueckert", "Emma C. Robinson"], "categories": ["cs.CV", "cs.LG", "q-bio.NC"], "doi": "", "_query": "property valuation computer vision", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/0906.5488v2", "title": "Relational Parametricity for Computational Effects", "abstract": "According to Strachey, a polymorphic program is parametric if it applies a uniform algorithm independently of the type instantiations at which it is applied. The notion of relational parametricity, introduced by Reynolds, is one possible mathematical formulation of this idea. Relational parametricity provides a powerful tool for establishing data abstraction properties, proving equivalences of datatypes, and establishing equalities of programs. Such properties have been well studied in a pure functional setting. Many programs, however, exhibit computational effects, and are not accounted for by the standard theory of relational parametricity. In this paper, we develop a foundational framework for extending the notion of relational parametricity to programming languages with effects.", "published": "2009-06-30T11:47:58Z", "authors": ["Rasmus Ejlers Møgelberg", "Alex Simpson"], "categories": ["cs.PL", "cs.LO"], "doi": "10.2168/LMCS-5(3:7)2009", "_query": "property valuation computer vision", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2406.05850v1", "title": "Scaling Graph Convolutions for Mobile Vision", "abstract": "To compete with existing mobile architectures, MobileViG introduces Sparse Vision Graph Attention (SVGA), a fast token-mixing operator based on the principles of GNNs. However, MobileViG scales poorly with model size, falling at most 1% behind models with similar latency. This paper introduces Mobile Graph Convolution (MGC), a new vision graph neural network (ViG) module that solves this scaling problem. Our proposed mobile vision architecture, MobileViGv2, uses MGC to demonstrate the effectiveness of our approach. MGC improves on SVGA by increasing graph sparsity and introducing conditional positional encodings to the graph operation. Our smallest model, MobileViGv2-Ti, achieves a 77.7% top-1 accuracy on ImageNet-1K, 2% higher than MobileViG-Ti, with 0.9 ms inference latency on the iPhone 13 Mini NPU. Our largest model, MobileViGv2-B, achieves an 83.4% top-1 accuracy, 0.8% higher than MobileViG-B, with 2.7 ms inference latency. Besides image classification, we show that MobileViGv2 generalizes well to other tasks. For object detection and instance segmentation on MS COCO 2017, MobileViGv2-M outperforms MobileViG-M by 1.2 $AP^{box}$ and 0.7 $AP^{mask}$, and MobileViGv2-B outperforms MobileViG-B by 1.0 $AP^{box}$ and 0.7 $AP^{mask}$. For semantic segmentation on ADE20K, MobileViGv2-M achieves 42.9% $mIoU$ and MobileViGv2-B achieves 44.3% $mIoU$. Our code can be found at \\url{https://github.com/SLDGroup/MobileViGv2}.", "published": "2024-06-09T16:49:19Z", "authors": ["William Avery", "Mustafa Munir", "Radu Marculescu"], "categories": ["cs.CV", "cs.LG"], "doi": "", "_query": "property valuation computer vision", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2603.19209v1", "title": "Do VLMs Need Vision Transformers? Evaluating State Space Models as Vision Encoders", "abstract": "Large vision--language models (VLMs) often use a frozen vision backbone, whose image features are mapped into a large language model through a lightweight connector. While transformer-based encoders are the standard visual backbone, we ask whether state space model (SSM) vision backbones can be a strong alternative. We systematically evaluate SSM vision backbones for VLMs in a controlled setting. Under matched ImageNet-1K initialization, the SSM backbone achieves the strongest overall performance across both VQA and grounding/localization. We further adapt both SSM and ViT-family backbones with detection or segmentation training and find that dense-task tuning generally improves performance across families; after this adaptation, the SSM backbone remains competitive while operating at a substantially smaller model scale. We further observe that (i) higher ImageNet accuracy or larger backbones do not reliably translate into better VLM performance, and (ii) some visual backbones are unstable in localization. Based on these findings, we propose stabilization strategies that improve robustness for both backbone families and highlight SSM backbones as a strong alternative to transformer-based vision encoders in VLMs.", "published": "2026-03-19T17:56:32Z", "authors": ["Shang-Jui Ray Kuo", "Paola Cascante-Bonilla"], "categories": ["cs.CV", "cs.LG"], "doi": "", "_query": "property valuation computer vision", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2308.04944v1", "title": "Gaussian Image Anomaly Detection with Greedy Eigencomponent Selection", "abstract": "Anomaly detection (AD) in images, identifying significant deviations from normality, is a critical issue in computer vision. This paper introduces a novel approach to dimensionality reduction for AD using pre-trained convolutional neural network (CNN) that incorporate EfficientNet models. We investigate the importance of component selection and propose two types of tree search approaches, both employing a greedy strategy, for optimal eigencomponent selection. Our study conducts three main experiments to evaluate the effectiveness of our approach. The first experiment explores the influence of test set performance on component choice, the second experiment examines the performance when we train on one anomaly type and evaluate on all other types, and the third experiment investigates the impact of using a minimum number of images for training and selecting them based on anomaly types. Our approach aims to find the optimal subset of components that deliver the highest performance score, instead of focusing solely on the proportion of variance explained by each component and also understand the components behaviour in different settings. Our results indicate that the proposed method surpasses both Principal Component Analysis (PCA) and Negated Principal Component Analysis (NPCA) in terms of detection accuracy, even when using fewer components. Thus, our approach provides a promising alternative to conventional dimensionality reduction techniques in AD, and holds potential to enhance the efficiency and effectiveness of AD systems.", "published": "2023-08-09T13:19:28Z", "authors": ["Tetiana Gula", "João P C Bertoldo"], "categories": ["cs.CV", "cs.AI"], "doi": "", "_query": "property valuation computer vision", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2102.02732v1", "title": "Computational identification of significant actors in paintings through symbols and attributes", "abstract": "The automatic analysis of fine art paintings presents a number of novel technical challenges to artificial intelligence, computer vision, machine learning, and knowledge representation quite distinct from those arising in the analysis of traditional photographs. The most important difference is that many realist paintings depict stories or episodes in order to convey a lesson, moral, or meaning. One early step in automatic interpretation and extraction of meaning in artworks is the identifications of figures (actors). In Christian art, specifically, one must identify the actors in order to identify the Biblical episode or story depicted, an important step in understanding the artwork. We designed an automatic system based on deep convolutional neural networks and simple knowledge database to identify saints throughout six centuries of Christian art based in large part upon saints symbols or attributes. Our work represents initial steps in the broad task of automatic semantic interpretation of messages and meaning in fine art.", "published": "2021-02-04T16:42:41Z", "authors": ["David G. Stork", "Anthony Bourached", "George H. Cann", "Ryan-Rhys Griffiths"], "categories": ["cs.CV"], "doi": "", "_query": "property valuation computer vision", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2504.08729v1", "title": "Steering CLIP's vision transformer with sparse autoencoders", "abstract": "While vision models are highly capable, their internal mechanisms remain poorly understood -- a challenge which sparse autoencoders (SAEs) have helped address in language, but which remains underexplored in vision. We address this gap by training SAEs on CLIP's vision transformer and uncover key differences between vision and language processing, including distinct sparsity patterns for SAEs trained across layers and token types. We then provide the first systematic analysis on the steerability of CLIP's vision transformer by introducing metrics to quantify how precisely SAE features can be steered to affect the model's output. We find that 10-15\\% of neurons and features are steerable, with SAEs providing thousands more steerable features than the base model. Through targeted suppression of SAE features, we then demonstrate improved performance on three vision disentanglement tasks (CelebA, Waterbirds, and typographic attacks), finding optimal disentanglement in middle model layers, and achieving state-of-the-art performance on defense against typographic attacks.", "published": "2025-04-11T17:56:09Z", "authors": ["Sonia Joseph", "Praneet Suresh", "Ethan Goldfarb", "Lorenz Hufe", "Yossi Gandelsman", "Robert Graham", "Danilo Bzdok", "Wojciech Samek", "Blake Aaron Richards"], "categories": ["cs.CV", "cs.AI", "cs.LG"], "doi": "", "_query": "property valuation computer vision", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/1711.10464v1", "title": "Openmv: A Python powered, extensible machine vision camera", "abstract": "Advances in semiconductor manufacturing processes and large scale integration keep pushing demanding applications further away from centralized processing, and closer to the edges of the network (i.e. Edge Computing). It has become possible to perform complex in-network image processing using low-power embedded smart cameras, enabling a multitude of new collaborative image processing applications. This paper introduces OpenMV, a new low-power smart camera that lends itself naturally to wireless sensor networks and machine vision applications. The uniqueness of this platform lies in running an embedded Python3 interpreter, allowing its peripherals and machine vision library to be scripted in Python. In addition, its hardware is extensible via modules that augment the platform with new capabilities, such as thermal imaging and networking modules.", "published": "2017-11-01T08:52:12Z", "authors": ["Ibrahim Abdelkader", "Yasser El-Sonbaty", "Mohamed El-Habrouk"], "categories": ["cs.CV", "cs.DC"], "doi": "", "_query": "property valuation computer vision", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2504.09960v1", "title": "Dual-Path Enhancements in Event-Based Eye Tracking: Augmented Robustness and Adaptive Temporal Modeling", "abstract": "Event-based eye tracking has become a pivotal technology for augmented reality and human-computer interaction. Yet, existing methods struggle with real-world challenges such as abrupt eye movements and environmental noise. Building on the efficiency of the Lightweight Spatiotemporal Network-a causal architecture optimized for edge devices-we introduce two key advancements. First, a robust data augmentation pipeline incorporating temporal shift, spatial flip, and event deletion improves model resilience, reducing Euclidean distance error by 12% (1.61 vs. 1.70 baseline) on challenging samples. Second, we propose KnightPupil, a hybrid architecture combining an EfficientNet-B3 backbone for spatial feature extraction, a bidirectional GRU for contextual temporal modeling, and a Linear Time-Varying State-Space Module to adapt to sparse inputs and noise dynamically. Evaluated on the 3ET+ benchmark, our framework achieved 1.61 Euclidean distance on the private test set of the Event-based Eye Tracking Challenge at CVPR 2025, demonstrating its effectiveness for practical deployment in AR/VR systems while providing a foundation for future innovations in neuromorphic vision.", "published": "2025-04-14T07:57:22Z", "authors": ["Hoang M. Truong", "Vinh-Thuan Ly", "Huy G. Tran", "Thuan-Phat Nguyen", "Tram T. Doan"], "categories": ["cs.CV"], "doi": "", "_query": "property valuation computer vision", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/1802.04546v1", "title": "Robust Deformation Estimation in Wood-Composite Materials using Variational Optical Flow", "abstract": "Wood-composite materials are widely used today as they homogenize humidity related directional deformations. Quantification of these deformations as coefficients is important for construction and engineering and topic of current research but still a manual process. This work introduces a novel computer vision approach that automatically extracts these properties directly from scans of the wooden specimens, taken at different humidity levels during the long lasting humidity conditioning process. These scans are used to compute a humidity dependent deformation field for each pixel, from which the desired coefficients can easily be calculated. The overall method includes automated registration of the wooden blocks, numerical optimization to compute a variational optical flow field which is further used to calculate dense strain fields and finally the engineering coefficients and their variance throughout the wooden blocks. The methods regularization is fully parameterizable which allows to model and suppress artifacts due to surface appearance changes of the specimens from mold, cracks, etc. that typically arise in the conditioning process.", "published": "2018-02-13T10:34:59Z", "authors": ["Markus Hofinger", "Thomas Pock", "Thomas Moosbrugger"], "categories": ["cs.CV"], "doi": "", "_query": "property valuation computer vision", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2406.10221v3", "title": "Long Story Short: Story-level Video Understanding from 20K Short Films", "abstract": "Recent developments in vision-language models have significantly advanced video understanding. Existing datasets and tasks, however, have notable limitations. Most datasets are confined to short videos with limited events and narrow narratives. For example, datasets with instructional and egocentric videos often depict the activities of one person in a single scene. Although existing movie datasets offer richer content, they are often limited to short-term tasks, lack publicly available videos, and frequently encounter data leakage issues given the use of subtitles and other information about commercial movies during LLM pretraining. To address the above limitations, we propose Short-Films 20K (SF20K), the largest publicly available movie dataset. SF20K consists of 20,143 amateur films, amounting to 3,582 hours of video, with an average of 12 minutes per movie. We accompany this dataset with SF20K-Test, a manual, open-ended question answering benchmark. SF20K-Test consists of 95 movies and 979 question-answer pairs. Our extensive analysis of SF20K-Test reveals limited data leakage, emphasizes the need for long-term reasoning, and demonstrates the strong performance of recent VLMs. Finally, we show that instruction tuning on the large-scale dataset substantially improves model performance, paving the way for future progress in long-term video understanding.", "published": "2024-06-14T17:54:54Z", "authors": ["Ridouane Ghermi", "Xi Wang", "Vicky Kalogeiton", "Ivan Laptev"], "categories": ["cs.CV", "cs.AI", "cs.CL"], "doi": "10.1007/s11263-026-02940-x", "_query": "property valuation computer vision", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2211.07638v1", "title": "Legged Locomotion in Challenging Terrains using Egocentric Vision", "abstract": "Animals are capable of precise and agile locomotion using vision. Replicating this ability has been a long-standing goal in robotics. The traditional approach has been to decompose this problem into elevation mapping and foothold planning phases. The elevation mapping, however, is susceptible to failure and large noise artifacts, requires specialized hardware, and is biologically implausible. In this paper, we present the first end-to-end locomotion system capable of traversing stairs, curbs, stepping stones, and gaps. We show this result on a medium-sized quadruped robot using a single front-facing depth camera. The small size of the robot necessitates discovering specialized gait patterns not seen elsewhere. The egocentric camera requires the policy to remember past information to estimate the terrain under its hind feet. We train our policy in simulation. Training has two phases - first, we train a policy using reinforcement learning with a cheap-to-compute variant of depth image and then in phase 2 distill it into the final policy that uses depth using supervised learning. The resulting policy transfers to the real world and is able to run in real-time on the limited compute of the robot. It can traverse a large variety of terrain while being robust to perturbations like pushes, slippery surfaces, and rocky terrain. Videos are at https://vision-locomotion.github.io", "published": "2022-11-14T18:59:58Z", "authors": ["Ananye Agarwal", "Ashish Kumar", "Jitendra Malik", "Deepak Pathak"], "categories": ["cs.RO", "cs.AI", "cs.CV", "cs.LG", "eess.SY"], "doi": "", "_query": "property valuation computer vision", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2104.12537v1", "title": "Computer vision in automated parking systems: Design, implementation and challenges", "abstract": "Automated driving is an active area of research in both industry and academia. Automated Parking, which is automated driving in a restricted scenario of parking with low speed manoeuvring, is a key enabling product for fully autonomous driving systems. It is also an important milestone from the perspective of a higher end system built from the previous generation driver assistance systems comprising of collision warning, pedestrian detection, etc. In this paper, we discuss the design and implementation of an automated parking system from the perspective of computer vision algorithms. Designing a low-cost system with functional safety is challenging and leads to a large gap between the prototype and the end product, in order to handle all the corner cases. We demonstrate how camera systems are crucial for addressing a range of automated parking use cases and also, to add robustness to systems based on active distance measuring sensors, such as ultrasonics and radar. The key vision modules which realize the parking use cases are 3D reconstruction, parking slot marking recognition, freespace and vehicle/pedestrian detection. We detail the important parking use cases and demonstrate how to combine the vision modules to form a robust parking system. To the best of the authors' knowledge, this is the first detailed discussion of a systemic view of a commercial automated parking system.", "published": "2021-04-26T13:18:02Z", "authors": ["Markus Heimberger", "Jonathan Horgan", "Ciaran Hughes", "John McDonald", "Senthil Yogamani"], "categories": ["cs.CV"], "doi": "10.1016/j.imavis.2017.07.002", "_query": "property valuation computer vision", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/1506.08518v2", "title": "Fast Computation of Abelian Runs", "abstract": "Given a word $w$ and a Parikh vector $\\mathcal{P}$, an abelian run of period $\\mathcal{P}$ in $w$ is a maximal occurrence of a substring of $w$ having abelian period $\\mathcal{P}$. Our main result is an online algorithm that, given a word $w$ of length $n$ over an alphabet of cardinality $σ$ and a Parikh vector $\\mathcal{P}$, returns all the abelian runs of period $\\mathcal{P}$ in $w$ in time $O(n)$ and space $O(σ+p)$, where $p$ is the norm of $\\mathcal{P}$, i.e., the sum of its components. We also present an online algorithm that computes all the abelian runs with periods of norm $p$ in $w$ in time $O(np)$, for any given norm $p$. Finally, we give an $O(n^2)$-time offline randomized algorithm for computing all the abelian runs of $w$. Its deterministic counterpart runs in $O(n^2\\logσ)$ time.", "published": "2015-06-29T06:24:08Z", "authors": ["Gabriele Fici", "Tomasz Kociumaka", "Thierry Lecroq", "Arnaud Lefebvre", "Elise Prieur-Gaston"], "categories": ["cs.DS"], "doi": "10.1016/j.tcs.2015.12.010", "_query": "property valuation computer vision", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/1608.02192v1", "title": "Playing for Data: Ground Truth from Computer Games", "abstract": "Recent progress in computer vision has been driven by high-capacity models trained on large datasets. Unfortunately, creating large datasets with pixel-level labels has been extremely costly due to the amount of human effort required. In this paper, we present an approach to rapidly creating pixel-accurate semantic label maps for images extracted from modern computer games. Although the source code and the internal operation of commercial games are inaccessible, we show that associations between image patches can be reconstructed from the communication between the game and the graphics hardware. This enables rapid propagation of semantic labels within and across images synthesized by the game, with no access to the source code or the content. We validate the presented approach by producing dense pixel-level semantic annotations for 25 thousand images synthesized by a photorealistic open-world computer game. Experiments on semantic segmentation datasets show that using the acquired data to supplement real-world images significantly increases accuracy and that the acquired data enables reducing the amount of hand-labeled real-world data: models trained with game data and just 1/3 of the CamVid training set outperform models trained on the complete CamVid training set.", "published": "2016-08-07T08:20:14Z", "authors": ["Stephan R. Richter", "Vibhav Vineet", "Stefan Roth", "Vladlen Koltun"], "categories": ["cs.CV"], "doi": "", "_query": "property valuation computer vision", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2401.09417v3", "title": "Vision Mamba: Efficient Visual Representation Learning with Bidirectional State Space Model", "abstract": "Recently the state space models (SSMs) with efficient hardware-aware designs, i.e., the Mamba deep learning model, have shown great potential for long sequence modeling. Meanwhile building efficient and generic vision backbones purely upon SSMs is an appealing direction. However, representing visual data is challenging for SSMs due to the position-sensitivity of visual data and the requirement of global context for visual understanding. In this paper, we show that the reliance on self-attention for visual representation learning is not necessary and propose a new generic vision backbone with bidirectional Mamba blocks (Vim), which marks the image sequences with position embeddings and compresses the visual representation with bidirectional state space models. On ImageNet classification, COCO object detection, and ADE20k semantic segmentation tasks, Vim achieves higher performance compared to well-established vision transformers like DeiT, while also demonstrating significantly improved computation & memory efficiency. For example, Vim is 2.8$\\times$ faster than DeiT and saves 86.8% GPU memory when performing batch inference to extract features on images with a resolution of 1248$\\times$1248. The results demonstrate that Vim is capable of overcoming the computation & memory constraints on performing Transformer-style understanding for high-resolution images and it has great potential to be the next-generation backbone for vision foundation models. Code is available at https://github.com/hustvl/Vim.", "published": "2024-01-17T18:56:18Z", "authors": ["Lianghui Zhu", "Bencheng Liao", "Qian Zhang", "Xinlong Wang", "Wenyu Liu", "Xinggang Wang"], "categories": ["cs.CV", "cs.LG"], "doi": "", "_query": "property valuation computer vision", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/1106.0962v1", "title": "An efficient circle detection scheme in digital images using ant system algorithm", "abstract": "Detection of geometric features in digital images is an important exercise in image analysis and computer vision. The Hough Transform techniques for detection of circles require a huge memory space for data processing hence requiring a lot of time in computing the locations of the data space, writing to and searching through the memory space. In this paper we propose a novel and efficient scheme for detecting circles in edge-detected grayscale digital images. We use Ant-system algorithm for this purpose which has not yet found much application in this field. The main feature of this scheme is that it can detect both intersecting as well as non-intersecting circles with a time efficiency that makes it useful in real time applications. We build up an ant system of new type which finds out closed loops in the image and then tests them for circles.", "published": "2011-06-06T05:52:09Z", "authors": ["K. Chattopadhyay", "J. Basu", "A. Konar"], "categories": ["cs.CV"], "doi": "", "_query": "property valuation computer vision", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2211.04785v1", "title": "Masked Vision-Language Transformers for Scene Text Recognition", "abstract": "Scene text recognition (STR) enables computers to recognize and read the text in various real-world scenes. Recent STR models benefit from taking linguistic information in addition to visual cues into consideration. We propose a novel Masked Vision-Language Transformers (MVLT) to capture both the explicit and the implicit linguistic information. Our encoder is a Vision Transformer, and our decoder is a multi-modal Transformer. MVLT is trained in two stages: in the first stage, we design a STR-tailored pretraining method based on a masking strategy; in the second stage, we fine-tune our model and adopt an iterative correction method to improve the performance. MVLT attains superior results compared to state-of-the-art STR models on several benchmarks. Our code and model are available at https://github.com/onealwj/MVLT.", "published": "2022-11-09T10:28:23Z", "authors": ["Jie Wu", "Ying Peng", "Shengming Zhang", "Weigang Qi", "Jian Zhang"], "categories": ["cs.CV"], "doi": "", "_query": "property valuation computer vision", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2512.16523v2", "title": "TTP: Test-Time Padding for Adversarial Detection and Robust Adaptation on Vision-Language Models", "abstract": "Vision-Language Models (VLMs), such as CLIP, have achieved impressive zero-shot recognition performance but remain highly susceptible to adversarial perturbations, posing significant risks in safety-critical scenarios. Previous training-time defenses rely on adversarial fine-tuning, which requires labeled data and costly retraining, while existing test-time strategies fail to reliably distinguish between clean and adversarial inputs, thereby preventing both adversarial robustness and clean accuracy from reaching their optimum. To address these limitations, we propose Test-Time Padding (TTP), a lightweight defense framework that performs adversarial detection followed by targeted adaptation at inference. TTP identifies adversarial inputs via the cosine similarity shift between CLIP feature embeddings computed before and after spatial padding, yielding a universal threshold for reliable detection across architectures and datasets. For detected adversarial cases, TTP employs trainable padding to restore disrupted attention patterns, coupled with a similarity-aware ensemble strategy for a more robust final prediction. For clean inputs, TTP leaves them unchanged by default or optionally integrates existing test-time adaptation techniques for further accuracy gains. Comprehensive experiments on diverse CLIP backbones and fine-grained benchmarks show that TTP consistently surpasses state-of-the-art test-time defenses, delivering substantial improvements in adversarial robustness without compromising clean accuracy. The code for this paper will be released soon.", "published": "2025-12-18T13:34:14Z", "authors": ["Zhiwei Li", "Yitian Pang", "Weining Wang", "Zhenan Sun", "Qi Li"], "categories": ["cs.CV", "cs.AI"], "doi": "", "_query": "property valuation computer vision", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2403.02308v3", "title": "Vision-RWKV: Efficient and Scalable Visual Perception with RWKV-Like Architectures", "abstract": "Transformers have revolutionized computer vision and natural language processing, but their high computational complexity limits their application in high-resolution image processing and long-context analysis. This paper introduces Vision-RWKV (VRWKV), a model adapted from the RWKV model used in the NLP field with necessary modifications for vision tasks. Similar to the Vision Transformer (ViT), our model is designed to efficiently handle sparse inputs and demonstrate robust global processing capabilities, while also scaling up effectively, accommodating both large-scale parameters and extensive datasets. Its distinctive advantage lies in its reduced spatial aggregation complexity, which renders it exceptionally adept at processing high-resolution images seamlessly, eliminating the necessity for windowing operations. Our evaluations demonstrate that VRWKV surpasses ViT's performance in image classification and has significantly faster speeds and lower memory usage processing high-resolution inputs. In dense prediction tasks, it outperforms window-based models, maintaining comparable speeds. These results highlight VRWKV's potential as a more efficient alternative for visual perception tasks. Code is released at https://github.com/OpenGVLab/Vision-RWKV.", "published": "2024-03-04T18:46:20Z", "authors": ["Yuchen Duan", "Weiyun Wang", "Zhe Chen", "Xizhou Zhu", "Lewei Lu", "Tong Lu", "Yu Qiao", "Hongsheng Li", "Jifeng Dai", "Wenhai Wang"], "categories": ["cs.CV"], "doi": "", "_query": "property valuation computer vision", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2102.05847v2", "title": "ZeroScatter: Domain Transfer for Long Distance Imaging and Vision through Scattering Media", "abstract": "Adverse weather conditions, including snow, rain, and fog, pose a major challenge for both human and computer vision. Handling these environmental conditions is essential for safe decision making, especially in autonomous vehicles, robotics, and drones. Most of today's supervised imaging and vision approaches, however, rely on training data collected in the real world that is biased towards good weather conditions, with dense fog, snow, and heavy rain as outliers in these datasets. Without training data, let alone paired data, existing autonomous vehicles often limit themselves to good conditions and stop when dense fog or snow is detected. In this work, we tackle the lack of supervised training data by combining synthetic and indirect supervision. We present ZeroScatter, a domain transfer method for converting RGB-only captures taken in adverse weather into clear daytime scenes. ZeroScatter exploits model-based, temporal, multi-view, multi-modal, and adversarial cues in a joint fashion, allowing us to train on unpaired, biased data. We assess the proposed method on in-the-wild captures, and the proposed method outperforms existing monocular descattering approaches by 2.8 dB PSNR on controlled fog chamber measurements.", "published": "2021-02-11T04:41:17Z", "authors": ["Zheng Shi", "Ethan Tseng", "Mario Bijelic", "Werner Ritter", "Felix Heide"], "categories": ["cs.CV"], "doi": "", "_query": "property valuation computer vision", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2408.09650v2", "title": "From Darkness to Detail: Frequency-Aware SSMs for Low-Light Vision", "abstract": "Low-light image enhancement remains a persistent challenge in computer vision, where state-of-the-art models are often hampered by hardware constraints and computational inefficiency, particularly at high resolutions. While foundational architectures like transformers and diffusion models have advanced the field, their computational complexity limits their deployment on edge devices. We introduce ExpoMamba, a novel architecture that integrates a frequency-aware state-space model within a modified U-Net. ExpoMamba is designed to address mixed-exposure challenges by decoupling the modeling of amplitude (intensity) and phase (structure) in the frequency domain. This allows for targeted enhancement, making it highly effective for real-time applications, including downstream tasks like object detection and segmentation. Our experiments on six benchmark datasets show that ExpoMamba is up to 2-3x faster than competing models and achieves a 6.8\\% PSNR improvement, establishing a new state-of-the-art in efficient, high-quality low-light enhancement. Source code: https://www.github.com/eashanadhikarla/ExpoMamba.", "published": "2024-08-19T02:16:47Z", "authors": ["Eashan Adhikarla", "Kai Zhang", "Gong Chen", "John Nicholson", "Brian D. Davison"], "categories": ["cs.CV", "cs.AI", "cs.MM", "eess.IV"], "doi": "", "_query": "property valuation computer vision", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/1111.7154v1", "title": "A Structural Approach to Reversible Computation", "abstract": "Reversibility is a key issue in the interface between computation and physics, and of growing importance as miniaturization progresses towards its physical limits. Most foundational work on reversible computing to date has focussed on simulations of low-level machine models. By contrast, we develop a more structural approach. We show how high-level functional programs can be mapped compositionally (i.e. in a syntax-directed fashion) into a simple kind of automata which are immediately seen to be reversible. The size of the automaton is linear in the size of the functional term. In mathematical terms, we are building a concrete model of functional computation. This construction stems directly from ideas arising in Geometry of Interaction and Linear Logic---but can be understood without any knowledge of these topics. In fact, it serves as an excellent introduction to them. At the same time, an interesting logical delineation between reversible and irreversible forms of computation emerges from our analysis.", "published": "2011-11-30T13:27:34Z", "authors": ["Samson Abramsky"], "categories": ["cs.LO", "math.LO", "quant-ph"], "doi": "10.1016/j.tcs.2005.07.002", "_query": "property valuation computer vision", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2506.07860v1", "title": "Egocentric Event-Based Vision for Ping Pong Ball Trajectory Prediction", "abstract": "In this paper, we present a real-time egocentric trajectory prediction system for table tennis using event cameras. Unlike standard cameras, which suffer from high latency and motion blur at fast ball speeds, event cameras provide higher temporal resolution, allowing more frequent state updates, greater robustness to outliers, and accurate trajectory predictions using just a short time window after the opponent's impact. We collect a dataset of ping-pong game sequences, including 3D ground-truth trajectories of the ball, synchronized with sensor data from the Meta Project Aria glasses and event streams. Our system leverages foveated vision, using eye-gaze data from the glasses to process only events in the viewer's fovea. This biologically inspired approach improves ball detection performance and significantly reduces computational latency, as it efficiently allocates resources to the most perceptually relevant regions, achieving a reduction factor of 10.81 on the collected trajectories. Our detection pipeline has a worst-case total latency of 4.5 ms, including computation and perception - significantly lower than a frame-based 30 FPS system, which, in the worst case, takes 66 ms solely for perception. Finally, we fit a trajectory prediction model to the estimated states of the ball, enabling 3D trajectory forecasting in the future. To the best of our knowledge, this is the first approach to predict table tennis trajectories from an egocentric perspective using event cameras.", "published": "2025-06-09T15:22:55Z", "authors": ["Ivan Alberico", "Marco Cannici", "Giovanni Cioffi", "Davide Scaramuzza"], "categories": ["cs.CV"], "doi": "", "_query": "property valuation computer vision", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/1904.07235v1", "title": "Focus Is All You Need: Loss Functions For Event-based Vision", "abstract": "Event cameras are novel vision sensors that output pixel-level brightness changes (\"events\") instead of traditional video frames. These asynchronous sensors offer several advantages over traditional cameras, such as, high temporal resolution, very high dynamic range, and no motion blur. To unlock the potential of such sensors, motion compensation methods have been recently proposed. We present a collection and taxonomy of twenty two objective functions to analyze event alignment in motion compensation approaches (Fig. 1). We call them Focus Loss Functions since they have strong connections with functions used in traditional shape-from-focus applications. The proposed loss functions allow bringing mature computer vision tools to the realm of event cameras. We compare the accuracy and runtime performance of all loss functions on a publicly available dataset, and conclude that the variance, the gradient and the Laplacian magnitudes are among the best loss functions. The applicability of the loss functions is shown on multiple tasks: rotational motion, depth and optical flow estimation. The proposed focus loss functions allow to unlock the outstanding properties of event cameras.", "published": "2019-04-15T15:40:56Z", "authors": ["Guillermo Gallego", "Mathias Gehrig", "Davide Scaramuzza"], "categories": ["cs.CV", "cs.LG", "cs.RO"], "doi": "10.1109/CVPR.2019.01256", "_query": "property valuation computer vision", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2410.00871v3", "title": "MAP: Unleashing Hybrid Mamba-Transformer Vision Backbone's Potential with Masked Autoregressive Pretraining", "abstract": "Hybrid Mamba-Transformer networks have recently garnered broad attention. These networks can leverage the scalability of Transformers while capitalizing on Mamba's strengths in long-context modeling and computational efficiency. However, the challenge of effectively pretraining such hybrid networks remains an open question. Existing methods, such as Masked Autoencoders (MAE) or autoregressive (AR) pretraining, primarily focus on single-type network architectures. In contrast, pretraining strategies for hybrid architectures must be effective for both Mamba and Transformer components. Based on this, we propose Masked Autoregressive Pretraining (MAP) to pretrain a hybrid Mamba-Transformer vision backbone network. This strategy combines the strengths of both MAE and Autoregressive pretraining, improving the performance of Mamba and Transformer modules within a unified paradigm. Experimental results show that the hybrid Mamba-Transformer vision backbone network pretrained with MAP significantly outperforms other pretraining strategies, achieving state-of-the-art performance. We validate the method's effectiveness on both 2D and 3D datasets and provide detailed ablation studies to support the design choices for each component. The code and checkpoints are available at https://github.com/yunzeliu/MAP", "published": "2024-10-01T17:05:08Z", "authors": ["Yunze Liu", "Li Yi"], "categories": ["cs.CV", "cs.AI"], "doi": "", "_query": "property valuation computer vision", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2207.09339v4", "title": "Vision Transformers: From Semantic Segmentation to Dense Prediction", "abstract": "The emergence of vision transformers (ViTs) in image classification has shifted the methodologies for visual representation learning. In particular, ViTs learn visual representation at full receptive field per layer across all the image patches, in comparison to the increasing receptive fields of CNNs across layers and other alternatives (e.g., large kernels and atrous convolution). In this work, for the first time we explore the global context learning potentials of ViTs for dense visual prediction (e.g., semantic segmentation). Our motivation is that through learning global context at full receptive field layer by layer, ViTs may capture stronger long-range dependency information, critical for dense prediction tasks. We first demonstrate that encoding an image as a sequence of patches, a vanilla ViT without local convolution and resolution reduction can yield stronger visual representation for semantic segmentation. For example, our model, termed as SEgmentation TRansformer (SETR), excels on ADE20K (50.28% mIoU, the first position in the test leaderboard on the day of submission) and performs competitively on Cityscapes. However, the basic ViT architecture falls short in broader dense prediction applications, such as object detection and instance segmentation, due to its lack of a pyramidal structure, high computational demand, and insufficient local context. For tackling general dense visual prediction tasks in a cost-effective manner, we further formulate a family of Hierarchical Local-Global (HLG) Transformers, characterized by local attention within windows and global-attention across windows in a pyramidal architecture. Extensive experiments show that our methods achieve appealing performance on a variety of dense prediction tasks (e.g., object detection and instance segmentation and semantic segmentation) as well as image classification.", "published": "2022-07-19T15:49:35Z", "authors": ["Li Zhang", "Jiachen Lu", "Sixiao Zheng", "Xinxuan Zhao", "Xiatian Zhu", "Yanwei Fu", "Tao Xiang", "Jianfeng Feng", "Philip H. S. Torr"], "categories": ["cs.CV"], "doi": "10.1007/s11263-024-02173-w", "_query": "property valuation computer vision", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2301.02211v1", "title": "Teaching Computer Vision for Ecology", "abstract": "Computer vision can accelerate ecology research by automating the analysis of raw imagery from sensors like camera traps, drones, and satellites. However, computer vision is an emerging discipline that is rarely taught to ecologists. This work discusses our experience teaching a diverse group of ecologists to prototype and evaluate computer vision systems in the context of an intensive hands-on summer workshop. We explain the workshop structure, discuss common challenges, and propose best practices. This document is intended for computer scientists who teach computer vision across disciplines, but it may also be useful to ecologists or other domain experts who are learning to use computer vision themselves.", "published": "2023-01-05T18:30:17Z", "authors": ["Elijah Cole", "Suzanne Stathatos", "Björn Lütjens", "Tarun Sharma", "Justin Kay", "Jason Parham", "Benjamin Kellenberger", "Sara Beery"], "categories": ["cs.CY", "cs.CV"], "doi": "", "_query": "property valuation computer vision", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2105.10384v1", "title": "FRaGenLP: A Generator of Random Linear Programming Problems for Cluster Computing Systems", "abstract": "The article presents and evaluates a scalable FRaGenLP algorithm for generating random linear programming problems of large dimension $n$ on cluster computing systems. To ensure the consistency of the problem and the boundedness of the feasible region, the constraint system includes $2n+1$ standard inequalities, called support inequalities. New random inequalities are generated and added to the system in a manner that ensures the consistency of the constraints. Furthermore, the algorithm uses two likeness metrics to prevent the addition of a new random inequality that is similar to one already present in the constraint system. The algorithm also rejects random inequalities that cannot affect the solution of the linear programming problem bounded by the support inequalities. The parallel implementation of the FRaGenLP algorithm is performed in C++ through the parallel BSF-skeleton, which encapsulates all aspects related to the MPI-based parallelization of the program. We provide the results of large-scale computational experiments on a cluster computing system to study the scalability of the FRaGenLP algorithm.", "published": "2021-05-18T14:55:24Z", "authors": ["Leonid B. Sokolinsky", "Irina M. Sokolinskaya"], "categories": ["cs.DC", "cs.MS", "math.OC"], "doi": "10.1007/978-3-030-81691-9_12", "_query": "property valuation computer vision", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/1509.05659v2", "title": "Type-based Self-stabilisation for Computational Fields", "abstract": "Emerging network scenarios require the development of solid large-scale situated systems. Unfortunately, the diffusion/aggregation computational processes therein often introduce a source of complexity that hampers predictability of the overall system behaviour. Computational fields have been introduced to help engineering such systems: they are spatially distributed data structures designed to adapt their shape to the topology of the underlying (mobile) network and to the events occurring in it, with notable applications to pervasive computing, sensor networks, and mobile robots. To assure behavioural correctness, namely, correspondence of micro-level specification (single device behaviour) with macro-level behaviour (resulting global spatial pattern), we investigate the issue of self-stabilisation for computational fields. We present a tiny, expressive, and type-sound calculus of computational fields, and define sufficient conditions for self-stabilisation, defined as the ability to react to changes in the environment finding a new stable state in finite time. A type-based approach is used to provide a correct checking procedure for self-stabilisation.", "published": "2015-09-18T15:19:59Z", "authors": ["Ferruccio Damiani", "Mirko Viroli"], "categories": ["cs.LO", "cs.PL"], "doi": "10.2168/LMCS-11(4:21)2015", "_query": "property valuation computer vision", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2604.23320v1", "title": "KAConvNet: Kolmogorov-Arnold Convolutional Networks for Vision Recognition", "abstract": "The Convolutional Neural Networks (CNNs) have been the dominant and effective approach for general computer vision tasks. Recently, Kolmogorov-Arnold neural networks (KANs), based on the Kolmogorov-Arnold representation theorem, have shown potential to replace Multi-Layer Perceptrons (MLPs) in deep learning. KANs, which use learnable nonlinear activations on edges and simple summation on nodes, offer fewer parameters and greater explainability compared to MLPs. However, there has been limited exploration of integrating the Kolmogorov-Arnold representation theorem with convolutional methods for computer vision tasks. Existing attempts have merely replaced learnable activation functions with weights, undermining KANs' theoretical foundation and limiting their potential effectiveness. Additionally, the B-spline curves used in KANs suffer from computational inefficiency and a tendency to overfit. In this paper, we propose a novel Kolmogorov-Arnold Convolutional Layer that deeply integrates the Kolmogorov-Arnold representation theorem with convolution. This layer provides stronger method interpretability because it is based on established mathematical theorems and its design has theoretical alignment. Building on the Kolmogorov-Arnold Convolutional Layer, we design an efficient network architecture called KAConvNet, which outperforms existing methods combining KAN and convolution, and achieves competitive performance compared to mainstream ViTs and CNNs. We believe that our work offers valuable insight into the field of artificial intelligence and will inspire the development of more innovative CNNs in the 2020s. The code is publicly available at https://github.com/UnicomAI/KAConvNet.", "published": "2026-04-25T14:14:41Z", "authors": ["Zhaoxiang Liu", "Zhicheng Ma", "Kaikai Zhao", "Kai Wang", "Shiguo Lian"], "categories": ["cs.CV"], "doi": "10.1016/j.imavis.2026.105983", "_query": "property valuation computer vision", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2305.08877v1", "title": "M$^2$DAR: Multi-View Multi-Scale Driver Action Recognition with Vision Transformer", "abstract": "Ensuring traffic safety and preventing accidents is a critical goal in daily driving, where the advancement of computer vision technologies can be leveraged to achieve this goal. In this paper, we present a multi-view, multi-scale framework for naturalistic driving action recognition and localization in untrimmed videos, namely M$^2$DAR, with a particular focus on detecting distracted driving behaviors. Our system features a weight-sharing, multi-scale Transformer-based action recognition network that learns robust hierarchical representations. Furthermore, we propose a new election algorithm consisting of aggregation, filtering, merging, and selection processes to refine the preliminary results from the action recognition module across multiple views. Extensive experiments conducted on the 7th AI City Challenge Track 3 dataset demonstrate the effectiveness of our approach, where we achieved an overlap score of 0.5921 on the A2 test set. Our source code is available at \\url{https://github.com/PurdueDigitalTwin/M2DAR}.", "published": "2023-05-13T02:38:15Z", "authors": ["Yunsheng Ma", "Liangqi Yuan", "Amr Abdelraouf", "Kyungtae Han", "Rohit Gupta", "Zihao Li", "Ziran Wang"], "categories": ["cs.CV"], "doi": "", "_query": "property valuation computer vision", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2411.09211v1", "title": "Dynamic Neural Communication: Convergence of Computer Vision and Brain-Computer Interface", "abstract": "Interpreting human neural signals to decode static speech intentions such as text or images and dynamic speech intentions such as audio or video is showing great potential as an innovative communication tool. Human communication accompanies various features, such as articulatory movements, facial expressions, and internal speech, all of which are reflected in neural signals. However, most studies only generate short or fragmented outputs, while providing informative communication by leveraging various features from neural signals remains challenging. In this study, we introduce a dynamic neural communication method that leverages current computer vision and brain-computer interface technologies. Our approach captures the user's intentions from neural signals and decodes visemes in short time steps to produce dynamic visual outputs. The results demonstrate the potential to rapidly capture and reconstruct lip movements during natural speech attempts from human neural signals, enabling dynamic neural communication through the convergence of computer vision and brain--computer interface.", "published": "2024-11-14T06:15:05Z", "authors": ["Ji-Ha Park", "Seo-Hyun Lee", "Soowon Kim", "Seong-Whan Lee"], "categories": ["cs.AI"], "doi": "", "_query": "property valuation computer vision", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/1702.00505v2", "title": "Algorithmic Performance-Accuracy Trade-off in 3D Vision Applications Using HyperMapper", "abstract": "In this paper we investigate an emerging application, 3D scene understanding, likely to be significant in the mobile space in the near future. The goal of this exploration is to reduce execution time while meeting our quality of result objectives. In previous work we showed for the first time that it is possible to map this application to power constrained embedded systems, highlighting that decision choices made at the algorithmic design-level have the most impact. As the algorithmic design space is too large to be exhaustively evaluated, we use a previously introduced multi-objective Random Forest Active Learning prediction framework dubbed HyperMapper, to find good algorithmic designs. We show that HyperMapper generalizes on a recent cutting edge 3D scene understanding algorithm and on a modern GPU-based computer architecture. HyperMapper is able to beat an expert human hand-tuning the algorithmic parameters of the class of Computer Vision applications taken under consideration in this paper automatically. In addition, we use crowd-sourcing using a 3D scene understanding Android app to show that the Pareto front obtained on an embedded system can be used to accelerate the same application on all the 83 smart-phones and tablets crowd-sourced with speedups ranging from 2 to over 12.", "published": "2017-02-02T00:01:46Z", "authors": ["Luigi Nardi", "Bruno Bodin", "Sajad Saeedi", "Emanuele Vespa", "Andrew J. Davison", "Paul H. J. Kelly"], "categories": ["cs.CV", "cs.DC", "cs.LG", "cs.PF"], "doi": "", "_query": "property valuation computer vision", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2112.03111v1", "title": "Ethics and Creativity in Computer Vision", "abstract": "This paper offers a retrospective of what we learnt from organizing the workshop *Ethical Considerations in Creative applications of Computer Vision* at CVPR 2021 conference and, prior to that, a series of workshops on *Computer Vision for Fashion, Art and Design* at ECCV 2018, ICCV 2019, and CVPR 2020. We hope this reflection will bring artists and machine learning researchers into conversation around the ethical and social dimensions of creative applications of computer vision.", "published": "2021-12-06T15:23:08Z", "authors": ["Negar Rostamzadeh", "Emily Denton", "Linda Petrini"], "categories": ["cs.CV", "cs.CY", "cs.LG"], "doi": "", "_query": "property valuation computer vision", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2610.05897v1", "title": "Fitting Vision Adapters at Frontier Scales", "abstract": "Training a small projector between a frozen vision encoder and language model is an established approach to multimodal learning. As the parameter count of language models scales dramatically, we revisit which vision capabilities this approach can add while keeping their pretrained weights fixed. Here we train a 50M parameter projector from the vision encoder of Kimi K2.6 to GLM 5.2 and 5.3, both models without native vision capabilities, and further present a reproducible recipe for training these adapters at scale. We study the following: (a) how vision capabilities of multimodal models scale as purely the language model side scales, and (b) what specific vision capabilities are able to be imbued into a pure language model at scale, and which ones remain limited. We evaluate on MMMU-Pro and BLINK, examining both overall performance and results on individual visual tasks.", "published": "2026-10-05T07:13:50Z", "authors": ["Jaehoon Lee", "Harry Partridge", "Mudith Jayasekara", "Charles O'Neill", "Max Kirkby", "Michael Psenka"], "categories": ["cs.CV", "cs.LG"], "doi": "", "_query": "property valuation computer vision", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2508.19294v2", "title": "Object Detection with Multimodal Large Vision-Language Models: An In-depth Review", "abstract": "The fusion of language and vision in large vision-language models (LVLMs) has revolutionized deep learning-based object detection by enhancing adaptability, contextual reasoning, and generalization beyond traditional architectures. This in-depth review presents a structured exploration of the state-of-the-art in LVLMs, systematically organized through a three-step research review process. First, we discuss the functioning of vision language models (VLMs) for object detection, describing how these models harness natural language processing (NLP) and computer vision (CV) techniques to revolutionize object detection and localization. We then explain the architectural innovations, training paradigms, and output flexibility of recent LVLMs for object detection, highlighting how they achieve advanced contextual understanding for object detection. The review thoroughly examines the approaches used in integration of visual and textual information, demonstrating the progress made in object detection using VLMs that facilitate more sophisticated object detection and localization strategies. This review presents comprehensive visualizations demonstrating LVLMs' effectiveness in diverse scenarios including localization and segmentation, and then compares their real-time performance, adaptability, and complexity to traditional deep learning systems. Based on the review, its is expected that LVLMs will soon meet or surpass the performance of conventional methods in object detection. The review also identifies a few major limitations of the current LVLM modes, proposes solutions to address those challenges, and presents a clear roadmap for the future advancement in this field. We conclude, based on this study, that the recent advancement in LVLMs have made and will continue to make a transformative impact on object detection and robotic applications in the future.", "published": "2025-08-25T17:21:00Z", "authors": ["Ranjan Sapkota", "Manoj Karkee"], "categories": ["cs.CV", "cs.AI", "cs.CL"], "doi": "10.1016/j.inffus.2025.103575", "_query": "property valuation computer vision", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2409.06945v1", "title": "FSMDet: Vision-guided feature diffusion for fully sparse 3D detector", "abstract": "Fully sparse 3D detection has attracted an increasing interest in the recent years. However, the sparsity of the features in these frameworks challenges the generation of proposals because of the limited diffusion process. In addition, the quest for efficiency has led to only few work on vision-assisted fully sparse models. In this paper, we propose FSMDet (Fully Sparse Multi-modal Detection), which use visual information to guide the LiDAR feature diffusion process while still maintaining the efficiency of the pipeline. Specifically, most of fully sparse works focus on complex customized center fusion diffusion/regression operators. However, we observed that if the adequate object completion is performed, even the simplest interpolation operator leads to satisfactory results. Inspired by this observation, we split the vision-guided diffusion process into two modules: a Shape Recover Layer (SRLayer) and a Self Diffusion Layer (SDLayer). The former uses RGB information to recover the shape of the visible part of an object, and the latter uses a visual prior to further spread the features to the center region. Experiments demonstrate that our approach successfully improves the performance of previous fully sparse models that use LiDAR only and reaches SOTA performance in multimodal models. At the same time, thanks to the sparse architecture, our method can be up to 5 times more efficient than previous SOTA methods in the inference process.", "published": "2024-09-11T01:55:45Z", "authors": ["Tianran Liu", "Morteza Mousa Pasandi", "Robert Laganiere"], "categories": ["cs.CV", "cs.AI"], "doi": "", "_query": "property valuation computer vision", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2306.07346v2", "title": "Learning to Mask and Permute Visual Tokens for Vision Transformer Pre-Training", "abstract": "The use of self-supervised pre-training has emerged as a promising approach to enhance the performance of many different visual tasks. In this context, recent approaches have employed the Masked Image Modeling paradigm, which pre-trains a backbone by reconstructing visual tokens associated with randomly masked image patches. This masking approach, however, introduces noise into the input data during pre-training, leading to discrepancies that can impair performance during the fine-tuning phase. Furthermore, input masking neglects the dependencies between corrupted patches, increasing the inconsistencies observed in downstream fine-tuning tasks. To overcome these issues, we propose a new self-supervised pre-training approach, named Masked and Permuted Vision Transformer (MaPeT), that employs autoregressive and permuted predictions to capture intra-patch dependencies. In addition, MaPeT employs auxiliary positional information to reduce the disparity between the pre-training and fine-tuning phases. In our experiments, we employ a fair setting to ensure reliable and meaningful comparisons and conduct investigations on multiple visual tokenizers, including our proposed $k$-CLIP which directly employs discretized CLIP features. Our results demonstrate that MaPeT achieves competitive performance on ImageNet, compared to baselines and competitors under the same model setting. We release an implementation of our code and models at https://github.com/aimagelab/MaPeT.", "published": "2023-06-12T18:12:19Z", "authors": ["Lorenzo Baraldi", "Roberto Amoroso", "Marcella Cornia", "Lorenzo Baraldi", "Andrea Pilzer", "Rita Cucchiara"], "categories": ["cs.CV", "cs.AI", "cs.MM"], "doi": "", "_query": "property valuation computer vision", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2512.08445v2", "title": "Uncertainty-Aware Subset Selection for Robust Visual Explainability under Distribution Shifts", "abstract": "Subset selection-based methods are widely used to explain deep vision models: they attribute predictions by highlighting the most influential image regions and support object-level explanations. While these methods perform well in in-distribution (ID) settings, their behavior under out-of-distribution (OOD) conditions remains poorly understood. Through extensive experiments across multiple ID-OOD sets, we find that reliability of the existing subset based methods degrades markedly, yielding redundant, unstable, and uncertainty-sensitive explanations. To address these shortcomings, we introduce a framework that combines submodular subset selection with layer-wise, gradient-based uncertainty estimation to improve robustness and fidelity without requiring additional training or auxiliary models. Our approach estimates uncertainty via adaptive weight perturbations and uses these estimates to guide submodular optimization, ensuring diverse and informative subset selection. Empirical evaluations show that, beyond mitigating the weaknesses of existing methods under OOD scenarios, our framework also yields improvements in ID settings. These findings highlight limitations of current subset-based approaches and demonstrate how uncertainty-driven optimization can enhance attribution and object-level interpretability, paving the way for more transparent and trustworthy AI in real-world vision applications.", "published": "2025-12-09T10:19:07Z", "authors": ["Madhav Gupta", "Vishak Prasad C", "Ganesh Ramakrishnan"], "categories": ["cs.CV", "cs.LG"], "doi": "", "_query": "property valuation computer vision", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2512.02512v2", "title": "Two-Stage Vision Transformer for Image Restoration: Colorization Pretraining + Residual Upsampling", "abstract": "In computer vision, Single Image Super-Resolution (SISR) is still a difficult problem. We present ViT-SR, a new technique to improve the performance of a Vision Transformer (ViT) employing a two-stage training strategy. In our method, the model learns rich, generalizable visual representations from the data itself through a self-supervised pretraining phase on a colourization task. The pre-trained model is then adjusted for 4x super-resolution. By predicting the addition of a high-frequency residual image to an initial bicubic interpolation, this design simplifies residual learning. ViT-SR, trained and evaluated on the DIV2K benchmark dataset, achieves an impressive SSIM of 0.712 and PSNR of 22.90 dB. These results demonstrate the efficacy of our two-stage approach and highlight the potential of self-supervised pre-training for complex image restoration tasks. Further improvements may be possible with larger ViT architectures or alternative pretext tasks.", "published": "2025-12-02T08:10:55Z", "authors": ["Aditya Chaudhary", "Prachet Dev Singh", "Ankit Jha"], "categories": ["cs.CV"], "doi": "", "_query": "property valuation computer vision", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2607.17052v1", "title": "Searching for Task-Specific Vision Paths: Evolutionary Block Pruning Across Vision-Language Models", "abstract": "Vision-language models normally execute the same complete vision encoder for every question, even when OCR, counting, object, attribute, and spatial queries may not require identical computation. We study whether fixed-budget combinations of vision blocks can be skipped without fine-tuning. A shared K-block route skips one searched set of exactly K blocks for every question, while a capability-specific K-block policy selects one same-size route using a known capability label. We introduce a source-balanced evolutionary search and compare it with independent ranking, contiguous removal, and random routes at matched budgets. Experiments use Qwen2.5-VL-3B-Instruct, SmolVLM2-2.2B-Instruct, and an 876-example image-disjoint selection split. Search transfers across architectures: on SmolVLM2, the searched shared four-block route beats independent construction by 4.91 percentage points. Capability specialization is less stable. On Qwen, the six-block capability policy beats the shared route by 2.17 points, driven by a 7.10-point OCR gain. On sealed IIIT5K, however, the SmolVLM2 OCR-specific route trails its shared route by 13.6 points. Combinatorial search reliably improves route construction, but capability labels do not define universally transferable vision pathways.", "published": "2026-07-19T03:43:26Z", "authors": ["Tarun Tomar"], "categories": ["cs.CV"], "doi": "", "_query": "property valuation computer vision", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/1911.12377v3", "title": "Multimodal Attention Networks for Low-Level Vision-and-Language Navigation", "abstract": "Vision-and-Language Navigation (VLN) is a challenging task in which an agent needs to follow a language-specified path to reach a target destination. The goal gets even harder as the actions available to the agent get simpler and move towards low-level, atomic interactions with the environment. This setting takes the name of low-level VLN. In this paper, we strive for the creation of an agent able to tackle three key issues: multi-modality, long-term dependencies, and adaptability towards different locomotive settings. To that end, we devise \"Perceive, Transform, and Act\" (PTA): a fully-attentive VLN architecture that leaves the recurrent approach behind and the first Transformer-like architecture incorporating three different modalities - natural language, images, and low-level actions for the agent control. In particular, we adopt an early fusion strategy to merge lingual and visual information efficiently in our encoder. We then propose to refine the decoding phase with a late fusion extension between the agent's history of actions and the perceptual modalities. We experimentally validate our model on two datasets: PTA achieves promising results in low-level VLN on R2R and achieves good performance in the recently proposed R4R benchmark. Our code is publicly available at https://github.com/aimagelab/perceive-transform-and-act.", "published": "2019-11-27T19:00:24Z", "authors": ["Federico Landi", "Lorenzo Baraldi", "Marcella Cornia", "Massimiliano Corsini", "Rita Cucchiara"], "categories": ["cs.CV", "cs.CL", "cs.LG"], "doi": "", "_query": "property valuation computer vision", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2602.05598v1", "title": "CAViT -- Channel-Aware Vision Transformer for Dynamic Feature Fusion", "abstract": "Vision Transformers (ViTs) have demonstrated strong performance across a range of computer vision tasks by modeling long-range spatial interactions via self-attention. However, channel-wise mixing in ViTs remains static, relying on fixed multilayer perceptrons (MLPs) that lack adaptability to input content. We introduce 'CAViT', a dual-attention architecture that replaces the static MLP with a dynamic, attention-based mechanism for feature interaction. Each Transformer block in CAViT performs spatial self-attention followed by channel-wise self-attention, allowing the model to dynamically recalibrate feature representations based on global image context. This unified and content-aware token mixing strategy enhances representational expressiveness without increasing depth or complexity. We validate CAViT across five benchmark datasets spanning both natural and medical domains, where it outperforms the standard ViT baseline by up to +3.6% in accuracy, while reducing parameter count and FLOPs by over 30%. Qualitative attention maps reveal sharper and semantically meaningful activation patterns, validating the effectiveness of our attention-driven token mixing.", "published": "2026-02-05T12:33:09Z", "authors": ["Aon Safdar", "Mohamed Saadeldin"], "categories": ["cs.CV", "cs.AI"], "doi": "", "_query": "property valuation computer vision", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2503.18013v1", "title": "Vision-R1: Evolving Human-Free Alignment in Large Vision-Language Models via Vision-Guided Reinforcement Learning", "abstract": "Large Vision-Language Models (LVLMs) typically follow a two-stage training paradigm-pretraining and supervised fine-tuning. Recently, preference optimization, derived from the language domain, has emerged as an effective post-training reinforcement strategy to enhance capabilities of LVLMs. However, constructing high-quality human-annotated preference data and developing robust reward models to mimic these preferences are both costly and challenging. Motivated by this observation, we propose Vision-R1, a novel vision-guided R1-like reinforcement learning algorithm for LVLMs that rewards models with definitive vision feedback. It only leverages curated instruction data, eliminating the need for specialized reward models and handcrafted preference datasets. We incorporate a criterion-driven reward function that further integrates multi-dimensional feedback to evaluate model completions comprehensively based on the vision task logic. Furthermore, we introduce a progressive rule refinement strategy that dynamically adjusts the reward criteria during training, enabling continuous model improvement and mitigating reward hacking. Extensive experiments on both in-distribution and out-of-distribution benchmarks demonstrate that fine-tuning the 7B LVLMs with Vision-R1 achieves consistent performance gains, with even up to 50% improvement and surpassing the state-of-the-art 10x size model.", "published": "2025-03-23T10:21:14Z", "authors": ["Yufei Zhan", "Yousong Zhu", "Shurong Zheng", "Hongyin Zhao", "Fan Yang", "Ming Tang", "Jinqiao Wang"], "categories": ["cs.CV", "cs.AI"], "doi": "", "_query": "property valuation computer vision", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/1912.05222v1", "title": "Automatic Analysis of Sewer Pipes Based on Unrolled Monocular Fisheye Images", "abstract": "The task of detecting and classifying damages in sewer pipes offers an important application area for computer vision algorithms. This paper describes a system, which is capable of accomplishing this task solely based on low quality and severely compressed fisheye images from a pipe inspection robot. Relying on robust image features, we estimate camera poses, model the image lighting, and exploit this information to generate high quality cylindrical unwraps of the pipes' surfaces.Based on the generated images, we apply semantic labeling based on deep convolutional neural networks to detect and classify defects as well as structural elements.", "published": "2019-12-11T10:32:35Z", "authors": ["Johannes Künzel", "Thomas Werner", "Ronja Möller", "Peter Eisert", "Jan Waschnewski", "Ralf Hilpert"], "categories": ["cs.CV"], "doi": "10.1109/WACV.2018.00223", "_query": "property valuation computer vision", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2308.09160v1", "title": "FedPerfix: Towards Partial Model Personalization of Vision Transformers in Federated Learning", "abstract": "Personalized Federated Learning (PFL) represents a promising solution for decentralized learning in heterogeneous data environments. Partial model personalization has been proposed to improve the efficiency of PFL by selectively updating local model parameters instead of aggregating all of them. However, previous work on partial model personalization has mainly focused on Convolutional Neural Networks (CNNs), leaving a gap in understanding how it can be applied to other popular models such as Vision Transformers (ViTs). In this work, we investigate where and how to partially personalize a ViT model. Specifically, we empirically evaluate the sensitivity to data distribution of each type of layer. Based on the insights that the self-attention layer and the classification head are the most sensitive parts of a ViT, we propose a novel approach called FedPerfix, which leverages plugins to transfer information from the aggregated model to the local client as a personalization. Finally, we evaluate the proposed approach on CIFAR-100, OrganAMNIST, and Office-Home datasets and demonstrate its effectiveness in improving the model's performance compared to several advanced PFL methods.", "published": "2023-08-17T19:22:30Z", "authors": ["Guangyu Sun", "Matias Mendieta", "Jun Luo", "Shandong Wu", "Chen Chen"], "categories": ["cs.CV", "cs.LG"], "doi": "", "_query": "property valuation computer vision", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2210.15722v1", "title": "PatchRot: A Self-Supervised Technique for Training Vision Transformers", "abstract": "Vision transformers require a huge amount of labeled data to outperform convolutional neural networks. However, labeling a huge dataset is a very expensive process. Self-supervised learning techniques alleviate this problem by learning features similar to supervised learning in an unsupervised way. In this paper, we propose a self-supervised technique PatchRot that is crafted for vision transformers. PatchRot rotates images and image patches and trains the network to predict the rotation angles. The network learns to extract both global and local features from an image. Our extensive experiments on different datasets showcase PatchRot training learns rich features which outperform supervised learning and compared baseline.", "published": "2022-10-27T18:55:12Z", "authors": ["Sachin Chhabra", "Prabal Bijoy Dutta", "Hemanth Venkateswara", "Baoxin Li"], "categories": ["cs.CV"], "doi": "", "_query": "property valuation computer vision", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2603.09771v2", "title": "Ego: Embedding-Guided Personalization of Vision-Language Models", "abstract": "AI assistants that support humans in daily life are becoming increasingly feasible, driven by the rapid advancements in multimodal language models. A key challenge lies in overcoming the generic nature of these models to deliver personalized experiences. Existing approaches to personalizing large vision language models often rely on additional training stages, which limit generality and scalability, or on engineered pipelines with external pre-trained modules, which hinder deployment efficiency. In this work, we propose an efficient personalization method that leverages the model's inherent ability to capture personalized concepts. Specifically, we extract visual tokens that predominantly represent the target concept by utilizing the model's internal attention mechanisms. These tokens serve as a memory of that specific concept, enabling the model to recall and describe it when it appears in test images. We conduct a comprehensive and unified evaluation of our approach and SOTA methods across various personalization settings including single-concept, multi-concept, and video personalization, demonstrating strong performance gains with minimal personalization overhead.", "published": "2026-03-10T15:10:41Z", "authors": ["Soroush Seifi", "Simon Gardier", "Vaggelis Dorovatas", "Daniel Olmeda Reino", "Rahaf Aljundi"], "categories": ["cs.CV", "cs.AI"], "doi": "", "_query": "property valuation computer vision", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2506.09237v2", "title": "PatchGuard: Adversarially Robust Anomaly Detection and Localization through Vision Transformers and Pseudo Anomalies", "abstract": "Anomaly Detection (AD) and Anomaly Localization (AL) are crucial in fields that demand high reliability, such as medical imaging and industrial monitoring. However, current AD and AL approaches are often susceptible to adversarial attacks due to limitations in training data, which typically include only normal, unlabeled samples. This study introduces PatchGuard, an adversarially robust AD and AL method that incorporates pseudo anomalies with localization masks within a Vision Transformer (ViT)-based architecture to address these vulnerabilities. We begin by examining the essential properties of pseudo anomalies, and follow it by providing theoretical insights into the attention mechanisms required to enhance the adversarial robustness of AD and AL systems. We then present our approach, which leverages Foreground-Aware Pseudo-Anomalies to overcome the deficiencies of previous anomaly-aware methods. Our method incorporates these crafted pseudo-anomaly samples into a ViT-based framework, with adversarial training guided by a novel loss function designed to improve model robustness, as supported by our theoretical analysis. Experimental results on well-established industrial and medical datasets demonstrate that PatchGuard significantly outperforms previous methods in adversarial settings, achieving performance gains of $53.2\\%$ in AD and $68.5\\%$ in AL, while also maintaining competitive accuracy in non-adversarial settings. The code repository is available at https://github.com/rohban-lab/PatchGuard .", "published": "2025-06-10T20:45:54Z", "authors": ["Mojtaba Nafez", "Amirhossein Koochakian", "Arad Maleki", "Jafar Habibi", "Mohammad Hossein Rohban"], "categories": ["cs.CV", "cs.LG"], "doi": "", "_query": "property valuation computer vision", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2206.11920v1", "title": "Agriculture-Vision Challenge 2022 -- The Runner-Up Solution for Agricultural Pattern Recognition via Transformer-based Models", "abstract": "The Agriculture-Vision Challenge in CVPR is one of the most famous and competitive challenges for global researchers to break the boundary between computer vision and agriculture sectors, aiming at agricultural pattern recognition from aerial images. In this paper, we propose our solution to the third Agriculture-Vision Challenge in CVPR 2022. We leverage a data pre-processing scheme and several Transformer-based models as well as data augmentation techniques to achieve a mIoU of 0.582, accomplishing the 2nd place in this challenge.", "published": "2022-06-23T18:02:12Z", "authors": ["Zhicheng Yang", "Jui-Hsin Lai", "Jun Zhou", "Hang Zhou", "Chen Du", "Zhongcheng Lai"], "categories": ["cs.CV"], "doi": "", "_query": "property valuation computer vision", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/1812.00725v3", "title": "CRAVES: Controlling Robotic Arm with a Vision-based Economic System", "abstract": "Training a robotic arm to accomplish real-world tasks has been attracting increasing attention in both academia and industry. This work discusses the role of computer vision algorithms in this field. We focus on low-cost arms on which no sensors are equipped and thus all decisions are made upon visual recognition, e.g., real-time 3D pose estimation. This requires annotating a lot of training data, which is not only time-consuming but also laborious. In this paper, we present an alternative solution, which uses a 3D model to create a large number of synthetic data, trains a vision model in this virtual domain, and applies it to real-world images after domain adaptation. To this end, we design a semi-supervised approach, which fully leverages the geometric constraints among keypoints. We apply an iterative algorithm for optimization. Without any annotations on real images, our algorithm generalizes well and produces satisfying results on 3D pose estimation, which is evaluated on two real-world datasets. We also construct a vision-based control system for task accomplishment, for which we train a reinforcement learning agent in a virtual environment and apply it to the real-world. Moreover, our approach, with merely a 3D model being required, has the potential to generalize to other types of multi-rigid-body dynamic systems. Website: https://qiuwch.github.io/craves.ai. Code: https://github.com/zuoym15/craves.ai", "published": "2018-12-03T13:28:29Z", "authors": ["Yiming Zuo", "Weichao Qiu", "Lingxi Xie", "Fangwei Zhong", "Yizhou Wang", "Alan L. Yuille"], "categories": ["cs.CV"], "doi": "", "_query": "property valuation computer vision", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2305.05238v1", "title": "Architectural Vision for Quantum Computing in the Edge-Cloud Continuum", "abstract": "Quantum processing units (QPUs) are currently exclusively available from cloud vendors. However, with recent advancements, hosting QPUs is soon possible everywhere. Existing work has yet to draw from research in edge computing to explore systems exploiting mobile QPUs, or how hybrid applications can benefit from distributed heterogeneous resources. Hence, this work presents an architecture for Quantum Computing in the edge-cloud continuum. We discuss the necessity, challenges, and solution approaches for extending existing work on classical edge computing to integrate QPUs. We describe how warm-starting allows defining workflows that exploit the hierarchical resources spread across the continuum. Then, we introduce a distributed inference engine with hybrid classical-quantum neural networks (QNNs) to aid system designers in accommodating applications with complex requirements that incur the highest degree of heterogeneity. We propose solutions focusing on classical layer partitioning and quantum circuit cutting to demonstrate the potential of utilizing classical and quantum computation across the continuum. To evaluate the importance and feasibility of our vision, we provide a proof of concept that exemplifies how extending a classical partition method to integrate quantum circuits can improve the solution quality. Specifically, we implement a split neural network with optional hybrid QNN predictors. Our results show that extending classical methods with QNNs is viable and promising for future work.", "published": "2023-05-09T08:00:10Z", "authors": ["Alireza Furutanpey", "Johanna Barzen", "Marvin Bechtold", "Schahram Dustdar", "Frank Leymann", "Philipp Raith", "Felix Truger"], "categories": ["quant-ph", "cs.DC", "cs.LG"], "doi": "10.1109/QSW59989.2023.00021", "_query": "property valuation computer vision", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2303.10093v2", "title": "Investigating the Role of Attribute Context in Vision-Language Models for Object Recognition and Detection", "abstract": "Vision-language alignment learned from image-caption pairs has been shown to benefit tasks like object recognition and detection. Methods are mostly evaluated in terms of how well object class names are learned, but captions also contain rich attribute context that should be considered when learning object alignment. It is unclear how methods use this context in learning, as well as whether models succeed when tasks require attribute and object understanding. To address this gap, we conduct extensive analysis of the role of attributes in vision-language models. We specifically measure model sensitivity to the presence and meaning of attribute context, gauging influence on object embeddings through unsupervised phrase grounding and classification via description methods. We further evaluate the utility of attribute context in training for open-vocabulary object detection, fine-grained text-region retrieval, and attribution tasks. Our results show that attribute context can be wasted when learning alignment for detection, attribute meaning is not adequately considered in embeddings, and describing classes by only their attributes is ineffective. A viable strategy that we find to increase benefits from attributes is contrastive training with adjective-based negative captions.", "published": "2023-03-17T16:14:37Z", "authors": ["Kyle Buettner", "Adriana Kovashka"], "categories": ["cs.CV", "cs.AI", "cs.CL", "cs.LG"], "doi": "", "_query": "property valuation computer vision", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2505.11282v2", "title": "MTevent: A Multi-Task Event Camera Dataset for 6D Pose Estimation and Moving Object Detection", "abstract": "Mobile robots are reaching unprecedented speeds, with platforms like Unitree B2, and Fraunhofer O3dyn achieving maximum speeds between 5 and 10 m/s. However, effectively utilizing such speeds remains a challenge due to the limitations of RGB cameras, which suffer from motion blur and fail to provide real-time responsiveness. Event cameras, with their asynchronous operation, and low-latency sensing, offer a promising alternative for high-speed robotic perception. In this work, we introduce MTevent, a dataset designed for 6D pose estimation and moving object detection in highly dynamic environments with large detection distances. Our setup consists of a stereo-event camera and an RGB camera, capturing 75 scenes, each on average 16 seconds, and featuring 16 unique objects under challenging conditions such as extreme viewing angles, varying lighting, and occlusions. MTevent is the first dataset to combine high-speed motion, long-range perception, and real-world object interactions, making it a valuable resource for advancing event-based vision in robotics. To establish a baseline, we evaluate the task of 6D pose estimation using NVIDIA's FoundationPose on RGB images, achieving an Average Recall of 0.22 with ground-truth masks, highlighting the limitations of RGB-based approaches in such dynamic settings. With MTevent, we provide a novel resource to improve perception models and foster further research in high-speed robotic vision. The dataset is available for download https://huggingface.co/datasets/anas-gouda/MTevent", "published": "2025-05-16T14:18:21Z", "authors": ["Shrutarv Awasthi", "Anas Gouda", "Sven Franke", "Jérôme Rutinowski", "Frank Hoffmann", "Moritz Roidl"], "categories": ["cs.CV"], "doi": "", "_query": "property valuation computer vision", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2511.08402v1", "title": "Anatomy-VLM: A Fine-grained Vision-Language Model for Medical Interpretation", "abstract": "Accurate disease interpretation from radiology remains challenging due to imaging heterogeneity. Achieving expert-level diagnostic decisions requires integration of subtle image features with clinical knowledge. Yet major vision-language models (VLMs) treat images as holistic entities and overlook fine-grained image details that are vital for disease diagnosis. Clinicians analyze images by utilizing their prior medical knowledge and identify anatomical structures as important region of interests (ROIs). Inspired from this human-centric workflow, we introduce Anatomy-VLM, a fine-grained, vision-language model that incorporates multi-scale information. First, we design a model encoder to localize key anatomical features from entire medical images. Second, these regions are enriched with structured knowledge for contextually-aware interpretation. Finally, the model encoder aligns multi-scale medical information to generate clinically-interpretable disease prediction. Anatomy-VLM achieves outstanding performance on both in- and out-of-distribution datasets. We also validate the performance of Anatomy-VLM on downstream image segmentation tasks, suggesting that its fine-grained alignment captures anatomical and pathology-related knowledge. Furthermore, the Anatomy-VLM's encoder facilitates zero-shot anatomy-wise interpretation, providing its strong expert-level clinical interpretation capabilities.", "published": "2025-11-11T16:18:01Z", "authors": ["Difei Gu", "Yunhe Gao", "Mu Zhou", "Dimitris Metaxas"], "categories": ["cs.CV", "cs.AI", "cs.LG"], "doi": "", "_query": "property valuation computer vision", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2607.01396v1", "title": "Computer Vision for Wildlife Monitoring: Detecting Brown Howler Monkeys using YOLO", "abstract": "Urban expansion threatens global biodiversity, especially affecting arboreal species due to the fragmentation of forest habitats. The movement of arboreal species across disjointed forest patches increases mortality risk and, thus, compromises their conservation. In this context, the installation of canopy bridges can be a viable strategy; yet continuous monitoring of their use by arboreal species is essential for ensuring their effectiveness, typically carried out with the aid of camera traps. However, this method often produces false-positive images that demand time from conservationists for review. In this context, computer vision algorithms can optimize the task of detecting target species using the canopy bridges. In this study, we explored the automatic detection of brown howler monkeys (Alouatta guariba) in videos obtained by camera traps. Given the need for a large number of annotated images of the target animals to train the algorithms, we tested the incorporation of auxiliary data to improve detection models, fine-tuning the YOLOv10 framework using varying proportions of them. The improvement of these automatic detection techniques contributes to conservation efforts, by providing automatic tools to monitor solutions that minimize the impact of human interference in animals habitats.", "published": "2026-07-01T18:57:09Z", "authors": ["Gabriel Ferri Schneider", "Guido Luis Glufke Mainardi", "Paulo Ricardo Knob", "Patrícia Dias", "Márcia Jardim", "Júlio César Bicca-Marques", "Soraia Raupp Musse"], "categories": ["cs.CV", "cs.GR"], "doi": "", "_query": "property valuation computer vision", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2212.05932v1", "title": "Automated Level Crossing System: A Computer Vision Based Approach with Raspberry Pi Microcontroller", "abstract": "In a rapidly flourishing country like Bangladesh, accidents in unmanned level crossings are increasing daily. This study presents a deep learning-based approach for automating level crossing junctions, ensuring maximum safety. Here, we develop a fully automated technique using computer vision on a microcontroller that will reduce and eliminate level-crossing deaths and accidents. A Raspberry Pi microcontroller detects impending trains using computer vision on live video, and the intersection is closed until the incoming train passes unimpeded. Live video activity recognition and object detection algorithms scan the junction 24/7. Self-regulating microcontrollers control the entire process. When persistent unauthorized activity is identified, authorities, such as police and fire brigade, are notified via automated messages and notifications. The microcontroller evaluates live rail-track data, and arrival and departure times to anticipate ETAs, train position, velocity, and track problems to avoid head-on collisions. This proposed scheme reduces level crossing accidents and fatalities at a lower cost than current market solutions. Index Terms: Deep Learning, Microcontroller, Object Detection, Railway Crossing, Raspberry Pi", "published": "2022-12-08T12:03:55Z", "authors": ["Rafid Umayer Murshed", "Sandip Kollol Dhruba", "Md. Tawheedul Islam Bhuian", "Mst. Rumi Akter"], "categories": ["cs.CV", "eess.IV"], "doi": "", "_query": "property valuation computer vision", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2602.16019v1", "title": "MedProbCLIP: Probabilistic Adaptation of Vision-Language Foundation Model for Reliable Radiograph-Report Retrieval", "abstract": "Vision-language foundation models have emerged as powerful general-purpose representation learners with strong potential for multimodal understanding, but their deterministic embeddings often fail to provide the reliability required for high-stakes biomedical applications. This work introduces MedProbCLIP, a probabilistic vision-language learning framework for chest X-ray and radiology report representation learning and bidirectional retrieval. MedProbCLIP models image and text representations as Gaussian embeddings through a probabilistic contrastive objective that explicitly captures uncertainty and many-to-many correspondences between radiographs and clinical narratives. A variational information bottleneck mitigates overconfident predictions, while MedProbCLIP employs multi-view radiograph encoding and multi-section report encoding during training to provide fine-grained supervision for clinically aligned correspondence, yet requires only a single radiograph and a single report at inference. Evaluated on the MIMIC-CXR dataset, MedProbCLIP outperforms deterministic and probabilistic baselines, including CLIP, CXR-CLIP, and PCME++, in both retrieval and zero-shot classification. Beyond accuracy, MedProbCLIP demonstrates superior calibration, risk-coverage behavior, selective retrieval reliability, and robustness to clinically relevant corruptions, underscoring the value of probabilistic vision-language modeling for improving the trustworthiness and safety of radiology image-text retrieval systems.", "published": "2026-02-17T21:20:32Z", "authors": ["Ahmad Elallaf", "Yu Zhang", "Yuktha Priya Masupalli", "Jeong Yang", "Young Lee", "Zechun Cao", "Gongbo Liang"], "categories": ["cs.CV", "cs.AI"], "doi": "", "_query": "property valuation computer vision", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2411.16407v1", "title": "A Study on Unsupervised Domain Adaptation for Semantic Segmentation in the Era of Vision-Language Models", "abstract": "Despite the recent progress in deep learning based computer vision, domain shifts are still one of the major challenges. Semantic segmentation for autonomous driving faces a wide range of domain shifts, e.g. caused by changing weather conditions, new geolocations and the frequent use of synthetic data in model training. Unsupervised domain adaptation (UDA) methods have emerged which adapt a model to a new target domain by only using unlabeled data of that domain. The variety of UDA methods is large but all of them use ImageNet pre-trained models. Recently, vision-language models have demonstrated strong generalization capabilities which may facilitate domain adaptation. We show that simply replacing the encoder of existing UDA methods like DACS by a vision-language pre-trained encoder can result in significant performance improvements of up to 10.0% mIoU on the GTA5-to-Cityscapes domain shift. For the generalization performance to unseen domains, the newly employed vision-language pre-trained encoder provides a gain of up to 13.7% mIoU across three unseen datasets. However, we find that not all UDA methods can be easily paired with the new encoder and that the UDA performance does not always likewise transfer into generalization performance. Finally, we perform our experiments on an adverse weather condition domain shift to further verify our findings on a pure real-to-real domain shift.", "published": "2024-11-25T14:12:24Z", "authors": ["Manuel Schwonberg", "Claus Werner", "Hanno Gottschalk", "Carsten Meyer"], "categories": ["cs.CV", "cs.AI"], "doi": "", "_query": "property valuation computer vision", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2006.13256v4", "title": "Rescaling Egocentric Vision", "abstract": "This paper introduces the pipeline to extend the largest dataset in egocentric vision, EPIC-KITCHENS. The effort culminates in EPIC-KITCHENS-100, a collection of 100 hours, 20M frames, 90K actions in 700 variable-length videos, capturing long-term unscripted activities in 45 environments, using head-mounted cameras. Compared to its previous version, EPIC-KITCHENS-100 has been annotated using a novel pipeline that allows denser (54% more actions per minute) and more complete annotations of fine-grained actions (+128% more action segments). This collection enables new challenges such as action detection and evaluating the \"test of time\" - i.e. whether models trained on data collected in 2018 can generalise to new footage collected two years later. The dataset is aligned with 6 challenges: action recognition (full and weak supervision), action detection, action anticipation, cross-modal retrieval (from captions), as well as unsupervised domain adaptation for action recognition. For each challenge, we define the task, provide baselines and evaluation metrics", "published": "2020-06-23T18:28:04Z", "authors": ["Dima Damen", "Hazel Doughty", "Giovanni Maria Farinella", "Antonino Furnari", "Evangelos Kazakos", "Jian Ma", "Davide Moltisanti", "Jonathan Munro", "Toby Perrett", "Will Price", "Michael Wray"], "categories": ["cs.CV", "cs.LG"], "doi": "10.5523/bris.2g1n6qdydwa9u22shpxqzp0t8m", "_query": "property valuation computer vision", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2302.08054v1", "title": "Spectral 3D Computer Vision -- A Review", "abstract": "Spectral 3D computer vision examines both the geometric and spectral properties of objects. It provides a deeper understanding of an object's physical properties by providing information from narrow bands in various regions of the electromagnetic spectrum. Mapping the spectral information onto the 3D model reveals changes in the spectra-structure space or enhances 3D representations with properties such as reflectance, chromatic aberration, and varying defocus blur. This emerging paradigm advances traditional computer vision and opens new avenues of research in 3D structure, depth estimation, motion analysis, and more. It has found applications in areas such as smart agriculture, environment monitoring, building inspection, geological exploration, and digital cultural heritage records. This survey offers a comprehensive overview of spectral 3D computer vision, including a unified taxonomy of methods, key application areas, and future challenges and prospects.", "published": "2023-02-16T03:29:40Z", "authors": ["Yajie Sun", "Ali Zia", "Vivien Rolland", "Charissa Yu", "Jun Zhou"], "categories": ["cs.CV", "cs.AI"], "doi": "", "_query": "property valuation computer vision", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2607.01086v1", "title": "LongVQUBench: Benchmarking Long-Term Video Quality Understanding of Vision-Language Models", "abstract": "The evaluation of long-term video quality understanding remains an open challenge for large vision-language models (LVLMs). Existing video quality benchmarks predominantly focus on short clips and isolated distortions, overlooking the temporal continuity, cumulative degradation, and reasoning complexity inherent in long-duration content. To address these limitations, we present LongVQUBench, a comprehensive benchmark for long-term video quality understanding. LongVQUBench contains over 1200 diverse videos spanning movies, documentaries, surveillance footage, egocentric recordings, and animated content, accompanied by 1500 multiple-choice and open-ended questions for validation and testing. To assess perceptual reasoning across different temporal scopes, we introduce three progressively complex evaluation levels: (i) local event quality understanding (LQU) for analyzing localized distortions; (ii) cross-event quality reasoning (CQR) for integrating multiple degraded events; and (iii) global quality understanding (GQU) for holistic perceptual evaluation over extended durations. Furthermore, a needle distortion question-answering (NDQA) paradigm is embedded across all three levels, where spatial or temporal artifacts are sparsely inserted to probe fine-grained detection and reasoning capabilities. Extensive experiments on 14 state-of-the-art LVLMs reveal significant performance degradation with increasing video length and reasoning depth, highlighting their limited capacity for long-range temporal integration and perceptual attribution. We envision LongVQUBench as a foundational step toward the systematic, hierarchical, and explainable evaluation of LVLMs' long-term video quality understanding.", "published": "2026-07-01T15:40:42Z", "authors": ["Arpita Nema", "Hanwei Zhu", "Xi Zhang", "Weisi Lin"], "categories": ["cs.CV", "cs.AI"], "doi": "", "_query": "property valuation computer vision", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2309.08612v1", "title": "Explaining Vision and Language through Graphs of Events in Space and Time", "abstract": "Artificial Intelligence makes great advances today and starts to bridge the gap between vision and language. However, we are still far from understanding, explaining and controlling explicitly the visual content from a linguistic perspective, because we still lack a common explainable representation between the two domains. In this work we come to address this limitation and propose the Graph of Events in Space and Time (GEST), by which we can represent, create and explain, both visual and linguistic stories. We provide a theoretical justification of our model and an experimental validation, which proves that GEST can bring a solid complementary value along powerful deep learning models. In particular, GEST can help improve at the content-level the generation of videos from text, by being easily incorporated into our novel video generation engine. Additionally, by using efficient graph matching techniques, the GEST graphs can also improve the comparisons between texts at the semantic level.", "published": "2023-08-29T07:25:06Z", "authors": ["Mihai Masala", "Nicolae Cudlenco", "Traian Rebedea", "Marius Leordeanu"], "categories": ["cs.AI", "cs.CL", "cs.CV"], "doi": "", "_query": "property valuation computer vision", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2409.11836v1", "title": "NT-ViT: Neural Transcoding Vision Transformers for EEG-to-fMRI Synthesis", "abstract": "This paper introduces the Neural Transcoding Vision Transformer (\\modelname), a generative model designed to estimate high-resolution functional Magnetic Resonance Imaging (fMRI) samples from simultaneous Electroencephalography (EEG) data. A key feature of \\modelname is its Domain Matching (DM) sub-module which effectively aligns the latent EEG representations with those of fMRI volumes, enhancing the model's accuracy and reliability. Unlike previous methods that tend to struggle with fidelity and reproducibility of images, \\modelname addresses these challenges by ensuring methodological integrity and higher-quality reconstructions which we showcase through extensive evaluation on two benchmark datasets; \\modelname outperforms the current state-of-the-art by a significant margin in both cases, e.g. achieving a $10\\times$ reduction in RMSE and a $3.14\\times$ increase in SSIM on the Oddball dataset. An ablation study also provides insights into the contribution of each component to the model's overall effectiveness. This development is critical in offering a new approach to lessen the time and financial constraints typically linked with high-resolution brain imaging, thereby aiding in the swift and precise diagnosis of neurological disorders. Although it is not a replacement for actual fMRI but rather a step towards making such imaging more accessible, we believe that it represents a pivotal advancement in clinical practice and neuroscience research. Code is available at \\url{https://github.com/rom42pla/ntvit}.", "published": "2024-09-18T09:38:08Z", "authors": ["Romeo Lanzino", "Federico Fontana", "Luigi Cinque", "Francesco Scarcello", "Atsuto Maki"], "categories": ["eess.IV", "cs.CV"], "doi": "", "_query": "property valuation computer vision", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2603.19516v2", "title": "Gastric-X: A Multimodal Multi-Phase Benchmark Dataset for Advancing Vision-Language Models in Gastric Cancer Analysis", "abstract": "Recent vision-language models (VLMs) have shown strong generalization and multimodal reasoning abilities in natural domains. However, their application to medical diagnosis remains limited by the lack of comprehensive and structured datasets that capture real clinical workflows. To advance the development of VLMs for clinical applications, particularly in gastric cancer, we introduce Gastric-X, a large-scale multimodal benchmark for gastric cancer analysis providing 1.7K cases. Each case in Gastric-X includes paired resting and dynamic CT scans, endoscopic image, a set of structured biochemical indicators, expert-authored diagnostic notes, and bounding box annotations of tumor regions, reflecting realistic clinical conditions. We systematically examine the capability of recent VLMs on five core tasks: Visual Question Answering (VQA), report generation, cross-modal retrieval, disease classification, and lesion localization. These tasks simulate critical stages of clinical workflow, from visual understanding and reasoning to multimodal decision support. Through this evaluation, we aim not only to assess model performance but also to probe the nature of VLM understanding: Can current VLMs meaningfully correlate biochemical signals with spatial tumor features and textual reports? We envision Gastric-X as a step toward aligning machine intelligence with the cognitive and evidential reasoning processes of physicians, and as a resource to inspire the development of next-generation medical VLMs.", "published": "2026-03-19T22:47:27Z", "authors": ["Sheng Lu", "Hao Chen", "Rui Yin", "Juyan Ba", "Yu Zhang", "Yuanzhe Li"], "categories": ["cs.CV", "cs.AI"], "doi": "", "_query": "property valuation computer vision", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2311.14435v3", "title": "Local Concept Embeddings for Analysis of Concept Distributions in Vision DNN Feature Spaces", "abstract": "Insights into the learned latent representations are imperative for verifying deep neural networks (DNNs) in critical computer vision (CV) tasks. Therefore, state-of-the-art supervised Concept-based eXplainable Artificial Intelligence (C-XAI) methods associate user-defined concepts like ``car'' each with a single vector in the DNN latent space (concept embedding vector). In the case of concept segmentation, these linearly separate between activation map pixels belonging to a concept and those belonging to background. Existing methods for concept segmentation, however, fall short of capturing implicitly learned sub-concepts (e.g., the DNN might split car into ``proximate car'' and ``distant car''), and overlap of user-defined concepts (e.g., between ``bus'' and ``truck''). In other words, they do not capture the full distribution of concept representatives in latent space. For the first time, this work shows that these simplifications are frequently broken and that distribution information can be particularly useful for understanding DNN-learned notions of sub-concepts, concept confusion, and concept outliers. To allow exploration of learned concept distributions, we propose a novel local concept analysis framework. Instead of optimizing a single global concept vector on the complete dataset, it generates a local concept embedding (LoCE) vector for each individual sample. We use the distribution formed by LoCEs to explore the latent concept distribution by fitting Gaussian mixture models (GMMs), hierarchical clustering, and concept-level information retrieval and outlier detection. Despite its context sensitivity, our method's concept segmentation performance is competitive to global baselines. Analysis results are obtained on three datasets and six diverse vision DNN architectures, including vision transformers (ViTs).", "published": "2023-11-24T12:22:00Z", "authors": ["Georgii Mikriukov", "Gesina Schwalbe", "Korinna Bade"], "categories": ["cs.CV", "cs.AI"], "doi": "10.1007/s11263-025-02446-y", "_query": "property valuation computer vision", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2504.19475v3", "title": "Prisma: An Open Source Toolkit for Mechanistic Interpretability in Vision and Video", "abstract": "Robust tooling and publicly available pre-trained models have helped drive recent advances in mechanistic interpretability for language models. However, similar progress in vision mechanistic interpretability has been hindered by the lack of accessible frameworks and pre-trained weights. We present Prisma (Access the codebase here: https://github.com/Prisma-Multimodal/ViT-Prisma), an open-source framework designed to accelerate vision mechanistic interpretability research, providing a unified toolkit for accessing 75+ vision and video transformers; support for sparse autoencoder (SAE), transcoder, and crosscoder training; a suite of 80+ pre-trained SAE weights; activation caching, circuit analysis tools, and visualization tools; and educational resources. Our analysis reveals surprising findings, including that effective vision SAEs can exhibit substantially lower sparsity patterns than language SAEs, and that in some instances, SAE reconstructions can decrease model loss. Prisma enables new research directions for understanding vision model internals while lowering barriers to entry in this emerging field.", "published": "2025-04-28T04:31:24Z", "authors": ["Sonia Joseph", "Praneet Suresh", "Lorenz Hufe", "Edward Stevinson", "Robert Graham", "Yash Vadi", "Danilo Bzdok", "Sebastian Lapuschkin", "Lee Sharkey", "Blake Aaron Richards"], "categories": ["cs.CV", "cs.AI", "cs.LG"], "doi": "", "_query": "property valuation computer vision", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2206.14528v1", "title": "Procrustes Analysis with Deformations: A Closed-Form Solution by Eigenvalue Decomposition", "abstract": "Generalized Procrustes Analysis (GPA) is the problem of bringing multiple shapes into a common reference by estimating transformations. GPA has been extensively studied for the Euclidean and affine transformations. We introduce GPA with deformable transformations, which forms a much wider and difficult problem. We specifically study a class of transformations called the Linear Basis Warps (LBWs), which contains the affine transformation and most of the usual deformation models, such as the Thin-Plate Spline (TPS). GPA with deformations is a nonconvex underconstrained problem. We resolve the fundamental ambiguities of deformable GPA using two shape constraints requiring the eigenvalues of the shape covariance. These eigenvalues can be computed independently as a prior or posterior. We give a closed-form and optimal solution to deformable GPA based on an eigenvalue decomposition. This solution handles regularization, favoring smooth deformation fields. It requires the transformation model to satisfy a fundamental property of free-translations, which asserts that the model can implement any translation. We show that this property fortunately holds true for most common transformation models, including the affine and TPS models. For the other models, we give another closed-form solution to GPA, which agrees exactly with the first solution for models with free-translation. We give pseudo-code for computing our solution, leading to the proposed DefGPA method, which is fast, globally optimal and widely applicable. We validate our method and compare it to previous work on six diverse 2D and 3D datasets, with special care taken to choose the hyperparameters from cross-validation.", "published": "2022-06-29T10:52:44Z", "authors": ["Fang Bai", "Adrien Bartoli"], "categories": ["cs.RO", "cs.CV", "cs.GR"], "doi": "10.1007/s11263-021-01571-8", "_query": "property valuation computer vision", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/1304.3824v13", "title": "Pricing and Valuation under the Real-World Measure", "abstract": "In general it is not clear which kind of information is supposed to be used for calculating the fair value of a contingent claim. Even if the information is specified, it is not guaranteed that the fair value is uniquely determined by the given information. A further problem is that asset prices are typically expressed in terms of a risk-neutral measure. This makes it difficult to transfer the fundamental results of financial mathematics to econometrics. I show that the aforementioned problems evaporate if the financial market is complete and sensitive. In this case, after an appropriate choice of the numeraire, the discounted price processes turn out to be uniformly integrable martingales under the real-world measure. This leads to a Law of One Price and a simple real-world valuation formula in a model-independent framework where the number of assets as well as the lifetime of the market can be finite or infinite.", "published": "2013-04-13T17:15:13Z", "authors": ["Gabriel Frahm"], "categories": ["q-fin.GN"], "doi": "", "_query": "property valuation computer vision", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2208.09602v2", "title": "Exploring Adversarial Robustness of Vision Transformers in the Spectral Perspective", "abstract": "The Vision Transformer has emerged as a powerful tool for image classification tasks, surpassing the performance of convolutional neural networks (CNNs). Recently, many researchers have attempted to understand the robustness of Transformers against adversarial attacks. However, previous researches have focused solely on perturbations in the spatial domain. This paper proposes an additional perspective that explores the adversarial robustness of Transformers against frequency-selective perturbations in the spectral domain. To facilitate comparison between these two domains, an attack framework is formulated as a flexible tool for implementing attacks on images in the spatial and spectral domains. The experiments reveal that Transformers rely more on phase and low frequency information, which can render them more vulnerable to frequency-selective attacks than CNNs. This work offers new insights into the properties and adversarial robustness of Transformers.", "published": "2022-08-20T04:14:27Z", "authors": ["Gihyun Kim", "Juyeop Kim", "Jong-Seok Lee"], "categories": ["cs.CV"], "doi": "", "_query": "property valuation computer vision", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/1905.12887v2", "title": "Does computer vision matter for action?", "abstract": "Computer vision produces representations of scene content. Much computer vision research is predicated on the assumption that these intermediate representations are useful for action. Recent work at the intersection of machine learning and robotics calls this assumption into question by training sensorimotor systems directly for the task at hand, from pixels to actions, with no explicit intermediate representations. Thus the central question of our work: Does computer vision matter for action? We probe this question and its offshoots via immersive simulation, which allows us to conduct controlled reproducible experiments at scale. We instrument immersive three-dimensional environments to simulate challenges such as urban driving, off-road trail traversal, and battle. Our main finding is that computer vision does matter. Models equipped with intermediate representations train faster, achieve higher task performance, and generalize better to previously unseen environments. A video that summarizes the work and illustrates the results can be found at https://youtu.be/4MfWa2yZ0Jc", "published": "2019-05-30T07:18:33Z", "authors": ["Brady Zhou", "Philipp Krähenbühl", "Vladlen Koltun"], "categories": ["cs.CV", "cs.AI", "cs.LG", "cs.RO"], "doi": "10.1126/scirobotics.aaw6661", "_query": "property valuation computer vision", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2505.24402v2", "title": "Leveraging Intermediate Features of Vision Transformer for Face Anti-Spoofing", "abstract": "Face recognition systems are designed to be robust against changes in head pose, illumination, and blurring during image capture. If a malicious person presents a face photo of the registered user, they may bypass the authentication process illegally. Such spoofing attacks need to be detected before face recognition. In this paper, we propose a spoofing attack detection method based on Vision Transformer (ViT) to detect minute differences between live and spoofed face images. The proposed method utilizes the intermediate features of ViT, which have a good balance between local and global features that are important for spoofing attack detection, for calculating loss in training and score in inference. The proposed method also introduces two data augmentation methods: face anti-spoofing data augmentation and patch-wise data augmentation, to improve the accuracy of spoofing attack detection. We demonstrate the effectiveness of the proposed method through experiments using the OULU-NPU and SiW datasets. The project page is available at: https://gsisaoki.github.io/FAS-ViT-CVPRW/ .", "published": "2025-05-30T09:33:01Z", "authors": ["Mika Feng", "Koichi Ito", "Takafumi Aoki", "Tetsushi Ohki", "Masakatsu Nishigaki"], "categories": ["cs.CV"], "doi": "", "_query": "property valuation computer vision", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2410.15927v1", "title": "GReFEL: Geometry-Aware Reliable Facial Expression Learning under Bias and Imbalanced Data Distribution", "abstract": "Reliable facial expression learning (FEL) involves the effective learning of distinctive facial expression characteristics for more reliable, unbiased and accurate predictions in real-life settings. However, current systems struggle with FEL tasks because of the variance in people's facial expressions due to their unique facial structures, movements, tones, and demographics. Biased and imbalanced datasets compound this challenge, leading to wrong and biased prediction labels. To tackle these, we introduce GReFEL, leveraging Vision Transformers and a facial geometry-aware anchor-based reliability balancing module to combat imbalanced data distributions, bias, and uncertainty in facial expression learning. Integrating local and global data with anchors that learn different facial data points and structural features, our approach adjusts biased and mislabeled emotions caused by intra-class disparity, inter-class similarity, and scale sensitivity, resulting in comprehensive, accurate, and reliable facial expression predictions. Our model outperforms current state-of-the-art methodologies, as demonstrated by extensive experiments on various datasets.", "published": "2024-10-21T11:55:06Z", "authors": ["Azmine Toushik Wasi", "Taki Hasan Rafi", "Raima Islam", "Karlo Serbetar", "Dong Kyu Chae"], "categories": ["cs.CV", "cs.AI", "cs.LG"], "doi": "10.1007/978-981-96-0911-6_27", "_query": "property valuation computer vision", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/1304.2367v1", "title": "Utility-Based Control for Computer Vision", "abstract": "Several key issues arise in implementing computer vision recognition of world objects in terms of Bayesian networks. Computational efficiency is a driving force. Perceptual networks are very deep, typically fifteen levels of structure. Images are wide, e.g., an unspecified-number of edges may appear anywhere in an image 512 x 512 pixels or larger. For efficiency, we dynamically instantiate hypotheses of observed objects. The network is not fixed, but is created incrementally at runtime. Generation of hypotheses of world objects and indexing of models for recognition are important, but they are not considered here [4,11]. This work is aimed at near-term implementation with parallel computation in a radar surveillance system, ADRIES [5, 15], and a system for industrial part recognition, SUCCESSOR [2]. For many applications, vision must be faster to be practical and so efficiently controlling the machine vision process is critical. Perceptual operators may scan megapixels and may require minutes of computation time. It is necessary to avoid unnecessary sensor actions and computation. Parallel computation is available at several levels of processor capability. The potential for parallel, distributed computation for high-level vision means distributing non-homogeneous computations. This paper addresses the problem of task control in machine vision systems based on Bayesian probability models. We separate control and inference to extend the previous work [3] to maximize utility instead of probability. Maximizing utility allows adopting perceptual strategies for efficient information gathering with sensors and analysis of sensor data. Results of controlling machine vision via utility to recognize military situations are presented in this paper. Future work extends this to industrial part recognition for SUCCESSOR.", "published": "2013-03-27T19:44:16Z", "authors": ["Tod S. Levitt", "Thomas O. Binford", "Gil J. Ettinger", "Patrice Gelband"], "categories": ["cs.CV", "cs.AI", "eess.SY"], "doi": "", "_query": "property valuation computer vision", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2107.08766v1", "title": "VisDrone-CC2020: The Vision Meets Drone Crowd Counting Challenge Results", "abstract": "Crowd counting on the drone platform is an interesting topic in computer vision, which brings new challenges such as small object inference, background clutter and wide viewpoint. However, there are few algorithms focusing on crowd counting on the drone-captured data due to the lack of comprehensive datasets. To this end, we collect a large-scale dataset and organize the Vision Meets Drone Crowd Counting Challenge (VisDrone-CC2020) in conjunction with the 16th European Conference on Computer Vision (ECCV 2020) to promote the developments in the related fields. The collected dataset is formed by $3,360$ images, including $2,460$ images for training, and $900$ images for testing. Specifically, we manually annotate persons with points in each video frame. There are $14$ algorithms from $15$ institutes submitted to the VisDrone-CC2020 Challenge. We provide a detailed analysis of the evaluation results and conclude the challenge. More information can be found at the website: \\url{http://www.aiskyeye.com/}.", "published": "2021-07-19T11:48:29Z", "authors": ["Dawei Du", "Longyin Wen", "Pengfei Zhu", "Heng Fan", "Qinghua Hu", "Haibin Ling", "Mubarak Shah", "Junwen Pan", "Ali Al-Ali", "Amr Mohamed", "Bakour Imene", "Bin Dong", "Binyu Zhang", "Bouchali Hadia Nesma", "Chenfeng Xu", "Chenzhen Duan", "Ciro Castiello", "Corrado Mencar", "Dingkang Liang", "Florian Krüger", "Gennaro Vessio", "Giovanna Castellano", "Jieru Wang", "Junyu Gao", "Khalid Abualsaud", "Laihui Ding", "Lei Zhao", "Marco Cianciotta", "Muhammad Saqib", "Noor Almaadeed", "Omar Elharrouss", "Pei Lyu", "Qi Wang", "Shidong Liu", "Shuang Qiu", "Siyang Pan", "Somaya Al-Maadeed", "Sultan Daud Khan", "Tamer Khattab", "Tao Han", "Thomas Golda", "Wei Xu", "Xiang Bai", "Xiaoqing Xu", "Xuelong Li", "Yanyun Zhao", "Ye Tian", "Yingnan Lin", "Yongchao Xu", "Yuehan Yao", "Zhenyu Xu", "Zhijian Zhao", "Zhipeng Luo", "Zhiwei Wei", "Zhiyuan Zhao"], "categories": ["cs.CV"], "doi": "", "_query": "property valuation computer vision", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2606.22383v1", "title": "Structured Hyperedge Adaptation for Parameter-Efficient Fine-Tuning of Vision Transformers", "abstract": "Parameter-efficient fine-tuning (PEFT) has become a practical solution for adapting large pretrained vision transformers (ViTs) to downstream tasks while updating only a small subset of parameters. However, existing adapter-based methods perform adaptation independently for each token, implicitly assuming that token refinements should be learned in isolation. This token-wise formulation overlooks the structured relationships among tokens that naturally arise in visual scenes, potentially leading to redundant updates and spatially inconsistent feature refinement. In this work, we revisit the design of parameter-efficient adapters and propose to perform adaptation in hyperedge space rather than token space. We introduce HyperAdapter, a hypergraph-based adapter architecture that enables structured, group-aware adaptation through soft token routing. HyperAdapter constructs a soft hypergraph over ViT tokens using prototype-based assignments, aggregates token features into latent hyperedge representations, applies lightweight bottleneck adaptation at the hyperedge level, and diffuses the resulting updates back to tokens via the hypergraph incidence structure. This design injects an explicit structural inductive bias into PEFT while preserving the modularity and efficiency of standard adapters. Extensive experiments across diverse visual benchmarks demonstrate that structured hyperedge adaptation consistently outperforms strong PEFT baselines under comparable parameter budgets, with particularly pronounced gains on tasks requiring structured reasoning. Our results suggest that the choice of adaptation space is a critical yet underexplored dimension in parameter-efficient transfer for ViTs.", "published": "2026-06-21T08:09:08Z", "authors": ["Edwin Kwadwo Tenagyei", "Lei Wang", "Ugochukwu Ejike Akpudo", "Jun Zhou", "Yongsheng Gao"], "categories": ["cs.CV", "cs.AI", "cs.LG"], "doi": "", "_query": "property valuation computer vision", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2606.22158v3", "title": "Improving Reasoning in Vision-Language Models via Perception Verified Self-Training", "abstract": "Achieving human-like reasoning in Vision-Language Models (VLMs) remains a long-standing challenge. Recent approaches leverage Chain-of-Thought (CoT) rationales generated by human annotators or proprietary models, which are costly and difficult to scale. Self-training offers a promising alternative but often suffers from visual hallucinations and language shortcuts because rationales are filtered only by answer correctness without verifying visual perception. We propose a perception-verified self-training framework that enforces visually grounded reasoning. Our method employs a CoT template (caption-reasoning-conclusion) that disentangles perception from reasoning, enabling independent verification of visual understanding. To compensate for the absence of ground-truth captions, we introduce PerceptEval, an unsupervised method that evaluates caption quality based on its alignment with visual and textual elements in the image. Using caption verification together with answer correctness, we partition the data into easy, medium, and hard subsets and design a two-stage curriculum learning strategy. Stage 1 trains on easy samples, while Stage 2 enhances medium samples by regenerating reasoning conditioned on verified captions and retaining only those with correct conclusions. This ensures training is performed exclusively on perceptually grounded reasoning, reducing hallucinations and language shortcuts. Extensive experiments across diverse domains and models demonstrate improvements of up to 16% over standard self-training baselines, showing that our framework provides a scalable and cost-effective solution for advancing multimodal reasoning without manually annotated CoT rationales.", "published": "2026-06-20T17:33:07Z", "authors": ["Sourabh Sharma", "Sonam Gupta", " Sadbhawna"], "categories": ["cs.CV"], "doi": "", "_query": "property valuation computer vision", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2412.07077v1", "title": "Retaining and Enhancing Pre-trained Knowledge in Vision-Language Models with Prompt Ensembling", "abstract": "The advancement of vision-language models, particularly the Contrastive Language-Image Pre-training (CLIP) model, has revolutionized the field of machine learning by enabling robust zero-shot learning capabilities. These capabilities allow models to understand and respond to previously unseen data without task-specific training. However, adapting CLIP to integrate specialized knowledge from various domains while retaining its zero-shot capabilities remains a significant challenge. To address this, we introduce a novel prompt ensemble learning approach called Group-wise Prompt Ensemble (GPE). This method aims to enhance CLIP's zero-shot capabilities by incorporating new domain knowledge while improving its adaptability and robustness against data distribution shifts. Our approach hinges on three main strategies: prompt grouping with masked attention to optimize CLIP's adaptability while safeguarding its zero-shot capabilities; the incorporation of auxiliary prompts for the seamless integration of new domain insights without disrupting the original model's representation; and an ensemble learning strategy that effectively merges original and new knowledge. Through rigorous experimentation, including more challenging cross-dataset transfer evaluations, our GPE method redefines the benchmarks for the adaptability and efficiency of vision-language models, surpassing existing models across various scenarios.", "published": "2024-12-10T00:40:31Z", "authors": ["Donggeun Kim", "Yujin Jo", "Myungjoo Lee", "Taesup Kim"], "categories": ["cs.CV"], "doi": "", "_query": "property valuation computer vision", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2603.26425v2", "title": "CPUBone: Efficient Vision Backbone Design for Devices with Low Parallelization Capabilities", "abstract": "Recent research on vision backbone architectures has predominantly focused on optimizing efficiency for hardware platforms with high parallel processing capabilities. This category increasingly includes embedded systems such as mobile phones and embedded AI accelerator modules. In contrast, CPUs do not have the possibility to parallelize operations in the same manner, wherefore models benefit from a specific design philosophy that balances amount of operations (MACs) and hardware-efficient execution by having high MACs per second (MACpS). In pursuit of this, we investigate two modifications to standard convolutions, aimed at reducing computational cost: grouping convolutions and reducing kernel sizes. While both adaptations substantially decrease the total number of MACs required for inference, sustaining low latency necessitates preserving hardware-efficiency. Our experiments across diverse CPU devices confirm that these adaptations successfully retain high hardware-efficiency on CPUs. Based on these insights, we introduce CPUBone, a new family of vision backbone models optimized for CPU-based inference. CPUBone achieves state-of-the-art Speed-Accuracy Trade-offs (SATs) across a wide range of CPU devices and effectively transfers its efficiency to downstream tasks such as object detection and semantic segmentation. Models and code are available at https://github.com/altair199797/CPUBone.", "published": "2026-03-27T13:55:06Z", "authors": ["Moritz Nottebaum", "Matteo Dunnhofer", "Christian Micheloni"], "categories": ["cs.CV", "cs.AI"], "doi": "", "_query": "property valuation computer vision", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2109.01134v6", "title": "Learning to Prompt for Vision-Language Models", "abstract": "Large pre-trained vision-language models like CLIP have shown great potential in learning representations that are transferable across a wide range of downstream tasks. Different from the traditional representation learning that is based mostly on discretized labels, vision-language pre-training aligns images and texts in a common feature space, which allows zero-shot transfer to a downstream task via prompting, i.e., classification weights are synthesized from natural language describing classes of interest. In this work, we show that a major challenge for deploying such models in practice is prompt engineering, which requires domain expertise and is extremely time-consuming -- one needs to spend a significant amount of time on words tuning since a slight change in wording could have a huge impact on performance. Inspired by recent advances in prompt learning research in natural language processing (NLP), we propose Context Optimization (CoOp), a simple approach specifically for adapting CLIP-like vision-language models for downstream image recognition. Concretely, CoOp models a prompt's context words with learnable vectors while the entire pre-trained parameters are kept fixed. To handle different image recognition tasks, we provide two implementations of CoOp: unified context and class-specific context. Through extensive experiments on 11 datasets, we demonstrate that CoOp requires as few as one or two shots to beat hand-crafted prompts with a decent margin and is able to gain significant improvements over prompt engineering with more shots, e.g., with 16 shots the average gain is around 15% (with the highest reaching over 45%). Despite being a learning-based approach, CoOp achieves superb domain generalization performance compared with the zero-shot model using hand-crafted prompts.", "published": "2021-09-02T17:57:31Z", "authors": ["Kaiyang Zhou", "Jingkang Yang", "Chen Change Loy", "Ziwei Liu"], "categories": ["cs.CV", "cs.AI", "cs.LG"], "doi": "10.1007/s11263-022-01653-1", "_query": "property valuation computer vision", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/1903.08636v1", "title": "An Efficient Schmidt-EKF for 3D Visual-Inertial SLAM", "abstract": "It holds great implications for practical applications to enable centimeter-accuracy positioning for mobile and wearable sensor systems. In this paper, we propose a novel, high-precision, efficient visual-inertial (VI)-SLAM algorithm, termed Schmidt-EKF VI-SLAM (SEVIS), which optimally fuses IMU measurements and monocular images in a tightly-coupled manner to provide 3D motion tracking with bounded error. In particular, we adapt the Schmidt Kalman filter formulation to selectively include informative features in the state vector while treating them as nuisance parameters (or Schmidt states) once they become matured. This change in modeling allows for significant computational savings by no longer needing to constantly update the Schmidt states (or their covariance), while still allowing the EKF to correctly account for their cross-correlations with the active states. As a result, we achieve linear computational complexity in terms of map size, instead of quadratic as in the standard SLAM systems. In order to fully exploit the map information to bound navigation drifts, we advocate efficient keyframe-aided 2D-to-2D feature matching to find reliable correspondences between current 2D visual measurements and 3D map features. The proposed SEVIS is extensively validated in both simulations and experiments.", "published": "2019-03-20T17:50:49Z", "authors": ["Patrick Geneva", "James Maley", "Guoquan Huang"], "categories": ["cs.CV", "cs.RO"], "doi": "", "_query": "property valuation computer vision", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2509.12989v1", "title": "PANORAMA: The Rise of Omnidirectional Vision in the Embodied AI Era", "abstract": "Omnidirectional vision, using 360-degree vision to understand the environment, has become increasingly critical across domains like robotics, industrial inspection, and environmental monitoring. Compared to traditional pinhole vision, omnidirectional vision provides holistic environmental awareness, significantly enhancing the completeness of scene perception and the reliability of decision-making. However, foundational research in this area has historically lagged behind traditional pinhole vision. This talk presents an emerging trend in the embodied AI era: the rapid development of omnidirectional vision, driven by growing industrial demand and academic interest. We highlight recent breakthroughs in omnidirectional generation, omnidirectional perception, omnidirectional understanding, and related datasets. Drawing on insights from both academia and industry, we propose an ideal panoramic system architecture in the embodied AI era, PANORAMA, which consists of four key subsystems. Moreover, we offer in-depth opinions related to emerging trends and cross-community impacts at the intersection of panoramic vision and embodied AI, along with the future roadmap and open challenges. This overview synthesizes state-of-the-art advancements and outlines challenges and opportunities for future research in building robust, general-purpose omnidirectional AI systems in the embodied AI era.", "published": "2025-09-16T11:54:37Z", "authors": ["Xu Zheng", "Chenfei Liao", "Ziqiao Weng", "Kaiyu Lei", "Zihao Dongfang", "Haocong He", "Yuanhuiyi Lyu", "Lutao Jiang", "Lu Qi", "Li Chen", "Danda Pani Paudel", "Kailun Yang", "Linfeng Zhang", "Luc Van Gool", "Xuming Hu"], "categories": ["cs.CV"], "doi": "", "_query": "property valuation computer vision", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2411.05964v1", "title": "Utilisation of Vision Systems and Digital Twin for Maintaining Cleanliness in Public Spaces", "abstract": "Nowadays, the increasing demand for maintaining high cleanliness standards in public spaces results in the search for innovative solutions. The deployment of CCTV systems equipped with modern cameras and software enables not only real-time monitoring of the cleanliness status but also automatic detection of impurities and optimisation of cleaning schedules. The Digital Twin technology allows for the creation of a virtual model of the space, facilitating the simulation, training, and testing of cleanliness management strategies before implementation in the real world. In this paper, we present the utilisation of advanced vision surveillance systems and the Digital Twin technology in cleanliness management, using a railway station as an example. The Digital Twin was created based on an actual 3D model in the Nvidia Omniverse Isaac Sim simulator. A litter detector, bin occupancy level detector, stain segmentation, and a human detector (including the cleaning crew) along with their movement analysis were implemented. A preliminary assessment was conducted, and potential modifications for further enhancement and future development of the system were identified.", "published": "2024-11-08T20:50:13Z", "authors": ["Mateusz Wasala", "Krzysztof Blachut", "Hubert Szolc", "Marcin Kowalczyk", "Michal Danilowicz", "Tomasz Kryjak"], "categories": ["cs.CV", "eess.IV"], "doi": "", "_query": "property valuation computer vision", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2512.21126v1", "title": "MarineEval: Assessing the Marine Intelligence of Vision-Language Models", "abstract": "We have witnessed promising progress led by large language models (LLMs) and further vision language models (VLMs) in handling various queries as a general-purpose assistant. VLMs, as a bridge to connect the visual world and language corpus, receive both visual content and various text-only user instructions to generate corresponding responses. Though great success has been achieved by VLMs in various fields, in this work, we ask whether the existing VLMs can act as domain experts, accurately answering marine questions, which require significant domain expertise and address special domain challenges/requirements. To comprehensively evaluate the effectiveness and explore the boundary of existing VLMs, we construct the first large-scale marine VLM dataset and benchmark called MarineEval, with 2,000 image-based question-answering pairs. During our dataset construction, we ensure the diversity and coverage of the constructed data: 7 task dimensions and 20 capacity dimensions. The domain requirements are specially integrated into the data construction and further verified by the corresponding marine domain experts. We comprehensively benchmark 17 existing VLMs on our MarineEval and also investigate the limitations of existing models in answering marine research questions. The experimental results reveal that existing VLMs cannot effectively answer the domain-specific questions, and there is still a large room for further performance improvements. We hope our new benchmark and observations will facilitate future research. Project Page: http://marineeval.hkustvgd.com/", "published": "2025-12-24T11:57:50Z", "authors": ["YuK-Kwan Wong", "Tuan-An To", "Jipeng Zhang", "Ziqiang Zheng", "Sai-Kit Yeung"], "categories": ["cs.CV", "cs.DB"], "doi": "", "_query": "property valuation computer vision", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2505.15970v1", "title": "Analyzing Hierarchical Structure in Vision Models with Sparse Autoencoders", "abstract": "The ImageNet hierarchy provides a structured taxonomy of object categories, offering a valuable lens through which to analyze the representations learned by deep vision models. In this work, we conduct a comprehensive analysis of how vision models encode the ImageNet hierarchy, leveraging Sparse Autoencoders (SAEs) to probe their internal representations. SAEs have been widely used as an explanation tool for large language models (LLMs), where they enable the discovery of semantically meaningful features. Here, we extend their use to vision models to investigate whether learned representations align with the ontological structure defined by the ImageNet taxonomy. Our results show that SAEs uncover hierarchical relationships in model activations, revealing an implicit encoding of taxonomic structure. We analyze the consistency of these representations across different layers of the popular vision foundation model DINOv2 and provide insights into how deep vision models internalize hierarchical category information by increasing information in the class token through each layer. Our study establishes a framework for systematic hierarchical analysis of vision model representations and highlights the potential of SAEs as a tool for probing semantic structure in deep networks.", "published": "2025-05-21T19:38:48Z", "authors": ["Matthew Lyle Olson", "Musashi Hinck", "Neale Ratzlaff", "Changbai Li", "Phillip Howard", "Vasudev Lal", "Shao-Yen Tseng"], "categories": ["cs.CV", "cs.LG"], "doi": "", "_query": "property valuation computer vision", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2408.02123v3", "title": "FovEx: Human-Inspired Explanations for Vision Transformers and Convolutional Neural Networks", "abstract": "Explainability in artificial intelligence (XAI) remains a crucial aspect for fostering trust and understanding in machine learning models. Current visual explanation techniques, such as gradient-based or class-activation-based methods, often exhibit a strong dependence on specific model architectures. Conversely, perturbation-based methods, despite being model-agnostic, are computationally expensive as they require evaluating models on a large number of forward passes. In this work, we introduce Foveation-based Explanations (FovEx), a novel XAI method inspired by human vision. FovEx seamlessly integrates biologically inspired perturbations by iteratively creating foveated renderings of the image and combines them with gradient-based visual explorations to determine locations of interest efficiently. These locations are selected to maximize the performance of the model to be explained with respect to the downstream task and then combined to generate an attribution map. We provide a thorough evaluation with qualitative and quantitative assessments on established benchmarks. Our method achieves state-of-the-art performance on both transformers (on 4 out of 5 metrics) and convolutional models (on 3 out of 5 metrics), demonstrating its versatility among various architectures. Furthermore, we show the alignment between the explanation map produced by FovEx and human gaze patterns (+14\\% in NSS compared to RISE, +203\\% in NSS compared to GradCAM). This comparison enhances our confidence in FovEx's ability to close the interpretation gap between humans and machines.", "published": "2024-08-04T19:37:30Z", "authors": ["Mahadev Prasad Panda", "Matteo Tiezzi", "Martina Vilas", "Gemma Roig", "Bjoern M. Eskofier", "Dario Zanca"], "categories": ["cs.CV", "cs.LG"], "doi": "10.1007/s11263-025-02543-y", "_query": "property valuation computer vision", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2012.03194v2", "title": "Computer Stereo Vision for Autonomous Driving", "abstract": "As an important component of autonomous systems, autonomous car perception has had a big leap with recent advances in parallel computing architectures. With the use of tiny but full-feature embedded supercomputers, computer stereo vision has been prevalently applied in autonomous cars for depth perception. The two key aspects of computer stereo vision are speed and accuracy. They are both desirable but conflicting properties, as the algorithms with better disparity accuracy usually have higher computational complexity. Therefore, the main aim of developing a computer stereo vision algorithm for resource-limited hardware is to improve the trade-off between speed and accuracy. In this chapter, we introduce both the hardware and software aspects of computer stereo vision for autonomous car systems. Then, we discuss four autonomous car perception tasks, including 1) visual feature detection, description and matching, 2) 3D information acquisition, 3) object detection/recognition and 4) semantic image segmentation. The principles of computer stereo vision and parallel computing on multi-threading CPU and GPU architectures are then detailed.", "published": "2020-12-06T06:54:03Z", "authors": ["Rui Fan", "Li Wang", "Mohammud Junaid Bocus", "Ioannis Pitas"], "categories": ["cs.CV"], "doi": "", "_query": "property valuation computer vision", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/1406.5020v1", "title": "Integration of Cloud Computing and Web2.0 Collaboration Technologies in E-Learning", "abstract": "Cloud computing technology is an emerging new computing paradigm for delivering computing services. Although it still in its early stage, it has changed the way how many applications are developed and accessed. This computing approach relies on a number of existing technologies, such as Web2.0, virtualization, Service oriented architecture SOA, Web services,etc.Cloud computing is growing rapidly and becoming an adoptable technology for the organizations especially education institutes, with its dynamic scalability and usage of virtualized resources as a service through the Internet.Today, eLearning is also becoming a very popular and powerful trend.However,in traditional web based eLearning systems,building and maintenance are located onsite in institutions or enterprises, which cause lot of problems to appear such as lacking the support of underlying infrastructure, which can dynamically allocate the needed calculation and storage resources for eLearning systems.As the need for e learning is increasing continuously and its necessary for eLearning systems to keep pace with the right technology needed for development and improvement.However, todays technologies such as Web 2.0, Cloud, etc.enable to build more successful and effective educational environment,that provide collaboration and interaction in eLearning environments.The challenge is to use and integrate these technologies in order to construct tools that allow the best possible learning results.Cloud computing and Web 2.0 are two areas that are starting to strongly effect how the development,deployment and usage of eLearning application.This paper presents the benefits of using cloud computing with the integration of Web 2.0 collaboration technologies in eLearning environment.", "published": "2014-06-19T12:24:30Z", "authors": ["Rasha Fouad AlCattan"], "categories": ["cs.CY", "cs.DC"], "doi": "", "_query": "property valuation computer vision", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2410.23219v1", "title": "DiaMond: Dementia Diagnosis with Multi-Modal Vision Transformers Using MRI and PET", "abstract": "Diagnosing dementia, particularly for Alzheimer's Disease (AD) and frontotemporal dementia (FTD), is complex due to overlapping symptoms. While magnetic resonance imaging (MRI) and positron emission tomography (PET) data are critical for the diagnosis, integrating these modalities in deep learning faces challenges, often resulting in suboptimal performance compared to using single modalities. Moreover, the potential of multi-modal approaches in differential diagnosis, which holds significant clinical importance, remains largely unexplored. We propose a novel framework, DiaMond, to address these issues with vision Transformers to effectively integrate MRI and PET. DiaMond is equipped with self-attention and a novel bi-attention mechanism that synergistically combine MRI and PET, alongside a multi-modal normalization to reduce redundant dependency, thereby boosting the performance. DiaMond significantly outperforms existing multi-modal methods across various datasets, achieving a balanced accuracy of 92.4% in AD diagnosis, 65.2% for AD-MCI-CN classification, and 76.5% in differential diagnosis of AD and FTD. We also validated the robustness of DiaMond in a comprehensive ablation study. The code is available at https://github.com/ai-med/DiaMond.", "published": "2024-10-30T17:11:00Z", "authors": ["Yitong Li", "Morteza Ghahremani", "Youssef Wally", "Christian Wachinger"], "categories": ["cs.CV", "cs.AI"], "doi": "", "_query": "property valuation computer vision", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2112.15111v3", "title": "Improving the Behaviour of Vision Transformers with Token-consistent Stochastic Layers", "abstract": "We introduce token-consistent stochastic layers in vision transformers, without causing any severe drop in performance. The added stochasticity improves network calibration, robustness and strengthens privacy. We use linear layers with token-consistent stochastic parameters inside the multilayer perceptron blocks, without altering the architecture of the transformer. The stochastic parameters are sampled from the uniform distribution, both during training and inference. The applied linear operations preserve the topological structure, formed by the set of tokens passing through the shared multilayer perceptron. This operation encourages the learning of the recognition task to rely on the topological structures of the tokens, instead of their values, which in turn offers the desired robustness and privacy of the visual features. The effectiveness of the token-consistent stochasticity is demonstrated on three different applications, namely, network calibration, adversarial robustness, and feature privacy, by boosting the performance of the respective established baselines.", "published": "2021-12-30T16:07:59Z", "authors": ["Nikola Popovic", "Danda Pani Paudel", "Thomas Probst", "Luc Van Gool"], "categories": ["cs.CV"], "doi": "", "_query": "property valuation computer vision", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2512.15957v1", "title": "Seeing is Believing (and Predicting): Context-Aware Multi-Human Behavior Prediction with Vision Language Models", "abstract": "Accurately predicting human behaviors is crucial for mobile robots operating in human-populated environments. While prior research primarily focuses on predicting actions in single-human scenarios from an egocentric view, several robotic applications require understanding multiple human behaviors from a third-person perspective. To this end, we present CAMP-VLM (Context-Aware Multi-human behavior Prediction): a Vision Language Model (VLM)-based framework that incorporates contextual features from visual input and spatial awareness from scene graphs to enhance prediction of humans-scene interactions. Due to the lack of suitable datasets for multi-human behavior prediction from an observer view, we perform fine-tuning of CAMP-VLM with synthetic human behavior data generated by a photorealistic simulator, and evaluate the resulting models on both synthetic and real-world sequences to assess their generalization capabilities. Leveraging Supervised Fine-Tuning (SFT) and Direct Preference Optimization (DPO), CAMP-VLM outperforms the best-performing baseline by up to 66.9% in prediction accuracy.", "published": "2025-12-17T20:44:32Z", "authors": ["Utsav Panchal", "Yuchen Liu", "Luigi Palmieri", "Ilche Georgievski", "Marco Aiello"], "categories": ["cs.CV", "cs.AI"], "doi": "", "_query": "property valuation computer vision", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2106.14153v2", "title": "Automatic Differentiation With Higher Infinitesimals, or Computational Smooth Infinitesimal Analysis in Weil Algebra", "abstract": "We propose an algorithm to compute the $C^\\infty$-ring structure of arbitrary Weil algebra. It allows us to do some analysis with higher infinitesimals numerically and symbolically. To that end, we first give a brief description of the (Forward-mode) automatic differentiation (AD) in terms of $C^\\infty$-rings. The notion of a $C^\\infty$-ring was introduced by Lawvere and used as the fundamental building block of smooth infinitesimal analysis and synthetic differential geometry. We argue that interpreting AD in terms of $C^\\infty$-rings gives us a unifying theoretical framework and modular ways to express multivariate partial derivatives. In particular, we can \"package\" higher-order Forward-mode AD as a Weil algebra, and take tensor products to compose them to achieve multivariate higher-order AD. The algorithms in the present paper can also be used for a pedagogical purpose in learning and studying smooth infinitesimal analysis as well.", "published": "2021-06-27T06:17:26Z", "authors": ["Hiromi Ishii"], "categories": ["cs.SC", "cs.MS", "math.CT", "math.DG", "math.NA"], "doi": "10.1007/978-3-030-85165-1_11", "_query": "property valuation computer vision", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2501.15371v2", "title": "Acquiring Submillimeter-Accurate Multi-Task Vision Datasets for Computer-Assisted Orthopedic Surgery", "abstract": "Advances in computer vision, particularly in optical image-based 3D reconstruction and feature matching, enable applications like marker-less surgical navigation and digitization of surgery. However, their development is hindered by a lack of suitable datasets with 3D ground truth. This work explores an approach to generating realistic and accurate ex vivo datasets tailored for 3D reconstruction and feature matching in open orthopedic surgery. A set of posed images and an accurately registered ground truth surface mesh of the scene are required to develop vision-based 3D reconstruction and matching methods suitable for surgery. We propose a framework consisting of three core steps and compare different methods for each step: 3D scanning, calibration of viewpoints for a set of high-resolution RGB images, and an optical-based method for scene registration. We evaluate each step of this framework on an ex vivo scoliosis surgery using a pig spine, conducted under real operating room conditions. A mean 3D Euclidean error of 0.35 mm is achieved with respect to the 3D ground truth. The proposed method results in submillimeter accurate 3D ground truths and surgical images with a spatial resolution of 0.1 mm. This opens the door to acquiring future surgical datasets for high-precision applications.", "published": "2025-01-26T02:52:46Z", "authors": ["Emma Most", "Jonas Hein", "Frédéric Giraud", "Nicola A. Cavalcanti", "Lukas Zingg", "Baptiste Brument", "Nino Louman", "Fabio Carrillo", "Philipp Fürnstahl", "Lilian Calvet"], "categories": ["cs.CV"], "doi": "", "_query": "property valuation computer vision", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2409.01109v1", "title": "SOOD-ImageNet: a Large-Scale Dataset for Semantic Out-Of-Distribution Image Classification and Semantic Segmentation", "abstract": "Out-of-Distribution (OOD) detection in computer vision is a crucial research area, with related benchmarks playing a vital role in assessing the generalizability of models and their applicability in real-world scenarios. However, existing OOD benchmarks in the literature suffer from two main limitations: (1) they often overlook semantic shift as a potential challenge, and (2) their scale is limited compared to the large datasets used to train modern models. To address these gaps, we introduce SOOD-ImageNet, a novel dataset comprising around 1.6M images across 56 classes, designed for common computer vision tasks such as image classification and semantic segmentation under OOD conditions, with a particular focus on the issue of semantic shift. We ensured the necessary scalability and quality by developing an innovative data engine that leverages the capabilities of modern vision-language models, complemented by accurate human checks. Through extensive training and evaluation of various models on SOOD-ImageNet, we showcase its potential to significantly advance OOD research in computer vision. The project page is available at https://github.com/bach05/SOODImageNet.git.", "published": "2024-09-02T09:37:39Z", "authors": ["Alberto Bacchin", "Davide Allegro", "Stefano Ghidoni", "Emanuele Menegatti"], "categories": ["cs.CV", "cs.LG"], "doi": "10.1007/978-3-031-91672-4_6", "_query": "property valuation computer vision", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/1811.11316v1", "title": "Using Computer Vision Techniques for Moving Poster Design", "abstract": "Graphic Design encompasses a wide range of activities from the design of traditional print media (e.g., books and posters) to site-specific (e.g., signage systems) and electronic media (e.g., interfaces). Its practice always explores the new possibilities of information and communication technologies. Therefore, interactivity and participation have become key features in the design process. Even in traditional print media, graphic designers are trying to enhance user experience and exploring new interaction models. Moving posters are an example of this. This type of posters combine the specific features of motion and print worlds in order to produce attractive forms of communication that explore and exploit the potential of digital screens. In our opinion, the next step towards the integration of moving posters with the surroundings, where they operate, is incorporating data from the environment, which also enables the seamless participation of the audience. As such, the adoption of computer vision techniques for moving poster design becomes a natural approach. Following this line of thought, we present a system wherein computer vision techniques are used to shape a moving poster. Although it is still a work in progress, the system is already able to sense the surrounding physical environment and translate the collected data into graphical information. The data is gathered from the environment in two ways: (1) directly using motion tracking; and (2) indirectly via contextual ambient data. In this sense, each user interaction with the system results in a different experience and in a unique poster design.", "published": "2018-11-27T23:59:46Z", "authors": ["Sérgio Rebelo", "Pedro Martins", "João Bicker", "Penousal Machado"], "categories": ["cs.HC"], "doi": "", "_query": "property valuation computer vision", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2307.07378v1", "title": "Defect Classification in Additive Manufacturing Using CNN-Based Vision Processing", "abstract": "The development of computer vision and in-situ monitoring using visual sensors allows the collection of large datasets from the additive manufacturing (AM) process. Such datasets could be used with machine learning techniques to improve the quality of AM. This paper examines two scenarios: first, using convolutional neural networks (CNNs) to accurately classify defects in an image dataset from AM and second, applying active learning techniques to the developed classification model. This allows the construction of a human-in-the-loop mechanism to reduce the size of the data required to train and generate training data.", "published": "2023-07-14T14:36:58Z", "authors": ["Xiao Liu", "Alessandra Mileo", "Alan F. Smeaton"], "categories": ["cs.CV", "cs.LG"], "doi": "10.5281/zenodo.8230600", "_query": "property valuation computer vision", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2605.18740v4", "title": "Vision-OPD: Learning to See Fine Details for Multimodal LLMs via On-Policy Self-Distillation", "abstract": "Multimodal Large Language Models (MLLMs) still struggle with fine-grained visual understanding, where answers often depend on small but decisive evidence in the full image. We observe a regional-to-global perception gap: the same MLLM answers fine-grained questions more accurately when conditioned on evidence-centered crops than on the corresponding full images, suggesting that many failures stem from difficulty to focus on relevant evidence rather than insufficient local recognition ability. Motivated by this observation, we propose Vision-OPD (Vision On-Policy Distillation), a regional-to-global self-distillation framework that transfers the model's own privileged regional perception to its full-image policy. Vision-OPD instantiates two conditional policies from the same MLLM: a crop-conditioned teacher and a full-image-conditioned student. The student generates on-policy rollouts, and Vision-OPD minimizes token-level divergence between the teacher and student next-token distributions along these rollouts. This enables the model to internalize the benefit of visual zooming without external teacher models, ground-truth labels, reward verifiers, or inference-time tool use. Experiments on multiple fine-grained visual understanding benchmarks show that Vision-OPD models achieve competitive or superior performance against much larger open-source, closed-source, and \"Thinking-with-Images\" agentic models. The code is available at https://github.com/VisionOPD/Vision-OPD", "published": "2026-05-18T17:57:04Z", "authors": ["Qianhao Yuan", "Jie Lou", "Xing Yu", "Hongyu Lin", "Le Sun", "Xianpei Han", "Yaojie Lu"], "categories": ["cs.CV", "cs.AI", "cs.CL", "cs.LG"], "doi": "", "_query": "property valuation computer vision", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2202.02674v4", "title": "Beurling's Theorem for Valuation Hilbert Modules and Several Complex Variables", "abstract": "We develop a theory of Valuation Hilbert Modules and prove a version of Beurling's theorem for these. Then we apply our version of Beurling's theorem to obtain complete descriptions of the closed invariant subspaces of a number of Hilbert spaces of analytic functions in several complex variables, including $H^2$ of the polydisk the ball, and bounded symmetric domains, and weighted $A^2$ spaces on complex analytic manifolds.", "published": "2022-02-06T01:36:56Z", "authors": ["Charles W. Neville"], "categories": ["math.CV"], "doi": "", "_query": "property valuation computer vision", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2407.17904v2", "title": "Exploring the Effect of Dataset Diversity in Self-Supervised Learning for Surgical Computer Vision", "abstract": "Over the past decade, computer vision applications in minimally invasive surgery have rapidly increased. Despite this growth, the impact of surgical computer vision remains limited compared to other medical fields like pathology and radiology, primarily due to the scarcity of representative annotated data. Whereas transfer learning from large annotated datasets such as ImageNet has been conventionally the norm to achieve high-performing models, recent advancements in self-supervised learning (SSL) have demonstrated superior performance. In medical image analysis, in-domain SSL pretraining has already been shown to outperform ImageNet-based initialization. Although unlabeled data in the field of surgical computer vision is abundant, the diversity within this data is limited. This study investigates the role of dataset diversity in SSL for surgical computer vision, comparing procedure-specific datasets against a more heterogeneous general surgical dataset across three different downstream surgical applications. The obtained results show that using solely procedure-specific data can lead to substantial improvements of 13.8%, 9.5%, and 36.8% compared to ImageNet pretraining. However, extending this data with more heterogeneous surgical data further increases performance by an additional 5.0%, 5.2%, and 2.5%, suggesting that increasing diversity within SSL data is beneficial for model performance. The code and pretrained model weights are made publicly available at https://github.com/TimJaspers0801/SurgeNet.", "published": "2024-07-25T09:49:04Z", "authors": ["Tim J. M. Jaspers", "Ronald L. P. D. de Jong", "Yasmina Al Khalil", "Tijn Zeelenberg", "Carolus H. J. Kusters", "Yiping Li", "Romy C. van Jaarsveld", "Franciscus H. A. Bakker", "Jelle P. Ruurda", "Willem M. Brinkman", "Peter H. N. De With", "Fons van der Sommen"], "categories": ["cs.CV"], "doi": "10.1007/978-3-031-73748-0_5", "_query": "property valuation computer vision", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2210.12785v1", "title": "An Improved RaftStereo Trained with A Mixed Dataset for the Robust Vision Challenge 2022", "abstract": "Stereo-matching is a fundamental problem in computer vision. Despite recent progress by deep learning, improving the robustness is ineluctable when deploying stereo-matching models to real-world applications. Different from the common practices, i.e., developing an elaborate model to achieve robustness, we argue that collecting multiple available datasets for training is a cheaper way to increase generalization ability. Specifically, this report presents an improved RaftStereo trained with a mixed dataset of seven public datasets for the robust vision challenge (denoted as iRaftStereo_RVC). When evaluated on the training sets of Middlebury, KITTI-2015, and ETH3D, the model outperforms its counterparts trained with only one dataset, such as the popular Sceneflow. After fine-tuning the pre-trained model on the three datasets of the challenge, it ranks at 2nd place on the stereo leaderboard, demonstrating the benefits of mixed dataset pre-training.", "published": "2022-10-23T17:01:34Z", "authors": ["Hualie Jiang", "Rui Xu", "Wenjie Jiang"], "categories": ["cs.CV"], "doi": "", "_query": "property valuation computer vision", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2211.16421v2", "title": "RGB no more: Minimally-decoded JPEG Vision Transformers", "abstract": "Most neural networks for computer vision are designed to infer using RGB images. However, these RGB images are commonly encoded in JPEG before saving to disk; decoding them imposes an unavoidable overhead for RGB networks. Instead, our work focuses on training Vision Transformers (ViT) directly from the encoded features of JPEG. This way, we can avoid most of the decoding overhead, accelerating data load. Existing works have studied this aspect but they focus on CNNs. Due to how these encoded features are structured, CNNs require heavy modification to their architecture to accept such data. Here, we show that this is not the case for ViTs. In addition, we tackle data augmentation directly on these encoded features, which to our knowledge, has not been explored in-depth for training in this setting. With these two improvements -- ViT and data augmentation -- we show that our ViT-Ti model achieves up to 39.2% faster training and 17.9% faster inference with no accuracy loss compared to the RGB counterpart.", "published": "2022-11-29T17:52:20Z", "authors": ["Jeongsoo Park", "Justin Johnson"], "categories": ["cs.CV", "eess.IV"], "doi": "", "_query": "property valuation computer vision", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2401.11790v3", "title": "Deep Learning for Computer Vision based Activity Recognition and Fall Detection of the Elderly: a Systematic Review", "abstract": "As the percentage of elderly people in developed countries increases worldwide, the healthcare of this collective is a worrying matter, especially if it includes the preservation of their autonomy. In this direction, many studies are being published on Ambient Assisted Living (AAL) systems, which help to reduce the preoccupations raised by the independent living of the elderly. In this study, a systematic review of the literature is presented on fall detection and Human Activity Recognition (HAR) for the elderly, as the two main tasks to solve to guarantee the safety of elderly people living alone. To address the current tendency to perform these two tasks, the review focuses on the use of Deep Learning (DL) based approaches on computer vision data. In addition, different collections of data like DL models, datasets or hardware (e.g. depth or thermal cameras) are gathered from the reviewed studies and provided for reference in future studies. Strengths and weaknesses of existing approaches are also discussed and, based on them, our recommendations for future works are provided.", "published": "2024-01-22T09:40:52Z", "authors": ["F. Xavier Gaya-Morey", "Cristina Manresa-Yee", "Jose M. Buades-Rubio"], "categories": ["cs.CV"], "doi": "10.1007/s10489-024-05645-1", "_query": "property valuation computer vision", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2511.18123v2", "title": "Bias Is a Subspace, Not a Coordinate: A Geometric Rethinking of Post-hoc Debiasing in Vision-Language Models", "abstract": "Vision-Language Models (VLMs) have become indispensable for multimodal reasoning, yet their representations often encode and amplify demographic biases, resulting in biased associations and misaligned predictions in downstream tasks. Such behavior undermines fairness and distorts the intended alignment between vision and language. Recent post-hoc approaches attempt to mitigate bias by replacing the most attribute-correlated embedding coordinates with neutral values. However, our systematic analysis reveals three critical limitations of this coordinate-wise approach: feature entanglement, poor cross-dataset generalization, and incomplete bias removal. We find that bias is not localized to a few coordinates but is instead distributed across a few linear subspaces. To address these limitations, we propose $\\textbf{S}$ubspace $\\textbf{P}$rojection $\\textbf{D}$ebiasing ($\\textbf{SPD}$), a geometrically principled framework that identifies and removes the entire subspace of linearly decodable bias while reinserting a neutral mean component to preserve semantic fidelity. Extensive experiments across zero-shot classification, text-to-image retrieval, and image generation validate the effectiveness of SPD: our method achieves more robust debiasing with an average improvement of $18.5\\%$ across four fairness metrics, while maintaining minimal loss in task performance compared to the best debiasing baseline.", "published": "2025-11-22T17:04:30Z", "authors": ["Dachuan Zhao", "Weiyue Li", "Zhenda Shen", "Yushu Qiu", "Bowen Xu", "Haoyu Chen", "Yongchao Chen"], "categories": ["cs.CV", "cs.AI", "cs.CL", "cs.LG"], "doi": "", "_query": "property valuation computer vision", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2606.29483v2", "title": "Fog Computing and Large Language Models: A vision for mutual beneficiaries", "abstract": "Fog computing utilizes proximal computational resources for sensor data processing and actuation, and addresses the latency, network load, and privacy issues of cloud-centric Internet of Things. On the other hand, Large Language Models (LLMs) are a type of deep learning AI models, which are trained on enormous text data, that perform various natural language processing tasks such as translation, question answering, text summarization, and code generation. LLMs are generally cloud-centric, requiring abundant GPU memory and computing capabilities, again face the same issues that led to fog computing. This pushes the necessity for LLM support in the proximity on fog infrastructure, requiring LLM optimizations such as parameter-weight quantization, pruning, low-rank adaptation etc. Meanwhile, fog computing also gets benefit from LLM's ability for code generation, in the dynamic deployment of fog-based applications. The paper addresses how both fog computing and LLMs can be mutual beneficiaries, discussing the state-of-the-art and future research scope.", "published": "2026-06-28T16:27:55Z", "authors": ["Satish Narayana Srirama"], "categories": ["cs.DC"], "doi": "10.1109/MC.2026.3708686", "_query": "property valuation computer vision", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2511.22019v2", "title": "Intra-Class Probabilistic Embeddings for Uncertainty Estimation in Vision-Language Models", "abstract": "Vision-language models (VLMs), such as CLIP, have gained popularity for their strong open vocabulary classification performance, but they are prone to assigning high confidence scores to misclassifications, limiting their reliability in safety-critical applications. We introduce a training-free, post-hoc uncertainty estimation method for contrastive VLMs that can be used to detect erroneous predictions. The key to our approach is to measure visual feature consistency within a class, using feature projection combined with multivariate Gaussians to create class-specific probabilistic embeddings. Our method is VLM-agnostic, requires no fine-tuning, demonstrates robustness to distribution shift, and works effectively with as few as 10 training images per class. Extensive experiments on ImageNet, Flowers102, Food101, EuroSAT and DTD show state-of-the-art error detection performance, significantly outperforming both deterministic and probabilistic VLM baselines. Code is available at https://github.com/zhenxianglin/ICPE.", "published": "2025-11-27T01:48:27Z", "authors": ["Zhenxiang Lin", "Maryam Haghighat", "Will Browne", "Dimity Miller"], "categories": ["cs.CV"], "doi": "", "_query": "property valuation computer vision", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2111.08772v1", "title": "Computer Vision for Supporting Image Search", "abstract": "Computer vision and multimedia information processing have made extreme progress within the last decade and many tasks can be done with a level of accuracy as if done by humans, or better. This is because we leverage the benefits of huge amounts of data available for training, we have enormous computer processing available and we have seen the evolution of machine learning as a suite of techniques to process data and deliver accurate vision-based systems. What kind of applications do we use this processing for ? We use this in autonomous vehicle navigation or in security applications, searching CCTV for example, and in medical image analysis for healthcare diagnostics. One application which is not widespread is image or video search directly by users. In this paper we present the need for such image finding or re-finding by examining human memory and when it fails, thus motivating the need for a different approach to image search which is outlined, along with the requirements of computer vision to support it.", "published": "2021-11-16T20:50:32Z", "authors": ["Alan F. Smeaton"], "categories": ["cs.CV", "cs.IR"], "doi": "10.1007/978-3-030-90235-3_1", "_query": "property valuation computer vision", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2307.08117v1", "title": "Domain Generalisation with Bidirectional Encoder Representations from Vision Transformers", "abstract": "Domain generalisation involves pooling knowledge from source domain(s) into a single model that can generalise to unseen target domain(s). Recent research in domain generalisation has faced challenges when using deep learning models as they interact with data distributions which differ from those they are trained on. Here we perform domain generalisation on out-of-distribution (OOD) vision benchmarks using vision transformers. Initially we examine four vision transformer architectures namely ViT, LeViT, DeiT, and BEIT on out-of-distribution data. As the bidirectional encoder representation from image transformers (BEIT) architecture performs best, we use it in further experiments on three benchmarks PACS, Home-Office and DomainNet. Our results show significant improvements in validation and test accuracy and our implementation significantly overcomes gaps between within-distribution and OOD data.", "published": "2023-07-16T17:50:37Z", "authors": ["Hamza Riaz", "Alan F. Smeaton"], "categories": ["cs.CV", "cs.LG"], "doi": "10.5281/zenodo.8246304", "_query": "property valuation computer vision", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2406.09711v1", "title": "AnimalFormer: Multimodal Vision Framework for Behavior-based Precision Livestock Farming", "abstract": "We introduce a multimodal vision framework for precision livestock farming, harnessing the power of GroundingDINO, HQSAM, and ViTPose models. This integrated suite enables comprehensive behavioral analytics from video data without invasive animal tagging. GroundingDINO generates accurate bounding boxes around livestock, while HQSAM segments individual animals within these boxes. ViTPose estimates key body points, facilitating posture and movement analysis. Demonstrated on a sheep dataset with grazing, running, sitting, standing, and walking activities, our framework extracts invaluable insights: activity and grazing patterns, interaction dynamics, and detailed postural evaluations. Applicable across species and video resolutions, this framework revolutionizes non-invasive livestock monitoring for activity detection, counting, health assessments, and posture analyses. It empowers data-driven farm management, optimizing animal welfare and productivity through AI-powered behavioral understanding.", "published": "2024-06-14T04:42:44Z", "authors": ["Ahmed Qazi", "Taha Razzaq", "Asim Iqbal"], "categories": ["cs.CV"], "doi": "", "_query": "property valuation computer vision", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2009.06360v1", "title": "PRAFlow_RVC: Pyramid Recurrent All-Pairs Field Transforms for Optical Flow Estimation in Robust Vision Challenge 2020", "abstract": "Optical flow estimation is an important computer vision task, which aims at estimating the dense correspondences between two frames. RAFT (Recurrent All Pairs Field Transforms) currently represents the state-of-the-art in optical flow estimation. It has excellent generalization ability and has obtained outstanding results across several benchmarks. To further improve the robustness and achieve accurate optical flow estimation, we present PRAFlow (Pyramid Recurrent All-Pairs Flow), which builds upon the pyramid network structure. Due to computational limitation, our proposed network structure only uses two pyramid layers. At each layer, the RAFT unit is used to estimate the optical flow at the current resolution. Our model was trained on several simulate and real-image datasets, submitted to multiple leaderboards using the same model and parameters, and won the 2nd place in the optical flow task of ECCV 2020 workshop: Robust Vision Challenge.", "published": "2020-09-14T12:27:52Z", "authors": ["Zhexiong Wan", "Yuxin Mao", "Yuchao Dai"], "categories": ["cs.CV"], "doi": "", "_query": "property valuation computer vision", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/1404.1836v1", "title": "Improved 3-Dimensional Security in Cloud Computing", "abstract": "Cloud computing is a trending technology in the field of Information Technology as it allows sharing of resources over a network. The reason Cloud computing gained traction so rapidly was because of its performance, availability and low cost among other features. Besides these features, companies are still refraining from binding their business with cloud computing due to the fear of data leakage. The focus of this paper is on the problem of data leakage. It proposes a framework which works in two phases. The first phase consists of data encryption and classification which is performed before storing the data. In this phase, the client may want to encrypt his data prior to uploading. After encryption, data is classified using three parameters namely Confidentiality [C], Integrity [I] and Availability [A]. With the help of proposed algorithm, criticality rating (Cr) of the data is calculated. According to the Cr, security will be provided on the basis of the 3 Dimensions proposed in this paper. The second phase consists of data retrieval by the client. As per the concept of 3D, users who want to access their data need to be authenticated, to avoid data from being compromised. Before every access to data, the users identity is verified for authorization. After the user is authorized for data access, if the data is encrypted, the user can decrypt the same.", "published": "2014-04-07T16:25:15Z", "authors": ["Sagar Tirodkar", "Yazad Baldawala", "Sagar Ulane", "Ashok Jori"], "categories": ["cs.CR", "cs.DC", "cs.NI"], "doi": "10.14445/22312803/IJCTT-V9P145", "_query": "property valuation computer vision", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/1406.4523v1", "title": "Quasi valuation and valuation derived from filtered ring and properties", "abstract": "In this paper we show if R is a filtered ring then we can define a quasi valuation. And if R is some kind of filtered ring then we can define a valuation. Then we prove some properties and relations for R.", "published": "2014-06-17T20:19:51Z", "authors": ["M. H. Anjom SHoa", "M. H. Hosseini"], "categories": ["math.RA"], "doi": "", "_query": "property valuation computer vision", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2208.02797v2", "title": "Vision-Centric BEV Perception: A Survey", "abstract": "In recent years, vision-centric Bird's Eye View (BEV) perception has garnered significant interest from both industry and academia due to its inherent advantages, such as providing an intuitive representation of the world and being conducive to data fusion. The rapid advancements in deep learning have led to the proposal of numerous methods for addressing vision-centric BEV perception challenges. However, there has been no recent survey encompassing this novel and burgeoning research field. To catalyze future research, this paper presents a comprehensive survey of the latest developments in vision-centric BEV perception and its extensions. It compiles and organizes up-to-date knowledge, offering a systematic review and summary of prevalent algorithms. Additionally, the paper provides in-depth analyses and comparative results on various BEV perception tasks, facilitating the evaluation of future works and sparking new research directions. Furthermore, the paper discusses and shares valuable empirical implementation details to aid in the advancement of related algorithms.", "published": "2022-08-04T17:53:17Z", "authors": ["Yuexin Ma", "Tai Wang", "Xuyang Bai", "Huitong Yang", "Yuenan Hou", "Yaming Wang", "Yu Qiao", "Ruigang Yang", "Dinesh Manocha", "Xinge Zhu"], "categories": ["cs.CV"], "doi": "", "_query": "property valuation computer vision", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2604.24380v1", "title": "Structural Pruning of Large Vision Language Models: A Comprehensive Study on Pruning Dynamics, Recovery, and Data Efficiency", "abstract": "While Large Vision Language Models (LVLMs) demonstrate impressive capabilities, their substantial computational and memory requirements pose deployment challenges on resource-constrained edge devices. Current parameter reduction techniques primarily involve training LVLMs from small language models, but these methods offer limited flexibility and remain computationally intensive. We study a complementary route: compressing existing LVLMs by applying structured pruning to the language model backbone, followed by lightweight recovery training. Specifically, we investigate two structural pruning paradigms: layerwise and widthwise pruning, and pair them with supervised finetuning and knowledge distillation on logits and hidden states. Additionally, we assess the feasibility of conducting recovery training with only a small fraction of the available data. Our results show that widthwise pruning generally maintains better performance in low-resource scenarios, where computational resources are limited or there is insufficient finetuning data. As for the recovery training, finetuning only the multimodal projector is sufficient at small compression levels. Furthermore, a combination of supervised finetuning and hidden-state distillation yields optimal recovery across various pruning levels. Notably, effective recovery can be achieved using just 5% of the original data, while retaining over 95% of the original performance. Through empirical study on three representative LVLM families ranging from 3B to 7B parameters, this study offers actionable insights for practitioners to compress LVLMs without extensive computation resources or sufficient data. The code base is available at https://github.com/YiranHuangIrene/VLMCompression.git.", "published": "2026-04-27T12:10:44Z", "authors": ["Yiran Huang", "Lukas Thede", "Massimiliano Mancini", "Wenjia Xu", "Zeynep Akata"], "categories": ["cs.CL"], "doi": "", "_query": "property valuation computer vision", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2510.23190v1", "title": "Evaluation of Vision-LLMs in Surveillance Video", "abstract": "The widespread use of cameras in our society has created an overwhelming amount of video data, far exceeding the capacity for human monitoring. This presents a critical challenge for public safety and security, as the timely detection of anomalous or criminal events is crucial for effective response and prevention. The ability for an embodied agent to recognize unexpected events is fundamentally tied to its capacity for spatial reasoning. This paper investigates the spatial reasoning of vision-language models (VLMs) by framing anomalous action recognition as a zero-shot, language-grounded task, addressing the embodied perception challenge of interpreting dynamic 3D scenes from sparse 2D video. Specifically, we investigate whether small, pre-trained vision--LLMs can act as spatially-grounded, zero-shot anomaly detectors by converting video into text descriptions and scoring labels via textual entailment. We evaluate four open models on UCF-Crime and RWF-2000 under prompting and privacy-preserving conditions. Few-shot exemplars can improve accuracy for some models, but may increase false positives, and privacy filters -- especially full-body GAN transforms -- introduce inconsistencies that degrade accuracy. These results chart where current vision--LLMs succeed (simple, spatially salient events) and where they falter (noisy spatial cues, identity obfuscation). Looking forward, we outline concrete paths to strengthen spatial grounding without task-specific training: structure-aware prompts, lightweight spatial memory across clips, scene-graph or 3D-pose priors during description, and privacy methods that preserve action-relevant geometry. This positions zero-shot, language-grounded pipelines as adaptable building blocks for embodied, real-world video understanding. Our implementation for evaluating VLMs is publicly available at: https://github.com/pascalbenschopTU/VLLM_AnomalyRecognition", "published": "2025-10-27T10:27:02Z", "authors": ["Pascal Benschop", "Cristian Meo", "Justin Dauwels", "Jelte P. Mense"], "categories": ["cs.CV"], "doi": "", "_query": "property valuation computer vision", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2105.02679v1", "title": "A 2.5D Vehicle Odometry Estimation for Vision Applications", "abstract": "This paper proposes a method to estimate the pose of a sensor mounted on a vehicle as the vehicle moves through the world, an important topic for autonomous driving systems. Based on a set of commonly deployed vehicular odometric sensors, with outputs available on automotive communication buses (e.g. CAN or FlexRay), we describe a set of steps to combine a planar odometry based on wheel sensors with a suspension model based on linear suspension sensors. The aim is to determine a more accurate estimate of the camera pose. We outline its usage for applications in both visualisation and computer vision.", "published": "2021-05-06T14:01:46Z", "authors": ["Paul Moran", "Leroy-Francisco Periera", "Anbuchezhiyan Selvaraju", "Tejash Prakash", "Pantelis Ermilios", "John McDonald", "Jonathan Horgan", "Ciarán Eising"], "categories": ["cs.RO", "cs.CV"], "doi": "", "_query": "property valuation computer vision", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2309.00035v1", "title": "FACET: Fairness in Computer Vision Evaluation Benchmark", "abstract": "Computer vision models have known performance disparities across attributes such as gender and skin tone. This means during tasks such as classification and detection, model performance differs for certain classes based on the demographics of the people in the image. These disparities have been shown to exist, but until now there has not been a unified approach to measure these differences for common use-cases of computer vision models. We present a new benchmark named FACET (FAirness in Computer Vision EvaluaTion), a large, publicly available evaluation set of 32k images for some of the most common vision tasks - image classification, object detection and segmentation. For every image in FACET, we hired expert reviewers to manually annotate person-related attributes such as perceived skin tone and hair type, manually draw bounding boxes and label fine-grained person-related classes such as disk jockey or guitarist. In addition, we use FACET to benchmark state-of-the-art vision models and present a deeper understanding of potential performance disparities and challenges across sensitive demographic attributes. With the exhaustive annotations collected, we probe models using single demographics attributes as well as multiple attributes using an intersectional approach (e.g. hair color and perceived skin tone). Our results show that classification, detection, segmentation, and visual grounding models exhibit performance disparities across demographic attributes and intersections of attributes. These harms suggest that not all people represented in datasets receive fair and equitable treatment in these vision tasks. We hope current and future results using our benchmark will contribute to fairer, more robust vision models. FACET is available publicly at https://facet.metademolab.com/", "published": "2023-08-31T17:59:48Z", "authors": ["Laura Gustafson", "Chloe Rolland", "Nikhila Ravi", "Quentin Duval", "Aaron Adcock", "Cheng-Yang Fu", "Melissa Hall", "Candace Ross"], "categories": ["cs.CV", "cs.AI"], "doi": "", "_query": "property valuation computer vision", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2411.19103v1", "title": "VARCO-VISION: Expanding Frontiers in Korean Vision-Language Models", "abstract": "In this paper, we introduce an open-source Korean-English vision-language model (VLM), VARCO-VISION. We incorporate a step-by-step training strategy that allows a model learn both linguistic and visual information while preserving the backbone model's knowledge. Our model demonstrates outstanding performance in diverse settings requiring bilingual image-text understanding and generation abilities compared to models of similar size. VARCO-VISION is also capable of grounding, referring, and OCR, expanding its usage and potential applications for real-world scenarios. In addition to the model, we release five Korean evaluation datasets, including four closed-set and one openset benchmarks. We anticipate that our milestone will broaden the opportunities for AI researchers aiming to train VLMs. VARCO-VISION is available at https://huggingface.co/NCSOFT/VARCO-VISION-14B.", "published": "2024-11-28T12:38:42Z", "authors": ["Jeongho Ju", "Daeyoung Kim", "SunYoung Park", "Youngjune Kim"], "categories": ["cs.CV", "cs.CL"], "doi": "", "_query": "property valuation computer vision", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2410.08397v2", "title": "VoxelPrompt: A Vision Agent for End-to-End Medical Image Analysis", "abstract": "We present VoxelPrompt, an end-to-end image analysis agent that tackles free-form radiological tasks. Given any number of volumetric medical images and a natural language prompt, VoxelPrompt integrates a language model that generates executable code to invoke a jointly-trained, adaptable vision network. This code further carries out analytical steps to address practical quantitative aims, such as measuring the growth of a tumor across visits. The pipelines generated by VoxelPrompt automate analyses that currently require practitioners to painstakingly combine multiple specialized vision and statistical tools. We evaluate VoxelPrompt using diverse neuroimaging tasks and show that it can delineate hundreds of anatomical and pathological features, measure complex morphological properties, and perform open-language analysis of lesion characteristics. VoxelPrompt performs these objectives with an accuracy similar to that of specialist single-task models for image analysis, while facilitating a broad range of compositional biomedical workflows.", "published": "2024-10-10T22:11:43Z", "authors": ["Andrew Hoopes", "Neel Dey", "Victor Ion Butoi", "John V. Guttag", "Adrian V. Dalca"], "categories": ["eess.IV", "cs.AI", "cs.CV"], "doi": "", "_query": "property valuation computer vision", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2212.06595v1", "title": "OAMixer: Object-aware Mixing Layer for Vision Transformers", "abstract": "Patch-based models, e.g., Vision Transformers (ViTs) and Mixers, have shown impressive results on various visual recognition tasks, alternating classic convolutional networks. While the initial patch-based models (ViTs) treated all patches equally, recent studies reveal that incorporating inductive bias like spatiality benefits the representations. However, most prior works solely focused on the location of patches, overlooking the scene structure of images. Thus, we aim to further guide the interaction of patches using the object information. Specifically, we propose OAMixer (object-aware mixing layer), which calibrates the patch mixing layers of patch-based models based on the object labels. Here, we obtain the object labels in unsupervised or weakly-supervised manners, i.e., no additional human-annotating cost is necessary. Using the object labels, OAMixer computes a reweighting mask with a learnable scale parameter that intensifies the interaction of patches containing similar objects and applies the mask to the patch mixing layers. By learning an object-centric representation, we demonstrate that OAMixer improves the classification accuracy and background robustness of various patch-based models, including ViTs, MLP-Mixers, and ConvMixers. Moreover, we show that OAMixer enhances various downstream tasks, including large-scale classification, self-supervised learning, and multi-object recognition, verifying the generic applicability of OAMixer", "published": "2022-12-13T14:14:48Z", "authors": ["Hyunwoo Kang", "Sangwoo Mo", "Jinwoo Shin"], "categories": ["cs.CV", "cs.LG"], "doi": "", "_query": "property valuation computer vision", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/1705.04352v3", "title": "Reconfiguring the Imaging Pipeline for Computer Vision", "abstract": "Advancements in deep learning have ignited an explosion of research on efficient hardware for embedded computer vision. Hardware vision acceleration, however, does not address the cost of capturing and processing the image data that feeds these algorithms. We examine the role of the image signal processing (ISP) pipeline in computer vision to identify opportunities to reduce computation and save energy. The key insight is that imaging pipelines should be designed to be configurable: to switch between a traditional photography mode and a low-power vision mode that produces lower-quality image data suitable only for computer vision. We use eight computer vision algorithms and a reversible pipeline simulation tool to study the imaging system's impact on vision performance. For both CNN-based and classical vision algorithms, we observe that only two ISP stages, demosaicing and gamma compression, are critical for task performance. We propose a new image sensor design that can compensate for skipping these stages. The sensor design features an adjustable resolution and tunable analog-to-digital converters (ADCs). Our proposed imaging system's vision mode disables the ISP entirely and configures the sensor to produce subsampled, lower-precision image data. This vision mode can save ~75% of the average energy of a baseline photography mode while having only a small impact on vision task accuracy.", "published": "2017-05-11T18:57:01Z", "authors": ["Mark Buckler", "Suren Jayasuriya", "Adrian Sampson"], "categories": ["cs.CV"], "doi": "", "_query": "property valuation computer vision", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2301.07583v1", "title": "A Survey of Advanced Computer Vision Techniques for Sports", "abstract": "Computer Vision developments are enabling significant advances in many fields, including sports. Many applications built on top of Computer Vision technologies, such as tracking data, are nowadays essential for every top-level analyst, coach, and even player. In this paper, we survey Computer Vision techniques that can help many sports-related studies gather vast amounts of data, such as Object Detection and Pose Estimation. We provide a use case for such data: building a model for shot speed estimation with pose data obtained using only Computer Vision models. Our model achieves a correlation of 67%. The possibility of estimating shot speeds enables much deeper studies about enabling the creation of new metrics and recommendation systems that will help athletes improve their performance, in any sport. The proposed methodology is easily replicable for many technical movements and is only limited by the availability of video data.", "published": "2023-01-18T15:01:36Z", "authors": ["Tiago Mendes-Neves", "Luís Meireles", "João Mendes-Moreira"], "categories": ["cs.CV", "cs.LG"], "doi": "", "_query": "property valuation computer vision", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2508.02977v1", "title": "Mamba-X: An End-to-End Vision Mamba Accelerator for Edge Computing Devices", "abstract": "Transformers have proven effective in language modeling but are limited by high computational and memory demands that grow quadratically with input sequence length. State space models (SSMs) offer a promising alternative by reducing attention complexity from $O(L^2)$ to $O(L)$ while also lowering overall memory consumption. Vision Mamba adapts the SSM approach for computer vision tasks, achieving lower latency and memory consumption than traditional transformer models. However, deploying Vision Mamba on edge devices is challenging due to its sequential scan operations, which hinder GPU efficiency. We propose Mamba-X, an end-to-end Vision Mamba accelerator that includes a systolic scan array to maximize parallelism and minimize memory traffic, along with a hybrid, hardware-friendly quantization technique to reduce memory usage and improve hardware efficiency without sacrificing accuracy.", "published": "2025-08-05T00:56:25Z", "authors": ["Dongho Yoon", "Gungyu Lee", "Jaewon Chang", "Yunjae Lee", "Dongjae Lee", "Minsoo Rhu"], "categories": ["cs.AR"], "doi": "", "_query": "property valuation computer vision", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2506.11140v3", "title": "Autonomous Computer Vision Development with Agentic AI", "abstract": "Agentic Artificial Intelligence (AI) systems leveraging Large Language Models (LLMs) exhibit significant potential for complex reasoning, planning, and tool utilization. We demonstrate that a specialized computer vision system can be built autonomously from a natural language prompt using Agentic AI methods. This involved extending SimpleMind (SM), an open-source Cognitive AI environment with configurable tools for medical image analysis, with an LLM-based agent, implemented using OpenManus, to automate the planning (tool configuration) for a particular computer vision task. We provide a proof-of-concept demonstration that an agentic system can interpret a computer vision task prompt, plan a corresponding SimpleMind workflow by decomposing the task and configuring appropriate tools. From the user input prompt, \"provide sm (SimpleMind) config for lungs, heart, and ribs segmentation for cxr (chest x-ray)\"), the agent LLM was able to generate the plan (tool configuration file in YAML format), and execute SM-Learn (training) and SM-Think (inference) scripts autonomously. The computer vision agent automatically configured, trained, and tested itself on 50 chest x-ray images, achieving mean dice scores of 0.96, 0.82, 0.83, for lungs, heart, and ribs, respectively. This work shows the potential for autonomous planning and tool configuration that has traditionally been performed by a data scientist in the development of computer vision applications.", "published": "2025-06-11T02:21:19Z", "authors": ["Jin Kim", "Muhammad Wahi-Anwa", "Sangyun Park", "Shawn Shin", "John M. Hoffman", "Matthew S. Brown"], "categories": ["cs.CV", "cs.AI", "cs.MA"], "doi": "", "_query": "property valuation computer vision", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2005.03318v1", "title": "A Review of Computer Vision Methods in Network Security", "abstract": "Network security has become an area of significant importance more than ever as highlighted by the eye-opening numbers of data breaches, attacks on critical infrastructure, and malware/ransomware/cryptojacker attacks that are reported almost every day. Increasingly, we are relying on networked infrastructure and with the advent of IoT, billions of devices will be connected to the internet, providing attackers with more opportunities to exploit. Traditional machine learning methods have been frequently used in the context of network security. However, such methods are more based on statistical features extracted from sources such as binaries, emails, and packet flows. On the other hand, recent years witnessed a phenomenal growth in computer vision mainly driven by the advances in the area of convolutional neural networks. At a glance, it is not trivial to see how computer vision methods are related to network security. Nonetheless, there is a significant amount of work that highlighted how methods from computer vision can be applied in network security for detecting attacks or building security solutions. In this paper, we provide a comprehensive survey of such work under three topics; i) phishing attempt detection, ii) malware detection, and iii) traffic anomaly detection. Next, we review a set of such commercial products for which public information is available and explore how computer vision methods are effectively used in those products. Finally, we discuss existing research gaps and future research directions, especially focusing on how network security research community and the industry can leverage the exponential growth of computer vision methods to build much secure networked systems.", "published": "2020-05-07T08:29:11Z", "authors": ["Jiawei Zhao", "Rahat Masood", "Suranga Seneviratne"], "categories": ["cs.NI", "cs.CR", "cs.CV"], "doi": "", "_query": "property valuation computer vision", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2403.04701v4", "title": "ObjectCompose: Evaluating Resilience of Vision-Based Models on Object-to-Background Compositional Changes", "abstract": "Given the large-scale multi-modal training of recent vision-based models and their generalization capabilities, understanding the extent of their robustness is critical for their real-world deployment. In this work, we evaluate the resilience of current vision-based models against diverse object-to-background context variations. The majority of robustness evaluation methods have introduced synthetic datasets to induce changes to object characteristics (viewpoints, scale, color) or utilized image transformation techniques (adversarial changes, common corruptions) on real images to simulate shifts in distributions. Recent works have explored leveraging large language models and diffusion models to generate changes in the background. However, these methods either lack in offering control over the changes to be made or distort the object semantics, making them unsuitable for the task. Our method, on the other hand, can induce diverse object-to-background changes while preserving the original semantics and appearance of the object. To achieve this goal, we harness the generative capabilities of text-to-image, image-to-text, and image-to-segment models to automatically generate a broad spectrum of object-to-background changes. We induce both natural and adversarial background changes by either modifying the textual prompts or optimizing the latents and textual embedding of text-to-image models. We produce various versions of standard vision datasets (ImageNet, COCO), incorporating either diverse and realistic backgrounds into the images or introducing color, texture, and adversarial changes in the background. We conduct extensive experiments to analyze the robustness of vision-based models against object-to-background context variations across diverse tasks. Code https://github.com/Muhammad-Huzaifaa/ObjectCompose.", "published": "2024-03-07T17:48:48Z", "authors": ["Hashmat Shadab Malik", "Muhammad Huzaifa", "Muzammal Naseer", "Salman Khan", "Fahad Shahbaz Khan"], "categories": ["cs.CV", "cs.AI"], "doi": "", "_query": "property valuation computer vision", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2604.08074v1", "title": "DinoRADE: Full Spectral Radar-Camera Fusion with Vision Foundation Model Features for Multi-class Object Detection in Adverse Weather", "abstract": "Reliable and weather-robust perception systems are essential for safe autonomous driving and typically employ multi-modal sensor configurations to achieve comprehensive environmental awareness. While recent automotive FMCW Radar-based approaches achieved remarkable performance on detection tasks in adverse weather conditions, they exhibited limitations in resolving fine-grained spatial details particularly critical for detecting smaller and vulnerable road users (VRUs). Furthermore, existing research has not adequately addressed VRU detection in adverse weather datasets such as K-Radar. We present DinoRADE, a Radar-centered detection pipeline that processes dense Radar tensors and aggregates vision features around transformed reference points in the camera perspective via deformable cross-attention. Vision features are provided by a DINOv3 Vision Foundation Model. We present a comprehensive performance evaluation on the K-Radar dataset in all weather conditions and are among the first to report detection performance individually for five object classes. Additionally, we compare our method with existing single-class detection approaches and outperform recent Radar-camera approaches by 12.1%. The code is available under https://github.com/chr-is-tof/RADE-Net.", "published": "2026-04-09T10:46:41Z", "authors": ["Christof Leitgeb", "Thomas Puchleitner", "Max Peter Ronecker", "Daniel Watzenig"], "categories": ["cs.CV"], "doi": "", "_query": "property valuation computer vision", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2507.22000v1", "title": "Staining and locking computer vision models without retraining", "abstract": "We introduce new methods of staining and locking computer vision models, to protect their owners' intellectual property. Staining, also known as watermarking, embeds secret behaviour into a model which can later be used to identify it, while locking aims to make a model unusable unless a secret trigger is inserted into input images. Unlike existing methods, our algorithms can be used to stain and lock pre-trained models without requiring fine-tuning or retraining, and come with provable, computable guarantees bounding their worst-case false positive rates. The stain and lock are implemented by directly modifying a small number of the model's weights and have minimal impact on the (unlocked) model's performance. Locked models are unlocked by inserting a small `trigger patch' into the corner of the input image. We present experimental results showing the efficacy of our methods and demonstrating their practical performance on a variety of computer vision models.", "published": "2025-07-29T16:47:34Z", "authors": ["Oliver J. Sutton", "Qinghua Zhou", "George Leete", "Alexander N. Gorban", "Ivan Y. Tyukin"], "categories": ["cs.CV", "cs.AI", "cs.LG"], "doi": "", "_query": "property valuation computer vision", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2108.04308v2", "title": "Do Datasets Have Politics? Disciplinary Values in Computer Vision Dataset Development", "abstract": "Data is a crucial component of machine learning. The field is reliant on data to train, validate, and test models. With increased technical capabilities, machine learning research has boomed in both academic and industry settings, and one major focus has been on computer vision. Computer vision is a popular domain of machine learning increasingly pertinent to real-world applications, from facial recognition in policing to object detection for autonomous vehicles. Given computer vision's propensity to shape machine learning research and impact human life, we seek to understand disciplinary practices around dataset documentation - how data is collected, curated, annotated, and packaged into datasets for computer vision researchers and practitioners to use for model tuning and development. Specifically, we examine what dataset documentation communicates about the underlying values of vision data and the larger practices and goals of computer vision as a field. To conduct this study, we collected a corpus of about 500 computer vision datasets, from which we sampled 114 dataset publications across different vision tasks. Through both a structured and thematic content analysis, we document a number of values around accepted data practices, what makes desirable data, and the treatment of humans in the dataset construction process. We discuss how computer vision datasets authors value efficiency at the expense of care; universality at the expense of contextuality; impartiality at the expense of positionality; and model work at the expense of data work. Many of the silenced values we identify sit in opposition with social computing practices. We conclude with suggestions on how to better incorporate silenced values into the dataset creation and curation process.", "published": "2021-08-09T19:07:58Z", "authors": ["Morgan Klaus Scheuerman", "Emily Denton", "Alex Hanna"], "categories": ["cs.CV", "cs.HC"], "doi": "10.1145/3476058", "_query": "property valuation computer vision", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2508.12453v1", "title": "Computing Approximately Proportional Allocations of Indivisible Goods: Beyond Additive and Monotone Valuations", "abstract": "Although approximate notions of envy-freeness-such as envy-freeness up to one good (EF1)-have been extensively studied for indivisible goods, the seemingly simpler fairness concept of proportionality up to one good (PROP1) has received far less attention. For additive valuations, every EF1 allocation is PROP1, and well-known algorithms such as Round-Robin and Envy-Cycle Elimination compute such allocations in polynomial time. PROP1 is also compatible with Pareto efficiency, as maximum Nash welfare allocations are EF1 and hence PROP1. We ask whether these favorable properties extend to non-additive valuations. We study a broad class of allocation instances with {\\em satiating goods}, where agents have non-negative valuation functions that need not be monotone, allowing for negative marginal values. We present the following results: - EF1 implies PROP1 for submodular valuations over satiating goods, ensuring existence and efficient computation via Envy-Cycle Elimination for monotone submodular valuations; - Round-robin computes a partial PROP1 allocation after the second-to-last round for satiating submodular goods and a complete PROP1 for monotone submodular valuations; - PROP1 allocations for satiating subadditive goods can be computed in polynomial-time; - Maximum Nash welfare allocations are PROP1 for monotone submodular goods, revealing yet another facet of their ``unreasonable fairness.''", "published": "2025-08-17T17:59:40Z", "authors": ["Martin Jupakkal Andersen", "Ioannis Caragiannis", "Anders Bo Ipsen", "Alexander Søltoft"], "categories": ["cs.GT", "cs.DS"], "doi": "", "_query": "property valuation computer vision", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2111.11432v1", "title": "Florence: A New Foundation Model for Computer Vision", "abstract": "Automated visual understanding of our diverse and open world demands computer vision models to generalize well with minimal customization for specific tasks, similar to human vision. Computer vision foundation models, which are trained on diverse, large-scale dataset and can be adapted to a wide range of downstream tasks, are critical for this mission to solve real-world computer vision applications. While existing vision foundation models such as CLIP, ALIGN, and Wu Dao 2.0 focus mainly on mapping images and textual representations to a cross-modal shared representation, we introduce a new computer vision foundation model, Florence, to expand the representations from coarse (scene) to fine (object), from static (images) to dynamic (videos), and from RGB to multiple modalities (caption, depth). By incorporating universal visual-language representations from Web-scale image-text data, our Florence model can be easily adapted for various computer vision tasks, such as classification, retrieval, object detection, VQA, image caption, video retrieval and action recognition. Moreover, Florence demonstrates outstanding performance in many types of transfer learning: fully sampled fine-tuning, linear probing, few-shot transfer and zero-shot transfer for novel images and objects. All of these properties are critical for our vision foundation model to serve general purpose vision tasks. Florence achieves new state-of-the-art results in majority of 44 representative benchmarks, e.g., ImageNet-1K zero-shot classification with top-1 accuracy of 83.74 and the top-5 accuracy of 97.18, 62.4 mAP on COCO fine tuning, 80.36 on VQA, and 87.8 on Kinetics-600.", "published": "2021-11-22T18:59:55Z", "authors": ["Lu Yuan", "Dongdong Chen", "Yi-Ling Chen", "Noel Codella", "Xiyang Dai", "Jianfeng Gao", "Houdong Hu", "Xuedong Huang", "Boxin Li", "Chunyuan Li", "Ce Liu", "Mengchen Liu", "Zicheng Liu", "Yumao Lu", "Yu Shi", "Lijuan Wang", "Jianfeng Wang", "Bin Xiao", "Zhen Xiao", "Jianwei Yang", "Michael Zeng", "Luowei Zhou", "Pengchuan Zhang"], "categories": ["cs.CV", "cs.AI", "cs.LG"], "doi": "", "_query": "property valuation computer vision", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2212.05598v3", "title": "Recurrent Vision Transformers for Object Detection with Event Cameras", "abstract": "We present Recurrent Vision Transformers (RVTs), a novel backbone for object detection with event cameras. Event cameras provide visual information with sub-millisecond latency at a high-dynamic range and with strong robustness against motion blur. These unique properties offer great potential for low-latency object detection and tracking in time-critical scenarios. Prior work in event-based vision has achieved outstanding detection performance but at the cost of substantial inference time, typically beyond 40 milliseconds. By revisiting the high-level design of recurrent vision backbones, we reduce inference time by a factor of 6 while retaining similar performance. To achieve this, we explore a multi-stage design that utilizes three key concepts in each stage: First, a convolutional prior that can be regarded as a conditional positional embedding. Second, local and dilated global self-attention for spatial feature interaction. Third, recurrent temporal feature aggregation to minimize latency while retaining temporal information. RVTs can be trained from scratch to reach state-of-the-art performance on event-based object detection - achieving an mAP of 47.2% on the Gen1 automotive dataset. At the same time, RVTs offer fast inference (<12 ms on a T4 GPU) and favorable parameter efficiency (5 times fewer than prior art). Our study brings new insights into effective design choices that can be fruitful for research beyond event-based vision.", "published": "2022-12-11T20:28:59Z", "authors": ["Mathias Gehrig", "Davide Scaramuzza"], "categories": ["cs.CV"], "doi": "", "_query": "property valuation computer vision", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2603.26127v1", "title": "Finding Distributed Object-Centric Properties in Self-Supervised Transformers", "abstract": "Self-supervised Vision Transformers (ViTs) like DINO show an emergent ability to discover objects, typically observed in [CLS] token attention maps of the final layer. However, these maps often contain spurious activations resulting in poor localization of objects. This is because the [CLS] token, trained on an image-level objective, summarizes the entire image instead of focusing on objects. This aggregation dilutes the object-centric information existing in the local, patch-level interactions. We analyze this by computing inter-patch similarity using patch-level attention components (query, key, and value) across all layers. We find that: (1) Object-centric properties are encoded in the similarity maps derived from all three components ($q, k, v$), unlike prior work that uses only key features or the [CLS] token. (2) This object-centric information is distributed across the network, not just confined to the final layer. Based on these insights, we introduce Object-DINO, a training-free method that extracts this distributed object-centric information. Object-DINO clusters attention heads across all layers based on the similarities of their patches and automatically identifies the object-centric cluster corresponding to all objects. We demonstrate Object-DINO's effectiveness on two applications: enhancing unsupervised object discovery (+3.6 to +12.4 CorLoc gains) and mitigating object hallucination in Multimodal Large Language Models by providing visual grounding. Our results demonstrate that using this distributed object-centric information improves downstream tasks without additional training.", "published": "2026-03-27T07:22:04Z", "authors": ["Samyak Rawlekar", "Amitabh Swain", "Yujun Cai", "Yiwei Wang", "Ming-Hsuan Yang", "Narendra Ahuja"], "categories": ["cs.CV", "cs.AI", "cs.CL", "cs.LG", "cs.MM"], "doi": "", "_query": "property valuation computer vision", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2108.10448v1", "title": "Fast Robust Tensor Principal Component Analysis via Fiber CUR Decomposition", "abstract": "We study the problem of tensor robust principal component analysis (TRPCA), which aims to separate an underlying low-multilinear-rank tensor and a sparse outlier tensor from their sum. In this work, we propose a fast non-convex algorithm, coined Robust Tensor CUR (RTCUR), for large-scale TRPCA problems. RTCUR considers a framework of alternating projections and utilizes the recently developed tensor Fiber CUR decomposition to dramatically lower the computational complexity. The performance advantage of RTCUR is empirically verified against the state-of-the-arts on the synthetic datasets and is further demonstrated on the real-world application such as color video background subtraction.", "published": "2021-08-23T23:49:40Z", "authors": ["HanQin Cai", "Zehan Chao", "Longxiu Huang", "Deanna Needell"], "categories": ["cs.LG", "cs.CV", "eess.IV", "math.OC"], "doi": "", "_query": "property valuation computer vision", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/1701.04674v1", "title": "Human perception in computer vision", "abstract": "Computer vision has made remarkable progress in recent years. Deep neural network (DNN) models optimized to identify objects in images exhibit unprecedented task-trained accuracy and, remarkably, some generalization ability: new visual problems can now be solved more easily based on previous learning. Biological vision (learned in life and through evolution) is also accurate and general-purpose. Is it possible that these different learning regimes converge to similar problem-dependent optimal computations? We therefore asked whether the human system-level computation of visual perception has DNN correlates and considered several anecdotal test cases. We found that perceptual sensitivity to image changes has DNN mid-computation correlates, while sensitivity to segmentation, crowding and shape has DNN end-computation correlates. Our results quantify the applicability of using DNN computation to estimate perceptual loss, and are consistent with the fascinating theoretical view that properties of human perception are a consequence of architecture-independent visual learning.", "published": "2017-01-17T14:00:30Z", "authors": ["Ron Dekel"], "categories": ["cs.CV", "q-bio.NC"], "doi": "", "_query": "property valuation computer vision", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2408.11448v1", "title": "Lookism: The overlooked bias in computer vision", "abstract": "In recent years, there have been significant advancements in computer vision which have led to the widespread deployment of image recognition and generation systems in socially relevant applications, from hiring to security screening. However, the prevalence of biases within these systems has raised significant ethical and social concerns. The most extensively studied biases in this context are related to gender, race and age. Yet, other biases are equally pervasive and harmful, such as lookism, i.e., the preferential treatment of individuals based on their physical appearance. Lookism remains under-explored in computer vision but can have profound implications not only by perpetuating harmful societal stereotypes but also by undermining the fairness and inclusivity of AI technologies. Thus, this paper advocates for the systematic study of lookism as a critical bias in computer vision models. Through a comprehensive review of existing literature, we identify three areas of intersection between lookism and computer vision. We illustrate them by means of examples and a user study. We call for an interdisciplinary approach to address lookism, urging researchers, developers, and policymakers to prioritize the development of equitable computer vision systems that respect and reflect the diversity of human appearances.", "published": "2024-08-21T09:07:20Z", "authors": ["Aditya Gulati", "Bruno Lepri", "Nuria Oliver"], "categories": ["cs.CV", "cs.AI", "cs.CY"], "doi": "", "_query": "property valuation computer vision", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/1907.04512v2", "title": "Computing Valuations of the Dieudonné Determinants", "abstract": "This paper addresses the problem of computing valuations of the Dieudonné determinants of matrices over discrete valuation skew fields (DVSFs). Under a reasonable computational model, we propose two algorithms for a class of DVSFs, called split. Our algorithms are extensions of the combinatorial relaxation of Murota (1995) and the matrix expansion by Moriyama--Murota (2013), both of which are based on combinatorial optimization. While our algorithms require an upper bound on the output, we give an estimation of the bound for skew polynomial matrices and show that the estimation is valid only for skew polynomial matrices. We consider two applications of this problem. The first one is the noncommutative weighted Edmonds' problem (nc-WEP), which is to compute the degree of the Dieudonné determinants of matrices having noncommutative symbols. We show that the presented algorithms reduce the nc-WEP to the unweighted problem in polynomial time. In particular, we show that the nc-WEP over the rational field is solvable in time polynomial in the input bit-length. We also present an application to analyses of degrees of freedom of linear time-varying systems by establishing formulas on the solution spaces of linear differential/difference equations.", "published": "2019-07-10T05:27:04Z", "authors": ["Taihei Oki"], "categories": ["cs.DS", "cs.SC"], "doi": "", "_query": "property valuation computer vision", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2309.06703v1", "title": "VLSlice: Interactive Vision-and-Language Slice Discovery", "abstract": "Recent work in vision-and-language demonstrates that large-scale pretraining can learn generalizable models that are efficiently transferable to downstream tasks. While this may improve dataset-scale aggregate metrics, analyzing performance around hand-crafted subgroups targeting specific bias dimensions reveals systemic undesirable behaviors. However, this subgroup analysis is frequently stalled by annotation efforts, which require extensive time and resources to collect the necessary data. Prior art attempts to automatically discover subgroups to circumvent these constraints but typically leverages model behavior on existing task-specific annotations and rapidly degrades on more complex inputs beyond \"tabular\" data, none of which study vision-and-language models. This paper presents VLSlice, an interactive system enabling user-guided discovery of coherent representation-level subgroups with consistent visiolinguistic behavior, denoted as vision-and-language slices, from unlabeled image sets. We show that VLSlice enables users to quickly generate diverse high-coherency slices in a user study (n=22) and release the tool publicly.", "published": "2023-09-13T04:02:38Z", "authors": ["Eric Slyman", "Minsuk Kahng", "Stefan Lee"], "categories": ["cs.CV", "cs.CL", "cs.HC", "cs.LG"], "doi": "10.1109/ICCV51070.2023.01403", "_query": "property valuation computer vision", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2610.13264v1", "title": "CV-OPD: Contrastive Vision On-policy Distillation", "abstract": "On-policy distillation (OPD) reveals versatility and convenience during VLM post-training, providing dense supervision for student from teacher. However, we observed an anomaly that the distilled VLM could still answer the questions accurately even when the related visual regions were masked or replaced with noise. Building on this observation, our quantitative and qualitative experiments demonstrate that OPD, which uses the answer as a hint, has failed to teach students to genuinely seek out and rely on visual evidence. Our theoretical analysis suggests that this failure arises from a shortcut enabled by the lack of visual constraints during distillation. To address the issue, we introduce Contrastive Vision On-policy Distillation (CV-OPD), which compares teacher distributions induced by positive and negative crops while conditioning on the same student prefix and augments optimization with a margin-based contrastive constraint, which is modulated with discrepancy gating across token and sample level. Specifically, CV-OPD first introduces a negative teacher unrelated to the answer and designs a Contrastive Divergence Loss (CDL) that constrains the feature distribution of student by contrasting the positive and negative teachers, correcting how the student exploits visual regions. The CV-OPD introduces a Discrepancy Aware Gate (DAG) that uses token-level and sample-level discrepancies to select visually relevant tokens for optimization, directing the student's attention toward visual content. Extensive experiments across a broad range of fine-grained visual benchmarks demonstrate that CV-OPD substantially alleviates shortcut and consistently improves the visual reasoning capabilities of existing VLMs.", "published": "2026-10-09T09:15:34Z", "authors": ["Haowei Wang", "Hao Lin", "Qi Zhu", "Jiayi Ji", "Deyi Ji", "Liujuan Cao", "Rongrong Ji"], "categories": ["cs.CV", "cs.LG", "cs.MM"], "doi": "", "_query": "property valuation computer vision", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2504.03653v2", "title": "A Survey on Heterogeneous Computing Using SmartNICs and Emerging Data Processing Units", "abstract": "The emergence of new, off-path smart network cards (SmartNICs), known generally as Data Processing Units (DPU), has opened a wide range of research opportunities. Of particular interest is the use of these and related devices in tandem with their host's CPU, creating a heterogeneous computing system with new properties and strengths to be explored, capable of accelerating a wide variety of workloads. This survey begins by providing the motivation and relevant background information for this new field, including its origins, a few current hardware offerings, major programming languages and frameworks for using them, and associated challenges. We then review and categorize a number of recent works in the field, covering a wide variety of studies, benchmarks, and application areas, such as data center infrastructure, commercial uses, and AI and ML acceleration. We conclude with a few observations.", "published": "2025-03-03T22:08:24Z", "authors": ["Nathan Tibbetts", "Sifat Ibtisum", "Satish Puri"], "categories": ["cs.DC", "cs.NI"], "doi": "10.1016/j.future.2025.108207", "_query": "property valuation computer vision", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2602.14662v1", "title": "Advances in Global Solvers for 3D Vision", "abstract": "Global solvers have emerged as a powerful paradigm for 3D vision, offering certifiable solutions to nonconvex geometric optimization problems traditionally addressed by local or heuristic methods. This survey presents the first systematic review of global solvers in geometric vision, unifying the field through a comprehensive taxonomy of three core paradigms: Branch-and-Bound (BnB), Convex Relaxation (CR), and Graduated Non-Convexity (GNC). We present their theoretical foundations, algorithmic designs, and practical enhancements for robustness and scalability, examining how each addresses the fundamental nonconvexity of geometric estimation problems. Our analysis spans ten core vision tasks, from Wahba problem to bundle adjustment, revealing the optimality-robustness-scalability trade-offs that govern solver selection. We identify critical future directions: scaling algorithms while maintaining guarantees, integrating data-driven priors with certifiable optimization, establishing standardized benchmarks, and addressing societal implications for safety-critical deployment. By consolidating theoretical foundations, practical advances, and broader impacts, this survey provides a unified perspective and roadmap toward certifiable, trustworthy perception for real-world applications. A continuously-updated literature summary and companion code tutorials are available at https://github.com/ericzzj1989/Awesome-Global-Solvers-for-3D-Vision.", "published": "2026-02-16T11:40:32Z", "authors": ["Zhenjun Zhao", "Heng Yang", "Bangyan Liao", "Yingping Zeng", "Shaocheng Yan", "Yingdong Gu", "Peidong Liu", "Yi Zhou", "Haoang Li", "Javier Civera"], "categories": ["cs.CV", "cs.RO"], "doi": "", "_query": "property valuation computer vision", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/1410.8623v1", "title": "Addressing the non-functional requirements of computer vision systems: A case study", "abstract": "Computer vision plays a major role in the robotics industry, where vision data is frequently used for navigation and high-level decision making. Although there is significant research in algorithms and functional requirements, there is a comparative lack of emphasis on how best to map these abstract concepts onto an appropriate software architecture. In this study, we distinguish between the functional and non-functional requirements of a computer vision system. Using a RoboCup humanoid robot system as a case study, we propose and develop a software architecture that fulfills the latter criteria. The modifiability of the proposed architecture is demonstrated by detailing a number of feature detection algorithms and emphasizing which aspects of the underlying framework were modified to support their integration. To demonstrate portability, we port our vision system (designed for an application-specific DARwIn-OP humanoid robot) to a general-purpose, Raspberry Pi computer. We evaluate performance on both platforms and compare them to a vision system optimised for functional requirements only. The architecture and implementation presented in this study provide a highly generalisable framework for computer vision system design that is of particular benefit in research and development, competition and other environments in which rapid system evolution is necessary.", "published": "2014-10-31T03:05:18Z", "authors": ["Shannon Fenn", "Alexandre Mendes", "David Budden"], "categories": ["cs.CV", "cs.RO", "cs.SE"], "doi": "", "_query": "property valuation computer vision", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2108.01390v5", "title": "Evo-ViT: Slow-Fast Token Evolution for Dynamic Vision Transformer", "abstract": "Vision transformers (ViTs) have recently received explosive popularity, but the huge computational cost is still a severe issue. Since the computation complexity of ViT is quadratic with respect to the input sequence length, a mainstream paradigm for computation reduction is to reduce the number of tokens. Existing designs include structured spatial compression that uses a progressive shrinking pyramid to reduce the computations of large feature maps, and unstructured token pruning that dynamically drops redundant tokens. However, the limitation of existing token pruning lies in two folds: 1) the incomplete spatial structure caused by pruning is not compatible with structured spatial compression that is commonly used in modern deep-narrow transformers; 2) it usually requires a time-consuming pre-training procedure. To tackle the limitations and expand the applicable scenario of token pruning, we present Evo-ViT, a self-motivated slow-fast token evolution approach for vision transformers. Specifically, we conduct unstructured instance-wise token selection by taking advantage of the simple and effective global class attention that is native to vision transformers. Then, we propose to update the selected informative tokens and uninformative tokens with different computation paths, namely, slow-fast updating. Since slow-fast updating mechanism maintains the spatial structure and information flow, Evo-ViT can accelerate vanilla transformers of both flat and deep-narrow structures from the very beginning of the training process. Experimental results demonstrate that our method significantly reduces the computational cost of vision transformers while maintaining comparable performance on image classification.", "published": "2021-08-03T09:56:07Z", "authors": ["Yifan Xu", "Zhijie Zhang", "Mengdan Zhang", "Kekai Sheng", "Ke Li", "Weiming Dong", "Liqing Zhang", "Changsheng Xu", "Xing Sun"], "categories": ["cs.CV"], "doi": "", "_query": "property valuation computer vision", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2401.11617v3", "title": "The State of Computer Vision Research in Africa", "abstract": "Despite significant efforts to democratize artificial intelligence (AI), computer vision which is a sub-field of AI, still lags in Africa. A significant factor to this, is the limited access to computing resources, datasets, and collaborations. As a result, Africa's contribution to top-tier publications in this field has only been 0.06% over the past decade. Towards improving the computer vision field and making it more accessible and inclusive, this study analyzes 63,000 Scopus-indexed computer vision publications from Africa. We utilize large language models to automatically parse their abstracts, to identify and categorize topics and datasets. This resulted in listing more than 100 African datasets. Our objective is to provide a comprehensive taxonomy of dataset categories to facilitate better understanding and utilization of these resources. We also analyze collaboration trends of researchers within and outside the continent. Additionally, we conduct a large-scale questionnaire among African computer vision researchers to identify the structural barriers they believe require urgent attention. In conclusion, our study offers a comprehensive overview of the current state of computer vision research in Africa, to empower marginalized communities to participate in the design and development of computer vision systems.", "published": "2024-01-21T22:50:44Z", "authors": ["Abdul-Hakeem Omotayo", "Ashery Mbilinyi", "Lukman Ismaila", "Houcemeddine Turki", "Mahmoud Abdien", "Karim Gamal", "Idriss Tondji", "Yvan Pimi", "Naome A. Etori", "Marwa M. Matar", "Clifford Broni-Bediako", "Abigail Oppong", "Mai Gamal", "Eman Ehab", "Gbetondji Dovonon", "Zainab Akinjobi", "Daniel Ajisafe", "Oluwabukola G. Adegboro", "Mennatullah Siam"], "categories": ["cs.CV"], "doi": "10.1613/jair.1.16653", "_query": "property valuation computer vision", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/cs/0511022v1", "title": "Does a Plane Imitate a Bird? Does Computer Vision Have to Follow Biological Paradigms?", "abstract": "We posit a new paradigm for image information processing. For the last 25 years, this task was usually approached in the frame of Treisman's two-stage paradigm [1]. The latter supposes an unsupervised, bottom-up directed process of preliminary information pieces gathering at the lower processing stages and a supervised, top-down directed process of information pieces binding and grouping at the higher stages. It is acknowledged that these sub-processes interact and intervene between them in a tricky and a complicated manner. Notwithstanding the prevalence of this paradigm in biological and computer vision, we nevertheless propose to replace it with a new one, which we would like to designate as a two-part paradigm. In it, information contained in an image is initially extracted in an independent top-down manner by one part of the system, and then it is examined and interpreted by another, separate system part. We argue that the new paradigm seems to be more plausible than its forerunner. We provide evidence from human attention vision studies and insights of Kolmogorov's complexity theory to support our arguments. We also provide some reasons in favor of separate image interpretation issues.", "published": "2005-11-04T15:08:47Z", "authors": ["Emanuel Diamant"], "categories": ["cs.NE"], "doi": "", "_query": "property valuation computer vision", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/0908.0994v1", "title": "A Secure Multi-Party Computation Protocol for Malicious Computation Prevention for preserving privacy during Data Mining", "abstract": "Secure Multi-Party Computation (SMC) allows parties with similar background to compute results upon their private data, minimizing the threat of disclosure. The exponential increase in sensitive data that needs to be passed upon networked computers and the stupendous growth of internet has precipitated vast opportunities for cooperative computation, where parties come together to facilitate computations and draw out conclusions that are mutually beneficial; at the same time aspiring to keep their private data secure. These computations are generally required to be done between competitors, who are obviously weary of each-others intentions. SMC caters not only to the needs of such parties but also provides plausible solutions to individual organizations for problems like privacy-preserving database query, privacy-preserving scientific computations, privacy-preserving intrusion detection and privacy-preserving data mining. This paper is an extension to a previously proposed protocol Encrytpo_Random, which presented a plain sailing yet effective approach to SMC and also put forward an aptly crafted architecture, whereby such an efficient protocol, involving the parties that have come forward for joint-computations and the third party who undertakes such computations, can be developed. Through this extended work an attempt has been made to further strengthen the existing protocol thus paving the way for a more secure multi-party computational process.", "published": "2009-08-07T07:22:06Z", "authors": ["Dr. Durgesh Kumar Mishra", "Neha Koria", "Nikhil Kapoor", "Ravish Bahety"], "categories": ["cs.CR", "cs.DB"], "doi": "", "_query": "property valuation computer vision", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2507.07620v4", "title": "ViLU: Learning Vision-Language Uncertainties for Failure Prediction", "abstract": "Reliable Uncertainty Quantification (UQ) and failure prediction remain open challenges for Vision-Language Models (VLMs). We introduce ViLU, a new Vision-Language Uncertainty quantification framework that contextualizes uncertainty estimates by leveraging all task-relevant textual representations. ViLU constructs an uncertainty-aware multi-modal representation by integrating the visual embedding, the predicted textual embedding, and an image-conditioned textual representation via cross-attention. Unlike traditional UQ methods based on loss prediction, ViLU trains an uncertainty predictor as a binary classifier to distinguish correct from incorrect predictions using a weighted binary cross-entropy loss, making it loss-agnostic. In particular, our proposed approach is well-suited for post-hoc settings, where only vision and text embeddings are available without direct access to the model itself. Extensive experiments on diverse datasets show the significant gains of our method compared to state-of-the-art failure prediction methods. We apply our method to standard classification datasets, such as ImageNet-1k, as well as large-scale image-caption datasets like CC12M and LAION-400M. Ablation studies highlight the critical role of our architecture and training in achieving effective uncertainty quantification. Our code is publicly available and can be found here: https://github.com/ykrmm/ViLU.", "published": "2025-07-10T10:41:13Z", "authors": ["Marc Lafon", "Yannis Karmim", "Julio Silva-Rodríguez", "Paul Couairon", "Clément Rambour", "Raphaël Fournier-Sniehotta", "Ismail Ben Ayed", "Jose Dolz", "Nicolas Thome"], "categories": ["cs.CV"], "doi": "", "_query": "property valuation computer vision", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/1302.1326v1", "title": "Cloud Computing framework for Computer Vision Research:An Introduction", "abstract": "Cloud computing offers the potential to help scientists to process massive number of computing resources often required in machine learning application such as computer vision problems. This proposal would like to show that which benefits can be obtained from cloud in order to help medical image analysis users (including scientists, clinicians, and research institutes). As security and privacy of algorithms are important for most of algorithms inventors, these algorithms can be hidden in a cloud to allow the users to use the algorithms as a package without any access to see/change their inside. In another word, in the user part, users send their images to the cloud and configure the algorithm via an interface. In the cloud part, the algorithms are applied to this image and the results are returned back to the user. My proposal has two parts: (1) investigate the potential of cloud computing for computer vision problems and (2) study the components of a proposed cloud-based framework for medical image analysis application and develop them (depending on the length of the internship). The investigation part will involve a study on several aspects of the problem including security, usability (for medical end users of the service), appropriate programming abstractions for vision problems, scalability and resource requirements. In the second part of this proposal I am going to thoroughly study of the proposed framework components and their relations and develop them. The proposed cloud-based framework includes an integrated environment to enable scientists and clinicians to access to the previous and current medical image analysis algorithms using a handful user interface without any access to the algorithm codes and procedures.", "published": "2013-02-06T11:41:26Z", "authors": ["Yu Zhou"], "categories": ["cs.CV", "cs.DC"], "doi": "", "_query": "property valuation computer vision", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2104.06757v3", "title": "VTGAN: Semi-supervised Retinal Image Synthesis and Disease Prediction using Vision Transformers", "abstract": "In Fluorescein Angiography (FA), an exogenous dye is injected in the bloodstream to image the vascular structure of the retina. The injected dye can cause adverse reactions such as nausea, vomiting, anaphylactic shock, and even death. In contrast, color fundus imaging is a non-invasive technique used for photographing the retina but does not have sufficient fidelity for capturing its vascular structure. The only non-invasive method for capturing retinal vasculature is optical coherence tomography-angiography (OCTA). However, OCTA equipment is quite expensive, and stable imaging is limited to small areas on the retina. In this paper, we propose a novel conditional generative adversarial network (GAN) capable of simultaneously synthesizing FA images from fundus photographs while predicting retinal degeneration. The proposed system has the benefit of addressing the problem of imaging retinal vasculature in a non-invasive manner as well as predicting the existence of retinal abnormalities. We use a semi-supervised approach to train our GAN using multiple weighted losses on different modalities of data. Our experiments validate that the proposed architecture exceeds recent state-of-the-art generative networks for fundus-to-angiography synthesis. Moreover, our vision transformer-based discriminators generalize quite well on out-of-distribution data sets for retinal disease prediction.", "published": "2021-04-14T10:32:36Z", "authors": ["Sharif Amit Kamran", "Khondker Fariha Hossain", "Alireza Tavakkoli", "Stewart Lee Zuckerbrod", "Salah A. Baker"], "categories": ["eess.IV", "cs.CV"], "doi": "10.1109/ICCVW54120.2021.00362", "_query": "property valuation computer vision", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2508.13957v3", "title": "ViT-FIQA: Assessing Face Image Quality using Vision Transformers", "abstract": "Face Image Quality Assessment (FIQA) aims to predict the utility of a face image for face recognition (FR) systems. State-of-the-art FIQA methods mainly rely on convolutional neural networks (CNNs), leaving the potential of Vision Transformer (ViT) architectures underexplored. This work proposes ViT-FIQA, a novel approach that extends standard ViT backbones, originally optimized for FR, through a learnable quality token designed to predict a scalar utility score for any given face image. The learnable quality token is concatenated with the standard image patch tokens, and the whole sequence is processed via global self-attention by the ViT encoders to aggregate contextual information across all patches. At the output of the backbone, ViT-FIQA branches into two heads: (1) the patch tokens are passed through a fully connected layer to learn discriminative face representations via a margin-penalty softmax loss, and (2) the quality token is fed into a regression head to learn to predict the face sample's utility. Extensive experiments on challenging benchmarks and several FR models, including both CNN- and ViT-based architectures, demonstrate that ViT-FIQA consistently achieves top-tier performance. These results underscore the effectiveness of transformer-based architectures in modeling face image utility and highlight the potential of ViTs as a scalable foundation for future FIQA research https://cutt.ly/irHlzXUC.", "published": "2025-08-19T15:50:07Z", "authors": ["Andrea Atzori", "Fadi Boutros", "Naser Damer"], "categories": ["cs.CV"], "doi": "", "_query": "property valuation computer vision", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/1611.00939v1", "title": "Recent Advances in Transient Imaging: A Computer Graphics and Vision Perspective", "abstract": "Transient imaging has recently made a huge impact in the computer graphics and computer vision fields. By capturing, reconstructing, or simulating light transport at extreme temporal resolutions, researchers have proposed novel techniques to show movies of light in motion, see around corners, detect objects in highly-scattering media, or infer material properties from a distance, to name a few. The key idea is to leverage the wealth of information in the temporal domain at the pico or nanosecond resolution, information usually lost during the capture-time temporal integration. This paper presents recent advances in this field of transient imaging from a graphics and vision perspective, including capture techniques, analysis, applications and simulation.", "published": "2016-11-03T10:11:10Z", "authors": ["Adrian Jarabo", "Belen Masia", "Julio Marco", "Diego Gutierrez"], "categories": ["cs.CV", "cs.GR"], "doi": "", "_query": "property valuation computer vision", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2212.05153v4", "title": "Algorithmic progress in computer vision", "abstract": "We investigate algorithmic progress in image classification on ImageNet, perhaps the most well-known test bed for computer vision. We estimate a model, informed by work on neural scaling laws, and infer a decomposition of progress into the scaling of compute, data, and algorithms. Using Shapley values to attribute performance improvements, we find that algorithmic improvements have been roughly as important as the scaling of compute for progress computer vision. Our estimates indicate that algorithmic innovations mostly take the form of compute-augmenting algorithmic advances (which enable researchers to get better performance from less compute), not data-augmenting algorithmic advances. We find that compute-augmenting algorithmic advances are made at a pace more than twice as fast as the rate usually associated with Moore's law. In particular, we estimate that compute-augmenting innovations halve compute requirements every nine months (95\\% confidence interval: 4 to 25 months).", "published": "2022-12-10T00:18:05Z", "authors": ["Ege Erdil", "Tamay Besiroglu"], "categories": ["cs.CV", "cs.LG"], "doi": "", "_query": "property valuation computer vision", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2406.00447v1", "title": "DroneVis: Versatile Computer Vision Library for Drones", "abstract": "This paper introduces DroneVis, a novel library designed to automate computer vision algorithms on Parrot drones. DroneVis offers a versatile set of features and provides a diverse range of computer vision tasks along with a variety of models to choose from. Implemented in Python, the library adheres to high-quality code standards, facilitating effortless customization and feature expansion according to user requirements. In addition, comprehensive documentation is provided, encompassing usage guidelines and illustrative use cases. Our documentation, code, and examples are available in https://github.com/ahmedheakl/drone-vis.", "published": "2024-06-01T14:06:46Z", "authors": ["Ahmed Heakl", "Fatma Youssef", "Victor Parque", "Walid Gomaa"], "categories": ["cs.CV", "cs.AI", "cs.CY", "cs.LG", "cs.RO"], "doi": "", "_query": "property valuation computer vision", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2305.01486v5", "title": "ARBEx: Attentive Feature Extraction with Reliability Balancing for Robust Facial Expression Learning", "abstract": "In this paper, we introduce a framework ARBEx, a novel attentive feature extraction framework driven by Vision Transformer with reliability balancing to cope against poor class distributions, bias, and uncertainty in the facial expression learning (FEL) task. We reinforce several data pre-processing and refinement methods along with a window-based cross-attention ViT to squeeze the best of the data. We also employ learnable anchor points in the embedding space with label distributions and multi-head self-attention mechanism to optimize performance against weak predictions with reliability balancing, which is a strategy that leverages anchor points, attention scores, and confidence values to enhance the resilience of label predictions. To ensure correct label classification and improve the models' discriminative power, we introduce anchor loss, which encourages large margins between anchor points. Additionally, the multi-head self-attention mechanism, which is also trainable, plays an integral role in identifying accurate labels. This approach provides critical elements for improving the reliability of predictions and has a substantial positive effect on final prediction capabilities. Our adaptive model can be integrated with any deep neural network to forestall challenges in various recognition tasks. Our strategy outperforms current state-of-the-art methodologies, according to extensive experiments conducted in a variety of contexts.", "published": "2023-05-02T15:10:01Z", "authors": ["Azmine Toushik Wasi", "Karlo Šerbetar", "Raima Islam", "Taki Hasan Rafi", "Dong-Kyu Chae"], "categories": ["cs.CV", "cs.AI"], "doi": "10.1007/978-981-96-0911-6_27", "_query": "property valuation computer vision", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2311.17315v3", "title": "Explaining CLIP's performance disparities on data from blind/low vision users", "abstract": "Large multi-modal models (LMMs) hold the potential to usher in a new era of automated visual assistance for people who are blind or low vision (BLV). Yet, these models have not been systematically evaluated on data captured by BLV users. We address this by empirically assessing CLIP, a widely-used LMM likely to underpin many assistive technologies. Testing 25 CLIP variants in a zero-shot classification task, we find that their accuracy is 15 percentage points lower on average for images captured by BLV users than web-crawled images. This disparity stems from CLIP's sensitivities to 1) image content (e.g. not recognizing disability objects as well as other objects); 2) image quality (e.g. not being robust to lighting variation); and 3) text content (e.g. not recognizing objects described by tactile adjectives as well as visual ones). We delve deeper with a textual analysis of three common pre-training datasets: LAION-400M, LAION-2B and DataComp-1B, showing that disability content is rarely mentioned. We then provide three examples that illustrate how the performance disparities extend to three downstream models underpinned by CLIP: OWL-ViT, CLIPSeg and DALL-E2. We find that few-shot learning with as few as 5 images can mitigate CLIP's quality-of-service disparities for BLV users in some scenarios, which we discuss alongside a set of other possible mitigations.", "published": "2023-11-29T02:10:31Z", "authors": ["Daniela Massiceti", "Camilla Longden", "Agnieszka Słowik", "Samuel Wills", "Martin Grayson", "Cecily Morrison"], "categories": ["cs.CV"], "doi": "", "_query": "property valuation computer vision", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2402.04563v1", "title": "Attention Guided CAM: Visual Explanations of Vision Transformer Guided by Self-Attention", "abstract": "Vision Transformer(ViT) is one of the most widely used models in the computer vision field with its great performance on various tasks. In order to fully utilize the ViT-based architecture in various applications, proper visualization methods with a decent localization performance are necessary, but these methods employed in CNN-based models are still not available in ViT due to its unique structure. In this work, we propose an attention-guided visualization method applied to ViT that provides a high-level semantic explanation for its decision. Our method selectively aggregates the gradients directly propagated from the classification output to each self-attention, collecting the contribution of image features extracted from each location of the input image. These gradients are additionally guided by the normalized self-attention scores, which are the pairwise patch correlation scores. They are used to supplement the gradients on the patch-level context information efficiently detected by the self-attention mechanism. This approach of our method provides elaborate high-level semantic explanations with great localization performance only with the class labels. As a result, our method outperforms the previous leading explainability methods of ViT in the weakly-supervised localization task and presents great capability in capturing the full instances of the target class object. Meanwhile, our method provides a visualization that faithfully explains the model, which is demonstrated in the perturbation comparison test.", "published": "2024-02-07T03:43:56Z", "authors": ["Saebom Leem", "Hyunseok Seo"], "categories": ["cs.CV", "cs.AI"], "doi": "", "_query": "property valuation computer vision", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2503.06749v4", "title": "Vision-R1: Incentivizing Reasoning Capability in Multimodal Large Language Models", "abstract": "DeepSeek-R1-Zero has successfully demonstrated the emergence of reasoning capabilities in LLMs purely through Reinforcement Learning (RL). Inspired by this breakthrough, we explore how RL can be utilized to enhance the reasoning capability of MLLMs. However, direct training with RL struggles to activate complex reasoning capabilities such as questioning and reflection in MLLMs, due to the absence of substantial high-quality multimodal reasoning data. To address this issue, we propose the reasoning MLLM, Vision-R1, to improve multimodal reasoning capability. Specifically, we first construct a high-quality multimodal CoT dataset without human annotations by leveraging an existing MLLM and DeepSeek-R1 through modality bridging and data filtering to obtain a 200K multimodal CoT dataset, Vision-R1-cold dataset. It serves as cold-start initialization data for Vision-R1. To mitigate the optimization challenges caused by overthinking after cold start, we propose Progressive Thinking Suppression Training (PTST) strategy and employ Group Relative Policy Optimization (GRPO) with the hard formatting result reward function to gradually refine the model's ability to learn correct and complex reasoning processes on a 10K multimodal math dataset. Comprehensive experiments show our model achieves an average improvement of $\\sim$6% across various multimodal math reasoning benchmarks. Vision-R1-7B achieves a 73.5% accuracy on the widely used MathVista benchmark, which is only 0.4% lower than the leading reasoning model, OpenAI O1. Scaling up the amount of multimodal math data in the RL training, Vision-R1-32B and Vison-R1-72B achieves 76.4% and 78.2% MathVista benchmark scores, respectively. The datasets and code will be released in: https://github.com/Osilly/Vision-R1 .", "published": "2025-03-09T20:06:45Z", "authors": ["Wenxuan Huang", "Bohan Jia", "Zijie Zhai", "Shaosheng Cao", "Zheyu Ye", "Fei Zhao", "Zhe Xu", "Xu Tang", "Yao Hu", "Shaohui Lin"], "categories": ["cs.CV", "cs.AI", "cs.CL", "cs.LG"], "doi": "", "_query": "property valuation computer vision", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/1809.10971v1", "title": "Computation of Pommaret Bases Using Syzygies", "abstract": "We investigate the application of syzygies for efficiently computing (finite) Pommaret bases. For this purpose, we first describe a non-trivial variant of Gerdt's algorithm to construct an involutive basis for the input ideal as well as an involutive basis for the syzygy module of the output basis. Then we apply this new algorithm in the context of Seiler's method to transform a given ideal into quasi stable position to ensure the existence of a finite Pommaret basis. This new approach allows us to avoid superfluous reductions in the iterative computation of Janet bases required by this method. We conclude the paper by proposing an involutive variant of the signature based algorithm of Gao et al. to compute simultaneously a Grobner basis for a given ideal and for the syzygy module of the input basis. All the presented algorithms have been implemented in Maple and their performance is evaluated via a set of benchmark ideals.", "published": "2018-09-28T11:39:46Z", "authors": ["Bentolhoda Binaei", "Amir Hashemi", "Werner M. Seiler"], "categories": ["math.AG", "cs.SC", "math.AC"], "doi": "", "_query": "property valuation computer vision", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2305.14986v1", "title": "Non-adversarial Robustness of Deep Learning Methods for Computer Vision", "abstract": "Non-adversarial robustness, also known as natural robustness, is a property of deep learning models that enables them to maintain performance even when faced with distribution shifts caused by natural variations in data. However, achieving this property is challenging because it is difficult to predict in advance the types of distribution shifts that may occur. To address this challenge, researchers have proposed various approaches, some of which anticipate potential distribution shifts, while others utilize knowledge about the shifts that have already occurred to enhance model generalizability. In this paper, we present a brief overview of the most recent techniques for improving the robustness of computer vision methods, as well as a summary of commonly used robustness benchmark datasets for evaluating the model's performance under data distribution shifts. Finally, we examine the strengths and limitations of the approaches reviewed and identify general trends in deep learning robustness improvement for computer vision.", "published": "2023-05-24T10:21:31Z", "authors": ["Gorana Gojić", "Vladimir Vincan", "Ognjen Kundačina", "Dragiša Mišković", "Dinu Dragan"], "categories": ["cs.LG", "cs.CV"], "doi": "", "_query": "property valuation computer vision", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/1905.13258v1", "title": "A survey of advances in vision-based vehicle re-identification", "abstract": "Vehicle re-identification (V-reID) has become significantly popular in the community due to its applications and research significance. In particular, the V-reID is an important problem that still faces numerous open challenges. This paper reviews different V-reID methods including sensor based methods, hybrid methods, and vision based methods which are further categorized into hand-crafted feature based methods and deep feature based methods. The vision based methods make the V-reID problem particularly interesting, and our review systematically addresses and evaluates these methods for the first time. We conduct experiments on four comprehensive benchmark datasets and compare the performances of recent hand-crafted feature based methods and deep feature based methods. We present the detail analysis of these methods in terms of mean average precision (mAP) and cumulative matching curve (CMC). These analyses provide objective insight into the strengths and weaknesses of these methods. We also provide the details of different V-reID datasets and critically discuss the challenges and future trends of V-reID methods.", "published": "2019-05-30T18:45:40Z", "authors": ["Sultan Daud Khan", "Habib Ullah"], "categories": ["cs.CV", "cs.AI"], "doi": "10.1016/j.cviu.2019.03.001", "_query": "property valuation computer vision", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/1811.10216v1", "title": "A Survey on Joint Object Detection and Pose Estimation using Monocular Vision", "abstract": "In this survey we present a complete landscape of joint object detection and pose estimation methods that use monocular vision. Descriptions of traditional approaches that involve descriptors or models and various estimation methods have been provided. These descriptors or models include chordiograms, shape-aware deformable parts model, bag of boundaries, distance transform templates, natural 3D markers and facet features whereas the estimation methods include iterative clustering estimation, probabilistic networks and iterative genetic matching. Hybrid approaches that use handcrafted feature extraction followed by estimation by deep learning methods have been outlined. We have investigated and compared, wherever possible, pure deep learning based approaches (single stage and multi stage) for this problem. Comprehensive details of the various accuracy measures and metrics have been illustrated. For the purpose of giving a clear overview, the characteristics of relevant datasets are discussed. The trends that prevailed from the infancy of this problem until now have also been highlighted.", "published": "2018-11-26T07:43:23Z", "authors": ["Aniruddha V Patil", "Pankaj Rabha"], "categories": ["cs.CV"], "doi": "", "_query": "property valuation computer vision", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/1905.07844v1", "title": "Implications of Computer Vision Driven Assistive Technologies Towards Individuals with Visual Impairment", "abstract": "Computer vision based technology is becoming ubiquitous in society. One application area that has seen an increase in computer vision is assistive technologies, specifically for those with visual impairment. Research has shown the ability of computer vision models to achieve tasks such provide scene captions, detect objects and recognize faces. Although assisting individuals with visual impairment with these tasks increases their independence and autonomy, concerns over bias, privacy and potential usefulness arise. This paper addresses the positive and negative implications computer vision based assistive technologies have on individuals with visual impairment, as well as considerations for computer vision researchers and developers in order to mitigate the amount of negative implications.", "published": "2019-05-20T02:00:56Z", "authors": ["Linda Wang", "Alexander Wong"], "categories": ["cs.CV", "cs.CY"], "doi": "", "_query": "property valuation computer vision", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/1907.10935v1", "title": "Convolutional Neural Networks on Randomized Data", "abstract": "Convolutional Neural Networks (CNNs) are build specifically for computer vision tasks for which it is known that the input data is a hierarchical structure based on locally correlated elements. The question that naturally arises is what happens with the performance of CNNs if one of the basic properties of the data is removed, e.g. what happens if the image pixels are randomly permuted? Intuitively one expects that the convolutional network performs poorly in these circumstances in contrast to a multilayer perceptron (MLPs) whose classification accuracy should not be affected by the pixel randomization. This work shows that by randomizing image pixels the hierarchical structure of the data is destroyed and long range correlations are introduced which standard CNNs are not able to capture. We show that their classification accuracy is heavily dependent on the class similarities as well as the pixel randomization process. We also indicate that dilated convolutions are able to recover some of the pixel correlations and improve the performance.", "published": "2019-07-25T09:59:27Z", "authors": ["Cristian Ivan"], "categories": ["cs.CV"], "doi": "", "_query": "property valuation computer vision", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/1703.05381v1", "title": "3D Vision Guided Robotic Charging Station for Electric and Plug-in Hybrid Vehicles", "abstract": "Electric vehicles (EVs) and plug-in hybrid vehicles (PHEVs) are rapidly gaining popularity on our roads. Besides a comparatively high purchasing price, the main two problems limiting their use are the short driving range and inconvenient charging process. In this paper we address the following by presenting an automatic robot-based charging station with 3D vision guidance for plugging and unplugging the charger. First of all, the whole system concept consisting of a 3D vision system, an UR10 robot and a charging station is presented. Then we show the shape-based matching methods used to successfully identify and get the exact pose of the charging port. The same approach is used to calibrate the camera-robot system by using just known structure of the connector plug and no additional markers. Finally, a three-step robot motion planning procedure for plug-in is presented and functionality is demonstrated in a series of successful experiments.", "published": "2017-03-15T20:49:14Z", "authors": ["Justinas Miseikis", "Matthias Ruther", "Bernhard Walzel", "Mario Hirz", "Helmut Brunner"], "categories": ["cs.RO", "cs.CV"], "doi": "", "_query": "property valuation computer vision", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2206.13718v1", "title": "The Third Place Solution for CVPR2022 AVA Accessibility Vision and Autonomy Challenge", "abstract": "The goal of AVA challenge is to provide vision-based benchmarks and methods relevant to accessibility. In this paper, we introduce the technical details of our submission to the CVPR2022 AVA Challenge. Firstly, we conducted some experiments to help employ proper model and data augmentation strategy for this task. Secondly, an effective training strategy was applied to improve the performance. Thirdly, we integrated the results from two different segmentation frameworks to improve the performance further. Experimental results demonstrate that our approach can achieve a competitive result on the AVA test set. Finally, our approach achieves 63.008\\%AP@0.50:0.95 on the test set of CVPR2022 AVA Challenge.", "published": "2022-06-28T03:05:37Z", "authors": ["Bo Yan", "Leilei Cao", "Zhuang Li", "Hongbin Wang"], "categories": ["cs.CV", "cs.AI"], "doi": "", "_query": "property valuation computer vision", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2609.25567v1", "title": "RootQuantV2: Adapting a Vision Foundation Model for Root-Trait Regression from Minirhizotron Imagery", "abstract": "A lack of high-throughput phenotyping solutions for root traits in field-grown crops has severely constrained understanding and improvement of below-ground traits and processes. Minirhizotrons are the standard non-destructive root-phenotyping method in field environments. Computer vision solutions are needed to allow automated trait estimation at scale, but training data is scarce and human annotations are often inaccessible because they reside in proprietary software that only exports per-image scalar totals of root length and surface area. Nevertheless, large numeric archives of these root traits already exist. RootQuant showed that the traits can be predicted directly from the whole image by regression, thus removing manually traced masks from the pipeline; RootQuantV2 takes that idea further by replacing RootQuant's CNN backbone with a self-supervised ViT. We adapt a frozen DINOv3 ViT-L/16 with a hybrid parameter-efficient scheme. Training only 11.9M parameters (3.78% of the model), RootQuantV2 achieves length and area $R^2$ of 0.950 and 0.930, respectively, while lowering length/area RMSE by 24.3%/20.7% over RootQuant. RootQuantV2 thus repurposes legacy numeric archives for high-throughput, automated root trait estimation.", "published": "2026-09-22T01:59:04Z", "authors": ["Kinjalk Parth", "Sebastian Varela", "Andrew D. B. Leakey"], "categories": ["cs.CV", "cs.AI", "cs.LG", "q-bio.QM"], "doi": "", "_query": "property valuation computer vision", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2012.12710v2", "title": "Existence and Computation of Maximin Fair Allocations Under Matroid-Rank Valuations", "abstract": "We study fair and economically efficient allocation of indivisible goods among agents whose valuations are rank functions of matroids. Such valuations constitute a well-studied class of submodular functions (i.e., they exhibit a diminishing returns property) and model preferences in several resource-allocation settings. We prove that, for matroid-rank valuations, a social welfare-maximizing allocation that gives each agent her maximin share always exists. Furthermore, such an allocation can be computed in polynomial time. We establish similar existential and algorithmic results for the pairwise maximin share guarantee as well. To complement these results, we show that if the agents have binary XOS valuations or weighted-rank valuations, then maximin fair allocations are not guaranteed to exist. Both of these valuation classes are immediate generalizations of matroid-rank functions.", "published": "2020-12-23T14:36:56Z", "authors": ["Siddharth Barman", "Paritosh Verma"], "categories": ["cs.GT"], "doi": "", "_query": "property valuation computer vision", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/0901.0765v1", "title": "The distribution of landed property", "abstract": "The distribution of property is established through various mechanisms. In this paper we study the acreage distribution of land plots owned by natural persons in the Zlín Region of the Czech Republic. We show that the data are explained in terms of a simple model in which the inheritance and market behavior are combined.", "published": "2009-01-07T07:12:11Z", "authors": ["Pavel Exner", "Petr Šeba", "Daniel Vašata"], "categories": ["physics.soc-ph"], "doi": "10.1016/j.physa.2009.07.019", "_query": "property valuation computer vision", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/1506.01144v6", "title": "What value do explicit high level concepts have in vision to language problems?", "abstract": "Much of the recent progress in Vision-to-Language (V2L) problems has been achieved through a combination of Convolutional Neural Networks (CNNs) and Recurrent Neural Networks (RNNs). This approach does not explicitly represent high-level semantic concepts, but rather seeks to progress directly from image features to text. We propose here a method of incorporating high-level concepts into the very successful CNN-RNN approach, and show that it achieves a significant improvement on the state-of-the-art performance in both image captioning and visual question answering. We also show that the same mechanism can be used to introduce external semantic information and that doing so further improves performance. In doing so we provide an analysis of the value of high level semantic information in V2L problems.", "published": "2015-06-03T07:06:11Z", "authors": ["Qi Wu", "Chunhua Shen", "Lingqiao Liu", "Anthony Dick", "Anton van den Hengel"], "categories": ["cs.CV"], "doi": "", "_query": "property valuation computer vision", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2301.01161v1", "title": "Procedural Humans for Computer Vision", "abstract": "Recent work has shown the benefits of synthetic data for use in computer vision, with applications ranging from autonomous driving to face landmark detection and reconstruction. There are a number of benefits of using synthetic data from privacy preservation and bias elimination to quality and feasibility of annotation. Generating human-centered synthetic data is a particular challenge in terms of realism and domain-gap, though recent work has shown that effective machine learning models can be trained using synthetic face data alone. We show that this can be extended to include the full body by building on the pipeline of Wood et al. to generate synthetic images of humans in their entirety, with ground-truth annotations for computer vision applications. In this report we describe how we construct a parametric model of the face and body, including articulated hands; our rendering pipeline to generate realistic images of humans based on this body model; an approach for training DNNs to regress a dense set of landmarks covering the entire body; and a method for fitting our body model to dense landmarks predicted from multiple views.", "published": "2023-01-03T15:44:48Z", "authors": ["Charlie Hewitt", "Tadas Baltrušaitis", "Erroll Wood", "Lohit Petikam", "Louis Florentin", "Hanz Cuevas Velasquez"], "categories": ["cs.CV", "cs.GR"], "doi": "", "_query": "property valuation computer vision", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2604.07132v1", "title": "CSA-Graphs: A Privacy-Preserving Structural Dataset for Child Sexual Abuse Research", "abstract": "Child Sexual Abuse Imagery (CSAI) classification is an important yet challenging problem for computer vision research due to the strict legal and ethical restrictions that prevent the public sharing of CSAI datasets. This limitation hinders reproducibility and slows progress in developing automated methods. In this work, we introduce CSA-Graphs, a privacy-preserving structural dataset. Instead of releasing the original images, we provide structural representations that remove explicit visual content while preserving contextual information. CSA-Graphs includes two complementary graph-based modalities: scene graphs describing object relationships and skeleton graphs encoding human pose. Experiments show that both representations retain useful information for classifying CSAI, and that combining them further improves performance. This dataset enables broader research on computer vision methods for child safety while respecting legal and ethical constraints.", "published": "2026-04-08T14:26:52Z", "authors": ["Carlos Caetano", "Camila Laranjeira", "Clara Ernesto", "Artur Barros", "João Macedo", "Leo S. F. Ribeiro", "Jefersson A. dos Santos", "Sandra Avila"], "categories": ["cs.CV", "cs.AI", "cs.LG"], "doi": "", "_query": "property valuation computer vision", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/1707.07210v1", "title": "Inspiring Computer Vision System Solutions", "abstract": "The \"digital Michelangelo project\" was a seminal computer vision project in the early 2000's that pushed the capabilities of acquisition systems and involved multiple people from diverse fields, many of whom are now leaders in industry and academia. Reviewing this project with modern eyes provides us with the opportunity to reflect on several issues, relevant now as then to the field of computer vision and research in general, that go beyond the technical aspects of the work. This article was written in the context of a reading group competition at the week-long International Computer Vision Summer School 2017 (ICVSS) on Sicily, Italy. To deepen the participants understanding of computer vision and to foster a sense of community, various reading groups were tasked to highlight important lessons which may be learned from provided literature, going beyond the contents of the paper. This report is the winning entry of this guided discourse (Fig. 1). The authors closely examined the origins, fruits and most importantly lessons about research in general which may be distilled from the \"digital Michelangelo project\". Discussions leading to this report were held within the group as well as with Hao Li, the group mentor.", "published": "2017-07-22T20:20:57Z", "authors": ["Julian Zilly", "Amit Boyarski", "Micael Carvalho", "Amir Atapour Abarghouei", "Konstantinos Amplianitis", "Aleksandr Krasnov", "Massimiliano Mancini", "Hernán Gonzalez", "Riccardo Spezialetti", "Carlos Sampedro Pérez", "Hao Li"], "categories": ["cs.CV", "cs.CY"], "doi": "", "_query": "property valuation computer vision", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2604.10017v1", "title": "What and Where to Adapt: Structure-Semantics Co-Tuning for Machine Vision Compression via Synergistic Adapters", "abstract": "Parameter-efficient fine-tuning of pre-trained codecs is a promising direction in image compression for human and machine vision. While most existing works have primarily focused on tuning the feature structure within the encoder-decoder backbones, the adaptation of the statistical semantics within the entropy model has received limited attention despite its function of predicting the probability distribution of latent features. Our analysis reveals that naive adapter insertion into the entropy model can lead to suboptimal outcomes, underscoring that the effectiveness of adapter-based tuning depends critically on the coordination between adapter type and placement across the compression pipeline. Therefore, we introduce Structure-Semantics Co-Tuning (S2-CoT), a novel framework that achieves this coordination via two specialized, synergistic adapters: the Structural Fidelity Adapter (SFA) and the Semantic Context Adapter (SCA). SFA is integrated into the encoder-decoder to preserve high-fidelity representations by dynamically fusing spatial and frequency information; meanwhile, the SCA adapts the entropy model to align with SFA-tuned features by refining the channel context for more efficient statistical coding. Through joint optimization, S2-CoT turns potential performance degradation into synergistic gains, achieving state-of-the-art results across four diverse base codecs with only a small fraction of trainable parameters, closely matching full fine-tuning performance. Code is available at https://github.com/Brock-bit4/S2-CoT.", "published": "2026-04-11T04:03:37Z", "authors": ["Shaobo Liu", "Haobo Xiong", "Kai Liu", "Yuna Lin"], "categories": ["cs.CV"], "doi": "", "_query": "property valuation computer vision", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2507.18660v1", "title": "Fuzzy Theory in Computer Vision: A Review", "abstract": "Computer vision applications are omnipresent nowadays. The current paper explores the use of fuzzy logic in computer vision, stressing its role in handling uncertainty, noise, and imprecision in image data. Fuzzy logic is able to model gradual transitions and human-like reasoning and provides a promising approach to computer vision. Fuzzy approaches offer a way to improve object recognition, image segmentation, and feature extraction by providing more adaptable and interpretable solutions compared to traditional methods. We discuss key fuzzy techniques, including fuzzy clustering, fuzzy inference systems, type-2 fuzzy sets, and fuzzy rule-based decision-making. The paper also discusses various applications, including medical imaging, autonomous systems, and industrial inspection. Additionally, we explore the integration of fuzzy logic with deep learning models such as convolutional neural networks (CNNs) to enhance performance in complex vision tasks. Finally, we examine emerging trends such as hybrid fuzzy-deep learning models and explainable AI.", "published": "2025-07-23T15:23:09Z", "authors": ["Adilet Yerkin", "Ayan Igali", "Elnara Kadyrgali", "Maksat Shagyrov", "Malika Ziyada", "Muragul Muratbekova", "Pakizar Shamoi"], "categories": ["cs.CV"], "doi": "", "_query": "property valuation computer vision", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2207.14396v1", "title": "Low Cost Embedded Vision System For Location And Tracking Of A Color Object", "abstract": "This paper describes the development of an embedded vision system for detection, location, and tracking of a color object; it makes use of a single 32-bit microprocessor to acquire image data, process, and perform actions according to the interpreted data. The system is intended for applications that need to make use of artificial vision for detection, location and tracking of a color object and its objective is to have achieve at reduced terms of size, power consumption, and cost.", "published": "2022-07-28T22:25:32Z", "authors": ["Diego Ayala", "Danilo Chavez", "Leopoldo Altamirano Robles"], "categories": ["cs.CV", "eess.IV"], "doi": "10.1007/978-3-031-11346-8_22", "_query": "property valuation computer vision", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/1506.04130v3", "title": "CloudCV: Large Scale Distributed Computer Vision as a Cloud Service", "abstract": "We are witnessing a proliferation of massive visual data. Unfortunately scaling existing computer vision algorithms to large datasets leaves researchers repeatedly solving the same algorithmic, logistical, and infrastructural problems. Our goal is to democratize computer vision; one should not have to be a computer vision, big data and distributed computing expert to have access to state-of-the-art distributed computer vision algorithms. We present CloudCV, a comprehensive system to provide access to state-of-the-art distributed computer vision algorithms as a cloud service through a Web Interface and APIs.", "published": "2015-06-12T19:50:07Z", "authors": ["Harsh Agrawal", "Clint Solomon Mathialagan", "Yash Goyal", "Neelima Chavali", "Prakriti Banik", "Akrit Mohapatra", "Ahmed Osman", "Dhruv Batra"], "categories": ["cs.CV", "cs.DC"], "doi": "", "_query": "property valuation computer vision", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2509.22283v1", "title": "Rule-Based Reinforcement Learning for Document Image Classification with Vision Language Models", "abstract": "Rule-based reinforcement learning has been gaining popularity ever since DeepSeek-R1 has demonstrated its success through simple verifiable rewards. In the domain of document analysis, reinforcement learning is not as prevalent, even though many downstream tasks may benefit from the emerging properties of reinforcement learning, particularly the enhanced reason capabilities. We study the effects of rule-based reinforcement learning with the task of Document Image Classification which is one of the most commonly studied downstream tasks in document analysis. We find that reinforcement learning tends to have better generalisation capabilities to out-of-distritbution data, which we examine in three different scenarios, namely out-of-distribution images, unseen classes and different modalities. Our code is available at https://github.com/jungomi/vision-finetune.", "published": "2025-09-26T12:46:24Z", "authors": ["Michael Jungo", "Andreas Fischer"], "categories": ["cs.CV"], "doi": "10.1007/978-3-032-09368-4_18", "_query": "property valuation computer vision", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/0909.1605v1", "title": "Kernel Spectral Curvature Clustering (KSCC)", "abstract": "Multi-manifold modeling is increasingly used in segmentation and data representation tasks in computer vision and related fields. While the general problem, modeling data by mixtures of manifolds, is very challenging, several approaches exist for modeling data by mixtures of affine subspaces (which is often referred to as hybrid linear modeling). We translate some important instances of multi-manifold modeling to hybrid linear modeling in embedded spaces, without explicitly performing the embedding but applying the kernel trick. The resulting algorithm, Kernel Spectral Curvature Clustering, uses kernels at two levels - both as an implicit embedding method to linearize nonflat manifolds and as a principled method to convert a multiway affinity problem into a spectral clustering one. We demonstrate the effectiveness of the method by comparing it with other state-of-the-art methods on both synthetic data and a real-world problem of segmenting multiple motions from two perspective camera views.", "published": "2009-09-09T01:58:23Z", "authors": ["G. Chen", "S. Atev", "G. Lerman"], "categories": ["cs.CV"], "doi": "10.1109/ICCVW.2009.5457627", "_query": "property valuation computer vision", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/1906.02337v1", "title": "MNIST-C: A Robustness Benchmark for Computer Vision", "abstract": "We introduce the MNIST-C dataset, a comprehensive suite of 15 corruptions applied to the MNIST test set, for benchmarking out-of-distribution robustness in computer vision. Through several experiments and visualizations we demonstrate that our corruptions significantly degrade performance of state-of-the-art computer vision models while preserving the semantic content of the test images. In contrast to the popular notion of adversarial robustness, our model-agnostic corruptions do not seek worst-case performance but are instead designed to be broad and diverse, capturing multiple failure modes of modern models. In fact, we find that several previously published adversarial defenses significantly degrade robustness as measured by MNIST-C. We hope that our benchmark serves as a useful tool for future work in designing systems that are able to learn robust feature representations that capture the underlying semantics of the input.", "published": "2019-06-05T22:23:43Z", "authors": ["Norman Mu", "Justin Gilmer"], "categories": ["cs.CV", "cs.LG"], "doi": "", "_query": "property valuation computer vision", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2405.04404v1", "title": "Vision Mamba: A Comprehensive Survey and Taxonomy", "abstract": "State Space Model (SSM) is a mathematical model used to describe and analyze the behavior of dynamic systems. This model has witnessed numerous applications in several fields, including control theory, signal processing, economics and machine learning. In the field of deep learning, state space models are used to process sequence data, such as time series analysis, natural language processing (NLP) and video understanding. By mapping sequence data to state space, long-term dependencies in the data can be better captured. In particular, modern SSMs have shown strong representational capabilities in NLP, especially in long sequence modeling, while maintaining linear time complexity. Notably, based on the latest state-space models, Mamba merges time-varying parameters into SSMs and formulates a hardware-aware algorithm for efficient training and inference. Given its impressive efficiency and strong long-range dependency modeling capability, Mamba is expected to become a new AI architecture that may outperform Transformer. Recently, a number of works have attempted to study the potential of Mamba in various fields, such as general vision, multi-modal, medical image analysis and remote sensing image analysis, by extending Mamba from natural language domain to visual domain. To fully understand Mamba in the visual domain, we conduct a comprehensive survey and present a taxonomy study. This survey focuses on Mamba's application to a variety of visual tasks and data types, and discusses its predecessors, recent advances and far-reaching impact on a wide range of domains. Since Mamba is now on an upward trend, please actively notice us if you have new findings, and new progress on Mamba will be included in this survey in a timely manner and updated on the Mamba project at https://github.com/lx6c78/Vision-Mamba-A-Comprehensive-Survey-and-Taxonomy.", "published": "2024-05-07T15:30:14Z", "authors": ["Xiao Liu", "Chenxu Zhang", "Lei Zhang"], "categories": ["cs.CV", "cs.AI", "cs.CL", "cs.LG"], "doi": "", "_query": "property valuation computer vision", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/1712.04236v4", "title": "Ultra valuations", "abstract": "This paper proposes an original exchange property of valuations.This property is shown to be equivalent to a property described by Dress and Terhalle in the context of discrete optimization and matroids and shown there to characterize the valuations for which the demand oracle can be implemented by a greedy algorithm. The same exchange property is also equivalent to a property described independently by Reijnierse, van Gellekom and Potters and by Lehmann, Lehmann and Nisan and shown there to be satisfied by substitutes valuations. It studies the family of valuations that satisfy this exchange property, the ultra valuations. Any substitutes valuation is an ultra valuation, but ultra valuations may exhibit complementarities. Any symmetric valuation is an ultra valuation. Substitutes valuations are exactly the submodular ultra valuations. Ultra valuations define ultrametrics on the set of items. The maximum of an ultra valuation on $n$ items can be found in $O(n^2)$ steps. Finding an efficient allocation among ultra valuations is NP-hard.", "published": "2017-12-12T11:14:52Z", "authors": ["Daniel Lehmann"], "categories": ["cs.GT"], "doi": "", "_query": "property valuation computer vision", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2203.15269v1", "title": "Vision Transformers in Medical Computer Vision -- A Contemplative Retrospection", "abstract": "Recent escalation in the field of computer vision underpins a huddle of algorithms with the magnificent potential to unravel the information contained within images. These computer vision algorithms are being practised in medical image analysis and are transfiguring the perception and interpretation of Imaging data. Among these algorithms, Vision Transformers are evolved as one of the most contemporary and dominant architectures that are being used in the field of computer vision. These are immensely utilized by a plenty of researchers to perform new as well as former experiments. Here, in this article we investigate the intersection of Vision Transformers and Medical images and proffered an overview of various ViTs based frameworks that are being used by different researchers in order to decipher the obstacles in Medical Computer Vision. We surveyed the application of Vision transformers in different areas of medical computer vision such as image-based disease classification, anatomical structure segmentation, registration, region-based lesion Detection, captioning, report generation, reconstruction using multiple medical imaging modalities that greatly assist in medical diagnosis and hence treatment process. Along with this, we also demystify several imaging modalities used in Medical Computer Vision. Moreover, to get more insight and deeper understanding, self-attention mechanism of transformers is also explained briefly. Conclusively, we also put some light on available data sets, adopted methodology, their performance measures, challenges and their solutions in form of discussion. We hope that this review article will open future directions for researchers in medical computer vision.", "published": "2022-03-29T06:32:43Z", "authors": ["Arshi Parvaiz", "Muhammad Anwaar Khalid", "Rukhsana Zafar", "Huma Ameer", "Muhammad Ali", "Muhammad Moazam Fraz"], "categories": ["eess.IV", "cs.CV", "cs.LG"], "doi": "", "_query": "property valuation computer vision", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2607.18673v3", "title": "MissingBench-Verified: Probing Vision-Language Models' Inability to Detect Missing Object Parts", "abstract": "Vision Language Models (VLMs) are well known for hallucinating non-existent objects in images. Objects with missing parts present a unique challenge for VLMs, stemming from both real-world knowledge bias and the scarcity of such images in training data. We present MissingBench-Verified, a benchmark designed to evaluate a specific and practically relevant scenario: when vision-language models fail to recognize that an essential component of an object has been removed. Across ten leading models, we observe consistent and significant failure rates that persist even when external tool evidence explicitly contradicts the model's visual perception. We further ask whether granting models access to image processing tools (e.g., cropping, contrast adjustment) enables autonomous inspection to resolve these failures. We find that existing mitigation strategies, including tool-assisted verification, autonomous visual reasoning, longer reasoning durations, and fine-tuning on an easier dataset, provide negligible improvement, indicating that this failure mode cannot be addressed through current prompting or post-hoc correction techniques. Our findings highlight a fundamental limitation of current VLM for inspection and monitoring tasks and underscore the need for architectural or training-level interventions that enable models to override internal expectations when confronted with contradictory evidence.", "published": "2026-07-21T03:43:50Z", "authors": ["Wenqi Marshall Guo", "Qingyun Qian", "Shiyu Zhou", "Guoping Luo", "Shan Du"], "categories": ["cs.CV"], "doi": "", "_query": "property valuation computer vision", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2306.05135v1", "title": "Does Image Anonymization Impact Computer Vision Training?", "abstract": "Image anonymization is widely adapted in practice to comply with privacy regulations in many regions. However, anonymization often degrades the quality of the data, reducing its utility for computer vision development. In this paper, we investigate the impact of image anonymization for training computer vision models on key computer vision tasks (detection, instance segmentation, and pose estimation). Specifically, we benchmark the recognition drop on common detection datasets, where we evaluate both traditional and realistic anonymization for faces and full bodies. Our comprehensive experiments reflect that traditional image anonymization substantially impacts final model performance, particularly when anonymizing the full body. Furthermore, we find that realistic anonymization can mitigate this decrease in performance, where our experiments reflect a minimal performance drop for face anonymization. Our study demonstrates that realistic anonymization can enable privacy-preserving computer vision development with minimal performance degradation across a range of important computer vision benchmarks.", "published": "2023-06-08T12:02:03Z", "authors": ["Håkon Hukkelås", "Frank Lindseth"], "categories": ["cs.CV", "cs.AI"], "doi": "", "_query": "property valuation computer vision", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2503.21254v1", "title": "Vision-to-Music Generation: A Survey", "abstract": "Vision-to-music Generation, including video-to-music and image-to-music tasks, is a significant branch of multimodal artificial intelligence demonstrating vast application prospects in fields such as film scoring, short video creation, and dance music synthesis. However, compared to the rapid development of modalities like text and images, research in vision-to-music is still in its preliminary stage due to its complex internal structure and the difficulty of modeling dynamic relationships with video. Existing surveys focus on general music generation without comprehensive discussion on vision-to-music. In this paper, we systematically review the research progress in the field of vision-to-music generation. We first analyze the technical characteristics and core challenges for three input types: general videos, human movement videos, and images, as well as two output types of symbolic music and audio music. We then summarize the existing methodologies on vision-to-music generation from the architecture perspective. A detailed review of common datasets and evaluation metrics is provided. Finally, we discuss current challenges and promising directions for future research. We hope our survey can inspire further innovation in vision-to-music generation and the broader field of multimodal generation in academic research and industrial applications. To follow latest works and foster further innovation in this field, we are continuously maintaining a GitHub repository at https://github.com/wzk1015/Awesome-Vision-to-Music-Generation.", "published": "2025-03-27T08:21:54Z", "authors": ["Zhaokai Wang", "Chenxi Bao", "Le Zhuo", "Jingrui Han", "Yang Yue", "Yihong Tang", "Victor Shea-Jay Huang", "Yue Liao"], "categories": ["cs.CV", "cs.AI", "cs.MM", "cs.SD", "eess.AS"], "doi": "", "_query": "property valuation computer vision", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2405.05574v2", "title": "Vision-Language Modeling with Regularized Spatial Transformer Networks for All Weather Crosswind Landing of Aircraft", "abstract": "The intrinsic capability of the Human Vision System (HVS) to perceive depth of field and failure of Instrument Landing Systems (ILS) stimulates a pilot to perform a vision-based manual landing over an autoland approach. However, harsh weather creates challenges, and a pilot must have a clear view of runway elements before the minimum decision altitude. To aid in manual landing, a vision-based system trained to clear weather-induced visual degradations requires a robust landing dataset under various climatic conditions. Nevertheless, to acquire a dataset, flying an aircraft in dangerous weather impacts safety. Also, this system fails to generate reliable warnings, as localization of runway elements suffers from projective distortion while landing at crosswind. To combat, we propose to synthesize harsh weather landing images by training a prompt-based climatic diffusion network. Also, we optimize a weather distillation model using a novel diffusion-distillation loss to learn to clear these visual degradations. Precisely, the distillation model learns an inverse relationship with the diffusion network. Inference time, pre-trained distillation network directly clears weather-impacted onboard camera images, which can be further projected to display devices for improved visibility.Then, to tackle crosswind landing, a novel Regularized Spatial Transformer Networks (RuSTaN) module accurately warps landing images. It minimizes the localization error of runway object detector and helps generate reliable internal software warnings. Finally, we curated an aircraft landing dataset (AIRLAD) by simulating a landing scenario under various weather degradations and experimentally validated our contributions.", "published": "2024-05-09T06:48:42Z", "authors": ["Debabrata Pal", "Anvita Singh", "Saumya Saumya", "Shouvik Das"], "categories": ["cs.CV"], "doi": "10.1145/3702250.3702255", "_query": "property valuation computer vision", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/1902.06804v1", "title": "Democratisation of Usable Machine Learning in Computer Vision", "abstract": "Many industries are now investing heavily in data science and automation to replace manual tasks and/or to help with decision making, especially in the realm of leveraging computer vision to automate many monitoring, inspection, and surveillance tasks. This has resulted in the emergence of the 'data scientist' who is conversant in statistical thinking, machine learning (ML), computer vision, and computer programming. However, as ML becomes more accessible to the general public and more aspects of ML become automated, applications leveraging computer vision are increasingly being created by non-experts with less opportunity for regulatory oversight. This points to the overall need for more educated responsibility for these lay-users of usable ML tools in order to mitigate potentially unethical ramifications. In this paper, we undertake a SWOT analysis to study the strengths, weaknesses, opportunities, and threats of building usable ML tools for mass adoption for important areas leveraging ML such as computer vision. The paper proposes a set of data science literacy criteria for educating and supporting lay-users in the responsible development and deployment of ML applications.", "published": "2019-02-18T21:22:45Z", "authors": ["Raymond Bond", "Ansgar Koene", "Alan Dix", "Jennifer Boger", "Maurice D. Mulvenna", "Mykola Galushka", "Bethany Waterhouse Bradley", "Fiona Browne", "Hui Wang", "Alexander Wong"], "categories": ["cs.CV", "cs.AI", "cs.LG"], "doi": "", "_query": "property valuation computer vision", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/1601.06615v1", "title": "A Taxonomy of Deep Convolutional Neural Nets for Computer Vision", "abstract": "Traditional architectures for solving computer vision problems and the degree of success they enjoyed have been heavily reliant on hand-crafted features. However, of late, deep learning techniques have offered a compelling alternative -- that of automatically learning problem-specific features. With this new paradigm, every problem in computer vision is now being re-examined from a deep learning perspective. Therefore, it has become important to understand what kind of deep networks are suitable for a given problem. Although general surveys of this fast-moving paradigm (i.e. deep-networks) exist, a survey specific to computer vision is missing. We specifically consider one form of deep networks widely used in computer vision - convolutional neural networks (CNNs). We start with \"AlexNet\" as our base CNN and then examine the broad variations proposed over time to suit different applications. We hope that our recipe-style survey will serve as a guide, particularly for novice practitioners intending to use deep-learning techniques for computer vision.", "published": "2016-01-25T14:25:07Z", "authors": ["Suraj Srinivas", "Ravi Kiran Sarvadevabhatla", "Konda Reddy Mopuri", "Nikita Prabhu", "Srinivas S S Kruthiventi", "R. Venkatesh Babu"], "categories": ["cs.CV", "cs.LG", "cs.MM"], "doi": "10.3389/frobt.2015.00036", "_query": "property valuation computer vision", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/1303.2071v2", "title": "Application of the SP theory of intelligence to the understanding of natural vision and the development of computer vision", "abstract": "The SP theory of intelligence aims to simplify and integrate concepts in computing and cognition, with information compression as a unifying theme. This article discusses how it may be applied to the understanding of natural vision and the development of computer vision. The theory, which is described quite fully elsewhere, is described here in outline but with enough detail to ensure that the rest of the article makes sense. Low level perceptual features such as edges or corners may be identified by the extraction of redundancy in uniform areas in a manner that is comparable with the run-length encoding technique for information compression. The concept of multiple alignment in the SP theory may be applied to the recognition of objects, and to scene analysis, with a hierarchy of parts and sub-parts, and at multiple levels of abstraction. The theory has potential for the unsupervised learning of visual objects and classes of objects, and suggests how coherent concepts may be derived from fragments. As in natural vision, both recognition and learning in the SP system is robust in the face of errors of omission, commission and substitution. The theory suggests how, via vision, we may piece together a knowledge of the three-dimensional structure of objects and of our environment, it provides an account of how we may see things that are not objectively present in an image, and how we recognise something despite variations in the size of its retinal image. And it has things to say about the phenomena of lightness constancy and colour constancy, the role of context in recognition, and ambiguities in visual perception. A strength of the SP theory is that it provides for the integration of vision with other sensory modalities and with other aspects of intelligence.", "published": "2013-03-08T18:21:17Z", "authors": ["J. Gerard Wolff"], "categories": ["cs.CV", "cs.AI"], "doi": "10.1186/2193-1801-3-552", "_query": "property valuation computer vision", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/1708.08169v1", "title": "ChainerCV: a Library for Deep Learning in Computer Vision", "abstract": "Despite significant progress of deep learning in the field of computer vision, there has not been a software library that covers these methods in a unifying manner. We introduce ChainerCV, a software library that is intended to fill this gap. ChainerCV supports numerous neural network models as well as software components needed to conduct research in computer vision. These implementations emphasize simplicity, flexibility and good software engineering practices. The library is designed to perform on par with the results reported in published papers and its tools can be used as a baseline for future research in computer vision. Our implementation includes sophisticated models like Faster R-CNN and SSD, and covers tasks such as object detection and semantic segmentation.", "published": "2017-08-28T02:54:11Z", "authors": ["Yusuke Niitani", "Toru Ogawa", "Shunta Saito", "Masaki Saito"], "categories": ["cs.CV"], "doi": "", "_query": "property valuation computer vision", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/1904.10016v1", "title": "The Profiling Potential of Computer Vision and the Challenge of Computational Empiricism", "abstract": "Computer vision and other biometrics data science applications have commenced a new project of profiling people. Rather than using 'transaction generated information', these systems measure the 'real world' and produce an assessment of the 'world state' - in this case an assessment of some individual trait. Instead of using proxies or scores to evaluate people, they increasingly deploy a logic of revealing the truth about reality and the people within it. While these profiling knowledge claims are sometimes tentative, they increasingly suggest that only through computation can these excesses of reality be captured and understood. This article explores the bases of those claims in the systems of measurement, representation, and classification deployed in computer vision. It asks if there is something new in this type of knowledge claim, sketches an account of a new form of computational empiricism being operationalised, and questions what kind of human subject is being constructed by these technological systems and practices. Finally, the article explores legal mechanisms for contesting the emergence of computational empiricism as the dominant knowledge platform for understanding the world and the people within it.", "published": "2019-04-22T18:20:38Z", "authors": ["Jake Goldenfein"], "categories": ["cs.CY", "cs.CV"], "doi": "10.1145/3287560.3287568", "_query": "property valuation computer vision", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2506.05883v1", "title": "HMVLM: Multistage Reasoning-Enhanced Vision-Language Model for Long-Tailed Driving Scenarios", "abstract": "We present HaoMo Vision-Language Model (HMVLM), an end-to-end driving framework that implements the slow branch of a cognitively inspired fast-slow architecture. A fast controller outputs low-level steering, throttle, and brake commands, while a slow planner-a large vision-language model-generates high-level intents such as \"yield to pedestrian\" or \"merge after the truck\" without compromising latency. HMVLM introduces three upgrades: (1) selective five-view prompting with an embedded 4s history of ego kinematics, (2) multi-stage chain-of-thought (CoT) prompting that enforces a Scene Understanding -> Driving Decision -> Trajectory Inference reasoning flow, and (3) spline-based trajectory post-processing that removes late-stage jitter and sharp turns. Trained on the Waymo Open Dataset, these upgrades enable HMVLM to achieve a Rater Feedback Score (RFS) of 7.7367, securing 2nd place in the 2025 Waymo Vision-based End-to-End (E2E) Driving Challenge and surpassing the public baseline by 2.77%.", "published": "2025-06-06T08:51:06Z", "authors": ["Daming Wang", "Yuhao Song", "Zijian He", "Kangliang Chen", "Xing Pan", "Lu Deng", "Weihao Gu"], "categories": ["cs.CV", "cs.AI"], "doi": "", "_query": "property valuation computer vision", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/1906.01529v6", "title": "Generative Adversarial Networks in Computer Vision: A Survey and Taxonomy", "abstract": "Generative adversarial networks (GANs) have been extensively studied in the past few years. Arguably their most significant impact has been in the area of computer vision where great advances have been made in challenges such as plausible image generation, image-to-image translation, facial attribute manipulation and similar domains. Despite the significant successes achieved to date, applying GANs to real-world problems still poses significant challenges, three of which we focus on here. These are: (1) the generation of high quality images, (2) diversity of image generation, and (3) stable training. Focusing on the degree to which popular GAN technologies have made progress against these challenges, we provide a detailed review of the state of the art in GAN-related research in the published scientific literature. We further structure this review through a convenient taxonomy we have adopted based on variations in GAN architectures and loss functions. While several reviews for GANs have been presented to date, none have considered the status of this field based on their progress towards addressing practical challenges relevant to computer vision. Accordingly, we review and critically discuss the most popular architecture-variant, and loss-variant GANs, for tackling these challenges. Our objective is to provide an overview as well as a critical analysis of the status of GAN research in terms of relevant progress towards important computer vision application requirements. As we do this we also discuss the most compelling applications in computer vision in which GANs have demonstrated considerable success along with some suggestions for future research directions. Code related to GAN-variants studied in this work is summarized on https://github.com/sheqi/GAN_Review.", "published": "2019-06-04T15:40:53Z", "authors": ["Zhengwei Wang", "Qi She", "Tomas E. Ward"], "categories": ["cs.LG", "cs.CV"], "doi": "", "_query": "property valuation computer vision", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2505.23043v2", "title": "Cross-Task Generalization Between Understanding and Generation in Unified Vision-Language Models: A Controlled Study", "abstract": "Unified vision-language models (VLMs) aim to support both visual understanding and generation within a single framework, but it remains unclear when mixed training benefits both capabilities and when it introduces conflicts. This paper presents a controlled empirical study of cross-task generalization between understanding and generation in unified VLMs. We construct two controllable image-text benchmarks, SmartWatch and modified CelebA, with paired VQA, captioning, and text-to-image generation tasks, and evaluate multiple LLM-based unified architectures built from SigLIP and VQ-VAE visual spaces. Our experiments show that mixed understanding-generation training can improve both tasks over task-specific training, but the benefit depends strongly on the relation between vision input and output spaces. Unified models with better aligned visual spaces exhibit stronger cross-task transfer, while reversible affine distortions of the input visual space substantially weaken this effect and can turn mutual benefits into conflicts. We further find that increasing data from one task can initially improve the other, but excessive imbalance between understanding and generation data may degrade the complementary task. By controlling attribute frequencies, we show that generation supervision can help recover underrepresented visual concepts for understanding. Adapter analyses suggest that this transfer is not primarily caused by richer visual adapter features, but by the base language model learning relationships that generalize across aligned visual spaces. A real-case experiment on LLaVA provides additional evidence that mixed understanding-generation training can benefit visual understanding beyond controlled benchmarks.", "published": "2025-05-29T03:40:21Z", "authors": ["Jihai Zhang", "Tianle Li", "Linjie Li", "Zhengyuan Yang", "Yu Cheng"], "categories": ["cs.CV", "cs.AI"], "doi": "", "_query": "property valuation computer vision", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2108.00045v1", "title": "Multi-Head Self-Attention via Vision Transformer for Zero-Shot Learning", "abstract": "Zero-Shot Learning (ZSL) aims to recognise unseen object classes, which are not observed during the training phase. The existing body of works on ZSL mostly relies on pretrained visual features and lacks the explicit attribute localisation mechanism on images. In this work, we propose an attention-based model in the problem settings of ZSL to learn attributes useful for unseen class recognition. Our method uses an attention mechanism adapted from Vision Transformer to capture and learn discriminative attributes by splitting images into small patches. We conduct experiments on three popular ZSL benchmarks (i.e., AWA2, CUB and SUN) and set new state-of-the-art harmonic mean results {on all the three datasets}, which illustrate the effectiveness of our proposed method.", "published": "2021-07-30T19:08:44Z", "authors": ["Faisal Alamri", "Anjan Dutta"], "categories": ["cs.CV", "cs.LG"], "doi": "", "_query": "property valuation computer vision", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/1804.02748v2", "title": "Scaling Egocentric Vision: The EPIC-KITCHENS Dataset", "abstract": "First-person vision is gaining interest as it offers a unique viewpoint on people's interaction with objects, their attention, and even intention. However, progress in this challenging domain has been relatively slow due to the lack of sufficiently large datasets. In this paper, we introduce EPIC-KITCHENS, a large-scale egocentric video benchmark recorded by 32 participants in their native kitchen environments. Our videos depict nonscripted daily activities: we simply asked each participant to start recording every time they entered their kitchen. Recording took place in 4 cities (in North America and Europe) by participants belonging to 10 different nationalities, resulting in highly diverse cooking styles. Our dataset features 55 hours of video consisting of 11.5M frames, which we densely labeled for a total of 39.6K action segments and 454.3K object bounding boxes. Our annotation is unique in that we had the participants narrate their own videos (after recording), thus reflecting true intention, and we crowd-sourced ground-truths based on these. We describe our object, action and anticipation challenges, and evaluate several baselines over two test splits, seen and unseen kitchens. Dataset and Project page: http://epic-kitchens.github.io", "published": "2018-04-08T20:07:13Z", "authors": ["Dima Damen", "Hazel Doughty", "Giovanni Maria Farinella", "Sanja Fidler", "Antonino Furnari", "Evangelos Kazakos", "Davide Moltisanti", "Jonathan Munro", "Toby Perrett", "Will Price", "Michael Wray"], "categories": ["cs.CV"], "doi": "", "_query": "property valuation computer vision", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/1709.00069v1", "title": "Learning Inference Models for Computer Vision", "abstract": "Computer vision can be understood as the ability to perform inference on image data. Breakthroughs in computer vision technology are often marked by advances in inference techniques. This thesis proposes novel inference schemes and demonstrates applications in computer vision. We propose inference techniques for both generative and discriminative vision models. The use of generative models in vision is often hampered by the difficulty of posterior inference. We propose techniques for improving inference in MCMC sampling and message-passing inference. Our inference strategy is to learn separate discriminative models that assist Bayesian inference in a generative model. Experiments on a range of generative models show that the proposed techniques accelerate the inference process and/or converge to better solutions. A main complication in the design of discriminative models is the inclusion of prior knowledge. We concentrate on CNN models and propose a generalization of standard spatial convolutions to bilateral convolutions. We generalize the existing use of bilateral filters and then propose new neural network architectures with learnable bilateral filters, which we call `Bilateral Neural Networks'. Experiments demonstrate the use of the bilateral networks on a wide range of image and video tasks and datasets. In summary, we propose techniques for better inference in several vision models ranging from inverse graphics to freely parameterized neural networks. In generative models, our inference techniques alleviate some of the crucial hurdles in Bayesian posterior inference, paving new ways for the use of model based machine learning in vision. In discriminative CNN models, the proposed filter generalizations aid in the design of new neural network architectures that can handle sparse high-dimensional data as well as provide a way to incorporate prior knowledge into CNNs.", "published": "2017-08-31T20:33:06Z", "authors": ["Varun Jampani"], "categories": ["cs.CV"], "doi": "10.15496/publikation-17888", "_query": "property valuation computer vision", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2505.07998v1", "title": "Vision Foundation Model Embedding-Based Semantic Anomaly Detection", "abstract": "Semantic anomalies are contextually invalid or unusual combinations of familiar visual elements that can cause undefined behavior and failures in system-level reasoning for autonomous systems. This work explores semantic anomaly detection by leveraging the semantic priors of state-of-the-art vision foundation models, operating directly on the image. We propose a framework that compares local vision embeddings from runtime images to a database of nominal scenarios in which the autonomous system is deemed safe and performant. In this work, we consider two variants of the proposed framework: one using raw grid-based embeddings, and another leveraging instance segmentation for object-centric representations. To further improve robustness, we introduce a simple filtering mechanism to suppress false positives. Our evaluations on CARLA-simulated anomalies show that the instance-based method with filtering achieves performance comparable to GPT-4o, while providing precise anomaly localization. These results highlight the potential utility of vision embeddings from foundation models for real-time anomaly detection in autonomous systems.", "published": "2025-05-12T19:00:29Z", "authors": ["Max Peter Ronecker", "Matthew Foutter", "Amine Elhafsi", "Daniele Gammelli", "Ihor Barakaiev", "Marco Pavone", "Daniel Watzenig"], "categories": ["cs.CV", "cs.LG"], "doi": "", "_query": "property valuation computer vision", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2111.07624v1", "title": "Attention Mechanisms in Computer Vision: A Survey", "abstract": "Humans can naturally and effectively find salient regions in complex scenes. Motivated by this observation, attention mechanisms were introduced into computer vision with the aim of imitating this aspect of the human visual system. Such an attention mechanism can be regarded as a dynamic weight adjustment process based on features of the input image. Attention mechanisms have achieved great success in many visual tasks, including image classification, object detection, semantic segmentation, video understanding, image generation, 3D vision, multi-modal tasks and self-supervised learning. In this survey, we provide a comprehensive review of various attention mechanisms in computer vision and categorize them according to approach, such as channel attention, spatial attention, temporal attention and branch attention; a related repository https://github.com/MenghaoGuo/Awesome-Vision-Attentions is dedicated to collecting related work. We also suggest future directions for attention mechanism research.", "published": "2021-11-15T09:18:40Z", "authors": ["Meng-Hao Guo", "Tian-Xing Xu", "Jiang-Jiang Liu", "Zheng-Ning Liu", "Peng-Tao Jiang", "Tai-Jiang Mu", "Song-Hai Zhang", "Ralph R. Martin", "Ming-Ming Cheng", "Shi-Min Hu"], "categories": ["cs.CV"], "doi": "10.1007/s41095-022-0271-y", "_query": "property valuation computer vision", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/1212.1095v5", "title": "The projector algorithm: a simple parallel algorithm for computing Voronoi diagrams and Delaunay graphs", "abstract": "The Voronoi diagram is a certain geometric data structure which has numerous applications in various scientific and technological fields. The theory of algorithms for computing 2D Euclidean Voronoi diagrams of point sites is rich and useful, with several different and important algorithms. However, this theory has been quite steady during the last few decades in the sense that no essentially new algorithms have entered the game. In addition, most of the known algorithms are serial in nature and hence cast inherent difficulties on the possibility to compute the diagram in parallel. In this paper we present the projector algorithm: a new and simple algorithm which enables the (combinatorial) computation of 2D Voronoi diagrams. The algorithm is significantly different from previous ones and some of the involved concepts in it are in the spirit of linear programming and optics. Parallel implementation is naturally supported since each Voronoi cell can be computed independently of the other cells. A new combinatorial structure for representing the cells (and any convex polytope) is described along the way and the computation of the induced Delaunay graph is obtained almost automatically.", "published": "2012-12-04T18:17:57Z", "authors": ["Daniel Reem"], "categories": ["cs.CG", "cs.DC", "cs.DS", "math.NA", "physics.comp-ph"], "doi": "10.1016/j.tcs.2023.114054", "_query": "property valuation computer vision", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/1705.04402v3", "title": "Negative Results in Computer Vision: A Perspective", "abstract": "A negative result is when the outcome of an experiment or a model is not what is expected or when a hypothesis does not hold. Despite being often overlooked in the scientific community, negative results are results and they carry value. While this topic has been extensively discussed in other fields such as social sciences and biosciences, less attention has been paid to it in the computer vision community. The unique characteristics of computer vision, particularly its experimental aspect, call for a special treatment of this matter. In this paper, I will address what makes negative results important, how they should be disseminated and incentivized, and what lessons can be learned from cognitive vision research in this regard. Further, I will discuss issues such as computer vision and human vision interaction, experimental design and statistical hypothesis testing, explanatory versus predictive modeling, performance evaluation, model comparison, as well as computer vision research culture.", "published": "2017-05-11T23:39:18Z", "authors": ["Ali Borji"], "categories": ["cs.CV"], "doi": "", "_query": "property valuation computer vision", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/1211.4907v2", "title": "Mahotas: Open source software for scriptable computer vision", "abstract": "Mahotas is a computer vision library for Python. It contains traditional image processing functionality such as filtering and morphological operations as well as more modern computer vision functions for feature computation, including interest point detection and local descriptors. The interface is in Python, a dynamic programming language, which is very appropriate for fast development, but the algorithms are implemented in C++ and are tuned for speed. The library is designed to fit in with the scientific software ecosystem in this language and can leverage the existing infrastructure developed in that language. Mahotas is released under a liberal open source license (MIT License) and is available from (http://github.com/luispedro/mahotas) and from the Python Package Index (http://pypi.python.org/pypi/mahotas).", "published": "2012-11-21T00:51:10Z", "authors": ["Luis Pedro Coelho"], "categories": ["cs.CV", "cs.SE"], "doi": "10.5334/jors.ac", "_query": "property valuation computer vision", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2609.39492v2", "title": "Front-to-Back: Benchmarking Vision-Language Models for Asymmetric Cross-View Vehicle Re-Identification", "abstract": "Matching the same vehicle across front and rear cameras is difficult because the cameras do not share a view and the vehicle's appearance changes substantially. We introduce Front2Back-ReID, a benchmark of 500 manually verified vehicle handovers from 20 recording sequences in South Africa. Each example asks a model to match a vehicle highlighted in a front-camera image to the same vehicle among at least three candidates in a later rear-camera image. We evaluate seven zero-shot vision-language models, four image-retrieval baselines, and 25 human participants. Models are tested using full front RGB images, cropped target vehicles, and binary silhouettes. The strongest VLM achieved 76.6 percent Rank-1 accuracy on target crops, compared with 74.0 percent for the frozen SigLIP2 baseline; this difference was not statistically clear. Human participants achieved 94.0 percent accuracy with full images and 92.2 percent with target crops. Under our evaluation setup, enabling reasoning improved accuracy across all three input conditions for every model evaluated in both modes. We also found that VLMs generally performed worse on full scenes than on target crops. These results show that general-purpose VLMs do not yet consistently outperform strong visual retrieval for front-to-rear vehicle matching, while humans remain substantially more reliable.", "published": "2026-09-30T10:53:40Z", "authors": ["Moseli Mots'oehli", "Thulani Babeli"], "categories": ["cs.CV"], "doi": "", "_query": "property valuation computer vision", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2411.16018v1", "title": "Style-Pro: Style-Guided Prompt Learning for Generalizable Vision-Language Models", "abstract": "Pre-trained Vision-language (VL) models, such as CLIP, have shown significant generalization ability to downstream tasks, even with minimal fine-tuning. While prompt learning has emerged as an effective strategy to adapt pre-trained VL models for downstream tasks, current approaches frequently encounter severe overfitting to specific downstream data distributions. This overfitting constrains the original behavior of the VL models to generalize to new domains or unseen classes, posing a critical challenge in enhancing the adaptability and generalization of VL models. To address this limitation, we propose Style-Pro, a novel style-guided prompt learning framework that mitigates overfitting and preserves the zero-shot generalization capabilities of CLIP. Style-Pro employs learnable style bases to synthesize diverse distribution shifts, guided by two specialized loss functions that ensure style diversity and content integrity. Then, to minimize discrepancies between unseen domains and the source domain, Style-Pro maps the unseen styles into the known style representation space as a weighted combination of style bases. Moreover, to maintain consistency between the style-shifted prompted model and the original frozen CLIP, Style-Pro introduces consistency constraints to preserve alignment in the learned embeddings, minimizing deviation during adaptation to downstream tasks. Extensive experiments across 11 benchmark datasets demonstrate the effectiveness of Style-Pro, consistently surpassing state-of-the-art methods in various settings, including base-to-new generalization, cross-dataset transfer, and domain generalization.", "published": "2024-11-25T00:20:53Z", "authors": ["Niloufar Alipour Talemi", "Hossein Kashiani", "Fatemeh Afghah"], "categories": ["cs.CV"], "doi": "", "_query": "property valuation computer vision", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2506.18985v3", "title": "GLIMPSE: Holistic Cross-Modal Explainability for Large Vision-Language Models", "abstract": "Recent large vision-language models (LVLMs) have advanced capabilities in visual question answering (VQA). However, interpreting where LVLMs direct their visual attention remains a significant challenge, yet is essential for understanding model behavior. We introduce GLIMPSE (Gradient-Layer Importance Mapping for Prompted Visual Saliency Explanation), a lightweight, model-agnostic framework that jointly attributes LVLM outputs to the most relevant visual evidence and textual signals that support open-ended generation. GLIMPSE fuses gradient-weighted attention, adaptive layer propagation, and relevance-weighted token aggregation to produce holistic response-level heat maps for interpreting cross-modal reasoning, outperforming prior methods in faithfulness and pushing the state-of-the-art in human-attention alignment. We demonstrate an analytic approach to uncover fine-grained insights into LVLM cross-modal attribution, trace reasoning dynamics, analyze systematic misalignment, diagnose hallucination and bias, and ensure transparency.", "published": "2025-06-23T18:00:04Z", "authors": ["Guanxi Shen"], "categories": ["cs.CV", "cs.AI"], "doi": "", "_query": "property valuation computer vision", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2301.13514v1", "title": "Fourier Sensitivity and Regularization of Computer Vision Models", "abstract": "Recent work has empirically shown that deep neural networks latch on to the Fourier statistics of training data and show increased sensitivity to Fourier-basis directions in the input. Understanding and modifying this Fourier-sensitivity of computer vision models may help improve their robustness. Hence, in this paper we study the frequency sensitivity characteristics of deep neural networks using a principled approach. We first propose a basis trick, proving that unitary transformations of the input-gradient of a function can be used to compute its gradient in the basis induced by the transformation. Using this result, we propose a general measure of any differentiable model's Fourier-sensitivity using the unitary Fourier-transform of its input-gradient. When applied to deep neural networks, we find that computer vision models are consistently sensitive to particular frequencies dependent on the dataset, training method and architecture. Based on this measure, we further propose a Fourier-regularization framework to modify the Fourier-sensitivities and frequency bias of models. Using our proposed regularizer-family, we demonstrate that deep neural networks obtain improved classification accuracy on robustness evaluations.", "published": "2023-01-31T10:05:35Z", "authors": ["Kiran Krishnamachari", "See-Kiong Ng", "Chuan-Sheng Foo"], "categories": ["cs.CV", "cs.AI", "cs.LG"], "doi": "", "_query": "property valuation computer vision", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2606.16870v1", "title": "Latent Space Reinforcement Learning for Inverse Material Estimation in Food Fracture Simulation", "abstract": "Realistic visual simulation of food manipulation requires accurate material parameters, yet these are difficult to measure directly and vary across the heterogeneous regions of a single food item. We address the inverse problem of estimating material parameters from a target description of fracture behavior in a non-differentiable continuum damage mechanics simulator. Using orange peeling as a test case, we train a neural surrogate on 2,000 forward simulations and compare Covariance Matrix Adaptation Evolution Strategy (CMA-ES, a gradient-free evolutionary optimizer) with Proximal Policy Optimization (PPO, a reinforcement learning algorithm) across the original 9-dimensional parameter space and two learned 4-dimensional latent representations. Since different oranges have different material properties, a practical inverse system must handle arbitrary targets without retraining. We train a goal-conditioned PPO policy that learns a general inverse mapping: given any target description of peeling behavior, the policy produces a material parameter estimate in a single forward pass (8 surrogate evaluations, approximately 10ms). Operating in a normalizing flow latent space with a shared surrogate evaluator, the goal-conditioned policy achieves 0.642 actual recovery when validated through the simulator, outperforming the original parameter space by 23%. A warm-start extension that initializes CMA-ES refinement from the policy's output further improves recovery to 0.828 with 540 evaluations. These findings provide a practical framework for inverse food physics and lay groundwork for vision-driven material identification from video observations of food manipulation.", "published": "2026-06-15T15:47:37Z", "authors": ["Adrian Ramlal", "Yuhao Chen", "John S. Zelek"], "categories": ["cs.CV", "cs.GR"], "doi": "", "_query": "property valuation computer vision", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2406.13008v1", "title": "ClaudesLens: Uncertainty Quantification in Computer Vision Models", "abstract": "In a world where more decisions are made using artificial intelligence, it is of utmost importance to ensure these decisions are well-grounded. Neural networks are the modern building blocks for artificial intelligence. Modern neural network-based computer vision models are often used for object classification tasks. Correctly classifying objects with \\textit{certainty} has become of great importance in recent times. However, quantifying the inherent \\textit{uncertainty} of the output from neural networks is a challenging task. Here we show a possible method to quantify and evaluate the uncertainty of the output of different computer vision models based on Shannon entropy. By adding perturbation of different levels, on different parts, ranging from the input to the parameters of the network, one introduces entropy to the system. By quantifying and evaluating the perturbed models on the proposed PI and PSI metrics, we can conclude that our theoretical framework can grant insight into the uncertainty of predictions of computer vision models. We believe that this theoretical framework can be applied to different applications for neural networks. We believe that Shannon entropy may eventually have a bigger role in the SOTA (State-of-the-art) methods to quantify uncertainty in artificial intelligence. One day we might be able to apply Shannon entropy to our neural systems.", "published": "2024-06-18T18:58:54Z", "authors": ["Mohamad Al Shaar", "Nils Ekström", "Gustav Gille", "Reza Rezvan", "Ivan Wely"], "categories": ["cs.CV", "cs.AI"], "doi": "", "_query": "property valuation computer vision", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2306.04225v2", "title": "Efficient Vision Transformer for Human Pose Estimation via Patch Selection", "abstract": "While Convolutional Neural Networks (CNNs) have been widely successful in 2D human pose estimation, Vision Transformers (ViTs) have emerged as a promising alternative to CNNs, boosting state-of-the-art performance. However, the quadratic computational complexity of ViTs has limited their applicability for processing high-resolution images. In this paper, we propose three methods for reducing ViT's computational complexity, which are based on selecting and processing a small number of most informative patches while disregarding others. The first two methods leverage a lightweight pose estimation network to guide the patch selection process, while the third method utilizes a set of learnable joint tokens to ensure that the selected patches contain the most important information about body joints. Experiments across six benchmarks show that our proposed methods achieve a significant reduction in computational complexity, ranging from 30% to 44%, with only a minimal drop in accuracy between 0% and 3.5%.", "published": "2023-06-07T08:02:17Z", "authors": ["Kaleab A. Kinfu", "Rene Vidal"], "categories": ["cs.CV", "cs.LG"], "doi": "", "_query": "property valuation computer vision", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2509.20777v1", "title": "CompressAI-Vision: Open-source software to evaluate compression methods for computer vision tasks", "abstract": "With the increasing use of neural network (NN)-based computer vision applications that process image and video data as input, interest has emerged in video compression technology optimized for computer vision tasks. In fact, given the variety of vision tasks, associated NN models and datasets, a consolidated platform is needed as a common ground to implement and evaluate compression methods optimized for downstream vision tasks. CompressAI-Vision is introduced as a comprehensive evaluation platform where new coding tools compete to efficiently compress the input of vision network while retaining task accuracy in the context of two different inference scenarios: \"remote\" and \"split\" inferencing. Our study showcases various use cases of the evaluation platform incorporated with standard codecs (under development) by examining the compression gain on several datasets in terms of bit-rate versus task accuracy. This evaluation platform has been developed as open-source software and is adopted by the Moving Pictures Experts Group (MPEG) for the development the Feature Coding for Machines (FCM) standard. The software is available publicly at https://github.com/InterDigitalInc/CompressAI-Vision.", "published": "2025-09-25T06:01:55Z", "authors": ["Hyomin Choi", "Heeji Han", "Chris Rosewarne", "Fabien Racapé"], "categories": ["cs.CV", "eess.IV"], "doi": "", "_query": "property valuation computer vision", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2108.00401v2", "title": "Advances in adversarial attacks and defenses in computer vision: A survey", "abstract": "Deep Learning (DL) is the most widely used tool in the contemporary field of computer vision. Its ability to accurately solve complex problems is employed in vision research to learn deep neural models for a variety of tasks, including security critical applications. However, it is now known that DL is vulnerable to adversarial attacks that can manipulate its predictions by introducing visually imperceptible perturbations in images and videos. Since the discovery of this phenomenon in 2013~[1], it has attracted significant attention of researchers from multiple sub-fields of machine intelligence. In [2], we reviewed the contributions made by the computer vision community in adversarial attacks on deep learning (and their defenses) until the advent of year 2018. Many of those contributions have inspired new directions in this area, which has matured significantly since witnessing the first generation methods. Hence, as a legacy sequel of [2], this literature review focuses on the advances in this area since 2018. To ensure authenticity, we mainly consider peer-reviewed contributions published in the prestigious sources of computer vision and machine learning research. Besides a comprehensive literature review, the article also provides concise definitions of technical terminologies for non-experts in this domain. Finally, this article discusses challenges and future outlook of this direction based on the literature reviewed herein and [2].", "published": "2021-08-01T08:54:47Z", "authors": ["Naveed Akhtar", "Ajmal Mian", "Navid Kardan", "Mubarak Shah"], "categories": ["cs.CV", "cs.CR", "cs.CY", "cs.LG"], "doi": "", "_query": "property valuation computer vision", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2310.17559v1", "title": "Instability of computer vision models is a necessary result of the task itself", "abstract": "Adversarial examples resulting from instability of current computer vision models are an extremely important topic due to their potential to compromise any application. In this paper we demonstrate that instability is inevitable due to a) symmetries (translational invariance) of the data, b) the categorical nature of the classification task, and c) the fundamental discrepancy of classifying images as objects themselves. The issue is further exacerbated by non-exhaustive labelling of the training data. Therefore we conclude that instability is a necessary result of how the problem of computer vision is currently formulated. While the problem cannot be eliminated, through the analysis of the causes, we have arrived at ways how it can be partially alleviated. These include i) increasing the resolution of images, ii) providing contextual information for the image, iii) exhaustive labelling of training data, and iv) preventing attackers from frequent access to the computer vision system.", "published": "2023-10-26T16:48:36Z", "authors": ["Oliver Turnbull", "George Cevora"], "categories": ["cs.CV", "cs.AI", "stat.ML"], "doi": "", "_query": "property valuation computer vision", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/1611.07879v1", "title": "Testing submodularity and other properties of valuation functions", "abstract": "We show that for any constant $ε> 0$ and $p \\ge 1$, it is possible to distinguish functions $f : \\{0,1\\}^n \\to [0,1]$ that are submodular from those that are $ε$-far from every submodular function in $\\ell_p$ distance with a constant number of queries. More generally, we extend the testing-by-implicit-learning framework of Diakonikolas et al. (2007) to show that every property of real-valued functions that is well-approximated in $\\ell_2$ distance by a class of $k$-juntas for some $k = O(1)$ can be tested in the $\\ell_p$-testing model with a constant number of queries. This result, combined with a recent junta theorem of Feldman and Vondrak (2016), yields the constant-query testability of submodularity. It also yields constant-query testing algorithms for a variety of other natural properties of valuation functions, including fractionally additive (XOS) functions, OXS functions, unit demand functions, coverage functions, and self-bounding functions.", "published": "2016-11-23T16:51:08Z", "authors": ["Eric Blais", "Abhinav Bommireddi"], "categories": ["cs.DS"], "doi": "", "_query": "property valuation computer vision", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/1904.07714v1", "title": "Low-Power Computer Vision: Status, Challenges, Opportunities", "abstract": "Computer vision has achieved impressive progress in recent years. Meanwhile, mobile phones have become the primary computing platforms for millions of people. In addition to mobile phones, many autonomous systems rely on visual data for making decisions and some of these systems have limited energy (such as unmanned aerial vehicles also called drones and mobile robots). These systems rely on batteries and energy efficiency is critical. This article serves two main purposes: (1) Examine the state-of-the-art for low-power solutions to detect objects in images. Since 2015, the IEEE Annual International Low-Power Image Recognition Challenge (LPIRC) has been held to identify the most energy-efficient computer vision solutions. This article summarizes 2018 winners' solutions. (2) Suggest directions for research as well as opportunities for low-power computer vision.", "published": "2019-04-15T17:48:48Z", "authors": ["Sergei Alyamkin", "Matthew Ardi", "Alexander C. Berg", "Achille Brighton", "Bo Chen", "Yiran Chen", "Hsin-Pai Cheng", "Zichen Fan", "Chen Feng", "Bo Fu", "Kent Gauen", "Abhinav Goel", "Alexander Goncharenko", "Xuyang Guo", "Soonhoi Ha", "Andrew Howard", "Xiao Hu", "Yuanjun Huang", "Donghyun Kang", "Jaeyoun Kim", "Jong Gook Ko", "Alexander Kondratyev", "Junhyeok Lee", "Seungjae Lee", "Suwoong Lee", "Zichao Li", "Zhiyu Liang", "Juzheng Liu", "Xin Liu", "Yang Lu", "Yung-Hsiang Lu", "Deeptanshu Malik", "Hong Hanh Nguyen", "Eunbyung Park", "Denis Repin", "Liang Shen", "Tao Sheng", "Fei Sun", "David Svitov", "George K. Thiruvathukal", "Baiwu Zhang", "Jingchi Zhang", "Xiaopeng Zhang", "Shaojie Zhuo"], "categories": ["cs.CV", "cs.AI", "cs.PF"], "doi": "", "_query": "property valuation computer vision", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2605.22903v1", "title": "Seeing without Looking: Do Vision-Language Benchmarks Really Test Vision?", "abstract": "Benchmark accuracy is often implicitly assumed to reflect grounded visual understanding in vision-language models (VLMs), yet it remains unclear to what extent such scores truly reflect reliance on visual evidence. Motivated by a surprising observation that removing a substantial fraction of image tokens only degrades model performance very slightly on a widely used hallucination benchmark, we systematically investigate this mismatch in a set of open-source VLMs. Our analysis spans multiple levels of granularity, spanning global visual degradation, localized occlusion, question reformulation, answer-space expansion, and decision-level analyses beyond standard accuracy. We further complement these behavioral results with a layer-wise analysis of vision-token geometry. Throughout the experiments, we find that although VLMs do incorporate visual input, their predictions are less sensitive to the loss of fine-grained visual evidence that standard accuracy should have suggested. Even when the final prediction remains unchanged, the model's internal support for the correct answer may already be weakened. We further complement a representation-level analysis, which shows increasing similarity among visual tokens in deeper layers, providing a possible explanation for our findings. Together, these results suggest that current benchmarks are not sufficient to reliably evaluate fine-grained visual grounding in VLMs.", "published": "2026-05-21T17:35:04Z", "authors": ["Zixuan Lan", "Luzhe Sun", "Matthew R. Walter", "Jiawei Zhou"], "categories": ["cs.CV", "cs.AI", "cs.CL"], "doi": "", "_query": "property valuation computer vision", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2609.39142v1", "title": "When Can Text Replace Vision? Structural Bottlenecks in Diagram Reasoning", "abstract": "Can structured text replace vision for diagram reasoning? A wrong answer after textualization can arise because the representation omits information the question needs, or because the solver fails to use information that is present. We introduce a diagnostic protocol to distinguish these explanations. Using the same solver model and generation settings, we compare three input conditions: the original image, question-blind structure extracted by a vision-language model, or gold structure derived from the diagram source. Validity-triggered recovery tests truncation and schema failure, question-relevant fidelity measures preservation of answer-critical structure, and matched edge interventions test the effect of error location. On a reserved holdout of 240 public FlowGen diagrams, evaluated under a frozen protocol, gold structure reaches 87% accuracy while direct vision and learned text both remain below 30%. The aggregate comparison includes source-derived relation labels that may not be printed in the image and uses different learned and gold graph encodings, so it does not isolate extraction error alone. Retrying only invalid extractions makes nearly every public representation schema-valid yet leaves accuracy essentially unchanged. The public learned-text deficit relative to gold more than doubles with structural difficulty. Question-relevant topology predicts correctness better than whole-graph topology. In an exposed intervention study, a single answer-relevant edge edit reduces the primary solver's original-answer accuracy to near zero, while matched irrelevant edits largely preserve it. Supplied structure requires fewer solving tokens than vision, but learned acquisition removes this advantage at single use. These comparisons motivate evaluating acquired text by the answer-relevant evidence it preserves and by the solver's ability to use that representation.", "published": "2026-09-30T07:08:43Z", "authors": ["Yunbei Zhang", "Janet Wang", "Jihun Hamm", "Chandan K Reddy"], "categories": ["cs.CV"], "doi": "", "_query": "property valuation computer vision", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2307.07147v1", "title": "Linking vision and motion for self-supervised object-centric perception", "abstract": "Object-centric representations enable autonomous driving algorithms to reason about interactions between many independent agents and scene features. Traditionally these representations have been obtained via supervised learning, but this decouples perception from the downstream driving task and could harm generalization. In this work we adapt a self-supervised object-centric vision model to perform object decomposition using only RGB video and the pose of the vehicle as inputs. We demonstrate that our method obtains promising results on the Waymo Open perception dataset. While object mask quality lags behind supervised methods or alternatives that use more privileged information, we find that our model is capable of learning a representation that fuses multiple camera viewpoints over time and successfully tracks many vehicles and pedestrians in the dataset. Code for our model is available at https://github.com/wayveai/SOCS.", "published": "2023-07-14T04:21:05Z", "authors": ["Kaylene C. Stocking", "Zak Murez", "Vijay Badrinarayanan", "Jamie Shotton", "Alex Kendall", "Claire Tomlin", "Christopher P. Burgess"], "categories": ["cs.CV"], "doi": "", "_query": "property valuation computer vision", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2303.15919v3", "title": "Fully Hyperbolic Convolutional Neural Networks for Computer Vision", "abstract": "Real-world visual data exhibit intrinsic hierarchical structures that can be represented effectively in hyperbolic spaces. Hyperbolic neural networks (HNNs) are a promising approach for learning feature representations in such spaces. However, current HNNs in computer vision rely on Euclidean backbones and only project features to the hyperbolic space in the task heads, limiting their ability to fully leverage the benefits of hyperbolic geometry. To address this, we present HCNN, a fully hyperbolic convolutional neural network (CNN) designed for computer vision tasks. Based on the Lorentz model, we generalize fundamental components of CNNs and propose novel formulations of the convolutional layer, batch normalization, and multinomial logistic regression. {Experiments on standard vision tasks demonstrate the promising performance of our HCNN framework in both hybrid and fully hyperbolic settings.} Overall, we believe our contributions provide a foundation for developing more powerful HNNs that can better represent complex structures found in image data. Our code is publicly available at https://github.com/kschwethelm/HyperbolicCV.", "published": "2023-03-28T12:20:52Z", "authors": ["Ahmad Bdeir", "Kristian Schwethelm", "Niels Landwehr"], "categories": ["cs.CV", "cs.LG"], "doi": "", "_query": "property valuation computer vision", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/1802.08195v3", "title": "Adversarial Examples that Fool both Computer Vision and Time-Limited Humans", "abstract": "Machine learning models are vulnerable to adversarial examples: small changes to images can cause computer vision models to make mistakes such as identifying a school bus as an ostrich. However, it is still an open question whether humans are prone to similar mistakes. Here, we address this question by leveraging recent techniques that transfer adversarial examples from computer vision models with known parameters and architecture to other models with unknown parameters and architecture, and by matching the initial processing of the human visual system. We find that adversarial examples that strongly transfer across computer vision models influence the classifications made by time-limited human observers.", "published": "2018-02-22T17:40:51Z", "authors": ["Gamaleldin F. Elsayed", "Shreya Shankar", "Brian Cheung", "Nicolas Papernot", "Alex Kurakin", "Ian Goodfellow", "Jascha Sohl-Dickstein"], "categories": ["cs.LG", "cs.CV", "q-bio.NC", "stat.ML"], "doi": "", "_query": "property valuation computer vision", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2111.10659v2", "title": "Are Vision Transformers Robust to Patch Perturbations?", "abstract": "Recent advances in Vision Transformer (ViT) have demonstrated its impressive performance in image classification, which makes it a promising alternative to Convolutional Neural Network (CNN). Unlike CNNs, ViT represents an input image as a sequence of image patches. The patch-based input image representation makes the following question interesting: How does ViT perform when individual input image patches are perturbed with natural corruptions or adversarial perturbations, compared to CNNs? In this work, we study the robustness of ViT to patch-wise perturbations. Surprisingly, we find that ViTs are more robust to naturally corrupted patches than CNNs, whereas they are more vulnerable to adversarial patches. Furthermore, we discover that the attention mechanism greatly affects the robustness of vision transformers. Specifically, the attention module can help improve the robustness of ViT by effectively ignoring natural corrupted patches. However, when ViTs are attacked by an adversary, the attention mechanism can be easily fooled to focus more on the adversarially perturbed patches and cause a mistake. Based on our analysis, we propose a simple temperature-scaling based method to improve the robustness of ViT against adversarial patches. Extensive qualitative and quantitative experiments are performed to support our findings, understanding, and improvement of ViT robustness to patch-wise perturbations across a set of transformer-based architectures.", "published": "2021-11-20T19:00:51Z", "authors": ["Jindong Gu", "Volker Tresp", "Yao Qin"], "categories": ["cs.CV", "eess.IV"], "doi": "", "_query": "property valuation computer vision", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2603.26900v1", "title": "Computer Vision with a Superpixelation Camera", "abstract": "Conventional cameras generate a lot of data that can be challenging to process in resource-constrained applications. Usually, cameras generate data streams on the order of the number of pixels in the image. However, most of this captured data is redundant for many downstream computer vision algorithms. We propose a novel camera design, which we call SuperCam, that adaptively processes captured data by performing superpixel segmentation on the fly. We show that SuperCam performs better than current state-of-the-art superpixel algorithms under memory-constrained situations. We also compare how well SuperCam performs when the compressed data is used for downstream computer vision tasks. Our results demonstrate that the proposed design provides superior output for image segmentation, object detection, and monocular depth estimation in situations where the available memory on the camera is limited. We posit that superpixel segmentation will play a crucial role as more computer vision inference models are deployed in edge devices. SuperCam would allow computer vision engineers to design more efficient systems for these applications.", "published": "2026-03-27T18:19:41Z", "authors": ["Sasidharan Mahalingam", "Rachel Brown", "Atul Ingle"], "categories": ["cs.CV"], "doi": "", "_query": "property valuation computer vision", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2506.10523v2", "title": "HP2C-DT: High-Precision High-Performance Computer-enabled Digital Twin", "abstract": "Digital twins are transforming the way we monitor, analyze, and control physical systems, but designing architectures that balance real-time responsiveness with heavy computational demands remains a challenge. Cloud-based solutions often struggle with latency and resource constraints, while edge-based approaches lack the processing power for complex simulations and data-driven optimizations. To address this problem, we propose the High-Precision High-Performance Computer-enabled Digital Twin (HP2C-DT) reference architecture, which integrates High-Performance Computing (HPC) into the computing continuum. Unlike traditional setups that use HPC only for offline simulations, HP2C-DT makes it an active part of digital twin workflows, dynamically assigning tasks to edge, cloud, or HPC resources based on urgency and computational needs. Furthermore, to bridge the gap between theory and practice, we introduce the HP2C-DT framework, a working implementation that uses COMPSs for seamless workload distribution across diverse infrastructures. We test it in a power grid use case, showing how it reduces communication bandwidth by an order of magnitude through edge-side data aggregation, improves response times by up to 2x via dynamic offloading, and maintains near-ideal strong scaling for compute-intensive workflows across a practical range of resources. These results demonstrate how an HPC-driven approach can push digital twins beyond their current limitations, making them smarter, faster, and more capable of handling real-world complexity.", "published": "2025-06-12T09:38:43Z", "authors": ["E. Iraola", "M. García-Lorenzo", "F. Lordan-Gomis", "F. Rossi", "E. Prieto-Araujo", "R. M. Badia"], "categories": ["cs.DC"], "doi": "10.1016/j.future.2025.108333", "_query": "property valuation computer vision", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2603.26900v1", "title": "Computer Vision with a Superpixelation Camera", "abstract": "Conventional cameras generate a lot of data that can be challenging to process in resource-constrained applications. Usually, cameras generate data streams on the order of the number of pixels in the image. However, most of this captured data is redundant for many downstream computer vision algorithms. We propose a novel camera design, which we call SuperCam, that adaptively processes captured data by performing superpixel segmentation on the fly. We show that SuperCam performs better than current state-of-the-art superpixel algorithms under memory-constrained situations. We also compare how well SuperCam performs when the compressed data is used for downstream computer vision tasks. Our results demonstrate that the proposed design provides superior output for image segmentation, object detection, and monocular depth estimation in situations where the available memory on the camera is limited. We posit that superpixel segmentation will play a crucial role as more computer vision inference models are deployed in edge devices. SuperCam would allow computer vision engineers to design more efficient systems for these applications.", "published": "2026-03-27T18:19:41Z", "authors": ["Sasidharan Mahalingam", "Rachel Brown", "Atul Ingle"], "categories": ["cs.CV"], "doi": "", "_query": "property valuation computer vision", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2405.11903v1", "title": "A comprehensive overview of deep learning techniques for 3D point cloud classification and semantic segmentation", "abstract": "Point cloud analysis has a wide range of applications in many areas such as computer vision, robotic manipulation, and autonomous driving. While deep learning has achieved remarkable success on image-based tasks, there are many unique challenges faced by deep neural networks in processing massive, unordered, irregular and noisy 3D points. To stimulate future research, this paper analyzes recent progress in deep learning methods employed for point cloud processing and presents challenges and potential directions to advance this field. It serves as a comprehensive review on two major tasks in 3D point cloud processing-- namely, 3D shape classification and semantic segmentation.", "published": "2024-05-20T09:33:27Z", "authors": ["Sushmita Sarker", "Prithul Sarker", "Gunner Stone", "Ryan Gorman", "Alireza Tavakkoli", "George Bebis", "Javad Sattarvand"], "categories": ["cs.CV"], "doi": "10.1007/s00138-024-01543-1", "_query": "property valuation computer vision", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2603.06569v2", "title": "Penguin-VL: Exploring the Efficiency Limits of VLM with LLM-based Vision Encoders", "abstract": "Vision Language Model (VLM) development has largely relied on scaling model size, which hinders deployment on compute-constrained mobile and edge devices such as smartphones and robots. In this work, we explore the performance limits of compact (e.g., 2B and 8B) VLMs. We challenge the prevailing practice that state-of-the-art VLMs must rely on vision encoders initialized via massive contrastive pretraining (e.g., CLIP/SigLIP). We identify an objective mismatch: contrastive learning, optimized for discrimination, enforces coarse and category-level invariances that suppress fine-grained visual cues needed for dense captioning and complex VLM reasoning. To address this issue, we present Penguin-VL, whose vision encoder is initialized from a text-only LLM. Our experiments reveal that Penguin-Encoder serves as a superior alternative to traditional contrastive pretraining, unlocking a higher degree of visual fidelity and data efficiency for multimodal understanding. Across various image and video benchmarks, Penguin-VL achieves performance comparable to leading VLMs (e.g., Qwen3-VL) in mathematical reasoning and surpasses them in tasks such as document understanding, visual knowledge, and multi-perspective video understanding. Notably, these gains are achieved with a lightweight architecture, demonstrating that improved visual representation rather than model scaling is the primary driver of performance. Our ablations show that Penguin-Encoder consistently outperforms contrastive-pretrained encoders, preserving fine-grained spatial and temporal cues that are critical for dense perception and complex reasoning. This makes it a strong drop-in alternative for compute-efficient VLMs and enables high performance in resource-constrained settings. Code: https://github.com/tencent-ailab/Penguin-VL", "published": "2026-03-06T18:58:04Z", "authors": ["Boqiang Zhang", "Lei Ke", "Ruihan Yang", "Qi Gao", "Tianyuan Qu", "Rossell Chen", "Dong Yu", " Leoweiliang"], "categories": ["cs.CV"], "doi": "", "_query": "property valuation computer vision", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2609.09417v3", "title": "Vision-language models see more about agriculture than they report, and test-time scaling remains bounded by parametric knowledge", "abstract": "Vision-language models (VLMs) show promise for agricultural diagnosis but fail unpredictably in zero-shot settings. We test whether this stems from poor visual features or an inability to verbalize what the model already perceives, using a benchmark spanning classification and detection drawn from 118 AgML classification and 54 detection datasets. Linear probing shows agricultural features are linearly separable in frozen VLM vision towers, with Gemma-4-31B's encoder reaching F1-macro of 0.60--0.78 across tasks and approaching the vision-only DINOv3 backbone, indicating the bottleneck lies downstream of perception. We then test whether test-time scaling, an iterative in-context skill-memory loop, can close this gap without updating weights. Gains are modest and inconsistent. Skill-memory improves classification accuracy by up to 3.1 points on three of four tasks while regressing on species, with gains concentrated on classes seen during optimization and unseen classes regressing. Detection F1 is effectively unchanged (0.412 to 0.421), though an oracle filtering condition reaches 0.432 F1, revealing real headroom unconstrained prompting fails to surface. These results indicate VLMs see more agricultural features than they report, but test-time scaling alone fails to surface that hidden parametric knowledge, motivating agentic approaches grounded in external, retrievable knowledge.", "published": "2026-09-08T20:15:53Z", "authors": ["Earl Ranario", "Jared Smith", "Lars Lundqvist", "Urmil Jatin Chandarana", "Bharath Sridhar", "J Mason Earles"], "categories": ["cs.CV"], "doi": "", "_query": "property valuation computer vision", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2303.02460v1", "title": "Extended Agriculture-Vision: An Extension of a Large Aerial Image Dataset for Agricultural Pattern Analysis", "abstract": "A key challenge for much of the machine learning work on remote sensing and earth observation data is the difficulty in acquiring large amounts of accurately labeled data. This is particularly true for semantic segmentation tasks, which are much less common in the remote sensing domain because of the incredible difficulty in collecting precise, accurate, pixel-level annotations at scale. Recent efforts have addressed these challenges both through the creation of supervised datasets as well as the application of self-supervised methods. We continue these efforts on both fronts. First, we generate and release an improved version of the Agriculture-Vision dataset (Chiu et al., 2020b) to include raw, full-field imagery for greater experimental flexibility. Second, we extend this dataset with the release of 3600 large, high-resolution (10cm/pixel), full-field, red-green-blue and near-infrared images for pre-training. Third, we incorporate the Pixel-to-Propagation Module Xie et al. (2021b) originally built on the SimCLR framework into the framework of MoCo-V2 Chen et al.(2020b). Finally, we demonstrate the usefulness of this data by benchmarking different contrastive learning approaches on both downstream classification and semantic segmentation tasks. We explore both CNN and Swin Transformer Liu et al. (2021a) architectures within different frameworks based on MoCo-V2. Together, these approaches enable us to better detect key agricultural patterns of interest across a field from aerial imagery so that farmers may be alerted to problematic areas in a timely fashion to inform their management decisions. Furthermore, the release of these datasets will support numerous avenues of research for computer vision in remote sensing for agriculture.", "published": "2023-03-04T17:35:24Z", "authors": ["Jing Wu", "David Pichler", "Daniel Marley", "David Wilson", "Naira Hovakimyan", "Jennifer Hobbs"], "categories": ["cs.CV"], "doi": "", "_query": "property valuation computer vision", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2303.17376v1", "title": "A Study of Autoregressive Decoders for Multi-Tasking in Computer Vision", "abstract": "There has been a recent explosion of computer vision models which perform many tasks and are composed of an image encoder (usually a ViT) and an autoregressive decoder (usually a Transformer). However, most of this work simply presents one system and its results, leaving many questions regarding design decisions and trade-offs of such systems unanswered. In this work, we aim to provide such answers. We take a close look at autoregressive decoders for multi-task learning in multimodal computer vision, including classification, captioning, visual question answering, and optical character recognition. Through extensive systematic experiments, we study the effects of task and data mixture, training and regularization hyperparameters, conditioning type and specificity, modality combination, and more. Importantly, we compare these to well-tuned single-task baselines to highlight the cost incurred by multi-tasking. A key finding is that a small decoder learned on top of a frozen pretrained encoder works surprisingly well. We call this setup locked-image tuning with decoder (LiT-decoder). It can be seen as teaching a decoder to interact with a pretrained vision model via natural language.", "published": "2023-03-30T13:42:58Z", "authors": ["Lucas Beyer", "Bo Wan", "Gagan Madan", "Filip Pavetic", "Andreas Steiner", "Alexander Kolesnikov", "André Susano Pinto", "Emanuele Bugliarello", "Xiao Wang", "Qihang Yu", "Liang-Chieh Chen", "Xiaohua Zhai"], "categories": ["cs.CV", "cs.AI", "cs.LG"], "doi": "", "_query": "property valuation computer vision", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/1806.06541v1", "title": "Partitioning Compute Units in CNN Acceleration for Statistical Memory Traffic Shaping", "abstract": "The design complexity of CNNs has been steadily increasing to improve accuracy. To cope with the massive amount of computation needed for such complex CNNs, the latest solutions utilize blocking of an image over the available dimensions and batching of multiple input images to improve data reuse in the memory hierarchy. While there has been numerous works on maximizing data reuse, only a few studies have focused on the memory bottleneck caused by limited bandwidth. Bandwidth bottleneck can easily occur in CNN acceleration as CNN layers have different sizes with varying computation needs and as batching is typically performed over each CNN layer for an ideal data reuse. In this case, the data transfer demand for a layer can be relatively low or high compared to the computation requirement of the layer, and hence temporal fluctuations in memory access can be induced eventually causing bandwidth problems. In this paper, we first show that there exists a high degree of fluctuation in memory access to computation ratio depending on CNN layers and functions in the layer being processed by the compute units (cores), where the units are tightly synchronized to maximize data reuse. Then we propose a strategy of partitioning the compute units where the cores within each partition process a batch of input data synchronously to maximize data reuse but different partitions run asynchronously. As the partitions stay asynchronous and typically process different CNN layers at any given moment, the memory access traffic sizes of the partitions become statistically shuffled. Thus, the partitioning of compute units and asynchronous use of them make the total memory access traffic size be smoothened over time. We call this smoothing statistical memory traffic shaping, and we show that it can lead to 8.0 percent of performance gain on a commercial 64-core processor when running ResNet-50.", "published": "2018-06-18T08:11:38Z", "authors": ["Daejin Jung", "Sunjung Lee", "Wonjong Rhee", "Jung Ho Ahn"], "categories": ["cs.DC"], "doi": "10.1109/LCA.2017.2773055", "_query": "property valuation computer vision", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2008.02522v1", "title": "Attaining High Bandwidth In Cloud Computing Through SDN-enabled Multi-tree Multicast", "abstract": "Achieving high bandwidth utilization in cloud computing is essential for better network performance. However, it is difficult to attain high bandwidth utilization in cloud computing due to the complex and distributed natures of cloud computing resources. Recently, a growing demand for multicast transmission is perceived in cloud computing, due to the explosive growth of multi-point communication applications, such as video conferencing, online gaming, etc. Nonetheless, the inherent complexity in multicast routing in cloud computing, existing multicast plans failed to produce effective and efficient protocol schemes, which limits the application of multicast communication on the Internet. In this paper, a technique is proposed in how the newly developed network architecture, Software Defined Network (SDN), can promote the design of the multicast protocol and improve the performance of the multicast transmission in the cloud computing. The approach is to use the SDN-cloud Computing-enabled multicast communication scheme with ultra-high bandwidth utilization. The bandwidth utilization is enhanced by measuring various routing trees for each multicast transmission session and distributing the traffic load over all available routes in the cloud computing resources. The SDN is utilized to tackle with various design hurdles in the cloud computing, including both the current ones with the conventional multicast pattern and the newly emerged ones with multi-tree multicast. The prototype implementation and experiments demonstrate the performance enhancement of the proposed approach in the cloud computing in compared to conventional single-tree multicast designs.", "published": "2020-08-06T08:45:26Z", "authors": ["Sayantan Guha", "Adel Alshamrani"], "categories": ["cs.NI"], "doi": "10.14445/22312803/IJCTT-V68I3P119", "_query": "property valuation computer vision", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2604.26772v1", "title": "TAP into the Patch Tokens: Leveraging Vision Foundation Model Features for AI-Generated Image Detection", "abstract": "Recent methods demonstrate that large-scale pretrained models, such as CLIP vision transformers, effectively detect AI-generated images (AIGIs) from unseen generative models when used as feature extractors. Many state-of-the-art methods for AI-generated image detection build upon the original CLIP-ViT to enhance this generalization. Since CLIP's release, numerous vision foundation models (VFMs) have emerged, incorporating architectural improvements and different training paradigms. Despite these advances, their potential for AIGI detection and AI image forensics remains largely unexplored. In this work, we present a comprehensive benchmark across multiple VFM families, covering diverse pretraining objectives, input resolutions, and model scales. We systematically evaluate their out-of-the-box performance for detecting fully-generated AI-images and AI-inpainted images, and discover that the best model outperforms the original CLIP by more than 12% in accuracy, beating established approaches in the process. To fully leverage the features of a modern VFM, we propose a simple redesign of the classifier head by utilizing tunable attention pooling (TAP), which aggregates output tokens into a refined global representation. Integrating TAP with the latest VFMs yields substantial performance gains across several AIGI detection benchmarks, establishing a new state-of-the-art on two challenging benchmarks for in-the-wild detection of AI-generated and -inpainted images.", "published": "2026-04-29T15:03:25Z", "authors": ["Ahmed Abdullah", "Nikolas Ebert", "Oliver Wasenmüller"], "categories": ["cs.CV"], "doi": "", "_query": "property valuation computer vision", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/1907.09233v1", "title": "Adapting Computer Vision Algorithms for Omnidirectional Video", "abstract": "Omnidirectional (360°) video has got quite popular because it provides a highly immersive viewing experience. For computer vision algorithms, it poses several challenges, like the special (equirectangular) projection commonly employed and the huge image size. In this work, we give a high-level overview of these challenges and outline strategies how to adapt computer vision algorithm for the specifics of omnidirectional video.", "published": "2019-07-22T11:12:35Z", "authors": ["Hannes Fassold"], "categories": ["cs.CV"], "doi": "", "_query": "property valuation computer vision", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2302.08242v1", "title": "Tuning computer vision models with task rewards", "abstract": "Misalignment between model predictions and intended usage can be detrimental for the deployment of computer vision models. The issue is exacerbated when the task involves complex structured outputs, as it becomes harder to design procedures which address this misalignment. In natural language processing, this is often addressed using reinforcement learning techniques that align models with a task reward. We adopt this approach and show its surprising effectiveness across multiple computer vision tasks, such as object detection, panoptic segmentation, colorization and image captioning. We believe this approach has the potential to be widely useful for better aligning models with a diverse range of computer vision tasks.", "published": "2023-02-16T11:49:48Z", "authors": ["André Susano Pinto", "Alexander Kolesnikov", "Yuge Shi", "Lucas Beyer", "Xiaohua Zhai"], "categories": ["cs.CV"], "doi": "", "_query": "property valuation computer vision", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2512.12307v5", "title": "MRD: Using Physically Based Differentiable Rendering to Probe Vision Models for 3D Scene Understanding", "abstract": "While deep learning methods have achieved impressive success in many vision benchmarks, it remains difficult to understand and explain the representations and decisions of these models. Though vision models are typically trained on 2D inputs, they are often assumed to develop an implicit representation of the underlying 3D scene (for example, showing tolerance to partial occlusion, or the ability to reason about relative depth). Here, we introduce MRD (metamers rendered differentiably), an approach that uses physically based differentiable rendering to probe vision models' implicit understanding of generative 3D scene properties, by finding 3D scene parameters that are physically different but produce the same model activation (i.e. are model metamers). Unlike previous pixel-based methods for evaluating model representations, these reconstruction results are always grounded in physical scene descriptions. This means we can, for example, probe a model's sensitivity to object shape while holding material and lighting constant. As a proof-of-principle, we assess multiple models in their ability to recover scene parameters of geometry (shape) and bidirectional reflectance distribution function (material). The results show high similarity in model activation between target and optimized scenes, with varying visual results. Qualitatively, these reconstructions help investigate the physical scene attributes to which models are sensitive or invariant. MRD holds promise for advancing our understanding of both computer and human vision by enabling analysis of how physical scene parameters drive changes in model responses.", "published": "2025-12-13T12:26:57Z", "authors": ["Benjamin Beilharz", "Thomas S. A. Wallis"], "categories": ["cs.CV", "cs.GR"], "doi": "", "_query": "property valuation computer vision", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2104.14124v1", "title": "Condensation-Net: Memory-Efficient Network Architecture with Cross-Channel Pooling Layers and Virtual Feature Maps", "abstract": "\"Lightweight convolutional neural networks\" is an important research topic in the field of embedded vision. To implement image recognition tasks on a resource-limited hardware platform, it is necessary to reduce the memory size and the computational cost. The contribution of this paper is stated as follows. First, we propose an algorithm to process a specific network architecture (Condensation-Net) without increasing the maximum memory storage for feature maps. The architecture for virtual feature maps saves 26.5% of memory bandwidth by calculating the results of cross-channel pooling before storing the feature map into the memory. Second, we show that cross-channel pooling can improve the accuracy of object detection tasks, such as face detection, because it increases the number of filter weights. Compared with Tiny-YOLOv2, the improvement of accuracy is 2.0% for quantized networks and 1.5% for full-precision networks when the false-positive rate is 0.1. Last but not the least, the analysis results show that the overhead to support the cross-channel pooling with the proposed hardware architecture is negligible small. The extra memory cost to support Condensation-Net is 0.2% of the total size, and the extra gate count is only 1.0% of the total size.", "published": "2021-04-29T05:44:02Z", "authors": ["Tse-Wei Chen", "Motoki Yoshinaga", "Hongxing Gao", "Wei Tao", "Dongchao Wen", "Junjie Liu", "Kinya Osa", "Masami Kato"], "categories": ["cs.CV", "cs.AR"], "doi": "10.1109/CVPRW.2019.00024", "_query": "property valuation computer vision", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/1909.13579v1", "title": "Meta-learning algorithms for Few-Shot Computer Vision", "abstract": "Few-Shot Learning is the challenge of training a model with only a small amount of data. Many solutions to this problem use meta-learning algorithms, i.e. algorithms that learn to learn. By sampling few-shot tasks from a larger dataset, we can teach these algorithms to solve new, unseen tasks. This document reports my work on meta-learning algorithms for Few-Shot Computer Vision. This work was done during my internship at Sicara, a French company building image recognition solutions for businesses. It contains: 1. an extensive review of the state-of-the-art in few-shot computer vision; 2. a benchmark of meta-learning algorithms for few-shot image classification; 3. the introduction to a novel meta-learning algorithm for few-shot object detection, which is still in development.", "published": "2019-09-30T10:51:16Z", "authors": ["Etienne Bennequin"], "categories": ["cs.CV", "cs.LG"], "doi": "", "_query": "property valuation computer vision", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/1708.07455v2", "title": "Review on Computer Vision Techniques in Emergency Situation", "abstract": "In emergency situations, actions that save lives and limit the impact of hazards are crucial. In order to act, situational awareness is needed to decide what to do. Geolocalized photos and video of the situations as they evolve can be crucial in better understanding them and making decisions faster. Cameras are almost everywhere these days, either in terms of smartphones, installed CCTV cameras, UAVs or others. However, this poses challenges in big data and information overflow. Moreover, most of the time there are no disasters at any given location, so humans aiming to detect sudden situations may not be as alert as needed at any point in time. Consequently, computer vision tools can be an excellent decision support. The number of emergencies where computer vision tools has been considered or used is very wide, and there is a great overlap across related emergency research. Researchers tend to focus on state-of-the-art systems that cover the same emergency as they are studying, obviating important research in other fields. In order to unveil this overlap, the survey is divided along four main axes: the types of emergencies that have been studied in computer vision, the objective that the algorithms can address, the type of hardware needed and the algorithms used. Therefore, this review provides a broad overview of the progress of computer vision covering all sorts of emergencies.", "published": "2017-08-24T15:24:47Z", "authors": ["Laura Lopez-Fuentes", "Joost van de Weijer", "Manuel Gonzalez-Hidalgo", "Harald Skinnemoen", "Andrew D. Bagdanov"], "categories": ["cs.CV"], "doi": "10.1007/s11042-017-5276-7", "_query": "property valuation computer vision", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2407.01400v2", "title": "GalLoP: Learning Global and Local Prompts for Vision-Language Models", "abstract": "Prompt learning has been widely adopted to efficiently adapt vision-language models (VLMs), e.g. CLIP, for few-shot image classification. Despite their success, most prompt learning methods trade-off between classification accuracy and robustness, e.g. in domain generalization or out-of-distribution (OOD) detection. In this work, we introduce Global-Local Prompts (GalLoP), a new prompt learning method that learns multiple diverse prompts leveraging both global and local visual features. The training of the local prompts relies on local features with an enhanced vision-text alignment. To focus only on pertinent features, this local alignment is coupled with a sparsity strategy in the selection of the local features. We enforce diversity on the set of prompts using a new ``prompt dropout'' technique and a multiscale strategy on the local prompts. GalLoP outperforms previous prompt learning methods on accuracy on eleven datasets in different few shots settings and with various backbones. Furthermore, GalLoP shows strong robustness performances in both domain generalization and OOD detection, even outperforming dedicated OOD detection methods. Code and instructions to reproduce our results: https://github.com/MarcLafon/gallop.", "published": "2024-07-01T15:50:15Z", "authors": ["Marc Lafon", "Elias Ramzi", "Clément Rambour", "Nicolas Audebert", "Nicolas Thome"], "categories": ["cs.CV"], "doi": "", "_query": "property valuation computer vision", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2609.07780v2", "title": "DroneGround: Open-Vocabulary Drone Payload Characterization Using Synthetic Data and Grounded Vision-Language Models", "abstract": "Automated drone surveillance has become increasingly important for public safety, critical infrastructure protection,and restricted airspace monitoring. While existing vision-based systems achieve strong performance for drone detection and tracking, reliable payload characterization remains highly challenging under long-range imaging conditions due to limited availability of annotated real-world datasets, and substantial distribution shifts encountered during deployment. Existing approaches formulate payload characterization as a closed-set object detection problem, limiting their ability to recognize previously unseen payloads and generalize beyond the training distribution. To address these challenges, we generate a photorealistic synthetic drone-payload dataset using Unreal Engine 5 and Cosys-AirSim and propose DroneGround: Grounded Vision-Language Payload Characterization, a two-stage framework for robust open-vocabulary payload analysis. DroneGround first employs a YOLO26s detector to localize drones and extract drone-centric image crops, followed by a LoRA-fine-tuned PaliGemma vision-language model that generates seman- tic descriptions of the detected drones and their attached payloads, enabling open-vocabulary payload characterization beyond predefined categories. An occlusion-based grounding module further provides interpretable payload localization by identifying image regions responsible for the generated descriptions. Extensive experiments on both synthetic and real-world drone imagery demonstrate that DroneGround substantially improves robustness under synthetic-to-real distribution shifts, outperforming a conventional closed-set payload detector by improving the F1-score from 82.5% to 96.3%, while achieving significantly better generalization to previously unseen payload categories (80.4%versus 42.7% F1). Dataset and code will be released upon request.", "published": "2026-09-07T17:28:53Z", "authors": ["Ami Pandat", "Rajasekhar Punna", "Gopika Vinod", "Rohit Shukla"], "categories": ["cs.CV"], "doi": "", "_query": "property valuation computer vision", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2511.08748v1", "title": "WiCV at CVPR 2025: The Women in Computer Vision Workshop", "abstract": "The Women in Computer Vision Workshop (WiCV@CVPR 2025) was held in conjunction with the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR 2025) in Nashville, Tennessee, United States. This report presents an overview of the workshop program, participation statistics, mentorship outcomes, and historical trends from previous WiCV editions. The goal is to document the impact and evolution of WiCV as a reference for future editions and for other initiatives aimed at advancing diversity, equity, and inclusion within the AI and computer vision communities. WiCV@CVPR 2025 marked the 16th edition of this long-standing event dedicated to increasing the visibility, inclusion, and professional growth of women and underrepresented minorities in the computer vision community. This year's workshop featured 14 accepted papers in the CVPR Workshop Proceedings out of 32 full-paper submissions. Five of these were selected for oral presentations, while all 14 were also presented as posters, along with 36 extended abstract posters accepted from 62 short-paper submissions, which are not included in the proceedings. The mentoring program matched 80 mentees with 37 mentors from both academia and industry. The 2025 edition attracted over 100 onsite participants, fostering rich technical and networking interactions across all career stages. Supported by 10 sponsors and approximately $44,000 USD in travel grants and diversity awards, WiCV continued its mission to empower emerging researchers and amplify diverse voices in computer vision.", "published": "2025-11-11T20:08:10Z", "authors": ["Estefania Talavera", "Deblina Bhattacharjee", "Himangi Mittal", "Mengwei Ren", "Karen Sanchez", "Carla Muntean", "JungEun Kim", "Mona Jalal"], "categories": ["cs.CV"], "doi": "", "_query": "property valuation computer vision", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2512.00714v1", "title": "Deep Learning-Based Computer Vision Models for Early Cancer Detection Using Multimodal Medical Imaging and Radiogenomic Integration Frameworks", "abstract": "Early cancer detection remains one of the most critical challenges in modern healthcare, where delayed diagnosis significantly reduces survival outcomes. Recent advancements in artificial intelligence, particularly deep learning, have enabled transformative progress in medical imaging analysis. Deep learning-based computer vision models, such as convolutional neural networks (CNNs), transformers, and hybrid attention architectures, can automatically extract complex spatial, morphological, and temporal patterns from multimodal imaging data including MRI, CT, PET, mammography, histopathology, and ultrasound. These models surpass traditional radiological assessment by identifying subtle tissue abnormalities and tumor microenvironment variations invisible to the human eye. At a broader scale, the integration of multimodal imaging with radiogenomics linking quantitative imaging features with genomics, transcriptomics, and epigenetic biomarkers has introduced a new paradigm for personalized oncology. This radiogenomic fusion allows the prediction of tumor genotype, immune response, molecular subtypes, and treatment resistance without invasive biopsies.", "published": "2025-11-30T03:28:48Z", "authors": ["Emmanuella Avwerosuoghene Oghenekaro"], "categories": ["cs.CV", "cs.AI"], "doi": "10.7753/IJCATR1411.1001", "_query": "property valuation computer vision", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2305.06611v1", "title": "Hyperbolic Deep Learning in Computer Vision: A Survey", "abstract": "Deep representation learning is a ubiquitous part of modern computer vision. While Euclidean space has been the de facto standard manifold for learning visual representations, hyperbolic space has recently gained rapid traction for learning in computer vision. Specifically, hyperbolic learning has shown a strong potential to embed hierarchical structures, learn from limited samples, quantify uncertainty, add robustness, limit error severity, and more. In this paper, we provide a categorization and in-depth overview of current literature on hyperbolic learning for computer vision. We research both supervised and unsupervised literature and identify three main research themes in each direction. We outline how hyperbolic learning is performed in all themes and discuss the main research problems that benefit from current advances in hyperbolic learning for computer vision. Moreover, we provide a high-level intuition behind hyperbolic geometry and outline open research questions to further advance research in this direction.", "published": "2023-05-11T07:14:23Z", "authors": ["Pascal Mettes", "Mina Ghadimi Atigh", "Martin Keller-Ressel", "Jeffrey Gu", "Serena Yeung"], "categories": ["cs.CV"], "doi": "", "_query": "property valuation computer vision", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2004.09420v2", "title": "Computer Vision For COVID-19 Control: A Survey", "abstract": "The COVID-19 pandemic has triggered an urgent need to contribute to the fight against an immense threat to the human population. Computer Vision, as a subfield of Artificial Intelligence, has enjoyed recent success in solving various complex problems in health care and has the potential to contribute to the fight of controlling COVID-19. In response to this call, computer vision researchers are putting their knowledge base at work to devise effective ways to counter COVID-19 challenge and serve the global community. New contributions are being shared with every passing day. It motivated us to review the recent work, collect information about available research resources and an indication of future research directions. We want to make it available to computer vision researchers to save precious time. This survey paper is intended to provide a preliminary review of the available literature on the computer vision efforts against COVID-19 pandemic.", "published": "2020-04-15T05:43:52Z", "authors": ["Anwaar Ulhaq", "Asim Khan", "Douglas Gomes", "Manoranjan Paul"], "categories": ["eess.IV", "cs.CV"], "doi": "10.1109/ACCESS.2020.3027685", "_query": "property valuation computer vision", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2109.08880v1", "title": "Computational Imaging and Artificial Intelligence: The Next Revolution of Mobile Vision", "abstract": "Signal capture stands in the forefront to perceive and understand the environment and thus imaging plays the pivotal role in mobile vision. Recent explosive progresses in Artificial Intelligence (AI) have shown great potential to develop advanced mobile platforms with new imaging devices. Traditional imaging systems based on the \"capturing images first and processing afterwards\" mechanism cannot meet this unprecedented demand. Differently, Computational Imaging (CI) systems are designed to capture high-dimensional data in an encoded manner to provide more information for mobile vision systems.Thanks to AI, CI can now be used in real systems by integrating deep learning algorithms into the mobile vision platform to achieve the closed loop of intelligent acquisition, processing and decision making, thus leading to the next revolution of mobile vision.Starting from the history of mobile vision using digital cameras, this work first introduces the advances of CI in diverse applications and then conducts a comprehensive review of current research topics combining CI and AI. Motivated by the fact that most existing studies only loosely connect CI and AI (usually using AI to improve the performance of CI and only limited works have deeply connected them), in this work, we propose a framework to deeply integrate CI and AI by using the example of self-driving vehicles with high-speed communication, edge computing and traffic planning. Finally, we outlook the future of CI plus AI by investigating new materials, brain science and new computing techniques to shed light on new directions of mobile vision systems.", "published": "2021-09-18T08:47:08Z", "authors": ["Jinli Suo", "Weihang Zhang", "Jin Gong", "Xin Yuan", "David J. Brady", "Qionghai Dai"], "categories": ["cs.CV", "cs.AI", "eess.IV"], "doi": "10.1109/JPROC.2023.3338272", "_query": "property valuation computer vision", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2212.02687v3", "title": "Vision Transformer Computation and Resilience for Dynamic Inference", "abstract": "State-of-the-art deep learning models for computer vision tasks are based on the transformer architecture and often deployed in real-time applications. In this scenario, the resources available for every inference can vary, so it is useful to be able to dynamically adapt execution to trade accuracy for efficiency. To create dynamic models, we leverage the resilience of vision transformers to pruning and switch between different scaled versions of a model. Surprisingly, we find that most FLOPs are generated by convolutions, not attention. These relative FLOP counts are not a good predictor of GPU performance since GPUs have special optimizations for convolutions. Some models are fairly resilient and their model execution can be adapted without retraining, while all models achieve better accuracy with retraining alternative execution paths. These insights mean that we can leverage CNN accelerators and these alternative execution paths to enable efficient and dynamic vision transformer inference. Our analysis shows that leveraging this type of dynamic execution can lead to saving 28\\% of energy with a 1.4\\% accuracy drop for SegFormer (63 GFLOPs), with no additional training, and 53\\% of energy for ResNet-50 (4 GFLOPs) with a 3.3\\% accuracy drop by switching between pretrained Once-For-All models.", "published": "2022-12-06T01:10:31Z", "authors": ["Kavya Sreedhar", "Jason Clemons", "Rangharajan Venkatesan", "Stephen W. Keckler", "Mark Horowitz"], "categories": ["cs.CV", "cs.AR"], "doi": "", "_query": "property valuation computer vision", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/1409.4326v2", "title": "Computing the Stereo Matching Cost with a Convolutional Neural Network", "abstract": "We present a method for extracting depth information from a rectified image pair. We train a convolutional neural network to predict how well two image patches match and use it to compute the stereo matching cost. The cost is refined by cross-based cost aggregation and semiglobal matching, followed by a left-right consistency check to eliminate errors in the occluded regions. Our stereo method achieves an error rate of 2.61 % on the KITTI stereo dataset and is currently (August 2014) the top performing method on this dataset.", "published": "2014-09-15T16:54:42Z", "authors": ["Jure Žbontar", "Yann LeCun"], "categories": ["cs.CV", "cs.LG", "cs.NE"], "doi": "10.1109/CVPR.2015.7298767", "_query": "property valuation computer vision", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2502.12357v3", "title": "The Japanese and universally Japanese properties for valuation rings and Prüfer domains", "abstract": "We discuss the Japanese and universally Japanese properties for valuation rings and Prüfer domains. These properties, regarding finiteness of integral closure, have been studied extensively for Noetherian rings, but very rarely, if ever, for non-Noetherian rings. Among other results, we show that for valuation rings and Prüfer domains, the Japanese and universally Japanese properties are equivalent. This result can be seen as a counterpart to Nagata's classical result for Noetherian rings. This result also tells us many non-Noetherian rings, including all absolutely integrally closed valuation rings and Prüfer domains, are universally Japanese.", "published": "2025-02-17T22:46:28Z", "authors": ["Shiji Lyu"], "categories": ["math.AC"], "doi": "", "_query": "property valuation computer vision", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2005.10430v1", "title": "Gender Slopes: Counterfactual Fairness for Computer Vision Models by Attribute Manipulation", "abstract": "Automated computer vision systems have been applied in many domains including security, law enforcement, and personal devices, but recent reports suggest that these systems may produce biased results, discriminating against people in certain demographic groups. Diagnosing and understanding the underlying true causes of model biases, however, are challenging tasks because modern computer vision systems rely on complex black-box models whose behaviors are hard to decode. We propose to use an encoder-decoder network developed for image attribute manipulation to synthesize facial images varying in the dimensions of gender and race while keeping other signals intact. We use these synthesized images to measure counterfactual fairness of commercial computer vision classifiers by examining the degree to which these classifiers are affected by gender and racial cues controlled in the images, e.g., feminine faces may elicit higher scores for the concept of nurse and lower scores for STEM-related concepts. We also report the skewed gender representations in an online search service on profession-related keywords, which may explain the origin of the biases encoded in the models.", "published": "2020-05-21T02:33:28Z", "authors": ["Jungseock Joo", "Kimmo Kärkkäinen"], "categories": ["cs.CV", "cs.AI", "cs.LG"], "doi": "", "_query": "property valuation computer vision", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2503.12344v1", "title": "EXPRESS: An LLM-Generated Explainable Property Valuation System with Neighbor Imputation", "abstract": "The demand for property valuation has attracted significant attention from sellers, buyers, and customers applying for loans. Reviews of existing approaches have revealed shortcomings in terms of not being able to handle missing value situations, as well as lacking interpretability, which means they cannot be used in real-world applications. To address these challenges, we propose an LLM-Generated EXplainable PRopErty valuation SyStem with neighbor imputation called EXPRESS, which provides the customizable missing value imputation technique, and addresses the opaqueness of prediction by providing the feature-wise explanation generated by LLM. The dynamic nearest neighbor search finds similar properties depending on different application scenarios by property configuration set by users (e.g., house age as criteria for the house in rural areas, and locations for buildings in urban areas). Motivated by the human appraisal procedure, we generate feature-wise explanations to provide users with a more intuitive understanding of the prediction results.", "published": "2025-03-16T03:49:52Z", "authors": ["Wei-Wei Du", "Yung-Chien Wang", "Wen-Chih Peng"], "categories": ["cs.LG"], "doi": "", "_query": "property valuation computer vision", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2204.03643v1", "title": "Total Variation Optimization Layers for Computer Vision", "abstract": "Optimization within a layer of a deep-net has emerged as a new direction for deep-net layer design. However, there are two main challenges when applying these layers to computer vision tasks: (a) which optimization problem within a layer is useful?; (b) how to ensure that computation within a layer remains efficient? To study question (a), in this work, we propose total variation (TV) minimization as a layer for computer vision. Motivated by the success of total variation in image processing, we hypothesize that TV as a layer provides useful inductive bias for deep-nets too. We study this hypothesis on five computer vision tasks: image classification, weakly supervised object localization, edge-preserving smoothing, edge detection, and image denoising, improving over existing baselines. To achieve these results we had to address question (b): we developed a GPU-based projected-Newton method which is $37\\times$ faster than existing solutions.", "published": "2022-04-07T17:59:27Z", "authors": ["Raymond A. Yeh", "Yuan-Ting Hu", "Zhongzheng Ren", "Alexander G. Schwing"], "categories": ["cs.CV"], "doi": "", "_query": "property valuation computer vision", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/1607.07405v3", "title": "gvnn: Neural Network Library for Geometric Computer Vision", "abstract": "We introduce gvnn, a neural network library in Torch aimed towards bridging the gap between classic geometric computer vision and deep learning. Inspired by the recent success of Spatial Transformer Networks, we propose several new layers which are often used as parametric transformations on the data in geometric computer vision. These layers can be inserted within a neural network much in the spirit of the original spatial transformers and allow backpropagation to enable end-to-end learning of a network involving any domain knowledge in geometric computer vision. This opens up applications in learning invariance to 3D geometric transformation for place recognition, end-to-end visual odometry, depth estimation and unsupervised learning through warping with a parametric transformation for image reconstruction error.", "published": "2016-07-25T18:57:17Z", "authors": ["Ankur Handa", "Michael Bloesch", "Viorica Patraucean", "Simon Stent", "John McCormac", "Andrew Davison"], "categories": ["cs.CV", "cs.LG"], "doi": "", "_query": "property valuation computer vision", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2001.02366v2", "title": "What can robotics research learn from computer vision research?", "abstract": "The computer vision and robotics research communities are each strong. However progress in computer vision has become turbo-charged in recent years due to big data, GPU computing, novel learning algorithms and a very effective research methodology. By comparison, progress in robotics seems slower. It is true that robotics came later to exploring the potential of learning -- the advantages over the well-established body of knowledge in dynamics, kinematics, planning and control is still being debated, although reinforcement learning seems to offer real potential. However, the rapid development of computer vision compared to robotics cannot be only attributed to the former's adoption of deep learning. In this paper, we argue that the gains in computer vision are due to research methodology -- evaluation under strict constraints versus experiments; bold numbers versus videos.", "published": "2020-01-08T04:32:10Z", "authors": ["Peter Corke", "Feras Dayoub", "David Hall", "John Skinner", "Niko Sünderhauf"], "categories": ["cs.RO", "cs.CV"], "doi": "", "_query": "property valuation computer vision", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/1707.03720v1", "title": "Multiband NFC for High-Throughput Wireless Computer Vision Sensor Network", "abstract": "Vision sensors lie in the heart of computer vision. In many computer vision applications, such as AR/VR, non-contacting near-field communication (NFC) with high throughput is required to transfer information to algorithms. In this work, we proposed a novel NFC system which utilizes multiple frequency bands to achieve high throughput.", "published": "2017-05-28T06:43:29Z", "authors": ["F. Li", "J. Du"], "categories": ["cs.NI", "cs.CV"], "doi": "", "_query": "property valuation computer vision", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/1905.00310v1", "title": "Towards computer vision powered color-nutrient assessment of pureed food", "abstract": "With one in four individuals afflicted with malnutrition, computer vision may provide a way of introducing a new level of automation in the nutrition field to reliably monitor food and nutrient intake. In this study, we present a novel approach to modeling the link between color and vitamin A content using transmittance imaging of a pureed foods dilution series in a computer vision powered nutrient sensing system via a fine-tuned deep autoencoder network, which in this case was trained to predict the relative concentration of sweet potato purees. Experimental results show the deep autoencoder network can achieve an accuracy of 80% across beginner (6 month) and intermediate (8 month) commercially prepared pureed sweet potato samples. Prediction errors may be explained by fundamental differences in optical properties which are further discussed.", "published": "2019-05-01T13:42:19Z", "authors": ["Kaylen J. Pfisterer", "Robert Amelard", "Braeden Syrnyk", "Alexander Wong"], "categories": ["cs.CV", "cs.NE", "eess.IV"], "doi": "", "_query": "property valuation computer vision", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2601.01891v1", "title": "Agentic AI in Remote Sensing: Foundations, Taxonomy, and Emerging Systems", "abstract": "The paradigm of Earth Observation analysis is shifting from static deep learning models to autonomous agentic AI. Although recent vision foundation models and multimodal large language models advance representation learning, they often lack the sequential planning and active tool orchestration required for complex geospatial workflows. This survey presents the first comprehensive review of agentic AI in remote sensing. We introduce a unified taxonomy distinguishing between single-agent copilots and multi-agent systems while analyzing architectural foundations such as planning mechanisms, retrieval-augmented generation, and memory structures. Furthermore, we review emerging benchmarks that move the evaluation from pixel-level accuracy to trajectory-aware reasoning correctness. By critically examining limitations in grounding, safety, and orchestration, this work outlines a strategic roadmap for the development of robust, autonomous geospatial intelligence.", "published": "2026-01-05T08:34:17Z", "authors": ["Niloufar Alipour Talemi", "Julia Boone", "Fatemeh Afghah"], "categories": ["cs.CV"], "doi": "", "_query": "property valuation computer vision", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2105.12754v1", "title": "Computer Vision and Conflicting Values: Describing People with Automated Alt Text", "abstract": "Scholars have recently drawn attention to a range of controversial issues posed by the use of computer vision for automatically generating descriptions of people in images. Despite these concerns, automated image description has become an important tool to ensure equitable access to information for blind and low vision people. In this paper, we investigate the ethical dilemmas faced by companies that have adopted the use of computer vision for producing alt text: textual descriptions of images for blind and low vision people, We use Facebook's automatic alt text tool as our primary case study. First, we analyze the policies that Facebook has adopted with respect to identity categories, such as race, gender, age, etc., and the company's decisions about whether to present these terms in alt text. We then describe an alternative -- and manual -- approach practiced in the museum community, focusing on how museums determine what to include in alt text descriptions of cultural artifacts. We compare these policies, using notable points of contrast to develop an analytic framework that characterizes the particular apprehensions behind these policy choices. We conclude by considering two strategies that seem to sidestep some of these concerns, finding that there are no easy ways to avoid the normative dilemmas posed by the use of computer vision to automate alt text.", "published": "2021-05-26T18:01:16Z", "authors": ["Margot Hanley", "Solon Barocas", "Karen Levy", "Shiri Azenkot", "Helen Nissenbaum"], "categories": ["cs.CY", "cs.AI", "cs.CV"], "doi": "10.1145/3461702.3462620", "_query": "property valuation computer vision", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2607.03346v1", "title": "Efficient Decentralized Multi-task Dataset Valuation via Model Merging", "abstract": "Accurate and efficient dataset valuation is essential for enabling fair and transparent data marketplaces, especially when multiple contributors provide data for training multi-task models. Most existing valuation methods, however, are limited to single-task settings, overlooking scenarios where a buyer aims to optimize performance across multiple downstream tasks. Moreover, traditional valuation approaches, such as Shapley-based or retraining-based methods, are computationally expensive and poorly suited for decentralized environments without a trusted central coordinator and with strict privacy constraints. We propose DMVM (Decentralized Multi-task Valuation via Model Merging), a novel framework that bypasses retraining and data sharing by leveraging task arithmetic to infer dataset contributions directly from model combinations. Instead of retraining or sharing raw data, DMVM quantifies how models trained on different datasets combine in parameter space to infer each dataset's marginal utility across multiple tasks. This formulation yields a valuation process that is scalable, computationally efficient, and explicitly aligned with multi-task generalization behavior. To support decentralized deployment, we introduce a secure aggregation protocol that enables collaborative valuation without revealing individual model parameters or private data. We also provide theoretical error bounds characterizing the approximation quality of DMVM and validate our framework through comprehensive experiments on computer vision and natural language processing tasks.", "published": "2026-07-03T14:01:53Z", "authors": ["Mohammadsajad Alipour", "Mohammad Mohammadi Amiri"], "categories": ["cs.CL", "cs.AI", "cs.CV", "cs.LG"], "doi": "", "_query": "property valuation computer vision", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/1304.3541v1", "title": "A modified dna computing approach to tackle the exponential solution space of the graph coloring problem", "abstract": "Although it has been evidenced that DNA computing is able to solve the graph coloring problem in a polynomial time complexity, but the exponential solution space is still a restrictive factor in applying this technique for solving really large problems. In this paper a modified DNA computing approach based on Adleman-Lipton model is proposed which tackles the mentioned restriction by coloring the vertices one by one. In each step, it expands the DNA strands encoding promising solutions and discards those which encode infeasible ones. A sample graph is colored by simulating the proposed approach and shows a notable reduction in the number of DNA strands used.", "published": "2013-04-12T05:34:44Z", "authors": ["Ramin Maazallahi", "Aliakbar Niknafs"], "categories": ["cs.CC", "cs.DS"], "doi": "10.5121/ijfcst.2013.3201", "_query": "property valuation computer vision", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2107.03436v1", "title": "Tensor Methods in Computer Vision and Deep Learning", "abstract": "Tensors, or multidimensional arrays, are data structures that can naturally represent visual data of multiple dimensions. Inherently able to efficiently capture structured, latent semantic spaces and high-order interactions, tensors have a long history of applications in a wide span of computer vision problems. With the advent of the deep learning paradigm shift in computer vision, tensors have become even more fundamental. Indeed, essential ingredients in modern deep learning architectures, such as convolutions and attention mechanisms, can readily be considered as tensor mappings. In effect, tensor methods are increasingly finding significant applications in deep learning, including the design of memory and compute efficient network architectures, improving robustness to random noise and adversarial attacks, and aiding the theoretical understanding of deep networks. This article provides an in-depth and practical review of tensors and tensor methods in the context of representation learning and deep learning, with a particular focus on visual data analysis and computer vision applications. Concretely, besides fundamental work in tensor-based visual data analysis methods, we focus on recent developments that have brought on a gradual increase of tensor methods, especially in deep learning architectures, and their implications in computer vision applications. To further enable the newcomer to grasp such concepts quickly, we provide companion Python notebooks, covering key aspects of the paper and implementing them, step-by-step with TensorLy.", "published": "2021-07-07T18:42:45Z", "authors": ["Yannis Panagakis", "Jean Kossaifi", "Grigorios G. Chrysos", "James Oldfield", "Mihalis A. Nicolaou", "Anima Anandkumar", "Stefanos Zafeiriou"], "categories": ["cs.CV"], "doi": "10.1109/JPROC.2021.3074329", "_query": "property valuation computer vision", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2411.18224v2", "title": "KANs for Computer Vision: An Experimental Study", "abstract": "This paper presents an experimental study of Kolmogorov-Arnold Networks (KANs) applied to computer vision tasks, particularly image classification. KANs introduce learnable activation functions on edges, offering flexible non-linear transformations compared to traditional pre-fixed activation functions with specific neural work like Multi-Layer Perceptrons (MLPs) and Convolutional Neural Networks (CNNs). While KANs have shown promise mostly in simplified or small-scale datasets, their effectiveness for more complex real-world tasks such as computer vision tasks remains less explored. To fill this gap, this experimental study aims to provide extended observations and insights into the strengths and limitations of KANs. We reveal that although KANs can perform well in specific vision tasks, they face significant challenges, including increased hyperparameter sensitivity and higher computational costs. These limitations suggest that KANs require architectural adaptations, such as integration with other architectures, to be practical for large-scale vision problems. This study focuses on empirical findings rather than proposing new methods, aiming to inform future research on optimizing KANs, in particular computer vision applications or alike.", "published": "2024-11-27T10:59:28Z", "authors": ["Karthik Mohan", "Hanxiao Wang", "Xiatian Zhu"], "categories": ["cs.CV"], "doi": "", "_query": "property valuation computer vision", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2501.10318v1", "title": "HiMix: Reducing Computational Complexity in Large Vision-Language Models", "abstract": "Benefiting from recent advancements in large language models and modality alignment techniques, existing Large Vision-Language Models(LVLMs) have achieved prominent performance across a wide range of scenarios. However, the excessive computational complexity limits the widespread use of these models in practical applications. We argue that one main bottleneck in computational complexity is caused by the involvement of redundant vision sequences in model computation. This is inspired by a reassessment of the efficiency of vision and language information transmission in the language decoder of LVLMs. Then, we propose a novel hierarchical vision-language interaction mechanism called Hierarchical Vision injection for Mixture Attention (HiMix). In HiMix, only the language sequence undergoes full forward propagation, while the vision sequence interacts with the language at specific stages within each language decoder layer. It is striking that our approach significantly reduces computational complexity with minimal performance loss. Specifically, HiMix achieves a 10x reduction in the computational cost of the language decoder across multiple LVLM models while maintaining comparable performance. This highlights the advantages of our method, and we hope our research brings new perspectives to the field of vision-language understanding. Project Page: https://xuange923.github.io/HiMix", "published": "2025-01-17T17:41:47Z", "authors": ["Xuange Zhang", "Dengjie Li", "Bo Liu", "Zenghao Bao", "Yao Zhou", "Baisong Yang", "Zhongying Liu", "Yujie Zhong", "Zheng Zhao", "Tongtong Yuan"], "categories": ["cs.CV"], "doi": "", "_query": "property valuation computer vision", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2203.04913v2", "title": "Leveling Down in Computer Vision: Pareto Inefficiencies in Fair Deep Classifiers", "abstract": "Algorithmic fairness is frequently motivated in terms of a trade-off in which overall performance is decreased so as to improve performance on disadvantaged groups where the algorithm would otherwise be less accurate. Contrary to this, we find that applying existing fairness approaches to computer vision improve fairness by degrading the performance of classifiers across all groups (with increased degradation on the best performing groups). Extending the bias-variance decomposition for classification to fairness, we theoretically explain why the majority of fairness classifiers designed for low capacity models should not be used in settings involving high-capacity models, a scenario common to computer vision. We corroborate this analysis with extensive experimental support that shows that many of the fairness heuristics used in computer vision also degrade performance on the most disadvantaged groups. Building on these insights, we propose an adaptive augmentation strategy that, uniquely, of all methods tested, improves performance for the disadvantaged groups.", "published": "2022-03-09T17:48:33Z", "authors": ["Dominik Zietlow", "Michael Lohaus", "Guha Balakrishnan", "Matthäus Kleindessner", "Francesco Locatello", "Bernhard Schölkopf", "Chris Russell"], "categories": ["cs.CV", "cs.LG"], "doi": "", "_query": "property valuation computer vision", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2602.03973v1", "title": "VLS: Steering Pretrained Robot Policies via Vision-Language Models", "abstract": "Why do pretrained diffusion or flow-matching policies fail when the same task is performed near an obstacle, on a shifted support surface, or amid mild clutter? Such failures rarely reflect missing motor skills; instead, they expose a limitation of imitation learning under train-test shifts, where action generation is tightly coupled to training-specific spatial configurations and task specifications. Retraining or fine-tuning to address these failures is costly and conceptually misaligned, as the required behaviors already exist but cannot be selectively adapted at test time. We propose Vision-Language Steering (VLS), a training-free framework for inference-time adaptation of frozen generative robot policies. VLS treats adaptation as an inference-time control problem, steering the sampling process of a pretrained diffusion or flow-matching policy in response to out-of-distribution observation-language inputs without modifying policy parameters. By leveraging vision-language models to synthesize trajectory-differentiable reward functions, VLS guides denoising toward action trajectories that satisfy test-time spatial and task requirements. Across simulation and real-world evaluations, VLS consistently outperforms prior steering methods, achieving a 31% improvement on CALVIN and a 13% gain on LIBERO-PRO. Real-world deployment on a Franka robot further demonstrates robust inference-time adaptation under test-time spatial and semantic shifts. Project page: https://vision-language-steering.github.io/webpage/", "published": "2026-02-03T19:50:16Z", "authors": ["Shuo Liu", "Ishneet Sukhvinder Singh", "Yiqing Xu", "Jiafei Duan", "Ranjay Krishna"], "categories": ["cs.RO", "cs.CV"], "doi": "", "_query": "property valuation computer vision", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2209.13232v4", "title": "A Survey on Graph Neural Networks and Graph Transformers in Computer Vision: A Task-Oriented Perspective", "abstract": "Graph Neural Networks (GNNs) have gained momentum in graph representation learning and boosted the state of the art in a variety of areas, such as data mining (\\emph{e.g.,} social network analysis and recommender systems), computer vision (\\emph{e.g.,} object detection and point cloud learning), and natural language processing (\\emph{e.g.,} relation extraction and sequence learning), to name a few. With the emergence of Transformers in natural language processing and computer vision, graph Transformers embed a graph structure into the Transformer architecture to overcome the limitations of local neighborhood aggregation while avoiding strict structural inductive biases. In this paper, we present a comprehensive review of GNNs and graph Transformers in computer vision from a task-oriented perspective. Specifically, we divide their applications in computer vision into five categories according to the modality of input data, \\emph{i.e.,} 2D natural images, videos, 3D data, vision + language, and medical images. In each category, we further divide the applications according to a set of vision tasks. Such a task-oriented taxonomy allows us to examine how each task is tackled by different GNN-based approaches and how well these approaches perform. Based on the necessary preliminaries, we provide the definitions and challenges of the tasks, in-depth coverage of the representative approaches, as well as discussions regarding insights, limitations, and future directions.", "published": "2022-09-27T08:10:14Z", "authors": ["Chaoqi Chen", "Yushuang Wu", "Qiyuan Dai", "Hong-Yu Zhou", "Mutian Xu", "Sibei Yang", "Xiaoguang Han", "Yizhou Yu"], "categories": ["cs.CV", "cs.AI", "cs.LG"], "doi": "", "_query": "property valuation computer vision", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2507.18650v1", "title": "Features extraction for image identification using computer vision", "abstract": "This study examines various feature extraction techniques in computer vision, the primary focus of which is on Vision Transformers (ViTs) and other approaches such as Generative Adversarial Networks (GANs), deep feature models, traditional approaches (SIFT, SURF, ORB), and non-contrastive and contrastive feature models. Emphasizing ViTs, the report summarizes their architecture, including patch embedding, positional encoding, and multi-head self-attention mechanisms with which they overperform conventional convolutional neural networks (CNNs). Experimental results determine the merits and limitations of both methods and their utilitarian applications in advancing computer vision.", "published": "2025-07-22T10:43:52Z", "authors": ["Venant Niyonkuru", "Sylla Sekou", "Jimmy Jackson Sinzinkayo"], "categories": ["cs.CV"], "doi": "10.30574/wjarr.2025.27.1.2647", "_query": "property valuation computer vision", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2003.01994v1", "title": "The iCub multisensor datasets for robot and computer vision applications", "abstract": "This document presents novel datasets, constructed by employing the iCub robot equipped with an additional depth sensor and color camera. We used the robot to acquire color and depth information for 210 objects in different acquisition scenarios. At this end, the results were large scale datasets for robot and computer vision applications: object representation, object recognition and classification, and action recognition.", "published": "2020-03-04T10:59:29Z", "authors": ["Murat Kirtay", "Ugo Albanese", "Lorenzo Vannucci", "Guido Schillaci", "Cecilia Laschi", "Egidio Falotico"], "categories": ["cs.RO", "cs.CV", "cs.LG"], "doi": "", "_query": "property valuation computer vision", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2308.10533v1", "title": "Joint learning of images and videos with a single Vision Transformer", "abstract": "In this study, we propose a method for jointly learning of images and videos using a single model. In general, images and videos are often trained by separate models. We propose in this paper a method that takes a batch of images as input to Vision Transformer IV-ViT, and also a set of video frames with temporal aggregation by late fusion. Experimental results on two image datasets and two action recognition datasets are presented.", "published": "2023-08-21T07:38:33Z", "authors": ["Shuki Shimizu", "Toru Tamaki"], "categories": ["cs.CV"], "doi": "", "_query": "property valuation computer vision", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2010.06188v1", "title": "When Wireless Communications Meet Computer Vision in Beyond 5G", "abstract": "This article articulates the emerging paradigm, sitting at the confluence of computer vision and wireless communication, to enable beyond-5G/6G mission-critical applications (autonomous/remote-controlled vehicles, visuo-haptic VR, and other cyber-physical applications). First, drawing on recent advances in machine learning and the availability of non-RF data, vision-aided wireless networks are shown to significantly enhance the reliability of wireless communication without sacrificing spectral efficiency. In particular, we demonstrate how computer vision enables {look-ahead} prediction in a millimeter-wave channel blockage scenario, before the blockage actually happens. From a computer vision perspective, we highlight how radio frequency (RF) based sensing and imaging are instrumental in robustifying computer vision applications against occlusion and failure. This is corroborated via an RF-based image reconstruction use case, showcasing a receiver-side image failure correction resulting in reduced retransmission and latency. Taken together, this article sheds light on the much-needed convergence of RF and non-RF modalities to enable ultra-reliable communication and truly intelligent 6G networks.", "published": "2020-10-13T05:25:35Z", "authors": ["Takayuki Nishio", "Yusuke Koda", "Jihong Park", "Mehdi Bennis", "Klaus Doppler"], "categories": ["cs.CV", "cs.NI"], "doi": "10.1109/MCOMSTD.001.2000047", "_query": "property valuation computer vision", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2202.02312v3", "title": "A Dataset for Interactive Vision-Language Navigation with Unknown Command Feasibility", "abstract": "Vision-language navigation (VLN), in which an agent follows language instruction in a visual environment, has been studied under the premise that the input command is fully feasible in the environment. Yet in practice, a request may not be possible due to language ambiguity or environment changes. To study VLN with unknown command feasibility, we introduce a new dataset Mobile app Tasks with Iterative Feedback (MoTIF), where the goal is to complete a natural language command in a mobile app. Mobile apps provide a scalable domain to study real downstream uses of VLN methods. Moreover, mobile app commands provide instruction for interactive navigation, as they result in action sequences with state changes via clicking, typing, or swiping. MoTIF is the first to include feasibility annotations, containing both binary feasibility labels and fine-grained labels for why tasks are unsatisfiable. We further collect follow-up questions for ambiguous queries to enable research on task uncertainty resolution. Equipped with our dataset, we propose the new problem of feasibility prediction, in which a natural language instruction and multimodal app environment are used to predict command feasibility. MoTIF provides a more realistic app dataset as it contains many diverse environments, high-level goals, and longer action sequences than prior work. We evaluate interactive VLN methods using MoTIF, quantify the generalization ability of current approaches to new app environments, and measure the effect of task feasibility on navigation performance.", "published": "2022-02-04T18:51:50Z", "authors": ["Andrea Burns", "Deniz Arsan", "Sanjna Agrawal", "Ranjitha Kumar", "Kate Saenko", "Bryan A. Plummer"], "categories": ["cs.CL", "cs.CV", "cs.HC"], "doi": "", "_query": "property valuation computer vision", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2105.07333v1", "title": "Computer-Mediated Consent to Sex: The Context of Tinder", "abstract": "This paper reports an interview study about how consent to sexual activity is computer-mediated. The study's context of online dating is chosen due to the prevalence of sexual violence, or nonconsensual sexual activity, that is associated with dating app-use. Participants (n=19) represent a range of gender identities and sexual orientations, and predominantly used the dating app Tinder. Findings reveal two computer-mediated consent processes: consent signaling and affirmative consent. With consent signaling, users employed Tinder's interface to infer and imply agreement to sex without any explicit confirmation before making sexual advances in-person. With affirmative consent, users employed the interface to establish patterns of overt discourse around sex and consent across online and offline modalities. The paper elucidates shortcomings of both computer-mediated consent processes that leave users susceptible to sexual violence and envisions dating apps as potential sexual violence prevention solutions if deliberately designed to mediate consent exchange.", "published": "2021-05-16T02:55:31Z", "authors": ["Douglas Zytko", "Nicholas Furlo", "Bailey Carlin", "Matthew Archer"], "categories": ["cs.HC"], "doi": "10.1145/3449288", "_query": "property valuation computer vision", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2108.11510v1", "title": "Deep Reinforcement Learning in Computer Vision: A Comprehensive Survey", "abstract": "Deep reinforcement learning augments the reinforcement learning framework and utilizes the powerful representation of deep neural networks. Recent works have demonstrated the remarkable successes of deep reinforcement learning in various domains including finance, medicine, healthcare, video games, robotics, and computer vision. In this work, we provide a detailed review of recent and state-of-the-art research advances of deep reinforcement learning in computer vision. We start with comprehending the theories of deep learning, reinforcement learning, and deep reinforcement learning. We then propose a categorization of deep reinforcement learning methodologies and discuss their advantages and limitations. In particular, we divide deep reinforcement learning into seven main categories according to their applications in computer vision, i.e. (i)landmark localization (ii) object detection; (iii) object tracking; (iv) registration on both 2D image and 3D image volumetric data (v) image segmentation; (vi) videos analysis; and (vii) other applications. Each of these categories is further analyzed with reinforcement learning techniques, network design, and performance. Moreover, we provide a comprehensive analysis of the existing publicly available datasets and examine source code availability. Finally, we present some open issues and discuss future research directions on deep reinforcement learning in computer vision", "published": "2021-08-25T23:01:48Z", "authors": ["Ngan Le", "Vidhiwar Singh Rathour", "Kashu Yamazaki", "Khoa Luu", "Marios Savvides"], "categories": ["cs.CV", "cs.AI"], "doi": "", "_query": "property valuation computer vision", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2302.12185v1", "title": "Scaling Up Computer Vision Neural Networks Using Fast Fourier Transform", "abstract": "Deep Learning-based Computer Vision field has recently been trying to explore larger kernels for convolution to effectively scale up Convolutional Neural Networks. Simultaneously, new paradigm of models such as Vision Transformers find it difficult to scale up to larger higher resolution images due to their quadratic complexity in terms of input sequence. In this report, Fast Fourier Transform is utilised in various ways to provide some solutions to these issues.", "published": "2023-02-02T19:19:10Z", "authors": ["Siddharth Agrawal"], "categories": ["cs.CV", "cs.LG"], "doi": "", "_query": "property valuation computer vision", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2306.16410v1", "title": "Towards Language Models That Can See: Computer Vision Through the LENS of Natural Language", "abstract": "We propose LENS, a modular approach for tackling computer vision problems by leveraging the power of large language models (LLMs). Our system uses a language model to reason over outputs from a set of independent and highly descriptive vision modules that provide exhaustive information about an image. We evaluate the approach on pure computer vision settings such as zero- and few-shot object recognition, as well as on vision and language problems. LENS can be applied to any off-the-shelf LLM and we find that the LLMs with LENS perform highly competitively with much bigger and much more sophisticated systems, without any multimodal training whatsoever. We open-source our code at https://github.com/ContextualAI/lens and provide an interactive demo.", "published": "2023-06-28T17:57:10Z", "authors": ["William Berrios", "Gautam Mittal", "Tristan Thrush", "Douwe Kiela", "Amanpreet Singh"], "categories": ["cs.CL", "cs.CV"], "doi": "", "_query": "property valuation computer vision", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2609.39627v1", "title": "Introduction to Computer Vision", "abstract": "This book presents a code-first introduction to computer vision, spanning classical 2D image processing, classical 3D vision, and deep learning. Organized as 44 short chapters across three parts, the book builds each topic from first principles: image arithmetic and morphology; convolution, pyramids, and frequency-domain filtering; feature detection, optical flow, and stereo; projective geometry, camera calibration, and structure from motion; and the full arc of modern deep learning, from a single neuron through convolutional networks, backpropagation, classic architectures, transfer learning, object detection, and semantic and instance segmentation, concluding with engineering considerations like mixed-precision and parallel training. Every technique is implemented directly in Python and NumPy or PyTorch and checked numerically against the corresponding OpenCV or PyTorch library function, so readers see not just the mathematics but its concrete behavior on real and synthetic data. The material was distilled with AI assistance from freely available online course notes, condensing extensive working code into concise mathematical exposition while preserving verified, reproducible results throughout. It is intended as a self-contained reference for students and practitioners who want to understand computer vision algorithms and their Python implementations.", "published": "2026-09-30T12:36:34Z", "authors": ["Stan Birchfield"], "categories": ["cs.CV"], "doi": "", "_query": "property valuation computer vision", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/1809.04659v2", "title": "Are object detection assessment criteria ready for maritime computer vision?", "abstract": "Maritime vessels equipped with visible and infrared cameras can complement other conventional sensors for object detection. However, application of computer vision techniques in maritime domain received attention only recently. The maritime environment offers its own unique requirements and challenges. Assessment of the quality of detections is a fundamental need in computer vision. However, the conventional assessment metrics suitable for usual object detection are deficient in the maritime setting. Thus, a large body of related work in computer vision appears inapplicable to the maritime setting at the first sight. We discuss the problem of defining assessment metrics suitable for maritime computer vision. We consider new bottom edge proximity metrics as assessment metrics for maritime computer vision. These metrics indicate that existing computer vision approaches are indeed promising for maritime computer vision and can play a foundational role in the emerging field of maritime computer vision.", "published": "2018-09-12T20:18:04Z", "authors": ["Dilip K. Prasad", "Huixu Dong", "Deepu Rajan", "Chai Quek"], "categories": ["cs.CV"], "doi": "", "_query": "property valuation computer vision", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2310.00093v3", "title": "DataDAM: Efficient Dataset Distillation with Attention Matching", "abstract": "Researchers have long tried to minimize training costs in deep learning while maintaining strong generalization across diverse datasets. Emerging research on dataset distillation aims to reduce training costs by creating a small synthetic set that contains the information of a larger real dataset and ultimately achieves test accuracy equivalent to a model trained on the whole dataset. Unfortunately, the synthetic data generated by previous methods are not guaranteed to distribute and discriminate as well as the original training data, and they incur significant computational costs. Despite promising results, there still exists a significant performance gap between models trained on condensed synthetic sets and those trained on the whole dataset. In this paper, we address these challenges using efficient Dataset Distillation with Attention Matching (DataDAM), achieving state-of-the-art performance while reducing training costs. Specifically, we learn synthetic images by matching the spatial attention maps of real and synthetic data generated by different layers within a family of randomly initialized neural networks. Our method outperforms the prior methods on several datasets, including CIFAR10/100, TinyImageNet, ImageNet-1K, and subsets of ImageNet-1K across most of the settings, and achieves improvements of up to 6.5% and 4.1% on CIFAR100 and ImageNet-1K, respectively. We also show that our high-quality distilled images have practical benefits for downstream applications, such as continual learning and neural architecture search.", "published": "2023-09-29T19:07:48Z", "authors": ["Ahmad Sajedi", "Samir Khaki", "Ehsan Amjadian", "Lucy Z. Liu", "Yuri A. Lawryshyn", "Konstantinos N. Plataniotis"], "categories": ["cs.CV", "cs.LG"], "doi": "", "_query": "property valuation computer vision", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/1211.4860v1", "title": "Domain Adaptations for Computer Vision Applications", "abstract": "A basic assumption of statistical learning theory is that train and test data are drawn from the same underlying distribution. Unfortunately, this assumption doesn't hold in many applications. Instead, ample labeled data might exist in a particular `source' domain while inference is needed in another, `target' domain. Domain adaptation methods leverage labeled data from both domains to improve classification on unseen data in the target domain. In this work we survey domain transfer learning methods for various application domains with focus on recent work in Computer Vision.", "published": "2012-11-20T20:54:30Z", "authors": ["Oscar Beijbom"], "categories": ["cs.CV", "cs.LG", "stat.ML"], "doi": "", "_query": "property valuation computer vision", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2402.09781v1", "title": "A Comprehensive Review on Computer Vision Analysis of Aerial Data", "abstract": "With the emergence of new technologies in the field of airborne platforms and imaging sensors, aerial data analysis is becoming very popular, capitalizing on its advantages over land data. This paper presents a comprehensive review of the computer vision tasks within the domain of aerial data analysis. While addressing fundamental aspects such as object detection and tracking, the primary focus is on pivotal tasks like change detection, object segmentation, and scene-level analysis. The paper provides the comparison of various hyper parameters employed across diverse architectures and tasks. A substantial section is dedicated to an in-depth discussion on libraries, their categorization, and their relevance to different domain expertise. The paper encompasses aerial datasets, the architectural nuances adopted, and the evaluation metrics associated with all the tasks in aerial data analysis. Applications of computer vision tasks in aerial data across different domains are explored, with case studies providing further insights. The paper thoroughly examines the challenges inherent in aerial data analysis, offering practical solutions. Additionally, unresolved issues of significance are identified, paving the way for future research directions in the field of aerial data analysis.", "published": "2024-02-15T08:10:09Z", "authors": ["Vivek Tetarwal", "Sandeep Kumar"], "categories": ["cs.CV", "cs.IT"], "doi": "", "_query": "property valuation computer vision", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2104.08188v1", "title": "I Find Your Lack of Uncertainty in Computer Vision Disturbing", "abstract": "Neural networks are used for many real world applications, but often they have problems estimating their own confidence. This is particularly problematic for computer vision applications aimed at making high stakes decisions with humans and their lives. In this paper we make a meta-analysis of the literature, showing that most if not all computer vision applications do not use proper epistemic uncertainty quantification, which means that these models ignore their own limitations. We describe the consequences of using models without proper uncertainty quantification, and motivate the community to adopt versions of the models they use that have proper calibrated epistemic uncertainty, in order to enable out of distribution detection. We close the paper with a summary of challenges on estimating uncertainty for computer vision applications and recommendations.", "published": "2021-04-16T15:58:27Z", "authors": ["Matias Valdenegro-Toro"], "categories": ["cs.CV", "cs.LG"], "doi": "", "_query": "property valuation computer vision", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2603.05012v1", "title": "Tell2Adapt: A Unified Framework for Source Free Unsupervised Domain Adaptation via Vision Foundation Model", "abstract": "Source Free Unsupervised Domain Adaptation (SFUDA) is critical for deploying deep learning models across diverse clinical settings. However, existing methods are typically designed for low-gap, specific domain shifts and cannot generalize into a unified, multi-modalities, and multi-target framework, which presents a major barrier to real-world application. To overcome this issue, we introduce Tell2Adapt, a novel SFUDA framework that harnesses the vast, generalizable knowledge of the Vision Foundation Model (VFM). Our approach ensures high-fidelity VFM prompts through Context-Aware Prompts Regularization (CAPR), which robustly translates varied text prompts into canonical instructions. This enables the generation of high-quality pseudo-labels for efficiently adapting the lightweight student model to target domain. To guarantee clinical reliability, the framework incorporates Visual Plausibility Refinement (VPR), which leverages the VFM's anatomical knowledge to re-ground the adapted model's predictions in target image's low-level visual features, effectively removing noise and false positives. We conduct one of the most extensive SFUDA evaluations to date, validating our framework across 10 domain adaptation directions and 22 anatomical targets, including brain, cardiac, polyp, and abdominal targets. Our results demonstrate that Tell2Adapt consistently outperforms existing approaches, achieving SOTA for a unified SFUDA framework in medical image segmentation. Code are avaliable at https://github.com/derekshiii/Tell2Adapt.", "published": "2026-03-05T10:01:22Z", "authors": ["Yulong Shi", "Shijie Li", "Ziyi Li", "Lin Qi"], "categories": ["cs.CV"], "doi": "", "_query": "property valuation computer vision", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2402.15448v1", "title": "Computer Vision for Multimedia Geolocation in Human Trafficking Investigation: A Systematic Literature Review", "abstract": "The task of multimedia geolocation is becoming an increasingly essential component of the digital forensics toolkit to effectively combat human trafficking, child sexual exploitation, and other illegal acts. Typically, metadata-based geolocation information is stripped when multimedia content is shared via instant messaging and social media. The intricacy of geolocating, geotagging, or finding geographical clues in this content is often overly burdensome for investigators. Recent research has shown that contemporary advancements in artificial intelligence, specifically computer vision and deep learning, show significant promise towards expediting the multimedia geolocation task. This systematic literature review thoroughly examines the state-of-the-art leveraging computer vision techniques for multimedia geolocation and assesses their potential to expedite human trafficking investigation. This includes a comprehensive overview of the application of computer vision-based approaches to multimedia geolocation, identifies their applicability in combating human trafficking, and highlights the potential implications of enhanced multimedia geolocation for prosecuting human trafficking. 123 articles inform this systematic literature review. The findings suggest numerous potential paths for future impactful research on the subject.", "published": "2024-02-23T17:23:06Z", "authors": ["Opeyemi Bamigbade", "John Sheppard", "Mark Scanlon"], "categories": ["cs.CV", "cs.AI", "cs.CY"], "doi": "", "_query": "property valuation computer vision", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2306.03421v2", "title": "Diversifying Joint Vision-Language Tokenization Learning", "abstract": "Building joint representations across images and text is an essential step for tasks such as Visual Question Answering and Video Question Answering. In this work, we find that the representations must not only jointly capture features from both modalities but should also be diverse for better generalization performance. To this end, we propose joint vision-language representation learning by diversifying the tokenization learning process, enabling tokens that are sufficiently disentangled from each other to be learned from both modalities. We observe that our approach outperforms the baseline models in a majority of settings and is competitive with state-of-the-art methods.", "published": "2023-06-06T05:41:42Z", "authors": ["Vardaan Pahuja", "AJ Piergiovanni", "Anelia Angelova"], "categories": ["cs.CV"], "doi": "", "_query": "property valuation computer vision", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2403.07839v1", "title": "MoPE-CLIP: Structured Pruning for Efficient Vision-Language Models with Module-wise Pruning Error Metric", "abstract": "Vision-language pre-trained models have achieved impressive performance on various downstream tasks. However, their large model sizes hinder their utilization on platforms with limited computational resources. We find that directly using smaller pre-trained models and applying magnitude-based pruning on CLIP models leads to inflexibility and inferior performance. Recent efforts for VLP compression either adopt uni-modal compression metrics resulting in limited performance or involve costly mask-search processes with learnable masks. In this paper, we first propose the Module-wise Pruning Error (MoPE) metric, accurately assessing CLIP module importance by performance decline on cross-modal tasks. Using the MoPE metric, we introduce a unified pruning framework applicable to both pre-training and task-specific fine-tuning compression stages. For pre-training, MoPE-CLIP effectively leverages knowledge from the teacher model, significantly reducing pre-training costs while maintaining strong zero-shot capabilities. For fine-tuning, consecutive pruning from width to depth yields highly competitive task-specific models. Extensive experiments in two stages demonstrate the effectiveness of the MoPE metric, and MoPE-CLIP outperforms previous state-of-the-art VLP compression methods.", "published": "2024-03-12T17:24:26Z", "authors": ["Haokun Lin", "Haoli Bai", "Zhili Liu", "Lu Hou", "Muyi Sun", "Linqi Song", "Ying Wei", "Zhenan Sun"], "categories": ["cs.CV", "cs.AI", "cs.MM"], "doi": "", "_query": "property valuation computer vision", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2004.11051v3", "title": "Proceedings of the ICLR Workshop on Computer Vision for Agriculture (CV4A) 2020", "abstract": "This is the proceedings of the Computer Vision for Agriculture (CV4A) Workshop that was held in conjunction with the International Conference on Learning Representations (ICLR) 2020. The Computer Vision for Agriculture (CV4A) 2020 workshop was scheduled to be held in Addis Ababa, Ethiopia, on April 26th, 2020. It was held virtually that same day due to the COVID-19 pandemic. The workshop was held in conjunction with the International Conference on Learning Representations (ICLR) 2020.", "published": "2020-04-23T10:11:52Z", "authors": ["Yannis Kalantidis", "Laura Sevilla-Lara", "Ernest Mwebaze", "Dina Machuve", "Hamed Alemohammad", "David Guerena"], "categories": ["cs.CV", "cs.AI"], "doi": "", "_query": "property valuation computer vision", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2604.07803v1", "title": "The Weaponization of Computer Vision: Tracing Military-Surveillance Ties through Conference Sponsorship", "abstract": "Computer vision, a core domain of artificial intelligence (AI), is the field that enables the computational analysis, understanding, and generation of visual data. Despite being historically rooted in military funding and increasingly deployed in warfare, the field tends to position itself as a neutral, purely technical endeavor, failing to engage in discussions about its dual-use applications. Yet it has been reported that computer vision systems are being systematically weaponized to assist in technologies that inflict harm, such as surveillance or warfare. Expanding on these concerns, we study the extent to which computer vision research is being used in the military and surveillance domains. We do so by collecting a dataset of tech companies with financial ties to the field's central research exchange platform: conferences. Conference sponsorship, we argue, not only serves as strong evidence of a company's investment in the field but also provides a privileged position for shaping its trajectory. By investigating sponsors' activities, we reveal that 44% of them have a direct connection with military or surveillance applications. We extend our analysis through two case studies in which we discuss the opportunities and limitations of sponsorship as a means for uncovering technological weaponization.", "published": "2026-04-09T04:54:44Z", "authors": ["Noa Garcia", "Amelia Katirai"], "categories": ["cs.CY", "cs.AI", "cs.CV"], "doi": "10.1145/3805689.3806535", "_query": "property valuation computer vision", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2103.13023v1", "title": "Can Vision Transformers Learn without Natural Images?", "abstract": "Can we complete pre-training of Vision Transformers (ViT) without natural images and human-annotated labels? Although a pre-trained ViT seems to heavily rely on a large-scale dataset and human-annotated labels, recent large-scale datasets contain several problems in terms of privacy violations, inadequate fairness protection, and labor-intensive annotation. In the present paper, we pre-train ViT without any image collections and annotation labor. We experimentally verify that our proposed framework partially outperforms sophisticated Self-Supervised Learning (SSL) methods like SimCLRv2 and MoCov2 without using any natural images in the pre-training phase. Moreover, although the ViT pre-trained without natural images produces some different visualizations from ImageNet pre-trained ViT, it can interpret natural image datasets to a large extent. For example, the performance rates on the CIFAR-10 dataset are as follows: our proposal 97.6 vs. SimCLRv2 97.4 vs. ImageNet 98.0.", "published": "2021-03-24T07:09:21Z", "authors": ["Kodai Nakashima", "Hirokatsu Kataoka", "Asato Matsumoto", "Kenji Iwata", "Nakamasa Inoue"], "categories": ["cs.CV"], "doi": "", "_query": "property valuation computer vision", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/1911.11845v2", "title": "Fast computation of soft tissue thermal response under deformation based on fast explicit dynamics finite element algorithm for surgical simulation", "abstract": "During thermal heating surgical procedures such as electrosurgery, thermal ablative treatment and hyperthermia, soft tissue deformation due to tool-tissue interaction and patients' motion can affect the distribution of induced thermal energy. Tissue temperature must be efficiently and accurately obtained from deformed tissues for precise thermal energy delivery; however, the classical Pennes bio-heat transfer can handle only the static non-moving state of soft tissue. This paper presents a formulation of bio-heat transfer under the effect of tissue deformation for fast or near real-time tissue temperature computation, based on fast explicit dynamics finite element algorithm for transient heat transfer. The proposed computation is achieved by transformation of the unknown deformed tissue state to the known initial non-moving state via a mapping function. The appropriateness and effectiveness of the proposed methodology are evaluated on a realistic virtual human liver with blood vessels to demonstrate a clinically relevant scenario of thermal ablation of hepatic cancer. Compared against the established non-linear procedures from commercial finite element analysis package, ABAQUS/CAE, the proposed methodology can achieve a typical 1.0e-3 level of normalized relative error at nodes and between 1.0e-4 and 1.0e-5 level of total errors, which is in good agreement with ABAQUS solutions. The proposed method consumes slightly more time than the formulation without soft tissue deformation, and computation performance of five different formulations are examined. The proposed method can be applied with bio-mechanical deformable models for fast or near real-time computation of non-linear bio-heat transfer, leading to translational potential in dynamic tissue temperature predictive analysis and thermal dosimetry computation for computer-integrated medical education and personalized treatments.", "published": "2019-11-26T21:45:11Z", "authors": ["Jinao Zhang", "Sunita Chauhan"], "categories": ["cs.CE"], "doi": "10.1016/j.cmpb.2019.105244", "_query": "property valuation computer vision", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/1712.01785v4", "title": "Towards Practical Verification of Machine Learning: The Case of Computer Vision Systems", "abstract": "Due to the increasing usage of machine learning (ML) techniques in security- and safety-critical domains, such as autonomous systems and medical diagnosis, ensuring correct behavior of ML systems, especially for different corner cases, is of growing importance. In this paper, we propose a generic framework for evaluating security and robustness of ML systems using different real-world safety properties. We further design, implement and evaluate VeriVis, a scalable methodology that can verify a diverse set of safety properties for state-of-the-art computer vision systems with only blackbox access. VeriVis leverage different input space reduction techniques for efficient verification of different safety properties. VeriVis is able to find thousands of safety violations in fifteen state-of-the-art computer vision systems including ten Deep Neural Networks (DNNs) such as Inception-v3 and Nvidia's Dave self-driving system with thousands of neurons as well as five commercial third-party vision APIs including Google vision and Clarifai for twelve different safety properties. Furthermore, VeriVis can successfully verify local safety properties, on average, for around 31.7% of the test images. VeriVis finds up to 64.8x more violations than existing gradient-based methods that, unlike VeriVis, cannot ensure non-existence of any violations. Finally, we show that retraining using the safety violations detected by VeriVis can reduce the average number of violations up to 60.2%.", "published": "2017-12-05T17:49:18Z", "authors": ["Kexin Pei", "Linjie Zhu", "Yinzhi Cao", "Junfeng Yang", "Carl Vondrick", "Suman Jana"], "categories": ["cs.CR", "cs.CV"], "doi": "", "_query": "property valuation computer vision", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2308.14179v1", "title": "Towards Vision-Language Mechanistic Interpretability: A Causal Tracing Tool for BLIP", "abstract": "Mechanistic interpretability seeks to understand the neural mechanisms that enable specific behaviors in Large Language Models (LLMs) by leveraging causality-based methods. While these approaches have identified neural circuits that copy spans of text, capture factual knowledge, and more, they remain unusable for multimodal models since adapting these tools to the vision-language domain requires considerable architectural changes. In this work, we adapt a unimodal causal tracing tool to BLIP to enable the study of the neural mechanisms underlying image-conditioned text generation. We demonstrate our approach on a visual question answering dataset, highlighting the causal relevance of later layer representations for all tokens. Furthermore, we release our BLIP causal tracing tool as open source to enable further experimentation in vision-language mechanistic interpretability by the community. Our code is available at https://github.com/vedantpalit/Towards-Vision-Language-Mechanistic-Interpretability.", "published": "2023-08-27T18:46:47Z", "authors": ["Vedant Palit", "Rohan Pandey", "Aryaman Arora", "Paul Pu Liang"], "categories": ["cs.CL", "cs.AI", "cs.CV"], "doi": "", "_query": "property valuation computer vision", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2304.06009v2", "title": "Literature Review: Computer Vision Applications in Transportation Logistics and Warehousing", "abstract": "Computer vision applications in transportation logistics and warehousing have a huge potential for process automation. We present a structured literature review on research in the field to help leverage this potential. The literature is categorized w.r.t. the application, i.e. the task it tackles and w.r.t. the computer vision techniques that are used. Regarding applications, we subdivide the literature in two areas: Monitoring, i.e. observing and retrieving relevant information from the environment, and manipulation, where approaches are used to analyze and interact with the environment. Additionally, we point out directions for future research and link to recent developments in computer vision that are suitable for application in logistics. Finally, we present an overview of existing datasets and industrial solutions. The results of our analysis are also available online at https://a-nau.github.io/cv-in-logistics.", "published": "2023-04-12T17:33:41Z", "authors": ["Alexander Naumann", "Felix Hertlein", "Laura Dörr", "Steffen Thoma", "Kai Furmans"], "categories": ["cs.CV", "cs.LG"], "doi": "", "_query": "property valuation computer vision", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2009.12088v1", "title": "Training CNNs in Presence of JPEG Compression: Multimedia Forensics vs Computer Vision", "abstract": "Convolutional Neural Networks (CNNs) have proved very accurate in multiple computer vision image classification tasks that required visual inspection in the past (e.g., object recognition, face detection, etc.). Motivated by these astonishing results, researchers have also started using CNNs to cope with image forensic problems (e.g., camera model identification, tampering detection, etc.). However, in computer vision, image classification methods typically rely on visual cues easily detectable by human eyes. Conversely, forensic solutions rely on almost invisible traces that are often very subtle and lie in the fine details of the image under analysis. For this reason, training a CNN to solve a forensic task requires some special care, as common processing operations (e.g., resampling, compression, etc.) can strongly hinder forensic traces. In this work, we focus on the effect that JPEG has on CNN training considering different computer vision and forensic image classification problems. Specifically, we consider the issues that rise from JPEG compression and misalignment of the JPEG grid. We show that it is necessary to consider these effects when generating a training dataset in order to properly train a forensic detector not losing generalization capability, whereas it is almost possible to ignore these effects for computer vision tasks.", "published": "2020-09-25T08:47:21Z", "authors": ["Sara Mandelli", "Nicolò Bonettini", "Paolo Bestagini", "Stefano Tubaro"], "categories": ["cs.CV", "cs.LG", "cs.MM", "eess.IV"], "doi": "", "_query": "property valuation computer vision", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/1910.02190v2", "title": "Kornia: an Open Source Differentiable Computer Vision Library for PyTorch", "abstract": "This work presents Kornia -- an open source computer vision library which consists of a set of differentiable routines and modules to solve generic computer vision problems. The package uses PyTorch as its main backend both for efficiency and to take advantage of the reverse-mode auto-differentiation to define and compute the gradient of complex functions. Inspired by OpenCV, Kornia is composed of a set of modules containing operators that can be inserted inside neural networks to train models to perform image transformations, camera calibration, epipolar geometry, and low level image processing techniques, such as filtering and edge detection that operate directly on high dimensional tensor representations. Examples of classical vision problems implemented using our framework are provided including a benchmark comparing to existing vision libraries.", "published": "2019-10-05T01:29:54Z", "authors": ["Edgar Riba", "Dmytro Mishkin", "Daniel Ponsa", "Ethan Rublee", "Gary Bradski"], "categories": ["cs.CV"], "doi": "", "_query": "property valuation computer vision", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2312.12865v3", "title": "RadEdit: stress-testing biomedical vision models via diffusion image editing", "abstract": "Biomedical imaging datasets are often small and biased, meaning that real-world performance of predictive models can be substantially lower than expected from internal testing. This work proposes using generative image editing to simulate dataset shifts and diagnose failure modes of biomedical vision models; this can be used in advance of deployment to assess readiness, potentially reducing cost and patient harm. Existing editing methods can produce undesirable changes, with spurious correlations learned due to the co-occurrence of disease and treatment interventions, limiting practical applicability. To address this, we train a text-to-image diffusion model on multiple chest X-ray datasets and introduce a new editing method RadEdit that uses multiple masks, if present, to constrain changes and ensure consistency in the edited images. We consider three types of dataset shifts: acquisition shift, manifestation shift, and population shift, and demonstrate that our approach can diagnose failures and quantify model robustness without additional data collection, complementing more qualitative tools for explainable AI.", "published": "2023-12-20T09:27:41Z", "authors": ["Fernando Pérez-García", "Sam Bond-Taylor", "Pedro P. Sanchez", "Boris van Breugel", "Daniel C. Castro", "Harshita Sharma", "Valentina Salvatelli", "Maria T. A. Wetscherek", "Hannah Richardson", "Matthew P. Lungren", "Aditya Nori", "Javier Alvarez-Valle", "Ozan Oktay", "Maximilian Ilse"], "categories": ["cs.CV", "cs.AI"], "doi": "10.1007/978-3-031-73254-6_21", "_query": "property valuation computer vision", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2503.17940v2", "title": "FisherTune: Fisher-Guided Robust Tuning of Vision Foundation Models for Domain Generalized Segmentation", "abstract": "Vision Foundation Models (VFMs) excel in generalization due to large-scale pretraining, but fine-tuning them for Domain Generalized Semantic Segmentation (DGSS) while maintaining this ability remains challenging. Existing approaches either selectively fine-tune parameters or freeze the VFMs and update only the adapters, both of which may underutilize the VFMs' full potential in DGSS tasks. We observe that domain-sensitive parameters in VFMs, arising from task and distribution differences, can hinder generalization. To address this, we propose \\textbf{FisherTune}, a robust fine-tuning method guided by the Domain-Related Fisher Information Matrix (DR-FIM). DR-FIM measures parameter sensitivity across tasks and domains, enabling selective updates that preserve generalization and enhance DGSS adaptability. FisherTune incorporates variational inference to stabilize DR-FIM estimation, treating parameters as Gaussian-distributed variables and leveraging pre-trained priors. Extensive experiments show that FisherTune achieves superior cross-domain segmentation while maintaining generalization, outperforming selective-parameter and adapter-based methods.", "published": "2025-03-23T04:47:15Z", "authors": ["Dong Zhao", "Jinlong Li", "Shuang Wang", "Mengyao Wu", "Qi Zang", "Nicu Sebe", "Zhun Zhong"], "categories": ["cs.CV"], "doi": "", "_query": "property valuation computer vision", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/1709.04411v1", "title": "Exploiting skeletal structure in computer vision annotation with Benders decomposition", "abstract": "Many annotation problems in computer vision can be phrased as integer linear programs (ILPs). The use of standard industrial solvers does not to exploit the underlying structure of such problems eg, the skeleton in pose estimation. The leveraging of the underlying structure in conjunction with industrial solvers promises increases in both speed and accuracy. Such structure can be exploited using Bender's decomposition, a technique from operations research, that solves complex ILPs or mixed integer linear programs by decomposing them into sub-problems that communicate via a master problem. The intuition is that conditioned on a small subset of the variables the solution to the remaining variables can be computed easily by taking advantage of properties of the ILP constraint matrix such as block structure. In this paper we apply Benders decomposition to a typical problem in computer vision where we have many sub-ILPs (eg, partitioning of detections, body-parts) coupled to a master ILP (eg, constructing skeletons). Dividing inference problems into a master problem and sub-problems motivates the development of a plethora of novel models, and inference approaches for the field of computer vision.", "published": "2017-09-13T16:43:07Z", "authors": ["Shaofei Wang", "Konrad Kording", "Julian Yarkony"], "categories": ["cs.CV"], "doi": "", "_query": "property valuation computer vision", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2312.12848v1", "title": "Quantum Annealing for Computer Vision Minimization Problems", "abstract": "Computer Vision (CV) labelling algorithms play a pivotal role in the domain of low-level vision. For decades, it has been known that these problems can be elegantly formulated as discrete energy minimization problems derived from probabilistic graphical models (such as Markov Random Fields). Despite recent advances in inference algorithms (such as graph-cut and message-passing algorithms), the resulting energy minimization problems are generally viewed as intractable. The emergence of quantum computations, which offer the potential for faster solutions to certain problems than classical methods, has led to an increased interest in utilizing quantum properties to overcome intractable problems. Recently, there has also been a growing interest in Quantum Computer Vision (QCV), with the hope of providing a credible alternative or assistant to deep learning solutions in the field. This study investigates a new Quantum Annealing based inference algorithm for CV discrete energy minimization problems. Our contribution is focused on Stereo Matching as a significant CV labeling problem. As a proof of concept, we also use a hybrid quantum-classical solver provided by D-Wave System to compare our results with the best classical inference algorithms in the literature.", "published": "2023-12-20T08:56:35Z", "authors": ["Shahrokh Heidari", "Michael J. Dinneen", "Patrice Delmas"], "categories": ["quant-ph", "cs.CV"], "doi": "", "_query": "property valuation computer vision", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2311.06565v2", "title": "Zhou valuations and jumping numbers", "abstract": "In this article, we prove that for any Zhou valuation $ν$, there exists a graded sequence of ideals $\\mathfrak{a}_{\\bullet}$ and a nonzero ideal $\\mathfrak{q}$ such that $ν$ $\\mathscr{A}-$computes the jumping number $\\mathrm{lct}^{\\mathfrak{q}}(\\mathfrak{a}_{\\bullet})$, and that for the subadditive sequence $\\mathfrak{b}^{\\varphi}_{\\bullet}$ related to a plurisubharmonic function $\\varphi$, there exists a Zhou valuation which $\\mathscr{A}-$computes $\\mathrm{lct}^{\\mathfrak{q}}(\\mathfrak{b}^{\\varphi}_{\\bullet})$, where the ``$\\mathscr{A}-$compute'' coincides with the ``compute'' in Jonsson-Mustaţă's Conjecture when the Zhou valuation $ν$ is quasimonomial. There are also some results obtained for Zhou valuations, including a characterization for a valuation being a Zhou valuation, and a denseness property of the cone of Zhou valuations.", "published": "2023-11-11T13:22:43Z", "authors": ["Shijie Bao", "Qi'an Guan", "Zheng Yuan"], "categories": ["math.CV", "math.AG"], "doi": "", "_query": "property valuation computer vision", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2204.08601v1", "title": "A Tour of Visualization Techniques for Computer Vision Datasets", "abstract": "We survey a number of data visualization techniques for analyzing Computer Vision (CV) datasets. These techniques help us understand properties and latent patterns in such data, by applying dataset-level analysis. We present various examples of how such analysis helps predict the potential impact of the dataset properties on CV models and informs appropriate mitigation of their shortcomings. Finally, we explore avenues for further visualization techniques of different modalities of CV datasets as well as ones that are tailored to support specific CV tasks and analysis needs.", "published": "2022-04-19T01:04:28Z", "authors": ["Bilal Alsallakh", "Pamela Bhattacharya", "Vanessa Feng", "Narine Kokhlikyan", "Orion Reblitz-Richardson", "Rahul Rajan", "David Yan"], "categories": ["cs.CV", "cs.GR"], "doi": "", "_query": "property valuation computer vision", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2103.00560v1", "title": "Perspectives on individual animal identification from biology and computer vision", "abstract": "Identifying individual animals is crucial for many biological investigations. In response to some of the limitations of current identification methods, new automated computer vision approaches have emerged with strong performance. Here, we review current advances of computer vision identification techniques to provide both computer scientists and biologists with an overview of the available tools and discuss their applications. We conclude by offering recommendations for starting an animal identification project, illustrate current limitations and propose how they might be addressed in the future.", "published": "2021-02-28T16:50:09Z", "authors": ["Maxime Vidal", "Nathan Wolf", "Beth Rosenberg", "Bradley P. Harris", "Alexander Mathis"], "categories": ["cs.CV", "q-bio.QM"], "doi": "10.1093/icb/icab107", "_query": "property valuation computer vision", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2511.01129v1", "title": "Boosting performance of computer vision applications through embedded GPUs on the edge", "abstract": "Computer vision applications, especially those using augmented reality technology, are becoming quite popular in mobile devices. However, this type of application is known as presenting significant demands regarding resources. In order to enable its utilization in devices with more modest resources, edge computing can be used to offload certain high intensive tasks. Still, edge computing is usually composed of devices with limited capacity, which may impact in users quality of experience when using computer vision applications. This work proposes the use of embedded devices with graphics processing units (GPUs) to overcome such limitation. Experiments performed shown that GPUs can attain a performance gain when compared to using only CPUs, which guarantee a better experience to users using such kind of application.", "published": "2025-11-03T00:38:14Z", "authors": ["Fabio Diniz Rossi"], "categories": ["cs.CV", "cs.DC"], "doi": "", "_query": "property valuation computer vision", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/quant-ph/0312003v3", "title": "An Algorithmic Argument for Nonadaptive Query Complexity Lower Bounds on Advised Quantum Computation", "abstract": "This paper employs a powerful argument, called an algorithmic argument, to prove lower bounds of the quantum query complexity of a multiple-block ordered search problem in which, given a block number i, we are to find a location of a target keyword in an ordered list of the i-th block. Apart from much studied polynomial and adversary methods for quantum query complexity lower bounds, our argument shows that the multiple-block ordered search needs a large number of nonadaptive oracle queries on a black-box model of quantum computation that is also supplemented with advice. Our argument is also applied to the notions of computational complexity theory: quantum truth-table reducibility and quantum truth-table autoreducibility.", "published": "2003-11-29T07:40:22Z", "authors": ["Harumichi Nishimura", "Tomoyuki Yamakami"], "categories": ["quant-ph", "cs.CC"], "doi": "10.1007/978-3-540-28629-5_65", "_query": "property valuation computer vision", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/1010.4983v2", "title": "Sequential Strong Measurements and Heat Vision", "abstract": "We study scenarios where a finite set of non-demolition von-Neumann measurements are available. We note that, in some situations, repeated application of such measurements allows estimating an infinite number of parameters of the initial quantum state, and illustrate the point with a physical example. We then move on to study how the system under observation is perturbed after several rounds of projective measurements. While in the finite dimensional case the effect of this perturbation always saturates, there are some instances of infinite dimensional systems where such a perturbation is accumulative, and the act of retrieving information about the system increases its energy indefinitely (i.e., we have `Heat Vision'). We analyze this effect and discuss a specific physical system with two dichotomic von-Neumann measurements where Heat Vision is expected to show.", "published": "2010-10-24T17:41:46Z", "authors": ["Miguel Navascues", "David Perez-Garcia"], "categories": ["quant-ph"], "doi": "10.1088/1367-2630/13/11/113038", "_query": "property valuation computer vision", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/1803.06312v2", "title": "EVA$^2$: Exploiting Temporal Redundancy in Live Computer Vision", "abstract": "Hardware support for deep convolutional neural networks (CNNs) is critical to advanced computer vision in mobile and embedded devices. Current designs, however, accelerate generic CNNs; they do not exploit the unique characteristics of real-time vision. We propose to use the temporal redundancy in natural video to avoid unnecessary computation on most frames. A new algorithm, activation motion compensation, detects changes in the visual input and incrementally updates a previously-computed output. The technique takes inspiration from video compression and applies well-known motion estimation techniques to adapt to visual changes. We use an adaptive key frame rate to control the trade-off between efficiency and vision quality as the input changes. We implement the technique in hardware as an extension to existing state-of-the-art CNN accelerator designs. The new unit reduces the average energy per frame by 54.2%, 61.7%, and 87.6% for three CNNs with less than 1% loss in vision accuracy.", "published": "2018-03-16T16:59:47Z", "authors": ["Mark Buckler", "Philip Bedoukian", "Suren Jayasuriya", "Adrian Sampson"], "categories": ["cs.CV", "eess.IV"], "doi": "", "_query": "property valuation computer vision", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/1405.6421v1", "title": "Filtered Ring Derived from Discrete Valuation Ring and Its Properties", "abstract": "In this paper we show that if R is a discrete valuation ring, then R is a filtered ring. We prove some properties and relation when R is a discrete valuation ring.", "published": "2014-05-25T19:39:30Z", "authors": ["Mohammad Hassan Anjom SHoa", "Mohammad Hossein Hosseini"], "categories": ["math.RA"], "doi": "", "_query": "property valuation computer vision", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2210.14981v1", "title": "Fast and Efficient Scene Categorization for Autonomous Driving using VAEs", "abstract": "Scene categorization is a useful precursor task that provides prior knowledge for many advanced computer vision tasks with a broad range of applications in content-based image indexing and retrieval systems. Despite the success of data driven approaches in the field of computer vision such as object detection, semantic segmentation, etc., their application in learning high-level features for scene recognition has not achieved the same level of success. We propose to generate a fast and efficient intermediate interpretable generalized global descriptor that captures coarse features from the image and use a classification head to map the descriptors to 3 scene categories: Rural, Urban and Suburban. We train a Variational Autoencoder in an unsupervised manner and map images to a constrained multi-dimensional latent space and use the latent vectors as compact embeddings that serve as global descriptors for images. The experimental results evidence that the VAE latent vectors capture coarse information from the image, supporting their usage as global descriptors. The proposed global descriptor is very compact with an embedding length of 128, significantly faster to compute, and is robust to seasonal and illuminational changes, while capturing sufficient scene information required for scene categorization.", "published": "2022-10-26T18:50:15Z", "authors": ["Saravanabalagi Ramachandran", "Jonathan Horgan", "Ganesh Sistu", "John McDonald"], "categories": ["cs.CV", "cs.LG"], "doi": "10.56541/SUHE3553", "_query": "property valuation computer vision", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2603.16592v1", "title": "On the Transfer of Collinearity to Computer Vision", "abstract": "Collinearity is a visual perception phenomenon in the human brain that amplifies spatially aligned edges arranged along a straight line. However, it is vague for which purpose humans might have this principle in the real-world, and its utilization in computer vision and engineering applications even is a largely unexplored field. In this work, our goal is to transfer the collinearity principle to computer vision, and we explore the potential usages of this novel principle for computer vision applications. We developed a prototype model to exemplify the principle, then tested it systematically, and benchmarked it in the context of four use cases. Our cases are selected to spawn a broad range of potential applications and scenarios: sketching the combination of collinearity with deep learning (case I and II), using collinearity with saliency models (case II), and as a feature detector (case I). In the first use case, we found that collinearity is able to improve the fault detection of wafers and obtain a performance increase by a factor 1.24 via collinearity (decrease of the error rate from 6.5% to 5.26%). In the second use case, we test the defect recognition in nanotechnology materials and achieve a performance increase by 3.2x via collinearity (deep learning, error from 21.65% to 6.64%), and also explore saliency models. As third experiment, we cover occlusions; while as fourth experiment, we test ImageNet and observe that it might not be very beneficial for ImageNet. Therefore, we can assemble a list of scenarios for which collinearity is beneficial (wafers, nanotechnology, occlusions), and for what is not beneficial (ImageNet). Hence, we infer collinearity might be suitable for industry applications as it helps if the image structures of interest are man-made because they often consist of lines. Our work provides another tool for CV, hope to capture the power of human processing.", "published": "2026-03-17T14:40:23Z", "authors": ["Frederik Beuth", "Danny Kowerko"], "categories": ["cs.CV"], "doi": "", "_query": "property valuation computer vision", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/1808.08275v1", "title": "Binary Image Features Proposed to Empower Computer Vision", "abstract": "This literature has proposed three fast and easy computable image features to improve computer vision by offering more human-like vision power. These features are not based on image pixels absolute or relative intensity; neither based on shape or colour. So, no complex pixel by pixel calculation is required. For human eyes, pixel by pixel calculation is like seeing an image with maximum zoom which is done only when a higher level of details is required. Normally, first we look at an image to get an overall idea about it to know whether it deserves further investigation or not. This capacity of getting an idea at a glance is analysed and three basic features are proposed to empower computer vision. Potential of proposed features is tested and established through different medical dataset. Achieved accuracy in classification demonstrates possibilities and potential of the use of the proposed features in image processing.", "published": "2018-08-14T06:39:58Z", "authors": ["Soumi Ray", "Vinod Kumar"], "categories": ["cs.CV"], "doi": "10.1002/ima.22418", "_query": "property valuation computer vision", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2210.12852v3", "title": "1st Place Solution of The Robust Vision Challenge 2022 Semantic Segmentation Track", "abstract": "This report describes the winning solution to the Robust Vision Challenge (RVC) semantic segmentation track at ECCV 2022. Our method adopts the FAN-B-Hybrid model as the encoder and uses SegFormer as the segmentation framework. The model is trained on a composite dataset consisting of images from 9 datasets (ADE20K, Cityscapes, Mapillary Vistas, ScanNet, VIPER, WildDash 2, IDD, BDD, and COCO) with a simple dataset balancing strategy. All the original labels are projected to a 256-class unified label space, and the model is trained using a cross-entropy loss. Without significant hyperparameter tuning or any specific loss weighting, our solution ranks the first place on all the testing semantic segmentation benchmarks from multiple domains (ADE20K, Cityscapes, Mapillary Vistas, ScanNet, VIPER, and WildDash 2). The proposed method can serve as a strong baseline for the multi-domain segmentation task and benefit future works. Code will be available at https://github.com/lambert-x/RVC_Segmentation.", "published": "2022-10-23T20:52:22Z", "authors": ["Junfei Xiao", "Zhichao Xu", "Shiyi Lan", "Zhiding Yu", "Alan Yuille", "Anima Anandkumar"], "categories": ["cs.CV"], "doi": "", "_query": "property valuation computer vision", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2205.06397v1", "title": "LANTERN-RD: Enabling Deep Learning for Mitigation of the Invasive Spotted Lanternfly", "abstract": "The Spotted Lanternfly (SLF) is an invasive planthopper that threatens the local biodiversity and agricultural economy of regions such as the Northeastern United States and Japan. As researchers scramble to study the insect, there is a great potential for computer vision tasks such as detection, pose estimation, and accurate identification to have important downstream implications in containing the SLF. However, there is currently no publicly available dataset for training such AI models. To enable computer vision applications and motivate advancements to challenge the invasive SLF problem, we propose LANTERN-RD, the first curated image dataset of the spotted lanternfly and its look-alikes, featuring images with varied lighting conditions, diverse backgrounds, and subjects in assorted poses. A VGG16-based baseline CNN validates the potential of this dataset for stimulating fresh computer vision applications to accelerate invasive SLF research. Additionally, we implement the trained model in a simple mobile classification application in order to directly empower responsible public mitigation efforts. The overarching mission of this work is to introduce a novel SLF image dataset and release a classification framework that enables computer vision applications, boosting studies surrounding the invasive SLF and assisting in minimizing its agricultural and economic damage.", "published": "2022-05-12T23:37:29Z", "authors": ["Srivatsa Kundurthy"], "categories": ["cs.CV", "cs.AI"], "doi": "", "_query": "property valuation computer vision", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2610.05237v1", "title": "Settling the Computational Complexity of Max-Min Allocation with Ternary Valuations", "abstract": "We study the problem of computing an allocation of indivisible items that maximizes egalitarian welfare, i.e., the utility of the worst-off agent, when agents' item values or marginal values belong to a small set. For additive valuations with values in $\\{p,q\\}$, where $q>p>0$ and $\\gcd(p,q)=1$, we give a polynomial-time algorithm when $p=2$ and prove constant-gap hardness when $p\\geq3$, already with exactly three high-valued goods per agent. We also give an $\\sqrt{3/2}$-approximation for common positive bi-valued additive valuations. For mixed additive valuations in $\\{-p,0,c\\}$, where $p\\in\\{1,2\\}$ and $c$ is a positive integer, a reduction to maximum-weight perfect matching resolves the conjectured tractability of $\\{-2,0,c\\}$-valuations. For submodular valuations with marginals in $\\{-2,0,c\\}$, where $c$ is odd, we establish an exact unit-gap hardness result and exponential value-query lower bounds, even when all but one agent are additive. Finally, for $\\{-1,0,1\\}$-submodular valuations, we prove that no finite multiplicative approximation exists unless $\\p=\\np$. Together, our results resolve open questions and provide a complete picture of the computational complexity of max-min allocation with ternary valuations.", "published": "2026-10-04T14:04:02Z", "authors": ["Thi Ngoc Anh Vu", "Trung Thanh Nguyen", "Khaled Elbassioni", "Jörg Rothe"], "categories": ["cs.GT", "cs.AI"], "doi": "", "_query": "property valuation computer vision", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2312.12872v1", "title": "Integration and Performance Analysis of Artificial Intelligence and Computer Vision Based on Deep Learning Algorithms", "abstract": "This paper focuses on the analysis of the application effectiveness of the integration of deep learning and computer vision technologies. Deep learning achieves a historic breakthrough by constructing hierarchical neural networks, enabling end-to-end feature learning and semantic understanding of images. The successful experiences in the field of computer vision provide strong support for training deep learning algorithms. The tight integration of these two fields has given rise to a new generation of advanced computer vision systems, significantly surpassing traditional methods in tasks such as machine vision image classification and object detection. In this paper, typical image classification cases are combined to analyze the superior performance of deep neural network models while also pointing out their limitations in generalization and interpretability, proposing directions for future improvements. Overall, the efficient integration and development trend of deep learning with massive visual data will continue to drive technological breakthroughs and application expansion in the field of computer vision, making it possible to build truly intelligent machine vision systems. This deepening fusion paradigm will powerfully promote unprecedented tasks and functions in computer vision, providing stronger development momentum for related disciplines and industries.", "published": "2023-12-20T09:37:06Z", "authors": ["Bo Liu", "Liqiang Yu", "Chang Che", "Qunwei Lin", "Hao Hu", "Xinyu Zhao"], "categories": ["cs.CV", "cs.AI"], "doi": "", "_query": "property valuation computer vision", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2603.25740v1", "title": "Drive My Way: Preference Alignment of Vision-Language-Action Model for Personalized Driving", "abstract": "Human driving behavior is inherently personal, which is shaped by long-term habits and influenced by short-term intentions. Individuals differ in how they accelerate, brake, merge, yield, and overtake across diverse situations. However, existing end-to-end autonomous driving systems either optimize for generic objectives or rely on fixed driving modes, lacking the ability to adapt to individual preferences or interpret natural language intent. To address this gap, we propose Drive My Way (DMW), a personalized Vision-Language-Action (VLA) driving framework that aligns with users' long-term driving habits and adapts to real-time user instructions. DMW learns a user embedding from our personalized driving dataset collected across multiple real drivers and conditions the policy on this embedding during planning, while natural language instructions provide additional short-term guidance. Closed-loop evaluation on the Bench2Drive benchmark demonstrates that DMW improves style instruction adaptation, and user studies show that its generated behaviors are recognizable as each driver's own style, highlighting personalization as a key capability for human-centered autonomous driving. Our data and code are available at https://dmw-cvpr.github.io/.", "published": "2026-03-26T17:59:54Z", "authors": ["Zehao Wang", "Huaide Jiang", "Shuaiwu Dong", "Yuping Wang", "Hang Qiu", "Jiachen Li"], "categories": ["cs.RO", "cs.AI", "cs.CV", "cs.LG", "cs.MA"], "doi": "", "_query": "property valuation computer vision", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2207.09847v3", "title": "Predicting Word Learning in Children from the Performance of Computer Vision Systems", "abstract": "For human children as well as machine learning systems, a key challenge in learning a word is linking the word to the visual phenomena it describes. We explore this aspect of word learning by using the performance of computer vision systems as a proxy for the difficulty of learning a word from visual cues. We show that the age at which children acquire different categories of words is correlated with the performance of visual classification and captioning systems, over and above the expected effects of word frequency. The performance of the computer vision systems is correlated with human judgments of the concreteness of words, which are in turn a predictor of children's word learning, suggesting that these models are capturing the relationship between words and visual phenomena.", "published": "2022-07-07T22:49:32Z", "authors": ["Sunayana Rane", "Mira L. Nencheva", "Zeyu Wang", "Casey Lew-Williams", "Olga Russakovsky", "Thomas L. Griffiths"], "categories": ["cs.CL", "cs.AI", "cs.CV"], "doi": "", "_query": "property valuation computer vision", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/1412.2672v1", "title": "When Computer Vision Gazes at Cognition", "abstract": "Joint attention is a core, early-developing form of social interaction. It is based on our ability to discriminate the third party objects that other people are looking at. While it has been shown that people can accurately determine whether another person is looking directly at them versus away, little is known about human ability to discriminate a third person gaze directed towards objects that are further away, especially in unconstraint cases where the looker can move her head and eyes freely. In this paper we address this question by jointly exploring human psychophysics and a cognitively motivated computer vision model, which can detect the 3D direction of gaze from 2D face images. The synthesis of behavioral study and computer vision yields several interesting discoveries. (1) Human accuracy of discriminating targets 8°-10° of visual angle apart is around 40% in a free looking gaze task; (2) The ability to interpret gaze of different lookers vary dramatically; (3) This variance can be captured by the computational model; (4) Human outperforms the current model significantly. These results collectively show that the acuity of human joint attention is indeed highly impressive, given the computational challenge of the natural looking task. Moreover, the gap between human and model performance, as well as the variability of gaze interpretation across different lookers, require further understanding of the underlying mechanisms utilized by humans for this challenging task.", "published": "2014-12-08T17:25:57Z", "authors": ["Tao Gao", "Daniel Harari", "Joshua Tenenbaum", "Shimon Ullman"], "categories": ["cs.AI", "cs.CV"], "doi": "", "_query": "property valuation computer vision", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2407.13111v1", "title": "PG-Attack: A Precision-Guided Adversarial Attack Framework Against Vision Foundation Models for Autonomous Driving", "abstract": "Vision foundation models are increasingly employed in autonomous driving systems due to their advanced capabilities. However, these models are susceptible to adversarial attacks, posing significant risks to the reliability and safety of autonomous vehicles. Adversaries can exploit these vulnerabilities to manipulate the vehicle's perception of its surroundings, leading to erroneous decisions and potentially catastrophic consequences. To address this challenge, we propose a novel Precision-Guided Adversarial Attack (PG-Attack) framework that combines two techniques: Precision Mask Perturbation Attack (PMP-Attack) and Deceptive Text Patch Attack (DTP-Attack). PMP-Attack precisely targets the attack region to minimize the overall perturbation while maximizing its impact on the target object's representation in the model's feature space. DTP-Attack introduces deceptive text patches that disrupt the model's understanding of the scene, further enhancing the attack's effectiveness. Our experiments demonstrate that PG-Attack successfully deceives a variety of advanced multi-modal large models, including GPT-4V, Qwen-VL, and imp-V1. Additionally, we won First-Place in the CVPR 2024 Workshop Challenge: Black-box Adversarial Attacks on Vision Foundation Models and codes are available at https://github.com/fuhaha824/PG-Attack.", "published": "2024-07-18T02:39:31Z", "authors": ["Jiyuan Fu", "Zhaoyu Chen", "Kaixun Jiang", "Haijing Guo", "Shuyong Gao", "Wenqiang Zhang"], "categories": ["cs.MM", "cs.CV"], "doi": "", "_query": "property valuation computer vision", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2305.18017v3", "title": "Trace models of concurrent valuation algebras", "abstract": "This paper introduces Concurrent Valuation Algebras (CVAs), a novel extension of ordered valuation algebras (OVAs). CVAs include two combine operators representing parallel and sequential products, adhering to a weak exchange law. This development offers theoretical and practical benefits for the specification and modelling of concurrent and distributed systems. As a presheaf on a space of domains, CVAs enable localised specifications, supporting modularity, compositionality, and the ability to represent large and complex systems. Furthermore, CVAs align with lattice-based refinement reasoning and are compatible with established methodologies such as Hoare and Rely-Guarantee logics. The flexibility of CVAs is explored through three trace models, illustrating distinct paradigms of concurrent/distributed computing, interrelated by morphisms. The paper also highlights the potential to incorporate a powerful local computation framework from valuation algebras for model checking in concurrent and distributed systems. The foundational results presented have been verified with the proof assistant Isabelle/HOL.", "published": "2023-05-29T11:18:18Z", "authors": ["Naso Evangelou-Oost", "Larissa Meinicke", "Callum Bannister", "Ian J. Hayes"], "categories": ["cs.LO"], "doi": "10.1007/978-981-99-7584-6_8", "_query": "property valuation computer vision", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2305.19743v1", "title": "Towards Monocular Shape from Refraction", "abstract": "Refraction is a common physical phenomenon and has long been researched in computer vision. Objects imaged through a refractive object appear distorted in the image as a function of the shape of the interface between the media. This hinders many computer vision applications, but can be utilized for obtaining the geometry of the refractive interface. Previous approaches for refractive surface recovery largely relied on various priors or additional information like multiple images of the analyzed surface. In contrast, we claim that a simple energy function based on Snell's law enables the reconstruction of an arbitrary refractive surface geometry using just a single image and known background texture and geometry. In the case of a single point, Snell's law has two degrees of freedom, therefore to estimate a surface depth, we need additional information. We show that solving for an entire surface at once introduces implicit parameter-free spatial regularization and yields convincing results when an intelligent initial guess is provided. We demonstrate our approach through simulations and real-world experiments, where the reconstruction shows encouraging results in the single-frame monocular setting.", "published": "2023-05-31T11:09:37Z", "authors": ["Antonin Sulc", "Imari Sato", "Bastian Goldluecke", "Tali Treibitz"], "categories": ["cs.CV"], "doi": "", "_query": "property valuation computer vision", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2408.02464v1", "title": "Fairness and Bias Mitigation in Computer Vision: A Survey", "abstract": "Computer vision systems have witnessed rapid progress over the past two decades due to multiple advances in the field. As these systems are increasingly being deployed in high-stakes real-world applications, there is a dire need to ensure that they do not propagate or amplify any discriminatory tendencies in historical or human-curated data or inadvertently learn biases from spurious correlations. This paper presents a comprehensive survey on fairness that summarizes and sheds light on ongoing trends and successes in the context of computer vision. The topics we discuss include 1) The origin and technical definitions of fairness drawn from the wider fair machine learning literature and adjacent disciplines. 2) Work that sought to discover and analyze biases in computer vision systems. 3) A summary of methods proposed to mitigate bias in computer vision systems in recent years. 4) A comprehensive summary of resources and datasets produced by researchers to measure, analyze, and mitigate bias and enhance fairness. 5) Discussion of the field's success, continuing trends in the context of multimodal foundation and generative models, and gaps that still need to be addressed. The presented characterization should help researchers understand the importance of identifying and mitigating bias in computer vision and the state of the field and identify potential directions for future research.", "published": "2024-08-05T13:44:22Z", "authors": ["Sepehr Dehdashtian", "Ruozhen He", "Yi Li", "Guha Balakrishnan", "Nuno Vasconcelos", "Vicente Ordonez", "Vishnu Naresh Boddeti"], "categories": ["cs.CV"], "doi": "", "_query": "property valuation computer vision", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2105.09137v1", "title": "TableZa -- A classical Computer Vision approach to Tabular Extraction", "abstract": "Computer aided Tabular Data Extraction has always been a very challenging and error prone task because it demands both Spectral and Spatial Sanity of data. In this paper we discuss an approach for Tabular Data Extraction in the realm of document comprehension. Given the different kinds of the Tabular formats that are often found across various documents, we discuss a novel approach using Computer Vision for extraction of tabular data from images or vector pdf(s) converted to image(s).", "published": "2021-05-19T13:55:33Z", "authors": ["Saumya Banthia", "Anantha Sharma", "Ravi Mangipudi"], "categories": ["cs.CL", "cs.CV", "cs.IR"], "doi": "", "_query": "property valuation computer vision", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2402.12536v1", "title": "Designing High-Performing Networks for Multi-Scale Computer Vision", "abstract": "Since the emergence of deep learning, the computer vision field has flourished with models improving at a rapid pace on more and more complex tasks. We distinguish three main ways to improve a computer vision model: (1) improving the data aspect by for example training on a large, more diverse dataset, (2) improving the training aspect by for example designing a better optimizer, and (3) improving the network architecture (or network for short). In this thesis, we chose to improve the latter, i.e. improving the network designs of computer vision models. More specifically, we investigate new network designs for multi-scale computer vision tasks, which are tasks requiring to make predictions about concepts at different scales. The goal of these new network designs is to outperform existing baseline designs from the literature. Specific care is taken to make sure the comparisons are fair, by guaranteeing that the different network designs were trained and evaluated with the same settings. Code is publicly available at https://github.com/CedricPicron/DetSeg.", "published": "2024-02-19T20:50:55Z", "authors": ["Cédric Picron"], "categories": ["cs.CV"], "doi": "", "_query": "property valuation computer vision", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2404.03474v1", "title": "Performance of computer vision algorithms for fine-grained classification using crowdsourced insect images", "abstract": "With fine-grained classification, we identify unique characteristics to distinguish among classes of the same super-class. We are focusing on species recognition in Insecta, as they are critical for biodiversity monitoring and at the base of many ecosystems. With citizen science campaigns, billions of images are collected in the wild. Once these are labelled, experts can use them to create distribution maps. However, the labelling process is time-consuming, which is where computer vision comes in. The field of computer vision offers a wide range of algorithms, each with its strengths and weaknesses; how do we identify the algorithm that is in line with our application? To answer this question, we provide a full and detailed evaluation of nine algorithms among deep convolutional networks (CNN), vision transformers (ViT), and locality-based vision transformers (LBVT) on 4 different aspects: classification performance, embedding quality, computational cost, and gradient activity. We offer insights that we haven't yet had in this domain proving to which extent these algorithms solve the fine-grained tasks in Insecta. We found that the ViT performs the best on inference speed and computational cost while the LBVT outperforms the others on performance and embedding quality; the CNN provide a trade-off among the metrics.", "published": "2024-04-04T14:26:58Z", "authors": ["Rita Pucci", "Vincent J. Kalkman", "Dan Stowell"], "categories": ["cs.CV", "cs.AI"], "doi": "", "_query": "property valuation computer vision", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2301.10608v1", "title": "Connecting metrics for shape-texture knowledge in computer vision", "abstract": "Modern artificial neural networks, including convolutional neural networks and vision transformers, have mastered several computer vision tasks, including object recognition. However, there are many significant differences between the behavior and robustness of these systems and of the human visual system. Deep neural networks remain brittle and susceptible to many changes in the image that do not cause humans to misclassify images. Part of this different behavior may be explained by the type of features humans and deep neural networks use in vision tasks. Humans tend to classify objects according to their shape while deep neural networks seem to rely mostly on texture. Exploring this question is relevant, since it may lead to better performing neural network architectures and to a better understanding of the workings of the vision system of primates. In this work, we advance the state of the art in our understanding of this phenomenon, by extending previous analyses to a much larger set of deep neural network architectures. We found that the performance of models in image classification tasks is highly correlated with their shape bias measured at the output and penultimate layer. Furthermore, our results showed that the number of neurons that represent shape and texture are strongly anti-correlated, thus providing evidence that there is competition between these two types of features. Finally, we observed that while in general there is a correlation between performance and shape bias, there are significant variations between architecture families.", "published": "2023-01-25T14:37:42Z", "authors": ["Tiago Oliveira", "Tiago Marques", "Arlindo L. Oliveira"], "categories": ["cs.CV", "cs.LG"], "doi": "", "_query": "property valuation computer vision", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2203.03682v2", "title": "Monocular Robot Navigation with Self-Supervised Pretrained Vision Transformers", "abstract": "In this work, we consider the problem of learning a perception model for monocular robot navigation using few annotated images. Using a Vision Transformer (ViT) pretrained with a label-free self-supervised method, we successfully train a coarse image segmentation model for the Duckietown environment using 70 training images. Our model performs coarse image segmentation at the 8x8 patch level, and the inference resolution can be adjusted to balance prediction granularity and real-time perception constraints. We study how best to adapt a ViT to our task and environment, and find that some lightweight architectures can yield good single-image segmentation at a usable frame rate, even on CPU. The resulting perception model is used as the backbone for a simple yet robust visual servoing agent, which we deploy on a differential drive mobile robot to perform two tasks: lane following and obstacle avoidance.", "published": "2022-03-07T19:47:52Z", "authors": ["Miguel Saavedra-Ruiz", "Sacha Morin", "Liam Paull"], "categories": ["cs.RO", "cs.AI", "cs.CV"], "doi": "", "_query": "property valuation computer vision", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2608.01343v1", "title": "DeVIT: Low-Power Vision Transformer Acceleration Using Delta Computation", "abstract": "The emergence of transformer-based deep learning models has brought unprecedented performance across various domains, particularly in natural language processing and computer vision. However, deploying these models, especially on resource-constrained devices, poses significant challenges due to their high computational complexity and large memory size and bandwidth requirements. This complexity has led researchers to use low-bit model weights to reduce memory usage and improve efficiency. In addition to reducing processing and memory demands, quantization introduces another useful property: value locality, where the extremely large number of parameters are restricted to a limited range of values. To fully take advantage of this locality, this paper presents DeVIT, an acceleration method for vision transformers that leverages differential computation to enable multiplier-less matrix multiplication.", "published": "2026-08-02T16:08:44Z", "authors": ["Reyhaneh Hosseinzadeh", "Parham Zilouchian Moghaddam", "Mehdi Modarressi"], "categories": ["cs.CV", "cs.AI", "cs.AR"], "doi": "", "_query": "property valuation computer vision", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2504.02351v1", "title": "Agglomerating Large Vision Encoders via Distillation for VFSS Segmentation", "abstract": "The deployment of foundation models for medical imaging has demonstrated considerable success. However, their training overheads associated with downstream tasks remain substantial due to the size of the image encoders employed, and the inference complexity is also significantly high. Although lightweight variants have been obtained for these foundation models, their performance is constrained by their limited model capacity and suboptimal training strategies. In order to achieve an improved tradeoff between complexity and performance, we propose a new framework to improve the performance of low complexity models via knowledge distillation from multiple large medical foundation models (e.g., MedSAM, RAD-DINO, MedCLIP), each specializing in different vision tasks, with the goal to effectively bridge the performance gap for medical image segmentation tasks. The agglomerated model demonstrates superior generalization across 12 segmentation tasks, whereas specialized models require explicit training for each task. Our approach achieved an average performance gain of 2\\% in Dice coefficient compared to simple distillation.", "published": "2025-04-03T07:38:09Z", "authors": ["Chengxi Zeng", "Yuxuan Jiang", "Fan Zhang", "Alberto Gambaruto", "Tilo Burghardt"], "categories": ["cs.CV", "cs.AI"], "doi": "", "_query": "property valuation computer vision", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2610.19347v1", "title": "Integrated Novelty Detection Systems: From Sensor Networks to Multi-Novelty Computer Vision", "abstract": "Novelty detection is crucial in various technological and scientific domains. Its importance spans from ensuring the reliability of sensor networks to enhancing the adaptability of computer vision systems. As AI systems become increasingly sophisticated and complex, effective anomaly detection is essential for maintaining reliability and accuracy. This work addresses novelty detection in weather sensor networks and computer vision. In weather sensor networks, particularly for monitoring rainfall, detecting blocked rain gauges can significantly impact data reliability, food security, and disaster management. This work evaluates four existing quality control methods for detecting blocked rain gauges and demonstrates their limitations, particularly in detecting clusters of faulty sensors. By comparing methods such as the Conditional Mixture Model (CMM), PSWQ (Fz-filter), Kernel Regression, and Gaussian Method, we identify their shortcomings and propose three novel quality control techniques that integrate probabilistic fault detection and consistency-based diagnosis framework to improve timely detection of blocked sensors, while reducing false alarms. In the realm of computer vision, current systems often fail to handle multiple types of novelty due to their focus on single-novelty scenarios. We highlight the need for integrated systems capable of handling multiple types of novelty. To support this, we introduce the Novel Snapshot Serengeti (NOVEL-SS) dataset, derived from the Snapshot Serengeti camera trap data, which includes various forms of novelty. We also present the DCA (Detect, Characterize, and Accommodate) baseline system, designed to handle multiple novelty by detecting, characterizing, and accommodating them. This work aims to advance the robustness and adaptability of novelty detection systems across both weather sensor networks and computer vision applications.", "published": "2026-10-14T00:41:14Z", "authors": ["Irené Tematelewo"], "categories": ["cs.LG", "cs.CV"], "doi": "", "_query": "property valuation computer vision", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2208.08315v3", "title": "Video-TransUNet: Temporally Blended Vision Transformer for CT VFSS Instance Segmentation", "abstract": "We propose Video-TransUNet, a deep architecture for instance segmentation in medical CT videos constructed by integrating temporal feature blending into the TransUNet deep learning framework. In particular, our approach amalgamates strong frame representation via a ResNet CNN backbone, multi-frame feature blending via a Temporal Context Module (TCM), non-local attention via a Vision Transformer, and reconstructive capabilities for multiple targets via a UNet-based convolutional-deconvolutional architecture with multiple heads. We show that this new network design can significantly outperform other state-of-the-art systems when tested on the segmentation of bolus and pharynx/larynx in Videofluoroscopic Swallowing Study (VFSS) CT sequences. On our VFSS2022 dataset it achieves a dice coefficient of 0.8796 and an average surface distance of 1.0379 pixels. Note that tracking the pharyngeal bolus accurately is a particularly important application in clinical practice since it constitutes the primary method for diagnostics of swallowing impairment. Our findings suggest that the proposed model can indeed enhance the TransUNet architecture via exploiting temporal information and improving segmentation performance by a significant margin. We publish key source code, network weights, and ground truth annotations for simplified performance reproduction.", "published": "2022-08-17T14:28:58Z", "authors": ["Chengxi Zeng", "Xinyu Yang", "Majid Mirmehdi", "Alberto M Gambaruto", "Tilo Burghardt"], "categories": ["eess.IV", "cs.CV"], "doi": "", "_query": "property valuation computer vision", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2009.11931v1", "title": "A Computer Vision Approach to Combat Lyme Disease", "abstract": "Lyme disease is an infectious disease transmitted to humans by a bite from an infected Ixodes species (blacklegged ticks). It is one of the fastest growing vector-borne illness in North America and is expanding its geographic footprint. Lyme disease treatment is time-sensitive, and can be cured by administering an antibiotic (prophylaxis) to the patient within 72 hours after a tick bite by the Ixodes species. However, the laboratory-based identification of each tick that might carry the bacteria is time-consuming and labour intensive and cannot meet the maximum turn-around-time of 72 hours for an effective treatment. Early identification of blacklegged ticks using computer vision technologies is a potential solution in promptly identifying a tick and administering prophylaxis within a crucial window period. In this work, we build an automated detection tool that can differentiate blacklegged ticks from other ticks species using advanced deep learning and computer vision approaches. We demonstrate the classification of tick species using Convolution Neural Network (CNN) models, trained end-to-end from tick images directly. Advanced knowledge transfer techniques within teacher-student learning frameworks are adopted to improve the performance of classification of tick species. Our best CNN model achieves 92% accuracy on test set. The tool can be integrated with the geography of exposure to determine the risk of Lyme disease infection and need for prophylaxis treatment.", "published": "2020-09-24T20:00:02Z", "authors": ["Sina Akbarian", "Tania Cawston", "Laurent Moreno", "Samir Patel", "Vanessa Allen", "Elham Dolatabadi"], "categories": ["cs.CV", "cs.LG", "eess.IV"], "doi": "", "_query": "property valuation computer vision", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2209.06399v1", "title": "A Survey on Evolutionary Computation for Computer Vision and Image Analysis: Past, Present, and Future Trends", "abstract": "Computer vision (CV) is a big and important field in artificial intelligence covering a wide range of applications. Image analysis is a major task in CV aiming to extract, analyse and understand the visual content of images. However, image-related tasks are very challenging due to many factors, e.g., high variations across images, high dimensionality, domain expertise requirement, and image distortions. Evolutionary computation (EC) approaches have been widely used for image analysis with significant achievement. However, there is no comprehensive survey of existing EC approaches to image analysis. To fill this gap, this paper provides a comprehensive survey covering all essential EC approaches to important image analysis tasks including edge detection, image segmentation, image feature analysis, image classification, object detection, and others. This survey aims to provide a better understanding of evolutionary computer vision (ECV) by discussing the contributions of different approaches and exploring how and why EC is used for CV and image analysis. The applications, challenges, issues, and trends associated to this research field are also discussed and summarised to provide further guidelines and opportunities for future research.", "published": "2022-09-14T03:35:25Z", "authors": ["Ying Bi", "Bing Xue", "Pablo Mesejo", "Stefano Cagnoni", "Mengjie Zhang"], "categories": ["cs.NE", "cs.AI", "cs.CV", "cs.LG"], "doi": "10.1109/TEVC.2022.3220747", "_query": "property valuation computer vision", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/1302.1546v1", "title": "Inference with Idempotent Valuations", "abstract": "Valuation based systems verifying an idempotent property are studied. A partial order is defined between the valuations giving them a lattice structure. Then, two different strategies are introduced to represent valuations: as infimum of the most informative valuations or as supremum of the least informative ones. It is studied how to carry out computations with both representations in an efficient way. The particular cases of finite sets and convex polytopes are considered.", "published": "2013-02-06T15:56:12Z", "authors": ["Luis D. Hernandez", "Serafin Moral"], "categories": ["cs.AI"], "doi": "", "_query": "property valuation computer vision", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2110.11403v1", "title": "SCENIC: A JAX Library for Computer Vision Research and Beyond", "abstract": "Scenic is an open-source JAX library with a focus on Transformer-based models for computer vision research and beyond. The goal of this toolkit is to facilitate rapid experimentation, prototyping, and research of new vision architectures and models. Scenic supports a diverse range of vision tasks (e.g., classification, segmentation, detection)and facilitates working on multi-modal problems, along with GPU/TPU support for multi-host, multi-device large-scale training. Scenic also offers optimized implementations of state-of-the-art research models spanning a wide range of modalities. Scenic has been successfully used for numerous projects and published papers and continues serving as the library of choice for quick prototyping and publication of new research ideas.", "published": "2021-10-18T08:41:17Z", "authors": ["Mostafa Dehghani", "Alexey Gritsenko", "Anurag Arnab", "Matthias Minderer", "Yi Tay"], "categories": ["cs.CV", "cs.AI", "cs.CL", "cs.LG"], "doi": "", "_query": "property valuation computer vision", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/1506.04654v2", "title": "Thin Structure Estimation with Curvature Regularization", "abstract": "Many applications in vision require estimation of thin structures such as boundary edges, surfaces, roads, blood vessels, neurons, etc. Unlike most previous approaches, we simultaneously detect and delineate thin structures with sub-pixel localization and real-valued orientation estimation. This is an ill-posed problem that requires regularization. We propose an objective function combining detection likelihoods with a prior minimizing curvature of the center-lines or surfaces. Unlike simple block-coordinate descent, we develop a novel algorithm that is able to perform joint optimization of location and detection variables more effectively. Our lower bound optimization algorithm applies to quadratic or absolute curvature. The proposed early vision framework is sufficiently general and it can be used in many higher-level applications. We illustrate the advantage of our approach on a range of 2D and 3D examples.", "published": "2015-06-15T16:28:01Z", "authors": ["Dmitrii Marin", "Yuri Boykov", "Yuchen Zhong"], "categories": ["cs.CV"], "doi": "10.1109/ICCV.2015.53", "_query": "property valuation computer vision", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/1501.03783v2", "title": "Effective zero-dimensionality for computable metric spaces", "abstract": "We begin to study classical dimension theory from the computable analysis (TTE) point of view. For computable metric spaces, several effectivisations of zero-dimensionality are shown to be equivalent. The part of this characterisation that concerns covering dimension extends to higher dimensions and to closed shrinkings of finite open covers. To deal with zero-dimensional subspaces uniformly, four operations (relative to the space and a class of subspaces) are defined; these correspond to definitions of inductive and covering dimensions and a countable basis condition. Finally, an effective retract characterisation of zero-dimensionality is proven under an effective compactness condition. In one direction this uses a version of the construction of bilocated sets.", "published": "2015-01-14T04:56:47Z", "authors": ["Robert Kenny"], "categories": ["math.LO", "cs.LO"], "doi": "10.2168/LMCS-11(1:11)2015", "_query": "property valuation computer vision", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2203.05825v1", "title": "WiCV 2021: The Eighth Women In Computer Vision Workshop", "abstract": "In this paper, we present the details of Women in Computer Vision Workshop - WiCV 2021, organized alongside the virtual CVPR 2021. It provides a voice to a minority (female) group in the computer vision community and focuses on increasing the visibility of these researchers, both in academia and industry. WiCV believes that such an event can play an important role in lowering the gender imbalance in the field of computer vision. WiCV is organized each year where it provides a)~opportunity for collaboration between researchers from minority groups, b)~mentorship to female junior researchers, c)~financial support to presenters to overcome monetary burden and d)~large and diverse choice of role models, who can serve as examples to younger researchers at the beginning of their careers. In this paper, we present a report on the workshop program, trends over the past years, a summary of statistics regarding presenters, attendees, and sponsorship for the WiCV 2021 workshop.", "published": "2022-03-11T10:03:13Z", "authors": ["Arushi Goel", "Niveditha Kalavakonda", "Nour Karessli", "Tejaswi Kasarla", "Kathryn Leonard", "Boyi Li", "Nermin Samet and", "Ghada Zamzmi"], "categories": ["cs.CV"], "doi": "", "_query": "property valuation computer vision", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2609.07318v1", "title": "PLATOS: A Power and Latency-Aware Task-Oriented Scheduling Strategy for Healthcare IoT in Fog Computing", "abstract": "Healthcare Internet of Things (HIoT) technology is revolutionising the healthcare industry by enabling real-time data collection and analysis for personalised patient care. However, the rapid expansion of HIoT technology introduces challenges such as increased latency and higher energy consumption in fog computing environments, particularly when managing battery-operated devices. To address these issues, this work proposes a novel scheduling strategy that optimises both power consumption and latency through task-oriented scheduling for HIoT tasks. The proposed strategy, named PLATOS (Power and Latency Aware Task Oriented Scheduling), is implemented in four sequential phases. In the first phase, HIoT tasks are categorised into three groups: priority-oriented, storage-oriented, and computational-oriented. The second phase focuses on latency optimisation by identifying the fog computing resources that yield the lowest execution delay for each task category. In the third phase, power optimisation is achieved by selecting the resources that minimise energy consumption. Finally, in the decision-making phase, high-performance fog resources are allocated to high-priority tasks while the remaining tasks are scheduled based on a mapped list derived from the latency and power optimisation phases. Simulation experiments conducted in iFogSim2 demonstrate that PLATOS reduces energy consumption by 18.72% and latency by 8.65% when compared to the state-of-the-art. These improvements enhance the efficiency and responsiveness of HIoT systems and contribute to more effective patient care and proactive healthcare service delivery.", "published": "2026-09-07T10:36:52Z", "authors": ["Mohammed Alaa Ala'anzy", "Zulfiqar Ahmad", "Zhanar Mukash"], "categories": ["cs.DC", "cs.AI"], "doi": "10.32523/2306-6172-2025-13-1-4-21", "_query": "property valuation computer vision", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2310.00958v1", "title": "Constant Approximation for Private Interdependent Valuations", "abstract": "The celebrated model of auctions with interdependent valuations, introduced by Milgrom and Weber in 1982, has been studied almost exclusively under private signals $s_1, \\ldots, s_n$ of the $n$ bidders and public valuation functions $v_i(s_1, \\ldots, s_n)$. Recent work in TCS has shown that this setting admits a constant approximation to the optimal social welfare if the valuations satisfy a natural property called submodularity over signals (SOS). More recently, Eden et al. (2022) have extended the analysis of interdependent valuations to include settings with private signals and private valuations, and established $O(\\log^2 n)$-approximation for SOS valuations. In this paper we show that this setting admits a constant factor approximation, settling the open question raised by Eden et al. (2022).", "published": "2023-10-02T07:51:35Z", "authors": ["Alon Eden", "Michal Feldman", "Kira Goldner", "Simon Mauras", "Divyarthi Mohan"], "categories": ["cs.GT", "cs.DS"], "doi": "", "_query": "property valuation computer vision", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2208.11388v1", "title": "WiCV 2022: The Tenth Women In Computer Vision Workshop", "abstract": "In this paper, we present the details of Women in Computer Vision Workshop - WiCV 2022, organized alongside the hybrid CVPR 2022 in New Orleans, Louisiana. It provides a voice to a minority (female) group in the computer vision community and focuses on increasing the visibility of these researchers, both in academia and industry. WiCV believes that such an event can play an important role in lowering the gender imbalance in the field of computer vision. WiCV is organized each year where it provides a) opportunity for collaboration between researchers from minority groups, b) mentorship to female junior researchers, c) financial support to presenters to overcome monetary burden and d) large and diverse choice of role models, who can serve as examples to younger researchers at the beginning of their careers. In this paper, we present a report on the workshop program, trends over the past years, a summary of statistics regarding presenters, attendees, and sponsorship for the WiCV 2022 workshop.", "published": "2022-08-24T09:12:36Z", "authors": ["Doris Antensteiner", "Silvia Bucci", "Arushi Goel", "Marah Halawa", "Niveditha Kalavakonda", "Tejaswi Kasarla", "Miaomiao Liu", "Nermin Samet", "Ivaxi Sheth"], "categories": ["cs.CV"], "doi": "", "_query": "property valuation computer vision", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2507.03283v1", "title": "MolVision: Molecular Property Prediction with Vision Language Models", "abstract": "Molecular property prediction is a fundamental task in computational chemistry with critical applications in drug discovery and materials science. While recent works have explored Large Language Models (LLMs) for this task, they primarily rely on textual molecular representations such as SMILES/SELFIES, which can be ambiguous and structurally less informative. In this work, we introduce MolVision, a novel approach that leverages Vision-Language Models (VLMs) by integrating both molecular structure as images and textual descriptions to enhance property prediction. We construct a benchmark spanning ten diverse datasets, covering classification, regression and description tasks. Evaluating nine different VLMs in zero-shot, few-shot, and fine-tuned settings, we find that visual information improves prediction performance, particularly when combined with efficient fine-tuning strategies such as LoRA. Our results reveal that while visual information alone is insufficient, multimodal fusion significantly enhances generalization across molecular properties. Adaptation of vision encoder for molecular images in conjunction with LoRA further improves the performance. The code and data is available at : $\\href{https://molvision.github.io/MolVision/}{https://molvision.github.io/MolVision/}$.", "published": "2025-07-04T04:15:31Z", "authors": ["Deepan Adak", "Yogesh Singh Rawat", "Shruti Vyas"], "categories": ["cs.CV"], "doi": "", "_query": "property valuation computer vision", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2002.06028v1", "title": "Constrained Dominant sets and Its applications in computer vision", "abstract": "In this thesis, we present new schemes which leverage a constrained clustering method to solve several computer vision tasks ranging from image retrieval, image segmentation and co-segmentation, to person re-identification. In the last decades clustering methods have played a vital role in computer vision applications; herein, we focus on the extension, reformulation, and integration of a well-known graph and game theoretic clustering method known as Dominant Sets. Thus, we have demonstrated the validity of the proposed methods with extensive experiments which are conducted on several benchmark datasets.", "published": "2020-02-12T20:19:44Z", "authors": ["Alemu Leulseged Tesfaye"], "categories": ["cs.CV"], "doi": "", "_query": "property valuation computer vision", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2409.04850v1", "title": "Deep Computer Vision for Solar Physics Big Data: Opportunities and Challenges", "abstract": "With recent missions such as advanced space-based observatories like the Solar Dynamics Observatory (SDO) and Parker Solar Probe, and ground-based telescopes like the Daniel K. Inouye Solar Telescope (DKIST), the volume, velocity, and variety of data have made solar physics enter a transformative era as solar physics big data (SPBD). With the recent advancement of deep computer vision, there are new opportunities in SPBD for tackling problems that were previously unsolvable. However, there are new challenges arising due to the inherent characteristics of SPBD and deep computer vision models. This vision paper presents an overview of the different types of SPBD, explores new opportunities in applying deep computer vision to SPBD, highlights the unique challenges, and outlines several potential future research directions.", "published": "2024-09-07T15:05:24Z", "authors": ["Bo Shen", "Marco Marena", "Chenyang Li", "Qin Li", "Haodi Jiang", "Mengnan Du", "Jiajun Xu", "Haimin Wang"], "categories": ["cs.CV", "astro-ph.SR"], "doi": "", "_query": "property valuation computer vision", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2609.20962v1", "title": "MemeTAG: Keyword-Driven Meme Classification through Tag Embedding Reconstruction", "abstract": "The proliferation of harmful internet memes poses a significant societal threat, yet their automated classification remains a formidable algorithmic challenge due to the nuanced, multimodal nature of their content. To address this, we introduce MemeTAG, a novel dual-objective framework that pioneers a keyword-aware approach to meme classification. Our core innovation is a two-part semantic guidance mechanism: first, we leverage a pretrained Vision-Language Model to generate a set of descriptive keywords, that capture the high-level semantics. Second, we introduce the Aggregated Tag Inference Network (ATIN), an attention-based module that distills these keywords into a single, rich semantic embedding. This embedding serves as a target for a novel auxiliary reconstruction loss, which compels the model to learn deeply aligned visual and textual features. This approach, combined with an efficient three-stage training strategy, establishes a new state-of-the-art on the HarMeme, Hateful Memes Challenge (HMC), and PrideMM datasets, decisively outperforming existing state-of-the-art methods.", "published": "2026-09-17T18:17:12Z", "authors": ["Akshit Sharma", "Prashant W. Patil"], "categories": ["cs.CV", "cs.MM"], "doi": "10.1109/WACV61042.2026.00741", "_query": "property valuation computer vision", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2306.01929v1", "title": "Recent Advances of Local Mechanisms in Computer Vision: A Survey and Outlook of Recent Work", "abstract": "Inspired by the fact that human brains can emphasize discriminative parts of the input and suppress irrelevant ones, substantial local mechanisms have been designed to boost the development of computer vision. They can not only focus on target parts to learn discriminative local representations, but also process information selectively to improve the efficiency. In terms of application scenarios and paradigms, local mechanisms have different characteristics. In this survey, we provide a systematic review of local mechanisms for various computer vision tasks and approaches, including fine-grained visual recognition, person re-identification, few-/zero-shot learning, multi-modal learning, self-supervised learning, Vision Transformers, and so on. Categorization of local mechanisms in each field is summarized. Then, advantages and disadvantages for every category are analyzed deeply, leaving room for exploration. Finally, future research directions about local mechanisms have also been discussed that may benefit future works. To the best our knowledge, this is the first survey about local mechanisms on computer vision. We hope that this survey can shed light on future research in the computer vision field.", "published": "2023-06-02T22:05:52Z", "authors": ["Qiangchang Wang", "Yilong Yin"], "categories": ["cs.CV"], "doi": "", "_query": "property valuation computer vision", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2204.02581v1", "title": "Banana Sub-Family Classification and Quality Prediction using Computer Vision", "abstract": "India is the second largest producer of fruits and vegetables in the world, and one of the largest consumers of fruits like Banana, Papaya and Mangoes through retail and ecommerce giants like BigBasket, Grofers and Amazon Fresh. However, adoption of technology in supply chain and retail stores is still low and there is a great potential to adopt computer-vision based technology for identification and classification of fruits. We have chosen banana fruit to build a computer vision based model to carry out the following three use-cases (a) Identify Banana from a given image (b) Determine sub-family or variety of Banana (c) Determine the quality of Banana. Successful execution of these use-cases using computer-vision model would greatly help with overall inventory management automation, quality control, quick and efficient weighing and billing which all are manual labor intensive currently. In this work, we suggest a machine learning pipeline that combines the ideas of CNNs, transfer learning, and data augmentation towards improving Banana fruit sub family and quality image classification. We have built a basic CNN and then went on to tune a MobileNet Banana classification model using a combination of self-curated and publicly-available dataset of 3064 images. The results show an overall 93.4% and 100% accuracy for sub-family/variety and for quality test classifications respectively.", "published": "2022-04-06T05:06:51Z", "authors": ["Narayana Darapaneni", "Arjun Tanndalam", "Mohit Gupta", "Neeta Taneja", "Prabu Purushothaman", "Swati Eswar", "Anwesh Reddy Paduri", "Thangaselvi Arichandrapandian"], "categories": ["cs.CV", "cs.AI"], "doi": "", "_query": "property valuation computer vision", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2408.07221v3", "title": "A Review of Pseudo-Labeling for Computer Vision", "abstract": "Deep neural models have achieved state of the art performance on a wide range of problems in computer science, especially in computer vision. However, deep neural networks often require large datasets of labeled samples to generalize effectively, and an important area of active research is semi-supervised learning, which attempts to instead utilize large quantities of (easily acquired) unlabeled samples. One family of methods in this space is pseudo-labeling, a class of algorithms that use model outputs to assign labels to unlabeled samples which are then used as labeled samples during training. Such assigned labels, called pseudo-labels, are most commonly associated with the field of semi-supervised learning. In this work we explore a broader interpretation of pseudo-labels within both self-supervised and unsupervised methods. By drawing the connection between these areas we identify new directions when advancements in one area would likely benefit others, such as curriculum learning and self-supervised regularization.", "published": "2024-08-13T22:17:48Z", "authors": ["Patrick Kage", "Jay C. Rothenberger", "Pavlos Andreadis", "Dimitrios I. Diochnos"], "categories": ["cs.CV", "cs.LG"], "doi": "10.1613/jair.1.19656", "_query": "property valuation computer vision", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2202.10108v2", "title": "ViTAEv2: Vision Transformer Advanced by Exploring Inductive Bias for Image Recognition and Beyond", "abstract": "Vision transformers have shown great potential in various computer vision tasks owing to their strong capability to model long-range dependency using the self-attention mechanism. Nevertheless, they treat an image as a 1D sequence of visual tokens, lacking an intrinsic inductive bias (IB) in modeling local visual structures and dealing with scale variance, which is instead learned implicitly from large-scale training data with longer training schedules. In this paper, we propose a Vision Transformer Advanced by Exploring intrinsic IB from convolutions, i.e., ViTAE. Technically, ViTAE has several spatial pyramid reduction modules to downsample and embed the input image into tokens with rich multi-scale context using multiple convolutions with different dilation rates. In this way, it acquires an intrinsic scale invariance IB and can learn robust feature representation for objects at various scales. Moreover, in each transformer layer, ViTAE has a convolution block parallel to the multi-head self-attention module, whose features are fused and fed into the feed-forward network. Consequently, it has the intrinsic locality IB and is able to learn local features and global dependencies collaboratively. The proposed two kinds of cells are stacked in both isotropic and multi-stage manners to formulate two families of ViTAE models, i.e., the vanilla ViTAE and ViTAEv2. Experiments on the ImageNet dataset as well as downstream tasks on the MS COCO, ADE20K, and AP10K datasets validate the superiority of our models over the baseline transformer models and concurrent works. Besides, we scale up our ViTAE model to 644M parameters and obtain the state-of-the-art classification performance, i.e., 88.5% Top-1 classification accuracy on ImageNet validation set and the best 91.2% Top-1 accuracy on ImageNet real validation set, without using extra private data.", "published": "2022-02-21T10:40:05Z", "authors": ["Qiming Zhang", "Yufei Xu", "Jing Zhang", "Dacheng Tao"], "categories": ["cs.CV"], "doi": "", "_query": "property valuation computer vision", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2306.09179v1", "title": "Neural World Models for Computer Vision", "abstract": "Humans navigate in their environment by learning a mental model of the world through passive observation and active interaction. Their world model allows them to anticipate what might happen next and act accordingly with respect to an underlying objective. Such world models hold strong promises for planning in complex environments like in autonomous driving. A human driver, or a self-driving system, perceives their surroundings with their eyes or their cameras. They infer an internal representation of the world which should: (i) have spatial memory (e.g. occlusions), (ii) fill partially observable or noisy inputs (e.g. when blinded by sunlight), and (iii) be able to reason about unobservable events probabilistically (e.g. predict different possible futures). They are embodied intelligent agents that can predict, plan, and act in the physical world through their world model. In this thesis we present a general framework to train a world model and a policy, parameterised by deep neural networks, from camera observations and expert demonstrations. We leverage important computer vision concepts such as geometry, semantics, and motion to scale world models to complex urban driving scenes. First, we propose a model that predicts important quantities in computer vision: depth, semantic segmentation, and optical flow. We then use 3D geometry as an inductive bias to operate in the bird's-eye view space. We present for the first time a model that can predict probabilistic future trajectories of dynamic agents in bird's-eye view from 360° surround monocular cameras only. Finally, we demonstrate the benefits of learning a world model in closed-loop driving. Our model can jointly predict static scene, dynamic scene, and ego-behaviour in an urban driving environment.", "published": "2023-06-15T14:58:21Z", "authors": ["Anthony Hu"], "categories": ["cs.CV", "cs.AI", "cs.RO"], "doi": "", "_query": "property valuation computer vision", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2505.21089v2", "title": "DisasterM3: A Remote Sensing Vision-Language Dataset for Disaster Damage Assessment and Response", "abstract": "Large vision-language models (VLMs) have made great achievements in Earth vision. However, complex disaster scenes with diverse disaster types, geographic regions, and satellite sensors have posed new challenges for VLM applications. To fill this gap, we curate a remote sensing vision-language dataset (DisasterM3) for global-scale disaster assessment and response. DisasterM3 includes 26,988 bi-temporal satellite images and 123k instruction pairs across 5 continents, with three characteristics: 1) Multi-hazard: DisasterM3 involves 36 historical disaster events with significant impacts, which are categorized into 10 common natural and man-made disasters. 2)Multi-sensor: Extreme weather during disasters often hinders optical sensor imaging, making it necessary to combine Synthetic Aperture Radar (SAR) imagery for post-disaster scenes. 3) Multi-task: Based on real-world scenarios, DisasterM3 includes 9 disaster-related visual perception and reasoning tasks, harnessing the full potential of VLM's reasoning ability with progressing from disaster-bearing body recognition to structural damage assessment and object relational reasoning, culminating in the generation of long-form disaster reports. We extensively evaluated 14 generic and remote sensing VLMs on our benchmark, revealing that state-of-the-art models struggle with the disaster tasks, largely due to the lack of a disaster-specific corpus, cross-sensor gap, and damage object counting insensitivity. Focusing on these issues, we fine-tune four VLMs using our dataset and achieve stable improvements across all tasks, with robust cross-sensor and cross-disaster generalization capabilities. The code and data are available at: https://github.com/Junjue-Wang/DisasterM3.", "published": "2025-05-27T12:16:07Z", "authors": ["Junjue Wang", "Weihao Xuan", "Heli Qi", "Zhihao Liu", "Kunyi Liu", "Yuhan Wu", "Hongruixuan Chen", "Jian Song", "Junshi Xia", "Zhuo Zheng", "Naoto Yokoya"], "categories": ["cs.CV"], "doi": "", "_query": "property valuation computer vision", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2303.17235v2", "title": "Kaizen: Practical Self-supervised Continual Learning with Continual Fine-tuning", "abstract": "Self-supervised learning (SSL) has shown remarkable performance in computer vision tasks when trained offline. However, in a Continual Learning (CL) scenario where new data is introduced progressively, models still suffer from catastrophic forgetting. Retraining a model from scratch to adapt to newly generated data is time-consuming and inefficient. Previous approaches suggested re-purposing self-supervised objectives with knowledge distillation to mitigate forgetting across tasks, assuming that labels from all tasks are available during fine-tuning. In this paper, we generalize self-supervised continual learning in a practical setting where available labels can be leveraged in any step of the SSL process. With an increasing number of continual tasks, this offers more flexibility in the pre-training and fine-tuning phases. With Kaizen, we introduce a training architecture that is able to mitigate catastrophic forgetting for both the feature extractor and classifier with a carefully designed loss function. By using a set of comprehensive evaluation metrics reflecting different aspects of continual learning, we demonstrated that Kaizen significantly outperforms previous SSL models in competitive vision benchmarks, with up to 16.5% accuracy improvement on split CIFAR-100. Kaizen is able to balance the trade-off between knowledge retention and learning from new data with an end-to-end model, paving the way for practical deployment of continual learning systems.", "published": "2023-03-30T09:08:57Z", "authors": ["Chi Ian Tang", "Lorena Qendro", "Dimitris Spathis", "Fahim Kawsar", "Cecilia Mascolo", "Akhil Mathur"], "categories": ["cs.LG"], "doi": "", "_query": "property valuation computer vision", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2305.18035v3", "title": "Physics-Informed Computer Vision: A Review and Perspectives", "abstract": "The incorporation of physical information in machine learning frameworks is opening and transforming many application domains. Here the learning process is augmented through the induction of fundamental knowledge and governing physical laws. In this work, we explore their utility for computer vision tasks in interpreting and understanding visual data. We present a systematic literature review of more than 250 papers on formulation and approaches to computer vision tasks guided by physical laws. We begin by decomposing the popular computer vision pipeline into a taxonomy of stages and investigate approaches to incorporate governing physical equations in each stage. Existing approaches in computer vision tasks are analyzed with regard to what governing physical processes are modeled and formulated, and how they are incorporated, i.e. modification of input data (observation bias), modification of network architectures (inductive bias), and modification of training losses (learning bias). The taxonomy offers a unified view of the application of the physics-informed capability, highlighting where physics-informed learning has been conducted and where the gaps and opportunities are. Finally, we highlight open problems and challenges to inform future research. While still in its early days, the study of physics-informed computer vision has the promise to develop better computer vision models that can improve physical plausibility, accuracy, data efficiency, and generalization in increasingly realistic applications.", "published": "2023-05-29T11:55:11Z", "authors": ["Chayan Banerjee", "Kien Nguyen", "Clinton Fookes", "George Karniadakis"], "categories": ["eess.IV", "cs.CV"], "doi": "", "_query": "property valuation computer vision", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2106.03805v1", "title": "3DB: A Framework for Debugging Computer Vision Models", "abstract": "We introduce 3DB: an extendable, unified framework for testing and debugging vision models using photorealistic simulation. We demonstrate, through a wide range of use cases, that 3DB allows users to discover vulnerabilities in computer vision systems and gain insights into how models make decisions. 3DB captures and generalizes many robustness analyses from prior work, and enables one to study their interplay. Finally, we find that the insights generated by the system transfer to the physical world. We are releasing 3DB as a library (https://github.com/3db/3db) alongside a set of example analyses, guides, and documentation: https://3db.github.io/3db/ .", "published": "2021-06-07T17:16:12Z", "authors": ["Guillaume Leclerc", "Hadi Salman", "Andrew Ilyas", "Sai Vemprala", "Logan Engstrom", "Vibhav Vineet", "Kai Xiao", "Pengchuan Zhang", "Shibani Santurkar", "Greg Yang", "Ashish Kapoor", "Aleksander Madry"], "categories": ["cs.CV", "cs.LG", "stat.ML"], "doi": "", "_query": "property valuation computer vision", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2104.05125v1", "title": "Shuffler: A Large Scale Data Management Tool for ML in Computer Vision", "abstract": "Datasets in the computer vision academic research community are primarily static. Once a dataset is accepted as a benchmark for a computer vision task, researchers working on this task will not alter it in order to make their results reproducible. At the same time, when exploring new tasks and new applications, datasets tend to be an ever changing entity. A practitioner may combine existing public datasets, filter images or objects in them, change annotations or add new ones to fit a task at hand, visualize sample images, or perhaps output statistics in the form of text or plots. In fact, datasets change as practitioners experiment with data as much as with algorithms, trying to make the most out of machine learning models. Given that ML and deep learning call for large volumes of data to produce satisfactory results, it is no surprise that the resulting data and software management associated to dealing with live datasets can be quite complex. As far as we know, there is no flexible, publicly available instrument to facilitate manipulating image data and their annotations throughout a ML pipeline. In this work, we present Shuffler, an open source tool that makes it easy to manage large computer vision datasets. It stores annotations in a relational, human-readable database. Shuffler defines over 40 data handling operations with annotations that are commonly useful in supervised learning applied to computer vision and supports some of the most well-known computer vision datasets. Finally, it is easily extensible, making the addition of new operations and datasets a task that is fast and easy to accomplish.", "published": "2021-04-11T22:27:28Z", "authors": ["Evgeny Toropov", "Paola A. Buitrago", "Jose M. F. Moura"], "categories": ["cs.CV", "cs.DC"], "doi": "10.1145/3332186.3333046", "_query": "property valuation computer vision", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2511.12625v1", "title": "A spatio-temporal statistical model for property valuation at country-scale with adjustments for regional submarkets", "abstract": "Valuing residential property is inherently complex, requiring consideration of numerous environmental, economic, and property-specific factors. These complexities present significant challenges for automated valuation models (AVMs), which are increasingly used to provide objective assessments for property taxation and mortgage financing. The challenge of obtaining accurate and objective valuations for properties at a country level, and not just within major cities, is further compounded by the presence of multiple localised submarkets-spanning urban, suburban, and rural contexts-where property features contribute differently to value. Existing AVMs often struggle in such settings: traditional hedonic regression models lack the flexibility to capture spatial variation, while advanced machine learning approaches demand extensive datasets that are rarely available. In this article, we address these limitations by developing a robust statistical framework for property valuation in the Irish housing market. We segment the country into six submarkets encompassing cities, large towns, and rural areas, and employ a generalized additive model that captures non-linear effects of property characteristics while allowing feature contributions to vary across submarkets. Our approach outperforms both machine learning-based and traditional hedonic regression models, particularly in data-sparse regions. In out-of-sample validation, our model achieves R-squared values of 0.70, 0.84, and 0.83 for rural areas, towns, and Dublin, respectively, compared to 0.52, 0.71, and 0.82 from a random forest benchmark. Furthermore, the temporal dynamics of our model align closely with reported inflation figures for the study period, providing additional validation of its accuracy.", "published": "2025-11-16T14:34:33Z", "authors": ["Brian O'Donovan", "Andrew Finley", "James Sweeney"], "categories": ["stat.AP"], "doi": "", "_query": "property valuation computer vision", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2602.23514v1", "title": "Modelling and Simulation of Neuromorphic Datasets for Anomaly Detection in Computer Vision", "abstract": "Limitations on the availability of Dynamic Vision Sensors (DVS) present a fundamental challenge to researchers of neuromorphic computer vision applications. In response, datasets have been created by the research community, but often contain a limited number of samples or scenarios. To address the lack of a comprehensive simulator of neuromorphic vision datasets, we introduce the Anomalous Neuromorphic Tool for Shapes (ANTShapes), a novel dataset simulation framework. Built in the Unity engine, ANTShapes simulates abstract, configurable 3D scenes populated by objects displaying randomly-generated behaviours describing attributes such as motion and rotation. The sampling of object behaviours, and the labelling of anomalously-acting objects, is a statistical process following central limit theorem principles. Datasets containing an arbitrary number of samples can be created and exported from ANTShapes, along with accompanying label and frame data, through the adjustment of a limited number of parameters within the software. ANTShapes addresses the limitations of data availability to researchers of event-based computer vision by allowing for the simulation of bespoke datasets to suit purposes including object recognition and localisation alongside anomaly detection.", "published": "2026-02-26T21:38:05Z", "authors": ["Mike Middleton", "Teymoor Ali", "Hakan Kayan", "Basabdatta Sen Bhattacharya", "Charith Perera", "Oliver Rhodes", "Elena Gheorghiu", "Mark Vousden", "Martin A. Trefzer"], "categories": ["cs.CV", "cs.AI", "cs.LG"], "doi": "", "_query": "property valuation computer vision", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2508.08317v1", "title": "Evaluation of State-of-the-Art Deep Learning Techniques for Plant Disease and Pest Detection", "abstract": "Addressing plant diseases and pests is critical for enhancing crop production and preventing economic losses. Recent advances in artificial intelligence (AI), machine learning (ML), and deep learning (DL) have significantly improved the precision and efficiency of detection methods, surpassing the limitations of manual identification. This study reviews modern computer-based techniques for detecting plant diseases and pests from images, including recent AI developments. The methodologies are organized into five categories: hyperspectral imaging, non-visualization techniques, visualization approaches, modified deep learning architectures, and transformer models. This structured taxonomy provides researchers with detailed, actionable insights for selecting advanced state-of-the-art detection methods. A comprehensive survey of recent work and comparative studies demonstrates the consistent superiority of modern AI-based approaches, which often outperform older image analysis methods in speed and accuracy. In particular, vision transformers such as the Hierarchical Vision Transformer (HvT) have shown accuracy exceeding 99.3% in plant disease detection, outperforming architectures like MobileNetV3. The study concludes by discussing system design challenges, proposing solutions, and outlining promising directions for future research.", "published": "2025-08-09T08:23:33Z", "authors": ["Saptarshi Banerjee", "Tausif Mallick", "Amlan Chakroborty", "Himadri Nath Saha", "Nityananda T. Takur"], "categories": ["cs.CV", "cs.AI"], "doi": "10.32604/cmc.2025.065250", "_query": "property valuation computer vision", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2609.06967v1", "title": "Re-calibrated Contrastive Loss for Transformation-Aware Prompt Conditioning in Vision-Language Models", "abstract": "Ensuring effective transfer learning for vision-language models without compromising their generalization performance is crucial. However, many existing methods overlook data characteristics and simply reuse the training strategies adopted during pre-training. Specifically, they treat same-class samples as distinct instances and transform images independently of their paired text prompts, which makes model learning more difficult. We address these limitations through transformation-aware prompt conditioning and a re-calibrated contrastive loss. Fixed text descriptors identify the transformations applied to paired images, providing transformation-level consistency without altering class semantics. This design aligns the image and text branches at the transformation level, enabling richer representations while preserving the models' ability to generalize. In addition, our loss function mitigates positive-gradient dilution in soft-target cross-entropy when each anchor has multiple valid positives. During transfer, our approach treats same-class samples as positives rather than distinct instances, enabling the model to learn domain-specific features more effectively. Experiments across distribution shift, transfer learning, and few-shot settings demonstrate consistent improvements over existing approaches. Source code for our method is available at https://github.com/SoongE/ReCalCon.", "published": "2026-09-07T03:04:38Z", "authors": ["Seungmin Oh", "Seunghun Kang", "Jongbin Ryu"], "categories": ["cs.CV", "cs.AI"], "doi": "", "_query": "property valuation computer vision", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2604.14629v1", "title": "Switch-KD: Visual-Switch Knowledge Distillation for Vision-Language Models", "abstract": "Vision-Language Models (VLMs) have shown remarkable capabilities in joint vision-language understanding, but their large scale poses significant challenges for deployment in resource-constrained scenarios. Knowledge Distillation (KD) offers a viable way to improve model capabilities without increasing model size or data requirements, making deployment more efficient. However, applying KD to VLMs is challenged by modality-specific supervision: although multimodal knowledge in VLMs is fused within the language space, current methods supervise each modality separately without explicitly addressing multimodal alignment, leading to inconsistent multimodal knowledge transfer. To address this, we propose Switch-KD, a visual-switch distillation framework that unifies vision-language knowledge transfer within a shared text-probability space. Switch-KD comprises two key components: (1) Visual-Switch Distillation, which switches the student's visual outputs into the teacher's language pathway to construct cross-modal probabilistic references for implicit visual knowledge transfer; and (2) Dynamic Bi-directional Logits Difference (DBiLD) loss, which adaptively aligns informative probability regions while preserving the distributional structures of teacher and student through bidirectional supervision. Guided by Switch-KD, a 0.5B TinyLLaVA effectively distills rich multimodal knowledge from its 3B teacher, yielding an average improvement of 3.6 points across 10 multimodal benchmarks without any architectural modification.", "published": "2026-04-16T05:13:57Z", "authors": ["Haoyi Sun", "Xiaoxiao Wang", "Ning Mao", "Qian Wang", "Lifu Mu", "Wen Zheng", "Tao Wei", "Wei Chen"], "categories": ["cs.CV"], "doi": "", "_query": "property valuation computer vision", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2512.05736v1", "title": "The Mathematics of Income Property Valuation", "abstract": "This paper surveys and generalizes the main valuation formulas used in real estate valuation and presents unified proofs. The results are otherwise scattered in obscure journals and books while the proofs are rarely available to researchers in the field. The material was originally developed so that it could be used by mathematically-trained appraisers and researchers in the former Soviet Union and in other transition economies that were starting their real estate valuation profession. Keywords: real estate valuations; six functions of one; Ellwood, Akerson, and Hoskold formulas; capitalization rate methods; amortization tables JEL: G12, R3", "published": "2025-12-05T14:15:50Z", "authors": ["David Ellerman"], "categories": ["econ.TH"], "doi": "", "_query": "property valuation computer vision", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2011.06089v2", "title": "Continuous Perception for Classifying Shapes and Weights of Garmentsfor Robotic Vision Applications", "abstract": "We present an approach to continuous perception for robotic laundry tasks. Our assumption is that the visual prediction of a garment's shapes and weights is possible via a neural network that learns the dynamic changes of garments from video sequences. Continuous perception is leveraged during training by inputting consecutive frames, of which the network learns how a garment deforms. To evaluate our hypothesis, we captured a dataset of 40K RGB and 40K depth video sequences while a garment is being manipulated. We also conducted ablation studies to understand whether the neural network learns the physical and dynamic properties of garments. Our findings suggest that a modified AlexNet-LSTM architecture has the best classification performance for the garment's shape and weights. To further provide evidence that continuous perception facilitates the prediction of the garment's shapes and weights, we evaluated our network on unseen video sequences and computed the 'Moving Average' over a sequence of predictions. We found that our network has a classification accuracy of 48% and 60% for shapes and weights of garments, respectively.", "published": "2020-11-11T21:55:58Z", "authors": ["Li Duan", "Gerardo Aragon-Camarasa"], "categories": ["cs.RO", "cs.CV"], "doi": "", "_query": "property valuation computer vision", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2408.00493v1", "title": "Explainable Emotion Decoding for Human and Computer Vision", "abstract": "Modern Machine Learning (ML) has significantly advanced various research fields, but the opaque nature of ML models hinders their adoption in several domains. Explainable AI (XAI) addresses this challenge by providing additional information to help users understand the internal decision-making process of ML models. In the field of neuroscience, enriching a ML model for brain decoding with attribution-based XAI techniques means being able to highlight which brain areas correlate with the task at hand, thus offering valuable insights to domain experts. In this paper, we analyze human and Computer Vision (CV) systems in parallel, training and explaining two ML models based respectively on functional Magnetic Resonance Imaging (fMRI) and movie frames. We do so by leveraging the \"StudyForrest\" dataset, which includes functional Magnetic Resonance Imaging (fMRI) scans of subjects watching the \"Forrest Gump\" movie, emotion annotations, and eye-tracking data. For human vision the ML task is to link fMRI data with emotional annotations, and the explanations highlight the brain regions strongly correlated with the label. On the other hand, for computer vision, the input data is movie frames, and the explanations are pixel-level heatmaps. We cross-analyzed our results, linking human attention (obtained through eye-tracking) with XAI saliency on CV models and brain region activations. We show how a parallel analysis of human and computer vision can provide useful information for both the neuroscience community (allocation theory) and the ML community (biological plausibility of convolutional models).", "published": "2024-08-01T11:53:44Z", "authors": ["Alessio Borriero", "Martina Milazzo", "Matteo Diano", "Davide Orsenigo", "Maria Chiara Villa", "Chiara Di Fazio", "Marco Tamietto", "Alan Perotti"], "categories": ["cs.CV", "eess.IV", "q-bio.NC"], "doi": "", "_query": "property valuation computer vision", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2506.16262v2", "title": "R3eVision: A Survey on Robust Rendering, Restoration, and Enhancement for 3D Low-Level Vision", "abstract": "Neural rendering methods such as Neural Radiance Fields (NeRF) and 3D Gaussian Splatting (3DGS) have achieved significant progress in photorealistic 3D scene reconstruction and novel view synthesis. However, most existing models assume clean and high-resolution (HR) multi-view inputs, which limits their robustness under real-world degradations such as noise, blur, low-resolution (LR), and weather-induced artifacts. To address these limitations, the emerging field of 3D Low-Level Vision (3D LLV) extends classical 2D Low-Level Vision tasks including super-resolution (SR), deblurring, weather degradation removal, restoration, and enhancement into the 3D spatial domain. This survey, referred to as R\\textsuperscript{3}eVision, provides a comprehensive overview of robust rendering, restoration, and enhancement for 3D LLV by formalizing the degradation-aware rendering problem and identifying key challenges related to spatio-temporal consistency and ill-posed optimization. Recent methods that integrate LLV into neural rendering frameworks are categorized to illustrate how they enable high-fidelity 3D reconstruction under adverse conditions. Application domains such as autonomous driving, AR/VR, and robotics are also discussed, where reliable 3D perception from degraded inputs is critical. By reviewing representative methods, datasets, and evaluation protocols, this work positions 3D LLV as a fundamental direction for robust 3D content generation and scene-level reconstruction in real-world environments.", "published": "2025-06-19T12:25:46Z", "authors": ["Weeyoung Kwon", "Jeahun Sung", "Minkyu Jeon", "Chanho Eom", "Jihyong Oh"], "categories": ["cs.CV"], "doi": "", "_query": "property valuation computer vision", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/1109.6442v2", "title": "ABHIVYAKTI: A Vision Based Intelligent System for Elder and Sick Persons", "abstract": "This paper describes an intelligent system ABHIVYAKTI, which would be pervasive in nature and based on the Computer Vision. It would be very easy in use and deployment. Elder and sick people who are not able to talk or walk, they are dependent on other human beings and need continuous monitoring, while our system provides flexibility to the sick or elder person to announce his or her need to their caretaker by just showing a particular gesture with the developed system, if the caretaker is not nearby. This system will use fingertip detection techniques for acquiring gesture and Artificial Neural Networks (ANNs) will be used for gesture recognition.", "published": "2011-09-29T08:59:29Z", "authors": ["Ankit Chaudhary", "Jagdish L. Raheja"], "categories": ["cs.CV"], "doi": "", "_query": "property valuation computer vision", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2504.13763v2", "title": "Decoding Vision Transformers: the Diffusion Steering Lens", "abstract": "Logit Lens is a widely adopted method for mechanistic interpretability of transformer-based language models, enabling the analysis of how internal representations evolve across layers by projecting them into the output vocabulary space. Although applying Logit Lens to Vision Transformers (ViTs) is technically straightforward, its direct use faces limitations in capturing the richness of visual representations. Building on the work of Toker et al. (2024)~\\cite{Toker2024-ve}, who introduced Diffusion Lens to visualize intermediate representations in the text encoders of text-to-image diffusion models, we demonstrate that while Diffusion Lens can effectively visualize residual stream representations in image encoders, it fails to capture the direct contributions of individual submodules. To overcome this limitation, we propose \\textbf{Diffusion Steering Lens} (DSL), a novel, training-free approach that steers submodule outputs and patches subsequent indirect contributions. We validate our method through interventional studies, showing that DSL provides an intuitive and reliable interpretation of the internal processing in ViTs.", "published": "2025-04-18T16:00:53Z", "authors": ["Ryota Takatsuki", "Sonia Joseph", "Ippei Fujisawa", "Ryota Kanai"], "categories": ["cs.CV", "cs.AI"], "doi": "", "_query": "property valuation computer vision", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2406.10935v1", "title": "Pick-or-Mix: Dynamic Channel Sampling for ConvNets", "abstract": "Channel pruning approaches for convolutional neural networks (ConvNets) deactivate the channels, statically or dynamically, and require special implementation. In addition, channel squeezing in representative ConvNets is carried out via 1x1 convolutions which dominates a large portion of computations and network parameters. Given these challenges, we propose an effective multi-purpose module for dynamic channel sampling, namely Pick-or-Mix (PiX), which does not require special implementation. PiX divides a set of channels into subsets and then picks from them, where the picking decision is dynamically made per each pixel based on the input activations. We plug PiX into prominent ConvNet architectures and verify its multi-purpose utilities. After replacing 1x1 channel squeezing layers in ResNet with PiX, the network becomes 25% faster without losing accuracy. We show that PiX allows ConvNets to learn better data representation than widely adopted approaches to enhance networks' representation power (e.g., SE, CBAM, AFF, SKNet, and DWP). We also show that PiX achieves state-of-the-art performance on network downscaling and dynamic channel pruning applications.", "published": "2024-06-16T13:33:09Z", "authors": ["Ashish Kumar", "Daneul Kim", "Jaesik Park", "Laxmidhar Behera"], "categories": ["cs.CV"], "doi": "", "_query": "property valuation computer vision", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2504.04242v2", "title": "Task-based Loss Functions in Computer Vision: A Comprehensive Review", "abstract": "Loss functions are at the heart of deep learning, shaping how models learn and perform across diverse tasks. They are used to quantify the difference between predicted outputs and ground truth labels, guiding the optimization process to minimize errors. Selecting the right loss function is critical, as it directly impacts model convergence, generalization, and overall performance across various applications, from computer vision to time series forecasting. This paper presents a comprehensive review of loss functions, covering fundamental metrics like Mean Squared Error and Cross-Entropy to advanced functions such as Adversarial and Diffusion losses. We explore their mathematical foundations, impact on model training, and strategic selection for various applications, including computer vision (Discriminative and generative), tabular data prediction, and time series forecasting. For each of these categories, we discuss the most used loss functions in the recent advancements of deep learning techniques. Also, this review explore the historical evolution, computational efficiency, and ongoing challenges in loss function design, underlining the need for more adaptive and robust solutions. Emphasis is placed on complex scenarios involving multi-modal data, class imbalances, and real-world constraints. Finally, we identify key future directions, advocating for loss functions that enhance interpretability, scalability, and generalization, leading to more effective and resilient deep learning models.", "published": "2025-04-05T18:07:20Z", "authors": ["Omar Elharrouss", "Yasir Mahmood", "Yassine Bechqito", "Mohamed Adel Serhani", "Elarbi Badidi", "Jamal Riffi", "Hamid Tairi"], "categories": ["cs.LG", "cs.CV"], "doi": "", "_query": "property valuation computer vision", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/1002.2184v1", "title": "The Fast Haar Wavelet Transform for Signal & Image Processing", "abstract": "A method for the design of Fast Haar wavelet for signal processing and image processing has been proposed. In the proposed work, the analysis bank and synthesis bank of Haar wavelet is modified by using polyphase structure. Finally, the Fast Haar wavelet was designed and it satisfies alias free and perfect reconstruction condition. Computational time and computational complexity is reduced in Fast Haar wavelet transform.", "published": "2010-02-10T19:27:25Z", "authors": ["V. Ashok", "T. Balakumaran", "C. Gowrishankar", "I. L. A. Vennila", "A. Nirmal kumar"], "categories": ["cs.MM", "cs.CV"], "doi": "", "_query": "property valuation computer vision", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/1812.04949v2", "title": "Subjective Annotations for Vision-Based Attention Level Estimation", "abstract": "Attention level estimation systems have a high potential in many use cases, such as human-robot interaction, driver modeling and smart home systems, since being able to measure a person's attention level opens the possibility to natural interaction between humans and computers. The topic of estimating a human's visual focus of attention has been actively addressed recently in the field of HCI. However, most of these previous works do not consider attention as a subjective, cognitive attentive state. New research within the field also faces the problem of the lack of annotated datasets regarding attention level in a certain context. The novelty of our work is two-fold: First, we introduce a new annotation framework that tackles the subjective nature of attention level and use it to annotate more than 100,000 images with three attention levels and second, we introduce a novel method to estimate attention levels, relying purely on extracted geometric features from RGB and depth images, and evaluate it with a deep learning fusion framework. The system achieves an overall accuracy of 80.02%. Our framework and attention level annotations are made publicly available.", "published": "2018-12-12T14:00:46Z", "authors": ["Andrea Coifman", "Péter Rohoska", "Miklas S. Kristoffersen", "Sven E. Shepstone", "Zheng-Hua Tan"], "categories": ["cs.CV", "cs.HC", "cs.LG"], "doi": "", "_query": "property valuation computer vision", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2210.11717v2", "title": "A Survey of Dataset Refinement for Problems in Computer Vision Datasets", "abstract": "Large-scale datasets have played a crucial role in the advancement of computer vision. However, they often suffer from problems such as class imbalance, noisy labels, dataset bias, or high resource costs, which can inhibit model performance and reduce trustworthiness. With the advocacy of data-centric research, various data-centric solutions have been proposed to solve the dataset problems mentioned above. They improve the quality of datasets by re-organizing them, which we call dataset refinement. In this survey, we provide a comprehensive and structured overview of recent advances in dataset refinement for problematic computer vision datasets. Firstly, we summarize and analyze the various problems encountered in large-scale computer vision datasets. Then, we classify the dataset refinement algorithms into three categories based on the refinement process: data sampling, data subset selection, and active learning. In addition, we organize these dataset refinement methods according to the addressed data problems and provide a systematic comparative description. We point out that these three types of dataset refinement have distinct advantages and disadvantages for dataset problems, which informs the choice of the data-centric method appropriate to a particular research objective. Finally, we summarize the current literature and propose potential future research topics.", "published": "2022-10-21T03:58:43Z", "authors": ["Zhijing Wan", "Zhixiang Wang", "CheukTing Chung", "Zheng Wang"], "categories": ["cs.CV"], "doi": "", "_query": "property valuation computer vision", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2604.14433v1", "title": "Zero-Ablation Overstates Register Content Dependence in DINO Vision Transformers", "abstract": "Zero-ablation -- replacing token activations with zero vectors -- is widely used to probe token function in vision transformers. Register zeroing in DINOv2+registers and DINOv3 produces large drops (up to $-36.6$\\,pp classification, $-30.9$\\,pp segmentation), suggesting registers are functionally indispensable. However, three replacement controls -- mean-substitution, noise-substitution, and cross-image register-shuffling -- preserve performance across classification, correspondence, and segmentation, remaining within ${\\sim}1$\\,pp of the unmodified baseline. Per-patch cosine similarity shows these replacements genuinely perturb internal representations, while zeroing causes disproportionately large perturbations, consistent with why it alone degrades tasks. We conclude that zero-ablation overstates dependence on exact register content. In the frozen-feature evaluations we test, performance depends on plausible register-like activations rather than on exact image-specific values. Registers nevertheless buffer dense features from \\texttt{[CLS]} dependence and are associated with compressed patch geometry. These findings, including the replacement-control results, replicate at ViT-B scale.", "published": "2026-04-15T21:24:42Z", "authors": ["Felipe Parodi", "Jordan Matelsky", "Melanie Segado"], "categories": ["cs.CV", "cs.LG"], "doi": "", "_query": "property valuation computer vision", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2608.10181v1", "title": "Human versus Computer Vision", "abstract": "Computer vision saliency models predict where people will look, one map per image, and a billion-dollar predicted-attention industry sells those maps in place of measuring real viewers. I test the leading models from the audience side, against 11.4 million webcam gaze points from 3,023 US adults recruited to national quotas, viewing circulating news photographs. I show that an untrained central marker outperforms every trained network, because the content the networks add on top of the center falls where these audiences never look. What accuracy remains is systematically biased, favoring younger, White, and moderate viewers over older, Black, and ideologically extreme ones. I propose a way forward and build on what a group's own gaze reveals about whether a model can learn that group at all, and I apply it across every demographic axis this sample supports. Ultimately, I show how systems that decide what people see can learn to see everyone, and this study supplies the standard by which such a claim should be judged.", "published": "2026-08-10T19:53:24Z", "authors": ["Elena Sirotkina"], "categories": ["cs.CV", "cs.CY"], "doi": "", "_query": "property valuation computer vision", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2609.35718v1", "title": "Hard Vision, Easy Vision: What GPT-6 Astra Reveals Across Computer Vision", "abstract": "Frontier general-purpose systems are rapidly expanding beyond visual understanding into capabilities traditionally handled by dedicated computer-vision models. As these capabilities expand, a central question for the computer-vision community is how far this reach extends, and what remains hard. We evaluate GPT-6 Astra alongside five frontier general-purpose AI systems across 34 capabilities and 55 benchmarks spanning nine areas of computer vision. We compare their performance with dedicated models and humans where suitable references are available. Astra demonstrates broad visual capability, with substantial gains over other frontier systems in visual and spatial reasoning and several forms of structured prediction. Across the state-of-the-art systems, a consistent pattern emerges. Capabilities involving semantic interpretation, reasoning, and object-centric prediction increasingly approach or reach available reference levels. In contrast, larger gaps remain when tasks require metric geometric accuracy, faithful reconstruction, temporally consistent dense prediction, or specialized fine-grained visual knowledge. Additional reasoning and specialist tools close selected gaps, but their benefits vary across capabilities. These results map a changing landscape of computer vision in which increasingly sophisticated visual tasks are accessible through a general-purpose interface, while precise and fidelity-sensitive perception remains an important frontier.", "published": "2026-09-28T17:46:57Z", "authors": ["Hanoona Rasheed", "Mohammed Irfan Kurpath", "Bin Ren", "Hisham Cholakkal", "Fahad Shahbaz Khan", "Salman Khan"], "categories": ["cs.CV"], "doi": "", "_query": "property valuation computer vision", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2312.09455v1", "title": "Integration of Robotics, Computer Vision, and Algorithm Design: A Chinese Poker Self-Playing Robot", "abstract": "This paper presents Chinese Poker Self-Playing Robot, an integrated system enabling a TM5-900 robotic arm to independently play the four-person card game Chinese poker. The robot uses a custom sucker mechanism to pick up and play cards. An object detection model based on YOLOv5 is utilized to recognize the suit and number of 13 cards dealt to the robot. A greedy algorithm is developed to divide the 13 cards into optimal hands of 3, 5, and 5 cards to play. Experiments demonstrate that the robot can successfully obtain the cards, identify them using computer vision, strategically select hands to play using the algorithm, and physically play the selected cards in the game. The system showcases effective integration of mechanical design, computer vision, algorithm design, and robotic control to accomplish the complex task of independently playing cards.", "published": "2023-11-28T06:53:34Z", "authors": ["Kuan-Huang Yu"], "categories": ["cs.RO", "cs.CV", "eess.SP"], "doi": "", "_query": "property valuation computer vision", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2401.16424v2", "title": "Computer Vision for Primate Behavior Analysis in the Wild", "abstract": "Advances in computer vision as well as increasingly widespread video-based behavioral monitoring have great potential for transforming how we study animal cognition and behavior. However, there is still a fairly large gap between the exciting prospects and what can actually be achieved in practice today, especially in videos from the wild. With this perspective paper, we want to contribute towards closing this gap, by guiding behavioral scientists in what can be expected from current methods and steering computer vision researchers towards problems that are relevant to advance research in animal behavior. We start with a survey of the state-of-the-art methods for computer vision problems that are directly relevant to the video-based study of animal behavior, including object detection, multi-individual tracking, individual identification, and (inter)action recognition. We then review methods for effort-efficient learning, which is one of the biggest challenges from a practical perspective. Finally, we close with an outlook into the future of the emerging field of computer vision for animal behavior, where we argue that the field should develop approaches to unify detection, tracking, identification and (inter)action recognition in a single, video-based framework.", "published": "2024-01-29T18:59:56Z", "authors": ["Richard Vogg", "Timo Lüddecke", "Jonathan Henrich", "Sharmita Dey", "Matthias Nuske", "Valentin Hassler", "Derek Murphy", "Julia Fischer", "Julia Ostner", "Oliver Schülke", "Peter M. Kappeler", "Claudia Fichtel", "Alexander Gail", "Stefan Treue", "Hansjörg Scherberger", "Florentin Wörgötter", "Alexander S. Ecker"], "categories": ["cs.CV", "q-bio.QM"], "doi": "", "_query": "property valuation computer vision", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2310.07300v1", "title": "uxSense: Supporting User Experience Analysis with Visualization and Computer Vision", "abstract": "Analyzing user behavior from usability evaluation can be a challenging and time-consuming task, especially as the number of participants and the scale and complexity of the evaluation grows. We propose uxSense, a visual analytics system using machine learning methods to extract user behavior from audio and video recordings as parallel time-stamped data streams. Our implementation draws on pattern recognition, computer vision, natural language processing, and machine learning to extract user sentiment, actions, posture, spoken words, and other features from such recordings. These streams are visualized as parallel timelines in a web-based front-end, enabling the researcher to search, filter, and annotate data across time and space. We present the results of a user study involving professional UX researchers evaluating user data using uxSense. In fact, we used uxSense itself to evaluate their sessions.", "published": "2023-10-11T08:35:03Z", "authors": ["Andrea Batch", "Yipeng Ji", "Mingming Fan", "Jian Zhao", "Niklas Elmqvist"], "categories": ["cs.HC"], "doi": "10.1109/TVCG.2023.3241581", "_query": "property valuation computer vision", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2009.00210v5", "title": "Semantics-aware Adaptive Knowledge Distillation for Sensor-to-Vision Action Recognition", "abstract": "Existing vision-based action recognition is susceptible to occlusion and appearance variations, while wearable sensors can alleviate these challenges by capturing human motion with one-dimensional time-series signal. For the same action, the knowledge learned from vision sensors and wearable sensors, may be related and complementary. However, there exists significantly large modality difference between action data captured by wearable-sensor and vision-sensor in data dimension, data distribution and inherent information content. In this paper, we propose a novel framework, named Semantics-aware Adaptive Knowledge Distillation Networks (SAKDN), to enhance action recognition in vision-sensor modality (videos) by adaptively transferring and distilling the knowledge from multiple wearable sensors. The SAKDN uses multiple wearable-sensors as teacher modalities and uses RGB videos as student modality. To preserve local temporal relationship and facilitate employing visual deep learning model, we transform one-dimensional time-series signals of wearable sensors to two-dimensional images by designing a gramian angular field based virtual image generation model. Then, we build a novel Similarity-Preserving Adaptive Multi-modal Fusion Module to adaptively fuse intermediate representation knowledge from different teacher networks. Finally, to fully exploit and transfer the knowledge of multiple well-trained teacher networks to the student network, we propose a novel Graph-guided Semantically Discriminative Mapping loss, which utilizes graph-guided ablation analysis to produce a good visual explanation highlighting the important regions across modalities and concurrently preserving the interrelations of original data. Experimental results on Berkeley-MHAD, UTD-MHAD and MMAct datasets well demonstrate the effectiveness of our proposed SAKDN.", "published": "2020-09-01T03:38:31Z", "authors": ["Yang Liu", "Keze Wang", "Guanbin Li", "Liang Lin"], "categories": ["cs.CV"], "doi": "10.1109/TIP.2021.3086590", "_query": "property valuation computer vision", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/1209.4172v1", "title": "Quasi-Valuations Extending a Valuation", "abstract": "Suppose $F$ is a field with valuation $v$ and valuation ring $O_{v}$, $E$ is a finite field extension and $w$ is a quasi-valuation on $E$ extending $v$. We study quasi-valuations on $E$ that extend $v$; in particular, their corresponding rings and their prime spectrums. We prove that these ring extensions satisfy INC (incomparability), LO (lying over), and GD (going down) over $O_{v}$; in particular, they have the same Krull Dimension. We also prove that every such quasi-valuation is dominated by some valuation extending $v$. Under the assumption that the value monoid of the quasi-valuation is a group we prove that these ring extensions satisfy GU (going up) over $O_{v}$, and a bound on the size of the prime spectrum is given. In addition, a 1:1 correspondence is obtained between exponential quasi-valuations and integrally closed quasi-valuation rings. Given $R$, an algebra over $O_{v}$, we construct a quasi-valuation on $R$; we also construct a quasi-valuation on $R \\otimes_{O_{v}} F$ which helps us prove our main Theorem. The main Theorem states that if $R \\subseteq E$ satisfies $R \\cap F=O_{v}$ and $E$ is the field of fractions of $R$, then $R$ and $v$ induce a quasi-valuation $w$ on $E$ such that $R=O_{w}$ and $w$ extends $v$; thus $R$ satisfies the properties of a quasi-valuation ring.", "published": "2012-09-19T07:56:11Z", "authors": ["Shai Sarussi"], "categories": ["math.AC"], "doi": "10.1016/j.jalgebra.2012.09.019", "_query": "property valuation computer vision", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2602.13880v2", "title": "VSAL: A Vision Solver with Adaptive Layouts for Graph Property Detection", "abstract": "Graph property detection aims to determine whether a graph exhibits certain structural properties, such as being Hamiltonian. Recently, learning-based approaches have shown great promise by leveraging data-driven models to detect graph properties efficiently. In particular, vision-based methods offer a visually intuitive solution by processing the visualizations of graphs. However, existing vision-based methods rely on fixed visual graph layouts, and therefore, the expressiveness of their pipeline is restricted. To overcome this limitation, we propose VSAL, a vision-based framework that incorporates an adaptive layout generator capable of dynamically producing informative graph visualizations tailored to individual instances, thereby improving graph property detection. Extensive experiments demonstrate that VSAL outperforms state-of-the-art vision-based methods on various tasks such as Hamiltonian cycle, planarity, claw-freeness, and tree detection.", "published": "2026-02-14T20:44:51Z", "authors": ["Jiahao Xie", "Guangmo Tong"], "categories": ["cs.AI", "cs.CV"], "doi": "", "_query": "property valuation computer vision", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/0906.4505v1", "title": "On valuation rings", "abstract": "In this paper we provide necessary and sufficient conditions for $ R=A\\propto E $ to be a valuation ring where $E$ is a non-torsion or finitely generated $A-$module. Also, we investigate the $ (n,d) $ property of the valuation ring.", "published": "2009-06-24T15:49:00Z", "authors": ["Mohammed Kabbour", "Najib Mahdou"], "categories": ["math.AC"], "doi": "", "_query": "property valuation computer vision", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/1902.03751v2", "title": "Taking a HINT: Leveraging Explanations to Make Vision and Language Models More Grounded", "abstract": "Many vision and language models suffer from poor visual grounding - often falling back on easy-to-learn language priors rather than basing their decisions on visual concepts in the image. In this work, we propose a generic approach called Human Importance-aware Network Tuning (HINT) that effectively leverages human demonstrations to improve visual grounding. HINT encourages deep networks to be sensitive to the same input regions as humans. Our approach optimizes the alignment between human attention maps and gradient-based network importances - ensuring that models learn not just to look at but rather rely on visual concepts that humans found relevant for a task when making predictions. We apply HINT to Visual Question Answering and Image Captioning tasks, outperforming top approaches on splits that penalize over-reliance on language priors (VQA-CP and robust captioning) using human attention demonstrations for just 6% of the training data.", "published": "2019-02-11T07:28:03Z", "authors": ["Ramprasaath R. Selvaraju", "Stefan Lee", "Yilin Shen", "Hongxia Jin", "Shalini Ghosh", "Larry Heck", "Dhruv Batra", "Devi Parikh"], "categories": ["cs.CV"], "doi": "", "_query": "property valuation computer vision", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2311.16673v1", "title": "Large Language Models Meet Computer Vision: A Brief Survey", "abstract": "Recently, the intersection of Large Language Models (LLMs) and Computer Vision (CV) has emerged as a pivotal area of research, driving significant advancements in the field of Artificial Intelligence (AI). As transformers have become the backbone of many state-of-the-art models in both Natural Language Processing (NLP) and CV, understanding their evolution and potential enhancements is crucial. This survey paper delves into the latest progressions in the domain of transformers and their subsequent successors, emphasizing their potential to revolutionize Vision Transformers (ViTs) and LLMs. This survey also presents a comparative analysis, juxtaposing the performance metrics of several leading paid and open-source LLMs, shedding light on their strengths and areas of improvement as well as a literature review on how LLMs are being used to tackle vision related tasks. Furthermore, the survey presents a comprehensive collection of datasets employed to train LLMs, offering insights into the diverse data available to achieve high performance in various pre-training and downstream tasks of LLMs. The survey is concluded by highlighting open directions in the field, suggesting potential venues for future research and development. This survey aims to underscores the profound intersection of LLMs on CV, leading to a new era of integrated and advanced AI models.", "published": "2023-11-28T10:39:19Z", "authors": ["Raby Hamadi"], "categories": ["cs.CV", "cs.AI"], "doi": "", "_query": "property valuation computer vision", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2005.13178v1", "title": "Generative Adversarial Networks (GANs): An Overview of Theoretical Model, Evaluation Metrics, and Recent Developments", "abstract": "One of the most significant challenges in statistical signal processing and machine learning is how to obtain a generative model that can produce samples of large-scale data distribution, such as images and speeches. Generative Adversarial Network (GAN) is an effective method to address this problem. The GANs provide an appropriate way to learn deep representations without widespread use of labeled training data. This approach has attracted the attention of many researchers in computer vision since it can generate a large amount of data without precise modeling of the probability density function (PDF). In GANs, the generative model is estimated via a competitive process where the generator and discriminator networks are trained simultaneously. The generator learns to generate plausible data, and the discriminator learns to distinguish fake data created by the generator from real data samples. Given the rapid growth of GANs over the last few years and their application in various fields, it is necessary to investigate these networks accurately. In this paper, after introducing the main concepts and the theory of GAN, two new deep generative models are compared, the evaluation metrics utilized in the literature and challenges of GANs are also explained. Moreover, the most remarkable GAN architectures are categorized and discussed. Finally, the essential applications in computer vision are examined.", "published": "2020-05-27T05:56:53Z", "authors": ["Pegah Salehi", "Abdolah Chalechale", "Maryam Taghizadeh"], "categories": ["cs.CV", "cs.LG", "eess.IV"], "doi": "", "_query": "property valuation computer vision", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2406.00081v1", "title": "From Structured to Unstructured:A Comparative Analysis of Computer Vision and Graph Models in solving Mesh-based PDEs", "abstract": "This article investigates the application of computer vision and graph-based models in solving mesh-based partial differential equations within high-performance computing environments. Focusing on structured, graded structured, and unstructured meshes, the study compares the performance and computational efficiency of three computer vision-based models against three graph-based models across three data\\-sets. The research aims to identify the most suitable models for different mesh topographies, particularly highlighting the exploration of graded meshes, a less studied area. Results demonstrate that computer vision-based models, notably U-Net, outperform the graph models in prediction performance and efficiency in two (structured and graded) out of three mesh topographies. The study also reveals the unexpected effectiveness of computer vision-based models in handling unstructured meshes, suggesting a potential shift in methodological approaches for data-driven partial differential equation learning. The article underscores deep learning as a viable and potentially sustainable way to enhance traditional high-performance computing methods, advocating for informed model selection based on the topography of the mesh.", "published": "2024-05-31T12:21:26Z", "authors": ["Jens Decke", "Olaf Wünsch", "Bernhard Sick", "Christian Gruhl"], "categories": ["cs.LG", "cs.CE", "cs.CV"], "doi": "", "_query": "property valuation computer vision", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2405.04969v2", "title": "A Review on Discriminative Self-supervised Learning Methods in Computer Vision", "abstract": "Self-supervised learning (SSL) has rapidly emerged as a transformative approach in computer vision, enabling the extraction of rich feature representations from vast amounts of unlabeled data and reducing reliance on costly manual annotations. This review presents a comprehensive analysis of discriminative SSL methods, which focus on learning representations by solving pretext tasks that do not require human labels. The paper systematically categorizes discriminative SSL approaches into five main groups: contrastive methods, clustering methods, self-distillation methods, knowledge distillation methods, and feature decorrelation methods. For each category, the review details the underlying principles, architectural components, loss functions, and representative algorithms, highlighting their unique mechanisms and contributions to the field. Extensive comparative evaluations are provided, including linear and semi-supervised protocols on standard benchmarks such as ImageNet, as well as transfer learning performance across diverse downstream tasks. The review also discusses theoretical foundations, scalability, efficiency, and practical challenges, such as computational demands and accessibility. By synthesizing recent advancements and identifying key trends, open challenges, and future research directions, this work serves as a valuable resource for researchers and practitioners aiming to leverage discriminative SSL for robust and generalizable computer vision models.", "published": "2024-05-08T11:15:20Z", "authors": ["Nikos Giakoumoglou", "Tania Stathaki", "Athanasios Gkelias"], "categories": ["cs.CV", "cs.AI"], "doi": "", "_query": "property valuation computer vision", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2406.08898v1", "title": "Computer Vision Approaches for Automated Bee Counting Application", "abstract": "Many application from the bee colony health state monitoring could be efficiently solved using a computer vision techniques. One of such challenges is an efficient way for counting the number of incoming and outcoming bees, which could be used to further analyse many trends, such as the bee colony health state, blooming periods, or for investigating the effects of agricultural spraying. In this paper, we compare three methods for the automated bee counting over two own datasets. The best performing method is based on the ResNet-50 convolutional neural network classifier, which achieved accuracy of 87% over the BUT1 dataset and the accuracy of 93% over the BUT2 dataset.", "published": "2024-06-13T07:51:08Z", "authors": ["Simon Bilik", "Ilona Janakova", "Adam Ligocki", "Dominik Ficek", "Karel Horak"], "categories": ["cs.CV", "cs.AI", "cs.LG"], "doi": "", "_query": "property valuation computer vision", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2404.00936v4", "title": "A Comprehensive Review of Knowledge Distillation in Computer Vision", "abstract": "Deep learning techniques have been demonstrated to surpass preceding cutting-edge machine learning techniques in recent years, with computer vision being one of the most prominent examples. However, deep learning models suffer from significant drawbacks when deployed in resource-constrained environments due to their large model size and high complexity. Knowledge Distillation is one of the prominent solutions to overcome this challenge. This review paper examines the current state of research on knowledge distillation, a technique for compressing complex models into smaller and simpler ones. The paper provides an overview of the major principles and techniques associated with knowledge distillation and reviews the applications of knowledge distillation in the domain of computer vision. The review focuses on the benefits of knowledge distillation, as well as the problems that must be overcome to improve its effectiveness.", "published": "2024-04-01T05:46:15Z", "authors": ["Gousia Habib", "Tausifa jan Saleem", "Sheikh Musa Kaleem", "Tufail Rouf", "Brejesh Lall"], "categories": ["cs.CV"], "doi": "", "_query": "property valuation computer vision", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2505.18855v1", "title": "Inference Compute-Optimal Video Vision Language Models", "abstract": "This work investigates the optimal allocation of inference compute across three key scaling factors in video vision language models: language model size, frame count, and the number of visual tokens per frame. While prior works typically focuses on optimizing model efficiency or improving performance without considering resource constraints, we instead identify optimal model configuration under fixed inference compute budgets. We conduct large-scale training sweeps and careful parametric modeling of task performance to identify the inference compute-optimal frontier. Our experiments reveal how task performance depends on scaling factors and finetuning data size, as well as how changes in data size shift the compute-optimal frontier. These findings translate to practical tips for selecting these scaling factors.", "published": "2025-05-24T20:09:04Z", "authors": ["Peiqi Wang", "ShengYun Peng", "Xuewen Zhang", "Hanchao Yu", "Yibo Yang", "Lifu Huang", "Fujun Liu", "Qifan Wang"], "categories": ["cs.CV", "cs.CL"], "doi": "", "_query": "property valuation computer vision", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2606.12371v1", "title": "A Turbo-Inference Strategy for Object Detection and Instance Segmentation", "abstract": "Object detection and instance segmentation tasks are closely related. Existing top-down instance segmentation methods usually follow a detect-then-segment paradigm, where an initial detector is used to recognize and localize objects with bounding boxes, followed by the segmentation of an instance mask within each bounding box. In such methods, the detection accuracy directly influences the subsequent segmentation performance. However, previous research has seldom explored the impact of the instance segmentation task on object detection. In this paper, we present a turbo-inference strategy for the top-down methods that leverages the complementary information between detection and segmentation tasks iteratively. Specifically we design two modules: turbo-detection head and turbo-segmentation head, which facilitate communication between the tasks. The two modules form a closed loop that interlaces the detection and segmentation results without retraining the model. Comprehensive experiments on the COCO, iFLYTEK, and Cityscapes datasets demonstrate that our method substantially enhances both detection and segmentation accuracies with a certain increase in computational cost. The proposed method represents a tradeoff between prediction accuracy and inference speed. Codes are available at https://github.com/zhaozhen2333/Turbo-Learning.git.", "published": "2026-06-10T17:38:22Z", "authors": ["Zhen Zhao", "Gang Zhang", "Xiaolin Hu", "Liang Tang"], "categories": ["cs.CV"], "doi": "10.1016/j.cviu.2026.104827", "_query": "property valuation computer vision", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2506.00721v2", "title": "Common Inpainted Objects In-N-Out of Context", "abstract": "We present Common Inpainted Objects In-N-Out of Context (COinCO), a novel dataset addressing the scarcity of out-of-context examples in existing vision datasets. By systematically replacing objects in COCO images through diffusion-based inpainting, we create 97,722 unique images featuring both contextually coherent and inconsistent scenes, enabling effective context learning. Each inpainted object is meticulously verified and categorized as in- or out-of-context through Large Vision Language Model assessments. We demonstrate three key tasks enabled by COinCO: (1) a fine-grained context reasoning approach that classifies objects as in- or out-of-context based on three criteria; (2) a novel Objects-from-Context prediction task that determines which new objects naturally belong in given scenes at both instance and clique level semantics, and (3) context-enhanced fake detection on state-of-the-art methods without fine-tuning. COinCO provides a controlled testbed with contextual variations, establishing a foundation for advancing context-aware visual understanding in computer vision, including image forensics. Code and dataset are available at https://co-in-co.github.io/.", "published": "2025-05-31T21:42:12Z", "authors": ["Tianze Yang", "Tyson Jordan", "Ruitong Sun", "Ninghao Liu", "Jin Sun"], "categories": ["cs.CV", "cs.LG"], "doi": "", "_query": "property valuation computer vision", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2307.00064v1", "title": "Situated Cameras, Situated Knowledges: Towards an Egocentric Epistemology for Computer Vision", "abstract": "In her influential 1988 paper, Situated Knowledges, Donna Haraway uses vision and perspective as a metaphor to discuss scientific knowledge. Today, egocentric computer vision discusses many of the same issues, except in a literal vision context. In this short position paper, we collapse that metaphor, and explore the interactions between feminist epistemology and egocentric CV as \"Egocentric Epistemology.\" Using this framework, we argue for the use of qualitative, human-centric methods as a complement to performance benchmarks, to center both the literal and metaphorical perspective of human crowd workers in CV.", "published": "2023-06-30T18:07:26Z", "authors": ["Samuel Goree", "David Crandall"], "categories": ["cs.CV"], "doi": "", "_query": "property valuation computer vision", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2501.18637v2", "title": "Machine learning of microstructure--property relationships in materials leveraging microstructure representation from foundational vision transformers", "abstract": "Machine learning of microstructure--property relationships from data is an emerging approach in computational materials science. Most existing machine learning efforts focus on the development of task-specific models for each microstructure--property relationship. We propose utilizing pre-trained foundational vision transformers for the extraction of task-agnostic microstructure features and subsequent light-weight machine learning of a microstructure-dependent property. We demonstrate our approach with pre-trained state-of-the-art vision transformers (CLIP, DINOv2, SAM) in two case studies on machine-learning: (i) elastic modulus of two-phase microstructures based on simulations data; and (ii) Vicker's hardness of Ni-base and Co-base superalloys based on experimental data published in literature. Our results show the potential of foundational vision transformers for robust microstructure representation and efficient machine learning of microstructure--property relationships without the need for expensive task-specific training or fine-tuning of bespoke deep learning models.", "published": "2025-01-28T17:06:47Z", "authors": ["Sheila E. Whitman", "Marat I. Latypov"], "categories": ["cs.CV", "cond-mat.mtrl-sci", "cs.LG", "physics.comp-ph"], "doi": "10.1016/j.actamat.2025.121217", "_query": "property valuation computer vision", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2412.09612v3", "title": "Olympus: A Universal Task Router for Computer Vision Tasks", "abstract": "We introduce Olympus, a new approach that transforms Multimodal Large Language Models (MLLMs) into a unified framework capable of handling a wide array of computer vision tasks. Utilizing a controller MLLM, Olympus delegates over 20 specialized tasks across images, videos, and 3D objects to dedicated modules. This instruction-based routing enables complex workflows through chained actions without the need for training heavy generative models. Olympus easily integrates with existing MLLMs, expanding their capabilities with comparable performance. Experimental results demonstrate that Olympus achieves an average routing accuracy of 94.75% across 20 tasks and precision of 91.82% in chained action scenarios, showcasing its effectiveness as a universal task router that can solve a diverse range of computer vision tasks. Project page: http://yuanze-lin.me/Olympus_page/", "published": "2024-12-12T18:59:40Z", "authors": ["Yuanze Lin", "Yunsheng Li", "Dongdong Chen", "Weijian Xu", "Ronald Clark", "Philip H. S. Torr"], "categories": ["cs.CV", "cs.AI", "cs.CL"], "doi": "", "_query": "property valuation computer vision", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2212.01569v1", "title": "Computer and Internet Literacy Course of the College of Computer Science for the Municipality of Agoo, La Union", "abstract": "The main objective of the study is to provide ICT awareness, literacy and skills development to the barangay officials of Agoo, La Union. Specifically, it aimed the following objectives: 1) to determine the profile of the respondents in terms of personal information, educational background and availability of computer unit and background in using computer; 2) to determine the effectiveness of the CILC in terms of services delivered, timeliness of the service, and improvement on the computer and internet knowledge of the trainees; and 3) to determine the level of relevance of the training sessions of the CILC. The study used a descriptive design. Data were gathered by using survey questionnaire and were analyzed by using statistical treatments such as frequency count, percentage and mean. As to the profile of the trainees, the study found that most of the trainees are female (88%); 84% are married, and 56% of them are at the age bracket of 30-39 years old. In terms of educational background, many are high school graduate (n= 17; 68%). In addition, most of them (84%) have background in computer. The result also shows that the CILC is at the high level of effectiveness (4.67) in terms of services delivered and is much relevant (4.45) in terms of its relevance.", "published": "2022-12-03T07:59:55Z", "authors": ["Clarisa V. Albarillo", "Emely A. Munar", "Maria Concepcion M. Balcita"], "categories": ["cs.CY"], "doi": "", "_query": "property valuation computer vision", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2509.10105v2", "title": "VARCO-VISION-2.0 Technical Report", "abstract": "We introduce VARCO-VISION-2.0, an open-weight bilingual vision-language model (VLM) for Korean and English with improved capabilities compared to the previous model VARCO-VISION-14B. The model supports multi-image understanding for complex inputs such as documents, charts, and tables, and delivers layoutaware OCR by predicting both textual content and its spatial location. Trained with a four-stage curriculum with memory-efficient techniques, the model achieves enhanced multimodal alignment, while preserving core language abilities and improving safety via preference optimization. Extensive benchmark evaluations demonstrate strong spatial grounding and competitive results for both languages, with the 14B model achieving 8th place on the OpenCompass VLM leaderboard among models of comparable scale. Alongside the 14B-scale model, we release a 1.7B version optimized for on-device deployment. We believe these models advance the development of bilingual VLMs and their practical applications. Two variants of VARCO-VISION-2.0 are available at Hugging Face: a full-scale 14B model and a lightweight 1.7B model.", "published": "2025-09-12T09:55:56Z", "authors": ["Young-rok Cha", "Jeongho Ju", "SunYoung Park", "Jong-Hyeon Lee", "Younghyun Yu", "Youngjune Kim"], "categories": ["cs.CV", "cs.CL"], "doi": "", "_query": "property valuation computer vision", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2006.16923v2", "title": "Large image datasets: A pyrrhic win for computer vision?", "abstract": "In this paper we investigate problematic practices and consequences of large scale vision datasets. We examine broad issues such as the question of consent and justice as well as specific concerns such as the inclusion of verifiably pornographic images in datasets. Taking the ImageNet-ILSVRC-2012 dataset as an example, we perform a cross-sectional model-based quantitative census covering factors such as age, gender, NSFW content scoring, class-wise accuracy, human-cardinality-analysis, and the semanticity of the image class information in order to statistically investigate the extent and subtleties of ethical transgressions. We then use the census to help hand-curate a look-up-table of images in the ImageNet-ILSVRC-2012 dataset that fall into the categories of verifiably pornographic: shot in a non-consensual setting (up-skirt), beach voyeuristic, and exposed private parts. We survey the landscape of harm and threats both society broadly and individuals face due to uncritical and ill-considered dataset curation practices. We then propose possible courses of correction and critique the pros and cons of these. We have duly open-sourced all of the code and the census meta-datasets generated in this endeavor for the computer vision community to build on. By unveiling the severity of the threats, our hope is to motivate the constitution of mandatory Institutional Review Boards (IRB) for large scale dataset curation processes.", "published": "2020-06-24T06:41:32Z", "authors": ["Vinay Uday Prabhu", "Abeba Birhane"], "categories": ["cs.CY", "stat.AP", "stat.ML"], "doi": "", "_query": "property valuation computer vision", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2510.07317v1", "title": "Quantum-enhanced Computer Vision: Going Beyond Classical Algorithms", "abstract": "Quantum-enhanced Computer Vision (QeCV) is a new research field at the intersection of computer vision, optimisation theory, machine learning and quantum computing. It has high potential to transform how visual signals are processed and interpreted with the help of quantum computing that leverages quantum-mechanical effects in computations inaccessible to classical (i.e. non-quantum) computers. In scenarios where existing non-quantum methods cannot find a solution in a reasonable time or compute only approximate solutions, quantum computers can provide, among others, advantages in terms of better time scalability for multiple problem classes. Parametrised quantum circuits can also become, in the long term, a considerable alternative to classical neural networks in computer vision. However, specialised and fundamentally new algorithms must be developed to enable compatibility with quantum hardware and unveil the potential of quantum computational paradigms in computer vision. This survey contributes to the existing literature on QeCV with a holistic review of this research field. It is designed as a quantum computing reference for the computer vision community, targeting computer vision students, scientists and readers with related backgrounds who want to familiarise themselves with QeCV. We provide a comprehensive introduction to QeCV, its specifics, and methodologies for formulations compatible with quantum hardware and QeCV methods, leveraging two main quantum computational paradigms, i.e. gate-based quantum computing and quantum annealing. We elaborate on the operational principles of quantum computers and the available tools to access, program and simulate them in the context of QeCV. Finally, we review existing quantum computing tools and learning materials and discuss aspects related to publishing and reviewing QeCV papers, open challenges and potential social implications.", "published": "2025-10-08T17:59:51Z", "authors": ["Natacha Kuete Meli", "Shuteng Wang", "Marcel Seelbach Benkner", "Michele Sasdelli", "Tat-Jun Chin", "Tolga Birdal", "Michael Moeller", "Vladislav Golyanik"], "categories": ["cs.CV"], "doi": "", "_query": "property valuation computer vision", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/1105.3559v4", "title": "Invariant Representative Cocycles of Cohomology Generators using Irregular Graph Pyramids", "abstract": "Structural pattern recognition describes and classifies data based on the relationships of features and parts. Topological invariants, like the Euler number, characterize the structure of objects of any dimension. Cohomology can provide more refined algebraic invariants to a topological space than does homology. It assigns `quantities' to the chains used in homology to characterize holes of any dimension. Graph pyramids can be used to describe subdivisions of the same object at multiple levels of detail. This paper presents cohomology in the context of structural pattern recognition and introduces an algorithm to efficiently compute representative cocycles (the basic elements of cohomology) in 2D using a graph pyramid. An extension to obtain scanning and rotation invariant cocycles is given.", "published": "2011-05-18T08:18:42Z", "authors": ["Rocio Gonzalez-Diaz", "Adrian Ion", "Mabel Iglesias-Ham", "Walter G. Kropatsch"], "categories": ["cs.CV"], "doi": "10.1016/j.cviu.2010.12.009", "_query": "property valuation computer vision", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2107.04902v3", "title": "Industry and Academic Research in Computer Vision", "abstract": "This work aims to study the dynamic between research in the industry and academia in computer vision. The results are demonstrated on a set of top-5 vision conferences that are representative of the field. Since data for such analysis was not readily available, significant effort was spent on gathering and processing meta-data from the original publications. First, this study quantifies the share of industry-sponsored research. Specifically, it shows that the proportion of papers published by industry-affiliated researchers is increasing and that more academics join companies or collaborate with them. Next, the possible impact of industry presence is further explored, namely in the distribution of research topics and citation patterns. The results indicate that the distribution of the research topics is similar in industry and academic papers. However, there is a strong preference towards citing industry papers. Finally, possible reasons for citation bias, such as code availability and influence, are investigated.", "published": "2021-07-10T20:09:52Z", "authors": ["Iuliia Kotseruba", "Manos Papagelis", "John K. Tsotsos"], "categories": ["cs.CV"], "doi": "", "_query": "property valuation computer vision", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2403.06322v2", "title": "Leveraging Computer Vision in the Intensive Care Unit (ICU) for Examining Visitation and Mobility", "abstract": "Despite the importance of closely monitoring patients in the Intensive Care Unit (ICU), many aspects are still assessed in a limited manner due to the time constraints imposed on healthcare providers. For example, although excessive visitations during rest hours can potentially exacerbate the risk of circadian rhythm disruption and delirium, it is not captured in the ICU. Likewise, while mobility can be an important indicator of recovery or deterioration in ICU patients, it is only captured sporadically or not captured at all. In the past few years, the computer vision field has found application in many domains by reducing the human burden. Using computer vision systems in the ICU can also potentially enable non-existing assessments or enhance the frequency and accuracy of existing assessments while reducing the staff workload. In this study, we leverage a state-of-the-art noninvasive computer vision system based on depth imaging to characterize ICU visitations and patients' mobility. We then examine the relationship between visitation and several patient outcomes, such as pain, acuity, and delirium. We found an association between deteriorating patient acuity and the incidence of delirium with increased visitations. In contrast, self-reported pain, reported using the Defense and Veteran Pain Rating Scale (DVPRS), was correlated with decreased visitations. Our findings highlight the feasibility and potential of using noninvasive autonomous systems to monitor ICU patients.", "published": "2024-03-10T21:43:47Z", "authors": ["Scott Siegel", "Jiaqing Zhang", "Sabyasachi Bandyopadhyay", "Subhash Nerella", "Brandon Silva", "Tezcan Baslanti", "Azra Bihorac", "Parisa Rashidi"], "categories": ["cs.CV", "cs.AI"], "doi": "", "_query": "property valuation computer vision", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2007.14933v1", "title": "OpenSBLI: Automated code-generation for heterogeneous computing architectures applied to compressible fluid dynamics on structured grids", "abstract": "OpenSBLI is an open-source code-generation system for compressible fluid dynamics (CFD) on heterogeneous computing architectures. Written in Python, OpenSBLI is an explicit high-order finite-difference solver on structured curvilinear meshes. Shock-capturing is performed by a choice of high-order Weighted Essentially Non-Oscillatory (WENO) or Targeted Essentially Non-Oscillatory (TENO) schemes. OpenSBLI generates a complete CFD solver in the Oxford Parallel Structured (OPS) domain specific language. The OPS library is embedded in C code, enabling massively-parallel execution of the code on a variety of high-performance-computing architectures, including GPUs. The present paper presents a code base that has been completely rewritten from the earlier proof of concept (Jacobs et al, JoCS 18 (2017), 12-23), allowing shock capturing, coordinate transformations for complex geometries, and a wide range of boundary conditions, including solid walls with and without heat transfer. A suite of validation and verification cases are presented, plus demonstration of a large-scale Direct Numerical Simulation (DNS) of a transitional Shockwave Boundary Layer Interaction (SBLI). The code is shown to have good weak and strong scaling on multi-GPU clusters. We demonstrate that code-generation and domain specific languages are suitable for performing efficient large-scale simulations of complex fluid flows on emerging computing architectures.", "published": "2020-07-29T16:07:21Z", "authors": ["David J. Lusher", "Satya P. Jammy", "Neil D. Sandham"], "categories": ["physics.comp-ph", "physics.flu-dyn"], "doi": "10.1016/j.cpc.2021.108063", "_query": "property valuation computer vision", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2403.02991v1", "title": "MADTP: Multimodal Alignment-Guided Dynamic Token Pruning for Accelerating Vision-Language Transformer", "abstract": "Vision-Language Transformers (VLTs) have shown great success recently, but are meanwhile accompanied by heavy computation costs, where a major reason can be attributed to the large number of visual and language tokens. Existing token pruning research for compressing VLTs mainly follows a single-modality-based scheme yet ignores the critical role of aligning different modalities for guiding the token pruning process, causing the important tokens for one modality to be falsely pruned in another modality branch. Meanwhile, existing VLT pruning works also lack the flexibility to dynamically compress each layer based on different input samples. To this end, we propose a novel framework named Multimodal Alignment-Guided Dynamic Token Pruning (MADTP) for accelerating various VLTs. Specifically, we first introduce a well-designed Multi-modality Alignment Guidance (MAG) module that can align features of the same semantic concept from different modalities, to ensure the pruned tokens are less important for all modalities. We further design a novel Dynamic Token Pruning (DTP) module, which can adaptively adjust the token compression ratio in each layer based on different input instances. Extensive experiments on various benchmarks demonstrate that MADTP significantly reduces the computational complexity of kinds of multimodal models while preserving competitive performance. Notably, when applied to the BLIP model in the NLVR2 dataset, MADTP can reduce the GFLOPs by 80% with less than 4% performance degradation.", "published": "2024-03-05T14:13:50Z", "authors": ["Jianjian Cao", "Peng Ye", "Shengze Li", "Chong Yu", "Yansong Tang", "Jiwen Lu", "Tao Chen"], "categories": ["cs.CV"], "doi": "", "_query": "property valuation computer vision", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2508.10171v1", "title": "SynSpill: Improved Industrial Spill Detection With Synthetic Data", "abstract": "Large-scale Vision-Language Models (VLMs) have transformed general-purpose visual recognition through strong zero-shot capabilities. However, their performance degrades significantly in niche, safety-critical domains such as industrial spill detection, where hazardous events are rare, sensitive, and difficult to annotate. This scarcity -- driven by privacy concerns, data sensitivity, and the infrequency of real incidents -- renders conventional fine-tuning of detectors infeasible for most industrial settings. We address this challenge by introducing a scalable framework centered on a high-quality synthetic data generation pipeline. We demonstrate that this synthetic corpus enables effective Parameter-Efficient Fine-Tuning (PEFT) of VLMs and substantially boosts the performance of state-of-the-art object detectors such as YOLO and DETR. Notably, in the absence of synthetic data (SynSpill dataset), VLMs still generalize better to unseen spill scenarios than these detectors. When SynSpill is used, both VLMs and detectors achieve marked improvements, with their performance becoming comparable. Our results underscore that high-fidelity synthetic data is a powerful means to bridge the domain gap in safety-critical applications. The combination of synthetic generation and lightweight adaptation offers a cost-effective, scalable pathway for deploying vision systems in industrial environments where real data is scarce/impractical to obtain. Project Page: https://synspill.vercel.app", "published": "2025-08-13T20:09:58Z", "authors": ["Aaditya Baranwal", "Abdul Mueez", "Jason Voelker", "Guneet Bhatia", "Shruti Vyas"], "categories": ["cs.CV", "cs.ET"], "doi": "10.1109/ICCVW69036.2025.00152", "_query": "property valuation computer vision", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/1604.04372v2", "title": "The Chow Form of the Essential Variety in Computer Vision", "abstract": "The Chow form of the essential variety in computer vision is calculated. Our derivation uses secant varieties, Ulrich sheaves and representation theory. Numerical experiments show that our formula can detect noisy point correspondences between two images.", "published": "2016-04-15T06:57:09Z", "authors": ["Gunnar Fløystad", "Joe Kileel", "Giorgio Ottaviani"], "categories": ["math.AG", "cs.CV", "math.AC"], "doi": "", "_query": "property valuation computer vision", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2010.13714v1", "title": "ActiveNet: A computer-vision based approach to determine lethargy", "abstract": "The outbreak of COVID-19 has forced everyone to stay indoors, fabricating a significant drop in physical activeness. Our work is constructed upon the idea to formulate a backbone mechanism, to detect levels of activeness in real-time, using a single monocular image of a target person. The scope can be generalized under many applications, be it in an interview, online classes, security surveillance, et cetera. We propose a Computer Vision based multi-stage approach, wherein the pose of a person is first detected, encoded with a novel approach, and then assessed by a classical machine learning algorithm to determine the level of activeness. An alerting system is wrapped around the approach to provide a solution to inhibit lethargy by sending notification alerts to individuals involved.", "published": "2020-10-26T16:54:03Z", "authors": ["Aitik Gupta", "Aadit Agarwal"], "categories": ["cs.CV", "cs.HC"], "doi": "10.1145/3430984.3430986", "_query": "property valuation computer vision", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2408.15178v1", "title": "A Review of Transformer-Based Models for Computer Vision Tasks: Capturing Global Context and Spatial Relationships", "abstract": "Transformer-based models have transformed the landscape of natural language processing (NLP) and are increasingly applied to computer vision tasks with remarkable success. These models, renowned for their ability to capture long-range dependencies and contextual information, offer a promising alternative to traditional convolutional neural networks (CNNs) in computer vision. In this review paper, we provide an extensive overview of various transformer architectures adapted for computer vision tasks. We delve into how these models capture global context and spatial relationships in images, empowering them to excel in tasks such as image classification, object detection, and segmentation. Analyzing the key components, training methodologies, and performance metrics of transformer-based models, we highlight their strengths, limitations, and recent advancements. Additionally, we discuss potential research directions and applications of transformer-based models in computer vision, offering insights into their implications for future advancements in the field.", "published": "2024-08-27T16:22:18Z", "authors": ["Gracile Astlin Pereira", "Muhammad Hussain"], "categories": ["cs.CV"], "doi": "", "_query": "property valuation computer vision", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2204.09817v4", "title": "Making the Most of Text Semantics to Improve Biomedical Vision--Language Processing", "abstract": "Multi-modal data abounds in biomedicine, such as radiology images and reports. Interpreting this data at scale is essential for improving clinical care and accelerating clinical research. Biomedical text with its complex semantics poses additional challenges in vision--language modelling compared to the general domain, and previous work has used insufficiently adapted models that lack domain-specific language understanding. In this paper, we show that principled textual semantic modelling can substantially improve contrastive learning in self-supervised vision--language processing. We release a language model that achieves state-of-the-art results in radiology natural language inference through its improved vocabulary and novel language pretraining objective leveraging semantics and discourse characteristics in radiology reports. Further, we propose a self-supervised joint vision--language approach with a focus on better text modelling. It establishes new state of the art results on a wide range of publicly available benchmarks, in part by leveraging our new domain-specific language model. We release a new dataset with locally-aligned phrase grounding annotations by radiologists to facilitate the study of complex semantic modelling in biomedical vision--language processing. A broad evaluation, including on this new dataset, shows that our contrastive learning approach, aided by textual-semantic modelling, outperforms prior methods in segmentation tasks, despite only using a global-alignment objective.", "published": "2022-04-21T00:04:35Z", "authors": ["Benedikt Boecking", "Naoto Usuyama", "Shruthi Bannur", "Daniel C. Castro", "Anton Schwaighofer", "Stephanie Hyland", "Maria Wetscherek", "Tristan Naumann", "Aditya Nori", "Javier Alvarez-Valle", "Hoifung Poon", "Ozan Oktay"], "categories": ["cs.CV", "cs.CL"], "doi": "10.1007/978-3-031-20059-5_1", "_query": "property valuation computer vision", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2411.15851v2", "title": "ResCLIP: Residual Attention for Training-free Dense Vision-language Inference", "abstract": "While vision-language models like CLIP have shown remarkable success in open-vocabulary tasks, their application is currently confined to image-level tasks, and they still struggle with dense predictions. Recent works often attribute such deficiency in dense predictions to the self-attention layers in the final block, and have achieved commendable results by modifying the original query-key attention to self-correlation attention, (e.g., query-query and key-key attention). However, these methods overlook the cross-correlation attention (query-key) properties, which capture the rich spatial correspondence. In this paper, we reveal that the cross-correlation of the self-attention in CLIP's non-final layers also exhibits localization properties. Therefore, we propose the Residual Cross-correlation Self-attention (RCS) module, which leverages the cross-correlation self-attention from intermediate layers to remold the attention in the final block. The RCS module effectively reorganizes spatial information, unleashing the localization potential within CLIP for dense vision-language inference. Furthermore, to enhance the focus on regions of the same categories and local consistency, we propose the Semantic Feedback Refinement (SFR) module, which utilizes semantic segmentation maps to further adjust the attention scores. By integrating these two strategies, our method, termed ResCLIP, can be easily incorporated into existing approaches as a plug-and-play module, significantly boosting their performance in dense vision-language inference. Extensive experiments across multiple standard benchmarks demonstrate that our method surpasses state-of-the-art training-free methods, validating the effectiveness of the proposed approach. Code is available at https://github.com/yvhangyang/ResCLIP.", "published": "2024-11-24T14:14:14Z", "authors": ["Yuhang Yang", "Jinhong Deng", "Wen Li", "Lixin Duan"], "categories": ["cs.CV"], "doi": "10.1109/CVPR52734.2025.02789", "_query": "property valuation computer vision", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/1902.02922v1", "title": "Informing Computer Vision with Optical Illusions", "abstract": "Illusions are fascinating and immediately catch people's attention and interest, but they are also valuable in terms of giving us insights into human cognition and perception. A good theory of human perception should be able to explain the illusion, and a correct theory will actually give quantifiable results. We investigate here the efficiency of a computational filtering model utilised for modelling the lateral inhibition of retinal ganglion cells and their responses to a range of Geometric Illusions using isotropic Differences of Gaussian filters. This study explores the way in which illusions have been explained and shows how a simple standard model of vision based on classical receptive fields can predict the existence of these illusions as well as the degree of effect. A fundamental contribution of this work is to link bottom-up processes to higher level perception and cognition consistent with Marr's theory of vision and edge map representation.", "published": "2019-02-08T03:06:29Z", "authors": ["Nasim Nematzadeh", "David M. W. Powers", "Trent Lewis"], "categories": ["cs.CV", "q-bio.NC"], "doi": "", "_query": "property valuation computer vision", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2510.00458v4", "title": "VLOD-TTA: Test-Time Adaptation of Vision-Language Object Detectors", "abstract": "Vision-language object detectors (VLODs) such as YOLO-World and Grounding DINO exhibit strong zero-shot generalization, but their performance degrades under distribution shift. Test-time adaptation (TTA) offers a practical way to adapt models during inference using only unlabeled target (test) data. However, while TTA has made substantial progress in vision-language classification, its application to VLODs remains largely unexplored. The only prior method relies on a mean-teacher framework that introduces significant latency and memory overhead. To this end, we introduce VLOD-TTA, a TTA method that leverages dense proposal overlap and image-conditioned prompts to adapt VLODs with low additional overhead. VLOD-TTA combines (i) an IoU-weighted entropy objective that emphasizes spatially coherent proposal clusters and mitigates confirmation bias from isolated boxes, and (ii) image-conditioned prompt selection that ranks prompts by image-level compatibility and aggregates the most informative prompt scores for detection. Our experiments across diverse distribution shifts, including artistic domains, adverse driving conditions, low-light imagery, and common corruptions, indicate that VLOD-TTA consistently outperforms standard TTA baselines and the prior state-of-the-art method using YOLO-World and Grounding DINO. Our code: https://github.com/imatif17/VLOD-TTA", "published": "2025-10-01T03:17:56Z", "authors": ["Atif Belal", "Heitor R. Medeiros", "Marco Pedersoli", "Eric Granger"], "categories": ["cs.CV"], "doi": "", "_query": "property valuation computer vision", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2310.19909v2", "title": "Battle of the Backbones: A Large-Scale Comparison of Pretrained Models across Computer Vision Tasks", "abstract": "Neural network based computer vision systems are typically built on a backbone, a pretrained or randomly initialized feature extractor. Several years ago, the default option was an ImageNet-trained convolutional neural network. However, the recent past has seen the emergence of countless backbones pretrained using various algorithms and datasets. While this abundance of choice has led to performance increases for a range of systems, it is difficult for practitioners to make informed decisions about which backbone to choose. Battle of the Backbones (BoB) makes this choice easier by benchmarking a diverse suite of pretrained models, including vision-language models, those trained via self-supervised learning, and the Stable Diffusion backbone, across a diverse set of computer vision tasks ranging from classification to object detection to OOD generalization and more. Furthermore, BoB sheds light on promising directions for the research community to advance computer vision by illuminating strengths and weakness of existing approaches through a comprehensive analysis conducted on more than 1500 training runs. While vision transformers (ViTs) and self-supervised learning (SSL) are increasingly popular, we find that convolutional neural networks pretrained in a supervised fashion on large training sets still perform best on most tasks among the models we consider. Moreover, in apples-to-apples comparisons on the same architectures and similarly sized pretraining datasets, we find that SSL backbones are highly competitive, indicating that future works should perform SSL pretraining with advanced architectures and larger pretraining datasets. We release the raw results of our experiments along with code that allows researchers to put their own backbones through the gauntlet here: https://github.com/hsouri/Battle-of-the-Backbones", "published": "2023-10-30T18:23:58Z", "authors": ["Micah Goldblum", "Hossein Souri", "Renkun Ni", "Manli Shu", "Viraj Prabhu", "Gowthami Somepalli", "Prithvijit Chattopadhyay", "Mark Ibrahim", "Adrien Bardes", "Judy Hoffman", "Rama Chellappa", "Andrew Gordon Wilson", "Tom Goldstein"], "categories": ["cs.CV", "cs.LG"], "doi": "", "_query": "property valuation computer vision", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2111.05080v3", "title": "Residual Quantity in Percentage of Factory Machines Using Computer Vision and Mathematical Methods", "abstract": "Computer vision has been thriving since AI development was gaining thrust. Using deep learning techniques has been the most popular way which computer scientists thought the solution of. However, deep learning techniques tend to show lower performance than manual processing. Using deep learning is not always the answer to a problem related to computer vision.", "published": "2021-11-09T12:26:57Z", "authors": ["Seunghyeon Kim", "Jihoon Ryoo", "Dongyeob Lee", "Youngho Kim"], "categories": ["cs.CV"], "doi": "", "_query": "property valuation computer vision", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2508.02765v1", "title": "The Architecture of Trust: A Framework for AI-Augmented Real Estate Valuation in the Era of Structured Data", "abstract": "The Uniform Appraisal Dataset (UAD) 3.6's mandatory 2026 implementation transforms residential property valuation from narrative reporting to structured, machine-readable formats. This paper provides the first comprehensive analysis of this regulatory shift alongside concurrent AI advances in computer vision, natural language processing, and autonomous systems. We develop a three-layer framework for AI-augmented valuation addressing technical implementation and institutional trust requirements. Our analysis reveals how regulatory standardization converging with AI capabilities enables fundamental market restructuring with profound implications for professional practice, efficiency, and systemic risk. We make four key contributions: (1) documenting institutional failures including inter-appraiser variability and systematic biases undermining valuation reliability; (2) developing an architectural framework spanning physical data acquisition, semantic understanding, and cognitive reasoning that integrates emerging technologies while maintaining professional oversight; (3) addressing trust requirements for high-stakes financial applications including regulatory compliance, algorithmic fairness, and uncertainty quantification; (4) proposing evaluation methodologies beyond generic AI benchmarks toward domain-specific protocols. Our findings indicate successful transformation requires not merely technological sophistication but careful human-AI collaboration, creating systems that augment rather than replace professional expertise while addressing historical biases and information asymmetries in real estate markets.", "published": "2025-08-04T05:24:25Z", "authors": ["Petteri Teikari", "Mike Jarrell", "Maryam Azh", "Harri Pesola"], "categories": ["cs.CY", "cs.AI", "cs.CV"], "doi": "", "_query": "property valuation computer vision", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2004.07691v2", "title": "In Search of Life: Learning from Synthetic Data to Detect Vital Signs in Videos", "abstract": "Automatically detecting vital signs in videos, such as the estimation of heart and respiration rates, is a challenging research problem in computer vision with important applications in the medical field. One of the key difficulties in tackling this task is the lack of sufficient supervised training data, which severely limits the use of powerful deep neural networks. In this paper we address this limitation through a novel deep learning approach, in which a recurrent deep neural network is trained to detect vital signs in the infrared thermal domain from purely synthetic data. What is most surprising is that our novel method for synthetic training data generation is general, relatively simple and uses almost no prior medical domain knowledge. Moreover, our system, which is trained in a purely automatic manner and needs no human annotation, also learns to predict the respiration or heart intensity signal for each moment in time and to detect the region of interest that is most relevant for the given task, e.g. the nose area in the case of respiration. We test the effectiveness of our proposed system on the recent LCAS dataset and obtain state-of-the-art results.", "published": "2020-04-16T15:02:46Z", "authors": ["Florin Condrea", "Victor-Andrei Ivan", "Marius Leordeanu"], "categories": ["cs.CV", "cs.LG", "eess.IV"], "doi": "", "_query": "property valuation computer vision", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2303.12249v1", "title": "State-of-the-art optical-based physical adversarial attacks for deep learning computer vision systems", "abstract": "Adversarial attacks can mislead deep learning models to make false predictions by implanting small perturbations to the original input that are imperceptible to the human eye, which poses a huge security threat to the computer vision systems based on deep learning. Physical adversarial attacks, which is more realistic, as the perturbation is introduced to the input before it is being captured and converted to a binary image inside the vision system, when compared to digital adversarial attacks. In this paper, we focus on physical adversarial attacks and further classify them into invasive and non-invasive. Optical-based physical adversarial attack techniques (e.g. using light irradiation) belong to the non-invasive category. As the perturbations can be easily ignored by humans as the perturbations are very similar to the effects generated by a natural environment in the real world. They are highly invisibility and executable and can pose a significant or even lethal threats to real systems. This paper focuses on optical-based physical adversarial attack techniques for computer vision systems, with emphasis on the introduction and discussion of optical-based physical adversarial attack techniques.", "published": "2023-03-22T01:14:52Z", "authors": ["Junbin Fang", "You Jiang", "Canjian Jiang", "Zoe L. Jiang", "Siu-Ming Yiu", "Chuanyi Liu"], "categories": ["cs.CV", "cs.CR", "eess.IV"], "doi": "", "_query": "property valuation computer vision", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2109.00426v1", "title": "Separating minimal valuations, point-continuous valuations and continuous valuations", "abstract": "We give two concrete examples of continuous valuations on dcpo's to separate minimal valuations, point-continuous valuations and continuous valuations: (1) Let $\\mathcal J$ be the Johnstone's non-sober dcpo, and $μ$ be the continuous valuation on $\\mathcal J$ with $μ(U) =1$ for nonempty Scott opens $U$ and $μ(U) = 0$ for $U=\\emptyset$. Then $μ$ is a point-continuous valuation on $\\mathcal J$ that is not minimal. (2) Lebesgue measure extends to a measure on the Sorgenfrey line $\\mathbb R_{l}$. Its restriction to the open subsets of $\\mathbb R_{l}$ is a continuous valuation $λ$. Then its image valuation $\\overlineλ$ through the embedding of $\\mathbb R_{l}$ into its Smyth powerdomain $\\mathcal Q\\mathbb R_{l}$ in the Scott topology is a continuous valuation that is not point-continuous. We believe that our construction $\\overlineλ$ might be useful in giving counterexamples displaying the failure of the general Fubini-type equations on dcpo's.", "published": "2021-09-01T15:19:53Z", "authors": ["Jean Goubault-Larrecq", "Xiaodong Jia"], "categories": ["cs.LO", "math.GN"], "doi": "", "_query": "property valuation computer vision", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2401.17061v1", "title": "OmniSCV: An Omnidirectional Synthetic Image Generator for Computer Vision", "abstract": "Omnidirectional and 360° images are becoming widespread in industry and in consumer society, causing omnidirectional computer vision to gain attention. Their wide field of view allows the gathering of a great amount of information about the environment from only an image. However, the distortion of these images requires the development of specific algorithms for their treatment and interpretation. Moreover, a high number of images is essential for the correct training of computer vision algorithms based on learning. In this paper, we present a tool for generating datasets of omnidirectional images with semantic and depth information. These images are synthesized from a set of captures that are acquired in a realistic virtual environment for Unreal Engine 4 through an interface plugin. We gather a variety of well-known projection models such as equirectangular and cylindrical panoramas, different fish-eye lenses, catadioptric systems, and empiric models. Furthermore, we include in our tool photorealistic non-central-projection systems as non-central panoramas and non-central catadioptric systems. As far as we know, this is the first reported tool for generating photorealistic non-central images in the literature. Moreover, since the omnidirectional images are made virtually, we provide pixel-wise information about semantics and depth as well as perfect knowledge of the calibration parameters of the cameras. This allows the creation of ground-truth information with pixel precision for training learning algorithms and testing 3D vision approaches. To validate the proposed tool, different computer vision algorithms are tested as line extractions from dioptric and catadioptric central images, 3D Layout recovery and SLAM using equirectangular panoramas, and 3D reconstruction from non-central panoramas.", "published": "2024-01-30T14:40:19Z", "authors": ["Bruno Berenguel-Baeta", "Jesus Bermudez-Cameo", "Jose J. Guerrero"], "categories": ["cs.CV"], "doi": "10.3390/s20072066", "_query": "property valuation computer vision", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2604.23012v1", "title": "On-Device Vision Training, Deployment, and Inference on a Thumb-Sized Microcontroller", "abstract": "This paper presents a complete, end-to-end on-device vision machine learning pipeline, comprising data acquisition, two-layer CNN training with Adam optimization, and real-time inference, executing entirely on a microcontroller-class device costing $15-40 USD. Unlike cloud-based workflows that require external infrastructure and conceal the computational pipeline from the practitioner, this system implements every step of the core ML lifecycle in approximately 1,750 lines of readable C++ that compiles in under one minute using the Arduino IDE, with no external ML dependencies. Running on the Seeed Studio ESP32-S3 XIAO ML Kit (8 MB PSRAM), the firmware achieves three-class 64x64 image classification in approximately 9 minutes per training run, with real-time inference at 6.3 FPS. Key contributions include: correct batch-level gradient accumulation; pre-computed resize lookup tables for inference; dual-format weight export for SD-free baked-in deployment; a three-tier weight priority system (SD binary > baked-in header > He-initialization) resolved automatically at boot; a single-constant network reconfiguration interface; and PSRAM-aware memory management suited to microcontroller constraints. All source code and reference datasets are released under the MIT License at https://github.com/webmcu-ai/on-device-vision-ai", "published": "2026-04-24T20:59:16Z", "authors": ["Jeremy Ellis"], "categories": ["cs.LG", "cs.CV"], "doi": "", "_query": "property valuation computer vision", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2403.07153v1", "title": "2023 Low-Power Computer Vision Challenge (LPCVC) Summary", "abstract": "This article describes the 2023 IEEE Low-Power Computer Vision Challenge (LPCVC). Since 2015, LPCVC has been an international competition devoted to tackling the challenge of computer vision (CV) on edge devices. Most CV researchers focus on improving accuracy, at the expense of ever-growing sizes of machine models. LPCVC balances accuracy with resource requirements. Winners must achieve high accuracy with short execution time when their CV solutions run on an embedded device, such as Raspberry PI or Nvidia Jetson Nano. The vision problem for 2023 LPCVC is segmentation of images acquired by Unmanned Aerial Vehicles (UAVs, also called drones) after disasters. The 2023 LPCVC attracted 60 international teams that submitted 676 solutions during the submission window of one month. This article explains the setup of the competition and highlights the winners' methods that improve accuracy and shorten execution time.", "published": "2024-03-11T20:51:18Z", "authors": ["Leo Chen", "Benjamin Boardley", "Ping Hu", "Yiru Wang", "Yifan Pu", "Xin Jin", "Yongqiang Yao", "Ruihao Gong", "Bo Li", "Gao Huang", "Xianglong Liu", "Zifu Wan", "Xinwang Chen", "Ning Liu", "Ziyi Zhang", "Dongping Liu", "Ruijie Shan", "Zhengping Che", "Fachao Zhang", "Xiaofeng Mou", "Jian Tang", "Maxim Chuprov", "Ivan Malofeev", "Alexander Goncharenko", "Andrey Shcherbin", "Arseny Yanchenko", "Sergey Alyamkin", "Xiao Hu", "George K. Thiruvathukal", "Yung Hsiang Lu"], "categories": ["cs.CV"], "doi": "", "_query": "property valuation computer vision", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2305.06133v1", "title": "When ChatGPT for Computer Vision Will Come? From 2D to 3D", "abstract": "ChatGPT and its improved variant GPT4 have revolutionized the NLP field with a single model solving almost all text related tasks. However, such a model for computer vision does not exist, especially for 3D vision. This article first provides a brief view on the progress of deep learning in text, image and 3D fields from the model perspective. Moreover, this work further discusses how AIGC evolves from the data perspective. On top of that, this work presents an outlook on the development of AIGC in 3D from the data perspective.", "published": "2023-05-10T13:29:51Z", "authors": ["Chenghao Li", "Chaoning Zhang"], "categories": ["cs.CV"], "doi": "", "_query": "property valuation computer vision", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2501.10343v1", "title": "3rd Workshop on Maritime Computer Vision (MaCVi) 2025: Challenge Results", "abstract": "The 3rd Workshop on Maritime Computer Vision (MaCVi) 2025 addresses maritime computer vision for Unmanned Surface Vehicles (USV) and underwater. This report offers a comprehensive overview of the findings from the challenges. We provide both statistical and qualitative analyses, evaluating trends from over 700 submissions. All datasets, evaluation code, and the leaderboard are available to the public at https://macvi.org/workshop/macvi25.", "published": "2025-01-17T18:34:47Z", "authors": ["Benjamin Kiefer", "Lojze Žust", "Jon Muhovič", "Matej Kristan", "Janez Perš", "Matija Teršek", "Uma Mudenagudi Chaitra Desai", "Arnold Wiliem", "Marten Kreis", "Nikhil Akalwadi", "Yitong Quan", "Zhiqiang Zhong", "Zhe Zhang", "Sujie Liu", "Xuran Chen", "Yang Yang", "Matej Fabijanić", "Fausto Ferreira", "Seongju Lee", "Junseok Lee", "Kyoobin Lee", "Shanliang Yao", "Runwei Guan", "Xiaoyu Huang", "Yi Ni", "Himanshu Kumar", "Yuan Feng", "Yi-Ching Cheng", "Tzu-Yu Lin", "Chia-Ming Lee", "Chih-Chung Hsu", "Jannik Sheikh", "Andreas Michel", "Wolfgang Gross", "Martin Weinmann", "Josip Šarić", "Yipeng Lin", "Xiang Yang", "Nan Jiang", "Yutang Lu", "Fei Feng", "Ali Awad", "Evan Lucas", "Ashraf Saleem", "Ching-Heng Cheng", "Yu-Fan Lin", "Tzu-Yu Lin", "Chih-Chung Hsu"], "categories": ["cs.CV", "cs.AI"], "doi": "", "_query": "property valuation computer vision", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2601.22634v1", "title": "What can Computer Vision learn from Ranganathan?", "abstract": "The Semantic Gap Problem (SGP) in Computer Vision (CV) arises from the misalignment between visual and lexical semantics leading to flawed CV dataset design and CV benchmarks. This paper proposes that classification principles of S.R. Ranganathan can offer a principled starting point to address SGP and design high-quality CV datasets. We elucidate how these principles, suitably adapted, underpin the vTelos CV annotation methodology. The paper also briefly presents experimental evidence showing improvements in CV annotation and accuracy, thereby, validating vTelos.", "published": "2026-01-30T06:51:13Z", "authors": ["Mayukh Bagchi", "Fausto Giunchiglia"], "categories": ["cs.CV", "cs.AI"], "doi": "", "_query": "property valuation computer vision", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2302.05011v1", "title": "Context Understanding in Computer Vision: A Survey", "abstract": "Contextual information plays an important role in many computer vision tasks, such as object detection, video action detection, image classification, etc. Recognizing a single object or action out of context could be sometimes very challenging, and context information may help improve the understanding of a scene or an event greatly. Appearance context information, e.g., colors or shapes of the background of an object can improve the recognition accuracy of the object in the scene. Semantic context (e.g. a keyboard on an empty desk vs. a keyboard next to a desktop computer ) will improve accuracy and exclude unrelated events. Context information that are not in the image itself, such as the time or location of an images captured, can also help to decide whether certain event or action should occur. Other types of context (e.g. 3D structure of a building) will also provide additional information to improve the accuracy. In this survey, different context information that has been used in computer vision tasks is reviewed. We categorize context into different types and different levels. We also review available machine learning models and image/video datasets that can employ context information. Furthermore, we compare context based integration and context-free integration in mainly two classes of tasks: image-based and video-based. Finally, this survey is concluded by a set of promising future directions in context learning and utilization.", "published": "2023-02-10T02:01:21Z", "authors": ["Xuan Wang", "Zhigang Zhu"], "categories": ["cs.CV"], "doi": "10.1016/j.cviu.2023.103646", "_query": "property valuation computer vision", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2408.16730v1", "title": "VideoLLM-MoD: Efficient Video-Language Streaming with Mixture-of-Depths Vision Computation", "abstract": "A well-known dilemma in large vision-language models (e.g., GPT-4, LLaVA) is that while increasing the number of vision tokens generally enhances visual understanding, it also significantly raises memory and computational costs, especially in long-term, dense video frame streaming scenarios. Although learnable approaches like Q-Former and Perceiver Resampler have been developed to reduce the vision token burden, they overlook the context causally modeled by LLMs (i.e., key-value cache), potentially leading to missed visual cues when addressing user queries. In this paper, we introduce a novel approach to reduce vision compute by leveraging redundant vision tokens \"skipping layers\" rather than decreasing the number of vision tokens. Our method, VideoLLM-MoD, is inspired by mixture-of-depths LLMs and addresses the challenge of numerous vision tokens in long-term or streaming video. Specifically, for each transformer layer, we learn to skip the computation for a high proportion (e.g., 80\\%) of vision tokens, passing them directly to the next layer. This approach significantly enhances model efficiency, achieving approximately \\textasciitilde42\\% time and \\textasciitilde30\\% memory savings for the entire training. Moreover, our method reduces the computation in the context and avoid decreasing the vision tokens, thus preserving or even improving performance compared to the vanilla model. We conduct extensive experiments to demonstrate the effectiveness of VideoLLM-MoD, showing its state-of-the-art results on multiple benchmarks, including narration, forecasting, and summarization tasks in COIN, Ego4D, and Ego-Exo4D datasets.", "published": "2024-08-29T17:21:58Z", "authors": ["Shiwei Wu", "Joya Chen", "Kevin Qinghong Lin", "Qimeng Wang", "Yan Gao", "Qianli Xu", "Tong Xu", "Yao Hu", "Enhong Chen", "Mike Zheng Shou"], "categories": ["cs.CV"], "doi": "", "_query": "property valuation computer vision", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2610.03772v1", "title": "Energy Variation in Training Modern Computer Vision Architectures", "abstract": "The rapid growth of deep learning has substantially increased the energy consumption associated with model training, making energy efficiency an increasingly relevant design criterion. This study empirically measures the energy variation of training seven modern computer vision architectures, MobileNetV3-Small, MobileNetV3-Large, EfficientNet-B0, EfficientNet-B1, ViT-B/32, ConvNeXt-Tiny, and ViT-B/16 for the ImageNet-1k classification task, using a homogeneous 10,000-image subset (ImageNet-10k) and a uniform 40-epoch baseline configuration executed on two NVIDIA Tesla P100 GPUs at the Bioinformatics and Computational Biology Center of Colombia (BIOS). Energy was recorded directly via NVML and contrasted with the computational complexity of each model. The results show a Pearson correlation of 0.85 between floating-point operations (GFLOPs) and energy consumption in kWh, indicating that computational complexity is a strong but imperfect predictor of energy expenditure: architectures with comparable GFLOPs exhibited consumption differing by up to 3.1x due to differences in the hardware efficiency of their dominant operations. The EfficientNet variants offered the best balance between classification performance (Val Top-5 up to 97.15%) and energy efficiency (0.457-0.611 kWh), while Vision Transformers exhibited the highest relative energy consumption and lower classification performance under the evaluated configuration. These findings guide architecture selection in energy-constrained computing environments.", "published": "2026-09-29T09:15:37Z", "authors": ["David Cortes", "Carlos Juiz", "Belen Bermejo"], "categories": ["cs.CV", "cs.PF"], "doi": "", "_query": "property valuation computer vision", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2307.13992v2", "title": "Causal reasoning in typical computer vision tasks", "abstract": "Deep learning has revolutionized the field of artificial intelligence. Based on the statistical correlations uncovered by deep learning-based methods, computer vision has contributed to tremendous growth in areas like autonomous driving and robotics. Despite being the basis of deep learning, such correlation is not stable and is susceptible to uncontrolled factors. In the absence of the guidance of prior knowledge, statistical correlations can easily turn into spurious correlations and cause confounders. As a result, researchers are now trying to enhance deep learning methods with causal theory. Causal theory models the intrinsic causal structure unaffected by data bias and is effective in avoiding spurious correlations. This paper aims to comprehensively review the existing causal methods in typical vision and vision-language tasks such as semantic segmentation, object detection, and image captioning. The advantages of causality and the approaches for building causal paradigms will be summarized. Future roadmaps are also proposed, including facilitating the development of causal theory and its application in other complex scenes and systems.", "published": "2023-07-26T07:01:57Z", "authors": ["Kexuan Zhang", "Qiyu Sun", "Chaoqiang Zhao", "Yang Tang"], "categories": ["cs.CV"], "doi": "", "_query": "property valuation computer vision", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2608.29395v1", "title": "GATE: Reliability-Gated Gaussian Evidence Fusion for Training-Free Test-Time Adaptation of Vision-Language Models", "abstract": "Vision-language models such as CLIP and SigLIP provide strong zero-shot recognition, but their predictions can degrade when deployed on target data that differ from the pretraining distribution. Test-time adaptation offers a practical way to improve robustness without source data or target labels, yet existing methods often rely on either prompt-side adaptation or image-side target evidence alone. In this work, we introduce GATE, a training-free two-pass transductive test-time adaptation framework that uses the unlabeled target set while keeping the image encoder, text encoder, and prompt parameters fully frozen. Instead of representing each class with a single prototype, GATE builds two complementary Gaussian sources of evidence in the shared vision-language feature space: a text Gaussian estimated from multiple language descriptions and an image Gaussian estimated from reliable unlabeled target samples. A class-wise reliability gate controls the influence of image-derived pseudo-evidence, and a score-level generalized Product-of-Experts fusion produces a normalized residual correction to the original zero-shot logits. Across fine-grained recognition datasets, ImageNet-family distribution shifts, multiple CLIP backbones, and SigLIP-B/16, GATE achieves the best average accuracy in every benchmark/backbone group. It improves zero-shot performance by an average of 5.41 points and outperforms the strongest non-GATE baseline by 1.94 points, demonstrating the benefit of reliability-gated distributional evidence for frozen VLM adaptation.", "published": "2026-08-29T18:33:23Z", "authors": ["Pedram MohajerAnsari", "Amir Salarpour", "Run Wang", "Mert D. Pesé"], "categories": ["cs.CV"], "doi": "", "_query": "property valuation computer vision", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2207.12392v3", "title": "Self-Distilled Vision Transformer for Domain Generalization", "abstract": "In the recent past, several domain generalization (DG) methods have been proposed, showing encouraging performance, however, almost all of them build on convolutional neural networks (CNNs). There is little to no progress on studying the DG performance of vision transformers (ViTs), which are challenging the supremacy of CNNs on standard benchmarks, often built on i.i.d assumption. This renders the real-world deployment of ViTs doubtful. In this paper, we attempt to explore ViTs towards addressing the DG problem. Similar to CNNs, ViTs also struggle in out-of-distribution scenarios and the main culprit is overfitting to source domains. Inspired by the modular architecture of ViTs, we propose a simple DG approach for ViTs, coined as self-distillation for ViTs. It reduces the overfitting of source domains by easing the learning of input-output mapping problem through curating non-zero entropy supervisory signals for intermediate transformer blocks. Further, it does not introduce any new parameters and can be seamlessly plugged into the modular composition of different ViTs. We empirically demonstrate notable performance gains with different DG baselines and various ViT backbones in five challenging datasets. Moreover, we report favorable performance against recent state-of-the-art DG methods. Our code along with pre-trained models are publicly available at: https://github.com/maryam089/SDViT.", "published": "2022-07-25T17:57:05Z", "authors": ["Maryam Sultana", "Muzammal Naseer", "Muhammad Haris Khan", "Salman Khan", "Fahad Shahbaz Khan"], "categories": ["cs.CV", "cs.AI", "cs.LG"], "doi": "", "_query": "property valuation computer vision", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/1402.1932v1", "title": "An EMUSIM Technique and its Components in Cloud Computing- A Review", "abstract": "Recent efforts to design and develop Cloud technologies focus on defining novel methods, policies and mechanisms for efficiently managing Cloud infrastructures. One key challenge potential Cloud customers have before renting resources is to know how their services will behave in a set of resources and the costs involved when growing and shrinking their resource pool. Most of the studies in this area rely on simulation-based experiments, which consider simplified modeling of applications and computing environment. In order to better predict service's behavior on Cloud platforms, an integrated architecture that is based on both simulation and emulation. The proposed architecture, named EMUSIM, automatically extracts information from application behavior via emulation and then uses this information to generate the corresponding simulation model. This paper presents brief overview of the EMUSIM technique and its components. The work in this paper focuses on architecture and operation details of Automated Emulation Framework (AEF), QAppDeployer and proposes Cloud Sim Application for Simulation techniques.", "published": "2014-02-09T10:10:48Z", "authors": ["Dr. Rahul Malhotra", "Prince Jain"], "categories": ["cs.DC"], "doi": "10.14445/2231-2803", "_query": "property valuation computer vision", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2003.13988v2", "title": "Fashion Meets Computer Vision: A Survey", "abstract": "Fashion is the way we present ourselves to the world and has become one of the world's largest industries. Fashion, mainly conveyed by vision, has thus attracted much attention from computer vision researchers in recent years. Given the rapid development, this paper provides a comprehensive survey of more than 200 major fashion-related works covering four main aspects for enabling intelligent fashion: (1) Fashion detection includes landmark detection, fashion parsing, and item retrieval, (2) Fashion analysis contains attribute recognition, style learning, and popularity prediction, (3) Fashion synthesis involves style transfer, pose transformation, and physical simulation, and (4) Fashion recommendation comprises fashion compatibility, outfit matching, and hairstyle suggestion. For each task, the benchmark datasets and the evaluation protocols are summarized. Furthermore, we highlight promising directions for future research.", "published": "2020-03-31T07:08:23Z", "authors": ["Wen-Huang Cheng", "Sijie Song", "Chieh-Yun Chen", "Shintami Chusnul Hidayati", "Jiaying Liu"], "categories": ["cs.CV"], "doi": "", "_query": "property valuation computer vision", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2108.11898v3", "title": "Supervised Compression for Resource-Constrained Edge Computing Systems", "abstract": "There has been much interest in deploying deep learning algorithms on low-powered devices, including smartphones, drones, and medical sensors. However, full-scale deep neural networks are often too resource-intensive in terms of energy and storage. As a result, the bulk part of the machine learning operation is therefore often carried out on an edge server, where the data is compressed and transmitted. However, compressing data (such as images) leads to transmitting information irrelevant to the supervised task. Another popular approach is to split the deep network between the device and the server while compressing intermediate features. To date, however, such split computing strategies have barely outperformed the aforementioned naive data compression baselines due to their inefficient approaches to feature compression. This paper adopts ideas from knowledge distillation and neural image compression to compress intermediate feature representations more efficiently. Our supervised compression approach uses a teacher model and a student model with a stochastic bottleneck and learnable prior for entropy coding (Entropic Student). We compare our approach to various neural image and feature compression baselines in three vision tasks and found that it achieves better supervised rate-distortion performance while maintaining smaller end-to-end latency. We furthermore show that the learned feature representations can be tuned to serve multiple downstream tasks.", "published": "2021-08-21T11:10:29Z", "authors": ["Yoshitomo Matsubara", "Ruihan Yang", "Marco Levorato", "Stephan Mandt"], "categories": ["cs.CV", "cs.LG"], "doi": "10.1109/WACV51458.2022.00100", "_query": "property valuation computer vision", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2605.06714v1", "title": "Edge Deep Learning in Computer Vision and Medical Diagnostics: A Comprehensive Survey", "abstract": "Edge deep learning, a paradigm change reconciling edge computing and deep learning, facilitates real-time decision making attuned to environmental factors through the close integration of computational resources and data sources. Here we provide a comprehensive review of the current state of the art in edge deep learning, focusing on computer vision applications, in particular medical diagnostics. An overview of the foundational principles and technical advantages of edge deep learning is presented, emphasising the capacity of this technology to revolutionise a wide range of domains. Furthermore, we present a novel categorisation of edge hardware platforms based on performance and usage scenarios, facilitating platform selection and operational effectiveness. Following this, we dive into approaches to effectively implement deep neural networks on edge devices, encompassing methods such as lightweight design and model compression. Reviewing practical applications in the fields of computer vision in general and medical diagnostics in particular, we demonstrate the profound impact edge-deployed deep learning models can have in real-life situations. Finally, we provide an analysis of potential future directions and obstacles to the adoption of edge deep learning, with the intention to stimulate further investigations and advancements of intelligent edge deep learning solutions. This survey provides researchers and practitioners with a comprehensive reference shedding light on the critical role deep learning plays in the advancement of edge computing applications.", "published": "2026-05-07T00:19:51Z", "authors": ["Yiwen Xu", "Tariq M. Khan", "Yang Song", "Erik Meijering"], "categories": ["cs.CV", "cs.AI"], "doi": "10.1007/s10462-024-11033-5", "_query": "property valuation computer vision", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2402.14958v1", "title": "EE3P: Event-based Estimation of Periodic Phenomena Properties", "abstract": "We introduce a novel method for measuring properties of periodic phenomena with an event camera, a device asynchronously reporting brightness changes at independently operating pixels. The approach assumes that for fast periodic phenomena, in any spatial window where it occurs, a very similar set of events is generated at the time difference corresponding to the frequency of the motion. To estimate the frequency, we compute correlations of spatio-temporal windows in the event space. The period is calculated from the time differences between the peaks of the correlation responses. The method is contactless, eliminating the need for markers, and does not need distinguishable landmarks. We evaluate the proposed method on three instances of periodic phenomena: (i) light flashes, (ii) vibration, and (iii) rotational speed. In all experiments, our method achieves a relative error lower than 0.04%, which is within the error margin of ground truth measurements.", "published": "2024-02-22T20:37:30Z", "authors": ["Jakub Kolář", "Radim Špetlík", "Jiří Matas"], "categories": ["cs.CV"], "doi": "", "_query": "property valuation computer vision", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/1806.01954v2", "title": "Mining for meaning: from vision to language through multiple networks consensus", "abstract": "Describing visual data into natural language is a very challenging task, at the intersection of computer vision, natural language processing and machine learning. Language goes well beyond the description of physical objects and their interactions and can convey the same abstract idea in many ways. It is both about content at the highest semantic level as well as about fluent form. Here we propose an approach to describe videos in natural language by reaching a consensus among multiple encoder-decoder networks. Finding such a consensual linguistic description, which shares common properties with a larger group, has a better chance to convey the correct meaning. We propose and train several network architectures and use different types of image, audio and video features. Each model produces its own description of the input video and the best one is chosen through an efficient, two-phase consensus process. We demonstrate the strength of our approach by obtaining state of the art results on the challenging MSR-VTT dataset.", "published": "2018-06-05T22:50:09Z", "authors": ["Iulia Duta", "Andrei Liviu Nicolicioiu", "Simion-Vlad Bogolin", "Marius Leordeanu"], "categories": ["cs.CV"], "doi": "", "_query": "property valuation computer vision", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2610.11818v1", "title": "From Pixels to Structure: Lightweight Vision-Language Models for Document OCR and Structured JSON Extraction", "abstract": "While massive, closed-source Vision-Language Models (VLMs) set strong benchmarks for document understanding, their dependence on commercial APIs limits adoption in institutional archives due to data autonomy concerns, recurring costs, and the environmental footprint of hyperscale computing. This is especially acute in heritage digitization, where documents include historical handwriting, domain-specific terminology (e.g., jewelry, prehistory, architecture), and non-standard layouts requiring high-dimensional structured extraction. We present a comparative study of eight open-source lightweight VLMs (up to 7B parameters) for Optical Character Recognition (OCR)-to-structure across three university heritage collections. Given a document image, models must extract text and generate schema-compliant JSON, enabling automatic validation and downstream use. We evaluate models under a constraint-aware protocol across zero-shot, few-shot, and fine-tuning settings, measuring extraction fidelity and structured-output quality using Character Error Rate (CER), Approximate Normalized Levenshtein Similarity (ANLS*), and mean Average Precision F1 (mAP-F1). Against a fine-tuning baseline, we further test the independent impact of (i) hyperparameter optimization, (ii) classical image preprocessing (illumination flattening, denoising, and CLAHE), and (iii) multi-stage training. Finally, we analyze the trade-off between dataset-specific fine-tuning and a single multi-dataset checkpoint, where joint training enables one model to operate across collections but can shift performance between datasets. Overall, we show that carefully adapted VLMs with up to 7B parameters can provide a sustainable, private, high-performing alternative to manual transcription or commercial black-box systems, and we offer actionable guidance for heritage institutions seeking institution-controlled OCR-to-JSON extraction.", "published": "2026-10-08T12:21:52Z", "authors": ["Uddipan Basu Bir", "Vincent Christlein", "Andreas Maier", "Mathias Zinnen"], "categories": ["cs.CV", "cs.AI"], "doi": "10.1007/978-3-032-36039-7_30", "_query": "property valuation computer vision", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2208.08190v1", "title": "DeepSportradar-v1: Computer Vision Dataset for Sports Understanding with High Quality Annotations", "abstract": "With the recent development of Deep Learning applied to Computer Vision, sport video understanding has gained a lot of attention, providing much richer information for both sport consumers and leagues. This paper introduces DeepSportradar-v1, a suite of computer vision tasks, datasets and benchmarks for automated sport understanding. The main purpose of this framework is to close the gap between academic research and real world settings. To this end, the datasets provide high-resolution raw images, camera parameters and high quality annotations. DeepSportradar currently supports four challenging tasks related to basketball: ball 3D localization, camera calibration, player instance segmentation and player re-identification. For each of the four tasks, a detailed description of the dataset, objective, performance metrics, and the proposed baseline method are provided. To encourage further research on advanced methods for sport understanding, a competition is organized as part of the MMSports workshop from the ACM Multimedia 2022 conference, where participants have to develop state-of-the-art methods to solve the above tasks. The four datasets, development kits and baselines are publicly available.", "published": "2022-08-17T09:55:02Z", "authors": ["Gabriel Van Zandycke", "Vladimir Somers", "Maxime Istasse", "Carlo Del Don", "Davide Zambrano"], "categories": ["cs.CV", "cs.LG", "eess.IV"], "doi": "10.1145/3552437.3555699", "_query": "property valuation computer vision", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2101.04804v1", "title": "Embedded Computer Vision System Applied to a Four-Legged Line Follower Robot", "abstract": "Robotics can be defined as the connection of perception to action. Taking this further, this project aims to drive a robot using an automated computer vision embedded system, connecting the robot's vision to its behavior. In order to implement a color recognition system on the robot, open source tools are chosen, such as Processing language, Android system, Arduino platform and Pixy camera. The constraints are clear: simplicity, replicability and financial viability. In order to integrate Robotics, Computer Vision and Image Processing, the robot is applied on a typical mobile robot's issue: line following. The problem of distinguishing the path from the background is analyzed through different approaches: the popular Otsu's Method, thresholding based on color combinations through experimentation and color tracking via hue and saturation. Decision making of where to move next is based on the line center of the path and is fully automated. Using a four-legged robot as platform and a camera as its only sensor, the robot is capable of successfully follow a line. From capturing the image to moving the robot, it's evident how integrative Robotics can be. The issue of this paper alone involves knowledge of Mechanical Engineering, Electronics, Control Systems and Programming. Everything related to this work was documented and made available on an open source online page, so it can be useful in learning and experimenting with robotics.", "published": "2021-01-12T23:52:53Z", "authors": ["Beatriz Arruda Asfora"], "categories": ["cs.RO", "cs.CV", "eess.IV", "eess.SY"], "doi": "10.20906/CPS/COB-2015-1649", "_query": "property valuation computer vision", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2109.13356v2", "title": "Efficient Computer Vision on Edge Devices with Pipeline-Parallel Hierarchical Neural Networks", "abstract": "Computer vision on low-power edge devices enables applications including search-and-rescue and security. State-of-the-art computer vision algorithms, such as Deep Neural Networks (DNNs), are too large for inference on low-power edge devices. To improve efficiency, some existing approaches parallelize DNN inference across multiple edge devices. However, these techniques introduce significant communication and synchronization overheads or are unable to balance workloads across devices. This paper demonstrates that the hierarchical DNN architecture is well suited for parallel processing on multiple edge devices. We design a novel method that creates a parallel inference pipeline for computer vision problems that use hierarchical DNNs. The method balances loads across the collaborating devices and reduces communication costs to facilitate the processing of multiple video frames simultaneously with higher throughput. Our experiments consider a representative computer vision problem where image recognition is performed on each video frame, running on multiple Raspberry Pi 4Bs. With four collaborating low-power edge devices, our approach achieves 3.21X higher throughput, 68% less energy consumption per device per frame, and 58% decrease in memory when compared with existing single-device hierarchical DNNs.", "published": "2021-09-27T21:27:41Z", "authors": ["Abhinav Goel", "Caleb Tung", "Xiao Hu", "George K. Thiruvathukal", "James C. Davis", "Yung-Hsiang Lu"], "categories": ["cs.CV", "cs.DC"], "doi": "", "_query": "property valuation computer vision", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/1912.05011v1", "title": "A psychophysics approach for quantitative comparison of interpretable computer vision models", "abstract": "The field of transparent Machine Learning (ML) has contributed many novel methods aiming at better interpretability for computer vision and ML models in general. But how useful the explanations provided by transparent ML methods are for humans remains difficult to assess. Most studies evaluate interpretability in qualitative comparisons, they use experimental paradigms that do not allow for direct comparisons amongst methods or they report only offline experiments with no humans in the loop. While there are clear advantages of evaluations with no humans in the loop, such as scalability, reproducibility and less algorithmic bias than with humans in the loop, these metrics are limited in their usefulness if we do not understand how they relate to other metrics that take human cognition into account. Here we investigate the quality of interpretable computer vision algorithms using techniques from psychophysics. In crowdsourced annotation tasks we study the impact of different interpretability approaches on annotation accuracy and task time. In order to relate these findings to quality measures for interpretability without humans in the loop we compare quality metrics with and without humans in the loop. Our results demonstrate that psychophysical experiments allow for robust quality assessment of transparency in machine learning. Interestingly the quality metrics computed without humans in the loop did not provide a consistent ranking of interpretability methods nor were they representative for how useful an explanation was for humans. These findings highlight the potential of methods from classical psychophysics for modern machine learning applications. We hope that our results provide convincing arguments for evaluating interpretability in its natural habitat, human-ML interaction, if the goal is to obtain an authentic assessment of interpretability.", "published": "2019-11-24T16:51:20Z", "authors": ["Felix Biessmann", "Dionysius Irza Refiano"], "categories": ["cs.CV", "cs.AI", "cs.HC"], "doi": "", "_query": "property valuation computer vision", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2302.08641v1", "title": "Transformer-based Generative Adversarial Networks in Computer Vision: A Comprehensive Survey", "abstract": "Generative Adversarial Networks (GANs) have been very successful for synthesizing the images in a given dataset. The artificially generated images by GANs are very realistic. The GANs have shown potential usability in several computer vision applications, including image generation, image-to-image translation, video synthesis, and others. Conventionally, the generator network is the backbone of GANs, which generates the samples and the discriminator network is used to facilitate the training of the generator network. The discriminator network is usually a Convolutional Neural Network (CNN). Whereas, the generator network is usually either an Up-CNN for image generation or an Encoder-Decoder network for image-to-image translation. The convolution-based networks exploit the local relationship in a layer, which requires the deep networks to extract the abstract features. Hence, CNNs suffer to exploit the global relationship in the feature space. However, recently developed Transformer networks are able to exploit the global relationship at every layer. The Transformer networks have shown tremendous performance improvement for several problems in computer vision. Motivated from the success of Transformer networks and GANs, recent works have tried to exploit the Transformers in GAN framework for the image/video synthesis. This paper presents a comprehensive survey on the developments and advancements in GANs utilizing the Transformer networks for computer vision applications. The performance comparison for several applications on benchmark datasets is also performed and analyzed. The conducted survey will be very useful to deep learning and computer vision community to understand the research trends \\& gaps related with Transformer-based GANs and to develop the advanced GAN architectures by exploiting the global and local relationships for different applications.", "published": "2023-02-17T01:13:58Z", "authors": ["Shiv Ram Dubey", "Satish Kumar Singh"], "categories": ["cs.CV", "eess.IV"], "doi": "", "_query": "property valuation computer vision", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2609.04168v1", "title": "Para-Pipe: Exploiting Hierarchical Operator Parallelism of ML Computational Graphs on SoCs", "abstract": "As edge-based deep learning applications become more complex, optimizing performance on heterogeneous System-on-Chips (SoCs) presents unique challenges. Traditional pipelining techniques distributing the computation across different on-chip processing units, while effective for throughput, do not address the latency demands posed by modern neural networks with complex interdependencies and extensive operator parallelism. There is a potential in leveraging operator parallelism to enable concurrent execution across multiple processing units, thereby reducing inference latency. However, prioritizing pipelining or parallel execution often necessitates a compromise, where optimizing one performance metric adversely impacts the other. This paper introduces Para-Pipe, a hierarchical mapping framework that integrates intra- and inter-stage operator parallelism within a pipelined architecture. Para-Pipe navigates the trade-off between throughput and latency by selectively fine-tuning parallelism levels within and across pipeline stages. This strategy can significantly reduce inter-processor communication overhead, significantly improving energy efficiency. Our evaluation demonstrates that Para-Pipe generates multiple Pareto-optimal configurations, achieving a balance between throughput and latency on an Amlogic SoC equipped with ARM big.LITTLE CPUs and GPU, as well as the Black Sesame Technology SoC featuring a deep learning accelerator and two DSPs. More importantly, throughput-optimized configurations under Para-Pipe on Amlogic SoC show an average energy efficiency improvement of 11.0% over purely pipelined strategies and 23.3% relative to non-pipelined parallel execution.", "published": "2026-09-03T17:53:44Z", "authors": ["Yujie Zhang", "Huiying Lan", "Ehsan Aghapour", "Zhiyuan Ning", "Peng Zan", "Weidong Shao", "Anuj Pathania", "Tulika Mitra"], "categories": ["cs.DC", "cs.LG", "cs.PF"], "doi": "10.1109/TCAD.2025.3568348", "_query": "property valuation computer vision", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/1808.08690v1", "title": "Stereo Computation for a Single Mixture Image", "abstract": "This paper proposes an original problem of \\emph{stereo computation from a single mixture image}-- a challenging problem that had not been researched before. The goal is to separate (\\ie, unmix) a single mixture image into two constitute image layers, such that the two layers form a left-right stereo image pair, from which a valid disparity map can be recovered. This is a severely illposed problem, from one input image one effectively aims to recover three (\\ie, left image, right image and a disparity map). In this work we give a novel deep-learning based solution, by jointly solving the two subtasks of image layer separation as well as stereo matching. Training our deep net is a simple task, as it does not need to have disparity maps. Extensive experiments demonstrate the efficacy of our method.", "published": "2018-08-27T05:03:37Z", "authors": ["Yiran Zhong", "Yuchao Dai", "Hongdong Li"], "categories": ["cs.CV"], "doi": "", "_query": "property valuation computer vision", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2602.18504v1", "title": "A Computer Vision Framework for Multi-Class Detection and Tracking in Soccer Broadcast Footage", "abstract": "Clubs with access to expensive multi-camera setups or GPS tracking systems gain a competitive advantage through detailed data, whereas lower-budget teams are often unable to collect similar information. This paper examines whether such data can instead be extracted directly from standard broadcast footage using a single-camera computer vision pipeline. This project develops an end-to-end system that combines a YOLO object detector with the ByteTrack tracking algorithm to identify and track players, referees, goalkeepers, and the ball throughout a match. Experimental results show that the pipeline achieves high performance in detecting and tracking players and officials, with strong precision, recall, and mAP50 scores, while ball detection remains the primary challenge. Despite this limitation, our findings demonstrate that AI can extract meaningful player-level spatial information from a single broadcast camera. By reducing reliance on specialized hardware, the proposed approach enables colleges, academies, and amateur clubs to adopt scalable, data-driven analysis methods previously accessible only to professional teams, highlighting the potential for affordable computer vision-based soccer analytics.", "published": "2026-02-17T21:44:09Z", "authors": ["Daniel Tshiani"], "categories": ["cs.CV", "cs.AI"], "doi": "", "_query": "property valuation computer vision", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2311.15108v2", "title": "Leveraging Diffusion Perturbations for Measuring Fairness in Computer Vision", "abstract": "Computer vision models have been known to encode harmful biases, leading to the potentially unfair treatment of historically marginalized groups, such as people of color. However, there remains a lack of datasets balanced along demographic traits that can be used to evaluate the downstream fairness of these models. In this work, we demonstrate that diffusion models can be leveraged to create such a dataset. We first use a diffusion model to generate a large set of images depicting various occupations. Subsequently, each image is edited using inpainting to generate multiple variants, where each variant refers to a different perceived race. Using this dataset, we benchmark several vision-language models on a multi-class occupation classification task. We find that images generated with non-Caucasian labels have a significantly higher occupation misclassification rate than images generated with Caucasian labels, and that several misclassifications are suggestive of racial biases. We measure a model's downstream fairness by computing the standard deviation in the probability of predicting the true occupation label across the different perceived identity groups. Using this fairness metric, we find significant disparities between the evaluated vision-and-language models. We hope that our work demonstrates the potential value of diffusion methods for fairness evaluations.", "published": "2023-11-25T19:40:13Z", "authors": ["Nicholas Lui", "Bryan Chia", "William Berrios", "Candace Ross", "Douwe Kiela"], "categories": ["cs.CV", "cs.AI"], "doi": "", "_query": "property valuation computer vision", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2309.13744v3", "title": "A Systematic Literature Review of Computer Vision Applications in Robotized Wire Harness Assembly", "abstract": "This article provides a systematic literature review of computer vision applications in robotized wire harness assembly.", "published": "2023-09-24T20:28:01Z", "authors": ["Hao Wang", "Omkar Salunkhe", "Walter Quadrini", "Dan Lämkull", "Fredrik Ore", "Mélanie Despeisse", "Luca Fumagalli", "Johan Stahre", "Björn Johansson"], "categories": ["cs.CV", "cs.AI", "cs.RO"], "doi": "10.1016/j.aei.2024.102596", "_query": "property valuation computer vision", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/1611.09942v2", "title": "Photographic home styles in Congress: a computer vision approach", "abstract": "While members of Congress now routinely communicate with constituents using images on a variety of internet platforms, little is known about how images are used as a means of strategic political communication. This is due primarily to computational limitations which have prevented large-scale, systematic analyses of image features. New developments in computer vision, however, are bringing the systematic study of images within reach. Here, we develop a framework for understanding visual political communication by extending Fenno's analysis of home style (Fenno 1978) to images and introduce \"photographic\" home styles. Using approximately 192,000 photographs collected from MCs Facebook profiles, we build machine learning software with convolutional neural networks and conduct an image manipulation experiment to explore how the race of people that MCs pose with shape photographic home styles. We find evidence that electoral pressures shape photographic home styles and demonstrate that Democratic and Republican members of Congress use images in very different ways.", "published": "2016-11-29T23:41:00Z", "authors": ["L. Jason Anastasopoulos", "Dhruvil Badani", "Crystal Lee", "Shiry Ginosar", "Jake Williams"], "categories": ["cs.SI", "cs.CV"], "doi": "", "_query": "property valuation computer vision", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2305.00434v2", "title": "EVREAL: Towards a Comprehensive Benchmark and Analysis Suite for Event-based Video Reconstruction", "abstract": "Event cameras are a new type of vision sensor that incorporates asynchronous and independent pixels, offering advantages over traditional frame-based cameras such as high dynamic range and minimal motion blur. However, their output is not easily understandable by humans, making the reconstruction of intensity images from event streams a fundamental task in event-based vision. While recent deep learning-based methods have shown promise in video reconstruction from events, this problem is not completely solved yet. To facilitate comparison between different approaches, standardized evaluation protocols and diverse test datasets are essential. This paper proposes a unified evaluation methodology and introduces an open-source framework called EVREAL to comprehensively benchmark and analyze various event-based video reconstruction methods from the literature. Using EVREAL, we give a detailed analysis of the state-of-the-art methods for event-based video reconstruction, and provide valuable insights into the performance of these methods under varying settings, challenging scenarios, and downstream tasks.", "published": "2023-04-30T09:28:38Z", "authors": ["Burak Ercan", "Onur Eker", "Aykut Erdem", "Erkut Erdem"], "categories": ["cs.CV"], "doi": "10.1109/CVPRW59228.2023.00410", "_query": "property valuation computer vision", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2407.21788v1", "title": "Vision-Language Model Based Handwriting Verification", "abstract": "Handwriting Verification is a critical in document forensics. Deep learning based approaches often face skepticism from forensic document examiners due to their lack of explainability and reliance on extensive training data and handcrafted features. This paper explores using Vision Language Models (VLMs), such as OpenAI's GPT-4o and Google's PaliGemma, to address these challenges. By leveraging their Visual Question Answering capabilities and 0-shot Chain-of-Thought (CoT) reasoning, our goal is to provide clear, human-understandable explanations for model decisions. Our experiments on the CEDAR handwriting dataset demonstrate that VLMs offer enhanced interpretability, reduce the need for large training datasets, and adapt better to diverse handwriting styles. However, results show that the CNN-based ResNet-18 architecture outperforms the 0-shot CoT prompt engineering approach with GPT-4o (Accuracy: 70%) and supervised fine-tuned PaliGemma (Accuracy: 71%), achieving an accuracy of 84% on the CEDAR AND dataset. These findings highlight the potential of VLMs in generating human-interpretable decisions while underscoring the need for further advancements to match the performance of specialized deep learning models.", "published": "2024-07-31T17:57:32Z", "authors": ["Mihir Chauhan", "Abhishek Satbhai", "Mohammad Abuzar Hashemi", "Mir Basheer Ali", "Bina Ramamurthy", "Mingchen Gao", "Siwei Lyu", "Sargur Srihari"], "categories": ["cs.CV", "cs.AI", "cs.CL", "cs.LG"], "doi": "", "_query": "property valuation computer vision", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2112.03444v2", "title": "GPU-Based Homotopy Continuation for Minimal Problems in Computer Vision", "abstract": "Systems of polynomial equations arise frequently in computer vision, especially in multiview geometry problems. Traditional methods for solving these systems typically aim to eliminate variables to reach a univariate polynomial, e.g., a tenth-order polynomial for 5-point pose estimation, using clever manipulations, or more generally using Grobner basis, resultants, and elimination templates, leading to successful algorithms for multiview geometry and other problems. However, these methods do not work when the problem is complex and when they do, they face efficiency and stability issues. Homotopy Continuation (HC) can solve more complex problems without the stability issues, and with guarantees of a global solution, but they are known to be slow. In this paper we show that HC can be parallelized on a GPU, showing significant speedups up to 26 times on polynomial benchmarks. We also show that GPU-HC can be generically applied to a range of computer vision problems, including 4-view triangulation and trifocal pose estimation with unknown focal length, which cannot be solved with elimination template but they can be efficiently solved with HC. GPU-HC opens the door to easy formulation and solution of a range of computer vision problems.", "published": "2021-12-07T01:45:12Z", "authors": ["Chiang-Heng Chien", "Hongyi Fan", "Ahmad Abdelfattah", "Elias Tsigaridas", "Stanimire Tomov", "Benjamin Kimia"], "categories": ["cs.CV"], "doi": "", "_query": "property valuation computer vision", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2305.07528v1", "title": "WEDGE: A multi-weather autonomous driving dataset built from generative vision-language models", "abstract": "The open road poses many challenges to autonomous perception, including poor visibility from extreme weather conditions. Models trained on good-weather datasets frequently fail at detection in these out-of-distribution settings. To aid adversarial robustness in perception, we introduce WEDGE (WEather images by DALL-E GEneration): a synthetic dataset generated with a vision-language generative model via prompting. WEDGE consists of 3360 images in 16 extreme weather conditions manually annotated with 16513 bounding boxes, supporting research in the tasks of weather classification and 2D object detection. We have analyzed WEDGE from research standpoints, verifying its effectiveness for extreme-weather autonomous perception. We establish baseline performance for classification and detection with 53.87% test accuracy and 45.41 mAP. Most importantly, WEDGE can be used to fine-tune state-of-the-art detectors, improving SOTA performance on real-world weather benchmarks (such as DAWN) by 4.48 AP for well-generated classes like trucks. WEDGE has been collected under OpenAI's terms of use and is released for public use under the CC BY-NC-SA 4.0 license. The repository for this work and dataset is available at https://infernolia.github.io/WEDGE.", "published": "2023-05-12T14:42:47Z", "authors": ["Aboli Marathe", "Deva Ramanan", "Rahee Walambe", "Ketan Kotecha"], "categories": ["cs.CV", "cs.AI"], "doi": "", "_query": "property valuation computer vision", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/1605.09527v2", "title": "Biconvex Relaxation for Semidefinite Programming in Computer Vision", "abstract": "Semidefinite programming is an indispensable tool in computer vision, but general-purpose solvers for semidefinite programs are often too slow and memory intensive for large-scale problems. We propose a general framework to approximately solve large-scale semidefinite problems (SDPs) at low complexity. Our approach, referred to as biconvex relaxation (BCR), transforms a general SDP into a specific biconvex optimization problem, which can then be solved in the original, low-dimensional variable space at low complexity. The resulting biconvex problem is solved using an efficient alternating minimization (AM) procedure. Since AM has the potential to get stuck in local minima, we propose a general initialization scheme that enables BCR to start close to a global optimum - this is key for our algorithm to quickly converge to optimal or near-optimal solutions. We showcase the efficacy of our approach on three applications in computer vision, namely segmentation, co-segmentation, and manifold metric learning. BCR achieves solution quality comparable to state-of-the-art SDP methods with speedups between 4X and 35X. At the same time, BCR handles a more general set of SDPs than previous approaches, which are more specialized.", "published": "2016-05-31T08:43:44Z", "authors": ["Sohil Shah", "Abhay Kumar", "Carlos Castillo", "David Jacobs", "Christoph Studer", "Tom Goldstein"], "categories": ["cs.CV", "math.NA", "math.OC"], "doi": "", "_query": "property valuation computer vision", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2308.02553v1", "title": "Survey on Computer Vision Techniques for Internet-of-Things Devices", "abstract": "Deep neural networks (DNNs) are state-of-the-art techniques for solving most computer vision problems. DNNs require billions of parameters and operations to achieve state-of-the-art results. This requirement makes DNNs extremely compute, memory, and energy-hungry, and consequently difficult to deploy on small battery-powered Internet-of-Things (IoT) devices with limited computing resources. Deployment of DNNs on Internet-of-Things devices, such as traffic cameras, can improve public safety by enabling applications such as automatic accident detection and emergency response.Through this paper, we survey the recent advances in low-power and energy-efficient DNN implementations that improve the deployability of DNNs without significantly sacrificing accuracy. In general, these techniques either reduce the memory requirements, the number of arithmetic operations, or both. The techniques can be divided into three major categories: neural network compression, network architecture search and design, and compiler and graph optimizations. In this paper, we survey both low-power techniques for both convolutional and transformer DNNs, and summarize the advantages, disadvantages, and open research problems.", "published": "2023-08-02T03:41:24Z", "authors": ["Ishmeet Kaur", "Adwaita Janardhan Jadhav"], "categories": ["cs.CV", "cs.LG"], "doi": "", "_query": "property valuation computer vision", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/1707.09332v6", "title": "Two Hilbert schemes in computer vision", "abstract": "We study multiview moduli problems that arise in computer vision. We show that these moduli spaces are always smooth and irreducible, in both the calibrated and uncalibrated cases, for any number of views. We also show that these moduli spaces always admit open immersions into Hilbert schemes for more than two views, extending and refining work of Aholt-Sturmfels-Thomas. We use these moduli spaces to study and extend the classical twisted pair covering of the essential variety.", "published": "2017-07-28T17:13:22Z", "authors": ["Max Lieblich", "Lucas Van Meter"], "categories": ["math.AG", "cs.CV"], "doi": "", "_query": "property valuation computer vision", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2408.14281v1", "title": "Uncertainties of Latent Representations in Computer Vision", "abstract": "Uncertainty quantification is a key pillar of trustworthy machine learning. It enables safe reactions under unsafe inputs, like predicting only when the machine learning model detects sufficient evidence, discarding anomalous data, or emitting warnings when an error is likely to be inbound. This is particularly crucial in safety-critical areas like medical image classification or self-driving cars. Despite the plethora of proposed uncertainty quantification methods achieving increasingly higher scores on performance benchmarks, uncertainty estimates are often shied away from in practice. Many machine learning projects start from pretrained latent representations that come without uncertainty estimates. Uncertainties would need to be trained by practitioners on their own, which is notoriously difficult and resource-intense. This thesis makes uncertainty estimates easily accessible by adding them to the latent representation vectors of pretrained computer vision models. Besides proposing approaches rooted in probability and decision theory, such as Monte-Carlo InfoNCE (MCInfoNCE) and loss prediction, we delve into both theoretical and empirical questions. We show that these unobservable uncertainties about unobservable latent representations are indeed provably correct. We also provide an uncertainty-aware representation learning (URL) benchmark to compare these unobservables against observable ground-truths. Finally, we compile our findings to pretrain lightweight representation uncertainties on large-scale computer vision models that transfer to unseen datasets in a zero-shot manner. Our findings do not only advance the current theoretical understanding of uncertainties over latent variables, but also facilitate the access to uncertainty quantification for future researchers inside and outside the field, enabling straightforward but trustworthy machine learning.", "published": "2024-08-26T14:02:30Z", "authors": ["Michael Kirchhof"], "categories": ["cs.LG", "cs.AI", "cs.CV"], "doi": "10.15496/publikation-98103", "_query": "property valuation computer vision", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2205.05493v1", "title": "Deep Learning and Computer Vision Techniques for Microcirculation Analysis: A Review", "abstract": "The analysis of microcirculation images has the potential to reveal early signs of life-threatening diseases like sepsis. Quantifying the capillary density and the capillary distribution in microcirculation images can be used as a biological marker to assist critically ill patients. The quantification of these biological markers is labor-intensive, time-consuming, and subject to interobserver variability. Several computer vision techniques with varying performance can be used to automate the analysis of these microcirculation images in light of the stated challenges. In this paper, we present a survey of over 50 research papers and present the most relevant and promising computer vision algorithms to automate the analysis of microcirculation images. Furthermore, we present a survey of the methods currently used by other researchers to automate the analysis of microcirculation images. This survey is of high clinical relevance because it acts as a guidebook of techniques for other researchers to develop their microcirculation analysis systems and algorithms.", "published": "2022-05-11T13:34:01Z", "authors": ["Maged Abdalla Helmy Mohamed Abdou", "Trung Tuyen Truong", "Eric Jul", "Paulo Ferreira"], "categories": ["cs.CV", "cs.AI"], "doi": "", "_query": "property valuation computer vision", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/1304.0160v8", "title": "Parallel Computation Is ESS", "abstract": "There are enormous amount of examples of Computation in nature, exemplified across multiple species in biology. One crucial aim for these computations across all life forms their ability to learn and thereby increase the chance of their survival. In the current paper a formal definition of autonomous learning is proposed. From that definition we establish a Turing Machine model for learning, where rule tables can be added or deleted, but can not be modified. Sequential and parallel implementations of this model are discussed. It is found that for general purpose learning based on this model, the implementations capable of parallel execution would be evolutionarily stable. This is proposed to be of the reasons why in Nature parallelism in computation is found in abundance.", "published": "2013-03-31T06:45:47Z", "authors": ["Nabarun Mondal", "Partha P. Ghosh"], "categories": ["cs.LG", "cs.AI", "cs.GT"], "doi": "", "_query": "property valuation computer vision", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/math/0512110v4", "title": "Computably Based Locally Compact Spaces", "abstract": "ASD (Abstract Stone Duality) is a re-axiomatisation of general topology in which the topology on a space is treated, not as an infinitary lattice, but as an exponential object of the same category as the original space, with an associated lambda-calculus. In this paper, this is shown to be equivalent to a notion of computable basis for locally compact sober spaces or locales, involving a family of open subspaces and accompanying family of compact ones. This generalises Smyth's effectively given domains and Jung's strong proximity lattices. Part of the data for a basis is the inclusion relation of compact subspaces within open ones, which is formulated in locale theory as the way-below relation on a continuous lattice. The finitary properties of this relation are characterised here, including the Wilker condition for the cover of a compact space by two open ones. The real line is used as a running example, being closely related to Scott's domain of intervals. ASD does not use the category of sets, but the full subcategory of overt discrete objects plays this role; it is an arithmetic universe (pretopos with lists). In particular, we use this subcategory to translate computable bases for classical spaces into objects in the ASD calculus.", "published": "2005-12-05T22:31:32Z", "authors": ["Paul Taylor"], "categories": ["math.GN", "cs.LO", "math.CT"], "doi": "10.2168/LMCS-2(1:1)2006", "_query": "property valuation computer vision", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2406.11069v1", "title": "WildVision: Evaluating Vision-Language Models in the Wild with Human Preferences", "abstract": "Recent breakthroughs in vision-language models (VLMs) emphasize the necessity of benchmarking human preferences in real-world multimodal interactions. To address this gap, we launched WildVision-Arena (WV-Arena), an online platform that collects human preferences to evaluate VLMs. We curated WV-Bench by selecting 500 high-quality samples from 8,000 user submissions in WV-Arena. WV-Bench uses GPT-4 as the judge to compare each VLM with Claude-3-Sonnet, achieving a Spearman correlation of 0.94 with the WV-Arena Elo. This significantly outperforms other benchmarks like MMVet, MMMU, and MMStar. Our comprehensive analysis of 20K real-world interactions reveals important insights into the failure cases of top-performing VLMs. For example, we find that although GPT-4V surpasses many other models like Reka-Flash, Opus, and Yi-VL-Plus in simple visual recognition and reasoning tasks, it still faces challenges with subtle contextual cues, spatial reasoning, visual imagination, and expert domain knowledge. Additionally, current VLMs exhibit issues with hallucinations and safety when intentionally provoked. We are releasing our chat and feedback data to further advance research in the field of VLMs.", "published": "2024-06-16T20:53:25Z", "authors": ["Yujie Lu", "Dongfu Jiang", "Wenhu Chen", "William Yang Wang", "Yejin Choi", "Bill Yuchen Lin"], "categories": ["cs.CV", "cs.AI", "cs.CL"], "doi": "", "_query": "property valuation computer vision", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2210.16901v1", "title": "Foreign Object Debris Detection for Airport Pavement Images based on Self-supervised Localization and Vision Transformer", "abstract": "Supervised object detection methods provide subpar performance when applied to Foreign Object Debris (FOD) detection because FOD could be arbitrary objects according to the Federal Aviation Administration (FAA) specification. Current supervised object detection algorithms require datasets that contain annotated examples of every to-be-detected object. While a large and expensive dataset could be developed to include common FOD examples, it is infeasible to collect all possible FOD examples in the dataset representation because of the open-ended nature of FOD. Limitations of the dataset could cause FOD detection systems driven by those supervised algorithms to miss certain FOD, which can become dangerous to airport operations. To this end, this paper presents a self-supervised FOD localization by learning to predict the runway images, which avoids the enumeration of FOD annotation examples. The localization method utilizes the Vision Transformer (ViT) to improve localization performance. The experiments show that the method successfully detects arbitrary FOD in real-world runway situations. The paper also provides an extension to the localization result to perform classification; a feature that can be useful to downstream tasks. To train the localization, this paper also presents a simple and realistic dataset creation framework that only collects clean runway images. The training and testing data for this method are collected at a local airport using unmanned aircraft systems (UAS). Additionally, the developed dataset is provided for public use and further studies.", "published": "2022-10-30T17:48:57Z", "authors": ["Travis Munyer", "Daniel Brinkman", "Xin Zhong", "Chenyu Huang", "Iason Konstantzos"], "categories": ["cs.CV", "cs.LG"], "doi": "", "_query": "property valuation computer vision", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2302.14383v3", "title": "Linear Spaces of Meanings: Compositional Structures in Vision-Language Models", "abstract": "We investigate compositional structures in data embeddings from pre-trained vision-language models (VLMs). Traditionally, compositionality has been associated with algebraic operations on embeddings of words from a pre-existing vocabulary. In contrast, we seek to approximate representations from an encoder as combinations of a smaller set of vectors in the embedding space. These vectors can be seen as \"ideal words\" for generating concepts directly within the embedding space of the model. We first present a framework for understanding compositional structures from a geometric perspective. We then explain what these compositional structures entail probabilistically in the case of VLM embeddings, providing intuitions for why they arise in practice. Finally, we empirically explore these structures in CLIP's embeddings and we evaluate their usefulness for solving different vision-language tasks such as classification, debiasing, and retrieval. Our results show that simple linear algebraic operations on embedding vectors can be used as compositional and interpretable methods for regulating the behavior of VLMs.", "published": "2023-02-28T08:11:56Z", "authors": ["Matthew Trager", "Pramuditha Perera", "Luca Zancato", "Alessandro Achille", "Parminder Bhatia", "Stefano Soatto"], "categories": ["cs.LG", "cs.AI", "cs.CL", "cs.CV"], "doi": "", "_query": "property valuation computer vision", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/1509.03354v3", "title": "Valuation Semirings", "abstract": "The main scope of this paper is to introduce valuation semirings in general and discrete valuation semirings in particular. In order to do that, first we define valuation maps and investigate them. Then we define valuation semirings with the help of valuation maps and prove that a multiplicatively cancellative semiring is a valuation semiring if and only if its ideals are totally ordered by inclusion. We also prove that if the unique maximal ideal of a valuation semiring is subtractive, then it is integrally closed. We end this paper by introducing discrete valuation semirings and show that a semiring is a discrete valuation semiring if and only if it is a multiplicatively cancellative principal ideal semiring possessing a nonzero unique maximal ideal. We also prove that a discrete valuation semiring is a Gaussian semiring if and only if its unique maximal ideal is subtractive.", "published": "2015-09-10T22:32:26Z", "authors": ["Peyman Nasehpour"], "categories": ["math.AC", "math.GR", "math.RA"], "doi": "10.1142/S0219498818500731", "_query": "property valuation computer vision", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2103.12438v3", "title": "Oriented pro-$\\ell$ groups with the Bogomolov-Positselski property", "abstract": "For a prime number $\\ell$ we say that an oriented pro-$\\ell$ group $(G,θ)$ has the Bogomolov-Positselski property if the kernel of the canonical projection on its maximal $θ$-abelian quotient $π^{ab}_{G,θ}\\colon G\\to G(θ)$ is a free pro-$\\ell$ group contained in the Frattini subgroup of $G$. We show that oriented pro-$\\ell$ groups of elementary type have the Bogomolov-Positselski property. This shows that Efrat's Elementary Type Conjecture implies a positive answer to Positselski's version of Bogomolov's Conjecture on maximal pro-$\\ell$ Galois groups of a field $K$ in case that $K^\\times/(K^\\times)^\\ell$ is finite. Secondly, it is shown that for an $H^\\bullet$-quadratic oriented pro-$\\ell$ group $(G,θ)$ the Bogomolov-Positselski property can be expressed by the injectivity of the transgression map $d_2^{2,1}$ in the Hochschild-Serre spectral sequence.", "published": "2021-03-23T10:31:28Z", "authors": ["Claudio Quadrelli", "Thomas S. Weigel"], "categories": ["math.GR", "math.NT"], "doi": "10.1007/s40993-022-00318-9", "_query": "property valuation computer vision", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2412.18321v1", "title": "Computer Vision-Driven Gesture Recognition: Toward Natural and Intuitive Human-Computer", "abstract": "This study mainly explores the application of natural gesture recognition based on computer vision in human-computer interaction, aiming to improve the fluency and naturalness of human-computer interaction through gesture recognition technology. In the fields of virtual reality, augmented reality and smart home, traditional input methods have gradually failed to meet the needs of users for interactive experience. As an intuitive and convenient interaction method, gestures have received more and more attention. This paper proposes a gesture recognition method based on a three-dimensional hand skeleton model. By simulating the three-dimensional spatial distribution of hand joints, a simplified hand skeleton structure is constructed. By connecting the palm and each finger joint, a dynamic and static gesture model of the hand is formed, which further improves the accuracy and efficiency of gesture recognition. Experimental results show that this method can effectively recognize various gestures and maintain high recognition accuracy and real-time response capabilities in different environments. In addition, combined with multimodal technologies such as eye tracking, the intelligence level of the gesture recognition system can be further improved, bringing a richer and more intuitive user experience. In the future, with the continuous development of computer vision, deep learning and multimodal interaction technology, natural interaction based on gestures will play an important role in a wider range of application scenarios and promote revolutionary progress in human-computer interaction.", "published": "2024-12-24T10:13:20Z", "authors": ["Fenghua Shao", "Tong Zhang", "Shang Gao", "Qi Sun", "Liuqingqing Yang"], "categories": ["cs.CV"], "doi": "", "_query": "property valuation computer vision", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2610.23967v1", "title": "Geo-vision: Native-Resolution Geometry and Sampled Topology for Vehicle-Part Damage Classification", "abstract": "Geo-vision classifies vehicle-part damage using a fixed representation combining appearance, multiscale geometry, orientation, and sampled digital topology. Connected components, holes, Euler characteristics, and spatial arrangement complement amplitude-sensitive pooling and a supervised tree classifier. We establish amplitude preservation, axial sign invariance, and conditional operator stability. Retrospective checkpoint evaluation on 55,303 crops from 4,916 source groups yields average precision 0.7577, exceeding 0.7458 for an ImageNet-pretrained frozen YOLO11n comparator on identical crops. The complete added structural block, comprising topology, occupancy, and component location, improves average precision by 0.00493 beyond appearance and geometry. Geo-vision uses no external encoder pretraining and has higher AP point estimates at every matched label budget. With approximately 25% fewer task labels, its AP point estimate exceeds full-data YOLO, although the adjusted interval includes zero. Complete serial CPU inference requires 26.98 ms per crop versus YOLO's 11.20 ms, with peak memory 185 versus 437 MiB. Independent external validation remains necessary.", "published": "2026-10-11T17:28:40Z", "authors": ["Usef Faghihi", "Amir Saki", "Serge Jonathan Kogbeti-Wongo"], "categories": ["cs.CV", "cs.LG"], "doi": "", "_query": "property valuation computer vision", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2112.05534v2", "title": "An Embarrassingly Pragmatic Introduction to Vision-based Autonomous Robots", "abstract": "Autonomous robots are currently one of the most popular Artificial Intelligence problems, having experienced significant advances in the last decade, from Self-driving cars and humanoids to delivery robots and drones. Part of the problem is to get a robot to emulate the perception of human beings, our sense of sight, replacing the eyes with cameras and the brain with mathematical models such as Neural Networks. Developing an AI able to drive a car without human intervention and a small robot to deliver packages in the city may seem like different problems, nevertheless from the point of view of perception and vision, both problems have several similarities. The main solutions we currently find focus on the environment perception through visual information using Computer Vision techniques, Machine Learning, and various algorithms to make the robot understand the environment or scene, move, adapt its trajectory and perform its tasks (maintenance, exploration, etc.) without the need for human intervention. In this work, we develop a small-scale autonomous vehicle from scratch, capable of understanding the scene using only visual information, navigating through industrial environments, detecting people and obstacles, or performing simple maintenance tasks. We review the state-of-the-art of fundamental problems and demonstrate that many methods employed at small-scale are similar to the ones employed in real Self-driving cars from companies like Tesla or Lyft. Finally, we discuss the current state of Robotics and autonomous driving and the technological and ethical limitations that we can find in this field.", "published": "2021-11-15T01:31:28Z", "authors": ["Marcos V. Conde"], "categories": ["cs.RO", "cs.CV"], "doi": "", "_query": "property valuation computer vision", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2510.17650v1", "title": "ZACH-ViT: A Zero-Token Vision Transformer with ShuffleStrides Data Augmentation for Robust Lung Ultrasound Classification", "abstract": "Differentiating cardiogenic pulmonary oedema (CPE) from non-cardiogenic and structurally normal lungs in lung ultrasound (LUS) videos remains challenging due to the high visual variability of non-cardiogenic inflammatory patterns (NCIP/ARDS-like), interstitial lung disease, and healthy lungs. This heterogeneity complicates automated classification as overlapping B-lines and pleural artefacts are common. We introduce ZACH-ViT (Zero-token Adaptive Compact Hierarchical Vision Transformer), a 0.25 M-parameter Vision Transformer variant that removes both positional embeddings and the [CLS] token, making it fully permutation-invariant and suitable for unordered medical image data. To enhance generalization, we propose ShuffleStrides Data Augmentation (SSDA), which permutes probe-view sequences and frame orders while preserving anatomical validity. ZACH-ViT was evaluated on 380 LUS videos from 95 critically ill patients against nine state-of-the-art baselines. Despite the heterogeneity of the non-cardiogenic group, ZACH-ViT achieved the highest validation and test ROC-AUC (0.80 and 0.79) with balanced sensitivity (0.60) and specificity (0.91), while all competing models collapsed to trivial classification. It trains 1.35x faster than Minimal ViT (0.62M parameters) with 2.5x fewer parameters, supporting real-time clinical deployment. These results show that aligning architectural design with data structure can outperform scale in small-data medical imaging.", "published": "2025-10-20T15:26:38Z", "authors": ["Athanasios Angelakis", "Amne Mousa", "Micah L. A. Heldeweg", "Laurens A. Biesheuvel", "Mark A. Haaksma", "Jasper M. Smit", "Pieter R. Tuinman", "Paul W. G. Elbers"], "categories": ["cs.LG", "cs.CV"], "doi": "", "_query": "property valuation computer vision", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/0912.4226v2", "title": "Computing Least Fixed Points of Probabilistic Systems of Polynomials", "abstract": "We study systems of equations of the form X1 = f1(X1, ..., Xn), ..., Xn = fn(X1, ..., Xn), where each fi is a polynomial with nonnegative coefficients that add up to 1. The least nonnegative solution, say mu, of such equation systems is central to problems from various areas, like physics, biology, computational linguistics and probabilistic program verification. We give a simple and strongly polynomial algorithm to decide whether mu=(1, ..., 1) holds. Furthermore, we present an algorithm that computes reliable sequences of lower and upper bounds on mu, converging linearly to mu. Our algorithm has these features despite using inexact arithmetic for efficiency. We report on experiments that show the performance of our algorithms.", "published": "2009-12-21T19:14:12Z", "authors": ["Javier Esparza", "Andreas Gaiser", "Stefan Kiefer"], "categories": ["cs.DS", "math.NA"], "doi": "", "_query": "property valuation computer vision", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/1604.05132v1", "title": "Using Self-Contradiction to Learn Confidence Measures in Stereo Vision", "abstract": "Learned confidence measures gain increasing importance for outlier removal and quality improvement in stereo vision. However, acquiring the necessary training data is typically a tedious and time consuming task that involves manual interaction, active sensing devices and/or synthetic scenes. To overcome this problem, we propose a new, flexible, and scalable way for generating training data that only requires a set of stereo images as input. The key idea of our approach is to use different view points for reasoning about contradictions and consistencies between multiple depth maps generated with the same stereo algorithm. This enables us to generate a huge amount of training data in a fully automated manner. Among other experiments, we demonstrate the potential of our approach by boosting the performance of three learned confidence measures on the KITTI2012 dataset by simply training them on a vast amount of automatically generated training data rather than a limited amount of laser ground truth data.", "published": "2016-04-18T13:26:46Z", "authors": ["Christian Mostegel", "Markus Rumpler", "Friedrich Fraundorfer", "Horst Bischof"], "categories": ["cs.CV"], "doi": "", "_query": "property valuation computer vision", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2211.13483v1", "title": "Towards computer vision technologies: Semi-automated reading of automated utility meters", "abstract": "In this report we analysed a possibility of using computer vision techniques for automated reading of utility meters. In our study, we focused on two computer vision techniques: an open-source solution Tensorflow Object Detection (Tensorflow) and a commercial solution Anyline. This report extends our previous publication: We start with presentation of a structured analysis of related approaches. After that we provide a detailed comparison of two computer vision technologies, Tensorflow Object Detection (Tensorflow) and Anyline, applied to semi-automated reading of utility meters. In this paper, we discuss limitations and benefits of each solution applied to utility meters reading, especially focusing on aspects such as accuracy and inference time. Our goal was to determine the solution that is the most suitable for this particular application area, where there are several specific challenges.", "published": "2022-11-24T09:05:52Z", "authors": ["Maria Spichkova", "Johan van Zyl"], "categories": ["cs.CV"], "doi": "", "_query": "property valuation computer vision", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2304.06133v1", "title": "Towards Evaluating Explanations of Vision Transformers for Medical Imaging", "abstract": "As deep learning models increasingly find applications in critical domains such as medical imaging, the need for transparent and trustworthy decision-making becomes paramount. Many explainability methods provide insights into how these models make predictions by attributing importance to input features. As Vision Transformer (ViT) becomes a promising alternative to convolutional neural networks for image classification, its interpretability remains an open research question. This paper investigates the performance of various interpretation methods on a ViT applied to classify chest X-ray images. We introduce the notion of evaluating faithfulness, sensitivity, and complexity of ViT explanations. The obtained results indicate that Layerwise relevance propagation for transformers outperforms Local interpretable model-agnostic explanations and Attention visualization, providing a more accurate and reliable representation of what a ViT has actually learned. Our findings provide insights into the applicability of ViT explanations in medical imaging and highlight the importance of using appropriate evaluation criteria for comparing them.", "published": "2023-04-12T19:37:28Z", "authors": ["Piotr Komorowski", "Hubert Baniecki", "Przemysław Biecek"], "categories": ["cs.CV", "cs.LG"], "doi": "10.1109/CVPRW59228.2023.00383", "_query": "property valuation computer vision", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2603.18456v2", "title": "The Local Lifting Property, Property FD, and stability of approximate representations", "abstract": "We establish Kirchberg's Local Lifting Property and Lubotzky--Shalom's Property FD for classes of finitely generated groups of central importance in geometric and combinatorial group theory: $3$-manifold groups, limit groups, and certain one-relator groups and right-angled Artin groups. We deduce that such groups are very flexibly stable, with respect to normalized unitarily invariant norms. In the appendix, we show that these groups also have Kechris's property (E)MD, and hence are stable in finite actions, in the selse of Gohla--Thom. The exposition is made accessible to operator algebraists and group theorists alike.", "published": "2026-03-19T03:36:15Z", "authors": ["Francesco Fournier-Facio", "Rufus Willett"], "categories": ["math.GR", "math.GT", "math.KT", "math.OA"], "doi": "", "_query": "property valuation computer vision", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2601.10587v3", "title": "Adversarial Evasion Attacks on Computer Vision using SHAP Values", "abstract": "The paper introduces a white-box attack on computer vision models using SHAP values. It demonstrates how adversarial evasion attacks can compromise the performance of deep learning models by reducing output confidence or inducing misclassifications. Such attacks are particularly insidious as they can deceive the perception of an algorithm while eluding human perception due to their imperceptibility to the human eye. The proposed attack leverages SHAP values to quantify the significance of individual inputs to the output at the inference stage. A comparison is drawn between the SHAP attack and the well-known Fast Gradient Sign Method. We find evidence that SHAP attacks are more robust in generating misclassifications particularly in gradient hiding scenarios.", "published": "2026-01-15T16:58:55Z", "authors": ["Frank Mollard", "Marcus Becker", "Florian Roehrbein"], "categories": ["cs.CV", "cs.AI"], "doi": "10.13140/RG.2.2.28762.40647", "_query": "property valuation computer vision", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2403.12981v1", "title": "Beyond Inference: Performance Analysis of DNN Server Overheads for Computer Vision", "abstract": "Deep neural network (DNN) inference has become an important part of many data-center workloads. This has prompted focused efforts to design ever-faster deep learning accelerators such as GPUs and TPUs. However, an end-to-end DNN-based vision application contains more than just DNN inference, including input decompression, resizing, sampling, normalization, and data transfer. In this paper, we perform a thorough evaluation of computer vision inference requests performed on a throughput-optimized serving system. We quantify the performance impact of server overheads such as data movement, preprocessing, and message brokers between two DNNs producing outputs at different rates. Our empirical analysis encompasses many computer vision tasks including image classification, segmentation, detection, depth-estimation, and more complex processing pipelines with multiple DNNs. Our results consistently demonstrate that end-to-end application performance can easily be dominated by data processing and data movement functions (up to 56% of end-to-end latency in a medium-sized image, and $\\sim$ 80% impact on system throughput in a large image), even though these functions have been conventionally overlooked in deep learning system design. Our work identifies important performance bottlenecks in different application scenarios, achieves 2.25$\\times$ better throughput compared to prior work, and paves the way for more holistic deep learning system design.", "published": "2024-03-02T02:35:08Z", "authors": ["Ahmed F. AbouElhamayed", "Susanne Balle", "Deshanand Singh", "Mohamed S. Abdelfattah"], "categories": ["cs.DC", "cs.AI", "cs.CV", "cs.LG"], "doi": "10.1145/3649329.3655960", "_query": "property valuation computer vision", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/1906.01620v3", "title": "Evaluating Scalable Bayesian Deep Learning Methods for Robust Computer Vision", "abstract": "While deep neural networks have become the go-to approach in computer vision, the vast majority of these models fail to properly capture the uncertainty inherent in their predictions. Estimating this predictive uncertainty can be crucial, for example in automotive applications. In Bayesian deep learning, predictive uncertainty is commonly decomposed into the distinct types of aleatoric and epistemic uncertainty. The former can be estimated by letting a neural network output the parameters of a certain probability distribution. Epistemic uncertainty estimation is a more challenging problem, and while different scalable methods recently have emerged, no extensive comparison has been performed in a real-world setting. We therefore accept this task and propose a comprehensive evaluation framework for scalable epistemic uncertainty estimation methods in deep learning. Our proposed framework is specifically designed to test the robustness required in real-world computer vision applications. We also apply this framework to provide the first properly extensive and conclusive comparison of the two current state-of-the-art scalable methods: ensembling and MC-dropout. Our comparison demonstrates that ensembling consistently provides more reliable and practically useful uncertainty estimates. Code is available at https://github.com/fregu856/evaluating_bdl.", "published": "2019-06-04T17:54:20Z", "authors": ["Fredrik K. Gustafsson", "Martin Danelljan", "Thomas B. Schön"], "categories": ["cs.LG", "cs.CV", "stat.ML"], "doi": "", "_query": "property valuation computer vision", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/1912.10107v1", "title": "Assessing Data Quality of Annotations with Krippendorff Alpha For Applications in Computer Vision", "abstract": "Current supervised deep learning frameworks rely on annotated data for modeling the underlying data distribution of a given task. In particular for computer vision algorithms powered by deep learning, the quality of annotated data is the most critical factor in achieving the desired algorithm performance. Data annotation is, typically, a manual process where the annotator follows guidelines and operates in a best-guess manner. Labeling criteria among annotators can show discrepancies in labeling results. This may impact the algorithm inference performance. Given the popularity and widespread use of deep learning among computer vision, more and more custom datasets are needed to train neural networks to tackle different kinds of tasks. Unfortunately, there is no full understanding of the factors that affect annotated data quality, and how it translates into algorithm performance. In this paper we studied this problem for object detection and recognition.We conducted several data annotation experiments to measure inter annotator agreement and consistency, as well as how the selection of ground truth impacts the perceived algorithm performance.We propose a methodology to monitor the quality of annotations during the labeling of images and how it can be used to measure performance. We also show that neglecting to monitor the annotation process can result in significant loss in algorithm precision. Through these experiments, we observe that knowledge of the labeling process, training data, and ground truth data used for algorithm evaluation are fundamental components to accurately assess trustworthiness of an AI system.", "published": "2019-12-20T21:23:42Z", "authors": ["Joseph Nassar", "Viveca Pavon-Harr", "Marc Bosch", "Ian McCulloh"], "categories": ["cs.CV", "cs.LG"], "doi": "", "_query": "property valuation computer vision", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2508.18188v1", "title": "Explain and Monitor Deep Learning Models for Computer Vision using Obz AI", "abstract": "Deep learning has transformed computer vision (CV), achieving outstanding performance in classification, segmentation, and related tasks. Such AI-based CV systems are becoming prevalent, with applications spanning from medical imaging to surveillance. State of the art models such as convolutional neural networks (CNNs) and vision transformers (ViTs) are often regarded as ``black boxes,'' offering limited transparency into their decision-making processes. Despite a recent advancement in explainable AI (XAI), explainability remains underutilized in practical CV deployments. A primary obstacle is the absence of integrated software solutions that connect XAI techniques with robust knowledge management and monitoring frameworks. To close this gap, we have developed Obz AI, a comprehensive software ecosystem designed to facilitate state-of-the-art explainability and observability for vision AI systems. Obz AI provides a seamless integration pipeline, from a Python client library to a full-stack analytics dashboard. With Obz AI, a machine learning engineer can easily incorporate advanced XAI methodologies, extract and analyze features for outlier detection, and continuously monitor AI models in real time. By making the decision-making mechanisms of deep models interpretable, Obz AI promotes observability and responsible deployment of computer vision systems.", "published": "2025-08-25T16:46:21Z", "authors": ["Neo Christopher Chung", "Jakub Binda"], "categories": ["cs.CV", "cs.AI", "cs.HC", "cs.SE"], "doi": "", "_query": "property valuation computer vision", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/math/0701867v3", "title": "Divisorial valuations via arcs", "abstract": "This paper shows a finiteness property of a divisorial valuation in terms of arcs. First we show that every divisorial valuation over an algebraic variety corresponds to an irreducible closed subset of the arc space. Then we define the codimension for this subset and give a formula of the codimension in terms of \"relative Mather canonical class\". By using this subset, we prove that a divisorial valuation is determined by assigning the values of finite functions. We also have a criterion for a divisorial valuation to be a monomial valuation by assigning the values of finite functions.", "published": "2007-01-30T05:16:19Z", "authors": ["Tommaso de Fernex", "Lawrence Ein", "Shihoko Ishii"], "categories": ["math.AG"], "doi": "", "_query": "property valuation computer vision", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2304.10764v1", "title": "Hyperbolic Geometry in Computer Vision: A Survey", "abstract": "Hyperbolic geometry, a Riemannian manifold endowed with constant sectional negative curvature, has been considered an alternative embedding space in many learning scenarios, \\eg, natural language processing, graph learning, \\etc, as a result of its intriguing property of encoding the data's hierarchical structure (like irregular graph or tree-likeness data). Recent studies prove that such data hierarchy also exists in the visual dataset, and investigate the successful practice of hyperbolic geometry in the computer vision (CV) regime, ranging from the classical image classification to advanced model adaptation learning. This paper presents the first and most up-to-date literature review of hyperbolic spaces for CV applications. To this end, we first introduce the background of hyperbolic geometry, followed by a comprehensive investigation of algorithms, with geometric prior of hyperbolic space, in the context of visual applications. We also conclude this manuscript and identify possible future directions.", "published": "2023-04-21T06:22:16Z", "authors": ["Pengfei Fang", "Mehrtash Harandi", "Trung Le", "Dinh Phung"], "categories": ["cs.CV"], "doi": "", "_query": "property valuation computer vision", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2408.06687v3", "title": "Masked Image Modeling: A Survey", "abstract": "In this work, we survey recent studies on masked image modeling (MIM), an approach that emerged as a powerful self-supervised learning technique in computer vision. The MIM task involves masking some information, e.g. pixels, patches, or even latent representations, and training a model, usually an autoencoder, to predicting the missing information by using the context available in the visible part of the input. We identify and formalize two categories of approaches on how to implement MIM as a pretext task, one based on reconstruction and one based on contrastive learning. Then, we construct a taxonomy and review the most prominent papers in recent years. We complement the manually constructed taxonomy with a dendrogram obtained by applying a hierarchical clustering algorithm. We further identify relevant clusters via manually inspecting the resulting dendrogram. Our review also includes datasets that are commonly used in MIM research. We aggregate the performance results of various masked image modeling methods on the most popular datasets, to facilitate the comparison of competing methods. Finally, we identify research gaps and propose several interesting directions of future work. We supplement our survey with the following public repository containing organized references: https://github.com/vladhondru25/MIM-Survey.", "published": "2024-08-13T07:27:02Z", "authors": ["Vlad Hondru", "Florinel Alin Croitoru", "Shervin Minaee", "Radu Tudor Ionescu", "Nicu Sebe"], "categories": ["cs.CV", "cs.AI", "cs.LG"], "doi": "", "_query": "property valuation computer vision", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2405.17464v2", "title": "Data Valuation by Fusing Global and Local Statistical Information", "abstract": "Data valuation has garnered increasing attention in recent years, given the critical role of high-quality data in various applications. Among diverse data valuation approaches, Shapley value-based methods are predominant due to their strong theoretical grounding. However, the exact computation of Shapley values is often computationally prohibitive, prompting the development of numerous approximation techniques. Despite notable advancements, existing methods generally neglect the incorporation of value distribution information and fail to account for dynamic data conditions, thereby compromising their performance and application potential. In this paper, we highlight the crucial role of both global and local statistical properties of value distributions in the context of data valuation for machine learning. First, we conduct a comprehensive analysis of these distributions across various simulated and real-world datasets, uncovering valuable insights and key patterns. Second, we propose an enhanced data valuation method that fuses the explored distribution characteristics into two regularization terms to refine Shapley value estimation. The proposed regularizers can be seamlessly incorporated into various existing data valuation methods. Third, we introduce a novel approach for dynamic data valuation that infers updated data values without recomputing Shapley values, thereby significantly improving computational efficiency. Extensive experiments have been conducted across a range of tasks, including Shapley value estimation, value-based data addition and removal, mislabeled data detection, and dynamic data valuation. The results showcase the consistent effectiveness and efficiency of our proposed methodologies, affirming the significant potential of global and local value distributions in data valuation.", "published": "2024-05-23T08:58:08Z", "authors": ["Xiaoling Zhou", "Ou Wu", "Michael K. Ng", "Hao Jiang"], "categories": ["cs.LG", "cs.AI", "stat.ML"], "doi": "", "_query": "property valuation computer vision", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2004.08708v1", "title": "Adaptive Attention Span in Computer Vision", "abstract": "Recent developments in Transformers for language modeling have opened new areas of research in computer vision. Results from late 2019 showed vast performance increases in both object detection and recognition when convolutions are replaced by local self-attention kernels. Models using local self-attention kernels were also shown to have less parameters and FLOPS compared to equivalent architectures that only use convolutions. In this work we propose a novel method for learning the local self-attention kernel size. We then compare its performance to fixed-size local attention and convolution kernels. The code for all our experiments and models is available at https://github.com/JoeRoussy/adaptive-attention-in-cv", "published": "2020-04-18T21:32:47Z", "authors": ["Jerrod Parker", "Shakti Kumar", "Joe Roussy"], "categories": ["cs.CV", "cs.LG"], "doi": "", "_query": "property valuation computer vision", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2008.11239v1", "title": "HoloLens 2 Research Mode as a Tool for Computer Vision Research", "abstract": "Mixed reality headsets, such as the Microsoft HoloLens 2, are powerful sensing devices with integrated compute capabilities, which makes it an ideal platform for computer vision research. In this technical report, we present HoloLens 2 Research Mode, an API and a set of tools enabling access to the raw sensor streams. We provide an overview of the API and explain how it can be used to build mixed reality applications based on processing sensor data. We also show how to combine the Research Mode sensor data with the built-in eye and hand tracking capabilities provided by HoloLens 2. By releasing the Research Mode API and a set of open-source tools, we aim to foster further research in the fields of computer vision as well as robotics and encourage contributions from the research community.", "published": "2020-08-25T19:05:38Z", "authors": ["Dorin Ungureanu", "Federica Bogo", "Silvano Galliani", "Pooja Sama", "Xin Duan", "Casey Meekhof", "Jan Stühmer", "Thomas J. Cashman", "Bugra Tekin", "Johannes L. Schönberger", "Pawel Olszta", "Marc Pollefeys"], "categories": ["cs.CV"], "doi": "", "_query": "property valuation computer vision", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2307.10790v1", "title": "Behavioral Analysis of Vision-and-Language Navigation Agents", "abstract": "To be successful, Vision-and-Language Navigation (VLN) agents must be able to ground instructions to actions based on their surroundings. In this work, we develop a methodology to study agent behavior on a skill-specific basis -- examining how well existing agents ground instructions about stopping, turning, and moving towards specified objects or rooms. Our approach is based on generating skill-specific interventions and measuring changes in agent predictions. We present a detailed case study analyzing the behavior of a recent agent and then compare multiple agents in terms of skill-specific competency scores. This analysis suggests that biases from training have lasting effects on agent behavior and that existing models are able to ground simple referring expressions. Our comparisons between models show that skill-specific scores correlate with improvements in overall VLN task performance.", "published": "2023-07-20T11:42:24Z", "authors": ["Zijiao Yang", "Arjun Majumdar", "Stefan Lee"], "categories": ["cs.CV", "cs.RO"], "doi": "", "_query": "property valuation computer vision", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2604.03334v1", "title": "Bridging the Dimensionality Gap: A Taxonomy and Survey of 2D Vision Model Adaptation for 3D Analysis", "abstract": "The remarkable success of Convolutional Neural Networks (CNNs) and Vision Transformers (ViTs) in 2D vision has spurred significant research in extending these architectures to the complex domain of 3D analysis. Yet, a core challenge arises from a fundamental dichotomy between the regular, dense grids of 2D images and the irregular, sparse nature of 3D data such as point clouds and meshes. This survey provides a comprehensive review and a unified taxonomy of adaptation strategies that bridge this gap, classifying them into three families: (1) Data-centric methods that project 3D data into 2D formats to leverage off-the-shelf 2D models, (2) Architecture-centric methods that design intrinsic 3D networks, and (3) Hybrid methods, which synergistically combine the two modeling paradigms to benefit from both rich visual priors of large 2D datasets and explicit geometric reasoning of 3D models. Through this framework, we qualitatively analyze the fundamental trade-offs between these families concerning computational complexity, reliance on large-scale pre-training, and the preservation of geometric inductive biases. We discuss key open challenges and outline promising future research directions, including the development of 3D foundation models, advancements in self-supervised learning (SSL) for geometric data, and the deeper integration of multi-modal signals.", "published": "2026-04-03T06:02:29Z", "authors": ["Akshat Pandya", "Bhavuk Jain"], "categories": ["cs.CV"], "doi": "10.5220/0014289900004084", "_query": "property valuation computer vision", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2403.19838v2", "title": "Multi-Frame, Lightweight & Efficient Vision-Language Models for Question Answering in Autonomous Driving", "abstract": "Vision-Language Models (VLMs) and Multi-Modal Language models (MMLMs) have become prominent in autonomous driving research, as these models can provide interpretable textual reasoning and responses for end-to-end autonomous driving safety tasks using traffic scene images and other data modalities. However, current approaches to these systems use expensive large language model (LLM) backbones and image encoders, making such systems unsuitable for real-time autonomous driving systems where tight memory constraints exist and fast inference time is necessary. To address these previous issues, we develop EM-VLM4AD, an efficient, lightweight, multi-frame vision language model which performs Visual Question Answering for autonomous driving. In comparison to previous approaches, EM-VLM4AD requires at least 10 times less memory and floating point operations, while also achieving higher CIDEr and ROUGE-L scores than the existing baseline on the DriveLM dataset. EM-VLM4AD also exhibits the ability to extract relevant information from traffic views related to prompts and can answer questions for various autonomous driving subtasks. We release our code to train and evaluate our model at https://github.com/akshaygopalkr/EM-VLM4AD.", "published": "2024-03-28T21:18:33Z", "authors": ["Akshay Gopalkrishnan", "Ross Greer", "Mohan Trivedi"], "categories": ["cs.CV", "cs.AI"], "doi": "", "_query": "property valuation computer vision", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2601.11404v2", "title": "ACoT-VLA: Action Chain-of-Thought for Vision-Language-Action Models", "abstract": "Vision-Language-Action models have emerged as essential generalist robot policies for diverse manipulation tasks, conventionally relying on directly translating multimodal inputs into actions via Vision-Language Model embeddings. Recent advancements have introduced explicit intermediary reasoning-such as sub-task prediction (language) or goal image synthesis (vision)-to guide action generation. However, these intermediate reasoning are often indirect and inherently limited in their capacity to convey the full, granular information required for precise action execution. Instead, we posit that the most effective form of reasoning is one that deliberates directly in the action space. We introduce Action Chain-of-Thought (ACoT), a paradigm where the reasoning process itself is formulated as a structured sequence of coarse action intents that guide the final policy. In this paper, we propose ACoT-VLA, a novel architecture that materializes the ACoT paradigm. Specifically, we introduce two complementary components: an Explicit Action Reasoner (EAR) and Implicit Action Reasoner (IAR). The former proposes coarse reference trajectories as explicit action-level reasoning steps, while the latter extracts latent action priors from internal representations of multimodal input, co-forming an ACoT that conditions the downstream action head to enable grounded policy learning. Extensive experiments in real-world and simulation environments demonstrate the superiority of our proposed method. Code is available at: https://github.com/AgibotTech/ACoT-VLA.", "published": "2026-01-16T16:17:06Z", "authors": ["Linqing Zhong", "Yi Liu", "Yifei Wei", "Ziyu Xiong", "Maoqing Yao", "Si Liu", "Guanghui Ren"], "categories": ["cs.RO"], "doi": "", "_query": "property valuation computer vision", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/1002.1691v1", "title": "Performance Evaluation of Unicast and Broadcast Mobile Ad hoc Network Routing Protocols", "abstract": "Efficient routing mechanism is a challenging issue for group oriented computing in Mobile Ad Hoc Networks (MANETs). The ability of MANETs to support adequate Quality of Service (QoS) for group communication is limited by the ability of the underlying ad-hoc routing protocols to provide consistent behavior despite the dynamic properties of mobile computing devices. In MANET QoS requirements can be quantified in terms of Packet Delivery Ratio (PDR), Data Latency, Packet Loss Probability, Routing Overhead, Medium Access Control (MAC) Overhead and Data Throughput etc. This paper presents an in depth study of one to many and many to many communications in MANETs and provides a comparative performance evaluation of unicast and broadcast routing protocols. Dynamic Source Routing protocol (DSR) is used as unicast protocol and BCAST is used to represent broadcast protocol. The performance differentials are analyzed using ns2 network simulator varying multicast group size (number of data senders and data receivers). Both protocols are simulated with identical traffic loads and mobility models. Simulation result shows that BCAST performs better than DSR in most cases.", "published": "2010-02-08T19:12:53Z", "authors": ["Sumon Kumar Debnath", "Foez Ahmed", "Nayeema Islam"], "categories": ["cs.NI", "cs.PF"], "doi": "", "_query": "property valuation computer vision", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2505.13281v1", "title": "Computer Vision Models Show Human-Like Sensitivity to Geometric and Topological Concepts", "abstract": "With the rapid improvement of machine learning (ML) models, cognitive scientists are increasingly asking about their alignment with how humans think. Here, we ask this question for computer vision models and human sensitivity to geometric and topological (GT) concepts. Under the core knowledge account, these concepts are innate and supported by dedicated neural circuitry. In this work, we investigate an alternative explanation, that GT concepts are learned ``for free'' through everyday interaction with the environment. We do so using computer visions models, which are trained on large image datasets. We build on prior studies to investigate the overall performance and human alignment of three classes of models -- convolutional neural networks (CNNs), transformer-based models, and vision-language models -- on an odd-one-out task testing 43 GT concepts spanning seven classes. Transformer-based models achieve the highest overall accuracy, surpassing that of young children. They also show strong alignment with children's performance, finding the same classes of concepts easy vs. difficult. By contrast, vision-language models underperform their vision-only counterparts and deviate further from human profiles, indicating that naïve multimodality might compromise abstract geometric sensitivity. These findings support the use of computer vision models to evaluate the sufficiency of the learning account for explaining human sensitivity to GT concepts, while also suggesting that integrating linguistic and visual representations might have unpredicted deleterious consequences.", "published": "2025-05-19T16:04:53Z", "authors": ["Zekun Wang", "Sashank Varma"], "categories": ["cs.CV"], "doi": "", "_query": "property valuation computer vision", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/cmp-lg/9406029v1", "title": "A Computational Model of Syntactic Processing: Ambiguity Resolution from Interpretation", "abstract": "Syntactic ambiguity abounds in natural language, yet humans have no difficulty coping with it. In fact, the process of ambiguity resolution is almost always unconscious. But it is not infallible, however, as example 1 demonstrates. 1. The horse raced past the barn fell. This sentence is perfectly grammatical, as is evident when it appears in the following context: 2. Two horses were being shown off to a prospective buyer. One was raced past a meadow. and the other was raced past a barn. ... Grammatical yet unprocessable sentences such as 1 are called `garden-path sentences.' Their existence provides an opportunity to investigate the human sentence processing mechanism by studying how and when it fails. The aim of this thesis is to construct a computational model of language understanding which can predict processing difficulty. The data to be modeled are known examples of garden path and non-garden path sentences, and other results from psycholinguistics. It is widely believed that there are two distinct loci of computation in sentence processing: syntactic parsing and semantic interpretation. One longstanding controversy is which of these two modules bears responsibility for the immediate resolution of ambiguity. My claim is that it is the latter, and that the syntactic processing module is a very simple device which blindly and faithfully constructs all possible analyses for the sentence up to the current point of processing. The interpretive module serves as a filter, occasionally discarding certain of these analyses which it deems less appropriate for the ongoing discourse than their competitors. This document is divided into three parts. The first is introductory, and reviews a selection of proposals from the sentence processing literature. The second part explores a body of data which has been adduced in support of a theory of structural preferences --- one that is inconsistent with the present claim. I show how the current proposal can be specified to account for the available data, and moreover to predict where structural preference theories will go wrong. The third part is a theoretical investigation of how well the proposed architecture can be realized using current conceptions of linguistic competence. In it, I present a parsing algorithm and a meaning-based ambiguity resolution method.", "published": "1994-06-20T19:57:34Z", "authors": ["Michael Niv"], "categories": ["cs.CL"], "doi": "", "_query": "property valuation computer vision", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2209.15455v1", "title": "Road Network Deterioration Monitoring Using Aerial Images and Computer Vision", "abstract": "Road maintenance is an essential process for guaranteeing the quality of transportation in any city. A crucial step towards effective road maintenance is the ability to update the inventory of the road network. We present a proof of concept of a protocol for maintaining said inventory based on the use of unmanned aerial vehicles to quickly collect images which are processed by a computer vision program that automatically identifies potholes and their severity. Our protocol aims to provide information to local governments to prioritise the road network maintenance budget, and to be able to detect early stages of road deterioration so as to minimise maintenance expenditure.", "published": "2022-09-30T13:05:03Z", "authors": ["Nicolas Parra-A", "Vladimir Vargas-Calderón", "Herbert Vinck-Posada", "Nicanor Vinck"], "categories": ["cs.CV", "cs.CY"], "doi": "", "_query": "property valuation computer vision", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2505.12425v1", "title": "Kornia-rs: A Low-Level 3D Computer Vision Library In Rust", "abstract": "We present \\textit{kornia-rs}, a high-performance 3D computer vision library written entirely in native Rust, designed for safety-critical and real-time applications. Unlike C++-based libraries like OpenCV or wrapper-based solutions like OpenCV-Rust, \\textit{kornia-rs} is built from the ground up to leverage Rust's ownership model and type system for memory and thread safety. \\textit{kornia-rs} adopts a statically-typed tensor system and a modular set of crates, providing efficient image I/O, image processing and 3D operations. To aid cross-platform compatibility, \\textit{kornia-rs} offers Python bindings, enabling seamless and efficient integration with Rust code. Empirical results show that \\textit{kornia-rs} achieves a 3~ 5 times speedup in image transformation tasks over native Rust alternatives, while offering comparable performance to C++ wrapper-based libraries. In addition to 2D vision capabilities, \\textit{kornia-rs} addresses a significant gap in the Rust ecosystem by providing a set of 3D computer vision operators. This paper presents the architecture and performance characteristics of \\textit{kornia-rs}, demonstrating its effectiveness in real-world computer vision applications.", "published": "2025-05-18T13:50:00Z", "authors": ["Edgar Riba", "Jian Shi", "Aditya Kumar", "Andrew Shen", "Gary Bradski"], "categories": ["cs.CV"], "doi": "", "_query": "property valuation computer vision", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2410.14078v2", "title": "Computational Social Choice: Parameterized Complexity and Challenges", "abstract": "We survey two key problems-Multi-Winner Determination and Hedonic Games in Computational Social Choice, with a special focus on their parameterized complexity, and propose some research challenges in the field.", "published": "2024-10-17T22:53:43Z", "authors": ["Jiehua Chen", "Christian Hatschka", "Sofia Simola"], "categories": ["cs.GT", "cs.CC", "cs.MA"], "doi": "", "_query": "property valuation computer vision", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/1507.06009v3", "title": "Valuations and Frobenius", "abstract": "The behavior of the Frobenius map is investigated for valuation rings of prime characteristic. We show that valuation rings are always F-pure. We introduce a generalization of the notion of strong F-regularity, which we call F-pure regularity, and show that a valuation ring is F-pure regular if and only if it is Noetherian. For valuations on function fields, we show that the Frobenius map is finite if and only if the valuation is divisorial; in this case the valuation ring is Frobenius split. For Noetherian valuation rings in function fields, we show that the valuation ring is Frobenius split if and only if Frobenius is finite, or equivalently, if and only if the valuation ring is excellent.", "published": "2015-07-21T23:04:19Z", "authors": ["Rankeya Datta", "Karen E. Smith"], "categories": ["math.AC"], "doi": "10.2140/ant.2016.10.1057", "_query": "property valuation computer vision", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2311.06613v1", "title": "Computer Vision for Particle Size Analysis of Coarse-Grained Soils", "abstract": "Particle size analysis (PSA) is a fundamental technique for evaluating the physical characteristics of soils. However, traditional methods like sieving can be time-consuming and labor-intensive. In this study, we present a novel approach that utilizes computer vision (CV) and the Python programming language for PSA of coarse-grained soils, employing a standard mobile phone camera. By eliminating the need for a high-performance camera, our method offers convenience and cost savings. Our methodology involves using the OPENCV library to detect and measure soil particles in digital photographs taken under ordinary lighting conditions. For accurate particle size determination, a calibration target with known dimensions is placed on a plain paper alongside 20 different sand samples. The proposed method is compared with traditional sieve analysis and exhibits satisfactory performance for soil particles larger than 2 mm, with a mean absolute percent error (MAPE) of approximately 6%. However, particles smaller than 2 mm result in higher MAPE, reaching up to 60%. To address this limitation, we recommend using a higher-resolution camera to capture images of the smaller soil particles. Furthermore, we discuss the advantages, limitations, and potential future improvements of our method. Remarkably, the program can be executed on a mobile phone, providing immediate results without the need to send soil samples to a laboratory. This field-friendly feature makes our approach highly convenient for on-site usage, outside of a traditional laboratory setting. Ultimately, this novel method represents an initial disruption to the industry, enabling efficient particle size analysis of soil without the reliance on laboratory-based sieve analysis. KEYWORDS: Computer vision, Grain size, ARUCO", "published": "2023-11-11T17:01:24Z", "authors": ["Sompote Youwai", "Parchya Makam"], "categories": ["cs.CV", "cs.LG"], "doi": "", "_query": "property valuation computer vision", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2410.12595v1", "title": "CMAL: A Novel Cross-Modal Associative Learning Framework for Vision-Language Pre-Training", "abstract": "With the flourishing of social media platforms, vision-language pre-training (VLP) recently has received great attention and many remarkable progresses have been achieved. The success of VLP largely benefits from the information complementation and enhancement between different modalities. However, most of recent studies focus on cross-modal contrastive learning (CMCL) to promote image-text alignment by pulling embeddings of positive sample pairs together while pushing those of negative pairs apart, which ignores the natural asymmetry property between different modalities and requires large-scale image-text corpus to achieve arduous progress. To mitigate this predicament, we propose CMAL, a Cross-Modal Associative Learning framework with anchor points detection and cross-modal associative learning for VLP. Specifically, we first respectively embed visual objects and textual tokens into separate hypersphere spaces to learn intra-modal hidden features, and then design a cross-modal associative prompt layer to perform anchor point masking and swap feature filling for constructing a hybrid cross-modal associative prompt. Afterwards, we exploit a unified semantic encoder to learn their cross-modal interactive features for context adaptation. Finally, we design an associative mapping classification layer to learn potential associative mappings between modalities at anchor points, within which we develop a fresh self-supervised associative mapping classification task to boost CMAL's performance. Experimental results verify the effectiveness of CMAL, showing that it achieves competitive performance against previous CMCL-based methods on four common downstream vision-and-language tasks, with significantly fewer corpus. Especially, CMAL obtains new state-of-the-art results on SNLI-VE and REC (testA).", "published": "2024-10-16T14:12:26Z", "authors": ["Zhiyuan Ma", "Jianjun Li", "Guohui Li", "Kaiyan Huang"], "categories": ["cs.CV"], "doi": "", "_query": "property valuation computer vision", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2604.17530v1", "title": "Real-Time Cellist Postural Evaluation With On-Device Computer Vision", "abstract": "Posture is a critical factor for beginning instrumental learners. Most students receive instruction only once a week, and during the intervals between lessons they have little or no feedback on their physical posture. As a result, posture often deteriorates, increasing the risk of musculoskeletal injury and inefficient technique. Recent advances in computer vision and machine learning make it possible to evaluate posture without the constant presence of a human expert. However, current solutions have been extremely limited in availability and convenience due to their reliance on computationally expensive hardware or multi-sensor setups. We present Cello Evaluator, a real-time postural feedback system for practicing cellists. Through this optimization for on-device computer vision inference, we provide access to cellist postural evaluation to anyone with a current generation Android phone and thus reduces the postural feedback voids within individual practice. To validate our mobile application, we conduct a heuristic evaluation consisting of cellist and UX experts. Overall feedback from the evaluation found the app to be user friendly and helpful.", "published": "2026-04-19T16:45:19Z", "authors": ["Paolo Wang", "Michael Zhang", "Shrinand Perumal", "Ekaterina Tszyao", "Luke Choi", "Kexin Sha", "Felix Lu", "Paige Lorenz", "Jackson P. Shields", "Sivamurugan Velmurugan", "Joshua Kamphuis", "William P. Jiang", "Gurtej Bagga", "Trevor Ju", "Raymond Otis Kwon", "Kristen Yeon-Ji Yun", "Yung-Hsiang Lu"], "categories": ["cs.HC", "cs.CV"], "doi": "", "_query": "property valuation computer vision", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2406.09087v2", "title": "Suitability of KANs for Computer Vision: A preliminary investigation", "abstract": "Kolmogorov-Arnold Networks (KANs) introduce a paradigm of neural modeling that implements learnable functions on the edges of the networks, diverging from the traditional node-centric activations in neural networks. This work assesses the applicability and efficacy of KANs in visual modeling, focusing on fundamental recognition and segmentation tasks. We mainly analyze the performance and efficiency of different network architectures built using KAN concepts along with conventional building blocks of convolutional and linear layers, enabling a comparative analysis with the conventional models. Our findings are aimed at contributing to understanding the potential of KANs in computer vision, highlighting both their strengths and areas for further research. Our evaluation point toward the fact that while KAN-based architectures perform in line with the original claims, it may often be important to employ more complex functions on the network edges to retain the performance advantage of KANs on more complex visual data.", "published": "2024-06-13T13:13:17Z", "authors": ["Basim Azam", "Naveed Akhtar"], "categories": ["cs.CV", "cs.AI"], "doi": "", "_query": "property valuation computer vision", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2606.04291v1", "title": "A Cookbook of 3D Vision: Data, Learning Paradigms, and Application", "abstract": "3D vision has rapidly evolved, driven by increasingly diverse data representations, learning paradigms, and modeling strategies. Yet the field remains fragmented across representations and benchmarks, making it difficult to develop unified perspectives on efficiency, fidelity, and scalability. This work provides a data-centric taxonomy of 3D vision that connects geometric representations, datasets, learning frameworks, and applications within a single conceptual map. We begin by analysing the principal structural representations of 3D data--point clouds, meshes, voxels, and 3D Gaussians--along with their acquisition pipelines. We then examine how dataset design, benchmark construction, and supervision regimes shape recent advances, spanning 2D-supervised 3D learning, implicit neural representations, and 4D world modeling. Through this integrative lens, we clarify the relationships among representations, learning paradigms, and downstream tasks in reconstruction, generation, and video modeling, offering a consolidated view of emerging trends toward balancing efficiency and fidelity and toward multimodal geometric grounding.", "published": "2026-06-02T23:41:52Z", "authors": ["Hongyang Du", "Zongxia Li", "Dawei Liu", "Runhao Li", "Haoyuan Song", "Qingyu Zhang", "Yubo Wang", "Jingcheng Ni", "Shihang Gui", "Congchao Dong", "Tao Hu"], "categories": ["cs.CV"], "doi": "", "_query": "property valuation computer vision", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2409.10533v4", "title": "Ethical Challenges in Computer Vision: Ensuring Privacy and Mitigating Bias in Publicly Available Datasets", "abstract": "This paper aims to shed light on the ethical problems of creating and deploying computer vision tech, particularly in using publicly available datasets. Due to the rapid growth of machine learning and artificial intelligence, computer vision has become a vital tool in many industries, including medical care, security systems, and trade. However, extensive use of visual data that is often collected without consent due to an informed discussion of its ramifications raises significant concerns about privacy and bias. The paper also examines these issues by analyzing popular datasets such as COCO, LFW, ImageNet, CelebA, PASCAL VOC, etc., that are usually used for training computer vision models. We offer a comprehensive ethical framework that addresses these challenges regarding the protection of individual rights, minimization of bias as well as openness and responsibility. We aim to encourage AI development that will take into account societal values as well as ethical standards to avoid any public harm.", "published": "2024-08-31T00:59:29Z", "authors": ["Ghalib Ahmed Tahir"], "categories": ["cs.CV", "cs.CR", "cs.CY"], "doi": "", "_query": "property valuation computer vision", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2506.03918v1", "title": "Learning from Noise: Enhancing DNNs for Event-Based Vision through Controlled Noise Injection", "abstract": "Event-based sensors offer significant advantages over traditional frame-based cameras, especially in scenarios involving rapid motion or challenging lighting conditions. However, event data frequently suffers from considerable noise, negatively impacting the performance and robustness of deep learning models. Traditionally, this problem has been addressed by applying filtering algorithms to the event stream, but this may also remove some of relevant data. In this paper, we propose a novel noise-injection training methodology designed to enhance the neural networks robustness against varying levels of event noise. Our approach introduces controlled noise directly into the training data, enabling models to learn noise-resilient representations. We have conducted extensive evaluations of the proposed method using multiple benchmark datasets (N-Caltech101, N-Cars, and Mini N-ImageNet) and various network architectures, including Convolutional Neural Networks, Vision Transformers, Spiking Neural Networks, and Graph Convolutional Networks. Experimental results show that our noise-injection training strategy achieves stable performance over a range of noise intensities, consistently outperforms event-filtering techniques, and achieves the highest average classification accuracy, making it a viable alternative to traditional event-data filtering methods in an object classification system. Code: https://github.com/vision-agh/DVS_Filtering", "published": "2025-06-04T13:10:26Z", "authors": ["Marcin Kowalczyk", "Kamil Jeziorek", "Tomasz Kryjak"], "categories": ["cs.CV"], "doi": "", "_query": "property valuation computer vision", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2308.13998v1", "title": "Computation-efficient Deep Learning for Computer Vision: A Survey", "abstract": "Over the past decade, deep learning models have exhibited considerable advancements, reaching or even exceeding human-level performance in a range of visual perception tasks. This remarkable progress has sparked interest in applying deep networks to real-world applications, such as autonomous vehicles, mobile devices, robotics, and edge computing. However, the challenge remains that state-of-the-art models usually demand significant computational resources, leading to impractical power consumption, latency, or carbon emissions in real-world scenarios. This trade-off between effectiveness and efficiency has catalyzed the emergence of a new research focus: computationally efficient deep learning, which strives to achieve satisfactory performance while minimizing the computational cost during inference. This review offers an extensive analysis of this rapidly evolving field by examining four key areas: 1) the development of static or dynamic light-weighted backbone models for the efficient extraction of discriminative deep representations; 2) the specialized network architectures or algorithms tailored for specific computer vision tasks; 3) the techniques employed for compressing deep learning models; and 4) the strategies for deploying efficient deep networks on hardware platforms. Additionally, we provide a systematic discussion on the critical challenges faced in this domain, such as network architecture design, training schemes, practical efficiency, and more realistic model compression approaches, as well as potential future research directions.", "published": "2023-08-27T03:55:28Z", "authors": ["Yulin Wang", "Yizeng Han", "Chaofei Wang", "Shiji Song", "Qi Tian", "Gao Huang"], "categories": ["cs.CV", "cs.AI", "cs.LG", "cs.MM"], "doi": "", "_query": "property valuation computer vision", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2512.05131v2", "title": "AREA3D: Active Reconstruction Agent with Unified Feed-Forward 3D Perception and Vision-Language Guidance", "abstract": "Active 3D reconstruction enables an agent to autonomously select viewpoints to efficiently obtain accurate and complete scene geometry, rather than passively reconstructing scenes from pre-collected images. However, existing active reconstruction methods often rely on hand-crafted geometric heuristics, which can lead to redundant observations without substantially improving reconstruction quality. To address this limitation, we propose AREA3D, an active reconstruction agent that leverages feed-forward 3D reconstruction models and vision-language guidance. Our framework decouples view-uncertainty modeling from the underlying feed-forward reconstructor, enabling precise uncertainty estimation without expensive online optimization. In addition, an integrated vision-language model provides high-level semantic guidance, encouraging informative and diverse viewpoints beyond purely geometric cues. Extensive experiments on both scene-level and object-level benchmarks demonstrate that AREA3D achieves state-of-the-art reconstruction accuracy, particularly in the sparse-view regime. Code will be made available at: https://github.com/TianlingXu/AREA3D .", "published": "2025-11-28T06:17:02Z", "authors": ["Tianling Xu", "Shengzhe Gan", "Leslie Gu", "Yuelei Li", "Fangneng Zhan", "Hanspeter Pfister"], "categories": ["cs.CV", "cs.AI", "cs.RO"], "doi": "", "_query": "property valuation computer vision", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2608.21067v2", "title": "AT-ViT: Area-Targeted Multi-View Vision Transformer with Cross-Attention and Multi-Scale Patching for Plant Trait Recognition in Herbarium Images", "abstract": "Automated plant traits recognition from herbarium images is essential for plant sciences, yet remains challenging because background elements (e.g., textual labels, mounting artifacts, and color charts) can introduce shortcut learning, leading models to rely on spurious non-plant cues rather than plant morphology. This bias degrades both generalization and interpretability. In this paper, we introduce AT-ViT, a dual-branch Vision Transformer that jointly encodes raw herbarium scans and their segmented-derived counterparts via a multi-scale, multi-view cross-attention fusion scheme. AT-ViT further incorporates a mask-guided patch weighting mechanism that amplifies plant-relevant regions and attenuates background-driven features. By learning from the original scans while being guided by segmentation masks through the mask-guided patch reweighting mechanism, the model is encouraged to focus on plant organs and learn plant-centric representations more effectively. Across multiple trait classification tasks (e.g., leaf base shape, thorns), AT-ViT delivers consistent accuracy gains, improves attention localization on plant regions, and exhibits increased robustness under synthetic background perturbations. Specifically, AT-ViT substantially improves spatial attention grounding, boosting plant-region alignment (Avg IoU_p: +15.66 to +18.03 pp) while reducing background overlap (Avg IoU_b: -27.92 to -31.02 pp) relative to CrossViT, and remains markedly more robust to background perturbations, outperforming ResNet101 by up to +32.32 accuracy points and CrossViT by up to +5.07 points under background-noise conditions.", "published": "2026-08-21T13:08:44Z", "authors": ["Amani Sedrat", "Takieddine Chehhat", "Youcef Sklab", "Hanane Ariouat", "Abderrazak Sebaa", "Eric Chenin", "Jean-Daniel Zucker", "Edi Prifti"], "categories": ["cs.CV", "cs.AI"], "doi": "10.1049/cvi2.70059", "_query": "property valuation computer vision", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2509.08712v1", "title": "Computational Imaging for Enhanced Computer Vision", "abstract": "This paper presents a comprehensive survey of computational imaging (CI) techniques and their transformative impact on computer vision (CV) applications. Conventional imaging methods often fail to deliver high-fidelity visual data in challenging conditions, such as low light, motion blur, or high dynamic range scenes, thereby limiting the performance of state-of-the-art CV systems. Computational imaging techniques, including light field imaging, high dynamic range (HDR) imaging, deblurring, high-speed imaging, and glare mitigation, address these limitations by enhancing image acquisition and reconstruction processes. This survey systematically explores the synergies between CI techniques and core CV tasks, including object detection, depth estimation, optical flow, face recognition, and keypoint detection. By analyzing the relationships between CI methods and their practical contributions to CV applications, this work highlights emerging opportunities, challenges, and future research directions. We emphasize the potential for task-specific, adaptive imaging pipelines that improve robustness, accuracy, and efficiency in real-world scenarios, such as autonomous navigation, surveillance, augmented reality, and robotics.", "published": "2025-09-10T16:02:42Z", "authors": ["Humera Shaikh", "Kaur Jashanpreet"], "categories": ["cs.CV"], "doi": "10.17577/IJERTV14IS050351", "_query": "property valuation computer vision", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/0804.2709v1", "title": "Subgroup theorem for valuated groups and the CSA property", "abstract": "A valuated group with normal forms is a group with an integer-valued length function satisfying some Lyndon's axioms and an additional axiom considered by Hurley. We prove a subgroup theorem for valuated groups with normal forms analogous to Grushko-Neumann's theorem. We study also the CSA property in such groups.", "published": "2008-04-17T01:38:49Z", "authors": ["Abderezak Ould Houcine"], "categories": ["math.GR"], "doi": "", "_query": "property valuation computer vision", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2306.01938v1", "title": "Self-supervised Interest Point Detection and Description for Fisheye and Perspective Images", "abstract": "Keypoint detection and matching is a fundamental task in many computer vision problems, from shape reconstruction, to structure from motion, to AR/VR applications and robotics. It is a well-studied problem with remarkable successes such as SIFT, and more recent deep learning approaches. While great robustness is exhibited by these techniques with respect to noise, illumination variation, and rigid motion transformations, less attention has been placed on image distortion sensitivity. In this work, we focus on the case when this is caused by the geometry of the cameras used for image acquisition, and consider the keypoint detection and matching problem between the hybrid scenario of a fisheye and a projective image. We build on a state-of-the-art approach and derive a self-supervised procedure that enables training an interest point detector and descriptor network. We also collected two new datasets for additional training and testing in this unexplored scenario, and we demonstrate that current approaches are suboptimal because they are designed to work in traditional projective conditions, while the proposed approach turns out to be the most effective.", "published": "2023-06-02T22:39:33Z", "authors": ["Marcela Mera-Trujillo", "Shivang Patel", "Yu Gu", "Gianfranco Doretto"], "categories": ["cs.CV", "cs.RO"], "doi": "", "_query": "property valuation computer vision", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2306.06048v3", "title": "How Does Fine-Tuning Impact Out-of-Distribution Detection for Vision-Language Models?", "abstract": "Recent large vision-language models such as CLIP have shown remarkable out-of-distribution (OOD) detection and generalization performance. However, their zero-shot in-distribution (ID) accuracy is often limited for downstream datasets. Recent CLIP-based fine-tuning methods such as prompt learning have demonstrated significant improvements in ID classification and OOD generalization where OOD labels are available. Nonetheless, it remains unclear whether the model is reliable to semantic shifts without OOD labels. In this paper, we aim to bridge the gap and present a comprehensive study to understand how fine-tuning impact OOD detection for few-shot downstream tasks. By framing OOD detection as multi-modal concept matching, we establish a connection between fine-tuning methods and various OOD scores. Our results suggest that a proper choice of OOD scores is essential for CLIP-based fine-tuning. In particular, the maximum concept matching (MCM) score provides a promising solution consistently. We also show that prompt learning demonstrates the state-of-the-art OOD detection performance over the zero-shot counterpart.", "published": "2023-06-09T17:16:50Z", "authors": ["Yifei Ming", "Yixuan Li"], "categories": ["cs.CV", "cs.CY", "cs.LG"], "doi": "10.1007/s11263-023-01895-7", "_query": "property valuation computer vision", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/1611.00265v3", "title": "Decomposition complexity with respect to coarse properties", "abstract": "We formalize the concept of a family of metric spaces satisfying a coarse property uniformly and we generalize finite decomposition complexity of Erik Guentner, Romain Tessera, and Guoliang Yu. Of particular interest are results determining sufficient conditions for a metric space to satisfy Property A of Guoliang Yu.", "published": "2016-11-01T15:15:47Z", "authors": ["Jerzy Dydak"], "categories": ["math.MG", "math.GT"], "doi": "", "_query": "property valuation computer vision", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2311.03747v1", "title": "SBCFormer: Lightweight Network Capable of Full-size ImageNet Classification at 1 FPS on Single Board Computers", "abstract": "Computer vision has become increasingly prevalent in solving real-world problems across diverse domains, including smart agriculture, fishery, and livestock management. These applications may not require processing many image frames per second, leading practitioners to use single board computers (SBCs). Although many lightweight networks have been developed for mobile/edge devices, they primarily target smartphones with more powerful processors and not SBCs with the low-end CPUs. This paper introduces a CNN-ViT hybrid network called SBCFormer, which achieves high accuracy and fast computation on such low-end CPUs. The hardware constraints of these CPUs make the Transformer's attention mechanism preferable to convolution. However, using attention on low-end CPUs presents a challenge: high-resolution internal feature maps demand excessive computational resources, but reducing their resolution results in the loss of local image details. SBCFormer introduces an architectural design to address this issue. As a result, SBCFormer achieves the highest trade-off between accuracy and speed on a Raspberry Pi 4 Model B with an ARM-Cortex A72 CPU. For the first time, it achieves an ImageNet-1K top-1 accuracy of around 80% at a speed of 1.0 frame/sec on the SBC. Code is available at https://github.com/xyongLu/SBCFormer.", "published": "2023-11-07T06:09:56Z", "authors": ["Xiangyong Lu", "Masanori Suganuma", "Takayuki Okatani"], "categories": ["cs.CV"], "doi": "", "_query": "property valuation computer vision", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2207.00449v3", "title": "Dissecting Self-Supervised Learning Methods for Surgical Computer Vision", "abstract": "The field of surgical computer vision has undergone considerable breakthroughs in recent years with the rising popularity of deep neural network-based methods. However, standard fully-supervised approaches for training such models require vast amounts of annotated data, imposing a prohibitively high cost; especially in the clinical domain. Self-Supervised Learning (SSL) methods, which have begun to gain traction in the general computer vision community, represent a potential solution to these annotation costs, allowing to learn useful representations from only unlabeled data. Still, the effectiveness of SSL methods in more complex and impactful domains, such as medicine and surgery, remains limited and unexplored. In this work, we address this critical need by investigating four state-of-the-art SSL methods (MoCo v2, SimCLR, DINO, SwAV) in the context of surgical computer vision. We present an extensive analysis of the performance of these methods on the Cholec80 dataset for two fundamental and popular tasks in surgical context understanding, phase recognition and tool presence detection. We examine their parameterization, then their behavior with respect to training data quantities in semi-supervised settings. Correct transfer of these methods to surgery, as described and conducted in this work, leads to substantial performance gains over generic uses of SSL - up to 7.4% on phase recognition and 20% on tool presence detection - as well as state-of-the-art semi-supervised phase recognition approaches by up to 14%. Further results obtained on a highly diverse selection of surgical datasets exhibit strong generalization properties. The code is available at https://github.com/CAMMA-public/SelfSupSurg.", "published": "2022-07-01T14:17:11Z", "authors": ["Sanat Ramesh", "Vinkle Srivastav", "Deepak Alapatt", "Tong Yu", "Aditya Murali", "Luca Sestini", "Chinedu Innocent Nwoye", "Idris Hamoud", "Saurav Sharma", "Antoine Fleurentin", "Georgios Exarchakis", "Alexandros Karargyris", "Nicolas Padoy"], "categories": ["cs.CV"], "doi": "", "_query": "property valuation computer vision", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2606.07683v1", "title": "Review the Code, Not the Story: A Vision and Protocol for Code-First Peer Review", "abstract": "Peer review in computational fields remains centered on author-written manuscripts, even though the decisive evidence for many claims resides in executable code, data, configurations, and experiment pipelines. This manuscript-first workflow gives authors substantial control over narrative framing while leaving reviewers with limited time to inspect implementation details, reproduce results, or detect unsupported claims. This vision and protocol paper proposes code-first peer review: authors submit executable research artifacts and minimal claim manifests; a venue-controlled AI system builds the environment, executes experiments, audits code paths, maps claims to evidence, and generates a standardized Review Package for human reviewers. The goal is not to replace reviewers or to give authors an automatic writing assistant. Instead, AI serves as review infrastructure that shifts the target of peer review from polished narratives to executable evidence. We formalize a claim-evidence contract, define the Generated Review View and Review Package abstractions, give a worked example, outline a system architecture, and analyze evaluation and governance challenges including AI bias, prompt injection, model instability, auditability, and author appeal.", "published": "2026-06-05T01:26:07Z", "authors": ["Jienan Chen"], "categories": ["cs.SE", "eess.SP"], "doi": "", "_query": "property valuation computer vision", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/1208.5092v1", "title": "Graph Degree Linkage: Agglomerative Clustering on a Directed Graph", "abstract": "This paper proposes a simple but effective graph-based agglomerative algorithm, for clustering high-dimensional data. We explore the different roles of two fundamental concepts in graph theory, indegree and outdegree, in the context of clustering. The average indegree reflects the density near a sample, and the average outdegree characterizes the local geometry around a sample. Based on such insights, we define the affinity measure of clusters via the product of average indegree and average outdegree. The product-based affinity makes our algorithm robust to noise. The algorithm has three main advantages: good performance, easy implementation, and high computational efficiency. We test the algorithm on two fundamental computer vision problems: image clustering and object matching. Extensive experiments demonstrate that it outperforms the state-of-the-arts in both applications.", "published": "2012-08-25T02:51:36Z", "authors": ["Wei Zhang", "Xiaogang Wang", "Deli Zhao", "Xiaoou Tang"], "categories": ["cs.CV", "cs.SI", "stat.ML"], "doi": "", "_query": "property valuation computer vision", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2608.24469v1", "title": "Low-Rank Ternary Adaptation for Fine-Tuning Transformers", "abstract": "Ternary transformers offer extreme memory and compute efficiency, but existing low-bit LoRA-based methods cannot directly fine-tune ternary weights. Current approaches either require dequantization, restoring low-bit base weights to higher precision to merge with adaptation weight, or update only quantization parameters, preventing a merged model that remains ternary. We propose ternary multiplicative adaptation, which represents discrete updates of ternary weights such as sign flips or zeroing through a low-rank Kronecker factorization into two small ternary matrices applied element-wise to ternary weights. This design is parameter-efficient and expressive, preserves the ternary domain, and supports direct merging without dequantization. Experiments on six models across language and vision, including ternarized LLaMA-3 1B and 3B and a ternary ViT-B/16, demonstrate that our method recovers much of the performance lost to quantization and outperforms strong low-bit and ternary baselines. Code is available at https://github.com/alexmanoo/ternary_adaptation.", "published": "2026-08-25T12:15:56Z", "authors": ["Alexandru-Dragos Manolache", "Yunqiang Li", "Jan van Gemert"], "categories": ["cs.CV", "cs.LG"], "doi": "10.1007/978-3-032-37242-0_10", "_query": "property valuation computer vision", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2204.13590v1", "title": "Computer Vision for Road Imaging and Pothole Detection: A State-of-the-Art Review of Systems and Algorithms", "abstract": "Computer vision algorithms have been prevalently utilized for 3-D road imaging and pothole detection for over two decades. Nonetheless, there is a lack of systematic survey articles on state-of-the-art (SoTA) computer vision techniques, especially deep learning models, developed to tackle these problems. This article first introduces the sensing systems employed for 2-D and 3-D road data acquisition, including camera(s), laser scanners, and Microsoft Kinect. Afterward, it thoroughly and comprehensively reviews the SoTA computer vision algorithms, including (1) classical 2-D image processing, (2) 3-D point cloud modeling and segmentation, and (3) machine/deep learning, developed for road pothole detection. This article also discusses the existing challenges and future development trends of computer vision-based road pothole detection approaches: classical 2-D image processing-based and 3-D point cloud modeling and segmentation-based approaches have already become history; and Convolutional neural networks (CNNs) have demonstrated compelling road pothole detection results and are promising to break the bottleneck with the future advances in self/un-supervised learning for multi-modal semantic segmentation. We believe that this survey can serve as practical guidance for developing the next-generation road condition assessment systems.", "published": "2022-04-28T16:00:39Z", "authors": ["Nachuan Ma", "Jiahe Fan", "Wenshuo Wang", "Jin Wu", "Yu Jiang", "Lihua Xie", "Rui Fan"], "categories": ["cs.CV", "cs.AI", "cs.LG", "cs.RO"], "doi": "10.1093/tse/tdac026", "_query": "property valuation computer vision", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/1512.00567v3", "title": "Rethinking the Inception Architecture for Computer Vision", "abstract": "Convolutional networks are at the core of most state-of-the-art computer vision solutions for a wide variety of tasks. Since 2014 very deep convolutional networks started to become mainstream, yielding substantial gains in various benchmarks. Although increased model size and computational cost tend to translate to immediate quality gains for most tasks (as long as enough labeled data is provided for training), computational efficiency and low parameter count are still enabling factors for various use cases such as mobile vision and big-data scenarios. Here we explore ways to scale up networks in ways that aim at utilizing the added computation as efficiently as possible by suitably factorized convolutions and aggressive regularization. We benchmark our methods on the ILSVRC 2012 classification challenge validation set demonstrate substantial gains over the state of the art: 21.2% top-1 and 5.6% top-5 error for single frame evaluation using a network with a computational cost of 5 billion multiply-adds per inference and with using less than 25 million parameters. With an ensemble of 4 models and multi-crop evaluation, we report 3.5% top-5 error on the validation set (3.6% error on the test set) and 17.3% top-1 error on the validation set.", "published": "2015-12-02T03:44:38Z", "authors": ["Christian Szegedy", "Vincent Vanhoucke", "Sergey Ioffe", "Jonathon Shlens", "Zbigniew Wojna"], "categories": ["cs.CV"], "doi": "", "_query": "property valuation computer vision", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2606.10594v1", "title": "Segment and Select: Vision-Language Segmentation in 3D Scenarios", "abstract": "3D vision-language segmentation aims to segment target objects in 3D scenarios according to the linguistic instructions and visual observations. Prior art heavily relies on the coarse superpoint representation to reduce the computation complexity, which suffers from poor segmentation quality and messy object boundaries. In this paper, we propose the SEGment-And-select (SEGA3D) paradigm for 3D visionlanguage segmentation that directly operates on the fine-grained visual information and is free from the superpoint dependency. Specifically, we first leverage a mask candidate generator to provide fine-grained categorical mask candidates, substantially improving the quality of candidate masks over the superpoint counterparts. Then, a Large Language Model (LLM) is utilized to generate the semantic and spatial information based on the linguistic description and visual features. The LLM output and visual features are fed to the Semantic-Spatial Selector (SSS) to produce the top-ranking mask candidates. Eventually, the Loopback Verification Module (LVM) is designed to yield the segmentation mask from the selected candidate masks. Our SEGA3D attains competitive performance on ScanRefer, ScanNet and Matterport3D benchmarks. Notably, our SEGA3D surpasses the top-performing counterpart by 8.3 mIoU and 5.3 mIoU on ScanNet and Matterport3D, respectively. Codes will be available upon publication.", "published": "2026-06-09T08:58:59Z", "authors": ["Yulin Chen", "Zhihang Zhong", "Yuenan Hou"], "categories": ["cs.CV"], "doi": "", "_query": "property valuation computer vision", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2207.09505v1", "title": "An Efficient Method for Face Quality Assessment on the Edge", "abstract": "Face recognition applications in practice are composed of two main steps: face detection and feature extraction. In a sole vision-based solution, the first step generates multiple detection for a single identity by ingesting a camera stream. A practical approach on edge devices should prioritize these detection of identities according to their conformity to recognition. In this perspective, we propose a face quality score regression by just appending a single layer to a face landmark detection network. With almost no additional cost, face quality scores are obtained by training this single layer to regress recognition scores with surveillance like augmentations. We implemented the proposed approach on edge GPUs with all face detection pipeline steps, including detection, tracking, and alignment. Comprehensive experiments show the proposed approach's efficiency through comparison with SOTA face quality regression models on different data sets and real-life scenarios.", "published": "2022-07-19T18:29:43Z", "authors": ["Sefa Burak Okcu", "Burak Oğuz Özkalaycı", "Cevahir Çığla"], "categories": ["cs.CV"], "doi": "10.1007/978-3-030-68238-5_5", "_query": "property valuation computer vision", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/1804.01310v1", "title": "Event-based Vision meets Deep Learning on Steering Prediction for Self-driving Cars", "abstract": "Event cameras are bio-inspired vision sensors that naturally capture the dynamics of a scene, filtering out redundant information. This paper presents a deep neural network approach that unlocks the potential of event cameras on a challenging motion-estimation task: prediction of a vehicle's steering angle. To make the best out of this sensor-algorithm combination, we adapt state-of-the-art convolutional architectures to the output of event sensors and extensively evaluate the performance of our approach on a publicly available large scale event-camera dataset (~1000 km). We present qualitative and quantitative explanations of why event cameras allow robust steering prediction even in cases where traditional cameras fail, e.g. challenging illumination conditions and fast motion. Finally, we demonstrate the advantages of leveraging transfer learning from traditional to event-based vision, and show that our approach outperforms state-of-the-art algorithms based on standard cameras.", "published": "2018-04-04T09:05:41Z", "authors": ["Ana I. Maqueda", "Antonio Loquercio", "Guillermo Gallego", "Narciso Garcia", "Davide Scaramuzza"], "categories": ["cs.CV", "cs.LG", "cs.RO"], "doi": "10.1109/CVPR.2018.00568", "_query": "property valuation computer vision", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2603.21432v1", "title": "Image-Based Structural Analysis Using Computer Vision and LLMs: PhotoBeamSolver", "abstract": "This paper presents the development of a documented program capable of solving idealized beam models, such as those commonly used in textbooks and academic exercises, from drawings made by a person. The system is based on computer vision and statistical learning techniques for the detection and visual interpretation of structural elements. Likewise, the main challenges and limitations associated with the integration of computer vision into structural analysis are analyzed, as well as the requirements necessary for its reliable application in the field of civil engineering. In this context, the implementation of the PhotoBeamSolver program is explored, and the current state of computer vision in civil engineering is discussed, particularly in relation to structural analysis, infrastructure inspection, and engineering decision-support systems.", "published": "2026-03-22T22:43:41Z", "authors": ["Altamirano-Muñiz Emilio Fernando"], "categories": ["cs.CV"], "doi": "", "_query": "property valuation computer vision", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/1901.08864v1", "title": "Vision-based inspection system employing computer vision & neural networks for detection of fractures in manufactured components", "abstract": "We are proceeding towards the age of automation and robotic integration of our production lines [5]. Effective quality-control systems have to be put in place to maintain the quality of manufactured components. Among different quality-control systems, vision-based inspection systems have gained considerable amount of popularity [8] due to developments in computing power and image processing techniques. In this paper, we present a vision-based inspection system (VBI) as a quality-control system, which not only detects the presence of defects, such as in conventional VBIs, but also leverage developments in machine learning to predict the presence of surface fractures and wearing. We use OpenCV, an open source computer-vision framework, and Tensorflow, an open source machine-learning framework developed by Google Inc., to accomplish the tasks of detection and prediction of presence of surface defects such as fractures of manufactured gears.", "published": "2019-01-25T13:18:04Z", "authors": ["Sarthak J Shetty"], "categories": ["cs.CV"], "doi": "", "_query": "property valuation computer vision", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2103.10619v2", "title": "Scalable Vision Transformers with Hierarchical Pooling", "abstract": "The recently proposed Visual image Transformers (ViT) with pure attention have achieved promising performance on image recognition tasks, such as image classification. However, the routine of the current ViT model is to maintain a full-length patch sequence during inference, which is redundant and lacks hierarchical representation. To this end, we propose a Hierarchical Visual Transformer (HVT) which progressively pools visual tokens to shrink the sequence length and hence reduces the computational cost, analogous to the feature maps downsampling in Convolutional Neural Networks (CNNs). It brings a great benefit that we can increase the model capacity by scaling dimensions of depth/width/resolution/patch size without introducing extra computational complexity due to the reduced sequence length. Moreover, we empirically find that the average pooled visual tokens contain more discriminative information than the single class token. To demonstrate the improved scalability of our HVT, we conduct extensive experiments on the image classification task. With comparable FLOPs, our HVT outperforms the competitive baselines on ImageNet and CIFAR-100 datasets. Code is available at https://github.com/MonashAI/HVT", "published": "2021-03-19T03:55:58Z", "authors": ["Zizheng Pan", "Bohan Zhuang", "Jing Liu", "Haoyu He", "Jianfei Cai"], "categories": ["cs.CV"], "doi": "", "_query": "property valuation computer vision", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2602.00865v2", "title": "Distill3R: A Pipeline for Democratizing 3D Foundation Models on Commodity Hardware", "abstract": "While multi-view 3D reconstruction has shifted toward large-scale foundation models capable of inferring globally consistent geometry, their reliance on massive computational clusters for training has created a significant barrier to entry for most academic laboratories. To bridge this compute divide, we introduce Distill3R, a framework designed to distill the geometric reasoning of 3D foundation models into compact students fully trainable on a single workstation. Our methodology centers on two primary innovations: (1) an offline caching pipeline that decouples heavy teacher inference from the training loop through compressed supervision signals, and (2) a confidence-aware distillation loss that leverages teacher uncertainty to enable training on commodity hardware. We propose a 72M-parameter student model which achieves a 9x reduction in parameters and a 5x inference speedup compared to its 650M-parameter teacher. The student is fully trainable in under 3 days on a single workstation, whereas its teacher requires massive GPU clusters for up to a week. We demonstrate that the student preserves the structural consistency and qualitative geometric understanding required for functional 3D awareness. By providing a reproducible, single-workstation training recipe, Distill3R serves as an exploratory entry point for democratized 3D vision research and efficient edge deployment. This work is not intended to compete with state-of-the-art foundation models, but to provide an accessible research baseline for laboratories without access to large-scale compute to train and specialize models on their own domain-specific data at minimal cost.", "published": "2026-01-31T18:56:45Z", "authors": ["Brandon Leblanc", "Charalambos Poullis"], "categories": ["cs.CV"], "doi": "10.21428/d82e957c.68efb4ef", "_query": "property valuation computer vision", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2211.03576v1", "title": "DAD vision: opto-electronic co-designed computer vision with division adjoint method", "abstract": "The miniaturization and mobility of computer vision systems are limited by the heavy computational burden and the size of optical lenses. Here, we propose to use a ultra-thin diffractive optical element to implement passive optical convolution. A division adjoint opto-electronic co-design method is also proposed. In our simulation experiments, the first few convolutional layers of the neural network can be replaced by optical convolution in a classification task on the CIFAR-10 dataset with no power consumption, while similar performance can be obtained.", "published": "2022-11-04T07:31:44Z", "authors": ["Zihan Zang", "Haoqiang Wang", "Yunpeng Xu"], "categories": ["cs.CV", "eess.IV", "physics.optics"], "doi": "", "_query": "property valuation computer vision", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2203.02281v2", "title": "A Comprehensive Review of Computer Vision in Sports: Open Issues, Future Trends and Research Directions", "abstract": "Recent developments in video analysis of sports and computer vision techniques have achieved significant improvements to enable a variety of critical operations. To provide enhanced information, such as detailed complex analysis in sports like soccer, basketball, cricket, badminton, etc., studies have focused mainly on computer vision techniques employed to carry out different tasks. This paper presents a comprehensive review of sports video analysis for various applications high-level analysis such as detection and classification of players, tracking player or ball in sports and predicting the trajectories of player or ball, recognizing the teams strategies, classifying various events in sports. The paper further discusses published works in a variety of application-specific tasks related to sports and the present researchers views regarding them. Since there is a wide research scope in sports for deploying computer vision techniques in various sports, some of the publicly available datasets related to a particular sport have been provided. This work reviews a detailed discussion on some of the artificial intelligence(AI)applications in sports vision, GPU-based work stations, and embedded platforms. Finally, this review identifies the research directions, probable challenges, and future trends in the area of visual recognition in sports.", "published": "2022-03-03T07:49:21Z", "authors": ["Banoth Thulasya Naik", "Mohammad Farukh Hashmi", "Neeraj Dhanraj Bokde"], "categories": ["cs.CV", "eess.IV"], "doi": "10.3390/app12094429", "_query": "property valuation computer vision", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2308.13558v1", "title": "Federated Learning for Computer Vision", "abstract": "Computer Vision (CV) is playing a significant role in transforming society by utilizing machine learning (ML) tools for a wide range of tasks. However, the need for large-scale datasets to train ML models creates challenges for centralized ML algorithms. The massive computation loads required for processing and the potential privacy risks associated with storing and processing data on central cloud servers put these algorithms under severe strain. To address these issues, federated learning (FL) has emerged as a promising solution, allowing privacy preservation by training models locally and exchanging them to improve overall performance. Additionally, the computational load is distributed across multiple clients, reducing the burden on central servers. This paper presents, to the best of the authors' knowledge, the first review discussing recent advancements of FL in CV applications, comparing them to conventional centralized training paradigms. It provides an overview of current FL applications in various CV tasks, emphasizing the advantages of FL and the challenges of implementing it in CV. To facilitate this, the paper proposes a taxonomy of FL techniques in CV, outlining their applications and security threats. It also discusses privacy concerns related to implementing blockchain in FL schemes for CV tasks and summarizes existing privacy preservation methods. Moving on, the paper identifies open research challenges and potential future research directions to further exploit the potential of FL and blockchain in CV applications.", "published": "2023-08-24T16:05:14Z", "authors": ["Yassine Himeur", "Iraklis Varlamis", "Hamza Kheddar", "Abbes Amira", "Shadi Atalla", "Yashbir Singh", "Faycal Bensaali", "Wathiq Mansoor"], "categories": ["cs.CV"], "doi": "", "_query": "property valuation computer vision", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2306.11582v2", "title": "Computing a human-like reaction time metric from stable recurrent vision models", "abstract": "The meteoric rise in the adoption of deep neural networks as computational models of vision has inspired efforts to \"align\" these models with humans. One dimension of interest for alignment includes behavioral choices, but moving beyond characterizing choice patterns to capturing temporal aspects of visual decision-making has been challenging. Here, we sketch a general-purpose methodology to construct computational accounts of reaction times from a stimulus-computable, task-optimized model. Specifically, we introduce a novel metric leveraging insights from subjective logic theory summarizing evidence accumulation in recurrent vision models. We demonstrate that our metric aligns with patterns of human reaction times for stimulus manipulations across four disparate visual decision-making tasks spanning perceptual grouping, mental simulation, and scene categorization. This work paves the way for exploring the temporal alignment of model and human visual strategies in the context of various other cognitive tasks toward generating testable hypotheses for neuroscience. Links to the code and data can be found on the project page: https://serre-lab.github.io/rnn_rts_site.", "published": "2023-06-20T14:56:02Z", "authors": ["Lore Goetschalckx", "Lakshmi Narasimhan Govindarajan", "Alekh Karkada Ashok", "Aarit Ahuja", "David L. Sheinberg", "Thomas Serre"], "categories": ["cs.CV", "cs.AI"], "doi": "", "_query": "property valuation computer vision", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2603.08827v1", "title": "Computer Vision-Based Vehicle Allotment System using Perspective Mapping", "abstract": "Smart city research envisions a future in which data-driven solutions and sustainable infrastructure work together to define urban living at the crossroads of urbanization and technology. Within this framework, smart parking systems play an important role in reducing urban congestion and supporting sustainable transportation. Automating parking solutions have considerable benefits, such as increased efficiency and less reliance on human involvement, but obstacles such as sensor limitations and integration complications remain. To overcome them, a more sophisticated car allotment system is required, particularly in heavily populated urban areas. Computer vision, with its higher accuracy and adaptability, outperforms traditional sensor-based systems for recognizing vehicles and vacant parking spaces. Unlike fixed sensor technologies, computer vision can dynamically assess a wide range of visual inputs while adjusting to changing parking layouts. This research presents a cost-effective, easy-to-implement smart parking system utilizing computer vision and object detection models like YOLOv8. Using inverse perspective mapping (IPM) to merge images from four camera views, we extract data on vacant spaces. The system simulates a 3D parking environment, representing available spots with a 3D Cartesian plot to guide users.", "published": "2026-03-09T18:37:46Z", "authors": ["Prachi Nandi", "Sonakshi Satapathy", "Suchismita Chinara"], "categories": ["cs.CV"], "doi": "", "_query": "property valuation computer vision", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2409.13828v1", "title": "ViTGuard: Attention-aware Detection against Adversarial Examples for Vision Transformer", "abstract": "The use of transformers for vision tasks has challenged the traditional dominant role of convolutional neural networks (CNN) in computer vision (CV). For image classification tasks, Vision Transformer (ViT) effectively establishes spatial relationships between patches within images, directing attention to important areas for accurate predictions. However, similar to CNNs, ViTs are vulnerable to adversarial attacks, which mislead the image classifier into making incorrect decisions on images with carefully designed perturbations. Moreover, adversarial patch attacks, which introduce arbitrary perturbations within a small area, pose a more serious threat to ViTs. Even worse, traditional detection methods, originally designed for CNN models, are impractical or suffer significant performance degradation when applied to ViTs, and they generally overlook patch attacks. In this paper, we propose ViTGuard as a general detection method for defending ViT models against adversarial attacks, including typical attacks where perturbations spread over the entire input and patch attacks. ViTGuard uses a Masked Autoencoder (MAE) model to recover randomly masked patches from the unmasked regions, providing a flexible image reconstruction strategy. Then, threshold-based detectors leverage distinctive ViT features, including attention maps and classification (CLS) token representations, to distinguish between normal and adversarial samples. The MAE model does not involve any adversarial samples during training, ensuring the effectiveness of our detectors against unseen attacks. ViTGuard is compared with seven existing detection methods under nine attacks across three datasets. The evaluation results show the superiority of ViTGuard over existing detectors. Finally, considering the potential detection evasion, we further demonstrate ViTGuard's robustness against adaptive attacks for evasion.", "published": "2024-09-20T18:11:56Z", "authors": ["Shihua Sun", "Kenechukwu Nwodo", "Shridatt Sugrim", "Angelos Stavrou", "Haining Wang"], "categories": ["cs.CV", "cs.CR"], "doi": "", "_query": "property valuation computer vision", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2606.04891v1", "title": "Hierarchical Space Partition for Surface Reconstruction", "abstract": "Generating compact polygonal models from point clouds is a key problem in 3D vision and computer graphics. However, due to inherent limitations of LiDAR scanning (e.g. range constraints and occlusions), critical scene information is often missing, leading to degraded reconstruction accuracy. To address this, we propose a plane assembling strategy that effectively recovers missing details while maintaining model compactness. We classify all the planes extracted from the scene into three categories: highly visible, barely visible, and invisible. The invisible planes, which are recovered by scene structure analysis, indicate the missing details. The three types of planes correspond to the three growth priorities. Each plane grows according to the priority level, and the space is partitioned progressively, namely, the hierarchical partition. Subsequently, we generate a watertight polygonal mesh from the partition via a min-cut-based optimization. Finally, comparisons on public datasets show the effectiveness and superiority of our method against mainstream approaches. The project page is available at https://hsr-3dv.github.io/.", "published": "2026-06-03T13:52:36Z", "authors": ["Minjie Tang", "Xiangfei Li"], "categories": ["cs.CV", "cs.CG"], "doi": "10.1109/3DV69130.2026.00027", "_query": "property valuation computer vision", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2308.14391v2", "title": "FIRE: Food Image to REcipe generation", "abstract": "Food computing has emerged as a prominent multidisciplinary field of research in recent years. An ambitious goal of food computing is to develop end-to-end intelligent systems capable of autonomously producing recipe information for a food image. Current image-to-recipe methods are retrieval-based and their success depends heavily on the dataset size and diversity, as well as the quality of learned embeddings. Meanwhile, the emergence of powerful attention-based vision and language models presents a promising avenue for accurate and generalizable recipe generation, which has yet to be extensively explored. This paper proposes FIRE, a novel multimodal methodology tailored to recipe generation in the food computing domain, which generates the food title, ingredients, and cooking instructions based on input food images. FIRE leverages the BLIP model to generate titles, utilizes a Vision Transformer with a decoder for ingredient extraction, and employs the T5 model to generate recipes incorporating titles and ingredients as inputs. We showcase two practical applications that can benefit from integrating FIRE with large language model prompting: recipe customization to fit recipes to user preferences and recipe-to-code transformation to enable automated cooking processes. Our experimental findings validate the efficacy of our proposed approach, underscoring its potential for future advancements and widespread adoption in food computing.", "published": "2023-08-28T08:14:20Z", "authors": ["Prateek Chhikara", "Dhiraj Chaurasia", "Yifan Jiang", "Omkar Masur", "Filip Ilievski"], "categories": ["cs.CV", "cs.CL"], "doi": "", "_query": "property valuation computer vision", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2305.08075v1", "title": "Analyzing Compression Techniques for Computer Vision", "abstract": "Compressing deep networks is highly desirable for practical use-cases in computer vision applications. Several techniques have been explored in the literature, and research has been done in finding efficient strategies for combining them. For this project, we aimed to explore three different basic compression techniques - knowledge distillation, pruning, and quantization for small-scale recognition tasks. Along with the basic methods, we also test the efficacy of combining them in a sequential manner. We analyze them using MNIST and CIFAR-10 datasets and present the results along with few observations inferred from them.", "published": "2023-05-14T05:17:32Z", "authors": ["Maniratnam Mandal", "Imran Khan"], "categories": ["cs.CV"], "doi": "", "_query": "property valuation computer vision", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/1009.1983v1", "title": "Evolutionary Computational Method of Facial Expression Analysis for Content-based Video Retrieval using 2-Dimensional Cellular Automata", "abstract": "In this paper, Deterministic Cellular Automata (DCA) based video shot classification and retrieval is proposed. The deterministic 2D Cellular automata model captures the human facial expressions, both spontaneous and posed. The determinism stems from the fact that the facial muscle actions are standardized by the encodings of Facial Action Coding System (FACS) and Action Units (AUs). Based on these encodings, we generate the set of evolutionary update rules of the DCA for each facial expression. We consider a Person-Independent Facial Expression Space (PIFES) to analyze the facial expressions based on Partitioned 2D-Cellular Automata which capture the dynamics of facial expressions and classify the shots based on it. Target video shot is retrieved by comparing the similar expression is obtained for the query frame's face with respect to the key faces expressions in the database video. Consecutive key face expressions in the database that are highly similar to the query frame's face, then the key faces are used to generate the set of retrieved video shots from the database. A concrete example of its application which realizes an affective interaction between the computer and the user is proposed. In the affective interaction, the computer can recognize the facial expression of any given video shot. This interaction endows the computer with certain ability to adapt to the user's feedback.", "published": "2010-09-10T11:25:17Z", "authors": ["P. Geetha", "Vasumathi Narayanan"], "categories": ["cs.CV"], "doi": "", "_query": "property valuation computer vision", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/1704.05519v3", "title": "Computer Vision for Autonomous Vehicles: Problems, Datasets and State of the Art", "abstract": "Recent years have witnessed enormous progress in AI-related fields such as computer vision, machine learning, and autonomous vehicles. As with any rapidly growing field, it becomes increasingly difficult to stay up-to-date or enter the field as a beginner. While several survey papers on particular sub-problems have appeared, no comprehensive survey on problems, datasets, and methods in computer vision for autonomous vehicles has been published. This book attempts to narrow this gap by providing a survey on the state-of-the-art datasets and techniques. Our survey includes both the historically most relevant literature as well as the current state of the art on several specific topics, including recognition, reconstruction, motion estimation, tracking, scene understanding, and end-to-end learning for autonomous driving. Towards this goal, we analyze the performance of the state of the art on several challenging benchmarking datasets, including KITTI, MOT, and Cityscapes. Besides, we discuss open problems and current research challenges. To ease accessibility and accommodate missing references, we also provide a website that allows navigating topics as well as methods and provides additional information.", "published": "2017-04-18T20:33:50Z", "authors": ["Joel Janai", "Fatma Güney", "Aseem Behl", "Andreas Geiger"], "categories": ["cs.CV", "cs.RO"], "doi": "", "_query": "property valuation computer vision", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/1905.07443v2", "title": "AutoDispNet: Improving Disparity Estimation With AutoML", "abstract": "Much research work in computer vision is being spent on optimizing existing network architectures to obtain a few more percentage points on benchmarks. Recent AutoML approaches promise to relieve us from this effort. However, they are mainly designed for comparatively small-scale classification tasks. In this work, we show how to use and extend existing AutoML techniques to efficiently optimize large-scale U-Net-like encoder-decoder architectures. In particular, we leverage gradient-based neural architecture search and Bayesian optimization for hyperparameter search. The resulting optimization does not require a large-scale compute cluster. We show results on disparity estimation that clearly outperform the manually optimized baseline and reach state-of-the-art performance.", "published": "2019-05-17T19:05:25Z", "authors": ["Tonmoy Saikia", "Yassine Marrakchi", "Arber Zela", "Frank Hutter", "Thomas Brox"], "categories": ["cs.CV", "cs.AI", "cs.LG"], "doi": "", "_query": "property valuation computer vision", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2301.05993v1", "title": "Empirical study of the modulus as activation function in computer vision applications", "abstract": "In this work we propose a new non-monotonic activation function: the modulus. The majority of the reported research on nonlinearities is focused on monotonic functions. We empirically demonstrate how by using the modulus activation function on computer vision tasks the models generalize better than with other nonlinearities - up to a 15% accuracy increase in CIFAR100 and 4% in CIFAR10, relative to the best of the benchmark activations tested. With the proposed activation function the vanishing gradient and dying neurons problems disappear, because the derivative of the activation function is always 1 or -1. The simplicity of the proposed function and its derivative make this solution specially suitable for TinyML and hardware applications.", "published": "2023-01-15T00:32:03Z", "authors": ["Iván Vallés-Pérez", "Emilio Soria-Olivas", "Marcelino Martínez-Sober", "Antonio J. Serrano-López", "Joan Vila-Francés", "Juan Gómez-Sanchís"], "categories": ["cs.CV", "cs.AI"], "doi": "", "_query": "property valuation computer vision", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/1904.10504v1", "title": "Understanding the efficacy, reliability and resiliency of computer vision techniques for malware detection and future research directions", "abstract": "My research lies in the intersection of security and machine learning. This overview summarizes one component of my research: combining computer vision with malware exploit detection for enhanced security solutions. I will present the perspectives of efficacy, reliability and resiliency to formulate threat detection as computer vision problems and develop state-of-the-art image-based malware classification. Representing malware binary as images provides a direct visualization of data samples, reduces the efforts for feature extraction, and consumes the whole binary for holistic structural analysis. Employing transfer learning of deep neural networks effective for large scale image classification to malware classification demonstrates superior classification efficacy compared with classical machine learning algorithms. To enhance reliability of these vision-based malware detectors, interpretation frameworks can be constructed on the malware visual representations and useful for extracting faithful explanation, so that security practitioners have confidence in the model before deployment. In cyber-security applications, we should always assume that a malware writer constantly modifies code to bypass detection. Addressing the resiliency of the malware detectors is equivalently important as efficacy and reliability. Via understanding the attack surfaces of machine learning models used for malware detection, we can greatly improve the robustness of the algorithms to combat malware adversaries in the wild. Finally I will discuss future research directions worth pursuing in this research community.", "published": "2019-04-03T18:34:20Z", "authors": ["Li Chen"], "categories": ["cs.CR", "cs.CV", "cs.LG"], "doi": "", "_query": "property valuation computer vision", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2103.09950v2", "title": "Learning to Resize Images for Computer Vision Tasks", "abstract": "For all the ways convolutional neural nets have revolutionized computer vision in recent years, one important aspect has received surprisingly little attention: the effect of image size on the accuracy of tasks being trained for. Typically, to be efficient, the input images are resized to a relatively small spatial resolution (e.g. 224x224), and both training and inference are carried out at this resolution. The actual mechanism for this re-scaling has been an afterthought: Namely, off-the-shelf image resizers such as bilinear and bicubic are commonly used in most machine learning software frameworks. But do these resizers limit the on task performance of the trained networks? The answer is yes. Indeed, we show that the typical linear resizer can be replaced with learned resizers that can substantially improve performance. Importantly, while the classical resizers typically result in better perceptual quality of the downscaled images, our proposed learned resizers do not necessarily give better visual quality, but instead improve task performance. Our learned image resizer is jointly trained with a baseline vision model. This learned CNN-based resizer creates machine friendly visual manipulations that lead to a consistent improvement of the end task metric over the baseline model. Specifically, here we focus on the classification task with the ImageNet dataset, and experiment with four different models to learn resizers adapted to each model. Moreover, we show that the proposed resizer can also be useful for fine-tuning the classification baselines for other vision tasks. To this end, we experiment with three different baselines to develop image quality assessment (IQA) models on the AVA dataset.", "published": "2021-03-17T23:43:44Z", "authors": ["Hossein Talebi", "Peyman Milanfar"], "categories": ["cs.CV", "cs.LG"], "doi": "", "_query": "property valuation computer vision", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2009.10521v1", "title": "A survey on Kornia: an Open Source Differentiable Computer Vision Library for PyTorch", "abstract": "This work presents Kornia, an open source computer vision library built upon a set of differentiable routines and modules that aims to solve generic computer vision problems. The package uses PyTorch as its main backend, not only for efficiency but also to take advantage of the reverse auto-differentiation engine to define and compute the gradient of complex functions. Inspired by OpenCV, Kornia is composed of a set of modules containing operators that can be integrated into neural networks to train models to perform a wide range of operations including image transformations,camera calibration, epipolar geometry, and low level image processing techniques, such as filtering and edge detection that operate directly on high dimensional tensor representations on graphical processing units, generating faster systems. Examples of classical vision problems implemented using our framework are provided including a benchmark comparing to existing vision libraries.", "published": "2020-09-21T08:48:28Z", "authors": ["E. Riba", "D. Mishkin", "J. Shi", "D. Ponsa", "F. Moreno-Noguer", "G. Bradski"], "categories": ["cs.CV"], "doi": "", "_query": "property valuation computer vision", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2306.09265v1", "title": "LVLM-eHub: A Comprehensive Evaluation Benchmark for Large Vision-Language Models", "abstract": "Large Vision-Language Models (LVLMs) have recently played a dominant role in multimodal vision-language learning. Despite the great success, it lacks a holistic evaluation of their efficacy. This paper presents a comprehensive evaluation of publicly available large multimodal models by building a LVLM evaluation Hub (LVLM-eHub). Our LVLM-eHub consists of $8$ representative LVLMs such as InstructBLIP and MiniGPT-4, which are thoroughly evaluated by a quantitative capability evaluation and an online arena platform. The former evaluates $6$ categories of multimodal capabilities of LVLMs such as visual question answering and embodied artificial intelligence on $47$ standard text-related visual benchmarks, while the latter provides the user-level evaluation of LVLMs in an open-world question-answering scenario. The study reveals several innovative findings. First, instruction-tuned LVLM with massive in-domain data such as InstructBLIP heavily overfits many existing tasks, generalizing poorly in the open-world scenario. Second, instruction-tuned LVLM with moderate instruction-following data may result in object hallucination issues (i.e., generate objects that are inconsistent with target images in the descriptions). It either makes the current evaluation metric such as CIDEr for image captioning ineffective or generates wrong answers. Third, employing a multi-turn reasoning evaluation framework can mitigate the issue of object hallucination, shedding light on developing an effective pipeline for LVLM evaluation. The findings provide a foundational framework for the conception and assessment of innovative strategies aimed at enhancing zero-shot multimodal techniques. Our LVLM-eHub will be available at https://github.com/OpenGVLab/Multi-Modality-Arena", "published": "2023-06-15T16:39:24Z", "authors": ["Peng Xu", "Wenqi Shao", "Kaipeng Zhang", "Peng Gao", "Shuo Liu", "Meng Lei", "Fanqing Meng", "Siyuan Huang", "Yu Qiao", "Ping Luo"], "categories": ["cs.CV", "cs.AI"], "doi": "", "_query": "property valuation computer vision", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/1403.4238v1", "title": "Computer Vision Accelerators for Mobile Systems based on OpenCL GPGPU Co-Processing", "abstract": "In this paper, we present an OpenCL-based heterogeneous implementation of a computer vision algorithm -- image inpainting-based object removal algorithm -- on mobile devices. To take advantage of the computation power of the mobile processor, the algorithm workflow is partitioned between the CPU and the GPU based on the profiling results on mobile devices, so that the computationally-intensive kernels are accelerated by the mobile GPGPU (general-purpose computing using graphics processing units). By exploring the implementation trade-offs and utilizing the proposed optimization strategies at different levels including algorithm optimization, parallelism optimization, and memory access optimization, we significantly speed up the algorithm with the CPU-GPU heterogeneous implementation, while preserving the quality of the output images. Experimental results show that heterogeneous computing based on GPGPU co-processing can significantly speed up the computer vision algorithms and makes them practical on real-world mobile devices.", "published": "2014-03-17T18:26:41Z", "authors": ["Guohui Wang", "Yingen Xiong", "Jay Yun", "Joseph R. Cavallaro"], "categories": ["cs.DC", "cs.CV", "cs.MS"], "doi": "10.1007/s11265-014-0878-z", "_query": "property valuation computer vision", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2004.03879v1", "title": "Monte-Carlo Siamese Policy on Actor for Satellite Image Super Resolution", "abstract": "In the past few years supervised and adversarial learning have been widely adopted in various complex computer vision tasks. It seems natural to wonder whether another branch of artificial intelligence, commonly known as Reinforcement Learning (RL) can benefit such complex vision tasks. In this study, we explore the plausible usage of RL in super resolution of remote sensing imagery. Guided by recent advances in super resolution, we propose a theoretical framework that leverages the benefits of supervised and reinforcement learning. We argue that a straightforward implementation of RL is not adequate to address ill-posed super resolution as the action variables are not fully known. To tackle this issue, we propose to parameterize action variables by matrices, and train our policy network using Monte-Carlo sampling. We study the implications of parametric action space in a model-free environment from theoretical and empirical perspective. Furthermore, we analyze the quantitative and qualitative results on both remote sensing and non-remote sensing datasets. Based on our experiments, we report considerable improvement over state-of-the-art methods by encapsulating supervised models in a reinforcement learning framework.", "published": "2020-04-08T08:39:08Z", "authors": ["Litu Rout", "Saumyaa Shah", "S Manthira Moorthi", "Debajyoti Dhar"], "categories": ["cs.CV"], "doi": "", "_query": "property valuation computer vision", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/1705.02689v1", "title": "AirDraw: Leveraging Smart Watch Motion Sensors for Mobile Human Computer Interactions", "abstract": "Wearable computing is one of the fastest growing technologies today. Smart watches are poised to take over at least of half the wearable devices market in the near future. Smart watch screen size, however, is a limiting factor for growth, as it restricts practical text input. On the other hand, wearable devices have some features, such as consistent user interaction and hands-free, heads-up operations, which pave the way for gesture recognition methods of text entry. This paper proposes a new text input method for smart watches, which utilizes motion sensor data and machine learning approaches to detect letters written in the air by a user. This method is less computationally intensive and less expensive when compared to computer vision approaches. It is also not affected by lighting factors, which limit computer vision solutions. The AirDraw system prototype developed to test this approach is presented. Additionally, experimental results close to 71% accuracy are presented.", "published": "2017-05-07T19:58:54Z", "authors": ["Seyed A Sajjadi", "Danial Moazen", "Ani Nahapetian"], "categories": ["cs.CV", "cs.AI", "cs.HC", "stat.ML"], "doi": "", "_query": "property valuation computer vision", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/cs/0401004v2", "title": "Cyborg Systems as Platforms for Computer-Vision Algorithm-Development for Astrobiology", "abstract": "Employing the allegorical imagery from the film \"The Matrix\", we motivate and discuss our `Cyborg Astrobiologist' research program. In this research program, we are using a wearable computer and video camcorder in order to test and train a computer-vision system to be a field-geologist and field-astrobiologist.", "published": "2004-01-02T12:39:15Z", "authors": ["Patrick C. McGuire", "J. A. Rodriguez-Manfredi", "E. Sebastian-Martinez", "J. Gomez-Elvira", "E. Diaz-Martinez", "J. Ormo", "K. Neuffer", "A. Giaquinta", "F. Camps-Martinez", "A. Lepinette-Malvitte", "J. Perez-Mercader", "H. Ritter", "M. Oesker", "J. Ontrup", "J. Walter"], "categories": ["cs.CV", "astro-ph", "cs.AI"], "doi": "", "_query": "property valuation computer vision", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2102.02320v1", "title": "One Label, One Billion Faces: Usage and Consistency of Racial Categories in Computer Vision", "abstract": "Computer vision is widely deployed, has highly visible, society altering applications, and documented problems with bias and representation. Datasets are critical for benchmarking progress in fair computer vision, and often employ broad racial categories as population groups for measuring group fairness. Similarly, diversity is often measured in computer vision datasets by ascribing and counting categorical race labels. However, racial categories are ill-defined, unstable temporally and geographically, and have a problematic history of scientific use. Although the racial categories used across datasets are superficially similar, the complexity of human race perception suggests the racial system encoded by one dataset may be substantially inconsistent with another. Using the insight that a classifier can learn the racial system encoded by a dataset, we conduct an empirical study of computer vision datasets supplying categorical race labels for face images to determine the cross-dataset consistency and generalization of racial categories. We find that each dataset encodes a substantially unique racial system, despite nominally equivalent racial categories, and some racial categories are systemically less consistent than others across datasets. We find evidence that racial categories encode stereotypes, and exclude ethnic groups from categories on the basis of nonconformity to stereotypes. Representing a billion humans under one racial category may obscure disparities and create new ones by encoding stereotypes of racial systems. The difficulty of adequately converting the abstract concept of race into a tool for measuring fairness underscores the need for a method more flexible and culturally aware than racial categories.", "published": "2021-02-03T22:50:04Z", "authors": ["Zaid Khan", "Yun Fu"], "categories": ["cs.CV", "cs.CY"], "doi": "10.1145/3442188.3445920", "_query": "property valuation computer vision", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2205.10469v1", "title": "Masterful: A Training Platform for Computer Vision Models", "abstract": "Masterful is a software platform to train deep learning computer vision models. Data and model architecture are inputs to the platform, and the output is a trained model. The platform's primary goal is to maximize a trained model's accuracy, which it achieves through its regularization and semi-supervised learning implementations. The platform's secondary goal is to minimize the amount of manual experimentation typically required to tune training hyperparameters, which it achieves via multiple metalearning algorithms which are custom built to control the platform's regularization and semi-supervised learning implementations. The platform's tertiary goal is to minimize the computing resources required to train a model, which it achieves via another set of metalearning algorithms which are purpose built to control Tensorflow's optimization implementations. The platform builds on top of Tensorflow's data management, architecture, automatic differentiation, and optimization implementations.", "published": "2022-05-21T00:20:09Z", "authors": ["Samuel Wookey", "Yaoshiang Ho", "Tom Rikert", "Juan David Gil Lopez", "Juan Manuel Muñoz Beancur", "Santiago Cortes", "Ray Tawil", "Aaron Sabin", "Jack Lynch", "Travis Harper", "Nikhil Gajendrakumar"], "categories": ["cs.CV", "cs.AI", "cs.LG"], "doi": "", "_query": "property valuation computer vision", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2008.05060v1", "title": "Online Graph Completion: Multivariate Signal Recovery in Computer Vision", "abstract": "The adoption of \"human-in-the-loop\" paradigms in computer vision and machine learning is leading to various applications where the actual data acquisition (e.g., human supervision) and the underlying inference algorithms are closely interwined. While classical work in active learning provides effective solutions when the learning module involves classification and regression tasks, many practical issues such as partially observed measurements, financial constraints and even additional distributional or structural aspects of the data typically fall outside the scope of this treatment. For instance, with sequential acquisition of partial measurements of data that manifest as a matrix (or tensor), novel strategies for completion (or collaborative filtering) of the remaining entries have only been studied recently. Motivated by vision problems where we seek to annotate a large dataset of images via a crowdsourced platform or alternatively, complement results from a state-of-the-art object detector using human feedback, we study the \"completion\" problem defined on graphs, where requests for additional measurements must be made sequentially. We design the optimization model in the Fourier domain of the graph describing how ideas based on adaptive submodularity provide algorithms that work well in practice. On a large set of images collected from Imgur, we see promising results on images that are otherwise difficult to categorize. We also show applications to an experimental design problem in neuroimaging.", "published": "2020-08-12T01:34:21Z", "authors": ["Won Hwa Kim", "Mona Jalal", "Seongjae Hwang", "Sterling C. Johnson", "Vikas Singh"], "categories": ["cs.CV", "cs.LG", "eess.SP", "stat.ML"], "doi": "10.1109/CVPR.2017.533", "_query": "property valuation computer vision", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2210.11318v2", "title": "A Survey of Computer Vision Technologies In Urban and Controlled-environment Agriculture", "abstract": "In the evolution of agriculture to its next stage, Agriculture 5.0, artificial intelligence will play a central role. Controlled-environment agriculture, or CEA, is a special form of urban and suburban agricultural practice that offers numerous economic, environmental, and social benefits, including shorter transportation routes to population centers, reduced environmental impact, and increased productivity. Due to its ability to control environmental factors, CEA couples well with computer vision (CV) in the adoption of real-time monitoring of the plant conditions and autonomous cultivation and harvesting. The objective of this paper is to familiarize CV researchers with agricultural applications and agricultural practitioners with the solutions offered by CV. We identify five major CV applications in CEA, analyze their requirements and motivation, and survey the state of the art as reflected in 68 technical papers using deep learning methods. In addition, we discuss five key subareas of computer vision and how they related to these CEA problems, as well as eleven vision-based CEA datasets. We hope the survey will help researchers quickly gain a bird-eye view of the striving research area and will spark inspiration for new research and development.", "published": "2022-10-20T14:51:01Z", "authors": ["Jiayun Luo", "Boyang Li", "Cyril Leung"], "categories": ["cs.CV"], "doi": "", "_query": "property valuation computer vision", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/1602.01233v1", "title": "Characterizing diophantine henselian valuation rings and valuation ideals", "abstract": "We give a characterization, in terms of the residue field, of those henselian valuation rings and those henselian valuation ideals that are diophantine. This characterization gives a common generalization of all the positive and negative results on diophantine henselian valuation rings and diophantine valuation ideals in the literature. We also treat questions of uniformity and we apply the results to show that a given field can carry at most one diophantine nontrivial equicharacteristic henselian valuation ring or valuation ideal.", "published": "2016-02-03T09:32:53Z", "authors": ["Sylvy Anscombe", "Arno Fehm"], "categories": ["math.LO", "math.AC", "math.NT"], "doi": "10.1112/plms.12042", "_query": "property valuation computer vision", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/quant-ph/0404143v1", "title": "An algorithm for simulating the Ising model on a type-II quantum computer", "abstract": "Presented here is an algorithm for a type-II quantum computer which simulates the Ising model in one and two dimensions. It is equivalent to the Metropolis Monte-Carlo method and takes advantage of quantum superposition for random number generation. This algorithm does not require the ensemble of states to be measured at the end of each iteration, as is required for other type-II algorithms. Only the binary result is measured at each node which means this algorithm could be implemented using a range of different quantum computing architectures. The Ising model provides an example of how cellular automata rules can be formulated to be run on a type-II quantum computer.", "published": "2004-04-26T07:37:18Z", "authors": ["J. H. Cole", "L. C. L. Hollenberg", "S. Prawer"], "categories": ["quant-ph"], "doi": "10.1016/j.cpc.2004.04.003", "_query": "property valuation computer vision", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2410.09563v2", "title": "Robust Optical Flow Computation: A Higher-Order Differential Approach", "abstract": "In the domain of computer vision, optical flow stands as a cornerstone for unraveling dynamic visual scenes. However, the challenge of accurately estimating optical flow under conditions of large nonlinear motion patterns remains an open question. The image flow constraint is vulnerable to substantial displacements, and rapid spatial transformations. Inaccurate approximations inherent in numerical differentiation techniques can further amplify such intricacies. In response, this research proposes an innovative algorithm for optical flow computation, utilizing the higher precision of second-order Taylor series approximation within the differential estimation framework. By embracing this mathematical underpinning, the research seeks to extract more information about the behavior of the function under complex real-world scenarios and estimate the motion of areas with a lack of texture. An impressive showcase of the algorithm's capabilities emerges through its performance on renowned optical flow benchmarks such as KITTI (2015) and Middlebury. The average endpoint error (AEE), which computes the Euclidian distance between the calculated flow field and the ground truth flow field, stands notably diminished, validating the effectiveness of the algorithm in handling complex motion patterns.", "published": "2024-10-12T15:20:11Z", "authors": ["Chanuka Algama", "Kasun Amarasinghe"], "categories": ["cs.CV"], "doi": "10.5220/0014297800004084", "_query": "property valuation computer vision", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2409.17330v1", "title": "VL4AD: Vision-Language Models Improve Pixel-wise Anomaly Detection", "abstract": "Semantic segmentation networks have achieved significant success under the assumption of independent and identically distributed data. However, these networks often struggle to detect anomalies from unknown semantic classes due to the limited set of visual concepts they are typically trained on. To address this issue, anomaly segmentation often involves fine-tuning on outlier samples, necessitating additional efforts for data collection, labeling, and model retraining. Seeking to avoid this cumbersome work, we take a different approach and propose to incorporate Vision-Language (VL) encoders into existing anomaly detectors to leverage the semantically broad VL pre-training for improved outlier awareness. Additionally, we propose a new scoring function that enables data- and training-free outlier supervision via textual prompts. The resulting VL4AD model, which includes max-logit prompt ensembling and a class-merging strategy, achieves competitive performance on widely used benchmark datasets, thereby demonstrating the potential of vision-language models for pixel-wise anomaly detection.", "published": "2024-09-25T20:12:10Z", "authors": ["Liangyu Zhong", "Joachim Sicking", "Fabian Hüger", "Hanno Gottschalk"], "categories": ["cs.CV"], "doi": "", "_query": "property valuation computer vision", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/1907.04100v2", "title": "calibDB: enabling web based computer vision through on-the-fly camera calibration", "abstract": "For many computer vision applications, the availability of camera calibration data is crucial as overall quality heavily depends on it. While calibration data is available on some devices through Augmented Reality (AR) frameworks like ARCore and ARKit, for most cameras this information is not available. Therefore, we propose a web based calibration service that not only aggregates calibration data, but also allows calibrating new cameras on-the-fly. We build upon a novel camera calibration framework that enables even novice users to perform a precise camera calibration in about 2 minutes. This allows general deployment of computer vision algorithms on the web, which was previously not possible due to lack of calibration data.", "published": "2019-07-09T11:51:44Z", "authors": ["Pavel Rojtberg", "Felix Gorschlüter"], "categories": ["cs.CV", "cs.NI", "eess.IV"], "doi": "10.1145/3329714.3338132", "_query": "property valuation computer vision", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2201.12693v2", "title": "Extracting Built Environment Features for Planning Research with Computer Vision: A Review and Discussion of State-of-the-Art Approaches", "abstract": "This is an extended abstract for a presentation at The 17th International Conference on CUPUM - Computational Urban Planning and Urban Management in June 2021. This study presents an interdisciplinary synthesis of the state-of-the-art approaches in computer vision technologies to extract built environment features that could improve the robustness of empirical research in planning. We discussed the findings from the review of studies in both planning and computer science.", "published": "2022-01-30T01:02:18Z", "authors": ["Meiqing Li", "Hao Sheng"], "categories": ["cs.CV", "cs.CY"], "doi": "", "_query": "property valuation computer vision", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/math/0511171v3", "title": "Theory of valuations on manifolds, IV. New properties of the multiplicative structure", "abstract": "This is the fourth part in the series of articles math.MG/0503397, math.MG/0503399, math.MG/0509512 where the theory of valuations on manifolds is developed. In this part it is shown that the filtration on valuations introduced in math.MG/0503399 is compatible with the product. Then it is proved that the Euler-Verdier involution on smooth valuations introduced in math.MG/0503399 is an automorphism of the algebra of valuations. Then an integration functional on valuations with compact support is introduced, and a property of selfduality of valuations is proved. Next a space of generalized valuations is defined, and some basic properties of it are proved. Finally a canonical imbedding of the space of constructible functions on a real analytic manifold into the space of generalized valuations is constructed, and various structures on valuations are compared with known structures on constructible functions.", "published": "2005-11-07T13:32:15Z", "authors": ["Semyon Alesker"], "categories": ["math.MG"], "doi": "", "_query": "property valuation computer vision", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2504.11662v1", "title": "Real-time Object and Event Detection Service through Computer Vision and Edge Computing", "abstract": "The World Health Organization suggests that road traffic crashes cost approximately 518 billion dollars globally each year, which accounts for 3% of the gross domestic product for most countries. Most fatal road accidents in urban areas involve Vulnerable Road Users (VRUs). Smart cities environments present innovative approaches to combat accidents involving cutting-edge technologies, that include advanced sensors, extensive datasets, Machine Learning (ML) models, communication systems, and edge computing. This paper proposes a strategy and an implementation of a system for road monitoring and safety for smart cities, based on Computer Vision (CV) and edge computing. Promising results were obtained by implementing vision algorithms and tracking using surveillance cameras, that are part of a Smart City testbed, the Aveiro Tech City Living Lab (ATCLL). The algorithm accurately detects and tracks cars, pedestrians, and bicycles, while predicting the road state, the distance between moving objects, and inferring on collision events to prevent collisions, in near real-time.", "published": "2025-04-15T23:11:42Z", "authors": ["Marcos Mendes", "Gonçalo Perna", "Pedro Rito", "Duarte Raposo", "Susana Sargento"], "categories": ["cs.CV"], "doi": "", "_query": "property valuation computer vision", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2404.16804v1", "title": "AAPL: Adding Attributes to Prompt Learning for Vision-Language Models", "abstract": "Recent advances in large pre-trained vision-language models have demonstrated remarkable performance on zero-shot downstream tasks. Building upon this, recent studies, such as CoOp and CoCoOp, have proposed the use of prompt learning, where context within a prompt is replaced with learnable vectors, leading to significant improvements over manually crafted prompts. However, the performance improvement for unseen classes is still marginal, and to tackle this problem, data augmentation has been frequently used in traditional zero-shot learning techniques. Through our experiments, we have identified important issues in CoOp and CoCoOp: the context learned through traditional image augmentation is biased toward seen classes, negatively impacting generalization to unseen classes. To address this problem, we propose adversarial token embedding to disentangle low-level visual augmentation features from high-level class information when inducing bias in learnable prompts. Through our novel mechanism called \"Adding Attributes to Prompt Learning\", AAPL, we guide the learnable context to effectively extract text features by focusing on high-level features for unseen classes. We have conducted experiments across 11 datasets, and overall, AAPL shows favorable performances compared to the existing methods in few-shot learning, zero-shot learning, cross-dataset, and domain generalization tasks.", "published": "2024-04-25T17:51:10Z", "authors": ["Gahyeon Kim", "Sohee Kim", "Seokju Lee"], "categories": ["cs.CV", "cs.AI", "cs.LG"], "doi": "", "_query": "property valuation computer vision", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2608.08840v1", "title": "SLAP: Selective Local Vision-Language Alignment for Fish Re-Identification via Partial Optimal Transport", "abstract": "Individual fish re-identification (ReID) is a fine-grained recognition problem in which identity-discriminative cues are often localized to specific body regions rather than distributed uniformly across the animal. Nevertheless, recent CLIP-based ReID methods rely predominantly on global image-text alignment, allowing background and weakly discriminative regions to contribute to cross-modal supervision. We propose a selective local vision-language alignment framework that establishes localized correspondences between visual patch embeddings and multiple identity-aware prompt embeddings through Partial Optimal Transport (POT). Rather than enforcing exhaustive correspondence, POT enables selective matching between visual patches and prompt embeddings, allowing the model to emphasize the strongest cross-modal correspondences while avoiding forced alignment of weakly matching regions, thereby yielding more discriminative visual representations for retrieval. The framework is trained end-to-end, while only the adapted visual encoder is retained during inference. Experiments on the longitudinal Symphodus melops dataset demonstrate consistent improvements over recent CLIP-based ReID methods under both closed-set and open-set evaluation protocols. Additional evaluations on other datasets further demonstrate the generalization capability of the proposed method across diverse marine ReID benchmarks.", "published": "2026-08-09T18:04:41Z", "authors": ["Cigdem Beyan", "Tonje Knutsen Sordalen", "Kim Tallaksen Halvorsen"], "categories": ["cs.CV"], "doi": "", "_query": "property valuation computer vision", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2105.11277v1", "title": "What is the State of the Art of Computer Vision-Assisted Cytology? A Systematic Literature Review", "abstract": "Cytology is a low-cost and non-invasive diagnostic procedure employed to support the diagnosis of a broad range of pathologies. Computer Vision technologies, by automatically generating quantitative and objective descriptions of examinations' contents, can help minimize the chances of misdiagnoses and shorten the time required for analysis. To identify the state-of-art of computer vision techniques currently applied to cytology, we conducted a Systematic Literature Review. We analyzed papers published in the last 5 years. The initial search was executed in September 2020 and resulted in 431 articles. After applying the inclusion/exclusion criteria, 157 papers remained, which we analyzed to build a picture of the tendencies and problems present in this research area, highlighting the computer vision methods, staining techniques, evaluation metrics, and the availability of the used datasets and computer code. As a result, we identified that the most used methods in the analyzed works are deep learning-based (70 papers), while fewer works employ classic computer vision only (101 papers). The most recurrent metric used for classification and object detection was the accuracy (33 papers and 5 papers), while for segmentation it was the Dice Similarity Coefficient (38 papers). Regarding staining techniques, Papanicolaou was the most employed one (130 papers), followed by H&E (20 papers) and Feulgen (5 papers). Twelve of the datasets used in the papers are publicly available, with the DTU/Herlev dataset being the most used one. We conclude that there still is a lack of high-quality datasets for many types of stains and most of the works are not mature enough to be applied in a daily clinical diagnostic routine. We also identified a growing tendency towards adopting deep learning-based approaches as the methods of choice.", "published": "2021-05-24T13:50:45Z", "authors": ["André Victória Matias", "João Gustavo Atkinson Amorim", "Luiz Antonio Buschetto Macarini", "Allan Cerentini", "Alexandre Sherlley Casimiro Onofre", "Fabiana Botelho de Miranda Onofre", "Felipe Perozzo Daltoé", "Marcelo Ricardo Stemmer", "Aldo von Wangenheim"], "categories": ["cs.CV", "eess.IV"], "doi": "10.1016/j.compmedimag.2021.101934", "_query": "property valuation computer vision", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/1807.10129v1", "title": "A Benchmark of Selected Algorithmic Differentiation Tools on Some Problems in Computer Vision and Machine Learning", "abstract": "Algorithmic differentiation (AD) allows exact computation of derivatives given only an implementation of an objective function. Although many AD tools are available, a proper and efficient implementation of AD methods is not straightforward. The existing tools are often too different to allow for a general test suite. In this paper, we compare fifteen ways of computing derivatives including eleven automatic differentiation tools implementing various methods and written in various languages (C++, F#, MATLAB, Julia and Python), two symbolic differentiation tools, finite differences, and hand-derived computation. We look at three objective functions from computer vision and machine learning. These objectives are for the most part simple, in the sense that no iterative loops are involved, and conditional statements are encapsulated in functions such as {\\tt abs} or {\\tt logsumexp}. However, it is important for the success of algorithmic differentiation that such `simple' objective functions are handled efficiently, as so many problems in computer vision and machine learning are of this form. Of course, our results depend on programmer skill, and familiarity with the tools. However, we contend that this paper presents an important datapoint: a skilled programmer devoting roughly a week to each tool produced the timings we present. We have made our implementations available as open source to allow the community to replicate and update these benchmarks.", "published": "2018-07-26T13:42:30Z", "authors": ["Filip Šrajer", "Zuzana Kukelova", "Andrew Fitzgibbon"], "categories": ["cs.MS", "cs.CV", "cs.LG"], "doi": "", "_query": "property valuation computer vision", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2006.06986v3", "title": "Quantum Robust Fitting", "abstract": "Many computer vision applications need to recover structure from imperfect measurements of the real world. The task is often solved by robustly fitting a geometric model onto noisy and outlier-contaminated data. However, recent theoretical analyses indicate that many commonly used formulations of robust fitting in computer vision are not amenable to tractable solution and approximation. In this paper, we explore the usage of quantum computers for robust fitting. To do so, we examine and establish the practical usefulness of a robust fitting formulation inspired by Fourier analysis of Boolean functions. We then investigate a quantum algorithm to solve the formulation and analyse the computational speed-up possible over the classical algorithm. Our work thus proposes one of the first quantum treatments of robust fitting for computer vision.", "published": "2020-06-12T08:00:55Z", "authors": ["Tat-Jun Chin", "David Suter", "Shin-Fang Chng", "James Quach"], "categories": ["cs.CV"], "doi": "", "_query": "property valuation computer vision", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/1504.07442v1", "title": "Embedded Platforms for Computer Vision-based Advanced Driver Assistance Systems: a Survey", "abstract": "Computer Vision, either alone or combined with other technologies such as radar or Lidar, is one of the key technologies used in Advanced Driver Assistance Systems (ADAS). Its role understanding and analysing the driving scene is of great importance as it can be noted by the number of ADAS applications that use this technology. However, porting a vision algorithm to an embedded automotive system is still very challenging, as there must be a trade-off between several design requisites. Furthermore, there is not a standard implementation platform, so different alternatives have been proposed by both the scientific community and the industry. This paper aims to review the requisites and the different embedded implementation platforms that can be used for Computer Vision-based ADAS, with a critical analysis and an outlook to future trends.", "published": "2015-04-28T12:19:45Z", "authors": ["Gorka Velez", "Oihana Otaegui"], "categories": ["cs.CV"], "doi": "", "_query": "property valuation computer vision", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/1803.10537v1", "title": "Context-aware Deep Feature Compression for High-speed Visual Tracking", "abstract": "We propose a new context-aware correlation filter based tracking framework to achieve both high computational speed and state-of-the-art performance among real-time trackers. The major contribution to the high computational speed lies in the proposed deep feature compression that is achieved by a context-aware scheme utilizing multiple expert auto-encoders; a context in our framework refers to the coarse category of the tracking target according to appearance patterns. In the pre-training phase, one expert auto-encoder is trained per category. In the tracking phase, the best expert auto-encoder is selected for a given target, and only this auto-encoder is used. To achieve high tracking performance with the compressed feature map, we introduce extrinsic denoising processes and a new orthogonality loss term for pre-training and fine-tuning of the expert auto-encoders. We validate the proposed context-aware framework through a number of experiments, where our method achieves a comparable performance to state-of-the-art trackers which cannot run in real-time, while running at a significantly fast speed of over 100 fps.", "published": "2018-03-28T11:38:12Z", "authors": ["Jongwon Choi", "Hyung Jin Chang", "Tobias Fischer", "Sangdoo Yun", "Kyuewang Lee", "Jiyeoup Jeong", "Yiannis Demiris", "Jin Young Choi"], "categories": ["cs.CV"], "doi": "10.1109/CVPR.2018.00057", "_query": "property valuation computer vision", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2305.06024v4", "title": "A Survey on the Robustness of Computer Vision Models against Common Corruptions", "abstract": "The performance of computer vision models are susceptible to unexpected changes in input images caused by sensor errors or extreme imaging environments, known as common corruptions (e.g. noise, blur, illumination changes). These corruptions can significantly hinder the reliability of these models when deployed in real-world scenarios, yet they are often overlooked when testing model generalization and robustness. In this survey, we present a comprehensive overview of methods that improve the robustness of computer vision models against common corruptions. We categorize methods into three groups based on the model components and training methods they target: data augmentation, learning strategies, and network components. We release a unified benchmark framework (available at \\url{https://github.com/nis-research/CorruptionBenchCV}) to compare robustness performance across several datasets, and we address the inconsistencies of evaluation practices in the literature. Our experimental analysis highlights the base corruption robustness of popular vision backbones, revealing that corruption robustness does not necessarily scale with model size and data size. Large models gain negligible robustness improvements, considering the increased computational requirements. To achieve generalizable and robust computer vision models, we foresee the need of developing new learning strategies that efficiently exploit limited data and mitigate unreliable learning behaviors.", "published": "2023-05-10T10:19:31Z", "authors": ["Shunxin Wang", "Raymond Veldhuis", "Christoph Brune", "Nicola Strisciuglio"], "categories": ["cs.CV"], "doi": "", "_query": "property valuation computer vision", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2411.00252v1", "title": "IO Transformer: Evaluating SwinV2-Based Reward Models for Computer Vision", "abstract": "Transformers and their derivatives have achieved state-of-the-art performance across text, vision, and speech recognition tasks. However, minimal effort has been made to train transformers capable of evaluating the output quality of other models. This paper examines SwinV2-based reward models, called the Input-Output Transformer (IO Transformer) and the Output Transformer. These reward models can be leveraged for tasks such as inference quality evaluation, data categorization, and policy optimization. Our experiments demonstrate highly accurate model output quality assessment across domains where the output is entirely dependent on the input, with the IO Transformer achieving perfect evaluation accuracy on the Change Dataset 25 (CD25). We also explore modified Swin V2 architectures. Ultimately Swin V2 remains on top with a score of 95.41 % on the IO Segmentation Dataset, outperforming the IO Transformer in scenarios where the output is not entirely dependent on the input. Our work expands the application of transformer architectures to reward modeling in computer vision and provides critical insights into optimizing these models for various tasks.", "published": "2024-10-31T23:16:09Z", "authors": ["Maxwell Meyer", "Jack Spruyt"], "categories": ["cs.CV", "cs.LG"], "doi": "", "_query": "property valuation computer vision", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/1107.2875v1", "title": "A Hilbert Scheme in Computer Vision", "abstract": "Multiview geometry is the study of two-dimensional images of three-dimensional scenes, a foundational subject in computer vision. We determine a universal Groebner basis for the multiview ideal of n generic cameras. As the cameras move, the multiview varieties vary in a family of dimension 11n-15. This family is the distinguished component of a multigraded Hilbert scheme with a unique Borel-fixed point. We present a combinatorial study of ideals lying on that Hilbert scheme.", "published": "2011-07-14T17:36:59Z", "authors": ["Chris Aholt", "Bernd Sturmfels", "Rekha Thomas"], "categories": ["math.AG", "cs.CV"], "doi": "10.4153/CJM-2012-023-2", "_query": "property valuation computer vision", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/1504.02840v1", "title": "siftservice.com - Turning a Computer Vision algorithm into a World Wide Web Service", "abstract": "Image features detection and description is a longstanding topic in computer vision and pattern recognition areas. The Scale Invariant Feature Transform (SIFT) is probably the most popular and widely demanded feature descriptor which facilitates a variety of computer vision applications such as image registration, object tracking, image forgery detection, and 3D surface reconstruction. This work introduces a Software as a Service (SaaS) based implementation of the SIFT algorithm which is freely available at http://siftservice.com for any academic, educational and research purposes. The service provides application-to-application interaction and aims Rapid Application Development (RAD) and also fast prototyping for computer vision students and researchers all around the world. An Internet connection is all they need!", "published": "2015-04-11T05:47:09Z", "authors": ["Ahmad Pahlavan Tafti", "Hamid Hassannia", "Zeyun Yu"], "categories": ["cs.CV"], "doi": "", "_query": "property valuation computer vision", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2209.11200v3", "title": "Attention is All They Need: Exploring the Media Archaeology of the Computer Vision Research Paper", "abstract": "Research papers, in addition to textual documents, are a designed interface through which researchers communicate. Recently, rapid growth has transformed that interface in many fields of computing. In this work, we examine the effects of this growth from a media archaeology perspective, through the changes to figures and tables in research papers. Specifically, we study these changes in computer vision over the past decade, as the deep learning revolution has driven unprecedented growth in the discipline. We ground our investigation through interviews with veteran researchers spanning computer vision, graphics, and visualization. Our analysis focuses on the research attention economy: how research paper elements contribute towards advertising, measuring, and disseminating an increasingly commodified \"contribution.\" Through this work, we seek to motivate future discussion surrounding the design of both the research paper itself as well as the larger sociotechnical research publishing system, including tools for finding, reading, and writing research papers.", "published": "2022-09-22T17:42:44Z", "authors": ["Samuel Goree", "Gabriel Appleby", "David Crandall", "Norman Su"], "categories": ["cs.CY", "cs.CV"], "doi": "10.1145/3686955", "_query": "property valuation computer vision", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2408.08250v1", "title": "Computer Vision Model Compression Techniques for Embedded Systems: A Survey", "abstract": "Deep neural networks have consistently represented the state of the art in most computer vision problems. In these scenarios, larger and more complex models have demonstrated superior performance to smaller architectures, especially when trained with plenty of representative data. With the recent adoption of Vision Transformer (ViT) based architectures and advanced Convolutional Neural Networks (CNNs), the total number of parameters of leading backbone architectures increased from 62M parameters in 2012 with AlexNet to 7B parameters in 2024 with AIM-7B. Consequently, deploying such deep architectures faces challenges in environments with processing and runtime constraints, particularly in embedded systems. This paper covers the main model compression techniques applied for computer vision tasks, enabling modern models to be used in embedded systems. We present the characteristics of compression subareas, compare different approaches, and discuss how to choose the best technique and expected variations when analyzing it on various embedded devices. We also share codes to assist researchers and new practitioners in overcoming initial implementation challenges for each subarea and present trends for Model Compression. Case studies for compression models are available at \\href{https://github.com/venturusbr/cv-model-compression}{https://github.com/venturusbr/cv-model-compression}.", "published": "2024-08-15T16:41:55Z", "authors": ["Alexandre Lopes", "Fernando Pereira dos Santos", "Diulhio de Oliveira", "Mauricio Schiezaro", "Helio Pedrini"], "categories": ["cs.CV"], "doi": "10.1016/j.cag.2024.104015", "_query": "property valuation computer vision", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2402.02941v1", "title": "Exploring the Synergies of Hybrid CNNs and ViTs Architectures for Computer Vision: A survey", "abstract": "The hybrid of Convolutional Neural Network (CNN) and Vision Transformers (ViT) architectures has emerged as a groundbreaking approach, pushing the boundaries of computer vision (CV). This comprehensive review provides a thorough examination of the literature on state-of-the-art hybrid CNN-ViT architectures, exploring the synergies between these two approaches. The main content of this survey includes: (1) a background on the vanilla CNN and ViT, (2) systematic review of various taxonomic hybrid designs to explore the synergy achieved through merging CNNs and ViTs models, (3) comparative analysis and application task-specific synergy between different hybrid architectures, (4) challenges and future directions for hybrid models, (5) lastly, the survey concludes with a summary of key findings and recommendations. Through this exploration of hybrid CV architectures, the survey aims to serve as a guiding resource, fostering a deeper understanding of the intricate dynamics between CNNs and ViTs and their collective impact on shaping the future of CV architectures.", "published": "2024-02-05T12:08:17Z", "authors": ["Haruna Yunusa", "Shiyin Qin", "Abdulrahman Hamman Adama Chukkol", "Abdulganiyu Abdu Yusuf", "Isah Bello", "Adamu Lawan"], "categories": ["cs.CV", "cs.LG"], "doi": "10.1016/j.engappai.2025.110057", "_query": "property valuation computer vision", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2604.07430v1", "title": "HY-Embodied-0.5: Embodied Foundation Models for Real-World Agents", "abstract": "We introduce HY-Embodied-0.5, a family of foundation models specifically designed for real-world embodied agents. To bridge the gap between general Vision-Language Models (VLMs) and the demands of embodied agents, our models are developed to enhance the core capabilities required by embodied intelligence: spatial and temporal visual perception, alongside advanced embodied reasoning for prediction, interaction, and planning. The HY-Embodied-0.5 suite comprises two primary variants: an efficient model with 2B activated parameters designed for edge deployment, and a powerful model with 32B activated parameters targeted for complex reasoning. To support the fine-grained visual perception essential for embodied tasks, we adopt a Mixture-of-Transformers (MoT) architecture to enable modality-specific computing. By incorporating latent tokens, this design effectively enhances the perceptual representation of the models. To improve reasoning capabilities, we introduce an iterative, self-evolving post-training paradigm. Furthermore, we employ on-policy distillation to transfer the advanced capabilities of the large model to the smaller variant, thereby maximizing the performance potential of the compact model. Extensive evaluations across 22 benchmarks, spanning visual perception, spatial reasoning, and embodied understanding, demonstrate the effectiveness of our approach. Our MoT-2B model outperforms similarly sized state-of-the-art models on 16 benchmarks, while the 32B variant achieves performance comparable to frontier models such as Gemini 3.0 Pro. In downstream robot control experiments, we leverage our robust VLM foundation to train an effective Vision-Language-Action (VLA) model, achieving compelling results in real-world physical evaluations. Code and models are open-sourced at https://github.com/Tencent-Hunyuan/HY-Embodied.", "published": "2026-04-08T17:59:48Z", "authors": ["Tencent Robotics X", "HY Vision Team", " :", "Xumin Yu", "Zuyan Liu", "Ziyi Wang", "He Zhang", "Yongming Rao", "Fangfu Liu", "Yani Zhang", "Ruowen Zhao", "Oran Wang", "Yves Liang", "Haitao Lin", "Minghui Wang", "Yubo Dong", "Kevin Cheng", "Bolin Ni", "Rui Huang", "Han Hu", "Zhengyou Zhang", " Linus", "Shunyu Yao"], "categories": ["cs.CV"], "doi": "", "_query": "property valuation computer vision", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2508.12813v1", "title": "SIS-Challenge: Event-based Spatio-temporal Instance Segmentation Challenge at the CVPR 2025 Event-based Vision Workshop", "abstract": "We present an overview of the Spatio-temporal Instance Segmentation (SIS) challenge held in conjunction with the CVPR 2025 Event-based Vision Workshop. The task is to predict accurate pixel-level segmentation masks of defined object classes from spatio-temporally aligned event camera and grayscale camera data. We provide an overview of the task, dataset, challenge details and results. Furthermore, we describe the methods used by the top-5 ranking teams in the challenge. More resources and code of the participants' methods are available here: https://github.com/tub-rip/MouseSIS/blob/main/docs/challenge_results.md", "published": "2025-08-18T10:49:06Z", "authors": ["Friedhelm Hamann", "Emil Mededovic", "Fabian Gülhan", "Yuli Wu", "Johannes Stegmaier", "Jing He", "Yiqing Wang", "Kexin Zhang", "Lingling Li", "Licheng Jiao", "Mengru Ma", "Hongxiang Huang", "Yuhao Yan", "Hongwei Ren", "Xiaopeng Lin", "Yulong Huang", "Bojun Cheng", "Se Hyun Lee", "Gyu Sung Ham", "Kanghan Oh", "Gi Hyun Lim", "Boxuan Yang", "Bowen Du", "Guillermo Gallego"], "categories": ["cs.CV", "cs.LG"], "doi": "", "_query": "property valuation computer vision", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/1505.05655v1", "title": "GPGPU Based Parallelized Client-Server Framework for Providing High Performance Computation Support", "abstract": "Parallel data processing has become indispensable for processing applications involving huge data sets. This brings into focus the Graphics Processing Units (GPUs) which emphasize on many-core computing. With the advent of General Purpose GPUs (GPGPU), applications not directly associated with graphics operations can also harness the computation capabilities of GPUs. Hence, it would be beneficial if the computing capabilities of a given GPGPU could be task optimized and made available. This paper describes a client-server framework in which users can choose a processing task and submit large data-sets for processing to a remote GPGPU and receive the results back, using well defined interfaces. The framework provides extensibility in terms of the number and type of tasks that the client can choose or submit for processing at the remote GPGPU server machine, with complete transparency to the underlying hardware and operating systems. Parallelization of user-submitted tasks on the GPGPU has been achieved using NVIDIA Compute Unified Device Architecture (CUDA).", "published": "2015-05-21T09:31:39Z", "authors": ["Poorna Banerjee", "Amit Dave"], "categories": ["cs.DC"], "doi": "", "_query": "property valuation computer vision", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/1407.1610v2", "title": "Analyzing the Performance of Multilayer Neural Networks for Object Recognition", "abstract": "In the last two years, convolutional neural networks (CNNs) have achieved an impressive suite of results on standard recognition datasets and tasks. CNN-based features seem poised to quickly replace engineered representations, such as SIFT and HOG. However, compared to SIFT and HOG, we understand much less about the nature of the features learned by large CNNs. In this paper, we experimentally probe several aspects of CNN feature learning in an attempt to help practitioners gain useful, evidence-backed intuitions about how to apply CNNs to computer vision problems.", "published": "2014-07-07T08:00:57Z", "authors": ["Pulkit Agrawal", "Ross Girshick", "Jitendra Malik"], "categories": ["cs.CV", "cs.NE"], "doi": "", "_query": "property valuation computer vision", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2312.09723v1", "title": "Tracking Skiers from the Top to the Bottom", "abstract": "Skiing is a popular winter sport discipline with a long history of competitive events. In this domain, computer vision has the potential to enhance the understanding of athletes' performance, but its application lags behind other sports due to limited studies and datasets. This paper makes a step forward in filling such gaps. A thorough investigation is performed on the task of skier tracking in a video capturing his/her complete performance. Obtaining continuous and accurate skier localization is preemptive for further higher-level performance analyses. To enable the study, the largest and most annotated dataset for computer vision in skiing, SkiTB, is introduced. Several visual object tracking algorithms, including both established methodologies and a newly introduced skier-optimized baseline algorithm, are tested using the dataset. The results provide valuable insights into the applicability of different tracking methods for vision-based skiing analysis. SkiTB, code, and results are available at https://machinelearning.uniud.it/datasets/skitb.", "published": "2023-12-15T11:53:17Z", "authors": ["Matteo Dunnhofer", "Luca Sordi", "Niki Martinel", "Christian Micheloni"], "categories": ["cs.CV", "cs.AI"], "doi": "10.1109/WACV57701.2024.00832", "_query": "property valuation computer vision", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/1809.03609v1", "title": "URBAN-i: From urban scenes to mapping slums, transport modes, and pedestrians in cities using deep learning and computer vision", "abstract": "Within the burgeoning expansion of deep learning and computer vision across the different fields of science, when it comes to urban development, deep learning and computer vision applications are still limited towards the notions of smart cities and autonomous vehicles. Indeed, a wide gap of knowledge appears when it comes to cities and urban regions in less developed countries where the chaos of informality is the dominant scheme. How can deep learning and Artificial Intelligence (AI) untangle the complexities of informality to advance urban modelling and our understanding of cities? Various questions and debates can be raised concerning the future of cities of the North and the South in the paradigm of AI and computer vision. In this paper, we introduce a new method for multipurpose realistic-dynamic urban modelling relying on deep learning and computer vision, using deep Convolutional Neural Networks (CNN), to sense and detect informality and slums in urban scenes from aerial and street view images in addition to detection of pedestrian and transport modes. The model has been trained on images of urban scenes in cities across the globe. The model shows a good validation of understanding a wide spectrum of nuances among the planned and the unplanned regions, including informal and slum areas. We attempt to advance urban modelling for better understanding the dynamics of city developments. We also aim to exemplify the significant impacts of AI in cities beyond how smart cities are discussed and perceived in the mainstream. The algorithms of the URBAN-i model are fully-coded in Python programming with the pre-trained deep learning models to be used as a tool for mapping and city modelling in the various corner of the globe, including informal settlements and slum regions.", "published": "2018-09-10T21:49:38Z", "authors": ["Mohamed R. Ibrahim", "James Haworth", "Tao Cheng"], "categories": ["cs.CV", "cs.AI"], "doi": "10.1177/2399808319846517", "_query": "property valuation computer vision", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/1608.03235v1", "title": "Gaze2Segment: A Pilot Study for Integrating Eye-Tracking Technology into Medical Image Segmentation", "abstract": "This study introduced a novel system, called Gaze2Segment, integrating biological and computer vision techniques to support radiologists' reading experience with an automatic image segmentation task. During diagnostic assessment of lung CT scans, the radiologists' gaze information were used to create a visual attention map. This map was then combined with a computer-derived saliency map, extracted from the gray-scale CT images. The visual attention map was used as an input for indicating roughly the location of a object of interest. With computer-derived saliency information, on the other hand, we aimed at finding foreground and background cues for the object of interest. At the final step, these cues were used to initiate a seed-based delineation process. Segmentation accuracy of the proposed Gaze2Segment was found to be 86% with dice similarity coefficient and 1.45 mm with Hausdorff distance. To the best of our knowledge, Gaze2Segment is the first true integration of eye-tracking technology into a medical image segmentation task without the need for any further user-interaction.", "published": "2016-08-10T16:44:00Z", "authors": ["Naji Khosravan", "Haydar Celik", "Baris Turkbey", "Ruida Cheng", "Evan McCreedy", "Matthew McAuliffe", "Sandra Bednarova", "Elizabeth Jones", "Xinjian Chen", "Peter L. Choyke", "Bradford J. Wood", "Ulas Bagci"], "categories": ["cs.CV", "cs.AI"], "doi": "", "_query": "property valuation computer vision", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2412.10995v1", "title": "RapidNet: Multi-Level Dilated Convolution Based Mobile Backbone", "abstract": "Vision transformers (ViTs) have dominated computer vision in recent years. However, ViTs are computationally expensive and not well suited for mobile devices; this led to the prevalence of convolutional neural network (CNN) and ViT-based hybrid models for mobile vision applications. Recently, Vision GNN (ViG) and CNN hybrid models have also been proposed for mobile vision tasks. However, all of these methods remain slower compared to pure CNN-based models. In this work, we propose Multi-Level Dilated Convolutions to devise a purely CNN-based mobile backbone. Using Multi-Level Dilated Convolutions allows for a larger theoretical receptive field than standard convolutions. Different levels of dilation also allow for interactions between the short-range and long-range features in an image. Experiments show that our proposed model outperforms state-of-the-art (SOTA) mobile CNN, ViT, ViG, and hybrid architectures in terms of accuracy and/or speed on image classification, object detection, instance segmentation, and semantic segmentation. Our fastest model, RapidNet-Ti, achieves 76.3\\% top-1 accuracy on ImageNet-1K with 0.9 ms inference latency on an iPhone 13 mini NPU, which is faster and more accurate than MobileNetV2x1.4 (74.7\\% top-1 with 1.0 ms latency). Our work shows that pure CNN architectures can beat SOTA hybrid and ViT models in terms of accuracy and speed when designed properly.", "published": "2024-12-14T23:39:03Z", "authors": ["Mustafa Munir", "Md Mostafijur Rahman", "Radu Marculescu"], "categories": ["cs.CV", "cs.AI"], "doi": "", "_query": "property valuation computer vision", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2206.12912v1", "title": "Woodscape Fisheye Object Detection for Autonomous Driving -- CVPR 2022 OmniCV Workshop Challenge", "abstract": "Object detection is a comprehensively studied problem in autonomous driving. However, it has been relatively less explored in the case of fisheye cameras. The strong radial distortion breaks the translation invariance inductive bias of Convolutional Neural Networks. Thus, we present the WoodScape fisheye object detection challenge for autonomous driving which was held as part of the CVPR 2022 Workshop on Omnidirectional Computer Vision (OmniCV). This is one of the first competitions focused on fisheye camera object detection. We encouraged the participants to design models which work natively on fisheye images without rectification. We used CodaLab to host the competition based on the publicly available WoodScape fisheye dataset. In this paper, we provide a detailed analysis on the competition which attracted the participation of 120 global teams and a total of 1492 submissions. We briefly discuss the details of the winning methods and analyze their qualitative and quantitative results.", "published": "2022-06-26T16:07:37Z", "authors": ["Saravanabalagi Ramachandran", "Ganesh Sistu", "Varun Ravi Kumar", "John McDonald", "Senthil Yogamani"], "categories": ["cs.CV"], "doi": "", "_query": "property valuation computer vision", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2112.00639v2", "title": "A Systematic Review of Robustness in Deep Learning for Computer Vision: Mind the gap?", "abstract": "Deep neural networks for computer vision are deployed in increasingly safety-critical and socially-impactful applications, motivating the need to close the gap in model performance under varied, naturally occurring imaging conditions. Robustness, ambiguously used in multiple contexts including adversarial machine learning, refers here to preserving model performance under naturally-induced image corruptions or alterations. We perform a systematic review to identify, analyze, and summarize current definitions and progress towards non-adversarial robustness in deep learning for computer vision. We find this area of research has received disproportionately less attention relative to adversarial machine learning, yet a significant robustness gap exists that manifests in performance degradation similar in magnitude to adversarial conditions. Toward developing a more transparent definition of robustness, we provide a conceptual framework based on a structural causal model of the data generating process and interpret non-adversarial robustness as pertaining to a model's behavior on corrupted images corresponding to low-probability samples from the unaltered data distribution. We identify key architecture-, data augmentation-, and optimization tactics for improving neural network robustness. This robustness perspective reveals that common practices in the literature correspond to causal concepts. We offer perspectives on how future research may mind this evident and significant non-adversarial robustness gap.", "published": "2021-12-01T16:42:38Z", "authors": ["Nathan Drenkow", "Numair Sani", "Ilya Shpitser", "Mathias Unberath"], "categories": ["cs.CV", "cs.AI", "cs.LG"], "doi": "", "_query": "property valuation computer vision", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2309.05148v2", "title": "Beyond Skin Tone: A Multidimensional Measure of Apparent Skin Color", "abstract": "This paper strives to measure apparent skin color in computer vision, beyond a unidimensional scale on skin tone. In their seminal paper Gender Shades, Buolamwini and Gebru have shown how gender classification systems can be biased against women with darker skin tones. Subsequently, fairness researchers and practitioners have adopted the Fitzpatrick skin type classification as a common measure to assess skin color bias in computer vision systems. While effective, the Fitzpatrick scale only focuses on the skin tone ranging from light to dark. Towards a more comprehensive measure of skin color, we introduce the hue angle ranging from red to yellow. When applied to images, the hue dimension reveals additional biases related to skin color in both computer vision datasets and models. We then recommend multidimensional skin color scales, relying on both skin tone and hue, for fairness assessments.", "published": "2023-09-10T21:52:47Z", "authors": ["William Thong", "Przemyslaw Joniak", "Alice Xiang"], "categories": ["cs.CV"], "doi": "", "_query": "property valuation computer vision", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2104.02215v2", "title": "When Pigs Fly: Contextual Reasoning in Synthetic and Natural Scenes", "abstract": "Context is of fundamental importance to both human and machine vision; e.g., an object in the air is more likely to be an airplane than a pig. The rich notion of context incorporates several aspects including physics rules, statistical co-occurrences, and relative object sizes, among others. While previous work has focused on crowd-sourced out-of-context photographs from the web to study scene context, controlling the nature and extent of contextual violations has been a daunting task. Here we introduce a diverse, synthetic Out-of-Context Dataset (OCD) with fine-grained control over scene context. By leveraging a 3D simulation engine, we systematically control the gravity, object co-occurrences and relative sizes across 36 object categories in a virtual household environment. We conducted a series of experiments to gain insights into the impact of contextual cues on both human and machine vision using OCD. We conducted psychophysics experiments to establish a human benchmark for out-of-context recognition, and then compared it with state-of-the-art computer vision models to quantify the gap between the two. We propose a context-aware recognition transformer model, fusing object and contextual information via multi-head attention. Our model captures useful information for contextual reasoning, enabling human-level performance and better robustness in out-of-context conditions compared to baseline models across OCD and other out-of-context datasets. All source code and data are publicly available at https://github.com/kreimanlab/WhenPigsFlyContext", "published": "2021-04-06T01:05:34Z", "authors": ["Philipp Bomatter", "Mengmi Zhang", "Dimitar Karev", "Spandan Madan", "Claire Tseng", "Gabriel Kreiman"], "categories": ["cs.CV", "cs.AI"], "doi": "", "_query": "property valuation computer vision", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2302.11713v5", "title": "Can Pre-trained Vision and Language Models Answer Visual Information-Seeking Questions?", "abstract": "Pre-trained vision and language models have demonstrated state-of-the-art capabilities over existing tasks involving images and texts, including visual question answering. However, it remains unclear whether these models possess the capability to answer questions that are not only querying visual content but knowledge-intensive and information-seeking. In this study, we introduce InfoSeek, a visual question answering dataset tailored for information-seeking questions that cannot be answered with only common sense knowledge. Using InfoSeek, we analyze various pre-trained visual question answering models and gain insights into their characteristics. Our findings reveal that state-of-the-art pre-trained multi-modal models (e.g., PaLI-X, BLIP2, etc.) face challenges in answering visual information-seeking questions, but fine-tuning on the InfoSeek dataset elicits models to use fine-grained knowledge that was learned during their pre-training. Furthermore, we show that accurate visual entity recognition can be used to improve performance on InfoSeek by retrieving relevant documents, showing a significant space for improvement.", "published": "2023-02-23T00:33:54Z", "authors": ["Yang Chen", "Hexiang Hu", "Yi Luan", "Haitian Sun", "Soravit Changpinyo", "Alan Ritter", "Ming-Wei Chang"], "categories": ["cs.CV", "cs.AI", "cs.CL"], "doi": "", "_query": "property valuation computer vision", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2401.06133v1", "title": "The possibility of making \\$138,000 from shredded banknote pieces using computer vision", "abstract": "Every country must dispose of old banknotes. At the Hong Kong Monetary Authority visitor center, visitors can buy a paperweight souvenir full of shredded banknotes. Even though the shredded banknotes are small, by using computer vision, it is possible to reconstruct the whole banknote like a jigsaw puzzle. Each paperweight souvenir costs \\$100 HKD, and it is claimed to contain shredded banknotes equivalent to 138 complete \\$1000 HKD banknotes. In theory, \\$138,000 HKD can be recovered by using computer vision. This paper discusses the technique of collecting shredded banknote pieces and applying a computer vision program.", "published": "2023-11-17T02:25:31Z", "authors": ["Chung To Kong"], "categories": ["cs.CV"], "doi": "", "_query": "property valuation computer vision", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2010.01968v1", "title": "Machine Learning and Computer Vision Techniques to Predict Thermal Properties of Particulate Composites", "abstract": "Accurate thermal analysis of composites and porous media requires detailed characterization of local thermal properties in small scale. For some important applications such as lithium-ion batteries, changes in the properties during the operation makes the analysis even more challenging, necessitating a rapid characterization. We propose a new method to characterize the thermal properties of particulate composites based on actual micro-images. Our computer-vision-based approach constructs 3D images from stacks of 2D SEM images and then extracts several representative elemental volumes (REVs) from the reconstructed images at random places, which leads to having a range of geometrical features for different REVs. A deep learning algorithm is designed based on convolutional neural nets to take the shape of the geometry and result in the effective conductivity of the REV. The training of the network is performed in two methods: First, based on implementing a coarser grid that uses the average values of conductivities from the fine grid and the resulted effective conductivity from the DNS solution of the fine grid. The other method uses conductivity values on cross sections from each REV in different directions. The results of training based on averaging show that using a coarser grid in the network does not have a meaningful effect on the network error; however, it decreases the training time up to three orders of magnitude. We showed that one general network can make accurate predictions using different types of electrode images, representing the difference in the geometry and constituents. Moreover, training based on averaging is more accurate than training based on cross sections. The study of the robustness of implementing a machine learning technique in predicting the thermal percolation shows the prediction error is almost half of the error from predictions based on the volume fraction.", "published": "2020-08-27T06:21:40Z", "authors": ["Fazlolah Mohaghegh", "Jayathi Murthy"], "categories": ["physics.app-ph", "cs.CV", "cs.LG", "physics.data-an"], "doi": "", "_query": "property valuation computer vision", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/1906.08988v3", "title": "A Fourier Perspective on Model Robustness in Computer Vision", "abstract": "Achieving robustness to distributional shift is a longstanding and challenging goal of computer vision. Data augmentation is a commonly used approach for improving robustness, however robustness gains are typically not uniform across corruption types. Indeed increasing performance in the presence of random noise is often met with reduced performance on other corruptions such as contrast change. Understanding when and why these sorts of trade-offs occur is a crucial step towards mitigating them. Towards this end, we investigate recently observed trade-offs caused by Gaussian data augmentation and adversarial training. We find that both methods improve robustness to corruptions that are concentrated in the high frequency domain while reducing robustness to corruptions that are concentrated in the low frequency domain. This suggests that one way to mitigate these trade-offs via data augmentation is to use a more diverse set of augmentations. Towards this end we observe that AutoAugment, a recently proposed data augmentation policy optimized for clean accuracy, achieves state-of-the-art robustness on the CIFAR-10-C benchmark.", "published": "2019-06-21T07:31:26Z", "authors": ["Dong Yin", "Raphael Gontijo Lopes", "Jonathon Shlens", "Ekin D. Cubuk", "Justin Gilmer"], "categories": ["cs.LG", "cs.CV", "stat.ML"], "doi": "", "_query": "property valuation computer vision", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2609.04088v1", "title": "Efficient Semantic Understanding from Digital Foveation", "abstract": "Dense semantic segmentation allocates computational resources uniformly across the entire image, regardless of scene complexity or task relevance. Inspired by biological vision, we investigate whether semantic understanding can be achieved more efficiently through digital foveated perception. We introduce a lightweight active-vision pipeline that combines saliency-driven fixation selection, high-resolution foveal observations, low-resolution contextual information, semantic accumulation, and adaptive computation. Beyond conventional dense prediction metrics, we use object-level evaluation to measure semantic understanding under sparse observations. On ADE20K-Object, a single foveated observation achieves 95.9% of the baseline Top-1 accuracy and 96.9% of the baseline Top-3 accuracy while requiring only 4.7% of the computational cost. At the scene level, semantic accumulation recovers 90.6% of the baseline object recall while using 58.6% of the computation. These results suggest that substantial semantic understanding can emerge from sparse observations when computation is allocated selectively, highlighting active vision as an efficient alternative to uniform dense processing and motivating evaluation protocols beyond conventional pixel-wise segmentation metrics.", "published": "2026-09-03T16:56:12Z", "authors": ["Caterina Caccavella", "Vittorio Fra", "Andreas Ziegler", "Giulia D'Angelo", "Yulia Sandamirskaya"], "categories": ["cs.CV"], "doi": "", "_query": "property valuation computer vision", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/1911.05931v1", "title": "VisionISP: Repurposing the Image Signal Processor for Computer Vision Applications", "abstract": "Traditional image signal processors (ISPs) are primarily designed and optimized to improve the image quality perceived by humans. However, optimal perceptual image quality does not always translate into optimal performance for computer vision applications. We propose a set of methods, which we collectively call VisionISP, to repurpose the ISP for machine consumption. VisionISP significantly reduces data transmission needs by reducing the bit-depth and resolution while preserving the relevant information. The blocks in VisionISP are simple, content-aware, and trainable. Experimental results show that VisionISP boosts the performance of a subsequent computer vision system trained to detect objects in an autonomous driving setting. The results demonstrate the potential and the practicality of VisionISP for computer vision applications.", "published": "2019-11-14T04:19:28Z", "authors": ["Chyuan-Tyng Wu", "Leo F. Isikdogan", "Sushma Rao", "Bhavin Nayak", "Timo Gerasimow", "Aleksandar Sutic", "Liron Ain-kedem", "Gilad Michael"], "categories": ["eess.IV", "cs.CV"], "doi": "10.1109/ICIP.2019.8803607", "_query": "property valuation computer vision", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2206.15351v1", "title": "Deep Learning to See: Towards New Foundations of Computer Vision", "abstract": "The remarkable progress in computer vision over the last few years is, by and large, attributed to deep learning, fueled by the availability of huge sets of labeled data, and paired with the explosive growth of the GPU paradigm. While subscribing to this view, this book criticizes the supposed scientific progress in the field and proposes the investigation of vision within the framework of information-based laws of nature. Specifically, the present work poses fundamental questions about vision that remain far from understood, leading the reader on a journey populated by novel challenges resonating with the foundations of machine learning. The central thesis is that for a deeper understanding of visual computational processes, it is necessary to look beyond the applications of general purpose machine learning algorithms and focus instead on appropriate learning theories that take into account the spatiotemporal nature of the visual signal.", "published": "2022-06-30T15:20:36Z", "authors": ["Alessandro Betti", "Marco Gori", "Stefano Melacci"], "categories": ["cs.CV"], "doi": "10.1007/978-3-030-90987-1", "_query": "property valuation computer vision", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/1908.10933v2", "title": "A Possible Reason for why Data-Driven Beats Theory-Driven Computer Vision", "abstract": "Why do some continue to wonder about the success and dominance of deep learning methods in computer vision and AI? Is it not enough that these methods provide practical solutions to many problems? Well no, it is not enough, at least for those who feel there should be a science that underpins all of this and that we should have a clear understanding of how this success was achieved. Here, this paper proposes that the dominance we are witnessing would not have been possible by the methods of deep learning alone: the tacit change has been the evolution of empirical practice in computer vision and AI over the past decades. We demonstrate this by examining the distribution of sensor settings in vision datasets and performance of both classic and deep learning algorithms under various camera settings. This reveals a strong mismatch between optimal performance ranges of classical theory-driven algorithms and sensor setting distributions in the common vision datasets, while data-driven models were trained for those datasets. The head-to-head comparisons between data-driven and theory-driven models were therefore unknowingly biased against the theory-driven models.", "published": "2019-08-28T20:19:41Z", "authors": ["John K. Tsotsos", "Iuliia Kotseruba", "Alexander Andreopoulos", "Yulong Wu"], "categories": ["cs.CV"], "doi": "", "_query": "property valuation computer vision", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2504.19682v1", "title": "Explaining Vision GNNs: A Semantic and Visual Analysis of Graph-based Image Classification", "abstract": "Graph Neural Networks (GNNs) have emerged as an efficient alternative to convolutional approaches for vision tasks such as image classification, leveraging patch-based representations instead of raw pixels. These methods construct graphs where image patches serve as nodes, and edges are established based on patch similarity or classification relevance. Despite their efficiency, the explainability of GNN-based vision models remains underexplored, even though graphs are naturally interpretable. In this work, we analyze the semantic consistency of the graphs formed at different layers of GNN-based image classifiers, focusing on how well they preserve object structures and meaningful relationships. A comprehensive analysis is presented by quantifying the extent to which inter-layer graph connections reflect semantic similarity and spatial coherence. Explanations from standard and adversarial settings are also compared to assess whether they reflect the classifiers' robustness. Additionally, we visualize the flow of information across layers through heatmap-based visualization techniques, thereby highlighting the models' explainability. Our findings demonstrate that the decision-making processes of these models can be effectively explained, while also revealing that their reasoning does not necessarily align with human perception, especially in deeper layers.", "published": "2025-04-28T11:13:40Z", "authors": ["Nikolaos Chaidos", "Angeliki Dimitriou", "Nikolaos Spanos", "Athanasios Voulodimos", "Giorgos Stamou"], "categories": ["cs.CV", "cs.LG"], "doi": "", "_query": "property valuation computer vision", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2206.01734v1", "title": "Using UAS Imagery and Computer Vision to Support Site-Specific Weed Control in Corn", "abstract": "Currently, weed control in a corn field is performed by a blanket application of herbicides that do not consider spatial distribution information of weeds and also uses an extensive amount of chemical herbicides. To reduce the amount of chemicals, we used drone-based high-resolution imagery and computer-vision techniques to perform site-specific weed control in corn.", "published": "2022-06-02T18:33:22Z", "authors": ["Ranjan Sapkota", "Paulo Flores"], "categories": ["cs.CV", "cs.AI", "eess.IV"], "doi": "", "_query": "property valuation computer vision", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2206.10253v1", "title": "Document Navigability: A Need for Print-Impaired", "abstract": "Printed documents continue to be a challenge for blind, low-vision, and other print-disabled (BLV) individuals. In this paper, we focus on the specific problem of (in-)accessibility of internal references to citations, footnotes, figures, tables and equations. While sighted users can flip to the referenced content and flip back in seconds, linear audio narration that BLV individuals rely on makes following these references extremely hard. We propose a vision based technique to locate the referenced content and extract metadata needed to (in subsequent work) inline a content summary into the audio narration. We apply our technique to citations in scientific documents and find it works well both on born-digital as well as scanned documents.", "published": "2022-06-21T11:01:34Z", "authors": ["Anukriti Kumar", "Tanuja Ganu", "Saikat Guha"], "categories": ["cs.CV", "cs.HC"], "doi": "", "_query": "property valuation computer vision", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2407.10044v1", "title": "Research Experience of an Undergraduate Student in Computer Vision and Robotics", "abstract": "This paper focuses on the educational journey of a computer engineering undergraduate student venturing into the domain of computer vision and robotics. It explores how optical flow and its applications can be used to detect moving objects when a camera undergoes translational motion, highlighting the challenges encountered and the strategies used to overcome them. Furthermore, the paper discusses not only the technical skills acquired by the student but also interpersonal skills as related to teamwork and diversity. In this paper, we detail the learning process, including the acquisition of technical and problem-solving skills, as well as out-of-the-box thinking.", "published": "2024-07-14T02:01:50Z", "authors": ["Ayush V. Gowda", "Juan D. Yepes", "Daniel Raviv"], "categories": ["cs.CV"], "doi": "", "_query": "property valuation computer vision", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2509.22720v1", "title": "LayoutAgent: A Vision-Language Agent Guided Compositional Diffusion for Spatial Layout Planning", "abstract": "Designing realistic multi-object scenes requires not only generating images, but also planning spatial layouts that respect semantic relations and physical plausibility. On one hand, while recent advances in diffusion models have enabled high-quality image generation, they lack explicit spatial reasoning, leading to unrealistic object layouts. On the other hand, traditional spatial planning methods in robotics emphasize geometric and relational consistency, but they struggle to capture semantic richness in visual scenes. To bridge this gap, in this paper, we propose LayoutAgent, an agentic framework that unifies vision-language reasoning with compositional diffusion for layout generation. Given multiple input images with target objects in them, our method first employs visual-language model to preprocess the inputs through segmentation, object size estimation, scene graph construction, and prompt rewriting. Then we leverage compositional diffusion-a method traditionally used in robotics-to synthesize bounding boxes that respect object relations encoded in the scene graph for spatial layouts. In the end, a foreground-conditioned image generator composes the complete scene by rendering the objects into the planned layout guided by designed prompts. Experiments demonstrate that LayoutAgent outperforms other state-of-the-art layout generation models in layout coherence, spatial realism and aesthetic alignment.", "published": "2025-09-24T20:41:04Z", "authors": ["Zezhong Fan", "Xiaohan Li", "Luyi Ma", "Kai Zhao", "Liang Peng", "Topojoy Biswas", "Evren Korpeoglu", "Kaushiki Nag", "Kannan Achan"], "categories": ["cs.CV", "cs.AI", "cs.LG"], "doi": "", "_query": "property valuation computer vision", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2604.22834v1", "title": "WebSerial Vision Training for Microcontrollers: A Browser-Based Companion to On-Device CNN Training", "abstract": "This paper presents webmcu-vision-web, a single-file, zero-install browser application for end-to-end TinyML vision model training and deployment on the Seeed Studio XIAO ESP32-S3 Sense (XIAO ML Kit, $15--40 USD). Acting as a browser-based companion to the on-device Arduino firmware of Paper 1 [1], it provides a private, fully local machine learning pipeline, from firmware flashing through image collection, CNN training, weight export, and live activation visualization, without any software installation beyond a Chromium-based browser. The system targets educators, small businesses, and researchers who need to train task-specific visual classifiers under their exact deployment conditions. Key capabilities include: in-browser firmware flashing via esptool-js; an SD card file browser with image preview and inline editing; config.json live-sync for zero-recompile hyperparameter adjustment; webcam and ESP32 OV2640 camera image capture; TensorFlow.js CNN training completing a three-class run (~30 images per class, 20 epochs) in approximately 1 minute browser-side versus 9 minutes on-device, enabling a complete collect-train-deploy cycle in under 10 minutes; weight export as myWeights.bin and myWeights.h; confusion matrix; and a live Conv2 activation heatmap streamed from the ESP32 during inference. No data leaves the local machine at any stage. A five-run consistency evaluation on the three-class reference problem (0Blank, 1Cup, 2Pen) demonstrates stable convergence with mean accuracy and standard deviation reported; all artefacts are released at the repository link below. The repository is a living template for LLM-assisted adaptation to new hardware and tasks. All source code is MIT-licensed at https://github.com/webmcu-ai/webmcu-vision-web.", "published": "2026-04-20T05:25:41Z", "authors": ["Jeremy Ellis"], "categories": ["cs.CV", "cs.LG"], "doi": "", "_query": "property valuation computer vision", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2110.00791v1", "title": "Optimizing Neural Network for Computer Vision task in Edge Device", "abstract": "The field of computer vision has grown very rapidly in the past few years due to networks like convolution neural networks and their variants. The memory required to store the model and computational expense are very high for such a network limiting it to deploy on the edge device. Many times, applications rely on the cloud but that makes it hard for working in real-time due to round-trip delays. We overcome these problems by deploying the neural network on the edge device itself. The computational expense for edge devices is reduced by reducing the floating-point precision of the parameters in the model. After this the memory required for the model decreases and the speed of the computation increases where the performance of the model is least affected. This makes an edge device to predict from the neural network all by itself.", "published": "2021-10-02T12:25:18Z", "authors": ["Ranjith M S", "S Parameshwara", "Pavan Yadav A", "Shriganesh Hegde"], "categories": ["cs.CV", "cs.AI"], "doi": "", "_query": "property valuation computer vision", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/1702.01894v1", "title": "CAAD: Computer Architecture for Autonomous Driving", "abstract": "We describe the computing tasks involved in autonomous driving, examine existing autonomous driving computing platform implementations. To enable autonomous driving, the computing stack needs to simultaneously provide high performance, low power consumption, and low thermal dissipation, at low cost. We discuss possible approaches to design computing platforms that will meet these needs.", "published": "2017-02-07T06:55:03Z", "authors": ["Shaoshan Liu", "Jie Tang", "Zhe Zhang", "Jean-Luc Gaudiot"], "categories": ["cs.AR", "cs.RO"], "doi": "", "_query": "property valuation computer vision", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2209.09676v1", "title": "Evaluation Framework for Computer Vision-Based Guidance of the Visually Impaired", "abstract": "Visually impaired persons have significant problems in their everyday movement. Therefore, some of our previous work involves computer vision in developing assistance systems for guiding the visually impaired in critical situations. Some of those situations includes crosswalks on road crossings and stairs in indoor and outdoor environment. This paper presents an evaluation framework for computer vision-based guiding of the visually impaired persons in such critical situations. Presented framework includes the interface for labeling and storing referent human decisions for guiding directions and compares them to computer vision-based decisions. Since strict evaluation methodology in this research field is not clearly defined and due to the specifics of the transfer of information to visually impaired persons, evaluation criterion for specific simplified guiding instructions is proposed.", "published": "2022-09-20T12:26:55Z", "authors": ["Krešimir Romić", "Irena Galić", "Marija Habijan", "Hrvoje Leventić"], "categories": ["cs.CV"], "doi": "", "_query": "property valuation computer vision", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2204.10384v2", "title": "Monocular Depth Estimation Using Cues Inspired by Biological Vision Systems", "abstract": "Monocular depth estimation (MDE) aims to transform an RGB image of a scene into a pixelwise depth map from the same camera view. It is fundamentally ill-posed due to missing information: any single image can have been taken from many possible 3D scenes. Part of the MDE task is, therefore, to learn which visual cues in the image can be used for depth estimation, and how. With training data limited by cost of annotation or network capacity limited by computational power, this is challenging. In this work we demonstrate that explicitly injecting visual cue information into the model is beneficial for depth estimation. Following research into biological vision systems, we focus on semantic information and prior knowledge of object sizes and their relations, to emulate the biological cues of relative size, familiar size, and absolute size. We use state-of-the-art semantic and instance segmentation models to provide external information, and exploit language embeddings to encode relational information between classes. We also provide a prior on the average real-world size of objects. This external information overcomes the limitation in data availability, and ensures that the limited capacity of a given network is focused on known-helpful cues, therefore improving performance. We experimentally validate our hypothesis and evaluate the proposed model on the widely used NYUD2 indoor depth estimation benchmark. The results show improvements in depth prediction when the semantic information, size prior and instance size are explicitly provided along with the RGB images, and our method can be easily adapted to any depth estimation system.", "published": "2022-04-21T19:42:36Z", "authors": ["Dylan Auty", "Krystian Mikolajczyk"], "categories": ["cs.CV"], "doi": "", "_query": "property valuation computer vision", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/1109.1057v1", "title": "Toward Designing Intelligent PDEs for Computer Vision: An Optimal Control Approach", "abstract": "Many computer vision and image processing problems can be posed as solving partial differential equations (PDEs). However, designing PDE system usually requires high mathematical skills and good insight into the problems. In this paper, we consider designing PDEs for various problems arising in computer vision and image processing in a lazy manner: \\emph{learning PDEs from real data via data-based optimal control}. We first propose a general intelligent PDE system which holds the basic translational and rotational invariance rule for most vision problems. By introducing a PDE-constrained optimal control framework, it is possible to use the training data resulting from multiple ways (ground truth, results from other methods, and manual results from humans) to learn PDEs for different computer vision tasks. The proposed optimal control based training framework aims at learning a PDE-based regressor to approximate the unknown (and usually nonlinear) mapping of different vision tasks. The experimental results show that the learnt PDEs can solve different vision problems reasonably well. In particular, we can obtain PDEs not only for problems that traditional PDEs work well but also for problems that PDE-based methods have never been tried before, due to the difficulty in describing those problems in a mathematical way.", "published": "2011-09-06T04:26:44Z", "authors": ["Risheng Liu", "Zhouchen Lin", "Wei Zhang", "Kewei Tang", "Zhixun Su"], "categories": ["cs.CV"], "doi": "", "_query": "property valuation computer vision", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2610.08418v1", "title": "From the Drosophila Visual Connectome to General-Purpose Computer Vision", "abstract": "Biological connectomes encode structured solutions to visual computation that may provide reusable inductive biases for artificial vision. We develop ConnectomeX around FlyVision, a trainable architecture that preserves parallel ON/OFF processing, recurrent computation and population-level graph interaction while scaling model capacity across tasks. FlyVision reached 99.34% accuracy on MNIST with 80,608 parameters and 78.03% on CIFAR-10 with 81,408 parameters. On ImageNet-1K, FlyVision Base and Large reached 60.79% and 66.25% top-1 accuracy with 1.8 and 3.7 million parameters, while a Large local-k7 model with a learned low-frequency branch reached 66.53%, compared with 69.25% for ResNet18 with 11.7 million parameters. On a 22-class skin-disease benchmark, FlyVision Large achieved 63.78% accuracy and 95.28% macro-AUROC with 2.99 million parameters. In four-class chest radiography, ImageNet-pretrained FlyVision Base and Large reached 92.60% and 92.76% accuracy with 1.33 and 2.97 million parameters, compared with 91.56% for ImageNet-pretrained ResNet18 with 11.18 million. BrainAGE extends FlyVision to volumetric T1-weighted MRI by applying a shared ImageNet-pretrained FlyVision Large encoder to 24 sagittal, coronal and axial slices per scan and combining slice-level age estimates by confidence-modulated Gaussian voting. On 433 held-out scans, three-axis fusion achieved a mean absolute error of 5.98 years and R^2 = 0.868. Across the 224x224 classification tasks, the best FlyVision configuration remained within three percentage points of ResNet18 on ImageNet-1K and skin-disease classification and exceeded it on chest radiography with substantially fewer parameters. These results show that a conserved connectome-informed computation can scale from compact recognition to large-scale natural and biomedical vision.", "published": "2026-10-06T14:22:00Z", "authors": ["Zongyu Li", "Akito Yamauchi", "Huaizhi Liu", "Vishwanatha Rao", "Jia Guo", "for the Frontotemporal Lobar Degeneration Neuroimaging Initiative", "for the Alzheimer's Disease Neuroimaging Initiative"], "categories": ["cs.CV", "cs.NE", "q-bio.NC"], "doi": "", "_query": "property valuation computer vision", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2205.10435v1", "title": "Towards Better Understanding Attribution Methods", "abstract": "Deep neural networks are very successful on many vision tasks, but hard to interpret due to their black box nature. To overcome this, various post-hoc attribution methods have been proposed to identify image regions most influential to the models' decisions. Evaluating such methods is challenging since no ground truth attributions exist. We thus propose three novel evaluation schemes to more reliably measure the faithfulness of those methods, to make comparisons between them more fair, and to make visual inspection more systematic. To address faithfulness, we propose a novel evaluation setting (DiFull) in which we carefully control which parts of the input can influence the output in order to distinguish possible from impossible attributions. To address fairness, we note that different methods are applied at different layers, which skews any comparison, and so evaluate all methods on the same layers (ML-Att) and discuss how this impacts their performance on quantitative metrics. For more systematic visualizations, we propose a scheme (AggAtt) to qualitatively evaluate the methods on complete datasets. We use these evaluation schemes to study strengths and shortcomings of some widely used attribution methods. Finally, we propose a post-processing smoothing step that significantly improves the performance of some attribution methods, and discuss its applicability.", "published": "2022-05-20T20:50:17Z", "authors": ["Sukrut Rao", "Moritz Böhle", "Bernt Schiele"], "categories": ["cs.CV", "cs.LG"], "doi": "10.1109/CVPR52688.2022.00998", "_query": "property valuation computer vision", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2312.01771v1", "title": "IMProv: Inpainting-based Multimodal Prompting for Computer Vision Tasks", "abstract": "In-context learning allows adapting a model to new tasks given a task description at test time. In this paper, we present IMProv - a generative model that is able to in-context learn visual tasks from multimodal prompts. Given a textual description of a visual task (e.g. \"Left: input image, Right: foreground segmentation\"), a few input-output visual examples, or both, the model in-context learns to solve it for a new test input. We train a masked generative transformer on a new dataset of figures from computer vision papers and their associated captions, together with a captioned large-scale image-text dataset. During inference time, we prompt the model with text and/or image task example(s) and have the model inpaint the corresponding output. We show that training our model with text conditioning and scaling the dataset size improves in-context learning for computer vision tasks by over +10\\% AP for Foreground Segmentation, over +5\\% gains in AP for Single Object Detection, and almost 20\\% lower LPIPS in Colorization. Our empirical results suggest that vision and language prompts are complementary and it is advantageous to use both to achieve better in-context learning performance. Project page is available at https://jerryxu.net/IMProv .", "published": "2023-12-04T09:48:29Z", "authors": ["Jiarui Xu", "Yossi Gandelsman", "Amir Bar", "Jianwei Yang", "Jianfeng Gao", "Trevor Darrell", "Xiaolong Wang"], "categories": ["cs.CV"], "doi": "", "_query": "property valuation computer vision", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2603.17879v2", "title": "Anatomy-Guided Vision-Language Learning with Angular Prototype Separation for Multi-Label Video Capsule Endoscopy Classification Under Class Imbalance", "abstract": "This work presents a multi-label temporal event detection framework for video capsule endoscopy (VCE) that addresses the extreme class imbalance inherent in the Galar dataset by combining two principal contributions: an Angular Separation Loss on class prototypes and a Biological State Machine temporal decoder. The backbone remains BiomedCLIP, a biomedical vision-language foundation model. Three consecutive frames are fused through a Local Differencing Attention module that amplifies transient pathological signals by suppressing static temporal redundancy. An Anatomy Context Head then conditions pathological predictions on soft anatomical activations, exploiting the known spatial co-occurrence structure of GI findings. Learnable text-feature prompts and prototype-based logit augmentation are trained alongside an Angular Separation Loss that penalizes off-diagonal cosine similarity between class prototypes, preventing the prototype collapse that afflicts rare classes under extreme imbalance. To counteract the skewed label distribution, the training regime combines asymmetric focal loss, inverse-frequency weighted sampling, temporal Mixup, Exponential Moving Average, and per-class threshold calibration. The Biological State Machine decoder replaces naive gap merging with a physiologically grounded forward-only state transition over anatomy labels, eliminating the fragmentation artefact that produced hundreds of spurious anatomy events per video in the prior approach and reducing per-video anatomy output to 2--3 clinically realistic events. On the held-out RARE-VISION test set comprising three NaviCam examinations (161,025 frames), the updated pipeline achieves an overall temporal mAP@0.5 of 0.3597 and mAP@0.95 of 0.3399, representing a relative improvement of 46% and 44% respectively over the prior submission, with total inference completed in approximately 21 minutes on a single GPU.", "published": "2026-03-18T16:04:50Z", "authors": ["Podakanti Satyajith Chary", "Nagarajan Ganapathy"], "categories": ["cs.CV", "cs.AI"], "doi": "", "_query": "property valuation computer vision", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2108.00114v1", "title": "On The State of Data In Computer Vision: Human Annotations Remain Indispensable for Developing Deep Learning Models", "abstract": "High-quality labeled datasets play a crucial role in fueling the development of machine learning (ML), and in particular the development of deep learning (DL). However, since the emergence of the ImageNet dataset and the AlexNet model in 2012, the size of new open-source labeled vision datasets has remained roughly constant. Consequently, only a minority of publications in the computer vision community tackle supervised learning on datasets that are orders of magnitude larger than Imagenet. In this paper, we survey computer vision research domains that study the effects of such large datasets on model performance across different vision tasks. We summarize the community's current understanding of those effects, and highlight some open questions related to training with massive datasets. In particular, we tackle: (a) The largest datasets currently used in computer vision research and the interesting takeaways from training on such datasets; (b) The effectiveness of pre-training on large datasets; (c) Recent advancements and hurdles facing synthetic datasets; (d) An overview of double descent and sample non-monotonicity phenomena; and finally, (e) A brief discussion of lifelong/continual learning and how it fares compared to learning from huge labeled datasets in an offline setting. Overall, our findings are that research on optimization for deep learning focuses on perfecting the training routine and thus making DL models less data hungry, while research on synthetic datasets aims to offset the cost of data labeling. However, for the time being, acquiring non-synthetic labeled data remains indispensable to boost performance.", "published": "2021-07-31T00:08:21Z", "authors": ["Zeyad Emam", "Andrew Kondrich", "Sasha Harrison", "Felix Lau", "Yushi Wang", "Aerin Kim", "Elliot Branson"], "categories": ["cs.CV", "cs.AI"], "doi": "", "_query": "property valuation computer vision", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2303.11684v2", "title": "SpikeCV: Open a Continuous Computer Vision Era", "abstract": "SpikeCV is a new open-source computer vision platform for the spike camera, which is a neuromorphic visual sensor that has developed rapidly in recent years. In the spike camera, each pixel position directly accumulates the light intensity and asynchronously fires spikes. The output binary spikes can reach a frequency of 40,000 Hz. As a new type of visual expression, spike sequence has high spatiotemporal completeness and preserves the continuous visual information of the external world. Taking advantage of the low latency and high dynamic range of the spike camera, many spike-based algorithms have made significant progress, such as high-quality imaging and ultra-high-speed target detection. To build up a community ecology for the spike vision to facilitate more users to take advantage of the spike camera, SpikeCV provides a variety of ultra-high-speed scene datasets, hardware interfaces, and an easy-to-use modules library. SpikeCV focuses on encapsulation for spike data, standardization for dataset interfaces, modularization for vision tasks, and real-time applications for challenging scenes. With the advent of the open-source Python ecosystem, modules of SpikeCV can be used as a Python library to fulfilled most of the numerical analysis needs of researchers. We demonstrate the efficiency of the SpikeCV on offline inference and real-time applications. The project repository address are \\url{https://openi.pcl.ac.cn/Cordium/SpikeCV} and \\url{https://github.com/Zyj061/SpikeCV", "published": "2023-03-21T09:00:12Z", "authors": ["Yajing Zheng", "Jiyuan Zhang", "Rui Zhao", "Jianhao Ding", "Shiyan Chen", "Ruiqin Xiong", "Zhaofei Yu", "Tiejun Huang"], "categories": ["cs.CV"], "doi": "10.1007/s11432-023-4565-6", "_query": "property valuation computer vision", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2301.05070v1", "title": "Wildfire Smoke Detection with Computer Vision", "abstract": "Wildfires are becoming more frequent and their effects more devastating every day. Climate change has directly and indirectly affected the occurrence of these, as well as social phenomena have increased the vulnerability of people. Consequently, and given the inevitable occurrence of these, it is important to have early warning systems that allow a timely and effective response. Artificial intelligence, machine learning and Computer Vision offer an effective and achievable alternative for opportune detection of wildfires and thus reduce the risk of disasters. YOLOv7 offers a simple, fast, and efficient algorithm for training object detection models which can be used in early detection of smoke columns in the initial stage wildfires. The developed model showed promising results, achieving a score of 0.74 in the F1 curve when the confidence level is 0.298, that is, a higher score at lower confidence levels was obtained. This means when the conditions are favorable for false positives. The metrics demonstrates the resilience and effectiveness of the model in detecting smoke columns.", "published": "2023-01-12T15:12:56Z", "authors": ["Eldan R. Daniel"], "categories": ["cs.CV", "cs.AI"], "doi": "", "_query": "property valuation computer vision", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2411.06727v2", "title": "Can KAN Work? Exploring the Potential of Kolmogorov-Arnold Networks in Computer Vision", "abstract": "Kolmogorov-Arnold Networks(KANs), as a theoretically efficient neural network architecture, have garnered attention for their potential in capturing complex patterns. However, their application in computer vision remains relatively unexplored. This study first analyzes the potential of KAN in computer vision tasks, evaluating the performance of KAN and its convolutional variants in image classification and semantic segmentation. The focus is placed on examining their characteristics across varying data scales and noise levels. Results indicate that while KAN exhibits stronger fitting capabilities, it is highly sensitive to noise, limiting its robustness. To address this challenge, we propose a smoothness regularization method and introduce a Segment Deactivation technique. Both approaches enhance KAN's stability and generalization, demonstrating its potential in handling complex visual data tasks.", "published": "2024-11-11T05:44:48Z", "authors": ["Yueyang Cang", "Yu hang liu", "Li Shi"], "categories": ["cs.CV"], "doi": "", "_query": "property valuation computer vision", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/1810.02320v1", "title": "Computer vision-based framework for extracting geological lineaments from optical remote sensing data", "abstract": "The extraction of geological lineaments from digital satellite data is a fundamental application in remote sensing. The location of geological lineaments such as faults and dykes are of interest for a range of applications, particularly because of their association with hydrothermal mineralization. Although a wide range of applications have utilized computer vision techniques, a standard workflow for application of these techniques to mineral exploration is lacking. We present a framework for extracting geological lineaments using computer vision techniques which is a combination of edge detection and line extraction algorithms for extracting geological lineaments using optical remote sensing data. It features ancillary computer vision techniques for reducing data dimensionality, removing noise and enhancing the expression of lineaments. We test the proposed framework on Landsat 8 data of a mineral-rich portion of the Gascoyne Province in Western Australia using different dimension reduction techniques and convolutional filters. To validate the results, the extracted lineaments are compared to our manual photointerpretation and geologically mapped structures by the Geological Survey of Western Australia (GSWA). The results show that the best correlation between our extracted geological lineaments and the GSWA geological lineament map is achieved by applying a minimum noise fraction transformation and a Laplacian filter. Application of a directional filter instead shows a stronger correlation with the output of our manual photointerpretation and known sites of hydrothermal mineralization. Hence, our framework using either filter can be used for mineral prospectivity mapping in other regions where faults are exposed and observable in optical remote sensing data.", "published": "2018-10-04T17:09:04Z", "authors": ["Ehsan Farahbakhsh", "Rohitash Chandra", "Hugo K. H. Olierook", "Richard Scalzo", "Chris Clark", "Steven M. Reddy", "R. Dietmar Muller"], "categories": ["cs.CV", "cs.AI"], "doi": "10.1080/01431161.2019.1674462", "_query": "property valuation computer vision", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2501.15122v3", "title": "Vision without Images: End-to-End Computer Vision from Single Compressive Measurements", "abstract": "Snapshot Compressed Imaging (SCI) offers high-speed, low-bandwidth, and energy-efficient image acquisition, but remains challenged by low-light and low signal-to-noise ratio (SNR) conditions. Moreover, practical hardware constraints in high-resolution sensors limit the use of large frame-sized masks, necessitating smaller, hardware-friendly designs. In this work, we present a novel SCI-based computer vision framework using pseudo-random binary masks of only 8$\\times$8 in size for physically feasible implementations. At its core is CompDAE, a Compressive Denoising Autoencoder built on the STFormer architecture, designed to perform downstream tasks--such as edge detection and depth estimation--directly from noisy compressive raw pixel measurements without image reconstruction. CompDAE incorporates a rate-constrained training strategy inspired by BackSlash to promote compact, compressible models. A shared encoder paired with lightweight task-specific decoders enables a unified multi-task platform. Extensive experiments across multiple datasets demonstrate that CompDAE achieves state-of-the-art performance with significantly lower complexity, especially under ultra-low-light conditions where traditional CMOS and SCI pipelines fail.", "published": "2025-01-25T08:20:30Z", "authors": ["Fengpu Pan", "Heting Gao", "Jiangtao Wen", "Yuxing Han"], "categories": ["cs.CV", "cs.AI"], "doi": "", "_query": "property valuation computer vision", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2201.00095v1", "title": "Computer Vision Based Parking Optimization System", "abstract": "An improvement in technology is linearly related to time and time-relevant problems. It has been seen that as time progresses, the number of problems humans face also increases. However, technology to resolve these problems tends to improve as well. One of the earliest existing problems which started with the invention of vehicles was parking. The ease of resolving this problem using technology has evolved over the years but the problem of parking still remains unsolved. The main reason behind this is that parking does not only involve one problem but it consists of a set of problems within itself. One of these problems is the occupancy detection of the parking slots in a distributed parking ecosystem. In a distributed system, users would find preferable parking spaces as opposed to random parking spaces. In this paper, we propose a web-based application as a solution for parking space detection in different parking spaces. The solution is based on Computer Vision (CV) and is built using the Django framework written in Python 3.0. The solution works to resolve the occupancy detection problem along with providing the user the option to determine the block based on availability and his preference. The evaluation results for our proposed system are promising and efficient. The proposed system can also be integrated with different systems and be used for solving other relevant parking problems.", "published": "2022-01-01T02:35:49Z", "authors": ["Siddharth Chandrasekaran", "Jeffrey Matthew Reginald", "Wei Wang", "Ting Zhu"], "categories": ["cs.CV", "cs.AI"], "doi": "", "_query": "property valuation computer vision", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2407.04859v1", "title": "Hybrid Primal Sketch: Combining Analogy, Qualitative Representations, and Computer Vision for Scene Understanding", "abstract": "One of the purposes of perception is to bridge between sensors and conceptual understanding. Marr's Primal Sketch combined initial edge-finding with multiple downstream processes to capture aspects of visual perception such as grouping and stereopsis. Given the progress made in multiple areas of AI since then, we have developed a new framework inspired by Marr's work, the Hybrid Primal Sketch, which combines computer vision components into an ensemble to produce sketch-like entities which are then further processed by CogSketch, our model of high-level human vision, to produce both more detailed shape representations and scene representations which can be used for data-efficient learning via analogical generalization. This paper describes our theoretical framework, summarizes several previous experiments, and outlines a new experiment in progress on diagram understanding.", "published": "2024-07-05T20:44:35Z", "authors": ["Kenneth D. Forbus", "Kezhen Chen", "Wangcheng Xu", "Madeline Usher"], "categories": ["cs.CV", "cs.AI"], "doi": "", "_query": "property valuation computer vision", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2507.06060v2", "title": "VisualSpeaker: Visually-Guided 3D Avatar Lip Synthesis", "abstract": "Realistic, high-fidelity 3D facial animations are crucial for expressive avatar systems in human-computer interaction and accessibility. Although prior methods show promising quality, their reliance on the mesh domain limits their ability to fully leverage the rapid visual innovations seen in 2D computer vision and graphics. We propose VisualSpeaker, a novel method that bridges this gap using photorealistic differentiable rendering, supervised by visual speech recognition, for improved 3D facial animation. Our contribution is a perceptual lip-reading loss, derived by passing photorealistic 3D Gaussian Splatting avatar renders through a pre-trained Visual Automatic Speech Recognition model during training. Evaluation on the MEAD dataset demonstrates that VisualSpeaker improves both the standard Lip Vertex Error metric by 56.1% and the perceptual quality of the generated animations, while retaining the controllability of mesh-driven animation. This perceptual focus naturally supports accurate mouthings, essential cues that disambiguate similar manual signs in sign language avatars.", "published": "2025-07-08T15:04:17Z", "authors": ["Alexandre Symeonidis-Herzig", "Özge Mercanoğlu Sincan", "Richard Bowden"], "categories": ["cs.CV", "cs.AI"], "doi": "", "_query": "property valuation computer vision", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/1402.0693v1", "title": "A survey on Human Computer Interaction Mechanism Using Finger Tracking", "abstract": "Human Computer Interaction (HCI) is a field in which developer makes a user friendly system. User can interact with a computer system without using any conventional peripheral devices. Marker is used to recognize hand movement accurately & successfully. Researchers establish the mechanism to interact with computer system using computer vision. The interaction is better than normal static keyboard and mouse. This paper represents most of innovative mechanisms of the finger tracking used to interact with a computer system using computer vision.", "published": "2014-02-04T11:13:56Z", "authors": ["Kinjal N. Shah", "Kirit R. Rathod", "Shardul J. Agravat"], "categories": ["cs.HC"], "doi": "10.14445/22312803/IJCTT-V7P148", "_query": "property valuation computer vision", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2407.04400v1", "title": "Hard-Attention Gates with Gradient Routing for Endoscopic Image Computing", "abstract": "To address overfitting and enhance model generalization in gastroenterological polyp size assessment, our study introduces Feature-Selection Gates (FSG) or Hard-Attention Gates (HAG) alongside Gradient Routing (GR) for dynamic feature selection. This technique aims to boost Convolutional Neural Networks (CNNs) and Vision Transformers (ViTs) by promoting sparse connectivity, thereby reducing overfitting and enhancing generalization. HAG achieves this through sparsification with learnable weights, serving as a regularization strategy. GR further refines this process by optimizing HAG parameters via dual forward passes, independently from the main model, to improve feature re-weighting. Our evaluation spanned multiple datasets, including CIFAR-100 for a broad impact assessment and specialized endoscopic datasets (REAL-Colon, Misawa, and SUN) focusing on polyp size estimation, covering over 200 polyps in more than 370,000 frames. The findings indicate that our HAG-enhanced networks substantially enhance performance in both binary and triclass classification tasks related to polyp sizing. Specifically, CNNs experienced an F1 Score improvement to 87.8% in binary classification, while in triclass classification, the ViT-T model reached an F1 Score of 76.5%, outperforming traditional CNNs and ViT-T models. To facilitate further research, we are releasing our codebase, which includes implementations for CNNs, multistream CNNs, ViT, and HAG-augmented variants. This resource aims to standardize the use of endoscopic datasets, providing public training-validation-testing splits for reliable and comparable research in gastroenterological polyp size estimation. The codebase is available at github.com/cosmoimd/feature-selection-gates.", "published": "2024-07-05T10:20:24Z", "authors": ["Giorgio Roffo", "Carlo Biffi", "Pietro Salvagnini", "Andrea Cherubini"], "categories": ["eess.IV", "cs.CV", "cs.LG"], "doi": "", "_query": "property valuation computer vision", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2004.02810v1", "title": "Computer Vision and Abnormal Patient Gait Assessment a Comparison of Machine Learning Models", "abstract": "Abnormal gait, its associated falls and complications have high patient morbidity, mortality. Computer vision detects, predicts patient gait abnormalities, assesses fall risk and serves as clinical decision support tool for physicians. This paper performs a systematic review of how computer vision, machine learning models perform an abnormal patient's gait assessment. Computer vision is beneficial in gait analysis, it helps capture the patient posture. Several literature suggests the use of different machine learning algorithms such as SVM, ANN, K-Star, Random Forest, KNN, among others to perform the classification on the features extracted to study patient gait abnormalities.", "published": "2020-03-22T02:00:15Z", "authors": ["Jasmin Hundall", "Benson A. Babu"], "categories": ["cs.CV"], "doi": "", "_query": "property valuation computer vision", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2205.13880v2", "title": "TraClets: Harnessing the power of computer vision for trajectory classification", "abstract": "Due to the advent of new mobile devices and tracking sensors in recent years, huge amounts of data are being produced every day. Therefore, novel methodologies need to emerge that dive through this vast sea of information and generate insights and meaningful information. To this end, researchers have developed several trajectory classification algorithms over the years that are able to annotate tracking data. Similarly, in this research, a novel methodology is presented that exploits image representations of trajectories, called TraClets, in order to classify trajectories in an intuitive humans way, through computer vision techniques. Several real-world datasets are used to evaluate the proposed approach and compare its classification performance to other state-of-the-art trajectory classification algorithms. Experimental results demonstrate that TraClets achieves a classification performance that is comparable to, or in most cases, better than the state-of-the-art, acting as a universal, high-accuracy approach for trajectory classification.", "published": "2022-05-27T10:28:05Z", "authors": ["Ioannis Kontopoulos", "Antonios Makris", "Konstantinos Tserpes", "Vania Bogorny"], "categories": ["cs.CV", "cs.LG"], "doi": "", "_query": "property valuation computer vision", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2012.06908v2", "title": "The Lottery Tickets Hypothesis for Supervised and Self-supervised Pre-training in Computer Vision Models", "abstract": "The computer vision world has been re-gaining enthusiasm in various pre-trained models, including both classical ImageNet supervised pre-training and recently emerged self-supervised pre-training such as simCLR and MoCo. Pre-trained weights often boost a wide range of downstream tasks including classification, detection, and segmentation. Latest studies suggest that pre-training benefits from gigantic model capacity. We are hereby curious and ask: after pre-training, does a pre-trained model indeed have to stay large for its downstream transferability? In this paper, we examine supervised and self-supervised pre-trained models through the lens of the lottery ticket hypothesis (LTH). LTH identifies highly sparse matching subnetworks that can be trained in isolation from (nearly) scratch yet still reach the full models' performance. We extend the scope of LTH and question whether matching subnetworks still exist in pre-trained computer vision models, that enjoy the same downstream transfer performance. Our extensive experiments convey an overall positive message: from all pre-trained weights obtained by ImageNet classification, simCLR, and MoCo, we are consistently able to locate such matching subnetworks at 59.04% to 96.48% sparsity that transfer universally to multiple downstream tasks, whose performance see no degradation compared to using full pre-trained weights. Further analyses reveal that subnetworks found from different pre-training tend to yield diverse mask structures and perturbation sensitivities. We conclude that the core LTH observations remain generally relevant in the pre-training paradigm of computer vision, but more delicate discussions are needed in some cases. Codes and pre-trained models will be made available at: https://github.com/VITA-Group/CV_LTH_Pre-training.", "published": "2020-12-12T21:53:55Z", "authors": ["Tianlong Chen", "Jonathan Frankle", "Shiyu Chang", "Sijia Liu", "Yang Zhang", "Michael Carbin", "Zhangyang Wang"], "categories": ["cs.LG", "cs.CV", "cs.NE"], "doi": "", "_query": "property valuation computer vision", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2205.10063v1", "title": "Uniform Masking: Enabling MAE Pre-training for Pyramid-based Vision Transformers with Locality", "abstract": "Masked AutoEncoder (MAE) has recently led the trends of visual self-supervision area by an elegant asymmetric encoder-decoder design, which significantly optimizes both the pre-training efficiency and fine-tuning accuracy. Notably, the success of the asymmetric structure relies on the \"global\" property of Vanilla Vision Transformer (ViT), whose self-attention mechanism reasons over arbitrary subset of discrete image patches. However, it is still unclear how the advanced Pyramid-based ViTs (e.g., PVT, Swin) can be adopted in MAE pre-training as they commonly introduce operators within \"local\" windows, making it difficult to handle the random sequence of partial vision tokens. In this paper, we propose Uniform Masking (UM), successfully enabling MAE pre-training for Pyramid-based ViTs with locality (termed \"UM-MAE\" for short). Specifically, UM includes a Uniform Sampling (US) that strictly samples $1$ random patch from each $2 \\times 2$ grid, and a Secondary Masking (SM) which randomly masks a portion of (usually $25\\%$) the already sampled regions as learnable tokens. US preserves equivalent elements across multiple non-overlapped local windows, resulting in the smooth support for popular Pyramid-based ViTs; whilst SM is designed for better transferable visual representations since US reduces the difficulty of pixel recovery pre-task that hinders the semantic learning. We demonstrate that UM-MAE significantly improves the pre-training efficiency (e.g., it speeds up and reduces the GPU memory by $\\sim 2\\times$) of Pyramid-based ViTs, but maintains the competitive fine-tuning performance across downstream tasks. For example using HTC++ detector, the pre-trained Swin-Large backbone self-supervised under UM-MAE only in ImageNet-1K can even outperform the one supervised in ImageNet-22K. The codes are available at https://github.com/implus/UM-MAE.", "published": "2022-05-20T10:16:30Z", "authors": ["Xiang Li", "Wenhai Wang", "Lingfeng Yang", "Jian Yang"], "categories": ["cs.CV"], "doi": "", "_query": "property valuation computer vision", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2409.07736v1", "title": "Transfer Learning Applied to Computer Vision Problems: Survey on Current Progress, Limitations, and Opportunities", "abstract": "The field of Computer Vision (CV) has faced challenges. Initially, it relied on handcrafted features and rule-based algorithms, resulting in limited accuracy. The introduction of machine learning (ML) has brought progress, particularly Transfer Learning (TL), which addresses various CV problems by reusing pre-trained models. TL requires less data and computing while delivering nearly equal accuracy, making it a prominent technique in the CV landscape. Our research focuses on TL development and how CV applications use it to solve real-world problems. We discuss recent developments, limitations, and opportunities.", "published": "2024-09-12T03:59:15Z", "authors": ["Aaryan Panda", "Damodar Panigrahi", "Shaswata Mitra", "Sudip Mittal", "Shahram Rahimi"], "categories": ["cs.CV", "cs.AI"], "doi": "", "_query": "property valuation computer vision", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2003.08485v1", "title": "Self-Supervised Contextual Bandits in Computer Vision", "abstract": "Contextual bandits are a common problem faced by machine learning practitioners in domains as diverse as hypothesis testing to product recommendations. There have been a lot of approaches in exploiting rich data representations for contextual bandit problems with varying degree of success. Self-supervised learning is a promising approach to find rich data representations without explicit labels. In a typical self-supervised learning scheme, the primary task is defined by the problem objective (e.g. clustering, classification, embedding generation etc.) and the secondary task is defined by the self-supervision objective (e.g. rotation prediction, words in neighborhood, colorization, etc.). In the usual self-supervision, we learn implicit labels from the training data for a secondary task. However, in the contextual bandit setting, we don't have the advantage of getting implicit labels due to lack of data in the initial phase of learning. We provide a novel approach to tackle this issue by combining a contextual bandit objective with a self supervision objective. By augmenting contextual bandit learning with self-supervision we get a better cumulative reward. Our results on eight popular computer vision datasets show substantial gains in cumulative reward. We provide cases where the proposed scheme doesn't perform optimally and give alternative methods for better learning in these cases.", "published": "2020-03-18T22:06:34Z", "authors": ["Aniket Anand Deshmukh", "Abhimanu Kumar", "Levi Boyles", "Denis Charles", "Eren Manavoglu", "Urun Dogan"], "categories": ["cs.CV", "cs.LG", "stat.ML"], "doi": "", "_query": "property valuation computer vision", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2402.12041v2", "title": "Surround-View Fisheye Optics in Computer Vision and Simulation: Survey and Challenges", "abstract": "In this paper, we provide a survey on automotive surround-view fisheye optics, with an emphasis on the impact of optical artifacts on computer vision tasks in autonomous driving and ADAS. The automotive industry has advanced in applying state-of-the-art computer vision to enhance road safety and provide automated driving functionality. When using camera systems on vehicles, there is a particular need for a wide field of view to capture the entire vehicle's surroundings, in areas such as low-speed maneuvering, automated parking, and cocoon sensing. However, one crucial challenge in surround-view cameras is the strong optical aberrations of the fisheye camera, which is an area that has received little attention in the literature. Additionally, a comprehensive dataset is needed for testing safety-critical scenarios in vehicle automation. The industry has turned to simulation as a cost-effective strategy for creating synthetic datasets with surround-view camera imagery. We examine different simulation methods (such as model-driven and data-driven simulations) and discuss the simulators' ability (or lack thereof) to model real-world optical performance. Overall, this paper highlights the optical aberrations in automotive fisheye datasets, and the limitations of optical reality in simulated fisheye datasets, with a focus on computer vision in surround-view optical systems.", "published": "2024-02-19T10:56:28Z", "authors": ["Daniel Jakab", "Brian Michael Deegan", "Sushil Sharma", "Eoin Martino Grua", "Jonathan Horgan", "Enda Ward", "Pepijn Van De Ven", "Anthony Scanlan", "Ciarán Eising"], "categories": ["cs.CV", "eess.IV"], "doi": "10.1109/TITS.2024.3368136", "_query": "property valuation computer vision", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2603.20985v1", "title": "Consistent but Dangerous: Per-Sample Safety Classification Reveals False Reliability in Medical Vision-Language Models", "abstract": "Consistency under paraphrase, the property that semantically equivalent prompts yield identical predictions, is increasingly used as a proxy for reliability when deploying medical vision-language models (VLMs). We show this proxy is fundamentally flawed: a model can achieve perfect consistency by relying on text patterns rather than the input image. We introduce a four-quadrant per-sample safety taxonomy that jointly evaluates consistency (stable predictions across paraphrased prompts) and image reliance (predictions that change when the image is removed). Samples are classified as Ideal (consistent and image-reliant), Fragile (inconsistent but image-reliant), Dangerous (consistent but not image-reliant), or Worst (inconsistent and not image-reliant). Evaluating five medical VLM configurations across two chest X-ray datasets (MIMIC-CXR, PadChest), we find that LoRA fine-tuning dramatically reduces flip rates but shifts a majority of samples into the Dangerous quadrant: LLaVA-Rad Base achieves a 1.5% flip rate on PadChest while 98.5% of its samples are Dangerous. Critically, Dangerous samples exhibit high accuracy (up to 99.6%) and low entropy, making them invisible to standard confidence-based screening. We observe a negative correlation between flip rate and Dangerous fraction (r = -0.89, n=10) and recommend that deployment evaluations always pair consistency checks with a text-only baseline: a single additional forward pass that exposes the false reliability trap.", "published": "2026-03-22T00:06:53Z", "authors": ["Binesh Sadanandan", "Vahid Behzadan"], "categories": ["cs.CV"], "doi": "", "_query": "property valuation computer vision", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2004.06180v1", "title": "Challenges and Opportunities for Computer Vision in Real-life Soccer Analytics", "abstract": "In this paper, we explore some of the applications of computer vision to sports analytics. Sport analytics deals with understanding and discovering patterns from a corpus of sports data. Analysing such data provides important performance metrics for the players, for instance in soccer matches, that could be useful for estimating their fitness and strengths. Team level statistics can also be estimated from such analysis. This paper mainly focuses on some the challenges and opportunities presented by sport video analysis in computer vision. Specifically, we use our multi-camera setup as a framework to discuss some of the real-life challenges for machine learning algorithms.", "published": "2020-04-13T20:06:23Z", "authors": ["Neha Bhargava", "Fabio Cuzzolin"], "categories": ["cs.CV"], "doi": "", "_query": "property valuation computer vision", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/0908.1033v1", "title": "Topological design of minimum cost survivable computer communication networks, Bipartite Graph Method", "abstract": "A good computer network is hard to disrupt. It is desired that the computer communication network remains connected even when some of the links or nodes fail. Since the communication links are expensive, one wants to achieve these goals with fewer links. The computer communication network is fault tolerant if it has alternative paths between vertices, the more disjoint paths, the better is the survivability. This paper presents a method for generating k-connected computer communication network with optimal number of links using bipartite graph concept.", "published": "2009-08-07T12:37:26Z", "authors": ["Kamalesh V. N", "S. K. Srivatsa"], "categories": ["cs.NI"], "doi": "", "_query": "property valuation computer vision", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2206.08833v1", "title": "A Comparative Study of Confidence Calibration in Deep Learning: From Computer Vision to Medical Imaging", "abstract": "Although deep learning prediction models have been successful in the discrimination of different classes, they can often suffer from poor calibration across challenging domains including healthcare. Moreover, the long-tail distribution poses great challenges in deep learning classification problems including clinical disease prediction. There are approaches proposed recently to calibrate deep prediction in computer vision, but there are no studies found to demonstrate how the representative models work in different challenging contexts. In this paper, we bridge the confidence calibration from computer vision to medical imaging with a comparative study of four high-impact calibration models. Our studies are conducted in different contexts (natural image classification and lung cancer risk estimation) including in balanced vs. imbalanced training sets and in computer vision vs. medical imaging. Our results support key findings: (1) We achieve new conclusions which are not studied under different learning contexts, e.g., combining two calibration models that both mitigate the overconfident prediction can lead to under-confident prediction, and simpler calibration models from the computer vision domain tend to be more generalizable to medical imaging. (2) We highlight the gap between general computer vision tasks and medical imaging prediction, e.g., calibration methods ideal for general computer vision tasks may in fact damage the calibration of medical imaging prediction. (3) We also reinforce previous conclusions in natural image classification settings. We believe that this study has merits to guide readers to choose calibration models and understand gaps between general computer vision and medical imaging domains.", "published": "2022-06-17T15:27:24Z", "authors": ["Riqiang Gao", "Thomas Li", "Yucheng Tang", "Zhoubing Xu", "Michael Kammer", "Sanja L. Antic", "Kim Sandler", "Fabien Moldonado", "Thomas A. Lasko", "Bennett Landman"], "categories": ["cs.CV"], "doi": "", "_query": "property valuation computer vision", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2610.11032v1", "title": "Frobenius splitting of valuation rings revisited", "abstract": "Let $K$ be a function field over an $F$-finite field $k$ of characteristic $p > 0$ and let $ν$ be a valuation of $K/k$. We show $ν$ is an Abhyankar valuation of $K/k$ if and only if the corresponding valuation ring is Frobenius split. The proof proceeds by analyzing when ideals of a valuation ring are uniformly $F$-compatible and establishing a general ramification-theoretic characterization of Frobenius split valuation rings of $F$-finite fields. In addition, when the ground field $k$ is not $F$-finite, we show that the equivalence between Frobenius splitting and the Abhyankar property can fail by constructing an example of an excellent Frobenius split DVR of a function field whose corresponding valuation is not divisorial. Our methods do not rely on local uniformization results.", "published": "2026-10-08T00:24:05Z", "authors": ["Rankeya Datta", "Karen E. Smith"], "categories": ["math.AC", "math.AG"], "doi": "", "_query": "property valuation computer vision", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2609.02184v1", "title": "CC-4DGS: Computational Deformation and Point-Cloud Compression for Storage-Efficient Dynamic Gaussian Splatting", "abstract": "Dynamic four-dimensional (4D) Gaussian Splatting has emerged as a powerful explicit representation for high-quality view synthesis, yet existing methods still require tens to hundreds of megabytes per scene due to their heavy reliance on large multi-resolution hash tables and high-dimensional Gaussian attributes. This paper presents CC-4DGS, a storage-efficient and scalable framework that rethinks both deformation modeling and canonical attribute storage. First, we introduce a computational deformation field (CDF) that replaces large multi-resolution learnable hash tables with deterministic dense hash encoding and compact neural decoders, enabling on-the-fly synthesis of deformation features while reducing deformation storage to only 1--3 MB per scene. Second, we propose a compression of canonical point-cloud attributes (CCA) pipeline that compresses high-dimensional spherical harmonic appearance terms and auxiliary Gaussian attributes via conditional autoencoding, selective quantization, and residual codebooks, achieving 3--5$\\times$ point-cloud reduction with negligible quality loss. Together, these components yield a unified representation that preserves real-time rendering performance while reducing total storage to 20--30 MB. Extensive experiments across the N3DV and Technicolor Light Field datasets demonstrate that CC-4DGS achieves reconstruction accuracy comparable to state-of-the-art methods such as Swift4D, while offering significantly improved storage efficiency and favorable runtime-memory trade-offs.", "published": "2026-09-02T06:47:19Z", "authors": ["Kyungdae Park", "Chae Eun Rhee"], "categories": ["cs.CV"], "doi": "10.1109/TVCG.2026.3721136", "_query": "property valuation computer vision", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2201.10865v2", "title": "On the Issues of TrueDepth Sensor Data for Computer Vision Tasks Across Different iPad Generations", "abstract": "In 2017 Apple introduced the TrueDepth sensor with the iPhone X release. Although its primary use case is biometric face recognition, the exploitation of accurate depth data for other computer vision tasks like segmentation, portrait image generation and metric 3D reconstruction seems natural and lead to the development of various applications. In this report, we investigate the reliability of TrueDepth data - accessed through two different APIs - on various devices including different iPhone and iPad generations and reveal two different and significant issues on all tested iPads.", "published": "2022-01-26T10:53:54Z", "authors": ["Steffen Urban", "Thomas Lindemeier", "David Dobbelstein", "Matthias Haenel"], "categories": ["cs.CV", "cs.RO"], "doi": "", "_query": "property valuation computer vision", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2404.00679v1", "title": "Weak-to-Strong 3D Object Detection with X-Ray Distillation", "abstract": "This paper addresses the critical challenges of sparsity and occlusion in LiDAR-based 3D object detection. Current methods often rely on supplementary modules or specific architectural designs, potentially limiting their applicability to new and evolving architectures. To our knowledge, we are the first to propose a versatile technique that seamlessly integrates into any existing framework for 3D Object Detection, marking the first instance of Weak-to-Strong generalization in 3D computer vision. We introduce a novel framework, X-Ray Distillation with Object-Complete Frames, suitable for both supervised and semi-supervised settings, that leverages the temporal aspect of point cloud sequences. This method extracts crucial information from both previous and subsequent LiDAR frames, creating Object-Complete frames that represent objects from multiple viewpoints, thus addressing occlusion and sparsity. Given the limitation of not being able to generate Object-Complete frames during online inference, we utilize Knowledge Distillation within a Teacher-Student framework. This technique encourages the strong Student model to emulate the behavior of the weaker Teacher, which processes simple and informative Object-Complete frames, effectively offering a comprehensive view of objects as if seen through X-ray vision. Our proposed methods surpass state-of-the-art in semi-supervised learning by 1-1.5 mAP and enhance the performance of five established supervised models by 1-2 mAP on standard autonomous driving datasets, even with default hyperparameters. Code for Object-Complete frames is available here: https://github.com/sakharok13/X-Ray-Teacher-Patching-Tools.", "published": "2024-03-31T13:09:06Z", "authors": ["Alexander Gambashidze", "Aleksandr Dadukin", "Maksim Golyadkin", "Maria Razzhivina", "Ilya Makarov"], "categories": ["cs.CV"], "doi": "", "_query": "property valuation computer vision", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2510.22370v1", "title": "BLIP-FusePPO: A Vision-Language Deep Reinforcement Learning Framework for Lane Keeping in Autonomous Vehicles", "abstract": "In this paper, we propose Bootstrapped Language-Image Pretraining-driven Fused State Representation in Proximal Policy Optimization (BLIP-FusePPO), a novel multimodal reinforcement learning (RL) framework for autonomous lane-keeping (LK), in which semantic embeddings generated by a vision-language model (VLM) are directly fused with geometric states, LiDAR observations, and Proportional-Integral-Derivative-based (PID) control feedback within the agent observation space. The proposed method lets the agent learn driving rules that are aware of their surroundings and easy to understand by combining high-level scene understanding from the VLM with low-level control and spatial signals. Our architecture brings together semantic, geometric, and control-aware representations to make policy learning more robust. A hybrid reward function that includes semantic alignment, LK accuracy, obstacle avoidance, and speed regulation helps learning to be more efficient and generalizable. Our method is different from the approaches that only use semantic models to shape rewards. Instead, it directly embeds semantic features into the state representation. This cuts down on expensive runtime inference and makes sure that semantic guidance is always available. The simulation results show that the proposed model is better at LK stability and adaptability than the best vision-based and multimodal RL baselines in a wide range of difficult driving situations. We make our code publicly available.", "published": "2025-10-25T17:27:08Z", "authors": ["Seyed Ahmad Hosseini Miangoleh", "Amin Jalal Aghdasian", "Farzaneh Abdollahi"], "categories": ["cs.RO", "cs.AI", "cs.CV", "cs.LG", "cs.SE"], "doi": "", "_query": "property valuation computer vision", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2208.09698v1", "title": "Fuse and Attend: Generalized Embedding Learning for Art and Sketches", "abstract": "While deep Embedding Learning approaches have witnessed widespread success in multiple computer vision tasks, the state-of-the-art methods for representing natural images need not necessarily perform well on images from other domains, such as paintings, cartoons, and sketch. This is because of the huge shift in the distribution of data from across these domains, as compared to natural images. Domains like sketch often contain sparse informative pixels. However, recognizing objects in such domains is crucial, given multiple relevant applications leveraging such data, for instance, sketch to image retrieval. Thus, achieving an Embedding Learning model that could perform well across multiple domains is not only challenging, but plays a pivotal role in computer vision. To this end, in this paper, we propose a novel Embedding Learning approach with the goal of generalizing across different domains. During training, given a query image from a domain, we employ gated fusion and attention to generate a positive example, which carries a broad notion of the semantics of the query object category (from across multiple domains). By virtue of Contrastive Learning, we pull the embeddings of the query and positive, in order to learn a representation which is robust across domains. At the same time, to teach the model to be discriminative against examples from different semantic categories (across domains), we also maintain a pool of negative embeddings (from different categories). We show the prowess of our method using the DomainBed framework, on the popular PACS (Photo, Art painting, Cartoon, and Sketch) dataset.", "published": "2022-08-20T14:44:11Z", "authors": ["Ujjal Kr Dutta"], "categories": ["cs.CV", "cs.AI", "cs.LG"], "doi": "", "_query": "property valuation computer vision", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/1407.8156v2", "title": "Uniformly defining $p$-henselian valuations", "abstract": "Admitting a non-trivial $p$-henselian valuation is a weaker assumption on a field than admitting a non-trivial henselian valuation. Unlike henselianity, $p$-henselianity is an elementary property in the language of rings. We are interested in the question when a field admits a non-trivial 0-definable $p$-henselian valuation (in the language of rings). We give a classification of elementary classes of fields in which the canonical $p$-henselian valuation is uniformly 0-definable. We then apply this to show that there is a definable valuation inducing the ($t$-)henselian topology on any ($t$-)henselian field which is neither separably nor real closed.", "published": "2014-07-30T18:50:30Z", "authors": ["Franziska Jahnke", "Jochen Koenigsmann"], "categories": ["math.LO", "math.AC"], "doi": "", "_query": "property valuation computer vision", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/1807.06653v4", "title": "Invariant Information Clustering for Unsupervised Image Classification and Segmentation", "abstract": "We present a novel clustering objective that learns a neural network classifier from scratch, given only unlabelled data samples. The model discovers clusters that accurately match semantic classes, achieving state-of-the-art results in eight unsupervised clustering benchmarks spanning image classification and segmentation. These include STL10, an unsupervised variant of ImageNet, and CIFAR10, where we significantly beat the accuracy of our closest competitors by 6.6 and 9.5 absolute percentage points respectively. The method is not specialised to computer vision and operates on any paired dataset samples; in our experiments we use random transforms to obtain a pair from each image. The trained network directly outputs semantic labels, rather than high dimensional representations that need external processing to be usable for semantic clustering. The objective is simply to maximise mutual information between the class assignments of each pair. It is easy to implement and rigorously grounded in information theory, meaning we effortlessly avoid degenerate solutions that other clustering methods are susceptible to. In addition to the fully unsupervised mode, we also test two semi-supervised settings. The first achieves 88.8% accuracy on STL10 classification, setting a new global state-of-the-art over all existing methods (whether supervised, semi-supervised or unsupervised). The second shows robustness to 90% reductions in label coverage, of relevance to applications that wish to make use of small amounts of labels. github.com/xu-ji/IIC", "published": "2018-07-17T20:17:29Z", "authors": ["Xu Ji", "João F. Henriques", "Andrea Vedaldi"], "categories": ["cs.CV", "cs.LG"], "doi": "", "_query": "property valuation computer vision", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2207.10741v1", "title": "Irrelevant Pixels are Everywhere: Find and Exclude Them for More Efficient Computer Vision", "abstract": "Computer vision is often performed using Convolutional Neural Networks (CNNs). CNNs are compute-intensive and challenging to deploy on power-contrained systems such as mobile and Internet-of-Things (IoT) devices. CNNs are compute-intensive because they indiscriminately compute many features on all pixels of the input image. We observe that, given a computer vision task, images often contain pixels that are irrelevant to the task. For example, if the task is looking for cars, pixels in the sky are not very useful. Therefore, we propose that a CNN be modified to only operate on relevant pixels to save computation and energy. We propose a method to study three popular computer vision datasets, finding that 48% of pixels are irrelevant. We also propose the focused convolution to modify a CNN's convolutional layers to reject the pixels that are marked irrelevant. On an embedded device, we observe no loss in accuracy, while inference latency, energy consumption, and multiply-add count are all reduced by about 45%.", "published": "2022-07-21T20:22:15Z", "authors": ["Caleb Tung", "Abhinav Goel", "Xiao Hu", "Nicholas Eliopoulos", "Emmanuel Amobi", "George K. Thiruvathukal", "Vipin Chaudhary", "Yung-Hsiang Lu"], "categories": ["cs.CV"], "doi": "", "_query": "property valuation computer vision", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/1707.07165v1", "title": "Coarse-to-Fine Lifted MAP Inference in Computer Vision", "abstract": "There is a vast body of theoretical research on lifted inference in probabilistic graphical models (PGMs). However, few demonstrations exist where lifting is applied in conjunction with top of the line applied algorithms. We pursue the applicability of lifted inference for computer vision (CV), with the insight that a globally optimal (MAP) labeling will likely have the same label for two symmetric pixels. The success of our approach lies in efficiently handling a distinct unary potential on every node (pixel), typical of CV applications. This allows us to lift the large class of algorithms that model a CV problem via PGM inference. We propose a generic template for coarse-to-fine (C2F) inference in CV, which progressively refines an initial coarsely lifted PGM for varying quality-time trade-offs. We demonstrate the performance of C2F inference by developing lifted versions of two near state-of-the-art CV algorithms for stereo vision and interactive image segmentation. We find that, against flat algorithms, the lifted versions have a much superior anytime performance, without any loss in final solution quality.", "published": "2017-07-22T13:45:28Z", "authors": ["Haroun Habeeb", "Ankit Anand", " Mausam", "Parag Singla"], "categories": ["cs.CV"], "doi": "10.24963/ijcai.2017/641", "_query": "property valuation computer vision", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/1903.07193v2", "title": "Robust superpixels using color and contour features along linear path", "abstract": "Superpixel decomposition methods are widely used in computer vision and image processing applications. By grouping homogeneous pixels, the accuracy can be increased and the decrease of the number of elements to process can drastically reduce the computational burden. For most superpixel methods, a trade-off is computed between 1) color homogeneity, 2) adherence to the image contours and 3) shape regularity of the decomposition. In this paper, we propose a framework that jointly enforces all these aspects and provides accurate and regular Superpixels with Contour Adherence using Linear Path (SCALP). During the decomposition, we propose to consider color features along the linear path between the pixel and the corresponding superpixel barycenter. A contour prior is also used to prevent the crossing of image boundaries when associating a pixel to a superpixel. Finally, in order to improve the decomposition accuracy and the robustness to noise, we propose to integrate the pixel neighborhood information, while preserving the same computational complexity. SCALP is extensively evaluated on standard segmentation dataset, and the obtained results outperform the ones of the state-of-the-art methods. SCALP is also extended for supervoxel decomposition on MRI images.", "published": "2019-03-17T23:00:13Z", "authors": ["Rémi Giraud", "Vinh-Thong Ta", "Nicolas Papadakis"], "categories": ["cs.CV"], "doi": "", "_query": "property valuation computer vision", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2104.08885v1", "title": "A survey of image labelling for computer vision applications", "abstract": "Supervised machine learning methods for image analysis require large amounts of labelled training data to solve computer vision problems. The recent rise of deep learning algorithms for recognising image content has led to the emergence of many ad-hoc labelling tools. With this survey, we capture and systematise the commonalities as well as the distinctions between existing image labelling software. We perform a structured literature review to compile the underlying concepts and features of image labelling software such as annotation expressiveness and degree of automation. We structure the manual labelling task by its organisation of work, user interface design options, and user support techniques to derive a systematisation schema for this survey. Applying it to available software and the body of literature, enabled us to uncover several application archetypes and key domains such as image retrieval or instance identification in healthcare or television.", "published": "2021-04-18T16:01:55Z", "authors": ["Christoph Sager", "Christian Janiesch", "Patrick Zschech"], "categories": ["cs.CV", "cs.HC", "cs.LG"], "doi": "10.1080/2573234X.2021.1908861", "_query": "property valuation computer vision", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2208.00085v3", "title": "Machine Learning and Computer Vision Techniques in Continuous Beehive Monitoring Applications: A survey", "abstract": "Wide use and availability of the machine learning and computer vision techniques allows development of relatively complex monitoring systems in many domains. Besides the traditional industrial domain, new application appears also in biology and agriculture, where we could speak about the detection of infections, parasites and weeds, but also about automated monitoring and early warning systems. This is also connected with the introduction of the easily accessible hardware and development kits such as Arduino, or RaspberryPi family. In this paper, we survey 50 existing papers focusing on the methods of automated beehive monitoring methods using the computer vision techniques, particularly on the pollen and Varroa mite detection together with the bee traffic monitoring. Such systems could also be used for the monitoring of the honeybee colonies and for the inspection of their health state, which could identify potentially dangerous states before the situation is critical, or to better plan periodic bee colony inspections and therefore save significant costs. Later, we also include analysis of the research trends in this application field and we outline the possible direction of the new explorations. Our paper is aimed also at veterinary and apidology professionals and experts, who might not be familiar with machine learning to introduce them to its possibilities, therefore each family of applications is opened by a brief theoretical introduction and motivation related to its base method. We hope that this paper will inspire other scientists to use machine learning techniques for other applications in beehive monitoring.", "published": "2022-07-29T21:56:59Z", "authors": ["Simon Bilik", "Tomas Zemcik", "Lukas Kratochvila", "Dominik Ricanek", "Milos Richter", "Sebastian Zambanini", "Karel Horak"], "categories": ["cs.CV", "cs.AI", "cs.LG"], "doi": "", "_query": "property valuation computer vision", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2212.10091v1", "title": "Computer Vision Methods for Automating Turbot Fish Cutting", "abstract": "This paper is about the design of an automated machine to cut turbot fish specimens. Machine vision is a key part of this project as it is used to compute a cutting curve for the specimen head. This task is impossible to be carried out by mechanical means. Machine vision is used to detect head boundary and a robot is used to cut the head. Binarization and mathematical morphology are used to detect fish boundary and this boundary is subsequently analyzed (using Hough transform and convex hull) to detect key points and thus defining the cutting curve. Afterwards, mechanical systems are used to slice fish to get an easy presentation for end consumer (as fish fillets than can be easily marketed and consumed).", "published": "2022-12-20T09:08:00Z", "authors": ["Fernando Martin-Rodriguez", "Fernando Isasi-de-Vicente", "Monica Fernandez-Barciela"], "categories": ["eess.IV", "cs.CV"], "doi": "", "_query": "property valuation computer vision", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2311.05304v3", "title": "Data Valuation and Detections in Federated Learning", "abstract": "Federated Learning (FL) enables collaborative model training while preserving the privacy of raw data. A challenge in this framework is the fair and efficient valuation of data, which is crucial for incentivizing clients to contribute high-quality data in the FL task. In scenarios involving numerous data clients within FL, it is often the case that only a subset of clients and datasets are pertinent to a specific learning task, while others might have either a negative or negligible impact on the model training process. This paper introduces a novel privacy-preserving method for evaluating client contributions and selecting relevant datasets without a pre-specified training algorithm in an FL task. Our proposed approach FedBary, utilizes Wasserstein distance within the federated context, offering a new solution for data valuation in the FL framework. This method ensures transparent data valuation and efficient computation of the Wasserstein barycenter and reduces the dependence on validation datasets. Through extensive empirical experiments and theoretical analyses, we demonstrate the potential of this data valuation method as a promising avenue for FL research.", "published": "2023-11-09T12:01:32Z", "authors": ["Wenqian Li", "Shuran Fu", "Fengrui Zhang", "Yan Pang"], "categories": ["cs.LG", "cs.AI", "cs.CR"], "doi": "", "_query": "property valuation computer vision", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/1807.08275v1", "title": "FastOrient: Lightweight Computer Vision for Wrist Control in Assistive Robotic Grasping", "abstract": "Wearable and Assistive robotics for human grasp support are broadly either tele-operated robotic arms or act through orthotic control of a paralyzed user's hand. Such devices require correct orientation for successful and efficient grasping. In many human-robot assistive settings, the end-user is required to explicitly control the many degrees of freedom making effective or efficient control problematic. Here we are demonstrating the off-loading of low-level control of assistive robotics and active orthotics, through automatic end-effector orientation control for grasping. This paper describes a compact algorithm implementing fast computer vision techniques to obtain the orientation of the target object to be grasped, by segmenting the images acquired with a camera positioned on top of the end-effector of the robotic device. The rotation needed that optimises grasping is directly computed from the object's orientation. The algorithm has been evaluated in 6 different scene backgrounds and end-effector approaches to 26 different objects. 94.8% of the objects were detected in all backgrounds. Grasping of the object was achieved in 91.1% of the cases and has been evaluated with a robot simulator confirming the performance of the algorithm.", "published": "2018-07-22T11:48:30Z", "authors": ["Mireia Ruiz Maymo", "Ali Shafti", "A. Aldo Faisal"], "categories": ["cs.RO", "cs.CV"], "doi": "", "_query": "property valuation computer vision", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/1510.04511v1", "title": "Orlicz valuations", "abstract": "In this paper, Orlicz valuations compatible with $SL(n)$ transforms are classified. Unlike their $L_p$ analogs, the identity operator and the reflection operator are the only $SL(n)$ compatible Orlicz valuations (up to dilations). It turns out that the Orlicz projection body operator, the Orlicz centroid body operator and the Orlicz difference body operator are not Orlicz valuations. The property that the Orlicz difference body operator is not an Orlicz valuation plays an important role in characterizing the identity operator and the reflection operator.", "published": "2015-10-15T12:49:12Z", "authors": ["Jin Li", "Gangsong Leng"], "categories": ["math.MG"], "doi": "", "_query": "property valuation computer vision", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2505.19451v3", "title": "Algebraic Zhou valuations", "abstract": "In this paper, we generalize Zhou valuations, originally defined on complex domains, to the framework of general schemes. We demonstrate that an algebraic version of the Jonsson--Mustaţă conjecture is equivalent to the statement that every Zhou valuation is quasi-monomial. By introducing a mixed version of jumping numbers and Tian functions associated with valuations, we obtain characterizations of a valuation being a Zhou valuation or computing some jumping number using the Tian functions. Furthermore, we establish the correspondence between Zhou valuations in algebraic settings and their counterparts in analytic settings.", "published": "2025-05-26T03:17:45Z", "authors": ["Shijie Bao", "Qi'an Guan", "Lin Zhou"], "categories": ["math.AG", "math.CV"], "doi": "", "_query": "property valuation computer vision", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2512.14141v1", "title": "TorchTraceAP: A New Benchmark Dataset for Detecting Performance Anti-Patterns in Computer Vision Models", "abstract": "Identifying and addressing performance anti-patterns in machine learning (ML) models is critical for efficient training and inference, but it typically demands deep expertise spanning system infrastructure, ML models and kernel development. While large tech companies rely on dedicated ML infrastructure engineers to analyze torch traces and benchmarks, such resource-intensive workflows are largely inaccessible to computer vision researchers in general. Among the challenges, pinpointing problematic trace segments within lengthy execution traces remains the most time-consuming task, and is difficult to automate with current ML models, including LLMs. In this work, we present the first benchmark dataset specifically designed to evaluate and improve ML models' ability to detect anti patterns in traces. Our dataset contains over 600 PyTorch traces from diverse computer vision models classification, detection, segmentation, and generation collected across multiple hardware platforms. We also propose a novel iterative approach: a lightweight ML model first detects trace segments with anti patterns, followed by a large language model (LLM) for fine grained classification and targeted feedback. Experimental results demonstrate that our method significantly outperforms unsupervised clustering and rule based statistical techniques for detecting anti pattern regions. Our method also effectively compensates LLM's limited context length and reasoning inefficiencies.", "published": "2025-12-16T06:54:20Z", "authors": ["Hanning Chen", "Keyu Man", "Kevin Zhu", "Chenguang Zhu", "Haonan Li", "Tongbo Luo", "Xizhou Feng", "Wei Sun", "Sreen Tallam", "Mohsen Imani", "Partha Kanuparthy"], "categories": ["cs.CV", "cs.AI"], "doi": "", "_query": "property valuation computer vision", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2107.04259v2", "title": "Unity Perception: Generate Synthetic Data for Computer Vision", "abstract": "We introduce the Unity Perception package which aims to simplify and accelerate the process of generating synthetic datasets for computer vision tasks by offering an easy-to-use and highly customizable toolset. This open-source package extends the Unity Editor and engine components to generate perfectly annotated examples for several common computer vision tasks. Additionally, it offers an extensible Randomization framework that lets the user quickly construct and configure randomized simulation parameters in order to introduce variation into the generated datasets. We provide an overview of the provided tools and how they work, and demonstrate the value of the generated synthetic datasets by training a 2D object detection model. The model trained with mostly synthetic data outperforms the model trained using only real data.", "published": "2021-07-09T07:09:00Z", "authors": ["Steve Borkman", "Adam Crespi", "Saurav Dhakad", "Sujoy Ganguly", "Jonathan Hogins", "You-Cyuan Jhang", "Mohsen Kamalzadeh", "Bowen Li", "Steven Leal", "Pete Parisi", "Cesar Romero", "Wesley Smith", "Alex Thaman", "Samuel Warren", "Nupur Yadav"], "categories": ["cs.CV"], "doi": "", "_query": "property valuation computer vision", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/1610.02431v1", "title": "ResearchDoom and CocoDoom: Learning Computer Vision with Games", "abstract": "In this short note we introduce ResearchDoom, an implementation of the Doom first-person shooter that can extract detailed metadata from the game. We also introduce the CocoDoom dataset, a collection of pre-recorded data extracted from Doom gaming sessions along with annotations in the MS Coco format. ResearchDoom and CocoDoom can be used to train and evaluate a variety of computer vision methods such as object recognition, detection and segmentation at the level of instances and categories, tracking, ego-motion estimation, monocular depth estimation and scene segmentation. The code and data are available at http://www.robots.ox.ac.uk/~vgg/research/researchdoom.", "published": "2016-10-07T21:35:02Z", "authors": ["A. Mahendran", "H. Bilen", "J. F. Henriques", "A. Vedaldi"], "categories": ["cs.CV"], "doi": "", "_query": "property valuation computer vision", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/1801.00553v3", "title": "Threat of Adversarial Attacks on Deep Learning in Computer Vision: A Survey", "abstract": "Deep learning is at the heart of the current rise of machine learning and artificial intelligence. In the field of Computer Vision, it has become the workhorse for applications ranging from self-driving cars to surveillance and security. Whereas deep neural networks have demonstrated phenomenal success (often beyond human capabilities) in solving complex problems, recent studies show that they are vulnerable to adversarial attacks in the form of subtle perturbations to inputs that lead a model to predict incorrect outputs. For images, such perturbations are often too small to be perceptible, yet they completely fool the deep learning models. Adversarial attacks pose a serious threat to the success of deep learning in practice. This fact has lead to a large influx of contributions in this direction. This article presents the first comprehensive survey on adversarial attacks on deep learning in Computer Vision. We review the works that design adversarial attacks, analyze the existence of such attacks and propose defenses against them. To emphasize that adversarial attacks are possible in practical conditions, we separately review the contributions that evaluate adversarial attacks in the real-world scenarios. Finally, we draw on the literature to provide a broader outlook of the research direction.", "published": "2018-01-02T05:22:06Z", "authors": ["Naveed Akhtar", "Ajmal Mian"], "categories": ["cs.CV"], "doi": "", "_query": "property valuation computer vision", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2509.17374v2", "title": "Revisiting Vision Language Foundations for No-Reference Image Quality Assessment", "abstract": "Large-scale vision language pre-training has recently shown promise for no-reference image-quality assessment (NR-IQA), yet the relative merits of modern Vision Transformer foundations remain poorly understood. In this work, we present the first systematic evaluation of six prominent pretrained backbones, CLIP, SigLIP2, DINOv2, DINOv3, Perception, and ResNet, for the task of No-Reference Image Quality Assessment (NR-IQA), each finetuned using an identical lightweight MLP head. Our study uncovers two previously overlooked factors: (1) SigLIP2 consistently achieves strong performance; and (2) the choice of activation function plays a surprisingly crucial role, particularly for enhancing the generalization ability of image quality assessment models. Notably, we find that simple sigmoid activations outperform commonly used ReLU and GELU on several benchmarks. Motivated by this finding, we introduce a learnable activation selection mechanism that adaptively determines the nonlinearity for each channel, eliminating the need for manual activation design, and achieving new state-of-the-art SRCC on CLIVE, KADID10K, and AGIQA3K. Extensive ablations confirm the benefits across architectures and regimes, establishing strong, resource-efficient NR-IQA baselines.", "published": "2025-09-22T06:24:42Z", "authors": ["Ankit Yadav", "Ta Duc Huy", "Lingqiao Liu"], "categories": ["cs.CV"], "doi": "", "_query": "property valuation computer vision", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2003.03396v1", "title": "Scalable Uncertainty for Computer Vision with Functional Variational Inference", "abstract": "As Deep Learning continues to yield successful applications in Computer Vision, the ability to quantify all forms of uncertainty is a paramount requirement for its safe and reliable deployment in the real-world. In this work, we leverage the formulation of variational inference in function space, where we associate Gaussian Processes (GPs) to both Bayesian CNN priors and variational family. Since GPs are fully determined by their mean and covariance functions, we are able to obtain predictive uncertainty estimates at the cost of a single forward pass through any chosen CNN architecture and for any supervised learning task. By leveraging the structure of the induced covariance matrices, we propose numerically efficient algorithms which enable fast training in the context of high-dimensional tasks such as depth estimation and semantic segmentation. Additionally, we provide sufficient conditions for constructing regression loss functions whose probabilistic counterparts are compatible with aleatoric uncertainty quantification.", "published": "2020-03-06T19:09:42Z", "authors": ["Eduardo D C Carvalho", "Ronald Clark", "Andrea Nicastro", "Paul H J Kelly"], "categories": ["cs.CV", "cs.LG"], "doi": "", "_query": "property valuation computer vision", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2007.03269v1", "title": "Single Storage Semi-Global Matching for Real Time Depth Processing", "abstract": "Depth-map is the key computation in computer vision and robotics. One of the most popular approach is via computation of disparity-map of images obtained from Stereo Camera. Semi Global Matching (SGM) method is a popular choice for good accuracy with reasonable computation time. To use such compute-intensive algorithms for real-time applications such as for autonomous aerial vehicles, blind Aid, etc. acceleration using GPU, FPGA is necessary. In this paper, we show the design and implementation of a stereo-vision system, which is based on FPGA-implementation of More Global Matching(MGM). MGM is a variant of SGM. We use 4 paths but store a single cumulative cost value for a corresponding pixel. Our stereo-vision prototype uses Zedboard containing an ARM-based Zynq-SoC, ZED-stereo-camera / ELP stereo-camera / Intel RealSense D435i, and VGA for visualization. The power consumption attributed to the custom FPGA-based acceleration of disparity map computation required for depth-map is just 0.72 watt. The update rate of the disparity map is realistic 10.5 fps.", "published": "2020-07-07T08:12:25Z", "authors": ["Prathmesh Sawant", "Yashwant Temburu", "Mandar Datar", "Imran Ahmed", "Vinayak Shriniwas", "Sachin Patkar"], "categories": ["cs.CV", "cs.AR", "cs.RO"], "doi": "", "_query": "property valuation computer vision", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2604.07912v1", "title": "ParkSense: Where Should a Delivery Driver Park? Leveraging Idle AV Compute and Vision-Language Models", "abstract": "Finding parking consumes a disproportionate share of food delivery time, yet no system addresses precise parking-spot selection relative to merchant entrances. We propose ParkSense, a framework that repurposes idle compute during low-risk AV states -- queuing at red lights, traffic congestion, parking-lot crawl -- to run a Vision-Language Model (VLM) on pre-cached satellite and street view imagery, identifying entrances and legal parking zones. We formalize the Delivery-Aware Precision Parking (DAPP) problem, show that a quantized 7B VLM completes inference in 4-8 seconds on HW4-class hardware, and estimate annual per-driver income gains of 3,000-8,000 USD in the U.S. Five open research directions are identified at this unexplored intersection of autonomous driving, computer vision, and last-mile logistics.", "published": "2026-04-09T07:28:57Z", "authors": ["Die Hu", "Henan Li"], "categories": ["cs.CV", "cs.RO"], "doi": "", "_query": "property valuation computer vision", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2606.29572v1", "title": "Valuation Reveals Uncertainty", "abstract": "This paper studies the recovery of uncertainty from dynamic sublinear valuation rules. A robust valuation assigns each payoff its worst-case expected value across plausible models under uncertainty and induces a dynamic sublinear valuation rule. While valuation rules are observable in practice, the underlying uncertainty structure is latent. First, we show that the latent uncertainty structure can be identified from an observed valuation rule and provide an explicit procedure for recovering it. Second, we develop the notion of time consistency for uncertainty structures as the uncertainty-side counterpart of time consistency in valuation. Third, we characterize all time-consistent uncertainty structures that represent a given valuation rule. Finally, we develop nonparametric estimators for recovering uncertainty from limited valuation data. These results overturn the traditional Knightian view that uncertainty is inherently non-measurable. Indeed, valuation contains sufficient information to identify, characterize, and statistically recover the uncertainty structures that generate it.", "published": "2026-06-28T19:25:38Z", "authors": ["Jongjin Park", "Hyungbin Park"], "categories": ["q-fin.MF", "econ.EM"], "doi": "", "_query": "property valuation computer vision", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2207.07522v1", "title": "Bi-PointFlowNet: Bidirectional Learning for Point Cloud Based Scene Flow Estimation", "abstract": "Scene flow estimation, which extracts point-wise motion between scenes, is becoming a crucial task in many computer vision tasks. However, all of the existing estimation methods utilize only the unidirectional features, restricting the accuracy and generality. This paper presents a novel scene flow estimation architecture using bidirectional flow embedding layers. The proposed bidirectional layer learns features along both forward and backward directions, enhancing the estimation performance. In addition, hierarchical feature extraction and warping improve the performance and reduce computational overhead. Experimental results show that the proposed architecture achieved a new state-of-the-art record by outperforming other approaches with large margin in both FlyingThings3D and KITTI benchmarks. Codes are available at https://github.com/cwc1260/BiFlow.", "published": "2022-07-15T15:14:53Z", "authors": ["Wencan Cheng", "Jong Hwan Ko"], "categories": ["cs.CV", "cs.AI"], "doi": "", "_query": "property valuation computer vision", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2405.06702v1", "title": "Malayalam Sign Language Identification using Finetuned YOLOv8 and Computer Vision Techniques", "abstract": "Technological advancements and innovations are advancing our daily life in all the ways possible but there is a larger section of society who are deprived of accessing the benefits due to their physical inabilities. To reap the real benefits and make it accessible to society, these talented and gifted people should also use such innovations without any hurdles. Many applications developed these days address these challenges, but localized communities and other constrained linguistic groups may find it difficult to use them. Malayalam, a Dravidian language spoken in the Indian state of Kerala is one of the twenty-two scheduled languages in India. Recent years have witnessed a surge in the development of systems and tools in Malayalam, addressing the needs of Kerala, but many of them are not empathetically designed to cater to the needs of hearing-impaired people. One of the major challenges is the limited or no availability of sign language data for the Malayalam language and sufficient efforts are not made in this direction. In this connection, this paper proposes an approach for sign language identification for the Malayalam language using advanced deep learning and computer vision techniques. We start by developing a labeled dataset for Malayalam letters and for the identification we use advanced deep learning techniques such as YOLOv8 and computer vision. Experimental results show that the identification accuracy is comparable to other sign language identification systems and other researchers in sign language identification can use the model as a baseline to develop advanced models.", "published": "2024-05-08T11:37:54Z", "authors": ["Abhinand K.", "Abhiram B. Nair", "Dhananjay C.", "Hanan Hamza", "Mohammed Fawaz J.", "Rahma Fahim K.", "Anoop V. S"], "categories": ["cs.CL", "cs.CV"], "doi": "", "_query": "property valuation computer vision", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2504.18719v1", "title": "Vysics: Object Reconstruction Under Occlusion by Fusing Vision and Contact-Rich Physics", "abstract": "We introduce Vysics, a vision-and-physics framework for a robot to build an expressive geometry and dynamics model of a single rigid body, using a seconds-long RGBD video and the robot's proprioception. While the computer vision community has built powerful visual 3D perception algorithms, cluttered environments with heavy occlusions can limit the visibility of objects of interest. However, observed motion of partially occluded objects can imply physical interactions took place, such as contact with a robot or the environment. These inferred contacts can supplement the visible geometry with \"physible geometry,\" which best explains the observed object motion through physics. Vysics uses a vision-based tracking and reconstruction method, BundleSDF, to estimate the trajectory and the visible geometry from an RGBD video, and an odometry-based model learning method, Physics Learning Library (PLL), to infer the \"physible\" geometry from the trajectory through implicit contact dynamics optimization. The visible and \"physible\" geometries jointly factor into optimizing a signed distance function (SDF) to represent the object shape. Vysics does not require pretraining, nor tactile or force sensors. Compared with vision-only methods, Vysics yields object models with higher geometric accuracy and better dynamics prediction in experiments where the object interacts with the robot and the environment under heavy occlusion. Project page: https://vysics-vision-and-physics.github.io/", "published": "2025-04-25T22:13:34Z", "authors": ["Bibit Bianchini", "Minghan Zhu", "Mengti Sun", "Bowen Jiang", "Camillo J. Taylor", "Michael Posa"], "categories": ["cs.RO"], "doi": "", "_query": "property valuation computer vision", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2207.11718v2", "title": "TIPS: Text-Induced Pose Synthesis", "abstract": "In computer vision, human pose synthesis and transfer deal with probabilistic image generation of a person in a previously unseen pose from an already available observation of that person. Though researchers have recently proposed several methods to achieve this task, most of these techniques derive the target pose directly from the desired target image on a specific dataset, making the underlying process challenging to apply in real-world scenarios as the generation of the target image is the actual aim. In this paper, we first present the shortcomings of current pose transfer algorithms and then propose a novel text-based pose transfer technique to address those issues. We divide the problem into three independent stages: (a) text to pose representation, (b) pose refinement, and (c) pose rendering. To the best of our knowledge, this is one of the first attempts to develop a text-based pose transfer framework where we also introduce a new dataset DF-PASS, by adding descriptive pose annotations for the images of the DeepFashion dataset. The proposed method generates promising results with significant qualitative and quantitative scores in our experiments.", "published": "2022-07-24T11:14:46Z", "authors": ["Prasun Roy", "Subhankar Ghosh", "Saumik Bhattacharya", "Umapada Pal", "Michael Blumenstein"], "categories": ["cs.CV", "cs.MM"], "doi": "", "_query": "property valuation computer vision", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2506.05210v2", "title": "Towards Vision-Language-Garment Models for Web Knowledge Garment Understanding and Generation", "abstract": "Multimodal foundation models have demonstrated strong generalization, yet their ability to transfer knowledge to specialized domains such as garment generation remains underexplored. We introduce VLG, a vision-language-garment model that synthesizes garments from textual descriptions and visual imagery. Our experiments assess VLG's zero-shot generalization, investigating its ability to transfer web-scale reasoning to unseen garment styles and prompts. Preliminary results indicate promising transfer capabilities, highlighting the potential for multimodal foundation models to adapt effectively to specialized domains like fashion design.", "published": "2025-06-05T16:22:17Z", "authors": ["Jan Ackermann", "Kiyohiro Nakayama", "Guandao Yang", "Tong Wu", "Gordon Wetzstein"], "categories": ["cs.CV"], "doi": "", "_query": "property valuation computer vision", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2503.23731v1", "title": "Investigation of intelligent barbell squat coaching system based on computer vision and machine learning", "abstract": "Purpose: Research has revealed that strength training can reduce the incidence of chronic diseases and physical deterioration at any age. Therefore, having a movement diagnostic system is crucial for training alone. Hence, this study developed an artificial intelligence and computer vision-based barbell squat coaching system with a real-time mode that immediately diagnoses the issue and provides feedback after each squat. In addition, a replay mode allows users to examine their previous squats and check their comments. Initially, four primary characteristics of the barbell squat were identified: body joint angles, dorsiflexion, the ratio of knee-to-hip movement, and barbell stability. Methods: We collect 8,151 squats from 77 participants, categorizing them as good squats and six issues. Then, we trained the diagnosis models with three machine-learning architectures. Furthermore, this research applied the SHapley Additive exPlanations (SHAP) method to enhance the accuracy of issue prediction and reduce the computation time by feature selection. Results: The F1 score of the six issues reached 86.86%, 69.01%, 77.42%, 90.74%, 95.83%, and 100%. Each squat diagnosis took less than 0.5 seconds. Finally, this study examined the efficacy of the proposed system with two groups of participants trained with and without the system. Subsequently, participants trained with the system exhibited substantial improvements in their squat technique, as assessed both by the system itself and by a professional weightlifting coach. Conclusion: This is a comprehensive study that integrates artificial intelligence, computer vision and multivariable processing technologies, aimed at building a real-time, user-friendly barbell squat feedback and training system.", "published": "2025-03-31T05:08:52Z", "authors": ["Yinq-Rong Chern", "Yuhao Lee", "Hsiao-Ching Lin", "Guan-Ting Chen", "Ying-Hsien Chen", "Fu-Sung Lin", "Chih-Yao Chuang", "Jenn-Jier James Lien", "Chih-Hsien Huang"], "categories": ["cs.CV", "cs.AI", "eess.IV"], "doi": "", "_query": "property valuation computer vision", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2509.04669v1", "title": "VCMamba: Bridging Convolutions with Multi-Directional Mamba for Efficient Visual Representation", "abstract": "Recent advances in Vision Transformers (ViTs) and State Space Models (SSMs) have challenged the dominance of Convolutional Neural Networks (CNNs) in computer vision. ViTs excel at capturing global context, and SSMs like Mamba offer linear complexity for long sequences, yet they do not capture fine-grained local features as effectively as CNNs. Conversely, CNNs possess strong inductive biases for local features but lack the global reasoning capabilities of transformers and Mamba. To bridge this gap, we introduce \\textit{VCMamba}, a novel vision backbone that integrates the strengths of CNNs and multi-directional Mamba SSMs. VCMamba employs a convolutional stem and a hierarchical structure with convolutional blocks in its early stages to extract rich local features. These convolutional blocks are then processed by later stages incorporating multi-directional Mamba blocks designed to efficiently model long-range dependencies and global context. This hybrid design allows for superior feature representation while maintaining linear complexity with respect to image resolution. We demonstrate VCMamba's effectiveness through extensive experiments on ImageNet-1K classification and ADE20K semantic segmentation. Our VCMamba-B achieves 82.6% top-1 accuracy on ImageNet-1K, surpassing PlainMamba-L3 by 0.3% with 37% fewer parameters, and outperforming Vision GNN-B by 0.3% with 64% fewer parameters. Furthermore, VCMamba-B obtains 47.1 mIoU on ADE20K, exceeding EfficientFormer-L7 by 2.0 mIoU while utilizing 62% fewer parameters. Code is available at https://github.com/Wertyuui345/VCMamba.", "published": "2025-09-04T21:32:27Z", "authors": ["Mustafa Munir", "Alex Zhang", "Radu Marculescu"], "categories": ["cs.CV", "cs.AI", "cs.LG"], "doi": "", "_query": "property valuation computer vision", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/0912.0947v1", "title": "A novel approach for implementing Steganography with computing power obtained by combining Cuda and Matlab", "abstract": "With the current development of multiprocessor systems, strive for computing data on such processor have also increased exponentially. If the multi core processors are not fully utilized, then even though we have the computing power the speed is not available to the end users for their respective applications. In accordance to this, the users or application designers also have to design newer applications taking care of the computing infrastructure available within. Our approach is to use the CUDA (Compute Unified Device Architecture) as backend and MATLAB as the front end to design an application for implementing steganography. Steganography is the term used for hiding information in the cover object like Image, Audio or Video data. As the computing required for multimedia data is much more than the text information, we have been successful in implementing image Steganography with the help of technology for the next generation.", "published": "2009-12-04T21:12:22Z", "authors": ["Samir B. Patel", "Shrikant N. Pradhan", "Saumitra U. Ambegaokar"], "categories": ["cs.CR"], "doi": "", "_query": "property valuation computer vision", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2205.11798v2", "title": "Symbolic Expression Transformer: A Computer Vision Approach for Symbolic Regression", "abstract": "Symbolic Regression (SR) is a type of regression analysis to automatically find the mathematical expression that best fits the data. Currently, SR still basically relies on various searching strategies so that a sample-specific model is required to be optimized for every expression, which significantly limits the model's generalization and efficiency. Inspired by the fact that human beings can infer a mathematical expression based on the curve of it, we propose Symbolic Expression Transformer (SET), a sample-agnostic model from the perspective of computer vision for SR. Specifically, the collected data is represented as images and an image caption model is employed for translating images to symbolic expressions. A large-scale dataset without overlap between training and testing sets in the image domain is released. Our results demonstrate the effectiveness of SET and suggest the promising direction of image-based model for solving the challenging SR problem.", "published": "2022-05-24T05:35:46Z", "authors": ["Jiachen Li", "Ye Yuan", "Hong-Bin Shen"], "categories": ["cs.CV", "cs.AI"], "doi": "", "_query": "property valuation computer vision", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2211.14819v2", "title": "Deep Active Learning for Computer Vision: Past and Future", "abstract": "As an important data selection schema, active learning emerges as the essential component when iterating an Artificial Intelligence (AI) model. It becomes even more critical given the dominance of deep neural network based models, which are composed of a large number of parameters and data hungry, in application. Despite its indispensable role for developing AI models, research on active learning is not as intensive as other research directions. In this paper, we present a review of active learning through deep active learning approaches from the following perspectives: 1) technical advancements in active learning, 2) applications of active learning in computer vision, 3) industrial systems leveraging or with potential to leverage active learning for data iteration, 4) current limitations and future research directions. We expect this paper to clarify the significance of active learning in a modern AI model manufacturing process and to bring additional research attention to active learning. By addressing data automation challenges and coping with automated machine learning systems, active learning will facilitate democratization of AI technologies by boosting model production at scale.", "published": "2022-11-27T13:07:14Z", "authors": ["Rinyoichi Takezoe", "Xu Liu", "Shunan Mao", "Marco Tianyu Chen", "Zhanpeng Feng", "Shiliang Zhang", "Xiaoyu Wang"], "categories": ["cs.LG", "cs.CV"], "doi": "10.1561/116.00000057", "_query": "property valuation computer vision", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2211.05565v2", "title": "Computer Vision on X-ray Data in Industrial Production and Security Applications: A Comprehensive Survey", "abstract": "X-ray imaging technology has been used for decades in clinical tasks to reveal the internal condition of different organs, and in recent years, it has become more common in other areas such as industry, security, and geography. The recent development of computer vision and machine learning techniques has also made it easier to automatically process X-ray images and several machine learning-based object (anomaly) detection, classification, and segmentation methods have been recently employed in X-ray image analysis. Due to the high potential of deep learning in related image processing applications, it has been used in most of the studies. This survey reviews the recent research on using computer vision and machine learning for X-ray analysis in industrial production and security applications and covers the applications, techniques, evaluation metrics, datasets, and performance comparison of those techniques on publicly available datasets. We also highlight some drawbacks in the published research and give recommendations for future research in computer vision-based X-ray analysis.", "published": "2022-11-10T13:37:36Z", "authors": ["Mehdi Rafiei", "Jenni Raitoharju", "Alexandros Iosifidis"], "categories": ["cs.CV"], "doi": "10.1109/ACCESS.2023.3234187", "_query": "property valuation computer vision", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2602.00289v1", "title": "Computer Vision and Its Relationship to Cognitive Science: A perspective from Bayes Decision Theory", "abstract": "This document presents an introduction to computer vision, and its relationship to Cognitive Science, from the perspective of Bayes Decision Theory (Berger 1985). Computer vision is a vast and complex field, so this overview has a narrow scope and provides a theoretical lens which captures many key concepts. BDT is rich enough to include two different approaches: (i) the Bayesian viewpoint, which gives a conceptually attractive framework for vision with concepts that resonate with Cognitive Science (Griffiths et al., 2024), and (ii) the Deep Neural Network approach whose successes in the real world have made Computer Vision into a trillion-dollar industry and which is motivated by the hierarchical structure of the visual ventral stream. The BDT framework relates and captures the strengths and weakness of these two approaches and, by discussing the limitations of BDT, points the way to how they can be combined in a richer framework.", "published": "2026-01-30T20:21:48Z", "authors": ["Alan Yuille", "Daniel Kersten"], "categories": ["cs.CV"], "doi": "", "_query": "property valuation computer vision", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/1809.05076v1", "title": "Computer Vision-aided Atom Tracking in STEM Imaging", "abstract": "To address the SMC'17 data challenge -- \"Data mining atomically resolved images for material properties\", we first used the classic \"blob detection\" algorithms developed in computer vision to identify all atom centers in each STEM image frame. With the help of nearest neighbor analysis, we then found and labeled every atom center common to all the STEM frames and tracked their movements through the given time interval for both Molybdenum or Selenium atoms.", "published": "2018-09-13T17:33:18Z", "authors": ["Yawei Hui", "Yaohua Liu"], "categories": ["cs.CV"], "doi": "", "_query": "property valuation computer vision", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/1603.06777v1", "title": "Energy-Efficient ConvNets Through Approximate Computing", "abstract": "Recently ConvNets or convolutional neural networks (CNN) have come up as state-of-the-art classification and detection algorithms, achieving near-human performance in visual detection. However, ConvNet algorithms are typically very computation and memory intensive. In order to be able to embed ConvNet-based classification into wearable platforms and embedded systems such as smartphones or ubiquitous electronics for the internet-of-things, their energy consumption should be reduced drastically. This paper proposes methods based on approximate computing to reduce energy consumption in state-of-the-art ConvNet accelerators. By combining techniques both at the system- and circuit level, we can gain energy in the systems arithmetic: up to 30x without losing classification accuracy and more than 100x at 99% classification accuracy, compared to the commonly used 16-bit fixed point number format.", "published": "2016-03-22T13:20:36Z", "authors": ["Bert Moons", "Bert De Brabandere", "Luc Van Gool", "Marian Verhelst"], "categories": ["cs.CV"], "doi": "10.1109/WACV.2016.7477614", "_query": "property valuation computer vision", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/1512.01413v2", "title": "Computational Imaging for VLBI Image Reconstruction", "abstract": "Very long baseline interferometry (VLBI) is a technique for imaging celestial radio emissions by simultaneously observing a source from telescopes distributed across Earth. The challenges in reconstructing images from fine angular resolution VLBI data are immense. The data is extremely sparse and noisy, thus requiring statistical image models such as those designed in the computer vision community. In this paper we present a novel Bayesian approach for VLBI image reconstruction. While other methods often require careful tuning and parameter selection for different types of data, our method (CHIRP) produces good results under different settings such as low SNR or extended emission. The success of our method is demonstrated on realistic synthetic experiments as well as publicly available real data. We present this problem in a way that is accessible to members of the community, and provide a dataset website (vlbiimaging.csail.mit.edu) that facilitates controlled comparisons across algorithms.", "published": "2015-12-04T14:11:46Z", "authors": ["Katherine L. Bouman", "Michael D. Johnson", "Daniel Zoran", "Vincent L. Fish", "Sheperd S. Doeleman", "William T. Freeman"], "categories": ["astro-ph.IM", "astro-ph.GA", "cs.CV"], "doi": "", "_query": "property valuation computer vision", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2404.03121v1", "title": "Utilizing Computer Vision for Continuous Monitoring of Vaccine Side Effects in Experimental Mice", "abstract": "The demand for improved efficiency and accuracy in vaccine safety assessments is increasing. Here, we explore the application of computer vision technologies to automate the monitoring of experimental mice for potential side effects after vaccine administration. Traditional observation methods are labor-intensive and lack the capability for continuous monitoring. By deploying a computer vision system, our research aims to improve the efficiency and accuracy of vaccine safety assessments. The methodology involves training machine learning models on annotated video data of mice behaviors pre- and post-vaccination. Preliminary results indicate that computer vision effectively identify subtle changes, signaling possible side effects. Therefore, our approach has the potential to significantly enhance the monitoring process in vaccine trials in animals, providing a practical solution to the limitations of human observation.", "published": "2024-04-03T23:59:59Z", "authors": ["Chuang Li", "Shuai Shao", "Willian Mikason", "Rubing Lin", "Yantong Liu"], "categories": ["cs.CV", "q-bio.NC"], "doi": "", "_query": "property valuation computer vision", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2508.17975v1", "title": "Enhanced Drift-Aware Computer Vision Architecture for Autonomous Driving", "abstract": "The use of computer vision in automotive is a trending research in which safety and security are a primary concern. In particular, for autonomous driving, preventing road accidents requires highly accurate object detection under diverse conditions. To address this issue, recently the International Organization for Standardization (ISO) released the 8800 norm, providing structured frameworks for managing associated AI relevant risks. However, challenging scenarios such as adverse weather or low lighting often introduce data drift, leading to degraded model performance and potential safety violations. In this work, we present a novel hybrid computer vision architecture trained with thousands of synthetic image data from the road environment to improve robustness in unseen drifted environments. Our dual mode framework utilized YOLO version 8 for swift detection and incorporated a five-layer CNN for verification. The system functioned in sequence and improved the detection accuracy by more than 90\\% when tested with drift-augmented road images. The focus was to demonstrate how such a hybrid model can provide better road safety when working together in a hybrid structure.", "published": "2025-08-25T12:43:29Z", "authors": ["Md Shahi Amran Hossain", "Abu Shad Ahammed", "Sayeri Mukherjee", "Roman Obermaisser"], "categories": ["cs.CV", "math.LO"], "doi": "", "_query": "property valuation computer vision", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2508.19477v1", "title": "Concurrent validity of computer-vision artificial intelligence player tracking software using broadcast footage", "abstract": "This study aimed to: (1) understand whether commercially available computer-vision and artificial intelligence (AI) player tracking software can accurately measure player position, speed and distance using broadcast footage and (2) determine the impact of camera feed and resolution on accuracy. Data were obtained from one match at the 2022 Qatar Federation Internationale de Football Association (FIFA) World Cup. Tactical, programme and camera 1 feeds were used. Three commercial tracking providers that use computer-vision and AI participated. Providers analysed instantaneous position (x, y coordinates) and speed (m\\,s^{-1}) of each player. Their data were compared with a high-definition multi-camera tracking system (TRACAB Gen 5). Root mean square error (RMSE) and mean bias were calculated. Position RMSE ranged from 1.68 to 16.39 m, while speed RMSE ranged from 0.34 to 2.38 m\\,s^{-1}. Total match distance mean bias ranged from -1745 m (-21.8%) to 1945 m (24.3%) across providers. Computer-vision and AI player tracking software offer the ability to track players with fair precision when players are detected by the software. Providers should use a tactical feed when tracking position and speed, which will maximise player detection, improving accuracy. Both 720p and 1080p resolutions are suitable, assuming appropriate computer-vision and AI models are implemented.", "published": "2025-08-26T23:40:23Z", "authors": ["Zachary L. Crang", "Rich D. Johnston", "Katie L. Mills", "Johsan Billingham", "Sam Robertson", "Michael H. Cole", "Jonathon Weakley", "Adam Hewitt and", "Grant M. Duthie"], "categories": ["cs.CV", "cs.AI"], "doi": "", "_query": "property valuation computer vision", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2109.11480v1", "title": "Improving Tuberculosis (TB) Prediction using Synthetically Generated Computed Tomography (CT) Images", "abstract": "The evaluation of infectious disease processes on radiologic images is an important and challenging task in medical image analysis. Pulmonary infections can often be best imaged and evaluated through computed tomography (CT) scans, which are often not available in low-resource environments and difficult to obtain for critically ill patients. On the other hand, X-ray, a different type of imaging procedure, is inexpensive, often available at the bedside and more widely available, but offers a simpler, two dimensional image. We show that by relying on a model that learns to generate CT images from X-rays synthetically, we can improve the automatic disease classification accuracy and provide clinicians with a different look at the pulmonary disease process. Specifically, we investigate Tuberculosis (TB), a deadly bacterial infectious disease that predominantly affects the lungs, but also other organ systems. We show that relying on synthetically generated CT improves TB identification by 7.50% and distinguishes TB properties up to 12.16% better than the X-ray baseline.", "published": "2021-09-23T16:35:15Z", "authors": ["Ashia Lewis", "Evanjelin Mahmoodi", "Yuyue Zhou", "Megan Coffee", "Elena Sizikova"], "categories": ["eess.IV", "cs.CV", "cs.LG"], "doi": "", "_query": "property valuation computer vision", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2403.18674v1", "title": "Deep Learning for Robust and Explainable Models in Computer Vision", "abstract": "Recent breakthroughs in machine and deep learning (ML and DL) research have provided excellent tools for leveraging enormous amounts of data and optimizing huge models with millions of parameters to obtain accurate networks for image processing. These developments open up tremendous opportunities for using artificial intelligence (AI) in the automation and human assisted AI industry. However, as more and more models are deployed and used in practice, many challenges have emerged. This thesis presents various approaches that address robustness and explainability challenges for using ML and DL in practice. Robustness and reliability are the critical components of any model before certification and deployment in practice. Deep convolutional neural networks (CNNs) exhibit vulnerability to transformations of their inputs, such as rotation and scaling, or intentional manipulations as described in the adversarial attack literature. In addition, building trust in AI-based models requires a better understanding of current models and developing methods that are more explainable and interpretable a priori. This thesis presents developments in computer vision models' robustness and explainability. Furthermore, this thesis offers an example of using vision models' feature response visualization (models' interpretations) to improve robustness despite interpretability and robustness being seemingly unrelated in the related research. Besides methodological developments for robust and explainable vision models, a key message of this thesis is introducing model interpretation techniques as a tool for understanding vision models and improving their design and robustness. In addition to the theoretical developments, this thesis demonstrates several applications of ML and DL in different contexts, such as medical imaging and affective computing.", "published": "2024-03-27T15:17:10Z", "authors": ["Mohammadreza Amirian"], "categories": ["cs.CV"], "doi": "10.18725/OPARU-51464", "_query": "property valuation computer vision", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/1910.12539v1", "title": "Virtual Piano using Computer Vision", "abstract": "In this research, Piano performances have been analyzed only based on visual information. Computer vision algorithms, e.g., Hough transform and binary thresholding, have been applied to find where the keyboard and specific keys are located. At the same time, Convolutional Neural Networks(CNNs) has been also utilized to find whether specific keys are pressed or not, and how much intensity the keys are pressed only based on visual information. Especially for detecting intensity, a new method of utilizing spatial, temporal CNNs model is devised. Early fusion technique is especially applied in temporal CNNs architecture to analyze hand movement. We also make a new dataset for training each model. Especially when finding an intensity of a pressed key, both of video frames and their optical flow images are used to train models to find effectiveness.", "published": "2019-10-28T10:36:30Z", "authors": ["Seongjae Kang", "Jaeyoon Kim", "Sung-eui Yoon"], "categories": ["cs.CV"], "doi": "", "_query": "property valuation computer vision", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/1609.09582v1", "title": "Digitizing Municipal Street Inspections Using Computer Vision", "abstract": "\"People want an authority to tell them how to value things. But they chose this authority not based on facts or results. They chose it because it seems authoritative and familiar.\" - The Big Short The pavement condition index is one such a familiar measure used by many US cities to measure street quality and justify billions of dollars spent every year on street repair. These billion-dollar decisions are based on evaluation criteria that are subjective and not representative. In this paper, we build upon our initial submission to D4GX 2015 that approaches this problem of information asymmetry in municipal decision-making. We describe a process to identify street-defects using computer vision techniques on data collected using the Street Quality Identification Device (SQUID). A User Interface to host a large quantity of image data towards digitizing the street inspection process and enabling actionable intelligence for a core public service is also described. This approach of combining device, data and decision-making around street repair enables cities make targeted decisions about street repair and could lead to an anticipatory response which can result in significant cost savings. Lastly, we share lessons learnt from the deployment of SQUID in the city of Syracuse, NY.", "published": "2016-09-30T03:36:03Z", "authors": ["Varun Adibhatla", "Shi Fan", "Krystof Litomisky", "Patrick Atwater"], "categories": ["cs.CY", "cs.CV"], "doi": "", "_query": "property valuation computer vision", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2212.11596v2", "title": "Deformable Surface Reconstruction via Riemannian Metric Preservation", "abstract": "Estimating the pose of an object from a monocular image is an inverse problem fundamental in computer vision. The ill-posed nature of this problem requires incorporating deformation priors to solve it. In practice, many materials do not perceptibly shrink or extend when manipulated, constituting a powerful and well-known prior. Mathematically, this translates to the preservation of the Riemannian metric. Neural networks offer the perfect playground to solve the surface reconstruction problem as they can approximate surfaces with arbitrary precision and allow the computation of differential geometry quantities. This paper presents an approach to inferring continuous deformable surfaces from a sequence of images, which is benchmarked against several techniques and obtains state-of-the-art performance without the need for offline training.", "published": "2022-12-22T10:45:08Z", "authors": ["Oriol Barbany", "Adrià Colomé", "Carme Torras"], "categories": ["cs.CV"], "doi": "", "_query": "property valuation computer vision", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2202.03163v4", "title": "Patch-Based Stochastic Attention for Image Editing", "abstract": "Attention mechanisms have become of crucial importance in deep learning in recent years. These non-local operations, which are similar to traditional patch-based methods in image processing, complement local convolutions. However, computing the full attention matrix is an expensive step with heavy memory and computational loads. These limitations curb network architectures and performances, in particular for the case of high resolution images. We propose an efficient attention layer based on the stochastic algorithm PatchMatch, which is used for determining approximate nearest neighbors. We refer to our proposed layer as a \"Patch-based Stochastic Attention Layer\" (PSAL). Furthermore, we propose different approaches, based on patch aggregation, to ensure the differentiability of PSAL, thus allowing end-to-end training of any network containing our layer. PSAL has a small memory footprint and can therefore scale to high resolution images. It maintains this footprint without sacrificing spatial precision and globality of the nearest neighbors, which means that it can be easily inserted in any level of a deep architecture, even in shallower levels. We demonstrate the usefulness of PSAL on several image editing tasks, such as image inpainting, guided image colorization, and single-image super-resolution. Our code is available at: https://github.com/ncherel/psal", "published": "2022-02-07T13:42:00Z", "authors": ["Nicolas Cherel", "Andrés Almansa", "Yann Gousseau", "Alasdair Newson"], "categories": ["cs.CV"], "doi": "10.1016/j.cviu.2023.103866", "_query": "property valuation computer vision", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2503.02675v1", "title": "State of play and future directions in industrial computer vision AI standards", "abstract": "The recent tremendous advancements in the areas of Artificial Intelligence (AI) and Deep Learning (DL) have also resulted into corresponding remarkable progress in the field of Computer Vision (CV), showcasing robust technological solutions in a wide range of application sectors of high industrial interest (e.g., healthcare, autonomous driving, automation, etc.). Despite the outstanding performance of CV systems in specific domains, their development and exploitation at industrial-scale necessitates, among other, the addressing of requirements related to the reliability, transparency, trustworthiness, security, safety, and robustness of the developed AI models. The latter raises the imperative need for the development of efficient, comprehensive and widely-adopted industrial standards. In this context, this study investigates the current state of play regarding the development of industrial computer vision AI standards, emphasizing on critical aspects, like model interpretability, data quality, and regulatory compliance. In particular, a systematic analysis of launched and currently developing CV standards, proposed by the main international standardization bodies (e.g. ISO/IEC, IEEE, DIN, etc.) is performed. The latter is complemented by a comprehensive discussion on the current challenges and future directions observed in this regularization endeavor.", "published": "2025-03-04T14:46:34Z", "authors": ["Artemis Stefanidou", "Panagiotis Radoglou-Grammatikis", "Vasileios Argyriou", "Panagiotis Sarigiannidis", "Iraklis Varlamis", "Georgios Th. Papadopoulos"], "categories": ["cs.CV", "cs.AI"], "doi": "", "_query": "property valuation computer vision", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2408.12396v1", "title": "Cross-Domain Foundation Model Adaptation: Pioneering Computer Vision Models for Geophysical Data Analysis", "abstract": "We explore adapting foundation models (FMs) from the computer vision domain to geoscience. FMs, large neural networks trained on massive datasets, excel in diverse tasks with remarkable adaptability and generality. However, geoscience faces challenges like lacking curated training datasets and high computational costs for developing specialized FMs. This study considers adapting FMs from computer vision to geoscience, analyzing their scale, adaptability, and generality for geoscientific data analysis. We introduce a workflow that leverages existing computer vision FMs, fine-tuning them for geoscientific tasks, reducing development costs while enhancing accuracy. Through experiments, we demonstrate this workflow's effectiveness in broad applications to process and interpret geoscientific data of lunar images, seismic data, DAS arrays and so on. Our findings introduce advanced ML techniques to geoscience, proving the feasibility and advantages of cross-domain FMs adaptation, driving further advancements in geoscientific data analysis and offering valuable insights for FMs applications in other scientific domains.", "published": "2024-08-22T13:41:18Z", "authors": ["Zhixiang Guo", "Xinming Wu", "Luming Liang", "Hanlin Sheng", "Nuo Chen", "Zhengfa Bi"], "categories": ["cs.CV", "physics.geo-ph"], "doi": "", "_query": "property valuation computer vision", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/1809.05992v1", "title": "Highly-Economized Multi-View Binary Compression for Scalable Image Clustering", "abstract": "How to economically cluster large-scale multi-view images is a long-standing problem in computer vision. To tackle this challenge, we introduce a novel approach named Highly-economized Scalable Image Clustering (HSIC) that radically surpasses conventional image clustering methods via binary compression. We intuitively unify the binary representation learning and efficient binary cluster structure learning into a joint framework. In particular, common binary representations are learned by exploiting both sharable and individual information across multiple views to capture their underlying correlations. Meanwhile, cluster assignment with robust binary centroids is also performed via effective discrete optimization under L21-norm constraint. By this means, heavy continuous-valued Euclidean distance computations can be successfully reduced by efficient binary XOR operations during the clustering procedure. To our best knowledge, HSIC is the first binary clustering work specifically designed for scalable multi-view image clustering. Extensive experimental results on four large-scale image datasets show that HSIC consistently outperforms the state-of-the-art approaches, whilst significantly reducing computational time and memory footprint.", "published": "2018-09-17T01:35:42Z", "authors": ["Zheng Zhang", "Li Liu", "Jie Qin", "Fan Zhu", "Fumin Shen", "Yong Xu", "Ling Shao", "Heng Tao Shen"], "categories": ["cs.CV", "cs.DM", "cs.LG"], "doi": "", "_query": "property valuation computer vision", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2412.20486v2", "title": "LSQCA: Resource-Efficient Load/Store Architecture for Limited-Scale Fault-Tolerant Quantum Computing", "abstract": "Current fault-tolerant quantum computer (FTQC) architectures utilize several encoding techniques to enable reliable logical operations with restricted qubit connectivity. However, such logical operations demand additional memory overhead to ensure fault tolerance. Since the main obstacle to practical quantum computing is the limited qubit count, our primary mission is to design floorplans that can reduce memory overhead without compromising computational capability. Despite extensive efforts to explore FTQC architectures, even the current state-of-the-art floorplan strategy devotes 50% of memory space to this overhead, not to data storage, to ensure unit-time random access to all logical qubits. In this paper, we propose an FTQC architecture based on a novel floorplan strategy, Load/Store Quantum Computer Architecture (LSQCA), which can achieve almost 100% memory density. The idea behind our architecture is to separate all memory regions into small computational space called Computational Registers (CR) and space-efficient memory space called Scan-Access Memory (SAM). We define an instruction set for these abstract structures and provide concrete designs named point-SAM and line-SAM architectures. With this design, we can improve the memory density by allowing variable-latency memory access while concealing the latency with other bottlenecks. We also propose optimization techniques to exploit properties of quantum programs observed in our static analysis, such as access locality in memory reference timestamps. Our numerical results indicate that LSQCA successfully leverages this idea. In a resource-restricted situation, a specific benchmark shows that we can achieve about 90% memory density with 5% increase in the execution time compared to a conventional floorplan, which achieves at most 50% memory density for unit-time random access. Our design ensures broad quantum applicability.", "published": "2024-12-29T14:58:23Z", "authors": ["Takumi Kobori", "Yasunari Suzuki", "Yosuke Ueno", "Teruo Tanimoto", "Synge Todo", "Yuuki Tokunaga"], "categories": ["quant-ph", "cs.AR"], "doi": "10.1109/HPCA61900.2025.00033", "_query": "property valuation computer vision", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2609.28262v1", "title": "RAMP: Robust Adaptive Mixed-Precision Quantization for Edge CPU Vision Models", "abstract": "Deploying deep learning models on edge CPUs is bottlenecked by computational and memory constraints. Mixed-precision quantization promises to reduce inference latency while preserving accuracy. However, quantization affects different layer types in inconsistent ways, so identifying where accuracy loss is minimized and latency reduction is maximized is critical, as the effect accumulates over a full deployment into substantial savings or unacceptable task degradation. Such identification relies on sensitivity metrics, proxies that estimate layer-wise degradation without evaluating the task accuracy of every candidate policy. Nevertheless, widely used metrics fail systematically on modern architectures. We present a systematic empirical study of 13 sensitivity metrics for layer-wise INT8 quantization across four distinctly different neural networks, and validate the resulting policies on two ARM64 platforms. Gradient-based sensitivity methods fail on 4 out of 8 model-hardware configurations and weight-based statistics on 2. In contrast, the Jensen-Shannon Divergence achieves zero catastrophic failures, reliably isolating the layers that cannot be safely quantized. A sensitivity metric alone does not define a policy, and the fixed thresholds typically used for that step are fragile over the highly skewed distributions of modern architectures. We address this with K-Means clustering, achieving near-lossless accuracy and a mean speed-up of $1.81\\times$ over the full-precision model. Finally, we reveal that excluding from quantization the layers whose speed-up is negligible, regardless of their sensitivity, can be counterproductive, as it induces computational graph fragmentation and disables operator fusion. Our results yield concrete allocation policies for practitioners and researchers deploying quantized vision models on heterogeneous edge CPUs, without GPU access or gradient computation.", "published": "2026-09-23T15:23:04Z", "authors": ["David Población-Criado", "Dario Garcia-Gasulla", "Eduardo Quinones"], "categories": ["cs.CV", "cs.LG", "cs.PF"], "doi": "", "_query": "property valuation computer vision", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2308.16435v1", "title": "Njobvu-AI: An open-source tool for collaborative image labeling and implementation of computer vision models", "abstract": "Practitioners interested in using computer vision models lack user-friendly and open-source software that combines features to label training data, allow multiple users, train new algorithms, review output, and implement new models. Labeling training data, such as images, is a key step to developing accurate object detection algorithms using computer vision. This step is often not compatible with many cloud-based services for marking or labeling image and video data due to limited internet bandwidth in many regions of the world. Desktop tools are useful for groups working in remote locations, but users often do not have the capability to combine projects developed locally by multiple collaborators. Furthermore, many tools offer features for labeling data or using pre-trained models for classification, but few allow researchers to combine these steps to create and apply custom models. Free, open-source, and user-friendly software that offers a full suite of features (e.g., ability to work locally and online, and train custom models) is desirable to field researchers and conservationists that may have limited coding skills. We developed Njobvu-AI, a free, open-source tool that can be run on both desktop and server hardware using Node.js, allowing users to label data, combine projects for collaboration and review, train custom algorithms, and implement new computer vision models. The name Njobvu-AI (pronounced N-joh-voo AI), incorporating the Chichewa word for elephant, is inspired by a wildlife monitoring program in Malawi that was a primary impetus for the development of this tool and references similarities between the powerful memory of elephants and properties of computer vision models.", "published": "2023-08-31T03:49:41Z", "authors": ["Jonathan S. Koning", "Ashwin Subramanian", "Mazen Alotaibi", "Cara L. Appel", "Christopher M. Sullivan", "Thon Chao", "Lisa Truong", "Robyn L. Tanguay", "Pankaj Jaiswal", "Taal Levi", "Damon B. Lesmeister"], "categories": ["cs.CV"], "doi": "", "_query": "property valuation computer vision", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2501.16384v1", "title": "MambaTron: Efficient Cross-Modal Point Cloud Enhancement using Aggregate Selective State Space Modeling", "abstract": "Point cloud enhancement is the process of generating a high-quality point cloud from an incomplete input. This is done by filling in the missing details from a reference like the ground truth via regression, for example. In addition to unimodal image and point cloud reconstruction, we focus on the task of view-guided point cloud completion, where we gather the missing information from an image, which represents a view of the point cloud and use it to generate the output point cloud. With the recent research efforts surrounding state-space models, originally in natural language processing and now in 2D and 3D vision, Mamba has shown promising results as an efficient alternative to the self-attention mechanism. However, there is limited research towards employing Mamba for cross-attention between the image and the input point cloud, which is crucial in multi-modal problems. In this paper, we introduce MambaTron, a Mamba-Transformer cell that serves as a building block for our network which is capable of unimodal and cross-modal reconstruction which includes view-guided point cloud completion.We explore the benefits of Mamba's long-sequence efficiency coupled with the Transformer's excellent analytical capabilities through MambaTron. This approach is one of the first attempts to implement a Mamba-based analogue of cross-attention, especially in computer vision. Our model demonstrates a degree of performance comparable to the current state-of-the-art techniques while using a fraction of the computation resources.", "published": "2025-01-25T05:25:20Z", "authors": ["Sai Tarun Inaganti", "Gennady Petrenko"], "categories": ["cs.CV", "cs.LG", "eess.IV"], "doi": "", "_query": "property valuation computer vision", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2412.09082v3", "title": "Towards Long-Horizon Vision-Language Navigation: Platform, Benchmark and Method", "abstract": "Existing Vision-Language Navigation (VLN) methods primarily focus on single-stage navigation, limiting their effectiveness in multi-stage and long-horizon tasks within complex and dynamic environments. To address these limitations, we propose a novel VLN task, named Long-Horizon Vision-Language Navigation (LH-VLN), which emphasizes long-term planning and decision consistency across consecutive subtasks. Furthermore, to support LH-VLN, we develop an automated data generation platform NavGen, which constructs datasets with complex task structures and improves data utility through a bidirectional, multi-granularity generation approach. To accurately evaluate complex tasks, we construct the Long-Horizon Planning and Reasoning in VLN (LHPR-VLN) benchmark consisting of 3,260 tasks with an average of 150 task steps, serving as the first dataset specifically designed for the long-horizon vision-language navigation task. Furthermore, we propose Independent Success Rate (ISR), Conditional Success Rate (CSR), and CSR weight by Ground Truth (CGT) metrics, to provide fine-grained assessments of task completion. To improve model adaptability in complex tasks, we propose a novel Multi-Granularity Dynamic Memory (MGDM) module that integrates short-term memory blurring with long-term memory retrieval to enable flexible navigation in dynamic environments. Our platform, benchmark and method supply LH-VLN with a robust data generation pipeline, comprehensive model evaluation dataset, reasonable metrics, and a novel VLN model, establishing a foundational framework for advancing LH-VLN.", "published": "2024-12-12T09:08:13Z", "authors": ["Xinshuai Song", "Weixing Chen", "Yang Liu", "Weikai Chen", "Guanbin Li", "Liang Lin"], "categories": ["cs.CV"], "doi": "", "_query": "property valuation computer vision", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2410.19164v1", "title": "HUE Dataset: High-Resolution Event and Frame Sequences for Low-Light Vision", "abstract": "Low-light environments pose significant challenges for image enhancement methods. To address these challenges, in this work, we introduce the HUE dataset, a comprehensive collection of high-resolution event and frame sequences captured in diverse and challenging low-light conditions. Our dataset includes 106 sequences, encompassing indoor, cityscape, twilight, night, driving, and controlled scenarios, each carefully recorded to address various illumination levels and dynamic ranges. Utilizing a hybrid RGB and event camera setup. we collect a dataset that combines high-resolution event data with complementary frame data. We employ both qualitative and quantitative evaluations using no-reference metrics to assess state-of-the-art low-light enhancement and event-based image reconstruction methods. Additionally, we evaluate these methods on a downstream object detection task. Our findings reveal that while event-based methods perform well in specific metrics, they may produce false positives in practical applications. This dataset and our comprehensive analysis provide valuable insights for future research in low-light vision and hybrid camera systems.", "published": "2024-10-24T21:15:15Z", "authors": ["Burak Ercan", "Onur Eker", "Aykut Erdem", "Erkut Erdem"], "categories": ["cs.CV"], "doi": "", "_query": "property valuation computer vision", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2502.16950v1", "title": "A survey of datasets for computer vision in agriculture", "abstract": "In agricultural research, there has been a recent surge in the amount of Computer Vision (CV) focused work. But unlike general CV research, large high-quality public datasets are sparsely available. This can be partially attributed to the high variability between different agricultural tasks, crops and environments as well as the complexity of data collection, but it is also influenced by the reticence to publish datasets by many authors. This, as well as the lack of a widely used agricultural data repository, are impactful factors that hinder research in applied CV for agriculture as well as the usage of agricultural data in general-purpose CV research. In this survey, we provide a large number of high-quality datasets of images taken on fields. Overall, we find 45 datasets, which are listed in this paper as well as in an online catalog on the project website: https://smartfarminglab.github.io/field_dataset_survey/.", "published": "2025-02-24T08:27:36Z", "authors": ["Nico Heider", "Lorenz Gunreben", "Sebastian Zürner", "Martin Schieck"], "categories": ["cs.CV"], "doi": "10.18420/giljt2025_02", "_query": "property valuation computer vision", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2002.01067v1", "title": "Valuation rings are derived splinters", "abstract": "We give three proofs that valuation rings are derived splinters: a geometric proof using the absolute integral closure, a homological proof which reduces the problem to checking that valuation rings are splinters (which is done in the second author's PhD thesis and which we reprise here), and a proof by approximation which reduces the problem to Bhatt's proof of the derived direct summand conjecture. The approximation property also shows that smooth algebras over valuation rings are splinters.", "published": "2020-02-04T00:38:24Z", "authors": ["Benjamin Antieau", "Rankeya Datta"], "categories": ["math.AG", "math.AC"], "doi": "", "_query": "property valuation computer vision", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/quant-ph/0508101v2", "title": "Qdensity - a Mathematica Quantum Computer Simulation", "abstract": "This Mathematica 5.2 package~\\footnote{QDENSITY is available at http://www.pitt.edu/~tabakin/QDENSITY} is a simulation of a Quantum Computer. The program provides a modular, instructive approach for generating the basic elements that make up a quantum circuit. The main emphasis is on using the density matrix, although an approach using state vectors is also implemented in the package. The package commands are defined in {\\it Qdensity.m} which contains the tools needed in quantum circuits, e.g. multiqubit kets, projectors, gates, etc. Selected examples of the basic commands are presented here and a tutorial notebook, {\\it Tutorial.nb} is provided with the package (available on our website) that serves as a full guide to the package. Finally, application is made to a variety of relevant cases, including Teleportation, Quantum Fourier transform, Grover's search and Shor's algorithm, in separate notebooks: {\\it QFT.nb}, {\\it Teleportation.nb}, {\\it Grover.nb} and {\\it Shor.nb} where each algorithm is explained in detail. Finally, two examples of the construction and manipulation of cluster states, which are part of ``one way computing\" ideas, are included as an additional tool in the notebook {\\it Cluster.nb}. A Mathematica palette containing most commands in QDENSITY is also included: {\\it QDENSpalette.nb} .", "published": "2005-08-15T14:48:12Z", "authors": ["B. Juliá-Díaz", "J. M. Burdis", "F. Tabakin"], "categories": ["quant-ph", "physics.comp-ph"], "doi": "10.1016/j.cpc.2005.12.021", "_query": "property valuation computer vision", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2406.10628v1", "title": "Public Computer Vision Datasets for Precision Livestock Farming: A Systematic Survey", "abstract": "Technology-driven precision livestock farming (PLF) empowers practitioners to monitor and analyze animal growth and health conditions for improved productivity and welfare. Computer vision (CV) is indispensable in PLF by using cameras and computer algorithms to supplement or supersede manual efforts for livestock data acquisition. Data availability is crucial for developing innovative monitoring and analysis systems through artificial intelligence-based techniques. However, data curation processes are tedious, time-consuming, and resource intensive. This study presents the first systematic survey of publicly available livestock CV datasets (https://github.com/Anil-Bhujel/Public-Computer-Vision-Dataset-A-Systematic-Survey). Among 58 public datasets identified and analyzed, encompassing different species of livestock, almost half of them are for cattle, followed by swine, poultry, and other animals. Individual animal detection and color imaging are the dominant application and imaging modality for livestock. The characteristics and baseline applications of the datasets are discussed, emphasizing the implications for animal welfare advocates. Challenges and opportunities are also discussed to inspire further efforts in developing livestock CV datasets. This study highlights that the limited quantity of high-quality annotated datasets collected from diverse environments, animals, and applications, the absence of contextual metadata, are a real bottleneck in PLF.", "published": "2024-06-15T13:22:41Z", "authors": ["Anil Bhujel", "Yibin Wang", "Yuzhen Lu", "Daniel Morris", "Mukesh Dangol"], "categories": ["cs.CV", "cs.AI"], "doi": "", "_query": "property valuation computer vision", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2409.08695v3", "title": "Precision Aquaculture: An Integrated Computer Vision and IoT Approach for Optimized Tilapia Feeding", "abstract": "Traditional fish farming practices often lead to inefficient feeding, resulting in environmental issues and reduced productivity. We developed an innovative system combining computer vision and IoT technologies for precise Tilapia feeding. Our solution uses real-time IoT sensors to monitor water quality parameters and computer vision algorithms to analyze fish size and count, determining optimal feed amounts. A mobile app enables remote monitoring and control. We utilized YOLOv8 for keypoint detection to measure Tilapia weight from length, achieving \\textbf{94\\%} precision on 3,500 annotated images. Pixel-based measurements were converted to centimeters using depth estimation for accurate feeding calculations. Our method, with data collection mirroring inference conditions, significantly improved results. Preliminary estimates suggest this approach could increase production up to 58 times compared to traditional farms. Our models, code, and dataset are open-source~\\footnote{The code, dataset, and models are available upon reasonable request.", "published": "2024-09-13T10:27:27Z", "authors": ["Rania Hossam", "Ahmed Heakl", "Walid Gomaa"], "categories": ["cs.CV", "cs.AI", "cs.LG", "cs.RO", "eess.SY"], "doi": "", "_query": "property valuation computer vision", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2211.14743v1", "title": "Searching for Uncollected Litter with Computer Vision", "abstract": "This study combines photo metadata and computer vision to quantify where uncollected litter is present. Images from the Trash Annotations in Context (TACO) dataset were used to teach an algorithm to detect 10 categories of garbage. Although it worked well with smartphone photos, it struggled when trying to process images from vehicle mounted cameras. However, increasing the variety of perspectives and backgrounds in the dataset will help it improve in unfamiliar situations. These data are plotted onto a map which, as accuracy improves, could be used for measuring waste management strategies and quantifying trends.", "published": "2022-11-27T06:30:03Z", "authors": ["Julian Hernandez", "Clark Fitzgerald"], "categories": ["cs.CV"], "doi": "", "_query": "property valuation computer vision", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2402.07945v1", "title": "ScreenAgent: A Vision Language Model-driven Computer Control Agent", "abstract": "Existing Large Language Models (LLM) can invoke a variety of tools and APIs to complete complex tasks. The computer, as the most powerful and universal tool, could potentially be controlled directly by a trained LLM agent. Powered by the computer, we can hopefully build a more generalized agent to assist humans in various daily digital works. In this paper, we construct an environment for a Vision Language Model (VLM) agent to interact with a real computer screen. Within this environment, the agent can observe screenshots and manipulate the Graphics User Interface (GUI) by outputting mouse and keyboard actions. We also design an automated control pipeline that includes planning, acting, and reflecting phases, guiding the agent to continuously interact with the environment and complete multi-step tasks. Additionally, we construct the ScreenAgent Dataset, which collects screenshots and action sequences when completing a variety of daily computer tasks. Finally, we trained a model, ScreenAgent, which achieved computer control capabilities comparable to GPT-4V and demonstrated more precise UI positioning capabilities. Our attempts could inspire further research on building a generalist LLM agent. The code is available at \\url{https://github.com/niuzaisheng/ScreenAgent}.", "published": "2024-02-09T02:33:45Z", "authors": ["Runliang Niu", "Jindong Li", "Shiqi Wang", "Yali Fu", "Xiyu Hu", "Xueyuan Leng", "He Kong", "Yi Chang", "Qi Wang"], "categories": ["cs.HC", "cs.AI", "cs.CV"], "doi": "10.24963/ijcai.2024/711", "_query": "property valuation computer vision", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2210.05451v1", "title": "Enabling ISP-less Low-Power Computer Vision", "abstract": "In order to deploy current computer vision (CV) models on resource-constrained low-power devices, recent works have proposed in-sensor and in-pixel computing approaches that try to partly/fully bypass the image signal processor (ISP) and yield significant bandwidth reduction between the image sensor and the CV processing unit by downsampling the activation maps in the initial convolutional neural network (CNN) layers. However, direct inference on the raw images degrades the test accuracy due to the difference in covariance of the raw images captured by the image sensors compared to the ISP-processed images used for training. Moreover, it is difficult to train deep CV models on raw images, because most (if not all) large-scale open-source datasets consist of RGB images. To mitigate this concern, we propose to invert the ISP pipeline, which can convert the RGB images of any dataset to its raw counterparts, and enable model training on raw images. We release the raw version of the COCO dataset, a large-scale benchmark for generic high-level vision tasks. For ISP-less CV systems, training on these raw images result in a 7.1% increase in test accuracy on the visual wake works (VWW) dataset compared to relying on training with traditional ISP-processed RGB datasets. To further improve the accuracy of ISP-less CV models and to increase the energy and bandwidth benefits obtained by in-sensor/in-pixel computing, we propose an energy-efficient form of analog in-pixel demosaicing that may be coupled with in-pixel CNN computations. When evaluated on raw images captured by real sensors from the PASCALRAW dataset, our approach results in a 8.1% increase in mAP. Lastly, we demonstrate a further 20.5% increase in mAP by using a novel application of few-shot learning with thirty shots each for the novel PASCALRAW dataset, constituting 3 classes.", "published": "2022-10-11T13:47:30Z", "authors": ["Gourav Datta", "Zeyu Liu", "Zihan Yin", "Linyu Sun", "Akhilesh R. Jaiswal", "Peter A. Beerel"], "categories": ["cs.CV", "eess.IV"], "doi": "", "_query": "property valuation computer vision", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/1907.10915v3", "title": "Self-supervised Domain Adaptation for Computer Vision Tasks", "abstract": "Recent progress of self-supervised visual representation learning has achieved remarkable success on many challenging computer vision benchmarks. However, whether these techniques can be used for domain adaptation has not been explored. In this work, we propose a generic method for self-supervised domain adaptation, using object recognition and semantic segmentation of urban scenes as use cases. Focusing on simple pretext/auxiliary tasks (e.g. image rotation prediction), we assess different learning strategies to improve domain adaptation effectiveness by self-supervision. Additionally, we propose two complementary strategies to further boost the domain adaptation accuracy on semantic segmentation within our method, consisting of prediction layer alignment and batch normalization calibration. The experimental results show adaptation levels comparable to most studied domain adaptation methods, thus, bringing self-supervision as a new alternative for reaching domain adaptation. The code is available at https://github.com/Jiaolong/self-supervised-da.", "published": "2019-07-25T09:20:29Z", "authors": ["Jiaolong Xu", "Liang Xiao", "Antonio M. Lopez"], "categories": ["cs.CV", "cs.LG"], "doi": "10.1109/ACCESS.2019.2949697", "_query": "property valuation computer vision", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2604.19054v3", "title": "Evaluation of Winning Solutions of 2025 Low Power Computer Vision Challenge", "abstract": "The IEEE Low-Power Computer Vision Challenge (LPCVC) aims to promote the development of efficient vision models for edge devices, balancing accuracy with constraints such as latency, memory capacity, and energy use. The 2025 challenge featured three tracks: (1) Image classification under various lighting conditions and styles, (2) Open-Vocabulary Segmentation with Text Prompt, and (3) Monocular Depth Estimation. This paper presents the design of LPCVC 2025, including its competition structure and evaluation framework, which integrates the Qualcomm AI Hub for consistent and reproducible benchmarking. The paper also introduces the top-performing solutions from each track and outlines key trends and observations. The paper concludes with suggestions for future computer vision competitions.", "published": "2026-04-21T04:00:55Z", "authors": ["Zihao Ye", "Yung-Hsiang Lu", "Xiao Hu", "Shuai Zhang", "Taotao Jing", "Xin Li", "Zhen Yao", "Bo Lang", "Zhihao Zheng", "Seungmin Oh", "Hankyul Kang", "Seunghun Kang", "Jongbin Ryu", "Kexin Chen", "Yuan Qi", "George K Thiruvathukal", "Mooi Choo Chuah"], "categories": ["cs.CV"], "doi": "", "_query": "property valuation computer vision", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2106.08952v1", "title": "Feedback on the ASTRONET Science Vision and Infrastructure Roadmap from the CTA Consortium", "abstract": "Feedback on the ASTRONET Science Vision and Infrastructure Roadmap from the CTA Consortium.", "published": "2021-06-11T11:41:05Z", "authors": ["A. Araudo", "A. Carosi", "W. Hofmann", "F. Iocco", "J. -P. Lenain", "E. Lindfors", "A. Lopez", "M. Meyer", "G. Morlino", "B. Olmi", "P. Romano", "M. Santander", "L. Tibaldo", "R. Zanin"], "categories": ["astro-ph.IM", "astro-ph.HE"], "doi": "", "_query": "property valuation computer vision", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/1612.01762v1", "title": "Computing tropical varieties over fields with valuation", "abstract": "We show how tropical varieties of ideals I over a field K with non-trivial valuation can be traced back to tropical varieties of ideals in R[[t]][x] over some dense subring R in its ring of integers. Moreover, for homogeneous ideals, we present algorithms on how the latter can be computed in finite time, provided that generators are polynomial in t. While doing so, we also comment on the computation of the Groebner polytope structure and p-adic Groebner bases using our framework. All algorithms solely rely on existing standard basis techniques.", "published": "2016-12-06T11:30:41Z", "authors": ["Thomas Markwig", "Yue Ren"], "categories": ["math.AG", "math.AC"], "doi": "", "_query": "property valuation computer vision", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2503.03395v1", "title": "AI-Driven Multi-Stage Computer Vision System for Defect Detection in Laser-Engraved Industrial Nameplates", "abstract": "Automated defect detection in industrial manufacturing is essential for maintaining product quality and minimizing production errors. In air disc brake manufacturing, ensuring the precision of laser-engraved nameplates is crucial for accurate product identification and quality control. Engraving errors, such as misprints or missing characters, can compromise both aesthetics and functionality, leading to material waste and production delays. This paper presents a proof of concept for an AI-driven computer vision system that inspects and verifies laser-engraved nameplates, detecting defects in logos and alphanumeric strings. The system integrates object detection using YOLOv7, optical character recognition (OCR) with Tesseract, and anomaly detection through a residual variational autoencoder (ResVAE) along with other computer vision methods to enable comprehensive inspections at multiple stages. Experimental results demonstrate the system's effectiveness, achieving 91.33% accuracy and 100% recall, ensuring that defective nameplates are consistently detected and addressed. This solution highlights the potential of AI-driven visual inspection to enhance quality control, reduce manual inspection efforts, and improve overall manufacturing efficiency.", "published": "2025-03-05T11:19:17Z", "authors": ["Adhish Anitha Vilasan", "Stephan Jäger", "Noah Klarmann"], "categories": ["cs.CV", "cs.AI"], "doi": "", "_query": "property valuation computer vision", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2508.01845v1", "title": "Beyond Vulnerabilities: A Survey of Adversarial Attacks as Both Threats and Defenses in Computer Vision Systems", "abstract": "Adversarial attacks against computer vision systems have emerged as a critical research area that challenges the fundamental assumptions about neural network robustness and security. This comprehensive survey examines the evolving landscape of adversarial techniques, revealing their dual nature as both sophisticated security threats and valuable defensive tools. We provide a systematic analysis of adversarial attack methodologies across three primary domains: pixel-space attacks, physically realizable attacks, and latent-space attacks. Our investigation traces the technical evolution from early gradient-based methods such as FGSM and PGD to sophisticated optimization techniques incorporating momentum, adaptive step sizes, and advanced transferability mechanisms. We examine how physically realizable attacks have successfully bridged the gap between digital vulnerabilities and real-world threats through adversarial patches, 3D textures, and dynamic optical perturbations. Additionally, we explore the emergence of latent-space attacks that leverage semantic structure in internal representations to create more transferable and meaningful adversarial examples. Beyond traditional offensive applications, we investigate the constructive use of adversarial techniques for vulnerability assessment in biometric authentication systems and protection against malicious generative models. Our analysis reveals critical research gaps, particularly in neural style transfer protection and computational efficiency requirements. This survey contributes a comprehensive taxonomy, evolution analysis, and identification of future research directions, aiming to advance understanding of adversarial vulnerabilities and inform the development of more robust and trustworthy computer vision systems.", "published": "2025-08-03T17:02:05Z", "authors": ["Zhongliang Guo", "Yifei Qian", "Yanli Li", "Weiye Li", "Chun Tong Lei", "Shuai Zhao", "Lei Fang", "Ognjen Arandjelović", "Chun Pong Lau"], "categories": ["cs.CV", "cs.AI", "cs.CR"], "doi": "", "_query": "property valuation computer vision", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/1308.3394v1", "title": "Superrosy fields and valuations", "abstract": "We prove that every non-trivial valuation on an infinite superrosy field of positive characteristic has divisible value group and algebraically closed residue field. In fact, we prove the following more general result. Let $K$ be a field such that for every finite extension $L$ of $K$ and for every natural number $n>0$ the index $[L^*:(L^*)^n]$ is finite and, if $char(K)=p>0$ and $f: L \\to L$ is given by $f(x)=x^p-x$, the index $[L^+:f[L]]$ is also finite. Then either there is a non-trivial definable valuation on $K$, or every non-trivial valuation on $K$ has divisible value group and, if $char(K)>0$, it has algebraically closed residue field. In the zero characteristic case, we get some partial results of this kind. We also notice that minimal fields have the property that every non-trivial valuation has divisible value group and algebraically closed residue field.", "published": "2013-08-15T13:46:46Z", "authors": ["Krzysztof Krupinski"], "categories": ["math.LO", "math.AC"], "doi": "", "_query": "property valuation computer vision", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2407.13455v1", "title": "Real Poincaré series of a plane divisorial valuation", "abstract": "Earlier, there was computed the Poincaré series of a valuation or of a collection of valuations on the ring of germs of holomorphic functions in two variables. For a collection of several plane curve valuations it appeared to coincide with the Alexander polynomial of the corresponding algebraic link. Recently, the authors defined Poincaré series of a valuation or of a collection of valuations in the real setting. (Actually, there were defined three versions of them, however, one of them was found to be ``not computable''.) These two Poincar'e series were computed for one plane curve valuation. Here we compute them for a plane divisorial valuation.", "published": "2024-07-18T12:33:28Z", "authors": ["Antonio Campillo", "Félix Delgado", "Sabir M. Gusein-Zade"], "categories": ["math.AG"], "doi": "", "_query": "property valuation computer vision", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2008.00813v1", "title": "Kinematics of motion tracking using computer vision", "abstract": "This paper describes the kinematics of the motion tracking of a rigid body using video recording. The novelty of the paper is on the adaptation of the methods and nomenclature used in Computer Vision to those used in Multibody System Dynamics. That way, the equations presented here can be used, for example, for inverse-dynamics multibody simulations driven by the motion tracking of selected bodies. This paper also adapts the well-known Zhang calibration method to the presented nomenclature.", "published": "2020-07-19T18:02:13Z", "authors": ["José L. Escalona"], "categories": ["cs.CV", "eess.IV"], "doi": "", "_query": "property valuation computer vision", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2505.17317v2", "title": "Optimizing Image Capture for Computer Vision-Powered Taxonomic Identification and Trait Recognition of Biodiversity Specimens", "abstract": "1) Biological collections house millions of specimens with digital images increasingly available through open-access platforms. However, most imaging protocols were developed for human interpretation without considering automated analysis requirements. As computer vision applications revolutionize taxonomic identification and trait extraction, a critical gap exists between current digitization practices and computational analysis needs. This review provides the first comprehensive practical framework for optimizing biological specimen imaging for computer vision applications. 2) Through interdisciplinary collaboration between taxonomists, collection managers, ecologists, and computer scientists, we synthesized evidence-based recommendations addressing fundamental computer vision concepts and practical imaging considerations. We provide immediately actionable implementation guidance while identifying critical areas requiring community standards development. 3) Our framework encompasses ten interconnected considerations for optimizing image capture for computer vision-powered taxonomic identification and trait extraction. We translate these into practical implementation checklists, equipment selection guidelines, and a roadmap for community standards development including filename conventions, pixel density requirements, and cross-institutional protocols. 4)By bridging biological and computational disciplines, this approach unlocks automated analysis potential for millions of existing specimens and guides future digitization efforts toward unprecedented analytical capabilities.", "published": "2025-05-22T22:11:05Z", "authors": ["Alyson East", "Elizabeth G. Campolongo", "Luke Meyers", "S M Rayeed", "Samuel Stevens", "Iuliia Zarubiieva", "Isadora E. Fluck", "Jennifer C. Girón", "Maximiliane Jousse", "Scott Lowe", "Kayla I Perry", "Isabelle Betancourt", "Noah Charney", "Evan Donoso", "Nathan Fox", "Kim J. Landsbergen", "Ekaterina Nepovinnykh", "Michelle Ramirez", "Parkash Singh", "Khum Thapa-Magar", "Matthew Thompson", "Evan Waite", "Tanya Berger-Wolf", "Hilmar Lapp", "Paula Mabee", "Charles Stewart", "Graham Taylor", "Sydne Record"], "categories": ["cs.CV", "eess.IV", "q-bio.QM"], "doi": "10.1111/2041-210X.70140", "_query": "property valuation computer vision", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/1705.05804v1", "title": "The Incremental Multiresolution Matrix Factorization Algorithm", "abstract": "Multiresolution analysis and matrix factorization are foundational tools in computer vision. In this work, we study the interface between these two distinct topics and obtain techniques to uncover hierarchical block structure in symmetric matrices -- an important aspect in the success of many vision problems. Our new algorithm, the incremental multiresolution matrix factorization, uncovers such structure one feature at a time, and hence scales well to large matrices. We describe how this multiscale analysis goes much farther than what a direct global factorization of the data can identify. We evaluate the efficacy of the resulting factorizations for relative leveraging within regression tasks using medical imaging data. We also use the factorization on representations learned by popular deep networks, providing evidence of their ability to infer semantic relationships even when they are not explicitly trained to do so. We show that this algorithm can be used as an exploratory tool to improve the network architecture, and within numerous other settings in vision.", "published": "2017-05-16T17:10:05Z", "authors": ["Vamsi K. Ithapu", "Risi Kondor", "Sterling C. Johnson", "Vikas Singh"], "categories": ["cs.CV", "math.NA", "stat.ML"], "doi": "", "_query": "property valuation computer vision", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2211.06004v1", "title": "A Comprehensive Survey of Transformers for Computer Vision", "abstract": "As a special type of transformer, Vision Transformers (ViTs) are used to various computer vision applications (CV), such as image recognition. There are several potential problems with convolutional neural networks (CNNs) that can be solved with ViTs. For image coding tasks like compression, super-resolution, segmentation, and denoising, different variants of the ViTs are used. The purpose of this survey is to present the first application of ViTs in CV. The survey is the first of its kind on ViTs for CVs to the best of our knowledge. In the first step, we classify different CV applications where ViTs are applicable. CV applications include image classification, object detection, image segmentation, image compression, image super-resolution, image denoising, and anomaly detection. Our next step is to review the state-of-the-art in each category and list the available models. Following that, we present a detailed analysis and comparison of each model and list its pros and cons. After that, we present our insights and lessons learned for each category. Moreover, we discuss several open research challenges and future research directions.", "published": "2022-11-11T05:11:03Z", "authors": ["Sonain Jamil", "Md. Jalil Piran", "Oh-Jin Kwon"], "categories": ["cs.CV"], "doi": "", "_query": "property valuation computer vision", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2510.08900v1", "title": "Few-shot Molecular Property Prediction: A Survey", "abstract": "AI-assisted molecular property prediction has become a promising technique in early-stage drug discovery and materials design in recent years. However, due to high-cost and complex wet-lab experiments, real-world molecules usually experience the issue of scarce annotations, leading to limited labeled data for effective supervised AI model learning. In light of this, few-shot molecular property prediction (FSMPP) has emerged as an expressive paradigm that enables learning from only a few labeled examples. Despite rapidly growing attention, existing FSMPP studies remain fragmented, without a coherent framework to capture methodological advances and domain-specific challenges. In this work, we present the first comprehensive and systematic survey of few-shot molecular property prediction. We begin by analyzing the few-shot phenomenon in molecular datasets and highlighting two core challenges: (1) cross-property generalization under distribution shifts, where each task corresponding to each property, may follow a different data distribution or even be inherently weakly related to others from a biochemical perspective, requiring the model to transfer knowledge across heterogeneous prediction tasks, and (2) cross-molecule generalization under structural heterogeneity, where molecules involved in different or same properties may exhibit significant structural diversity, making model difficult to achieve generalization. Then, we introduce a unified taxonomy that organizes existing methods into data, model, and learning paradigm levels, reflecting their strategies for extracting knowledge from scarce supervision in few-shot molecular property prediction. Next, we compare representative methods, summarize benchmark datasets and evaluation protocols. In the end, we identify key trends and future directions for advancing the continued research on FSMPP.", "published": "2025-10-10T01:24:54Z", "authors": ["Zeyu Wang", "Tianyi Jiang", "Huanchang Ma", "Yao Lu", "Xiaoze Bao", "Shanqing Yu", "Qi Xuan", "Shirui Pan", "Xin Zheng"], "categories": ["cs.CE"], "doi": "", "_query": "property valuation computer vision", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2211.13508v2", "title": "1st Workshop on Maritime Computer Vision (MaCVi) 2023: Challenge Results", "abstract": "The 1$^{\\text{st}}$ Workshop on Maritime Computer Vision (MaCVi) 2023 focused on maritime computer vision for Unmanned Aerial Vehicles (UAV) and Unmanned Surface Vehicle (USV), and organized several subchallenges in this domain: (i) UAV-based Maritime Object Detection, (ii) UAV-based Maritime Object Tracking, (iii) USV-based Maritime Obstacle Segmentation and (iv) USV-based Maritime Obstacle Detection. The subchallenges were based on the SeaDronesSee and MODS benchmarks. This report summarizes the main findings of the individual subchallenges and introduces a new benchmark, called SeaDronesSee Object Detection v2, which extends the previous benchmark by including more classes and footage. We provide statistical and qualitative analyses, and assess trends in the best-performing methodologies of over 130 submissions. The methods are summarized in the appendix. The datasets, evaluation code and the leaderboard are publicly available at https://seadronessee.cs.uni-tuebingen.de/macvi.", "published": "2022-11-24T09:59:13Z", "authors": ["Benjamin Kiefer", "Matej Kristan", "Janez Perš", "Lojze Žust", "Fabio Poiesi", "Fabio Augusto de Alcantara Andrade", "Alexandre Bernardino", "Matthew Dawkins", "Jenni Raitoharju", "Yitong Quan", "Adem Atmaca", "Timon Höfer", "Qiming Zhang", "Yufei Xu", "Jing Zhang", "Dacheng Tao", "Lars Sommer", "Raphael Spraul", "Hangyue Zhao", "Hongpu Zhang", "Yanyun Zhao", "Jan Lukas Augustin", "Eui-ik Jeon", "Impyeong Lee", "Luca Zedda", "Andrea Loddo", "Cecilia Di Ruberto", "Sagar Verma", "Siddharth Gupta", "Shishir Muralidhara", "Niharika Hegde", "Daitao Xing", "Nikolaos Evangeliou", "Anthony Tzes", "Vojtěch Bartl", "Jakub Špaňhel", "Adam Herout", "Neelanjan Bhowmik", "Toby P. Breckon", "Shivanand Kundargi", "Tejas Anvekar", "Chaitra Desai", "Ramesh Ashok Tabib", "Uma Mudengudi", "Arpita Vats", "Yang Song", "Delong Liu", "Yonglin Li", "Shuman Li", "Chenhao Tan", "Long Lan", "Vladimir Somers", "Christophe De Vleeschouwer", "Alexandre Alahi", "Hsiang-Wei Huang", "Cheng-Yen Yang", "Jenq-Neng Hwang", "Pyong-Kun Kim", "Kwangju Kim", "Kyoungoh Lee", "Shuai Jiang", "Haiwen Li", "Zheng Ziqiang", "Tuan-Anh Vu", "Hai Nguyen-Truong", "Sai-Kit Yeung", "Zhuang Jia", "Sophia Yang", "Chih-Chung Hsu", "Xiu-Yu Hou", "Yu-An Jhang", "Simon Yang", "Mau-Tsuen Yang"], "categories": ["cs.CV", "cs.AI", "cs.LG", "cs.RO"], "doi": "", "_query": "property valuation computer vision", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/0911.0894v1", "title": "A New Computational Schema for Euphonic Conjunctions in Sanskrit Processing", "abstract": "Automated language processing is central to the drive to enable facilitated referencing of increasingly available Sanskrit E texts. The first step towards processing Sanskrit text involves the handling of Sanskrit compound words that are an integral part of Sanskrit texts. This firstly necessitates the processing of euphonic conjunctions or sandhis, which are points in words or between words, at which adjacent letters coalesce and transform. The ancient Sanskrit grammarian Panini's codification of the Sanskrit grammar is the accepted authority in the subject. His famed sutras or aphorisms, numbering approximately four thousand, tersely, precisely and comprehensively codify the rules of the grammar, including all the rules pertaining to sandhis. This work presents a fresh new approach to processing sandhis in terms of a computational schema. This new computational model is based on Panini's complex codification of the rules of grammar. The model has simple beginnings and is yet powerful, comprehensive and computationally lean.", "published": "2009-11-04T19:33:21Z", "authors": ["N. Rama", "Meenakshi Lakshmanan"], "categories": ["cs.CL"], "doi": "", "_query": "property valuation computer vision", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2003.11066v1", "title": "A Survey of Methods for Low-Power Deep Learning and Computer Vision", "abstract": "Deep neural networks (DNNs) are successful in many computer vision tasks. However, the most accurate DNNs require millions of parameters and operations, making them energy, computation and memory intensive. This impedes the deployment of large DNNs in low-power devices with limited compute resources. Recent research improves DNN models by reducing the memory requirement, energy consumption, and number of operations without significantly decreasing the accuracy. This paper surveys the progress of low-power deep learning and computer vision, specifically in regards to inference, and discusses the methods for compacting and accelerating DNN models. The techniques can be divided into four major categories: (1) parameter quantization and pruning, (2) compressed convolutional filters and matrix factorization, (3) network architecture search, and (4) knowledge distillation. We analyze the accuracy, advantages, disadvantages, and potential solutions to the problems with the techniques in each category. We also discuss new evaluation metrics as a guideline for future research.", "published": "2020-03-24T18:47:24Z", "authors": ["Abhinav Goel", "Caleb Tung", "Yung-Hsiang Lu", "George K. Thiruvathukal"], "categories": ["cs.CV"], "doi": "", "_query": "property valuation computer vision", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2210.07436v1", "title": "Smart Headset, Computer Vision and Machine Learning for Efficient Prawn Farm Management", "abstract": "Understanding the growth and distribution of the prawns is critical for optimising the feed and harvest strategies. An inadequate understanding of prawn growth can lead to reduced financial gain, for example, crops are harvested too early. The key to maintaining a good understanding of prawn growth is frequent sampling. However, the most commonly adopted sampling practice, the cast net approach, is unable to sample the prawns at a high frequency as it is expensive and laborious. An alternative approach is to sample prawns from feed trays that farm workers inspect each day. This will allow growth data collection at a high frequency (each day). But measuring prawns manually each day is a laborious task. In this article, we propose a new approach that utilises smart glasses, depth camera, computer vision and machine learning to detect prawn distribution and growth from feed trays. A smart headset was built to allow farmers to collect prawn data while performing daily feed tray checks. A computer vision + machine learning pipeline was developed and demonstrated to detect the growth trends of prawns in 4 prawn ponds over a growing season.", "published": "2022-10-14T00:39:52Z", "authors": ["Mingze Xi", "Ashfaqur Rahman", "Chuong Nguyen", "Stuart Arnold", "John McCulloch"], "categories": ["cs.CV", "cs.HC", "cs.LG"], "doi": "", "_query": "property valuation computer vision", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2603.13782v1", "title": "Your Vision-Language-Action Model Already Has Attention Heads For Path Deviation Detection", "abstract": "Vision-Language-Action (VLA) models have demonstrated strong potential for predicting semantic actions in navigation tasks, demonstrating the ability to reason over complex linguistic instructions and visual contexts. However, they are fundamentally hindered by visual-reasoning hallucinations that lead to trajectory deviations. Addressing this issue has conventionally required training external critic modules or relying on complex uncertainty heuristics. In this work, we discover that monitoring a few attention heads within a frozen VLA model can accurately detect path deviations without incurring additional computational overhead. We refer to these heads, which inherently capture the spatiotemporal causality between historical visual sequences and linguistic instructions, as Navigation Heads. Using these heads, we propose an intuitive, training-free anomaly-detection framework that monitors their signals to detect hallucinations in real time. Surprisingly, among over a thousand attention heads, a combination of just three is sufficient to achieve a 44.6 % deviation detection rate with a low false-positive rate of 11.7 %. Furthermore, upon detecting a deviation, we bypass the heavy VLA model and trigger a lightweight Reinforcement Learning (RL) policy to safely execute a shortest-path rollback. By integrating this entire detection-to-recovery pipeline onto a physical robot, we demonstrate its practical robustness. All source code will be publicly available.", "published": "2026-03-14T06:26:11Z", "authors": ["Jaehwan Jeong", "Evelyn Zhu", "Jinying Lin", "Emmanuel Jaimes", "Tuan-Anh Vu", "Jungseock Joo", "Sangpil Kim", "M. Khalid Jawed"], "categories": ["cs.RO", "cs.CV"], "doi": "", "_query": "property valuation computer vision", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/1502.07446v1", "title": "Estimating the Potential Speedup of Computer Vision Applications on Embedded Multiprocessors", "abstract": "Computer vision applications constitute one of the key drivers for embedded multicore architectures. Although the number of available cores is increasing in new architectures, designing an application to maximize the utilization of the platform is still a challenge. In this sense, parallel performance prediction tools can aid developers in understanding the characteristics of an application and finding the most adequate parallelization strategy. In this work, we present a method for early parallel performance estimation on embedded multiprocessors from sequential application traces. We describe its implementation in Parana, a fast trace-driven simulator targeting OpenMP applications on the STMicroelectronics' STxP70 Application-Specific Multiprocessor (ASMP). Results for the FAST key point detector application show an error margin of less than 10% compared to the reference cycle-approximate simulator, with lower modeling effort and up to 20x faster execution time.", "published": "2015-02-26T06:13:47Z", "authors": ["Vítor Schwambach", "Sébastien Cleyet-Merle", "Alain Issard", "Stéphane Mancini"], "categories": ["cs.CV", "cs.DC", "cs.PF"], "doi": "", "_query": "property valuation computer vision", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2103.12231v1", "title": "On the Role of System Software in Energy Management of Neuromorphic Computing", "abstract": "Neuromorphic computing systems such as DYNAPs and Loihi have recently been introduced to the computing community to improve performance and energy efficiency of machine learning programs, especially those that are implemented using Spiking Neural Network (SNN). The role of a system software for neuromorphic systems is to cluster a large machine learning model (e.g., with many neurons and synapses) and map these clusters to the computing resources of the hardware. In this work, we formulate the energy consumption of a neuromorphic hardware, considering the power consumed by neurons and synapses, and the energy consumed in communicating spikes on the interconnect. Based on such formulation, we first evaluate the role of a system software in managing the energy consumption of neuromorphic systems. Next, we formulate a simple heuristic-based mapping approach to place the neurons and synapses onto the computing resources to reduce energy consumption. We evaluate our approach with 10 machine learning applications and demonstrate that the proposed mapping approach leads to a significant reduction of energy consumption of neuromorphic computing systems.", "published": "2021-03-22T23:31:43Z", "authors": ["Twisha Titirsha", "Shihao Song", "Adarsha Balaji", "Anup Das"], "categories": ["cs.NE", "cs.AR"], "doi": "", "_query": "property valuation computer vision", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2009.03224v1", "title": "A Review on Near Duplicate Detection of Images using Computer Vision Techniques", "abstract": "Nowadays, digital content is widespread and simply redistributable, either lawfully or unlawfully. For example, after images are posted on the internet, other web users can modify them and then repost their versions, thereby generating near-duplicate images. The presence of near-duplicates affects the performance of the search engines critically. Computer vision is concerned with the automatic extraction, analysis and understanding of useful information from digital images. The main application of computer vision is image understanding. There are several tasks in image understanding such as feature extraction, object detection, object recognition, image cleaning, image transformation, etc. There is no proper survey in literature related to near duplicate detection of images. In this paper, we review the state-of-the-art computer vision-based approaches and feature extraction methods for the detection of near duplicate images. We also discuss the main challenges in this field and how other researchers addressed those challenges. This review provides research directions to the fellow researchers who are interested to work in this field.", "published": "2020-09-07T16:41:46Z", "authors": ["K. K. Thyagharajan", "G. Kalaiarasi"], "categories": ["cs.CV"], "doi": "10.1007/s11831-020-09400-w", "_query": "property valuation computer vision", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2003.08763v1", "title": "Shape retrieval of non-rigid 3d human models", "abstract": "3D models of humans are commonly used within computer graphics and vision, and so the ability to distinguish between body shapes is an important shape retrieval problem. We extend our recent paper which provided a benchmark for testing non-rigid 3D shape retrieval algorithms on 3D human models. This benchmark provided a far stricter challenge than previous shape benchmarks. We have added 145 new models for use as a separate training set, in order to standardise the training data used and provide a fairer comparison. We have also included experiments with the FAUST dataset of human scans. All participants of the previous benchmark study have taken part in the new tests reported here, many providing updated results using the new data. In addition, further participants have also taken part, and we provide extra analysis of the retrieval results. A total of 25 different shape retrieval methods.", "published": "2020-03-01T20:03:16Z", "authors": ["David Pickup", "Xianfang Sun", "Paul L Rosin", "Ralph R Martin", "Z Cheng", "Zhouhui Lian", "Masaki Aono", "A Ben Hamza", "A Bronstein", "M Bronstein", "S Bu", "Umberto Castellani", "S Cheng", "Valeria Garro", "Andrea Giachetti", "Afzal Godil", "Luca Isaia", "J Han", "Henry Johan", "L Lai", "Bo Li", "C Li", "Haisheng Li", "Roee Litman", "X Liu", "Z Liu", "Yijuan Lu", "L Sun", "G Tam", "Atsushi Tatsuma", "J Ye"], "categories": ["cs.CV", "cs.IR", "cs.LG", "stat.ML"], "doi": "", "_query": "property valuation computer vision", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2308.13910v1", "title": "Exploring Human Crowd Patterns and Categorization in Video Footage for Enhanced Security and Surveillance using Computer Vision and Machine Learning", "abstract": "Computer vision and machine learning have brought revolutionary shifts in perception for researchers, scientists, and the general populace. Once thought to be unattainable, these technologies have achieved the seemingly impossible. Their exceptional applications in diverse fields like security, agriculture, and education are a testament to their impact. However, the full potential of computer vision remains untapped. This paper explores computer vision's potential in security and surveillance, presenting a novel approach to track motion in videos. By categorizing motion into Arcs, Lanes, Converging/Diverging, and Random/Block motions using Motion Information Images and Blockwise dominant motion data, the paper examines different optical flow techniques, CNN models, and machine learning models. Successfully achieving its objectives with promising accuracy, the results can train anomaly-detection models, provide behavioral insights based on motion, and enhance scene comprehension.", "published": "2023-08-26T16:09:20Z", "authors": ["Afnan Alazbah", "Khalid Fakeeh", "Osama Rabie"], "categories": ["cs.CV", "cs.AI"], "doi": "", "_query": "property valuation computer vision", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/1501.06563v2", "title": "On Lazard's Valuation and CAD Construction", "abstract": "In 1990 Lazard proposed an improved projection operation for cylindrical algebraic decomposition (CAD). For the proof he introduced a certain notion of valuation of a multivariate Puiseux series at a point. However a gap in one of the key supporting results for the improved projection was subsequently noticed. In this report we study a more limited but rigorous concept of Lazard's valuation: namely, we study Lazard's valuation of a multivariate polynomial at a point. We prove some basic properties of the limited Lazard valuation and identify some relationships between valuation-invariance and order-invariance.", "published": "2015-01-26T20:57:00Z", "authors": ["Scott McCallum", "Hoon Hong"], "categories": ["math.AG"], "doi": "", "_query": "property valuation computer vision", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2507.22398v3", "title": "On the Reliability of Vision-Language Models Under Adversarial Frequency-Domain Perturbations", "abstract": "Vision-Language Models (VLMs) are increasingly used as perceptual modules for visual content reasoning, including through captioning and DeepFake detection. In this work, we expose a critical vulnerability of VLMs when exposed to subtle, structured perturbations in the frequency domain. Specifically, we highlight how these feature transformations undermine authenticity/DeepFake detection and automated image captioning tasks. We design targeted image transformations, operating in the frequency domain to systematically adjust VLM outputs when exposed to frequency-perturbed real and synthetic images. We demonstrate that the perturbation injection method generalizes across five state-of-the-art VLMs which includes different-parameter Qwen2/2.5 and BLIP models. Experimenting across ten real and generated image datasets reveals that VLM judgments are sensitive to frequency-based cues and may not wholly align with semantic content. Crucially, we show that visually-imperceptible spatial frequency transformations expose the fragility of VLMs deployed for automated image captioning and authenticity detection tasks. Our findings under realistic, black-box constraints challenge the reliability of VLMs, underscoring the need for robust multimodal perception systems.", "published": "2025-07-30T05:41:29Z", "authors": ["Jordan Vice", "Naveed Akhtar", "Yansong Gao", "Richard Hartley", "Ajmal Mian"], "categories": ["cs.CV"], "doi": "", "_query": "property valuation computer vision", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2504.10745v1", "title": "Interactivity x Explainability: Toward Understanding How Interactivity Can Improve Computer Vision Explanations", "abstract": "Explanations for computer vision models are important tools for interpreting how the underlying models work. However, they are often presented in static formats, which pose challenges for users, including information overload, a gap between semantic and pixel-level information, and limited opportunities for exploration. We investigate interactivity as a mechanism for tackling these issues in three common explanation types: heatmap-based, concept-based, and prototype-based explanations. We conducted a study (N=24), using a bird identification task, involving participants with diverse technical and domain expertise. We found that while interactivity enhances user control, facilitates rapid convergence to relevant information, and allows users to expand their understanding of the model and explanation, it also introduces new challenges. To address these, we provide design recommendations for interactive computer vision explanations, including carefully selected default views, independent input controls, and constrained output spaces.", "published": "2025-04-14T22:35:26Z", "authors": ["Indu Panigrahi", "Sunnie S. Y. Kim", "Amna Liaqat", "Rohan Jinturkar", "Olga Russakovsky", "Ruth Fong", "Parastoo Abtahi"], "categories": ["cs.HC", "cs.CV"], "doi": "", "_query": "property valuation computer vision", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2202.00190v4", "title": "Sketching stochastic valuation functions", "abstract": "We consider the problem of sketching set valuation functions, defined as the expectation of a valuation function applied to independent random item values. For valuation functions that are monotone and either subadditive or submodular, and that satisfy a weak homogeneity condition (or other structural conditions), we show that there exist discretized versions of the item value distributions -- each with support size $O(k \\log k)$ -- that yield a sketch valuation function providing a constant-factor approximation to the true valuation for any subset of items of size at most $k$. These discretized distributions can be computed efficiently for each item independently, making the approach highly scalable. Our results apply broadly to valuation functions commonly encountered in practice, including team performance based on the best member (e.g., maximum functions), constant elasticity of substitution (CES) production functions with diminishing returns in economics, and others. Sketch valuation functions are especially useful in optimization problems such as best set selection and welfare maximization, where exact value computations are costly or intricate. They enable efficient approximate evaluation of value oracle queries while preserving provable approximation guarantees for the original stochastic optimization problem.", "published": "2022-02-01T02:41:23Z", "authors": ["Milan Vojnović", "Yiliu Wang"], "categories": ["math.ST", "stat.ME"], "doi": "", "_query": "property valuation computer vision", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2201.02028v1", "title": "A Light in the Dark: Deep Learning Practices for Industrial Computer Vision", "abstract": "In recent years, large pre-trained deep neural networks (DNNs) have revolutionized the field of computer vision (CV). Although these DNNs have been shown to be very well suited for general image recognition tasks, application in industry is often precluded for three reasons: 1) large pre-trained DNNs are built on hundreds of millions of parameters, making deployment on many devices impossible, 2) the underlying dataset for pre-training consists of general objects, while industrial cases often consist of very specific objects, such as structures on solar wafers, 3) potentially biased pre-trained DNNs raise legal issues for companies. As a remedy, we study neural networks for CV that we train from scratch. For this purpose, we use a real-world case from a solar wafer manufacturer. We find that our neural networks achieve similar performances as pre-trained DNNs, even though they consist of far fewer parameters and do not rely on third-party datasets.", "published": "2022-01-06T12:36:35Z", "authors": ["Maximilian Harl", "Marvin Herchenbach", "Sven Kruschel", "Nico Hambauer", "Patrick Zschech", "Mathias Kraus"], "categories": ["cs.CV", "cs.AI", "cs.LG"], "doi": "", "_query": "property valuation computer vision", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2508.12802v1", "title": "Morphological classification of eclipsing binary stars using computer vision methods", "abstract": "We present an application of computer vision methods to classify the light curves of eclipsing binaries (EB). We have used pre-trained models based on convolutional neural networks ($\\textit{ResNet50}$) and vision transformers ($\\textit{vit\\_base\\_patch16\\_224}$), which were fine-tuned on images created from synthetic datasets. To improve model generalisation and reduce overfitting, we developed a novel image representation by transforming phase-folded light curves into polar coordinates combined with hexbin visualisation. Our hierarchical approach in the first stage classifies systems into detached and overcontact types, and in the second stage identifies the presence or absence of spots. The binary classification models achieved high accuracy ($>96\\%$) on validation data across multiple passbands (Gaia~$G$, $I$, and $TESS$) and demonstrated strong performance ($>94\\%$, up to $100\\%$ for $TESS$) when tested on extensive observational data from the OGLE, DEBCat, and WUMaCat catalogues. While the primary binary classification was highly successful, the secondary task of automated spot detection performed poorly, revealing a significant limitation of our models for identifying subtle photometric features. This study highlights the potential of computer vision for EB morphological classification in large-scale surveys, but underscores the need for further research into robust, automated spot detection.", "published": "2025-08-18T10:29:19Z", "authors": ["Štefan Parimucha", "Maksim Gabdeev", "Yanna Markus", "Martin Vaňko", "Pavol Gajdoš"], "categories": ["cs.CV", "astro-ph.IM", "astro-ph.SR"], "doi": "", "_query": "property valuation computer vision", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/1510.05142v1", "title": "Memory-Efficient Design Strategy for a Parallel Embedded Integral Image Computation Engine", "abstract": "In embedded vision systems, parallel computation of the integral image presents several design challenges in terms of hardware resources, speed and power consumption. Although recursive equations significantly reduce the number of operations for computing the integral image, the required internal memory becomes prohibitively large for an embedded integral image computation engine for increasing image sizes. With the objective of achieving high-throughput with minimum hardware resources, this paper proposes a memory-efficient design strategy for a parallel embedded integral image computation engine. Results show that the design achieves nearly 35% reduction in memory for common HD video.", "published": "2015-10-17T16:04:33Z", "authors": ["Shoaib Ehsan", "Adrian F. Clark", "Wah M. Cheung", "Arjunsingh M. Bais", "Bayar I. Menzat", "Nadia Kanwal", "Klaus D. McDonald-Maier"], "categories": ["cs.CV"], "doi": "10.1109/IMVIP.2011.29", "_query": "property valuation computer vision", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/1604.01802v2", "title": "Learning to Track at 100 FPS with Deep Regression Networks", "abstract": "Machine learning techniques are often used in computer vision due to their ability to leverage large amounts of training data to improve performance. Unfortunately, most generic object trackers are still trained from scratch online and do not benefit from the large number of videos that are readily available for offline training. We propose a method for offline training of neural networks that can track novel objects at test-time at 100 fps. Our tracker is significantly faster than previous methods that use neural networks for tracking, which are typically very slow to run and not practical for real-time applications. Our tracker uses a simple feed-forward network with no online training required. The tracker learns a generic relationship between object motion and appearance and can be used to track novel objects that do not appear in the training set. We test our network on a standard tracking benchmark to demonstrate our tracker's state-of-the-art performance. Further, our performance improves as we add more videos to our offline training set. To the best of our knowledge, our tracker is the first neural-network tracker that learns to track generic objects at 100 fps.", "published": "2016-04-06T20:39:34Z", "authors": ["David Held", "Sebastian Thrun", "Silvio Savarese"], "categories": ["cs.CV", "cs.AI", "cs.LG", "cs.RO"], "doi": "", "_query": "property valuation computer vision", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/1607.04100v1", "title": "Insurance valuation: a computable multi-period cost-of-capital approach", "abstract": "We present an approach to market-consistent multi-period valuation of insurance liability cash flows based on a two-stage valuation procedure. First, a portfolio of traded financial instrument aimed at replicating the liability cash flow is fixed. Then the residual cash flow is managed by repeated one-period replication using only cash funds. The latter part takes capital requirements and costs into account, as well as limited liability and risk averseness of capital providers. The cost-of-capital margin is the value of the residual cash flow. We set up a general framework for the cost-of-capital margin and relate it to dynamic risk measurement. Moreover, we present explicit formulas and properties of the cost-of-capital margin under further assumptions on the model for the liability cash flow and on the conditional risk measures and utility functions. Finally, we highlight computational aspects of the cost-of-capital margin, and related quantities, in terms of an example from life insurance.", "published": "2016-07-14T11:58:28Z", "authors": ["Hampus Engsner", "Mathias Lindholm", "Filip Lindskog"], "categories": ["q-fin.RM"], "doi": "", "_query": "property valuation computer vision", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/1308.1374v1", "title": "Bayesian ensemble learning for image denoising", "abstract": "Natural images are often affected by random noise and image denoising has long been a central topic in Computer Vision. Many algorithms have been introduced to remove the noise from the natural images, such as Gaussian, Wiener filtering and wavelet thresholding. However, many of these algorithms remove the fine edges and make them blur. Recently, many promising denoising algorithms have been introduced such as Non-local Means, Fields of Experts, and BM3D. In this paper, we explore Bayesian method of ensemble learning for image denoising. Ensemble methods seek to combine multiple different algorithms to retain the strengths of all methods and the weaknesses of none. Bayesian ensemble models are Non-local Means and Fields of Experts, the very successful recent algorithms. The Non-local Means presumes that the image contains an extensive amount of self-similarity. The approach of the Fields of Experts model extends traditional Markov Random Field model by learning potential functions over extended pixel neighborhoods. The two models are implemented and image denoising is performed on natural images. The experimental results obtained are used to compare with the single algorithm and discuss the ensemble learning and their approaches. Comparing to the results of Non-local Means and Fields of Experts, Ensemble learning showed improvement nearly 1dB.", "published": "2013-08-06T18:46:18Z", "authors": ["Hyuntaek Oh"], "categories": ["cs.CV"], "doi": "", "_query": "property valuation computer vision", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2401.06960v2", "title": "Transformer for Object Re-Identification: A Survey", "abstract": "Object Re-identification (Re-ID) aims to identify specific objects across different times and scenes, which is a widely researched task in computer vision. For a prolonged period, this field has been predominantly driven by deep learning technology based on convolutional neural networks. In recent years, the emergence of Vision Transformers has spurred a growing number of studies delving deeper into Transformer-based Re-ID, continuously breaking performance records and witnessing significant progress in the Re-ID field. Offering a powerful, flexible, and unified solution, Transformers cater to a wide array of Re-ID tasks with unparalleled efficacy. This paper provides a comprehensive review and in-depth analysis of the Transformer-based Re-ID. In categorizing existing works into Image/Video-Based Re-ID, Re-ID with limited data/annotations, Cross-Modal Re-ID, and Special Re-ID Scenarios, we thoroughly elucidate the advantages demonstrated by the Transformer in addressing a multitude of challenges across these domains. Considering the trending unsupervised Re-ID, we propose a new Transformer baseline, UntransReID, achieving state-of-the-art performance on both single/cross modal tasks. For the under-explored animal Re-ID, we devise a standardized experimental benchmark and conduct extensive experiments to explore the applicability of Transformer for this task and facilitate future research. Finally, we discuss some important yet under-investigated open issues in the large foundation model era, we believe it will serve as a new handbook for researchers in this field. A periodically updated website will be available at https://github.com/mangye16/ReID-Survey.", "published": "2024-01-13T03:17:57Z", "authors": ["Mang Ye", "Shuoyi Chen", "Chenyue Li", "Wei-Shi Zheng", "David Crandall", "Bo Du"], "categories": ["cs.CV", "cs.AI"], "doi": "", "_query": "property valuation computer vision", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/1403.4334v3", "title": "Bregman Divergences for Infinite Dimensional Covariance Matrices", "abstract": "We introduce an approach to computing and comparing Covariance Descriptors (CovDs) in infinite-dimensional spaces. CovDs have become increasingly popular to address classification problems in computer vision. While CovDs offer some robustness to measurement variations, they also throw away part of the information contained in the original data by only retaining the second-order statistics over the measurements. Here, we propose to overcome this limitation by first mapping the original data to a high-dimensional Hilbert space, and only then compute the CovDs. We show that several Bregman divergences can be computed between the resulting CovDs in Hilbert space via the use of kernels. We then exploit these divergences for classification purposes. Our experiments demonstrate the benefits of our approach on several tasks, such as material and texture recognition, person re-identification, and action recognition from motion capture data.", "published": "2014-03-18T03:55:33Z", "authors": ["Mehrtash Harandi", "Mathieu Salzmann", "Fatih Porikli"], "categories": ["cs.CV"], "doi": "", "_query": "property valuation computer vision", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2412.00076v1", "title": "Flaws of ImageNet, Computer Vision's Favourite Dataset", "abstract": "Since its release, ImageNet-1k dataset has become a gold standard for evaluating model performance. It has served as the foundation for numerous other datasets and training tasks in computer vision. As models have improved in accuracy, issues related to label correctness have become increasingly apparent. In this blog post, we analyze the issues in the ImageNet-1k dataset, including incorrect labels, overlapping or ambiguous class definitions, training-evaluation domain shifts, and image duplicates. The solutions for some problems are straightforward. For others, we hope to start a broader conversation about refining this influential dataset to better serve future research.", "published": "2024-11-26T16:47:59Z", "authors": ["Nikita Kisel", "Illia Volkov", "Katerina Hanzelkova", "Klara Janouskova", "Jiri Matas"], "categories": ["cs.CV"], "doi": "", "_query": "property valuation computer vision", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2201.10110v4", "title": "A Hybrid Quantum-Classical Algorithm for Robust Fitting", "abstract": "Fitting geometric models onto outlier contaminated data is provably intractable. Many computer vision systems rely on random sampling heuristics to solve robust fitting, which do not provide optimality guarantees and error bounds. It is therefore critical to develop novel approaches that can bridge the gap between exact solutions that are costly, and fast heuristics that offer no quality assurances. In this paper, we propose a hybrid quantum-classical algorithm for robust fitting. Our core contribution is a novel robust fitting formulation that solves a sequence of integer programs and terminates with a global solution or an error bound. The combinatorial subproblems are amenable to a quantum annealer, which helps to tighten the bound efficiently. While our usage of quantum computing does not surmount the fundamental intractability of robust fitting, by providing error bounds our algorithm is a practical improvement over randomised heuristics. Moreover, our work represents a concrete application of quantum computing in computer vision. We present results obtained using an actual quantum computer (D-Wave Advantage) and via simulation. Source code: https://github.com/dadung/HQC-robust-fitting", "published": "2022-01-25T05:59:24Z", "authors": ["Anh-Dzung Doan", "Michele Sasdelli", "David Suter", "Tat-Jun Chin"], "categories": ["cs.CV"], "doi": "", "_query": "property valuation computer vision", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2503.00442v1", "title": "Detection of Customer Interested Garments in Surveillance Video using Computer Vision", "abstract": "One of the basic requirements of humans is clothing and this approach aims to identify the garments selected by customer during shopping, from surveillance video. The existing approaches to detect garments were developed on western wear using datasets of western clothing. They do not address Indian garments due to the increased complexity. In this work, we propose a computer vision based framework to address this problem through video surveillance. The proposed framework uses the Mixture of Gaussians background subtraction algorithm to identify the foreground present in a video frame. The visual information present in this foreground is analysed using computer vision techniques such as image segmentation to detect the various garments, the customer is interested in. The framework was tested on a dataset, that comprises of CCTV videos from a garments store. When presented with raw surveillance footage, the proposed framework demonstrated its effectiveness in detecting the interest of customer in choosing their garments by achieving a high precision and recall.", "published": "2025-03-01T10:39:50Z", "authors": ["Earnest Paul Ijjina", "Aniruddha Srinivas Joshi", "Goutham Kanahasabai"], "categories": ["cs.CV"], "doi": "10.1109/ICCCNT49239.2020.9225571", "_query": "property valuation computer vision", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2306.12174v2", "title": "OphGLM: Training an Ophthalmology Large Language-and-Vision Assistant based on Instructions and Dialogue", "abstract": "Large multimodal language models (LMMs) have achieved significant success in general domains. However, due to the significant differences between medical images and text and general web content, the performance of LMMs in medical scenarios is limited. In ophthalmology, clinical diagnosis relies on multiple modalities of medical images, but unfortunately, multimodal ophthalmic large language models have not been explored to date. In this paper, we study and construct an ophthalmic large multimodal model. Firstly, we use fundus images as an entry point to build a disease assessment and diagnosis pipeline to achieve common ophthalmic disease diagnosis and lesion segmentation. Then, we establish a new ophthalmic multimodal instruction-following and dialogue fine-tuning dataset based on disease-related knowledge data and publicly available real-world medical dialogue. We introduce visual ability into the large language model to complete the ophthalmic large language and vision assistant (OphGLM). Our experimental results demonstrate that the OphGLM model performs exceptionally well, and it has the potential to revolutionize clinical applications in ophthalmology. The dataset, code, and models will be made publicly available at https://github.com/ML-AILab/OphGLM.", "published": "2023-06-21T11:09:48Z", "authors": ["Weihao Gao", "Zhuo Deng", "Zhiyuan Niu", "Fuju Rong", "Chucheng Chen", "Zheng Gong", "Wenze Zhang", "Daimin Xiao", "Fang Li", "Zhenjie Cao", "Zhaoyi Ma", "Wenbin Wei", "Lan Ma"], "categories": ["cs.CV"], "doi": "", "_query": "property valuation computer vision", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2405.14030v3", "title": "Refining Skewed Perceptions in Vision-Language Contrastive Models through Visual Representations", "abstract": "Large vision-language contrastive models (VLCMs), such as CLIP, have become foundational, demonstrating remarkable success across a variety of downstream tasks. Despite their advantages, these models, akin to other foundational systems, inherit biases from the disproportionate distribution of real-world data, leading to misconceptions about the actual environment. Prevalent datasets like ImageNet are often riddled with non-causal, spurious correlations that can diminish VLCM performance in scenarios where these contextual elements are absent. This study presents an investigation into how a simple linear probe can effectively distill task-specific core features from CLIP's embedding for downstream applications. Our analysis reveals that the CLIP text representations are often tainted by spurious correlations, inherited in the biased pre-training dataset. Empirical evidence suggests that relying on visual representations from CLIP, as opposed to text embedding, is more effective to refine the skewed perceptions in VLCMs, emphasizing the superior utility of visual representations in overcoming embedded biases. Our code can be found here.", "published": "2024-05-22T22:03:11Z", "authors": ["Haocheng Dai", "Sarang Joshi"], "categories": ["cs.CV", "cs.CL"], "doi": "", "_query": "property valuation computer vision", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2303.13540v2", "title": "Artificial Intelligence for Sustainability: Facilitating Sustainable Smart Product-Service Systems with Computer Vision", "abstract": "The usage and impact of deep learning for cleaner production and sustainability purposes remain little explored. This work shows how deep learning can be harnessed to increase sustainability in production and product usage. Specifically, we utilize deep learning-based computer vision to determine the wear states of products. The resulting insights serve as a basis for novel product-service systems with improved integration and result orientation. Moreover, these insights are expected to facilitate product usage improvements and R&D innovations. We demonstrate our approach on two products: machining tools and rotating X-ray anodes. From a technical standpoint, we show that it is possible to recognize the wear state of these products using deep-learning-based computer vision. In particular, we detect wear through microscopic images of the two products. We utilize a U-Net for semantic segmentation to detect wear based on pixel granularity. The resulting mean dice coefficients of 0.631 and 0.603 demonstrate the feasibility of the proposed approach. Consequently, experts can now make better decisions, for example, to improve the machining process parameters. To assess the impact of the proposed approach on environmental sustainability, we perform life cycle assessments that show gains for both products. The results indicate that the emissions of CO2 equivalents are reduced by 12% for machining tools and by 44% for rotating anodes. This work can serve as a guideline and inspire researchers and practitioners to utilize computer vision in similar scenarios to develop sustainable smart product-service systems and enable cleaner production.", "published": "2023-03-17T16:01:39Z", "authors": ["Jannis Walk", "Niklas Kühl", "Michael Saidani", "Jürgen Schatte"], "categories": ["cs.LG", "cs.AI", "cs.CV", "eess.IV"], "doi": "10.1016/j.jclepro.2023.136748", "_query": "property valuation computer vision", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2403.07244v1", "title": "Time-Efficient Light-Field Acquisition Using Coded Aperture and Events", "abstract": "We propose a computational imaging method for time-efficient light-field acquisition that combines a coded aperture with an event-based camera. Different from the conventional coded-aperture imaging method, our method applies a sequence of coding patterns during a single exposure for an image frame. The parallax information, which is related to the differences in coding patterns, is recorded as events. The image frame and events, all of which are measured in a single exposure, are jointly used to computationally reconstruct a light field. We also designed an algorithm pipeline for our method that is end-to-end trainable on the basis of deep optics and compatible with real camera hardware. We experimentally showed that our method can achieve more accurate reconstruction than several other imaging methods with a single exposure. We also developed a hardware prototype with the potential to complete the measurement on the camera within 22 msec and demonstrated that light fields from real 3-D scenes can be obtained with convincing visual quality. Our software and supplementary video are available from our project website.", "published": "2024-03-12T02:04:17Z", "authors": ["Shuji Habuchi", "Keita Takahashi", "Chihiro Tsutake", "Toshiaki Fujii", "Hajime Nagahara"], "categories": ["cs.CV", "eess.IV"], "doi": "", "_query": "property valuation computer vision", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2501.03121v2", "title": "Distributed and heterogeneous tensor-vector contraction algorithms for high performance computing", "abstract": "The tensor-vector contraction (TVC) is the most memory-bound operation of its class and a core component of the higher-order power method (HOPM). This paper brings distributed-memory parallelization to a native TVC algorithm for dense tensors that overall remains oblivious to contraction mode, tensor splitting and tensor order. Similarly, we propose a novel distributed HOPM, namely dHOPM3, that can save up to one order of magnitude of streamed memory and is about twice as costly in terms of data movement as a distributed TVC operation (dTVC) when using task-based parallelization. The numerical experiments carried out in this work on three different architectures featuring multi-core and accelerators confirm that the performances of dTVC and dHOPM3 remain relatively close to the peak system memory bandwidth (50%-80%, depending on the architecture) and on par with STREAM benchmark figures. On strong scalability scenarios, our native multi-core implementations of these two algorithms can achieve similar and sometimes even greater performance figures than those based upon state-of-the-art CUDA batched kernels. Finally, we demonstrate that both computation and communication can benefit from mixed precision arithmetic also in cases where the hardware does not support low precision data types natively.", "published": "2025-01-06T16:29:19Z", "authors": ["Pedro J. Martinez-Ferrer", "Albert-Jan Yzelman", "Vicenç Beltran"], "categories": ["cs.DC"], "doi": "10.1016/j.future.2024.107698", "_query": "property valuation computer vision", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2209.01545v2", "title": "OneQ: A Compilation Framework for Photonic One-Way Quantum Computation", "abstract": "In this paper, we propose OneQ, the first optimizing compilation framework for one-way quantum computation towards realistic photonic quantum architectures. Unlike previous compilation efforts for solid-state qubit technologies, our innovative framework addresses a unique set of challenges in photonic quantum computing. Specifically, this includes the dynamic generation of qubits over time, the need to perform all computation through measurements instead of relying on 1-qubit and 2-qubit gates, and the fact that photons are instantaneously destroyed after measurements. As pioneers in this field, we demonstrate the vast optimization potential of photonic one-way quantum computing, showcasing the remarkable ability of OneQ to reduce computing resource requirements by orders of magnitude.", "published": "2022-09-04T07:08:56Z", "authors": ["Hezi Zhang", "Anbang Wu", "Yuke Wang", "Gushu Li", "Hassan Shapourian", "Alireza Shabani", "Yufei Ding"], "categories": ["quant-ph"], "doi": "10.1145/3579371.3589047", "_query": "property valuation computer vision", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2304.14358v1", "title": "Structure Analysis of the FRP Rebar Using Computer Vision Techniques", "abstract": "In this paper we present a method to analyze the inner structure of the composite FRP rebar, namely the shift of the real center of gravity with a respect to the geometrical center of rebar and changes of cross-sectional characteristics. We propose an automated pipeline based on classical computer vision techniques and on the ratio between the glass fibers and epoxy filament in the analyzed cross-section to compute the shift vector of the real center of gravity in respect to the geometrical center together with the cross-section area and its principal moments. We discuss the achieved results over two cross sections in a different portion of the rebar and in the end, we suggest possible direction and improvements for our future work. We also made our code publicly available.", "published": "2023-04-27T17:37:23Z", "authors": ["Juraj Lagin", "Simon Bilik"], "categories": ["cs.CV", "math.NA"], "doi": "", "_query": "property valuation computer vision", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2004.12152v2", "title": "Semi-Lexical Languages -- A Formal Basis for Unifying Machine Learning and Symbolic Reasoning in Computer Vision", "abstract": "Human vision is able to compensate imperfections in sensory inputs from the real world by reasoning based on prior knowledge about the world. Machine learning has had a significant impact on computer vision due to its inherent ability in handling imprecision, but the absence of a reasoning framework based on domain knowledge limits its ability to interpret complex scenarios. We propose semi-lexical languages as a formal basis for dealing with imperfect tokens provided by the real world. The power of machine learning is used to map the imperfect tokens into the alphabet of the language and symbolic reasoning is used to determine the membership of input in the language. Semi-lexical languages also have bindings that prevent the variations in which a semi-lexical token is interpreted in different parts of the input, thereby leaning on deduction to enhance the quality of recognition of individual tokens. We present case studies that demonstrate the advantage of using such a framework over pure machine learning and pure symbolic methods.", "published": "2020-04-25T14:37:24Z", "authors": ["Briti Gangopadhyay", "Somnath Hazra", "Pallab Dasgupta"], "categories": ["cs.AI", "cs.CV"], "doi": "", "_query": "property valuation computer vision", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2607.14548v1", "title": "HyMobileAgent: Data-Environment Co-Scaling for Efficient GUI Agents", "abstract": "As large multimodal models move from understanding content to operating on digital environments, mobile GUI has emerged as a challenging and consequential testbed for digital embodied intelligence. Mobile agents operate under three coupled constraints: precise perception of complex interfaces, scalable acquisition of high-quality interaction data, and robust long-horizon decision making under compounding execution errors. This report presents HyMobileAgent, a mobile GUI agent built on Hy3.0-VL-A3B, a vision-native foundation model featuring native any-resolution input, an A3B-scale deployment budget, and a 32K context window to model extended interaction histories. Rather than relying solely on model scaling, we develop a joint data and environment centric scaling framework to address the key bottlenecks of mobile interaction. Our framework integrates a GUI perception flywheel combining mock-interface synthesis, rejection sampling, and icon-specific augmentation; a knowledge pipeline that transforms tutorial videos into structured interaction data; a million-scale action data pipeline deployed across more than 2000 sandbox and real-device instances with automated failure attribution; the PhoneWorld Mock App Factory, providing a resettable training environment with 34 mock applications and over 34000 tasks; and a structured Planning-and-Reflection mechanism with explicit dead-loop detection for reliable long-horizon execution. We also introduce a progressive training recipe consisting of mid-training, supervised fine-tuning, and reinforcement learning with task-specific reward designs.", "published": "2026-07-16T04:12:42Z", "authors": [" Hy Vision Team", "Huawen Shen", "Zhengyang Tang", "Shangpin Peng", "Liang Wu", "Anran Zhang", "Weinong Wang", "Yiduo Guo", "Chenxin Li", "Zhengyao Fang", "Yang Ding", "Junyi Li", "Fei Tang", "Zheng Ruan", "Yi Zhang", "Xingran Zhou", "Dingchen Yang", "Sunqi Fan", "Zhiyi Wan", "Han Hu", "Xin Lai", "Pengyuan Lyu", "Chengquan Zhang"], "categories": ["cs.CV"], "doi": "", "_query": "property valuation computer vision", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2003.07060v4", "title": "Finding Fair and Efficient Allocations When Valuations Don't Add Up", "abstract": "In this paper, we present new results on the fair and efficient allocation of indivisible goods to agents whose preferences correspond to {\\em matroid rank functions}. This is a versatile valuation class with several desirable properties (such as monotonicity and submodularity), which naturally lends itself to a number of real-world domains. We use these properties to our advantage; first, we show that when agent valuations are matroid rank functions, a socially optimal (i.e. utilitarian social welfare-maximizing) allocation that achieves envy-freeness up to one item (EF1) exists and is computationally tractable. We also prove that the Nash welfare-maximizing and the leximin allocations both exhibit this fairness/efficiency combination, by showing that they can be achieved by minimizing any symmetric strictly convex function over utilitarian optimal outcomes. To the best of our knowledge, this is the first valuation function class not subsumed by additive valuations for which it has been established that an allocation maximizing Nash welfare is EF1. Moreover, for a subclass of these valuation functions based on maximum (unweighted) bipartite matching, we show that a leximin allocation can be computed in polynomial time. Additionally, we explore possible extensions of our results to fairness criteria other than EF1 as well as to generalizations of the above valuation classes.", "published": "2020-03-16T07:42:27Z", "authors": ["Nawal Benabbou", "Mithun Chakraborty", "Ayumi Igarashi", "Yair Zick"], "categories": ["cs.AI", "cs.GT"], "doi": "10.1007/978-3-030-57980-7_3", "_query": "property valuation computer vision", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/1808.03998v1", "title": "Enhancing camera surveillance using computer vision: a research note", "abstract": "$\\mathbf{Purpose}$ - The growth of police operated surveillance cameras has out-paced the ability of humans to monitor them effectively. Computer vision is a possible solution. An ongoing research project on the application of computer vision within a municipal police department is described. The paper aims to discuss these issues. $\\mathbf{Design/methodology/approach}$ - Following the demystification of computer vision technology, its potential for police agencies is developed within a focus on computer vision as a solution for two common surveillance camera tasks (live monitoring of multiple surveillance cameras and summarizing archived video files). Three unaddressed research questions (can specialized computer vision applications for law enforcement be developed at this time, how will computer vision be utilized within existing public safety camera monitoring rooms, and what are the system-wide impacts of a computer vision capability on local criminal justice systems) are considered. $\\mathbf{Findings}$ - Despite computer vision becoming accessible to law enforcement agencies the impact of computer vision has not been discussed or adequately researched. There is little knowledge of computer vision or its potential in the field. $\\mathbf{Originality/value}$ - This paper introduces and discusses computer vision from a law enforcement perspective and will be valuable to police personnel tasked with monitoring large camera networks and considering computer vision as a system upgrade.", "published": "2018-08-12T20:01:37Z", "authors": ["Haroon Idrees", "Mubarak Shah", "Ray Surette"], "categories": ["cs.CY"], "doi": "", "_query": "property valuation computer vision", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2407.03755v2", "title": "A Computer Vision Approach to Estimate the Localized Sea State", "abstract": "This research presents a novel application of computer vision (CV) and deep learning methods for real-time sea state recognition, aiming to contribute to improving the operational safety and energy efficiency of seagoing vessels, key factors in meeting the legislative carbon reduction targets. Our work focuses on utilizing sea images in operational envelopes captured by a single stationary camera mounted on the ship bridge. The collected images are used to train a deep learning model to automatically recognize the state of the sea based on the Beaufort scale. To recognize the sea state, we used 4 state-of-the-art deep neural networks with different characteristics that proved useful in various computer vision tasks: Resnet-101, NASNet, MobileNet_v2, and Transformer ViT-b32. Furthermore, we have defined a unique large-scale dataset, collected over a broad range of sea conditions from an ocean-going vessel prepared for machine learning. We used the transfer learning approach to fine-tune the models on our dataset. The obtained results demonstrate the potential for this approach to complement traditional methods, particularly where in-situ measurements are unfeasible or interpolated weather buoy data is insufficiently accurate. This study sets the groundwork for further development of sea state classification models to address recognized gaps in maritime research and enable safer and more efficient maritime operations.", "published": "2024-07-04T09:07:25Z", "authors": ["Aleksandar Vorkapic", "Miran Pobar", "Marina Ivasic-Kos"], "categories": ["cs.CV"], "doi": "10.1016/j.oceaneng.2024.118318", "_query": "property valuation computer vision", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2509.18391v1", "title": "Does Embodiment Matter to Biomechanics and Function? A Comparative Analysis of Head-Mounted and Hand-Held Assistive Devices for Individuals with Blindness and Low Vision", "abstract": "Visual assistive technologies, such as Microsoft Seeing AI, can improve access to environmental information for persons with blindness or low vision (pBLV). Yet, the physical and functional implications of different device embodiments remain unclear. In this study, 11 pBLV participants used Seeing AI on a hand-held smartphone and on a head-mounted ARx Vision system to perform six activities of daily living, while their movements were captured with Xsens motion capture. Functional outcomes included task time, success rate, and number of attempts, and biomechanical measures included joint range of motion, angular path length, working volume, and movement smoothness. The head-mounted system generally reduced upper-body movement and task time, especially for document-scanning style tasks, whereas the hand-held system yielded higher success rates for tasks involving small or curved text. These findings indicate that both embodiments are viable, but they differ in terms of physical demands and ease of use. Incorporating biomechanical measures into assistive technology evaluations can inform designs that optimise user experience by balancing functional efficiency, physical sustainability, and intuitive interaction.", "published": "2025-09-22T20:23:33Z", "authors": ["Gaurav Seth", "Hoa Pham", "Giles Hamilton-Fletcher", "Charles Leclercq", "John-Ross Rizzo"], "categories": ["cs.HC", "cs.CV"], "doi": "10.1080/10447318.2026.2650557", "_query": "property valuation computer vision", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2503.06472v2", "title": "CalliReader: Contextualizing Chinese Calligraphy via an Embedding-Aligned Vision-Language Model", "abstract": "Chinese calligraphy, a UNESCO Heritage, remains computationally challenging due to visual ambiguity and cultural complexity. Existing AI systems fail to contextualize their intricate scripts, because of limited annotated data and poor visual-semantic alignment. We propose CalliReader, a vision-language model (VLM) that solves the Chinese Calligraphy Contextualization (CC$^2$) problem through three innovations: (1) character-wise slicing for precise character extraction and sorting, (2) CalliAlign for visual-text token compression and alignment, (3) embedding instruction tuning (e-IT) for improving alignment and addressing data scarcity. We also build CalliBench, the first benchmark for full-page calligraphic contextualization, addressing three critical issues in previous OCR and VQA approaches: fragmented context, shallow reasoning, and hallucination. Extensive experiments including user studies have been conducted to verify our CalliReader's \\textbf{superiority to other state-of-the-art methods and even human professionals in page-level calligraphy recognition and interpretation}, achieving higher accuracy while reducing hallucination. Comparisons with reasoning models highlight the importance of accurate recognition as a prerequisite for reliable comprehension. Quantitative analyses validate CalliReader's efficiency; evaluations on document and real-world benchmarks confirm its robust generalization ability.", "published": "2025-03-09T06:19:32Z", "authors": ["Yuxuan Luo", "Jiaqi Tang", "Chenyi Huang", "Feiyang Hao", "Zhouhui Lian"], "categories": ["cs.CV", "cs.MM"], "doi": "", "_query": "property valuation computer vision", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/1005.5181v1", "title": "Compression Rate Method for Empirical Science and Application to Computer Vision", "abstract": "This philosophical paper proposes a modified version of the scientific method, in which large databases are used instead of experimental observations as the necessary empirical ingredient. This change in the source of the empirical data allows the scientific method to be applied to several aspects of physical reality that previously resisted systematic interrogation. Under the new method, scientific theories are compared by instantiating them as compression programs, and examining the codelengths they achieve on a database of measurements related to a phenomenon of interest. Because of the impossibility of compressing random data, \"real world\" data can only be compressed by discovering and exploiting the empirical structure it exhibits. The method also provides a new way of thinking about two longstanding issues in the philosophy of science: the problem of induction and the problem of demarcation. The second part of the paper proposes to reformulate computer vision as an empirical science of visual reality, by applying the new method to large databases of natural images. The immediate goal of the proposed reformulation is to repair the chronic difficulties in evaluation experienced by the field of computer vision. The reformulation should bring a wide range of benefits, including a substantially increased degree of methodological rigor, the ability to justify complex theories without overfitting, a scalable evaluation paradigm, and the potential to make systematic progress. A crucial argument is that the change is not especially drastic, because most computer vision tasks can be reformulated as specialized image compression techniques. Finally, a concrete proposal is discussed in which a database is produced by recording from a roadside video camera, and compression is achieved by developing a computational understanding of the appearance of moving cars.", "published": "2010-05-27T21:27:43Z", "authors": ["Daniel Burfoot"], "categories": ["cs.CV"], "doi": "", "_query": "property valuation computer vision", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2108.08995v1", "title": "Discriminative Domain-Invariant Adversarial Network for Deep Domain Generalization", "abstract": "Domain generalization approaches aim to learn a domain invariant prediction model for unknown target domains from multiple training source domains with different distributions. Significant efforts have recently been committed to broad domain generalization, which is a challenging and topical problem in machine learning and computer vision communities. Most previous domain generalization approaches assume that the conditional distribution across the domains remain the same across the source domains and learn a domain invariant model by minimizing the marginal distributions. However, the assumption of a stable conditional distribution of the training source domains does not really hold in practice. The hyperplane learned from the source domains will easily misclassify samples scattered at the boundary of clusters or far from their corresponding class centres. To address the above two drawbacks, we propose a discriminative domain-invariant adversarial network (DDIAN) for domain generalization. The discriminativeness of the features are guaranteed through a discriminative feature module and domain-invariant features are guaranteed through the global domain and local sub-domain alignment modules. Extensive experiments on several benchmarks show that DDIAN achieves better prediction on unseen target data during training compared to state-of-the-art domain generalization approaches.", "published": "2021-08-20T04:24:12Z", "authors": ["Mohammad Mahfujur Rahman", "Clinton Fookes", "Sridha Sridharan"], "categories": ["cs.CV", "cs.AI", "cs.LG"], "doi": "", "_query": "property valuation computer vision", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2310.00108v1", "title": "Practical Membership Inference Attacks Against Large-Scale Multi-Modal Models: A Pilot Study", "abstract": "Membership inference attacks (MIAs) aim to infer whether a data point has been used to train a machine learning model. These attacks can be employed to identify potential privacy vulnerabilities and detect unauthorized use of personal data. While MIAs have been traditionally studied for simple classification models, recent advancements in multi-modal pre-training, such as CLIP, have demonstrated remarkable zero-shot performance across a range of computer vision tasks. However, the sheer scale of data and models presents significant computational challenges for performing the attacks. This paper takes a first step towards developing practical MIAs against large-scale multi-modal models. We introduce a simple baseline strategy by thresholding the cosine similarity between text and image features of a target point and propose further enhancing the baseline by aggregating cosine similarity across transformations of the target. We also present a new weakly supervised attack method that leverages ground-truth non-members (e.g., obtained by using the publication date of a target model and the timestamps of the open data) to further enhance the attack. Our evaluation shows that CLIP models are susceptible to our attack strategies, with our simple baseline achieving over $75\\%$ membership identification accuracy. Furthermore, our enhanced attacks outperform the baseline across multiple models and datasets, with the weakly supervised attack demonstrating an average-case performance improvement of $17\\%$ and being at least $7$X more effective at low false-positive rates. These findings highlight the importance of protecting the privacy of multi-modal foundational models, which were previously assumed to be less susceptible to MIAs due to less overfitting. Our code is available at https://github.com/ruoxi-jia-group/CLIP-MIA.", "published": "2023-09-29T19:38:40Z", "authors": ["Myeongseob Ko", "Ming Jin", "Chenguang Wang", "Ruoxi Jia"], "categories": ["cs.LG", "cs.CV"], "doi": "", "_query": "property valuation computer vision", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/1703.03156v1", "title": "Face-to-BMI: Using Computer Vision to Infer Body Mass Index on Social Media", "abstract": "A person's weight status can have profound implications on their life, ranging from mental health, to longevity, to financial income. At the societal level, \"fat shaming\" and other forms of \"sizeism\" are a growing concern, while increasing obesity rates are linked to ever raising healthcare costs. For these reasons, researchers from a variety of backgrounds are interested in studying obesity from all angles. To obtain data, traditionally, a person would have to accurately self-report their body-mass index (BMI) or would have to see a doctor to have it measured. In this paper, we show how computer vision can be used to infer a person's BMI from social media images. We hope that our tool, which we release, helps to advance the study of social aspects related to body weight.", "published": "2017-03-09T06:48:47Z", "authors": ["Enes Kocabey", "Mustafa Camurcu", "Ferda Ofli", "Yusuf Aytar", "Javier Marin", "Antonio Torralba", "Ingmar Weber"], "categories": ["cs.HC", "cs.CV", "cs.CY"], "doi": "", "_query": "property valuation computer vision", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2403.00689v1", "title": "Hydra: Computer Vision for Data Quality Monitoring", "abstract": "Hydra is a system which utilizes computer vision to perform near real time data quality management, initially developed for Hall-D in 2019. Since then, it has been deployed across all experimental halls at Jefferson Lab, with the CLAS12 collaboration in Hall-B being the first outside of GlueX to fully utilize Hydra. The system comprises back end processes that manage the models, their inferences, and the data flow. The front-end components, accessible via web pages, allow detector experts and shift crews to view and interact with the system. This talk will give an overview of the Hydra system as well as highlight significant developments in Hydra's feature set, acute challenges with operating Hydra in all halls, and lessons learned along the way.", "published": "2024-03-01T17:20:58Z", "authors": ["Thomas Britton", "Torri Jeske", "David Lawrence", "Kishansingh Rajput"], "categories": ["cs.CV", "nucl-ex", "physics.ins-det"], "doi": "", "_query": "property valuation computer vision", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2109.11369v4", "title": "Recent Advances of Continual Learning in Computer Vision: An Overview", "abstract": "In contrast to batch learning where all training data is available at once, continual learning represents a family of methods that accumulate knowledge and learn continuously with data available in sequential order. Similar to the human learning process with the ability of learning, fusing, and accumulating new knowledge coming at different time steps, continual learning is considered to have high practical significance. Hence, continual learning has been studied in various artificial intelligence tasks. In this paper, we present a comprehensive review of the recent progress of continual learning in computer vision. In particular, the works are grouped by their representative techniques, including regularization, knowledge distillation, memory, generative replay, parameter isolation, and a combination of the above techniques. For each category of these techniques, both its characteristics and applications in computer vision are presented. At the end of this overview, several subareas, where continuous knowledge accumulation is potentially helpful while continual learning has not been well studied, are discussed.", "published": "2021-09-23T13:30:18Z", "authors": ["Haoxuan Qu", "Hossein Rahmani", "Li Xu", "Bryan Williams", "Jun Liu"], "categories": ["cs.CV"], "doi": "", "_query": "property valuation computer vision", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2411.02445v1", "title": "WiCV@CVPR2024: The Thirteenth Women In Computer Vision Workshop at the Annual CVPR Conference", "abstract": "In this paper, we present the details of Women in Computer Vision Workshop - WiCV 2024, organized alongside the CVPR 2024 in Seattle, Washington, United States. WiCV aims to amplify the voices of underrepresented women in the computer vision community, fostering increased visibility in both academia and industry. We believe that such events play a vital role in addressing gender imbalances within the field. The annual WiCV@CVPR workshop offers a)~opportunity for collaboration between researchers from minority groups, b) mentorship for female junior researchers, c) financial support to presenters to alleviate financial burdens and d)~a diverse array of role models who can inspire younger researchers at the outset of their careers. In this paper, we present a comprehensive report on the workshop program, historical trends from the past WiCV@CVPR events, and a summary of statistics related to presenters, attendees, and sponsorship for the WiCV 2024 workshop.", "published": "2024-11-03T00:45:10Z", "authors": ["Asra Aslam", "Sachini Herath", "Ziqi Huang", "Estefania Talavera", "Deblina Bhattacharjee", "Himangi Mittal", "Vanessa Staderini", "Mengwei Ren", "Azade Farshad"], "categories": ["cs.CV"], "doi": "", "_query": "property valuation computer vision", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/1903.10793v2", "title": "Valuations and henselization", "abstract": "We study the extension of valuations centered in a local domain to its henseliza-tion. We prove that a valuation $ν$ centered in a local domain R uniquely determines a minimal prime H($ν$) of the henselization R h of R and an extension of $ν$ centered in R h /H($ν$), which has the same value group as $ν$. Our method, which assumes neither that R is noetherian nor that it is integrally closed, is to reduce the problem to the extension of the valuation to a quotient of a standard {é}tale local R-algebra and in that situation to draw valuative consequences from the observation that the Newton-Hensel algorithm for constructing roots of polynomials produces sequences that are always pseudo-convergent in the sense of Ostrowski. We then apply this method to the study of the approximation of elements of the henselization of a valued field by elements of the field and give a characterization of the henselian property of a local domain (R, m R) in terms of the limits of certain pseudo-convergent sequences of elements of m R for a valuation centered in it. Another consequence of our work is to establish in full generality a bijective correspondence between the minimal primes of the henselization of a local domain R and the connected components of the Riemann-Zariski space of valuations centered in R.", "published": "2019-03-26T11:06:12Z", "authors": ["Ana Belén de Felipe Paramio", "Bernard H Teissier"], "categories": ["math.AG", "math.AC"], "doi": "", "_query": "property valuation computer vision", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2411.01001v1", "title": "Automated Assessment of Residual Plots with Computer Vision Models", "abstract": "Plotting the residuals is a recommended procedure to diagnose deviations from linear model assumptions, such as non-linearity, heteroscedasticity, and non-normality. The presence of structure in residual plots can be tested using the lineup protocol to do visual inference. There are a variety of conventional residual tests, but the lineup protocol, used as a statistical test, performs better for diagnostic purposes because it is less sensitive and applies more broadly to different types of departures. However, the lineup protocol relies on human judgment which limits its scalability. This work presents a solution by providing a computer vision model to automate the assessment of residual plots. It is trained to predict a distance measure that quantifies the disparity between the residual distribution of a fitted classical normal linear regression model and the reference distribution, based on Kullback-Leibler divergence. From extensive simulation studies, the computer vision model exhibits lower sensitivity than conventional tests but higher sensitivity than human visual tests. It is slightly less effective on non-linearity patterns. Several examples from classical papers and contemporary data illustrate the new procedures, highlighting its usefulness in automating the diagnostic process and supplementing existing methods.", "published": "2024-11-01T19:51:44Z", "authors": ["Weihao Li", "Dianne Cook", "Emi Tanaka", "Susan VanderPlas", "Klaus Ackermann"], "categories": ["stat.ML", "cs.CV", "cs.LG"], "doi": "", "_query": "property valuation computer vision", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2406.02612v1", "title": "Is Data Valuation Learnable and Interpretable?", "abstract": "Measuring the value of individual samples is critical for many data-driven tasks, e.g., the training of a deep learning model. Recent literature witnesses the substantial efforts in developing data valuation methods. The primary data valuation methodology is based on the Shapley value from game theory, and various methods are proposed along this path. {Even though Shapley value-based valuation has solid theoretical basis, it is entirely an experiment-based approach and no valuation model has been constructed so far.} In addition, current data valuation methods ignore the interpretability of the output values, despite an interptable data valuation method is of great helpful for applications such as data pricing. This study aims to answer an important question: is data valuation learnable and interpretable? A learned valuation model have several desirable merits such as fixed number of parameters and knowledge reusability. An intrepretable data valuation model can explain why a sample is valuable or invaluable. To this end, two new data value modeling frameworks are proposed, in which a multi-layer perception~(MLP) and a new regression tree are utilized as specific base models for model training and interpretability, respectively. Extensive experiments are conducted on benchmark datasets. {The experimental results provide a positive answer for the question.} Our study opens up a new technical path for the assessing of data values. Large data valuation models can be built across many different data-driven tasks, which can promote the widespread application of data valuation.", "published": "2024-06-03T08:13:47Z", "authors": ["Ou Wu", "Weiyao Zhu", "Mengyang Li"], "categories": ["cs.LG", "cs.AI"], "doi": "", "_query": "property valuation computer vision", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/1605.04218v1", "title": "Anytime Inference in Valuation Algebras", "abstract": "Anytime inference is inference performed incrementally, with the accuracy of the inference being controlled by a tunable parameter, usually time. Such anytime inference algorithms are also usually interruptible, gradually converging to the exact inference value until terminated. While anytime inference algorithms for specific domains like probability potentials exist in the literature, our objective in this article is to obtain an anytime inference algorithm which is sufficiently generic to cover a wide range of domains. For this we utilise the theory of generic inference as a basis for constructing an anytime inference algorithm, and in particular, extending work done on ordered valuation algebras. The novel contribution of this work is the construction of anytime algorithms in a generic framework, which automatically gives us instantiations in various useful domains. We also show how to apply this generic framework for anytime inference in semiring induced valuation algebras, an important subclass of valuation algebras, which includes instances like probability potentials, disjunctive normal forms and distributive lattices. Keywords: Approximation; Anytime algorithms; Resource-bounded computation; Generic inference; Valuation algebras; Local computation; Binary join trees.", "published": "2016-05-13T15:40:10Z", "authors": ["Abhishek Dasgupta", "Samson Abramsky"], "categories": ["cs.AI"], "doi": "", "_query": "property valuation computer vision", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2402.02826v1", "title": "SynthVision -- Harnessing Minimal Input for Maximal Output in Computer Vision Models using Synthetic Image data", "abstract": "Rapid development of disease detection computer vision models is vital in response to urgent medical crises like epidemics or events of bioterrorism. However, traditional data gathering methods are too slow for these scenarios necessitating innovative approaches to generate reliable models quickly from minimal data. We demonstrate our new approach by building a comprehensive computer vision model for detecting Human Papilloma Virus Genital warts using only synthetic data. In our study, we employed a two phase experimental design using diffusion models. In the first phase diffusion models were utilized to generate a large number of diverse synthetic images from 10 HPV guide images explicitly focusing on accurately depicting genital warts. The second phase involved the training and testing vision model using this synthetic dataset. This method aimed to assess the effectiveness of diffusion models in rapidly generating high quality training data and the subsequent impact on the vision model performance in medical image recognition. The study findings revealed significant insights into the performance of the vision model trained on synthetic images generated through diffusion models. The vision model showed exceptional performance in accurately identifying cases of genital warts. It achieved an accuracy rate of 96% underscoring its effectiveness in medical image classification. For HPV cases the model demonstrated a high precision of 99% and a recall of 94%. In normal cases the precision was 95% with an impressive recall of 99%. These metrics indicate the model capability to correctly identify true positive cases and minimize false positives. The model achieved an F1 Score of 96% for HPV cases and 97% for normal cases. The high F1 Score across both categories highlights the balanced nature of the model precision and recall ensuring reliability and robustness in its predictions.", "published": "2024-02-05T09:18:49Z", "authors": ["Yudara Kularathne", "Prathapa Janitha", "Sithira Ambepitiya", "Thanveer Ahamed", "Dinuka Wijesundara", "Prarththanan Sothyrajah"], "categories": ["cs.CV", "cs.AI", "cs.LG"], "doi": "", "_query": "property valuation computer vision", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2202.08452v1", "title": "PCB Component Detection using Computer Vision for Hardware Assurance", "abstract": "Printed Circuit Board (PCB) assurance in the optical domain is a crucial field of study. Though there are many existing PCB assurance methods using image processing, computer vision (CV), and machine learning (ML), the PCB field is complex and increasingly evolving so new techniques are required to overcome the emerging problems. Existing ML-based methods outperform traditional CV methods, however they often require more data, have low explainability, and can be difficult to adapt when a new technology arises. To overcome these challenges, CV methods can be used in tandem with ML methods. In particular, human-interpretable CV algorithms such as those that extract color, shape, and texture features increase PCB assurance explainability. This allows for incorporation of prior knowledge, which effectively reduce the number of trainable ML parameters and thus, the amount of data needed to achieve high accuracy when training or retraining an ML model. Hence, this study explores the benefits and limitations of a variety of common computer vision-based features for the task of PCB component detection using semantic data. Results of this study indicate that color features demonstrate promising performance for PCB component detection. The purpose of this paper is to facilitate collaboration between the hardware assurance, computer vision, and machine learning communities.", "published": "2022-02-17T05:46:53Z", "authors": ["Wenwei Zhao", "Suprith Gurudu", "Shayan Taheri", "Shajib Ghosh", "Mukhil Azhagan Mallaiyan Sathiaseelan", "Navid Asadizanjani"], "categories": ["cs.CV"], "doi": "", "_query": "property valuation computer vision", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2409.12939v1", "title": "Accelerating AI and Computer Vision for Satellite Pose Estimation on the Intel Myriad X Embedded SoC", "abstract": "The challenging deployment of Artificial Intelligence (AI) and Computer Vision (CV) algorithms at the edge pushes the community of embedded computing to examine heterogeneous System-on-Chips (SoCs). Such novel computing platforms provide increased diversity in interfaces, processors and storage, however, the efficient partitioning and mapping of AI/CV workloads still remains an open issue. In this context, the current paper develops a hybrid AI/CV system on Intel's Movidius Myriad X, which is an heterogeneous Vision Processing Unit (VPU), for initializing and tracking the satellite's pose in space missions. The space industry is among the communities examining alternative computing platforms to comply with the tight constraints of on-board data processing, while it is also striving to adopt functionalities from the AI domain. At algorithmic level, we rely on the ResNet-50-based UrsoNet network along with a custom classical CV pipeline. For efficient acceleration, we exploit the SoC's neural compute engine and 16 vector processors by combining multiple parallelization and low-level optimization techniques. The proposed single-chip, robust-estimation, and real-time solution delivers a throughput of up to 5 FPS for 1-MegaPixel RGB images within a limited power envelope of 2W.", "published": "2024-09-19T17:50:50Z", "authors": ["Vasileios Leon", "Panagiotis Minaidis", "George Lentaris", "Dimitrios Soudris"], "categories": ["cs.AR", "cs.CV"], "doi": "10.1016/j.micpro.2023.104947", "_query": "property valuation computer vision", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/1902.11128v1", "title": "FixyNN: Efficient Hardware for Mobile Computer Vision via Transfer Learning", "abstract": "The computational demands of computer vision tasks based on state-of-the-art Convolutional Neural Network (CNN) image classification far exceed the energy budgets of mobile devices. This paper proposes FixyNN, which consists of a fixed-weight feature extractor that generates ubiquitous CNN features, and a conventional programmable CNN accelerator which processes a dataset-specific CNN. Image classification models for FixyNN are trained end-to-end via transfer learning, with the common feature extractor representing the transfered part, and the programmable part being learnt on the target dataset. Experimental results demonstrate FixyNN hardware can achieve very high energy efficiencies up to 26.6 TOPS/W ($4.81 \\times$ better than iso-area programmable accelerator). Over a suite of six datasets we trained models via transfer learning with an accuracy loss of $<1\\%$ resulting in up to 11.2 TOPS/W - nearly $2 \\times$ more efficient than a conventional programmable CNN accelerator of the same area.", "published": "2019-02-27T02:42:33Z", "authors": ["Paul N. Whatmough", "Chuteng Zhou", "Patrick Hansen", "Shreyas Kolala Venkataramanaiah", "Jae-sun Seo", "Matthew Mattina"], "categories": ["cs.CV", "cs.AR", "cs.LG", "stat.ML"], "doi": "", "_query": "property valuation computer vision", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2211.13137v1", "title": "Pruned Lightweight Encoders for Computer Vision", "abstract": "Latency-critical computer vision systems, such as autonomous driving or drone control, require fast image or video compression when offloading neural network inference to a remote computer. To ensure low latency on a near-sensor edge device, we propose the use of lightweight encoders with constant bitrate and pruned encoding configurations, namely, ASTC and JPEG XS. Pruning introduces significant distortion which we show can be recovered by retraining the neural network with compressed data after decompression. Such an approach does not modify the network architecture or require coding format modifications. By retraining with compressed datasets, we reduced the classification accuracy and segmentation mean intersection over union (mIoU) degradation due to ASTC compression to 4.9-5.0 percentage points (pp) and 4.4-4.0 pp, respectively. With the same method, the mIoU lost due to JPEG XS compression at the main profile was restored to 2.7-2.3 pp. In terms of encoding speed, our ASTC encoder implementation is 2.3x faster than JPEG. Even though the JPEG XS reference encoder requires optimizations to reach low latency, we showed that disabling significance flag coding saves 22-23% of encoding time at the cost of 0.4-0.3 mIoU after retraining.", "published": "2022-11-23T17:11:48Z", "authors": ["Jakub Žádník", "Markku Mäkitalo", "Pekka Jääskeläinen"], "categories": ["cs.CV"], "doi": "10.1109/mmsp55362.2022.9949477", "_query": "property valuation computer vision", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2407.11668v1", "title": "Learning to Make Keypoints Sub-Pixel Accurate", "abstract": "This work addresses the challenge of sub-pixel accuracy in detecting 2D local features, a cornerstone problem in computer vision. Despite the advancements brought by neural network-based methods like SuperPoint and ALIKED, these modern approaches lag behind classical ones such as SIFT in keypoint localization accuracy due to their lack of sub-pixel precision. We propose a novel network that enhances any detector with sub-pixel precision by learning an offset vector for detected features, thereby eliminating the need for designing specialized sub-pixel accurate detectors. This optimization directly minimizes test-time evaluation metrics like relative pose error. Through extensive testing with both nearest neighbors matching and the recent LightGlue matcher across various real-world datasets, our method consistently outperforms existing methods in accuracy. Moreover, it adds only around 7 ms to the time of a particular detector. The code is available at https://github.com/KimSinjeong/keypt2subpx .", "published": "2024-07-16T12:39:56Z", "authors": ["Shinjeong Kim", "Marc Pollefeys", "Daniel Barath"], "categories": ["cs.CV"], "doi": "", "_query": "property valuation computer vision", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2210.03289v1", "title": "Scalable Self-Supervised Representation Learning from Spatiotemporal Motion Trajectories for Multimodal Computer Vision", "abstract": "Self-supervised representation learning techniques utilize large datasets without semantic annotations to learn meaningful, universal features that can be conveniently transferred to solve a wide variety of downstream supervised tasks. In this work, we propose a self-supervised method for learning representations of geographic locations from unlabeled GPS trajectories to solve downstream geospatial computer vision tasks. Tiles resulting from a raster representation of the earth's surface are modeled as nodes on a graph or pixels of an image. GPS trajectories are modeled as allowed Markovian paths on these nodes. A scalable and distributed algorithm is presented to compute image-like representations, called reachability summaries, of the spatial connectivity patterns between tiles and their neighbors implied by the observed Markovian paths. A convolutional, contractive autoencoder is trained to learn compressed representations, called reachability embeddings, of reachability summaries for every tile. Reachability embeddings serve as task-agnostic, feature representations of geographic locations. Using reachability embeddings as pixel representations for five different downstream geospatial tasks, cast as supervised semantic segmentation problems, we quantitatively demonstrate that reachability embeddings are semantically meaningful representations and result in 4-23% gain in performance, as measured using area under the precision-recall curve (AUPRC) metric, when compared to baseline models that use pixel representations that do not account for the spatial connectivity between tiles. Reachability embeddings transform sequential, spatiotemporal mobility data into semantically meaningful tensor representations that can be combined with other sources of imagery and are designed to facilitate multimodal learning in geospatial computer vision.", "published": "2022-10-07T02:41:02Z", "authors": ["Swetava Ganguli", "C. V. Krishnakumar Iyer", "Vipul Pandey"], "categories": ["cs.CV", "cs.AI", "cs.LG"], "doi": "10.1109/MDM55031.2022.00028", "_query": "property valuation computer vision", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2505.23597v1", "title": "Bridging Classical and Modern Computer Vision: PerceptiveNet for Tree Crown Semantic Segmentation", "abstract": "The accurate semantic segmentation of tree crowns within remotely sensed data is crucial for scientific endeavours such as forest management, biodiversity studies, and carbon sequestration quantification. However, precise segmentation remains challenging due to complexities in the forest canopy, including shadows, intricate backgrounds, scale variations, and subtle spectral differences among tree species. Compared to the traditional methods, Deep Learning models improve accuracy by extracting informative and discriminative features, but often fall short in capturing the aforementioned complexities. To address these challenges, we propose PerceptiveNet, a novel model incorporating a Logarithmic Gabor-parameterised convolutional layer with trainable filter parameters, alongside a backbone that extracts salient features while capturing extensive context and spatial information through a wider receptive field. We investigate the impact of Log-Gabor, Gabor, and standard convolutional layers on semantic segmentation performance through extensive experimentation. Additionally, we conduct an ablation study to assess the contributions of individual layers and their combinations to overall model performance, and we evaluate PerceptiveNet as a backbone within a novel hybrid CNN-Transformer model. Our results outperform state-of-the-art models, demonstrating significant performance improvements on a tree crown dataset while generalising across domains, including two benchmark aerial scene semantic segmentation datasets with varying complexities.", "published": "2025-05-29T16:11:08Z", "authors": ["Georgios Voulgaris"], "categories": ["cs.CV"], "doi": "", "_query": "property valuation computer vision", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2201.05729v3", "title": "CLIP-TD: CLIP Targeted Distillation for Vision-Language Tasks", "abstract": "Contrastive language-image pretraining (CLIP) links vision and language modalities into a unified embedding space, yielding the tremendous potential for vision-language (VL) tasks. While early concurrent works have begun to study this potential on a subset of tasks, important questions remain: 1) What is the benefit of CLIP on unstudied VL tasks? 2) Does CLIP provide benefit in low-shot or domain-shifted scenarios? 3) Can CLIP improve existing approaches without impacting inference or pretraining complexity? In this work, we seek to answer these questions through two key contributions. First, we introduce an evaluation protocol that includes Visual Commonsense Reasoning (VCR), Visual Entailment (SNLI-VE), and Visual Question Answering (VQA), across a variety of data availability constraints and conditions of domain shift. Second, we propose an approach, named CLIP Targeted Distillation (CLIP-TD), to intelligently distill knowledge from CLIP into existing architectures using a dynamically weighted objective applied to adaptively selected tokens per instance. Experiments demonstrate that our proposed CLIP-TD leads to exceptional gains in the low-shot (up to 51.9%) and domain-shifted (up to 71.3%) conditions of VCR, while simultaneously improving performance under standard fully-supervised conditions (up to 2%), achieving state-of-art performance on VCR compared to other single models that are pretrained with image-text data only. On SNLI-VE, CLIP-TD produces significant gains in low-shot conditions (up to 6.6%) as well as fully supervised (up to 3%). On VQA, CLIP-TD provides improvement in low-shot (up to 9%), and in fully-supervised (up to 1.3%). Finally, CLIP-TD outperforms concurrent works utilizing CLIP for finetuning, as well as baseline naive distillation approaches. Code will be made available.", "published": "2022-01-15T01:54:01Z", "authors": ["Zhecan Wang", "Noel Codella", "Yen-Chun Chen", "Luowei Zhou", "Jianwei Yang", "Xiyang Dai", "Bin Xiao", "Haoxuan You", "Shih-Fu Chang", "Lu Yuan"], "categories": ["cs.CV", "cs.AI", "cs.CL", "cs.LG", "cs.MM"], "doi": "", "_query": "property valuation computer vision", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2102.10854v1", "title": "Contour Loss for Instance Segmentation via k-step Distance Transformation Image", "abstract": "Instance segmentation aims to locate targets in the image and segment each target area at pixel level, which is one of the most important tasks in computer vision. Mask R-CNN is a classic method of instance segmentation, but we find that its predicted masks are unclear and inaccurate near contours. To cope with this problem, we draw on the idea of contour matching based on distance transformation image and propose a novel loss function, called contour loss. Contour loss is designed to specifically optimize the contour parts of the predicted masks, thus can assure more accurate instance segmentation. In order to make the proposed contour loss to be jointly trained under modern neural network frameworks, we design a differentiable k-step distance transformation image calculation module, which can approximately compute truncated distance transformation images of the predicted mask and corresponding ground-truth mask online. The proposed contour loss can be integrated into existing instance segmentation methods such as Mask R-CNN, and combined with their original loss functions without modification of the inference network structures, thus has strong versatility. Experimental results on COCO show that contour loss is effective, which can further improve instance segmentation performances.", "published": "2021-02-22T09:35:35Z", "authors": ["Xiaolong Guo", "Xiaosong Lan", "Kunfeng Wang", "Shuxiao Li"], "categories": ["cs.CV"], "doi": "", "_query": "property valuation computer vision", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2606.00095v1", "title": "Bridging the 2D-3D Gap: A Hierarchical Semantic-Geometric Map for Vision Language Navigation", "abstract": "Vision-Language Navigation (VLN) enables embodied agents to reach target locations in unseen environments by following language instructions. Despite recent progress with vision-language models (VLMs), a critical semantic-geometric gap remains: while VLMs excel at language and 2D visual understanding, they struggle with 3D spatial reasoning and fail to capture the causal dynamics between actions and spatial transitions, resulting in unreliable navigation, particularly in zero-shot settings. To bridge this gap, we propose a Hierarchical Semantic-Geometric Map (HSGM) that transforms 3D geometric information into a structured representation compatible with VLMs, effectively linking them to the physical world. Specifically, HSGM is represented as a multi-channel top-down map organized into three levels: (1) geometric level that records navigable regions and obstacles, (2) semantic level that represents objects and their relations, and (3) decision level that supports high-level task reasoning and goal selection. During navigation, the VLM acts as a high-level semantic planner, interpreting the spatial layout encoded in the HSGM to select geometrically valid waypoints, while low-level, collision-free movements between waypoints are executed by a classical path-planning algorithm, fully decoupling semantic reasoning from action execution. Additionally, complex instructions are decomposed into subtasks to alleviate the problem of progress forgetting or hallucinating in long-horizon navigation. Extensive experiments on R2R-CE and RxR-CE benchmarks demonstrate that our zero-shot framework achieves state-of-the-art performance and even outperforms several supervised methods. Code is available at https://github.com/Teacher-Tom/HSGM_public.", "published": "2026-05-25T08:53:21Z", "authors": ["Kailing Li", "Tianwen Qian", "Lijin Yang", "Yuqian Fu", "Jingyu Gong", "Xiaoling Wang", "Liang He"], "categories": ["cs.CV", "cs.AI", "cs.CL", "cs.RO"], "doi": "", "_query": "property valuation computer vision", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/1403.1974v1", "title": "Designing an FPGA Synthesizable Computer Vision Algorithm to Detect the Greening of Potatoes", "abstract": "Potato quality control has improved in the last years thanks to automation techniques like machine vision, mainly making the classification task between different quality degrees faster, safer and less subjective. In our study we are going to design a computer vision algorithm for grading of potatoes according to the greening of the surface color of potato. The ratio of green pixels to the total number of pixels of the potato surface is found. The higher the ratio the worse is the potato. First the image is converted into serial data and then processing is done in RGB colour space. Green part of the potato is also shown by de-serializing the output. The same algorithm is then synthesized on FPGA and the result shows thousand times speed improvement in case of hardware synthesis.", "published": "2014-03-08T14:59:46Z", "authors": ["Jaspinder Pal Singh"], "categories": ["cs.CV"], "doi": "10.14445/22315381/IJETT-V8P275", "_query": "property valuation computer vision", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2405.00470v2", "title": "Valuations, bijections, and bases", "abstract": "The aim of this paper is to build a theory of commutative and noncommutative {\\it injective} valuations of various algebras (including algebras with zero divisors). The targets of our valuations are (well-)ordered commutative and noncommutative (partial and entire) semigroups including any sub-semigroups of the free monoid $F_n$ on $n$ generators and various quotients. When the range of a valuation of an algebra $A$ is a finitely generated (partial) semigroup, we construct a generalization of the standard monomial bases in $A$, which seems to be new in noncommutative case. Quite remarkably, for any pair of well-ordered valuations one has a canonical bijection between the valuation semigroups, which serves as an analog of the celebrated Jordan-Hölder correspondences and these bijections are ``almost\" homomorphisms of the involved semigroups. A spectacular demonstration of this remarkable property of JH-bijections for quantum Schubert cells $A=U_q(w)$ results in mysterious \"symplectomorphisms\" of involved skew symmetric forms.", "published": "2024-05-01T12:13:54Z", "authors": ["Arkady Berenstein", "Dima Grigoriev"], "categories": ["math.RA", "math.RT"], "doi": "", "_query": "property valuation computer vision", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2204.00452v2", "title": "Vision Transformer with Cross-attention by Temporal Shift for Efficient Action Recognition", "abstract": "Feature shifts have been shown to be useful for action recognition with CNN-based models since Temporal Shift Module (TSM) was proposed. It is based on frame-wise feature extraction with late fusion, and layer features are shifted along the time direction for the temporal interaction. TokenShift, a recent model based on Vision Transformer (ViT), also uses the temporal feature shift mechanism, which, however, does not fully exploit the structure of Multi-head Self-Attention (MSA) in ViT. In this paper, we propose Multi-head Self/Cross-Attention (MSCA), which fully utilizes the attention structure. TokenShift is based on a frame-wise ViT with features temporally shifted with successive frames (at time t+1 and t-1). In contrast, the proposed MSCA replaces MSA in the frame-wise ViT, and some MSA heads attend to successive frames instead of the current frame. The computation cost is the same as the frame-wise ViT and TokenShift as it simply changes the target to which the attention is taken. There is a choice about which of key, query, and value are taken from the successive frames, then we experimentally compared these variants with Kinetics400. We also investigate other variants in which the proposed MSCA is used along the patch dimension of ViT, instead of the head dimension. Experimental results show that a variant, MSCA-KV, shows the best performance and is better than TokenShift by 0.1% and then ViT by 1.2%.", "published": "2022-04-01T14:06:19Z", "authors": ["Ryota Hashiguchi", "Toru Tamaki"], "categories": ["cs.CV"], "doi": "", "_query": "property valuation computer vision", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2107.14061v1", "title": "The Need and Status of Sea Turtle Conservation and Survey of Associated Computer Vision Advances", "abstract": "For over hundreds of millions of years, sea turtles and their ancestors have swum in the vast expanses of the ocean. They have undergone a number of evolutionary changes, leading to speciation and sub-speciation. However, in the past few decades, some of the most notable forces driving the genetic variance and population decline have been global warming and anthropogenic impact ranging from large-scale poaching, collecting turtle eggs for food, besides dumping trash including plastic waste into the ocean. This leads to severe detrimental effects in the sea turtle population, driving them to extinction. This research focusses on the forces causing the decline in sea turtle population, the necessity for the global conservation efforts along with its successes and failures, followed by an in-depth analysis of the modern advances in detection and recognition of sea turtles, involving Machine Learning and Computer Vision systems, aiding the conservation efforts.", "published": "2021-07-29T14:53:47Z", "authors": ["Aditya Jyoti Paul"], "categories": ["cs.CV", "cs.AI", "cs.LG", "eess.IV"], "doi": "", "_query": "property valuation computer vision", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2202.03716v1", "title": "Binary Neural Networks as a general-propose compute paradigm for on-device computer vision", "abstract": "For binary neural networks (BNNs) to become the mainstream on-device computer vision algorithm, they must achieve a superior speed-vs-accuracy tradeoff than 8-bit quantization and establish a similar degree of general applicability in vision tasks. To this end, we propose a BNN framework comprising 1) a minimalistic inference scheme for hardware-friendliness, 2) an over-parameterized training scheme for high accuracy, and 3) a simple procedure to adapt to different vision tasks. The resultant framework overtakes 8-bit quantization in the speed-vs-accuracy tradeoff for classification, detection, segmentation, super-resolution and matching: our BNNs not only retain the accuracy levels of their 8-bit baselines but also showcase 1.3-2.4$\\times$ faster FPS on mobile CPUs. Similar conclusions can be drawn for prototypical systolic-array-based AI accelerators, where our BNNs promise 2.8-7$\\times$ fewer execution cycles than 8-bit and 2.1-2.7$\\times$ fewer cycles than alternative BNN designs. These results suggest that the time for large-scale BNN adoption could be upon us.", "published": "2022-02-08T08:38:22Z", "authors": ["Guhong Nie", "Lirui Xiao", "Menglong Zhu", "Dongliang Chu", "Yue Shen", "Peng Li", "Kang Yang", "Li Du", "Bo Chen"], "categories": ["cs.CV"], "doi": "", "_query": "property valuation computer vision", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2309.17401v5", "title": "Adversarial Attacks to Latent Representations of Distributed Neural Networks in Split Computing", "abstract": "Distributed deep neural networks (DNNs) have been shown to reduce the computational burden of mobile devices and decrease the end-to-end inference latency in edge computing scenarios. While distributed DNNs have been studied, to the best of our knowledge, the resilience of distributed DNNs to adversarial action remains an open problem. In this paper, we fill the existing research gap by rigorously analyzing the robustness of distributed DNNs against adversarial action. We cast this problem in the context of information theory and rigorously proved that (i) the compressed latent dimension improves the robustness but also affect task-oriented performance; and (ii) the deeper splitting point enhances the robustness but also increases the computational burden. These two trade-offs provide a novel perspective to design robust distributed DNN. To test our theoretical findings, we perform extensive experimental analysis by considering 6 different DNN architectures, 6 different approaches for distributed DNN and 10 different adversarial attacks using the ImageNet-1K dataset.", "published": "2023-09-29T17:01:29Z", "authors": ["Milin Zhang", "Mohammad Abdi", "Jonathan Ashdown", "Francesco Restuccia"], "categories": ["cs.LG", "cs.AI"], "doi": "", "_query": "property valuation computer vision", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2304.00303v1", "title": "An algorithm for computing syzygies on $V[X]$ when $V$ is a valuation domain", "abstract": "We give an algorithm for computing the V-saturation of any finitely-generated submodule of a power of V[X], where V is a valuation domain. Our algorithm is based on a notion of \"echelon form\" which ensures its correctness. This allows us to compute a finite system of generators for the syzygy module of any finitely generated submodule of a power of V[X].", "published": "2023-04-01T12:11:49Z", "authors": ["Henri Lombardi", "Claude Quitté", "Ihsen Yengui"], "categories": ["math.AC"], "doi": "10.1080/00927872.2013.794360", "_query": "property valuation computer vision", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/1809.08391v1", "title": "Understanding Fake Faces", "abstract": "Face recognition research is one of the most active topics in computer vision (CV), and deep neural networks (DNN) are now filling the gap between human-level and computer-driven performance levels in face verification algorithms. However, although the performance gap appears to be narrowing in terms of accuracy-based expectations, a curious question has arisen; specifically, \"Face understanding of AI is really close to that of human?\" In the present study, in an effort to confirm the brain-driven concept, we conduct image-based detection, classification, and generation using an in-house created fake face database. This database has two configurations: (i) false positive face detections produced using both the Viola Jones (VJ) method and convolutional neural networks (CNN), and (ii) simulacra that have fundamental characteristics that resemble faces but are completely artificial. The results show a level of suggestive knowledge that indicates the continuing existence of a gap between the capabilities of recent vision-based face recognition algorithms and human-level performance. On a positive note, however, we have obtained knowledge that will advance the progress of face-understanding models.", "published": "2018-09-22T06:42:34Z", "authors": ["Ryota Natsume", "Kazuki Inoue", "Yoshihiro Fukuhara", "Shintaro Yamamoto", "Shigeo Morishima", "Hirokatsu Kataoka"], "categories": ["cs.CV", "cs.AI"], "doi": "", "_query": "property valuation computer vision", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2505.21472v2", "title": "Mitigating Hallucination in Large Vision-Language Models via Adaptive Attention Calibration", "abstract": "Large vision-language models (LVLMs) achieve impressive performance on multimodal tasks but often suffer from hallucination, and confidently describe objects or attributes not present in the image. Current training-free interventions struggle to maintain accuracy in open-ended and long-form generation scenarios. We introduce the Confidence-Aware Attention Calibration (CAAC) framework to address this challenge by targeting two key biases: spatial perception bias, which distributes attention disproportionately across image tokens, and modality bias, which shifts focus from visual to textual inputs over time. CAAC employs a two-step approach: Visual-Token Calibration (VTC) to balance attention across visual tokens, and Adaptive Attention Re-Scaling (AAR) to reinforce visual grounding guided by the model's confidence. This confidence-driven adjustment ensures consistent visual alignment during generation. Experiments on CHAIR, AMBER, and POPE benchmarks demonstrate that CAAC outperforms baselines, particularly in long-form generations, effectively reducing hallucination.", "published": "2025-05-27T17:45:21Z", "authors": ["Mehrdad Fazli", "Bowen Wei", "Ahmet Sari", "Ziwei Zhu"], "categories": ["cs.CV", "cs.CL", "cs.MM"], "doi": "", "_query": "property valuation computer vision", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2103.00481v6", "title": "Contrast Sensitivity Functions in Autoencoders", "abstract": "Three decades ago, Atick et al. suggested that human frequency sensitivity may emerge from the enhancement required for a more efficient analysis of retinal images. Here we reassess the relevance of low-level vision tasks in the explanation of the Contrast Sensitivity Functions (CSFs) in light of (1) the current trend of using artificial neural networks for studying vision, and (2) the current knowledge of retinal image representations. As a first contribution, we show that a very popular type of convolutional neural networks (CNNs), called autoencoders, may develop human-like CSFs in the spatio-temporal and chromatic dimensions when trained to perform some basic low-level vision tasks (like retinal noise and optical blur removal), but not others (like chromatic adaptation or pure reconstruction after simple bottlenecks). As an illustrative example, the best CNN (in the considered set of simple architectures for enhancement of the retinal signal) reproduces the CSFs with an RMSE error of 11\\% of the maximum sensitivity. As a second contribution, we provide experimental evidence of the fact that, for some functional goals (at low abstraction level), deeper CNNs that are better in reaching the quantitative goal are actually worse in replicating human-like phenomena (such as the CSFs). This low-level result (for the explored networks) is not necessarily in contradiction with other works that report advantages of deeper nets in modeling higher-level vision goals. However, in line with a growing body of literature, our results suggests another word of caution about CNNs in vision science since the use of simplified units or unrealistic architectures in goal optimization may be a limitation for the modeling and understanding of human vision.", "published": "2021-02-28T12:19:02Z", "authors": ["Qiang Li", "Alex Gomez-Villa", "Marcelo Bertalmio", "Jesus Malo"], "categories": ["q-bio.NC"], "doi": "10.1167/jov.22.6.8.", "_query": "property valuation computer vision", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2508.10523v2", "title": "Reasoning in Computer Vision: Taxonomy, Models, Tasks, and Methodologies", "abstract": "Visual reasoning matters for many computer vision tasks that go beyond surface-level object detection and classification. Despite progress in relational, symbolic, temporal, causal, and commonsense reasoning, existing surveys typically cover only one part of the problem, such as visual question answering, scene-graph generation, neuro-symbolic AI, or multimodal chain-of-thought, and rarely analyze reasoning types, methodologies, and evaluation protocols together. This survey addresses that gap. Following a structured literature review, we group visual reasoning into five major types (relational, symbolic, temporal, causal, and commonsense) and examine how each is implemented across methods that range from graph-based models, memory networks, attention mechanisms, and neuro-symbolic systems to reasoning with vision-language models (VLMs) and multimodal large language models (MLLMs), including visual chain-of-thought, visual programming, and tool-augmented and test-time reasoning. We then review evaluation protocols for functional correctness, structural consistency, and causal validity, and we analyze their limits in generalizability, reproducibility, faithfulness, and explanatory power. We also identify open challenges: scaling to complex scenes, integrating symbolic and neural paradigms more deeply, the shortage of comprehensive benchmarks, language-prior shortcuts and hallucination in foundation models, and reasoning under weak supervision. Finally, we set out a research agenda for vision systems and argue that connecting perception and reasoning is necessary for transparent, trustworthy, and cross-domain models, especially in high-stakes settings such as autonomous driving and medical diagnostics.", "published": "2025-08-14T10:53:35Z", "authors": ["Ayushman Sarkar", "Zhenyu Yu", "Mohd Yamani Idna Idris"], "categories": ["cs.CV"], "doi": "", "_query": "property valuation computer vision", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2604.11993v1", "title": "Ultra-low-light computer vision using trained photon correlations", "abstract": "Illumination using correlated photon sources has been established as an approach to allowing high-fidelity images to be reconstructed from noisy camera frames by taking advantage of the knowledge that signal photons are spatially correlated whereas detector clicks due to noise are uncorrelated. However, in computer-vision tasks, the goal is often not ultimately to reconstruct an image, but to make inferences about a scene -- such as what object is present. Here we show how correlated-photon illumination can be used to gain an advantage in a hybrid optical-electronic computer-vision pipeline for object recognition. We demonstrate correlation-aware training (CAT): end-to-end optimization of a trainable correlated-photon illumination source and a Transformer backend in a way that the Transformer can learn to benefit from the correlations, using a small number (<= 100) of shots. We show a classification accuracy enhancement of up to 15 percentage points over conventional, uncorrelated-illumination-based computer vision in ultra-low-light and noisy imaging conditions, as well as an improvement over using untrained correlated-photon illumination. Our work illustrates how specializing to a computer-vision task -- object recognition -- and training the pattern of photon correlations in conjunction with a digital backend allows us to push the limits of accuracy in highly photon-budget-constrained scenarios beyond existing methods focused on image reconstruction.", "published": "2026-04-13T19:34:23Z", "authors": ["Mandar M. Sohoni", "Jérémie Laydevant", "Mathieu Ouellet", "Shi-Yuan Ma", "Ryotatsu Yanagimoto", "Benjamin A. Ash", "Tatsuhiro Onodera", "Tianyu Wang", "Logan G. Wright", "Peter L. McMahon"], "categories": ["cs.CV", "physics.optics"], "doi": "", "_query": "property valuation computer vision", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2110.02551v4", "title": "A Survey of Fish Tracking Techniques Based on Computer Vision", "abstract": "Fish tracking is a key technology for obtaining movement trajectories and identifying abnormal behavior. However, it faces considerable challenges, including occlusion, multi-scale tracking, and fish deformation. Notably, extant reviews have focused more on behavioral analysis rather than providing a comprehensive overview of computer vision-based fish tracking approaches. This paper presents a comprehensive review of the advancements of fish tracking technologies over the past seven years (2017-2023). It explores diverse fish tracking techniques with an emphasis on fundamental localization and tracking methods. Auxiliary plugins commonly integrated into fish tracking systems, such as underwater image enhancement and re-identification, are also examined. Additionally, this paper summarizes open-source datasets, evaluation metrics, challenges, and applications in fish tracking research. Finally, a comprehensive discussion offers insights and future directions for vision-based fish tracking techniques. We hope that our work could provide a partial reference in the development of fish tracking algorithms.", "published": "2021-10-06T07:46:35Z", "authors": ["Weiran Li", "Zhenbo Li", "Fei Li", "Meng Yuan", "Chaojun Cen", "Yanyu Qi", "Qiannan Guo", "You Li"], "categories": ["cs.CV"], "doi": "", "_query": "property valuation computer vision", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2007.08364v1", "title": "A high fidelity synthetic face framework for computer vision", "abstract": "Analysis of faces is one of the core applications of computer vision, with tasks ranging from landmark alignment, head pose estimation, expression recognition, and face recognition among others. However, building reliable methods requires time-consuming data collection and often even more time-consuming manual annotation, which can be unreliable. In our work we propose synthesizing such facial data, including ground truth annotations that would be almost impossible to acquire through manual annotation at the consistency and scale possible through use of synthetic data. We use a parametric face model together with hand crafted assets which enable us to generate training data with unprecedented quality and diversity (varying shape, texture, expression, pose, lighting, and hair).", "published": "2020-07-16T14:40:28Z", "authors": ["Tadas Baltrusaitis", "Erroll Wood", "Virginia Estellers", "Charlie Hewitt", "Sebastian Dziadzio", "Marek Kowalski", "Matthew Johnson", "Thomas J. Cashman", "Jamie Shotton"], "categories": ["cs.CV", "cs.LG"], "doi": "", "_query": "property valuation computer vision", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/1201.4139v1", "title": "Image decomposition with anisotropic diffusion applied to leaf-texture analysis", "abstract": "Texture analysis is an important field of investigation that has received a great deal of interest from computer vision community. In this paper, we propose a novel approach for texture modeling based on partial differential equation (PDE). Each image $f$ is decomposed into a family of derived sub-images. $f$ is split into the $u$ component, obtained with anisotropic diffusion, and the $v$ component which is calculated by the difference between the original image and the $u$ component. After enhancing the texture attribute $v$ of the image, Gabor features are computed as descriptors. We validate the proposed approach on two texture datasets with high variability. We also evaluate our approach on an important real-world application: leaf-texture analysis. Experimental results indicate that our approach can be used to produce higher classification rates and can be successfully employed for different texture applications.", "published": "2012-01-19T18:39:41Z", "authors": ["Bruno Brandoli Machado", "Wesley Nunes Gonçalves", "Odemir Martinez Bruno"], "categories": ["cs.CV"], "doi": "", "_query": "property valuation computer vision", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/1410.2320v1", "title": "Computing minimum area homologies", "abstract": "Calculating and categorizing the similarity of curves is a fundamental problem which has generated much recent interest. However, to date there are no implementations of these algorithms for curves on surfaces with provable guarantees on the quality of the measure. In this paper, we present a similarity measure for any two cycles that are homologous, where we calculate the minimum area of any homology (or connected bounding chain) between the two cycles. The minimum area homology exists for broader classes of cycles than previous measures which are based on homotopy. It is also much easier to compute than previously defined measures, yielding an efficient implementation that is based on linear algebra tools. We demonstrate our algorithm on a range of inputs, showing examples which highlight the feasibility of this similarity measure.", "published": "2014-10-09T00:40:45Z", "authors": ["Erin Wolf Chambers", "Mikael Vejdemo-Johansson"], "categories": ["cs.CG", "cs.GR", "math.AT"], "doi": "", "_query": "property valuation computer vision", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/1411.5007v1", "title": "A Unified View of Large-scale Zero-sum Equilibrium Computation", "abstract": "The task of computing approximate Nash equilibria in large zero-sum extensive-form games has received a tremendous amount of attention due mainly to the Annual Computer Poker Competition. Immediately after its inception, two competing and seemingly different approaches emerged---one an application of no-regret online learning, the other a sophisticated gradient method applied to a convex-concave saddle-point formulation. Since then, both approaches have grown in relative isolation with advancements on one side not effecting the other. In this paper, we rectify this by dissecting and, in a sense, unify the two views.", "published": "2014-11-18T20:43:39Z", "authors": ["Kevin Waugh", "J. Andrew Bagnell"], "categories": ["cs.AI", "cs.GT"], "doi": "", "_query": "property valuation computer vision", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2407.09083v2", "title": "BKDSNN: Enhancing the Performance of Learning-based Spiking Neural Networks Training with Blurred Knowledge Distillation", "abstract": "Spiking neural networks (SNNs), which mimic biological neural system to convey information via discrete spikes, are well known as brain-inspired models with excellent computing efficiency. By utilizing the surrogate gradient estimation for discrete spikes, learning-based SNN training methods that can achieve ultra-low inference latency (number of time-step) emerge recently. Nevertheless, due to the difficulty in deriving precise gradient estimation for discrete spikes using learning-based method, a distinct accuracy gap persists between SNN and its artificial neural networks (ANNs) counterpart. To address the aforementioned issue, we propose a blurred knowledge distillation (BKD) technique, which leverages random blurred SNN feature to restore and imitate the ANN feature. Note that, our BKD is applied upon the feature map right before the last layer of SNN, which can also mix with prior logits-based knowledge distillation for maximized accuracy boost. To our best knowledge, in the category of learning-based methods, our work achieves state-of-the-art performance for training SNNs on both static and neuromorphic datasets. On ImageNet dataset, BKDSNN outperforms prior best results by 4.51% and 0.93% with the network topology of CNN and Transformer respectively.", "published": "2024-07-12T08:17:24Z", "authors": ["Zekai Xu", "Kang You", "Qinghai Guo", "Xiang Wang", "Zhezhi He"], "categories": ["cs.NE"], "doi": "", "_query": "property valuation computer vision", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2006.11379v1", "title": "Improving Train Track Safety using Drones, Computer Vision and Machine Learning", "abstract": "Millions of human casualties resulting from train accidents globally are caused by the inefficient, manual track inspections. Government agencies are seriously concerned about the safe operations of the rail industry after series of accidents reported across e USA and around the globe, mainly attributed to track defects. Casualties resulting from track defects result in billions of dollars loss in public and private investments and loss of revenue due to downtime, ultimately resulting in loss of the public's confidence. The manual, mundane, and expensive monitoring of rail track safety can be transform through the use of drones, computer vision, and machine learning. The primary goal of this study is to develop multiple algorithms that implement supervised and semi-supervised learning that accurately analyze whether a track is safe or unsafe based on simulated training data of train tracks. This includes being able to develop a Convolutional Neural Network that can identify track defects using supervised learning without having to specify a particular algorithm for detecting those defects, and that the new model would both speed up and improve the quality of the track defect detection process, accompanied with a computer vision image-processing algorithm. Our other goals included designing and building a prototype representation of train tracks to simulate track defects, to precisely and consistently conduct the visual inspection using drones. Ultimately, the goal demonstrates that the state of good repairs in railway tracks can be attained through the use of drones, computer vision and machine learning.", "published": "2020-06-04T23:17:23Z", "authors": ["Kirthi Kumar", "Anuraag Kaashyap"], "categories": ["cs.CV"], "doi": "", "_query": "property valuation computer vision", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2501.02040v2", "title": "A Separable Self-attention Inspired by the State Space Model for Computer Vision", "abstract": "Mamba is an efficient State Space Model (SSM) with linear computational complexity. Although SSMs are not suitable for handling non-causal data, Vision Mamba (ViM) methods still demonstrate good performance in tasks such as image classification and object detection. Recent studies have shown that there is a rich theoretical connection between state space models and attention variants. We propose a novel separable self attention method, for the first time introducing some excellent design concepts of Mamba into separable self-attention. To ensure a fair comparison with ViMs, we introduce VMINet, a simple yet powerful prototype architecture, constructed solely by stacking our novel attention modules with the most basic down-sampling layers. Notably, VMINet differs significantly from the conventional Transformer architecture. Our experiments demonstrate that VMINet has achieved competitive results on image classification and high-resolution dense prediction tasks.Code is available at: https://github.com/yws-wxs/VMINet.", "published": "2025-01-03T15:23:36Z", "authors": ["Juntao Zhang", "Shaogeng Liu", "Kun Bian", "You Zhou", "Pei Zhang", "Jianning Liu", "Jun Zhou", "Bingyan Liu"], "categories": ["cs.CV", "cs.AI"], "doi": "", "_query": "property valuation computer vision", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2602.23217v2", "title": "Multidimensional Task Learning: A Unified Tensor Framework for Computer Vision Tasks", "abstract": "This paper introduces Multidimensional Task Learning (MTL), a unified mathematical framework based on Generalized Einstein MLPs (GE-MLPs) that operate directly on tensors via the Einstein product. We argue that current computer vision task formulations are inherently constrained by matrix-based thinking: standard architectures rely on matrix-valued weights and vectorvalued biases, requiring structural flattening that restricts the space of naturally expressible tasks. GE-MLPs lift this constraint by operating with tensor-valued parameters, enabling explicit control over which dimensions are preserved or contracted without information loss. Through rigorous mathematical derivations, we demonstrate that classification, segmentation, and detection are special cases of MTL, differing only in their dimensional configuration within a formally defined task space. We further prove that this task space is strictly larger than what matrix-based formulations can natively express, enabling principled task configurations such as spatiotemporal or cross modal predictions that require destructive flattening under conventional approaches. This work provides a mathematical foundation for understanding, comparing, and designing computer vision tasks through the lens of tensor algebra.", "published": "2026-02-26T17:00:45Z", "authors": ["Alaa El Ichi", "Khalide Jbilou"], "categories": ["cs.CV", "math.NA"], "doi": "", "_query": "property valuation computer vision", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2310.11475v2", "title": "Tracking and Mapping in Medical Computer Vision: A Review", "abstract": "As computer vision algorithms increase in capability, their applications in clinical systems will become more pervasive. These applications include: diagnostics, such as colonoscopy and bronchoscopy; guiding biopsies, minimally invasive interventions, and surgery; automating instrument motion; and providing image guidance using pre-operative scans. Many of these applications depend on the specific visual nature of medical scenes and require designing algorithms to perform in this environment. In this review, we provide an update to the field of camera-based tracking and scene mapping in surgery and diagnostics in medical computer vision. We begin with describing our review process, which results in a final list of 515 papers that we cover. We then give a high-level summary of the state of the art and provide relevant background for those who need tracking and mapping for their clinical applications. After which, we review datasets provided in the field and the clinical needs that motivate their design. Then, we delve into the algorithmic side, and summarize recent developments. This summary should be especially useful for algorithm designers and to those looking to understand the capability of off-the-shelf methods. We maintain focus on algorithms for deformable environments while also reviewing the essential building blocks in rigid tracking and mapping since there is a large amount of crossover in methods. With the field summarized, we discuss the current state of the tracking and mapping methods along with needs for future algorithms, needs for quantification, and the viability of clinical applications. We then provide some research directions and questions. We conclude that new methods need to be designed or combined to support clinical applications in deformable environments, and more focus needs to be put into collecting datasets for training and evaluation.", "published": "2023-10-17T03:36:28Z", "authors": ["Adam Schmidt", "Omid Mohareri", "Simon DiMaio", "Michael C. Yip", "Septimiu E. Salcudean"], "categories": ["cs.CV"], "doi": "10.1016/j.media.2024.103131", "_query": "property valuation computer vision", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2411.10535v1", "title": "Advancing Autonomous Driving Perception: Analysis of Sensor Fusion and Computer Vision Techniques", "abstract": "In autonomous driving, perception systems are piv otal as they interpret sensory data to understand the envi ronment, which is essential for decision-making and planning. Ensuring the safety of these perception systems is fundamental for achieving high-level autonomy, allowing us to confidently delegate driving and monitoring tasks to machines. This re port aims to enhance the safety of perception systems by examining and summarizing the latest advancements in vision based systems, and metrics for perception tasks in autonomous driving. The report also underscores significant achievements and recognized challenges faced by current research in this field. This project focuses on enhancing the understanding and navigation capabilities of self-driving robots through depth based perception and computer vision techniques. Specifically, it explores how we can perform better navigation into unknown map 2D map with existing detection and tracking algorithms and on top of that how depth based perception can enhance the navigation capabilities of the wheel based bots to improve autonomous driving perception.", "published": "2024-11-15T19:11:58Z", "authors": ["Urvishkumar Bharti", "Vikram Shahapur"], "categories": ["cs.RO", "cs.CV"], "doi": "", "_query": "property valuation computer vision", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/0711.0784v1", "title": "Addendum to Research MMMCV; A Man/Microbio/Megabio/Computer Vision", "abstract": "In October 2007, a Research Proposal for the University of Sydney, Australia, the author suggested that biovie-physical phenomenon as `electrodynamic dependant biological vision', is governed by relativistic quantum laws and biovision. The phenomenon on the basis of `biovielectroluminescence', satisfies man/microbio/megabio/computer vision (MMMCV), as a robust candidate for physical and visual sciences. The general aim of this addendum is to present a refined text of Sections 1-3 of that proposal and highlighting the contents of its Appendix in form of a `Mechanisms' Section. We then briefly remind in an article aimed for December 2007, by appending two more equations into Section 3, a theoretical II-time scenario as a time model well-proposed for the phenomenon. The time model within the core of the proposal, plays a significant role in emphasizing the principle points on Objectives no. 1-8, Sub-hypothesis 3.1.2, mentioned in Article [arXiv:0710.0410]. It also expresses the time concept in terms of causing quantized energy f(|E|) of time |t|, emit in regard to shortening the probability of particle loci as predictable patterns of particle's un-occurred motion, a solution to Heisenberg's uncertainty principle (HUP) into a simplistic manner. We conclude that, practical frames via a time algorithm to this model, fixates such predictable patterns of motion of scenery bodies onto recordable observation points of a MMMCV system. It even suppresses/predicts superposition phenomena coming from a human subject and/or other bio-subjects for any decision making event, e.g., brainwave quantum patterns based on vision. Maintaining the existential probability of Riemann surfaces of II-time scenarios in the context of biovielectroluminescence, makes motion-prediction a possibility.", "published": "2007-11-06T19:41:22Z", "authors": ["Philip B. Alipour"], "categories": ["cs.CV", "cs.CE"], "doi": "", "_query": "property valuation computer vision", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2005.06089v1", "title": "Apple Defect Detection Using Deep Learning Based Object Detection For Better Post Harvest Handling", "abstract": "The inclusion of Computer Vision and Deep Learning technologies in Agriculture aims to increase the harvest quality, and productivity of farmers. During postharvest, the export market and quality evaluation are affected by assorting of fruits and vegetables. In particular, apples are susceptible to a wide range of defects that can occur during harvesting or/and during the post-harvesting period. This paper aims to help farmers with post-harvest handling by exploring if recent computer vision and deep learning methods such as the YOLOv3 (Redmon & Farhadi (2018)) can help in detecting healthy apples from apples with defects.", "published": "2020-05-12T23:34:43Z", "authors": ["Paolo Valdez"], "categories": ["cs.CV", "eess.IV"], "doi": "", "_query": "property valuation computer vision", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/1608.01668v1", "title": "Self-Organising Maps in Computer Security", "abstract": "Some argue that biologically inspired algorithms are the future of solving difficult problems in computer science. Others strongly believe that the future lies in the exploration of mathematical foundations of problems at hand. The field of computer security tends to accept the latter view as a more appropriate approach due to its more workable validation and verification possibilities. The lack of rigorous scientific practices prevalent in biologically inspired security research does not aid in presenting bio-inspired security approaches as a viable way of dealing with complex security problems. This chapter introduces a biologically inspired algorithm, called the Self Organising Map (SOM), that was developed by Teuvo Kohonen in 1981. Since the algorithm's inception it has been scrutinised by the scientific community and analysed in more than 4000 research papers, many of which dealt with various computer security issues, from anomaly detection, analysis of executables all the way to wireless network monitoring. In this chapter a review of security related SOM research undertaken in the past is presented and analysed. The algorithm's biological analogies are detailed and the author's view on the future possibilities of this successful bio-inspired approach are given. The SOM algorithm's close relation to a number of vital functions of the human brain and the emergence of multi-core computer architectures are the two main reasons behind our assumption that the future of the SOM algorithm and its variations is promising, notably in the field of computer security.", "published": "2016-08-05T05:51:24Z", "authors": ["Jan Feyereisl", "Uwe Aickelin"], "categories": ["cs.AI", "cs.CR"], "doi": "", "_query": "property valuation computer vision", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/1508.00051v1", "title": "A Simplified Phase Model for Oscillator Based Computing", "abstract": "Building oscillator based computing systems with emerging nano-device technologies has become a promising solution for unconventional computing tasks like computer vision and pattern recognition. However, simulation and analysis of these systems is both time and compute intensive due to the nonlinearity of new devices and the complex behavior of coupled oscillators. In order to speed up the simulation of coupled oscillator systems, we propose a simplified phase model to perform phase and frequency synchronization prediction based on a synthesis of earlier models. Our model can predict the frequency locking behavior with several orders of magnitude speedup compared to direct evaluation, enabling the effective and efficient simulation of the large numbers of oscillators required for practical computing systems.", "published": "2015-07-31T23:26:11Z", "authors": ["Yan Fang", "Victor V. Yashin", "Donald M. Chiarulli", "Steven P. Levitan"], "categories": ["cs.ET"], "doi": "10.1109/ISVLSI.2015.44", "_query": "property valuation computer vision", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/1901.04937v2", "title": "Computation of residual polynomial operators of inductive valuations", "abstract": "Let $(K,v)$ be a valued field, and $μ$ an inductive valuation on $K[x]$ extending $v$. Let $G_μ$ be the graded algebra of $μ$ over $K[x]$, and $κ$ the maximal subfield of the subring of $G_μ$ formed by the homogeneous elements of degree zero. In this paper, we find an algorithm to compute the field $κ$ and the residual polynomial operator $R_μ: K[x]\\toκ[y]$, where $y$ is another indeterminate, without any need to perform computations in the graded algebra. This leads to an OM algorithm to compute the factorization of separable defectless polynomials over henselian fields.", "published": "2019-01-15T17:04:19Z", "authors": ["Nathália Moraes de Oliveira", "Enric Nart"], "categories": ["math.AG", "math.NT"], "doi": "", "_query": "property valuation computer vision", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2404.00043v1", "title": "Improve accessibility for Low Vision and Blind people using Machine Learning and Computer Vision", "abstract": "With the ever-growing expansion of mobile technology worldwide, there is an increasing need for accommodation for those who are disabled. This project explores how machine learning and computer vision could be utilized to improve accessibility for people with visual impairments. There have been many attempts to develop various software that would improve accessibility in the day-to-day lives of blind people. However, applications on the market have low accuracy and only provide audio feedback. This project will concentrate on building a mobile application that helps blind people to orient in space by receiving audio and haptic feedback, e.g. vibrations, about their surroundings in real-time. The mobile application will have 3 main features. The initial feature is scanning text from the camera and reading it to a user. This feature can be used on paper with text, in the environment, and on road signs. The second feature is detecting objects around the user, and providing audio feedback about those objects. It also includes providing the description of the objects and their location, and giving haptic feedback if the user is too close to an object. The last feature is currency detection which provides a total amount of currency value to the user via the camera.", "published": "2024-03-24T21:19:17Z", "authors": ["Jasur Shukurov"], "categories": ["cs.HC", "cs.CV", "cs.LG"], "doi": "", "_query": "property valuation computer vision", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2009.01717v2", "title": "Multi-Loss Weighting with Coefficient of Variations", "abstract": "Many interesting tasks in machine learning and computer vision are learned by optimising an objective function defined as a weighted linear combination of multiple losses. The final performance is sensitive to choosing the correct (relative) weights for these losses. Finding a good set of weights is often done by adopting them into the set of hyper-parameters, which are set using an extensive grid search. This is computationally expensive. In this paper, we propose a weighting scheme based on the coefficient of variations and set the weights based on properties observed while training the model. The proposed method incorporates a measure of uncertainty to balance the losses, and as a result the loss weights evolve during training without requiring another (learning based) optimisation. In contrast to many loss weighting methods in literature, we focus on single-task multi-loss problems, such as monocular depth estimation and semantic segmentation, and show that multi-task approaches for loss weighting do not work on those single-tasks. The validity of the approach is shown empirically for depth estimation and semantic segmentation on multiple datasets.", "published": "2020-09-03T14:51:19Z", "authors": ["Rick Groenendijk", "Sezer Karaoglu", "Theo Gevers", "Thomas Mensink"], "categories": ["cs.CV", "cs.AI"], "doi": "", "_query": "property valuation computer vision", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/1702.05564v1", "title": "The Ciona17 Dataset for Semantic Segmentation of Invasive Species in a Marine Aquaculture Environment", "abstract": "An original dataset for semantic segmentation, Ciona17, is introduced, which to the best of the authors' knowledge, is the first dataset of its kind with pixel-level annotations pertaining to invasive species in a marine environment. Diverse outdoor illumination, a range of object shapes, colour, and severe occlusion provide a significant real world challenge for the computer vision community. An accompanying ground-truthing tool for superpixel labeling, Truth and Crop, is also introduced. Finally, we provide a baseline using a variant of Fully Convolutional Networks, and report results in terms of the standard mean intersection over union (mIoU) metric.", "published": "2017-02-18T03:40:33Z", "authors": ["Angus Galloway", "Graham W. Taylor", "Aaron Ramsay", "Medhat Moussa"], "categories": ["cs.CV"], "doi": "10.5683/SP/NTUOK9", "_query": "property valuation computer vision", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/1407.2776v1", "title": "What you need to know about the state-of-the-art computational models of object-vision: A tour through the models", "abstract": "Models of object vision have been of great interest in computer vision and visual neuroscience. During the last decades, several models have been developed to extract visual features from images for object recognition tasks. Some of these were inspired by the hierarchical structure of primate visual system, and some others were engineered models. The models are varied in several aspects: models that are trained by supervision, models trained without supervision, and models (e.g. feature extractors) that are fully hard-wired and do not need training. Some of the models come with a deep hierarchical structure consisting of several layers, and some others are shallow and come with only one or two layers of processing. More recently, new models have been developed that are not hand-tuned but trained using millions of images, through which they learn how to extract informative task-related features. Here I will survey all these different models and provide the reader with an intuitive, as well as a more detailed, understanding of the underlying computations in each of the models.", "published": "2014-07-10T13:15:18Z", "authors": ["Seyed-Mahdi Khaligh-Razavi"], "categories": ["cs.CV", "cs.AI", "cs.LG", "q-bio.NC"], "doi": "", "_query": "property valuation computer vision", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2203.10213v1", "title": "Volkit: A Performance-Portable Computer Vision Library for 3D Volumetric Data", "abstract": "We present volkit, an open source library with high performance implementations of image manipulation and computer vision algorithms that focus on 3D volumetric representations. Volkit implements a cross-platform, performance-portable API targeting both CPUs and GPUs that defers data and resource movement and hides them from the application developer using a managed API. We use volkit to process medical and simulation data that is rendered in VR and consequently integrated the library into the C++ virtual reality software CalVR. The paper presents case studies and performance results and by that demonstrates the library's effectiveness and the efficiency of this approach.", "published": "2022-03-19T01:52:08Z", "authors": ["Stefan Zellmann", "Giovanni Aguirre", "Jürgen P. Schulze"], "categories": ["cs.CV", "cs.GR"], "doi": "", "_query": "property valuation computer vision", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2205.09180v4", "title": "Learning Rate Curriculum", "abstract": "Most curriculum learning methods require an approach to sort the data samples by difficulty, which is often cumbersome to perform. In this work, we propose a novel curriculum learning approach termed Learning Rate Curriculum (LeRaC), which leverages the use of a different learning rate for each layer of a neural network to create a data-agnostic curriculum during the initial training epochs. More specifically, LeRaC assigns higher learning rates to neural layers closer to the input, gradually decreasing the learning rates as the layers are placed farther away from the input. The learning rates increase at various paces during the first training iterations, until they all reach the same value. From this point on, the neural model is trained as usual. This creates a model-level curriculum learning strategy that does not require sorting the examples by difficulty and is compatible with any neural network, generating higher performance levels regardless of the architecture. We conduct comprehensive experiments on 12 data sets from the computer vision (CIFAR-10, CIFAR-100, Tiny ImageNet, ImageNet-200, Food-101, UTKFace, PASCAL VOC), language (BoolQ, QNLI, RTE) and audio (ESC-50, CREMA-D) domains, considering various convolutional (ResNet-18, Wide-ResNet-50, DenseNet-121, YOLOv5), recurrent (LSTM) and transformer (CvT, BERT, SepTr) architectures. We compare our approach with the conventional training regime, as well as with Curriculum by Smoothing (CBS), a state-of-the-art data-agnostic curriculum learning approach. Unlike CBS, our performance improvements over the standard training regime are consistent across all data sets and models. Furthermore, we significantly surpass CBS in terms of training time (there is no additional cost over the standard training regime for LeRaC). Our code is freely available at: https://github.com/CroitoruAlin/LeRaC.", "published": "2022-05-18T18:57:36Z", "authors": ["Florinel-Alin Croitoru", "Nicolae-Catalin Ristea", "Radu Tudor Ionescu", "Nicu Sebe"], "categories": ["cs.LG", "cs.CL", "cs.CV"], "doi": "", "_query": "property valuation computer vision", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/1904.13307v1", "title": "Survey of Computer Vision and Machine Learning in Gastrointestinal Endoscopy", "abstract": "This paper attempts to provide the reader a place to begin studying the application of computer vision and machine learning to gastrointestinal (GI) endoscopy. They have been classified into 18 categories. It should be be noted by the reader that this is a review from pre-deep learning era. A lot of deep learning based applications have not been covered in this thesis.", "published": "2019-04-26T12:46:03Z", "authors": ["Anant S. Vemuri"], "categories": ["physics.med-ph", "cs.CV", "eess.IV"], "doi": "", "_query": "property valuation computer vision", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/1305.0775v3", "title": "Residual ideals of MacLane valuations", "abstract": "Let $K$ be a field equipped with a discrete valuation $v$. In a pioneering work, S. MacLane determined all valuations on $K(x)$ extending $v$. His work was recently reviewed and generalized by M. Vaquié, by using the graded algebra of a valuation. We extend Vaquié's approach by studying residual ideals of the graded algebra of a valuation as an abstract counterpart of certain residual polynomials which play a key role in the computational applications of the theory. As a consequence, we determine the structure of the graded algebra of the discrete valuations on $K(x)$ and we show how these valuations may be used to parameterize irreducible polynomials over local fields up to Okutsu equivalence.", "published": "2013-05-03T16:54:34Z", "authors": ["Julio Fernández", "Jordi Guàrdia", "Jesús Montes", "Enric Nart"], "categories": ["math.NT"], "doi": "", "_query": "property valuation computer vision", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2006.03677v4", "title": "Visual Transformers: Token-based Image Representation and Processing for Computer Vision", "abstract": "Computer vision has achieved remarkable success by (a) representing images as uniformly-arranged pixel arrays and (b) convolving highly-localized features. However, convolutions treat all image pixels equally regardless of importance; explicitly model all concepts across all images, regardless of content; and struggle to relate spatially-distant concepts. In this work, we challenge this paradigm by (a) representing images as semantic visual tokens and (b) running transformers to densely model token relationships. Critically, our Visual Transformer operates in a semantic token space, judiciously attending to different image parts based on context. This is in sharp contrast to pixel-space transformers that require orders-of-magnitude more compute. Using an advanced training recipe, our VTs significantly outperform their convolutional counterparts, raising ResNet accuracy on ImageNet top-1 by 4.6 to 7 points while using fewer FLOPs and parameters. For semantic segmentation on LIP and COCO-stuff, VT-based feature pyramid networks (FPN) achieve 0.35 points higher mIoU while reducing the FPN module's FLOPs by 6.5x.", "published": "2020-06-05T20:49:49Z", "authors": ["Bichen Wu", "Chenfeng Xu", "Xiaoliang Dai", "Alvin Wan", "Peizhao Zhang", "Zhicheng Yan", "Masayoshi Tomizuka", "Joseph Gonzalez", "Kurt Keutzer", "Peter Vajda"], "categories": ["cs.CV", "cs.LG", "eess.IV"], "doi": "", "_query": "property valuation computer vision", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/1304.0840v1", "title": "A Fast Semidefinite Approach to Solving Binary Quadratic Problems", "abstract": "Many computer vision problems can be formulated as binary quadratic programs (BQPs). Two classic relaxation methods are widely used for solving BQPs, namely, spectral methods and semidefinite programming (SDP), each with their own advantages and disadvantages. Spectral relaxation is simple and easy to implement, but its bound is loose. Semidefinite relaxation has a tighter bound, but its computational complexity is high for large scale problems. We present a new SDP formulation for BQPs, with two desirable properties. First, it has a similar relaxation bound to conventional SDP formulations. Second, compared with conventional SDP methods, the new SDP formulation leads to a significantly more efficient and scalable dual optimization approach, which has the same degree of complexity as spectral methods. Extensive experiments on various applications including clustering, image segmentation, co-segmentation and registration demonstrate the usefulness of our SDP formulation for solving large-scale BQPs.", "published": "2013-04-03T04:31:10Z", "authors": ["Peng Wang", "Chunhua Shen", "Anton van den Hengel"], "categories": ["cs.CV", "cs.LG"], "doi": "10.1109/CVPR.2013.173", "_query": "property valuation computer vision", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2307.14527v2", "title": "Open Problems in Computer Vision for Wilderness SAR and The Search for Patricia Wu-Murad", "abstract": "This paper details the challenges in applying two computer vision systems, an EfficientDET supervised learning model and the unsupervised RX spectral classifier, to 98.9 GB of drone imagery from the Wu-Murad wilderness search and rescue (WSAR) effort in Japan and identifies 3 directions for future research. There have been at least 19 proposed approaches and 3 datasets aimed at locating missing persons in drone imagery, but only 3 approaches (2 unsupervised and 1 of an unknown structure) are referenced in the literature as having been used in an actual WSAR operation. Of these proposed approaches, the EfficientDET architecture and the unsupervised spectral RX classifier were selected as the most appropriate for this setting. The EfficientDET model was applied to the HERIDAL dataset and despite achieving performance that is statistically equivalent to the state-of-the-art, the model fails to translate to the real world in terms of false positives (e.g., identifying tree limbs and rocks as people), and false negatives (e.g., failing to identify members of the search team). The poor results in practice for algorithms that showed good results on datasets suggest 3 areas of future research: more realistic datasets for wilderness SAR, computer vision models that are capable of seamlessly handling the variety of imagery that can be collected during actual WSAR operations, and better alignment on performance measures.", "published": "2023-07-26T22:09:29Z", "authors": ["Thomas Manzini", "Robin Murphy"], "categories": ["cs.CV", "cs.AI", "cs.LG"], "doi": "", "_query": "property valuation computer vision", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/1908.06726v1", "title": "Some Aspects of Geometric Computer Vision for Analysing Dynamical Scenes focusing Automotive Applications", "abstract": "This draft summarizes some basics about geometric computer vision needed to implement efficient computer vision algorithms for applications that use measurements from at least one digital camera mounted on a moving platform with a special focus on automotive applications processing image streams taken from cameras mounted on a car. Our intention is twofold: On the one hand, we would like to introduce well-known basic geometric relations in a compact way that can also be found in lecture books about geometric computer vision like [1, 2]. On the other hand, we would like to share some experience about subtleties that should be taken into account in order to set up quite simple but robust and fast vision algorithms that are able to run in real time. We added a conglomeration of literature, we found to be relevant when implementing basic algorithms like optical flow, visual odometry and structure from motion. The reader should get some feeling about how the estimates of these algorithms are interrelated, which parts of the algorithms are critical in terms of robustness and what kind of additional assumptions can be useful to constrain the solution space of the underlying usually non-convex optimization problems.", "published": "2019-08-19T12:12:57Z", "authors": ["Volker Willert", "Martin Buczko"], "categories": ["cs.CV"], "doi": "", "_query": "property valuation computer vision", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2607.16992v1", "title": "Automated Cardiac Adipose Tissue Segmentation in Computed Tomography: A Literature Review", "abstract": "This review provides an overview of recent advancements in automated segmentation methods on Computed Tomography (CT) for two types of cardiac fat: Epicardial adipose Tissue (EAT) and Pericardial Adipose Tissue (PAT). These fat deposits, separated by the pericardium, have been linked to various cardiovascular diseases, with EAT receiving the most research attention. Their complex anatomical context makes manual quantification highly time-consuming and prone to considerable inter-observer variability. Automated methods effectively address these complications, offering a more efficient and consistent solution. This study encompasses a broad range of methods, spanning AI as well as non-AI approaches. Additionally, it presents the remaining challenges, including the need for larger annotated public datasets and optimized attenuation thresholds for contrast-enhanced CT. It is demonstrated that automated methods are able to achieve segmentation results comparable to the quality of human annotation, proving their potential as a clinical tool for discovering new biomarkers and enhancing patient outcomes.", "published": "2026-07-18T22:52:05Z", "authors": ["Andreas W. Aspe", "Jonas Jalili Pedersen", "Andreas Ohrt Johansen", "Klaus Fuglsang Kofoed", "Kristine Aavild Sørensen", "Rasmus Reinhold Paulsen", "Josefine Vilsbøll Sundgaard"], "categories": ["cs.CV", "cs.AI"], "doi": "10.1007/978-3-031-95918-9_17", "_query": "property valuation computer vision", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2209.07221v1", "title": "Number of Attention Heads vs Number of Transformer-Encoders in Computer Vision", "abstract": "Determining an appropriate number of attention heads on one hand and the number of transformer-encoders, on the other hand, is an important choice for Computer Vision (CV) tasks using the Transformer architecture. Computing experiments confirmed the expectation that the total number of parameters has to satisfy the condition of overdetermination (i.e., number of constraints significantly exceeding the number of parameters). Then, good generalization performance can be expected. This sets the boundaries within which the number of heads and the number of transformers can be chosen. If the role of context in images to be classified can be assumed to be small, it is favorable to use multiple transformers with a low number of heads (such as one or two). In classifying objects whose class may heavily depend on the context within the image (i.e., the meaning of a patch being dependent on other patches), the number of heads is equally important as that of transformers.", "published": "2022-09-15T11:26:44Z", "authors": ["Tomas Hrycej", "Bernhard Bermeitinger", "Siegfried Handschuh"], "categories": ["cs.CV", "cs.LG"], "doi": "10.5220/0011578000003335", "_query": "property valuation computer vision", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2207.11837v2", "title": "Inter-model Interpretability: Self-supervised Models as a Case Study", "abstract": "Since early machine learning models, metrics such as accuracy and precision have been the de facto way to evaluate and compare trained models. However, a single metric number doesn't fully capture the similarities and differences between models, especially in the computer vision domain. A model with high accuracy on a certain dataset might provide a lower accuracy on another dataset, without any further insights. To address this problem we build on a recent interpretability technique called Dissect to introduce \\textit{inter-model interpretability}, which determines how models relate or complement each other based on the visual concepts they have learned (such as objects and materials). Towards this goal, we project 13 top-performing self-supervised models into a Learned Concepts Embedding (LCE) space that reveals proximities among models from the perspective of learned concepts. We further crossed this information with the performance of these models on four computer vision tasks and 15 datasets. The experiment allowed us to categorize the models into three categories and revealed for the first time the type of visual concepts different tasks requires. This is a step forward for designing cross-task learning algorithms.", "published": "2022-07-24T22:50:18Z", "authors": ["Ahmad Mustapha", "Wael Khreich", "Wes Masri"], "categories": ["cs.CV"], "doi": "", "_query": "property valuation computer vision", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2101.11217v1", "title": "Automated Crop Field Surveillance using Computer Vision", "abstract": "Artificial Intelligence is everywhere today. But unfortunately, Agriculture has not been able to get that much attention from Artificial Intelligence (AI). A lack of automation persists in the agriculture industry. For over many years, farmers and crop field owners have been facing a problem of trespassing of wild animals for which no feasible solution has been provided. Installing a fence or barrier like structure is neither feasible nor efficient due to the large areas covered by the fields. Also, if the landowner can afford to build a wall or barrier, government policies for building walls are often very irksome. The paper intends to give a simple intelligible solution to the problem with Automated Crop Field Surveillance using Computer Vision. The solution will significantly reduce the cost of crops destroyed annually and completely automate the security of the field.", "published": "2021-01-27T05:58:28Z", "authors": ["Tejas Atul Khare", "Anuradha C. Phadke"], "categories": ["cs.CV"], "doi": "10.1109/DISCOVER50404.2020.9278072", "_query": "property valuation computer vision", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/1911.12116v1", "title": "Analysis of Explainers of Black Box Deep Neural Networks for Computer Vision: A Survey", "abstract": "Deep Learning is a state-of-the-art technique to make inference on extensive or complex data. As a black box model due to their multilayer nonlinear structure, Deep Neural Networks are often criticized to be non-transparent and their predictions not traceable by humans. Furthermore, the models learn from artificial datasets, often with bias or contaminated discriminating content. Through their increased distribution, decision-making algorithms can contribute promoting prejudge and unfairness which is not easy to notice due to lack of transparency. Hence, scientists developed several so-called explanators or explainers which try to point out the connection between input and output to represent in a simplified way the inner structure of machine learning black boxes. In this survey we differ the mechanisms and properties of explaining systems for Deep Neural Networks for Computer Vision tasks. We give a comprehensive overview about taxonomy of related studies and compare several survey papers that deal with explainability in general. We work out the drawbacks and gaps and summarize further research ideas.", "published": "2019-11-27T12:58:52Z", "authors": ["Vanessa Buhrmester", "David Münch", "Michael Arens"], "categories": ["cs.AI", "cs.CV"], "doi": "", "_query": "property valuation computer vision", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2301.12531v2", "title": "PhyCV: The First Physics-inspired Computer Vision Library", "abstract": "PhyCV is the first computer vision library which utilizes algorithms directly derived from the equations of physics governing physical phenomena. The algorithms appearing in the current release emulate, in a metaphoric sense, the propagation of light through a physical medium with natural and engineered diffractive properties followed by coherent detection. Unlike traditional algorithms that are a sequence of hand-crafted empirical rules or deep learning algorithms that are usually data-driven and computationally heavy, physics-inspired algorithms leverage physical laws of nature as blueprints for inventing algorithms. PhyCV features low-dimensionality and high- efficiency, making it ideal for edge computing applications. We demonstrate real-time video processing on NVIDIA Jetson Nano using PhyCV. In addition, these algorithms have the potential to be implemented in real physical devices for fast and efficient computation in the form of analog computing. The open-sourced code is available at https://github.com/JalaliLabUCLA/phycv", "published": "2023-01-29T20:16:15Z", "authors": ["Yiming Zhou", "Callen MacPhee", "Madhuri Suthar", "Bahram Jalali"], "categories": ["eess.IV", "cs.CV"], "doi": "", "_query": "property valuation computer vision", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2004.04081v2", "title": "Satellite-based Prediction of Forage Conditions for Livestock in Northern Kenya", "abstract": "This paper introduces the first dataset of satellite images labeled with forage quality by on-the-ground experts and provides proof of concept for applying computer vision methods to index-based drought insurance. We also present the results of a collaborative benchmark tool used to crowdsource an accurate machine learning model on the dataset. Our methods significantly outperform the existing technology for an insurance program in Northern Kenya, suggesting that a computer vision-based approach could substantially benefit pastoralists, whose exposure to droughts is severe and worsening with climate change.", "published": "2020-04-08T16:03:50Z", "authors": ["Andrew Hobbs", "Stacey Svetlichnaya"], "categories": ["cs.CV"], "doi": "", "_query": "property valuation computer vision", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2501.10547v1", "title": "HyperCam: Low-Power Onboard Computer Vision for IoT Cameras", "abstract": "We present HyperCam, an energy-efficient image classification pipeline that enables computer vision tasks onboard low-power IoT camera systems. HyperCam leverages hyperdimensional computing to perform training and inference efficiently on low-power microcontrollers. We implement a low-power wireless camera platform using off-the-shelf hardware and demonstrate that HyperCam can achieve an accuracy of 93.60%, 84.06%, 92.98%, and 72.79% for MNIST, Fashion-MNIST, Face Detection, and Face Identification tasks, respectively, while significantly outperforming other classifiers in resource efficiency. Specifically, it delivers inference latency of 0.08-0.27s while using 42.91-63.00KB flash memory and 22.25KB RAM at peak. Among other machine learning classifiers such as SVM, xgBoost, MicroNets, MobileNetV3, and MCUNetV3, HyperCam is the only classifier that achieves competitive accuracy while maintaining competitive memory footprint and inference latency that meets the resource requirements of low-power camera systems.", "published": "2025-01-17T20:41:35Z", "authors": ["Chae Young Lee", " Pu", " Yi", "Maxwell Fite", "Tejus Rao", "Sara Achour", "Zerina Kapetanovic"], "categories": ["cs.CV", "cs.LG", "cs.NE", "eess.IV"], "doi": "", "_query": "property valuation computer vision", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2312.11380v1", "title": "Orientation-Constrained System for Lamp Detection in Buildings Based on Computer Vision", "abstract": "Computer vision is used in this work to detect lighting elements in buildings with the goal of improving the accuracy of previous methods to provide a precise inventory of the location and state of lamps. Using the framework developed in our previous works, we introduce two new modifications to enhance the system: first, a constraint on the orientation of the detected poses in the optimization methods for both the initial and the refined estimates based on the geometric information of the building information modelling (BIM) model; second, an additional reprojection error filtering step to discard the erroneous poses introduced with the orientation restrictions, keeping the identification and localization errors low while greatly increasing the number of detections. These~enhancements are tested in five different case studies with more than 30,000 images, with results showing improvements in the number of detections, the percentage of correct model and state identifications, and the distance between detections and reference positions", "published": "2023-12-18T17:43:55Z", "authors": ["Francisco Troncoso-Pastoriza", "Pablo Eguía-Oller", "Rebeca P. Díaz-Redondo", "Enrique Granada-Álvarez", "Aitor Erkoreka"], "categories": ["cs.CV", "cs.AI"], "doi": "10.3390/s19071516", "_query": "property valuation computer vision", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2411.00831v1", "title": "Saliency-Based diversity and fairness Metric and FaceKeepOriginalAugment: A Novel Approach for Enhancing Fairness and Diversity", "abstract": "Data augmentation has become a pivotal tool in enhancing the performance of computer vision tasks, with the KeepOriginalAugment method emerging as a standout technique for its intelligent incorporation of salient regions within less prominent areas, enabling augmentation in both regions. Despite its success in image classification, its potential in addressing biases remains unexplored. In this study, we introduce an extension of the KeepOriginalAugment method, termed FaceKeepOriginalAugment, which explores various debiasing aspects-geographical, gender, and stereotypical biases-in computer vision models. By maintaining a delicate balance between data diversity and information preservation, our approach empowers models to exploit both diverse salient and non-salient regions, thereby fostering increased diversity and debiasing effects. We investigate multiple strategies for determining the placement of the salient region and swapping perspectives to decide which part undergoes augmentation. Leveraging the Image Similarity Score (ISS), we quantify dataset diversity across a range of datasets, including Flickr Faces HQ (FFHQ), WIKI, IMDB, Labelled Faces in the Wild (LFW), UTK Faces, and Diverse Dataset. We evaluate the effectiveness of FaceKeepOriginalAugment in mitigating gender bias across CEO, Engineer, Nurse, and School Teacher datasets, utilizing the Image-Image Association Score (IIAS) in convolutional neural networks (CNNs) and vision transformers (ViTs). Our findings shows the efficacy of FaceKeepOriginalAugment in promoting fairness and inclusivity within computer vision models, demonstrated by reduced gender bias and enhanced overall fairness. Additionally, we introduce a novel metric, Saliency-Based Diversity and Fairness Metric, which quantifies both diversity and fairness while handling data imbalance across various datasets.", "published": "2024-10-29T13:49:23Z", "authors": ["Teerath Kumar", "Alessandra Mileo", "Malika Bendechache"], "categories": ["cs.CV", "cs.AI", "cs.MM"], "doi": "", "_query": "property valuation computer vision", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/1811.05006v2", "title": "A new approach for pedestrian density estimation using moving sensors and computer vision", "abstract": "An understanding of pedestrian dynamics is indispensable for numerous urban applications including the design of transportation networks and planing for business development. Pedestrian counting often requires utilizing manual or technical means to count individuals in each location of interest. However, such methods do not scale to the size of a city and a new approach to fill this gap is here proposed. In this project, we used a large dense dataset of images of New York City along with computer vision techniques to construct a spatio-temporal map of relative person density. Due to the limitations of state of the art computer vision methods, such automatic detection of person is inherently subject to errors. We model these errors as a probabilistic process, for which we provide theoretical analysis and thorough numerical simulations. We demonstrate that, within our assumptions, our methodology can supply a reasonable estimate of person densities and provide theoretical bounds for the resulting error.", "published": "2018-11-12T21:19:38Z", "authors": ["Eric K. Tokuda", "Yitzchak Lockerman", "Gabriel B. A. Ferreira", "Ethan Sorrelgreen", "David Boyle", "Roberto M. Cesar-Jr.", "Claudio T. Silva"], "categories": ["cs.CV"], "doi": "10.1145/3397575", "_query": "property valuation computer vision", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2511.15029v1", "title": "Computer Vision Modeling of the Development of Geometric and Numerical Concepts in Humans", "abstract": "Mathematical thinking is a fundamental aspect of human cognition. Cognitive scientists have investigated the mechanisms that underlie our ability to thinking geometrically and numerically, to take two prominent examples, and developmental scientists have documented the trajectories of these abilities over the lifespan. Prior research has shown that computer vision (CV) models trained on the unrelated task of image classification nevertheless learn latent representations of geometric and numerical concepts similar to those of adults. Building on this demonstrated cognitive alignment, the current study investigates whether CV models also show developmental alignment: whether their performance improvements across training to match the developmental progressions observed in children. In a detailed case study of the ResNet-50 model, we show that this is the case. For the case of geometry and topology, we find developmental alignment for some classes of concepts (Euclidean Geometry, Geometrical Figures, Metric Properties, Topology) but not others (Chiral Figures, Geometric Transformations, Symmetrical Figures). For the case of number, we find developmental alignment in the emergence of a human-like ``mental number line'' representation with experience. These findings show the promise of computer vision models for understanding the development of mathematical understanding in humans. They point the way to future research exploring additional model architectures and building larger benchmarks.", "published": "2025-11-19T01:50:35Z", "authors": ["Zekun Wang", "Sashank Varma"], "categories": ["cs.CV"], "doi": "", "_query": "property valuation computer vision", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2207.02598v1", "title": "Predicting is not Understanding: Recognizing and Addressing Underspecification in Machine Learning", "abstract": "Machine learning (ML) models are typically optimized for their accuracy on a given dataset. However, this predictive criterion rarely captures all desirable properties of a model, in particular how well it matches a domain expert's understanding of a task. Underspecification refers to the existence of multiple models that are indistinguishable in their in-domain accuracy, even though they differ in other desirable properties such as out-of-distribution (OOD) performance. Identifying these situations is critical for assessing the reliability of ML models. We formalize the concept of underspecification and propose a method to identify and partially address it. We train multiple models with an independence constraint that forces them to implement different functions. They discover predictive features that are otherwise ignored by standard empirical risk minimization (ERM), which we then distill into a global model with superior OOD performance. Importantly, we constrain the models to align with the data manifold to ensure that they discover meaningful features. We demonstrate the method on multiple datasets in computer vision (collages, WILDS-Camelyon17, GQA) and discuss general implications of underspecification. Most notably, in-domain performance cannot serve for OOD model selection without additional assumptions.", "published": "2022-07-06T11:20:40Z", "authors": ["Damien Teney", "Maxime Peyrard", "Ehsan Abbasnejad"], "categories": ["cs.LG", "cs.CV"], "doi": "", "_query": "property valuation computer vision", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2604.27715v1", "title": "Improving Calibration in Test-Time Prompt Tuning for Vision-Language Models via Data-Free Flatness-Aware Prompt Pretraining", "abstract": "Test-time prompt tuning (TPT) has emerged as a promising technique for enhancing the adaptability of vision-language models by optimizing textual prompts using unlabeled test data. However, prior studies have observed that TPT often produces poorly calibrated models, raising concerns about the reliability of their predictions. Recent works address this issue by incorporating additional regularization terms that constrain model outputs, which improve calibration but often degrade performance. In this work, we reveal that these regularization strategies implicitly encourage optimization toward flatter minima, and that the sharpness of the loss landscape around adapted prompts is a key factor governing calibration quality. Motivated by this observation, we introduce Flatness-aware Prompt Pretraining (FPP), a simple yet effective pretraining framework for TPT that initializes prompts within flatter regions of the loss landscape prior to adaptation. We show that simply replacing the initialization in existing TPT pipelines--without modifying any other components--is sufficient to improve both calibration and performance. Notably, FPP requires no labeled data and incurs no additional computational costs during test-time tuning, making it highly practical for real-world deployment. The code is available at: https://github.com/YonseiML/fpp.", "published": "2026-04-30T11:01:23Z", "authors": ["Hyeonseo Jang", "Jaebyeong Jeon", "Joong-Won Hwang", "Kibok Lee"], "categories": ["cs.CV"], "doi": "", "_query": "property valuation computer vision", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2303.10741v2", "title": "Computer Vision Estimation of Emotion Reaction Intensity in the Wild", "abstract": "Emotions play an essential role in human communication. Developing computer vision models for automatic recognition of emotion expression can aid in a variety of domains, including robotics, digital behavioral healthcare, and media analytics. There are three types of emotional representations which are traditionally modeled in affective computing research: Action Units, Valence Arousal (VA), and Categorical Emotions. As part of an effort to move beyond these representations towards more fine-grained labels, we describe our submission to the newly introduced Emotional Reaction Intensity (ERI) Estimation challenge in the 5th competition for Affective Behavior Analysis in-the-Wild (ABAW). We developed four deep neural networks trained in the visual domain and a multimodal model trained with both visual and audio features to predict emotion reaction intensity. Our best performing model on the Hume-Reaction dataset achieved an average Pearson correlation coefficient of 0.4080 on the test set using a pre-trained ResNet50 model. This work provides a first step towards the development of production-grade models which predict emotion reaction intensities rather than discrete emotion categories.", "published": "2023-03-19T19:09:41Z", "authors": ["Yang Qian", "Ali Kargarandehkordi", "Onur Cezmi Mutlu", "Saimourya Surabhi", "Mohammadmahdi Honarmand", "Dennis Paul Wall", "Peter Washington"], "categories": ["cs.CV", "cs.LG"], "doi": "", "_query": "property valuation computer vision", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2202.04215v1", "title": "QAC: Quantum-computing Aided Composition", "abstract": "In this chapter I will discuss the role of quantum computing in computer music and how it can be integrated to better serve the creative artists. I will start by considering different approaches in current computer music and quantum computing tools, as well as reviewing some previous attempts to integrate them. Then, I will reflect on the meaning of this integration and present what I coined as QAC (Quantum-computing Aided Composition) as well as an early attempt at realizing it. This chapter will also introduce The QAC Toolkit Max package, analyze its performance, and explore some examples of what it can offer to realtime creative practice. Lastly, I will present a real case scenario of QAC in the creative work Disklavier Prelude #3.", "published": "2022-02-09T01:17:21Z", "authors": ["Omar Costa Hamido"], "categories": ["cs.ET", "cs.HC", "cs.SD", "eess.AS", "quant-ph"], "doi": "10.1007/978-3-031-13909-3_8", "_query": "property valuation computer vision", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2504.03611v3", "title": "On the small boundary property and $\\mathcal Z$-absorption", "abstract": "We introduce Property (C) for a unital commutative sub-C*-algebra $D$ of a unital C*-algebra $A$, which is a version of the relative comparison property using almost normalizers. In the case that $D = \\mathrm{C}(X)$ and $A = \\mathrm{C}(X) \\rtimesΓ$, where $(X, Γ)$ is a free and minimal dynamical system with uniform Rokhlin property, it turns out that this Property (C) is equivalent to the small boundary property of $(X, Γ)$.", "published": "2025-04-04T17:32:55Z", "authors": ["George A. Elliott", "Zhuang Niu"], "categories": ["math.OA", "math.DS"], "doi": "", "_query": "property valuation computer vision", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2208.09535v3", "title": "On computing Discretized Ricci curvatures of graphs: local algorithms and (localized) fine-grained reductions", "abstract": "Characterizing shapes of high-dimensional objects via Ricci curvatures plays a critical role in many research areas in mathematics and physics. However, even though several discretizations of Ricci curvatures for discrete combinatorial objects such as networks have been proposed and studied by mathematicians, the computational complexity aspects of these discretizations have escaped the attention of theoretical computer scientists to a large extent. In this paper, we study one such discretization, namely the Ollivier-Ricci curvature, from the perspective of efficient computation by fine-grained reductions and local query-based algorithms. Our main contributions are the following. (a) We relate our curvature computation problem to minimum weight perfect matching problem on complete bipartite graphs via fine-grained reduction. (b) We formalize the computational aspects of the curvature computation problems in suitable frameworks so that they can be studied by researchers in local algorithms. (c) We provide the first known lower and upper bounds on queries for query-based algorithms for the curvature computation problems in our local algorithms framework. En route, we also illustrate a localized version of our fine-grained reduction. We believe that our results bring forth an intriguing set of research questions, motivated both in theory and practice, regarding designing efficient algorithms for curvatures of objects.", "published": "2022-08-19T20:02:20Z", "authors": ["Bhaskar DasGupta", "Elena Grigorescu", "Tamalika Mukherjee"], "categories": ["cs.DS", "cs.CC"], "doi": "", "_query": "property valuation computer vision", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/1510.05069v2", "title": "Considering Time in Designing Large-Scale Systems for Scientific Computing", "abstract": "High performance computing (HPC) has driven collaborative science discovery for decades. Exascale computing platforms, currently in the design stage, will be deployed around 2022. The next generation of supercomputers is expected to utilize radically different computational paradigms, necessitating fundamental changes in how the community of scientific users will make the most efficient use of these powerful machines. However, there have been few studies of how scientists work with exascale or close-to-exascale HPC systems. Time as a metaphor is so pervasive in the discussions and valuation of computing within the HPC community that it is worthy of close study. We utilize time as a lens to conduct an ethnographic study of scientists interacting with HPC systems. We build upon recent CSCW work to consider temporal rhythms and collective time within the HPC sociotechnical ecosystem and provide considerations for future system design.", "published": "2015-10-17T05:06:26Z", "authors": ["Nan-Chen Chen", "Sarah S. Poon", "Lavanya Ramakrishnan", "Cecilia R. Aragon"], "categories": ["cs.HC"], "doi": "10.1145/2818048.2819988", "_query": "property valuation computer vision", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2406.05004v2", "title": "The Choquet-Deny Property for Groupoids", "abstract": "A countable discrete group is called Choquet-Deny if for any non-degenerate probability measure on the group, the corresponding space of bounded harmonic functions is trivial. Building on the previous work of Jaworski, a complete characterization of Choquet-Deny groups was recently achieved by Frisch, Hartman, Tamuz, and Ferdowski. In this article, we extend the study of the Choquet-Deny property to the framework of discrete measured groupoids. Our primary result offers a complete characterization of this property in terms of the isotropy groups and the equivalence relation associated with the given groupoid. Additionally, we use the implications derived from our main theorem to classify the Choquet-Deny property of transformation groupoids.", "published": "2024-06-07T15:19:33Z", "authors": ["Tey Berendschot", "Soham Chakraborty", "Milan Donvil", "Se-Jin Kim", "Mario Klisse"], "categories": ["math.FA", "math.DS", "math.GR", "math.OA", "math.PR"], "doi": "", "_query": "property valuation computer vision", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2505.05091v1", "title": "DispBench: Benchmarking Disparity Estimation to Synthetic Corruptions", "abstract": "Deep learning (DL) has surpassed human performance on standard benchmarks, driving its widespread adoption in computer vision tasks. One such task is disparity estimation, estimating the disparity between matching pixels in stereo image pairs, which is crucial for safety-critical applications like medical surgeries and autonomous navigation. However, DL-based disparity estimation methods are highly susceptible to distribution shifts and adversarial attacks, raising concerns about their reliability and generalization. Despite these concerns, a standardized benchmark for evaluating the robustness of disparity estimation methods remains absent, hindering progress in the field. To address this gap, we introduce DispBench, a comprehensive benchmarking tool for systematically assessing the reliability of disparity estimation methods. DispBench evaluates robustness against synthetic image corruptions such as adversarial attacks and out-of-distribution shifts caused by 2D Common Corruptions across multiple datasets and diverse corruption scenarios. We conduct the most extensive performance and robustness analysis of disparity estimation methods to date, uncovering key correlations between accuracy, reliability, and generalization. Open-source code for DispBench: https://github.com/shashankskagnihotri/benchmarking_robustness/tree/disparity_estimation/final/disparity_estimation", "published": "2025-05-08T09:40:17Z", "authors": ["Shashank Agnihotri", "Amaan Ansari", "Annika Dackermann", "Fabian Rösch", "Margret Keuper"], "categories": ["cs.CV", "cs.LG"], "doi": "", "_query": "property valuation computer vision", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/1011.3699v3", "title": "Valuations and asymptotic invariants for sequences of ideals", "abstract": "We study asymptotic jumping numbers for graded sequences of ideals, and show that every such invariant is computed by a suitable real valuation of the function field. We conjecture that every valuation that computes an asymptotic jumping number is necessarily quasi-monomial. This conjecture holds in dimension two. In general, we reduce it to the case of affine space and to graded sequences of valuation ideals. Along the way, we study the structure of a suitable valuation space.", "published": "2010-11-16T14:07:58Z", "authors": ["Mattias Jonsson", "Mircea Mustata"], "categories": ["math.AG", "math.AC"], "doi": "", "_query": "property valuation computer vision", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2208.03209v1", "title": "Bias and Fairness in Computer Vision Applications of the Criminal Justice System", "abstract": "Discriminatory practices involving AI-driven police work have been the subject of much controversies in the past few years, with algorithms such as COMPAS, PredPol and ShotSpotter being accused of unfairly impacting minority groups. At the same time, the issues of fairness in machine learning, and in particular in computer vision, have been the subject of a growing number of academic works. In this paper, we examine how these area intersect. We provide information on how these practices have come to exist and the difficulties in alleviating them. We then examine three applications currently in development to understand what risks they pose to fairness and how those risks can be mitigated.", "published": "2022-08-05T14:48:52Z", "authors": ["Sophie Noiret", "Jennifer Lumetzberger", "Martin Kampel"], "categories": ["cs.CV"], "doi": "10.1109/SSCI50451.2021.9660177", "_query": "property valuation computer vision", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/math/0307238v1", "title": "Monomial discrete valuations in k[[X]]", "abstract": "Let v be a rank m discrete valuation of k[[X1,...,Xn]] with dimension n-m. We prove that there exists an inmediate extension L of K where the valuation is monomial. Therefore we compute explicitly the residue field of the valuation.", "published": "2003-07-17T10:13:41Z", "authors": ["Francisco Javier Herrera Govantes", "Miguel Angel Olalla Acosta", "Jose Luis Vicente Cordoba"], "categories": ["math.AC", "math.AG"], "doi": "", "_query": "property valuation computer vision", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/1811.10847v1", "title": "Algae Detection Using Computer Vision and Deep Learning", "abstract": "A disconcerting ramification of water pollution caused by burgeoning populations, rapid industrialization and modernization of agriculture, has been the exponential increase in the incidence of algal growth across the globe. Harmful algal blooms (HABs) have devastated fisheries, contaminated drinking water and killed livestock, resulting in economic losses to the tune of millions of dollars. Therefore, it is important to constantly monitor water bodies and identify any algae build-up so that prompt action against its accumulation can be taken and the harmful consequences can be avoided. In this paper, we propose a computer vision system based on deep learning for algae monitoring. The proposed system is fast, accurate and cheap, and it can be installed on any robotic platforms such as USVs and UAVs for autonomous algae monitoring. The experimental results demonstrate that the proposed system can detect algae in distinct environments regardless of the underlying hardware with high accuracy and in real time.", "published": "2018-11-27T07:31:26Z", "authors": ["Arabinda Samantaray", "Baijian Yang", "J. Eric Dietz", "Byung-Cheol Min"], "categories": ["cs.CV", "cs.LG", "cs.RO"], "doi": "", "_query": "property valuation computer vision", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2608.28310v1", "title": "Transparency Rendering in Computer-Aided Design: Methodologies, Trade-offs, and Challenges", "abstract": "This paper surveys the state of transparency rendering in Computer-Aided Design (CAD), with a focus on both practical deployment in industrial systems and the underlying algorithms. We first review current approaches to transparency rendering in CAD environments and outline application scenarios in which accurate and performant transparency is critical for design inspection, communication, and decision-making. We then analyze the trade-offs between approximate and exact transparency techniques, comparing their performance-quality balance on desktop and mobile platforms and discussing criteria for selecting appropriate methods. The survey further identifies the need for robust benchmarks, quality metrics, and evaluation methodologies tailored to CAD-specific visualization tasks. We examine techniques for emphasizing important interior components such as importance-driven transparency, silhouette-based methods, and related approaches to support effective spatial understanding in complex assemblies. Finally, we discuss the unique challenges of rendering transparent constructive solid geometry (CSG) objects, including robustness, correctness, and integration with modern rendering pipelines. Collectively, these contributions characterize current capabilities, systematize open problems, and outline future research directions for transparency rendering in CAD visualization.", "published": "2026-08-28T13:18:35Z", "authors": ["Grigoris Tsopouridis", "Ioannis Fudos"], "categories": ["cs.GR"], "doi": "10.14733/cadaps.2027.133-151", "_query": "property valuation computer vision", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2405.06196v2", "title": "VLSM-Adapter: Finetuning Vision-Language Segmentation Efficiently with Lightweight Blocks", "abstract": "Foundation Vision-Language Models (VLMs) trained using large-scale open-domain images and text pairs have recently been adapted to develop Vision-Language Segmentation Models (VLSMs) that allow providing text prompts during inference to guide image segmentation. If robust and powerful VLSMs can be built for medical images, it could aid medical professionals in many clinical tasks where they must spend substantial time delineating the target structure of interest. VLSMs for medical images resort to fine-tuning base VLM or VLSM pretrained on open-domain natural image datasets due to fewer annotated medical image datasets; this fine-tuning is resource-consuming and expensive as it usually requires updating all or a significant fraction of the pretrained parameters. Recently, lightweight blocks called adapters have been proposed in VLMs that keep the pretrained model frozen and only train adapters during fine-tuning, substantially reducing the computing resources required. We introduce a novel adapter, VLSM-Adapter, that can fine-tune pretrained vision-language segmentation models using transformer encoders. Our experiments in widely used CLIP-based segmentation models show that with only 3 million trainable parameters, the VLSM-Adapter outperforms state-of-the-art and is comparable to the upper bound end-to-end fine-tuning. The source code is available at: https://github.com/naamiinepal/vlsm-adapter.", "published": "2024-05-10T02:23:56Z", "authors": ["Manish Dhakal", "Rabin Adhikari", "Safal Thapaliya", "Bishesh Khanal"], "categories": ["cs.CV", "cs.AI", "cs.CL", "cs.LG"], "doi": "", "_query": "property valuation computer vision", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2409.15809v1", "title": "A Computer Vision Approach for Autonomous Cars to Drive Safe at Construction Zone", "abstract": "To build a smarter and safer city, a secure, efficient, and sustainable transportation system is a key requirement. The autonomous driving system (ADS) plays an important role in the development of smart transportation and is considered one of the major challenges facing the automotive sector in recent decades. A car equipped with an autonomous driving system (ADS) comes with various cutting-edge functionalities such as adaptive cruise control, collision alerts, automated parking, and more. A primary area of research within ADAS involves identifying road obstacles in construction zones regardless of the driving environment. This paper presents an innovative and highly accurate road obstacle detection model utilizing computer vision technology that can be activated in construction zones and functions under diverse drift conditions, ultimately contributing to build a safer road transportation system. The model developed with the YOLO framework achieved a mean average precision exceeding 94\\% and demonstrated an inference time of 1.6 milliseconds on the validation dataset, underscoring the robustness of the methodology applied to mitigate hazards and risks for autonomous vehicles.", "published": "2024-09-24T07:11:00Z", "authors": ["Abu Shad Ahammed", "Md Shahi Amran Hossain", "Roman Obermaisser"], "categories": ["cs.CV", "cs.RO"], "doi": "", "_query": "property valuation computer vision", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2211.14736v1", "title": "Attribution-based XAI Methods in Computer Vision: A Review", "abstract": "The advancements in deep learning-based methods for visual perception tasks have seen astounding growth in the last decade, with widespread adoption in a plethora of application areas from autonomous driving to clinical decision support systems. Despite their impressive performance, these deep learning-based models remain fairly opaque in their decision-making process, making their deployment in human-critical tasks a risky endeavor. This in turn makes understanding the decisions made by these models crucial for their reliable deployment. Explainable AI (XAI) methods attempt to address this by offering explanations for such black-box deep learning methods. In this paper, we provide a comprehensive survey of attribution-based XAI methods in computer vision and review the existing literature for gradient-based, perturbation-based, and contrastive methods for XAI, and provide insights on the key challenges in developing and evaluating robust XAI methods.", "published": "2022-11-27T05:56:36Z", "authors": ["Kumar Abhishek", "Deeksha Kamath"], "categories": ["cs.CV", "cs.AI"], "doi": "", "_query": "property valuation computer vision", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2403.10883v2", "title": "Improving Adversarial Transferability of Vision-Language Pre-training Models through Collaborative Multimodal Interaction", "abstract": "Despite the substantial advancements in Vision-Language Pre-training (VLP) models, their susceptibility to adversarial attacks poses a significant challenge. Existing work rarely studies the transferability of attacks on VLP models, resulting in a substantial performance gap from white-box attacks. We observe that prior work overlooks the interaction mechanisms between modalities, which plays a crucial role in understanding the intricacies of VLP models. In response, we propose a novel attack, called Collaborative Multimodal Interaction Attack (CMI-Attack), leveraging modality interaction through embedding guidance and interaction enhancement. Specifically, attacking text at the embedding level while preserving semantics, as well as utilizing interaction image gradients to enhance constraints on perturbations of texts and images. Significantly, in the image-text retrieval task on Flickr30K dataset, CMI-Attack raises the transfer success rates from ALBEF to TCL, $\\text{CLIP}_{\\text{ViT}}$ and $\\text{CLIP}_{\\text{CNN}}$ by 8.11%-16.75% over state-of-the-art methods. Moreover, CMI-Attack also demonstrates superior performance in cross-task generalization scenarios. Our work addresses the underexplored realm of transfer attacks on VLP models, shedding light on the importance of modality interaction for enhanced adversarial robustness.", "published": "2024-03-16T10:32:24Z", "authors": ["Jiyuan Fu", "Zhaoyu Chen", "Kaixun Jiang", "Haijing Guo", "Jiafeng Wang", "Shuyong Gao", "Wenqiang Zhang"], "categories": ["cs.CV", "cs.CR", "cs.MM"], "doi": "", "_query": "property valuation computer vision", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2303.15444v1", "title": "Quantum Multi-Model Fitting", "abstract": "Geometric model fitting is a challenging but fundamental computer vision problem. Recently, quantum optimization has been shown to enhance robust fitting for the case of a single model, while leaving the question of multi-model fitting open. In response to this challenge, this paper shows that the latter case can significantly benefit from quantum hardware and proposes the first quantum approach to multi-model fitting (MMF). We formulate MMF as a problem that can be efficiently sampled by modern adiabatic quantum computers without the relaxation of the objective function. We also propose an iterative and decomposed version of our method, which supports real-world-sized problems. The experimental evaluation demonstrates promising results on a variety of datasets. The source code is available at: https://github.com/FarinaMatteo/qmmf.", "published": "2023-03-27T17:59:54Z", "authors": ["Matteo Farina", "Luca Magri", "Willi Menapace", "Elisa Ricci", "Vladislav Golyanik", "Federica Arrigoni"], "categories": ["cs.CV"], "doi": "", "_query": "property valuation computer vision", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/1910.07486v2", "title": "LOST: A flexible framework for semi-automatic image annotation", "abstract": "State-of-the-art computer vision approaches rely on huge amounts of annotated data. The collection of such data is a time consuming process since it is mainly performed by humans. The literature shows that semi-automatic annotation approaches can significantly speed up the annotation process by the automatic generation of annotation proposals to support the annotator. In this paper we present a framework that allows for a quick and flexible design of semi-automatic annotation pipelines. We show that a good design of the process will speed up the collection of annotations. Our contribution is a new approach to image annotation that allows for the combination of different annotation tools and machine learning algorithms in one process. We further present potential applications of our approach. The source code of our framework called LOST (Label Objects and Save Time) is available at: https://github.com/l3p-cv/lost.", "published": "2019-10-16T17:34:27Z", "authors": ["Jonas Jäger", "Gereon Reus", "Joachim Denzler", "Viviane Wolff", "Klaus Fricke-Neuderth"], "categories": ["cs.CV"], "doi": "", "_query": "property valuation computer vision", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2304.11196v1", "title": "Fast GraspNeXt: A Fast Self-Attention Neural Network Architecture for Multi-task Learning in Computer Vision Tasks for Robotic Grasping on the Edge", "abstract": "Multi-task learning has shown considerable promise for improving the performance of deep learning-driven vision systems for the purpose of robotic grasping. However, high architectural and computational complexity can result in poor suitability for deployment on embedded devices that are typically leveraged in robotic arms for real-world manufacturing and warehouse environments. As such, the design of highly efficient multi-task deep neural network architectures tailored for computer vision tasks for robotic grasping on the edge is highly desired for widespread adoption in manufacturing environments. Motivated by this, we propose Fast GraspNeXt, a fast self-attention neural network architecture tailored for embedded multi-task learning in computer vision tasks for robotic grasping. To build Fast GraspNeXt, we leverage a generative network architecture search strategy with a set of architectural constraints customized to achieve a strong balance between multi-task learning performance and embedded inference efficiency. Experimental results on the MetaGraspNet benchmark dataset show that the Fast GraspNeXt network design achieves the highest performance (average precision (AP), accuracy, and mean squared error (MSE)) across multiple computer vision tasks when compared to other efficient multi-task network architecture designs, while having only 17.8M parameters (about >5x smaller), 259 GFLOPs (as much as >5x lower) and as much as >3.15x faster on a NVIDIA Jetson TX2 embedded processor.", "published": "2023-04-21T18:07:14Z", "authors": ["Alexander Wong", "Yifan Wu", "Saad Abbasi", "Saeejith Nair", "Yuhao Chen", "Mohammad Javad Shafiee"], "categories": ["cs.CV", "cs.AI", "cs.LG"], "doi": "", "_query": "property valuation computer vision", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/1501.02825v1", "title": "A Survey on Recent Advances of Computer Vision Algorithms for Egocentric Video", "abstract": "Recent technological advances have made lightweight, head mounted cameras both practical and affordable and products like Google Glass show first approaches to introduce the idea of egocentric (first-person) video to the mainstream. Interestingly, the computer vision community has only recently started to explore this new domain of egocentric vision, where research can roughly be categorized into three areas: Object recognition, activity detection/recognition, video summarization. In this paper, we try to give a broad overview about the different problems that have been addressed and collect and compare evaluation results. Moreover, along with the emergence of this new domain came the introduction of numerous new and versatile benchmark datasets, which we summarize and compare as well.", "published": "2015-01-12T21:14:56Z", "authors": ["Sven Bambach"], "categories": ["cs.CV"], "doi": "", "_query": "property valuation computer vision", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/1511.07917v1", "title": "Context-aware CNNs for person head detection", "abstract": "Person detection is a key problem for many computer vision tasks. While face detection has reached maturity, detecting people under a full variation of camera view-points, human poses, lighting conditions and occlusions is still a difficult challenge. In this work we focus on detecting human heads in natural scenes. Starting from the recent local R-CNN object detector, we extend it with two types of contextual cues. First, we leverage person-scene relations and propose a Global CNN model trained to predict positions and scales of heads directly from the full image. Second, we explicitly model pairwise relations among objects and train a Pairwise CNN model using a structured-output surrogate loss. The Local, Global and Pairwise models are combined into a joint CNN framework. To train and test our full model, we introduce a large dataset composed of 369,846 human heads annotated in 224,740 movie frames. We evaluate our method and demonstrate improvements of person head detection against several recent baselines in three datasets. We also show improvements of the detection speed provided by our model.", "published": "2015-11-24T23:23:18Z", "authors": ["Tuan-Hung Vu", "Anton Osokin", "Ivan Laptev"], "categories": ["cs.CV", "cs.LG"], "doi": "", "_query": "property valuation computer vision", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/1907.13426v2", "title": "Expectation-Maximization Attention Networks for Semantic Segmentation", "abstract": "Self-attention mechanism has been widely used for various tasks. It is designed to compute the representation of each position by a weighted sum of the features at all positions. Thus, it can capture long-range relations for computer vision tasks. However, it is computationally consuming. Since the attention maps are computed w.r.t all other positions. In this paper, we formulate the attention mechanism into an expectation-maximization manner and iteratively estimate a much more compact set of bases upon which the attention maps are computed. By a weighted summation upon these bases, the resulting representation is low-rank and deprecates noisy information from the input. The proposed Expectation-Maximization Attention (EMA) module is robust to the variance of input and is also friendly in memory and computation. Moreover, we set up the bases maintenance and normalization methods to stabilize its training procedure. We conduct extensive experiments on popular semantic segmentation benchmarks including PASCAL VOC, PASCAL Context and COCO Stuff, on which we set new records.", "published": "2019-07-31T11:35:54Z", "authors": ["Xia Li", "Zhisheng Zhong", "Jianlong Wu", "Yibo Yang", "Zhouchen Lin", "Hong Liu"], "categories": ["cs.CV"], "doi": "", "_query": "property valuation computer vision", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2110.01774v2", "title": "HighlightMe: Detecting Highlights from Human-Centric Videos", "abstract": "We present a domain- and user-preference-agnostic approach to detect highlightable excerpts from human-centric videos. Our method works on the graph-based representation of multiple observable human-centric modalities in the videos, such as poses and faces. We use an autoencoder network equipped with spatial-temporal graph convolutions to detect human activities and interactions based on these modalities. We train our network to map the activity- and interaction-based latent structural representations of the different modalities to per-frame highlight scores based on the representativeness of the frames. We use these scores to compute which frames to highlight and stitch contiguous frames to produce the excerpts. We train our network on the large-scale AVA-Kinetics action dataset and evaluate it on four benchmark video highlight datasets: DSH, TVSum, PHD2, and SumMe. We observe a 4-12% improvement in the mean average precision of matching the human-annotated highlights over state-of-the-art methods in these datasets, without requiring any user-provided preferences or dataset-specific fine-tuning.", "published": "2021-10-05T01:18:15Z", "authors": ["Uttaran Bhattacharya", "Gang Wu", "Stefano Petrangeli", "Viswanathan Swaminathan", "Dinesh Manocha"], "categories": ["cs.CV"], "doi": "10.1109/ICCV48922.2021.00805", "_query": "property valuation computer vision", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/1812.03713v1", "title": "$t$-local domains and valuation domains", "abstract": "In a valuation domain $(V,M)$ every nonzero finitely generated ideal $J$ is principal and so, in particular, $J=J^t$, hence the maximal ideal $M$ is a $t$-ideal. Therefore, the $t$-local domains (i.e., the local domains, with maximal ideal being a $t$-ideal) are \"cousins\" of valuation domains, but, as we will see in detail, not so close. Indeed, for instance, a localization of a $t$-local domain is not necessarily $t$-local, but of course a localization of a valuation domain is a valuation domain. So it is natural to ask under what conditions is a $t$-local domain a valuation domain? The main purpose of the present paper is to address this question, surveying in part previous work by various authors containing useful properties for applying them to our goal.", "published": "2018-12-10T10:13:01Z", "authors": ["Marco Fontana", "Muhammad Zafrullah"], "categories": ["math.AC", "math.AG", "math.RA"], "doi": "", "_query": "property valuation computer vision", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2608.07726v2", "title": "Vision Meets WiFi: Physics-Grounded Estimation of Volumetric Mechanical Properties", "abstract": "Estimating volumetric mechanical properties, including Young's modulus, Poisson's ratio, and density at each voxel, is intrinsically ambiguous from vision alone, as visually similar objects may have substantially different material compositions and physical behavior. Existing approaches predict these properties independently across voxels, overlooking the piecewise-constant material structure of real objects and producing noisy or inconsistent estimates for voxels that share the same material, while lacking an explicit mechanism to resolve visual ambiguity. We introduce ViWi (Vision Meets WiFi), an object-centric framework for volumetric mechanical-property estimation. ViWi represents each object using a compact set of material slots that aggregate evidence from voxels with a shared material identity and produce coherent slot-level property predictions. To complement visual appearance, ViWi incorporates a compact RF descriptor generated through WiFi-band electromagnetic simulation using permittivity and conductivity. The RF descriptor conditions the material slots with global composition cues that may be unavailable from images, while visual features preserve voxel-level spatial localization. On GVM, ViWi improves over the prior state of the art on four of six per-voxel metrics, while its vision-only variant improves all reported mass-estimation metrics on ABO-500. These results demonstrate that combining object-centric material structure with complementary RF evidence enables more accurate and physically coherent volumetric property estimation beyond what is possible from visual appearance alone.", "published": "2026-08-07T19:30:43Z", "authors": ["Ali Bahri", "Hongliang Li", "Soufiane Lamghari", "Jie Chuai", "Zhitang Chen"], "categories": ["cs.CV"], "doi": "", "_query": "property valuation computer vision", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2608.26015v1", "title": "Isometry invariant valuations on spherical polytopes", "abstract": "We show that every continuous and isometry invariant valuation on spherical polytopes is a linear combination of the spherical intrinsic volumes. The proof relies on a weak differentiability property satisfied by valuations on polytopes in $\\mathbb{R}^n$ with a natural smoothness property with respect to the action of the affine group. This enables us to transfer several results established by Alesker for quasi-smooth valuations to the polytopal setting, and to reduce the problem to the translation invariant case of measurable valuations on polytopes.", "published": "2026-08-26T16:57:33Z", "authors": ["Jonas Knoerr"], "categories": ["math.MG"], "doi": "", "_query": "property valuation computer vision", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2502.10489v1", "title": "LiveVal: Time-aware Data Valuation via Adaptive Reference Points", "abstract": "Time-aware data valuation enhances training efficiency and model robustness, as early detection of harmful samples could prevent months of wasted computation. However, existing methods rely on model retraining or convergence assumptions or fail to capture long-term training dynamics. We propose LiveVal, an efficient time-aware data valuation method with three key designs: 1) seamless integration with SGD training for efficient data contribution monitoring; 2) reference-based valuation with normalization for reliable benchmark establishment; and 3) adaptive reference point selection for real-time updating with optimized memory usage. We establish theoretical guarantees for LiveVal's stability and prove that its valuations are bounded and directionally aligned with optimization progress. Extensive experiments demonstrate that LiveVal provides efficient data valuation across different modalities and model scales, achieving 180 speedup over traditional methods while maintaining robust detection performance.", "published": "2025-02-14T12:41:20Z", "authors": ["Jie Xu", "Zihan Wu", "Cong Wang", "Xiaohua Jia"], "categories": ["cs.LG", "cs.AI"], "doi": "", "_query": "property valuation computer vision", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/math/0606189v2", "title": "Computing Groebner bases of ideals of few points in high dimensions", "abstract": "A contemporary and exciting application of Groebner bases is their use in computational biology, particularly in the reverse engineering of gene regulatory networks from experimental data. In this setting, the data are typically limited to tens of points, while the number of genes or variables is potentially in the thousands. As such data sets vastly underdetermine the biological network, many models may fit the same data and reverse engineering programs often require the use of methods for choosing parsimonious models. Grobner bases have recently been employed as a selection tool for polynomial dynamical systems that are characterized by maps in a vector space over a finite field. While there are numerous existing algorithms to compute Groebner bases, to date none has been specifically designed to cope with large numbers of variables and few distinct data points. In this paper, we present an algorithm for computing Grobner bases of zero-dimensional ideals that is optimized for the case when the number m of points is much smaller than the number n of indeterminates. The algorithm identifies those variables that are essential, that is, in the support of the standard monomials associated to a polynomial ideal, and computes the relations in the Groebner bases in terms of these variables. When n is much larger than m, the complexity is dominated by nm^3. The algorithm has been implemented and tested in the computer algebra system Macaulay 2. We provide a comparison of its performance to the Buchberger-Moeller algorithm, as built into the system.", "published": "2006-06-08T14:26:38Z", "authors": ["Winfried Just", "Brandilyn Stigler"], "categories": ["math.AC"], "doi": "10.1145/1279721.1279722", "_query": "property valuation computer vision", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/1902.07474v2", "title": "Spatially-Adaptive Filter Units for Compact and Efficient Deep Neural Networks", "abstract": "Convolutional neural networks excel in a number of computer vision tasks. One of their most crucial architectural elements is the effective receptive field size, that has to be manually set to accommodate a specific task. Standard solutions involve large kernels, down/up-sampling and dilated convolutions. These require testing a variety of dilation and down/up-sampling factors and result in non-compact representations and excessive number of parameters. We address this issue by proposing a new convolution filter composed of displaced aggregation units (DAU). DAUs learn spatial displacements and adapt the receptive field sizes of individual convolution filters to a given problem, thus eliminating the need for hand-crafted modifications. DAUs provide a seamless substitution of convolutional filters in existing state-of-the-art architectures, which we demonstrate on AlexNet, ResNet50, ResNet101, DeepLab and SRN-DeblurNet. The benefits of this design are demonstrated on a variety of computer vision tasks and datasets, such as image classification (ILSVRC 2012), semantic segmentation (PASCAL VOC 2011, Cityscape) and blind image de-blurring (GOPRO). Results show that DAUs efficiently allocate parameters resulting in up to four times more compact networks at similar or better performance.", "published": "2019-02-20T09:49:55Z", "authors": ["Domen Tabernik", "Matej Kristan", "Aleš Leonardis"], "categories": ["cs.CV"], "doi": "10.1007/s11263-019-01282-1", "_query": "property valuation computer vision", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2005.14260v1", "title": "Overview: Computer vision and machine learning for microstructural characterization and analysis", "abstract": "The characterization and analysis of microstructure is the foundation of microstructural science, connecting the materials structure to its composition, process history, and properties. Microstructural quantification traditionally involves a human deciding a priori what to measure and then devising a purpose-built method for doing so. However, recent advances in data science, including computer vision (CV) and machine learning (ML) offer new approaches to extracting information from microstructural images. This overview surveys CV approaches to numerically encode the visual information contained in a microstructural image, which then provides input to supervised or unsupervised ML algorithms that find associations and trends in the high-dimensional image representation. CV/ML systems for microstructural characterization and analysis span the taxonomy of image analysis tasks, including image classification, semantic segmentation, object detection, and instance segmentation. These tools enable new approaches to microstructural analysis, including the development of new, rich visual metrics and the discovery of processing-microstructure-property relationships.", "published": "2020-05-28T19:51:23Z", "authors": ["Elizabeth A. Holm", "Ryan Cohn", "Nan Gao", "Andrew R. Kitahara", "Thomas P. Matson", "Bo Lei", "Srujana Rao Yarasi"], "categories": ["cs.CV", "cond-mat.mtrl-sci"], "doi": "10.1007/s11661-020-06008-4", "_query": "property valuation computer vision", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/1611.08998v5", "title": "DeepSetNet: Predicting Sets with Deep Neural Networks", "abstract": "This paper addresses the task of set prediction using deep learning. This is important because the output of many computer vision tasks, including image tagging and object detection, are naturally expressed as sets of entities rather than vectors. As opposed to a vector, the size of a set is not fixed in advance, and it is invariant to the ordering of entities within it. We define a likelihood for a set distribution and learn its parameters using a deep neural network. We also derive a loss for predicting a discrete distribution corresponding to set cardinality. Set prediction is demonstrated on the problem of multi-class image classification. Moreover, we show that the proposed cardinality loss can also trivially be applied to the tasks of object counting and pedestrian detection. Our approach outperforms existing methods in all three cases on standard datasets.", "published": "2016-11-28T06:42:56Z", "authors": ["S. Hamid Rezatofighi", "Vijay Kumar B G", "Anton Milan", "Ehsan Abbasnejad", "Anthony Dick", "Ian Reid"], "categories": ["cs.CV", "cs.AI", "cs.LG"], "doi": "", "_query": "property valuation computer vision", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2310.07110v1", "title": "Valuation Duration of the Stock Market", "abstract": "At the peak of the tech bubble, only 0.57% of market valuation comes from dividends in the next year. Taking the ratio of total market value to the value of one-year dividends, we obtain a valuation-based duration of 175 years. In contrast, at the height of the global financial crisis, more than 2.2% of market value is from dividends in the next year, implying a duration of 46 years. What drives valuation duration? We find that market participants have limited information about cash flow beyond one year. Therefore, an increase in valuation duration is due to a decrease in the discount rate rather than good news about long-term growth. Accordingly, valuation duration negatively predicts annual market return with an out-of-sample R2 of 15%, robustly outperforming other predictors in the literature. While the price-dividend ratio reflects the overall valuation level, our valuation-based measure of duration captures the slope of the valuation term structure. We show that valuation duration, as a discount rate proxy, is a critical state variable that augments the price-dividend ratio in spanning the (latent) state space for stock-market dynamics.", "published": "2023-10-11T01:12:18Z", "authors": ["Ye Li", "Chen Wang"], "categories": ["q-fin.PR", "q-fin.GN"], "doi": "", "_query": "property valuation computer vision", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/0911.0497v1", "title": "A Context-based Trust Management Model for Pervasive Computing Systems", "abstract": "Trust plays an important role in making collaborative decisions about service evaluation and service selection in pervasive computing. Context is a fundamental concept in pervasive systems, which is based on the interpretation of environment and systems. The dynamic nature of context can strongly affect trust management and service selection. In this paper, we present a context-based trust management model for pervasive computing systems. The concept of context is considered in basic components of the model such as trust computation module, recommender assessment module, transaction management module, and request responder. In order to measure a predicted trustworthiness according to the fuzzy nature of trust in pervasive environments, fuzzy concepts are integrated in the proposed model.", "published": "2009-11-03T05:03:39Z", "authors": ["Negin Razavi", "Amir Masoud Rahmani", "Mehran Mohsenzadeh"], "categories": ["cs.CR"], "doi": "", "_query": "property valuation computer vision", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2204.06681v3", "title": "MINSU (Mobile Inventory And Scanning Unit):Computer Vision and AI", "abstract": "The MINSU(Mobile Inventory and Scanning Unit) algorithm uses the computational vision analysis method to record the residual quantity/fullness of the cabinet. To do so, it goes through a five-step method: object detection, foreground subtraction, K-means clustering, percentage estimation, and counting. The input image goes through the object detection method to analyze the specific position of the cabinets in terms of coordinates. After doing so, it goes through the foreground subtraction method to make the image more focus-able to the cabinet itself by removing the background (some manual work may have to be done such as selecting the parts that were not grab cut by the algorithm). In the K-means clustering method, the multi-colored image turns into a 3 colored monotonous image for quicker and more accurate analysis. At last, the image goes through percentage estimation and counting. In these two methods, the proportion that the material inside the cabinet is found in percentage which then is used to approximate the number of materials inside. Had this project been successful, the residual quantity management could solve the problem addressed earlier in the introduction.", "published": "2022-04-14T00:21:14Z", "authors": ["Jihoon Ryoo", "Byungkon Kang", "Dongyeob Lee", "Seunghyeon Kim", "Youngho Kim"], "categories": ["cs.CV", "cs.AI"], "doi": "", "_query": "property valuation computer vision", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2406.05612v3", "title": "Which Backbone to Use: A Resource-efficient Domain Specific Comparison for Computer Vision", "abstract": "In contemporary computer vision applications, particularly image classification, architectural backbones pre-trained on large datasets like ImageNet are commonly employed as feature extractors. Despite the widespread use of these pre-trained convolutional neural networks (CNNs), there remains a gap in understanding the performance of various resource-efficient backbones across diverse domains and dataset sizes. Our study systematically evaluates multiple lightweight, pre-trained CNN backbones under consistent training settings across a variety of datasets, including natural images, medical images, galaxy images, and remote sensing images. This comprehensive analysis aims to aid machine learning practitioners in selecting the most suitable backbone for their specific problem, especially in scenarios involving small datasets where fine-tuning a pre-trained network is crucial. Even though attention-based architectures are gaining popularity, we observed that they tend to perform poorly under low data finetuning tasks compared to CNNs. We also observed that some CNN architectures such as ConvNeXt, RegNet and EfficientNet performs well compared to others on a diverse set of domains consistently. Our findings provide actionable insights into the performance trade-offs and effectiveness of different backbones, facilitating informed decision-making in model selection for a broad spectrum of computer vision domains. Our code is available here: https://github.com/pranavphoenix/Backbones", "published": "2024-06-09T02:01:25Z", "authors": ["Pranav Jeevan", "Amit Sethi"], "categories": ["cs.CV", "cs.AI", "cs.LG"], "doi": "", "_query": "property valuation computer vision", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/1409.3288v2", "title": "Reconciliation of RDF* and Property Graphs", "abstract": "Both the notion of Property Graphs (PG) and the Resource Description Framework (RDF) are commonly used models for representing graph-shaped data. While there exist some system-specific solutions to convert data from one model to the other, these solutions are not entirely compatible with one another and none of them appears to be based on a formal foundation. In fact, for the PG model, there does not even exist a commonly agreed-upon formal definition. The aim of this document is to reconcile both models formally. To this end, the document proposes a formalization of the PG model and introduces well-defined transformations between PGs and RDF. As a result, the document provides a basis for the following two innovations: On one hand, by implementing the RDF-to-PG transformations defined in this document, PG-based systems can enable their users to load RDF data and make it accessible in a compatible, system-independent manner using, e.g., the graph traversal language Gremlin or the declarative graph query language Cypher. On the other hand, the PG-to-RDF transformation in this document enables RDF data management systems to support compatible, system-independent queries over the content of Property Graphs by using the standard RDF query language SPARQL. Additionally, this document represents a foundation for systematic research on relationships between the two models and between their query languages.", "published": "2014-09-11T01:15:06Z", "authors": ["Olaf Hartig"], "categories": ["cs.DB"], "doi": "", "_query": "property valuation computer vision", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/1512.06492v1", "title": "Remote Health Coaching System and Human Motion Data Analysis for Physical Therapy with Microsoft Kinect", "abstract": "This paper summarizes the recent progress we have made for the computer vision technologies in physical therapy with the accessible and affordable devices. We first introduce the remote health coaching system we build with Microsoft Kinect. Since the motion data captured by Kinect is noisy, we investigate the data accuracy of Kinect with respect to the high accuracy motion capture system. We also propose an outlier data removal algorithm based on the data distribution. In order to generate the kinematic parameter from the noisy data captured by Kinect, we propose a kinematic filtering algorithm based on Unscented Kalman Filter and the kinematic model of human skeleton. The proposed algorithm can obtain smooth kinematic parameter with reduced noise compared to the kinematic parameter generated from the raw motion data from Kinect.", "published": "2015-12-21T04:58:36Z", "authors": ["Qifei Wang", "Gregorij Kurillo", "Ferda Ofli", "Ruzena Bajcsy"], "categories": ["cs.CV", "cs.AI"], "doi": "", "_query": "property valuation computer vision", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2608.13343v1", "title": "AmalthAI: An Open-Source Computer Vision Platform for Cultural Heritage", "abstract": "Computer vision (CV) and machine learning (ML) offer new tools for cultural heritage (CH) artifact analysis, but the CV/ML pipeline remains largely inaccessible to CH domain experts, who lack the background to configure, train, or assess models. We present AmalthAI, an open-source CV platform that bridges this gap, enabling non-ML CH experts to independently produce and validate archaeologically meaningful findings. The interface covers dataset management, training, and inference for classification, segmentation, and object detection, with Kubeflow and Katib handling scalable training and hyperparameter search. Grad-CAM localizes the image region behind a prediction, and a vision-language model (VLM) adds a text description of it for expert review. Since archaeological data is often state-owned or rights-encumbered and cannot leave institutional custody, AmalthAI's self-hostable deployment ensures sensitive data is kept within premises. We test the platform on an archaeological use case built on a custom dataset of clay textile imprints, where CH experts trained and validated segmentation, and classification models for hypothesis testing. We provide the implementation code at https://github.com/TEXTaiLES/AmalthAI.", "published": "2026-08-13T15:14:48Z", "authors": ["Christos Chatzisavvas", "Stelios Alvanos", "Efstratios Politis", "Panagiotis Rigas", "Thomas Pappas", "Ioannis Giannoukos", "Nikolaos Mitianoudis", "Agata Ulanowska", "Katarzyna Żebrowska", "Nazarij Buławka", "Christina Margariti", "George Pavlidis", "Chairi Kiourt", "Anestis Koutsoudis", "Vassilis Katsouros", "George Ioannakis"], "categories": ["cs.CV"], "doi": "", "_query": "property valuation computer vision", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2508.05990v3", "title": "Efficient Bayer-Domain Video Computer Vision with Fast Motion Estimation and Learned Perception Residual", "abstract": "Video computer vision systems face substantial computational burdens arising from two fundamental challenges: eliminating unnecessary processing and reducing temporal redundancy in back-end inference while maintaining accuracy with minimal extra computation. To address these issues, we propose an efficient video computer vision framework that jointly optimizes both the front end and back end of the pipeline. On the front end, we remove the traditional image signal processor (ISP) and feed Bayer raw measurements directly into Bayer-domain vision models, avoiding costly human-oriented ISP operations. On the back end, we introduce a fast and highly parallel motion estimation algorithm that extracts inter-frame temporal correspondence to avoid redundant computation. To mitigate artifacts caused by motion inaccuracies, we further employ lightweight perception residual networks that directly learn perception-level residuals and refine the propagated features. Experiments across multiple models and tasks demonstrate that our system achieves substantial acceleration with only minor performance degradation.", "published": "2025-08-08T03:55:19Z", "authors": ["Haichao Wang", "Jiangtao Wen", "Yuxing Han"], "categories": ["cs.CV"], "doi": "", "_query": "property valuation computer vision", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/1708.01034v1", "title": "What Will I Do Next? The Intention from Motion Experiment", "abstract": "In computer vision, video-based approaches have been widely explored for the early classification and the prediction of actions or activities. However, it remains unclear whether this modality (as compared to 3D kinematics) can still be reliable for the prediction of human intentions, defined as the overarching goal embedded in an action sequence. Since the same action can be performed with different intentions, this problem is more challenging but yet affordable as proved by quantitative cognitive studies which exploit the 3D kinematics acquired through motion capture systems. In this paper, we bridge cognitive and computer vision studies, by demonstrating the effectiveness of video-based approaches for the prediction of human intentions. Precisely, we propose Intention from Motion, a new paradigm where, without using any contextual information, we consider instantaneous grasping motor acts involving a bottle in order to forecast why the bottle itself has been reached (to pass it or to place in a box, or to pour or to drink the liquid inside). We process only the grasping onsets casting intention prediction as a classification framework. Leveraging on our multimodal acquisition (3D motion capture data and 2D optical videos), we compare the most commonly used 3D descriptors from cognitive studies with state-of-the-art video-based techniques. Since the two analyses achieve an equivalent performance, we demonstrate that computer vision tools are effective in capturing the kinematics and facing the cognitive problem of human intention prediction.", "published": "2017-08-03T07:37:58Z", "authors": ["Andrea Zunino", "Jacopo Cavazza", "Atesh Koul", "Andrea Cavallo", "Cristina Becchio", "Vittorio Murino"], "categories": ["cs.CV"], "doi": "10.1109/CVPRW.2017.7", "_query": "property valuation computer vision", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2511.07325v1", "title": "Garbage Vulnerable Point Monitoring using IoT and Computer Vision", "abstract": "This paper proposes a smart way to manage municipal solid waste by using the Internet of Things (IoT) and computer vision (CV) to monitor illegal waste dumping at garbage vulnerable points (GVPs) in urban areas. The system can quickly detect and monitor dumped waste using a street-level camera and object detection algorithm. Data was collected from the Sangareddy district in Telangana, India. A series of comprehensive experiments was carried out using the proposed dataset to assess the accuracy and overall performance of various object detection models. Specifically, we performed an in-depth evaluation of YOLOv8, YOLOv10, YOLO11m, and RT-DETR on our dataset. Among these models, YOLO11m achieved the highest accuracy of 92.39\\% in waste detection, demonstrating its effectiveness in detecting waste. Additionally, it attains an mAP@50 of 0.91, highlighting its high precision. These findings confirm that the object detection model is well-suited for monitoring and tracking waste dumping events at GVP locations. Furthermore, the system effectively captures waste disposal patterns, including hourly, daily, and weekly dumping trends, ensuring comprehensive daily and nightly monitoring.", "published": "2025-11-10T17:27:51Z", "authors": ["R. Kumar", "A. Lall", "S. Chaudhari", "M. Kale", "A. Vattem"], "categories": ["cs.CV", "cs.LG"], "doi": "", "_query": "property valuation computer vision", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2603.00342v1", "title": "Challenges in Enabling Private Data Valuation", "abstract": "Data valuation methods quantify how individual training examples contribute to a model's behavior, and are increasingly used for dataset curation, auditing, and emerging data markets. As these techniques become operational, they raise serious privacy concerns: valuation scores can reveal whether a person's data was included in training, whether it was unusually influential, or what sensitive patterns exist in proprietary datasets. This motivates the study of privacy-preserving data valuation. However, privacy is fundamentally in tension with valuation utility under differential privacy (DP). DP requires outputs to be insensitive to any single record, while valuation methods are explicitly designed to measure per-record influence. As a result, naive privatization often destroys the fine-grained distinctions needed to rank or attribute value, particularly in heterogeneous datasets where rare examples exert outsized effects. In this work, we analyze the feasibility of DP-compatible data valuation. We identify the core algorithmic primitives across common valuation frameworks that induce prohibitive sensitivity, explaining why straightforward DP mechanisms fail. We further derive design principles for more privacy-amenable valuation procedures and empirically characterize how privacy constraints degrade ranking fidelity across representative methods and datasets. Our results clarify the limits of current approaches and provide a foundation for developing valuation methods that remain useful under rigorous privacy guarantees.", "published": "2026-02-27T22:21:14Z", "authors": ["Yiwei Fu", "Tianhao Wang", "Varun Chandrasekaran"], "categories": ["cs.CR", "cs.AI", "cs.LG"], "doi": "", "_query": "property valuation computer vision", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2406.00490v2", "title": "Research on the Application of Computer Vision Based on Deep Learning in Autonomous Driving Technology", "abstract": "This research aims to explore the application of deep learning in autonomous driving computer vision technology and its impact on improving system performance. By using advanced technologies such as convolutional neural networks (CNN), multi-task joint learning methods, and deep reinforcement learning, this article analyzes in detail the application of deep learning in image recognition, real-time target tracking and classification, environment perception and decision support, and path planning and navigation. Application process in key areas. Research results show that the proposed system has an accuracy of over 98% in image recognition, target tracking and classification, and also demonstrates efficient performance and practicality in environmental perception and decision support, path planning and navigation. The conclusion points out that deep learning technology can significantly improve the accuracy and real-time response capabilities of autonomous driving systems. Although there are still challenges in environmental perception and decision support, with the advancement of technology, it is expected to achieve wider applications and greater capabilities in the future. potential.", "published": "2024-06-01T16:41:24Z", "authors": ["Jingyu Zhang", "Jin Cao", "Jinghao Chang", "Xinjin Li", "Houze Liu", "Zhenglin Li"], "categories": ["cs.CV", "cs.AI"], "doi": "", "_query": "property valuation computer vision", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2111.01851v1", "title": "Private Interdependent Valuations", "abstract": "We consider the single-item interdependent value setting, where there is a monopolist, $n$ buyers, and each buyer has a private signal $s_i$ describing a piece of information about the item. Each bidder $i$ also has a valuation function $v_i(s_1,\\ldots,s_n)$ mapping the (private) signals of all buyers to a positive real number representing their value for the item. This setting captures scenarios where the item's information is asymmetric or dispersed among agents, such as in competitions for oil drilling rights, or in auctions for art pieces. Due to the increased complexity of this model compared to standard private values, it is generally assumed that each bidder's valuation function $v_i$ is public knowledge. But in many situations, the seller may not know how a bidder aggregates signals into a valuation. In this paper, we design mechanisms that guarantee approximately-optimal social welfare while satisfying ex-post incentive compatibility and individual rationality for the case where the valuation functions are private to the bidders. When the valuations are public, it is possible for optimal social welfare to be attained by a deterministic mechanism under a single-crossing condition. In contrast, when the valuations are the bidders' private information, we show that no finite bound can be achieved by any deterministic mechanism even under single-crossing. Moreover, no randomized mechanism can guarantee better than an $n$-approximation. We thus consider valuation functions that are submodular over signals (SOS), introduced in the context of combinatorial auctions in a recent breakthrough paper by Eden et al. [EC'19]. Our main result is an $O(\\log^2 n)$-approximation for buyers with private signals and valuations under the SOS condition. We also give a tight $Θ(k)$-approximation for the case each agent's valuation depends on at most $k$ other signals even for unknown $k$.", "published": "2021-11-02T19:15:06Z", "authors": ["Alon Eden", "Kira Goldner", "Shuran Zheng"], "categories": ["cs.GT", "cs.DS"], "doi": "", "_query": "property valuation computer vision", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/1904.02242v1", "title": "Unpaired Thermal to Visible Spectrum Transfer using Adversarial Training", "abstract": "Thermal Infrared (TIR) cameras are gaining popularity in many computer vision applications due to their ability to operate under low-light conditions. Images produced by TIR cameras are usually difficult for humans to perceive visually, which limits their usability. Several methods in the literature were proposed to address this problem by transforming TIR images into realistic visible spectrum (VIS) images. However, existing TIR-VIS datasets suffer from imperfect alignment between TIR-VIS image pairs which degrades the performance of supervised methods. We tackle this problem by learning this transformation using an unsupervised Generative Adversarial Network (GAN) which trains on unpaired TIR and VIS images. When trained and evaluated on KAIST-MS dataset, our proposed methods was shown to produce significantly more realistic and sharp VIS images than the existing state-of-the-art supervised methods. In addition, our proposed method was shown to generalize very well when evaluated on a new dataset of new environments.", "published": "2019-04-03T21:20:10Z", "authors": ["Adam Nyberg", "Abdelrahman Eldesokey", "David Bergström", "David Gustafsson"], "categories": ["cs.CV"], "doi": "", "_query": "property valuation computer vision", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2605.16704v1", "title": "Convex Dataset Valuation for Post-Training", "abstract": "Improving LLM performance on downstream tasks sometimes requires leveraging auxiliary datasets during post-training. In practice, however, developers face constraints on compute, labeling, and licensing costs that preclude using all available data, necessitating principled dataset-level selection. These constraints are increasingly shaped by dataset marketplaces, where data acquisition is governed by budgets and negotiation. We study dataset valuation as a subset selection problem during LLM post-training. Our goal is to identify and weight auxiliary datasets so as to maximize target task performance given constrained budgets. We first show that commonly used gradient alignment scores provide a reasonable yet incomplete valuation signal, as they ignore redundancy among datasets. To address this, we propose a scalable convex dataset-level valuation method based on kernel mean matching (KMM) in gradient space, which jointly accounts for alignment with the target task and redundancy across auxiliary datasets. Through extensive experiments across diverse post-training settings and tasks, we show that our approach consistently outperforms existing valuation baselines, achieving stronger performance with low computational overhead. Our results position dataset valuation as a practical decision tool for post-training data selection in market-constrained large language model settings. The code is available at https://github.com/uiuctml/convex_data_valuation.", "published": "2026-05-15T23:35:07Z", "authors": ["Siqi Zeng", "Christopher Jung", "Rui Li", "Zhe Kang", "Ming Li", "Nima Noorshams", "Zhigang Wang", "Fuchun Peng", "Han Zhao", "Xue Feng"], "categories": ["cs.LG"], "doi": "", "_query": "property valuation computer vision", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2312.10529v1", "title": "Transformers in Unsupervised Structure-from-Motion", "abstract": "Transformers have revolutionized deep learning based computer vision with improved performance as well as robustness to natural corruptions and adversarial attacks. Transformers are used predominantly for 2D vision tasks, including image classification, semantic segmentation, and object detection. However, robots and advanced driver assistance systems also require 3D scene understanding for decision making by extracting structure-from-motion (SfM). We propose a robust transformer-based monocular SfM method that learns to predict monocular pixel-wise depth, ego vehicle's translation and rotation, as well as camera's focal length and principal point, simultaneously. With experiments on KITTI and DDAD datasets, we demonstrate how to adapt different vision transformers and compare them against contemporary CNN-based methods. Our study shows that transformer-based architecture, though lower in run-time efficiency, achieves comparable performance while being more robust against natural corruptions, as well as untargeted and targeted attacks.", "published": "2023-12-16T20:00:34Z", "authors": ["Hemang Chawla", "Arnav Varma", "Elahe Arani", "Bahram Zonooz"], "categories": ["cs.CV", "cs.AI"], "doi": "10.1007/978-3-031-45725-8_14", "_query": "property valuation computer vision", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2204.06825v1", "title": "The Vision of Self-Evolving Computing Systems", "abstract": "Computing systems are omnipresent; their sustainability has become crucial for our society. A key aspect of this sustainability is the ability of computing systems to cope with the continuous change they face, ranging from dynamic operating conditions, to changing goals, and technological progress. While we are able to engineer smart computing systems that autonomously deal with various types of changes, handling unanticipated changes requires system evolution, which remains in essence a human-centered process. This will eventually become unmanageable. To break through the status quo, we put forward an arguable opinion for the vision of self-evolving computing systems that are equipped with an evolutionary engine enabling them to evolve autonomously. Specifically, when a self-evolving computing system detects conditions outside its operational domain, such as an anomaly or a new goal, it activates an evolutionary engine that runs online experiments to determine how the system needs to evolve to deal with the changes, thereby evolving its architecture. During this process the engine can integrate new computing elements that are provided by computing warehouses. These computing elements provide specifications and procedures enabling their automatic integration. We motivate the need for self-evolving computing systems in light of the state of the art, outline a conceptual architecture of self-evolving computing systems, and illustrate the architecture for a future smart city mobility system that needs to evolve continuously with changing conditions. To conclude, we highlight key research challenges to realize the vision of self-evolving computing systems.", "published": "2022-04-14T08:53:23Z", "authors": ["Danny Weyns", "Thomas Baeck", "Rene Vidal", "Xin Yao", "Ahmed Nabil Belbachir"], "categories": ["cs.SE", "cs.LG"], "doi": "", "_query": "property valuation computer vision", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/1405.0601v1", "title": "Supervised Descent Method for Solving Nonlinear Least Squares Problems in Computer Vision", "abstract": "Many computer vision problems (e.g., camera calibration, image alignment, structure from motion) are solved with nonlinear optimization methods. It is generally accepted that second order descent methods are the most robust, fast, and reliable approaches for nonlinear optimization of a general smooth function. However, in the context of computer vision, second order descent methods have two main drawbacks: (1) the function might not be analytically differentiable and numerical approximations are impractical, and (2) the Hessian may be large and not positive definite. To address these issues, this paper proposes generic descent maps, which are average \"descent directions\" and rescaling factors learned in a supervised fashion. Using generic descent maps, we derive a practical algorithm - Supervised Descent Method (SDM) - for minimizing Nonlinear Least Squares (NLS) problems. During training, SDM learns a sequence of decent maps that minimize the NLS. In testing, SDM minimizes the NLS objective using the learned descent maps without computing the Jacobian or the Hessian. We prove the conditions under which the SDM is guaranteed to converge. We illustrate the effectiveness and accuracy of SDM in three computer vision problems: rigid image alignment, non-rigid image alignment, and 3D pose estimation. In particular, we show how SDM achieves state-of-the-art performance in the problem of facial feature detection. The code has been made available at www.humansensing.cs.cmu.edu/intraface.", "published": "2014-05-03T15:37:27Z", "authors": ["Xuehan Xiong", "Fernando De la Torre"], "categories": ["cs.CV"], "doi": "", "_query": "property valuation computer vision", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/1709.09384v1", "title": "Globally-Optimal Inlier Set Maximisation for Simultaneous Camera Pose and Feature Correspondence", "abstract": "Estimating the 6-DoF pose of a camera from a single image relative to a pre-computed 3D point-set is an important task for many computer vision applications. Perspective-n-Point (PnP) solvers are routinely used for camera pose estimation, provided that a good quality set of 2D-3D feature correspondences are known beforehand. However, finding optimal correspondences between 2D key-points and a 3D point-set is non-trivial, especially when only geometric (position) information is known. Existing approaches to the simultaneous pose and correspondence problem use local optimisation, and are therefore unlikely to find the optimal solution without a good pose initialisation, or introduce restrictive assumptions. Since a large proportion of outliers are common for this problem, we instead propose a globally-optimal inlier set cardinality maximisation approach which jointly estimates optimal camera pose and optimal correspondences. Our approach employs branch-and-bound to search the 6D space of camera poses, guaranteeing global optimality without requiring a pose prior. The geometry of SE(3) is used to find novel upper and lower bounds for the number of inliers and local optimisation is integrated to accelerate convergence. The evaluation empirically supports the optimality proof and shows that the method performs much more robustly than existing approaches, including on a large-scale outdoor data-set.", "published": "2017-09-27T08:36:07Z", "authors": ["Dylan Campbell", "Lars Petersson", "Laurent Kneip", "Hongdong Li"], "categories": ["cs.CV", "cs.RO"], "doi": "", "_query": "property valuation computer vision", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2503.09100v1", "title": "Tacchi 2.0: A Low Computational Cost and Comprehensive Dynamic Contact Simulator for Vision-based Tactile Sensors", "abstract": "With the development of robotics technology, some tactile sensors, such as vision-based sensors, have been applied to contact-rich robotics tasks. However, the durability of vision-based tactile sensors significantly increases the cost of tactile information acquisition. Utilizing simulation to generate tactile data has emerged as a reliable approach to address this issue. While data-driven methods for tactile data generation lack robustness, finite element methods (FEM) based approaches require significant computational costs. To address these issues, we integrated a pinhole camera model into the low computational cost vision-based tactile simulator Tacchi that used the Material Point Method (MPM) as the simulated method, completing the simulation of marker motion images. We upgraded Tacchi and introduced Tacchi 2.0. This simulator can simulate tactile images, marked motion images, and joint images under different motion states like pressing, slipping, and rotating. Experimental results demonstrate the reliability of our method and its robustness across various vision-based tactile sensors.", "published": "2025-03-12T06:34:12Z", "authors": ["Yuhao Sun", "Shixin Zhang", "Wenzhuang Li", "Jie Zhao", "Jianhua Shan", "Zirong Shen", "Zixi Chen", "Fuchun Sun", "Di Guo", "Bin Fang"], "categories": ["cs.RO", "cs.CV"], "doi": "", "_query": "property valuation computer vision", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/1411.7891v1", "title": "Log-Concavity Properties of Minkowski Valuations", "abstract": "New Orlicz Brunn-Minkowski inequalities are established for rigid motion compatible Minkowski valuations of arbitrary degree. These extend classical log-concavity properties of intrinsic volumes and generalize seminal results of Lutwak and others. Two different approaches which refine previously employed techniques are explored. It is shown that both lead to the same class of Minkowski valuations for which these inequalities hold. An appendix by Semyon Alesker contains the proof of a new classification of generalized translation invariant valuations.", "published": "2014-11-28T14:54:11Z", "authors": ["Astrid Berg", "Lukas Parapatits", "Franz E. Schuster", "Manuel Weberndorfer"], "categories": ["math.MG", "math.DG", "math.FA"], "doi": "", "_query": "property valuation computer vision", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2407.15646v2", "title": "SS-SFR: Synthetic Scenes Spatial Frequency Response on Virtual KITTI and Degraded Automotive Simulations for Object Detection", "abstract": "Automotive simulation can potentially compensate for a lack of training data in computer vision applications. However, there has been little to no image quality evaluation of automotive simulation and the impact of optical degradations on simulation is little explored. In this work, we investigate Virtual KITTI and the impact of applying variations of Gaussian blur on image sharpness. Furthermore, we consider object detection, a common computer vision application on three different state-of-the-art models, thus allowing us to characterize the relationship between object detection and sharpness. It was found that while image sharpness (MTF50) degrades from an average of 0.245cy/px to approximately 0.119cy/px; object detection performance stays largely robust within 0.58\\%(Faster RCNN), 1.45\\%(YOLOF) and 1.93\\%(DETR) across all respective held-out test sets.", "published": "2024-07-22T14:03:29Z", "authors": ["Daniel Jakab", "Alexander Braun", "Cathaoir Agnew", "Reenu Mohandas", "Brian Michael Deegan", "Dara Molloy", "Enda Ward", "Tony Scanlan", "Ciarán Eising"], "categories": ["cs.CV"], "doi": "", "_query": "property valuation computer vision", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/1703.03872v3", "title": "Deep Image Matting", "abstract": "Image matting is a fundamental computer vision problem and has many applications. Previous algorithms have poor performance when an image has similar foreground and background colors or complicated textures. The main reasons are prior methods 1) only use low-level features and 2) lack high-level context. In this paper, we propose a novel deep learning based algorithm that can tackle both these problems. Our deep model has two parts. The first part is a deep convolutional encoder-decoder network that takes an image and the corresponding trimap as inputs and predict the alpha matte of the image. The second part is a small convolutional network that refines the alpha matte predictions of the first network to have more accurate alpha values and sharper edges. In addition, we also create a large-scale image matting dataset including 49300 training images and 1000 testing images. We evaluate our algorithm on the image matting benchmark, our testing set, and a wide variety of real images. Experimental results clearly demonstrate the superiority of our algorithm over previous methods.", "published": "2017-03-10T23:43:17Z", "authors": ["Ning Xu", "Brian Price", "Scott Cohen", "Thomas Huang"], "categories": ["cs.CV"], "doi": "", "_query": "property valuation computer vision", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2412.14272v1", "title": "Split Learning in Computer Vision for Semantic Segmentation Delay Minimization", "abstract": "In this paper, we propose a novel approach to minimize the inference delay in semantic segmentation using split learning (SL), tailored to the needs of real-time computer vision (CV) applications for resource-constrained devices. Semantic segmentation is essential for applications such as autonomous vehicles and smart city infrastructure, but faces significant latency challenges due to high computational and communication loads. Traditional centralized processing methods are inefficient for such scenarios, often resulting in unacceptable inference delays. SL offers a promising alternative by partitioning deep neural networks (DNNs) between edge devices and a central server, enabling localized data processing and reducing the amount of data required for transmission. Our contribution includes the joint optimization of bandwidth allocation, cut layer selection of the edge devices' DNN, and the central server's processing resource allocation. We investigate both parallel and serial data processing scenarios and propose low-complexity heuristic solutions that maintain near-optimal performance while reducing computational requirements. Numerical results show that our approach effectively reduces inference delay, demonstrating the potential of SL for improving real-time CV applications in dynamic, resource-constrained environments.", "published": "2024-12-18T19:07:25Z", "authors": ["Nikos G. Evgenidis", "Nikos A. Mitsiou", "Sotiris A. Tegos", "Panagiotis D. Diamantoulakis", "George K. Karagiannidis"], "categories": ["cs.CV", "cs.AI", "cs.DC", "cs.IT", "cs.LG"], "doi": "", "_query": "property valuation computer vision", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2212.08989v3", "title": "Deep learning applied to computational mechanics: A comprehensive review, state of the art, and the classics", "abstract": "Three recent breakthroughs due to AI in arts and science serve as motivation: An award winning digital image, protein folding, fast matrix multiplication. Many recent developments in artificial neural networks, particularly deep learning (DL), applied and relevant to computational mechanics (solid, fluids, finite-element technology) are reviewed in detail. Both hybrid and pure machine learning (ML) methods are discussed. Hybrid methods combine traditional PDE discretizations with ML methods either (1) to help model complex nonlinear constitutive relations, (2) to nonlinearly reduce the model order for efficient simulation (turbulence), or (3) to accelerate the simulation by predicting certain components in the traditional integration methods. Here, methods (1) and (2) relied on Long-Short-Term Memory (LSTM) architecture, with method (3) relying on convolutional neural networks. Pure ML methods to solve (nonlinear) PDEs are represented by Physics-Informed Neural network (PINN) methods, which could be combined with attention mechanism to address discontinuous solutions. Both LSTM and attention architectures, together with modern and generalized classic optimizers to include stochasticity for DL networks, are extensively reviewed. Kernel machines, including Gaussian processes, are provided to sufficient depth for more advanced works such as shallow networks with infinite width. Not only addressing experts, readers are assumed familiar with computational mechanics, but not with DL, whose concepts and applications are built up from the basics, aiming at bringing first-time learners quickly to the forefront of research. History and limitations of AI are recounted and discussed, with particular attention at pointing out misstatements or misconceptions of the classics, even in well-known references. Positioning and pointing control of a large-deformable beam is given as an example.", "published": "2022-12-18T02:03:00Z", "authors": ["Loc Vu-Quoc", "Alexander Humer"], "categories": ["cs.LG"], "doi": "10.32604/cmes.2023.028130", "_query": "property valuation computer vision", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2607.10797v1", "title": "Compositional Context Fine-Tuning Vision-Language Model for Complex Assembly Action Understanding from Videos", "abstract": "Assembly action understanding is a key enabler for effective human-robot collaborative assembly, yet it remains challenging due to subtle motions and fine-grained hand-object interactions. We adapt vision-language models (VLMs) to this challenging domain with Compositional Context Fine-Tuning (CCFT), a method that decomposes assembly actions into semantic elements (Verb, Object, Tool) and fine-tunes VLMs to recognize each action element using templated question-answering pairs. This approach ensures near-deterministic outputs. To enable efficient and effective multi-task learning under limited data, a Layer-Partitioned Alternating Training (LP-AT) method is presented, which assigns distinct model layers to recognize specific action elements through element-specific low-rank adapters. LP-AT alternates weight updates across element-specific adapters, reducing cross-task interference while enabling per-adapter hyperparameter optimization. Furthermore, we create HA-ViD-VQA and IKEA-ASM-VQA datasets from existing assembly video datasets. Extensive experiments on these datasets demonstrate that our method consistently outperforms strong action recognition baselines while providing interpretable element-level predictions that can support diverse downstream applications.", "published": "2026-07-12T15:09:33Z", "authors": ["Hao Zheng", "Jinyi Huang", "Tiantian Zheng", "Xun Xu", "Tuka Alhanai"], "categories": ["cs.CV"], "doi": "", "_query": "property valuation computer vision", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2405.03945v1", "title": "Role of Sensing and Computer Vision in 6G Wireless Communications", "abstract": "Recently, we are witnessing the remarkable progress and widespread adoption of sensing technologies in autonomous driving, robotics, and metaverse. Considering the rapid advancement of computer vision (CV) technology to analyze the sensing information, we anticipate a proliferation of wireless applications exploiting the sensing and CV technologies in 6G. In this article, we provide a holistic overview of the sensing and CV-aided wireless communications (SVWC) framework for 6G. By analyzing the high-resolution sensing information through the powerful CV techniques, SVWC can quickly and accurately understand the wireless environments and then perform the wireless tasks. To demonstrate the efficacy of SVWC, we design the whole process of SVWC including the sensing dataset collection, DL model training, and execution of realistic wireless tasks. From the numerical evaluations on 6G communication scenarios, we show that SVWC achieves considerable performance gains over the conventional 5G systems in terms of positioning accuracy, data rate, and access latency.", "published": "2024-05-07T02:10:30Z", "authors": ["Seungnyun Kim", "Jihoon Moon", "Jinhong Kim", "Yongjun Ahn", "Donghoon Kim", "Sunwoo Kim", "Kyuhong Shim", "Byonghyo Shim"], "categories": ["cs.CV", "cs.NI"], "doi": "10.1109/MWC.016.2300526", "_query": "property valuation computer vision", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2205.04387v3", "title": "Co-Designed Architectures for Modular Superconducting Quantum Computers", "abstract": "Noisy, Intermediate Scale Quantum (NISQ) computers have reached the point where they can show the potential for quantum advantage over classical computing. Unfortunately, NISQ machines introduce sufficient noise that even for moderate size quantum circuits the results can be unreliable. We propose a co-designed superconducting quantum computer using a Superconducting Nonlinear Asymmetric Inductive eLement (SNAIL) modulator. The SNAIL modulator is designed by considering both the ideal fundamental qubit gate operation while maximizing the qubit coupling capabilities. First, the SNAIL natively implements $\\sqrt[n]{\\texttt{iSWAP}}$ gates realized through proportionally scaled pulse lengths. This naturally includes $\\sqrt{\\texttt{iSWAP}}$, which provides an advantage over $\\texttt{CNOT}$ as a basis gate. Second, the SNAIL enables high-degree couplings that allow rich and highly parallel qubit connection topologies without suffering from frequency crowding. Building on our previously demonstrated SNAIL-based quantum state router we propose a quantum 4-ary tree and a hypercube inspired corral built from interconnected quantum modules. We compare their advantage in data movement based on necessary \\texttt{SWAP} gates to the traditional lattice and heavy-hex lattice used in latest commercial quantum computers. We demonstrate the co-design advantage of our SNAIL-based machine with $\\sqrt{\\texttt{iSWAP}}$ basis gates and rich topologies against $\\texttt{CNOT}$/heavy-hex and $\\texttt{FSIM}$/lattice for 16-20 qubit and extrapolated designs circa 80 qubit architectures. We compare total circuit time and total gate count to understand fidelity for systems dominated by decoherence and control imperfections, respectively. Finally, we provide a gate duration sensitivity study on further decreasing the SNAIL pulse length to realize $\\sqrt[n]{\\texttt{iSWAP}}$ qubit systems to reduce decoherence times.", "published": "2022-05-09T15:42:54Z", "authors": ["Evan McKinney", "Mingkang Xia", "Chao Zhou", "Pinlei Lu", "Michael Hatridge", "Alex K. Jones"], "categories": ["quant-ph"], "doi": "10.1109/HPCA56546.2023.10071036", "_query": "property valuation computer vision", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2403.08789v1", "title": "Bridging Human Concepts and Computer Vision for Explainable Face Verification", "abstract": "With Artificial Intelligence (AI) influencing the decision-making process of sensitive applications such as Face Verification, it is fundamental to ensure the transparency, fairness, and accountability of decisions. Although Explainable Artificial Intelligence (XAI) techniques exist to clarify AI decisions, it is equally important to provide interpretability of these decisions to humans. In this paper, we present an approach to combine computer and human vision to increase the explanation's interpretability of a face verification algorithm. In particular, we are inspired by the human perceptual process to understand how machines perceive face's human-semantic areas during face comparison tasks. We use Mediapipe, which provides a segmentation technique that identifies distinct human-semantic facial regions, enabling the machine's perception analysis. Additionally, we adapted two model-agnostic algorithms to provide human-interpretable insights into the decision-making processes.", "published": "2024-01-30T09:13:49Z", "authors": ["Miriam Doh", "Caroline Mazini Rodrigues", "Nicolas Boutry", "Laurent Najman", "Matei Mancas", "Hugues Bersini"], "categories": ["cs.CV", "cs.AI", "cs.HC", "cs.LG"], "doi": "", "_query": "property valuation computer vision", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/1903.05244v1", "title": "Learning Feature Aggregation in Temporal Domain for Re-Identification", "abstract": "Person re-identification is a standard and established problem in the computer vision community. In recent years, vehicle re-identification is also getting more attention. In this paper, we focus on both these tasks and propose a method for aggregation of features in temporal domain as it is common to have multiple observations of the same object. The aggregation is based on weighting different elements of the feature vectors by different weights and it is trained in an end-to-end manner by a Siamese network. The experimental results show that our method outperforms other existing methods for feature aggregation in temporal domain on both vehicle and person re-identification tasks. Furthermore, to push research in vehicle re-identification further, we introduce a novel dataset CarsReId74k. The dataset is not limited to frontal/rear viewpoints. It contains 17,681 unique vehicles, 73,976 observed tracks, and 277,236 positive pairs. The dataset was captured by 66 cameras from various angles.", "published": "2019-03-12T22:34:21Z", "authors": ["Jakub Špaňhel", "Jakub Sochor", "Roman Juránek", "Petr Dobeš", "Vojtěch Bartl", "Adam Herout"], "categories": ["cs.CV"], "doi": "", "_query": "property valuation computer vision", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2404.00191v1", "title": "Optimal Blackjack Strategy Recommender: A Comprehensive Study on Computer Vision Integration for Enhanced Gameplay", "abstract": "This research project investigates the application of several computer vision techniques for playing card detection and recognition in the context of the popular casino game, blackjack. The primary objective is to develop a robust system that is capable of detecting and accurately classifying playing cards in real-time, and displaying the optimal move recommendation based on the given image of the current game. The proposed methodology involves using K-Means for image segmentation, card reprojection and feature extraction, training of the KNN classifier using a labeled dataset, and integration of the detection system into a Blackjack Basic Strategy recommendation algorithm. Further, the study aims to observe the effectiveness of this approach in detecting various card designs under different lighting conditions and occlusions. Overall, the project examines the potential benefits of incorporating computer vision techniques, with a specific focus on card detection, into commonly played games aiming to enhance player decision-making and optimize strategic outcomes. The results obtained from our experimental evaluations with models developed under considerable time constraints, highlight the potential for practical implementation in real-world casino environments and across other similarly structured games.", "published": "2024-03-29T23:17:49Z", "authors": ["Krishnanshu Gupta", "Devon Bolt", "Ben Hinchliff"], "categories": ["cs.CV"], "doi": "", "_query": "property valuation computer vision", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2108.13465v2", "title": "Full-Cycle Energy Consumption Benchmark for Low-Carbon Computer Vision", "abstract": "The energy consumption of deep learning models is increasing at a breathtaking rate, which raises concerns due to potential negative effects on carbon neutrality in the context of global warming and climate change. With the progress of efficient deep learning techniques, e.g., model compression, researchers can obtain efficient models with fewer parameters and smaller latency. However, most of the existing efficient deep learning methods do not explicitly consider energy consumption as a key performance indicator. Furthermore, existing methods mostly focus on the inference costs of the resulting efficient models, but neglect the notable energy consumption throughout the entire life cycle of the algorithm. In this paper, we present the first large-scale energy consumption benchmark for efficient computer vision models, where a new metric is proposed to explicitly evaluate the full-cycle energy consumption under different model usage intensity. The benchmark can provide insights for low carbon emission when selecting efficient deep learning algorithms in different model usage scenarios.", "published": "2021-08-30T18:22:36Z", "authors": ["Bo Li", "Xinyang Jiang", "Donglin Bai", "Yuge Zhang", "Ningxin Zheng", "Xuanyi Dong", "Lu Liu", "Yuqing Yang", "Dongsheng Li"], "categories": ["cs.CV", "cs.LG"], "doi": "", "_query": "property valuation computer vision", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2105.08582v1", "title": "Vision Transformer for Fast and Efficient Scene Text Recognition", "abstract": "Scene text recognition (STR) enables computers to read text in natural scenes such as object labels, road signs and instructions. STR helps machines perform informed decisions such as what object to pick, which direction to go, and what is the next step of action. In the body of work on STR, the focus has always been on recognition accuracy. There is little emphasis placed on speed and computational efficiency which are equally important especially for energy-constrained mobile machines. In this paper we propose ViTSTR, an STR with a simple single stage model architecture built on a compute and parameter efficient vision transformer (ViT). On a comparable strong baseline method such as TRBA with accuracy of 84.3%, our small ViTSTR achieves a competitive accuracy of 82.6% (84.2% with data augmentation) at 2.4x speed up, using only 43.4% of the number of parameters and 42.2% FLOPS. The tiny version of ViTSTR achieves 80.3% accuracy (82.1% with data augmentation), at 2.5x the speed, requiring only 10.9% of the number of parameters and 11.9% FLOPS. With data augmentation, our base ViTSTR outperforms TRBA at 85.2% accuracy (83.7% without augmentation) at 2.3x the speed but requires 73.2% more parameters and 61.5% more FLOPS. In terms of trade-offs, nearly all ViTSTR configurations are at or near the frontiers to maximize accuracy, speed and computational efficiency all at the same time.", "published": "2021-05-18T15:08:54Z", "authors": ["Rowel Atienza"], "categories": ["cs.CV"], "doi": "", "_query": "property valuation computer vision", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2210.02533v1", "title": "Towards Semi-automatic Detection and Localization of Indoor Accessibility Issues using Mobile Depth Scanning and Computer Vision", "abstract": "To help improve the safety and accessibility of indoor spaces, researchers and health professionals have created assessment instruments that enable homeowners and trained experts to audit and improve homes. With advances in computer vision, augmented reality (AR), and mobile sensors, new approaches are now possible. We introduce RASSAR (Room Accessibility and Safety Scanning in Augmented Reality), a new proof-of-concept prototype for semi-automatically identifying, categorizing, and localizing indoor accessibility and safety issues using LiDAR + camera data, machine learning, and AR. We present an overview of the current RASSAR prototype and a preliminary evaluation in a single home.", "published": "2022-10-05T20:07:05Z", "authors": ["Xia Su", "Kaiming Cheng", "Han Zhang", "Jaewook Lee", "Jon E. Froehlich"], "categories": ["cs.HC", "cs.CV", "cs.CY"], "doi": "", "_query": "property valuation computer vision", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2610.03389v1", "title": "From Patching to Pruning Visual Computation in Vision Language Models", "abstract": "Vision language models (VLMs) incur substantial inference cost because every visual token is processed by the attention and MLP projections of every decoder layer, even when token-specific visual computation is unnecessary at many depths. We introduce Patch-to-Prune (P2P), inspired by Mechanistic Interpretability, a training-free framework that converts activation patching from a diagnostic tool into an inference-time computation bypass. P2P performs validation-guided forward and backward layer sweeps to identify decoder regions whose visual-token projection outputs can be replaced by fixed neutral proxy activation vectors within a user-specified accuracy tolerance. Unlike conventional token-pruning methods, P2P preserves the sequence length, token order, positional information, attention mask, and residual pathways, thereby pruning computation without removing tokens or modifying the pretrained model weights. We evaluate P2P on four VLMs from the Qwen2.5-VL and LLaVA families across seven multi-modal benchmarks using mutually disjoint calibration, validation, and test partitions. P2P at a 3% tolerance retains around 94% of dense accuracy while reducing FLOPs by 55%. Beyond these efficiency gains, our layer-wise analysis suggests that visual processing in VLMs is non-uniformly distributed across decoder depth: early and late layers often require little token-specific visual computation, whereas intermediate layers appear to perform most task-relevant visual integration, enabling later reasoning to rely largely on visual information already embedded in shared residual and textual representations. This makes P2P both an efficient inference framework and a causal lens into visual information processing in VLMs.", "published": "2026-10-02T14:37:53Z", "authors": ["Rahul Chowdhury", "Timothy A Rupprecht", "Xuan Shen", "Shaoyi Huang", "Pu Zhao", "Yanzhi Wang"], "categories": ["cs.CV", "cs.LG"], "doi": "", "_query": "property valuation computer vision", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2502.20256v2", "title": "Do computer vision foundation models learn the low-level characteristics of the human visual system?", "abstract": "Computer vision foundation models, such as DINO or OpenCLIP, are trained in a self-supervised manner on large image datasets. Analogously, substantial evidence suggests that the human visual system (HVS) is influenced by the statistical distribution of colors and patterns in the natural world, characteristics also present in the training data of foundation models. The question we address in this paper is whether foundation models trained on natural images mimic some of the low-level characteristics of the human visual system, such as contrast detection, contrast masking, and contrast constancy. Specifically, we designed a protocol comprising nine test types to evaluate the image encoders of 45 foundation and generative models. Our results indicate that some foundation models (e.g., DINO, DINOv2, and OpenCLIP), share some of the characteristics of human vision, but other models show little resemblance. Foundation models tend to show smaller sensitivity to low contrast and rather irregular responses to contrast across frequencies. The foundation models show the best agreement with human data in terms of contrast masking. Our findings suggest that human vision and computer vision may take both similar and different paths when learning to interpret images of the real world. Overall, while differences remain, foundation models trained on vision tasks start to align with low-level human vision, with DINOv2 showing the closest resemblance.", "published": "2025-02-27T16:43:56Z", "authors": ["Yancheng Cai", "Fei Yin", "Dounia Hammou", "Rafal Mantiuk"], "categories": ["cs.CV"], "doi": "", "_query": "property valuation computer vision", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2308.04402v4", "title": "Person Re-Identification without Identification via Event Anonymization", "abstract": "Wide-scale use of visual surveillance in public spaces puts individual privacy at stake while increasing resource consumption (energy, bandwidth, and computation). Neuromorphic vision sensors (event-cameras) have been recently considered a valid solution to the privacy issue because they do not capture detailed RGB visual information of the subjects in the scene. However, recent deep learning architectures have been able to reconstruct images from event cameras with high fidelity, reintroducing a potential threat to privacy for event-based vision applications. In this paper, we aim to anonymize event-streams to protect the identity of human subjects against such image reconstruction attacks. To achieve this, we propose an end-to-end network architecture jointly optimized for the twofold objective of preserving privacy and performing a downstream task such as person ReId. Our network learns to scramble events, enforcing the degradation of images recovered from the privacy attacker. In this work, we also bring to the community the first ever event-based person ReId dataset gathered to evaluate the performance of our approach. We validate our approach with extensive experiments and report results on the synthetic event data simulated from the publicly available SoftBio dataset and our proposed Event-ReId dataset.", "published": "2023-08-08T17:04:53Z", "authors": ["Shafiq Ahmad", "Pietro Morerio", "Alessio Del Bue"], "categories": ["cs.CV"], "doi": "", "_query": "property valuation computer vision", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/1705.00816v1", "title": "Central and peripheral vision for scene recognition: A neurocomputational modeling exploration", "abstract": "What are the roles of central and peripheral vision in human scene recognition? Larson and Loschky (2009) showed that peripheral vision contributes more than central vision in obtaining maximum scene recognition accuracy. However, central vision is more efficient for scene recognition than peripheral, based on the amount of visual area needed for accurate recognition. In this study, we model and explain the results of Larson and Loschky (2009) using a neurocomputational modeling approach. We show that the advantage of peripheral vision in scene recognition, as well as the efficiency advantage for central vision, can be replicated using state-of-the-art deep neural network models. In addition, we propose and provide support for the hypothesis that the peripheral advantage comes from the inherent usefulness of peripheral features. This result is consistent with data presented by Thibaut, Tran, Szaffarczyk, and Boucart (2014), who showed that patients with central vision loss can still categorize natural scenes efficiently. Furthermore, by using a deep mixture-of-experts model (\"The Deep Model,\" or TDM) that receives central and peripheral visual information on separate channels simultaneously, we show that the peripheral advantage emerges naturally in the learning process: When trained to categorize scenes, the model weights the peripheral pathway more than the central pathway. As we have seen in our previous modeling work, learning creates a transform that spreads different scene categories into different regions in representational space. Finally, we visualize the features for the two pathways, and find that different preferences for scene categories emerge for the two pathways during the training process.", "published": "2017-05-02T06:44:02Z", "authors": ["Panqu Wang", "Garrison W. Cottrell"], "categories": ["q-bio.NC"], "doi": "10.1167/17.4.9", "_query": "property valuation computer vision", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/1712.00311v2", "title": "Folded Recurrent Neural Networks for Future Video Prediction", "abstract": "Future video prediction is an ill-posed Computer Vision problem that recently received much attention. Its main challenges are the high variability in video content, the propagation of errors through time, and the non-specificity of the future frames: given a sequence of past frames there is a continuous distribution of possible futures. This work introduces bijective Gated Recurrent Units, a double mapping between the input and output of a GRU layer. This allows for recurrent auto-encoders with state sharing between encoder and decoder, stratifying the sequence representation and helping to prevent capacity problems. We show how with this topology only the encoder or decoder needs to be applied for input encoding and prediction, respectively. This reduces the computational cost and avoids re-encoding the predictions when generating a sequence of frames, mitigating the propagation of errors. Furthermore, it is possible to remove layers from an already trained model, giving an insight to the role performed by each layer and making the model more explainable. We evaluate our approach on three video datasets, outperforming state of the art prediction results on MMNIST and UCF101, and obtaining competitive results on KTH with 2 and 3 times less memory usage and computational cost than the best scored approach.", "published": "2017-12-01T13:31:56Z", "authors": ["Marc Oliu", "Javier Selva", "Sergio Escalera"], "categories": ["cs.CV", "stat.ML"], "doi": "", "_query": "property valuation computer vision", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2601.20196v1", "title": "Automated Marine Biofouling Assessment: Benchmarking Computer Vision and Multimodal LLMs on the Level of Fouling Scale", "abstract": "Marine biofouling on vessel hulls poses major ecological, economic, and biosecurity risks. Traditional survey methods rely on diver inspections, which are hazardous and limited in scalability. This work investigates automated classification of biofouling severity on the Level of Fouling (LoF) scale using both custom computer vision models and large multimodal language models (LLMs). Convolutional neural networks, transformer-based segmentation, and zero-shot LLMs were evaluated on an expert-labelled dataset from the New Zealand Ministry for Primary Industries. Computer vision models showed high accuracy at extreme LoF categories but struggled with intermediate levels due to dataset imbalance and image framing. LLMs, guided by structured prompts and retrieval, achieved competitive performance without training and provided interpretable outputs. The results demonstrate complementary strengths across approaches and suggest that hybrid methods integrating segmentation coverage with LLM reasoning offer a promising pathway toward scalable and interpretable biofouling assessment.", "published": "2026-01-28T02:46:21Z", "authors": ["Brayden Hamilton", "Tim Cashmore", "Peter Driscoll", "Trevor Gee", "Henry Williams"], "categories": ["cs.CV"], "doi": "", "_query": "property valuation computer vision", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2603.28149v1", "title": "BlankSkip: Early-exit Object Detection onboard Nano-drones", "abstract": "Deploying tiny computer vision Deep Neural Networks (DNNs) on-board nano-sized drones is key for achieving autonomy, but is complicated by the extremely tight constraints of their computational platforms (approximately 10 MiB memory, 1 W power budget). Early-exit adaptive DNNs that dial down the computational effort for \"easy-to-process\" input frames represent a promising way to reduce the average inference latency. However, while this approach is extensively studied for classification, its application to dense tasks like object detection (OD) is not straightforward. In this paper, we propose BlankSkip, an adaptive network for on-device OD that leverages a simple auxiliary classification task for early exit, i.e., identifying frames with no objects of interest. With experiments using a real-world nano-drone platform, the Bitcraze Crazyflie 2.1, we achieve up to 24% average throughput improvement with a limited 0.015 mean Average Precision (mAP) drop compared to a static MobileNet-SSD detector, on a state-of-the-art nano-drones OD dataset.", "published": "2026-03-30T08:14:46Z", "authors": ["Carlo Marra", "Beatrice Alessandra Motetti", "Alessio Burrello", "Enrico Macii", "Massimo Poncino", "Daniele Jahier Pagliari"], "categories": ["cs.CV"], "doi": "", "_query": "property valuation computer vision", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2005.02460v1", "title": "A new design of a flying robot, with advanced computer vision techniques to perform self-maintenance of smart grids", "abstract": "In this paper, we present a full design of a flying robot to investigate the state of power grid components and to perform the appropriate maintenance procedures according to each fail or defect that could be recognized. To realize this purpose; different types of sensors including thermal and aerial vision-based systems are employed in this design. The main features and technical specifications of this robot are presented and discussed here in detail. Some essential and advanced computer vision techniques are exploited in this work to take some readings and measurements from the robot's surroundings. From each given image, many sub-images containing different electrical components are extracted using a new region proposal approach that relies on Discrete Wavelet Transform, to be classified later by utilizing a Convolutional Neural Network.", "published": "2020-05-05T20:06:32Z", "authors": ["Abdullatif Baba"], "categories": ["cs.RO", "cs.CV", "eess.IV"], "doi": "10.1016/j.jksuci.2020.07.009", "_query": "property valuation computer vision", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2610.00666v1", "title": "VisionQ: VLM-as-a-Judge Taxonomy, Dataset and Benchmark for Qualitative Analysis in Computer Vision", "abstract": "Qualitative comparison figures are central evidence in computer vision papers, and vision-language models (VLMs) are increasingly used to judge them. Yet existing benchmarks score only scalar quality or overall preference, so a judge can be rewarded for picking the preferred image for the wrong visual reason. We introduce VisionQ, the first benchmark built from peer-reviewed CV comparison figures that grounds every judgment in a named visual criterion: each question states the criterion, and a judge is credited only when it selects the output the authors identify as best on that criterion. We call this task criterion-conditioned visual discrimination. VisionQ comprises (1) a corpus of 1,409 CVPR and ICCV papers with 1,800+ validated comparison figures and 3,911 hand-annotated data points linking method crops to author-stated visual claims; (2) a six-axis, 51-leaf taxonomy of the visual criteria behind qualitative judgment; (3) a criterion-conditioned evaluation protocol that hides method names, captions, and paper identity and reports accuracy per criterion; and (4) VisionQ-Judge, a DPO-tuned Gemma-4-E4B judge trained on symmetric evidence pairs, which reduces last-option predictions by 7.0pp and improves accuracy by 2.5pp on a held-out test set. Evaluating 20 open- and closed-source VLM judges, we find that the strongest reach only 63.1% accuracy (chance 32.2%) and that reliability varies sharply across criteria. Code: https://github.com/ReML-AI/visionq. Data: https://huggingface.co/datasets/visionq-anon-2026/VisionQ-1k.", "published": "2026-09-30T20:03:52Z", "authors": ["Vu Dinh Xuan", "Duc-Hai Nguyen", "Minh-Dung Dao", "Vu Quynh Giao", "Quang Hong Nguyen", "Binh-Son Hua", "Barry O'Sullivan", "David Murphy", "Hoang D. Nguyen"], "categories": ["cs.CV", "cs.AI"], "doi": "", "_query": "property valuation computer vision", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2410.16471v3", "title": "Valuation Ideal Factorization Domains", "abstract": "An integral domain $D$ is a {\\em valuation ideal factorization domain} (VIFD) if each nonzero principal ideal of $D$ can be written as a finite product of valuation ideals. Clearly, $π$-domains are VIFDs. We study the ring-theoretic properties of VIFDs and the $*$-operation analogs of VIFDs. Among them, we show that if $D$ is treed (resp., $*$-treed), then $D$ is a VIFD (resp., $*$-VIFD) if and only if $D$ is an ${\\rm h}$-local Prüfer domain (resp., a $*$-${\\rm h}$-local P$*$MD) if and only if every nonzero prime ideal of $D$ contains an invertible (resp., a $*$-invertible) valuation ideal. We also study integral domains $D$ such that for each nonzero nonunit $a\\in D$, there is a positive integer $n$ such that $a^n$ can be written as a finite product of valuation elements.", "published": "2024-10-21T19:58:55Z", "authors": ["Gyu Whan Chang", "Andreas Reinhart"], "categories": ["math.AC"], "doi": "", "_query": "property valuation computer vision", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2404.09502v1", "title": "SparseOcc: Rethinking Sparse Latent Representation for Vision-Based Semantic Occupancy Prediction", "abstract": "Vision-based perception for autonomous driving requires an explicit modeling of a 3D space, where 2D latent representations are mapped and subsequent 3D operators are applied. However, operating on dense latent spaces introduces a cubic time and space complexity, which limits scalability in terms of perception range or spatial resolution. Existing approaches compress the dense representation using projections like Bird's Eye View (BEV) or Tri-Perspective View (TPV). Although efficient, these projections result in information loss, especially for tasks like semantic occupancy prediction. To address this, we propose SparseOcc, an efficient occupancy network inspired by sparse point cloud processing. It utilizes a lossless sparse latent representation with three key innovations. Firstly, a 3D sparse diffuser performs latent completion using spatially decomposed 3D sparse convolutional kernels. Secondly, a feature pyramid and sparse interpolation enhance scales with information from others. Finally, the transformer head is redesigned as a sparse variant. SparseOcc achieves a remarkable 74.9% reduction on FLOPs over the dense baseline. Interestingly, it also improves accuracy, from 12.8% to 14.1% mIOU, which in part can be attributed to the sparse representation's ability to avoid hallucinations on empty voxels.", "published": "2024-04-15T06:45:06Z", "authors": ["Pin Tang", "Zhongdao Wang", "Guoqing Wang", "Jilai Zheng", "Xiangxuan Ren", "Bailan Feng", "Chao Ma"], "categories": ["cs.CV"], "doi": "", "_query": "property valuation computer vision", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2108.08189v1", "title": "FOX-NAS: Fast, On-device and Explainable Neural Architecture Search", "abstract": "Neural architecture search can discover neural networks with good performance, and One-Shot approaches are prevalent. One-Shot approaches typically require a supernet with weight sharing and predictors that predict the performance of architecture. However, the previous methods take much time to generate performance predictors thus are inefficient. To this end, we propose FOX-NAS that consists of fast and explainable predictors based on simulated annealing and multivariate regression. Our method is quantization-friendly and can be efficiently deployed to the edge. The experiments on different hardware show that FOX-NAS models outperform some other popular neural network architectures. For example, FOX-NAS matches MobileNetV2 and EfficientNet-Lite0 accuracy with 240% and 40% less latency on the edge CPU. FOX-NAS is the 3rd place winner of the 2020 Low-Power Computer Vision Challenge (LPCVC), DSP classification track. See all evaluation results at https://lpcv.ai/competitions/2020. Search code and pre-trained models are released at https://github.com/great8nctu/FOX-NAS.", "published": "2021-08-14T16:23:13Z", "authors": ["Chia-Hsiang Liu", "Yu-Shin Han", "Yuan-Yao Sung", "Yi Lee", "Hung-Yueh Chiang", "Kai-Chiang Wu"], "categories": ["cs.LG", "cs.CV"], "doi": "", "_query": "property valuation computer vision", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/1409.0814v1", "title": "CoMOGrad and PHOG: From Computer Vision to Fast and Accurate Protein Tertiary Structure Retrieval", "abstract": "Due to the advancements in technology number of entries in the structural database of proteins are increasing day by day. Methods for retrieving protein tertiary structures from this large database is the key to comparative analysis of structures which plays an important role to understand proteins and their function. In this paper, we present fast and accurate methods for the retrieval of proteins from a large database with tertiary structures similar to a query protein. Our proposed methods borrow ideas from the field of computer vision. The speed and accuracy of our methods comes from the two newly introduced features, the co-occurrence matrix of the oriented gradient and pyramid histogram of oriented gradient and from the use of Euclidean distance as the distance measure. Experimental results clearly indicate the superiority of our approach in both running time and accuracy. Our method is readily available for use from this website: http://research.buet.ac.bd:8080/Comograd/.", "published": "2014-09-02T18:26:50Z", "authors": ["Rezaul Karim", "Mohd. Momin Al Aziz", "Swakkhar Shatabda", "M. Sohel Rahman", "Md. Abul Kashem Mia", "Farhana Zaman", "Salman Rakin"], "categories": ["cs.CV", "cs.CE", "cs.IR"], "doi": "", "_query": "property valuation computer vision", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2204.06972v2", "title": "The multi-modal universe of fast-fashion: the Visuelle 2.0 benchmark", "abstract": "We present Visuelle 2.0, the first dataset useful for facing diverse prediction problems that a fast-fashion company has to manage routinely. Furthermore, we demonstrate how the use of computer vision is substantial in this scenario. Visuelle 2.0 contains data for 6 seasons / 5355 clothing products of Nuna Lie, a famous Italian company with hundreds of shops located in different areas within the country. In particular, we focus on a specific prediction problem, namely short-observation new product sale forecasting (SO-fore). SO-fore assumes that the season has started and a set of new products is on the shelves of the different stores. The goal is to forecast the sales for a particular horizon, given a short, available past (few weeks), since no earlier statistics are available. To be successful, SO-fore approaches should capture this short past and exploit other modalities or exogenous data. To these aims, Visuelle 2.0 is equipped with disaggregated data at the item-shop level and multi-modal information for each clothing item, allowing computer vision approaches to come into play. The main message that we deliver is that the use of image data with deep networks boosts performances obtained when using the time series in long-term forecasting scenarios, ameliorating the WAPE and MAE by up to 5.48% and 7% respectively compared to competitive baseline methods. The dataset is available at https://humaticslab.github.io/forecasting/visuelle", "published": "2022-04-14T13:53:46Z", "authors": ["Geri Skenderi", "Christian Joppi", "Matteo Denitto", "Berniero Scarpa", "Marco Cristani"], "categories": ["cs.CV", "cs.LG"], "doi": "", "_query": "property valuation computer vision", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2010.01131v1", "title": "Embedded Systems and Computer Vision Techniques utilized in Spray Painting Robots: A Review", "abstract": "The advent of the era of machines has limited human interaction and this has increased their presence in the last decade. The requirement to increase the effectiveness, durability and reliability in the robots has also risen quite drastically too. Present paper covers the various embedded system and computer vision methodologies, techniques and innovations used in the field of spray painting robots. There have been many advancements in the sphere of painting robots utilized for high rise buildings, wall painting, road marking paintings, etc. Review focuses on image processing, computational and computer vision techniques that can be applied in the product to increase efficiency of the performance drastically. Image analysis, filtering, enhancement, object detection, edge detection methods, path and localization methods and fine tuning of parameters are being discussed in depth to use while developing such products. Dynamic system design is being deliberated by using which results in reduction of human interaction, environment sustainability and better quality of work in detail. Embedded systems involving the micro-controllers, processors, communicating devices, sensors and actuators, soft-ware to use them; is being explained for end-to-end development and enhancement of accuracy and precision in Spray Painting Robots.", "published": "2020-10-02T17:59:03Z", "authors": ["Soham Shah", "Siddhi Vinayak Pandey", "Archit Sorathiya", "Raj Sheth", "Alok Kumar Singh", "Jignesh Thaker"], "categories": ["cs.CV"], "doi": "", "_query": "property valuation computer vision", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/1501.04506v1", "title": "Defining coarsenings of valuations", "abstract": "We study the question which henselian fields admit definable henselian valuations (with or without parameters). We show that every field which admits a henselian valuation with non-divisible value group admits a parameter-definable (non-trivial) henselian valuation. In equicharacteristic $0$, we give a complete characterization of henselian fields admitting a parameter-definable (non-trivial) henselian valuation. We also obtain partial characterization results of fields admitting 0-definable (non-trivial) henselian valuations. We then draw some Galois-theoretic conclusions from our results.", "published": "2015-01-19T14:40:30Z", "authors": ["Franziska Jahnke", "Jochen Koenigsmann"], "categories": ["math.LO"], "doi": "", "_query": "property valuation computer vision", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2203.05724v2", "title": "Information-Theoretic Odometry Learning", "abstract": "In this paper, we propose a unified information theoretic framework for learning-motivated methods aimed at odometry estimation, a crucial component of many robotics and vision tasks such as navigation and virtual reality where relative camera poses are required in real time. We formulate this problem as optimizing a variational information bottleneck objective function, which eliminates pose-irrelevant information from the latent representation. The proposed framework provides an elegant tool for performance evaluation and understanding in information-theoretic language. Specifically, we bound the generalization errors of the deep information bottleneck framework and the predictability of the latent representation. These provide not only a performance guarantee but also practical guidance for model design, sample collection, and sensor selection. Furthermore, the stochastic latent representation provides a natural uncertainty measure without the needs for extra structures or computations. Experiments on two well-known odometry datasets demonstrate the effectiveness of our method.", "published": "2022-03-11T02:37:35Z", "authors": ["Sen Zhang", "Jing Zhang", "Dacheng Tao"], "categories": ["cs.CV"], "doi": "10.1007/s11263-022-01659-9", "_query": "property valuation computer vision", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2104.08609v1", "title": "On truncations of valuations", "abstract": "In this paper we study the truncation $ν_q$ of a valuation $ν$ on a polynomial $q$. It is known that when $q$ is a key polynomial, then $ν_q$ is a valuation. It is also known that the converse does not hold. We show that when $q$ is a key polynomial, then $ν_q$ is the restriction of the truncation given by an optimizing root of $q$. We also discuss which conditions assure that $ν_q=ν$. Finally, we assume that $ν_q$ is a valuation and present some conditions, given in terms of the graded algebra, to assure that $q$ is a key polynomial.", "published": "2021-04-17T18:23:04Z", "authors": ["Josnei Antonio Novacoski", "Caio Henrique Silva de Souza"], "categories": ["math.AC"], "doi": "", "_query": "property valuation computer vision", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2107.02894v1", "title": "Adversarial Machine Learning for Cybersecurity and Computer Vision: Current Developments and Challenges", "abstract": "We provide a comprehensive overview of adversarial machine learning focusing on two application domains, i.e., cybersecurity and computer vision. Research in adversarial machine learning addresses a significant threat to the wide application of machine learning techniques -- they are vulnerable to carefully crafted attacks from malicious adversaries. For example, deep neural networks fail to correctly classify adversarial images, which are generated by adding imperceptible perturbations to clean images.We first discuss three main categories of attacks against machine learning techniques -- poisoning attacks, evasion attacks, and privacy attacks. Then the corresponding defense approaches are introduced along with the weakness and limitations of the existing defense approaches. We notice adversarial samples in cybersecurity and computer vision are fundamentally different. While adversarial samples in cybersecurity often have different properties/distributions compared with training data, adversarial images in computer vision are created with minor input perturbations. This further complicates the development of robust learning techniques, because a robust learning technique must withstand different types of attacks.", "published": "2021-06-30T03:05:58Z", "authors": ["Bowei Xi"], "categories": ["cs.CR", "cs.LG"], "doi": "", "_query": "property valuation computer vision", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2411.18674v2", "title": "Active Data Curation Effectively Distills Large-Scale Multimodal Models", "abstract": "Knowledge distillation (KD) is the de facto standard for compressing large-scale models into smaller ones. Prior works have explored ever more complex KD strategies involving different objective functions, teacher-ensembles, and weight inheritance. In this work we explore an alternative, yet simple approach -- active data curation as effective distillation for contrastive multimodal pretraining. Our simple online batch selection method, ACID, outperforms strong KD baselines across various model-, data- and compute-configurations. Further, we find such an active data curation strategy to in fact be complementary to standard KD, and can be effectively combined to train highly performant inference-efficient models. Our simple and scalable pretraining framework, ACED, achieves state-of-the-art results across 27 zero-shot classification and retrieval tasks with upto 11% less inference FLOPs. We further demonstrate that our ACED models yield strong vision-encoders for training generative multimodal models in the LiT-Decoder setting, outperforming larger vision encoders for image-captioning and visual question-answering tasks.", "published": "2024-11-27T18:50:15Z", "authors": ["Vishaal Udandarao", "Nikhil Parthasarathy", "Muhammad Ferjad Naeem", "Talfan Evans", "Samuel Albanie", "Federico Tombari", "Yongqin Xian", "Alessio Tonioni", "Olivier J. Hénaff"], "categories": ["cs.CV", "cs.LG"], "doi": "", "_query": "property valuation computer vision", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2310.01998v2", "title": "A Formalization of Complete Discrete Valuation Rings and Local Fields", "abstract": "Local fields, and fields complete with respect to a discrete valuation, are essential objects in commutative algebra, with applications to number theory and algebraic geometry. We formalize in Lean the basic theory of discretely valued fields. In particular, we prove that the unit ball with respect to a discrete valuation on a field is a discrete valuation ring and, conversely, that the adic valuation on the field of fractions of a discrete valuation ring is discrete. We define finite extensions of valuations and of discrete valuation rings, and prove some global-to-local results. Building on this general theory, we formalize the abstract definition and some fundamental properties of local fields. As an application, we show that finite extensions of the field $\\mathbb{Q}_p$ of $p$-adic numbers and of the field $\\mathbb{F}_p(\\!(X)\\!)$ of Laurent series over $\\mathbb{F}_p$ are local fields.", "published": "2023-10-03T12:15:29Z", "authors": ["María Inés de Frutos-Fernández", "Filippo Alberto Edoardo Nuccio Mortarino Majno Di Capriglio"], "categories": ["cs.LO", "math.NT"], "doi": "10.1145/3636501.3636942", "_query": "property valuation computer vision", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2603.03857v1", "title": "DeepScan: A Training-Free Framework for Visually Grounded Reasoning in Large Vision-Language Models", "abstract": "Humans can robustly localize visual evidence and provide grounded answers even in noisy environments by identifying critical cues and then relating them to the full context in a bottom-up manner. Inspired by this, we propose DeepScan, a training-free framework that combines Hierarchical Scanning, Refocusing, and Evidence-Enhanced Reasoning for visually grounded reasoning in Large Vision-Language Models (LVLMs). Unlike existing methods that pursue one-shot localization of complete evidence, Hierarchical Scanning performs local cue exploration and multi-scale evidence extraction to recover evidence in a bottom-up manner, effectively mitigating the impacts of distractive context. Refocusing then optimizes the localized evidence view through collaboration of LVLMs and visual experts. Finally, Evidence-Enhanced Reasoning aggregates multi-granular views via a hybrid evidence memory and yields accurate and interpretable answers. Experimental results demonstrate that DeepScan significantly boosts LVLMs in diverse visual tasks, especially in fine-grained visual understanding. It achieves 90.6% overall accuracy on V* when integrated with Qwen2.5-VL-7B. Moreover, DeepScan provides consistent improvements for LVLMs across various architectures and model scales without additional adaptation cost.", "published": "2026-03-04T09:06:47Z", "authors": ["Yangfu Li", "Hongjian Zhan", "Jiawei Chen", "Yuning Gong", "Qi Liu", "Yue Lu"], "categories": ["cs.CV"], "doi": "", "_query": "property valuation computer vision", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/1907.04433v2", "title": "GluonCV and GluonNLP: Deep Learning in Computer Vision and Natural Language Processing", "abstract": "We present GluonCV and GluonNLP, the deep learning toolkits for computer vision and natural language processing based on Apache MXNet (incubating). These toolkits provide state-of-the-art pre-trained models, training scripts, and training logs, to facilitate rapid prototyping and promote reproducible research. We also provide modular APIs with flexible building blocks to enable efficient customization. Leveraging the MXNet ecosystem, the deep learning models in GluonCV and GluonNLP can be deployed onto a variety of platforms with different programming languages. The Apache 2.0 license has been adopted by GluonCV and GluonNLP to allow for software distribution, modification, and usage.", "published": "2019-07-09T21:59:44Z", "authors": ["Jian Guo", "He He", "Tong He", "Leonard Lausen", "Mu Li", "Haibin Lin", "Xingjian Shi", "Chenguang Wang", "Junyuan Xie", "Sheng Zha", "Aston Zhang", "Hang Zhang", "Zhi Zhang", "Zhongyue Zhang", "Shuai Zheng", "Yi Zhu"], "categories": ["cs.LG", "cs.CL", "cs.CV", "stat.ML"], "doi": "", "_query": "property valuation computer vision", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/cs/0110043v1", "title": "An Overview of Computer security", "abstract": "As more business activities are being automated and an increasing number of computers are being used to store vital and sensitive information the need for secure computer systems becomes more apparent. These systems can be achieved only through systematic design; they cannot be achieved through haphazard seat-of-the-pants methods.This paper introduces some known threats to the computer security, categorizes the threats, and analyses protection mechanisms and techniques for countering the threats. The threats have been classified more so as definitions and then followed by the classifications of these threats. Also mentioned are the protection mechanisms.", "published": "2001-10-21T13:57:20Z", "authors": ["Shireesh Reddy Annam"], "categories": ["cs.CY", "cs.NI"], "doi": "", "_query": "property valuation computer vision", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/1510.00681v1", "title": "Valuation on filtered module and relations", "abstract": "In this paper we show if R is a filtered ring and M a filtered R module then we can define a valuation on a module for M. Then we show that we can find an skeleton of valuation on M, and we prove some properties such that derived form it for a filtered module.", "published": "2015-10-02T18:52:13Z", "authors": ["M. H Anjom SHoa", "M. H Hosseini"], "categories": ["math.RA"], "doi": "", "_query": "property valuation computer vision", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/1908.00309v1", "title": "A Framework for Depth Estimation and Relative Localization of Ground Robots using Computer Vision", "abstract": "The 3D depth estimation and relative pose estimation problem within a decentralized architecture is a challenging problem that arises in missions that require coordination among multiple vision-controlled robots. The depth estimation problem aims at recovering the 3D information of the environment. The relative localization problem consists of estimating the relative pose between two robots, by sensing each other's pose or sharing information about the perceived environment. Most solutions for these problems use a set of discrete data without taking into account the chronological order of the events. This paper builds on recent results on continuous estimation to propose a framework that estimates the depth and relative pose between two non-holonomic vehicles. The basic idea consists in estimating the depth of the points by explicitly considering the dynamics of the camera mounted on a ground robot, and feeding the estimates of 3D points observed by both cameras in a filter that computes the relative pose between the robots. We evaluate the convergence for a set of simulated scenarios and show experimental results validating the proposed framework.", "published": "2019-08-01T10:28:23Z", "authors": ["Romulo T. Rodrigues", "Pedro Miraldo", "Dimos V. Dimarogonas", "A. Pedro Aguiar"], "categories": ["cs.RO", "cs.CV"], "doi": "", "_query": "property valuation computer vision", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2110.12408v1", "title": "Quantum Computer Music: Foundations and Initial Experiments", "abstract": "Quantum computing is a nascent technology, which is advancing rapidly. There is a long history of research into using computers for music. Nowadays computers are absolutely essential for the music economy. Thus, it is very likely that quantum computers will impact the music industry in time to come. This chapter lays the foundations of the new field of 'Quantum Computer Music'. It begins with an introduction to algorithmic computer music and methods to program computers to generate music, such as Markov chains and random walks. Then, it presents quantum computing versions of those methods. The discussions are supported by detailed explanations of quantum computing concepts and walk-through examples. A bespoke generative music algorithm is presented, the Basak-Miranda algorithm, which leverages a property of quantum mechanics known as constructive and destructive interference to operate a musical Markov chain. An Appendix introducing the fundamentals of quantum computing deemed necessary to understand the chapter and a link to access Jupyter Notebooks with examples are also provided.", "published": "2021-10-24T10:56:07Z", "authors": ["Eduardo R. Miranda", "Suchitra T. Basak"], "categories": ["cs.ET", "cs.SD", "eess.AS", "quant-ph"], "doi": "", "_query": "property valuation computer vision", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/cond-mat/0004080v2", "title": "SUE: A Special Purpose Computer for Spin Glass Models", "abstract": "The use of last generation Programmable Electronic Components makes possible the construction of very powerful and competitive special purpose computers. We have designed, constructed and tested a three-dimensional Spin Glass model dedicated machine, which consists of 12 identical boards. Each single board can simulate 8 different systems, updating all the systems at every clock cycle. The update speed of the whole machine is 217ps/spin with 48 MHz clock frequency. A device devoted to fast random number generation has been developed and included in every board. The on-board reprogrammability permits us to change easily the lattice size, or even the update algorithm or the action. We present here a detailed description of the machine and the first runs using the Heat Bath algorithm.", "published": "2000-04-05T19:13:08Z", "authors": ["A. Cruz", "J. Pech", "A. Tarancon", "P. Tellez", "C. L. Ullod", "C. Ungil"], "categories": ["cond-mat.dis-nn", "physics.comp-ph"], "doi": "10.1016/S0010-4655(00)00170-3", "_query": "property valuation computer vision", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/1912.03531v2", "title": "How to Distribute Computation in Networks", "abstract": "In network function computation is as a means to reduce the required communication flow in terms of number of bits transmitted per source symbol. However, the rate region for the function computation problem in general topologies is an open problem, and has only been considered under certain restrictive assumptions (e.g. tree networks, linear functions, etc.). In this paper, we propose a new perspective for distributing computation, and formulate a flow-based delay cost minimization problem that jointly captures the costs of communications and computation. We introduce the notion of entropic surjectivity as a measure to determine how sparse the function is and to understand the limits of computation. Exploiting Little's law for stationary systems, we provide a connection between this new notion and the computation processing factor that reflects the proportion of flow that requires communications. This connection gives us an understanding of how much a node (in isolation) should compute to communicate the desired function within the network without putting any assumptions on the topology. Our analysis characterizes the functions only via their entropic surjectivity, and provides insight into how to distribute computation. We numerically test our technique for search, MapReduce, and classification tasks, and infer for each task how sensitive the processing factor to the entropic surjectivity is.", "published": "2019-12-07T17:01:00Z", "authors": ["Derya Malak", "Alejandro Cohen", "Muriel Medard"], "categories": ["cs.IT", "cs.NI"], "doi": "", "_query": "property valuation computer vision", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2210.03529v2", "title": "Mesh-Tension Driven Expression-Based Wrinkles for Synthetic Faces", "abstract": "Recent advances in synthesizing realistic faces have shown that synthetic training data can replace real data for various face-related computer vision tasks. A question arises: how important is realism? Is the pursuit of photorealism excessive? In this work, we show otherwise. We boost the realism of our synthetic faces by introducing dynamic skin wrinkles in response to facial expressions and observe significant performance improvements in downstream computer vision tasks. Previous approaches for producing such wrinkles either required prohibitive artist effort to scale across identities and expressions or were not capable of reconstructing high-frequency skin details with sufficient fidelity. Our key contribution is an approach that produces realistic wrinkles across a large and diverse population of digital humans. Concretely, we formalize the concept of mesh-tension and use it to aggregate possible wrinkles from high-quality expression scans into albedo and displacement texture maps. At synthesis, we use these maps to produce wrinkles even for expressions not represented in the source scans. Additionally, to provide a more nuanced indicator of model performance under deformations resulting from compressed expressions, we introduce the 300W-winks evaluation subset and the Pexels dataset of closed eyes and winks.", "published": "2022-10-05T18:00:13Z", "authors": ["Chirag Raman", "Charlie Hewitt", "Erroll Wood", "Tadas Baltrusaitis"], "categories": ["cs.CV", "cs.GR"], "doi": "", "_query": "property valuation computer vision", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/1510.05963v1", "title": "Semantic, Cognitive, and Perceptual Computing: Advances toward Computing for Human Experience", "abstract": "The World Wide Web continues to evolve and serve as the infrastructure for carrying massive amounts of multimodal and multisensory observations. These observations capture various situations pertinent to people's needs and interests along with all their idiosyncrasies. To support human-centered computing that empower people in making better and timely decisions, we look towards computation that is inspired by human perception and cognition. Toward this goal, we discuss computing paradigms of semantic computing, cognitive computing, and an emerging aspect of computing, which we call perceptual computing. In our view, these offer a continuum to make the most out of vast, growing, and diverse data pertinent to human needs and interests. We propose details of perceptual computing characterized by interpretation and exploration operations comparable to the interleaving of bottom and top brain processing. This article consists of two parts. First we describe semantic computing, cognitive computing, and perceptual computing to lay out distinctions while acknowledging their complementary capabilities. We then provide a conceptual overview of the newest of these three paradigms--perceptual computing. For further insights, we focus on an application scenario of asthma management converting massive, heterogeneous and multimodal (big) data into actionable information or smart data.", "published": "2015-10-20T16:57:49Z", "authors": ["Amit Sheth", "Pramod Anantharam", "Cory Henson"], "categories": ["cs.AI"], "doi": "", "_query": "property valuation computer vision", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2207.01323v1", "title": "Computer vision application for improved product traceability in the granite manufacturing industry", "abstract": "The traceability of granite blocks consists in identifying each block with a finite number of color bands which represent a numerical code. This code has to be read several times throughout the manufacturing process, but its accuracy is subject to human errors, leading to cause faults in the traceability system. A computer vision system is presented to address this problem through color detection and the decryption of the associated code. The system developed makes use of color space transformations, and several thresholds for the isolation of the colors. Computer vision methods are implemented, along with contour detection procedures for color identification. Lastly, the analysis of geometrical features is used to decrypt the color code captured. The proposed algorithm is trained on a set of 109 pictures taken in different environmental conditions and validated on a set of 21 images. The outcome shows promising results with an accuracy rate of 75.00% in the validation process. Therefore, the application presented can help employees reduce the number of mistakes on product tracking.", "published": "2022-07-04T11:04:03Z", "authors": ["Xurxo Rigueira", "Javier Martinez", "Maria Araujo", "Antonio Recaman"], "categories": ["cs.CV"], "doi": "", "_query": "property valuation computer vision", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2207.04724v1", "title": "Interpretability by design using computer vision for behavioral sensing in child and adolescent psychiatry", "abstract": "Observation is an essential tool for understanding and studying human behavior and mental states. However, coding human behavior is a time-consuming, expensive task, in which reliability can be difficult to achieve and bias is a risk. Machine learning (ML) methods offer ways to improve reliability, decrease cost, and scale up behavioral coding for application in clinical and research settings. Here, we use computer vision to derive behavioral codes or concepts of a gold standard behavioral rating system, offering familiar interpretation for mental health professionals. Features were extracted from videos of clinical diagnostic interviews of children and adolescents with and without obsessive-compulsive disorder. Our computationally-derived ratings were comparable to human expert ratings for negative emotions, activity-level/arousal and anxiety. For the attention and positive affect concepts, our ML ratings performed reasonably. However, results for gaze and vocalization indicate a need for improved data quality or additional data modalities.", "published": "2022-07-11T09:07:08Z", "authors": ["Flavia D. Frumosu", "Nicole N. Lønfeldt", "A. -R. Cecilie Mora-Jensen", "Sneha Das", "Nicklas Leander Lund", "A. Katrine Pagsberg", "Line K. H. Clemmensen"], "categories": ["cs.CV", "cs.LG"], "doi": "", "_query": "property valuation computer vision", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2401.08581v1", "title": "Temporal Embeddings: Scalable Self-Supervised Temporal Representation Learning from Spatiotemporal Data for Multimodal Computer Vision", "abstract": "There exists a correlation between geospatial activity temporal patterns and type of land use. A novel self-supervised approach is proposed to stratify landscape based on mobility activity time series. First, the time series signal is transformed to the frequency domain and then compressed into task-agnostic temporal embeddings by a contractive autoencoder, which preserves cyclic temporal patterns observed in time series. The pixel-wise embeddings are converted to image-like channels that can be used for task-based, multimodal modeling of downstream geospatial tasks using deep semantic segmentation. Experiments show that temporal embeddings are semantically meaningful representations of time series data and are effective across different tasks such as classifying residential area and commercial areas. Temporal embeddings transform sequential, spatiotemporal motion trajectory data into semantically meaningful image-like tensor representations that can be combined (multimodal fusion) with other data modalities that are or can be transformed into image-like tensor representations (for e.g., RBG imagery, graph embeddings of road networks, passively collected imagery like SAR, etc.) to facilitate multimodal learning in geospatial computer vision. Multimodal computer vision is critical for training machine learning models for geospatial feature detection to keep a geospatial mapping service up-to-date in real-time and can significantly improve user experience and above all, user safety.", "published": "2023-10-16T02:53:29Z", "authors": ["Yi Cao", "Swetava Ganguli", "Vipul Pandey"], "categories": ["cs.CV", "cs.AI", "cs.LG"], "doi": "", "_query": "property valuation computer vision", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2309.13745v3", "title": "Overview of Computer Vision Techniques in Robotized Wire Harness Assembly: Current State and Future Opportunities", "abstract": "Wire harnesses are essential hardware for electronic systems in modern automotive vehicles. With a shift in the automotive industry towards electrification and autonomous driving, more and more automotive electronics are responsible for energy transmission and safety-critical functions such as maneuvering, driver assistance, and safety system. This paradigm shift places more demand on automotive wire harnesses from the safety perspective and stresses the greater importance of high-quality wire harness assembly in vehicles. However, most of the current operations of wire harness assembly are still performed manually by skilled workers, and some of the manual processes are problematic in terms of quality control and ergonomics. There is also a persistent demand in the industry to increase competitiveness and gain market share. Hence, assuring assembly quality while improving ergonomics and optimizing labor costs is desired. Robotized assembly, accomplished by robots or in human-robot collaboration, is a key enabler for fulfilling the increasingly demanding quality and safety as it enables more replicable, transparent, and comprehensible processes than completely manual operations. However, robotized assembly of wire harnesses is challenging in practical environments due to the flexibility of the deformable objects, though many preliminary automation solutions have been proposed under simplified industrial configurations. Previous research efforts have proposed the use of computer vision technology to facilitate robotized automation of wire harness assembly, enabling the robots to better perceive and manipulate the flexible wire harness. This article presents an overview of computer vision technology proposed for robotized wire harness assembly and derives research gaps that require further study to facilitate a more practical robotized assembly of wire harnesses.", "published": "2023-09-24T20:28:19Z", "authors": ["Hao Wang", "Omkar Salunkhe", "Walter Quadrini", "Dan Lämkull", "Fredrik Ore", "Björn Johansson", "Johan Stahre"], "categories": ["cs.RO", "cs.AI", "cs.CV"], "doi": "10.1016/j.procir.2023.09.127", "_query": "property valuation computer vision", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2505.09842v1", "title": "Valuations on Superrings", "abstract": "A valuation theory for superrings is developed, extending classical constructions from commutative algebra to the $\\mathbb Z_2$-graded and supercommutative setting. We define valuations on superrings, investigate their fundamental properties, and explore the construction of Zariski-Riemann superspaces.", "published": "2025-05-14T22:54:49Z", "authors": ["Pedro Rizzo", "Joel Torres del Valle", "Alexander Torres-Gomez"], "categories": ["math.RA", "math.AG"], "doi": "", "_query": "property valuation computer vision", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/math/0509542v1", "title": "Valuation Extensions of Filtered and Graded Algebras", "abstract": "In this note we relate the valuations of the algebras appearing in the non-commutative geometry of quantized algebras to properties of sub-lattices in some vector spaces. We consider the case of algebras with $PBW$-bases and prove that under some mild assumptions the valuations of the ground field extend to a non-commutative valuation. Later we introduce the notion of $F$-reductor and graded reductor and reduce the problem of finding an extending non-commutative valuation to finding a reductor in an associated graded ring having a domain for its reduction.", "published": "2005-09-23T09:33:19Z", "authors": ["C. Baetica", "F. Van Oystaeyen"], "categories": ["math.RA", "math.QA"], "doi": "", "_query": "property valuation computer vision", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2603.06228v4", "title": "Low-Latency Event-Based Object Detection with Spatially-Sparse Linear Attention", "abstract": "Event cameras provide sequential visual data with spatial sparsity and high temporal resolution, making them attractive for low-latency object detection. Existing asynchronous event-based neural networks exploit this low-latency advantage by updating predictions event by event, but still suffer from two bottlenecks: recurrent architectures are difficult to train efficiently on long sequences, and improving accuracy often increases per-event computation and latency. Linear attention is appealing because it enables parallel training and recurrent inference. However, its dense state updates make per-event computation scale with the state size, yielding a poor accuracy-efficiency trade-off for object detection, where accurate localization requires fine-grained spatial states. The key challenge is therefore to introduce sparse state activation that exploits the spatial sparsity of events while preserving efficient parallel training. We propose Spatially-Sparse Linear Attention (SSLA), which introduces a mixture-of-spaces state decomposition and a scatter-compute-gather training procedure, enabling state-level sparsity as well as training parallelism. Building on SSLA, we develop an end-to-end asynchronous linear attention model, SSLA-Det, for low-latency event-based object detection. On Gen1 and N-Caltech101, SSLA-Det achieves state-of-the-art accuracy among asynchronous methods, reaching 0.375 mAP and 0.515 mAP, respectively, while reducing per-event computation by over 20 times compared with the strongest prior asynchronous baseline, demonstrating the potential of linear attention for low-latency event-based vision.", "published": "2026-03-06T12:44:00Z", "authors": ["Haiqing Hao", "Zhipeng Sui", "Rong Zou", "Zijia Dai", "Nikola Zubić", "Davide Scaramuzza", "Wenhui Wang"], "categories": ["cs.CV"], "doi": "", "_query": "property valuation computer vision", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2403.09199v2", "title": "Task-Specific Adaptation of Segmentation Foundation Model via Prompt Learning", "abstract": "Recently, foundation models trained on massive datasets to adapt to a wide range of tasks have attracted considerable attention and are actively being explored within the computer vision community. Among these, the Segment Anything Model (SAM) stands out for its remarkable progress in generalizability and flexibility for image segmentation tasks, achieved through prompt-based object mask generation. However, despite its strength, SAM faces two key limitations when applied to instance segmentation that segments specific objects or those in unique environments (e.g., task-specific adaptation for out-of-distribution objects) not typically present in the training data: 1) the ambiguity inherent in input prompts and 2) the necessity for extensive additional training to achieve optimal segmentation. To address these challenges, we propose a task-specific adaptation (i.e., customization) of the segmentation foundation model via prompt learning tailored to SAM. Our method involves a prompt learning module (PLM), which adjusts input prompts into the embedding space to better align with peculiarities of the target task, thereby enabling more efficient training. Furthermore, we introduce a point matching module (PMM) to enhance the feature representation for finer segmentation by ensuring detailed alignment with ground truth boundaries. Experimental results on various customized segmentation scenarios demonstrate the effectiveness of the proposed method.", "published": "2024-03-14T09:13:51Z", "authors": ["Hyung-Il Kim", "Kimin Yun", "Jun-Seok Yun", "Yuseok Bae"], "categories": ["cs.CV", "cs.AI"], "doi": "", "_query": "property valuation computer vision", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/1512.08017v1", "title": "An Analytical Evaluation of Matricizing Least-Square-Errors Curve Fitting to Support High Performance Computation on Large Datasets", "abstract": "The procedure of Least Square-Errors curve fitting is extensively used in many computer applications for fitting a polynomial curve of a given degree to approximate a set of data. Although various methodologies exist to carry out curve fitting on data, most of them have shortcomings with respect to efficiency especially where huge datasets are involved. This paper proposes and analyzes a matricized approach to the Least Square-Errors curve fitting with the primary objective of parallelizing the whole algorithm so that high performance efficiency can be achieved when algorithmic execution takes place on colossal datasets.", "published": "2015-12-25T16:53:57Z", "authors": ["Poorna Banerjee Dasgupta"], "categories": ["cs.DC"], "doi": "10.14445/22312803/IJCTT-V30P120", "_query": "property valuation computer vision", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2011.02839v1", "title": "Chasing Carbon: The Elusive Environmental Footprint of Computing", "abstract": "Given recent algorithm, software, and hardware innovation, computing has enabled a plethora of new applications. As computing becomes increasingly ubiquitous, however, so does its environmental impact. This paper brings the issue to the attention of computer-systems researchers. Our analysis, built on industry-reported characterization, quantifies the environmental effects of computing in terms of carbon emissions. Broadly, carbon emissions have two sources: operational energy consumption, and hardware manufacturing and infrastructure. Although carbon emissions from the former are decreasing thanks to algorithmic, software, and hardware innovations that boost performance and power efficiency, the overall carbon footprint of computer systems continues to grow. This work quantifies the carbon output of computer systems to show that most emissions related to modern mobile and data-center equipment come from hardware manufacturing and infrastructure. We therefore outline future directions for minimizing the environmental impact of computing systems.", "published": "2020-10-28T18:15:22Z", "authors": ["Udit Gupta", "Young Geun Kim", "Sylvia Lee", "Jordan Tse", "Hsien-Hsin S. Lee", "Gu-Yeon Wei", "David Brooks", "Carole-Jean Wu"], "categories": ["cs.AR", "cs.CY"], "doi": "", "_query": "property valuation computer vision", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/1804.03928v1", "title": "Deep Learning For Computer Vision Tasks: A review", "abstract": "Deep learning has recently become one of the most popular sub-fields of machine learning owing to its distributed data representation with multiple levels of abstraction. A diverse range of deep learning algorithms are being employed to solve conventional artificial intelligence problems. This paper gives an overview of some of the most widely used deep learning algorithms applied in the field of computer vision. It first inspects the various approaches of deep learning algorithms, followed by a description of their applications in image classification, object identification, image extraction and semantic segmentation in the presence of noise. The paper concludes with the discussion of the future scope and challenges for construction and training of deep neural networks.", "published": "2018-04-11T11:13:35Z", "authors": ["Rajat Kumar Sinha", "Ruchi Pandey", "Rohan Pattnaik"], "categories": ["cs.CV"], "doi": "", "_query": "property valuation computer vision", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2212.08121v1", "title": "Backdoor Attack Detection in Computer Vision by Applying Matrix Factorization on the Weights of Deep Networks", "abstract": "The increasing importance of both deep neural networks (DNNs) and cloud services for training them means that bad actors have more incentive and opportunity to insert backdoors to alter the behavior of trained models. In this paper, we introduce a novel method for backdoor detection that extracts features from pre-trained DNN's weights using independent vector analysis (IVA) followed by a machine learning classifier. In comparison to other detection techniques, this has a number of benefits, such as not requiring any training data, being applicable across domains, operating with a wide range of network architectures, not assuming the nature of the triggers used to change network behavior, and being highly scalable. We discuss the detection pipeline, and then demonstrate the results on two computer vision datasets regarding image classification and object detection. Our method outperforms the competing algorithms in terms of efficiency and is more accurate, helping to ensure the safe application of deep learning and AI.", "published": "2022-12-15T20:20:18Z", "authors": ["Khondoker Murad Hossain", "Tim Oates"], "categories": ["cs.CV", "cs.AI"], "doi": "", "_query": "property valuation computer vision", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2005.05490v1", "title": "Monotone Boolean Functions, Feasibility/Infeasibility, LP-type problems and MaxCon", "abstract": "This paper outlines connections between Monotone Boolean Functions, LP-Type problems and the Maximum Consensus Problem. The latter refers to a particular type of robust fitting characterisation, popular in Computer Vision (MaxCon). Indeed, this is our main motivation but we believe the results of the study of these connections are more widely applicable to LP-type problems (at least 'thresholded versions', as we describe), and perhaps even more widely. We illustrate, with examples from Computer Vision, how the resulting perspectives suggest new algorithms. Indeed, we focus, in the experimental part, on how the Influence (a property of Boolean Functions that takes on a special form if the function is Monotone) can guide a search for the MaxCon solution.", "published": "2020-05-11T23:51:15Z", "authors": ["David Suter", "Ruwan Tennakoon", "Erchuan Zhang", "Tat-Jun Chin", "Alireza Bab-Hadiashar"], "categories": ["cs.LG", "cs.AI", "cs.CG", "cs.CV"], "doi": "", "_query": "property valuation computer vision", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2204.04705v1", "title": "SplitNets: Designing Neural Architectures for Efficient Distributed Computing on Head-Mounted Systems", "abstract": "We design deep neural networks (DNNs) and corresponding networks' splittings to distribute DNNs' workload to camera sensors and a centralized aggregator on head mounted devices to meet system performance targets in inference accuracy and latency under the given hardware resource constraints. To achieve an optimal balance among computation, communication, and performance, a split-aware neural architecture search framework, SplitNets, is introduced to conduct model designing, splitting, and communication reduction simultaneously. We further extend the framework to multi-view systems for learning to fuse inputs from multiple camera sensors with optimal performance and systemic efficiency. We validate SplitNets for single-view system on ImageNet as well as multi-view system on 3D classification, and show that the SplitNets framework achieves state-of-the-art (SOTA) performance and system latency compared with existing approaches.", "published": "2022-04-10T15:22:08Z", "authors": ["Xin Dong", "Barbara De Salvo", "Meng Li", "Chiao Liu", "Zhongnan Qu", "H. T. Kung", "Ziyun Li"], "categories": ["cs.LG", "cs.AI", "cs.DC"], "doi": "", "_query": "property valuation computer vision", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2503.07252v1", "title": "Semantic Communications with Computer Vision Sensing for Edge Video Transmission", "abstract": "Despite the widespread adoption of vision sensors in edge applications, such as surveillance, the transmission of video data consumes substantial spectrum resources. Semantic communication (SC) offers a solution by extracting and compressing information at the semantic level, preserving the accuracy and relevance of transmitted data while significantly reducing the volume of transmitted information. However, traditional SC methods face inefficiencies due to the repeated transmission of static frames in edge videos, exacerbated by the absence of sensing capabilities, which results in spectrum inefficiency. To address this challenge, we propose a SC with computer vision sensing (SCCVS) framework for edge video transmission. The framework first introduces a compression ratio (CR) adaptive SC (CRSC) model, capable of adjusting CR based on whether the frames are static or dynamic, effectively conserving spectrum resources. Additionally, we implement an object detection and semantic segmentation models-enabled sensing (OSMS) scheme, which intelligently senses the changes in the scene and assesses the significance of each frame through in-context analysis. Hence, The OSMS scheme provides CR prompts to the CRSC model based on real-time sensing results. Moreover, both CRSC and OSMS are designed as lightweight models, ensuring compatibility with resource-constrained sensors commonly used in practical edge applications. Experimental simulations validate the effectiveness of the proposed SCCVS framework, demonstrating its ability to enhance transmission efficiency without sacrificing critical semantic information.", "published": "2025-03-10T12:34:22Z", "authors": ["Yubo Peng", "Luping Xiang", "Kun Yang", "Kezhi Wang", "Merouane Debbah"], "categories": ["cs.CV", "eess.IV", "eess.SP"], "doi": "", "_query": "property valuation computer vision", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2311.15884v3", "title": "Elementary Quantum Recursion Schemes That Capture Quantum Polylogarithmic Time Computability of Quantum Functions", "abstract": "Quantum computing has been studied over the past four decades based on two computational models of quantum circuits and quantum Turing machines. To capture quantum polynomial-time computability, a new recursion-theoretic approach was taken lately by Yamakami [J. Symb. Logic 80, pp.~1546--1587, 2020] by way of recursion schematic definition, which constitutes six initial quantum functions and three construction schemes of composition, branching, and multi-qubit quantum recursion. By taking a similar approach, we look into quantum polylogarithmic-time computability and further explore the expressing power of elementary schemes designed for such quantum computation. In particular, we introduce an elementary form of the quantum recursion, called the fast quantum recursion, and formulate $EQS$ (elementary quantum schemes) of ``elementary'' quantum functions. This class $EQS$ captures exactly quantum polylogarithmic-time computability, which forms the complexity class BQPOLYLOGTIME. We also demonstrate the separation of BQPOLYLOGTIME from NLOGTIME and PPOLYLOGTIME. As a natural extension of $EQS$, we further consider an algorithmic procedural scheme that implements the well-known divide-and-conquer strategy. This divide-and-conquer scheme helps compute the parity function but the scheme cannot be realized within our system $EQS$.", "published": "2023-11-27T14:53:45Z", "authors": ["Tomoyuki Yamakami"], "categories": ["cs.CC", "quant-ph"], "doi": "", "_query": "property valuation computer vision", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/1504.02648v2", "title": "Time-causal and time-recursive spatio-temporal receptive fields", "abstract": "We present an improved model and theory for time-causal and time-recursive spatio-temporal receptive fields, based on a combination of Gaussian receptive fields over the spatial domain and first-order integrators or equivalently truncated exponential filters coupled in cascade over the temporal domain. Compared to previous spatio-temporal scale-space formulations in terms of non-enhancement of local extrema or scale invariance, these receptive fields are based on different scale-space axiomatics over time by ensuring non-creation of new local extrema or zero-crossings with increasing temporal scale. Specifically, extensions are presented about (i) parameterizing the intermediate temporal scale levels, (ii) analysing the resulting temporal dynamics, (iii) transferring the theory to a discrete implementation, (iv) computing scale-normalized spatio-temporal derivative expressions for spatio-temporal feature detection and (v) computational modelling of receptive fields in the lateral geniculate nucleus (LGN) and the primary visual cortex (V1) in biological vision. We show that by distributing the intermediate temporal scale levels according to a logarithmic distribution, we obtain much faster temporal response properties (shorter temporal delays) compared to a uniform distribution. Specifically, these kernels converge very rapidly to a limit kernel possessing true self-similar scale-invariant properties over temporal scales, thereby allowing for true scale invariance over variations in the temporal scale, although the underlying temporal scale-space representation is based on a discretized temporal scale parameter. We show how scale-normalized temporal derivatives can be defined for these time-causal scale-space kernels and how the composed theory can be used for computing basic types of scale-normalized spatio-temporal derivative expressions in a computationally efficient manner.", "published": "2015-04-10T12:06:27Z", "authors": ["Tony Lindeberg"], "categories": ["cs.CV", "q-bio.NC"], "doi": "10.1007/s10851-015-0613-9", "_query": "property valuation computer vision", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2212.04223v3", "title": "Vicious Classifiers: Assessing Inference-time Data Reconstruction Risk in Edge Computing", "abstract": "Privacy-preserving inference in edge computing paradigms encourages the users of machine-learning services to locally run a model on their private input and only share the models outputs for a target task with the server. We study how a vicious server can reconstruct the input data by observing only the models outputs while keeping the target accuracy very close to that of a honest server by jointly training a target model (to run at users' side) and an attack model for data reconstruction (to secretly use at servers' side). We present a new measure to assess the inference-time reconstruction risk. Evaluations on six benchmark datasets show the model's input can be approximately reconstructed from the outputs of a single inference. We propose a primary defense mechanism to distinguish vicious versus honest classifiers at inference time. By studying such a risk associated with emerging ML services our work has implications for enhancing privacy in edge computing. We discuss open challenges and directions for future studies and release our code as a benchmark for the community at https://github.com/mmalekzadeh/vicious-classifiers .", "published": "2022-12-08T12:05:50Z", "authors": ["Mohammad Malekzadeh", "Deniz Gunduz"], "categories": ["cs.LG", "cs.CR", "cs.IT"], "doi": "", "_query": "property valuation computer vision", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2410.09831v1", "title": "LoLI-Street: Benchmarking Low-Light Image Enhancement and Beyond", "abstract": "Low-light image enhancement (LLIE) is essential for numerous computer vision tasks, including object detection, tracking, segmentation, and scene understanding. Despite substantial research on improving low-quality images captured in underexposed conditions, clear vision remains critical for autonomous vehicles, which often struggle with low-light scenarios, signifying the need for continuous research. However, paired datasets for LLIE are scarce, particularly for street scenes, limiting the development of robust LLIE methods. Despite using advanced transformers and/or diffusion-based models, current LLIE methods struggle in real-world low-light conditions and lack training on street-scene datasets, limiting their effectiveness for autonomous vehicles. To bridge these gaps, we introduce a new dataset LoLI-Street (Low-Light Images of Streets) with 33k paired low-light and well-exposed images from street scenes in developed cities, covering 19k object classes for object detection. LoLI-Street dataset also features 1,000 real low-light test images for testing LLIE models under real-life conditions. Furthermore, we propose a transformer and diffusion-based LLIE model named \"TriFuse\". Leveraging the LoLI-Street dataset, we train and evaluate our TriFuse and SOTA models to benchmark on our dataset. Comparing various models, our dataset's generalization feasibility is evident in testing across different mainstream datasets by significantly enhancing images and object detection for practical applications in autonomous driving and surveillance systems. The complete code and dataset is available on https://github.com/tanvirnwu/TriFuse.", "published": "2024-10-13T13:11:56Z", "authors": ["Md Tanvir Islam", "Inzamamul Alam", "Simon S. Woo", "Saeed Anwar", "IK Hyun Lee", "Khan Muhammad"], "categories": ["cs.CV", "cs.AI", "cs.CE"], "doi": "", "_query": "property valuation computer vision", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/1510.08980v1", "title": "The Complexity of Equilibria for Risk-Modeling Valuations", "abstract": "We study the complexity of deciding the existence of mixed equilibria for minimization games where players use valuations other than expectation to evaluate their costs. We consider risk-averse players seeking to minimize the sum ${\\mathsf{V}} = {\\mathsf{E}} + {\\mathsf{R}}$ of expectation ${\\mathsf{E}}$ and a risk valuation ${\\mathsf{R}}$ of their costs; ${\\mathsf{R}}$ is non-negative and vanishes exactly when the cost incurred to a player is constant over all choices of strategies by the other players. In a ${\\mathsf{V}}$-equilibrium, no player could unilaterally reduce her cost. Say that ${\\mathsf{V}}$ has the Weak-Equilibrium-for-Expectation property if all strategies supported in a player's best-response mixed strategy incur the same conditional expectation of her cost. We introduce ${\\mathsf{E}}$-strict concavity and observe that every ${\\mathsf{E}}$-strictly concave valuation has the Weak-Equilibrium-for-Expectation property. We focus on a broad class of valuations shown to have the Weak-Equilibrium-for-Expectation property, which we exploit to prove two main complexity results, the first of their kind, for the two simplest cases of the problem: games with two strategies, or games with two players. For each case, we show that deciding the existence of a ${\\mathsf{V}}$-equilibrium is strongly ${\\mathcal{NP}}$-hard for certain choices of significant valuations (including variance and standard deviation).", "published": "2015-10-30T06:31:47Z", "authors": ["Marios Mavronicolas", "Burkhard Monien"], "categories": ["cs.GT", "cs.CC"], "doi": "", "_query": "property valuation computer vision", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2111.15592v1", "title": "MapReader: A Computer Vision Pipeline for the Semantic Exploration of Maps at Scale", "abstract": "We present MapReader, a free, open-source software library written in Python for analyzing large map collections (scanned or born-digital). This library transforms the way historians can use maps by turning extensive, homogeneous map sets into searchable primary sources. MapReader allows users with little or no computer vision expertise to i) retrieve maps via web-servers; ii) preprocess and divide them into patches; iii) annotate patches; iv) train, fine-tune, and evaluate deep neural network models; and v) create structured data about map content. We demonstrate how MapReader enables historians to interpret a collection of $\\approx$16K nineteenth-century Ordnance Survey map sheets ($\\approx$30.5M patches), foregrounding the challenge of translating visual markers into machine-readable data. We present a case study focusing on British rail infrastructure and buildings as depicted on these maps. We also show how the outputs from the MapReader pipeline can be linked to other, external datasets, which we use to evaluate as well as enrich and interpret the results. We release $\\approx$62K manually annotated patches used here for training and evaluating the models.", "published": "2021-11-30T17:37:01Z", "authors": ["Kasra Hosseini", "Daniel C. S. Wilson", "Kaspar Beelen", "Katherine McDonough"], "categories": ["cs.CV", "cs.LG"], "doi": "", "_query": "property valuation computer vision", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2507.12195v1", "title": "Revealing the Ancient Beauty: Digital Reconstruction of Temple Tiles using Computer Vision", "abstract": "Modern digitised approaches have dramatically changed the preservation and restoration of cultural treasures, integrating computer scientists into multidisciplinary projects with ease. Machine learning, deep learning, and computer vision techniques have revolutionised developing sectors like 3D reconstruction, picture inpainting,IoT-based methods, genetic algorithms, and image processing with the integration of computer scientists into multidisciplinary initiatives. We suggest three cutting-edge techniques in recognition of the special qualities of Indian monuments, which are famous for their architectural skill and aesthetic appeal. First is the Fractal Convolution methodology, a segmentation method based on image processing that successfully reveals subtle architectural patterns within these irreplaceable cultural buildings. The second is a revolutionary Self-Sensitive Tile Filling (SSTF) method created especially for West Bengal's mesmerising Bankura Terracotta Temples with a brand-new data augmentation method called MosaicSlice on the third. Furthermore, we delve deeper into the Super Resolution strategy to upscale the images without losing significant amount of quality. Our methods allow for the development of seamless region-filling and highly detailed tiles while maintaining authenticity using a novel data augmentation strategy within affordable costs introducing automation. By providing effective solutions that preserve the delicate balance between tradition and innovation, this study improves the subject and eventually ensures unrivalled efficiency and aesthetic excellence in cultural heritage protection. The suggested approaches advance the field into an era of unmatched efficiency and aesthetic quality while carefully upholding the delicate equilibrium between tradition and innovation.", "published": "2025-07-16T12:46:04Z", "authors": ["Arkaprabha Basu"], "categories": ["cs.CV", "cs.AI"], "doi": "", "_query": "property valuation computer vision", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/1708.00069v1", "title": "Learning Robust Representations for Computer Vision", "abstract": "Unsupervised learning techniques in computer vision often require learning latent representations, such as low-dimensional linear and non-linear subspaces. Noise and outliers in the data can frustrate these approaches by obscuring the latent spaces. Our main goal is deeper understanding and new development of robust approaches for representation learning. We provide a new interpretation for existing robust approaches and present two specific contributions: a new robust PCA approach, which can separate foreground features from dynamic background, and a novel robust spectral clustering method, that can cluster facial images with high accuracy. Both contributions show superior performance to standard methods on real-world test sets.", "published": "2017-07-31T20:50:01Z", "authors": ["Peng Zheng", "Aleksandr Y. Aravkin", "Karthikeyan Natesan Ramamurthy", "Jayaraman Jayaraman Thiagarajan"], "categories": ["stat.ML", "cs.CV", "cs.LG"], "doi": "", "_query": "property valuation computer vision", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2109.00783v4", "title": "Computer Vision Self-supervised Learning Methods on Time Series", "abstract": "Self-supervised learning (SSL) has had great success in both computer vision. Most of the current mainstream computer vision SSL frameworks are based on Siamese network architecture. These approaches often rely on cleverly crafted loss functions and training setups to avoid feature collapse. In this study, we evaluate if those computer-vision SSL frameworks are also effective on a different modality (\\textit{i.e.,} time series). The effectiveness is experimented and evaluated on the UCR and UEA archives, and we show that the computer vision SSL frameworks can be effective even for time series. In addition, we propose a new method that improves on the recently proposed VICReg method. Our method improves on a \\textit{covariance} term proposed in VICReg, and in addition we augment the head of the architecture by an iterative normalization layer that accelerates the convergence of the model.", "published": "2021-09-02T08:45:53Z", "authors": ["Daesoo Lee", "Erlend Aune"], "categories": ["cs.LG", "cs.AI", "stat.ML"], "doi": "", "_query": "property valuation computer vision", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2012.06608v4", "title": "Estimating the contribution of early and late noise in vision from psychophysical data", "abstract": "In many psychophysical detection and discrimination tasks human performance is thought to be limited by internal or inner noise when neuronal activity is converted into an overt behavioural response. It is unclear, however, to what extent the behaviourally limiting inner noise arises from early noise in the photoreceptors and the retina, or from late noise in cortex at or immediately prior to the decision stage. Presumably, the behaviourally limiting inner noise is a non-trivial combination of both early and late noises. Here we propose a method to quantify the contributions of early and late noise purely from psychophysical data. Our analysis generalizes classical results for linear systems (Burgess and Colborne, 1988) by combining the theory of noise propagation through a nonlinear network (Ahumada, 1987) with the expressions to obtain the perceptual metric along the nonlinear network (Malo and Simoncelli, 2006; Laparra et al., 2010). We show that from threshold-only data the relative contribution of early and late noise can only be determined if the experiments include substantial external noise in some of the stimuli used during experiments. If experimenters collected full psychometric functions, however, then early and late noise sources can be quantified even in the absence of external noise. Our psychophysical estimate of the magnitude of the early noise assuming a standard cascade of linear and nonlinear model stages is substantially lower than the noise in cone photocurrents computed via an accurate model of retinal physiology (Brainard and Wandell, 2020, ISETBIO). This is consistent with the idea that one of the fundamental tasks of early vision is to reduce the comparatively large retinal noise.", "published": "2020-12-11T19:25:46Z", "authors": ["Jesus Malo", "Jose Juan Esteve-Taboada", "Guillermo Aguilar", "Marianne Maertens", "Felix A. Wichmann"], "categories": ["q-bio.NC"], "doi": "", "_query": "property valuation computer vision", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2308.09987v1", "title": "ClothesNet: An Information-Rich 3D Garment Model Repository with Simulated Clothes Environment", "abstract": "We present ClothesNet: a large-scale dataset of 3D clothes objects with information-rich annotations. Our dataset consists of around 4400 models covering 11 categories annotated with clothes features, boundary lines, and keypoints. ClothesNet can be used to facilitate a variety of computer vision and robot interaction tasks. Using our dataset, we establish benchmark tasks for clothes perception, including classification, boundary line segmentation, and keypoint detection, and develop simulated clothes environments for robotic interaction tasks, including rearranging, folding, hanging, and dressing. We also demonstrate the efficacy of our ClothesNet in real-world experiments. Supplemental materials and dataset are available on our project webpage.", "published": "2023-08-19T11:34:40Z", "authors": ["Bingyang Zhou", "Haoyu Zhou", "Tianhai Liang", "Qiaojun Yu", "Siheng Zhao", "Yuwei Zeng", "Jun Lv", "Siyuan Luo", "Qiancai Wang", "Xinyuan Yu", "Haonan Chen", "Cewu Lu", "Lin Shao"], "categories": ["cs.RO", "cs.AI", "cs.CV"], "doi": "", "_query": "property valuation computer vision", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2104.13874v2", "title": "Exploring Relational Context for Multi-Task Dense Prediction", "abstract": "The timeline of computer vision research is marked with advances in learning and utilizing efficient contextual representations. Most of them, however, are targeted at improving model performance on a single downstream task. We consider a multi-task environment for dense prediction tasks, represented by a common backbone and independent task-specific heads. Our goal is to find the most efficient way to refine each task prediction by capturing cross-task contexts dependent on tasks' relations. We explore various attention-based contexts, such as global and local, in the multi-task setting and analyze their behavior when applied to refine each task independently. Empirical findings confirm that different source-target task pairs benefit from different context types. To automate the selection process, we propose an Adaptive Task-Relational Context (ATRC) module, which samples the pool of all available contexts for each task pair using neural architecture search and outputs the optimal configuration for deployment. Our method achieves state-of-the-art performance on two important multi-task benchmarks, namely NYUD-v2 and PASCAL-Context. The proposed ATRC has a low computational toll and can be used as a drop-in refinement module for any supervised multi-task architecture.", "published": "2021-04-28T16:45:56Z", "authors": ["David Bruggemann", "Menelaos Kanakis", "Anton Obukhov", "Stamatios Georgoulis", "Luc Van Gool"], "categories": ["cs.CV"], "doi": "", "_query": "property valuation computer vision", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/math/0504263v1", "title": "Value Monoids of Zero-Dimensional Valuations of Rank One", "abstract": "Classically, Groebner bases are computed by first prescribing a set monomial order. Moss Sweedler suggested an alternative and developed a framework to perform such computations by using valuation rings in place of monomial orders. We build on these ideas by providing a class of valuations on rational function fields of two variables that are suitable for this framework. For these valuations, we explicitly compute the image of an underlying polynomial ring and use this to perform computations concerning ideals in the polynomial ring. Interestingly, for these valuations, some ideals have a finite Groebner basis with respect to the valuation that is not a Groebner basis with respect to any monomial order, whereas other ideals only have Groebner bases that are infinite with respect to the valuation.", "published": "2005-04-13T05:21:17Z", "authors": ["Edward Mosteig"], "categories": ["math.AC"], "doi": "", "_query": "property valuation computer vision", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2308.05168v1", "title": "A Unified Interactive Model Evaluation for Classification, Object Detection, and Instance Segmentation in Computer Vision", "abstract": "Existing model evaluation tools mainly focus on evaluating classification models, leaving a gap in evaluating more complex models, such as object detection. In this paper, we develop an open-source visual analysis tool, Uni-Evaluator, to support a unified model evaluation for classification, object detection, and instance segmentation in computer vision. The key idea behind our method is to formulate both discrete and continuous predictions in different tasks as unified probability distributions. Based on these distributions, we develop 1) a matrix-based visualization to provide an overview of model performance; 2) a table visualization to identify the problematic data subsets where the model performs poorly; 3) a grid visualization to display the samples of interest. These visualizations work together to facilitate the model evaluation from a global overview to individual samples. Two case studies demonstrate the effectiveness of Uni-Evaluator in evaluating model performance and making informed improvements.", "published": "2023-08-09T18:11:28Z", "authors": ["Changjian Chen", "Yukai Guo", "Fengyuan Tian", "Shilong Liu", "Weikai Yang", "Zhaowei Wang", "Jing Wu", "Hang Su", "Hanspeter Pfister", "Shixia Liu"], "categories": ["cs.CV", "cs.HC"], "doi": "", "_query": "property valuation computer vision", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2412.05976v2", "title": "LightOcc: Lightweight Spatial Embedding for Efficient Vision-based 3D Occupancy Prediction", "abstract": "Occupancy prediction has garnered increasing attention in recent years for its comprehensive fine-grained environmental representation and strong generalization to open-set objects. Nevertheless, mainstream occupancy prediction methods employ cumbersome voxel features as the scene representation, incurring substantial overheads in both memory and computation. When comparing the occupancy distribution in each spatial dimension, we find that the information entropy of the height dimension is much lower than the other two dimensions that constitute the Bird's Eye View (BEV) plane, which indicates that the height distribution of occupancy is easier to learn and predict. Accordingly, we propose Lightweight Spatial Embedding that can represent complete height information in a more compact way than voxel features, thus significantly enhancing its deployability. First, Single-Channel Occupancy is sampled from the multi-view depth distributions, which is then processed by Spatial-to-Channel mechanism to extract Lightweight Spatial Embeddings of different views by 2D convolution. These embeddings will interact with each other through the Lightweight Cross-View Interaction module to obtain the Unified Embedding, which can directly supplement BEV features with height information. Furthermore, we extract Edge-aware Spatial Embedding and apply Geometric Supervision on Spatial Embeddings, aiming to enhance their capability to represent spatial information. We also propose BEV-CutMix, a feature-level data augmentation strategy, to increase the diversity of the driving scenes. We integrate these innovative components into a pure 2D convolutional model, namely LightOcc. Sufficient experimental results show that LightOcc achieves state-of-the-art performance on multiple benchmarks while demonstrating significant efficiency advantages.", "published": "2024-12-08T15:49:35Z", "authors": ["Jinqing Zhang", "Yanan Zhang", "Wenrui Cai", "Qingjie Liu", "Yunhong Wang"], "categories": ["cs.CV"], "doi": "", "_query": "property valuation computer vision", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2108.05305v2", "title": "ConvNets vs. Transformers: Whose Visual Representations are More Transferable?", "abstract": "Vision transformers have attracted much attention from computer vision researchers as they are not restricted to the spatial inductive bias of ConvNets. However, although Transformer-based backbones have achieved much progress on ImageNet classification, it is still unclear whether the learned representations are as transferable as or even more transferable than ConvNets' features. To address this point, we systematically investigate the transfer learning ability of ConvNets and vision transformers in 15 single-task and multi-task performance evaluations. Given the strong correlation between the performance of pre-trained models and transfer learning, we include 2 residual ConvNets (i.e., R-101x3 and R-152x4) and 3 Transformer-based visual backbones (i.e., ViT-B, ViT-L and Swin-B), which have close error rates on ImageNet, that indicate similar transfer learning performance on downstream datasets. We observe consistent advantages of Transformer-based backbones on 13 downstream tasks (out of 15), including but not limited to fine-grained classification, scene recognition (classification, segmentation and depth estimation), open-domain classification, face recognition, etc. More specifically, we find that two ViT models heavily rely on whole network fine-tuning to achieve performance gains while Swin Transformer does not have such a requirement. Moreover, vision transformers behave more robustly in multi-task learning, i.e., bringing more improvements when managing mutually beneficial tasks and reducing performance losses when tackling irrelevant tasks. We hope our discoveries can facilitate the exploration and exploitation of vision transformers in the future.", "published": "2021-08-11T16:20:38Z", "authors": ["Hong-Yu Zhou", "Chixiang Lu", "Sibei Yang", "Yizhou Yu"], "categories": ["cs.CV"], "doi": "", "_query": "property valuation computer vision", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2107.08246v1", "title": "Woodscape Fisheye Semantic Segmentation for Autonomous Driving -- CVPR 2021 OmniCV Workshop Challenge", "abstract": "We present the WoodScape fisheye semantic segmentation challenge for autonomous driving which was held as part of the CVPR 2021 Workshop on Omnidirectional Computer Vision (OmniCV). This challenge is one of the first opportunities for the research community to evaluate the semantic segmentation techniques targeted for fisheye camera perception. Due to strong radial distortion standard models don't generalize well to fisheye images and hence the deformations in the visual appearance of objects and entities needs to be encoded implicitly or as explicit knowledge. This challenge served as a medium to investigate the challenges and new methodologies to handle the complexities with perception on fisheye images. The challenge was hosted on CodaLab and used the recently released WoodScape dataset comprising of 10k samples. In this paper, we provide a summary of the competition which attracted the participation of 71 global teams and a total of 395 submissions. The top teams recorded significantly improved mean IoU and accuracy scores over the baseline PSPNet with ResNet-50 backbone. We summarize the methods of winning algorithms and analyze the failure cases. We conclude by providing future directions for the research.", "published": "2021-07-17T14:32:58Z", "authors": ["Saravanabalagi Ramachandran", "Ganesh Sistu", "John McDonald", "Senthil Yogamani"], "categories": ["cs.CV", "cs.RO"], "doi": "", "_query": "property valuation computer vision", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2509.15490v1", "title": "SmolRGPT: Efficient Spatial Reasoning for Warehouse Environments with 600M Parameters", "abstract": "Recent advances in vision-language models (VLMs) have enabled powerful multimodal reasoning, but state-of-the-art approaches typically rely on extremely large models with prohibitive computational and memory requirements. This makes their deployment challenging in resource-constrained environments such as warehouses, robotics, and industrial applications, where both efficiency and robust spatial understanding are critical. In this work, we present SmolRGPT, a compact vision-language architecture that explicitly incorporates region-level spatial reasoning by integrating both RGB and depth cues. SmolRGPT employs a three-stage curriculum that progressively align visual and language features, enables spatial relationship understanding, and adapts to task-specific datasets. We demonstrate that with only 600M parameters, SmolRGPT achieves competitive results on challenging warehouse spatial reasoning benchmarks, matching or exceeding the performance of much larger alternatives. These findings highlight the potential for efficient, deployable multimodal intelligence in real-world settings without sacrificing core spatial reasoning capabilities. The code of the experimentation will be available at: https://github.com/abtraore/SmolRGPT", "published": "2025-09-18T23:55:51Z", "authors": ["Abdarahmane Traore", "Éric Hervet", "Andy Couturier"], "categories": ["cs.CV", "cs.AI"], "doi": "", "_query": "property valuation computer vision", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2201.09105v3", "title": "Credit Valuation Adjustment with Replacement Closeout: Theory and Algorithms", "abstract": "The replacement closeout convention has drawn more and more attention since the 2008 financial crisis. Compared with the conventional risk-free closeout, the replacement closeout convention incorporates the creditworthiness of the counterparty and thus providing a more accurate estimate of the Mark-to-market value of a financial claim. In contrast to the risk-free closeout, the replacement closeout renders a nonlinear valuation system, which constitutes the major difficulty in the valuation of the counterparty credit risk. In this paper, we show how to address the nonlinearity attributed to the replacement closeout in the theoretical and computational analysis. In the theoretical part, we prove the unique solvability of the nonlinear valuation system and study the impact of the replacement closeout on the credit valuation adjustment. In the computational part, we propose a neural network-based algorithm for solving the (high dimensional) nonlinear valuation system and effectively alleviating the curse of dimensionality. We numerically compare the computational cost for the valuations with risk-free and replacement closeouts. The numerical tests confirm both the accuracy and the computational efficiency of our proposed algorithm for the valuation of the replacement closeout.", "published": "2022-01-22T18:01:56Z", "authors": ["Chaofan Sun", "Ken Seng Tan", "Wei Wei"], "categories": ["q-fin.MF"], "doi": "", "_query": "property valuation computer vision", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/1809.03175v1", "title": "Geoseg: A Computer Vision Package for Automatic Building Segmentation and Outline Extraction", "abstract": "Recently, deep learning algorithms, especially fully convolutional network based methods, are becoming very popular in the field of remote sensing. However, these methods are implemented and evaluated through various datasets and deep learning frameworks. There has not been a package that covers these methods in a unifying manner. In this study, we introduce a computer vision package termed Geoseg that focus on building segmentation and outline extraction. Geoseg implements over nine state-of-the-art models as well as utility scripts needed to conduct model training, logging, evaluating and visualization. The implementation of Geoseg emphasizes unification, simplicity, and flexibility. The performance and computational efficiency of all implemented methods are evaluated by comparison experiment through a unified, high-quality aerial image dataset.", "published": "2018-09-10T08:29:42Z", "authors": ["Guangming Wu", "Zhiling Guo"], "categories": ["cs.CV"], "doi": "", "_query": "property valuation computer vision", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2202.03131v1", "title": "Transformers in Self-Supervised Monocular Depth Estimation with Unknown Camera Intrinsics", "abstract": "The advent of autonomous driving and advanced driver assistance systems necessitates continuous developments in computer vision for 3D scene understanding. Self-supervised monocular depth estimation, a method for pixel-wise distance estimation of objects from a single camera without the use of ground truth labels, is an important task in 3D scene understanding. However, existing methods for this task are limited to convolutional neural network (CNN) architectures. In contrast with CNNs that use localized linear operations and lose feature resolution across the layers, vision transformers process at constant resolution with a global receptive field at every stage. While recent works have compared transformers against their CNN counterparts for tasks such as image classification, no study exists that investigates the impact of using transformers for self-supervised monocular depth estimation. Here, we first demonstrate how to adapt vision transformers for self-supervised monocular depth estimation. Thereafter, we compare the transformer and CNN-based architectures for their performance on KITTI depth prediction benchmarks, as well as their robustness to natural corruptions and adversarial attacks, including when the camera intrinsics are unknown. Our study demonstrates how transformer-based architecture, though lower in run-time efficiency, achieves comparable performance while being more robust and generalizable.", "published": "2022-02-07T13:17:29Z", "authors": ["Arnav Varma", "Hemang Chawla", "Bahram Zonooz", "Elahe Arani"], "categories": ["cs.CV", "cs.AI"], "doi": "10.5220/0010884000003124", "_query": "property valuation computer vision", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/1909.01056v1", "title": "STaDA: Style Transfer as Data Augmentation", "abstract": "The success of training deep Convolutional Neural Networks (CNNs) heavily depends on a significant amount of labelled data. Recent research has found that neural style transfer algorithms can apply the artistic style of one image to another image without changing the latter's high-level semantic content, which makes it feasible to employ neural style transfer as a data augmentation method to add more variation to the training dataset. The contribution of this paper is a thorough evaluation of the effectiveness of the neural style transfer as a data augmentation method for image classification tasks. We explore the state-of-the-art neural style transfer algorithms and apply them as a data augmentation method on Caltech 101 and Caltech 256 dataset, where we found around 2% improvement from 83% to 85% of the image classification accuracy with VGG16, compared with traditional data augmentation strategies. We also combine this new method with conventional data augmentation approaches to further improve the performance of image classification. This work shows the potential of neural style transfer in computer vision field, such as helping us to reduce the difficulty of collecting sufficient labelled data and improve the performance of generic image-based deep learning algorithms.", "published": "2019-09-03T10:48:42Z", "authors": ["Xu Zheng", "Tejo Chalasani", "Koustav Ghosal", "Sebastian Lutz", "Aljosa Smolic"], "categories": ["cs.CV", "cs.LG"], "doi": "10.5220/0007353401070114", "_query": "property valuation computer vision", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2602.21699v1", "title": "SF3D-RGB: Scene Flow Estimation from Monocular Camera and Sparse LiDAR", "abstract": "Scene flow estimation is an extremely important task in computer vision to support the perception of dynamic changes in the scene. For robust scene flow, learning-based approaches have recently achieved impressive results using either image-based or LiDAR-based modalities. However, these methods have tended to focus on the use of a single modality. To tackle these problems, we present a deep learning architecture, SF3D-RGB, that enables sparse scene flow estimation using 2D monocular images and 3D point clouds (e.g., acquired by LiDAR) as inputs. Our architecture is an end-to-end model that first encodes information from each modality into features and fuses them together. Then, the fused features enhance a graph matching module for better and more robust mapping matrix computation to generate an initial scene flow. Finally, a residual scene flow module further refines the initial scene flow. Our model is designed to strike a balance between accuracy and efficiency. Furthermore, experiments show that our proposed method outperforms single-modality methods and achieves better scene flow accuracy on real-world datasets while using fewer parameters compared to other state-of-the-art methods with fusion.", "published": "2026-02-25T09:03:42Z", "authors": ["Rajai Alhimdiat", "Ramy Battrawy", "René Schuster", "Didier Stricker", "Wesam Ashour"], "categories": ["cs.CV"], "doi": "", "_query": "property valuation computer vision", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2505.11845v1", "title": "ElderFallGuard: Real-Time IoT and Computer Vision-Based Fall Detection System for Elderly Safety", "abstract": "For the elderly population, falls pose a serious and increasing risk of serious injury and loss of independence. In order to overcome this difficulty, we present ElderFallGuard: A Computer Vision Based IoT Solution for Elderly Fall Detection and Notification, a cutting-edge, non-invasive system intended for quick caregiver alerts and real-time fall detection. Our approach leverages the power of computer vision, utilizing MediaPipe for accurate human pose estimation from standard video streams. We developed a custom dataset comprising 7200 samples across 12 distinct human poses to train and evaluate various machine learning classifiers, with Random Forest ultimately selected for its superior performance. ElderFallGuard employs a specific detection logic, identifying a fall when a designated prone pose (\"Pose6\") is held for over 3 seconds coupled with a significant drop in motion detected for more than 2 seconds. Upon confirmation, the system instantly dispatches an alert, including a snapshot of the event, to a designated Telegram group via a custom bot, incorporating cooldown logic to prevent notification overload. Rigorous testing on our dataset demonstrated exceptional results, achieving 100% accuracy, precision, recall, and F1-score. ElderFallGuard offers a promising, vision-based IoT solution to enhance elderly safety and provide peace of mind for caregivers through intelligent, timely alerts.", "published": "2025-05-17T05:09:47Z", "authors": ["Tasrifur Riahi", "Md. Azizul Hakim Bappy", "Md. Mehedi Islam"], "categories": ["cs.CV"], "doi": "", "_query": "property valuation computer vision", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2508.09732v1", "title": "Predictive Uncertainty for Runtime Assurance of a Real-Time Computer Vision-Based Landing System", "abstract": "Recent advances in data-driven computer vision have enabled robust autonomous navigation capabilities for civil aviation, including automated landing and runway detection. However, ensuring that these systems meet the robustness and safety requirements for aviation applications remains a major challenge. In this work, we present a practical vision-based pipeline for aircraft pose estimation from runway images that represents a step toward the ability to certify these systems for use in safety-critical aviation applications. Our approach features three key innovations: (i) an efficient, flexible neural architecture based on a spatial Soft Argmax operator for probabilistic keypoint regression, supporting diverse vision backbones with real-time inference; (ii) a principled loss function producing calibrated predictive uncertainties, which are evaluated via sharpness and calibration metrics; and (iii) an adaptation of Residual-based Receiver Autonomous Integrity Monitoring (RAIM), enabling runtime detection and rejection of faulty model outputs. We implement and evaluate our pose estimation pipeline on a dataset of runway images. We show that our model outperforms baseline architectures in terms of accuracy while also producing well-calibrated uncertainty estimates with sub-pixel precision that can be used downstream for fault detection.", "published": "2025-08-13T11:56:22Z", "authors": ["Romeo Valentin", "Sydney M. Katz", "Artur B. Carneiro", "Don Walker", "Mykel J. Kochenderfer"], "categories": ["cs.CV", "cs.RO"], "doi": "", "_query": "property valuation computer vision", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/1002.2222v1", "title": "An innovative platform to improve the performance of exact string matching algorithms", "abstract": "Exact String Matching is an essential issue in many computer science applications. Unfortunately, the performance of Exact String Matching algorithms, namely, executing time, does not address the needs of these applications. This paper proposes a general platform for improving the existing Exact String Matching algorithms executing time, called the PXSMAlg platform. The function of this platform is to parallelize the Exact String Matching algorithms using the MPI model over the Master or Slaves paradigms. The PXSMAlg platform parallelization process is done by dividing the Text into several parts and working on these parts simultaneously. This improves the executing time of the Exact String Matching algorithms. We have simulated the PXSMAlg platform in order to show its competence, through applying the Quick Search algorithm on the PXSMAlg platform. The simulation result showed significant improvement in the Quick Search executing time, and therefore extreme competence in the PXSMAlg platform.", "published": "2010-02-11T20:31:03Z", "authors": ["Mosleh M. Abu Alhaj", "M. Halaiyqah", "Muhannad A. Abu Hashem", "Adnan A. Hnaif", "O. Abouabdalla", "Ahmed M. Manasrah"], "categories": ["cs.DC", "cs.DS"], "doi": "", "_query": "property valuation computer vision", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2402.11836v1", "title": "DIO: Dataset of 3D Mesh Models of Indoor Objects for Robotics and Computer Vision Applications", "abstract": "The creation of accurate virtual models of real-world objects is imperative to robotic simulations and applications such as computer vision, artificial intelligence, and machine learning. This paper documents the different methods employed for generating a database of mesh models of real-world objects. These methods address the tedious and time-intensive process of manually generating the models using CAD software. Essentially, DSLR/phone cameras were employed to acquire images of target objects. These images were processed using a photogrammetry software known as Meshroom to generate a dense surface reconstruction of the scene. The result produced by Meshroom was edited and simplified using MeshLab, a mesh-editing software to produce the final model. Based on the obtained models, this process was effective in modelling the geometry and texture of real-world objects with high fidelity. An active 3D scanner was also utilized to accelerate the process for large objects. All generated models and captured images are made available on the website of the project.", "published": "2024-02-19T04:58:40Z", "authors": ["Nillan Nimal", "Wenbin Li", "Ronald Clark", "Sajad Saeedi"], "categories": ["cs.RO", "cs.CV"], "doi": "", "_query": "property valuation computer vision", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/1902.02288v3", "title": "The space of persistence diagrams fails to have Yu's property A", "abstract": "We define a simple obstruction to Yu's property A that we call $k$-prisms. This structure allows for a straightforward proof that the space of persistence diagrams fails to have property A in a Wasserstein metric.", "published": "2019-02-06T17:26:09Z", "authors": ["Greg Bell", "Austin Lawson", "C. Neil Pritchard", "Dan Yasaki"], "categories": ["math.MG", "math.GT"], "doi": "", "_query": "property valuation computer vision", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/1805.04687v2", "title": "BDD100K: A Diverse Driving Dataset for Heterogeneous Multitask Learning", "abstract": "Datasets drive vision progress, yet existing driving datasets are impoverished in terms of visual content and supported tasks to study multitask learning for autonomous driving. Researchers are usually constrained to study a small set of problems on one dataset, while real-world computer vision applications require performing tasks of various complexities. We construct BDD100K, the largest driving video dataset with 100K videos and 10 tasks to evaluate the exciting progress of image recognition algorithms on autonomous driving. The dataset possesses geographic, environmental, and weather diversity, which is useful for training models that are less likely to be surprised by new conditions. Based on this diverse dataset, we build a benchmark for heterogeneous multitask learning and study how to solve the tasks together. Our experiments show that special training strategies are needed for existing models to perform such heterogeneous tasks. BDD100K opens the door for future studies in this important venue.", "published": "2018-05-12T09:24:21Z", "authors": ["Fisher Yu", "Haofeng Chen", "Xin Wang", "Wenqi Xian", "Yingying Chen", "Fangchen Liu", "Vashisht Madhavan", "Trevor Darrell"], "categories": ["cs.CV"], "doi": "", "_query": "property valuation computer vision", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/1601.06823v1", "title": "Survey on the attention based RNN model and its applications in computer vision", "abstract": "The recurrent neural networks (RNN) can be used to solve the sequence to sequence problem, where both the input and the output have sequential structures. Usually there are some implicit relations between the structures. However, it is hard for the common RNN model to fully explore the relations between the sequences. In this survey, we introduce some attention based RNN models which can focus on different parts of the input for each output item, in order to explore and take advantage of the implicit relations between the input and the output items. The different attention mechanisms are described in detail. We then introduce some applications in computer vision which apply the attention based RNN models. The superiority of the attention based RNN model is shown by the experimental results. At last some future research directions are given.", "published": "2016-01-25T21:54:02Z", "authors": ["Feng Wang", "David M. J. Tax"], "categories": ["cs.CV", "cs.LG"], "doi": "", "_query": "property valuation computer vision", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2112.09190v1", "title": "Monitoring crop phenology with street-level imagery using computer vision", "abstract": "Street-level imagery holds a significant potential to scale-up in-situ data collection. This is enabled by combining the use of cheap high quality cameras with recent advances in deep learning compute solutions to derive relevant thematic information. We present a framework to collect and extract crop type and phenological information from street level imagery using computer vision. During the 2018 growing season, high definition pictures were captured with side-looking action cameras in the Flevoland province of the Netherlands. Each month from March to October, a fixed 200-km route was surveyed collecting one picture per second resulting in a total of 400,000 geo-tagged pictures. At 220 specific parcel locations detailed on the spot crop phenology observations were recorded for 17 crop types. Furthermore, the time span included specific pre-emergence parcel stages, such as differently cultivated bare soil for spring and summer crops as well as post-harvest cultivation practices, e.g. green manuring and catch crops. Classification was done using TensorFlow with a well-known image recognition model, based on transfer learning with convolutional neural networks (MobileNet). A hypertuning methodology was developed to obtain the best performing model among 160 models. This best model was applied on an independent inference set discriminating crop type with a Macro F1 score of 88.1% and main phenological stage at 86.9% at the parcel level. Potential and caveats of the approach along with practical considerations for implementation and improvement are discussed. The proposed framework speeds up high quality in-situ data collection and suggests avenues for massive data collection via automated classification using computer vision.", "published": "2021-12-16T20:36:45Z", "authors": ["Raphaël d'Andrimont", "Momchil Yordanov", "Laura Martinez-Sanchez", "Marijn van der Velde"], "categories": ["cs.CV", "stat.AP"], "doi": "", "_query": "property valuation computer vision", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/1803.07985v1", "title": "BioTracker: An Open-Source Computer Vision Framework for Visual Animal Tracking", "abstract": "The study of animal behavior increasingly relies on (semi-) automatic methods for the extraction of relevant behavioral features from video or picture data. To date, several specialized software products exist to detect and track animals' positions in simple (laboratory) environments. Tracking animals in their natural environments, however, often requires substantial customization of the image processing algorithms to the problem-specific image characteristics. Here we introduce BioTracker, an open-source computer vision framework, that provides programmers with core functionalities that are essential parts of a tracking software, such as video I/O, graphics overlays and mouse and keyboard interfaces. BioTracker additionally provides a number of different tracking algorithms suitable for a variety of image recording conditions. The main feature of BioTracker is however the straightforward implementation of new problem-specific tracking modules and vision algorithms that can build upon BioTracker's core functionalities. With this open-source framework the scientific community can accelerate their research and focus on the development of new vision algorithms.", "published": "2018-03-21T16:12:18Z", "authors": ["Hauke Jürgen Mönck", "Andreas Jörg", "Tobias von Falkenhausen", "Julian Tanke", "Benjamin Wild", "David Dormagen", "Jonas Piotrowski", "Claudia Winklmayr", "David Bierbach", "Tim Landgraf"], "categories": ["cs.CV"], "doi": "", "_query": "property valuation computer vision", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/1710.00925v5", "title": "Fine-Grained Head Pose Estimation Without Keypoints", "abstract": "Estimating the head pose of a person is a crucial problem that has a large amount of applications such as aiding in gaze estimation, modeling attention, fitting 3D models to video and performing face alignment. Traditionally head pose is computed by estimating some keypoints from the target face and solving the 2D to 3D correspondence problem with a mean human head model. We argue that this is a fragile method because it relies entirely on landmark detection performance, the extraneous head model and an ad-hoc fitting step. We present an elegant and robust way to determine pose by training a multi-loss convolutional neural network on 300W-LP, a large synthetically expanded dataset, to predict intrinsic Euler angles (yaw, pitch and roll) directly from image intensities through joint binned pose classification and regression. We present empirical tests on common in-the-wild pose benchmark datasets which show state-of-the-art results. Additionally we test our method on a dataset usually used for pose estimation using depth and start to close the gap with state-of-the-art depth pose methods. We open-source our training and testing code as well as release our pre-trained models.", "published": "2017-10-02T22:01:20Z", "authors": ["Nataniel Ruiz", "Eunji Chong", "James M. Rehg"], "categories": ["cs.CV"], "doi": "", "_query": "property valuation computer vision", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2306.04736v1", "title": "BU-CVKit: Extendable Computer Vision Framework for Species Independent Tracking and Analysis", "abstract": "A major bottleneck of interdisciplinary computer vision (CV) research is the lack of a framework that eases the reuse and abstraction of state-of-the-art CV models by CV and non-CV researchers alike. We present here BU-CVKit, a computer vision framework that allows the creation of research pipelines with chainable Processors. The community can create plugins of their work for the framework, hence improving the re-usability, accessibility, and exposure of their work with minimal overhead. Furthermore, we provide MuSeqPose Kit, a user interface for the pose estimation package of BU-CVKit, which automatically scans for installed plugins and programmatically generates an interface for them based on the metadata provided by the user. It also provides software support for standard pose estimation features such as annotations, 3D reconstruction, reprojection, and camera calibration. Finally, we show examples of behavioral neuroscience pipelines created through the sample plugins created for our framework.", "published": "2023-06-07T19:12:03Z", "authors": ["Mahir Patel", "Lucas Carstensen", "Yiwen Gu", "Michael E. Hasselmo", "Margrit Betke"], "categories": ["cs.CV"], "doi": "", "_query": "property valuation computer vision", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2509.01882v2", "title": "HydroVision: Predicting Optically Active Parameters in Surface Water Using Computer Vision", "abstract": "Ongoing advancements in computer vision, particularly in pattern recognition and scene classification, have enabled new applications in environmental monitoring. Deep learning now offers non-contact methods for assessing water quality and detecting contamination, both critical for disaster response and public health protection. This work introduces HydroVision, a deep learning-based scene classification framework that estimates optically active water quality parameters including Chlorophyll-Alpha, Chlorophylls, Colored Dissolved Organic Matter (CDOM), Phycocyanins, Suspended Sediments, and Turbidity from standard Red-Green-Blue (RGB) images of surface water. HydroVision supports early detection of contamination trends and strengthens monitoring by regulatory agencies during external environmental stressors, industrial activities, and force majeure events. The model is trained on more than 500,000 seasonally varied images collected from the United States Geological Survey Hydrologic Imagery Visualization and Information System between 2022 and 2024. This approach leverages widely available RGB imagery as a scalable, cost-effective alternative to traditional multispectral and hyperspectral remote sensing. Four state-of-the-art convolutional neural networks (VGG-16, ResNet50, MobileNetV2, DenseNet121) and a Vision Transformer are evaluated through transfer learning to identify the best-performing architecture. DenseNet121 achieves the highest validation performance, with an R2 score of 0.89 in predicting CDOM, demonstrating the framework's promise for real-world water quality monitoring across diverse conditions. While the current model is optimized for well-lit imagery, future work will focus on improving robustness under low-light and obstructed scenarios to expand its operational utility.", "published": "2025-09-02T02:12:52Z", "authors": ["Shubham Laxmikant Deshmukh", "Matthew Wilchek", "Feras A. Batarseh"], "categories": ["cs.CV", "cs.AI"], "doi": "", "_query": "property valuation computer vision", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/1708.01741v1", "title": "Learning Discriminative Alpha-Beta-divergence for Positive Definite Matrices (Extended Version)", "abstract": "Symmetric positive definite (SPD) matrices are useful for capturing second-order statistics of visual data. To compare two SPD matrices, several measures are available, such as the affine-invariant Riemannian metric, Jeffreys divergence, Jensen-Bregman logdet divergence, etc.; however, their behaviors may be application dependent, raising the need of manual selection to achieve the best possible performance. Further and as a result of their overwhelming complexity for large-scale problems, computing pairwise similarities by clever embedding of SPD matrices is often preferred to direct use of the aforementioned measures. In this paper, we propose a discriminative metric learning framework, Information Divergence and Dictionary Learning (IDDL), that not only learns application specific measures on SPD matrices automatically, but also embeds them as vectors using a learned dictionary. To learn the similarity measures (which could potentially be distinct for every dictionary atom), we use the recently introduced alpha-beta-logdet divergence, which is known to unify the measures listed above. We propose a novel IDDL objective, that learns the parameters of the divergence and the dictionary atoms jointly in a discriminative setup and is solved efficiently using Riemannian optimization. We showcase extensive experiments on eight computer vision datasets, demonstrating state-of-the-art performances.", "published": "2017-08-05T09:38:20Z", "authors": ["Anoop Cherian", "Panagiotis Stanitsas", "Mehrtash Harandi", "Vassilios Morellas", "Nikolaos Papanikolopoulos"], "categories": ["cs.CV"], "doi": "", "_query": "property valuation computer vision", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2505.15414v1", "title": "Efficient Data Driven Mixture-of-Expert Extraction from Trained Networks", "abstract": "Vision Transformers have emerged as the state-of-the-art models in various Computer Vision tasks, but their high computational and resource demands pose significant challenges. While Mixture-of-Experts (MoE) can make these models more efficient, they often require costly retraining or even training from scratch. Recent developments aim to reduce these computational costs by leveraging pretrained networks. These have been shown to produce sparse activation patterns in the Multi-Layer Perceptrons (MLPs) of the encoder blocks, allowing for conditional activation of only relevant subnetworks for each sample. Building on this idea, we propose a new method to construct MoE variants from pretrained models. Our approach extracts expert subnetworks from the model's MLP layers post-training in two phases. First, we cluster output activations to identify distinct activation patterns. In the second phase, we use these clusters to extract the corresponding subnetworks responsible for producing them. On ImageNet-1k recognition tasks, we demonstrate that these extracted experts can perform surprisingly well out of the box and require only minimal fine-tuning to regain 98% of the original performance, all while reducing MACs and model size, by up to 36% and 32% respectively.", "published": "2025-05-21T11:55:25Z", "authors": ["Uranik Berisha", "Jens Mehnert", "Alexandru Paul Condurache"], "categories": ["cs.CV"], "doi": "", "_query": "property valuation computer vision", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2301.08639v1", "title": "Notes on valuation theory for Krasner hyperfields", "abstract": "The main aim of this article is to study and develop valuation theory for Krasner hyperfields. In analogy with classical valuation theory for fields, we generalise the formalism of valuation rings to describe equivalence of valuations on hyperfields. After proving basic results and discussing several examples, we focus on the valued hyperfields that Krasner originally defined in 1957. We find that these must have a particular additive structure which in turns implies the existence of a valuation a'la Krasner. We note that given such a valued hyperfield $(F,v)$, the valuation induced by its additive structure does not have to be equivalent to $v$. We discuss the cases in which it does.", "published": "2023-01-20T15:40:00Z", "authors": ["Alessandro Linzi"], "categories": ["math.AC"], "doi": "", "_query": "property valuation computer vision", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/1710.01288v1", "title": "Decoding visemes: improving machine lipreading", "abstract": "Machine lipreading (MLR) is speech recognition from visual cues and a niche research problem in speech processing & computer vision. Current challenges fall into two groups: the content of the video, such as rate of speech or; the parameters of the video recording e.g, video resolution. We show that HD video is not needed to successfully lipread with a computer. The term \"viseme\" is used in machine lipreading to represent a visual cue or gesture which corresponds to a subgroup of phonemes where the phonemes are visually indistinguishable. A phoneme is the smallest sound one can utter, because there are more phonemes per viseme, maps between units show a many-to-one relationship. Many maps have been presented, we compare these and our results show Lee's is best. We propose a new method of speaker-dependent phoneme-to-viseme maps and compare these to Lee's. Our results show the sensitivity of phoneme clustering and we use our new knowledge to augment a conventional MLR system. It has been observed in MLR, that classifiers need training on test subjects to achieve accuracy. Thus machine lipreading is highly speaker-dependent. Conversely speaker independence is robust classification of non-training speakers. We investigate the dependence of phoneme-to-viseme maps between speakers and show there is not a high variability of visemes, but there is high variability in trajectory between visemes of individual speakers with the same ground truth. This implies a dependency upon the number of visemes within each set for each individual. We show that prior phoneme-to-viseme maps rarely have enough visemes and the optimal size, which varies by speaker, ranges from 11-35. Finally we decode from visemes back to phonemes and into words. Our novel approach uses the optimum range visemes within hierarchical training of phoneme classifiers and demonstrates a significant increase in classification accuracy.", "published": "2017-10-03T17:29:24Z", "authors": ["Helen L Bear"], "categories": ["cs.CV", "eess.AS"], "doi": "", "_query": "property valuation computer vision", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2311.06976v1", "title": "CD-COCO: A Versatile Complex Distorted COCO Database for Scene-Context-Aware Computer Vision", "abstract": "The recent development of deep learning methods applied to vision has enabled their increasing integration into real-world applications to perform complex Computer Vision (CV) tasks. However, image acquisition conditions have a major impact on the performance of high-level image processing. A possible solution to overcome these limitations is to artificially augment the training databases or to design deep learning models that are robust to signal distortions. We opt here for the first solution by enriching the database with complex and realistic distortions which were ignored until now in the existing databases. To this end, we built a new versatile database derived from the well-known MS-COCO database to which we applied local and global photo-realistic distortions. These new local distortions are generated by considering the scene context of the images that guarantees a high level of photo-realism. Distortions are generated by exploiting the depth information of the objects in the scene as well as their semantics. This guarantees a high level of photo-realism and allows to explore real scenarios ignored in conventional databases dedicated to various CV applications. Our versatile database offers an efficient solution to improve the robustness of various CV tasks such as Object Detection (OD), scene segmentation, and distortion-type classification methods. The image database, scene classification index, and distortion generation codes are publicly available \\footnote{\\url{https://github.com/Aymanbegh/CD-COCO}}", "published": "2023-11-12T22:28:19Z", "authors": ["Ayman Beghdadi", "Azeddine Beghdadi", "Malik Mallem", "Lotfi Beji", "Faouzi Alaya Cheikh"], "categories": ["cs.CV", "cs.DB"], "doi": "", "_query": "property valuation computer vision", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/1602.06189v1", "title": "Accrual valuation and mark to market adjustment", "abstract": "This paper provides intuition on the relationship of accrual and mark-to-market valuation for cash and forward interest rate trades. Discounted cashflow valuation is compared to spread-based valuation for forward trades, which explains the trader's view on valuation. This is followed by Taylor series approximation for cash trades, uncovering simple intuition behind accrual valuation and mark-to-market adjustment. It is followed by the PNL example modelled in R. Within the Taylor approximation framework, theta and delta are explained. The concept of deferral is explained taking Forward Rate Agreement (FRA) as an example.", "published": "2016-02-18T19:56:45Z", "authors": ["Alexey Bakshaev"], "categories": ["q-fin.PR"], "doi": "", "_query": "property valuation computer vision", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/1307.5501v1", "title": "Value Functions and Dubrovin Valuation Rings on Simple Algebras", "abstract": "In this paper we prove relationships between two generalizations of commutative valuation theory for noncommutative central simple algebras: (1) Dubrovin valuation rings; and (2) the value functions called gauges introduced by Tignol and Wadsworth in [TW1] and [TW2]. We show that if v is a valuation on a field F with associated valuation ring V and v is defectless in a central simple F-algebra A, and C is a subring of A, then the following are equivalent: (a) C is the gauge ring of some minimal v-gauge on A, i.e., a gauge with the minimal number of simple components of C/J(C); (b) C is integral over V with C = B_1 \\cap ... \\cap B_xi$ where each B_i is a Dubrovin valuation ring of A with center V, and the B_i satisfy Graeter's Intersection Property. Along the way we prove the existence of minimal gauges whenever possible and we show how gauges on simple algebras are built from gauges on central simple algebras.", "published": "2013-07-21T07:35:56Z", "authors": ["Mauricio A. Ferreira", "Adrian R. Wadsworth"], "categories": ["math.RA"], "doi": "", "_query": "property valuation computer vision", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2509.15441v1", "title": "Computing Linear Regions in Neural Networks with Skip Connections", "abstract": "Neural networks are important tools in machine learning. Representing piecewise linear activation functions with tropical arithmetic enables the application of tropical geometry. Algorithms are presented to compute regions where the neural networks are linear maps. Through computational experiments, we provide insights on the difficulty to train neural networks, in particular on the problems of overfitting and on the benefits of skip connections.", "published": "2025-09-18T21:27:43Z", "authors": ["Johnny Joyce", "Jan Verschelde"], "categories": ["cs.LG", "cs.SC"], "doi": "", "_query": "property valuation computer vision", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2604.18075v1", "title": "Enhancing Continual Learning of Vision-Language Models via Dynamic Prefix Weighting", "abstract": "We investigate recently introduced domain-class incremental learning scenarios for vision-language models (VLMs). Recent works address this challenge using parameter-efficient methods, such as prefix-tuning or adapters, which facilitate model adaptation to downstream tasks by incorporating task-specific information into input tokens through additive vectors. However, previous approaches often normalize the weights of these vectors, disregarding the fact that different input tokens require different degrees of adjustment. To overcome this issue, we propose Dynamic Prefix Weighting (DPW), a framework that dynamically assigns weights to prefixes, complemented by adapters. DPW consists of 1) a gating module that adjusts the weights of each prefix based on the importance of the corresponding input token, and 2) a weighting mechanism that derives adapter output weights as a residual of prefix-tuning weights, ensuring that adapters are utilized only when necessary. Experimental results demonstrate that our method achieves state-of-the-art performance in domain-class incremental learning scenarios for VLMs. The code is available at: https://github.com/YonseiML/dpw.", "published": "2026-04-20T10:46:09Z", "authors": ["Hyeonseo Jang", "Hyuk Kwon", "Kibok Lee"], "categories": ["cs.CV"], "doi": "", "_query": "property valuation computer vision", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2609.24249v1", "title": "Reinforcement Learning Inspired Black-box Adversarial Attacks for Computer Vision", "abstract": "Neural networks, both convolution or transformer based, are essential for modern computer vision systems. However, they are vulnerable to small perturbations, almost imperceptible to humans, which significantly alter the model's prediction. These adversarial attacks are often considered to be a significant threat to the implementation of neural networks in safety-critical applications. Most attacks utilize the white-box threat model and therefore require full access to the target model, making them unrealistic to use in practice. We propose a novel approach under the more realistic black-box threat model that utilizes concepts from reinforcement learning to optimize perturbations with a non-differentiable target model. Reinforcement learning algorithms have already been optimized to be query efficient, making them an ideal starting point when designing black-box adversarial attacks. We show the success of our reinforcement learning inspired black-box adversarial attack (RIBA) in generating adversarial perturbations using only a small number of queries to the target model, by comparing it to state of the art attacks on different models on the Cifar10 and ImageNet data sets. RIBA takes $25.4\\%$ fewer median queries to generate attacked images against a ResNet-18 on Cifar10 and $22.5\\%$ fewer median queries to fool a Vit-B/16 model on ImageNet. Additionally, we demonstrate that RIBA can match the performance of white-box attacks on an adversarially trained model.", "published": "2026-09-21T08:14:44Z", "authors": ["Florian Krone", "Elena Hoemann", "Sven Hallerbach"], "categories": ["cs.LG", "cs.CR", "cs.CV"], "doi": "", "_query": "property valuation computer vision", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2610.06327v1", "title": "Dual Variational Autoencoders for Efficient Sim-to-Real Transfer in Low-Cost Robotic Navigation", "abstract": "Vision-based autonomous navigation for low-cost robots remains a fundamental challenge, primarily due to the significant gap between simulated training environments and real-world operational conditions. Direct policy transfer from simulation is often ineffective, while training exclusively on real data is impractical. We propose a hybrid transfer learning framework that effectively bridges the sim-to-real gap by combining domain randomization with feature-level domain adaptation. Our method employs a dual convolutional variational autoencoder architecture with a shared decoder, trained on an extensive set of 45225 simulated images and a minimal set of only 4556 real-world samples. This architecture learns a compact, common latent representation space that aligns the distributions of both domains. The adaptation process is further enhanced by two complementary data augmentation techniques designed to expand the limited real-world data. Experimental evaluation demonstrates that our method achieves an average success rate of almost 91% on image classification tasks for real-world indoor navigation, significantly outperforming both simulation-only and real-world-only training. We validate these findings through a direct, real-world deployment, where the proposed policy successfully guides a low-cost robot in a reactive exploration task. Furthermore, we validate the model's efficiency through a rigorous computational estimation, confirming its suitability for resource-constrained embedded platforms such as the Raspberry Pi 4 and NVIDIA Jetson Nano. This work presents a practical solution for developing effective and efficient navigation policies for low-cost robotic systems.", "published": "2026-10-05T13:38:26Z", "authors": ["Álvaro Díez", "Fidel Aznar"], "categories": ["cs.RO", "cs.CV", "cs.LG"], "doi": "10.1016/j.imavis.2026.106121", "_query": "property valuation computer vision", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2312.05975v3", "title": "FM-G-CAM: A Holistic Approach for Explainable AI in Computer Vision", "abstract": "Explainability is a vital aspect of modern AI for real-world impact and usability. The main objective of this paper is to emphasise the need to understand the predictions of Computer Vision models, specifically Convolutional Neural Network (CNN) models. Existing methods for explaining CNN predictions are largely based on Gradient-weighted Class Activation Maps (Grad-CAM) and focus solely on a single target class; this assumption about the target class selection neglects a large portion of the predictor CNN's prediction process. In this paper, we present an exhaustive methodology, called Fused Multi-class Gradient-weighted Class Activation Map (FM-G-CAM), that considers multiple top-predicted classes and provides a holistic explanation of the predictor CNN's rationale. We also provide a detailed mathematical and algorithmic description of our method. Furthermore, alongside a concise comparison of existing methods, we compare FM-G-CAM with Grad-CAM, quantitatively and qualitatively highlighting its benefits through real-world practical use cases. Finally, we present an open-source Python library with an FM-G-CAM implementation to conveniently generate saliency maps for CNN-based model predictions.", "published": "2023-12-10T19:33:40Z", "authors": ["Ravidu Suien Rammuni Silva", "Jordan J. Bird"], "categories": ["cs.CV", "cs.AI", "cs.LG"], "doi": "", "_query": "property valuation computer vision", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2505.08336v1", "title": "A computer vision-based model for occupancy detection using low-resolution thermal images", "abstract": "Occupancy plays an essential role in influencing the energy consumption and operation of heating, ventilation, and air conditioning (HVAC) systems. Traditional HVAC typically operate on fixed schedules without considering occupancy. Advanced occupant-centric control (OCC) adopted occupancy status in regulating HVAC operations. RGB images combined with computer vision (CV) techniques are widely used for occupancy detection, however, the detailed facial and body features they capture raise significant privacy concerns. Low-resolution thermal images offer a non-invasive solution that mitigates privacy issues. The study developed an occupancy detection model utilizing low-resolution thermal images and CV techniques, where transfer learning was applied to fine-tune the You Only Look Once version 5 (YOLOv5) model. The developed model ultimately achieved satisfactory performance, with precision, recall, mAP50, and mAP50 values approaching 1.000. The contributions of this model lie not only in mitigating privacy concerns but also in reducing computing resource demands.", "published": "2025-05-13T08:27:50Z", "authors": ["Xue Cui", "Vincent Gbouna Zakka", "Minhyun Lee"], "categories": ["cs.CV", "cs.AI"], "doi": "", "_query": "property valuation computer vision", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2604.04080v1", "title": "Intelligent Traffic Monitoring with YOLOv11: A Case Study in Real-Time Vehicle Detection", "abstract": "Recent advancements in computer vision, driven by artificial intelligence, have significantly enhanced monitoring systems. One notable application is traffic monitoring, which leverages computer vision alongside deep learning-based object detection and counting. We present an offline, real-time traffic monitoring system that couples a pre-trained YOLOv11 detector with BoT-SORT/ByteTrack for multi-object tracking, implemented in PyTorch/OpenCV and wrapped in a Qt-based desktop UI. The CNN pipeline enables efficient vehicle detection and counting from video streams without cloud dependencies. Across diverse scenes, the system achieves (66.67-95.83%) counting accuracy. Class-wise detection yields high precision (cars: 0.97-1.00; trucks: 1.00) with strong recall (cars: 0.82-1.00; trucks: 0.70-1.00), resulting in F1 scores of (0.90-1.00 for cars and 0.82-1.00 for trucks). While adverse weather conditions may negatively impact this performance, results remain robust in typical conditions. By integrating lightweight models with an accessible, cloud-independent interface, this paper contributes to the modernization and development of future smart cities by showing the capacity of AI-driven traffic monitoring systems.", "published": "2026-04-05T11:57:28Z", "authors": ["Shkelqim Sherifi"], "categories": ["cs.CV", "cs.AI", "cs.LG"], "doi": "10.1109/ICCA66035.2025.11430921", "_query": "property valuation computer vision", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2312.06709v5", "title": "AM-RADIO: Agglomerative Vision Foundation Model -- Reduce All Domains Into One", "abstract": "A handful of visual foundation models (VFMs) have recently emerged as the backbones for numerous downstream tasks. VFMs like CLIP, DINOv2, SAM are trained with distinct objectives, exhibiting unique characteristics for various downstream tasks. We find that despite their conceptual differences, these models can be effectively merged into a unified model through multi-teacher distillation. We name this approach AM-RADIO (Agglomerative Model -- Reduce All Domains Into One). This integrative approach not only surpasses the performance of individual teacher models but also amalgamates their distinctive features, such as zero-shot vision-language comprehension, detailed pixel-level understanding, and open vocabulary segmentation capabilities. In pursuit of the most hardware-efficient backbone, we evaluated numerous architectures in our multi-teacher distillation pipeline using the same training recipe. This led to the development of a novel architecture (E-RADIO) that exceeds the performance of its predecessors and is at least 7x faster than the teacher models. Our comprehensive benchmarking process covers downstream tasks including ImageNet classification, ADE20k semantic segmentation, COCO object detection and LLaVa-1.5 framework. Code: https://github.com/NVlabs/RADIO", "published": "2023-12-10T17:07:29Z", "authors": ["Mike Ranzinger", "Greg Heinrich", "Jan Kautz", "Pavlo Molchanov"], "categories": ["cs.CV"], "doi": "", "_query": "property valuation computer vision", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2508.10180v3", "title": "For-Value: Efficient Forward-Only Data Valuation for finetuning LLMs and VLMs", "abstract": "Data valuation is essential for enhancing the transparency and accountability of large language models (LLMs) and vision-language models (VLMs). However, existing methods typically rely on gradient computations, making them computationally prohibitive for billion-parameter models and precluding batch parallelization. In this work, we introduce For-Value, a forward-only data valuation framework that enables efficient batch-scalable value estimation while maintaining effectiveness. Leveraging the expressive power of pretrained LLMs/VLMs, we theoretically demonstrate that data valuation can be captured by the alignment between the final hidden representations and prediction errors at the last layer. In light of this insight, For-Value computes data value using a simple closed-form expression with a single forward pass, eliminating the need for costly backpropagation and enabling efficient batch calculating at scale. Extensive experiments show that For-Value matches or outperforms gradient-based baselines in detecting influential data and mislabeled data, while achieving significant efficiency improvements.", "published": "2025-08-13T20:33:06Z", "authors": ["Wenlong Deng", "Qi Zeng", "Jiaming Zhang", "Minghui Chen", "Zixin Ding", "Christos Thrampoulidis", "Boying Gong", "Xiaoxiao Li"], "categories": ["cs.CL"], "doi": "", "_query": "property valuation computer vision", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2502.14858v1", "title": "The $p$-adic Galois Cohomology of Valuation Fields", "abstract": "We compute the Galois cohomology of any $p$-adic valuation field extension of a pre-perfectoid field. Moreover, we obtain a generalization and also a new proof of the classical results of Tate and Hyodo on discrete valuation fields, without using higher ramification group, local class field theory or Epp's elimination of ramifications. A key ingredient is Gabber-Ramero's computation of cotangent complexes for valuation rings.", "published": "2025-02-20T18:59:07Z", "authors": ["Tongmu He"], "categories": ["math.AG", "math.AC", "math.NT"], "doi": "", "_query": "property valuation computer vision", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2511.00205v1", "title": "Fix: externalizing network I/O in serverless computing", "abstract": "We describe a system for serverless computing where users, programs, and the underlying platform share a common representation of a computation: a deterministic procedure, run in an environment of well-specified data or the outputs of other computations. This representation externalizes I/O: data movement over the network is performed exclusively by the platform. Applications can describe the precise data needed at each stage, helping the provider schedule tasks and network transfers to reduce starvation. The design suggests an end-to-end argument for outsourced computing, shifting the service model from ``pay-for-effort'' to ``pay-for-results.''", "published": "2025-10-31T19:05:42Z", "authors": ["Yuhan Deng", "Akshay Srivatsan", "Sebastian Ingino", "Francis Chua", "Yasmine Mitchell", "Matthew Vilaysack", "Keith Winstein"], "categories": ["cs.OS", "cs.DC"], "doi": "10.1145/3767295.3769387", "_query": "property valuation computer vision", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/1303.1477v1", "title": "Valuation Networks and Conditional Independence", "abstract": "Valuation networks have been proposed as graphical representations of valuation-based systems (VBSs). The VBS framework is able to capture many uncertainty calculi including probability theory, Dempster-Shafer's belief-function theory, Spohn's epistemic belief theory, and Zadeh's possibility theory. In this paper, we show how valuation networks encode conditional independence relations. For the probabilistic case, the class of probability models encoded by valuation networks includes undirected graph models, directed acyclic graph models, directed balloon graph models, and recursive causal graph models.", "published": "2013-03-06T14:20:35Z", "authors": ["Prakash P. Shenoy"], "categories": ["cs.AI"], "doi": "", "_query": "property valuation computer vision", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2601.04182v2", "title": "Saturation property fails for Schubert coefficients", "abstract": "The saturation property for Littlewood--Richardson coefficients was established by Knutson and Tao in 1999. In 2004, Kirillov conjectured that the saturation property extends to Schubert coefficients. We disprove this conjecture in a strong form, by showing that it fails for a large family of instances. We also discuss computational complexity implications.", "published": "2026-01-07T18:49:59Z", "authors": ["Igor Pak", "Colleen Robichaux"], "categories": ["math.CO"], "doi": "", "_query": "property valuation computer vision", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2312.05270v1", "title": "Image and AIS Data Fusion Technique for Maritime Computer Vision Applications", "abstract": "Deep learning object detection methods, like YOLOv5, are effective in identifying maritime vessels but often lack detailed information important for practical applications. In this paper, we addressed this problem by developing a technique that fuses Automatic Identification System (AIS) data with vessels detected in images to create datasets. This fusion enriches ship images with vessel-related data, such as type, size, speed, and direction. Our approach associates detected ships to their corresponding AIS messages by estimating distance and azimuth using a homography-based method suitable for both fixed and periodically panning cameras. This technique is useful for creating datasets for waterway traffic management, encounter detection, and surveillance. We introduce a novel dataset comprising of images taken in various weather conditions and their corresponding AIS messages. This dataset offers a stable baseline for refining vessel detection algorithms and trajectory prediction models. To assess our method's performance, we manually annotated a portion of this dataset. The results are showing an overall association accuracy of 74.76 %, with the association accuracy for fixed cameras reaching 85.06 %. This demonstrates the potential of our approach in creating datasets for vessel detection, pose estimation and auto-labelling pipelines.", "published": "2023-12-07T20:54:49Z", "authors": ["Emre Gülsoylu", "Paul Koch", "Mert Yıldız", "Manfred Constapel", "André Peter Kelm"], "categories": ["cs.CV"], "doi": "", "_query": "property valuation computer vision", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2308.16139v5", "title": "MedShapeNet -- A Large-Scale Dataset of 3D Medical Shapes for Computer Vision", "abstract": "Prior to the deep learning era, shape was commonly used to describe the objects. Nowadays, state-of-the-art (SOTA) algorithms in medical imaging are predominantly diverging from computer vision, where voxel grids, meshes, point clouds, and implicit surface models are used. This is seen from numerous shape-related publications in premier vision conferences as well as the growing popularity of ShapeNet (about 51,300 models) and Princeton ModelNet (127,915 models). For the medical domain, we present a large collection of anatomical shapes (e.g., bones, organs, vessels) and 3D models of surgical instrument, called MedShapeNet, created to facilitate the translation of data-driven vision algorithms to medical applications and to adapt SOTA vision algorithms to medical problems. As a unique feature, we directly model the majority of shapes on the imaging data of real patients. As of today, MedShapeNet includes 23 dataset with more than 100,000 shapes that are paired with annotations (ground truth). Our data is freely accessible via a web interface and a Python application programming interface (API) and can be used for discriminative, reconstructive, and variational benchmarks as well as various applications in virtual, augmented, or mixed reality, and 3D printing. Exemplary, we present use cases in the fields of classification of brain tumors, facial and skull reconstructions, multi-class anatomy completion, education, and 3D printing. In future, we will extend the data and improve the interfaces. The project pages are: https://medshapenet.ikim.nrw/ and https://github.com/Jianningli/medshapenet-feedback", "published": "2023-08-30T16:52:20Z", "authors": ["Jianning Li", "Zongwei Zhou", "Jiancheng Yang", "Antonio Pepe", "Christina Gsaxner", "Gijs Luijten", "Chongyu Qu", "Tiezheng Zhang", "Xiaoxi Chen", "Wenxuan Li", "Marek Wodzinski", "Paul Friedrich", "Kangxian Xie", "Yuan Jin", "Narmada Ambigapathy", "Enrico Nasca", "Naida Solak", "Gian Marco Melito", "Viet Duc Vu", "Afaque R. Memon", "Christopher Schlachta", "Sandrine De Ribaupierre", "Rajnikant Patel", "Roy Eagleson", "Xiaojun Chen", "Heinrich Mächler", "Jan Stefan Kirschke", "Ezequiel de la Rosa", "Patrick Ferdinand Christ", "Hongwei Bran Li", "David G. Ellis", "Michele R. Aizenberg", "Sergios Gatidis", "Thomas Küstner", "Nadya Shusharina", "Nicholas Heller", "Vincent Andrearczyk", "Adrien Depeursinge", "Mathieu Hatt", "Anjany Sekuboyina", "Maximilian Löffler", "Hans Liebl", "Reuben Dorent", "Tom Vercauteren", "Jonathan Shapey", "Aaron Kujawa", "Stefan Cornelissen", "Patrick Langenhuizen", "Achraf Ben-Hamadou", "Ahmed Rekik", "Sergi Pujades", "Edmond Boyer", "Federico Bolelli", "Costantino Grana", "Luca Lumetti", "Hamidreza Salehi", "Jun Ma", "Yao Zhang", "Ramtin Gharleghi", "Susann Beier", "Arcot Sowmya", "Eduardo A. Garza-Villarreal", "Thania Balducci", "Diego Angeles-Valdez", "Roberto Souza", "Leticia Rittner", "Richard Frayne", "Yuanfeng Ji", "Vincenzo Ferrari", "Soumick Chatterjee", "Florian Dubost", "Stefanie Schreiber", "Hendrik Mattern", "Oliver Speck", "Daniel Haehn", "Christoph John", "Andreas Nürnberger", "João Pedrosa", "Carlos Ferreira", "Guilherme Aresta", "António Cunha", "Aurélio Campilho", "Yannick Suter", "Jose Garcia", "Alain Lalande", "Vicky Vandenbossche", "Aline Van Oevelen", "Kate Duquesne", "Hamza Mekhzoum", "Jef Vandemeulebroucke", "Emmanuel Audenaert", "Claudia Krebs", "Timo van Leeuwen", "Evie Vereecke", "Hauke Heidemeyer", "Rainer Röhrig", "Frank Hölzle", "Vahid Badeli", "Kathrin Krieger", "Matthias Gunzer", "Jianxu Chen", "Timo van Meegdenburg", "Amin Dada", "Miriam Balzer", "Jana Fragemann", "Frederic Jonske", "Moritz Rempe", "Stanislav Malorodov", "Fin H. Bahnsen", "Constantin Seibold", "Alexander Jaus", "Zdravko Marinov", "Paul F. Jaeger", "Rainer Stiefelhagen", "Ana Sofia Santos", "Mariana Lindo", "André Ferreira", "Victor Alves", "Michael Kamp", "Amr Abourayya", "Felix Nensa", "Fabian Hörst", "Alexander Brehmer", "Lukas Heine", "Yannik Hanusrichter", "Martin Weßling", "Marcel Dudda", "Lars E. Podleska", "Matthias A. Fink", "Julius Keyl", "Konstantinos Tserpes", "Moon-Sung Kim", "Shireen Elhabian", "Hans Lamecker", "Dženan Zukić", "Beatriz Paniagua", "Christian Wachinger", "Martin Urschler", "Luc Duong", "Jakob Wasserthal", "Peter F. Hoyer", "Oliver Basu", "Thomas Maal", "Max J. H. Witjes", "Gregor Schiele", "Ti-chiun Chang", "Seyed-Ahmad Ahmadi", "Ping Luo", "Bjoern Menze", "Mauricio Reyes", "Thomas M. Deserno", "Christos Davatzikos", "Behrus Puladi", "Pascal Fua", "Alan L. Yuille", "Jens Kleesiek", "Jan Egger"], "categories": ["cs.CV", "cs.DB", "cs.LG"], "doi": "10.1515/bmt-2024-0396", "_query": "property valuation computer vision", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/1907.02506v1", "title": "Security modeling and efficient computation offloading for service workflow in mobile edge computing", "abstract": "It is a big challenge for resource-limited mobile devices (MDs) to execute various complex and energy-consumed mobile applications. Fortunately, as a novel computing paradigm, edge computing (MEC) can provide abundant computing resources to execute all or parts of the tasks of MDs and thereby can greatly reduce the energy of MD and improve the QoS of applications. However, offloading workflow tasks to the MEC servers are liable to external security threats (e.g., snooping, alteration). In this paper, we propose a security and energy efficient computation offloading (SEECO) strategy for service workflows in MEC environment, the goal of which is to optimize the energy consumption under the risk probability and deadline constraints. First, we build a security overhead model to measure the execution time of security services. Then, we formulate the computation offloading problem by incorporating the security, energy consumption and execution time of workflow application. Finally, based on the genetic algorithm (GA), the corresponding coding strategies of SEECO are devised by considering tasks execution order and location and security services selection. Extensive experiments with the variety of workflow parameters demonstrate that SEECO strategy can achieve the security and energy efficiency for the mobile applications.", "published": "2019-07-04T17:40:30Z", "authors": ["Binbin Huang", "Zhongjin Lia", "Peng Tang", "Shangguang Wang", "Jun Zhao", "Haiyang Hua", "Wanqing Lia", "Victor Chang"], "categories": ["cs.DC", "cs.CR", "cs.NI"], "doi": "10.1016/j.future.2019.03.011", "_query": "property valuation computer vision", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/1107.4637v2", "title": "Efficient variational inference in large-scale Bayesian compressed sensing", "abstract": "We study linear models under heavy-tailed priors from a probabilistic viewpoint. Instead of computing a single sparse most probable (MAP) solution as in standard deterministic approaches, the focus in the Bayesian compressed sensing framework shifts towards capturing the full posterior distribution on the latent variables, which allows quantifying the estimation uncertainty and learning model parameters using maximum likelihood. The exact posterior distribution under the sparse linear model is intractable and we concentrate on variational Bayesian techniques to approximate it. Repeatedly computing Gaussian variances turns out to be a key requisite and constitutes the main computational bottleneck in applying variational techniques in large-scale problems. We leverage on the recently proposed Perturb-and-MAP algorithm for drawing exact samples from Gaussian Markov random fields (GMRF). The main technical contribution of our paper is to show that estimating Gaussian variances using a relatively small number of such efficiently drawn random samples is much more effective than alternative general-purpose variance estimation techniques. By reducing the problem of variance estimation to standard optimization primitives, the resulting variational algorithms are fully scalable and parallelizable, allowing Bayesian computations in extremely large-scale problems with the same memory and time complexity requirements as conventional point estimation techniques. We illustrate these ideas with experiments in image deblurring.", "published": "2011-07-22T22:10:38Z", "authors": ["George Papandreou", "Alan Yuille"], "categories": ["cs.CV", "cs.IT", "stat.ML"], "doi": "10.1109/ICCVW.2011.6130406", "_query": "property valuation computer vision", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2509.22631v1", "title": "LABELING COPILOT: A Deep Research Agent for Automated Data Curation in Computer Vision", "abstract": "Curating high-quality, domain-specific datasets is a major bottleneck for deploying robust vision systems, requiring complex trade-offs between data quality, diversity, and cost when researching vast, unlabeled data lakes. We introduce Labeling Copilot, the first data curation deep research agent for computer vision. A central orchestrator agent, powered by a large multimodal language model, uses multi-step reasoning to execute specialized tools across three core capabilities: (1) Calibrated Discovery sources relevant, in-distribution data from large repositories; (2) Controllable Synthesis generates novel data for rare scenarios with robust filtering; and (3) Consensus Annotation produces accurate labels by orchestrating multiple foundation models via a novel consensus mechanism incorporating non-maximum suppression and voting. Our large-scale validation proves the effectiveness of Labeling Copilot's components. The Consensus Annotation module excels at object discovery: on the dense COCO dataset, it averages 14.2 candidate proposals per image-nearly double the 7.4 ground-truth objects-achieving a final annotation mAP of 37.1%. On the web-scale Open Images dataset, it navigated extreme class imbalance to discover 903 new bounding box categories, expanding its capability to over 1500 total. Concurrently, our Calibrated Discovery tool, tested at a 10-million sample scale, features an active learning strategy that is up to 40x more computationally efficient than alternatives with equivalent sample efficiency. These experiments validate that an agentic workflow with optimized, scalable tools provides a robust foundation for curating industrial-scale datasets.", "published": "2025-09-26T17:55:26Z", "authors": ["Debargha Ganguly", "Sumit Kumar", "Ishwar Balappanawar", "Weicong Chen", "Shashank Kambhatla", "Srinivasan Iyengar", "Shivkumar Kalyanaraman", "Ponnurangam Kumaraguru", "Vipin Chaudhary"], "categories": ["cs.CV", "cs.CL"], "doi": "", "_query": "property valuation computer vision", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2401.17736v1", "title": "Leveraging Human-Machine Interactions for Computer Vision Dataset Quality Enhancement", "abstract": "Large-scale datasets for single-label multi-class classification, such as \\emph{ImageNet-1k}, have been instrumental in advancing deep learning and computer vision. However, a critical and often understudied aspect is the comprehensive quality assessment of these datasets, especially regarding potential multi-label annotation errors. In this paper, we introduce a lightweight, user-friendly, and scalable framework that synergizes human and machine intelligence for efficient dataset validation and quality enhancement. We term this novel framework \\emph{Multilabelfy}. Central to Multilabelfy is an adaptable web-based platform that systematically guides annotators through the re-evaluation process, effectively leveraging human-machine interactions to enhance dataset quality. By using Multilabelfy on the ImageNetV2 dataset, we found that approximately $47.88\\%$ of the images contained at least two labels, underscoring the need for more rigorous assessments of such influential datasets. Furthermore, our analysis showed a negative correlation between the number of potential labels per image and model top-1 accuracy, illuminating a crucial factor in model evaluation and selection. Our open-source framework, Multilabelfy, offers a convenient, lightweight solution for dataset enhancement, emphasizing multi-label proportions. This study tackles major challenges in dataset integrity and provides key insights into model performance evaluation. Moreover, it underscores the advantages of integrating human expertise with machine capabilities to produce more robust models and trustworthy data development. The source code for Multilabelfy will be available at https://github.com/esla/Multilabelfy. \\keywords{Computer Vision \\and Dataset Quality Enhancement \\and Dataset Validation \\and Human-Computer Interaction \\and Multi-label Annotation.}", "published": "2024-01-31T10:57:07Z", "authors": ["Esla Timothy Anzaku", "Hyesoo Hong", "Jin-Woo Park", "Wonjun Yang", "Kangmin Kim", "JongBum Won", "Deshika Vinoshani Kumari Herath", "Arnout Van Messem", "Wesley De Neve"], "categories": ["cs.CV"], "doi": "", "_query": "property valuation computer vision", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/1805.00326v2", "title": "I Know How You Feel: Emotion Recognition with Facial Landmarks", "abstract": "Classification of human emotions remains an important and challenging task for many computer vision algorithms, especially in the era of humanoid robots which coexist with humans in their everyday life. Currently proposed methods for emotion recognition solve this task using multi-layered convolutional networks that do not explicitly infer any facial features in the classification phase. In this work, we postulate a fundamentally different approach to solve emotion recognition task that relies on incorporating facial landmarks as a part of the classification loss function. To that end, we extend a recently proposed Deep Alignment Network (DAN), that achieves state-of-the-art results in the recent facial landmark recognition challenge, with a term related to facial features. Thanks to this simple modification, our model called EmotionalDAN is able to outperform state-of-the-art emotion classification methods on two challenging benchmark dataset by up to 5%.", "published": "2018-04-22T19:06:50Z", "authors": ["Ivona Tautkute", "Tomasz Trzcinski", "Adam Bielski"], "categories": ["cs.CV", "cs.AI"], "doi": "", "_query": "property valuation computer vision", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2204.02573v1", "title": "Detecting key Soccer match events to create highlights using Computer Vision", "abstract": "The research and data science community has been fascinated with the development of automatic systems for the detection of key events in a video. Special attention in this field is given to sports video analytics which could help in identifying key events during a match and help in preparing a strategy for the games going forward. For this paper, we have chosen Football (soccer) as a sport where we would want to create highlights for a given match video, through a computer vision model that aims to identify important events in a Soccer match to create highlights of the match. We built the models based on Faster RCNN and YoloV5 architectures and noticed that for the amount of data we used for training Faster RCNN did better than YoloV5 in detecting the events in the match though it was much slower. Within Faster RCNN using ResNet50 as a base model gave a better class accuracy of 95.5% as compared to 92% with VGG16 as base model completely outperforming YoloV5 for our training dataset. We tested with an original video of size 23 minutes and our model could reduce it to 4:50 minutes of highlights capturing almost all important events in the match.", "published": "2022-04-06T04:28:27Z", "authors": ["Narayana Darapaneni", "Prashant Kumar", "Nikhil Malhotra", "Vigneswaran Sundaramurthy", "Abhaya Thakur", "Shivam Chauhan", "Krishna Chaitanya Thangeda", "Anwesh Reddy Paduri"], "categories": ["cs.CV", "cs.AI"], "doi": "", "_query": "property valuation computer vision", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2309.12768v1", "title": "WiCV@CVPR2023: The Eleventh Women In Computer Vision Workshop at the Annual CVPR Conference", "abstract": "In this paper, we present the details of Women in Computer Vision Workshop - WiCV 2023, organized alongside the hybrid CVPR 2023 in Vancouver, Canada. WiCV aims to amplify the voices of underrepresented women in the computer vision community, fostering increased visibility in both academia and industry. We believe that such events play a vital role in addressing gender imbalances within the field. The annual WiCV@CVPR workshop offers a) opportunity for collaboration between researchers from minority groups, b) mentorship for female junior researchers, c) financial support to presenters to alleviate finanacial burdens and d) a diverse array of role models who can inspire younger researchers at the outset of their careers. In this paper, we present a comprehensive report on the workshop program, historical trends from the past WiCV@CVPR events, and a summary of statistics related to presenters, attendees, and sponsorship for the WiCV 2023 workshop.", "published": "2023-09-22T10:15:38Z", "authors": ["Doris Antensteiner", "Marah Halawa", "Asra Aslam", "Ivaxi Sheth", "Sachini Herath", "Ziqi Huang", "Sunnie S. Y. Kim", "Aparna Akula", "Xin Wang"], "categories": ["cs.CV"], "doi": "", "_query": "property valuation computer vision", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2603.26727v1", "title": "The Nonverbal Gap: Toward Affective Computer Vision for Safer and More Equitable Online Dating", "abstract": "Online dating has become the dominant way romantic relationships begin, yet current platforms strip the nonverbal cues: gaze, facial expression, body posture, response timing, that humans rely on to signal comfort, disinterest, and consent, creating a communication gap with disproportionate safety consequences for women. We argue that this gap represents both a technical opportunity and a moral responsibility for the computer vision community, which has developed the affective tools, facial action unit detection, gaze estimation, engagement modeling, and multimodal affect recognition, needed to begin addressing it, yet has largely ignored the dating domain as a research context. We propose a fairness-first research agenda organized around four capability areas: real-time discomfort detection, engagement asymmetry modeling between partners, consent-aware interaction design, and longitudinal interaction summarization, each grounded in established CV methodology and motivated by the social psychology of romantic communication. We argue that responsible pursuit of this agenda requires purpose-built datasets collected under dyadic consent protocols, fairness evaluation disaggregated across race, gender identity, neurotype, and cultural background, and architectural commitments to on-device processing that prevent affective data from becoming platform surveillance infrastructure. This vision paper calls on the WICV community, whose members are uniquely positioned to understand both the technical opportunity and the human stakes, to establish online dating safety as a first-class research domain before commercial deployment outpaces ethical deliberation.", "published": "2026-03-20T03:31:07Z", "authors": ["Ratna Kandala", "Niva Manchanda", "Akshata Kishore Moharir"], "categories": ["cs.CV", "cs.AI"], "doi": "", "_query": "property valuation computer vision", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2502.00760v1", "title": "Privacy Preserving Properties of Vision Classifiers", "abstract": "Vision classifiers are often trained on proprietary datasets containing sensitive information, yet the models themselves are frequently shared openly under the privacy-preserving assumption. Although these models are assumed to protect sensitive information in their training data, the extent to which this assumption holds for different architectures remains unexplored. This assumption is challenged by inversion attacks which attempt to reconstruct training data from model weights, exposing significant privacy vulnerabilities. In this study, we systematically evaluate the privacy-preserving properties of vision classifiers across diverse architectures, including Multi-Layer Perceptrons (MLPs), Convolutional Neural Networks (CNNs), and Vision Transformers (ViTs). Using network inversion-based reconstruction techniques, we assess the extent to which these architectures memorize and reveal training data, quantifying the relative ease of reconstruction across models. Our analysis highlights how architectural differences, such as input representation, feature extraction mechanisms, and weight structures, influence privacy risks. By comparing these architectures, we identify which are more resilient to inversion attacks and examine the trade-offs between model performance and privacy preservation, contributing to the development of secure and privacy-respecting machine learning models for sensitive applications. Our findings provide actionable insights into the design of secure and privacy-aware machine learning systems, emphasizing the importance of evaluating architectural decisions in sensitive applications involving proprietary or personal data.", "published": "2025-02-02T11:50:00Z", "authors": ["Pirzada Suhail", "Amit Sethi"], "categories": ["cs.LG", "cs.CR", "cs.CV"], "doi": "", "_query": "property valuation computer vision", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2006.03725v1", "title": "Applied Awareness: Test-Driven GUI Development using Computer Vision and Cryptography", "abstract": "Graphical user interface testing is significantly challenging, and automating it even more so. Test-driven development is impractical: it generally requires an initial implementation of the GUI to generate golden images or to construct interactive test scenarios, and subsequent maintenance is costly. While computer vision has been applied to several aspects of GUI testing, we demonstrate a novel and immediately applicable approach of interpreting GUI presentation in terms of backend communications, modeling \"awareness\" in the fashion employed by cryptographic proofs of security. This focus on backend communication circumvents deficiencies in typical testing methodologies that rely on platform-dependent UI affordances or accessibility features. Our interdisciplinary work is ready for off-the-shelf practice: we report self-contained, practical implementation with both online and offline validation, using simple designer specifications at the outset and specifically avoiding any requirements for a bootstrap implementation or golden images. In addition to practical implementation, ties to formal verification methods in cryptography are explored and explained, providing fertile perspectives on assurance in UI and interpretability in AI.", "published": "2020-06-05T22:46:48Z", "authors": ["Donald Beaver"], "categories": ["cs.SE", "cs.CV"], "doi": "", "_query": "property valuation computer vision", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2601.11876v1", "title": "AI for Green Spaces: Leveraging Autonomous Navigation and Computer Vision for Park Litter Removal", "abstract": "There are 50 billion pieces of litter in the U.S. alone. Grass fields contribute to this problem because picnickers tend to leave trash on the field. We propose building a robot that can autonomously navigate, identify, and pick up trash in parks. To autonomously navigate the park, we used a Spanning Tree Coverage (STC) algorithm to generate a coverage path the robot could follow. To navigate this path, we successfully used Real-Time Kinematic (RTK) GPS, which provides a centimeter-level reading every second. For computer vision, we utilized the ResNet50 Convolutional Neural Network (CNN), which detects trash with 94.52% accuracy. For trash pickup, we tested multiple design concepts. We select a new pickup mechanism that specifically targets the trash we encounter on the field. Our solution achieved an overall success rate of 80%, demonstrating that autonomous trash pickup robots on grass fields are a viable solution.", "published": "2026-01-17T02:05:05Z", "authors": ["Christopher Kao", "Akhil Pathapati", "James Davis"], "categories": ["cs.RO", "cs.AI", "cs.CV", "eess.SY"], "doi": "10.1109/SII59315.2025.10870583", "_query": "property valuation computer vision", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2608.09052v1", "title": "Triple Expert Learning from Noisy Labels for Semi-Supervised Vision Foundation Model Adaptation", "abstract": "Semi-supervised adaptation of vision foundation models (VFMs) commonly freezes the pretrained backbone and updates lightweight modules such as LoRA. However, pseudo-labels have mixed reliability, and a single LoRA adapter must absorb reliable, ambiguous, and noisy gradients in the same low-rank space. This can make VFM adaptation sensitive to pseudo-label noise. We propose \\textbf{TriNoL}, a \\textbf{Tri}ple-expert learning framework from \\textbf{No}isy \\textbf{L}abels for semi-supervised VFM adaptation. TriNoL routes unlabeled samples into three confidence regions and assigns them to three LoRA experts: a Positive Expert for high-confidence pseudo-labels, an Alignment Expert for medium-confidence ambiguous samples, and a Negative Expert for low-confidence noisy samples. The VFM backbone remains frozen, and only the LoRA experts and classifier head are updated. By separating different pseudo-label reliability regions into specialized adaptation paths, TriNoL improves robustness to noisy supervision while keeping the training cost low.", "published": "2026-08-10T03:00:17Z", "authors": ["Xuanyu Liu", "Zheng Fang", "Hongyang He", "Yundi Hong", "Daizong Liu"], "categories": ["cs.CV", "cs.AI"], "doi": "", "_query": "property valuation computer vision", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2311.11992v1", "title": "Exploring Lip Segmentation Techniques in Computer Vision: A Comparative Analysis", "abstract": "Lip segmentation is crucial in computer vision, especially for lip reading. Despite extensive face segmentation research, lip segmentation has received limited attention. The aim of this study is to compare state-of-the-art lip segmentation models using a standardized setting and a publicly available dataset. Five techniques, namely EHANet, Mask2Former, BiSeNet V2, PIDNet, and STDC1, are qualitatively selected based on their reported performance, inference time, code availability, recency, and popularity. The CelebAMask-HQ dataset, comprising manually annotated face images, is used to fairly assess the lip segmentation performance of the selected models. Inference experiments are conducted on a Raspberry Pi4 to emulate limited computational resources. The results show that Mask2Former and EHANet have the best performances in terms of mIoU score. BiSeNet V2 demonstrate competitive performance, while PIDNet excels in recall but has lower precision. Most models present inference time ranging from 1000 to around 3000 milliseconds on a Raspberry Pi4, with PIDNet having the lowest mean inference time. This study provides a comprehensive evaluation of lip segmentation models, highlighting their performance and inference times. The findings contribute to the development of lightweight techniques and establish benchmarks for future advances in lip segmentation, especially in IoT and edge computing scenarios.", "published": "2023-11-20T18:23:41Z", "authors": ["Pietro B. S. Masur", "Francisco Braulio Oliveira", "Lucas Moreira Medino", "Emanuel Huber", "Milene Haraguchi Padilha", "Cassio de Alcantara", "Renata Sellaro"], "categories": ["cs.CV", "cs.AI", "cs.LG"], "doi": "", "_query": "property valuation computer vision", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/1507.04851v1", "title": "Convolution of valuations on manifolds", "abstract": "We introduce the new notion of convolution of a (smooth or generalized) valuation on a group $G$ and a valuation on a manifold $M$ acted upon by the group. In the case of a transitive group action, we prove that the spaces of smooth and generalized valuations on $M$ are modules over the algebra of compactly supported generalized valuations on $G$ satisfying some technical condition of tameness. The case of a vector space acting on itself is studied in detail. We prove explicit formulas in this case and show that the new convolution is an extension of the convolution on smooth translation invariant valuations introduced by J.~Fu and the second named author.", "published": "2015-07-17T07:19:59Z", "authors": ["Semyon Alesker", "Andreas Bernig"], "categories": ["math.DG"], "doi": "", "_query": "property valuation computer vision", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/physics/0608167v1", "title": "Electricity Real Options Valuation", "abstract": "In this paper a real option approach for the valuation of real assets is presented. Two continuous time models used for valuation are described: geometric Brownian motion model and interest rate model. The valuation for electricity spread option under Vasicek interest model is placed and the formulas for parameter estimators are calculated. The theoretical part is confronted with real data from electricity market.", "published": "2006-08-16T13:18:44Z", "authors": ["Ewa Broszkiewicz-Suwaj"], "categories": ["physics.ao-ph", "math-ph", "physics.data-an"], "doi": "", "_query": "property valuation computer vision", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/1206.3521v1", "title": "The constructible topology on spaces of valuation domains", "abstract": "We consider properties and applications of a compact, Hausdorff topology called the \"ultrafilter topology\" defined on an {\\sl arbitrary spectral space} and we observe that this topology coincides with the constructible topology. If $K$ is a field and $A$ a subring of $K$, we show that the space Zar$(K|A)$ of all valuation domains, having $K$ as quotient field and containing $A$, (endowed with the Zariski topology) is a spectral space by giving in this general setting the explicit construction of a ring whose Zariski spectrum is homeomorphic to Zar$(K|A)$. We extend results regarding spectral topologies on the spaces of all valuation domains and apply the theory developed to study representations of integrally closed domains as intersections of valuation overrings. As a very particular case, we prove that two collections of valuation domains of $K$ with the same ultrafilter closure represent, as an intersection, the same integrally closed domain.", "published": "2012-06-15T17:20:03Z", "authors": ["Carmelo Finocchiaro", "Marco Fontana", "K. Alan Loper"], "categories": ["math.AC", "math.AG"], "doi": "", "_query": "property valuation computer vision", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2105.01456v1", "title": "Computer vision for liquid samples in hospitals and medical labs using hierarchical image segmentation and relations prediction", "abstract": "This work explores the use of computer vision for image segmentation and classification of medical fluid samples in transparent containers (for example, tubes, syringes, infusion bags). Handling fluids such as infusion fluids, blood, and urine samples is a significant part of the work carried out in medical labs and hospitals. The ability to accurately identify and segment the liquids and the vessels that contain them from images can help in automating such processes. Modern computer vision typically involves training deep neural nets on large datasets of annotated images. This work presents a new dataset containing 1,300 annotated images of medical samples involving vessels containing liquids and solid material. The images are annotated with the type of liquid (e.g., blood, urine), the phase of the material (e.g., liquid, solid, foam, suspension), the type of vessel (e.g., syringe, tube, cup, infusion bottle/bag), and the properties of the vessel (transparent, opaque). In addition, vessel parts such as corks, labels, spikes, and valves are annotated. Relations and hierarchies between vessels and materials are also annotated, such as which vessel contains which material or which vessels are linked or contain each other. Three neural networks are trained on the dataset: One network learns to detect vessels, a second net detects the materials and parts inside each vessel, and a third net identifies relationships and connectivity between vessels.", "published": "2021-05-04T12:39:39Z", "authors": ["Sagi Eppel", "Haoping Xu", "Alan Aspuru-Guzik"], "categories": ["cs.CV"], "doi": "", "_query": "property valuation computer vision", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2505.15408v3", "title": "Mouse Lockbox Dataset: Behavior Recognition for Mice Solving Lockboxes", "abstract": "Machine learning and computer vision methods have a major impact on the study of natural animal behavior, as they enable the (semi-)automatic analysis of vast amounts of video data. Mice are the standard mammalian model system in most research fields, but the datasets available today to refine such methods focus either on simple or social behaviors. In this work, we present a video dataset of individual mice solving complex mechanical puzzles, so-called lockboxes. The more than 110 hours of total playtime show their behavior recorded from three different perspectives. As a benchmark for frame-level action classification methods, we provide human-annotated labels for all videos of two different mice, that equal 13% of our dataset. Our keypoint (pose) tracking-based action classification framework illustrates the challenges of automated labeling of fine-grained behaviors, such as the manipulation of objects. We hope that our work will help accelerate the advancement of automated action and behavior classification in the computational neuroscience community. Our dataset is publicly available at https://doi.org/10.14279/depositonce-23850", "published": "2025-05-21T11:51:51Z", "authors": ["Patrik Reiske", "Marcus N. Boon", "Niek Andresen", "Sole Traverso", "Katharina Hohlbaum", "Lars Lewejohann", "Christa Thöne-Reineke", "Olaf Hellwich", "Henning Sprekeler"], "categories": ["cs.CV"], "doi": "10.1007/s11263-026-02908-x", "_query": "property valuation computer vision", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/1704.03568v2", "title": "Beyond Planar Symmetry: Modeling human perception of reflection and rotation symmetries in the wild", "abstract": "Humans take advantage of real world symmetries for various tasks, yet capturing their superb symmetry perception mechanism with a computational model remains elusive. Motivated by a new study demonstrating the extremely high inter-person accuracy of human perceived symmetries in the wild, we have constructed the first deep-learning neural network for reflection and rotation symmetry detection (Sym-NET), trained on photos from MS-COCO (Microsoft-Common Object in COntext) dataset with nearly 11K consistent symmetry-labels from more than 400 human observers. We employ novel methods to convert discrete human labels into symmetry heatmaps, capture symmetry densely in an image and quantitatively evaluate Sym-NET against multiple existing computer vision algorithms. On CVPR 2013 symmetry competition testsets and unseen MS-COCO photos, Sym-NET significantly outperforms all other competitors. Beyond mathematically well-defined symmetries on a plane, Sym-NET demonstrates abilities to identify viewpoint-varied 3D symmetries, partially occluded symmetrical objects, and symmetries at a semantic level.", "published": "2017-04-11T23:25:25Z", "authors": ["Christopher Funk", "Yanxi Liu"], "categories": ["cs.CV", "q-bio.NC", "stat.ML"], "doi": "", "_query": "property valuation computer vision", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2201.01466v1", "title": "Challenges of Artificial Intelligence -- From Machine Learning and Computer Vision to Emotional Intelligence", "abstract": "Artificial intelligence (AI) has become a part of everyday conversation and our lives. It is considered as the new electricity that is revolutionizing the world. AI is heavily invested in both industry and academy. However, there is also a lot of hype in the current AI debate. AI based on so-called deep learning has achieved impressive results in many problems, but its limits are already visible. AI has been under research since the 1940s, and the industry has seen many ups and downs due to over-expectations and related disappointments that have followed. The purpose of this book is to give a realistic picture of AI, its history, its potential and limitations. We believe that AI is a helper, not a ruler of humans. We begin by describing what AI is and how it has evolved over the decades. After fundamentals, we explain the importance of massive data for the current mainstream of artificial intelligence. The most common representations for AI, methods, and machine learning are covered. In addition, the main application areas are introduced. Computer vision has been central to the development of AI. The book provides a general introduction to computer vision, and includes an exposure to the results and applications of our own research. Emotions are central to human intelligence, but little use has been made in AI. We present the basics of emotional intelligence and our own research on the topic. We discuss super-intelligence that transcends human understanding, explaining why such achievement seems impossible on the basis of present knowledge,and how AI could be improved. Finally, a summary is made of the current state of AI and what to do in the future. In the appendix, we look at the development of AI education, especially from the perspective of contents at our own university.", "published": "2022-01-05T06:00:22Z", "authors": ["Matti Pietikäinen", "Olli Silven"], "categories": ["cs.AI", "cs.CV", "cs.LG"], "doi": "", "_query": "property valuation computer vision", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/1701.03151v1", "title": "Guaranteed Parameter Estimation for Discrete Energy Minimization", "abstract": "Structural learning, a method to estimate the parameters for discrete energy minimization, has been proven to be effective in solving computer vision problems, especially in 3D scene parsing. As the complexity of the models increases, structural learning algorithms turn to approximate inference to retain tractability. Unfortunately, such methods often fail because the approximation can be arbitrarily poor. In this work, we propose a method to overcome this limitation through exploiting the properties of the joint problem of training time inference and learning. With the help of the learning framework, we transform the inapproximable inference problem into a polynomial time solvable one, thereby enabling tractable exact inference while still allowing an arbitrary graph structure and full potential interactions. Our learning algorithm is guaranteed to return a solution with a bounded error to the global optimal within the feasible parameter space. We demonstrate the effectiveness of this method on two point cloud scene parsing datasets. Our approach runs much faster and solves a problem that is intractable for previous, well-known approaches.", "published": "2017-01-11T20:41:14Z", "authors": ["Mengtian Li", "Daniel Huber"], "categories": ["cs.CV"], "doi": "", "_query": "property valuation computer vision", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2304.14408v3", "title": "Using Scalable Computer Vision to Automate High-throughput Semiconductor Characterization", "abstract": "High-throughput materials synthesis methods have risen in popularity due to their potential to accelerate the design and discovery of novel functional materials, such as solution-processed semiconductors. After synthesis, key material properties must be measured and characterized to validate discovery and provide feedback to optimization cycles. However, with the boom in development of high-throughput synthesis tools that champion production rates up to $10^4$ samples per hour with flexible form factors, most sample characterization methods are either slow (conventional rates of $10^1$ samples per hour, approximately 1000x slower) or rigid (e.g., designed for standard-size microplates), resulting in a bottleneck that impedes the materials-design process. To overcome this challenge, we propose a set of automated material property characterization (autocharacterization) tools that leverage the adaptive, parallelizable, and scalable nature of computer vision to accelerate the throughput of characterization by 85x compared to the non-automated workflow. We demonstrate a generalizable composition mapping tool for high-throughput synthesized binary material systems as well as two scalable autocharacterization algorithms that (1) autonomously compute the band gap of 200 unique compositions in 6 minutes and (2) autonomously compute the degree of degradation in 200 unique compositions in 20 minutes, generating ultra-high compositional resolution trends of band gap and stability. We demonstrate that the developed band gap and degradation detection autocharacterization methods achieve 98.5% accuracy and 96.9% accuracy, respectively, on the FA$_{1-x}$MA$_{x}$PbI$_3$, $0\\leq x \\leq 1$ perovskite semiconductor system.", "published": "2023-03-16T17:30:51Z", "authors": ["Alexander E. Siemenn", "Eunice Aissi", "Fang Sheng", "Armi Tiihonen", "Hamide Kavak", "Basita Das", "Tonio Buonassisi"], "categories": ["eess.IV", "cs.CV"], "doi": "10.1038/s41467-024-48768-2", "_query": "property valuation computer vision", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2411.02136v3", "title": "Advanced computer vision for extracting georeferenced vehicle trajectories from drone imagery", "abstract": "This paper presents a framework for extracting georeferenced vehicle trajectories from high-altitude drone imagery, addressing key challenges in urban traffic monitoring and the limitations of traditional ground-based systems. Our approach integrates several novel contributions, including a tailored object detector optimized for high-altitude bird's-eye view perspectives, a unique track stabilization method that uses detected vehicle bounding boxes as exclusion masks during image registration, and an orthophoto and master frame-based georeferencing strategy that enhances consistent alignment across multiple drone viewpoints. Additionally, our framework features robust vehicle dimension estimation and detailed road segmentation, enabling comprehensive traffic analysis. Conducted in the Songdo International Business District, South Korea, the study utilized a multi-drone experiment covering 20 intersections, capturing approximately 12TB of 4K video data over four days. The framework produced two high-quality datasets: the Songdo Traffic dataset, comprising approximately 700,000 unique vehicle trajectories, and the Songdo Vision dataset, containing over 5,000 human-annotated images with about 300,000 vehicle instances in four classes. Comparisons with high-precision sensor data from an instrumented probe vehicle highlight the accuracy and consistency of our extraction pipeline in dense urban environments. The public release of Songdo Traffic and Songdo Vision, and the complete source code for the extraction pipeline, establishes new benchmarks in data quality, reproducibility, and scalability in traffic research. Results demonstrate the potential of integrating drone technology with advanced computer vision for precise and cost-effective urban traffic monitoring, providing valuable resources for developing intelligent transportation systems and enhancing traffic management strategies.", "published": "2024-11-04T14:49:01Z", "authors": ["Robert Fonod", "Haechan Cho", "Hwasoo Yeo", "Nikolas Geroliminis"], "categories": ["cs.CV", "cs.AI", "cs.LG"], "doi": "10.1016/j.trc.2025.105205", "_query": "property valuation computer vision", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2006.11316v1", "title": "SqueezeBERT: What can computer vision teach NLP about efficient neural networks?", "abstract": "Humans read and write hundreds of billions of messages every day. Further, due to the availability of large datasets, large computing systems, and better neural network models, natural language processing (NLP) technology has made significant strides in understanding, proofreading, and organizing these messages. Thus, there is a significant opportunity to deploy NLP in myriad applications to help web users, social networks, and businesses. In particular, we consider smartphones and other mobile devices as crucial platforms for deploying NLP models at scale. However, today's highly-accurate NLP neural network models such as BERT and RoBERTa are extremely computationally expensive, with BERT-base taking 1.7 seconds to classify a text snippet on a Pixel 3 smartphone. In this work, we observe that methods such as grouped convolutions have yielded significant speedups for computer vision networks, but many of these techniques have not been adopted by NLP neural network designers. We demonstrate how to replace several operations in self-attention layers with grouped convolutions, and we use this technique in a novel network architecture called SqueezeBERT, which runs 4.3x faster than BERT-base on the Pixel 3 while achieving competitive accuracy on the GLUE test set. The SqueezeBERT code will be released.", "published": "2020-06-19T18:40:29Z", "authors": ["Forrest N. Iandola", "Albert E. Shaw", "Ravi Krishna", "Kurt W. Keutzer"], "categories": ["cs.CL", "cs.CV", "cs.LG"], "doi": "", "_query": "property valuation computer vision", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2111.09024v1", "title": "Geometric valuation theory", "abstract": "A brief introduction to geometric valuation theory is given. The focus is on classification results for valuations on convex bodies and on function spaces.", "published": "2021-11-17T10:36:33Z", "authors": ["Monika Ludwig"], "categories": ["math.MG", "math.FA"], "doi": "10.4171/8ECM/25", "_query": "property valuation computer vision", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/1407.1267v1", "title": "Calibration of Multiple Fish-Eye Cameras Using a Wand", "abstract": "Fish-eye cameras are becoming increasingly popular in computer vision, but their use for 3D measurement is limited partly due to the lack of an accurate, efficient and user-friendly calibration procedure. For such a purpose, we propose a method to calibrate the intrinsic and extrinsic parameters (including radial distortion parameters) of two/multiple fish-eye cameras simultaneously by using a wand under general motions. Thanks to the generic camera model used, the proposed calibration method is also suitable for two/multiple conventional cameras and mixed cameras (e.g. two conventional cameras and a fish-eye camera). Simulation and real experiments demonstrate the effectiveness of the proposed method. Moreover, we develop the camera calibration toolbox, which is available online.", "published": "2014-07-04T15:58:23Z", "authors": ["Qiang Fu", "Quan Quan", "Kai-Yuan Cai"], "categories": ["cs.CV"], "doi": "", "_query": "property valuation computer vision", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2105.04383v1", "title": "A framework for the automation of testing computer vision systems", "abstract": "Vision systems, i.e., systems that allow to detect and track objects in images, have gained substantial importance over the past decades. They are used in quality assurance applications, e.g., for finding surface defects in products during manufacturing, surveillance, but also automated driving, requiring reliable behavior. Interestingly, there is only little work on quality assurance and especially testing of vision systems in general. In this paper, we contribute to the area of testing vision software, and present a framework for the automated generation of tests for systems based on vision and image recognition. The framework makes use of existing libraries allowing to modify original images and to obtain similarities between the original and modified images. We show how such a framework can be used for testing a particular industrial application on identifying defects on riblet surfaces and present preliminary results from the image classification domain.", "published": "2021-05-10T14:02:42Z", "authors": ["Franz Wotawa", "Lorenz Klampfl", "Ledio Jahaj"], "categories": ["cs.SE", "cs.CV"], "doi": "", "_query": "property valuation computer vision", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/1703.04977v2", "title": "What Uncertainties Do We Need in Bayesian Deep Learning for Computer Vision?", "abstract": "There are two major types of uncertainty one can model. Aleatoric uncertainty captures noise inherent in the observations. On the other hand, epistemic uncertainty accounts for uncertainty in the model -- uncertainty which can be explained away given enough data. Traditionally it has been difficult to model epistemic uncertainty in computer vision, but with new Bayesian deep learning tools this is now possible. We study the benefits of modeling epistemic vs. aleatoric uncertainty in Bayesian deep learning models for vision tasks. For this we present a Bayesian deep learning framework combining input-dependent aleatoric uncertainty together with epistemic uncertainty. We study models under the framework with per-pixel semantic segmentation and depth regression tasks. Further, our explicit uncertainty formulation leads to new loss functions for these tasks, which can be interpreted as learned attenuation. This makes the loss more robust to noisy data, also giving new state-of-the-art results on segmentation and depth regression benchmarks.", "published": "2017-03-15T07:27:12Z", "authors": ["Alex Kendall", "Yarin Gal"], "categories": ["cs.CV"], "doi": "", "_query": "property valuation computer vision", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2406.11403v2", "title": "Multimodal Structured Generation: CVPR's 2nd MMFM Challenge Technical Report", "abstract": "Multimodal Foundation Models (MMFMs) have demonstrated strong performance in both computer vision and natural language processing tasks. However, their performance diminishes in tasks that require a high degree of integration between these modalities, such as document understanding. Moreover, finetuning these models and deploying them requires significantly more compute and more engineering effort than unimodal models. In this work, we present Multimodal Structured Generation, a framework that forces (frozen) MMFMs to produce outputs in a strictly structured format by applying hard constraints directly to the output logits. This approach not only ensures that the model generates parseable outputs that downstream APIs can easily ingest but also allows us to force the model to reason before answering, which significantly boosts performance without the need for expensive fine-tuning. We demonstrate the effectiveness of our method through competitive results in the CVPR 2nd MMFM Challenge, highlighting that carefully designed lightweight engineering can outperform expensive and complicated modeling approaches. All of our scripts, deployment steps, and evaluation results can be accessed in https://github.com/leloykun/MMFM-Challenge", "published": "2024-06-17T10:45:47Z", "authors": ["Franz Louis Cesista"], "categories": ["cs.CV", "cs.CL"], "doi": "", "_query": "property valuation computer vision", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2005.03298v1", "title": "Augmented Valuation and Minimal Pair", "abstract": "Let $(K, ν)$ be a valued field, the notions of \\emph{augmented valuation}, of \\emph{limit augmented valuation} and of \\emph{admissible family} of valuations enable to give a description of any valuation $μ$ of $K [x]$ extending $ν$. In the case where the field $K$ is algebraically closed, this description is particularly simple and we can reduce it to the notions of \\emph{minimal pair} and \\emph{pseudo-convergent family}. Let $(K, ν)$ be a henselian valued field and $\\barν$ the unique extension of $ν$ to the algebraic closure $\\bar K$ of $K$ and let $μ$ be a valuation of $ K [x]$ extending $ν$, we study the extensions $\\barμ$ from $μ$ to $\\bar K [x]$ and we give a description of the valuations $\\barμ_i$ of $\\bar K [x]$ which are the extensions of the valuations $μ_i$ belonging to the admissible family associated with $μ$.", "published": "2020-05-07T07:42:28Z", "authors": ["Michel Vaquié"], "categories": ["math.AC", "math.AG"], "doi": "", "_query": "property valuation computer vision", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2405.13954v1", "title": "What is Your Data Worth to GPT? LLM-Scale Data Valuation with Influence Functions", "abstract": "Large language models (LLMs) are trained on a vast amount of human-written data, but data providers often remain uncredited. In response to this issue, data valuation (or data attribution), which quantifies the contribution or value of each data to the model output, has been discussed as a potential solution. Nevertheless, applying existing data valuation methods to recent LLMs and their vast training datasets has been largely limited by prohibitive compute and memory costs. In this work, we focus on influence functions, a popular gradient-based data valuation method, and significantly improve its scalability with an efficient gradient projection strategy called LoGra that leverages the gradient structure in backpropagation. We then provide a theoretical motivation of gradient projection approaches to influence functions to promote trust in the data valuation process. Lastly, we lower the barrier to implementing data valuation systems by introducing LogIX, a software package that can transform existing training code into data valuation code with minimal effort. In our data valuation experiments, LoGra achieves competitive accuracy against more expensive baselines while showing up to 6,500x improvement in throughput and 5x reduction in GPU memory usage when applied to Llama3-8B-Instruct and the 1B-token dataset.", "published": "2024-05-22T19:39:05Z", "authors": ["Sang Keun Choe", "Hwijeen Ahn", "Juhan Bae", "Kewen Zhao", "Minsoo Kang", "Youngseog Chung", "Adithya Pratapa", "Willie Neiswanger", "Emma Strubell", "Teruko Mitamura", "Jeff Schneider", "Eduard Hovy", "Roger Grosse", "Eric Xing"], "categories": ["cs.LG", "cs.AI", "cs.CL"], "doi": "", "_query": "property valuation computer vision", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2602.11448v3", "title": "Hierarchical Concept Embedding & Pursuit for Interpretable Image Classification", "abstract": "Interpretable-by-design models are gaining traction in computer vision because they provide faithful explanations for their predictions. In image classification, these models typically recover human-interpretable concepts from an image and use them for classification. Sparse concept recovery methods leverage the latent space of vision-language models to represent image embeddings as sparse combinations of concept embeddings. However, by ignoring the hierarchical structure of semantic concepts, these methods may produce correct predictions with explanations that are inconsistent with the hierarchy. In this work, we propose Hierarchical Concept Embedding & Pursuit (HCEP), a framework that induces a hierarchy of concept embeddings in the latent space and performs hierarchical sparse coding to recover the concepts present in an image. Given a hierarchy of semantic concepts, we introduce a geometric construction for the corresponding hierarchy of embeddings. Under the assumption that the true concepts form a rooted path in the hierarchy, we derive sufficient conditions for their recovery in the embedding space. We further show that hierarchical sparse coding reliably recovers hierarchical concept embeddings, whereas standard sparse coding fails. Experiments on real-world datasets show that HCEP improves concept precision and recall compared to existing methods while maintaining competitive classification accuracy. Moreover, when the number of samples available for concept estimation and classifier training is limited, HCEP achieves superior classification accuracy and concept recovery. Our results demonstrate that incorporating hierarchical structure into sparse concept recovery leads to more faithful and interpretable image classification models.", "published": "2026-02-11T23:53:15Z", "authors": ["Nghia Nguyen", "Tianjiao Ding", "René Vidal"], "categories": ["cs.LG", "cs.CV"], "doi": "", "_query": "property valuation computer vision", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2604.08722v1", "title": "AI Driven Soccer Analysis Using Computer Vision", "abstract": "Sport analysis is crucial for team performance since it provides actionable data that can inform coaching decisions, improve player performance, and enhance team strategies. To analyze more complex features from game footage, a computer vision model can be used to identify and track key entities from the field. We propose the use of an object detection and tracking system to predict player positioning throughout the game. To translate this to positioning in relation to the field dimensions, we use a point prediction model to identify key points on the field and combine these with known field dimensions to extract actual distances. For the player-identification model, object detection models like YOLO and Faster R-CNN are evaluated on the accuracy of our custom video footage using multiple different evaluation metrics. The goal is to identify the best model for object identification to obtain the most accurate results when paired with SAM2 (Segment Anything Model 2) for segmentation and tracking. For the key point detection model, we use a CNN model to find consistent locations in the soccer field. Through homography, the positions of points and objects in the camera perspective will be transformed to a real-ground perspective. The segmented player masks from SAM2 are transformed from camera perspective to real-world field coordinates through homography, regardless of camera angle or movement. The transformed real-world coordinates can be used to calculate valuable tactical insights including player speed, distance covered, positioning heatmaps, and more complex team statistics, providing coaches and players with actionable performance data previously unavailable from standard video analysis.", "published": "2026-04-09T19:19:40Z", "authors": ["Adrian Manchado", "Tanner Cellio", "Jonathan Keane", "Yiyang Wang"], "categories": ["cs.CV", "cs.AI"], "doi": "", "_query": "property valuation computer vision", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2211.01226v2", "title": "DEArt: Dataset of European Art", "abstract": "Large datasets that were made publicly available to the research community over the last 20 years have been a key enabling factor for the advances in deep learning algorithms for NLP or computer vision. These datasets are generally pairs of aligned image / manually annotated metadata, where images are photographs of everyday life. Scholarly and historical content, on the other hand, treat subjects that are not necessarily popular to a general audience, they may not always contain a large number of data points, and new data may be difficult or impossible to collect. Some exceptions do exist, for instance, scientific or health data, but this is not the case for cultural heritage (CH). The poor performance of the best models in computer vision - when tested over artworks - coupled with the lack of extensively annotated datasets for CH, and the fact that artwork images depict objects and actions not captured by photographs, indicate that a CH-specific dataset would be highly valuable for this community. We propose DEArt, at this point primarily an object detection and pose classification dataset meant to be a reference for paintings between the XIIth and the XVIIIth centuries. It contains more than 15000 images, about 80% non-iconic, aligned with manual annotations for the bounding boxes identifying all instances of 69 classes as well as 12 possible poses for boxes identifying human-like objects. Of these, more than 50 classes are CH-specific and thus do not appear in other datasets; these reflect imaginary beings, symbolic entities and other categories related to art. Additionally, existing datasets do not include pose annotations. Our results show that object detectors for the cultural heritage domain can achieve a level of precision comparable to state-of-art models for generic images via transfer learning.", "published": "2022-11-02T16:05:35Z", "authors": ["Artem Reshetnikov", "Maria-Cristina Marinescu", "Joaquim More Lopez"], "categories": ["cs.CV"], "doi": "", "_query": "property valuation computer vision", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2005.07289v2", "title": "Taskology: Utilizing Task Relations at Scale", "abstract": "Many computer vision tasks address the problem of scene understanding and are naturally interrelated e.g. object classification, detection, scene segmentation, depth estimation, etc. We show that we can leverage the inherent relationships among collections of tasks, as they are trained jointly, supervising each other through their known relationships via consistency losses. Furthermore, explicitly utilizing the relationships between tasks allows improving their performance while dramatically reducing the need for labeled data, and allows training with additional unsupervised or simulated data. We demonstrate a distributed joint training algorithm with task-level parallelism, which affords a high degree of asynchronicity and robustness. This allows learning across multiple tasks, or with large amounts of input data, at scale. We demonstrate our framework on subsets of the following collection of tasks: depth and normal prediction, semantic segmentation, 3D motion and ego-motion estimation, and object tracking and 3D detection in point clouds. We observe improved performance across these tasks, especially in the low-label regime.", "published": "2020-05-14T22:53:46Z", "authors": ["Yao Lu", "Sören Pirk", "Jan Dlabal", "Anthony Brohan", "Ankita Pasad", "Zhao Chen", "Vincent Casser", "Anelia Angelova", "Ariel Gordon"], "categories": ["cs.CV", "cs.LG"], "doi": "", "_query": "property valuation computer vision", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/1802.02181v1", "title": "Applications of a Graph Theoretic Based Clustering Framework in Computer Vision and Pattern Recognition", "abstract": "Recently, several clustering algorithms have been used to solve variety of problems from different discipline. This dissertation aims to address different challenging tasks in computer vision and pattern recognition by casting the problems as a clustering problem. We proposed novel approaches to solve multi-target tracking, visual geo-localization and outlier detection problems using a unified underlining clustering framework, i.e., dominant set clustering and its extensions, and presented a superior result over several state-of-the-art approaches.", "published": "2018-01-07T09:35:12Z", "authors": ["Yonatan Tariku Tesfaye"], "categories": ["cs.CV"], "doi": "", "_query": "property valuation computer vision", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2412.00429v1", "title": "Learner Attentiveness and Engagement Analysis in Online Education Using Computer Vision", "abstract": "In recent times, online education and the usage of video-conferencing platforms have experienced massive growth. Due to the limited scope of a virtual classroom, it may become difficult for instructors to analyze learners' attention and comprehension in real time while teaching. In the digital mode of education, it would be beneficial for instructors to have an automated feedback mechanism to be informed regarding learners' attentiveness at any given time. This research presents a novel computer vision-based approach to analyze and quantify learners' attentiveness, engagement, and other affective states within online learning scenarios. This work presents the development of a multiclass multioutput classification method using convolutional neural networks on a publicly available dataset - DAiSEE. A machine learning-based algorithm is developed on top of the classification model that outputs a comprehensive attentiveness index of the learners. Furthermore, an end-to-end pipeline is proposed through which learners' live video feed is processed, providing detailed attentiveness analytics of the learners to the instructors. By comparing the experimental outcomes of the proposed method against those of previous methods, it is demonstrated that the proposed method exhibits better attentiveness detection than state-of-the-art methods. The proposed system is a comprehensive, practical, and real-time solution that is deployable and easy to use. The experimental results also demonstrate the system's efficiency in gauging learners' attentiveness.", "published": "2024-11-30T10:54:08Z", "authors": ["Sharva Gogawale", "Madhura Deshpande", "Parteek Kumar", "Irad Ben-Gal"], "categories": ["cs.CV", "cs.AI"], "doi": "", "_query": "property valuation computer vision", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2303.04720v2", "title": "Medical Waste Sorting: a computer vision approach for assisted primary sorting", "abstract": "Medical waste, i.e. waste produced during medical activities in hospitals, clinics and laboratories, represents hazardous waste whose management involves special care and high costs. However, this kind of waste contains a significant fraction of highly valued materials that can enter a circular economy process. To this end, in this paper, we propose a computer vision approach for assisting in the primary sorting of medical waste. The feasibility of our approach is demonstrated on a representative dataset we collected and made available to the community, with which we have trained a model that achieves 100\\% accuracy, and a new dataset on which the trained model exhibits good generalization.", "published": "2023-03-08T17:07:02Z", "authors": ["A. Bruno", "C. Caudai", "G. R. Leone", "M. Martinelli", "D. Moroni", "F. Crotti"], "categories": ["cs.CV"], "doi": "", "_query": "property valuation computer vision", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2508.14375v1", "title": "Computing-In-Memory Dataflow for Minimal Buffer Traffic", "abstract": "Computing-In-Memory (CIM) offers a potential solution to the memory wall issue and can achieve high energy efficiency by minimizing data movement, making it a promising architecture for edge AI devices. Lightweight models like MobileNet and EfficientNet, which utilize depthwise convolution for feature extraction, have been developed for these devices. However, CIM macros often face challenges in accelerating depthwise convolution, including underutilization of CIM memory and heavy buffer traffic. The latter, in particular, has been overlooked despite its significant impact on latency and energy consumption. To address this, we introduce a novel CIM dataflow that significantly reduces buffer traffic by maximizing data reuse and improving memory utilization during depthwise convolution. The proposed dataflow is grounded in solid theoretical principles, fully demonstrated in this paper. When applied to MobileNet and EfficientNet models, our dataflow reduces buffer traffic by 77.4-87.0%, leading to a total reduction in data traffic energy and latency by 10.1-17.9% and 15.6-27.8%, respectively, compared to the baseline (conventional weight-stationary dataflow).", "published": "2025-08-20T03:05:40Z", "authors": ["Choongseok Song", "Doo Seok Jeong"], "categories": ["cs.AR", "cs.AI"], "doi": "", "_query": "property valuation computer vision", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/1003.1462v1", "title": "Implementing New-age Authentication Techniques using OpenID for Security Automation", "abstract": "Security of any software can be enhanced manifolds if multiple factors for authorization and authentication are used .The main aim of this work was to design and implement an Academy Automation Software for IPS Academy which uses OpenID and Windows CardSpace as Authentication Techniques in addition to Role Based Authentication (RBA) System to ensure that only authentic users can access the predefined roles as per their Authorization level. The Automation covers different computing hardware and software that can be used to digitally create, manipulate, collect, store, and relay Academy information needed for accomplishing basic Operation like admissions and registration, student and faculty interaction, online library, medical and business development. Raw data storage, electronic transfer, and the management of electronic business information comprise the basic activities of the Academy automation system. Further Transport Layer Security (TLS) protocol has been implemented to provide security and data integrity for communications over networks. TLS encrypts the segments of network connections at the Transport", "published": "2010-03-07T12:33:36Z", "authors": ["Dharmendra Choukse", "Umesh Kumar Singh", "Deepak Sukheja", "Rekha Shahapurkar"], "categories": ["cs.CR"], "doi": "", "_query": "property valuation computer vision", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2601.10742v1", "title": "Line-based Event Preprocessing: Towards Low-Energy Neuromorphic Computer Vision", "abstract": "Neuromorphic vision made significant progress in recent years, thanks to the natural match between spiking neural networks and event data in terms of biological inspiration, energy savings, latency and memory use for dynamic visual data processing. However, optimising its energy requirements still remains a challenge within the community, especially for embedded applications. One solution may reside in preprocessing events to optimise data quantity thus lowering the energy cost on neuromorphic hardware, proportional to the number of synaptic operations. To this end, we extend an end-to-end neuromorphic line detection mechanism to introduce line-based event data preprocessing. Our results demonstrate on three benchmark event-based datasets that preprocessing leads to an advantageous trade-off between energy consumption and classification performance. Depending on the line-based preprocessing strategy and the complexity of the classification task, we show that one can maintain or increase the classification accuracy while significantly reducing the theoretical energy consumption. Our approach systematically leads to a significant improvement of the neuromorphic classification efficiency, thus laying the groundwork towards a more frugal neuromorphic computer vision thanks to event preprocessing.", "published": "2026-01-10T12:30:34Z", "authors": ["Amélie Gruel", "Pierre Lewden", "Adrien F. Vincent", "Sylvain Saïghi"], "categories": ["cs.NE", "cs.AI", "cs.CV", "eess.IV"], "doi": "10.1088/2634-4386/ae5128", "_query": "property valuation computer vision", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2402.08025v1", "title": "Beyond the Mud: Datasets and Benchmarks for Computer Vision in Off-Road Racing", "abstract": "Despite significant progress in optical character recognition (OCR) and computer vision systems, robustly recognizing text and identifying people in images taken in unconstrained \\emph{in-the-wild} environments remain an ongoing challenge. However, such obstacles must be overcome in practical applications of vision systems, such as identifying racers in photos taken during off-road racing events. To this end, we introduce two new challenging real-world datasets - the off-road motorcycle Racer Number Dataset (RND) and the Muddy Racer re-iDentification Dataset (MUDD) - to highlight the shortcomings of current methods and drive advances in OCR and person re-identification (ReID) under extreme conditions. These two datasets feature over 6,300 images taken during off-road competitions which exhibit a variety of factors that undermine even modern vision systems, namely mud, complex poses, and motion blur. We establish benchmark performance on both datasets using state-of-the-art models. Off-the-shelf models transfer poorly, reaching only 15% end-to-end (E2E) F1 score on text spotting, and 33% rank-1 accuracy on ReID. Fine-tuning yields major improvements, bringing model performance to 53% F1 score for E2E text spotting and 79% rank-1 accuracy on ReID, but still falls short of good performance. Our analysis exposes open problems in real-world OCR and ReID that necessitate domain-targeted techniques. With these datasets and analysis of model limitations, we aim to foster innovations in handling real-world conditions like mud and complex poses to drive progress in robust computer vision. All data was sourced from PerformancePhoto.co, a website used by professional motorsports photographers, racers, and fans. The top-performing text spotting and ReID models are deployed on this platform to power real-time race photo search.", "published": "2024-02-12T19:42:05Z", "authors": ["Jacob Tyo", "Motolani Olarinre", "Youngseog Chung", "Zachary C. Lipton"], "categories": ["cs.CV"], "doi": "", "_query": "property valuation computer vision", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/1002.1678v1", "title": "Scenario Based Worm Trace Pattern Identification Technique", "abstract": "The number of malware variants is growing tremendously and the study of malware attacks on the Internet is still a demanding research domain. In this research, various logs from different OSI layer are explore to identify the traces leave on the attacker and victim logs, and the attack worm trace pattern are establish in order to reveal true attacker or victim. For the purpose of this paper, it will only concentrate on cybercrime that caused by malware network intrusion and used the traditional worm namely blaster worm variants. This research creates the concept of trace pattern by fusing the attackers and victims perspective. Therefore, the objective of this paper is to propose on attackers, victims and multistep, attacker or victim, trace patterns by combining both perspectives. These three proposed worm trace patterns can be extended into research areas in alert correlation and computer forensic investigation.", "published": "2010-02-08T18:49:17Z", "authors": ["S. Siti Rahayu", "Y. Robiah", "S. Shahrin", "Mohd M. Zaki", "R. Irda", "M. A. Faizal"], "categories": ["cs.CR"], "doi": "", "_query": "property valuation computer vision", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2606.14773v1", "title": "Double-Helix Vision (DH-V2): A Geometry-Based Visual Sampler for Bandwidth-Constrained Perception", "abstract": "We present Double-Helix Vision (DH), a geometry-based visual sampler that compresses 2D images into compact 1D signals using paired golden-ratio-inspired spiral trajectories. Rather than processing every pixel uniformly, DH employs two phase-shifted helices (Alpha and Beta, offset by 180 degrees) to sample the image with biologically-inspired foveation: high density at the center, sparse coverage at the periphery. At 4K resolution, DH achieves a 1,433x compression ratio (99.93% reduction) while preserving the geometric structure of the scene. The full perception pipeline -- including spatial mapping, temporal collision detection, and intra-frame structural disparity estimation -- runs in 0.52 ms at 1080p on CPU-only hardware, with no neural network dependencies. On CIFAR-10 at extreme sampling budgets (K=128 points per helix), DH achieves a +6.03% accuracy gain over uniform random sampling. A JSON-serializable Robotics API is provided, delivering sub-millisecond spatial perception reports in 2.7 KB packets. Code and benchmarks are available under the MIT License.", "published": "2026-06-09T16:38:24Z", "authors": ["Jinwen Wen"], "categories": ["cs.CV", "cs.AI"], "doi": "", "_query": "property valuation computer vision", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/1811.06336v2", "title": "State Complexity Characterizations of Parameterized Degree-Bounded Graph Connectivity, Sub-Linear Space Computation, and the Linear Space Hypothesis", "abstract": "The linear space hypothesis is a practical working hypothesis, which originally states the insolvability of a restricted 2CNF Boolean formula satisfiability problem parameterized by the number of Boolean variables. From this hypothesis, it naturally follows that the degree-3 directed graph connectivity problem (3DSTCON) parameterized by the number of vertices in a given graph cannot belong to PsubLIN, composed of all parameterized decision problems computable by polynomial-time, sub-linear-space deterministic Turing machines. This hypothesis immediately implies L$\\neq$NL and it was used as a solid foundation to obtain new lower bounds on the computational complexity of various NL search and NL optimization problems. The state complexity of transformation refers to the cost of converting one type of finite automata to another type, where the cost is measured in terms of the increase of the number of inner states of the converted automata from that of the original automata. We relate the linear space hypothesis to the state complexity of transforming restricted 2-way nondeterministic finite automata to computationally equivalent 2-way alternating finite automata having narrow computation graphs. For this purpose, we present state complexity characterizations of 3DSTCON and PsubLIN. We further characterize a nonuniform version of the linear space hypothesis in terms of the state complexity of transformation.", "published": "2018-11-15T13:28:59Z", "authors": ["Tomoyuki Yamakami"], "categories": ["cs.CC", "cs.FL"], "doi": "", "_query": "property valuation computer vision", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/1704.06610v1", "title": "Context-based Object Viewpoint Estimation: A 2D Relational Approach", "abstract": "The task of object viewpoint estimation has been a challenge since the early days of computer vision. To estimate the viewpoint (or pose) of an object, people have mostly looked at object intrinsic features, such as shape or appearance. Surprisingly, informative features provided by other, extrinsic elements in the scene, have so far mostly been ignored. At the same time, contextual cues have been proven to be of great benefit for related tasks such as object detection or action recognition. In this paper, we explore how information from other objects in the scene can be exploited for viewpoint estimation. In particular, we look at object configurations by following a relational neighbor-based approach for reasoning about object relations. We show that, starting from noisy object detections and viewpoint estimates, exploiting the estimated viewpoint and location of other objects in the scene can lead to improved object viewpoint predictions. Experiments on the KITTI dataset demonstrate that object configurations can indeed be used as a complementary cue to appearance-based viewpoint estimation. Our analysis reveals that the proposed context-based method can improve object viewpoint estimation by reducing specific types of viewpoint estimation errors commonly made by methods that only consider local information. Moreover, considering contextual information produces superior performance in scenes where a high number of object instances occur. Finally, our results suggest that, following a cautious relational neighbor formulation brings improvements over its aggressive counterpart for the task of object viewpoint estimation.", "published": "2017-04-21T15:55:54Z", "authors": ["Jose Oramas", "Luc De Raedt", "Tinne Tuytelaars"], "categories": ["cs.CV"], "doi": "10.1016/j.cviu.2017.04.005", "_query": "property valuation computer vision", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2506.14265v2", "title": "Self-supervised Representation Learning with Local Aggregation for Image-based Profiling", "abstract": "Image-based cell profiling aims to create informative representations of cell images. This technique is critical in drug discovery and has greatly advanced with recent improvements in computer vision. Inspired by recent developments in non-contrastive Self-Supervised Learning (SSL), this paper provides an initial exploration into training a generalizable feature extractor for cell images using such methods. However, there are two major challenges: 1) Unlike typical scenarios where each representation is based on a single image, cell profiling often involves multiple input images, making it difficult to effectively fuse all available information; and 2) There is a large difference between the distributions of cell images and natural images, causing the view-generation process in existing SSL methods to fail. To address these issues, we propose a self-supervised framework with local aggregation to improve cross-site consistency of cell representations. We introduce specialized data augmentation and representation post-processing methods tailored to cell images, which effectively address the issues mentioned above and result in a robust feature extractor. With these improvements, the proposed framework won the Cell Line Transferability challenge at CVPR 2025.", "published": "2025-06-17T07:25:57Z", "authors": ["Siran Dai", "Qianqian Xu", "Peisong Wen", "Yang Liu", "Qingming Huang"], "categories": ["cs.CV"], "doi": "", "_query": "property valuation computer vision", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/1601.06274v1", "title": "Solving Dense Image Matching in Real-Time using Discrete-Continuous Optimization", "abstract": "Dense image matching is a fundamental low-level problem in Computer Vision, which has received tremendous attention from both discrete and continuous optimization communities. The goal of this paper is to combine the advantages of discrete and continuous optimization in a coherent framework. We devise a model based on energy minimization, to be optimized by both discrete and continuous algorithms in a consistent way. In the discrete setting, we propose a novel optimization algorithm that can be massively parallelized. In the continuous setting we tackle the problem of non-convex regularizers by a formulation based on differences of convex functions. The resulting hybrid discrete-continuous algorithm can be efficiently accelerated by modern GPUs and we demonstrate its real-time performance for the applications of dense stereo matching and optical flow.", "published": "2016-01-23T14:59:08Z", "authors": ["Alexander Shekhovtsov", "Christian Reinbacher", "Gottfried Graber", "Thomas Pock"], "categories": ["cs.CV"], "doi": "", "_query": "property valuation computer vision", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2512.07664v1", "title": "A Framework for Data Valuation and Monetisation", "abstract": "As organisations increasingly recognise data as a strategic resource, they face the challenge of translating informational assets into measurable business value. Existing valuation approaches remain fragmented, often separating economic, governance, and strategic perspectives and lacking operational mechanisms suitable for real settings. This paper introduces a unified valuation framework that integrates these perspectives into a coherent decision-support model. Building on two artefacts from the Horizon Europe DATAMITE project, a taxonomy of data-quality and performance metrics, and an Analytic Network Process (ANP) tool for deriving relative importance, we develop a hybrid valuation model. The model combines qualitative scoring, cost- and utility-based estimation, relevance/quality indexing, and multi-criteria weighting to define data value transparently and systematically. Anchored in the Balanced Scorecard (BSC), the framework aligns indicators and valuation outcomes with organisational strategy, enabling firms to assess monetisation potential across Data-as-a-Service, Information-as-a-Service, and Answers-as-a-Service pathways. Methodologically, the study follows a Design Science approach complemented by embedded case studies with industrial partners, which informed continual refinement of the model. Because the evaluation is connected to a high-level taxonomy, the approach also reveals how valuation considerations map to BSC perspectives. Across the analysed use cases, the framework demonstrated flexibility, transparency, and reduced arbitrariness in valuation, offering organisations a structured basis for linking data assets to strategic and economic outcomes.", "published": "2025-12-08T15:57:26Z", "authors": ["Eduardo Vyhmeister", "Bastien Pietropaoli", " UdoBub", "Rob Schneider", "Andrea Visentin"], "categories": ["cs.CY", "cs.ET"], "doi": "", "_query": "property valuation computer vision", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/1305.3215v1", "title": "A computer algebra user interface manifesto", "abstract": "Many computer algebra systems have more than 1000 built-in functions, making expertise difficult. Using mock dialog boxes, this article describes a proposed interactive general-purpose wizard for organizing optional transformations and allowing easy fine grain control over the form of the result even by amateurs. This wizard integrates ideas including: * flexible subexpression selection; * complete control over the ordering of variables and commutative operands, with well-chosen defaults; * interleaving the choice of successively less main variables with applicable function choices to provide detailed control without incurring a combinatorial number of applicable alternatives at any one level; * quick applicability tests to reduce the listing of inapplicable transformations; * using an organizing principle to order the alternatives in a helpful manner; * labeling quickly-computed alternatives in dialog boxes with a preview of their results, * using ellipsis elisions if necessary or helpful; * allowing the user to retreat from a sequence of choices to explore other branches of the tree of alternatives or to return quickly to branches already visited; * allowing the user to accumulate more than one of the alternative forms; * integrating direct manipulation into the wizard; and * supporting not only the usual input-result pair mode, but also the useful alternative derivational and in situ replacement modes in a unified window.", "published": "2013-05-14T17:21:29Z", "authors": ["David R. Stoutemyer"], "categories": ["cs.SC", "cs.MS"], "doi": "", "_query": "property valuation computer vision", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/1604.04024v3", "title": "Towards Automated Melanoma Screening: Proper Computer Vision & Reliable Results", "abstract": "In this paper we survey, analyze and criticize current art on automated melanoma screening, reimplementing a baseline technique, and proposing two novel ones. Melanoma, although highly curable when detected early, ends as one of the most dangerous types of cancer, due to delayed diagnosis and treatment. Its incidence is soaring, much faster than the number of trained professionals able to diagnose it. Automated screening appears as an alternative to make the most of those professionals, focusing their time on the patients at risk while safely discharging the other patients. However, the potential of automated melanoma diagnosis is currently unfulfilled, due to the emphasis of current literature on outdated computer vision models. Even more problematic is the irreproducibility of current art. We show how streamlined pipelines based upon current Computer Vision outperform conventional models - a model based on an advanced bags of words reaches an AUC of 84.6%, and a model based on deep neural networks reaches 89.3%, while the baseline (a classical bag of words) stays at 81.2%. We also initiate a dialog to improve reproducibility in our community", "published": "2016-04-14T03:26:28Z", "authors": ["Michel Fornaciali", "Micael Carvalho", "Flávia Vasques Bittencourt", "Sandra Avila", "Eduardo Valle"], "categories": ["cs.CV"], "doi": "", "_query": "property valuation computer vision", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2506.23349v2", "title": "A case for data valuation transparency via DValCards", "abstract": "Following the rise in popularity of data-centric machine learning (ML), various data valuation methods have been proposed to quantify the contribution of each datapoint to desired ML model performance metrics (e.g., accuracy). Beyond the technical applications of data valuation methods (e.g., data cleaning, data acquisition, etc.), it has been suggested that within the context of data markets, data buyers might utilize such methods to fairly compensate data owners. Here we demonstrate that data valuation metrics are inherently biased and unstable under simple algorithmic design choices, resulting in both technical and ethical implications. By analyzing 9 tabular classification datasets and 6 data valuation methods, we illustrate how (1) common and inexpensive data pre-processing techniques can drastically alter estimated data values; (2) subsampling via data valuation metrics may increase class imbalance; and (3) data valuation metrics may undervalue underrepresented group data. Consequently, we argue in favor of increased transparency associated with data valuation in-the-wild and introduce the novel Data Valuation Cards (DValCards) framework towards this aim. The proliferation of DValCards will reduce misuse of data valuation metrics, including in data pricing, and build trust in responsible ML systems.", "published": "2025-06-29T17:53:00Z", "authors": ["Keziah Naggita", "Julienne LaChance"], "categories": ["cs.LG"], "doi": "", "_query": "property valuation computer vision", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2211.00392v1", "title": "Expansion of Visual Hints for Improved Generalization in Stereo Matching", "abstract": "We introduce visual hints expansion for guiding stereo matching to improve generalization. Our work is motivated by the robustness of Visual Inertial Odometry (VIO) in computer vision and robotics, where a sparse and unevenly distributed set of feature points characterizes a scene. To improve stereo matching, we propose to elevate 2D hints to 3D points. These sparse and unevenly distributed 3D visual hints are expanded using a 3D random geometric graph, which enhances the learning and inference process. We evaluate our proposal on multiple widely adopted benchmarks and show improved performance without access to additional sensors other than the image sequence. To highlight practical applicability and symbiosis with visual odometry, we demonstrate how our methods run on embedded hardware.", "published": "2022-11-01T11:30:26Z", "authors": ["Andrea Pilzer", "Yuxin Hou", "Niki Loppi", "Arno Solin", "Juho Kannala"], "categories": ["cs.CV"], "doi": "", "_query": "property valuation computer vision", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2402.01943v2", "title": "Precedence-Constrained Winter Value for Effective Graph Data Valuation", "abstract": "Data valuation is essential for quantifying data's worth, aiding in assessing data quality and determining fair compensation. While existing data valuation methods have proven effective in evaluating the value of Euclidean data, they face limitations when applied to the increasingly popular graph-structured data. Particularly, graph data valuation introduces unique challenges, primarily stemming from the intricate dependencies among nodes and the exponential growth in value estimation costs. To address the challenging problem of graph data valuation, we put forth an innovative solution, Precedence-Constrained Winter (PC-Winter) Value, to account for the complex graph structure. Furthermore, we develop a variety of strategies to address the computational challenges and enable efficient approximation of PC-Winter. Extensive experiments demonstrate the effectiveness of PC-Winter across diverse datasets and tasks.", "published": "2024-02-02T22:39:50Z", "authors": ["Hongliang Chi", "Wei Jin", "Charu Aggarwal", "Yao Ma"], "categories": ["cs.LG"], "doi": "", "_query": "property valuation computer vision", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2512.13411v1", "title": "Computer vision training dataset generation for robotic environments using Gaussian splatting", "abstract": "This paper introduces a novel pipeline for generating large-scale, highly realistic, and automatically labeled datasets for computer vision tasks in robotic environments. Our approach addresses the critical challenges of the domain gap between synthetic and real-world imagery and the time-consuming bottleneck of manual annotation. We leverage 3D Gaussian Splatting (3DGS) to create photorealistic representations of the operational environment and objects. These assets are then used in a game engine where physics simulations create natural arrangements. A novel, two-pass rendering technique combines the realism of splats with a shadow map generated from proxy meshes. This map is then algorithmically composited with the image to add both physically plausible shadows and subtle highlights, significantly enhancing realism. Pixel-perfect segmentation masks are generated automatically and formatted for direct use with object detection models like YOLO. Our experiments show that a hybrid training strategy, combining a small set of real images with a large volume of our synthetic data, yields the best detection and segmentation performance, confirming this as an optimal strategy for efficiently achieving robust and accurate models.", "published": "2025-12-15T15:00:17Z", "authors": ["Patryk Niżeniec", "Marcin Iwanowski"], "categories": ["cs.CV", "cs.GR"], "doi": "", "_query": "property valuation computer vision", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2304.10701v9", "title": "GMValuator: Similarity-based Data Valuation for Generative Models", "abstract": "Data valuation plays a crucial role in machine learning. Existing data valuation methods, mainly focused on discriminative models, overlook generative models that have gained attention recently. In generative models, data valuation measures the impact of training data on generated datasets. Very few existing attempts at data valuation methods designed for deep generative models either concentrate on specific models or lack robustness in their outcomes. Moreover, efficiency still reveals vulnerable shortcomings. We formulate the data valuation problem in generative models from a similarity matching perspective to bridge the gaps. Specifically, we introduce Generative Model Valuator (GMValuator), the first training-free and model-agnostic approach to providing data valuation for image generation tasks. It empowers efficient data valuation through our innovative similarity matching module, calibrates biased contributions by incorporating image quality assessment, and attributes credits to all training samples based on their contributions to the generated samples. Additionally, we introduce four evaluation criteria for assessing data valuation methods in generative models. GMValuator is extensively evaluated on benchmark and high-resolution datasets and various mainstream generative architectures to demonstrate its effectiveness.", "published": "2023-04-21T02:02:02Z", "authors": ["Jiaxi Yang", "Wenglong Deng", "Benlin Liu", "Yangsibo Huang", "James Zou", "Xiaoxiao Li"], "categories": ["cs.CV", "cs.LG"], "doi": "", "_query": "property valuation computer vision", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2308.11718v1", "title": "Products of $p$-Adic Valuation Trees", "abstract": "The study of prime divisibility plays a crucial role in number theory. The $p$-adic valuation of a number is the highest power of a prime, $p$, that divides that number. Using this valuation, we construct $p$-adic valuation trees to visually represent the valuations of a sequence. We investigate how nodes split on trees generated by linear functions with rational coefficients, as well as those formed from a product of linear or lower degree polynomials. We describe the infinite branches of these polynomial trees and the valuations of their terminating nodes.", "published": "2023-08-22T18:07:41Z", "authors": ["Dillon Snyder"], "categories": ["math.NT"], "doi": "", "_query": "property valuation computer vision", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2202.07242v1", "title": "Neural Architecture Search for Dense Prediction Tasks in Computer Vision", "abstract": "The success of deep learning in recent years has lead to a rising demand for neural network architecture engineering. As a consequence, neural architecture search (NAS), which aims at automatically designing neural network architectures in a data-driven manner rather than manually, has evolved as a popular field of research. With the advent of weight sharing strategies across architectures, NAS has become applicable to a much wider range of problems. In particular, there are now many publications for dense prediction tasks in computer vision that require pixel-level predictions, such as semantic segmentation or object detection. These tasks come with novel challenges, such as higher memory footprints due to high-resolution data, learning multi-scale representations, longer training times, and more complex and larger neural architectures. In this manuscript, we provide an overview of NAS for dense prediction tasks by elaborating on these novel challenges and surveying ways to address them to ease future research and application of existing methods to novel problems.", "published": "2022-02-15T08:06:50Z", "authors": ["Thomas Elsken", "Arber Zela", "Jan Hendrik Metzen", "Benedikt Staffler", "Thomas Brox", "Abhinav Valada", "Frank Hutter"], "categories": ["cs.CV", "cs.LG"], "doi": "", "_query": "property valuation computer vision", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2212.10881v1", "title": "In-Sensor & Neuromorphic Computing are all you need for Energy Efficient Computer Vision", "abstract": "Due to the high activation sparsity and use of accumulates (AC) instead of expensive multiply-and-accumulates (MAC), neuromorphic spiking neural networks (SNNs) have emerged as a promising low-power alternative to traditional DNNs for several computer vision (CV) applications. However, most existing SNNs require multiple time steps for acceptable inference accuracy, hindering real-time deployment and increasing spiking activity and, consequently, energy consumption. Recent works proposed direct encoding that directly feeds the analog pixel values in the first layer of the SNN in order to significantly reduce the number of time steps. Although the overhead for the first layer MACs with direct encoding is negligible for deep SNNs and the CV processing is efficient using SNNs, the data transfer between the image sensors and the downstream processing costs significant bandwidth and may dominate the total energy. To mitigate this concern, we propose an in-sensor computing hardware-software co-design framework for SNNs targeting image recognition tasks. Our approach reduces the bandwidth between sensing and processing by 12-96x and the resulting total energy by 2.32x compared to traditional CV processing, with a 3.8% reduction in accuracy on ImageNet.", "published": "2022-12-21T09:47:52Z", "authors": ["Gourav Datta", "Zeyu Liu", "Md Abdullah-Al Kaiser", "Souvik Kundu", "Joe Mathai", "Zihan Yin", "Ajey P. Jacob", "Akhilesh R. Jaiswal", "Peter A. Beerel"], "categories": ["cs.CV"], "doi": "", "_query": "property valuation computer vision", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2610.26734v1", "title": "CAVA: Complementary-Label-Aware Data Valuation", "abstract": "Complementary-label learning (CLL) trains a classifier from labels that name a class an instance does not belong to. Such labels are cheap to collect from crowdworkers or vision-language models (VLMs), making them attractive for large-scale annotation. However, both sources can mistakenly assign the true class as the complementary label. Such annotation errors are particularly harmful in CLL because the model is explicitly trained to suppress the correct answer. Optimal transport (OT) data valuation identifies harmful samples by comparing training data with a clean validation set, but transferring it directly to CLL creates a semantic mismatch: label $c$ means not class $c$ in the training but class $c$ in the validation. We show that this makes the OT label groups noisy mixtures whose behavior depends on the noise rate. The transferred score is informative only at sufficiently high noise, becomes uninformative at the crossover rate $1/M$ for $M$ classes, and reverses below it, where practical CLL annotation typically operates. We propose CAVA, which introduces a complementary label cost and a label margin that isolates the contribution of each complementary label to the transport potential. The margin keeps a consistent direction across noise rates, detects diagonal noise with a parameter-free threshold of closed-form precision, and allows detected samples to be recycled as ordinary-label supervision. On synthetic and human-annotated benchmarks, CAVA separates diagonal noise almost perfectly where directly transferred OT valuation performs worse than random. With only a fraction of the clean validation data, it also improves every CLL objective we evaluate, showing that OT-based valuation becomes reliable once the semantics of complementary supervision are modeled explicitly.", "published": "2026-10-19T17:00:37Z", "authors": ["Tan-Ha Mai", "Hsuan-Tien Lin", "Vu Nguyen"], "categories": ["cs.LG", "cs.CV"], "doi": "", "_query": "property valuation computer vision", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2010.07222v1", "title": "Domain Shift in Computer Vision models for MRI data analysis: An Overview", "abstract": "Machine learning and computer vision methods are showing good performance in medical imagery analysis. Yetonly a few applications are now in clinical use and one of the reasons for that is poor transferability of themodels to data from different sources or acquisition domains. Development of new methods and algorithms forthe transfer of training and adaptation of the domain in multi-modal medical imaging data is crucial for thedevelopment of accurate models and their use in clinics. In present work, we overview methods used to tackle thedomain shift problem in machine learning and computer vision. The algorithms discussed in this survey includeadvanced data processing, model architecture enhancing and featured training, as well as predicting in domaininvariant latent space. The application of the autoencoding neural networks and their domain-invariant variationsare heavily discussed in a survey. We observe the latest methods applied to the magnetic resonance imaging(MRI) data analysis and conclude on their performance as well as propose directions for further research.", "published": "2020-10-14T16:34:21Z", "authors": ["Ekaterina Kondrateva", "Marina Pominova", "Elena Popova", "Maxim Sharaev", "Alexander Bernstein", "Evgeny Burnaev"], "categories": ["eess.IV", "cs.CV"], "doi": "", "_query": "property valuation computer vision", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2403.02240v5", "title": "Quantum Computing: Vision and Challenges", "abstract": "The recent development of quantum computing, which uses entanglement, superposition, and other quantum fundamental concepts, can provide substantial processing advantages over traditional computing. These quantum features help solve many complex problems that cannot be solved otherwise with conventional computing methods. These problems include modeling quantum mechanics, logistics, chemical-based advances, drug design, statistical science, sustainable energy, banking, reliable communication, and quantum chemical engineering. The last few years have witnessed remarkable progress in quantum software and algorithm creation and quantum hardware research, which has significantly advanced the prospect of realizing quantum computers. It would be helpful to have comprehensive literature research on this area to grasp the current status and find outstanding problems that require considerable attention from the research community working in the quantum computing industry. To better understand quantum computing, this paper examines the foundations and vision based on current research in this area. We discuss cutting-edge developments in quantum computer hardware advancement and subsequent advances in quantum cryptography, quantum software, and high-scalability quantum computers. Many potential challenges and exciting new trends for quantum technology research and development are highlighted in this paper for a broader debate.", "published": "2024-03-04T17:33:18Z", "authors": ["Sukhpal Singh Gill", "Oktay Cetinkaya", "Stefano Marrone", "Daniel Claudino", "David Haunschild", "Leon Schlote", "Huaming Wu", "Carlo Ottaviani", "Xiaoyuan Liu", "Sree Pragna Machupalli", "Kamalpreet Kaur", "Priyansh Arora", "Ji Liu", "Ahmed Farouk", "Houbing Herbert Song", "Steve Uhlig", "Kotagiri Ramamohanarao"], "categories": ["cs.DC", "cs.ET", "quant-ph"], "doi": "10.1016/B978-0-443-29096-1.00008-8", "_query": "property valuation computer vision", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/1806.11293v2", "title": "Proof-of-work certificates that can be efficiently computed in the cloud", "abstract": "In an emerging computing paradigm, computational capabilities, from processing power to storage capacities, are offered to users over communication networks as a cloud-based service. There, demanding computations are outsourced in order to limit infrastructure costs. The idea of verifiable computing is to associate a data structure, a proof-of-work certificate, to the result of the outsourced computation. This allows a verification algorithm to prove the validity of the result, faster than by recomputing it. We talk about a Prover (the server performing the computations) and a Verifier. Goldwasser, Kalai and Rothblum gave in 2008 a generic method to verify any parallelizable computation, in almost linear time in the size of the, potentially structured, inputs and the result. However, the extra cost of the computations for the Prover (and therefore the extra cost to the customer), although only almost a constant factor of the overall work, is nonetheless prohibitive in practice. Differently, we will here present problem-specific procedures in computer algebra, e.g. for exact linear algebra computations, that are Prover-optimal, that is that have much less financial overhead.", "published": "2018-06-29T08:13:29Z", "authors": ["Jean-Guillaume Dumas"], "categories": ["cs.SC", "cs.CR"], "doi": "", "_query": "property valuation computer vision", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2305.03601v1", "title": "Human Attention-Guided Explainable Artificial Intelligence for Computer Vision Models", "abstract": "We examined whether embedding human attention knowledge into saliency-based explainable AI (XAI) methods for computer vision models could enhance their plausibility and faithfulness. We first developed new gradient-based XAI methods for object detection models to generate object-specific explanations by extending the current methods for image classification models. Interestingly, while these gradient-based methods worked well for explaining image classification models, when being used for explaining object detection models, the resulting saliency maps generally had lower faithfulness than human attention maps when performing the same task. We then developed Human Attention-Guided XAI (HAG-XAI) to learn from human attention how to best combine explanatory information from the models to enhance explanation plausibility by using trainable activation functions and smoothing kernels to maximize XAI saliency map's similarity to human attention maps. While for image classification models, HAG-XAI enhanced explanation plausibility at the expense of faithfulness, for object detection models it enhanced plausibility and faithfulness simultaneously and outperformed existing methods. The learned functions were model-specific, well generalizable to other databases.", "published": "2023-05-05T15:05:07Z", "authors": ["Guoyang Liu", "Jindi Zhang", "Antoni B. Chan", "Janet H. Hsiao"], "categories": ["cs.CV", "cs.AI"], "doi": "", "_query": "property valuation computer vision", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2203.06463v1", "title": "A Systematic Review on Computer Vision-Based Parking Lot Management Applied on Public Datasets", "abstract": "Computer vision-based parking lot management methods have been extensively researched upon owing to their flexibility and cost-effectiveness. To evaluate such methods authors often employ publicly available parking lot image datasets. In this study, we surveyed and compared robust publicly available image datasets specifically crafted to test computer vision-based methods for parking lot management approaches and consequently present a systematic and comprehensive review of existing works that employ such datasets. The literature review identified relevant gaps that require further research, such as the requirement of dataset-independent approaches and methods suitable for autonomous detection of position of parking spaces. In addition, we have noticed that several important factors such as the presence of the same cars across consecutive images, have been neglected in most studies, thereby rendering unrealistic assessment protocols. Furthermore, the analysis of the datasets also revealed that certain features that should be present when developing new benchmarks, such as the availability of video sequences and images taken in more diverse conditions, including nighttime and snow, have not been incorporated.", "published": "2022-03-12T15:35:29Z", "authors": ["Paulo Ricardo Lisboa de Almeida", "Jeovane Honório Alves", "Rafael Stubs Parpinelli", "Jean Paul Barddal"], "categories": ["cs.CV", "cs.AI", "cs.LG"], "doi": "10.1016/j.eswa.2022.116731", "_query": "property valuation computer vision", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2407.04190v1", "title": "Computer Vision for Clinical Gait Analysis: A Gait Abnormality Video Dataset", "abstract": "Clinical gait analysis (CGA) using computer vision is an emerging field in artificial intelligence that faces barriers of accessible, real-world data, and clear task objectives. This paper lays the foundation for current developments in CGA as well as vision-based methods and datasets suitable for gait analysis. We introduce The Gait Abnormality in Video Dataset (GAVD) in response to our review of over 150 current gait-related computer vision datasets, which highlighted the need for a large and accessible gait dataset clinically annotated for CGA. GAVD stands out as the largest video gait dataset, comprising 1874 sequences of normal, abnormal and pathological gaits. Additionally, GAVD includes clinically annotated RGB data sourced from publicly available content on online platforms. It also encompasses over 400 subjects who have undergone clinical grade visual screening to represent a diverse range of abnormal gait patterns, captured in various settings, including hospital clinics and urban uncontrolled outdoor environments. We demonstrate the validity of the dataset and utility of action recognition models for CGA using pretrained models Temporal Segment Networks(TSN) and SlowFast network to achieve video abnormality detection of 94% and 92% respectively when tested on GAVD dataset. A GitHub repository https://github.com/Rahmyyy/GAVD consisting of convenient URL links, and clinically relevant annotation for CGA is provided for over 450 online videos, featuring diverse subjects performing a range of normal, pathological, and abnormal gait patterns.", "published": "2024-07-05T00:18:40Z", "authors": ["Rahm Ranjan", "David Ahmedt-Aristizabal", "Mohammad Ali Armin", "Juno Kim"], "categories": ["cs.CV"], "doi": "10.1109/ACCESS.2025.3545787", "_query": "property valuation computer vision", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2609.24075v1", "title": "A paired synthetic construction-site image dataset for robust computer vision under adverse conditions", "abstract": "Computer-vision systems used for construction monitoring can degrade under adverse environmental and visual conditions, yet such conditions remain underrepresented in existing construction image datasets. We present ConSynth-X, a paired synthetic construction-site image dataset containing 34,199 images derived from 3,109 real-world source scenes. The dataset comprises 11 condition-specific subsets spanning precipitation, fog, nighttime illumination, adverse weather at night, and small-object or long-distance views. Each synthetic image is linked to its corresponding source scene, enabling controlled comparison across environmental and visual conditions. ConSynth-X includes source-derived annotations, generation metadata, provenance information, and image-quality indicators, supporting object detection, image captioning, visual grounding, and visual question answering. Technical validation evaluates source-synthetic fidelity and alignment with real adverse-condition imagery using embedding-based similarity and distributional analyses. The dataset provides a structured resource for evaluating and improving the robustness of construction vision and vision-language models under challenging field conditions.", "published": "2026-09-21T04:01:41Z", "authors": ["Viet Huy Duong", "Ruoxin Xiong", "Md Abdullah Al Forhad", "Weishi Shi"], "categories": ["cs.CV"], "doi": "", "_query": "property valuation computer vision", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2506.21537v1", "title": "ResQ: A Novel Framework to Implement Residual Neural Networks on Analog Rydberg Atom Quantum Computers", "abstract": "Research in quantum machine learning has recently proliferated due to the potential of quantum computing to accelerate machine learning. An area of machine learning that has not yet been explored is neural ordinary differential equation (neural ODE) based residual neural networks (ResNets), which aim to improve the effectiveness of neural networks using the principles of ordinary differential equations. In this work, we present our insights about why analog Rydberg atom quantum computers are especially well-suited for ResNets. We also introduce ResQ, a novel framework to optimize the dynamics of Rydberg atom quantum computers to solve classification problems in machine learning using analog quantum neural ODEs.", "published": "2025-06-26T17:55:12Z", "authors": ["Nicholas S. DiBrita", "Jason Han", "Tirthak Patel"], "categories": ["quant-ph", "cs.CV", "cs.ET"], "doi": "", "_query": "property valuation computer vision", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2211.14054v1", "title": "CAD2Render: A Modular Toolkit for GPU-accelerated Photorealistic Synthetic Data Generation for the Manufacturing Industry", "abstract": "The use of computer vision for product and assembly quality control is becoming ubiquitous in the manufacturing industry. Lately, it is apparent that machine learning based solutions are outperforming classical computer vision algorithms in terms of performance and robustness. However, a main drawback is that they require sufficiently large and labeled training datasets, which are often not available or too tedious and too time consuming to acquire. This is especially true for low-volume and high-variance manufacturing. Fortunately, in this industry, CAD models of the manufactured or assembled products are available. This paper introduces CAD2Render, a GPU-accelerated synthetic data generator based on the Unity High Definition Render Pipeline (HDRP). CAD2Render is designed to add variations in a modular fashion, making it possible for high customizable data generation, tailored to the needs of the industrial use case at hand. Although CAD2Render is specifically designed for manufacturing use cases, it can be used for other domains as well. We validate CAD2Render by demonstrating state of the art performance in two industrial relevant setups. We demonstrate that the data generated by our approach can be used to train object detection and pose estimation models with a high enough accuracy to direct a robot. The code for CAD2Render is available at https://github.com/EDM-Research/CAD2Render.", "published": "2022-11-25T12:17:35Z", "authors": ["Steven Moonen", "Bram Vanherle", "Joris de Hoog", "Taoufik Bourgana", "Abdellatif Bey-Temsamani", "Nick Michiels"], "categories": ["cs.CV", "cs.GR", "cs.LG"], "doi": "10.1109/WACVW58289.2023.00065", "_query": "property valuation computer vision", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/1707.04318v1", "title": "Discriminative Optimization: Theory and Applications to Computer Vision Problems", "abstract": "Many computer vision problems are formulated as the optimization of a cost function. This approach faces two main challenges: (i) designing a cost function with a local optimum at an acceptable solution, and (ii) developing an efficient numerical method to search for one (or multiple) of these local optima. While designing such functions is feasible in the noiseless case, the stability and location of local optima are mostly unknown under noise, occlusion, or missing data. In practice, this can result in undesirable local optima or not having a local optimum in the expected place. On the other hand, numerical optimization algorithms in high-dimensional spaces are typically local and often rely on expensive first or second order information to guide the search. To overcome these limitations, this paper proposes Discriminative Optimization (DO), a method that learns search directions from data without the need of a cost function. Specifically, DO explicitly learns a sequence of updates in the search space that leads to stationary points that correspond to desired solutions. We provide a formal analysis of DO and illustrate its benefits in the problem of 3D point cloud registration, camera pose estimation, and image denoising. We show that DO performed comparably or outperformed state-of-the-art algorithms in terms of accuracy, robustness to perturbations, and computational efficiency.", "published": "2017-07-13T20:58:32Z", "authors": ["Jayakorn Vongkulbhisal", "Fernando De la Torre", "João P. Costeira"], "categories": ["cs.CV"], "doi": "10.1109/TPAMI.2018.2826536", "_query": "property valuation computer vision", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2007.08592v1", "title": "Advances in Deep Learning for Hyperspectral Image Analysis--Addressing Challenges Arising in Practical Imaging Scenarios", "abstract": "Deep neural networks have proven to be very effective for computer vision tasks, such as image classification, object detection, and semantic segmentation -- these are primarily applied to color imagery and video. In recent years, there has been an emergence of deep learning algorithms being applied to hyperspectral and multispectral imagery for remote sensing and biomedicine tasks. These multi-channel images come with their own unique set of challenges that must be addressed for effective image analysis. Challenges include limited ground truth (annotation is expensive and extensive labeling is often not feasible), and high dimensional nature of the data (each pixel is represented by hundreds of spectral bands), despite being presented by a large amount of unlabeled data and the potential to leverage multiple sensors/sources that observe the same scene. In this chapter, we will review recent advances in the community that leverage deep learning for robust hyperspectral image analysis despite these unique challenges -- specifically, we will review unsupervised, semi-supervised and active learning approaches to image analysis, as well as transfer learning approaches for multi-source (e.g. multi-sensor, or multi-temporal) image analysis.", "published": "2020-07-16T19:51:02Z", "authors": ["Xiong Zhou", "Saurabh Prasad"], "categories": ["cs.CV", "cs.LG", "eess.IV"], "doi": "10.1007/978-3-030-38617-7_5", "_query": "property valuation computer vision", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2605.21033v1", "title": "Efficient Banzhaf-Based Data Valuation for $k$-Nearest Neighbors Classification", "abstract": "Data valuation, the task of quantifying the contribution of individual data points to model performance, has emerged as a fundamental challenge in machine learning. Game-theoretic approaches, such as the Banzhaf value, offer principled frameworks for fair data valuation; however, they suffer from exponential computational complexity. We address this challenge by developing efficient algorithms specifically tailored for computing Banzhaf values in $k$-nearest neighbor ($k$NN) classifiers. We first establish the theoretical hardness of the problem by proving that it is \\#P-hard. Despite this intractability, we exploit the locality properties of $k$NN classifiers to develop practical exact algorithms. Our main contribution is a dynamic programming framework that achieves significant computational improvements: we present a pseudo-polynomial algorithm with $O(Wkn^2)$ time complexity for weighted $k$NN classifiers, where $W$ is the maximum sum of top-$k$ weights, and a specialized algorithm for unweighted $k$NN that achieves $O(nk^2)$ time complexity, that is, linear in the number of data points. We also offer efficient Monte Carlo estimation methods. Extensive experiments on real-world datasets demonstrate the practical efficiency of our approach and its effectiveness in data valuation applications.", "published": "2026-05-20T11:10:15Z", "authors": ["Guangyi Zhang", "Lutz Oettershagen", "Lixu Wang", "Aristides Gionis"], "categories": ["cs.LG", "cs.DS"], "doi": "", "_query": "property valuation computer vision", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2301.06443v2", "title": "Sparse resultant based minimal solvers in computer vision and their connection with the action matrix", "abstract": "Many computer vision applications require robust and efficient estimation of camera geometry from a minimal number of input data measurements, i.e., solving minimal problems in a RANSAC framework. Minimal problems are usually formulated as complex systems of sparse polynomials. The systems usually are overdetermined and consist of polynomials with algebraically constrained coefficients. Most state-of-the-art efficient polynomial solvers are based on the action matrix method that has been automated and highly optimized in recent years. On the other hand, the alternative theory of sparse resultants and Newton polytopes has been less successful for generating efficient solvers, primarily because the polytopes do not respect the constraints on the coefficients. Therefore, in this paper, we propose a simple iterative scheme to test various subsets of the Newton polytopes and search for the most efficient solver. Moreover, we propose to use an extra polynomial with a special form to further improve the solver efficiency via a Schur complement computation. We show that for some camera geometry problems our extra polynomial-based method leads to smaller and more stable solvers than the state-of-the-art Grobner basis-based solvers. The proposed method can be fully automated and incorporated into existing tools for automatic generation of efficient polynomial solvers. It provides a competitive alternative to popular Grobner basis-based methods for minimal problems in computer vision. We also study the conditions under which the minimal solvers generated by the state-of-the-art action matrix-based methods and the proposed extra polynomial resultant-based method, are equivalent. Specifically we consider a step-by-step comparison between the approaches based on the action matrix and the sparse resultant, followed by a set of substitutions, which would lead to equivalent minimal solvers.", "published": "2023-01-16T14:25:19Z", "authors": ["Snehal Bhayani", "Janne Heikkilä", "Zuzana Kukelova"], "categories": ["cs.CV"], "doi": "", "_query": "property valuation computer vision", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2001.06675v2", "title": "An Interdisciplinary Guideline for the Production of Videos and Vision Videos by Software Professionals", "abstract": "Background and Motivation: In recent years, the topic of applying videos in requirements engineering has been discussed and its contributions are of interesting potential. In the last 35 years, several researchers proposed approaches for applying videos in requirements engineering due to their communication richness and effectiveness. However, these approaches mainly use videos but omit the details about how to produce them. This lack of guidance is one crucial reason why videos are not an established documentation option for successful requirements communication and thus shared understanding. Software professionals are not directors and thus they do not necessarily know what constitutes a good video in general and for an existing approach. Therefore, this lack of knowledge and skills on how to produce and use videos for visual communication impedes the application of videos by software professionals in requirements engineering. How to Create Effective Videos and Vision Videos?: This technical report addresses this lack of knowledge and skills by software professionals. We provide two guidelines that can be used as checklists to avoid frequent flaws in the production and use of videos respectively vision videos. Software professionals without special training should be able to follow these guidelines to achieve the basic capabilities to produce (vision) videos that are accepted by their stakeholders. These guidelines represent a core set of those capabilities in the preproduction, shooting, postproduction, and viewing of (vision) videos. We do not strive for perfection in any of these capabilities, .e.g., technical handling of video equipment, storytelling, or video editing. Instead, these guidelines support all steps of the (vision) video production and use process to a balanced way.", "published": "2020-01-18T14:49:01Z", "authors": ["Oliver Karras", "Kurt Schneider"], "categories": ["cs.SE"], "doi": "", "_query": "property valuation computer vision", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2104.04167v2", "title": "The Road to Know-Where: An Object-and-Room Informed Sequential BERT for Indoor Vision-Language Navigation", "abstract": "Vision-and-Language Navigation (VLN) requires an agent to find a path to a remote location on the basis of natural-language instructions and a set of photo-realistic panoramas. Most existing methods take the words in the instructions and the discrete views of each panorama as the minimal unit of encoding. However, this requires a model to match different nouns (e.g., TV, table) against the same input view feature. In this work, we propose an object-informed sequential BERT to encode visual perceptions and linguistic instructions at the same fine-grained level, namely objects and words. Our sequential BERT also enables the visual-textual clues to be interpreted in light of the temporal context, which is crucial to multi-round VLN tasks. Additionally, we enable the model to identify the relative direction (e.g., left/right/front/back) of each navigable location and the room type (e.g., bedroom, kitchen) of its current and final navigation goal, as such information is widely mentioned in instructions implying the desired next and final locations. We thus enable the model to know-where the objects lie in the images, and to know-where they stand in the scene. Extensive experiments demonstrate the effectiveness compared against several state-of-the-art methods on three indoor VLN tasks: REVERIE, NDH, and R2R. Project repository: https://github.com/YuankaiQi/ORIST", "published": "2021-04-09T02:44:39Z", "authors": ["Yuankai Qi", "Zizheng Pan", "Yicong Hong", "Ming-Hsuan Yang", "Anton van den Hengel", "Qi Wu"], "categories": ["cs.CL", "cs.CV"], "doi": "", "_query": "property valuation computer vision", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2601.10250v1", "title": "Cell Behavior Video Classification Challenge, a benchmark for computer vision methods in time-lapse microscopy", "abstract": "The classification of microscopy videos capturing complex cellular behaviors is crucial for understanding and quantifying the dynamics of biological processes over time. However, it remains a frontier in computer vision, requiring approaches that effectively model the shape and motion of objects without rigid boundaries, extract hierarchical spatiotemporal features from entire image sequences rather than static frames, and account for multiple objects within the field of view. To this end, we organized the Cell Behavior Video Classification Challenge (CBVCC), benchmarking 35 methods based on three approaches: classification of tracking-derived features, end-to-end deep learning architectures to directly learn spatiotemporal features from the entire video sequence without explicit cell tracking, or ensembling tracking-derived with image-derived features. We discuss the results achieved by the participants and compare the potential and limitations of each approach, serving as a basis to foster the development of computer vision methods for studying cellular dynamics.", "published": "2026-01-15T10:14:16Z", "authors": ["Raffaella Fiamma Cabini", "Deborah Barkauskas", "Guangyu Chen", "Zhi-Qi Cheng", "David E Cicchetti", "Judith Drazba", "Rodrigo Fernandez-Gonzalez", "Raymond Hawkins", "Yujia Hu", "Jyoti Kini", "Charles LeWarne", "Xufeng Lin", "Sai Preethi Nakkina", "John W Peterson", "Koert Schreurs", "Ayushi Singh", "Kumaran Bala Kandan Viswanathan", "Inge MN Wortel", "Sanjian Zhang", "Rolf Krause", "Santiago Fernandez Gonzalez", "Diego Ulisse Pizzagalli"], "categories": ["eess.IV", "cs.CV", "q-bio.QM"], "doi": "", "_query": "property valuation computer vision", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2307.06659v1", "title": "A Comprehensive Analysis of Blockchain Applications for Securing Computer Vision Systems", "abstract": "Blockchain (BC) and Computer Vision (CV) are the two emerging fields with the potential to transform various sectors.The ability of BC can help in offering decentralized and secure data storage, while CV allows machines to learn and understand visual data. This integration of the two technologies holds massive promise for developing innovative applications that can provide solutions to the challenges in various sectors such as supply chain management, healthcare, smart cities, and defense. This review explores a comprehensive analysis of the integration of BC and CV by examining their combination and potential applications. It also provides a detailed analysis of the fundamental concepts of both technologies, highlighting their strengths and limitations. This paper also explores current research efforts that make use of the benefits offered by this combination. The effort includes how BC can be used as an added layer of security in CV systems and also ensure data integrity, enabling decentralized image and video analytics using BC. The challenges and open issues associated with this integration are also identified, and appropriate potential future directions are also proposed.", "published": "2023-07-13T10:03:40Z", "authors": ["Ramalingam M", "Chemmalar Selvi", "Nancy Victor", "Rajeswari Chengoden", "Sweta Bhattacharya", "Praveen Kumar Reddy Maddikunta", "Duehee Lee", "Md. Jalil Piran", "Neelu Khare", "Gokul Yendri", "Thippa Reddy Gadekallu"], "categories": ["cs.CR", "cs.CV", "cs.CY"], "doi": "", "_query": "property valuation computer vision", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2011.02904v1", "title": "Hyperrealistic Image Inpainting with Hypergraphs", "abstract": "Image inpainting is a non-trivial task in computer vision due to multiple possibilities for filling the missing data, which may be dependent on the global information of the image. Most of the existing approaches use the attention mechanism to learn the global context of the image. This attention mechanism produces semantically plausible but blurry results because of incapability to capture the global context. In this paper, we introduce hypergraph convolution on spatial features to learn the complex relationship among the data. We introduce a trainable mechanism to connect nodes using hyperedges for hypergraph convolution. To the best of our knowledge, hypergraph convolution have never been used on spatial features for any image-to-image tasks in computer vision. Further, we introduce gated convolution in the discriminator to enforce local consistency in the predicted image. The experiments on Places2, CelebA-HQ, Paris Street View, and Facades datasets, show that our approach achieves state-of-the-art results.", "published": "2020-11-05T15:28:57Z", "authors": ["Gourav Wadhwa", "Abhinav Dhall", "Subrahmanyam Murala", "Usman Tariq"], "categories": ["cs.CV"], "doi": "", "_query": "property valuation computer vision", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2407.18695v1", "title": "PIV3CAMS: a multi-camera dataset for multiple computer vision problems and its application to novel view-point synthesis", "abstract": "The modern approaches for computer vision tasks significantly rely on machine learning, which requires a large number of quality images. While there is a plethora of image datasets with a single type of images, there is a lack of datasets collected from multiple cameras. In this thesis, we introduce Paired Image and Video data from three CAMeraS, namely PIV3CAMS, aimed at multiple computer vision tasks. The PIV3CAMS dataset consists of 8385 pairs of images and 82 pairs of videos taken from three different cameras: Canon D5 Mark IV, Huawei P20, and ZED stereo camera. The dataset includes various indoor and outdoor scenes from different locations in Zurich (Switzerland) and Cheonan (South Korea). Some of the computer vision applications that can benefit from the PIV3CAMS dataset are image/video enhancement, view interpolation, image matching, and much more. We provide a careful explanation of the data collection process and detailed analysis of the data. The second part of this thesis studies the usage of depth information in the view synthesizing task. In addition to the regeneration of a current state-of-the-art algorithm, we investigate several proposed alternative models that integrate depth information geometrically. Through extensive experiments, we show that the effect of depth is crucial in small view changes. Finally, we apply our model to the introduced PIV3CAMS dataset to synthesize novel target views as an example application of PIV3CAMS.", "published": "2024-07-26T12:18:29Z", "authors": ["Sohyeong Kim", "Martin Danelljan", "Radu Timofte", "Luc Van Gool", "Jean-Philippe Thiran"], "categories": ["cs.CV"], "doi": "", "_query": "property valuation computer vision", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2109.01879v4", "title": "Moving Object Detection for Event-based Vision using k-means Clustering", "abstract": "Moving object detection is important in computer vision. Event-based cameras are bio-inspired cameras that work by mimicking the working of the human eye. These cameras have multiple advantages over conventional frame-based cameras, like reduced latency, HDR, reduced motion blur during high motion, low power consumption, etc. In spite of these advantages, event-based cameras are noise-sensitive and have low resolution. Moreover, the task of moving object detection in these cameras is difficult, as event-based sensors lack useful visual features like texture and color. In this paper, we investigate the application of the k-means clustering technique in detecting moving objects in event-based data.", "published": "2021-09-04T14:43:14Z", "authors": ["Anindya Mondal", "Mayukhmali Das"], "categories": ["cs.CV", "cs.AI", "eess.IV"], "doi": "10.1109/UPCON52273.2021.9667636", "_query": "property valuation computer vision", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2411.15366v1", "title": "Personalization of Wearable Sensor-Based Joint Kinematic Estimation Using Computer Vision for Hip Exoskeleton Applications", "abstract": "Accurate lower-limb joint kinematic estimation is critical for applications such as patient monitoring, rehabilitation, and exoskeleton control. While previous studies have employed wearable sensor-based deep learning (DL) models for estimating joint kinematics, these methods often require extensive new datasets to adapt to unseen gait patterns. Meanwhile, researchers in computer vision have advanced human pose estimation models, which are easy to deploy and capable of real-time inference. However, such models are infeasible in scenarios where cameras cannot be used. To address these limitations, we propose a computer vision-based DL adaptation framework for real-time joint kinematic estimation. This framework requires only a small dataset (i.e., 1-2 gait cycles) and does not depend on professional motion capture setups. Using transfer learning, we adapted our temporal convolutional network (TCN) to stiff knee gait data, allowing the model to further reduce root mean square error by 9.7% and 19.9% compared to a TCN trained on only able-bodied and stiff knee datasets, respectively. Our framework demonstrates a potential for smartphone camera-trained DL models to estimate real-time joint kinematics across novel users in clinical populations with applications in wearable robots.", "published": "2024-11-22T22:17:42Z", "authors": ["Changseob Song", "Bogdan Ivanyuk-Skulskyi", "Adrian Krieger", "Kaitao Luo", "Inseung Kang"], "categories": ["cs.RO", "cs.CV"], "doi": "", "_query": "property valuation computer vision", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2004.04504v1", "title": "A Proposed IoT Smart Trap using Computer Vision for Sustainable Pest Control in Coffee Culture", "abstract": "The Internet of Things (IoT) is emerging as a multi-purpose technology with enormous potential for improving the quality of life in several areas. In particular, IoT has been applied in agriculture to make it more sustainable ecologically. For instance, electronic traps have the potential to perform pest control without any pesticide. In this paper, a smart trap with IoT capabilities that uses computer vision to identify the insect of interest is proposed. The solution includes 1) an embedded system with camera, GPS sensor and motor actuators; 2) an IoT middleware as database service provider, and 3) a Web application to present data by a configurable heat map. The demonstration of proposed solution is exposed and the main conclusions are the perception about pest concentration at the plantation and the viability as alternative pest control over traditional control based on pesticides.", "published": "2020-04-09T12:04:15Z", "authors": ["Vitor Alexandre Campos Figueiredo", "Samuel Mafra", "Joel Rodrigues"], "categories": ["cs.CV", "cs.NI"], "doi": "", "_query": "property valuation computer vision", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2212.08996v1", "title": "Smart Face Shield: A Sensor-Based Wearable Face Shield Utilizing Computer Vision Algorithms", "abstract": "The study aims the development of a wearable device to combat the onslaught of covid-19. Likewise, to enhance the regular face shield available in the market. Furthermore, to raise awareness of the health and safety protocols initiated by the government and its affiliates in the enforcement of social distancing with the integration of computer vision algorithms. The wearable device was composed of various hardware and software components such as a transparent polycarbonate face shield, microprocessor, sensors, camera, thin-film transistor on-screen display, jumper wires, power bank, and python programming language. The algorithm incorporated in the study was object detection under computer vision machine learning. The front camera with OpenCV technology determines the distance of a person in front of the user. Utilizing TensorFlow, the target object identifies and detects the image or live feed to get its bounding boxes. The focal length lens requires the determination of the distance from the camera to the target object. To get the focal length, multiply the pixel width by the known distance and divide it by the known width (Rosebrock, 2020). The deployment of unit testing ensures that the parameters are valid in terms of design and specifications.", "published": "2022-12-18T03:23:38Z", "authors": ["Manuel Luis C. Delos Santos", "Ronaldo S. Tinio", "Darwin B. Diaz", "Karlene Emily I. Tolosa"], "categories": ["cs.CV"], "doi": "10.25147/ijcsr.2017.001.1.118", "_query": "property valuation computer vision", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2010.16307v1", "title": "Automatic Counting and Identification of Train Wagons Based on Computer Vision and Deep Learning", "abstract": "In this work, we present a robust and efficient solution for counting and identifying train wagons using computer vision and deep learning. The proposed solution is cost-effective and can easily replace solutions based on radiofrequency identification (RFID), which are known to have high installation and maintenance costs. According to our experiments, our two-stage methodology achieves impressive results on real-world scenarios, i.e., 100% accuracy in the counting stage and 99.7% recognition rate in the identification one. Moreover, the system is able to automatically reject some of the train wagons successfully counted, as they have damaged identification codes. The results achieved were surprising considering that the proposed system requires low processing power (i.e., it can run in low-end setups) and that we used a relatively small number of images to train our Convolutional Neural Network (CNN) for character recognition. The proposed method is registered, under number BR512020000808-9, with the National Institute of Industrial Property (Brazil).", "published": "2020-10-30T14:56:54Z", "authors": ["Rayson Laroca", "Alessander Cidral Boslooper", "David Menotti"], "categories": ["cs.CV"], "doi": "", "_query": "property valuation computer vision", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/0712.3870v3", "title": "Substitute Valuations: Generation and Structure", "abstract": "Substitute valuations (in some contexts called gross substitute valuations) are prominent in combinatorial auction theory. An algorithm is given in this paper for generating a substitute valuation through Monte Carlo simulation. In addition, the geometry of the set of all substitute valuations for a fixed number of goods K is investigated. The set consists of a union of polyhedrons, and the maximal polyhedrons are identified for K=4. It is shown that the maximum dimension of the maximal polyhedrons increases with K nearly as fast as two to the power K. Consequently, under broad conditions, if a combinatorial algorithm can present an arbitrary substitute valuation given a list of input numbers, the list must grow nearly as fast as two to the power K.", "published": "2007-12-22T16:52:39Z", "authors": ["Bruce Hajek"], "categories": ["cs.GT", "cs.PF"], "doi": "10.1016/j.peva.2008.07.001", "_query": "property valuation computer vision", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2412.08321v3", "title": "TGOSPA Metric Parameters Selection and Evaluation for Visual Multi-object Tracking", "abstract": "Multi-object tracking algorithms are deployed in various applications, each with different performance requirements. For example, track switches pose significant challenges for offline scene understanding, as they hinder the accuracy of data interpretation. Conversely, in online surveillance applications, their impact is often minimal. This disparity underscores the need for application-specific performance evaluations that are both simple and mathematically sound. The trajectory generalized optimal sub-pattern assignment (TGOSPA) metric offers a principled approach to evaluate multi-object tracking performance. It accounts for localization errors, the number of missed and false objects, and the number of track switches, providing a comprehensive assessment framework. This paper illustrates the effective use of the TGOSPA metric in computer vision tasks, addressing challenges posed by the need for application-specific scoring methodologies. By exploring the TGOSPA parameter selection, we enable users to compare, comprehend, and optimize the performance of algorithms tailored for specific tasks, such as target tracking and training of detector or re-ID modules.", "published": "2024-12-11T11:57:05Z", "authors": ["Jan Krejčí", "Oliver Kost", "Ondřej Straka", "Yuxuan Xia", "Lennart Svensson", "Ángel F. García-Fernández"], "categories": ["eess.SY", "cs.CV"], "doi": "", "_query": "property valuation computer vision", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2609.16858v1", "title": "TecoPrompt: Temporal-Conservative Prompt Learning for Vision-Language Models", "abstract": "Prompt learning adapts vision-language models, such as CLIP, by adjusting a small set of context tokens. However, under few-shot supervision, even moderate label noise can disrupt prompt optimization. To address this issue, we propose TecoPrompt, a closed-loop robust prompt-learning framework that revisits optimal transport (OT) pseudo-labeling from a temporal perspective. TecoPrompt employs an entropic OT plan in the CLIP semantic space to obtain globally consistent label candidates. It verifies the reliability of these candidates by examining trajectory stability: a noisy label is only rewritten if the OT candidate remains unchanged within a K-epoch temporal stability window and passes a confidence gate based on Exponential Moving Average (EMA). This approach helps reduce confirmation bias. The rewritten labels are then integrated back into prompt training using a tri-group objective that includes three loss functions aligned with clean, mid, and noisy subsets. Experiments on seven datasets with synthetic symmetric and asymmetric noise, as well as Food101N, demonstrate significant performance improvements. For example, on the OxfordPets dataset, with 50% asymmetric noise, TecoPrompt achieves an accuracy of 0.843, up from 0.775.", "published": "2026-09-15T08:49:07Z", "authors": ["Zeyi Shao", "Haowen Hua", "Jiaxin Zhang", "John See", "Zeyd Boukhers", "Cong Yang"], "categories": ["cs.CV"], "doi": "", "_query": "property valuation computer vision", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2008.01392v1", "title": "Learning Visual Representations with Caption Annotations", "abstract": "Pretraining general-purpose visual features has become a crucial part of tackling many computer vision tasks. While one can learn such features on the extensively-annotated ImageNet dataset, recent approaches have looked at ways to allow for noisy, fewer, or even no annotations to perform such pretraining. Starting from the observation that captioned images are easily crawlable, we argue that this overlooked source of information can be exploited to supervise the training of visual representations. To do so, motivated by the recent progresses in language models, we introduce {\\em image-conditioned masked language modeling} (ICMLM) -- a proxy task to learn visual representations over image-caption pairs. ICMLM consists in predicting masked words in captions by relying on visual cues. To tackle this task, we propose hybrid models, with dedicated visual and textual encoders, and we show that the visual representations learned as a by-product of solving this task transfer well to a variety of target tasks. Our experiments confirm that image captions can be leveraged to inject global and localized semantic information into visual representations. Project website: https://europe.naverlabs.com/icmlm.", "published": "2020-08-04T08:04:16Z", "authors": ["Mert Bulent Sariyildiz", "Julien Perez", "Diane Larlus"], "categories": ["cs.CV"], "doi": "", "_query": "property valuation computer vision", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2607.11381v1", "title": "Common extensions of valuations to rational function fields", "abstract": "Let (K(X)|K,w) be a valuation transcendental extension of rational function fields and take a minimal pair of definition (a, gamma). In this paper, we characterize those K-conjugates a' of a such that the monomial valuation induced by the pair (a', gamma) restricts to w on K(X). In particular, we show that a' satisfies this if and only if a' and a are conjugates over the henselization of (K,v). The second part of the paper concerns abstract key polynomials. We introduce the notion of a regular limit key polynomial, and more generally, that of a regular complete sequence of key polynomials. We prove that regularity is equivalent to the property that every root of every key polynomial determines the corresponding truncated valuation. This extends earlier work of Mahboub, Mansour and Spivakovsky by allowing both limit key polynomials and valuation algebraic extensions. As a consequence, we obtain that w always admits a regular complete sequence of key polynomials whenever (K,v) is dense in its henselization.", "published": "2026-07-13T10:42:28Z", "authors": ["Arpan Dutta", "Wael Mahboub"], "categories": ["math.AC", "math.AG"], "doi": "", "_query": "property valuation computer vision", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2212.07778v2", "title": "Efficient Visual Computing with Camera RAW Snapshots", "abstract": "Conventional cameras capture image irradiance on a sensor and convert it to RGB images using an image signal processor (ISP). The images can then be used for photography or visual computing tasks in a variety of applications, such as public safety surveillance and autonomous driving. One can argue that since RAW images contain all the captured information, the conversion of RAW to RGB using an ISP is not necessary for visual computing. In this paper, we propose a novel $ρ$-Vision framework to perform high-level semantic understanding and low-level compression using RAW images without the ISP subsystem used for decades. Considering the scarcity of available RAW image datasets, we first develop an unpaired CycleR2R network based on unsupervised CycleGAN to train modular unrolled ISP and inverse ISP (invISP) models using unpaired RAW and RGB images. We can then flexibly generate simulated RAW images (simRAW) using any existing RGB image dataset and finetune different models originally trained for the RGB domain to process real-world camera RAW images. We demonstrate object detection and image compression capabilities in RAW-domain using RAW-domain YOLOv3 and RAW image compressor (RIC) on snapshots from various cameras. Quantitative results reveal that RAW-domain task inference provides better detection accuracy and compression compared to RGB-domain processing. Furthermore, the proposed \\r{ho}-Vision generalizes across various camera sensors and different task-specific models. Additional advantages of the proposed $ρ$-Vision that eliminates the ISP are the potential reductions in computations and processing times.", "published": "2022-12-15T12:54:21Z", "authors": ["Zhihao Li", "Ming Lu", "Xu Zhang", "Xin Feng", "M. Salman Asif", "Zhan Ma"], "categories": ["cs.CV", "eess.IV"], "doi": "10.1109/TPAMI.2024.3359326", "_query": "property valuation computer vision", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2109.13796v2", "title": "Actuarial-consistency and two-step actuarial valuations: a new paradigm to insurance valuation", "abstract": "This paper introduces new valuation schemes called actuarial-consistent valuations for insurance liabilities which depend on both financial and actuarial risks, which imposes that all actuarial risks are priced via standard actuarial principles. We propose to extend standard actuarial principles by a new actuarial-consistent procedure, which we call \"two-step actuarial valuations\". \\bluebis{In the case valuations are coherent}, we show that actuarial-consistent valuations are equivalent to two-step actuarial valuations. We also discuss the connection with \"two-step market-consistent valuations\" from Pelsser and Stadje (2014). In particular, we discuss how the dependence structure between actuarial and financial risks impacts both actuarial-consistent and market-consistent valuations.", "published": "2021-08-30T07:22:47Z", "authors": ["Karim Barigou", "Daniël Linders", "Fan Yang"], "categories": ["q-fin.PR", "math.PR"], "doi": "", "_query": "property valuation computer vision", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2212.10207v1", "title": "Graph Neural Networks in Computer Vision -- Architectures, Datasets and Common Approaches", "abstract": "Graph Neural Networks (GNNs) are a family of graph networks inspired by mechanisms existing between nodes on a graph. In recent years there has been an increased interest in GNN and their derivatives, i.e., Graph Attention Networks (GAT), Graph Convolutional Networks (GCN), and Graph Recurrent Networks (GRN). An increase in their usability in computer vision is also observed. The number of GNN applications in this field continues to expand; it includes video analysis and understanding, action and behavior recognition, computational photography, image and video synthesis from zero or few shots, and many more. This contribution aims to collect papers published about GNN-based approaches towards computer vision. They are described and summarized from three perspectives. Firstly, we investigate the architectures of Graph Neural Networks and their derivatives used in this area to provide accurate and explainable recommendations for the ensuing investigations. As for the other aspect, we also present datasets used in these works. Finally, using graph analysis, we also examine relations between GNN-based studies in computer vision and potential sources of inspiration identified outside of this field.", "published": "2022-12-20T12:40:29Z", "authors": ["Maciej Krzywda", "Szymon Łukasik", "Amir H. Gandomi"], "categories": ["cs.LG", "cs.AI"], "doi": "10.1109/IJCNN55064.2022.9892658", "_query": "property valuation computer vision", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2410.03686v2", "title": "LCM: Log Conformal Maps for Robust Representation Learning to Mitigate Perspective Distortion", "abstract": "Perspective distortion (PD) leads to substantial alterations in the shape, size, orientation, angles, and spatial relationships of visual elements in images. Accurately determining camera intrinsic and extrinsic parameters is challenging, making it hard to synthesize perspective distortion effectively. The current distortion correction methods involve removing distortion and learning vision tasks, thus making it a multi-step process, often compromising performance. Recent work leverages the Möbius transform for mitigating perspective distortions (MPD) to synthesize perspective distortions without estimating camera parameters. Möbius transform requires tuning multiple interdependent and interrelated parameters and involving complex arithmetic operations, leading to substantial computational complexity. To address these challenges, we propose Log Conformal Maps (LCM), a method leveraging the logarithmic function to approximate perspective distortions with fewer parameters and reduced computational complexity. We provide a detailed foundation complemented with experiments to demonstrate that LCM with fewer parameters approximates the MPD. We show that LCM integrates well with supervised and self-supervised representation learning, outperform standard models, and matches the state-of-the-art performance in mitigating perspective distortion over multiple benchmarks, namely Imagenet-PD, Imagenet-E, and Imagenet-X. Further LCM demonstrate seamless integration with person re-identification and improved the performance. Source code is made publicly available at https://github.com/meenakshi23/Log-Conformal-Maps.", "published": "2024-09-20T18:07:47Z", "authors": ["Meenakshi Subhash Chippa", "Prakash Chandra Chhipa", "Kanjar De", "Marcus Liwicki", "Rajkumar Saini"], "categories": ["cs.CV"], "doi": "", "_query": "property valuation computer vision", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/1611.05744v1", "title": "Compensating for Large In-Plane Rotations in Natural Images", "abstract": "Rotation invariance has been studied in the computer vision community primarily in the context of small in-plane rotations. This is usually achieved by building invariant image features. However, the problem of achieving invariance for large rotation angles remains largely unexplored. In this work, we tackle this problem by directly compensating for large rotations, as opposed to building invariant features. This is inspired by the neuro-scientific concept of mental rotation, which humans use to compare pairs of rotated objects. Our contributions here are three-fold. First, we train a Convolutional Neural Network (CNN) to detect image rotations. We find that generic CNN architectures are not suitable for this purpose. To this end, we introduce a convolutional template layer, which learns representations for canonical 'unrotated' images. Second, we use Bayesian Optimization to quickly sift through a large number of candidate images to find the canonical 'unrotated' image. Third, we use this method to achieve robustness to large angles in an image retrieval scenario. Our method is task-agnostic, and can be used as a pre-processing step in any computer vision system.", "published": "2016-11-17T15:50:36Z", "authors": ["Lokesh Boominathan", "Suraj Srinivas", "R. Venkatesh Babu"], "categories": ["cs.CV"], "doi": "", "_query": "property valuation computer vision", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2208.12694v1", "title": "Hardware-aware mobile building block evaluation for computer vision", "abstract": "In this work we propose a methodology to accurately evaluate and compare the performance of efficient neural network building blocks for computer vision in a hardware-aware manner. Our comparison uses pareto fronts based on randomly sampled networks from a design space to capture the underlying accuracy/complexity trade-offs. We show that our approach allows to match the information obtained by previous comparison paradigms, but provides more insights in the relationship between hardware cost and accuracy. We use our methodology to analyze different building blocks and evaluate their performance on a range of embedded hardware platforms. This highlights the importance of benchmarking building blocks as a preselection step in the design process of a neural network. We show that choosing the right building block can speed up inference by up to a factor of 2x on specific hardware ML accelerators.", "published": "2022-08-26T14:44:17Z", "authors": ["Maxim Bonnaerens", "Matthias Freiberger", "Marian Verhelst", "Joni Dambre"], "categories": ["cs.CV"], "doi": "", "_query": "property valuation computer vision", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/0908.2795v1", "title": "Fibered knots and Property 2R, II", "abstract": "A knot K in the 3-sphere is said to have Property nR if, whenever K is a component of an n-component link L and some integral surgery on L produces the connected sum of n copies of S^1 x S^2, there is a sequence of handle slides on L that converts L into a 0-framed unlink. The Generalized Property R Conjecture is that all knots have Property nR for all n. The simplest plausible counterexample could be the square knot. Exploiting the remarkable symmetry of the square knot, we characterize all two-component links that contain it and which surger to S^1 x S^2 # S^1 x S^2. We argue that at least one such link probably cannot be reduced to the unlink by a series of handle-slides, so the square knot probably does not have Property 2R. This example is based on a classic construction of the first author. On the other hand, the square knot may well satisfy a somewhat weaker property, which is still useful in 4-manifold theory. For the weaker property, copies of canceling Hopf pairs may be added to the link before the handle slides and then removed after the handle slides. Beyond our specific example, we discuss the mechanics of how addition and later removal of a Hopf pair can be used to reduce the complexity of a surgery description.", "published": "2009-08-19T18:26:55Z", "authors": ["Robert E. Gompf", "Martin Scharlemann"], "categories": ["math.GT"], "doi": "", "_query": "property valuation computer vision", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2004.13060v3", "title": "GIMP-ML: Python Plugins for using Computer Vision Models in GIMP", "abstract": "This paper introduces GIMP-ML v1.1, a set of Python plugins for the widely popular GNU Image Manipulation Program (GIMP). It enables the use of recent advances in computer vision to the conventional image editing pipeline. Applications from deep learning such as monocular depth estimation, semantic segmentation, mask generative adversarial networks, image super-resolution, de-noising, de-hazing, matting, enlightening and coloring have been incorporated with GIMP through Python-based plugins. Additionally, operations on images such as k-means based color clustering have also been added. GIMP-ML relies on standard Python packages such as numpy, pytorch, open-cv, scipy. Apart from these, several image manipulation techniques using these plugins have been compiled and demonstrated in the YouTube channel (https://youtube.com/user/kritiksoman) with the objective of demonstrating the use-cases for machine learning based image modification. In addition, GIMP-ML also aims to bring the benefits of using deep learning networks used for computer vision tasks to routine image processing workflows. The code and installation procedure for configuring these plugins is available at https://github.com/kritiksoman/GIMP-ML.", "published": "2020-04-27T18:00:37Z", "authors": ["Kritik Soman"], "categories": ["cs.CV"], "doi": "", "_query": "property valuation computer vision", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/0904.1613v1", "title": "On the closed-form solution of the rotation matrix arising in computer vision problems", "abstract": "We show the closed-form solution to the maximization of trace(A'R), where A is given and R is unknown rotation matrix. This problem occurs in many computer vision tasks involving optimal rotation matrix estimation. The solution has been continuously reinvented in different fields as part of specific problems. We summarize the historical evolution of the problem and present the general proof of the solution. We contribute to the proof by considering the degenerate cases of A and discuss the uniqueness of R.", "published": "2009-04-09T22:15:25Z", "authors": ["Andriy Myronenko", "Xubo Song"], "categories": ["cs.CV"], "doi": "", "_query": "property valuation computer vision", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2510.23100v2", "title": "Group equivariant Radon-Nikodým property and its characterizations", "abstract": "We introduce and study equivariant versions of the Radon-Nikodým property for Banach spaces, together with the closely related notions such as dentability, the Bishop-Phelps and Krein-Milman properties, and Lindenstrauss' property A, all considered in the presence of a continuous group action by linear isometries. While in the classical setting the Radon-Nikodým property, the Bishop-Phelps property and dentability are equivalent, the equivariant situation turns out to depend essentially on the acting group and requires non-trivial tools from abstract harmonic analysis and representation theory. We establish several implications among the equivariant counterparts of these properties. Namely, given a compact group $G$, the $G$-Bishop-Phelps property implies strong $G$-dentability, which in turn implies the $G$-Krein-Milman property and the classical Bishop-Phelps property, for any $G$-Banach space. Moreover, given a locally compact and second countable group $G$, the $G$-Radon-Nikodým property is equivalent to the classical Radon-Nikodým property, for any $G$-Banach space.", "published": "2025-10-27T08:15:23Z", "authors": ["Sheldon Dantas", "Michal Doucha", "Mingu Jung", "Tomáš Raunig"], "categories": ["math.FA"], "doi": "", "_query": "property valuation computer vision", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2605.08153v1", "title": "Temporal-Decay Shapley: A Time-Aware Data Valuation Framework for Time-Series Data", "abstract": "With the rapid development of machine learning applications on time-series data, accurately assessing the value of training samples has become essential for data selection, noise detection, and model optimization. However, traditional data valuation methods usually assume that samples are independent and identically distributed, and thus ignore the time-varying nature of sample value in time-series data. This paper proposes an improved temporal Shapley data valuation method that enables accurate sample valuation for time-series data through a temporal decay mechanism and a multi-scale fusion strategy. Specifically, we propose three progressively enhanced temporal Shapley methods. Temporal-Decay Shapley (TDS) incorporates temporal information into Shapley value computation through exponential decay weights; the improved TDS adopts power exponential decay to better adapt to nonlinear temporal drift; and Multi-Scale Temporal-Decay Shapley (MS-TDS) constructs a multi-scale fusion mechanism that balances the value of short-term hotspot samples and long-term foundational samples through parallel multi-scale valuation and sample-level adaptive fusion. Experimental results show that the proposed methods generally outperform traditional methods in noise detection and high-value data identification tasks, with more evident advantages under most strongly temporal settings, thereby effectively improving the accuracy and robustness of data valuation.", "published": "2026-05-04T04:32:33Z", "authors": ["Chuwen Pang", "Bing Mi", "Kongyang Chen"], "categories": ["cs.LG", "cs.GT"], "doi": "", "_query": "property valuation computer vision", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2202.00418v2", "title": "Review of Serial and Parallel Min-Cut/Max-Flow Algorithms for Computer Vision", "abstract": "Minimum cut/maximum flow (min-cut/max-flow) algorithms solve a variety of problems in computer vision and thus significant effort has been put into developing fast min-cut/max-flow algorithms. As a result, it is difficult to choose an ideal algorithm for a given problem. Furthermore, parallel algorithms have not been thoroughly compared. In this paper, we evaluate the state-of-the-art serial and parallel min-cut/max-flow algorithms on the largest set of computer vision problems yet. We focus on generic algorithms, i.e., for unstructured graphs, but also compare with the specialized GridCut implementation. When applicable, GridCut performs best. Otherwise, the two pseudoflow algorithms, Hochbaum pseudoflow and excesses incremental breadth first search, achieves the overall best performance. The most memory efficient implementation tested is the Boykov-Kolmogorov algorithm. Amongst generic parallel algorithms, we find the bottom-up merging approach by Liu and Sun to be best, but no method is dominant. Of the generic parallel methods, only the parallel preflow push-relabel algorithm is able to efficiently scale with many processors across problem sizes, and no generic parallel method consistently outperforms serial algorithms. Finally, we provide and evaluate strategies for algorithm selection to obtain good expected performance. We make our dataset and implementations publicly available for further research.", "published": "2022-02-01T14:06:27Z", "authors": ["Patrick M. Jensen", "Niels Jeppesen", "Anders B. Dahl", "Vedrana A. Dahl"], "categories": ["cs.CV"], "doi": "", "_query": "property valuation computer vision", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2503.09361v1", "title": "Deep Learning for Climate Action: Computer Vision Analysis of Visual Narratives on X", "abstract": "Climate change is one of the most pressing challenges of the 21st century, sparking widespread discourse across social media platforms. Activists, policymakers, and researchers seek to understand public sentiment and narratives while access to social media data has become increasingly restricted in the post-API era. In this study, we analyze a dataset of climate change-related tweets from X (formerly Twitter) shared in 2019, containing 730k tweets along with the shared images. Our approach integrates statistical analysis, image classification, object detection, and sentiment analysis to explore visual narratives in climate discourse. Additionally, we introduce a graphical user interface (GUI) to facilitate interactive data exploration. Our findings reveal key themes in climate communication, highlight sentiment divergence between images and text, and underscore the strengths and limitations of foundation models in analyzing social media imagery. By releasing our code and tools, we aim to support future research on the intersection of climate change, social media, and computer vision.", "published": "2025-03-12T13:03:49Z", "authors": ["Katharina Prasse", "Marcel Kleinmann", "Inken Adam", "Kerstin Beckersjuergen", "Andreas Edte", "Jona Frroku", "Timotheus Gumpp", "Steffen Jung", "Isaac Bravo", "Stefanie Walter", "Margret Keuper"], "categories": ["cs.CV", "cs.SI"], "doi": "", "_query": "property valuation computer vision", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2104.08702v1", "title": "Reconsidering CO2 emissions from Computer Vision", "abstract": "Climate change is a pressing issue that is currently affecting and will affect every part of our lives. It's becoming incredibly vital we, as a society, address the climate crisis as a universal effort, including those in the Computer Vision (CV) community. In this work, we analyze the total cost of CO2 emissions by breaking it into (1) the architecture creation cost and (2) the life-time evaluation cost. We show that over time, these costs are non-negligible and are having a direct impact on our future. Importantly, we conduct an ethical analysis of how the CV-community is unintentionally overlooking its own ethical AI principles by emitting this level of CO2. To address these concerns, we propose adding \"enforcement\" as a pillar of ethical AI and provide some recommendations for how architecture designers and broader CV community can curb the climate crisis.", "published": "2021-04-18T04:01:40Z", "authors": ["Andre Fu", "Mahdi S. Hosseini", "Konstantinos N. Plataniotis"], "categories": ["cs.CV"], "doi": "", "_query": "property valuation computer vision", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/1406.4845v2", "title": "Computer Vision Approach for Low Cost, High Precision Measurement of Grapevine Trunk Diameter in Outdoor Conditions", "abstract": "Trunk diameter is a variable of agricultural interest, used mainly in the prediction of fruit trees production. It is correlated with leaf area and biomass of trees, and consequently gives a good estimate of the potential production of the plants. This work presents a low cost, high precision method for the measurement of trunk diameter of grapevines based on Computer Vision techniques. Several methods based on Computer Vision and other techniques are introduced in the literature. These methods present different advantages for crop management: they are amenable to be operated by unknowledgeable personnel, with lower operational costs; they result in lower stress levels to knowledgeable personnel, avoiding the deterioration of the measurement quality over time; and they make the measurement process amenable to be embedded in larger autonomous systems, allowing more measurements to be taken with equivalent costs. To date, all existing autonomous methods are either of low precision, or have a prohibitive cost for massive agricultural adoption, leaving the manual Vernier caliper or tape measure as the only choice in most situations. In this work we present a semi-autonomous measurement method that is susceptible to be fully automated, cost effective for mass adoption, and its precision is competitive (with slight improvements) over the caliper manual method.", "published": "2014-05-16T18:44:58Z", "authors": ["Diego Sebastián Pérez", "Facundo Bromberg", "Francisco Gonzalez Antivilo"], "categories": ["cs.CV"], "doi": "", "_query": "property valuation computer vision", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2409.16538v1", "title": "Source-Free Domain Adaptation for YOLO Object Detection", "abstract": "Source-free domain adaptation (SFDA) is a challenging problem in object detection, where a pre-trained source model is adapted to a new target domain without using any source domain data for privacy and efficiency reasons. Most state-of-the-art SFDA methods for object detection have been proposed for Faster-RCNN, a detector that is known to have high computational complexity. This paper focuses on domain adaptation techniques for real-world vision systems, particularly for the YOLO family of single-shot detectors known for their fast baselines and practical applications. Our proposed SFDA method - Source-Free YOLO (SF-YOLO) - relies on a teacher-student framework in which the student receives images with a learned, target domain-specific augmentation, allowing the model to be trained with only unlabeled target data and without requiring feature alignment. A challenge with self-training using a mean-teacher architecture in the absence of labels is the rapid decline of accuracy due to noisy or drifting pseudo-labels. To address this issue, a teacher-to-student communication mechanism is introduced to help stabilize the training and reduce the reliance on annotated target data for model selection. Despite its simplicity, our approach is competitive with state-of-the-art detectors on several challenging benchmark datasets, even sometimes outperforming methods that use source data for adaptation.", "published": "2024-09-25T01:22:10Z", "authors": ["Simon Varailhon", "Masih Aminbeidokhti", "Marco Pedersoli", "Eric Granger"], "categories": ["cs.CV", "cs.AI", "cs.LG"], "doi": "", "_query": "property valuation computer vision", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2501.18098v3", "title": "Disentangling Safe and Unsafe Corruptions via Anisotropy and Locality", "abstract": "State-of-the-art machine learning systems are vulnerable to small perturbations to their input, where ``small'' is defined according to a threat model that assigns a positive threat to each perturbation. Most prior works define a task-agnostic, isotropic, and global threat, like the $\\ell_p$ norm, where the magnitude of the perturbation fully determines the degree of the threat and neither the direction of the attack nor its position in space matter. However, common corruptions in computer vision, such as blur, compression, or occlusions, are not well captured by such threat models. This paper proposes a novel threat model called \\texttt{Projected Displacement} (PD) to study robustness beyond existing isotropic and global threat models. The proposed threat model measures the threat of a perturbation via its alignment with \\textit{unsafe directions}, defined as directions in the input space along which a perturbation of sufficient magnitude changes the ground truth class label. Unsafe directions are identified locally for each input based on observed training data. In this way, the PD threat model exhibits anisotropy and locality. Experiments on Imagenet-1k data indicate that, for any input, the set of perturbations with small PD threat includes \\textit{safe} perturbations of large $\\ell_p$ norm that preserve the true label, such as noise, blur and compression, while simultaneously excluding \\textit{unsafe} perturbations that alter the true label. Unlike perceptual threat models based on embeddings of large-vision models, the PD threat model can be readily computed for arbitrary classification tasks without pre-training or finetuning. Further additional task annotation such as sensitivity to image regions or concept hierarchies can be easily integrated into the assessment of threat and thus the PD threat model presents practitioners with a flexible, task-driven threat specification.", "published": "2025-01-30T02:21:07Z", "authors": ["Ramchandran Muthukumar", "Ambar Pal", "Jeremias Sulam", "Rene Vidal"], "categories": ["cs.CV", "cs.LG"], "doi": "", "_query": "property valuation computer vision", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/0909.0583v1", "title": "Computational Complexities and Breaches in Authentication Frameworks of Broadband Wireless Access", "abstract": "Secure access of communication networks has become an increasingly important area of consideration for the communication service providers of present day. Broadband Wireless Access (BWA) networks are proving to be an efficient and cost effective solution for the provisioning of high rate wireless traffic links in static and mobile domains. The secure access of these networks is necessary to ensure their superior operation and revenue efficacy. Although authentication process is a key to secure access in BWA networks, the breaches present in them limit the networks performance. In this paper, the vulnerabilities in the authentication frameworks of BWA networks have been unveiled. Moreover, this paper also describes the limitations of these protocols and of the solutions proposed to them due to the involved computational complexities and overheads. The possible attacks on privacy and performance of BWA networks have been discussed and explained in detail.", "published": "2009-09-03T07:56:44Z", "authors": ["Raheel Maqsood Hashmi", "Arooj Mubashara Siddiqui", "Memoona Jabeen", "Khurram S. Alimgeer", "Shahid A. Khan"], "categories": ["cs.CR"], "doi": "", "_query": "property valuation computer vision", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/1605.03621v3", "title": "ASP Vision: Optically Computing the First Layer of Convolutional Neural Networks using Angle Sensitive Pixels", "abstract": "Deep learning using convolutional neural networks (CNNs) is quickly becoming the state-of-the-art for challenging computer vision applications. However, deep learning's power consumption and bandwidth requirements currently limit its application in embedded and mobile systems with tight energy budgets. In this paper, we explore the energy savings of optically computing the first layer of CNNs. To do so, we utilize bio-inspired Angle Sensitive Pixels (ASPs), custom CMOS diffractive image sensors which act similar to Gabor filter banks in the V1 layer of the human visual cortex. ASPs replace both image sensing and the first layer of a conventional CNN by directly performing optical edge filtering, saving sensing energy, data bandwidth, and CNN FLOPS to compute. Our experimental results (both on synthetic data and a hardware prototype) for a variety of vision tasks such as digit recognition, object recognition, and face identification demonstrate using ASPs while achieving similar performance compared to traditional deep learning pipelines.", "published": "2016-05-11T21:11:30Z", "authors": ["Huaijin Chen", "Suren Jayasuriya", "Jiyue Yang", "Judy Stephen", "Sriram Sivaramakrishnan", "Ashok Veeraraghavan", "Alyosha Molnar"], "categories": ["cs.CV"], "doi": "", "_query": "property valuation computer vision", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2411.00711v1", "title": "Debiasify: Self-Distillation for Unsupervised Bias Mitigation", "abstract": "Simplicity bias poses a significant challenge in neural networks, often leading models to favor simpler solutions and inadvertently learn decision rules influenced by spurious correlations. This results in biased models with diminished generalizability. While many current approaches depend on human supervision, obtaining annotations for various bias attributes is often impractical. To address this, we introduce Debiasify, a novel self-distillation approach that requires no prior knowledge about the nature of biases. Our method leverages a new distillation loss to transfer knowledge within the network, from deeper layers containing complex, highly-predictive features to shallower layers with simpler, attribute-conditioned features in an unsupervised manner. This enables Debiasify to learn robust, debiased representations that generalize effectively across diverse biases and datasets, improving both worst-group performance and overall accuracy. Extensive experiments on computer vision and medical imaging benchmarks demonstrate the effectiveness of our approach, significantly outperforming previous unsupervised debiasing methods (e.g., a 10.13% improvement in worst-group accuracy for Wavy Hair classification in CelebA) and achieving comparable or superior performance to supervised approaches. Our code is publicly available at the following link: Debiasify.", "published": "2024-11-01T16:25:05Z", "authors": ["Nourhan Bayasi", "Jamil Fayyad", "Ghassan Hamarneh", "Rafeef Garbi", "Homayoun Najjaran"], "categories": ["cs.CV", "cs.LG"], "doi": "", "_query": "property valuation computer vision", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2104.08732v1", "title": "Application of Computer Vision and Machine Learning for Digitized Herbarium Specimens: A Systematic Literature Review", "abstract": "Herbarium contains treasures of millions of specimens which have been preserved for several years for scientific studies. To speed up more scientific discoveries, a digitization of these specimens is currently on going to facilitate easy access and sharing of its data to a wider scientific community. Online digital repositories such as IDigBio and GBIF have already accumulated millions of specimen images yet to be explored. This presents a perfect time to automate and speed up more novel discoveries using machine learning and computer vision. In this study, a thorough analysis and comparison of more than 50 peer-reviewed studies which focus on application of computer vision and machine learning techniques to digitized herbarium specimen have been examined. The study categorizes different techniques and applications which have been commonly used and it also highlights existing challenges together with their possible solutions. It is our hope that the outcome of this study will serve as a strong foundation for beginners of the relevant field and will also shed more light for both computer science and ecology experts.", "published": "2021-04-18T06:08:51Z", "authors": ["Burhan Rashid Hussein", "Owais Ahmed Malik", "Wee-Hong Ong", "Johan Willem Frederik Slik"], "categories": ["cs.CV"], "doi": "", "_query": "property valuation computer vision", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/1201.5083v1", "title": "Atomic Pseudo-Valuation Domains", "abstract": "Pseudo-valuation domains have been studied since their introduction in 1978 by Hedstrom and Houston. Related objects, boundary valuation domains, were introduced by Maney in 2004. Here, it is shown that the class of atomic pseudo-valuation domains coincides with the class of boundary valuation domains. It is also shown that power series rings and generalized power series rings give examples of pseudo-valuation domains whose congruence lattices can be characterized. The paper also introduces, and makes use of, a sufficient condition on the group of divisibility of a domain to guarantee that it is a pseudo-valuation domain.", "published": "2012-01-24T18:39:18Z", "authors": ["Elijah Stines"], "categories": ["math.RA", "math.AC"], "doi": "", "_query": "property valuation computer vision", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2105.01803v1", "title": "DeepRT: A Soft Real Time Scheduler for Computer Vision Applications on the Edge", "abstract": "The ubiquity of smartphone cameras and IoT cameras, together with the recent boom of deep learning and deep neural networks, proliferate various computer vision driven mobile and IoT applications deployed on the edge. This paper focuses on applications which make soft real time requests to perform inference on their data - they desire prompt responses within designated deadlines, but occasional deadline misses are acceptable. Supporting soft real time applications on a multi-tenant edge server is not easy, since the requests sharing the limited GPU computing resources of an edge server interfere with each other. In order to tackle this problem, we comprehensively evaluate how latency and throughput respond to different GPU execution plans. Based on this analysis, we propose a GPU scheduler, DeepRT, which provides latency guarantee to the requests while maintaining high overall system throughput. The key component of DeepRT, DisBatcher, batches data from different requests as much as possible while it is proven to provide latency guarantee for requests admitted by an Admission Control Module. DeepRT also includes an Adaptation Module which tackles overruns. Our evaluation results show that DeepRT outperforms state-of-the-art works in terms of the number of deadline misses and throughput.", "published": "2021-05-05T00:08:17Z", "authors": ["Zhe Yang", "Klara Nahrstedt", "Hongpeng Guo", "Qian Zhou"], "categories": ["cs.DC", "cs.CV"], "doi": "", "_query": "property valuation computer vision", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2205.02551v1", "title": "Biologically inspired deep residual networks for computer vision applications", "abstract": "Deep neural network has been ensured as a key technology in the field of many challenging and vigorously researched computer vision tasks. Furthermore, classical ResNet is thought to be a state-of-the-art convolutional neural network (CNN) and was observed to capture features which can have good generalization ability. In this work, we propose a biologically inspired deep residual neural network where the hexagonal convolutions are introduced along the skip connections. The performance of different ResNet variants using square and hexagonal convolution are evaluated with the competitive training strategy mentioned by [1]. We show that the proposed approach advances the baseline image classification accuracy of vanilla ResNet architectures on CIFAR-10 and the same was observed over multiple subsets of the ImageNet 2012 dataset. We observed an average improvement by 1.35% and 0.48% on baseline top-1 accuracies for ImageNet 2012 and CIFAR-10, respectively. The proposed biologically inspired deep residual networks were observed to have improved generalized performance and this could be a potential research direction to improve the discriminative ability of state-of-the-art image classification networks.", "published": "2022-05-05T10:23:43Z", "authors": ["Prathibha Varghese", "G. Arockia Selva Saroja"], "categories": ["cs.CV", "cs.NE", "eess.IV"], "doi": "", "_query": "property valuation computer vision", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/1009.3078v1", "title": "Asymmetric Totally-corrective Boosting for Real-time Object Detection", "abstract": "Real-time object detection is one of the core problems in computer vision. The cascade boosting framework proposed by Viola and Jones has become the standard for this problem. In this framework, the learning goal for each node is asymmetric, which is required to achieve a high detection rate and a moderate false positive rate. We develop new boosting algorithms to address this asymmetric learning problem. We show that our methods explicitly optimize asymmetric loss objectives in a totally corrective fashion. The methods are totally corrective in the sense that the coefficients of all selected weak classifiers are updated at each iteration. In contract, conventional boosting like AdaBoost is stage-wise in that only the current weak classifier's coefficient is updated. At the heart of the totally corrective boosting is the column generation technique. Experiments on face detection show that our methods outperform the state-of-the-art asymmetric boosting methods.", "published": "2010-09-16T02:45:59Z", "authors": ["Peng Wang", "Chunhua Shen", "Nick Barnes", "Hong Zheng", "Zhang Ren"], "categories": ["cs.CV"], "doi": "", "_query": "property valuation computer vision", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2508.18205v3", "title": "An RBF-based method for computational electromagnetics with reduced numerical dispersion", "abstract": "The finite difference time domain method is one of the simplest and most popular methods in computational electromagnetics. This work considers two possible ways of generalising it to a meshless setting by employing local radial basis function interpolation. The resulting methods remain fully explicit and are convergent if properly chosen hyperviscosity terms are added to the update equations. We demonstrate that increasing the stencil size of the approximation has a desirable effect on numerical dispersion. Furthermore, our proposed methods can exhibit a decreased dispersion anisotropy compared to the finite difference time domain method.", "published": "2025-08-25T17:03:17Z", "authors": ["Andrej Kolar-Požun", "Gregor Kosec"], "categories": ["physics.comp-ph", "math.NA"], "doi": "10.1016/j.cma.2026.118865", "_query": "property valuation computer vision", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2202.09300v2", "title": "Exploring Adversarially Robust Training for Unsupervised Domain Adaptation", "abstract": "Unsupervised Domain Adaptation (UDA) methods aim to transfer knowledge from a labeled source domain to an unlabeled target domain. UDA has been extensively studied in the computer vision literature. Deep networks have been shown to be vulnerable to adversarial attacks. However, very little focus is devoted to improving the adversarial robustness of deep UDA models, causing serious concerns about model reliability. Adversarial Training (AT) has been considered to be the most successful adversarial defense approach. Nevertheless, conventional AT requires ground-truth labels to generate adversarial examples and train models, which limits its effectiveness in the unlabeled target domain. In this paper, we aim to explore AT to robustify UDA models: How to enhance the unlabeled data robustness via AT while learning domain-invariant features for UDA? To answer this question, we provide a systematic study into multiple AT variants that can potentially be applied to UDA. Moreover, we propose a novel Adversarially Robust Training method for UDA accordingly, referred to as ARTUDA. Extensive experiments on multiple adversarial attacks and UDA benchmarks show that ARTUDA consistently improves the adversarial robustness of UDA models. Code is available at https://github.com/shaoyuanlo/ARTUDA", "published": "2022-02-18T17:05:19Z", "authors": ["Shao-Yuan Lo", "Vishal M. Patel"], "categories": ["cs.CV", "cs.LG"], "doi": "", "_query": "property valuation computer vision", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/1102.1241v2", "title": "On modules over valuations", "abstract": "Recently an algebra of smooth valuations was attached to any smooth manifold. Roughly put, a smooth valuation is finitely additive measure on compact submanifolds with corners which satisfies some extra properties. In this note we initiate a study of modules over smooth valuations. More specifically we study finitely generated projective modules in analogy to the study of vector bundles on a manifold. In particular it is shown that on a compact manifold there exists a canonical isomorphism between the $K$-ring constructed out of finitely generated projective modules over valuations and the classical topological $K^0$-ring constructed out of vector bundles.", "published": "2011-02-07T08:03:34Z", "authors": ["Semyon Alesker"], "categories": ["math.MG", "math.KT"], "doi": "", "_query": "property valuation computer vision", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2306.02071v3", "title": "DU-Shapley: A Shapley Value Proxy for Efficient Dataset Valuation", "abstract": "We consider the dataset valuation problem, that is, the problem of quantifying the incremental gain, to some relevant pre-defined utility of a machine learning task, of aggregating an individual dataset to others. The Shapley value is a natural tool to perform dataset valuation due to its formal axiomatic justification, which can be combined with Monte Carlo integration to overcome the computational tractability challenges. Such generic approximation methods, however, remain expensive in some cases. In this paper, we exploit the knowledge about the structure of the dataset valuation problem to devise more efficient Shapley value estimators. We propose a novel approximation, referred to as discrete uniform Shapley, which is expressed as an expectation under a discrete uniform distribution with support of reasonable size. We justify the relevancy of the proposed framework via asymptotic and non-asymptotic theoretical guarantees and illustrate its benefits via an extensive set of numerical experiments.", "published": "2023-06-03T10:22:50Z", "authors": ["Felipe Garrido-Lucero", "Benjamin Heymann", "Maxime Vono", "Patrick Loiseau", "Vianney Perchet"], "categories": ["cs.AI", "cs.GT", "stat.CO", "stat.ML"], "doi": "", "_query": "property valuation computer vision", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2511.19575v2", "title": "HunyuanOCR Technical Report", "abstract": "This paper presents HunyuanOCR, a commercial-grade, open-source, and lightweight (1B parameters) Vision-Language Model (VLM) dedicated to OCR tasks. The architecture comprises a Native Vision Transformer (ViT) and a lightweight LLM connected via an MLP adapter. HunyuanOCR demonstrates superior performance, outperforming commercial APIs, traditional pipelines, and larger models (e.g., Qwen3-VL-4B). Specifically, it surpasses current public solutions in perception tasks (Text Spotting, Parsing) and excels in semantic tasks (IE, Text Image Translation), securing first place in the ICDAR 2025 DIMT Challenge (Small Model Track). Furthermore, it achieves state-of-the-art (SOTA) results on OCRBench among VLMs with fewer than 3B parameters. HunyuanOCR achieves breakthroughs in three key aspects: 1) Unifying Versatility and Efficiency: We implement comprehensive support for core capabilities including spotting, parsing, IE, VQA, and translation within a lightweight framework. This addresses the limitations of narrow \"OCR expert models\" and inefficient \"General VLMs\". 2) Streamlined End-to-End Architecture: Adopting a pure end-to-end paradigm eliminates dependencies on pre-processing modules (e.g., layout analysis). This fundamentally resolves error propagation common in traditional pipelines and simplifies system deployment. 3) Data-Driven and RL Strategies: We confirm the critical role of high-quality data and, for the first time in the industry, demonstrate that Reinforcement Learning (RL) strategies yield significant performance gains in OCR tasks. HunyuanOCR is officially open-sourced on HuggingFace. We also provide a high-performance deployment solution based on vLLM, placing its production efficiency in the top tier. We hope this model will advance frontier research and provide a solid foundation for industrial applications.", "published": "2025-11-24T17:59:59Z", "authors": [" Hunyuan Vision Team", "Pengyuan Lyu", "Xingyu Wan", "Gengluo Li", "Shangpin Peng", "Weinong Wang", "Liang Wu", "Huawen Shen", "Yu Zhou", "Canhui Tang", "Qi Yang", "Qiming Peng", "Bin Luo", "Hower Yang", "Xinsong Zhang", "Jinnian Zhang", "Houwen Peng", "Hongming Yang", "Senhao Xie", "Longsha Zhou", "Ge Pei", "Binghong Wu", "Rui Yan", "Kan Wu", "Jieneng Yang", "Bochao Wang", "Kai Liu", "Jianchen Zhu", "Jie Jiang", " Linus", "Han Hu", "Chengquan Zhang"], "categories": ["cs.CV", "cs.AI"], "doi": "", "_query": "property valuation computer vision", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2111.08251v2", "title": "Enabling equivariance for arbitrary Lie groups", "abstract": "Although provably robust to translational perturbations, convolutional neural networks (CNNs) are known to suffer from extreme performance degradation when presented at test time with more general geometric transformations of inputs. Recently, this limitation has motivated a shift in focus from CNNs to Capsule Networks (CapsNets). However, CapsNets suffer from admitting relatively few theoretical guarantees of invariance. We introduce a rigourous mathematical framework to permit invariance to any Lie group of warps, exclusively using convolutions (over Lie groups), without the need for capsules. Previous work on group convolutions has been hampered by strong assumptions about the group, which precludes the application of such techniques to common warps in computer vision such as affine and homographic. Our framework enables the implementation of group convolutions over any finite-dimensional Lie group. We empirically validate our approach on the benchmark affine-invariant classification task, where we achieve 30% improvement in accuracy against conventional CNNs while outperforming most CapsNets. As further illustration of the generality of our framework, we train a homography-convolutional model which achieves superior robustness on a homography-perturbed dataset, where CapsNet results degrade.", "published": "2021-11-16T06:23:44Z", "authors": ["Lachlan Ewen MacDonald", "Sameera Ramasinghe", "Simon Lucey"], "categories": ["cs.CV", "cs.LG", "cs.NE"], "doi": "", "_query": "property valuation computer vision", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2408.02398v1", "title": "Tensorial template matching for fast cross-correlation with rotations and its application for tomography", "abstract": "Object detection is a main task in computer vision. Template matching is the reference method for detecting objects with arbitrary templates. However, template matching computational complexity depends on the rotation accuracy, being a limiting factor for large 3D images (tomograms). Here, we implement a new algorithm called tensorial template matching, based on a mathematical framework that represents all rotations of a template with a tensor field. Contrary to standard template matching, the computational complexity of the presented algorithm is independent of the rotation accuracy. Using both, synthetic and real data from tomography, we demonstrate that tensorial template matching is much faster than template matching and has the potential to improve its accuracy", "published": "2024-08-05T11:42:41Z", "authors": ["Antonio Martinez-Sanchez", "Ulrike Homberg", "José María Almira", "Harold Phelippeau"], "categories": ["cs.CV", "q-bio.QM"], "doi": "10.1007/978-3-031-73383-3_2", "_query": "property valuation computer vision", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2209.02691v1", "title": "Statistical Foundation Behind Machine Learning and Its Impact on Computer Vision", "abstract": "This paper revisits the principle of uniform convergence in statistical learning, discusses how it acts as the foundation behind machine learning, and attempts to gain a better understanding of the essential problem that current deep learning algorithms are solving. Using computer vision as an example domain in machine learning, the discussion shows that recent research trends in leveraging increasingly large-scale data to perform pre-training for representation learning are largely to reduce the discrepancy between a practically tractable empirical loss and its ultimately desired but intractable expected loss. Furthermore, this paper suggests a few future research directions, predicts the continued increase of data, and argues that more fundamental research is needed on robustness, interpretability, and reasoning capabilities of machine learning by incorporating structure and knowledge.", "published": "2022-09-06T17:59:04Z", "authors": ["Lei Zhang", "Heung-Yeung Shum"], "categories": ["cs.CV"], "doi": "", "_query": "property valuation computer vision", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2307.03254v1", "title": "Vision Language Transformers: A Survey", "abstract": "Vision language tasks, such as answering questions about or generating captions that describe an image, are difficult tasks for computers to perform. A relatively recent body of research has adapted the pretrained transformer architecture introduced in \\citet{vaswani2017attention} to vision language modeling. Transformer models have greatly improved performance and versatility over previous vision language models. They do so by pretraining models on a large generic datasets and transferring their learning to new tasks with minor changes in architecture and parameter values. This type of transfer learning has become the standard modeling practice in both natural language processing and computer vision. Vision language transformers offer the promise of producing similar advancements in tasks which require both vision and language. In this paper, we provide a broad synthesis of the currently available research on vision language transformer models and offer some analysis of their strengths, limitations and some open questions that remain.", "published": "2023-07-06T19:08:56Z", "authors": ["Clayton Fields", "Casey Kennington"], "categories": ["cs.CV", "cs.AI", "cs.CL", "cs.LG"], "doi": "", "_query": "property valuation computer vision", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2305.17328v3", "title": "Zero-TPrune: Zero-Shot Token Pruning through Leveraging of the Attention Graph in Pre-Trained Transformers", "abstract": "Deployment of Transformer models on edge devices is becoming increasingly challenging due to the exponentially growing inference cost that scales quadratically with the number of tokens in the input sequence. Token pruning is an emerging solution to address this challenge due to its ease of deployment on various Transformer backbones. However, most token pruning methods require computationally expensive fine-tuning, which is undesirable in many edge deployment cases. In this work, we propose Zero-TPrune, the first zero-shot method that considers both the importance and similarity of tokens in performing token pruning. It leverages the attention graph of pre-trained Transformer models to produce an importance distribution for tokens via our proposed Weighted Page Rank (WPR) algorithm. This distribution further guides token partitioning for efficient similarity-based pruning. Due to the elimination of the fine-tuning overhead, Zero-TPrune can prune large models at negligible computational cost, switch between different pruning configurations at no computational cost, and perform hyperparameter tuning efficiently. We evaluate the performance of Zero-TPrune on vision tasks by applying it to various vision Transformer backbones and testing them on ImageNet. Without any fine-tuning, Zero-TPrune reduces the FLOPs cost of DeiT-S by 34.7% and improves its throughput by 45.3% with only 0.4% accuracy loss. Compared with state-of-the-art pruning methods that require fine-tuning, Zero-TPrune not only eliminates the need for fine-tuning after pruning but also does so with only 0.1% accuracy loss. Compared with state-of-the-art fine-tuning-free pruning methods, Zero-TPrune reduces accuracy loss by up to 49% with similar FLOPs budgets. Project webpage: https://jha-lab.github.io/zerotprune.", "published": "2023-05-27T02:08:51Z", "authors": ["Hongjie Wang", "Bhishma Dedhia", "Niraj K. Jha"], "categories": ["cs.CV", "cs.AI", "cs.LG", "eess.IV"], "doi": "", "_query": "property valuation computer vision", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2301.09451v1", "title": "A Simple Recipe for Competitive Low-compute Self supervised Vision Models", "abstract": "Self-supervised methods in vision have been mostly focused on large architectures as they seem to suffer from a significant performance drop for smaller architectures. In this paper, we propose a simple self-supervised distillation technique that can train high performance low-compute neural networks. Our main insight is that existing joint-embedding based SSL methods can be repurposed for knowledge distillation from a large self-supervised teacher to a small student model. Thus, we call our method Replace one Branch (RoB) as it simply replaces one branch of the joint-embedding training with a large teacher model. RoB is widely applicable to a number of architectures such as small ResNets, MobileNets and ViT, and pretrained models such as DINO, SwAV or iBOT. When pretraining on the ImageNet dataset, RoB yields models that compete with supervised knowledge distillation. When applied to MSN, RoB produces students with strong semi-supervised capabilities. Finally, our best ViT-Tiny models improve over prior SSL state-of-the-art on ImageNet by $2.3\\%$ and are on par or better than a supervised distilled DeiT on five downstream transfer tasks (iNaturalist, CIFAR, Clevr/Count, Clevr/Dist and Places). We hope RoB enables practical self-supervision at smaller scale.", "published": "2023-01-23T14:20:01Z", "authors": ["Quentin Duval", "Ishan Misra", "Nicolas Ballas"], "categories": ["cs.CV", "cs.AI", "cs.LG"], "doi": "", "_query": "property valuation computer vision", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2503.19100v1", "title": "Anomaly Detection Using Computer Vision: A Comparative Analysis of Class Distinction and Performance Metrics", "abstract": "This paper showcases an experimental study on anomaly detection using computer vision. The study focuses on class distinction and performance evaluation, combining OpenCV with deep learning techniques while employing a TensorFlow-based convolutional neural network for real-time face recognition and classification. The system effectively distinguishes among three classes: authorized personnel (admin), intruders, and non-human entities. A MobileNetV2-based deep learning model is utilized to optimize real-time performance, ensuring high computational efficiency without compromising accuracy. Extensive dataset preprocessing, including image augmentation and normalization, enhances the models generalization capabilities. Our analysis demonstrates classification accuracies of 90.20% for admin, 98.60% for intruders, and 75.80% for non-human detection, while maintaining an average processing rate of 30 frames per second. The study leverages transfer learning, batch normalization, and Adam optimization to achieve stable and robust learning, and a comparative analysis of class differentiation strategies highlights the impact of feature extraction techniques and training methodologies. The results indicate that advanced feature selection and data augmentation significantly enhance detection performance, particularly in distinguishing human from non-human scenes. As an experimental study, this research provides critical insights into optimizing deep learning-based surveillance systems for high-security environments and improving the accuracy and efficiency of real-time anomaly detection.", "published": "2025-03-24T19:36:47Z", "authors": ["Md. Barkat Ullah Tusher", "Shartaz Khan Akash", "Amirul Islam Showmik"], "categories": ["cs.CV", "cs.AI"], "doi": "", "_query": "property valuation computer vision", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2608.18166v1", "title": "TractoGraphVLM: A Unified Vision-Language Framework for White Matter Tractography", "abstract": "Vision language models have transformed 2D medical imaging, yet extending them to 3D white matter tractography remains challenging due to the complex topology of fiber bundles. We introduce TractoGraphVLM, a unified framework for four tasks, bundle classification, text-to-tract retrieval, anatomical captioning, and visual question answering, built on a shared GPS architecture, training procedure, and read-out design. Fiber bundles are represented as streamline graphs whose nodes encode 3D position and tangent orientation. A General, Powerful, Scalable (GPS) graph transformer produces bundle embeddings aligned with a frozen BiomedBERT text encoder via contrastive learning, while a BioGPT decoder with visual prefix tokens generates captions and answers. A single shared encoder and decoder is trained jointly across all four tasks and evaluated from one checkpoint. Trained on HCP Young Adult subjects, TractoGraphVLM achieves 91.8% bundle classification accuracy, 84.7% retrieval R@1, BLEU-4=20.1, ROUGE-L=66.8, and 66.4% VQA accuracy on a held-out test set. The same checkpoints transfer zero-shot to HCP Aging subjects, with a modest drop on discriminative tasks and a larger drop on generative tasks, showing robustness to age and acquisition shift. Language supervision yields richer representations than label-only training, recovering structure like hemisphere and fiber family, carried by captions but never given as a label. Swapping only the visual encoder, graphs preserving fiber orientation outperform volumetric baselines, with GPS giving the best balance. Generative metrics measure consistency with a structured knowledge base rather than independent clinical text; even so, TractoGraphVLM shows that classifying, retrieving, describing, and answering questions about a white matter bundle can be served by one jointly trained model that learns transferable neuroanatomy from language alone.", "published": "2026-08-16T16:14:48Z", "authors": ["Gurucharan Marthi Krishna Kumar", "Janine Dale Mendola", "Amir Shmuel"], "categories": ["eess.IV", "cs.CV"], "doi": "", "_query": "property valuation computer vision", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2105.08499v3", "title": "Assessing bikeability with street view imagery and computer vision", "abstract": "Studies evaluating bikeability usually compute spatial indicators shaping cycling conditions and conflate them in a quantitative index. Much research involves site visits or conventional geospatial approaches, and few studies have leveraged street view imagery (SVI) for conducting virtual audits. These have assessed a limited range of aspects, and not all have been automated using computer vision (CV). Furthermore, studies have not yet zeroed in on gauging the usability of these technologies thoroughly. We investigate, with experiments at a fine spatial scale and across multiple geographies (Singapore and Tokyo), whether we can use SVI and CV to assess bikeability comprehensively. Extending related work, we develop an exhaustive index of bikeability composed of 34 indicators. The results suggest that SVI and CV are adequate to evaluate bikeability in cities comprehensively. As they outperformed non-SVI counterparts by a wide margin, SVI indicators are also found to be superior in assessing urban bikeability, and potentially can be used independently, replacing traditional techniques. However, the paper exposes some limitations, suggesting that the best way forward is combining both SVI and non-SVI approaches. The new bikeability index presents a contribution in transportation and urban analytics, and it is scalable to assess cycling appeal widely.", "published": "2021-05-13T14:08:58Z", "authors": ["Koichi Ito", "Filip Biljecki"], "categories": ["cs.CV"], "doi": "10.1016/j.trc.2021.103371", "_query": "property valuation computer vision", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/1705.07632v3", "title": "Computer vision-based food calorie estimation: dataset, method, and experiment", "abstract": "Computer vision has been introduced to estimate calories from food images. But current food image data sets don't contain volume and mass records of foods, which leads to an incomplete calorie estimation. In this paper, we present a novel food image data set with volume and mass records of foods, and a deep learning method for food detection, to make a complete calorie estimation. Our data set includes 2978 images, and every image contains corresponding each food's annotation, volume and mass records, as well as a certain calibration reference. To estimate calorie of food in the proposed data set, a deep learning method using Faster R-CNN first is put forward to detect the food. And the experiment results show our method is effective to estimate calories and our data set contains adequate information for calorie estimation. Our data set is the first released food image data set which can be used to evaluate computer vision-based calorie estimation methods.", "published": "2017-05-22T09:47:29Z", "authors": ["Yanchao Liang", "Jianhua Li"], "categories": ["cs.CV"], "doi": "", "_query": "property valuation computer vision", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/1405.4943v1", "title": "Classical Control of Large-Scale Quantum Computers", "abstract": "The accelerated development of quantum technology has reached a pivotal point. Early in 2014, several results were published demonstrating that several experimental technologies are now accurate enough to satisfy the requirements of fault-tolerant, error corrected quantum computation. While there are many technological and experimental issues that still need to be solved, the ability of experimental systems to now have error rates low enough to satisfy the fault-tolerant threshold for several error correction models is a tremendous milestone. Consequently, it is now a good time for the computer science and classical engineering community to examine the {\\em classical} problems associated with compiling quantum algorithms and implementing them on future quantum hardware. In this paper, we will review the basic operational rules of a topological quantum computing architecture and outline one of the most important classical problems that need to be solved; the decoding of error correction data for a large-scale quantum computer. We will endeavour to present these problems independently from the underlying physics as much of this work can be effectively solved by non-experts in quantum information or quantum mechanics.", "published": "2014-05-20T03:40:46Z", "authors": ["Simon J. Devitt"], "categories": ["quant-ph"], "doi": "", "_query": "property valuation computer vision", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/1809.02875v1", "title": "A Supervised Learning Methodology for Real-Time Disguised Face Recognition in the Wild", "abstract": "Facial recognition has always been a challeng- ing task for computer vision scientists and experts. Despite complexities arising due to variations in camera parameters, illumination and face orientations, significant progress has been made in the field with deep learning algorithms now competing with human-level accuracy. But in contrast to the recent advances in face recognition techniques, Disguised Facial Identification continues to be a tougher challenge in the field of computer vision. The modern day scenario, where security is of prime concern, regular face identification techniques do not perform as required when the faces are disguised, which calls for a different approach to handle situations where intruders have their faces masked. Along the same lines, we propose a deep learning architecture for disguised facial recognition (DFR). The algorithm put forward in this paper detects 20 facial key-points in the first stage, using a 14-layered convolutional neural network (CNN). These facial key-points are later utilized by a support vector machine (SVM) for classifying the disguised faces based on the euclidean distance ratios and angles between different facial key-points. This overall architecture imparts a basic intelligence to our system. Our key-point feature prediction accuracy is 65% while the classification rate is 72.4%. Moreover, the architecture works at 19 FPS, thereby performing in almost real-time. The efficiency of our approach is also compared with the state-of-the-art Disguised Facial Identification methods.", "published": "2018-09-08T20:53:19Z", "authors": ["Saumya Kumaar", "Abhinandan Dogra", "Abrar Majeedi", "Hanan Gani", "Ravi M. Vishwanath", "S N Omkar"], "categories": ["cs.CV"], "doi": "", "_query": "property valuation computer vision", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/1907.10559v1", "title": "QRMODA and BRMODA: Novel Models for Face Recognition Accuracy in Computer Vision Systems with Adapted Video Streams", "abstract": "A major challenge facing Computer Vision systems is providing the ability to accurately detect threats and recognize subjects and/or objects under dynamically changing network conditions. We propose two novel models that characterize the face recognition accuracy in terms of video encoding parameters. Specifically, we model the accuracy in terms of video resolution, quantization, and actual bit rate. We validate the models using two distinct video datasets and a large image dataset by conducting 1, 668 experiments that involve simultaneously varying combinations of encoding parameters. We show that both models hold true for the deep learning and statistical based face recognition. Furthermore, we show that the models can be used to capture different accuracy metrics, specifically the recall, precision, and F1-score. Ultimately, we provide meaningful insights on the factors affecting the constants of each proposed model.", "published": "2019-07-24T16:59:56Z", "authors": ["Hayder Hamandi", "Nabil Sarhan"], "categories": ["cs.CV", "cs.MM"], "doi": "", "_query": "property valuation computer vision", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2103.01856v3", "title": "Spatial-Phase Shallow Learning: Rethinking Face Forgery Detection in Frequency Domain", "abstract": "The remarkable success in face forgery techniques has received considerable attention in computer vision due to security concerns. We observe that up-sampling is a necessary step of most face forgery techniques, and cumulative up-sampling will result in obvious changes in the frequency domain, especially in the phase spectrum. According to the property of natural images, the phase spectrum preserves abundant frequency components that provide extra information and complement the loss of the amplitude spectrum. To this end, we present a novel Spatial-Phase Shallow Learning (SPSL) method, which combines spatial image and phase spectrum to capture the up-sampling artifacts of face forgery to improve the transferability, for face forgery detection. And we also theoretically analyze the validity of utilizing the phase spectrum. Moreover, we notice that local texture information is more crucial than high-level semantic information for the face forgery detection task. So we reduce the receptive fields by shallowing the network to suppress high-level features and focus on the local region. Extensive experiments show that SPSL can achieve the state-of-the-art performance on cross-datasets evaluation as well as multi-class classification and obtain comparable results on single dataset evaluation.", "published": "2021-03-02T16:45:08Z", "authors": ["Honggu Liu", "Xiaodan Li", "Wenbo Zhou", "Yuefeng Chen", "Yuan He", "Hui Xue", "Weiming Zhang", "Nenghai Yu"], "categories": ["cs.CV"], "doi": "", "_query": "property valuation computer vision", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/1004.4998v1", "title": "Computing in Coq with Infinite Algebraic Data Structures", "abstract": "Computational content encoded into constructive type theory proofs can be used to make computing experiments over concrete data structures. In this paper, we explore this possibility when working in Coq with chain complexes of infinite type (that is to say, generated by infinite sets) as a part of the formalization of a hierarchy of homological algebra structures.", "published": "2010-04-28T11:04:47Z", "authors": ["César Domínguez", "Julio Rubio"], "categories": ["cs.LO"], "doi": "", "_query": "property valuation computer vision", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2103.05086v1", "title": "How Privacy-Preserving are Line Clouds? Recovering Scene Details from 3D Lines", "abstract": "Visual localization is the problem of estimating the camera pose of a given image with respect to a known scene. Visual localization algorithms are a fundamental building block in advanced computer vision applications, including Mixed and Virtual Reality systems. Many algorithms used in practice represent the scene through a Structure-from-Motion (SfM) point cloud and use 2D-3D matches between a query image and the 3D points for camera pose estimation. As recently shown, image details can be accurately recovered from SfM point clouds by translating renderings of the sparse point clouds to images. To address the resulting potential privacy risks for user-generated content, it was recently proposed to lift point clouds to line clouds by replacing 3D points by randomly oriented 3D lines passing through these points. The resulting representation is unintelligible to humans and effectively prevents point cloud-to-image translation. This paper shows that a significant amount of information about the 3D scene geometry is preserved in these line clouds, allowing us to (approximately) recover the 3D point positions and thus to (approximately) recover image content. Our approach is based on the observation that the closest points between lines can yield a good approximation to the original 3D points. Code is available at https://github.com/kunalchelani/Line2Point.", "published": "2021-03-08T21:32:43Z", "authors": ["Kunal Chelani", "Fredrik Kahl", "Torsten Sattler"], "categories": ["cs.CV"], "doi": "", "_query": "property valuation computer vision", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2408.10949v3", "title": "Cotlar Properties, Hyperbolicity, and Unconditional Fourier Decompositions", "abstract": "Let a discrete group $Γ$ act by isometries on a metric space $X$. We show that actions on geodesic hyperbolic spaces satisfy a branch Cotlar property. Under a finite low-orbit-frequency hypothesis, this yields uniform $L^4$-unconditionality for branch subspaces associated with sufficiently separated subsets; when the union of these branches has finite complement, the corresponding signed projections are uniformly bounded on $L^p(\\widehatΓ)$ for every $1<p<\\infty$. Finally, for connected vertex-transitive graphs, we prove that a scale-wise branch Cotlar property characterizes hyperbolicity of $X$.", "published": "2024-08-20T15:41:36Z", "authors": ["Tao Mei"], "categories": ["math.FA"], "doi": "", "_query": "property valuation computer vision", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2501.13518v1", "title": "Text-driven Online Action Detection", "abstract": "Detecting actions as they occur is essential for applications like video surveillance, autonomous driving, and human-robot interaction. Known as online action detection, this task requires classifying actions in streaming videos, handling background noise, and coping with incomplete actions. Transformer architectures are the current state-of-the-art, yet the potential of recent advancements in computer vision, particularly vision-language models (VLMs), remains largely untapped for this problem, partly due to high computational costs. In this paper, we introduce TOAD: a Text-driven Online Action Detection architecture that supports zero-shot and few-shot learning. TOAD leverages CLIP (Contrastive Language-Image Pretraining) textual embeddings, enabling efficient use of VLMs without significant computational overhead. Our model achieves 82.46% mAP on the THUMOS14 dataset, outperforming existing methods, and sets new baselines for zero-shot and few-shot performance on the THUMOS14 and TVSeries datasets.", "published": "2025-01-23T10:06:52Z", "authors": ["Manuel Benavent-Lledo", "David Mulero-Pérez", "David Ortiz-Perez", "Jose Garcia-Rodriguez"], "categories": ["cs.CV"], "doi": "10.1177/10692509241308069", "_query": "property valuation computer vision", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2208.00881v1", "title": "Computer vision-based analysis of buildings and built environments: A systematic review of current approaches", "abstract": "Analysing 88 sources published from 2011 to 2021, this paper presents a first systematic review of the computer vision-based analysis of buildings and the built environments to assess its value to architectural and urban design studies. Following a multi-stage selection process, the types of algorithms and data sources used are discussed in respect to architectural applications such as a building classification, detail classification, qualitative environmental analysis, building condition survey, and building value estimation. This reveals current research gaps and trends, and highlights two main categories of research aims. First, to use or optimise computer vision methods for architectural image data, which can then help automate time-consuming, labour-intensive, or complex tasks of visual analysis. Second, to explore the methodological benefits of machine learning approaches to investigate new questions about the built environment by finding patterns and relationships between visual, statistical, and qualitative data, which can overcome limitations of conventional manual analysis. The growing body of research offers new methods to architectural and design studies, with the paper identifying future challenges and directions of research.", "published": "2022-08-01T14:17:51Z", "authors": ["Małgorzata B. Starzyńska", "Robin Roussel", "Sam Jacoby", "Ali Asadipour"], "categories": ["cs.CV"], "doi": "", "_query": "property valuation computer vision", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/1701.02632v3", "title": "Midgar: Detection of people through computer vision in the Internet of Things scenarios to improve the security in Smart Cities, Smart Towns, and Smart Homes", "abstract": "Could we use Computer Vision in the Internet of Things for using pictures as sensors? This is the principal hypothesis that we want to resolve. Currently, in order to create safety areas, cities, or homes, people use IP cameras. Nevertheless, this system needs people who watch the camera images, watch the recording after something occurred, or watch when the camera notifies them of any movement. These are the disadvantages. Furthermore, there are many Smart Cities and Smart Homes around the world. This is why we thought of using the idea of the Internet of Things to add a way of automating the use of IP cameras. In our case, we propose the analysis of pictures through Computer Vision to detect people in the analysed pictures. With this analysis, we are able to obtain if these pictures contain people and handle the pictures as if they were sensors with two possible states. Notwithstanding, Computer Vision is a very complicated field. This is why we needed a second hypothesis: Could we work with Computer Vision in the Internet of Things with a good accuracy to automate or semi-automate this kind of events? The demonstration of these hypotheses required a testing over our Computer Vision module to check the possibilities that we have to use this module in a possible real environment with a good accuracy. Our proposal, as a possible solution, is the analysis of entire sequence instead of isolated pictures for using pictures as sensors in the Internet of Things.", "published": "2017-01-10T15:19:14Z", "authors": ["Cristian González García", "Daniel Meana-Llorián", "B. Cristina Pelayo G-Bustelo", "Juan Manuel Cueva Lovelle", "Néstor Garcia-Fernandez"], "categories": ["cs.CV"], "doi": "10.1016/j.future.2016.12.033", "_query": "property valuation computer vision", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/1503.02318v3", "title": "Understanding Image Virality", "abstract": "Virality of online content on social networking websites is an important but esoteric phenomenon often studied in fields like marketing, psychology and data mining. In this paper we study viral images from a computer vision perspective. We introduce three new image datasets from Reddit, and define a virality score using Reddit metadata. We train classifiers with state-of-the-art image features to predict virality of individual images, relative virality in pairs of images, and the dominant topic of a viral image. We also compare machine performance to human performance on these tasks. We find that computers perform poorly with low level features, and high level information is critical for predicting virality. We encode semantic information through relative attributes. We identify the 5 key visual attributes that correlate with virality. We create an attribute-based characterization of images that can predict relative virality with 68.10% accuracy (SVM+Deep Relative Attributes) -- better than humans at 60.12%. Finally, we study how human prediction of image virality varies with different `contexts' in which the images are viewed, such as the influence of neighbouring images, images recently viewed, as well as the image title or caption. This work is a first step in understanding the complex but important phenomenon of image virality. Our datasets and annotations will be made publicly available.", "published": "2015-03-08T20:29:28Z", "authors": ["Arturo Deza", "Devi Parikh"], "categories": ["cs.SI", "cs.CV"], "doi": "10.1109/CVPR.2015.7298791", "_query": "property valuation computer vision", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/1306.0404v2", "title": "Iterative Grassmannian Optimization for Robust Image Alignment", "abstract": "Robust high-dimensional data processing has witnessed an exciting development in recent years, as theoretical results have shown that it is possible using convex programming to optimize data fit to a low-rank component plus a sparse outlier component. This problem is also known as Robust PCA, and it has found application in many areas of computer vision. In image and video processing and face recognition, the opportunity to process massive image databases is emerging as people upload photo and video data online in unprecedented volumes. However, data quality and consistency is not controlled in any way, and the massiveness of the data poses a serious computational challenge. In this paper we present t-GRASTA, or \"Transformed GRASTA (Grassmannian Robust Adaptive Subspace Tracking Algorithm)\". t-GRASTA iteratively performs incremental gradient descent constrained to the Grassmann manifold of subspaces in order to simultaneously estimate a decomposition of a collection of images into a low-rank subspace, a sparse part of occlusions and foreground objects, and a transformation such as rotation or translation of the image. We show that t-GRASTA is 4 $\\times$ faster than state-of-the-art algorithms, has half the memory requirement, and can achieve alignment for face images as well as jittered camera surveillance images.", "published": "2013-06-03T13:49:14Z", "authors": ["Jun He", "Dejiao Zhang", "Laura Balzano", "Tao Tao"], "categories": ["cs.CV", "math.OC", "stat.ML"], "doi": "10.1016/j.imavis.2014.02.015", "_query": "property valuation computer vision", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/1606.08894v3", "title": "Existence of valuations with smallest normalized volume", "abstract": "Li introduced the normalized volume of a valuation due to its relation to K-semistability. He conjectured that over a klt singularity there exists a valuation with smallest normalized volume. We prove this conjecture and provide an example to show that such a valuation need not be divisorial.", "published": "2016-06-28T21:34:08Z", "authors": ["Harold Blum"], "categories": ["math.AG"], "doi": "10.1112/S0010437X17008016", "_query": "property valuation computer vision", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/1912.04219v1", "title": "FaultNet: Faulty Rail-Valves Detection using Deep Learning and Computer Vision", "abstract": "Regular inspection of rail valves and engines is an important task to ensure the safety and efficiency of railway networks around the globe. Over the past decade, computer vision and pattern recognition based techniques have gained traction for such inspection and defect detection tasks. An automated end-to-end trained system can potentially provide a low-cost, high throughput, and cheap alternative to manual visual inspection of these components. However, such systems require a huge amount of defective images for networks to understand complex defects. In this paper, a multi-phase deep learning based technique is proposed to perform accurate fault detection of rail-valves. Our approach uses a two-step method to perform high precision image segmentation of rail-valves resulting in pixel-wise accurate segmentation. Thereafter, a computer vision technique is used to identify faulty valves. We demonstrate that the proposed approach results in improved detection performance when compared to current state-of-theart techniques used in fault detection.", "published": "2019-11-09T03:33:53Z", "authors": ["Ramanpreet Singh Pahwa", "Jin Chao", "Jestine Paul", "Yiqun Li", "Ma Tin Lay Nwe", "Shudong Xie", "Ashish James", "Arulmurugan Ambikapathi", "Zeng Zeng", "Vijay Ramaseshan Chandrasekhar"], "categories": ["cs.CV"], "doi": "", "_query": "property valuation computer vision", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2301.05886v3", "title": "Efficient Risk Estimation for the Credit Valuation Adjustment", "abstract": "The valuation of over-the-counter derivatives is subject to a series of valuation adjustments known as xVA, which pose additional risks for financial institutions. Associated risk measures, such as the value-at-risk of an underlying valuation adjustment, play an important role in managing these risks. Monte Carlo methods are often regarded as inefficient for computing such measures. As an example, we consider the value-at-risk of the Credit Valuation Adjustment (CVA-VaR), which can be expressed using a triple nested expectation. Traditional Monte Carlo methods are often inefficient at handling several nested expectations. Utilising recent developments in multilevel nested simulation for probabilities, we construct a hierarchical estimator of the CVA-VaR which reduces the computational complexity by 3 orders of magnitude compared to standard Monte Carlo.", "published": "2023-01-14T10:49:10Z", "authors": ["Michael B. Giles", "Abdul-Lateef Haji-Ali", "Jonathan Spence"], "categories": ["q-fin.CP"], "doi": "", "_query": "property valuation computer vision", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2412.10710v2", "title": "Virtual Trial Room with Computer Vision and Machine Learning", "abstract": "Online shopping has revolutionized the retail industry, providing customers with convenience and accessibility. However, customers often hesitate to purchase wearable products such as watches, jewelry, glasses, shoes, and clothes due to the lack of certainty regarding fit and suitability. This leads to significant return rates, causing problems for both customers and vendors. To address this issue, a platform called the Virtual Trial Room with Computer Vision and Machine Learning is designed which enables customers to easily check whether a product will fit and suit them or not. To achieve this, an AI-generated 3D model of the human head was created from a single 2D image using the DECA model. This 3D model was then superimposed with a custom-made 3D model of glass which is based on real-world measurements and fitted over the human head. To replicate the real-world look and feel, the model was retouched with textures, lightness, and smoothness. Furthermore, a full-stack application was developed utilizing various fornt-end and back-end technologies. This application enables users to view 3D-generated results on the website, providing an immersive and interactive experience.", "published": "2024-12-14T06:50:10Z", "authors": ["Tulashi Prasad Joshi", "Amrendra Kumar Yadav", "Arjun Chhetri", "Suraj Agrahari", "Umesh Kanta Ghimire"], "categories": ["cs.CV"], "doi": "", "_query": "property valuation computer vision", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2005.12834v3", "title": "Blockchain and Fog Computing for Cyberphysical Systems: The Case of Smart Industry", "abstract": "Blockchain has revolutionized how transactions are conducted by ensuring secure and auditable peer-to-peer coordination. This is due to both the development of decentralization, and the promotion of trust among peers. Blockchain and fog computing are currently being evaluated as potential support for software and a wide spectrum of applications, ranging from banking practices and digital transactions to cyber-physical systems. These systems are designed to work in highly complex, sometimes even adversarial, environments, and to synchronize heterogeneous machines and manufacturing facilities in cyber computational space, and address critical challenges such as computational complexity, security, trust, and data management. Coupling blockchain with fog computing technologies has the potential to identify and overcome these issues. Thus, this paper presents the knowledge of blockchain and fog computing required to improve cyber-physical systems in terms of quality-of-service, data storage, computing and security.", "published": "2020-05-26T16:12:41Z", "authors": ["Ouns Bouachir", "Moayad Aloqaily", "Lewis Tseng", "Azzedine Boukerche"], "categories": ["cs.NI", "cs.CR"], "doi": "10.1109/MC.2020.2996212", "_query": "property valuation computer vision", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/math/0310192v1", "title": "Completions of valuation rings", "abstract": "Let k be a field of characteristic zero, K an algebraic function field over k, and V a k-valuation ring of K. Zariski's theorem of local uniformization shows that there exist algebraic regular local rings R_i with quotient field K which are dominated by V, and such that the direct union of the R_i's is V. We investigate the ring T, which is the direct union of the completions of the R_i's. We give necessary and sufficient conditions for T to be a valuation ring. We then focus on the case in which the valuation has rank one.", "published": "2003-10-13T20:08:22Z", "authors": ["Steven Dale Cutkosky", "Laura Ghezzi"], "categories": ["math.AC", "math.AG"], "doi": "", "_query": "property valuation computer vision", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/1612.06615v1", "title": "Deep Motion Features for Visual Tracking", "abstract": "Robust visual tracking is a challenging computer vision problem, with many real-world applications. Most existing approaches employ hand-crafted appearance features, such as HOG or Color Names. Recently, deep RGB features extracted from convolutional neural networks have been successfully applied for tracking. Despite their success, these features only capture appearance information. On the other hand, motion cues provide discriminative and complementary information that can improve tracking performance. Contrary to visual tracking, deep motion features have been successfully applied for action recognition and video classification tasks. Typically, the motion features are learned by training a CNN on optical flow images extracted from large amounts of labeled videos. This paper presents an investigation of the impact of deep motion features in a tracking-by-detection framework. We further show that hand-crafted, deep RGB, and deep motion features contain complementary information. To the best of our knowledge, we are the first to propose fusing appearance information with deep motion features for visual tracking. Comprehensive experiments clearly suggest that our fusion approach with deep motion features outperforms standard methods relying on appearance information alone.", "published": "2016-12-20T11:33:31Z", "authors": ["Susanna Gladh", "Martin Danelljan", "Fahad Shahbaz Khan", "Michael Felsberg"], "categories": ["cs.CV"], "doi": "", "_query": "property valuation computer vision", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2112.08364v3", "title": "Data Valuation for Vertical Federated Learning: A Model-free and Privacy-preserving Method", "abstract": "Vertical Federated learning (VFL) is a promising paradigm for predictive analytics, empowering an organization (i.e., task party) to enhance its predictive models through collaborations with multiple data suppliers (i.e., data parties) in a decentralized and privacy-preserving way. Despite the fast-growing interest in VFL, the lack of effective and secure tools for assessing the value of data owned by data parties hinders the application of VFL in business contexts. In response, we propose FedValue, a privacy-preserving, task-specific but model-free data valuation method for VFL, which consists of a data valuation metric and a federated computation method. Specifically, we first introduce a novel data valuation metric, namely MShapley-CMI. The metric evaluates a data party's contribution to a predictive analytics task without the need of executing a machine learning model, making it well-suited for real-world applications of VFL. Next, we develop an innovative federated computation method that calculates the MShapley-CMI value for each data party in a privacy-preserving manner. Extensive experiments conducted on six public datasets validate the efficacy of FedValue for data valuation in the context of VFL. In addition, we illustrate the practical utility of FedValue with a case study involving federated movie recommendations.", "published": "2021-12-15T02:42:28Z", "authors": ["Xiao Han", "Leye Wang", "Junjie Wu", "Xiao Fang"], "categories": ["cs.LG", "cs.AI"], "doi": "", "_query": "property valuation computer vision", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/1507.01220v1", "title": "Moments and Valuations", "abstract": "All measurable and $\\operatorname{SL}(n)$-covariant vector valued valuations on convex polytopes containing the origin in their interiors are completely classified. The moment vector is shown to be essentially the only such valuation.", "published": "2015-07-05T13:41:17Z", "authors": ["Christoph Haberl", "Lukas Parapatits"], "categories": ["math.MG"], "doi": "", "_query": "property valuation computer vision", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2412.09856v2", "title": "LinGen: Towards High-Resolution Minute-Length Text-to-Video Generation with Linear Computational Complexity", "abstract": "Text-to-video generation enhances content creation but is highly computationally intensive: The computational cost of Diffusion Transformers (DiTs) scales quadratically in the number of pixels. This makes minute-length video generation extremely expensive, limiting most existing models to generating videos of only 10-20 seconds length. We propose a Linear-complexity text-to-video Generation (LinGen) framework whose cost scales linearly in the number of pixels. For the first time, LinGen enables high-resolution minute-length video generation on a single GPU without compromising quality. It replaces the computationally-dominant and quadratic-complexity block, self-attention, with a linear-complexity block called MATE, which consists of an MA-branch and a TE-branch. The MA-branch targets short-to-long-range correlations, combining a bidirectional Mamba2 block with our token rearrangement method, Rotary Major Scan, and our review tokens developed for long video generation. The TE-branch is a novel TEmporal Swin Attention block that focuses on temporal correlations between adjacent tokens and medium-range tokens. The MATE block addresses the adjacency preservation issue of Mamba and improves the consistency of generated videos significantly. Experimental results show that LinGen outperforms DiT (with a 75.6% win rate) in video quality with up to 15$\\times$ (11.5$\\times$) FLOPs (latency) reduction. Furthermore, both automatic metrics and human evaluation demonstrate our LinGen-4B yields comparable video quality to state-of-the-art models (with a 50.5%, 52.1%, 49.1% win rate with respect to Gen-3, LumaLabs, and Kling, respectively). This paves the way to hour-length movie generation and real-time interactive video generation. We provide 68s video generation results and more examples in our project website: https://lineargen.github.io/.", "published": "2024-12-13T04:55:10Z", "authors": ["Hongjie Wang", "Chih-Yao Ma", "Yen-Cheng Liu", "Ji Hou", "Tao Xu", "Jialiang Wang", "Felix Juefei-Xu", "Yaqiao Luo", "Peizhao Zhang", "Tingbo Hou", "Peter Vajda", "Niraj K. Jha", "Xiaoliang Dai"], "categories": ["cs.CV", "cs.AI", "cs.LG", "eess.IV"], "doi": "", "_query": "property valuation computer vision", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/1510.05138v1", "title": "Integral Images: Efficient Algorithms for Their Computation and Storage in Resource-Constrained Embedded Vision Systems", "abstract": "The integral image, an intermediate image representation, has found extensive use in multi-scale local feature detection algorithms, such as Speeded-Up Robust Features (SURF), allowing fast computation of rectangular features at constant speed, independent of filter size. For resource-constrained real-time embedded vision systems, computation and storage of integral image presents several design challenges due to strict timing and hardware limitations. Although calculation of the integral image only consists of simple addition operations, the total number of operations is large owing to the generally large size of image data. Recursive equations allow substantial decrease in the number of operations but require calculation in a serial fashion. This paper presents two new hardware algorithms that are based on the decomposition of these recursive equations, allowing calculation of up to four integral image values in a row-parallel way without significantly increasing the number of operations. An efficient design strategy is also proposed for a parallel integral image computation unit to reduce the size of the required internal memory (nearly 35% for common HD video). Addressing the storage problem of integral image in embedded vision systems, the paper presents two algorithms which allow substantial decrease (at least 44.44%) in the memory requirements. Finally, the paper provides a case study that highlights the utility of the proposed architectures in embedded vision systems.", "published": "2015-10-17T15:41:26Z", "authors": ["Shoaib Ehsan", "Adrian F. Clark", "Naveed ur Rehman", "Klaus D. McDonald-Maier"], "categories": ["cs.CV"], "doi": "", "_query": "property valuation computer vision", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/1901.00211v1", "title": "Mapping Areas using Computer Vision Algorithms and Drones", "abstract": "The goal of this paper is to implement a system, titled as Drone Map Creator (DMC) using Computer Vision techniques. DMC can process visual information from an HD camera in a drone and automatically create a map by stitching together visual information captured by a drone. The proposed approach employs the Speeded up robust features (SURF) method to detect the key points for each image frame; then the corresponding points between the frames are identified by maximizing the determinant of a Hessian matrix. Finally, two images are stitched together by using the identified points. Our results show that despite some limitations from the external environment, we could have successfully stitched images together along video sequences.", "published": "2019-01-01T21:24:07Z", "authors": ["Bashar Alhafni", "Saulo Fernando Guedes", "Lays Cavalcante Ribeiro", "Juhyun Park", "Jeongkyu Lee"], "categories": ["cs.CV"], "doi": "", "_query": "property valuation computer vision", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2211.12392v2", "title": "Continuous R-valuations", "abstract": "We introduce continuous $R$-valuations on directed-complete posets (dcpos, for short), as a generalization of continuous valuations in domain theory, by extending values of continuous valuations from reals to so-called Abelian d-rags $R$. Like the valuation monad $\\mathbf{V}$ introduced by Jones and Plotkin, we show that the construction of continuous $R$-valuations extends to a strong monad $\\mathbf{V}^R$ on the category of dcpos and Scott-continuous maps. Additionally, and as in recent work by the two authors and C. Théron, and by the second author, B. Lindenhovius, M. Mislove and V. Zamdzhiev, we show that we can extract a commutative monad $\\mathbf{V}^R_m$ out of it, whose elements we call minimal $R$-valuations. We also show that continuous $R$-valuations have close connections to measures when $R$ is taken to be $\\mathbf{I}\\mathbb{R}^\\star_+$, the interval domain of the extended nonnegative reals: (1) On every coherent topological space, every non-zero, bounded $τ$-smooth measure $μ$ (defined on the Borel $σ$-algebra), canonically determines a continuous $\\mathbf{I}\\mathbb{R}^\\star_+$-valuation; and (2) such a continuous $\\mathbf{I}\\mathbb{R}^\\star_+$-valuation is the most precise (in a certain sense) continuous $\\mathbf{I}\\mathbb{R}^\\star_+$-valuation that approximates $μ$, when the support of $μ$ is a compact Hausdorff subspace of a second-countable stably compact topological space. This in particular applies to Lebesgue measure on the unit interval. As a result, the Lebesgue measure can be identified as a continuous $\\mathbf{I}\\mathbb{R}^\\star_+$-valuation. Additionally, we show that the latter is minimal.", "published": "2022-11-22T16:39:17Z", "authors": ["Jean Goubault-Larrecq", "Xiaodong Jia"], "categories": ["math.GN", "cs.LO"], "doi": "10.46298/entics.10351", "_query": "property valuation computer vision", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2212.01506v2", "title": "Autonomous Apple Fruitlet Sizing and Growth Rate Tracking using Computer Vision", "abstract": "In this paper, we present a computer vision-based approach to measure the sizes and growth rates of apple fruitlets. Measuring the growth rates of apple fruitlets is important because it allows apple growers to determine when to apply chemical thinners to their crops in order to optimize yield. The current practice of obtaining growth rates involves using calipers to record sizes of fruitlets across multiple days. Due to the number of fruitlets needed to be sized, this method is laborious, time-consuming, and prone to human error. With images collected by a hand-held stereo camera, our system, segments, clusters, and fits ellipses to fruitlets to measure their diameters. The growth rates are then calculated by temporally associating clustered fruitlets across days. We provide quantitative results on data collected in an apple orchard, and demonstrate that our system is able to predict abscise rates within 3.5% of the current method with a 6 times improvement in speed, while requiring significantly less manual effort. Moreover, we provide results on images captured by a robotic system in the field, and discuss the next steps required to make the process fully autonomous.", "published": "2022-12-03T01:59:46Z", "authors": ["Harry Freeman", "Mohamad Qadri", "Abhisesh Silwal", "Paul O'Connor", "Zachary Rubinstein", "Daniel Cooley", "George Kantor"], "categories": ["cs.RO", "cs.CV"], "doi": "", "_query": "property valuation computer vision", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2505.21964v2", "title": "UI-Evol: Automatic Knowledge Evolving for Computer Use Agents", "abstract": "External knowledge has played a crucial role in the recent development of computer use agents. We identify a critical knowledge-execution gap: retrieved knowledge often fails to translate into effective real-world task execution. Our analysis shows even 90% correct knowledge yields only 41% execution success rate. To bridge this gap, we propose UI-Evol, a plug-and-play module for autonomous GUI knowledge evolution. UI-Evol consists of two stages: a Retrace Stage that extracts faithful objective action sequences from actual agent-environment interactions, and a Critique Stage that refines existing knowledge by comparing these sequences against external references. We conduct comprehensive experiments on the OSWorld benchmark with the state-of-the-art Agent S2. Our results demonstrate that UI-Evol not only significantly boosts task performance but also addresses a previously overlooked issue of high behavioral standard deviation in computer use agents, leading to superior performance on computer use tasks and substantially improved agent reliability.", "published": "2025-05-28T04:32:05Z", "authors": ["Ziyun Zhang", "Xinyi Liu", "Xiaoyi Zhang", "Jun Wang", "Gang Chen", "Yan Lu"], "categories": ["cs.HC", "cs.CL"], "doi": "", "_query": "property valuation computer vision", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2302.07917v1", "title": "Evaluating Trade-offs in Computer Vision Between Attribute Privacy, Fairness and Utility", "abstract": "This paper investigates to what degree and magnitude tradeoffs exist between utility, fairness and attribute privacy in computer vision. Regarding privacy, we look at this important problem specifically in the context of attribute inference attacks, a less addressed form of privacy. To create a variety of models with different preferences, we use adversarial methods to intervene on attributes relating to fairness and privacy. We see that that certain tradeoffs exist between fairness and utility, privacy and utility, and between privacy and fairness. The results also show that those tradeoffs and interactions are more complex and nonlinear between the three goals than intuition would suggest.", "published": "2023-02-15T19:20:51Z", "authors": ["William Paul", "Philip Mathew", "Fady Alajaji", "Philippe Burlina"], "categories": ["cs.CV"], "doi": "", "_query": "property valuation computer vision", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2602.22683v2", "title": "SUPERGLASSES: Benchmarking Vision Language Models as Intelligent Agents for AI Smart Glasses", "abstract": "The rapid advancement of AI-powered smart glasses-one of the hottest wearable devices-has unlocked new frontiers for multimodal interaction, with Visual Question Answering (VQA) over external knowledge sources emerging as a core application. Existing Vision Language Models (VLMs) adapted to smart glasses are typically trained and evaluated on traditional multimodal datasets; however, these datasets lack the variety and realism needed to reflect smart glasses usage scenarios and diverge from their specific challenges, where accurately identifying the object of interest must precede any external knowledge retrieval. To bridge this gap, we introduce SUPER- GLASSES, the first comprehensive VQA benchmark built on real-world data entirely collected by smart glasses devices. SUPERGLASSES comprises 2,422 egocentric image-question pairs spanning 14 image domains and 8 query categories, enriched with full search trajectories and reasoning annotations. We evaluate 26 representative VLMs on this benchmark, revealing significant performance gaps. To address the limitations of existing models, we further propose the SUPERLENS, a multimodal smart glasses agent that enables retrieval-augmented answer generation by integrating automatic object detection, query decoupling, and multimodal web search. SUPERLENS achieves state-of-the-art performance, outperforming GPT-4o by 2.19%, underscoring the need for task-specific solutions in smart glasses VQA. Our dataset is publicly available at https://huggingface.co/datasets/xandery/SuperGlasses.", "published": "2026-02-26T06:55:48Z", "authors": ["Zhuohang Jiang", "Xu Yuan", "Haohao Qu", "Shanru Lin", "Kanglong Liu", "Wenqi Fan", "Qing Li"], "categories": ["cs.CV", "cs.AI"], "doi": "", "_query": "property valuation computer vision", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/math/0503399v4", "title": "Theory of valuations on manifolds, II", "abstract": "This article is the second part in the series of articles where we are developing theory of valuations on manifolds. Roughly speaking valuations could be thought as finitely additive measures on a class of nice subsets of a manifold which satisfy some additional assumptions. The goal of this article is to introduce a notion of a smooth valuation on an arbitrary smooth manifold and establish some of the basic properties of it.", "published": "2005-03-19T11:38:33Z", "authors": ["Semyon Alesker"], "categories": ["math.MG"], "doi": "", "_query": "property valuation computer vision", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2606.14578v1", "title": "A Qualitative Review of GenAI-Based Methods for Data Generation and Augmentation in Industrial Computer Vision Applications", "abstract": "AI-driven computer vision applications require a profound database to ensure predictable behaviors and performance. Such predictable behaviors are especially important for industrial applications in gaining trust from users. However, such a database is not readily available in industrial applications, and its acquisition is not trivial either. Active learning methods can be applied to ramp up data within a project deployment to iteratively increase the database, and thus the application predictability. Unfortunately, we observe that this often leads to a loss of user trust in the application, which is difficult to regain once lost. This leads to a \"chicken-and-egg\" dilemma in which neither the database nor the application is developed. In this work, we review state-of-the-art methods and approaches to further boost the database the initial active data ramp-up phase. Here, we focus on recent advancements in GenAI-based data generation and augmentation methods and review their adaptability on an industrial computer vision classification use case. Although we observe a potential for automatic data ramp-up, we also see a domain miss match in between the source (training environment) and target (industrial use-case) - regarding context defined in natural language and object characteristics.", "published": "2026-06-12T15:57:51Z", "authors": ["Paul Koch", "Paul Hofmann", "Ferdinand Waßelewsky", "Adem Karakurt", "Andre Sérs", "Jörg Krüger"], "categories": ["cs.CV"], "doi": "", "_query": "property valuation computer vision", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2005.05287v2", "title": "Using Computer Vision to enhance Safety of Workforce in Manufacturing in a Post COVID World", "abstract": "The COVID-19 pandemic forced governments across the world to impose lockdowns to prevent virus transmissions. This resulted in the shutdown of all economic activity and accordingly the production at manufacturing plants across most sectors was halted. While there is an urgency to resume production, there is an even greater need to ensure the safety of the workforce at the plant site. Reports indicate that maintaining social distancing and wearing face masks while at work clearly reduces the risk of transmission. We decided to use computer vision on CCTV feeds to monitor worker activity and detect violations which trigger real time voice alerts on the shop floor. This paper describes an efficient and economic approach of using AI to create a safe environment in a manufacturing setup. We demonstrate our approach to build a robust social distancing measurement algorithm using a mix of modern-day deep learning and classic projective geometry techniques. We have deployed our solution at manufacturing plants across the Aditya Birla Group (ABG). We have also described our face mask detection approach which provides a high accuracy across a range of customized masks.", "published": "2020-05-11T17:40:58Z", "authors": ["Prateek Khandelwal", "Anuj Khandelwal", "Snigdha Agarwal", "Deep Thomas", "Naveen Xavier", "Arun Raghuraman"], "categories": ["cs.CV", "cs.LG", "stat.ML"], "doi": "", "_query": "property valuation computer vision", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2505.16147v2", "title": "Losing is for Cherishing: Data Valuation Based on Machine Unlearning and Shapley Value", "abstract": "The proliferation of large models has intensified the need for efficient data valuation methods to quantify the contribution of individual data providers. Traditional approaches, such as game-theory-based Shapley value and influence-function-based techniques, face prohibitive computational costs or require access to full data and model training details, making them hardly achieve partial data valuation. To address this, we propose Unlearning Shapley, a novel framework that leverages machine unlearning to estimate data values efficiently. By unlearning target data from a pretrained model and measuring performance shifts on a reachable test set, our method computes Shapley values via Monte Carlo sampling, avoiding retraining and eliminating dependence on full data. Crucially, Unlearning Shapley supports both full and partial data valuation, making it scalable for large models (e.g., LLMs) and practical for data markets. Experiments on benchmark datasets and large-scale text corpora demonstrate that our approach matches the accuracy of state-of-the-art methods while reducing computational overhead by orders of magnitude. Further analysis confirms a strong correlation between estimated values and the true impact of data subsets, validating its reliability in real-world scenarios. This work bridges the gap between data valuation theory and practical deployment, offering a scalable, privacy-compliant solution for modern AI ecosystems.", "published": "2025-05-22T02:46:03Z", "authors": ["Le Ma", "Shirao Yang", "Zihao Wang", "Yinggui Wang", "Lei Wang", "Tao Wei", "Kejun Zhang"], "categories": ["cs.AI", "cs.LG"], "doi": "", "_query": "property valuation computer vision", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/1704.08671v3", "title": "Construction of the Lindström valuation of an algebraic extension", "abstract": "Recently, Bollen, Draisma, and Pendavingh have introduced the Lindström valuation on the algebraic matroid of a field extension of characteristic p. Their construction passes through what they call a matroid flock and builds on some of the associated theory of matroid flocks which they develop. In this paper, we give a direct construction of the Lindström valuated matroid using the theory of inseparable field extensions. In particular, we give a description of the valuation, the valuated circuits, and the valuated cocircuits.", "published": "2017-04-27T17:34:17Z", "authors": ["Dustin Cartwright"], "categories": ["math.CO"], "doi": "10.1016/j.jcta.2018.03.003", "_query": "property valuation computer vision", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/1002.2182v1", "title": "Detection of Microcalcification in Mammograms Using Wavelet Transform and Fuzzy Shell Clustering", "abstract": "Microcalcifications in mammogram have been mainly targeted as a reliable earliest sign of breast cancer and their early detection is vital to improve its prognosis. Since their size is very small and may be easily overlooked by the examining radiologist, computer-based detection output can assist the radiologist to improve the diagnostic accuracy. In this paper, we have proposed an algorithm for detecting microcalcification in mammogram. The proposed microcalcification detection algorithm involves mammogram quality enhancement using multirresolution analysis based on the dyadic wavelet transform and microcalcification detection by fuzzy shell clustering. It may be possible to detect nodular components such as microcalcification accurately by introducing shape information. The effectiveness of the proposed algorithm for microcalcification detection is confirmed by experimental results.", "published": "2010-02-10T19:22:25Z", "authors": ["T. Balakumaran", "I. L. A. Vennila", "C. Gowri Shankar"], "categories": ["cs.CV"], "doi": "", "_query": "property valuation computer vision", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2311.06964v1", "title": "Adaptive recurrent vision performs zero-shot computation scaling to unseen difficulty levels", "abstract": "Humans solving algorithmic (or) reasoning problems typically exhibit solution times that grow as a function of problem difficulty. Adaptive recurrent neural networks have been shown to exhibit this property for various language-processing tasks. However, little work has been performed to assess whether such adaptive computation can also enable vision models to extrapolate solutions beyond their training distribution's difficulty level, with prior work focusing on very simple tasks. In this study, we investigate a critical functional role of such adaptive processing using recurrent neural networks: to dynamically scale computational resources conditional on input requirements that allow for zero-shot generalization to novel difficulty levels not seen during training using two challenging visual reasoning tasks: PathFinder and Mazes. We combine convolutional recurrent neural networks (ConvRNNs) with a learnable halting mechanism based on Graves (2016). We explore various implementations of such adaptive ConvRNNs (AdRNNs) ranging from tying weights across layers to more sophisticated biologically inspired recurrent networks that possess lateral connections and gating. We show that 1) AdRNNs learn to dynamically halt processing early (or late) to solve easier (or harder) problems, 2) these RNNs zero-shot generalize to more difficult problem settings not shown during training by dynamically increasing the number of recurrent iterations at test time. Our study provides modeling evidence supporting the hypothesis that recurrent processing enables the functional advantage of adaptively allocating compute resources conditional on input requirements and hence allowing generalization to harder difficulty levels of a visual reasoning problem without training.", "published": "2023-11-12T21:07:04Z", "authors": ["Vijay Veerabadran", "Srinivas Ravishankar", "Yuan Tang", "Ritik Raina", "Virginia R. de Sa"], "categories": ["cs.CV", "cs.LG"], "doi": "", "_query": "property valuation computer vision", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2402.15351v2", "title": "AutoMMLab: Automatically Generating Deployable Models from Language Instructions for Computer Vision Tasks", "abstract": "Automated machine learning (AutoML) is a collection of techniques designed to automate the machine learning development process. While traditional AutoML approaches have been successfully applied in several critical steps of model development (e.g. hyperparameter optimization), there lacks a AutoML system that automates the entire end-to-end model production workflow for computer vision. To fill this blank, we propose a novel request-to-model task, which involves understanding the user's natural language request and execute the entire workflow to output production-ready models. This empowers non-expert individuals to easily build task-specific models via a user-friendly language interface. To facilitate development and evaluation, we develop a new experimental platform called AutoMMLab and a new benchmark called LAMP for studying key components in the end-to-end request-to-model pipeline. Hyperparameter optimization (HPO) is one of the most important components for AutoML. Traditional approaches mostly rely on trial-and-error, leading to inefficient parameter search. To solve this problem, we propose a novel LLM-based HPO algorithm, called HPO-LLaMA. Equipped with extensive knowledge and experience in model hyperparameter tuning, HPO-LLaMA achieves significant improvement of HPO efficiency. Dataset and code are available at https://github.com/yang-ze-kang/AutoMMLab.", "published": "2024-02-23T14:38:19Z", "authors": ["Zekang Yang", "Wang Zeng", "Sheng Jin", "Chen Qian", "Ping Luo", "Wentao Liu"], "categories": ["cs.LG", "cs.CV"], "doi": "", "_query": "property valuation computer vision", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2011.10839v1", "title": "Deep Learning-Based Computer Vision for Real Time Intravenous Drip Infusion Monitoring", "abstract": "This paper explores the use of deep learning-based computer vision for real-time monitoring of the flow in intravenous (IV) infusions. IV infusions are among the most common therapies in hospitalized patients and, given that both over-infusion and under-infusion can cause severe damages, monitoring the flow rate of the fluid being administered to patients is very important for their safety. The proposed system uses a camera to film the IV drip infusion kit and a deep learning-based algorithm to classify acquired frames into two different states: frames with a drop that has just begun to take shape and frames with a well-formed drop. The alternation of these two states is used to count drops and derive a measurement of the flow rate of the drip. The usage of a camera as sensing element makes the proposed system safe in medical environments and easier to be integrated into current health facilities. Experimental results are reported in the paper that confirm the accuracy of the system and its capability to produce real-time estimates. The proposed method can be therefore effectively adopted to implement IV infusion monitoring and control systems.", "published": "2020-11-21T18:26:44Z", "authors": ["Nicola Giaquinto", "Marco Scarpetta", "Maurizio Spadavecchia", "Gregorio Andria"], "categories": ["cs.CV", "cs.LG"], "doi": "10.1109/JSEN.2020.3039009", "_query": "property valuation computer vision", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2309.01904v1", "title": "Improving Drone Imagery For Computer Vision/Machine Learning in Wilderness Search and Rescue", "abstract": "This paper describes gaps in acquisition of drone imagery that impair the use with computer vision/machine learning (CV/ML) models and makes five recommendations to maximize image suitability for CV/ML post-processing. It describes a notional work process for the use of drones in wilderness search and rescue incidents. The large volume of data from the wide area search phase offers the greatest opportunity for CV/ML techniques because of the large number of images that would otherwise have to be manually inspected. The 2023 Wu-Murad search in Japan, one of the largest missing person searches conducted in that area, serves as a case study. Although drone teams conducting wide area searches may not know in advance if the data they collect is going to be used for CV/ML post-processing, there are data collection procedures that can improve the search in general with automated collection software. If the drone teams do expect to use CV/ML, then they can exploit knowledge about the model to further optimize flights.", "published": "2023-09-05T02:31:04Z", "authors": ["Robin Murphy", "Thomas Manzini"], "categories": ["cs.RO", "cs.CV"], "doi": "", "_query": "property valuation computer vision", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/1308.3108v2", "title": "On Lattices over Valuation Rings of Arbitrary Rank", "abstract": "We show how several results about p-adic lattices generalize easily to lattices over valuation ring of arbitrary rank having only the Henselian property for quadratic polynomial. If 2 is invertible we obtain the uniqueness of the Jordan decomposition and the Witt Cancelation Theorem. We show that the isomorphism classes of indecomposable rank 2 lattices over such a ring in which 2 is not invertible are characterized by two invariants, provided that the lattices contain a primitive norm divisible by 2 of maximal valuation.", "published": "2013-08-14T13:02:12Z", "authors": ["Shaul Zemel"], "categories": ["math.AC"], "doi": "10.1016/j.jalgebra.2014.10.022", "_query": "property valuation computer vision", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/1108.5669v2", "title": "Learning Valuation Functions", "abstract": "In this paper we study the approximate learnability of valuations commonly used throughout economics and game theory for the quantitative encoding of agent preferences. We provide upper and lower bounds regarding the learnability of important subclasses of valuation functions that express no-complementarities. Our main results concern their approximate learnability in the distributional learning (PAC-style) setting. We provide nearly tight lower and upper bounds of $\\tildeΘ(n^{1/2})$ on the approximation factor for learning XOS and subadditive valuations, both widely studied superclasses of submodular valuations. Interestingly, we show that the $\\tildeΩ(n^{1/2})$ lower bound can be circumvented for XOS functions of polynomial complexity; we provide an algorithm for learning the class of XOS valuations with a representation of polynomial size achieving an $O(n^{\\eps})$ approximation factor in time $O(n^{1/\\eps})$ for any $\\eps > 0$. This highlights the importance of considering the complexity of the target function for polynomial time learning. We also provide new learning results for interesting subclasses of submodular functions. Our upper bounds for distributional learning leverage novel structural results for all these valuation classes. We show that many of these results provide new learnability results in the Goemans et al. model (SODA 2009) of approximate learning everywhere via value queries. We also introduce a new model that is more realistic in economic settings, in which the learner can set prices and observe purchase decisions at these prices rather than observing the valuation function directly. In this model, most of our upper bounds continue to hold despite the fact that the learner receives less information (both for learning in the distributional setting and with value queries), while our lower bounds naturally extend.", "published": "2011-08-29T17:48:28Z", "authors": ["Maria Florina Balcan", "Florin Constantin", "Satoru Iwata", "Lei Wang"], "categories": ["cs.GT", "cs.DS", "stat.ML"], "doi": "", "_query": "property valuation computer vision", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2510.26684v1", "title": "Process Integrated Computer Vision for Real-Time Failure Prediction in Steel Rolling Mill", "abstract": "We present a long-term deployment study of a machine vision-based anomaly detection system for failure prediction in a steel rolling mill. The system integrates industrial cameras to monitor equipment operation, alignment, and hot bar motion in real time along the process line. Live video streams are processed on a centralized video server using deep learning models, enabling early prediction of equipment failures and process interruptions, thereby reducing unplanned breakdown costs. Server-based inference minimizes the computational load on industrial process control systems (PLCs), supporting scalable deployment across production lines with minimal additional resources. By jointly analyzing sensor data from data acquisition systems and visual inputs, the system identifies the location and probable root causes of failures, providing actionable insights for proactive maintenance. This integrated approach enhances operational reliability, productivity, and profitability in industrial manufacturing environments.", "published": "2025-10-30T16:54:16Z", "authors": ["Vaibhav Kurrey", "Sivakalyan Pujari", "Gagan Raj Gupta"], "categories": ["cs.CV", "cs.AI"], "doi": "", "_query": "property valuation computer vision", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/1112.6024v1", "title": "Valuation of Zynga", "abstract": "On December 16, Zynga, the well-known social game developing company went public. This event is following other recent IPOs in the world of social networking companies, such as Groupon, Linkedin or Pandora to cite a few. With a valuation close to 7 billion USD at the time when it went public, Zynga has become the biggest web IPO since Google. This recent enthusiasm for social networking companies, and in particular Zynga, brings up the question whether or not they are overvalued. The common denominator of all these IPOs is that a lot of estimates about their valuation have been circulating, without any specifics given about the methodology or assumptions used to obtain those numbers. To bring more substance to the debate, we propose a two-tiered approach. First, we introduce a new model to forecast the global user base of Zynga, based on the analysis of the individual dynamics of its major games. Next, we model the revenues per user using a logistic growth function, a standard model for growth in competition. This leads to bracket the valuation of Zynga using three different scenarios (base one, optimistic and very optimistic): 4.17 billion USD in the base case, 5.16 billion in the high growth and 7.02 billion in the extreme growth scenario respectively. Thus, only the unlikely extreme growth scenario could potentially justify today's 6.6 billion USD valuation of Zynga. This suggests that Zynga at its IPO has been overpriced.", "published": "2011-12-27T21:22:10Z", "authors": ["Zalán Forró", "Peter Cauwels", "Didier Sornette"], "categories": ["q-fin.GN"], "doi": "", "_query": "property valuation computer vision", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/1809.09539v3", "title": "The Zariski-Riemann space of valuation domains associated to pseudo-convergent sequences", "abstract": "Let $V$ be a valuation domain with quotient field $K$. Given a pseudo-convergent sequence $E$ in $K$, we study two constructions associating to $E$ a valuation domain of $K(X)$ lying over $V$, especially when $V$ has rank one. The first one has been introduced by Ostrowski, the second one more recently by Loper and Werner. We describe the main properties of these valuation domains, and we give a notion of equivalence on the set of pseudo-convergent sequences of $K$ characterizing when the associated valuation domains are equal. Then, we analyze the topological properties of the Zariski-Riemann spaces formed by these valuation domains.", "published": "2018-09-25T15:19:28Z", "authors": ["Giulio Peruginelli", "Dario Spirito"], "categories": ["math.AC"], "doi": "10.1090/tran/8185", "_query": "property valuation computer vision", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/1505.07508v1", "title": "Valuations in Nilpotent Minimum Logic", "abstract": "The Euler characteristic can be defined as a special kind of valuation on finite distributive lattices. This work begins with some brief consideration on the role of the Euler characteristic on NM algebras, the algebraic counterpart of Nilpotent Minimum logic. Then, we introduce a new valuation, a modified version of the Euler characteristic we call idempotent Euler characteristic. We show that the new valuation encodes information about the formulæ in NM propositional logic.", "published": "2015-05-27T22:49:19Z", "authors": ["Pietro Codara", "Diego Valota"], "categories": ["cs.LO", "math.CO", "math.LO"], "doi": "10.1109/ISMVL.2015.19", "_query": "property valuation computer vision", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/1910.02319v2", "title": "Covariance-free Partial Least Squares: An Incremental Dimensionality Reduction Method", "abstract": "Dimensionality reduction plays an important role in computer vision problems since it reduces computational cost and is often capable of yielding more discriminative data representation. In this context, Partial Least Squares (PLS) has presented notable results in tasks such as image classification and neural network optimization. However, PLS is infeasible on large datasets, such as ImageNet, because it requires all the data to be in memory in advance, which is often impractical due to hardware limitations. Additionally, this requirement prevents us from employing PLS on streaming applications where the data are being continuously generated. Motivated by this, we propose a novel incremental PLS, named Covariance-free Incremental Partial Least Squares (CIPLS), which learns a low-dimensional representation of the data using a single sample at a time. In contrast to other state-of-the-art approaches, instead of adopting a partially-discriminative or SGD-based model, we extend Nonlinear Iterative Partial Least Squares (NIPALS) -- the standard algorithm used to compute PLS -- for incremental processing. Among the advantages of this approach are the preservation of discriminative information across all components, the possibility of employing its score matrices for feature selection, and its computational efficiency. We validate CIPLS on face verification and image classification tasks, where it outperforms several other incremental dimensionality reduction techniques. In the context of feature selection, CIPLS achieves comparable results when compared to state-of-the-art techniques.", "published": "2019-10-05T19:45:50Z", "authors": ["Artur Jordao", "Maiko Lie", "Victor Hugo Cunha de Melo", "William Robson Schwartz"], "categories": ["cs.CV", "cs.LG"], "doi": "", "_query": "property valuation computer vision", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2507.16683v1", "title": "QRetinex-Net: Quaternion-Valued Retinex Decomposition for Low-Level Computer Vision Applications", "abstract": "Images taken in low light often show color shift, low contrast, noise, and other artifacts that hurt computer-vision accuracy. Retinex theory addresses this by viewing an image S as the pixel-wise product of reflectance R and illumination I, mirroring the way people perceive stable object colors under changing light. The decomposition is ill-posed, and classic Retinex models have four key flaws: (i) they treat the red, green, and blue channels independently; (ii) they lack a neuroscientific model of color vision; (iii) they cannot perfectly rebuild the input image; and (iv) they do not explain human color constancy. We introduce the first Quaternion Retinex formulation, in which the scene is written as the Hamilton product of quaternion-valued reflectance and illumination. To gauge how well reflectance stays invariant, we propose the Reflectance Consistency Index. Tests on low-light crack inspection, face detection under varied lighting, and infrared-visible fusion show gains of 2-11 percent over leading methods, with better color fidelity, lower noise, and higher reflectance stability.", "published": "2025-07-22T15:17:24Z", "authors": ["Sos Agaian", "Vladimir Frants"], "categories": ["cs.CV"], "doi": "", "_query": "property valuation computer vision", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/1112.4060v1", "title": "A real time vehicles detection algorithm for vision based sensors", "abstract": "A vehicle detection plays an important role in the traffic control at signalised intersections. This paper introduces a vision-based algorithm for vehicles presence recognition in detection zones. The algorithm uses linguistic variables to evaluate local attributes of an input image. The image attributes are categorised as vehicle, background or unknown features. Experimental results on complex traffic scenes show that the proposed algorithm is effective for a real-time vehicles detection.", "published": "2011-12-17T14:50:50Z", "authors": ["Bartłomiej Płaczek"], "categories": ["cs.CV"], "doi": "10.1007/978-3-642-15907-7_26", "_query": "property valuation computer vision", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2410.04751v1", "title": "Intriguing Properties of Large Language and Vision Models", "abstract": "Recently, large language and vision models (LLVMs) have received significant attention and development efforts due to their remarkable generalization performance across a wide range of tasks requiring perception and cognitive abilities. A key factor behind their success is their simple architecture, which consists of a vision encoder, a projector, and a large language model (LLM). Despite their achievements in advanced reasoning tasks, their performance on fundamental perception-related tasks (e.g., MMVP) remains surprisingly low. This discrepancy raises the question of how LLVMs truly perceive images and exploit the advantages of the vision encoder. To address this, we systematically investigate this question regarding several aspects: permutation invariance, robustness, math reasoning, alignment preserving and importance, by evaluating the most common LLVM's families (i.e., LLaVA) across 10 evaluation benchmarks. Our extensive experiments reveal several intriguing properties of current LLVMs: (1) they internally process the image in a global manner, even when the order of visual patch sequences is randomly permuted; (2) they are sometimes able to solve math problems without fully perceiving detailed numerical information; (3) the cross-modal alignment is overfitted to complex reasoning tasks, thereby, causing them to lose some of the original perceptual capabilities of their vision encoder; (4) the representation space in the lower layers (<25%) plays a crucial role in determining performance and enhancing visual understanding. Lastly, based on the above observations, we suggest potential future directions for building better LLVMs and constructing more challenging evaluation benchmarks.", "published": "2024-10-07T05:07:01Z", "authors": ["Young-Jun Lee", "Byungsoo Ko", "Han-Gyu Kim", "Yechan Hwang", "Ho-Jin Choi"], "categories": ["cs.CV", "cs.CL"], "doi": "", "_query": "property valuation computer vision", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2512.01658v1", "title": "Computing Treedepth Obstructions", "abstract": "The graph parameter treedepth is minor-monotone; hence, the class of graphs with treedepth at most $k$ is minor-closed. By the Graph Minor Theorem, such a class is characterized by a finite set of forbidden minors. A conjecture of Dvořák, Giannopoulou, and Thilikos states that every such forbidden minor has at most $2^k$ vertices. We present an algorithm that, given $n, k \\in \\mathbb{N}$, computes the set of forbidden minors, forbidden subgraphs, and forbidden induced subgraphs on at most $n$ vertices, for the class of graphs of treedepth at most $k$. Applying this algorithm to $k = 4$ and $n = 16$, we enumerate 1546 forbidden minors, 1718 forbidden subgraphs, and 12204 forbidden induced subgraphs. Assuming the above conjecture holds, these sets constitute the complete obstruction sets for graphs of treedepth at most 4.", "published": "2025-12-01T13:30:53Z", "authors": ["Kolja Kühn"], "categories": ["cs.DM", "cs.DS", "math.CO"], "doi": "", "_query": "property valuation computer vision", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/0910.1845v1", "title": "Parallel Computation of Finite Element Navier-Stokes codes using MUMPS Solver", "abstract": "The study deals with the parallelization of 2D and 3D finite element based Navier-Stokes codes using direct solvers. Development of sparse direct solvers using multifrontal solvers has significantly reduced the computational time of direct solution methods. Although limited by its stringent memory requirements, multifrontal solvers can be computationally efficient. First the performance of MUltifrontal Massively Parallel Solver (MUMPS) is evaluated for both 2D and 3D codes in terms of memory requirements and CPU times. The scalability of both Newton and modified Newton algorithms is tested.", "published": "2009-10-09T20:11:36Z", "authors": ["Mandhapati P. Raju"], "categories": ["cs.MS"], "doi": "", "_query": "property valuation computer vision", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2311.04888v1", "title": "Towards Few-Annotation Learning in Computer Vision: Application to Image Classification and Object Detection tasks", "abstract": "In this thesis, we develop theoretical, algorithmic and experimental contributions for Machine Learning with limited labels, and more specifically for the tasks of Image Classification and Object Detection in Computer Vision. In a first contribution, we are interested in bridging the gap between theory and practice for popular Meta-Learning algorithms used in Few-Shot Classification. We make connections to Multi-Task Representation Learning, which benefits from solid theoretical foundations, to verify the best conditions for a more efficient meta-learning. Then, to leverage unlabeled data when training object detectors based on the Transformer architecture, we propose both an unsupervised pretraining and a semi-supervised learning method in two other separate contributions. For pretraining, we improve Contrastive Learning for object detectors by introducing the localization information. Finally, our semi-supervised method is the first tailored to transformer-based detectors.", "published": "2023-11-08T18:50:04Z", "authors": ["Quentin Bouniot"], "categories": ["cs.CV", "cs.AI", "cs.LG", "stat.ML"], "doi": "", "_query": "property valuation computer vision", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/1911.08076v1", "title": "IFQ-Net: Integrated Fixed-point Quantization Networks for Embedded Vision", "abstract": "Deploying deep models on embedded devices has been a challenging problem since the great success of deep learning based networks. Fixed-point networks, which represent their data with low bits fixed-point and thus give remarkable savings on memory usage, are generally preferred. Even though current fixed-point networks employ relative low bits (e.g. 8-bits), the memory saving is far from enough for the embedded devices. On the other hand, quantization deep networks, for example XNOR-Net and HWGQNet, quantize the data into 1 or 2 bits resulting in more significant memory savings but still contain lots of floatingpoint data. In this paper, we propose a fixed-point network for embedded vision tasks through converting the floatingpoint data in a quantization network into fixed-point. Furthermore, to overcome the data loss caused by the conversion, we propose to compose floating-point data operations across multiple layers (e.g. convolution, batch normalization and quantization layers) and convert them into fixedpoint. We name the fixed-point network obtained through such integrated conversion as Integrated Fixed-point Quantization Networks (IFQ-Net). We demonstrate that our IFQNet gives 2.16x and 18x more savings on model size and runtime feature map memory respectively with similar accuracy on ImageNet. Furthermore, based on YOLOv2, we design IFQ-Tinier-YOLO face detector which is a fixed-point network with 256x reduction in model size (246k Bytes) than Tiny-YOLO. We illustrate the promising performance of our face detector in terms of detection rate on Face Detection Data Set and Bencmark (FDDB) and qualitative results of detecting small faces of Wider Face dataset.", "published": "2019-11-19T03:29:03Z", "authors": ["Hongxing Gao", "Wei Tao", "Dongchao Wen", "Tse-Wei Chen", "Kinya Osa", "Masami Kato"], "categories": ["cs.CV"], "doi": "", "_query": "property valuation computer vision", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2103.14672v2", "title": "Multi-Modal RGB-D Scene Recognition Across Domains", "abstract": "Scene recognition is one of the basic problems in computer vision research with extensive applications in robotics. When available, depth images provide helpful geometric cues that complement the RGB texture information and help to identify discriminative scene image features. Depth sensing technology developed fast in the last years and a great variety of 3D cameras have been introduced, each with different acquisition properties. However, those properties are often neglected when targeting big data collections, so multi-modal images are gathered disregarding their original nature. In this work, we put under the spotlight the existence of a possibly severe domain shift issue within multi-modality scene recognition datasets. As a consequence, a scene classification model trained on one camera may not generalize on data from a different camera, only providing a low recognition performance. Starting from the well-known SUN RGB-D dataset, we designed an experimental testbed to study this problem and we use it to benchmark the performance of existing methods. Finally, we introduce a novel adaptive scene recognition approach that leverages self-supervised translation between modalities. Indeed, learning to go from RGB to depth and vice-versa is an unsupervised procedure that can be trained jointly on data of multiple cameras and may help to bridge the gap among the extracted feature distributions. Our experimental results confirm the effectiveness of the proposed approach.", "published": "2021-03-26T18:20:29Z", "authors": ["Andrea Ferreri", "Silvia Bucci", "Tatiana Tommasi"], "categories": ["cs.CV", "cs.RO"], "doi": "", "_query": "property valuation computer vision", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2606.05149v1", "title": "An Open-Source Two-Stage Computer Vision Pipeline for Fine-Grained Vehicle Classification using Vision Transformers", "abstract": "Vehicle body type is a significant determinant of cyclist injury severity in overtaking crashes, yet automated tools for classifying vehicles into injury-risk-relevant categories from naturalistic roadway video do not exist in the open literature. Standard object detection benchmarks provide only coarse vehicle labels (car, truck, bus, motorcycle), while existing fine-grained recognition systems are trained on controlled imagery and lack evaluation for deployment robustness across recording sites. This paper presents an open-source two-stage computer vision pipeline combining a pre-trained RT-DETR detector for coarse vehicle localization with a fine-tuned Vision Transformer (ViT-Base/16) for six-category body-type classification: passenger car, SUV, pickup truck, minivan, large van, and commercial truck. A confidence-based abstention mechanism withholds Stage 2 predictions when softmax output falls below 0.60, producing unknown labels rather than silent misclassifications. Evaluated on 3,805 annotated overtaking events from a bicycle-lane corridor in Ann Arbor, Michigan (in-distribution), the pipeline achieved 0.94 accuracy with per-class F1 scores from 0.91 (minivan) to 0.97 (SUV). On an independent out-of-distribution evaluation of 311 events from an open cycling dataset without retraining, accuracy was 0.89. Three of four well-represented categories maintained F1 at or above 0.90 under domain shift. The largest degradation was observed for minivan (F1 = 0.72), driven by abstention rate rising from 2.4% to 25.0% rather than active misclassification, consistent with the mechanism propagating genuine model uncertainty. The full pipeline, including inference scripts, training code, evaluation utilities, and model weights, is released as open-source software to support reproducibility and reuse across roadside video archives and cycling safety research.", "published": "2026-06-03T17:53:33Z", "authors": ["Gandhimathi Padmanaban", "Fred Feng"], "categories": ["cs.CV", "cs.LG", "eess.IV"], "doi": "", "_query": "property valuation computer vision", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/1810.04452v1", "title": "AI Learns to Recognize Bengali Handwritten Digits: Bengali.AI Computer Vision Challenge 2018", "abstract": "Solving problems with Artificial intelligence in a competitive manner has long been absent in Bangladesh and Bengali-speaking community. On the other hand, there has not been a well structured database for Bengali Handwritten digits for mass public use. To bring out the best minds working in machine learning and use their expertise to create a model which can easily recognize Bengali Handwritten digits, we organized Bengali.AI Computer Vision Challenge.The challenge saw both local and international teams participating with unprecedented efforts.", "published": "2018-10-10T10:59:28Z", "authors": ["Sharif Amit Kamran", "Ahmed Imtiaz Humayun", "Samiul Alam", "Rashed Mohammad Doha", "Manash Kumar Mandal", "Tahsin Reasat", "Fuad Rahman"], "categories": ["cs.CV"], "doi": "", "_query": "property valuation computer vision", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/1803.09492v1", "title": "Latency and Throughput Characterization of Convolutional Neural Networks for Mobile Computer Vision", "abstract": "We study performance characteristics of convolutional neural networks (CNN) for mobile computer vision systems. CNNs have proven to be a powerful and efficient approach to implement such systems. However, the system performance depends largely on the utilization of hardware accelerators, which are able to speed up the execution of the underlying mathematical operations tremendously through massive parallelism. Our contribution is performance characterization of multiple CNN-based models for object recognition and detection with several different hardware platforms and software frameworks, using both local (on-device) and remote (network-side server) computation. The measurements are conducted using real workloads and real processing platforms. On the platform side, we concentrate especially on TensorFlow and TensorRT. Our measurements include embedded processors found on mobile devices and high-performance processors that can be used on the network side of mobile systems. We show that there exists significant latency--throughput trade-offs but the behavior is very complex. We demonstrate and discuss several factors that affect the performance and yield this complex behavior.", "published": "2018-03-26T09:49:03Z", "authors": ["Jussi Hanhirova", "Teemu Kämäräinen", "Sipi Seppälä", "Matti Siekkinen", "Vesa Hirvisalo", "Antti Ylä-Jääski"], "categories": ["cs.CV"], "doi": "", "_query": "property valuation computer vision", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2106.09177v1", "title": "Insights into Data through Model Behaviour: An Explainability-driven Strategy for Data Auditing for Responsible Computer Vision Applications", "abstract": "In this study, we take a departure and explore an explainability-driven strategy to data auditing, where actionable insights into the data at hand are discovered through the eyes of quantitative explainability on the behaviour of a dummy model prototype when exposed to data. We demonstrate this strategy by auditing two popular medical benchmark datasets, and discover hidden data quality issues that lead deep learning models to make predictions for the wrong reasons. The actionable insights gained from this explainability driven data auditing strategy is then leveraged to address the discovered issues to enable the creation of high-performing deep learning models with appropriate prediction behaviour. The hope is that such an explainability-driven strategy can be complimentary to data-driven strategies to facilitate for more responsible development of machine learning algorithms for computer vision applications.", "published": "2021-06-16T23:46:39Z", "authors": ["Alexander Wong", "Adam Dorfman", "Paul McInnis", "Hayden Gunraj"], "categories": ["cs.CV", "cs.LG"], "doi": "", "_query": "property valuation computer vision", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2112.02891v3", "title": "Seeing Objects in dark with Continual Contrastive Learning", "abstract": "Object Detection, a fundamental computer vision problem, has paramount importance in smart camera systems. However, a truly reliable camera system could be achieved if and only if the underlying object detection component is robust enough across varying imaging conditions (or domains), for instance, different times of the day, adverse weather conditions, etc. In an effort to achieving a reliable camera system, in this paper, we make an attempt to train such a robust detector. Unfortunately, to build a well-performing detector across varying imaging conditions, one would require labeled training images (often in large numbers) from a plethora of corner cases. As manually obtaining such a large labeled dataset may be infeasible, we suggest using synthetic images, to mimic different training image domains. We propose a novel, contrastive learning method to align the latent representations of a pair of real and synthetic images, to make the detector robust to the different domains. However, we found that merely contrasting the embeddings may lead to catastrophic forgetting of the information essential for object detection. Hence, we employ a continual learning based penalty, to alleviate the issue of forgetting, while contrasting the representations. We showcase that our proposed method outperforms a wide range of alternatives to address the extremely challenging, yet under-studied scenario of object detection at night-time.", "published": "2021-12-06T09:28:45Z", "authors": ["Ujjal Kr Dutta"], "categories": ["cs.CV", "cs.AI", "cs.LG"], "doi": "", "_query": "property valuation computer vision", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2403.18587v1", "title": "The Impact of Uniform Inputs on Activation Sparsity and Energy-Latency Attacks in Computer Vision", "abstract": "Resource efficiency plays an important role for machine learning nowadays. The energy and decision latency are two critical aspects to ensure a sustainable and practical application. Unfortunately, the energy consumption and decision latency are not robust against adversaries. Researchers have recently demonstrated that attackers can compute and submit so-called sponge examples at inference time to increase the energy consumption and decision latency of neural networks. In computer vision, the proposed strategy crafts inputs with less activation sparsity which could otherwise be used to accelerate the computation. In this paper, we analyze the mechanism how these energy-latency attacks reduce activation sparsity. In particular, we find that input uniformity is a key enabler. A uniform image, that is, an image with mostly flat, uniformly colored surfaces, triggers more activations due to a specific interplay of convolution, batch normalization, and ReLU activation. Based on these insights, we propose two new simple, yet effective strategies for crafting sponge examples: sampling images from a probability distribution and identifying dense, yet inconspicuous inputs in natural datasets. We empirically examine our findings in a comprehensive evaluation with multiple image classification models and show that our attack achieves the same sparsity effect as prior sponge-example methods, but at a fraction of computation effort. We also show that our sponge examples transfer between different neural networks. Finally, we discuss applications of our findings for the good by improving efficiency by increasing sparsity.", "published": "2024-03-27T14:11:23Z", "authors": ["Andreas Müller", "Erwin Quiring"], "categories": ["cs.CR", "cs.CV", "cs.LG"], "doi": "", "_query": "property valuation computer vision", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2503.13236v2", "title": "Gradient Extrapolation for Debiased Representation Learning", "abstract": "Machine learning classification models trained with empirical risk minimization (ERM) often inadvertently rely on spurious correlations. When absent in the test data, these unintended associations between non-target attributes and target labels lead to poor generalization. This paper addresses this problem from a model optimization perspective and proposes a novel method, Gradient Extrapolation for Debiased Representation Learning (GERNE), designed to learn debiased representations in both known and unknown attribute training cases. GERNE uses two distinct batches with different amounts of spurious correlations and defines the target gradient as a linear extrapolation of the gradients computed from each batch's loss. Our analysis shows that when the extrapolated gradient points toward the batch gradient with fewer spurious correlations, it effectively guides training toward learning a debiased model. GERNE serves as a general framework for debiasing, encompassing ERM and Resampling methods as special cases. We derive the theoretical upper and lower bounds of the extrapolation factor employed by GERNE. By tuning this factor, GERNE can adapt to maximize either Group-Balanced Accuracy (GBA) or Worst-Group Accuracy (WGA). We validate GERNE on five vision and one NLP benchmarks, demonstrating competitive and often superior performance compared to state-of-the-art baselines. The project page is available at: https://gerne-debias.github.io/.", "published": "2025-03-17T14:48:57Z", "authors": ["Ihab Asaad", "Maha Shadaydeh", "Joachim Denzler"], "categories": ["cs.LG", "cs.CV"], "doi": "", "_query": "property valuation computer vision", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2204.13195v1", "title": "Stream Iterative Distributed Coded Computing for Learning Applications in Heterogeneous Systems", "abstract": "To improve the utility of learning applications and render machine learning solutions feasible for complex applications, a substantial amount of heavy computations is needed. Thus, it is essential to delegate the computations among several workers, which brings up the major challenge of coping with delays and failures caused by the system's heterogeneity and uncertainties. In particular, minimizing the end-to-end job in-order execution delay, from arrival to delivery, is of great importance for real-world delay-sensitive applications. In this paper, for computation of each job iteration in a stochastic heterogeneous distributed system where the workers vary in their computing and communicating powers, we present a novel joint scheduling-coding framework that optimally split the coded computational load among the workers. This closes the gap between the workers' response time, and is critical to maximize the resource utilization. To further reduce the in-order execution delay, we also incorporate redundant computations in each iteration of a distributed computational job. Our simulation results demonstrate that the delay obtained using the proposed solution is dramatically lower than the uniform split which is oblivious to the system's heterogeneity and, in fact, is very close to an ideal lower bound just by introducing a small percentage of redundant computations.", "published": "2022-04-27T21:08:29Z", "authors": ["Homa Esfahanizadeh", "Alejandro Cohen", "Muriel Medard"], "categories": ["cs.DC", "cs.IT"], "doi": "", "_query": "property valuation computer vision", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2407.10709v1", "title": "Detecting Omissions in Geographic Maps through Computer Vision", "abstract": "This paper explores the application of computer vision technologies to the analysis of maps, an area with substantial historical, cultural, and political significance. Our focus is on developing and evaluating a method for automatically identifying maps that depict specific regions and feature landmarks with designated names, a task that involves complex challenges due to the diverse styles and methods used in map creation. We address three main subtasks: differentiating maps from non-maps, verifying the accuracy of the region depicted, and confirming the presence or absence of particular landmark names through advanced text recognition techniques. Our approach utilizes a Convolutional Neural Network and transfer learning to differentiate maps from non-maps, verify the accuracy of depicted regions, and confirm landmark names through advanced text recognition. We also introduce the VinMap dataset, containing annotated map images of Vietnam, to train and test our method. Experiments on this dataset demonstrate that our technique achieves F1-score of 85.51% for identifying maps excluding specific territorial landmarks. This result suggests practical utility and indicates areas for future improvement.", "published": "2024-07-15T13:26:58Z", "authors": ["Phuc D. A. Nguyen", "Anh Do", "Minh Hoai"], "categories": ["cs.CV"], "doi": "", "_query": "property valuation computer vision", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2103.12980v1", "title": "On a realization of motion and similarity group equivalence classes of labeled points in $\\mathbb R^k$ with applications to computer vision", "abstract": "We study a realization of motion and similarity group equivalence classes of $n\\geq 1$ labeled points in $\\mathbb R^k,\\, k\\geq 1$ as a metric space with a computable metric. Our study is motivated by applications in computer vision.", "published": "2021-03-24T04:25:12Z", "authors": ["Steven B. Damelin", "David L. Ragozin", "Michael Werman"], "categories": ["cs.CV", "math.GR", "math.OC"], "doi": "", "_query": "property valuation computer vision", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/1906.04721v3", "title": "Data-Free Quantization Through Weight Equalization and Bias Correction", "abstract": "We introduce a data-free quantization method for deep neural networks that does not require fine-tuning or hyperparameter selection. It achieves near-original model performance on common computer vision architectures and tasks. 8-bit fixed-point quantization is essential for efficient inference on modern deep learning hardware. However, quantizing models to run in 8-bit is a non-trivial task, frequently leading to either significant performance reduction or engineering time spent on training a network to be amenable to quantization. Our approach relies on equalizing the weight ranges in the network by making use of a scale-equivariance property of activation functions. In addition the method corrects biases in the error that are introduced during quantization. This improves quantization accuracy performance, and can be applied to many common computer vision architectures with a straight forward API call. For common architectures, such as the MobileNet family, we achieve state-of-the-art quantized model performance. We further show that the method also extends to other computer vision architectures and tasks such as semantic segmentation and object detection.", "published": "2019-06-11T17:47:51Z", "authors": ["Markus Nagel", "Mart van Baalen", "Tijmen Blankevoort", "Max Welling"], "categories": ["cs.LG", "cs.CV", "stat.ML"], "doi": "", "_query": "property valuation computer vision", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/1708.00634v1", "title": "Dual-Glance Model for Deciphering Social Relationships", "abstract": "Since the beginning of early civilizations, social relationships derived from each individual fundamentally form the basis of social structure in our daily life. In the computer vision literature, much progress has been made in scene understanding, such as object detection and scene parsing. Recent research focuses on the relationship between objects based on its functionality and geometrical relations. In this work, we aim to study the problem of social relationship recognition, in still images. We have proposed a dual-glance model for social relationship recognition, where the first glance fixates at the individual pair of interest and the second glance deploys attention mechanism to explore contextual cues. We have also collected a new large scale People in Social Context (PISC) dataset, which comprises of 22,670 images and 76,568 annotated samples from 9 types of social relationship. We provide benchmark results on the PISC dataset, and qualitatively demonstrate the efficacy of the proposed model.", "published": "2017-08-02T08:13:28Z", "authors": ["Junnan Li", "Yongkang Wong", "Qi Zhao", "Mohan S. Kankanhalli"], "categories": ["cs.CV"], "doi": "", "_query": "property valuation computer vision", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/1709.05731v1", "title": "Facial Feature Tracking under Varying Facial Expressions and Face Poses based on Restricted Boltzmann Machines", "abstract": "Facial feature tracking is an active area in computer vision due to its relevance to many applications. It is a nontrivial task, since faces may have varying facial expressions, poses or occlusions. In this paper, we address this problem by proposing a face shape prior model that is constructed based on the Restricted Boltzmann Machines (RBM) and their variants. Specifically, we first construct a model based on Deep Belief Networks to capture the face shape variations due to varying facial expressions for near-frontal view. To handle pose variations, the frontal face shape prior model is incorporated into a 3-way RBM model that could capture the relationship between frontal face shapes and non-frontal face shapes. Finally, we introduce methods to systematically combine the face shape prior models with image measurements of facial feature points. Experiments on benchmark databases show that with the proposed method, facial feature points can be tracked robustly and accurately even if faces have significant facial expressions and poses.", "published": "2017-09-18T00:11:25Z", "authors": ["Yue Wu", "Zuoguan Wang", "Qiang Ji"], "categories": ["cs.CV"], "doi": "", "_query": "property valuation computer vision", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/1811.03595v2", "title": "The ordinal generated by an ordinal grammar is computable", "abstract": "A prefix grammar is a context-free grammar whose nonterminals generate prefix-free languages. A prefix grammar $G$ is an ordinal grammar if the language $L(G)$ is well-ordered with respect to the lexicographic ordering. It is known that from a finite system of parametric fixed point equations one can construct an ordinal grammar $G$ such that the lexicographic order of $G$ is isomorphic with the least solution of the system, if this solution is well-ordered. In this paper we show that given an ordinal grammar, one can compute (the Cantor normal form of) the order type of the lexicographic order of its language, yielding that least solutions of fixed point equation systems defining algebraic ordinals are effectively computable (and thus, their isomorphism problem is also decidable).", "published": "2018-11-08T18:30:44Z", "authors": ["Kitti Gelle", "Szabolcs Ivan"], "categories": ["cs.FL"], "doi": "", "_query": "property valuation computer vision", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/1911.10037v1", "title": "Computer Vision-based Accident Detection in Traffic Surveillance", "abstract": "Computer vision-based accident detection through video surveillance has become a beneficial but daunting task. In this paper, a neoteric framework for detection of road accidents is proposed. The proposed framework capitalizes on Mask R-CNN for accurate object detection followed by an efficient centroid based object tracking algorithm for surveillance footage. The probability of an accident is determined based on speed and trajectory anomalies in a vehicle after an overlap with other vehicles. The proposed framework provides a robust method to achieve a high Detection Rate and a low False Alarm Rate on general road-traffic CCTV surveillance footage. This framework was evaluated on diverse conditions such as broad daylight, low visibility, rain, hail, and snow using the proposed dataset. This framework was found effective and paves the way to the development of general-purpose vehicular accident detection algorithms in real-time.", "published": "2019-11-22T13:38:06Z", "authors": ["Earnest Paul Ijjina", "Dhananjai Chand", "Savyasachi Gupta", "Goutham K"], "categories": ["cs.CV"], "doi": "10.1109/ICCCNT45670.2019.8944469", "_query": "property valuation computer vision", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/0910.1585v2", "title": "Distributed Computing with Adaptive Heuristics", "abstract": "We use ideas from distributed computing to study dynamic environments in which computational nodes, or decision makers, follow adaptive heuristics (Hart 2005), i.e., simple and unsophisticated rules of behavior, e.g., repeatedly \"best replying\" to others' actions, and minimizing \"regret\", that have been extensively studied in game theory and economics. We explore when convergence of such simple dynamics to an equilibrium is guaranteed in asynchronous computational environments, where nodes can act at any time. Our research agenda, distributed computing with adaptive heuristics, lies on the borderline of computer science (including distributed computing and learning) and game theory (including game dynamics and adaptive heuristics). We exhibit a general non-termination result for a broad class of heuristics with bounded recall---that is, simple rules of behavior that depend only on recent history of interaction between nodes. We consider implications of our result across a wide variety of interesting and timely applications: game theory, circuit design, social networks, routing and congestion control. We also study the computational and communication complexity of asynchronous dynamics and present some basic observations regarding the effects of asynchrony on no-regret dynamics. We believe that our work opens a new avenue for research in both distributed computing and game theory.", "published": "2009-10-08T22:39:39Z", "authors": ["Aaron D. Jaggard", "Michael Schapira", "Rebecca N. Wright"], "categories": ["cs.DC", "cs.GT"], "doi": "", "_query": "property valuation computer vision", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/1805.02755v2", "title": "EngineCL: Usability and Performance in Heterogeneous Computing", "abstract": "Heterogeneous systems have become one of the most common architectures today, thanks to their excellent performance and energy consumption. However, due to their heterogeneity they are very complex to program and even more to achieve performance portability on different devices. This paper presents EngineCL, a new OpenCL-based runtime system that outstandingly simplifies the co-execution of a single massive data-parallel kernel on all the devices of a heterogeneous system. It performs a set of low level tasks regarding the management of devices, their disjoint memory spaces and scheduling the workload between the system devices while providing a layered API. EngineCL has been validated in two compute nodes (HPC and commodity system), that combine six devices with different architectures. Experimental results show that it has excellent usability compared with OpenCL; a maximum 2.8% of overhead compared to the native version under loads of less than a second of execution and a tendency towards zero for longer execution times; and it can reach an average efficiency of 0.89 when balancing the load.", "published": "2018-05-07T21:37:03Z", "authors": ["Raúl Nozal", "Jose Luis Bosque", "Ramón Beivide"], "categories": ["cs.DC"], "doi": "10.1016/j.future.2020.02.016", "_query": "property valuation computer vision", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/1210.7615v4", "title": "Definable henselian valuations", "abstract": "In this note we investigate the question whether a henselian valued field carries a non-trivial 0-definable henselian valuation (in the language of rings). It follows from the work of Prestel and Ziegler that there are henselian valued fields which do not admit a 0-definable non-trivial henselian valuation. We give conditions on the residue field which ensure the existence of a parameter-free definiton. In particular, we show that a henselian valued field admits a non-trivial 0-definable valuation when the residue field is separably closed or sufficiently non-henselian, or when the absolute Galois group of the (residue) field is non-universal.", "published": "2012-10-29T10:52:35Z", "authors": ["Franziska Jahnke", "Jochen Koenigsmann"], "categories": ["math.LO"], "doi": "", "_query": "property valuation computer vision", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2609.20869v2", "title": "TAPe+ML: A Compact Structured Representation for Multi-Task Computer Vision", "abstract": "We present TAPe+ML v3, a compact computer vision system based on TAPe (Theory of Active Perception), a structured representation that encodes relations among perceptual elements before recognition. Instead of operating directly on pixel tensors, the system uses a shared TAPe representation and a modular recognition architecture for image classification, object detection, and instance segmentation. TAPe+ML v3 combines background and contour processing, local object localization, prototype-based classification, and a coordinator for specialized submodels. Across the reported experiments, it uses fewer than 100,000 parameters. On COCO object detection, it obtains 84.7 mAP50 and 65.3 mAP50-95. On COCO instance segmentation, it obtains 80.7 mask mAP50 and 58.4 mask mAP50-95. In classification experiments, it reaches 92 percent validation accuracy on Imagenette under an identical-training comparison with a raw-pixel baseline, and 89.9 percent Top-1 accuracy on ImageNet-Real. We also evaluate compactness in video scene detection and adaptation under distribution shift in an industrial pilot. The results suggest that shifting part of the modeling burden from network parameters to a structured input representation can support compact multi-task vision systems with reduced data, memory, and compute requirements.", "published": "2026-09-15T14:08:43Z", "authors": ["Sergey Kurinov", "Alexey Upatov"], "categories": ["cs.CV", "eess.IV"], "doi": "", "_query": "property valuation computer vision", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2605.22942v1", "title": "Improved Vision-to-Chart Buoy Association with Learned World-to-Image Projection", "abstract": "This report presents a lightweight modification to the DETR-based fusion transformer baseline for the MaCVi 2026 Vision-to-Chart data association challenge. The challenge baseline decoder receives per-buoy queries encoding world-space distance and bearing, forcing the transformer to implicitly learn the complex geometric projection from world coordinates to image pixels. Instead, this work trains an additional dedicated MLP, QueryMLP, to explicitly predict the buoy's waterline contact point in the image from chart measurements and IMU orientation data. The predicted pixel coordinates are appended to the baseline decoder query vector, providing a direct spatial prior per buoy and reducing the geometric reasoning burden on the transformer decoder. On the challenge leaderboard, the presented approach achieves an Overall score of 0.7386, with F1 = 0.8055 and mIoU = 0.6718, on the held-out test set, placing second among all submissions.", "published": "2026-05-21T18:17:55Z", "authors": ["Borja Carrillo-Perez"], "categories": ["cs.CV"], "doi": "", "_query": "property valuation computer vision", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/1809.07674v1", "title": "The Open Vision Computer: An Integrated Sensing and Compute System for Mobile Robots", "abstract": "In this paper we describe the Open Vision Computer (OVC) which was designed to support high speed, vision guided autonomous drone flight. In particular our aim was to develop a system that would be suitable for relatively small-scale flying platforms where size, weight, power consumption and computational performance were all important considerations. This manuscript describes the primary features of our OVC system and explains how they are used to support fully autonomous indoor and outdoor exploration and navigation operations on our Falcon 250 quadrotor platform.", "published": "2018-09-20T15:31:06Z", "authors": ["Morgan Quigley", "Kartik Mohta", "Shreyas S. Shivakumar", "Michael Watterson", "Yash Mulgaonkar", "Mikael Arguedas", "Ke Sun", "Sikang Liu", "Bernd Pfrommer", "Vijay Kumar", "Camillo J. Taylor"], "categories": ["cs.RO"], "doi": "", "_query": "property valuation computer vision", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2512.01310v1", "title": "Lost in Distortion: Uncovering the Domain Gap Between Computer Vision and Brain Imaging -- A Study on Pretraining for Age Prediction", "abstract": "Large-scale brain imaging datasets provide unprecedented opportunities for developing domain foundation models through pretraining. However, unlike natural image datasets in computer vision, these neuroimaging data often exhibit high heterogeneity in quality, ranging from well-structured scans to severely distorted or incomplete brain volumes. This raises a fundamental question: can noise or low-quality scans contribute meaningfully to pretraining, or do they instead hinder model learning? In this study, we systematically explore the role of data quality level in pretraining and its impact on downstream tasks. Specifically, we perform pretraining on datasets with different quality levels and perform fine-tuning for brain age prediction on external cohorts. Our results show significant performance differences across quality levels, revealing both opportunities and limitations. We further discuss the gap between computer vision practices and clinical neuroimaging standards, emphasizing the necessity of domain-aware curation to ensure trusted and generalizable domain-specific foundation models.", "published": "2025-12-01T06:11:31Z", "authors": ["Yanteng Zhang", "Songheng Li", "Zeyu Shen", "Qizhen Lan", "Lipei Zhang", "Yang Liu", "Vince Calhoun"], "categories": ["cs.CV"], "doi": "", "_query": "property valuation computer vision", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2608.24652v3", "title": "Warped cones associated to isometric free actions do not have geometric property (T)", "abstract": "Suppose that $G$ is a finitely generated group, $M$ is a compact Riemannian manifold and $G\\curvearrowright M$ is a free and isometric action. We prove that the associated discretized warped cone does not have geometric property (T). In contrast, we show that the discretized warped cone associated to the natural action $SL_m(\\mathbb Z)\\curvearrowright \\mathbb T^m$ has geometric property (T) for $m\\geq 3$. We also prove that, for free probability-measure-preserving Lipschitz actions on compact Riemannian manifolds, geometric property (T) of the discretized warped cone implies Kazhdan's property (T) of the acting group.", "published": "2026-08-25T14:59:14Z", "authors": ["Ryo Toyota"], "categories": ["math.OA", "math.DS", "math.GR"], "doi": "", "_query": "property valuation computer vision", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2410.06982v1", "title": "Structure-Centric Robust Monocular Depth Estimation via Knowledge Distillation", "abstract": "Monocular depth estimation, enabled by self-supervised learning, is a key technique for 3D perception in computer vision. However, it faces significant challenges in real-world scenarios, which encompass adverse weather variations, motion blur, as well as scenes with poor lighting conditions at night. Our research reveals that we can divide monocular depth estimation into three sub-problems: depth structure consistency, local texture disambiguation, and semantic-structural correlation. Our approach tackles the non-robustness of existing self-supervised monocular depth estimation models to interference textures by adopting a structure-centered perspective and utilizing the scene structure characteristics demonstrated by semantics and illumination. We devise a novel approach to reduce over-reliance on local textures, enhancing robustness against missing or interfering patterns. Additionally, we incorporate a semantic expert model as the teacher and construct inter-model feature dependencies via learnable isomorphic graphs to enable aggregation of semantic structural knowledge. Our approach achieves state-of-the-art out-of-distribution monocular depth estimation performance across a range of public adverse scenario datasets. It demonstrates notable scalability and compatibility, without necessitating extensive model engineering. This showcases the potential for customizing models for diverse industrial applications.", "published": "2024-10-09T15:20:29Z", "authors": ["Runze Chen", "Haiyong Luo", "Fang Zhao", "Jingze Yu", "Yupeng Jia", "Juan Wang", "Xuepeng Ma"], "categories": ["cs.CV"], "doi": "", "_query": "property valuation computer vision", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/1512.06532v1", "title": "Path computation in multi-layer multi-domain networks: A language theoretic approach", "abstract": "Multi-layer networks are networks in which several protocols may coexist at different layers. The Pseudo-Wire architecture provides encapsulation and de-capsulation functions of protocols over Packet-Switched Networks. In a multi-domain context, computing a path to support end-to-end services requires the consideration of encapsulation and decapsulation capabilities. It appears that graph models are not expressive enough to tackle this problem. In this paper, we propose a new model of heterogeneous networks using Automata Theory. A network is modeled as a Push-Down Automaton (PDA) which is able to capture the encapsulation and decapsulation capabilities, the PDA stack corresponding to the stack of encapsulated protocols. We provide polynomial algorithms that compute the shortest path either in hops or in the number of encapsulations and decapsulations along the inter-domain path, the latter reducing manual configurations and possible loops in the path.", "published": "2015-12-21T08:55:41Z", "authors": ["Mohamed Lamine Lamali", "Hélia Pouyllau", "Dominique Barth"], "categories": ["cs.DS", "cs.FL", "cs.NI"], "doi": "10.1016/j.comcom.2012.11.009", "_query": "property valuation computer vision", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/1501.04691v3", "title": "Tracing the boundaries of materials in transparent vessels using computer vision", "abstract": "Visual recognition of material boundaries in transparent vessels is valuable for numerous applications. Such recognition is essential for estimation of fill-level, volume and phase-boundaries as well as for tracking of such chemical processes as precipitation, crystallization, condensation, evaporation and phase-separation. The problem of material boundary recognition in images is particularly complex for materials with non-flat surfaces, i.e., solids, powders and viscous fluids, in which the material interfaces have unpredictable shapes. This work demonstrates a general method for finding the boundaries of materials inside transparent containers in images. The method uses an image of the transparent vessel containing the material and the boundary of the vessel in this image. The recognition is based on the assumption that the material boundary appears in the image in the form of a curve (with various constraints) whose endpoints are both positioned on the vessel contour. The probability that a curve matches the material boundary in the image is evaluated using a cost function based on some image properties along this curve. Several image properties were examined as indicators for the material boundary. The optimal boundary curve was found using Dijkstra's algorithm. The method was successfully examined for recognition of various types of phase-boundaries, including liquid-air, solid-air and solid-liquid interfaces, as well as for various types of glassware containers from everyday life and the chemistry laboratory (i.e., bottles, beakers, flasks, jars, columns, vials and separation-funnels). In addition, the method can be easily extended to materials carried on top of carrier vessels (i.e., plates, spoons, spatulas).", "published": "2015-01-20T01:19:33Z", "authors": ["Sagi Eppel"], "categories": ["cs.CV"], "doi": "", "_query": "property valuation computer vision", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/1806.07197v1", "title": "Computer-aided proofs for multiparty computation with active security", "abstract": "Secure multi-party computation (MPC) is a general cryptographic technique that allows distrusting parties to compute a function of their individual inputs, while only revealing the output of the function. It has found applications in areas such as auctioning, email filtering, and secure teleconference. Given its importance, it is crucial that the protocols are specified and implemented correctly. In the programming language community it has become good practice to use computer proof assistants to verify correctness proofs. In the field of cryptography, EasyCrypt is the state of the art proof assistant. It provides an embedded language for probabilistic programming, together with a specialized logic, embedded into an ambient general purpose higher-order logic. It allows us to conveniently express cryptographic properties. EasyCrypt has been used successfully on many applications, including public-key encryption, signatures, garbled circuits and differential privacy. Here we show for the first time that it can also be used to prove security of MPC against a malicious adversary. We formalize additive and replicated secret sharing schemes and apply them to Maurer's MPC protocol for secure addition and multiplication. Our method extends to general polynomial functions. We follow the insights from EasyCrypt that security proofs can be often be reduced to proofs about program equivalence, a topic that is well understood in the verification of programming languages. In particular, we show that in the passive case the non-interference-based definition is equivalent to a standard game-based security definition. For the active case we provide a new NI definition, which we call input independence.", "published": "2018-06-19T12:55:38Z", "authors": ["Helene Haagh", "Aleksandr Karbyshev", "Sabine Oechsner", "Bas Spitters", "Pierre-Yves Strub"], "categories": ["cs.LO", "cs.CR"], "doi": "10.1109/CSF.2018.00016", "_query": "property valuation computer vision", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2510.16765v2", "title": "WaMaIR: Image Restoration via Multiscale Wavelet Convolutions and Mamba-based Channel Modeling with Texture Enhancement", "abstract": "Image restoration is a fundamental and challenging task in computer vision, where CNN-based frameworks demonstrate significant computational efficiency. However, previous CNN-based methods often face challenges in adequately restoring fine texture details, which are limited by the small receptive field of CNN structures and the lack of channel feature modeling. In this paper, we propose WaMaIR, which is a novel framework with a large receptive field for image perception and improves the reconstruction of texture details in restored images. Specifically, we introduce the Global Multiscale Wavelet Transform Convolutions (GMWTConvs) for expandding the receptive field to extract image features, preserving and enriching texture features in model inputs. Meanwhile, we propose the Mamba-Based Channel-Aware Module (MCAM), explicitly designed to capture long-range dependencies within feature channels, which enhancing the model sensitivity to color, edges, and texture information. Additionally, we propose Multiscale Texture Enhancement Loss (MTELoss) for image restoration to guide the model in preserving detailed texture structures effectively. Extensive experiments confirm that WaMaIR outperforms state-of-the-art methods, achieving better image restoration and efficient computational performance of the model.", "published": "2025-10-19T09:11:58Z", "authors": ["Shengyu Zhu", "Congyi Fan", "Fuxuan Zhang"], "categories": ["cs.CV"], "doi": "", "_query": "property valuation computer vision", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/1906.05352v1", "title": "Uncovering Dominant Social Class in Neighborhoods through Building Footprints: A Case Study of Residential Zones in Massachusetts using Computer Vision", "abstract": "In urban theory, urban form is related to social and economic status. This paper explores to uncover zip-code level income through urban form by analyzing figure-ground map, a simple, prevailing and precise representation of urban form in the field of urban study. Deep learning in computer vision enables such representation maps to be studied at a large scale. We propose to train a DCNN model to identify and uncover the internal bridge between social class and urban form. Further, using hand-crafted informative visual features related with urban form properties (building size, building density, etc.), we apply a random forest classifier to interpret how morphological properties are related with social class.", "published": "2019-06-12T19:47:09Z", "authors": ["Qianhui Liang", "Zhoutong Wang"], "categories": ["cs.CV", "cs.CY"], "doi": "", "_query": "property valuation computer vision", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2507.23357v2", "title": "Foundations and Models in Modern Computer Vision: Key Building Blocks in Landmark Architectures", "abstract": "This report analyzes the evolution of key design patterns in computer vision by examining six influential papers. The analysis begins with foundational architectures for image recognition. We review ResNet, which introduced residual connections to overcome the vanishing gradient problem and enable effective training of significantly deeper convolutional networks. Subsequently, we examine the Vision Transformer (ViT), which established a new paradigm by applying the Transformer architecture to sequences of image patches, demonstrating the efficacy of attention-based models for large-scale image recognition. Building on these visual representation backbones, we investigate generative models. Generative Adversarial Networks (GANs) are analyzed for their novel adversarial training process, which challenges a generator against a discriminator to learn complex data distributions. Then, Latent Diffusion Models (LDMs) are covered, which improve upon prior generative methods by performing a sequential denoising process in a perceptually compressed latent space. LDMs achieve high-fidelity synthesis with greater computational efficiency, representing the current state-of-the-art for image generation. Finally, we explore self-supervised learning techniques that reduce dependency on labeled data. DINO is a self-distillation framework in which a student network learns to match the output of a momentum-updated teacher, yielding features with strong k-NN classification performance. We conclude with Masked Autoencoders (MAE), which utilize an asymmetric encoder-decoder design to reconstruct heavily masked inputs, providing a highly scalable and effective method for pre-training large-scale vision models.", "published": "2025-07-31T09:08:11Z", "authors": ["Radu-Andrei Bourceanu", "Neil De La Fuente", "Jan Grimm", "Andrei Jardan", "Andriy Manucharyan", "Cornelius Weiss", "Daniel Cremers", "Roman Pflugfelder"], "categories": ["cs.CV"], "doi": "", "_query": "property valuation computer vision", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2509.12047v2", "title": "A Computer Vision Pipeline for Individual-Level Behavior Analysis: Benchmarking on the Edinburgh Pig Dataset", "abstract": "Animal behavior analysis plays a crucial role in understanding animal welfare, health status, and productivity in agricultural settings. However, traditional manual observation methods are time-consuming, subjective, and limited in scalability. We present a modular pipeline that leverages open-sourced state-of-the-art computer vision techniques to automate animal behavior analysis in a group housing environment. Our approach combines state-of-the-art models for zero-shot object detection, motion-aware segmentation and tracking, and advanced feature extraction using vision transformers for robust behavior recognition. The pipeline addresses challenges including animal occlusions and group housing scenarios, as demonstrated in indoor pig monitoring. We validated our system on the Edinburgh Pig Behavior Video Dataset for multiple behavioral tasks. Our temporal model achieved 94.2% overall accuracy, representing a 21.2 percentage point improvement over existing methods. The pipeline demonstrated robust tracking capabilities with a 93.3% identity preservation (IDF1) score and an 89.3% average precision (AP) for object detection. The modular design suggests potential for adaptation to other contexts, though further validation across species would be required. The open-source implementation provides a scalable solution for behavior monitoring, contributing to precision pig farming and welfare assessment through automated, objective, and continuous analysis.", "published": "2025-09-15T15:31:12Z", "authors": ["Haiyu Yang", "Enhong Liu", "Jennifer Sun", "Sumit Sharma", "Meike van Leerdam", "Sebastien Franceschini", "Puchun Niu", "Miel Hostens"], "categories": ["cs.CV", "cs.AI"], "doi": "10.1038/s41598-026-67803-4", "_query": "property valuation computer vision", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2201.10700v2", "title": "Deep Image Deblurring: A Survey", "abstract": "Image deblurring is a classic problem in low-level computer vision with the aim to recover a sharp image from a blurred input image. Advances in deep learning have led to significant progress in solving this problem, and a large number of deblurring networks have been proposed. This paper presents a comprehensive and timely survey of recently published deep-learning based image deblurring approaches, aiming to serve the community as a useful literature review. We start by discussing common causes of image blur, introduce benchmark datasets and performance metrics, and summarize different problem formulations. Next, we present a taxonomy of methods using convolutional neural networks (CNN) based on architecture, loss function, and application, offering a detailed review and comparison. In addition, we discuss some domain-specific deblurring applications including face images, text, and stereo image pairs. We conclude by discussing key challenges and future research directions.", "published": "2022-01-26T01:31:30Z", "authors": ["Kaihao Zhang", "Wenqi Ren", "Wenhan Luo", "Wei-Sheng Lai", "Bjorn Stenger", "Ming-Hsuan Yang", "Hongdong Li"], "categories": ["cs.CV"], "doi": "", "_query": "property valuation computer vision", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/1612.06340v2", "title": "Computing Human-Understandable Strategies", "abstract": "Algorithms for equilibrium computation generally make no attempt to ensure that the computed strategies are understandable by humans. For instance the strategies for the strongest poker agents are represented as massive binary files. In many situations, we would like to compute strategies that can actually be implemented by humans, who may have computational limitations and may only be able to remember a small number of features or components of the strategies that have been computed. We study poker games where private information distributions can be arbitrary. We create a large training set of game instances and solutions, by randomly selecting the information probabilities, and present algorithms that learn from the training instances in order to perform well in games with unseen information distributions. We are able to conclude several new fundamental rules about poker strategy that can be easily implemented by humans.", "published": "2016-12-19T20:40:19Z", "authors": ["Sam Ganzfried", "Farzana Yusuf"], "categories": ["cs.GT", "cs.AI", "cs.LG", "cs.MA", "stat.ML"], "doi": "", "_query": "property valuation computer vision", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2103.15358v2", "title": "Multi-Scale Vision Longformer: A New Vision Transformer for High-Resolution Image Encoding", "abstract": "This paper presents a new Vision Transformer (ViT) architecture Multi-Scale Vision Longformer, which significantly enhances the ViT of \\cite{dosovitskiy2020image} for encoding high-resolution images using two techniques. The first is the multi-scale model structure, which provides image encodings at multiple scales with manageable computational cost. The second is the attention mechanism of vision Longformer, which is a variant of Longformer \\cite{beltagy2020longformer}, originally developed for natural language processing, and achieves a linear complexity w.r.t. the number of input tokens. A comprehensive empirical study shows that the new ViT significantly outperforms several strong baselines, including the existing ViT models and their ResNet counterparts, and the Pyramid Vision Transformer from a concurrent work \\cite{wang2021pyramid}, on a range of vision tasks, including image classification, object detection, and segmentation. The models and source code are released at \\url{https://github.com/microsoft/vision-longformer}.", "published": "2021-03-29T06:23:20Z", "authors": ["Pengchuan Zhang", "Xiyang Dai", "Jianwei Yang", "Bin Xiao", "Lu Yuan", "Lei Zhang", "Jianfeng Gao"], "categories": ["cs.CV", "cs.AI", "cs.LG"], "doi": "", "_query": "property valuation computer vision", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/math/9902151v5", "title": "Homotopy invariants of higher dimensional categories and concurrency in computer science", "abstract": "The strict globular $ω$-categories formalize the execution paths of a parallel automaton and the homotopies between them. One associates to such (and any) $ω$-category $\\C$ three homology theories. The first one is called the globular homology. It contains the oriented loops of $\\C$. The two other ones are called the negative (resp. positive) corner homology. They contain in a certain manner the branching areas of execution paths or negative corners (resp. the merging areas of execution paths or positive corners) of $\\C$. Two natural linear maps called the negative (resp. the positive) Hurewicz morphism from the globular homology to the negative (resp. positive) corner homology are constructed. We explain the reason why these constructions allow to reinterprete some geometric problems coming from computer science.", "published": "1999-02-26T10:06:27Z", "authors": ["Philippe Gaucher"], "categories": ["math.CT", "cs.OH", "math.AT"], "doi": "10.1017/S0960129500003182", "_query": "property valuation computer vision", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/1802.01516v1", "title": "3D non-rigid registration using color: Color Coherent Point Drift", "abstract": "Research into object deformations using computer vision techniques has been under intense study in recent years. A widely used technique is 3D non-rigid registration to estimate the transformation between two instances of a deforming structure. Despite many previous developments on this topic, it remains a challenging problem. In this paper we propose a novel approach to non-rigid registration combining two data spaces in order to robustly calculate the correspondences and transformation between two data sets. In particular, we use point color as well as 3D location as these are the common outputs of RGB-D cameras. We have propose the Color Coherent Point Drift (CCPD) algorithm (an extension of the CPD method [1]). Evaluation is performed using synthetic and real data. The synthetic data includes easy shapes that allow evaluation of the effect of noise, outliers and missing data. Moreover, an evaluation of realistic figures obtained using Blensor is carried out. Real data acquired using a general purpose Primesense Carmine sensor is used to validate the CCPD for real shapes. For all tests, the proposed method is compared to the original CPD showing better results in registration accuracy in most cases.", "published": "2018-02-05T17:14:09Z", "authors": ["Marcelo Saval-Calvo", "Jorge Azorin-Lopez", "Andres Fuster-Guillo", "Victor Villena-Martinez", "Robert B. Fisher"], "categories": ["cs.CV"], "doi": "10.1016/j.cviu.2018.01.008", "_query": "property valuation computer vision", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/math/0304338v1", "title": "Algebraic structures on valuations, their properties and applications", "abstract": "We describe various structures of algebraic nature on the space of continuous valuations on convex sets, their properties (like versions of Poincaré duality and hard Lefschetz theorem), and their relations and applications to integral geometry.", "published": "2003-04-22T21:27:27Z", "authors": ["Semyon Alesker"], "categories": ["math.MG"], "doi": "", "_query": "property valuation computer vision", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/1806.05984v1", "title": "Ego-Lane Analysis System (ELAS): Dataset and Algorithms", "abstract": "Decreasing costs of vision sensors and advances in embedded hardware boosted lane related research detection, estimation, and tracking in the past two decades. The interest in this topic has increased even more with the demand for advanced driver assistance systems (ADAS) and self-driving cars. Although extensively studied independently, there is still need for studies that propose a combined solution for the multiple problems related to the ego-lane, such as lane departure warning (LDW), lane change detection, lane marking type (LMT) classification, road markings detection and classification, and detection of adjacent lanes (i.e., immediate left and right lanes) presence. In this paper, we propose a real-time Ego-Lane Analysis System (ELAS) capable of estimating ego-lane position, classifying LMTs and road markings, performing LDW and detecting lane change events. The proposed vision-based system works on a temporal sequence of images. Lane marking features are extracted in perspective and Inverse Perspective Mapping (IPM) images that are combined to increase robustness. The final estimated lane is modeled as a spline using a combination of methods (Hough lines with Kalman filter and spline with particle filter). Based on the estimated lane, all other events are detected. To validate ELAS and cover the lack of lane datasets in the literature, a new dataset with more than 20 different scenes (in more than 15,000 frames) and considering a variety of scenarios (urban road, highways, traffic, shadows, etc.) was created. The dataset was manually annotated and made publicly available to enable evaluation of several events that are of interest for the research community (i.e., lane estimation, change, and centering; road markings; intersections; LMTs; crosswalks and adjacent lanes). ELAS achieved high detection rates in all real-world events and proved to be ready for real-time applications.", "published": "2018-06-15T14:02:44Z", "authors": ["Rodrigo F. Berriel", "Edilson de Aguiar", "Alberto F. de Souza", "Thiago Oliveira-Santos"], "categories": ["cs.CV"], "doi": "10.1016/J.IMAVIS.2017.07.005", "_query": "property valuation computer vision", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2106.02874v3", "title": "RDA: Robust Domain Adaptation via Fourier Adversarial Attacking", "abstract": "Unsupervised domain adaptation (UDA) involves a supervised loss in a labeled source domain and an unsupervised loss in an unlabeled target domain, which often faces more severe overfitting (than classical supervised learning) as the supervised source loss has clear domain gap and the unsupervised target loss is often noisy due to the lack of annotations. This paper presents RDA, a robust domain adaptation technique that introduces adversarial attacking to mitigate overfitting in UDA. We achieve robust domain adaptation by a novel Fourier adversarial attacking (FAA) method that allows large magnitude of perturbation noises but has minimal modification of image semantics, the former is critical to the effectiveness of its generated adversarial samples due to the existence of 'domain gaps'. Specifically, FAA decomposes images into multiple frequency components (FCs) and generates adversarial samples by just perturbating certain FCs that capture little semantic information. With FAA-generated samples, the training can continue the 'random walk' and drift into an area with a flat loss landscape, leading to more robust domain adaptation. Extensive experiments over multiple domain adaptation tasks show that RDA can work with different computer vision tasks with superior performance.", "published": "2021-06-05T11:38:41Z", "authors": ["Jiaxing Huang", "Dayan Guan", "Aoran Xiao", "Shijian Lu"], "categories": ["cs.CV"], "doi": "", "_query": "property valuation computer vision", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2608.19976v2", "title": "Random valuations", "abstract": "A valuation is a finitely additive function on the family of compact convex sets in $\\mathbb{R}^d$. We study non-negative infinitely divisible random valuations, with particular emphasis on monotone, $σ$-continuous models with independent increments along nested families. After separating the deterministic part, we show that the Lévy measure of such a valuation is generated by pairs $(F,r)$, where $F$ is a non-empty closed convex set and $r>0$, with each pair contributing $r\\mathbf{1}_{F\\cap K=\\emptyset}$. This yields a Poisson representation and an equivalent formulation through a pure-jump completely random measure on the space of closed convex sets. For stationary valuations, we derive a cylinder-Grassmannian representation of the Lévy measure. In the stationary isotropic case, we obtain a McMullen-type decomposition, at the level of one-dimensional distributions, into independent components stable under dilation of the argument.", "published": "2026-08-20T12:50:11Z", "authors": ["Andrii Ilienko", "Ilya Molchanov", "Tommaso Visonà"], "categories": ["math.PR", "math.MG"], "doi": "", "_query": "property valuation computer vision", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2105.15041v1", "title": "Scorpion detection and classification systems based on computer vision and deep learning for health security purposes", "abstract": "In this paper, two novel automatic and real-time systems for the detection and classification of two genera of scorpions found in La Plata city (Argentina) were developed using computer vision and deep learning techniques. The object detection technique was implemented with two different methods, YOLO (You Only Look Once) and MobileNet, based on the shape features of the scorpions. High accuracy values of 88% and 91%, and high recall values of 90% and 97%, have been achieved for both models, respectively, which guarantees that they can successfully detect scorpions. In addition, the MobileNet method has been shown to have excellent performance to detect scorpions within an uncontrolled environment and to perform multiple detections. The MobileNet model was also used for image classification in order to successfully distinguish between dangerous scorpion (Tityus) and non-dangerous scorpion (Bothriurus) with the purpose of providing a health security tool. Applications for smartphones were developed, with the advantage of the portability of the systems, which can be used as a help tool for emergency services, or for biological research purposes. The developed systems can be easily scalable to other genera and species of scorpions to extend the region where these applications can be used.", "published": "2021-05-31T15:26:09Z", "authors": ["Francisco Luis Giambelluca", "Marcelo A. Cappelletti", "Jorge Osio", "Luis A. Giambelluca"], "categories": ["cs.CV", "cs.AI", "eess.IV"], "doi": "", "_query": "property valuation computer vision", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2503.14430v1", "title": "Joint Image-Instance Spatial-Temporal Attention for Few-shot Action Recognition", "abstract": "Few-shot Action Recognition (FSAR) constitutes a crucial challenge in computer vision, entailing the recognition of actions from a limited set of examples. Recent approaches mainly focus on employing image-level features to construct temporal dependencies and generate prototypes for each action category. However, a considerable number of these methods utilize mainly image-level features that incorporate background noise and focus insufficiently on real foreground (action-related instances), thereby compromising the recognition capability, particularly in the few-shot scenario. To tackle this issue, we propose a novel joint Image-Instance level Spatial-temporal attention approach (I2ST) for Few-shot Action Recognition. The core concept of I2ST is to perceive the action-related instances and integrate them with image features via spatial-temporal attention. Specifically, I2ST consists of two key components: Action-related Instance Perception and Joint Image-Instance Spatial-temporal Attention. Given the basic representations from the feature extractor, the Action-related Instance Perception is introduced to perceive action-related instances under the guidance of a text-guided segmentation model. Subsequently, the Joint Image-Instance Spatial-temporal Attention is used to construct the feature dependency between instances and images...", "published": "2025-03-11T12:52:05Z", "authors": ["Zefeng Qian", "Chongyang Zhang", "Yifei Huang", "Gang Wang", "Jiangyong Ying"], "categories": ["cs.CV"], "doi": "10.1016/j.cviu.2025.104322", "_query": "property valuation computer vision", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2307.13361v1", "title": "Of Mice and Pose: 2D Mouse Pose Estimation from Unlabelled Data and Synthetic Prior", "abstract": "Numerous fields, such as ecology, biology, and neuroscience, use animal recordings to track and measure animal behaviour. Over time, a significant volume of such data has been produced, but some computer vision techniques cannot explore it due to the lack of annotations. To address this, we propose an approach for estimating 2D mouse body pose from unlabelled images using a synthetically generated empirical pose prior. Our proposal is based on a recent self-supervised method for estimating 2D human pose that uses single images and a set of unpaired typical 2D poses within a GAN framework. We adapt this method to the limb structure of the mouse and generate the empirical prior of 2D poses from a synthetic 3D mouse model, thereby avoiding manual annotation. In experiments on a new mouse video dataset, we evaluate the performance of the approach by comparing pose predictions to a manually obtained ground truth. We also compare predictions with those from a supervised state-of-the-art method for animal pose estimation. The latter evaluation indicates promising results despite the lack of paired training data. Finally, qualitative results using a dataset of horse images show the potential of the setting to adapt to other animal species.", "published": "2023-07-25T09:31:55Z", "authors": ["Jose Sosa", "Sharn Perry", "Jane Alty", "David Hogg"], "categories": ["cs.CV"], "doi": "", "_query": "property valuation computer vision", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2404.01654v1", "title": "AI WALKUP: A Computer-Vision Approach to Quantifying MDS-UPDRS in Parkinson's Disease", "abstract": "Parkinson's Disease (PD) is the second most common neurodegenerative disorder. The existing assessment method for PD is usually the Movement Disorder Society - Unified Parkinson's Disease Rating Scale (MDS-UPDRS) to assess the severity of various types of motor symptoms and disease progression. However, manual assessment suffers from high subjectivity, lack of consistency, and high cost and low efficiency of manual communication. We want to use a computer vision based solution to capture human pose images based on a camera, reconstruct and perform motion analysis using algorithms, and extract the features of the amount of motion through feature engineering. The proposed approach can be deployed on different smartphones, and the video recording and artificial intelligence analysis can be done quickly and easily through our APP.", "published": "2024-04-02T05:53:34Z", "authors": ["Xiang Xiang", "Zihan Zhang", "Jing Ma", "Yao Deng"], "categories": ["cs.CV", "cs.AI", "eess.IV", "eess.SP"], "doi": "", "_query": "property valuation computer vision", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/1802.00509v1", "title": "Learning Semantic Segmentation with Diverse Supervision", "abstract": "Models based on deep convolutional neural networks (CNN) have significantly improved the performance of semantic segmentation. However, learning these models requires a large amount of training images with pixel-level labels, which are very costly and time-consuming to collect. In this paper, we propose a method for learning CNN-based semantic segmentation models from images with several types of annotations that are available for various computer vision tasks, including image-level labels for classification, box-level labels for object detection and pixel-level labels for semantic segmentation. The proposed method is flexible and can be used together with any existing CNN-based semantic segmentation networks. Experimental evaluation on the challenging PASCAL VOC 2012 and SIFT-flow benchmarks demonstrate that the proposed method can effectively make use of diverse training data to improve the performance of the learned models.", "published": "2018-02-01T22:26:24Z", "authors": ["Linwei Ye", "Zhi Liu", "Yang Wang"], "categories": ["cs.CV"], "doi": "", "_query": "property valuation computer vision", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/1904.03472v1", "title": "Few-Shot Learning via Saliency-guided Hallucination of Samples", "abstract": "Learning new concepts from a few of samples is a standard challenge in computer vision. The main directions to improve the learning ability of few-shot training models include (i) a robust similarity learning and (ii) generating or hallucinating additional data from the limited existing samples. In this paper, we follow the latter direction and present a novel data hallucination model. Currently, most datapoint generators contain a specialized network (i.e., GAN) tasked with hallucinating new datapoints, thus requiring large numbers of annotated data for their training in the first place. In this paper, we propose a novel less-costly hallucination method for few-shot learning which utilizes saliency maps. To this end, we employ a saliency network to obtain the foregrounds and backgrounds of available image samples and feed the resulting maps into a two-stream network to hallucinate datapoints directly in the feature space from viable foreground-background combinations. To the best of our knowledge, we are the first to leverage saliency maps for such a task and we demonstrate their usefulness in hallucinating additional datapoints for few-shot learning. Our proposed network achieves the state of the art on publicly available datasets.", "published": "2019-04-06T15:33:39Z", "authors": ["Hongguang Zhang", "Jing Zhang", "Piotr Koniusz"], "categories": ["cs.CV"], "doi": "", "_query": "property valuation computer vision", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2504.14292v1", "title": "Optimal Operation and Valuation of Electricity Storages", "abstract": "This paper applies computational techniques of convex stochastic optimization to optimal operation and valuation of electricity storages in the face of uncertain electricity prices. Our approach is applicable to various specifications of storages, and it allows for e.g.\\ hard constraints on storage capacity and charging speed. Our valuations are based on the indifference pricing principle, which builds on optimal trading strategies and calibrates to the user's initial position, market views and risk preferences. We illustrate the effects of storage capacity and charging speed by numerically computing the valuations using stochastic dual dynamic programming.", "published": "2025-04-19T13:35:33Z", "authors": ["Jean-Philippe Chancelier", "Michel De Lara", "François Pacaud", "Teemu Pennanen", "Ari-Pekka Perkkiö"], "categories": ["math.OC"], "doi": "", "_query": "property valuation computer vision", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2603.12823v1", "title": "Adaptive Vision-Language Model Routing for Computer Use Agents", "abstract": "Computer Use Agents (CUAs) translate natural-language instructions into Graphical User Interface (GUI) actions such as clicks, keystrokes, and scrolls by relying on a Vision-Language Model (VLM) to interpret screenshots and predict grounded tool calls. However, grounding accuracy varies dramatically across VLMs, while current CUA systems typically route every action to a single fixed model regardless of difficulty. We propose \\textbf{Adaptive VLM Routing} (AVR), a framework that inserts a lightweight semantic routing layer between the CUA orchestrator and a pool of VLMs. For each tool call, AVR estimates action difficulty from multimodal embeddings, probes a small VLM to measure confidence, and routes the action to the cheapest model whose predicted accuracy satisfies a target reliability threshold. For \\textit{warm} agents with memory of prior UI interactions, retrieved context further narrows the capability gap between small and large models, allowing many actions to be handled without escalation. We formalize routing as a cost--accuracy trade-off, derive a threshold-based policy for model selection, and evaluate AVR using ScreenSpot-Pro grounding data together with the OpenClaw agent routing benchmark. Across these settings, AVR projects inference cost reductions of up to 78\\% while staying within 2 percentage points of an all-large-model baseline. When combined with the Visual Confused Deputy guardrail, AVR also escalates high-risk actions directly to the strongest available model, unifying efficiency and safety within a single routing framework. Materials are also provided Model, benchmark, and code: https://github.com/vllm-project/semantic-router.", "published": "2026-03-13T09:21:25Z", "authors": ["Xunzhuo Liu", "Bowei He", "Xue Liu", "Andy Luo", "Haichen Zhang", "Huamin Chen"], "categories": ["cs.CL", "cs.CV"], "doi": "", "_query": "property valuation computer vision", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2006.12709v1", "title": "CIE XYZ Net: Unprocessing Images for Low-Level Computer Vision Tasks", "abstract": "Cameras currently allow access to two image states: (i) a minimally processed linear raw-RGB image state (i.e., raw sensor data) or (ii) a highly-processed nonlinear image state (e.g., sRGB). There are many computer vision tasks that work best with a linear image state, such as image deblurring and image dehazing. Unfortunately, the vast majority of images are saved in the nonlinear image state. Because of this, a number of methods have been proposed to \"unprocess\" nonlinear images back to a raw-RGB state. However, existing unprocessing methods have a drawback because raw-RGB images are sensor-specific. As a result, it is necessary to know which camera produced the sRGB output and use a method or network tailored for that sensor to properly unprocess it. This paper addresses this limitation by exploiting another camera image state that is not available as an output, but it is available inside the camera pipeline. In particular, cameras apply a colorimetric conversion step to convert the raw-RGB image to a device-independent space based on the CIE XYZ color space before they apply the nonlinear photo-finishing. Leveraging this canonical image state, we propose a deep learning framework, CIE XYZ Net, that can unprocess a nonlinear image back to the canonical CIE XYZ image. This image can then be processed by any low-level computer vision operator and re-rendered back to the nonlinear image. We demonstrate the usefulness of the CIE XYZ Net on several low-level vision tasks and show significant gains that can be obtained by this processing framework. Code and dataset are publicly available at https://github.com/mahmoudnafifi/CIE_XYZ_NET.", "published": "2020-06-23T02:59:11Z", "authors": ["Mahmoud Afifi", "Abdelrahman Abdelhamed", "Abdullah Abuolaim", "Abhijith Punnappurath", "Michael S. Brown"], "categories": ["cs.CV", "eess.IV"], "doi": "", "_query": "property valuation computer vision", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2508.12794v2", "title": "Vehicle detection from GSV imagery: Predicting travel behaviour for cycling and motorcycling using Computer Vision", "abstract": "Transportation influence health by shaping exposure to physical activity, air pollution and injury risk. Comparative data on cycling and motorcycling behaviours is scarce, particularly at a global scale. Street view imagery, such as Google Street View (GSV), combined with computer vision, is a valuable resource for efficiently capturing travel behaviour data. This study demonstrates a novel approach using deep learning on street view images to estimate cycling and motorcycling levels across diverse cities worldwide. We utilized data from 185 global cities. The data on mode shares of cycling and motorcycling estimated using travel surveys or censuses. We used GSV images to detect cycles and motorcycles in sampled locations, using 8000 images per city. The YOLOv4 model, fine-tuned using images from six cities, achieved a mean average precision of 89% for detecting cycles and motorcycles. A global prediction model was developed using beta regression with city-level mode shares as outcome, with log transformed explanatory variables of counts of GSV-detected images with cycles and motorcycles, while controlling for population density. We found strong correlations between GSV motorcycle counts and motorcycle mode share (0.78) and moderate correlations between GSV cycle counts and cycling mode share (0.51). Beta regression models predicted mode shares with $R^2$ values of 0.614 for cycling and 0.612 for motorcycling, achieving median absolute errors (MDAE) of 1.3% and 1.4%, respectively. Scatterplots demonstrated consistent prediction accuracy, though cities like Utrecht and Cali were outliers. The model was applied to 60 cities globally for which we didn't have recent mode share data. We provided estimates for some cities in the Middle East, Latin America and East Asia. With computer vision, GSV images capture travel modes and activity, providing insights alongside traditional data sources.", "published": "2025-08-18T10:17:30Z", "authors": [" Kyriaki", " Kokka", "Rahul Goel", "Ali Abbas", "Kerry A. Nice", "Luca Martial", "SM Labib", "Rihuan Ke", "Carola Bibiane Schönlieb", "James Woodcock"], "categories": ["cs.CV", "cs.AI"], "doi": "", "_query": "property valuation computer vision", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2305.15097v1", "title": "Computer Vision for Construction Progress Monitoring: A Real-Time Object Detection Approach", "abstract": "Construction progress monitoring (CPM) is essential for effective project management, ensuring on-time and on-budget delivery. Traditional CPM methods often rely on manual inspection and reporting, which are time-consuming and prone to errors. This paper proposes a novel approach for automated CPM using state-of-the-art object detection algorithms. The proposed method leverages e.g. YOLOv8's real-time capabilities and high accuracy to identify and track construction elements within site images and videos. A dataset was created, consisting of various building elements and annotated with relevant objects for training and validation. The performance of the proposed approach was evaluated using standard metrics, such as precision, recall, and F1-score, demonstrating significant improvement over existing methods. The integration of Computer Vision into CPM provides stakeholders with reliable, efficient, and cost-effective means to monitor project progress, facilitating timely decision-making and ultimately contributing to the successful completion of construction projects.", "published": "2023-05-24T12:27:42Z", "authors": ["Jiesheng Yang", "Andreas Wilde", "Karsten Menzel", "Md Zubair Sheikh", "Boris Kuznetsov"], "categories": ["cs.CV", "cs.AI"], "doi": "", "_query": "property valuation computer vision", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2203.08215v2", "title": "Auto-Gait: Automatic Ataxia Risk Assessment with Computer Vision on Gait Task Videos", "abstract": "In this paper, we investigated whether we can 1) detect participants with ataxia-specific gait characteristics (risk-prediction), and 2) assess severity of ataxia from gait (severity-assessment) using computer vision. We created a dataset of 155 videos from 89 participants, 24 controls and 65 diagnosed with (or are pre-manifest) spinocerebellar ataxias (SCAs), performing the gait task of the Scale for the Assessment and Rating of Ataxia (SARA) from 11 medical sites located in 8 different states across the United States. We develop a computer vision pipeline to detect, track, and separate out the participants from their surroundings and construct several features from their body pose coordinates to capture gait characteristics like step width, step length, swing, stability, speed, etc. Our risk-prediction model achieves 83.06% accuracy and an 80.23% F1 score. Similarly, our severity-assessment model achieves a mean absolute error (MAE) score of 0.6225 and a Pearson's correlation coefficient score of 0.7268. Our models still performed competitively when evaluated on data from sites not used during training. Furthermore, through feature importance analysis, we found that our models associate wider steps, decreased walking speed, and increased instability with greater ataxia severity, which is consistent with previously established clinical knowledge. Our models create possibilities for remote ataxia assessment in non-clinical settings in the future, which could significantly improve accessibility of ataxia care. Furthermore, our underlying dataset was assembled from a geographically diverse cohort, highlighting its potential to further increase equity. The code used in this study is open to the public, and the anonymized body pose landmark dataset is also available upon request.", "published": "2022-03-15T19:28:10Z", "authors": ["Wasifur Rahman", "Masum Hasan", "Md Saiful Islam", "Titilayo Olubajo", "Jeet Thaker", "Abdelrahman Abdelkader", "Phillip Yang", "Tetsuo Ashizawa", "Ehsan Hoque"], "categories": ["cs.CV", "cs.LG"], "doi": "", "_query": "property valuation computer vision", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2402.08922v3", "title": "The Mirrored Influence Hypothesis: Efficient Data Influence Estimation by Harnessing Forward Passes", "abstract": "Large-scale black-box models have become ubiquitous across numerous applications. Understanding the influence of individual training data sources on predictions made by these models is crucial for improving their trustworthiness. Current influence estimation techniques involve computing gradients for every training point or repeated training on different subsets. These approaches face obvious computational challenges when scaled up to large datasets and models. In this paper, we introduce and explore the Mirrored Influence Hypothesis, highlighting a reciprocal nature of influence between training and test data. Specifically, it suggests that evaluating the influence of training data on test predictions can be reformulated as an equivalent, yet inverse problem: assessing how the predictions for training samples would be altered if the model were trained on specific test samples. Through both empirical and theoretical validations, we demonstrate the wide applicability of our hypothesis. Inspired by this, we introduce a new method for estimating the influence of training data, which requires calculating gradients for specific test samples, paired with a forward pass for each training point. This approach can capitalize on the common asymmetry in scenarios where the number of test samples under concurrent examination is much smaller than the scale of the training dataset, thus gaining a significant improvement in efficiency compared to existing approaches. We demonstrate the applicability of our method across a range of scenarios, including data attribution in diffusion models, data leakage detection, analysis of memorization, mislabeled data detection, and tracing behavior in language models. Our code will be made available at https://github.com/ruoxi-jia-group/Forward-INF.", "published": "2024-02-14T03:43:05Z", "authors": ["Myeongseob Ko", "Feiyang Kang", "Weiyan Shi", "Ming Jin", "Zhou Yu", "Ruoxi Jia"], "categories": ["cs.LG", "stat.ML"], "doi": "", "_query": "property valuation computer vision", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2012.08375v1", "title": "Practical Auto-Calibration for Spatial Scene-Understanding from Crowdsourced Dashcamera Videos", "abstract": "Spatial scene-understanding, including dense depth and ego-motion estimation, is an important problem in computer vision for autonomous vehicles and advanced driver assistance systems. Thus, it is beneficial to design perception modules that can utilize crowdsourced videos collected from arbitrary vehicular onboard or dashboard cameras. However, the intrinsic parameters corresponding to such cameras are often unknown or change over time. Typical manual calibration approaches require objects such as a chessboard or additional scene-specific information. On the other hand, automatic camera calibration does not have such requirements. Yet, the automatic calibration of dashboard cameras is challenging as forward and planar navigation results in critical motion sequences with reconstruction ambiguities. Structure reconstruction of complete visual-sequences that may contain tens of thousands of images is also computationally untenable. Here, we propose a system for practical monocular onboard camera auto-calibration from crowdsourced videos. We show the effectiveness of our proposed system on the KITTI raw, Oxford RobotCar, and the crowdsourced D$^2$-City datasets in varying conditions. Finally, we demonstrate its application for accurate monocular dense depth and ego-motion estimation on uncalibrated videos.", "published": "2020-12-15T15:38:17Z", "authors": ["Hemang Chawla", "Matti Jukola", "Shabbir Marzban", "Elahe Arani", "Bahram Zonooz"], "categories": ["cs.CV", "cs.RO"], "doi": "10.5220/0010255808690880", "_query": "property valuation computer vision", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2506.00836v1", "title": "Advancing from Automated to Autonomous Beamline by Leveraging Computer Vision", "abstract": "The synchrotron light source, a cutting-edge large-scale user facility, requires autonomous synchrotron beamline operations, a crucial technique that should enable experiments to be conducted automatically, reliably, and safely with minimum human intervention. However, current state-of-the-art synchrotron beamlines still heavily rely on human safety oversight. To bridge the gap between automated and autonomous operation, a computer vision-based system is proposed, integrating deep learning and multiview cameras for real-time collision detection. The system utilizes equipment segmentation, tracking, and geometric analysis to assess potential collisions with transfer learning that enhances robustness. In addition, an interactive annotation module has been developed to improve the adaptability to new object classes. Experiments on a real beamline dataset demonstrate high accuracy, real-time performance, and strong potential for autonomous synchrotron beamline operations.", "published": "2025-06-01T04:53:55Z", "authors": ["Baolu Li", "Hongkai Yu", "Huiming Sun", "Jin Ma", "Yuewei Lin", "Lu Ma", "Yonghua Du"], "categories": ["cs.CV"], "doi": "", "_query": "property valuation computer vision", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2409.10104v1", "title": "A Comparative Study of Open Source Computer Vision Models for Application on Small Data: The Case of CFRP Tape Laying", "abstract": "In the realm of industrial manufacturing, Artificial Intelligence (AI) is playing an increasing role, from automating existing processes to aiding in the development of new materials and techniques. However, a significant challenge arises in smaller, experimental processes characterized by limited training data availability, questioning the possibility to train AI models in such small data contexts. In this work, we explore the potential of Transfer Learning to address this challenge, specifically investigating the minimum amount of data required to develop a functional AI model. For this purpose, we consider the use case of quality control of Carbon Fiber Reinforced Polymer (CFRP) tape laying in aerospace manufacturing using optical sensors. We investigate the behavior of different open-source computer vision models with a continuous reduction of the training data. Our results show that the amount of data required to successfully train an AI model can be drastically reduced, and the use of smaller models does not necessarily lead to a loss of performance.", "published": "2024-09-16T09:07:31Z", "authors": ["Thomas Fraunholz", "Dennis Rall", "Tim Köhler", "Alfons Schuster", "Monika Mayer", "Lars Larsen"], "categories": ["cs.CV", "cs.LG"], "doi": "", "_query": "property valuation computer vision", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/1803.11493v1", "title": "3D Pose Estimation and 3D Model Retrieval for Objects in the Wild", "abstract": "We propose a scalable, efficient and accurate approach to retrieve 3D models for objects in the wild. Our contribution is twofold. We first present a 3D pose estimation approach for object categories which significantly outperforms the state-of-the-art on Pascal3D+. Second, we use the estimated pose as a prior to retrieve 3D models which accurately represent the geometry of objects in RGB images. For this purpose, we render depth images from 3D models under our predicted pose and match learned image descriptors of RGB images against those of rendered depth images using a CNN-based multi-view metric learning approach. In this way, we are the first to report quantitative results for 3D model retrieval on Pascal3D+, where our method chooses the same models as human annotators for 50% of the validation images on average. In addition, we show that our method, which was trained purely on Pascal3D+, retrieves rich and accurate 3D models from ShapeNet given RGB images of objects in the wild.", "published": "2018-03-30T14:47:49Z", "authors": ["Alexander Grabner", "Peter M. Roth", "Vincent Lepetit"], "categories": ["cs.CV", "cs.AI"], "doi": "", "_query": "property valuation computer vision", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2310.00922v1", "title": "How Close are Other Computer Vision Tasks to Deepfake Detection?", "abstract": "In this paper, we challenge the conventional belief that supervised ImageNet-trained models have strong generalizability and are suitable for use as feature extractors in deepfake detection. We present a new measurement, \"model separability,\" for visually and quantitatively assessing a model's raw capacity to separate data in an unsupervised manner. We also present a systematic benchmark for determining the correlation between deepfake detection and other computer vision tasks using pre-trained models. Our analysis shows that pre-trained face recognition models are more closely related to deepfake detection than other models. Additionally, models trained using self-supervised methods are more effective in separation than those trained using supervised methods. After fine-tuning all models on a small deepfake dataset, we found that self-supervised models deliver the best results, but there is a risk of overfitting. Our results provide valuable insights that should help researchers and practitioners develop more effective deepfake detection models.", "published": "2023-10-02T06:32:35Z", "authors": ["Huy H. Nguyen", "Junichi Yamagishi", "Isao Echizen"], "categories": ["cs.CV"], "doi": "", "_query": "property valuation computer vision", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2312.01540v1", "title": "Robust Computer Vision in an Ever-Changing World: A Survey of Techniques for Tackling Distribution Shifts", "abstract": "AI applications are becoming increasingly visible to the general public. There is a notable gap between the theoretical assumptions researchers make about computer vision models and the reality those models face when deployed in the real world. One of the critical reasons for this gap is a challenging problem known as distribution shift. Distribution shifts tend to vary with complexity of the data, dataset size, and application type. In our paper, we discuss the identification of such a prominent gap, exploring the concept of distribution shift and its critical significance. We provide an in-depth overview of various types of distribution shifts, elucidate their distinctions, and explore techniques within the realm of the data-centric domain employed to address them. Distribution shifts can occur during every phase of the machine learning pipeline, from the data collection stage to the stage of training a machine learning model to the stage of final model deployment. As a result, it raises concerns about the overall robustness of the machine learning techniques for computer vision applications that are deployed publicly for consumers. Different deep learning models each tailored for specific type of data and tasks, architectural pipelines; highlighting how variations in data preprocessing and feature extraction can impact robustness., data augmentation strategies (e.g. geometric, synthetic and learning-based); demonstrating their role in enhancing model generalization, and training mechanisms (e.g. transfer learning, zero-shot) fall under the umbrella of data-centric methods. Each of these components form an integral part of the neural-network we analyze contributing uniquely to strengthening model robustness against distribution shifts. We compare and contrast numerous AI models that are built for mitigating shifts in hidden stratification and spurious correlations, ...", "published": "2023-12-03T23:40:12Z", "authors": ["Eashan Adhikarla", "Kai Zhang", "Jun Yu", "Lichao Sun", "John Nicholson", "Brian D. Davison"], "categories": ["cs.CV"], "doi": "", "_query": "property valuation computer vision", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2506.23783v1", "title": "Mamba-FETrack V2: Revisiting State Space Model for Frame-Event based Visual Object Tracking", "abstract": "Combining traditional RGB cameras with bio-inspired event cameras for robust object tracking has garnered increasing attention in recent years. However, most existing multimodal tracking algorithms depend heavily on high-complexity Vision Transformer architectures for feature extraction and fusion across modalities. This not only leads to substantial computational overhead but also limits the effectiveness of cross-modal interactions. In this paper, we propose an efficient RGB-Event object tracking framework based on the linear-complexity Vision Mamba network, termed Mamba-FETrack V2. Specifically, we first design a lightweight Prompt Generator that utilizes embedded features from each modality, together with a shared prompt pool, to dynamically generate modality-specific learnable prompt vectors. These prompts, along with the modality-specific embedded features, are then fed into a Vision Mamba-based FEMamba backbone, which facilitates prompt-guided feature extraction, cross-modal interaction, and fusion in a unified manner. Finally, the fused representations are passed to the tracking head for accurate target localization. Extensive experimental evaluations on multiple RGB-Event tracking benchmarks, including short-term COESOT dataset and long-term datasets, i.e., FE108 and FELT V2, demonstrate the superior performance and efficiency of the proposed tracking framework. The source code and pre-trained models will be released on https://github.com/Event-AHU/Mamba_FETrack", "published": "2025-06-30T12:24:01Z", "authors": ["Shiao Wang", "Ju Huang", "Qingchuan Ma", "Jinfeng Gao", "Chunyi Xu", "Xiao Wang", "Lan Chen", "Bo Jiang"], "categories": ["cs.CV", "cs.AI", "cs.LG"], "doi": "", "_query": "property valuation computer vision", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2103.00191v2", "title": "FisheyeSuperPoint: Keypoint Detection and Description Network for Fisheye Images", "abstract": "Keypoint detection and description is a commonly used building block in computer vision systems particularly for robotics and autonomous driving. However, the majority of techniques to date have focused on standard cameras with little consideration given to fisheye cameras which are commonly used in urban driving and automated parking. In this paper, we propose a novel training and evaluation pipeline for fisheye images. We make use of SuperPoint as our baseline which is a self-supervised keypoint detector and descriptor that has achieved state-of-the-art results on homography estimation. We introduce a fisheye adaptation pipeline to enable training on undistorted fisheye images. We evaluate the performance on the HPatches benchmark, and, by introducing a fisheye based evaluation method for detection repeatability and descriptor matching correctness, on the Oxford RobotCar dataset.", "published": "2021-02-27T11:26:34Z", "authors": ["Anna Konrad", "Ciarán Eising", "Ganesh Sistu", "John McDonald", "Rudi Villing", "Senthil Yogamani"], "categories": ["cs.CV", "cs.RO"], "doi": "", "_query": "property valuation computer vision", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/1903.04407v2", "title": "Accuracy Booster: Performance Boosting using Feature Map Re-calibration", "abstract": "Convolution Neural Networks (CNN) have been extremely successful in solving intensive computer vision tasks. The convolutional filters used in CNNs have played a major role in this success, by extracting useful features from the inputs. Recently researchers have tried to boost the performance of CNNs by re-calibrating the feature maps produced by these filters, e.g., Squeeze-and-Excitation Networks (SENets). These approaches have achieved better performance by Exciting up the important channels or feature maps while diminishing the rest. However, in the process, architectural complexity has increased. We propose an architectural block that introduces much lower complexity than the existing methods of CNN performance boosting while performing significantly better than them. We carry out experiments on the CIFAR, ImageNet and MS-COCO datasets, and show that the proposed block can challenge the state-of-the-art results. Our method boosts the ResNet-50 architecture to perform comparably to the ResNet-152 architecture, which is a three times deeper network, on classification. We also show experimentally that our method is not limited to classification but also generalizes well to other tasks such as object detection.", "published": "2019-03-11T16:16:03Z", "authors": ["Pravendra Singh", "Pratik Mazumder", "Vinay P. Namboodiri"], "categories": ["cs.CV", "cs.AI", "cs.LG"], "doi": "", "_query": "property valuation computer vision", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/1310.3153v1", "title": "Valuation Compressions in VCG-Based Combinatorial Auctions", "abstract": "The focus of classic mechanism design has been on truthful direct-revelation mechanisms. In the context of combinatorial auctions the truthful direct-revelation mechanism that maximizes social welfare is the VCG mechanism. For many valuation spaces computing the allocation and payments of the VCG mechanism, however, is a computationally hard problem. We thus study the performance of the VCG mechanism when bidders are forced to choose bids from a subspace of the valuation space for which the VCG outcome can be computed efficiently. We prove improved upper bounds on the welfare loss for restrictions to additive bids and upper and lower bounds for restrictions to non-additive bids. These bounds show that the welfare loss increases in expressiveness. All our bounds apply to equilibrium concepts that can be computed in polynomial time as well as to learning outcomes.", "published": "2013-10-11T14:58:51Z", "authors": ["Paul Duetting", "Monika Henzinger", "Martin Starnberger"], "categories": ["cs.GT"], "doi": "", "_query": "property valuation computer vision", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/cs/9901007v1", "title": "Universal Object Oriented Languages and Computer Algebra", "abstract": "The universal object oriented languages made programming more simple and efficient. In the article is considered possibilities of using similar methods in computer algebra. A clear and powerful universal language is useful if particular problem was not implemented in standard software packages like REDUCE, MATHEMATICA, etc. and if the using of internal programming languages of the packages looks not very efficient. Functional languages like LISP had some advantages and traditions for algebraic and symbolic manipulations. Functional and object oriented programming are not incompatible ones. An extension of the model of an object for manipulation with pure functions and algebraic expressions is considered.", "published": "1999-01-15T19:28:58Z", "authors": ["Alexander Yu. Vlasov"], "categories": ["cs.PL"], "doi": "", "_query": "property valuation computer vision", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2207.02062v3", "title": "Image Amodal Completion: A Survey", "abstract": "Existing computer vision systems can compete with humans in understanding the visible parts of objects, but still fall far short of humans when it comes to depicting the invisible parts of partially occluded objects. Image amodal completion aims to equip computers with human-like amodal completion functions to understand an intact object despite it being partially occluded. The main purpose of this survey is to provide an intuitive understanding of the research hotspots, key technologies and future trends in the field of image amodal completion. Firstly, we present a comprehensive review of the latest literature in this emerging field, exploring three key tasks in image amodal completion, including amodal shape completion, amodal appearance completion, and order perception. Then we examine popular datasets related to image amodal completion along with their common data collection methods and evaluation metrics. Finally, we discuss real-world applications and future research directions for image amodal completion, facilitating the reader's understanding of the challenges of existing technologies and upcoming research trends.", "published": "2022-07-05T14:13:22Z", "authors": ["Jiayang Ao", "Qiuhong Ke", "Krista A. Ehinger"], "categories": ["cs.CV", "cs.LG"], "doi": "10.1016/j.cviu.2023.103661", "_query": "property valuation computer vision", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/1809.04704v1", "title": "Do-It-Yourself Single Camera 3D Pointer Input Device", "abstract": "We present a new algorithm for single camera 3D reconstruction, or 3D input for human-computer interfaces, based on precise tracking of an elongated object, such as a pen, having a pattern of colored bands. To configure the system, the user provides no more than one labelled image of a handmade pointer, measurements of its colored bands, and the camera's pinhole projection matrix. Other systems are of much higher cost and complexity, requiring combinations of multiple cameras, stereocameras, and pointers with sensors and lights. Instead of relying on information from multiple devices, we examine our single view more closely, integrating geometric and appearance constraints to robustly track the pointer in the presence of occlusion and distractor objects. By probing objects of known geometry with the pointer, we demonstrate acceptable accuracy of 3D localization.", "published": "2018-09-12T23:07:43Z", "authors": ["Bernard Llanos", "Yee-Hong Yang"], "categories": ["cs.CV"], "doi": "10.1109/CRV.2018.00038", "_query": "property valuation computer vision", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2609.18279v1", "title": "Decoder-Agnostic Token Merging for Vision Transformers: A Systematic Study of G2TM", "abstract": "Vision Transformers (ViTs) have achieved state-of-the-art performance across a range of computer vision tasks, mainly thanks to the self-attention mechanism. However, its complexity, increasing quadratically with the number of tokens, remains the major obstacle to ViT efficiency and deployment at scale. Token merging reduces this cost by aggregating redundant tokens. Yet existing methods are typically evaluated within a single architecture, leaving open whether their effectiveness stems from the merging mechanism itself or from the specific decoder they are paired with. We extend Graph-Guided Token Merging (G2TM), a single module inserted early in a ViT-based network, beyond its original Segmenter setting. We evaluate G2TM across three semantic segmentation frameworks (Segmenter, SETR, EoMT) and three decoder families (Linear, Transformer-, convolution-based), as well as standard ViT image classification. Our results show that G2TM's behavior and accuracy-efficiency trade-off are consistent across every tested architecture for a given backbone size, indicating that its effectiveness is a property of the encoder rather than the decoder. G2TM also generalizes well to image classification, achieving an even smaller degradation in accuracy compared to semantic segmentation. We further find that G2TM's optimal hyperparameters, resulting in a consistent drop in GFLOPs of 22-47% and an increase in throughput by up to 74% for segmentation models on ADE20K dataset, depend primarily on the backbone's pre-training recipe and on the target dataset, rather than on the decoder choice.", "published": "2026-09-16T08:02:37Z", "authors": ["Victor Bercy", "Martyna Poreba", "Michal Szczepanski", "Samia Bouchafa"], "categories": ["cs.CV"], "doi": "10.5220/0014267600004084", "_query": "property valuation computer vision", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2602.03883v1", "title": "Explainable Computer Vision Framework for Automated Pore Detection and Criticality Assessment in Additive Manufacturing", "abstract": "Internal porosity remains a critical defect mode in additively manufactured components, compromising structural performance and limiting industrial adoption. Automated defect detection methods exist but lack interpretability, preventing engineers from understanding the physical basis of criticality predictions. This study presents an explainable computer vision framework for pore detection and criticality assessment in three-dimensional tomographic volumes. Sequential grayscale slices were reconstructed into volumetric datasets, and intensity-based thresholding with connected component analysis identified 500 individual pores. Each pore was characterized using geometric descriptors including size, aspect ratio, extent, and spatial position relative to the specimen boundary. A pore interaction network was constructed using percentile-based Euclidean distance criteria, yielding 24,950 inter-pore connections. Machine learning models predicted pore criticality scores from extracted features, and SHAP analysis quantified individual feature contributions. Results demonstrate that normalized surface distance dominates model predictions, contributing more than an order of magnitude greater importance than all other descriptors. Pore size provides minimal influence, while geometric parameters show negligible impact. The strong inverse relationship between surface proximity and criticality reveals boundary-driven failure mechanisms. This interpretable framework enables transparent defect assessment and provides actionable insights for process optimization and quality control in additive manufacturing.", "published": "2026-02-03T02:28:31Z", "authors": ["Akshansh Mishra", "Rakesh Morisetty"], "categories": ["cs.CV", "cs.AI", "cs.CE", "cs.LG"], "doi": "", "_query": "property valuation computer vision", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/1607.04499v2", "title": "Selecting Algorithms for Black Box Matrices: Checking for Matrix Properties That Can Simplify Computations", "abstract": "Processes to automate the selection of appropriate algorithms for various matrix computations are described. In particular, processes to check for, and certify, various matrix properties of black box matrices are presented. These include sparsity patterns and structural properties that allow \"superfast\" algorithms to be used in place of black-box algorithms. Matrix properties that hold generically, and allow the use of matrix preconditioning to be reduced or eliminated, can also be checked for and certified - notably including in the small-field case, where this presently has the greatest impact on the efficiency of the computation.", "published": "2016-07-15T13:17:30Z", "authors": ["Wayne Eberly"], "categories": ["math.NA"], "doi": "", "_query": "property valuation computer vision", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2309.03311v1", "title": "Default Process Modeling and Credit Valuation Adjustment", "abstract": "This paper presents a convenient framework for modeling default process and pricing derivative securities involving credit risk. The framework provides an integrated view of credit valuation adjustment by linking distance-to-default, default probability, survival probability, and default correlation together. We show that risky valuation is Martingale in our model. The framework reduces the technical issues of performing risky valuation to the same issues faced when performing the ordinary valuation. The numerical results show that the model prediction is consistent with the historical observations.", "published": "2023-09-06T18:48:00Z", "authors": ["David Xiao"], "categories": ["q-fin.PR"], "doi": "", "_query": "property valuation computer vision", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2411.04865v4", "title": "ZAHA: Introducing the Level of Facade Generalization and the Large-Scale Point Cloud Facade Semantic Segmentation Benchmark Dataset", "abstract": "Facade semantic segmentation is a long-standing challenge in photogrammetry and computer vision. Although the last decades have witnessed the influx of facade segmentation methods, there is a lack of comprehensive facade classes and data covering the architectural variability. In ZAHA, we introduce Level of Facade Generalization (LoFG), novel hierarchical facade classes designed based on international urban modeling standards, ensuring compatibility with real-world challenging classes and uniform methods' comparison. Realizing the LoFG, we present to date the largest semantic 3D facade segmentation dataset, providing 601 million annotated points at five and 15 classes of LoFG2 and LoFG3, respectively. Moreover, we analyze the performance of baseline semantic segmentation methods on our introduced LoFG classes and data, complementing it with a discussion on the unresolved challenges for facade segmentation. We firmly believe that ZAHA shall facilitate further development of 3D facade semantic segmentation methods, enabling robust segmentation indispensable in creating urban digital twins.", "published": "2024-11-07T16:58:18Z", "authors": ["Olaf Wysocki", "Yue Tan", "Thomas Froech", "Yan Xia", "Magdalena Wysocki", "Ludwig Hoegner", "Daniel Cremers", "Christoph Holst"], "categories": ["cs.CV", "cs.AI"], "doi": "", "_query": "property valuation computer vision", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/1111.3165v1", "title": "Effective Ways of Secure, Private and Trusted Cloud Computing", "abstract": "Cloud computing is an Internet-based computing, where shared resources, software and information, are provided to computers and devices on-demand. It provides people the way to share distributed resources and services that belong to different organization. Since cloud computing uses distributed resources in open environment, thus it is important to provide the security and trust to share the data for developing cloud computing applications. In this paper we assess how can cloud providers earn their customers' trust and provide the security, privacy and reliability, when a third party is processing sensitive data in a remote machine located in various countries? A concept of utility cloud has been represented to provide the various services to the users. Emerging technologies can help address the challenges of Security, Privacy and Trust in cloud computing.", "published": "2011-11-14T10:28:59Z", "authors": ["Pardeep Kumar", "Vivek Kumar Sehgal", "Durg Singh Chauhan", "P. K. Gupta", "Manoj Diwakar"], "categories": ["cs.DC", "cs.CR"], "doi": "", "_query": "property valuation computer vision", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/math/0004016v1", "title": "On the valuation of Paris options: foundational results", "abstract": "This paper adresses the valuation of the Paris barrier options proposed by Yor, Jeanblanc-Picque, and Chesnay (Advances in Applied Probability, 29(1997), 165-184) using the Laplace transform approach. Based on suggestions by Pliska the notion of Paris options is extended such that their valuation is possible at any point during their lifespan. The Laplace transforms derived by Yor et al. are modified when necessary, and their basic analytic properties are discussed.", "published": "2000-04-04T15:30:14Z", "authors": ["Michael Schröder"], "categories": ["math.CA", "math.PR", "q-fin.CP"], "doi": "", "_query": "property valuation computer vision", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2609.37881v1", "title": "Global Structure and Local Specifications in Sublinear Valuation", "abstract": "This work studies the relationships among sublinear valuation rules, uncertainty structures, and local specifications in a time-homogeneous Markovian framework with killing. These objects are linked, under finiteness and locality of the upper generator and a Lyapunov condition, by three maps: robust valuation, globalization, and localization. First, we show that the corresponding classes of uncertainty structures and sublinear valuation rules are order-isomorphic via the robust valuation map. Second, our analysis clarifies how local specifications constrain sublinear valuation and uncertainty structures, as well as what information localization and globalization preserve. In particular, the compositions of localization and globalization need not recover the original objects but yield canonical extremal elements. Third, the sublinear valuation generated by a local specification is characterized by the greatest viscosity subsolution of the associated Hamilton--Jacobi--Bellman equation. Finally, each uncertainty structure with killing admits a unique representation by a family of pairs consisting of a cumulative discounting process and an underlying state law. These results provide a framework for order relations, probabilistic and PDE-based representations, and model recovery in sublinear valuation without requiring uniqueness of the underlying martingale problems or a viscosity comparison principle.", "published": "2026-09-29T15:53:57Z", "authors": ["Jongjin Park", "David Criens", "Hyungbin Park"], "categories": ["q-fin.MF", "math.PR"], "doi": "", "_query": "property valuation computer vision", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2107.06961v4", "title": "On complete classes of valuated matroids", "abstract": "We characterize a rich class of valuated matroids, called R-minor valuated matroids that includes the indicator functions of matroids, and is closed under operations such as taking minors, duality, and induction by network. We exhibit a family of valuated matroids that are not R-minor based on sparse paving matroids. Valuated matroids are inherently related to gross substitute valuations in mathematical economics. By the same token we refute the Matroid Based Valuation Conjecture by Ostrovsky and Paes Leme (Theoretical Economics 2015) asserting that every gross substitute valuation arises from weighted matroid rank functions by repeated applications of merge and endowment operations. Our result also has implications in the context of Lorentzian polynomials: it reveals the limitations of known construction operations.", "published": "2021-07-14T19:47:19Z", "authors": ["Edin Husić", "Georg Loho", "Ben Smith", "László A. Végh"], "categories": ["math.CO", "cs.DM", "cs.GT", "math.OC"], "doi": "10.46298/theoretics.24.24", "_query": "property valuation computer vision", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2203.15331v2", "title": "CNN Filter DB: An Empirical Investigation of Trained Convolutional Filters", "abstract": "Currently, many theoretical as well as practically relevant questions towards the transferability and robustness of Convolutional Neural Networks (CNNs) remain unsolved. While ongoing research efforts are engaging these problems from various angles, in most computer vision related cases these approaches can be generalized to investigations of the effects of distribution shifts in image data. In this context, we propose to study the shifts in the learned weights of trained CNN models. Here we focus on the properties of the distributions of dominantly used 3x3 convolution filter kernels. We collected and publicly provide a dataset with over 1.4 billion filters from hundreds of trained CNNs, using a wide range of datasets, architectures, and vision tasks. In a first use case of the proposed dataset, we can show highly relevant properties of many publicly available pre-trained models for practical applications: I) We analyze distribution shifts (or the lack thereof) between trained filters along different axes of meta-parameters, like visual category of the dataset, task, architecture, or layer depth. Based on these results, we conclude that model pre-training can succeed on arbitrary datasets if they meet size and variance conditions. II) We show that many pre-trained models contain degenerated filters which make them less robust and less suitable for fine-tuning on target applications. Data & Project website: https://github.com/paulgavrikov/cnn-filter-db", "published": "2022-03-29T08:25:42Z", "authors": ["Paul Gavrikov", "Janis Keuper"], "categories": ["cs.CV", "cs.AI", "cs.LG"], "doi": "10.1109/CVPR52688.2022.01848", "_query": "property valuation computer vision", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2408.04471v2", "title": "What could go wrong? Discovering and describing failure modes in computer vision", "abstract": "Deep learning models are effective, yet brittle. Even carefully trained, their behavior tends to be hard to predict when confronted with out-of-distribution samples. In this work, our goal is to propose a simple yet effective solution to predict and describe via natural language potential failure modes of computer vision models. Given a pretrained model and a set of samples, our aim is to find sentences that accurately describe the visual conditions in which the model underperforms. In order to study this important topic and foster future research on it, we formalize the problem of Language-Based Error Explainability (LBEE) and propose a set of metrics to evaluate and compare different methods for this task. We propose solutions that operate in a joint vision-and-language embedding space, and can characterize through language descriptions model failures caused, e.g., by objects unseen during training or adverse visual conditions. We experiment with different tasks, such as classification under the presence of dataset bias and semantic segmentation in unseen environments, and show that the proposed methodology isolates nontrivial sentences associated with specific error causes. We hope our work will help practitioners better understand the behavior of models, increasing their overall safety and interpretability.", "published": "2024-08-08T14:01:12Z", "authors": ["Gabriela Csurka", "Tyler L. Hayes", "Diane Larlus", "Riccardo Volpi"], "categories": ["cs.CV"], "doi": "", "_query": "property valuation computer vision", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/1301.4338v1", "title": "Quasi-valuations - topology and the weak approximation theorem", "abstract": "Suppose $F$ is a field with a nontrivial valuation $v$ and valuation ring $O_{v}$, $E$ is a finite field extension and $w$ is a quasi-valuation on $E$ extending $v$. We study the topology induced by $w$. We prove that the quasi-valuation ring determines the topology, independent of the choice of its quasi-valuation. Moreover, we prove the weak approximation theorem for quasi-valuations.", "published": "2013-01-18T10:16:36Z", "authors": ["Shai Sarussi"], "categories": ["math.GN", "math.RA"], "doi": "", "_query": "property valuation computer vision", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/1904.00320v1", "title": "NM-Net: Mining Reliable Neighbors for Robust Feature Correspondences", "abstract": "Feature correspondence selection is pivotal to many feature-matching based tasks in computer vision. Searching for spatially k-nearest neighbors is a common strategy for extracting local information in many previous works. However, there is no guarantee that the spatially k-nearest neighbors of correspondences are consistent because the spatial distribution of false correspondences is often irregular. To address this issue, we present a compatibility-specific mining method to search for consistent neighbors. Moreover, in order to extract and aggregate more reliable features from neighbors, we propose a hierarchical network named NM-Net with a series of convolution layers taking the generated graph as input, which is insensitive to the order of correspondences. Our experimental results have shown the proposed method achieves the state-of-the-art performance on four datasets with various inlier ratios and varying numbers of feature consistencies.", "published": "2019-03-31T01:50:37Z", "authors": ["Chen Zhao", "Zhiguo Cao", "Chi Li", "Xin Li", "Jiaqi Yang"], "categories": ["cs.CV"], "doi": "", "_query": "property valuation computer vision", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2407.08293v1", "title": "Generating sequences of rank 1 valuations", "abstract": "We consider a rank 1 valuation $ν$ centered in a regular 3-dimensional local ring $R$. We assume that the residue field $k$ of $ν$ is contained in $R$. An algorithm for constructing a generating sequences for $ν$ in $R$ is provided. This is a generalization of our previous algorithm where only valuations of rational rank 1 were considered. We then give a comparison example of a rational rank 3 valuation centered in a ring of polynomials in three variables for which we explicitly compute a set of key polynomials and a generating sequence.", "published": "2024-07-11T08:39:25Z", "authors": ["Olga Kashcheyeva"], "categories": ["math.AC"], "doi": "", "_query": "property valuation computer vision", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/1709.05732v1", "title": "A Hierarchical Probabilistic Model for Facial Feature Detection", "abstract": "Facial feature detection from facial images has attracted great attention in the field of computer vision. It is a nontrivial task since the appearance and shape of the face tend to change under different conditions. In this paper, we propose a hierarchical probabilistic model that could infer the true locations of facial features given the image measurements even if the face is with significant facial expression and pose. The hierarchical model implicitly captures the lower level shape variations of facial components using the mixture model. Furthermore, in the higher level, it also learns the joint relationship among facial components, the facial expression, and the pose information through automatic structure learning and parameter estimation of the probabilistic model. Experimental results on benchmark databases demonstrate the effectiveness of the proposed hierarchical probabilistic model.", "published": "2017-09-18T00:20:20Z", "authors": ["Yue Wu", "Ziheng Wang", "Qiang Ji"], "categories": ["cs.CV"], "doi": "", "_query": "property valuation computer vision", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2306.10577v3", "title": "OpenDataVal: a Unified Benchmark for Data Valuation", "abstract": "Assessing the quality and impact of individual data points is critical for improving model performance and mitigating undesirable biases within the training dataset. Several data valuation algorithms have been proposed to quantify data quality, however, there lacks a systemic and standardized benchmarking system for data valuation. In this paper, we introduce OpenDataVal, an easy-to-use and unified benchmark framework that empowers researchers and practitioners to apply and compare various data valuation algorithms. OpenDataVal provides an integrated environment that includes (i) a diverse collection of image, natural language, and tabular datasets, (ii) implementations of eleven different state-of-the-art data valuation algorithms, and (iii) a prediction model API that can import any models in scikit-learn. Furthermore, we propose four downstream machine learning tasks for evaluating the quality of data values. We perform benchmarking analysis using OpenDataVal, quantifying and comparing the efficacy of state-of-the-art data valuation approaches. We find that no single algorithm performs uniformly best across all tasks, and an appropriate algorithm should be employed for a user's downstream task. OpenDataVal is publicly available at https://opendataval.github.io with comprehensive documentation. Furthermore, we provide a leaderboard where researchers can evaluate the effectiveness of their own data valuation algorithms.", "published": "2023-06-18T14:38:29Z", "authors": ["Kevin Fu Jiang", "Weixin Liang", "James Zou", "Yongchan Kwon"], "categories": ["cs.LG", "stat.ML"], "doi": "", "_query": "property valuation computer vision", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/1311.6493v3", "title": "Monomial Valuations, Cusp Singularities, and Continued Fractions", "abstract": "This paper explores the relationship between real valued monomial valuations on $k(x,y)$, the resolution of cusp singularities, and continued fractions. It is shown that up to equivalence there is a one to one correspondence between real valued monomial valuations on $k(x,y)$ and continued fraction expansions of real numbers between zero and one. This relationship with continued fractions is then used to provide a characterization of the valuation rings for real valued monomial valuations on $k(x,y)$. In the case when the monomial valuation is equivalent to an integral monomial valuation, we exhibit explicit generators of the valuation rings. Finally, we demonstrate that if $ν$ is a monomial valuation such that $ν(x)=a$ and $ν(y)=b$, where $a$ and $b$ are relatively prime positive integers larger than one, then $ν$ governs a resolution of the singularities of the plane curve $x^{b}=y^{a}$ in a way we make explicit. Further, we provide an exact bound on the number of blow ups needed to resolve singularities in terms of the continued fraction of $a/b$", "published": "2013-11-25T21:39:47Z", "authors": ["Juliette Bruce", "Molly Logue", "Robert Walker"], "categories": ["math.AG", "math.AC"], "doi": "10.1216/JCA-2015-7-4-495", "_query": "property valuation computer vision", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2210.12843v1", "title": "Delving into Masked Autoencoders for Multi-Label Thorax Disease Classification", "abstract": "Vision Transformer (ViT) has become one of the most popular neural architectures due to its great scalability, computational efficiency, and compelling performance in many vision tasks. However, ViT has shown inferior performance to Convolutional Neural Network (CNN) on medical tasks due to its data-hungry nature and the lack of annotated medical data. In this paper, we pre-train ViTs on 266,340 chest X-rays using Masked Autoencoders (MAE) which reconstruct missing pixels from a small part of each image. For comparison, CNNs are also pre-trained on the same 266,340 X-rays using advanced self-supervised methods (e.g., MoCo v2). The results show that our pre-trained ViT performs comparably (sometimes better) to the state-of-the-art CNN (DenseNet-121) for multi-label thorax disease classification. This performance is attributed to the strong recipes extracted from our empirical studies for pre-training and fine-tuning ViT. The pre-training recipe signifies that medical reconstruction requires a much smaller proportion of an image (10% vs. 25%) and a more moderate random resized crop range (0.5~1.0 vs. 0.2~1.0) compared with natural imaging. Furthermore, we remark that in-domain transfer learning is preferred whenever possible. The fine-tuning recipe discloses that layer-wise LR decay, RandAug magnitude, and DropPath rate are significant factors to consider. We hope that this study can direct future research on the application of Transformers to a larger variety of medical imaging tasks.", "published": "2022-10-23T20:14:57Z", "authors": ["Junfei Xiao", "Yutong Bai", "Alan Yuille", "Zongwei Zhou"], "categories": ["cs.CV"], "doi": "", "_query": "property valuation computer vision", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/1810.01175v2", "title": "Line Drawings from 3D Models", "abstract": "This tutorial describes the geometry and algorithms for generating line drawings from 3D models, focusing on occluding contours. The geometry of occluding contours on meshes and on smooth surfaces is described in detail, together with algorithms for extracting contours, computing their visibility, and creating stylized renderings and animations. Exact methods and hardware-accelerated fast methods are both described, and the trade-offs between different methods are discussed. The tutorial brings together and organizes material that, at present, is scattered throughout the literature. It also includes some novel explanations, and implementation tips. A thorough survey of the field of non-photorealistic 3D rendering is also included, covering other kinds of line drawings and artistic shading.", "published": "2018-10-02T11:28:10Z", "authors": ["Pierre Bénard", "Aaron Hertzmann"], "categories": ["cs.GR"], "doi": "10.1561/0600000075", "_query": "property valuation computer vision", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/1709.08130v1", "title": "Simultaneous Facial Landmark Detection, Pose and Deformation Estimation under Facial Occlusion", "abstract": "Facial landmark detection, head pose estimation, and facial deformation analysis are typical facial behavior analysis tasks in computer vision. The existing methods usually perform each task independently and sequentially, ignoring their interactions. To tackle this problem, we propose a unified framework for simultaneous facial landmark detection, head pose estimation, and facial deformation analysis, and the proposed model is robust to facial occlusion. Following a cascade procedure augmented with model-based head pose estimation, we iteratively update the facial landmark locations, facial occlusion, head pose and facial de- formation until convergence. The experimental results on benchmark databases demonstrate the effectiveness of the proposed method for simultaneous facial landmark detection, head pose and facial deformation estimation, even if the images are under facial occlusion.", "published": "2017-09-23T23:53:44Z", "authors": ["Yue Wu", "Chao Gou", "Qiang Ji"], "categories": ["cs.CV"], "doi": "", "_query": "property valuation computer vision", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/1810.10090v1", "title": "NestDNN: Resource-Aware Multi-Tenant On-Device Deep Learning for Continuous Mobile Vision", "abstract": "Mobile vision systems such as smartphones, drones, and augmented-reality headsets are revolutionizing our lives. These systems usually run multiple applications concurrently and their available resources at runtime are dynamic due to events such as starting new applications, closing existing applications, and application priority changes. In this paper, we present NestDNN, a framework that takes the dynamics of runtime resources into account to enable resource-aware multi-tenant on-device deep learning for mobile vision systems. NestDNN enables each deep learning model to offer flexible resource-accuracy trade-offs. At runtime, it dynamically selects the optimal resource-accuracy trade-off for each deep learning model to fit the model's resource demand to the system's available runtime resources. In doing so, NestDNN efficiently utilizes the limited resources in mobile vision systems to jointly maximize the performance of all the concurrently running applications. Our experiments show that compared to the resource-agnostic status quo approach, NestDNN achieves as much as 4.2% increase in inference accuracy, 2.0x increase in video frame processing rate and 1.7x reduction on energy consumption.", "published": "2018-10-23T21:07:42Z", "authors": ["Biyi Fang", "Xiao Zeng", "Mi Zhang"], "categories": ["cs.CV"], "doi": "10.1145/3241539.3241559", "_query": "property valuation computer vision", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2407.14823v2", "title": "Scaling Up Single Image Dehazing Algorithm by Cross-Data Vision Alignment for Richer Representation Learning and Beyond", "abstract": "In recent years, deep neural networks tasks have increasingly relied on high-quality image inputs. With the development of high-resolution representation learning, the task of image dehazing has received significant attention. Previously, many methods collect diverse image data for large-scale training to boost the performance on a target scene. Ignoring the domain gap between different data, former de-hazing methods simply adopt multiple datasets for explicit large-scale training, which often makes the methods themselves be violated. To address this problem, we propose a novel method of cross-data vision alignment for richer representation learning to improve the existing dehazing methodology. Specifically, we call for the internal- and external knowledge should be further adapted with a self-supervised manner to fill up the domain gap. By using cross-data external alignment, the datasets inherit samples from different domains that are firmly aligned, making the model learn more robust and generalizable features. By using the internal augmentation method, the model can fully exploit local information within the images, and then obtaining more image details. To demonstrate the effectiveness of our proposed method, we conduct training on the Natural Image Dataset (NID). Experimental results show that our method clearly resolves the domain gap in different dehazing datasets and presents a new pipeline for large-scale training in the dehazing task. Our approach significantly outperforms other advanced methods in dehazing and produces dehazed images that are closest to real haze-free images.", "published": "2024-07-20T10:00:20Z", "authors": ["Yukai Shi", "Zhipeng Weng", "Yupei Lin", "Cidan Shi", "Xiaojun Yang", "Liang Lin"], "categories": ["cs.CV", "cs.AI", "cs.LG", "cs.MM", "eess.IV"], "doi": "", "_query": "property valuation computer vision", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2501.04333v1", "title": "Gravitational physics in the context of Indian astronomy: A vision document", "abstract": "Contributions from the Indian gravity community have played a significant role in shaping several branches of astronomy and astrophysics. This document reviews some of the most important contributions and presents a vision for gravity research in the context of astronomy \\& astrophysics in India. This is an expanded version of one of the chapters in the recently released Vision Document of the Astronomical Society of India.", "published": "2025-01-08T08:10:38Z", "authors": ["P. Ajith", "K. G. Arun", "Sukanta Bose", "Sumanta Chakraborty", "Shantanu Desai", "A. Gopakumar", "Sanved Kolekar", "Rajesh Nayak", "Archana Pai", "Sudipta Sarkar", "Jasjeet Singh Bagla", "Patrick Das Gupta", "Rahul Kashyap", "Prashant Kocherlakota", "Prayush Kumar", "Banibrata Mukhopadhyay"], "categories": ["astro-ph.IM", "astro-ph.HE", "gr-qc"], "doi": "10.1007/s12036-024-10031-x", "_query": "property valuation computer vision", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2210.00497v1", "title": "Approximate Computing and the Efficient Machine Learning Expedition", "abstract": "Approximate computing (AxC) has been long accepted as a design alternative for efficient system implementation at the cost of relaxed accuracy requirements. Despite the AxC research activities in various application domains, AxC thrived the past decade when it was applied in Machine Learning (ML). The by definition approximate notion of ML models but also the increased computational overheads associated with ML applications-that were effectively mitigated by corresponding approximations-led to a perfect matching and a fruitful synergy. AxC for AI/ML has transcended beyond academic prototypes. In this work, we enlighten the synergistic nature of AxC and ML and elucidate the impact of AxC in designing efficient ML systems. To that end, we present an overview and taxonomy of AxC for ML and use two descriptive application scenarios to demonstrate how AxC boosts the efficiency of ML systems.", "published": "2022-10-02T12:10:39Z", "authors": ["Jörg Henkel", "Hai Li", "Anand Raghunathan", "Mehdi B. Tahoori", "Swagath Venkataramani", "Xiaoxuan Yang", "Georgios Zervakis"], "categories": ["cs.AR", "cs.LG"], "doi": "10.1145/3508352.3561105", "_query": "property valuation computer vision", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2012.10516v1", "title": "Computer Vision based Tomography of Structures Using 3D Digital Image Correlation", "abstract": "Internal properties of a sample can be observed by medical imaging tools, such as ultrasound devices, magnetic resonance imaging (MRI) and optical coherence tomography (OCT) which are based on relying on changes in material density or chemical composition [1-21]. As a preliminary investigation, the feasibility to detect interior defects inferred from the discrepancy in elasticity modulus distribution of a three-dimensional heterogeneous sample using only surface full-field measurements and finite element model updating as an inverse optimization algorithm without any assumption about local homogeneities and also the elasticity modulus distribution is investigated. Recently, the authors took advantages of the digital image correlation technique as a full field measurement in constitutive property identification of a full-scale steel component [22-27]. To the extension of previous works, in this brief technical note, the new idea intended at recovering unseen volumetric defect distributions within the interior of three-dimensional heterogeneous space of the structural component using 3D-Digital Image Correlation for structural identification [28-57]. As a proof of concept, the results of this paper illustrate the potential to identify invisible internal defect by the proposed computer vision technique establishes the potential for new opportunities to characterize internal heterogeneous materials for their mechanical property distribution and condition state.", "published": "2020-12-18T21:17:00Z", "authors": ["Mehrdad Shafiei Dizaji", "Devin Harris"], "categories": ["cs.CV", "eess.IV"], "doi": "", "_query": "property valuation computer vision", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/1809.02651v1", "title": "Reservoir Computing based Neural Image Filters", "abstract": "Clean images are an important requirement for machine vision systems to recognize visual features correctly. However, the environment, optics, electronics of the physical imaging systems can introduce extreme distortions and noise in the acquired images. In this work, we explore the use of reservoir computing, a dynamical neural network model inspired from biological systems, in creating dynamic image filtering systems that extracts signal from noise using inverse modeling. We discuss the possibility of implementing these networks in hardware close to the sensors.", "published": "2018-09-07T19:58:53Z", "authors": ["Samiran Ganguly", "Yunfei Gu", "Yunkun Xie", "Mircea R. Stan", "Avik W. Ghosh", "Nibir K. Dhar"], "categories": ["cs.CV", "cs.ET", "cs.NE"], "doi": "", "_query": "property valuation computer vision", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2003.01196v1", "title": "DEEVA: A Deep Learning and IoT Based Computer Vision System to Address Safety and Security of Production Sites in Energy Industry", "abstract": "When it comes to addressing the safety/security related needs at different production/construction sites, accurate detection of the presence of workers, vehicles, equipment important and formed an integral part of computer vision-based surveillance systems (CVSS). Traditional CVSS systems focus on the use of different computer vision and pattern recognition algorithms overly reliant on manual extraction of features and small datasets, limiting their usage because of low accuracy, need for expert knowledge and high computational costs. The main objective of this paper is to provide decision makers at sites with a practical yet comprehensive deep learning and IoT based solution to tackle various computer vision related problems such as scene classification, object detection in scenes, semantic segmentation, scene captioning etc. Our overarching goal is to address the central question of What is happening at this site and where is it happening in an automated fashion minimizing the need for human resources dedicated to surveillance. We developed Deep ExxonMobil Eye for Video Analysis (DEEVA) package to handle scene classification, object detection, semantic segmentation and captioning of scenes in a hierarchical approach. The results reveal that transfer learning with the RetinaNet object detector is able to detect the presence of workers, different types of vehicles/construction equipment, safety related objects at a high level of accuracy (above 90%). With the help of deep learning to automatically extract features and IoT technology to automatic capture, transfer and process vast amount of realtime images, this framework is an important step towards the development of intelligent surveillance systems aimed at addressing myriads of open ended problems in the realm of security/safety monitoring, productivity assessments and future decision making.", "published": "2020-03-02T21:26:00Z", "authors": ["Nimish M. Awalgaonkar", "Haining Zheng", "Christopher S. Gurciullo"], "categories": ["cs.CV"], "doi": "", "_query": "property valuation computer vision", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2203.07425v1", "title": "Computational Perspective of the Fog Node", "abstract": "Fog computing is a recent computational paradigm that was proposed to solve some weaknesses in cloud-based systems. For this reason, this technology has been extensively studied by several technology areas. It is still in a maturing stage, so there is no consensus in academia about its concepts and definitions, and each area adopts the ones that are convenient for each use case. This article proposes a definition and a classification relying on a computational perspective for the fog node, which is a fundamental element in a fog computing environment. In addition, the main challenges related to the fog node are also presented.", "published": "2022-03-14T18:36:53Z", "authors": ["João Bachiega", "Breno Costa", "Aleteia P. F. Araujo"], "categories": ["cs.DC"], "doi": "", "_query": "property valuation computer vision", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/1811.10681v2", "title": "Matching Features without Descriptors: Implicitly Matched Interest Points", "abstract": "The extraction and matching of interest points is a prerequisite for many geometric computer vision problems. Traditionally, matching has been achieved by assigning descriptors to interest points and matching points that have similar descriptors. In this paper, we propose a method by which interest points are instead already implicitly matched at detection time. With this, descriptors do not need to be calculated, stored, communicated, or matched any more. This is achieved by a convolutional neural network with multiple output channels and can be thought of as a collection of a variety of detectors, each specialized to specific visual features. This paper describes how to design and train such a network in a way that results in successful relative pose estimation performance despite the limitation on interest point count. While the overall matching score is slightly lower than with traditional methods, the approach is descriptor free and thus enables localization systems with a significantly smaller memory footprint and multi-agent localization systems with lower bandwidth requirements. The network also outputs the confidence for a specific interest point resulting in a valid match. We evaluate performance relative to state-of-the-art alternatives.", "published": "2018-11-26T20:45:58Z", "authors": ["Titus Cieslewski", "Michael Bloesch", "Davide Scaramuzza"], "categories": ["cs.CV"], "doi": "", "_query": "property valuation computer vision", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2101.01169v5", "title": "Transformers in Vision: A Survey", "abstract": "Astounding results from Transformer models on natural language tasks have intrigued the vision community to study their application to computer vision problems. Among their salient benefits, Transformers enable modeling long dependencies between input sequence elements and support parallel processing of sequence as compared to recurrent networks e.g., Long short-term memory (LSTM). Different from convolutional networks, Transformers require minimal inductive biases for their design and are naturally suited as set-functions. Furthermore, the straightforward design of Transformers allows processing multiple modalities (e.g., images, videos, text and speech) using similar processing blocks and demonstrates excellent scalability to very large capacity networks and huge datasets. These strengths have led to exciting progress on a number of vision tasks using Transformer networks. This survey aims to provide a comprehensive overview of the Transformer models in the computer vision discipline. We start with an introduction to fundamental concepts behind the success of Transformers i.e., self-attention, large-scale pre-training, and bidirectional encoding. We then cover extensive applications of transformers in vision including popular recognition tasks (e.g., image classification, object detection, action recognition, and segmentation), generative modeling, multi-modal tasks (e.g., visual-question answering, visual reasoning, and visual grounding), video processing (e.g., activity recognition, video forecasting), low-level vision (e.g., image super-resolution, image enhancement, and colorization) and 3D analysis (e.g., point cloud classification and segmentation). We compare the respective advantages and limitations of popular techniques both in terms of architectural design and their experimental value. Finally, we provide an analysis on open research directions and possible future works.", "published": "2021-01-04T18:57:24Z", "authors": ["Salman Khan", "Muzammal Naseer", "Munawar Hayat", "Syed Waqas Zamir", "Fahad Shahbaz Khan", "Mubarak Shah"], "categories": ["cs.CV", "cs.AI", "cs.LG"], "doi": "10.1145/3505244", "_query": "property valuation computer vision", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/1807.09828v1", "title": "ADVIO: An authentic dataset for visual-inertial odometry", "abstract": "The lack of realistic and open benchmarking datasets for pedestrian visual-inertial odometry has made it hard to pinpoint differences in published methods. Existing datasets either lack a full six degree-of-freedom ground-truth or are limited to small spaces with optical tracking systems. We take advantage of advances in pure inertial navigation, and develop a set of versatile and challenging real-world computer vision benchmark sets for visual-inertial odometry. For this purpose, we have built a test rig equipped with an iPhone, a Google Pixel Android phone, and a Google Tango device. We provide a wide range of raw sensor data that is accessible on almost any modern-day smartphone together with a high-quality ground-truth track. We also compare resulting visual-inertial tracks from Google Tango, ARCore, and Apple ARKit with two recent methods published in academic forums. The data sets cover both indoor and outdoor cases, with stairs, escalators, elevators, office environments, a shopping mall, and metro station.", "published": "2018-07-25T19:13:58Z", "authors": ["Santiago Cortés", "Arno Solin", "Esa Rahtu", "Juho Kannala"], "categories": ["cs.CV"], "doi": "", "_query": "property valuation computer vision", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2409.13550v2", "title": "A preliminary study on continual learning in computer vision using Kolmogorov-Arnold Networks", "abstract": "Deep learning has long been dominated by multi-layer perceptrons (MLPs), which have demonstrated superiority over other optimizable models in various domains. Recently, a new alternative to MLPs has emerged - Kolmogorov-Arnold Networks (KAN)- which are based on a fundamentally different mathematical framework. According to their authors, KANs address several major issues in MLPs, such as catastrophic forgetting in continual learning scenarios. However, this claim has only been supported by results from a regression task on a toy 1D dataset. In this paper, we extend the investigation by evaluating the performance of KANs in continual learning tasks within computer vision, specifically using the MNIST datasets. To this end, we conduct a structured analysis of the behavior of MLPs and two KAN-based models in a class-incremental learning scenario, ensuring that the architectures involved have the same number of trainable parameters. Our results demonstrate that an efficient version of KAN outperforms both traditional MLPs and the original KAN implementation. We further analyze the influence of hyperparameters in MLPs and KANs, as well as the impact of certain trainable parameters in KANs, such as bias and scale weights. Additionally, we provide a preliminary investigation of recent KAN-based convolutional networks and compare their performance with that of traditional convolutional neural networks. Our codes can be found at https://github.com/MrPio/KAN-Continual_Learning_tests.", "published": "2024-09-20T14:49:21Z", "authors": ["Alessandro Cacciatore", "Valerio Morelli", "Federica Paganica", "Emanuele Frontoni", "Lucia Migliorelli", "Daniele Berardini"], "categories": ["cs.CV", "cs.LG"], "doi": "", "_query": "property valuation computer vision", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/1812.04377v2", "title": "Deep Reader: Information extraction from Document images via relation extraction and Natural Language", "abstract": "Recent advancements in the area of Computer Vision with state-of-art Neural Networks has given a boost to Optical Character Recognition (OCR) accuracies. However, extracting characters/text alone is often insufficient for relevant information extraction as documents also have a visual structure that is not captured by OCR. Extracting information from tables, charts, footnotes, boxes, headings and retrieving the corresponding structured representation for the document remains a challenge and finds application in a large number of real-world use cases. In this paper, we propose a novel enterprise based end-to-end framework called DeepReader which facilitates information extraction from document images via identification of visual entities and populating a meta relational model across different entities in the document image. The model schema allows for an easy to understand abstraction of the entities detected by the deep vision models and the relationships between them. DeepReader has a suite of state-of-the-art vision algorithms which are applied to recognize handwritten and printed text, eliminate noisy effects, identify the type of documents and detect visual entities like tables, lines and boxes. Deep Reader maps the extracted entities into a rich relational schema so as to capture all the relevant relationships between entities (words, textboxes, lines etc) detected in the document. Relevant information and fields can then be extracted from the document by writing SQL queries on top of the relationship tables. A natural language based interface is added on top of the relationship schema so that a non-technical user, specifying the queries in natural language, can fetch the information with minimal effort. In this paper, we also demonstrate many different capabilities of Deep Reader and report results on a real-world use case.", "published": "2018-12-11T13:09:13Z", "authors": ["Vishwanath D", "Rohit Rahul", "Gunjan Sehgal", " Swati", "Arindam Chowdhury", "Monika Sharma", "Lovekesh Vig", "Gautam Shroff", "Ashwin Srinivasan"], "categories": ["cs.CV", "cs.CL"], "doi": "", "_query": "property valuation computer vision", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/0707.3482v1", "title": "A Bayesian Framework for Combining Valuation Estimates", "abstract": "Obtaining more accurate equity value estimates is the starting point for stock selection, value-based indexing in a noisy market, and beating benchmark indices through tactical style rotation. Unfortunately, discounted cash flow, method of comparables, and fundamental analysis typically yield discrepant valuation estimates. Moreover, the valuation estimates typically disagree with market price. Can one form a superior valuation estimate by averaging over the individual estimates, including market price? This article suggests a Bayesian framework for combining two or more estimates into a superior valuation estimate. The framework justifies the common practice of averaging over several estimates to arrive at a final point estimate.", "published": "2007-07-24T05:04:53Z", "authors": ["Kenton K. Yee"], "categories": ["q-fin.ST", "cs.CE", "nlin.AO", "nlin.CD", "nlin.SI", "physics.pop-ph", "physics.soc-ph", "stat.AP"], "doi": "", "_query": "property valuation computer vision", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2110.06571v1", "title": "Hyperspectral 3D Mapping of Underwater Environments", "abstract": "Hyperspectral imaging has been increasingly used for underwater survey applications over the past years. As many hyperspectral cameras work as push-broom scanners, their use is usually limited to the creation of photo-mosaics based on a flat surface approximation and by interpolating the camera pose from dead-reckoning navigation. Yet, because of drift in the navigation and the mostly wrong flat surface assumption, the quality of the obtained photo-mosaics is often too low to support adequate analysis.In this paper we present an initial method for creating hyperspectral 3D reconstructions of underwater environments. By fusing the data gathered by a classical RGB camera, an inertial navigation system and a hyperspectral push-broom camera, we show that the proposed method creates highly accurate 3D reconstructions with hyperspectral textures. We propose to combine techniques from simultaneous localization and mapping, structure-from-motion and 3D reconstruction and advantageously use them to create 3D models with hyperspectral texture, allowing us to overcome the flat surface assumption and the classical limitation of dead-reckoning navigation.", "published": "2021-10-13T08:37:22Z", "authors": ["Maxime Ferrera", "Aurélien Arnaubec", "Klemen Istenic", "Nuno Gracias", "Touria Bajjouk"], "categories": ["cs.CV", "cs.RO"], "doi": "", "_query": "property valuation computer vision", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2404.07188v1", "title": "GCV-Turbo: End-to-end Acceleration of GNN-based Computer Vision Tasks on FPGA", "abstract": "Graph neural networks (GNNs) have recently empowered various novel computer vision (CV) tasks. In GNN-based CV tasks, a combination of CNN layers and GNN layers or only GNN layers are employed. This paper introduces GCV-Turbo, a domain-specific accelerator on FPGA for end-to-end acceleration of GNN-based CV tasks. GCV-Turbo consists of two key components: (1) a \\emph{novel} hardware architecture optimized for the computation kernels in both CNNs and GNNs using the same set of computation resources. (2) a PyTorch-compatible compiler that takes a user-defined model as input, performs end-to-end optimization for the computation graph of a given GNN-based CV task, and produces optimized code for hardware execution. The hardware architecture and the compiler work synergistically to support a variety of GNN-based CV tasks. We implement GCV-Turbo on a state-of-the-art FPGA and evaluate its performance across six representative GNN-based CV tasks with diverse input data modalities (e.g., image, human skeleton, point cloud). Compared with state-of-the-art CPU (GPU) implementations, GCV-Turbo achieves an average latency reduction of $68.4\\times$ ($4.1\\times$) on these six GNN-based CV tasks. Moreover, GCV-Turbo supports the execution of the standalone CNNs or GNNs, achieving performance comparable to that of state-of-the-art CNN (GNN) accelerators for widely used CNN-only (GNN-only) models.", "published": "2024-04-10T17:41:41Z", "authors": ["Bingyi Zhang", "Rajgopal Kannan", "Carl Busart", "Viktor Prasanna"], "categories": ["cs.DC", "cs.CV", "eess.IV"], "doi": "", "_query": "property valuation computer vision", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2008.07418v2", "title": "Improving Emergency Response during Hurricane Season using Computer Vision", "abstract": "We have developed a framework for crisis response and management that incorporates the latest technologies in computer vision (CV), inland flood prediction, damage assessment and data visualization. The framework uses data collected before, during, and after the crisis to enable rapid and informed decision making during all phases of disaster response. Our computer-vision model analyzes spaceborne and airborne imagery to detect relevant features during and after a natural disaster and creates metadata that is transformed into actionable information through web-accessible mapping tools. In particular, we have designed an ensemble of models to identify features including water, roads, buildings, and vegetation from the imagery. We have investigated techniques to bootstrap and reduce dependency on large data annotation efforts by adding use of open source labels including OpenStreetMaps and adding complementary data sources including Height Above Nearest Drainage (HAND) as a side channel to the network's input to encourage it to learn other features orthogonal to visual characteristics. Modeling efforts include modification of connected U-Nets for (1) semantic segmentation, (2) flood line detection, and (3) for damage assessment. In particular for the case of damage assessment, we added a second encoder to U-Net so that it could learn pre-event and post-event image features simultaneously. Through this method, the network is able to learn the difference between the pre- and post-disaster images, and therefore more effectively classify the level of damage. We have validated our approaches using publicly available data from the National Oceanic and Atmospheric Administration (NOAA)'s Remote Sensing Division, which displays the city and street-level details as mosaic tile images as well as data released as part of the Xview2 challenge.", "published": "2020-08-17T15:42:02Z", "authors": ["Marc Bosch", "Christian Conroy", "Benjamin Ortiz", "Philip Bogden"], "categories": ["cs.CV", "eess.IV"], "doi": "", "_query": "property valuation computer vision", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2503.14553v5", "title": "Redefining non-IID Data in Federated Learning for Computer Vision Tasks: Migrating from Labels to Embeddings for Task-Specific Data Distributions", "abstract": "Federated Learning (FL) has emerged as one of the prominent paradigms for distributed machine learning (ML). However, it is well-established that its performance can degrade significantly under non-IID (non-independent and identically distributed) data distributions across clients. To study this effect, the existing works predominantly emulate data heterogeneity by imposing label distribution skew across clients. In this paper, we show that label distribution skew fails to fully capture the data heterogeneity in computer vision tasks beyond classification, exposing an overlooked gap in the literature. Motivated by this, by utilizing pre-trained deep neural networks to extract task-specific data embeddings, we define task-specific data heterogeneity through the lens of each vision task and introduce a new level of data heterogeneity called embedding-based data heterogeneity. Our methodology involves clustering data points based on embeddings and distributing them among clients using the Dirichlet distribution. Through extensive experiments, we evaluate the performance of different FL methods under our revamped notion of data heterogeneity, introducing new benchmark performance measures to the literature. For instance, across seven representative computer vision tasks, our embedding-based heterogeneity formulation leads to up to around 60% increase in the observed loss under FedAvg, indicating that it more accurately exposes the performance degradation caused by data heterogeneity. We further unveil a series of open research directions that can be pursued. (Code: https://github.com/KasraBorazjani/task-perspective-het.git)", "published": "2025-03-17T22:16:53Z", "authors": ["Kasra Borazjani", "Payam Abdisarabshali", "Naji Khosravan", "Seyyedali Hosseinalipour"], "categories": ["cs.CV", "cs.LG"], "doi": "", "_query": "property valuation computer vision", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/1807.09436v3", "title": "Deterministic consensus maximization with biconvex programming", "abstract": "Consensus maximization is one of the most widely used robust fitting paradigms in computer vision, and the development of algorithms for consensus maximization is an active research topic. In this paper, we propose an efficient deterministic optimization algorithm for consensus maximization. Given an initial solution, our method conducts a deterministic search that forcibly increases the consensus of the initial solution. We show how each iteration of the update can be formulated as an instance of biconvex programming, which we solve efficiently using a novel biconvex optimization algorithm. In contrast to our algorithm, previous consensus improvement techniques rely on random sampling or relaxations of the objective function, which reduce their ability to significantly improve the initial consensus. In fact, on challenging instances, the previous techniques may even return a worse off solution. Comprehensive experiments show that our algorithm can consistently and greatly improve the quality of the initial solution, without substantial cost.", "published": "2018-07-25T04:54:57Z", "authors": ["Zhipeng Cai", "Tat-Jun Chin", "Huu Le", "David Suter"], "categories": ["cs.CV"], "doi": "", "_query": "property valuation computer vision", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2107.12859v2", "title": "RGL-NET: A Recurrent Graph Learning framework for Progressive Part Assembly", "abstract": "Autonomous assembly of objects is an essential task in robotics and 3D computer vision. It has been studied extensively in robotics as a problem of motion planning, actuator control and obstacle avoidance. However, the task of developing a generalized framework for assembly robust to structural variants remains relatively unexplored. In this work, we tackle this problem using a recurrent graph learning framework considering inter-part relations and the progressive update of the part pose. Our network can learn more plausible predictions of shape structure by accounting for priorly assembled parts. Compared to the current state-of-the-art, our network yields up to 10% improvement in part accuracy and up to 15% improvement in connectivity accuracy on the PartNet dataset. Moreover, our resulting latent space facilitates exciting applications such as shape recovery from the point-cloud components. We conduct extensive experiments to justify our design choices and demonstrate the effectiveness of the proposed framework.", "published": "2021-07-27T14:47:43Z", "authors": ["Abhinav Narayan Harish", "Rajendra Nagar", "Shanmuganathan Raman"], "categories": ["cs.CV"], "doi": "", "_query": "property valuation computer vision", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2507.05020v2", "title": "Adaptation of Multi-modal Representation Models for Multi-task Surgical Computer Vision", "abstract": "Surgical AI often involves multiple tasks within a single procedure, like phase recognition or assessing the Critical View of Safety in laparoscopic cholecystectomy. Traditional models, built for one task at a time, lack flexibility, requiring a separate model for each. To address this, we introduce MML-SurgAdapt, a unified multi-task framework with Vision-Language Models (VLMs), specifically CLIP, to handle diverse surgical tasks through natural language supervision. A key challenge in multi-task learning is the presence of partial annotations when integrating different tasks. To overcome this, we employ Single Positive Multi-Label (SPML) learning, which traditionally reduces annotation burden by training models with only one positive label per instance. Our framework extends this approach to integrate data from multiple surgical tasks within a single procedure, enabling effective learning despite incomplete or noisy annotations. We demonstrate the effectiveness of our model on a combined dataset consisting of Cholec80, Endoscapes2023, and CholecT50, utilizing custom prompts. Extensive evaluation shows that MML-SurgAdapt performs comparably to task-specific benchmarks, with the added advantage of handling noisy annotations. It also outperforms the existing SPML frameworks for the task. By reducing the required labels by 23%, our approach proposes a more scalable and efficient labeling process, significantly easing the annotation burden on clinicians. To our knowledge, this is the first application of SPML to integrate data from multiple surgical tasks, presenting a novel and generalizable solution for multi-task learning in surgical computer vision. Implementation is available at: https://github.com/CAMMA-public/MML-SurgAdapt", "published": "2025-07-07T14:03:10Z", "authors": ["Soham Walimbe", "Britty Baby", "Vinkle Srivastav", "Nicolas Padoy"], "categories": ["cs.CV", "cs.AI"], "doi": "", "_query": "property valuation computer vision", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2101.09744v3", "title": "Classic versus deep learning approaches to address computer vision challenges", "abstract": "Computer vision and image processing address many challenging applications. While the last decade has seen deep neural network architectures revolutionizing those fields, early methods relied on 'classic', i.e., non-learned approaches. In this study, we explore the differences between classic and deep learning (DL) algorithms to gain new insight regarding which is more suitable for a given application. The focus is on two challenging ill-posed problems, namely faint edge detection and multispectral image registration, studying recent state-of-the-art DL and classic solutions. While those DL algorithms outperform classic methods in terms of accuracy and development time, they tend to have higher resource requirements and are unable to perform outside their training space. Moreover, classic algorithms are more transparent, which facilitates their adoption for real-life applications. As both classes of approaches have unique strengths and limitations, the choice of a solution is clearly application dependent.", "published": "2021-01-24T16:27:23Z", "authors": ["Nati Ofir", "Jean-Christophe Nebel"], "categories": ["cs.CV"], "doi": "", "_query": "property valuation computer vision", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2109.05226v1", "title": "Evaluating Computer Vision Techniques for Urban Mobility on Large-Scale, Unconstrained Roads", "abstract": "Conventional approaches for addressing road safety rely on manual interventions or immobile CCTV infrastructure. Such methods are expensive in enforcing compliance to traffic rules and do not scale to large road networks. This paper proposes a simple mobile imaging setup to address several common problems in road safety at scale. We use recent computer vision techniques to identify possible irregularities on roads, the absence of street lights, and defective traffic signs using videos from a moving camera-mounted vehicle. Beyond the inspection of static road infrastructure, we also demonstrate the mobile imaging solution's applicability to spot traffic violations. Before deploying our system in the real-world, we investigate the strengths and shortcomings of computer vision techniques on thirteen condition-based hierarchical labels. These conditions include different timings, road type, traffic density, and state of road damage. Our demonstrations are then carried out on 2000 km of unconstrained road scenes, captured across an entire city. Through this, we quantitatively measure the overall safety of roads in the city through carefully constructed metrics. We also show an interactive dashboard for visually inspecting and initiating action in a time, labor and cost-efficient manner. Code, models, and datasets used in this work will be publicly released.", "published": "2021-09-11T09:07:56Z", "authors": ["Harish Rithish", "Raghava Modhugu", "Ranjith Reddy", "Rohit Saluja", "C. V. Jawahar"], "categories": ["cs.CV"], "doi": "", "_query": "property valuation computer vision", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2604.16482v1", "title": "A Survey of Spatial Memory Representations for Efficient Robot Navigation", "abstract": "As vision-based robots navigate larger environments, their spatial memory grows without bound, eventually exhausting computational resources, particularly on embedded platforms (8-16GB shared memory, $<$30W) where adding hardware is not an option. This survey examines the spatial memory efficiency problem across 88 references spanning 52 systems (1989-2025), from occupancy grids to neural implicit representations. We introduce the $α= M_{\\text{peak}} / M_{\\text{map}}$, the ratio of peak runtime memory (the total RAM or GPU memory consumed during operation) to saved map size (the persistent checkpoint written to disk), exposing the gap between published map sizes and actual deployment cost. Independent profiling on an NVIDIA A100 GPU reveals that $α$ spans two orders of magnitude within neural methods alone, ranging from 2.3 (Point-SLAM) to 215 (NICE-SLAM, whose 47,MB map requires 10GB at runtime), showing that memory architecture, not paradigm label, determines deployment feasibility. We propose a standardized evaluation protocol comprising memory growth rate, query latency, memory-completeness curves, and throughput degradation, none of which current benchmarks capture. Through a Pareto frontier analysis with explicit benchmark separation, we show that no single paradigm dominates within its evaluation regime: 3DGS methods achieve the best absolute accuracy at 90-254,MB map size on Replica, while scene graphs provide semantic abstraction at predictable cost. We provide the first independently measured $α$ reference values and an $α$-aware budgeting algorithm enabling practitioners to assess deployment feasibility on target hardware prior to implementation.", "published": "2026-04-13T02:12:17Z", "authors": ["Ma. Madecheen S. Pangaliman", "Steven S. Sison", "Erwin P. Quilloy", "Rowel Atienza"], "categories": ["cs.CV", "cs.RO"], "doi": "", "_query": "property valuation computer vision", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2407.17480v4", "title": "Dynamic Universal Approximation Theory: The Basic Theory for Deep Learning-Based Computer Vision Models", "abstract": "Computer vision (CV) is one of the most crucial fields in artificial intelligence. In recent years, a variety of deep learning models based on convolutional neural networks (CNNs) and Transformers have been designed to tackle diverse problems in CV. These algorithms have found practical applications in areas such as robotics and facial recognition. Despite the increasing power of current CV models, several fundamental questions remain unresolved: Why do CNNs require deep layers? What ensures the generalization ability of CNNs? Why do residual-based networks outperform fully convolutional networks like VGG? What is the fundamental difference between residual-based CNNs and Transformer-based networks? Why can CNNs utilize LoRA and pruning techniques? The root cause of these questions lies in the lack of a robust theoretical foundation for deep learning models in CV. To address these critical issues and techniques, we employ the Universal Approximation Theorem (UAT) to provide a theoretical basis for convolution- and Transformer-based models in CV. By doing so, we aim to elucidate these questions from a theoretical perspective.", "published": "2024-07-02T06:08:30Z", "authors": ["Wei Wang", "Qing Li"], "categories": ["cs.CV", "cs.AI"], "doi": "", "_query": "property valuation computer vision", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2106.09701v2", "title": "Always Be Dreaming: A New Approach for Data-Free Class-Incremental Learning", "abstract": "Modern computer vision applications suffer from catastrophic forgetting when incrementally learning new concepts over time. The most successful approaches to alleviate this forgetting require extensive replay of previously seen data, which is problematic when memory constraints or data legality concerns exist. In this work, we consider the high-impact problem of Data-Free Class-Incremental Learning (DFCIL), where an incremental learning agent must learn new concepts over time without storing generators or training data from past tasks. One approach for DFCIL is to replay synthetic images produced by inverting a frozen copy of the learner's classification model, but we show this approach fails for common class-incremental benchmarks when using standard distillation strategies. We diagnose the cause of this failure and propose a novel incremental distillation strategy for DFCIL, contributing a modified cross-entropy training and importance-weighted feature distillation, and show that our method results in up to a 25.1% increase in final task accuracy (absolute difference) compared to SOTA DFCIL methods for common class-incremental benchmarks. Our method even outperforms several standard replay based methods which store a coreset of images.", "published": "2021-06-17T17:56:08Z", "authors": ["James Smith", "Yen-Chang Hsu", "Jonathan Balloch", "Yilin Shen", "Hongxia Jin", "Zsolt Kira"], "categories": ["cs.CV", "cs.LG"], "doi": "", "_query": "property valuation computer vision", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2301.00394v2", "title": "Deep Learning Technique for Human Parsing: A Survey and Outlook", "abstract": "Human parsing aims to partition humans in image or video into multiple pixel-level semantic parts. In the last decade, it has gained significantly increased interest in the computer vision community and has been utilized in a broad range of practical applications, from security monitoring, to social media, to visual special effects, just to name a few. Although deep learning-based human parsing solutions have made remarkable achievements, many important concepts, existing challenges, and potential research directions are still confusing. In this survey, we comprehensively review three core sub-tasks: single human parsing, multiple human parsing, and video human parsing, by introducing their respective task settings, background concepts, relevant problems and applications, representative literature, and datasets. We also present quantitative performance comparisons of the reviewed methods on benchmark datasets. Additionally, to promote sustainable development of the community, we put forward a transformer-based human parsing framework, providing a high-performance baseline for follow-up research through universal, concise, and extensible solutions. Finally, we point out a set of under-investigated open issues in this field and suggest new directions for future study. We also provide a regularly updated project page, to continuously track recent developments in this fast-advancing field: https://github.com/soeaver/awesome-human-parsing.", "published": "2023-01-01T12:39:57Z", "authors": ["Lu Yang", "Wenhe Jia", "Shan Li", "Qing Song"], "categories": ["cs.CV"], "doi": "10.1007/s11263-024-02031-9", "_query": "property valuation computer vision", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2606.01635v1", "title": "AlphaToken: Decoupling Adaptation and Stability for Path-Aware Response Token Valuation in LLM Post-Training", "abstract": "Token selection is pivotal for effective LLM post-training. However, existing methods mostly rely on local heuristics and rarely formulate token selection as a principled valuation of individual response tokens. We introduce $\\textbf{AlphaToken}$, a response token valuation framework that decouples valuation into $\\textbf{adaptation}$ (promoting target-task learning) and $\\textbf{stability}$ (preserving pre-trained capabilities), and makes each objective $\\textbf{path-aware}$ by combining the direct-path signal from local token gradients with the downstream causal-path signal in autoregressive generation. Since retention data are typically unavailable, AlphaToken approximates stability via a $\\textbf{Fisher-drift proxy}$ anchored at the pre-trained reference model. For efficient computation, we extend Ghost Dot-Product to token-level valuation. AlphaToken masks low-value response tokens during fine-tuning and preference optimization, concentrating training signals on more valuable positions. Experiments show that AlphaToken improves post-training performance and mitigates catastrophic forgetting.", "published": "2026-06-01T03:40:35Z", "authors": ["Liu Qing", "Ou Wu", "Yi Du"], "categories": ["cs.CL", "cs.AI"], "doi": "", "_query": "property valuation computer vision", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2109.08203v2", "title": "Torch.manual_seed(3407) is all you need: On the influence of random seeds in deep learning architectures for computer vision", "abstract": "In this paper I investigate the effect of random seed selection on the accuracy when using popular deep learning architectures for computer vision. I scan a large amount of seeds (up to $10^4$) on CIFAR 10 and I also scan fewer seeds on Imagenet using pre-trained models to investigate large scale datasets. The conclusions are that even if the variance is not very large, it is surprisingly easy to find an outlier that performs much better or much worse than the average.", "published": "2021-09-16T20:10:12Z", "authors": ["David Picard"], "categories": ["cs.CV"], "doi": "", "_query": "property valuation computer vision", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2010.15303v1", "title": "Automatic joint damage quantification using computer vision and deep learning", "abstract": "Joint raveled or spalled damage (henceforth called joint damage) can affect the safety and long-term performance of concrete pavements. It is important to assess and quantify the joint damage over time to assist in building action plans for maintenance, predicting maintenance costs, and maximize the concrete pavement service life. A framework for the accurate, autonomous, and rapid quantification of joint damage with a low-cost camera is proposed using a computer vision technique with a deep learning (DL) algorithm. The DL model is employed to train 263 images of sawcuts with joint damage. The trained DL model is used for pixel-wise color-masking joint damage in a series of query 2D images, which are used to reconstruct a 3D image using open-source structure from motion algorithm. Another damage quantification algorithm using a color threshold is applied to detect and compute the surface area of the damage in the 3D reconstructed image. The effectiveness of the framework was validated through inspecting joint damage at four transverse contraction joints in Illinois, USA, including three acceptable joints and one unacceptable joint by visual inspection. The results show the framework achieves 76% recall and 10% error.", "published": "2020-10-29T01:41:20Z", "authors": ["Quang Tran", "Jeffery R. Roesler"], "categories": ["cs.CV"], "doi": "", "_query": "property valuation computer vision", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2408.01895v2", "title": "Computational Trichromacy Reconstruction: Empowering the Color-Vision Deficient to Recognize Colors Using Augmented Reality", "abstract": "We propose an assistive technology that helps individuals with Color Vision Deficiencies (CVD) to recognize/name colors. A dichromat's color perception is a reduced two-dimensional (2D) subset of a normal trichromat's three dimensional color (3D) perception, leading to confusion when visual stimuli that appear identical to the dichromat are referred to by different color names. Using our proposed system, CVD individuals can interactively induce distinct perceptual changes to originally confusing colors via a computational color space transformation. By combining their original 2D precepts for colors with the discriminative changes, a three dimensional color space is reconstructed, where the dichromat can learn to resolve color name confusions and accurately recognize colors. Our system is implemented as an Augmented Reality (AR) interface on smartphones, where users interactively control the rotation through swipe gestures and observe the induced color shifts in the camera view or in a displayed image. Through psychophysical experiments and a longitudinal user study, we demonstrate that such rotational color shifts have discriminative power (initially confusing colors become distinct under rotation) and exhibit structured perceptual shifts dichromats can learn with modest training. The AR App is also evaluated in two real-world scenarios (building with lego blocks and interpreting artistic works); users all report positive experience in using the App to recognize object colors that they otherwise could not.", "published": "2024-08-04T01:34:22Z", "authors": ["Yuhao Zhu", "Ethan Chen", "Colin Hascup", "Yukang Yan", "Gaurav Sharma"], "categories": ["cs.HC", "cs.CV"], "doi": "10.1145/3654777.3676415", "_query": "property valuation computer vision", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/1704.02268v3", "title": "Deep Unsupervised Similarity Learning using Partially Ordered Sets", "abstract": "Unsupervised learning of visual similarities is of paramount importance to computer vision, particularly due to lacking training data for fine-grained similarities. Deep learning of similarities is often based on relationships between pairs or triplets of samples. Many of these relations are unreliable and mutually contradicting, implying inconsistencies when trained without supervision information that relates different tuples or triplets to each other. To overcome this problem, we use local estimates of reliable (dis-)similarities to initially group samples into compact surrogate classes and use local partial orders of samples to classes to link classes to each other. Similarity learning is then formulated as a partial ordering task with soft correspondences of all samples to classes. Adopting a strategy of self-supervision, a CNN is trained to optimally represent samples in a mutually consistent manner while updating the classes. The similarity learning and grouping procedure are integrated in a single model and optimized jointly. The proposed unsupervised approach shows competitive performance on detailed pose estimation and object classification.", "published": "2017-04-07T15:44:51Z", "authors": ["Miguel A Bautista", "Artsiom Sanakoyeu", "Björn Ommer"], "categories": ["cs.CV"], "doi": "", "_query": "property valuation computer vision", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/1406.6946v2", "title": "An improved computer vision method for detecting white blood cells", "abstract": "The automatic detection of White Blood Cells (WBC) still remains as an unsolved issue in medical imaging. The analysis of WBC images has engaged researchers from fields of medicine and computer vision alike. Since WBC can be approximated by an ellipsoid form, an ellipse detector algorithm may be successfully applied in order to recognize them. This paper presents an algorithm for the automatic detection of WBC embedded into complicated and cluttered smear images that considers the complete process as a multi-ellipse detection problem. The approach, based on the Differential Evolution (DE) algorithm, transforms the detection task into an optimization problem where individuals emulate candidate ellipses. An objective function evaluates if such candidate ellipses are really present in the edge image of the smear. Guided by the values of such function, the set of encoded candidate ellipses (individuals) are evolved using the DE algorithm so that they can fit into the WBC enclosed within the edge-only map of the image. Experimental results from white blood cell images with a varying range of complexity are included to validate the efficiency of the proposed technique in terms of accuracy and robustness.", "published": "2014-06-26T17:01:32Z", "authors": ["Erik Cuevas", "Margarita Diaz", "Miguel Manzanares", "Daniel Zaldivar", "Marco Perez"], "categories": ["cs.CV"], "doi": "", "_query": "property valuation computer vision", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2511.17576v1", "title": "Multimodal AI for Body Fat Estimation: Computer Vision and Anthropometry with DEXA Benchmarks", "abstract": "Tracking body fat percentage is essential for effective weight management, yet gold-standard methods such as DEXA scans remain expensive and inaccessible for most people. This study evaluates the feasibility of artificial intelligence (AI) models as low-cost alternatives using frontal body images and basic anthropometric data. The dataset consists of 535 samples: 253 cases with recorded anthropometric measurements (weight, height, neck, ankle, and wrist) and 282 images obtained via web scraping from Reddit posts with self-reported body fat percentages, including some reported as DEXA-derived by the original posters. Because no public datasets exist for computer-vision-based body fat estimation, this dataset was compiled specifically for this study. Two approaches were developed: (1) ResNet-based image models and (2) regression models using anthropometric measurements. A multimodal fusion framework is also outlined for future expansion once paired datasets become available. The image-based model achieved a Root Mean Square Error (RMSE) of 4.44% and a Coefficient of Determination (R^2) of 0.807. These findings demonstrate that AI-assisted models can offer accessible and low-cost body fat estimates, supporting future consumer applications in health and fitness.", "published": "2025-11-15T00:20:24Z", "authors": ["Rayan Aldajani"], "categories": ["cs.CV", "cs.AI", "cs.LG"], "doi": "", "_query": "property valuation computer vision", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2610.03445v1", "title": "Corrupted but Correct: Why Vision-Language Models Lie to Themselves Internally", "abstract": "A targeted adversarial perturbation can drive a vision-language model's (VLM's) teacher-forced training loss for a fixed target caption to near zero, yet the same model, allowed to generate freely, produces the original, correct description with no trace of the target. We call this dissociation the train/inference gap, and give it a precise mechanistic account on Qwen2.5-VL-7B-Instruct using a controlled two-stage PGD attack on 200 held-out COCO images. First, we show that image-level pixel statistics, including a correctly re-implemented, texture-based attackability measure from the CNN robustness literature, have essentially no predictive power over which images are corrupted (best predictor r=-0.050, p=0.484; ridge regression R^2=0.069). Second, using the logit lens, we localise the gap to a single autoregressive step: the rank of the target token, conditioned on the correct first token already being generated, is fixed at exactly 3,488 out of 152,064 vocabulary entries for every image and every condition, with zero variance. Third, tracking target-token rank across all 28 LLM decoder layers reveals that the visual encoder corrupts every image's representation by a comparable margin regardless of eventual outcome, but the language model decoder then differentially arbitrates: amplifying the corrupted signal for susceptible images and actively suppressing it, past its clean-image baseline, for resistant ones (p<0.001, rank-biserial r=0.579). A linear probe on the merger hidden state separates these two outcomes with AUC=0.858, though we flag a circularity concern in this estimate. Together these results argue that adversarial robustness in autoregressive VLMs is substantially a property of the language decoder's prior, not the visual encoder, with direct implications for where faithfulness evaluations and defenses for deployed VLM systems should be targeted.", "published": "2026-10-02T15:27:28Z", "authors": ["Arun Josephraj Arokiaraj", "Zekun Wu", "Adriano Koshiyama"], "categories": ["cs.CV", "cs.AI"], "doi": "", "_query": "property valuation computer vision", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/1704.01085v3", "title": "Deep Depth From Focus", "abstract": "Depth from focus (DFF) is one of the classical ill-posed inverse problems in computer vision. Most approaches recover the depth at each pixel based on the focal setting which exhibits maximal sharpness. Yet, it is not obvious how to reliably estimate the sharpness level, particularly in low-textured areas. In this paper, we propose `Deep Depth From Focus (DDFF)' as the first end-to-end learning approach to this problem. One of the main challenges we face is the hunger for data of deep neural networks. In order to obtain a significant amount of focal stacks with corresponding groundtruth depth, we propose to leverage a light-field camera with a co-calibrated RGB-D sensor. This allows us to digitally create focal stacks of varying sizes. Compared to existing benchmarks our dataset is 25 times larger, enabling the use of machine learning for this inverse problem. We compare our results with state-of-the-art DFF methods and we also analyze the effect of several key deep architectural components. These experiments show that our proposed method `DDFFNet' achieves state-of-the-art performance in all scenes, reducing depth error by more than 75% compared to the classical DFF methods.", "published": "2017-04-04T16:15:54Z", "authors": ["Caner Hazirbas", "Sebastian Georg Soyer", "Maximilian Christian Staab", "Laura Leal-Taixé", "Daniel Cremers"], "categories": ["cs.CV"], "doi": "", "_query": "property valuation computer vision", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2203.06145v2", "title": "Neuromorphic Data Augmentation for Training Spiking Neural Networks", "abstract": "Developing neuromorphic intelligence on event-based datasets with Spiking Neural Networks (SNNs) has recently attracted much research attention. However, the limited size of event-based datasets makes SNNs prone to overfitting and unstable convergence. This issue remains unexplored by previous academic works. In an effort to minimize this generalization gap, we propose Neuromorphic Data Augmentation (NDA), a family of geometric augmentations specifically designed for event-based datasets with the goal of significantly stabilizing the SNN training and reducing the generalization gap between training and test performance. The proposed method is simple and compatible with existing SNN training pipelines. Using the proposed augmentation, for the first time, we demonstrate the feasibility of unsupervised contrastive learning for SNNs. We conduct comprehensive experiments on prevailing neuromorphic vision benchmarks and show that NDA yields substantial improvements over previous state-of-the-art results. For example, the NDA-based SNN achieves accuracy gain on CIFAR10-DVS and N-Caltech 101 by 10.1% and 13.7%, respectively. Code is available on GitHub https://github.com/Intelligent-Computing-Lab-Yale/NDA_SNN", "published": "2022-03-11T18:17:19Z", "authors": ["Yuhang Li", "Youngeun Kim", "Hyoungseob Park", "Tamar Geller", "Priyadarshini Panda"], "categories": ["cs.CV"], "doi": "", "_query": "property valuation computer vision", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/1404.6215v9", "title": "Valuation and Divisibility", "abstract": "In this paper, we explain how some basic facts about valuation can help clarify many questions about divisibility in integral domains.", "published": "2014-04-24T18:29:34Z", "authors": ["Nicholas Phat Nguyen"], "categories": ["math.AC"], "doi": "", "_query": "property valuation computer vision", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/cs/0201021v1", "title": "Learning to Play Games in Extensive Form by Valuation", "abstract": "A valuation for a player in a game in extensive form is an assignment of numeric values to the players moves. The valuation reflects the desirability moves. We assume a myopic player, who chooses a move with the highest valuation. Valuations can also be revised, and hopefully improved, after each play of the game. Here, a very simple valuation revision is considered, in which the moves made in a play are assigned the payoff obtained in the play. We show that by adopting such a learning process a player who has a winning strategy in a win-lose game can almost surely guarantee a win in a repeated game. When a player has more than two payoffs, a more elaborate learning procedure is required. We consider one that associates with each move the average payoff in the rounds in which this move was made. When all players adopt this learning procedure, with some perturbations, then, with probability 1, strategies that are close to subgame perfect equilibrium are played after some time. A single player who adopts this procedure can guarantee only her individually rational payoff.", "published": "2002-01-23T11:58:17Z", "authors": ["Philippe Jehiel", "Dov Samet"], "categories": ["cs.LG", "cs.GT"], "doi": "", "_query": "property valuation computer vision", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2110.12521v2", "title": "Reachability Embeddings: Scalable Self-Supervised Representation Learning from Mobility Trajectories for Multimodal Geospatial Computer Vision", "abstract": "Self-supervised representation learning techniques utilize large datasets without semantic annotations to learn meaningful, universal features that can be conveniently transferred to solve a wide variety of downstream supervised tasks. In this paper, we propose a self-supervised method for learning representations of geographic locations from unlabeled GPS trajectories to solve downstream geospatial computer vision tasks. Tiles resulting from a raster representation of the earth's surface are modeled as nodes on a graph or pixels of an image. GPS trajectories are modeled as allowed Markovian paths on these nodes. A scalable and distributed algorithm is presented to compute image-like tensors, called reachability summaries, of the spatial connectivity patterns between tiles and their neighbors implied by the observed Markovian paths. A convolutional, contractive autoencoder is trained to learn compressed representations, called reachability embeddings, of reachability summaries for every tile. Reachability embeddings serve as task-agnostic, feature representations of geographic locations. Using reachability embeddings as pixel representations for five different downstream geospatial tasks, cast as supervised semantic segmentation problems, we quantitatively demonstrate that reachability embeddings are semantically meaningful representations and result in 4-23% gain in performance, while using upto 67% less trajectory data, as measured using area under the precision-recall curve (AUPRC) metric, when compared to baseline models that use pixel representations that do not account for the spatial connectivity between tiles. Reachability embeddings transform sequential, spatiotemporal mobility data into semantically meaningful image-like tensor representations that can be combined with other sources of imagery and are designed to facilitate multimodal learning in geospatial computer vision.", "published": "2021-10-24T20:10:22Z", "authors": ["Swetava Ganguli", "C. V. Krishnakumar Iyer", "Vipul Pandey"], "categories": ["cs.CV", "cs.AI", "cs.LG"], "doi": "10.1109/MDM55031.2022.00028", "_query": "property valuation computer vision", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2505.10590v2", "title": "Anchoring AI Capabilities in Market Valuations: The Capability Realization Rate Model and Valuation Misalignment Risk", "abstract": "Recent breakthroughs in artificial intelligence (AI) have triggered surges in market valuations for AI-related companies, often outpacing the realization of underlying capabilities. We examine the anchoring effect of AI capabilities on equity valuations and propose a Capability Realization Rate (CRR) model to quantify the gap between AI potential and realized performance. Using data from the 2023--2025 generative AI boom, we analyze sector-level sensitivity and conduct case studies (OpenAI, Adobe, NVIDIA, Meta, Microsoft, Goldman Sachs) to illustrate patterns of valuation premium and misalignment. Our findings indicate that AI-native firms commanded outsized valuation premiums anchored to future potential, while traditional companies integrating AI experienced re-ratings subject to proof of tangible returns. We argue that CRR can help identify valuation misalignment risk-where market prices diverge from realized AI-driven value. We conclude with policy recommendations to improve transparency, mitigate speculative bubbles, and align AI innovation with sustainable market value.", "published": "2025-05-15T01:06:06Z", "authors": ["Xinmin Fang", "Lingfeng Tao", "Zhengxiong Li"], "categories": ["cs.CY", "cs.AI"], "doi": "", "_query": "property valuation computer vision", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2506.19939v1", "title": "Computer Vision based Automated Quantification of Agricultural Sprayers Boom Displacement", "abstract": "Application rate errors when using self-propelled agricultural sprayers for agricultural production remain a concern. Among other factors, spray boom instability is one of the major contributors to application errors. Spray booms' width of 38m, combined with 30 kph driving speeds, varying terrain, and machine dynamics when maneuvering complex field boundaries, make controls of these booms very complex. However, there is no quantitative knowledge on the extent of boom movement to systematically develop a solution that might include boom designs and responsive boom control systems. Therefore, this study was conducted to develop an automated computer vision system to quantify the boom movement of various agricultural sprayers. A computer vision system was developed to track a target on the edge of the sprayer boom in real time. YOLO V7, V8, and V11 neural network models were trained to track the boom's movements in field operations to quantify effective displacement in the vertical and transverse directions. An inclinometer sensor was mounted on the boom to capture boom angles and validate the neural network model output. The results showed that the model could detect the target with more than 90 percent accuracy, and distance estimates of the target on the boom were within 0.026 m of the inclinometer sensor data. This system can quantify the boom movement on the current sprayer and potentially on any other sprayer with minor modifications. The data can be used to make design improvements to make sprayer booms more stable and achieve greater application accuracy.", "published": "2025-06-24T18:30:18Z", "authors": ["Aryan Singh Dalal", "Sidharth Rai", "Rahul Singh", "Treman Singh Kaloya", "Rahul Harsha Cheppally", "Ajay Sharda"], "categories": ["cs.CV"], "doi": "", "_query": "property valuation computer vision", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/1111.5548v1", "title": "Computation of generalized inverses using Php/MySql environment", "abstract": "The main aim of this paper is to develop a client/server-based model for computing the weighted Moore-Penrose inverse using the partitioning method as well as for storage of generated results. The web application is developed in the PHP/MySQL environment. The source code is open and free for testing by using a web browser. Influence of different matrix representations and storage systems on the computational time is investigated. The CPU time for searching the previously stored pseudo-inverses is compared with the CPU time spent for new computation of the same inverses.", "published": "2011-11-23T16:47:10Z", "authors": ["Milan B. Tasić", "Predrag S. Stanimirović", "Selver H. Pepić"], "categories": ["cs.DB", "cs.DS", "math.NA"], "doi": "10.1080/00207160.2010.541453", "_query": "property valuation computer vision", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/1509.07884v2", "title": "Berline-Vergne valuation and generalized permutohedra", "abstract": "Generalizing a conjecture by De Loera et al., we conjecture that integral generalized permutohedra all have positive Ehrhart coefficients. Berline and Vergne construct a valuation that assigns values to faces of polytopes, which provides a way to write Ehrhart coefficients of a polytope as positive sums of these values. Based on available results, we pose a stronger conjecture: Berline-Vergne's valuation is always positive on permutohedra, which implies our first conjecture. This article proves that our strong conjecture on Berline-Vergne's valuation is true for dimension up to 6, and is true if we restrict to faces of codimension up to 3. In addition to investigating the positivity conjectures, we study the Berline-Vergne's valuation, and show that it is the unique construction for McMullen's formula used to describe number of lattice points in permutohedra under certain symmetry constraints. We also give an equivalent statement to the strong conjecture in terms of mixed valuations.", "published": "2015-09-25T20:24:06Z", "authors": ["Federico Castillo", "Fu Liu"], "categories": ["math.CO"], "doi": "", "_query": "property valuation computer vision", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/math/0303200v1", "title": "Valuations, Deformations, and Toric Geometry", "abstract": "A study of the relation between a noetherian local domain with a given valuation and its associated graded ring with respect to the valuation, which in some cases is an esentially toric variety, possibly of infinite embedding dimension, but of finite Krull dimension. After extension of the valuation to a suitable completion (whose existence is not established in the paper) the relation becomes much more precise, and suggests a possible way to establish local uniformization for an excellent equicharacteristic local domain with an algebraically closed residue field, by deforming a partial toric embedded resolution of the essentially toric spectrum of the associated graded ring.", "published": "2003-03-17T14:30:50Z", "authors": ["Bernard Teissier"], "categories": ["math.AC", "math.AG"], "doi": "", "_query": "property valuation computer vision", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/1805.11815v1", "title": "Enabling Pedestrian Safety using Computer Vision Techniques: A Case Study of the 2018 Uber Inc. Self-driving Car Crash", "abstract": "Human lives are important. The decision to allow self-driving vehicles operate on our roads carries great weight. This has been a hot topic of debate between policy-makers, technologists and public safety institutions. The recent Uber Inc. self-driving car crash, resulting in the death of a pedestrian, has strengthened the argument that autonomous vehicle technology is still not ready for deployment on public roads. In this work, we analyze the Uber car crash and shed light on the question, \"Could the Uber Car Crash have been avoided?\". We apply state-of-the-art Computer Vision models to this highly practical scenario. More generally, our experimental results are an evaluation of various image enhancement and object recognition techniques for enabling pedestrian safety in low-lighting conditions using the Uber crash as a case study.", "published": "2018-05-30T05:38:30Z", "authors": ["Puneet Kohli", "Anjali Chadha"], "categories": ["cs.CV", "cs.AI"], "doi": "10.1007/978-3-030-12388-8_19", "_query": "property valuation computer vision", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2501.04267v1", "title": "A 5G-Edge Architecture for Computational Offloading of Computer Vision Applications", "abstract": "Processing computer vision applications (CVA) on mobile devices is challenging due to limited battery life and computing power. While cloud-based remote processing of CVA offers abundant computational resources, it introduces latency issues that can hinder real-time applications. To overcome this problem, computational offloading to edge servers has been adopted by industry and academic research. Furthermore, 5G access can also benefit CVA with lower latency and higher bandwidth than previous cellular generations. As the number of Mobile Operators and Internet Service providers relying on 5G access is growing, it is of paramount importance to elaborate a solution for supporting real time applications with the assistance of the edge computing. Besides that, open-source based platforms for Multi-access Edge Computing (MEC) and 5G core can be deployed to rapid prototyping and testing applications. This paper aims at providing an end-to-end solution of open-source MEC and 5G Core platforms along with a commercial 5G Radio. We first conceived a 5G-edge computing environment to assist near to user processing of computer vision applications. Then a sentiment analysis application is developed and integrated to the proposed 5G-Edge architecture. Finally, we conducted a performance evaluation of the proposed solution and compare it against a remote cloud-based approach in order to highlight the benefits of our proposal. The proposed architecture achieved a 260\\% throughput performance increase and reduced response time by 71.3\\% compared to the remote-cloud-based offloading.", "published": "2025-01-08T04:20:36Z", "authors": ["Marcelo V. B. da Silva", "Maria Barbosa", "Anderson Queiroz", "Kelvin L. Dias"], "categories": ["cs.NI"], "doi": "", "_query": "property valuation computer vision", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2104.11600v1", "title": "A Picture is Worth a Collaboration: Accumulating Design Knowledge for Computer-Vision-based Hybrid Intelligence Systems", "abstract": "Computer vision (CV) techniques try to mimic human capabilities of visual perception to support labor-intensive and time-consuming tasks like the recognition and localization of critical objects. Nowadays, CV increasingly relies on artificial intelligence (AI) to automatically extract useful information from images that can be utilized for decision support and business process automation. However, the focus of extant research is often exclusively on technical aspects when designing AI-based CV systems while neglecting socio-technical facets, such as trust, control, and autonomy. For this purpose, we consider the design of such systems from a hybrid intelligence (HI) perspective and aim to derive prescriptive design knowledge for CV-based HI systems. We apply a reflective, practice-inspired design science approach and accumulate design knowledge from six comprehensive CV projects. As a result, we identify four design-related mechanisms (i.e., automation, signaling, modification, and collaboration) that inform our derived meta-requirements and design principles. This can serve as a basis for further socio-technical research on CV-based HI systems.", "published": "2021-04-23T13:47:57Z", "authors": ["Patrick Zschech", "Jannis Walk", "Kai Heinrich", "Michael Vössing", "Niklas Kühl"], "categories": ["cs.CV", "cs.AI"], "doi": "", "_query": "property valuation computer vision", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2512.15765v4", "title": "Shapley-based Data Valuation for LLM Alignment via Sequential Preference Optimization", "abstract": "Data valuation is a natural framework for understanding which data sources matter most when aligning a Large Language Model (LLM) from multiple sources. The standard game-theoretic approach treats each source, or equivalently each preference dataset, as a player in a cooperative game and assigns it a contribution score through the Shapley value. In practice, however, Shapley-based valuation is computationally prohibitive because it requires aligning a separate model for every possible coalition of sources, i.e., an exponential number of alignments. We address this challenge for Direct Alignment Algorithms (DAAs), including IPO, which learn through log-policy ratios with respect to a reference policy. We show that, when a model is aligned sequentially source by source, exact optimization makes each stage contribute additively to the log-probability of a full response, up to a prompt-dependent normalization constant. This allows the log-probability assigned by any coalition to a fixed response to be reconstructed from the base policy and the policies trained on each source individually. This reduces the alignment cost of Shapley-based valuation from exponential to linear, since only one model per source needs to be trained to evaluate coalition scores. We test whether this theoretical property remains approximately valid under finite training across several base models and real-world data sources. We finally compute the Shapley values of these sources under multiple reward models, showing how their estimated contributions vary across evaluation criteria.", "published": "2025-12-12T10:13:54Z", "authors": ["Mélissa Tamine", "Otmane Sakhi", "Benjamin Heymann", "Maxime Vono", "Patrick Loiseau"], "categories": ["cs.LG", "cs.GT", "stat.ML"], "doi": "", "_query": "property valuation computer vision", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2103.15214v2", "title": "Computational Complexity of Covering Two-vertex Multigraphs with Semi-edges", "abstract": "We initiate the study of computational complexity of graph coverings, aka locally bijective graph homomorphisms, for {\\em graphs with semi-edges}. The notion of graph covering is a discretization of coverings between surfaces or topological spaces, a notion well known and deeply studied in classical topology. Graph covers have found applications in discrete mathematics for constructing highly symmetric graphs, and in computer science in the theory of local computations. In 1991, Abello, Fellows, and Stillwell asked for a classification of the computational complexity of deciding if an input graph covers a fixed target graph, in the ordinary setting (of graphs with only edges). Although many general results are known, the full classification is still open. In spite of that, we propose to study the more general case of covering graphs composed of normal edges (including multiedges and loops) and so-called semi-edges. Semi-edges are becoming increasingly popular in modern topological graph theory, as well as in mathematical physics. They also naturally occur in the local computation setting, since they are lifted to matchings in the covering graph. We show some solvable cases and, in particular, completely characterize the complexity of the already very nontrivial problem of covering one- and two-vertex (multi)graphs with semi-edges. Our NP-hardness results are proven for simple input graphs, and in the case of regular two-vertex target graphs, even for bipartite ones. We remark that our new characterization results also strengthen previously known results for covering graphs without semi-edges, and they in turn apply to an infinite class of simple target graphs with at most two vertices of degree more than two. Some of the results are moreover proven in a more general setting (e.g., finding $k$-tuples of pairwise disjoint perfect matchings in regular graphs).", "published": "2021-03-28T20:14:54Z", "authors": ["Jan Bok", "Jiří Fiala", "Petr Hliněný", "Nikola Jedličková", "Jan Kratochvíl"], "categories": ["cs.DM", "cs.CC", "math.CO"], "doi": "", "_query": "property valuation computer vision", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2603.06576v2", "title": "BEVLM: Distilling Semantic Knowledge from LLMs into Bird's-Eye View Representations", "abstract": "The integration of Large Language Models (LLMs) into autonomous driving has attracted growing interest for their strong reasoning and semantic understanding abilities, which are essential for handling complex decision-making and long-tail scenarios. However, existing methods typically feed LLMs with tokens from multi-view and multi-frame images independently, leading to redundant computation and limited spatial consistency. This separation in visual processing hinders accurate 3D spatial reasoning and fails to maintain geometric coherence across views. On the other hand, Bird's-Eye View (BEV) representations learned from geometrically annotated tasks (e.g., object detection) provide spatial structure but lack the semantic richness of foundation vision encoders. To bridge this gap, we propose BEVLM, a framework that connects a spatially consistent and semantically distilled BEV representation with LLMs. Through extensive experiments, we show that BEVLM enables LLMs to reason more effectively in cross-view driving scenes, improving accuracy by 46.0%, by leveraging BEV features as unified inputs. Furthermore, by distilling semantic knowledge from LLMs into BEV representations, BEVLM significantly improves closed-loop end-to-end driving performance in safety-critical scenarios across UniAD and VAD, with gains of up to 28.2%.", "published": "2026-03-06T18:59:55Z", "authors": ["Thomas Monninger", "Shaoyuan Xie", "Qi Alfred Chen", "Sihao Ding"], "categories": ["cs.CV", "cs.AI", "cs.LG", "cs.RO"], "doi": "", "_query": "property valuation computer vision", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2005.00486v2", "title": "The support of dually epi-translation invariant valuations on convex functions", "abstract": "We study dually epi-translation invariant valuations on cones of convex functions containing the space of finite-valued convex functions. The existence of a homogeneous decomposition is used to associate a distribution to every valuation of this type similar to the Goodey-Weil embedding for translation invariant valuations on convex bodies. The relation between the valuation and its associated distribution is used to establish a notion of support for valuations. As an application, we show that there are no $\\mathrm{SL}(n)$ or translation invariant valuations except constant valuations in this class and we discuss which valuations on finite-valued convex functions can be extended to larger cones. In addition, we examine some topological properties of spaces of valuations with compact support.", "published": "2020-04-30T12:07:08Z", "authors": ["Jonas Knoerr"], "categories": ["math.MG", "math.FA"], "doi": "10.1016/j.jfa.2021.109059", "_query": "property valuation computer vision", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/1905.07836v1", "title": "Enabling Computer Vision Driven Assistive Devices for the Visually Impaired via Micro-architecture Design Exploration", "abstract": "Recent improvements in object detection have shown potential to aid in tasks where previous solutions were not able to achieve. A particular area is assistive devices for individuals with visual impairment. While state-of-the-art deep neural networks have been shown to achieve superior object detection performance, their high computational and memory requirements make them cost prohibitive for on-device operation. Alternatively, cloud-based operation leads to privacy concerns, both not attractive to potential users. To address these challenges, this study investigates creating an efficient object detection network specifically for OLIV, an AI-powered assistant for object localization for the visually impaired, via micro-architecture design exploration. In particular, we formulate the problem of finding an optimal network micro-architecture as an numerical optimization problem, where we find the set of hyperparameters controlling the MobileNetV2-SSD network micro-architecture that maximizes a modified NetScore objective function for the MSCOCO-OLIV dataset of indoor objects. Experimental results show that such a micro-architecture design exploration strategy leads to a compact deep neural network with a balanced trade-off between accuracy, size, and speed, making it well-suited for enabling on-device computer vision driven assistive devices for the visually impaired.", "published": "2019-05-20T01:30:15Z", "authors": ["Linda Wang", "Alexander Wong"], "categories": ["cs.CV", "cs.NE"], "doi": "", "_query": "property valuation computer vision", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2111.13131v1", "title": "Scene Graph Generation with Geometric Context", "abstract": "Scene Graph Generation has gained much attention in computer vision research with the growing demand in image understanding projects like visual question answering, image captioning, self-driving cars, crowd behavior analysis, activity recognition, and more. Scene graph, a visually grounded graphical structure of an image, immensely helps to simplify the image understanding tasks. In this work, we introduced a post-processing algorithm called Geometric Context to understand the visual scenes better geometrically. We use this post-processing algorithm to add and refine the geometric relationships between object pairs to a prior model. We exploit this context by calculating the direction and distance between object pairs. We use Knowledge Embedded Routing Network (KERN) as our baseline model, extend the work with our algorithm, and show comparable results on the recent state-of-the-art algorithms.", "published": "2021-11-25T15:42:21Z", "authors": ["Vishal Kumar", "Albert Mundu", "Satish Kumar Singh"], "categories": ["cs.CV", "cs.AI", "cs.LG"], "doi": "", "_query": "property valuation computer vision", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2103.11968v2", "title": "A (possibly new) structure without the canonical base property", "abstract": "In this short note, we introduce a generalization of the canonical base property, called transfer of internality on quotients. A structural study of groups definable in theories with this property yields as a consequence infinitely many new uncountably categorical additive covers of the complex numbers without the canonical base property.", "published": "2021-03-22T16:16:15Z", "authors": ["Michael Loesch"], "categories": ["math.LO"], "doi": "", "_query": "property valuation computer vision", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/1805.00400v1", "title": "Computing Environments for Reproducibility: Capturing the \"Whole Tale\"", "abstract": "The act of sharing scientific knowledge is rapidly evolving away from traditional articles and presentations to the delivery of executable objects that integrate the data and computational details (e.g., scripts and workflows) upon which the findings rely. This envisioned coupling of data and process is essential to advancing science but faces technical and institutional barriers. The Whole Tale project aims to address these barriers by connecting computational, data-intensive research efforts with the larger research process--transforming the knowledge discovery and dissemination process into one where data products are united with research articles to create \"living publications\" or \"tales\". The Whole Tale focuses on the full spectrum of science, empowering users in the long tail of science, and power users with demands for access to big data and compute resources. We report here on the design, architecture, and implementation of the Whole Tale environment.", "published": "2018-05-01T15:32:39Z", "authors": ["Adam Brinckman", "Kyle Chard", "Niall Gaffney", "Mihael Hategan", "Matthew B. Jones", "Kacper Kowalik", "Sivakumar Kulasekaran", "Bertram Ludäscher", "Bryce D. Mecum", "Jarek Nabrzyski", "Victoria Stodden", "Ian J. Taylor", "Matthew J. Turk", "Kandace Turner"], "categories": ["cs.CY"], "doi": "", "_query": "property valuation computer vision", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/1603.03012v1", "title": "Capital Valuation Adjustment and Funding Valuation Adjustment", "abstract": "In the aftermath of the 2007 global financial crisis, banks started reflecting into derivative pricing the cost of capital and collateral funding through XVA metrics. Here XVA is a catch-all acronym whereby X is replaced by a letter such as C for credit, D for debt, F for funding, K for capital and so on, and VA stands for valuation adjustment. This behaviour is at odds with economies where markets for contingent claims are complete, whereby trades clear at fair valuations and the costs for capital and collateral are both irrelevant to investment decisions. In this paper, we set forth a mathematical formalism for derivative portfolio management in incomplete markets for banks. A particular emphasis is given to the problem of finding optimal strategies for retained earnings which ensure a sustainable dividend policy.", "published": "2016-03-09T19:45:08Z", "authors": ["Claudio Albanese", "Simone Caenazzo", "Stéphane Crépey"], "categories": ["q-fin.CP"], "doi": "", "_query": "property valuation computer vision", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2109.05616v1", "title": "On the $p$-adic valuation of a hyperfactorial", "abstract": "In this document will be proved a formula to compute the $p$-adic valuation of a hyperfactorial. We call a hyperfactorial the result of multiplying a given number of consecutive integers from 1 to the given number,each raised to its own power. For example, the hyperfactorial of $n$ is equal to: $1^1 2^2 3^3\\dots n^n$ . Lots of studies have been done about the hyperfactorial function, in particular two mathematicians: Glaisher and Kinkelin, who have found the asymptotic behaviour of this function as $n$ that approaches infinity (finding a costant, the Glaisher-Kinkelin costant, which has a lot of expressions using the Euler Gamma function and the Riemann Zeta function). In particular in this document I'll write about the p-adic valuation of this function, or rather the maximum exponent of $p$($p$ a prime integer) such that $p$ raised to that power divides the hyperfactorial of $n$. The formula which I will present uses the famous De-Polignac formula for the $p$-adic valuation of the simple factorial. Then I'll discuss about the asymptotic analysis of our result.", "published": "2021-09-12T21:24:19Z", "authors": ["Luca Onnis"], "categories": ["math.NT"], "doi": "", "_query": "property valuation computer vision", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2504.08154v1", "title": "Investigating Vision-Language Model for Point Cloud-based Vehicle Classification", "abstract": "Heavy-duty trucks pose significant safety challenges due to their large size and limited maneuverability compared to passenger vehicles. A deeper understanding of truck characteristics is essential for enhancing the safety perspective of cooperative autonomous driving. Traditional LiDAR-based truck classification methods rely on extensive manual annotations, which makes them labor-intensive and costly. The rapid advancement of large language models (LLMs) trained on massive datasets presents an opportunity to leverage their few-shot learning capabilities for truck classification. However, existing vision-language models (VLMs) are primarily trained on image datasets, which makes it challenging to directly process point cloud data. This study introduces a novel framework that integrates roadside LiDAR point cloud data with VLMs to facilitate efficient and accurate truck classification, which supports cooperative and safe driving environments. This study introduces three key innovations: (1) leveraging real-world LiDAR datasets for model development, (2) designing a preprocessing pipeline to adapt point cloud data for VLM input, including point cloud registration for dense 3D rendering and mathematical morphological techniques to enhance feature representation, and (3) utilizing in-context learning with few-shot prompting to enable vehicle classification with minimally labeled training data. Experimental results demonstrate encouraging performance of this method and present its potential to reduce annotation efforts while improving classification accuracy.", "published": "2025-04-10T22:37:27Z", "authors": ["Yiqiao Li", "Jie Wei", "Camille Kamga"], "categories": ["cs.CV"], "doi": "", "_query": "property valuation computer vision", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2606.21194v1", "title": "MEDLAYXPLAIN: Benchmarking the Expert-Lay Gap in Medical Vision-Language Models", "abstract": "Medical Vision-Language Models (Med-VLMs) achieve strong expert-level performance, yet their ability to generate patient-accessible descriptions remains underexplored. With the 21st Century Cures Act now mandating immediate patient access to diagnostic imaging results, evaluating whether Med-VLMs can bridge this Expert-Lay Gap is both urgent and clinically consequential for patient education and shared decision-making. To this end, we introduce MedLayXPlain, the first large-scale multimodal benchmark and evaluation framework for Medical Lay Language Generation (MLLG). MedLayXPlain-122K provides 122,789 region-grounded samples across 8 imaging modalities from 12 publicly available source datasets, each comprising a medical image with paired expert and lay captions anchored in a three-level Unified Medical Language System (UMLS) ontology hierarchy spanning 7 semantic groups, 43 semantic types, and 2,411 medical concepts. Lay captions are constructed via Hierarchical Ontology-Verified Refinement (HOVER), a three-step pipeline combining patient-centric vocabulary mapping, LLM-based constrained rewriting, and cross-model visual verification to enforce semantic equivalence while preventing hallucination. We further introduce MedLayEval, a lightweight 3B evaluator distilled from a 27B verifier that scores expert-lay alignment across five clinically grounded attributes, addressing the poor correlation between standard NLG metrics and clinical judgment. Benchmarking 33 VLMs on MedLayXPlain-122K reveals a systematic Expert-Lay Gap: medical VLMs achieve strong expert captioning but suffer significant lay-register degradation, while general-purpose VLMs produce more accessible language yet lack clinical precision, confirming that neither current paradigm adequately serves patient-facing communication.", "published": "2026-06-19T08:06:09Z", "authors": ["Han Jang", "Junhyeok Lee", "Songsoo Kim", "Chae Young Lim", "Hyeonjin Goh", "Heeseong Eum", "Kyu Sung Choi"], "categories": ["cs.CV", "cs.AI", "cs.CL"], "doi": "", "_query": "property valuation computer vision", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2507.19878v1", "title": "Efficient Self-Supervised Neuro-Analytic Visual Servoing for Real-time Quadrotor Control", "abstract": "This work introduces a self-supervised neuro-analytical, cost efficient, model for visual-based quadrotor control in which a small 1.7M parameters student ConvNet learns automatically from an analytical teacher, an improved image-based visual servoing (IBVS) controller. Our IBVS system solves numerical instabilities by reducing the classical visual servoing equations and enabling efficient stable image feature detection. Through knowledge distillation, the student model achieves 11x faster inference compared to the teacher IBVS pipeline, while demonstrating similar control accuracy at a significantly lower computational and memory cost. Our vision-only self-supervised neuro-analytic control, enables quadrotor orientation and movement without requiring explicit geometric models or fiducial markers. The proposed methodology leverages simulation-to-reality transfer learning and is validated on a small drone platform in GPS-denied indoor environments. Our key contributions include: (1) an analytical IBVS teacher that solves numerical instabilities inherent in classical approaches, (2) a two-stage segmentation pipeline combining YOLOv11 with a U-Net-based mask splitter for robust anterior-posterior vehicle segmentation to correctly estimate the orientation of the target, and (3) an efficient knowledge distillation dual-path system, which transfers geometric visual servoing capabilities from the analytical IBVS teacher to a compact and small student neural network that outperforms the teacher, while being suitable for real-time onboard deployment.", "published": "2025-07-26T09:17:38Z", "authors": ["Sebastian Mocanu", "Sebastian-Ion Nae", "Mihai-Eugen Barbu", "Marius Leordeanu"], "categories": ["cs.CV", "cs.RO"], "doi": "", "_query": "property valuation computer vision", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2609.29785v1", "title": "Lightweight Vision Transformer-Based U-Net for Brain Tumor Segmentation from MRI", "abstract": "Accurate brain tumor segmentation from Magnetic Resonance Imaging is essential for diagnosis, treatment planning, and surgical guidance. Although Convolutional Neural Networks, particularly UNet, have achieved significant success in medical image segmentation, they often struggle to capture the long-range spatial dependencies required to model tumors with irregular shapes and complex boundaries. This paper proposes a lightweight Vision Transformer UNet that combines the hierarchical feature extraction capability of UNet with the global context modeling of Vision Transformers. The proposed architecture incorporates a compact ViT bottleneck within a U-Net encoder-decoder framework, enabling effective learning of both local and global features while maintaining computational efficiency with only 2.6 million trainable parameters. The model was evaluated on the TCGA LGG MRI Segmentation dataset, achieving a mean Intersection over Union of 0.8100 and a Dice score of 0.8446, outperforming the baseline UNet by 3.75% and 3.15%, respectively. Extensive quantitative and qualitative analyses, including confusion matrix evaluation, precision recall curves, per-image performance distribution, and tumor size dependency analysis, demonstrate the effectiveness and robustness of the proposed method for brain tumor segmentation.", "published": "2026-09-24T13:26:08Z", "authors": ["Sheekar Banerjee", "Md. Srabon Chowdhury", "Md. Mahbub Hasan Akash", "Ishtiak Al Mamoon"], "categories": ["cs.CV"], "doi": "", "_query": "property valuation computer vision", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/1307.3088v1", "title": "The Declaratron, semantic specification for scientific computation using MathML", "abstract": "We introduce the Declaratron, a system which takes a declarative approach to specifying mathematically based scientific computation. This uses displayable mathematical notation (Content MathML) and is both executable and semantically well defined. We combine domain specific representations of physical science (e.g. CML, Chemical Markup Language), MathML formulae and computational specifications (DeXML) to create executable documents which include scientific data and mathematical formulae. These documents preserve the provenance of the data used, and build tight semantic links between components of mathematical formulae and domain objects---in effect grounding the mathematical semantics in the scientific domain. The Declaratron takes these specifications and i) carries out entity resolution and decoration to prepare for computation ii) uses a MathML execution engine to run calculations over the revised tree iii) outputs domain objects and the complete document to give both results and an encapsulated history of the computation. A short description of a case study is given to illustrate how the system can be used. Many scientific problems require frequent change of the mathematical functional form and the Declaratron provides this without requiring changes to code. Additionally, it supports reproducible science, machine indexing and semantic search of computations, makes implicit assumptions visible, and separates domain knowledge from computational techniques. We believe that the Declaratron could replace much conventional procedural code in science.", "published": "2013-07-11T12:35:11Z", "authors": ["Dave Murray-Rust", "Peter Murray-Rust"], "categories": ["cs.MS"], "doi": "", "_query": "property valuation computer vision", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2601.17062v1", "title": "A Computer Vision Pipeline for Iterative Bullet Hole Tracking in Rifle Zeroing", "abstract": "Adjusting rifle sights, a process commonly called \"zeroing,\" requires shooters to identify and differentiate bullet holes from multiple firing iterations. Traditionally, this process demands physical inspection, introducing delays due to range safety protocols and increasing the risk of human error. We present an end-to-end computer vision system for automated bullet hole detection and iteration-based tracking directly from images taken at the firing line. Our approach combines YOLOv8 for accurate small-object detection with Intersection over Union (IoU) analysis to differentiate bullet holes across sequential images. To address the scarcity of labeled sequential data, we propose a novel data augmentation technique that removes rather than adds objects to simulate realistic firing sequences. Additionally, we introduce a preprocessing pipeline that standardizes target orientation using ORB-based perspective correction, improving model accuracy. Our system achieves 97.0% mean average precision on bullet hole detection and 88.8% accuracy in assigning bullet holes to the correct firing iteration. While designed for rifle zeroing, this framework offers broader applicability in domains requiring the temporal differentiation of visually similar objects.", "published": "2026-01-22T15:52:28Z", "authors": ["Robert M. Belcher", "Brendan C. Degryse", "Leonard R. Kosta", "Christopher J. Lowrance"], "categories": ["cs.CV", "cs.AI"], "doi": "", "_query": "property valuation computer vision", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2511.13315v1", "title": "Computer Vision based group activity detection and action spotting", "abstract": "Group activity detection in multi-person scenes is challenging due to complex human interactions, occlusions, and variations in appearance over time. This work presents a computer vision based framework for group activity recognition and action spotting using a combination of deep learning models and graph based relational reasoning. The system first applies Mask R-CNN to obtain accurate actor localization through bounding boxes and instance masks. Multiple backbone networks, including Inception V3, MobileNet, and VGG16, are used to extract feature maps, and RoIAlign is applied to preserve spatial alignment when generating actor specific features. The mask information is then fused with the feature maps to obtain refined masked feature representations for each actor. To model interactions between individuals, we construct Actor Relation Graphs that encode appearance similarity and positional relations using methods such as normalized cross correlation, sum of absolute differences, and dot product. Graph Convolutional Networks operate on these graphs to reason about relationships and predict both individual actions and group level activities. Experiments on the Collective Activity dataset demonstrate that the combination of mask based feature refinement, robust similarity search, and graph neural network reasoning leads to improved recognition performance across both crowded and non crowded scenarios. This approach highlights the potential of integrating segmentation, feature extraction, and relational graph reasoning for complex video understanding tasks.", "published": "2025-11-17T12:52:22Z", "authors": ["Narthana Sivalingam", "Santhirarajah Sivasthigan", "Thamayanthi Mahendranathan", "G. M. R. I. Godaliyadda", "M. P. B. Ekanayake", "H. M. V. R. Herath"], "categories": ["cs.CV", "cs.AI"], "doi": "", "_query": "property valuation computer vision", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2404.15523v2", "title": "Understanding Hyperbolic Metric Learning through Hard Negative Sampling", "abstract": "In recent years, there has been a growing trend of incorporating hyperbolic geometry methods into computer vision. While these methods have achieved state-of-the-art performance on various metric learning tasks using hyperbolic distance measurements, the underlying theoretical analysis supporting this superior performance remains under-exploited. In this study, we investigate the effects of integrating hyperbolic space into metric learning, particularly when training with contrastive loss. We identify a need for a comprehensive comparison between Euclidean and hyperbolic spaces regarding the temperature effect in the contrastive loss within the existing literature. To address this gap, we conduct an extensive investigation to benchmark the results of Vision Transformers (ViTs) using a hybrid objective function that combines loss from Euclidean and hyperbolic spaces. Additionally, we provide a theoretical analysis of the observed performance improvement. We also reveal that hyperbolic metric learning is highly related to hard negative sampling, providing insights for future work. This work will provide valuable data points and experience in understanding hyperbolic image embeddings. To shed more light on problem-solving and encourage further investigation into our approach, our code is available online (https://github.com/YunYunY/HypMix).", "published": "2024-04-23T21:11:30Z", "authors": ["Yun Yue", "Fangzhou Lin", "Guanyi Mou", "Ziming Zhang"], "categories": ["cs.CV"], "doi": "10.1109/WACV57701.2024.00190", "_query": "property valuation computer vision", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2507.12988v2", "title": "Variance-Based Pruning for Accelerating and Compressing Trained Networks", "abstract": "Increasingly expensive training of ever larger models such as Vision Transfomers motivate reusing the vast library of already trained state-of-the-art networks. However, their latency, high computational costs and memory demands pose significant challenges for deployment, especially on resource-constrained hardware. While structured pruning methods can reduce these factors, they often require costly retraining, sometimes for up to hundreds of epochs, or even training from scratch to recover the lost accuracy resulting from the structural modifications. Maintaining the provided performance of trained models after structured pruning and thereby avoiding extensive retraining remains a challenge. To solve this, we introduce Variance-Based Pruning, a simple and structured one-shot pruning technique for efficiently compressing networks, with minimal finetuning. Our approach first gathers activation statistics, which are used to select neurons for pruning. Simultaneously the mean activations are integrated back into the model to preserve a high degree of performance. On ImageNet-1k recognition tasks, we demonstrate that directly after pruning DeiT-Base retains over 70% of its original performance and requires only 10 epochs of fine-tuning to regain 99% of the original accuracy while simultaneously reducing MACs by 35% and model size by 36%, thus speeding up the model by 1.44x. The code is available at: https://github.com/boschresearch/variance-based-pruning", "published": "2025-07-17T10:54:17Z", "authors": ["Uranik Berisha", "Jens Mehnert", "Alexandru Paul Condurache"], "categories": ["cs.CV", "cs.LG"], "doi": "", "_query": "property valuation computer vision", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2211.13644v1", "title": "Seeds Don't Lie: An Adaptive Watermarking Framework for Computer Vision Models", "abstract": "In recent years, various watermarking methods were suggested to detect computer vision models obtained illegitimately from their owners, however they fail to demonstrate satisfactory robustness against model extraction attacks. In this paper, we present an adaptive framework to watermark a protected model, leveraging the unique behavior present in the model due to a unique random seed initialized during the model training. This watermark is used to detect extracted models, which have the same unique behavior, indicating an unauthorized usage of the protected model's intellectual property (IP). First, we show how an initial seed for random number generation as part of model training produces distinct characteristics in the model's decision boundaries, which are inherited by extracted models and present in their decision boundaries, but aren't present in non-extracted models trained on the same data-set with a different seed. Based on our findings, we suggest the Robust Adaptive Watermarking (RAW) Framework, which utilizes the unique behavior present in the protected and extracted models to generate a watermark key-set and verification model. We show that the framework is robust to (1) unseen model extraction attacks, and (2) extracted models which undergo a blurring method (e.g., weight pruning). We evaluate the framework's robustness against a naive attacker (unaware that the model is watermarked), and an informed attacker (who employs blurring strategies to remove watermarked behavior from an extracted model), and achieve outstanding (i.e., >0.9) AUC values. Finally, we show that the framework is robust to model extraction attacks with different structure and/or architecture than the protected model.", "published": "2022-11-24T14:48:40Z", "authors": ["Jacob Shams", "Ben Nassi", "Ikuya Morikawa", "Toshiya Shimizu", "Asaf Shabtai", "Yuval Elovici"], "categories": ["cs.CV"], "doi": "", "_query": "property valuation computer vision", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2605.30713v1", "title": "Diversity Matters: Revisiting Test-Time Compute in Vision-Language Models", "abstract": "Test-time compute (TTC) strategies have emerged as a lightweight approach to boost reasoning in large language models (LLMs). However, their application and benefits for vision-language models (VLMs) remain underexplored. We present a systematic study of TTC across seven VLMs and six benchmarks, specifically analyzing feature-based scoring and majority voting methods. We find that feature heuristics fail and voting yields only modest gains in single-model settings. We theoretically show that this limitation stems from a lack of prediction diversity: when outputs are highly correlated, voting provides little benefit. In contrast, multi-model ensembles offer richer diversity, yet standard majority voting fails to account for varying model capabilities. To address this, we propose Entropy-based TTC (ETTC), which selects the most confident prediction based on predictive entropy. Our method reduces to majority voting in the single-model case, but in model ensembles, it leverages confidence disparities to prioritize stronger models. We prove that ETTC outperforms majority voting under mild assumptions and empirically demonstrate that it consistently surpasses both voting and the best individual model. Crucially, our results show that smaller models can synergistically enhance larger ones, unlocking ensembling gains not achievable with standard strategies.", "published": "2026-05-29T01:06:38Z", "authors": ["Yijie Tong", "Yifan Hou", "Shaobo Cui", "Antoine Bosselut", "Mrinmaya Sachan"], "categories": ["cs.LG", "cs.CV", "cs.MM"], "doi": "", "_query": "property valuation computer vision", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2204.08790v6", "title": "ELEVATER: A Benchmark and Toolkit for Evaluating Language-Augmented Visual Models", "abstract": "Learning visual representations from natural language supervision has recently shown great promise in a number of pioneering works. In general, these language-augmented visual models demonstrate strong transferability to a variety of datasets and tasks. However, it remains challenging to evaluate the transferablity of these models due to the lack of easy-to-use evaluation toolkits and public benchmarks. To tackle this, we build ELEVATER (Evaluation of Language-augmented Visual Task-level Transfer), the first benchmark and toolkit for evaluating(pre-trained) language-augmented visual models. ELEVATER is composed of three components. (i) Datasets. As downstream evaluation suites, it consists of 20 image classification datasets and 35 object detection datasets, each of which is augmented with external knowledge. (ii) Toolkit. An automatic hyper-parameter tuning toolkit is developed to facilitate model evaluation on downstream tasks. (iii) Metrics. A variety of evaluation metrics are used to measure sample-efficiency (zero-shot and few-shot) and parameter-efficiency (linear probing and full model fine-tuning). ELEVATER is a platform for Computer Vision in the Wild (CVinW), and is publicly released at at https://computer-vision-in-the-wild.github.io/ELEVATER/", "published": "2022-04-19T10:23:42Z", "authors": ["Chunyuan Li", "Haotian Liu", "Liunian Harold Li", "Pengchuan Zhang", "Jyoti Aneja", "Jianwei Yang", "Ping Jin", "Houdong Hu", "Zicheng Liu", "Yong Jae Lee", "Jianfeng Gao"], "categories": ["cs.CV", "cs.CL", "cs.LG"], "doi": "", "_query": "property valuation computer vision", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2409.10876v5", "title": "Coordinate-based Speed of Sound Recovery for Aberration-Corrected Photoacoustic Computed Tomography", "abstract": "Photoacoustic computed tomography (PACT) is a non-invasive imaging modality, similar to ultrasound, with wide-ranging medical applications. Conventional PACT images are degraded by wavefront distortion caused by the heterogeneous speed of sound (SOS) in tissue. Accounting for these effects can improve image quality and provide medically useful information, but measuring the SOS directly is burdensome and the existing joint reconstruction method is computationally expensive. Traditional supervised learning techniques are currently inaccessible in this data-starved domain. In this work, we introduce an efficient, self-supervised joint reconstruction method that recovers SOS and high-quality images for ring array PACT systems. To solve this semi-blind inverse problem, we parametrize the SOS using either a pixel grid or a neural field (NF) and update it directly by backpropagating the gradients through a differentiable imaging forward model. Our method removes SOS aberrations more accurately and 35x faster than the current SOTA. We demonstrate the success of our method quantitatively in simulation and qualitatively on experimentally-collected and in vivo data. Our code and synthetic numerical phantoms are available on our project page: https://lukeli0425.github.io/Coord-SoS-PACT/.", "published": "2024-09-17T04:11:20Z", "authors": ["Tianao Li", "Manxiu Cui", "Cheng Ma", "Emma Alexander"], "categories": ["eess.IV", "cs.CV", "eess.SP"], "doi": "", "_query": "property valuation computer vision", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2404.01116v1", "title": "Intelligent Robotic Control System Based on Computer Vision Technology", "abstract": "The article explores the intersection of computer vision technology and robotic control, highlighting its importance in various fields such as industrial automation, healthcare, and environmental protection. Computer vision technology, which simulates human visual observation, plays a crucial role in enabling robots to perceive and understand their surroundings, leading to advancements in tasks like autonomous navigation, object recognition, and waste management. By integrating computer vision with robot control, robots gain the ability to interact intelligently with their environment, improving efficiency.", "published": "2024-04-01T13:46:23Z", "authors": ["Chang Che", "Haotian Zheng", "Zengyi Huang", "Wei Jiang", "Bo Liu"], "categories": ["cs.RO"], "doi": "", "_query": "property valuation computer vision", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/1202.5363v1", "title": "A Valuation Theoretic Approach to Essential Dimension", "abstract": "In this essay we explore the notion of essential dimension using the theory of valuations of fields. Given a field extension K/k and a valuation on K that is trivial on k, we prove that the rank of the valuation cannot exceed the transcendence degree trdeg K. We use this inequality to prove lower bounds on the essential dimension in some interesting situations. We study orbits of a torus action and find a formula for the essential dimension of the functor of these orbits.", "published": "2012-02-24T02:02:44Z", "authors": ["Aurel Meyer"], "categories": ["math.AG"], "doi": "", "_query": "property valuation computer vision", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/1411.6714v1", "title": "The Digital Humanities Unveiled: Perceptions Held by Art Historians and Computer Scientists about Computer Vision Technology", "abstract": "Although computer scientists are generally familiar with the achievements of computer vision technology in art history, these accomplishments are little known and often misunderstood by scholars in the humanities. To clarify the parameters of this seeming disjuncture, we have addressed the concerns that one example of the digitization of the humanities poses on social, philosophical, and practical levels. In support of our assessment of the perceptions held by computer scientists and art historians about the use of computer vision technology to examine art, we based our interpretations on two surveys that were distributed in August 2014. In this paper, the development of these surveys and their results are discussed in the context of the major philosophical conclusions of our research in this area to date.", "published": "2014-11-25T03:16:02Z", "authors": ["Emily L. Spratt", "Ahmed Elgammal"], "categories": ["cs.CY"], "doi": "", "_query": "property valuation computer vision", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/1905.06658v4", "title": "Vision-based Robotic Grasping From Object Localization, Object Pose Estimation to Grasp Estimation for Parallel Grippers: A Review", "abstract": "This paper presents a comprehensive survey on vision-based robotic grasping. We conclude three key tasks during vision-based robotic grasping, which are object localization, object pose estimation and grasp estimation. In detail, the object localization task contains object localization without classification, object detection and object instance segmentation. This task provides the regions of the target object in the input data. The object pose estimation task mainly refers to estimating the 6D object pose and includes correspondence-based methods, template-based methods and voting-based methods, which affords the generation of grasp poses for known objects. The grasp estimation task includes 2D planar grasp methods and 6DoF grasp methods, where the former is constrained to grasp from one direction. These three tasks could accomplish the robotic grasping with different combinations. Lots of object pose estimation methods need not object localization, and they conduct object localization and object pose estimation jointly. Lots of grasp estimation methods need not object localization and object pose estimation, and they conduct grasp estimation in an end-to-end manner. Both traditional methods and latest deep learning-based methods based on the RGB-D image inputs are reviewed elaborately in this survey. Related datasets and comparisons between state-of-the-art methods are summarized as well. In addition, challenges about vision-based robotic grasping and future directions in addressing these challenges are also pointed out.", "published": "2019-05-16T11:12:01Z", "authors": ["Guoguang Du", "Kai Wang", "Shiguo Lian", "Kaiyong Zhao"], "categories": ["cs.RO", "cs.CV"], "doi": "10.1007/s10462-020-09888-5", "_query": "property valuation computer vision", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2603.23037v2", "title": "YOLO with Kolmogorov-Arnold networks and vision-language foundation models for interpretable object detection with trustworthy multimodal AI in computer vision perception", "abstract": "The trustworthy object detection capabilities of a novel Kolmogorov-Arnold network framework are examined here. The approach addresses a key limitation in computer vision for vehicle detection perception, and beyond. These systems offer limited transparency regarding the reliability of their confidence scores in visually degraded or ambiguous scenes. To this end, a Kolmogorov-Arnold network is employed as an interpretable post-hoc surrogate to model the trustworthiness of the You Only Look Once (Yolov10) detections using seven geometric and semantic features. The additive spline-based structure of the Kolmogorov-Arnold network enables direct visualisation of each feature's influence. This produces smooth and transparent functional mappings that reveal when the model's confidence is well supported and when it is unreliable. Furthermore, a bootstrapped language-image (BLIP) foundation model generates descriptive captions of each scene. This tool enables a lightweight multimodal interface without affecting the interpretability layer. Experiments on both Common Objects in Context (COCO), and images from the University of Bath campus demonstrate that the framework accurately identifies low-trust predictions under blur, occlusion, or low texture. This provides actionable insights for acceptance, review, or downstream risk mitigation. The resulting system delivers interpretable object detection with trustworthy confidence estimates. It offers a powerful tool for transparent and practical perception component for autonomous and multimodal artificial intelligence applications.", "published": "2026-03-24T10:13:43Z", "authors": ["Marios Impraimakis", "Daniel Vazquez", "Feiyu Zhou"], "categories": ["cs.CV", "cs.AI", "cs.CL", "cs.LG", "cs.RO"], "doi": "10.1038/s41598-026-57596-x", "_query": "property valuation computer vision", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2503.19347v1", "title": "Stop Walking in Circles! Bailing Out Early in Projected Gradient Descent", "abstract": "Projected Gradient Descent (PGD) under the $L_\\infty$ ball has become one of the defacto methods used in adversarial robustness evaluation for computer vision (CV) due to its reliability and efficacy, making a strong and easy-to-implement iterative baseline. However, PGD is computationally demanding to apply, especially when using thousands of iterations is the current best-practice recommendation to generate an adversarial example for a single image. In this work, we introduce a simple novel method for early termination of PGD based on cycle detection by exploiting the geometry of how PGD is implemented in practice and show that it can produce large speedup factors while providing the \\emph{exact} same estimate of model robustness as standard PGD. This method substantially speeds up PGD without sacrificing any attack strength, enabling evaluations of robustness that were previously computationally intractable.", "published": "2025-03-25T04:51:44Z", "authors": ["Philip Doldo", "Derek Everett", "Amol Khanna", "Andre T Nguyen", "Edward Raff"], "categories": ["cs.CV", "cs.LG", "stat.ML"], "doi": "", "_query": "property valuation computer vision", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2301.10759v1", "title": "Efficient Flow-Guided Multi-frame De-fencing", "abstract": "Taking photographs ''in-the-wild'' is often hindered by fence obstructions that stand between the camera user and the scene of interest, and which are hard or impossible to avoid. De-fencing is the algorithmic process of automatically removing such obstructions from images, revealing the invisible parts of the scene. While this problem can be formulated as a combination of fence segmentation and image inpainting, this often leads to implausible hallucinations of the occluded regions. Existing multi-frame approaches rely on propagating information to a selected keyframe from its temporal neighbors, but they are often inefficient and struggle with alignment of severely obstructed images. In this work we draw inspiration from the video completion literature and develop a simplified framework for multi-frame de-fencing that computes high quality flow maps directly from obstructed frames and uses them to accurately align frames. Our primary focus is efficiency and practicality in a real-world setting: the input to our algorithm is a short image burst (5 frames) - a data modality commonly available in modern smartphones - and the output is a single reconstructed keyframe, with the fence removed. Our approach leverages simple yet effective CNN modules, trained on carefully generated synthetic data, and outperforms more complicated alternatives real bursts, both quantitatively and qualitatively, while running real-time.", "published": "2023-01-25T18:42:59Z", "authors": ["Stavros Tsogkas", "Fengjia Zhang", "Allan Jepson", "Alex Levinshtein"], "categories": ["cs.CV"], "doi": "", "_query": "property valuation computer vision", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2004.10237v2", "title": "Quadratic Gorenstein algebras with many surprising properties", "abstract": "Let $k$ be a field of characteristic $0$. Using the method of idealization, we show that there is a non-Koszul, quadratic, Artinian, Gorenstein, standard graded $k$-algebra of regularity $3$ and codimension $8$, answering a question of Mastroeni, Schenck, and Stillman. We also show that this example is minimal in the sense that no other idealization that is non-Koszul, quadratic, Artinian, Gorenstein algebra, with regularity $3$ has smaller codimension. We also construct an infinite family of graded, quadratic, Artinian, Gorenstein algebras $A_m$, indexed by an integer $m \\ge 2$, with the following properties: (1) there are minimal first syzygies of the defining ideal in degree $m+2$, (2) for $m \\ge 3$, $A_m$ is not Koszul, (3) for $m \\ge 7$, the Hilbert function of $A_m$ is not unimodal, and thus (4) for $m \\ge 7$, $A_m$ does not satisfy the weak or strong Lefschetz properties. In particular, the subadditivity property fails for quadratic Gorenstein ideals. Finally, we show that the idealization of a construction of Roos yields non-Koszul quadratic Gorenstein algebras such that the residue field $k$ has a linear resolution for precisely $α$ steps for any integer $α\\ge 2$. Thus there is no finite test for the Koszul property even for quadratic Gorenstein algebras.", "published": "2020-04-21T18:41:34Z", "authors": ["Jason McCullough", "Alexandra Seceleanu"], "categories": ["math.AC"], "doi": "", "_query": "property valuation computer vision", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2004.07007v1", "title": "Visual Descriptor Learning from Monocular Video", "abstract": "Correspondence estimation is one of the most widely researched and yet only partially solved area of computer vision with many applications in tracking, mapping, recognition of objects and environment. In this paper, we propose a novel way to estimate dense correspondence on an RGB image where visual descriptors are learned from video examples by training a fully convolutional network. Most deep learning methods solve this by training the network with a large set of expensive labeled data or perform labeling through strong 3D generative models using RGB-D videos. Our method learns from RGB videos using contrastive loss, where relative labeling is estimated from optical flow. We demonstrate the functionality in a quantitative analysis on rendered videos, where ground truth information is available. Not only does the method perform well on test data with the same background, it also generalizes to situations with a new background. The descriptors learned are unique and the representations determined by the network are global. We further show the applicability of the method to real-world videos.", "published": "2020-04-15T11:19:57Z", "authors": ["Umashankar Deekshith", "Nishit Gajjar", "Max Schwarz", "Sven Behnke"], "categories": ["cs.CV"], "doi": "", "_query": "property valuation computer vision", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2510.23409v3", "title": "Eigen-Value: Efficient Domain-Robust Data Valuation via Eigenvalue-Based Approach", "abstract": "Data valuation has become central in the era of data-centric AI. It drives efficient training pipelines and enables objective pricing in data markets by assigning a numeric value to each data point. Most existing data valuation methods estimate the effect of removing individual data points by evaluating changes in model validation performance under in-distribution (ID) settings, as opposed to out-of-distribution (OOD) scenarios where data follow different patterns. Since ID and OOD data behave differently, data valuation methods based on ID loss often fail to generalize to OOD settings, particularly when the validation set contains no OOD data. Furthermore, although OOD-aware methods exist, they involve heavy computational costs, which hinder practical deployment. To address these challenges, we introduce \\emph{Eigen-Value} (EV), a plug-and-play data valuation framework for OOD robustness that uses only an ID data subset, including during validation. EV provides a new spectral approximation of domain discrepancy, which is the gap of loss between ID and OOD using ratios of eigenvalues of ID data's covariance matrix. EV then estimates the marginal contribution of each data point to this discrepancy via perturbation theory, alleviating the computational burden. Subsequently, EV plugs into ID loss-based methods by adding an EV term without any additional training loop. We demonstrate that EV achieves improved OOD robustness and stable value rankings across real-world datasets, while remaining computationally lightweight. These results indicate that EV is practical for large-scale settings with domain shift, offering an efficient path to OOD-robust data valuation.", "published": "2025-10-27T15:12:49Z", "authors": ["Youngjun Choi", "Joonseong Kang", "Sungjun Lim", "Kyungwoo Song"], "categories": ["cs.LG", "cs.AI"], "doi": "", "_query": "property valuation computer vision", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/1501.06293v2", "title": "Haagerup approximation property via bimodules", "abstract": "The Haagerup approximation property (HAP) is defined for finite von Neumann algebras in such a way that the group von Neumann algebra of a discrete group has the HAP if and only if the group itself has the Haagerup property. The HAP has been studied extensively for finite von Neumann algebras and it is recently generalized for arbitrary von Neumann algebras by Caspers-Skalski and Okayasu-Tomatsu. One of the motivations behind the generalization is the fact that quantum group von Neumann algebras are often infinite even though the Haagerup property has been defined successfully for locally compact quantum groups by Daws-Fima-Skalski-White. In this paper, we partly fill this gap by proving that the von Neumann algebra of a locally compact quantum group with the Haagerup property has the HAP. This is new even for genuine locally compact groups.", "published": "2015-01-26T09:23:56Z", "authors": ["Rui Okayasu", "Narutaka Ozawa", "Reiji Tomatsu"], "categories": ["math.OA", "math.GR", "math.QA"], "doi": "", "_query": "property valuation computer vision", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2211.05120v1", "title": "Deep Learning based Computer Vision Methods for Complex Traffic Environments Perception: A Review", "abstract": "Computer vision applications in intelligent transportation systems (ITS) and autonomous driving (AD) have gravitated towards deep neural network architectures in recent years. While performance seems to be improving on benchmark datasets, many real-world challenges are yet to be adequately considered in research. This paper conducted an extensive literature review on the applications of computer vision in ITS and AD, and discusses challenges related to data, models, and complex urban environments. The data challenges are associated with the collection and labeling of training data and its relevance to real world conditions, bias inherent in datasets, the high volume of data needed to be processed, and privacy concerns. Deep learning (DL) models are commonly too complex for real-time processing on embedded hardware, lack explainability and generalizability, and are hard to test in real-world settings. Complex urban traffic environments have irregular lighting and occlusions, and surveillance cameras can be mounted at a variety of angles, gather dirt, shake in the wind, while the traffic conditions are highly heterogeneous, with violation of rules and complex interactions in crowded scenarios. Some representative applications that suffer from these problems are traffic flow estimation, congestion detection, autonomous driving perception, vehicle interaction, and edge computing for practical deployment. The possible ways of dealing with the challenges are also explored while prioritizing practical deployment.", "published": "2022-11-09T05:16:01Z", "authors": ["Talha Azfar", "Jinlong Li", "Hongkai Yu", "Ruey Long Cheu", "Yisheng Lv", "Ruimin Ke"], "categories": ["cs.CV", "eess.IV"], "doi": "", "_query": "property valuation computer vision", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/1305.1163v1", "title": "A Computer Vision System for Attention Mapping in SLAM based 3D Models", "abstract": "The study of human factors in the frame of interaction studies has been relevant for usability engi-neering and ergonomics for decades. Today, with the advent of wearable eye-tracking and Google glasses, monitoring of human factors will soon become ubiquitous. This work describes a computer vision system that enables pervasive mapping and monitoring of human attention. The key contribu-tion is that our methodology enables full 3D recovery of the gaze pointer, human view frustum and associated human centred measurements directly into an automatically computed 3D model in real-time. We apply RGB-D SLAM and descriptor matching methodologies for the 3D modelling, locali-zation and fully automated annotation of ROIs (regions of interest) within the acquired 3D model. This innovative methodology will open new avenues for attention studies in real world environments, bringing new potential into automated processing for human factors technologies.", "published": "2013-05-06T12:35:52Z", "authors": ["Lucas Paletta", "Katrin Santner", "Gerald Fritz", "Albert Hofmann", "Gerald Lodron", "Georg Thallinger", "Heinz Mayer"], "categories": ["cs.CV"], "doi": "", "_query": "property valuation computer vision", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/1802.08793v1", "title": "Multispectral Image Intrinsic Decomposition via Low Rank Constraint", "abstract": "Multispectral images contain many clues of surface characteristics of the objects, thus can be widely used in many computer vision tasks, e.g., recolorization and segmentation. However, due to the complex illumination and the geometry structure of natural scenes, the spectra curves of a same surface can look very different. In this paper, a Low Rank Multispectral Image Intrinsic Decomposition model (LRIID) is presented to decompose the shading and reflectance from a single multispectral image. We extend the Retinex model, which is proposed for RGB image intrinsic decomposition, for multispectral domain. Based on this, a low rank constraint is proposed to reduce the ill-posedness of the problem and make the algorithm solvable. A dataset of 12 images is given with the ground truth of shadings and reflectance, so that the objective evaluations can be conducted. The experiments demonstrate the effectiveness of proposed method.", "published": "2018-02-24T04:04:39Z", "authors": ["Qian Huang", "Weixin Zhu", "Yang Zhao", "Linsen Chen", "Yao Wang", "Tao Yue", "Xun Cao"], "categories": ["cs.CV"], "doi": "", "_query": "property valuation computer vision", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/1512.02311v1", "title": "Direct Intrinsics: Learning Albedo-Shading Decomposition by Convolutional Regression", "abstract": "We introduce a new approach to intrinsic image decomposition, the task of decomposing a single image into albedo and shading components. Our strategy, which we term direct intrinsics, is to learn a convolutional neural network (CNN) that directly predicts output albedo and shading channels from an input RGB image patch. Direct intrinsics is a departure from classical techniques for intrinsic image decomposition, which typically rely on physically-motivated priors and graph-based inference algorithms. The large-scale synthetic ground-truth of the MPI Sintel dataset plays a key role in training direct intrinsics. We demonstrate results on both the synthetic images of Sintel and the real images of the classic MIT intrinsic image dataset. On Sintel, direct intrinsics, using only RGB input, outperforms all prior work, including methods that rely on RGB+Depth input. Direct intrinsics also generalizes across modalities; it produces quite reasonable decompositions on the real images of the MIT dataset. Our results indicate that the marriage of CNNs with synthetic training data may be a powerful new technique for tackling classic problems in computer vision.", "published": "2015-12-08T03:38:52Z", "authors": ["Takuya Narihira", "Michael Maire", "Stella X. Yu"], "categories": ["cs.CV"], "doi": "", "_query": "property valuation computer vision", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/1909.06887v1", "title": "Learning an Effective Equivariant 3D Descriptor Without Supervision", "abstract": "Establishing correspondences between 3D shapes is a fundamental task in 3D Computer Vision, typically addressed by matching local descriptors. Recently, a few attempts at applying the deep learning paradigm to the task have shown promising results. Yet, the only explored way to learn rotation invariant descriptors has been to feed neural networks with highly engineered and invariant representations provided by existing hand-crafted descriptors, a path that goes in the opposite direction of end-to-end learning from raw data so successfully deployed for 2D images. In this paper, we explore the benefits of taking a step back in the direction of end-to-end learning of 3D descriptors by disentangling the creation of a robust and distinctive rotation equivariant representation, which can be learned from unoriented input data, and the definition of a good canonical orientation, required only at test time to obtain an invariant descriptor. To this end, we leverage two recent innovations: spherical convolutional neural networks to learn an equivariant descriptor and plane folding decoders to learn without supervision. The effectiveness of the proposed approach is experimentally validated by outperforming hand-crafted and learned descriptors on a standard benchmark.", "published": "2019-09-15T21:27:23Z", "authors": ["Riccardo Spezialetti", "Samuele Salti", "Luigi Di Stefano"], "categories": ["cs.CV"], "doi": "", "_query": "property valuation computer vision", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/1503.01392v2", "title": "Valuations of Semirings", "abstract": "We develop notions of valuations on a semiring, with a view toward extending the classical theory of abstract nonsingular curves and discrete valuation rings to this general algebraic setting; the novelty of our approach lies in the implementation of hyperrings to yield a new definition (\\emph{hyperfield valuation}). In particular, we classify valuations on the semifield $\\mathbb{Q}_{max}$ (the max-plus semifield of rational numbers) and also valuations on the `function field' $\\mathbb{Q}_{max}(T)$ (the semifield of rational functions over $\\mathbb{Q}_{max}$) which are trivial on $\\mathbb{Q}_{max}$. We construct and study the abstract curve associated to $\\mathbb{Q}_{max}(T)$ in relation to the projective line $\\mathbb{P}^1_{\\mathbb{F}_1}$ over the field with one element $\\mathbb{F}_{1}$ and the tropical projective line. Finally, we discuss possible connections to tropical curves and Berkovich's theory of analytic spaces.", "published": "2015-03-04T17:15:00Z", "authors": ["Jaiung Jun"], "categories": ["math.AG", "math.AC"], "doi": "", "_query": "property valuation computer vision", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/0808.3558v1", "title": "Market-Oriented Cloud Computing: Vision, Hype, and Reality for Delivering IT Services as Computing Utilities", "abstract": "This keynote paper: presents a 21st century vision of computing; identifies various computing paradigms promising to deliver the vision of computing utilities; defines Cloud computing and provides the architecture for creating market-oriented Clouds by leveraging technologies such as VMs; provides thoughts on market-based resource management strategies that encompass both customer-driven service management and computational risk management to sustain SLA-oriented resource allocation; presents some representative Cloud platforms especially those developed in industries along with our current work towards realising market-oriented resource allocation of Clouds by leveraging the 3rd generation Aneka enterprise Grid technology; reveals our early thoughts on interconnecting Clouds for dynamically creating an atmospheric computing environment along with pointers to future community research; and concludes with the need for convergence of competing IT paradigms for delivering our 21st century vision.", "published": "2008-08-26T17:16:11Z", "authors": ["Rajkumar Buyya", "Chee Shin Yeo", "Srikumar Venugopal"], "categories": ["cs.DC"], "doi": "10.1109/HPCC.2008.172", "_query": "property valuation computer vision", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2502.17471v1", "title": "The mean value property (corrected version)", "abstract": "The paper deals with some elementary problems about various mean value properties and their connections to harmonic functions and random walks.", "published": "2025-02-13T12:10:17Z", "authors": ["Vilmos Totik"], "categories": ["math.HO", "math.CA"], "doi": "", "_query": "property valuation computer vision", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2008.05524v1", "title": "Mitigating Dataset Imbalance via Joint Generation and Classification", "abstract": "Supervised deep learning methods are enjoying enormous success in many practical applications of computer vision and have the potential to revolutionize robotics. However, the marked performance degradation to biases and imbalanced data questions the reliability of these methods. In this work we address these questions from the perspective of dataset imbalance resulting out of severe under-representation of annotated training data for certain classes and its effect on both deep classification and generation methods. We introduce a joint dataset repairment strategy by combining a neural network classifier with Generative Adversarial Networks (GAN) that makes up for the deficit of training examples from the under-representated class by producing additional training examples. We show that the combined training helps to improve the robustness of both the classifier and the GAN against severe class imbalance. We show the effectiveness of our proposed approach on three very different datasets with different degrees of imbalance in them. The code is available at https://github.com/AadSah/ImbalanceCycleGAN .", "published": "2020-08-12T18:40:38Z", "authors": ["Aadarsh Sahoo", "Ankit Singh", "Rameswar Panda", "Rogerio Feris", "Abir Das"], "categories": ["cs.CV"], "doi": "", "_query": "property valuation computer vision", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2005.10100v1", "title": "Reduction of valuation risk by Kalman filtering in business valuation models", "abstract": "A recursive free cash flow model (FCFF) is proposed to determine the corporate value of a company in an efficient market in which new market and company-specific information is modelled by additive white noise. The stochastic equations of the FCFF model are solved explicitly to obtain the average corporate value and valuation risk. It is pointed out that valuation risk can be reduced significantly by implementing a conventional two-step Kalman filter in the recursive FCFF model, thus improving its predictive power. Systematic errors of the Kalman filter, caused by intermediate changes in risk and hence in the weighted average cost of capital (WACC), are detected by measuring the residuals. By including an additional adjustment step in the conventional Kalman filtering algorithm, it is shown that systematic errors can be eliminated by recursively adjusting the WACC. The performance of the three-step adaptive Kalman filter is tested by Monte Carlo simulation which demonstrates the reliability and robustness against systematic errors. It is also proved that the conventional and adaptive Kalman filtering algorithms can be implemented into other valuation models such as the economic value added model (EVA) and free cash flow to equity model (FCFE).", "published": "2020-05-20T15:01:13Z", "authors": ["Rene Scheurwater"], "categories": ["q-fin.CP"], "doi": "", "_query": "property valuation computer vision", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/1001.2160v2", "title": "Evolving MultiAlgebras unify all usual sequential computation models", "abstract": "It is well-known that Abstract State Machines (ASMs) can simulate \"step-by-step\" any type of machines (Turing machines, RAMs, etc.). We aim to overcome two facts: 1) simulation is not identification, 2) the ASMs simulating machines of some type do not constitute a natural class among all ASMs. We modify Gurevich's notion of ASM to that of EMA (\"Evolving MultiAlgebra\") by replacing the program (which is a syntactic object) by a semantic object: a functional which has to be very simply definable over the static part of the ASM. We prove that very natural classes of EMAs correspond via \"literal identifications\" to slight extensions of the usual machine models and also to grammar models. Though we modify these models, we keep their computation approach: only some contingencies are modified. Thus, EMAs appear as the mathematical model unifying all kinds of sequential computation paradigms.", "published": "2010-01-13T13:35:20Z", "authors": ["Serge Grigorieff", "Pierre Valarcher"], "categories": ["cs.FL", "cs.LO"], "doi": "", "_query": "property valuation computer vision", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2008.01121v3", "title": "The Universal Valuation of Coxeter Matroids", "abstract": "Coxeter matroids generalize matroids just as flag varieties of Lie groups generalize Grassmannians. Valuations of Coxeter matroids are functions that behave well with respect to subdivisions of a Coxeter matroid into smaller ones. We compute the universal valuative invariant of Coxeter matroids. A key ingredient is the family of Coxeter Schubert matroids, which correspond to the Bruhat cells of flag varieties. In the process, we compute the universal valuation of generalized Coxeter permutohedra, a larger family of polyhedra that model Coxeter analogues of combinatorial objects such as matroids, clusters, and posets.", "published": "2020-08-03T18:28:35Z", "authors": ["Christopher Eur", "Mario Sanchez", "Mariel Supina"], "categories": ["math.CO"], "doi": "10.1112/blms.12461", "_query": "property valuation computer vision", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2608.20056v1", "title": "Gravity-aware partially calibrated absolute pose estimation from affine- or rotation-covariant features", "abstract": "Inertial measurement units (IMUs) are now standard in most consumer devices, such as smartphones, drones, and extended reality (XR) headsets. By fusing visual and inertial data, localization systems gain significantly in speed and robustness compared to vision-only or IMU-only approaches. However, traditional pose estimation methods fail to utilize the local geometric information embedded in feature descriptors like SIFT. Recent work has proved the advantages of leveraging this information for relative and absolute pose estimation, but its application to partially calibrated absolute pose estimation remains unexplored. In this paper, we derive novel constraints for joint estimation of absolute pose and focal length, making use of a gravity vector obtained from IMU data and the feature-induced local geometry, which we use to construct two efficient solvers: UP1PfAC, that operates given a single affine correspondence and UP2PfORI, which requires two orientation-covariant features. Unlike traditional, semi-calibrated absolute pose methods requiring four point correspondences, our solvers benefit from fewer samples and lower computational cost, simplifying robust estimation in modern RANSAC-like frameworks. We evaluate the proposed solvers against the state-of-the-art on large-scale public datasets and demonstrate that our method achieves fast and accurate localization and focal length estimation.", "published": "2026-08-20T13:52:54Z", "authors": ["Marcus Valtonen Örnhag", "Alberto Jaenal", "Stefan Adalbjörnsson"], "categories": ["cs.CV"], "doi": "10.1007/978-3-032-37211-6_19", "_query": "property valuation computer vision", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2407.16636v3", "title": "Velocity Driven Vision: Asynchronous Sensor Fusion Birds Eye View Models for Autonomous Vehicles", "abstract": "Fusing different sensor modalities can be a difficult task, particularly if they are asynchronous. Asynchronisation may arise due to long processing times or improper synchronisation during calibration, and there must exist a way to still utilise this previous information for the purpose of safe driving, and object detection in ego vehicle/ multi-agent trajectory prediction. Difficulties arise in the fact that the sensor modalities have captured information at different times and also at different positions in space. Therefore, they are not spatially nor temporally aligned. This paper will investigate the challenge of radar and LiDAR sensors being asynchronous relative to the camera sensors, for various time latencies. The spatial alignment will be resolved before lifting into BEV space via the transformation of the radar/LiDAR point clouds into the new ego frame coordinate system. Only after this can we concatenate the radar/LiDAR point cloud and lifted camera features. Temporal alignment will be remedied for radar data only, we will implement a novel method of inferring the future radar point positions using the velocity information. Our approach to resolving the issue of sensor asynchrony yields promising results. We demonstrate velocity information can drastically improve IoU for asynchronous datasets, as for a time latency of 360 milliseconds (ms), IoU improves from 49.54 to 53.63. Additionally, for a time latency of 550ms, the camera+radar (C+R) model outperforms the camera+LiDAR (C+L) model by 0.18 IoU. This is an advancement in utilising the often-neglected radar sensor modality, which is less favoured than LiDAR for autonomous driving purposes.", "published": "2024-07-23T16:52:42Z", "authors": ["Seamie Hayes", "Sushil Sharma", "Ciarán Eising"], "categories": ["cs.RO", "cs.CV"], "doi": "", "_query": "property valuation computer vision", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2205.10700v3", "title": "On invariant von Neumann subalgebras rigidity property", "abstract": "We say that a countable discrete group $Γ$ satisfies the invariant von Neumann subalgebras rigidity (ISR) property if every $Γ$- invariant von Neumann subalgebra $\\mathcal{M}$ in $L(Γ)$ is of the form $L(Λ)$ for some normal subgroup $Λ\\lhd Γ$. We show many ``negatively curved\" groups, including all torsion free non-amenable hyperbolic groups and torsion free groups with positive first $L^2$-Betti number under a mild assumption, and certain finite direct product of them have this property. We also discuss whether the torsion-free assumption can be relaxed.", "published": "2022-05-22T01:01:34Z", "authors": ["Tattwamasi Amrutam", "Yongle Jiang"], "categories": ["math.OA", "math.GR"], "doi": "", "_query": "property valuation computer vision", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2206.14484v3", "title": "Countability constraints in order-theoretic approaches to computability", "abstract": "Computability on uncountable sets has no standard formalization, unlike that on countable sets, which is given by Turing machines. Some of the approaches to define computability in these sets rely on order-theoretic structures to translate such notions from Turing machines to uncountable spaces. Since these machines are used as a baseline for computability in these approaches, countability restrictions on the ordered structures are fundamental. Here, we show several relations between the usual countability restrictions in order-theoretic theories of computability and some more common order-theoretic countability constraints, like order density properties and functional characterizations of the order structure in terms of multi-utilities. As a result, we show how computability can be introduced in some order structures via countability order density and multi-utility constraints.", "published": "2022-06-29T09:18:48Z", "authors": ["Pedro Hack", "Daniel A. Braun", "Sebastian Gottwald"], "categories": ["math.LO", "cs.LO"], "doi": "10.1017/S0960129524000173", "_query": "property valuation computer vision", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/1512.06473v3", "title": "Quantized Convolutional Neural Networks for Mobile Devices", "abstract": "Recently, convolutional neural networks (CNN) have demonstrated impressive performance in various computer vision tasks. However, high performance hardware is typically indispensable for the application of CNN models due to the high computation complexity, which prohibits their further extensions. In this paper, we propose an efficient framework, namely Quantized CNN, to simultaneously speed-up the computation and reduce the storage and memory overhead of CNN models. Both filter kernels in convolutional layers and weighting matrices in fully-connected layers are quantized, aiming at minimizing the estimation error of each layer's response. Extensive experiments on the ILSVRC-12 benchmark demonstrate 4~6x speed-up and 15~20x compression with merely one percentage loss of classification accuracy. With our quantized CNN model, even mobile devices can accurately classify images within one second.", "published": "2015-12-21T02:26:46Z", "authors": ["Jiaxiang Wu", "Cong Leng", "Yuhang Wang", "Qinghao Hu", "Jian Cheng"], "categories": ["cs.CV"], "doi": "", "_query": "property valuation computer vision", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/1611.03345v1", "title": "Radial continuous valuations on star bodies and star sets", "abstract": "We show that a radial continuous valuation defined on the $n$-dimensional star bodies extends uniquely to a continuous valuation on the $n$-dimensional bounded star sets. Moreover, we provide an integral representation of every such valuation, in terms of the radial function, which is valid on the dense subset of the simple Borel star sets. We also show that every radial continuous valuation defined on the $n$-dimensional star bodies can be decomposed as a sum $V=V^+-V^-$, where both $V^+$ and $V^-$ are positive radial continuous valuations.", "published": "2016-11-10T15:28:23Z", "authors": ["Pedro Tradacete", "Ignacio Villanueva"], "categories": ["math.MG", "math.FA"], "doi": "", "_query": "property valuation computer vision", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2407.04133v1", "title": "Computing Clipped Products", "abstract": "Sometimes only some digits of a numerical product or some terms of a polynomial or series product are required. Frequently these constitute the most significant or least significant part of the value, for example when computing initial values or refinement steps in iterative approximation schemes. Other situations require the middle portion. In this paper we provide algorithms for the general problem of computing a given span of coefficients within a product, that is the terms within a range of degrees for univariate polynomials or range digits of an integer. This generalizes the \"middle product\" concept of Hanrot, Quercia and Zimmerman. We are primarily interested in problems of modest size where constant speed up factors can improve overall system performance, and therefore focus the discussion on classical and Karatsuba multiplication and how methods may be combined.", "published": "2024-07-04T19:38:34Z", "authors": ["Arthur C. Norman", "Stephen M. Watt"], "categories": ["cs.SC", "math.NA"], "doi": "", "_query": "property valuation computer vision", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/1202.2792v1", "title": "On the Hardness of Welfare Maximization in Combinatorial Auctions with Submodular Valuations", "abstract": "We present a new type of monotone submodular functions: \\emph{multi-peak submodular functions}. Roughly speaking, given a family of sets $\\cF$, we construct a monotone submodular function $f$ with a high value $f(S)$ for every set $S \\in {\\cF}$ (a \"peak\"), and a low value on every set that does not intersect significantly any set in $\\cF$. We use this construction to show that a better than $(1-\\frac{1}{2e})$-approximation ($\\simeq 0.816$) for welfare maximization in combinatorial auctions with submodular valuations is (1) impossible in the communication model, (2) NP-hard in the computational model where valuations are given explicitly. Establishing a constant approximation hardness for this problem in the communication model was a long-standing open question. The valuations we construct for the hardness result in the computational model depend only on a constant number of items, and hence the result holds even if the players can answer arbitrary queries about their valuation, including demand queries. We also study two other related problems that received some attention recently: max-min allocation (for which we also get hardness of $(1-\\frac 1 {2e}+ε)$-approximation, in both models), and combinatorial public projects (for which we prove hardness of $(3/4+ε)$-approximation in the communication model, and hardness of $(1 -\\frac 1 e+ε)$-approximation in the computational model, using constant size valuations).", "published": "2012-02-13T17:09:58Z", "authors": ["Shahar Dobzinski", "Jan Vondrak"], "categories": ["cs.DS", "cs.GT"], "doi": "", "_query": "property valuation computer vision", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2511.11648v2", "title": "Lightweight Time Series Data Valuation on Time Series Foundation Models via In-Context Finetuning", "abstract": "Time series foundation models (TSFMs) have demonstrated increasing capabilities due to their extensive pretraining on large volumes of diverse time series data. Consequently, the quality of time series data is crucial to TSFM performance, rendering an accurate and efficient data valuation of time series for TSFMs indispensable. However, traditional data valuation methods, such as influence functions, face severe computational bottlenecks due to their poor scalability with growing TSFM model sizes and often fail to preserve temporal dependencies. In this paper, we propose LTSV, a Lightweight Time Series Valuation on TSFMS via in-context finetuning. Grounded in the theoretical evidence that in-context finetuning approximates the influence function, LTSV estimates a sample's contribution by measuring the change in context loss after in-context finetuning, leveraging the strong generalization capabilities of TSFMs to produce robust and transferable data valuations. To capture temporal dependencies, we introduce temporal block aggregation, which integrates per-block influence scores across overlapping time windows. Experiments across multiple time series datasets and models demonstrate that LTSV consistently provides reliable and strong valuation performance, while maintaining manageable computational requirements. Our results suggest that in-context finetuning on time series foundation models provides a practical and effective bridge between data attribution and model generalization in time series learning.", "published": "2025-11-10T13:06:46Z", "authors": ["Shunyu Wu", "Tianyue Li", "Yixuan Leng", "Jingyi Suo", "Jian Lou", "Dan Li", "See-Kiong Ng"], "categories": ["cs.LG", "cs.AI"], "doi": "", "_query": "property valuation computer vision", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2307.12194v1", "title": "LIST: Learning Implicitly from Spatial Transformers for Single-View 3D Reconstruction", "abstract": "Accurate reconstruction of both the geometric and topological details of a 3D object from a single 2D image embodies a fundamental challenge in computer vision. Existing explicit/implicit solutions to this problem struggle to recover self-occluded geometry and/or faithfully reconstruct topological shape structures. To resolve this dilemma, we introduce LIST, a novel neural architecture that leverages local and global image features to accurately reconstruct the geometric and topological structure of a 3D object from a single image. We utilize global 2D features to predict a coarse shape of the target object and then use it as a base for higher-resolution reconstruction. By leveraging both local 2D features from the image and 3D features from the coarse prediction, we can predict the signed distance between an arbitrary point and the target surface via an implicit predictor with great accuracy. Furthermore, our model does not require camera estimation or pixel alignment. It provides an uninfluenced reconstruction from the input-view direction. Through qualitative and quantitative analysis, we show the superiority of our model in reconstructing 3D objects from both synthetic and real-world images against the state of the art.", "published": "2023-07-23T01:01:27Z", "authors": ["Mohammad Samiul Arshad", "William J. Beksi"], "categories": ["cs.CV", "cs.GR"], "doi": "", "_query": "property valuation computer vision", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/1909.08148v1", "title": "AdaCompress: Adaptive Compression for Online Computer Vision Services", "abstract": "With the growth of computer vision based applications and services, an explosive amount of images have been uploaded to cloud servers which host such computer vision algorithms, usually in the form of deep learning models. JPEG has been used as the {\\em de facto} compression and encapsulation method before one uploads the images, due to its wide adaptation. However, standard JPEG configuration does not always perform well for compressing images that are to be processed by a deep learning model, e.g., the standard quality level of JPEG leads to 50\\% of size overhead (compared with the best quality level selection) on ImageNet under the same inference accuracy in popular computer vision models including InceptionNet, ResNet, etc. Knowing this, designing a better JPEG configuration for online computer vision services is still extremely challenging: 1) Cloud-based computer vision models are usually a black box to end-users; thus it is difficult to design JPEG configuration without knowing their model structures. 2) JPEG configuration has to change when different users use it. In this paper, we propose a reinforcement learning based JPEG configuration framework. In particular, we design an agent that adaptively chooses the compression level according to the input image's features and backend deep learning models. Then we train the agent in a reinforcement learning way to adapt it for different deep learning cloud services that act as the {\\em interactive training environment} and feeding a reward with comprehensive consideration of accuracy and data size. In our real-world evaluation on Amazon Rekognition, Face++ and Baidu Vision, our approach can reduce the size of images by 1/2 -- 1/3 while the overall classification accuracy only decreases slightly.", "published": "2019-09-17T23:45:28Z", "authors": ["Hongshan Li", "Yu Guo", "Zhi Wang", "Shutao Xia", "Wenwu Zhu"], "categories": ["cs.MM", "eess.IV"], "doi": "10.1145/3343031.3350874", "_query": "property valuation computer vision", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2011.14749v2", "title": "A valuation theorem for Noetherian rings", "abstract": "Let A and B be integral domains. Suppose A is Noetherian and B is a finitely generated A-algebra that contains A. Denote by A' the integral closure of A in B. We show that A' is determined by finitely many unique discrete valuation rings. Our result generalizes Rees' classical valuation theorem for ideals. We also obtain a variant of Zariski's main theorem.", "published": "2020-11-30T13:05:19Z", "authors": ["Antoni Rangachev"], "categories": ["math.AC", "math.AG"], "doi": "", "_query": "property valuation computer vision", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2406.08822v1", "title": "Computer vision-based model for detecting turning lane features on Florida's public roadways", "abstract": "Efficient and current roadway geometry data collection is critical to transportation agencies in road planning, maintenance, design, and rehabilitation. Data collection methods are divided into land-based and aerial-based. Land-based methods for extensive highway networks are tedious, costly, pose safety risks. Therefore, there is the need for efficient, safe, and economical data acquisition methodologies. The rise of computer vision and object detection technologies have made automated extraction of roadway geometry features feasible. This study detects roadway features on Florida's public roads from high-resolution aerial images using AI. The developed model achieved an average accuracy of 80.4 percent when compared with ground truth data. The extracted roadway geometry data can be integrated with crash and traffic data to provide valuable insights to policymakers and roadway users.", "published": "2024-06-13T05:28:53Z", "authors": ["Richard Boadu Antwi", "Samuel Takyi", "Kimollo Michael", "Alican Karaer", "Eren Erman Ozguven", "Ren Moses", "Maxim A. Dulebenets", "Thobias Sando"], "categories": ["cs.CV", "cs.AI"], "doi": "", "_query": "property valuation computer vision", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2410.02806v1", "title": "Investigating the Impact of Randomness on Reproducibility in Computer Vision: A Study on Applications in Civil Engineering and Medicine", "abstract": "Reproducibility is essential for scientific research. However, in computer vision, achieving consistent results is challenging due to various factors. One influential, yet often unrecognized, factor is CUDA-induced randomness. Despite CUDA's advantages for accelerating algorithm execution on GPUs, if not controlled, its behavior across multiple executions remains non-deterministic. While reproducibility issues in ML being researched, the implications of CUDA-induced randomness in application are yet to be understood. Our investigation focuses on this randomness across one standard benchmark dataset and two real-world datasets in an isolated environment. Our results show that CUDA-induced randomness can account for differences up to 4.77% in performance scores. We find that managing this variability for reproducibility may entail increased runtime or reduce performance, but that disadvantages are not as significant as reported in previous studies.", "published": "2024-09-19T11:06:06Z", "authors": ["Bahadır Eryılmaz", "Osman Alperen Koraş", "Jörg Schlötterer", "Christin Seifert"], "categories": ["cs.CV", "cs.AI"], "doi": "", "_query": "property valuation computer vision", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/1402.6635v1", "title": "Tensor computations in computer algebra systems", "abstract": "This paper considers three types of tensor computations. On their basis, we attempt to formulate criteria that must be satisfied by a computer algebra system dealing with tensors. We briefly overview the current state of tensor computations in different computer algebra systems. The tensor computations are illustrated with appropriate examples implemented in specific systems: Cadabra and Maxima.", "published": "2014-02-22T15:47:58Z", "authors": ["A. V. Korolkova", "D. S. Kulyabov", "L. A. Sevastyanov"], "categories": ["cs.SC", "cs.MS", "gr-qc"], "doi": "10.1134/S0361768813030031", "_query": "property valuation computer vision", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2208.14657v1", "title": "EViT: Privacy-Preserving Image Retrieval via Encrypted Vision Transformer in Cloud Computing", "abstract": "Image retrieval systems help users to browse and search among extensive images in real-time. With the rise of cloud computing, retrieval tasks are usually outsourced to cloud servers. However, the cloud scenario brings a daunting challenge of privacy protection as cloud servers cannot be fully trusted. To this end, image-encryption-based privacy-preserving image retrieval schemes have been developed, which first extract features from cipher-images, and then build retrieval models based on these features. Yet, most existing approaches extract shallow features and design trivial retrieval models, resulting in insufficient expressiveness for the cipher-images. In this paper, we propose a novel paradigm named Encrypted Vision Transformer (EViT), which advances the discriminative representations capability of cipher-images. First, in order to capture comprehensive ruled information, we extract multi-level local length sequence and global Huffman-code frequency features from the cipher-images which are encrypted by stream cipher during JPEG compression process. Second, we design the Vision Transformer-based retrieval model to couple with the multi-level features, and propose two adaptive data augmentation methods to improve representation power of the retrieval model. Our proposal can be easily adapted to unsupervised and supervised settings via self-supervised contrastive learning manner. Extensive experiments reveal that EViT achieves both excellent encryption and retrieval performance, outperforming current schemes in terms of retrieval accuracy by large margins while protecting image privacy effectively. Code is publicly available at \\url{https://github.com/onlinehuazai/EViT}.", "published": "2022-08-31T07:07:21Z", "authors": ["Qihua Feng", "Peiya Li", "Zhixun Lu", "Chaozhuo Li", "Zefang Wang", "Zhiquan Liu", "Chunhui Duan", "Feiran Huang"], "categories": ["cs.CV", "cs.MM"], "doi": "", "_query": "property valuation computer vision", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/1201.4769v2", "title": "On algebraic volume density property", "abstract": "A smooth affine algebraic variety $X$ equipped with an algebraic volume form $ω$ has the algebraic volume density property (AVDP) if the Lie algebra generated by completely integrable algebraic vector fields of $ω$-divergence zero coincides with the space of all algebraic vector fields of $ω$-divergence zero. We develop an effective criterion of verifying whether a given $X$ has AVDP. As an application of this method we establish AVDP for any homogeneous space $X=G/R$ that admits a $G$-invariant algebraic volume form where $G$ is a linear algebraic group and $R$ is a closed reductive subgroup of $G$.", "published": "2012-01-23T17:03:13Z", "authors": ["Shulim Kaliman", "Frank Kutzschebauch"], "categories": ["math.AG"], "doi": "", "_query": "property valuation computer vision", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2405.01725v1", "title": "Development of Skip Connection in Deep Neural Networks for Computer Vision and Medical Image Analysis: A Survey", "abstract": "Deep learning has made significant progress in computer vision, specifically in image classification, object detection, and semantic segmentation. The skip connection has played an essential role in the architecture of deep neural networks,enabling easier optimization through residual learning during the training stage and improving accuracy during testing. Many neural networks have inherited the idea of residual learning with skip connections for various tasks, and it has been the standard choice for designing neural networks. This survey provides a comprehensive summary and outlook on the development of skip connections in deep neural networks. The short history of skip connections is outlined, and the development of residual learning in deep neural networks is surveyed. The effectiveness of skip connections in the training and testing stages is summarized, and future directions for using skip connections in residual learning are discussed. Finally, we summarize seminal papers, source code, models, and datasets that utilize skip connections in computer vision, including image classification, object detection, semantic segmentation, and image reconstruction. We hope this survey could inspire peer researchers in the community to develop further skip connections in various forms and tasks and the theory of residual learning in deep neural networks. The project page can be found at https://github.com/apple1986/Residual_Learning_For_Images", "published": "2024-05-02T20:43:58Z", "authors": ["Guoping Xu", "Xiaxia Wang", "Xinglong Wu", "Xuesong Leng", "Yongchao Xu"], "categories": ["eess.IV", "cs.CV", "cs.LG"], "doi": "10.1016/j.engappai.2024.109890", "_query": "property valuation computer vision", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/1510.04860v1", "title": "Measurement of Road Traffic Parameters Based on Multi-Vehicle Tracking", "abstract": "Development of computing power and cheap video cameras enabled today's traffic management systems to include more cameras and computer vision applications for transportation system monitoring and control. Combined with image processing algorithms cameras are used as sensors to measure road traffic parameters like flow volume, origin-destination matrices, classify vehicles, etc. In this paper we propose a system for measurement of road traffic parameters (basic motion model parameters and macro-scopic traffic parameters). The system is based on Local Binary Pattern (LBP) image features classification with a cascade of Gentle Adaboost (GAB) classifiers to determine vehicle existence and its location in an image. Additionally, vehicle tracking and counting in a road traffic video is performed by using Extended Kalman Filter (EKF) and virtual markers. The newly proposed system is compared with a system based on background subtraction. Comparison is performed by the means of execution time and accuracy.", "published": "2015-10-16T12:26:29Z", "authors": ["Kristian Kovačić", "Edouard Ivanjko", "Niko Jelušić"], "categories": ["cs.CV"], "doi": "", "_query": "property valuation computer vision", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2006.03870v3", "title": "Towards large-scale, automated, accurate detection of CCTV camera objects using computer vision. Applications and implications for privacy, safety, and cybersecurity. (Preprint)", "abstract": "In order to withstand the ever-increasing invasion of privacy by CCTV cameras and technologies, on par CCTV-aware solutions must exist that provide privacy, safety, and cybersecurity features. We argue that a first important step towards such CCTV-aware solutions must be a mapping system (e.g., Google Maps, OpenStreetMap) that provides both privacy and safety routing and navigation options. However, this in turn requires that the mapping system contains updated information on CCTV cameras' exact geo-location, coverage area, and possibly other meta-data (e.g., resolution, facial recognition features, operator). Such information is however missing from current mapping systems, and there are several ways to fix this. One solution is to perform CCTV camera detection on geo-location tagged images, e.g., street view imagery on various platforms, user images publicly posted in image sharing platforms such as Flickr. Unfortunately, to the best of our knowledge, there are no computer vision models for CCTV camera object detection as well as no mapping system that supports privacy and safety routing options. To close these gaps, with this paper we introduce CCTVCV -- the first and only computer vision MS COCO-compatible models that are able to accurately detect CCTV and video surveillance cameras in images and video frames. To this end, our best detectors were built using 8387 images that were manually reviewed and annotated to contain 10419 CCTV camera instances, and achieve an accuracy of up to 98.7%. Moreover, we build and evaluate multiple models, present a comprehensive comparison of their performance, and outline core challenges associated with such research.", "published": "2020-06-06T13:49:09Z", "authors": ["Hannu Turtiainen", "Andrei Costin", "Tuomo Lahtinen", "Lauri Sintonen", "Timo Hamalainen"], "categories": ["cs.CV", "cs.CR"], "doi": "", "_query": "property valuation computer vision", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2605.21268v3", "title": "Vision Transformers and Convolutional Neural Networks for Land Use Scene Classification", "abstract": "Land Use Scene Classification (LUSC) from remote sensing imagery plays a critical role in environmental monitoring, urban planning, and sustainable resource management. In recent years, deep learning methods have significantly advanced the state of the art, with Convolutional Neural Networks (CNNs) dominating the field because of their strong ability to capture local spatial features. However, the emergence of Vision Transformers (ViTs) has introduced a new paradigm that models long-range dependencies through self-attention mechanisms, potentially enabling improved global context understanding. This paper presents a comparative assessment of Vision Transformers and CNN-based architecture for remote sensing land use scene classification. Representative CNN models, such as AlexNet, is evaluated alongside the Vision Transformer (ViT) using benchmark remote sensing datasets, including the UC Merced Land Use and EuroSAT Land Use datasets. The study examines classification accuracy, precision, recall, F1-score, and computational complexity to provide a comprehensive performance comparison. Experimental results demonstrate that CNNs perform robustly on datasets with limited training samples and strong local texture characteristics, whereas Vision Transformers exhibit superior performance in capturing global spatial relationships in complex scenes when sufficient training data are available. However, ViTs typically require greater computational resources and larger training datasets to achieve optimal performance. The findings of this study provide insights into the strengths and limitations of both architectures and offer guidance for selecting appropriate models for remote sensing land use scene classification applications.", "published": "2026-05-20T14:57:16Z", "authors": ["Arun D. Kulkarni"], "categories": ["cs.CV"], "doi": "10.14569/IJACSA.2026.0170701", "_query": "property valuation computer vision", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/1907.01859v1", "title": "Essentially finite generation of valuation rings in terms of classical invariants", "abstract": "The main goal of this paper is to study some properties of an extension of valuations from classical invariants. More specifically, we consider a valued field $(K,ν)$ and an extension $ω$ of $ν$ to a finite extension $L$ of $K$. Then we study when the valuation ring of $ω$ is essentially finitely generated over the valuation ring of $ν$. We present a necessary condition in terms of classic invariants of the extension by Hagen Knaf and show that in some particular cases, this condition is also sufficient. We also study when the corresponding extension of graded algebras is finitely generated. For this problem we present an equivalent condition (which is weaker than the one for the finite generation of the valuation rings).", "published": "2019-07-03T11:24:18Z", "authors": ["Steven Dale Cutkosky", "Josnei Novacoski"], "categories": ["math.AC"], "doi": "", "_query": "property valuation computer vision", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/1606.05164v3", "title": "Network Valuation in Financial Systems", "abstract": "We introduce a general model for the balance-sheet consistent valuation of interbank claims within an interconnected financial system. Our model represents an extension of clearing models of interdependent liabilities to account for the presence of uncertainty on banks' external assets. At the same time, it also provides a natural extension of classic structural credit risk models to the case of an interconnected system. We characterize the existence and uniqueness of a valuation that maximises individual and total equity values for all banks. We apply our model to the assessment of systemic risk, and in particular for the case of stress-testing. Further, we provide a fixed-point algorithm to carry out the network valuation and the conditions for its convergence.", "published": "2016-06-16T12:45:11Z", "authors": ["Paolo Barucca", "Marco Bardoscia", "Fabio Caccioli", "Marco D'Errico", "Gabriele Visentin", "Guido Caldarelli", "Stefano Battiston"], "categories": ["q-fin.RM", "physics.soc-ph"], "doi": "10.1111/mafi.12272", "_query": "property valuation computer vision", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/1508.03362v1", "title": "Ramification of valuations and local rings", "abstract": "In this paper we consider birational properties of ramification in excellent local rings. We extend earlier results of the author with Olivier Piltant to show that strong local monomialization is true along a valuation dominating a defectless extension of two dimensional excellent local rings. We also obtain general results on the structure of the extension of associated graded rings along a valuation, and show that the invariants alpha and beta of stable forms of two dimensional extensions in characteristic p of the author and Olivier Piltant are not eventually constant.", "published": "2015-08-09T00:44:54Z", "authors": ["Steven Dale Cutkosky"], "categories": ["math.AG", "math.AC"], "doi": "", "_query": "property valuation computer vision", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2209.12155v1", "title": "Discriminative feature encoding for intrinsic image decomposition", "abstract": "Intrinsic image decomposition is an important and long-standing computer vision problem. Given an input image, recovering the physical scene properties is ill-posed. Several physically motivated priors have been used to restrict the solution space of the optimization problem for intrinsic image decomposition. This work takes advantage of deep learning, and shows that it can solve this challenging computer vision problem with high efficiency. The focus lies in the feature encoding phase to extract discriminative features for different intrinsic layers from an input image. To achieve this goal, we explore the distinctive characteristics of different intrinsic components in the high dimensional feature embedding space. We define feature distribution divergence to efficiently separate the feature vectors of different intrinsic components. The feature distributions are also constrained to fit the real ones through a feature distribution consistency. In addition, a data refinement approach is provided to remove data inconsistency from the Sintel dataset, making it more suitable for intrinsic image decomposition. Our method is also extended to intrinsic video decomposition based on pixel-wise correspondences between adjacent frames. Experimental results indicate that our proposed network structure can outperform the existing state-of-the-art.", "published": "2022-09-25T05:51:49Z", "authors": ["Zongji Wang", "Yunfei Liu", "Feng Lu"], "categories": ["cs.CV"], "doi": "", "_query": "property valuation computer vision", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2502.12027v1", "title": "Enhancing Transparent Object Pose Estimation: A Fusion of GDR-Net and Edge Detection", "abstract": "Object pose estimation of transparent objects remains a challenging task in the field of robot vision due to the immense influence of lighting, background, and reflections. However, the edges of clear objects have the highest contrast, which leads to stable and prominent features. We propose a novel approach by incorporating edge detection in a pre-processing step for the tasks of object detection and object pose estimation. We conducted experiments to investigate the effect of edge detectors on transparent objects. We examine the performance of the state-of-the-art 6D object pose estimation pipeline GDR-Net and the object detector YOLOX when applying different edge detectors as pre-processing steps (i.e., Canny edge detection with and without color information, and holistically-nested edges (HED)). We evaluate the physically-based rendered dataset Trans6D-32 K of transparent objects with parameters proposed by the BOP Challenge. Our results indicate that applying edge detection as a pre-processing enhances performance for certain objects.", "published": "2025-02-17T16:59:37Z", "authors": ["Tessa Pulli", "Peter Hönig", "Stefan Thalhammer", "Matthias Hirschmanner", "Markus Vincze"], "categories": ["cs.CV"], "doi": "", "_query": "property valuation computer vision", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/1908.10455v1", "title": "Self-Supervised Representation Learning via Neighborhood-Relational Encoding", "abstract": "In this paper, we propose a novel self-supervised representation learning by taking advantage of a neighborhood-relational encoding (NRE) among the training data. Conventional unsupervised learning methods only focused on training deep networks to understand the primitive characteristics of the visual data, mainly to be able to reconstruct the data from a latent space. They often neglected the relation among the samples, which can serve as an important metric for self-supervision. Different from the previous work, NRE aims at preserving the local neighborhood structure on the data manifold. Therefore, it is less sensitive to outliers. We integrate our NRE component with an encoder-decoder structure for learning to represent samples considering their local neighborhood information. Such discriminative and unsupervised representation learning scheme is adaptable to different computer vision tasks due to its independence from intense annotation requirements. We evaluate our proposed method for different tasks, including classification, detection, and segmentation based on the learned latent representations. In addition, we adopt the auto-encoding capability of our proposed method for applications like defense against adversarial example attacks and video anomaly detection. Results confirm the performance of our method is better or at least comparable with the state-of-the-art for each specific application, but with a generic and self-supervised approach.", "published": "2019-08-27T20:26:01Z", "authors": ["Mohammad Sabokrou", "Mohammad Khalooei", "Ehsan Adeli"], "categories": ["cs.CV"], "doi": "", "_query": "property valuation computer vision", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2502.00494v3", "title": "Data Overvaluation Attack and Truthful Data Valuation in Federated Learning", "abstract": "In collaborative machine learning (CML), data valuation, i.e., evaluating the contribution of each client's data to the machine learning model, has become a critical task for incentivizing and selecting positive data contributions. However, existing studies often assume that clients engage in data valuation truthfully, overlooking the practical motivation for clients to exaggerate their contributions. To unlock this threat, this paper introduces the data overvaluation attack, enabling strategic clients to have their data significantly overvalued in federated learning, a widely adopted paradigm for decentralized CML. Furthermore, we propose a Bayesian truthful data valuation metric, named Truth-Shapley. Truth-Shapley is the unique metric that guarantees some promising axioms for data valuation while ensuring that clients' optimal strategy is to perform truthful data valuation under certain conditions. Our experiments demonstrate the vulnerability of existing data valuation metrics to the proposed attack and validate the robustness and effectiveness of Truth-Shapley.", "published": "2025-02-01T17:05:37Z", "authors": ["Shuyuan Zheng", "Sudong Cai", "Chuan Xiao", "Yang Cao", "Jianbin Qin", "Masatoshi Yoshikawa", "Makoto Onizuka"], "categories": ["cs.CR", "cs.AI", "cs.LG"], "doi": "", "_query": "property valuation computer vision", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2610.00159v1", "title": "Maximin Share Allocations Beyond Weakly Lexicographic Valuations", "abstract": "We study the fair division of indivisible goods among agents with nonnegative additive valuations. An agent's maximin share (MMS) is the value she can guarantee by partitioning the goods into as many bundles as there are agents and receiving a least-valued bundle. We prove the existence of exact MMS allocations for a valuation domain that generalizes several few-valued domains as well as weakly lexicographic valuations. For each agent, the lower-valued goods, after a positive rescaling, may take values in $\\{0,1,d,d+1\\}$, $\\{0,1,2,2e\\}$, or $\\{0,1,2,3,4\\}$, where $d,e\\ge 2$ are integers. Above these goods, the valuation may contain arbitrarily many value classes, subject to the condition that a single good in each class is worth at least the total value of all strictly lower-valued goods. The value classes, scaling factors, and parameters may vary across agents. We give an algorithm that computes an MMS allocation in $O(nm\\log^2(2+m))$ time, where $n$ is the number of agents and $m$ is the number of goods.", "published": "2026-09-12T15:40:17Z", "authors": ["Nicholas Teh"], "categories": ["cs.GT"], "doi": "", "_query": "property valuation computer vision", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2309.01824v1", "title": "On the fly Deep Neural Network Optimization Control for Low-Power Computer Vision", "abstract": "Processing visual data on mobile devices has many applications, e.g., emergency response and tracking. State-of-the-art computer vision techniques rely on large Deep Neural Networks (DNNs) that are usually too power-hungry to be deployed on resource-constrained edge devices. Many techniques improve the efficiency of DNNs by using sparsity or quantization. However, the accuracy and efficiency of these techniques cannot be adapted for diverse edge applications with different hardware constraints and accuracy requirements. This paper presents a novel technique to allow DNNs to adapt their accuracy and energy consumption during run-time, without the need for any re-training. Our technique called AdaptiveActivation introduces a hyper-parameter that controls the output range of the DNNs' activation function to dynamically adjust the sparsity and precision in the DNN. AdaptiveActivation can be applied to any existing pre-trained DNN to improve their deployability in diverse edge environments. We conduct experiments on popular edge devices and show that the accuracy is within 1.5% of the baseline. We also show that our approach requires 10%--38% less memory than the baseline techniques leading to more accuracy-efficiency tradeoff options", "published": "2023-09-04T21:26:26Z", "authors": ["Ishmeet Kaur", "Adwaita Janardhan Jadhav"], "categories": ["cs.CV", "cs.LG"], "doi": "", "_query": "property valuation computer vision", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2407.06513v1", "title": "Computer vision tasks for intelligent aerospace missions: An overview", "abstract": "Computer vision tasks are crucial for aerospace missions as they help spacecraft to understand and interpret the space environment, such as estimating position and orientation, reconstructing 3D models, and recognizing objects, which have been extensively studied to successfully carry out the missions. However, traditional methods like Kalman Filtering, Structure from Motion, and Multi-View Stereo are not robust enough to handle harsh conditions, leading to unreliable results. In recent years, deep learning (DL)-based perception technologies have shown great potential and outperformed traditional methods, especially in terms of their robustness to changing environments. To further advance DL-based aerospace perception, various frameworks, datasets, and strategies have been proposed, indicating significant potential for future applications. In this survey, we aim to explore the promising techniques used in perception tasks and emphasize the importance of DL-based aerospace perception. We begin by providing an overview of aerospace perception, including classical space programs developed in recent years, commonly used sensors, and traditional perception methods. Subsequently, we delve into three fundamental perception tasks in aerospace missions: pose estimation, 3D reconstruction, and recognition, as they are basic and crucial for subsequent decision-making and control. Finally, we discuss the limitations and possibilities in current research and provide an outlook on future developments, including the challenges of working with limited datasets, the need for improved algorithms, and the potential benefits of multi-source information fusion.", "published": "2024-07-09T02:50:54Z", "authors": ["Huilin Chen", "Qiyu Sun", "Fangfei Li", "Yang Tang"], "categories": ["cs.CV"], "doi": "", "_query": "property valuation computer vision", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2302.09560v1", "title": "Deep Selector-JPEG: Adaptive JPEG Image Compression for Computer Vision in Image classification with Human Vision Criteria", "abstract": "With limited storage/bandwidth resources, input images to Computer Vision (CV) applications that use Deep Neural Networks (DNNs) are often encoded with JPEG that is tailored to Human Vision (HV). This paper presents Deep Selector-JPEG, an adaptive JPEG compression method that targets image classification while satisfying HV criteria. For each image, Deep Selector-JPEG selects adaptively a Quality Factor (QF) to compress the image so that a good trade-off between the Compression Ratio (CR) and DNN classifier Accuracy (Rate-Accuracy performance) can be achieved over a set of images for a variety of DNN classifiers while the MS-SSIM of such compressed image is greater than a threshold value predetermined by HV with a high probability. Deep Selector-JPEG is designed via light-weighted or heavy-weighted selector architectures. Experimental results show that in comparison with JPEG at the same CR, Deep Selector-JPEG achieves better Rate-Accuracy performance over the ImageNet validation set for all tested DNN classifiers with gains in classification accuracy between 0.2% and 1% at the same CRs while satisfying HV constraints. Deep Selector-JPEG can also roughly provide the original classification accuracy at higher CRs.", "published": "2023-02-19T12:38:20Z", "authors": ["Hossam Amer", "Sepideh Shaterian", "En-hui Yang"], "categories": ["eess.IV", "cs.CV", "cs.LG", "cs.MM"], "doi": "", "_query": "property valuation computer vision", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2502.16152v1", "title": "DUPRE: Data Utility Prediction for Efficient Data Valuation", "abstract": "Data valuation is increasingly used in machine learning (ML) to decide the fair compensation for data owners and identify valuable or harmful data for improving ML models. Cooperative game theory-based data valuation, such as Data Shapley, requires evaluating the data utility (e.g., validation accuracy) and retraining the ML model for multiple data subsets. While most existing works on efficient estimation of the Shapley values have focused on reducing the number of subsets to evaluate, our framework, \\texttt{DUPRE}, takes an alternative yet complementary approach that reduces the cost per subset evaluation by predicting data utilities instead of evaluating them by model retraining. Specifically, given the evaluated data utilities of some data subsets, \\texttt{DUPRE} fits a \\emph{Gaussian process} (GP) regression model to predict the utility of every other data subset. Our key contribution lies in the design of our GP kernel based on the sliced Wasserstein distance between empirical data distributions. In particular, we show that the kernel is valid and positive semi-definite, encodes prior knowledge of similarities between different data subsets, and can be efficiently computed. We empirically verify that \\texttt{DUPRE} introduces low prediction error and speeds up data valuation for various ML models, datasets, and utility functions.", "published": "2025-02-22T08:53:39Z", "authors": ["Kieu Thao Nguyen Pham", "Rachael Hwee Ling Sim", "Quoc Phong Nguyen", "See Kiong Ng", "Bryan Kian Hsiang Low"], "categories": ["cs.LG", "cs.GT"], "doi": "10.5555/3709347.3743790", "_query": "property valuation computer vision", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/1310.4891v1", "title": "Dictionary Learning and Sparse Coding on Grassmann Manifolds: An Extrinsic Solution", "abstract": "Recent advances in computer vision and machine learning suggest that a wide range of problems can be addressed more appropriately by considering non-Euclidean geometry. In this paper we explore sparse dictionary learning over the space of linear subspaces, which form Riemannian structures known as Grassmann manifolds. To this end, we propose to embed Grassmann manifolds into the space of symmetric matrices by an isometric mapping, which enables us to devise a closed-form solution for updating a Grassmann dictionary, atom by atom. Furthermore, to handle non-linearity in data, we propose a kernelised version of the dictionary learning algorithm. Experiments on several classification tasks (face recognition, action recognition, dynamic texture classification) show that the proposed approach achieves considerable improvements in discrimination accuracy, in comparison to state-of-the-art methods such as kernelised Affine Hull Method and graph-embedding Grassmann discriminant analysis.", "published": "2013-10-18T03:04:47Z", "authors": ["Mehrtash Harandi", "Conrad Sanderson", "Chunhua Shen", "Brian C. Lovell"], "categories": ["cs.CV"], "doi": "", "_query": "property valuation computer vision", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2201.13027v2", "title": "BOAT: Bilateral Local Attention Vision Transformer", "abstract": "Vision Transformers achieved outstanding performance in many computer vision tasks. Early Vision Transformers such as ViT and DeiT adopt global self-attention, which is computationally expensive when the number of patches is large. To improve efficiency, recent Vision Transformers adopt local self-attention mechanisms, where self-attention is computed within local windows. Despite the fact that window-based local self-attention significantly boosts efficiency, it fails to capture the relationships between distant but similar patches in the image plane. To overcome this limitation of image-space local attention, in this paper, we further exploit the locality of patches in the feature space. We group the patches into multiple clusters using their features, and self-attention is computed within every cluster. Such feature-space local attention effectively captures the connections between patches across different local windows but still relevant. We propose a Bilateral lOcal Attention vision Transformer (BOAT), which integrates feature-space local attention with image-space local attention. We further integrate BOAT with both Swin and CSWin models, and extensive experiments on several benchmark datasets demonstrate that our BOAT-CSWin model clearly and consistently outperforms existing state-of-the-art CNN models and vision Transformers.", "published": "2022-01-31T07:09:50Z", "authors": ["Tan Yu", "Gangming Zhao", "Ping Li", "Yizhou Yu"], "categories": ["cs.CV", "cs.LG"], "doi": "", "_query": "property valuation computer vision", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2606.14658v1", "title": "Giving AI a Headache: Acoustic Adversarial Attacks to Computer Vision Applications", "abstract": "Artificial Intelligence (AI) is increasingly used to automate a variety of real-world computer vision (CV) applications, such as autonomous vehicle control, facial recognition, and security cameras. Recent research has shown that acoustic vibration can induce real physical motion in cameras, interfering with their internal stabilization mechanisms. Because the motion falls outside the conditions the stabilization system was designed to handle, the system introduces artifacts into the frame, causing AI-based CV models to misclassify, miss targets, or hallucinate objects. Previous work used ultrasonic frequencies (>20 kHz) to perform short-range attacks, which limits them to short distances due to the attenuation exhibited by high frequencies. In this work, we investigate acoustic attacks using lower frequencies in the audible range (<20 kHz), and we further expand our analysis to include how various image and object features are affected by the attacks. Specifically, we performed physical experiments to demonstrate the viability of our attacks on an off-the-shelf object detection model (YOLO11) by resonating a commercially available camera with various frequencies. Based on our results, we provide insights into several factors that make an AI CV system more vulnerable to these attacks, which could help inform the development of future mitigation strategies.", "published": "2026-06-12T17:22:53Z", "authors": ["Nicole Villavicencio-Garduño", "Maksim Ekin Eren", "Milo Prisbrey", "Ben Migliori", "Michael Teti"], "categories": ["cs.CV", "cs.AI"], "doi": "10.1117/12.3093699", "_query": "property valuation computer vision", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2409.09530v1", "title": "An Augmentation-based Model Re-adaptation Framework for Robust Image Segmentation", "abstract": "Image segmentation is a crucial task in computer vision, with wide-ranging applications in industry. The Segment Anything Model (SAM) has recently attracted intensive attention; however, its application in industrial inspection, particularly for segmenting commercial anti-counterfeit codes, remains challenging. Unlike open-source datasets, industrial settings often face issues such as small sample sizes and complex textures. Additionally, computational cost is a key concern due to the varying number of trainable parameters. To address these challenges, we propose an Augmentation-based Model Re-adaptation Framework (AMRF). This framework leverages data augmentation techniques during training to enhance the generalisation of segmentation models, allowing them to adapt to newly released datasets with temporal disparity. By observing segmentation masks from conventional models (FCN and U-Net) and a pre-trained SAM model, we determine a minimal augmentation set that optimally balances training efficiency and model performance. Our results demonstrate that the fine-tuned FCN surpasses its baseline by 3.29% and 3.02% in cropping accuracy, and 5.27% and 4.04% in classification accuracy on two temporally continuous datasets. Similarly, the fine-tuned U-Net improves upon its baseline by 7.34% and 4.94% in cropping, and 8.02% and 5.52% in classification. Both models outperform the top-performing SAM models (ViT-Large and ViT-Base) by an average of 11.75% and 9.01% in cropping accuracy, and 2.93% and 4.83% in classification accuracy, respectively.", "published": "2024-09-14T21:01:49Z", "authors": ["Zheming Zuo", "Joseph Smith", "Jonathan Stonehouse", "Boguslaw Obara"], "categories": ["cs.CV"], "doi": "", "_query": "property valuation computer vision", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/1608.07021v2", "title": "Multiple Exchange Property for M$^\\natural$-concave Functions and Valuated Matroids", "abstract": "The multiple exchange property for matroid bases is generalized for valuated matroids and M$^\\natural$-concave set functions. The proof is based on the Fenchel-type duality theorem in discrete convex analysis. The present result has an implication in economics: The strong no complementarities (SNC) condition of Gul and Stacchetti is in fact equivalent to the gross substitutes (GS) condition of Kelso and Crawford.", "published": "2016-08-25T06:03:43Z", "authors": ["Kazuo Murota"], "categories": ["math.CO"], "doi": "", "_query": "property valuation computer vision", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2004.03218v3", "title": "Property (T) and actions on infinite measure spaces", "abstract": "The aim of the article is to provide a characterization of Kazhdan's property (T) for locally compact, second countable pairs of groups $H\\subset G$ in terms of actions on infinite, $σ$-finite measure spaces. It is inspired by the recent characterization of the Haagerup property by similar actions due to T. Delabie, A. Zumbrunnen and the author.", "published": "2020-04-07T09:17:28Z", "authors": ["Paul Jolissaint"], "categories": ["math.GR"], "doi": "", "_query": "property valuation computer vision", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/1907.04408v2", "title": "SAT Solvers and Computer Algebra Systems: A Powerful Combination for Mathematics", "abstract": "Over the last few decades, many distinct lines of research aimed at automating mathematics have been developed, including computer algebra systems (CASs) for mathematical modelling, automated theorem provers for first-order logic, SAT/SMT solvers aimed at program verification, and higher-order proof assistants for checking mathematical proofs. More recently, some of these lines of research have started to converge in complementary ways. One success story is the combination of SAT solvers and CASs (SAT+CAS) aimed at resolving mathematical conjectures. Many conjectures in pure and applied mathematics are not amenable to traditional proof methods. Instead, they are best addressed via computational methods that involve very large combinatorial search spaces. SAT solvers are powerful methods to search through such large combinatorial spaces---consequently, many problems from a variety of mathematical domains have been reduced to SAT in an attempt to resolve them. However, solvers traditionally lack deep repositories of mathematical domain knowledge that can be crucial to pruning such large search spaces. By contrast, CASs are deep repositories of mathematical knowledge but lack efficient general search capabilities. By combining the search power of SAT with the deep mathematical knowledge in CASs we can solve many problems in mathematics that no other known methods seem capable of solving. We demonstrate the success of the SAT+CAS paradigm by highlighting many conjectures that have been disproven, verified, or partially verified using our tool MathCheck. These successes indicate that the paradigm is positioned to become a standard method for solving problems requiring both a significant amount of search and deep mathematical reasoning. For example, the SAT+CAS paradigm has recently been used by Heule, Kauers, and Seidl to find many new algorithms for $3\\times3$ matrix multiplication.", "published": "2019-07-09T20:49:14Z", "authors": ["Curtis Bright", "Ilias Kotsireas", "Vijay Ganesh"], "categories": ["cs.LO", "cs.AI", "cs.SC", "math.CO"], "doi": "", "_query": "property valuation computer vision", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/1202.5985v1", "title": "Fast computation of the performance evaluation of biometric systems: application to multibiometric", "abstract": "The performance evaluation of biometric systems is a crucial step when designing and evaluating such systems. The evaluation process uses the Equal Error Rate (EER) metric proposed by the International Organization for Standardization (ISO/IEC). The EER metric is a powerful metric which allows easily comparing and evaluating biometric systems. However, the computation time of the EER is, most of the time, very intensive. In this paper, we propose a fast method which computes an approximated value of the EER. We illustrate the benefit of the proposed method on two applications: the computing of non parametric confidence intervals and the use of genetic algorithms to compute the parameters of fusion functions. Experimental results show the superiority of the proposed EER approximation method in term of computing time, and the interest of its use to reduce the learning of parameters with genetic algorithms. The proposed method opens new perspectives for the development of secure multibiometrics systems by speeding up their computation time.", "published": "2012-02-27T16:05:10Z", "authors": ["Romain Giot", "Mohamad El-Abed", "Christophe Rosenberger"], "categories": ["cs.DS"], "doi": "10.1016/j.future.2012.02.003", "_query": "property valuation computer vision", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2405.14554v2", "title": "SearchLVLMs: A Plug-and-Play Framework for Augmenting Large Vision-Language Models by Searching Up-to-Date Internet Knowledge", "abstract": "Large vision-language models (LVLMs) are ignorant of the up-to-date knowledge, such as LLaVA series, because they cannot be updated frequently due to the large amount of resources required, and therefore fail in many cases. For example, if a LVLM was released on January 2024, and it wouldn't know the singer of the theme song for the new Detective Conan movie, which wasn't released until April 2024. To solve the problem, a promising solution motivated by retrieval-augmented generation (RAG) is to provide LVLMs with up-to-date knowledge via internet search during inference, i.e., internet-augmented generation (IAG), which is already integrated in some closed-source commercial LVLMs such as GPT-4V. However, the specific mechanics underpinning them remain a mystery. In this paper, we propose a plug-and-play framework, for augmenting existing LVLMs in handling visual question answering (VQA) about up-to-date knowledge, dubbed SearchLVLMs. A hierarchical filtering model is trained to effectively and efficiently find the most helpful content from the websites returned by a search engine to prompt LVLMs with up-to-date knowledge. To train the model and evaluate our framework's performance, we propose a pipeline to automatically generate news-related VQA samples to construct a dataset, dubbed UDK-VQA. A multi-model voting mechanism is introduced to label the usefulness of website/content for VQA samples to construct the training set. Experimental results demonstrate the effectiveness of our framework, outperforming GPT-4V by about 25% in accuracy.", "published": "2024-05-23T13:32:07Z", "authors": ["Chuanhao Li", "Zhen Li", "Chenchen Jing", "Shuo Liu", "Wenqi Shao", "Yuwei Wu", "Ping Luo", "Yu Qiao", "Kaipeng Zhang"], "categories": ["cs.CV", "cs.AI"], "doi": "", "_query": "property valuation computer vision", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/1809.06846v4", "title": "MNIST Dataset Classification Utilizing k-NN Classifier with Modified Sliding-window Metric", "abstract": "The MNIST dataset of the handwritten digits is known as one of the commonly used datasets for machine learning and computer vision research. We aim to study a widely applicable classification problem and apply a simple yet efficient K-nearest neighbor classifier with an enhanced heuristic. We evaluate the performance of the K-nearest neighbor classification algorithm on the MNIST dataset where the $L2$ Euclidean distance metric is compared to a modified distance metric which utilizes the sliding window technique in order to avoid performance degradation due to slight spatial misalignments. The accuracy metric and confusion matrices are used as the performance indicators to compare the performance of the baseline algorithm versus the enhanced sliding window method and results show significant improvement using this proposed method.", "published": "2018-09-18T17:55:53Z", "authors": ["Divas Grover", "Behrad Toghi"], "categories": ["cs.CV"], "doi": "", "_query": "property valuation computer vision", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2501.11836v1", "title": "Data-driven Detection and Evaluation of Damages in Concrete Structures: Using Deep Learning and Computer Vision", "abstract": "Structural integrity is vital for maintaining the safety and longevity of concrete infrastructures such as bridges, tunnels, and walls. Traditional methods for detecting damages like cracks and spalls are labor-intensive, time-consuming, and prone to human error. To address these challenges, this study explores advanced data-driven techniques using deep learning for automated damage detection and analysis. Two state-of-the-art instance segmentation models, YOLO-v7 instance segmentation and Mask R-CNN, were evaluated using a dataset comprising 400 images, augmented to 10,995 images through geometric and color-based transformations to enhance robustness. The models were trained and validated using a dataset split into 90% training set, validation and test set 10%. Performance metrics such as precision, recall, mean average precision (mAP@0.5), and frames per second (FPS) were used for evaluation. YOLO-v7 achieved a superior mAP@0.5 of 96.1% and processed 40 FPS, outperforming Mask R-CNN, which achieved a mAP@0.5 of 92.1% with a slower processing speed of 18 FPS. The findings recommend YOLO-v7 instance segmentation model for real-time, high-speed structural health monitoring, while Mask R-CNN is better suited for detailed offline assessments. This study demonstrates the potential of deep learning to revolutionize infrastructure maintenance, offering a scalable and efficient solution for automated damage detection.", "published": "2025-01-21T02:44:05Z", "authors": ["Saeid Ataei", "Saeed Adibnazari", "Seyyed Taghi Ataei"], "categories": ["cs.CV", "cs.AI", "cs.LG"], "doi": "", "_query": "property valuation computer vision", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2106.00161v1", "title": "Integrative Use of Computer Vision and Unmanned Aircraft Technologies in Public Inspection: Foreign Object Debris Image Collection", "abstract": "Unmanned Aircraft Systems (UAS) have become an important resource for public service providers and smart cities. The purpose of this study is to expand this research area by integrating computer vision and UAS technology to automate public inspection. As an initial case study for this work, a dataset of common foreign object debris (FOD) is developed to assess the potential of light-weight automated detection. This paper presents the rationale and creation of this dataset. Future iterations of our work will include further technical details analyzing experimental implementation. At a local airport, UAS and portable cameras are used to collect the data contained in the initial version of this dataset. After collecting these videos of FOD, they were split into individual frames and stored as several thousand images. These frames are then annotated following standard computer vision format and stored in a folder-structure that reflects our creation method. The dataset annotations are validated using a custom tool that could be abstracted to fit future applications. Initial detection models were successfully created using the famous You Only Look Once algorithm, which indicates the practicality of the proposed data. Finally, several potential scenarios that could utilize either this dataset or similar methods for other public service are presented.", "published": "2021-06-01T00:45:32Z", "authors": ["Travis J. E. Munyer", "Daniel Brinkman", "Chenyu Huang", "Xin Zhong"], "categories": ["cs.CV"], "doi": "10.1145/3463677.3463743", "_query": "property valuation computer vision", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2101.08337v2", "title": "Essential finite generation of extensions of valuation rings", "abstract": "Given a generically finite local extension of valuation rings $V \\subset W$, the question of whether $W$ is the localization of a finitely generated $V$-algebra is significant for approaches to the problem of local uniformization of valuations using ramification theory. Hagen Knaf proposed a characterization of when $W$ is essentially of finite type over $V$ in terms of classical invariants of the extension of associated valuations. Knaf's conjecture has been verified in important special cases by Cutkosky and Novacoski using local uniformization of Abhyankar valuations and resolution of singularities of excellent surfaces in arbitrary characteristic, and by Cutkosky for valuation rings of function fields of characteristic $0$ using embedded resolution of singularities. In this paper we prove Knaf's conjecture in full generality.", "published": "2021-01-20T21:56:50Z", "authors": ["Rankeya Datta"], "categories": ["math.AC", "math.AG"], "doi": "10.1002/mana.202100190", "_query": "property valuation computer vision", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/1905.01489v3", "title": "WoodScape: A multi-task, multi-camera fisheye dataset for autonomous driving", "abstract": "Fisheye cameras are commonly employed for obtaining a large field of view in surveillance, augmented reality and in particular automotive applications. In spite of their prevalence, there are few public datasets for detailed evaluation of computer vision algorithms on fisheye images. We release the first extensive fisheye automotive dataset, WoodScape, named after Robert Wood who invented the fisheye camera in 1906. WoodScape comprises of four surround view cameras and nine tasks including segmentation, depth estimation, 3D bounding box detection and soiling detection. Semantic annotation of 40 classes at the instance level is provided for over 10,000 images and annotation for other tasks are provided for over 100,000 images. With WoodScape, we would like to encourage the community to adapt computer vision models for fisheye camera instead of using naive rectification.", "published": "2019-05-04T13:14:12Z", "authors": ["Senthil Yogamani", "Ciaran Hughes", "Jonathan Horgan", "Ganesh Sistu", "Padraig Varley", "Derek O'Dea", "Michal Uricar", "Stefan Milz", "Martin Simon", "Karl Amende", "Christian Witt", "Hazem Rashed", "Sumanth Chennupati", "Sanjaya Nayak", "Saquib Mansoor", "Xavier Perroton", "Patrick Perez"], "categories": ["cs.CV", "cs.AI", "cs.LG", "cs.RO", "stat.ML"], "doi": "", "_query": "property valuation computer vision", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2001.04732v1", "title": "Fine-grained Image Classification and Retrieval by Combining Visual and Locally Pooled Textual Features", "abstract": "Text contained in an image carries high-level semantics that can be exploited to achieve richer image understanding. In particular, the mere presence of text provides strong guiding content that should be employed to tackle a diversity of computer vision tasks such as image retrieval, fine-grained classification, and visual question answering. In this paper, we address the problem of fine-grained classification and image retrieval by leveraging textual information along with visual cues to comprehend the existing intrinsic relation between the two modalities. The novelty of the proposed model consists of the usage of a PHOC descriptor to construct a bag of textual words along with a Fisher Vector Encoding that captures the morphology of text. This approach provides a stronger multimodal representation for this task and as our experiments demonstrate, it achieves state-of-the-art results on two different tasks, fine-grained classification and image retrieval.", "published": "2020-01-14T12:06:12Z", "authors": ["Andres Mafla", "Sounak Dey", "Ali Furkan Biten", "Lluis Gomez", "Dimosthenis Karatzas"], "categories": ["cs.CV"], "doi": "", "_query": "property valuation computer vision", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2507.23021v1", "title": "Modeling Human Gaze Behavior with Diffusion Models for Unified Scanpath Prediction", "abstract": "Predicting human gaze scanpaths is crucial for understanding visual attention, with applications in human-computer interaction, autonomous systems, and cognitive robotics. While deep learning models have advanced scanpath prediction, most existing approaches generate averaged behaviors, failing to capture the variability of human visual exploration. In this work, we present ScanDiff, a novel architecture that combines diffusion models with Vision Transformers to generate diverse and realistic scanpaths. Our method explicitly models scanpath variability by leveraging the stochastic nature of diffusion models, producing a wide range of plausible gaze trajectories. Additionally, we introduce textual conditioning to enable task-driven scanpath generation, allowing the model to adapt to different visual search objectives. Experiments on benchmark datasets show that ScanDiff surpasses state-of-the-art methods in both free-viewing and task-driven scenarios, producing more diverse and accurate scanpaths. These results highlight its ability to better capture the complexity of human visual behavior, pushing forward gaze prediction research. Source code and models are publicly available at https://aimagelab.github.io/ScanDiff.", "published": "2025-07-30T18:36:09Z", "authors": ["Giuseppe Cartella", "Vittorio Cuculo", "Alessandro D'Amelio", "Marcella Cornia", "Giuseppe Boccignone", "Rita Cucchiara"], "categories": ["cs.CV", "cs.AI"], "doi": "", "_query": "property valuation computer vision", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2502.21201v3", "title": "The PanAf-FGBG Dataset: Understanding the Impact of Backgrounds in Wildlife Behaviour Recognition", "abstract": "Computer vision analysis of camera trap video footage is essential for wildlife conservation, as captured behaviours offer some of the earliest indicators of changes in population health. Recently, several high-impact animal behaviour datasets and methods have been introduced to encourage their use; however, the role of behaviour-correlated background information and its significant effect on out-of-distribution generalisation remain unexplored. In response, we present the PanAf-FGBG dataset, featuring 20 hours of wild chimpanzee behaviours, recorded at over 350 individual camera locations. Uniquely, it pairs every video with a chimpanzee (referred to as a foreground video) with a corresponding background video (with no chimpanzee) from the same camera location. We present two views of the dataset: one with overlapping camera locations and one with disjoint locations. This setup enables, for the first time, direct evaluation of in-distribution and out-of-distribution conditions, and for the impact of backgrounds on behaviour recognition models to be quantified. All clips come with rich behavioural annotations and metadata including unique camera IDs and detailed textual scene descriptions. Additionally, we establish several baselines and present a highly effective latent-space normalisation technique that boosts out-of-distribution performance by +5.42% mAP for convolutional and +3.75% mAP for transformer-based models. Finally, we provide an in-depth analysis on the role of backgrounds in out-of-distribution behaviour recognition, including the so far unexplored impact of background durations (i.e., the count of background frames within foreground videos).", "published": "2025-02-28T16:18:57Z", "authors": ["Otto Brookes", "Maksim Kukushkin", "Majid Mirmehdi", "Colleen Stephens", "Paula Dieguez", "Thurston C. Hicks", "Sorrel Jones", "Kevin Lee", "Maureen S. McCarthy", "Amelia Meier", "Emmanuelle Normand", "Erin G. Wessling", "Roman M. Wittig", "Kevin Langergraber", "Klaus Zuberbühler", "Lukas Boesch", "Thomas Schmid", "Mimi Arandjelovic", "Hjalmar Kühl", "Tilo Burghardt"], "categories": ["cs.CV", "cs.AI"], "doi": "", "_query": "property valuation computer vision", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2307.11558v1", "title": "Advancing Visual Grounding with Scene Knowledge: Benchmark and Method", "abstract": "Visual grounding (VG) aims to establish fine-grained alignment between vision and language. Ideally, it can be a testbed for vision-and-language models to evaluate their understanding of the images and texts and their reasoning abilities over their joint space. However, most existing VG datasets are constructed using simple description texts, which do not require sufficient reasoning over the images and texts. This has been demonstrated in a recent study~\\cite{luo2022goes}, where a simple LSTM-based text encoder without pretraining can achieve state-of-the-art performance on mainstream VG datasets. Therefore, in this paper, we propose a novel benchmark of \\underline{S}cene \\underline{K}nowledge-guided \\underline{V}isual \\underline{G}rounding (SK-VG), where the image content and referring expressions are not sufficient to ground the target objects, forcing the models to have a reasoning ability on the long-form scene knowledge. To perform this task, we propose two approaches to accept the triple-type input, where the former embeds knowledge into the image features before the image-query interaction; the latter leverages linguistic structure to assist in computing the image-text matching. We conduct extensive experiments to analyze the above methods and show that the proposed approaches achieve promising results but still leave room for improvement, including performance and interpretability. The dataset and code are available at \\url{https://github.com/zhjohnchan/SK-VG}.", "published": "2023-07-21T13:06:02Z", "authors": ["Zhihong Chen", "Ruifei Zhang", "Yibing Song", "Xiang Wan", "Guanbin Li"], "categories": ["cs.CV", "cs.CL"], "doi": "", "_query": "property valuation computer vision", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2209.05834v1", "title": "Computer vision system to count crustacean larvae", "abstract": "Fish products account for about 16 percent of the human diet worldwide, as of 2017. The counting action is a significant component in growing and producing these products. Growers must count the fish accurately, to do so technological solutions are needed. Two computer vision systems to automatically count crustacean larvae grown in industrial ponds were developed. The first system included an iPhone 11 camera with 3024X4032 resolution which acquired images from an industrial pond in indoor conditions. Two experiments were performed with this system, the first one included 200 images acquired in one day on growth stages 9,10 with an iPhone 11 camera on specific illumination condition. In the second experiment, a larvae industrial pond was photographed for 11 days with two devices an iPhone 11 and a SONY DSCHX90V cameras. With the first device (iPhone 11) two illumination conditions were tested. In each condition, 110 images were acquired. That system resulted in an accuracy of 88.4 percent image detection. The second system included a DSLR Nikon D510 camera with a 2000X2000 resolution with which seven experiments were performed outside the industrial pond. Images were acquired on day 1 of larvae growing stage resulting in the acquisition of a total of 700 images. That system resulted in an accuracy of 86 percent for a density of 50. An algorithm that automatically counts the number of larvae was developed for both cases based on the YOLOv5 CNN model. In addition, in this study, a larvae growth function was developed. Daily, several larvae were taken manually from the industrial pond and analyzed under a microscope. Once the growth stage was determined, images of the larva were acquired. Each larva's length was measured manually from the images. The most suitable model was the Gompertz model with a goodness of fit index of R squared of 0.983.", "published": "2022-09-13T09:18:13Z", "authors": ["Chen Rothschild"], "categories": ["cs.CV", "eess.IV"], "doi": "", "_query": "property valuation computer vision", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/1906.04131v3", "title": "Algebraic Overshear Density Property", "abstract": "We introduce the notion of the algebraic overshear density property which implies both the algebraic notion of flexibility and the holomorphic notion of the density property. We investigate basic consequences of this stronger property, and propose further research directions in this borderland between affine algebraic geometry and elliptic holomorphic geometry. As an application, we show that any smoothly bordered Riemann surface with finitely many boundary components that is embedded in a complex affine surface with the algebraic overshear density property admits a proper holomorphic embedding.", "published": "2019-06-10T17:09:16Z", "authors": ["Rafael B. Andrist", "Frank Kutzschebauch"], "categories": ["math.CV"], "doi": "", "_query": "property valuation computer vision", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2606.10666v1", "title": "Analyzing Training-Free Corruption Detection for Object Detection Datasets", "abstract": "Annotation errors are widespread in computer vision datasets and can significantly degrade the performance of systems trained on them, particularly in complex tasks such as object detection. Several approaches exist to identify annotation errors, including training-free feature-space methods which provide a fast and interpretable way to analyze annotations. However, the behavior on object detection annotations, which include semantic and spatial information, remains largely unexplored. In this work we analyze the applicability of feature-space-based approaches for detecting annotation errors in object detection datasets. By adapting an existing feature-space method, we show that such approaches reliably expose semantic mislabel, while positional errors remain difficult to detect. We evaluate this behavior across multiple pretrained embedding models, synthetic noise types (symmetric, asymmetric, and positional), and real-world annotation errors using VOC2012 and KITTI. All code and real-world corruptions are publicly available at the following repository: https://github.com/ ChristianSieberichs/BoundingBox\\_corruption\\_detection", "published": "2026-06-09T10:17:45Z", "authors": ["Christian Sieberichs", "Simon Geerkens", "Thomas Waschulzik", "Viswanathan Ramesh", "Alexander Braun"], "categories": ["cs.CV", "cs.DB"], "doi": "", "_query": "property valuation computer vision", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2212.04665v1", "title": "A Computer Vision Method for Estimating Velocity from Jumps", "abstract": "Athletes routinely undergo fitness evaluations to evaluate their training progress. Typically, these evaluations require a trained professional who utilizes specialized equipment like force plates. For the assessment, athletes perform drop and squat jumps, and key variables are measured, e.g. velocity, flight time, and time to stabilization, to name a few. However, amateur athletes may not have access to professionals or equipment that can provide these assessments. Here, we investigate the feasibility of estimating key variables using video recordings. We focus on jump velocity as a starting point because it is highly correlated with other key variables and is important for determining posture and lower-limb capacity. We find that velocity can be estimated with a high degree of precision across a range of athletes, with an average R-value of 0.71 (SD = 0.06).", "published": "2022-12-09T04:40:36Z", "authors": ["Soumyadip Roy", "Chaitanya Roygaga", "Nathaniel Blanchard", "Aparna Bharati"], "categories": ["cs.CV"], "doi": "", "_query": "property valuation computer vision", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/0809.1437v1", "title": "How applicable is Python as first computer language for teaching programming in a pre-university educational environment, from a teacher's point of view?", "abstract": "This project report attempts to evaluate the educational properties of the Python computer language, in practice. This is done by examining computer language evolution history, related scientific background work, the existing educational research on computer languages and Python's experimental application in higher secondary education in Greece, during first half of year 2002. This Thesis Report was delivered in advance of a thesis defense for a Masters/Doctorandus (MSc/Drs) title with the Amstel Institute/Universiteit van Amsterdam, during the same year.", "published": "2008-09-09T14:39:57Z", "authors": ["Fotis Georgatos"], "categories": ["cs.PL", "cs.CY"], "doi": "", "_query": "property valuation computer vision", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2001.10130v1", "title": "Interpreting Cloud Computer Vision Pain-Points: A Mining Study of Stack Overflow", "abstract": "Intelligent services are becoming increasingly more pervasive; application developers want to leverage the latest advances in areas such as computer vision to provide new services and products to users, and large technology firms enable this via RESTful APIs. While such APIs promise an easy-to-integrate on-demand machine intelligence, their current design, documentation and developer interface hides much of the underlying machine learning techniques that power them. Such APIs look and feel like conventional APIs but abstract away data-driven probabilistic behaviour - the implications of a developer treating these APIs in the same way as other, traditional cloud services, such as cloud storage, is of concern. The objective of this study is to determine the various pain-points developers face when implementing systems that rely on the most mature of these intelligent services, specifically those that provide computer vision. We use Stack Overflow to mine indications of the frustrations that developers appear to face when using computer vision services, classifying their questions against two recent classification taxonomies (documentation-related and general questions). We find that, unlike mature fields like mobile development, there is a contrast in the types of questions asked by developers. These indicate a shallow understanding of the underlying technology that empower such systems. We discuss several implications of these findings via the lens of learning taxonomies to suggest how the software engineering community can improve these services and comment on the nature by which developers use them.", "published": "2020-01-28T00:56:51Z", "authors": ["Alex Cummaudo", "Rajesh Vasa", "Scott Barnett", "John Grundy", "Mohamed Abdelrazek"], "categories": ["cs.SE", "cs.AI"], "doi": "", "_query": "property valuation computer vision", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2411.08666v2", "title": "A Survey on Vision Autoregressive Model", "abstract": "Autoregressive models have demonstrated great performance in natural language processing (NLP) with impressive scalability, adaptability and generalizability. Inspired by their notable success in NLP field, autoregressive models have been intensively investigated recently for computer vision, which perform next-token predictions by representing visual data as visual tokens and enables autoregressive modelling for a wide range of vision tasks, ranging from visual generation and visual understanding to the very recent multimodal generation that unifies visual generation and understanding with a single autoregressive model. This paper provides a systematic review of vision autoregressive models, including the development of a taxonomy of existing methods and highlighting their major contributions, strengths, and limitations, covering various vision tasks such as image generation, video generation, image editing, motion generation, medical image analysis, 3D generation, robotic manipulation, unified multimodal generation, etc. Besides, we investigate and analyze the latest advancements in autoregressive models, including thorough benchmarking and discussion of existing methods across various evaluation datasets. Finally, we outline key challenges and promising directions for future research, offering a roadmap to guide further advancements in vision autoregressive models.", "published": "2024-11-13T14:59:41Z", "authors": ["Kai Jiang", "Jiaxing Huang"], "categories": ["cs.CV", "cs.AI"], "doi": "", "_query": "property valuation computer vision", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2108.05580v1", "title": "perf4sight: A toolflow to model CNN training performance on Edge GPUs", "abstract": "The increased memory and processing capabilities of today's edge devices create opportunities for greater edge intelligence. In the domain of vision, the ability to adapt a Convolutional Neural Network's (CNN) structure and parameters to the input data distribution leads to systems with lower memory footprint, latency and power consumption. However, due to the limited compute resources and memory budget on edge devices, it is necessary for the system to be able to predict the latency and memory footprint of the training process in order to identify favourable training configurations of the network topology and device combination for efficient network adaptation. This work proposes perf4sight, an automated methodology for developing accurate models that predict CNN training memory footprint and latency given a target device and network. This enables rapid identification of network topologies that can be retrained on the edge device with low resource consumption. With PyTorch as the framework and NVIDIA Jetson TX2 as the target device, the developed models predict training memory footprint and latency with 95% and 91% accuracy respectively for a wide range of networks, opening the path towards efficient network adaptation on edge GPUs.", "published": "2021-08-12T07:55:37Z", "authors": ["Aditya Rajagopal", "Christos-Savvas Bouganis"], "categories": ["cs.LG", "cs.AI", "cs.CV"], "doi": "", "_query": "property valuation computer vision", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/1309.2685v1", "title": "Complete Valuations on Finite Distributive Lattices", "abstract": "We characterize the finite distributive lattices which admit a complete valuation, that is bijective over a set of consecutive natural numbers, with the additional conditions of completeness (Definition 2.3). We prove that such lattices are downset lattices of finite posets of dimension at most two, and determine a realizer through a recursive relation between weights on the poset associated to valuation. The relation shows that the weights count chains in the complementary poset. Conversely, we prove that a valuation defined on a poset of dimension at most two, through the weight function which counts chains in the complementary poset, is complete.", "published": "2013-09-10T22:40:54Z", "authors": ["Francesco Marigo"], "categories": ["cs.DM", "math.CO"], "doi": "", "_query": "property valuation computer vision", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2411.05902v2", "title": "Autoregressive Models in Vision: A Survey", "abstract": "Autoregressive modeling has been a huge success in the field of natural language processing (NLP). Recently, autoregressive models have emerged as a significant area of focus in computer vision, where they excel in producing high-quality visual content. Autoregressive models in NLP typically operate on subword tokens. However, the representation strategy in computer vision can vary in different levels, i.e., pixel-level, token-level, or scale-level, reflecting the diverse and hierarchical nature of visual data compared to the sequential structure of language. This survey comprehensively examines the literature on autoregressive models applied to vision. To improve readability for researchers from diverse research backgrounds, we start with preliminary sequence representation and modeling in vision. Next, we divide the fundamental frameworks of visual autoregressive models into three general sub-categories, including pixel-based, token-based, and scale-based models based on the representation strategy. We then explore the interconnections between autoregressive models and other generative models. Furthermore, we present a multifaceted categorization of autoregressive models in computer vision, including image generation, video generation, 3D generation, and multimodal generation. We also elaborate on their applications in diverse domains, including emerging domains such as embodied AI and 3D medical AI, with about 250 related references. Finally, we highlight the current challenges to autoregressive models in vision with suggestions about potential research directions. We have also set up a Github repository to organize the papers included in this survey at: https://github.com/ChaofanTao/Autoregressive-Models-in-Vision-Survey.", "published": "2024-11-08T17:15:12Z", "authors": ["Jing Xiong", "Gongye Liu", "Lun Huang", "Chengyue Wu", "Taiqiang Wu", "Yao Mu", "Yuan Yao", "Hui Shen", "Zhongwei Wan", "Jinfa Huang", "Chaofan Tao", "Shen Yan", "Huaxiu Yao", "Lingpeng Kong", "Hongxia Yang", "Mi Zhang", "Guillermo Sapiro", "Jiebo Luo", "Ping Luo", "Ngai Wong"], "categories": ["cs.CV", "cs.CL"], "doi": "", "_query": "property valuation computer vision", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2401.06019v1", "title": "Automatic UAV-based Airport Pavement Inspection Using Mixed Real and Virtual Scenarios", "abstract": "Runway and taxiway pavements are exposed to high stress during their projected lifetime, which inevitably leads to a decrease in their condition over time. To make sure airport pavement condition ensure uninterrupted and resilient operations, it is of utmost importance to monitor their condition and conduct regular inspections. UAV-based inspection is recently gaining importance due to its wide range monitoring capabilities and reduced cost. In this work, we propose a vision-based approach to automatically identify pavement distress using images captured by UAVs. The proposed method is based on Deep Learning (DL) to segment defects in the image. The DL architecture leverages the low computational capacities of embedded systems in UAVs by using an optimised implementation of EfficientNet feature extraction and Feature Pyramid Network segmentation. To deal with the lack of annotated data for training we have developed a synthetic dataset generation methodology to extend available distress datasets. We demonstrate that the use of a mixed dataset composed of synthetic and real training images yields better results when testing the training models in real application scenarios.", "published": "2024-01-11T16:30:07Z", "authors": ["Pablo Alonso", "Jon Ander Iñiguez de Gordoa", "Juan Diego Ortega", "Sara García", "Francisco Javier Iriarte", "Marcos Nieto"], "categories": ["cs.CV"], "doi": "10.1117/12.2679734", "_query": "property valuation computer vision", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/1512.00486v2", "title": "Loss Functions for Top-k Error: Analysis and Insights", "abstract": "In order to push the performance on realistic computer vision tasks, the number of classes in modern benchmark datasets has significantly increased in recent years. This increase in the number of classes comes along with increased ambiguity between the class labels, raising the question if top-1 error is the right performance measure. In this paper, we provide an extensive comparison and evaluation of established multiclass methods comparing their top-k performance both from a practical as well as from a theoretical perspective. Moreover, we introduce novel top-k loss functions as modifications of the softmax and the multiclass SVM losses and provide efficient optimization schemes for them. In the experiments, we compare on various datasets all of the proposed and established methods for top-k error optimization. An interesting insight of this paper is that the softmax loss yields competitive top-k performance for all k simultaneously. For a specific top-k error, our new top-k losses lead typically to further improvements while being faster to train than the softmax.", "published": "2015-12-01T21:22:35Z", "authors": ["Maksim Lapin", "Matthias Hein", "Bernt Schiele"], "categories": ["stat.ML", "cs.CV", "cs.LG"], "doi": "", "_query": "property valuation computer vision", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2310.07252v1", "title": "A Comparative Study of Pre-trained CNNs and GRU-Based Attention for Image Caption Generation", "abstract": "Image captioning is a challenging task involving generating a textual description for an image using computer vision and natural language processing techniques. This paper proposes a deep neural framework for image caption generation using a GRU-based attention mechanism. Our approach employs multiple pre-trained convolutional neural networks as the encoder to extract features from the image and a GRU-based language model as the decoder to generate descriptive sentences. To improve performance, we integrate the Bahdanau attention model with the GRU decoder to enable learning to focus on specific image parts. We evaluate our approach using the MSCOCO and Flickr30k datasets and show that it achieves competitive scores compared to state-of-the-art methods. Our proposed framework can bridge the gap between computer vision and natural language and can be extended to specific domains.", "published": "2023-10-11T07:30:01Z", "authors": ["Rashid Khan", "Bingding Huang", "Haseeb Hassan", "Asim Zaman", "Zhongfu Ye"], "categories": ["cs.CV", "cs.LG"], "doi": "", "_query": "property valuation computer vision", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2209.06044v3", "title": "On the finite generation of valuation semigroups on toric surfaces", "abstract": "We provide a combinatorial criterion for the finite generation of a valuation semigroup associated with an ample divisor on a smooth toric surface and a non-toric valuation of maximal rank. As an application, we construct a lattice polytope such that none of the valuation semigroups of the associated polarized toric variety coming from one-parameter subgroups and centered at a non-toric point are finitely generated.", "published": "2022-09-13T14:51:54Z", "authors": ["Klaus Altmann", "Christian Haase", "Alex Küronya", "Karin Schaller", "Lena Walter"], "categories": ["math.AG"], "doi": "10.46298/epiga.2024.11407", "_query": "property valuation computer vision", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2404.02370v1", "title": "Enhancing Human-Computer Interaction in Chest X-ray Analysis using Vision and Language Model with Eye Gaze Patterns", "abstract": "Recent advancements in Computer Assisted Diagnosis have shown promising performance in medical imaging tasks, particularly in chest X-ray analysis. However, the interaction between these models and radiologists has been primarily limited to input images. This work proposes a novel approach to enhance human-computer interaction in chest X-ray analysis using Vision-Language Models (VLMs) enhanced with radiologists' attention by incorporating eye gaze data alongside textual prompts. Our approach leverages heatmaps generated from eye gaze data, overlaying them onto medical images to highlight areas of intense radiologist's focus during chest X-ray evaluation. We evaluate this methodology in tasks such as visual question answering, chest X-ray report automation, error detection, and differential diagnosis. Our results demonstrate the inclusion of eye gaze information significantly enhances the accuracy of chest X-ray analysis. Also, the impact of eye gaze on fine-tuning was confirmed as it outperformed other medical VLMs in all tasks except visual question answering. This work marks the potential of leveraging both the VLM's capabilities and the radiologist's domain knowledge to improve the capabilities of AI models in medical imaging, paving a novel way for Computer Assisted Diagnosis with a human-centred AI.", "published": "2024-04-03T00:09:05Z", "authors": ["Yunsoo Kim", "Jinge Wu", "Yusuf Abdulle", "Yue Gao", "Honghan Wu"], "categories": ["cs.CV", "cs.AI", "cs.CL"], "doi": "", "_query": "property valuation computer vision", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/0802.2079v2", "title": "Zero dimensional arc valuations on smooth varieties", "abstract": "For a normalized transcendence degree zero arc valuation v on a nonsingular variety X (with dim X > 1), we describe the maximal irreducible subset C(v) of the arc space of X such that the valuation given by the order of vanishing along a general arc of C(v) equals v. We describe C(v) both algebraically, in terms of the sequence of valuation ideals of v, and geometrically, in terms of the sequence of infinitely near points associated to v. When X is a surface, our construction also applies to any divisorial valuation v, and in this case C(v) coincides with a subset Ein, Lazarsfeld, and Mustata associate to v.", "published": "2008-02-14T18:45:42Z", "authors": ["Yogesh More"], "categories": ["math.AG"], "doi": "", "_query": "property valuation computer vision", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/1810.10341v1", "title": "Visions of a generalized probability theory", "abstract": "In this Book we argue that the fruitful interaction of computer vision and belief calculus is capable of stimulating significant advances in both fields. From a methodological point of view, novel theoretical results concerning the geometric and algebraic properties of belief functions as mathematical objects are illustrated and discussed in Part II, with a focus on both a perspective 'geometric approach' to uncertainty and an algebraic solution to the issue of conflicting evidence. In Part III we show how these theoretical developments arise from important computer vision problems (such as articulated object tracking, data association and object pose estimation) to which, in turn, the evidential formalism is able to provide interesting new solutions. Finally, some initial steps towards a generalization of the notion of total probability to belief functions are taken, in the perspective of endowing the theory of evidence with a complete battery of estimation and inference tools to the benefit of all scientists and practitioners.", "published": "2018-10-18T14:00:48Z", "authors": ["Fabio Cuzzolin"], "categories": ["cs.CV", "cs.AI", "math.PR"], "doi": "", "_query": "property valuation computer vision", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/1802.06208v1", "title": "On the Extensions of a Discrete Valuation in a Number Field", "abstract": "Let $K$ be a number field defined by a monic irreducible polynomial $F(X) \\in \\mathbb{Z}[X]$, $p$ a fixed rational prime, and $ν_p$ the discrete valuation associated to $p$. Assume that $\\overline{F}(X)$ factors modulo $p$ into the product of powers of $r$ distinct monic irreducible polynomials. We present in this paper a condition, weaker than the known ones, which guarantees the existence of exactly $r$ valuations of $K$ extending $ν_p$. We further specify the ramification indices and residue degrees of these extended valuations in such a way that generalizes the known estimates. Some useful remarks and computational examples are also given to highlight some improvements due to our result.", "published": "2018-02-17T08:36:12Z", "authors": ["Abdulaziz Deajim", "Lhoussain El Fadil"], "categories": ["math.NT"], "doi": "", "_query": "property valuation computer vision", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/1310.2880v7", "title": "Feature Selection with Annealing for Computer Vision and Big Data Learning", "abstract": "Many computer vision and medical imaging problems are faced with learning from large-scale datasets, with millions of observations and features. In this paper we propose a novel efficient learning scheme that tightens a sparsity constraint by gradually removing variables based on a criterion and a schedule. The attractive fact that the problem size keeps dropping throughout the iterations makes it particularly suitable for big data learning. Our approach applies generically to the optimization of any differentiable loss function, and finds applications in regression, classification and ranking. The resultant algorithms build variable screening into estimation and are extremely simple to implement. We provide theoretical guarantees of convergence and selection consistency. In addition, one dimensional piecewise linear response functions are used to account for nonlinearity and a second order prior is imposed on these functions to avoid overfitting. Experiments on real and synthetic data show that the proposed method compares very well with other state of the art methods in regression, classification and ranking while being computationally very efficient and scalable.", "published": "2013-10-10T16:47:22Z", "authors": ["Adrian Barbu", "Yiyuan She", "Liangjing Ding", "Gary Gramajo"], "categories": ["stat.ML", "cs.CV", "cs.LG", "math.ST"], "doi": "10.1109/TPAMI.2016.2544315", "_query": "property valuation computer vision", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2303.02869v1", "title": "Enhancing Border Security and Countering Terrorism Through Computer Vision: a Field of Artificial Intelligence", "abstract": "Border security had been a persistent problem in international border especially when it get to the issue of preventing illegal movement of weapons, contraband, drugs, and combating issue of illegal or undocumented immigrant while at the same time ensuring that lawful trade, economic prosperity coupled with national sovereignty across the border is maintained. In this research work, we used open source computer vision (Open CV) and adaboost algorithm to develop a model which can detect a moving object a far off, classify it, automatically snap full image and face of the individual separately, and then run a background check on them against worldwide databases while making a prediction about an individual being a potential threat, intending immigrant, potential terrorists or extremist and then raise sound alarm. Our model can be deployed on any camera device and be mounted at any international border. There are two stages involved, we first developed a model based on open CV computer vision algorithm, with the ability to detect human movement from afar, it will automatically snap both the face and the full image of the person separately, and the second stage is the automatic triggering of background check against the moving object. This ensures it check the moving object against several databases worldwide and is able to determine the admissibility of the person afar off. If the individual is inadmissible, it will automatically alert the border officials with the image of the person and other details, and if the bypass the border officials, the system is able to detect and alert the authority with his images and other details. All these operations will be done afar off by the AI powered camera before the individual reach the border", "published": "2023-03-06T03:37:43Z", "authors": ["Tosin Ige", "Abosede Kolade", "Olukunle Kolade"], "categories": ["cs.CV"], "doi": "10.1007/978-3-031-21438-7_54", "_query": "property valuation computer vision", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/1310.2880v7", "title": "Feature Selection with Annealing for Computer Vision and Big Data Learning", "abstract": "Many computer vision and medical imaging problems are faced with learning from large-scale datasets, with millions of observations and features. In this paper we propose a novel efficient learning scheme that tightens a sparsity constraint by gradually removing variables based on a criterion and a schedule. The attractive fact that the problem size keeps dropping throughout the iterations makes it particularly suitable for big data learning. Our approach applies generically to the optimization of any differentiable loss function, and finds applications in regression, classification and ranking. The resultant algorithms build variable screening into estimation and are extremely simple to implement. We provide theoretical guarantees of convergence and selection consistency. In addition, one dimensional piecewise linear response functions are used to account for nonlinearity and a second order prior is imposed on these functions to avoid overfitting. Experiments on real and synthetic data show that the proposed method compares very well with other state of the art methods in regression, classification and ranking while being computationally very efficient and scalable.", "published": "2013-10-10T16:47:22Z", "authors": ["Adrian Barbu", "Yiyuan She", "Liangjing Ding", "Gary Gramajo"], "categories": ["stat.ML", "cs.CV", "cs.LG", "math.ST"], "doi": "10.1109/TPAMI.2016.2544315", "_query": "property valuation computer vision", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/1802.06208v1", "title": "On the Extensions of a Discrete Valuation in a Number Field", "abstract": "Let $K$ be a number field defined by a monic irreducible polynomial $F(X) \\in \\mathbb{Z}[X]$, $p$ a fixed rational prime, and $ν_p$ the discrete valuation associated to $p$. Assume that $\\overline{F}(X)$ factors modulo $p$ into the product of powers of $r$ distinct monic irreducible polynomials. We present in this paper a condition, weaker than the known ones, which guarantees the existence of exactly $r$ valuations of $K$ extending $ν_p$. We further specify the ramification indices and residue degrees of these extended valuations in such a way that generalizes the known estimates. Some useful remarks and computational examples are also given to highlight some improvements due to our result.", "published": "2018-02-17T08:36:12Z", "authors": ["Abdulaziz Deajim", "Lhoussain El Fadil"], "categories": ["math.NT"], "doi": "", "_query": "property valuation computer vision", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2604.18957v1", "title": "Bridging Foundation Models and ASTM Metallurgical Standards for Automated Grain Size Estimation from Microscopy Images", "abstract": "Extracting standardized metallurgical metrics from microscopy images remains challenging due to complex grain morphology and the data demands of supervised segmentation. To bridge foundational computer vision with practical metallurgical evaluation, we propose an automated pipeline for dense instance segmentation and grain size estimation that adapts Cellpose-SAM to microstructures and integrates its topology-aware gradient tracking with an ASTM E112 Jeffries planimetric module. We systematically benchmark this pipeline against a classical convolutional network (U-Net), an adaptive-prompting vision foundation model (MatSAM) and a contemporary vision-language model (Qwen2.5-VL-7B). Our evaluations reveal that while the out-of-the-box vision-language model struggles with the localized spatial reasoning required for dense microscopic counting and MatSAM suffers from over-segmentation despite its domain-specific prompt generation, our adapted pipeline successfully maintains topological separation. Furthermore, experiments across progressively reduced training splits demonstrate exceptional few-shot scalability; utilizing only two training samples, the proposed system predicts the ASTM grain size number (G) with a mean absolute percentage error (MAPE) as low as 1.50%, while robustness testing across varying target grain counts empirically validates the ASTM 50-grain sampling minimum. These results highlight the efficacy of application-level foundation model integration for highly accurate, automated materials characterization. Our project repository is available at https://github.com/mueez-overflow/ASTM-Grain-Size-Estimator.", "published": "2026-04-21T01:13:03Z", "authors": ["Abdul Mueez", "Shruti Vyas"], "categories": ["cs.CV"], "doi": "", "_query": "property valuation computer vision", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/0905.4271v2", "title": "The Science Vision for the Stratospheric Observatory for Infrared Astronomy (SOFIA)", "abstract": "An updated Science Vision for the SOFIA project is presented, including an overview of the characteristics and capabilities of the observatory and first generation instruments. A primary focus is placed on four science themes: 'The Formation of Stars and Planets', 'The Interstellar Medium of the Milky Way', 'Galaxies and the Galactic Center' and 'Planetary Science'.", "published": "2009-05-26T19:39:22Z", "authors": ["T. L. Roellig", "E. E. Becklin", "N. J. Evans", "J. M. De Buizer", "M. Meixner", "A. G. G. M. Tielens", "G. J. Stacey", "W. D. Vacca", "J. N. Cuzzi", "D. E. Backman"], "categories": ["astro-ph.IM", "astro-ph.CO", "astro-ph.EP", "astro-ph.GA", "astro-ph.SR"], "doi": "", "_query": "property valuation computer vision", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/1912.02304v1", "title": "Almost discrete valuation domains", "abstract": "Let $D$ be an integral domain. Then $D$ is an almost valuation (AV-)domain if for $a, b\\in D\\setminus \\{0\\}$ there exists a natural number $n$ with $a^{n}\\mid b^{n}$ or $b^{n}\\mid a^{n}$. AV-domains are closely related to valuation domains, for example, $D$ is an AV-domain if and only if the integral closure $\\bar{D}$ is a valuation domain and $D\\subseteq \\bar{D}$ is a root extension. In this note we explore various generalizations of DVRs (which we might call almost DVRs) such as Noetherian AV-domains, AV-domains with $\\bar{D}$ a DVR, and quasilocal and local API-domains (i.e., for $\\{a_α\\}_{α\\in Λ}\\subseteq D$, there exists an $n$ with $(\\{a_α^{n}\\}_{α\\in Λ})$ principal). The structure of complete local AV-domains and API-domains is determined.", "published": "2019-12-04T23:30:40Z", "authors": ["Daniel D. Anderson", "Shiqi Xing", "Muhammad Zafrullah"], "categories": ["math.AC"], "doi": "", "_query": "property valuation computer vision", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2603.03672v2", "title": "Local Shapley: Model-Induced Locality and Optimal Reuse in Data Valuation", "abstract": "The Shapley value provides a principled foundation for data valuation, but exact computation is #P-hard due to the exponential coalition space. Existing accelerations remain global and ignore a structural property of modern predictors: for a given test instance, only a small subset of training points influences the prediction. We formalize this model-induced locality through support sets defined by the model's computational pathway (e.g., neighbors in KNN, leaves in trees, receptive fields in GNNs), showing that Shapley computation can be projected onto these supports without loss when locality is exact. This reframes Shapley evaluation as a structured data processing problem over overlapping support-induced subset families rather than exhaustive coalition enumeration. We prove that the intrinsic complexity of Local Shapley is governed by the number of distinct influential subsets, establishing an information-theoretic lower bound on retraining operations. Guided by this result, we propose LSMR (Local Shapley via Model Reuse), an optimal subset-centric algorithm that trains each influential subset exactly once via support mapping and pivot scheduling. For larger supports, we develop LSMR-A, a reuse-aware Monte Carlo estimator that remains unbiased with exponential concentration, with runtime determined by the number of distinct sampled subsets rather than total draws. Experiments across multiple model families demonstrate substantial retraining reductions and speedups while preserving high valuation fidelity.", "published": "2026-03-04T02:59:23Z", "authors": ["Xuan Yang", "Hsi-Wen Chen", "Ming-Syan Chen", "Jian Pei"], "categories": ["cs.LG", "cs.AI", "cs.DB", "cs.GT"], "doi": "", "_query": "property valuation computer vision", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2208.04149v1", "title": "Computer Vision Methods for the Microstructural Analysis of Materials: The State-of-the-art and Future Perspectives", "abstract": "Finding quantitative descriptors representing the microstructural features of a given material is an ongoing research area in the paradigm of Materials-by-Design. Historically, microstructural analysis mostly relies on qualitative descriptions. However, to build a robust and accurate process-structure-properties relationship, which is required for designing new advanced high-performance materials, the extraction of quantitative and meaningful statistical data from the microstructural analysis is a critical step. In recent years, computer vision (CV) methods, especially those which are centered around convolutional neural network (CNN) algorithms have shown promising results for this purpose. This review paper focuses on the state-of-the-art CNN-based techniques that have been applied to various multi-scale microstructural image analysis tasks, including classification, object detection, segmentation, feature extraction, and reconstruction. Additionally, we identified the main challenges with regard to the application of these methods to materials science research. Finally, we discussed some possible future directions of research in this area. In particular, we emphasized the application of transformer-based models and their capabilities to improve the microstructural analysis of materials.", "published": "2022-07-29T15:27:47Z", "authors": ["Khaled Alrfou", "Amir Kordijazi", "Tian Zhao"], "categories": ["cond-mat.mtrl-sci", "cs.CV", "physics.app-ph", "physics.data-an"], "doi": "", "_query": "property valuation computer vision", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/1710.02238v2", "title": "How Much Chemistry Does a Deep Neural Network Need to Know to Make Accurate Predictions?", "abstract": "The meteoric rise of deep learning models in computer vision research, having achieved human-level accuracy in image recognition tasks is firm evidence of the impact of representation learning of deep neural networks. In the chemistry domain, recent advances have also led to the development of similar CNN models, such as Chemception, that is trained to predict chemical properties using images of molecular drawings. In this work, we investigate the effects of systematically removing and adding localized domain-specific information to the image channels of the training data. By augmenting images with only 3 additional basic information, and without introducing any architectural changes, we demonstrate that an augmented Chemception (AugChemception) outperforms the original model in the prediction of toxicity, activity, and solvation free energy. Then, by altering the information content in the images, and examining the resulting model's performance, we also identify two distinct learning patterns in predicting toxicity/activity as compared to solvation free energy. These patterns suggest that Chemception is learning about its tasks in the manner that is consistent with established knowledge. Thus, our work demonstrates that advanced chemical knowledge is not a pre-requisite for deep learning models to accurately predict complex chemical properties.", "published": "2017-10-05T23:53:59Z", "authors": ["Garrett B. Goh", "Charles Siegel", "Abhinav Vishnu", "Nathan O. Hodas", "Nathan Baker"], "categories": ["stat.ML", "cs.AI", "cs.CV", "cs.LG"], "doi": "", "_query": "property valuation computer vision", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/1102.5309v3", "title": "A Nearly-Quadratic Gap Between Adaptive and Non-Adaptive Property Testers", "abstract": "We show that for all integers $t\\geq 8$ and arbitrarily small $ε>0$, there exists a graph property $Π$ (which depends on $ε$) such that $ε$-testing $Π$ has non-adaptive query complexity $Q=\\~Θ(q^{2-2/t})$, where $q=\\~Θ(ε^{-1})$ is the adaptive query complexity. This resolves the question of how beneficial adaptivity is, in the context of proximity-dependent properties (\\cite{benefits-of-adaptivity}). This also gives evidence that the canonical transformation of Goldreich and Trevisan (\\cite{canonical-testers}) is essentially optimal when converting an adaptive property tester to a non-adaptive property tester. To do so, we provide optimal adaptive and non-adaptive testers for the combined property of having maximum degree $O(εN)$ and being a \\emph{blow-up collection} of an arbitrary base graph $H$.", "published": "2011-02-25T18:54:33Z", "authors": ["Jeremy Hurwitz"], "categories": ["cs.DS", "cs.CC", "cs.DM"], "doi": "", "_query": "property valuation computer vision", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2301.13675v1", "title": "On Tight Submodules of Modules over Valuation Domains", "abstract": "This note offers an unusual approach of studying a class of modules inasmuch as it is investigating a subclass of the category of modules over a valuation domain. This class is far from being a full subcategory, it is not even a category. Our concern is the subclass consisting of modules of projective dimension not exceeding one, admitting only morphisms whose kernels and cokernels are also objects in this subclass. This class is still tractable, several features are in general simpler than in module categories, but lots of familiar properties are lost. A number of results on modules in this class are similar to those on modules over rank one discrete valuation domains (where the global dimension is 1). The study is considerably simplified by taking advantage of the general theory of modules over valuation domains available in the literature, e.g. in [14]-[15]. Our main goal is to establish the basic features and have a closer look at injectivity, pure-injectivity, and cotorsionness, but we do not wish to enter into an in-depth study of these properties.", "published": "2023-01-31T14:49:01Z", "authors": ["Peter Danchev", "Laszlo Fuchs"], "categories": ["math.AC", "math.RA"], "doi": "", "_query": "property valuation computer vision", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/1902.01831v2", "title": "Face Alignment using a 3D Deeply-initialized Ensemble of Regression Trees", "abstract": "Face alignment algorithms locate a set of landmark points in images of faces taken in unrestricted situations. State-of-the-art approaches typically fail or lose accuracy in the presence of occlusions, strong deformations, large pose variations and ambiguous configurations. In this paper we present 3DDE, a robust and efficient face alignment algorithm based on a coarse-to-fine cascade of ensembles of regression trees. It is initialized by robustly fitting a 3D face model to the probability maps produced by a convolutional neural network. With this initialization we address self-occlusions and large face rotations. Further, the regressor implicitly imposes a prior face shape on the solution, addressing occlusions and ambiguous face configurations. Its coarse-to-fine structure tackles the combinatorial explosion of parts deformation. In the experiments performed, 3DDE improves the state-of-the-art in 300W, COFW, AFLW and WFLW data sets. Finally, we perform cross-dataset experiments that reveal the existence of a significant data set bias in these benchmarks.", "published": "2019-02-05T18:07:17Z", "authors": ["Roberto Valle", "José M. Buenaposada", "Antonio Valdés", "Luis Baumela"], "categories": ["cs.CV"], "doi": "10.1016/j.cviu.2019.102846", "_query": "property valuation computer vision", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/1911.12114v2", "title": "Exoplanet Vision 2050", "abstract": "Is there any hope for us to draw a plausible picture of the future of exoplanet research? Here we extrapolate from the first 25 years of exoplanet discovery into the year 2050. If the power law for the cumulative exoplanet count continues, then almost 100,000,000 exoplanets would be known by 2050. Although this number sounds ridiculously large, we find that the power law could plausibly continue until at least as far as 2030, when Gaia and WFIRST will have discovered on the order of 100,000 exoplanets. After an early era of radial velocity detection, we are now in the transit era, which might be followed by a transit and astrometry era dominated by the WFIRST and Gaia missions. And then? Maybe more is not better. A small and informal survey among astronomers at the \"Exoplanet Vision 2050\" workshop in Budapest suggests that astrobiological topics might influence the future of exoplanet research.", "published": "2019-11-27T12:46:04Z", "authors": ["René Heller", "László Kiss"], "categories": ["astro-ph.EP", "astro-ph.IM"], "doi": "", "_query": "property valuation computer vision", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/1610.03484v2", "title": "Multi-scale computational homogenisation to predict the long-term durability of composite structures", "abstract": "A coupled hygro-thermo-mechanical computational model is proposed for fibre reinforced polymers, formulated within the framework of Computational Homogenisation (CH). At each macrostructure Gauss point, constitutive matrices for thermal, moisture transport and mechanical responses are calculated from CH of the underlying representative volume element (RVE). A degradation model, developed from experimental data relating evolution of mechanical properties over time for a given exposure temperature and moisture concentration is also developed and incorporated in the proposed computational model. A unified approach is used to impose the RVE boundary conditions, which allows convenient switching between linear Dirichlet, uniform Neumann and periodic boundary conditions. A plain weave textile composite RVE consisting of yarns embedded in a matrix is considered in this case. Matrix and yarns are considered as isotropic and transversely isotropic materials respectively. Furthermore, the computational framework utilises hierarchic basis functions and designed to take advantage of distributed memory high-performance computing.", "published": "2016-10-11T15:47:23Z", "authors": ["Zahur Ullah", "Lukasz Kaczmarczyk", "Sotirios Grammatikos", "Mark Evernden", "Chris Pearce"], "categories": ["cs.CE"], "doi": "", "_query": "property valuation computer vision", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/1202.2451v1", "title": "Ultrafilter and Constructible topologies on spaces of valuation domains", "abstract": "Let $K$ be a field and let $A$ be a subring of $K$. We consider properties and applications of a compact, Hausdorff topology called the \"ultrafilter topology\" defined on the space Zar$(K|A)$ of all valuation domains having $K$ as quotient field and containing $A$. We show that the ultrafilter topology coincides with the constructible topology on the abstract Riemann-Zariski surface Zar$(K|A)$. We extend results regarding distinguished spectral topologies on spaces of valuation domains.", "published": "2012-02-11T16:54:09Z", "authors": ["Carmelo A. Finocchiaro", "Marco Fontana", "K. Alan Loper"], "categories": ["math.AC", "math.AG"], "doi": "", "_query": "property valuation computer vision", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/1411.4843v1", "title": "A generalization of the Abhyankar Jung theorem to associated graded rings of valuations", "abstract": "Suppose that R\\rightarrow S is an extension of local domains and ν^* is a valuation dominating S. We consider the natural extension of associated graded rings along the valuation gr_{ν^*}(R)\\rightarrow gr_{ν^*}(S). We give examples showing that in general, this extension does not share good properties of the extension $\\rightarrow S, but after enough blow ups above the valuations, good properties of the extension R\\rightarrow S are reflected in the extension of associated graded rings. Stable properties of this extension (after blowing up) are much better in characteristic zero than in positive characteristic. Our main result is a generalization of the Abhyankar-Jung theorem which holds for extensions of associated graded rings along the valuation, after enough blowing up.", "published": "2014-11-18T13:49:13Z", "authors": ["Steven Dale Cutkosky"], "categories": ["math.AG", "math.AC"], "doi": "", "_query": "property valuation computer vision", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/1312.3756v1", "title": "Arithmetical pseudo-valuations associated to Dubrovin valuation rings and prime divisors of bounded Krull domains", "abstract": "We look at value functions of primes in simple Artinian rings and associate arithmetical pseudo-valuations to Dubrovin valuation rings which, in the Noetherian case, are $\\mathbb{Z}$-valued. This allows a divisor theory for bounded Krull domains.", "published": "2013-12-13T10:17:08Z", "authors": ["Freddy Van Oystaeyen", "Nikolaas Verhulst"], "categories": ["math.RA"], "doi": "", "_query": "property valuation computer vision", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2006.15102v1", "title": "ULSAM: Ultra-Lightweight Subspace Attention Module for Compact Convolutional Neural Networks", "abstract": "The capability of the self-attention mechanism to model the long-range dependencies has catapulted its deployment in vision models. Unlike convolution operators, self-attention offers infinite receptive field and enables compute-efficient modeling of global dependencies. However, the existing state-of-the-art attention mechanisms incur high compute and/or parameter overheads, and hence unfit for compact convolutional neural networks (CNNs). In this work, we propose a simple yet effective \"Ultra-Lightweight Subspace Attention Mechanism\" (ULSAM), which infers different attention maps for each feature map subspace. We argue that leaning separate attention maps for each feature subspace enables multi-scale and multi-frequency feature representation, which is more desirable for fine-grained image classification. Our method of subspace attention is orthogonal and complementary to the existing state-of-the-arts attention mechanisms used in vision models. ULSAM is end-to-end trainable and can be deployed as a plug-and-play module in the pre-existing compact CNNs. Notably, our work is the first attempt that uses a subspace attention mechanism to increase the efficiency of compact CNNs. To show the efficacy of ULSAM, we perform experiments with MobileNet-V1 and MobileNet-V2 as backbone architectures on ImageNet-1K and three fine-grained image classification datasets. We achieve $\\approx$13% and $\\approx$25% reduction in both the FLOPs and parameter counts of MobileNet-V2 with a 0.27% and more than 1% improvement in top-1 accuracy on the ImageNet-1K and fine-grained image classification datasets (respectively). Code and trained models are available at https://github.com/Nandan91/ULSAM.", "published": "2020-06-26T17:05:43Z", "authors": ["Rajat Saini", "Nandan Kumar Jha", "Bedanta Das", "Sparsh Mittal", "C. Krishna Mohan"], "categories": ["cs.CV"], "doi": "10.1109/WACV45572.2020.9093341", "_query": "property valuation computer vision", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2605.14689v1", "title": "Are Candidate Models Really Needed for Active Learning?", "abstract": "Deep learning has profoundly impacted domains such as computer vision and natural language processing by uncovering complex patterns in vast datasets. However, the reliance on extensive labeled data poses significant challenges, including resource constraints and annotation errors, particularly in training Convolutional Neural Networks (CNNs) and transformers due to a larger number of parameters. Active learning offers a promising solution to reduce labeling burdens by strategically selecting the most informative samples for annotation. However, the current active learning frameworks are time-intensive which select the samples iteratively with the help of initial candidate models. This study investigates the feasibility of using CNNs and transformers with randomly initialized weights, eliminating the need for initial candidate models while achieving results comparable to active learning frameworks that depend on such candidate models. We evaluate three confidence-based sampling strategies: high confidence (HC), low confidence (LC), and a combination of high confidence in the early stages of training and low confidence at later stages of training (HCLC). Among these, mostly LC demonstrated the best performance in our experiments, showcasing its effectiveness as an active learning strategy without the need for candidate models. Further, extensive experiments verify the robustness of the proposed active learning methods. By challenging traditional frameworks, the proposed work introduces a streamlined approach to active learning, advancing efficiency and flexibility across diverse datasets and domains.", "published": "2026-05-14T11:03:29Z", "authors": ["Harshini Mridula Mohan", "Maanya Manjunath", "Vipul Arya", "S. H. Shabbeer Basha", "Nitin Cheekatla"], "categories": ["cs.CV"], "doi": "", "_query": "property valuation computer vision", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2604.24583v1", "title": "Improving Vision-language Models with Perception-centric Process Reward Models", "abstract": "Recent advancements in reinforcement learning with verifiable rewards (RLVR) have significantly improved the complex reasoning ability of vision-language models (VLMs). However, its outcome-level supervision is too coarse to diagnose and correct errors within the reasoning chain. To this end, we propose Perceval, a process reward model (PRM) that enables token-level error grounding, which can extract image-related claims from the response and compare them one by one with the visual evidence in the image, ultimately returning claims that contain perceptual errors. Perceval is trained with perception-intensive supervised training data. We then integrate Perceval into the RL training process to train the policy models. Specifically, compared to traditional GRPO, which applies sequence-level advantages, we apply token-level advantages by targeting penalties on hallucinated spans identified by Perceval, thus enabling fine-grained supervision signals. In addition to augmenting the training process, Perceval can also assist VLMs during the inference stage. Using Perceval, we can truncate the erroneous portions of the model's response, and then either have the model regenerate the response directly or induce the model to reflect on its previous output. This process can be repeated multiple times to achieve test-time scaling. Experiments show significant improvements on benchmarks from various domains across multiple reasoning VLMs trained with RL, highlighting the promise of perception-centric supervision as a general-purpose strategy. For test-time scaling, it also demonstrates consistent performance gains over other strategies, such as major voting. Our code and data will be publicly released at https://github.com/RUCAIBox/Perceval.", "published": "2026-04-27T15:08:02Z", "authors": ["Yingqian Min", "Kun Zhou", "Yifan Li", "Yuhuan Wu", "Han Peng", "Yifan Du", "Wayne Xin Zhao", "Min Yang", "Ji-Rong Wen"], "categories": ["cs.CV"], "doi": "", "_query": "property valuation computer vision", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2308.02525v2", "title": "Can Self-Supervised Representation Learning Methods Withstand Distribution Shifts and Corruptions?", "abstract": "Self-supervised learning in computer vision aims to leverage the inherent structure and relationships within data to learn meaningful representations without explicit human annotation, enabling a holistic understanding of visual scenes. Robustness in vision machine learning ensures reliable and consistent performance, enhancing generalization, adaptability, and resistance to noise, variations, and adversarial attacks. Self-supervised paradigms, namely contrastive learning, knowledge distillation, mutual information maximization, and clustering, have been considered to have shown advances in invariant learning representations. This work investigates the robustness of learned representations of self-supervised learning approaches focusing on distribution shifts and image corruptions in computer vision. Detailed experiments have been conducted to study the robustness of self-supervised learning methods on distribution shifts and image corruptions. The empirical analysis demonstrates a clear relationship between the performance of learned representations within self-supervised paradigms and the severity of distribution shifts and corruptions. Notably, higher levels of shifts and corruptions are found to significantly diminish the robustness of the learned representations. These findings highlight the critical impact of distribution shifts and image corruptions on the performance and resilience of self-supervised learning methods, emphasizing the need for effective strategies to mitigate their adverse effects. The study strongly advocates for future research in the field of self-supervised representation learning to prioritize the key aspects of safety and robustness in order to ensure practical applicability. The source code and results are available on GitHub.", "published": "2023-07-31T13:07:56Z", "authors": ["Prakash Chandra Chhipa", "Johan Rodahl Holmgren", "Kanjar De", "Rajkumar Saini", "Marcus Liwicki"], "categories": ["cs.CV"], "doi": "", "_query": "property valuation computer vision", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2608.23119v1", "title": "Quantum Reservoir Computing with Physics-Informed Correction for Reduced-Order PDE Forecasting", "abstract": "We study a hybrid proposal--correction architecture for reduced-order PDE forecasting in which a pure-state quantum reservoir computer (QRC) predicts latent coefficient dynamics and a PINN-based physics-informed corrector (PIC) refines local rollout windows. The method is evaluated on Burgers and Kuramoto--Sivashinsky (KS), with KS as the primary chaotic benchmark. On KS, QRC+PIC consistently improves over QRC alone in RMSE, NRMSE, and PDE residual, while Burgers highlights a regime in which simple baselines remain strong. These results suggest that QRC proposals with local physics-informed correction are a viable benchmark-dependent reduced-order forecasting strategy.", "published": "2026-08-24T11:28:49Z", "authors": ["Krishna Bhatia", " Harsh", "Shalini Devendrababu"], "categories": ["quant-ph", "cs.LG"], "doi": "", "_query": "property valuation computer vision", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/1605.06457v1", "title": "Virtual Worlds as Proxy for Multi-Object Tracking Analysis", "abstract": "Modern computer vision algorithms typically require expensive data acquisition and accurate manual labeling. In this work, we instead leverage the recent progress in computer graphics to generate fully labeled, dynamic, and photo-realistic proxy virtual worlds. We propose an efficient real-to-virtual world cloning method, and validate our approach by building and publicly releasing a new video dataset, called Virtual KITTI (see http://www.xrce.xerox.com/Research-Development/Computer-Vision/Proxy-Virtual-Worlds), automatically labeled with accurate ground truth for object detection, tracking, scene and instance segmentation, depth, and optical flow. We provide quantitative experimental evidence suggesting that (i) modern deep learning algorithms pre-trained on real data behave similarly in real and virtual worlds, and (ii) pre-training on virtual data improves performance. As the gap between real and virtual worlds is small, virtual worlds enable measuring the impact of various weather and imaging conditions on recognition performance, all other things being equal. We show these factors may affect drastically otherwise high-performing deep models for tracking.", "published": "2016-05-20T18:03:07Z", "authors": ["Adrien Gaidon", "Qiao Wang", "Yohann Cabon", "Eleonora Vig"], "categories": ["cs.CV", "cs.LG", "cs.NE", "stat.ML"], "doi": "", "_query": "property valuation computer vision", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2605.20802v1", "title": "ELSA: An ELastic SNN Inference Architecture for Efficient Neuromorphic Computing", "abstract": "Spiking neural networks (SNNs) exploit event-driven and addition-only computation to substantially improve efficiency for intelligent computation. A key temporal property of SNNs, elastic inference, allows outputs to emerge progressively, enabling responses to salient inputs much earlier than full evaluation. However, existing SNN-specific accelerators cannot capitalize on this property. Layer-by-layer designs emit outputs only after all layers are complete, while time-step-by-time-step designs rely on coarse-grained, layer-wise pipelines that require synchronizing all spines/tokens within a layer. This barrier prevents results from being forwarded immediately, delaying the earliest possible response and forfeiting the benefits of elastic inference. To address these challenges, we propose ELSA, a near-SRAM dataflow architecture that realizes true elastic inference through a fine-grained spine/token-wise pipeline and hardware optimizations tailored to SNNs. ELSA forwards each spine/token immediately upon production, forming a continuous streaming pipeline that substantially reduces the latency to the first response. To enhance this lightweight execution, ELSA introduces a bundled address event representation protocol to lower communication traffic of network-on-chip (NoC), and leverages mini-batch spiking Gustavson-product to cut memory access and exploit inherent sparsity. Combined with mapping and scheduling optimizations, ELSA achieves efficient, event-driven computation without compromising accuracy. Experiments show that SNNs can outperform quantized artificial neural networks (QANNs) while maintaining on-par accuracy. For a 4-bit ResNet-50, ELSA achieves 3.4$\\times$ speedup and 13.6$\\times$ higher energy efficiency over the SOTA QANN accelerator (ANT), and 2.9$\\times$ speedup and 22.1$\\times$ energy efficiency gains over the SOTA SNN accelerator (PAICORE).", "published": "2026-05-20T06:47:57Z", "authors": ["Kang You", "Chen Nie", "Lee Jun Yan", "Ziling Wei", "Cheng Zou", "Zekai Xu", "Yu Feng", "Honglan Jiang", "Zhezhi He"], "categories": ["cs.AR", "cs.AI"], "doi": "", "_query": "property valuation computer vision", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/1405.0515v3", "title": "KVA: Capital Valuation Adjustment", "abstract": "Credit (CVA), Debit (DVA) and Funding Valuation Adjustments (FVA) are now familiar valuation adjustments made to the value of a portfolio of derivatives to account for credit risks and funding costs. However, recent changes in the regulatory regime and the increases in regulatory capital requirements has led many banks to include the cost of capital in derivative pricing. This paper formalises the addition of cost of capital by extending the Burgard-Kjaer (2013) semi-replication approach to CVA and FVA to include an addition capital term, Capital Valuation Adjustment (KVA, i.e. Kapital Valuation Adjustment to distinguish from CVA.) The utilization of the capital for funding purposes is also considered. The use of the semi-replication approach means that the flexibility around the treatment of self-default is carried over into this analysis. The paper further considers the practical calculation of KVA with reference to the Basel II (BCBS-128) and Basel III (BCBS-189) capital regimes and their implementation via CRD IV. The paper also assesses how KVA may be hedged, given that any hedging transactions themselves lead to regulatory capital requirements and hence capital costs. Finally a number of numerical examples are presented to gauge the cost impact of KVA on vanilla derivative products.", "published": "2014-05-02T21:09:30Z", "authors": ["Andrew Green", "Chris Kenyon"], "categories": ["q-fin.PR"], "doi": "", "_query": "property valuation computer vision", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2312.09880v2", "title": "Information Extraction from Unstructured data using Augmented-AI and Computer Vision", "abstract": "Information extraction (IE) from unstructured documents remains a critical challenge in data processing pipelines. Traditional optical character recognition (OCR) methods and conventional parsing engines demonstrate limited effectiveness when processing large-scale document datasets. This paper presents a comprehensive framework for information extraction that combines Augmented Intelligence (A2I) with computer vision and natural language processing techniques. Our approach addresses the limitations of conventional methods by leveraging deep learning architectures for object detection, particularly for tabular data extraction, and integrating cloud-based services for scalable document processing. The proposed methodology demonstrates improved accuracy and efficiency in extracting structured information from diverse document formats including PDFs, images, and scanned documents. Experimental validation shows significant improvements over traditional OCR-based approaches, particularly in handling complex document layouts and multi-modal content extraction.", "published": "2023-12-15T15:27:41Z", "authors": ["Aditya Parikh"], "categories": ["cs.CV"], "doi": "", "_query": "property valuation computer vision", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2601.02289v1", "title": "Rank-based Geographical Regularization: Revisiting Contrastive Self-Supervised Learning for Multispectral Remote Sensing Imagery", "abstract": "Self-supervised learning (SSL) has become a powerful paradigm for learning from large, unlabeled datasets, particularly in computer vision (CV). However, applying SSL to multispectral remote sensing (RS) images presents unique challenges and opportunities due to the geographical and temporal variability of the data. In this paper, we introduce GeoRank, a novel regularization method for contrastive SSL that improves upon prior techniques by directly optimizing spherical distances to embed geographical relationships into the learned feature space. GeoRank outperforms or matches prior methods that integrate geographical metadata and consistently improves diverse contrastive SSL algorithms (e.g., BYOL, DINO). Beyond this, we present a systematic investigation of key adaptations of contrastive SSL for multispectral RS images, including the effectiveness of data augmentations, the impact of dataset cardinality and image size on performance, and the task dependency of temporal views. Code is available at https://github.com/tomburgert/georank.", "published": "2026-01-05T17:24:50Z", "authors": ["Tom Burgert", "Leonard Hackel", "Paolo Rota", "Begüm Demir"], "categories": ["cs.CV"], "doi": "", "_query": "property valuation computer vision", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2111.07370v3", "title": "Co-segmentation Inspired Attention Module for Video-based Computer Vision Tasks", "abstract": "Video-based computer vision tasks can benefit from estimation of the salient regions and interactions between those regions. Traditionally, this has been done by identifying the object regions in the images by utilizing pre-trained models to perform object detection, object segmentation and/or object pose estimation. Although using pre-trained models is a viable approach, it has several limitations in the need for an exhaustive annotation of object categories, a possible domain gap between datasets, and a bias that is typically present in pre-trained models. In this work, we propose to utilize the common rationale that a sequence of video frames capture a set of common objects and interactions between them, thus a notion of co-segmentation between the video frame features may equip the model with the ability to automatically focus on task-specific salient regions and improve the underlying task's performance in an end-to-end manner. In this regard, we propose a generic module called ``Co-Segmentation inspired Attention Module'' (COSAM) that can be plugged in to any CNN model to promote the notion of co-segmentation based attention among a sequence of video frame features. We show the application of COSAM in three video-based tasks namely: 1) Video-based person re-ID, 2) Video captioning, & 3) Video action classification and demonstrate that COSAM is able to capture the task-specific salient regions in video frames, thus leading to notable performance improvements along with interpretable attention maps for a variety of video-based vision tasks, with possible application to other video-based vision tasks as well.", "published": "2021-11-14T15:35:37Z", "authors": ["Arulkumar Subramaniam", "Jayesh Vaidya", "Muhammed Abdul Majeed Ameen", "Athira Nambiar", "Anurag Mittal"], "categories": ["cs.CV"], "doi": "", "_query": "property valuation computer vision", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2211.13218v2", "title": "CODA-Prompt: COntinual Decomposed Attention-based Prompting for Rehearsal-Free Continual Learning", "abstract": "Computer vision models suffer from a phenomenon known as catastrophic forgetting when learning novel concepts from continuously shifting training data. Typical solutions for this continual learning problem require extensive rehearsal of previously seen data, which increases memory costs and may violate data privacy. Recently, the emergence of large-scale pre-trained vision transformer models has enabled prompting approaches as an alternative to data-rehearsal. These approaches rely on a key-query mechanism to generate prompts and have been found to be highly resistant to catastrophic forgetting in the well-established rehearsal-free continual learning setting. However, the key mechanism of these methods is not trained end-to-end with the task sequence. Our experiments show that this leads to a reduction in their plasticity, hence sacrificing new task accuracy, and inability to benefit from expanded parameter capacity. We instead propose to learn a set of prompt components which are assembled with input-conditioned weights to produce input-conditioned prompts, resulting in a novel attention-based end-to-end key-query scheme. Our experiments show that we outperform the current SOTA method DualPrompt on established benchmarks by as much as 4.5% in average final accuracy. We also outperform the state of art by as much as 4.4% accuracy on a continual learning benchmark which contains both class-incremental and domain-incremental task shifts, corresponding to many practical settings. Our code is available at https://github.com/GT-RIPL/CODA-Prompt", "published": "2022-11-23T18:57:11Z", "authors": ["James Seale Smith", "Leonid Karlinsky", "Vyshnavi Gutta", "Paola Cascante-Bonilla", "Donghyun Kim", "Assaf Arbelle", "Rameswar Panda", "Rogerio Feris", "Zsolt Kira"], "categories": ["cs.CV", "cs.AI", "cs.LG"], "doi": "", "_query": "property valuation computer vision", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2205.06836v1", "title": "A Framework for Event-based Computer Vision on a Mobile Device", "abstract": "We present the first publicly available Android framework to stream data from an event camera directly to a mobile phone. Today's mobile devices handle a wider range of workloads than ever before and they incorporate a growing gamut of sensors that make devices smarter, more user friendly and secure. Conventional cameras in particular play a central role in such tasks, but they cannot record continuously, as the amount of redundant information recorded is costly to process. Bio-inspired event cameras on the other hand only record changes in a visual scene and have shown promising low-power applications that specifically suit mobile tasks such as face detection, gesture recognition or gaze tracking. Our prototype device is the first step towards embedding such an event camera into a battery-powered handheld device. The mobile framework allows us to stream events in real-time and opens up the possibilities for always-on and on-demand sensing on mobile phones. To liaise the asynchronous event camera output with synchronous von Neumann hardware, we look at how buffering events and processing them in batches can benefit mobile applications. We evaluate our framework in terms of latency and throughput and show examples of computer vision tasks that involve both event-by-event and pre-trained neural network methods for gesture recognition, aperture robust optical flow and grey-level image reconstruction from events. The code is available at https://github.com/neuromorphic-paris/frog", "published": "2022-05-13T18:06:20Z", "authors": ["Gregor Lenz", "Serge Picaud", "Sio-Hoi Ieng"], "categories": ["cs.CV"], "doi": "", "_query": "property valuation computer vision", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2302.03087v3", "title": "Dividing Good and Better Items Among Agents with Bivalued Submodular Valuations", "abstract": "We study the problem of fairly allocating a set of indivisible goods among agents with {\\em bivalued submodular valuations} -- each good provides a marginal gain of either $a$ or $b$ ($a < b$) and goods have decreasing marginal gains. This is a natural generalization of two well-studied valuation classes -- bivalued additive valuations and binary submodular valuations. We present a simple sequential algorithmic framework, based on the recently introduced Yankee Swap mechanism, that can be adapted to compute a variety of solution concepts, including max Nash welfare (MNW), leximin and $p$-mean welfare maximizing allocations when $a$ divides $b$. This result is complemented by an existing result on the computational intractability of MNW and leximin allocations when $a$ does not divide $b$. We show that MNW and leximin allocations guarantee each agent at least $\\frac25$ and $\\frac{a}{b+2a}$ of their maximin share, respectively, when $a$ divides $b$. We also show that neither the leximin nor the MNW allocation is guaranteed to be envy free up to one good (EF1). This is surprising since for the simpler classes of bivalued additive valuations and binary submodular valuations, MNW allocations are known to be envy free up to any good (EFX).", "published": "2023-02-06T19:41:28Z", "authors": ["Cyrus Cousins", "Vignesh Viswanathan", "Yair Zick"], "categories": ["cs.GT", "cs.AI"], "doi": "", "_query": "property valuation computer vision", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2505.08605v3", "title": "Leveraging Multi-Modal Information to Enhance Dataset Distillation", "abstract": "Dataset distillation aims to create a small and highly representative synthetic dataset that preserves the essential information of a larger real dataset. Beyond reducing storage and computational costs, related approaches offer a promising avenue for privacy preservation in computer vision by eliminating the need to store or share sensitive real-world images. Existing methods focus solely on optimizing visual representations, overlooking the potential of multi-modal information. In this work, we propose a multi-modal dataset distillation framework that incorporates two key enhancements: caption-guided supervision and object-centric masking. To leverage textual information, we introduce two strategies: caption concatenation, which fuses caption embeddings with visual features during classification, and caption matching, which enforces semantic alignment between real and synthetic data through a caption-based loss. To improve data utility and reduce unnecessary background noise, we employ segmentation masks to isolate target objects and introduce two novel losses: masked feature alignment and masked gradient matching, both aimed at promoting object-centric learning. Extensive evaluations demonstrate that our approach improves downstream performance while promoting privacy protection by minimizing exposure to real data.", "published": "2025-05-13T14:20:11Z", "authors": ["Zhe Li", "Hadrien Reynaud", "Bernhard Kainz"], "categories": ["cs.CV"], "doi": "", "_query": "property valuation computer vision", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2211.12009v1", "title": "Deep-Learning-Based Computer Vision Approach For The Segmentation Of Ball Deliveries And Tracking In Cricket", "abstract": "There has been a significant increase in the adoption of technology in cricket recently. This trend has created the problem of duplicate work being done in similar computer vision-based research works. Our research tries to solve one of these problems by segmenting ball deliveries in a cricket broadcast using deep learning models, MobileNet and YOLO, thus enabling researchers to use our work as a dataset for their research. The output from our research can be used by cricket coaches and players to analyze ball deliveries which are played during the match. This paper presents an approach to segment and extract video shots in which only the ball is being delivered. The video shots are a series of continuous frames that make up the whole scene of the video. Object detection models are applied to reach a high level of accuracy in terms of correctly extracting video shots. The proof of concept for building large datasets of video shots for ball deliveries is proposed which paves the way for further processing on those shots for the extraction of semantics. Ball tracking in these video shots is also done using a separate RetinaNet model as a sample of the usefulness of the proposed dataset. The position on the cricket pitch where the ball lands is also extracted by tracking the ball along the y-axis. The video shot is then classified as a full-pitched, good-length or short-pitched delivery.", "published": "2022-11-22T04:55:58Z", "authors": ["Kumail Abbas", "Muhammad Saeed", "M. Imad Khan", "Khandakar Ahmed", "Hua Wang"], "categories": ["cs.CV", "cs.AI"], "doi": "", "_query": "property valuation computer vision", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2107.06935v1", "title": "Object Retrieval and Localization in Large Art Collections using Deep Multi-Style Feature Fusion and Iterative Voting", "abstract": "The search for specific objects or motifs is essential to art history as both assist in decoding the meaning of artworks. Digitization has produced large art collections, but manual methods prove to be insufficient to analyze them. In the following, we introduce an algorithm that allows users to search for image regions containing specific motifs or objects and find similar regions in an extensive dataset, helping art historians to analyze large digitized art collections. Computer vision has presented efficient methods for visual instance retrieval across photographs. However, applied to art collections, they reveal severe deficiencies because of diverse motifs and massive domain shifts induced by differences in techniques, materials, and styles. In this paper, we present a multi-style feature fusion approach that successfully reduces the domain gap and improves retrieval results without labelled data or curated image collections. Our region-based voting with GPU-accelerated approximate nearest-neighbour search allows us to find and localize even small motifs within an extensive dataset in a few seconds. We obtain state-of-the-art results on the Brueghel dataset and demonstrate its generalization to inhomogeneous collections with a large number of distractors.", "published": "2021-07-14T18:40:49Z", "authors": ["Nikolai Ufer", "Sabine Lang", "Björn Ommer"], "categories": ["cs.CV"], "doi": "10.1007/978-3-030-66096-3_12", "_query": "property valuation computer vision", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2204.07356v5", "title": "Vision-and-Language Pretrained Models: A Survey", "abstract": "Pretrained models have produced great success in both Computer Vision (CV) and Natural Language Processing (NLP). This progress leads to learning joint representations of vision and language pretraining by feeding visual and linguistic contents into a multi-layer transformer, Visual-Language Pretrained Models (VLPMs). In this paper, we present an overview of the major advances achieved in VLPMs for producing joint representations of vision and language. As the preliminaries, we briefly describe the general task definition and genetic architecture of VLPMs. We first discuss the language and vision data encoding methods and then present the mainstream VLPM structure as the core content. We further summarise several essential pretraining and fine-tuning strategies. Finally, we highlight three future directions for both CV and NLP researchers to provide insightful guidance.", "published": "2022-04-15T07:33:06Z", "authors": ["Siqu Long", "Feiqi Cao", "Soyeon Caren Han", "Haiqin Yang"], "categories": ["cs.CV", "cs.CL"], "doi": "", "_query": "property valuation computer vision", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2001.03409v1", "title": "Superbubbles as an Empirical Characteristic of Directed Networks", "abstract": "Superbubbles are acyclic induced subgraphs of a digraph with single entrance and exit that naturally arise in the context of genome assembly and the analysis of genome alignments in computational biology. These structures can be computed in linear time and are confined to non-symmetric digraphs. We demonstrate empirically that graph parameters derived from superbubbles provide a convenient means of distinguishing different classes of real-world graphical models, while being largely unrelated to simple, commonly used parameters.", "published": "2020-01-10T12:56:03Z", "authors": ["Fabian Gärtner", "Felix Kühnl", "Carsten R. Seemann", "The Students of the Graphs", "Networks Computer Lab 2018/19", "Christian Höner zu Siederdissen", "Peter F. Stadler"], "categories": ["cs.SI"], "doi": "10.1017/nws.2020.32", "_query": "property valuation computer vision", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2309.13578v1", "title": "A SAM-based Solution for Hierarchical Panoptic Segmentation of Crops and Weeds Competition", "abstract": "Panoptic segmentation in agriculture is an advanced computer vision technique that provides a comprehensive understanding of field composition. It facilitates various tasks such as crop and weed segmentation, plant panoptic segmentation, and leaf instance segmentation, all aimed at addressing challenges in agriculture. Exploring the application of panoptic segmentation in agriculture, the 8th Workshop on Computer Vision in Plant Phenotyping and Agriculture (CVPPA) hosted the challenge of hierarchical panoptic segmentation of crops and weeds using the PhenoBench dataset. To tackle the tasks presented in this competition, we propose an approach that combines the effectiveness of the Segment AnyThing Model (SAM) for instance segmentation with prompt input from object detection models. Specifically, we integrated two notable approaches in object detection, namely DINO and YOLO-v8. Our best-performing model achieved a PQ+ score of 81.33 based on the evaluation metrics of the competition.", "published": "2023-09-24T08:34:12Z", "authors": ["Khoa Dang Nguyen", "Thanh-Hai Phung", "Hoang-Giang Cao"], "categories": ["cs.CV"], "doi": "", "_query": "property valuation computer vision", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2308.08276v1", "title": "Computer vision-enriched discrete choice models, with an application to residential location choice", "abstract": "Visual imagery is indispensable to many multi-attribute decision situations. Examples of such decision situations in travel behaviour research include residential location choices, vehicle choices, tourist destination choices, and various safety-related choices. However, current discrete choice models cannot handle image data and thus cannot incorporate information embedded in images into their representations of choice behaviour. This gap between discrete choice models' capabilities and the real-world behaviour it seeks to model leads to incomplete and, possibly, misleading outcomes. To solve this gap, this study proposes \"Computer Vision-enriched Discrete Choice Models\" (CV-DCMs). CV-DCMs can handle choice tasks involving numeric attributes and images by integrating computer vision and traditional discrete choice models. Moreover, because CV-DCMs are grounded in random utility maximisation principles, they maintain the solid behavioural foundation of traditional discrete choice models. We demonstrate the proposed CV-DCM by applying it to data obtained through a novel stated choice experiment involving residential location choices. In this experiment, respondents faced choice tasks with trade-offs between commute time, monthly housing cost and street-level conditions, presented using images. As such, this research contributes to the growing body of literature in the travel behaviour field that seeks to integrate discrete choice modelling and machine learning.", "published": "2023-08-16T10:33:24Z", "authors": ["Sander van Cranenburgh", "Francisco Garrido-Valenzuela"], "categories": ["cs.CV", "econ.EM"], "doi": "", "_query": "property valuation computer vision", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/0909.0245v1", "title": "Enhanced Mode Selection Algorithm for H.264 encoder for Application in Low Computational power devices", "abstract": "The intent of the H.264 AVC project was to create a standard capable of providing good video quality at substantially lower bit rates than previous standards without increasing the complexity of design so much that it would be impractical or excessively expensive to implement. An additional goal was to provide enough flexibility to allow the standard to be applied to a wide variety of applications. To achieve better coding efficiency, H.264 AVC uses several techniques such as inter mode and intra mode prediction with variable size motion compensation, which adopts Rate Distortion Optimization (RDO). This increases the computational complexity of the encoder especially for devices with lower processing capabilities such as mobile and other handheld devices. In this paper, we propose an algorithm to reduce the number of mode and sub mode evaluations in inter mode prediction. Experimental results show that this fast intra mode selection algorithm can lessen about 75 percent encoding time with little loss of bit rate and visual quality.", "published": "2009-09-01T18:57:06Z", "authors": ["Sourabh Rungta", "Kshitij Verma", "Neeta Tripathi", "Anupam Shukla"], "categories": ["cs.MM"], "doi": "", "_query": "property valuation computer vision", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2311.00995v1", "title": "A Chronological Survey of Theoretical Advancements in Generative Adversarial Networks for Computer Vision", "abstract": "Generative Adversarial Networks (GANs) have been workhorse generative models for last many years, especially in the research field of computer vision. Accordingly, there have been many significant advancements in the theory and application of GAN models, which are notoriously hard to train, but produce good results if trained well. There have been many a surveys on GANs, organizing the vast GAN literature from various focus and perspectives. However, none of the surveys brings out the important chronological aspect: how the multiple challenges of employing GAN models were solved one-by-one over time, across multiple landmark research works. This survey intends to bridge that gap and present some of the landmark research works on the theory and application of GANs, in chronological order.", "published": "2023-11-02T05:11:47Z", "authors": ["Hrishikesh Sharma"], "categories": ["cs.CV", "eess.IV"], "doi": "", "_query": "property valuation computer vision", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2302.08374v3", "title": "Efficiency 360: Efficient Vision Transformers", "abstract": "Transformers are widely used for solving tasks in natural language processing, computer vision, speech, and music domains. In this paper, we talk about the efficiency of transformers in terms of memory (the number of parameters), computation cost (number of floating points operations), and performance of models, including accuracy, the robustness of the model, and fair \\& bias-free features. We mainly discuss the vision transformer for the image classification task. Our contribution is to introduce an efficient 360 framework, which includes various aspects of the vision transformer, to make it more efficient for industrial applications. By considering those applications, we categorize them into multiple dimensions such as privacy, robustness, transparency, fairness, inclusiveness, continual learning, probabilistic models, approximation, computational complexity, and spectral complexity. We compare various vision transformer models based on their performance, the number of parameters, and the number of floating point operations (FLOPs) on multiple datasets.", "published": "2023-02-16T15:43:32Z", "authors": ["Badri N. Patro", "Vijay Srinivas Agneeswaran"], "categories": ["cs.CV", "cs.AI", "cs.CL", "cs.LG"], "doi": "", "_query": "property valuation computer vision", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/quant-ph/0107123v1", "title": "A Topos Perspective on the Kochen-Specker Theorem: IV. Interval Valuations", "abstract": "We extend the topos-theoretic treatment given in previous papers of assigning values to quantities in quantum theory. In those papers, the main idea was to assign a sieve as a partial and contextual truth value to a proposition that the value of a quantity lies in a certain set $Δ\\subseteq \\mathR$. Here we relate such sieve-valued valuations to valuations that assign to quantities subsets, rather than single elements, of their spectra (we call these `interval' valuations). There are two main results. First, there is a natural correspondence between these two kinds of valuation, which uses the notion of a state's support for a quantity (Section 3). Second, if one starts with a more general notion of interval valuation, one sees that our interval valuations based on the notion of support (and correspondingly, our sieve-valued valuations) are a simple way to secure certain natural properties of valuations, such as monotonicity (Section 4).", "published": "2001-07-24T15:06:23Z", "authors": ["J. Butterfield", "C. J. Isham"], "categories": ["quant-ph", "gr-qc"], "doi": "", "_query": "property valuation computer vision", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/1802.07563v1", "title": "Laplace transforms and valuations", "abstract": "It is proved that the classical Laplace transform is a continuous valuation which is positively GL$(n)$ covariant and logarithmic translation covariant. Conversely, these properties turn out to be sufficient to characterize this transform.", "published": "2018-02-21T13:33:04Z", "authors": ["Jin Li", "Dan Ma"], "categories": ["math.MG", "math.FA"], "doi": "10.1016/j.jfa.2016.09.011", "_query": "property valuation computer vision", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2502.18555v1", "title": "Application of Attention Mechanism with Bidirectional Long Short-Term Memory (BiLSTM) and CNN for Human Conflict Detection using Computer Vision", "abstract": "The automatic detection of human conflicts through videos is a crucial area in computer vision, with significant applications in monitoring and public safety policies. However, the scarcity of public datasets and the complexity of human interactions make this task challenging. This study investigates the integration of advanced deep learning techniques, including Attention Mechanism, Convolutional Neural Networks (CNNs), and Bidirectional Long ShortTerm Memory (BiLSTM), to improve the detection of violent behaviors in videos. The research explores how the use of the attention mechanism can help focus on the most relevant parts of the video, enhancing the accuracy and robustness of the model. The experiments indicate that the combination of CNNs with BiLSTM and the attention mechanism provides a promising solution for conflict monitoring, offering insights into the effectiveness of different strategies. This work opens new possibilities for the development of automated surveillance systems that can operate more efficiently in real-time detection of violent events.", "published": "2025-02-25T18:48:34Z", "authors": ["Erick da Silva Farias", "Eduardo Palhares Junior"], "categories": ["cs.CV", "cs.AI"], "doi": "", "_query": "property valuation computer vision", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2410.00807v1", "title": "WiGNet: Windowed Vision Graph Neural Network", "abstract": "In recent years, Graph Neural Networks (GNNs) have demonstrated strong adaptability to various real-world challenges, with architectures such as Vision GNN (ViG) achieving state-of-the-art performance in several computer vision tasks. However, their practical applicability is hindered by the computational complexity of constructing the graph, which scales quadratically with the image size. In this paper, we introduce a novel Windowed vision Graph neural Network (WiGNet) model for efficient image processing. WiGNet explores a different strategy from previous works by partitioning the image into windows and constructing a graph within each window. Therefore, our model uses graph convolutions instead of the typical 2D convolution or self-attention mechanism. WiGNet effectively manages computational and memory complexity for large image sizes. We evaluate our method in the ImageNet-1k benchmark dataset and test the adaptability of WiGNet using the CelebA-HQ dataset as a downstream task with higher-resolution images. In both of these scenarios, our method achieves competitive results compared to previous vision GNNs while keeping memory and computational complexity at bay. WiGNet offers a promising solution toward the deployment of vision GNNs in real-world applications. We publicly released the code at https://github.com/EIDOSLAB/WiGNet.", "published": "2024-10-01T15:54:07Z", "authors": ["Gabriele Spadaro", "Marco Grangetto", "Attilio Fiandrotti", "Enzo Tartaglione", "Jhony H. Giraldo"], "categories": ["cs.CV", "cs.AI"], "doi": "", "_query": "property valuation computer vision", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2004.04917v1", "title": "Multimodal Categorization of Crisis Events in Social Media", "abstract": "Recent developments in image classification and natural language processing, coupled with the rapid growth in social media usage, have enabled fundamental advances in detecting breaking events around the world in real-time. Emergency response is one such area that stands to gain from these advances. By processing billions of texts and images a minute, events can be automatically detected to enable emergency response workers to better assess rapidly evolving situations and deploy resources accordingly. To date, most event detection techniques in this area have focused on image-only or text-only approaches, limiting detection performance and impacting the quality of information delivered to crisis response teams. In this paper, we present a new multimodal fusion method that leverages both images and texts as input. In particular, we introduce a cross-attention module that can filter uninformative and misleading components from weak modalities on a sample by sample basis. In addition, we employ a multimodal graph-based approach to stochastically transition between embeddings of different multimodal pairs during training to better regularize the learning process as well as dealing with limited training data by constructing new matched pairs from different samples. We show that our method outperforms the unimodal approaches and strong multimodal baselines by a large margin on three crisis-related tasks.", "published": "2020-04-10T06:31:30Z", "authors": ["Mahdi Abavisani", "Liwei Wu", "Shengli Hu", "Joel Tetreault", "Alejandro Jaimes"], "categories": ["cs.LG", "cs.AI", "cs.CL", "cs.CV"], "doi": "", "_query": "property valuation computer vision", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2203.02197v1", "title": "The 2-adic valuation of the general degree 2 polynomial in 2 variables", "abstract": "The p-adic valuation of a polynomial can be given by its valuation tree. This work describes the 2-adic valuation tree of the general degree 2 polynomial in 2 variables.", "published": "2022-03-04T09:13:31Z", "authors": [" Shubham"], "categories": ["math.NT"], "doi": "10.7546/nntdm.2023.29.4.737-751", "_query": "property valuation computer vision", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2508.00367v1", "title": "Representation Shift: Unifying Token Compression with FlashAttention", "abstract": "Transformers have demonstrated remarkable success across vision, language, and video. Yet, increasing task complexity has led to larger models and more tokens, raising the quadratic cost of self-attention and the overhead of GPU memory access. To reduce the computation cost of self-attention, prior work has proposed token compression techniques that drop redundant or less informative tokens. Meanwhile, fused attention kernels such as FlashAttention have been developed to alleviate memory overhead by avoiding attention map construction and its associated I/O to HBM. This, however, makes it incompatible with most training-free token compression methods, which rely on attention maps to determine token importance. Here, we propose Representation Shift, a training-free, model-agnostic metric that measures the degree of change in each token's representation. This seamlessly integrates token compression with FlashAttention, without attention maps or retraining. Our method further generalizes beyond Transformers to CNNs and state space models. Extensive experiments show that Representation Shift enables effective token compression compatible with FlashAttention, yielding significant speedups of up to 5.5% and 4.4% in video-text retrieval and video QA, respectively. Code is available at https://github.com/mlvlab/Representation-Shift.", "published": "2025-08-01T06:53:55Z", "authors": ["Joonmyung Choi", "Sanghyeok Lee", "Byungoh Ko", "Eunseo Kim", "Jihyung Kil", "Hyunwoo J. Kim"], "categories": ["cs.CV"], "doi": "", "_query": "property valuation computer vision", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/1610.05849v1", "title": "Finite Computational Structures and Implementations", "abstract": "What is computable with limited resources? How can we verify the correctness of computations? How to measure computational power with precision? Despite the immense scientific and engineering progress in computing, we still have only partial answers to these questions. In order to make these problems more precise, we describe an abstract algebraic definition of classical computation, generalizing traditional models to semigroups. The mathematical abstraction also allows the investigation of different computing paradigms (e.g. cellular automata, reversible computing) in the same framework. Here we summarize the main questions and recent results of the research of finite computation.", "published": "2016-10-19T02:25:50Z", "authors": ["Attila Egri-Nagy"], "categories": ["cs.OH", "math.GR"], "doi": "", "_query": "property valuation computer vision", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/1904.03775v2", "title": "ANTNets: Mobile Convolutional Neural Networks for Resource Efficient Image Classification", "abstract": "Deep convolutional neural networks have achieved remarkable success in computer vision. However, deep neural networks require large computing resources to achieve high performance. Although depthwise separable convolution can be an efficient module to approximate a standard convolution, it often leads to reduced representational power of networks. In this paper, under budget constraints such as computational cost (MAdds) and the parameter count, we propose a novel basic architectural block, ANTBlock. It boosts the representational power by modeling, in a high dimensional space, interdependency of channels between a depthwise convolution layer and a projection layer in the ANTBlocks. Our experiments show that ANTNet built by a sequence of ANTBlocks, consistently outperforms state-of-the-art low-cost mobile convolutional neural networks across multiple datasets. On CIFAR100, our model achieves 75.7% top-1 accuracy, which is 1.5% higher than MobileNetV2 with 8.3% fewer parameters and 19.6% less computational cost. On ImageNet, our model achieves 72.8% top-1 accuracy, which is 0.8% improvement, with 157.7ms (20% faster) on iPhone 5s over MobileNetV2.", "published": "2019-04-07T23:43:24Z", "authors": ["Yunyang Xiong", "Hyunwoo J. Kim", "Varsha Hedau"], "categories": ["cs.CV"], "doi": "", "_query": "property valuation computer vision", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2204.07722v1", "title": "Searching Intrinsic Dimensions of Vision Transformers", "abstract": "It has been shown by many researchers that transformers perform as well as convolutional neural networks in many computer vision tasks. Meanwhile, the large computational costs of its attention module hinder further studies and applications on edge devices. Some pruning methods have been developed to construct efficient vision transformers, but most of them have considered image classification tasks only. Inspired by these results, we propose SiDT, a method for pruning vision transformer backbones on more complicated vision tasks like object detection, based on the search of transformer dimensions. Experiments on CIFAR-100 and COCO datasets show that the backbones with 20\\% or 40\\% dimensions/parameters pruned can have similar or even better performance than the unpruned models. Moreover, we have also provided the complexity analysis and comparisons with the previous pruning methods.", "published": "2022-04-16T05:16:35Z", "authors": ["Fanghui Xue", "Biao Yang", "Yingyong Qi", "Jack Xin"], "categories": ["cs.CV", "cs.LG"], "doi": "", "_query": "property valuation computer vision", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/1709.08304v3", "title": "Positivity of valuations on convex bodies and invariant valuations by linear actions", "abstract": "In this paper, we endow the space of continuous translation invariant valuation on convex sets generated by mixed volumes coupled with a suitable Radon measure on tuples of convex bodies with two appropriate norms. This enables us to construct a continuous extension of the convolution operator on smooth valuations to non-smooth valuations, which are in the completion of the spaces of valuations with respect to these norms. The novelty of our approach lies in the fact that our proof does not rely on the general theory of wave fronts, but on geometric inequalities deduced from optimal transport methods. We apply this result to prove a variant of Minkowski's existence theorem, and generalize a theorem of Favre-Wulcan and Lin in complex dynamics over toric varieties by studying the linear actions on the Banach spaces of valuations and by studying their corresponding eigenspaces.", "published": "2017-09-25T03:33:07Z", "authors": ["Nguyen-Bac Dang", "Jian Xiao"], "categories": ["math.DG", "math.DS", "math.MG"], "doi": "", "_query": "property valuation computer vision", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2305.16450v2", "title": "Investigation of UAV Detection in Images with Complex Backgrounds and Rainy Artifacts", "abstract": "To detect unmanned aerial vehicles (UAVs) in real-time, computer vision and deep learning approaches are evolving research areas. Interest in this problem has grown due to concerns regarding the possible hazards and misuse of employing UAVs in many applications. These include potential privacy violations. To address the concerns, vision-based object detection methods have been developed for UAV detection. However, UAV detection in images with complex backgrounds and weather artifacts like rain has yet to be reasonably studied. Hence, for this purpose, we prepared two training datasets. The first dataset has the sky as its background and is called the Sky Background Dataset (SBD). The second training dataset has more complex scenes (with diverse backgrounds) and is named the Complex Background Dataset (CBD). Additionally, two test sets were prepared: one containing clear images and the other with images with three rain artifacts, named the Rainy Test Set (RTS). This work also focuses on benchmarking state-of-the-art object detection models, and to the best of our knowledge, it is the first to investigate the performance of recent and popular vision-based object detection methods for UAV detection under challenging conditions such as complex backgrounds, varying UAV sizes, and low-to-heavy rainy conditions. The findings presented in the paper shall help provide insights concerning the performance of the selected models for UAV detection under challenging conditions and pave the way to develop more robust UAV detection methods. The codes and datasets are available at: https://github.com/AdnanMunir294/UAVD-CBRA.", "published": "2023-05-25T19:54:33Z", "authors": ["Adnan Munir", "Abdul Jabbar Siddiqui", "Saeed Anwar"], "categories": ["cs.CV", "cs.MM", "eess.IV"], "doi": "", "_query": "property valuation computer vision", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/1501.01578v1", "title": "GammaCHI: a package for the inversion and computation of the gamma and chi-square cumulative distribution functions (central and noncentral)", "abstract": "A Fortran 90 module (GammaCHI) for computing and inverting the gamma and chi-square cumulative distribution functions (central and noncentral) is presented. The main novelty of this package are the reliable and accurate inversion routines for the noncentral cumulative distribution functions. Additionally, the package also provides routines for computing the gamma function, the error function and other functions related to the gamma function. The module includes the routines cdfgamC, invcdfgamC, cdfgamNC, invcdfgamNC, errorfunction, inverfc, gamma, loggam, gamstar and quotgamm for the computation of the central gamma distribution function (and its complementary function), the inversion of the central gamma distribution function, the computation of the noncentral gamma distribution function (and its complementary function), the inversion of the noncentral gamma distribution function, the computation of the error function and its complementary function, the inversion of the complementary error function, the computation of: the gamma function, the logarithm of the gamma function, the regulated gamma function and the ratio of two gamma functions, respectively.", "published": "2015-01-07T18:06:17Z", "authors": ["A. Gil", "J. Segura", "N. M. Temme"], "categories": ["cs.MS", "math.CA", "math.NA"], "doi": "10.1016/j.cpc.2015.01.004", "_query": "property valuation computer vision", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2311.04482v1", "title": "Optimizing Distributed Networking with Big Data Scheduling and Cloud Computing", "abstract": "With the rapid transformation of computer hardware and algorithms, mobile networking has evolved from low data carrying capacity and high latency to better-optimized networks, either by enhancing the digital network or using different approaches to reduce network traffic. This paper discusses the big data applications and scheduling in the distributed networking and analyzes the opportunities and challenges of data management systems. The analysis shows that the big data scheduling in the cloud computing environment produces the most efficient way to transfer and synchronize data. Since scheduling problems and cloud models are very complex to analyze in different settings, we set it to the typical software defined networks. The development of cloud management models and coflow scheduling algorithm is proved to be the priority of the digital communications and networks development in the future.", "published": "2023-11-08T06:22:44Z", "authors": ["Wenbo Zhu"], "categories": ["cs.NI", "cs.DC"], "doi": "10.1117/12.2642577", "_query": "property valuation computer vision", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/math/0504193v3", "title": "A note on quotients of word hyperbolic groups with Property (T)", "abstract": "Every discrete group with Kazhdan's Property (T) is a quotient of a torsion-free, word hyperbolic group with Property (T).", "published": "2005-04-10T13:17:15Z", "authors": ["Yves de Cornulier"], "categories": ["math.GR"], "doi": "", "_query": "property valuation computer vision", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2410.19810v1", "title": "Training Compute-Optimal Vision Transformers for Brain Encoding", "abstract": "The optimal training of a vision transformer for brain encoding depends on three factors: model size, data size, and computational resources. This study investigates these three pillars, focusing on the effects of data scaling, model scaling, and high-performance computing on brain encoding results. Using VideoGPT to extract efficient spatiotemporal features from videos and training a Ridge model to predict brain activity based on these features, we conducted benchmark experiments with varying data sizes (10k, 100k, 1M, 6M) and different model configurations of GPT-2, including hidden layer dimensions, number of layers, and number of attention heads. We also evaluated the effects of training models with 32-bit vs 16-bit floating point representations. Our results demonstrate that increasing the hidden layer dimensions significantly improves brain encoding performance, as evidenced by higher Pearson correlation coefficients across all subjects. In contrast, the number of attention heads does not have a significant effect on the encoding results. Additionally, increasing the number of layers shows some improvement in brain encoding correlations, but the trend is not as consistent as that observed with hidden layer dimensions. The data scaling results show that larger training datasets lead to improved brain encoding performance, with the highest Pearson correlation coefficients observed for the largest dataset size (6M). These findings highlight that the effects of data scaling are more significant compared to model scaling in enhancing brain encoding performance. Furthermore, we explored the impact of floating-point precision by comparing 32-bit and 16-bit representations. Training with 16-bit precision yielded the same brain encoding accuracy as 32-bit, while reducing training time by 1.17 times, demonstrating its efficiency for high-performance computing tasks.", "published": "2024-10-17T12:54:50Z", "authors": ["Sana Ahmadi", "Francois Paugam", "Tristan Glatard", "Pierre Lune Bellec"], "categories": ["eess.IV", "cs.CV", "cs.LG", "q-bio.NC"], "doi": "", "_query": "property valuation computer vision", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2406.06813v1", "title": "Stable Neighbor Denoising for Source-free Domain Adaptive Segmentation", "abstract": "We study source-free unsupervised domain adaptation (SFUDA) for semantic segmentation, which aims to adapt a source-trained model to the target domain without accessing the source data. Many works have been proposed to address this challenging problem, among which uncertainty-based self-training is a predominant approach. However, without comprehensive denoising mechanisms, they still largely fall into biased estimates when dealing with different domains and confirmation bias. In this paper, we observe that pseudo-label noise is mainly contained in unstable samples in which the predictions of most pixels undergo significant variations during self-training. Inspired by this, we propose a novel mechanism to denoise unstable samples with stable ones. Specifically, we introduce the Stable Neighbor Denoising (SND) approach, which effectively discovers highly correlated stable and unstable samples by nearest neighbor retrieval and guides the reliable optimization of unstable samples by bi-level learning. Moreover, we compensate for the stable set by object-level object paste, which can further eliminate the bias caused by less learned classes. Our SND enjoys two advantages. First, SND does not require a specific segmentor structure, endowing its universality. Second, SND simultaneously addresses the issues of class, domain, and confirmation biases during adaptation, ensuring its effectiveness. Extensive experiments show that SND consistently outperforms state-of-the-art methods in various SFUDA semantic segmentation settings. In addition, SND can be easily integrated with other approaches, obtaining further improvements.", "published": "2024-06-10T21:44:52Z", "authors": ["Dong Zhao", "Shuang Wang", "Qi Zang", "Licheng Jiao", "Nicu Sebe", "Zhun Zhong"], "categories": ["cs.CV"], "doi": "", "_query": "property valuation computer vision", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2512.09463v1", "title": "Privacy-Preserving Computer Vision for Industry: Three Case Studies in Human-Centric Manufacturing", "abstract": "The adoption of AI-powered computer vision in industry is often constrained by the need to balance operational utility with worker privacy. Building on our previously proposed privacy-preserving framework, this paper presents its first comprehensive validation on real-world data collected directly by industrial partners in active production environments. We evaluate the framework across three representative use cases: woodworking production monitoring, human-aware AGV navigation, and multi-camera ergonomic risk assessment. The approach employs learned visual transformations that obscure sensitive or task-irrelevant information while retaining features essential for task performance. Through both quantitative evaluation of the privacy-utility trade-off and qualitative feedback from industrial partners, we assess the framework's effectiveness, deployment feasibility, and trust implications. Results demonstrate that task-specific obfuscation enables effective monitoring with reduced privacy risks, establishing the framework's readiness for real-world adoption and providing cross-domain recommendations for responsible, human-centric AI deployment in industry.", "published": "2025-12-10T09:33:03Z", "authors": ["Sander De Coninck", "Emilio Gamba", "Bart Van Doninck", "Abdellatif Bey-Temsamani", "Sam Leroux", "Pieter Simoens"], "categories": ["cs.CV", "cs.AI"], "doi": "", "_query": "property valuation computer vision", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2102.13343v3", "title": "Are Gross Substitutes a Substitute for Submodular Valuations?", "abstract": "The class of gross substitutes (GS) set functions plays a central role in Economics and Computer Science. GS belongs to the hierarchy of {\\em complement free} valuations introduced by Lehmann, Lehmann and Nisan, along with other prominent classes: $GS \\subsetneq Submodular \\subsetneq XOS \\subsetneq Subadditive$. The GS class has always been more enigmatic than its counterpart classes, both in its definition and in its relation to the other classes. For example, while it is well understood how closely the Submodular, XOS and Subadditive classes (point-wise) approximate one another, approximability of these classes by GS remained wide open. Our main result is the existence of a submodular valuation (one that is also budget additive) that cannot be approximated by GS within a ratio better than $Ω(\\frac{\\log m}{\\log\\log m})$, where $m$ is the number of items. En route, we uncover a new symmetrization operation that preserves GS, which may be of independent interest. We show that our main result is tight with respect to budget additive valuations. Additionally, for a class of submodular functions that we refer to as {\\em concave of Rado} valuations (this class greatly extends budget additive valuations), we show approximability by GS within an $O(\\log^2m)$ factor.", "published": "2021-02-26T07:46:30Z", "authors": ["Shahar Dobzinski", "Uriel Feige", "Michal Feldman", "Renato Paes Leme"], "categories": ["cs.GT"], "doi": "", "_query": "property valuation computer vision", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2012.10878v1", "title": "Computer Vision based Animal Collision Avoidance Framework for Autonomous Vehicles", "abstract": "Animals have been a common sighting on roads in India which leads to several accidents between them and vehicles every year. This makes it vital to develop a support system for driverless vehicles that assists in preventing these forms of accidents. In this paper, we propose a neoteric framework for avoiding vehicle-to-animal collisions by developing an efficient approach for the detection of animals on highways using deep learning and computer vision techniques on dashcam video. Our approach leverages the Mask R-CNN model for detecting and identifying various commonly found animals. Then, we perform lane detection to deduce whether a detected animal is on the vehicle's lane or not and track its location and direction of movement using a centroid based object tracking algorithm. This approach ensures that the framework is effective at determining whether an animal is obstructing the path or not of an autonomous vehicle in addition to predicting its movement and giving feedback accordingly. This system was tested under various lighting and weather conditions and was observed to perform relatively well, which leads the way for prominent driverless vehicle's support systems for avoiding vehicular collisions with animals on Indian roads in real-time.", "published": "2020-12-20T09:51:23Z", "authors": ["Savyasachi Gupta", "Dhananjai Chand", "Ilaiah Kavati"], "categories": ["cs.CV"], "doi": "", "_query": "property valuation computer vision", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2508.09344v2", "title": "Blink-to-code: real-time Morse code communication via eye blink detection and classification", "abstract": "This study proposes a real-time system that translates voluntary eye blinks into Morse code, enabling communication for individuals with severe motor impairments. Using a standard webcam and computer vision, the system detects and classifies blinks as short (dot) or long (dash), then decodes them into alphanumeric characters. Experiments with five participants show 62% decoding accuracy and 18-20 seconds response times, demonstrating a viable, low-cost assistive communication method.", "published": "2025-08-12T21:07:28Z", "authors": ["Anushka Bhatt"], "categories": ["cs.CV"], "doi": "", "_query": "property valuation computer vision", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2504.06163v1", "title": "Action Valuation in Sports: A Survey", "abstract": "Action Valuation (AV) has emerged as a key topic in Sports Analytics, offering valuable insights by assigning scores to individual actions based on their contribution to desired outcomes. Despite a few surveys addressing related concepts such as Player Valuation, there is no comprehensive review dedicated to an in-depth analysis of AV across different sports. In this survey, we introduce a taxonomy with nine dimensions related to the AV task, encompassing data, methodological approaches, evaluation techniques, and practical applications. Through this analysis, we aim to identify the essential characteristics of effective AV methods, highlight existing gaps in research, and propose future directions for advancing the field.", "published": "2025-04-08T15:59:19Z", "authors": ["Artur Xarles", "Sergio Escalera", "Thomas B. Moeslund", "Albert Clapés"], "categories": ["cs.CV"], "doi": "", "_query": "property valuation computer vision", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2412.05996v1", "title": "Paddy Disease Detection and Classification Using Computer Vision Techniques: A Mobile Application to Detect Paddy Disease", "abstract": "Plant diseases significantly impact our food supply, causing problems for farmers, economies reliant on agriculture, and global food security. Accurate and timely plant disease diagnosis is crucial for effective treatment and minimizing yield losses. Despite advancements in agricultural technology, a precise and early diagnosis remains a challenge, especially in underdeveloped regions where agriculture is crucial and agricultural experts are scarce. However, adopting Deep Learning applications can assist in accurately identifying diseases without needing plant pathologists. In this study, the effectiveness of various computer vision models for detecting paddy diseases is evaluated and proposed the best deep learning-based disease detection system. Both classification and detection using the Paddy Doctor dataset, which contains over 20,000 annotated images of paddy leaves for disease diagnosis are tested and evaluated. For detection, we utilized the YOLOv8 model-based model were used for paddy disease detection and CNN models and the Vision Transformer were used for disease classification. The average mAP50 of 69% for detection tasks was achieved and the Vision Transformer classification accuracy was 99.38%. It was found that detection models are effective at identifying multiple diseases simultaneously with less computing power, whereas classification models, though computationally expensive, exhibit better performance for classifying single diseases. Additionally, a mobile application was developed to enable farmers to identify paddy diseases instantly. Experiments with the app showed encouraging results in utilizing the trained models for both disease classification and treatment guidance.", "published": "2024-12-08T17:08:27Z", "authors": ["Bimarsha Khanal", "Paras Poudel", "Anish Chapagai", "Bijan Regmi", "Sitaram Pokhrel", "Salik Ram Khanal"], "categories": ["cs.CV"], "doi": "", "_query": "property valuation computer vision", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/1307.2131v1", "title": "Axioms for the Lefschetz number as a lattice valuation", "abstract": "We give new axioms for the Lefschetz number based on Hadwiger's characterization of the Euler characteristic as the unique lattice valuation on polyhedra which takes value 1 on simplices. In the setting of maps on abstract simplicial complexes, we show that the Lefschetz number is unique with respect to a valuation axiom and an axiom specifying the value on a simplex. These axioms lead naturally to the classical computation of the Lefschetz number as a trace in homology. We then extend this approach to continuous maps of polyhedra, assuming an extra homotopy invariance axiom. We also show that this homotopy axiom can be weakened.", "published": "2013-07-08T15:37:31Z", "authors": ["P. Christopher Staecker"], "categories": ["math.GN", "math.AT"], "doi": "", "_query": "property valuation computer vision", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2412.20210v2", "title": "Towards Real-Time 2D Mapping: Harnessing Drones, AI, and Computer Vision for Advanced Insights", "abstract": "This paper presents an advanced mapping system that combines drone imagery with machine learning and computer vision to overcome challenges in speed, accuracy, and adaptability across diverse terrains. By automating processes like feature detection, image matching, and stitching, the system produces seamless, high-resolution maps with minimal latency, offering strategic advantages in defense operations. Developed in Python, the system utilizes OpenCV for image processing, NumPy for efficient computations, and Concurrent[dot]futures for parallel execution. ORB (Oriented FAST and Rotated BRIEF) is employed for feature detection, while FLANN (Fast Library for Approximate Nearest Neighbors) ensures accurate keypoint matching. Homography transformations align overlapping images, resulting in distortion-free maps in real time. This automation eliminates manual intervention, enabling live updates essential in rapidly changing environments. Designed for versatility, the system performs reliably under various lighting conditions and rugged terrains, making it highly suitable for aerospace and defense applications. Testing has shown notable improvements in processing speed and accuracy compared to conventional methods, enhancing situational awareness and informed decision-making. This scalable solution leverages cutting-edge technologies to provide actionable, reliable data for mission-critical operations.", "published": "2024-12-28T16:47:18Z", "authors": ["Bharath Kumar Agnur"], "categories": ["cs.CV", "cs.AI", "cs.LG"], "doi": "", "_query": "property valuation computer vision", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2509.09720v1", "title": "Australian Supermarket Object Set (ASOS): A Benchmark Dataset of Physical Objects and 3D Models for Robotics and Computer Vision", "abstract": "This paper introduces the Australian Supermarket Object Set (ASOS), a comprehensive dataset comprising 50 readily available supermarket items with high-quality 3D textured meshes designed for benchmarking in robotics and computer vision applications. Unlike existing datasets that rely on synthetic models or specialized objects with limited accessibility, ASOS provides a cost-effective collection of common household items that can be sourced from a major Australian supermarket chain. The dataset spans 10 distinct categories with diverse shapes, sizes, and weights. 3D meshes are acquired by a structure-from-motion techniques with high-resolution imaging to generate watertight meshes. The dataset's emphasis on accessibility and real-world applicability makes it valuable for benchmarking object detection, pose estimation, and robotics applications.", "published": "2025-09-09T22:26:01Z", "authors": ["Akansel Cosgun", "Lachlan Chumbley", "Benjamin J. Meyer"], "categories": ["cs.CV", "cs.RO", "eess.IV"], "doi": "", "_query": "property valuation computer vision", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2407.14402v1", "title": "The Vision of Autonomic Computing: Can LLMs Make It a Reality?", "abstract": "The Vision of Autonomic Computing (ACV), proposed over two decades ago, envisions computing systems that self-manage akin to biological organisms, adapting seamlessly to changing environments. Despite decades of research, achieving ACV remains challenging due to the dynamic and complex nature of modern computing systems. Recent advancements in Large Language Models (LLMs) offer promising solutions to these challenges by leveraging their extensive knowledge, language understanding, and task automation capabilities. This paper explores the feasibility of realizing ACV through an LLM-based multi-agent framework for microservice management. We introduce a five-level taxonomy for autonomous service maintenance and present an online evaluation benchmark based on the Sock Shop microservice demo project to assess our framework's performance. Our findings demonstrate significant progress towards achieving Level 3 autonomy, highlighting the effectiveness of LLMs in detecting and resolving issues within microservice architectures. This study contributes to advancing autonomic computing by pioneering the integration of LLMs into microservice management frameworks, paving the way for more adaptive and self-managing computing systems. The code will be made available at https://aka.ms/ACV-LLM.", "published": "2024-07-19T15:30:32Z", "authors": ["Zhiyang Zhang", "Fangkai Yang", "Xiaoting Qin", "Jue Zhang", "Qingwei Lin", "Gong Cheng", "Dongmei Zhang", "Saravan Rajmohan", "Qi Zhang"], "categories": ["cs.AI", "cs.CL", "cs.DC", "cs.MA", "cs.SE"], "doi": "", "_query": "property valuation computer vision", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2309.12675v1", "title": "Vision Transformers for Computer Go", "abstract": "Motivated by the success of transformers in various fields, such as language understanding and image analysis, this investigation explores their application in the context of the game of Go. In particular, our study focuses on the analysis of the Transformer in Vision. Through a detailed analysis of numerous points such as prediction accuracy, win rates, memory, speed, size, or even learning rate, we have been able to highlight the substantial role that transformers can play in the game of Go. This study was carried out by comparing them to the usual Residual Networks.", "published": "2023-09-22T07:35:37Z", "authors": ["Amani Sagri", "Tristan Cazenave", "Jérôme Arjonilla", "Abdallah Saffidine"], "categories": ["cs.AI", "cs.CV"], "doi": "", "_query": "property valuation computer vision", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2204.09576v2", "title": "The valuation pairing on an upper cluster algebra", "abstract": "It is known that many (upper) cluster algebras are not unique factorization domains. We exhibit the local factorization properties with respect to any given seed $t$: any non-zero element in a full rank upper cluster algebra can be uniquely written as the product of a cluster monomial in $t$ and another element not divisible by the cluster variables in $t$. Our approach is based on introducing the valuation pairing on an upper cluster algebra: it counts the maximal multiplicity of a cluster variable among the factorizations of any given element. We apply the valuation pairing to obtain many results concerning factoriality, $d$-vectors, $F$-polynomials and the combinatorics of cluster Poisson variables. In particular, we obtain that full rank and primitive upper cluster algebras are factorial; an explanation of $d$-vectors using valuation pairing; a cluster monomial in non-initial cluster variables is determined by its $F$-polynomial; the $F$-polynomials of non-initial cluster variables are irreducible; and the cluster Poisson variables parametrize the exchange pairs of the corresponding upper cluster algebra.", "published": "2022-04-20T16:16:25Z", "authors": ["Peigen Cao", "Bernhard Keller", "Fan Qin"], "categories": ["math.RT", "math.RA"], "doi": "10.1515/crelle-2023-0080", "_query": "property valuation computer vision", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/1808.04593v1", "title": "Unsupervised learning of foreground object detection", "abstract": "Unsupervised learning poses one of the most difficult challenges in computer vision today. The task has an immense practical value with many applications in artificial intelligence and emerging technologies, as large quantities of unlabeled videos can be collected at relatively low cost. In this paper, we address the unsupervised learning problem in the context of detecting the main foreground objects in single images. We train a student deep network to predict the output of a teacher pathway that performs unsupervised object discovery in videos or large image collections. Our approach is different from published methods on unsupervised object discovery. We move the unsupervised learning phase during training time, then at test time we apply the standard feed-forward processing along the student pathway. This strategy has the benefit of allowing increased generalization possibilities during training, while remaining fast at testing. Our unsupervised learning algorithm can run over several generations of student-teacher training. Thus, a group of student networks trained in the first generation collectively create the teacher at the next generation. In experiments our method achieves top results on three current datasets for object discovery in video, unsupervised image segmentation and saliency detection. At test time the proposed system is fast, being one to two orders of magnitude faster than published unsupervised methods.", "published": "2018-08-14T09:12:28Z", "authors": ["Ioana Croitoru", "Simion-Vlad Bogolin", "Marius Leordeanu"], "categories": ["cs.CV"], "doi": "10.1007/s11263-019-01183-3", "_query": "property valuation computer vision", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/1905.01745v2", "title": "Faster polytope rounding, sampling, and volume computation via a sublinear \"Ball Walk\"", "abstract": "We study the problem of \"isotropically rounding\" a polytope $K\\subset\\mathbb{R}^n$, that is, computing a linear transformation which makes the uniform distribution on the polytope have roughly identity covariance matrix. We assume $K$ is defined by $m$ linear inequalities, with guarantee that $rB\\subset K\\subset RB$, where $B$ is the unit ball. We introduce a new variant of the ball walk Markov chain and show that, roughly, the expected number of arithmetic operations per-step of this Markov chain is $O(m)$ that is sublinear in the input size $mn$--the per-step time of all prior Markov chains. Subsequently, we give a rounding algorithm that succeeds with probability $1-\\varepsilon$ in $\\tilde{O}(mn^{4.5}\\mbox{polylog}(\\frac{1}{\\varepsilon},\\frac{R}{r}))$ arithmetic operations. This gives a factor of $\\sqrt{n}$ improvement on the previous bound of $\\tilde{O}(mn^5\\mbox{polylog}(\\frac{1}{\\varepsilon},\\frac{R}{r}))$ for rounding, which uses the hit-and-run algorithm. Since the rounding preprocessing step is in many cases the bottleneck in improving sampling or volume computation, our results imply these tasks can also be achieved in roughly $\\tilde{O}(mn^{4.5}\\mbox{polylog}(\\frac{1}{\\varepsilon},\\frac{R}{r})+mn^4δ^{-2})$ operations for computing the volume of $K$ up to a factor $1+δ$ and $\\tilde{O}(mn^{4.5}\\mbox{polylog}(\\frac{1}{\\varepsilon},\\frac{R}{r})))$ for uniformly sampling on $K$ with TV error $\\varepsilon$. This improves on the previous bounds of $\\tilde{O}(mn^5\\mbox{polylog}(\\frac{1}{\\varepsilon},\\frac{R}{r})+mn^4δ^{-2})$ for volume computation when roughly $m\\geq n^{2.5}$, and $\\tilde{O}(mn^5\\mbox{polylog}(\\frac{1}{\\varepsilon},\\frac{R}{r}))$ for sampling when roughly $m\\geq n^{1.5}$. We achieve this improvement by a novel method of computing polytope membership, where one avoids checking inequalities estimated to have a very low probability of being violated.", "published": "2019-05-05T20:52:25Z", "authors": ["Oren Mangoubi", "Nisheeth K. Vishnoi"], "categories": ["cs.DS", "cs.LG", "math.PR", "stat.CO", "stat.ML"], "doi": "", "_query": "property valuation computer vision", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/1411.5319v2", "title": "Fashion Apparel Detection: The Role of Deep Convolutional Neural Network and Pose-dependent Priors", "abstract": "In this work, we propose and address a new computer vision task, which we call fashion item detection, where the aim is to detect various fashion items a person in the image is wearing or carrying. The types of fashion items we consider in this work include hat, glasses, bag, pants, shoes and so on. The detection of fashion items can be an important first step of various e-commerce applications for fashion industry. Our method is based on state-of-the-art object detection method pipeline which combines object proposal methods with a Deep Convolutional Neural Network. Since the locations of fashion items are in strong correlation with the locations of body joints positions, we incorporate contextual information from body poses in order to improve the detection performance. Through the experiments, we demonstrate the effectiveness of the proposed method.", "published": "2014-11-19T19:09:00Z", "authors": ["Kota Hara", "Vignesh Jagadeesh", "Robinson Piramuthu"], "categories": ["cs.CV"], "doi": "10.1109/WACV.2016.7477611", "_query": "property valuation computer vision", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/1305.7120v2", "title": "Computations by fly-automata beyond monadic second-order logic", "abstract": "We present logically based methods for constructing XP and FPT graph algorithms, parametrized by tree-width or clique-width. We will use fly-automata introduced in a previous article. They make possible to check properties that are not monadic second-order expressible because their states may include counters, so that their sets of states may be infinite. We equip these automata with output functions, so that they can compute values associated with terms or graphs. Rather than new algorithmic results we present tools for constructing easily certain dynamic programming algorithms by combining predefined automata for basic functions and properties.", "published": "2013-05-30T14:14:30Z", "authors": ["Bruno Courcelle", "Irène Durand"], "categories": ["cs.LO"], "doi": "", "_query": "property valuation computer vision", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2602.06300v1", "title": "Accelerating Vision Transformers on Brain Processing Unit", "abstract": "With the advancement of deep learning technologies, specialized neural processing hardware such as Brain Processing Units (BPUs) have emerged as dedicated platforms for CNN acceleration, offering optimized INT8 computation capabilities for convolutional operations. Meanwhile, Vision Transformer (ViT) models, such as the Data-efficient Image Transformer (DeiT), have demonstrated superior performance and play increasingly crucial roles in computer vision tasks. However, due to the architectural mismatch between CNN-optimized hardware and Vision Transformer computation characteristics--namely, that linear layers in Transformers operate on three-dimensional data while BPU acceleration is designed for four-dimensional convolution operations-it is difficult or even impossible to leverage BPU's advantages when deploying Vision Transformers. To address this challenge, we propose a novel approach that restructures the Vision Transformer by replacing linear layers and layer normalization operations with carefully designed convolutional operators. This enables DeiT to fully utilize the acceleration capabilities of BPUs, while allowing the original weight parameters to be inherited by the restructured models without retraining or fine-tuning. To the best of our knowledge, this is the first successful deployment of Vision Transformers that fully leverages BPU classification datasets demonstrate the effectiveness of our approach. Specifically, the quantized DeiT-Base model achieves 80.4% accuracy on ImageNet, compared to the original 81.8%, while obtaining up to a 3.8* inference speedup. Our finetuned DeiT model on the flower classification dataset also achieves excellent performance, with only a 0.5% accuracy drop for the DeiT-Base model, further demonstrating the effectiveness of our method.", "published": "2026-02-06T01:48:54Z", "authors": ["Jinchi Tang", "Yan Guo"], "categories": ["cs.CV", "cs.AI"], "doi": "", "_query": "property valuation computer vision", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/1605.05272v1", "title": "Fast and Accurate Algorithm for Eye Localization for Gaze Tracking in Low Resolution Images", "abstract": "Iris centre localization in low-resolution visible images is a challenging problem in computer vision community due to noise, shadows, occlusions, pose variations, eye blinks, etc. This paper proposes an efficient method for determining iris centre in low-resolution images in the visible spectrum. Even low-cost consumer-grade webcams can be used for gaze tracking without any additional hardware. A two-stage algorithm is proposed for iris centre localization. The proposed method uses geometrical characteristics of the eye. In the first stage, a fast convolution based approach is used for obtaining the coarse location of iris centre (IC). The IC location is further refined in the second stage using boundary tracing and ellipse fitting. The algorithm has been evaluated in public databases like BioID, Gi4E and is found to outperform the state of the art methods.", "published": "2016-05-17T18:10:43Z", "authors": ["Anjith George", "Aurobinda Routray"], "categories": ["cs.CV"], "doi": "10.1049/iet-cvi.2015.0316", "_query": "property valuation computer vision", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/1609.00829v1", "title": "Efficient computation of Laguerre polynomials", "abstract": "An efficient algorithm and a Fortran 90 module (LaguerrePol) for computing Laguerre polynomials $L^{(α)}_n(z)$ are presented. The standard three-term recurrence relation satisfied by the polynomials and different types of asymptotic expansions valid for $n$ large and $α$ small, are used depending on the parameter region. Based on tests of contiguous relations in the parameter $α$ and the degree $n$ satisfied by the polynomials, we claim that a relative accuracy close or better than $10^{-12}$ can be obtained using the module LaguerrePol for computing the functions $L^{(α)}_n(z)$ in the parameter range $z \\ge 0$, $-1 < α\\le 5$, $n \\ge 0$.", "published": "2016-09-03T13:59:43Z", "authors": ["A. Gil", "J. Segura", "N. M. Temme"], "categories": ["math.NA", "cs.MS", "math.CA"], "doi": "10.1016/j.cpc.2016.09.002", "_query": "property valuation computer vision", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/cs/0410067v1", "title": "Computational Unification: a Vision for Connecting Researchers", "abstract": "The extent to which the benefits of science can be fully realized depends critically upon the quality of the connection between researchers themselves and between researchers and members of the public. We believe that it is now possible to improve these connections on a community-wide and even world-wide basis through the use of an appropriate information management system. In this paper we explore the concepts and challenges, and propose an architecture for the implementation of such a system. \"One of the greatest visions for science is a computational unification in which every researcher can interact with all other researchers through use of their own research system.\" \"These features not only enable research collaboration on a scale never previously envisaged, they also enable sharing and dissemination of scientific knowledge to the public at large with a sophistication unparalleled in history.\"", "published": "2004-10-26T18:21:54Z", "authors": ["Richard M. Troy"], "categories": ["cs.DC", "cs.CY"], "doi": "", "_query": "property valuation computer vision", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2208.09122v1", "title": "Towards Unbiased Label Distribution Learning for Facial Pose Estimation Using Anisotropic Spherical Gaussian", "abstract": "Facial pose estimation refers to the task of predicting face orientation from a single RGB image. It is an important research topic with a wide range of applications in computer vision. Label distribution learning (LDL) based methods have been recently proposed for facial pose estimation, which achieve promising results. However, there are two major issues in existing LDL methods. First, the expectations of label distributions are biased, leading to a biased pose estimation. Second, fixed distribution parameters are applied for all learning samples, severely limiting the model capability. In this paper, we propose an Anisotropic Spherical Gaussian (ASG)-based LDL approach for facial pose estimation. In particular, our approach adopts the spherical Gaussian distribution on a unit sphere which constantly generates unbiased expectation. Meanwhile, we introduce a new loss function that allows the network to learn the distribution parameter for each learning sample flexibly. Extensive experimental results show that our method sets new state-of-the-art records on AFLW2000 and BIWI datasets.", "published": "2022-08-19T02:12:36Z", "authors": ["Zhiwen Cao", "Dongfang Liu", "Qifan Wang", "Yingjie Chen"], "categories": ["cs.CV"], "doi": "", "_query": "property valuation computer vision", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/1602.00484v2", "title": "Mobile Edge Computing, Fog et al.: A Survey and Analysis of Security Threats and Challenges", "abstract": "For various reasons, the cloud computing paradigm is unable to meet certain requirements (e.g. low latency and jitter, context awareness, mobility support) that are crucial for several applications (e.g. vehicular networks, augmented reality). To fulfil these requirements, various paradigms, such as fog computing, mobile edge computing, and mobile cloud computing, have emerged in recent years. While these edge paradigms share several features, most of the existing research is compartmentalised; no synergies have been explored. This is especially true in the field of security, where most analyses focus only on one edge paradigm, while ignoring the others. The main goal of this study is to holistically analyse the security threats, challenges, and mechanisms inherent in all edge paradigms, while highlighting potential synergies and venues of collaboration. In our results, we will show that all edge paradigms should consider the advances in other paradigms.", "published": "2016-02-01T11:46:41Z", "authors": ["Rodrigo Roman", "Javier Lopez", "Masahiro Mambo"], "categories": ["cs.CR", "cs.NI"], "doi": "10.1016/j.future.2016.11.009", "_query": "property valuation computer vision", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/1911.00386v1", "title": "Risk Neutral Valuation of Inflation-Linked Interest Rate Derivatives", "abstract": "We propose a model for the joint evolution of European inflation, the European Central Bank official interest rate and the short-term interest rate, in a stochastic, continuous time setting. We derive the valuation equation for a contingent claim depending potentially on all three factors. This valuation equation reduces to a finite number of Cauchy problems for a degenerate parabolic PDE with non-local terms. We show that the price of the contingent claim is the only viscosity solution of the valuation equation. We also provide an efficient numerical scheme to compute the price and implement it in an example.", "published": "2019-11-01T14:04:02Z", "authors": ["Flavia Antonacci", "Cristina Costantini", "Fernanda D'Ippoliti", "Marco Papi"], "categories": ["q-fin.MF"], "doi": "", "_query": "property valuation computer vision", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2208.02443v1", "title": "Credal Valuation Networks for Machine Reasoning Under Uncertainty", "abstract": "Contemporary undertakings provide limitless opportunities for widespread application of machine reasoning and artificial intelligence in situations characterised by uncertainty, hostility and sheer volume of data. The paper develops a valuation network as a graphical system for higher-level fusion and reasoning under uncertainty in support of the human operators. Valuations, which are mathematical representation of (uncertain) knowledge and collected data, are expressed as credal sets, defined as coherent interval probabilities in the framework of imprecise probability theory. The basic operations with such credal sets, combination and marginalisation, are defined to satisfy the axioms of a valuation algebra. A practical implementation of the credal valuation network is discussed and its utility demonstrated on a small scale example.", "published": "2022-08-04T04:33:16Z", "authors": ["Branko Ristic", "Alessio Benavoli", "Sanjeev Arulampalam"], "categories": ["cs.AI"], "doi": "", "_query": "property valuation computer vision", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/1912.10869v1", "title": "Famille admise associée à une valuation de K(X)", "abstract": "Let K be a field with a valuation $ν$ and let L = K(x) be a transcendental extension of K, then any valuation $μ$ of L which extends $ν$ is determined by its restriction to the polynomial ring K[x]. We know how to associate to this valuation $μ$ a family of valuations A = ($μ$i)i$\\in$I of K[x], called the associated admise family, which converges in a certain sense towards the valuation $μ$. Although the definition of this family, as well as the notion of convergence, essentially imply the structure of the polynomial ring, in particular the degree of polynomials, we show in this note that the family A of valuations of L do not depend on the chosen generator x.", "published": "2019-12-23T14:31:18Z", "authors": ["Michel Vaquié"], "categories": ["math.AC", "math.AG"], "doi": "10.1112/blms.12378", "_query": "property valuation computer vision", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2609.14592v1", "title": "AI Deployment Accountability Engineering: A Vision for Accountable AI in Safety-Critical Socio-Technical Systems", "abstract": "Artificial intelligence systems are rapidly becoming critical components in healthcare, finance, public services, and other safety-critical domains. Yet the engineering practices used to evaluate these systems remain predominantly model-centric, emphasizing properties such as accuracy, robustness, fairness, and interpretability before deployment. These properties are necessary but insufficient once an AI system operates within an ever changing socio-technical environment characterized by distribution shifts, institutional constraints, human feedback loops, privacy requirements, and interactions among multiple AI agents. This vision paper introduces AI Deployment Accountability Engineering (ADAE), a proposed AI engineering subdiscipline concerned with establishing measurable, continuous, and actionable accountability for deployed AI systems. ADAE treats accountability as a deployment-layer property rather than solely as a property of an individual model. It seeks to determine whether an AI-enabled system continues to operate within acceptable risk limits, identify the contexts in which failures emerge, attribute failures across interacting technical and human components, translate technical failures into downstream consequences, and support timely intervention. We articulate a research agenda built around four interconnected pillars: structured discovery of context-dependent failure modes, privacy-preserving accountability measurement, system-level risk analysis for agentic AI, and translation of technical failures into operational, and institutional risks. The broader goal is to establish foundational principles, mathematical tools, and system architectures for accountable AI deployment across safety-critical applications.", "published": "2026-09-13T15:18:41Z", "authors": ["Murat Kantarcioglu"], "categories": ["cs.AI", "cs.CR", "cs.CY"], "doi": "", "_query": "property valuation computer vision", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2310.19522v2", "title": "Are Natural Domain Foundation Models Useful for Medical Image Classification?", "abstract": "The deep learning field is converging towards the use of general foundation models that can be easily adapted for diverse tasks. While this paradigm shift has become common practice within the field of natural language processing, progress has been slower in computer vision. In this paper we attempt to address this issue by investigating the transferability of various state-of-the-art foundation models to medical image classification tasks. Specifically, we evaluate the performance of five foundation models, namely SAM, SEEM, DINOv2, BLIP, and OpenCLIP across four well-established medical imaging datasets. We explore different training settings to fully harness the potential of these models. Our study shows mixed results. DINOv2 consistently outperforms the standard practice of ImageNet pretraining. However, other foundation models failed to consistently beat this established baseline indicating limitations in their transferability to medical image classification tasks.", "published": "2023-10-30T13:21:56Z", "authors": ["Joana Palés Huix", "Adithya Raju Ganeshan", "Johan Fredin Haslum", "Magnus Söderberg", "Christos Matsoukas", "Kevin Smith"], "categories": ["cs.CV"], "doi": "", "_query": "property valuation computer vision", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2607.02819v1", "title": "Vision Token Manipulation Attacks on Cloud-Edge Inference of Large Vision-Language Models", "abstract": "Cloud-edge Large Vision-Language Model (LVLM) inference enables efficient deployment by splitting computation between edge devices and cloud servers. In this process, intermediate vision tokens are transmitted from the edge to the cloud over a communication link, thereby exposing a new attack surface. We study vision token manipulation attack (VTM-Attack) under a black-box man-in-the-middle setting, where an adversary intercepts and manipulates a subset of transmitted vision tokens under a budget constraint. We propose four naïve attack strategies and an optimization-based token selection method. Experiments on 6 state-of-the-art LVLMs (3B-72B) across 4 benchmarks show that manipulating only 10\\% of vision tokens can reduce accuracy by up to 88.31\\%. These results reveal a critical vulnerability in cloud-edge LVLM inference.", "published": "2026-07-02T23:16:02Z", "authors": ["Zikai Zhang", "Rui Hu", "Olivera Kotevska", "Jiahao Xu"], "categories": ["cs.CR", "cs.AI", "cs.CV"], "doi": "", "_query": "property valuation computer vision", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2303.07560v1", "title": "Machine Learning Computer Vision Applications for Spatial AI Object Recognition in Orange County, California", "abstract": "We provide an integrated and systematic automation approach to spatial object recognition and positional detection using AI machine learning and computer vision algorithms for Orange County, California. We describe a comprehensive methodology for multi-sensor, high-resolution field data acquisition, along with post-field processing and pre-analysis processing tasks. We developed a series of algorithmic formulations and workflows that integrate convolutional deep neural network learning with detected object positioning estimation in 360° equirectancular photosphere imagery. We provide examples of application processing more than 800 thousand cardinal directions in photosphere images across two areas in Orange County, and present detection results for stop-sign and fire hydrant object recognition. We discuss the efficiency and effectiveness of our approach, along with broader inferences related to the performance and implications of this approach for future technological innovations, including automation of spatial data and public asset inventories, and near real-time AI field data systems.", "published": "2023-03-14T00:57:11Z", "authors": ["Kostas Alexandridis"], "categories": ["cs.CV", "cs.LG"], "doi": "", "_query": "property valuation computer vision", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2107.12646v1", "title": "Computer Vision-Based Guidance Assistance Concept for Plowing Using RGB-D Camera", "abstract": "This paper proposes a concept of computer vision-based guidance assistance for agricultural vehicles to increase the accuracy in plowing and reduce driver's cognitive burden in long-lasting tillage operations. Plowing is a common agricultural practice to prepare the soil for planting in many countries and it can take place both in the spring and the fall. Since plowing operation requires high traction forces, it causes increased energy consumption. Moreover, longer operation time due to unnecessary maneuvers leads to higher fuel consumption. To provide necessary information for the driver and the control unit of the tractor, a first concept of furrow detection system based on an RGB-D camera was developed.", "published": "2021-07-27T07:55:58Z", "authors": ["Erkin Türköz", "Ertug Olcay", "Timo Oksanen"], "categories": ["cs.CV"], "doi": "", "_query": "property valuation computer vision", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/1903.03889v3", "title": "Fast Single Image Reflection Suppression via Convex Optimization", "abstract": "Removing undesired reflections from images taken through the glass is of great importance in computer vision. It serves as a means to enhance the image quality for aesthetic purposes as well as to preprocess images in machine learning and pattern recognition applications. We propose a convex model to suppress the reflection from a single input image. Our model implies a partial differential equation with gradient thresholding, which is solved efficiently using Discrete Cosine Transform. Extensive experiments on synthetic and real-world images demonstrate that our approach achieves desirable reflection suppression results and dramatically reduces the execution time.", "published": "2019-03-10T00:01:36Z", "authors": ["Yang Yang", "Wenye Ma", "Yin Zheng", "Jian-Feng Cai", "Weiyu Xu"], "categories": ["cs.CV"], "doi": "", "_query": "property valuation computer vision", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/1603.09196v1", "title": "Definable Valuations Induced by Definable Subgroups", "abstract": "In his unpublished preprint \"Definable Valuations\" Koenigsmann shows that every field that admits a t-henselian topology is either real closed or separably closed or admits a definable valuation inducing the t-henselian topology. To show this Koenigsmann investigates valuation rings induced by certain (definable) subgroups of the field. The aim of this paper, based on the authors PhD thesis, is to look at the methods used in the preprint in greater detail and correct a mistake in the original paper based on a paper of Jahnke and Koenigsmann.", "published": "2016-03-30T13:55:27Z", "authors": ["Katharina Dupont"], "categories": ["math.LO", "math.GR"], "doi": "", "_query": "property valuation computer vision", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/1705.08182v3", "title": "Unmasking the abnormal events in video", "abstract": "We propose a novel framework for abnormal event detection in video that requires no training sequences. Our framework is based on unmasking, a technique previously used for authorship verification in text documents, which we adapt to our task. We iteratively train a binary classifier to distinguish between two consecutive video sequences while removing at each step the most discriminant features. Higher training accuracy rates of the intermediately obtained classifiers represent abnormal events. To the best of our knowledge, this is the first work to apply unmasking for a computer vision task. We compare our method with several state-of-the-art supervised and unsupervised methods on four benchmark data sets. The empirical results indicate that our abnormal event detection framework can achieve state-of-the-art results, while running in real-time at 20 frames per second.", "published": "2017-05-23T11:14:17Z", "authors": ["Radu Tudor Ionescu", "Sorina Smeureanu", "Bogdan Alexe", "Marius Popescu"], "categories": ["cs.CV"], "doi": "", "_query": "property valuation computer vision", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/1708.01022v1", "title": "When Kernel Methods meet Feature Learning: Log-Covariance Network for Action Recognition from Skeletal Data", "abstract": "Human action recognition from skeletal data is a hot research topic and important in many open domain applications of computer vision, thanks to recently introduced 3D sensors. In the literature, naive methods simply transfer off-the-shelf techniques from video to the skeletal representation. However, the current state-of-the-art is contended between to different paradigms: kernel-based methods and feature learning with (recurrent) neural networks. Both approaches show strong performances, yet they exhibit heavy, but complementary, drawbacks. Motivated by this fact, our work aims at combining together the best of the two paradigms, by proposing an approach where a shallow network is fed with a covariance representation. Our intuition is that, as long as the dynamics is effectively modeled, there is no need for the classification network to be deep nor recurrent in order to score favorably. We validate this hypothesis in a broad experimental analysis over 6 publicly available datasets.", "published": "2017-08-03T06:52:51Z", "authors": ["Jacopo Cavazza", "Pietro Morerio", "Vittorio Murino"], "categories": ["cs.CV"], "doi": "10.1109/CVPRW.2017.165", "_query": "property valuation computer vision", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/1712.08562v2", "title": "Extensions of Valuations to the Henselization and Completion", "abstract": "We show that if $R$ is a local domain which is dominated by a valuation $ν$, then there does not always exist a regular local ring $R'$ which birationally dominates R and is dominated by $ν$ and an extension of $ν$ to the Henselization $(R')^h$ of $R'$ such that the associated graded rings of $R'$ and $(R')^h$ along the valuations are equal. We also show that there does not always exist $R'$, a prime ideal $p$ of the completion $\\widehat{R'}$ such that $p\\cap R'=(0)$ and an extension of $ν$ to $\\widehat{R'}$ such that the associated graded rings of $R'$ and $\\widehat{R'}/p$ along the valuation are equal.", "published": "2017-12-22T16:41:07Z", "authors": ["Steven Dale Cutkosky"], "categories": ["math.AG", "math.AC"], "doi": "", "_query": "property valuation computer vision", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/cs/0505043v2", "title": "Estimacao Temporal da Deformacao entre Objectos utilizando uma Metodologia Fisica", "abstract": "In this paper, it is presented a methodology to estimate the deformation involved between two objects attending to its physical properties. This methodology can be used, for example, in Computational Vision or Computer Graphics applications, and consists in physically modeling the objects, by means of the Finite Elements Method, establishing correspondences between some of its data points, by using Modal Matching, and finally, determining the displacement field, that is the intermediate shapes, through the resolution of the Lagrange Dynamic Equilibrium Equation. As in many of the possible applications of the methodology to present, it is necessary to quantify the existing deformation, as well as to estimate only the non rigid component of the involved global deformation. The solutions adopted to satisfy such intentions will be also presented.", "published": "2005-05-16T02:20:19Z", "authors": ["Joao Manuel R. S. Tavares", "Raquel R. Pinho"], "categories": ["cs.GR", "cs.CG"], "doi": "", "_query": "property valuation computer vision", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2101.05107v2", "title": "Relatively Lazy: Indoor-Outdoor Navigation Using Vision and GNSS", "abstract": "Visual Teach and Repeat has shown relative navigation is a robust and efficient solution for autonomous vision-based path following in difficult environments. Adding additional absolute sensors such as Global Navigation Satellite Systems (GNSS) has the potential to expand the domain of Visual Teach and Repeat to environments where the ability to visually localize is not guaranteed. Our method of lazy mapping and delaying estimation until a path-tracking error is needed avoids the need to estimate absolute states. As a result, map optimization is not required and paths can be driven immediately after being taught. We validate our approach on a real robot through an experiment in a joint indoor-outdoor environment comprising 3.5km of autonomous route repeating across a variety of lighting conditions. We achieve smooth error signals throughout the runs despite large sections of dropout for each sensor.", "published": "2021-01-13T14:43:45Z", "authors": ["Benjamin Congram", "Timothy D. Barfoot"], "categories": ["cs.RO", "cs.CV"], "doi": "10.1109/CRV52889.2021.00015", "_query": "property valuation computer vision", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/1211.1564v1", "title": "Funded Bilateral Valuation Adjustment", "abstract": "We show how the cost of funding the collateral in a particular set up can be equal to the Bilateral Valuation Adjustment with the \"funded\" probability of default, leading to the definition of a Funded Bilateral Valuation Adjustment (FBVA). That set up can also be viewed by an investor as an effective way to restructure the counterparty risk arising from an uncollateralized transaction with a counterparty, mitigating or even avoiding entirely the additional capital charge introduced by the new Basel III framework.", "published": "2012-11-07T14:52:14Z", "authors": ["Lorenzo Giada", "Claudio Nordio"], "categories": ["q-fin.PR", "q-fin.RM"], "doi": "", "_query": "property valuation computer vision", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2201.02885v2", "title": "Agricultural Plant Cataloging and Establishment of a Data Framework from UAV-based Crop Images by Computer Vision", "abstract": "UAV-based image retrieval in modern agriculture enables gathering large amounts of spatially referenced crop image data. In large-scale experiments, however, UAV images suffer from containing a multitudinous amount of crops in a complex canopy architecture. Especially for the observation of temporal effects, this complicates the recognition of individual plants over several images and the extraction of relevant information tremendously. In this work, we present a hands-on workflow for the automatized temporal and spatial identification and individualization of crop images from UAVs abbreviated as \"cataloging\" based on comprehensible computer vision methods. We evaluate the workflow on two real-world datasets. One dataset is recorded for observation of Cercospora leaf spot - a fungal disease - in sugar beet over an entire growing cycle. The other one deals with harvest prediction of cauliflower plants. The plant catalog is utilized for the extraction of single plant images seen over multiple time points. This gathers large-scale spatio-temporal image dataset that in turn can be applied to train further machine learning models including various data layers. The presented approach improves analysis and interpretation of UAV data in agriculture significantly. By validation with some reference data, our method shows an accuracy that is similar to more complex deep learning-based recognition techniques. Our workflow is able to automatize plant cataloging and training image extraction, especially for large datasets.", "published": "2022-01-08T21:14:07Z", "authors": ["Maurice Günder", "Facundo R. Ispizua Yamati", "Jana Kierdorf", "Ribana Roscher", "Anne-Katrin Mahlein", "Christian Bauckhage"], "categories": ["cs.CV", "cs.CE", "cs.LG"], "doi": "10.1093/gigascience/giac054", "_query": "property valuation computer vision", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2607.06560v1", "title": "Vision as Unified Multimodal Generation", "abstract": "We formulate computer vision as unified multimodal generation, where heterogeneous visual tasks are expressed in the native text and image generation spaces of a unified multimodal model, without task-specific architectures. Under this formulation, SenseNova-Vision uses natural-language instructions and optional visual prompts to specify tasks, target regions or views, and decoding conventions, and generates responses as text for symbolic outputs, images for dense spatial predictions, or mixed text-and-image outputs for compositional tasks. To support large-scale training, we convert diverse computer vision annotations into instruction-response examples compatible with these generation spaces, resulting in the SenseNova-Vision Corpus, a computer-vision instruction-response corpus spanning text, image, and mixed targets. Starting from an off-the-shelf pretrained unified multimodal model, SenseNova-Vision is trained primarily on this corpus, with auxiliary multimodal data used as a capability-preserving mixture, and requires no task-specific prediction heads or architectural modifications. The resulting model covers a broad range of vision tasks, including detection, OCR, keypoint estimation, segmentation, depth estimation, surface normal prediction, point maps, and camera pose estimation, while supporting language-defined variants that combine category, color, region, and other visual cues. Experiments show that a single unified model can match leading task-specialized systems across structured visual understanding, dense geometric prediction, segmentation, and multi-view visual geometry. These results suggest unified multimodal generation as a scalable route for integrating computer vision capabilities into general-purpose foundation models. The model and corpus are publicly available.", "published": "2026-07-07T17:58:33Z", "authors": ["Xiaoyang Han", "Jianhua Li", "Kewang Deng", "Zukai Chen", "Xuanke Shi", "Sihan Wang", "Boxuan Li", "Linyan Wang", "Siyi Xie", "Xin You", "Jinsheng Quan", "Zhongang Cai", "Haiwen Diao", "Ziwei Liu", "Lei Yang", "Dahua Lin", "Quan Wang"], "categories": ["cs.CV"], "doi": "", "_query": "property valuation computer vision", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2106.05974v1", "title": "Scaling Vision with Sparse Mixture of Experts", "abstract": "Sparsely-gated Mixture of Experts networks (MoEs) have demonstrated excellent scalability in Natural Language Processing. In Computer Vision, however, almost all performant networks are \"dense\", that is, every input is processed by every parameter. We present a Vision MoE (V-MoE), a sparse version of the Vision Transformer, that is scalable and competitive with the largest dense networks. When applied to image recognition, V-MoE matches the performance of state-of-the-art networks, while requiring as little as half of the compute at inference time. Further, we propose an extension to the routing algorithm that can prioritize subsets of each input across the entire batch, leading to adaptive per-image compute. This allows V-MoE to trade-off performance and compute smoothly at test-time. Finally, we demonstrate the potential of V-MoE to scale vision models, and train a 15B parameter model that attains 90.35% on ImageNet.", "published": "2021-06-10T17:10:56Z", "authors": ["Carlos Riquelme", "Joan Puigcerver", "Basil Mustafa", "Maxim Neumann", "Rodolphe Jenatton", "André Susano Pinto", "Daniel Keysers", "Neil Houlsby"], "categories": ["cs.CV", "cs.LG", "stat.ML"], "doi": "", "_query": "property valuation computer vision", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2104.08500v4", "title": "Vision Transformer Pruning", "abstract": "Vision transformer has achieved competitive performance on a variety of computer vision applications. However, their storage, run-time memory, and computational demands are hindering the deployment to mobile devices. Here we present a vision transformer pruning approach, which identifies the impacts of dimensions in each layer of transformer and then executes pruning accordingly. By encouraging dimension-wise sparsity in the transformer, important dimensions automatically emerge. A great number of dimensions with small importance scores can be discarded to achieve a high pruning ratio without significantly compromising accuracy. The pipeline for vision transformer pruning is as follows: 1) training with sparsity regularization; 2) pruning dimensions of linear projections; 3) fine-tuning. The reduced parameters and FLOPs ratios of the proposed algorithm are well evaluated and analyzed on ImageNet dataset to demonstrate the effectiveness of our proposed method.", "published": "2021-04-17T09:49:24Z", "authors": ["Mingjian Zhu", "Yehui Tang", "Kai Han"], "categories": ["cs.CV"], "doi": "", "_query": "property valuation computer vision", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2106.02938v4", "title": "Energy-Based Learning for Cooperative Games, with Applications to Valuation Problems in Machine Learning", "abstract": "Valuation problems, such as feature interpretation, data valuation and model valuation for ensembles, become increasingly more important in many machine learning applications. Such problems are commonly solved by well-known game-theoretic criteria, such as Shapley value or Banzhaf value. In this work, we present a novel energy-based treatment for cooperative games, with a theoretical justification by the maximum entropy framework. Surprisingly, by conducting variational inference of the energy-based model, we recover various game-theoretic valuation criteria through conducting one-step fixed point iteration for maximizing the mean-field ELBO objective. This observation also verifies the rationality of existing criteria, as they are all attempting to decouple the correlations among the players through the mean-field approach. By running fixed point iteration for multiple steps, we achieve a trajectory of the valuations, among which we define the valuation with the best conceivable decoupling error as the Variational Index. We prove that under uniform initializations, these variational valuations all satisfy a set of game-theoretic axioms. We experimentally demonstrate that the proposed Variational Index enjoys lower decoupling error and better valuation performance on certain synthetic and real-world valuation problems.", "published": "2021-06-05T17:39:04Z", "authors": ["Yatao Bian", "Yu Rong", "Tingyang Xu", "Jiaxiang Wu", "Andreas Krause", "Junzhou Huang"], "categories": ["cs.LG"], "doi": "", "_query": "property valuation computer vision", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2511.20439v2", "title": "Object-Centric Vision Token Pruning for Vision Language Models", "abstract": "In Vision Language Models (VLMs), vision tokens are quantity-heavy yet information-dispersed compared with language tokens, thus consume too much unnecessary computation. Pruning redundant vision tokens for high VLM inference efficiency has been continuously studied but all existing methods resort to indirect and non-guaranteed ways. We propose OC-VTP, a direct and guaranteed approach to select the most representative vision tokens for high-efficiency yet accuracy-preserving VLM inference. Our OC-VTP requires merely light-weight pre-training of a small object-centric vision token pruner, which can then be inserted into existing VLMs, without fine-tuning of any models on any datasets. It is gauranteed that the most representative vision tokens are kept by minimizing the error in reconstructing the original unpruned tokens from the selected ones. Across any vision pruning ratios, i.e., inference efficiency, our OC-VTP consistently helps mainstream VLMs to preserve the highest inference accuracy. Our pruning also demonstrates interesting interpretability. Our codes are available at https://github.com/GarryLarry010131/OC-VTP.", "published": "2025-11-25T16:12:32Z", "authors": ["Guangyuan Li", "Rongzhen Zhao", "Jinhong Deng", "Yanbo Wang", "Joni Pajarinen"], "categories": ["cs.CV", "cs.AI"], "doi": "", "_query": "property valuation computer vision", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2503.14867v1", "title": "DVHGNN: Multi-Scale Dilated Vision HGNN for Efficient Vision Recognition", "abstract": "Recently, Vision Graph Neural Network (ViG) has gained considerable attention in computer vision. Despite its groundbreaking innovation, Vision Graph Neural Network encounters key issues including the quadratic computational complexity caused by its K-Nearest Neighbor (KNN) graph construction and the limitation of pairwise relations of normal graphs. To address the aforementioned challenges, we propose a novel vision architecture, termed Dilated Vision HyperGraph Neural Network (DVHGNN), which is designed to leverage multi-scale hypergraph to efficiently capture high-order correlations among objects. Specifically, the proposed method tailors Clustering and Dilated HyperGraph Construction (DHGC) to adaptively capture multi-scale dependencies among the data samples. Furthermore, a dynamic hypergraph convolution mechanism is proposed to facilitate adaptive feature exchange and fusion at the hypergraph level. Extensive qualitative and quantitative evaluations of the benchmark image datasets demonstrate that the proposed DVHGNN significantly outperforms the state-of-the-art vision backbones. For instance, our DVHGNN-S achieves an impressive top-1 accuracy of 83.1% on ImageNet-1K, surpassing ViG-S by +1.0% and ViHGNN-S by +0.6%.", "published": "2025-03-19T03:45:23Z", "authors": ["Caoshuo Li", "Tanzhe Li", "Xiaobin Hu", "Donghao Luo", "Taisong Jin"], "categories": ["cs.CV"], "doi": "", "_query": "property valuation computer vision", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2008.02767v7", "title": "The Grothendieck--Serre conjecture over valuation rings", "abstract": "In this article, we establish the Grothendieck-Serre conjecture over valuation rings: for a reductive group scheme $G$ over a valuation ring $V$ with fraction field $K$, a $G$-torsor over $V$ is trivial if it is trivial over $K$. This result is predicted by the original Grothendieck-Serre conjecture and the resolution of singularities. The novelty of our proof lies in overcoming subtleties brought by general nondiscrete valuation rings. By using flasque resolutions and inducting with local cohomology, we prove a non-Noetherian counterpart of Colliot-Thélène-Sansuc's case of tori. Then, taking advantage of techniques in algebraization, we obtain the passage to the Henselian rank one case. Finally, we induct on Levi subgroups and use the integrality of rational points of anisotropic groups to reduce to the semisimple anisotropic case, in which we appeal to properties of parahoric subgroups in Bruhat-Tits theory to conclude. In the last section, by using properties of reflexive sheaves, we also prove a variant of Nisnevich's purity conjecture.", "published": "2020-08-06T17:11:50Z", "authors": ["Ning Guo"], "categories": ["math.AG"], "doi": "", "_query": "property valuation computer vision", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/1802.07559v1", "title": "$L_p$-Blaschke Valuations", "abstract": "In this article, a classification of continuous, linearly intertwining, symmetric $L_p$-Blaschke ($p>1$) valuations is established as an extension of Haberl's work on Blaschke valuations. More precisely, we show that for dimensions $n\\geq 3$, the only continuous, linearly intertwining, normalized symmetric $L_p$-Blaschke valuation is the normalized $L_p$-curvature image operator, while for dimension $n = 2 $, a rotated normalized $L_p$-curvature image operator is an only additional one. One of the advantages of our approach is that we deal with normalized symmetric $L_p$-Blaschke valuations, which makes it possible to handle the case $p=n$. The cases where $p \\not =n$ are also discussed by studying the relations between symmetric $L_p$-Blaschke valuations and normalized ones.", "published": "2018-02-21T13:23:36Z", "authors": ["Jin Li", "Shufeng Yuan", "Gangsong Leng"], "categories": ["math.MG"], "doi": "10.1090/S0002-9947-2015-06047-4", "_query": "property valuation computer vision", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/1803.07781v1", "title": "Exploiting deep residual networks for human action recognition from skeletal data", "abstract": "The computer vision community is currently focusing on solving action recognition problems in real videos, which contain thousands of samples with many challenges. In this process, Deep Convolutional Neural Networks (D-CNNs) have played a significant role in advancing the state-of-the-art in various vision-based action recognition systems. Recently, the introduction of residual connections in conjunction with a more traditional CNN model in a single architecture called Residual Network (ResNet) has shown impressive performance and great potential for image recognition tasks. In this paper, we investigate and apply deep ResNets for human action recognition using skeletal data provided by depth sensors. Firstly, the 3D coordinates of the human body joints carried in skeleton sequences are transformed into image-based representations and stored as RGB images. These color images are able to capture the spatial-temporal evolutions of 3D motions from skeleton sequences and can be efficiently learned by D-CNNs. We then propose a novel deep learning architecture based on ResNets to learn features from obtained color-based representations and classify them into action classes. The proposed method is evaluated on three challenging benchmark datasets including MSR Action 3D, KARD, and NTU-RGB+D datasets. Experimental results demonstrate that our method achieves state-of-the-art performance for all these benchmarks whilst requiring less computation resource. In particular, the proposed method surpasses previous approaches by a significant margin of 3.4% on MSR Action 3D dataset, 0.67% on KARD dataset, and 2.5% on NTU-RGB+D dataset.", "published": "2018-03-21T07:43:53Z", "authors": ["Huy-Hieu Pham", "Louahdi Khoudour", "Alain Crouzil", "Pablo Zegers", "Sergio A. Velastin"], "categories": ["cs.CV"], "doi": "10.1016/j.cviu.2018.03.003", "_query": "property valuation computer vision", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2210.14518v1", "title": "Which Factors Matter Most? Can Startup Valuation be Micro-Targeted?", "abstract": "While startup valuations are influenced by revenues, risks, age, and macroeconomic conditions, specific causality is traditionally a black box. Because valuations are not disclosed, roles played by other factors (industry, geography, and intellectual property) can often only be guessed at. VC valuation research indicates the importance of establishing a factor-hierarchy to better understand startup valuations and their dynamics, suggesting the wisdom of hiring data-scientists for this purpose. Bespoke understanding can be established via construction of hierarchical prediction models based on decision trees and random forests. These have the advantage of understanding which factors matter most. In combination with OLS, the also tell us the circumstances of when specific causalities apply. This study explores the deterministic role of categorical variables on the valuation of start-ups (i.e. the joint-combination geographic, urban, and sectoral denomination-variables), in order to be able to build a generalized valuation scorecard approach. Using a dataset of 1,091 venture-capital investments, containing 1,044 unique EU and EEA, this study examines microeconomic, sectoral, and local-level impacts on startup valuation. In principle, the study relies on Fixedeffects and Joint-fixed-effects regressions as well as the analysis and exploration of divergent micropopulations and fault-lines by means of non-parametric approaches combining econometric and machinelearning techniques.", "published": "2022-10-26T07:09:01Z", "authors": ["Max Berre"], "categories": ["q-fin.GN"], "doi": "", "_query": "property valuation computer vision", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2102.06448v4", "title": "The MSR-Video to Text Dataset with Clean Annotations", "abstract": "Video captioning automatically generates short descriptions of the video content, usually in form of a single sentence. Many methods have been proposed for solving this task. A large dataset called MSR Video to Text (MSR-VTT) is often used as the benchmark dataset for testing the performance of the methods. However, we found that the human annotations, i.e., the descriptions of video contents in the dataset are quite noisy, e.g., there are many duplicate captions and many captions contain grammatical problems. These problems may pose difficulties to video captioning models for learning underlying patterns. We cleaned the MSR-VTT annotations by removing these problems, then tested several typical video captioning models on the cleaned dataset. Experimental results showed that data cleaning boosted the performances of the models measured by popular quantitative metrics. We recruited subjects to evaluate the results of a model trained on the original and cleaned datasets. The human behavior experiment demonstrated that trained on the cleaned dataset, the model generated captions that were more coherent and more relevant to the contents of the video clips.", "published": "2021-02-12T11:14:56Z", "authors": ["Haoran Chen", "Jianmin Li", "Simone Frintrop", "Xiaolin Hu"], "categories": ["cs.CV", "cs.LG"], "doi": "10.1016/j.cviu.2022.103581", "_query": "property valuation computer vision", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2401.06884v3", "title": "Definable henselian valuations in positive residue characteristic", "abstract": "We study the question of $\\mathcal{L}_{\\mathrm{ring}}$-definability of non-trivial henselian valuation rings. Building on previous work of Jahnke and Koenigsmann, we provide a characterization of henselian fields that admit a non-trivial definable henselian valuation. In particular, we treat cases where the canonical henselian valuation has positive residue characteristic, using techniques from the model theory and algebra of tame fields.", "published": "2024-01-12T20:30:14Z", "authors": ["Margarete Ketelsen", "Simone Ramello", "Piotr Szewczyk"], "categories": ["math.LO", "math.AC"], "doi": "10.1017/jsl.2024.55", "_query": "property valuation computer vision", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2604.02182v1", "title": "ViT-Explainer: An Interactive Walkthrough of the Vision Transformer Pipeline", "abstract": "Transformer-based architectures have become the shared backbone of natural language processing and computer vision. However, understanding how these models operate remains challenging, particularly in vision settings, where images are processed as sequences of patch tokens. Existing interpretability tools often focus on isolated components or expert-oriented analysis, leaving a gap in guided, end-to-end understanding of the full inference pipeline. To bridge this gap, we present ViT-Explainer, a web-based interactive system that provides an integrated visualization of Vision Transformer inference, from patch tokenization to final classification. The system combines animated walkthroughs, patch-level attention overlays, and a vision-adapted Logit Lens within both guided and free exploration modes. A user study with six participants suggests that ViT-Explainer is easy to learn and use, helping users interpret and understand Vision Transformer behavior.", "published": "2026-04-02T15:44:23Z", "authors": ["Juan Manuel Hernandez", "Mariana Fernandez-Espinosa", "Denis Parra", "Diego Gomez-Zara"], "categories": ["cs.CV", "cs.HC"], "doi": "", "_query": "property valuation computer vision", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/math/0702315v1", "title": "Gersten's conjecture for commutative discrete valuation rings", "abstract": "The purpose of this article is to prove that Gersten's conjecture for a commutative discrete valuation ring is true. Combining with the result of \\cite{GL87}, we learn that Gersten's conjecture is true if the ring is a commutative regular local, smooth over a commutative discrete valuation ring.", "published": "2007-02-12T09:51:20Z", "authors": ["Satoshi Mochizuki"], "categories": ["math.KT", "math.AC", "math.AG"], "doi": "", "_query": "property valuation computer vision", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/1711.02808v1", "title": "Less-Expensive Valuation of Long Term Annuities Linked to Mortality, Cash and Equity", "abstract": "This paper proposes a paradigm shift in the valuation of long term annuities, away from classical no-arbitrage valuation towards valuation under the real world probability measure. Furthermore, we apply this valuation method to two examples of annuity products, one having annual payments linked to a mortality index and the savings account and the other having annual payments linked to a mortality index and an equity index with a guarantee that is linked to the same mortality index and the savings account. Out-of-sample hedge simulations demonstrate the effectiveness of real world valuation. In contrast to risk neutral valuation, which is a form of relative valuation, the long term average excess return of the equity market comes into play. Instead of the savings account, the numéraire portfolio is employed as the fundamental unit of value in the analysis. The numéraire portfolio is the strictly positive, tradable portfolio that when used as benchmark makes all benchmarked nonnegative portfolios supermartingales. The benchmarked real world value of a benchmarked contingent claim equals its real world conditional expectation. This yields the minimal possible value for its hedgeable part and minimizes the fluctuations for its benchmarked hedge error. Under classical assumptions, actuarial and risk neutral valuation emerge as special cases of the proposed real world valuation. In long term liability and asset valuation, the proposed real world valuation can lead to significantly lower values than suggested by classical approaches when an equivalent risk neutral probability measure does not exist.", "published": "2017-11-08T02:58:45Z", "authors": ["Kevin Fergusson", "Eckhard Platen"], "categories": ["q-fin.MF", "q-fin.PR"], "doi": "", "_query": "property valuation computer vision", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2208.08768v2", "title": "TSCom-Net: Coarse-to-Fine 3D Textured Shape Completion Network", "abstract": "Reconstructing 3D human body shapes from 3D partial textured scans remains a fundamental task for many computer vision and graphics applications -- e.g., body animation, and virtual dressing. We propose a new neural network architecture for 3D body shape and high-resolution texture completion -- BCom-Net -- that can reconstruct the full geometry from mid-level to high-level partial input scans. We decompose the overall reconstruction task into two stages - first, a joint implicit learning network (SCom-Net and TCom-Net) that takes a voxelized scan and its occupancy grid as input to reconstruct the full body shape and predict vertex textures. Second, a high-resolution texture completion network, that utilizes the predicted coarse vertex textures to inpaint the missing parts of the partial 'texture atlas'. A thorough experimental evaluation on 3DBodyTex.V2 dataset shows that our method achieves competitive results with respect to the state-of-the-art while generalizing to different types and levels of partial shapes. The proposed method has also ranked second in the track1 of SHApe Recovery from Partial textured 3D scans (SHARP [38,1]) 2022 challenge1.", "published": "2022-08-18T11:06:10Z", "authors": ["Ahmet Serdar Karadeniz", "Sk Aziz Ali", "Anis Kacem", "Elona Dupont", "Djamila Aouada"], "categories": ["cs.CV"], "doi": "", "_query": "property valuation computer vision", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2406.01130v2", "title": "SAVA: Scalable Learning-Agnostic Data Valuation", "abstract": "Selecting data for training machine learning models is crucial since large, web-scraped, real datasets contain noisy artifacts that affect the quality and relevance of individual data points. These noisy artifacts will impact model performance. We formulate this problem as a data valuation task, assigning a value to data points in the training set according to how similar or dissimilar they are to a clean and curated validation set. Recently, LAVA demonstrated the use of optimal transport (OT) between a large noisy training dataset and a clean validation set, to value training data efficiently, without the dependency on model performance. However, the LAVA algorithm requires the entire dataset as an input, this limits its application to larger datasets. Inspired by the scalability of stochastic (gradient) approaches which carry out computations on batches of data points instead of the entire dataset, we analogously propose SAVA, a scalable variant of LAVA with its computation on batches of data points. Intuitively, SAVA follows the same scheme as LAVA which leverages the hierarchically defined OT for data valuation. However, while LAVA processes the whole dataset, SAVA divides the dataset into batches of data points, and carries out the OT problem computation on those batches. Moreover, our theoretical derivations on the trade-off of using entropic regularization for OT problems include refinements of prior work. We perform extensive experiments, to demonstrate that SAVA can scale to large datasets with millions of data points and does not trade off data valuation performance.", "published": "2024-06-03T09:17:35Z", "authors": ["Samuel Kessler", "Tam Le", "Vu Nguyen"], "categories": ["cs.LG"], "doi": "", "_query": "property valuation computer vision", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2206.07162v2", "title": "Category-Agnostic 6D Pose Estimation with Conditional Neural Processes", "abstract": "We present a novel meta-learning approach for 6D pose estimation on unknown objects. In contrast to ``instance-level\" and ``category-level\" pose estimation methods, our algorithm learns object representation in a category-agnostic way, which endows it with strong generalization capabilities across object categories. Specifically, we employ a neural process-based meta-learning approach to train an encoder to capture texture and geometry of an object in a latent representation, based on very few RGB-D images and ground-truth keypoints. The latent representation is then used by a simultaneously meta-trained decoder to predict the 6D pose of the object in new images. Furthermore, we propose a novel geometry-aware decoder for the keypoint prediction using a Graph Neural Network (GNN), which explicitly takes geometric constraints specific to each object into consideration. To evaluate our algorithm, extensive experiments are conducted on the \\linemod dataset, and on our new fully-annotated synthetic datasets generated from Multiple Categories in Multiple Scenes (MCMS). Experimental results demonstrate that our model performs well on unseen objects with very different shapes and appearances. Remarkably, our model also shows robust performance on occluded scenes although trained fully on data without occlusion. To our knowledge, this is the first work exploring \\textbf{cross-category level} 6D pose estimation.", "published": "2022-06-14T20:46:09Z", "authors": ["Yumeng Li", "Ning Gao", "Hanna Ziesche", "Gerhard Neumann"], "categories": ["cs.CV", "cs.AI", "cs.LG", "cs.RO"], "doi": "", "_query": "property valuation computer vision", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/1908.05877v1", "title": "Zero-Shot Crowd Behavior Recognition", "abstract": "Understanding crowd behavior in video is challenging for computer vision. There have been increasing attempts on modeling crowded scenes by introducing ever larger property ontologies (attributes) and annotating ever larger training datasets. However, in contrast to still images, manually annotating video attributes needs to consider spatiotemporal evolution which is inherently much harder and more costly. Critically, the most interesting crowd behaviors captured in surveillance videos (e.g., street fighting, flash mobs) are either rare, thus have few examples for model training, or unseen previously. Existing crowd analysis techniques are not readily scalable to recognize novel (unseen) crowd behaviors. To address this problem, we investigate and develop methods for recognizing visual crowd behavioral attributes without any training samples, i.e., zero-shot learning crowd behavior recognition. To that end, we relax the common assumption that each individual crowd video instance is only associated with a single crowd attribute. Instead, our model learns to jointly recognize multiple crowd behavioral attributes in each video instance by exploring multiattribute cooccurrence as contextual knowledge for optimizing individual crowd attribute recognition. Joint multilabel attribute prediction in zero-shot learning is inherently nontrivial because cooccurrence statistics does not exist for unseen attributes. To solve this problem, we learn to predict cross-attribute cooccurrence from both online text corpus and multilabel annotation of videos with known attributes. Our experiments show that this approach to modeling multiattribute context not only improves zero-shot crowd behavior recognition on the WWW crowd video dataset, but also generalizes to novel behavior (violence) detection cross-domain in the Violence Flow video dataset.", "published": "2019-08-16T08:02:48Z", "authors": ["Xun Xu", "Shaogang Gong", "Timothy Hospedales"], "categories": ["cs.CV"], "doi": "10.1016/B978-0-12-809276-7.00018-7", "_query": "property valuation computer vision", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/1710.07173v1", "title": "Fields with automorphism and valuation", "abstract": "Abraham Robinson's method for finding model completions is refined and and generalized for model companions and is applied to the theory of fields equipped with both a valuation and an automorphism.", "published": "2017-10-19T15:09:30Z", "authors": ["Özlem Beyarslan", "Daniel Max Hoffmann", "Gönenç Onay", "David Pierce"], "categories": ["math.LO"], "doi": "10.1007/s00153-020-00728-5", "_query": "property valuation computer vision", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2206.15301v1", "title": "Definable valuations on ordered fields", "abstract": "We study the definability of convex valuations on ordered fields, with a particular focus on the distinguished subclass of henselian valuations. In the setting of ordered fields, one can consider definability both in the language of rings $\\mathcal{L}_{\\mathrm{r}}$ and in the richer language of ordered rings $\\mathcal{L}_{\\mathrm{or}}$. We analyse and compare definability in both languages and show the following contrary results: while there are convex valuations that are definable in the language $\\mathcal{L}_{\\mathrm{or}}$ but not in the language $\\mathcal{L}_{\\mathrm{r}}$, any $\\mathcal{L}_{\\mathrm{or}}$-definable henselian valuation is already $\\mathcal{L}_{\\mathrm{r}}$-definable. To prove the latter, we show that the value group and the ordered residue field of an ordered henselian valued field are stably embedded (as an ordered abelian group, respectively as an ordered field). Moreover, we show that in almost real closed fields any $\\mathcal{L}_{\\mathrm{or}}$-definable valuation is henselian.", "published": "2022-06-30T14:09:43Z", "authors": ["Philip Dittmann", "Franziska Jahnke", "Lothar Sebastian Krapp", "Salma Kuhlmann"], "categories": ["math.LO", "math.AC"], "doi": "10.2140/mt.2023.2.101", "_query": "property valuation computer vision", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2507.14811v7", "title": "SegQuant: A Semantics-Aware and Generalizable Quantization Framework for Diffusion Models", "abstract": "Diffusion models have demonstrated exceptional generative capabilities but are computationally intensive, posing significant challenges for deployment in resource-constrained or latency-sensitive environments. Quantization offers an effective means to reduce model size and computational cost, with post-training quantization (PTQ) being particularly appealing due to its compatibility with pre-trained models without requiring retraining or training data. However, existing PTQ methods for diffusion models often rely on architecture-specific heuristics that limit their generalizability and hinder integration with industrial deployment pipelines. To address these limitations, we propose SegQuant, a unified quantization framework that adaptively combines complementary techniques to enhance cross-model versatility. SegQuant consists of a segment-aware, graph-based quantization strategy (SegLinear) that captures structural semantics and spatial heterogeneity, along with a dual-scale quantization scheme (DualScale) that preserves polarity-asymmetric activations, which is crucial for maintaining visual fidelity in generated outputs. SegQuant is broadly applicable beyond Transformer-based diffusion models, achieving strong performance while ensuring seamless compatibility with mainstream deployment tools.", "published": "2025-07-20T04:00:53Z", "authors": ["Jiaji Zhang", "Ruichao Sun", "Hailiang Zhao", "Jiaju Wu", "Peng Chen", "Hao Li", "Yuying Liu", "Kingsum Chow", "Gang Xiong", "Shuiguang Deng"], "categories": ["cs.CV", "cs.AI"], "doi": "", "_query": "property valuation computer vision", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2304.05090v1", "title": "CrowdSim2: an Open Synthetic Benchmark for Object Detectors", "abstract": "Data scarcity has become one of the main obstacles to developing supervised models based on Artificial Intelligence in Computer Vision. Indeed, Deep Learning-based models systematically struggle when applied in new scenarios never seen during training and may not be adequately tested in non-ordinary yet crucial real-world situations. This paper presents and publicly releases CrowdSim2, a new synthetic collection of images suitable for people and vehicle detection gathered from a simulator based on the Unity graphical engine. It consists of thousands of images gathered from various synthetic scenarios resembling the real world, where we varied some factors of interest, such as the weather conditions and the number of objects in the scenes. The labels are automatically collected and consist of bounding boxes that precisely localize objects belonging to the two object classes, leaving out humans from the annotation pipeline. We exploited this new benchmark as a testing ground for some state-of-the-art detectors, showing that our simulated scenarios can be a valuable tool for measuring their performances in a controlled environment.", "published": "2023-04-11T09:35:57Z", "authors": ["Paweł Foszner", "Agnieszka Szczęsna", "Luca Ciampi", "Nicola Messina", "Adam Cygan", "Bartosz Bizoń", "Michał Cogiel", "Dominik Golba", "Elżbieta Macioszek", "Michał Staniszewski"], "categories": ["cs.CV"], "doi": "10.5220/0011692500003417", "_query": "property valuation computer vision", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2203.11933v4", "title": "A Prompt Array Keeps the Bias Away: Debiasing Vision-Language Models with Adversarial Learning", "abstract": "Vision-language models can encode societal biases and stereotypes, but there are challenges to measuring and mitigating these multimodal harms due to lacking measurement robustness and feature degradation. To address these challenges, we investigate bias measures and apply ranking metrics for image-text representations. We then investigate debiasing methods and show that prepending learned embeddings to text queries that are jointly trained with adversarial debiasing and a contrastive loss reduces various bias measures with minimal degradation to the image-text representation.", "published": "2022-03-22T17:59:04Z", "authors": ["Hugo Berg", "Siobhan Mackenzie Hall", "Yash Bhalgat", "Wonsuk Yang", "Hannah Rose Kirk", "Aleksandar Shtedritski", "Max Bain"], "categories": ["cs.LG", "cs.CL", "cs.CV", "cs.CY"], "doi": "", "_query": "property valuation computer vision", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2004.03590v1", "title": "Multimodal Image Synthesis with Conditional Implicit Maximum Likelihood Estimation", "abstract": "Many tasks in computer vision and graphics fall within the framework of conditional image synthesis. In recent years, generative adversarial nets (GANs) have delivered impressive advances in quality of synthesized images. However, it remains a challenge to generate both diverse and plausible images for the same input, due to the problem of mode collapse. In this paper, we develop a new generic multimodal conditional image synthesis method based on Implicit Maximum Likelihood Estimation (IMLE) and demonstrate improved multimodal image synthesis performance on two tasks, single image super-resolution and image synthesis from scene layouts. We make our implementation publicly available.", "published": "2020-04-07T03:06:55Z", "authors": ["Ke Li", "Shichong Peng", "Tianhao Zhang", "Jitendra Malik"], "categories": ["cs.CV", "cs.GR", "cs.LG", "cs.NE", "eess.IV"], "doi": "", "_query": "property valuation computer vision", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/1510.05275v1", "title": "Real-time Tracking Based on Neuromrophic Vision", "abstract": "Real-time tracking is an important problem in computer vision in which most methods are based on the conventional cameras. Neuromorphic vision is a concept defined by incorporating neuromorphic vision sensors such as silicon retinas in vision processing system. With the development of the silicon technology, asynchronous event-based silicon retinas that mimic neuro-biological architectures has been developed in recent years. In this work, we combine the vision tracking algorithm of computer vision with the information encoding mechanism of event-based sensors which is inspired from the neural rate coding mechanism. The real-time tracking of single object with the advantage of high speed of 100 time bins per second is successfully realized. Our method demonstrates that the computer vision methods could be used for the neuromorphic vision processing and we can realize fast real-time tracking using neuromorphic vision sensors compare to the conventional camera.", "published": "2015-10-18T16:27:36Z", "authors": ["Hongmin Li", "Pei Jing", "Guoqi Li"], "categories": ["cs.CV"], "doi": "", "_query": "property valuation computer vision", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2112.09290v2", "title": "PeopleSansPeople: A Synthetic Data Generator for Human-Centric Computer Vision", "abstract": "In recent years, person detection and human pose estimation have made great strides, helped by large-scale labeled datasets. However, these datasets had no guarantees or analysis of human activities, poses, or context diversity. Additionally, privacy, legal, safety, and ethical concerns may limit the ability to collect more human data. An emerging alternative to real-world data that alleviates some of these issues is synthetic data. However, creation of synthetic data generators is incredibly challenging and prevents researchers from exploring their usefulness. Therefore, we release a human-centric synthetic data generator PeopleSansPeople which contains simulation-ready 3D human assets, a parameterized lighting and camera system, and generates 2D and 3D bounding box, instance and semantic segmentation, and COCO pose labels. Using PeopleSansPeople, we performed benchmark synthetic data training using a Detectron2 Keypoint R-CNN variant [1]. We found that pre-training a network using synthetic data and fine-tuning on various sizes of real-world data resulted in a keypoint AP increase of $+38.03$ ($44.43 \\pm 0.17$ vs. $6.40$) for few-shot transfer (limited subsets of COCO-person train [2]), and an increase of $+1.47$ ($63.47 \\pm 0.19$ vs. $62.00$) for abundant real data regimes, outperforming models trained with the same real data alone. We also found that our models outperformed those pre-trained with ImageNet with a keypoint AP increase of $+22.53$ ($44.43 \\pm 0.17$ vs. $21.90$) for few-shot transfer and $+1.07$ ($63.47 \\pm 0.19$ vs. $62.40$) for abundant real data regimes. This freely-available data generator should enable a wide range of research into the emerging field of simulation to real transfer learning in the critical area of human-centric computer vision.", "published": "2021-12-17T02:33:31Z", "authors": ["Salehe Erfanian Ebadi", "You-Cyuan Jhang", "Alex Zook", "Saurav Dhakad", "Adam Crespi", "Pete Parisi", "Steven Borkman", "Jonathan Hogins", "Sujoy Ganguly"], "categories": ["cs.CV", "cs.AI", "cs.DB", "cs.GR", "cs.LG"], "doi": "", "_query": "property valuation computer vision", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/1909.09832v2", "title": "Upper Ramification Groups for Arbitrary Valuation Rings", "abstract": "T. Saito established a ramification theory for ring extensions locally of complete intersection. We show that for a Henselian valuation ring $A$ with field of fractions $K$ and for a finite Galois extension $L$ of $K$, the integral closure $B$ of $A$ in $L$ is a filtered union of subrings of $B$ which are of complete intersection over $A$. By this, we can obtain a ramification theory of Henselian valuation rings as the limit of the ramification theory of Saito. Our theory generalizes the ramification theory of complete discrete valuation rings of Abbes-Saito. We study \"defect extensions\" which are not treated in these previous works.", "published": "2019-09-21T14:16:26Z", "authors": ["Kazuya Kato", "Vaidehee Thatte"], "categories": ["math.NT", "math.AG"], "doi": "", "_query": "property valuation computer vision", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2108.04177v1", "title": "Novel scorpion detection system combining computer vision and fluorescence", "abstract": "In this work, a fully automatic and real-time system for the detection of scorpions was developed using computer vision and deep learning techniques. This system is based on the implementation of a double validation process using the shape features and the fluorescent characteristics of scorpions when exposed to ultraviolet (UV) light. The Haar Cascade Classifier (HCC) and YOLO (You Only Look Once) models have been used and compared as the first mechanism for the scorpion shape detection. The detection of the fluorescence emitted by the scorpions under UV light has been used as a second detection mechanism in order to increase the accuracy and precision of the system. The results obtained show that the system can accurately and reliably detect the presence of scorpions. In addition, values obtained of recall of 100% is essential with the purpose of providing a health security tool. Although the developed system can only be used at night or in dark environment, where the fluorescence emitted by the scorpions can be visualized, the nocturnal activity of scorpions justifies the incorporation of this second validation mechanism.", "published": "2021-08-09T17:00:52Z", "authors": ["Francisco Luis Giambelluca", "Jorge Osio", "Luis A. Giambelluca", "Marcelo A. Cappelletti"], "categories": ["cs.CV", "eess.IV"], "doi": "", "_query": "property valuation computer vision", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2111.09171v1", "title": "Automated Approach for Computer Vision-based Vehicle Movement Classification at Traffic Intersections", "abstract": "Movement specific vehicle classification and counting at traffic intersections is a crucial component for various traffic management activities. In this context, with recent advancements in computer-vision based techniques, cameras have emerged as a reliable data source for extracting vehicular trajectories from traffic scenes. However, classifying these trajectories by movement type is quite challenging as characteristics of motion trajectories obtained this way vary depending on camera calibrations. Although some existing methods have addressed such classification tasks with decent accuracies, the performance of these methods significantly relied on manual specification of several regions of interest. In this study, we proposed an automated classification method for movement specific classification (such as right-turn, left-turn and through movements) of vision-based vehicle trajectories. Our classification framework identifies different movement patterns observed in a traffic scene using an unsupervised hierarchical clustering technique Thereafter a similarity-based assignment strategy is adopted to assign incoming vehicle trajectories to identified movement groups. A new similarity measure was designed to overcome the inherent shortcomings of vision-based trajectories. Experimental results demonstrated the effectiveness of the proposed classification approach and its ability to adapt to different traffic scenarios without any manual intervention.", "published": "2021-11-17T15:02:43Z", "authors": ["Udita Jana", "Jyoti Prakash Das Karmakar", "Pranamesh Chakraborty", "Tingting Huang", "Dave Ness", "Duane Ritcher", "Anuj Sharma"], "categories": ["cs.CV"], "doi": "", "_query": "property valuation computer vision", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/1612.03182v1", "title": "Arch2030: A Vision of Computer Architecture Research over the Next 15 Years", "abstract": "Application trends, device technologies and the architecture of systems drive progress in information technologies. However, the former engines of such progress - Moore's Law and Dennard Scaling - are rapidly reaching the point of diminishing returns. The time has come for the computing community to boldly confront a new challenge: how to secure a foundational future for information technology's continued progress. The computer architecture community engaged in several visioning exercises over the years. Five years ago, we released a white paper, 21st Century Computer Architecture, which influenced funding programs in both academia and industry. More recently, the IEEE Rebooting Computing Initiative explored the future of computing systems in the architecture, device, and circuit domains. This report stems from an effort to continue this dialogue, reach out to the applications and devices/circuits communities, and understand their trends and vision. We aim to identify opportunities where architecture research can bridge the gap between the application and device domains.", "published": "2016-12-09T21:02:13Z", "authors": ["Luis Ceze", "Mark D. Hill", "Thomas F. Wenisch"], "categories": ["cs.AR", "cs.CY"], "doi": "", "_query": "property valuation computer vision", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2007.09178v1", "title": "AutoCount: Unsupervised Segmentation and Counting of Organs in Field Images", "abstract": "Counting plant organs such as heads or tassels from outdoor imagery is a popular benchmark computer vision task in plant phenotyping, which has been previously investigated in the literature using state-of-the-art supervised deep learning techniques. However, the annotation of organs in field images is time-consuming and prone to errors. In this paper, we propose a fully unsupervised technique for counting dense objects such as plant organs. We use a convolutional network-based unsupervised segmentation method followed by two post-hoc optimization steps. The proposed technique is shown to provide competitive counting performance on a range of organ counting tasks in sorghum (S. bicolor) and wheat (T. aestivum) with no dataset-dependent tuning or modifications.", "published": "2020-07-17T18:27:47Z", "authors": ["Jordan Ubbens", "Tewodros Ayalew", "Steve Shirtliffe", "Anique Josuttes", "Curtis Pozniak", "Ian Stavness"], "categories": ["cs.CV"], "doi": "", "_query": "property valuation computer vision", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2106.04560v2", "title": "Scaling Vision Transformers", "abstract": "Attention-based neural networks such as the Vision Transformer (ViT) have recently attained state-of-the-art results on many computer vision benchmarks. Scale is a primary ingredient in attaining excellent results, therefore, understanding a model's scaling properties is a key to designing future generations effectively. While the laws for scaling Transformer language models have been studied, it is unknown how Vision Transformers scale. To address this, we scale ViT models and data, both up and down, and characterize the relationships between error rate, data, and compute. Along the way, we refine the architecture and training of ViT, reducing memory consumption and increasing accuracy of the resulting models. As a result, we successfully train a ViT model with two billion parameters, which attains a new state-of-the-art on ImageNet of 90.45% top-1 accuracy. The model also performs well for few-shot transfer, for example, reaching 84.86% top-1 accuracy on ImageNet with only 10 examples per class.", "published": "2021-06-08T17:47:39Z", "authors": ["Xiaohua Zhai", "Alexander Kolesnikov", "Neil Houlsby", "Lucas Beyer"], "categories": ["cs.CV", "cs.AI", "cs.LG"], "doi": "", "_query": "property valuation computer vision", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2001.01259v1", "title": "A Robust Pose Transformational GAN for Pose Guided Person Image Synthesis", "abstract": "Generating photorealistic images of human subjects in any unseen pose have crucial applications in generating a complete appearance model of the subject. However, from a computer vision perspective, this task becomes significantly challenging due to the inability of modelling the data distribution conditioned on pose. Existing works use a complicated pose transformation model with various additional features such as foreground segmentation, human body parsing etc. to achieve robustness that leads to computational overhead. In this work, we propose a simple yet effective pose transformation GAN by utilizing the Residual Learning method without any additional feature learning to generate a given human image in any arbitrary pose. Using effective data augmentation techniques and cleverly tuning the model, we achieve robustness in terms of illumination, occlusion, distortion and scale. We present a detailed study, both qualitative and quantitative, to demonstrate the superiority of our model over the existing methods on two large datasets.", "published": "2020-01-05T15:32:35Z", "authors": ["Arnab Karmakar", "Deepak Mishra"], "categories": ["cs.CV", "cs.LG", "eess.IV"], "doi": "", "_query": "property valuation computer vision", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2511.07177v1", "title": "A Choice-free look at algebraic extensions of valuations", "abstract": "In this paper, we study extensions of valuations over algebraic field extensions without the use of the Axiom of Choice. We show a bijection between the extensions of a valuation and the maximal ideals of the relative integral closure of its valuation ring. In the case of a finite extension, we show that these maximal ideals exist. We conclude with an elementary proof of the fundamental inequality.", "published": "2025-11-10T15:06:38Z", "authors": ["Cédric Aïd"], "categories": ["math.AC"], "doi": "", "_query": "property valuation computer vision", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/math/0206228v1", "title": "A divisorial valuation with irrational volume", "abstract": "In this paper we present a divisorial valuation with irrational volume using an algebro-geometric construction.", "published": "2002-06-21T13:28:05Z", "authors": ["Alex Kuronya"], "categories": ["math.AG", "math.AC"], "doi": "", "_query": "property valuation computer vision", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2405.16234v2", "title": "Vision Language Models for Spreadsheet Understanding: Challenges and Opportunities", "abstract": "This paper explores capabilities of Vision Language Models on spreadsheet comprehension. We propose three self-supervised challenges with corresponding evaluation metrics to comprehensively evaluate VLMs on Optical Character Recognition (OCR), spatial perception, and visual format recognition. Additionally, we utilize the spreadsheet table detection task to assess the overall performance of VLMs by integrating these challenges. To probe VLMs more finely, we propose three spreadsheet-to-image settings: column width adjustment, style change, and address augmentation. We propose variants of prompts to address the above tasks in different settings. Notably, to leverage the strengths of VLMs in understanding text rather than two-dimensional positioning, we propose to decode cell values on the four boundaries of the table in spreadsheet boundary detection. Our findings reveal that VLMs demonstrate promising OCR capabilities but produce unsatisfactory results due to cell omission and misalignment, and they notably exhibit insufficient spatial and format recognition skills, motivating future work to enhance VLMs' spreadsheet data comprehension capabilities using our methods to generate extensive spreadsheet-image pairs in various settings.", "published": "2024-05-25T13:51:48Z", "authors": ["Shiyu Xia", "Junyu Xiong", "Haoyu Dong", "Jianbo Zhao", "Yuzhang Tian", "Mengyu Zhou", "Yeye He", "Shi Han", "Dongmei Zhang"], "categories": ["cs.CV"], "doi": "10.18653/v1/2024.alvr-1.10", "_query": "property valuation computer vision", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2302.11890v1", "title": "Characterizations of Sequential Valuation Rules", "abstract": "Approval-based committee (ABC) voting rules elect a fixed size subset of the candidates, a so-called committee, based on the voters' approval ballots over the candidates. While these rules have recently attracted significant attention, axiomatic characterizations are largely missing so far. We address this problem by characterizing ABC voting rules within the broad and intuitive class of sequential valuation rules. These rules compute the winning committees by sequentially adding candidates that increase the score of the chosen committee the most. In more detail, we first characterize almost the full class of sequential valuation rules based on mild standard conditions and a new axiom called consistent committee monotonicity. This axiom postulates that the winning committees of size k can be derived from those of size k-1 by only adding candidates and that these new candidates are chosen consistently. By requiring additional conditions, we derive from this result also a characterization of the prominent class of sequential Thiele rules. Finally, we refine our results to characterize three well-known ABC voting rules, namely sequential approval voting, sequential proportional approval voting, and sequential Chamberlin-Courant approval voting.", "published": "2023-02-23T09:53:21Z", "authors": ["Chris Dong", "Patrick Lederer"], "categories": ["cs.GT", "econ.TH"], "doi": "", "_query": "property valuation computer vision", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/1704.07177v1", "title": "Tensor valuations on lattice polytopes", "abstract": "The Ehrhart polynomial and the reciprocity theorems by Ehrhart \\& Macdonald are extended to tensor valuations on lattice polytopes. A complete classification is established of tensor valuations of rank up to eight that are equivariant with respect to the special linear group over the integers and translation covariant. Every such valuation is a linear combination of the Ehrhart tensors which is shown to no longer hold true for rank nine.", "published": "2017-04-24T12:34:05Z", "authors": ["Monika Ludwig", "Laura Silverstein"], "categories": ["math.MG", "math.CO"], "doi": "10.1016/j.aim.2017.08.015", "_query": "property valuation computer vision", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2202.06774v1", "title": "Random zonotopes and valuations", "abstract": "We define a random zonotope in Euclidean space, by adding finitely many random segments, which are independently and identically distributed. For this random polytope, we determine, under a mild assumption on the distribution, the expectations of the intrinsic volumes, more generally, the expectations of suitable valuations. We also prove a central limit theorem for a valuation evaluated at these random zonotopes.", "published": "2022-02-14T14:50:57Z", "authors": ["Rolf Schneider"], "categories": ["math.PR", "math.MG"], "doi": "", "_query": "property valuation computer vision", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/1306.2137v1", "title": "Minkowski valuations in a 2-dimensional complex vector space", "abstract": "The classification of continuous, translation invariant Minkowski valuations which are contravariant (or covariant) with respect to the complex special linear group is established in a 2-dimensional complex vector space. Every such valuation is given by the sum of a valuation of degree of homogeneity 1 and 3. In dimensions $m\\geq 3$ such a classification was previously established and only valuations of a degree of homogeneity 2m-1 appear.", "published": "2013-06-10T08:38:16Z", "authors": ["Judit Abardia"], "categories": ["math.DG"], "doi": "10.1093/imrn/rnt251", "_query": "property valuation computer vision", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/1409.0158v1", "title": "Computers Should Be Uniters Not Dividers: A Vision of Computer-Enhanced Happy Future", "abstract": "This manifesto provides a vision of how computers can be used to bring people together, to enhance people's use of their natural creativity, and thus, make them happier.", "published": "2014-08-30T19:55:55Z", "authors": ["Alexander Titovets", "Philip Mills", "Vladik Kreinovich"], "categories": ["cs.CY"], "doi": "", "_query": "property valuation computer vision", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2511.06463v3", "title": "Error Estimate and Convergence Analysis for Data Valuation", "abstract": "Data valuation quantifies data importance, but existing methods cannot ensure validity in a single training process. The neural dynamic data valuation (NDDV) method [3] addresses this limitation. Based on NDDV, we are the first to explore error estimation and convergence analysis in data valuation. Under Lipschitz and smoothness assumptions, we derive quadratic error bounds for loss differences that scale inversely with time steps and quadratically with control variations, ensuring stability. We also prove that the expected squared gradient norm for the training loss vanishes asymptotically, and that the meta loss converges sublinearly over iterations. In particular, NDDV achieves sublinear convergence.", "published": "2025-11-09T17:15:40Z", "authors": ["Zhangyong Liang", "Huanhuan Gao", "Ji Zhang"], "categories": ["cs.LG"], "doi": "", "_query": "property valuation computer vision", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/1507.04669v1", "title": "Constructing examples of semigroups of valuations", "abstract": "We work with rational rank 1 valuations centered in regular local rings. Given an algebraic function field $K$ of transcendence degree 3 over $k$, a regular local ring $R$ with $QF(R)=K$ and a $k$-valuation $ν$ of $K$, we provide an algorithm for constructing a generating sequences for $ν$ in $R$. We then develop a method for determining a valuation $ν$ on $k(x,y,z)$ through the sequence of defining values. Using the above results we construct examples of valuations centered in $k[x,y,z]_{(x,y,z)}$ and investigate their semigroups of values.", "published": "2015-07-16T17:44:40Z", "authors": ["Olga Kashcheyeva"], "categories": ["math.AC"], "doi": "", "_query": "property valuation computer vision", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2305.09072v1", "title": "Skin Deep: Investigating Subjectivity in Skin Tone Annotations for Computer Vision Benchmark Datasets", "abstract": "To investigate the well-observed racial disparities in computer vision systems that analyze images of humans, researchers have turned to skin tone as more objective annotation than race metadata for fairness performance evaluations. However, the current state of skin tone annotation procedures is highly varied. For instance, researchers use a range of untested scales and skin tone categories, have unclear annotation procedures, and provide inadequate analyses of uncertainty. In addition, little attention is paid to the positionality of the humans involved in the annotation process--both designers and annotators alike--and the historical and sociological context of skin tone in the United States. Our work is the first to investigate the skin tone annotation process as a sociotechnical project. We surveyed recent skin tone annotation procedures and conducted annotation experiments to examine how subjective understandings of skin tone are embedded in skin tone annotation procedures. Our systematic literature review revealed the uninterrogated association between skin tone and race and the limited effort to analyze annotator uncertainty in current procedures for skin tone annotation in computer vision evaluation. Our experiments demonstrated that design decisions in the annotation procedure such as the order in which the skin tone scale is presented or additional context in the image (i.e., presence of a face) significantly affected the resulting inter-annotator agreement and individual uncertainty of skin tone annotations. We call for greater reflexivity in the design, analysis, and documentation of procedures for evaluation using skin tone.", "published": "2023-05-15T23:55:56Z", "authors": ["Teanna Barrett", "Quan Ze Chen", "Amy X. Zhang"], "categories": ["cs.CV"], "doi": "10.1145/3593013.3594114", "_query": "property valuation computer vision", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2104.06935v1", "title": "Stereo Radiance Fields (SRF): Learning View Synthesis for Sparse Views of Novel Scenes", "abstract": "Recent neural view synthesis methods have achieved impressive quality and realism, surpassing classical pipelines which rely on multi-view reconstruction. State-of-the-Art methods, such as NeRF, are designed to learn a single scene with a neural network and require dense multi-view inputs. Testing on a new scene requires re-training from scratch, which takes 2-3 days. In this work, we introduce Stereo Radiance Fields (SRF), a neural view synthesis approach that is trained end-to-end, generalizes to new scenes, and requires only sparse views at test time. The core idea is a neural architecture inspired by classical multi-view stereo methods, which estimates surface points by finding similar image regions in stereo images. In SRF, we predict color and density for each 3D point given an encoding of its stereo correspondence in the input images. The encoding is implicitly learned by an ensemble of pair-wise similarities -- emulating classical stereo. Experiments show that SRF learns structure instead of overfitting on a scene. We train on multiple scenes of the DTU dataset and generalize to new ones without re-training, requiring only 10 sparse and spread-out views as input. We show that 10-15 minutes of fine-tuning further improve the results, achieving significantly sharper, more detailed results than scene-specific models. The code, model, and videos are available at https://virtualhumans.mpi-inf.mpg.de/srf/.", "published": "2021-04-14T15:38:57Z", "authors": ["Julian Chibane", "Aayush Bansal", "Verica Lazova", "Gerard Pons-Moll"], "categories": ["cs.CV", "cs.LG"], "doi": "", "_query": "property valuation computer vision", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/1002.2189v1", "title": "Performance Evaluation of TCP over Mobile Ad hoc Networks", "abstract": "With the proliferation of mobile computing devices, the demand for continuous network connectivity regardless of physical location has spurred interest in the use of mobile ad hoc networks. Since Transmission Control Protocol (TCP) is the standard network protocol for communication in the internet, any wireless network with Internet service need to be compatible with TCP. TCP is tuned to perform well in traditional wired networks, where packet losses occur mostly because of congestion. However, TCP connections in Ad-hoc mobile networks are plagued by problems such as high bit error rates, frequent route changes, multipath routing and temporary network partitions. The throughput of TCP over such connection is not satisfactory, because TCP misinterprets the packet loss or delay as congestion and invokes congestion control and avoidance algorithm. In this research, the performance of TCP in Adhoc mobile network with high Bit Error rate (BER) and mobility is studied and investigated. Simulation model is implemented and experiments are performed using the Network Simulatior 2 (NS2).", "published": "2010-02-10T19:42:53Z", "authors": ["Foez Ahmed", "Sateesh Kumar Pradhan", "Nayeema Islam", "Sumon Kumar Debnath"], "categories": ["cs.NI"], "doi": "", "_query": "property valuation computer vision", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/1908.09747v2", "title": "Hard-Mining Loss based Convolutional Neural Network for Face Recognition", "abstract": "Face Recognition is one of the prominent problems in the computer vision domain. Witnessing advances in deep learning, significant work has been observed in face recognition, which touched upon various parts of the recognition framework like Convolutional Neural Network (CNN), Layers, Loss functions, etc. Various loss functions such as Cross-Entropy, Angular-Softmax and ArcFace have been introduced to learn the weights of network for face recognition. However, these loss functions do not give high priority to the hard samples as compared to the easy samples. Moreover, their learning process is biased due to a number of easy examples compared to hard examples. In this paper, we address this issue by considering hard examples with more priority. In order to do so, We propose a Hard-Mining loss by increasing the loss for harder examples and decreasing the loss for easy examples. The proposed concept is generic and can be used with any existing loss function. We test the Hard-Mining loss with different losses such as Cross-Entropy, Angular-Softmax and ArcFace. The proposed Hard-Mining loss is tested over widely used Labeled Faces in the Wild (LFW) and YouTube Faces (YTF) datasets. The training is performed over CASIA-WebFace and MS-Celeb-1M datasets. We use the residual network (i.e., ResNet18) for the experimental analysis. The experimental results suggest that the performance of existing loss functions is boosted when used in the framework of the proposed Hard-Mining loss.", "published": "2019-08-09T06:55:45Z", "authors": ["Yash Srivastava", "Vaishnav Murali", "Shiv Ram Dubey"], "categories": ["cs.CV", "cs.LG"], "doi": "", "_query": "property valuation computer vision", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2306.08842v2", "title": "ViP: A Differentially Private Foundation Model for Computer Vision", "abstract": "Artificial intelligence (AI) has seen a tremendous surge in capabilities thanks to the use of foundation models trained on internet-scale data. On the flip side, the uncurated nature of internet-scale data also poses significant privacy and legal risks, as they often contain personal information or copyrighted material that should not be trained on without permission. In this work, we propose as a mitigation measure a recipe to train foundation vision models with differential privacy (DP) guarantee. We identify masked autoencoders as a suitable learning algorithm that aligns well with DP-SGD, and train ViP -- a Vision transformer with differential Privacy -- under a strict privacy budget of $ε=8$ on the LAION400M dataset. We evaluate the quality of representation learned by ViP using standard downstream vision tasks; in particular, ViP achieves a (non-private) linear probing accuracy of $55.7\\%$ on ImageNet, comparable to that of end-to-end trained AlexNet (trained and evaluated on ImageNet). Our result suggests that scaling to internet-scale data can be practical for private learning. Code is available at \\url{https://github.com/facebookresearch/ViP-MAE}.", "published": "2023-06-15T04:06:24Z", "authors": ["Yaodong Yu", "Maziar Sanjabi", "Yi Ma", "Kamalika Chaudhuri", "Chuan Guo"], "categories": ["cs.CV", "cs.CR", "cs.LG"], "doi": "", "_query": "property valuation computer vision", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/1704.04882v3", "title": "Monoidal computer III: A coalgebraic view of computability and complexity", "abstract": "Monoidal computer is a categorical model of intensional computation, where many different programs correspond to the same input-output behavior. The upshot of yet another model of computation is that a categorical formalism should provide a much needed high level language for theory of computation, flexible enough to allow abstracting away the low level implementation details when they are irrelevant, or taking them into account when they are genuinely needed. A salient feature of the approach through monoidal categories is the formal graphical language of string diagrams, which supports visual reasoning about programs and computations. In the present paper, we provide a coalgebraic characterization of monoidal computer. It turns out that the availability of interpreters and specializers, that make a monoidal category into a monoidal computer, is equivalent with the existence of a *universal state space*, that carries a weakly final state machine for any pair of input and output types. Being able to program state machines in monoidal computers allows us to represent Turing machines, to capture their execution, count their steps, as well as, e.g., the memory cells that they use. The coalgebraic view of monoidal computer thus provides a convenient diagrammatic language for studying computability and complexity.", "published": "2017-04-17T06:27:29Z", "authors": ["Dusko Pavlovic", "Muzamil Yahia"], "categories": ["cs.LO", "cs.CC", "math.CT", "math.LO"], "doi": "10.1007/978-3-030-00389-0_10", "_query": "property valuation computer vision", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/1601.04533v1", "title": "Gauss-Bonnet for multi-linear valuations", "abstract": "We prove Gauss-Bonnet and Poincare-Hopf formulas for multi-linear valuations on finite simple graphs G=(V,E) and answer affirmatively a conjecture of Gruenbaum from 1970 by constructing higher order Dehn-Sommerville valuations which vanish for all d-graphs without boundary. An example of a quadratic valuation is the Wu characteristic w(G) which sums (-1)^(dim(x)+dim(y)) over all intersecting pairs of complete subgraphs x,y of a G. More generally, an intersection number w(A,B) sums (-1)^(dim(x)+dim(y)) over pairs x,y, where x is in A and y is in B and x,y intersect. w(G) is a quadratic Euler characteristic X(G), where X sums (-1)^dim(x) over all complete subgraphs x of G. We prove that w is multiplicative, like Euler characteristic: w(G x H) = w(G) w(H) for any two graphs and that w is invariant under Barycentric refinements. We construct a curvature K satisfying Gauss-Bonnet w(G) = sum K(a). We also prove w(G) = X(G)-X(dG) for Euler characteristic X which holds for any d-graph G with boundary dG. We also show higher order Poincare-Hopf formulas: there is for every multi-linear valuation X and function f an index i(a) such that sum i(a)=X(G). For d-graphs G and X=w it agrees with the Euler curvature. For the vanishing multi-valuations which were conjectured to exist, like for the quadratic valuation X(G) = (V X) Y with X=(1,-1,1,-1,1),Y=(0,-2,3,-4,5) on 4-graphs, discrete 4 manifolds, where V_{ij}(G) is the f-matrix counting the number of i and j-simplices in G intersecting, the curvature is constant zero. For all graphs and multi-linear Dehn-Sommerville relations, the Dehn-Sommerville curvature K(v) at a vertex is a Dehn-Sommerville valuation on the unit sphere S(v). We show X V(G) Y = v(G) Y for any linear valuation Y of a d-graph G with f-vector v(G). This provides examples for the Gruenbaum conjecture.", "published": "2016-01-18T14:36:46Z", "authors": ["Oliver Knill"], "categories": ["cs.DM", "math.CO", "math.GN"], "doi": "", "_query": "property valuation computer vision", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2308.13957v2", "title": "Differentiable Weight Masks for Domain Transfer", "abstract": "One of the major drawbacks of deep learning models for computer vision has been their inability to retain multiple sources of information in a modular fashion. For instance, given a network that has been trained on a source task, we would like to re-train this network on a similar, yet different, target task while maintaining its performance on the source task. Simultaneously, researchers have extensively studied modularization of network weights to localize and identify the set of weights culpable for eliciting the observed performance on a given task. One set of works studies the modularization induced in the weights of a neural network by learning and analysing weight masks. In this work, we combine these fields to study three such weight masking methods and analyse their ability to mitigate \"forgetting'' on the source task while also allowing for efficient finetuning on the target task. We find that different masking techniques have trade-offs in retaining knowledge in the source task without adversely affecting target task performance.", "published": "2023-08-26T20:45:52Z", "authors": ["Samar Khanna", "Skanda Vaidyanath", "Akash Velu"], "categories": ["cs.CV", "cs.AI", "cs.LG"], "doi": "", "_query": "property valuation computer vision", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2609.19719v1", "title": "SeetaPsych v1.0: An Open-source Computer Vision Toolkit for Behavior-based Psychological Measurement", "abstract": "Automated visual analysis opens new avenues for behavior--based psychological measurement. Nevertheless, existing technological modules are typically scattered across task specific systems with heterogeneous interfaces and disparate deployment requirements. In this work, we present SeetaPsych v1.0, an open source, unified and extensible computer vision toolkit designed to extract psychologically relevant signals from facial images and/or face based videos. The current release encompasses four major core modules aiming at behavior--based physiological perception: unified face based emotion analysis (simultaneous facial expression recognition, facial action unit detection, and valence--arousal estimation), camera based heart rate estimation, screen point--of--gaze estimation, and scene gaze following. A suite of auxiliary preprocessing modules for human centric visual analysis is also included, comprising face detection, facial landmark detection, and head detection. These functionalities are encapsulated within a modular Pipeline/Runner architecture that automatically resolves attribute dependencies, constructs computation graphs, and support intermediate result sharing among modules. SeetaPsych provides standardized Python APIs to facilitate reproducible, large scale analyses, alongside an interactive WebUI for rapid, code--free method evaluation. Overall, SeetaPsych offers an integrated and accessible visual measurement platform for research in psychology, behavioral science, human computer interaction, and related fields.", "published": "2026-09-17T05:26:10Z", "authors": ["Jiabei Zeng", "Chiqin Li", "Kaizhou Li", "Fei Chang", "Yong Li", "Yuanhao Zhao", "Dan Han", "Wenqiang Yang", "Xilin Chen", "Shiguang Shan"], "categories": ["cs.CV"], "doi": "", "_query": "property valuation computer vision", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2012.10332v4", "title": "2-adic Valuations of Quadratic Sequences", "abstract": "Properties of 2-adic valuation sequences for general quadratic polynomials with integer coefficients are determined directly from the coefficients. These properties include boundedness or unboundedness, periodicity, and valuations at terminating nodes. We completely describe the periodic sequences in the bounded case. Throughout, we frame results in terms of trees and sequences.", "published": "2020-12-18T16:22:05Z", "authors": ["Will Boultinghouse", "Jane Long", "Olena Kozhushkina", "Justin Trulen"], "categories": ["math.NT"], "doi": "", "_query": "property valuation computer vision", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/cs/0609003v1", "title": "In Quest of Image Semantics: Are We Looking for It Under the Right Lamppost?", "abstract": "In the last years we witness a dramatic growth of research focused on semantic image understanding. Indeed, without understanding image content successful accomplishment of any image-processing task is simply incredible. Up to the recent times, the ultimate need for such understanding has been met by the knowledge that a domain expert or a vision system supervisor have contributed to every image-processing application. The advent of the Internet has drastically changed this situation. Internet sources of visual information are diffused and dispersed over the whole Web, so the duty of information content discovery and evaluation must be relegated now to an image understanding agent (a machine or a computer program) capable to perform image content assessment at a remote image location. Development of Content Based Image Retrieval (CBIR) techniques was a right move in a right direction, launched about ten years ago. Unfortunately, very little progress has been made since then. The reason for this can be seen in a rank of long lasting misconceptions that CBIR designers are continuing to adhere to. I hope, my arguments will help them to change their minds.", "published": "2006-09-02T11:50:29Z", "authors": ["Emanuel Diamant"], "categories": ["cs.CV", "cs.IR"], "doi": "", "_query": "property valuation computer vision", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2209.14262v3", "title": "A Survey on Physical Adversarial Attack in Computer Vision", "abstract": "Over the past decade, deep learning has revolutionized conventional tasks that rely on hand-craft feature extraction with its strong feature learning capability, leading to substantial enhancements in traditional tasks. However, deep neural networks (DNNs) have been demonstrated to be vulnerable to adversarial examples crafted by malicious tiny noise, which is imperceptible to human observers but can make DNNs output the wrong result. Existing adversarial attacks can be categorized into digital and physical adversarial attacks. The former is designed to pursue strong attack performance in lab environments while hardly remaining effective when applied to the physical world. In contrast, the latter focus on developing physical deployable attacks, thus exhibiting more robustness in complex physical environmental conditions. Recently, with the increasing deployment of the DNN-based system in the real world, strengthening the robustness of these systems is an emergency, while exploring physical adversarial attacks exhaustively is the precondition. To this end, this paper reviews the evolution of physical adversarial attacks against DNN-based computer vision tasks, expecting to provide beneficial information for developing stronger physical adversarial attacks. Specifically, we first proposed a taxonomy to categorize the current physical adversarial attacks and grouped them. Then, we discuss the existing physical attacks and focus on the technique for improving the robustness of physical attacks under complex physical environmental conditions. Finally, we discuss the issues of the current physical adversarial attacks to be solved and give promising directions.", "published": "2022-09-28T17:23:52Z", "authors": ["Donghua Wang", "Wen Yao", "Tingsong Jiang", "Guijian Tang", "Xiaoqian Chen"], "categories": ["cs.CV"], "doi": "", "_query": "property valuation computer vision", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/1601.00039v3", "title": "The weak ideal property and topological dimension zero", "abstract": "Following up on previous work, we prove a number of results for C*-algebras with the weak ideal property or topological dimension zero, and some results for C*-algebras with related properties. Some of the more important results include: The weak ideal property implies topological dimension zero. For a separable C*-algebra~A, topological dimension zero is equivalent to RR (O_2 \\otimes A) = 0, to D \\otimes A having the ideal property for some (or any) Kirchberg algebra~D, and to A being residually hereditarily in the class of all C*-algebras B such that O_{\\infty} \\otimes B contains a nonzero projection. Extending the known result for Z_2, the classes of C*-algebras with topological dimension zero, with the weak ideal property, and with residual (SP) are closed under crossed products by arbitrary actions of abelian 2-groups. If A and B are separable, one of them is exact, A has the ideal property, and B has the weak ideal property, then A \\otimes_{min} B has the weak ideal property. If X is a totally disconnected locally compact Hausdorff space and A is a C_0 (X)-algebra all of whose fibers have one of the weak ideal property, topological dimension zero, residual (SP), or the combination of pure infiniteness and the ideal property, then A also has the corresponding property (for topological dimension zero, provided A is separable). Topological dimension zero, the weak ideal property, and the ideal property are all equivalent for a substantial class of separable C*-algebras including all separable locally AH algebras. The weak ideal property does not imply the ideal property for separable Z-stable C*-algebras. We give other related results, as well as counterexamples to several other statements one might hope for.", "published": "2016-01-01T02:54:20Z", "authors": ["Cornel Pasnicu", "N. Christopher Phillips"], "categories": ["math.OA"], "doi": "10.4153/CJM-2017-012-4", "_query": "property valuation computer vision", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2408.11649v1", "title": "Video-to-Text Pedestrian Monitoring (VTPM): Leveraging Computer Vision and Large Language Models for Privacy-Preserve Pedestrian Activity Monitoring at Intersections", "abstract": "Computer vision has advanced research methodologies, enhancing system services across various fields. It is a core component in traffic monitoring systems for improving road safety; however, these monitoring systems don't preserve the privacy of pedestrians who appear in the videos, potentially revealing their identities. Addressing this issue, our paper introduces Video-to-Text Pedestrian Monitoring (VTPM), which monitors pedestrian movements at intersections and generates real-time textual reports, including traffic signal and weather information. VTPM uses computer vision models for pedestrian detection and tracking, achieving a latency of 0.05 seconds per video frame. Additionally, it detects crossing violations with 90.2% accuracy by incorporating traffic signal data. The proposed framework is equipped with Phi-3 mini-4k to generate real-time textual reports of pedestrian activity while stating safety concerns like crossing violations, conflicts, and the impact of weather on their behavior with latency of 0.33 seconds. To enhance comprehensive analysis of the generated textual reports, Phi-3 medium is fine-tuned for historical analysis of these generated textual reports. This fine-tuning enables more reliable analysis about the pedestrian safety at intersections, effectively detecting patterns and safety critical events. The proposed VTPM offers a more efficient alternative to video footage by using textual reports reducing memory usage, saving up to 253 million percent, eliminating privacy issues, and enabling comprehensive interactive historical analysis.", "published": "2024-08-21T14:21:53Z", "authors": ["Ahmed S. Abdelrahman", "Mohamed Abdel-Aty", "Dongdong Wang"], "categories": ["cs.CV", "cs.AI"], "doi": "", "_query": "property valuation computer vision", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2204.09435v1", "title": "Hephaestus: A large scale multitask dataset towards InSAR understanding", "abstract": "Synthetic Aperture Radar (SAR) data and Interferometric SAR (InSAR) products in particular, are one of the largest sources of Earth Observation data. InSAR provides unique information on diverse geophysical processes and geology, and on the geotechnical properties of man-made structures. However, there are only a limited number of applications that exploit the abundance of InSAR data and deep learning methods to extract such knowledge. The main barrier has been the lack of a large curated and annotated InSAR dataset, which would be costly to create and would require an interdisciplinary team of experts experienced on InSAR data interpretation. In this work, we put the effort to create and make available the first of its kind, manually annotated dataset that consists of 19,919 individual Sentinel-1 interferograms acquired over 44 different volcanoes globally, which are split into 216,106 InSAR patches. The annotated dataset is designed to address different computer vision problems, including volcano state classification, semantic segmentation of ground deformation, detection and classification of atmospheric signals in InSAR imagery, interferogram captioning, text to InSAR generation, and InSAR image quality assessment.", "published": "2022-04-20T12:58:18Z", "authors": ["Nikolaos Ioannis Bountos", "Ioannis Papoutsis", "Dimitrios Michail", "Andreas Karavias", "Panagiotis Elias", "Isaak Parcharidis"], "categories": ["cs.CV", "astro-ph.IM", "cs.LG"], "doi": "", "_query": "property valuation computer vision", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2209.00961v1", "title": "LiteDepth: Digging into Fast and Accurate Depth Estimation on Mobile Devices", "abstract": "Monocular depth estimation is an essential task in the computer vision community. While tremendous successful methods have obtained excellent results, most of them are computationally expensive and not applicable for real-time on-device inference. In this paper, we aim to address more practical applications of monocular depth estimation, where the solution should consider not only the precision but also the inference time on mobile devices. To this end, we first develop an end-to-end learning-based model with a tiny weight size (1.4MB) and a short inference time (27FPS on Raspberry Pi 4). Then, we propose a simple yet effective data augmentation strategy, called R2 crop, to boost the model performance. Moreover, we observe that the simple lightweight model trained with only one single loss term will suffer from performance bottleneck. To alleviate this issue, we adopt multiple loss terms to provide sufficient constraints during the training stage. Furthermore, with a simple dynamic re-weight strategy, we can avoid the time-consuming hyper-parameter choice of loss terms. Finally, we adopt the structure-aware distillation to further improve the model performance. Notably, our solution named LiteDepth ranks 2nd in the MAI&AIM2022 Monocular Depth Estimation Challenge}, with a si-RMSE of 0.311, an RMSE of 3.79, and the inference time is 37$ms$ tested on the Raspberry Pi 4. Notably, we provide the fastest solution to the challenge. Codes and models will be released at \\url{https://github.com/zhyever/LiteDepth}.", "published": "2022-09-02T11:38:28Z", "authors": ["Zhenyu Li", "Zehui Chen", "Jialei Xu", "Xianming Liu", "Junjun Jiang"], "categories": ["cs.CV"], "doi": "", "_query": "property valuation computer vision", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/1810.01163v1", "title": "An Entropic Optimal Transport Loss for Learning Deep Neural Networks under Label Noise in Remote Sensing Images", "abstract": "Deep neural networks have established as a powerful tool for large scale supervised classification tasks. The state-of-the-art performances of deep neural networks are conditioned to the availability of large number of accurately labeled samples. In practice, collecting large scale accurately labeled datasets is a challenging and tedious task in most scenarios of remote sensing image analysis, thus cheap surrogate procedures are employed to label the dataset. Training deep neural networks on such datasets with inaccurate labels easily overfits to the noisy training labels and degrades the performance of the classification tasks drastically. To mitigate this effect, we propose an original solution with entropic optimal transportation. It allows to learn in an end-to-end fashion deep neural networks that are, to some extent, robust to inaccurately labeled samples. We empirically demonstrate on several remote sensing datasets, where both scene and pixel-based hyperspectral images are considered for classification. Our method proves to be highly tolerant to significant amounts of label noise and achieves favorable results against state-of-the-art methods.", "published": "2018-10-02T10:31:37Z", "authors": ["Bharath Bhushan Damodaran", "Rémi Flamary", "Viven Seguy", "Nicolas Courty"], "categories": ["cs.CV", "cs.LG", "stat.ML"], "doi": "10.1016/j.cviu.2019.102863", "_query": "property valuation computer vision", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/1301.3172v1", "title": "How to determine the sign of a valuation on C[x,y]?", "abstract": "Given a divisorial discrete valuation 'centered at infinity' on C[x,y], we show that its sign on C[x,y] (i.e. whether it is negative or non-positive on non-constant polynomials) is completely determined by the sign of its value on the 'last key form' (key forms being the avatar of 'key polynomials' of valuations (introduced by [Maclane, 1936]) in 'global coordinates'). The proof involves computations related to the cone of curves on certain compactifications of C^2 and gives a characterization of the divisorial valuations centered at infinity whose 'skewness' can be interpreted in terms of the 'slope' of an extremal ray of these cones, yielding a generalization of a result of [Favre-Jonsson, 2007]. A by-product of these arguments is a characterization of valuations which 'determine' normal compactifications of C^2 with one irreducible curve at infinity in terms of an associated 'semigroup of values'.", "published": "2013-01-14T22:11:40Z", "authors": ["Pinaki Mondal"], "categories": ["math.AC", "math.AG"], "doi": "", "_query": "property valuation computer vision", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2211.11747v2", "title": "NEVIS'22: A Stream of 100 Tasks Sampled from 30 Years of Computer Vision Research", "abstract": "A shared goal of several machine learning communities like continual learning, meta-learning and transfer learning, is to design algorithms and models that efficiently and robustly adapt to unseen tasks. An even more ambitious goal is to build models that never stop adapting, and that become increasingly more efficient through time by suitably transferring the accrued knowledge. Beyond the study of the actual learning algorithm and model architecture, there are several hurdles towards our quest to build such models, such as the choice of learning protocol, metric of success and data needed to validate research hypotheses. In this work, we introduce the Never-Ending VIsual-classification Stream (NEVIS'22), a benchmark consisting of a stream of over 100 visual classification tasks, sorted chronologically and extracted from papers sampled uniformly from computer vision proceedings spanning the last three decades. The resulting stream reflects what the research community thought was meaningful at any point in time, and it serves as an ideal test bed to assess how well models can adapt to new tasks, and do so better and more efficiently as time goes by. Despite being limited to classification, the resulting stream has a rich diversity of tasks from OCR, to texture analysis, scene recognition, and so forth. The diversity is also reflected in the wide range of dataset sizes, spanning over four orders of magnitude. Overall, NEVIS'22 poses an unprecedented challenge for current sequential learning approaches due to the scale and diversity of tasks, yet with a low entry barrier as it is limited to a single modality and well understood supervised learning problems. Moreover, we provide a reference implementation including strong baselines and an evaluation protocol to compare methods in terms of their trade-off between accuracy and compute.", "published": "2022-11-15T18:57:46Z", "authors": ["Jorg Bornschein", "Alexandre Galashov", "Ross Hemsley", "Amal Rannen-Triki", "Yutian Chen", "Arslan Chaudhry", "Xu Owen He", "Arthur Douillard", "Massimo Caccia", "Qixuang Feng", "Jiajun Shen", "Sylvestre-Alvise Rebuffi", "Kitty Stacpoole", "Diego de las Casas", "Will Hawkins", "Angeliki Lazaridou", "Yee Whye Teh", "Andrei A. Rusu", "Razvan Pascanu", "Marc'Aurelio Ranzato"], "categories": ["cs.LG", "cs.CV"], "doi": "", "_query": "property valuation computer vision", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2209.15179v4", "title": "Physical Adversarial Attack meets Computer Vision: A Decade Survey", "abstract": "Despite the impressive achievements of Deep Neural Networks (DNNs) in computer vision, their vulnerability to adversarial attacks remains a critical concern. Extensive research has demonstrated that incorporating sophisticated perturbations into input images can lead to a catastrophic degradation in DNNs' performance. This perplexing phenomenon not only exists in the digital space but also in the physical world. Consequently, it becomes imperative to evaluate the security of DNNs-based systems to ensure their safe deployment in real-world scenarios, particularly in security-sensitive applications. To facilitate a profound understanding of this topic, this paper presents a comprehensive overview of physical adversarial attacks. Firstly, we distill four general steps for launching physical adversarial attacks. Building upon this foundation, we uncover the pervasive role of artifacts carrying adversarial perturbations in the physical world. These artifacts influence each step. To denote them, we introduce a new term: adversarial medium. Then, we take the first step to systematically evaluate the performance of physical adversarial attacks, taking the adversarial medium as a first attempt. Our proposed evaluation metric, hiPAA, comprises six perspectives: Effectiveness, Stealthiness, Robustness, Practicability, Aesthetics, and Economics. We also provide comparative results across task categories, together with insightful observations and suggestions for future research directions.", "published": "2022-09-30T01:59:53Z", "authors": ["Hui Wei", "Hao Tang", "Xuemei Jia", "Zhixiang Wang", "Hanxun Yu", "Zhubo Li", "Shin'ichi Satoh", "Luc Van Gool", "Zheng Wang"], "categories": ["cs.CV"], "doi": "10.1109/TPAMI.2024.3430860", "_query": "property valuation computer vision", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/1003.5712v1", "title": "Overview of utility-based valuation", "abstract": "We review the utility-based valuation method for pricing derivative securities in incomplete markets. In particular, we review the practical approach to the utility-based pricing by the means of computing the first order expansion of marginal utility-based prices with respect to a small number of random endowments.", "published": "2010-03-30T01:19:50Z", "authors": ["David German"], "categories": ["q-fin.GN", "q-fin.PR"], "doi": "", "_query": "property valuation computer vision", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/1803.10842v1", "title": "Deep Learning Object Detection Methods for Ecological Camera Trap Data", "abstract": "Deep learning methods for computer vision tasks show promise for automating the data analysis of camera trap images. Ecological camera traps are a common approach for monitoring an ecosystem's animal population, as they provide continual insight into an environment without being intrusive. However, the analysis of camera trap images is expensive, labour intensive, and time consuming. Recent advances in the field of deep learning for object detection show promise towards automating the analysis of camera trap images. Here, we demonstrate their capabilities by training and comparing two deep learning object detection classifiers, Faster R-CNN and YOLO v2.0, to identify, quantify, and localize animal species within camera trap images using the Reconyx Camera Trap and the self-labeled Gold Standard Snapshot Serengeti data sets. When trained on large labeled datasets, object recognition methods have shown success. We demonstrate their use, in the context of realistically sized ecological data sets, by testing if object detection methods are applicable for ecological research scenarios when utilizing transfer learning. Faster R-CNN outperformed YOLO v2.0 with average accuracies of 93.0\\% and 76.7\\% on the two data sets, respectively. Our findings show promising steps towards the automation of the labourious task of labeling camera trap images, which can be used to improve our understanding of the population dynamics of ecosystems across the planet.", "published": "2018-03-28T20:30:39Z", "authors": ["Stefan Schneider", "Graham W. Taylor", "Stefan C. Kremer"], "categories": ["cs.CV"], "doi": "", "_query": "property valuation computer vision", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/1405.0320v1", "title": "Computing all Affine Solution Sets of Binomial Systems", "abstract": "To compute solutions of sparse polynomial systems efficiently we have to exploit the structure of their Newton polytopes. While the application of polyhedral methods naturally excludes solutions with zero components, an irreducible decomposition of a variety is typically understood in affine space, including also those components with zero coordinates. For the problem of computing solution sets in the intersection of some coordinate planes, the direct application of a polyhedral method fails, because the original facial structure of the Newton polytopes may alter completely when selected variables become zero. Our new proposed method enumerates all factors contributing to a generalized permanent and toric solutions as a special case of this enumeration. For benchmark problems such as the adjacent 2-by-2 minors of a general matrix, our methods scale much better than the witness set representations of numerical algebraic geometry.", "published": "2014-05-01T23:00:24Z", "authors": ["Danko Adrovic", "Jan Verschelde"], "categories": ["cs.SC", "cs.MS", "math.AG"], "doi": "", "_query": "property valuation computer vision", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2506.04379v1", "title": "Visualizing and Controlling Cortical Responses Using Voxel-Weighted Activation Maximization", "abstract": "Deep neural networks (DNNs) trained on visual tasks develop feature representations that resemble those in the human visual system. Although DNN-based encoding models can accurately predict brain responses to visual stimuli, they offer limited insight into the specific features driving these responses. Here, we demonstrate that activation maximization -- a technique designed to interpret vision DNNs -- can be applied to DNN-based encoding models of the human brain. We extract and adaptively downsample activations from multiple layers of a pretrained Inception V3 network, then use linear regression to predict fMRI responses. This yields a full image-computable model of brain responses. Next, we apply activation maximization to generate images optimized for predicted responses in individual cortical voxels. We find that these images contain visual characteristics that qualitatively correspond with known selectivity and enable exploration of selectivity across the visual cortex. We further extend our method to whole regions of interest (ROIs) of the brain and validate its efficacy by presenting these images to human participants in an fMRI study. We find that the generated images reliably drive activity in targeted regions across both low- and high-level visual areas and across subjects. These results demonstrate that activation maximization can be successfully applied to DNN-based encoding models. By addressing key limitations of alternative approaches that require natively generative models, our approach enables flexible characterization and modulation of responses across the human visual system.", "published": "2025-06-04T18:48:08Z", "authors": ["Matthew W. Shinkle", "Mark D. Lescroart"], "categories": ["cs.CV", "cs.AI", "q-bio.NC"], "doi": "", "_query": "property valuation computer vision", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2105.10497v3", "title": "Intriguing Properties of Vision Transformers", "abstract": "Vision transformers (ViT) have demonstrated impressive performance across various machine vision problems. These models are based on multi-head self-attention mechanisms that can flexibly attend to a sequence of image patches to encode contextual cues. An important question is how such flexibility in attending image-wide context conditioned on a given patch can facilitate handling nuisances in natural images e.g., severe occlusions, domain shifts, spatial permutations, adversarial and natural perturbations. We systematically study this question via an extensive set of experiments encompassing three ViT families and comparisons with a high-performing convolutional neural network (CNN). We show and analyze the following intriguing properties of ViT: (a) Transformers are highly robust to severe occlusions, perturbations and domain shifts, e.g., retain as high as 60% top-1 accuracy on ImageNet even after randomly occluding 80% of the image content. (b) The robust performance to occlusions is not due to a bias towards local textures, and ViTs are significantly less biased towards textures compared to CNNs. When properly trained to encode shape-based features, ViTs demonstrate shape recognition capability comparable to that of human visual system, previously unmatched in the literature. (c) Using ViTs to encode shape representation leads to an interesting consequence of accurate semantic segmentation without pixel-level supervision. (d) Off-the-shelf features from a single ViT model can be combined to create a feature ensemble, leading to high accuracy rates across a range of classification datasets in both traditional and few-shot learning paradigms. We show effective features of ViTs are due to flexible and dynamic receptive fields possible via the self-attention mechanism.", "published": "2021-05-21T17:59:18Z", "authors": ["Muzammal Naseer", "Kanchana Ranasinghe", "Salman Khan", "Munawar Hayat", "Fahad Shahbaz Khan", "Ming-Hsuan Yang"], "categories": ["cs.CV", "cs.AI", "cs.LG"], "doi": "", "_query": "property valuation computer vision", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2403.13298v2", "title": "Rotary Position Embedding for Vision Transformer", "abstract": "Rotary Position Embedding (RoPE) performs remarkably on language models, especially for length extrapolation of Transformers. However, the impacts of RoPE on computer vision domains have been underexplored, even though RoPE appears capable of enhancing Vision Transformer (ViT) performance in a way similar to the language domain. This study provides a comprehensive analysis of RoPE when applied to ViTs, utilizing practical implementations of RoPE for 2D vision data. The analysis reveals that RoPE demonstrates impressive extrapolation performance, i.e., maintaining precision while increasing image resolution at inference. It eventually leads to performance improvement for ImageNet-1k, COCO detection, and ADE-20k segmentation. We believe this study provides thorough guidelines to apply RoPE into ViT, promising improved backbone performance with minimal extra computational overhead. Our code and pre-trained models are available at https://github.com/naver-ai/rope-vit", "published": "2024-03-20T04:47:13Z", "authors": ["Byeongho Heo", "Song Park", "Dongyoon Han", "Sangdoo Yun"], "categories": ["cs.CV", "cs.LG"], "doi": "", "_query": "property valuation computer vision", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2304.01451v1", "title": "q-Partitioning Valuations: Exploring the Space Between Subadditive and Fractionally Subadditive Valuations", "abstract": "For a set $M$ of $m$ elements, we define a decreasing chain of classes of normalized monotone-increasing valuation functions from $2^M$ to $\\mathbb{R}_{\\geq 0}$, parameterized by an integer $q \\in [2,m]$. For a given $q$, we refer to the class as $q$-partitioning. A valuation function is subadditive if and only if it is $2$-partitioning, and fractionally subadditive if and only if it is $m$-partitioning. Thus, our chain establishes an interpolation between subadditive and fractionally subadditive valuations. We show that this interpolation is smooth, interpretable , and non-trivial. We interpolate prior results that separate subadditive and fractionally subadditive for all $q \\in \\{2,\\ldots, m\\}$. Two highlights are the following:(i) An $Ω\\left(\\frac{\\log \\log q}{\\log \\log m}\\right)$-competitive posted price mechanism for $q$-partitioning valuations. Note that this matches asymptotically the state-of-the-art for both subadditive ($q=2$) [DKL20], and fractionally subadditive ($q=m$) [FGL15]. (ii)Two upper-tail concentration inequalities on $1$-Lipschitz, $q$-partitioning valuations over independent items. One extends the state-of-the-art for $q=m$ to $q<m$, the other improves the state-of-the-art for $q=2$ for $q > 2$. Our concentration inequalities imply several corollaries that interpolate between subadditive and fractionally subadditive, for example: $\\mathbb{E}[v(S)]\\le (1 + 1/\\log q)\\text{Median}[v(S)] + O(\\log q)$. To prove this, we develop a new isoperimetric inequality using Talagrand's method of control by $q$ points, which may be of independent interest. We also discuss other probabilistic inequalities and game-theoretic applications of $q$-partitioning valuations, and connections to subadditive MPH-$k$ valuations [EFNTW19].", "published": "2023-04-04T01:51:05Z", "authors": ["Kiril Bangachev", "S. Matthew Weinberg"], "categories": ["cs.GT"], "doi": "", "_query": "property valuation computer vision", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2307.16528v1", "title": "Deep Learning and Computer Vision for Glaucoma Detection: A Review", "abstract": "Glaucoma is the leading cause of irreversible blindness worldwide and poses significant diagnostic challenges due to its reliance on subjective evaluation. However, recent advances in computer vision and deep learning have demonstrated the potential for automated assessment. In this paper, we survey recent studies on AI-based glaucoma diagnosis using fundus, optical coherence tomography, and visual field images, with a particular emphasis on deep learning-based methods. We provide an updated taxonomy that organizes methods into architectural paradigms and includes links to available source code to enhance the reproducibility of the methods. Through rigorous benchmarking on widely-used public datasets, we reveal performance gaps in generalizability, uncertainty estimation, and multimodal integration. Additionally, our survey curates key datasets while highlighting limitations such as scale, labeling inconsistencies, and bias. We outline open research challenges and detail promising directions for future studies. This survey is expected to be useful for both AI researchers seeking to translate advances into practice and ophthalmologists aiming to improve clinical workflows and diagnosis using the latest AI outcomes.", "published": "2023-07-31T09:49:51Z", "authors": ["Mona Ashtari-Majlan", "Mohammad Mahdi Dehshibi", "David Masip"], "categories": ["eess.IV", "cs.CV", "cs.LG"], "doi": "", "_query": "property valuation computer vision", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2508.12755v1", "title": "CLAIRE-DSA: Fluoroscopic Image Classification for Quality Assurance of Computer Vision Pipelines in Acute Ischemic Stroke", "abstract": "Computer vision models can be used to assist during mechanical thrombectomy (MT) for acute ischemic stroke (AIS), but poor image quality often degrades performance. This work presents CLAIRE-DSA, a deep learning--based framework designed to categorize key image properties in minimum intensity projections (MinIPs) acquired during MT for AIS, supporting downstream quality control and workflow optimization. CLAIRE-DSA uses pre-trained ResNet backbone models, fine-tuned to predict nine image properties (e.g., presence of contrast, projection angle, motion artefact severity). Separate classifiers were trained on an annotated dataset containing $1,758$ fluoroscopic MinIPs. The model achieved excellent performance on all labels, with ROC-AUC ranging from $0.91$ to $0.98$, and precision ranging from $0.70$ to $1.00$. The ability of CLAIRE-DSA to identify suitable images was evaluated on a segmentation task by filtering poor quality images and comparing segmentation performance on filtered and unfiltered datasets. Segmentation success rate increased from $42%$ to $69%$, $p < 0.001$. CLAIRE-DSA demonstrates strong potential as an automated tool for accurately classifying image properties in DSA series of acute ischemic stroke patients, supporting image annotation and quality control in clinical and research applications. Source code is available at https://gitlab.com/icai-stroke-lab/wp3_neurointerventional_ai/claire-dsa.", "published": "2025-08-18T09:28:58Z", "authors": ["Cristo J. van den Berg", "Frank G. te Nijenhuis", "Mirre J. Blaauboer", "Daan T. W. van Erp", "Carlijn M. Keppels", "Matthijs van der Sluijs", "Bob Roozenbeek", "Wim van Zwam", "Sandra Cornelissen", "Danny Ruijters", "Ruisheng Su", "Theo van Walsum"], "categories": ["cs.CV", "cs.AI"], "doi": "10.1007/978-3-032-07945-9_4", "_query": "property valuation computer vision", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2307.13541v1", "title": "Group Activity Recognition in Computer Vision: A Comprehensive Review, Challenges, and Future Perspectives", "abstract": "Group activity recognition is a hot topic in computer vision. Recognizing activities through group relationships plays a vital role in group activity recognition. It holds practical implications in various scenarios, such as video analysis, surveillance, automatic driving, and understanding social activities. The model's key capabilities encompass efficiently modeling hierarchical relationships within a scene and accurately extracting distinctive spatiotemporal features from groups. Given this technology's extensive applicability, identifying group activities has garnered significant research attention. This work examines the current progress in technology for recognizing group activities, with a specific focus on global interactivity and activities. Firstly, we comprehensively review the pertinent literature and various group activity recognition approaches, from traditional methodologies to the latest methods based on spatial structure, descriptors, non-deep learning, hierarchical recurrent neural networks (HRNN), relationship models, and attention mechanisms. Subsequently, we present the relational network and relational architectures for each module. Thirdly, we investigate methods for recognizing group activity and compare their performance with state-of-the-art technologies. We summarize the existing challenges and provide comprehensive guidance for newcomers to understand group activity recognition. Furthermore, we review emerging perspectives in group activity recognition to explore new directions and possibilities.", "published": "2023-07-25T14:44:41Z", "authors": ["Chuanchuan Wang", "Ahmad Sufril Azlan Mohamed"], "categories": ["cs.CV", "cs.AI"], "doi": "", "_query": "property valuation computer vision", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2406.13847v1", "title": "Locating and measuring marine aquaculture production from space: a computer vision approach in the French Mediterranean", "abstract": "Aquaculture production -- the cultivation of aquatic plants and animals -- has grown rapidly since the 1990s, but sparse, self-reported and aggregate production data limits the effective understanding and monitoring of the industry's trends and potential risks. Building on a manual survey of aquaculture production from remote sensing imagery, we train a computer vision model to identify marine aquaculture cages from aerial and satellite imagery, and generate a spatially explicit dataset of finfish production locations in the French Mediterranean from 2000-2021 that includes 4,010 cages (69m2 average cage area). We demonstrate the value of our method as an easily adaptable, cost-effective approach that can improve the speed and reliability of aquaculture surveys, and enables downstream analyses relevant to researchers and regulators. We illustrate its use to compute independent estimates of production, and develop a flexible framework to quantify uncertainty in these estimates. Overall, our study presents an efficient, scalable and highly adaptable method for monitoring aquaculture production from remote sensing imagery.", "published": "2024-06-19T21:19:44Z", "authors": ["Sebastian Quaade", "Andrea Vallebueno", "Olivia D. N. Alcabes", "Kit T. Rodolfa", "Daniel E. Ho"], "categories": ["cs.CV"], "doi": "", "_query": "property valuation computer vision", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2302.09108v1", "title": "ViTA: A Vision Transformer Inference Accelerator for Edge Applications", "abstract": "Vision Transformer models, such as ViT, Swin Transformer, and Transformer-in-Transformer, have recently gained significant traction in computer vision tasks due to their ability to capture the global relation between features which leads to superior performance. However, they are compute-heavy and difficult to deploy in resource-constrained edge devices. Existing hardware accelerators, including those for the closely-related BERT transformer models, do not target highly resource-constrained environments. In this paper, we address this gap and propose ViTA - a configurable hardware accelerator for inference of vision transformer models, targeting resource-constrained edge computing devices and avoiding repeated off-chip memory accesses. We employ a head-level pipeline and inter-layer MLP optimizations, and can support several commonly used vision transformer models with changes solely in our control logic. We achieve nearly 90% hardware utilization efficiency on most vision transformer models, report a power of 0.88W when synthesised with a clock of 150 MHz, and get reasonable frame rates - all of which makes ViTA suitable for edge applications.", "published": "2023-02-17T19:35:36Z", "authors": ["Shashank Nag", "Gourav Datta", "Souvik Kundu", "Nitin Chandrachoodan", "Peter A. Beerel"], "categories": ["cs.AR", "cs.CV", "cs.LG"], "doi": "10.1109/ISCAS46773.2023.10181988", "_query": "property valuation computer vision", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2503.19006v1", "title": "Computational Thinking with Computer Vision: Developing AI Competency in an Introductory Computer Science Course", "abstract": "Developing competency in artificial intelligence is becoming increasingly crucial for computer science (CS) students at all levels of the CS curriculum. However, most previous research focuses on advanced CS courses, as traditional introductory courses provide limited opportunities to develop AI skills and knowledge. This paper introduces an introductory CS course where students learn computational thinking through computer vision, a sub-field of AI, as an application context. The course aims to achieve computational thinking outcomes alongside critical thinking outcomes that expose students to AI approaches and their societal implications. Through experiential activities such as individual projects and reading discussions, our course seeks to balance technical learning and critical thinking goals. Our evaluation, based on pre-and post-course surveys, shows an improved sense of belonging, self-efficacy, and AI ethics awareness among students. The results suggest that an AI-focused context can enhance participation and employability, student-selected projects support self-efficacy, and ethically grounded AI instruction can be effective for interdisciplinary audiences. Students' discussions on reading assignments demonstrated deep engagement with the complex challenges in today's AI landscape. Finally, we share insights on scaling such courses for larger cohorts and improving the learning experience for introductory CS students.", "published": "2025-03-24T15:49:37Z", "authors": ["Tahiya Chowdhury"], "categories": ["cs.CY", "cs.AI"], "doi": "", "_query": "property valuation computer vision", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/1711.00005v2", "title": "Performance Optimization and Parallelization of a Parabolic Equation Solver in Computational Ocean Acoustics on Modern Many-core Computer", "abstract": "As one of open-source codes widely used in computational ocean acoustics, FOR3D can provide a very good estimate for underwater acoustic propagation. In this paper, we propose a performance optimization and parallelization to speed up the running of FOR3D. We utilized a variety of methods to enhance the entire performance, such as using a multi-threaded programming model to exploit the potential capability of the many-core node of high-performance computing (HPC) system, tuning compile options, using efficient tuned mathematical library and utilizing vectorization optimization instruction. In addition, we extended the application from single-frequency calculation to multi-frequency calculation successfully by using OpenMP+MPI hybrid programming techniques on the mainstream HPC platform. A detailed performance evaluation was performed and the results showed that the proposed parallelization obtained good accelerated effect of 25.77X when testing a typical three-dimensional medium-sized case on Tianhe-2 supercomputer. It also showed that the tuned parallel version has a weak-scalability. The speed of calculation of underwater sound field can be greatly improved by the strategy mentioned in this paper. The method used in this paper is not only applicable to other similar computing models in computational ocean acoustics but also a guideline of performance enhancement for scientific and engineering application running on modern many-core-computing platform.", "published": "2017-10-31T13:58:48Z", "authors": ["Min Xu", "Yongxian Wang", "Anthony Theodore Chronopoulos", "Hao Yue"], "categories": ["cs.MS", "cs.CE", "cs.PF"], "doi": "10.12783/dtcse/csae2017/17546", "_query": "property valuation computer vision", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2209.05911v1", "title": "Computer vision based vehicle tracking as a complementary and scalable approach to RFID tagging", "abstract": "Logging of incoming/outgoing vehicles serves as a piece of critical information for root-cause analysis to combat security breach incidents in various sensitive organizations. RFID tagging hampers the scalability of vehicle tracking solutions on both logistics as well as technical fronts. For instance, requiring each incoming vehicle(departmental or private) to be RFID tagged is a severe constraint and coupling video analytics with RFID to detect abnormal vehicle movement is non-trivial. We leverage publicly available implementations of computer vision algorithms to develop an interpretable vehicle tracking algorithm using finite-state machine formalism. The state-machine consumes input from the cascaded object detection and optical character recognition(OCR) models for state transitions. We evaluated the proposed method on 75 video clips of 285 vehicles from our system deployment site. We observed that the detection rate is most affected by the speed and the type of vehicle. The highest detection rate is achieved when the vehicle movement is restricted to follow a movement restrictions(SOP) at the checkpoint similar to RFID tagging. We further analyzed 700 vehicle tracking predictions on live-data and identified that the majority of vehicle number prediction errors are due to illegible-text, image-blur, text occlusion and out-of-vocab letters in vehicle numbers. Towards system deployment and performance enhancement, we expect our ongoing system monitoring to provide evidences to establish a higher vehicle-throughput SOP at the security checkpoint as well as to drive the fine-tuning of the deployed computer-vision models and the state-machine to establish the proposed approach as a promising alternative to RFID-tagging.", "published": "2022-09-13T11:49:38Z", "authors": ["Pranav Kant Gaur", "Abhilash Bhardwaj", "Pritam Shete", "Mohini Laghate", "Dinesh M Sarode"], "categories": ["cs.CV"], "doi": "", "_query": "property valuation computer vision", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2603.06362v1", "title": "Computer vision-based estimation of invertebrate biomass", "abstract": "The ability to estimate invertebrate biomass using only images could help scaling up quantitative biodiversity monitoring efforts. Computer vision-based methods have the potential to omit the manual, time-consuming, and destructive process of dry weighing specimens. We present two approaches for dry mass estimation that do not require additional manual effort apart from imaging the specimens: fitting a linear model with novel predictors, automatically calculated by an imaging device, and training a family of end-to-end deep neural networks for the task, using single-view, multi-view, and metadata-aware architectures. We propose using area and sinking speed as predictors. These can be calculated with BIODISCOVER, which is a dual-camera system that captures image sequences of specimens sinking in an ethanol column. For this study, we collected a large dataset of dry mass measurement and image sequence pairs to train and evaluate models. We show that our methods can estimate specimen dry mass even with complex and visually diverse specimen morphologies. Combined with automatic taxonomic classification, our approach is an accurate method for group-level dry mass estimation, with a median percentage error of 10-20% for individuals. We highlight the importance of choosing appropriate evaluation metrics, and encourage using both percentage errors and absolute errors as metrics, because they measure different properties. We also explore different optimization losses, data augmentation methods, and model architectures for training deep-learning models.", "published": "2026-03-06T15:12:40Z", "authors": ["Mikko Impiö", "Philipp M. Rehsen", "Jarrett Blair", "Cecilie Mielec", "Arne J. Beermann", "Florian Leese", "Toke T. Høye", "Jenni Raitoharju"], "categories": ["cs.CV"], "doi": "", "_query": "property valuation computer vision", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2506.02693v2", "title": "Poincaré series of valuations on functions over a field", "abstract": "For a subfield $\\K$ of the field $\\C$ of complex numbers, we consider curve and divisorial valuations on the algebra $\\K[[x,y]]$ of formal power series in two variables with the coeficients in $\\K$. We compute the semigroup Poincaré series and the classical Poincaré series of a valuation.", "published": "2025-06-03T09:43:34Z", "authors": ["Antonio Campillo", "Felix Delgado", "Sabir Gusein-Zade"], "categories": ["math.AG"], "doi": "", "_query": "property valuation computer vision", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/1508.05277v2", "title": "Canonical Valuations and the Birational Section Conjecture", "abstract": "We develop a notion of a `canonical $\\mathcal{C}$-henselian valuation' for a class $\\mathcal{C}$ of field extensions, generalizing the construction of the canonical henselian valuation of a field. We use this to show that the $p$-adic valuation on a finite extension $F$ of $\\mathbb{Q}_p$ can be recovered entirely (or up to some indeterminacy of the residue field) from various small quotients of $G_F$, the absolute Galois group of $F$. In particular, it can be recovered fully from the maximal solvable quotient. We use this to prove several versions of the birational section conjecture for varieties over $p$-adic fields.", "published": "2015-08-21T14:19:27Z", "authors": ["Kristian Strommen"], "categories": ["math.NT"], "doi": "", "_query": "property valuation computer vision", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/1804.03247v1", "title": "Fine-grained Activity Recognition in Baseball Videos", "abstract": "In this paper, we introduce a challenging new dataset, MLB-YouTube, designed for fine-grained activity detection. The dataset contains two settings: segmented video classification as well as activity detection in continuous videos. We experimentally compare various recognition approaches capturing temporal structure in activity videos, by classifying segmented videos and extending those approaches to continuous videos. We also compare models on the extremely difficult task of predicting pitch speed and pitch type from broadcast baseball videos. We find that learning temporal structure is valuable for fine-grained activity recognition.", "published": "2018-04-09T21:32:36Z", "authors": ["AJ Piergiovanni", "Michael S. Ryoo"], "categories": ["cs.CV"], "doi": "", "_query": "property valuation computer vision", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2507.12490v1", "title": "Spatially Grounded Explanations in Vision Language Models for Document Visual Question Answering", "abstract": "We introduce EaGERS, a fully training-free and model-agnostic pipeline that (1) generates natural language rationales via a vision language model, (2) grounds these rationales to spatial sub-regions by computing multimodal embedding similarities over a configurable grid with majority voting, and (3) restricts the generation of responses only from the relevant regions selected in the masked image. Experiments on the DocVQA dataset demonstrate that our best configuration not only outperforms the base model on exact match accuracy and Average Normalized Levenshtein Similarity metrics but also enhances transparency and reproducibility in DocVQA without additional model fine-tuning.", "published": "2025-07-15T20:05:25Z", "authors": ["Maximiliano Hormazábal Lagos", "Héctor Cerezo-Costas", "Dimosthenis Karatzas"], "categories": ["cs.CV", "cs.AI", "cs.CL", "cs.LG"], "doi": "", "_query": "property valuation computer vision", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2507.11267v1", "title": "YOLOatr : Deep Learning Based Automatic Target Detection and Localization in Thermal Infrared Imagery", "abstract": "Automatic Target Detection (ATD) and Recognition (ATR) from Thermal Infrared (TI) imagery in the defense and surveillance domain is a challenging computer vision (CV) task in comparison to the commercial autonomous vehicle perception domain. Limited datasets, peculiar domain-specific and TI modality-specific challenges, i.e., limited hardware, scale invariance issues due to greater distances, deliberate occlusion by tactical vehicles, lower sensor resolution and resultant lack of structural information in targets, effects of weather, temperature, and time of day variations, and varying target to clutter ratios all result in increased intra-class variability and higher inter-class similarity, making accurate real-time ATR a challenging CV task. Resultantly, contemporary state-of-the-art (SOTA) deep learning architectures underperform in the ATR domain. We propose a modified anchor-based single-stage detector, called YOLOatr, based on a modified YOLOv5s, with optimal modifications to the detection heads, feature fusion in the neck, and a custom augmentation profile. We evaluate the performance of our proposed model on a comprehensive DSIAC MWIR dataset for real-time ATR over both correlated and decorrelated testing protocols. The results demonstrate that our proposed model achieves state-of-the-art ATR performance of up to 99.6%.", "published": "2025-07-15T12:41:01Z", "authors": ["Aon Safdar", "Usman Akram", "Waseem Anwar", "Basit Malik", "Mian Ibad Ali"], "categories": ["cs.CV", "cs.AI"], "doi": "10.5281/zenodo.8264062", "_query": "property valuation computer vision", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2604.11530v3", "title": "Beyond Attention Scores: SVD-Based Vision Token Pruning for Efficient Vision-Language Models", "abstract": "Vision-Language Models (VLMs) have revolutionized multi-modal learning by jointly processing visual and textual information. Yet, they face significant challenges due to the high computational and memory demands of processing long sequences of vision tokens. Many existing methods rely on local heuristics, such as attention scores or token norms. However, these criteria suffer from positional bias and information dispersion, limiting their ability to preserve essential content at high pruning ratios and leading to performance degradation on visually detailed images. To address these issues, we propose SVD-Prune, a training-free, plug-and-play token pruning method based on Singular Value Decomposition. It decomposes the vision token feature matrix and selects the top-k tokens using statistical leverage scores, ensuring only tokens contributing most to the dominant global variance are preserved. Experiments show that SVD-Prune consistently outperforms prior pruning methods under extreme vision token budgets, maintaining strong performance even with 32 and 16 vision tokens.", "published": "2026-04-13T14:30:13Z", "authors": ["Yvon Apedo", "Martyna Poreba", "Michal Szczepanski", "Samia Bouchafa"], "categories": ["cs.CV", "cs.AI"], "doi": "", "_query": "property valuation computer vision", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2502.06574v4", "title": "On the Impact of the Utility in Semivalue-based Data Valuation", "abstract": "Semivalue-based data valuation uses cooperative-game theory intuitions to assign each data point a value reflecting its contribution to a downstream task. Still, those values depend on the practitioner's choice of utility, raising the question: How robust is semivalue-based data valuation to changes in the utility? This issue is critical when the utility is set as a trade-off between several criteria and when practitioners must select among multiple equally valid utilities. We address this by introducing the notion of a dataset's spatial signature: given a semivalue, we embed each data point into a lower-dimensional space in which any utility becomes a linear functional, making the data valuation framework amenable to a simpler geometric picture. Building on this, we propose a practical methodology centered on an explicit robustness metric that informs practitioners whether and by how much their data valuation results will shift as the utility changes. We validate this approach across diverse datasets and semivalues, demonstrating strong agreement with rank-correlation analyses and offering analytical insight into how choosing a semivalue can amplify or diminish robustness.", "published": "2025-02-10T15:42:38Z", "authors": ["Mélissa Tamine", "Benjamin Heymann", "Maxime Vono", "Patrick Loiseau"], "categories": ["cs.AI", "cs.GT", "cs.LG"], "doi": "", "_query": "property valuation computer vision", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/1811.08824v3", "title": "Early Fusion for Goal Directed Robotic Vision", "abstract": "Building perceptual systems for robotics which perform well under tight computational budgets requires novel architectures which rethink the traditional computer vision pipeline. Modern vision architectures require the agent to build a summary representation of the entire scene, even if most of the input is irrelevant to the agent's current goal. In this work, we flip this paradigm, by introducing EarlyFusion vision models that condition on a goal to build custom representations for downstream tasks. We show that these goal specific representations can be learned more quickly, are substantially more parameter efficient, and more robust than existing attention mechanisms in our domain. We demonstrate the effectiveness of these methods on a simulated robotic item retrieval problem that is trained in a fully end-to-end manner via imitation learning.", "published": "2018-11-21T16:55:17Z", "authors": ["Aaron Walsman", "Yonatan Bisk", "Saadia Gabriel", "Dipendra Misra", "Yoav Artzi", "Yejin Choi", "Dieter Fox"], "categories": ["cs.CV", "cs.RO"], "doi": "", "_query": "property valuation computer vision", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/1409.1654v2", "title": "Polymorphic Worms Collection in Cloud Computing", "abstract": "In the past few years, computer worms are seen as one of significant challenges of cloud computing. Worms are rapidly changing and getting more sophisticated to evade detection. One major issue to defend against computer worms is collecting worms' payloads to generate their signature and study their behavior. To collect worms' payloads, we identified challenges for detecting and collecting worms' payloads and proposed high-interactive honeypot to collect payloads of zero-day polymorphic worms in homogeneous and heterogeneous cloud computing platforms. Virtual machine (VM) memory and VM disk image are inspected from outside using open-source forensics tools and VMWare Virtual Disk Development Kit. Our experiments show that the proposed approach overcomes the identified challenges.", "published": "2014-09-05T03:14:47Z", "authors": ["Ashraf A. Shahin"], "categories": ["cs.DC", "cs.CR"], "doi": "", "_query": "property valuation computer vision", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2510.21785v1", "title": "Multi-Agent Pose Uncertainty: A Differentiable Rendering Cramér-Rao Bound", "abstract": "Pose estimation is essential for many applications within computer vision and robotics. Despite its uses, few works provide rigorous uncertainty quantification for poses under dense or learned models. We derive a closed-form lower bound on the covariance of camera pose estimates by treating a differentiable renderer as a measurement function. Linearizing image formation with respect to a small pose perturbation on the manifold yields a render-aware Cramér-Rao bound. Our approach reduces to classical bundle-adjustment uncertainty, ensuring continuity with vision theory. It also naturally extends to multi-agent settings by fusing Fisher information across cameras. Our statistical formulation has downstream applications for tasks such as cooperative perception and novel view synthesis without requiring explicit keypoint correspondences.", "published": "2025-10-18T23:21:02Z", "authors": ["Arun Muthukkumar"], "categories": ["cs.CV", "cs.GR", "cs.LG", "cs.RO"], "doi": "", "_query": "property valuation computer vision", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/1004.4025v1", "title": "Computers and the Conservation of Energy", "abstract": "The purpose of this report is to show that computer and allied technologies can be used to increase energy efficiency. The report is divided into transport, industrial, commercial and domestic sections, which correspond to the major energy consuming sectors of the economy. Each section considers the various ways in which energy can be saved by the use of the computer. The report concludes that it is economic to incorporate computer based energy management systems in a wide variety of applications and that it is important that this capability is realised on a large scale. A comprehensive reference list and a bibliography are included.", "published": "2010-04-22T23:13:50Z", "authors": ["Grenville J. Croll"], "categories": ["cs.CY"], "doi": "", "_query": "property valuation computer vision", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2005.06037v1", "title": "Computer Vision Toolkit for Non-invasive Monitoring of Factory Floor Artifacts", "abstract": "Digitization has led to smart, connected technologies be an integral part of businesses, governments and communities. For manufacturing digitization, there has been active research and development with a focus on Cloud Manufacturing (CM) and the Industrial Internet of Things (IIoT). This work presents a computer vision toolkit (CV Toolkit) for non-invasive digitization of the factory floor in line with Industry 4.0 requirements for factory data collection. Currently, technical challenges persist towards digitization of legacy systems due to the limitation for changes in their design and sensors. This novel toolkit is developed to facilitate easy integration of legacy production machinery and factory floor artifacts with the digital and smart manufacturing environment with no requirement of any physical changes in the machines. The system developed is modular, and allows real-time monitoring of production machinery. Modularity aspect allows the incorporation of new software applications in the current framework of CV Toolkit. To allow connectivity of this toolkit with manufacturing floors in a simple, deployable and cost-effective manner, the toolkit is integrated with a known manufacturing data standard, MTConnect, to \"translate\" the digital inputs into data streams that can be read by commercial status tracking and reporting software solutions. The proposed toolkit is demonstrated using a mock-panel environment developed in house at the University of Cincinnati to highlight its usability.", "published": "2020-05-12T20:25:34Z", "authors": ["Aditya M. Deshpande", "Anil Kumar Telikicherla", "Vinay Jakkali", "David A. Wickelhaus", "Manish Kumar", "Sam Anand"], "categories": ["cs.CV", "eess.SY"], "doi": "10.1016/j.promfg.2020.05.141", "_query": "property valuation computer vision", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2204.03365v1", "title": "Valuations with infinite limit-depth", "abstract": "For a certain field $K$, we construct a valuation-algebraic valuation on the polynomial ring $K[x]$, whose Maclane--Vaquié chain consists of an infinite (countable) number of limit augmentations", "published": "2022-04-07T11:24:09Z", "authors": ["Maria Alberich-Carramiñana", "Jordi Guàrdia", "Enric Nart", "Joaquim Roé"], "categories": ["math.AC"], "doi": "", "_query": "property valuation computer vision", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2604.24193v1", "title": "Computer Vision-Based Early Detection of Container Loss at Sea", "abstract": "Containerised shipping underpins global trade, yet container loss at sea remains a persistent safety, environmental, and economic challenge. Despite compliance with Cargo Securing Manuals, dynamic maritime conditions such as vessel motion, wind loading, and severe sea states can progressively destabilise container stacks, leading to overboard losses. With the new International Maritime Organisation's (IMO) mandatory reporting requirements for lost containers, there is an urgent need for a reliable, evidence-based early detection solution for destabilised containers. This study showcases a low-cost, retrofittable computer vision-based system for early detection of destabilised containers using existing onboard cameras. The framework integrates object segmentation to isolate container stacks, temporal object tracking using optical flow and individual objects' residual motion extraction to quantify relative movement. Experimental evaluation on real onboard ship footage demonstrates that the proposed pipeline effectively isolates container-level motion under challenging conditions of varying sea states and visibility conditions. By enabling early alerts for crew intervention and navigational adjustment, the proposed approach enhances cargo safety, operational resilience, and regulatory compliance.", "published": "2026-04-27T08:53:05Z", "authors": ["Vishakha Lall", "Capt. Stanley S Pinto", "Capt. Chu Xing Peng", "Wu Kaiwen"], "categories": ["cs.CV"], "doi": "", "_query": "property valuation computer vision", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/1804.06508v1", "title": "UCNN: Exploiting Computational Reuse in Deep Neural Networks via Weight Repetition", "abstract": "Convolutional Neural Networks (CNNs) have begun to permeate all corners of electronic society (from voice recognition to scene generation) due to their high accuracy and machine efficiency per operation. At their core, CNN computations are made up of multi-dimensional dot products between weight and input vectors. This paper studies how weight repetition ---when the same weight occurs multiple times in or across weight vectors--- can be exploited to save energy and improve performance during CNN inference. This generalizes a popular line of work to improve efficiency from CNN weight sparsity, as reducing computation due to repeated zero weights is a special case of reducing computation due to repeated weights. To exploit weight repetition, this paper proposes a new CNN accelerator called the Unique Weight CNN Accelerator (UCNN). UCNN uses weight repetition to reuse CNN sub-computations (e.g., dot products) and to reduce CNN model size when stored in off-chip DRAM ---both of which save energy. UCNN further improves performance by exploiting sparsity in weights. We evaluate UCNN with an accelerator-level cycle and energy model and with an RTL implementation of the UCNN processing element. On three contemporary CNNs, UCNN improves throughput-normalized energy consumption by 1.2x - 4x, relative to a similarly provisioned baseline accelerator that uses Eyeriss-style sparsity optimizations. At the same time, the UCNN processing element adds only 17-24% area overhead relative to the same baseline.", "published": "2018-04-18T00:11:38Z", "authors": ["Kartik Hegde", "Jiyong Yu", "Rohit Agrawal", "Mengjia Yan", "Michael Pellauer", "Christopher W. Fletcher"], "categories": ["cs.NE", "cs.LG"], "doi": "", "_query": "property valuation computer vision", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2312.00269v1", "title": "Adaptability of Computer Vision at the Tactical Edge: Addressing Environmental Uncertainty", "abstract": "Computer Vision (CV) systems are increasingly being adopted into Command and Control (C2) systems to improve intelligence analysis on the battlefield, the tactical edge. CV systems leverage Artificial Intelligence (AI) algorithms to help visualize and interpret the environment, enhancing situational awareness. However, the adaptability of CV systems at the tactical edge remains challenging due to rapidly changing environments and objects which can confuse the deployed models. A CV model leveraged in this environment can become uncertain in its predictions, as the environment and the objects existing in the environment begin to change. Additionally, mission objectives can rapidly change leading to adjustments in technology, camera angles, and image resolutions. All of which can negatively affect the performance of and potentially introduce uncertainty into the system. When the training environment and/or technology differs from the deployment environment, CV models can perform unexpectedly. Unfortunately, most scenarios at the tactical edge do not incorporate Uncertainty Quantification (UQ) into their deployed C2 and CV systems. This concept paper explores the idea of synchronizing robust data operations and model fine-tuning driven by UQ all at the tactical edge. Specifically, curating datasets and training child models based on the residuals of predictions, using these child models to calculate prediction intervals (PI), and then using these PI to calibrate the deployed models. By incorporating UQ into the core operations surrounding C2 and CV systems at the tactical edge, we can help drive purposeful adaptability on the battlefield.", "published": "2023-12-01T01:03:59Z", "authors": ["Hayden Moore"], "categories": ["cs.CV"], "doi": "", "_query": "property valuation computer vision", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2603.20519v1", "title": "End-to-End Optimization of Polarimetric Measurement and Material Classifier", "abstract": "Material classification is a fundamental problem in computer vision and plays a crucial role in scene understanding. Previous studies have explored various material recognition methods based on reflection properties such as color, texture, specularity, and scattering. Among these cues, polarization is particularly valuable because it provides rich material information and enables recognition even at distances where capturing high-resolution texture is impractical. However, measuring polarimetric reflectance properties typically requires multiple modulations of the polarization state of the incident light, making the process time-consuming and often unnecessary for certain recognition tasks. While material classification can be achieved using only a subset of polarimetric measurements, the optimal configuration of measurement angles remains unclear. In this study, we propose an end-to-end optimization framework that jointly learns a material classifier and determines the optimal combinations of rotation angles for polarization elements that control both the incident and reflected light states. Using our Mueller-matrix material dataset, we demonstrate that our method achieves high-accuracy material classification even with a limited number of measurements.", "published": "2026-03-20T21:37:17Z", "authors": ["Ryota Maeda", "Naoki Arikawa", "Yutaka No", "Shinsaku Hiura"], "categories": ["cs.CV"], "doi": "", "_query": "property valuation computer vision", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/1703.08778v2", "title": "Valuations on convex functions and convex sets and Monge-Ampere operators", "abstract": "The notion of a valuation on convex bodies is very classical. The notion of a valuation on a class of functions was recently introduced and studied by M. Ludwig and others. We study an explicit relation between continuous valuations on convex functions which are invariant under adding arbitrary linear functionals, and translations invariant continuous valuations on convex bodies. More precisely, we construct a natural linear map from the former space to the latter and prove that it has dense image and infinite dimensional kernel. The proof uses the author's irreducibility theorem and few properties of the real Monge-Ampere operators due to A.D. Alexandrov and Z. Blocki. Fur- thermore we show how to use complex, quaternionic, and octonionic Monge-Ampere operators to construct more examples of continuous valuations on convex functions in an analogous way.", "published": "2017-03-26T07:08:37Z", "authors": ["Semyon Alesker"], "categories": ["math.MG"], "doi": "", "_query": "property valuation computer vision", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2510.26151v1", "title": "MV-MLM: Bridging Multi-View Mammography and Language for Breast Cancer Diagnosis and Risk Prediction", "abstract": "Large annotated datasets are essential for training robust Computer-Aided Diagnosis (CAD) models for breast cancer detection or risk prediction. However, acquiring such datasets with fine-detailed annotation is both costly and time-consuming. Vision-Language Models (VLMs), such as CLIP, which are pre-trained on large image-text pairs, offer a promising solution by enhancing robustness and data efficiency in medical imaging tasks. This paper introduces a novel Multi-View Mammography and Language Model for breast cancer classification and risk prediction, trained on a dataset of paired mammogram images and synthetic radiology reports. Our MV-MLM leverages multi-view supervision to learn rich representations from extensive radiology data by employing cross-modal self-supervision across image-text pairs. This includes multiple views and the corresponding pseudo-radiology reports. We propose a novel joint visual-textual learning strategy to enhance generalization and accuracy performance over different data types and tasks to distinguish breast tissues or cancer characteristics(calcification, mass) and utilize these patterns to understand mammography images and predict cancer risk. We evaluated our method on both private and publicly available datasets, demonstrating that the proposed model achieves state-of-the-art performance in three classification tasks: (1) malignancy classification, (2) subtype classification, and (3) image-based cancer risk prediction. Furthermore, the model exhibits strong data efficiency, outperforming existing fully supervised or VLM baselines while trained on synthetic text reports and without the need for actual radiology reports.", "published": "2025-10-30T05:12:29Z", "authors": ["Shunjie-Fabian Zheng", "Hyeonjun Lee", "Thijs Kooi", "Ali Diba"], "categories": ["cs.CV", "cs.AI"], "doi": "", "_query": "property valuation computer vision", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2508.04102v2", "title": "AR as an Evaluation Playground: Bridging Metrics and Visual Perception of Computer Vision Models", "abstract": "Quantitative metrics are central to evaluating computer vision (CV) models, but they often fail to capture real-world performance due to protocol inconsistencies and ground-truth noise. While visual perception studies can complement these metrics, they often require end-to-end systems that are time-consuming to implement and setups that are difficult to reproduce. We systematically summarize key challenges in evaluating CV models and present the design of ARCADE, an evaluation platform that leverages augmented reality (AR) to enable easy, reproducible, and human-centered CV evaluation. ARCADE uses a modular architecture that provides cross-platform data collection, pluggable model inference, and interactive AR tasks, supporting both metric and visual perception evaluation. We demonstrate ARCADE through a user study with 15 participants and case studies on two representative CV tasks, depth and lighting estimation, showing that ARCADE can reveal perceptual flaws in model quality that are often missed by traditional metrics. We also evaluate ARCADE's usability and performance, showing its flexibility as a reliable real-time platform.", "published": "2025-08-06T05:44:22Z", "authors": ["Ashkan Ganj", "Yiqin Zhao", "Tian Guo"], "categories": ["cs.CV"], "doi": "", "_query": "property valuation computer vision", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2505.03623v2", "title": "Bounding Box-Guided Diffusion for Synthesizing Industrial Images and Segmentation Map", "abstract": "Synthetic dataset generation in Computer Vision, particularly for industrial applications, is still underexplored. Industrial defect segmentation, for instance, requires highly accurate labels, yet acquiring such data is costly and time-consuming. To address this challenge, we propose a novel diffusion-based pipeline for generating high-fidelity industrial datasets with minimal supervision. Our approach conditions the diffusion model on enriched bounding box representations to produce precise segmentation masks, ensuring realistic and accurately localized defect synthesis. Compared to existing layout-conditioned generative methods, our approach improves defect consistency and spatial accuracy. We introduce two quantitative metrics to evaluate the effectiveness of our method and assess its impact on a downstream segmentation task trained on real and synthetic data. Our results demonstrate that diffusion-based synthesis can bridge the gap between artificial and real-world industrial data, fostering more reliable and cost-efficient segmentation models. The code is publicly available at https://github.com/covisionlab/diffusion_labeling.", "published": "2025-05-06T15:21:36Z", "authors": ["Emanuele Caruso", "Alessandro Simoni", "Francesco Pelosin"], "categories": ["cs.CV"], "doi": "", "_query": "property valuation computer vision", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2603.21583v2", "title": "HACMatch Semi-Supervised Rotation Regression with Hardness-Aware Curriculum Pseudo Labeling", "abstract": "Regressing 3D rotations of objects from 2D images is a crucial yet challenging task, with broad applications in autonomous driving, virtual reality, and robotic control. Existing rotation regression models often rely on large amounts of labeled data for training or require additional information beyond 2D images, such as point clouds or CAD models. Therefore, exploring semi-supervised rotation regression using only a limited number of labeled 2D images is highly valuable. While recent work FisherMatch introduces semi-supervised learning to rotation regression, it suffers from rigid entropy-based pseudo-label filtering that fails to effectively distinguish between reliable and unreliable unlabeled samples. To address this limitation, we propose a hardness-aware curriculum learning framework that dynamically selects pseudo-labeled samples based on their difficulty, progressing from easy to complex examples. We introduce both multi-stage and adaptive curriculum strategies to replace fixed-threshold filtering with more flexible, hardness-aware mechanisms. Additionally, we present a novel structured data augmentation strategy specifically tailored for rotation estimation, which assembles composite images from augmented patches to introduce feature diversity while preserving critical geometric integrity. Comprehensive experiments on PASCAL3D+ and ObjectNet3D demonstrate that our method outperforms existing supervised and semi-supervised baselines, particularly in low-data regimes, validating the effectiveness of our curriculum learning framework and structured augmentation approach.", "published": "2026-03-23T05:24:03Z", "authors": ["Mei Li", "Huayi Zhou", "Suizhi Huang", "Yuxiang Lu", "Yue Ding", "Hongtao Lu"], "categories": ["cs.CV"], "doi": "10.1016/j.cviu.2026.104742", "_query": "property valuation computer vision", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/1705.03441v1", "title": "Improved Computation of Involutive Bases", "abstract": "In this paper, we describe improved algorithms to compute Janet and Pommaret bases. To this end, based on the method proposed by Moller et al., we present a more efficient variant of Gerdt's algorithm (than the algorithm presented by Gerdt-Hashemi-M.Alizadeh) to compute minimal involutive bases. Further, by using the involutive version of Hilbert driven technique, along with the new variant of Gerdt's algorithm, we modify the algorithm, given by Seiler, to compute a linear change of coordinates for a given homogeneous ideal so that the new ideal (after performing this change) possesses a finite Pommaret basis. All the proposed algorithms have been implemented in Maple and their efficiency is discussed via a set of benchmark polynomials.", "published": "2017-05-09T17:34:08Z", "authors": ["Bentolhoda Binaei", "Amir Hashemi", "Werner M. Seiler"], "categories": ["cs.SC", "math.AC", "math.AG"], "doi": "", "_query": "property valuation computer vision", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2212.03213v2", "title": "Twisted homology stability of O_n for valuation rings", "abstract": "In this article, we extend an argument of Vogtmann in order to show homology stability of the Euclidean orthogonal group $O_n(A)$ when $A$ is a valuation ring subject to arithmetic conditions on either its residue or its quotient field. In particular, it is shown that if $A$ is a henselian valuation ring, then the groups $O_n(A)$ exhibit homology stability if the residue field of $A$ has finite Pythagoras number. Our results include those of Vogtmann, and hold with various twisted coefficients. Using these results, we give analogues for fields $F\\neq\\mathbb R$ of some computations that appear in the study of scissor congruences.", "published": "2022-12-06T18:36:16Z", "authors": ["Oscar Harr"], "categories": ["math.AT", "math.RT"], "doi": "10.1016/j.jalgebra.2025.10.046", "_query": "property valuation computer vision", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2410.04386v1", "title": "Data Distribution Valuation", "abstract": "Data valuation is a class of techniques for quantitatively assessing the value of data for applications like pricing in data marketplaces. Existing data valuation methods define a value for a discrete dataset. However, in many use cases, users are interested in not only the value of the dataset, but that of the distribution from which the dataset was sampled. For example, consider a buyer trying to evaluate whether to purchase data from different vendors. The buyer may observe (and compare) only a small preview sample from each vendor, to decide which vendor's data distribution is most useful to the buyer and purchase. The core question is how should we compare the values of data distributions from their samples? Under a Huber characterization of the data heterogeneity across vendors, we propose a maximum mean discrepancy (MMD)-based valuation method which enables theoretically principled and actionable policies for comparing data distributions from samples. We empirically demonstrate that our method is sample-efficient and effective in identifying valuable data distributions against several existing baselines, on multiple real-world datasets (e.g., network intrusion detection, credit card fraud detection) and downstream applications (classification, regression).", "published": "2024-10-06T07:56:53Z", "authors": ["Xinyi Xu", "Shuaiqi Wang", "Chuan-Sheng Foo", "Bryan Kian Hsiang Low", "Giulia Fanti"], "categories": ["cs.LG"], "doi": "", "_query": "property valuation computer vision", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2107.12081v1", "title": "Towards the Unseen: Iterative Text Recognition by Distilling from Errors", "abstract": "Visual text recognition is undoubtedly one of the most extensively researched topics in computer vision. Great progress have been made to date, with the latest models starting to focus on the more practical \"in-the-wild\" setting. However, a salient problem still hinders practical deployment -- prior arts mostly struggle with recognising unseen (or rarely seen) character sequences. In this paper, we put forward a novel framework to specifically tackle this \"unseen\" problem. Our framework is iterative in nature, in that it utilises predicted knowledge of character sequences from a previous iteration, to augment the main network in improving the next prediction. Key to our success is a unique cross-modal variational autoencoder to act as a feedback module, which is trained with the presence of textual error distribution data. This module importantly translate a discrete predicted character space, to a continuous affine transformation parameter space used to condition the visual feature map at next iteration. Experiments on common datasets have shown competitive performance over state-of-the-arts under the conventional setting. Most importantly, under the new disjoint setup where train-test labels are mutually exclusive, ours offers the best performance thus showcasing the capability of generalising onto unseen words.", "published": "2021-07-26T10:06:42Z", "authors": ["Ayan Kumar Bhunia", "Pinaki Nath Chowdhury", "Aneeshan Sain", "Yi-Zhe Song"], "categories": ["cs.CV"], "doi": "", "_query": "property valuation computer vision", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/1904.03321v2", "title": "Computer Vision Approach to Study Deformation of Materials", "abstract": "Characterization of the deformation of materials across different length scales has continuously attracted enormous attention from the mechanics and materials communities. In this study, the possibility of utilizing a computer vision algorithm to extract deformation information of materials has been explored, which greatly expands the use of computer vision approaches to studying mechanics of materials and potentially opens new dialogues between the two communities. The computer vision algorithm is first developed and tested on computationally deformed images, before evaluating experimentally collected images on speckle painted samples before and after deformation. Moreover, a virtual experiment has also shown the feasibility of mapping surface strain of a sample based on its natural pattern with significantly improved accuracy compared to DIC result, which provides new opportunities in experimentation and computer algorithms to study deformation mechanics of materials. Validation experiments include evaluating the performance of strain mapping using the computer vision approach in the uniaxial tensile test and three-point bending test, compared with extensometer reading and digital image correlation respectively.", "published": "2019-04-05T23:51:38Z", "authors": ["Chaoyi Zhu", "Haoren Wang", "Kevin Kaufmann", "Kenneth Vecchio"], "categories": ["physics.comp-ph", "cond-mat.mtrl-sci"], "doi": "10.1088/1361-6501/ab65d9", "_query": "property valuation computer vision", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/1003.5689v1", "title": "Valuation theoretic and model theoretic aspects of local uniformization", "abstract": "This paper gives a survey on a valuation theoretical approach to local uniformization in positive characteristic, the model theory of valued fields in positive characteristic, and their connection with the valuation theoretical phenomenon of defect.", "published": "2010-03-29T21:46:52Z", "authors": ["Franz-Viktor Kuhlmann"], "categories": ["math.AC"], "doi": "", "_query": "property valuation computer vision", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2411.07684v1", "title": "AI enhanced diagnosis of Peyronies disease a novel approach using Computer Vision", "abstract": "This study presents an innovative AI-driven tool for diagnosing Peyronie's Disease (PD), a condition that affects between 0.3% and 13.1% of men worldwide. Our method uses key point detection on both images and videos to measure penile curvature angles, utilizing advanced computer vision techniques. This tool has demonstrated high accuracy in identifying anatomical landmarks, validated against conventional goniometer measurements. Traditional PD diagnosis often involves subjective and invasive methods, which can lead to patient discomfort and inaccuracies. Our approach offers a precise, reliable, and non-invasive diagnostic tool to address these drawbacks. The model distinguishes between PD and normal anatomical changes with a sensitivity of 96.7% and a specificity of 100%. This advancement represents a significant improvement in urological diagnostics, greatly enhancing the efficacy and convenience of PD assessment for healthcare providers and patients.", "published": "2024-11-12T09:56:42Z", "authors": ["Yudara Kularathne", "Janitha Prathapa", "Prarththanan Sothyrajah", "Salomi Arasaratnam", "Sithira Ambepitiya", "Thanveer Ahamed", "Dinuka Wijesundara"], "categories": ["eess.IV", "cs.AI", "cs.CV"], "doi": "", "_query": "property valuation computer vision", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/1908.04130v2", "title": "Jointly Aligning Millions of Images with Deep Penalised Reconstruction Congealing", "abstract": "Extrapolating fine-grained pixel-level correspondences in a fully unsupervised manner from a large set of misaligned images can benefit several computer vision and graphics problems, e.g. co-segmentation, super-resolution, image edit propagation, structure-from-motion, and 3D reconstruction. Several joint image alignment and congealing techniques have been proposed to tackle this problem, but robustness to initialisation, ability to scale to large datasets, and alignment accuracy seem to hamper their wide applicability. To overcome these limitations, we propose an unsupervised joint alignment method leveraging a densely fused spatial transformer network to estimate the warping parameters for each image and a low-capacity auto-encoder whose reconstruction error is used as an auxiliary measure of joint alignment. Experimental results on digits from multiple versions of MNIST (i.e., original, perturbed, affNIST and infiMNIST) and faces from LFW, show that our approach is capable of aligning millions of images with high accuracy and robustness to different levels and types of perturbation. Moreover, qualitative and quantitative results suggest that the proposed method outperforms state-of-the-art approaches both in terms of alignment quality and robustness to initialisation.", "published": "2019-08-12T12:55:31Z", "authors": ["Roberto Annunziata", "Christos Sagonas", "Jacques Cali"], "categories": ["cs.CV"], "doi": "", "_query": "property valuation computer vision", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/1505.07581v4", "title": "Algebraic properties of ideals of poset homomorphisms", "abstract": "Given finite posets $P$ and $Q$, we consider a specific ideal $L(P,Q)$, whose minimal monomial generators correspond to order-preserving maps $φ:P\\rightarrow Q$. We study algebraic invariants of those ideals. In particular, sharp lower and upper bounds for the Castelnuovo-Mumford regularity and the projective dimension are provided. Precise formulas are obtained for a large subclass of these ideals. Moreover, we provide complete characterizations for several algebraic properties of $L(P,Q)$, including being Buchsbaum, Cohen-Macaulay, Gorenstein and having a linear resolution. We also give a partial characterization for Golod property of $L(P,Q)$. Using those results, we derive applications for other important classes of monomial ideals, such as initial ideals of determinantal ideals and multichain ideals.", "published": "2015-05-28T08:03:48Z", "authors": ["Martina Juhnke-Kubitzke", "Lukas Katthän", "Sara Saeedi Madani"], "categories": ["math.AC", "math.CO"], "doi": "", "_query": "property valuation computer vision", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2003.08857v2", "title": "Examples of property (T) II$_1$ factors with trivial fundamental group", "abstract": "In this article we provide the first examples of property (T) $\\rm II_1$ factors $\\mathcal N$ with trivial fundamental group, $\\mathcal F (\\mathcal N)=1$. Our examples arise as group factors $\\mathcal N=\\mathcal L(G)$ where $G$ belong to two distinct families of property (T) groups previously studied in the literature: the groups introduced by Valette in \\cite{Va04} and the ones introduced recently in \\cite{CDK19} using the Belegradek-Osin Rips construction from \\cite{BO06}. In particular, our results provide a continuum of explicit pairwise non-isomorphic property (T) factors.", "published": "2020-03-19T15:09:52Z", "authors": ["Ionut Chifan", "Sayan Das", "Cyril Houdayer", "Krishnendu Khan"], "categories": ["math.OA", "math.GR"], "doi": "", "_query": "property valuation computer vision", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/1708.02204v1", "title": "Curvature sensing by vision and touch", "abstract": "Brain representations of curvature may be formed on the basis of either vision or touch. Experimental and theoretical work by the author and her colleagues has shown that the processing underlying such representations directly depends on specific two-dimensional geometric properties of the curved object, and on the symmetry of curvature. Virtual representations of curves with mirror symmetry were displayed in 2D on a computer screen to sighted observers for visual scaling. For tactile (haptic) scaling, the physical counterparts of these curves were placed in the two hands of sighted observers, who were blindfolded during the sensing experiment, and of congenitally blind observers, who never had any visual experience. All results clearly show that curvature, whether haptically or visually sensed, is statistically linked to the same curve properties. Sensation is expressed psychophysically as a power function of any symmetrical curve's aspect ratio, a scale invariant geometric property of physical objects. The results of the author's work support biologically motivated models of sensory integration for curvature processing. They also promote the idea of a universal power law for adaptive brain control and balancing of motor responses to environmental stimuli across sensory modalities.", "published": "2017-08-07T17:02:51Z", "authors": ["Birgitta Dresp-Langley"], "categories": ["q-bio.NC"], "doi": "10.1155/2013/634168", "_query": "property valuation computer vision", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2008.00992v2", "title": "An Exploration of Target-Conditioned Segmentation Methods for Visual Object Trackers", "abstract": "Visual object tracking is the problem of predicting a target object's state in a video. Generally, bounding-boxes have been used to represent states, and a surge of effort has been spent by the community to produce efficient causal algorithms capable of locating targets with such representations. As the field is moving towards binary segmentation masks to define objects more precisely, in this paper we propose to extensively explore target-conditioned segmentation methods available in the computer vision community, in order to transform any bounding-box tracker into a segmentation tracker. Our analysis shows that such methods allow trackers to compete with recently proposed segmentation trackers, while performing quasi real-time.", "published": "2020-08-03T16:21:18Z", "authors": ["Matteo Dunnhofer", "Niki Martinel", "Christian Micheloni"], "categories": ["cs.CV"], "doi": "10.1007/978-3-030-68238-5_41", "_query": "property valuation computer vision", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2512.02458v2", "title": "Vision to Geometry: 3D Spatial Memory for Sequential Embodied MLLM Reasoning and Exploration", "abstract": "Embodied agents are expected to assist humans by actively exploring unknown environments and reasoning about spatial contexts. When deployed in real life, agents often face sequential tasks where each new task follows the completion of the previous one and may include infeasible objectives, such as searching for non-existent objects. However, most existing research focuses on isolated goals, overlooking the core challenge of sequential tasks: the ability to reuse spatial knowledge accumulated from previous explorations to guide subsequent reasoning and exploration. In this work, we investigate this underexplored yet practically significant embodied AI challenge. Specifically, we propose 3DSPMR, a 3D SPatial Memory Reasoning framework that utilizes Field-of-View (FoV) coverage as an explicit geometric prior. By integrating FoV-based constraints, 3DSPMR significantly enhances an agent's memory, reasoning, and exploration capabilities across sequential tasks. To facilitate research in this area, we further introduce SEER-Bench, a novel Sequential Embodied Exploration and Reasoning Benchmark that spans two foundational tasks: Embodied Question Answering (EQA) and Embodied Multi-modal Navigation (EMN). SEER-Bench uniquely incorporates both feasible and infeasible tasks to provide a rigorous and comprehensive evaluation of agent performance. Extensive experiments verify that 3DSPMR achieves substantial performance gains on both sequential EQA and EMN tasks.", "published": "2025-12-02T06:35:30Z", "authors": ["Zhongyi Cai", "Yi Du", "Chen Wang", "Yu Kong"], "categories": ["cs.CV"], "doi": "", "_query": "property valuation computer vision", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2604.27230v1", "title": "Now's the Time: Computer Science Must Evolve to Emphasize Software and Systems Engineering with Artificial Intelligence (AI)", "abstract": "Computer science (CS) education needs to evolve to support software and artificial intelligence (AI) systems engineering, and it needs to happen now -- precisely because the core intellectual contributions of CS have never been more important. We argue that traditional curricula, built around programming, data structures, and algorithms as ends in themselves, must be reframed so that these topics become foundational building blocks within a systems- and engineering-centered education. Graduates should be prepared not to compete with AI on routine coding tasks, but to design, orchestrate, verify, and own complex AI-enabled systems operating under real-world constraints. More importantly, computer science education should be geared toward preparing students for future disruptions. The broad history of computing is marked by one disruptive technology after another, requiring us to rise to the moment instead of merely acquiescing to it.", "published": "2026-04-29T21:55:45Z", "authors": ["Chandra N. Sekharan", "George K. Thiruvathukal"], "categories": ["cs.SE"], "doi": "", "_query": "property valuation computer vision", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2308.11223v1", "title": "LDP-Feat: Image Features with Local Differential Privacy", "abstract": "Modern computer vision services often require users to share raw feature descriptors with an untrusted server. This presents an inherent privacy risk, as raw descriptors may be used to recover the source images from which they were extracted. To address this issue, researchers recently proposed privatizing image features by embedding them within an affine subspace containing the original feature as well as adversarial feature samples. In this paper, we propose two novel inversion attacks to show that it is possible to (approximately) recover the original image features from these embeddings, allowing us to recover privacy-critical image content. In light of such successes and the lack of theoretical privacy guarantees afforded by existing visual privacy methods, we further propose the first method to privatize image features via local differential privacy, which, unlike prior approaches, provides a guaranteed bound for privacy leakage regardless of the strength of the attacks. In addition, our method yields strong performance in visual localization as a downstream task while enjoying the privacy guarantee.", "published": "2023-08-22T06:28:55Z", "authors": ["Francesco Pittaluga", "Bingbing Zhuang"], "categories": ["cs.CV", "cs.CR"], "doi": "", "_query": "property valuation computer vision", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2607.16838v1", "title": "TopoGS: Planar Reconstruction via Topology-aware 3D Gaussian Splatting", "abstract": "Extracting structured, parametric 3D representations from raw images remains a fundamental challenge in computer vision and graphics. While recent advancements in the 3D Gaussian Splatting (3DGS) pipeline integrate planar primitives to yield compact and editable geometry, these approaches typically treat planes as isolated, discrete sets. This lack of topological connectivity hinders robust geometric reasoning, leading to fragmented reconstructions and misaligned boundaries that fall short of the precision for rigorous spatial analysis and professional design workflows. To address this, we introduce TopoGS, the first 3DGS framework to explicitly integrate both planar and topological constraints for coherent 3D reconstruction. Specifically, we extract global 2D topological relationships from multi-view image segmentations and anchor Gaussian primitives to these structural elements. This formulation enables the joint optimization of plane parameters, rendering fidelity, and topological adjacency. By enforcing strict multi-view consistency alongside these topological constraints, our method significantly mitigates geometric misalignments and produces connected, structured 3D models. Extensive evaluations on the ScanNet++ dataset demonstrate that TopoGS achieves state-of-the-art performance, providing a highly robust solution for generating accurate, topologically sound, and visually faithful scene representations.", "published": "2026-07-18T14:27:49Z", "authors": ["Shanshan Pan", "Jiale Chen", "Yilin Liu", "Hui Huang"], "categories": ["cs.CV", "cs.GR"], "doi": "", "_query": "property valuation computer vision", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2104.11227v1", "title": "Multiscale Vision Transformers", "abstract": "We present Multiscale Vision Transformers (MViT) for video and image recognition, by connecting the seminal idea of multiscale feature hierarchies with transformer models. Multiscale Transformers have several channel-resolution scale stages. Starting from the input resolution and a small channel dimension, the stages hierarchically expand the channel capacity while reducing the spatial resolution. This creates a multiscale pyramid of features with early layers operating at high spatial resolution to model simple low-level visual information, and deeper layers at spatially coarse, but complex, high-dimensional features. We evaluate this fundamental architectural prior for modeling the dense nature of visual signals for a variety of video recognition tasks where it outperforms concurrent vision transformers that rely on large scale external pre-training and are 5-10x more costly in computation and parameters. We further remove the temporal dimension and apply our model for image classification where it outperforms prior work on vision transformers. Code is available at: https://github.com/facebookresearch/SlowFast", "published": "2021-04-22T17:59:45Z", "authors": ["Haoqi Fan", "Bo Xiong", "Karttikeya Mangalam", "Yanghao Li", "Zhicheng Yan", "Jitendra Malik", "Christoph Feichtenhofer"], "categories": ["cs.CV", "cs.AI", "cs.LG"], "doi": "", "_query": "property valuation computer vision", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2409.09330v1", "title": "VOMTC: Vision Objects for Millimeter and Terahertz Communications", "abstract": "Recent advances in sensing and computer vision (CV) technologies have opened the door for the application of deep learning (DL)-based CV technologies in the realm of 6G wireless communications. For the successful application of this emerging technology, it is crucial to have a qualified vision dataset tailored for wireless applications (e.g., RGB images containing wireless devices such as laptops and cell phones). An aim of this paper is to propose a large-scale vision dataset referred to as Vision Objects for Millimeter and Terahertz Communications (VOMTC). The VOMTC dataset consists of 20,232 pairs of RGB and depth images obtained from a camera attached to the base station (BS), with each pair labeled with three representative object categories (person, cell phone, and laptop) and bounding boxes of the objects. Through experimental studies of the VOMTC datasets, we show that the beamforming technique exploiting the VOMTC-trained object detector outperforms conventional beamforming techniques.", "published": "2024-09-14T06:18:51Z", "authors": ["Sunwoo Kim", "Yongjun Ahn", "Daeyoung Park", "Byonghyo Shim"], "categories": ["cs.NI", "cs.CV"], "doi": "10.1109/TCCN.2024.3435909", "_query": "property valuation computer vision", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2012.06573v2", "title": "Risk & returns around FOMC press conferences: a novel perspective from computer vision", "abstract": "I propose a new tool to characterize the resolution of uncertainty around FOMC press conferences. It relies on the construction of a measure capturing the level of discussion complexity between the Fed Chair and reporters during the Q&A sessions. I show that complex discussions are associated with higher equity returns and a drop in realized volatility. The method creates an attention score by quantifying how much the Chair needs to rely on reading internal documents to be able to answer a question. This is accomplished by building a novel dataset of video images of the press conferences and leveraging recent deep learning algorithms from computer vision. This alternative data provides new information on nonverbal communication that cannot be extracted from the widely analyzed FOMC transcripts. This paper can be seen as a proof of concept that certain videos contain valuable information for the study of financial markets.", "published": "2020-12-11T18:59:47Z", "authors": ["Alexis Marchal"], "categories": ["stat.ML", "cs.CV", "cs.LG", "q-fin.GN"], "doi": "", "_query": "property valuation computer vision", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2408.16916v2", "title": "A Computational Framework for Modeling Emergence of Color Vision in the Human Brain", "abstract": "It is a mystery how the brain decodes color vision purely from the optic nerve signals it receives, with a core inferential challenge being how it disentangles internal perception with the correct color dimensionality from the unknown encoding properties of the eye. In this paper, we introduce a computational framework for modeling this emergence of human color vision by simulating both the eye and the cortex. Existing research often overlooks how the cortex develops color vision or represents color space internally, assuming that the color dimensionality is known a priori; however, we argue that the visual cortex has the capability and the challenge of inferring the color dimensionality purely from fluctuations in the optic nerve signals. To validate our theory, we introduce a simulation engine for biological eyes based on established vision science and generate optic nerve signals resulting from looking at natural images. Further, we propose a bio-plausible model of cortical learning based on self-supervised prediction of optic nerve signal fluctuations under natural eye motions. We show that this model naturally learns to generate color vision by disentangling retinal invariants from the sensory signals. When the retina contains N types of color photoreceptors, our simulation shows that N-dimensional color vision naturally emerges, verified through formal colorimetry. Using this framework, we also present the first simulation work that successfully boosts the color dimensionality, as observed in gene therapy on squirrel monkeys, and demonstrates the possibility of enhancing human color vision from 3D to 4D.", "published": "2024-08-29T21:27:06Z", "authors": ["Atsunobu Kotani", "Ren Ng"], "categories": ["cs.NE", "q-bio.NC"], "doi": "", "_query": "property valuation computer vision", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2208.00761v2", "title": "AI Augmented Edge and Fog Computing: Trends and Challenges", "abstract": "In recent years, the landscape of computing paradigms has witnessed a gradual yet remarkable shift from monolithic computing to distributed and decentralized paradigms such as Internet of Things (IoT), Edge, Fog, Cloud, and Serverless. The frontiers of these computing technologies have been boosted by shift from manually encoded algorithms to Artificial Intelligence (AI)-driven autonomous systems for optimum and reliable management of distributed computing resources. Prior work focuses on improving existing systems using AI across a wide range of domains, such as efficient resource provisioning, application deployment, task placement, and service management. This survey reviews the evolution of data-driven AI-augmented technologies and their impact on computing systems. We demystify new techniques and draw key insights in Edge, Fog and Cloud resource management-related uses of AI methods and also look at how AI can innovate traditional applications for enhanced Quality of Service (QoS) in the presence of a continuum of resources. We present the latest trends and impact areas such as optimizing AI models that are deployed on or for computing systems. We layout a roadmap for future research directions in areas such as resource management for QoS optimization and service reliability. Finally, we discuss blue-sky ideas and envision this work as an anchor point for future research on AI-driven computing systems.", "published": "2022-08-01T11:41:04Z", "authors": ["Shreshth Tuli", "Fatemeh Mirhakimi", "Samodha Pallewatta", "Syed Zawad", "Giuliano Casale", "Bahman Javadi", "Feng Yan", "Rajkumar Buyya", "Nicholas R. Jennings"], "categories": ["cs.DC"], "doi": "", "_query": "property valuation computer vision", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2005.11362v2", "title": "Stable and expressive recurrent vision models", "abstract": "Primate vision depends on recurrent processing for reliable perception. A growing body of literature also suggests that recurrent connections improve the learning efficiency and generalization of vision models on classic computer vision challenges. Why then, are current large-scale challenges dominated by feedforward networks? We posit that the effectiveness of recurrent vision models is bottlenecked by the standard algorithm used for training them, \"back-propagation through time\" (BPTT), which has O(N) memory-complexity for training an N step model. Thus, recurrent vision model design is bounded by memory constraints, forcing a choice between rivaling the enormous capacity of leading feedforward models or trying to compensate for this deficit through granular and complex dynamics. Here, we develop a new learning algorithm, \"contractor recurrent back-propagation\" (C-RBP), which alleviates these issues by achieving constant O(1) memory-complexity with steps of recurrent processing. We demonstrate that recurrent vision models trained with C-RBP can detect long-range spatial dependencies in a synthetic contour tracing task that BPTT-trained models cannot. We further show that recurrent vision models trained with C-RBP to solve the large-scale Panoptic Segmentation MS-COCO challenge outperform the leading feedforward approach, with fewer free parameters. C-RBP is a general-purpose learning algorithm for any application that can benefit from expansive recurrent dynamics. Code and data are available at https://github.com/c-rbp.", "published": "2020-05-22T19:31:28Z", "authors": ["Drew Linsley", "Alekh Karkada Ashok", "Lakshmi Narasimhan Govindarajan", "Rex Liu", "Thomas Serre"], "categories": ["cs.CV", "cs.LG"], "doi": "", "_query": "property valuation computer vision", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2510.15602v1", "title": "Quantized FCA: Efficient Zero-Shot Texture Anomaly Detection", "abstract": "Zero-shot anomaly localization is a rising field in computer vision research, with important progress in recent years. This work focuses on the problem of detecting and localizing anomalies in textures, where anomalies can be defined as the regions that deviate from the overall statistics, violating the stationarity assumption. The main limitation of existing methods is their high running time, making them impractical for deployment in real-world scenarios, such as assembly line monitoring. We propose a real-time method, named QFCA, which implements a quantized version of the feature correspondence analysis (FCA) algorithm. By carefully adapting the patch statistics comparison to work on histograms of quantized values, we obtain a 10x speedup with little to no loss in accuracy. Moreover, we introduce a feature preprocessing step based on principal component analysis, which enhances the contrast between normal and anomalous features, improving the detection precision on complex textures. Our method is thoroughly evaluated against prior art, comparing favorably with existing methods. Project page: https://reality.tf.fau.de/pub/ardelean2025quantized.html", "published": "2025-10-17T12:48:59Z", "authors": ["Andrei-Timotei Ardelean", "Patrick Rückbeil", "Tim Weyrich"], "categories": ["cs.CV"], "doi": "10.2312/vmv.20251240", "_query": "property valuation computer vision", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2608.09961v1", "title": "On the Relationship Between Semiring-Induced Valuation Algebra Isomorphisms and Semiring Isomorphisms", "abstract": "This paper establishes a bidirectional relationship between isomorphisms of semirings and isomorphisms of their induced valuation algebras. We show that a semiring isomorphism is sufficient for a valuation algebra isomorphism. Conversely, for additively idempotent semirings without multiplicatively idempotent zero divisors, the existence of a valuation algebra isomorphism implies that the underlying semirings are isomorphic.", "published": "2026-07-25T00:54:34Z", "authors": ["Xuechong Guan"], "categories": ["math.RA", "math.AC"], "doi": "", "_query": "property valuation computer vision", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2506.12619v1", "title": "Semivalue-based data valuation is arbitrary and gameable", "abstract": "The game-theoretic notion of the semivalue offers a popular framework for credit attribution and data valuation in machine learning. Semivalues have been proposed for a variety of high-stakes decisions involving data, such as determining contributor compensation, acquiring data from external sources, or filtering out low-value datapoints. In these applications, semivalues depend on the specification of a utility function that maps subsets of data to a scalar score. While it is broadly agreed that this utility function arises from a composition of a learning algorithm and a performance metric, its actual instantiation involves numerous subtle modeling choices. We argue that this underspecification leads to varying degrees of arbitrariness in semivalue-based valuations. Small, but arguably reasonable changes to the utility function can induce substantial shifts in valuations across datapoints. Moreover, these valuation methodologies are also often gameable: low-cost adversarial strategies exist to exploit this ambiguity and systematically redistribute value among datapoints. Through theoretical constructions and empirical examples, we demonstrate that a bad-faith valuator can manipulate utility specifications to favor preferred datapoints, and that a good-faith valuator is left without principled guidance to justify any particular specification. These vulnerabilities raise ethical and epistemic concerns about the use of semivalues in several applications. We conclude by highlighting the burden of justification that semivalue-based approaches place on modelers and discuss important considerations for identifying appropriate uses.", "published": "2025-06-14T20:20:15Z", "authors": ["Hannah Diehl", "Ashia C. Wilson"], "categories": ["cs.LG", "cs.GT"], "doi": "", "_query": "property valuation computer vision", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2302.00416v1", "title": "Valuations on Convex Bodies and Functions", "abstract": "An introduction to geometric valuation theory is given. The focus is on classification results for $\\operatorname{SL}(n)$ invariant and rigid motion invariant valuations on convex bodies and on convex functions.", "published": "2023-02-01T13:02:26Z", "authors": ["Monika Ludwig", "Fabian Mussnig"], "categories": ["math.MG", "math.FA"], "doi": "10.1007/978-3-031-37883-6_2", "_query": "property valuation computer vision", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/1903.08384v2", "title": "Combinatorial Auctions with Interdependent Valuations: SOS to the Rescue", "abstract": "We study combinatorial auctions with interdependent valuations. In such settings, each agent $i$ has a private signal $s_i$ that captures her private information, and the valuation function of every agent depends on the entire signal profile, ${\\bf s}=(s_1,\\ldots,s_n)$. The literature in economics shows that the interdependent model gives rise to strong impossibility results, and identifies assumptions under which optimal solutions can be attained. The computer science literature provides approximation results for simple single-parameter settings (mostly single item auctions, or matroid feasibility constraints). Both bodies of literature focus largely on valuations satisfying a technical condition termed {\\em single crossing} (or variants thereof). We consider the class of {\\em submodular over signals} (SOS) valuations (without imposing any single-crossing type assumption), and provide the first welfare approximation guarantees for multi-dimensional combinatorial auctions, achieved by universally ex-post IC-IR mechanisms. Our main results are: $(i)$ 4-approximation for any single-parameter downward-closed setting with single-dimensional signals and SOS valuations; $(ii)$ 4-approximation for any combinatorial auction with multi-dimensional signals and {\\em separable}-SOS valuations; and $(iii)$ $(k+3)$- and $(2\\log(k)+4)$-approximation for any combinatorial auction with single-dimensional signals, with $k$-sized signal space, for SOS and strong-SOS valuations, respectively. All of our results extend to a parameterized version of SOS, $d$-SOS, while losing a factor that depends on $d$.", "published": "2019-03-20T08:34:13Z", "authors": ["Alon Eden", "Michal Feldman", "Amos Fiat", "Kira Goldner", "Anna R. Karlin"], "categories": ["cs.GT"], "doi": "", "_query": "property valuation computer vision", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2510.14434v1", "title": "The valuation of the discriminant of a hypersurface", "abstract": "Let $R$ be a discrete valuation ring, with valuation $v \\colon R \\twoheadrightarrow \\mathbb{Z}_{\\ge 0} \\cup \\{\\infty\\}$ and residue field $k$. Let $H$ be a hypersurface $\\operatorname{Proj}(R[x_0,\\ldots,x_n]/\\langle f \\rangle)$. Let $H_k$ be the special fiber, and let $(H_k)_{\\mathrm{sing}}$ be its singular subscheme. Let $Δ(f)$ be the discriminant of $f$. We use Zariski's main theorem and degeneration arguments to prove that $v(Δ(f))=1$ if and only if $H$ is regular and $(H_k)_{\\mathrm{sing}}$ consists of a nondegenerate double point over $k$. We also give lower bounds on $v(Δ(f))$ when $H_k$ has multiple singularities or a positive-dimensional singularity.", "published": "2025-10-16T08:36:20Z", "authors": ["Bjorn Poonen", "Michael Stoll"], "categories": ["math.AG", "math.NT"], "doi": "", "_query": "property valuation computer vision", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2108.05789v1", "title": "Presenting an extensive lab- and field-image dataset of crops and weeds for computer vision tasks in agriculture", "abstract": "We present two large datasets of labelled plant-images that are suited towards the training of machine learning and computer vision models. The first dataset encompasses as the day of writing over 1.2 million images of indoor-grown crops and weeds common to the Canadian Prairies and many US states. The second dataset consists of over 540,000 images of plants imaged in farmland. All indoor plant images are labelled by species and we provide rich etadata on the level of individual images. This comprehensive database allows to filter the datasets under user-defined specifications such as for example the crop-type or the age of the plant. Furthermore, the indoor dataset contains images of plants taken from a wide variety of angles, including profile shots, top-down shots, and angled perspectives. The images taken from plants in fields are all from a top-down perspective and contain usually multiple plants per image. For these images metadata is also available. In this paper we describe both datasets' characteristics with respect to plant variety, plant age, and number of images. We further introduce an open-access sample of the indoor-dataset that contains 1,000 images of each species covered in our dataset. These, in total 14,000 images, had been selected, such that they form a representative sample with respect to plant age and ndividual plants per species. This sample serves as a quick entry point for new users to the dataset, allowing them to explore the data on a small scale and find the parameters of data most useful for their application without having to deal with hundreds of thousands of individual images.", "published": "2021-08-12T15:06:32Z", "authors": ["Michael A. Beck", "Chen-Yi Liu", "Christopher P. Bidinosti", "Christopher J. Henry", "Cara M. Godee", "Manisha Ajmani"], "categories": ["cs.CV", "cs.AI", "cs.RO"], "doi": "", "_query": "property valuation computer vision", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2203.01907v1", "title": "Computer Vision Aided Blockage Prediction in Real-World Millimeter Wave Deployments", "abstract": "This paper provides the first real-world evaluation of using visual (RGB camera) data and machine learning for proactively predicting millimeter wave (mmWave) dynamic link blockages before they happen. Proactively predicting line-of-sight (LOS) link blockages enables mmWave/sub-THz networks to make proactive network management decisions, such as proactive beam switching and hand-off) before a link failure happens. This can significantly enhance the network reliability and latency while efficiently utilizing the wireless resources. To evaluate this gain in reality, this paper (i) develops a computer vision based solution that processes the visual data captured by a camera installed at the infrastructure node and (ii) studies the feasibility of the proposed solution based on the large-scale real-world dataset, DeepSense 6G, that comprises multi-modal sensing and communication data. Based on the adopted real-world dataset, the developed solution achieves $\\approx 90\\%$ accuracy in predicting blockages happening within the future $0.1$s and $\\approx 80\\%$ for blockages happening within $1$s, which highlights a promising solution for mmWave/sub-THz communication networks.", "published": "2022-03-03T18:38:10Z", "authors": ["Gouranga Charan", "Ahmed Alkhateeb"], "categories": ["eess.SP", "cs.CV"], "doi": "", "_query": "property valuation computer vision", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2303.16963v1", "title": "Fairness-Aware Data Valuation for Supervised Learning", "abstract": "Data valuation is a ML field that studies the value of training instances towards a given predictive task. Although data bias is one of the main sources of downstream model unfairness, previous work in data valuation does not consider how training instances may influence both performance and fairness of ML models. Thus, we propose Fairness-Aware Data vauatiOn (FADO), a data valuation framework that can be used to incorporate fairness concerns into a series of ML-related tasks (e.g., data pre-processing, exploratory data analysis, active learning). We propose an entropy-based data valuation metric suited to address our two-pronged goal of maximizing both performance and fairness, which is more computationally efficient than existing metrics. We then show how FADO can be applied as the basis for unfairness mitigation pre-processing techniques. Our methods achieve promising results -- up to a 40 p.p. improvement in fairness at a less than 1 p.p. loss in performance compared to a baseline -- and promote fairness in a data-centric way, where a deeper understanding of data quality takes center stage.", "published": "2023-03-29T18:51:13Z", "authors": ["José Pombal", "Pedro Saleiro", "Mário A. T. Figueiredo", "Pedro Bizarro"], "categories": ["cs.LG", "cs.CY"], "doi": "", "_query": "property valuation computer vision", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/1605.06425v2", "title": "Extension of valuations in characteristic one", "abstract": "We develop an extension of valuations theorem for suitable extensions of idempotent semirings. As an application, we give a new proof for the classical case of fields. Along the way, we develop characteristic one analogues of some central results in the theory of valuation rings.", "published": "2016-05-20T16:28:49Z", "authors": ["Jeffrey Tolliver"], "categories": ["math.RA", "math.NT"], "doi": "", "_query": "property valuation computer vision", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2410.15817v2", "title": "LaMP-Val: Large Language Models Empower Personalized Valuation in Auction", "abstract": "Auctions are a vital economic mechanism used to determine the market value of goods or services through competitive bidding within a specific framework. However, much of the current research primarily focuses on the bidding algorithms used within auction mechanisms. This often neglects the potential benefits of incorporating individual users' unique preferences into the valuation process. Our theoretical and empirical analysis demonstrates that valuation errors can significantly impact the overall utility. To bridge this gap, we propose a personalized valuation framework, namely Large \\underline{La}nguage \\underline{M}odels-powered \\underline{P}ersonalized \\underline{Val}uation (LaMP-Val), which integrates Large Language Models to incorporate personalized semantic preference into users valuation process. LaMP-Val integrating three components: data, learning, and evaluation. The data component tackles the challenge of building a novel dataset specifically for LLMs fine-tuning in personalized valuation modeling. The learning component introduces a diversity template to enhance LLMs' capacity for modeling fine-grained personal valuation patterns. The evaluation component establishes a closed-loop system where LLM-generated valuations interact with bidding strategies and auction. It proposes two novel metrics to quantify valuation precision and bidding intention accuracy in personalized scenarios. Extensive experiments show that LaMP-Val more accurately captures personalized values and achieves greater profits than baseline approaches.", "published": "2024-10-21T09:31:51Z", "authors": ["Jie Sun", "Tianyu Zhang", "Houcheng Jiang", "Kexin Huang", "Xiang Shu", "Zhibo Zhu", "Lintao Ma", "Xingyu Lu", "Jun Zhou", "Junkang Wu", "Chi Luo", "An Zhang", "Junkang Wu", "Jiancan Wu", "Xiang Wang"], "categories": ["cs.CE"], "doi": "", "_query": "property valuation computer vision", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/1808.00118v1", "title": "Toward Multimodal Interaction in Scalable Visual Digital Evidence Visualization Using Computer Vision Techniques and ISS", "abstract": "Visualization requirements in Forensic Lucid have to do with different levels of case knowledge abstraction, representation, aggregation, as well as the operational aspects as the final long-term goal of this proposal. It encompasses anything from the finer detailed representation of hierarchical contexts to Forensic Lucid programs, to the documented evidence and its management, its linkage to programs, to evaluation, and to the management of GIPSY software networks. This includes an ability to arbitrarily switch between those views combined with usable multimodal interaction. The purpose is to determine how the findings can be applied to Forensic Lucid and investigation case management. It is also natural to want a convenient and usable evidence visualization, its semantic linkage and the reasoning machinery for it. Thus, we propose a scalable management, visualization, and evaluation of digital evidence using the modified interactive 3D documentary system - Illimitable Space System - (ISS) to represent, semantically link, and provide a usable interface to digital investigators that is navigable via different multimodal interaction techniques using Computer Vision techniques including gestures, as well as eye-gaze and audio.", "published": "2018-08-01T00:28:32Z", "authors": ["Serguei A. Mokhov", "Miao Song", "Jashanjot Singh", "Joey Paquet", "Mourad Debbabi", "Sudhir Mudur"], "categories": ["cs.CV", "cs.CR", "cs.HC"], "doi": "", "_query": "property valuation computer vision", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2302.04869v1", "title": "Reversible Vision Transformers", "abstract": "We present Reversible Vision Transformers, a memory efficient architecture design for visual recognition. By decoupling the GPU memory requirement from the depth of the model, Reversible Vision Transformers enable scaling up architectures with efficient memory usage. We adapt two popular models, namely Vision Transformer and Multiscale Vision Transformers, to reversible variants and benchmark extensively across both model sizes and tasks of image classification, object detection and video classification. Reversible Vision Transformers achieve a reduced memory footprint of up to 15.5x at roughly identical model complexity, parameters and accuracy, demonstrating the promise of reversible vision transformers as an efficient backbone for hardware resource limited training regimes. Finally, we find that the additional computational burden of recomputing activations is more than overcome for deeper models, where throughput can increase up to 2.3x over their non-reversible counterparts. Full code and trained models are available at https://github.com/facebookresearch/slowfast. A simpler, easy to understand and modify version is also available at https://github.com/karttikeya/minREV", "published": "2023-02-09T18:59:54Z", "authors": ["Karttikeya Mangalam", "Haoqi Fan", "Yanghao Li", "Chao-Yuan Wu", "Bo Xiong", "Christoph Feichtenhofer", "Jitendra Malik"], "categories": ["cs.CV", "cs.AI"], "doi": "", "_query": "property valuation computer vision", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2403.19969v2", "title": "Separate, Dynamic and Differentiable (SMART) Pruner for Block/Output Channel Pruning on Computer Vision Tasks", "abstract": "Block pruning, which eliminates contiguous blocks of weights, is a structural pruning method that can significantly enhance the performance of neural processing units (NPUs). In industrial applications, an ideal block pruning algorithm should meet three key requirements: (1) maintain high accuracy across diverse models and tasks, as machine learning deployments on edge devices are typically accuracy-critical; (2) offer precise control over resource constraints to facilitate user adoption; and (3) provide convergence guarantees to prevent performance instability. However, to the best of our knowledge, no existing block pruning algorithm satisfies all three requirements simultaneously. In this paper, we introduce SMART (Separate, Dynamic, and Differentiable) pruning, a novel algorithm designed to address this gap. SMART leverages both weight and activation information to enhance accuracy, employs a differentiable top-k operator for precise control of resource constraints, and offers convergence guarantees under mild conditions. Extensive experiments involving seven models, four datasets, three different block types, and three computer vision tasks demonstrate that SMART pruning achieves state-of-the-art performance in block pruning.", "published": "2024-03-29T04:28:06Z", "authors": ["Guanhua Ding", "Zexi Ye", "Zhen Zhong", "Gang Li", "David Shao"], "categories": ["cs.CV", "cs.LG"], "doi": "", "_query": "property valuation computer vision", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/1906.07328v2", "title": "Losing Confidence in Quality: Unspoken Evolution of Computer Vision Services", "abstract": "Recent advances in artificial intelligence (AI) and machine learning (ML), such as computer vision, are now available as intelligent services and their accessibility and simplicity is compelling. Multiple vendors now offer this technology as cloud services and developers want to leverage these advances to provide value to end-users. However, there is no firm investigation into the maintenance and evolution risks arising from use of these intelligent services; in particular, their behavioural consistency and transparency of their functionality. We evaluated the responses of three different intelligent services (specifically computer vision) over 11 months using 3 different data sets, verifying responses against the respective documentation and assessing evolution risk. We found that there are: (1) inconsistencies in how these services behave; (2) evolution risk in the responses; and (3) a lack of clear communication that documents these risks and inconsistencies. We propose a set of recommendations to both developers and intelligent service providers to inform risk and assist maintainability.", "published": "2019-06-18T01:11:43Z", "authors": ["Alex Cummaudo", "Rajesh Vasa", "John Grundy", "Mohamed Abdelrazek", "Andrew Cain"], "categories": ["cs.SE", "cs.AI"], "doi": "", "_query": "property valuation computer vision", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2312.11413v2", "title": "DeRDaVa: Deletion-Robust Data Valuation for Machine Learning", "abstract": "Data valuation is concerned with determining a fair valuation of data from data sources to compensate them or to identify training examples that are the most or least useful for predictions. With the rising interest in personal data ownership and data protection regulations, model owners will likely have to fulfil more data deletion requests. This raises issues that have not been addressed by existing works: Are the data valuation scores still fair with deletions? Must the scores be expensively recomputed? The answer is no. To avoid recomputations, we propose using our data valuation framework DeRDaVa upfront for valuing each data source's contribution to preserving robust model performance after anticipated data deletions. DeRDaVa can be efficiently approximated and will assign higher values to data that are more useful or less likely to be deleted. We further generalize DeRDaVa to Risk-DeRDaVa to cater to risk-averse/seeking model owners who are concerned with the worst/best-cases model utility. We also empirically demonstrate the practicality of our solutions.", "published": "2023-12-18T18:17:51Z", "authors": ["Xiao Tian", "Rachael Hwee Ling Sim", "Jue Fan", "Bryan Kian Hsiang Low"], "categories": ["cs.LG", "cs.AI"], "doi": "", "_query": "property valuation computer vision", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/1609.05236v3", "title": "Minimal plane valuations", "abstract": "We consider the last value $\\hatμ (ν)$ of the vanishing sequence of $H^0(L)$ along a divisorial or irrational valuation $ν$ centered at $\\mathcal{O}_{\\mathbb{P}^2,p}$, where $L$ resp. $p$ is a line resp. a point of the projective plane $\\mathbb{P}^2$ over an algebraically closed field. This value contains, for valuations, similar information as that given by Seshadri constants for points. It is always true that $\\hatμ (ν) \\geq \\sqrt{1 / \\mathrm{vol}(ν)}$ and minimal valuations are those satisfying the equality. In this paper, we prove that the Greuel-Lossen-Shustin Conjecture implies a variation of the Nagata Conjecture involving minimal valuations (that extends the one stated in the paper \"Very general monomial valuations of $\\mathbb{P}^2$ and a Nagata type conjecture\" by Dumnicki et al. to the whole set of divisorial and irrational valuations of the projective plane) which also implies the original one. We also provide infinitely many families of very general minimal valuations with an arbitrary number of Puiseux exponents, and an asymptotic result that can be considered as an evidence in the direction of the mentioned conjecture by Dumnicki et al.", "published": "2016-09-16T21:01:23Z", "authors": ["Carlos Galindo", "Francisco Monserrat", "Julio José Moyano-Fernández"], "categories": ["math.AG", "math.AC"], "doi": "", "_query": "property valuation computer vision", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2605.14645v1", "title": "Vision-Based Water Level and Flow Estimation", "abstract": "With the rapid evolution of computer vision, vision-based methodologies for water level and river surface velocity estimation have reached significant maturity. Compared to traditional sensing, these techniques offer superior interpretability, automated data archiving, and enhanced system robustness. However, challenges such as environmental sensitivity, limited precision, and complex site calibration persist. This work proposes an integrated framework that synergizes state-of-the-art (SOTA) vision models with statistical modeling. By leveraging physical priors and robust filtering strategies, we improve the accuracy of water level detection and flow estimation. Code will be available at https://github.com/sunzx97/Vision_Based_Water_Level_and_Flow_Estimation.git", "published": "2026-05-14T10:01:59Z", "authors": ["ZhiXin Sun"], "categories": ["cs.CV", "cs.AI"], "doi": "", "_query": "property valuation computer vision", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2505.15325v3", "title": "SoftHGNN: Soft Hypergraph Neural Networks for General Visual Recognition", "abstract": "Visual recognition relies on understanding the semantics of image tokens and their complex interactions. Mainstream self-attention methods, while effective at modeling global pair-wise relations, fail to capture high-order associations inherent in real-world scenes and often suffer from redundant computation. Hypergraphs extend conventional graphs by modeling high-order interactions and offer a promising framework for addressing these limitations. However, existing hypergraph neural networks typically rely on static and hard hyperedge assignments, which lead to redundant hyperedges and overlooking the continuity of visual semantics. In this work, we present Soft Hypergraph Neural Networks (SoftHGNN), a lightweight plug-and-play hypergraph computation method for late-stage semantic reasoning in existing vision pipelines. Our SoftHGNN introduces the concept of soft hyperedges, where each vertex is associated with hyperedges via continuous and differentiable participation weights rather than hard binary assignments. These weights are produced by measuring similarities between vertex features and a small set of learnable hyperedge prototypes, yielding input-adaptive and semantically rich soft hyperedges. Using soft hyperedges as the medium for message aggregation and dissemination, SoftHGNN enriches feature representations with high-order contextual associations. To further enhance efficiency when scaling up the number of soft hyperedges, we incorporate a sparse hyperedge selection mechanism that activates only the top-k important hyperedges, along with a load-balancing regularizer to ensure adequate and balanced hyperedge utilization. Experimental results across three tasks on five datasets demonstrate that SoftHGNN efficiently captures high-order associations in visual scenes, achieving significant performance improvements. The code is available at: https://github.com/Mengqi-Lei/SoftHGNN.", "published": "2025-05-21T10:01:30Z", "authors": ["Mengqi Lei", "Yihong Wu", "Siqi Li", "Xinhu Zheng", "Juan Wang", "Shaoyi Du", "Yue Gao"], "categories": ["cs.CV"], "doi": "", "_query": "property valuation computer vision", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2410.00267v1", "title": "KPCA-CAM: Visual Explainability of Deep Computer Vision Models using Kernel PCA", "abstract": "Deep learning models often function as black boxes, providing no straightforward reasoning for their predictions. This is particularly true for computer vision models, which process tensors of pixel values to generate outcomes in tasks such as image classification and object detection. To elucidate the reasoning of these models, class activation maps (CAMs) are used to highlight salient regions that influence a model's output. This research introduces KPCA-CAM, a technique designed to enhance the interpretability of Convolutional Neural Networks (CNNs) through improved class activation maps. KPCA-CAM leverages Principal Component Analysis (PCA) with the kernel trick to capture nonlinear relationships within CNN activations more effectively. By mapping data into higher-dimensional spaces with kernel functions and extracting principal components from this transformed hyperplane, KPCA-CAM provides more accurate representations of the underlying data manifold. This enables a deeper understanding of the features influencing CNN decisions. Empirical evaluations on the ILSVRC dataset across different CNN models demonstrate that KPCA-CAM produces more precise activation maps, providing clearer insights into the model's reasoning compared to existing CAM algorithms. This research advances CAM techniques, equipping researchers and practitioners with a powerful tool to gain deeper insights into CNN decision-making processes and overall behaviors.", "published": "2024-09-30T22:36:37Z", "authors": ["Sachin Karmani", "Thanushon Sivakaran", "Gaurav Prasad", "Mehmet Ali", "Wenbo Yang", "Sheyang Tang"], "categories": ["cs.CV", "cs.AI"], "doi": "", "_query": "property valuation computer vision", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2410.16349v1", "title": "Large Language Models in Computer Science Education: A Systematic Literature Review", "abstract": "Large language models (LLMs) are becoming increasingly better at a wide range of Natural Language Processing tasks (NLP), such as text generation and understanding. Recently, these models have extended their capabilities to coding tasks, bridging the gap between natural languages (NL) and programming languages (PL). Foundational models such as the Generative Pre-trained Transformer (GPT) and LLaMA series have set strong baseline performances in various NL and PL tasks. Additionally, several models have been fine-tuned specifically for code generation, showing significant improvements in code-related applications. Both foundational and fine-tuned models are increasingly used in education, helping students write, debug, and understand code. We present a comprehensive systematic literature review to examine the impact of LLMs in computer science and computer engineering education. We analyze their effectiveness in enhancing the learning experience, supporting personalized education, and aiding educators in curriculum development. We address five research questions to uncover insights into how LLMs contribute to educational outcomes, identify challenges, and suggest directions for future research.", "published": "2024-10-21T17:49:50Z", "authors": ["Nishat Raihan", "Mohammed Latif Siddiq", "Joanna C. S. Santos", "Marcos Zampieri"], "categories": ["cs.LG", "cs.HC"], "doi": "", "_query": "property valuation computer vision", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2111.01353v2", "title": "Can Vision Transformers Perform Convolution?", "abstract": "Several recent studies have demonstrated that attention-based networks, such as Vision Transformer (ViT), can outperform Convolutional Neural Networks (CNNs) on several computer vision tasks without using convolutional layers. This naturally leads to the following questions: Can a self-attention layer of ViT express any convolution operation? In this work, we prove that a single ViT layer with image patches as the input can perform any convolution operation constructively, where the multi-head attention mechanism and the relative positional encoding play essential roles. We further provide a lower bound on the number of heads for Vision Transformers to express CNNs. Corresponding with our analysis, experimental results show that the construction in our proof can help inject convolutional bias into Transformers and significantly improve the performance of ViT in low data regimes.", "published": "2021-11-02T03:30:17Z", "authors": ["Shanda Li", "Xiangning Chen", "Di He", "Cho-Jui Hsieh"], "categories": ["cs.CV", "cs.LG"], "doi": "", "_query": "property valuation computer vision", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/1901.01151v1", "title": "Learning From Less Data: A Unified Data Subset Selection and Active Learning Framework for Computer Vision", "abstract": "Supervised machine learning based state-of-the-art computer vision techniques are in general data hungry. Their data curation poses the challenges of expensive human labeling, inadequate computing resources and larger experiment turn around times. Training data subset selection and active learning techniques have been proposed as possible solutions to these challenges. A special class of subset selection functions naturally model notions of diversity, coverage and representation and can be used to eliminate redundancy thus lending themselves well for training data subset selection. They can also help improve the efficiency of active learning in further reducing human labeling efforts by selecting a subset of the examples obtained using the conventional uncertainty sampling based techniques. In this work, we empirically demonstrate the effectiveness of two diversity models, namely the Facility-Location and Dispersion models for training-data subset selection and reducing labeling effort. We demonstrate this across the board for a variety of computer vision tasks including Gender Recognition, Face Recognition, Scene Recognition, Object Detection and Object Recognition. Our results show that diversity based subset selection done in the right way can increase the accuracy by upto 5 - 10% over existing baselines, particularly in settings in which less training data is available. This allows the training of complex machine learning models like Convolutional Neural Networks with much less training data and labeling costs while incurring minimal performance loss.", "published": "2019-01-03T14:07:08Z", "authors": ["Vishal Kaushal", "Rishabh Iyer", "Suraj Kothawade", "Rohan Mahadev", "Khoshrav Doctor", "Ganesh Ramakrishnan"], "categories": ["cs.CV"], "doi": "", "_query": "property valuation computer vision", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/1901.07851v2", "title": "Computer Vision and Metrics Learning for Hypothesis Testing: An Application of Q-Q Plot for Normality Test", "abstract": "This paper proposes a new deep-learning method to construct test statistics by computer vision and metrics learning. The application highlighted in this paper is applying computer vision on Q-Q plot to construct a new test statistic for normality test. To the best of our knowledge, there is no similar application documented in the literature. Traditionally, there are two families of approaches for verifying the probability distribution of a random variable. Researchers either subjectively assess the Q-Q plot or objectively use a mathematical formula, such as Kolmogorov-Smirnov test, to formally conduct a normality test. Graphical assessment by human beings is not rigorous whereas normality test statistics may not be accurate enough when the uniformly most powerful test does not exist. It may take tens of years for statistician to develop a new test statistic that is more powerful statistically. Our proposed method integrates four components based on deep learning: an image representation learning component of a Q-Q plot, a dimension reduction component, a metrics learning component that best quantifies the differences between two Q-Q plots for normality test, and a new normality hypothesis testing process. Our experimentation results show that the machine-learning-based test statistics can outperform several widely-used traditional normality tests. This study provides convincing evidence that the proposed method could objectively create a powerful test statistic based on Q-Q plots and this method could be modified to construct many more powerful test statistics for other applications in the future.", "published": "2019-01-23T12:38:17Z", "authors": ["Ke-Wei Huang", "Mengke Qiao", "Xuanqi Liu", "Siyuan Liu", "Mingxi Dai"], "categories": ["cs.CV"], "doi": "", "_query": "property valuation computer vision", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2403.12172v2", "title": "Graph-Jigsaw Conditioned Diffusion Model for Skeleton-based Video Anomaly Detection", "abstract": "Skeleton-based video anomaly detection (SVAD) is a crucial task in computer vision. Accurately identifying abnormal patterns or events enables operators to promptly detect suspicious activities, thereby enhancing safety. Achieving this demands a comprehensive understanding of human motions, both at body and region levels, while also accounting for the wide variations of performing a single action. However, existing studies fail to simultaneously address these crucial properties. This paper introduces a novel, practical and lightweight framework, namely Graph-Jigsaw Conditioned Diffusion Model for Skeleton-based Video Anomaly Detection (GiCiSAD) to overcome the challenges associated with SVAD. GiCiSAD consists of three novel modules: the Graph Attention-based Forecasting module to capture the spatio-temporal dependencies inherent in the data, the Graph-level Jigsaw Puzzle Maker module to distinguish subtle region-level discrepancies between normal and abnormal motions, and the Graph-based Conditional Diffusion model to generate a wide spectrum of human motions. Extensive experiments on four widely used skeleton-based video datasets show that GiCiSAD outperforms existing methods with significantly fewer training parameters, establishing it as the new state-of-the-art.", "published": "2024-03-18T18:42:32Z", "authors": ["Ali Karami", "Thi Kieu Khanh Ho", "Narges Armanfard"], "categories": ["cs.CV", "cs.AI"], "doi": "", "_query": "property valuation computer vision", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2011.08185v1", "title": "Assistive Diagnostic Tool for Brain Tumor Detection using Computer Vision", "abstract": "Today, over 700,000 people are living with brain tumors in the United States. Brain tumors can spread very quickly to other parts of the brain and the spinal cord unless necessary preventive action is taken. Thus, the survival rate for this disease is less than 40% for both men and women. A conclusive and early diagnosis of a brain tumor could be the difference between life and death for some. However, brain tumor detection and segmentation are tedious and time-consuming processes as it can only be done by radiologists and clinical experts. The use of computer vision techniques, such as Mask R Convolutional Neural Network (Mask R CNN), to detect and segment brain tumors can mitigate the possibility of human error while increasing prediction accuracy rates. The goal of this project is to create an assistive diagnostics tool for brain tumor detection and segmentation. Transfer learning was used with the Mask R CNN, and necessary parameters were accordingly altered, as a starting point. The model was trained with 20 epochs and later tested. The prediction segmentation matched 90% with the ground truth. This suggests that the model was able to perform at a high level. Once the model was finalized, the application running on Flask was created. The application will serve as a tool for medical professionals. It allows doctors to upload patient brain tumor MRI images in order to receive immediate results on the diagnosis and segmentation for each patient.", "published": "2020-11-17T04:58:33Z", "authors": ["Sahithi Ankireddy"], "categories": ["cs.CV", "eess.IV"], "doi": "", "_query": "property valuation computer vision", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2104.02286v2", "title": "The Brauer group of a valuation ring", "abstract": "Let $V$ be a valuation ring and $K$ be its field of fraction. We show that the canonical map $\\Br(V) \\to \\Br(K)$ is injective.", "published": "2021-04-06T04:58:14Z", "authors": ["Vivek Sadhu"], "categories": ["math.AG"], "doi": "", "_query": "property valuation computer vision", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2307.03802v1", "title": "Student Teacher Interaction While Learning Computer Science: Early Results from an Experiment on Undergraduates", "abstract": "The scope of this paper was to find out how the students in Computer Science perceive different teaching styles and how the teaching style impacts the learning desire and interest in the course. To find out, we designed and implemented an experiment in which the same groups of students (86 students) were exposed to different teaching styles (presented by the same teacher at a difference of two weeks between lectures). We tried to minimize external factors' impact by carefully selecting the dates (close ones), having the courses in the same classroom and on the same day of the week, at the same hour, and checking the number and the complexity of the introduced items to be comparable. We asked for students' feedback and we define a set of countable body signs for their involvement in the course. The results were comparable by both metrics (body language) and text analysis results, students prefer a more interactive course, with a relaxing atmosphere, and are keener to learn in these conditions.", "published": "2023-07-07T19:08:59Z", "authors": ["Manuela Petrescu", "Kuderna Bentasup"], "categories": ["cs.HC", "cs.CY"], "doi": "10.5220/0011844400003470", "_query": "property valuation computer vision", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2401.00263v2", "title": "A framework for the valuation of insurance liabilities by production cost", "abstract": "This paper sets out a framework for the valuation of insurance liabilities that is intended to be economically realistic, elementary, reasonably practically applicable, and as a special case to provide a basis for the valuation in regulatory solvency systems such as Solvency II and the SST. The valuation framework is based on the cost of producing the liabilities to an insurance company that is subject to solvency regulation (regulatory solvency capital requirements) and insolvency laws (consequences of failure) in finite discrete time. Starting from the replication approach of classical no-arbitrage theory, the framework additionally considers the nature and cost of capital (expressed by a ``financiability condition\"), that the liabilities may be required to be fulfilled only ``in sufficiently many cases\" (expressed by a ``fulfillment condition\"), production using ``fully illiquid\" assets in addition to tradables, and the asymmetry between assets and liabilities. We identify necessary and sufficient conditions on the capital investment under which the framework recovers the market prices of tradables, investigate extending production to take account of insolvency, implications of using illiquid assets in the production, and show how Solvency II and SST valuation can be derived with specific assumptions.", "published": "2023-12-30T15:30:44Z", "authors": ["Christoph Moehr"], "categories": ["q-fin.PR"], "doi": "", "_query": "property valuation computer vision", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2202.08892v2", "title": "Developing Imperceptible Adversarial Patches to Camouflage Military Assets From Computer Vision Enabled Technologies", "abstract": "Convolutional neural networks (CNNs) have demonstrated rapid progress and a high level of success in object detection. However, recent evidence has highlighted their vulnerability to adversarial attacks. These attacks are calculated image perturbations or adversarial patches that result in object misclassification or detection suppression. Traditional camouflage methods are impractical when applied to disguise aircraft and other large mobile assets from autonomous detection in intelligence, surveillance and reconnaissance technologies and fifth generation missiles. In this paper we present a unique method that produces imperceptible patches capable of camouflaging large military assets from computer vision-enabled technologies. We developed these patches by maximising object detection loss whilst limiting the patch's colour perceptibility. This work also aims to further the understanding of adversarial examples and their effects on object detection algorithms.", "published": "2022-02-17T20:31:51Z", "authors": ["Chris Wise", "Jo Plested"], "categories": ["cs.CV"], "doi": "", "_query": "property valuation computer vision", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2111.14344v1", "title": "On the implicit constant fields and key polynomials for valuation algebraic extensions", "abstract": "This article is a natural construction of our previous works. In this article, we employ similar ideas due to MacLane to provide an estimate of IC(K(X)|K,v) when (K(X)|K,v) is a valuation algebraic extension. Our central result is an analogue of the estimate obtained in the valuation transcendental case. We further provide a natural construction of a complete sequence of key polynomials for v over K in the setting of valuation algebraic extensions.", "published": "2021-11-29T06:26:11Z", "authors": ["Arpan Dutta"], "categories": ["math.AG", "math.AC"], "doi": "", "_query": "property valuation computer vision", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2504.12515v2", "title": "Event Quality Score (EQS): Assessing the Realism of Simulated Event Camera Streams via Distances in Latent Space", "abstract": "Event cameras promise a paradigm shift in vision sensing with their low latency, high dynamic range, and asynchronous nature of events. Unfortunately, the scarcity of high-quality labeled datasets hinders their widespread adoption in deep learning-driven computer vision. To mitigate this, several simulators have been proposed to generate synthetic event data for training models for detection and estimation tasks. However, the fundamentally different sensor design of event cameras compared to traditional frame-based cameras poses a challenge for accurate simulation. As a result, most simulated data fail to mimic data captured by real event cameras. Inspired by existing work on using deep features for image comparison, we introduce event quality score (EQS), a quality metric that utilizes activations of the RVT architecture. Through sim-to-real experiments on the DSEC driving dataset, it is shown that a higher EQS implies improved generalization to real-world data after training on simulated events. Thus, optimizing for EQS can lead to developing more realistic event camera simulators, effectively reducing the simulation gap. EQS is available at https://github.com/eventbasedvision/EQS.", "published": "2025-04-16T22:25:57Z", "authors": ["Kaustav Chanda", "Aayush Atul Verma", "Arpitsinh Vaghela", "Yezhou Yang", "Bharatesh Chakravarthi"], "categories": ["cs.CV"], "doi": "", "_query": "property valuation computer vision", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/1803.10862v1", "title": "A Survey on Deep Learning Methods for Robot Vision", "abstract": "Deep learning has allowed a paradigm shift in pattern recognition, from using hand-crafted features together with statistical classifiers to using general-purpose learning procedures for learning data-driven representations, features, and classifiers together. The application of this new paradigm has been particularly successful in computer vision, in which the development of deep learning methods for vision applications has become a hot research topic. Given that deep learning has already attracted the attention of the robot vision community, the main purpose of this survey is to address the use of deep learning in robot vision. To achieve this, a comprehensive overview of deep learning and its usage in computer vision is given, that includes a description of the most frequently used neural models and their main application areas. Then, the standard methodology and tools used for designing deep-learning based vision systems are presented. Afterwards, a review of the principal work using deep learning in robot vision is presented, as well as current and future trends related to the use of deep learning in robotics. This survey is intended to be a guide for the developers of robot vision systems.", "published": "2018-03-28T21:37:14Z", "authors": ["Javier Ruiz-del-Solar", "Patricio Loncomilla", "Naiomi Soto"], "categories": ["cs.CV"], "doi": "", "_query": "property valuation computer vision", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/1411.6844v1", "title": "On the quantifier complexity of definable canonical henselian valuations", "abstract": "We discuss definability in the language of rings without parameters of the unique canonical henselian valuation of a field. We show that in most cases where the canonical henselian valuation is definable, it is already definable by a universal-existential or an existential-universal formula.", "published": "2014-11-25T12:55:38Z", "authors": ["Arno Fehm", "Franziska Jahnke"], "categories": ["math.LO"], "doi": "", "_query": "property valuation computer vision", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/astro-ph/9709082v2", "title": "A New Vision of the Coma Cluster: Conference Summary", "abstract": "Summary of the highlights and main themes of a conference on the Coma cluster, \"A New Vision of an Old Cluster: Untangling Coma Berenices\".", "published": "1997-09-10T06:46:08Z", "authors": ["Matthew Colless"], "categories": ["astro-ph"], "doi": "", "_query": "property valuation computer vision", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2603.13240v1", "title": "Gloss-Free Sign Language Translation: An Unbiased Evaluation of Progress in the Field", "abstract": "Sign Language Translation (SLT) aims to automatically convert visual sign language videos into spoken language text and vice versa. While recent years have seen rapid progress, the true sources of performance improvements often remain unclear. Do reported performance gains come from methodological novelty, or from the choice of a different backbone, training optimizations, hyperparameter tuning, or even differences in the calculation of evaluation metrics? This paper presents a comprehensive study of recent gloss-free SLT models by re-implementing key contributions in a unified codebase. We ensure fair comparison by standardizing preprocessing, video encoders, and training setups across all methods. Our analysis shows that many of the performance gains reported in the literature often diminish when models are evaluated under consistent conditions, suggesting that implementation details and evaluation setups play a significant role in determining results. We make the codebase publicly available here (https://github.com/ozgemercanoglu/sltbaselines) to support transparency and reproducibility in SLT research.", "published": "2026-02-18T08:40:31Z", "authors": ["Ozge Mercanoglu Sincan", "Jian He Low", "Sobhan Asasi", "Richard Bowden"], "categories": ["cs.CV", "cs.CL"], "doi": "10.1016/j.cviu.2025.104498", "_query": "property valuation computer vision", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2609.27710v2", "title": "FFM-CP: Cross-Backbone Fusion of Vision-Language Foundation Models for Few-Shot Computational Pathology", "abstract": "Pathology vision-language foundation models vary in performance across diseases and tasks, with no single model consistently performing best. The high cost of expert pathology annotation can also limit the labeled data available for task-specific adaptation. Combining complementary pretrained representations is a potential approach to these limitations, yet learning an effective fusion from few labeled examples remains challenging. We introduce Few-shot Fusion Foundation Models of Computational Pathology (FFM-CP), which is a framework that combines multiple pathology vision-language models in the few-shot learning setting. The framework first aligns heterogeneous representations using a closed-form Orthogonal Procrustes transformation estimated from corresponding support images. This alignment preserves within-model feature geometry without training an additional alignment network. Within the aligned space, a unified graph enables information exchange across backbones by jointly refining support-image features and visual and textual class prototypes. These refined representations support complementary text-prototype and case-retrieval branches that capture semantic class knowledge and within-class visual variation, respectively. Each branch learns to combine predictions from all ordered backbone pairs, allowing queries encoded by one model to draw on evidence represented by another. We evaluate three backbone combinations on six histopathology datasets at 4, 8, and 16 shots per class. FFM-CP achieves higher mean macro-F1 than the strongest individually adapted member of each fused set in 50 of 54 comparisons. These findings suggest that combining complementary pretrained representations can improve histopathological classification when annotations are limited.", "published": "2026-09-23T11:28:09Z", "authors": ["Anh-Tien Nguyen", "Trung DQ. Dang", "Nghiem Tuong Diep", "Bui Ngoc Han Nguyen", "Tan-Ha Mai", "Miriam Cindy Maurer", "Phuong Hoa Nguyen", "Thi Thuy Uyen Nguyen", "Youngjun Park", "Daniel Sonntag", "Duy Minh Ho Nguyen", "Anne-Christin Hauschild"], "categories": ["cs.CV", "cs.LG"], "doi": "", "_query": "property valuation computer vision", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/1908.07656v2", "title": "Survey on Deep Neural Networks in Speech and Vision Systems", "abstract": "This survey presents a review of state-of-the-art deep neural network architectures, algorithms, and systems in vision and speech applications. Recent advances in deep artificial neural network algorithms and architectures have spurred rapid innovation and development of intelligent vision and speech systems. With availability of vast amounts of sensor data and cloud computing for processing and training of deep neural networks, and with increased sophistication in mobile and embedded technology, the next-generation intelligent systems are poised to revolutionize personal and commercial computing. This survey begins by providing background and evolution of some of the most successful deep learning models for intelligent vision and speech systems to date. An overview of large-scale industrial research and development efforts is provided to emphasize future trends and prospects of intelligent vision and speech systems. Robust and efficient intelligent systems demand low-latency and high fidelity in resource-constrained hardware platforms such as mobile devices, robots, and automobiles. Therefore, this survey also provides a summary of key challenges and recent successes in running deep neural networks on hardware-restricted platforms, i.e. within limited memory, battery life, and processing capabilities. Finally, emerging applications of vision and speech across disciplines such as affective computing, intelligent transportation, and precision medicine are discussed. To our knowledge, this paper provides one of the most comprehensive surveys on the latest developments in intelligent vision and speech applications from the perspectives of both software and hardware systems. Many of these emerging technologies using deep neural networks show tremendous promise to revolutionize research and development for future vision and speech systems.", "published": "2019-08-16T16:40:49Z", "authors": ["Mahbubul Alam", "Manar D. Samad", "Lasitha Vidyaratne", "Alexander Glandon", "Khan M. Iftekharuddin"], "categories": ["cs.CV", "cs.LG", "cs.NE", "cs.SD", "eess.AS", "eess.SP", "stat.ML"], "doi": "", "_query": "property valuation computer vision", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2308.09621v1", "title": "Canonicity and Computability in Homotopy Type Theory", "abstract": "This dissertation gives an overview of Martin Lof's dependant type theory, focusing on its computational content and addressing a question of possibility of fully canonical and computable semantic presentation.", "published": "2023-08-18T15:23:33Z", "authors": ["Dmitry Filippov"], "categories": ["cs.LO"], "doi": "", "_query": "property valuation computer vision", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/1807.04521v1", "title": "Eurolab-4-HPC Long-Term Vision on High-Performance Computing", "abstract": "Radical changes in computing are foreseen for the next decade. The US IEEE society wants to \"reboot computing\" and the HiPEAC Vision 2017 sees the time to \"re-invent computing\", both by challenging its basic assumptions. This document presents the \"EuroLab-4-HPC Long-Term Vision on High-Performance Computing\" of August 2017, a road mapping effort within the EC CSA1 Eurolab-4-HPC that targets potential changes in hardware, software, and applications in High-Performance Computing (HPC). The objective of the Eurolab-4-HPC vision is to provide a long-term roadmap from 2023 to 2030 for High-Performance Computing (HPC). Because of the long-term perspective and its speculative nature, the authors started with an assessment of future computing technologies that could influence HPC hardware and software. The proposal on research topics is derived from the report and discussions within the road mapping expert group. We prefer the term \"vision\" over \"roadmap\", firstly because timings are hard to predict given the long-term perspective, and secondly because EuroLab-4-HPC will have no direct control over the realization of its vision.", "published": "2018-07-11T15:50:44Z", "authors": ["Theo Ungerer", "Paul Carpenter"], "categories": ["cs.CE"], "doi": "", "_query": "property valuation computer vision", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2412.08108v3", "title": "Exploiting Vision Encoder Vulnerabilities for Universal Adversarial Perturbations on Large Vision-Language Models", "abstract": "Large Vision-Language Models (LVLMs) have achieved remarkable performance on multimodal tasks but remain highly vulnerable to small adversarial perturbations in input images. Existing attacks typically target the vision encoder's final output embeddings, implicitly treating the encoder as a uniform attack surface, while a systematic analysis of which internal components are most vulnerable has remained largely unexplored. We show such analysis is essential, as adversarial vulnerability in LVLM vision encoders is structurally concentrated rather than uniformly distributed. Building on this, we propose Vision Encoder Vulnerable-Component-Targeted Universal Adversarial Perturbation (VEV-UAP), a task-agnostic and cost-efficient attack framework. Through a component- and layer-wise analysis of attention mechanisms, we identify the value components in middle layers as critical vulnerabilities that strongly influence downstream language model behavior. VEV-UAP selectively targets these components to generate a single universal perturbation shared across images, without involving textual inputs or the language model during optimization. Experiments across multiple LVLMs and tasks show VEV-UAP achieves state-of-the-art attack success rates with reduced computational overhead. Moreover, a single VEV-UAP transfers across LVLMs sharing the same vision encoder, even when paired with different language models, making it a practical framework for scalable robustness evaluation.", "published": "2024-12-11T05:23:34Z", "authors": ["Hee-Seon Kim", "Minbeom Kim", "Seokil Ham", "Changick Kim"], "categories": ["cs.CV", "cs.CL", "cs.CR"], "doi": "", "_query": "property valuation computer vision", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/1506.07631v1", "title": "Dynamic Mechanism Design with Interdependent Valuations", "abstract": "We consider an infinite horizon dynamic mechanism design problem with interdependent valuations. In this setting the type of each agent is assumed to be evolving according to a first order Markov process and is independent of the types of other agents. However, the valuation of an agent can depend on the types of other agents, which makes the problem fall into an interdependent valuation setting. Designing truthful mechanisms in this setting is non-trivial in view of an impossibility result which says that for interdependent valuations, any efficient and ex-post incentive compatible mechanism must be a constant mechanism, even in a static setting. Mezzetti (2004) circumvents this problem by splitting the decisions of allocation and payment into two stages. However, Mezzetti's result is limited to a static setting and moreover in the second stage of that mechanism, agents are weakly indifferent about reporting their valuations truthfully. This paper provides a first attempt at designing a dynamic mechanism which is efficient, `strict' ex-post incentive compatible and ex-post individually rational in a setting with interdependent values and Markovian type evolution.", "published": "2015-06-25T06:47:52Z", "authors": ["Swaprava Nath", "Onno Zoeter", "Y. Narahari", "Christopher R. Dance"], "categories": ["cs.GT"], "doi": "", "_query": "property valuation computer vision", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2302.11431v1", "title": "A Note on \"Towards Efficient Data Valuation Based on the Shapley Value''", "abstract": "The Shapley value (SV) has emerged as a promising method for data valuation. However, computing or estimating the SV is often computationally expensive. To overcome this challenge, Jia et al. (2019) propose an advanced SV estimation algorithm called ``Group Testing-based SV estimator'' which achieves favorable asymptotic sample complexity. In this technical note, we present several improvements in the analysis and design choices of this SV estimator. Moreover, we point out that the Group Testing-based SV estimator does not fully reuse the collected samples. Our analysis and insights contribute to a better understanding of the challenges in developing efficient SV estimation algorithms for data valuation.", "published": "2023-02-22T15:13:45Z", "authors": ["Jiachen T. Wang", "Ruoxi Jia"], "categories": ["stat.ML", "cs.LG"], "doi": "", "_query": "property valuation computer vision", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/1812.06829v1", "title": "Discriminant Patch Representation for RGB-D Face Recognition Using Convolutional Neural Networks", "abstract": "This paper focuses on designing data-driven models to learn a discriminant representation space for face recognition using RGB-D data. Unlike hand-crafted representations, learned models can extract and organize the discriminant information from the data, and can automatically adapt to build new compute vision applications faster. We proposed an effective way to train Convolutional Neural Networks to learn face patch discriminant features. The proposed solution was tested and validated on state-of-the-art RGB-D datasets and showed competitive and promising results relatively to standard hand-crafted feature extractors.", "published": "2018-12-17T15:19:21Z", "authors": ["Nesrine Grati", "Achraf Ben-Hamadou", "Mohamed Hammami"], "categories": ["cs.CV"], "doi": "", "_query": "property valuation computer vision", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2007.08920v1", "title": "Vision-based Estimation of MDS-UPDRS Gait Scores for Assessing Parkinson's Disease Motor Severity", "abstract": "Parkinson's disease (PD) is a progressive neurological disorder primarily affecting motor function resulting in tremor at rest, rigidity, bradykinesia, and postural instability. The physical severity of PD impairments can be quantified through the Movement Disorder Society Unified Parkinson's Disease Rating Scale (MDS-UPDRS), a widely used clinical rating scale. Accurate and quantitative assessment of disease progression is critical to developing a treatment that slows or stops further advancement of the disease. Prior work has mainly focused on dopamine transport neuroimaging for diagnosis or costly and intrusive wearables evaluating motor impairments. For the first time, we propose a computer vision-based model that observes non-intrusive video recordings of individuals, extracts their 3D body skeletons, tracks them through time, and classifies the movements according to the MDS-UPDRS gait scores. Experimental results show that our proposed method performs significantly better than chance and competing methods with an F1-score of 0.83 and a balanced accuracy of 81%. This is the first benchmark for classifying PD patients based on MDS-UPDRS gait severity and could be an objective biomarker for disease severity. Our work demonstrates how computer-assisted technologies can be used to non-intrusively monitor patients and their motor impairments. The code is available at https://github.com/mlu355/PD-Motor-Severity-Estimation.", "published": "2020-07-17T11:49:30Z", "authors": ["Mandy Lu", "Kathleen Poston", "Adolf Pfefferbaum", "Edith V. Sullivan", "Li Fei-Fei", "Kilian M. Pohl", "Juan Carlos Niebles", "Ehsan Adeli"], "categories": ["cs.CV", "cs.LG", "eess.IV"], "doi": "", "_query": "property valuation computer vision", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/0707.3104v1", "title": "The 2-adic valuations of Stirling numbers", "abstract": "The 2-adic valuation of the Stirling numbres is examined. We conjecture pattrens about the distributions of these valuations in residue classes modulo powers of 2.", "published": "2007-07-20T16:30:46Z", "authors": ["Tewodros Amdeberhan", "Dante Manna", "Victor H. Moll"], "categories": ["math.NT"], "doi": "", "_query": "property valuation computer vision", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2201.02658v2", "title": "Fair and efficient contribution valuation for vertical federated learning", "abstract": "Federated learning is an emerging technology for training machine learning models across decentralized data sources without sharing data. Vertical federated learning, also known as feature-based federated learning, applies to scenarios where data sources have the same sample IDs but different feature sets. To ensure fairness among data owners, it is critical to objectively assess the contributions from different data sources and compensate the corresponding data owners accordingly. The Shapley value is a provably fair contribution valuation metric originating from cooperative game theory. However, its straight-forward computation requires extensively retraining a model on each potential combination of data sources, leading to prohibitively high communication and computation overheads due to multiple rounds of federated learning. To tackle this challenge, we propose a contribution valuation metric called vertical federated Shapley value (VerFedSV) based on the classic Shapley value. We show that VerFedSV not only satisfies many desirable properties of fairness but is also efficient to compute. Moreover, VerFedSV can be adapted to both synchronous and asynchronous vertical federated learning algorithms. Both theoretical analysis and extensive experimental results demonstrate the fairness, efficiency, adaptability, and effectiveness of VerFedSV.", "published": "2022-01-07T19:57:15Z", "authors": ["Zhenan Fan", "Huang Fang", "Xinglu Wang", "Zirui Zhou", "Jian Pei", "Michael P. Friedlander", "Yong Zhang"], "categories": ["cs.LG"], "doi": "", "_query": "property valuation computer vision", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/1007.3539v1", "title": "Single Parameter Combinatorial Auctions with Partially Public Valuations", "abstract": "We consider the problem of designing truthful auctions, when the bidders' valuations have a public and a private component. In particular, we consider combinatorial auctions where the valuation of an agent $i$ for a set $S$ of items can be expressed as $v_if(S)$, where $v_i$ is a private single parameter of the agent, and the function $f$ is publicly known. Our motivation behind studying this problem is two-fold: (a) Such valuation functions arise naturally in the case of ad-slots in broadcast media such as Television and Radio. For an ad shown in a set $S$ of ad-slots, $f(S)$ is, say, the number of {\\em unique} viewers reached by the ad, and $v_i$ is the valuation per-unique-viewer. (b) From a theoretical point of view, this factorization of the valuation function simplifies the bidding language, and renders the combinatorial auction more amenable to better approximation factors. We present a general technique, based on maximal-in-range mechanisms, that converts any $α$-approximation non-truthful algorithm ($α\\leq 1$) for this problem into $Ω(\\fracα{\\log{n}})$ and $Ω(α)$-approximate truthful mechanisms which run in polynomial time and quasi-polynomial time, respectively.", "published": "2010-07-20T23:53:55Z", "authors": ["Gagan Goel", "Chinmay Karande", "Lei Wang"], "categories": ["cs.GT"], "doi": "10.1007/978-3-642-16170-4_21", "_query": "property valuation computer vision", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2607.04781v1", "title": "Renewing Reliability: Valuation and Credit Risk Adjustments for Renewable Power Purchase Agreements", "abstract": "Power Purchase Agreements (PPAs) are bilateral over-the-counter contracts central to renewable energy financing. While their capacity to stabilise revenues and hedge price risk is well recognised, their OTC structure exposes both parties to counterparty credit risk. This is a dimension yet to be explored in the literature, particularly given the dual price and volumetric uncertainty inherent in renewable sources. This paper develops a framework for the pricing and valuation of wind power PPAs and for quantifying this risk through Credit Valuation Adjustment (CVA) and Debit Valuation Adjustment (DVA). We model the joint dynamics of electricity spot prices and renewable output, incorporate default probabilities, and compute valuation adjustments that reflect the fair value of bilateral credit risk. The framework provides market participants with a transparent metric for PPA valuation under counterparty risk. While initiatives such as the European Investment Bank's pilot guarantee scheme aim to mitigate credit risk for certain offtakers, such interventions do not cover all PPA transactions. Rigorous internal credit risk assessment therefore remains indispensable for lenders, producers, and offtakers alike.", "published": "2026-07-06T08:10:42Z", "authors": ["Nicola Bartolini", "Silvia Romagnoli", "Amia Santini"], "categories": ["q-fin.PR"], "doi": "", "_query": "property valuation computer vision", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2301.00930v2", "title": "Data Valuation Without Training of a Model", "abstract": "Many recent works on understanding deep learning try to quantify how much individual data instances influence the optimization and generalization of a model. Such attempts reveal characteristics and importance of individual instances, which may provide useful information in diagnosing and improving deep learning. However, most of the existing works on data valuation require actual training of a model, which often demands high-computational cost. In this paper, we provide a training-free data valuation score, called complexity-gap score, which is a data-centric score to quantify the influence of individual instances in generalization of two-layer overparameterized neural networks. The proposed score can quantify irregularity of the instances and measure how much each data instance contributes in the total movement of the network parameters during training. We theoretically analyze and empirically demonstrate the effectiveness of the complexity-gap score in finding `irregular or mislabeled' data instances, and also provide applications of the score in analyzing datasets and diagnosing training dynamics. Our code is publicly available at https://github.com/JJchy/CG_score", "published": "2023-01-03T02:19:20Z", "authors": ["Nohyun Ki", "Hoyong Choi", "Hye Won Chung"], "categories": ["cs.LG"], "doi": "", "_query": "property valuation computer vision", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2208.12513v4", "title": "Perspective-1-Ellipsoid: Formulation, Analysis and Solutions of the Camera Pose Estimation Problem from One Ellipse-Ellipsoid Correspondence", "abstract": "In computer vision, camera pose estimation from correspondences between 3D geometric entities and their projections into the image has been a widely investigated problem. Although most state-of-the-art methods exploit low-level primitives such as points or lines, the emergence of very effective CNN-based object detectors in the recent years has paved the way to the use of higher-level features carrying semantically meaningful information. Pioneering works in that direction have shown that modelling 3D objects by ellipsoids and 2D detections by ellipses offers a convenient manner to link 2D and 3D data. However, the mathematical formalism most often used in the related litterature does not enable to easily distinguish ellipsoids and ellipses from other quadrics and conics, leading to a loss of specificity potentially detrimental in some developments. Moreover, the linearization process of the projection equation creates an over-representation of the camera parameters, also possibly causing an efficiency loss. In this paper, we therefore introduce an ellipsoid-specific theoretical framework and demonstrate its beneficial properties in the context of pose estimation. More precisely, we first show that the proposed formalism enables to reduce the pose estimation problem to a position or orientation-only estimation problem in which the remaining unknowns can be derived in closed-form. Then, we demonstrate that it can be further reduced to a 1 Degree-of-Freedom (1DoF) problem and provide the analytical derivations of the pose as a function of that unique scalar unknown. We illustrate our theoretical considerations by visual examples and include a discussion on the practical aspects. Finally, we release this paper along with the corresponding source code in order to contribute towards more efficient resolutions of ellipsoid-related pose estimation problems.", "published": "2022-08-26T09:15:20Z", "authors": ["Vincent Gaudillière", "Gilles Simon", "Marie-Odile Berger"], "categories": ["cs.CV"], "doi": "", "_query": "property valuation computer vision", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2607.11529v1", "title": "Parse, Search, and Confirmation: Training-Free Aerial Vision-and-Dialog Navigation with Chain-of-Thought Reasoning and Structured Spatial Memory", "abstract": "In this paper, we tackle the Aerial Vision-and-Dialog Navigation (AVDN) task in the training-free setting for resource-efficient high-altitude UAV navigation.Naively applying MLLMs leads to unreliable navigation due to weak directional grounding and the lack of explicit spatial memory.To address these issues, we propose PSC-AVDN, a training-free framework that tightly couples a three-stage Parsing-Search-Confirmation reasoning pipeline with a Structured Spatial Memory (SSM).The parsing stage uses an LLM to convert ambiguous dialogue instructions into stable geometric directional and destination cues.A Search Chain-of-Thought (S-CoT) then performs stepwise target exploration under high-altitude observations, and a Confirmation Chain-of-Thought (C-CoT) conducts fine-grained verification around candidate regions to resolve visual ambiguity.Meanwhile, SSM integrates three complementary sources of spatial cues, including multi-scale visual observation, spatial visual memory, and structured geometric memory to provide global spatial context and long-horizon consistency.Extensive experiments on ANDH and ANDH-Full show that PSC-AVDN establishes new state-of-the-art performance in the training-free setting, matching or surpassing several finetuned methods.Code will be publicly available at: https://github.com/QY6616/PSC-AVDN", "published": "2026-07-13T13:14:20Z", "authors": ["Yu Qi", "Hongyu Li", "Shaofei Huang", "Tianrui Hui", "Yaxiong Wang", "Lechao Cheng", "Zhun Zhong", "Si Liu", "Meng Wang"], "categories": ["cs.CV"], "doi": "", "_query": "property valuation computer vision", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2005.09459v2", "title": "Review on Computer Vision in Gastric Cancer: Potential Efficient Tools for Diagnosis", "abstract": "Rapid diagnosis of gastric cancer is a great challenge for clinical doctors. Dramatic progress of computer vision on gastric cancer has been made recently and this review focuses on advances during the past five years. Different methods for data generation and augmentation are presented, and various approaches to extract discriminative features compared and evaluated. Classification and segmentation techniques are carefully discussed for assisting more precise diagnosis and timely treatment. For classification, various methods have been developed to better proceed specific images, such as images with rotation and estimated real-timely (endoscopy), high resolution images (histopathology), low diagnostic accuracy images (X-ray), poor contrast images of the soft-tissue with cavity (CT) or those images with insufficient annotation. For detection and segmentation, traditional methods and machine learning methods are compared. Application of those methods will greatly reduce the labor and time consumption for the diagnosis of gastric cancers.", "published": "2020-05-17T16:14:15Z", "authors": ["Yihua Sun"], "categories": ["eess.IV", "cs.CV"], "doi": "", "_query": "property valuation computer vision", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2410.20064v1", "title": "Some restricted partition functions in terms of 2-adic valuation", "abstract": "The 2-adic valuation of an integer n which is the exponent of the highest power of 2 that divides n. In this paper, we give representations of certain restricted partition functions in terms of 2-adic valuation.", "published": "2024-10-26T04:01:25Z", "authors": ["Sabi Biswas", "Nipen Saikia"], "categories": ["math.NT"], "doi": "", "_query": "property valuation computer vision", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2411.00915v5", "title": "Empower Vision Applications with LoRA LMM", "abstract": "Large Multimodal Models (LMMs) have shown significant progress in various complex vision tasks with the solid linguistic and reasoning capacity inherited from large language models (LMMs). Low-rank adaptation (LoRA) offers a promising method to integrate external knowledge into LMMs, compensating for their limitations on domain-specific tasks. However, the existing LoRA model serving is excessively computationally expensive and causes extremely high latency. In this paper, we present an end-to-end solution that empowers diverse vision tasks and enriches vision applications with LoRA LMMs. Our system, VaLoRA, enables accurate and efficient vision tasks by 1) an accuracy-aware LoRA adapter generation approach that generates LoRA adapters rich in domain-specific knowledge to meet application-specific accuracy requirements, 2) an adaptive-tiling LoRA adapters batching operator that efficiently computes concurrent heterogeneous LoRA adapters, and 3) a flexible LoRA adapter orchestration mechanism that manages application requests and LoRA adapters to achieve the lowest average response latency. We prototype VaLoRA on five popular vision tasks on three LMMs. Experiment results reveal that VaLoRA improves 24-62% of the accuracy compared to the original LMMs and reduces 20-89% of the latency compared to the state-of-the-art LoRA model serving systems.", "published": "2024-11-01T13:43:33Z", "authors": ["Liang Mi", "Weijun Wang", "Wenming Tu", "Qingfeng He", "Rui Kong", "Xinyu Fang", "Yazhu Dong", "Yikang Zhang", "Yunchun Li", "Meng Li", "Haipeng Dai", "Guihai Chen", "Yunxin Liu"], "categories": ["cs.CV", "cs.AI"], "doi": "", "_query": "property valuation computer vision", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2301.05323v2", "title": "Salient Object Detection for Images Taken by People With Vision Impairments", "abstract": "Salient object detection is the task of producing a binary mask for an image that deciphers which pixels belong to the foreground object versus background. We introduce a new salient object detection dataset using images taken by people who are visually impaired who were seeking to better understand their surroundings, which we call VizWiz-SalientObject. Compared to seven existing datasets, VizWiz-SalientObject is the largest (i.e., 32,000 human-annotated images) and contains unique characteristics including a higher prevalence of text in the salient objects (i.e., in 68\\% of images) and salient objects that occupy a larger ratio of the images (i.e., on average, $\\sim$50\\% coverage). We benchmarked seven modern salient object detection methods on our dataset and found they struggle most with images featuring salient objects that are large, have less complex boundaries, and lack text as well as for lower quality images. We invite the broader community to work on our new dataset challenge by publicly sharing the dataset at https://vizwiz.org/tasks-and-datasets/salient-object .", "published": "2023-01-12T22:33:01Z", "authors": ["Jarek Reynolds", "Chandra Kanth Nagesh", "Danna Gurari"], "categories": ["cs.CV"], "doi": "", "_query": "property valuation computer vision", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/1708.02546v1", "title": "A principal ideal theorem for compact sets of rank one valuation rings", "abstract": "Let $F$ be a field, and let Zar$(F)$ be the space of valuation rings of $F$ with respect to the Zariski topology. We prove that if $X$ is a quasicompact set of rank one valuation rings in Zar$(F)$ whose maximal ideals do not intersect to $0$, then the intersection of the rings in $X$ is an integral domain with quotient field $F$ such that every finitely generated ideal is a principal ideal. To prove this result, we develop a duality between (a) quasicompact sets of rank one valuation rings whose maximal ideals do not intersect to $0$, and (b) one-dimensional Prüfer domains with nonzero Jacobson radical and quotient field $F$. The necessary restriction in all these cases to collections of valuation rings whose maximal ideals do not intersect to $0$ is motivated by settings in which the valuation rings considered all dominate a given local ring.", "published": "2017-08-08T16:20:46Z", "authors": ["Bruce Olberding"], "categories": ["math.AC"], "doi": "", "_query": "property valuation computer vision", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2211.02208v1", "title": "Automated Logging Drone: A Computer Vision Drone Implementation", "abstract": "In recent years, Artificial Intelligence (AI) and Computer Vision (CV) have become the pinnacle of technology with new developments seemingly every day. This technology along with more powerful drone technology have made autonomous surveillance more sought after. Here an overview of the Automated Logging Drone (ALD) project is presented along with examples of how this project can be used with more refining and added features.", "published": "2022-11-04T01:36:32Z", "authors": ["Aaron Yagnik", "Adrian S. -W. Tam"], "categories": ["cs.HC"], "doi": "", "_query": "property valuation computer vision", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/1601.01954v4", "title": "(Non)Vanishing results on local cohomology of valuation rings", "abstract": "We examine local cohomology in the setting of valuation rings. The novelty of this investigation stems from the fact that valuation rings are usually non-Noetherian, whereas local cohomology has been extensively developed mostly in a Noetherian setting. We prove various vanishing results on local cohomology for valuation rings of finite Krull dimension. These vanishing results stem from a uniform bound on the global dimension of such rings. Our investigation reveals differences in the sheaf theoretic definition of local cohomology, and the algebraic definition in terms of a limit of certain Ext functors.", "published": "2016-01-08T17:37:55Z", "authors": ["Rankeya Datta"], "categories": ["math.AC"], "doi": "10.1016/j.jalgebra.2016.12.032", "_query": "property valuation computer vision", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/1410.7033v1", "title": "Valuations and Surface Area Measures", "abstract": "We consider valuations defined on polytopes containing the origin which have measures on the sphere as values. We show that the classical surface area measure is essentially the only such valuation which is SL(n) contravariant of degree one. Moreover, for all real $p$, an $L_p$ version of the above result is established for GL(n) contravariant valuations of degree $p$. This provides a characterization of the $L_p$ surface area measures from the $L_p$ Brunn-Minkowski theory.", "published": "2014-10-26T14:31:03Z", "authors": ["Christoph Haberl", "Lukas Parapatits"], "categories": ["math.MG"], "doi": "", "_query": "property valuation computer vision", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/1610.09809v1", "title": "Valuation theoretic methods in the birational geometry of algebraic varieties", "abstract": "In this paper, we give a valuation formula for rational top differential forms of function fields in characteristic zero for arbitrary Abhyankar places generalizing the classical valuation at prime divisors. This enables us to define log discrepancies for log pairs for arbitrary Abhyankar places. If the Abhyankar place has dimension greater than zero we restrict rational top differential forms with valuation zero to the residue field of the Abhyankar place, generalizing the classical restriction of a top differential form with a simple pole along a smooth divisor. This opens up the door to generalize the classical adjunction machinery to arbitrary Abhyankar places.", "published": "2016-10-31T07:39:50Z", "authors": ["Stefan Günther"], "categories": ["math.AG"], "doi": "", "_query": "property valuation computer vision", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2012.06948v1", "title": "Using Computer Vision to Automate Hand Detection and Tracking of Surgeon Movements in Videos of Open Surgery", "abstract": "Open, or non-laparoscopic surgery, represents the vast majority of all operating room procedures, but few tools exist to objectively evaluate these techniques at scale. Current efforts involve human expert-based visual assessment. We leverage advances in computer vision to introduce an automated approach to video analysis of surgical execution. A state-of-the-art convolutional neural network architecture for object detection was used to detect operating hands in open surgery videos. Automated assessment was expanded by combining model predictions with a fast object tracker to enable surgeon-specific hand tracking. To train our model, we used publicly available videos of open surgery from YouTube and annotated these with spatial bounding boxes of operating hands. Our model's spatial detections of operating hands significantly outperforms the detections achieved using pre-existing hand-detection datasets, and allow for insights into intra-operative movement patterns and economy of motion.", "published": "2020-12-13T03:10:09Z", "authors": ["Michael Zhang", "Xiaotian Cheng", "Daniel Copeland", "Arjun Desai", "Melody Y. Guan", "Gabriel A. Brat", "Serena Yeung"], "categories": ["cs.CV"], "doi": "", "_query": "property valuation computer vision", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2608.01730v1", "title": "Learning Where to Look and How to Judge: Resolution-agnostic Image Quality Assessment with Quality-aware Saliency", "abstract": "No-reference image quality assessment (NR IQA) has recently benefited from deep and multimodal models, yet many SOTA systems still violate at least one basic requirement: they either discard critical quality cues via aggressive resizing, fail to generalize across resolutions, cannot be jointly trained on heterogeneous IQA datasets with mismatched MOS scales, or require prohibitive computation. We present \\textbf{ReLIQS}, a model for \\textbf{Re}solution-agnostic \\textbf{L}earning for \\textbf{I}mage \\textbf{Q}uality with \\textbf{S}aliency, which is resolution-agnostic, preserves original-resolution quality cues, learns from multiple subjective studies, and remains computationally efficient and budget-adaptive. ReLIQS is a CLIP-based multiscale patch-driven architecture that learns both \\emph{where to look} and \\emph{how to judge} quality. Fixed-size patches are sampled across multiple resolutions, including the original resolution, and encoded with a CLIP vision backbone. A lightweight Perceptual Importance Estimator then predicts IQA-specific importance maps to select a small set of informative patches, and a Latent Quality Axis Module aggregates their embeddings into a single image-level score. Across authentic, synthetic, and AIGC benchmarks spanning diverse resolutions and distortions, ReLIQS generalizes better than strong CNN-, CLIP-, and MLLM-based baselines with matching or reduced computational cost.", "published": "2026-08-03T05:54:06Z", "authors": ["Hakan Emre Gedik", "Shashank Gupta", "Alan Bovik"], "categories": ["cs.CV"], "doi": "", "_query": "property valuation computer vision", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/1903.11068v2", "title": "Full-rank Valuations and Toric Initial Ideals", "abstract": "Let $V(I)$ be a polarized projective variety or a subvariety of a product of projective spaces and let $A$ be its (multi-)homogeneous coordinate ring. Given a full-rank valuation $\\mathfrak v$ on $A$ we associate weights to the coordinates of the projective space, respectively, the product of projective spaces. Let $w_{\\mathfrak v}$ be the vector whose entries are these weights. Our main result is that the value semi-group of $\\mathfrak v$ is generated by the images of the generators of $A$ if and only if the initial ideal of $I$ with respect to $w_{\\mathfrak v}$ is prime. We further show that $w_{\\mathfrak v}$ always lies in the tropicalization of $I$. Applying our result to string valuations for flag varieties, we solve a conjecture by \\cite{BLMM} connecting the Minkowski property of string cones with the tropical flag variety. For Rietsch-Williams' valuation for Grassmannians our results give a criterion for when the Plücker coordinates form a Khovanskii basis. Further, as a corollary we obtain that the weight vectors defined in \\cite{BFFHL} lie in the tropical Grassmannian.", "published": "2019-03-26T17:41:24Z", "authors": ["Lara Bossinger"], "categories": ["math.AG", "math.AC", "math.RT"], "doi": "10.1093/imrn/rnaa071", "_query": "property valuation computer vision", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2208.04309v1", "title": "3D Vision with Transformers: A Survey", "abstract": "The success of the transformer architecture in natural language processing has recently triggered attention in the computer vision field. The transformer has been used as a replacement for the widely used convolution operators, due to its ability to learn long-range dependencies. This replacement was proven to be successful in numerous tasks, in which several state-of-the-art methods rely on transformers for better learning. In computer vision, the 3D field has also witnessed an increase in employing the transformer for 3D convolution neural networks and multi-layer perceptron networks. Although a number of surveys have focused on transformers in vision in general, 3D vision requires special attention due to the difference in data representation and processing when compared to 2D vision. In this work, we present a systematic and thorough review of more than 100 transformers methods for different 3D vision tasks, including classification, segmentation, detection, completion, pose estimation, and others. We discuss transformer design in 3D vision, which allows it to process data with various 3D representations. For each application, we highlight key properties and contributions of proposed transformer-based methods. To assess the competitiveness of these methods, we compare their performance to common non-transformer methods on 12 3D benchmarks. We conclude the survey by discussing different open directions and challenges for transformers in 3D vision. In addition to the presented papers, we aim to frequently update the latest relevant papers along with their corresponding implementations at: https://github.com/lahoud/3d-vision-transformers.", "published": "2022-08-08T17:59:11Z", "authors": ["Jean Lahoud", "Jiale Cao", "Fahad Shahbaz Khan", "Hisham Cholakkal", "Rao Muhammad Anwer", "Salman Khan", "Ming-Hsuan Yang"], "categories": ["cs.CV"], "doi": "", "_query": "property valuation computer vision", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/1308.4743v1", "title": "Quasi-valuations and algebras over valuation domains", "abstract": "Suppose $F$ is a field with valuation $v$ and valuation domain $O_{v}$, and $R$ is an $O_{v}-$algebra. We prove that $R$ satisfies SGB (strong going between) over $O_{v}$. We give a necessary and sufficient condition for $R$ to satisfy LO (lying over) over $O_{v}$. Using the filter \\qv constructed in [Sa1], we show that if $R$ is torsion-free over $O_{v}$ then $R$ satisfies GD (going down) over $O_{v}$. In particular, if $R$ is torsion-free and $(R^{\\times} \\cap O_{v}) \\subseteq O_{v}^{\\times}$, then for any chain in $\\text{Spec}(O_v)$ there exists a chain in $\\text{Spec}(R)$ covering it. Assuming $R$ is torsion-free over $O_{v}$ and $[R \\otimes_{O_{v}}F:F]< \\infty$, we prove that $R$ satisfies INC (incomparabilty) over $O_{v}$. Assuming in addition that $(R^{\\times} \\cap O_{v}) \\subseteq O_{v}^{\\times}$, we deduce that $R$ and $O_{v}$ have the same Krull dimension and a bound on the size of the prime spectrum of $R$ is given. Under certain assumptions on $R$ and a \\qv defined on it, we prove that the \\qv ring satisfies GU (going up) over $O_{v}$. Combining these five properties together, we deduce that any maximal chain of prime ideals of the \\qv ring is lying over $\\text{Spec}(O_{v})$, in a one-to-one correspondence.", "published": "2013-08-22T00:09:15Z", "authors": ["Shai Sarussi"], "categories": ["math.RA"], "doi": "", "_query": "property valuation computer vision", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2104.12753v3", "title": "Vision Transformers with Patch Diversification", "abstract": "Vision transformer has demonstrated promising performance on challenging computer vision tasks. However, directly training the vision transformers may yield unstable and sub-optimal results. Recent works propose to improve the performance of the vision transformers by modifying the transformer structures, e.g., incorporating convolution layers. In contrast, we investigate an orthogonal approach to stabilize the vision transformer training without modifying the networks. We observe the instability of the training can be attributed to the significant similarity across the extracted patch representations. More specifically, for deep vision transformers, the self-attention blocks tend to map different patches into similar latent representations, yielding information loss and performance degradation. To alleviate this problem, in this work, we introduce novel loss functions in vision transformer training to explicitly encourage diversity across patch representations for more discriminative feature extraction. We empirically show that our proposed techniques stabilize the training and allow us to train wider and deeper vision transformers. We further show the diversified features significantly benefit the downstream tasks in transfer learning. For semantic segmentation, we enhance the state-of-the-art (SOTA) results on Cityscapes and ADE20k. Our code is available at https://github.com/ChengyueGongR/PatchVisionTransformer.", "published": "2021-04-26T17:43:04Z", "authors": ["Chengyue Gong", "Dilin Wang", "Meng Li", "Vikas Chandra", "Qiang Liu"], "categories": ["cs.CV", "cs.LG"], "doi": "", "_query": "property valuation computer vision", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2006.01909v1", "title": "SL($n$) contravariant vector valuations", "abstract": "All SL($n$) contravariant vector valuations on polytopes in $\\mathbb R^n$ are completely classified without any additional assumptions. The facet vector is defined. It turns out to be the unique such valuation for $n\\geq3$. In dimension two, the classification corresponds to the case of SL(2) covariant valuations.", "published": "2020-06-02T19:43:02Z", "authors": ["Jin Li", "Dan Ma", "Wei Wang"], "categories": ["math.MG"], "doi": "", "_query": "property valuation computer vision", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/1505.02302v2", "title": "Periodicity in the $p$-adic valuation of a polynomial", "abstract": "For a prime $p$ and an integer $x$, the $p$-adic valuation of $x$ is denoted by $ν_{p}(x)$. For a polynomial $Q$ with integer coefficients, the sequence of valuations $ν_{p}(Q(n))$ is shown to be either periodic or unbounded. The first case corresponds to the situation where $Q$ has no roots in the ring of $p$-adic integers. In the periodic situation, the period length is determined.", "published": "2015-05-09T18:35:48Z", "authors": ["Luis A. Medina", "Victor H. Moll", "Eric Rowland"], "categories": ["math.NT"], "doi": "10.1016/j.jnt.2017.03.006", "_query": "property valuation computer vision", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2112.08281v2", "title": "Detecting Object States vs Detecting Objects: A New Dataset and a Quantitative Experimental Study", "abstract": "The detection of object states in images (State Detection - SD) is a problem of both theoretical and practical importance and it is tightly interwoven with other important computer vision problems, such as action recognition and affordance detection. It is also highly relevant to any entity that needs to reason and act in dynamic domains, such as robotic systems and intelligent agents. Despite its importance, up to now, the research on this problem has been limited. In this paper, we attempt a systematic study of the SD problem. First, we introduce the Object State Detection Dataset (OSDD), a new publicly available dataset consisting of more than 19,000 annotations for 18 object categories and 9 state classes. Second, using a standard deep learning framework used for Object Detection (OD), we conduct a number of appropriately designed experiments, towards an in-depth study of the behavior of the SD problem. This study enables the setup of a baseline on the performance of SD, as well as its relative performance in comparison to OD, in a variety of scenarios. Overall, the experimental outcomes confirm that SD is harder than OD and that tailored SD methods need to be developed for addressing effectively this significant problem.", "published": "2021-12-15T17:19:14Z", "authors": ["Filippos Gouidis", "Theodore Patkos", "Antonis Argyros", "Dimitris Plexousakis"], "categories": ["cs.CV"], "doi": "", "_query": "property valuation computer vision", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2305.05659v2", "title": "Green-Lazarsfeld property $N_p$ for Segre product of Hibi rings", "abstract": "In this article, we prove that if a Hibi ring satisfies property $N_2$, then its Segre product with a polynomial ring in finitely many variables also satisfies property $N_2$. When the polynomial ring is in two variables, we also prove the above statement for $N_3$. Moreover, we study the minimal Koszul relations of the second syzygy module of Hibi rings.", "published": "2023-05-09T17:53:16Z", "authors": ["Dharm Veer"], "categories": ["math.AC"], "doi": "", "_query": "property valuation computer vision", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/1707.05242v1", "title": "Minkowski valuations on convex functions", "abstract": "A classification of SL$(n)$ contravariant Minkowski valuations on convex functions and a characterization of the projection body operator are established. The associated LYZ measure is characterized. In addition, a new SL$(n)$ covariant Minkowski valuation on convex functions is defined and characterized.", "published": "2017-07-17T15:46:26Z", "authors": ["Andrea Colesanti", "Monika Ludwig", "Fabian Mussnig"], "categories": ["math.FA", "math.MG"], "doi": "10.1007/s00526-017-1243-4", "_query": "property valuation computer vision", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2406.06152v1", "title": "On the Complexity of Inverse Bivariate Multi-unit Assignment Valuation Problems", "abstract": "Inverse and bilevel optimization problems play a central role in both theory and applications. These two classes are known to be closely related due to the pioneering work of Dempe and Lohse (2006), and thus have often been discussed together ever since. In this paper, we consider inverse problems for multi-unit assignment valuations. Multi-unit assignment valuations form a subclass of strong-substitutes valuations that can be represented by edge-weighted complete bipartite graphs. These valuations play a key role in auction theory as the strong substitutes condition implies the existence of a Walrasian equilibrium. A recent line of research concentrated on the problem of deciding whether a bivariate valuation function is an assignment valuation or not. In this paper, we consider an \\emph{inverse} variant of the problem: we are given a bivariate function $g$, and our goal is to find a bivariate multi-unit assignment valuation function $f$ that is as close to $g$ as possible. The difference between $f$ and $g$ can be measured either in $\\ell_1$- or $\\ell_\\infty$-norm. Using tools from discrete convex analysis, we show that the problem is strongly NP-hard. On the other hand, we derive linear programming formulations that solve relaxed versions of the problem.", "published": "2024-06-10T10:25:36Z", "authors": ["Kristóf Bérczi", "Lydia Mirabel Mendoza-Cadena"], "categories": ["math.OC"], "doi": "", "_query": "property valuation computer vision", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2409.11028v2", "title": "Estimating the distribution of numerosity and non-numerical visual magnitudes in natural scenes using computer vision", "abstract": "Humans share with many animal species the ability to perceive and approximately represent the number of objects in visual scenes. This ability improves throughout childhood, suggesting that learning and development play a key role in shaping our number sense. This hypothesis is further supported by computational investigations based on deep learning, which have shown that numerosity perception can spontaneously emerge in neural networks that learn the statistical structure of images with a varying number of items. However, neural network models are usually trained using synthetic datasets that might not faithfully reflect the statistical structure of natural environments, and there is also growing interest in using more ecological visual stimuli to investigate numerosity perception in humans. In this work, we exploit recent advances in computer vision algorithms to design and implement an original pipeline that can be used to estimate the distribution of numerosity and non-numerical magnitudes in large-scale datasets containing thousands of real images depicting objects in daily life situations. We show that in natural visual scenes the frequency of appearance of different numerosities follows a power law distribution. Moreover, we show that the correlational structure for numerosity and continuous magnitudes is stable across datasets and scene types (homogeneous vs. heterogeneous object sets). We suggest that considering such \"ecological\" pattern of covariance is important to understand the influence of non-numerical visual cues on numerosity judgements.", "published": "2024-09-17T09:49:29Z", "authors": ["Kuinan Hou", "Marco Zorzi", "Alberto Testolin"], "categories": ["cs.CV"], "doi": "10.1007/s00426-024-02064-2", "_query": "property valuation computer vision", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2408.16357v3", "title": "Law of Vision Representation in MLLMs", "abstract": "We present the \"Law of Vision Representation\" in multimodal large language models (MLLMs). It reveals a strong correlation between the combination of cross-modal alignment, correspondence in vision representation, and MLLM performance. We quantify the two factors using the cross-modal Alignment and Correspondence score (AC score). Through extensive experiments involving thirteen different vision representation settings and evaluations across eight benchmarks, we find that the AC score is linearly correlated to model performance. By leveraging this relationship, we are able to identify and train the optimal vision representation only, which does not require finetuning the language model every time, resulting in a 99.7% reduction in computational cost.", "published": "2024-08-29T08:56:48Z", "authors": ["Shijia Yang", "Bohan Zhai", "Quanzeng You", "Jianbo Yuan", "Hongxia Yang", "Chenfeng Xu"], "categories": ["cs.CV"], "doi": "", "_query": "property valuation computer vision", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/cs/9809019v1", "title": "Distributed Computation as Hierarchy", "abstract": "This paper presents a new distributed computational model of distributed systems called the phase web that extends V. Pratt's orthocurrence relation from 1986. The model uses mutual-exclusion to express sequence, and a new kind of hierarchy to replace event sequences, posets, and pomsets. The model explicitly connects computation to a discrete Clifford algebra that is in turn extended into homology and co-homology, wherein the recursive nature of objects and boundaries becomes apparent and itself subject to hierarchical recursion. Topsy, a programming environment embodying the phase web, is available from www.cs.auc.dk/topsy.", "published": "1998-09-14T15:25:42Z", "authors": ["Michael Manthey"], "categories": ["cs.DC", "cs.NE"], "doi": "", "_query": "property valuation computer vision", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/1711.09080v1", "title": "Intrinsic valuation entropy", "abstract": "We extend the notion of intrinsic entropy for endomorphisms of Abelian groups to endomorphisms of modules over an Archimedean non-discrete valuation domain $R$, using the natural non-discrete length function introduced by Northcott and Reufel for such a category of modules. We prove that this notion of entropy is a length function for the category of $R[X]$-modules, it satisfies (a suitably adapted version of) the Intrinsic Algebraic Yuzvinski Formula and that it is essentially the unique invariant for $Mod(R[X])$ with these properties.", "published": "2017-11-24T18:50:15Z", "authors": ["Luigi Salce", "Simone Virili"], "categories": ["math.RA"], "doi": "", "_query": "property valuation computer vision", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2411.16219v2", "title": "Weakly Supervised Panoptic Segmentation for Defect-Based Grading of Fresh Produce", "abstract": "Visual inspection for defect grading in agricultural supply chains is crucial but traditionally labor-intensive and error-prone. Automated computer vision methods typically require extensively annotated datasets, which are often unavailable in decentralized supply chains. We address this challenge by evaluating the Segment Anything Model (SAM) to generate dense panoptic segmentation masks from sparse annotations. These dense predictions are then used to train a supervised panoptic segmentation model. Focusing on banana surface defects (bruises and scars), we validate our approach using 476 field images annotated with 1440 defects. While SAM-generated masks generally align with human annotations, substantially reducing annotation effort, we explicitly identify failure cases associated with specific defect sizes and shapes. Despite these limitations, our approach offers practical estimates of defect number and relative size from panoptic masks, underscoring the potential and current boundaries of foundation models for defect quantification in low-data agricultural scenarios. GitHub: https://github.com/manuelknott/banana-defect-segmentation", "published": "2024-11-25T09:28:58Z", "authors": ["Manuel Knott", "Divinefavour Odion", "Sameer Sontakke", "Anup Karwa", "Thijs Defraeye"], "categories": ["cs.CV"], "doi": "", "_query": "property valuation computer vision", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/1104.2175v1", "title": "Extracting Parts of 2D Shapes Using Local and Global Interactions Simultaneously", "abstract": "Perception research provides strong evidence in favor of part based representation of shapes in human visual system. Despite considerable differences among different theories in terms of how part boundaries are found, there is substantial agreement on that the process depends on many local and global geometric factors. This poses an important challenge from the computational point of view. In the first part of the chapter, I present a novel decomposition method by taking both local and global interactions within the shape domain into account. At the top of the partitioning hierarchy, the shape gets split into two parts capturing, respectively, the gross structure and the peripheral structure. The gross structure may be conceived as the least deformable part of the shape which remains stable under visual transformations. The peripheral structure includes limbs, protrusions, and boundary texture. Such a separation is in accord with the behavior of the artists who start with a gross shape and enrich it with details. The method is particularly interesting from the computational point of view as it does not resort to any geometric notions (e.g. curvature, convexity) explicitly. In the second part of the chapter, I relate the new method to PDE based shape representation schemes.", "published": "2011-04-12T11:33:20Z", "authors": ["Sibel Tari"], "categories": ["cs.CV"], "doi": "", "_query": "property valuation computer vision", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/1805.02273v1", "title": "Extensions of integral domains and quasi-valuations", "abstract": "Let $S$ be an integral domain with field of fractions $F$ and let $A$ be an $F$-algebra having an $S$-stable basis. We prove the existence of an $S$-subalgebra $R$ of $A$ lying over $S$ whose localization with respect to $S$ is $A$ (we call such $R$ an $S$-nice subalgebra of $A$). We also show that there is no such minimal $S$-nice subalgebra of $A$. Given a valuation $v$ on $F$ with a corresponding valuation domain $O_v$, and an $O_v$-stable basis of $A$ over $F$, we prove the existence of a quasi-valuation on $A$ extending $v$ on $F$. Moreover, we prove the existence of an infinite decreasing chain of quasi-valuations on $A$, all of which extend $v$. Finally, we present applications for the above existence theorems; for example, we show that if $A$ is commutative and $\\mathcal C$ is any chain of prime ideals of $S$, then there exists an $S$-nice subalgebra of $A$, having a chain of prime ideals covering $\\mathcal C$.", "published": "2018-05-06T20:05:21Z", "authors": ["Shai Sarussi"], "categories": ["math.RA"], "doi": "", "_query": "property valuation computer vision", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2310.02544v1", "title": "SlowFormer: Universal Adversarial Patch for Attack on Compute and Energy Efficiency of Inference Efficient Vision Transformers", "abstract": "Recently, there has been a lot of progress in reducing the computation of deep models at inference time. These methods can reduce both the computational needs and power usage of deep models. Some of these approaches adaptively scale the compute based on the input instance. We show that such models can be vulnerable to a universal adversarial patch attack, where the attacker optimizes for a patch that when pasted on any image, can increase the compute and power consumption of the model. We run experiments with three different efficient vision transformer methods showing that in some cases, the attacker can increase the computation to the maximum possible level by simply pasting a patch that occupies only 8\\% of the image area. We also show that a standard adversarial training defense method can reduce some of the attack's success. We believe adaptive efficient methods will be necessary for the future to lower the power usage of deep models, so we hope our paper encourages the community to study the robustness of these methods and develop better defense methods for the proposed attack.", "published": "2023-10-04T02:57:01Z", "authors": ["KL Navaneet", "Soroush Abbasi Koohpayegani", "Essam Sleiman", "Hamed Pirsiavash"], "categories": ["cs.CV"], "doi": "", "_query": "property valuation computer vision", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2407.15546v1", "title": "Personalization of Dataset Retrieval Results using a Metadata-based Data Valuation Method", "abstract": "In this paper, we propose a novel data valuation method for a Dataset Retrieval (DR) use case in Ireland's National mapping agency. To the best of our knowledge, data valuation has not yet been applied to Dataset Retrieval. By leveraging metadata and a user's preferences, we estimate the personal value of each dataset to facilitate dataset retrieval and filtering. We then validated the data value-based ranking against the stakeholders' ranking of the datasets. The proposed data valuation method and use case demonstrated that data valuation is promising for dataset retrieval. For instance, the outperforming dataset retrieval based on our approach obtained 0.8207 in terms of NDCG@5 (the truncated Normalized Discounted Cumulative Gain at 5). This study is unique in its exploration of a data valuation-based approach to dataset retrieval and stands out because, unlike most existing methods, our approach is validated using the stakeholders ranking of the datasets.", "published": "2024-07-22T11:13:07Z", "authors": ["Malick Ebiele", "Malika Bendechache", "Eamonn Clinton", "Rob Brennan"], "categories": ["cs.IR", "cs.DB"], "doi": "", "_query": "property valuation computer vision", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2607.26886v1", "title": "Hearsay: Vision-Language Medical Diagnoses Without an Image", "abstract": "When asked to describe a medical image that was never attached, frontier vision-language models do not abstain: they confabulate a diagnosis. We show that this confabulation is not random. It is structured by who the patient is said to be. Across chest X-ray, brain MRI, and dermatology, Claude Opus-4.7, GPT-5.4, and Gemini-3.1-Pro are each queried with only a demographic descriptor and no image, and changing the descriptor systematically shifts the diagnosis returned. Claude concentrates sharply: a 65-year-old white man asking about a skin mole receives Melanoma in nearly every response, and a 32-year-old Black woman asking about her chest X-ray receives a Sarcoidosis diagnosis whose reasoning reads \"suspected, based on demographics and classic pattern.'' GPT-5.4's effect is broader, fabricating across every demographic cell we test, most conspicuously naming Sarcoidosis for young Black patients on chest X-ray. Two structural findings sharpen the problem. A hedged regime appears in which the prose acknowledges the missing image while the structured diagnosis field nevertheless names a disease, a dissociation invisible to prose-only audits. And Claude's dermatology effect collapses entirely when 'skin mole' is swapped for 'skin lesion' while GPT-5.4's is preserved, indicating that mirage is a family of distinct failure modes rather than a single phenomenon. Trustworthy VLM deployment in clinical pipelines requires auditing the structured output channel directly, and probe-word sensitivity should be treated as a first-class evaluation dimension", "published": "2026-07-29T13:15:23Z", "authors": ["Siddharth Vohra"], "categories": ["cs.CV", "cs.AI", "cs.CL", "cs.CY"], "doi": "", "_query": "property valuation computer vision", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2305.09407v1", "title": "A Novel Strategy for Improving Robustness in Computer Vision Manufacturing Defect Detection", "abstract": "Visual quality inspection in high performance manufacturing can benefit from automation, due to cost savings and improved rigor. Deep learning techniques are the current state of the art for generic computer vision tasks like classification and object detection. Manufacturing data can pose a challenge for deep learning because data is highly repetitive and there are few images of defects or deviations to learn from. Deep learning models trained with such data can be fragile and sensitive to context, and can under-detect new defects not found in the training data. In this work, we explore training defect detection models to learn specific defects out of context, so that they are more likely to be detected in new situations. We demonstrate how models trained on diverse images containing a common defect type can pick defects out in new circumstances. Such generic models could be more robust to new defects not found data collected for training, and can reduce data collection impediments to implementing visual inspection on production lines. Additionally, we demonstrate that object detection models trained to predict a label and bounding box outperform classifiers that predict a label only on held out test data typical of manufacturing inspection tasks. Finally, we studied the factors that affect generalization in order to train models that work under a wider range of conditions.", "published": "2023-05-16T12:51:51Z", "authors": ["Ahmad Mohamad Mezher", "Andrew E. Marble"], "categories": ["cs.CV"], "doi": "", "_query": "property valuation computer vision", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2008.08791v1", "title": "Facial movement synergies and Action Unit detection from distal wearable Electromyography and Computer Vision", "abstract": "Distal facial Electromyography (EMG) can be used to detect smiles and frowns with reasonable accuracy. It capitalizes on volume conduction to detect relevant muscle activity, even when the electrodes are not placed directly on the source muscle. The main advantage of this method is to prevent occlusion and obstruction of the facial expression production, whilst allowing EMG measurements. However, measuring EMG distally entails that the exact source of the facial movement is unknown. We propose a novel method to estimate specific Facial Action Units (AUs) from distal facial EMG and Computer Vision (CV). This method is based on Independent Component Analysis (ICA), Non-Negative Matrix Factorization (NNMF), and sorting of the resulting components to determine which is the most likely to correspond to each CV-labeled action unit (AU). Performance on the detection of AU06 (Orbicularis Oculi) and AU12 (Zygomaticus Major) was estimated by calculating the agreement with Human Coders. The results of our proposed algorithm showed an accuracy of 81% and a Cohen's Kappa of 0.49 for AU6; and accuracy of 82% and a Cohen's Kappa of 0.53 for AU12. This demonstrates the potential of distal EMG to detect individual facial movements. Using this multimodal method, several AU synergies were identified. We quantified the co-occurrence and timing of AU6 and AU12 in posed and spontaneous smiles using the human-coded labels, and for comparison, using the continuous CV-labels. The co-occurrence analysis was also performed on the EMG-based labels to uncover the relationship between muscle synergies and the kinematics of visible facial movement.", "published": "2020-08-20T06:09:03Z", "authors": ["Monica Perusquia-Hernandez", "Felix Dollack", "Chun Kwang Tan", "Shushi Namba", "Saho Ayabe-Kanamura", "Kenji Suzuki"], "categories": ["cs.HC", "cs.CV", "eess.IV"], "doi": "", "_query": "property valuation computer vision", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2606.02153v1", "title": "Ultra Diffusion Poser: Diffusion-Based Human Motion Tracking From Sparse Inertial Sensors and Ranging-Based Between-Sensor Distances", "abstract": "Methods using inertial measurement units (IMUs) provide a wearable alternative to camera-based motion capture. To mitigate drift from inertial signals, recent sparse inertial pose estimators integrate inter-sensor distances measured by ultra-wideband (UWB) ranging. So far, UWB distances have only been used as an additional input feature, ignoring the physical constraints they impose on sensor positions. However, these distances can also be used to reconstruct the underlying 3D sensor layout, which in turn provides more informative input for pose reconstruction. We propose Ultra Diffusion Poser, a diffusion model that explicitly models these geometric constraints. It includes a Spatial Layout Module that analytically reconstructs the 3D sensor positions from UWB measurements. These sensor positions are used alongside IMU signals and UWB distances as a conditioning signal during diffusion. Still, network predictions can violate inter-sensor distance measurements. To address this, we introduce UWB-Diffusion Guidance, which encourages alignment between predicted poses and measured distances during diffusion sampling. Together, these contributions enable our model to achieve state-of-the-art performance, reducing joint position error by up to 22% over prior work.", "published": "2026-06-01T12:20:31Z", "authors": ["Dominik Hollidt", "Tommaso Bendinelli", "Christian Holz"], "categories": ["cs.CV", "cs.GR"], "doi": "", "_query": "property valuation computer vision", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/1905.00307v2", "title": "3DFaceGAN: Adversarial Nets for 3D Face Representation, Generation, and Translation", "abstract": "Over the past few years, Generative Adversarial Networks (GANs) have garnered increased interest among researchers in Computer Vision, with applications including, but not limited to, image generation, translation, imputation, and super-resolution. Nevertheless, no GAN-based method has been proposed in the literature that can successfully represent, generate or translate 3D facial shapes (meshes). This can be primarily attributed to two facts, namely that (a) publicly available 3D face databases are scarce as well as limited in terms of sample size and variability (e.g., few subjects, little diversity in race and gender), and (b) mesh convolutions for deep networks present several challenges that are not entirely tackled in the literature, leading to operator approximations and model instability, often failing to preserve high-frequency components of the distribution. As a result, linear methods such as Principal Component Analysis (PCA) have been mainly utilized towards 3D shape analysis, despite being unable to capture non-linearities and high frequency details of the 3D face - such as eyelid and lip variations. In this work, we present 3DFaceGAN, the first GAN tailored towards modeling the distribution of 3D facial surfaces, while retaining the high frequency details of 3D face shapes. We conduct an extensive series of both qualitative and quantitative experiments, where the merits of 3DFaceGAN are clearly demonstrated against other, state-of-the-art methods in tasks such as 3D shape representation, generation, and translation.", "published": "2019-05-01T13:31:01Z", "authors": ["Stylianos Moschoglou", "Stylianos Ploumpis", "Mihalis Nicolaou", "Athanasios Papaioannou", "Stefanos Zafeiriou"], "categories": ["cs.CV"], "doi": "", "_query": "property valuation computer vision", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/1707.01026v2", "title": "Polyhedra and parameter spaces for matroids over valuation rings", "abstract": "In this paper we address two of the major foundational questions in the theory of matroids over rings. First, we provide a cryptomorphic axiomatisation, by introducing an analogue of the base polytope for matroids. Second, we describe a parameter space for matroids over a valuation ring, which turns out to be a tropical version of the Bott-Samelson varieties for the full flag variety. Thus a matroid over a valuation ring is a sequence of flags of tropical linear spaces a.k.a. valuated matroids.", "published": "2017-07-04T14:59:34Z", "authors": ["Alex Fink", "Luca Moci"], "categories": ["math.CO", "math.AG"], "doi": "", "_query": "property valuation computer vision", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/1511.08164v4", "title": "Minimizing normalized volumes of valuations", "abstract": "For any $\\mathbb{Q}$-Gorenstein klt singularity $(X,o)$, we introduce a normalized volume function $\\widehat{\\rm vol}$ that is defined on the space of real valuations centered at $o$ and consider the problem of minimizing $\\widehat{\\rm vol}$. We prove that the normalized volume has a uniform positive lower bound by proving an Izumi type estimate for any $\\mathbb{Q}$-Gorenstein klt singularity. Furthermore, by proving a properness estimate, we show that the set of real valuations with uniformly bounded normalized volumes is compact, and hence reduce the existence of minimizers for the normalized volume functional $\\widehat{\\rm vol}$ to a conjectural lower semicontinuity property. We calculate candidate minimizers in several examples to show that this is an interesting and nontrivial problem. In particular, by using an inequality of de-Fernex-Ein-Mustaţă, we show that the divisorial valuation associated to the exceptional divisor of the standard blow up is a minimizer of $\\widehat{\\rm vol}$ for a smooth point. Finally the relation to Fujita's work on divisorial stability is also pointed out.", "published": "2015-11-25T19:10:30Z", "authors": ["Chi Li"], "categories": ["math.AG", "math.AC", "math.DG"], "doi": "", "_query": "property valuation computer vision", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2107.12087v2", "title": "Text is Text, No Matter What: Unifying Text Recognition using Knowledge Distillation", "abstract": "Text recognition remains a fundamental and extensively researched topic in computer vision, largely owing to its wide array of commercial applications. The challenging nature of the very problem however dictated a fragmentation of research efforts: Scene Text Recognition (STR) that deals with text in everyday scenes, and Handwriting Text Recognition (HTR) that tackles hand-written text. In this paper, for the first time, we argue for their unification -- we aim for a single model that can compete favourably with two separate state-of-the-art STR and HTR models. We first show that cross-utilisation of STR and HTR models trigger significant performance drops due to differences in their inherent challenges. We then tackle their union by introducing a knowledge distillation (KD) based framework. This is however non-trivial, largely due to the variable-length and sequential nature of text sequences, which renders off-the-shelf KD techniques that mostly works with global fixed-length data inadequate. For that, we propose three distillation losses all of which are specifically designed to cope with the aforementioned unique characteristics of text recognition. Empirical evidence suggests that our proposed unified model performs on par with individual models, even surpassing them in certain cases. Ablative studies demonstrate that naive baselines such as a two-stage framework, and domain adaption/generalisation alternatives do not work as well, further verifying the appropriateness of our design.", "published": "2021-07-26T10:10:34Z", "authors": ["Ayan Kumar Bhunia", "Aneeshan Sain", "Pinaki Nath Chowdhury", "Yi-Zhe Song"], "categories": ["cs.CV"], "doi": "", "_query": "property valuation computer vision", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/1002.2420v1", "title": "High Performance Hybrid Two Layer Router Architecture for FPGAs Using Network On Chip", "abstract": "Networks on Chip is a recent solution paradigm adopted to increase the performance of Multicore designs. The key idea is to interconnect various computation modules (IP cores) in a network fashion and transport packets simultaneously across them, thereby gaining performance. In addition to improving performance by having multiple packets in flight, NoCs also present a host of other advantages including scalability, power efficiency, and component reuse through modular design. This work focuses on design and development of high performance communication architectures for FPGAs using NoCs Once completely developed, the above methodology could be used to augment the current FPGA design flow for implementing multicore SoC applications. We design and implement an NoC framework for FPGAs, MultiClock OnChip Network for Reconfigurable Systems (MoCReS). We propose a novel microarchitecture for a hybrid two layer router that supports both packetswitched communications, across its local and directional ports, as well as, time multiplexed circuitswitched communications among the multiple IP cores directly connected to it. Results from place and route VHDL models of the advanced router architecture show an average improvement of 20.4 percent in NoC bandwidth (maximum of 24 percent compared to a traditional NoC). We parameterize the hybrid router model over the number of ports, channel width and bRAM depth and develop a library of network components (MoClib Library). For your paper to be published in the conference proceedings, you must use this document as both an instruction set and as a template into which you can type your own text. If your paper does not conform to the required format, you will be asked to fix it.", "published": "2010-02-11T20:22:18Z", "authors": ["P. Ezhumalai", "S. Manojkumar", "C. Arun", "P. Sakthivel", "D. Sridharan"], "categories": ["cs.NI"], "doi": "", "_query": "property valuation computer vision", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2201.01249v1", "title": "ExAID: A Multimodal Explanation Framework for Computer-Aided Diagnosis of Skin Lesions", "abstract": "One principal impediment in the successful deployment of AI-based Computer-Aided Diagnosis (CAD) systems in clinical workflows is their lack of transparent decision making. Although commonly used eXplainable AI methods provide some insight into opaque algorithms, such explanations are usually convoluted and not readily comprehensible except by highly trained experts. The explanation of decisions regarding the malignancy of skin lesions from dermoscopic images demands particular clarity, as the underlying medical problem definition is itself ambiguous. This work presents ExAID (Explainable AI for Dermatology), a novel framework for biomedical image analysis, providing multi-modal concept-based explanations consisting of easy-to-understand textual explanations supplemented by visual maps justifying the predictions. ExAID relies on Concept Activation Vectors to map human concepts to those learnt by arbitrary Deep Learning models in latent space, and Concept Localization Maps to highlight concepts in the input space. This identification of relevant concepts is then used to construct fine-grained textual explanations supplemented by concept-wise location information to provide comprehensive and coherent multi-modal explanations. All information is comprehensively presented in a diagnostic interface for use in clinical routines. An educational mode provides dataset-level explanation statistics and tools for data and model exploration to aid medical research and education. Through rigorous quantitative and qualitative evaluation of ExAID, we show the utility of multi-modal explanations for CAD-assisted scenarios even in case of wrong predictions. We believe that ExAID will provide dermatologists an effective screening tool that they both understand and trust. Moreover, it will be the basis for similar applications in other biomedical imaging fields.", "published": "2022-01-04T17:11:28Z", "authors": ["Adriano Lucieri", "Muhammad Naseer Bajwa", "Stephan Alexander Braun", "Muhammad Imran Malik", "Andreas Dengel", "Sheraz Ahmed"], "categories": ["cs.AI", "cs.LG", "eess.IV"], "doi": "", "_query": "property valuation computer vision", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/1805.04142v1", "title": "Towards Budget-Driven Hardware Optimization for Deep Convolutional Neural Networks using Stochastic Computing", "abstract": "Recently, Deep Convolutional Neural Network (DCNN) has achieved tremendous success in many machine learning applications. Nevertheless, the deep structure has brought significant increases in computation complexity. Largescale deep learning systems mainly operate in high-performance server clusters, thus restricting the application extensions to personal or mobile devices. Previous works on GPU and/or FPGA acceleration for DCNNs show increasing speedup, but ignore other constraints, such as area, power, and energy. Stochastic Computing (SC), as a unique data representation and processing technique, has the potential to enable the design of fully parallel and scalable hardware implementations of large-scale deep learning systems. This paper proposed an automatic design allocation algorithm driven by budget requirement considering overall accuracy performance. This systematic method enables the automatic design of a DCNN where all design parameters are jointly optimized. Experimental results demonstrate that proposed algorithm can achieve a joint optimization of all design parameters given the comprehensive budget of a DCNN.", "published": "2018-05-10T19:21:39Z", "authors": ["Zhe Li", "Ji Li", "Ao Ren", "Caiwen Ding", "Jeffrey Draper", "Qinru Qiu", "Bo Yuan", "Yanzhi Wang"], "categories": ["cs.NE", "cs.ET"], "doi": "", "_query": "property valuation computer vision", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2606.03730v3", "title": "Beyond False Stability: High-Noise Drift Gating for Test-Time Adversarial Defenses in Vision-Language Models", "abstract": "Vision-language models (VLMs) such as CLIP show strong zero-shot generalization but remain highly vulnerable to adversarial attacks. Adversarial training improves robustness but is computationally expensive, motivating test-time defenses. Recent approaches exploit how CLIP's visual representations respond to stochastic perturbations: aggregating predictions across noisy views, constructing Gaussian noise-averaged anchors and interpolating features toward them, or applying counter-perturbations. These strategies improve robustness but often degrade clean accuracy, yielding an unfavorable clean-robust trade-off. We revisit stochastic test-time defenses and identify an underexplored noise-regime transition in CLIP's representation space. Prior work explored perturbations mainly in the weak-noise regime, where adversarial examples can appear unusually stable (false stability). Our analysis shows this reverses as perturbation strength grows: beyond the weak-noise regime, adversarial representations become markedly more unstable than clean ones, giving a clearer separation signal. The transition is consistent across uniform and Gaussian noise, photometric and geometric transforms, datasets, and diverse attacks. It largely disappears in adversarially trained models, suggesting it is tied to the fragile local-basin geometry of adversarial representations in non-robust CLIP. We propose a training-free, plug-in drift-gated mechanism that uses high-noise feature drift as a lightweight gating signal to trigger existing test-time defenses only when adversarial-like instability is detected. Across 13 datasets it consistently improves the clean-robust trade-off. On eight fine-grained datasets, mean clean+adversarial accuracy rises from 65.7% to 71.4% for counterattack defenses and 68.4% to 73.2% for noise-anchoring; on ImageNet and four shifted variants, from 56.1% to 66.2% and 62.1% to 67.6%.", "published": "2026-06-02T14:49:04Z", "authors": ["Hashmat Shadab Malik", "Muzammal Naseer", "Salman Khan"], "categories": ["cs.CV"], "doi": "", "_query": "property valuation computer vision", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/1004.0844v1", "title": "Quantum Portfolios of Observables and the Risk Neutral Valuation Model", "abstract": "Quantum Portfolios of quantum algorithms encoded on qbits have recently been reported. In this paper a discussion of the continuous variables version of quantum portfolios is presented. A risk neutral valuation model for options dependent on the measured values of the observables, analogous to the traditional Black-Scholes valuation model, is obtained from the underlying stochastic equations. The quantum algorithms are here encoded on simple harmonic oscillator (SHO) states, and a Fokker-Planck equation for the Glauber P-representation is obtained as a starting point for the analysis. A discussion of the observation of the polarization of a portfolio of qbits is also obtained and the resultant Fokker-Planck equation is used to obtain the risk neutral valuation of the qbit polarization portfolio.", "published": "2010-04-02T06:14:37Z", "authors": ["Fredrick Michael"], "categories": ["q-fin.GN"], "doi": "", "_query": "property valuation computer vision", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2307.14487v1", "title": "Technical note: ShinyAnimalCV: open-source cloud-based web application for object detection, segmentation, and three-dimensional visualization of animals using computer vision", "abstract": "Computer vision (CV), a non-intrusive and cost-effective technology, has furthered the development of precision livestock farming by enabling optimized decision-making through timely and individualized animal care. The availability of affordable two- and three-dimensional camera sensors, combined with various machine learning and deep learning algorithms, has provided a valuable opportunity to improve livestock production systems. However, despite the availability of various CV tools in the public domain, applying these tools to animal data can be challenging, often requiring users to have programming and data analysis skills, as well as access to computing resources. Moreover, the rapid expansion of precision livestock farming is creating a growing need to educate and train animal science students in CV. This presents educators with the challenge of efficiently demonstrating the complex algorithms involved in CV. Thus, the objective of this study was to develop ShinyAnimalCV, an open-source cloud-based web application. This application provides a user-friendly interface for performing CV tasks, including object segmentation, detection, three-dimensional surface visualization, and extraction of two- and three-dimensional morphological features. Nine pre-trained CV models using top-view animal data are included in the application. ShinyAnimalCV has been deployed online using cloud computing platforms. The source code of ShinyAnimalCV is available on GitHub, along with detailed documentation on training CV models using custom data and deploying ShinyAnimalCV locally to allow users to fully leverage the capabilities of the application. ShinyAnimalCV can contribute to CV research and teaching in the animal science community.", "published": "2023-07-26T20:25:29Z", "authors": ["Jin Wang", "Yu Hu", "Lirong Xiang", "Gota Morota", "Samantha A. Brooks", "Carissa L. Wickens", "Emily K. Miller-Cushon", "Haipeng Yu"], "categories": ["cs.CV", "cs.AI"], "doi": "10.1093/jas/skad416", "_query": "property valuation computer vision", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2409.11499v2", "title": "Robotic Optimization of Powdered Beverages Leveraging Computer Vision and Bayesian Optimization", "abstract": "The growing demand for innovative research in the food industry is driving the adoption of robots in large-scale experimentation, as it offers increased precision, replicability, and efficiency in product manufacturing and evaluation. To this end, we introduce a robotic system designed to optimize food product quality, focusing on powdered cappuccino preparation as a case study. By leveraging optimization algorithms and computer vision, the robot explores the parameter space to identify the ideal conditions for producing a cappuccino with the best foam quality. The system also incorporates computer vision-driven feedback in a closed-loop control to further improve the beverage. Our findings demonstrate the effectiveness of robotic automation in achieving high repeatability and extensive parameter exploration, paving the way for more advanced and reliable food product development.", "published": "2024-09-17T19:00:46Z", "authors": ["Emilia Szymanska", "Josie Hughes"], "categories": ["cs.RO"], "doi": "", "_query": "property valuation computer vision", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2308.10198v2", "title": "Structure and computability of preimages in the Game of Life", "abstract": "Conway's Game of Life is a two-dimensional cellular automaton. As a dynamical system, it is well-known to be computationally universal, i.e.\\ capable of simulating an arbitrary Turing machine. We show that in a sense taking a single backwards step of the Game of Life is a computationally universal process, by constructing patterns whose preimage computation encodes an arbitrary circuit-satisfaction problem, or, equivalently, any tiling problem. As a corollary, we obtain for example that the set of orphans is coNP-complete, exhibit a $6210 \\times 37800$-periodic configuration whose preimage is nonempty but contains no periodic configurations, and prove that the existence of a preimage for a periodic point is undecidable. Our constructions were obtained by a combination of computer searches and manual design.", "published": "2023-08-20T08:09:04Z", "authors": ["Ville Salo", "Ilkka Törmä"], "categories": ["cs.FL", "cs.DM", "math.DS"], "doi": "", "_query": "property valuation computer vision", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/math/0202303v1", "title": "Uniform Approximation of Abhyankar Valuation Ideals in Smooth Function Fields", "abstract": "In this paper we use the theory of multiplier ideals to show that the valuation ideals of a rank one Abhyankar valuation centered at a smooth point of a complex algebraic variety are approximated, in a quite strong sense, by sequences of powers of fixed ideals. Fix a rank one valuation v centered at a smooth point x on an algebraic variety over a field of characteristic zero. Assume that v is Abhyankar, that is, that its rational rank plus its transcendence degree equal the dimension of the variety. Let a_m denote the ideal of elements in the local ring of x whose valuations are at least m. Our main theorem is that there exists e>0 such that a_{mn} is contained in (a_{m-e})^n for all m and n. This can be viewed as a greatly strengthened form of Izumi's Theorem for Abhyankar valuations centered on smooth complex varieties.", "published": "2002-02-28T15:52:31Z", "authors": ["Lawrence Ein", "Robert Lazarsfeld", "Karen E. Smith"], "categories": ["math.AG", "math.AC"], "doi": "", "_query": "property valuation computer vision", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2310.16764v1", "title": "ConvNets Match Vision Transformers at Scale", "abstract": "Many researchers believe that ConvNets perform well on small or moderately sized datasets, but are not competitive with Vision Transformers when given access to datasets on the web-scale. We challenge this belief by evaluating a performant ConvNet architecture pre-trained on JFT-4B, a large labelled dataset of images often used for training foundation models. We consider pre-training compute budgets between 0.4k and 110k TPU-v4 core compute hours, and train a series of networks of increasing depth and width from the NFNet model family. We observe a log-log scaling law between held out loss and compute budget. After fine-tuning on ImageNet, NFNets match the reported performance of Vision Transformers with comparable compute budgets. Our strongest fine-tuned model achieves a Top-1 accuracy of 90.4%.", "published": "2023-10-25T16:52:13Z", "authors": ["Samuel L. Smith", "Andrew Brock", "Leonard Berrada", "Soham De"], "categories": ["cs.CV", "cs.LG", "cs.NE"], "doi": "", "_query": "property valuation computer vision", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2512.11260v2", "title": "Do We Need Reformer for Vision? An Experimental Comparison with Vision Transformers", "abstract": "Transformers have recently demonstrated strong performance in computer vision, with Vision Transformers (ViTs) leveraging self-attention to capture both low-level and high-level image features. However, standard ViTs remain computationally expensive, since global self-attention scales quadratically with the number of tokens, which limits their practicality for high-resolution inputs and resource-constrained settings. In this work, we investigate the Reformer architecture as an alternative vision backbone. By combining patch-based tokenization with locality-sensitive hashing (LSH) attention, our model approximates global self-attention while reducing its theoretical time complexity from $\\mathcal{O}(n^2)$ to $\\mathcal{O}(n \\log n)$ in the sequence length $n$. We evaluate the proposed Reformer-based vision model on CIFAR-10 to assess its behavior on small-scale datasets, on ImageNet-100 to study its accuracy--efficiency trade-off in a more realistic setting, and on a high-resolution medical imaging dataset to evaluate the model under longer token sequences. While the Reformer achieves higher accuracy on CIFAR-10 compared to our ViT-style baseline, the ViT model consistently outperforms the Reformer in our experiments in terms of practical efficiency and end-to-end computation time across the larger and higher-resolution settings. These results suggest that, despite the theoretical advantages of LSH-based attention, meaningful computation gains require sequence lengths substantially longer than those produced by typical high-resolution images.", "published": "2025-12-12T03:49:55Z", "authors": ["Ali El Bellaj", "Mohammed-Amine Cheddadi", "Rhassan Berber"], "categories": ["cs.CV"], "doi": "", "_query": "property valuation computer vision", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2511.06274v1", "title": "Why `Fair Market Valuations' are Inappropirate for Employee-Owned Firms and Partnerships", "abstract": "The usual formulas for the fair market valuation of a firm at time $t$ include the profits accruing to the shares at time $t$ from the use of wage or salaried labor in the future. But in employee-owned firms or partnerships, the future worker-members or partners are the residual claimants at those future times, so in those cases, the future residuals do not accrue to the current shareholder/residual-claimants. Hence any `fair market valuation' of an employee-owned firm or partnership that assumes those future residuals accrue to the current shareholder/residual-claimants is inappropriate. Keywords: fair market valuations, residual claimants, property rights, personal rights, Miller-Modigliani valuations.", "published": "2025-11-09T08:24:37Z", "authors": ["David Ellerman"], "categories": ["econ.TH", "q-fin.PR"], "doi": "", "_query": "property valuation computer vision", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2503.20680v1", "title": "Vision as LoRA", "abstract": "We introduce Vision as LoRA (VoRA), a novel paradigm for transforming an LLM into an MLLM. Unlike prevalent MLLM architectures that rely on external vision modules for vision encoding, VoRA internalizes visual capabilities by integrating vision-specific LoRA layers directly into the LLM. This design allows the added parameters to be seamlessly merged into the LLM during inference, eliminating structural complexity and minimizing computational overhead. Moreover, inheriting the LLM's ability of handling flexible context, VoRA can process inputs at arbitrary resolutions. To further strengthen VoRA's visual capabilities, we introduce a block-wise distillation method that transfers visual priors from a pre-trained ViT into the LoRA layers, effectively accelerating training by injecting visual knowledge. Additionally, we apply bi-directional attention masks to better capture the context information of an image. We successfully demonstrate that with additional pre-training data, VoRA can perform comparably with conventional encode-based MLLMs. All training data, codes, and model weights will be released at https://github.com/Hon-Wong/VoRA.", "published": "2025-03-26T16:15:42Z", "authors": ["Han Wang", "Yongjie Ye", "Bingru Li", "Yuxiang Nie", "Jinghui Lu", "Jingqun Tang", "Yanjie Wang", "Can Huang"], "categories": ["cs.CV", "cs.CL"], "doi": "", "_query": "property valuation computer vision", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2309.03598v1", "title": "Enhancing Sample Utilization through Sample Adaptive Augmentation in Semi-Supervised Learning", "abstract": "In semi-supervised learning, unlabeled samples can be utilized through augmentation and consistency regularization. However, we observed certain samples, even undergoing strong augmentation, are still correctly classified with high confidence, resulting in a loss close to zero. It indicates that these samples have been already learned well and do not provide any additional optimization benefits to the model. We refer to these samples as ``naive samples\". Unfortunately, existing SSL models overlook the characteristics of naive samples, and they just apply the same learning strategy to all samples. To further optimize the SSL model, we emphasize the importance of giving attention to naive samples and augmenting them in a more diverse manner. Sample adaptive augmentation (SAA) is proposed for this stated purpose and consists of two modules: 1) sample selection module; 2) sample augmentation module. Specifically, the sample selection module picks out {naive samples} based on historical training information at each epoch, then the naive samples will be augmented in a more diverse manner in the sample augmentation module. Thanks to the extreme ease of implementation of the above modules, SAA is advantageous for being simple and lightweight. We add SAA on top of FixMatch and FlexMatch respectively, and experiments demonstrate SAA can significantly improve the models. For example, SAA helped improve the accuracy of FixMatch from 92.50% to 94.76% and that of FlexMatch from 95.01% to 95.31% on CIFAR-10 with 40 labels.", "published": "2023-09-07T09:50:45Z", "authors": ["Guan Gui", "Zhen Zhao", "Lei Qi", "Luping Zhou", "Lei Wang", "Yinghuan Shi"], "categories": ["cs.CV"], "doi": "", "_query": "property valuation computer vision", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2512.23078v1", "title": "Deep Learning for Art Market Valuation", "abstract": "We study how deep learning can improve valuation in the art market by incorporating the visual content of artworks into predictive models. Using a large repeated-sales dataset from major auction houses, we benchmark classical hedonic regressions and tree-based methods against modern deep architectures, including multi-modal models that fuse tabular and image data. We find that while artist identity and prior transaction history dominate overall predictive power, visual embeddings provide a distinct and economically meaningful contribution for fresh-to-market works where historical anchors are absent. Interpretability analyses using Grad-CAM and embedding visualizations show that models attend to compositional and stylistic cues. Our findings demonstrate that multi-modal deep learning delivers significant value precisely when valuation is hardest, namely first-time sales, and thus offers new insights for both academic research and practice in art market valuation.", "published": "2025-12-28T21:04:09Z", "authors": ["Jianping Mei", "Michael Moses", "Jan Waelty", "Yucheng Yang"], "categories": ["q-fin.GN", "cs.AI", "cs.CV", "cs.LG", "econ.GN"], "doi": "", "_query": "property valuation computer vision", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2603.29104v1", "title": "Regular rings over valuation rings", "abstract": "Bertin (1972) defined regularity for coherent local rings, and Knaf (2004) studied the property for a local ring $A$ essentially finitely presented over a valuation ring $V$. We discuss several properties of this notion of regularity for such $A$, obtaining results parallel to results for regularity of Noetherian local rings. We include classical and modern topics: openness of loci, perfectoid big Cohen--Macaulay algebras, and cotangent complexes. We also give an application to Noetherian rings, showing a version of Kodaira's vanishing theorem in large enough residue characteristics.", "published": "2026-03-31T00:51:07Z", "authors": ["Shiji Lyu"], "categories": ["math.AC"], "doi": "", "_query": "property valuation computer vision", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/1503.02200v3", "title": "Sequential Posted Price Mechanisms with Correlated Valuations", "abstract": "We study the revenue performance of sequential posted price mechanisms and some natural extensions, for a general setting where the valuations of the buyers are drawn from a correlated distribution. Sequential posted price mechanisms are conceptually simple mechanisms that work by proposing a take-it-or-leave-it offer to each buyer. We apply sequential posted price mechanisms to single-parameter multi-unit settings in which each buyer demands only one item and the mechanism can assign the service to at most k of the buyers. For standard sequential posted price mechanisms, we prove that with the valuation distribution having finite support, no sequential posted price mechanism can extract a constant fraction of the optimal expected revenue, even with unlimited supply. We extend this result to the the case of a continuous valuation distribution when various standard assumptions hold simultaneously. In fact, it turns out that the best fraction of the optimal revenue that is extractable by a sequential posted price mechanism is proportional to ratio of the highest and lowest possible valuation. We prove that for two simple generalizations of these mechanisms, a better revenue performance can be achieved: if the sequential posted price mechanism has for each buyer the option of either proposing an offer or asking the buyer for its valuation, then a Omega(1/max{1,d}) fraction of the optimal revenue can be extracted, where d denotes the degree of dependence of the valuations, ranging from complete independence (d=0) to arbitrary dependence (d=n-1). Moreover, when we generalize the sequential posted price mechanisms further, such that the mechanism has the ability to make a take-it-or-leave-it offer to the i-th buyer that depends on the valuations of all buyers except i's, we prove that a constant fraction (2-sqrt{e})/4~0.088 of the optimal revenue can be always be extracted.", "published": "2015-03-07T19:02:16Z", "authors": ["Marek Adamczyk", "Allan Borodin", "Diodato Ferraioli", "Bart de Keijzer", "Stefano Leonardi"], "categories": ["cs.GT"], "doi": "", "_query": "property valuation computer vision", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/1908.03391v1", "title": "Distinguishing Individual Red Pandas from Their Faces", "abstract": "Individual identification is essential to animal behavior and ecology research and is of significant importance for protecting endangered species. Red pandas, among the world's rarest animals, are currently identified mainly by visual inspection and microelectronic chips, which are costly and inefficient. Motivated by recent advancement in computer-vision-based animal identification, in this paper, we propose an automatic framework for identifying individual red pandas based on their face images. We implement the framework by exploring well-established deep learning models with necessary adaptation for effectively dealing with red panda images. Based on a database of red panda images constructed by ourselves, we evaluate the effectiveness of the proposed automatic individual red panda identification method. The evaluation results show the promising potential of automatically recognizing individual red pandas from their faces. We are going to release our database and model in the public domain to promote the research on automatic animal identification and particularly on the technique for protecting red pandas.", "published": "2019-08-09T10:01:28Z", "authors": ["Qi He", "Qijun Zhao", "Ning Liu", "Peng Chen", "Zhihe Zhang", "Rong Hou"], "categories": ["cs.CV"], "doi": "", "_query": "property valuation computer vision", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2602.15794v1", "title": "Service Orchestration in the Computing Continuum: Structural Challenges and Vision", "abstract": "The Computing Continuum (CC) integrates different layers of processing infrastructure, from Edge to Cloud, to optimize service quality through ubiquitous and reliable computation. Compared to central architectures, however, heterogeneous and dynamic infrastructure increases the complexity for service orchestration. To guide research, this article first summarizes structural problems of the CC, and then, envisions an ideal solution for autonomous service orchestration across the CC. As one instantiation, we show how Active Inference, a concept from neuroscience, can support self-organizing services in continuously interpreting their environment to optimize service quality. Still, we conclude that no existing solution achieves our vision, but that research on service orchestration faces several structural challenges. Most notably: provide standardized simulation and evaluation environments for comparing the performance of orchestration mechanisms. Together, the challenges outline a research roadmap toward resilient and scalable service orchestration in the CC.", "published": "2026-02-17T18:34:07Z", "authors": ["Boris Sedlak", "Víctor Casamayor Pujol", "Ildefons Magrans de Abril", "Praveen Kumar Donta", "Adel N. Toosi", "Schahram Dustdar"], "categories": ["cs.DC", "cs.ET", "eess.SY"], "doi": "", "_query": "property valuation computer vision", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2603.10578v1", "title": "R4-CGQA: Retrieval-based Vision Language Models for Computer Graphics Image Quality Assessment", "abstract": "Immersive Computer Graphics (CGs) rendering has become ubiquitous in modern daily life. However, comprehensively evaluating CG quality remains challenging for two reasons: First, existing CG datasets lack systematic descriptions of rendering quality; and second existing CG quality assessment methods cannot provide reasonable text-based explanations. To address these issues, we first identify six key perceptual dimensions of CG quality from the user perspective and construct a dataset of 3500 CG images with corresponding quality descriptions. Each description covers CG style, content, and perceived quality along the selected dimensions. Furthermore, we use a subset of the dataset to build several question-answer benchmarks based on the descriptions in order to evaluate the responses of existing Vision Language Models (VLMs). We find that current VLMs are not sufficiently accurate in judging fine-grained CG quality, but that descriptions of visually similar images can significantly improve a VLM's understanding of a given CG image. Motivated by this observation, we adopt retrieval-augmented generation and propose a two-stream retrieval framework that effectively enhances the CG quality assessment capabilities of VLMs. Experiments on several representative VLMs demonstrate that our method substantially improves their performance on CG quality assessment.", "published": "2026-03-11T09:28:49Z", "authors": ["Zhuangzi Li", "Jian Jin", "Shilv Cai", "Weisi Lin"], "categories": ["cs.CV", "cs.DB"], "doi": "", "_query": "property valuation computer vision", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2406.11730v3", "title": "CHG Shapley: Efficient Data Valuation and Selection towards Trustworthy Machine Learning", "abstract": "Understanding the decision-making process of machine learning models is crucial for ensuring trustworthy machine learning. Data Shapley, a landmark study on data valuation, advances this understanding by assessing the contribution of each datum to model performance. However, the resource-intensive and time-consuming nature of multiple model retraining poses challenges for applying Data Shapley to large datasets. To address this, we propose the CHG (compound of Hardness and Gradient) utility function, which approximates the utility of each data subset on model performance in every training epoch. By deriving the closed-form Shapley value for each data point using the CHG utility function, we reduce the computational complexity to that of a single model retraining, achieving a quadratic improvement over existing marginal contribution-based methods. We further leverage CHG Shapley for real-time data selection, conducting experiments across three settings: standard datasets, label noise datasets, and class imbalance datasets. These experiments demonstrate its effectiveness in identifying high-value and noisy data. By enabling efficient data valuation, CHG Shapley promotes trustworthy model training through a novel data-centric perspective. Our codes are available at https://github.com/caihuaiguang/CHG-Shapley-for-Data-Valuation and https://github.com/caihuaiguang/CHG-Shapley-for-Data-Selection.", "published": "2024-06-17T16:48:31Z", "authors": ["Huaiguang Cai"], "categories": ["cs.GT", "cs.LG"], "doi": "", "_query": "property valuation computer vision", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/1704.02729v1", "title": "DeepPermNet: Visual Permutation Learning", "abstract": "We present a principled approach to uncover the structure of visual data by solving a novel deep learning task coined visual permutation learning. The goal of this task is to find the permutation that recovers the structure of data from shuffled versions of it. In the case of natural images, this task boils down to recovering the original image from patches shuffled by an unknown permutation matrix. Unfortunately, permutation matrices are discrete, thereby posing difficulties for gradient-based methods. To this end, we resort to a continuous approximation of these matrices using doubly-stochastic matrices which we generate from standard CNN predictions using Sinkhorn iterations. Unrolling these iterations in a Sinkhorn network layer, we propose DeepPermNet, an end-to-end CNN model for this task. The utility of DeepPermNet is demonstrated on two challenging computer vision problems, namely, (i) relative attributes learning and (ii) self-supervised representation learning. Our results show state-of-the-art performance on the Public Figures and OSR benchmarks for (i) and on the classification and segmentation tasks on the PASCAL VOC dataset for (ii).", "published": "2017-04-10T06:59:00Z", "authors": ["Rodrigo Santa Cruz", "Basura Fernando", "Anoop Cherian", "Stephen Gould"], "categories": ["cs.CV"], "doi": "", "_query": "property valuation computer vision", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2309.09473v1", "title": "Self-supervised Multi-view Clustering in Computer Vision: A Survey", "abstract": "Multi-view clustering (MVC) has had significant implications in cross-modal representation learning and data-driven decision-making in recent years. It accomplishes this by leveraging the consistency and complementary information among multiple views to cluster samples into distinct groups. However, as contrastive learning continues to evolve within the field of computer vision, self-supervised learning has also made substantial research progress and is progressively becoming dominant in MVC methods. It guides the clustering process by designing proxy tasks to mine the representation of image and video data itself as supervisory information. Despite the rapid development of self-supervised MVC, there has yet to be a comprehensive survey to analyze and summarize the current state of research progress. Therefore, this paper explores the reasons and advantages of the emergence of self-supervised MVC and discusses the internal connections and classifications of common datasets, data issues, representation learning methods, and self-supervised learning methods. This paper does not only introduce the mechanisms for each category of methods but also gives a few examples of how these techniques are used. In the end, some open problems are pointed out for further investigation and development.", "published": "2023-09-18T04:11:18Z", "authors": ["Jiatai Wang", "Zhiwei Xu", "Xuewen Yang", "Hailong Li", "Bo Li", "Xuying Meng"], "categories": ["cs.CV"], "doi": "", "_query": "property valuation computer vision", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/1903.03372v1", "title": "Semantically Tied Paired Cycle Consistency for Zero-Shot Sketch-based Image Retrieval", "abstract": "Zero-shot sketch-based image retrieval (SBIR) is an emerging task in computer vision, allowing to retrieve natural images relevant to sketch queries that might not been seen in the training phase. Existing works either require aligned sketch-image pairs or inefficient memory fusion layer for mapping the visual information to a semantic space. In this work, we propose a semantically aligned paired cycle-consistent generative (SEM-PCYC) model for zero-shot SBIR, where each branch maps the visual information to a common semantic space via an adversarial training. Each of these branches maintains a cycle consistency that only requires supervision at category levels, and avoids the need of highly-priced aligned sketch-image pairs. A classification criteria on the generators' outputs ensures the visual to semantic space mapping to be discriminating. Furthermore, we propose to combine textual and hierarchical side information via a feature selection auto-encoder that selects discriminating side information within a same end-to-end model. Our results demonstrate a significant boost in zero-shot SBIR performance over the state-of-the-art on the challenging Sketchy and TU-Berlin datasets.", "published": "2019-03-08T11:20:39Z", "authors": ["Anjan Dutta", "Zeynep Akata"], "categories": ["cs.CV"], "doi": "", "_query": "property valuation computer vision", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/0801.0449v1", "title": "Semigroups of valuations dominating local domains", "abstract": "A new criterion is given for a semigroup to be the semigroup of a valuation dominating an equicharacteristic local domain. The criterion is used to construct examples of well ordered subsemigroups of the positive rational numbers which are of ordinal type omega, but are not the value semigroup of a valuation on an equicharacteristic noetherian local domain. This shows that the necessary conditions on value semigroups given in Appendix 3 to Zariski and Samuel's book ``Commutative Algebra'' are not sufficient.", "published": "2008-01-02T22:24:04Z", "authors": ["Steven Dale Cutkosky"], "categories": ["math.AC", "math.AG"], "doi": "", "_query": "property valuation computer vision", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/1808.00257v1", "title": "Subitizing with Variational Autoencoders", "abstract": "Numerosity, the number of objects in a set, is a basic property of a given visual scene. Many animals develop the perceptual ability to subitize: the near-instantaneous identification of the numerosity in small sets of visual items. In computer vision, it has been shown that numerosity emerges as a statistical property in neural networks during unsupervised learning from simple synthetic images. In this work, we focus on more complex natural images using unsupervised hierarchical neural networks. Specifically, we show that variational autoencoders are able to spontaneously perform subitizing after training without supervision on a large amount images from the Salient Object Subitizing dataset. While our method is unable to outperform supervised convolutional networks for subitizing, we observe that the networks learn to encode numerosity as basic visual property. Moreover, we find that the learned representations are likely invariant to object area; an observation in alignment with studies on biological neural networks in cognitive neuroscience.", "published": "2018-08-01T10:45:52Z", "authors": ["Rijnder Wever", "Tom F. H. Runia"], "categories": ["cs.CV"], "doi": "", "_query": "property valuation computer vision", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/1003.3325v1", "title": "Resource Pricing In A Dynamic Multi-Commodity Market For Computational Resources", "abstract": "The adoption of market-based principles in resource management systems for computational infrastructures such as grids and clusters allows for matching demand and supply for resources in a utility maximizing manner. As such, they offer a promise of producing more efficient resource allocations, compared to traditional system-centric approaches that do not allow consumers and providers to express their valuations for computational resources. In this paper, we investigate the pricing of resources in grids through the use of a computational commodity market of CPU resources, where resource prices are determined through the computation of a supply-and-demand equilibrium. In particular, we introduce several categories of CPUs characterized by their execution speed. These differ in cost and performance but may be used interchangeably in executing jobs and thus represent so-called substitutable resources. We investigate the performance of the algorithms for computing the supply-and-demand equilibrium in this multi-commodity setting under dynamically varying consumer and provider populations.", "published": "2010-03-17T08:48:43Z", "authors": ["K. Abdelkader", "J. Broeckhove", "K. Vanmechelen"], "categories": ["cs.GT", "cs.DC", "cs.NI"], "doi": "10.5121/ijcnc.2010.2205", "_query": "property valuation computer vision", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/1805.04025v4", "title": "Deep Nets: What have they ever done for Vision?", "abstract": "This is an opinion paper about the strengths and weaknesses of Deep Nets for vision. They are at the heart of the enormous recent progress in artificial intelligence and are of growing importance in cognitive science and neuroscience. They have had many successes but also have several limitations and there is limited understanding of their inner workings. At present Deep Nets perform very well on specific visual tasks with benchmark datasets but they are much less general purpose, flexible, and adaptive than the human visual system. We argue that Deep Nets in their current form are unlikely to be able to overcome the fundamental problem of computer vision, namely how to deal with the combinatorial explosion, caused by the enormous complexity of natural images, and obtain the rich understanding of visual scenes that the human visual achieves. We argue that this combinatorial explosion takes us into a regime where \"big data is not enough\" and where we need to rethink our methods for benchmarking performance and evaluating vision algorithms. We stress that, as vision algorithms are increasingly used in real world applications, that performance evaluation is not merely an academic exercise but has important consequences in the real world. It is impractical to review the entire Deep Net literature so we restrict ourselves to a limited range of topics and references which are intended as entry points into the literature. The views expressed in this paper are our own and do not necessarily represent those of anybody else in the computer vision community.", "published": "2018-05-10T15:43:44Z", "authors": ["Alan L. Yuille", "Chenxi Liu"], "categories": ["cs.CV", "cs.AI", "cs.LG"], "doi": "", "_query": "property valuation computer vision", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2603.27741v1", "title": "Segmenting Superbubbles in a Simulated Multiphase Interstellar Medium using Computer Vision", "abstract": "We developed a computer vision-based methodology to achieve precise 3D segmentation and tracking of superbubbles within magnetohydrodynamic simulations of the supernova-driven interstellar medium. Leveraging advanced 3D transformer models, our approach effectively captures the complex morphology and dynamic evolution of these astrophysical structures. To demonstrate the technique, we specifically focused on a superbubble exhibiting interesting interactions with its surrounding medium, driven by a series of successive supernova explosions. Our model successfully generated detailed 3D segmentation masks, enabling us to visualize and analyze the bubble's structural evolution over time. The results reveal insights into the superbubble's growth patterns, energy retention, and interactions with surrounding interstellar matter. This interdisciplinary approach not only enhances our understanding of superbubble dynamics but also offers a robust framework for investigating other complex phenomena in the cosmos.", "published": "2026-03-29T15:44:20Z", "authors": ["Jing-Wen Chen", "Alex S. Hill", "Anna Ordog", "Rebecca A. Booth", "Mohamed S. Shehata"], "categories": ["astro-ph.GA", "cs.CV"], "doi": "", "_query": "property valuation computer vision", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/math/0110157v1", "title": "Some Applications of Algebraic Curves to Computational Vision", "abstract": "We introduce a new formalism and a number of new results in the context of geometric computational vision. The classical scope of the research in geometric computer vision is essentially limited to static configurations of points and lines in $P^3$ . By using some well known material from algebraic geometry, we open new branches to computational vision. We introduce algebraic curves embedded in $P^3$ as the building blocks from which the tensor of a couple of cameras (projections) can be computed. In the process we address dimensional issues and as a result establish the minimal number of algebraic curves required for the tensor variety to be discrete as a function of their degree and genus. We then establish new results on the reconstruction of an algebraic curves in $P^3$ from multiple projections on projective planes embedded in $P^3$ . We address three different presentations of the curve: (i) definition by a set of equations, for which we show that for a generic configuration, two projections of a curve of degree d defines a curve in $P^3$ with two irreducible components, one of degree d and the other of degree $d(d - 1)$, (ii) the dual presentation in the dual space $P^{3*}$, for which we derive a lower bound for the number of projections necessary for linear reconstruction as a function of the degree and the genus, and (iii) the presentation as an hypersurface of $P^5$, defined by the set of lines in $P^3$ meeting the curve, for which we also derive lower bounds for the number of projections necessary for linear reconstruction as a function of the degree (of the curve). Moreover we show that the latter representation yields a new and efficient algorithm for dealing with mixed configurations of static and moving points in $P^3$.", "published": "2001-10-15T18:37:04Z", "authors": ["Michael Fryers", "Jeremy Yirmeyahu Kaminski", "Mina Teicher"], "categories": ["math.AG", "cs.IT"], "doi": "", "_query": "property valuation computer vision", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/0809.3507v1", "title": "Growth of rank 1 valuation semigroups", "abstract": "We consider the question of which semigroups can occur as the semigroup $S_R(ν)$ of positive values of a rank 1 valuation dominating a Noetherian local ring $R$. We give a number of bounds of polynomial type on the growth of $φ(n)=S_R(ν)\\cap (0,n)$ for $n\\in\\NN$, starting with the upper bound of $P_R(n)$, where $P_R(n)$ is the Hilbert function of $R$. This bound is generalized to an extremely general bound for arbitrary rank valuations in the paper \"Semigroups of valuations on local rings, II\", by Cutkosky and Teissier, arXiv:0805.3788. This bound is already enough to give simple examples of rank 1 well ordered semigroups which are not the value semigroup $S_R(ν)$ of a valuation dominating a Noetherian local ring. In the case of rank 1, it is possible to give more precise estimates of $φ(n)$, which we prove in this paper. We also give examples showing that many different rates of growth are possible for $φ(n)$ on a regular local ring of dimension 2, such as $n(α$ for any rational $α$ with $1\\leα\\le 2$, and $n{log}(n)$.", "published": "2008-09-20T14:54:31Z", "authors": ["Steven Dale Cutkosky", "Kia Dalili", "Olga Kashcheyeva"], "categories": ["math.AC", "math.AG"], "doi": "", "_query": "property valuation computer vision", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/1709.08959v1", "title": "Continuity and representation of valuations on star bodies", "abstract": "It is shown that every continuous valuation defined on the $n$-dimensional star bodies has an integral representation in terms of the radial function. Our argument is based on the non-trivial fact that continuous valuations are uniformly continuous on bounded sets. We also characterize the continuous valuations on the $n$-dimensional star bodies that arise as the restriction of a measure on $\\mathbb R^n$.", "published": "2017-09-26T12:10:52Z", "authors": ["Pedro Tradacete", "Ignacio Villanueva"], "categories": ["math.MG", "math.FA"], "doi": "", "_query": "property valuation computer vision", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2103.16652v2", "title": "Robustness Certification for Point Cloud Models", "abstract": "The use of deep 3D point cloud models in safety-critical applications, such as autonomous driving, dictates the need to certify the robustness of these models to real-world transformations. This is technically challenging, as it requires a scalable verifier tailored to point cloud models that handles a wide range of semantic 3D transformations. In this work, we address this challenge and introduce 3DCertify, the first verifier able to certify the robustness of point cloud models. 3DCertify is based on two key insights: (i) a generic relaxation based on first-order Taylor approximations, applicable to any differentiable transformation, and (ii) a precise relaxation for global feature pooling, which is more complex than pointwise activations (e.g., ReLU or sigmoid) but commonly employed in point cloud models. We demonstrate the effectiveness of 3DCertify by performing an extensive evaluation on a wide range of 3D transformations (e.g., rotation, twisting) for both classification and part segmentation tasks. For example, we can certify robustness against rotations by $\\pm$60° for 95.7% of point clouds, and our max pool relaxation increases certification by up to 15.6%.", "published": "2021-03-30T19:52:07Z", "authors": ["Tobias Lorenz", "Anian Ruoss", "Mislav Balunović", "Gagandeep Singh", "Martin Vechev"], "categories": ["cs.LG", "cs.AI", "cs.CV"], "doi": "", "_query": "property valuation computer vision", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2403.04914v1", "title": "Improving the Equation of Exchange for Cryptoasset Valuation Using Empirical Data", "abstract": "In the evolving domain of cryptocurrency markets, accurate token valuation remains a critical aspect influencing investment decisions and policy development. Whilst the prevailing equation of exchange pricing model offers a quantitative valuation approach based on the interplay between token price, transaction volume, supply, and either velocity or holding time, it exhibits intrinsic shortcomings. Specifically, the model may not consistently delineate the relationship between average token velocity and holding time. This paper aims to refine this equation, enhancing the depth of insight into token valuation methodologies.", "published": "2024-03-07T22:00:17Z", "authors": ["Stylianos Kampakis", "Melody Yuan", "Oritsebawo Paul Ikpobe", "Linas Stankevicius"], "categories": ["cs.CE", "stat.OT"], "doi": "", "_query": "property valuation computer vision", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/1811.04256v5", "title": "Scene Text Detection and Recognition: The Deep Learning Era", "abstract": "With the rise and development of deep learning, computer vision has been tremendously transformed and reshaped. As an important research area in computer vision, scene text detection and recognition has been inescapably influenced by this wave of revolution, consequentially entering the era of deep learning. In recent years, the community has witnessed substantial advancements in mindset, approach and performance. This survey is aimed at summarizing and analyzing the major changes and significant progresses of scene text detection and recognition in the deep learning era. Through this article, we devote to: (1) introduce new insights and ideas; (2) highlight recent techniques and benchmarks; (3) look ahead into future trends. Specifically, we will emphasize the dramatic differences brought by deep learning and the grand challenges still remained. We expect that this review paper would serve as a reference book for researchers in this field. Related resources are also collected and compiled in our Github repository: https://github.com/Jyouhou/SceneTextPapers.", "published": "2018-11-10T13:56:31Z", "authors": ["Shangbang Long", "Xin He", "Cong Yao"], "categories": ["cs.CV"], "doi": "", "_query": "property valuation computer vision", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/1408.4816v1", "title": "Uniform definability of henselian valuation rings in the Macintyre language", "abstract": "We discuss definability of henselian valuation rings in the Macintyre language $\\mathcal{L}_{\\rm Mac}$, the language of rings expanded by n-th power predicates. In particular, we show that henselian valuation rings with finite or Hilbertian residue field are uniformly $\\exists$-$\\emptyset$-definable in $\\mathcal{L}_{\\rm Mac}$, and henselian valuation rings with value group $\\mathbb{Z}$ are uniformly $\\exists\\forall$-$\\emptyset$-definable in the ring language, but not uniformly $\\exists$-$\\emptyset$-definable in $\\mathcal{L}_{\\rm Mac}$. We apply these results to local fields $\\mathbb{Q}_p$ and $\\mathbb{F}_p((t))$, as well as to higher dimensional local fields.", "published": "2014-08-20T20:41:25Z", "authors": ["Arno Fehm", "Alexander Prestel"], "categories": ["math.AC", "math.LO"], "doi": "10.1112/blms/bdv042", "_query": "property valuation computer vision", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2110.08037v1", "title": "Tensor-to-Image: Image-to-Image Translation with Vision Transformers", "abstract": "Transformers gain huge attention since they are first introduced and have a wide range of applications. Transformers start to take over all areas of deep learning and the Vision transformers paper also proved that they can be used for computer vision tasks. In this paper, we utilized a vision transformer-based custom-designed model, tensor-to-image, for the image to image translation. With the help of self-attention, our model was able to generalize and apply to different problems without a single modification.", "published": "2021-10-06T17:57:45Z", "authors": ["Yiğit Gündüç"], "categories": ["cs.CV", "cs.AI", "cs.LG"], "doi": "", "_query": "property valuation computer vision", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2005.02814v1", "title": "The Information Content of Taster's Valuation in Tea Auctions of India", "abstract": "Tea auctions across India occur as an ascending open auction, conducted online. Before the auction, a sample of the tea lot is sent to potential bidders and a group of tea tasters. The seller's reserve price is a confidential function of the tea taster's valuation, which also possibly acts as a signal to the bidders. In this paper, we work with the dataset from a single tea auction house, J Thomas, of tea dust category, on 49 weeks in the time span of 2018-2019, with the following objectives in mind: $\\bullet$ Objective classification of the various categories of tea dust (25) into a more manageable, and robust classification of the tea dust, based on source and grades. $\\bullet$ Predict which tea lots would be sold in the auction market, and a model for the final price conditioned on sale. $\\bullet$ To study the distribution of price and ratio of the sold tea auction lots. $\\bullet$ Make a detailed analysis of the information obtained from the tea taster's valuation and its impact on the final auction price. The model used has shown various promising results on cross-validation. The importance of valuation is firmly established through analysis of causal relationship between the valuation and the actual price. The authors hope that this study of the properties and the detailed analysis of the role played by the various factors, would be significant in the decision making process for the players of the auction game, pave the way to remove the manual interference in an attempt to automate the auction procedure, and improve tea quality in markets.", "published": "2020-05-04T16:39:47Z", "authors": ["Abhinandan Dalal", "Diganta Mukherjee", "Subhrajyoty Roy"], "categories": ["stat.AP", "econ.EM"], "doi": "", "_query": "property valuation computer vision", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/1003.4881v2", "title": "The Validity of Company Valuation Using Discounted Cash Flow Methods", "abstract": "This paper closely examines theoretical and practical aspects of the widely used discounted cash flows (DCF) valuation method. It assesses its potentials as well as several weaknesses. A special emphasize is being put on the valuation of companies using the DCF method. The paper finds that the discounted cash flow method is a powerful tool to analyze even complex situations. However, the DCF method is subject to massive assumption bias and even slight changes in the underlying assumptions of an analysis can drastically alter the valuation results. A practical example of these implications is given using a scenario analysis.", "published": "2010-03-25T13:13:42Z", "authors": ["Florian Steiger"], "categories": ["q-fin.GN", "q-fin.PR"], "doi": "", "_query": "property valuation computer vision", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2608.18602v1", "title": "Teach a Molmo2Fish: Towards interactive fish tracking with natural language guidance", "abstract": "Computer vision is increasingly used to automate recognition tasks in large ecological datasets, but more complex tasks such as multi-object tracking continue to pose challenges. As researchers seek to incorporate vision models in ecology workflows, various lines of research have explored how to make imperfect predictions useful through human-in-the-loop processes. We propose a new approach to working with imperfect tracking predictions through an interactive prediction correction workflow taking place as a conversation with a multimodal large language model, which we tailor to a sonar fish tracking dataset as an initial proof of concept. We investigate the performance of the tool, Molmo2Fish, across guided and unguided tasks, correcting its own predicted tracks and external tracks. We find that Molmo2Fish achieves high performance on fish tracking and track correction tasks, but there is still much room to improve on incorporating natural language guidance. The code and data are publicly available at https://github.com/tidalove/molmo2fish.", "published": "2026-08-19T06:44:42Z", "authors": ["Kai Van Brunt", "Justin Kay", "Sara Beery"], "categories": ["cs.CV"], "doi": "", "_query": "property valuation computer vision", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/1405.0508v2", "title": "MVA: Initial Margin Valuation Adjustment by Replication and Regression", "abstract": "Initial margin requirements are becoming an increasingly common feature of derivative markets. However, while the valuation of derivatives under collateralisation (Piterbarg 2010, Piterbarg2012), under counterparty risk with unsecured funding costs (FVA) (Burgard2011, Burgard2011, Burgard2013) and in the presence of regulatory capital (KVA) (Green2014) are established through valuation adjustments, hitherto initial margin has not been considered. This paper further extends the semi-replication framework of (Burgard2013a), itself later extended by (Green2014), to cover the cost of initial margin, leading to Margin Valuation Adjustment (MVA). Initial margin requirements are typically generated through the use of VAR or CVAR models. Given the form of MVA as an integral over the expected initial margin profile this would lead to excessive computational costs if a brute force calculation were to be used. Hence we also propose a computationally efficient approach to the calculation of MVA through the use of regression techniques, Longstaff-Schwartz Augmented Compression (LSAC).", "published": "2014-05-02T20:49:38Z", "authors": ["Andrew Green", "Chris Kenyon"], "categories": ["q-fin.PR", "q-fin.GN", "q-fin.RM"], "doi": "", "_query": "property valuation computer vision", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/1303.0729v2", "title": "Groebner bases over fields with valuations", "abstract": "Let K be a field with a valuation and let S be the polynomial ring S:= K[x_1,..., x_n]. We discuss the extension of Groebner theory to ideals in S, taking the valuations of coefficients into account, and describe the Buchberger algorithm in this context. In addition we discuss some implementation and complexity issues. The main motivation comes from tropical geometry, as tropical varieties can be defined using these Groebner bases, but we also give examples showing that the resulting Groebner bases can be substantially smaller than traditional Groebner bases. In the case K = Q with the p-adic valuation the algorithms have been implemented in a Macaulay 2 package.", "published": "2013-03-04T15:25:37Z", "authors": ["Andrew J. Chan", "Diane Maclagan"], "categories": ["math.AC"], "doi": "", "_query": "property valuation computer vision", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2508.00549v2", "title": "Your other Left! Vision-Language Models Fail to Identify Relative Positions in Medical Images", "abstract": "Clinical decision-making relies heavily on understanding relative positions of anatomical structures and anomalies. Therefore, for Vision-Language Models (VLMs) to be applicable in clinical practice, the ability to accurately determine relative positions on medical images is a fundamental prerequisite. Despite its importance, this capability remains highly underexplored. To address this gap, we evaluate the ability of state-of-the-art VLMs, GPT-4o, Llama3.2, Pixtral, and JanusPro, and find that all models fail at this fundamental task. Inspired by successful approaches in computer vision, we investigate whether visual prompts, such as alphanumeric or colored markers placed on anatomical structures, can enhance performance. While these markers provide moderate improvements, results remain significantly lower on medical images compared to observations made on natural images. Our evaluations suggest that, in medical imaging, VLMs rely more on prior anatomical knowledge than on actual image content for answering relative position questions, often leading to incorrect conclusions. To facilitate further research in this area, we introduce the MIRP , Medical Imaging Relative Positioning, benchmark dataset, designed to systematically evaluate the capability to identify relative positions in medical images.", "published": "2025-08-01T11:44:06Z", "authors": ["Daniel Wolf", "Heiko Hillenhagen", "Billurvan Taskin", "Alex Bäuerle", "Meinrad Beer", "Michael Götz", "Timo Ropinski"], "categories": ["cs.CV"], "doi": "", "_query": "property valuation computer vision", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2304.06589v1", "title": "Tube formulas for valuations in complex space forms", "abstract": "Given an isometry invariant valuation on a complex space form we compute its value on the tubes of sufficiently small radii around a set of positive reach. This generalizes classical formulas of Weyl, Gray and others about the volume of tubes. We also develop a general framework on tube formulas for valuations in riemannian manifolds.", "published": "2023-04-13T14:52:46Z", "authors": ["Gil Solanes", "Juan Andrés Trillo"], "categories": ["math.DG", "math.MG"], "doi": "", "_query": "property valuation computer vision", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2303.13018v1", "title": "MonoATT: Online Monocular 3D Object Detection with Adaptive Token Transformer", "abstract": "Mobile monocular 3D object detection (Mono3D) (e.g., on a vehicle, a drone, or a robot) is an important yet challenging task. Existing transformer-based offline Mono3D models adopt grid-based vision tokens, which is suboptimal when using coarse tokens due to the limited available computational power. In this paper, we propose an online Mono3D framework, called MonoATT, which leverages a novel vision transformer with heterogeneous tokens of varying shapes and sizes to facilitate mobile Mono3D. The core idea of MonoATT is to adaptively assign finer tokens to areas of more significance before utilizing a transformer to enhance Mono3D. To this end, we first use prior knowledge to design a scoring network for selecting the most important areas of the image, and then propose a token clustering and merging network with an attention mechanism to gradually merge tokens around the selected areas in multiple stages. Finally, a pixel-level feature map is reconstructed from heterogeneous tokens before employing a SOTA Mono3D detector as the underlying detection core. Experiment results on the real-world KITTI dataset demonstrate that MonoATT can effectively improve the Mono3D accuracy for both near and far objects and guarantee low latency. MonoATT yields the best performance compared with the state-of-the-art methods by a large margin and is ranked number one on the KITTI 3D benchmark.", "published": "2023-03-23T03:45:03Z", "authors": ["Yunsong Zhou", "Hongzi Zhu", "Quan Liu", "Shan Chang", "Minyi Guo"], "categories": ["cs.CV"], "doi": "", "_query": "property valuation computer vision", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/1602.08760v2", "title": "Valuation theory of indefinite orthogonal groups", "abstract": "Let $\\mathrm{SO}^+(p,q)$ denote the identity connected component of the real orthogonal group with signature $(p,q)$. We give a complete description of the spaces of continuous and generalized translation- and $\\mathrm{SO}^+(p,q)$-invariant valuations, generalizing Hadwiger's classification of Euclidean isometry-invariant valuations. As a result of independent interest, we identify within the space of translation-invariant valuations the class of Klain-Schneider continuous valuations, which strictly contains all continuous translation-invariant valuations. The operations of pull-back and push-forward by a linear map extend naturally to this class.", "published": "2016-02-28T19:34:26Z", "authors": ["Andreas Bernig", "Dmitry Faifman"], "categories": ["math.DG", "math.MG"], "doi": "10.1016/j.jfa.2017.06.005", "_query": "property valuation computer vision", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2011.03543v1", "title": "XVA Valuation under Market Illiquidity", "abstract": "Before the 2008 financial crisis, most research in financial mathematics focused on pricing options without considering the effects of counterparties' defaults, illiquidity problems, and the role of the sale and repurchase agreement (Repo) market. Recently, models were proposed to address this by computing a total valuation adjustment (XVA) of derivatives; however without considering a potential crisis in the market. In this article, we include a possible crisis by using an alternating renewal process to describe the switching between a normal financial regime and a financial crisis. We develop a framework to price the XVA of a European claim in this state-dependent situation. The price is characterized as a solution to a backward stochastic differential equation (BSDE), and we prove the existence and uniqueness of this solution. In a numerical study based on a deep learning algorithm for BSDEs, we compare the effect of different parameters on the valuation of the XVA.", "published": "2020-11-06T18:41:09Z", "authors": ["Weijie Pang", "Stephan Sturm"], "categories": ["q-fin.PR", "math.PR"], "doi": "", "_query": "property valuation computer vision", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/1410.3171v2", "title": "Ramification Theory for Artin-Schreier Extensions of Valuation Rings", "abstract": "The goal of this paper is to generalize and refine the classical ramification theory of complete discrete valuation rings to more general valuation rings, in the case of Artin-Schreier extensions. We define refined versions of invariants of ramification in the classical ramification theory and compare them. Furthermore, we can treat the defect case.", "published": "2014-10-13T00:47:48Z", "authors": ["Vaidehee Thatte"], "categories": ["math.NT"], "doi": "", "_query": "property valuation computer vision", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/1804.05312v2", "title": "Local Descriptors Optimized for Average Precision", "abstract": "Extraction of local feature descriptors is a vital stage in the solution pipelines for numerous computer vision tasks. Learning-based approaches improve performance in certain tasks, but still cannot replace handcrafted features in general. In this paper, we improve the learning of local feature descriptors by optimizing the performance of descriptor matching, which is a common stage that follows descriptor extraction in local feature based pipelines, and can be formulated as nearest neighbor retrieval. Specifically, we directly optimize a ranking-based retrieval performance metric, Average Precision, using deep neural networks. This general-purpose solution can also be viewed as a listwise learning to rank approach, which is advantageous compared to recent local ranking approaches. On standard benchmarks, descriptors learned with our formulation achieve state-of-the-art results in patch verification, patch retrieval, and image matching.", "published": "2018-04-15T07:08:24Z", "authors": ["Kun He", "Yan Lu", "Stan Sclaroff"], "categories": ["cs.CV"], "doi": "", "_query": "property valuation computer vision", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/1104.2753v1", "title": "Monoid Valuations and Value Ordered Supervaluations", "abstract": "We complement two papers on supertropical valuation theory ([IKR1],[IKR2]) by providing natural examples of m-valuations (= monoid valuations), after that of supervaluations and transmissions between them. The supervaluations discussed have values in totally ordered supertropical semirings, and the transmissions discussed respect the orderings. Basics of a theory of such semirings and transmissions are developed as far as needed.", "published": "2011-04-14T13:07:08Z", "authors": ["Zur Izhakian", "Manfred Knebusch", "Louis Rowen"], "categories": ["math.AC", "math.AG", "math.RA"], "doi": "", "_query": "property valuation computer vision", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/1605.07431v3", "title": "Combinatorial mixed valuations", "abstract": "Combinatorial mixed valuations associated to translation-invariant valuations on polytopes are introduced. In contrast to the construction of mixed valuations via polarization, combinatorial mixed valuations reflect and often inherit properties of inhomogeneous valuations. In particular, it is shown that under mild assumptions combinatorial mixed valuations are monotone and hence nonnegative. For combinatorially positive valuations, this has strong computational implications. Applied to the discrete volume, the results generalize and strengthen work of Bihan (2015) on discrete mixed volumes. For rational polytopes, it is proved that combinatorial mixed monotonicity is equivalent to monotonicity. Stronger even, a conjecture is substantiated that combinatorial mixed monotonicity implies the homogeneous monotonicity in the sense of Bernig--Fu (2011).", "published": "2016-05-24T12:55:20Z", "authors": ["Katharina Jochemko", "Raman Sanyal"], "categories": ["math.CO", "math.MG"], "doi": "", "_query": "property valuation computer vision", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2210.15769v1", "title": "Fully-attentive and interpretable: vision and video vision transformers for pain detection", "abstract": "Pain is a serious and costly issue globally, but to be treated, it must first be detected. Vision transformers are a top-performing architecture in computer vision, with little research on their use for pain detection. In this paper, we propose the first fully-attentive automated pain detection pipeline that achieves state-of-the-art performance on binary pain detection from facial expressions. The model is trained on the UNBC-McMaster dataset, after faces are 3D-registered and rotated to the canonical frontal view. In our experiments we identify important areas of the hyperparameter space and their interaction with vision and video vision transformers, obtaining 3 noteworthy models. We analyse the attention maps of one of our models, finding reasonable interpretations for its predictions. We also evaluate Mixup, an augmentation technique, and Sharpness-Aware Minimization, an optimizer, with no success. Our presented models, ViT-1 (F1 score 0.55 +- 0.15), ViViT-1 (F1 score 0.55 +- 0.13), and ViViT-2 (F1 score 0.49 +- 0.04), all outperform earlier works, showing the potential of vision transformers for pain detection. Code is available at https://github.com/IPDTFE/ViT-McMaster", "published": "2022-10-27T21:01:40Z", "authors": ["Giacomo Fiorentini", "Itir Onal Ertugrul", "Albert Ali Salah"], "categories": ["cs.CV"], "doi": "", "_query": "property valuation computer vision", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/1510.02078v1", "title": "Leveraging Context to Support Automated Food Recognition in Restaurants", "abstract": "The pervasiveness of mobile cameras has resulted in a dramatic increase in food photos, which are pictures reflecting what people eat. In this paper, we study how taking pictures of what we eat in restaurants can be used for the purpose of automating food journaling. We propose to leverage the context of where the picture was taken, with additional information about the restaurant, available online, coupled with state-of-the-art computer vision techniques to recognize the food being consumed. To this end, we demonstrate image-based recognition of foods eaten in restaurants by training a classifier with images from restaurant's online menu databases. We evaluate the performance of our system in unconstrained, real-world settings with food images taken in 10 restaurants across 5 different types of food (American, Indian, Italian, Mexican and Thai).", "published": "2015-10-07T19:51:23Z", "authors": ["Vinay Bettadapura", "Edison Thomaz", "Aman Parnami", "Gregory Abowd", "Irfan Essa"], "categories": ["cs.CV"], "doi": "10.1109/WACV.2015.83", "_query": "property valuation computer vision", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2004.11146v1", "title": "On the computation of the M{ö}bius transform", "abstract": "The M{ö}bius transform is a crucial transformation into the Boolean world; it allows to change the Boolean representation between the True Table and Algebraic Normal Form. In this work, we introduce a new algebraic point of view of this transformation based on the polynomial form of Boolean functions. It appears that we can perform a new notion: the M{ö}bius computation variable by variable and new computation properties. As a consequence, we propose new algorithms which can produce a huge speed up of the M{ö}bius computation for sub-families of Boolean function. Furthermore we compute directly the M{ö}bius transformation of some particular Boolean functions. Finally, we show that for some of them the Hamming weight is directly related to the algebraic degree of specific factors.", "published": "2020-04-15T15:21:38Z", "authors": ["Morgan Barbier", "Hayat Cheballah", "Jean-Marie Le Bars"], "categories": ["cs.DS", "cs.CR"], "doi": "", "_query": "property valuation computer vision", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2211.06800v1", "title": "CS-Shapley: Class-wise Shapley Values for Data Valuation in Classification", "abstract": "Data valuation, or the valuation of individual datum contributions, has seen growing interest in machine learning due to its demonstrable efficacy for tasks such as noisy label detection. In particular, due to the desirable axiomatic properties, several Shapley value approximation methods have been proposed. In these methods, the value function is typically defined as the predictive accuracy over the entire development set. However, this limits the ability to differentiate between training instances that are helpful or harmful to their own classes. Intuitively, instances that harm their own classes may be noisy or mislabeled and should receive a lower valuation than helpful instances. In this work, we propose CS-Shapley, a Shapley value with a new value function that discriminates between training instances' in-class and out-of-class contributions. Our theoretical analysis shows the proposed value function is (essentially) the unique function that satisfies two desirable properties for evaluating data values in classification. Further, our experiments on two benchmark evaluation tasks (data removal and noisy label detection) and four classifiers demonstrate the effectiveness of CS-Shapley over existing methods. Lastly, we evaluate the \"transferability\" of data values estimated from one classifier to others, and our results suggest Shapley-based data valuation is transferable for application across different models.", "published": "2022-11-13T03:32:33Z", "authors": ["Stephanie Schoch", "Haifeng Xu", "Yangfeng Ji"], "categories": ["cs.LG"], "doi": "", "_query": "property valuation computer vision", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2409.04345v1", "title": "Computer-Generated Sand Mixtures and Sand-based Images", "abstract": "This paper aims to verify the effectiveness of the software implementation of the proposed algorithm in creating computer-generated images of sand mixtures using a photograph of sand as an input and its effectiveness in converting digital pictures into sand-based images out of the mixtures it generated. The method of this paper is to visually compare the photographed image of the actual mixtures to its computer-generated counterpart to verify if the mixture generation produces results as expected and compare the computer-generated sand-based images with its source to verify image reproduction maintains same image content. The results of the mixture comparison shows that the actual and the computer-generated ones have similar overall shade and color. Still, the generated one has a rougher texture and higher contrast due to the method of inheriting visual features by pixel, not by individual sand particles. The comparison of the sand-based image and its source has demonstrated the software's ability to maintain the essence of its contents during conversion while replacing its texture with the visual properties of the generated sand mixture. The result have shown that the software implementation of the proposed algorithm can effectively use the images of sand to generate images of its mixtures and use those mixture images to convert a digital picture into a computer-generated sand-based image.", "published": "2024-09-06T15:27:51Z", "authors": ["Ryan A. Subong", "Alma Jean D. Subong"], "categories": ["cs.CV", "eess.IV"], "doi": "10.25147/ijcsr.2017.001.1.207", "_query": "property valuation computer vision", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2403.08128v2", "title": "Singular loci of algebras over ramified discrete valuation rings", "abstract": "When $k$ is a field, the classical Jacobian criterion computes the singular locus of an equidimensional, finitely generated $k$-algebra as the closed subset of an ideal generated by appropriate minors of the so-called Jacobian matrix. Recently, Hochster-Jeffries and Saito have extended this result for algebras over any unramified discrete valuation ring of mixed characteristic via the use of $p$-derivations. Motivated by these results, in this paper, we state and prove an analogous Jacobian criterion for algebras over a ramified discrete valuation ring of mixed characteristic.", "published": "2024-03-12T23:34:04Z", "authors": ["Nawaj KC"], "categories": ["math.AC"], "doi": "", "_query": "property valuation computer vision", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2608.19110v1", "title": "Rigid motion invariant valuations on polytopes", "abstract": "We show that any measurable, translation and $\\mathrm{SO}(n)$-invariant valuation on polytopes in $\\mathbb{R}^n$ is a linear combination of the intrinsic volumes, which extends Hadwiger's classical characterization of rigid motion invariant continuous valuations on convex bodies. This result is based on a novel regularity result for translation invariant, measurable, $1$-homogeneous, and simple valuations. As a consequence, we obtain a similar characterization of the Steiner point map on polytopes.", "published": "2026-08-19T17:03:29Z", "authors": ["Jonas Knoerr"], "categories": ["math.MG"], "doi": "", "_query": "property valuation computer vision", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/1709.05056v1", "title": "Learning Compact Geometric Features", "abstract": "We present an approach to learning features that represent the local geometry around a point in an unstructured point cloud. Such features play a central role in geometric registration, which supports diverse applications in robotics and 3D vision. Current state-of-the-art local features for unstructured point clouds have been manually crafted and none combines the desirable properties of precision, compactness, and robustness. We show that features with these properties can be learned from data, by optimizing deep networks that map high-dimensional histograms into low-dimensional Euclidean spaces. The presented approach yields a family of features, parameterized by dimension, that are both more compact and more accurate than existing descriptors.", "published": "2017-09-15T04:44:28Z", "authors": ["Marc Khoury", "Qian-Yi Zhou", "Vladlen Koltun"], "categories": ["cs.CV", "cs.GR", "cs.LG"], "doi": "", "_query": "property valuation computer vision", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/1309.0135v1", "title": "Ramification of local rings along valuations", "abstract": "In this paper we discuss stable forms of extensions of algebraic local rings along a valuation in all dimensions over a field k of characteristic zero, and generalize a formula of Ghezzi, Hà and Kashcheyeva describing the extension of associated graded rings along the valuation for stable extensions of regular algebraic local rings of dimension two to arbitrary ground fields k of characteristic zero. We discuss the failure of this result in positive characteristic.", "published": "2013-08-31T17:06:01Z", "authors": ["Steven Dale Cutkosky", "Pham An Vinh"], "categories": ["math.AC"], "doi": "", "_query": "property valuation computer vision", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2405.17665v3", "title": "Improving Robotic Arms through Natural Language Processing, Computer Vision, and Edge Computing", "abstract": "This paper introduces a prototype for a new approach to assistive robotics, integrating edge computing with Natural Language Processing (NLP) and computer vision to enhance the interaction between humans and robotic systems. Our proof of concept demonstrates the feasibility of using large language models (LLMs) and vision systems in tandem for interpreting and executing complex commands conveyed through natural language. This integration aims to improve the intuitiveness and accessibility of assistive robotic systems, making them more adaptable to the nuanced needs of users with disabilities. By leveraging the capabilities of edge computing, our system has the potential to minimize latency and support offline capability, enhancing the autonomy and responsiveness of assistive robots. Experimental results from our implementation on a robotic arm show promising outcomes in terms of accurate intent interpretation and object manipulation based on verbal commands. This research lays the groundwork for future developments in assistive robotics, focusing on creating highly responsive, user-centric systems that can significantly improve the quality of life for individuals with disabilities. For video demonstrations and source code, please refer to: https://tinyurl.com/EnhancedArmEdgeNLP.", "published": "2024-05-27T21:33:56Z", "authors": ["Pascal Sikorski", "Kaleb Yu", "Lucy Billadeau", "Flavio Esposito", "Hadi AliAkbarpour", "Madi Babaiasl"], "categories": ["cs.RO"], "doi": "", "_query": "property valuation computer vision", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2404.02067v1", "title": "Red-Teaming Segment Anything Model", "abstract": "Foundation models have emerged as pivotal tools, tackling many complex tasks through pre-training on vast datasets and subsequent fine-tuning for specific applications. The Segment Anything Model is one of the first and most well-known foundation models for computer vision segmentation tasks. This work presents a multi-faceted red-teaming analysis that tests the Segment Anything Model against challenging tasks: (1) We analyze the impact of style transfer on segmentation masks, demonstrating that applying adverse weather conditions and raindrops to dashboard images of city roads significantly distorts generated masks. (2) We focus on assessing whether the model can be used for attacks on privacy, such as recognizing celebrities' faces, and show that the model possesses some undesired knowledge in this task. (3) Finally, we check how robust the model is to adversarial attacks on segmentation masks under text prompts. We not only show the effectiveness of popular white-box attacks and resistance to black-box attacks but also introduce a novel approach - Focused Iterative Gradient Attack (FIGA) that combines white-box approaches to construct an efficient attack resulting in a smaller number of modified pixels. All of our testing methods and analyses indicate a need for enhanced safety measures in foundation models for image segmentation.", "published": "2024-04-02T16:07:50Z", "authors": ["Krzysztof Jankowski", "Bartlomiej Sobieski", "Mateusz Kwiatkowski", "Jakub Szulc", "Michal Janik", "Hubert Baniecki", "Przemyslaw Biecek"], "categories": ["cs.CV", "cs.AI", "cs.LG"], "doi": "", "_query": "property valuation computer vision", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2606.18729v3", "title": "TimeLAVA: Learning-Agnostic Valuation for Time Series Data", "abstract": "Data valuation quantifies the intrinsic quality of individual samples to enable principled data curation, quality control, and robust learning. For time series in critical domains such as healthcare, finance, and industrial monitoring, effective valuation methods are essential yet fundamentally lacking. Existing approaches are either model-dependent, limiting their generalizability, or designed for i.i.d. data and thus fail to capture temporal dependencies, multi-scale patterns, and non-stationary dynamics inherent to sequential data. We introduce TimeLAVA, a learning-agnostic framework that values temporal segments by their marginal contribution to minimizing distributional discrepancy between evaluated and reference data. At its core is a novel Selective Wavelet-based Wasserstein discrepancy combining multi-scale wavelet transforms for temporal localization with unbalanced optimal transport for robustness to distributional shifts. Segment values are efficiently computed via sensitivity analysis without requiring model training and aggregated into point-wise scores. We provide theoretical guarantees linking valuation to model-agnostic generalization and prove bounded sensitivity to outlier contamination. Extensive experiments across anomaly detection, data pruning, and label noise detection demonstrate that TimeLAVA produces significantly more informative value scores than existing methods on diverse real-world datasets.", "published": "2026-06-17T06:16:16Z", "authors": ["Wenqin Liu", "Weizhi Quan", "Aoqi Zuo", "Erdun Gao", "Vu Nguyen", "Dino Sejdinovic", "Howard Bondell", "Mingming Gong"], "categories": ["stat.ML", "cs.LG"], "doi": "", "_query": "property valuation computer vision", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2012.08323v2", "title": "Improved Image Matting via Real-time User Clicks and Uncertainty Estimation", "abstract": "Image matting is a fundamental and challenging problem in computer vision and graphics. Most existing matting methods leverage a user-supplied trimap as an auxiliary input to produce good alpha matte. However, obtaining high-quality trimap itself is arduous, thus restricting the application of these methods. Recently, some trimap-free methods have emerged, however, the matting quality is still far behind the trimap-based methods. The main reason is that, without the trimap guidance in some cases, the target network is ambiguous about which is the foreground target. In fact, choosing the foreground is a subjective procedure and depends on the user's intention. To this end, this paper proposes an improved deep image matting framework which is trimap-free and only needs several user click interactions to eliminate the ambiguity. Moreover, we introduce a new uncertainty estimation module that can predict which parts need polishing and a following local refinement module. Based on the computation budget, users can choose how many local parts to improve with the uncertainty guidance. Quantitative and qualitative results show that our method performs better than existing trimap-free methods and comparably to state-of-the-art trimap-based methods with minimal user effort.", "published": "2020-12-15T14:32:36Z", "authors": ["Tianyi Wei", "Dongdong Chen", "Wenbo Zhou", "Jing Liao", "Hanqing Zhao", "Weiming Zhang", "Nenghai Yu"], "categories": ["cs.CV"], "doi": "", "_query": "property valuation computer vision", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2504.04787v1", "title": "Dynamic Vision Mamba", "abstract": "Mamba-based vision models have gained extensive attention as a result of being computationally more efficient than attention-based models. However, spatial redundancy still exists in these models, represented by token and block redundancy. For token redundancy, we analytically find that early token pruning methods will result in inconsistency between training and inference or introduce extra computation for inference. Therefore, we customize token pruning to fit the Mamba structure by rearranging the pruned sequence before feeding it into the next Mamba block. For block redundancy, we allow each image to select SSM blocks dynamically based on an empirical observation that the inference speed of Mamba-based vision models is largely affected by the number of SSM blocks. Our proposed method, Dynamic Vision Mamba (DyVM), effectively reduces FLOPs with minor performance drops. We achieve a reduction of 35.2\\% FLOPs with only a loss of accuracy of 1.7\\% on Vim-S. It also generalizes well across different Mamba vision model architectures and different vision tasks. Our code will be made public.", "published": "2025-04-07T07:31:28Z", "authors": ["Mengxuan Wu", "Zekai Li", "Zhiyuan Liang", "Moyang Li", "Xuanlei Zhao", "Samir Khaki", "Zheng Zhu", "Xiaojiang Peng", "Konstantinos N. Plataniotis", "Kai Wang", "Wangbo Zhao", "Yang You"], "categories": ["cs.CV", "cs.AI"], "doi": "", "_query": "property valuation computer vision", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2312.13620v1", "title": "A Comprehensive End-to-End Computer Vision Framework for Restoration and Recognition of Low-Quality Engineering Drawings", "abstract": "The digitization of engineering drawings is crucial for efficient reuse, distribution, and archiving. Existing computer vision approaches for digitizing engineering drawings typically assume the input drawings have high quality. However, in reality, engineering drawings are often blurred and distorted due to improper scanning, storage, and transmission, which may jeopardize the effectiveness of existing approaches. This paper focuses on restoring and recognizing low-quality engineering drawings, where an end-to-end framework is proposed to improve the quality of the drawings and identify the graphical symbols on them. The framework uses K-means clustering to classify different engineering drawing patches into simple and complex texture patches based on their gray level co-occurrence matrix statistics. Computer vision operations and a modified Enhanced Super-Resolution Generative Adversarial Network (ESRGAN) model are then used to improve the quality of the two types of patches, respectively. A modified Faster Region-based Convolutional Neural Network (Faster R-CNN) model is used to recognize the quality-enhanced graphical symbols. Additionally, a multi-stage task-driven collaborative learning strategy is proposed to train the modified ESRGAN and Faster R-CNN models to improve the resolution of engineering drawings in the direction that facilitates graphical symbol recognition, rather than human visual perception. A synthetic data generation method is also proposed to construct quality-degraded samples for training the framework. Experiments on real-world electrical diagrams show that the proposed framework achieves an accuracy of 98.98% and a recall of 99.33%, demonstrating its superiority over previous approaches. Moreover, the framework is integrated into a widely-used power system software application to showcase its practicality.", "published": "2023-12-21T07:22:25Z", "authors": ["Lvyang Yang", "Jiankang Zhang", "Huaiqiang Li", "Longfei Ren", "Chen Yang", "Jingyu Wang", "Dongyuan Shi"], "categories": ["cs.CV", "eess.IV"], "doi": "", "_query": "property valuation computer vision", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2602.08726v1", "title": "SynSacc: A Blender-to-V2E Pipeline for Synthetic Neuromorphic Eye-Movement Data and Sim-to-Real Spiking Model Training", "abstract": "The study of eye movements, particularly saccades and fixations, are fundamental to understanding the mechanisms of human cognition and perception. Accurate classification of these movements requires sensing technologies capable of capturing rapid dynamics without distortion. Event cameras, also known as Dynamic Vision Sensors (DVS), provide asynchronous recordings of changes in light intensity, thereby eliminating motion blur inherent in conventional frame-based cameras and offering superior temporal resolution and data efficiency. In this study, we introduce a synthetic dataset generated with Blender to simulate saccades and fixations under controlled conditions. Leveraging Spiking Neural Networks (SNNs), we evaluate its robustness by training two architectures and finetuning on real event data. The proposed models achieve up to 0.83 accuracy and maintain consistent performance across varying temporal resolutions, demonstrating stability in eye movement classification. Moreover, the use of SNNs with synthetic event streams yields substantial computational efficiency gains over artificial neural network (ANN) counterparts, underscoring the utility of synthetic data augmentation in advancing event-based vision. All code and datasets associated with this work is available at https: //github.com/Ikhadija-5/SynSacc-Dataset.", "published": "2026-02-09T14:34:31Z", "authors": ["Khadija Iddrisu", "Waseem Shariff", "Suzanne Little", "Noel OConnor"], "categories": ["cs.CV"], "doi": "", "_query": "property valuation computer vision", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2103.13630v3", "title": "A Survey of Quantization Methods for Efficient Neural Network Inference", "abstract": "As soon as abstract mathematical computations were adapted to computation on digital computers, the problem of efficient representation, manipulation, and communication of the numerical values in those computations arose. Strongly related to the problem of numerical representation is the problem of quantization: in what manner should a set of continuous real-valued numbers be distributed over a fixed discrete set of numbers to minimize the number of bits required and also to maximize the accuracy of the attendant computations? This perennial problem of quantization is particularly relevant whenever memory and/or computational resources are severely restricted, and it has come to the forefront in recent years due to the remarkable performance of Neural Network models in computer vision, natural language processing, and related areas. Moving from floating-point representations to low-precision fixed integer values represented in four bits or less holds the potential to reduce the memory footprint and latency by a factor of 16x; and, in fact, reductions of 4x to 8x are often realized in practice in these applications. Thus, it is not surprising that quantization has emerged recently as an important and very active sub-area of research in the efficient implementation of computations associated with Neural Networks. In this article, we survey approaches to the problem of quantizing the numerical values in deep Neural Network computations, covering the advantages/disadvantages of current methods. With this survey and its organization, we hope to have presented a useful snapshot of the current research in quantization for Neural Networks and to have given an intelligent organization to ease the evaluation of future research in this area.", "published": "2021-03-25T06:57:11Z", "authors": ["Amir Gholami", "Sehoon Kim", "Zhen Dong", "Zhewei Yao", "Michael W. Mahoney", "Kurt Keutzer"], "categories": ["cs.CV"], "doi": "", "_query": "property valuation computer vision", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2301.03831v1", "title": "Dynamic Grained Encoder for Vision Transformers", "abstract": "Transformers, the de-facto standard for language modeling, have been recently applied for vision tasks. This paper introduces sparse queries for vision transformers to exploit the intrinsic spatial redundancy of natural images and save computational costs. Specifically, we propose a Dynamic Grained Encoder for vision transformers, which can adaptively assign a suitable number of queries to each spatial region. Thus it achieves a fine-grained representation in discriminative regions while keeping high efficiency. Besides, the dynamic grained encoder is compatible with most vision transformer frameworks. Without bells and whistles, our encoder allows the state-of-the-art vision transformers to reduce computational complexity by 40%-60% while maintaining comparable performance on image classification. Extensive experiments on object detection and segmentation further demonstrate the generalizability of our approach. Code is available at https://github.com/StevenGrove/vtpack.", "published": "2023-01-10T07:55:29Z", "authors": ["Lin Song", "Songyang Zhang", "Songtao Liu", "Zeming Li", "Xuming He", "Hongbin Sun", "Jian Sun", "Nanning Zheng"], "categories": ["cs.CV"], "doi": "", "_query": "property valuation computer vision", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2502.00594v1", "title": "Fast Vision Mamba: Pooling Spatial Dimensions for Accelerated Processing", "abstract": "State Space Models (SSMs) with selective scan (Mamba) have been adapted into efficient vision models. Mamba, unlike Vision Transformers, achieves linear complexity for token interactions through a recurrent hidden state process. This sequential processing is enhanced by a parallel scan algorithm, which reduces the computational time of recurrent steps from $L$ sequential steps to $log(L)$ parallel steps with respect to the number of input tokens ($L$). In this work, we propose Fast Vision Mamba (FastVim), that further reduces the computational time of the SSM block by reducing the number of recurrent steps in Vision Mamba models while still retaining model performance. By alternately pooling tokens along image dimensions across Mamba blocks, we obtain a 2$\\times$ reduction in the number of parallel steps in SSM block. Our model offers up to $72.5\\%$ speedup in inference speed compared to baseline Vision Mamba models on high resolution (2048$\\times$2048) images. Our experiments demonstrate state-of-the-art performance with dramatically improved throughput in a range of tasks such as image classification, cell perturbation prediction, segmentation, and object detection. Code is made available at https://github.com/insitro/FastVim", "published": "2025-02-01T23:35:20Z", "authors": ["Saarthak Kapse", "Robin Betz", "Srinivasan Sivanandan"], "categories": ["cs.CV", "cs.AI"], "doi": "", "_query": "property valuation computer vision", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/cs/0501031v2", "title": "From truth to computability II", "abstract": "Computability logic is a formal theory of computational tasks and resources. Formulas in it represent interactive computational problems, and \"truth\" is understood as algorithmic solvability. Interactive computational problems, in turn, are defined as a certain sort games between a machine and its environment, with logical operators standing for operations on such games. Within the ambitious program of finding axiomatizations for incrementally rich fragments of this semantically introduced logic, the earlier article \"From truth to computability I\" proved soundness and completeness for system CL3, whose language has the so called parallel connectives (including negation), choice connectives, choice quantifiers, and blind quantifiers. The present paper extends that result to the significantly more expressive system CL4 with the same collection of logical operators. What makes CL4 expressive is the presence of two sorts of atoms in its language: elementary atoms, representing elementary computational problems (i.e. predicates, i.e. problems of zero degree of interactivity), and general atoms, representing arbitrary computational problems. CL4 conservatively extends CL3, with the latter being nothing but the general-atom-free fragment of the former. Removing the blind (classical) group of quantifiers from the language of CL4 is shown to yield a decidable logic despite the fact that the latter is still first-order. A comprehensive online source on computability logic can be found at http://www.cis.upenn.edu/~giorgi/cl.html", "published": "2005-01-16T13:47:47Z", "authors": ["Giorgi Japaridze"], "categories": ["cs.LO", "cs.AI", "math.LO"], "doi": "10.1016/j.tcs.2007.01.004", "_query": "property valuation computer vision", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2510.05927v4", "title": "Computational Complexity in Property Testing", "abstract": "We initiate a systematic study of the computational complexity of property testing, focusing on the relationship between query and time complexity. While traditional work in property testing has emphasized query complexity, relatively little is known about the computational hardness of property testers. Our goal is to chart the landscape of time-query interplay and develop tools for proving time complexity lower bounds. Our first contribution is a pair of time-query hierarchy theorems for property testing. For all suitable nondecreasing functions $q(n)$ and $t(n)$ with $t(n)\\geq q(n)$, we construct properties with query complexity $\\tildeΘ(q(n))$ and time complexity $\\tildeΩ(t(n))$. Our weak hierarchy holds unconditionally, whereas the strong version-assuming the Strong Exponential Time Hypothesis-provides better control over the time complexity of the constructed properties. We then turn to halfspaces in $\\mathbb{R}^d$, a fundamental class in property testing and learning theory. We study the problem of approximating the distance from the input function to the nearest halfspace within additive error $ε$. For the distribution-free distance approximation problem, known algorithms achieve query complexity $O(d/ε^2)$, but take time $\\tildeΘ(1/ε^d)$. We provide a fine-grained justification for this gap: assuming the $k$-SUM conjecture, any algorithm must have running time $Ω(1/ε^{d/2})$. This fine-grained lower bound yields a provable separation between query and time complexity for a natural and well-studied (tolerant) testing problem. We also prove that any Statistical Query (SQ) algorithm under the standard Gaussian distribution requires $(1/ε)^{Ω(d)}$ queries if the queries are answered with additive error up to $ε^{Ω(d)}$, revealing a fundamental barrier even in the distribution-specific setting.", "published": "2025-10-07T13:40:01Z", "authors": ["Renato Ferreira Pinto", "Diptaksho Palit", "Sofya Raskhodnikova"], "categories": ["cs.CC", "cs.DS"], "doi": "", "_query": "property valuation computer vision", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2503.04139v2", "title": "Robust Computer-Vision based Construction Site Detection for Assistive-Technology Applications", "abstract": "Purpose: Navigating urban environments poses significant challenges for individuals who are blind or have low vision, especially in areas affected by construction. Construction zones introduce hazards such as uneven surfaces, barriers, hazardous materials, excessive noise, and altered routes that obstruct familiar paths and compromise safety. Although navigation tools assist in trip planning, they often overlook these temporary obstacles. Existing hazard detection systems also struggle with the visual variability of construction sites. Methods: We developed a computer vision--based assistive system integrating three modules: an open-vocabulary object detector to identify diverse construction-related elements, a YOLO-based model specialized in detecting scaffolding and poles, and an optical character recognition module to interpret construction signage. Results: In static testing at seven construction sites using images from multiple stationary viewpoints, the system achieved 88.56% overall accuracy. It consistently identified relevant objects within 2--10 meters and at approach angles up to 75$^{\\circ}$. At 2--4 meters, detection was perfect (100%) across all angles. Even at 10 meters, six of seven sites remained detectable within a 15$^{\\circ}$ approach. In dynamic testing along a 0.5-mile urban route containing eight construction sites, the system analyzed every frame of a first-person walking video. It achieved 87.26% accuracy in distinguishing construction from non-construction areas, rising to 92.0% with a 50-frame majority vote filter. Conclusion: The system can reliably detect construction sites in real time and at sufficient distances to provide advance warnings, enabling individuals with visual impairments to make safer mobility decisions such as proceeding with caution or rerouting.", "published": "2025-03-06T06:35:19Z", "authors": ["Junchi Feng", "Giles Hamilton-Fletcher", "Nikhil Ballem", "Michael Batavia", "Yifei Wang", "Jiuling Zhong", "Maurizio Porfiri", "John-Ross Rizzo"], "categories": ["cs.CV"], "doi": "", "_query": "property valuation computer vision", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/1401.4813v2", "title": "Definable henselian valuation rings", "abstract": "We give model theoretic criteria for $\\exists \\forall$ and $\\forall \\exists$- formulas in the ring language to define uniformly the valuation rings $\\mathcal{O}$ of models $(K, \\mathcal{O})$ of an elementary theory $Σ$ of henselian valued fields. As one of the applications we obtain the existence of an $\\exists \\forall$-formula defining uniformly the valuation rings $\\mathcal{O}$ of valued henselian fields $(K, \\mathcal{O})$ whose residue class field $k$ is finite, pseudo-finite, or hilbertian. We also obtain $\\forall \\exists$-formulas $\\varphi_2$ and $\\varphi_4$ such that $\\varphi_2$ defines uniformly $k[[t]]$ in $k((t))$ whenever $k$ is finite or the function field of a real or complex curve, and $\\varphi_4$ does the job if $k$ is any number field.", "published": "2014-01-20T07:45:23Z", "authors": ["Alexander Prestel"], "categories": ["math.AC", "math.LO"], "doi": "", "_query": "property valuation computer vision", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/1604.05933v1", "title": "Parametric Object Motion from Blur", "abstract": "Motion blur can adversely affect a number of vision tasks, hence it is generally considered a nuisance. We instead treat motion blur as a useful signal that allows to compute the motion of objects from a single image. Drawing on the success of joint segmentation and parametric motion models in the context of optical flow estimation, we propose a parametric object motion model combined with a segmentation mask to exploit localized, non-uniform motion blur. Our parametric image formation model is differentiable w.r.t. the motion parameters, which enables us to generalize marginal-likelihood techniques from uniform blind deblurring to localized, non-uniform blur. A two-stage pipeline, first in derivative space and then in image space, allows to estimate both parametric object motion as well as a motion segmentation from a single image alone. Our experiments demonstrate its ability to cope with very challenging cases of object motion blur.", "published": "2016-04-20T13:00:30Z", "authors": ["Jochen Gast", "Anita Sellent", "Stefan Roth"], "categories": ["cs.CV"], "doi": "", "_query": "property valuation computer vision", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2305.00054v3", "title": "LAVA: Data Valuation without Pre-Specified Learning Algorithms", "abstract": "Traditionally, data valuation (DV) is posed as a problem of equitably splitting the validation performance of a learning algorithm among the training data. As a result, the calculated data values depend on many design choices of the underlying learning algorithm. However, this dependence is undesirable for many DV use cases, such as setting priorities over different data sources in a data acquisition process and informing pricing mechanisms in a data marketplace. In these scenarios, data needs to be valued before the actual analysis and the choice of the learning algorithm is still undetermined then. Another side-effect of the dependence is that to assess the value of individual points, one needs to re-run the learning algorithm with and without a point, which incurs a large computation burden. This work leapfrogs over the current limits of data valuation methods by introducing a new framework that can value training data in a way that is oblivious to the downstream learning algorithm. Our main results are as follows. (1) We develop a proxy for the validation performance associated with a training set based on a non-conventional class-wise Wasserstein distance between training and validation sets. We show that the distance characterizes the upper bound of the validation performance for any given model under certain Lipschitz conditions. (2) We develop a novel method to value individual data based on the sensitivity analysis of the class-wise Wasserstein distance. Importantly, these values can be directly obtained for free from the output of off-the-shelf optimization solvers when computing the distance. (3) We evaluate our new data valuation framework over various use cases related to detecting low-quality data and show that, surprisingly, the learning-agnostic feature of our framework enables a significant improvement over SOTA performance while being orders of magnitude faster.", "published": "2023-04-28T19:05:16Z", "authors": ["Hoang Anh Just", "Feiyang Kang", "Jiachen T. Wang", "Yi Zeng", "Myeongseob Ko", "Ming Jin", "Ruoxi Jia"], "categories": ["cs.LG", "cs.AI", "stat.ML"], "doi": "", "_query": "property valuation computer vision", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/1505.00110v1", "title": "The Cross-Depiction Problem: Computer Vision Algorithms for Recognising Objects in Artwork and in Photographs", "abstract": "The cross-depiction problem is that of recognising visual objects regardless of whether they are photographed, painted, drawn, etc. It is a potentially significant yet under-researched problem. Emulating the remarkable human ability to recognise objects in an astonishingly wide variety of depictive forms is likely to advance both the foundations and the applications of Computer Vision. In this paper we benchmark classification, domain adaptation, and deep learning methods; demonstrating that none perform consistently well in the cross-depiction problem. Given the current interest in deep learning, the fact such methods exhibit the same behaviour as all but one other method: they show a significant fall in performance over inhomogeneous databases compared to their peak performance, which is always over data comprising photographs only. Rather, we find the methods that have strong models of spatial relations between parts tend to be more robust and therefore conclude that such information is important in modelling object classes regardless of appearance details.", "published": "2015-05-01T07:38:52Z", "authors": ["Hongping Cai", "Qi Wu", "Tadeo Corradi", "Peter Hall"], "categories": ["cs.CV"], "doi": "", "_query": "property valuation computer vision", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2210.11443v2", "title": "Snapshot of Algebraic Vision", "abstract": "In this survey article, we present interactions between algebraic geometry and computer vision, which have recently come under the header of algebraic vision. The subject has given new insights in multiple view geometry and its application to 3D scene reconstruction and carried a host of novel problems and ideas back into algebraic geometry.", "published": "2022-10-20T17:45:22Z", "authors": ["Joe Kileel", "Kathlén Kohn"], "categories": ["math.AG", "cs.CV"], "doi": "", "_query": "property valuation computer vision", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/1907.11432v3", "title": "Exploiting the Redundancy in Convolutional Filters for Parameter Reduction", "abstract": "Convolutional Neural Networks (CNNs) have achieved state-of-the-art performance in many computer vision tasks over the years. However, this comes at the cost of heavy computation and memory intensive network designs, suggesting potential improvements in efficiency. Convolutional layers of CNNs partly account for such an inefficiency, as they are known to learn redundant features. In this work, we exploit this redundancy, observing it as the correlation between convolutional filters of a layer, and propose an alternative approach to reproduce it efficiently. The proposed 'LinearConv' layer learns a set of orthogonal filters, and a set of coefficients that linearly combines them to introduce a controlled redundancy. We introduce a correlation-based regularization loss to achieve such flexibility over redundancy, and control the number of parameters in turn. This is designed as a plug-and-play layer to conveniently replace a conventional convolutional layer, without any additional changes required in the network architecture or the hyperparameter settings. Our experiments verify that LinearConv models achieve a performance on-par with their counterparts, with almost a 50% reduction in parameters on average, and the same computational requirement and speed at inference.", "published": "2019-07-26T08:39:31Z", "authors": ["Kumara Kahatapitiya", "Ranga Rodrigo"], "categories": ["cs.CV"], "doi": "", "_query": "property valuation computer vision", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2506.04026v1", "title": "On the Usage of Gaussian Process for Efficient Data Valuation", "abstract": "In machine learning, knowing the impact of a given datum on model training is a fundamental task referred to as Data Valuation. Building on previous works from the literature, we have designed a novel canonical decomposition allowing practitioners to analyze any data valuation method as the combination of two parts: a utility function that captures characteristics from a given model and an aggregation procedure that merges such information. We also propose to use Gaussian Processes as a means to easily access the utility function on ``sub-models'', which are models trained on a subset of the training set. The strength of our approach stems from both its theoretical grounding in Bayesian theory, and its practical reach, by enabling fast estimation of valuations thanks to efficient update formulae.", "published": "2025-06-04T14:53:51Z", "authors": ["Clément Bénesse", "Patrick Mesana", "Athénaïs Gautier", "Sébastien Gambs"], "categories": ["cs.LG"], "doi": "", "_query": "property valuation computer vision", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2411.11505v4", "title": "LaVin-DiT: Large Vision Diffusion Transformer", "abstract": "This paper presents the Large Vision Diffusion Transformer (LaVin-DiT), a scalable and unified foundation model designed to tackle over 20 computer vision tasks in a generative framework. Unlike existing large vision models directly adapted from natural language processing architectures, which rely on less efficient autoregressive techniques and disrupt spatial relationships essential for vision data, LaVin-DiT introduces key innovations to optimize generative performance for vision tasks. First, to address the high dimensionality of visual data, we incorporate a spatial-temporal variational autoencoder that encodes data into a continuous latent space. Second, for generative modeling, we develop a joint diffusion transformer that progressively produces vision outputs. Third, for unified multi-task training, in-context learning is implemented. Input-target pairs serve as task context, which guides the diffusion transformer to align outputs with specific tasks within the latent space. During inference, a task-specific context set and test data as queries allow LaVin-DiT to generalize across tasks without fine-tuning. Trained on extensive vision datasets, the model is scaled from 0.1B to 3.4B parameters, demonstrating substantial scalability and state-of-the-art performance across diverse vision tasks. This work introduces a novel pathway for large vision foundation models, underscoring the promising potential of diffusion transformers. The code and models are available.", "published": "2024-11-18T12:05:27Z", "authors": ["Zhaoqing Wang", "Xiaobo Xia", "Runnan Chen", "Dongdong Yu", "Changhu Wang", "Mingming Gong", "Tongliang Liu"], "categories": ["cs.CV"], "doi": "", "_query": "property valuation computer vision", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2609.04788v1", "title": "Shapley Valuation of Finite-Copy Quantum Data Depends on Physical Access", "abstract": "Data valuation asks how learning utility should be attributed to training data contributors. Most classical formulations begin after data have become reusable records, so the physical readout of the data is effectively fixed. Finite-copy quantum data are different: unknown states are consumable physical systems, and the same supplied states and downstream task can yield different Shapley values under different physical access models. Our framework makes this dependence explicit by treating physical access as a component of quantum data valuation itself. We establish an exact connection between physical-access advantage and contributor-level data valuation. For nested access models, we prove that the maximal downstream utility gain enabled by richer physical access exactly determines the largest symmetric Shapley ranking-reversal margin. More generally, for arbitrary access-model pairs, including non-nested ones, we derive an exact geometric characterization of the possible shifts of the full Shapley attribution vector. For fixed learning pipelines, we further obtain an operational Shapley-observable representation for finite-copy valuation. Numerical experiments demonstrate that identical quantum samples can receive different values and rankings when only the physical access model is changed. These results establish that quantum data value is not an intrinsic property of the underlying states alone, but emerges from the interaction between quantum states, physical access, and the downstream learning task.", "published": "2026-09-04T06:29:08Z", "authors": ["Qipeng Qian", "Yuntao Qian"], "categories": ["quant-ph"], "doi": "", "_query": "property valuation computer vision", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2511.17920v1", "title": "Animated Territorial Data Extractor (ATDE): A Computer-Vision Method for Extracting Territorial Data from Animated Historical Maps", "abstract": "We present Animated Territorial Data Extractor (ATDE), a computer vision tool that extracts quantitative territorial data from animated historical map videos. ATDE employs HSV-based color segmentation, RGB channel filtering, and Direct-Neighbor Filtering to identify and count pixels representing territorial control. Combined with preprocessing for temporal alignment and cross-video scaling, the pipeline converts animated videos into structured time-series data. We demonstrate the tool on ten Chinese dynasties (200 BCE - 1912 CE), producing year-by-year pixel counts that align with expected historical patterns. While not a substitute for authoritative historical datasets, ATDE is well-suited for educational demonstrations, preliminary data exploration, and comparative analysis of territorial dynamics. The tool requires no pre-existing shapefiles and can be applied to any animated map video given seed colors and basic configuration. Code and examples are available on GitHub.", "published": "2025-11-22T05:19:05Z", "authors": ["Hamza Alshamy", "Isaiah Woram", "Advay Mishra", "Zihan Xia", "Pascal Wallisch"], "categories": ["cs.CY", "cs.CV"], "doi": "", "_query": "property valuation computer vision", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2312.00114v3", "title": "Un-EVIMO: Unsupervised Event-Based Independent Motion Segmentation", "abstract": "Event cameras are a novel type of biologically inspired vision sensor known for their high temporal resolution, high dynamic range, and low power consumption. Because of these properties, they are well-suited for processing fast motions that require rapid reactions. Although event cameras have recently shown competitive performance in unsupervised optical flow estimation, performance in detecting independently moving objects (IMOs) is lacking behind, although event-based methods would be suited for this task based on their low latency and HDR properties. Previous approaches to event-based IMO segmentation have been heavily dependent on labeled data. However, biological vision systems have developed the ability to avoid moving objects through daily tasks without being given explicit labels. In this work, we propose the first event framework that generates IMO pseudo-labels using geometric constraints. Due to its unsupervised nature, our method can handle an arbitrary number of not predetermined objects and is easily scalable to datasets where expensive IMO labels are not readily available. We evaluate our approach on the EVIMO dataset and show that it performs competitively with supervised methods, both quantitatively and qualitatively.", "published": "2023-11-30T18:59:32Z", "authors": ["Ziyun Wang", "Jinyuan Guo", "Kostas Daniilidis"], "categories": ["cs.CV"], "doi": "", "_query": "property valuation computer vision", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2309.03895v1", "title": "InstructDiffusion: A Generalist Modeling Interface for Vision Tasks", "abstract": "We present InstructDiffusion, a unifying and generic framework for aligning computer vision tasks with human instructions. Unlike existing approaches that integrate prior knowledge and pre-define the output space (e.g., categories and coordinates) for each vision task, we cast diverse vision tasks into a human-intuitive image-manipulating process whose output space is a flexible and interactive pixel space. Concretely, the model is built upon the diffusion process and is trained to predict pixels according to user instructions, such as encircling the man's left shoulder in red or applying a blue mask to the left car. InstructDiffusion could handle a variety of vision tasks, including understanding tasks (such as segmentation and keypoint detection) and generative tasks (such as editing and enhancement). It even exhibits the ability to handle unseen tasks and outperforms prior methods on novel datasets. This represents a significant step towards a generalist modeling interface for vision tasks, advancing artificial general intelligence in the field of computer vision.", "published": "2023-09-07T17:56:57Z", "authors": ["Zigang Geng", "Binxin Yang", "Tiankai Hang", "Chen Li", "Shuyang Gu", "Ting Zhang", "Jianmin Bao", "Zheng Zhang", "Han Hu", "Dong Chen", "Baining Guo"], "categories": ["cs.CV"], "doi": "", "_query": "property valuation computer vision", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2405.08217v1", "title": "Data Valuation with Gradient Similarity", "abstract": "High-quality data is crucial for accurate machine learning and actionable analytics, however, mislabeled or noisy data is a common problem in many domains. Distinguishing low- from high-quality data can be challenging, often requiring expert knowledge and considerable manual intervention. Data Valuation algorithms are a class of methods that seek to quantify the value of each sample in a dataset based on its contribution or importance to a given predictive task. These data values have shown an impressive ability to identify mislabeled observations, and filtering low-value data can boost machine learning performance. In this work, we present a simple alternative to existing methods, termed Data Valuation with Gradient Similarity (DVGS). This approach can be easily applied to any gradient descent learning algorithm, scales well to large datasets, and performs comparably or better than baseline valuation methods for tasks such as corrupted label discovery and noise quantification. We evaluate the DVGS method on tabular, image and RNA expression datasets to show the effectiveness of the method across domains. Our approach has the ability to rapidly and accurately identify low-quality data, which can reduce the need for expert knowledge and manual intervention in data cleaning tasks.", "published": "2024-05-13T22:10:00Z", "authors": ["Nathaniel J. Evans", "Gordon B. Mills", "Guanming Wu", "Xubo Song", "Shannon McWeeney"], "categories": ["cs.LG", "q-bio.GN", "q-bio.QM", "stat.ML"], "doi": "", "_query": "property valuation computer vision", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/0901.2682v1", "title": "Self-stabilizing Numerical Iterative Computation", "abstract": "Many challenging tasks in sensor networks, including sensor calibration, ranking of nodes, monitoring, event region detection, collaborative filtering, collaborative signal processing, {\\em etc.}, can be formulated as a problem of solving a linear system of equations. Several recent works propose different distributed algorithms for solving these problems, usually by using linear iterative numerical methods. The main problem with previous approaches is that once the problem inputs change during the process of computation, the computation may output unexpected results. In real life settings, sensor measurements are subject to varying environmental conditions and to measurement noise. We present a simple iterative scheme called SS-Iterative for solving systems of linear equations, and examine its properties in the self-stabilizing perspective. We analyze the behavior of the proposed scheme under changing input sequences using two different assumptions on the input: a box bound, and a probabilistic distribution. As a case study, we discuss the sensor calibration problem and provide simulation results to support the applicability of our approach.", "published": "2009-01-18T06:56:59Z", "authors": ["Danny Bickson", "Ezra N. Hoch", "Harel Avissar", "Danny Dolev"], "categories": ["math.NA", "cs.DC"], "doi": "", "_query": "property valuation computer vision", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2208.07311v3", "title": "A General Framework for Fair Allocation under Matroid Rank Valuations", "abstract": "We study the problem of fairly allocating a set of indivisible goods among agents with matroid rank valuations -- every good provides a marginal value of $0$ or $1$ when added to a bundle and valuations are submodular. We generalize the Yankee Swap algorithm to create a simple framework, called General Yankee Swap, that can efficiently compute allocations that maximize any justice criterion (or fairness objective) satisfying some mild assumptions. Along with maximizing a justice criterion, General Yankee Swap is guaranteed to maximize utilitarian social welfare, ensure strategyproofness and use at most a quadratic number of valuation queries. We show how General Yankee Swap can be used to compute allocations for five different well-studied justice criteria: (a) Prioritized Lorenz dominance, (b) Maximin fairness, (c) Weighted leximin, (d) Max weighted Nash welfare, and (e) Max weighted $p$-mean welfare. In particular, our framework provides the first polynomial time algorithms to compute weighted leximin, max weighted Nash welfare and max weighted $p$-mean welfare allocations for agents with matroid rank valuations.", "published": "2022-08-15T16:18:02Z", "authors": ["Vignesh Viswanathan", "Yair Zick"], "categories": ["cs.GT"], "doi": "", "_query": "property valuation computer vision", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2201.11264v4", "title": "Properties of the Non-Autonomous Lattice Sine-Gordon Equation: Consistency around a Broken Cube Property", "abstract": "The lattice sine-Gordon equation is an integrable partial difference equation on ${\\mathbb Z}^2$, which approaches the sine-Gordon equation in a continuum limit. In this paper, we show that the non-autonomous lattice sine-Gordon equation has the consistency around a broken cube property as well as its autonomous version. Moreover, we construct two new Lax pairs of the non-autonomous case by using the consistency property.", "published": "2022-01-27T01:34:39Z", "authors": ["Nobutaka Nakazono"], "categories": ["nlin.SI", "math-ph"], "doi": "10.3842/SIGMA.2022.032", "_query": "property valuation computer vision", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/1210.7956v1", "title": "Implementation of a Vision System for a Landmine Detecting Robot Using Artificial Neural Network", "abstract": "Landmines, specifically anti-tank mines, cluster bombs, and unexploded ordnance form a serious problem in many countries. Several landmine sweeping techniques are used for minesweeping. This paper presents the design and the implementation of the vision system of an autonomous robot for landmines localization. The proposed work develops state-of-the-art techniques in digital image processing for pre-processing captured images of the contaminated area. After enhancement, Artificial Neural Network (ANN) is used in order to identify, recognize and classify the landmines' make and model. The Back-Propagation algorithm is used for training the network. The proposed work proved to be able to identify and classify different types of landmines under various conditions (rotated landmine, partially covered landmine) with a success rate of up to 90%.", "published": "2012-10-30T10:41:30Z", "authors": ["Roger Achkar", "Michel Owayjan"], "categories": ["cs.NE", "cs.CV"], "doi": "10.5121/ijaia.2012.3507", "_query": "property valuation computer vision", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/1802.05870v1", "title": "The dynamic impact of monetary policy on regional housing prices in the US: Evidence based on factor-augmented vector autoregressions", "abstract": "In this study interest centers on regional differences in the response of housing prices to monetary policy shocks in the US. We address this issue by analyzing monthly home price data for metropolitan regions using a factor-augmented vector autoregression (FAVAR) model. Bayesian model estimation is based on Gibbs sampling with Normal-Gamma shrinkage priors for the autoregressive coefficients and factor loadings, while monetary policy shocks are identified using high-frequency surprises around policy announcements as external instruments. The empirical results indicate that monetary policy actions typically have sizeable and significant positive effects on regional housing prices, revealing differences in magnitude and duration. The largest effects are observed in regions located in states on both the East and West Coasts, notably California, Arizona and Florida.", "published": "2018-02-16T09:08:34Z", "authors": ["Manfred M. Fischer", "Florian Huber", "Michael Pfarrhofer", "Petra Staufer-Steinnocher"], "categories": ["econ.EM"], "doi": "", "_query": "housing price text listing", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/1103.5971v1", "title": "Housing risk and return: Evidence from a housing asset-pricing model", "abstract": "This paper investigates the risk-return relationship in determination of housing asset pricing. In so doing, the paper evaluates behavioral hypotheses advanced by Case and Shiller (1988, 2002, 2009) in studies of boom and post-boom housing markets. The paper specifies and tests a multi-factor housing asset pricing model. In that model, we evaluate whether the market factor as well as other measures of risk, including idiosyncratic risk, momentum, and MSA size effects, have explanatory power for metropolitan-specific housing returns. Further, we test the robustness of the asset pricing results to inclusion of controls for socioeconomic variables commonly represented in the house price literature, including changes in employment, affordability, and foreclosure incidence. We find a sizable and statistically significant influence of the market factor on MSA house price returns. Moreover we show that market betas have varied substantially over time. Also, results are largely robust to the inclusion of other explanatory variables, including standard measures of risk and other housing market fundamentals. Additional tests of model validity using the Fama-MacBeth framework offer further strong support of a positive risk and return relationship in housing. Our findings are supportive of the application of a housing investment risk-return framework in explanation of variation in metro-area cross-section and time-series US house price returns. Further, results strongly corroborate Case-Shiller survey research indicating the importance of speculative forces in the determination of U.S. housing returns.", "published": "2011-03-30T15:55:56Z", "authors": ["Karl Case", "John Cotter", "Stuart Gabriel"], "categories": ["q-fin.PM", "q-fin.PR", "q-fin.ST"], "doi": "", "_query": "housing price text listing", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2402.04082v1", "title": "An Optimal House Price Prediction Algorithm: XGBoost", "abstract": "An accurate prediction of house prices is a fundamental requirement for various sectors including real estate and mortgage lending. It is widely recognized that a property value is not solely determined by its physical attributes but is significantly influenced by its surrounding neighbourhood. Meeting the diverse housing needs of individuals while balancing budget constraints is a primary concern for real estate developers. To this end, we addressed the house price prediction problem as a regression task and thus employed various machine learning techniques capable of expressing the significance of independent variables. We made use of the housing dataset of Ames City in Iowa, USA to compare support vector regressor, random forest regressor, XGBoost, multilayer perceptron and multiple linear regression algorithms for house price prediction. Afterwards, we identified the key factors that influence housing costs. Our results show that XGBoost is the best performing model for house price prediction.", "published": "2024-02-06T15:36:06Z", "authors": ["Hemlata Sharma", "Hitesh Harsora", "Bayode Ogunleye"], "categories": ["cs.LG", "cs.AI", "stat.AP", "stat.ME"], "doi": "10.3390/analytics3010003", "_query": "housing price text listing", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2604.18605v1", "title": "Exploring Drivers of Extreme Housing Prices in Australia", "abstract": "In recent years Australia has observed a growing, unexplained resilience of increasing house price trends. Here, we seek to understand what is driving Australia's indestructible asset using insights from market experts. We construct a differential equation model of house price to develop intuition for its historical behaviour and responsiveness to changes in mortgage rates. Using this model, we identify a point of 'decoupling' between house price and mortgage rate in the system with supply limitations found to be the main driver for this change. From there, modern extreme value techniques are implemented on real-world data to investigate how the effectiveness of mortgage rate in moderating extreme house price has changed before and after this historical decoupling. We find that without an increase in the housing supply chain, through either deregulation or reduced competition with government building, an 11\\% increase in mortgage rate will be needed to slow extreme housing costs.", "published": "2026-04-10T01:43:04Z", "authors": ["Grace Burtenshaw", "Ashley Burtenshaw", "Meagan Carney"], "categories": ["q-fin.GN", "math.PR", "stat.AP"], "doi": "", "_query": "housing price text listing", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/1809.03834v1", "title": "House Price Modeling with Digital Census", "abstract": "Urban house prices are strongly associated with local socioeconomic factors. In literature, house price modeling is based on socioeconomic variables from traditional census, which is not real-time, dynamic and comprehensive. Inspired by the emerging concept of \"digital census\" - using large-scale digital records of human activities to measure urban population dynamics and socioeconomic conditions, we introduce three typical datasets, namely 311 complaints, crime complaints and taxi trips, into house price modeling. Based on the individual housing sales data in New York City, we provide comprehensive evidence that these digital census datasets can substantially improve the modeling performances on both house price levels and changes, regardless whether traditional census is included or not. Hence, digital census can serve as both effective alternatives and complements to traditional census for house price modeling.", "published": "2018-08-29T16:13:58Z", "authors": ["Enwei Zhu", "Stanislav Sobolevsky"], "categories": ["econ.EM", "physics.soc-ph"], "doi": "", "_query": "housing price text listing", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2204.09050v1", "title": "House Price Prediction Based On Deep Learning", "abstract": "Since ancient times, what Chinese people have been pursuing is very simple, which is nothing more than \"to live and work happily, to eat and dress comfortable\". Today, more than 40 years after the reform and opening, people have basically solved the problem of food and clothing, and the urgent problem is housing. Nowadays, due to the storm of long-term rental apartment intermediary platforms such as eggshell, increasing the sense of insecurity of renters, as well as the urbanization in recent years and the scramble for people in major cities, this will make the future real estate market competition more intense. In order to better grasp the real estate price, let consumers buy a house reasonably, and provide a reference for the government to formulate policies, this paper summarizes the existing methods of house price prediction and proposes a house price prediction method based on mixed depth vision and text features.", "published": "2022-04-19T20:12:16Z", "authors": ["Yuying Wu", "Youshan Zhang"], "categories": ["cs.LG"], "doi": "", "_query": "housing price text listing", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2410.16745v1", "title": "Characterizing the top trading cycles rule for housing markets with lexicographic preferences", "abstract": "We consider a housing market model with limited externalities where agents care both about their own consumption via demand preferences and about the agent who receives their endowment via supply preferences (we extend the associated lexicographic preference domains introduced in Klaus and Meo, 2023). If preferences are demand lexicographic, then our model extends the classical Shapley-Scarf housing market (Shapley and Scarf, 1974) with strict preferences model. Our main result is a characterization of the corresponding top trading cycles (TTC) rule by individual rationality, pair efficiency, and strategy-proofness (Theorem 1), which extends that of Ekici (2024) from classical Shapley-Scarf housing markets with strict preferences to our model. Two further characterizations are immediately obtained by strengthening pair efficiency to either Pareto efficiency or pairwise stability (Corollaries 1 and 2). Finally, we show that as soon as we extend the preference domain to include demand lexicographic as well as supply lexicographic preferences (e.g., when preferences are separable), no rule satisfying individual rationality, pair efficiency, and strategy-proofness exists (Theorem 2).", "published": "2024-10-22T06:58:48Z", "authors": ["Bettina Klaus"], "categories": ["econ.TH", "cs.GT"], "doi": "10.1007/s00355-024-01556-9", "_query": "housing price text listing", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2412.14624v2", "title": "The Diffusive Nature of Housing Prices", "abstract": "We analyze the French housing market prices in the period 1970-2022, with high-resolution data from 2018 to 2022. The spatial correlation of the observed price field exhibits logarithmic decay characteristic of the two-dimensional random diffusion equation -- local interactions may create long-range correlations. We introduce a stylized model, used in the past to model spatial regularities in voting patterns, that accounts for both spatial and temporal correlations with reasonable values of parameters. Our analysis reveals that price shocks are persistent in time and their amplitude is strongly heterogeneous in space. Our study confirms and quantifies the diffusive nature of housing prices that was anticipated long ago (Clapp et al. 1994, Pollakowski et al. 1997), albeit on much restricted, local data sets.", "published": "2024-12-19T08:21:51Z", "authors": ["Antoine-Cyrus Becharat", "Michael Benzaquen", "Jean-Philippe Bouchaud"], "categories": ["cond-mat.stat-mech", "econ.GN", "physics.soc-ph"], "doi": "", "_query": "housing price text listing", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2609.10856v1", "title": "Following the Preference, Missing the Optimum: Compliance Without Optimization in AI Housing Recommendation", "abstract": "Large language models are becoming the first point of contact for consumer search in domains where the stakes are material and the law is explicit. Existing audits show that models steer housing seekers by perceived identity, but none can say what a user loses when a recommender overlooks a suitable option, for want of an enumerated inventory to score omissions against. We audit AI housing recommendation against a verifiable ground truth. For each of 150 synthetic renter scenarios in New York City we build a pool of 120 real listings with known rent, bedrooms and GTFS-computed transit commute, compute the exact set satisfying the renter's stated constraints, and derive its Pareto frontier. The primary outcome assumes no utility function: a recommendation is strictly dominated if the same pool holds a listing cheaper, faster to commute from and no smaller in bedrooms. Across 9,945 calls to three models from two vendors, compliance is near-perfect (1.8% violation against a 66.6% random floor), yet 39.0% of recommendations are strictly dominated, and the dominating listing is a median 900 USD/month cheaper and 3.5 minutes closer. A within-scenario manipulation separates two capabilities usually conflated: changing one sentence moves median recommended rent by 646 USD/month in the correct direction, so preferences are honored, yet recommendations still sit 606 USD/month above the five cheapest qualifying listings on the same screen, and an unambiguous lexicographic instruction gives no improvement under equivalence testing against a pre-specified 50 USD/month bound. The gap widens with candidate-set size and replicates across OpenAI and Anthropic models to within 3 USD. We characterize the failure as compliance without optimization, propose dominance-rate instrumentation as a deployable diagnostic, and release all code, prompts and per-call results.", "published": "2026-09-09T21:51:15Z", "authors": ["Hsuan Lo"], "categories": ["cs.CY", "cs.IR"], "doi": "", "_query": "housing price text listing", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/1104.2719v1", "title": "An autoregressive approach to house price modeling", "abstract": "A statistical model for predicting individual house prices and constructing a house price index is proposed utilizing information regarding sale price, time of sale and location (ZIP code). This model is composed of a fixed time effect and a random ZIP (postal) code effect combined with an autoregressive component. The former two components are applied to all home sales, while the latter is applied only to homes sold repeatedly. The time effect can be converted into a house price index. To evaluate the proposed model and the resulting index, single-family home sales for twenty US metropolitan areas from July 1985 through September 2004 are analyzed. The model is shown to have better predictive abilities than the benchmark S&P/Case--Shiller model, which is a repeat sales model, and a conventional mixed effects model. Finally, Los Angeles, CA, is used to illustrate a historical housing market downturn.", "published": "2011-04-14T10:54:29Z", "authors": ["Chaitra H. Nagaraja", "Lawrence D. Brown", "Linda H. Zhao"], "categories": ["stat.AP"], "doi": "10.1214/10-AOAS380", "_query": "housing price text listing", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2405.14503v1", "title": "Radial analysis and scaling of housing prices in French urban areas", "abstract": "Urban scaling laws summarize how urban attributes evolve with city size. Recent criticism questions notably the aggregate view of this approach, which leads to neglecting the internal structure of cities. This is all the more relevant for housing prices due to their important variations across space. Based on a dataset compiling millions of real estate transactions over the period 2017-2021, we investigate the regularities of the radial (center-periphery) profiles of housing prices across cities, with respect to their size. Results are threefold. First, they corroborate prior findings in the urban scaling literature stating that largest cities agglomerate higher housing prices. Second, we find that housing price radial profiles scale in three dimensions with the power 1/5 of city population. After rescaling, great regularities between radial profiles can be observed, although some locational amenities have a significant impact on prices. Third, it appears that our rescaled profiles approach fails to explain housing price variations in the city center across cities. In fact, prices near the city center rise much faster with city size than those in the periphery. This has strong implications for low-income households seeking homeownership, because prohibitive prices in the center may contribute to pushing them out into peripheral locations.", "published": "2024-05-23T12:38:20Z", "authors": ["Gaëtan Laziou", "Rémi Lemoy", "Marion Le Texier"], "categories": ["physics.soc-ph"], "doi": "", "_query": "housing price text listing", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2503.18332v2", "title": "Regional House Price Dynamics in Australia: Insights into Lifestyle and Mining Dynamics through PCA", "abstract": "This report applies Principal Component Analysis (PCA) to regional house price indexes to uncover dominant trends in Australia's housing market. Regions are assigned PCA-derived scores that reveal which underlying market forces are most influential in each area, enabling broad classification of local housing markets. The approach highlights where price movements tend to align across regions, even those geographically distant. The three most dominant trends are described in detail and, together with the regional scores, provide objective tools for policymakers, researchers, and real estate professionals.", "published": "2025-03-24T04:31:17Z", "authors": ["Willem Sijp"], "categories": ["econ.GN"], "doi": "", "_query": "housing price text listing", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2602.18592v1", "title": "A Roof Over Risk: A House Price-at-Risk Framework for Hungary", "abstract": "This paper develops a House Price-at-Risk framework to examine how housing subsidies, credit conditions, and supply factors influence the distribution of house price growth in Hungary. Using quantile regression with adaptive LASSO variable selection, we identify variables driving downside versus upside risks across multiple horizons. Financial stress dominates the lower tail at short horizons, while unemployment and affordability constraints become the primary drivers of downside risk at longer horizons. Housing subsidies exhibit pro-cyclical characteristics, concentrating significant positive effects on the upper quantiles while leaving the lower tail largely unaffected. Supply-side variables display horizon-dependent sign reversals, with construction permits exerting upward pressure on prices in the short run but moderating them as supply materialises. Uncertainty decomposition reveals persistent left-tail dominance across all horizons. These findings suggest that macroprudential frameworks should account for the distributional effects of housing subsidies, particularly their pro-cyclical influence on house price growth.", "published": "2026-02-20T19:57:51Z", "authors": ["Tibor Szendrei", "Nikolett Vágó", "Katalin Varga"], "categories": ["econ.EM"], "doi": "", "_query": "housing price text listing", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/1612.08486v1", "title": "Understanding the Impacts of Dark Pools on Price Discovery", "abstract": "This paper investigates the impact of dark pools on price discovery (the efficiency of prices on stock exchanges to aggregate information). Assets are traded in either an exchange or a dark pool, with the dark pool offering better prices but lower execution rates. Informed traders receive noisy and heterogeneous signals about an asset's fundamental. We find that informed traders use dark pools to mitigate their information risk and there is a sorting effect: in equilibrium, traders with strong signals trade in exchanges, traders with moderate signals trade in dark pools, and traders with weak signals do not trade. As a result, dark pools have an amplification effect on price discovery. That is, when information precision is high (information risk is low), the majority of informed traders trade in the exchange hence adding a dark pool enhances price discovery, whereas when information precision is low (information risk is high), the majority of the informed traders trade in the dark pool hence adding a dark pool impairs price discovery. The paper reconciles the conflicting empirical evidence and produces novel empirical predictions. The paper also provides regulatory suggestions with dark pools on current equity markets and in emerging markets.", "published": "2016-12-27T03:26:29Z", "authors": ["Linlin Ye"], "categories": ["q-fin.GN", "q-fin.TR"], "doi": "", "_query": "housing price text listing", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2509.21460v2", "title": "Forecasting House Prices", "abstract": "This article identifies the factors that drove house prices in 13 advanced countries over the past 35 years. It does so based on Breiman s (2001) random forest model. Shapley values indicate that annual house price growth across countries is explained first and foremost by price momentum, initial valuations (proxied by price to rent ratios) and household credit growth. Partial effects of explanatory variables are also elicited and suggest important non-linearities, for instance as to what concerns the effects of CPI inflation on house price growth. The out-of-sample forecast test reveals that the random forest model delivers 44% lower house price variation root square mean errors (RMSEs) and 45% lower mean absolute errors (MAEs) when compared to an OLS model that uses the same set of 10 pre-determined explanatory variables. Notably, the same model works well for all countries, as the random forest attributes minimal values to country fixed effects.", "published": "2025-09-25T19:27:37Z", "authors": ["Emanuel Kohlscheen"], "categories": ["econ.GN"], "doi": "", "_query": "housing price text listing", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2504.13459v1", "title": "The impact of institutional quality on the relation between FDI and house prices in ASEAN emerging countries", "abstract": "This study investigates the relationship between house prices and capital inflows in ASEAN emerging economies, and the impact of institutional quality on that relation. Using a unique balanced panel data set of six emerging countries in ASEAN from 2009 to 2019, we employ various econometric techniques to examine the impact of foreign direct investment (FDI) on the house price index. Our findings indicate a long-run relationship and Granger causality from FDI to the house price index in these markets, and we also find evidence of co-movement between the stock price index and the house price index. Additionally, our results suggest that better institutions reduce the impact of FDI on host country housing markets in the context of ASEAN emerging economies. This is one of the first studies to shed light on the role of institutional quality in the effect of FDIs on housing prices in this region.", "published": "2025-04-18T04:33:07Z", "authors": ["Hoang-Anh Le"], "categories": ["econ.GN"], "doi": "", "_query": "housing price text listing", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2310.08133v1", "title": "Multi Level Dense Layer Neural Network Model for Housing Price Prediction", "abstract": "Predicting the price of a house remains a challenging issue that needs to be addressed. Research has attempted to establish a model with different methods and algorithms to predict the housing price, from the traditional hedonic model to a neural network algorithm. However, many existing algorithms in the literature are proposed without any finetuning and customization in the model. In this paper, the author attempted to propose a novel neural network-based model to improve the performance of housing price prediction. Inspired by the modular neural network, the proposed model consists of a three-level neural network that is capable to process information in parallel. The author compared several state-of-the-art algorithms available in the literature on the Boston housing dataset to evaluate the effectiveness of the proposed model. The results show that the proposed model provides better accuracy and outperforms existing algorithms in different evaluation metrics. The code for the implementation is available https://github.com/wijayarobert/MultiLevelDenseLayerNN", "published": "2023-10-12T08:46:26Z", "authors": ["Robert Wijaya"], "categories": ["cs.CY"], "doi": "", "_query": "housing price text listing", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2204.06967v1", "title": "JUE Insight: The (Non-)Effect of Opportunity Zones on Housing Prices", "abstract": "Will the Opportunity Zones (OZ) program, America's largest new place-based policy in decades, generate neighborhood change? We compare single-family housing price growth in OZs with price growth in areas that were eligible but not included in the program. We also compare OZs to their nearest geographic neighbors. Our most credible estimates rule out price impacts greater than 0.5 percentage points with 95% confidence, suggesting that, so far, home buyers don't believe that this subsidy will generate major neighborhood change. OZ status reduces prices in areas with little employment, perhaps because buyers think that subsidizing new investment will increase housing supply. Mixed evidence suggests that OZs may have increased residential permitting.", "published": "2022-04-14T13:48:40Z", "authors": ["Jiafeng Chen", "Edward Glaeser", "David Wessel"], "categories": ["econ.GN"], "doi": "10.1016/j.jue.2022.103451", "_query": "housing price text listing", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/1609.08399v1", "title": "House price estimation from visual and textual features", "abstract": "Most existing automatic house price estimation systems rely only on some textual data like its neighborhood area and the number of rooms. The final price is estimated by a human agent who visits the house and assesses it visually. In this paper, we propose extracting visual features from house photographs and combining them with the house's textual information. The combined features are fed to a fully connected multilayer Neural Network (NN) that estimates the house price as its single output. To train and evaluate our network, we have collected the first houses dataset (to our knowledge) that combines both images and textual attributes. The dataset is composed of 535 sample houses from the state of California, USA. Our experiments showed that adding the visual features increased the R-value by a factor of 3 and decreased the Mean Square Error (MSE) by one order of magnitude compared with textual-only features. Additionally, when trained on the benchmark textual-only features housing dataset, our proposed NN still outperformed the existing model published results.", "published": "2016-09-27T13:15:31Z", "authors": ["Eman Ahmed", "Mohamed Moustafa"], "categories": ["cs.CV"], "doi": "", "_query": "housing price text listing", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2108.02442v1", "title": "House Price Determinants and Market Segmentation in Boulder, Colorado: A Hedonic Price Approach", "abstract": "In this research we perform hedonic regression model to examine the residential property price determinants in the city of Boulder in the state of Colorado, USA. The urban housing markets are too compounded to be considered as homogeneous markets. The heterogeneity of an urban property market requires creation of market segmentation. To test whether residential properties in the real estate market in the city of Boulder are analyzed and predicted in the disaggregate level or at an aggregate level we stratify the housing market based on both property types and location and estimate separate hedonic price models for each submarket. The results indicate that the implicit values of the property characteristics are not identical across property types and locations in the city of Boulder and market segmentation exists.", "published": "2021-08-05T08:19:48Z", "authors": ["Mahdieh Yazdani"], "categories": ["econ.GN"], "doi": "", "_query": "housing price text listing", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2312.14325v1", "title": "Exploring Distributions of House Prices and House Price Indices", "abstract": "We use house prices (HP) and house price indices (HPI) as a proxy to income distribution. Specifically, we analyze sale prices in the 1970-2010 window of over 116,000 single-family homes in Hamilton County, Ohio, including Cincinnati metro area of about 2.2 million people. We also analyze HPI, published by Federal Housing Finance Agency (FHFA), for nearly 18,000 US ZIP codes that cover a period of over 40 years starting in 1980's. If HP can be viewed as a first derivative of income, HPI can be viewed as its second derivative. We use generalized beta (GB) family of functions to fit distributions of HP and HPI since GB naturally arises from the models of economic exchange described by stochastic differential equations. Our main finding is that HP and multi-year HPI exhibit a negative Dragon King (nDK) behavior, wherein power-law distribution tail gives way to an abrupt decay to a finite upper limit value, which is similar to our recent findings for realized volatility of S\\&P500 index in the US stock market. This type of tail behavior is best fitted by a modified GB (mGB) distribution. Tails of single-year HPI appear to show more consistency with power-law behavior, which is better described by a GB Prime (GB2) distribution. We supplement full distribution fits by mGB and GB2 with direct linear fits (LF) of the tails. Our numerical procedure relies on evaluation of confidence intervals (CI) of the fits, as well as of p-values that give the likelihood that data come from the fitted distributions.", "published": "2023-12-21T22:38:24Z", "authors": ["Jiong Liu", "Hamed Farahani", "R. A. Serota"], "categories": ["econ.EM", "econ.TH"], "doi": "10.3390/economies12020047", "_query": "housing price text listing", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2202.11416v1", "title": "Price formation in financial markets: a game-theoretic perspective", "abstract": "We propose two novel frameworks to study the price formation of an asset negotiated in an order book. Specifically, we develop a game-theoretic model in many-person games and mean-field games, considering costs stemming from limited liquidity. We derive analytical formulas for the formed price in terms of the realized order flow. We also identify appropriate conditions that ensure the convergence of the price we find in the finite population game to that of its mean-field counterpart. We numerically assess our results with a large experiment using high-frequency data from ten stocks listed in the NASDAQ, a stock listed in B3 in Brazil, and a cryptocurrency listed in Binance.", "published": "2022-02-23T10:59:35Z", "authors": ["David Evangelista", "Yuri Saporito", "Yuri Thamsten"], "categories": ["q-fin.TR"], "doi": "", "_query": "housing price text listing", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2004.02706v1", "title": "What do online listings tell us about the housing market?", "abstract": "Traditional data sources for the analysis of housing markets show several limitations, that recently started to be overcome using data coming from housing sales advertisements (ads) websites. In this paper, using a large dataset of ads in Italy, we provide the first comprehensive analysis of the problems and potential of these data. The main problem is that multiple ads (\"duplicates\") can correspond to the same housing unit. We show that this issue is mainly caused by sellers' attempt to increase visibility of their listings. Duplicates lead to misrepresentation of the volume and composition of housing supply, but this bias can be corrected by identifying duplicates with machine learning tools. We then focus on the potential of these data. We show that the timeliness, granularity, and online nature of these data allow monitoring of housing demand, supply and liquidity, and that the (asking) prices posted on the website can be more informative than transaction prices.", "published": "2020-04-06T14:40:09Z", "authors": ["Michele Loberto", "Andrea Luciani", "Marco Pangallo"], "categories": ["econ.EM", "econ.GN"], "doi": "", "_query": "housing price text listing", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2409.05335v1", "title": "A Multi-Modal Deep Learning Based Approach for House Price Prediction", "abstract": "Accurate prediction of house price, a vital aspect of the residential real estate sector, is of substantial interest for a wide range of stakeholders. However, predicting house prices is a complex task due to the significant variability influenced by factors such as house features, location, neighborhood, and many others. Despite numerous attempts utilizing a wide array of algorithms, including recent deep learning techniques, to predict house prices accurately, existing approaches have fallen short of considering a wide range of factors such as textual and visual features. This paper addresses this gap by comprehensively incorporating attributes, such as features, textual descriptions, geo-spatial neighborhood, and house images, typically showcased in real estate listings in a house price prediction system. Specifically, we propose a multi-modal deep learning approach that leverages different types of data to learn more accurate representation of the house. In particular, we learn a joint embedding of raw house attributes, geo-spatial neighborhood, and most importantly from textual description and images representing the house; and finally use a downstream regression model to predict the house price from this jointly learned embedding vector. Our experimental results with a real-world dataset show that the text embedding of the house advertisement description and image embedding of the house pictures in addition to raw attributes and geo-spatial embedding, can significantly improve the house price prediction accuracy. The relevant source code and dataset are publicly accessible at the following URL: https://github.com/4P0N/mhpp", "published": "2024-09-09T05:26:33Z", "authors": ["Md Hasebul Hasan", "Md Abid Jahan", "Mohammed Eunus Ali", "Yuan-Fang Li", "Timos Sellis"], "categories": ["cs.LG", "cs.AI", "cs.CV"], "doi": "", "_query": "housing price text listing", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/1805.12101v1", "title": "Unravelling Airbnb Predicting Price for New Listing", "abstract": "This paper analyzes Airbnb listings in the city of San Francisco to better understand how different attributes such as bedrooms, location, house type amongst others can be used to accurately predict the price of a new listing that optimal in terms of the host's profitability yet affordable to their guests. This model is intended to be helpful to the internal pricing tools that Airbnb provides to its hosts. Furthermore, additional analysis is performed to ascertain the likelihood of a listings availability for potential guests to consider while making a booking. The analysis begins with exploring and examining the data to make necessary transformations that can be conducive for a better understanding of the problem at large while helping us make hypothesis. Moving further, machine learning models are built that are intuitive to use to validate the hypothesis on pricing and availability and run experiments in that context to arrive at a viable solution. The paper then concludes with a discussion on the business implications, associated risks and future scope.", "published": "2018-05-25T14:07:17Z", "authors": ["Paridhi Choudhary", "Aniket Jain", "Rahul Baijal"], "categories": ["q-fin.GN"], "doi": "", "_query": "housing price text listing", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2112.06192v1", "title": "Housing Price Prediction Model Selection Based on Lorenz and Concentration Curves: Empirical Evidence from Tehran Housing Market", "abstract": "This study contributes a house price prediction model selection in Tehran City based on the area between Lorenz curve (LC) and concentration curve (CC) of the predicted price by using 206,556 observed transaction data over the period from March 21, 2018, to February 19, 2021. Several different methods such as generalized linear models (GLM) and recursive partitioning and regression trees (RPART), random forests (RF) regression models, and neural network (NN) models were examined house price prediction. We used 90% of all data samples which were chosen randomly to estimate the parameters of pricing models and 10% of remaining datasets to test the accuracy of prediction. Results showed that the area between the LC and CC curves (which are known as ABC criterion) of real and predicted prices in the test data sample of the random forest regression model was less than by other models under study. The comparison of the calculated ABC criteria leads us to conclude that the nonlinear regression models such as RF regression models give an accurate prediction of house prices in Tehran City.", "published": "2021-12-12T09:44:28Z", "authors": ["Mohammad Mirbagherijam"], "categories": ["econ.EM"], "doi": "", "_query": "housing price text listing", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/1803.00919v1", "title": "House Price Modeling over Heterogeneous Regions with Hierarchical Spatial Functional Analysis", "abstract": "Online real-estate information systems such as Zillow and Trulia have gained increasing popularity in recent years. One important feature offered by these systems is the online home price estimate through automated data-intensive computation based on housing information and comparative market value analysis. State-of-the-art approaches model house prices as a combination of a latent land desirability surface and a regression from house features. However, by using uniformly damping kernels, they are unable to handle irregularly shaped regions or capture land value discontinuities within the same region due to the existence of implicit sub-communities, which are common in real-world scenarios. In this paper, we explore the novel application of recent advances in spatial functional analysis to house price modeling and propose the Hierarchical Spatial Functional Model (HSFM), which decomposes house values into land desirability at both the global scale and hidden local scales as well as the feature regression component. We propose statistical learning algorithms based on finite-element spatial functional analysis and spatial constrained clustering to train our model. Extensive evaluations based on housing data in a major Canadian city show that our proposed approach can reduce the mean relative house price estimation error down to 6.60%.", "published": "2018-03-01T03:41:55Z", "authors": ["Bang Liu", "Borislav Mavrin", "Di Niu", "Linglong Kong"], "categories": ["cs.CE"], "doi": "10.1109/ICDM.2016.0134", "_query": "housing price text listing", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2105.06060v1", "title": "House Price Prediction using Satellite Imagery", "abstract": "In this paper we show how using satellite images can improve the accuracy of housing price estimation models. Using Los Angeles County's property assessment dataset, by transferring learning from an Inception-v3 model pretrained on ImageNet, we could achieve an improvement of ~10% in R-squared score compared to two baseline models that only use non-image features of the house.", "published": "2021-05-13T03:25:32Z", "authors": ["Sina Jandaghi Semnani", "Hoormazd Rezaei"], "categories": ["cs.LG", "stat.ML"], "doi": "", "_query": "housing price text listing", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/1503.05550v1", "title": "Club Convergence of House Prices: Evidence from China's Ten Key Cities", "abstract": "The latest global financial tsunami and its follow-up global economic recession has uncovered the crucial impact of housing markets on financial and economic systems. The Chinese stock market experienced a markedly fall during the global financial tsunami and China's economy has also slowed down by about 2\\%-3\\% when measured in GDP. Nevertheless, the housing markets in diverse Chinese cities seemed to continue the almost nonstop mania for more than ten years. However, the structure and dynamics of the Chinese housing market are less studied. Here we perform an extensive study of the Chinese housing market by analyzing ten representative key cities based on both linear and nonlinear econophysical and econometric methods. We identify a common collective driving force which accounts for 96.5\\% of the house price growth, indicating very high systemic risk in the Chinese housing market. The ten key cities can be categorized into clubs and the house prices of the cities in the same club exhibit an evident convergence. These findings from different methods are basically consistent with each other. The identified city clubs are also consistent with the conventional classification of city tiers. The house prices of the first-tier cities grow the fastest, and those of the third- and fourth-tier cities rise the slowest, which illustrates the possible presence of a ripple effect in the diffusion of house prices in different cities.", "published": "2015-03-18T03:46:56Z", "authors": ["Hao Meng", "Wen-Jie Xie", "Wei-Xing Zhou"], "categories": ["q-fin.ST", "physics.soc-ph"], "doi": "10.1142/S0217979215501817", "_query": "housing price text listing", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2207.04455v1", "title": "The Price of Stability for First Price Auction", "abstract": "This paper establishes the Price of Stability (PoS) for First Price Auctions, for all equilibrium concepts that have been studied in the literature: Bayes Nash Equilibrium $\\subsetneq$ Bayes Correlated Equilibrium $\\subsetneq$ Bayes Coarse Correlated Equilibrium} $\\bullet$ Bayes Nash Equilibrium: For independent valuations, the tight PoS is $1 - 1/ e^{2} \\approx 0.8647$, matching the counterpart Price of Anarchy (PoA) bound \\cite{JL22}. For correlated valuations, the tight $\\PoS$ is $1 - 1 / e \\approx 0.6321$, matching the counterpart PoA bound \\cite{ST13,S14}. This result indicates that, in the worst cases, efficiency degradation depends not on different selections among Bayes Nash Equilibria. $\\bullet$ Bayesian Coarse Correlated Equilibrium: For independent or correlated valuations, the tight PoS is always $1 = 100\\%$, i.e., no efficiency degradation, different from the counterpart PoA bound $1 - 1 / e \\approx 0.6321$ \\cite{ST13,S14}. This result indicates that First Price Auctions can be fully efficient when we allow the more general equilibrium concepts.", "published": "2022-07-10T12:49:36Z", "authors": ["Yaonan Jin", "Pinyan Lu"], "categories": ["cs.GT"], "doi": "", "_query": "housing price text listing", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2102.02392v1", "title": "Lapsing Quickly into Fatalism: Bell on Backward Causation", "abstract": "This is a dialogue between Huw Price and Travis Norsen, loosely inspired by a letter that Price received from J. S. Bell in 1988. The main topic of discussion is Bell's views about retrocausal approaches to quantum theory, and their relevance to contemporary issues.", "published": "2021-02-04T03:22:50Z", "authors": ["Travis Norsen", "Huw Price"], "categories": ["quant-ph", "physics.hist-ph"], "doi": "", "_query": "housing price text listing", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2505.01591v2", "title": "Machine Learning Fairness in House Price Prediction: A Case Study of America's Expanding Metropolises", "abstract": "As a basic human need, housing plays a key role in enhancing health, well-being, and educational outcome in society, and the housing market is a major factor for promoting quality of life and ensuring social equity. To improve the housing conditions, there has been extensive research on building Machine Learning (ML)-driven house price prediction solutions to accurately forecast the future conditions, and help inform actions and policies in the field. In spite of their success in developing high-accuracy models, there is a gap in our understanding of the extent to which various ML-driven house price prediction approaches show ethnic and/or racial bias, which in turn is essential for the responsible use of ML, and ensuring that the ML-driven solutions do not exacerbate inequity. To fill this gap, this paper develops several ML models from a combination of structural and neighborhood-level attributes, and conducts comprehensive assessments on the fairness of ML models under various definitions of privileged groups. As a result, it finds that the ML-driven house price prediction models show various levels of bias towards protected attributes (i.e., race and ethnicity in this study). Then, it investigates the performance of different bias mitigation solutions, and the experimental results show their various levels of effectiveness on different ML-driven methods. However, in general, the in-processing bias mitigation approach tends to be more effective than the pre-processing one in this problem domain. Our code is available at https://github.com/wahab1412/housing_fairness.", "published": "2025-05-02T21:26:21Z", "authors": ["Abdalwahab Almajed", "Maryam Tabar", "Peyman Najafirad"], "categories": ["cs.LG"], "doi": "", "_query": "housing price text listing", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2210.06261v1", "title": "Predicting housing prices and analyzing real estate market in the Chicago suburbs using Machine Learning", "abstract": "The pricing of housing properties is determined by a variety of factors. However, post-pandemic markets have experienced volatility in the Chicago suburb area, which have affected house prices greatly. In this study, analysis was done on the Naperville/Bolingbrook real estate market to predict property prices based on these housing attributes through machine learning models, and to evaluate the effectiveness of such models in a volatile market space. Gathering data from Redfin, a real estate website, sales data from 2018 up until the summer season of 2022 were collected for research. By analyzing these sales in this range of time, we can also look at the state of the housing market and identify trends in price. For modeling the data, the models used were linear regression, support vector regression, decision tree regression, random forest regression, and XGBoost regression. To analyze results, comparison was made on the MAE, RMSE, and R-squared values for each model. It was found that the XGBoost model performs the best in predicting house prices despite the additional volatility sponsored by post-pandemic conditions. After modeling, Shapley Values (SHAP) were used to evaluate the weights of the variables in constructing models.", "published": "2022-10-12T14:41:53Z", "authors": ["Kevin Xu", "Hieu Nguyen"], "categories": ["cs.LG"], "doi": "", "_query": "housing price text listing", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2405.08398v2", "title": "Exploring the spatial segmentation of housing markets from online listings", "abstract": "The real estate market shows an inherent connection to space. Real estate agencies unevenly operate and specialize across space, price and type of properties, thereby segmenting the market into submarkets. We introduce here a methodology based on multipartite networks to detect the spatial segmentation emerging from data on housing online listings. Considering the spatial information of the listings, we build a bipartite network that connects agencies and spatial units. This bipartite network is projected into a network of spatial units, whose connections account for similarities in the agency ecosystem. We then apply clustering methods to this network to segment markets into spatially-coherent regions, which are found to be robust across different clustering detection algorithms, discretization of space and spatial scales, and across countries with case studies in France and Spain. This methodology addresses the long-standing issue of housing market segmentation, relevant in disciplines such as urban studies and spatial economics, and with implications for policymaking.", "published": "2024-05-14T07:52:42Z", "authors": ["David Abella", "Johann H. Martínez", "Mattia Mazzoli", "Thibault Le Corre", "Julien Migozzi", "Eduard Alonso-Paulí", "Rafel Crespí-Cladera", "Thomas Louail", "José J. Ramasco"], "categories": ["physics.soc-ph"], "doi": "", "_query": "housing price text listing", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2507.08641v1", "title": "Pricing and hedging the prepayment option of mortgages under stochastic housing market activity", "abstract": "Prepayment risk embedded in fixed-rate mortgages forms a significant fraction of a financial institution's exposure. The embedded prepayment option bears the same interest rate risk as an exotic interest rate swap with a suitable stochastic notional. Focusing on penalty-free prepayment because of the contract owner's relocation to a new house, we model the prepayment option value as an European-type interest rate receiver swaption with stochastic maturity matching the stochastic time of relocation. This is a convenient representation since it allows us to compute the prepayment option value in terms of well-known pricing formulas for European-type swaptions. We investigate the effect of a stochastic housing market activity as the explanatory variable for the distribution of the relocation time, as opposed to the conventional assumption of a deterministic housing market activity. We prove that the housing market covariance drives the prepayment option price difference between the stochastic setting and its deterministic counterpart. The prepayment option exposure is hedged using market instruments based on Delta-Gamma replication. Furthermore, since the housing market activity is a non-tradable risk factor, we perform non-standard actuarial hedging focusing on controlling the prepayment option exposure yield by risky housing market scenarios.", "published": "2025-07-11T14:43:02Z", "authors": ["Leonardo Perotti", "Lech A. Grzelak", "Cornelis W. Oosterlee"], "categories": ["q-fin.PR"], "doi": "", "_query": "housing price text listing", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/1012.3039v2", "title": "London house prices are power-law distributed", "abstract": "We explore the house price distributions for the English cities of London, Manchester, Bristol, Newcastle, Birmingham and Leeds. We find Pareto (power law) behaviour in their upper tails, which is clearly distinct from lognormal and gamma distributions in the cases of London, Manchester and Newcastle. For London, the city with the lowest power, this is a striking match with that found in the wealth distribution of the super-rich. We propose an index of Housing Wealth Inequality based on the Pareto exponent and analogous to the Gini coefficient, and comment on its possible uses.", "published": "2010-12-14T14:24:45Z", "authors": ["Niall MacKay"], "categories": ["physics.soc-ph"], "doi": "", "_query": "housing price text listing", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2506.09539v1", "title": "Disentangling Spatial and Structural Drivers of Housing Prices through Bayesian Networks: A Case Study of Madrid, Barcelona, and Valencia", "abstract": "Understanding how housing prices respond to spatial accessibility, structural attributes, and typological distinctions is central to contemporary urban research and policy. In cities marked by affordability stress and market segmentation, models that offer both predictive capability and interpretive clarity are increasingly needed. This study applies discrete Bayesian networks to model residential price formation across Madrid, Barcelona, and Valencia using over 180,000 geo-referenced housing listings. The resulting probabilistic structures reveal distinct city-specific logics. Madrid exhibits amenity-driven stratification, Barcelona emphasizes typology and classification, while Valencia is shaped by spatial and structural fundamentals. By enabling joint inference, scenario simulation, and sensitivity analysis within a transparent framework, the approach advances housing analytics toward models that are not only accurate but actionable, interpretable, and aligned with the demands of equitable urban governance.", "published": "2025-06-11T09:15:02Z", "authors": ["Alvaro Garcia Murga", "Manuele Leonelli"], "categories": ["stat.AP"], "doi": "", "_query": "housing price text listing", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2501.06241v1", "title": "Predicting House Rental Prices in Ghana Using Machine Learning", "abstract": "This study investigates the efficacy of machine learning models for predicting house rental prices in Ghana, addressing the need for accurate and accessible housing market information. Utilising a comprehensive dataset of rental listings, we trained and evaluated various models, including CatBoost, XGBoost, and Random Forest. CatBoost emerged as the best-performing model, achieving an $R^2$ of 0.876, demonstrating its ability to effectively capture complex relationships within the housing market. Feature importance analysis revealed that location-based features, number of bedrooms, bathrooms, and furnishing status are key drivers of rental prices. Our findings provide valuable insights for stakeholders, including real estate professionals, investors, and policymakers, while also highlighting opportunities for future research, such as incorporating temporal data and exploring regional variations.", "published": "2025-01-08T15:40:46Z", "authors": ["Philip Adzanoukpe"], "categories": ["cs.LG", "stat.AP"], "doi": "10.20944/preprints202412.1927.v1", "_query": "housing price text listing", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/1008.1376v1", "title": "House Price Distributions of Taiwan: A Preliminary Study", "abstract": "The house price distributions of Taiwan are analyzed. The tail of the cumulative distribution function (CDF) follows an approximate power law with an exponent equals to -2.4 while the distribution of the house price per unit area displays a lognormal distribution. Implications of the results are also discussed.", "published": "2010-08-08T01:23:28Z", "authors": ["Chung-I Chou", "S. P. Li"], "categories": ["physics.soc-ph", "physics.data-an"], "doi": "", "_query": "housing price text listing", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/1709.08432v1", "title": "House Price Prediction Using LSTM", "abstract": "In this paper, we use the house price data ranging from January 2004 to October 2016 to predict the average house price of November and December in 2016 for each district in Beijing, Shanghai, Guangzhou and Shenzhen. We apply Autoregressive Integrated Moving Average model to generate the baseline while LSTM networks to build prediction model. These algorithms are compared in terms of Mean Squared Error. The result shows that the LSTM model has excellent properties with respect to predict time series. Also, stateful LSTM networks and stack LSTM networks are employed to further study the improvement of accuracy of the house prediction model.", "published": "2017-09-25T11:31:50Z", "authors": ["Xiaochen Chen", "Lai Wei", "Jiaxin Xu"], "categories": ["cs.LG", "stat.ML"], "doi": "", "_query": "housing price text listing", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2112.07273v1", "title": "Urban Housing Prices and Migration's Fertility Intentions: Based on the 2018 China Migrants' Dynamic Survey", "abstract": "While the size of China's mobile population continues to expand, the fertility rate is significantly lower than the stable generation replacement level of the population, and the structural imbalance of human resource supply has attracted widespread attention. This paper uses LPM and Probit models to estimate the impact of house prices on the fertility intentions of the mobile population based on data from the 2018 National Mobile Population Dynamics Monitoring Survey. The lagged land sales price is used as an instrumental variable of house price to mitigate the potential endogeneity problem. The results show that for every 100\\% increase in the ratio of house price to household income of mobile population, the fertility intention of the female mobile population of working age at the inflow location will decrease by 4.42\\%, and the marginal effect of relative house price on labor force fertility intention is EXP(-0.222); the sensitivity of mobile population fertility intention to house price is affected by the moderating effect of infrastructure construction at the inflow location. The willingness to have children in the inflow area is higher for female migrants of working age with lower age, smaller family size and higher education. Based on the above findings, the study attempts to provide a new practical perspective for the mainline institutional change and balanced economic development in China's economic transition phase.", "published": "2021-12-14T10:09:08Z", "authors": ["Jingwen Tan", "Shixi Kang"], "categories": ["econ.GN"], "doi": "", "_query": "housing price text listing", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/1807.07155v2", "title": "Take a Look Around: Using Street View and Satellite Images to Estimate House Prices", "abstract": "When an individual purchases a home, they simultaneously purchase its structural features, its accessibility to work, and the neighborhood amenities. Some amenities, such as air quality, are measurable while others, such as the prestige or the visual impression of a neighborhood, are difficult to quantify. Despite the well-known impacts intangible housing features have on house prices, limited attention has been given to systematically quantifying these difficult to measure amenities. Two issues have led to this neglect. Not only do few quantitative methods exist that can measure the urban environment, but that the collection of such data is both costly and subjective. We show that street image and satellite image data can capture these urban qualities and improve the estimation of house prices. We propose a pipeline that uses a deep neural network model to automatically extract visual features from images to estimate house prices in London, UK. We make use of traditional housing features such as age, size, and accessibility as well as visual features from Google Street View images and Bing aerial images in estimating the house price model. We find encouraging results where learning to characterize the urban quality of a neighborhood improves house price prediction, even when generalizing to previously unseen London boroughs. We explore the use of non-linear vs. linear methods to fuse these cues with conventional models of house pricing, and show how the interpretability of linear models allows us to directly extract proxy variables for visual desirability of neighborhoods that are both of interest in their own right, and could be used as inputs to other econometric methods. This is particularly valuable as once the network has been trained with the training data, it can be applied elsewhere, allowing us to generate vivid dense maps of the visual appeal of London streets.", "published": "2018-07-18T21:10:08Z", "authors": ["Stephen Law", "Brooks Paige", "Chris Russell"], "categories": ["econ.EM", "cs.CV"], "doi": "10.1145/3342240", "_query": "housing price text listing", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2511.13200v1", "title": "A Price to Enter: Anticipatory Housing Market Sorting and Access Inequality under New York's Congestion Pricing", "abstract": "This study examines how congestion pricing shapes housing market outcomes and spatial equity in New York City. Using high-frequency sales and rental data and a combination of propensity score matching difference-in-differences, geographic regression discontinuity, and event study designs, the analysis identifies distinct short-run adjustment patterns triggered by the policy announcement. Housing prices inside the toll zone fell by about 3.3% and rents by 3%, with the sharpest declines occurring immediately after the announcement. These effects weakened over time, and price resilience emerged among premium properties, indicating early market sorting and growing segmentation. The Geo-RDD results show a clear boundary penalty, with properties just inside the cordon experiencing more pronounced declines than otherwise similar properties just outside. Renters and lower-value segments were more exposed to early adjustment pressures, while implementation-stage effects were limited. The findings suggest that congestion pricing can reshape urban space not only by altering mobility incentives but also by redistributing access and opportunity. Equity-oriented design that includes early-stage support for boundary neighborhoods and renters, along with reinvestment of revenues into untolled transit access, is important for ensuring that the benefits of congestion pricing are shared rather than concentrated.", "published": "2025-11-17T10:09:37Z", "authors": ["Mingzhi Xiao", "Yuki Takayama"], "categories": ["econ.GN"], "doi": "", "_query": "housing price text listing", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/1812.09452v1", "title": "The Price of BitCoin: GARCH Evidence from High Frequency Data", "abstract": "This is the first paper that estimates the price determinants of BitCoin in a Generalised Autoregressive Conditional Heteroscedasticity framework using high frequency data. Derived from a theoretical model, we estimate BitCoin transaction demand and speculative demand equations in a GARCH framework using hourly data for the period 2013-2018. In line with the theoretical model, our empirical results confirm that both the BitCoin transaction demand and speculative demand have a statistically significant impact on the BitCoin price formation. The BitCoin price responds negatively to the BitCoin velocity, whereas positive shocks to the BitCoin stock, interest rate and the size of the BitCoin economy exercise an upward pressure on the BitCoin price.", "published": "2018-12-22T05:25:15Z", "authors": ["Pavel Ciaian", "d'Artis Kancs", "Miroslava Rajcaniova"], "categories": ["q-fin.ST", "econ.GN"], "doi": "", "_query": "housing price text listing", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/1404.0879v1", "title": "Utility indifference pricing of derivatives written on industrial loss indexes", "abstract": "We consider the problem of pricing derivatives written on some industrial loss index via utility indifference pricing. The industrial loss index is modelled by a compound Poisson process and the insurer can adjust her portfolio by choosing the risk loading, which in turn determines the demand. We compute the price of a CAT(spread) option written on that index using utility indifference pricing.", "published": "2014-04-03T12:34:01Z", "authors": ["Gunther Leobacher", "Philip Ngare"], "categories": ["q-fin.PR", "q-fin.PM", "q-fin.RM"], "doi": "", "_query": "housing price text listing", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/1806.00037v1", "title": "MULTIGRAIN: Simulating mixtures of multiple dust grains and gas with SPH", "abstract": "We present MULTIGRAIN, an algorithm for simulating multiple phases of small dust grains embedded in a gas, building on our earlier work in simulating two-phase mixtures of gas and dust in SPH (Laibe & Price 2012a,b; Price & Laibe 2015). The MULTIGRAIN method (Hutchison, Price & Laibe 2018) is more accurate than single-phase simulations because the gas experiences a backreaction from each dust phase and communicates this change to the other phases, thereby indirectly coupling the dust phases together. The MULTIGRAIN method is fast, explicit and low storage, requiring only an array of dust fractions and their derivatives defined for each resolution element. We demonstrate the MULTIGRAIN algorithm on test problems related to the settling of dust in the discs of gas around young stars, where solar systems are born. Finally I will discuss possible extensions of the method to incorporate both large and small grains, together with recent improvements in our numerical techniques for gas and dust mixtures. In particular, I will show how the 'overdamping' problem identified by Laibe & Price (2012a) can be solved.", "published": "2018-05-31T18:22:21Z", "authors": ["Daniel J. Price", "Mark Hutchison", "Guillaume Laibe"], "categories": ["astro-ph.IM"], "doi": "", "_query": "housing price text listing", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2102.07655v2", "title": "Dense for the Price of Sparse: Improved Performance of Sparsely Initialized Networks via a Subspace Offset", "abstract": "That neural networks may be pruned to high sparsities and retain high accuracy is well established. Recent research efforts focus on pruning immediately after initialization so as to allow the computational savings afforded by sparsity to extend to the training process. In this work, we introduce a new `DCT plus Sparse' layer architecture, which maintains information propagation and trainability even with as little as 0.01% trainable kernel parameters remaining. We show that standard training of networks built with these layers, and pruned at initialization, achieves state-of-the-art accuracy for extreme sparsities on a variety of benchmark network architectures and datasets. Moreover, these results are achieved using only simple heuristics to determine the locations of the trainable parameters in the network, and thus without having to initially store or compute with the full, unpruned network, as is required by competing prune-at-initialization algorithms. Switching from standard sparse layers to DCT plus Sparse layers does not increase the storage footprint of a network and incurs only a small additional computational overhead.", "published": "2021-02-12T00:05:02Z", "authors": ["Ilan Price", "Jared Tanner"], "categories": ["cs.LG"], "doi": "", "_query": "housing price text listing", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/1112.4632v4", "title": "The Price of Matching Selfish Vertices", "abstract": "We analyze the setting of minimum-cost perfect matchings with selfish vertices through the price of anarchy (PoA) and price of stability (PoS) lens. The underlying solution concept used for this analysis is the Gale-Shapley stable matching notion, where the preferences are determined so that each player (vertex) wishes to minimize the cost of her own matching edge.", "published": "2011-12-20T09:57:00Z", "authors": ["Yuval Emek", "Tobias Langner", "Roger Wattenhofer"], "categories": ["cs.CG"], "doi": "", "_query": "housing price text listing", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2009.00254v1", "title": "Boosting House Price Predictions using Geo-Spatial Network Embedding", "abstract": "Real estate contributes significantly to all major economies around the world. In particular, house prices have a direct impact on stakeholders, ranging from house buyers to financing companies. Thus, a plethora of techniques have been developed for real estate price prediction. Most of the existing techniques rely on different house features to build a variety of prediction models to predict house prices. Perceiving the effect of spatial dependence on house prices, some later works focused on introducing spatial regression models for improving prediction performance. However, they fail to take into account the geo-spatial context of the neighborhood amenities such as how close a house is to a train station, or a highly-ranked school, or a shopping center. Such contextual information may play a vital role in users' interests in a house and thereby has a direct influence on its price. In this paper, we propose to leverage the concept of graph neural networks to capture the geo-spatial context of the neighborhood of a house. In particular, we present a novel method, the Geo-Spatial Network Embedding (GSNE), that learns the embeddings of houses and various types of Points of Interest (POIs) in the form of multipartite networks, where the houses and the POIs are represented as attributed nodes and the relationships between them as edges. Extensive experiments with a large number of regression techniques show that the embeddings produced by our proposed GSNE technique consistently and significantly improve the performance of the house price prediction task regardless of the downstream regression model.", "published": "2020-09-01T06:17:21Z", "authors": ["Sarkar Snigdha Sarathi Das", "Mohammed Eunus Ali", "Yuan-Fang Li", "Yong-Bin Kang", "Timos Sellis"], "categories": ["cs.LG", "stat.ML"], "doi": "", "_query": "housing price text listing", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2601.08957v2", "title": "The Connection Between Monetary Policy and Housing Prices: Public Perception and Expert Communication", "abstract": "We study how the general public perceives the link between monetary policy and housing markets. Using a large-scale, cross-country survey experiment in Austria, Germany, Italy, Sweden, and the United Kingdom, we examine households' understanding of monetary policy, their beliefs about its impact on house prices, and how these beliefs respond to expert information. We find that while most respondents grasp the basic mechanisms of conventional monetary policy and recognize the connection between interest rates and house prices, literacy regarding unconventional monetary policy is very low. Beliefs about the monetary policy-housing nexus are malleable and respond to information, particularly when it is provided by academic economists rather than central bankers. Monetary policy literacy is strongly related to education, gender, age, and experience in housing and mortgage markets. Our results highlight the central role of housing in how households interpret monetary policy and point to the importance of credible and inclusive communication strategies for effective policy transmission.", "published": "2026-01-13T19:51:06Z", "authors": ["Philipp Poyntner", "Sofie R. Waltl"], "categories": ["econ.GN"], "doi": "", "_query": "housing price text listing", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2405.17505v1", "title": "Predicting Rental Price of Lane Houses in Shanghai with Machine Learning Methods and Large Language Models", "abstract": "Housing has emerged as a crucial concern among young individuals residing in major cities, including Shanghai. Given the unprecedented surge in property prices in this metropolis, young people have increasingly resorted to the rental market to address their housing needs. This study utilizes five traditional machine learning methods: multiple linear regression (MLR), ridge regression (RR), lasso regression (LR), decision tree (DT), and random forest (RF), along with a Large Language Model (LLM) approach using ChatGPT, for predicting the rental prices of lane houses in Shanghai. It applies these methods to examine a public data sample of about 2,609 lane house rental transactions in 2021 in Shanghai, and then compares the results of these methods. In terms of predictive power, RF has achieved the best performance among the traditional methods. However, the LLM approach, particularly in the 10-shot scenario, shows promising results that surpass traditional methods in terms of R-Squared value. The three performance metrics: mean squared error (MSE), mean absolute error (MAE), and R-Squared, are used to evaluate the models. Our conclusion is that while traditional machine learning models offer robust techniques for rental price prediction, the integration of LLM such as ChatGPT holds significant potential for enhancing predictive accuracy.", "published": "2024-05-26T07:01:33Z", "authors": ["Tingting Chen", "Shijing Si"], "categories": ["cs.LG", "cs.CL"], "doi": "", "_query": "housing price text listing", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/1411.5497v1", "title": "Time-warped growth processes, with applications to the modeling of boom-bust cycles in house prices", "abstract": "House price increases have been steady over much of the last 40 years, but there have been occasional declines, most notably in the recent housing bust that started around 2007, on the heels of the preceding housing bubble. We introduce a novel growth model that is motivated by time-warping models in functional data analysis and includes a nonmonotone time-warping component that allows the inclusion and description of boom-bust cycles and facilitates insights into the dynamics of asset bubbles. The underlying idea is to model longitudinal growth trajectories for house prices and other phenomena, where temporal setbacks and deflation may be encountered, by decomposing such trajectories into two components. A first component corresponds to underlying steady growth driven by inflation that anchors the observed trajectories on a simple first order linear differential equation, while a second boom-bust component is implemented as time warping. Time warping is a commonly encountered phenomenon and reflects random variation along the time axis. Our approach to time warping is more general than previous approaches by admitting the inclusion of nonmonotone warping functions. The anchoring of the trajectories on an underlying linear dynamic system also makes the time-warping component identifiable and enables straightforward estimation procedures for all model components. The application to the dynamics of housing prices as observed for 19 metropolitan areas in the U.S. from December 1998 to July 2013 reveals that the time setbacks corresponding to nonmonotone time warping vary substantially across markets and we find indications that they are related to market-specific growth rates.", "published": "2014-11-20T10:42:29Z", "authors": ["Jie Peng", "Debashis Paul", "Hans-Georg Müller"], "categories": ["stat.AP"], "doi": "10.1214/14-AOAS740", "_query": "housing price text listing", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/1605.05397v4", "title": "New Insights into Rental Housing Markets across the United States: Web Scraping and Analyzing Craigslist Rental Listings", "abstract": "Current sources of data on rental housing - such as the census or commercial databases that focus on large apartment complexes - do not reflect recent market activity or the full scope of the U.S. rental market. To address this gap, we collected, cleaned, analyzed, mapped, and visualized 11 million Craigslist rental housing listings. The data reveal fine-grained spatial and temporal patterns within and across metropolitan housing markets in the U.S. We find some metropolitan areas have only single-digit percentages of listings below fair market rent. Nontraditional sources of volunteered geographic information offer planners real-time, local-scale estimates of rent and housing characteristics currently lacking in alternative sources, such as census data.", "published": "2016-05-17T23:29:58Z", "authors": ["Geoff Boeing", "Paul Waddell"], "categories": ["stat.AP", "stat.CO", "stat.ME"], "doi": "10.1177/0739456X16664789", "_query": "housing price text listing", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/1505.01164v1", "title": "Achieving a Hyperlocal Housing Price Index: Overcoming Data Sparsity by Bayesian Dynamical Modeling of Multiple Data Streams", "abstract": "Understanding how housing values evolve over time is important to policy makers, consumers and real estate professionals. Existing methods for constructing housing indices are computed at a coarse spatial granularity, such as metropolitan regions, which can mask or distort price dynamics apparent in local markets, such as neighborhoods and census tracts. A challenge in moving to estimates at, for example, the census tract level is the sparsity of spatiotemporally localized house sales observations. Our work aims at addressing this challenge by leveraging observations from multiple census tracts discovered to have correlated valuation dynamics. Our proposed Bayesian nonparametric approach builds on the framework of latent factor models to enable a flexible, data-driven method for inferring the clustering of correlated census tracts. We explore methods for scalability and parallelizability of computations, yielding a housing valuation index at the level of census tract rather than zip code, and on a monthly basis rather than quarterly. Our analysis is provided on a large Seattle metropolitan housing dataset.", "published": "2015-05-05T20:01:08Z", "authors": ["You Ren", "Emily B. Fox", "Andrew Bruce"], "categories": ["stat.AP", "stat.ME", "stat.ML"], "doi": "", "_query": "housing price text listing", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2305.08887v1", "title": "Covariate-distance Weighted Regression (CWR): A Case Study for Estimation of House Prices", "abstract": "Geographically weighted regression (GWR) is a popular tool for modeling spatial heterogeneity in a regression model. However, the current weighting function used in GWR only considers the geographical distance, while the attribute similarity is totally ignored. In this study, we proposed a covariate weighting function that combines the geographical distance and attribute distance. The covariate-distance weighted regression (CWR) is the extension of GWR including geographical distance and attribute distance. House prices are affected by numerous factors, such as house age, floor area, and land use. Prediction model is used to help understand the characteristics of regional house prices. The CWR was used to understand the relationship between the house price and controlling factors. The CWR can consider the geological and attribute distances, and produce accurate estimates of house price that preserve the weight matrix for geological and attribute distance functions. Results show that the house attributes/conditions and the characteristics of the house, such as floor area and house age, might affect the house price. After factor selection, in which only house age and floor area of a building are considered, the RMSE of the CWR model can be improved by 2.9%-26.3% for skyscrapers when compared to the GWR. CWR can effectively reduce estimation errors from traditional spatial regression models and provide novel and feasible models for spatial estimation.", "published": "2023-05-15T03:05:57Z", "authors": ["Hone-Jay Chu", "Po-Hung Chen", "Sheng-Mao Chang", "Muhammad Zeeshan Ali", "Sumriti Ranjan Patra"], "categories": ["cs.LG"], "doi": "", "_query": "housing price text listing", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2509.17367v1", "title": "Scale-free Characteristics of Multilingual Legal Texts and the Limitations of LLMs", "abstract": "We present a comparative analysis of text complexity across domains using scale-free metrics. We quantify linguistic complexity via Heaps' exponent $β$ (vocabulary growth), Taylor's exponent $α$ (word-frequency fluctuation scaling), compression rate $r$ (redundancy), and entropy. Our corpora span three domains: legal documents (statutes, cases, deeds) as a specialized domain, general natural language texts (literature, Wikipedia), and AI-generated (GPT) text. We find that legal texts exhibit slower vocabulary growth (lower $β$) and higher term consistency (higher $α$) than general texts. Within legal domain, statutory codes have the lowest $β$ and highest $α$, reflecting strict drafting conventions, while cases and deeds show higher $β$ and lower $α$. In contrast, GPT-generated text shows the statistics more aligning with general language patterns. These results demonstrate that legal texts exhibit domain-specific structures and complexities, which current generative models do not fully replicate.", "published": "2025-09-22T05:34:15Z", "authors": ["Haoyang Chen", "Kumiko Tanaka-Ishii"], "categories": ["cs.CL"], "doi": "10.1007/978-3-032-02551-7_10", "_query": "housing price text listing", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/physics/0606064v1", "title": "A Roof over your Head; House Price Peaks in the UK and Ireland", "abstract": "We analyse, following recent work of Roehner, changes in house prices for both the UK and Ireland. We conclude that prices in London have reached a tipping point and prices relative to inflation are set to fall over the next few years. If inflation does not rise then a hard landing seems likely. House prices in the Irish Republic are shown to have broken away from the moderate rise still to be found in Northern Ireland and Dublin has emerged as another global 'hot spot'. An evolution of Dublin house prices similar to that in London can be anticipated. Keywords: Econophysics, house prices, real estate, prediction PACS: 89.65.Gh, 89.90.+n", "published": "2006-06-07T14:07:28Z", "authors": ["Peter Richmond"], "categories": ["physics.soc-ph", "physics.pop-ph"], "doi": "10.1016/j.physa.2006.09.003", "_query": "housing price text listing", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/1002.0650v2", "title": "Magnetic fields and radiative feedback in the star formation process", "abstract": "Star formation is a complex process involving the interplay of many physical effects, including gravity, turbulent gas dynamics, magnetic fields and radiation. Our understanding of the process has improved substantially in recent years, primarily as a result of our increased ability to incorporate the relevant physics in numerical calculations of the star formation process. In this contribution we present an overview of our recent studies of star cluster formation in turbulent, magnetised clouds using self-gravitating radiation-magnetohydrodynamics calculations (Price and Bate 2008, 2009). Our incorporation of magnetic fields and radiative transfer into the Smoothed Particle Hydrodynamics method are discussed. We highlight how magnetic fields and radiative heating of the gas around newborn stars can solve several of the key puzzles in star formation, including an explanation for why star formation is such a slow and inefficient process. However, the presence of magnetic fields at observed strengths in collapsing protostellar cores also leads to problems on smaller scales, including a difficulty in forming protostellar discs and binary stars (Price and Bate 2007, Hennebelle and Teyssier 2008), which suggests that our understanding of the role of magnetic fields in star formation is not yet complete.", "published": "2010-02-03T05:55:54Z", "authors": ["Daniel J. Price", "Matthew R. Bate"], "categories": ["astro-ph.GA", "astro-ph.SR"], "doi": "10.1063/1.3460126", "_query": "housing price text listing", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/1310.4260v1", "title": "A Switch for Artificial Resistivity and Other Dissipation Terms", "abstract": "We describe a new switch to reduce dissipation from artificial resistivity in Smoothed Particle Magnetohydrodynamics simulations. The switch utilises the gradient of the magnetic field to detect shocks, setting alpha_B = h |gradB| / |B|. This measures the relative degree of discontinuity, and the switch is not dependent on the absolute field strength. We present results comparing the new resistivity switch to the switch of Price & Monaghan (2005), showing that it is more robust in capturing shocks (especially in weak fields), while leading to less overall dissipation. The design of this switch is generalised to create similar switches for artificial viscosity and thermal conduction, with proof of concept tests conducted on a Sod shock tube and Kelvin-Helmholtz instabilities.", "published": "2013-10-16T04:07:02Z", "authors": ["Terrence Tricco", "Daniel Price"], "categories": ["astro-ph.IM", "physics.comp-ph"], "doi": "", "_query": "housing price text listing", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2302.09438v1", "title": "Does Machine Learning Amplify Pricing Errors in the Housing Market? -- The Economics of Machine Learning Feedback Loops", "abstract": "Machine learning algorithms are increasingly employed to price or value homes for sale, properties for rent, rides for hire, and various other goods and services. Machine learning-based prices are typically generated by complex algorithms trained on historical sales data. However, displaying these prices to consumers anchors the realized sales prices, which will in turn become training samples for future iterations of the algorithms. The economic implications of this machine learning \"feedback loop\" - an indirect human-algorithm interaction - remain relatively unexplored. In this work, we develop an analytical model of machine learning feedback loops in the context of the housing market. We show that feedback loops lead machine learning algorithms to become overconfident in their own accuracy (by underestimating its error), and leads home sellers to over-rely on possibly erroneous algorithmic prices. As a consequence at the feedback loop equilibrium, sale prices can become entirely erratic (relative to true consumer preferences in absence of ML price interference). We then identify conditions (choice of ML models, seller characteristics and market characteristics) where the economic payoffs for home sellers at the feedback loop equilibrium is worse off than no machine learning. We also empirically validate primitive building blocks of our analytical model using housing market data from Zillow. We conclude by prescribing algorithmic corrective strategies to mitigate the effects of machine learning feedback loops, discuss the incentives for platforms to adopt these strategies, and discuss the role of policymakers in regulating the same.", "published": "2023-02-18T23:20:57Z", "authors": ["Nikhil Malik", "Emaad Manzoor"], "categories": ["econ.TH", "cs.CY"], "doi": "", "_query": "housing price text listing", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/1611.04989v2", "title": "Recurrent Neural Network based Part-of-Speech Tagger for Code-Mixed Social Media Text", "abstract": "This paper describes Centre for Development of Advanced Computing's (CDACM) submission to the shared task-'Tool Contest on POS tagging for Code-Mixed Indian Social Media (Facebook, Twitter, and Whatsapp) Text', collocated with ICON-2016. The shared task was to predict Part of Speech (POS) tag at word level for a given text. The code-mixed text is generated mostly on social media by multilingual users. The presence of the multilingual words, transliterations, and spelling variations make such content linguistically complex. In this paper, we propose an approach to POS tag code-mixed social media text using Recurrent Neural Network Language Model (RNN-LM) architecture. We submitted the results for Hindi-English (hi-en), Bengali-English (bn-en), and Telugu-English (te-en) code-mixed data.", "published": "2016-11-15T19:02:35Z", "authors": ["Raj Nath Patel", "Prakash B. Pimpale", "M Sasikumar"], "categories": ["cs.CL"], "doi": "", "_query": "housing price text listing", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2005.06562v1", "title": "A solution to the overdamping problem when simulating dust-gas mixtures with smoothed particle hydrodynamics", "abstract": "We present a fix to the overdamping problem found by Laibe & Price (2012) when simulating strongly coupled dust-gas mixtures using two different sets of particles using smoothed particle hydrodynamics. Our solution is to compute the drag at the barycentre between gas and dust particle pairs when computing the drag force by reconstructing the velocity field, similar to the procedure in Godunov-type solvers. This fixes the overdamping problem at negligible computational cost, but with additional memory required to store velocity derivatives. We employ slope limiters to avoid spurious oscillations at shocks, finding the van Leer Monotonized Central limiter most effective.", "published": "2020-05-13T19:56:04Z", "authors": ["Daniel J. Price", "Guillaume Laibe"], "categories": ["astro-ph.IM", "astro-ph.EP", "physics.comp-ph"], "doi": "10.1093/mnras/staa1366", "_query": "housing price text listing", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/astro-ph/0310790v1", "title": "Smoothed Particle Magnetohydrodynamics II. Variational principles and variable smoothing length terms", "abstract": "In this paper we show how a Lagrangian variational principle can be used to derive the SPMHD (smoothed particle magnetohydrodynamics) equations for ideal MHD. We also consider the effect of a variable smoothing length in the SPH kernels after which we demonstrate by numerical tests that the consistent treatment of terms relating to the gradient of the smoothing length in the SPMHD equations significantly improves the accuracy of the algorithm. Our results complement those obtained in a companion paper (Price and Monaghan 2003a, paper I) for non ideal MHD where artificial dissipative terms were included to handle shocks.", "published": "2003-10-28T12:31:58Z", "authors": ["D. J. Price", "J. J. Monaghan"], "categories": ["astro-ph"], "doi": "10.1111/j.1365-2966.2004.07346.x", "_query": "housing price text listing", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/quant-ph/9602020v1", "title": "Locality, Independence and the Pro-Liberty Bell", "abstract": "Construed as an argument against hidden variable theories, Bell's Theorem assumes that hidden variables would be independent of future measurement settings. This Independence Assumption (IA) is rarely questioned. Bell considered relaxing it to avoid non-locality, but thought that the resulting view left no room for free will. However, Bell seems to have failed to distinguish two different strategies for giving up IA. One strategy takes for granted the Principle of the Common Cause, which requires that a correlation between hidden variables and measurement settings be explained by a joint correlation with some unknown factor in their common past. The other strategy rejects the Principle of the Common Cause, and argues that the required correlation might be due to the known interaction between the object system and the measuring device in their common future. Bell and most others who have discussed these issues have focussed on the former strategy, but because the two approaches have not been properly distinguished, it has not been well appreciated that there is a quite different way to relax IA. This paper distinguishes the two strategies, and argues that the latter is considerably more appealing than the former.", "published": "1996-02-28T00:54:30Z", "authors": ["Huw Price"], "categories": ["quant-ph"], "doi": "", "_query": "housing price text listing", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2102.12112v2", "title": "Modeling Price Clustering in High-Frequency Prices", "abstract": "The price clustering phenomenon manifesting itself as an increased occurrence of specific prices is widely observed and well-documented for various financial instruments and markets. In the literature, however, it is rarely incorporated into price models. We consider that there are several types of agents trading only in specific multiples of the tick size resulting in an increased occurrence of these multiples in prices. For example, stocks on the NYSE and NASDAQ exchanges are traded with precision to one cent but multiples of five cents and ten cents occur much more often in prices. To capture this behavior, we propose a discrete price model based on a mixture of double Poisson distributions with dynamic volatility and dynamic proportions of agent types. The model is estimated by the maximum likelihood method. In an empirical study of DJIA stocks, we find that higher instantaneous volatility leads to weaker price clustering at the ultra-high frequency. This is in sharp contrast with results at low frequencies which show that daily realized volatility has a positive impact on price clustering.", "published": "2021-02-24T08:06:36Z", "authors": ["Vladimír Holý", "Petra Tomanová"], "categories": ["q-fin.ST"], "doi": "10.1080/14697688.2022.2050285", "_query": "housing price text listing", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/quant-ph/9610036v1", "title": "Time Symmetry in Microphysics", "abstract": "Physics takes for granted that interacting physical systems with no common history are independent, before their interaction. This principle is time-asymmetric, for no such restriction applies to systems with no common future, after an interaction. The time-asymmetry is normally attributed to boundary conditions. I argue that there are two distinct independence principles of this kind at work in contemporary physics, one of which cannot be attributed to boundary conditions, and therefore conflicts with the assumed T (or CPT) symmetry of microphysics. I note that this may have interesting ramifications in quantum mechanics.", "published": "1996-10-22T23:16:19Z", "authors": ["Huw Price"], "categories": ["quant-ph"], "doi": "", "_query": "housing price text listing", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2212.06986v4", "title": "Why entanglement?", "abstract": "In this piece, written for a general audience, we propose a mechanism for quantum entanglement. The key ingredient is collider bias. In the language of causal models, a collider is a variable causally influenced by two or more other variables. Conditioning on a collider typically produces non-causal correlations between its contributing causes. This phenomenon can produce associations analogous to Bell correlations, in suitable post-selected ensembles. Such collider artefacts may become real connections, resembling causality, if a collider is 'constrained' (e.g., by a future boundary condition). We consider the time-reversed analogues of these points in the context of retrocausal models of QM. Retrocausality yields a collider at the source of an EPR-Bell particle pair, and in this case constraint of the collider is possible by normal methods of experimental preparation. It follows that connections resembling causality may emerge across such colliders, from one branch of the experiment to the other. Our hypothesis is that this constrained retrocausal collider bias is the origin of entanglement. This piece is based on a suggestion first made in arXiv:2101.05370v4 [quant-ph], and is an ancestor of an essay now published online in Aeon magazine [Price & Wharton 2023a]. In an updated version of the argument in arXiv:2309.10969 [quant-ph] we (i) demonstrate its application in a real Bell experiment; and (ii) show that we can do without an explicit postulate of retrocausality", "published": "2022-12-14T02:46:22Z", "authors": ["Huw Price", "Ken Wharton"], "categories": ["quant-ph", "physics.hist-ph", "physics.pop-ph"], "doi": "", "_query": "housing price text listing", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2209.03936v2", "title": "Shift in house price estimates during COVID-19 reveals effect of crisis on collective speculation", "abstract": "We exploit a city-level panel comprised of individual house price estimates to estimate the impact of COVID-19 on both small and big real-estate markets in California USA. Descriptive analysis of spot house price estimates, including contemporaneous price uncertainty and 30-day price change for individual properties listed on the online real-estate platform Zillow.com, together facilitate quantifying both the excess valuation and valuation confidence attributable to this global socio-economic shock. Our quasi-experimental pre-/post-COVID-19 design spans several years around 2020 and leverages contemporaneous price estimates of rental properties - i.e., real estate entering the habitation market, just not for purchase (off-market) and hence free of speculation - as an appropriate counterfactual to properties listed for sale, which are subject to on-market speculation. Combining unit-level matching and multivariate difference-in-difference regression approaches, we obtain consistent estimates regarding the sign and magnitude of excess price growth observed after the pandemic onset. Specifically, our results indicate that properties listed for sale appreciated an additional 1% per month above what would be expected in the absence of the pandemic. This corresponds to an excess annual price growth of roughly 12.7 percentage points, which accounts for more than half of the actual annual price growth in 2021 observed across the studied regions. Simultaneously, uncertainty in price estimates decreased, signaling the irrational confidence characteristic of prior asset bubbles. We explore how these two trends are related to market size, local market supply and borrowing costs, which altogether lend support for the counterintuitive roles of uncertainty and interruptions in decision-making.", "published": "2022-07-21T17:47:20Z", "authors": ["Alexander M. Petersen"], "categories": ["q-fin.GN", "physics.soc-ph"], "doi": "10.1140/epjds/s13688-024-00488-9", "_query": "housing price text listing", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/1110.4119v1", "title": "Integration and Contagion in US Housing Markets", "abstract": "This paper explores integration and contagion among US metropolitan housing markets. The analysis applies Federal Housing Finance Agency (FHFA) house price repeat sales indexes from 384 metropolitan areas to estimate a multi-factor model of U.S. housing market integration. It then identifies statistical jumps in metropolitan house price returns as well as MSA contemporaneous and lagged jump correlations. Finally, the paper evaluates contagion in housing markets via parametric assessment of MSA house price spatial dynamics. A R-squared measure reveals an upward trend in MSA housing market integration over the 2000s to approximately .83 in 2010. Among California MSAs, the trend was especially pronounced, as average integration increased from about .55 in 1997 to close to .95 in 2008! The 2000s bubble period similarly was characterized by elevated incidence of statistical jumps in housing returns. Again, jump incidence and MSA jump correlations were especially high in California. Analysis of contagion among California markets indicates that house price returns in San Francisco often led those of surrounding communities; in contrast, southern California MSA house price returns appeared to move largely in lock step. The high levels of housing market integration evidenced in the analysis suggest limited investor opportunity to diversify away MSA-specific housing risk. Further, results suggest that macro and policy shocks propagate through a large number of MSA housing markets. Research findings are relevant to all market participants, including institutional investors in MBS as well as those who regulate housing, the housing GSEs, mortgage lenders, and related financial institutions.", "published": "2011-10-18T20:42:59Z", "authors": ["John Cotter", "Stuart Gabriel", "Richard Roll"], "categories": ["q-fin.GN"], "doi": "", "_query": "housing price text listing", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2302.13034v1", "title": "Does Noise Affect Housing Prices? A Case Study in the Urban Area of Thessaloniki", "abstract": "Real estate markets depend on various methods to predict housing prices, including models that have been trained on datasets of residential or commercial properties. Most studies endeavor to create more accurate machine learning models by utilizing data such as basic property characteristics as well as urban features like distances from amenities and road accessibility. Even though environmental factors like noise pollution can potentially affect prices, the research around this topic is limited. One of the reasons is the lack of data. In this paper, we reconstruct and make publicly available a general purpose noise pollution dataset based on published studies conducted by the Hellenic Ministry of Environment and Energy for the city of Thessaloniki, Greece. Then, we train ensemble machine learning models, like XGBoost, on property data for different areas of Thessaloniki to investigate the way noise influences prices through interpretability evaluation techniques. Our study provides a new noise pollution dataset that not only demonstrates the impact noise has on housing prices, but also indicates that the influence of noise on prices significantly varies among different areas of the same city.", "published": "2023-02-25T09:11:57Z", "authors": ["Georgios Kamtziridis", "Dimitris Vrakas", "Grigorios Tsoumakas"], "categories": ["cs.LG"], "doi": "", "_query": "housing price text listing", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2502.12116v3", "title": "Floods do not sink prices, historical memory does: How flood risk impacts the Italian housing market", "abstract": "Do home prices incorporate flood risk in the immediate aftermath of specific flood events, or is it the repeated exposure over the years that plays a more significant role? We address this question through the first systematic study of the Italian housing market, which is an ideal case study because it is highly exposed to floods, though unevenly distributed across the national territory. Using a novel dataset containing about 550,000 mortgage-financed transactions between 2016 and 2024, as well as hedonic regressions and a difference-in-difference design, we find that: (i) specific floods do not decrease home prices in areas at risk; (ii) the repeated exposure to floods in flood-prone areas leads to a price decline, up to 4\\% in the most frequently flooded regions; (iii) responses are heterogeneous by buyers' income and age. Young buyers (with limited exposure to prior floods) do not obtain any price reduction for settling in risky areas, while experienced buyers do. At the same time, buyers who settle in risky areas have lower incomes than buyers in safe areas in the most affected regions. Our results emphasize the importance of cultural and institutional factors in understanding how flood risk affects the housing market and socioeconomic outcomes.", "published": "2025-02-17T18:41:23Z", "authors": ["Anna Bellaver", "Lorenzo Costantini", "Ariadna Fosch", "Anna Monticelli", "David Scala", "Marco Pangallo"], "categories": ["econ.GN"], "doi": "", "_query": "housing price text listing", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/astro-ph/0310789v1", "title": "Smoothed Particle Magnetohydrodynamics I. Algorithms and tests in one dimension", "abstract": "In this paper we show how the Smoothed Particle Hydrodynamics (SPH) equations for ideal magnetohydrodynamics (MHD) can be written in conservation form with the positivity of the dissipation guaranteed. We call the resulting algorithm Smoothed Particle Magnetohydrodynamics (SPMHD). The equations appear to be accurate, robust and easy to apply and do not suffer from the instabilities known to exist previously in formulations of the SPMHD equations. In addition we formulate our MHD equations such that errors associated with non-zero divergence of the magnetic field are naturally propagated by the flow and should therefore remain small. In this and a companion paper (Price and Monaghan 2003b) we present a wide range of numerical tests in one dimension to show that the algorithm gives very good results for one dimensional flows in both adiabatic and isothermal MHD. For the one dimensional tests the field structure is either two or three dimensional. The algorithm has many astrophysical applications and is particularly suited to star formation problems.", "published": "2003-10-28T12:27:40Z", "authors": ["D. J. Price", "J. J. Monaghan"], "categories": ["astro-ph"], "doi": "10.1111/j.1365-2966.2004.07345.x", "_query": "housing price text listing", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/1306.3531v1", "title": "The convergence of regional house prices in the USA in the context of the stress testing of financial institutions", "abstract": "I studied the convergence of regional house prices to national prices in USA by analyzing time-series of house price indices of 9 Census Divisions. I found the evidence of the convergence in some parts of the country using asymmetric unit root tests. The fact that the evidence of the convergence is not present in large parts of the country raises an issue of execution and interpretation of results of Federal Reserve Bank's annual stress testing of the US banking system.", "published": "2013-06-14T23:57:54Z", "authors": ["Argyn Kuketayev"], "categories": ["q-fin.RM", "stat.AP"], "doi": "", "_query": "housing price text listing", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2405.18434v1", "title": "Modeling the Feedback of AI Price Estimations on Actual Market Values", "abstract": "Public availability of Artificial Intelligence generated information can change the markets forever, and its factoring into economical dynamics may take economists by surprise, out-dating models and schools of thought. Real estate hyper-inflation is not a new phenomenon but its consistent and almost monotonous persistence over 12 years, coinciding with prominence of public estimation information from Zillow, a successful Mass Real Estate Estimator (MREE), could not escape unobserved. What we model is a repetitive theoretical game between the MREE and the home owners, where each player has secret information and expertise. If the intention is to keep housing affordable and maintain old American lifestyle with broad home-ownership, new challenges are defined. Simulations show that a simple restriction of MREE-style price estimation availability to opt-in properties may help partially reduce feedback loop by acting on its likely causes, as suggested by experimental simulation models. The conjecture that the MREE pressure on real estate inflation rate is correlated with the absolute MREE estimation errors, which is logically explainable, is then validated in simulations.", "published": "2024-03-13T03:44:13Z", "authors": ["Viorel Silaghi", "Zobaida Alssadi", "Ben Mathew", "Majed Alotaibi", "Ali Alqarni", "Marius Silaghi"], "categories": ["econ.GN", "cs.AI", "cs.CE", "cs.CY", "cs.GT"], "doi": "", "_query": "housing price text listing", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/1610.07292v1", "title": "Population growth, interest rate, and housing tax in the transitional China", "abstract": "This paper combines and develops the models in Lastrapes (2002) and Mankiw & Weil (1989), which enables us to analyze the effects of interest rate and population growth shocks on housing price in one integrated framework. Based on this model, we carry out policy simulations to examine whether the housing (stock or flow) tax reduces the housing price fluctuations caused by interest rate or population growth shocks. Simulation results imply that the choice of housing tax tools depends on the kind of shock that housing market faces. In the situation where the housing price volatility is caused by the population growth shock, the flow tax can reduce the volatility of housing price while the stock tax makes no difference to it. If the shock is resulting from the interest rate, the policy maker should not impose any kind of the housing taxes. Furthermore, the effect of one kind of the housing tax can be strengthened by that of the other type of housing tax.", "published": "2016-10-24T06:22:26Z", "authors": ["Ling-Yun He", "Xing-Chun Wen"], "categories": ["q-fin.GN", "econ.GN"], "doi": "10.1016/j.physa.2016.11.057", "_query": "housing price text listing", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2404.05178v1", "title": "Estimating granular house price distributions in the Australian market using Gaussian mixtures", "abstract": "A new methodology is proposed to approximate the time-dependent house price distribution at a fine regional scale using Gaussian mixtures. The means, variances and weights of the mixture components are related to time, location and dwelling type through a non linear function trained by a deep functional approximator. Price indices are derived as means, medians, quantiles or other functions of the estimated distributions. Price densities for larger regions, such as a city, are calculated via a weighted sum of the component density functions. The method is applied to a data set covering all of Australia at a fine spatial and temporal resolution. In addition to enabling a detailed exploration of the data, the proposed index yields lower prediction errors in the practical task of individual dwelling price projection from previous sales values within the three major Australian cities. The estimated quantiles are also found to be well calibrated empirically, capturing the complexity of house price distributions.", "published": "2024-04-08T04:01:43Z", "authors": ["Willem P Sijp", "Anastasios Panagiotelis"], "categories": ["econ.EM"], "doi": "", "_query": "housing price text listing", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2405.07456v1", "title": "Boosting House Price Estimations with Multi-Head Gated Attention", "abstract": "Evaluating house prices is crucial for various stakeholders, including homeowners, investors, and policymakers. However, traditional spatial interpolation methods have limitations in capturing the complex spatial relationships that affect property values. To address these challenges, we have developed a new method called Multi-Head Gated Attention for spatial interpolation. Our approach builds upon attention-based interpolation models and incorporates multiple attention heads and gating mechanisms to capture spatial dependencies and contextual information better. Importantly, our model produces embeddings that reduce the dimensionality of the data, enabling simpler models like linear regression to outperform complex ensembling models. We conducted extensive experiments to compare our model with baseline methods and the original attention-based interpolation model. The results show a significant improvement in the accuracy of house price predictions, validating the effectiveness of our approach. This research advances the field of spatial interpolation and provides a robust tool for more precise house price evaluation. Our GitHub repository.contains the data and code for all datasets, which are available for researchers and practitioners interested in replicating or building upon our work.", "published": "2024-05-13T04:12:03Z", "authors": ["Zakaria Abdellah Sellam", "Cosimo Distante", "Abdelmalik Taleb-Ahmed", "Pier Luigi Mazzeo"], "categories": ["cs.LG"], "doi": "", "_query": "housing price text listing", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2201.04003v1", "title": "Housing Market Forecasting using Home Showing Events", "abstract": "Both buyers and sellers face uncertainty in real estate transactions in about when to time a transaction and at what cost. Both buyers and sellers make decisions without knowing the present and future state of the large and dynamic real estate market. Current approaches rely on analysis of historic transactions to price a property. However, as we show in this paper, the transaction data alone cannot be used to forecast demand. We develop a housing demand index based on microscopic home showings events data that can provide decision-making support for buyers and sellers on a very granular time and spatial scale. We use statistical modeling to develop a housing market demand forecast up to twenty weeks using high-volume, high-velocity data on home showings, listing events, and historic sales data. We demonstrate our analysis using data from seven million individual records sourced from a unique, proprietary dataset that has not previously been explored in application to the real estate market. We then employ a series of predictive models to estimate current and forecast future housing demand. A housing demand index provides insight into the level of demand for a home on the market and to what extent current demand represents future expectation. As a result, these indices provide decision-making support into important questions about when to sell or buy, or the elasticity present in the housing demand market, which impact price negotiations, price-taking and price-setting expectations. This forecast is especially valuable because it helps buyers and sellers to know on a granular and timely basis if they should engage in a home transaction or adjust their home price both in current and future states based on our forecasted housing demand index.", "published": "2022-01-06T21:51:54Z", "authors": ["Yuanyuan Zha", "Susan T. Parker", "James J. Foster", "Vadim Sokolov"], "categories": ["stat.AP"], "doi": "", "_query": "housing price text listing", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2012.06008v1", "title": "Price Suggestion for Online Second-hand Items with Texts and Images", "abstract": "This paper presents an intelligent price suggestion system for online second-hand listings based on their uploaded images and text descriptions. The goal of price prediction is to help sellers set effective and reasonable prices for their second-hand items with the images and text descriptions uploaded to the online platforms. Specifically, we design a multi-modal price suggestion system which takes as input the extracted visual and textual features along with some statistical item features collected from the second-hand item shopping platform to determine whether the image and text of an uploaded second-hand item are qualified for reasonable price suggestion with a binary classification model, and provide price suggestions for second-hand items with qualified images and text descriptions with a regression model. To satisfy different demands, two different constraints are added into the joint training of the classification model and the regression model. Moreover, a customized loss function is designed for optimizing the regression model to provide price suggestions for second-hand items, which can not only maximize the gain of the sellers but also facilitate the online transaction. We also derive a set of metrics to better evaluate the proposed price suggestion system. Extensive experiments on a large real-world dataset demonstrate the effectiveness of the proposed multi-modal price suggestion system.", "published": "2020-12-10T22:50:42Z", "authors": ["Liang Han", "Zhaozheng Yin", "Zhurong Xia", "Mingqian Tang", "Rong Jin"], "categories": ["cs.AI"], "doi": "", "_query": "housing price text listing", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/astro-ph/0509083v1", "title": "Smoothed Particle Magnetohydrodynamics III. Multidimensional tests and the div B = 0 constraint", "abstract": "In two previous papers (Price & Monaghan 2004a,b) (papers I,II) we have described an algorithm for solving the equations of Magnetohydrodynamics (MHD) using the Smoothed Particle Hydrodynamics (SPH) method. The algorithm uses dissipative terms in order to capture shocks and has been tested on a wide range of one dimensional problems in both adiabatic and isothermal MHD. In this paper we investigate multidimensional aspects of the algorithm, refining many of the aspects considered in papers I and II and paying particular attention to the code's ability to maintain the div B = 0 constraint associated with the magnetic field. In particular we implement a hyperbolic divergence cleaning method recently proposed by Dedner et al. (2002) in combination with the consistent formulation of the MHD equations in the presence of non-zero magnetic divergence derived in papers I and II. Various projection methods for maintaining the divergence-free condition are also examined. Finally the algorithm is tested against a wide range of multidimensional problems used to test recent grid-based MHD codes. A particular finding of these tests is that in SPMHD the magnitude of the divergence error is dependent on the number of neighbours used to calculate a particle's properties and only weakly dependent on the total number of particles. Whilst many improvements could still be made to the algorithm, our results suggest that the method is ripe for application to problems of current theoretical interest, such as that of star formation.", "published": "2005-09-05T11:45:39Z", "authors": ["D. J. Price", "J. J. Monaghan"], "categories": ["astro-ph"], "doi": "10.1111/j.1365-2966.2005.09576.x", "_query": "housing price text listing", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/1707.04868v1", "title": "Forecasting the U.S. Real House Price Index", "abstract": "The 2006 sudden and immense downturn in U.S. House Prices sparked the 2007 global financial crisis and revived the interest about forecasting such imminent threats for economic stability. In this paper we propose a novel hybrid forecasting methodology that combines the Ensemble Empirical Mode Decomposition (EEMD) from the field of signal processing with the Support Vector Regression (SVR) methodology that originates from machine learning. We test the forecasting ability of the proposed model against a Random Walk (RW) model, a Bayesian Autoregressive and a Bayesian Vector Autoregressive model. The proposed methodology outperforms all the competing models with half the error of the RW model with and without drift in out-of-sample forecasting. Finally, we argue that this new methodology can be used as an early warning system for forecasting sudden house prices drops with direct policy implications.", "published": "2017-07-16T11:36:16Z", "authors": ["Vasilios Plakandaras", "Rangan Gupta", "Periklis Gogas", "Theophilos Papadimitriou"], "categories": ["q-fin.CP"], "doi": "", "_query": "housing price text listing", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/1306.6137v1", "title": "Effects Of Zoning On Housing Option Value", "abstract": "The research explores the subject of zoning effect on price value of a house in a certain designated zone. Zoning is defined as a land under planned use. This results in price value of the land. As the area of the land decreases an increase in price occurs. This research does not specify the magnitude of a certain unit by which a housing price will increase or decrease but it estimates the housing option value. This research helps the policy makers to improve long term policy decisions. This research will showcase zoning effects on housing option value for the specific zones in the city of Normal. The observations are taken for different parcels of total neighborhoods in the city of Normal. This research will use hedonic model for conducting a regression analysis on the subject. The hypothesis taken into consideration before starting the research is to prove that more 50 percent variation in the housing price is affected by the zoning characteristics. This research will try to prove the hypothesis by comparing the assessed values with the predicted values from the model.", "published": "2013-06-26T05:43:46Z", "authors": ["Prathamesh Muzumdar"], "categories": ["stat.AP"], "doi": "", "_query": "housing price text listing", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2004.07571v1", "title": "Explaining herding and volatility in the cyclical price dynamics of urban housing markets using a large scale agent-based model", "abstract": "Urban housing markets, along with markets of other assets, universally exhibit periods of strong price increases followed by sharp corrections. The mechanisms generating such non-linearities are not yet well understood. We develop an agent-based model populated by a large number of heterogeneous households. The agents' behavior is compatible with economic rationality, with the trend-following behavior found to be essential in replicating market dynamics. The model is calibrated using several large and distributed datasets of the Greater Sydney region (demographic, economic and financial) across three specific and diverse periods since 2006. The model is not only capable of explaining price dynamics during these periods, but also reproduces the novel behavior actually observed immediately prior to the market peak in 2017, namely a sharp increase in the variability of prices. This novel behavior is related to a combination of trend-following aptitude of the household agents (rational herding) and their propensity to borrow.", "published": "2020-04-16T10:17:50Z", "authors": ["Kirill S. Glavatskiy", "Mikhail Prokopenko", "Adrian Carro", "Paul Ormerod", "Michael Harre"], "categories": ["q-fin.CP", "nlin.AO", "physics.soc-ph"], "doi": "", "_query": "housing price text listing", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/1309.5437v1", "title": "A switch to reduce resistivity in smoothed particle magnetohydrodynamics", "abstract": "Artificial resistivity is included in Smoothed Particle Magnetohydrodynamics simulations to capture shocks and discontinuities in the magnetic field. Here we present a new method for adapting the strength of the applied resistivity so that shocks are captured but the dissipation of the magnetic field away from shocks is minimised. Our scheme utilises the gradient of the magnetic field as a shock indicator, setting α_B = h|gradB|/|B|, such that resistivity is switched on only where strong discontinuities are present. The advantage to this approach is that the resistivity parameter does not depend on the absolute field strength. The new switch is benchmarked on a series of shock tube tests demonstrating its ability to capture shocks correctly. It is compared against a previous switch proposed by Price & Monaghan (2005), showing that it leads to lower dissipation of the field, and in particular, that it succeeds at capturing shocks in the regime where the Alfvén speed is much less than the sound speed (i.e., when the magnetic field is very weak). It is also simpler. We also demonstrate that our recent constrained divergence cleaning algorithm has no difficulty with shock tube tests, in contrast to other implementations.", "published": "2013-09-21T05:12:52Z", "authors": ["Terrence S. Tricco", "Daniel J. Price"], "categories": ["astro-ph.IM", "astro-ph.CO", "astro-ph.GA", "astro-ph.HE", "astro-ph.SR"], "doi": "10.1093/mnras/stt1776", "_query": "housing price text listing", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2101.02033v1", "title": "Boarding House Renting Price Prediction Using Deep Neural Network Regression on Mobile Apps", "abstract": "Boarding house is the most important requirement, especially for college students who live far away from the city, place of his origin or house. However, the problem we see now is the uneven distribution of study places in Indonesia which 75% of the best top educational institutions come from the island of Java. So, students who are looking for boarding houses rent requires more effort in comparing the various aspects desired. They need to survey one by one to the boarding house they want, even though they can survey online, it still requires more effort to pay attention to the desired facilities one by one. Therefore, we then created an Mobile Application that can predict prices based on student needs by comparing several variables, namely city, area, type of boarding house, and facilities. So, students can easily estimate the ideal price. The results of this study prove that we have succeeded in predicting prices for boarding houses rent well based on the variables we have determined, and modeling that variables using Deep Neural Network Regression.", "published": "2020-12-31T15:12:10Z", "authors": ["Malik Abdul Aziz", "Fahmi Nurrahim", "Prastyo Eko Susanto", "Yurio Windiatmoko"], "categories": ["cs.CY", "cs.LG"], "doi": "", "_query": "housing price text listing", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2510.22817v1", "title": "Wildfire and house prices: A synthetic control case study of Altadena (Jan 2025)", "abstract": "This study uses the Synthetic Control Method (SCM) to estimate the causal impact of a January 2025 wildfire on housing prices in Altadena, California. We construct a 'synthetic' Altadena from a weighted average of peer cities to serve as a counterfactual; this approach assumes no spillover effects on the donor pool. The results reveal a substantial negative price effect that intensifies over time. Over the six months following the event, we estimate an average monthly loss of $32,125. The statistical evidence for this effect is nuanced. Based on the robust post-to-pre-treatment RMSPE ratio, the result is statistically significant at the 10% level (p = 0.0508). In contrast, the effect is not statistically significant when measured by the average post-treatment gap (p = 0.3220). This analysis highlights the significant financial risks faced by communities in fire-prone regions and demonstrates SCM's effectiveness in evaluating disaster-related economic damages.", "published": "2025-10-26T20:16:28Z", "authors": ["Yibo Sun"], "categories": ["econ.GN"], "doi": "", "_query": "housing price text listing", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2609.06308v1", "title": "Housing Price Appreciation, Housing Affordability, and the Spatial Restructuring of Toronto Commuting", "abstract": "This study investigates the spatial relationships among housing-market appreciation, housing affordability, and automobile commuting to Toronto across 23 census metropolitan areas and municipalities in southern Ontario from 2016 to 2021. Utilizing Statistics Canada Census data, the analysis employed linear and quadratic ordinary least-squares regression, Global and Local Moran's I, and residual spatial diagnostics to assess geographic patterns in average dwelling values, owner shelter-cost burden, and lone-driver commuting. The study found that average dwelling values increased significantly throughout the region, maintaining a strong nonlinear relationship with distance from Toronto, which indicates a persistent metropolitan accessibility gradient. Commuter volumes also demonstrated pronounced nonlinear distance gradients; however, housing appreciation was not significantly correlated with percentage changes in commuting ($R^2$ = .045, p = .331), suggesting that the expansion of the housing market did not result in proportional growth in Toronto-bound automobile travel. The shelter-cost burden exhibited weaker distance relationships and greater residual spatial dependence, indicating localized affordability influences not fully explained by distance or dwelling values. Overall, the findings suggest an increasingly extensive regional housing market with spatially heterogeneous affordability and commuting outcomes, highlighting the importance of integrating housing, transportation, and regional planning.", "published": "2026-09-05T23:36:50Z", "authors": ["Shawn Berry"], "categories": ["econ.GN", "stat.OT"], "doi": "", "_query": "housing price text listing", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/1206.6159v2", "title": "Constrained Hyperbolic Divergence Cleaning for Smoothed Particle Magnetohydrodynamics", "abstract": "We present a constrained formulation of Dedner et al's hyperbolic/parabolic divergence cleaning scheme for enforcing the \\nabla\\dot B = 0 constraint in Smoothed Particle Magnetohydrodynamics (SPMHD) simulations. The constraint we impose is that energy removed must either be conserved or dissipated, such that the scheme is guaranteed to decrease the overall magnetic energy. This is shown to require use of conjugate numerical operators for evaluating \\nabla\\dot B and \\nablaψ in the SPMHD cleaning equations. The resulting scheme is shown to be stable at density jumps and free boundaries, in contrast to an earlier implementation by Price & Monaghan (2005). Optimal values of the damping parameter are found to be σ = 0.2-0.3 in 2D and σ = 0.8-1.2 in 3D. With these parameters, our constrained Hamiltonian formulation is found to provide an effective means of enforcing the divergence constraint in SPMHD, typically maintaining average values of h |\\nabla\\dot B| / |B| to 0.1-1%, up to an order of magnitude better than artificial resistivity without the associated dissipation in the physical field. Furthermore, when applied to realistic, 3D simulations we find an improvement of up to two orders of magnitude in momentum conservation with a corresponding improvement in numerical stability at essentially zero additional computational expense.", "published": "2012-06-27T02:56:22Z", "authors": ["Terrence S. Tricco", "Daniel J. Price"], "categories": ["astro-ph.IM", "astro-ph.GA", "astro-ph.SR", "physics.comp-ph"], "doi": "10.1016/j.jcp.2012.06.039", "_query": "housing price text listing", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2502.11521v2", "title": "Detecting Various DeFi Price Manipulations with LLM Reasoning", "abstract": "DeFi (Decentralized Finance) is one of the most important applications of today's cryptocurrencies and smart contracts. It manages hundreds of billions in Total Value Locked (TVL) on-chain, yet it remains susceptible to common DeFi price manipulation attacks. Despite state-of-the-art (SOTA) systems like DeFiRanger and DeFort, we found that they are less effective to non-standard price models in custom DeFi protocols, which account for 44.2% of the 95 DeFi price manipulation attacks reported over the past three years. In this paper, we introduce the first LLM-based approach, DeFiScope, for detecting DeFi price manipulation attacks in both standard and custom price models. Our insight is that large language models (LLMs) have certain intelligence to abstract price calculation from smart contract source code and infer the trend of token price changes based on the extracted price models. To further strengthen LLMs in this aspect, we leverage Foundry to synthesize on-chain data and use it to fine-tune a DeFi price-specific LLM. Together with the high-level DeFi operations recovered from low-level transaction data, DeFiScope detects various DeFi price manipulations according to systematically mined patterns. Experimental results show that DeFiScope achieves a high recall of 80% on real-world attacks, a precision of 96% on suspicious transactions, and zero false alarms on benign transactions, significantly outperforming SOTA approaches. Moreover, we evaluate DeFiScope's cost-effectiveness and demonstrate its practicality by helping our industry partner confirm 147 real-world price manipulation attacks, including discovering 81 previously unknown historical incidents.", "published": "2025-02-17T07:45:03Z", "authors": ["Juantao Zhong", "Daoyuan Wu", "Ye Liu", "Maoyi Xie", "Yang Liu", "Yi Li", "Ning Liu"], "categories": ["cs.CR", "cs.AI"], "doi": "", "_query": "housing price text listing", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/1903.11530v2", "title": "Market Dynamics: On Directional Information Derived From (Time, Execution Price, Shares Traded) Transaction Sequences", "abstract": "A new approach to obtaining market--directional information, based on a non-stationary solution to the dynamic equation \"future price tends to the value that maximizes the number of shares traded per unit time\" [1] is presented. In our previous work[2], we established that it is the share execution flow ($I=dV/dt$) and not the share trading volume ($V$) that is the driving force of the market, and that asset prices are much more sensitive to the execution flow $I$ (the dynamic impact) than to the traded volume $V$ (the regular impact). In this paper, an important advancement is achieved: we define the \"scalp-price\" ${\\cal P}$ as the sum of only those price moves that are relevant to market dynamics; the criterion of relevance is a high $I$. Thus, only \"follow the market\" (and not \"little bounce\") events are included in ${\\cal P}$. Changes in the scalp-price defined this way indicate a market trend change - not a bear market rally or a bull market sell-off; the approach can be further extended to non-local price change. The software calculating the scalp--price given market observations triples (time, execution price, shares traded) is available from the authors.", "published": "2019-03-27T16:29:04Z", "authors": ["Vladislav Gennadievich Malyshkin"], "categories": ["q-fin.TR", "q-fin.CP"], "doi": "", "_query": "housing price text listing", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2001.06365v1", "title": "Exhaustive List of Topological Hourglass Band Crossings in 230 Space Groups", "abstract": "Topological semimetals with band crossings (BCs) near the Fermi level have attracted intense research activities in the past several years. Among various BCs, those enforced by an hourglass-like connectivity pattern, which are just located at the vertex in the neck of an hourglass and thus called hourglass BCs (HBCs), show interesting topological properties and are intimately related with the space group symmetry. Through checking compatibility relations in the Brillouin zone (BZ), we list all possible HBCs for all 230 space groups by identifying positions of HBCs as well as the compatibility relations related with the HBCs.The HBCs can be coexisting with conventional topological BCs such as Dirac andWeyl fermions and based on our exhaustive list, the dimensionality and degeneracy of the HBCs can be quickly identified. It is also found that the HBCs can be classified into two categories: one contains essential HBCs which are guaranteed to exist, while the HBCs in the other category may be tuned to disappear. Our results can help in efficiently predicting hourglass semimetals combined with first-principles calculations as well as studying transitions among various topological crystalline phases.", "published": "2020-01-17T15:17:58Z", "authors": ["Lin Wu", "Feng Tang", "Xiangang Wan"], "categories": ["cond-mat.mtrl-sci"], "doi": "10.1103/PhysRevB.102.035106", "_query": "housing price text listing", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/1303.2915v2", "title": "Modeling US housing prices by spatial dynamic structural equation models", "abstract": "This article proposes a spatial dynamic structural equation model for the analysis of housing prices at the State level in the USA. The study contributes to the existing literature by extending the use of dynamic factor models to the econometric analysis of multivariate lattice data. One of the main advantages of our model formulation is that by modeling the spatial variation via spatially structured factor loadings, we entertain the possibility of identifying similarity \"regions\" that share common time series components. The factor loadings are modeled as conditionally independent multivariate Gaussian Markov Random Fields, while the common components are modeled by latent dynamic factors. The general model is proposed in a state-space formulation where both stationary and nonstationary autoregressive distributed-lag processes for the latent factors are considered. For the latent factors which exhibit a common trend, and hence are cointegrated, an error correction specification of the (vector) autoregressive distributed-lag process is proposed. Full probabilistic inference for the model parameters is facilitated by adapting standard Markov chain Monte Carlo (MCMC) algorithms for dynamic linear models to our model formulation. The fit of the model is discussed for a data set of 48 States for which we model the relationship between housing prices and the macroeconomy, using State level unemployment and per capita personal income.", "published": "2013-03-12T15:23:05Z", "authors": ["Pasquale Valentini", "Luigi Ippoliti", "Lara Fontanella"], "categories": ["stat.AP"], "doi": "10.1214/12-AOAS613", "_query": "housing price text listing", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2404.05543v1", "title": "Optimal Allocation of Tasks and Price of Anarchy of Distributed Optimization in Networked Computing Facilities", "abstract": "The allocation of computing tasks for networked distributed services poses a question to service providers on whether centralized allocation management be worth its cost. Existing analytical models were conceived for users accessing computing resources with practically indistinguishable (hence irrelevant for the allocation decision) delays, which is typical of services located in the same distant data center. However, with the rise of the edge-cloud continuum, a simple analysis of the sojourn time that computing tasks observe at the server misses the impact of diverse latency values imposed by server locations. We therefore study the optimization of computing task allocation with a new model that considers both distance of servers and sojourn time in servers. We derive exact algorithms to optimize the system and we show, through numerical analysis and real experiments, that differences in server location in the edge-cloud continuum cannot be neglected. By means of algorithmic game theory, we study the price of anarchy of a distributed implementation of the computing task allocation problem and unveil important practical properties such as the fact that the price of anarchy tends to be small -- except when the system is overloaded -- and its maximum can be computed with low complexity.", "published": "2024-04-08T14:10:14Z", "authors": ["Vincenzo Mancuso", "Paolo Castagno", "Leonardo Badia", "Matteo Sereno", "Marco Ajmone Marsan"], "categories": ["cs.GT", "cs.DC", "cs.PF"], "doi": "", "_query": "housing price text listing", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/1511.01207v2", "title": "Trajectory based models. Evaluation of minmax pricing bounds", "abstract": "The paper studies sub and super-replication price bounds for contingent claims defined on general trajectory based market models. No prior probabilistic or topological assumptions are placed on the trajectory space, trading is assumed to take place at a finite number of occasions but not bounded in number nor necessarily equally spaced in time. For a given option, there exists an interval bounding the set of possible fair prices; such interval exists under more general conditions than the usual no-arbitrage requirement. The paper develops a backward recursive method to evaluate the option bounds; the global minmax optimization, defining the price interval, is reduced to a local minmax optimization via dynamic programming. Trajectory sets are introduced for which existing non-probabilistic markets models are nested as a particular case. Several examples are presented, the effect of the presence of arbitrage on the price bounds is illustrated.", "published": "2015-11-04T04:47:01Z", "authors": ["Ivan Degano", "Sebastian Ferrando", "Alfredo Gonzalez"], "categories": ["q-fin.MF"], "doi": "", "_query": "housing price text listing", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/1807.04516v1", "title": "A Bayesian Nonparametric Approach to Geographic Regression Discontinuity Designs: Do School Districts Affect NYC House Prices?", "abstract": "Most research on regression discontinuity designs (RDDs) has focused on univariate cases, where only those units with a \"forcing\" variable on one side of a threshold value receive a treatment. Geographical regression discontinuity designs (GeoRDDs) extend the RDD to multivariate settings with spatial forcing variables. We propose a framework for analysing GeoRDDs, which we implement using Gaussian process regression. This yields a Bayesian posterior distribution of the treatment effect at every point along the border. We address nuances of having a functional estimand defind on a border with potentially intricate topology, particularly when defining and estimating causal estimands of the local average treatment effect (LATE). The Bayesian estimate of the LATE can also be used as a test statistic in a hypothesis test with good frequentist properties, which we validate using simulations and placebo tests. We demonstrate our methodology with a dataset of property sales in New York City, to assess whether there is a discontinuity in housing prices at the border between two school district. We find a statistically significant difference in price across the border between the districts with $p$=0.002, and estimate a 20% higher price on average for a house on the more desirable side.", "published": "2018-07-11T09:57:29Z", "authors": ["Maxime Rischard", "Zach Branson", "Luke Miratrix", "Luke Bornn"], "categories": ["stat.AP"], "doi": "", "_query": "housing price text listing", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2607.17633v1", "title": "Social Integration and Housing Behaviours of Immigrants: Evidence from Singapore's Public Housing Market", "abstract": "This study investigates the impact of social integration on immigrants' housing behaviours from a temporal perspective, using Singapore's differential public housing policies on immigrants as a quasi-natural experiment. With the support of a local town council, we conducted a survey on social integration among 1,128 immigrant and local households living in public housing estates. In the public open rental housing market - primarily accommodating yet-to-integrate immigrants - we find immigrant renters live up to 3.04% farther from their workplace and pay lower rents up to 0.67% per additional year of residency. Such impacts are more substantial among minority ethnic groups. The results remain robust when using alternative subjective or objective measures of social integration. However, in the public resale housing market - primarily accommodating native and well-integrated naturalised citizens - we find that naturalised citizens face no price premiums relative to native homebuyers, implying no further effect of integration on housing prices after well-integration. This study extends the literature of spatial assimilation focusing on ethnic residential segregations and is generalizable to cities with few ethnic enclaves.", "published": "2026-07-20T07:40:03Z", "authors": ["Yi Fan", "Ho Pin Teo", "Yong Tu", "Wayne Xinwei Wan"], "categories": ["econ.GN"], "doi": "10.1016/j.cities.2026.107220", "_query": "housing price text listing", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2202.04358v1", "title": "House Price Valuation Model Based on Geographically Neural Network Weighted Regression: The Case Study of Shenzhen, China", "abstract": "Confronted with the spatial heterogeneity of real estate market, some traditional research utilized Geographically Weighted Regression (GWR) to estimate the house price. However, its kernel function is non-linear, elusive, and complex to opt bandwidth, the predictive power could also be improved. Consequently, a novel technique, Geographical Neural Network Weighted Regression (GNNWR), has been applied to improve the accuracy of real estate appraisal with the help of neural networks. Based on Shenzhen house price dataset, this work conspicuously captures the weight distribution of different variants at Shenzhen real estate market, which GWR is difficult to materialize. Moreover, we focus on the performance of GNNWR, verify its robustness and superiority, refine the experiment process with 10-fold cross-validation, extend its application area from natural to socioeconomic geospatial data. It's a practical and trenchant way to assess house price, and we demonstrate the effectiveness of GNNWR on a complex socioeconomic dataset.", "published": "2022-02-09T09:40:54Z", "authors": ["Zimo Wang", "Yicheng Wang", "Sensen Wu"], "categories": ["stat.AP", "cs.LG"], "doi": "", "_query": "housing price text listing", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2112.04906v3", "title": "Enhancing Column Generation by a Machine-Learning-Based Pricing Heuristic for Graph Coloring", "abstract": "Column Generation (CG) is an effective method for solving large-scale optimization problems. CG starts by solving a sub-problem with a subset of columns (i.e., variables) and gradually includes new columns that can improve the solution of the current subproblem. The new columns are generated as needed by repeatedly solving a pricing problem, which is often NP-hard and is a bottleneck of the CG approach. To tackle this, we propose a Machine-Learning-based Pricing Heuristic (MLPH)that can generate many high-quality columns efficiently. In each iteration of CG, our MLPH leverages an ML model to predict the optimal solution of the pricing problem, which is then used to guide a sampling method to efficiently generate multiple high-quality columns. Using the graph coloring problem, we empirically show that MLPH significantly enhancesCG as compared to six state-of-the-art methods, and the improvement in CG can lead to substantially better performance of the branch-and-price exact method.", "published": "2021-12-08T03:58:25Z", "authors": ["Yunzhuang Shen", "Yuan Sun", "Xiaodong Li", "Andrew Eberhard", "Andreas Ernst"], "categories": ["math.OC", "cs.AI", "cs.LG"], "doi": "", "_query": "housing price text listing", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2503.15046v3", "title": "Refined enumeration of planar Eulerian orientations", "abstract": "We address the enumeration of Eulerian orientations of 4-valent planar maps according to three parameters: the number of vertices, the number of alternating vertices (having in/out/in/out incident edges), and the number of clockwise oriented faces. This is a refinement of the six vertex model studied by Kostov, then Zinn-Justin and Elvey Price, where one only considers the first two parameters. Via a bijection of Ambjorn and Budd, our problem is equivalent to the enumeration of Eulerian partial orientations of general planar maps, counted by the number of edges, the number of undirected edges, and the number of vertices. We first derive from combinatorial arguments a system of functional equations characterising the associated trivariate series $Q(t,ω,v)$. We then derive from this system a compact characterisation of this series. We use it to determine $Q(t,ω,v)$ in three two-parameter cases. The first two cases correspond to setting the variable $ω$ counting alternating vertices (or undirected edges after the AB bijection) to $0$ or $1$: when $ω=0$ we count Eulerian orientations of general planar maps by edges and vertices, and when $ω=1$ we count Eulerian orientations of quartic maps by vertices and clockwise faces. The final forms of these two series, namely $Q(t,0, v)$ and $Q(t,1,v)$, refine those obtained by the authors in an earlier paper for $v=1$. The third case that we solve, namely $v=1$ (but $ω$ arbitrary), is the standard six-vertex model, for which we provide a new proof of the formula of Elvey Price and Zinn-Justin involving Jacobi theta functions. This new derivation remains purely in the world of formal power series, not relying on complex analysis. Our results also use a more direct approach to solving the functional equations, in contrast to the guess and check approaches used in previous work.", "published": "2025-03-19T09:33:45Z", "authors": ["Mireille Bousquet-Mélou", "Andrew Elvey Price"], "categories": ["math.CO"], "doi": "", "_query": "housing price text listing", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2407.10176v1", "title": "On shock capturing in smoothed particle hydrodynamics", "abstract": "For the past 20 years, our approach to shock capturing in smoothed particle hydrodynamics (SPH) has been to use artificial viscosity and conductivity terms supplemented by switches to control excess dissipation away from shocks (Monaghan 1997; Morris & Monaghan 1997). This approach has been demonstrated to be superior to approximate Riemann solvers in a recent comparison (Puri & Ramachandran 2014). The Cullen & Dehnen (2010) switch is regarded as the state of the art. But are we missing something? I will present a novel approach to shock capturing in SPH that utilises the philosophy of approximate Riemann solvers but provides a direct improvement on the ability to reduce excess dissipation away from shocks while preserving the fidelity of the shock itself.", "published": "2024-07-14T12:15:41Z", "authors": ["Daniel J. Price"], "categories": ["astro-ph.IM"], "doi": "", "_query": "housing price text listing", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/1204.1515v1", "title": "Natural selection. IV. The Price equation", "abstract": "The Price equation partitions total evolutionary change into two components. The first component provides an abstract expression of natural selection. The second component subsumes all other evolutionary processes, including changes during transmission. The natural selection component is often used in applications. Those applications attract widespread interest for their simplicity of expression and ease of interpretation. Those same applications attract widespread criticism by dropping the second component of evolutionary change and by leaving unspecified the detailed assumptions needed for a complete study of dynamics. Controversies over approximation and dynamics have nothing to do with the Price equation itself, which is simply a mathematical equivalence relation for total evolutionary change expressed in an alternative form. Disagreements about approach have to do with the tension between the relative valuation of abstract versus concrete analyses. The Price equation's greatest value has been on the abstract side, particularly the invariance relations that illuminate the understanding of natural selection. Those abstract insights lay the foundation for applications in terms of kin selection, information theory interpretations of natural selection, and partitions of causes by path analysis. I discuss recent critiques of the Price equation by Nowak and van Veelen.", "published": "2012-04-06T17:02:46Z", "authors": ["Steven A. Frank"], "categories": ["q-bio.PE"], "doi": "10.1111/j.1420-9101.2012.02498.x", "_query": "housing price text listing", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2505.16287v1", "title": "Machine learning approach to stock price crash risk", "abstract": "In this study, we propose a novel machine-learning-based measure for stock price crash risk, utilizing the minimum covariance determinant methodology. Employing this newly introduced dependent variable, we predict stock price crash risk through cross-sectional regression analysis. The findings confirm that the proposed method effectively captures stock price crash risk, with the model demonstrating strong performance in terms of both statistical significance and economic relevance. Furthermore, leveraging a newly developed firm-specific investor sentiment index, the analysis identifies a positive correlation between stock price crash risk and firm-specific investor sentiment. Specifically, higher levels of sentiment are associated with an increased likelihood of stock price crash risk. This relationship remains robust across different firm sizes and when using the detoned version of the firm-specific investor sentiment index, further validating the reliability of the proposed approach.", "published": "2025-05-22T06:33:35Z", "authors": ["Abdullah Karasan", "Ozge Sezgin Alp", "Gerhard-Wilhelm Weber"], "categories": ["q-fin.CP"], "doi": "10.1007/s10479-025-06596-7", "_query": "housing price text listing", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/1201.6429v2", "title": "Bounding the inefficiency of outcomes in generalized second price auctions", "abstract": "The Generalized Second Price (GSP) auction is the primary auction used for monetizing the use of the Internet. It is well-known that truthtelling is not a dominant strategy in this auction and that inefficient equilibria can arise. In this paper we study the space of equilibria in GSP, and quantify the efficiency loss that can arise in equilibria under a wide range of sources of uncertainty, as well as in the full information setting. The traditional Bayesian game models uncertainty in the valuations (types) of the participants. The Generalized Second Price (GSP) auction gives rise to a further form of uncertainty: the selection of quality factors resulting in uncertainty about the behavior of the underlying ad allocation algorithm. The bounds we obtain apply to both forms of uncertainty, and are robust in the sense that they apply under various perturbations of the solution concept, extending to models with information asymmetries and bounded rationality in the form of learning strategies. We present a constant bound (2.927) on the factor of the efficiency loss (\\emph{price of anarchy}) of the corresponding game for the Bayesian model of partial information about other participants and about ad quality factors. For the full information setting, we prove a surprisingly low upper bound of 1.282 on the price of anarchy over pure Nash equilibria, nearly matching a lower bound of 1.259 for the case of three advertisers. Further, we do not require that the system reaches equilibrium, and give similarly low bounds also on the quality degradation for any no-regret learning outcome. Our conclusion is that the number of advertisers in the auction has almost no impact on the price of anarchy, and that the efficiency of GSP is very robust with respect to the belief and rationality assumptions imposed on the participants.", "published": "2012-01-31T03:36:26Z", "authors": ["Ioannis Caragiannis", "Christos Kaklamanis", "Panagiotis Kanellopoulos", "Maria Kyropoulou", "Brendan Lucier", "Renato Paes Leme", "Éva Tardos"], "categories": ["cs.GT"], "doi": "", "_query": "housing price text listing", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2107.07148v1", "title": "What Image Features Boost Housing Market Predictions?", "abstract": "The attractiveness of a property is one of the most interesting, yet challenging, categories to model. Image characteristics are used to describe certain attributes, and to examine the influence of visual factors on the price or timeframe of the listing. In this paper, we propose a set of techniques for the extraction of visual features for efficient numerical inclusion in modern-day predictive algorithms. We discuss techniques such as Shannon's entropy, calculating the center of gravity, employing image segmentation, and using Convolutional Neural Networks. After comparing these techniques as applied to a set of property-related images (indoor, outdoor, and satellite), we conclude the following: (i) the entropy is the most efficient single-digit visual measure for housing price prediction; (ii) image segmentation is the most important visual feature for the prediction of housing lifespan; and (iii) deep image features can be used to quantify interior characteristics and contribute to captivation modeling. The set of 40 image features selected here carries a significant amount of predictive power and outperforms some of the strongest metadata predictors. Without any need to replace a human expert in a real-estate appraisal process, we conclude that the techniques presented in this paper can efficiently describe visible characteristics, thus introducing perceived attractiveness as a quantitative measure into the predictive modeling of housing.", "published": "2021-07-15T06:32:10Z", "authors": ["Zona Kostic", "Aleksandar Jevremovic"], "categories": ["cs.CV", "cs.LG"], "doi": "10.1109/TMM.2020.2966890", "_query": "housing price text listing", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2508.03156v1", "title": "Unveiling Location-Specific Price Drivers: A Two-Stage Cluster Analysis for Interpretable House Price Predictions", "abstract": "House price valuation remains challenging due to localized market variations. Existing approaches often rely on black-box machine learning models, which lack interpretability, or simplistic methods like linear regression (LR), which fail to capture market heterogeneity. To address this, we propose a machine learning approach that applies two-stage clustering, first grouping properties based on minimal location-based features before incorporating additional features. Each cluster is then modeled using either LR or a generalized additive model (GAM), balancing predictive performance with interpretability. Constructing and evaluating our models on 43,309 German house property listings from 2023, we achieve a 36% improvement for the GAM and 58% for LR in mean absolute error compared to models without clustering. Additionally, graphical analyses unveil pattern shifts between clusters. These findings emphasize the importance of cluster-specific insights, enhancing interpretability and offering practical value for buyers, sellers, and real estate analysts seeking more reliable property valuations.", "published": "2025-08-05T07:00:42Z", "authors": ["Paul Gümmer", "Julian Rosenberger", "Mathias Kraus", "Patrick Zschech", "Nico Hambauer"], "categories": ["cs.LG"], "doi": "", "_query": "housing price text listing", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2006.10092v1", "title": "Housing Market Prediction Problem using Different Machine Learning Algorithms: A Case Study", "abstract": "Developing an accurate prediction model for housing prices is always needed for socio-economic development and well-being of citizens. In this paper, a diverse set of machine learning algorithms such as XGBoost, CatBoost, Random Forest, Lasso, Voting Regressor, and others, are being employed to predict the housing prices using public available datasets. The housing datasets of 62,723 records from January 2015 to November 2019 are obtained from Florida Volusia County Property Appraiser website. The records are publicly available and include the real estate or economic database, maps, and other associated information. The database is usually updated weekly according to the State of Florida regulations. Then, the housing price prediction models using machine learning techniques are developed and their regression model performances are compared. Finally, an improved housing price prediction model for assisting the housing market is proposed. Particularly, a house seller or buyer, or a real estate broker can get insight in making better-informed decisions considering the housing price prediction. The empirical results illustrate that based on prediction model performance, Coefficient of Determination (R2), Mean Square Error (MSE), Mean Absolute Error (MAE), and computational time, the XGBoost algorithm performs superior to the other models to predict the housing price.", "published": "2020-06-17T18:16:24Z", "authors": ["Shashi Bhushan Jha", "Radu F. Babiceanu", "Vijay Pandey", "Rajesh Kumar Jha"], "categories": ["cs.LG", "stat.ML"], "doi": "", "_query": "housing price text listing", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/1610.04805v1", "title": "Beyond Spatial Auto-Regressive Models: Predicting Housing Prices with Satellite Imagery", "abstract": "When modeling geo-spatial data, it is critical to capture spatial correlations for achieving high accuracy. Spatial Auto-Regression (SAR) is a common tool used to model such data, where the spatial contiguity matrix (W) encodes the spatial correlations. However, the efficacy of SAR is limited by two factors. First, it depends on the choice of contiguity matrix, which is typically not learnt from data, but instead, is assumed to be known apriori. Second, it assumes that the observations can be explained by linear models. In this paper, we propose a Convolutional Neural Network (CNN) framework to model geo-spatial data (specifi- cally housing prices), to learn the spatial correlations automatically. We show that neighborhood information embedded in satellite imagery can be leveraged to achieve the desired spatial smoothing. An additional upside of our framework is the relaxation of linear assumption on the data. Specific challenges we tackle while implementing our framework include, (i) how much of the neighborhood is relevant while estimating housing prices? (ii) what is the right approach to capture multiple resolutions of satellite imagery? and (iii) what other data-sources can help improve the estimation of spatial correlations? We demonstrate a marked improvement of 57% on top of the SAR baseline through the use of features from deep neural networks for the cities of London, Birmingham and Liverpool.", "published": "2016-10-16T01:17:19Z", "authors": ["Archith J. Bency", "Swati Rallapalli", "Raghu K. Ganti", "Mudhakar Srivatsa", "B. S. Manjunath"], "categories": ["cs.CV"], "doi": "", "_query": "housing price text listing", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2208.01969v7", "title": "Regulation and Frontier Housing Supply", "abstract": "Regulation is a major driver of housing supply, yet often difficult to observe directly. We show that frontier cost, the non-land cost of producing housing absent regulation, is identified from prices and quantities alone, without instruments even when quantity is endogenous in a mean regression. Identification requires regulation to enter as a nonnegative wedge with zero in its conditional support. The difference between price and frontier cost yields the regulatory tax. We apply the approach to new multi-floor, multi-family residential construction in Israel. Accounting for random housing quality, we estimate economies of scale at low heights (minimum efficient scale about five floors), nearly constant marginal cost at middle heights, and an elasticity of substitution between land and non-land inputs of about 0.15 to 0.2 at the greatest heights. The estimated mean regulatory tax is 47% of housing prices, with substantial variation across locations, and is positively correlated with centrality, density, and prices. We also construct a lower bound allowing quality to differ systematically over location and time, assuming weak complementarity between quality and demand. In 2017, when prices were highest in our sample and the bound is most informative, we bound the mean regulatory tax between 38% (using a 2km radius) and 53%.", "published": "2022-08-03T10:45:41Z", "authors": ["Dan Ben-Moshe", "David Genesove"], "categories": ["econ.GN"], "doi": "", "_query": "housing price text listing", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/1803.08265v2", "title": "The generating function of planar Eulerian orientations", "abstract": "The enumeration of planar maps equipped with an Eulerian orientation has attracted attention in both combinatorics and theoretical physics since at least 2000. The case of 4-valent maps is particularly interesting: these orientations are in bijection with properly 3-coloured quadrangulations, while in physics they correspond to configurations of the ice model. We solve both problems -- namely the enumeration of planar Eulerian orientations and of 4-valent planar Eulerian orientations -- by expressing the associated generating functions as the inverses (for the composition of series) of simple hypergeometric series. Using these expressions, we derive the asymptotic behaviour of the number of planar Eulerian orientations, thus proving earlier predictions of Kostov, Zinn-Justin, Elvey Price and Guttmann. This behaviour, $μ^n /(n \\log n)^2$, prevents the associated generating functions from being D-finite. Still, these generating functions are differentially algebraic, as they satisfy non-linear differential equations of order $2$. Differential algebraicity has recently been proved for other map problems, in particular for maps equipped with a Potts model. Our solutions mix recursive and bijective ingredients. In particular, a preliminary bijection transforms our oriented maps into maps carrying a height function on their vertices. In the 4-valent case, we also observe an unexpected connection with the enumeration of maps equipped with a spanning tree that is internally inactive in the sense of Tutte. This connection remains to be explained combinatorially.", "published": "2018-03-22T08:39:15Z", "authors": ["Mireille Bousquet-Mélou", "Andrew Elvey Price"], "categories": ["math.CO"], "doi": "10.1016/j.jcta.2019.105183", "_query": "housing price text listing", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2609.17398v1", "title": "Enhancing Accessibility of Medical Texts through Large Language Model-Driven Plain Language Adaptation", "abstract": "This paper addresses the challenge of making complex healthcare information more accessible through automated Plain Language Adaptation (PLA). PLA aims to simplify technical medical language, bridging a critical gap between the complexity of healthcare texts and patients' reading comprehension. Recent advances in Large Language Models (LLMs), such as GPT and BART, have opened new possibilities for PLA, especially in zero-shot and few-shot learning contexts where task-specific data is limited. In this work, we leverage the capabilities of LLMs such as GPT-4o-mini, Gemini-1.5-pro, and LLaMA for text simplification. Additionally, we incorporate Mixture-of-Agents (MoA) techniques to enhance adaptability and robustness in PLA tasks. Key contributions include a comparative analysis of prompting strategies, finetuning with QLoRA on different LLMs, and the integration of MoA technique. Our findings demonstrate the effectiveness of LLM-driven PLA, showcasing its potential in making healthcare information more comprehensible while preserving essential content.", "published": "2026-09-15T16:33:38Z", "authors": ["Ting-Wei Chang", "Hen-Hsen Huang", "Hsin-Hsi Chen"], "categories": ["cs.CL"], "doi": "10.6028/nist.sp.1329plaba-ntu_nlp", "_query": "housing price text listing", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/1202.4325v1", "title": "Scalar curvature and vector bundles", "abstract": "In the first part we use Gromov's K--area to define the K--area homology which stabilizes into singular homology on the category of pairs of compact smooth manifolds. The second part treats the questions of certain curvature gaps. For instance, the $L^\\infty $--curvature gap of complex vector bundles on a compact manifold is positive if and only if the K--area homology coincides with the reduced singular homology in all even degrees. In the third part we give some upper bounds of the scalar curvature on compact manifolds. In particular, we generalize results by Llarull and Goette, Semmelmann.", "published": "2012-02-20T13:52:45Z", "authors": ["Mario Listing"], "categories": ["math.DG"], "doi": "", "_query": "housing price text listing", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2407.15905v1", "title": "A divide-and-conquer approach for spatio-temporal analysis of large house price data from Greater London", "abstract": "Statistical research in real estate markets, particularly in understanding the spatio-temporal dynamics of house prices, has garnered significant attention in recent times. Although Bayesian methods are common in spatio-temporal modeling, standard Markov chain Monte Carlo (MCMC) techniques are usually slow for large datasets such as house price data. To tackle this problem, we propose a divide-and-conquer spatio-temporal modeling approach. This method involves partitioning the data into multiple subsets and applying an appropriate Gaussian process model to each subset in parallel. The results from each subset are then combined using the Wasserstein barycenter technique to obtain the global parameters for the original problem. The proposed methodology allows for multiple observations per spatial and time unit, thereby offering added benefits for practitioners. As a real-life application, we analyze house price data of more than 0.6 million transactions from 983 middle layer super output areas in London over a period of eight years. The methodology provides insightful findings about the effects of various amenities, trend patterns, and the relationship between prices and carbon emissions. Furthermore, as demonstrated through a cross-validation study, it shows good predictive accuracy while balancing computational efficiency.", "published": "2024-07-22T14:29:13Z", "authors": ["Kapil Gupta", "Soudeep Deb"], "categories": ["stat.AP"], "doi": "", "_query": "housing price text listing", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2606.09564v4", "title": "Option prices from operational-time reaction-boundary lattices", "abstract": "We consider the role of a continuum operational time $u$, its mapping to calendar time $t$, and their relation to event time in option-pricing problems. We derive option-pricing equations from an operational-time Markov lattice rather than from a calendar-time diffusion. The primitive model is a homogeneous nearest-neighbour log-price lattice; state-dependent local variance is represented through its general local-kernel extension. Its Chapman--Kolmogorov decomposition yields discrete forward and backward equations. In price variables, the backward equation gives a generalized European pricing PDE and reduces to Black--Scholes--Merton under the risk-neutral drift restriction and constant volatility. Interpreted as a reaction boundary, the lattice gives a structural route from boundary variance to projected local volatility. The key point is the separation of the operational kernel, the calendar-time clock projection, and the pricing-measure choice. Within the deterministic one-factor local-volatility class, an option surface identifies the projected clock--variance combination rather than its operational variance and clock components separately. The resulting hierarchy also distinguishes full calendar-generator equivalence from weaker terminal or continuum matching, and clarifies why incompleteness concerns unspanned clock, jump, or renewal risks rather than random time alone.", "published": "2026-06-08T14:38:58Z", "authors": ["Chris Angstmann", "Tim Gebbie"], "categories": ["q-fin.PR", "q-fin.MF", "q-fin.TR"], "doi": "", "_query": "housing price text listing", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2609.01628v1", "title": "Marginal Expected Revenue for Jointly Ranking Auction and Fixed-Price Listings in E-Commerce Sponsored Search", "abstract": "E-commerce search ranking must balance multiple objectives--relevance, user engagement, and platform revenue--when allocating impression slots to competing listings. Estimating the expected revenue component is well understood for fixed-price items, but becomes challenging when marketplace inventory includes mixed listing formats such as pure auctions and hybrid \"Auction with Buy It Now\" (ABIN) items, where prices evolve dynamically and the final transaction value is unknown at ranking time. Yet auction and ABIN listings account for a meaningful share of inventory and transaction volume on platforms such as eBay, and are a popular format for individual sellers and for unique items with unclear value. We extend the standard Expected Cost-per-Mille (eCPM) framework to auction and ABIN listings by deriving a marginal eCPM (meCPM) that captures the incremental value of showing one more impression of an item whose price is still evolving. The resulting formulation extends the familiar fixed-price eCPM--which is already inherently marginal--to auction dynamics, allowing unified ranking of fixed-price, auction, and ABIN listings under a single objective. We then describe a practical production implementation that approximates this objective, addressing cold-start challenges by bootstrapping from existing engagement models. Online A/B tests at a large e-commerce platform showed positive revenue gains and statistically significant improvements to user metrics, and the system was deployed to production.", "published": "2026-08-08T17:15:34Z", "authors": ["Greg Kocher", "Sanjana Arun"], "categories": ["cs.IR", "cs.LG"], "doi": "", "_query": "housing price text listing", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2303.18161v2", "title": "Nash equilibria for relative investors with (non)linear price impact", "abstract": "We consider the strategic interaction of $n$ investors who are able to influence a stock price process and at the same time measure their utilities relative to the other investors. Our main aim is to find Nash equilibrium investment strategies in this setting in a financial market driven by a Brownian motion and investigate the influence the price impact has on the equilibrium. We consider both CRRA and CARA utility functions. Our findings show that the problem is well-posed as long as the price impact is at most linear. Moreover, numerical results reveal that the investors behave very aggressively when the price impact is beyond a critical parameter.", "published": "2023-03-31T15:54:36Z", "authors": ["Nicole Bäuerle", "Tamara Göll"], "categories": ["math.OC", "q-fin.MF"], "doi": "10.1007/s11579-024-00356-0", "_query": "housing price text listing", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2108.11915v1", "title": "Cooling Measures and Housing Wealth: Evidence from Singapore", "abstract": "Excessive house price growth was at the heart of the financial crisis in 2007/08. Since then, many countries have added cooling measures to their regulatory frameworks. It has been found that these measures can indeed control price growth, but no one has examined whether this has adverse consequences for the housing wealth distribution. We examine this for Singapore, which started in 2009 to target price growth over ten rounds in total. We find that welfare from housing wealth in the last round might not be higher than before 2009. This depends on the deflator used to convert nominal into real prices. Irrespective of the deflator, we can reject that welfare increased monotonically over the different rounds.", "published": "2021-08-26T17:11:35Z", "authors": ["Wolfgang Karl Härdle", "Rainer Schulz", "Taojun Sie"], "categories": ["q-fin.GN"], "doi": "", "_query": "housing price text listing", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2107.10455v1", "title": "Time Varying Risk in U.S. Housing Sector and Real Estate Investment Trusts Equity Return", "abstract": "This study examines how housing sector volatilities affect real estate investment trust (REIT) equity return in the United States. I argue that unexpected changes in housing variables can be a source of aggregate housing risk, and the first principal component extracted from the volatilities of U.S. housing variables can predict the expected REIT equity returns. I propose and construct a factor-based housing risk index as an additional factor in asset price models that uses the time-varying conditional volatility of housing variables within the U.S. housing sector. The findings show that the proposed housing risk index is economically and theoretically consistent with the risk-return relationship of the conditional Intertemporal Capital Asset Pricing Model (ICAPM) of Merton (1973), which predicts an average maximum of 5.6 percent of risk premium in REIT equity return. In subsample analyses, the positive relationship is not affected by sample periods' choice but shows higher housing risk beta values for the 2009-18 sample period. The relationship remains significant after controlling for VIX, Fama-French three factors, and a broad set of macroeconomic and financial variables. Moreover, the proposed housing beta also accurately forecasts U.S. macroeconomic and financial conditions.", "published": "2021-07-22T04:57:39Z", "authors": ["Masud Alam"], "categories": ["econ.GN"], "doi": "", "_query": "housing price text listing", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/1110.3250v5", "title": "On a stochastic differential equation arising in a price impact model", "abstract": "We provide sufficient conditions for the existence and uniqueness of solutions to a stochastic differential equation which arises in a price impact model. These conditions are stated as smoothness and boundedness requirements on utility functions or Malliavin differentiability of payoffs and endowments.", "published": "2011-10-14T15:33:51Z", "authors": ["Peter Bank", "Dmitry Kramkov"], "categories": ["q-fin.TR", "math.PR"], "doi": "10.1016/j.spa.2012.10.011", "_query": "housing price text listing", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2103.16388v1", "title": "Text Mining of Stocktwits Data for Predicting Stock Prices", "abstract": "Stock price prediction can be made more efficient by considering the price fluctuations and understanding the sentiments of people. A limited number of models understand financial jargon or have labelled datasets concerning stock price change. To overcome this challenge, we introduced FinALBERT, an ALBERT based model trained to handle financial domain text classification tasks by labelling Stocktwits text data based on stock price change. We collected Stocktwits data for over ten years for 25 different companies, including the major five FAANG (Facebook, Amazon, Apple, Netflix, Google). These datasets were labelled with three labelling techniques based on stock price changes. Our proposed model FinALBERT is fine-tuned with these labels to achieve optimal results. We experimented with the labelled dataset by training it on traditional machine learning, BERT, and FinBERT models, which helped us understand how these labels behaved with different model architectures. Our labelling method competitive advantage is that it can help analyse the historical data effectively, and the mathematical function can be easily customised to predict stock movement.", "published": "2021-03-13T03:29:14Z", "authors": ["Mukul Jaggi", "Priyanka Mandal", "Shreya Narang", "Usman Naseem", "Matloob Khushi"], "categories": ["q-fin.ST", "cs.LG"], "doi": "10.3390/asi4010013", "_query": "housing price text listing", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2511.10808v1", "title": "Seasonality in the U.S. Housing Market: Post-Pandemic Shifts and Regional Dynamics", "abstract": "Seasonality has traditionally shaped the U.S. housing market, with activity peaking in spring-summer and declining in autumn-winter. However, recent disruptions, particularly post-COVID-19, raise questions about shift in these patterns. This study analyzes housing market date (1991-2024) to examine evolving seasonality and regional heterogeneity. Using Housing Price Index (HPI), inventory and sales data from the Federal Housing Finance Agency and U.S. Census Bureau, seasonal components are extracted via the X-13-ARIMA procedure, and statistical tests assess variations across regions. The results confirm seasonal fluctuations in prices and volumes, with recent shifts toward earlier annual peak (March-April) and amplified seasonal effects. Regional variations align with differences in climate and market structure, while prices and sales volumes exhibit in-phase movement, suggesting thick-market momentum behaviour. These findings highlight key implications for policymakers, realtors and investors navigating post-pandemic market dynamics, offering insights into the timing and interpretation of housing market activities.", "published": "2025-11-13T21:20:54Z", "authors": ["Yihan Hu", "Yifei Huang", "Weizhao Wang"], "categories": ["econ.GN", "stat.AP"], "doi": "", "_query": "housing price text listing", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/1904.07444v2", "title": "Equilibria in a large production economy with an infinite dimensional commodity space and price dependent preferences", "abstract": "We prove the existence of a competitive equilibrium in a production economy with infinitely many commodities and a measure space of agents whose preferences are price dependent. We employ a saturated measure space for the set of agents and apply recent results for an infinite dimensional separable Banach space such as Lyapunov's convexity theorem and an exact Fatou's lemma to obtain the result.", "published": "2019-04-16T03:57:58Z", "authors": ["Hyo Seok Jang", "Sangjik Lee"], "categories": ["econ.TH", "math.OC"], "doi": "", "_query": "housing price text listing", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2411.19206v2", "title": "A general framework for pricing and hedging under local viability", "abstract": "In this paper, a new approach for solving the problems of pricing and hedging derivatives is introduced in a general frictionless market setting. The method is applicable even in cases where an equivalent local martingale measure fails to exist. Our main results include a new superhedging duality for American options when wealth processes can be negative and trading strategies are subject to a cone constraint. This answers one of the questions raised by Fernholz, Karatzas and Kardaras.", "published": "2024-11-28T15:25:50Z", "authors": ["Huy N. Chau", "Miklos Rasonyi"], "categories": ["q-fin.PR"], "doi": "", "_query": "housing price text listing", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/1902.07599v1", "title": "Fast, Small, and Simple Document Listing on Repetitive Text Collections", "abstract": "Document listing on string collections is the task of finding all documents where a pattern appears. It is regarded as the most fundamental document retrieval problem, and is useful in various applications. Many of the fastest-growing string collections are composed of very similar documents, such as versioned code and document collections, genome repositories, etc. Plain pattern-matching indexes designed for repetitive text collections achieve orders-of-magnitude reductions in space. Instead, there are not many analogous indexes for document retrieval. In this paper we present a simple document listing index for repetitive string collections of total length $n$ that lists the $ndoc$ distinct documents where a pattern of length $m$ appears in time $\\mathcal{O}(m+ndoc \\cdot \\log n)$. We exploit the repetitiveness of the document array (i.e., the suffix array coarsened to document identifiers) to grammar-compress it while precomputing the answers to nonterminals, and store them in grammar-compressed form as well. Our experimental results show that our index sharply outperforms existing alternatives in the space/time tradeoff map.", "published": "2019-02-20T15:34:06Z", "authors": ["Dustin Cobas", "Gonzalo Navarro"], "categories": ["cs.DS", "cs.IR", "cs.IT"], "doi": "", "_query": "housing price text listing", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2303.11365v5", "title": "Housing Bubbles with Phase Transitions", "abstract": "We analyze how equilibrium housing prices are determined in the process of economic development within an overlapping generations model with perfect housing and rental markets. We characterize the rent growth rate in all equilibria. The economy exhibits a two-stage phase transition: as incomes of home buyers rise, the equilibrium regime changes from fundamental to bubble possibility, where fundamental and bubbly equilibria coexist. With even higher incomes, fundamental equilibria disappear and housing bubbles become a necessity. We also discuss extensions and refinements such as equilibrium uniqueness, multiple savings vehicles, welfare implications, credit- and expectation-driven bubbles, and testable implications of our theory.", "published": "2023-03-20T18:07:22Z", "authors": ["Tomohiro Hirano", "Alexis Akira Toda"], "categories": ["econ.TH"], "doi": "", "_query": "housing price text listing", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2610.02256v2", "title": "TRACE: A Reproducible Benchmark for Electricity Price Forecasting with Official Operational Text", "abstract": "Electricity price forecasting (EPF) supports scheduling, bidding, and risk management in electricity markets, yet existing benchmarks focus mainly on numerical inputs, leaving the forecasting value of forecast-time textual context insufficiently evaluated. We introduce TRACE, a reproducible benchmark of 7,300 zone--day instances pairing prices from five zones in a major U.S. market with official operational text available at the forecast cutoff. TRACE reconstructs official operational text at each cutoff, preventing post-cutoff information leakage. We evaluate TRACE for semantic alignment and forecasting value. Semantic assessments align with central movement and both tail risks in ground-truth prices, most consistently for upper-tail price risk. Forecasting value is reflected in a median 7.4\\% reduction in upper-tail pinball loss across time-series foundation models. A controlled cross-day text-mismatch ablation reverses the gains, falling below the no-text baseline.", "published": "2026-09-30T20:44:33Z", "authors": ["Xinyi Yi", "Zhangdie Yuan", "Ioannis Lestas"], "categories": ["cs.LG", "stat.ML"], "doi": "", "_query": "housing price text listing", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/1306.2831v1", "title": "Systemic risk and spatiotemporal dynamics of the US housing market", "abstract": "Housing markets play a crucial role in economies and the collapse of a real-estate bubble usually destabilizes the financial system and causes economic recessions. We investigate the systemic risk and spatiotemporal dynamics of the US housing market (1975-2011) at the state level based on the Random Matrix Theory (RMT). We identify rich economic information in the largest eigenvalues deviating from RMT predictions and unveil that the component signs of the eigenvectors contain either geographical information or the extent of differences in house price growth rates or both. Our results show that the US housing market experienced six different regimes, which is consistent with the evolution of state clusters identified by the box clustering algorithm and the consensus clustering algorithm on the partial correlation matrices. Our analysis uncovers that dramatic increases in the systemic risk are usually accompanied with regime shifts, which provides a means of early detection of housing bubbles.", "published": "2013-06-12T14:05:13Z", "authors": ["Hao Meng", "Wen-Jie Xie", "Zhi-Qiang Jiang", "Boris Podobnik", "Wei-Xing Zhou", "H. Eugene Stanley"], "categories": ["q-fin.ST"], "doi": "10.1038/srep03655", "_query": "housing price text listing", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2208.14207v1", "title": "Understanding intra-day price formation process by agent-based financial market simulation: calibrating the extended chiarella model", "abstract": "This article presents XGB-Chiarella, a powerful new approach for deploying agent-based models to generate realistic intra-day artificial financial price data. This approach is based on agent-based models, calibrated by XGBoost machine learning surrogate. Following the Extended Chiarella model, three types of trading agents are introduced in this agent-based model: fundamental traders, momentum traders, and noise traders. In particular, XGB-Chiarella focuses on configuring the simulation to accurately reflect real market behaviours. Instead of using the original Expectation-Maximisation algorithm for parameter estimation, the agent-based Extended Chiarella model is calibrated using XGBoost machine learning surrogate. It is shown that the machine learning surrogate learned in the proposed method is an accurate proxy of the true agent-based market simulation. The proposed calibration method is superior to the original Expectation-Maximisation parameter estimation in terms of the distance between historical and simulated stylised facts. With the same underlying model, the proposed methodology is capable of generating realistic price time series in various stocks listed at three different exchanges, which indicates the universality of intra-day price formation process. For the time scale (minutes) chosen in this paper, one agent per category is shown to be sufficient to capture the intra-day price formation process. The proposed XGB-Chiarella approach provides insights that the price formation process is comprised of the interactions between momentum traders, fundamental traders, and noise traders. It can also be used to enhance risk management by practitioners.", "published": "2022-08-29T15:35:39Z", "authors": ["Kang Gao", "Perukrishnen Vytelingum", "Stephen Weston", "Wayne Luk", "Ce Guo"], "categories": ["q-fin.CP", "q-fin.GN"], "doi": "10.1002/wilm.11014", "_query": "housing price text listing", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/1905.03111v1", "title": "Parallel and Distributed Algorithms for the housing allocation Problem", "abstract": "We give parallel and distributed algorithms for the housing allocation problem. In this problem, there is a set of agents and a set of houses. Each agent has a strict preference list for a subset of houses. We need to find a matching such that some criterion is optimized. One such criterion is Pareto Optimality. A matching is Pareto optimal if no coalition of agents can be strictly better off by exchanging houses among themselves. We also study the housing market problem, a variant of the housing allocation problem, where each agent initially owns a house. In addition to Pareto optimality, we are also interested in finding the core of a housing market. A matching is in the core if there is no coalition of agents that can be better off by breaking away from other agents and switching houses only among themselves. In the first part of this work, we show that computing a Pareto optimal matching of a house allocation is in {\\bf CC} and computing the core of a housing market is {\\bf CC}-hard. Given a matching, we also show that verifying whether it is in the core can be done in {\\bf NC}. We then give an algorithm to show that computing a maximum Pareto optimal matching for the housing allocation problem is in {\\bf RNC}^2 and quasi-{\\bf NC}^2. In the second part of this work, we present a distributed version of the top trading cycle algorithm for finding the core of a housing market. To that end, we first present two algorithms for finding all the disjoint cycles in a functional graph: a Las Vegas algorithm which terminates in $O(\\log l)$ rounds with high probability, where $l$ is the length of the longest cycle, and a deterministic algorithm which terminates in $O(\\log^* n \\log l)$ rounds, where $n$ is the number of nodes in the graph. Both algorithms work in the synchronous distributed model and use messages of size $O(\\log n)$.", "published": "2019-05-08T14:46:32Z", "authors": ["Xiong Zheng", "Vijay Garg"], "categories": ["cs.DC"], "doi": "", "_query": "housing price text listing", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/1812.00040v1", "title": "A Branch and Price Algorithm for List Coloring Problem", "abstract": "Coloring problems in graphs have been used to model a wide range of real applications. In particular, the List Coloring Problem generalizes the well-known Graph Coloring Problem for which many exact algorithms have been developed. In this work, we present a Branch-and-Price algorithm for the weighted version of the List Coloring Problem, based on the one developed by Mehrotra and Trick (1996) for the Graph Coloring Problem. This version considers non-negative weights associated to each color and it is required to assign a color to each vertex from predetermined lists in such a way the sum of weights of the assigned colors is minimum. Computational experiments show the good performance of our approach, being able to comfortably solve instances whose graphs have up to seventy vertices. These experiences also bring out that the hardness of the instances of the List Coloring Problem does not seem to depend only on quantitative parameters such as the size of the graph, its density, and the size of list of colors, but also on the distribution of colors present in the lists.", "published": "2018-11-30T20:18:42Z", "authors": ["Mauro Lucci", "Graciela Nasini", "Daniel Severín"], "categories": ["cs.DM"], "doi": "10.1016/j.entcs.2019.08.054", "_query": "housing price text listing", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2603.03260v2", "title": "Does Entry of Food-and-Drink Establishments Raise Local House Prices? Event-Study Evidence from London", "abstract": "Restaurants, cafes, pubs, and takeaways are among the most visible markers of neighborhood change, yet whether their arrival is capitalised into nearby housing values remains empirically unsettled. We assemble a London-wide panel linking Land Registry prices, non-domestic EPC lodgement timings for food-and-drink establishments, and neighborhood amenity measures at the LSOA level. Our preferred annual event-study design defines treatment as the first clean-onset year in which an LSOA records at least two eligible EPC lodgements for food-and-drink establishments, after a two-year lookback with no prior entries. In this specification, pre-trend tests are not rejected in either the stacked or Sun-Abraham estimators, and log house prices rise gradually from about 0.5% in the event year to roughly 3.4--3.7% by years four and five. The results are consistent with local amenity capitalization following commercial entry, while remaining appropriately cautious about endogenous siting and concurrent redevelopment.", "published": "2026-03-03T18:51:20Z", "authors": ["Wanqi Liu", "Rong Zhao"], "categories": ["physics.soc-ph", "econ.GN"], "doi": "", "_query": "housing price text listing", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2003.11565v2", "title": "The Millennial Boom, the Baby Bust, and the Housing Market", "abstract": "As baby boomers have begun to downsize and retire, their preferences now overlap with millennials' predilection for urban amenities and smaller living spaces. This confluence in tastes between the two largest age segments of the U.S. population has meaningfully changed the evolution of home prices in the United States. Utilizing a Bartik shift-share instrument for demography-driven demand shocks, we show that from 2000 to 2018 (i) the price growth of four- and five-bedroom houses has lagged the prices of one- and two-bedroom homes, (ii) within local labor markets, the relative home prices in baby boomer-rich zip codes have declined compared with millennial-rich neighborhoods, and (iii) the zip codes with the largest relative share of smaller homes have grown fastest. These patterns have become more pronounced during the latest economic cycle. We show that the effects are concentrated in areas where housing supply is most inelastic. If this pattern in the housing market persists or expands, the approximately 16.5 trillion in real estate wealth held by households headed by those aged 55 or older will be significantly affected. We find little evidence that these upcoming changes have been incorporated into current prices.", "published": "2020-03-25T18:03:45Z", "authors": ["Marijn A. Bolhuis", "Judd N. L. Cramer"], "categories": ["econ.GN"], "doi": "", "_query": "housing price text listing", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2505.09620v1", "title": "The impact of economic policies on housing prices. Approximations and predictions in the UK, the US, France, and Switzerland from the 1980s to today", "abstract": "I show that house prices can be modeled using machine learning (kNN and tree-bagging) and a small dataset composed of macro-economic factors (MEF), including an inflation metric (CPI), US treasury rates (10-yr), Gross Domestic Product (GDP), and portfolio size of central banks (ECB, FED). This set of parameters covers all the parties involved in a transaction (buyer, seller, and financing facility) while ignoring the intrinsic properties of each asset and encompassing local (inflation) and liquidity issues that may impede each transaction composing a market. The model here takes the point of view of a real estate trader who is interested in both the financing and the price of the transaction. Machine Learning allows for the discrimination of two periods within the dataset. Unconventional policies of central banks may have allowed some institutional investors to arbitrage between real estate returns and other bond markets (sovereign and corporate). Finally, to assess the models' relative performances, I performed various sensitivity tests, which tend to constrain the possibilities of each approach for each need. I also show that some models can predict the evolution of prices over the next 4 quarters with uncertainties that outperform existing index uncertainties.", "published": "2025-04-10T12:46:54Z", "authors": ["Nicolas Houlié"], "categories": ["q-fin.ST"], "doi": "", "_query": "housing price text listing", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/1510.07129v1", "title": "Bayesian Inference for High Dimensional Changing Linear Regression with Application to Minnesota House Price Index Data", "abstract": "In many applications, the dataset under investigation exhibits heterogeneous regimes that are more appropriately modeled using piece-wise linear models for each of the data segments separated by change-points. Although there have been much work on change point linear regression for the low dimensional case, high-dimensional change point regression is severely underdeveloped. Motivated by the analysis of Minnesota House Price Index data, we propose a fully Bayesian framework for fitting changing linear regression models in high-dimensional settings. Using segment-specific shrinkage and diffusion priors, we deliver full posterior inference for the change points and simultaneously obtain posterior probabilities of variable selection in each segment via an efficient Gibbs sampler. Additionally, our method can detect an unknown number of change points and accommodate different variable selection constraints like grouping or partial selection. We substantiate the accuracy of our method using simulation experiments for a wide range of scenarios. We apply our approach for a macro-economic analysis of Minnesota house price index data. The results strongly favor the change point model over a homogeneous (no change point) high-dimensional regression model.", "published": "2015-10-24T10:28:14Z", "authors": ["Abhirup Datta", "Hui Zou", "Sudipto Banerjee"], "categories": ["math.ST", "stat.AP"], "doi": "", "_query": "housing price text listing", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2307.02260v1", "title": "Does Transport Inequality Perpetuate Housing Insecurity?", "abstract": "With trends of urbanisation on the rise, providing adequate housing to individuals remains a complex issue to be addressed. Often, the slow output of relevant housing policies, coupled with quickly increasing housing costs, leaves individuals with the burden of finding housing that is affordable and safe. In this paper, we unveil how urban planning, not just housing policies, can prevent individuals from accessing better housing conditions. We begin by proposing a clustering approach to characterising levels of housing insecurity in a city, by considering multiple dimensions of housing. Then we define levels of transit efficiency in 20 US cities by comparing public transit journeys to car-based journeys. Finally, we use geospatial autocorrelation to highlight how commuting to areas associated with better housing conditions results in transit commute times of over 30 minutes in most cities, and commute times of over an hour in some cases. Ultimately, we show the role that public transportation plays in locking vulnerable demographics into a cycle of poverty, thus motivating a more holistic approach to addressing housing insecurity that extends beyond changing housing policies.", "published": "2023-07-05T13:00:17Z", "authors": ["Nandini Iyer", "Ronaldo Menezes", "Hugo Barbosa"], "categories": ["physics.soc-ph"], "doi": "", "_query": "housing price text listing", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/1401.8142v1", "title": "The Integrated Size and Price Optimization Problem", "abstract": "We present the Integrated Size and Price Optimization Problem (ISPO) for a fashion discounter with many branches. Based on a two-stage stochastic programming model with recourse, we develop an exact algorithm and a production-compliant heuristic that produces small optimality gaps. In a field study we show that a distribution of supply over branches and sizes based on ISPO solutions is significantly better than a one-stage optimization of the distribution ignoring the possibility of optimal pricing.", "published": "2014-01-31T11:50:44Z", "authors": ["Miriam Kießling", "Sascha Kurz", "Jörg Rambau"], "categories": ["math.OC", "q-fin.GN"], "doi": "10.3934/naco.2012.2.669", "_query": "housing price text listing", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/0907.1853v1", "title": "Housing Market Microstructure", "abstract": "In this article, we develop a model for the evolution of real estate prices. A wide range of inputs, including stochastic interest rates and changing demands for the asset, are considered. Maximizing their expected utility, home owners make optimal sale decisions given these changing market conditions. Using these optimal sale decisions, we simulate the implied evolution of housing prices providing insights into the recent subprime lending crisis.", "published": "2009-07-10T15:54:17Z", "authors": ["Hazer Inaltekin", "Robert Jarrow", "Mehmet Saglam", "Yildiray Yildirim"], "categories": ["q-fin.TR"], "doi": "", "_query": "housing price text listing", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/1210.0986v1", "title": "The First Observations with the GRT at Parkes", "abstract": "In this contribution Marcus Price gives a first hand account of some of the very first scientific observations undertaken with the \"Giant Radio Telescope\" at Parkes. It was clearly a very exciting time of discovery, enabled by superb engineering.", "published": "2012-10-03T05:10:39Z", "authors": ["R. M. Price"], "categories": ["astro-ph.IM"], "doi": "", "_query": "housing price text listing", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2503.01884v2", "title": "Contextual Quantum Neural Networks for Stock Price Prediction", "abstract": "In this paper, we apply quantum machine learning (QML) to predict the stock prices of multiple assets using a contextual quantum neural network. Our approach captures recent trends to predict future stock price distributions, moving beyond traditional models that focus on entire historical data, enhancing adaptability and precision. Utilizing the principles of quantum superposition, we introduce a new training technique called the quantum batch gradient update (QBGU), which accelerates the standard stochastic gradient descent (SGD) in quantum applications and improves convergence. Consequently, we propose a quantum multi-task learning (QMTL) architecture, specifically, the share-and-specify ansatz, that integrates task-specific operators controlled by quantum labels, enabling the simultaneous and efficient training of multiple assets on the same quantum circuit as well as enabling efficient portfolio representation with logarithmic overhead in the number of qubits. This architecture represents the first of its kind in quantum finance, offering superior predictive power and computational efficiency for multi-asset stock price forecasting. Through extensive experimentation on S\\&P 500 data for Apple, Google, Microsoft, and Amazon stocks, we demonstrate that our approach not only outperforms quantum single-task learning (QSTL) models but also effectively captures inter-asset correlations, leading to enhanced prediction accuracy. Our findings highlight the transformative potential of QML in financial applications, paving the way for more advanced, resource-efficient quantum algorithms in stock price prediction and other complex financial modeling tasks.", "published": "2025-02-26T22:39:23Z", "authors": ["Sharan Mourya", "Hannes Leipold", "Bibhas Adhikari"], "categories": ["cs.LG", "cs.AI"], "doi": "10.1038/s41598-025-34413-5", "_query": "housing price text listing", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2010.00413v2", "title": "Resilient Urban Housing Markets: Shocks vs. Fundamentals", "abstract": "In the face of a pandemic, urban protests, and an affordability crisis, is the desirability of dense urban settings at a turning point? Assessing cities' long term trends remains challenging. The first part of this chapter describes the short-run dynamics of the housing market in 2020. Evidence from prices and price-to-rent ratios suggests expectations of resilience. Zip-level evidence suggests a short-run trend towards suburbanization, and some impacts of urban protests on house prices. The second part of the chapter analyzes the long-run dynamics of urban growth between 1970 and 2010. It analyzes what, in such urban growth, is explained by short-run shocks as opposed to fundamentals such as education, industrial specialization, industrial diversification, urban segregation, and housing supply elasticity. This chapter's original results as well as a large established body of literature suggest that fundamentals are the key drivers of growth. The chapter illustrates this finding with two case studies: the New York City housing market after September 11, 2001; and the San Francisco Bay Area in the aftermath of the 1989 Loma Prieta earthquake. Both areas rebounded strongly after these shocks, suggesting the resilience of the urban metropolis.", "published": "2020-10-01T13:59:53Z", "authors": ["Amine Ouazad"], "categories": ["econ.GN", "physics.soc-ph"], "doi": "", "_query": "housing price text listing", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2304.06720v4", "title": "Expressive Text-to-Image Generation with Rich Text", "abstract": "Plain text has become a prevalent interface for text-to-image synthesis. However, its limited customization options hinder users from accurately describing desired outputs. For example, plain text makes it hard to specify continuous quantities, such as the precise RGB color value or importance of each word. Furthermore, creating detailed text prompts for complex scenes is tedious for humans to write and challenging for text encoders to interpret. To address these challenges, we propose using a rich-text editor supporting formats such as font style, size, color, and footnote. We extract each word's attributes from rich text to enable local style control, explicit token reweighting, precise color rendering, and detailed region synthesis. We achieve these capabilities through a region-based diffusion process. We first obtain each word's region based on attention maps of a diffusion process using plain text. For each region, we enforce its text attributes by creating region-specific detailed prompts and applying region-specific guidance, and maintain its fidelity against plain-text generation through region-based injections. We present various examples of image generation from rich text and demonstrate that our method outperforms strong baselines with quantitative evaluations.", "published": "2023-04-13T17:59:55Z", "authors": ["Songwei Ge", "Taesung Park", "Jun-Yan Zhu", "Jia-Bin Huang"], "categories": ["cs.CV", "cs.GR", "cs.LG"], "doi": "", "_query": "housing price text listing", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2604.12659v1", "title": "Do VLMs Truly \"Read\" Candlesticks? A Multi-Scale Benchmark for Visual Stock Price Forecasting", "abstract": "Vision-language models(VLMs) are increasingly applied to visual stock price forecasting, yet existing benchmarks inadequately evaluate their understanding of stock price in candlestick charts. First, prior studies fail to isolate VLMs' comprehension of visual inputs genuinely improves predictive performance and whether VLMs truly comprehend candlestick patterns. Further, most existing datasets and evaluation setups are designed around single-period or tabular inputs. However, human analysts strongly rely on multi-scale candlestick charts, where longer-term horizons capture trend direction and shorter-term horizons provide cues for inflection points, making it difficult to systematically assess VLMs' ability to integrate short-term and long-term visual market dynamics. To bridge this gap, we construct a multi-scale candlestick charts dataset and a standardized evaluation framework to assess VLMs' ability to utilize multi-scale visual market signals. Evaluation combines confusion-matrix-based diagnostics with information coefficient(IC) time series metrics and includes XGBoost as a feature-based temporal baseline. Using this dataset, we benchmark representative VLMs and analyze their ability to leverage multi-scale stock price data. Experimental results show that most VLMs perform well only under persistent uptrend or downtrend conditions, while exhibiting weak predictive capability in more common market scenarios. We also identify significant prediction biases and limited sensitivity to explicitly specified forecast horizons in prompts, indicating inherent limitations in precise temporal reasoning.", "published": "2026-04-14T12:26:34Z", "authors": ["Kaiqi Hu", "Linda Xiao", "Shiyue Xu", "Ziyi Tang", "Mingwen Liu"], "categories": ["cs.LG", "cs.CL"], "doi": "", "_query": "housing price text listing", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2604.15580v1", "title": "Housing Decisions under Mobility Risk: A Stochastic Threshold Approach", "abstract": "We develop a stochastic free-boundary model of housing tenure decisions in markets with high mobility risk, such as areas near military installations. Housing prices and rents follow correlated diffusion processes, and households face an uncertain relocation horizon. We derive a closed-form characterization of the optimal buy-versus-rent boundary in terms of the price-to-rent ratio. The model highlights how mobility risk reduces the value of ownership by shortening the effective holding period and increasing uncertainty. As a result, identical price-to-rent ratios can imply different optimal decisions across locations. Numerical illustrations show how variations in volatility and relocation intensity shift the threshold, providing a structural interpretation of observed cross-market heterogeneity. The framework offers a tractable tool for analyzing housing decisions under uncertainty and for interpreting price-to-rent ratios in high-mobility environments.", "published": "2026-04-16T23:25:14Z", "authors": ["Hui Wu"], "categories": ["math.OC"], "doi": "", "_query": "housing price text listing", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/1907.06118v2", "title": "Online Rental Housing Market Representation and the Digital Reproduction of Urban Inequality", "abstract": "As the rental housing market moves online, the Internet offers divergent possible futures: either the promise of more-equal access to information for previously marginalized homeseekers, or a reproduction of longstanding information inequalities. Biases in online listings' representativeness could impact different communities' access to housing search information, reinforcing traditional information segregation patterns through a digital divide. They could also circumscribe housing practitioners' and researchers' ability to draw broad market insights from listings to understand rental supply and affordability. This study examines millions of Craigslist rental listings across the US and finds that they spatially concentrate and over-represent whiter, wealthier, and better-educated communities. Other significant demographic differences exist in age, language, college enrollment, rent, poverty rate, and household size. Most cities' online housing markets are digitally segregated by race and class, and we discuss various implications for residential mobility, community legibility, gentrification, housing voucher utilization, and automated monitoring and analytics in the smart cities paradigm. While Craigslist contains valuable crowdsourced data to better understand affordability and available rental supply in real-time, it does not evenly represent all market segments. The Internet promises information democratization, and online listings can reduce housing search costs and increase choice sets. However, technology access/preferences and information channel segregation can concentrate such information-broadcasting benefits in already-advantaged communities, reproducing traditional inequalities and reinforcing residential sorting and segregation dynamics. Technology platforms like Craigslist construct new institutions with the power to shape spatial economies.", "published": "2019-07-13T18:56:57Z", "authors": ["Geoff Boeing"], "categories": ["econ.GN", "stat.AP"], "doi": "10.1177/0308518X19869678", "_query": "housing price text listing", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/1912.04661v1", "title": "Adaptive Dynamic Model Averaging with an Application to House Price Forecasting", "abstract": "Dynamic model averaging (DMA) combines the forecasts of a large number of dynamic linear models (DLMs) to predict the future value of a time series. The performance of DMA critically depends on the appropriate choice of two forgetting factors. The first of these controls the speed of adaptation of the coefficient vector of each DLM, while the second enables time variation in the model averaging stage. In this paper we develop a novel, adaptive dynamic model averaging (ADMA) methodology. The proposed methodology employs a stochastic optimisation algorithm that sequentially updates the forgetting factor of each DLM, and uses a state-of-the-art non-parametric model combination algorithm from the prediction with expert advice literature, which offers finite-time performance guarantees. An empirical application to quarterly UK house price data suggests that ADMA produces more accurate forecasts than the benchmark autoregressive model, as well as competing DMA specifications.", "published": "2019-12-10T12:03:58Z", "authors": ["Alisa Yusupova", "Nicos G. Pavlidis", "Efthymios G. Pavlidis"], "categories": ["econ.EM", "stat.ME"], "doi": "", "_query": "housing price text listing", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/1310.4161v1", "title": "Remark on the Novikov conjecture and the homotopy invariance of the Hirzebruch L-class", "abstract": "We use K-area homology to summarize some results about the Novikov conjecture and the Hirzebruch L-class. In fact, we provide necessary and sufficient conditions for closed manifolds to have a homotopy invariant L-class. In order to obtain additional properties we also introduce the cohomology of infinite K--area which preserves the cup product of singular cohomology.", "published": "2013-10-15T19:48:00Z", "authors": ["Mario Listing"], "categories": ["math.DG", "math.GT"], "doi": "", "_query": "housing price text listing", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/math/0408092v1", "title": "Scalar curvature rigidity of almost Hermitian manifolds which are asymptotic to $\\mathbb{C}H^{2n}$", "abstract": "We show that an almost Hermitian manifold $(M,g)$ of real dimension $4n$ which is strongly asymptotic to $\\mathbb{C}H^{2n}$ and satisfies a certain scalar curvature bound must be isometric to the complex hyperbolic space. Assuming Kähler instead of almost Hermitian this gives the already known rigidity result by H. Boualem and M. Herlich proved in \\emph{Ann. Scuola Norm. Sup Pisa (Ser. V)}, vol. 1(2).", "published": "2004-08-06T18:16:03Z", "authors": ["Mario Listing"], "categories": ["math.DG"], "doi": "", "_query": "housing price text listing", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2510.16898v2", "title": "Adaptive Online Learning with LSTM Networks for Energy Price Prediction", "abstract": "Accurate prediction of electricity prices is crucial for stakeholders in the energy market, particularly for grid operators, energy producers, and consumers. This study focuses on developing a predictive model leveraging Long Short-Term Memory (LSTM) networks to forecast day-ahead electricity prices in the California energy market. The model incorporates a variety of features, including historical price data, weather conditions, and the energy generation mix. A novel custom loss function that integrates Mean Absolute Error (MAE), Jensen-Shannon Divergence (JSD), and a smoothness penalty is introduced to enhance the prediction accuracy and interpretability. Additionally, an adaptive online learning framework is implemented to allow the model to adapt to new data incrementally, ensuring continuous relevance and accuracy. The results demonstrate that the custom loss function can improve the model's performance, aligning predicted prices more closely with actual values, particularly during peak intervals. The adaptive online learning framework achieves the best overall performance, reducing MSE, MAE, and RMSE by approximately 23.0%, 3.4%, and 12.2%, respectively, compared with the next-best model, namely the static model, which is trained once and kept fixed during testing. The inclusion of the energy generation mix further enhances the model's predictive capabilities, highlighting the importance of comprehensive feature integration. This research provides a robust framework for electricity price forecasting, which offers valuable insights and tools for better decision-making in dynamic electricity markets.", "published": "2025-10-19T15:48:38Z", "authors": ["Salih Salihoglu", "Ibrahim Ahmed", "Afshin Asadi"], "categories": ["cs.LG", "cs.AI"], "doi": "10.1007/s41060-026-01251-1", "_query": "housing price text listing", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/1606.00424v3", "title": "Residential income segregation: A behavioral model of the housing market", "abstract": "We represent the functioning of the housing market and study the relation between income segregation, income inequality and house prices by introducing a spatial Agent-Based Model (ABM). Differently from traditional models in urban economics, we explicitly specify the behavior of buyers and sellers and the price formation mechanism. Buyers who differ by income select among heterogeneous neighborhoods using a probabilistic model of residential choice; sellers employ an aspiration level heuristic to set their reservation offer price; prices are determined through a continuous double auction. We first provide an approximate analytical solution of the ABM, shedding light on the structure of the model and on the effect of the parameters. We then simulate the ABM and find that: (i) a more unequal income distribution lowers the prices globally, but implies stronger segregation; (ii) a spike of the demand in one part of the city increases the prices all over the city; (iii) subsidies are more efficient than taxes in fostering social mixing.", "published": "2016-06-01T19:53:26Z", "authors": ["Marco Pangallo", "Jean Pierre Nadal", "Annick Vignes"], "categories": ["econ.GN", "nlin.AO"], "doi": "", "_query": "housing price text listing", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2308.15498v1", "title": "Chunked Lists versus Extensible Arrays for Text Inversion", "abstract": "In our 2017 work on in-memory list-based text inversion [Hawking and Billerbeck. Efficient In-Memory, List-Based Text Inversion. ADCS 2017] we compared memory use and indexing speed of a considerable number of variants of chunked linked lists. In the present work we compare the best performing of those variants (FBB - dynamic Fibonacci chunking) with the extensible SQ array technique (SQA) presented in [Moffat and Mackenzie. Immediate-Access Indexing Using Space-Efficient Extensible Arrays. ADCS 2023].", "published": "2023-08-29T02:42:52Z", "authors": ["David Hawking", "Bodo Billerbeck"], "categories": ["cs.IR"], "doi": "", "_query": "housing price text listing", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/1802.09959v1", "title": "Valuation, Liquidity Price, and Stability of Cryptocurrencies", "abstract": "Cryptocurrencies are examined through the asset flow equations and experimental asset markets. Since tangible value of a typical cryptocurrency is non-existent, the theory suggests that price will gravitate toward liquidity value, i.e., the total amount of cash available for purchase of the asset divided by the number of units. Thus it is unlikely that cryptocurrencies in their current form will be stable in the absence of a mechanism of a link to value.", "published": "2018-02-27T15:17:46Z", "authors": ["Carey Caginalp", "Gunduz Caginalp"], "categories": ["q-fin.MF", "cs.CR"], "doi": "10.1073/pnas.1722031115", "_query": "housing price text listing", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2502.15905v1", "title": "Improving ex ante accuracy assessment in predicting house price dispersion: evidence from the USA", "abstract": "The study focuses on improving the ex ante prediction accuracy assessment in the case of forecasting various house price dispersion measures in the USA. It addresses a critical gap in real estate market forecasting by proposing a novel method for assessing ex ante prediction accuracy under unanticipated shocks. The proposal is based on a parametric bootstrap approach under a misspecified model, allowing for the simulation of future values and estimation of prediction errors in case of unexpected price changes. The study highlights the limitations of the traditional approach that fails to account for unforeseen market events and provides a more in-depth understanding of how prediction accuracy changes under unexpected scenarios. The proposed methods offers valuable insights for real estate market management by enabling more robust risk assessment and decision-making in the face of unexpected market fluctuations. Real data application is based on longitudinal U.S. data on real estate transactions.", "published": "2025-02-21T19:45:29Z", "authors": ["Adam Chwila", "Monika Hadaś-Dyduch", "Małgorzata Krzciuk", "Tomasz Stachurski", "Alicja Wolny-Dominiak", "Tomasz Żądło"], "categories": ["stat.ME", "stat.AP"], "doi": "", "_query": "housing price text listing", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/1710.10400v1", "title": "Total-Text: A Comprehensive Dataset for Scene Text Detection and Recognition", "abstract": "Text in curve orientation, despite being one of the common text orientations in real world environment, has close to zero existence in well received scene text datasets such as ICDAR2013 and MSRA-TD500. The main motivation of Total-Text is to fill this gap and facilitate a new research direction for the scene text community. On top of the conventional horizontal and multi-oriented texts, it features curved-oriented text. Total-Text is highly diversified in orientations, more than half of its images have a combination of more than two orientations. Recently, a new breed of solutions that casted text detection as a segmentation problem has demonstrated their effectiveness against multi-oriented text. In order to evaluate its robustness against curved text, we fine-tuned DeconvNet and benchmark it on Total-Text. Total-Text with its annotation is available at https://github.com/cs-chan/Total-Text-Dataset", "published": "2017-10-28T06:39:43Z", "authors": ["Chee Kheng Chng", "Chee Seng Chan"], "categories": ["cs.CV"], "doi": "", "_query": "housing price text listing", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/1506.07312v4", "title": "Universal expressions of population change by the Price equation: natural selection, information, and maximum entropy production", "abstract": "The Price equation shows the unity between the fundamental expressions of change in biology, in information and entropy descriptions of populations, and in aspects of thermodynamics. The Price equation partitions the change in the average value of a metric between two populations. A population may be composed of organisms or particles or any members of a set to which we can assign probabilities. A metric may be biological fitness or physical energy or the output of an arbitrarily complicated function that assigns quantitative values to members of the population. The first part of the Price equation describes how directly applied forces change the probabilities assigned to members of the population when holding constant the metrical values of the members---a fixed metrical frame of reference. The second part describes how the metrical values change, altering the metrical frame of reference. In canonical examples, the direct forces balance the changing metrical frame of reference, leaving the average or total metrical values unchanged. In biology, relative reproductive success (fitness) remains invariant as a simple consequence of the conservation of total probability. In physics, systems often conserve total energy. Nonconservative metrics can be described by starting with conserved metrics, and then studying how coordinate transformations between conserved and nonconserved metrics alter the geometry of the dynamics and the aggregate values of populations. From this abstract perspective, key results from different subjects appear more simply as universal geometric principles for the dynamics of populations subject to the constraints of particular conserved quantities", "published": "2015-06-24T10:41:22Z", "authors": ["Steven A. Frank"], "categories": ["cond-mat.stat-mech", "q-bio.PE"], "doi": "10.1002/ece3.2922", "_query": "housing price text listing", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/math/0402142v2", "title": "Scalar curvature rigidity of almost Hermitian spin manifolds which are asymptotically complex hyperbolic", "abstract": "This paper generalizes a rigidity result of complex hyperbolic spaces by M. Herzlich. We prove that an almost Hermitian spin manifold $(M,g)$ of real dimension $4n+2$ which is strongly asymptotic to $\\hyp{\\C}^{2n+1}$ and satisfies a certain scalar curvature bound must be isometric to the complex hyperbolic space. The fact that we do not assume $g$ to be Kähler reflects in the inequality for the scalar curvature.", "published": "2004-02-09T16:35:34Z", "authors": ["Mario Listing"], "categories": ["math.DG"], "doi": "", "_query": "housing price text listing", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2004.01664v2", "title": "A sharp version of Price's law for wave decay on asymptotically flat spacetimes", "abstract": "We prove Price's law with an explicit leading order term for solutions $φ(t,x)$ of the scalar wave equation on a class of stationary asymptotically flat $(3+1)$-dimensional spacetimes including subextremal Kerr black holes. Our precise asymptotics in the full forward causal cone imply in particular that $φ(t,x)=c t^{-3}+\\mathcal O(t^{-4+})$ for bounded $|x|$, where $c\\in\\mathbb C$ is an explicit constant. This decay also holds along the event horizon on Kerr spacetimes and thus renders a result by Luk-Sbierski on the linear scalar instability of the Cauchy horizon unconditional. We moreover prove inverse quadratic decay of the radiation field, with explicit leading order term. We establish analogous results for scattering by stationary potentials with inverse cubic spatial decay. On the Schwarzschild spacetime, we prove pointwise $t^{-2 l-3}$ decay for waves with angular frequency at least $l$, and $t^{-2 l-4}$ decay for waves which are in addition initially static. This definitively settles Price's law for linear scalar waves in full generality. The heart of the proof is the analysis of the resolvent at low energies. Rather than constructing its Schwartz kernel explicitly, we proceed more directly using the geometric microlocal approach to the limiting absorption principle pioneered by Melrose and recently extended to the zero energy limit by Vasy.", "published": "2020-04-03T16:37:34Z", "authors": ["Peter Hintz"], "categories": ["math.AP", "gr-qc"], "doi": "10.1007/s00220-021-04276-8", "_query": "housing price text listing", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2501.15916v2", "title": "Online Housing Market", "abstract": "This paper studies an online variant of the celebrated housing market problem, where each agent has a single house and seeks to exchange it for another based on her preferences. In this online setting, agents may arrive and depart at any time, meaning that not all agents are present on the housing market simultaneously. I extend the well known serial dictatorship and Gale s top trading cycle mechanisms to this online scenario, aiming to retain their desirable properties such as Pareto efficiency, individual rationality, and strategy proofness. These extensions also seek to prevent agents from strategically delaying their arrival or advancing their departure. I demonstrate that achieving all of these properties simultaneously is impossible in the online context, and I present several variants that achieve different subsets of these properties.", "published": "2025-01-27T10:05:49Z", "authors": ["Julien Lesca"], "categories": ["cs.GT", "cs.AI"], "doi": "", "_query": "housing price text listing", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2512.04959v2", "title": "Towards a Fully Automated Differential $\\text{NNLO}_\\text{EW}$ Generator for Lepton Colliders", "abstract": "Future proposed lepton collider experiments will reach unprecedented levels of accuracy. To ensure the success of these experiments, and to fully exploit their wealth of data, the precision of theory calculations must reach comparable or even better levels. One bottleneck in achieving this precision target lies in the systematic, process-independent inclusion of higher-order corrections at Next-to-Next-to-Leading Order in the electroweak coupling $\\text{NNLO}_\\text{EW}$ while ensuring the correct matching with modern all-orders resummation techniques. Here, we present a solution to this problem, based on the Yennie-Frautschi-Suura theorem, which employs a local infrared (IR) subtraction to remove divergences and its matching to an all-order resummation of the soft and soft-collinear logarithms.", "published": "2025-12-04T16:29:01Z", "authors": ["Alan Price", "Frank Krauss"], "categories": ["hep-ph", "hep-ex", "hep-th"], "doi": "10.1103/rt7d-7jf9", "_query": "housing price text listing", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2512.15720v1", "title": "Hidden Order in Trades Predicts the Size of Price Moves", "abstract": "Financial markets exhibit an apparent paradox: while directional price movements remain largely unpredictable--consistent with weak-form efficiency--the magnitude of price changes displays systematic structure. Here we demonstrate that real-time order-flow entropy, computed from a 15-state Markov transition matrix at second resolution, predicts the magnitude of intraday returns without providing directional information. Analysis of 38.5 million SPY trades over 36 trading days reveals that conditioning on entropy below the 5th percentile increases subsequent 5-minute absolute returns by a factor of 2.89 (t = 12.41, p < 0.0001), while directional accuracy remains at 45.0%--statistically indistinguishable from chance (p = 0.12). This decoupling arises from a fundamental symmetry: entropy is invariant under sign permutation, detecting the presence of informed trading without revealing its direction. Walk-forward validation across five non-overlapping test periods confirms out-of-sample predictability, and label-permutation placebo tests yield z = 14.4 against the null. These findings suggest that information-theoretic measures may serve as volatility state variables in market microstructure, though the limited sample (36 days, single instrument) requires extended validation.", "published": "2025-12-02T23:20:46Z", "authors": ["Mainak Singha"], "categories": ["q-fin.TR", "q-fin.ST", "stat.AP", "stat.ME"], "doi": "", "_query": "housing price text listing", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/1310.0755v1", "title": "Relative K-area homology and applications", "abstract": "We show an Uhlenbeck type estimate for closed simply connected manifolds which provides the existence of certain exact sequences in K-area homology. This leads to the behavior of the K-area homology under surgery. Moreover, we give an index theoretic obstruction to positive scalar curvature on compact spin manifolds with boundary.", "published": "2013-10-02T16:32:10Z", "authors": ["Mario Listing"], "categories": ["math.DG", "math.AT"], "doi": "", "_query": "housing price text listing", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2502.03012v1", "title": "A Framework to Monitor the Effects of External Shocks on Housing Markets", "abstract": "We develop a framework to holistically test for and monitor the impact of different types of events affecting a country's housing market, yet originating from housing-external sources. We classify events along three dimensions leading to testable hypotheses: prices versus quantities, supply versus demand, and immediate versus gradually evolving. These dimensions translate into guidance about which data type, statistical measure and testing strategy should be used. To perform such test suitable statistical models are needed which we implement as a hierarchical hedonic price model and a complementary count model. These models are amended by regime and contextual variables as suggested by our classification strategy. We apply this framework to the Austrian real estate market together with three disruptive events triggered by the COVID-19 pandemic, a policy tightening mortgage lending standards, as well as the cost-of-living crisis that came along with increased financing costs. The tests yield the expected results and, by that, some housing market puzzles are resolved. Deviating from the prior classification exercise means that some developments would have been undetected. Further, adopting our framework consistently when performing empirical research on residential real estate would lead to better comparable research results and, by that, would allow researchers to draw meta-conclusions from the bulk of studies available across time and space.", "published": "2025-02-05T09:13:33Z", "authors": ["Anja Hahn", "Sanela Omerovic", "Sofie Waltl"], "categories": ["econ.GN"], "doi": "", "_query": "housing price text listing", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2406.17777v1", "title": "Text-Animator: Controllable Visual Text Video Generation", "abstract": "Video generation is a challenging yet pivotal task in various industries, such as gaming, e-commerce, and advertising. One significant unresolved aspect within T2V is the effective visualization of text within generated videos. Despite the progress achieved in Text-to-Video~(T2V) generation, current methods still cannot effectively visualize texts in videos directly, as they mainly focus on summarizing semantic scene information, understanding, and depicting actions. While recent advances in image-level visual text generation show promise, transitioning these techniques into the video domain faces problems, notably in preserving textual fidelity and motion coherence. In this paper, we propose an innovative approach termed Text-Animator for visual text video generation. Text-Animator contains a text embedding injection module to precisely depict the structures of visual text in generated videos. Besides, we develop a camera control module and a text refinement module to improve the stability of generated visual text by controlling the camera movement as well as the motion of visualized text. Quantitative and qualitative experimental results demonstrate the superiority of our approach to the accuracy of generated visual text over state-of-the-art video generation methods. The project page can be found at https://laulampaul.github.io/text-animator.html.", "published": "2024-06-25T17:59:41Z", "authors": ["Lin Liu", "Quande Liu", "Shengju Qian", "Yuan Zhou", "Wengang Zhou", "Houqiang Li", "Lingxi Xie", "Qi Tian"], "categories": ["cs.CV"], "doi": "", "_query": "housing price text listing", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/math/0408224v1", "title": "Conformally invariant Cotton and Bach tensor in N-dimensions", "abstract": "This paper presents conformal invariants for Riemannian manifolds of dimension greater than or equal to four whose vanishing is necessary for a Riemannian manifold to be conformally related to an Einstein space. One of the invariants is a modification of the Cotton tensor, the other is a $n$--dimensional version of the Bach tensor. In general both tensors are smooth only on an open and dense subset of $M$, but this subset is invariant under conformal transformations. Moreover, we generalize the main result of \"Conformal Einstein Spaces in $N$--Dimensions\" published in \\emph{Ann. Global Anal. Geom.} {\\bf 20}(2) (2001).", "published": "2004-08-17T18:02:17Z", "authors": ["Mario Listing"], "categories": ["math.DG"], "doi": "", "_query": "housing price text listing", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2605.21358v1", "title": "From Summer to Spring: A Shift in US Housing Market Seasonality", "abstract": "The US housing market exhibits pronounced seasonal cycles: prices and sales rise through spring, peak in summer, and decline through autumn and winter. Since 2021, this pattern has shifted earlier in the calendar year, with spring strengthening at the expense of the traditional summer peak. A leading explanation for housing market seasonality is the search-and-matching model of Ngai and Tenreyro (2014), which links these cycles to household mobility through a thick-market mechanism. In this framework, periods with higher mobility generate thicker markets and higher prices and transaction volumes. Viewed through this lens, a shift in the seasonal cycle of prices and sales raises the question of whether the timing of household moves has changed. Did residential mobility shift earlier in the calendar year after 2021? We find that it did. Using SIPP data, and corroborating evidence from Google Trends indicators, we document a post-2021 shift in mobility toward spring. We extend the model to a monthly frequency, prove the existence and uniqueness of the equilibrium, and calibrate it to the observed mobility patterns. The calibrated model reproduces the spring shift in both prices and transaction volumes, consistent with the view that a change in the timing of household mobility alone can account for the recent shift in housing market seasonality.", "published": "2026-05-20T16:23:25Z", "authors": ["Yihan Hu", "Cemil Selcuk"], "categories": ["econ.GN"], "doi": "", "_query": "housing price text listing", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2211.11350v1", "title": "Rooms with Text: A Dataset for Overlaying Text Detection", "abstract": "In this paper, we introduce a new dataset of room interior pictures with overlaying and scene text, totalling to 4836 annotated images in 25 product categories. We provide details on the collection and annotation process of our dataset, and analyze its statistics. Furthermore, we propose a baseline method for overlaying text detection, that leverages the character region-aware text detection framework to guide the classification model. We validate our approach and show its efficiency in terms of binary classification metrics, reaching the final performance of 0.95 F1 score, with false positive and false negative rates of 0.02 and 0.06 correspondingly.", "published": "2022-11-21T11:04:41Z", "authors": ["Oleg Smirnov", "Aditya Tewari"], "categories": ["cs.CV"], "doi": "", "_query": "housing price text listing", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/1007.3166v2", "title": "Homology of finite K-area", "abstract": "We use Gromov's K--area to define a generalized homology theory on compact smooth manifolds. In fact, this theory collects obstructions to the enlargeability of the manifold and its nontrivial submanifolds. Moreover, using the K--area homology we can rephrase some classic results about positive scalar curvature.", "published": "2010-07-19T14:50:10Z", "authors": ["Mario Listing"], "categories": ["math.DG", "math.KT"], "doi": "", "_query": "housing price text listing", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2407.21209v3", "title": "Algorithm-Assisted Decision Making and Racial Disparities in Housing: A Study of the Allegheny Housing Assessment Tool", "abstract": "The demand for housing assistance across the United States far exceeds the supply, leaving housing providers the task of prioritizing clients for receipt of this limited resource. To be eligible for federal funding, local homelessness systems are required to implement assessment tools as part of their prioritization processes. The Vulnerability Index Service Prioritization Decision Assistance Tool (VI-SPDAT) is the most commonly used assessment tool nationwide. Recent studies have criticized the VI-SPDAT as exhibiting racial bias, which may lead to unwarranted racial disparities in housing provision. In response to these criticisms, some jurisdictions have developed alternative tools, such as the Allegheny Housing Assessment (AHA), which uses algorithms to assess clients' risk levels. Drawing on data from its deployment, we conduct descriptive and quantitative analyses to evaluate whether replacing the VI-SPDAT with the AHA affects racial disparities in housing allocation. We find that the VI-SPDAT tended to assign higher risk scores to white clients and lower risk scores to Black clients, and that white clients were served at a higher rates pre-AHA deployment. While post-deployment service decisions became better aligned with the AHA score, and the distribution of AHA scores is similar across racial groups, we do not find evidence of a corresponding decrease in disparities in service rates. We attribute the persistent disparity to the use of Alt-AHA, a survey-based tool that is used in cases of low data quality, as well as group differences in eligibility-related factors, such as chronic homelessness and veteran status. We discuss the implications for housing service systems seeking to reduce racial disparities in their service delivery.", "published": "2024-07-30T21:44:48Z", "authors": ["Lingwei Cheng", "Cameron Drayton", "Alexandra Chouldechova", "Rhema Vaithianathan"], "categories": ["cs.HC", "econ.GN"], "doi": "", "_query": "housing price text listing", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2203.01477v1", "title": "A Matching Mechanism for Provision of Housing to the Marginalized", "abstract": "During this pandemic, there have been unprecedented community and local government efforts to slow down the spread of the coronavirus, and also to protect our local economies. One such effort is California's project Roomkey that provided emergency housing to over 2000 vulnerable persons but fell short of the set goal of 15,000. It is projected that the homelessness problem will only get worse after the pandemic. With that in mind, we borrow from efforts like project Roomkey and suggest a solution that looks to improve upon these efforts to efficiently assign housing to the unhoused in our communities. The pandemic, together with the project Roomkey, shed light on the underlying supply demand mismatch that presents an opportunity for a matching mechanism solution to assigning housing options to the unhoused in a way that maximizes social welfare and minimizes susceptibility to strategic manipulation. Additionally, we argue that this automated solution would cut down on the amount of funding and personnel required for the assignment of housing to unhoused persons. Our solution is not intended to replace current solutions to homeless housing assignments but rather improve upon them. We can not postpone a proper solution to homelessness anymore, the time is now as the need for an efficient solution is most dire.", "published": "2022-03-03T01:34:36Z", "authors": ["J Ceasar Aguma"], "categories": ["cs.GT", "cs.CY", "econ.GN"], "doi": "", "_query": "housing price text listing", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/1509.03915v3", "title": "An Impossibility Result for Housing Markets with Fractional Endowments", "abstract": "The housing market setting constitutes a fundamental model of exchange economies of goods. Most of the work concerning housing markets does not cater for randomized assignments or allocation of time-shares. House allocation with fractional endowments of houses was considered by Athanassoglou and Sethuraman (2011) who posed the open problem whether individual rationality, weak strategyproofness, and efficiency are compatible for the setting. We show that the three axioms are incompatible.", "published": "2015-09-14T00:22:54Z", "authors": ["Haris Aziz"], "categories": ["cs.GT", "cs.DS"], "doi": "", "_query": "housing price text listing", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2312.01938v1", "title": "DSText V2: A Comprehensive Video Text Spotting Dataset for Dense and Small Text", "abstract": "Recently, video text detection, tracking, and recognition in natural scenes are becoming very popular in the computer vision community. However, most existing algorithms and benchmarks focus on common text cases (e.g., normal size, density) and single scenario, while ignoring extreme video text challenges, i.e., dense and small text in various scenarios. In this paper, we establish a video text reading benchmark, named DSText V2, which focuses on Dense and Small text reading challenges in the video with various scenarios. Compared with the previous datasets, the proposed dataset mainly include three new challenges: 1) Dense video texts, a new challenge for video text spotters to track and read. 2) High-proportioned small texts, coupled with the blurriness and distortion in the video, will bring further challenges. 3) Various new scenarios, e.g., Game, Sports, etc. The proposed DSText V2 includes 140 video clips from 7 open scenarios, supporting three tasks, i.e., video text detection (Task 1), video text tracking (Task 2), and end-to-end video text spotting (Task 3). In this article, we describe detailed statistical information of the dataset, tasks, evaluation protocols, and the results summaries. Most importantly, a thorough investigation and analysis targeting three unique challenges derived from our dataset are provided, aiming to provide new insights. Moreover, we hope the benchmark will promise video text research in the community. DSText v2 is built upon DSText v1, which was previously introduced to organize the ICDAR 2023 competition for dense and small video text.", "published": "2023-11-29T09:13:27Z", "authors": ["Weijia Wu", "Yiming Zhang", "Yefei He", "Luoming Zhang", "Zhenyu Lou", "Hong Zhou", "Xiang Bai"], "categories": ["cs.CV"], "doi": "", "_query": "housing price text listing", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2210.10146v1", "title": "Housing Forecasts via Stock Market Indicators", "abstract": "Through the reinterpretation of housing data as candlesticks, we extend Nature Scientific Reports' article by Liang and Unwin [LU22] on stock market indicators for COVID-19 data, and utilize some of the most prominent technical indicators from the stock market to estimate future changes in the housing market, comparing the findings to those one would obtain from studying real estate ETF's. By providing an analysis of MACD, RSI, and Candlestick indicators (Bullish Engulfing, Bearish Engulfing, Hanging Man, and Hammer), we exhibit their statistical significance in making predictions for USA data sets (using Zillow Housing data) and also consider their applications within three different scenarios: a stable housing market, a volatile housing market, and a saturated market. In particular, we show that bearish indicators have a much higher statistical significance then bullish indicators, and we further illustrate how in less stable or more populated countries, bearish trends are only slightly more statistically present compared to bullish trends.", "published": "2022-10-18T20:23:43Z", "authors": ["Varun Mittal", "Laura P. Schaposnik"], "categories": ["econ.GN"], "doi": "10.1016/j.heliyon.2023.e16286", "_query": "housing price text listing", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/1007.1832v1", "title": "Scalar Curvature on Compact Symmetric Spaces", "abstract": "A classic result by Gromov and Lawson states that a Riemannian metric of non--negative scalar curvature on the Torus must be flat. The analogous rigidity result for the standard sphere was shown by Llarull. Later Goette and Semmelmann generalized it to locally symmetric spaces of compact type and nontrivial Euler characteristic. In this paper we improve the results by Llarull and Goette, Semmelmann. In fact we show that if $(M,g_0)$ is a locally symmetric space of compact type with $χ(M)\\neq 0$ and $g$ is a Riemannian metric on $M$ with $\\mathrm{scal}_g\\cdot g\\geq \\mathrm{scal}_0\\cdot g_0$, then $g$ is a constant multiple of $g_0$. The previous results by Llarull and Goette, Semmelmann always needed the two inequalities $g\\geq g_0$ and $\\mathrm{scal}_g\\geq \\mathrm{scal}_0$ in order to conclude $g=g_0$. Moreover, if $(S^{2m},g_0)$ is the standard sphere, we improve this result further and show that any metric $g$ on $S^{2m}$ of scalar curvature $\\mathrm{scal}_g\\geq (2m-1)\\mathrm{tr}_g(g_0)$ is a constant multiple of $g_0$.", "published": "2010-07-12T07:11:41Z", "authors": ["Mario Listing"], "categories": ["math.DG"], "doi": "", "_query": "housing price text listing", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/1904.12640v2", "title": "TextCohesion: Detecting Text for Arbitrary Shapes", "abstract": "In this paper, we propose a pixel-wise method named TextCohesion for scene text detection, which splits a text instance into five key components: a Text Skeleton and four Directional Pixel Regions. These components are easier to handle than the entire text instance. A confidence scoring mechanism is designed to filter characters that are similar to text. Our method can integrate text contexts intensively when backgrounds are complex. Experiments on two curved challenging benchmarks demonstrate that TextCohesion outperforms state-of-the-art methods, achieving the F-measure of 84.6% on Total-Text and bfseries86.3% on SCUT-CTW1500.", "published": "2019-04-22T13:21:38Z", "authors": ["Weijia Wu", "Jici Xing", "Hong Zhou"], "categories": ["cs.CV"], "doi": "", "_query": "housing price text listing", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2002.01578v1", "title": "Rental Housing Spot Markets: How Online Information Exchanges Can Supplement Transacted-Rents Data", "abstract": "Traditional US rental housing data sources such as the American Community Survey and the American Housing Survey report on the transacted market - what existing renters pay each month. They do not explicitly tell us about the spot market - i.e., the asking rents that current homeseekers must pay to acquire housing - though they are routinely used as a proxy. This study compares governmental data to millions of contemporaneous rental listings and finds that asking rents diverge substantially from these most recent estimates. Conventional housing data understate current market conditions and affordability challenges, especially in cities with tight and expensive rental markets.", "published": "2020-02-04T23:23:35Z", "authors": ["Geoff Boeing", "Jake Wegmann", "Junfeng Jiao"], "categories": ["econ.GN", "cs.CY", "stat.AP"], "doi": "10.1177/0739456X20904435", "_query": "housing price text listing", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2609.21478v1", "title": "Bricks or Cash? Externalities of Housing Upgrading in High-density Cities", "abstract": "We estimate housing externalities in a high-density city, exploiting the staggered rollout of Singapore's nationwide Main Upgrading Programme for public housing. Controlling for nonrandom neighborhood exposure, we find that upgrading raises treated buildings' prices by 11.5% upon completion and neighboring buildings' resale prices by about 2% within 500 meters, decaying to zero beyond. A model with distance-decaying externalities shows that in dense settings spillovers justify the distortions of in-kind provision; this advantage diminishes and reverses at lower densities. Administrative data on over 2 million residents show that upgrading disproportionately retains older incumbents, suggesting age-specific amenities as an underexplored externality channel.", "published": "2026-09-18T08:26:44Z", "authors": ["Sumit Agarwal", "Ying Deng", "Yi Fan", "Qi Gao", "Jing Li", "Lin Ma"], "categories": ["econ.GN"], "doi": "", "_query": "housing price text listing", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2205.01255v1", "title": "Incorporating Financial Hardship in Measuring the Mental Health Impact of Housing Stress", "abstract": "Housing expenditure tends to be sticky and costly to adjust, and makes up a large proportion of household expenditure. Additionally, the loss of housing can have catastrophic consequences. These specific features of housing expenditure imply that housing stress could cause negative mental health impacts. This research investigates the effects of housing stress on mental health, contributing to the literature by nesting housing stress within a measure of financial hardship, thus improving robustness to omitted variables and creating a natural comparison group for matching. Fixed effects (FE) regressions and a difference-in-differences (DID) methodology are estimated utilising data from the Household Income and Labour Dynamics in Australia (HILDA) Survey. The results show that renters who are in housing stress have a significant decline in self-reported mental health, with those in prior financial hardship being more severely affected. In contrast, there is little to no evidence of housing stress impacting on owners with a mortgage. The results also suggest that the mental health impact of housing stress is more important than some, but not all, aspects of financial hardship.", "published": "2022-05-03T00:38:56Z", "authors": ["Timothy Ludlow", "Jonas Fooken", "Christiern Rose", "Kam Tang"], "categories": ["econ.GN"], "doi": "", "_query": "housing price text listing", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2604.24576v1", "title": "BuyTheBy: A dataset of 18,710 text-based paper mill advertisements with 51,812 timestamped prices", "abstract": "The study of paper mills and similar businesses operating in the market for academic and education fraud services is frustrated by the lack of market price data on their various offerings. Here, we assemble BuyTheBy, a large, annotated dataset of timestamped, text-based paper mill advertisements from seven businesses operating out of seven different countries. The dataset consists of 18,710 individual advertisements, of which 15,839 have prices listed. Among these there are 20,598 positions listed as for sale on 5,567 unique products in 14 different product categories with 51,812 timestamped price data points. We perform elementary analysis of this dataset to demonstrate its utility for quantitative understanding of markets for academic fraud services and suggest future use cases.", "published": "2026-04-21T21:29:40Z", "authors": ["Reese AK Richardson", "Spencer S Hong", "Anna Abalkina"], "categories": ["cs.CY"], "doi": "", "_query": "housing price text listing", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/1406.6100v1", "title": "Probabilistic flows of inhabitants in urban areas and self-organization in housing markets", "abstract": "We propose a simple probabilistic model to explain the spatial structure of the rent distribution of housing market in city of Sapporo. Here we modify the mathematical model proposed by Gauvin et. al. Especially, we consider the competition between two distances, namely, the distance between house and center, and the distance between house and office. Computer simulations are carried out to reveal the self-organized spatial structure appearing in the rent distribution. We also compare the resulting distribution with empirical rent distribution in Sapporo as an example of cities designated by ordinance. We find that the lowest ranking agents (from the viewpoint of the lowest `willing to pay') are swept away from relatively attractive regions and make several their own `communities' at low offering price locations in the city.", "published": "2014-06-23T22:11:26Z", "authors": ["Takao Hishikawa", "Jun-ichi Inoue"], "categories": ["physics.soc-ph", "q-fin.GN"], "doi": "", "_query": "housing price text listing", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2606.06694v2", "title": "The Geography of Algorithmic Judgment: LLM Intermediaries, Place Identity, and Racial Steering in Housing Search", "abstract": "Large language models (LLMs) are rapidly assuming an intermediary role in housing search through the integration of listing platforms within conversational interfaces, mediating access to information, search, and recommendations within urban settings. We expand on prior work on racial steering in LLMs by conducting a behavioral audit of seven open-weight and closed-source LLMs across four U.S. cities, testing location recommendations across three iterative prompting conditions that progressively add lifestyle preference context and reflect fair housing paired-testing methodologies. We find that steering is an emergent behavior of the model's interpretive license rather than primarily a static property. Steering results from the interaction of a user's identity, preference articulation, and the spatial logic that a model has internalized about learned representations of place, preference, and opportunity in a given city, and how different types of users relate to it. While steering was present, it was not uniform in direction or magnitude across evaluated conditions. Preference-conditioned testing often increased or reconfigured the number of models that exhibited steering behaviors relative to baseline conditions, suggesting that LLMs may interpret what the same housing preference means differently depending on the racial identity of the user. Our findings also demonstrate that the city is not a neutral testing unit for LLM evaluation in place-based sectors, and results from one local market cannot be assumed to generalize to another. Local and domain expertise will be required in the housing sector to ensure that legal and institutional commitments to fair housing are not undermined while adopting AI tools that mediate spatial access.", "published": "2026-06-04T20:17:58Z", "authors": ["Hana Samad", "Trung Lam", "Christoph Mügge-Durum", "Michael Akinwumi"], "categories": ["cs.LG", "cs.AI", "cs.CY"], "doi": "10.1609/aies.v9i4.43303", "_query": "housing price text listing", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/1208.0371v1", "title": "Can Metropolitan Housing Risk be Diversified? A Cautionary Tale from the Recent Boom and Bust", "abstract": "Geographic diversification is fundamental to risk mitigation among investors and insurers of housing, mortgages, and mortgage-related derivatives. To characterize diversification potential, we provide estimates of integration, spatial correlation, and contagion among US metropolitan housing markets. Results reveal a high and increasing level of integration among US markets over the decade of the 2000s, especially in California. We apply integration results to assess the risk of alternative housing investment portfolios. Portfolio simulation indicates reduced diversification potential and increased risk in the wake of estimated increases in metropolitan housing market integration. Research findings provide new insights regarding the synchronous non-performance of geographically-disparate MBS investments during the late 2000s.", "published": "2012-08-01T21:42:56Z", "authors": ["John Cotter", "Stuart Gabriel", "Richard Roll"], "categories": ["q-fin.PM"], "doi": "", "_query": "housing price text listing", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2308.06929v1", "title": "Predicting Listing Prices In Dynamic Short Term Rental Markets Using Machine Learning Models", "abstract": "Our research group wanted to take on the difficult task of predicting prices in a dynamic market. And short term rentals such as Airbnb listings seemed to be the perfect proving ground to do such a thing. Airbnb has revolutionized the travel industry by providing a platform for homeowners to rent out their properties to travelers. The pricing of Airbnb rentals is prone to high fluctuations, with prices changing frequently based on demand, seasonality, and other factors. Accurate prediction of Airbnb rental prices is crucial for hosts to optimize their revenue and for travelers to make informed booking decisions. In this project, we aim to predict the prices of Airbnb rentals using a machine learning modeling approach. Our project expands on earlier research in the area of analyzing Airbnb rental prices by taking a methodical machine learning approach as well as incorporating sentiment analysis into our feature engineering. We intend to gain a deeper understanding on periodic changes of Airbnb rental prices. The primary objective of this study is to construct an accurate machine learning model for predicting Airbnb rental prices specifically in Austin, Texas. Our project's secondary objective is to identify the key factors that drive Airbnb rental prices and to investigate how these factors vary across different locations and property types.", "published": "2023-08-14T04:15:09Z", "authors": ["Sam Chapman", "Seifey Mohammad", "Kimberly Villegas"], "categories": ["cs.LG"], "doi": "", "_query": "housing price text listing", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/1903.11205v3", "title": "Airbnb's disruption of the housing structure in London", "abstract": "This paper explores Airbnb, a peer-to-peer platform for short-term rental of housing accommodation, examining the geographical pattern of those establishments using data from London. Our purpose is to analyse whether or not the diversity of dwelling types correlate with the distribution of listings. We use a measure of spread based on entropy to indicate the diversity of dwelling types and look at its relationship with the distribution of Airbnb establishments, as well as the type of home ownership using correlation analysis. It is important to note that our study only considers domestic building types, and excludes any information on the diversity of land uses. Two important findings emerge from our analysis. Firstly, the spatial location of Airbnb rentals is negatively correlated with the diversity of dwelling types, and positively correlated with a single dwelling type, which corresponds in general to purpose built flats, conversions and flats in commercial buildings. Secondly, Airbnb is associated with areas that have a high proportion of privately rented properties, detracting more than 1.4% of the housing supply into short-term rentals. Such a phenomenon can reach up to 20% in some neighbourhoods, further exacerbating the process of gentrification. Finally, we discuss the implications of these findings as instruments to inform policies associated with the 'sharing' economy in relation to the disruption of the housing structure.", "published": "2019-03-27T00:49:15Z", "authors": ["Zahratu Shabrina", "Elsa Arcaute", "Michael Batty"], "categories": ["cs.CY", "physics.soc-ph"], "doi": "", "_query": "housing price text listing", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2110.12394v1", "title": "Housing property rights and social integration of migrant population: based on the 2017 china migrants' dynamic survey", "abstract": "Push-pull theory, one of the most important macro theories in demography, argues that population migration is driven by a combination of push (repulsive) forces at the place of emigration and pull (attractive) forces at the place of emigration. Based on the push-pull theory, this paper shows another practical perspective of the theory by measuring the reverse push and pull forces from the perspective of housing property rights. We use OLS and sequential Probit models to analyze the impact of urban and rural property rights factors on the social integration of the migrant population-based, on \"China Migrants' Dynamic Survey\". We found that after controlling for personal and urban characteristics, there is a significant negative effect of rural property rights (homestead) ownership of the mobile population on their socio-economic integration, and cultural and psychological integration in the inflow area. The effect of urban house price on social integration of the migrant population is consistent with the \"inverted U-shaped\" nonlinear assumption: when the house price to income ratio of the migrant population in the inflow area increases beyond the inflection point, its social integration level decreases. That is, there is an inverse push force and pull force mechanism of housing property rights on population mobility.", "published": "2021-10-24T09:07:35Z", "authors": ["Jingwen Tan", "Shixi Kang"], "categories": ["econ.GN"], "doi": "", "_query": "housing price text listing", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2406.06524v2", "title": "Gas Fees on the Ethereum Blockchain: From Foundations to Derivatives Valuations", "abstract": "The gas fee, paid for inclusion in the blockchain, is analyzed in two parts. First, we consider how effort in terms of resources required to process and store a transaction turns into a gas limit, which, through a fee, comprised of the base and priority fee in the current version of Ethereum, is converted into the cost paid by the user. We adhere closely to the Ethereum protocol to simplify the analysis and to constrain the design choices when considering multidimensional gas. Second, we assume that the gas price is given deus ex machina by a fractional Ornstein-Uhlenbeck process and evaluate various derivatives. These contracts can, for example, mitigate gas cost volatility. The ability to price and trade forwards besides the existing spot inclusion into the blockchain could enable users to hedge against future cost fluctuations. Overall, this paper offers a comprehensive analysis of gas fee dynamics on the Ethereum blockchain, integrating supply-side constraints with demand-side modelling to enhance the predictability and stability of transaction costs.", "published": "2024-06-10T17:59:46Z", "authors": ["Bernhard K Meister", "Henry CW Price"], "categories": ["q-fin.PM", "q-fin.GN"], "doi": "", "_query": "housing price text listing", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2302.13997v3", "title": "Host Community Respecting Refugee Housing", "abstract": "We propose a novel model for refugee housing respecting the preferences of the accepting community and refugees themselves. In particular, we are given a topology representing the local community, a set of inhabitants occupying some vertices of the topology, and a set of refugees that should be housed on the empty vertices of the graph. Both the inhabitants and the refugees have preferences over the structure of their neighborhood. We are specifically interested in the problem of finding housing such that the preferences of every individual are met; using game-theoretical words, we are looking for housing that is stable with respect to some well-defined notion of stability. We investigate conditions under which the existence of equilibria is guaranteed and study the computational complexity of finding such a stable outcome. As the problem is NP-hard even in very simple settings, we employ the parameterized complexity framework to give a finer-grained view of the problem's complexity with respect to natural parameters and structural restrictions of the given topology.", "published": "2023-02-27T17:42:03Z", "authors": ["Dušan Knop", "Šimon Schierreich"], "categories": ["cs.GT", "cs.DS", "econ.TH"], "doi": "", "_query": "housing price text listing", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2403.17998v1", "title": "Text Is MASS: Modeling as Stochastic Embedding for Text-Video Retrieval", "abstract": "The increasing prevalence of video clips has sparked growing interest in text-video retrieval. Recent advances focus on establishing a joint embedding space for text and video, relying on consistent embedding representations to compute similarity. However, the text content in existing datasets is generally short and concise, making it hard to fully describe the redundant semantics of a video. Correspondingly, a single text embedding may be less expressive to capture the video embedding and empower the retrieval. In this study, we propose a new stochastic text modeling method T-MASS, i.e., text is modeled as a stochastic embedding, to enrich text embedding with a flexible and resilient semantic range, yielding a text mass. To be specific, we introduce a similarity-aware radius module to adapt the scale of the text mass upon the given text-video pairs. Plus, we design and develop a support text regularization to further control the text mass during the training. The inference pipeline is also tailored to fully exploit the text mass for accurate retrieval. Empirical evidence suggests that T-MASS not only effectively attracts relevant text-video pairs while distancing irrelevant ones, but also enables the determination of precise text embeddings for relevant pairs. Our experimental results show a substantial improvement of T-MASS over baseline (3% to 6.3% by R@1). Also, T-MASS achieves state-of-the-art performance on five benchmark datasets, including MSRVTT, LSMDC, DiDeMo, VATEX, and Charades.", "published": "2024-03-26T17:59:52Z", "authors": ["Jiamian Wang", "Guohao Sun", "Pichao Wang", "Dongfang Liu", "Sohail Dianat", "Majid Rabbani", "Raghuveer Rao", "Zhiqiang Tao"], "categories": ["cs.CV"], "doi": "", "_query": "housing price text listing", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2605.05535v2", "title": "Housing Potential Common Data Model and City Digital Twin", "abstract": "The evaluation of housing potential requires consideration of a location from multiple perspectives, ranging from zoning and land use to population characteristics and access to services. This research introduces the Housing Potential Common Data Model (HPCDM) to overcome existing data silos, serving as a standard to support integration and interoperability across the diverse range of datasets that are required for housing potential analysis. This report details the evaluation of the model along with the creation of a City Digital Twin for housing and a pilot dashboard application to demonstrate a practical implementation. Beyond the technical framework, this work identifies critical barriers to adoption and provides actionable mitigation strategies for urban planners and stakeholders.", "published": "2026-05-07T00:27:47Z", "authors": ["Megan Katsumi", "Mark Fox", "Anderson Wong", "Divnoor Chatha"], "categories": ["cs.AI"], "doi": "", "_query": "housing price text listing", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2508.20396v2", "title": "BiListing: Modality Alignment for Listings", "abstract": "Airbnb is a leader in offering travel accommodations. Airbnb has historically relied on structured data to understand, rank, and recommend listings to guests due to the limited capabilities and associated complexity arising from extracting meaningful information from text and images. With the rise of representation learning, leveraging rich information from text and photos has become easier. A popular approach has been to create embeddings for text documents and images to enable use cases of computing similarities between listings or using embeddings as features in an ML model. However, an Airbnb listing has diverse unstructured data: multiple images, various unstructured text documents such as title, description, and reviews, making this approach challenging. Specifically, it is a non-trivial task to combine multiple embeddings of different pieces of information to reach a single representation. This paper proposes BiListing, for Bimodal Listing, an approach to align text and photos of a listing by leveraging large-language models and pretrained language-image models. The BiListing approach has several favorable characteristics: capturing unstructured data into a single embedding vector per listing and modality, enabling zero-shot capability to search inventory efficiently in user-friendly semantics, overcoming the cold start problem, and enabling listing-to-listing search along a single modality, or both. We conducted offline and online tests to leverage the BiListing embeddings in the Airbnb search ranking model, and successfully deployed it in production, achieved 0.425% of NDCB gain, and drove tens of millions in incremental revenue.", "published": "2025-08-28T03:47:31Z", "authors": ["Guillaume Guy", "Mihajlo Grbovic", "Chun How Tan", "Han Zhao"], "categories": ["cs.LG"], "doi": "10.1145/3746252.3761577", "_query": "housing price text listing", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2001.11585v1", "title": "Housing Search in the Age of Big Data: Smarter Cities or the Same Old Blind Spots?", "abstract": "Housing scholars stress the importance of the information environment in shaping housing search behavior and outcomes. Rental listings have increasingly moved online over the past two decades and, in turn, online platforms like Craigslist are now central to the search process. Do these technology platforms serve as information equalizers or do they reflect traditional information inequalities that correlate with neighborhood sociodemographics? We synthesize and extend analyses of millions of US Craigslist rental listings and find they supply significantly different volumes, quality, and types of information in different communities. Technology platforms have the potential to broaden, diversify, and equalize housing search information, but they rely on landlord behavior and, in turn, likely will not reach this potential without a significant redesign or policy intervention. Smart cities advocates hoping to build better cities through technology must critically interrogate technology platforms and big data for systematic biases.", "published": "2020-01-30T22:10:29Z", "authors": ["Geoff Boeing", "Max Besbris", "Ariela Schachter", "John Kuk"], "categories": ["stat.AP", "cs.CY", "econ.GN"], "doi": "10.1080/10511482.2019.1684336", "_query": "housing price text listing", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/1012.2606v2", "title": "Modeling urban housing market dynamics: can the socio-spatial segregation preserve some social diversity?", "abstract": "Addressing issues of social diversity, we introduce a model of housing transactions between agents who are heterogeneous in their willingness to pay. A key assumption is that agents' preferences for a location depend on both an intrinsic attractiveness and on the social characteristics of the neighborhood. The stationary space distribution of income is analytically and numerically characterized. The main results are that socio-spatial segregation occurs if -- and only if -- the social influence is strong enough, but even so, some social diversity is preserved at most locations. Comparison with data on the Paris housing market shows that the results reproduce general trends of price distribution and spatial income segregation.", "published": "2010-12-13T00:34:34Z", "authors": ["Laetitia Gauvin", "Annick Vignes", "Jean-Pierre Nadal"], "categories": ["physics.soc-ph", "cs.SI"], "doi": "", "_query": "housing price text listing", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2207.02896v1", "title": "Mortgage-Rate-Adjusted Home Prices", "abstract": "In this paper, we investigate the impact of mortgage rates on home prices, and how the impact may be used to help property purchase discussions at individual buyer level and to adjust home price indices across time. A mortgage-rate-adjusted \"effective price\" is derived to measure near term property price in the presence of (expected) mortgage rate changes. A price-mortgage rate neutrality line is then constructed based on the \"effective price\" to help differentiate various market scenarios in the near term, which can be used by prospective buyers in their \"to-buy or not-to-buy\" deliberations. At the market level, effective home prices allow for neutralization of mortgage rates on the movement of the housing market. An application of the neutralization strategy to the Case-Shiller Home Price Index (HPI) indicates that the U.S. housing market has been considerably affected by the dynamics of mortgage rates in a long run. But mortgage rates do no appear a primary driver of the extraordinary home price increase during the COVID-19 pandemic.", "published": "2022-07-06T18:16:44Z", "authors": ["Honggao Cao"], "categories": ["q-fin.GN"], "doi": "", "_query": "housing price text listing", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/1609.09066v1", "title": "Refugee Resettlement Housing Scout", "abstract": "According to the United States High Commission for Refugees (UNHCr), there are 65.3 million forcibly displaced people in the world today, 21.5 million of them being refugees. This has led to an unprecedented refugee crisis which has led countries to accept refugee families and to resettle them. Diverse agencies are helping refugees coming to US to resettle and start their new life in the country. One of the first and most challenging steps of this process is to find affordable housing that also meets a suite of additional constraints and priorities. These include being within a mile of public transportation and near schools, faith centers and international grocery stores. We detail an interactive data-driven web-based tool, which incorporates in one consolidated platform most of the needed information. The tool searches, filters and demonstrates a list of possible housing locations, and allows for the dynamic prioritization based on user-specified importance weights on the diverse criteria. The platform was created in a partnership with New American Pathways, a nonprofit that supports refugee resettlement in the metro Atlanta, but exemplifies a methodology that can help many other organizations with similar goals.", "published": "2016-09-28T04:48:20Z", "authors": ["Unaiza Ahsan", "Oleksandra Sopova", "Wes Stayton", "Bistra Dilkina"], "categories": ["cs.CY"], "doi": "", "_query": "housing price text listing", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2003.08632v2", "title": "Unsupervised deep learning for text line segmentation", "abstract": "We present an unsupervised deep learning method for text line segmentation that is inspired by the relative variance between text lines and spaces among text lines. Handwritten text line segmentation is important for the efficiency of further processing. A common method is to train a deep learning network for embedding the document image into an image of blob lines that are tracing the text lines. Previous methods learned such embedding in a supervised manner, requiring the annotation of many document images. This paper presents an unsupervised embedding of document image patches without a need for annotations. The number of foreground pixels over the text lines is relatively different from the number of foreground pixels over the spaces among text lines. Generating similar and different pairs relying on this principle definitely leads to outliers. However, as the results show, the outliers do not harm the convergence and the network learns to discriminate the text lines from the spaces between text lines. Remarkably, with a challenging Arabic handwritten text line segmentation dataset, VML-AHTE, we achieved superior performance over the supervised methods. Additionally, the proposed method was evaluated on the ICDAR 2017 and ICFHR 2010 handwritten text line segmentation datasets.", "published": "2020-03-19T08:57:53Z", "authors": ["Berat Kurar Barakat", "Ahmad Droby", "Rym Alasam", "Boraq Madi", "Irina Rabaev", "Raed Shammes", "Jihad El-Sana"], "categories": ["cs.CV", "eess.IV"], "doi": "", "_query": "housing price text listing", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2208.09231v1", "title": "Shift Variance in Scene Text Detection", "abstract": "Theory of convolutional neural networks suggests the property of shift equivariance, i.e., that a shifted input causes an equally shifted output. In practice, however, this is not always the case. This poses a great problem for scene text detection for which a consistent spatial response is crucial, irrespective of the position of the text in the scene. Using a simple synthetic experiment, we demonstrate the inherent shift variance of a state-of-the-art fully convolutional text detector. Furthermore, using the same experimental setting, we show how small architectural changes can lead to an improved shift equivariance and less variation of the detector output. We validate the synthetic results using a real-world training schedule on the text detection network. To quantify the amount of shift variability, we propose a metric based on well-established text detection benchmarks. While the proposed architectural changes are not able to fully recover shift equivariance, adding smoothing filters can substantially improve shift consistency on common text datasets. Considering the potentially large impact of small shifts, we propose to extend the commonly used text detection metrics by the metric described in this work, in order to be able to quantify the consistency of text detectors.", "published": "2022-08-19T09:11:28Z", "authors": ["Markus Glitzner", "Jan-Hendrik Neudeck", "Philipp Härtinger"], "categories": ["cs.CV"], "doi": "", "_query": "housing price text listing", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2004.12791v1", "title": "Bank financial stability, bank valuation and international oil prices: Evidence from listed Russian public banks", "abstract": "Using data on 17 listed public banks from Russia over the period 2008 to 2016, we analyze whether international oil prices affect the bank stability in an oil-dependent country. We posit that a decrease in international oil prices has a negative long-run macroeconomic impact for an oil-exporting country, which further deteriorates the bank financial stability. More specifically, a decrease in international oil prices leads for an oil-exporting country as Russia to a currency depreciation and to a deterioration of the fiscal stance. In addition, given the positive correlation of oil and stock prices documented by numerous previous studies, a decrease in international oil prices represents a negative signal for the stock markets investors, negatively affecting banks' share prices and thus, their capacity to generate sustainable earnings. In this context, the bank financial stability can be menaced. With a focus on public listed banks and using a Pool Mean Group (PMG) estimator, we show that an increase in international oil prices and in the price to book value ratio has a long-run positive effect on Russian public banks stability, and conversely. While positive oil-price shocks contribute to bank stability in the long run, an opposite effect is recorded for negative shocks. However, no significant impact is documented in the short run. Our findings are robust to different bank stability specifications, different samples and control variables.", "published": "2020-04-27T13:36:20Z", "authors": ["Claudiu Albulescu"], "categories": ["q-fin.CP"], "doi": "", "_query": "housing price text listing", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2101.00913v1", "title": "Credit Crunch: The Role of Household Lending Capacity in the Dutch Housing Boom and Bust 1995-2018", "abstract": "What causes house prices to rise and fall? Economists identify household access to credit as a crucial factor. \"Loan-to-Value\" and \"Debt-to-GDP\" ratios are the standard measures for credit access. However, these measures fail to explain the depth of the Dutch housing bust after the 2009 Financial Crisis. This work is the first to model household lending capacity based on the formulas that Dutch banks use in the mortgage application process. We compare the ability of regression models to forecast housing prices when different measures of credit access are utilised. We show that our measure of household lending capacity is a forward-looking, highly predictive variable that outperforms `Loan-to-Value' and debt ratios in forecasting the Dutch crisis. Sharp declines in lending capacity foreshadow the market deceleration.", "published": "2021-01-04T12:10:07Z", "authors": ["Menno Schellekens", "Taha Yasseri"], "categories": ["stat.AP", "q-fin.GN"], "doi": "", "_query": "housing price text listing", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/1904.05328v1", "title": "PropTech for Proactive Pricing of Houses in Classified Advertisements in the Indian Real Estate Market", "abstract": "Property Technology (PropTech) is the next big thing that is going to disrupt the real estate market. Nowadays, we see applications of Machine Learning (ML) and Artificial Intelligence (AI) in almost all the domains but for a long time the real estate industry was quite slow in adopting data science and machine learning for problem solving and improving their processes. However, things are changing quite fast as we see a lot of adoption of AI and ML in the US and European real estate markets. But the Indian real estate market has to catch-up a lot. This paper proposes a machine learning approach for solving the house price prediction problem in the classified advertisements. This study focuses on the Indian real estate market. We apply advanced machine learning algorithms such as Random forest, Gradient boosting and Artificial neural networks on a real world dataset and compare the performance of these methods. We find that the Random forest method is the best performer in terms of prediction accuracy.", "published": "2019-03-28T03:38:05Z", "authors": ["Sayan Putatunda"], "categories": ["cs.SI"], "doi": "", "_query": "housing price text listing", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2510.18279v2", "title": "Text or Pixels? It Takes Half: On the Token Efficiency of Visual Text Inputs in Multimodal LLMs", "abstract": "Large language models (LLMs) and their multimodal variants can now process visual inputs, including images of text. This raises an intriguing question: can we compress textual inputs by feeding them as images to reduce token usage while preserving performance? In this paper, we show that visual text representations are a practical and surprisingly effective form of input compression for decoder LLMs. We exploit the idea of rendering long text inputs as a single image and provide it directly to the model. This leads to dramatically reduced number of decoder tokens required, offering a new form of input compression. Through experiments on two distinct benchmarks RULER (long-context retrieval) and CNN/DailyMail (document summarization) we demonstrate that this text-as-image method yields substantial token savings (often nearly half) without degrading task performance.", "published": "2025-10-21T04:07:20Z", "authors": ["Yanhong Li", "Zixuan Lan", "Jiawei Zhou"], "categories": ["cs.CL", "cs.AI"], "doi": "", "_query": "housing price text listing", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2208.06997v1", "title": "Combining deep learning and crowdsourcing geo-images to predict housing quality in rural China", "abstract": "Housing quality is an essential proxy for regional wealth, security and health. Understanding the distribution of housing quality is crucial for unveiling rural development status and providing political proposals. However,present rural house quality data highly depends on a top-down, time-consuming survey at the national or provincial level but fails to unpack the housing quality at the village level. To fill the gap between accurately depicting rural housing quality conditions and deficient data,we collect massive rural images and invite users to assess their housing quality at scale. Furthermore, a deep learning framework is proposed to automatically and efficiently predict housing quality based on crowd-sourcing rural images.", "published": "2022-08-15T03:58:03Z", "authors": ["Weipan Xu", "Yu Gu", "Yifan Chen", "Yongtian Wang", "Weihuan Deng", "Xun Li"], "categories": ["cs.LG"], "doi": "", "_query": "housing price text listing", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2112.07513v1", "title": "CORE-Text: Improving Scene Text Detection with Contrastive Relational Reasoning", "abstract": "Localizing text instances in natural scenes is regarded as a fundamental challenge in computer vision. Nevertheless, owing to the extremely varied aspect ratios and scales of text instances in real scenes, most conventional text detectors suffer from the sub-text problem that only localizes the fragments of text instance (i.e., sub-texts). In this work, we quantitatively analyze the sub-text problem and present a simple yet effective design, COntrastive RElation (CORE) module, to mitigate that issue. CORE first leverages a vanilla relation block to model the relations among all text proposals (sub-texts of multiple text instances) and further enhances relational reasoning via instance-level sub-text discrimination in a contrastive manner. Such way naturally learns instance-aware representations of text proposals and thus facilitates scene text detection. We integrate the CORE module into a two-stage text detector of Mask R-CNN and devise our text detector CORE-Text. Extensive experiments on four benchmarks demonstrate the superiority of CORE-Text. Code is available: \\url{https://github.com/jylins/CORE-Text}.", "published": "2021-12-14T16:22:25Z", "authors": ["Jingyang Lin", "Yingwei Pan", "Rongfeng Lai", "Xuehang Yang", "Hongyang Chao", "Ting Yao"], "categories": ["cs.CV", "cs.AI", "cs.MM"], "doi": "", "_query": "housing price text listing", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2110.07151v1", "title": "Machine Learning, Deep Learning, and Hedonic Methods for Real Estate Price Prediction", "abstract": "In recent years several complaints about racial discrimination in appraising home values have been accumulating. For several decades, to estimate the sale price of the residential properties, appraisers have been walking through the properties, observing the property, collecting data, and making use of the hedonic pricing models. However, this method bears some costs and by nature is subjective and biased. To minimize human involvement and the biases in the real estate appraisals and boost the accuracy of the real estate market price prediction models, in this research we design data-efficient learning machines capable of learning and extracting the relation or patterns between the inputs (features for the house) and output (value of the houses). We compare the performance of some machine learning and deep learning algorithms, specifically artificial neural networks, random forest, and k nearest neighbor approaches to that of hedonic method on house price prediction in the city of Boulder, Colorado. Even though this study has been done over the houses in the city of Boulder it can be generalized to the housing market in any cities. The results indicate non-linear association between the dwelling features and dwelling prices. In light of these findings, this study demonstrates that random forest and artificial neural networks algorithms can be better alternatives over the hedonic regression analysis for prediction of the house prices in the city of Boulder, Colorado.", "published": "2021-10-14T04:43:04Z", "authors": ["Mahdieh Yazdani"], "categories": ["econ.EM"], "doi": "", "_query": "housing price text listing", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2011.14021v1", "title": "Rethinking Text Segmentation: A Novel Dataset and A Text-Specific Refinement Approach", "abstract": "Text segmentation is a prerequisite in many real-world text-related tasks, e.g., text style transfer, and scene text removal. However, facing the lack of high-quality datasets and dedicated investigations, this critical prerequisite has been left as an assumption in many works, and has been largely overlooked by current research. To bridge this gap, we proposed TextSeg, a large-scale fine-annotated text dataset with six types of annotations: word- and character-wise bounding polygons, masks and transcriptions. We also introduce Text Refinement Network (TexRNet), a novel text segmentation approach that adapts to the unique properties of text, e.g. non-convex boundary, diverse texture, etc., which often impose burdens on traditional segmentation models. In our TexRNet, we propose text specific network designs to address such challenges, including key features pooling and attention-based similarity checking. We also introduce trimap and discriminator losses that show significant improvement on text segmentation. Extensive experiments are carried out on both our TextSeg dataset and other existing datasets. We demonstrate that TexRNet consistently improves text segmentation performance by nearly 2% compared to other state-of-the-art segmentation methods. Our dataset and code will be made available at https://github.com/SHI-Labs/Rethinking-Text-Segmentation.", "published": "2020-11-27T22:50:09Z", "authors": ["Xingqian Xu", "Zhifei Zhang", "Zhaowen Wang", "Brian Price", "Zhonghao Wang", "Humphrey Shi"], "categories": ["cs.CV"], "doi": "", "_query": "housing price text listing", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/0809.0979v1", "title": "A housing-demographic multi-layered nonlinear model to test regulation strategies", "abstract": "We propose a novel multi-layered nonlinear model that is able to capture and predict the housing-demographic dynamics of the real-state market by simulating the transitions of owners among price-based house layers. This model allows us to determine which parameters are most effective to smoothen the severity of a potential market crisis. The International Monetary Fund (IMF) has issued severe warnings about the current real-state bubble in the United States, the United Kingdom, Ireland, the Netherlands, Australia and Spain in the last years. Madrid (Spain), in particular, is an extreme case of this bubble. It is, therefore, an excellent test case to analyze housing dynamics in the context of the empirical data provided by the Spanish National Institute of Statistics and other sources of data. The model is able to predict the mean house occupancy, and shows that i) the house market conditions in Madrid are unstable but not critical; and ii) the regulation of the construction rate is more effective than interest rate changes. Our results indicate that to accommodate the construction rate to the total population of first-time buyers is the most effective way to maintain the system near equilibrium conditions. In addition, we show that to raise interest rates will heavily affect the poorest housing bands of the population while the middle class layers remain nearly unaffected.", "published": "2008-09-05T08:51:28Z", "authors": ["Ramon Huerta", "Fernando Corbacho", "Luis F. Lago-Fernandez"], "categories": ["nlin.CG", "q-fin.GN"], "doi": "", "_query": "housing price text listing", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2110.06875v3", "title": "The core of housing markets from an agent's perspective: Is it worth sprucing up your home?", "abstract": "We study housing markets as introduced by Shapley and Scarf (1974). We investigate the computational complexity of various questions regarding the situation of an agent $a$ in a housing market $H$: we show that it is $\\mathsf{NP}$-hard to find an allocation in the core of $H$ where (i) $a$ receives a certain house, (ii) $a$ does not receive a certain house, or (iii) $a$ receives a house other than her own. We prove that the core of housing markets respects improvement in the following sense: given an allocation in the core of $H$ where agent $a$ receives a house $h$, if the value of the house owned by $a$ increases, then the resulting housing market admits an allocation in its core in which $a$ receives either $h$, or a house that $a$ prefers to $h$; moreover, such an allocation can be found efficiently. We further show an analogous result in the Stable Roommates setting by proving that stable matchings in a one-sided market also respect improvement.", "published": "2021-10-13T17:11:06Z", "authors": ["Ildikó Schlotter", "Péter Biró", "Tamás Fleiner"], "categories": ["cs.GT", "cs.DS"], "doi": "", "_query": "housing price text listing", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2604.00556v1", "title": "HabitatAgent: An End-to-End Multi-Agent System for Housing Consultation", "abstract": "Housing selection is a high-stakes and largely irreversible decision problem. We study housing consultation as a decision-support interface for housing selection. Existing housing platforms and many LLM-based assistants often reduce this process to ranking or recommendation, resulting in opaque reasoning, brittle multi-constraint handling, and limited guarantees on factuality. We present HabitatAgent, the first LLM-powered multi-agent architecture for end-to-end housing consultation. HabitatAgent comprises four specialized agent roles: Memory, Retrieval, Generation, and Validation. The Memory Agent maintains multi-layer user memory through internal stages for constraint extraction, memory fusion, and verification-gated updates; the Retrieval Agent performs hybrid vector--graph retrieval (GraphRAG); the Generation Agent produces evidence-referenced recommendations and explanations; and the Validation Agent applies multi-tier verification and targeted remediation. Together, these agents provide an auditable and reliable workflow for end-to-end housing consultation. We evaluate HabitatAgent on 100 real user consultation scenarios (300 multi-turn question--answer pairs) under an end-to-end correctness protocol. A strong single-stage baseline (Dense+Rerank) achieves 75% accuracy, while HabitatAgent reaches 95%.", "published": "2026-04-01T06:59:50Z", "authors": ["Hongyang Yang", "Yanxin Zhang", "Yang She", "Yue Xiao", "Hao Wu", "Yiyang Zhang", "Jiapeng Hou", "Rongshan Zhang"], "categories": ["cs.LG", "cs.AI", "cs.ET", "q-fin.CP", "q-fin.RM"], "doi": "", "_query": "housing price text listing", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2312.12232v1", "title": "Brush Your Text: Synthesize Any Scene Text on Images via Diffusion Model", "abstract": "Recently, diffusion-based image generation methods are credited for their remarkable text-to-image generation capabilities, while still facing challenges in accurately generating multilingual scene text images. To tackle this problem, we propose Diff-Text, which is a training-free scene text generation framework for any language. Our model outputs a photo-realistic image given a text of any language along with a textual description of a scene. The model leverages rendered sketch images as priors, thus arousing the potential multilingual-generation ability of the pre-trained Stable Diffusion. Based on the observation from the influence of the cross-attention map on object placement in generated images, we propose a localized attention constraint into the cross-attention layer to address the unreasonable positioning problem of scene text. Additionally, we introduce contrastive image-level prompts to further refine the position of the textual region and achieve more accurate scene text generation. Experiments demonstrate that our method outperforms the existing method in both the accuracy of text recognition and the naturalness of foreground-background blending.", "published": "2023-12-19T15:18:40Z", "authors": ["Lingjun Zhang", "Xinyuan Chen", "Yaohui Wang", "Yue Lu", "Yu Qiao"], "categories": ["cs.CV"], "doi": "", "_query": "housing price text listing", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/1611.07636v1", "title": "Mechanism Design for Multi-Type Housing Markets", "abstract": "We study multi-type housing markets, where there are $p\\ge 2$ types of items, each agent is initially endowed one item of each type, and the goal is to design mechanisms without monetary transfer to (re)allocate items to the agents based on their preferences over bundles of items, such that each agent gets one item of each type. In sharp contrast to classical housing markets, previous studies in multi-type housing markets have been hindered by the lack of natural solution concepts, because the strict core might be empty. We break the barrier in the literature by leveraging AI techniques and making natural assumptions on agents' preferences. We show that when agents' preferences are lexicographic, even with different importance orders, the classical top-trading-cycles mechanism can be extended while preserving most of its nice properties. We also investigate computational complexity of checking whether an allocation is in the strict core and checking whether the strict core is empty. Our results convey an encouragingly positive message: it is possible to design good mechanisms for multi-type housing markets under natural assumptions on preferences.", "published": "2016-11-23T04:30:42Z", "authors": ["Sibel Adali", "Sujoy Sikdar", "Lirong Xia"], "categories": ["cs.GT"], "doi": "", "_query": "housing price text listing", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2110.01890v1", "title": "De-rendering Stylized Texts", "abstract": "Editing raster text is a promising but challenging task. We propose to apply text vectorization for the task of raster text editing in display media, such as posters, web pages, or advertisements. In our approach, instead of applying image transformation or generation in the raster domain, we learn a text vectorization model to parse all the rendering parameters including text, location, size, font, style, effects, and hidden background, then utilize those parameters for reconstruction and any editing task. Our text vectorization takes advantage of differentiable text rendering to accurately reproduce the input raster text in a resolution-free parametric format. We show in the experiments that our approach can successfully parse text, styling, and background information in the unified model, and produces artifact-free text editing compared to a raster baseline.", "published": "2021-10-05T09:23:39Z", "authors": ["Wataru Shimoda", "Daichi Haraguchi", "Seiichi Uchida", "Kota Yamaguchi"], "categories": ["cs.CV"], "doi": "", "_query": "housing price text listing", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2112.08171v1", "title": "Text Gestalt: Stroke-Aware Scene Text Image Super-Resolution", "abstract": "In the last decade, the blossom of deep learning has witnessed the rapid development of scene text recognition. However, the recognition of low-resolution scene text images remains a challenge. Even though some super-resolution methods have been proposed to tackle this problem, they usually treat text images as general images while ignoring the fact that the visual quality of strokes (the atomic unit of text) plays an essential role for text recognition. According to Gestalt Psychology, humans are capable of composing parts of details into the most similar objects guided by prior knowledge. Likewise, when humans observe a low-resolution text image, they will inherently use partial stroke-level details to recover the appearance of holistic characters. Inspired by Gestalt Psychology, we put forward a Stroke-Aware Scene Text Image Super-Resolution method containing a Stroke-Focused Module (SFM) to concentrate on stroke-level internal structures of characters in text images. Specifically, we attempt to design rules for decomposing English characters and digits at stroke-level, then pre-train a text recognizer to provide stroke-level attention maps as positional clues with the purpose of controlling the consistency between the generated super-resolution image and high-resolution ground truth. The extensive experimental results validate that the proposed method can indeed generate more distinguishable images on TextZoom and manually constructed Chinese character dataset Degraded-IC13. Furthermore, since the proposed SFM is only used to provide stroke-level guidance when training, it will not bring any time overhead during the test phase. Code is available at https://github.com/FudanVI/FudanOCR/tree/main/text-gestalt.", "published": "2021-12-13T15:26:10Z", "authors": ["Jingye Chen", "Haiyang Yu", "Jianqi Ma", "Bin Li", "Xiangyang Xue"], "categories": ["cs.CV", "cs.AI"], "doi": "", "_query": "housing price text listing", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2605.17031v1", "title": "A Joint Synthetic Housing-Household Inventory", "abstract": "Accurately understanding the interactions between humans and the built environment requires integrated representations of both the buildings and the populations that occupy them. However, high-fidelity datasets that jointly capture detailed housing structures and demographic characteristics at the household level do not currently exist. This paper presents a framework for constructing a joint housing-household inventory that explicitly links individuals and households to compatible housing units from the National Structure Inventory (NSI), while preserving realistic population densities and demographic distributions. The framework integrates three components: (i) synthetic population generation from American Community Survey (ACS) Public Use Microdata Sample (PUMS) records that preserve complex intra-household relationships; (ii) a deep contrastive learning model that quantifies housing-household compatibility; and (iii) a hierarchical optimization-based allocation procedure that enforces building-level capacity and block-group-level demographic constraints. The generated synthetic population attains high statistical realism relative to the census microdata, and the contrastive learning model identifies compatible housing-household pairs with high predictive accuracy. Applied to coastal North Carolina, evaluations at building, neighborhood, and regional scales show that the joint inventory matches block-group-level demographic distributions, reproduces observed spatial population patterns without systematic bias, and maintains consistent allocation quality across urban, suburban, and rural contexts. By enabling coupled household- and building-level analyses, the resulting inventory supports a broad range of applications, including disaster resilience planning, housing and affordability analysis, energy-use assessment, and public health research.", "published": "2026-05-16T14:58:44Z", "authors": ["Xiao Qian", "Shangjia Dong", "Rachel Davidson"], "categories": ["cs.CY"], "doi": "", "_query": "housing price text listing", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2308.09501v2", "title": "Towards More Realistic Models for Refugee Integration: Anonymous Refugee Housing with Upper-Bounds", "abstract": "[Knop and Schierreich; AAMAS~'23] recently introduced a novel model for refugee housing, where we are given a city's topology represented as a graph, a set of inhabitants who occupy some vertices of the topology, and a set of refugees that we need to house in unoccupied vertices. Moreover, each inhabitant approves specific numbers of refugees in his neighbourhood, and our goal is to find housing such that every inhabitant approves the number of refugees housed in his neighbourhood. However, such a model suffers from several problems: (i) it is computationally hard to find desirable housing, and (ii) the inhabitants' preferences are not necessarily continuous, which hardly occurs in practice. To avoid these objections, we introduce a restricted variant of the problem that is more closely aligned with practical scenarios of refugee housing and study its complexity.", "published": "2023-08-18T12:16:36Z", "authors": ["Šimon Schierreich"], "categories": ["cs.GT"], "doi": "", "_query": "housing price text listing", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2108.10826v2", "title": "S&P 500 Stock Price Prediction Using Technical, Fundamental and Text Data", "abstract": "We summarized both common and novel predictive models used for stock price prediction and combined them with technical indices, fundamental characteristics and text-based sentiment data to predict S&P stock prices. A 66.18% accuracy in S&P 500 index directional prediction and 62.09% accuracy in individual stock directional prediction was achieved by combining different machine learning models such as Random Forest and LSTM together into state-of-the-art ensemble models. The data we use contains weekly historical prices, finance reports, and text information from news items associated with 518 different common stocks issued by current and former S&P 500 large-cap companies, from January 1, 2000 to December 31, 2019. Our study's innovation includes utilizing deep language models to categorize and infer financial news item sentiment; fusing different models containing different combinations of variables and stocks to jointly make predictions; and overcoming the insufficient data problem for machine learning models in time series by using data across different stocks.", "published": "2021-08-24T16:18:52Z", "authors": ["Shan Zhong", "David B. Hitchcock"], "categories": ["stat.ML", "cs.LG"], "doi": "10.19139/soic-2310-5070-1362", "_query": "housing price text listing", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2306.15039v1", "title": "An Integral-based Approach for the Vector Potential in Smoothed Particle Magnetohydrodynamics", "abstract": "A new implementation for the time evolution of the magnetic vector potential is obtained for smoothed particle magnetohydrodynamics by considering the induction equation in integral form. Galilean invariance is achieved through proper gauge choice. This new discretisation is tested using the Orszag-Tang MHD vortex in a 3D configuration. The corresponding conservative equations of motion are derived, but are not found to solve the MHD equations in the continuum limit. Tests are performed using a hybrid approach instead, whereby the equations of motion based on the magnetic field instead of vector potential are used. Test results experience the same numerical instability as with the Price (2010) formulation. We conclude that this new formulation is non-viable.", "published": "2023-06-26T19:57:49Z", "authors": ["Terrence S. Tricco", "Daniel J. Price"], "categories": ["astro-ph.IM", "physics.plasm-ph"], "doi": "", "_query": "housing price text listing", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2501.15834v2", "title": "The Strong Core of Housing Markets with Partial Order Preferences", "abstract": "We study the strong core of housing markets when agents' preferences over houses are expressed as partial orders. We provide a structural characterization of the strong core, and propose an efficient algorithm that finds an allocation in the strong core or decides that it is empty, even in the presence of forced and forbidden arcs. The algorithm satisfies the property of group-strategyproofness. Additionally, we show that certain results known for the strong core in the case when agents' preferences are weak orders can be extended to the setting with partial order preferences; among others, we show that the strong core in such housing markets satisfies the property of respecting improvements.", "published": "2025-01-27T07:42:37Z", "authors": ["Ildikó Schlotter", "Lydia Mirabel Mendoza-Cadena"], "categories": ["cs.GT"], "doi": "", "_query": "housing price text listing", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2303.10832v1", "title": "A Strategy-proof Mechanism For Networked Housing Markets", "abstract": "This paper studies a house allocation problem in a networked housing market, where agents can invite others to join the system in order to enrich their options. Top Trading Cycle is a well-known matching mechanism that achieves a set of desirable properties in a market without invitations. However, under a tree-structured networked market, existing agents have to strategically propagate the barter market as their invitees may compete in the same house with them. Our impossibility result shows that TTC cannot work properly in a networked housing market. Hence, we characterize the possible competitions between inviters and invitees, which lead agents to fail to refer others truthfully (strategy-proof). We then present a novel mechanism based on TTC, avoiding the aforementioned competition to ensure all agents report preference and propagate the barter market truthfully. Unlike the existing mechanisms, the agents' preferences are less restricted under our mechanism. Furthermore, we show by simulations that our mechanism outperforms the existing matching mechanisms in terms of the number of swaps and agents' satisfaction.", "published": "2023-03-20T02:35:41Z", "authors": ["Youjia Zhang", "Pingzhong Tang"], "categories": ["cs.GT"], "doi": "", "_query": "housing price text listing", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/1911.01196v1", "title": "Spherical Text Embedding", "abstract": "Unsupervised text embedding has shown great power in a wide range of NLP tasks. While text embeddings are typically learned in the Euclidean space, directional similarity is often more effective in tasks such as word similarity and document clustering, which creates a gap between the training stage and usage stage of text embedding. To close this gap, we propose a spherical generative model based on which unsupervised word and paragraph embeddings are jointly learned. To learn text embeddings in the spherical space, we develop an efficient optimization algorithm with convergence guarantee based on Riemannian optimization. Our model enjoys high efficiency and achieves state-of-the-art performances on various text embedding tasks including word similarity and document clustering.", "published": "2019-11-04T18:36:12Z", "authors": ["Yu Meng", "Jiaxin Huang", "Guangyuan Wang", "Chao Zhang", "Honglei Zhuang", "Lance Kaplan", "Jiawei Han"], "categories": ["cs.CL", "cs.LG", "stat.ML"], "doi": "", "_query": "housing price text listing", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/1802.08238v1", "title": "What are the most important factors that influence the changes in London Real Estate Prices? How to quantify them?", "abstract": "In recent years, real estate industry has captured government and public attention around the world. The factors influencing the prices of real estate are diversified and complex. However, due to the limitations and one-sidedness of their respective views, they did not provide enough theoretical basis for the fluctuation of house price and its influential factors. The purpose of this paper is to build a housing price model to make the scientific and objective analysis of London's real estate market trends from the year 1996 to 2016 and proposes some countermeasures to reasonably control house prices. Specifically, the paper analyzes eight factors which affect the house prices from two aspects: housing supply and demand and find out the factor which is of vital importance to the increase of housing price per square meter. The problem of a high level of multicollinearity between them is solved by using principal components analysis.", "published": "2018-02-22T18:50:51Z", "authors": ["Yiyang Gu"], "categories": ["stat.AP", "q-fin.GN"], "doi": "", "_query": "housing price text listing", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2108.08229v1", "title": "Tilted Platforms: Rental Housing Technology and the Rise of Urban Big Data Oligopolies", "abstract": "This article interprets emerging scholarship on rental housing platforms -- particularly the most well-known and used short- and long-term rental housing platforms - and considers how the technological processes connecting both short-term and long-term rentals to the platform economy are transforming cities. It discusses potential policy approaches to more equitably distribute benefits and mitigate harms. We argue that information technology is not value-neutral. While rental housing platforms may empower data analysts and certain market participants, the same cannot be said for all users or society at large. First, user-generated online data frequently reproduce the systematic biases found in traditional sources of housing information. Evidence is growing that the information broadcasting potential of rental housing platforms may increase rather than mitigate sociospatial inequality. Second, technology platforms curate and shape information according to their creators' own financial and political interests. The question of which data -- and people -- are hidden or marginalized on these platforms is just as important as the question of which data are available. Finally, important differences in benefits and drawbacks exist between short-term and long-term rental housing platforms, but are underexplored in the literature: this article unpacks these differences and proposes policy recommendations.", "published": "2021-08-18T16:36:51Z", "authors": ["Geoff Boeing", "Max Besbris", "David Wachsmuth", "Jake Wegmann"], "categories": ["econ.GN", "cs.CY"], "doi": "10.1186/s42854-021-00024-2", "_query": "housing price text listing", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2402.08484v2", "title": "The Computational Complexity of the Housing Market", "abstract": "We prove that the classic problem of finding a competitive equilibrium in an exchange economy with indivisible goods, money, and unit-demand agents is PPAD-complete. In this \"housing market\", agents have preferences over the house and amount of money they end up with, but can experience income effects. Our results contrast with the existence of polynomial-time algorithms for related problems: Top Trading Cycles for the \"housing exchange\" problem in which there are no transfers and the Hungarian algorithm for the \"housing assignment\" problem in which agents' utilities are linear in money. Along the way, we prove that the Rainbow-KKM problem, a total search problem based on a generalization by Gale of the Knaster-Kuratowski-Mazurkiewicz lemma, is PPAD-complete. Our reductions also imply bounds on the query complexity of finding competitive equilibrium.", "published": "2024-02-13T14:20:41Z", "authors": ["Edwin Lock", "Zephyr Qiu", "Alexander Teytelboym"], "categories": ["cs.GT", "cs.CC"], "doi": "", "_query": "housing price text listing", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2409.06011v2", "title": "Investigating the effects of housing instability on depression, anxiety, and mental health treatment in childhood and adolescence", "abstract": "Housing instability is a widespread phenomenon in the United States. In combination with other social determinants of health, housing instability affects children's overall health and development. Drawing on data from the 2022 National Survey of Children's Health, we employed multiple logistic regression models to understand how sociodemographic factors, especially housing instability, affect mental health outcomes and treatment access for youth aged 6-17 years. Our results show that youth facing housing instability have a higher likelihood of experiencing anxiety (OR: 1.42, p<0.001) and depression (OR: 1.57, p<0.001). Furthermore, youth experiencing both mental health conditions and housing instability are significantly less likely to receive mental health services in the past year, indicating the substantial barriers they face in accessing mental health care. Based on our findings, we highlight opportunities for digital mental health interventions to provide children experiencing housing instability with more accessible and consistent mental health services.", "published": "2024-09-09T19:10:42Z", "authors": ["Rachael Zehrung", "Di Hu", "Yawen Guo", "Kai Zheng", "Yunan Chen"], "categories": ["cs.HC"], "doi": "", "_query": "housing price text listing", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2104.07225v2", "title": "Text Guide: Improving the quality of long text classification by a text selection method based on feature importance", "abstract": "The performance of text classification methods has improved greatly over the last decade for text instances of less than 512 tokens. This limit has been adopted by most state-of-the-research transformer models due to the high computational cost of analyzing longer text instances. To mitigate this problem and to improve classification for longer texts, researchers have sought to resolve the underlying causes of the computational cost and have proposed optimizations for the attention mechanism, which is the key element of every transformer model. In our study, we are not pursuing the ultimate goal of long text classification, i.e., the ability to analyze entire text instances at one time while preserving high performance at a reasonable computational cost. Instead, we propose a text truncation method called Text Guide, in which the original text length is reduced to a predefined limit in a manner that improves performance over naive and semi-naive approaches while preserving low computational costs. Text Guide benefits from the concept of feature importance, a notion from the explainable artificial intelligence domain. We demonstrate that Text Guide can be used to improve the performance of recent language models specifically designed for long text classification, such as Longformer. Moreover, we discovered that parameter optimization is the key to Text Guide performance and must be conducted before the method is deployed. Future experiments may reveal additional benefits provided by this new method.", "published": "2021-04-15T04:10:08Z", "authors": ["Krzysztof Fiok", "Waldemar Karwowski", "Edgar Gutierrez", "Mohammad Reza Davahli", "Maciej Wilamowski", "Tareq Ahram", "Awad Al-Juaid", "Jozef Zurada"], "categories": ["cs.AI"], "doi": "10.1109/ACCESS.2021.3099758", "_query": "housing price text listing", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2010.03997v1", "title": "Unconstrained Text Detection in Manga", "abstract": "The detection and recognition of unconstrained text is an open problem in research. Text in comic books has unusual styles that raise many challenges for text detection. This work aims to identify text characters at a pixel level in a comic genre with highly sophisticated text styles: Japanese manga. To overcome the lack of a manga dataset with individual character level annotations, we create our own. Most of the literature in text detection use bounding box metrics, which are unsuitable for pixel-level evaluation. Thus, we implemented special metrics to evaluate performance. Using these resources, we designed and evaluated a deep network model, outperforming current methods for text detection in manga in most metrics.", "published": "2020-10-07T13:28:13Z", "authors": ["Julián Del Gobbo", "Rosana Matuk Herrera"], "categories": ["cs.CV"], "doi": "", "_query": "housing price text listing", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/1811.06418v1", "title": "Adversarial Examples from Cryptographic Pseudo-Random Generators", "abstract": "In our recent work (Bubeck, Price, Razenshteyn, arXiv:1805.10204) we argued that adversarial examples in machine learning might be due to an inherent computational hardness of the problem. More precisely, we constructed a binary classification task for which (i) a robust classifier exists; yet no non-trivial accuracy can be obtained with an efficient algorithm in (ii) the statistical query model. In the present paper we significantly strengthen both (i) and (ii): we now construct a task which admits (i') a maximally robust classifier (that is it can tolerate perturbations of size comparable to the size of the examples themselves); and moreover we prove computational hardness of learning this task under (ii') a standard cryptographic assumption.", "published": "2018-11-15T15:08:12Z", "authors": ["Sébastien Bubeck", "Yin Tat Lee", "Eric Price", "Ilya Razenshteyn"], "categories": ["cs.LG", "cs.CC", "cs.CR", "stat.ML"], "doi": "", "_query": "housing price text listing", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2102.02593v1", "title": "The housing problem and revealed preference theory: duality and an application", "abstract": "This paper exhibits a duality between the theory of Revealed Preference of Afriat and the housing allocation problem of Shapley and Scarf. In particular, it is shown that Afriat's theorem can be interpreted as a second welfare theorem in the housing problem. Using this duality, the revealed preference problem is connected to an optimal assignment problem, and a geometrical characterization of the rationalizability of experiment data is given. This allows in turn to give new indices of rationalizability of the data, and to define weaker notions of rationalizability, in the spirit of Afriat's efficiency index.", "published": "2021-02-04T13:09:50Z", "authors": ["Ivar Ekeland", "Alfred Galichon"], "categories": ["econ.GN"], "doi": "10.1007/s00199-012-0719-x", "_query": "housing price text listing", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2001.10338v1", "title": "Short Text Classification via Term Graph", "abstract": "Short text classi cation is a method for classifying short sentence with prede ned labels. However, short text is limited in shortness in text length that leads to a challenging problem of sparse features. Most of existing methods treat each short sentences as independently and identically distributed (IID), local context only in the sentence itself is focused and the relational information between sentences are lost. To overcome these limitations, we propose a PathWalk model that combine the strength of graph networks and short sentences to solve the sparseness of short text. Experimental results on four different available datasets show that our PathWalk method achieves the state-of-the-art results, demonstrating the efficiency and robustness of graph networks for short text classification.", "published": "2020-01-20T04:32:13Z", "authors": ["Wei Pang"], "categories": ["cs.IR", "cs.LG", "stat.ML"], "doi": "", "_query": "housing price text listing", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/1209.2540v1", "title": "Modelling of c-C2H4O formation on grain surfaces", "abstract": "Despite its potential reactivity due to ring strain, ethylene oxide (c-C2H4O) is a complex molecule that seems to be stable under the physical conditions of an interstellar dense core; indeed it has been detected towards several high-mass star forming regions with a column density of the order of 10e13cm-2 (Ikeda et al. 2001). To date, its observational abundances cannot be reproduced by chemical models and this may be due to the significant contribution played by its chemistry on grain surfaces. Recently, Ward and Price (2011) have performed experiments in order to investigate the surface formation of ethylene oxide starting with oxygen atoms and ethylene ice as reactants. We present a chemical model which includes the most recent experimental results from Ward and Price (2011) on the formation of c-C2H4O. We study the influence of the physical parameters of dense cores on the abundances of c-C2H4O. We verify that ethylene oxide can indeed be formed during the cold phase (when the ISM dense cores are formed), via addition of an oxygen atom across the C=C double bond of the ethylene molecule, and released by thermal desorption during the hot core phase. A qualitative comparison between our theoretical results and those from the observations shows that we are able to reproduce the abundances of ethylene oxide towards high-mass star-forming regions.", "published": "2012-09-12T09:53:52Z", "authors": ["A. Occhiogrosso", "S. Viti", "M. D. Ward", "S. D. Price"], "categories": ["astro-ph.GA", "astro-ph.SR"], "doi": "10.1111/j.1365-2966.2012.22112.x", "_query": "housing price text listing", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2412.14816v3", "title": "TextSleuth: Towards Explainable Tampered Text Detection", "abstract": "Recently, tampered text detection has attracted increasing attention due to its essential role in information security. Although existing methods can detect the tampered text region, the interpretation of such detection remains unclear, making the prediction unreliable. To address this problem, we propose to explain the basis of tampered text detection with natural language via large multimodal models. To fill the data gap for this task, we propose a large-scale, comprehensive dataset, ETTD, which contains both pixel-level annotations for tampered text region and natural language annotations describing the anomaly of the tampered text. Multiple methods are employed to improve the quality of the proposed data. For example, elaborate queries are introduced to generate high-quality anomaly descriptions with GPT4o. A fused mask prompt is proposed to reduce confusion when querying GPT4o to generate anomaly descriptions. To automatically filter out low-quality annotations, we also propose to prompt GPT4o to recognize tampered texts before describing the anomaly, and to filter out the responses with low OCR accuracy. To further improve explainable tampered text detection, we propose a simple yet effective model called TextSleuth, which achieves improved fine-grained perception and cross-domain generalization by focusing on the suspected region, with a two-stage analysis paradigm and an auxiliary grounding prompt. Extensive experiments on both the ETTD dataset and the public dataset have verified the effectiveness of the proposed methods. In-depth analysis is also provided to inspire further research. Our dataset and code will be open-source.", "published": "2024-12-19T13:10:03Z", "authors": ["Chenfan Qu", "Jian Liu", "Haoxing Chen", "Baihan Yu", "Jingjing Liu", "Weiqiang Wang", "Lianwen Jin"], "categories": ["cs.CV"], "doi": "", "_query": "housing price text listing", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2403.15490v2", "title": "Increasing retrofit device adoption in social housing: evidence from two field experiments in Belgium", "abstract": "Energy efficient technologies are particularly important for social housing settings: they offer the potential to improve tenants' wellbeing through monetary savings and comfort, while reducing emissions of entire communities. Slow uptake of innovative energy technology in social housing has been associated with a lack of trust and the perceived risks of adoption. To counteract both, we designed a communication campaign for a retrofit technology for heating including social norms for technology adoption and concretely experienced benefits. We report two randomized controlled trials (RCT) in two different social housing communities in Belgium. In the first study, randomization was on housing block level: the communication led to significant higher uptake rates compared to the control group, (b = 1.7, p = .024). In the second study randomization occurred on apartment level, again yielding a significant increase (b = 1.62, p = 0.02), when an interaction with housing blocks was considered. We discuss challenges of conducting randomized controlled trials in social housing communities.", "published": "2024-03-21T14:49:05Z", "authors": ["Mona Bielig", "Celina Kacperski", "Florian Kutzner"], "categories": ["cs.CY", "cs.HC"], "doi": "10.1016/j.jenvp.2024.102284", "_query": "housing price text listing", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2302.03873v1", "title": "Geometric Perception based Efficient Text Recognition", "abstract": "Every Scene Text Recognition (STR) task consists of text localization \\& text recognition as the prominent sub-tasks. However, in real-world applications with fixed camera positions such as equipment monitor reading, image-based data entry, and printed document data extraction, the underlying data tends to be regular scene text. Hence, in these tasks, the use of generic, bulky models comes up with significant disadvantages compared to customized, efficient models in terms of model deployability, data privacy \\& model reliability. Therefore, this paper introduces the underlying concepts, theory, implementation, and experiment results to develop models, which are highly specialized for the task itself, to achieve not only the SOTA performance but also to have minimal model weights, shorter inference time, and high model reliability. We introduce a novel deep learning architecture (GeoTRNet), trained to identify digits in a regular scene image, only using the geometrical features present, mimicking human perception over text recognition. The code is publicly available at https://github.com/ACRA-FL/GeoTRNet", "published": "2023-02-08T04:19:24Z", "authors": ["P. N. Deelaka", "D. R. Jayakodi", "D. Y. Silva"], "categories": ["cs.CV", "cs.AI"], "doi": "", "_query": "housing price text listing", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2303.16289v2", "title": "Long-term experimental study of price responsive predictive control in a real occupied single-family house with heat pump", "abstract": "The continuous introduction of renewable electricity and increased consumption through electrification of the transport and heating sector challenges grid stability. This study investigates load shifting through demand side management as a solution. We present a four-month experimental study of a low-complexity, hierarchical Model Predictive Control approach for demand side management in a near-zero emission occupied single-family house in Denmark. The control algorithm uses a price signal, weather forecast, a single-zone building model, and a non-linear heat pump efficiency model to generate a space-heating schedule. The weather-compensated, commercial heat pump is made to act smart grid-ready through outdoor temperature input override to enable heat boosting and forced stops to accommodate the heating schedule. The cost reduction from the controller ranged from 2-33% depending on the chosen comfort level. The experiment demonstrates that load shifting is feasible and cost-effective, even without energy storage, and that the current price scheme provides an incentive for Danish end-consumers to shift heating loads. However, issues related to controlling the heat pump through input-manipulation were identified, and the authors propose a more promising path forward involving coordination with manufacturers and regulators to make commercial heat pumps truly smart grid-ready.", "published": "2023-03-28T20:08:20Z", "authors": ["Simon Thorsteinsson", "Alex Arash Sand Kalaee", "Pierre Vogler-Finck", "Henrik Lund Stærmose", "Ivan Katic", "Jan Dimon Bendtsen"], "categories": ["eess.SY"], "doi": "10.1016/j.apenergy.2023.121398", "_query": "housing price text listing", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/1111.3089v1", "title": "Dusty gas with SPH - II. Implicit timestepping and astrophysical drag regimes", "abstract": "In a companion paper (Laibe & Price 2011b), we have presented an algorithm for simulating two-fluid gas and dust mixtures in Smoothed Particle Hydrodynamics (SPH). In this paper, we develop an implicit timestepping method that preserves the exact conservation of the both linear and angular momentum in the underlying SPH algorithm, but unlike previous schemes, allows the iterations to converge to arbitrary accuracy and is suited to the treatment of non- linear drag regimes. The algorithm presented in Paper I is also extended to deal with realistic astrophysical drag regimes, including both linear and non-linear Epstein and Stokes drag. The scheme is benchmarked against the test suite presented in Paper I, including i) the analytic solutions of the dustybox problem and ii) solutions of the dustywave, dustyshock, dustysedov and dustydisc obtained with explicit timestepping. We find that the implicit method is 1- 10 times faster than the explicit temporal integration when the ratio r between the the timestep and the drag stopping time is 1 < r < 1000.", "published": "2011-11-14T02:28:49Z", "authors": ["Guillaume Laibe", "Daniel J. Price"], "categories": ["astro-ph.IM"], "doi": "10.1111/j.1365-2966.2011.20201.x", "_query": "housing price text listing", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2309.00410v2", "title": "Selective Scene Text Removal", "abstract": "Scene text removal (STR) is the image transformation task to remove text regions in scene images. The conventional STR methods remove all scene text. This means that the existing methods cannot select text to be removed. In this paper, we propose a novel task setting named selective scene text removal (SSTR) that removes only target words specified by the user. Although SSTR is a more complex task than STR, the proposed multi-module structure enables efficient training for SSTR. Experimental results show that the proposed method can remove target words as expected.", "published": "2023-09-01T12:07:40Z", "authors": ["Hayato Mitani", "Akisato Kimura", "Seiichi Uchida"], "categories": ["cs.CV", "cs.LG"], "doi": "", "_query": "housing price text listing", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/0810.0332v1", "title": "Enhanced Integrated Scoring for Cleaning Dirty Texts", "abstract": "An increasing number of approaches for ontology engineering from text are gearing towards the use of online sources such as company intranet and the World Wide Web. Despite such rise, not much work can be found in aspects of preprocessing and cleaning dirty texts from online sources. This paper presents an enhancement of an Integrated Scoring for Spelling error correction, Abbreviation expansion and Case restoration (ISSAC). ISSAC is implemented as part of a text preprocessing phase in an ontology engineering system. New evaluations performed on the enhanced ISSAC using 700 chat records reveal an improved accuracy of 98% as compared to 96.5% and 71% based on the use of only basic ISSAC and of Aspell, respectively.", "published": "2008-10-02T03:42:12Z", "authors": ["Wilson Wong", "Wei Liu", "Mohammed Bennamoun"], "categories": ["cs.AI"], "doi": "", "_query": "housing price text listing", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2101.04480v1", "title": "Text analysis in financial disclosures", "abstract": "Financial disclosure analysis and Knowledge extraction is an important financial analysis problem. Prevailing methods depend predominantly on quantitative ratios and techniques, which suffer from limitations like window dressing and past focus. Most of the information in a firm's financial disclosures is in unstructured text and contains valuable information about its health. Humans and machines fail to analyze it satisfactorily due to the enormous volume and unstructured nature, respectively. Researchers have started analyzing text content in disclosures recently. This paper covers the previous work in unstructured data analysis in Finance and Accounting. It also explores the state of art methods in computational linguistics and reviews the current methodologies in Natural Language Processing (NLP). Specifically, it focuses on research related to text source, linguistic attributes, firm attributes, and mathematical models employed in the text analysis approach. This work contributes to disclosure analysis methods by highlighting the limitations of the current focus on sentiment metrics and highlighting broader future research areas", "published": "2021-01-06T17:45:40Z", "authors": ["Sridhar Ravula"], "categories": ["q-fin.TR", "cs.LG", "q-fin.GN"], "doi": "", "_query": "housing price text listing", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2012.03739v1", "title": "Exploring the Usage of Online Food Delivery Data for Intra-Urban Job and Housing Mobility Detection and Characterization", "abstract": "Human mobility plays a critical role in urban planning and policy-making. However, at certain spatial and temporal resolutions, it is very challenging to track, for example, job and housing mobility. In this study, we explore the usage of a new modality of dataset, online food delivery data, to detect job and housing mobility. By leveraging millions of meal orders from a popular online food ordering and delivery service in Beijing, China, we are able to detect job and housing moves at much higher spatial and temporal resolutions than using traditional data sources. Popular moving seasons and origins/destinations can be well identified. More importantly, we match the detected moves to both macro- and micro-level factors so as to characterize job and housing dynamics. Our findings suggest that commuting distance is a major factor for job and housing mobility. We also observe that: (1) For home movers, there is a trade-off between lower housing cost and shorter commuting distance given the urban spatial structure; (2) For job hoppers, those who frequently work overtime are more likely to reduce their working hours by switching jobs. While this new modality of dataset has its limitations, we believe that ensemble approaches would be promising, where a mash-up of multiple datasets with different characteristic limitations can provide a more comprehensive picture of job and housing dynamics. Our work demonstrates the effectiveness of utilizing food delivery data to detect and analyze job and housing mobility, and contributes to realizing the full potential of ensemble-based approaches.", "published": "2020-12-04T02:48:53Z", "authors": ["Yawen Zhang", "Seth Spielman", "Qi Liu", "Si Shen", "Jason Shuo Zhang", "Qin Lv"], "categories": ["cs.CY"], "doi": "", "_query": "housing price text listing", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2308.06199v1", "title": "Weakly Supervised Text Classification on Free Text Comments in Patient-Reported Outcome Measures", "abstract": "Free text comments (FTC) in patient-reported outcome measures (PROMs) data are typically analysed using manual methods, such as content analysis, which is labour-intensive and time-consuming. Machine learning analysis methods are largely unsupervised, necessitating post-analysis interpretation. Weakly supervised text classification (WSTC) can be a valuable method of analysis to classify domain-specific text data in which there is limited labelled data. In this paper, we apply five WSTC techniques to FTC in PROMs data to identify health-related quality of life (HRQoL) themes reported by colorectal cancer patients. The WSTC methods label all the themes mentioned in the FTC. The results showed moderate performance on the PROMs data, mainly due to the precision of the models, and variation between themes. Evaluation of the classification performance illustrated the potential and limitations of keyword based WSTC to label PROMs FTC when labelled data is limited.", "published": "2023-08-11T15:47:49Z", "authors": ["Anna-Grace Linton", "Vania Dimitrova", "Amy Downing", "Richard Wagland", "Adam Glaser"], "categories": ["cs.CL"], "doi": "", "_query": "housing price text listing", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2604.14124v1", "title": "Icy Volatile Enhancements in Evolving Protoplanetary Disks", "abstract": "Protoplanetary disk ice lines shape a multitude of planet formation processes, setting the environmental composition through evolution. Ice line locations depend on molecular sublimation and deposition properties, but in dynamic disks where temperature and density structures change, so do the expected compositions of planets and planetesimals. In turbulent viscous disks with particle drift, thermal evolution, and desorption/adsorption, Price et al. 2021 demonstrated that the CO/H$_2$O ice ratio beyond the CO ice line can become enhanced by $\\sim10\\times$. We expand on their work by incorporating additional carbon, nitrogen, and oxygen species, more particle sizes, and a broader disk parameter exploration. We find that before $\\sim0.5$Myr, volatile ices are enhanced relative to H$_2$O as the outer disk is desiccated by drift, while at later disk times outward advection and volatile deposition further increase relative volatile icy enhancements beyond the evolving critical disk radius. The outcome of these combined relative icy enhancement to H$_2$O mechanisms is solid C/O $\\sim$ N/O $\\sim1$ beyond the hypervolatile ice lines, much higher than expected in static disks. Hypervolatiles (N$_2$, CO, and CH$_4$) robustly increase to $\\sim100\\times$ across the explored parameter space, while mid-volatiles (CO$_2$ and NH$_3$) are sensitive to model choices, with enhancements ranging from $\\sim2-50\\times$. Together these results demonstrate that coupling disk dynamics with simple sublimation and deposition chemistry is fundamental to predicting grain, planetesimal, and planetary compositions, particularly the role of advection in redistributing volatiles across disk radii.", "published": "2026-04-15T17:49:26Z", "authors": ["Elizabeth Yunerman", "Ellen Price", "Karin Öberg"], "categories": ["astro-ph.EP"], "doi": "10.3847/1538-4357/ae60fe", "_query": "housing price text listing", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2308.14989v3", "title": "Efficiency in Multiple-Type Housing Markets", "abstract": "We consider multiple-type housing markets (Moulin, 1995), which extend Shapley-Scarf housing markets (Shapley and Scarf, 1974) from one dimension to higher dimensions. In this model, Pareto efficiency is incompatible with individual rationality and strategy-proofness (Konishi et al., 2001). Therefore, we consider two weaker efficiency properties: coordinatewise efficiency and pairwise efficiency. We show that these two properties both (i) are compatible with individual rationality and strategy-proofness, and (ii) help us to identify two specific mechanisms. To be more precise, on various domains of preference profiles, together with other well-studied properties (individual rationality, strategy-proofness, and non-bossiness), coordinatewise efficiency and pairwise efficiency respectively characterize two extensions of the top-trading-cycles mechanism (TTC): the coordinatewise top-trading-cycles mechanism (cTTC) and the bundle top-trading-cycles mechanism (bTTC). Moreover, we propose several variations of our efficiency properties, and we find that each of them is either satisfied by cTTC or bTTC, or leads to an impossibility result (together with individual rationality and strategy-proofness). Therefore, our characterizations can be primarily interpreted as a compatibility test: any reasonable efficiency property that is not satisfied by cTTC or bTTC could be considered incompatible with individual rationality and strategy-proofness. The external validity of our results in the context of general environments is also discussed. For multiple-type housing markets with strict preferences, our characterization of bTTC constitutes the first characterization of an extension of the prominent TTC mechanism", "published": "2023-08-29T02:38:23Z", "authors": ["Di Feng"], "categories": ["econ.TH"], "doi": "", "_query": "housing price text listing", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/1607.01999v1", "title": "Inferring the contiguity matrix for spatial autoregressive analysis with applications to house price prediction", "abstract": "Inference methods in traditional statistics, machine learning and data mining assume that data is generated from an independent and identically distributed (iid) process. Spatial data exhibits behavior for which the iid assumption must be relaxed. For example, the standard approach in spatial regression is to assume the existence of a contiguity matrix which captures the spatial autoregressive properties of the data. However all spatial methods, till now, have assumed that the contiguity matrix is given apriori or can be estimated by using a spatial similarity function. In this paper we propose a convex optimization formulation to solve the spatial autoregressive regression (SAR) model in which both the contiguity matrix and the non-spatial regression parameters are unknown and inferred from the data. We solve the problem using the alternating direction method of multipliers (ADMM) which provides a solution which is both robust and efficient. While our approach is general we use data from housing markets of Boston and Sydney to both guide the analysis and validate our results. A novel side effect of our approach is the automatic discovery of spatial clusters which translate to submarkets in the housing data sets.", "published": "2016-07-07T13:14:43Z", "authors": ["Somwrita Sarkar", "Sanjay Chawla"], "categories": ["econ.GN"], "doi": "", "_query": "housing price text listing", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2205.06457v2", "title": "ViT5: Pretrained Text-to-Text Transformer for Vietnamese Language Generation", "abstract": "We present ViT5, a pretrained Transformer-based encoder-decoder model for the Vietnamese language. With T5-style self-supervised pretraining, ViT5 is trained on a large corpus of high-quality and diverse Vietnamese texts. We benchmark ViT5 on two downstream text generation tasks, Abstractive Text Summarization and Named Entity Recognition. Although Abstractive Text Summarization has been widely studied for the English language thanks to its rich and large source of data, there has been minimal research into the same task in Vietnamese, a much lower resource language. In this work, we perform exhaustive experiments on both Vietnamese Abstractive Summarization and Named Entity Recognition, validating the performance of ViT5 against many other pretrained Transformer-based encoder-decoder models. Our experiments show that ViT5 significantly outperforms existing models and achieves state-of-the-art results on Vietnamese Text Summarization. On the task of Named Entity Recognition, ViT5 is competitive against previous best results from pretrained encoder-based Transformer models. Further analysis shows the importance of context length during the self-supervised pretraining on downstream performance across different settings.", "published": "2022-05-13T06:08:35Z", "authors": ["Long Phan", "Hieu Tran", "Hieu Nguyen", "Trieu H. Trinh"], "categories": ["cs.CL", "cs.AI"], "doi": "", "_query": "housing price text listing", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2312.10306v1", "title": "Mapping Housing Stock Characteristics from Drone Images for Climate Resilience in the Caribbean", "abstract": "Comprehensive information on housing stock is crucial for climate adaptation initiatives aiming to reduce the adverse impacts of climate-extreme hazards in high-risk regions like the Caribbean. In this study, we propose a workflow for rapidly generating critical baseline housing stock data using very high-resolution drone images and deep learning techniques. Specifically, our work leverages the Segment Anything Model and convolutional neural networks for the automated generation of building footprints and roof classification maps. By strengthening local capacity within government agencies to leverage AI and Earth Observation-based solutions, this work seeks to improve the climate resilience of the housing sector in small island developing states in the Caribbean.", "published": "2023-12-16T03:49:40Z", "authors": ["Isabelle Tingzon", "Nuala Margaret Cowan", "Pierre Chrzanowski"], "categories": ["cs.CV"], "doi": "10.1017/eds.2024.46", "_query": "housing price text listing", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2601.20079v1", "title": "Techno-economic optimization of a heat-pipe microreactor, part II: multi-objective optimization analysis", "abstract": "Heat-pipe microreactors (HPMRs) are compact and transportable nuclear power systems exhibiting inherent safety, well-suited for deployment in remote regions where access is limited and reliance on costly fossil fuels is prevalent. In prior work, we developed a design optimization framework that incorporates techno-economic considerations through surrogate modeling and reinforcement learning (RL)-based optimization, focusing solely on minimizing the levelized cost of electricity (LCOE) by using a bottom-up cost estimation approach. In this study, we extend that framework to a multi-objective optimization that uses the Pareto Envelope Augmented with Reinforcement Learning (PEARL) algorithm. The objectives include minimizing both the rod-integrated peaking factor ($F_{Δh}$) and LCOE -- subject to safety and operational constraints. We evaluate three cost scenarios: (1) a high-cost axial and drum reflectors, (2) a low-cost axial reflector, and (3) low-cost axial and drum reflectors. Our findings indicate that reducing the solid moderator radius, pin pitch, and drum coating angle -- all while increasing the fuel height -- effectively lowers $F_{Δh}$. Across all three scenarios, four key strategies consistently emerged for optimizing LCOE: (1) minimizing the axial reflector contribution when costly, (2) reducing control drum reliance, (3) substituting expensive tri-structural isotropic (TRISO) fuel with axial reflector material priced at the level of graphite, and (4) maximizing fuel burnup. While PEARL demonstrates promise in navigating trade-offs across diverse design scenarios, discrepancies between surrogate model predictions and full-order simulations remain. Further improvements are anticipated through constraint relaxation and surrogate development, constituting an ongoing area of investigation.", "published": "2026-01-27T21:54:25Z", "authors": ["Paul Seurin", "Dean Price"], "categories": ["cs.LG", "physics.comp-ph"], "doi": "", "_query": "housing price text listing", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/physics/0701065v1", "title": "Gearboxes: indirect identification of dynamic forces transmitted to housing through bearings", "abstract": "In order to determine indirectly the generalized forces applied on the gearbox, two approaches are tested, one is a numerical method, the other is experimental. For the numerical method, the vibration response of the gearbox can be evaluated with use of a finite element analysis and a spectral and iterative method. It's confirmed that applying the generalized forces transmitted through the bearings on the empty housing allows to rebuild the housing response (reciprocity principle). The simulation on the time- and space-averaged mean square vibration velocity shows the vibration response of the housing is induced principally by the axial exciting forces, and secondarily by the moments Mx and My. transmitted on the free side (the 6 mm thick face). Realistic estimation of these excitation forces needs a modelling of the whole gearbox taking into account the mechanical properties of the housing. For the experimental approach, an inverse method based upon the FRF measurement and characterisation of gearbox vibration response is developed. Beginning from the equivalent forces obtained by this inverse method, a resulting force and a moment are derived for representing the distributed force acting on each bearing. The comparison between the results obtained by these two methods shows the effectiveness of this indirect force determination methodology.", "published": "2007-01-05T15:04:55Z", "authors": ["Gu Xiang Zhang", "Emmanuel Rigaud", "Jean-Claude Pascal", "Jean Sabot"], "categories": ["physics.class-ph"], "doi": "", "_query": "housing price text listing", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/1210.3312v1", "title": "Artex is AnotheR TEXt summarizer", "abstract": "This paper describes Artex, another algorithm for Automatic Text Summarization. In order to rank sentences, a simple inner product is calculated between each sentence, a document vector (text topic) and a lexical vector (vocabulary used by a sentence). Summaries are then generated by assembling the highest ranked sentences. No ruled-based linguistic post-processing is necessary in order to obtain summaries. Tests over several datasets (coming from Document Understanding Conferences (DUC), Text Analysis Conferences (TAC), evaluation campaigns, etc.) in French, English and Spanish have shown that summarizer achieves interesting results.", "published": "2012-10-11T18:21:01Z", "authors": ["Juan-Manuel Torres-Moreno"], "categories": ["cs.IR", "cs.AI", "cs.CL"], "doi": "", "_query": "housing price text listing", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2506.00765v2", "title": "HouseTS: A Large-Scale, Multimodal Spatiotemporal U.S. Housing Dataset and Benchmark", "abstract": "Accurate long-horizon house-price forecasting requires benchmarks that capture temporal dynamics together with time-varying local context. However, existing public resources remain fragmented: many datasets have limited spatial coverage, temporal depth, or multimodal alignment; the robustness of modern deep forecasters and time-series foundation models on housing data is not well characterized; and aerial imagery is rarely leveraged in a time-aware and interpretable manner at scale. To bridge these gaps, we present HouseTS (House Time Series), a multimodal spatiotemporal dataset for ZIP-code-level housing-market analysis, covering monthly signals from March 2012 to December 2023 across over 6,000 ZIP codes in 30 major U.S. metropolitan areas. HouseTS aligns monthly housing-market indicators, monthly POI dynamics, and annual census-based socioeconomic variables under a unified schema, and includes time-stamped annual aerial imagery. Building on HouseTS, we define standardized long-horizon forecasting tasks for univariate and multivariate prediction and benchmark 16 model families spanning statistical methods, classical machine learning, deep neural networks, and time-series foundation models in both zero-shot and fine-tuned modes. We also provide image-derived textual change annotations from multi-year aerial image sequences via a vision--language pipeline with LLM-as-judge and human verification to support scalable interpretability analyses. HouseTS is available on Kaggle, with code and documentation on GitHub.", "published": "2025-06-01T00:52:41Z", "authors": ["Shengkun Wang", "Yanshen Sun", "Fanglan Chen", "Linhan Wang", "Naren Ramakrishnan", "Chang-Tien Lu", "Yinlin Chen"], "categories": ["cs.AI"], "doi": "", "_query": "housing price text listing", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/cmp-lg/9407010v1", "title": "Combining Knowledge Sources to Reorder N-Best Speech Hypothesis Lists", "abstract": "A simple and general method is described that can combine different knowledge sources to reorder N-best lists of hypotheses produced by a speech recognizer. The method is automatically trainable, acquiring information from both positive and negative examples. Experiments are described in which it was tested on a 1000-utterance sample of unseen ATIS data.", "published": "1994-07-12T16:39:05Z", "authors": ["Manny Rayner", "David Carter", "Vassilios Digalakis", "Patti Price"], "categories": ["cs.CL"], "doi": "", "_query": "housing price text listing", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2206.12647v1", "title": "Feedback Dynamics of the Low-Income Rental Housing Market: Exploring Policy Responses to COVID-19", "abstract": "The COVID-19 recession threatens mass housing insecurity that undermines economic recovery. Unprecedented federal policy responses halt court-ordered evictions, but questions remain whether policies adequately account for dynamics that drive landlord-tenant interactions including accumulations of rental and mortgage arrears, rental unit availability, and low-income housing options. A system dynamics model probes complex feedback dynamics driving tenant and landlord decision-making in the low-income rental housing market pre- and post-pandemic protections. Feedback loops highlight tradeoffs considered by low-income tenants and landlords in the context of scarcity and uncertainty. Simulations suggest the eviction moratorium reduced evictions by 51% before its expiration in late 2021, but rental arrears, overcrowding, and homelessness remained high. Federal emergency rental assistance further contributed to keeping evictions low, but showed only modest effects on housing insecurity and homelessness. More rapid allocation of funds would substantially increase impact. Failure to address underlying financial hardship and limited affordable housing undermine COVID recovery.", "published": "2022-06-25T13:09:37Z", "authors": ["Katherine Marcal", "Patrick J. Fowler", "Peter S. Hovmand"], "categories": ["cs.CY"], "doi": "", "_query": "housing price text listing", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2308.10408v1", "title": "Turning a CLIP Model into a Scene Text Spotter", "abstract": "We exploit the potential of the large-scale Contrastive Language-Image Pretraining (CLIP) model to enhance scene text detection and spotting tasks, transforming it into a robust backbone, FastTCM-CR50. This backbone utilizes visual prompt learning and cross-attention in CLIP to extract image and text-based prior knowledge. Using predefined and learnable prompts, FastTCM-CR50 introduces an instance-language matching process to enhance the synergy between image and text embeddings, thereby refining text regions. Our Bimodal Similarity Matching (BSM) module facilitates dynamic language prompt generation, enabling offline computations and improving performance. FastTCM-CR50 offers several advantages: 1) It can enhance existing text detectors and spotters, improving performance by an average of 1.7% and 1.5%, respectively. 2) It outperforms the previous TCM-CR50 backbone, yielding an average improvement of 0.2% and 0.56% in text detection and spotting tasks, along with a 48.5% increase in inference speed. 3) It showcases robust few-shot training capabilities. Utilizing only 10% of the supervised data, FastTCM-CR50 improves performance by an average of 26.5% and 5.5% for text detection and spotting tasks, respectively. 4) It consistently enhances performance on out-of-distribution text detection and spotting datasets, particularly the NightTime-ArT subset from ICDAR2019-ArT and the DOTA dataset for oriented object detection. The code is available at https://github.com/wenwenyu/TCM.", "published": "2023-08-21T01:25:48Z", "authors": ["Wenwen Yu", "Yuliang Liu", "Xingkui Zhu", "Haoyu Cao", "Xing Sun", "Xiang Bai"], "categories": ["cs.CV"], "doi": "", "_query": "housing price text listing", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2510.21835v1", "title": "A Multimodal, Multitask System for Generating E Commerce Text Listings from Images", "abstract": "Manually generating catchy descriptions and names is labor intensive and a slow process for retailers. Although generative AI provides an automation solution in form of Vision to Language Models (VLM), the current VLMs are prone to factual \"hallucinations\". Siloed, single task models are not only inefficient but also fail to capture interdependent relationships between features. To address these challenges, we propose an end to end, multi task system that generates factually grounded textual listings from a single image. The contributions of this study are two proposals for the model architecture. First, application of multi task learning approach for fine tuning a vision encoder where a single vision backbone is jointly trained on attribute prediction such as color, hemline and neck style and price regression. Second, introduction of a hierarchical generation process where the model's own predicted attributes are embedded in a prompt and fed to the text decoder to improve factual consistency. The experiments demonstrate the superiority of this architecture. The multi tasking approach outperforms both the independent price regression, with a 3.6% better R2 Value and attribute classification, with a 6.6% improvement F1 score. Critically, the hierarchical generation process proves highly effective, slashing the factual hallucination rate from 12.7% to 7.1%, a 44.5% relative reduction, compared to a non hierarchical ablation. The hierarchical approach also reduces the latency of the autoregressive text generation process by a factor of 3.5 when compared to direct vision to language model of similar size. One minor caveat is that the model does perform 3.5% worse than direct vision-to-language model on ROUGE-L score.", "published": "2025-10-22T11:50:49Z", "authors": ["Nayan Kumar Singh"], "categories": ["cs.LG", "cs.AI", "cs.CL", "cs.CV"], "doi": "", "_query": "housing price text listing", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2510.01196v1", "title": "Location Matters: Leveraging Multi-Resolution Geo-Embeddings for Housing Search", "abstract": "QuintoAndar Group is Latin America's largest housing platform, revolutionizing property rentals and sales. Headquartered in Brazil, it simplifies the housing process by eliminating paperwork and enhancing accessibility for tenants, buyers, and landlords. With thousands of houses available for each city, users struggle to find the ideal home. In this context, location plays a pivotal role, as it significantly influences property value, access to amenities, and life quality. A great location can make even a modest home highly desirable. Therefore, incorporating location into recommendations is essential for their effectiveness. We propose a geo-aware embedding framework to address sparsity and spatial nuances in housing recommendations on digital rental platforms. Our approach integrates an hierarchical H3 grid at multiple levels into a two-tower neural architecture. We compare our method with a traditional matrix factorization baseline and a single-resolution variant using interaction data from our platform. Embedding specific evaluation reveals richer and more balanced embedding representations, while offline ranking simulations demonstrate a substantial uplift in recommendation quality.", "published": "2025-09-01T18:49:03Z", "authors": ["Ivo Silva", "Pedro Nogueira", "Guilherme Bonaldo"], "categories": ["cs.IR", "cs.LG"], "doi": "10.1145/3705328.3748136", "_query": "housing price text listing", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2209.15041v1", "title": "Summarizing text to embed qualitative data into visualizations", "abstract": "Qualitative data can be conveyed with strings of text. Fitting longer text into visualizations requires a) space to place the text inside the visualization; and b) appropriate text to fit the space available. For quantitative visualizations, space is available in area marks; or within visualization layouts where the marks have an implied space (e.g. bar charts). For qualitative visualizations, space is defined in common text layouts such as prose paragraphs. To fit text within these layouts is a function for emerging NLP capabilities such as summarization.", "published": "2022-09-29T18:25:42Z", "authors": ["Richard Brath"], "categories": ["cs.HC"], "doi": "", "_query": "housing price text listing", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2507.03477v1", "title": "REAL: Benchmarking Abilities of Large Language Models for Housing Transactions and Services", "abstract": "The development of large language models (LLMs) has greatly promoted the progress of chatbot in multiple fields. There is an urgent need to evaluate whether LLMs can play the role of agent in housing transactions and services as well as humans. We present Real Estate Agent Large Language Model Evaluation (REAL), the first evaluation suite designed to assess the abilities of LLMs in the field of housing transactions and services. REAL comprises 5,316 high-quality evaluation entries across 4 topics: memory, comprehension, reasoning and hallucination. All these entries are organized as 14 categories to assess whether LLMs have the knowledge and ability in housing transactions and services scenario. Additionally, the REAL is used to evaluate the performance of most advanced LLMs. The experiment results indicate that LLMs still have significant room for improvement to be applied in the real estate field.", "published": "2025-07-04T11:05:44Z", "authors": ["Kexin Zhu", "Yang Han"], "categories": ["cs.AI"], "doi": "", "_query": "housing price text listing", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2605.12559v1", "title": "Coordination Failures and Stackelberg Leadership in Housing Development with Network Effects", "abstract": "I study coordination failures in housing development markets with network effects, where the value of building depends on aggregate supply. When network effects are sufficiently strong and convex, multiple equilibria arise: a low-supply coordination failure and a high-supply outcome. Without a coordination mechanism, equilibrium is indeterminate. I introduce a large developer who moves first in a Stackelberg game, committing to housing supply before atomistic developers make entry decisions. The main result is that the large developer always commits at least to the high-supply equilibrium, eliminating the coordination failure by pushing past the unstable threshold that separates the low and high outcomes. The result is unconditional; it holds for general demand functions and cost distributions, and does not depend on which stable continuation equilibrium materializes. The leader's commitment inverts standard monopoly intuition: first-mover commitment can improve welfare by resolving a coordination problem that atomistic markets cannot solve on their own. I also characterize when the developer builds beyond the high equilibrium into a monopoly region, and show that the market underprovides housing relative to the social optimum.", "published": "2026-05-11T21:03:38Z", "authors": ["Vaibhav Rangan"], "categories": ["econ.TH"], "doi": "", "_query": "housing price text listing", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2511.07022v1", "title": "Fair Societies: Algorithms for House Allocations", "abstract": "House Allocations concern with matchings involving one-sided preferences, where houses serve as a proxy encoding valuable indivisible resources (e.g. organs, course seats, subsidized public housing units) to be allocated among the agents. Every agent must receive exactly one resource. We study algorithmic approaches towards ensuring fairness in such settings. Minimizing the number of envious agents is known to be NP-complete (Kamiyama et al. 2021). We present two tractable approaches to deal with the computational hardness. When the agents are presented with an initial allocation of houses, we aim to refine this allocation by reallocating a bounded number of houses to reduce the number of envious agents. We show an efficient algorithm when the agents express preference for a bounded number of houses. Next, we consider single peaked preference domain and present a polynomial time algorithm for finding an allocation that minimize the number of envious agents. We further extend it to satisfy Pareto efficiency. Our former algorithm works for other measures of envy such as total envy, or maximum envy, with suitable modifications. Finally, we present an empirical analysis recording the fairness-welfare trade-off of our algorithms.", "published": "2025-11-10T12:09:27Z", "authors": ["Hadi Hosseini", "Sanjukta Roy", "Aditi Sethia"], "categories": ["cs.GT"], "doi": "", "_query": "housing price text listing", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2409.05990v1", "title": "FairHome: A Fair Housing and Fair Lending Dataset", "abstract": "We present a Fair Housing and Fair Lending dataset (FairHome): A dataset with around 75,000 examples across 9 protected categories. To the best of our knowledge, FairHome is the first publicly available dataset labeled with binary labels for compliance risk in the housing domain. We demonstrate the usefulness and effectiveness of such a dataset by training a classifier and using it to detect potential violations when using a large language model (LLM) in the context of real-estate transactions. We benchmark the trained classifier against state-of-the-art LLMs including GPT-3.5, GPT-4, LLaMA-3, and Mistral Large in both zero-shot and few-shot contexts. Our classifier outperformed with an F1-score of 0.91, underscoring the effectiveness of our dataset.", "published": "2024-09-09T18:34:26Z", "authors": ["Anusha Bagalkotkar", "Aveek Karmakar", "Gabriel Arnson", "Ondrej Linda"], "categories": ["cs.LG", "cs.CY"], "doi": "", "_query": "housing price text listing", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2503.07691v2", "title": "Fair Text Classification via Transferable Representations", "abstract": "Group fairness is a central research topic in text classification, where reaching fair treatment between sensitive groups (e.g., women and men) remains an open challenge. We propose an approach that extends the use of the Wasserstein Dependency Measure for learning unbiased neural text classifiers. Given the challenge of distinguishing fair from unfair information in a text encoder, we draw inspiration from adversarial training by inducing independence between representations learned for the target label and those for a sensitive attribute. We further show that Domain Adaptation can be efficiently leveraged to remove the need for access to the sensitive attributes in the dataset we cure. We provide both theoretical and empirical evidence that our approach is well-founded.", "published": "2025-03-10T16:52:45Z", "authors": ["Thibaud Leteno", "Michael Perrot", "Charlotte Laclau", "Antoine Gourru", "Christophe Gravier"], "categories": ["cs.LG", "cs.CL"], "doi": "", "_query": "housing price text listing", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/1910.04666v1", "title": "Set systems related to a house allocation problem", "abstract": "We are given a set $A$ of buyers, a set $B$ of houses, and for each buyer a preference list, i.e., an ordering of the houses. A house allocation is an injective mapping $τ$ from $A$ to $B$, and $τ$ is strictly better than another house allocation $τ'\\neq τ$ if for every buyer $i$, $τ'(i)$ does not come before $τ(i)$ in the preference list of $i$. A house allocation is Pareto optimal if there is no strictly better house allocation. Let $s(τ)$ be the image of $τ$ (i.e., the set of houses sold in the house allocation $τ$). We are interested in the largest possible cardinality $f(m)$ of the family of sets $s(τ)$ for Pareto optimal mappings $τ$ taken over all sets of preference lists of $m$ buyers. We improve the earlier upper bound on $f(m)$ given by Asinowski, Keszegh and Miltzow by making a connection between this problem and some problems in extremal set theory.", "published": "2019-10-10T16:05:08Z", "authors": ["Dániel Gerbner", "Balázs Keszegh", "Abhishek Methuku", "Dániel T. Nagy", "Balázs Patkós", "Casey Tompkins", "Chuanqi Xiao"], "categories": ["math.CO"], "doi": "10.1112/jlms.12484", "_query": "housing price text listing", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/1805.07274v1", "title": "Language Expansion In Text-Based Games", "abstract": "Text-based games are suitable test-beds for designing agents that can learn by interaction with the environment in the form of natural language text. Very recently, deep reinforcement learning based agents have been successfully applied for playing text-based games. In this paper, we explore the possibility of designing a single agent to play several text-based games and of expanding the agent's vocabulary using the vocabulary of agents trained for multiple games. To this extent, we explore the application of recently proposed policy distillation method for video games to the text-based game setting. We also use text-based games as a test-bed to analyze and hence understand policy distillation approach in detail.", "published": "2018-05-17T10:43:04Z", "authors": ["Ghulam Ahmed Ansari", "Sagar J P", "Sarath Chandar", "Balaraman Ravindran"], "categories": ["cs.CL", "cs.AI"], "doi": "", "_query": "housing price text listing", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/1307.4990v2", "title": "Video Text Localization using Wavelet and Shearlet Transforms", "abstract": "Text in video is useful and important in indexing and retrieving the video documents efficiently and accurately. In this paper, we present a new method of text detection using a combined dictionary consisting of wavelets and a recently introduced transform called shearlets. Wavelets provide optimally sparse expansion for point-like structures and shearlets provide optimally sparse expansions for curve-like structures. By combining these two features we have computed a high frequency sub-band to brighten the text part. Then K-means clustering is used for obtaining text pixels from the Standard Deviation (SD) of combined coefficient of wavelets and shearlets as well as the union of wavelets and shearlets features. Text parts are obtained by grouping neighboring regions based on geometric properties of the classified output frame of unsupervised K-means classification. The proposed method tested on a standard as well as newly collected database shows to be superior to some existing methods.", "published": "2013-07-18T15:58:19Z", "authors": ["Purnendu Banerjee", "B. B. Chaudhuri"], "categories": ["cs.CV"], "doi": "10.1117/12.2036077", "_query": "housing price text listing", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2108.01368v2", "title": "Robust Compressed Sensing MRI with Deep Generative Priors", "abstract": "The CSGM framework (Bora-Jalal-Price-Dimakis'17) has shown that deep generative priors can be powerful tools for solving inverse problems. However, to date this framework has been empirically successful only on certain datasets (for example, human faces and MNIST digits), and it is known to perform poorly on out-of-distribution samples. In this paper, we present the first successful application of the CSGM framework on clinical MRI data. We train a generative prior on brain scans from the fastMRI dataset, and show that posterior sampling via Langevin dynamics achieves high quality reconstructions. Furthermore, our experiments and theory show that posterior sampling is robust to changes in the ground-truth distribution and measurement process. Our code and models are available at: \\url{https://github.com/utcsilab/csgm-mri-langevin}.", "published": "2021-08-03T08:52:06Z", "authors": ["Ajil Jalal", "Marius Arvinte", "Giannis Daras", "Eric Price", "Alexandros G. Dimakis", "Jonathan I. Tamir"], "categories": ["cs.LG", "cs.CV", "cs.IT", "stat.ML"], "doi": "", "_query": "housing price text listing", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2209.06730v1", "title": "MUST-VQA: MUltilingual Scene-text VQA", "abstract": "In this paper, we present a framework for Multilingual Scene Text Visual Question Answering that deals with new languages in a zero-shot fashion. Specifically, we consider the task of Scene Text Visual Question Answering (STVQA) in which the question can be asked in different languages and it is not necessarily aligned to the scene text language. Thus, we first introduce a natural step towards a more generalized version of STVQA: MUST-VQA. Accounting for this, we discuss two evaluation scenarios in the constrained setting, namely IID and zero-shot and we demonstrate that the models can perform on a par on a zero-shot setting. We further provide extensive experimentation and show the effectiveness of adapting multilingual language models into STVQA tasks.", "published": "2022-09-14T15:37:56Z", "authors": ["Emanuele Vivoli", "Ali Furkan Biten", "Andres Mafla", "Dimosthenis Karatzas", "Lluis Gomez"], "categories": ["cs.CV"], "doi": "", "_query": "housing price text listing", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2304.05568v1", "title": "Improving Diffusion Models for Scene Text Editing with Dual Encoders", "abstract": "Scene text editing is a challenging task that involves modifying or inserting specified texts in an image while maintaining its natural and realistic appearance. Most previous approaches to this task rely on style-transfer models that crop out text regions and feed them into image transfer models, such as GANs. However, these methods are limited in their ability to change text style and are unable to insert texts into images. Recent advances in diffusion models have shown promise in overcoming these limitations with text-conditional image editing. However, our empirical analysis reveals that state-of-the-art diffusion models struggle with rendering correct text and controlling text style. To address these problems, we propose DIFFSTE to improve pre-trained diffusion models with a dual encoder design, which includes a character encoder for better text legibility and an instruction encoder for better style control. An instruction tuning framework is introduced to train our model to learn the mapping from the text instruction to the corresponding image with either the specified style or the style of the surrounding texts in the background. Such a training method further brings our method the zero-shot generalization ability to the following three scenarios: generating text with unseen font variation, e.g., italic and bold, mixing different fonts to construct a new font, and using more relaxed forms of natural language as the instructions to guide the generation task. We evaluate our approach on five datasets and demonstrate its superior performance in terms of text correctness, image naturalness, and style controllability. Our code is publicly available. https://github.com/UCSB-NLP-Chang/DiffSTE", "published": "2023-04-12T02:08:34Z", "authors": ["Jiabao Ji", "Guanhua Zhang", "Zhaowen Wang", "Bairu Hou", "Zhifei Zhang", "Brian Price", "Shiyu Chang"], "categories": ["cs.CV", "cs.AI"], "doi": "", "_query": "housing price text listing", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2504.20429v3", "title": "Estimating the housing production function with unobserved land heterogeneity", "abstract": "Housing supply in dense cities depends on the ability of builders to substitute capital for scarce land. This margin is difficult to estimate because builders choose capital after observing microgeographic conditions that are only imperfectly observed by researchers. This paper develops a method for estimating revenue-based housing production functions in this setting. Because observed capital variation reflects both technological substitution and endogenous responses to latent local conditions, existing estimators can transmit unobserved heterogeneity into the estimated production function. The method treats the unobserved local conditions that affect capital choice as a scalar Markov state and combines the capital share equation with Markov moments implemented using repeated cross-sectional construction data. Monte Carlo simulations show that the estimator recovers capital and land elasticities under flexible production technologies when capital choices respond to latent local conditions. An application to newly constructed housing in Tokyo's 23 special wards illustrates how the method can be implemented in a dense single-city setting. The results show that explicitly modeling latent local heterogeneity matters for estimated capital-land elasticities and implies returns to scale close to one.", "published": "2025-04-29T04:51:35Z", "authors": ["Yusuke Adachi"], "categories": ["econ.GN"], "doi": "", "_query": "housing price text listing", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/1407.3569v1", "title": "Dust and gas mixtures with multiple grain species - a one-fluid approach", "abstract": "We derive the single-fluid evolution equations describing a mixture made of a gas phase and an arbitrary number of dust phases, generalising the approach developed in Laibe & Price (2014a). A generalisation for continuous dust distributions as well as analytic approximations for strong drag regimes are also provided. This formalism lays the foundation for numeri- cal simulations of dust populations in a wide range of astrophysical systems while avoiding limitations associated with a multiple-fluid treatment. The usefulness of the formalism is illustrated on a series of analytical problems, namely the dustybox, dustyshock and dustywave problems as well as the radial drift of grains and the streaming instability in protoplanetary discs. We find physical effects specific to the presence of several dust phases and multiple drag timescales, including non-monotonic evolution of the differential velocity between phases and increased efficiency of the linear growth of the streaming instability. Interestingly, it is found that under certain conditions, large grains can migrate outwards in protoplanetary discs. This may explain the presence of small pebbles at several hundreds of astronomical units from their central star.", "published": "2014-07-14T08:51:39Z", "authors": ["Guillaume Laibe", "Daniel J. Price"], "categories": ["astro-ph.EP"], "doi": "10.1093/mnras/stu1367", "_query": "housing price text listing", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2401.07641v3", "title": "SwinTextSpotter v2: Towards Better Synergy for Scene Text Spotting", "abstract": "End-to-end scene text spotting, which aims to read the text in natural images, has garnered significant attention in recent years. However, recent state-of-the-art methods usually incorporate detection and recognition simply by sharing the backbone, which does not directly take advantage of the feature interaction between the two tasks. In this paper, we propose a new end-to-end scene text spotting framework termed SwinTextSpotter v2, which seeks to find a better synergy between text detection and recognition. Specifically, we enhance the relationship between two tasks using novel Recognition Conversion and Recognition Alignment modules. Recognition Conversion explicitly guides text localization through recognition loss, while Recognition Alignment dynamically extracts text features for recognition through the detection predictions. This simple yet effective design results in a concise framework that requires neither an additional rectification module nor character-level annotations for the arbitrarily-shaped text. Furthermore, the parameters of the detector are greatly reduced without performance degradation by introducing a Box Selection Schedule. Qualitative and quantitative experiments demonstrate that SwinTextSpotter v2 achieved state-of-the-art performance on various multilingual (English, Chinese, and Vietnamese) benchmarks. The code will be available at \\href{https://github.com/mxin262/SwinTextSpotterv2}{SwinTextSpotter v2}.", "published": "2024-01-15T12:33:00Z", "authors": ["Mingxin Huang", "Dezhi Peng", "Hongliang Li", "Zhenghao Peng", "Chongyu Liu", "Dahua Lin", "Yuliang Liu", "Xiang Bai", "Lianwen Jin"], "categories": ["cs.CV"], "doi": "", "_query": "housing price text listing", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2205.15696v1", "title": "An Informational Space Based Semantic Analysis for Scientific Texts", "abstract": "One major problem in Natural Language Processing is the automatic analysis and representation of human language. Human language is ambiguous and deeper understanding of semantics and creating human-to-machine interaction have required an effort in creating the schemes for act of communication and building common-sense knowledge bases for the 'meaning' in texts. This paper introduces computational methods for semantic analysis and the quantifying the meaning of short scientific texts. Computational methods extracting semantic feature are used to analyse the relations between texts of messages and 'representations of situations' for a newly created large collection of scientific texts, Leicester Scientific Corpus. The representation of scientific-specific meaning is standardised by replacing the situation representations, rather than psychological properties, with the vectors of some attributes: a list of scientific subject categories that the text belongs to. First, this paper introduces 'Meaning Space' in which the informational representation of the meaning is extracted from the occurrence of the word in texts across the scientific categories, i.e., the meaning of a word is represented by a vector of Relative Information Gain about the subject categories. Then, the meaning space is statistically analysed for Leicester Scientific Dictionary-Core and we investigate 'Principal Components of the Meaning' to describe the adequate dimensions of the meaning. The research in this paper conducts the base for the geometric representation of the meaning of texts.", "published": "2022-05-31T11:19:32Z", "authors": ["Neslihan Suzen", "Alexander N. Gorban", "Jeremy Levesley", "Evgeny M. Mirkes"], "categories": ["cs.CL", "cs.AI", "cs.HC", "cs.IT"], "doi": "", "_query": "housing price text listing", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2311.09133v1", "title": "Explainable Text Classification Techniques in Legal Document Review: Locating Rationales without Using Human Annotated Training Text Snippets", "abstract": "US corporations regularly spend millions of dollars reviewing electronically-stored documents in legal matters. Recently, attorneys apply text classification to efficiently cull massive volumes of data to identify responsive documents for use in these matters. While text classification is regularly used to reduce the discovery costs of legal matters, it also faces a perception challenge: amongst lawyers, this technology is sometimes looked upon as a \"black box\". Put simply, no extra information is provided for attorneys to understand why documents are classified as responsive. In recent years, explainable machine learning has emerged as an active research area. In an explainable machine learning system, predictions or decisions made by a machine learning model are human understandable. In legal 'document review' scenarios, a document is responsive, because one or more of its small text snippets are deemed responsive. In these scenarios, if these responsive snippets can be located, then attorneys could easily evaluate the model's document classification decisions - this is especially important in the field of responsible AI. Our prior research identified that predictive models created using annotated training text snippets improved the precision of a model when compared to a model created using all of a set of documents' text as training. While interesting, manually annotating training text snippets is not generally practical during a legal document review. However, small increases in precision can drastically decrease the cost of large document reviews. Automating the identification of training text snippets without human review could then make the application of training text snippet-based models a practical approach.", "published": "2023-11-15T17:24:56Z", "authors": ["Christian Mahoney", "Peter Gronvall", "Nathaniel Huber-Fliflet", "Jianping Zhang"], "categories": ["cs.IR"], "doi": "", "_query": "housing price text listing", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2105.05455v1", "title": "MT: Multi-Perspective Feature Learning Network for Scene Text Detection", "abstract": "Text detection, the key technology for understanding scene text, has become an attractive research topic. For detecting various scene texts, researchers propose plenty of detectors with different advantages: detection-based models enjoy fast detection speed, and segmentation-based algorithms are not limited by text shapes. However, for most intelligent systems, the detector needs to detect arbitrary-shaped texts with high speed and accuracy simultaneously. Thus, in this study, we design an efficient pipeline named as MT, which can detect adhesive arbitrary-shaped texts with only a single binary mask in the inference stage. This paper presents the contributions on three aspects: (1) a light-weight detection framework is designed to speed up the inference process while keeping high detection accuracy; (2) a multi-perspective feature module is proposed to learn more discriminative representations to segment the mask accurately; (3) a multi-factor constraints IoU minimization loss is introduced for training the proposed model. The effectiveness of MT is evaluated on four real-world scene text datasets, and it surpasses all the state-of-the-art competitors to a large extent.", "published": "2021-05-12T06:41:34Z", "authors": ["Chuang Yang", "Mulin Chen", "Yuan Yuan", "Qi Wang"], "categories": ["cs.CV"], "doi": "", "_query": "housing price text listing", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/1904.12387v1", "title": "TMIXT: A process flow for Transcribing MIXed handwritten and machine-printed Text", "abstract": "Handling large corpuses of documents is of significant importance in many fields, no more so than in the areas of crime investigation and defence, where an organisation may be presented with a large volume of scanned documents which need to be processed in a finite time. However, this problem is exacerbated both by the volume, in terms of scanned documents and the complexity of the pages, which need to be processed. Often containing many different elements, which each need to be processed and understood. Text recognition, which is a primary task of this process, is usually dependent upon the type of text, being either handwritten or machine-printed. Accordingly, the recognition involves prior classification of the text category, before deciding on the recognition method to be applied. This poses a more challenging task if a document contains both handwritten and machine-printed text. In this work, we present a generic process flow for text recognition in scanned documents containing mixed handwritten and machine-printed text without the need to classify text in advance. We realize the proposed process flow using several open-source image processing and text recognition packages1. The evaluation is performed using a specially developed variant, presented in this work, of the IAM handwriting database, where we achieve an average transcription accuracy of nearly 80% for pages containing both printed and handwritten text.", "published": "2019-04-28T21:46:39Z", "authors": ["Fady Medhat", "Mahnaz Mohammadi", "Sardar Jaf", "Chris G. Willcocks", "Toby P. Breckon", "Peter Matthews", "Andrew Stephen McGough", "Georgios Theodoropoulos", "Boguslaw Obara"], "categories": ["cs.LG", "cs.CV", "cs.IR"], "doi": "10.1109/BigData.2018.8622136", "_query": "housing price text listing", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2609.37902v1", "title": "You Cannot Pick a Provider From the Price List: Market-Aware Routing for Open-Weight LLM Inference", "abstract": "Existing LLM routers choose among models using static per-model costs. We show that open-weight inference markets introduce a second, largely ignored decision axis: after choosing a model, a client must still choose which provider serves it. Measuring live endpoints across [nummodels] open models, competing providers, multiple task types, and three measurement waves, we find that provider choice cannot be inferred from the price list. The same model can vary sharply in quality, latency, availability, and price across providers; higher-priced providers are consistently faster, but price does not reliably predict quality or availability; and provider feasibility is task-selective, with one deployment nearly normal on knowledge tasks but catastrophically degraded on multi-step reasoning. We formulate same-model provider selection as a price-taker market-aware routing problem. A simple measured-map policy routes to the cheapest provider that is both quality-equivalent and healthy, yielding matched-quality savings while avoiding degraded endpoints. Because the map drifts, we introduce FACET, an online provider router that certifies per-(provider x task) feasibility facets and fails safe to an anchor before serving uncertified arms. Across relaxed deployment assumptions, FACET tolerates imperfect task assignment and sparse feedback, while systematic evaluator bias exposes a quality-signal trust boundary that can be mitigated with ground-truth probes or audits. Live provider runs further confirm that certification can move real traffic from a premium anchor to a substantially cheaper certified endpoint. Our results suggest that market-aware LLM routing must measure not only which model to use, but also who serves it.", "published": "2026-09-29T16:02:18Z", "authors": ["Liang He", "Jingbo Wen", "Yixiong Chen", "Yue Yang", "Qizhen Lan", "Kangning Cui", "Xilu Wang"], "categories": ["cs.AI"], "doi": "", "_query": "housing price text listing", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/1308.3243v1", "title": "Arabic Text Recognition in Video Sequences", "abstract": "In this paper, we propose a robust approach for text extraction and recognition from Arabic news video sequence. The text included in video sequences is an important needful for indexing and searching system. However, this text is difficult to detect and recognize because of the variability of its size, their low resolution characters and the complexity of the backgrounds. To solve these problems, we propose a system performing in two main tasks: extraction and recognition of text. Our system is tested on a varied database composed of different Arabic news programs and the obtained results are encouraging and show the merits of our approach.", "published": "2013-08-14T20:15:44Z", "authors": ["M. Ben Halima", "H. Karray", "A. M. Alimi"], "categories": ["cs.MM", "cs.CL", "cs.CV"], "doi": "", "_query": "housing price text listing", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2301.01222v1", "title": "A Multi-Source Information Learning Framework for Airbnb Price Prediction", "abstract": "With the development of technology and sharing economy, Airbnb as a famous short-term rental platform, has become the first choice for many young people to select. The issue of Airbnb's pricing has always been a problem worth studying. While the previous studies achieve promising results, there are exists deficiencies to solve. Such as, (1) the feature attributes of rental are not rich enough; (2) the research on rental text information is not deep enough; (3) there are few studies on predicting the rental price combined with the point of interest(POI) around the house. To address the above challenges, we proposes a multi-source information embedding(MSIE) model to predict the rental price of Airbnb. Specifically, we first selects the statistical feature to embed the original rental data. Secondly, we generates the word feature vector and emotional score combination of three different text information to form the text feature embedding. Thirdly, we uses the points of interest(POI) around the rental house information generates a variety of spatial network graphs, and learns the embedding of the network to obtain the spatial feature embedding. Finally, this paper combines the three modules into multi source rental representations, and uses the constructed fully connected neural network to predict the price. The analysis of the experimental results shows the effectiveness of our proposed model.", "published": "2023-01-01T11:07:55Z", "authors": ["Lu Jiang", "Yuanhan Li", "Na Luo", "Jianan Wang", "Qiao Ning"], "categories": ["cs.LG", "cs.IR"], "doi": "", "_query": "housing price text listing", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/1706.04041v3", "title": "Text Extraction From Texture Images Using Masked Signal Decomposition", "abstract": "Text extraction is an important problem in image processing with applications from optical character recognition to autonomous driving. Most of the traditional text segmentation algorithms consider separating text from a simple background (which usually has a different color from texts). In this work we consider separating texts from a textured background, that has similar color to texts. We look at this problem from a signal decomposition perspective, and consider a more realistic scenario where signal components are overlaid on top of each other (instead of adding together). When the signals are overlaid, to separate signal components, we need to find a binary mask which shows the support of each component. Because directly solving the binary mask is intractable, we relax this problem to the approximated continuous problem, and solve it by alternating optimization method. We show that the proposed algorithm achieves significantly better results than other recent works on several challenging images.", "published": "2017-06-11T20:52:00Z", "authors": ["Shervin Minaee", "Yao Wang"], "categories": ["cs.CV"], "doi": "", "_query": "housing price text listing", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2104.00567v6", "title": "Text to Image Generation with Semantic-Spatial Aware GAN", "abstract": "Text-to-image synthesis (T2I) aims to generate photo-realistic images which are semantically consistent with the text descriptions. Existing methods are usually built upon conditional generative adversarial networks (GANs) and initialize an image from noise with sentence embedding, and then refine the features with fine-grained word embedding iteratively. A close inspection of their generated images reveals a major limitation: even though the generated image holistically matches the description, individual image regions or parts of somethings are often not recognizable or consistent with words in the sentence, e.g. \"a white crown\". To address this problem, we propose a novel framework Semantic-Spatial Aware GAN for synthesizing images from input text. Concretely, we introduce a simple and effective Semantic-Spatial Aware block, which (1) learns semantic-adaptive transformation conditioned on text to effectively fuse text features and image features, and (2) learns a semantic mask in a weakly-supervised way that depends on the current text-image fusion process in order to guide the transformation spatially. Experiments on the challenging COCO and CUB bird datasets demonstrate the advantage of our method over the recent state-of-the-art approaches, regarding both visual fidelity and alignment with input text description.", "published": "2021-04-01T15:48:01Z", "authors": ["Kai Hu", "Wentong Liao", "Michael Ying Yang", "Bodo Rosenhahn"], "categories": ["cs.CV", "cs.LG"], "doi": "", "_query": "housing price text listing", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2001.01641v1", "title": "Housing Investment, Stock Market Participation and Household Portfolio choice: Evidence from China's Urban Areas", "abstract": "This paper employs the survey data of CHFS (2013) to investigate the impact of housing investment on household stock market participation and portfolio choice. The results show that larger housing investment encourages the household participation in the stock market, but reduces the proportion of their stockholding. The above conclusion remains true even when the endogeneity problem is controlled with risk attitude classification, Heckman model test and subsample regression. This study shows that the growth in the housing market will not lead to stock market development because of lack of household financial literacy and the low expected yield on stock market.", "published": "2020-01-06T16:07:04Z", "authors": ["Huirong Liu"], "categories": ["econ.GN"], "doi": "", "_query": "housing price text listing", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2605.14766v1", "title": "Streaming Speech-to-Text Translation with a SpeechLLM", "abstract": "Normally, a system that translates speech into text consists of separate modules for speech recognition and text-to-text translation. Combining those tasks into a SpeechLLM promises to exploit paralinguistic information in the speech and to reduce cascaded errors. But existing SpeechLLM systems are slow since they do not work in a real streaming fashion: they wait for a complete utterance of audio before outputting a translation, or output tokens at fixed intervals, which is not suitable for real applications. This work proposes an LLM-based architecture for real streaming speech-to-text translation. The LLM learns not just to emit output tokens, but also to decide whether it has seen enough audio to do so. The system is trained using automatic alignments of the input speech and the output text. In experiments on different language pairs, the system achieves a translation quality close to the non-streaming baseline, but with a latency of only 1-2 seconds.", "published": "2026-05-14T12:32:57Z", "authors": ["Titouan Parcollet", "Shucong Zhang", "Xianrui Zheng", "Rogier C. van Dalen"], "categories": ["cs.CL", "cs.AI", "eess.AS"], "doi": "", "_query": "housing price text listing", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2304.12487v1", "title": "Queue Routing Strategies to Improve Equitable Housing Coordination in New York City", "abstract": "Runaway and homeless youth (RHY) are a group of youth and young adults who are at high risk of being exploited through human trafficking. Although access to housing and support services is an effective way to decrease their vulnerability to being exploited, research reveals that coordination of these services provided to RHY by non-profit and government organizations is neither standardized, nor efficient. This situation often causes decreased, delayed, and inequitable access to these scarce housing resources. In this study, we aim to increase the housing system efficiency and reduce the barriers that are contributing to inequitable access to housing through simulation modeling and analyses. Specifically, we simulate a set of crisis and emergency shelters in New York City, funded by a single governmental organization, considering a queuing network with pools of multiple parallel servers, servers with demographic eligibility criteria, stochastic RHY arrival, impatient youth behaviour (possibility of abandonment), and a decision-maker (coordinator) that determines which server pool RHY is routed to. This simulation allows us to evaluate the impact of different queue routing strategies. Our simulation results show that by changing the way RHY is routed to shelters, we can reduce the average wait time by approximately a day and decrease the proportion of RHY abandoning the shelters by 13%.", "published": "2023-04-24T22:58:51Z", "authors": ["Yaren Bilge Kaya", "Kayse Lee Maass"], "categories": ["math.NA", "math.PR"], "doi": "", "_query": "housing price text listing", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/1610.06370v1", "title": "Clinical Text Prediction with Numerically Grounded Conditional Language Models", "abstract": "Assisted text input techniques can save time and effort and improve text quality. In this paper, we investigate how grounded and conditional extensions to standard neural language models can bring improvements in the tasks of word prediction and completion. These extensions incorporate a structured knowledge base and numerical values from the text into the context used to predict the next word. Our automated evaluation on a clinical dataset shows extended models significantly outperform standard models. Our best system uses both conditioning and grounding, because of their orthogonal benefits. For word prediction with a list of 5 suggestions, it improves recall from 25.03% to 71.28% and for word completion it improves keystroke savings from 34.35% to 44.81%, where theoretical bound for this dataset is 58.78%. We also perform a qualitative investigation of how models with lower perplexity occasionally fare better at the tasks. We found that at test time numbers have more influence on the document level than on individual word probabilities.", "published": "2016-10-20T11:48:30Z", "authors": ["Georgios P. Spithourakis", "Steffen E. Petersen", "Sebastian Riedel"], "categories": ["cs.CL", "cs.HC", "cs.NE"], "doi": "", "_query": "housing price text listing", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2011.03713v1", "title": "Naturalization of Text by the Insertion of Pauses and Filler Words", "abstract": "In this article, we introduce a set of methods to naturalize text based on natural human speech. Voice-based interactions provide a natural way of interfacing with electronic systems and are seeing a widespread adaptation of late. These computerized voices can be naturalized to some degree by inserting pauses and filler words at appropriate positions. The first proposed text transformation method uses the frequency of bigrams in the training data to make appropriate insertions in the input sentence. It uses a probability distribution to choose the insertions from a set of all possible insertions. This method is fast and can be included before a Text-To-Speech module. The second method uses a Recurrent Neural Network to predict the next word to be inserted. It confirms the insertions given by the bigram method. Additionally, the degree of naturalization can be controlled in both these methods. On the conduction of a blind survey, we conclude that the output of these text transformation methods is comparable to natural speech.", "published": "2020-11-07T06:56:54Z", "authors": ["Richa Sharma", "Parth Vipul Shah", "Ashwini M. Joshi"], "categories": ["cs.CL"], "doi": "", "_query": "housing price text listing", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2112.07395v1", "title": "Handwritten text generation and strikethrough characters augmentation", "abstract": "We introduce two data augmentation techniques, which, used with a Resnet-BiLSTM-CTC network, significantly reduce Word Error Rate (WER) and Character Error Rate (CER) beyond best-reported results on handwriting text recognition (HTR) tasks. We apply a novel augmentation that simulates strikethrough text (HandWritten Blots) and a handwritten text generation method based on printed text (StackMix), which proved to be very effective in HTR tasks. StackMix uses weakly-supervised framework to get character boundaries. Because these data augmentation techniques are independent of the network used, they could also be applied to enhance the performance of other networks and approaches to HTR. Extensive experiments on ten handwritten text datasets show that HandWritten Blots augmentation and StackMix significantly improve the quality of HTR models", "published": "2021-12-14T13:41:10Z", "authors": ["Alex Shonenkov", "Denis Karachev", "Max Novopoltsev", "Mark Potanin", "Denis Dimitrov", "Andrey Chertok"], "categories": ["cs.CV"], "doi": "", "_query": "housing price text listing", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2407.18069v4", "title": "$\\text{C}^2\\text{P}$: Featuring Large Language Models with Causal Reasoning", "abstract": "Causal reasoning is one of the primary bottlenecks that Large Language Models (LLMs) must overcome to attain human-level intelligence. Recent studies indicate that LLMs display near-random performance on reasoning tasks. To address this, we introduce the Causal Chain of Prompting ($\\text{C}^2\\text{P}$), a reasoning framework that aims to equip current LLMs with causal reasoning capabilities as the first framework of its kind operating autonomously without relying on external tools or modules during both the causal learning and reasoning phases. To evaluate the performance of $\\text{C}^2\\text{P}$, we first demonstrate that reasoning accuracy improved by over $30.7\\%$ and $25.9\\%$ for GPT-4 Turbo and LLaMA 3.1, respectively, when using our framework, compared to the same models without $\\text{C}^2\\text{P}$ on a synthetic benchmark dataset. Then, using few-shot learning of the same LLMs with $\\text{C}^2\\text{P}$, the reasoning accuracy increased by more than $20.05\\%$ and $20.89\\%$, respectively, with as few as ten examples, compared to the corresponding LLMs without $\\text{C}^2\\text{P}$ on the same dataset. To evaluate $\\text{C}^2\\text{P}$ in realistic scenarios, we utilized another benchmark dataset containing natural stories across various fields, including healthcare, medicine, economics, education, social sciences, environmental science, and marketing. The results show improved reasoning when $\\text{C}^2\\text{P}$ is applied, compared to cases where our framework is not used, which often leads to random and hallucinated responses. By showing the improved performance of few-shot learned GPT-4 Turbo and LLaMA 3.1 with $\\text{C}^2\\text{P}$, we demonstrate the generalizability of our framework.", "published": "2024-07-25T14:24:57Z", "authors": ["Abdolmahdi Bagheri", "Matin Alinejad", "Mahdi Dehshiri", "Kevin Bello", "Alireza Akhondi-Asl"], "categories": ["cs.LO"], "doi": "", "_query": "housing price text listing", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2304.10995v1", "title": "Solving the List Coloring Problem through a Branch-and-Price algorithm", "abstract": "In this work, we present a branch-and-price algorithm to solve the weighted version of the List Coloring Problem, based on a vertex cover formulation by stable sets. This problem is interesting for its applications and also for the many other problems that it generalizes, including the well-known Graph Coloring Problem. With the introduction of the concept of indistinguishable colors, some theoretical results are presented which are later incorporated into the algorithm. We propose two branching strategies based on others for the Graph Coloring Problem, the first is an adaptation of the one used by Mehrotra and Trick in their pioneering branch-and-price algorithm, and the other is inspired by the one used by Méndez-Díaz and Zabala in their branch-and-cut algorithm. The rich structure of this problem makes both branching strategies robust. Extended computation experimentation on a wide variety of instances shows the effectiveness of this approach and evidences the different behaviors that the algorithm can have according to the structure of each type of instance.", "published": "2023-04-21T14:44:23Z", "authors": ["Mauro Lucci", "Daniel Severin", "Graciela Nasini"], "categories": ["cs.DS", "cs.DM"], "doi": "10.1016/j.ejor.2024.01.038", "_query": "housing price text listing", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/1004.1957v1", "title": "Modelling Shear Flows with SPH and Grid Based Methods", "abstract": "Given the importance of shear flows for astrophysical gas dynamics, we study the evolution of the Kelvin-Helmholtz instability (KHI) analytically and numerically. We derive the dispersion relation for the two-dimensional KHI including viscous dissipation. The resulting expression for the growth rate is then used to estimate the intrinsic viscosity of four numerical schemes depending on code-specific as well as on physical parameters. Our set of numerical schemes includes the Tree-SPH code VINE, an alternative SPH formulation developed by Price (2008), and the finite-volume grid codes FLASH and PLUTO. In the first part, we explicitly demonstrate the effect of dissipation-inhibiting mechanisms such as the Balsara viscosity on the evolution of the KHI. With VINE, increasing density contrasts lead to a continuously increasing suppression of the KHI (with complete suppression from a contrast of 6:1 or higher). The alternative SPH formulation including an artificial thermal conductivity reproduces the analytically expected growth rates up to a density contrast of 10:1. The second part addresses the shear flow evolution with FLASH and PLUTO. Both codes result in a consistent non-viscous evolution (in the equal as well as in the different density case) in agreement with the analytical prediction. The viscous evolution studied with FLASH shows minor deviations from the analytical prediction.", "published": "2010-04-12T13:55:19Z", "authors": ["Veronika Junk", "Stefanie Walch", "Fabian Heitsch", "Andreas Burkert", "Markus Wetzstein", "Marc Schartmann", "Daniel Price"], "categories": ["astro-ph.GA"], "doi": "10.1111/j.1365-2966.2010.17039.x", "_query": "housing price text listing", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2006.13268v1", "title": "Automating Text Naturalness Evaluation of NLG Systems", "abstract": "Automatic methods and metrics that assess various quality criteria of automatically generated texts are important for developing NLG systems because they produce repeatable results and allow for a fast development cycle. We present here an attempt to automate the evaluation of text naturalness which is a very important characteristic of natural language generation methods. Instead of relying on human participants for scoring or labeling the text samples, we propose to automate the process by using a human likeliness metric we define and a discrimination procedure based on large pretrained language models with their probability distributions. We analyze the text probability fractions and observe how they are influenced by the size of the generative and discriminative models involved in the process. Based on our results, bigger generators and larger pretrained discriminators are more appropriate for a better evaluation of text naturalness. A comprehensive validation procedure with human participants is required as follow up to check how well this automatic evaluation scheme correlates with human judgments.", "published": "2020-06-23T18:48:33Z", "authors": ["Erion Çano", "Ondřej Bojar"], "categories": ["cs.CL", "cs.LG"], "doi": "", "_query": "housing price text listing", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/1509.09093v1", "title": "A Sentence Meaning Based Alignment Method for Parallel Text Corpora Preparation", "abstract": "Text alignment is crucial to the accuracy of Machine Translation (MT) systems, some NLP tools or any other text processing tasks requiring bilingual data. This research proposes a language independent sentence alignment approach based on Polish (not position-sensitive language) to English experiments. This alignment approach was developed on the TED Talks corpus, but can be used for any text domain or language pair. The proposed approach implements various heuristics for sentence recognition. Some of them value synonyms and semantic text structure analysis as a part of additional information. Minimization of data loss was ensured. The solution is compared to other sentence alignment implementations. Also an improvement in MT system score with text processed with described tool is shown.", "published": "2015-09-30T09:29:51Z", "authors": ["Krzysztof Wołk", "Krzysztof Marasek"], "categories": ["cs.CL", "cs.IR"], "doi": "", "_query": "housing price text listing", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2208.12419v1", "title": "Arbitrary Shape Text Detection via Segmentation with Probability Maps", "abstract": "Arbitrary shape text detection is a challenging task due to the significantly varied sizes and aspect ratios, arbitrary orientations or shapes, inaccurate annotations, etc. Due to the scalability of pixel-level prediction, segmentation-based methods can adapt to various shape texts and hence attracted considerable attention recently. However, accurate pixel-level annotations of texts are formidable, and the existing datasets for scene text detection only provide coarse-grained boundary annotations. Consequently, numerous misclassified text pixels or background pixels inside annotations always exist, degrading the performance of segmentation-based text detection methods. Generally speaking, whether a pixel belongs to text or not is highly related to the distance with the adjacent annotation boundary. With this observation, in this paper, we propose an innovative and robust segmentation-based detection method via probability maps for accurately detecting text instances. To be concrete, we adopt a Sigmoid Alpha Function (SAF) to transfer the distances between boundaries and their inside pixels to a probability map. However, one probability map can not cover complex probability distributions well because of the uncertainty of coarse-grained text boundary annotations. Therefore, we adopt a group of probability maps computed by a series of Sigmoid Alpha Functions to describe the possible probability distributions. In addition, we propose an iterative model to learn to predict and assimilate probability maps for providing enough information to reconstruct text instances. Finally, simple region growth algorithms are adopted to aggregate probability maps to complete text instances. Experimental results demonstrate that our method achieves state-of-the-art performance in terms of detection accuracy on several benchmarks.", "published": "2022-08-26T03:29:00Z", "authors": ["Shi-Xue Zhang", "Xiaobin Zhu", "Lei Chen", "Jie-Bo Hou", "Xu-Cheng Yin"], "categories": ["cs.CV"], "doi": "", "_query": "housing price text listing", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2403.07938v1", "title": "Text-to-Audio Generation Synchronized with Videos", "abstract": "In recent times, the focus on text-to-audio (TTA) generation has intensified, as researchers strive to synthesize audio from textual descriptions. However, most existing methods, though leveraging latent diffusion models to learn the correlation between audio and text embeddings, fall short when it comes to maintaining a seamless synchronization between the produced audio and its video. This often results in discernible audio-visual mismatches. To bridge this gap, we introduce a groundbreaking benchmark for Text-to-Audio generation that aligns with Videos, named T2AV-Bench. This benchmark distinguishes itself with three novel metrics dedicated to evaluating visual alignment and temporal consistency. To complement this, we also present a simple yet effective video-aligned TTA generation model, namely T2AV. Moving beyond traditional methods, T2AV refines the latent diffusion approach by integrating visual-aligned text embeddings as its conditional foundation. It employs a temporal multi-head attention transformer to extract and understand temporal nuances from video data, a feat amplified by our Audio-Visual ControlNet that adeptly merges temporal visual representations with text embeddings. Further enhancing this integration, we weave in a contrastive learning objective, designed to ensure that the visual-aligned text embeddings resonate closely with the audio features. Extensive evaluations on the AudioCaps and T2AV-Bench demonstrate that our T2AV sets a new standard for video-aligned TTA generation in ensuring visual alignment and temporal consistency.", "published": "2024-03-08T22:27:38Z", "authors": ["Shentong Mo", "Jing Shi", "Yapeng Tian"], "categories": ["cs.SD", "cs.AI", "cs.CV", "cs.LG", "cs.MM", "eess.AS"], "doi": "", "_query": "housing price text listing", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2603.23070v2", "title": "Knowledge management in House of Graphs", "abstract": "The House of Graphs is an online database of graphs which can be accessed at https://houseofgraphs.org/. It serves as a central repository for complete lists of graphs for various graph classes. However, its main feature is a searchable database of so-called \"interesting\" graphs. The development of the original House of Graphs started in 2010 and it was completely rebuilt in 2021-2022. Each graph in the database is accompanied by a significant amount of meta-data such as a name, drawings, precomputed graph invariants, and comments. Given this amount of information and the importance of reliability in the scientific world, robust data management is essential to ensure accuracy and consistency across the database. In this article, we therefore focus on knowledge management in the House of Graphs and describe the inner workings of the House of Graphs and how we ensure that its data is coherent, qualitative and stable.", "published": "2026-03-24T11:07:05Z", "authors": ["Gauvain Devillez", "Sven D'hondt", "Jan Goedgebeur"], "categories": ["math.CO", "cs.DB", "cs.DM"], "doi": "", "_query": "housing price text listing", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/1710.07210v1", "title": "Multi-Task Label Embedding for Text Classification", "abstract": "Multi-task learning in text classification leverages implicit correlations among related tasks to extract common features and yield performance gains. However, most previous works treat labels of each task as independent and meaningless one-hot vectors, which cause a loss of potential information and makes it difficult for these models to jointly learn three or more tasks. In this paper, we propose Multi-Task Label Embedding to convert labels in text classification into semantic vectors, thereby turning the original tasks into vector matching tasks. We implement unsupervised, supervised and semi-supervised models of Multi-Task Label Embedding, all utilizing semantic correlations among tasks and making it particularly convenient to scale and transfer as more tasks are involved. Extensive experiments on five benchmark datasets for text classification show that our models can effectively improve performances of related tasks with semantic representations of labels and additional information from each other.", "published": "2017-10-17T23:35:58Z", "authors": ["Honglun Zhang", "Liqiang Xiao", "Wenqing Chen", "Yongkun Wang", "Yaohui Jin"], "categories": ["cs.CL"], "doi": "", "_query": "housing price text listing", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2405.07029v2", "title": "A framework of text-dependent speaker verification for chinese numerical string corpus", "abstract": "The Chinese numerical string corpus, serves as a valuable resource for speaker verification, particularly in financial transactions. Researches indicate that in short speech scenarios, text-dependent speaker verification (TD-SV) consistently outperforms text-independent speaker verification (TI-SV). However, TD-SV potentially includes the validation of text information, that can be negatively impacted by reading rhythms and pauses. To address this problem, we propose an end-to-end speaker verification system that enhances TD-SV by decoupling speaker and text information. Our system consists of a text embedding extractor, a speaker embedding extractor and a fusion module. In the text embedding extractor, we employ an enhanced Transformer and introduce a triple loss including text classification loss, connectionist temporal classification (CTC) loss and decoder loss; while in the speaker embedding extractor, we create a multi-scale pooling method by combining sliding window attentive statistics pooling (SWASP) with attentive statistics pooling (ASP). To mitigate the scarcity of data, we have recorded a publicly available Chinese numerical corpus named SHALCAS22A (hereinafter called SHAL), which can be accessed on Open-SLR. Moreover, we employ data augmentation techniques using Tacotron2 and HiFi-GAN. Our method achieves an equal error rate (EER) performance improvement of 49.2% on Hi-Mia and 75.0% on SHAL, respectively.", "published": "2024-05-11T15:02:06Z", "authors": ["Litong Zheng", "Feng Hong", "Weijie Xu", "Wan Zheng"], "categories": ["cs.SD", "eess.AS"], "doi": "", "_query": "housing price text listing", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2105.07822v1", "title": "Acquisitive Crimes, Time of Day, and Multiunit Housing in the City of Milwaukee", "abstract": "According to \"Social Disorganization\" theory, criminal activity increases if the societal institutions that might be responsible for maintaining order are weakened. Do large apartment buildings, which often have fairly transient populations and low levels of community involvement, have disproportionately high rates of crime? Do these rates differ during the daytime or nighttime, depending when residents are present, or away from their property? This study examines four types of \"acquisitive\" crime in Milwaukee during 2014. Overall, nighttime crimes are shown to be more dispersed than daytime crimes. A spatial regression estimation finds that the density of multiunit housing is positively related to all types of crime except burglaries, but not for all times of day. Daytime robberies, in particular, increase as the density of multiunit housing increases.", "published": "2021-05-13T00:51:06Z", "authors": ["Scott W. Hegerty"], "categories": ["econ.GN"], "doi": "", "_query": "housing price text listing", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/1905.01976v1", "title": "TextKD-GAN: Text Generation using KnowledgeDistillation and Generative Adversarial Networks", "abstract": "Text generation is of particular interest in many NLP applications such as machine translation, language modeling, and text summarization. Generative adversarial networks (GANs) achieved a remarkable success in high quality image generation in computer vision,and recently, GANs have gained lots of interest from the NLP community as well. However, achieving similar success in NLP would be more challenging due to the discrete nature of text. In this work, we introduce a method using knowledge distillation to effectively exploit GAN setup for text generation. We demonstrate how autoencoders (AEs) can be used for providing a continuous representation of sentences, which is a smooth representation that assign non-zero probabilities to more than one word. We distill this representation to train the generator to synthesize similar smooth representations. We perform a number of experiments to validate our idea using different datasets and show that our proposed approach yields better performance in terms of the BLEU score and Jensen-Shannon distance (JSD) measure compared to traditional GAN-based text generation approaches without pre-training.", "published": "2019-04-23T15:15:12Z", "authors": ["Md. Akmal Haidar", "Mehdi Rezagholizadeh"], "categories": ["cs.CL"], "doi": "", "_query": "housing price text listing", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/1712.05404v1", "title": "SEE: Towards Semi-Supervised End-to-End Scene Text Recognition", "abstract": "Detecting and recognizing text in natural scene images is a challenging, yet not completely solved task. In recent years several new systems that try to solve at least one of the two sub-tasks (text detection and text recognition) have been proposed. In this paper we present SEE, a step towards semi-supervised neural networks for scene text detection and recognition, that can be optimized end-to-end. Most existing works consist of multiple deep neural networks and several pre-processing steps. In contrast to this, we propose to use a single deep neural network, that learns to detect and recognize text from natural images, in a semi-supervised way. SEE is a network that integrates and jointly learns a spatial transformer network, which can learn to detect text regions in an image, and a text recognition network that takes the identified text regions and recognizes their textual content. We introduce the idea behind our novel approach and show its feasibility, by performing a range of experiments on standard benchmark datasets, where we achieve competitive results.", "published": "2017-12-14T12:59:26Z", "authors": ["Christian Bartz", "Haojin Yang", "Christoph Meinel"], "categories": ["cs.CV"], "doi": "", "_query": "housing price text listing", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/1903.12473v2", "title": "Shape Robust Text Detection with Progressive Scale Expansion Network", "abstract": "Scene text detection has witnessed rapid progress especially with the recent development of convolutional neural networks. However, there still exists two challenges which prevent the algorithm into industry applications. On the one hand, most of the state-of-art algorithms require quadrangle bounding box which is in-accurate to locate the texts with arbitrary shape. On the other hand, two text instances which are close to each other may lead to a false detection which covers both instances. Traditionally, the segmentation-based approach can relieve the first problem but usually fail to solve the second challenge. To address these two challenges, in this paper, we propose a novel Progressive Scale Expansion Network (PSENet), which can precisely detect text instances with arbitrary shapes. More specifically, PSENet generates the different scale of kernels for each text instance, and gradually expands the minimal scale kernel to the text instance with the complete shape. Due to the fact that there are large geometrical margins among the minimal scale kernels, our method is effective to split the close text instances, making it easier to use segmentation-based methods to detect arbitrary-shaped text instances. Extensive experiments on CTW1500, Total-Text, ICDAR 2015 and ICDAR 2017 MLT validate the effectiveness of PSENet. Notably, on CTW1500, a dataset full of long curve texts, PSENet achieves a F-measure of 74.3% at 27 FPS, and our best F-measure (82.2%) outperforms state-of-art algorithms by 6.6%. The code will be released in the future.", "published": "2019-03-28T06:04:44Z", "authors": ["Wenhai Wang", "Enze Xie", "Xiang Li", "Wenbo Hou", "Tong Lu", "Gang Yu", "Shuai Shao"], "categories": ["cs.CV"], "doi": "", "_query": "housing price text listing", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2508.16657v1", "title": "Leveraging Multi-Source Textural UGC for Neighbourhood Housing Quality Assessment: A GPT-Enhanced Framework", "abstract": "This study leverages GPT-4o to assess neighbourhood housing quality using multi-source textural user-generated content (UGC) from Dianping, Weibo, and the Government Message Board. The analysis involves filtering relevant texts, extracting structured evaluation units, and conducting sentiment scoring. A refined housing quality assessment system with 46 indicators across 11 categories was developed, highlighting an objective-subjective method gap and platform-specific differences in focus. GPT-4o outperformed rule-based and BERT models, achieving 92.5% accuracy in fine-tuned settings. The findings underscore the value of integrating UGC and GPT-driven analysis for scalable, resident-centric urban assessments, offering practical insights for policymakers and urban planners.", "published": "2025-08-20T08:29:40Z", "authors": ["Qiyuan Hong", "Huimin Zhao", "Ying Long"], "categories": ["cs.CY", "cs.CL"], "doi": "", "_query": "housing price text listing", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2305.03737v1", "title": "Tuning Traditional Language Processing Approaches for Pashto Text Classification", "abstract": "Today text classification becomes critical task for concerned individuals for numerous purposes. Hence, several researches have been conducted to develop automatic text classification for national and international languages. However, the need for an automatic text categorization system for local languages is felt. The main aim of this study is to establish a Pashto automatic text classification system. In order to pursue this work, we built a Pashto corpus which is a collection of Pashto documents due to the unavailability of public datasets of Pashto text documents. Besides, this study compares several models containing both statistical and neural network machine learning techniques including Multilayer Perceptron (MLP), Support Vector Machine (SVM), K Nearest Neighbor (KNN), decision tree, gaussian naïve Bayes, multinomial naïve Bayes, random forest, and logistic regression to discover the most effective approach. Moreover, this investigation evaluates two different feature extraction methods including unigram, and Time Frequency Inverse Document Frequency (IFIDF). Subsequently, this research obtained average testing accuracy rate 94% using MLP classification algorithm and TFIDF feature extraction method in this context.", "published": "2023-05-04T22:57:45Z", "authors": ["Jawid Ahmad Baktash", "Mursal Dawodi", "Mohammad Zarif Joya", "Nematullah Hassanzada"], "categories": ["cs.CL", "cs.AI", "cs.LG"], "doi": "10.5121/ijci.2023.120222", "_query": "housing price text listing", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2607.12205v1", "title": "A Unified Credit Expansion Theory on Housing Cycle: Causal Evidence for Within- and Cross-Metro Patterns in the Prior, Boom, Bust, and Recovery Periods", "abstract": "During the 1999-2019 U.S. housing cycle, three empirical facts present a puzzle: in the boom period, the correlation between income growth and mortgage growth is (1) negative across ZIP codes within a metropolitan area, but (2) positive across metropolitan areas, and (3) the metropolitan areas that experience the worst bust also show the strongest recovery. I develop a unified credit expansion theory that explains both within- and cross-metro patterns in the prior, boom, bust, and recovery periods (including the three facts above) and generates new testable implications of ``double differences\" (cross ZIP codes and cross metros) for the four periods. Following the idea of ``Economic Base Theory\", I construct local economic exposure to net export growth as the driving force of local economy and credit expansion. For the identification strategy, I use a new instrumental variable approach from the International trade literature for the following empirical results. First, I show that high-net-export-growth metros experience a stronger boom-bust-recovery housing cycle due to credit expansion in private-label mortgages (PLMs), rather than in government-sponsored enterprise mortgages (GSEMs), because only the former can legally respond to local economic conditions. Second, for the ``double differences\", I define a low-minus-high (LMH) factor as the private-label mortgage (and house price) growth in low-income ZIP codes minus that in high-income ZIP codes within the same metropolitan area. I show that this low-minus-high factor (as a measure of credit expansion) in the high-net-export-growth metros is more positive during the boom period, more negative during the bust period, and slightly more positive in the recovery period than in the low-net-export-growth metros. Lastly, I employ five tests to demonstrate that ``speculation\" is unlikely to play a dominant role in this housing cycle.", "published": "2026-07-13T23:15:50Z", "authors": ["Bo Li"], "categories": ["q-fin.GN"], "doi": "", "_query": "housing price text listing", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/1706.07721v1", "title": "Investigating prescriptions for artificial resistivity in smoothed particle magnetohydrodynamics", "abstract": "In numerical simulations, artificial terms are applied to the evolution equations for stability. To prove their validity, these terms are thoroughly tested in test problems where the results are well known. However, they are seldom tested in production-quality simulations at high resolution where they interact with a plethora of physical and numerical algorithms. We test three artificial resistivities in both the Orszag-Tang vortex and in a star formation simulation. From the Orszag-Tang vortex, the Price et. al. (2017) artificial resistivity is the least dissipative thus captures the density and magnetic features; in the star formation algorithm, each artificial resistivity algorithm interacts differently with the sink particle to produce various results, including gas bubbles, dense discs, and migrating sink particles. The star formation simulations suggest that it is important to rely upon physical resistivity rather than artificial resistivity for convergence.", "published": "2017-06-23T14:26:59Z", "authors": ["James Wurster", "Matthew R. Bate", "Daniel J. Price", "Terrence S. Tricco"], "categories": ["astro-ph.IM"], "doi": "", "_query": "housing price text listing", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2302.05808v1", "title": "Long-term option pricing with a lower reflecting barrier", "abstract": "This paper considers the pricing of long-term options on assets such as housing, where either government intervention or the economic nature of the asset is assumed to limit large falls in prices. The observed asset price is modelled by a geometric Brownian motion (the 'notional price') reflected at a lower barrier. The resulting observed price has standard dynamics but with localised intervention at the barrier, which allows arbitrage with interim losses; this is funded by the government's unlimited powers of intervention, and its exploitation is subject to credit constraints. Despite the lack of an equivalent martingale measure for the observed price, options on this price can be expressed as compound options on the arbitrage-free notional price, to which standard risk-neutral arguments can be applied. Because option deltas tend to zero when the observed price approaches the barrier, hedging with the observed price gives the same results as hedging with the notional price, and so exactly replicates option payoffs. Hedging schemes are not unique, with the cheapest scheme for any derivative being the one which best exploits the interventions at the barrier. The price of a put is clear: direct replication has a lower initial cost than synthetic replication, and the replication portfolio always has positive value. The price of a call is ambiguous: synthetic replication has a lower initial cost than direct replication, but the replication portfolio may give interim losses, and so the preferred replication strategy (and hence price) of a call may depend on what margin payments need to be made on these losses.", "published": "2023-02-11T22:59:34Z", "authors": ["R. Guy Thomas"], "categories": ["q-fin.PR", "q-fin.MF", "q-fin.RM"], "doi": "10.1017/S1748499522000227", "_query": "housing price text listing", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2209.14022v1", "title": "Leveraging machine learning for less developed languages: Progress on Urdu text detection", "abstract": "Text detection in natural scene images has applications for autonomous driving, navigation help for elderly and blind people. However, the research on Urdu text detection is usually hindered by lack of data resources. We have developed a dataset of scene images with Urdu text. We present the use of machine learning methods to perform detection of Urdu text from the scene images. We extract text regions using channel enhanced Maximally Stable Extremal Region (MSER) method. First, we classify text and noise based on their geometric properties. Next, we use a support vector machine for early discarding of non-text regions. To further remove the non-text regions, we use histogram of oriented gradients (HoG) features obtained and train a second SVM classifier. This improves the overall performance on text region detection within the scene images. To support research on Urdu text, We aim to make the data freely available for research use. We also aim to highlight the challenges and the research gap for Urdu text detection.", "published": "2022-09-28T12:00:34Z", "authors": ["Hazrat Ali"], "categories": ["cs.CV", "cs.AI", "cs.LG", "eess.IV"], "doi": "", "_query": "housing price text listing", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2511.12629v2", "title": "Bandit Learning in Housing Markets", "abstract": "The housing market, also known as one-sided matching market, is a classic exchange economy model where each agent on the demand side initially owns an indivisible good (a house) and has a personal preference over all goods. The goal is to find a core-stable allocation that exhausts all mutually beneficial exchanges among subgroups of agents. While this model has been extensively studied in economics and computer science due to its broad applications, little attention has been paid to settings where preferences are unknown and must be learned through repeated interactions. In this paper, we propose a statistical learning model within the multi-player multi-armed bandit framework, where players (agents) learn their preferences over arms (goods) from stochastic rewards. We introduce the notion of \\emph{core regret} for each player as the market objective. We study both centralized and decentralized approaches, proving $O(\\log T / Δ^2)$ upper bounds on regret, where $T$ is the time horizon and $Δ$ is the minimum preference gap among players. For the decentralized setting, we also establish a matching lower bound, demonstrating that our algorithm is order-optimal.", "published": "2025-11-16T14:50:44Z", "authors": ["Shiyun Lin"], "categories": ["cs.GT"], "doi": "", "_query": "housing price text listing", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2009.09132v2", "title": "Prior Art Search and Reranking for Generated Patent Text", "abstract": "Generative models, such as GPT-2, have demonstrated impressive results recently. A fundamental question we'd like to address is: where did the generated text come from? This work is our initial effort toward answering the question by using prior art search. The purpose of the prior art search is to find the most similar prior text in the training data of GPT-2. We take a reranking approach and apply it to the patent domain. Specifically, we pre-train GPT-2 models from scratch by using the patent data from the USPTO. The input for the prior art search is the patent text generated by the GPT-2 model. We also pre-trained BERT models from scratch for converting patent text to embeddings. The steps of reranking are: (1) search the most similar text in the training data of GPT-2 by taking a bag-of-word ranking approach (BM25), (2) convert the search results in text format to BERT embeddings, and (3) provide the final result by ranking the BERT embeddings based on their similarities with the patent text generated by GPT-2. The experiments in this work show that such reranking is better than ranking with embeddings alone. However, our mixed results also indicate that calculating the semantic similarities among long text spans is still challenging. To our knowledge, this work is the first to implement a reranking system to identify retrospectively the most similar inputs to a GPT model based on its output.", "published": "2020-09-19T01:16:18Z", "authors": ["Jieh-Sheng Lee", "Jieh Hsiang"], "categories": ["cs.CL"], "doi": "", "_query": "housing price text listing", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/1904.07695v1", "title": "Short Text Topic Modeling Techniques, Applications, and Performance: A Survey", "abstract": "Analyzing short texts infers discriminative and coherent latent topics that is a critical and fundamental task since many real-world applications require semantic understanding of short texts. Traditional long text topic modeling algorithms (e.g., PLSA and LDA) based on word co-occurrences cannot solve this problem very well since only very limited word co-occurrence information is available in short texts. Therefore, short text topic modeling has already attracted much attention from the machine learning research community in recent years, which aims at overcoming the problem of sparseness in short texts. In this survey, we conduct a comprehensive review of various short text topic modeling techniques proposed in the literature. We present three categories of methods based on Dirichlet multinomial mixture, global word co-occurrences, and self-aggregation, with example of representative approaches in each category and analysis of their performance on various tasks. We develop the first comprehensive open-source library, called STTM, for use in Java that integrates all surveyed algorithms within a unified interface, benchmark datasets, to facilitate the expansion of new methods in this research field. Finally, we evaluate these state-of-the-art methods on many real-world datasets and compare their performance against one another and versus long text topic modeling algorithm.", "published": "2019-04-13T09:08:46Z", "authors": ["Qiang Jipeng", "Qian Zhenyu", "Li Yun", "Yuan Yunhao", "Wu Xindong"], "categories": ["cs.IR", "cs.CL"], "doi": "", "_query": "housing price text listing", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2007.05316v1", "title": "On Distributed Listing of Cliques", "abstract": "We show an $\\tilde{O}(n^{p/(p+2)})$-round algorithm in the \\congest model for \\emph{listing} of $K_p$ (a clique with $p$ nodes), for all $p =4, p\\geq 6$. For $p = 5$, we show an $\\tilde{O}(n^{3/4})$-round algorithm. For $p=4$ and $p=5$, our results improve upon the previous state-of-the-art of $O(n^{5/6+o(1)})$ and $O(n^{21/22+o(1)})$, respectively, by Eden et al. [DISC 2019]. For all $p\\geq 6$, ours is the first sub-linear round algorithm for $K_p$ listing. We leverage the recent expander decomposition algorithm of Chang et al. [SODA 2019] to create clusters with a good mixing time. Three key novelties in our algorithm are: (1) we carefully iterate our listing process with coupled values of min-degree within the clusters and arboricity outside the clusters, (2) all the listing is done within the cluster, which necessitates new techniques for bringing into the cluster the information about \\emph{all} edges that can potentially form $K_p$ instances with the cluster edges, and (3) within each cluster we use a sparsity-aware listing algorithm, which is faster than a general listing algorithm and which we can allow the cluster to use since we make sure to sparsify the graph as the iterations proceed. As a byproduct of our algorithm, we show an \\emph{optimal} sparsity-aware algorithm for $K_p$ listing, which runs in $\\tildeΘ(1 + m/n^{1 + 2/p})$ rounds in the \\clique model. Previously, Pandurangan et al. [SPAA 2018], Chang et al. [SODA 2019], and Censor-Hillel et al. [TCS 2020] showed sparsity-aware algorithms for the case of $p = 3$, yet ours is the first such sparsity aware algorithm for $p \\geq 4$.", "published": "2020-07-10T11:27:32Z", "authors": ["Keren Censor-Hillel", "François Le Gall", "Dean Leitersdorf"], "categories": ["cs.DC", "cs.DS"], "doi": "10.1145/3382734.3405742", "_query": "housing price text listing", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2307.07887v3", "title": "Handwritten and Printed Text Segmentation: A Signature Case Study", "abstract": "While analyzing scanned documents, handwritten text can overlap with printed text. This overlap causes difficulties during the optical character recognition (OCR) and digitization process of documents, and subsequently, hurts downstream NLP tasks. Prior research either focuses solely on the binary classification of handwritten text or performs a three-class segmentation of the document, i.e., recognition of handwritten, printed, and background pixels. This approach results in the assignment of overlapping handwritten and printed pixels to only one of the classes, and thus, they are not accounted for in the other class. Thus, in this research, we develop novel approaches to address the challenges of handwritten and printed text segmentation. Our objective is to recover text from different classes in their entirety, especially enhancing the segmentation performance on overlapping sections. To support this task, we introduce a new dataset, SignaTR6K, collected from real legal documents, as well as a new model architecture for the handwritten and printed text segmentation task. Our best configuration outperforms prior work on two different datasets by 17.9% and 7.3% on IoU scores. The SignaTR6K dataset is accessible for download via the following link: https://forms.office.com/r/2a5RDg7cAY.", "published": "2023-07-15T21:49:22Z", "authors": ["Sina Gholamian", "Ali Vahdat"], "categories": ["cs.CV", "cs.AI", "cs.LG"], "doi": "", "_query": "housing price text listing", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/0704.3662v1", "title": "An Automated Evaluation Metric for Chinese Text Entry", "abstract": "In this paper, we propose an automated evaluation metric for text entry. We also consider possible improvements to existing text entry evaluation metrics, such as the minimum string distance error rate, keystrokes per character, cost per correction, and a unified approach proposed by MacKenzie, so they can accommodate the special characteristics of Chinese text. Current methods lack an integrated concern about both typing speed and accuracy for Chinese text entry evaluation. Our goal is to remove the bias that arises due to human factors. First, we propose a new metric, called the correction penalty (P), based on Fitts' law and Hick's law. Next, we transform it into the approximate amortized cost (AAC) of information theory. An analysis of the AAC of Chinese text input methods with different context lengths is also presented.", "published": "2007-04-27T05:34:10Z", "authors": ["Mike Tian-Jian Jiang", "James Zhan", "Jaimie Lin", "Jerry Lin", "Wen-Lien Hsu"], "categories": ["cs.HC", "cs.CL"], "doi": "", "_query": "housing price text listing", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2305.05972v1", "title": "Coding for IBLTs with Listing Guarantees", "abstract": "The Invertible Bloom Lookup Table (IBLT) is a probabilistic data structure for set representation, with applications in network and traffic monitoring. It is known for its ability to list its elements, an operation that succeeds with high probability for sufficiently large table. However, listing can fail even for relatively small sets. This paper extends recent work on the worst-case analysis of IBLT, which guarantees successful listing for all sets of a certain size, by introducing more general IBLT schemes. These schemes allow for greater freedom in the implementation of the insert, delete, and listing operations and demonstrate that the IBLT memory can be reduced while still maintaining successful listing guarantees. The paper also explores the time-memory trade-off of these schemes, some of which are based on linear codes and \\(B_h\\)-sequences over finite fields.", "published": "2023-05-10T08:29:40Z", "authors": ["Daniella Bar-Lev", "Avi Mizrahi", "Tuvi Etzion", "Ori Rottenstreich", "Eitan Yaakobi"], "categories": ["cs.IT", "cs.DS"], "doi": "", "_query": "housing price text listing", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/1710.03088v1", "title": "Finger Based Techniques for Nonvisual Touchscreen Text Entry", "abstract": "This research proposes Finger Based Technique (FBT) for non-visual touch screen device interaction designed for blind users. Based on the proposed technique, the blind user can access virtual keys based on finger holding positions. Three different models have been proposed. They are Single Digit Finger-Digit Input (FDI), Double Digit FDI for digital text entry, and Finger-Text Input (FTI) for normal text entry. All the proposed models were implemented with voice feedback while enabling touch as the input gesture. The models were evaluated with 7 blind participants with Samsung Galaxy S2 apparatus. The results show that Single Digit FDI is substantially faster and more accurate than Double Digit FDI and iPhone voice-over. FTI also looks promising for text entry. Our study also reveals 11 accessible regions to place widgets for quick access by blind users in flat touch screen based smartphones. Identification of these accessible regions will promote dynamic interactions for blind users and serve as a usability design framework for touch screen applications.", "published": "2017-10-02T12:36:49Z", "authors": ["Mohammed Fakrudeen", "Sufian Yousef", "Mahdi H. Miraz", "AbdelRahman Hamza Hussein"], "categories": ["cs.HC"], "doi": "10.1142/9789814667364_0028", "_query": "housing price text listing", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/1610.09226v1", "title": "Text Segmentation using Named Entity Recognition and Co-reference Resolution in English and Greek Texts", "abstract": "In this paper we examine the benefit of performing named entity recognition (NER) and co-reference resolution to an English and a Greek corpus used for text segmentation. The aim here is to examine whether the combination of text segmentation and information extraction can be beneficial for the identification of the various topics that appear in a document. NER was performed manually in the English corpus and was compared with the output produced by publicly available annotation tools while, an already existing tool was used for the Greek corpus. Produced annotations from both corpora were manually corrected and enriched to cover four types of named entities. Co-reference resolution i.e., substitution of every reference of the same instance with the same named entity identifier was subsequently performed. The evaluation, using five text segmentation algorithms for the English corpus and four for the Greek corpus leads to the conclusion that, the benefit highly depends on the segment's topic, the number of named entity instances appearing in it, as well as the segment's length.", "published": "2016-10-28T14:09:33Z", "authors": ["Pavlina Fragkou"], "categories": ["cs.CL", "cs.IR"], "doi": "", "_query": "housing price text listing", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2007.15769v2", "title": "Instrument variable detection with graph learning : an application to high dimensional GIS-census data for house pricing", "abstract": "Endogeneity bias and instrument variable validation have always been important topics in statistics and econometrics. In the era of big data, such issues typically combine with dimensionality issues and, hence, require even more attention. In this paper, we merge two well-known tools from machine learning and biostatistics---variable selection algorithms and probablistic graphs---to estimate house prices and the corresponding causal structure using 2010 data on Sydney. The estimation uses a 200-gigabyte ultrahigh dimensional database consisting of local school data, GIS information, census data, house characteristics and other socio-economic records. Using \"big data\", we show that it is possible to perform a data-driven instrument selection efficiently and purge out the invalid instruments. Our approach improves the sparsity of variable selection, stability and robustness in the presence of high dimensionality, complicated causal structures and the consequent multicollinearity, and recovers a sparse and intuitive causal structure. The approach also reveals an efficiency and effectiveness in endogeneity detection, instrument validation, weak instrument pruning and the selection of valid instruments. From the perspective of machine learning, the estimation results both align with and confirms the facts of Sydney house market, the classical economic theories and the previous findings of simultaneous equations modeling. Moreover, the estimation results are consistent with and supported by classical econometric tools such as two-stage least square regression and different instrument tests. All the code may be found at \\url{https://github.com/isaac2math/solar_graph_learning}.", "published": "2020-07-30T23:11:54Z", "authors": ["Ning Xu", "Timothy C. G. Fisher", "Jian Hong"], "categories": ["stat.ML", "cs.LG", "stat.AP"], "doi": "", "_query": "housing price text listing", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2403.09062v1", "title": "TBI Image/Text (TBI-IT): Comprehensive Text and Image Datasets for Traumatic Brain Injury Research", "abstract": "In this paper, we introduce a new dataset in the medical field of Traumatic Brain Injury (TBI), called TBI-IT, which includes both electronic medical records (EMRs) and head CT images. This dataset is designed to enhance the accuracy of artificial intelligence in the diagnosis and treatment of TBI. This dataset, built upon the foundation of standard text and image data, incorporates specific annotations within the EMRs, extracting key content from the text information, and categorizes the annotation content of imaging data into five types: brain midline, hematoma, left cerebral ventricle, right cerebral ventricle and fracture. TBI-IT aims to be a foundational dataset for feature learning in image segmentation tasks and named entity recognition.", "published": "2024-03-14T03:07:49Z", "authors": ["Jie Li", "Jiaying Wen", "Tongxin Yang", "Fenglin Cai", "Miao Wei", "Zhiwei Zhang", "Li Jiang"], "categories": ["eess.IV", "cs.CV"], "doi": "", "_query": "housing price text listing", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/1502.01710v5", "title": "Text Understanding from Scratch", "abstract": "This article demontrates that we can apply deep learning to text understanding from character-level inputs all the way up to abstract text concepts, using temporal convolutional networks (ConvNets). We apply ConvNets to various large-scale datasets, including ontology classification, sentiment analysis, and text categorization. We show that temporal ConvNets can achieve astonishing performance without the knowledge of words, phrases, sentences and any other syntactic or semantic structures with regards to a human language. Evidence shows that our models can work for both English and Chinese.", "published": "2015-02-05T20:45:19Z", "authors": ["Xiang Zhang", "Yann LeCun"], "categories": ["cs.LG", "cs.CL"], "doi": "", "_query": "housing price text listing", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/1706.08205v1", "title": "Metrics for Bengali Text Entry Research", "abstract": "With the intention of bringing uniformity to Bengali text entry research, here we present a new approach for calculating the most popular English text entry evaluation metrics for Bengali. To demonstrate our approach, we conducted a user study where we evaluated four popular Bengali text entry techniques.", "published": "2017-06-26T02:07:11Z", "authors": ["Sayan Sarcar", "Ahmed Sabbir Arif", "Ali Mazalek"], "categories": ["cs.HC"], "doi": "", "_query": "housing price text listing", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2310.14575v1", "title": "Listing 6-Cycles", "abstract": "Listing copies of small subgraphs (such as triangles, $4$-cycles, small cliques) in the input graph is an important and well-studied problem in algorithmic graph theory. In this paper, we give a simple algorithm that lists $t$ (non-induced) $6$-cycles in an $n$-node undirected graph in $\\tilde O(n^2+t)$ time. This nearly matches the fastest known algorithm for detecting a $6$-cycle in $O(n^2)$ time by Yuster and Zwick (1997). Previously, a folklore $O(n^2+t)$-time algorithm was known for the task of listing $4$-cycles.", "published": "2023-10-23T05:18:24Z", "authors": ["Ce Jin", "Virginia Vassilevska Williams", "Renfei Zhou"], "categories": ["cs.DS"], "doi": "", "_query": "housing price text listing", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/1204.5171v1", "title": "ConocoPhillips' share price model revisited", "abstract": "Three years ago we found a statistically reliable link between ConocoPhillips' (NYSE: COP) stock price and the difference between the core and headline CPI in the United States. In this article, the original relationship is revisited with new data available since 2009. The agreement between the observed monthly closing price (adjusted for dividends and splits) and that predicted from the CPI difference is confirmed. The original quantitative link is validated. In order to improve the accuracy of the COP price prediction a series of advanced models is developed. The original set of two major CPIs is extended by smaller components of the headline CPIs (e.g. the CPIs of motor fuel and housing energy) and several PPIs (e.g. the PPIs of crude oil and coal) which may be inherently related to ConocoPhillips and other energy companies. These advanced models have demonstrated much lower modeling errors with better statistical properties. The earlier reported quasi-linear trend in the CPI difference is also revisited. This trend allows for an accurate prediction of the COP prices at a five to ten year horizon.", "published": "2012-04-21T07:45:54Z", "authors": ["Ivan Kitov"], "categories": ["q-fin.GN"], "doi": "", "_query": "housing price text listing", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2408.15257v1", "title": "Text classification optimization algorithm based on graph neural network", "abstract": "In the field of natural language processing, text classification, as a basic task, has important research value and application prospects. Traditional text classification methods usually rely on feature representations such as the bag of words model or TF-IDF, which overlook the semantic connections between words and make it challenging to grasp the deep structural details of the text. Recently, GNNs have proven to be a valuable asset for text classification tasks, thanks to their capability to handle non-Euclidean data efficiently. However, the existing text classification methods based on GNN still face challenges such as complex graph structure construction and high cost of model training. This paper introduces a text classification optimization algorithm utilizing graph neural networks. By introducing adaptive graph construction strategy and efficient graph convolution operation, the accuracy and efficiency of text classification are effectively improved. The experimental results demonstrate that the proposed method surpasses traditional approaches and existing GNN models across multiple public datasets, highlighting its superior performance and feasibility for text classification tasks.", "published": "2024-08-09T23:25:37Z", "authors": ["Erdi Gao", "Haowei Yang", "Dan Sun", "Haohao Xia", "Yuhan Ma", "Yuanjing Zhu"], "categories": ["cs.CL", "cs.AI"], "doi": "", "_query": "housing price text listing", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/1901.11477v1", "title": "Text line Segmentation in Compressed Representation of Handwritten Document using Tunneling Algorithm", "abstract": "In this research work, we perform text line segmentation directly in compressed representation of an unconstrained handwritten document image. In this relation, we make use of text line terminal points which is the current state-of-the-art. The terminal points spotted along both margins (left and right) of a document image for every text line are considered as source and target respectively. The tunneling algorithm uses a single agent (or robot) to identify the coordinate positions in the compressed representation to perform text-line segmentation of the document. The agent starts at a source point and progressively tunnels a path routing in between two adjacent text lines and reaches the probable target. The agent's navigation path from source to the target bypassing obstacles, if any, results in segregating the two adjacent text lines. However, the target point would be known only when the agent reaches the destination; this is applicable for all source points and henceforth we could analyze the correspondence between source and target nodes. Artificial Intelligence in Expert systems, dynamic programming and greedy strategies are employed for every search space while tunneling. An exhaustive experimentation is carried out on various benchmark datasets including ICDAR13 and the performances are reported.", "published": "2019-01-03T05:19:38Z", "authors": ["Amarnath R", "P Nagabhushan"], "categories": ["cs.CV", "cs.AI", "cs.DS"], "doi": "10.18201/ijisae.2018448451", "_query": "housing price text listing", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2602.20356v1", "title": "Prevalence of Food and Housing Insecurity among Direct Support Professionals in New York", "abstract": "Background. Low earnings are associated with household insecurity. Direct Support Professionals (DSPs) provide support for people with intellectual and developmental disabilities, typically for wages close to state minimums, and may experience insecurity. Objective. The purpose of the study was to evaluate the prevalence of food and housing insecurity among DSPs. Methods. We conducted a statewide, cross-sectional survey of DSPs in New York State (2022-2023). Measures included detailed questions about food and housing insecurity. We used chi-square analyses and logistic regressions to examine relationships between insecurity and demographic characteristics as proxies for social determinants of health. A total of 4,503 DSPs responded to the survey. The analytic sample contained 2,766 respondents with complete data for all relevant variables. Results. Overall, 62.6% experienced food and/or housing insecurity, with over half of those respondents experiencing both. Insecurity was highest among DSPs with a disability (76.2%), DSPs of color (75.7%), and those with lower income (72.4%), but over 50% of DSPs across demographic groups experienced insecurity. Conclusions. The widespread insecurity this study demonstrates is an occupational hazard that reduces worker welfare. At the macro-level, household insecurity is a critical threat to the stability of the care and support delivery system. The human services sector is projected to grow rapidly in the future. If growth continues along low wage lines, it implies an equally rapid expansion of worker insecurity. Government action to raise pay and interventions that enhance food and housing security are needed to support workers in the care delivery system for people with intellectual and developmental disabilities.", "published": "2026-02-23T21:03:31Z", "authors": ["Jennifer Cohen", "Yana Rodgers"], "categories": ["econ.GN"], "doi": "10.1016/j.dhjo.2024.101769", "_query": "housing price text listing", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2009.10532v1", "title": "A rapidly updating stratified mix-adjusted median property price index model", "abstract": "Homeowners, first-time buyers, banks, governments and construction companies are highly interested in following the state of the property market. Currently, property price indexes are published several months out of date and hence do not offer the up-to-date information which housing market stakeholders need in order to make informed decisions. In this article, we present an updated version of a central-price tendency based property price index which uses geospatial property data and stratification in order to compare similar houses. The expansion of the algorithm to include additional parameters owing to a new data structure implementation and a richer dataset allows for the construction of a far smoother and more robust index than the original algorithm produced.", "published": "2020-09-22T13:23:03Z", "authors": ["Robert Miller", "Phil Maguire"], "categories": ["stat.AP", "stat.CO"], "doi": "", "_query": "housing price text listing", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2410.09632v1", "title": "SciGisPy: a Novel Metric for Biomedical Text Simplification via Gist Inference Score", "abstract": "Biomedical literature is often written in highly specialized language, posing significant comprehension challenges for non-experts. Automatic text simplification (ATS) offers a solution by making such texts more accessible while preserving critical information. However, evaluating ATS for biomedical texts is still challenging due to the limitations of existing evaluation metrics. General-domain metrics like SARI, BLEU, and ROUGE focus on surface-level text features, and readability metrics like FKGL and ARI fail to account for domain-specific terminology or assess how well the simplified text conveys core meanings (gist). To address this, we introduce SciGisPy, a novel evaluation metric inspired by Gist Inference Score (GIS) from Fuzzy-Trace Theory (FTT). SciGisPy measures how well a simplified text facilitates the formation of abstract inferences (gist) necessary for comprehension, especially in the biomedical domain. We revise GIS for this purpose by introducing domain-specific enhancements, including semantic chunking, Information Content (IC) theory, and specialized embeddings, while removing unsuitable indexes. Our experimental evaluation on the Cochrane biomedical text simplification dataset demonstrates that SciGisPy outperforms the original GIS formulation, with a significant increase in correctly identified simplified texts (84% versus 44.8%). The results and a thorough ablation study confirm that SciGisPy better captures the essential meaning of biomedical content, outperforming existing approaches.", "published": "2024-10-12T19:53:56Z", "authors": ["Chen Lyu", "Gabriele Pergola"], "categories": ["cs.CL"], "doi": "", "_query": "housing price text listing", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2112.15093v2", "title": "Benchmarking Chinese Text Recognition: Datasets, Baselines, and an Empirical Study", "abstract": "The flourishing blossom of deep learning has witnessed the rapid development of text recognition in recent years. However, the existing text recognition methods are mainly proposed for English texts. As another widely-spoken language, Chinese text recognition (CTR) in all ways has extensive application markets. Based on our observations, we attribute the scarce attention on CTR to the lack of reasonable dataset construction standards, unified evaluation protocols, and results of the existing baselines. To fill this gap, we manually collect CTR datasets from publicly available competitions, projects, and papers. According to application scenarios, we divide the collected datasets into four categories including scene, web, document, and handwriting datasets. Besides, we standardize the evaluation protocols in CTR. With unified evaluation protocols, we evaluate a series of representative text recognition methods on the collected datasets to provide baselines. The experimental results indicate that the performance of baselines on CTR datasets is not as good as that on English datasets due to the characteristics of Chinese texts that are quite different from the Latin alphabet. Moreover, we observe that by introducing radical-level supervision as an auxiliary task, the performance of baselines can be further boosted. The code and datasets are made publicly available at https://github.com/FudanVI/benchmarking-chinese-text-recognition", "published": "2021-12-30T15:30:52Z", "authors": ["Haiyang Yu", "Jingye Chen", "Bin Li", "Jianqi Ma", "Mengnan Guan", "Xixi Xu", "Xiaocong Wang", "Shaobo Qu", "Xiangyang Xue"], "categories": ["cs.CV"], "doi": "", "_query": "housing price text listing", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2606.22760v1", "title": "Pricing the Unpriced Asset: A Standards-Based Method for Valuing Enterprise Data under IAS 38 and IAS 2", "abstract": "The recognition and measurement of data assets under current accounting standards presents significant challenges. While International Accounting Standard 38 (IAS 38) provides a framework for intangible asset recognition, data assets frequently fail to meet capitalisation criteria due to difficulties in demonstrating separability, establishing reliable cost measurement, and proving probable future economic benefits. The widespread failure to easily and reliably value data causes mispricing and allocative distortions across data and artificial intelligence markets. This paper introduces a two-layer valuation progression for authenticated data assets, that is, datasets that have met IAS 38 recognition criteria through established legal provenance and contractual boundaries. The first layer, D-Val, is the auditable cost-basis valuation consistent with IAS 38. D-Val is defined as D-Val = Cp * Avt, where Cp is the reliably measurable production cost and Avt is the appreciation or depreciation factor applied over time. Under prevailing interpretations of IAS 38, Av is constrained to values less than or equal to one absent an active market revaluation, rendering D-Val a strictly cost-less-amortisation figure. The second layer, A-Val, is a theoretically grounded commercial valuation that incorporates scarcity, rivalry, completeness, accuracy, and explicit premia for provenance authentication and independent audit. A-Val is not auditable as fair value under current practice but serves as a defensible commercial valuation during the period before active markets for authenticated data assets mature. As authenticated data markets mature parameter assumptions improve providing a foundation for iterative refinement of the model.", "published": "2026-06-22T01:59:04Z", "authors": ["Natasha E. Blycha", "James Myint", "Dean Arden", "Michael Small", "Conor Blycha", "Schellie-Jayne Price", "Steve Bailey", "Ryan Feng", "Rachael Johnson", "Adam Myer"], "categories": ["cs.CE"], "doi": "", "_query": "housing price text listing", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/1712.09518v1", "title": "Improving Text Normalization by Optimizing Nearest Neighbor Matching", "abstract": "Text normalization is an essential task in the processing and analysis of social media that is dominated with informal writing. It aims to map informal words to their intended standard forms. Previously proposed text normalization approaches typically require manual selection of parameters for improved performance. In this paper, we present an automatic optimizationbased nearest neighbor matching approach for text normalization. This approach is motivated by the observation that text normalization is essentially a matching problem and nearest neighbor matching with an adaptive similarity function is the most direct procedure for it. Our similarity function incorporates weighted contributions of contextual, string, and phonetic similarity, and the nearest neighbor matching involves a minimum similarity threshold. These four parameters are tuned efficiently using grid search. We evaluate the performance of our approach on two benchmark datasets. The results demonstrate that parameter tuning on small sized labeled datasets produce state-of-the-art text normalization performances. Thus, this approach allows practically easy construction of evolving domain-specific normalization lexicons", "published": "2017-12-27T08:02:26Z", "authors": ["Salman Ahmad Ansari", "Usman Zafar", "Asim Karim"], "categories": ["cs.CL"], "doi": "", "_query": "housing price text listing", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/1809.01964v1", "title": "Named Entity Recognition on Noisy Data using Images and Text (1-page abstract)", "abstract": "Named Entity Recognition (NER) is an important subtask of information extraction that seeks to locate and recognise named entities. Despite recent achievements, we still face limitations in correctly detecting and classifying entities, prominently in short and noisy text, such as Twitter. An important negative aspect in most of NER approaches is the high dependency on hand-crafted features and domain-specific knowledge, necessary to achieve state-of-the-art results. Thus, devising models to deal with such linguistically complex contexts is still challenging. In this paper, we propose a novel multi-level architecture that does not rely on any specific linguistic resource or encoded rule. Unlike traditional approaches, we use features extracted from images and text to classify named entities. Experimental tests against state-of-the-art NER for Twitter on the Ritter dataset present competitive results (0.59 F-measure), indicating that this approach may lead towards better NER models.", "published": "2018-09-03T08:59:59Z", "authors": ["Diego Esteves"], "categories": ["cs.IR"], "doi": "", "_query": "housing price text listing", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/1803.04552v1", "title": "Text Data Mining from the Author's Perspective: Whose Text, Whose Mining, and to Whose Benefit?", "abstract": "Given the many technical, social, and policy shifts in access to scholarly content since the early days of text data mining, it is time to expand the conversation about text data mining from concerns of the researcher wishing to mine data to include concerns of researcher-authors about how their data are mined, by whom, for what purposes, and to whose benefits.", "published": "2018-03-12T22:11:40Z", "authors": ["Christine L. Borgman"], "categories": ["cs.DL"], "doi": "", "_query": "housing price text listing", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2012.03468v1", "title": "An Empirical Survey of Unsupervised Text Representation Methods on Twitter Data", "abstract": "The field of NLP has seen unprecedented achievements in recent years. Most notably, with the advent of large-scale pre-trained Transformer-based language models, such as BERT, there has been a noticeable improvement in text representation. It is, however, unclear whether these improvements translate to noisy user-generated text, such as tweets. In this paper, we present an experimental survey of a wide range of well-known text representation techniques for the task of text clustering on noisy Twitter data. Our results indicate that the more advanced models do not necessarily work best on tweets and that more exploration in this area is needed.", "published": "2020-12-07T06:14:13Z", "authors": ["Lili Wang", "Chongyang Gao", "Jason Wei", "Weicheng Ma", "Ruibo Liu", "Soroush Vosoughi"], "categories": ["cs.CL", "cs.LG"], "doi": "10.18653/v1/2020.wnut-1.27", "_query": "housing price text listing", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/1908.08207v1", "title": "Mask TextSpotter: An End-to-End Trainable Neural Network for Spotting Text with Arbitrary Shapes", "abstract": "Unifying text detection and text recognition in an end-to-end training fashion has become a new trend for reading text in the wild, as these two tasks are highly relevant and complementary. In this paper, we investigate the problem of scene text spotting, which aims at simultaneous text detection and recognition in natural images. An end-to-end trainable neural network named as Mask TextSpotter is presented. Different from the previous text spotters that follow the pipeline consisting of a proposal generation network and a sequence-to-sequence recognition network, Mask TextSpotter enjoys a simple and smooth end-to-end learning procedure, in which both detection and recognition can be achieved directly from two-dimensional space via semantic segmentation. Further, a spatial attention module is proposed to enhance the performance and universality. Benefiting from the proposed two-dimensional representation on both detection and recognition, it easily handles text instances of irregular shapes, for instance, curved text. We evaluate it on four English datasets and one multi-language dataset, achieving consistently superior performance over state-of-the-art methods in both detection and end-to-end text recognition tasks. Moreover, we further investigate the recognition module of our method separately, which significantly outperforms state-of-the-art methods on both regular and irregular text datasets for scene text recognition.", "published": "2019-08-22T05:31:53Z", "authors": ["Minghui Liao", "Pengyuan Lyu", "Minghang He", "Cong Yao", "Wenhao Wu", "Xiang Bai"], "categories": ["cs.CV"], "doi": "", "_query": "housing price text listing", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2005.08254v2", "title": "Analyzing the relationship between text features and research proposal productivity", "abstract": "Predicting the output of research grants is of considerable relevance to research funding bodies, scientific entities and government agencies. In this study, we investigate whether text features extracted from projects title and abstracts are able to identify productive grants. Our analysis was conducted in three distinct areas, namely Medicine, Dentistry and Veterinary Medicine. Topical and complexity text features were used to identify predictors of productivity. The results indicate that there is a statistically significant relationship between text features and grants productivity, however such a dependence is weak. A feature relevance analysis revealed that the abstract text length and metrics derived from lexical diversity are among the most discriminative features. We also found that the prediction accuracy has a dependence on the considered project language and that topical features are more discriminative than text complexity measurements. Our findings suggest that text features should be used in combination with other features to assist the identification of relevant research ideas.", "published": "2020-05-17T13:51:55Z", "authors": ["Jorge A. V. Tohalino", "Laura V. C. Quispe", "Diego R. Amancio"], "categories": ["cs.DL"], "doi": "10.1007/s11192-021-03926-x", "_query": "housing price text listing", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2306.10414v1", "title": "KEST: Kernel Distance Based Efficient Self-Training for Improving Controllable Text Generation", "abstract": "Self-training (ST) has come to fruition in language understanding tasks by producing pseudo labels, which reduces the labeling bottleneck of language model fine-tuning. Nevertheless, in facilitating semi-supervised controllable language generation, ST faces two key challenges. First, augmented by self-generated pseudo text, generation models tend to over-exploit the previously learned text distribution, suffering from mode collapse and poor generation diversity. Second, generating pseudo text in each iteration is time-consuming, severely decelerating the training process. In this work, we propose KEST, a novel and efficient self-training framework to handle these problems. KEST utilizes a kernel-based loss, rather than standard cross entropy, to learn from the soft pseudo text produced by a shared non-autoregressive generator. We demonstrate both theoretically and empirically that KEST can benefit from more diverse pseudo text in an efficient manner, which allows not only refining and exploiting the previously fitted distribution but also enhanced exploration towards a larger potential text space, providing a guarantee of improved performance. Experiments on three controllable generation tasks demonstrate that KEST significantly improves control accuracy while maintaining comparable text fluency and generation diversity against several strong baselines.", "published": "2023-06-17T19:40:57Z", "authors": ["Yuxi Feng", "Xiaoyuan Yi", "Laks V. S. Lakshmanan", "Xing Xie"], "categories": ["cs.CL"], "doi": "", "_query": "housing price text listing", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2410.02978v1", "title": "An explainable approach to detect case law on housing and eviction issues within the HUDOC database", "abstract": "Case law is instrumental in shaping our understanding of human rights, including the right to adequate housing. The HUDOC database provides access to the textual content of case law from the European Court of Human Rights (ECtHR), along with some metadata. While this metadata includes valuable information, such as the application number and the articles addressed in a case, it often lacks detailed substantive insights, such as the specific issues a case covers. This underscores the need for detailed analysis to extract such information. However, given the size of the database - containing over 40,000 cases - an automated solution is essential. In this study, we focus on the right to adequate housing and aim to build models to detect cases related to housing and eviction issues. Our experiments show that the resulting models not only provide performance comparable to more sophisticated approaches but are also interpretable, offering explanations for their decisions by highlighting the most influential words. The application of these models led to the identification of new cases that were initially overlooked during data collection. This suggests that NLP approaches can be effectively applied to categorise case law based on the specific issues they address.", "published": "2024-10-03T20:39:51Z", "authors": ["Mohammad Mohammadi", "Martijn Wieling", "Michel Vols"], "categories": ["cs.LG", "cs.AI"], "doi": "", "_query": "housing price text listing", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/1712.06994v1", "title": "DeepNorm-A Deep Learning Approach to Text Normalization", "abstract": "This paper presents an simple yet sophisticated approach to the challenge by Sproat and Jaitly (2016)- given a large corpus of written text aligned to its normalized spoken form, train an RNN to learn the correct normalization function. Text normalization for a token seems very straightforward without it's context. But given the context of the used token and then normalizing becomes tricky for some classes. We present a novel approach in which the prediction of our classification algorithm is used by our sequence to sequence model to predict the normalized text of the input token. Our approach takes very less time to learn and perform well unlike what has been reported by Google (5 days on their GPU cluster). We have achieved an accuracy of 97.62 which is impressive given the resources we use. Our approach is using the best of both worlds, gradient boosting - state of the art in most classification tasks and sequence to sequence learning - state of the art in machine translation. We present our experiments and report results with various parameter settings.", "published": "2017-12-17T18:31:26Z", "authors": ["Maryam Zare", "Shaurya Rohatgi"], "categories": ["cs.CL"], "doi": "", "_query": "housing price text listing", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/1802.09426v2", "title": "Tone Biased MMR Text Summarization", "abstract": "Text summarization is an interesting area for researchers to develop new techniques to provide human like summaries for vast amounts of information. Summarization techniques tend to focus on providing accurate representation of content, and often the tone of the content is ignored. Tone of the content sets a baseline for how a reader perceives the content. As such being able to generate summary with tone that is appropriate for the reader is important. In our work we implement Maximal Marginal Relevance [MMR] based multi-document text summarization and propose a naive model to change tone of the summarization by setting a bias to specific set of words and restricting other words in the summarization output. This bias towards a specified set of words produces a summary whose tone is same as tone of specified words.", "published": "2018-02-26T16:12:40Z", "authors": ["Mayank Chaudhari", "Aakash Nelson Mattukoyya"], "categories": ["cs.IR", "cs.CL"], "doi": "", "_query": "housing price text listing", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/1905.11975v4", "title": "On Variational Learning of Controllable Representations for Text without Supervision", "abstract": "The variational autoencoder (VAE) can learn the manifold of natural images on certain datasets, as evidenced by meaningful interpolating or extrapolating in the continuous latent space. However, on discrete data such as text, it is unclear if unsupervised learning can discover similar latent space that allows controllable manipulation. In this work, we find that sequence VAEs trained on text fail to properly decode when the latent codes are manipulated, because the modified codes often land in holes or vacant regions in the aggregated posterior latent space, where the decoding network fails to generalize. Both as a validation of the explanation and as a fix to the problem, we propose to constrain the posterior mean to a learned probability simplex, and performs manipulation within this simplex. Our proposed method mitigates the latent vacancy problem and achieves the first success in unsupervised learning of controllable representations for text. Empirically, our method outperforms unsupervised baselines and strong supervised approaches on text style transfer, and is capable of performing more flexible fine-grained control over text generation than existing methods.", "published": "2019-05-28T17:49:47Z", "authors": ["Peng Xu", "Jackie Chi Kit Cheung", "Yanshuai Cao"], "categories": ["cs.CL", "cs.LG"], "doi": "", "_query": "housing price text listing", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/1605.05496v1", "title": "The Design of a Drag-Free CubeSat and the Housing for its Gravitational Reference Sensor", "abstract": "A Drag-Free CubeSat mission has been proposed to demonstrate the feasibility of a Gravitational Reference Sensor (GRS) with an optical readout for a 3 units (3U) spacecraft. A purely drag-free object is defined by the absence of all external forces other than gravity, which are shielded by the spacecraft. In a real case, the TM will still be affected by disturbances. Several of them are passively reduced by the design of the TM housing. This system is a thick-walled aluminium box that holds the shadow sensors and shields the TM. The housing has an effect on the mechanical, thermal and magnetic environment around the TM. All of them have been analysed. The mechanical vibrations have to fit the launch environment and the modes have to be outside of the measurement range (0.0001 - 1 Hz). The magnetic field has to be reduced by a 0.01 factor. The temperature difference between internal opposing surfaces, determining pressure on the TM, has to be below 10^-3(1 mHz/f)1/3 K Hz^-1/2. The housing, together with the TM, the sensors and the UV LEDs for charging control, constitutes the GRS, which would then fit into a 1U. The other 2Us are occupied by the caging mechanism that constraints the TM during launch, the thrusters, the Attitude Determination And Control System (ADACS) and the electronics. The Drag-Free CubeSat will be the result of the combined efforts of Stanford, University of Florida, KACST and NASA and will be the first drag-free mission with an optical readout and the first GRS designed within the limits of a 3U small satellite. In the first section, this paper briefly updates on the main characteristics and systems of the project. Particular emphasis is then given to the recently designed housing, its expected performance and the open issues.", "published": "2016-05-18T09:41:18Z", "authors": ["Carlo Zanoni", "Abdul Alfauwaz", "Ahmad Aljadaan", "Salman Althubiti", "Karthik Balakrishnan", "Sasha Buchman", "Robert L. Byer", "John W. Conklin", "Grant D. Cutler", "Dan DeBra", "Eric Hultgren", "John A. Lipa", "Shailendhar Saraf", "Andreas Zoellner"], "categories": ["astro-ph.IM", "physics.ins-det"], "doi": "", "_query": "housing price text listing", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2209.07250v1", "title": "Answering Count Questions with Structured Answers from Text", "abstract": "In this work we address the challenging case of answering count queries in web search, such as ``number of songs by John Lennon''. Prior methods merely answer these with a single, and sometimes puzzling number or return a ranked list of text snippets with different numbers. This paper proposes a methodology for answering count queries with inference, contextualization and explanatory evidence. Unlike previous systems, our method infers final answers from multiple observations, supports semantic qualifiers for the counts, and provides evidence by enumerating representative instances. Experiments with a wide variety of queries, including existing benchmark show the benefits of our method, and the influence of specific parameter settings. Our code, data and an interactive system demonstration are publicly available at https://github.com/ghoshs/CoQEx and https://nlcounqer.mpi-inf.mpg.de/.", "published": "2022-09-15T12:35:32Z", "authors": ["Shrestha Ghosh", "Simon Razniewski", "Gerhard Weikum"], "categories": ["cs.IR"], "doi": "10.1016/j.websem.2022.100769", "_query": "housing price text listing", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2205.10569v1", "title": "HLATR: Enhance Multi-stage Text Retrieval with Hybrid List Aware Transformer Reranking", "abstract": "Deep pre-trained language models (e,g. BERT) are effective at large-scale text retrieval task. Existing text retrieval systems with state-of-the-art performance usually adopt a retrieve-then-reranking architecture due to the high computational cost of pre-trained language models and the large corpus size. Under such a multi-stage architecture, previous studies mainly focused on optimizing single stage of the framework thus improving the overall retrieval performance. However, how to directly couple multi-stage features for optimization has not been well studied. In this paper, we design Hybrid List Aware Transformer Reranking (HLATR) as a subsequent reranking module to incorporate both retrieval and reranking stage features. HLATR is lightweight and can be easily parallelized with existing text retrieval systems so that the reranking process can be performed in a single yet efficient processing. Empirical experiments on two large-scale text retrieval datasets show that HLATR can efficiently improve the ranking performance of existing multi-stage text retrieval methods.", "published": "2022-05-21T11:38:33Z", "authors": ["Yanzhao Zhang", "Dingkun Long", "Guangwei Xu", "Pengjun Xie"], "categories": ["cs.IR", "cs.CL"], "doi": "", "_query": "housing price text listing", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2408.02339v2", "title": "Modeling the impact of Climate transition on real estate prices", "abstract": "In this work, we propose a model to quantify the impact of the climate transition on a property in housing market. We begin by noting that property is an asset in an economy. That economy is organized in sectors, driven by its productivity which is a multidimensional Ornstein-Uhlenbeck process, while the climate transition is declined thanks to the carbon price, a continuous deterministic process. We then extend the sales comparison approach and the income approach to valuate an energy inefficient real estate asset. We obtain its value as the difference between the price of an equivalent efficient building following an exponential Ornstein-Uhlenbeck as well as the actualized renovation costs and the actualized sum of the future additional energy costs (before and after the renovation date). These costs are due to the inefficiency of the building, before an optimal renovation date which depends on the carbon price process. Moreover, since the renovation increases the efficiency of the building, which is random, the future additional energy costs become smaller and even zero if the optimal energy efficiency is reached. Finally, we carry out simulations based on the French economy and the house price index of France. The findings support the conclusion that the order of magnitude of the depreciation obtained by our model is the same as the empirical observations.", "published": "2024-08-05T09:34:47Z", "authors": ["Lionel Sopgoui"], "categories": ["q-fin.RM", "econ.GN", "q-fin.GN"], "doi": "", "_query": "housing price text listing", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2506.04381v1", "title": "Hierarchical Text Classification Using Contrastive Learning Informed Path Guided Hierarchy", "abstract": "Hierarchical Text Classification (HTC) has recently gained traction given the ability to handle complex label hierarchy. This has found applications in domains like E- commerce, customer care and medicine industry among other real-world applications. Existing HTC models either encode label hierarchy separately and mix it with text encoding or guide the label hierarchy structure in the text encoder. Both approaches capture different characteristics of label hierarchy and are complementary to each other. In this paper, we propose a Hierarchical Text Classification using Contrastive Learning Informed Path guided hierarchy (HTC-CLIP), which learns hierarchy-aware text representation and text informed path guided hierarchy representation using contrastive learning. During the training of HTC-CLIP, we learn two different sets of class probabilities distributions and during inference, we use the pooled output of both probabilities for each class to get the best of both representations. Our results show that the two previous approaches can be effectively combined into one architecture to achieve improved performance. Tests on two public benchmark datasets showed an improvement of 0.99 - 2.37% in Macro F1 score using HTC-CLIP over the existing state-of-the-art models.", "published": "2025-06-04T18:51:34Z", "authors": ["Neeraj Agrawal", "Saurabh Kumar", "Priyanka Bhatt", "Tanishka Agarwal"], "categories": ["cs.CL", "cs.LG"], "doi": "10.3233/FAIA230249", "_query": "housing price text listing", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2603.26525v1", "title": "Who burdens the welfare state? Migration and ageing in housing, education, and healthcare demand", "abstract": "Political discourse attributes the pressure on European welfare systems to foreign nationals. Yet projections of service demand rarely disaggregate service demand by citizenship status. We develop a structural demographic model and project healthcare, education, and housing demand in Austria through 2050, disaggregated by citizenship status and regions across migration scenarios. We find that migration, ageing, and fertility shape each sector differently. In healthcare, the ageing of Austrian nationals contributes 4.7 times more to demand growth than immigration, with the most acute pressures in rural, low-migration regions. In housing, migration accounts for the entire net growth in demand, concentrated in metropolitan hubs. In education, aggregate demand contracts regardless of migration assumptions, whereas future needs are driven more by the births of foreigners in Austria than by new arrivals. Foreign nationals consume services in proportion to their demographic weight, with deviations explained by age structure rather than over-utilisation. These results show that the drivers of service demand are sector-specific: migration restrictions could ease housing pressure, but would not address ageing-driven healthcare demand and may accelerate contraction in the education system.", "published": "2026-03-27T15:38:23Z", "authors": ["Guillermo Prieto-Viertel", "Carsten Källner", "Elma Dervic", "Ola Ali", "Andrea Vismara", "Rafael Prieto-Curiel"], "categories": ["physics.soc-ph", "econ.GN"], "doi": "", "_query": "housing price text listing", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2305.19747v1", "title": "Analyzing Text Representations by Measuring Task Alignment", "abstract": "Textual representations based on pre-trained language models are key, especially in few-shot learning scenarios. What makes a representation good for text classification? Is it due to the geometric properties of the space or because it is well aligned with the task? We hypothesize the second claim. To test it, we develop a task alignment score based on hierarchical clustering that measures alignment at different levels of granularity. Our experiments on text classification validate our hypothesis by showing that task alignment can explain the classification performance of a given representation.", "published": "2023-05-31T11:20:48Z", "authors": ["Cesar Gonzalez-Gutierrez", "Audi Primadhanty", "Francesco Cazzaro", "Ariadna Quattoni"], "categories": ["cs.CL"], "doi": "", "_query": "housing price text listing", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2407.04776v2", "title": "Quantifying Privacy Risks of Public Statistics to Residents of Subsidized Housing", "abstract": "As the U.S. Census Bureau implements its controversial new disclosure avoidance system, researchers and policymakers debate the necessity of new privacy protections for public statistics. With experiments on both public statistics and synthetic microdata, we explore a particular privacy concern: respondents in subsidized housing may deliberately not mention unauthorized children and other household members for fear of being discovered and evicted. By combining public statistics from the Decennial Census and the Department of Housing and Urban Development, we demonstrate a simple, inexpensive reconstruction attack that could identify subsidized households living in violation of occupancy guidelines in 2010. Experiments on synthetic data suggest that a random swapping mechanism similar to the Census Bureau's 2010 disclosure avoidance measures does not significantly reduce the precision of this attack, while a differentially private mechanism similar to the 2020 disclosure avoidance system does. Our results provide a valuable example for policymakers seeking trustworthy public statistics.", "published": "2024-07-05T18:00:02Z", "authors": ["Ryan Steed", "Diana Qing", "Zhiwei Steven Wu"], "categories": ["cs.CY"], "doi": "10.1162/99608f92.39d8bfa4", "_query": "housing price text listing", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2012.02294v1", "title": "Accelerating Text Mining Using Domain-Specific Stop Word Lists", "abstract": "Text preprocessing is an essential step in text mining. Removing words that can negatively impact the quality of prediction algorithms or are not informative enough is a crucial storage-saving technique in text indexing and results in improved computational efficiency. Typically, a generic stop word list is applied to a dataset regardless of the domain. However, many common words are different from one domain to another but have no significance within a particular domain. Eliminating domain-specific common words in a corpus reduces the dimensionality of the feature space, and improves the performance of text mining tasks. In this paper, we present a novel mathematical approach for the automatic extraction of domain-specific words called the hyperplane-based approach. This new approach depends on the notion of low dimensional representation of the word in vector space and its distance from hyperplane. The hyperplane-based approach can significantly reduce text dimensionality by eliminating irrelevant features. We compare the hyperplane-based approach with other feature selection methods, namely \\c{hi}2 and mutual information. An experimental study is performed on three different datasets and five classification algorithms, and measure the dimensionality reduction and the increase in the classification performance. Results indicate that the hyperplane-based approach can reduce the dimensionality of the corpus by 90% and outperforms mutual information. The computational time to identify the domain-specific words is significantly lower than mutual information.", "published": "2020-11-18T17:42:32Z", "authors": ["Farah Alshanik", "Amy Apon", "Alexander Herzog", "Ilya Safro", "Justin Sybrandt"], "categories": ["cs.IR", "cs.LG"], "doi": "", "_query": "housing price text listing", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2311.11047v1", "title": "CLIPSwarm: Converting text into formations of robots", "abstract": "We present CLIPSwarm, an algorithm to generate robot swarm formations from natural language descriptions. CLIPSwarm receives an input text and finds the position of the robots to form a shape that corresponds to the given text. To do so, we implement a variation of the Montecarlo particle filter to obtain a matching formation iteratively. In every iteration, we generate a set of new formations and evaluate their Clip Similarity with the given text, selecting the best formations according to this metric. This metric is obtained using Clip, [1], an existing foundation model trained to encode images and texts into vectors within a common latent space. The comparison between these vectors determines how likely the given text describes the shapes. Our initial proof of concept shows the potential of this solution to generate robot swarm formations just from natural language descriptions and demonstrates a novel application of foundation models, such as CLIP, in the field of multi-robot systems. In this first approach, we create formations using a Convex-Hull approach. Next steps include more robust and generic representation and optimization steps in the process of obtaining a suitable swarm formation.", "published": "2023-11-18T11:46:42Z", "authors": ["Pablo Pueyo", "Eduardo Montijano", "Ana C. Murillo", "Mac Schwager"], "categories": ["cs.RO"], "doi": "", "_query": "housing price text listing", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2408.09798v1", "title": "Enhance Modality Robustness in Text-Centric Multimodal Alignment with Adversarial Prompting", "abstract": "Converting different modalities into generalized text, which then serves as input prompts for large language models (LLMs), is a common approach for aligning multimodal models, particularly when pairwise data is limited. Text-centric alignment method leverages the unique properties of text as a modality space, transforming diverse inputs into a unified textual representation, thereby enabling downstream models to effectively interpret various modal inputs. This study evaluates the quality and robustness of multimodal representations in the face of noise imperfections, dynamic input order permutations, and missing modalities, revealing that current text-centric alignment methods can compromise downstream robustness. To address this issue, we propose a new text-centric adversarial training approach that significantly enhances robustness compared to traditional robust training methods and pre-trained multimodal foundation models. Our findings underscore the potential of this approach to improve the robustness and adaptability of multimodal representations, offering a promising solution for dynamic and real-world applications.", "published": "2024-08-19T08:44:55Z", "authors": ["Yun-Da Tsai", "Ting-Yu Yen", "Keng-Te Liao", "Shou-De Lin"], "categories": ["cs.LG"], "doi": "", "_query": "housing price text listing", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2606.02183v2", "title": "Efficiently Listing Projected Trees, and Equivalence of Listing and Enumeration", "abstract": "The subgraph isomorphism problem and its generalizations, such as conjunctive queries where some nodes are projected, are among the most fundamental problems in graph algorithms and database theory. In this paper, we study the listing and enumeration variants of these problems and present two main results. The first result is an algorithm for enumerating projected trees with preprocessing time $\\widetilde{O}(n^{17.42})$ and delay $\\mathrm{polylog}(n)$. Prior to this work, for trees on $k$ nodes all algorithms in the literature required preprocessing time $n^{Ω(k)}$ or delay $n^{Ω(1)}$ or assumed $ω=2$. Our result generalizes to arbitrary projected hypergraphs, achieving enumeration in preprocessing time $\\widetilde{O}(m^{17.42 \\, \\mathrm{subw}(H)})$ and polylogarithmic delay, where $\\mathrm{subw}(H)$ is the submodular width of the pattern hypergraph $H$. We heavily rely on fast (rectangular and output-sensitive) matrix multiplication, which we complement by fine-grained lower bounds indicating that any algorithm beating preprocessing time $n^{Ω(k)}$ with polylogarithmic delay must rely on fast matrix multiplication. The second result is a generic enumeration-to-listing reduction, establishing that listing and enumeration are equivalent under natural assumptions. For (colored) subgraph isomorphism, our reduction transforms any listing algorithm running in time $O(f(n,m) + t \\cdot g(n,m))$ into an enumeration algorithm with preprocessing time $O\\left( (f(n,m)+g(n,m)+n+m) \\log^2 n \\right)$ and delay $O(g(n,m))$. We utilize this reduction to prove our first main result, and we expect that our generic reduction will find many future applications.", "published": "2026-06-01T12:38:26Z", "authors": ["Karl Bringmann", "Nick Fischer", "Yanheng Wang"], "categories": ["cs.DS", "cs.DB"], "doi": "", "_query": "housing price text listing", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2301.01635v4", "title": "SPTS v2: Single-Point Scene Text Spotting", "abstract": "End-to-end scene text spotting has made significant progress due to its intrinsic synergy between text detection and recognition. Previous methods commonly regard manual annotations such as horizontal rectangles, rotated rectangles, quadrangles, and polygons as a prerequisite, which are much more expensive than using single-point. Our new framework, SPTS v2, allows us to train high-performing text-spotting models using a single-point annotation. SPTS v2 reserves the advantage of the auto-regressive Transformer with an Instance Assignment Decoder (IAD) through sequentially predicting the center points of all text instances inside the same predicting sequence, while with a Parallel Recognition Decoder (PRD) for text recognition in parallel, which significantly reduces the requirement of the length of the sequence. These two decoders share the same parameters and are interactively connected with a simple but effective information transmission process to pass the gradient and information. Comprehensive experiments on various existing benchmark datasets demonstrate the SPTS v2 can outperform previous state-of-the-art single-point text spotters with fewer parameters while achieving 19$\\times$ faster inference speed. Within the context of our SPTS v2 framework, our experiments suggest a potential preference for single-point representation in scene text spotting when compared to other representations. Such an attempt provides a significant opportunity for scene text spotting applications beyond the realms of existing paradigms. Code is available at: https://github.com/Yuliang-Liu/SPTSv2.", "published": "2023-01-04T14:20:14Z", "authors": ["Yuliang Liu", "Jiaxin Zhang", "Dezhi Peng", "Mingxin Huang", "Xinyu Wang", "Jingqun Tang", "Can Huang", "Dahua Lin", "Chunhua Shen", "Xiang Bai", "Lianwen Jin"], "categories": ["cs.CV", "cs.AI"], "doi": "", "_query": "housing price text listing", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2202.02013v2", "title": "A Benchmark Corpus for the Detection of Automatically Generated Text in Academic Publications", "abstract": "Automatic text generation based on neural language models has achieved performance levels that make the generated text almost indistinguishable from those written by humans. Despite the value that text generation can have in various applications, it can also be employed for malicious tasks. The diffusion of such practices represent a threat to the quality of academic publishing. To address these problems, we propose in this paper two datasets comprised of artificially generated research content: a completely synthetic dataset and a partial text substitution dataset. In the first case, the content is completely generated by the GPT-2 model after a short prompt extracted from original papers. The partial or hybrid dataset is created by replacing several sentences of abstracts with sentences that are generated by the Arxiv-NLP model. We evaluate the quality of the datasets comparing the generated texts to aligned original texts using fluency metrics such as BLEU and ROUGE. The more natural the artificial texts seem, the more difficult they are to detect and the better is the benchmark. We also evaluate the difficulty of the task of distinguishing original from generated text by using state-of-the-art classification models.", "published": "2022-02-04T08:16:56Z", "authors": ["Vijini Liyanage", "Davide Buscaldi", "Adeline Nazarenko"], "categories": ["cs.CL"], "doi": "", "_query": "housing price text listing", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2309.11354v2", "title": "Self-supervised learning unveils change in urban housing from street-level images", "abstract": "Cities around the world face a critical shortage of affordable and decent housing. Despite its critical importance for policy, our ability to effectively monitor and track progress in urban housing is limited. Deep learning-based computer vision methods applied to street-level images have been successful in the measurement of socioeconomic and environmental inequalities but did not fully utilize temporal images to track urban change as time-varying labels are often unavailable. We used self-supervised methods to measure change in London using 15 million street images taken between 2008 and 2021. Our novel adaptation of Barlow Twins, Street2Vec, embeds urban structure while being invariant to seasonal and daily changes without manual annotations. It outperformed generic embeddings, successfully identified point-level change in London's housing supply from street-level images, and distinguished between major and minor change. This capability can provide timely information for urban planning and policy decisions toward more liveable, equitable, and sustainable cities.", "published": "2023-09-20T14:35:23Z", "authors": ["Steven Stalder", "Michele Volpi", "Nicolas Büttner", "Stephen Law", "Kenneth Harttgen", "Esra Suel"], "categories": ["cs.CV", "cs.LG"], "doi": "10.1016/j.compenvurbsys.2024.102156", "_query": "housing price text listing", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2510.08246v1", "title": "Does everyone have a price? Understanding people's attitude towards online and offline price discrimination", "abstract": "Online stores can present a different price to each customer. Such algorithmic personalised pricing can lead to advanced forms of price discrimination based on the characteristics and behaviour of individual consumers. We conducted two consumer surveys among a representative sample of the Dutch population (N=1233 and N=1202), to analyse consumer attitudes towards a list of examples of price discrimination and dynamic pricing. A vast majority finds online price discrimination unfair and unacceptable, and thinks it should be banned. However, some pricing strategies that have been used by companies for decades are almost equally unpopular. We analyse the results to better understand why people dislike many types of price discrimination.", "published": "2025-10-09T14:05:04Z", "authors": ["Joost Poort", "Frederik J. Zuiderveen Borgesius"], "categories": ["cs.CY"], "doi": "10.14763/2019.1.1383", "_query": "housing price text listing", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2411.07499v2", "title": "Listing 6-Cycles in Sparse Graphs", "abstract": "This work considers the problem of output-sensitive listing of occurrences of $2k$-cycles for fixed constant $k\\geq 2$ in an undirected host graph with $m$ edges and $t$ $2k$-cycles. Recent work of Jin and Xu (and independently Abboud, Khoury, Leibowitz, and Safier) [STOC 2023] gives an $O(m^{4/3}+t)$ time algorithm for listing $4$-cycles, and recent work by Jin, Vassilevska Williams and Zhou [SOSA 2024] gives an $\\widetilde{O}(n^2+t)$ time algorithm for listing $6$-cycles in $n$ node graphs. We focus on resolving the next natural question: obtaining listing algorithms for $6$-cycles in the sparse setting, i.e., in terms of $m$ rather than $n$. Previously, the best known result here is the better of Jin, Vassilevska Williams and Zhou's $\\widetilde{O}(n^2+t)$ algorithm and Alon, Yuster and Zwick's $O(m^{5/3}+t)$ algorithm. We give an algorithm for listing $6$-cycles with running time $\\widetilde{O}(m^{1.6}+t)$. Our algorithm is a natural extension of Dahlgaard, Knudsen and Stöckel's [STOC 2017] algorithm for detecting a $2k$-cycle. Our main technical contribution is the analysis of the algorithm which involves a type of ``supersaturation'' lemma relating the number of $2k$-cycles in a bipartite graph to the sizes of the parts in the bipartition and the number of edges. We also give a simplified analysis of Dahlgaard, Knudsen and Stöckel's $2k$-cycle detection algorithm (with a small polylogarithmic increase in the running time), which is helpful in analyzing our listing algorithm.", "published": "2024-11-12T02:55:40Z", "authors": ["Virginia Vassilevska Williams", "Alek Westover"], "categories": ["cs.DS"], "doi": "", "_query": "housing price text listing", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2210.03028v1", "title": "Detecting Narrative Elements in Informational Text", "abstract": "Automatic extraction of narrative elements from text, combining narrative theories with computational models, has been receiving increasing attention over the last few years. Previous works have utilized the oral narrative theory by Labov and Waletzky to identify various narrative elements in personal stories texts. Instead, we direct our focus to informational texts, specifically news stories. We introduce NEAT (Narrative Elements AnnoTation) - a novel NLP task for detecting narrative elements in raw text. For this purpose, we designed a new multi-label narrative annotation scheme, better suited for informational text (e.g. news media), by adapting elements from the narrative theory of Labov and Waletzky (Complication and Resolution) and adding a new narrative element of our own (Success). We then used this scheme to annotate a new dataset of 2,209 sentences, compiled from 46 news articles from various category domains. We trained a number of supervised models in several different setups over the annotated dataset to identify the different narrative elements, achieving an average F1 score of up to 0.77. The results demonstrate the holistic nature of our annotation scheme as well as its robustness to domain category.", "published": "2022-10-06T16:23:33Z", "authors": ["Effi Levi", "Guy Mor", "Tamir Sheafer", "Shaul R. Shenhav"], "categories": ["cs.CL"], "doi": "10.18653/v1/2022.findings-naacl.133", "_query": "housing price text listing", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2511.22027v1", "title": "Local unanimity in Shapley-Scarf housing markets", "abstract": "In the housing market model introduced by Shapley and Scarf (1974), we propose a new axiom, local unanimity, that extends the unanimity condition widely used in social choice theory. It applies the unanimity condition to any subset of agents in the model who unanimously agree on the best exchange of their endowments. Building on this axiom, we provide several concise characterizations of the Top Trading Cycles (TTC) mechanism under both strict and weak preference domains.", "published": "2025-11-27T02:19:06Z", "authors": ["Xinquan Hu", "Jun Zhang"], "categories": ["econ.TH"], "doi": "", "_query": "housing price text listing", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2302.10791v1", "title": "Estimating Bibliometric Links using Google Scholar: A Semi-Systematic Literature Mapping of Migration and Housing", "abstract": "As the number of empirical studies increases unprecedentedly in line with expansions in higher education, theoretical developments in population studies suffer due to a discoverability crisis of related work. The systematic use of previous research is common in the medical sciences through various types of structured reviews. However, these are less common in the social sciences, despite their potential, especially in cross-disciplinary fields such as migration. We use Google Scholar to examine the niche of housing research within migration studies through a broad range of documents. The contribution of this meta-analysis is threefold. Firstly, we illustrate the association of keywords across the corpus of literature related to migration and housing and map the growth of migration literature since the 1960s. Secondly, we highlight key bridging documents using network measures. Finally, we estimate the distance in reading time between documents. Our findings suggest that the corpus of previous work at the intersection of migration and housing consists of many additive case studies, indicating a gap in integrative approaches replicable across a growing knowledge landscape.", "published": "2023-02-20T17:30:01Z", "authors": ["Boyana Buyuklieva", "Juste Raimbault"], "categories": ["cs.DL"], "doi": "", "_query": "housing price text listing", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/1807.09602v1", "title": "Text Classification based on Multiple Block Convolutional Highways", "abstract": "In the Text Classification areas of Sentiment Analysis, Subjectivity/Objectivity Analysis, and Opinion Polarity, Convolutional Neural Networks have gained special attention because of their performance and accuracy. In this work, we applied recent advances in CNNs and propose a novel architecture, Multiple Block Convolutional Highways (MBCH), which achieves improved accuracy on multiple popular benchmark datasets, compared to previous architectures. The MBCH is based on new techniques and architectures including highway networks, DenseNet, batch normalization and bottleneck layers. In addition, to cope with the limitations of existing pre-trained word vectors which are used as inputs for the CNN, we propose a novel method, Improved Word Vectors (IWV). The IWV improves the accuracy of CNNs which are used for text classification tasks.", "published": "2018-07-23T13:58:38Z", "authors": ["Seyed Mahdi Rezaeinia", "Ali Ghodsi", "Rouhollah Rahmani"], "categories": ["cs.CL"], "doi": "", "_query": "housing price text listing", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/1406.1580v1", "title": "Machine learning approach for text and document mining", "abstract": "Text Categorization (TC), also known as Text Classification, is the task of automatically classifying a set of text documents into different categories from a predefined set. If a document belongs to exactly one of the categories, it is a single-label classification task; otherwise, it is a multi-label classification task. TC uses several tools from Information Retrieval (IR) and Machine Learning (ML) and has received much attention in the last years from both researchers in the academia and industry developers. In this paper, we first categorize the documents using KNN based machine learning approach and then return the most relevant documents.", "published": "2014-06-06T04:37:19Z", "authors": ["Vishwanath Bijalwan", "Pinki Kumari", "Jordan Pascual", "Vijay Bhaskar Semwal"], "categories": ["cs.IR", "cs.LG"], "doi": "", "_query": "housing price text listing", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/1901.10746v1", "title": "Reference-less Quality Estimation of Text Simplification Systems", "abstract": "The evaluation of text simplification (TS) systems remains an open challenge. As the task has common points with machine translation (MT), TS is often evaluated using MT metrics such as BLEU. However, such metrics require high quality reference data, which is rarely available for TS. TS has the advantage over MT of being a monolingual task, which allows for direct comparisons to be made between the simplified text and its original version. In this paper, we compare multiple approaches to reference-less quality estimation of sentence-level text simplification systems, based on the dataset used for the QATS 2016 shared task. We distinguish three different dimensions: gram-maticality, meaning preservation and simplicity. We show that n-gram-based MT metrics such as BLEU and METEOR correlate the most with human judgment of grammaticality and meaning preservation, whereas simplicity is best evaluated by basic length-based metrics.", "published": "2019-01-30T10:21:04Z", "authors": ["Louis Martin", "Samuel Humeau", "Pierre-Emmanuel Mazaré", "Antoine Bordes", "Éric Villemonte de La Clergerie", "Benoît Sagot"], "categories": ["cs.CL"], "doi": "", "_query": "housing price text listing", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/1302.2718v2", "title": "Text Steganographic Approaches: A Comparison", "abstract": "This paper presents three novel approaches of text steganography. The first approach uses the theme of missing letter puzzle where each character of message is hidden by missing one or more letters in a word of cover. The average Jaro score was found to be 0.95 indicating closer similarity between cover and stego file. The second approach hides a message in a wordlist where ASCII value of embedded character determines length and starting letter of a word. The third approach conceals a message, without degrading cover, by using start and end letter of words of the cover. For enhancing the security of secret message, the message is scrambled using one-time pad scheme before being concealed and cipher text is then concealed in cover. We also present an empirical comparison of the proposed approaches with some of the popular text steganographic approaches and show that our approaches outperform the existing approaches.", "published": "2013-02-12T07:03:02Z", "authors": ["Monika Agarwal"], "categories": ["cs.CR"], "doi": "10.5121/ijnsa.2013.5107", "_query": "housing price text listing", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/1606.08984v1", "title": "Optimal Consumption, Investment and Housing with Means-tested Public Pension in Retirement", "abstract": "In this paper, we develop an expected utility model for the retirement behavior in the decumulation phase of Australian retirees with sequential family status subject to consumption, housing, investment, bequest and government provided means-tested Age Pension. We account for mortality risk and risky investment assets, and introduce a health proxy to capture the decreasing level of consumption for older retirees. Then we find optimal housing at retirement, and optimal consumption and optimal risky asset allocation depending on age and wealth. The model is solved numerically as a stochastic control problem, and is calibrated using the maximum likelihood method on empirical data of consumption and housing from the Australian Bureau of Statistics 2009-2010 Survey. The model fits the characteristics of the data well to explain the behavior of Australian retirees. The key findings are the following: First, the optimal policy is highly sensitive to means-tested Age Pension early in retirement but this sensitivity fades with age. Secondly, the allocation to risky assets shows a complex relationship with the means-tested Age Pension that disappears once minimum withdrawal rules are enforced. As a general rule, when wealth decreases the proportion allocated to risky assets increases, due to the Age Pension working as a buffer against investment losses. Finally, couples can be more aggressive with risky allocations due to their longer life expectancy compared with singles.", "published": "2016-06-29T07:32:28Z", "authors": ["Johan G. Andreasson", "Pavel V. Shevchenko", "Alex Novikov"], "categories": ["econ.GN", "q-fin.RM"], "doi": "", "_query": "housing price text listing", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2305.09662v1", "title": "Make-An-Animation: Large-Scale Text-conditional 3D Human Motion Generation", "abstract": "Text-guided human motion generation has drawn significant interest because of its impactful applications spanning animation and robotics. Recently, application of diffusion models for motion generation has enabled improvements in the quality of generated motions. However, existing approaches are limited by their reliance on relatively small-scale motion capture data, leading to poor performance on more diverse, in-the-wild prompts. In this paper, we introduce Make-An-Animation, a text-conditioned human motion generation model which learns more diverse poses and prompts from large-scale image-text datasets, enabling significant improvement in performance over prior works. Make-An-Animation is trained in two stages. First, we train on a curated large-scale dataset of (text, static pseudo-pose) pairs extracted from image-text datasets. Second, we fine-tune on motion capture data, adding additional layers to model the temporal dimension. Unlike prior diffusion models for motion generation, Make-An-Animation uses a U-Net architecture similar to recent text-to-video generation models. Human evaluation of motion realism and alignment with input text shows that our model reaches state-of-the-art performance on text-to-motion generation.", "published": "2023-05-16T17:58:43Z", "authors": ["Samaneh Azadi", "Akbar Shah", "Thomas Hayes", "Devi Parikh", "Sonal Gupta"], "categories": ["cs.CV", "cs.AI"], "doi": "", "_query": "housing price text listing", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2110.05694v1", "title": "Computing Hecke operators for arithmetic subgroups of $\\text{Sp}_4$", "abstract": "We outline an algorithm for computing Hecke operators on equivariant cohomology $H^\\ast_{Γ_{\\text{Sp}}}(X_{\\text{Sp}};ρ)$ for the symplectic group $\\text{Sp}_4(\\mathbb{R})$. To do this, we define a new acyclic cell complex for $\\text{Sp}_4(\\mathbb{R})$ and make use of the well-tempered complex.", "published": "2021-10-12T02:26:23Z", "authors": ["Dylan Galt", "Mark McConnell"], "categories": ["math.NT"], "doi": "", "_query": "housing price text listing", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2406.15306v1", "title": "Advanced Multimodal Deep Learning Architecture for Image-Text Matching", "abstract": "Image-text matching is a key multimodal task that aims to model the semantic association between images and text as a matching relationship. With the advent of the multimedia information age, image, and text data show explosive growth, and how to accurately realize the efficient and accurate semantic correspondence between them has become the core issue of common concern in academia and industry. In this study, we delve into the limitations of current multimodal deep learning models in processing image-text pairing tasks. Therefore, we innovatively design an advanced multimodal deep learning architecture, which combines the high-level abstract representation ability of deep neural networks for visual information with the advantages of natural language processing models for text semantic understanding. By introducing a novel cross-modal attention mechanism and hierarchical feature fusion strategy, the model achieves deep fusion and two-way interaction between image and text feature space. In addition, we also optimize the training objectives and loss functions to ensure that the model can better map the potential association structure between images and text during the learning process. Experiments show that compared with existing image-text matching models, the optimized new model has significantly improved performance on a series of benchmark data sets. In addition, the new model also shows excellent generalization and robustness on large and diverse open scenario datasets and can maintain high matching performance even in the face of previously unseen complex situations.", "published": "2024-06-13T08:32:24Z", "authors": ["Jinyin Wang", "Haijing Zhang", "Yihao Zhong", "Yingbin Liang", "Rongwei Ji", "Yiru Cang"], "categories": ["cs.LG", "cs.CL", "cs.CV"], "doi": "", "_query": "housing price text listing", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2107.05279v1", "title": "ICDAR 2021 Competition on Integrated Circuit Text Spotting and Aesthetic Assessment", "abstract": "With hundreds of thousands of electronic chip components are being manufactured every day, chip manufacturers have seen an increasing demand in seeking a more efficient and effective way of inspecting the quality of printed texts on chip components. The major problem that deters this area of research is the lacking of realistic text on chips datasets to act as a strong foundation. Hence, a text on chips dataset, ICText is used as the main target for the proposed Robust Reading Challenge on Integrated Circuit Text Spotting and Aesthetic Assessment (RRC-ICText) 2021 to encourage the research on this problem. Throughout the entire competition, we have received a total of 233 submissions from 10 unique teams/individuals. Details of the competition and submission results are presented in this report.", "published": "2021-07-12T09:29:02Z", "authors": ["Chun Chet Ng", "Akmalul Khairi Bin Nazaruddin", "Yeong Khang Lee", "Xinyu Wang", "Yuliang Liu", "Chee Seng Chan", "Lianwen Jin", "Yipeng Sun", "Lixin Fan"], "categories": ["cs.CV"], "doi": "", "_query": "housing price text listing", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2507.13614v3", "title": "Linguistic and Embedding-Based Profiling of Texts generated by Humans and Large Language Models", "abstract": "The rapid advancements in large language models (LLMs) have significantly improved their ability to generate natural language, making texts generated by LLMs increasingly indistinguishable from human-written texts. While recent research has primarily focused on using LLMs to classify text as either human-written or machine-generated texts, our study focuses on characterizing these texts using a set of linguistic features across different linguistic levels such as morphology, syntax, and semantics. We select a dataset of human-written and machine-generated texts spanning 8 domains and produced by 11 different LLMs. We calculate different linguistic features such as dependency length and emotionality, and we use them for characterizing human-written and machine-generated texts along with different sampling strategies, repetition controls, and model release dates. Our statistical analysis reveals that human-written texts tend to exhibit simpler syntactic structures and more diverse semantic content. Furthermore, we calculate the variability of our set of features across models and domains. Both human- and machine-generated texts show stylistic diversity across domains, with human-written texts displaying greater variation in our features. Finally, we apply style embeddings to further test variability among human-written and machine-generated texts. Notably, newer models output text that is similarly variable, pointing to a homogenization of machine-generated texts.", "published": "2025-07-18T02:46:55Z", "authors": ["Sergio E. Zanotto", "Segun Aroyehun"], "categories": ["cs.CL", "cs.AI"], "doi": "", "_query": "housing price text listing", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2202.02280v1", "title": "Portfolio Choice with Indivisible and Illiquid Housing Assets: The Case of Spain", "abstract": "This paper studies the investment decision of the Spanish households using a unique data set, the Spanish Survey of Household Finance (EFF). We propose a theoretical model in which households, given a fixed investment in housing, allocate their net wealth across bank time deposits, stocks, and mortgage. Besides considering housing as an indivisible and illiquid asset that restricts the portfolio choice decision, we take into account the financial constraints that households face when they apply for external funding. For every representative household in the EFF we solve this theoretical problem and obtain the theoretically optimal portfolio that is compared with households' actual choices. We find that households significantly underinvest in stocks and deposits while the optimal and actual mortgage investments are alike. Considering the three types of financial assets at once, we find that the households headed by highly financially sophisticated, older, retired, richer, and unconstrained persons are the ones investing more efficiently.", "published": "2022-02-04T18:10:33Z", "authors": ["Sergio Mayordomo", "María Rodriguez-Moreno", "Juan Ignacio Peña"], "categories": ["q-fin.PM"], "doi": "", "_query": "housing price text listing", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/1911.06147v2", "title": "t-SS3: a text classifier with dynamic n-grams for early risk detection over text streams", "abstract": "A recently introduced classifier, called SS3, has shown to be well suited to deal with early risk detection (ERD) problems on text streams. It obtained state-of-the-art performance on early depression and anorexia detection on Reddit in the CLEF's eRisk open tasks. SS3 was created to deal with ERD problems naturally since: it supports incremental training and classification over text streams, and it can visually explain its rationale. However, SS3 processes the input using a bag-of-word model lacking the ability to recognize important word sequences. This aspect could negatively affect the classification performance and also reduces the descriptiveness of visual explanations. In the standard document classification field, it is very common to use word n-grams to try to overcome some of these limitations. Unfortunately, when working with text streams, using n-grams is not trivial since the system must learn and recognize which n-grams are important \"on the fly\". This paper introduces t-SS3, an extension of SS3 that allows it to recognize useful patterns over text streams dynamically. We evaluated our model in the eRisk 2017 and 2018 tasks on early depression and anorexia detection. Experimental results suggest that t-SS3 is able to improve both current results and the richness of visual explanations.", "published": "2019-11-11T22:06:40Z", "authors": ["Sergio G. Burdisso", "Marcelo Errecalde", "Manuel Montes-y-Gómez"], "categories": ["cs.CL", "cs.IR", "cs.LG"], "doi": "10.1016/j.patrec.2020.07.001", "_query": "housing price text listing", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2004.03589v1", "title": "Salience Estimation with Multi-Attention Learning for Abstractive Text Summarization", "abstract": "Attention mechanism plays a dominant role in the sequence generation models and has been used to improve the performance of machine translation and abstractive text summarization. Different from neural machine translation, in the task of text summarization, salience estimation for words, phrases or sentences is a critical component, since the output summary is a distillation of the input text. Although the typical attention mechanism can conduct text fragment selection from the input text conditioned on the decoder states, there is still a gap to conduct direct and effective salience detection. To bring back direct salience estimation for summarization with neural networks, we propose a Multi-Attention Learning framework which contains two new attention learning components for salience estimation: supervised attention learning and unsupervised attention learning. We regard the attention weights as the salience information, which means that the semantic units with large attention value will be more important. The context information obtained based on the estimated salience is incorporated with the typical attention mechanism in the decoder to conduct summary generation. Extensive experiments on some benchmark datasets in different languages demonstrate the effectiveness of the proposed framework for the task of abstractive summarization.", "published": "2020-04-07T02:38:56Z", "authors": ["Piji Li", "Lidong Bing", "Zhongyu Wei", "Wai Lam"], "categories": ["cs.CL", "cs.IR", "cs.LG"], "doi": "", "_query": "housing price text listing", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2412.05861v1", "title": "Depression detection from Social Media Bangla Text Using Recurrent Neural Networks", "abstract": "Emotion artificial intelligence is a field of study that focuses on figuring out how to recognize emotions, especially in the area of text mining. Today is the age of social media which has opened a door for us to share our individual expressions, emotions, and perspectives on any event. We can analyze sentiment on social media posts to detect positive, negative, or emotional behavior toward society. One of the key challenges in sentiment analysis is to identify depressed text from social media text that is a root cause of mental ill-health. Furthermore, depression leads to severe impairment in day-to-day living and is a major source of suicide incidents. In this paper, we apply natural language processing techniques on Facebook texts for conducting emotion analysis focusing on depression using multiple machine learning algorithms. Preprocessing steps like stemming, stop word removal, etc. are used to clean the collected data, and feature extraction techniques like stylometric feature, TF-IDF, word embedding, etc. are applied to the collected dataset which consists of 983 texts collected from social media posts. In the process of class prediction, LSTM, GRU, support vector machine, and Naive-Bayes classifiers have been used. We have presented the results using the primary classification metrics including F1-score, and accuracy. This work focuses on depression detection from social media posts to help psychologists to analyze sentiment from shared posts which may reduce the undesirable behaviors of depressed individuals through diagnosis and treatment.", "published": "2024-12-08T08:53:51Z", "authors": ["Sultan Ahmed", "Salman Rakin", "Mohammad Washeef Ibn Waliur", "Nuzhat Binte Islam", "Billal Hossain", "Md. Mostofa Akbar"], "categories": ["cs.HC", "cs.CL", "cs.LG"], "doi": "", "_query": "housing price text listing", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/1312.3552v1", "title": "Counting Popular Matchings in House Allocation Problems", "abstract": "We study the problem of counting the number of popular matchings in a given instance. A popular matching instance consists of agents A and houses H, where each agent ranks a subset of houses according to their preferences. A matching is an assignment of agents to houses. A matching M is more popular than matching M' if the number of agents that prefer M to M' is more than the number of people that prefer M' to M. A matching M is called popular if there exists no matching more popular than M. McDermid and Irving gave a poly-time algorithm for counting the number of popular matchings when the preference lists are strictly ordered. We first consider the case of ties in preference lists. Nasre proved that the problem of counting the number of popular matching is #P-hard when there are ties. We give an FPRAS for this problem. We then consider the popular matching problem where preference lists are strictly ordered but each house has a capacity associated with it. We give a switching graph characterization of popular matchings in this case. Such characterizations were studied earlier for the case of strictly ordered preference lists (McDermid and Irving) and for preference lists with ties (Nasre). We use our characterization to prove that counting popular matchings in capacitated case is #P-hard.", "published": "2013-12-12T17:21:20Z", "authors": ["Rupam Acharyya", "Sourav Chakraborty", "Nitesh Jha"], "categories": ["cs.DS"], "doi": "", "_query": "housing price text listing", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2503.16437v1", "title": "Haunted House: A text-based game for comparing the flexibility of mental models in humans and LLMs", "abstract": "This study introduces \"Haunted House\" a novel text-based game designed to compare the performance of humans and large language models (LLMs) in model-based reasoning. Players must escape from a house containing nine rooms in a 3x3 grid layout while avoiding the ghost. They are guided by verbal clues that they get each time they move. In Study 1, the results from 98 human participants revealed a success rate of 31.6%, significantly outperforming seven state-of-the-art LLMs tested. Out of 140 attempts across seven LLMs, only one attempt resulted in a pass by Claude 3 Opus. Preliminary results suggested that GPT o3-mini-high performance might be higher, but not at the human level. Further analysis of 29 human participants' moves in Study 2 indicated that LLMs frequently struggled with random and illogical moves, while humans exhibited such errors less frequently. Our findings suggest that current LLMs encounter difficulties in tasks that demand active model-based reasoning, offering inspiration for future benchmarks.", "published": "2025-02-12T16:19:40Z", "authors": ["Brett Puppart", "Paul-Henry Paltmann", "Jaan Aru"], "categories": ["cs.HC", "cs.AI", "q-bio.NC"], "doi": "", "_query": "housing price text listing", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/1705.09061v1", "title": "Triangle Finding and Listing in CONGEST Networks", "abstract": "Triangle-free graphs play a central role in graph theory, and triangle detection (or triangle finding) as well as triangle enumeration (triangle listing) play central roles in the field of graph algorithms. In distributed computing, algorithms with sublinear round complexity for triangle finding and listing have recently been developed in the powerful CONGEST clique model, where communication is allowed between any two nodes of the network. In this paper we present the first algorithms with sublinear complexity for triangle finding and triangle listing in the standard CONGEST model, where the communication topology is the same as the topology of the network. More precisely, we give randomized algorithms for triangle finding and listing with round complexity $O(n^{2/3}(\\log n)^{2/3})$ and $O(n^{3/4}\\log n)$, respectively, where $n$ denotes the number of nodes of the network. We also show a lower bound $Ω(n^{1/3}/\\log n)$ on the round complexity of triangle listing, which also holds for the CONGEST clique model.", "published": "2017-05-25T06:24:25Z", "authors": ["Taisuke Izumi", "François Le Gall"], "categories": ["cs.DC", "cs.DS"], "doi": "10.1145/3087801.3087811", "_query": "housing price text listing", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/1810.12738v1", "title": "Visual Re-ranking with Natural Language Understanding for Text Spotting", "abstract": "Many scene text recognition approaches are based on purely visual information and ignore the semantic relation between scene and text. In this paper, we tackle this problem from natural language processing perspective to fill the gap between language and vision. We propose a post-processing approach to improve scene text recognition accuracy by using occurrence probabilities of words (unigram language model), and the semantic correlation between scene and text. For this, we initially rely on an off-the-shelf deep neural network, already trained with a large amount of data, which provides a series of text hypotheses per input image. These hypotheses are then re-ranked using word frequencies and semantic relatedness with objects or scenes in the image. As a result of this combination, the performance of the original network is boosted with almost no additional cost. We validate our approach on ICDAR'17 dataset.", "published": "2018-10-29T10:41:45Z", "authors": ["Ahmed Sabir", "Francesc Moreno-Noguer", "Lluís Padró"], "categories": ["cs.CV"], "doi": "", "_query": "housing price text listing", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2305.19957v2", "title": "DeepSolo++: Let Transformer Decoder with Explicit Points Solo for Multilingual Text Spotting", "abstract": "End-to-end text spotting aims to integrate scene text detection and recognition into a unified framework. Dealing with the relationship between the two sub-tasks plays a pivotal role in designing effective spotters. Although Transformer-based methods eliminate the heuristic post-processing, they still suffer from the synergy issue between the sub-tasks and low training efficiency. Besides, they overlook the exploring on multilingual text spotting which requires an extra script identification task. In this paper, we present DeepSolo++, a simple DETR-like baseline that lets a single decoder with explicit points solo for text detection, recognition, and script identification simultaneously. Technically, for each text instance, we represent the character sequence as ordered points and model them with learnable explicit point queries. After passing a single decoder, the point queries have encoded requisite text semantics and locations, thus can be further decoded to the center line, boundary, script, and confidence of text via very simple prediction heads in parallel. Furthermore, we show the surprisingly good extensibility of our method, in terms of character class, language type, and task. On the one hand, our method not only performs well in English scenes but also masters the transcription with complex font structure and a thousand-level character classes, such as Chinese. On the other hand, our DeepSolo++ achieves better performance on the additionally introduced script identification task with a simpler training pipeline compared with previous methods. In addition, our models are also compatible with line annotations, which require much less annotation cost than polygons. The code is available at \\url{https://github.com/ViTAE-Transformer/DeepSolo}.", "published": "2023-05-31T15:44:00Z", "authors": ["Maoyuan Ye", "Jing Zhang", "Shanshan Zhao", "Juhua Liu", "Tongliang Liu", "Bo Du", "Dacheng Tao"], "categories": ["cs.CV"], "doi": "", "_query": "housing price text listing", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2106.03161v2", "title": "Identifying Populist Paragraphs in Text: A machine-learning approach", "abstract": "Abstract: In this paper we present an approach to develop a text-classification model which would be able to identify populist content in text. The developed BERT-based model is largely successful in identifying populist content in text and produces only a negligible amount of False Negatives, which makes it well-suited as a content analysis automation tool, which shortlists potentially relevant content for human validation.", "published": "2021-06-06T15:58:34Z", "authors": ["Jogilė Ulinskaitė", "Lukas Pukelis"], "categories": ["cs.CL"], "doi": "", "_query": "housing price text listing", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/1703.00955v4", "title": "Toward Controlled Generation of Text", "abstract": "Generic generation and manipulation of text is challenging and has limited success compared to recent deep generative modeling in visual domain. This paper aims at generating plausible natural language sentences, whose attributes are dynamically controlled by learning disentangled latent representations with designated semantics. We propose a new neural generative model which combines variational auto-encoders and holistic attribute discriminators for effective imposition of semantic structures. With differentiable approximation to discrete text samples, explicit constraints on independent attribute controls, and efficient collaborative learning of generator and discriminators, our model learns highly interpretable representations from even only word annotations, and produces realistic sentences with desired attributes. Quantitative evaluation validates the accuracy of sentence and attribute generation.", "published": "2017-03-02T21:23:47Z", "authors": ["Zhiting Hu", "Zichao Yang", "Xiaodan Liang", "Ruslan Salakhutdinov", "Eric P. Xing"], "categories": ["cs.LG", "cs.AI", "cs.CL", "stat.ML"], "doi": "", "_query": "housing price text listing", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2609.28451v1", "title": "Inter-Temporal Price Constraints in Dynamic Pricing: Performance Guarantees Under Price Monotonicity and Promotion Fatigue", "abstract": "We study dynamic pricing problems under inter-temporal price constraints. We have resources with limited capacities. At each time period, we decide which products to make available and what prices to charge for the available products. The sale probability for a product depends on its price. If we make a sale for a product, then we collect a revenue reflecting the price and consume the capacities of a combination of resources. We work with two types of inter-temporal constraints. In price monotonicity, the prices charged for a product at different time periods have to be monotone. In promotion fatigue, we can discount a product at most once over each time interval of a fixed length. Computing the optimal policy is intractable. We use fluid approximations to construct policies. Traditionally, policies from fluid approximations make randomized decisions at each time period by following an optimal solution to the fluid approximation, but such randomized decisions easily violate price monotonicity or promotion fatigue constraints. We develop policies that sample price paths according to an optimal solution to the fluid approximation, while satisfying the inter-temporal constraints. Letting $c_{\\min}$ be the smallest initial capacity of a resource and $L$ be the maximum number of resources used by a product, our policies have a performance guarantee of $\\max\\Big\\{ \\frac{1}{8L}, \\, \\frac{1}{2} - \\sqrt{\\frac{\\log c_{\\min}}{2 \\,c_{\\min}}} - \\frac{L}{c_{\\min}}\\Big\\}$. Thus, under large resource capacities, our policies are guaranteed to obtain at least half of the optimal total expected revenue. The latter performance guarantee is tight in the sense that no policy can, in general, obtain more than half of the optimal objective value of the fluid approximation even under large resource capacities. We unify our approach to open the path for extensions to other inter-temporal price constraints.", "published": "2026-09-23T17:47:41Z", "authors": ["Weiyuan Li", "Paat Rusmevichientong", "Huseyin Topaloglu"], "categories": ["math.OC", "cs.DM"], "doi": "", "_query": "housing price text listing", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2009.00678v1", "title": "Text and Style Conditioned GAN for Generation of Offline Handwriting Lines", "abstract": "This paper presents a GAN for generating images of handwritten lines conditioned on arbitrary text and latent style vectors. Unlike prior work, which produce stroke points or single-word images, this model generates entire lines of offline handwriting. The model produces variable-sized images by using style vectors to determine character widths. A generator network is trained with GAN and autoencoder techniques to learn style, and uses a pre-trained handwriting recognition network to induce legibility. A study using human evaluators demonstrates that the model produces images that appear to be written by a human. After training, the encoder network can extract a style vector from an image, allowing images in a similar style to be generated, but with arbitrary text.", "published": "2020-09-01T20:19:42Z", "authors": ["Brian Davis", "Chris Tensmeyer", "Brian Price", "Curtis Wigington", "Bryan Morse", "Rajiv Jain"], "categories": ["cs.CV"], "doi": "", "_query": "housing price text listing", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2503.19848v1", "title": "Guarding against artificial intelligence--hallucinated citations: the case for full-text reference deposit", "abstract": "The tendency of generative artificial intelligence (AI) systems to \"hallucinate\" false information is well-known; AI-generated citations to non-existent sources have made their way into the reference lists of peer-reviewed publications. Here, I propose a solution to this problem, taking inspiration from the Transparency and Openness Promotion (TOP) data sharing guidelines, the clash of generative AI with the American judiciary, and the precedent set by submissions of prior art to the United States Patent and Trademark Office. Journals should require authors to submit the full text of each cited source along with their manuscripts, thereby preventing authors from citing any material whose full text they cannot produce. This solution requires limited additional work on the part of authors or editors while effectively immunizing journals against hallucinated references.", "published": "2025-03-25T17:12:38Z", "authors": ["Alex Glynn"], "categories": ["cs.DL", "cs.AI"], "doi": "10.3897/ese.2025.e153973", "_query": "housing price text listing", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2112.01846v1", "title": "A Proposal of Automatic Error Correction in Text", "abstract": "The great amount of information that can be stored in electronic media is growing up daily. Many of them is got mainly by typing, such as the huge of information obtained from web 2.0 sites; or scaned and processing by an Optical Character Recognition software, like the texts of libraries and goverment offices. Both processes introduce error in texts, so it is difficult to use the data for other purposes than just to read it, i.e. the processing of those texts by other applications like e-learning, learning of languages, electronic tutorials, data minning, information retrieval and even more specialized systems such as tiflologic software, specifically blinded people-oriented applications like automatic reading, where the text would be error free as possible in order to make easier the text to speech task, and so on. In this paper it is showed an application of automatic recognition and correction of ortographic errors in electronic texts. This task is composed of three stages: a) error detection; b) candidate corrections generation; and c) correction -selection of the best candidate. The proposal is based in part of speech text categorization, word similarity, word diccionaries, statistical measures, morphologic analisys and n-grams based language model of Spanish.", "published": "2021-09-24T17:17:56Z", "authors": ["Wulfrano A. Luna-Ramírez", "Carlos R. Jaimez-González"], "categories": ["cs.CL", "cs.AI"], "doi": "", "_query": "housing price text listing", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/1707.06100v1", "title": "Discovering topics in text datasets by visualizing relevant words", "abstract": "When dealing with large collections of documents, it is imperative to quickly get an overview of the texts' contents. In this paper we show how this can be achieved by using a clustering algorithm to identify topics in the dataset and then selecting and visualizing relevant words, which distinguish a group of documents from the rest of the texts, to summarize the contents of the documents belonging to each topic. We demonstrate our approach by discovering trending topics in a collection of New York Times article snippets.", "published": "2017-07-18T15:46:47Z", "authors": ["Franziska Horn", "Leila Arras", "Grégoire Montavon", "Klaus-Robert Müller", "Wojciech Samek"], "categories": ["cs.CL"], "doi": "", "_query": "housing price text listing", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2406.17882v1", "title": "Cyber Security Operations Educational Gamification Application Listing", "abstract": "This listing contains a total of 80 gamification applications (GA)s used in cyber security operations (CSO) undergraduate education, from 74 publications, published between 2007 and June 2022. The listing outlines each GA identified and provides a short overview of each. This listing serves as both a comprehensive repository of existing GAs in cybersecurity undergraduate education, and as a starting point for adding new CSO GAs to the list. Contact the first author to add a CSO GA to the next version of the list.", "published": "2024-06-25T18:42:52Z", "authors": ["Sherri Weitl-Harms", "Adam Spanier", "John D. Hastings"], "categories": ["cs.CR", "cs.GT", "cs.HC"], "doi": "", "_query": "housing price text listing", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2005.01279v1", "title": "Improving Adversarial Text Generation by Modeling the Distant Future", "abstract": "Auto-regressive text generation models usually focus on local fluency, and may cause inconsistent semantic meaning in long text generation. Further, automatically generating words with similar semantics is challenging, and hand-crafted linguistic rules are difficult to apply. We consider a text planning scheme and present a model-based imitation-learning approach to alleviate the aforementioned issues. Specifically, we propose a novel guider network to focus on the generative process over a longer horizon, which can assist next-word prediction and provide intermediate rewards for generator optimization. Extensive experiments demonstrate that the proposed method leads to improved performance.", "published": "2020-05-04T05:45:13Z", "authors": ["Ruiyi Zhang", "Changyou Chen", "Zhe Gan", "Wenlin Wang", "Dinghan Shen", "Guoyin Wang", "Zheng Wen", "Lawrence Carin"], "categories": ["cs.CL", "cs.LG"], "doi": "", "_query": "housing price text listing", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2202.10304v1", "title": "Real-Time Scene Text Detection with Differentiable Binarization and Adaptive Scale Fusion", "abstract": "Recently, segmentation-based scene text detection methods have drawn extensive attention in the scene text detection field, because of their superiority in detecting the text instances of arbitrary shapes and extreme aspect ratios, profiting from the pixel-level descriptions. However, the vast majority of the existing segmentation-based approaches are limited to their complex post-processing algorithms and the scale robustness of their segmentation models, where the post-processing algorithms are not only isolated to the model optimization but also time-consuming and the scale robustness is usually strengthened by fusing multi-scale feature maps directly. In this paper, we propose a Differentiable Binarization (DB) module that integrates the binarization process, one of the most important steps in the post-processing procedure, into a segmentation network. Optimized along with the proposed DB module, the segmentation network can produce more accurate results, which enhances the accuracy of text detection with a simple pipeline. Furthermore, an efficient Adaptive Scale Fusion (ASF) module is proposed to improve the scale robustness by fusing features of different scales adaptively. By incorporating the proposed DB and ASF with the segmentation network, our proposed scene text detector consistently achieves state-of-the-art results, in terms of both detection accuracy and speed, on five standard benchmarks.", "published": "2022-02-21T15:30:14Z", "authors": ["Minghui Liao", "Zhisheng Zou", "Zhaoyi Wan", "Cong Yao", "Xiang Bai"], "categories": ["cs.CV"], "doi": "", "_query": "housing price text listing", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2505.17099v1", "title": "Learning Interpretable Representations Leads to Semantically Faithful EEG-to-Text Generation", "abstract": "Pretrained generative models have opened new frontiers in brain decoding by enabling the synthesis of realistic texts and images from non-invasive brain recordings. However, the reliability of such outputs remains questionable--whether they truly reflect semantic activation in the brain, or are merely hallucinated by the powerful generative models. In this paper, we focus on EEG-to-text decoding and address its hallucination issue through the lens of posterior collapse. Acknowledging the underlying mismatch in information capacity between EEG and text, we reframe the decoding task as semantic summarization of core meanings rather than previously verbatim reconstruction of stimulus texts. To this end, we propose the Generative Language Inspection Model (GLIM), which emphasizes learning informative and interpretable EEG representations to improve semantic grounding under heterogeneous and small-scale data conditions. Experiments on the public ZuCo dataset demonstrate that GLIM consistently generates fluent, EEG-grounded sentences without teacher forcing. Moreover, it supports more robust evaluation beyond text similarity, through EEG-text retrieval and zero-shot semantic classification across sentiment categories, relation types, and corpus topics. Together, our architecture and evaluation protocols lay the foundation for reliable and scalable benchmarking in generative brain decoding.", "published": "2025-05-21T05:29:55Z", "authors": ["Xiaozhao Liu", "Dinggang Shen", "Xihui Liu"], "categories": ["cs.CL"], "doi": "", "_query": "housing price text listing", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2309.14994v1", "title": "Measurement Models For Sailboats Price vs. Features And Regional Areas", "abstract": "In this study, we investigated the relationship between sailboat technical specifications and their prices, as well as regional pricing influences. Utilizing a dataset encompassing characteristics like length, beam, draft, displacement, sail area, and waterline, we applied multiple machine learning models to predict sailboat prices. The gradient descent model demonstrated superior performance, producing the lowest MSE and MAE. Our analysis revealed that monohulled boats are generally more affordable than catamarans, and that certain specifications such as length, beam, displacement, and sail area directly correlate with higher prices. Interestingly, lower draft was associated with higher listing prices. We also explored regional price determinants and found that the United States tops the list in average sailboat prices, followed by Europe, Hong Kong, and the Caribbean. Contrary to our initial hypothesis, a country's GDP showed no direct correlation with sailboat prices. Utilizing a 50% cross-validation method, our models yielded consistent results across test groups. Our research offers a machine learning-enhanced perspective on sailboat pricing, aiding prospective buyers in making informed decisions.", "published": "2023-09-26T15:03:05Z", "authors": ["Jiaqi Weng", "Chunlin Feng", "Yihan Shao"], "categories": ["cs.LG", "cs.AI"], "doi": "", "_query": "housing price text listing", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/1708.05545v1", "title": "Spotting Separator Points at Line Terminals in Compressed Document Images for Text-line Segmentation", "abstract": "Line separators are used to segregate text-lines from one another in document image analysis. Finding the separator points at every line terminal in a document image would enable text-line segmentation. In particular, identifying the separators in handwritten text could be a thrilling exercise. Obviously it would be challenging to perform this in the compressed version of a document image and that is the proposed objective in this research. Such an effort would prevent the computational burden of decompressing a document for text-line segmentation. Since document images are generally compressed using run length encoding (RLE) technique as per the CCITT standards, the first column in the RLE will be a white column. The value (depth) in the white column is very low when a particular line is a text line and the depth could be larger at the point of text line separation. A longer consecutive sequence of such larger depth should indicate the gap between the text lines, which provides the separator region. In case of over separation and under separation issues, corrective actions such as deletion and insertion are suggested respectively. An extensive experimentation is conducted on the compressed images of the benchmark datasets of ICDAR13 and Alireza et al [17] to demonstrate the efficacy.", "published": "2017-08-18T09:51:17Z", "authors": ["Amarnath R", "P. Nagabhushan"], "categories": ["cs.CV"], "doi": "10.5120/ijca2017915133", "_query": "housing price text listing", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2402.05152v2", "title": "Is the price right? Reconceptualizing price and income elasticity to anticipate price perception issues", "abstract": "Price perception by consumers represents a challenge to the ability of a business to correctly and profitably price and sell their products or services in a given market and any new target market. Complicating the perception of prices is the dynamics of price and income elasticity, both of which are key for estimating demand. This article proposes a novel and elegant identity that conceptualizes elasticity as a means of quantifying the potential for price perception problems in a market. Elasticity studies from 1990 to 2023 (n=30) were sampled to evaluate the relationship between price and income elasticity for various consumer commodities using the identity. The results suggest that, given known price and income elasticity values, a business can anticipate pricing perception problems in advance and address the potential for damaging distortion of their value proposition. Further, the business can use this insight to correctly choose a strategic pricing approach.", "published": "2024-02-07T13:42:23Z", "authors": ["Shawn Berry"], "categories": ["econ.GN"], "doi": "", "_query": "housing price text listing", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2210.17253v1", "title": "House of Graphs 2.0: a database of interesting graphs and more", "abstract": "In 2012 we announced the House of Graphs (https://houseofgraphs.org) [Discrete Appl. Math. 161 (2013), 311-314], which was a new database of graphs. The House of Graphs hosts complete lists of graphs of various graph classes, but its main feature is a searchable database of so called \"interesting\" graphs, which includes graphs that already occurred as extremal graphs or as counterexamples to conjectures. An important aspect of this database is that it can be extended by users of the website. Over the years, several new features and graph invariants were added to the House of Graphs and users uploaded many interesting graphs to the website. But as the development of the original House of Graphs website started in 2010, the underlying frameworks and technologies of the website became outdated. This is why we completely rebuilt the House of Graphs using modern frameworks to build a maintainable and expandable web application that is future-proof. On top of this, several new functionalities were added to improve the application and the user experience. This article describes the changes and new features of the new House of Graphs website.", "published": "2022-10-31T12:18:26Z", "authors": ["Kris Coolsaet", "Sven D'hondt", "Jan Goedgebeur"], "categories": ["math.CO", "cs.DM"], "doi": "10.1016/j.dam.2022.10.013", "_query": "housing price text listing", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/1711.08799v1", "title": "Impact of Cross-Listing Chinese Stock Returns. A and N Shares Rate of Return Comparison", "abstract": "The paper examines the Chinese market reaction to the ADR issue by comparing returns and their stochastic variances of the Chinese firms cross-listed in the U.S. stock market. First, It was implemented capital asset pricing model (CAPM) to determine expected returns A and N shares. The CAPM provided with a methodology to quantify risk and translate that risk into estimates of expected return on equity. Overall findings document that N shares of Chinese entities listed on U.S. market were greatly affected by economic turmoil during the period of World Financial Crises 2007-2008 than the A shares listed on the local market. After in order to test the hypothesis of beneficial cross-listing, it was implemented an event study method and the returns was modeled following GARCH process, which assumes homoscedasticity in residual returns. The results indicate a significant negative abnormal market return on an ADR listing date. The return volatilities after the listing date are compared to those before the listing. Four out of ten companies experienced increased volatility of local return after the cross-listing. Keywords: cross-listing, ADR, rate of return, volatility, CAPM, GARCH model, N shares, A shares", "published": "2017-11-23T18:30:08Z", "authors": ["Kamilla Sabitova"], "categories": ["q-fin.PR", "q-fin.RM", "q-fin.ST", "q-fin.TR"], "doi": "10.13140/RG.2.2.12115.68642", "_query": "housing price text listing", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2212.13812v1", "title": "Invertible Bloom Lookup Tables with Listing Guarantees", "abstract": "The Invertible Bloom Lookup Table (IBLT) is a probabilistic concise data structure for set representation that supports a listing operation as the recovery of the elements in the represented set. Its applications can be found in network synchronization and traffic monitoring as well as in error-correction codes. IBLT can list its elements with probability affected by the size of the allocated memory and the size of the represented set, such that it can fail with small probability even for relatively small sets. While previous works only studied the failure probability of IBLT, this work initiates the worst case analysis of IBLT that guarantees successful listing for all sets of a certain size. The worst case study is important since the failure of IBLT imposes high overhead. We describe a novel approach that guarantees successful listing when the set satisfies a tunable upper bound on its size. To allow that, we develop multiple constructions that are based on various coding techniques such as stopping sets and the stopping redundancy of error-correcting codes, Steiner systems, and covering arrays as well as new methodologies we develop. We analyze the sizes of IBLTs with listing guarantees obtained by the various methods as well as their mapping memory consumption. Lastly, we study lower bounds on the achievable sizes of IBLT with listing guarantees and verify the results in the paper by simulations.", "published": "2022-12-28T13:18:08Z", "authors": ["Avi Mizrahi", "Daniella Bar-Lev", "Eitan Yaakobi", "Ori Rottenstreich"], "categories": ["cs.IT", "cs.DS"], "doi": "", "_query": "housing price text listing", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2307.10230v4", "title": "Prompt Tuning on Graph-augmented Low-resource Text Classification", "abstract": "Text classification is a fundamental problem in information retrieval with many real-world applications, such as predicting the topics of online articles and the categories of e-commerce product descriptions. However, low-resource text classification, with no or few labeled samples, presents a serious concern for supervised learning. Meanwhile, many text data are inherently grounded on a network structure, such as a hyperlink/citation network for online articles, and a user-item purchase network for e-commerce products. These graph structures capture rich semantic relationships, which can potentially augment low-resource text classification. In this paper, we propose a novel model called Graph-Grounded Pre-training and Prompting (G2P2) to address low-resource text classification in a two-pronged approach. During pre-training, we propose three graph interaction-based contrastive strategies to jointly pre-train a graph-text model; during downstream classification, we explore handcrafted discrete prompts and continuous prompt tuning for the jointly pre-trained model to achieve zero- and few-shot classification, respectively. Moreover, we explore the possibility of employing continuous prompt tuning for zero-shot inference. Specifically, we aim to generalize continuous prompts to unseen classes while leveraging a set of base classes. To this end, we extend G2P2 into G2P2$^*$, hinging on a new architecture of conditional prompt tuning. Extensive experiments on four real-world datasets demonstrate the strength of G2P2 in zero- and few-shot low-resource text classification tasks, and illustrate the advantage of G2P2$^*$ in dealing with unseen classes.", "published": "2023-07-15T11:49:43Z", "authors": ["Zhihao Wen", "Yuan Fang"], "categories": ["cs.IR"], "doi": "", "_query": "housing price text listing", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/1502.06219v1", "title": "Video Text Localization with an emphasis on Edge Features", "abstract": "The text detection and localization plays a major role in video analysis and understanding. The scene text embedded in video consist of high-level semantics and hence contributes significantly to visual content analysis and retrieval. This paper proposes a novel method to robustly localize the texts in natural scene images and videos based on sobel edge emphasizing approach. The input image is preprocessed and edge emphasis is done to detect the text clusters. Further, a set of rules have been devised using morphological operators for false positive elimination and connected component analysis is performed to detect the text regions and hence text localization is performed. The experimental results obtained on publicly available standard datasets illustrate that the proposed method can detect and localize the texts of various sizes, fonts and colors.", "published": "2015-02-22T12:32:18Z", "authors": ["B. H. Shekar", "Smitha M. L."], "categories": ["cs.CV"], "doi": "", "_query": "housing price text listing", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/1909.07145v1", "title": "ICDAR2019 Robust Reading Challenge on Arbitrary-Shaped Text (RRC-ArT)", "abstract": "This paper reports the ICDAR2019 Robust Reading Challenge on Arbitrary-Shaped Text (RRC-ArT) that consists of three major challenges: i) scene text detection, ii) scene text recognition, and iii) scene text spotting. A total of 78 submissions from 46 unique teams/individuals were received for this competition. The top performing score of each challenge is as follows: i) T1 - 82.65%, ii) T2.1 - 74.3%, iii) T2.2 - 85.32%, iv) T3.1 - 53.86%, and v) T3.2 - 54.91%. Apart from the results, this paper also details the ArT dataset, tasks description, evaluation metrics and participants methods. The dataset, the evaluation kit as well as the results are publicly available at https://rrc.cvc.uab.es/?ch=14", "published": "2019-09-16T12:19:00Z", "authors": ["Chee-Kheng Chng", "Yuliang Liu", "Yipeng Sun", "Chun Chet Ng", "Canjie Luo", "Zihan Ni", "ChuanMing Fang", "Shuaitao Zhang", "Junyu Han", "Errui Ding", "Jingtuo Liu", "Dimosthenis Karatzas", "Chee Seng Chan", "Lianwen Jin"], "categories": ["cs.CV"], "doi": "", "_query": "housing price text listing", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2310.08785v3", "title": "DeltaSpace: A Semantic-aligned Feature Space for Flexible Text-guided Image Editing", "abstract": "Text-guided image editing faces significant challenges when considering training and inference flexibility. Much literature collects large amounts of annotated image-text pairs to train text-conditioned generative models from scratch, which is expensive and not efficient. After that, some approaches that leverage pre-trained vision-language models have been proposed to avoid data collection, but they are limited by either per text-prompt optimization or inference-time hyper-parameters tuning. To address these issues, we investigate and identify a specific space, referred to as CLIP DeltaSpace, where the CLIP visual feature difference of two images is semantically aligned with the CLIP textual feature difference of their corresponding text descriptions. Based on DeltaSpace, we propose a novel framework called DeltaEdit, which maps the CLIP visual feature differences to the latent space directions of a generative model during the training phase, and predicts the latent space directions from the CLIP textual feature differences during the inference phase. And this design endows DeltaEdit with two advantages: (1) text-free training; (2) generalization to various text prompts for zero-shot inference. Extensive experiments validate the effectiveness and versatility of DeltaEdit with different generative models, including both the GAN model and the diffusion model, in achieving flexible text-guided image editing. Code is available at https://github.com/Yueming6568/DeltaEdit.", "published": "2023-10-12T15:43:12Z", "authors": ["Yueming Lyu", "Kang Zhao", "Bo Peng", "Huafeng Chen", "Yue Jiang", "Yingya Zhang", "Jing Dong", "Caifeng Shan"], "categories": ["cs.CV", "cs.AI"], "doi": "", "_query": "housing price text listing", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2004.03585v1", "title": "Bi-interpretability with $\\mathbb{Z}$ and models of the complete elementary theories of $\\text{SL}_n(\\mathcal{O})$, $\\text{T}_n(\\mathcal{O})$ and $\\text{GL}_n(\\mathcal{O})$, $n\\geq 3$", "abstract": "Let $\\mathcal{O}$ be the ring of integers of a number field, and let $n\\geq 3$. This paper studies bi-interpretability of the ring of integers $\\mathbb{Z}$ with the special linear group $\\text{SL}_n(\\mathcal{O})$, the general linear group $\\text{GL}_n(\\mathcal{O})$ and solvable group of all invertible uppertriangular matrices over $\\mathcal{O}$, $\\text{T}_n(\\mathcal{O})$. For each of these groups we provide a complete characterization of arbitrary models of their complete elementary theories.", "published": "2020-04-07T00:09:01Z", "authors": ["Mahmood Sohrabi", "Alexei G. Myasnikov"], "categories": ["math.GR", "math.LO"], "doi": "", "_query": "housing price text listing", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2607.20477v1", "title": "Semi-Supervised Text-Attributed Graph Distillation", "abstract": "{\\em Text-Attributed Graphs} (TAGs) have emerged as an expressive data model for integrating graph topology with rich textual semantics. Existing representation learning methods over TAGs suffer from severe scalability bottlenecks, particularly together with {\\em Large Language Models} (LLMs). While data distillation offers a promising data-centric solution, existing methods fail to capture the complex interplay between graph and text modalities, struggle with the label scarcity inherent in semi-supervised settings, and lack the ability to produce the human-readable textual attributes required for downstream LLM-based tasks. To address these challenges, we propose \\algo{}, a unified semi-supervised framework guided by the {\\em Wasserstein Distance} (WSD). Grounded in our empirical findings on real TAGs, \\algo{} introduces a graph-text collaborative encoding module that utilizes dual-pathway encoders (graph-aware and -free) within a collaborative self-training scheme to harvest reliable pseudo-labels and fuse complementary graph-text features. Furthermore, we develop a theoretically grounded WSD-based graph sketching algorithm and a cost-effective LLM text synthesis module, which leverages cluster-based keyword extraction to generate coherent, human-readable summaries for condensed nodes. Extensive experiments on benchmark datasets demonstrate that \\algo{} achieves a state-of-the-art performance-compression trade-off in terms of both GNN- and LLM-based downstream tasks, enabling effective and efficient TAG learning or analytics.", "published": "2026-05-27T14:52:24Z", "authors": ["Yurui Lai", "Samir Moustafa", "Renchi Yang", "Tsz Nam Chan"], "categories": ["cs.AI"], "doi": "", "_query": "housing price text listing", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2104.08910v1", "title": "Towards Open-World Text-Guided Face Image Generation and Manipulation", "abstract": "The existing text-guided image synthesis methods can only produce limited quality results with at most \\mbox{$\\text{256}^2$} resolution and the textual instructions are constrained in a small Corpus. In this work, we propose a unified framework for both face image generation and manipulation that produces diverse and high-quality images with an unprecedented resolution at 1024 from multimodal inputs. More importantly, our method supports open-world scenarios, including both image and text, without any re-training, fine-tuning, or post-processing. To be specific, we propose a brand new paradigm of text-guided image generation and manipulation based on the superior characteristics of a pretrained GAN model. Our proposed paradigm includes two novel strategies. The first strategy is to train a text encoder to obtain latent codes that align with the hierarchically semantic of the aforementioned pretrained GAN model. The second strategy is to directly optimize the latent codes in the latent space of the pretrained GAN model with guidance from a pretrained language model. The latent codes can be randomly sampled from a prior distribution or inverted from a given image, which provides inherent supports for both image generation and manipulation from multi-modal inputs, such as sketches or semantic labels, with textual guidance. To facilitate text-guided multi-modal synthesis, we propose the Multi-Modal CelebA-HQ, a large-scale dataset consisting of real face images and corresponding semantic segmentation map, sketch, and textual descriptions. Extensive experiments on the introduced dataset demonstrate the superior performance of our proposed method. Code and data are available at https://github.com/weihaox/TediGAN.", "published": "2021-04-18T16:56:07Z", "authors": ["Weihao Xia", "Yujiu Yang", "Jing-Hao Xue", "Baoyuan Wu"], "categories": ["cs.CV", "cs.MM"], "doi": "", "_query": "housing price text listing", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/1912.09501v1", "title": "A Framework for Explainable Text Classification in Legal Document Review", "abstract": "Companies regularly spend millions of dollars producing electronically-stored documents in legal matters. Recently, parties on both sides of the 'legal aisle' are accepting the use of machine learning techniques like text classification to cull massive volumes of data and to identify responsive documents for use in these matters. While text classification is regularly used to reduce the discovery costs in legal matters, it also faces a peculiar perception challenge: amongst lawyers, this technology is sometimes looked upon as a \"black box\", little information provided for attorneys to understand why documents are classified as responsive. In recent years, a group of AI and ML researchers have been actively researching Explainable AI, in which actions or decisions are human understandable. In legal document review scenarios, a document can be identified as responsive, if one or more of its text snippets are deemed responsive. In these scenarios, if text classification can be used to locate these snippets, then attorneys could easily evaluate the model's classification decision. When deployed with defined and explainable results, text classification can drastically enhance overall quality and speed of the review process by reducing the review time. Moreover, explainable predictive coding provides lawyers with greater confidence in the results of that supervised learning task. This paper describes a framework for explainable text classification as a valuable tool in legal services: for enhancing the quality and efficiency of legal document review and for assisting in locating responsive snippets within responsive documents. This framework has been implemented in our legal analytics product, which has been used in hundreds of legal matters. We also report our experimental results using the data from an actual legal matter that used this type of document review.", "published": "2019-12-19T19:07:23Z", "authors": ["Christian J. Mahoney", "Jianping Zhang", "Nathaniel Huber-Fliflet", "Peter Gronvall", "Haozhen Zhao"], "categories": ["cs.IR", "cs.AI", "cs.LG"], "doi": "", "_query": "housing price text listing", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2307.15871v2", "title": "Towards Optimal Output-Sensitive Clique Listing or: Listing Cliques from Smaller Cliques", "abstract": "We study finding and listing $k$-cliques in a graph, for constant $k\\geq 3$, a fundamental problem of both theoretical and practical importance. Our main contribution is a new output-sensitive algorithm for listing $k$-cliques in graphs, for arbitrary $k\\geq 3$, coupled with lower bounds based on standard fine-grained assumptions, showing that our algorithm's running time is tight. Previously, the only known conditionally optimal output-sensitive algorithms were for the case of $3$-cliques by Björklund, Pagh, Vassilevska W. and Zwick [ICALP'14]. Typical inputs to subgraph isomorphism or listing problems are measured by the number of nodes $n$ or the number of edges $m$. Our framework is very general in that it gives $k$-clique listing algorithms whose running times are measured in terms of the number of $\\ell$-cliques $Δ_\\ell$ in the graph for any $1\\leq \\ell<k$. This generalizes the typical parameterization in terms of $n$ (the number of $1$-cliques) and $m$ (the number of $2$-cliques). If the matrix multiplication exponent $ω$ is $2$, and if the size of the output, $Δ_k$, is sufficiently large, then for every $\\ell<k$, the running time of our algorithm for listing $k$-cliques is $$\\tilde{O}\\left(Δ_\\ell^{\\frac{2}{\\ell (k - \\ell)}}Δ_k^{1-\\frac{2}{k(k-\\ell)}}\\right).$$ For sufficiently large $Δ_k$, we prove that this runtime is in fact {\\em optimal} for all $1 \\leq \\ell < k$ under the Exact $k$-Clique hypothesis. In the special cases of $k = 4$ and $5$, our algorithm in terms of $n$ is conditionally optimal for all values of $Δ_k$ if $ω= 2$. Moreover, our framework is powerful enough to provide an improvement upon the 19-year old runtimes for $4$ and $5$-clique detection in $m$-edge graphs, as a function of $m$ [Eisenbrand and Grandoni, TCS'04].", "published": "2023-07-29T02:37:32Z", "authors": ["Mina Dalirrooyfard", "Surya Mathialagan", "Virginia Vassilevska Williams", "Yinzhan Xu"], "categories": ["cs.DS"], "doi": "", "_query": "housing price text listing", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2309.02459v2", "title": "Text-Only Domain Adaptation for End-to-End Speech Recognition through Down-Sampling Acoustic Representation", "abstract": "Mapping two modalities, speech and text, into a shared representation space, is a research topic of using text-only data to improve end-to-end automatic speech recognition (ASR) performance in new domains. However, the length of speech representation and text representation is inconsistent. Although the previous method up-samples the text representation to align with acoustic modality, it may not match the expected actual duration. In this paper, we proposed novel representations match strategy through down-sampling acoustic representation to align with text modality. By introducing a continuous integrate-and-fire (CIF) module generating acoustic representations consistent with token length, our ASR model can learn unified representations from both modalities better, allowing for domain adaptation using text-only data of the target domain. Experiment results of new domain data demonstrate the effectiveness of the proposed method.", "published": "2023-09-04T08:52:59Z", "authors": ["Jiaxu Zhu", "Weinan Tong", "Yaoxun Xu", "Changhe Song", "Zhiyong Wu", "Zhao You", "Dan Su", "Dong Yu", "Helen Meng"], "categories": ["cs.SD", "cs.CL", "eess.AS"], "doi": "10.21437/Interspeech.2023-1378", "_query": "housing price text listing", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/1911.09026v1", "title": "Weak Supervision for Generating Pixel-Level Annotations in Scene Text Segmentation", "abstract": "Providing pixel-level supervisions for scene text segmentation is inherently difficult and costly, so that only few small datasets are available for this task. To face the scarcity of training data, previous approaches based on Convolutional Neural Networks (CNNs) rely on the use of a synthetic dataset for pre-training. However, synthetic data cannot reproduce the complexity and variability of natural images. In this work, we propose to use a weakly supervised learning approach to reduce the domain-shift between synthetic and real data. Leveraging the bounding-box supervision of the COCO-Text and the MLT datasets, we generate weak pixel-level supervisions of real images. In particular, the COCO-Text-Segmentation (COCO_TS) and the MLT-Segmentation (MLT_S) datasets are created and released. These two datasets are used to train a CNN, the Segmentation Multiscale Attention Network (SMANet), which is specifically designed to face some peculiarities of the scene text segmentation task. The SMANet is trained end-to-end on the proposed datasets, and the experiments show that COCO_TS and MLT_S are a valid alternative to synthetic images, allowing to use only a fraction of the training samples and improving significantly the performances.", "published": "2019-11-19T09:55:27Z", "authors": ["Simone Bonechi", "Paolo Andreini", "Monica Bianchini", "Franco Scarselli"], "categories": ["cs.CV", "cs.LG", "eess.IV"], "doi": "10.1016/j.patrec.2020.06.023", "_query": "housing price text listing", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2608.04076v1", "title": "Forced Displacement of People Experiencing Homelessness: Housing and Movement Outcomes after Encampment Clearances", "abstract": "The 2024 Grants Pass decision newly emboldens US cities to manage unsheltered homelessness through forced displacement. Although literature demonstrates the harmful health and material impacts of this tactic, the longer-term results for housing outcomes and migration patterns remain less clear. In response, this study leverages longitudinal street outreach data to investigate where people move following encampment clearances. We specifically employ relational event models to predict the likelihood of various outcomes post-removal, such as relocating tracts, entering shelter, or obtaining housing. Results suggest that displaced residents do not travel far, yet clearances may still reduce visible homelessness by decreasing the size of camp communities. Furthermore, people appear unlikely to move indoors and instead face high risks of losing contact with service providers. These trends hold regardless of individual demographics, although people with mental health conditions demonstrate stronger attachments to their original sites. Evidence additionally indicates that neighborhood conditions could influence these migration behaviors. Such findings corroborate broader literature on place attachments, residential mobility, and invisibilization of poverty. This paper ultimately addresses the urgent need for a deeper understanding of how forced displacement impacts homelessness and pathways to housing.", "published": "2026-08-04T17:49:07Z", "authors": ["Brandon Morande", "Kim Serry", "Amy Hagopian", "Zack W. Almquist"], "categories": ["cs.SI", "stat.AP"], "doi": "", "_query": "housing price text listing", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/1308.6635v1", "title": "Efficiently Listing Combinatorial Patterns in Graphs", "abstract": "Graphs are extremely versatile and ubiquitous mathematical structures with potential to model a wide range of domains. For this reason, graph problems have been of interest since the early days of computer science. Some of these problems consider substructures of a graph that have certain properties. These substructures of interest, generally called patterns, are often meaningful in the domain being modeled. Classic examples of patterns include spanning trees, cycles and subgraphs. This thesis focuses on the topic of explicitly listing all the patterns existing in an input graph. One of the defining features of this problem is that the number of patterns is frequently exponential on the size of the input graph. Thus, the time complexity of listing algorithms is parameterized by the size of the output. The main contribution of this work is the presentation of optimal algorithms for four different problems of listing patterns in graphs, namely the listing of k-subtrees, k-subgraphs, st-paths and cycles. The algorithms presented are framed within the same generic approach, based in a recursive partition of the search space that divides the problem into subproblems. The key to an efficient implementation of this approach is to avoid recursing into subproblems that do not list any patterns. With this goal in sight, a dynamic data structure, called the certificate, is introduced and maintained throughout the recursion. Moreover, properties of the recursion tree and lower bounds on the number of patterns are used to amortize the cost of the algorithm on the size of the output.", "published": "2013-08-30T02:10:19Z", "authors": ["Rui Ferreira"], "categories": ["cs.DS"], "doi": "", "_query": "housing price text listing", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2108.01343v3", "title": "I3CL:Intra- and Inter-Instance Collaborative Learning for Arbitrary-shaped Scene Text Detection", "abstract": "Existing methods for arbitrary-shaped text detection in natural scenes face two critical issues, i.e., 1) fracture detections at the gaps in a text instance; and 2) inaccurate detections of arbitrary-shaped text instances with diverse background context. To address these issues, we propose a novel method named Intra- and Inter-Instance Collaborative Learning (I3CL). Specifically, to address the first issue, we design an effective convolutional module with multiple receptive fields, which is able to collaboratively learn better character and gap feature representations at local and long ranges inside a text instance. To address the second issue, we devise an instance-based transformer module to exploit the dependencies between different text instances and a global context module to exploit the semantic context from the shared background, which are able to collaboratively learn more discriminative text feature representation. In this way, I3CL can effectively exploit the intra- and inter-instance dependencies together in a unified end-to-end trainable framework. Besides, to make full use of the unlabeled data, we design an effective semi-supervised learning method to leverage the pseudo labels via an ensemble strategy. Without bells and whistles, experimental results show that the proposed I3CL sets new state-of-the-art results on three challenging public benchmarks, i.e., an F-measure of 77.5% on ICDAR2019-ArT, 86.9% on Total-Text, and 86.4% on CTW-1500. Notably, our I3CL with the ResNeSt-101 backbone ranked 1st place on the ICDAR2019-ArT leaderboard. The source code will be available at https://github.com/ViTAE-Transformer/ViTAE-Transformer-Scene-Text-Detection.", "published": "2021-08-03T07:48:12Z", "authors": ["Bo Du", "Jian Ye", "Jing Zhang", "Juhua Liu", "Dacheng Tao"], "categories": ["cs.CV"], "doi": "", "_query": "housing price text listing", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/cs/0109088v1", "title": "Value of Usage and Seller's Listing Behavior in Internet Auctions", "abstract": "In this paper, we aim to empirically examine the value of website usage and sellers' listing behavior in the two leading Internet auctions sites, eBay and Yahoo!Auctions. The descriptive data analysis of the seller's equilibrium listing behavior indicates that a seller's higher expected auction revenue from eBay is correlated with a lager number of potential bidders measured by website usage per listing. Our estimation results, based on the logarithm specifications of sellers' expected auction revenues and potential bidders' website usage, show that in a median case, (i) 1 percent increase of the unique visitors (page views) per listed item induces 0.022 (0.007) percent increase of a seller's expected auction revenue; and (ii) 1 percent increase of sellers' listings induces 1.99 (4.74) percent increase of the unique visitors (page views). Since increased expected auction revenues will induce more listings, we can infer positive feedback effects between the number of listings and website usage. Consequently, Yahoo!Auctions, which has substantially less listings, has greater incentives to increase listings via these feedback effects which are reflected in its fee schedules.", "published": "2001-09-24T21:28:34Z", "authors": ["Sangin Park"], "categories": ["cs.CY"], "doi": "", "_query": "housing price text listing", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2509.18212v1", "title": "Personalised Pricing: The Demise of the Fixed Price?", "abstract": "An online seller or platform is technically able to offer every consumer a different price for the same product, based on information it has about the customers. Such online price discrimination exacerbates concerns regarding the fairness and morality of price discrimination, and the possible need for regulation. In this chapter, we discuss the underlying basis of price discrimination in economic theory, and its popular perception. Our surveys show that consumers are critical and suspicious of online price discrimination. A majority consider it unacceptable and unfair, and are in favour of a ban. When stores apply online price discrimination, most consumers think they should be informed about it. We argue that the General Data Protection Regulation (GDPR) applies to the most controversial forms of online price discrimination, and not only requires companies to disclose their use of price discrimination, but also requires companies to ask customers for their prior consent. Industry practice, however, does not show any adoption of these two principles.", "published": "2025-09-21T11:39:08Z", "authors": ["Joost Poort", "Frederik Zuiderveen Borgesius"], "categories": ["cs.CY"], "doi": "", "_query": "housing price text listing", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2102.08162v1", "title": "Integrating Floor Plans into Hedonic Models for Rent Price Appraisal", "abstract": "Online real estate platforms have become significant marketplaces facilitating users' search for an apartment or a house. Yet it remains challenging to accurately appraise a property's value. Prior works have primarily studied real estate valuation based on hedonic price models that take structured data into account while accompanying unstructured data is typically ignored. In this study, we investigate to what extent an automated visual analysis of apartment floor plans on online real estate platforms can enhance hedonic rent price appraisal. We propose a tailored two-staged deep learning approach to learn price-relevant designs of floor plans from historical price data. Subsequently, we integrate the floor plan predictions into hedonic rent price models that account for both structural and locational characteristics of an apartment. Our empirical analysis based on a unique dataset of 9174 real estate listings suggests that current hedonic models underutilize the available data. We find that (1) the visual design of floor plans has significant explanatory power regarding rent prices - even after controlling for structural and locational apartment characteristics, and (2) harnessing floor plans results in an up to 10.56% lower out-of-sample prediction error. We further find that floor plans yield a particularly high gain in prediction performance for older and smaller apartments. Altogether, our empirical findings contribute to the existing research body by establishing the link between the visual design of floor plans and real estate prices. Moreover, our approach has important implications for online real estate platforms, which can use our findings to enhance user experience in their real estate listings.", "published": "2021-02-16T14:05:33Z", "authors": ["Kirill Solovev", "Nicolas Pröllochs"], "categories": ["cs.LG", "econ.GN", "stat.AP"], "doi": "10.1145/3442381.3449967", "_query": "housing price text listing", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2508.15248v1", "title": "Estimating profitable price bounds for prescriptive price optimization", "abstract": "Pricing of products and services, which has a significant impact on consumer demand, is one of the most important factors in maximizing business profits. Prescriptive price optimization is a prominent data-driven pricing methodology consisting of two phases: demand forecasting and price optimization. In the practice of prescriptive price optimization, the price of each item is typically set within a predetermined range defined by lower and upper bounds. Narrow price ranges can lead to missed opportunities, while wide price ranges run the risk of proposing unrealistic prices; therefore, determining profitable price bounds while maintaining the reliability of the suggested prices is a critical challenge that directly affects the effectiveness of prescriptive price optimization. We propose two methods for estimating price bounds in prescriptive price optimization so that future total revenue derived from the optimized prices will be maximized. Our first method for price bounds estimation uses the bootstrap procedure to estimate confidence intervals for optimal prices. Our second method uses the Nelder--Mead simplex method for black-box price bounds optimization that maximizes total revenue estimated through $K$-fold cross-validation. Experimental results with synthetic price--demand datasets demonstrate that our methods successfully narrowed down the price range while maintaining high revenues, particularly when the number of items was small or the demand noise level was low. Moreover, as more data accumulated, the comparative advantage of our methods further increased.", "published": "2025-08-21T05:19:26Z", "authors": ["Masato Inokuma", "Shunnosuke Ikeda", "Yuichi Takano"], "categories": ["math.OC", "cs.CE"], "doi": "", "_query": "housing price text listing", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2609.29691v1", "title": "Online House Allocation with Subsidy", "abstract": "House allocation is a fundamental problem in which each agent is assigned exactly one house. While the classical model assumes that all houses are available before the allocation is computed, many practical settings require decisions to be made as houses become available over time. We introduce the online house allocation problem, where houses arrive sequentially and the algorithm must maintain an allocation without knowledge of future arrivals. Unlike online fair division, the one-house-per-agent constraint makes recourse an inherent part of the problem, as accepting a newly arrived house may require reassigning previously allocated houses. We study online house allocation under subsidy-based fairness, where monetary subsidies eliminate envy among agents. We show that envy-freeability can always be maintained online using bounded recourse and that reassignment chains of length linear in the number of agents are unavoidable in the worst case. In contrast, minimizing the total subsidy is fundamentally harder: no deterministic online algorithm against an adaptive adversary, and no randomized online algorithm against a non-adaptive adversary, admits a bounded competitive ratio, even for two agents and four houses. We complement these impossibilities by showing that exact online subsidy minimization is possible whenever there is at most one extra house beyond the number of agents, and that this guarantee is best possible with respect to the number of extra houses. Finally, we develop learning-augmented algorithms that recover the offline optimum under accurate predictions while providing explicit robustness guarantees when predictions are inaccurate.", "published": "2026-08-31T01:14:42Z", "authors": ["Nicholas Teh", "Saar Cohen", "Karen Frilya Celine", "Benjamin Yu", "Michael J. Wooldridge"], "categories": ["cs.GT"], "doi": "", "_query": "housing price text listing", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2007.06160v1", "title": "Nested Dirichlet Process For Population Size Estimation From Multi-list Recapture Data", "abstract": "Heterogeneity of response patterns is important in estimating the size of a closed population from multiple recapture data when capture patterns are different over time and location. In this paper, we extend the non-parametric one layer latent class model for multiple recapture data proposed by Manrique-Vallier (2016) to a nested latent class model with the first layer modeling individual heterogeneity and the second layer modeling location-time differences. Location-time groups with similar recording patterns are in the same top layer latent class and individuals within each top layer class are dependent. The nested latent class model incorporates hierarchical heterogeneity into the modeling to estimate population size from multi-list recapture data. This approach leads to more accurate population size estimation and reduced uncertainty. We apply the method to estimating casualties from the Syrian conflict.", "published": "2020-07-13T02:52:19Z", "authors": ["Shuaimin Kang", "Krista Gile", "Megan Price"], "categories": ["stat.AP", "stat.ME"], "doi": "", "_query": "housing price text listing", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2202.06397v1", "title": "Transformer-based Approaches for Legal Text Processing", "abstract": "In this paper, we introduce our approaches using Transformer-based models for different problems of the COLIEE 2021 automatic legal text processing competition. Automated processing of legal documents is a challenging task because of the characteristics of legal documents as well as the limitation of the amount of data. With our detailed experiments, we found that Transformer-based pretrained language models can perform well with automated legal text processing problems with appropriate approaches. We describe in detail the processing steps for each task such as problem formulation, data processing and augmentation, pretraining, finetuning. In addition, we introduce to the community two pretrained models that take advantage of parallel translations in legal domain, NFSP and NMSP. In which, NFSP achieves the state-of-the-art result in Task 5 of the competition. Although the paper focuses on technical reporting, the novelty of its approaches can also be an useful reference in automated legal document processing using Transformer-based models.", "published": "2022-02-13T19:59:15Z", "authors": ["Ha-Thanh Nguyen", "Minh-Phuong Nguyen", "Thi-Hai-Yen Vuong", "Minh-Quan Bui", "Minh-Chau Nguyen", "Tran-Binh Dang", "Vu Tran", "Le-Minh Nguyen", "Ken Satoh"], "categories": ["cs.CL", "cs.AI", "cs.NE"], "doi": "10.1007/s12626-022-00102-2", "_query": "housing price text listing", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2104.10281v1", "title": "Nonlinear Pricing with Misspecified and Arbitrary Perception of the Marginal Price", "abstract": "In the context of nonlinear prices, the empirical evidence suggests that the consumers have cognitive biases represented in a limited understanding of nonlinear price structures, and they respond to some alternative perceptions of the marginal prices. In particular, consumers usually make choices based more on the average than the marginal prices, which can result in a suboptimal behavior. Taking the misspecification in the marginal price as exogenous, this document analyzes how is the optimal quadratic price scheme for a monopolist and the optimal quadratic price scheme that maximizes welfare when there is a continuum of representative consumers with an arbitrary perception of the marginal price. Under simple preferences and costs functional forms and very straightforward hypotheses, the results suggest that the bias in the marginal price doesn't affect the maximum welfare attainable with quadratic price schemes, and it has a negligible effect over the efficiency cost caused by the monopolist, so the misspecification in the marginal price is not relevant for welfare increasing policies. However, almost always the misspecification in the marginal price is beneficial for the monopolist. An interesting result is that under these functional forms, more misspecification in the marginal price is beneficial for both the consumers and the monopolist if the level of bias is low, so not always is socially optima to have educated consumers about the real marginal price. Finally, the document shows that the bias in the marginal price has a negative effect on reduction of aggregate consumption using two-tier increasing tariffs, which are commonly used for reduction of aggregate consumption.", "published": "2021-04-20T23:22:42Z", "authors": ["Diego Alejandro Murillo Taborda"], "categories": ["econ.GN"], "doi": "", "_query": "housing price text listing", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2308.06571v1", "title": "ModelScope Text-to-Video Technical Report", "abstract": "This paper introduces ModelScopeT2V, a text-to-video synthesis model that evolves from a text-to-image synthesis model (i.e., Stable Diffusion). ModelScopeT2V incorporates spatio-temporal blocks to ensure consistent frame generation and smooth movement transitions. The model could adapt to varying frame numbers during training and inference, rendering it suitable for both image-text and video-text datasets. ModelScopeT2V brings together three components (i.e., VQGAN, a text encoder, and a denoising UNet), totally comprising 1.7 billion parameters, in which 0.5 billion parameters are dedicated to temporal capabilities. The model demonstrates superior performance over state-of-the-art methods across three evaluation metrics. The code and an online demo are available at \\url{https://modelscope.cn/models/damo/text-to-video-synthesis/summary}.", "published": "2023-08-12T13:53:10Z", "authors": ["Jiuniu Wang", "Hangjie Yuan", "Dayou Chen", "Yingya Zhang", "Xiang Wang", "Shiwei Zhang"], "categories": ["cs.CV", "cs.AI"], "doi": "", "_query": "housing price text listing", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2604.27398v1", "title": "Why Mean Pooling Works: Quantifying Second-Order Collapse in Text Embeddings", "abstract": "For constructing text embeddings, mean pooling, which averages token embeddings, is the standard approach. This paper examines whether mean pooling actually works well in real models. First, we note that mean pooling can collapse information beyond the first-order statistics of the token embeddings, such as second-order statistics that capture their spatial structure, potentially mapping distinct token embedding distributions to similar text embeddings. Motivated by this concern, we propose a simple metric to quantify such a collapse induced by mean pooling. Then, using this metric, we empirically measure how often this collapse occurs in actual models and texts, and find that modern text encoders are robust to this collapse. In particular, contrastive fine-tuned text encoders tend to be less prone to the collapse than their pretrained backbone models. We also find that the robustness of these text encoders lies in the concentration of token embeddings within each text. In addition, we find that robustness to the collapse, as quantified by our proposed metric, correlates with downstream task performance. Overall, our findings offer a new perspective on why modern text encoders remain effective despite relying on seemingly coarse mean pooling.", "published": "2026-04-30T04:09:26Z", "authors": ["Tomomasa Hara", "Hiroto Kurita", "Masaaki Imaizumi", "Kentaro Inui", "Sho Yokoi"], "categories": ["cs.CL"], "doi": "", "_query": "housing price text listing", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2306.03557v2", "title": "Take the Hint: Improving Arabic Diacritization with Partially-Diacritized Text", "abstract": "Automatic Arabic diacritization is useful in many applications, ranging from reading support for language learners to accurate pronunciation predictor for downstream tasks like speech synthesis. While most of the previous works focused on models that operate on raw non-diacritized text, production systems can gain accuracy by first letting humans partly annotate ambiguous words. In this paper, we propose 2SDiac, a multi-source model that can effectively support optional diacritics in input to inform all predictions. We also introduce Guided Learning, a training scheme to leverage given diacritics in input with different levels of random masking. We show that the provided hints during test affect more output positions than those annotated. Moreover, experiments on two common benchmarks show that our approach i) greatly outperforms the baseline also when evaluated on non-diacritized text; and ii) achieves state-of-the-art results while reducing the parameter count by over 60%.", "published": "2023-06-06T10:18:17Z", "authors": ["Parnia Bahar", "Mattia Di Gangi", "Nick Rossenbach", "Mohammad Zeineldeen"], "categories": ["cs.CL", "cs.AI"], "doi": "", "_query": "housing price text listing", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2202.06238v1", "title": "Multimodal Depression Classification Using Articulatory Coordination Features And Hierarchical Attention Based Text Embeddings", "abstract": "Multimodal depression classification has gained immense popularity over the recent years. We develop a multimodal depression classification system using articulatory coordination features extracted from vocal tract variables and text transcriptions obtained from an automatic speech recognition tool that yields improvements of area under the receiver operating characteristics curve compared to uni-modal classifiers (7.5% and 13.7% for audio and text respectively). We show that in the case of limited training data, a segment-level classifier can first be trained to then obtain a session-wise prediction without hindering the performance, using a multi-stage convolutional recurrent neural network. A text model is trained using a Hierarchical Attention Network (HAN). The multimodal system is developed by combining embeddings from the session-level audio model and the HAN text model", "published": "2022-02-13T07:37:09Z", "authors": ["Nadee Seneviratne", "Carol Espy-Wilson"], "categories": ["eess.AS", "cs.CL"], "doi": "", "_query": "housing price text listing", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/0810.4616v1", "title": "Assembling Actor-based Mind-Maps from Text Stream", "abstract": "For human beings, the processing of text streams of unknown size leads generally to problems because e.g. noise must be selected out, information be tested for its relevance or redundancy, and linguistic phenomenon like ambiguity or the resolution of pronouns be advanced. Putting this into simulation by using an artificial mind-map is a challenge, which offers the gate for a wide field of applications like automatic text summarization or punctual retrieval. In this work we present a framework that is a first step towards an automatic intellect. It aims at assembling a mind-map based on incoming text streams and on a subject-verb-object strategy, having the verb as an interconnection between the adjacent nouns. The mind-map's performance is enriched by a pronoun resolution engine that bases on the work of D. Klein, and C. D. Manning.", "published": "2008-10-25T16:00:08Z", "authors": ["Claudine Brucks", "Christoph Schommer"], "categories": ["cs.CL", "cs.DL"], "doi": "", "_query": "housing price text listing", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2502.11205v1", "title": "Deep Contrastive Learning for Feature Alignment: Insights from Housing-Household Relationship Inference", "abstract": "Housing and household characteristics are key determinants of social and economic well-being, yet our understanding of their interrelationships remains limited. This study addresses this knowledge gap by developing a deep contrastive learning (DCL) model to infer housing-household relationships using the American Community Survey (ACS) Public Use Microdata Sample (PUMS). More broadly, the proposed model is suitable for a class of problems where the goal is to learn joint relationships between two distinct entities without explicitly labeled ground truth data. Our proposed dual-encoder DCL approach leverages co-occurrence patterns in PUMS and introduces a bisect K-means clustering method to overcome the absence of ground truth labels. The dual-encoder DCL architecture is designed to handle the semantic differences between housing (building) and household (people) features while mitigating noise introduced by clustering. To validate the model, we generate a synthetic ground truth dataset and conduct comprehensive evaluations. The model further demonstrates its superior performance in capturing housing-household relationships in Delaware compared to state-of-the-art methods. A transferability test in North Carolina confirms its generalizability across diverse sociodemographic and geographic contexts. Finally, the post-hoc explainable AI analysis using SHAP values reveals that tenure status and mortgage information play a more significant role in housing-household matching than traditionally emphasized factors such as the number of persons and rooms.", "published": "2025-02-16T17:06:06Z", "authors": ["Xiao Qian", "Shangjia Dong", "Rachel Davidson"], "categories": ["cs.LG", "cs.CY"], "doi": "", "_query": "housing price text listing", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2212.04979v3", "title": "VideoCoCa: Video-Text Modeling with Zero-Shot Transfer from Contrastive Captioners", "abstract": "We explore an efficient approach to establish a foundational video-text model. We present VideoCoCa that maximally reuses a pretrained image-text contrastive captioner (CoCa) model and adapt it to video-text tasks with minimal extra training. While previous works adapt image-text models with various cross-frame fusion modules, we find that the generative attentional pooling and contrastive attentional pooling layers in CoCa are instantly adaptable to flattened frame embeddings, yielding state-of-the-art results on zero-shot video classification and zero-shot text-to-video retrieval. Furthermore, we explore lightweight finetuning on top of VideoCoCa, and achieve strong results on video question-answering and video captioning.", "published": "2022-12-09T16:39:09Z", "authors": ["Shen Yan", "Tao Zhu", "Zirui Wang", "Yuan Cao", "Mi Zhang", "Soham Ghosh", "Yonghui Wu", "Jiahui Yu"], "categories": ["cs.CV", "cs.LG", "cs.MM"], "doi": "", "_query": "housing price text listing", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2405.04955v1", "title": "Improving Long Text Understanding with Knowledge Distilled from Summarization Model", "abstract": "Long text understanding is important yet challenging for natural language processing. A long article or document usually contains many redundant words that are not pertinent to its gist and sometimes can be regarded as noise. With recent advances of abstractive summarization, we propose our \\emph{Gist Detector} to leverage the gist detection ability of a summarization model and integrate the extracted gist into downstream models to enhance their long text understanding ability. Specifically, Gist Detector first learns the gist detection knowledge distilled from a summarization model, and then produces gist-aware representations to augment downstream models. We evaluate our method on three different tasks: long document classification, distantly supervised open-domain question answering, and non-parallel text style transfer. The experimental results show that our method can significantly improve the performance of baseline models on all tasks.", "published": "2024-05-08T10:49:39Z", "authors": ["Yan Liu", "Yazheng Yang", "Xiaokang Chen"], "categories": ["cs.CL", "cs.AI"], "doi": "", "_query": "housing price text listing", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2311.11655v2", "title": "Peeking Inside the Schufa Blackbox: Explaining the German Housing Scoring System", "abstract": "Explainable Artificial Intelligence is a concept aimed at making complex algorithms transparent to users through a uniform solution. Researchers have highlighted the importance of integrating domain specific contexts to develop explanations tailored to end users. In this study, we focus on the Schufa housing scoring system in Germany and investigate how users information needs and expectations for explanations vary based on their roles. Using the speculative design approach, we asked business information students to imagine user interfaces that provide housing credit score explanations from the perspectives of both tenants and landlords. Our preliminary findings suggest that although there are general needs that apply to all users, there are also conflicting needs that depend on the practical realities of their roles and how credit scores affect them. We contribute to Human centered XAI research by proposing future research directions that examine users explanatory needs considering their roles and agencies.", "published": "2023-11-20T10:42:31Z", "authors": ["Dean-Robin Kern", "Gunnar Stevens", "Erik Dethier", "Sidra Naveed", "Fatemeh Alizadeh", "Delong Du", "Md Shajalal"], "categories": ["cs.AI", "cs.HC"], "doi": "", "_query": "housing price text listing", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2601.18788v1", "title": "Unsupervised Text Segmentation via Kernel Change-Point Detection on Sentence Embeddings", "abstract": "Unsupervised text segmentation is crucial because boundary labels are expensive, subjective, and often fail to transfer across domains and granularity choices. We propose Embed-KCPD, a training-free method that represents sentences as embedding vectors and estimates boundaries by minimizing a penalized KCPD objective. Beyond the algorithmic instantiation, we develop, to our knowledge, the first dependence-aware theory for KCPD under $m$-dependent sequences, a finite-memory abstraction of short-range dependence common in language. We prove an oracle inequality for the population penalized risk and a localization guarantee showing that each true change point is recovered within a window that is small relative to segment length. To connect theory to practice, we introduce an LLM-based simulation framework that generates synthetic documents with controlled finite-memory dependence and known boundaries, validating the predicted scaling behavior. Across standard segmentation benchmarks, Embed-KCPD often outperforms strong unsupervised baselines. A case study on Taylor Swift's tweets illustrates that Embed-KCPD combines strong theoretical guarantees, simulated reliability, and practical effectiveness for text segmentation.", "published": "2026-01-26T18:54:34Z", "authors": ["Mumin Jia", "Jairo Diaz-Rodriguez"], "categories": ["cs.CL", "cs.LG", "stat.ML"], "doi": "", "_query": "housing price text listing", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2410.19889v1", "title": "Ensembling Finetuned Language Models for Text Classification", "abstract": "Finetuning is a common practice widespread across different communities to adapt pretrained models to particular tasks. Text classification is one of these tasks for which many pretrained models are available. On the other hand, ensembles of neural networks are typically used to boost performance and provide reliable uncertainty estimates. However, ensembling pretrained models for text classification is not a well-studied avenue. In this paper, we present a metadataset with predictions from five large finetuned models on six datasets, and report results of different ensembling strategies from these predictions. Our results shed light on how ensembling can improve the performance of finetuned text classifiers and incentivize future adoption of ensembles in such tasks.", "published": "2024-10-25T09:15:54Z", "authors": ["Sebastian Pineda Arango", "Maciej Janowski", "Lennart Purucker", "Arber Zela", "Frank Hutter", "Josif Grabocka"], "categories": ["cs.CL", "cs.LG"], "doi": "", "_query": "housing price text listing", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/1611.00097v3", "title": "Scientific Literature Text Mining and the Case for Open Access", "abstract": "\"Open access\" has become a central theme of journal reform in academic publishing. In this article, I examine the relationship between open access publishing and an important infrastructural element of a modern research enterprise, scientific literature text mining, or the use of data analytic techniques to conduct meta-analyses and investigations into the scientific corpus. I give a brief history of the open access movement, discuss novel journalistic practices, and an overview of data-driven investigation of the scientific corpus. I argue that particularly in an era where the veracity of many research studies has been called into question, scientific literature text mining should be one of the key motivations for open access publishing, not only in the basic sciences, but in the engineering and applied sciences as well. The enormous benefits of unrestricted access to the research literature should prompt scholars from all disciplines to lend their vocal support to enabling legal, wholesale access to the scientific literature as part of a data science pipeline.", "published": "2016-11-01T01:15:53Z", "authors": ["Gopal P. Sarma"], "categories": ["cs.CY", "cs.DL", "physics.soc-ph"], "doi": "10.21428/14888", "_query": "housing price text listing", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2006.16805v4", "title": "Pricing Fresh Data", "abstract": "We introduce the concept of {\\it fresh data trading}, in which a destination user requests, and pays for, fresh data updates from a source provider, and data freshness is captured by the {\\it age of information} (AoI) metric. Keeping data fresh relies on frequent data updates by the source, which motivates the source to {\\it price fresh data}. In this work, the destination incurs an age-related cost, modeled as a general increasing function of the AoI. The source designs a pricing mechanism to maximize its profit; the destination chooses a data update schedule to trade off its payments to the source and its age-related cost. Depending on different real-time applications and scenarios, we study both a predictable-deadline and an unpredictable-deadline models. The key challenge of designing the optimal pricing scheme lies in the destination's time-interdependent valuations, due to the nature of AoI and the infinite-dimensional and dynamic optimization. To this end, we consider three pricing schemes that exploit and understand the profitability of three different dimensions in designing pricing: a {\\it time-dependent} pricing scheme, in which the price for each update depends on when it is requested; a {\\it quantity-based} pricing scheme, in which the price of each update depends on how many updates have been previously requested; a {\\it subscription-based} pricing scheme, in which the price for each update is flat-rate but the source charges an additional subscription fee. Our analysis reveals that the optimal subscription-based pricing maximizes the source's profit among all possible pricing schemes under both predictable deadline and unpredictable deadline models; the optimal quantity-based pricing scheme is only optimal with a predictable deadline; the time-dependent pricing scheme, under the unpredictable deadline, is asymptotically optimal under significant time discounting.", "published": "2020-06-28T23:51:59Z", "authors": ["Meng Zhang", "Ahmed Arafa", "Jianwei Huang", "H. Vincent Poor"], "categories": ["cs.NI", "cs.IT"], "doi": "", "_query": "housing price text listing", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2406.09744v2", "title": "The Cost and Complexity of Minimizing Envy in House Allocation", "abstract": "We study almost-envy-freeness in house allocation, where $m$ houses are to be allocated among $n$ agents so that every agent receives exactly one house. An envy-free allocation need not exist, and therefore we may have to settle for relaxations of envy-freeness. But typical relaxations such as envy-free up to one good do not make sense for house allocation, as every agent is required to receive exactly one house. Hence we turn to different aggregate measures of envy as markers of fairness. In particular, we define the amount of envy experienced by an agent $a$ w.r.t. an allocation to be the number of agents that agent $a$ envies under that allocation. We quantify the envy generated by an allocation using three different metrics: 1) the number of agents who are envious; 2) the maximum amount of envy experienced by any agent; and 3) the total amount of envy experienced by all agents, and look for allocations that minimize one of the three metrics. We thus study three computational problems corresponding to each of the three metrics and prove a host of algorithmic and hardness results. We also suggest practical approaches for these problems via integer linear program (ILP) formulations and report the findings of our experimental evaluation of ILPs. Finally, we study the price of fairness (PoF), which quantifies the loss of welfare we must suffer due to the fairness requirements, and we prove a number of results on PoF, including tight bounds as well as algorithms that simultaneously optimize both welfare and fairness.", "published": "2024-06-14T06:23:59Z", "authors": ["Jayakrishnan Madathil", "Neeldhara Misra", "Aditi Sethia"], "categories": ["cs.GT"], "doi": "", "_query": "housing price text listing", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2506.16264v1", "title": "Pricing under the Benchmark Approach", "abstract": "The paper summarizes key results of the benchmark approach with a focus on the concept of benchmark-neutral pricing. It applies these results to the pricing of an extreme-maturity European put option on a well-diversified stock index. The growth optimal portfolio of the stocks is approximated by a well-diversified stock portfolio and modeled by a drifted time-transformed squared Bessel process of dimension four. It is shown that the benchmark-neutral price of a European put option is theoretically the minimal possible price and the respective risk-neutral put price turns out to be significantly more expensive.", "published": "2025-06-19T12:26:40Z", "authors": ["Eckhard Platen"], "categories": ["q-fin.MF", "q-fin.GN"], "doi": "", "_query": "housing price text listing", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2112.06430v1", "title": "Predicting Airbnb Rental Prices Using Multiple Feature Modalities", "abstract": "Figuring out the price of a listed Airbnb rental is an important and difficult task for both the host and the customer. For the former, it can enable them to set a reasonable price without compromising on their profits. For the customer, it helps understand the key drivers for price and also provides them with similarly priced places. This price prediction regression task can also have multiple downstream uses, such as in recommendation of similar rentals based on price. We propose to use geolocation, temporal, visual and natural language features to create a reliable and accurate price prediction algorithm.", "published": "2021-12-13T05:57:05Z", "authors": ["Aditya Ahuja", "Aditya Lahiri", "Aniruddha Das"], "categories": ["cs.LG"], "doi": "", "_query": "housing price text listing", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2002.00171v1", "title": "Novel Language Resources for Hindi: An Aesthetics Text Corpus and a Comprehensive Stop Lemma List", "abstract": "This paper is an effort to complement the contributions made by researchers working toward the inclusion of non-English languages in natural language processing studies. Two novel Hindi language resources have been created and released for public consumption. The first resource is a corpus consisting of nearly thousand pre-processed fictional and nonfictional texts spanning over hundred years. The second resource is an exhaustive list of stop lemmas created from 12 corpora across multiple domains, consisting of over 13 million words, from which more than 200,000 lemmas were generated, and 11 publicly available stop word lists comprising over 1000 words, from which nearly 400 unique lemmas were generated. This research lays emphasis on the use of stop lemmas instead of stop words owing to the presence of various, but not all morphological forms of a word in stop word lists, as opposed to the presence of only the root form of the word, from which variations could be derived if required. It was also observed that stop lemmas were more consistent across multiple sources as compared to stop words. In order to generate a stop lemma list, the parts of speech of the lemmas were investigated but rejected as it was found that there was no significant correlation between the rank of a word in the frequency list and its part of speech. The stop lemma list was assessed using a comparative method. A formal evaluation method is suggested as future work arising from this study.", "published": "2020-02-01T08:49:17Z", "authors": ["Gayatri Venugopal-Wairagade", "Jatinderkumar R. Saini", "Dhanya Pramod"], "categories": ["cs.CL"], "doi": "10.14569/IJACSA.2020.0110130", "_query": "housing price text listing", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2303.07909v3", "title": "Text-to-image Diffusion Models in Generative AI: A Survey", "abstract": "This survey reviews the progress of diffusion models in generating images from text, ~\\textit{i.e.} text-to-image diffusion models. As a self-contained work, this survey starts with a brief introduction of how diffusion models work for image synthesis, followed by the background for text-conditioned image synthesis. Based on that, we present an organized review of pioneering methods and their improvements on text-to-image generation. We further summarize applications beyond image generation, such as text-guided generation for various modalities like videos, and text-guided image editing. Beyond the progress made so far, we discuss existing challenges and promising future directions.", "published": "2023-03-14T13:49:54Z", "authors": ["Chenshuang Zhang", "Chaoning Zhang", "Mengchun Zhang", "In So Kweon", "Junmo Kim"], "categories": ["cs.CV", "cs.AI", "cs.LG"], "doi": "", "_query": "housing price text listing", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/1911.12598v1", "title": "Online Pricing with Reserve Price Constraint for Personal Data Markets", "abstract": "The society's insatiable appetites for personal data are driving the emergency of data markets, allowing data consumers to launch customized queries over the datasets collected by a data broker from data owners. In this paper, we study how the data broker can maximize her cumulative revenue by posting reasonable prices for sequential queries. We thus propose a contextual dynamic pricing mechanism with the reserve price constraint, which features the properties of ellipsoid for efficient online optimization, and can support linear and non-linear market value models with uncertainty. In particular, under low uncertainty, our pricing mechanism provides a worst-case regret logarithmic in the number of queries. We further extend to other similar application scenarios, including hospitality service, online advertising, and loan application, and extensively evaluate three pricing instances of noisy linear query, accommodation rental, and impression over MovieLens 20M dataset, Airbnb listings in U.S. major cities, and Avazu mobile ad click dataset, respectively. The analysis and evaluation results reveal that our proposed pricing mechanism incurs low practical regret, online latency, and memory overhead, and also demonstrate that the existence of reserve price can mitigate the cold-start problem in a posted price mechanism, and thus can reduce the cumulative regret.", "published": "2019-11-28T08:55:47Z", "authors": ["Chaoyue Niu", "Zhenzhe Zheng", "Fan Wu", "Shaojie Tang", "Guihai Chen"], "categories": ["cs.CE", "cs.GT", "cs.LG"], "doi": "", "_query": "housing price text listing", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2411.17724v1", "title": "Incentives to Build Houses, Trade Houses, or Trade House Building Skills in Simulated Worlds under Various Governing Systems or Institutions: Comparing Multi-agent Reinforcement Learning to Generative Agent-based Model", "abstract": "It has been shown that social institutions impact human motivations to produce different behaviours, such as amount of working or specialisation in labor. With advancement in artificial intelligence (AI), specifically large language models (LLMs), now it is possible to perform in-silico simulations to test various hypotheses around this topic. Here, I simulate two somewhat similar worlds using multi-agent reinforcement learning (MARL) framework of the AI-Economist and generative agent-based model (GABM) framework of the Concordia. In the extended versions of the AI-Economist and Concordia, the agents are able to build houses, trade houses, and trade house building skill. Moreover, along the individualistic-collectivists axis, there are a set of three governing systems: Full-Libertarian, Semi-Libertarian/Utilitarian, and Full-Utilitarian. Additionally, in the extended AI-Economist, the Semi-Libertarian/Utilitarian system is further divided to a set of three governing institutions along the discriminative axis: Inclusive, Arbitrary, and Extractive. Building on these, I am able to show that among governing systems and institutions of the extended AI-Economist, under the Semi-Libertarian/Utilitarian and Inclusive government, the ratios of building houses to trading houses and trading house building skill are higher than the rest. Furthermore, I am able to show that in the extended Concordia when the central government care about equality in the society, the Full-Utilitarian system generates agents building more houses and trading more house building skill. In contrast, these economic activities are higher under the Full-Libertarian system when the central government cares about productivity in the society. Overall, the focus of this paper is to compare and contrast two advanced techniques of AI, MARL and GABM, to simulate a similar social phenomena with limitations.", "published": "2024-11-21T08:52:42Z", "authors": ["Aslan S. Dizaji"], "categories": ["cs.MA"], "doi": "", "_query": "housing price text listing", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2310.12341v1", "title": "Price dispersion across online platforms: Evidence from hotel room prices in London (UK)", "abstract": "This paper studies the widespread price dispersion of homogeneous products across different online platforms, even when consumers can easily access price information from comparison websites. We collect data for the 200 most popular hotels in London (UK) and document that prices vary widely across booking sites while making reservations for a hotel room. Additionally, we find that prices listed across different platforms tend to converge as the booking date gets closer to the date of stay. However, the price dispersion persists until the date of stay, implying that the \"law of one price\" does not hold. We present a simple theoretical model to explain this and show that in the presence of aggregate demand uncertainty and capacity constraints, price dispersion could exist even when products are homogeneous, consumers are homogeneous, all agents have perfect information about the market structure, and consumers face no search costs to acquire information about the products. Our theoretical intuition and robust empirical evidence provide additional insights into price dispersion across online platforms in different institutional settings. Our study complements the existing literature that relies on consumer search costs to explain the price dispersion phenomenon.", "published": "2023-10-18T21:40:08Z", "authors": ["Debashrita Mohapatra", "Debi Prasad Mohapatra", "Ram Sewak Dubey"], "categories": ["econ.GN"], "doi": "", "_query": "housing price text listing", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2305.06475v1", "title": "A Model for Translation of Text from Indian Languages to Bharti Braille Characters", "abstract": "People who are visually impaired face a lot of difficulties while studying. One of the major causes to this is lack of available text in Bharti Braille script. In this paper, we have suggested a scheme to convert text in major Indian languages into Bharti Braille. The system uses a hybrid approach where at first the text in Indian language is given to a rule based system and in case if there is any ambiguity then it is resolved by applying a LSTM based model. The developed model has also been tested and found to have produced near accurate results.", "published": "2023-05-05T09:21:13Z", "authors": ["Nisheeth Joshi", "Pragya Katyayan"], "categories": ["cs.CL", "cs.AI"], "doi": "10.1109/ISCON57294.2023.10112021", "_query": "housing price text listing", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/1901.01774v1", "title": "Location-Centered House Price Prediction: A Multi-Task Learning Approach", "abstract": "Accurate house prediction is of great significance to various real estate stakeholders such as house owners, buyers, investors, and agents. We propose a location-centered prediction framework that differs from existing work in terms of data profiling and prediction model. Regarding data profiling, we define and capture a fine-grained location profile powered by a diverse range of location data sources, such as transportation profile (e.g., distance to nearest train station), education profile (e.g., school zones and ranking), suburb profile based on census data, facility profile (e.g., nearby hospitals, supermarkets). Regarding the choice of prediction model, we observe that a variety of approaches either consider the entire house data for modeling, or split the entire data and model each partition independently. However, such modeling ignores the relatedness between partitions, and for all prediction scenarios, there may not be sufficient training samples per partition for the latter approach. We address this problem by conducting a careful study of exploiting the Multi-Task Learning (MTL) model. Specifically, we map the strategies for splitting the entire house data to the ways the tasks are defined in MTL, and each partition obtained is aligned with a task. Furthermore, we select specific MTL-based methods with different regularization terms to capture and exploit the relatedness between tasks. Based on real-world house transaction data collected in Melbourne, Australia. We design extensive experimental evaluations, and the results indicate a significant superiority of MTL-based methods over state-of-the-art approaches. Meanwhile, we conduct an in-depth analysis on the impact of task definitions and method selections in MTL on the prediction performance, and demonstrate that the impact of task definitions on prediction performance far exceeds that of method selections.", "published": "2019-01-07T12:36:57Z", "authors": ["Guangliang Gao", "Zhifeng Bao", "Jie Cao", "A. K. Qin", "Timos Sellis", "Zhiang Wu"], "categories": ["cs.LG", "stat.ML"], "doi": "", "_query": "housing price text listing", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2306.17309v1", "title": "Retail Pricing Format and Rigidity of Regular Prices", "abstract": "We study the price rigidity of regular and sale prices, and how it is affected by pricing formats (pricing strategies). We use data from three large Canadian stores with different pricing formats (Every-Day-Low-Price, Hi-Lo, and Hybrid) that are located within a 1 km radius of each other. Our data contains both the actual transaction prices and actual regular prices as displayed on the store shelves. We combine these data with two generated regular price series (filtered prices and reference prices) and study their rigidity. Regular price rigidity varies with store formats because different format stores treat sale prices differently, and consequently define regular prices differently. Correspondingly, the meanings of price cuts and sale prices vary across store formats. To interpret the findings, we consider the store pricing format distribution across the US.", "published": "2023-06-29T21:35:32Z", "authors": ["Sourav Ray", "Avichai Snir", "Daniel Levy"], "categories": ["econ.GN"], "doi": "", "_query": "housing price text listing", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2212.12843v1", "title": "A Note on Improved Results for One Round Distributed Clique Listing", "abstract": "In this note, we investigate listing cliques of arbitrary sizes in bandwidth-limited, dynamic networks. The problem of detecting and listing triangles and cliques was originally studied in great detail by Bonne and Censor-Hillel (ICALP 2019). We extend this study to dynamic graphs where more than one update may occur as well as resolve an open question posed by Bonne and Censor-Hillel (2019). Our algorithms and results are based on some simple observations about listing triangles under various settings and we show that we can list larger cliques using such facts. Specifically, we show that our techniques can be used to solve an open problem posed in the original paper: we show that detecting and listing cliques (of any size) can be done using $O(1)$-bandwidth after one round of communication under node insertions and node/edge deletions. We conclude with an extension of our techniques to obtain a small bandwidth $1$-round algorithm for listing cliques when more than one node insertion/deletion and/or edge deletion update occurs at any time.", "published": "2022-12-25T02:58:39Z", "authors": ["Quanquan C. Liu"], "categories": ["cs.DS"], "doi": "", "_query": "housing price text listing", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/1809.05352v1", "title": "Put a Ring on It: Text Entry Performance on a Grip Ring Attached Smartphone", "abstract": "This paper presents results of a study investing effects of grip rings on text entry. Results revealed that grip rings do not affect text entry performance in terms of speed, accuracy, or keystrokes per character. It then reflects on future research directions based on the results and observations from the study. The purpose of this work is to stress the necessity of classifying and evaluating low-cost mobile phone accessories.", "published": "2018-09-14T11:05:21Z", "authors": ["Monwen Shen", "Gulnar Rakhmetulla", "Ahmed Sabbir Arif"], "categories": ["cs.HC"], "doi": "", "_query": "housing price text listing", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2211.10022v1", "title": "Listing 4-Cycles", "abstract": "In this note we present an algorithm that lists all $4$-cycles in a graph in time $\\tilde{O}(\\min(n^2,m^{4/3})+t)$ where $t$ is their number. Notably, this separates $4$-cycle listing from triangle-listing, since the latter has a $(\\min(n^3,m^{3/2})+t)^{1-o(1)}$ lower bound under the $3$-SUM Conjecture. Our upper bound is conditionally tight because (1) $O(n^2,m^{4/3})$ is the best known bound for detecting if the graph has any $4$-cycle, and (2) it matches a recent $(\\min(n^3,m^{3/2})+t)^{1-o(1)}$ $3$-SUM lower bound for enumeration algorithms. The latter lower bound was proved very recently by Abboud, Bringmann, and Fischer [arXiv, 2022] and independently by Jin and Xu [arXiv, 2022]. In an independent work, Jin and Xu [arXiv, 2022] also present an algorithm with the same time bound.", "published": "2022-11-18T04:09:20Z", "authors": ["Amir Abboud", "Seri Khoury", "Oree Leibowitz", "Ron Safier"], "categories": ["cs.DS"], "doi": "", "_query": "housing price text listing", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2507.09208v1", "title": "The calibration house in JUNO", "abstract": "As an auxiliary system within the calibration system of the Jiangmen Underground Neutrino Observatory, a calibration house is designed to provide interfaces for connecting the central detector and accommodating various calibration sub-systems. Onsite installation has demonstrated that the calibration house interfaces are capable of effectively connecting to the central detector and supporting the installation of complex and sophisticated calibration sub-systems. Additionally, controlling the levels of radon and oxygen within the calibration house is critical. Radon can increase the experimental background, while oxygen can degrade the quality of the liquid scintillator. The oxygen concentration can be maintained at levels below 10 parts per million, and the radon concentration can be kept below 15 mBq/m$^{3}$. This paper will provide detailed information on the calibration house and its methods for radon and oxygen concentration control.", "published": "2025-07-12T09:02:44Z", "authors": ["J. Hui", "R. Li", "Y. Wu", "T. Zhang", "Z. Chen", "A. Freegard", "J. Huang", "H. Lai", "Y. Liao", "J. Liu", "Y. Meng", "A. Takenaka", "Z. Xiang", "P. Zhang", "Y. Zhang"], "categories": ["physics.ins-det", "hep-ex"], "doi": "", "_query": "housing price text listing", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2310.15721v2", "title": "An interpolation between special linear and general algebraic cobordism $\\text{MSL}$ and $\\text{MGL}$", "abstract": "Conner and Floyd determined the torsion in the special unitary bordism $\\text{MSU}$ back in the late 1960s. One of the ingredients of their work was an interpolation between $\\text{MSU}$ and unitary bordism $\\text{MU}$. In this work, we prove an exactly similar relation between the special, and general linear algebraic cobordism $\\text{MSL}$, and $\\text{MGL}$ in the stable motivic homotopy category over any Noetherian base scheme of finite Krull dimension. This gives a filtration of $\\text{MGL}$ in terms of $\\text{MSL}$, and the skeletal filtration of $\\mathbb{P}^{\\infty}$. Using that, we compute the first slice of $\\text{MSL}$ over characteristic zero fields. We also compute the first line of the stable homotopy groups of $\\text{MSL}$ over fields of characteristic zero.", "published": "2023-10-24T10:54:50Z", "authors": ["Ahina Nandy"], "categories": ["math.AT", "math.AG"], "doi": "", "_query": "housing price text listing", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/1203.5298v2", "title": "Dynamical fluctuations in a simple housing market model", "abstract": "We consider a simple stochastic model of a urban rental housing market, in which the interaction of tenants and landlords induces rent fluctuations. We simulate the model numerically and measure the equilibrium rent distribution, which is found to be close to a lognormal law. We also study the influence of the density of agents (or equivalently, the vacancy rate) on the rent distribution. A simplified version of the model, amenable to analytical treatment, is studied and leads to a lognormal distribution of rents. The predicted equilibrium value agrees quantitatively with numerical simulations, while a qualitative agreement is obtained for the standard deviation. The connection with non-equilibrium statistical physics models like ratchets is also emphasized.", "published": "2012-03-23T17:07:58Z", "authors": ["Rémi Lemoy", "Eric Bertin"], "categories": ["q-fin.GN", "cond-mat.stat-mech", "physics.soc-ph"], "doi": "10.1088/1742-5468/2012/12/P12007", "_query": "housing price text listing", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2309.09188v2", "title": "Simon Conjecture and the $\\text{v}$-number of monomial ideals", "abstract": "Let $I\\subset S$ be a graded ideal of a standard graded polynomial ring $S$ with coefficients in a field $K$, and let $\\text{v}(I)$ be the $\\text{v}$-number of $I$. In previous work, we showed that for any graded ideal $I\\subset S$ generated in a single degree, then $\\text{v}(I^k)=α(I)k+b$, for all $k\\gg0$, where $α(I)$ is the initial degree of $I$ and $b$ is a suitable integer. In the present paper, using polarization, we extend Simon conjecture to any monomial ideal. As a consequence, if Simon conjecture holds, and all powers of $I$ have linear quotients, then $b\\in\\{-1,0\\}$. This fact suggest that if $I$ is an equigenerated monomial ideal with linear powers, then $\\text{v}(I^k)=α(I)k-1$, for all $k\\ge1$. We verify this conjecture for monomial ideals with linear powers having $\\text{depth}S/I=0$, edge ideals with linear resolution, polymatroidal ideals, and Hibi ideals.", "published": "2023-09-17T07:26:13Z", "authors": ["Antonino Ficarra"], "categories": ["math.AC", "math.CO"], "doi": "", "_query": "housing price text listing", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2410.09566v2", "title": "Bridging Text and Image for Artist Style Transfer via Contrastive Learning", "abstract": "Image style transfer has attracted widespread attention in the past few years. Despite its remarkable results, it requires additional style images available as references, making it less flexible and inconvenient. Using text is the most natural way to describe the style. More importantly, text can describe implicit abstract styles, like styles of specific artists or art movements. In this paper, we propose a Contrastive Learning for Artistic Style Transfer (CLAST) that leverages advanced image-text encoders to control arbitrary style transfer. We introduce a supervised contrastive training strategy to effectively extract style descriptions from the image-text model (i.e., CLIP), which aligns stylization with the text description. To this end, we also propose a novel and efficient adaLN based state space models that explore style-content fusion. Finally, we achieve a text-driven image style transfer. Extensive experiments demonstrate that our approach outperforms the state-of-the-art methods in artistic style transfer. More importantly, it does not require online fine-tuning and can render a 512x512 image in 0.03s.", "published": "2024-10-12T15:27:57Z", "authors": ["Zhi-Song Liu", "Li-Wen Wang", "Jun Xiao", "Vicky Kalogeiton"], "categories": ["cs.CV", "cs.HC"], "doi": "", "_query": "housing price text listing", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2305.00044v4", "title": "Hedonic Prices and Quality Adjusted Price Indices Powered by AI", "abstract": "We develop empirical models that efficiently process large amounts of unstructured product data (text, images, prices, quantities) to produce accurate hedonic price estimates and derived indices. To achieve this, we generate abstract product attributes (or ``features'') from descriptions and images using deep neural networks. These attributes are then used to estimate the hedonic price function. To demonstrate the effectiveness of this approach, we apply the models to Amazon's data for first-party apparel sales, and estimate hedonic prices. The resulting models have a very high out-of-sample predictive accuracy, with $R^2$ ranging from $80\\%$ to $90\\%$. Finally, we construct the AI-based hedonic Fisher price index, chained at the year-over-year frequency, and contrast it with the CPI and other electronic indices.", "published": "2023-04-28T18:37:59Z", "authors": ["Patrick Bajari", "Zhihao Cen", "Victor Chernozhukov", "Manoj Manukonda", "Suhas Vijaykumar", "Jin Wang", "Ramon Huerta", "Junbo Li", "Ling Leng", "George Monokroussos", "Shan Wang"], "categories": ["econ.GN", "cs.LG"], "doi": "10.1016/j.jeconom.2025.106052", "_query": "housing price text listing", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/1912.05164v3", "title": "Third-Degree Price Discrimination Versus Uniform Pricing", "abstract": "We compare the profit of the optimal third-degree price discrimination policy against a uniform pricing policy. A uniform pricing policy offers the same price to all segments of the market. Our main result establishes that for a broad class of third-degree price discrimination problems with concave profit functions (in the price space) and common support, a uniform price is guaranteed to achieve one half of the optimal monopoly profits. This profit bound holds for any number of segments and prices that the seller might use under third-degree price discrimination. We establish that these conditions are tight and that weakening either common support or concavity can lead to arbitrarily poor profit comparisons even for regular or monotone hazard rate distributions.", "published": "2019-12-11T08:15:49Z", "authors": ["Dirk Bergemann", "Francisco Castro", "Gabriel Weintraub"], "categories": ["econ.GN", "cs.GT", "econ.TH"], "doi": "", "_query": "housing price text listing", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/1503.05139v1", "title": "Pricing of Warrants with Stock Price Dependent Threshold Conditions", "abstract": "Warrants with stock price dependent threshold conditions give the right to buy specially issued stocks, if the performance of the stock price satisfies some requirements. Existence of these derivatives changes the price process of the underlying. We show that in the presence of such warrants one cannot assume that the stock market is arbitrage free and that the stock is tradeable at every time moment with the same price for buying and selling. This means that the usual methods for deriving fair prices for such warrants cannot be used. We start from a simple model for the firm's value process and discuss some ways to specify a related model for the stock price process in the presence of warrants with threshold conditions. We also discuss how indifference pricing approach can be used for pricing such warrants.", "published": "2015-03-17T17:53:01Z", "authors": ["Ander Olvik", "Raul Kangro"], "categories": ["q-fin.PR"], "doi": "", "_query": "housing price text listing", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2407.01157v1", "title": "Unaligning Everything: Or Aligning Any Text to Any Image in Multimodal Models", "abstract": "Utilizing a shared embedding space, emerging multimodal models exhibit unprecedented zero-shot capabilities. However, the shared embedding space could lead to new vulnerabilities if different modalities can be misaligned. In this paper, we extend and utilize a recently developed effective gradient-based procedure that allows us to match the embedding of a given text by minimally modifying an image. Using the procedure, we show that we can align the embeddings of distinguishable texts to any image through unnoticeable adversarial attacks in joint image-text models, revealing that semantically unrelated images can have embeddings of identical texts and at the same time visually indistinguishable images can be matched to the embeddings of very different texts. Our technique achieves 100\\% success rate when it is applied to text datasets and images from multiple sources. Without overcoming the vulnerability, multimodal models cannot robustly align inputs from different modalities in a semantically meaningful way. \\textbf{Warning: the text data used in this paper are toxic in nature and may be offensive to some readers.}", "published": "2024-07-01T10:25:47Z", "authors": ["Shaeke Salman", "Md Montasir Bin Shams", "Xiuwen Liu"], "categories": ["cs.CV", "cs.AI", "cs.CL", "cs.LG"], "doi": "", "_query": "housing price text listing", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/1707.05489v3", "title": "Vision-based Real Estate Price Estimation", "abstract": "Since the advent of online real estate database companies like Zillow, Trulia and Redfin, the problem of automatic estimation of market values for houses has received considerable attention. Several real estate websites provide such estimates using a proprietary formula. Although these estimates are often close to the actual sale prices, in some cases they are highly inaccurate. One of the key factors that affects the value of a house is its interior and exterior appearance, which is not considered in calculating automatic value estimates. In this paper, we evaluate the impact of visual characteristics of a house on its market value. Using deep convolutional neural networks on a large dataset of photos of home interiors and exteriors, we develop a method for estimating the luxury level of real estate photos. We also develop a novel framework for automated value assessment using the above photos in addition to home characteristics including size, offered price and number of bedrooms. Finally, by applying our proposed method for price estimation to a new dataset of real estate photos and metadata, we show that it outperforms Zillow's estimates.", "published": "2017-07-18T06:29:02Z", "authors": ["Omid Poursaeed", "Tomas Matera", "Serge Belongie"], "categories": ["cs.CV", "cs.LG"], "doi": "10.1007/s00138-018-0922-2", "_query": "housing price text listing", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/1204.3549v2", "title": "House of Graphs: a database of interesting graphs", "abstract": "In this note we present House of Graphs (http://hog.grinvin.org) which is a new database of graphs. The key principle is to have a searchable database and offer -- next to complete lists of some graph classes -- also a list of special graphs that already turned out to be interesting and relevant in the study of graph theoretic problems or as counterexamples to conjectures. This list can be extended by users of the database.", "published": "2012-04-16T16:06:47Z", "authors": ["Gunnar Brinkmann", "Kris Coolsaet", "Jan Goedgebeur", "Hadrien Melot"], "categories": ["math.CO", "cs.DM"], "doi": "10.1016/j.dam.2012.07.018", "_query": "housing price text listing", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2405.14909v2", "title": "Interpretable Price Bounds Estimation with Shape Constraints in Price Optimization", "abstract": "This study addresses the interpretable estimation of price bounds in the context of price optimization. In recent years, price-optimization methods have become indispensable for maximizing revenue and profits. However, effective application of these methods to real-world pricing operations remains a significant challenge. It is crucial for operators responsible for setting prices to utilize reasonable price bounds that are not only interpretable but also acceptable. Despite this necessity, most studies assume that price bounds are given constant values, and few have explored reasonable determinations of these bounds. Therefore, we propose a comprehensive framework for determining price bounds that includes both the estimation and adjustment of these bounds. Specifically, we first estimate price bounds using three distinct approaches based on historical pricing data. Then, we adjust the estimated price bounds by solving an optimization problem that incorporates shape constraints. This method allows the implementation of price optimization under practical and reasonable price bounds suitable for real-world applications. We report the effectiveness of our proposed method through numerical experiments using historical pricing data from actual services.", "published": "2024-05-23T10:30:16Z", "authors": ["Shunnosuke Ikeda", "Naoki Nishimura", "Shunji Umetani"], "categories": ["cs.GT", "cs.AI", "math.OC"], "doi": "", "_query": "housing price text listing", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/1802.04774v2", "title": "Asset Price Volatility and Price Extrema", "abstract": "The relationship between price volatilty and a market extremum is examined using a fundamental economics model of supply and demand. By examining randomness through a microeconomic setting, we obtain the implications of randomness in the supply and demand, rather than assuming that price has randomness on an empirical basis. Within a very general setting the volatility has an extremum that precedes the extremum of the price. A key issue is that randomness arises from the supply and demand, and the variance in the stochastic differential equation govening the logarithm of price must reflect this. Analogous results are obtained by further assuming that the supply and demand are dependent on the deviation from fundamental value of the asset.", "published": "2018-02-13T18:07:43Z", "authors": ["Carey Caginalp", "Gunduz Caginalp"], "categories": ["q-fin.MF", "math.PR", "math.ST"], "doi": "", "_query": "housing price text listing", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2507.21340v1", "title": "StructText: A Synthetic Table-to-Text Approach for Benchmark Generation with Multi-Dimensional Evaluation", "abstract": "Extracting structured information from text, such as key-value pairs that could augment tabular data, is quite useful in many enterprise use cases. Although large language models (LLMs) have enabled numerous automated pipelines for converting natural language into structured formats, there is still a lack of benchmarks for evaluating their extraction quality, especially in specific domains or focused documents specific to a given organization. Building such benchmarks by manual annotations is labour-intensive and limits the size and scalability of the benchmarks. In this work, we present StructText, an end-to-end framework for automatically generating high-fidelity benchmarks for key-value extraction from text using existing tabular data. It uses available tabular data as structured ground truth, and follows a two-stage ``plan-then-execute'' pipeline to synthetically generate corresponding natural-language text. To ensure alignment between text and structured source, we introduce a multi-dimensional evaluation strategy that combines (a) LLM-based judgments on factuality, hallucination, and coherence and (b) objective extraction metrics measuring numeric and temporal accuracy. We evaluated the proposed method on 71,539 examples across 49 datasets. Results reveal that while LLMs achieve strong factual accuracy and avoid hallucination, they struggle with narrative coherence in producing extractable text. Notably, models presume numerical and temporal information with high fidelity yet this information becomes embedded in narratives that resist automated extraction. We release a framework, including datasets, evaluation tools, and baseline extraction systems, to support continued research.", "published": "2025-07-28T21:20:44Z", "authors": ["Satyananda Kashyap", "Sola Shirai", "Nandana Mihindukulasooriya", "Horst Samulowitz"], "categories": ["cs.CL", "cs.AI", "cs.DB", "cs.IR"], "doi": "", "_query": "housing price text listing", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/1205.2766v2", "title": "Optimal Listing of Cycles and st-Paths in Undirected Graphs", "abstract": "We present the first optimal algorithm for the classical problem of listing all the cycles in an undirected graph. We exploit their properties so that the total cost is the time taken to read the input graph plus the time to list the output, namely, the edges in each of the cycles. The algorithm uses a reduction to the problem of listing all the paths from a vertex s to a vertex t which we also solve optimally.", "published": "2012-05-12T11:12:10Z", "authors": ["Rui Ferreira", "Roberto Grossi", "Andrea Marino", "Nadia Pisanti", "Romeo Rizzi", "Gustavo Sacomoto"], "categories": ["cs.DS"], "doi": "", "_query": "housing price text listing", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/1810.00681v1", "title": "Learning Robust, Transferable Sentence Representations for Text Classification", "abstract": "Despite deep recurrent neural networks (RNNs) demonstrate strong performance in text classification, training RNN models are often expensive and requires an extensive collection of annotated data which may not be available. To overcome the data limitation issue, existing approaches leverage either pre-trained word embedding or sentence representation to lift the burden of training RNNs from scratch. In this paper, we show that jointly learning sentence representations from multiple text classification tasks and combining them with pre-trained word-level and sentence level encoders result in robust sentence representations that are useful for transfer learning. Extensive experiments and analyses using a wide range of transfer and linguistic tasks endorse the effectiveness of our approach.", "published": "2018-09-28T05:40:20Z", "authors": ["Wasi Uddin Ahmad", "Xueying Bai", "Nanyun Peng", "Kai-Wei Chang"], "categories": ["cs.CL"], "doi": "", "_query": "housing price text listing", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2212.11685v2", "title": "Text Generation with Diffusion Language Models: A Pre-training Approach with Continuous Paragraph Denoise", "abstract": "In this paper, we introduce a novel dIffusion language modEl pre-training framework for text generation, which we call GENIE. GENIE is a large-scale pretrained diffusion language model that consists of an encoder and a diffusion-based decoder, which can generate text by gradually transforming a random noise sequence into a coherent text sequence. To pre-train GENIE on a large-scale language corpus, we design a new continuous paragraph denoise objective, which encourages the diffusion-decoder to reconstruct a clean text paragraph from a corrupted version, while preserving the semantic and syntactic coherence. We evaluate GENIE on four downstream text generation benchmarks, namely XSum, CNN/DailyMail, Gigaword, and CommonGen. Our experimental results show that GENIE achieves comparable performance with the state-of-the-art autoregressive models on these benchmarks, and generates more diverse text samples. The code and models of GENIE are available at https://github.com/microsoft/ProphetNet/tree/master/GENIE.", "published": "2022-12-22T13:17:11Z", "authors": ["Zhenghao Lin", "Yeyun Gong", "Yelong Shen", "Tong Wu", "Zhihao Fan", "Chen Lin", "Nan Duan", "Weizhu Chen"], "categories": ["cs.CL", "cs.LG"], "doi": "", "_query": "housing price text listing", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/1809.04019v1", "title": "Training and Prediction Data Discrepancies: Challenges of Text Classification with Noisy, Historical Data", "abstract": "Industry datasets used for text classification are rarely created for that purpose. In most cases, the data and target predictions are a by-product of accumulated historical data, typically fraught with noise, present in both the text-based document, as well as in the targeted labels. In this work, we address the question of how well performance metrics computed on noisy, historical data reflect the performance on the intended future machine learning model input. The results demonstrate the utility of dirty training datasets used to build prediction models for cleaner (and different) prediction inputs.", "published": "2018-09-11T16:43:52Z", "authors": ["Emilia Apostolova", "R. Andrew Kreek"], "categories": ["cs.IR", "cs.CL", "cs.LG", "stat.ML"], "doi": "", "_query": "housing price text listing", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/1903.01655v1", "title": "Cross-shareholding networks and stock price synchronicity: Evidence from China", "abstract": "This paper investigates the effect of cross-shareholding on stock price synchronicity, as a measure of price informativeness, of the listed firms in the Chinese stock market. We gauge firms' levels of cross-shareholdings in terms of centrality in the cross-shareholding network. It is confirmed that it is through a noise-reducing process that cross-shareholding promotes price synchronicity and reduces price delay. More importantly, this effect on price informativeness is pronounced for large firms and in the periods of market downturns. Overall, our analyses provide insights into the relation between the ownership structure and price informativeness.", "published": "2019-03-05T04:05:21Z", "authors": ["Fenghua Wen", "Yujie Yuan", "Wei-Xing Zhou"], "categories": ["q-fin.ST"], "doi": "10.1002/ijfe.1828", "_query": "housing price text listing", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2607.08437v2", "title": "Does online sustainability communication shape public discourse? Insights from six years of tenant-housing provider interactions", "abstract": "Authorities increasingly rely on social media to advance sustainability transitions, infrastructure investment, and service reform. Yet how citizens respond to these digital communications remains poorly understood. Existing approaches rely on aggregate engagement metrics (e.g., likes), providing limited insight into discourse structure and quality. We developed a data-driven, multidimensional framework to analyse how social media communication shapes the content of discourse, focusing on sustainability-related engagement in Dutch public housing. We analysed 792 posts and 3,197 tenant comments from the Facebook pages of 92 housing providers (2018-2023). A machine-learning pipeline classified comments into recurring discourse configurations across three dimensions - communicative intent, sentiment, and semantic relatedness. Multinomial logistic regression estimated the effects of post-design and organisational characteristics on discourse. Tenant comments were significantly more semantically aligned with their corresponding posts than with randomly paired content, indicating that organisational communication structures responses to topics. Six discourse types emerged, with critical and inquiry-driven engagement increasing over time. Post-level features did not significantly explain variation; organisational characteristics dominated. Larger housing associations attracted more substantive responses, while lower-rent organisations received fewer evaluative comments. While applied to housing associations, our methodology provides a scalable approach to analyse online discourse dynamics, quality, and content across organisations and contexts.", "published": "2026-07-09T12:59:03Z", "authors": ["Shray Juneja", "Suzan Verberne", "Ioulia V. Ossokina"], "categories": ["cs.CY"], "doi": "", "_query": "housing price text listing", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2508.11151v1", "title": "The core in the housing market model with fractional endowments", "abstract": "We explore the core concept in a generalization of the housing market model where agents own fractional endowments while maintaining ordinal preferences. Recognizing that individuals are easier than coalitions to block an allocation, we adopt a definition in which individuals block an allocation if their received assignments do not first-order stochastically dominate their endowment, while a non-singleton coalition blocks an allocation if they can reallocate their endowments to obtain new assignments that first-order stochastically dominate their original assignments. Our findings show that, unlike the original model, the strong core may be empty, while the weak core is nonempty. The weak core always contains elements that satisfy equal treatment of equals, but it may not contain elements satisfying equal-endowment no envy.", "published": "2025-08-15T01:49:14Z", "authors": ["Jingsheng Yu", "Jun Zhang"], "categories": ["econ.TH"], "doi": "", "_query": "housing price text listing", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2210.16586v1", "title": "NTULM: Enriching Social Media Text Representations with Non-Textual Units", "abstract": "On social media, additional context is often present in the form of annotations and meta-data such as the post's author, mentions, Hashtags, and hyperlinks. We refer to these annotations as Non-Textual Units (NTUs). We posit that NTUs provide social context beyond their textual semantics and leveraging these units can enrich social media text representations. In this work we construct an NTU-centric social heterogeneous network to co-embed NTUs. We then principally integrate these NTU embeddings into a large pretrained language model by fine-tuning with these additional units. This adds context to noisy short-text social media. Experiments show that utilizing NTU-augmented text representations significantly outperforms existing text-only baselines by 2-5\\% relative points on many downstream tasks highlighting the importance of context to social media NLP. We also highlight that including NTU context into the initial layers of language model alongside text is better than using it after the text embedding is generated. Our work leads to the generation of holistic general purpose social media content embedding.", "published": "2022-10-29T12:18:04Z", "authors": ["Jinning Li", "Shubhanshu Mishra", "Ahmed El-Kishky", "Sneha Mehta", "Vivek Kulkarni"], "categories": ["cs.CL", "cs.AI", "cs.LG", "cs.SI"], "doi": "", "_query": "housing price text listing", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/1803.05662v1", "title": "Structure Regularized Neural Network for Entity Relation Classification for Chinese Literature Text", "abstract": "Relation classification is an important semantic processing task in the field of natural language processing. In this paper, we propose the task of relation classification for Chinese literature text. A new dataset of Chinese literature text is constructed to facilitate the study in this task. We present a novel model, named Structure Regularized Bidirectional Recurrent Convolutional Neural Network (SR-BRCNN), to identify the relation between entities. The proposed model learns relation representations along the shortest dependency path (SDP) extracted from the structure regularized dependency tree, which has the benefits of reducing the complexity of the whole model. Experimental results show that the proposed method significantly improves the F1 score by 10.3, and outperforms the state-of-the-art approaches on Chinese literature text.", "published": "2018-03-15T09:45:58Z", "authors": ["Ji Wen", "Xu Sun", "Xuancheng Ren", "Qi Su"], "categories": ["cs.CL"], "doi": "", "_query": "housing price text listing", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2410.22756v2", "title": "Exploring the $ φ{\\text -}α$ interaction via femtoscopic study", "abstract": "Very recently the Wood-Saxon (WS) type interaction in the single-folding potential approach are constructed to simulate the $ φ{\\text -}α$ potentials. One set of the $ φ{\\text -}α$ potentials are based on the first principle HAL QCD $φ{\\text -}N $ interactions in $^{4}S_{3/2}$ channel, and in another set, the $φ$-meson-nucleus potentials were calculated by employing the quark-meson coupling (QMC) model. By utilizing these two set of $ φ{\\text -}α$ potentials, the two-particle momentum correlation of $ φ{\\text -}α$ in high-energy heavy ion collisions is explored. The numerical results show that the correlation functions at small source size (high density nuclear medium) depends on the employed potential model. Also, the correlation functions are obtained within the Lednicky-Lyuboshits (LL) formalism. For small source size, it is found that, the LL formula returns significantly different values due to the large interaction range of the $φ{\\text -}α$ potential.", "published": "2024-10-30T07:26:58Z", "authors": ["Faisal Etminan"], "categories": ["nucl-th", "hep-ph"], "doi": "10.1016/j.physletb.2025.139564", "_query": "housing price text listing", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2202.01211v2", "title": "An Adaptive Deep Clustering Pipeline to Inform Text Labeling at Scale", "abstract": "Mining the latent intentions from large volumes of natural language inputs is a key step to help data analysts design and refine Intelligent Virtual Assistants (IVAs) for customer service and sales support. We created a flexible and scalable clustering pipeline within the Verint Intent Manager (VIM) that integrates the fine-tuning of language models, a high performing k-NN library and community detection techniques to help analysts quickly surface and organize relevant user intentions from conversational texts. The fine-tuning step is necessary because pre-trained language models cannot encode texts to efficiently surface particular clustering structures when the target texts are from an unseen domain or the clustering task is not topic detection. We describe the pipeline and demonstrate its performance and ability to scale on three real-world text mining tasks. As deployed in the VIM application, this clustering pipeline produces high quality results, improving the performance of data analysts and reducing the time it takes to surface intentions from customer service data, thereby reducing the time it takes to build and deploy IVAs in new domains.", "published": "2022-02-01T22:54:18Z", "authors": ["Xinyu Chen", "Ian Beaver"], "categories": ["cs.CL", "cs.LG"], "doi": "", "_query": "housing price text listing", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2306.09529v1", "title": "House-Swapping with Objective Indifferences", "abstract": "We study the classic house-swapping problem of Shapley and Scarf (1974) in a setting where agents may have \"objective\" indifferences, i.e., indifferences that are shared by all agents. In other words, if any one agent is indifferent between two houses, then all agents are indifferent between those two houses. The most direct interpretation is the presence of multiple copies of the same object. Our setting is a special case of the house-swapping problem with general indifferences. We derive a simple, easily interpretable algorithm that produces the unique strict core allocation of the house-swapping market, if it exists. Our algorithm runs in square polynomial time, a substantial improvement over the cubed time methods for the more general problem.", "published": "2023-06-15T22:16:22Z", "authors": ["Will Sandholtz", "Andrew Tai"], "categories": ["econ.TH", "cs.DM", "cs.GT"], "doi": "", "_query": "housing price text listing", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/1707.03367v1", "title": "Wextractor: Follow-up of the evolution of prices in web pages", "abstract": "In the e-commerce world, the follow-up of prices in detail web pages is of great interest for things like buying a product when it falls below some threshold. For doing this task, instead of bookmarking the pages and revisiting them, in this paper we propose a novel web data extraction system, called Wextractor. It consists of an extraction method and a web app for listing the retrieved prices. As for the final user, the main feature of Wextractor is usability because (s)he only has to signal the pages of interest and our system automatically extracts the price from the page.", "published": "2017-07-11T17:12:50Z", "authors": ["Jorge Lloret-Gazo"], "categories": ["cs.IR"], "doi": "", "_query": "housing price text listing", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2609.05235v1", "title": "PRICE: A Systematic Study of LLM Adaptation Choices for Bitcoin Price Forecasting", "abstract": "Cryptocurrency markets exhibit extreme volatility and non-stationary dynamics that challenge conventional forecasting methods. Although Large Language Models (LLMs) have shown promise for time series forecasting, the combined effects of adaptation choices remain largely unexplored in financial settings. This study introduces PRICE, a structured approach for adapting LLMs to short-term Bitcoin price forecasting. Built on a 4-bit quantized LLaMA-3 8B model, PRICE investigates how fine-tuning, numerical representation, prompting, inference, and decoding jointly influence forecasting performance. PRICE integrates Parameter-efficient fine-tuning with Low-Rank Adaptation (LoRA), Recursive multi-step inference, Integer-rounded numerical representation, Context-Task-Format (CTF) prompting, and Exact zero-temperature decoding. Ablation studies show that each component contributes to forecasting accuracy and reliability. LoRA enables efficient training on limited hardware, recursive inference improves accuracy, integer-rounded values reduce errors, CTF prompting outperforms Chain-of-Thought, Implicit Chain-of-Thought (iCoT), and few-shot prompting, and zero-temperature decoding improves stability during recursive forecasting. Comparative evaluation against eight transformer-based and time-series foundation models shows that PRICE achieves the lowest forecasting errors on both validation and test sets while maintaining robust performance across evaluation periods. Despite being based on a model primarily pretrained on text rather than time-series data, PRICE achieves competitive or superior performance relative to specialized foundation models. These findings demonstrate that adaptation choices critically determine the accuracy and robustness of LLMs for numerical time-series forecasting.", "published": "2026-09-04T15:01:38Z", "authors": ["Maryam Fakhari", "Mehran Safayani"], "categories": ["cs.LG", "cs.AI", "math.NA"], "doi": "", "_query": "housing price text listing", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2210.01482v1", "title": "Transformer-based Subject Entity Detection in Wikipedia Listings", "abstract": "In tasks like question answering or text summarisation, it is essential to have background knowledge about the relevant entities. The information about entities - in particular, about long-tail or emerging entities - in publicly available knowledge graphs like DBpedia or CaLiGraph is far from complete. In this paper, we present an approach that exploits the semi-structured nature of listings (like enumerations and tables) to identify the main entities of the listing items (i.e., of entries and rows). These entities, which we call subject entities, can be used to increase the coverage of knowledge graphs. Our approach uses a transformer network to identify subject entities at the token-level and surpasses an existing approach in terms of performance while being bound by fewer limitations. Due to a flexible input format, it is applicable to any kind of listing and is, unlike prior work, not dependent on entity boundaries as input. We demonstrate our approach by applying it to the complete Wikipedia corpus and extracting 40 million mentions of subject entities with an estimated precision of 71% and recall of 77%. The results are incorporated in the most recent version of CaLiGraph.", "published": "2022-10-04T09:14:51Z", "authors": ["Nicolas Heist", "Heiko Paulheim"], "categories": ["cs.IR"], "doi": "", "_query": "housing price text listing", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/1512.03950v1", "title": "A Hidden Markov Model Based System for Entity Extraction from Social Media English Text at FIRE 2015", "abstract": "This paper presents the experiments carried out by us at Jadavpur University as part of the participation in FIRE 2015 task: Entity Extraction from Social Media Text - Indian Languages (ESM-IL). The tool that we have developed for the task is based on Trigram Hidden Markov Model that utilizes information like gazetteer list, POS tag and some other word level features to enhance the observation probabilities of the known tokens as well as unknown tokens. We submitted runs for English only. A statistical HMM (Hidden Markov Models) based model has been used to implement our system. The system has been trained and tested on the datasets released for FIRE 2015 task: Entity Extraction from Social Media Text - Indian Languages (ESM-IL). Our system is the best performer for English language and it obtains precision, recall and F-measures of 61.96, 39.46 and 48.21 respectively.", "published": "2015-12-12T18:57:11Z", "authors": ["Kamal Sarkar"], "categories": ["cs.CL"], "doi": "", "_query": "housing price text listing", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/1905.13622v2", "title": "Characterizing Shadow Price via Lagrangian Multiplier for Nonsmooth Problem", "abstract": "In this paper, a relation between shadow price and the Lagrangian multiplier for nonsmooth problem is explored. It is shown that the Lagrangian Multiplier is the upper bound of shadow price for convex optimization and a class of Lipschtzian optimizations. This work can be used in shadow pricing for nonsmooth situation. The several nonsmooth functions involved in this class of Lipschtzian optimizations is listed. Finally, an application to electricity pricing is discussed.", "published": "2019-05-31T13:57:14Z", "authors": ["Yan Gao"], "categories": ["econ.TH"], "doi": "", "_query": "housing price text listing", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2111.09111v1", "title": "Forecasting Crude Oil Price Using Event Extraction", "abstract": "Research on crude oil price forecasting has attracted tremendous attention from scholars and policymakers due to its significant effect on the global economy. Besides supply and demand, crude oil prices are largely influenced by various factors, such as economic development, financial markets, conflicts, wars, and political events. Most previous research treats crude oil price forecasting as a time series or econometric variable prediction problem. Although recently there have been researches considering the effects of real-time news events, most of these works mainly use raw news headlines or topic models to extract text features without profoundly exploring the event information. In this study, a novel crude oil price forecasting framework, AGESL, is proposed to deal with this problem. In our approach, an open domain event extraction algorithm is utilized to extract underlying related events, and a text sentiment analysis algorithm is used to extract sentiment from massive news. Then a deep neural network integrating the news event features, sentimental features, and historical price features is built to predict future crude oil prices. Empirical experiments are performed on West Texas Intermediate (WTI) crude oil price data, and the results show that our approach obtains superior performance compared with several benchmark methods.", "published": "2021-11-14T08:48:43Z", "authors": ["Jiangwei Liu", "Xiaohong Huang"], "categories": ["cs.LG", "cs.AI", "cs.CL", "econ.GN", "stat.AP"], "doi": "10.1109/ACCESS.2021.3124802", "_query": "housing price text listing", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/1810.05972v3", "title": "DDSL: Efficient Subgraph Listing on Distributed and Dynamic Graphs", "abstract": "Subgraph listing is a fundamental problem in graph theory and has wide applications in areas like sociology, chemistry, and social networks. Modern graphs can usually be large-scale as well as highly dynamic, which challenges the efficiency of existing subgraph listing algorithms. Recent works have shown the benefits of partitioning and processing big graphs in a distributed system, however, there is only few work targets subgraph listing on dynamic graphs in a distributed environment. In this paper, we propose an efficient approach, called Distributed and Dynamic Subgraph Listing (DDSL), which can incrementally update the results instead of running from scratch. DDSL follows a general distributed join framework. In this framework, we use a Neighbor-Preserved storage for data graphs, which takes bounded extra space and supports dynamic updating. After that, we propose a comprehensive cost model to estimate the I/O cost of listing subgraphs. Then based on this cost model, we develop an algorithm to find the optimal join tree for a given pattern. To handle dynamic graphs, we propose an efficient left-deep join algorithm to incrementally update the join results. Extensive experiments are conducted on real-world datasets. The results show that DDSL outperforms existing methods in dealing with both static dynamic graphs in terms of the responding time.", "published": "2018-10-14T04:58:58Z", "authors": ["Xun Jian", "Yue Wang", "Xiayu Lei", "Yanyan Shen", "Lei Chen"], "categories": ["cs.DB"], "doi": "", "_query": "housing price text listing", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2307.04687v1", "title": "Congruences for the partition function $\\text{PDO}_t(n)$ modulo powers of $2$ and $3$", "abstract": "Lin introduced the partition function $\\text{PDO}_t(n)$, which counts the total number of tagged parts over all the partitions of $n$ with designated summands in which all parts are odd. For $k\\geq0$, Lin conjectured congruences for $\\text{PDO}_t(8\\cdot3^kn)$ and $\\text{PDO}_t(12\\cdot3^kn)$ modulo $3^{k+2}$. In this article, we develop a new approach to study these congruences. We study the generating functions of $\\text{PDO}_t(8\\cdot3^kn)$ and $\\text{PDO}_t(12\\cdot3^kn)$ modulo $3^{k+3}$ for certain values of $k$. We also study $\\text{PDO}_t(n)$ modulo powers of $2$. We establish infinitely many congruences for $\\text{PDO}_t(n)$ modulo $8$ and $32$. We prove several congruences modulo small powers of $2$ and discuss the existence of congruences modulo arbitrary powers of $2$ similar to those in Lin's conjecture. In reference to this, we also pose some problems for future work.", "published": "2023-07-10T16:42:48Z", "authors": ["Gurinder Singh", "Rupam Barman"], "categories": ["math.NT"], "doi": "", "_query": "housing price text listing", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2108.06614v1", "title": "The SelectGen Challenge: Finding the Best Training Samples for Few-Shot Neural Text Generation", "abstract": "We propose a shared task on training instance selection for few-shot neural text generation. Large-scale pretrained language models have led to dramatic improvements in few-shot text generation. Nonetheless, almost all previous work simply applies random sampling to select the few-shot training instances. Little to no attention has been paid to the selection strategies and how they would affect model performance. The study of the selection strategy can help us to (1) make the most use of our annotation budget in downstream tasks and (2) better benchmark few-shot text generative models. We welcome submissions that present their selection strategies and the effects on the generation quality.", "published": "2021-08-14T21:20:35Z", "authors": ["Ernie Chang", "Xiaoyu Shen", "Alex Marin", "Vera Demberg"], "categories": ["cs.CL"], "doi": "", "_query": "housing price text listing", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2011.07405v1", "title": "Tight Distributed Listing of Cliques", "abstract": "Much progress has recently been made in understanding the complexity landscape of subgraph finding problems in the CONGEST model of distributed computing. However, so far, very few tight bounds are known in this area. For triangle (i.e., 3-clique) listing, an optimal $\\tilde{O}(n^{1/3})$-round distributed algorithm has been constructed by Chang et al.~[SODA 2019, PODC 2019]. Recent works of Eden et al.~[DISC 2019] and of Censor-Hillel et al.~[PODC 2020] have shown sublinear algorithms for $K_p$-listing, for each $p \\geq 4$, but still leaving a significant gap between the upper bounds and the known lower bounds of the problem. In this paper, we completely close this gap. We show that for each $p \\geq 4$, there is an $\\tilde{O}(n^{1 - 2/p})$-round distributed algorithm that lists all $p$-cliques $K_p$ in the communication network. Our algorithm is \\emph{optimal} up to a polylogarithmic factor, due to the $\\tildeΩ(n^{1 - 2/p})$-round lower bound of Fischer et al.~[SPAA 2018], which holds even in the CONGESTED CLIQUE model. Together with the triangle-listing algorithm by Chang et al.~[SODA 2019, PODC 2019], our result thus shows that the round complexity of $K_p$-listing, for all $p$, is the same in both the CONGEST and CONGESTED CLIQUE models, at $\\tildeΘ(n^{1 - 2/p})$ rounds. For $p=4$, our result additionally matches the $\\tildeΩ(n^{1/2})$ lower bound for $K_4$-\\emph{detection} by Czumaj and Konrad [DISC 2018], implying that the round complexities for detection and listing of $K_4$ are equivalent in the CONGEST model.", "published": "2020-11-14T22:44:01Z", "authors": ["Keren Censor-Hillel", "Yi-Jun Chang", "François Le Gall", "Dean Leitersdorf"], "categories": ["cs.DC"], "doi": "10.1137/1.9781611976465.171", "_query": "housing price text listing", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2607.01669v1", "title": "UT-AISTimprt submission for ICME 2026 Grand Challenge on Academic Text-to-Music Generation", "abstract": "This work investigates the effect of batch sampling strategies during training for text-to-audio music generation under low-data and small-scale model settings. This paper describes our approach and findings for the ICME 2026 Grand Challenge on Academic Text-to-Music Generation. Training data are clustered using either text embeddings or audio embeddings, and samples with similar characteristics are grouped within the same mini-batch to mitigate gradient interference. The effects of modality and cluster granularity on clustering are analyzed. Results show that clustering based on text embeddings achieves better performance on objective evaluation metrics than clustering based on audio embeddings. In addition, different cluster granularity leads to different behaviors across evaluation criteria: a moderate number of clusters performs best on objective metrics, while a larger number of clusters tends to exhibit music with more coherent structure in listening tests.", "published": "2026-07-02T03:51:01Z", "authors": ["Shunsuke Yoshida", "Yu-Hua Chen", "Satoru Fukayama"], "categories": ["cs.SD"], "doi": "", "_query": "housing price text listing", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2109.01034v1", "title": "Scene Text recognition with Full Normalization", "abstract": "Scene text recognition has made significant progress in recent years and has become an important part of the work-flow. The widespread use of mobile devices opens up wide possibilities for using OCR technologies in everyday life. However, lack of training data for new research in this area remains relevant. In this article, we present a new dataset consisting of real shots on smartphones and demonstrate the effectiveness of profile normalization in this task. In addition, the influence of various augmentations during the training of models for analyzing document images on smartphones is studied in detail. Our dataset is publicly available.", "published": "2021-07-13T17:36:30Z", "authors": ["Nathan Zachary", "Gerald Carl", "Russell Elijah", "Hessi Roma", "Robert Leer", "James Amelia"], "categories": ["cs.CV"], "doi": "", "_query": "housing price text listing", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2108.09363v1", "title": "A Pivot Gray Code Listing for the Spanning Trees of the Fan Graph", "abstract": "We use a greedy strategy to list the spanning trees of the fan graph, $F_n$, such that successive trees differ by pivoting a single edge around a vertex. It is the first greedy algorithm for exhaustively generating spanning trees using such a minimal change operation. The resulting listing is then studied to find a recursive algorithm that produces the same listing in $O(1)$-amortized time using $O(n)$ space. Additionally, we present $O(n)$-time algorithms for ranking and unranking the spanning trees for our listing; an improvement over the generic $O(n^3)$-time algorithm for ranking and unranking spanning trees of an arbitrary graph.", "published": "2021-08-20T20:45:51Z", "authors": ["Ben Cameron", "Aaron Grubb", "Joe Sawada"], "categories": ["cs.DS", "cs.DM", "math.CO"], "doi": "", "_query": "housing price text listing", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2302.12468v3", "title": "Adapting Knowledge for Few-shot Table-to-Text Generation", "abstract": "Pretrained language models (PLMs) have made remarkable progress in table-to-text generation tasks. However, the lack of domain-specific knowledge makes it challenging to bridge the topological gap between tabular data and text, especially in real-world applications with limited resources. To mitigate the limitation of insufficient labeled data, we propose a novel framework: Adapt-Knowledge-to-Generate (AKG). The core insight of AKG is to adapt unlabeled domain-specific knowledge into the model, which brings at least three benefits: (1) it injects representation of normal table-related descriptions to bridge the topological gap between tabular data and texts; (2) it enables us to use large amounts of unlabeled domain-specific knowledge fully, which can alleviate the PLMs' inherent shortcomings of lacking domain knowledge; (3) it allows us to design various tasks to employ the domain-specific knowledge. Extensive experiments and analyses are conducted on three open-domain, few-shot natural language generation (NLG) data sets: Humans, Songs, and Books. Compared to previous state-of-the-art approaches, our model achieves superior performance in terms of both fluency and accuracy as judged by human and automatic evaluations.", "published": "2023-02-24T05:48:53Z", "authors": ["Zhixin Guo", "Minyxuan Yan", "Jiexing Qi", "Jianping Zhou", "Ziwei He", "Guanjie Zheng", "Xinbing Wang"], "categories": ["cs.CL"], "doi": "", "_query": "housing price text listing", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/1904.05270v1", "title": "Google Street View image of a house predicts car accident risk of its resident", "abstract": "Road traffic injuries are a leading cause of death worldwide. Proper estimation of car accident risk is critical for appropriate allocation of resources in healthcare, insurance, civil engineering, and other industries. We show how images of houses are predictive of car accidents. We analyze 20,000 addresses of insurance company clients, collect a corresponding house image using Google Street View, and annotate house features such as age, type, and condition. We find that this information substantially improves car accident risk prediction compared to the state-of-the-art risk model of the insurance company and could be used for price discrimination. From this perspective, public availability of house images raises legal and social concerns, as they can be a proxy of ethnicity, religion and other sensitive data.", "published": "2019-04-10T16:16:31Z", "authors": ["Kinga Kita", "Łukasz Kidziński"], "categories": ["stat.AP"], "doi": "", "_query": "housing price text listing", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2401.03854v2", "title": "TIER: Text-Image Encoder-based Regression for AIGC Image Quality Assessment", "abstract": "Recently, AIGC image quality assessment (AIGCIQA), which aims to assess the quality of AI-generated images (AIGIs) from a human perception perspective, has emerged as a new topic in computer vision. Unlike common image quality assessment tasks where images are derived from original ones distorted by noise, blur, and compression, \\textit{etc.}, in AIGCIQA tasks, images are typically generated by generative models using text prompts. Considerable efforts have been made in the past years to advance AIGCIQA. However, most existing AIGCIQA methods regress predicted scores directly from individual generated images, overlooking the information contained in the text prompts of these images. This oversight partially limits the performance of these AIGCIQA methods. To address this issue, we propose a text-image encoder-based regression (TIER) framework. Specifically, we process the generated images and their corresponding text prompts as inputs, utilizing a text encoder and an image encoder to extract features from these text prompts and generated images, respectively. To demonstrate the effectiveness of our proposed TIER method, we conduct extensive experiments on several mainstream AIGCIQA databases, including AGIQA-1K, AGIQA-3K, and AIGCIQA2023. The experimental results indicate that our proposed TIER method generally demonstrates superior performance compared to baseline in most cases.", "published": "2024-01-08T12:35:15Z", "authors": ["Jiquan Yuan", "Xinyan Cao", "Jinming Che", "Qinyuan Wang", "Sen Liang", "Wei Ren", "Jinlong Lin", "Xixin Cao"], "categories": ["cs.CV", "cs.AI"], "doi": "", "_query": "housing price text listing", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/1811.01734v1", "title": "Transductive Learning with String Kernels for Cross-Domain Text Classification", "abstract": "For many text classification tasks, there is a major problem posed by the lack of labeled data in a target domain. Although classifiers for a target domain can be trained on labeled text data from a related source domain, the accuracy of such classifiers is usually lower in the cross-domain setting. Recently, string kernels have obtained state-of-the-art results in various text classification tasks such as native language identification or automatic essay scoring. Moreover, classifiers based on string kernels have been found to be robust to the distribution gap between different domains. In this paper, we formally describe an algorithm composed of two simple yet effective transductive learning approaches to further improve the results of string kernels in cross-domain settings. By adapting string kernels to the test set without using the ground-truth test labels, we report significantly better accuracy rates in cross-domain English polarity classification.", "published": "2018-11-02T08:05:36Z", "authors": ["Radu Tudor Ionescu", "Andrei M. Butnaru"], "categories": ["cs.CL", "cs.LG"], "doi": "", "_query": "housing price text listing", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2306.05540v1", "title": "DetectLLM: Leveraging Log Rank Information for Zero-Shot Detection of Machine-Generated Text", "abstract": "With the rapid progress of large language models (LLMs) and the huge amount of text they generated, it becomes more and more impractical to manually distinguish whether a text is machine-generated. Given the growing use of LLMs in social media and education, it prompts us to develop methods to detect machine-generated text, preventing malicious usage such as plagiarism, misinformation, and propaganda. Previous work has studied several zero-shot methods, which require no training data. These methods achieve good performance, but there is still a lot of room for improvement. In this paper, we introduce two novel zero-shot methods for detecting machine-generated text by leveraging the log rank information. One is called DetectLLM-LRR, which is fast and efficient, and the other is called DetectLLM-NPR, which is more accurate, but slower due to the need for perturbations. Our experiments on three datasets and seven language models show that our proposed methods improve over the state of the art by 3.9 and 1.75 AUROC points absolute. Moreover, DetectLLM-NPR needs fewer perturbations than previous work to achieve the same level of performance, which makes it more practical for real-world use. We also investigate the efficiency--performance trade-off based on users preference on these two measures and we provide intuition for using them in practice effectively. We release the data and the code of both methods in https://github.com/mbzuai-nlp/DetectLLM", "published": "2023-05-23T11:18:30Z", "authors": ["Jinyan Su", "Terry Yue Zhuo", "Di Wang", "Preslav Nakov"], "categories": ["cs.CL", "cs.AI"], "doi": "", "_query": "housing price text listing", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2106.06925v1", "title": "On the Complexity of Fair House Allocation", "abstract": "We study fairness in house allocation, where $m$ houses are to be allocated among $n$ agents so that every agent receives one house. We show that maximizing the number of envy-free agents is hard to approximate to within a factor of $n^{1-γ}$ for any constant $γ>0$, and that the exact version is NP-hard even for binary utilities. Moreover, we prove that deciding whether a proportional allocation exists is computationally hard, whereas the corresponding problem for equitability can be solved efficiently.", "published": "2021-06-13T05:45:21Z", "authors": ["Naoyuki Kamiyama", "Pasin Manurangsi", "Warut Suksompong"], "categories": ["cs.GT", "cs.CC"], "doi": "10.1016/j.orl.2021.06.006", "_query": "housing price text listing", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2306.10572v2", "title": "Quantum Algorithms for the Shortest Common Superstring and Text Assembling Problems", "abstract": "In this paper, we consider two versions of the Text Assembling problem. We are given a sequence of strings $s^1,\\dots,s^n$ of total length $L$ that is a dictionary, and a string $t$ of length $m$ that is texts. The first version of the problem is assembling $t$ from the dictionary. The second version is the ``Shortest Superstring Problem''(SSP) or the ``Shortest Common Superstring Problem''(SCS). In this case, $t$ is not given, and we should construct the shortest string (we call it superstring) that contains each string from the given sequence as a substring. These problems are connected with the sequence assembly method for reconstructing a long DNA sequence from small fragments. For both problems, we suggest new quantum algorithms that work better than their classical counterparts. In the first case, we present a quantum algorithm with $O(m+\\log m\\sqrt{nL})$ running time. In the case of SSP, we present a quantum algorithm with running time $O(n^3 1.728^n +L +\\sqrt{L}n^{1.5}+\\sqrt{L}n\\log^2L\\log^2n)$.", "published": "2023-06-18T14:16:49Z", "authors": ["Kamil Khadiev", "Carlos Manuel Bosch Machado", "Zeyu Chen", "Junde Wu"], "categories": ["quant-ph", "cs.DS"], "doi": "10.26421/QIC24.3-4-4", "_query": "housing price text listing", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/1808.04701v2", "title": "Measuring Market Performance with Stochastic Demand: Price of Anarchy and Price of Uncertainty", "abstract": "Globally operating suppliers face the rising challenge of wholesale pricing under scarce data about retail demand, in contrast to better informed, locally operating retailers. At the same time, as local businesses proliferate, markets congest and retail competition increases. To capture these strategic considerations, we employ the classic Cournot model and extend it to a two-stage supply chain with an upstream supplier who operates under demand uncertainty and multiple downstream retailers who compete over quantity. The supplier's belief about retail demand is modeled via a continuous probability distribution function F. If F has the decreasing generalized mean residual life property, then the supplier's optimal pricing policy exists and is the unique fixed point of the mean residual life function. We evaluate the realized Price of Uncertainty and show that there exist demand levels for which market performs better when the supplier prices under demand uncertainty. In general, performance worsens for lower values of realized demand. We examine the effects of increasing competition on supply chain efficiency via the realized Price of Anarchy and complement our findings with numerical results.", "published": "2018-08-11T07:00:55Z", "authors": ["Costis Melolidakis", "Stefanos Leonardos", "Constandina Koki"], "categories": ["cs.GT"], "doi": "10.1007/978-3-319-99383-6_21", "_query": "housing price text listing", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2502.16857v1", "title": "Sarang at DEFACTIFY 4.0: Detecting AI-Generated Text Using Noised Data and an Ensemble of DeBERTa Models", "abstract": "This paper presents an effective approach to detect AI-generated text, developed for the Defactify 4.0 shared task at the fourth workshop on multimodal fact checking and hate speech detection. The task consists of two subtasks: Task-A, classifying whether a text is AI generated or human written, and Task-B, classifying the specific large language model that generated the text. Our team (Sarang) achieved the 1st place in both tasks with F1 scores of 1.0 and 0.9531, respectively. The methodology involves adding noise to the dataset to improve model robustness and generalization. We used an ensemble of DeBERTa models to effectively capture complex patterns in the text. The result indicates the effectiveness of our noise-driven and ensemble-based approach, setting a new standard in AI-generated text detection and providing guidance for future developments.", "published": "2025-02-24T05:32:00Z", "authors": ["Avinash Trivedi", "Sangeetha Sivanesan"], "categories": ["cs.CL", "cs.AI"], "doi": "", "_query": "housing price text listing", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2512.02564v1", "title": "Retail Price Ripples", "abstract": "Much like small ripples in a stream, which get lost in the larger waves, small changes in retail prices often fly under the radar of public perceptions, while large price changes appear as marketing moves associated with demand and competition. Unnoticed, these could increase consumers out of pocket expenses. Indeed, retailers could boost their profits by making numerous small price increases or by obfuscating large price increases with numerous small price decreases, thereby bypassing the consumers full attention and consideration, and triggering consumer fairness concerns. Yet only a handful of papers study small price changes. Extant results are often based on a single retailer, limited products, short time span, and legacy datasets dating back to the 1980s and 1990s, leaving their current practical relevance questionable. Researchers have also questioned whether the reported observations of small price changes are artifacts of measurement errors driven by data aggregation. In a series of analyses of a large dataset containing almost 79 billion weekly price observations from 2006 to 2015, covering 527 products, and about 35,000 stores across 161 retailers, we find robust evidence of asymmetric pricing in the small, where small price increases outnumber small price decreases, but no such asymmetry is present in the large. We also document the reverse phenomenon, where small price decreases outnumber small price increases. Our results are robust to several possible measurement issues. Importantly, our findings indicate a greater current relevance and generalizability of such asymmetric pricing practices than the existing literature recognizes.", "published": "2025-12-02T09:36:00Z", "authors": ["Xiao Ling", "Sourav Ray", "Daniel Levy"], "categories": ["econ.GN"], "doi": "10.13140/RG.2.2.24328.07686", "_query": "housing price text listing", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2406.08068v2", "title": "Large Language Models Meet Text-Centric Multimodal Sentiment Analysis: A Survey", "abstract": "Compared to traditional sentiment analysis, which only considers text, multimodal sentiment analysis needs to consider emotional signals from multimodal sources simultaneously and is therefore more consistent with the way how humans process sentiment in real-world scenarios. It involves processing emotional information from various sources such as natural language, images, videos, audio, physiological signals, etc. However, although other modalities also contain diverse emotional cues, natural language usually contains richer contextual information and therefore always occupies a crucial position in multimodal sentiment analysis. The emergence of ChatGPT has opened up immense potential for applying large language models (LLMs) to text-centric multimodal tasks. However, it is still unclear how existing LLMs can adapt better to text-centric multimodal sentiment analysis tasks. This survey aims to (1) present a comprehensive review of recent research in text-centric multimodal sentiment analysis tasks, (2) examine the potential of LLMs for text-centric multimodal sentiment analysis, outlining their approaches, advantages, and limitations, (3) summarize the application scenarios of LLM-based multimodal sentiment analysis technology, and (4) explore the challenges and potential research directions for multimodal sentiment analysis in the future.", "published": "2024-06-12T10:36:27Z", "authors": ["Hao Yang", "Yanyan Zhao", "Yang Wu", "Shilong Wang", "Tian Zheng", "Hongbo Zhang", "Zongyang Ma", "Wanxiang Che", "Bing Qin"], "categories": ["cs.CL"], "doi": "", "_query": "housing price text listing", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/1909.09799v1", "title": "On the Price of Anarchy for High-Price Links", "abstract": "We study Nash equilibria and the price of anarchy in the classic model of Network Creation Games introduced by Fabrikant, Luthra, Maneva, Papadimitriou and Shenker in 2003. This is a selfish network creation model where players correspond to nodes in a network and each of them can create links to the other $n-1$ players at a prefixed price $α> 0$. The player's goal is to minimise the sum of her cost buying edges and her cost for using the resulting network. One of the main conjectures for this model states that the price of anarchy, i.e. the relative cost of the lack of coordination, is constant for all $α$. This conjecture has been confirmed for $α= O(n^{1-δ})$ with $δ\\geq 1/\\log n$ and for $α> 4n-13$. The best known upper bound on the price of anarchy for the remaining range is $2^{O(\\sqrt{\\log n})}$. We give new insights into the structure of the Nash equilibria for $α> n$ and we enlarge the range of the parameter $α$ for which the price of anarchy is constant. Specifically, we prove that for any small $ε>0$, the price of anarchy is constant for $α> n(1+ε)$ by showing that any biconnected component of any non-trivial Nash equilibrium, if it exists, has at most a constant number of nodes.", "published": "2019-09-21T09:10:16Z", "authors": ["Carme Àlvarez", "Arnau Messegué"], "categories": ["cs.GT"], "doi": "", "_query": "housing price text listing", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/1207.6205v3", "title": "Option prices with call prices", "abstract": "There exist several methods how more general options can be priced with call prices. In this article, we extend these results to cover a wider class of options and market models. In particular, we introduce a new pricing formula which can be used to price more general options if prices for call options and digital options are known for every strike price. Moreover, we derive similar results for barrier type options. As a consequence, we obtain a static hedging for general options in the general class of models. Our result can be utilised in several significant applications. As a simple example, we derive an upper bound for the value of a general American option with convex payoff and characterise conditions under which the value of this option equals to the value of the corresponding European option.", "published": "2012-07-26T08:53:08Z", "authors": ["Lauri Viitasaari"], "categories": ["q-fin.PR", "math.PR"], "doi": "", "_query": "housing price text listing", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2203.16825v1", "title": "indic-punct: An automatic punctuation restoration and inverse text normalization framework for Indic languages", "abstract": "Automatic Speech Recognition (ASR) generates text which is most of the times devoid of any punctuation. Absence of punctuation is text can affect readability. Also, down stream NLP tasks such as sentiment analysis, machine translation, greatly benefit by having punctuation and sentence boundary information. We present an approach for automatic punctuation of text using a pretrained IndicBERT model. Inverse text normalization is done by hand writing weighted finite state transducer (WFST) grammars. We have developed this tool for 11 Indic languages namely Hindi, Tamil, Telugu, Kannada, Gujarati, Marathi, Odia, Bengali, Assamese, Malayalam and Punjabi. All code and data is publicly. available", "published": "2022-03-31T06:18:43Z", "authors": ["Anirudh Gupta", "Neeraj Chhimwal", "Ankur Dhuriya", "Rishabh Gaur", "Priyanshi Shah", "Harveen Singh Chadha", "Vivek Raghavan"], "categories": ["cs.CL"], "doi": "", "_query": "housing price text listing", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2107.04638v1", "title": "Robust Clearing Price Mechanisms for Reserve Price Optimization", "abstract": "Setting an effective reserve price for strategic bidders in repeated auctions is a central question in online advertising. In this paper, we investigate how to set an anonymous reserve price in repeated auctions based on historical bids in a way that balances revenue and incentives to misreport. We propose two simple and computationally efficient methods to set reserve prices based on the notion of a clearing price and make them robust to bidder misreports. The first approach adds random noise to the reserve price, drawing on techniques from differential privacy. The second method applies a smoothing technique by adding noise to the training bids used to compute the reserve price. We provide theoretical guarantees on the trade-offs between the revenue performance and bid-shading incentives of these two mechanisms. Finally, we empirically evaluate our mechanisms on synthetic data to validate our theoretical findings.", "published": "2021-07-09T19:20:48Z", "authors": ["Zhe Feng", "Sébastien Lahaie"], "categories": ["cs.GT"], "doi": "", "_query": "housing price text listing", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/0906.0307v1", "title": "Conformal House", "abstract": "We investigate the gauge dynamics of nonsupersymmetric SU(N) gauge theories featuring the simultaneous presence of fermionic matter transforming according to two distinct representations of the underlying gauge group. We bound the regions of flavors and colors which can yield a physical infrared fixed point. As a consistency check we recover the previously investigated conformal windows bounds when restricting to a single matter representation. The earlier conformal windows can be imagined to be part now of the new conformal house. We predict the nonperturbative anomalous dimensions at the infrared fixed points. We further investigate the effects of adding mass terms to the condensates on the conformal house chiral dynamics and construct the simplest instanton induced effective Lagrangian terms.", "published": "2009-06-01T15:46:51Z", "authors": ["Thomas A Ryttov", "Francesco Sannino"], "categories": ["hep-ph", "hep-lat", "hep-th"], "doi": "10.1142/S0217751X10050391", "_query": "housing price text listing", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2106.07704v4", "title": "Efficient (Soft) Q-Learning for Text Generation with Limited Good Data", "abstract": "Maximum likelihood estimation (MLE) is the predominant algorithm for training text generation models. This paradigm relies on direct supervision examples, which is not applicable to many emerging applications, such as generating adversarial attacks or generating prompts to control language models. Reinforcement learning (RL) on the other hand offers a more flexible solution by allowing users to plug in arbitrary task metrics as reward. Yet previous RL algorithms for text generation, such as policy gradient (on-policy RL) and Q-learning (off-policy RL), are often notoriously inefficient or unstable to train due to the large sequence space and the sparse reward received only at the end of sequences. In this paper, we introduce a new RL formulation for text generation from the soft Q-learning (SQL) perspective. It enables us to draw from the latest RL advances, such as path consistency learning, to combine the best of on-/off-policy updates, and learn effectively from sparse reward. We apply the approach to a wide range of novel text generation tasks, including learning from noisy/negative examples, adversarial attacks, and prompt generation. Experiments show our approach consistently outperforms both task-specialized algorithms and the previous RL methods.", "published": "2021-06-14T18:48:40Z", "authors": ["Han Guo", "Bowen Tan", "Zhengzhong Liu", "Eric P. Xing", "Zhiting Hu"], "categories": ["cs.CL", "cs.LG"], "doi": "", "_query": "housing price text listing", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2605.21194v1", "title": "Listing the hyperarithmetical functions", "abstract": "Given a countable Turing ideal $\\mathcal{I} \\subseteq ω^ω$, we say that $x$ is a list (resp. weak list) of $\\mathcal{I}$ if $\\mathcal{I}=\\{x^{[n]} : n \\in ω\\}$ (resp. if $\\mathcal{I} \\subseteq \\{x^{[n]} :n \\in ω\\}$). We show that, for several natural ideals $\\mathcal{I}$, $x$ computes a list of $\\mathcal{I}$ if and only if it computes a function dominating all the functions in $\\mathcal{I}$. On the other hand, we provide reals which are $\\mathsf{HYP}$-strongly null engulfing (and hence $\\mathsf{HYP}$-dominating, by results of Greenberg, Kuyper and Turetsky) but which cannot compute a weak list for $\\mathsf{HYP}$, solving a problem left open in a recent paper by Greenberg and the second author. This result can be generalized to any countable ideal which is downward closed under $\\leq_{\\mathsf{HYP}}$. We also give a characterization of reals which compute a list of $\\mathsf{HYP}$: $x$ computes a list of $\\mathsf{HYP}$ if and only if $x$ is $\\mathsf{HYP}$-dominating and $\\mathcal{O}$ is $Σ^0_2(x)$.", "published": "2026-05-20T13:56:42Z", "authors": ["Joseph S. Miller", "Gian Marco Osso", "Isabella Scott"], "categories": ["math.LO"], "doi": "", "_query": "housing price text listing", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2204.07506v2", "title": "Price and Payoff Autocorrelations in a Multi-Period Consumption-Based Asset Pricing Model", "abstract": "This paper highlights the hidden dependence of the basic pricing equation of a multi-period consumption-based asset pricing model on price and payoff autocorrelations. We obtain the approximations of the basic pricing equation that describe the mean price \"to-day,\" mean payoff \"next-day,\" price and payoff volatilities, and price and payoff autocorrelations. The deep conjunction of the consumption-based model with other versions of asset pricing, such as ICAPM, APM, etc. (Cochrane, 2001), emphasizes that our results are valid for other pricing models.", "published": "2022-03-05T19:39:10Z", "authors": ["Victor Olkhov"], "categories": ["econ.GN", "q-fin.PR"], "doi": "", "_query": "housing price text listing", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/1907.07673v1", "title": "Network Based Pricing for 3D Printing Services in Two-Sided Manufacturing-as-a-Service Marketplace", "abstract": "This paper presents approaches to determine a network based pricing for 3D printing services in the context of a two-sided manufacturing-as-a-service marketplace. The intent is to provide cost analytics to enable service bureaus to better compete in the market by moving away from setting ad-hoc and subjective prices. A data mining approach with machine learning methods is used to estimate a price range based on the profile characteristics of 3D printing service suppliers. The model considers factors such as supplier experience, supplier capabilities, customer reviews and ratings from past orders, and scale of operations among others to estimate a price range for suppliers' services. Data was gathered from existing marketplace websites, which was then used to train and test the model. The model demonstrates an accuracy of 65% for US based suppliers and 59% for Europe based suppliers to classify a supplier's 3D Printer listing in one of the seven price categories. The improvement over baseline accuracy of 25% demonstrates that machine learning based methods are promising for network based pricing in manufacturing marketplaces. Conventional methodologies for pricing services through activity based costing are inefficient in strategically pricing 3D printing service offering in a connected marketplace. As opposed to arbitrarily determining prices, this work proposes an approach to determine prices through data mining methods to estimate competitive prices. Such tools can be built into online marketplaces to help independent service bureaus to determine service price rates.", "published": "2019-07-17T19:14:55Z", "authors": ["Deepak Pahwa", "Binil Starly"], "categories": ["cs.LG", "stat.ML"], "doi": "10.1108/RPJ-01-2019-0018", "_query": "housing price text listing", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2605.27658v1", "title": "Historical Developments in Probability Measures for Asset Pricing: From State Prices to Modern Pricing Kernels", "abstract": "This review summarizes the historical development of probability measures in asset pricing, from early mathematical finance and state price theory to risk-neutral valuation, martingale measures, forward measures, stochastic discount factors, incomplete-market measure selection, benchmark pricing, robust and nonlinear pricing, and modern data-driven probability transformations. The central theme is that asset pricing is not merely an exercise in estimating physical probabilities. Instead, pricing theory constructs, transforms, or selects probability measures so that market prices can be represented as expectations after discounting, numeraire normalization, marginal utility weighting, entropy penalization, calibration, or information conditioning. The paper emphasizes landmark contributions including Bachelier's probabilistic model of speculation, Arrow-Debreu state-contingent claims, Black-Scholes-Merton option pricing, Harrison-Kreps and Harrison-Pliska's martingale formalization, Delbaen and Schachermayer's fundamental theorem, Breeden-Litzenberger implied state price densities, change of numeraire methods, Hansen-Jagannathan stochastic discount factor restrictions, Cochrane's SDF synthesis, and recent empirical and machine learning work on learned pricing kernels. Text-, attention-, and sentiment-based probability transformations are treated as recent information-adjusted forecasting extensions that complement, rather than replace, martingale, numeraire, SDF, and incomplete-market frameworks. The paper also collects key formulas for state prices, stochastic discount factors, Radon-Nikodym densities, Girsanov changes of measure, risk-neutral valuation, forward measures, implied densities, coherent risk measures, benchmark pricing, learned SDFs, and information-adjusted forecasting.", "published": "2026-05-26T20:22:20Z", "authors": ["Zhang Chen", "Chen Kay"], "categories": ["q-fin.MF"], "doi": "", "_query": "housing price text listing", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/1905.05174v1", "title": "48 Crepant Paths to $\\text{SU}(2)\\!\\times\\!\\text{SU}(3)$", "abstract": "We study crepant resolutions of Weierstrass models of $\\text{SU}(2)\\!\\times\\!\\text{SU}(3)$-models, whose gauge group describes the non-abelian sector of the Standard Model. The $\\text{SU}(2)\\!\\times\\!\\text{SU}(3)$-models are elliptic fibrations characterized by the collision of two Kodaira fibers with dual graphs that are affine Dynkin diagrams of type $\\widetilde{\\text{A}}_1$ and $\\widetilde{\\text{A}}_2$. Once we eliminate those collisions that do not have crepant resolutions, we are left with six distinct collisions that are related to each other by deformations. Each of these six collisions has eight distinct crepant resolutions whose flop diagram is a hexagon with two legs attached to two adjacent nodes. Hence, we consider 48 distinct resolutions that are connected to each other by deformations and flops. We determine topological invariants---such as Euler characteristics, Hodge numbers, and triple intersections of fibral divisors---for each of the crepant resolutions. We analyze the physics of these fibrations when used as compactifications of M-theory and F-theory on Calabi--Yau threefolds yielding 5d ${\\mathcal N}=1$ and 6d ${\\mathcal N}=(1,0)$ supergravity theories respectively. We study the 5d prepotential in the Coulomb branch of the theory and check that the six-dimensional theory is anomaly-free and compatible with a 6d uplift from a 5d theory.", "published": "2019-05-13T17:59:59Z", "authors": ["Mboyo Esole", "Ravi Jagadeesan", "Monica Jinwoo Kang"], "categories": ["hep-th", "math-ph", "math.AG"], "doi": "", "_query": "housing price text listing", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2409.03583v1", "title": "Text-Guided Mixup Towards Long-Tailed Image Categorization", "abstract": "In many real-world applications, the frequency distribution of class labels for training data can exhibit a long-tailed distribution, which challenges traditional approaches of training deep neural networks that require heavy amounts of balanced data. Gathering and labeling data to balance out the class label distribution can be both costly and time-consuming. Many existing solutions that enable ensemble learning, re-balancing strategies, or fine-tuning applied to deep neural networks are limited by the inert problem of few class samples across a subset of classes. Recently, vision-language models like CLIP have been observed as effective solutions to zero-shot or few-shot learning by grasping a similarity between vision and language features for image and text pairs. Considering that large pre-trained vision-language models may contain valuable side textual information for minor classes, we propose to leverage text supervision to tackle the challenge of long-tailed learning. Concretely, we propose a novel text-guided mixup technique that takes advantage of the semantic relations between classes recognized by the pre-trained text encoder to help alleviate the long-tailed problem. Our empirical study on benchmark long-tailed tasks demonstrates the effectiveness of our proposal with a theoretical guarantee. Our code is available at https://github.com/rsamf/text-guided-mixup.", "published": "2024-09-05T14:37:43Z", "authors": ["Richard Franklin", "Jiawei Yao", "Deyang Zhong", "Qi Qian", "Juhua Hu"], "categories": ["cs.CV"], "doi": "", "_query": "housing price text listing", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/1903.12478v1", "title": "Item Listing Optimization for E-commerce Websites based on Diversity", "abstract": "For e-commerce websites, deciding the manner in which items are listed on webpages is an important issue because it can dramatically affect item sales. One of the simplest strategies of listing items to improve the overall sales is to do so in a descending order of sales or sales numbers. However, in lists generated using this strategy, items with high similarity are often placed consecutively. In other words, the generated item list might be biased toward a specific preference. Therefore, this study employs penalties for items with high similarity being placed next to each other in the list and transforms the item listing problem to a quadratic assignment problem (QAP). The QAP is well-known as an NP-hard problem that cannot be solved in polynomial time. To solve the QAP, we employ quantum annealing (QA), which exploits the quantum tunneling effect to efficiently solve an optimization problem. In addition, we propose a problem decomposition method based on the structure of the item listing problem because the quantum annealer we use (i.e., D-Wave 2000Q) has a limited number of quantum bits. Our experimental results indicate that we can create an item list that considers both sales and diversity. In addition, we observe that using the problem decomposition method based on a problem structure can lead to a better solution with the quantum annealer in comparison with the existing problem decomposition method.", "published": "2019-03-27T18:00:01Z", "authors": ["Naoki Nishimura", "Kotaro Tanahashi", "Koji Suganuma", "Masamichi J. Miyama", "Masayuki Ohzeki"], "categories": ["quant-ph", "math.OC"], "doi": "10.3389/fcomp.2019.00002", "_query": "housing price text listing", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/1710.11027v1", "title": "Named Entity Recognition in Twitter using Images and Text", "abstract": "Named Entity Recognition (NER) is an important subtask of information extraction that seeks to locate and recognise named entities. Despite recent achievements, we still face limitations with correctly detecting and classifying entities, prominently in short and noisy text, such as Twitter. An important negative aspect in most of NER approaches is the high dependency on hand-crafted features and domain-specific knowledge, necessary to achieve state-of-the-art results. Thus, devising models to deal with such linguistically complex contexts is still challenging. In this paper, we propose a novel multi-level architecture that does not rely on any specific linguistic resource or encoded rule. Unlike traditional approaches, we use features extracted from images and text to classify named entities. Experimental tests against state-of-the-art NER for Twitter on the Ritter dataset present competitive results (0.59 F-measure), indicating that this approach may lead towards better NER models.", "published": "2017-10-30T15:56:03Z", "authors": ["Diego Esteves", "Rafael Peres", "Jens Lehmann", "Giulio Napolitano"], "categories": ["cs.IR", "cs.CL"], "doi": "", "_query": "housing price text listing", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2211.13499v1", "title": "On the triangulated category of framed motives $\\text{DFr}_{-}^{eff}(k)$", "abstract": "The category of framed correspondences $Fr_*(k)$ was invented by Voevodsky in his notes in order to give another framework for SH(k) more amenable to explicit calculations. Based on that notes and on their JAMS paper Garkusha and the author introduced in a very recent paper a triangulated category of framed bispectra $\\text{SH}_{nis}^{fr}(k)$. It is shown in the latter paper that $\\text{SH}_{nis}^{fr}(k)$ recovers classical Morel-Voevodsky triangulated category of bispectra $\\text{SH}(k)$. For any infinite perfect field $k$ a triangulated category of $\\mathbb{F}\\text{r}$-motives $\\text{D}\\mathbb{F}\\text{r}_{-}^{eff}(k)$ is constructed in the style of the Voevodsky construction of the category $\\text{DM}_-^{eff}(k)$. In our approach the Voevodsky category of Nisnevich sheaves with transfers is replaced with the category of $\\mathbb{F}\\text{r}$-modules. To each smooth $k$-variety $X$ the $\\mathbb{F}\\text{r}$-motive $\\text{M}_{\\mathbb{F}\\text{r}}(X)$ is associated in the category $\\text{D}\\mathbb{F}\\text{r}_{-}^{eff}(k)$. We identify the triangulated category $\\text{D}\\mathbb{F}\\text{r}_{-}^{eff}(k)$ with the full triangulated subcategory $\\text{SH}^{eff}_{-}(k)$ of the classical Morel-Voevodsky triangulated category $\\text{SH}^{eff}(k)$ of effective motivic bispectra. Moreover, the triangulated category $\\text{D}\\mathbb{F}\\text{r}_{-}^{eff}(k)$ is naturally symmetric monoidal. The mentioned identification of the triangulated categories respects the symmetric monoidal structures on both sides.", "published": "2022-11-24T09:42:37Z", "authors": ["Ivan Panin"], "categories": ["math.KT"], "doi": "", "_query": "housing price text listing", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2604.17576v1", "title": "Strategic Pricing and Consumer Welfare under One-Sided Price Regulation", "abstract": "Motivated by Germany's April 2026 fuel price regulation, in this note I study a two-period pricing problem with demand uncertainty and a rule that prohibits more than one price increase during the day. Under flexible pricing, the firm chooses the static monopoly price in each period. Under the regulation, by contrast, it may price strategically high in period 1 to preserve flexibility in period 2. I show that the regulation weakly raises expected average prices. The increase is strict when future high demand is sufficiently likely and the gap between high and low demand is large; otherwise, expected average prices are unchanged. Consumer surplus rises when expected prices do not, and decreases otherwise.", "published": "2026-04-19T18:44:05Z", "authors": ["Philipp Denter"], "categories": ["econ.TH"], "doi": "", "_query": "housing price text listing", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2608.28042v1", "title": "SimpCue: Cue-Based Prompting for Multilingual Text Simplification", "abstract": "Text simplification aims to make complex texts easier to understand while preserving their original meaning. Recent large language models can perform simplification through prompting, but it remains unclear whether adding explicit linguistic information about sentence complexity to the prompt improves their outputs. We investigate this question for multilingual sentence-level Easy-to-Read simplification in Catalan, Spanish, and Italian. Using Qwen3-8B, we compare a baseline prompt, a gold-cue prompt enriched with gold linguistic cues, and a predicted-cue prompt enriched with automatically predicted cues. We evaluate the outputs using SARI, BLEU, chrF, and BERTScore, and complement this evaluation with a manual qualitative analysis. Predicted-cue prompting obtains the best overall scores across all four metrics, although the gains over the baseline are small. Gold-cue prompting does not consistently improve over the baseline, and results vary across languages. These findings indicate that cue-based prompting can influence multilingual Easy-to-Read simplification, but its benefits are modest, metric-dependent, and language-dependent.", "published": "2026-08-28T08:05:04Z", "authors": ["Mehrzad Tareh", "Horacio Saggion", "Stefan Bott"], "categories": ["cs.CL", "cs.AI"], "doi": "", "_query": "housing price text listing", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/1503.08167v2", "title": "Normalization of Non-Standard Words in Croatian Texts", "abstract": "This paper presents text normalization which is an integral part of any text-to-speech synthesis system. Text normalization is a set of methods with a task to write non-standard words, like numbers, dates, times, abbreviations, acronyms and the most common symbols, in their full expanded form are presented. The whole taxonomy for classification of non-standard words in Croatian language together with rule-based normalization methods combined with a lookup dictionary are proposed. Achieved token rate for normalization of Croatian texts is 95%, where 80% of expanded words are in correct morphological form.", "published": "2015-03-27T17:57:00Z", "authors": ["Slobodan Beliga", "Miran Pobar", "Sanda Martinčić-Ipšić"], "categories": ["cs.CL"], "doi": "", "_query": "housing price text listing", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2310.15687v1", "title": "Reducing residential emissions: carbon pricing vs. subsidizing retrofits", "abstract": "In this paper, we compare different mitigation policies when housing investments are irreversible. We use a general equilibrium model with non-homothetic preferences and an elaborate setup of the residential housing and energy production sector. In the first-best transition, the energy demand plays only a secondary role. However, this changes when optimal carbon taxes are not available. While providing subsidies for retrofits results in the lowest direct costs for households, it ultimately leads to the highest aggregate costs and proves to be an ineffective way to decarbonize the economy. In the second-best context, a phased-in carbon price outperforms the subsidy-based transition.", "published": "2023-10-24T09:59:53Z", "authors": ["Alkis Blanz", "Beatriz Gaitan"], "categories": ["econ.GN"], "doi": "", "_query": "housing price text listing", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2403.08915v1", "title": "Cross-Modal Learning of Housing Quality in Amsterdam", "abstract": "In our research we test data and models for the recognition of housing quality in the city of Amsterdam from ground-level and aerial imagery. For ground-level images we compare Google StreetView (GSV) to Flickr images. Our results show that GSV predicts the most accurate building quality scores, approximately 30% better than using only aerial images. However, we find that through careful filtering and by using the right pre-trained model, Flickr image features combined with aerial image features are able to halve the performance gap to GSV features from 30% to 15%. Our results indicate that there are viable alternatives to GSV for liveability factor prediction, which is encouraging as GSV images are more difficult to acquire and not always available.", "published": "2024-03-13T19:11:58Z", "authors": ["Alex Levering", "Diego Marcos", "Devis Tuia"], "categories": ["cs.CV", "cs.AI"], "doi": "10.1145/3486635.3491067", "_query": "housing price text listing", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/cs/0611113v1", "title": "An Anthological Review of Research Utilizing MontyLingua, a Python-Based End-to-End Text Processor", "abstract": "MontyLingua, an integral part of ConceptNet which is currently the largest commonsense knowledge base, is an English text processor developed using Python programming language in MIT Media Lab. The main feature of MontyLingua is the coverage for all aspects of English text processing from raw input text to semantic meanings and summary generation, yet each component in MontyLingua is loosely-coupled to each other at the architectural and code level, which enabled individual components to be used independently or substituted. However, there has been no review exploring the role of MontyLingua in recent research work utilizing it. This paper aims to review the use of and roles played by MontyLingua and its components in research work published in 19 articles between October 2004 and August 2006. We had observed a diversified use of MontyLingua in many different areas, both generic and domain-specific. Although the use of text summarizing component had not been observe, we are optimistic that it will have a crucial role in managing the current trend of information overload in future research.", "published": "2006-11-22T03:24:54Z", "authors": ["Maurice HT Ling"], "categories": ["cs.CL"], "doi": "", "_query": "housing price text listing", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/cond-mat/0312560v2", "title": "Power law for the calm-time interval of price changes", "abstract": "In this paper, we describe a newly discovered statistical property of time series data for daily price changes. We conducted quantitative investigation of the {\\it calm-time intervals} of price changes for 800 companies listed in the Tokyo Stock Exchange, and for the Nikkei 225 index over a 27-year period from January 4, 1975 to December 28, 2001. A calm-time interval is defined as the interval between two successive price changes above a fixed threshold. We found that the calm-time interval distribution of price changes obeys a power law decay. Furthermore, we show that the power-law exponent decreases monotonically with respect to the threshold. Keyword: econophysics, stock price changes, calm time interval, power-laws; PACS: 89.90.+n; 05.40.Df;", "published": "2003-12-21T17:49:59Z", "authors": ["Taisei Kaizoji", "Michiyo Kaizoji"], "categories": ["cond-mat.stat-mech", "physics.soc-ph", "q-fin.ST"], "doi": "10.1016/j.physa.2003.12.054", "_query": "housing price text listing", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/1702.06810v3", "title": "Pricing average price advertising options when underlying spot market prices are discontinuous", "abstract": "Advertising options have been recently studied as a special type of guaranteed contracts in online advertising, which are an alternative sales mechanism to real-time auctions. An advertising option is a contract which gives its buyer a right but not obligation to enter into transactions to purchase page views or link clicks at one or multiple pre-specified prices in a specific future period. Different from typical guaranteed contracts, the option buyer pays a lower upfront fee but can have greater flexibility and more control of advertising. Many studies on advertising options so far have been restricted to the situations where the option payoff is determined by the underlying spot market price at a specific time point and the price evolution over time is assumed to be continuous. The former leads to a biased calculation of option payoff and the latter is invalid empirically for many online advertising slots. This paper addresses these two limitations by proposing a new advertising option pricing framework. First, the option payoff is calculated based on an average price over a specific future period. Therefore, the option becomes path-dependent. The average price is measured by the power mean, which contains several existing option payoff functions as its special cases. Second, jump-diffusion stochastic models are used to describe the movement of the underlying spot market price, which incorporate several important statistical properties including jumps and spikes, non-normality, and absence of autocorrelations. A general option pricing algorithm is obtained based on Monte Carlo simulation. In addition, an explicit pricing formula is derived for the case when the option payoff is based on the geometric mean. This pricing formula is also a generalized version of several other option pricing models discussed in related studies.", "published": "2017-02-22T14:26:31Z", "authors": ["Bowei Chen", "Mohan Kankanhalli"], "categories": ["cs.GT"], "doi": "10.1109/TKDE.2018.2867027", "_query": "housing price text listing", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/1403.7830v1", "title": "Pseudo Linear Pricing Rule for Utility Indifference Valuation", "abstract": "This paper considers exponential utility indifference pricing for a multidimensional non-traded assets model, and provides two linear approximations for the utility indifference price. The key tool is a probabilistic representation for the utility indifference price by the solution of a functional differential equation, which is termed \\emph{pseudo linear pricing rule}. We also provide an alternative derivation of the quadratic BSDE representation for the utility indifference price.", "published": "2014-03-30T23:18:17Z", "authors": ["Vicky Henderson", "Gechun Liang"], "categories": ["q-fin.PM", "math.PR"], "doi": "", "_query": "housing price text listing", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/1411.0756v1", "title": "Greatest solutions of equations in $\\text{CLL}_R$ and its application", "abstract": "This paper explores the process calculus $\\text{CLL}_R$ furtherly. First, we prove that for any equation $X=_{RS} t_X$ such that $X$ is strongly guarded in $t_X$, $\\langle X|X=t_X \\rangle$ is the largest solution w.r.t $\\sqsubseteq_{RS}$. Second, we encode a fragment of action-based CTL in $\\text{CLL}_R$.", "published": "2014-11-04T02:10:09Z", "authors": ["Yan Zhang", "Zhaohui Zhu", "Jinjin Zhang"], "categories": ["cs.LO"], "doi": "", "_query": "housing price text listing", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/1805.07467v2", "title": "Unsupervised Cross-Modal Alignment of Speech and Text Embedding Spaces", "abstract": "Recent research has shown that word embedding spaces learned from text corpora of different languages can be aligned without any parallel data supervision. Inspired by the success in unsupervised cross-lingual word embeddings, in this paper we target learning a cross-modal alignment between the embedding spaces of speech and text learned from corpora of their respective modalities in an unsupervised fashion. The proposed framework learns the individual speech and text embedding spaces, and attempts to align the two spaces via adversarial training, followed by a refinement procedure. We show how our framework could be used to perform spoken word classification and translation, and the results on these two tasks demonstrate that the performance of our unsupervised alignment approach is comparable to its supervised counterpart. Our framework is especially useful for developing automatic speech recognition (ASR) and speech-to-text translation systems for low- or zero-resource languages, which have little parallel audio-text data for training modern supervised ASR and speech-to-text translation models, but account for the majority of the languages spoken across the world.", "published": "2018-05-18T22:59:18Z", "authors": ["Yu-An Chung", "Wei-Hung Weng", "Schrasing Tong", "James Glass"], "categories": ["cs.CL", "cs.SD", "eess.AS"], "doi": "", "_query": "housing price text listing", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2608.18547v1", "title": "Measuring Proof Burden in Public Bounty Listings: A RentAHuman Case Study", "abstract": "Online bounty markets let requesters advertise paid tasks. Workers may be asked not just to complete a task but to prove it, and proof can mean exposure: revealing identity or location, using a personal account, posting publicly, acting in the physical world, or repeated evidence at later checks, none disclosed by the posted price. We call these advertised requirements proof burden and measure them on RentAHuman, a 2026 market publicized as a place for AI agents to hire humans. We study what listings request, not what workers submit or experience. We manually audited a nonrandom May 31, 2026 snapshot: every listing our searches returned from RentAHuman and Human Pages, another such market (981 listings, all but one from RentAHuman). Two independent coders recorded 13 features (11 kinds of evidence, recurring monitoring, physical-world action) and our 0-5 Proof Burden Score; a blinded third resolved all disagreements. A planned content screen leaves 779 bounty/task listings as the primary population; 438 (56.2%) score 4 or 5, spanning 154 distinct feature combinations: a checklist, not a single score, tells workers what a listing entails. Platform metadata labels some requester accounts as agents or bots. Exploratory comparisons show physical-world action, location proof, or recurring monitoring in 75.0% of agent-or-bot-labeled versus 55.3% of human-labeled listings, though score-4-or-5 shares did not clearly differ. The labels are self-reported or platform-assigned, the agent-or-bot-labeled listings come from only 20 displayed names, and the comparison was chosen post hoc, after seeing the data: a hypothesis, not a confirmed difference. We contribute the 13-requirement vocabulary, the adjudicated manual audit, and this descriptive case study; the score is a secondary screening summary. The study offers no worker-validated measure or automated detector yet.", "published": "2026-08-19T05:08:21Z", "authors": ["Iman YeckehZaare"], "categories": ["cs.HC", "cs.CY"], "doi": "10.1145/3834580.3838744", "_query": "housing price text listing", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/1706.08188v1", "title": "Listing Words in Free Groups", "abstract": "Lists of equivalence classes of words under rotation or rotation plus reversal (i.e., necklaces and bracelets) have many uses, and efficient algorithms for generating these lists exist. In combinatorial group theory elements of a group are typically written as words in the generators and their inverses, and necklaces and bracelets correspond to conjugacy classes and relators respectively. We present algorithms to generate lists of freely and cyclically reduced necklaces and bracelets in free groups. Experimental evidence suggests that these algorithms are CAT -- that is, they run in constant amortized time.", "published": "2017-06-25T23:21:15Z", "authors": ["Colin Ramsay"], "categories": ["math.CO"], "doi": "", "_query": "housing price text listing", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2209.02407v1", "title": "Predict stock prices with ARIMA and LSTM", "abstract": "MAE, MSE and RMSE performance indicators are used to analyze the performance of different stocks predicted by LSTM and ARIMA models in this paper. 50 listed company stocks from finance.yahoo.com are selected as the research object in the experiments. The dataset used in this work consists of the highest price on transaction days, corresponding to the period from 01 January 2010 to 31 December 2018. For LSTM model, the data from 01 January 2010 to 31 December 2015 are selected as the training set, the data from 01 January 2016 to 31 December 2017 as the validation set and the data from 01 January 2018 to 31 December 2018 as the test set. In term of ARIMA model, the data from 01 January 2016 to 31 December 2017 are selected as the training set, and the data from 01 January 2018 to 31 December 2018 as the test set. For both models, 60 days of data are used to predict the next day. After analysis, it is suggested that both ARIMA and LSTM models can predict stock prices, and the prediction results are generally consistent with the actual results;and LSTM has better performance in predicting stock prices(especially in expressing stock price changes), while the application of ARIMA is more convenient.", "published": "2022-08-31T17:05:44Z", "authors": ["Ruochen Xiao", "Yingying Feng", "Lei Yan", "Yihan Ma"], "categories": ["q-fin.ST", "cs.CE"], "doi": "", "_query": "housing price text listing", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2403.16584v2", "title": "Can Large Language Models (or Humans) Disentangle Text?", "abstract": "We investigate the potential of large language models (LLMs) to disentangle text variables--to remove the textual traces of an undesired forbidden variable in a task sometimes known as text distillation and closely related to the fairness in AI and causal inference literature. We employ a range of various LLM approaches in an attempt to disentangle text by identifying and removing information about a target variable while preserving other relevant signals. We show that in the strong test of removing sentiment, the statistical association between the processed text and sentiment is still detectable to machine learning classifiers post-LLM-disentanglement. Furthermore, we find that human annotators also struggle to disentangle sentiment while preserving other semantic content. This suggests there may be limited separability between concept variables in some text contexts, highlighting limitations of methods relying on text-level transformations and also raising questions about the robustness of disentanglement methods that achieve statistical independence in representation space.", "published": "2024-03-25T09:51:54Z", "authors": ["Nicolas Audinet de Pieuchon", "Adel Daoud", "Connor Thomas Jerzak", "Moa Johansson", "Richard Johansson"], "categories": ["cs.CL"], "doi": "", "_query": "housing price text listing", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2512.02192v1", "title": "Story2MIDI: Emotionally Aligned Music Generation from Text", "abstract": "In this paper, we introduce Story2MIDI, a sequence-to-sequence Transformer-based model for generating emotion-aligned music from a given piece of text. To develop this model, we construct the Story2MIDI dataset by merging existing datasets for sentiment analysis from text and emotion classification in music. The resulting dataset contains pairs of text blurbs and music pieces that evoke the same emotions in the reader or listener. Despite the small scale of our dataset and limited computational resources, our results indicate that our model effectively learns emotion-relevant features in music and incorporates them into its generation process, producing samples with diverse emotional responses. We evaluate the generated outputs using objective musical metrics and a human listening study, confirming the model's ability to capture intended emotional cues.", "published": "2025-12-01T20:35:18Z", "authors": ["Mohammad Shokri", "Alexandra C. Salem", "Gabriel Levine", "Johanna Devaney", "Sarah Ita Levitan"], "categories": ["cs.SD", "cs.AI", "cs.CL"], "doi": "", "_query": "housing price text listing", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/1809.09228v1", "title": "Non-trivial topology of the quasi-one-dimensional triplons in the quantum antiferromagnet ${\\text{BiCu}}_{2}{\\text{PO}}_{6}$", "abstract": "Topological properties of physical systems are attracting tremendous interest. Recently, magnetic solid state compounds with and without magnetic order have become a focus. We show that ${\\text{BiCu}}_{2}{\\text{PO}}_{6}$ is the first gapful quantum antiferromagnet with a finite Zak phase, which characterises one-dimensional systems, and only the second with topological non-trivial triplon excitations. Surprisingly, in spite of the bulk-boundary correspondence no localised edge mode occurs. This unexpected behaviour is explained by the distinction between direct and indirect gaps among the triplon bands.", "published": "2018-09-24T21:17:28Z", "authors": ["Maik Malki", "Leanna Splinter", "Götz S. Uhrig"], "categories": ["cond-mat.str-el"], "doi": "10.1103/PhysRevResearch.1.033197", "_query": "housing price text listing", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2501.01156v2", "title": "TexAVi: Generating Stereoscopic VR Video Clips from Text Descriptions", "abstract": "While generative models such as text-to-image, large language models and text-to-video have seen significant progress, the extension to text-to-virtual-reality remains largely unexplored, due to a deficit in training data and the complexity of achieving realistic depth and motion in virtual environments. This paper proposes an approach to coalesce existing generative systems to form a stereoscopic virtual reality video from text. Carried out in three main stages, we start with a base text-to-image model that captures context from an input text. We then employ Stable Diffusion on the rudimentary image produced, to generate frames with enhanced realism and overall quality. These frames are processed with depth estimation algorithms to create left-eye and right-eye views, which are stitched side-by-side to create an immersive viewing experience. Such systems would be highly beneficial in virtual reality production, since filming and scene building often require extensive hours of work and post-production effort. We utilize image evaluation techniques, specifically Fréchet Inception Distance and CLIP Score, to assess the visual quality of frames produced for the video. These quantitative measures establish the proficiency of the proposed method. Our work highlights the exciting possibilities of using natural language-driven graphics in fields like virtual reality simulations.", "published": "2025-01-02T09:21:03Z", "authors": ["Vriksha Srihari", "R. Bhavya", "Shruti Jayaraman", "V. Mary Anita Rajam"], "categories": ["cs.CV", "cs.AI", "cs.LG"], "doi": "10.1109/CVMI61877.2024.10782691", "_query": "housing price text listing", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/1908.11597v1", "title": "An Empirical Study into the Success of Listed Smart Contracts in Ethereum", "abstract": "Since it takes time and effort to put a new product or service on the market, one would like to predict whether it will be a success. In general this is not possible, but it is possible to follow best practices in order to maximise the chance of success. A smart contract is intended to encode business logic and is therefore at the heart of every new business on the Ethereum blockchain. We have investigated how to measure the success of smart contracts, and whether successful smart contracts have characteristics that less successful smart contracts lack. The appearance of a smart contract on a listing website such as Etherscan or StateoftheDapps is such a characteristic. In this paper, we present a three-pronged analysis of the relative success of listed smart contracts. First, we have used statistical analysis on the publicly visible transaction history of the Ethereum blockchain to determine that listed contracts are significantly more successful than their unlisted counterparts. Next, we have conducted a survey among more than 200 developers via an anonymous online survey about their experience with the listing process. A significant majority of respondents do not believe that listing a contract itself contributes to its success, but they believe that the extra attention that is typically paid in tandem with the listing process does contribute. Finally, based on the respondents' answers, we have drafted 10 recommendations for developers and validated them by submitting them to an international panel of experts.", "published": "2019-08-30T08:55:44Z", "authors": ["Pieter Hartel", "Ivan Homoliak", "Daniël Reijsbergen"], "categories": ["cs.SE"], "doi": "", "_query": "housing price text listing", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/1904.09949v1", "title": "A note on the effective listing of complete types", "abstract": "We use the \"geometric axioms\" point of view to give an effective listing of the complete types of the theory $DCF_{0}$ of differentially closed fields of characteristic $0$. This gives another account of observations made in earlier papers.", "published": "2019-04-22T16:50:11Z", "authors": ["Anand Pillay"], "categories": ["math.LO", "math.AG"], "doi": "", "_query": "housing price text listing", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/1905.00468v2", "title": "Envy-Freeness in House Allocation Problems", "abstract": "We consider the house allocation problem, where $m$ houses are to be assigned to $n$ agents so that each agent gets exactly one house. We present a polynomial-time algorithm that determines whether an envy-free assignment exists, and if so, computes one such assignment. We also show that an envy-free assignment exists with high probability if the number of houses exceeds the number of agents by a logarithmic factor.", "published": "2019-05-01T19:45:40Z", "authors": ["Jiarui Gan", "Warut Suksompong", "Alexandros A. Voudouris"], "categories": ["cs.GT"], "doi": "10.1016/j.mathsocsci.2019.07.005", "_query": "housing price text listing", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/1803.05342v2", "title": "Zassenhaus Conjecture on torsion units holds for $\\text{SL}(2,p)$ and $\\text{SL}(2,p^2)$", "abstract": "H.J. Zassenhaus conjectured that any unit of finite order and augmentation $1$ in the integral group ring $\\mathbb{Z}G$ of a finite group $G$ is conjugate in the rational group algebra $\\mathbb{Q}G$ to an element of $G$. We prove the Zassenhaus Conjecture for the groups $\\text{SL}(2,p)$ and $\\text{SL}(2,p^2)$ with $p$ a prime number. This is the first infinite family of non-solvable groups for which the Zassenhaus Conjecture has been proved. We also prove that if $G=\\text{SL}(2,p^f)$, with $f$ arbitrary and $u$ is a torsion unit of $\\mathbb{Z}G$ with augmentation $1$ and order coprime with $p$ then $u$ is conjugate in $\\mathbb{Q}G$ to an element of $G$. By known results, this reduces the proof of the Zassenhaus Conjecture for this groups to prove that every unit of $\\mathbb{Z}G$ of order multiple of $p$ and augmentation $1$ has actually order $p$.", "published": "2018-03-14T15:09:40Z", "authors": ["Ángel del Río", "Mariano Serrano"], "categories": ["math.GR"], "doi": "", "_query": "housing price text listing", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2101.00718v1", "title": "Text Searching Allowing for Non-Overlapping Adjacent Unbalanced Translocations", "abstract": "In this paper we investigate the \\emph{approximate string matching problem} when the allowed edit operations are \\emph{non-overlapping unbalanced translocations of adjacent factors}. Such kind of edit operations take place when two adjacent sub-strings of the text swap, resulting in a modified string. The two involved substrings are allowed to be of different lengths. Such large-scale modifications on strings have various applications. They are among the most frequent chromosomal alterations, accounted for 30\\% of all losses of heterozygosity, a major genetic event causing inactivation of cancer suppressor genes. In addition, among other applications, they are frequent modifications accounted in musical or in natural language information retrieval. However, despite of their central role in so many fields of text processing, little attention has been devoted to the problem of matching strings allowing for this kind of edit operation. In this paper we present three algorithms for solving the problem, all of them with a $\\bigO(nm^3)$ worst-case and a $\\bigO(m^2)$-space complexity, where $m$ and $n$ are the length of the pattern and of the text, respectively. % In particular, our first algorithm is based on the dynamic-programming approach. Our second solution improves the previous one by making use of the Directed Acyclic Word Graph of the pattern. Finally our third algorithm is based on an alignment procedure. We also show that under the assumptions of equiprobability and independence of characters, our second algorithm has a $\\bigO(n\\log^2_σ m)$ average time complexity, for an alphabet of size $σ\\geq 4$.", "published": "2021-01-03T22:21:51Z", "authors": ["Domenico Cantone", "Simone Faro", "Arianna Pavone"], "categories": ["cs.DS", "cs.LO"], "doi": "", "_query": "housing price text listing", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2208.02819v1", "title": "Model Blending for Text Classification", "abstract": "Deep neural networks (DNNs) have proven successful in a wide variety of applications such as speech recognition and synthesis, computer vision, machine translation, and game playing, to name but a few. However, existing deep neural network models are computationally expensive and memory intensive, hindering their deployment in devices with low memory resources or in applications with strict latency requirements. Therefore, a natural thought is to perform model compression and acceleration in deep networks without significantly decreasing the model performance, which is what we call reducing the complexity. In the following work, we try reducing the complexity of state of the art LSTM models for natural language tasks such as text classification, by distilling their knowledge to CNN based models, thus reducing the inference time(or latency) during testing.", "published": "2022-08-05T05:07:45Z", "authors": ["Ramit Pahwa"], "categories": ["cs.LG", "cs.AI"], "doi": "", "_query": "housing price text listing", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/1706.04997v1", "title": "Extracting Formal Models from Normative Texts", "abstract": "We are concerned with the analysis of normative texts - documents based on the deontic notions of obligation, permission, and prohibition. Our goal is to make queries about these notions and verify that a text satisfies certain properties concerning causality of actions and timing constraints. This requires taking the original text and building a representation (model) of it in a formal language, in our case the C-O Diagram formalism. We present an experimental, semi-automatic aid that helps to bridge the gap between a normative text in natural language and its C-O Diagram representation. Our approach consists of using dependency structures obtained from the state-of-the-art Stanford Parser, and applying our own rules and heuristics in order to extract the relevant components. The result is a tabular data structure where each sentence is split into suitable fields, which can then be converted into a C-O Diagram. The process is not fully automatic however, and some post-editing is generally required of the user. We apply our tool and perform experiments on documents from different domains, and report an initial evaluation of the accuracy and feasibility of our approach.", "published": "2017-06-15T07:24:23Z", "authors": ["John J. Camilleri", "Normunds Grūzītis", "Gerardo Schneider"], "categories": ["cs.CL"], "doi": "", "_query": "housing price text listing", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2009.10535v1", "title": "3D Printing an External Cavity Diode Laser Housing", "abstract": "The ability to control the frequency of an external-cavity diode laser (ECDL) is an essential component for undergraduate laboratories and atomic physics research. Typically the housing for the ECDL's diffraction grating and piezoelectric transducer is either purchased commercially or machined from metal. Here, we present an alternative to these commonly used options that utilizes 3D printing, a tool available in many physics departments. We characterize the performance of our ECDL system using atomic spectroscopy and self-heterodyne interferometry and show that it is sufficient for use in undergraduate spectroscopy experiments and a number of research applications where extremely narrow laser linewidths are not necessary. The performance and affordability of 3D-printed designs make them an appealing option for future use.", "published": "2020-09-18T17:10:12Z", "authors": ["E. Brekke", "T. Bennett", "H. Rook", "E. L. Hazlett"], "categories": ["physics.ed-ph", "physics.atom-ph"], "doi": "10.1119/10.0002135", "_query": "housing price text listing", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2106.03158v2", "title": "Transferring Knowledge from Text to Video: Zero-Shot Anticipation for Procedural Actions", "abstract": "Can we teach a robot to recognize and make predictions for activities that it has never seen before? We tackle this problem by learning models for video from text. This paper presents a hierarchical model that generalizes instructional knowledge from large-scale text corpora and transfers the knowledge to video. Given a portion of an instructional video, our model recognizes and predicts coherent and plausible actions multiple steps into the future, all in rich natural language. To demonstrate the capabilities of our model, we introduce the \\emph{Tasty Videos Dataset V2}, a collection of 4022 recipes for zero-shot learning, recognition and anticipation. Extensive experiments with various evaluation metrics demonstrate the potential of our method for generalization, given limited video data for training models.", "published": "2021-06-06T15:43:39Z", "authors": ["Fadime Sener", "Rishabh Saraf", "Angela Yao"], "categories": ["cs.CV"], "doi": "", "_query": "housing price text listing", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2002.04212v1", "title": "Quantum coupled-wave theory of price formation in financial markets: price measurement, dynamics and ergodicity", "abstract": "We explore nature of price formation in financial markets and develop a theory of bid and ask price dynamics in which the two prices form due to quantum-chaotic interaction between buy and sell orders. In this model bid and ask prices are represented by eigenvalues of a 2x2 price operator corresponding to 'bid' and 'ask' eigenstates, while randomness of price operator results in price fluctuations that destroy oscillatory effects. We show that this theory adequately captures behavior of bid-ask spread and allows to model bid and ask price dynamics in a coordinated way. We also discuss ergodicity properties of price formation and show how directional price movement occurs due to ergodicity violation in a quantum process instead of the commonly believed forces acting on price. This theory has wide range of applications such as trade execution modeling, large order pricing and risk valuation for illiquid securities.", "published": "2020-02-11T05:37:31Z", "authors": ["Jack Sarkissian"], "categories": ["q-fin.TR"], "doi": "10.1016/j.physa.2020.124300", "_query": "housing price text listing", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2210.07423v1", "title": "Task Grouping for Multilingual Text Recognition", "abstract": "Most existing OCR methods focus on alphanumeric characters due to the popularity of English and numbers, as well as their corresponding datasets. On extending the characters to more languages, recent methods have shown that training different scripts with different recognition heads can greatly improve the end-to-end recognition accuracy compared to combining characters from all languages in the same recognition head. However, we postulate that similarities between some languages could allow sharing of model parameters and benefit from joint training. Determining language groupings, however, is not immediately obvious. To this end, we propose an automatic method for multilingual text recognition with a task grouping and assignment module using Gumbel-Softmax, introducing a task grouping loss and weighted recognition loss to allow for simultaneous training of the models and grouping modules. Experiments on MLT19 lend evidence to our hypothesis that there is a middle ground between combining every task together and separating every task that achieves a better configuration of task grouping/separation.", "published": "2022-10-13T23:54:23Z", "authors": ["Jing Huang", "Kevin J Liang", "Rama Kovvuri", "Tal Hassner"], "categories": ["cs.CV"], "doi": "", "_query": "housing price text listing", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2310.11771v2", "title": "Perpetual Futures Pricing", "abstract": "Perpetual futures are contracts without expiration date in which the anchoring of the futures price to the spot price is ensured by periodic funding payments from long to short. We derive explicit expressions for the no-arbitrage price of various perpetual contracts, including linear, inverse, and quantos futures in both discrete and continuous-time. In particular, we show that the futures price is given by the risk-neutral expectation of the spot sampled at a random time that reflects the intensity of the price anchoring. Furthermore, we identify funding specifications that guarantee the coincidence of futures and spot prices, and show that for such specifications perpetual futures contracts can be replicated by dynamic trading in primitive securities.", "published": "2023-10-18T07:57:37Z", "authors": ["Damien Ackerer", "Julien Hugonnier", "Urban Jermann"], "categories": ["q-fin.PR", "q-fin.MF"], "doi": "", "_query": "housing price text listing", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2206.07328v2", "title": "A Survey : Neural Networks for AMR-to-Text", "abstract": "AMR-to-text is one of the key techniques in the NLP community that aims at generating sentences from the Abstract Meaning Representation (AMR) graphs. Since AMR was proposed in 2013, the study on AMR-to-Text has become increasingly prevalent as an essential branch of structured data to text because of the unique advantages of AMR as a high-level semantic description of natural language. In this paper, we provide a brief survey of AMR-to-Text. Firstly, we introduce the current scenario of this technique and point out its difficulties. Secondly, based on the methods used in previous studies, we roughly divided them into five categories according to their respective mechanisms, i.e., Rules-based, Seq-to-Seq-based, Graph-to-Seq-based, Transformer-based, and Pre-trained Language Model (PLM)-based. In particular, we detail the neural network-based method and present the latest progress of AMR-to-Text, which refers to AMR reconstruction, Decoder optimization, etc. Furthermore, we present the benchmarks and evaluation methods of AMR-to-Text. Eventually, we provide a summary of current techniques and the outlook for future research.", "published": "2022-06-15T07:20:28Z", "authors": ["Hongyu Hao", "Guangtong Li", "Zhiming Hu", "Huafeng Wang"], "categories": ["cs.CL", "cs.LG"], "doi": "", "_query": "housing price text listing", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/1403.2990v1", "title": "A Framework for Pricing Schemes for Networks under Complete Information", "abstract": "The revenue maximization problem of service provider is considered and different pricing schemes to solve the above problem are implemented. The service provider can choose an apt pricing scheme subjected to limited resources, if he knows the utility function and identity of the user. The complete price differentiation can achieve a large revenue gain but has high implementation complexity. The partial price differentiation scheme to overcome the high implementational complexity of complete price differentiation scheme is also studied. A polynomial- time algorithm is designed for partial price differentiation scheme that can compute the optimal partial differentiation prices. The willingness of the users to pay is also considered while designing price differentiation schemes.", "published": "2014-03-12T15:53:41Z", "authors": ["Aditya Goyal", "Akanksha Tyagi", "Manisha Bhende", "Swapnil Kawade"], "categories": ["cs.NI"], "doi": "10.14445/22315381/IJETT-V9P214", "_query": "housing price text listing", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2202.09323v2", "title": "Market-Based Price Autocorrelation", "abstract": "This paper assumes that the randomness of market trade values and volumes determines the properties of stochastic market prices. We derive the direct dependence of the first two price statistical moments and price volatility on statistical moments, volatilities, and correlations of market trade values and volumes. That helps describe the dependence of market-based price autocorrelation between times t and t-τ on statistical moments and correlations between trade values and volumes. That highlights the impact of the randomness of the size of market deals on price statistical moments and autocorrelation. Statistical moments and correlations of market trade values and volumes are assessed by conventional frequency-based probabilities. The distinctions between market-based price autocorrelation and autocorrelation that are assessed by the frequency-based probability analysis of price time series reveal the different approaches to the definitions of price probabilities. To forecast market-based price autocorrelation, one should predict the statistical moments and correlations of trade values and volumes.", "published": "2022-02-15T19:40:10Z", "authors": ["Victor Olkhov"], "categories": ["econ.GN", "q-fin.GN", "q-fin.PR", "q-fin.ST"], "doi": "", "_query": "housing price text listing", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2406.09972v1", "title": "A Better LLM Evaluator for Text Generation: The Impact of Prompt Output Sequencing and Optimization", "abstract": "This research investigates prompt designs of evaluating generated texts using large language models (LLMs). While LLMs are increasingly used for scoring various inputs, creating effective prompts for open-ended text evaluation remains challenging due to model sensitivity and subjectivity in evaluation of text generation. Our study experimented with different prompt structures, altering the sequence of output instructions and including explanatory reasons. We found that the order of presenting reasons and scores significantly influences LLMs' scoring, with a different level of rule understanding in the prompt. An additional optimization may enhance scoring alignment if sufficient data is available. This insight is crucial for improving the accuracy and consistency of LLM-based evaluations.", "published": "2024-06-14T12:31:44Z", "authors": ["KuanChao Chu", "Yi-Pei Chen", "Hideki Nakayama"], "categories": ["cs.CL"], "doi": "10.11517/pjsai.JSAI2024.0_2G5GS604", "_query": "housing price text listing", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2204.13102v1", "title": "The Price and Cost of Bitcoin", "abstract": "Explaining changes in bitcoin's price and predicting its future have been the foci of many research studies. In contrast, far less attention has been paid to the relationship between bitcoin's mining costs and its price. One popular notion is the cost of bitcoin creation provides a support level below which this cryptocurrency's price should never fall because if it did, mining would become unprofitable and threaten the maintenance of bitcoin's public ledger. Other research has used mining costs to explain or forecast bitcoin's price movements. Competing econometric analyses have debunked this idea, showing that changes in mining costs follow changes in bitcoin's price rather than preceding them, but the reason for this behavior remains unexplained in these analyses. This research aims to employ economic theory to explain why econometric studies have failed to predict bitcoin prices and why mining costs follow movements in bitcoin prices rather than precede them. We do so by explaining the chain of causality connecting a bitcoin's price to its mining costs.", "published": "2022-04-27T17:57:55Z", "authors": ["John E. Marthinsen", "Steven R. Gordon"], "categories": ["econ.GN"], "doi": "10.1016/j.qref.2022.04.003", "_query": "housing price text listing", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/1712.02820v1", "title": "A Deep Network Model for Paraphrase Detection in Short Text Messages", "abstract": "This paper is concerned with paraphrase detection. The ability to detect similar sentences written in natural language is crucial for several applications, such as text mining, text summarization, plagiarism detection, authorship authentication and question answering. Given two sentences, the objective is to detect whether they are semantically identical. An important insight from this work is that existing paraphrase systems perform well when applied on clean texts, but they do not necessarily deliver good performance against noisy texts. Challenges with paraphrase detection on user generated short texts, such as Twitter, include language irregularity and noise. To cope with these challenges, we propose a novel deep neural network-based approach that relies on coarse-grained sentence modeling using a convolutional neural network and a long short-term memory model, combined with a specific fine-grained word-level similarity matching model. Our experimental results show that the proposed approach outperforms existing state-of-the-art approaches on user-generated noisy social media data, such as Twitter texts, and achieves highly competitive performance on a cleaner corpus.", "published": "2017-12-07T19:10:45Z", "authors": ["Basant Agarwal", "Heri Ramampiaro", "Helge Langseth", "Massimiliano Ruocco"], "categories": ["cs.IR", "cs.AI", "cs.CL"], "doi": "10.1016/j.ipm.2018.06.005", "_query": "housing price text listing", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/1809.08960v1", "title": "On a gap between rational annuitization price for producer and price for customer", "abstract": "The paper studies pricing of insurance products focusing on the pricing of annuities under uncertainty. This pricing problem is crucial for financial decision making and was studied intensively, however, many open questions still remain. In particular, there is a so-called \"annuity puzzle\" related to certain inconsistency of existing financial theory with the empirical observations for the annuities market. The paper suggests a pricing method based on the risk minimization such that both producer and customer seek to minimize the mean square hedging error accepted as a measure of risk. This leads to two different versions of the pricing problem: the selection of the annuity price given the rate of regular payments, and the selection of the rate of payments given the annuity price. It appears that solutions of these two problems are different. This can contribute to explanation for the \"annuity puzzle\".", "published": "2018-09-24T14:16:48Z", "authors": ["Nikolai Dokuchaev"], "categories": ["econ.GN", "q-fin.MF"], "doi": "10.1057/s41272-018-00163-5", "_query": "housing price text listing", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/1806.06424v1", "title": "The House of a Reciprocal Algebraic Integer", "abstract": "Let $α$ be an algebraic integer of degree $d$, which is reciprocal. The house of $α$ is the largest modulus of its conjugates. We compute the minimum of the houses of all reciprocal algebraic integers of degree $d$ which are not roots of unity, say $\\mathrm{mr}(d)$, for $d$ at most 34. We prove several lemmata and use them to avoid unnecessary calculations. The computations suggest several conjectures. The direct consequence of the last one is the conjecture of Schinzel and Zassenhaus. We demonstrate the utility of $d$-th power of the house of $α$.", "published": "2018-06-17T18:26:07Z", "authors": ["Dragan Stankov"], "categories": ["math.NT"], "doi": "", "_query": "housing price text listing", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/cs/0602076v1", "title": "Exploring term-document matrices from matrix models in text mining", "abstract": "We explore a matrix-space model, that is a natural extension to the vector space model for Information Retrieval. Each document can be represented by a matrix that is based on document extracts (e.g. sentences, paragraphs, sections). We focus on the performance of this model for the specific case in which documents are originally represented as term-by-sentence matrices. We use the singular value decomposition to approximate the term-by-sentence matrices and assemble these results to form the pseudo-``term-document'' matrix that forms the basis of a text mining method alternative to traditional VSM and LSI. We investigate the singular values of this matrix and provide experimental evidence suggesting that the method can be particularly effective in terms of accuracy for text collections with multi-topic documents, such as web pages with news.", "published": "2006-02-21T16:14:16Z", "authors": ["Ioannis Antonellis", "Efstratios Gallopoulos"], "categories": ["cs.IR", "cs.DB", "cs.DL"], "doi": "", "_query": "housing price text listing", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/1610.05562v1", "title": "Do Price Ranges Increase Click-throughs?", "abstract": "An important goal of online comparison shopping services is to \"convert\" a viewer from general product category pages (for example product groups such as \"smartphones\" or \"air-conditioners\") to detailed product pages and ultimately to order pages. Comparison shopping websites provide a familiar web interface as well as a chance for consumers to purchase items at competitive prices. In return for providing access to a large market of potential consumers, the comparison shopping service usually receives financial compensation for product clicks and orders. This study looked at 2.5 million product listing visits at price.com.hk to determine whether a modification in the way prices are displayed on general category pages resulted in more \"conversions\" to product detail pages. We found a statistically significant improvement over-all as a result of the new price display resulting in 3.6% more product clicks over all categories. Additional analysis showed that the effect is heterogeneous among different categories, and in a few cases there may be some categories negatively affected by the display modification.", "published": "2016-05-15T03:58:06Z", "authors": ["Patrick Ng", "Drew Plant", "Yiran Sheng"], "categories": ["cs.CY"], "doi": "", "_query": "housing price text listing", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2203.00856v1", "title": "E-Polynomials of Generic $\\text{GL}_n\\rtimes\\!<\\!σ\\!>\\!~$-Character Varieties: Unbranched Case", "abstract": "For any unbranched double covering of compact Riemann surfaces, we study the associated character varieties that are unitary in the global sense, which we call $\\text{GL}_n\\rtimes\\!<\\!σ\\!>\\!~$-character varieties. We introduce $k>0$ punctures on the surface, and restrict the monodromies around the punctures to generic semi-simple conjugacy classes in $\\text{GL}_n$, and compute the E-polynomials of these character varieties using the character table of $\\text{GL}_n(q)$. The result is expressed as the inner product of certain symmetric functions. We are then led to a conjectural formula for the mixed Hodge polynomial, which is built out of (modified) Macdonald polynomials, their self-pairings, and self-pairings of wreath Macdonald polynomials.", "published": "2022-03-02T04:14:01Z", "authors": ["Cheng Shu"], "categories": ["math.AG"], "doi": "", "_query": "housing price text listing", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2105.13903v4", "title": "Three Remarks On Asset Pricing", "abstract": "We consider the consumption-based asset pricing model, derive a new modified basic pricing equation, and present its successive approximations using the Taylor series expansions of the investor's utility during the averaging time interval. For linear and quadratic Taylor approximations, we derive new expressions for the mean price, mean payoff, volatility, skewness, and the asset's amount that define the maximum of the investor's utility. We discuss the market-based origin of price probability. We use volume weighted average price (VWAP) as a market-based average price and introduce market-based price volatility. The use of VWAP results in zero correlations between the price p and trade volume U. We derive a correlation between price p and squares of trade volume {U^2} and between squares of price {p^2} and volume {U^2}. To predict market-based price volatility, one should forecast the 2-d statistical moments of the market trade values and volumes at the same horizon T.", "published": "2021-05-24T17:22:24Z", "authors": ["Victor Olkhov"], "categories": ["econ.GN", "q-fin.GN", "q-fin.PR"], "doi": "", "_query": "housing price text listing", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2304.14262v2", "title": "A flow-based ascending auction to compute buyer-optimal Walrasian prices", "abstract": "We consider a market where a set of objects is sold to a set of buyers, each equipped with a valuation function for the objects. The goal of the auctioneer is to determine reasonable prices together with a stable allocation. One definition of \"reasonable\" and \"stable\" is a Walrasian equilibrium, which is a tuple consisting of a price vector together with an allocation satisfying the following desirable properties: (i) the allocation is market-clearing in the sense that as much as possible is sold, and (ii) the allocation is stable in the sense that every buyer ends up with an optimal set with respect to the given prices. Moreover, \"buyer-optimal\" means that the prices are smallest possible among all Walrasian prices. In this paper, we present a combinatorial network flow algorithm to compute buyer-optimal Walrasian prices in a multi-unit matching market with additive valuation functions and buyer demands. The algorithm can be seen as a generalization of the classical housing market auction and mimics the very natural procedure of an ascending auction. We use our structural insights to prove monotonicity of the buyer-optimal Walrasian prices with respect to changes in supply or demand.", "published": "2023-04-27T15:19:33Z", "authors": ["Katharina Eickhoff", "S. Thomas McCormick", "Britta Peis", "Niklas Rieken", "Laura Vargas Koch"], "categories": ["cs.GT"], "doi": "10.1002/net.22218", "_query": "housing price text listing", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2403.05131v3", "title": "Sora as a World Model? A Complete Survey on Text-to-Video Generation", "abstract": "The evolution of video generation from text, from animating MNIST to simulating the world with Sora, has progressed at a breakneck speed. Here, we systematically discuss how far text-to-video generation technology supports essential requirements in world modeling. We curate 250+ studies on text-based video synthesis and world modeling. We then observe that recent models increasingly support spatial, action, and strategic intelligences in world modeling through adherence to completeness, consistency, invention, as well as human interaction and control. We conclude that text-to-video generation is adept at world modeling, although homework in several aspects, such as the diversity-consistency trade-offs, remains to be addressed.", "published": "2024-03-08T07:58:13Z", "authors": ["Fachrina Dewi Puspitasari", "Chaoning Zhang", "Joseph Cho", "Adnan Haider", "Noor Ul Eman", "Omer Amin", "Alexis Mankowski", "Muhammad Umair", "Jingyao Zheng", "Sheng Zheng", "Lik-Hang Lee", "Caiyan Qin", "Tae-Ho Kim", "Choong Seon Hong", "Yang Yang", "Heng Tao Shen"], "categories": ["cs.AI", "cs.CV"], "doi": "", "_query": "housing price text listing", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/1003.2692v1", "title": "Modeling share prices of banks and bankrupts", "abstract": "Share prices of financial companies from the S&P 500 list have been modeled by a linear function of consumer price indices in the USA. The Johansen and Engle-Granger tests for cointegration both demonstrated the presence of an equilibrium long-term relation between observed and predicted time series. Econometrically, the pricing concept is valid. For several companies, share prices are defined only by CPI readings in the past. Therefore, our empirical pricing model is a deterministic one. For a few companies, including Lehman Brothers, AIG, Freddie Mac and Fannie Mae, negative share prices could be foreseen in May-September 2008. One might interpret the negative share prices as a sign of approaching bankruptcies.", "published": "2010-03-13T08:52:47Z", "authors": ["Ivan O. Kitov"], "categories": ["q-fin.ST"], "doi": "", "_query": "housing price text listing", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2105.14445v1", "title": "Modeling Text-visual Mutual Dependency for Multi-modal Dialog Generation", "abstract": "Multi-modal dialog modeling is of growing interest. In this work, we propose frameworks to resolve a specific case of multi-modal dialog generation that better mimics multi-modal dialog generation in the real world, where each dialog turn is associated with the visual context in which it takes place. Specifically, we propose to model the mutual dependency between text-visual features, where the model not only needs to learn the probability of generating the next dialog utterance given preceding dialog utterances and visual contexts, but also the probability of predicting the visual features in which a dialog utterance takes place, leading the generated dialog utterance specific to the visual context. We observe significant performance boosts over vanilla models when the mutual dependency between text and visual features is modeled. Code is available at https://github.com/ShannonAI/OpenViDial.", "published": "2021-05-30T07:20:28Z", "authors": ["Shuhe Wang", "Yuxian Meng", "Xiaofei Sun", "Fei Wu", "Rongbin Ouyang", "Rui Yan", "Tianwei Zhang", "Jiwei Li"], "categories": ["cs.CL"], "doi": "", "_query": "housing price text listing", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2108.07160v2", "title": "Computing and Listing Avoidable Vertices and Paths", "abstract": "A simplicial vertex of a graph is a vertex whose neighborhood is a clique. It is known that listing all simplicial vertices can be done in $O(nm)$ time or $O(n^ω)$ time, where $O(n^ω)$ is the time needed to perform a fast matrix multiplication. The notion of avoidable vertices generalizes the concept of simplicial vertices in the following way: a vertex $u$ is avoidable if every induced path on three vertices with middle vertex $u$ is contained in an induced cycle. We present algorithms for listing all avoidable vertices of a graph through the notion of minimal triangulations and common neighborhood detection. In particular we give algorithms with running times $O(n^{2}m)$ and $O(n^{1+ω})$, respectively. Additionally, based on a simplified graph traversal we propose a fast algorithm that runs in time $O(n^2 + m^2)$ and matches the corresponding running time of listing all simplicial vertices on sparse graphs with $m=O(n)$. Moreover, we show that our algorithms cannot be improved significantly, as we prove that under plausible complexity assumptions there is no truly subquadratic algorithm for recognizing an avoidable vertex. To complement our results, we consider their natural generalizations of avoidable edges and avoidable paths. We propose an $O(nm)$-time algorithm that recognizes whether a given induced path is avoidable.", "published": "2021-08-16T15:29:11Z", "authors": ["Charis Papadopoulos", "Athanasios Zisis"], "categories": ["cs.DS"], "doi": "", "_query": "housing price text listing", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2509.09721v1", "title": "A Multimodal RAG Framework for Housing Damage Assessment: Collaborative Optimization of Image Encoding and Policy Vector Retrieval", "abstract": "After natural disasters, accurate evaluations of damage to housing are important for insurance claims response and planning of resources. In this work, we introduce a novel multimodal retrieval-augmented generation (MM-RAG) framework. On top of classical RAG architecture, we further the framework to devise a two-branch multimodal encoder structure that the image branch employs a visual encoder composed of ResNet and Transformer to extract the characteristic of building damage after disaster, and the text branch harnesses a BERT retriever for the text vectorization of posts as well as insurance policies and for the construction of a retrievable restoration index. To impose cross-modal semantic alignment, the model integrates a cross-modal interaction module to bridge the semantic representation between image and text via multi-head attention. Meanwhile, in the generation module, the introduced modal attention gating mechanism dynamically controls the role of visual evidence and text prior information during generation. The entire framework takes end-to-end training, and combines the comparison loss, the retrieval loss and the generation loss to form multi-task optimization objectives, and achieves image understanding and policy matching in collaborative learning. The results demonstrate superior performance in retrieval accuracy and classification index on damage severity, where the Top-1 retrieval accuracy has been improved by 9.6%.", "published": "2025-09-10T01:58:07Z", "authors": ["Jiayi Miao", "Dingxin Lu", "Zhuqi Wang"], "categories": ["cs.CV", "cs.AI", "cs.LG"], "doi": "", "_query": "housing price text listing", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2401.04052v1", "title": "The Role of Text in Visualizations: How Annotations Shape Perceptions of Bias and Influence Predictions", "abstract": "This paper investigates the role of text in visualizations, specifically the impact of text position, semantic content, and biased wording. Two empirical studies were conducted based on two tasks (predicting data trends and appraising bias) using two visualization types (bar and line charts). While the addition of text had a minimal effect on how people perceive data trends, there was a significant impact on how biased they perceive the authors to be. This finding revealed a relationship between the degree of bias in textual information and the perception of the authors' bias. Exploratory analyses support an interaction between a person's prediction and the degree of bias they perceived. This paper also develops a crowdsourced method for creating chart annotations that range from neutral to highly biased. This research highlights the need for designers to mitigate potential polarization of readers' opinions based on how authors' ideas are expressed.", "published": "2024-01-08T17:45:01Z", "authors": ["Chase Stokes", "Cindy Xiong Bearfield", "Marti A. Hearst"], "categories": ["cs.HC"], "doi": "10.1109/TVCG.2023.3338451", "_query": "housing price text listing", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2609.38253v1", "title": "CollageAttack: Exploiting Cross-Modal Alignment Flaws in T2I Models through Spatial Text Composition", "abstract": "Text-to-image (T2I) models have substantially improved in language understanding, in-image text rendering, and visual composition, while their safety mechanisms do not always keep pace with these capabilities. This creates a cross-modal attack surface in which harmful semantics can remain inconspicuous in a serialized prompt yet emerge through image-level composition. We propose CollageAttack, an automated single-prompt black-box jailbreak that shifts semantic assembly into the image plane by combining context-relevant scenes, scene-grounded textual carriers, and spatially distributed text fragments. Experiments across multiple open-weight and commercial T2I models show that CollageAttack achieves attack success rates of up to 86.0%, outperforming the strongest baseline on the same model by 18.5 percentage points, while consistently producing more harmful outputs and preserving the source intent. We further find that distributed textual fragments can reconstruct the intended semantics after generation, with visual composition producing stronger communicative impact than text alone. These results reveal a cross-modal safety gap in which harmful meaning emerges from the composition of individually less explicit elements.", "published": "2026-09-29T08:23:01Z", "authors": ["Zhiyi Mou", "Yao Lu", "Wangze Ni", "Di Hong", "Dakun Shen", "Haoyang Li", "Chen Jason Zhang", "Alexander Zhou", "Kui Ren"], "categories": ["cs.CR", "cs.AI"], "doi": "", "_query": "housing price text listing", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2602.15547v2", "title": "jina-embeddings-v5-text: Task-Targeted Embedding Distillation", "abstract": "Text embedding models are widely used for semantic similarity tasks, including information retrieval, clustering, and classification. General-purpose models are typically trained with single- or multi-stage processes using contrastive loss functions. We introduce a novel training regimen that combines model distillation techniques with task-specific contrastive loss to produce compact, high-performance embedding models. Our findings suggest that this approach is more effective for training small models than purely contrastive or distillation-based training paradigms alone. Benchmark scores for the resulting models, jina-embeddings-v5-text-small and jina-embeddings-v5-text-nano, exceed or match the state-of-the-art for models of similar size. jina-embeddings-v5-text models additionally support long texts (up to 32k tokens) in many languages, and generate embeddings that remain robust under truncation and binary quantization. Model weights are publicly available, hopefully inspiring further advances in embedding model development.", "published": "2026-02-17T12:50:50Z", "authors": ["Mohammad Kalim Akram", "Saba Sturua", "Nastia Havriushenko", "Quentin Herreros", "Michael Günther", "Maximilian Werk", "Han Xiao"], "categories": ["cs.CL"], "doi": "", "_query": "housing price text listing", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2505.18061v1", "title": "Posted Pricing and Competition in Large Markets", "abstract": "Posted price mechanisms are prevalent in allocating goods within online marketplaces due to their simplicity and practical efficiency. We explore a fundamental scenario where buyers' valuations are independent and identically distributed, focusing specifically on the allocation of a single unit. Inspired by the rapid growth and scalability of modern online marketplaces, we investigate optimal performance guarantees under the assumption of a significantly large market. We show a large market benefit when using fixed prices, improving the known guarantee of $1-1/e\\approx 0.632$ to $0.712$. We then study the case of selling $k$ identical units, and we prove that the optimal fixed price guarantee approaches $1-1/\\sqrt{2k π}$, which implies that the large market advantage vanishes as $k$ grows. We use real-world auction data to test our fixed price policies in the large market regime. Next, under the large market assumption, we show that the competition complexity for the optimal posted price mechanism is constant, and we identify precise scaling factors for the number of bidders that enable it to match benchmark performance. Remarkably, our findings break previously established worst-case impossibility results, underscoring the practical robustness and efficiency of posted pricing in large-scale marketplaces.", "published": "2025-05-23T16:07:56Z", "authors": ["José Correa", "Vasilis Livanos", "Dana Pizarro", "Victor Verdugo"], "categories": ["cs.GT"], "doi": "", "_query": "housing price text listing", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2301.11847v1", "title": "A Comparative Study of Pretrained Language Models for Long Clinical Text", "abstract": "Objective: Clinical knowledge enriched transformer models (e.g., ClinicalBERT) have state-of-the-art results on clinical NLP (natural language processing) tasks. One of the core limitations of these transformer models is the substantial memory consumption due to their full self-attention mechanism, which leads to the performance degradation in long clinical texts. To overcome this, we propose to leverage long-sequence transformer models (e.g., Longformer and BigBird), which extend the maximum input sequence length from 512 to 4096, to enhance the ability to model long-term dependencies in long clinical texts. Materials and Methods: Inspired by the success of long sequence transformer models and the fact that clinical notes are mostly long, we introduce two domain enriched language models, Clinical-Longformer and Clinical-BigBird, which are pre-trained on a large-scale clinical corpus. We evaluate both language models using 10 baseline tasks including named entity recognition, question answering, natural language inference, and document classification tasks. Results: The results demonstrate that Clinical-Longformer and Clinical-BigBird consistently and significantly outperform ClinicalBERT and other short-sequence transformers in all 10 downstream tasks and achieve new state-of-the-art results. Discussion: Our pre-trained language models provide the bedrock for clinical NLP using long texts. We have made our source code available at https://github.com/luoyuanlab/Clinical-Longformer, and the pre-trained models available for public download at: https://huggingface.co/yikuan8/Clinical-Longformer. Conclusion: This study demonstrates that clinical knowledge enriched long-sequence transformers are able to learn long-term dependencies in long clinical text. Our methods can also inspire the development of other domain-enriched long-sequence transformers.", "published": "2023-01-27T16:50:29Z", "authors": ["Yikuan Li", "Ramsey M. Wehbe", "Faraz S. Ahmad", "Hanyin Wang", "Yuan Luo"], "categories": ["cs.CL", "cs.AI"], "doi": "10.1093/jamia/ocac225", "_query": "housing price text listing", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2610.01016v1", "title": "Scaling and Distilling Text Embeddings for Better Diffusibility", "abstract": "Diffusion language models (DLMs) offer a promising alternative to autoregressive (AR) language generation. Recent advances in continuous DLMs, which apply latent diffusion to continuous text embeddings, raise a practical question: which embedding makes the best latent space, i.e., the most diffusible? To answer this, we search through different embeddings and find that scaling the embedding model to stronger ones within the same family (T5 to T5Gemma-1 to T5Gemma-2) greatly improves generative performance. But the raw T5Gemma-2 embeddings are still not optimal. They are so discriminative that even the embeddings of plausible alternative words are separated, which makes the generation vulnerable to imperfect sampling. Consequently, continuous diffusion often fails to reach any of them and ends up at an invalid embedding instead. To address this, we distill T5Gemma-2 into a student encoder that learns the teacher's decoded probabilities as soft labels. Learning from such soft labels makes the student pull the alternative embeddings closer while maintaining the encoding-decoding mechanism. The distilled embeddings form a more connected and diffusible latent space, improving over the vanilla T5Gemma-2 embeddings. As a result, our medium-sized DLM achieves Gen. PPL 17.8 (against real-text PPL 15.4) at real-text entropy on OpenWebText, outperforming GPT-2-M on Gen. PPL.", "published": "2026-10-01T03:59:36Z", "authors": ["Zekai Zhang", "Yunjie Tian", "Yanjin He", "Xiaoyan Zhang", "Dongdi Zhao", "Qing Qu", "Di Fu"], "categories": ["cs.CL"], "doi": "", "_query": "housing price text listing", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2411.18085v1", "title": "MONOPOLY: Learning to Price Public Facilities for Revaluing Private Properties with Large-Scale Urban Data", "abstract": "The value assessment of private properties is an attractive but challenging task which is widely concerned by a majority of people around the world. A prolonged topic among us is ``\\textit{how much is my house worth?}''. To answer this question, most experienced agencies would like to price a property given the factors of its attributes as well as the demographics and the public facilities around it. However, no one knows the exact prices of these factors, especially the values of public facilities which may help assess private properties. In this paper, we introduce our newly launched project ``Monopoly'' (named after a classic board game) in which we propose a distributed approach for revaluing private properties by learning to price public facilities (such as hospitals etc.) with the large-scale urban data we have accumulated via Baidu Maps. To be specific, our method organizes many points of interest (POIs) into an undirected weighted graph and formulates multiple factors including the virtual prices of surrounding public facilities as adaptive variables to parallelly estimate the housing prices we know. Then the prices of both public facilities and private properties can be iteratively updated according to the loss of prediction until convergence. We have conducted extensive experiments with the large-scale urban data of several metropolises in China. Results show that our approach outperforms several mainstream methods with significant margins. Further insights from more in-depth discussions demonstrate that the ``Monopoly'' is an innovative application in the interdisciplinary field of business intelligence and urban computing, and it will be beneficial to tens of millions of our users for investments and to the governments for urban planning as well as taxation.", "published": "2024-11-27T06:44:41Z", "authors": ["Miao Fan", "Jizhou Huang", "An Zhuo", "Ying Li", "Ping Li", "Haifeng Wang"], "categories": ["cs.AI", "cs.SI"], "doi": "", "_query": "housing price text listing", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/1402.0410v1", "title": "Minimum Price in Search Model", "abstract": "This paper investigates the effects of a low bound price. To do so, a popular and empirically proven model (Stahl (89') \\cite{Stahl89}) is used. The model is extended to include an exogenously given bound on prices sellers can offer, excluding prices below such bound. The finding are rather surprising - when the bound is set sufficiently high expected price offered (EPO) by sellers drops significantly. The result seem to be robust in the parameters of the model, and driven by the information provided to consumers by such legislation step: when the limitation is set at sufficiently high levels all consumers anticipate the bound price, and searchers reject any price above it. As a result sellers offer the bound price as a pure strategy.", "published": "2014-02-03T16:04:19Z", "authors": ["Sergey Kuniavsky"], "categories": ["cs.GT"], "doi": "", "_query": "housing price text listing", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2508.17301v2", "title": "Price Regulation and Network Spillovers", "abstract": "We study price regulation for a monopolist operating in networked markets with demand spillovers. Achieving efficiency requires price reductions proportional to consumers' Katz-Bonacich centralities, which generally cannot be implemented by commonly used price regulations. Moreover, these regulations become asymptotically welfare neutral as spillovers grow. Nevertheless, some price regulations may still benefit consumers. In particular, average-price regulation robustly increases consumer surplus. By contrast, banning price discrimination increases consumer surplus only when more central consumers have higher intrinsic willingness to pay.", "published": "2025-08-24T11:08:18Z", "authors": ["Chengqing Li", "Junjie Zhou"], "categories": ["econ.TH"], "doi": "", "_query": "housing price text listing", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/1806.00898v1", "title": "Competitive pricing despite search costs if lower price signals quality", "abstract": "I show that firms price almost competitively and consumers can infer product quality from prices in markets where firms differ in quality and production cost, and learning prices is costly. Bankruptcy risk or regulation links higher quality to lower cost. If high-quality firms have lower cost, then they can signal quality by cutting prices. Then the low-quality firms must cut prices to retain customers. This price-cutting race to the bottom ends in a separating equilibrium in which the low-quality firms charge their competitive price and the high-quality firms charge slightly less.", "published": "2018-06-03T23:52:22Z", "authors": ["Sander Heinsalu"], "categories": ["econ.GN", "econ.TH"], "doi": "", "_query": "housing price text listing", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/1502.03629v1", "title": "On the greatest solution of equations in $\\text{CLL}_R$", "abstract": "It is shown that, for any equation $X=_{RS} t_X$ in the LLTS-oriented process calculus $\\text{CLL}_R$, if $X$ is strongly guarded in $t_X$, then the recursive term $\\langle X|X=t_X \\rangle$ is the greatest solution of this equation w.r.t Lüttgen and Vogler's ready simulation.", "published": "2015-02-12T12:32:21Z", "authors": ["Yan Zhang", "Zhaohui Zhu", "Jinjin Zhang"], "categories": ["cs.LO"], "doi": "", "_query": "housing price text listing", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/1702.00144v1", "title": "Zipf's law for share price and company fundamentals", "abstract": "We statistically investigate the distribution of share price and the distributions of three common financial indicators using data from approximately 8,000 companies publicly listed worldwide for the period 2004-2013. We find that the distribution of share price follows Zipf's law; that is, it can be approximated by a power law distribution with exponent equal to 1. An examination of the distributions of dividends per share, cash flow per share, and book value per share - three financial indicators that can be assumed to influence corporate value (i.e. share price) - shows that these distributions can also be approximated by a power law distribution with power-law exponent equal to 1. We estimate a panel regression model in which share price is the dependent variable and the three financial indicators are explanatory variables. The two-way fixed effects model that was selected as the best model has quite high power for explaining the actual data. From these results, we can surmise that the reason why share price follows Zipf's law is that corporate value, i.e. company fundamentals, follows Zipf's law.", "published": "2017-02-01T06:22:09Z", "authors": ["Taisei Kaizoji", "Michiko Miyano"], "categories": ["econ.GN", "q-fin.ST"], "doi": "", "_query": "housing price text listing", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2407.10351v1", "title": "Comparing Complex Concepts with Transformers: Matching Patent Claims Against Natural Language Text", "abstract": "A key capability in managing patent applications or a patent portfolio is comparing claims to other text, e.g. a patent specification. Because the language of claims is different from language used elsewhere in the patent application or in non-patent text, this has been challenging for computer based natural language processing. We test two new LLM-based approaches and find that both provide substantially better performance than previously published values. The ability to match dense information from one domain against much more distributed information expressed in a different vocabulary may also be useful beyond the intellectual property space.", "published": "2024-07-14T22:31:07Z", "authors": ["Matthias Blume", "Ghobad Heidari", "Christoph Hewel"], "categories": ["cs.CL"], "doi": "", "_query": "housing price text listing", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/1606.09222v1", "title": "Penambahan emosi menggunakan metode manipulasi prosodi untuk sistem text to speech bahasa Indonesia", "abstract": "Adding an emotions using prosody manipulation method for Indonesian text to speech system. Text To Speech (TTS) is a system that can convert text in one language into speech, accordance with the reading of the text in the language used. The focus of this research is a natural sounding concept, the make \"humanize\" for the pronunciation of voice synthesis system Text To Speech. Humans have emotions / intonation that may affect the sound produced. The main requirement for the system used Text To Speech in this research is eSpeak, the database MBROLA using id1, Human Speech Corpus database from a website that summarizes the words with the highest frequency (Most Common Words) used in a country. And there are 3 types of emotional / intonation designed base. There is a happy, angry and sad emotion. Method for develop the emotional filter is manipulate the relevant features of prosody (especially pitch and duration value) using a predetermined rate factor that has been established by analyzing the differences between the standard output Text To Speech and voice recording with emotional prosody / a particular intonation. The test results for the perception tests of Human Speech Corpus for happy emotion is 95 %, 96.25 % for angry emotion and 98.75 % for sad emotions. For perception test system carried by intelligibility and naturalness test. Intelligibility test for the accuracy of sound with the original sentence is 93.3%, and for clarity rate for each sentence is 62.8%. For naturalness, accuracy emotional election amounted to 75.6 % for happy emotion, 73.3 % for angry emotion, and 60 % for sad emotions. ----- Text To Speech (TTS) merupakan suatu sistem yang dapat mengonversi teks dalam format suatu bahasa menjadi ucapan sesuai dengan pembacaan teks dalam bahasa yang digunakan.", "published": "2016-06-29T19:06:48Z", "authors": ["Salita Ulitia Prini", "Ary Setijadi Prihatmanto"], "categories": ["cs.SD", "cs.CL", "cs.RO"], "doi": "10.13140/RG.2.1.4140.6165", "_query": "housing price text listing", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/0803.4416v1", "title": "Consistent price systems and face-lifting pricing under transaction costs", "abstract": "In markets with transaction costs, consistent price systems play the same role as martingale measures in frictionless markets. We prove that if a continuous price process has conditional full support, then it admits consistent price systems for arbitrarily small transaction costs. This result applies to a large class of Markovian and non-Markovian models, including geometric fractional Brownian motion. Using the constructed price systems, we show, under very general assumptions, the following ``face-lifting'' result: the asymptotic superreplication price of a European contingent claim $g(S_T)$ equals $\\hat{g}(S_0)$, where $\\hat{g}$ is the concave envelope of $g$ and $S_t$ is the price of the asset at time $t$. This theorem generalizes similar results obtained for diffusion processes to processes with conditional full support.", "published": "2008-03-31T09:55:40Z", "authors": ["Paolo Guasoni", "Miklós Rásonyi", "Walter Schachermayer"], "categories": ["q-fin.PR", "math.PR"], "doi": "10.1214/07-AAP461", "_query": "housing price text listing", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2009.01317v1", "title": "Towards Earnings Call and Stock Price Movement", "abstract": "Earnings calls are hosted by management of public companies to discuss the company's financial performance with analysts and investors. Information disclosed during an earnings call is an essential source of data for analysts and investors to make investment decisions. Thus, we leverage earnings call transcripts to predict future stock price dynamics. We propose to model the language in transcripts using a deep learning framework, where an attention mechanism is applied to encode the text data into vectors for the discriminative network classifier to predict stock price movements. Our empirical experiments show that the proposed model is superior to the traditional machine learning baselines and earnings call information can boost the stock price prediction performance.", "published": "2020-08-23T20:38:14Z", "authors": ["Zhiqiang Ma", "Grace Bang", "Chong Wang", "Xiaomo Liu"], "categories": ["q-fin.ST", "cs.CE", "cs.CL", "cs.LG"], "doi": "", "_query": "housing price text listing", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2508.11816v1", "title": "LLM-Guided Planning and Summary-Based Scientific Text Simplification: DS@GT at CLEF 2025 SimpleText", "abstract": "In this paper, we present our approach for the CLEF 2025 SimpleText Task 1, which addresses both sentence-level and document-level scientific text simplification. For sentence-level simplification, our methodology employs large language models (LLMs) to first generate a structured plan, followed by plan-driven simplification of individual sentences. At the document level, we leverage LLMs to produce concise summaries and subsequently guide the simplification process using these summaries. This two-stage, LLM-based framework enables more coherent and contextually faithful simplifications of scientific text.", "published": "2025-08-15T21:44:52Z", "authors": ["Krishna Chaitanya Marturi", "Heba H. Elwazzan"], "categories": ["cs.CL"], "doi": "", "_query": "housing price text listing", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2203.08902v1", "title": "Price Tracing: Linking Nodal Prices in Optimized Power Systems", "abstract": "Optimizing the total cost of power systems is a common tool for network operation and planning. Besides valuable information about how to run and possibly expand a power system, the optimization provides an optimal Locational Marginal Price per node and time step. This price can be seen as the price of electricity paid by consumers and purchased by suppliers, while maximizing social welfare. Naturally, it is a direct result of the optimization problem, and therefore does not give any information about its internal composition. This paper shows that by applying Flow Tracing, an algorithm for tracking flows in complex networks, it is possible to interlink Locational Marginal Prices in a coherent mathematical way. This does not only lead to important insights into the price structure, but also provides an intuitive decomposition and allocation of all system costs. Then individual consumers see how much they have to pay to individual generators and transmission lines in the power system. This method, introduced as Price Tracing, outperforms similar approaches provided by the literature, since the resulting cost allocations are transparent, plausible, and consistent with the Locational Marginal Prices from the optimization. The Price Tracing method is applied and discussed on behalf of a power system model of Germany with a high share of renewable power. The presented analysis and its implications can help in finding a more efficient market design to promote renewable power supply.", "published": "2022-03-16T19:25:48Z", "authors": ["Fabian Hofmann", "Markus Schlott"], "categories": ["physics.soc-ph"], "doi": "", "_query": "housing price text listing", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/1807.07966v1", "title": "Exploring Combinations of Ontological Features and Keywords for Text Retrieval", "abstract": "Named entities have been considered and combined with keywords to enhance information retrieval performance. However, there is not yet a formal and complete model that takes into account entity names, classes, and identifiers together. Our work explores various adaptations of the traditional Vector Space Model that combine different ontological features with keywords, and in different ways. It shows better performance of the proposed models as compared to the keyword-based Lucene, and their advantages for both text retrieval and representation of documents and queries.", "published": "2018-07-20T10:30:26Z", "authors": ["Tru H. Cao", "Khanh C. Le", "Vuong M. Ngo"], "categories": ["cs.IR"], "doi": "", "_query": "housing price text listing", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/1811.08803v1", "title": "Distinguishing correlation from causation using genome-wide association studies", "abstract": "Genome-wide association studies (GWAS) have emerged as a rich source of genetic clues into disease biology, and they have revealed strong genetic correlations among many diseases and traits. Some of these genetic correlations may reflect causal relationships. We developed a method to quantify causal relationships between genetically correlated traits using GWAS summary association statistics. In particular, our method quantifies what part of the genetic component of trait 1 is also causal for trait 2 using mixed fourth moments $E(α_1^2α_1α_2)$ and $E(α_2^2α_1α_2)$ of the bivariate effect size distribution. If trait 1 is causal for trait 2, then SNPs affecting trait 1 (large $α_1^2$) will have correlated effects on trait 2 (large $α_1α_2$), but not vice versa. We validated this approach in extensive simulations. Across 52 traits (average $N=331$k), we identified 30 putative genetically causal relationships, many novel, including an effect of LDL cholesterol on decreased bone mineral density. More broadly, we demonstrate that it is possible to distinguish between genetic correlation and causation using genetic association data.", "published": "2018-11-21T16:01:51Z", "authors": ["Luke J. O'Connor", "Alkes L. Price"], "categories": ["stat.ME", "cs.LG", "stat.ML"], "doi": "", "_query": "housing price text listing", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2408.07845v1", "title": "Enhancing Equitable Access to AI in Housing and Homelessness System of Care through Federated Learning", "abstract": "The top priority of a Housing and Homelessness System of Care (HHSC) is to connect people experiencing homelessness to supportive housing. An HHSC typically consists of many agencies serving the same population. Information technology platforms differ in type and quality between agencies, so their data are usually isolated from one agency to another. Larger agencies may have sufficient data to train and test artificial intelligence (AI) tools but smaller agencies typically do not. To address this gap, we introduce a Federated Learning (FL) approach enabling all agencies to train a predictive model collaboratively without sharing their sensitive data. We demonstrate how FL can be used within an HHSC to provide all agencies equitable access to quality AI and further assist human decision-makers in the allocation of resources within HHSC. This is achieved while preserving the privacy of the people within the data by not sharing identifying information between agencies without their consent. Our experimental results using real-world HHSC data from Calgary, Alberta, demonstrate that our FL approach offers comparable performance with the idealized scenario of training the predictive model with data fully shared and linked between agencies.", "published": "2024-08-14T23:01:02Z", "authors": ["Musa Taib", "Jiajun Wu", "Steve Drew", "Geoffrey G. Messier"], "categories": ["cs.LG", "cs.AI", "cs.CY"], "doi": "", "_query": "housing price text listing", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2601.13911v1", "title": "Designing sustainable barn-type houses: Optimal shapes for minimal envelope and energy use", "abstract": "Barn-type houses have become one of the most popular single-family housing typologies in Poland and across Europe due to their simplicity, functionality, and potential for energy efficiency. Despite their widespread use, systematic methods for optimizing their geometry in terms of envelope area and energy performance remain limited. This paper develops a rigorous mathematical framework for determining the optimal proportions of barn-type houses with respect to minimizing the external surface area while satisfying constraints of either fixed volume or fixed floor area. Closed-form solutions for the optimal width, length, and height are derived as explicit functions of the roof slope, together with formulas for the minimal achievable surface. A recently introduced dimensionless compactness measure is also calculated, allowing quantitative assessment of how far a given design deviates from the theoretical optimum. The methodology is applied to case studies of three existing houses, showing that while some designs deviate significantly from optimal compactness, others already closely approximate it. The results confirm that theoretical optimization can lead to meaningful reductions in construction costs and energy demand. To support practical implementation, two original freely available software tools were developed, enabling architects and engineers to perform optimization analyses.", "published": "2026-01-20T12:40:24Z", "authors": ["Ewa Rokita-Magdziarz", "Barbara Gronostajska", "Marcin Magdziarz"], "categories": ["math.OC"], "doi": "", "_query": "housing price text listing", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2511.22185v2", "title": "Textual semantics and machine learning methods for data product pricing", "abstract": "Reasonable pricing of data products enables data trading platforms to maximize revenue and foster the growth of the data trading market. The textual semantics of data products are vital for pricing and contain significant value that remains largely underexplored. Therefore, to investigate how textual features influence data product pricing, we employ five prevalent text representation techniques to encode the descriptive text of data products. And then, we employ six machine learning methods to predict data product prices, including linear regression, neural networks, decision trees, support vector machines, random forests, and XGBoost. Our empirical design consists of two tasks: a regression task that predicts the continuous price of data products, and a classification task that discretizes price into ordered categories. Furthermore, we conduct feature importance analysis by the mRMR feature selection method and SHAP-based interpretability techniques. Based on empirical data from the AWA Data Exchange, we find that for predicting continuous prices, Word2Vec text representations capturing semantic similarity yield superior performance. In contrast, for price-tier classification tasks, simpler representations that do not rely on semantic similarity, such as Bag-of-Words and TF-IDF, perform better. SHAP analysis reveals that semantic features related to healthcare and demographics tend to increase prices, whereas those associated with weather and environmental topics are linked to lower prices. This analytical framework significantly enhances the interpretability of pricing models.", "published": "2025-11-27T07:51:31Z", "authors": ["Ruize Gao", "Feng Xiao", "Jinpu Li", "Shaoze Cui"], "categories": ["cs.CE"], "doi": "10.4310/RME.260221154937", "_query": "housing price text listing", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/1609.09412v2", "title": "Experimental Measurement of the Berry Curvature from Anomalous Transport", "abstract": "Geometrical properties of energy bands underlie fascinating phenomena in a wide-range of systems, including solid-state materials, ultracold gases and photonics. Most famously, local geometrical characteristics like the Berry curvature can be related to global topological invariants such as those classifying quantum Hall states or topological insulators. Regardless of the band topology, however, any non-zero Berry curvature can have important consequences, such as in the semi-classical evolution of a wave packet. Here, we experimentally demonstrate for the first time that wave packet dynamics can be used to directly map out the Berry curvature. To this end, we use optical pulses in two coupled fibre loops to study the discrete time-evolution of a wave packet in a 1D geometrical \"charge\" pump, where the Berry curvature leads to an anomalous displacement of the wave packet under pumping. This is both the first direct observation of Berry curvature effects in an optical system, and, more generally, the proof-of-principle demonstration that semi-classical dynamics can serve as a high-resolution tool for mapping out geometrical properties.", "published": "2016-09-29T16:24:13Z", "authors": ["Martin Wimmer", "Hannah M. Price", "Iacopo Carusotto", "Ulf Peschel"], "categories": ["physics.optics", "cond-mat.quant-gas"], "doi": "10.1038/nphys4050", "_query": "housing price text listing", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2202.07919v3", "title": "HousE: Knowledge Graph Embedding with Householder Parameterization", "abstract": "The effectiveness of knowledge graph embedding (KGE) largely depends on the ability to model intrinsic relation patterns and mapping properties. However, existing approaches can only capture some of them with insufficient modeling capacity. In this work, we propose a more powerful KGE framework named HousE, which involves a novel parameterization based on two kinds of Householder transformations: (1) Householder rotations to achieve superior capacity of modeling relation patterns; (2) Householder projections to handle sophisticated relation mapping properties. Theoretically, HousE is capable of modeling crucial relation patterns and mapping properties simultaneously. Besides, HousE is a generalization of existing rotation-based models while extending the rotations to high-dimensional spaces. Empirically, HousE achieves new state-of-the-art performance on five benchmark datasets. Our code is available at https://github.com/anrep/HousE.", "published": "2022-02-16T08:13:23Z", "authors": ["Rui Li", "Jianan Zhao", "Chaozhuo Li", "Di He", "Yiqi Wang", "Yuming Liu", "Hao Sun", "Senzhang Wang", "Weiwei Deng", "Yanming Shen", "Xing Xie", "Qi Zhang"], "categories": ["cs.AI"], "doi": "", "_query": "housing price text listing", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2203.02089v1", "title": "A Robust Statistical Analysis of the Role of Hydropower on the System Electricity Price and Price Volatility", "abstract": "Hydroelectric power (hydropower) is unique in that it can function as both a conventional source of electricity and as backup storage (pumped hydroelectric storage) for providing energy in times of high demand on the grid. This study examines the impact of hydropower on system electricity price and price volatility in the region served by the New England Independent System Operator (ISONE) from 2014 - 2020. We perform a robust holistic analysis of the mean and quantile effects, as well as the marginal contributing effects of hydropower in the presence of solar and wind resources. First, the price data is adjusted for deterministic temporal trends, correcting for seasonal, weekend, and diurnal effects that may obscure actual representative trends in the data. Using multiple linear regression and quantile regression, we observe that hydropower contributes to a reduction in the system electricity price and price volatility. While hydropower has a weak impact on decreasing price and volatility at the mean, it has greater impact at extreme quantiles (> 70th percentile). At these higher percentiles, we find that hydropower provides a stabilizing effect on price volatility in the presence of volatile resources such as wind. We conclude with a discussion of the observed relationship between hydropower and system electricity price and volatility.", "published": "2022-03-04T01:58:13Z", "authors": ["Olukunle O. Owolabi", "Kathryn Lawson", "Sanhita Sengupta", "Yingsi Huang", "Lan Wang", "Chaopeng Shen", "Mila Getmansky Sherman", "Deborah A. Sunter"], "categories": ["q-fin.GN"], "doi": "", "_query": "housing price text listing", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/1607.02598v1", "title": "Security Pricing as an Enabler of Cyber-Insurance: A First Look at Differentiated Pricing Markets", "abstract": "Despite the promising potential of network risk management services (e.g., cyber-insurance) to improve information security, their deployment is relatively scarce, primarily due to such service companies being unable to guarantee profitability. As a novel approach to making cyber-insurance services more viable, we explore a symbiotic relationship between security vendors (e.g., Symantec) capable of price differentiating their clients, and cyber-insurance agencies having possession of information related to the security investments of their clients. The goal of this relationship is to (i) allow security vendors to price differentiate their clients based on security investment information from insurance agencies, (ii) allow the vendors to make more profit than in homogeneous pricing settings, and (iii) subsequently transfer some of the extra profit to cyber-insurance agencies to make insurance services more viable. \\noindent In this paper, we perform a theoretical study of a market for differentiated security product pricing, primarily with a view to ensuring that security vendors (SVs) make more profit in the differentiated pricing case as compared to the case of non-differentiated pricing. In order to practically realize such pricing markets, we propose novel and \\emph{computationally efficient} consumer differentiated pricing mechanisms for SVs based on (i) the market structure, (ii) the communication network structure of SV consumers captured via a consumer's \\emph{Bonacich centrality} in the network, and (iii) security investment amounts made by SV consumers.", "published": "2016-07-09T11:07:07Z", "authors": ["Ranjan Pal", "Leana Golubchik", "Konstantinos Psounis", "Pan Hui"], "categories": ["cs.GT"], "doi": "", "_query": "housing price text listing", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2506.14708v1", "title": "Discrete time shadow price revisited", "abstract": "In the paper discrete time shadow price is constructed for the market with several assets with given bid and ask prices. Shadow price is the price such that the problem of optimal utility from terminal wealth on the market without transaction costs gives the same value function as in the case of bid and ask prices. In the paper we solve first static problem for two assets and then we construct the shadow price for dynamic model. Finally we present construction of the shadow price for several assets.", "published": "2025-06-17T16:47:57Z", "authors": ["Tomasz Rogala", "Łukasz Stettner"], "categories": ["math.OC"], "doi": "", "_query": "housing price text listing", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/1602.04950v3", "title": "Deviations in expected price impact for small transaction volumes under fee restructuring", "abstract": "We report on the occurrence of an anomaly in the price impacts of small transaction volumes following a change in the fee structure of an electronic market. We first review evidence for the existence of a master curve for price impact on the Johannesburg Stock Exchange (JSE). On attempting to re-estimate a master curve after fee reductions, it is found that the price impact corresponding to smaller volume trades is greater than expected relative to prior estimates for a range of listed stocks. We show that a master curve for price impact can be found following rescaling by an appropriate liquidity proxy, providing a means for practitioners to approximate price impact curves without onerous processing of tick data.", "published": "2016-02-16T09:04:50Z", "authors": ["Michael Harvey", "Dieter Hendricks", "Tim Gebbie", "Diane Wilcox"], "categories": ["q-fin.TR"], "doi": "10.1016/j.physa.2016.11.042", "_query": "housing price text listing", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/0903.4833v2", "title": "Recovering a time-homogeneous stock price process from perpetual option prices", "abstract": "It is well known how to determine the price of perpetual American options if the underlying stock price is a time-homogeneous diffusion. In the present paper we consider the inverse problem, that is, given prices of perpetual American options for different strikes, we show how to construct a time-homogeneous stock price model which reproduces the given option prices.", "published": "2009-03-27T15:53:31Z", "authors": ["Erik Ekström", "David Hobson"], "categories": ["math.PR", "q-fin.PR"], "doi": "10.1214/10-AAP720", "_query": "housing price text listing", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2306.05432v1", "title": "Towards End-to-end Speech-to-text Summarization", "abstract": "Speech-to-text (S2T) summarization is a time-saving technique for filtering and keeping up with the broadcast news uploaded online on a daily basis. The rise of large language models from deep learning with impressive text generation capabilities has placed the research focus on summarization systems that produce paraphrased compact versions of the document content, also known as abstractive summaries. End-to-end (E2E) modelling of S2T abstractive summarization is a promising approach that offers the possibility of generating rich latent representations that leverage non-verbal and acoustic information, as opposed to the use of only linguistic information from automatically generated transcripts in cascade systems. However, the few literature on E2E modelling of this task fails on exploring different domains, namely broadcast news, which is challenging domain where large and diversified volumes of data are presented to the user every day. We model S2T summarization both with a cascade and an E2E system for a corpus of broadcast news in French. Our novel E2E model leverages external data by resorting to transfer learning from a pre-trained T2T summarizer. Experiments show that both our cascade and E2E abstractive summarizers are stronger than an extractive baseline. However, the performance of the E2E model still lies behind the cascade one, which is object of an extensive analysis that includes future directions to close that gap.", "published": "2023-06-06T15:22:16Z", "authors": ["Raul Monteiro", "Diogo Pernes"], "categories": ["cs.CL", "cs.AI", "cs.LG", "eess.AS"], "doi": "", "_query": "housing price text listing", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/1310.2775v1", "title": "On price of symmetrisation", "abstract": "We introduce the price of symmetrisation, a concept that aims to compare fundamental differences (gap and quotient) between values of a given graph invariant for digraphs and the values of the same invariant of the symmetric versions of these digraphs. Basically, given some invariant our goal is to characterise digraphs that maximise price of symmetrisation. In particular, we show that for some invariants, as diameter or domination number, the problem is easy. The main contribution of this paper is about (partial) results on the price of symmetrisation of the average distance. It appears to be much more intricate than the simple cases mentioned above. First, we state a conjecture about digraphs that maximise this price of symmetrisation. Then, we prove that this conjecture is true for some particular class of digraphs (called bags) but it remains open for general digraphs. Moreover, we study several graph transformations in order to remove some configurations that do not appear in the conjectured extremal digraphs.", "published": "2013-10-10T11:22:45Z", "authors": ["Absil Romain", "Hadrien Mélot"], "categories": ["cs.DM", "math.CO"], "doi": "", "_query": "housing price text listing", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2403.01162v1", "title": "Envy-Free House Allocation with Minimum Subsidy", "abstract": "House allocation refers to the problem where $m$ houses are to be allocated to $n$ agents so that each agent receives one house. Since an envy-free house allocation does not always exist, we consider finding such an allocation in the presence of subsidy. We show that computing an envy-free allocation with minimum subsidy is NP-hard in general, but can be done efficiently if $m$ differs from $n$ by an additive constant or if the agents have identical utilities.", "published": "2024-03-02T10:31:27Z", "authors": ["Davin Choo", "Yan Hao Ling", "Warut Suksompong", "Nicholas Teh", "Jian Zhang"], "categories": ["cs.GT", "cs.CC"], "doi": "10.1016/j.orl.2024.107103", "_query": "housing price text listing", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/1406.2710v1", "title": "A Multiplicative Model for Learning Distributed Text-Based Attribute Representations", "abstract": "In this paper we propose a general framework for learning distributed representations of attributes: characteristics of text whose representations can be jointly learned with word embeddings. Attributes can correspond to document indicators (to learn sentence vectors), language indicators (to learn distributed language representations), meta-data and side information (such as the age, gender and industry of a blogger) or representations of authors. We describe a third-order model where word context and attribute vectors interact multiplicatively to predict the next word in a sequence. This leads to the notion of conditional word similarity: how meanings of words change when conditioned on different attributes. We perform several experimental tasks including sentiment classification, cross-lingual document classification, and blog authorship attribution. We also qualitatively evaluate conditional word neighbours and attribute-conditioned text generation.", "published": "2014-06-10T20:29:10Z", "authors": ["Ryan Kiros", "Richard S. Zemel", "Ruslan Salakhutdinov"], "categories": ["cs.LG", "cs.CL"], "doi": "", "_query": "housing price text listing", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2606.09032v1", "title": "Bridging the Agent-World Gap: Text World Models for LLM-based Agents", "abstract": "Large language model (LLM)-based agents are increasingly used in interactive textual environments, from web navigation and code editing to tool use and long-horizon dialogue. Yet many remain largely reactive, mapping observations to actions without an explicit model of how these environments are structured and evolve. This motivates text world models (TWMs): transition models over textual states that, given a state and a candidate action, predict the resulting webpage, terminal output, API response, or user reply, thereby supporting planning, efficient learning, and principled evaluation. We systematically review text world models for LLM-based agents, organized around a formal framework and the agent lifecycle: (1) Foundations, defining text world models and characterizing them by state representation and grounding domain; (2) Construction, taxonomizing LLM-as-WM and code-as-WM paradigms and reviewing methods for building them; (3) Application, examining how world models support agents at training time through experience synthesis and at inference time through planning, verification, and adaptation; and (4) Evaluation, covering both evaluation of the world model itself and its use as an evaluation environment for agents. We aim to consolidate this rapidly developing area, clarify its design space, and highlight open challenges for future research.", "published": "2026-06-08T04:58:52Z", "authors": ["Yixia Li", "Hongru Wang", "Peng Lai", "Zhiwen Ruan", "He Zhu", "Youxin Zhu", "Ganlong Zhao", "Minda Hu", "Yun Chen", "Sibei Yang", "Peng Li", "Jeff Z. Pan", "Jia Pan", "Guanhua Chen", "Yang Liu", "Guanbin Li"], "categories": ["cs.CL"], "doi": "", "_query": "housing price text listing", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2506.13967v1", "title": "High-Dimensional Spatial-Plus-Vertical Price Relationships and Price Transmission: A Machine Learning Approach", "abstract": "Price transmission has been studied extensively in agricultural economics through the lens of spatial and vertical price relationships. Classical time series econometric techniques suffer from the \"curse of dimensionality\" and are applied almost exclusively to small sets of price series, either prices of one commodity in a few regions or prices of a few commodities in one region. However, an agrifood supply chain usually contains several commodities (e.g., cattle and beef) and spans numerous regions. Failing to jointly examine multi-region, multi-commodity price relationships limits researchers' ability to derive insights from increasingly high-dimensional price datasets of agrifood supply chains. We apply a machine-learning method - specifically, regularized regression - to augment the classical vector error correction model (VECM) and study large spatial-plus-vertical price systems. Leveraging weekly provincial-level data on the piglet-hog-pork supply chain in China, we uncover economically interesting changes in price relationships in the system before and after the outbreak of a major hog disease. To quantify price transmission in the large system, we rely on the spatial-plus-vertical price relationships identified by the regularized VECM to visualize comprehensive spatial and vertical price transmission of hypothetical shocks through joint impulse response functions. Price transmission shows considerable heterogeneity across regions and commodities as the VECM outcomes imply and display different dynamics over time.", "published": "2025-06-16T20:16:56Z", "authors": ["Mindy L. Mallory", "Rundong Peng", "Meilin Ma", "H. Holly Wang"], "categories": ["econ.EM"], "doi": "", "_query": "housing price text listing", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2403.04279v2", "title": "Controllable Generation with Text-to-Image Diffusion Models: A Survey", "abstract": "In the rapidly advancing realm of visual generation, diffusion models have revolutionized the landscape, marking a significant shift in capabilities with their impressive text-guided generative functions. However, relying solely on text for conditioning these models does not fully cater to the varied and complex requirements of different applications and scenarios. Acknowledging this shortfall, a variety of studies aim to control pre-trained text-to-image (T2I) models to support novel conditions. In this survey, we undertake a thorough review of the literature on controllable generation with T2I diffusion models, covering both the theoretical foundations and practical advancements in this domain. Our review begins with a brief introduction to the basics of denoising diffusion probabilistic models (DDPMs) and widely used T2I diffusion models. We then reveal the controlling mechanisms of diffusion models, theoretically analyzing how novel conditions are introduced into the denoising process for conditional generation. Additionally, we offer a detailed overview of research in this area, organizing it into distinct categories from the condition perspective: generation with specific conditions, generation with multiple conditions, and universal controllable generation. For an exhaustive list of the controllable generation literature surveyed, please refer to our curated repository at https://github.com/PRIV-Creation/Awesome-Controllable-T2I-Diffusion-Models.", "published": "2024-03-07T07:24:18Z", "authors": ["Pu Cao", "Feng Zhou", "Qing Song", "Lu Yang"], "categories": ["cs.CV"], "doi": "10.1109/TPAMI.2025.3646548", "_query": "housing price text listing", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/1711.06970v1", "title": "How much is my car worth? A methodology for predicting used cars prices using Random Forest", "abstract": "Cars are being sold more than ever. Developing countries adopt the lease culture instead of buying a new car due to affordability. Therefore, the rise of used cars sales is exponentially increasing. Car sellers sometimes take advantage of this scenario by listing unrealistic prices owing to the demand. Therefore, arises a need for a model that can assign a price for a vehicle by evaluating its features taking the prices of other cars into consideration. In this paper, we use supervised learning method namely Random Forest to predict the prices of used cars. The model has been chosen after careful exploratory data analysis to determine the impact of each feature on price. A Random Forest with 500 Decision Trees were created to train the data. From experimental results, the training accuracy was found out to be 95.82%, and the testing accuracy was 83.63%. The the model can predict the price of cars accurately by choosing the most correlated features.", "published": "2017-11-19T05:26:38Z", "authors": ["Nabarun Pal", "Priya Arora", "Dhanasekar Sundararaman", "Puneet Kohli", "Sai Sumanth Palakurthy"], "categories": ["cs.CY", "cs.LG"], "doi": "", "_query": "housing price text listing", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2005.02068v1", "title": "Establishing Baselines for Text Classification in Low-Resource Languages", "abstract": "While transformer-based finetuning techniques have proven effective in tasks that involve low-resource, low-data environments, a lack of properly established baselines and benchmark datasets make it hard to compare different approaches that are aimed at tackling the low-resource setting. In this work, we provide three contributions. First, we introduce two previously unreleased datasets as benchmark datasets for text classification and low-resource multilabel text classification for the low-resource language Filipino. Second, we pretrain better BERT and DistilBERT models for use within the Filipino setting. Third, we introduce a simple degradation test that benchmarks a model's resistance to performance degradation as the number of training samples are reduced. We analyze our pretrained model's degradation speeds and look towards the use of this method for comparing models aimed at operating within the low-resource setting. We release all our models and datasets for the research community to use.", "published": "2020-05-05T11:17:07Z", "authors": ["Jan Christian Blaise Cruz", "Charibeth Cheng"], "categories": ["cs.CL"], "doi": "", "_query": "housing price text listing", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2511.23455v2", "title": "The Price of Progress: Price Performance and the Future of AI", "abstract": "Language models have seen enormous progress on advanced benchmarks in recent years, but much of this progress has only been possible by using more costly models. Benchmarks may therefore present a warped picture of progress in practical capabilities *per dollar*. To remedy this, we use data from Artificial Analysis and Epoch AI to form the largest dataset of current and historical prices to run benchmarks to date. We find that the price for a given level of benchmark performance has decreased remarkably fast, around $5\\times$ to $10\\times$ per year, for frontier models on knowledge, reasoning, math, and software engineering benchmarks. These reductions in the cost of AI inference are due to economic forces, hardware efficiency improvements, and algorithmic efficiency improvements. Isolating out open models to control for competition effects and dividing by hardware price declines, we estimate that algorithmic efficiency progress is around $3\\times$ per year. However, at the same time, the price of running frontier models is rising between $3\\times$ to $18\\times$ per year due to bigger models and larger reasoning demands. Finally, we recommend that evaluators both publicize and take into account the price of benchmarking as an essential part of measuring the real-world impact of AI.", "published": "2025-11-28T18:47:33Z", "authors": ["Hans Gundlach", "Jayson Lynch", "Matthias Mertens", "Neil Thompson"], "categories": ["cs.LG", "cs.AI", "cs.CY"], "doi": "", "_query": "housing price text listing", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2309.10220v1", "title": "Comparing effects of price limit and circuit breaker in stock exchanges by an agent-based model", "abstract": "The prevention of rapidly and steeply falling market prices is vital to avoid financial crisis. To this end, some stock exchanges implement a price limit or a circuit breaker, and there has been intensive investigation into which regulation best prevents rapid and large variations in price. In this study, we examine this question using an artificial market model that is an agent-based model for a financial market. Our findings show that the price limit and the circuit breaker basically have the same effect when the parameters, limit price range and limit time range, are the same. However, the price limit is less effective when limit the time range is smaller than the cancel time range. With the price limit, many sell orders are accumulated around the lower limit price, and when the lower limit price is changed before the accumulated sell orders are cancelled, it leads to the accumulation of sell orders of various prices. These accumulated sell orders essentially act as a wall against buy orders, thereby preventing price from rising. Caution should be taken in the sense that these results pertain to a limited situation. Specifically, our finding that the circuit breaker is better than the price limit should be adapted only in cases where the reason for falling prices is erroneous orders and when individual stocks are regulated.", "published": "2023-09-19T00:23:25Z", "authors": ["Takanobu Mizuta", "Isao Yagi"], "categories": ["q-fin.CP", "q-fin.TR"], "doi": "", "_query": "housing price text listing", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2209.06825v2", "title": "An Exploration of Hands-free Text Selection for Virtual Reality Head-Mounted Displays", "abstract": "Hand-based interaction, such as using a handheld controller or making hand gestures, has been widely adopted as the primary method for interacting with both virtual reality (VR) and augmented reality (AR) head-mounted displays (HMDs). In contrast, hands-free interaction avoids the need for users' hands and although it can afford additional benefits, there has been limited research in exploring and evaluating hands-free techniques for these HMDs. As VR HMDs become ubiquitous, people will need to do text editing, which requires selecting text segments. Similar to hands-free interaction, text selection is underexplored. This research focuses on both, text selection via hands-free interaction. Our exploration involves a user study with 24 participants to investigate the performance, user experience, and workload of three hands-free selection mechanisms (Dwell, Blink, Voice) to complement head-based pointing. Results indicate that Blink outperforms Dwell and Voice in completion time. Users' subjective feedback also shows that Blink is the preferred technique for text selection. This work is the first to explore hands-free interaction for text selection in VR HMDs. Our results provide a solid platform for further research in this important area.", "published": "2022-09-14T09:25:54Z", "authors": ["Xuanru Meng", "Wenge Xu", "Hai-Ning Liang"], "categories": ["cs.HC"], "doi": "", "_query": "housing price text listing", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2303.11989v2", "title": "Text2Room: Extracting Textured 3D Meshes from 2D Text-to-Image Models", "abstract": "We present Text2Room, a method for generating room-scale textured 3D meshes from a given text prompt as input. To this end, we leverage pre-trained 2D text-to-image models to synthesize a sequence of images from different poses. In order to lift these outputs into a consistent 3D scene representation, we combine monocular depth estimation with a text-conditioned inpainting model. The core idea of our approach is a tailored viewpoint selection such that the content of each image can be fused into a seamless, textured 3D mesh. More specifically, we propose a continuous alignment strategy that iteratively fuses scene frames with the existing geometry to create a seamless mesh. Unlike existing works that focus on generating single objects or zoom-out trajectories from text, our method generates complete 3D scenes with multiple objects and explicit 3D geometry. We evaluate our approach using qualitative and quantitative metrics, demonstrating it as the first method to generate room-scale 3D geometry with compelling textures from only text as input.", "published": "2023-03-21T16:21:02Z", "authors": ["Lukas Höllein", "Ang Cao", "Andrew Owens", "Justin Johnson", "Matthias Nießner"], "categories": ["cs.CV"], "doi": "", "_query": "housing price text listing", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2008.11806v2", "title": "The Time Function of Stock Price", "abstract": "This paper tends to define the quantitative relationship between the stock price and time as a time function. Based on the empirical evidence that the log-return of a stock is the series of white noise, a mathematical model of the integral white noise is established to describe the phenomenon of stock price movement. A deductive approach is used to derive the auto-correlation function, displacement formula and power spectral density of the stock price movement, which reveals not only the characteristics and rules of the movement but also the predictability of the stock price. The deductive fundamental is provided for the price analysis, prediction and risk management of portfolio investment.", "published": "2020-08-11T03:02:14Z", "authors": ["Shengfeng Mei", "Hong Gao"], "categories": ["q-fin.ST"], "doi": "", "_query": "housing price text listing", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2008.05880v1", "title": "Lifelong Property Price Prediction: A Case Study for the Toronto Real Estate Market", "abstract": "We present Luce, the first life-long predictive model for automated property valuation. Luce addresses two critical issues of property valuation: the lack of recent sold prices and the sparsity of house data. It is designed to operate on a limited volume of recent house transaction data. As a departure from prior work, Luce organizes the house data in a heterogeneous information network (HIN) where graph nodes are house entities and attributes that are important for house price valuation. We employ a Graph Convolutional Network (GCN) to extract the spatial information from the HIN for house-related data like geographical locations, and then use a Long Short Term Memory (LSTM) network to model the temporal dependencies for house transaction data over time. Unlike prior work, Luce can make effective use of the limited house transactions data in the past few months to update valuation information for all house entities within the HIN. By providing a complete and up-to-date house valuation dataset, Luce thus massively simplifies the downstream valuation task for the targeting properties. We demonstrate the benefit of Luce by applying it to large, real-life datasets obtained from the Toronto real estate market. Extensive experimental results show that Luce not only significantly outperforms prior property valuation methods but also often reaches and sometimes exceeds the valuation accuracy given by independent experts when using the actual realization price as the ground truth.", "published": "2020-08-12T07:32:16Z", "authors": ["Hao Peng", "Jianxin Li", "Zheng Wang", "Renyu Yang", "Mingzhe Liu", "Mingming Zhang", "Philip S. Yu", "Lifang He"], "categories": ["cs.LG"], "doi": "", "_query": "housing price text listing", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2506.19953v1", "title": "An experiment in price perception error", "abstract": "The process of consumer decision-making is multidimensional, and price perception is a very important but still not well-understood dimension for both marketers and consumers. Although heuristics or mental shortcuts are seen as biased and can cause decision errors, consumers tend to use price knowledge heuristics for purchase decisions, sometimes relying on old information. This study examined the effects of individual attitudinal and demographic factors on the ability of 351 respondents to correctly guess the prices of 13 products and services using ANOVA and the development of a seven-factor price perception model using latent variable analysis. While most respondents preferred to either research prices first or compare prices to a similar product, they either systematically underestimated or overestimated the prices of the products. In the ANOVA, brand loyalty, importance of substitute products and knockoff products, how financially well-off the respondent household was growing up, product quality importance, haggling tendency, and level of income were statistically significant predictors of price perception error. Latent variable analysis revealed that demographics, decision making, and price sensitivity factors had the greatest influence on price perception error. Level of education and income, frequency of regret, coupon importance, and how respondents chose to save or spend money were significant latent variables.", "published": "2025-06-24T19:02:49Z", "authors": ["Shawn Berry"], "categories": ["econ.GN"], "doi": "", "_query": "housing price text listing", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2106.16177v3", "title": "\"Stabilizer\" or \"catalyst\"? How green technology innovation affects the risk of stock price crashes: an analysis based on the quantity and quality of patents", "abstract": "To explore the relationship between corporate green technological innovation and the risk of stock price crashes, we first analyzed the data of listed companies in China from 2008 to 2018 and constructed indicators for the quantity and quality of corporate green technology innovation. The study found that the quantity of green technology innovation is not related to the risk of stock price crashes, while the quality of green technology innovation is negatively related to the risk of stock price crashes. Second, we studied the impact of corporate ownership on the relationship between the quality of green technological innovation and the risk of stock price crashes and found that in nonstate-owned enterprises, the quality of green technological innovation is negatively correlated with the risk of a stock price collapse, while in state-owned enterprises, the quality of green technological innovation and the risk of a stock price collapse are positive and not significant. Furthermore, we studied the mediating effect of the number of negative news reports in the media of listed companies on the relationship between the quality of corporate green technology innovation and the stock price crash and found that the quality of green technology innovation is positively correlated with the number of negative news reports in the media of listed companies, while the number of negative news reports in the media of listed companies is positively correlated with the risk of a stock price collapse. Finally, we conducted a DID regression by using the impact of exogenous policy shocks on the quality of green technology innovation, and the main results passed the robustness test.", "published": "2021-06-30T16:12:19Z", "authors": ["Ge-zhi Wu", "Da-ming You"], "categories": ["econ.GN"], "doi": "", "_query": "housing price text listing", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2512.23352v2", "title": "Revealed Preference Analysis of Shapley-Scarf Housing Markets", "abstract": "We ask what the reallocation of indivisible objects reveals about the market that produced it. A central authority assigns the objects, after which recipients exchange them among themselves. Preferences are never observed, and individuals of the same type have identical preferences. A reallocation is rationalizable as Pareto efficient and individually rational exactly when a directed graph built from the data is acyclic, and as weak core-stable exactly when a mixed graph admits an orientation acyclic within types and free of alternating cycles. Deciding the latter is NP-complete, and on the single-crossing domain, although the test becomes more restrictive, it turns out to be more useful in practice. Both properties are less demanding than the strict core, so their rejection eliminates a broader set of possibilities. We apply the tests to administrative data on social housing lettings in England.", "published": "2025-12-29T10:26:56Z", "authors": ["Umutcan Salman", "Michele Lombardi", "Francesco Ciardiello", "Riccardo Saulle"], "categories": ["econ.TH"], "doi": "", "_query": "housing price text listing", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2308.12291v1", "title": "The Quantum-House Effect and Its Demonstration on SpinQ Gemini", "abstract": "We introduce the quantum-house effect, a non-local quantum phenomenon which goes against classical intuition. We show how the effect can be achieved with any bipartite quantum state where neither subsystem is in a pure state. Besides its theoretical description, the quantum-house effect is also demonstrated on SpinQ Gemini, a 2-qubit liquid-state NMR desktop quantum computer.", "published": "2023-07-22T18:48:50Z", "authors": ["Tamás Varga"], "categories": ["physics.gen-ph"], "doi": "10.1007/s12045-023-1544-1", "_query": "housing price text listing", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2511.21158v4", "title": "Rectified strong core in Shapley-Scarf housing markets with indifferences", "abstract": "We study core concepts in the Shapley-Scarf housing market model under unrestricted weak preferences. Among standard concepts, the strong core may be empty while the nonempty weak core may contain Pareto inefficient allocations. We propose the rectified strong core by adding a single condition: an unaffected agent may join a blocking coalition only if every object he views as indifferent to his current assignment is owned by the coalition. The rectified strong core is always nonempty and Pareto efficient, lies between the weak and strong cores, and coincides with the strong core whenever the latter is nonempty. This condition is the most permissive admissibility requirement for unaffected blocking agents that guarantees nonemptiness, and extends the behavioral foundation of the strong core to weak preferences.", "published": "2025-11-26T08:18:07Z", "authors": ["Jun Zhang"], "categories": ["econ.TH"], "doi": "", "_query": "housing price text listing", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2106.14572v1", "title": "Dynamic Urban Planning: an Agent-Based Model Coupling Mobility Mode and Housing Choice. Use case Kendall Square", "abstract": "As cities become increasingly populated, urban planning plays a key role in ensuring the equitable and inclusive development of metropolitan areas. MIT City Science group created a data-driven tangible platform, CityScope, to help different stakeholders, such as government representatives, urban planners, developers, and citizens, collaboratively shape the urban scenario through the real-time impact analysis of different urban interventions. This paper presents an agent-based model that characterizes citizens' behavioural patterns with respect to housing and mobility choice that will constitute the first step in the development of a dynamic incentive system for an open interactive governance process. The realistic identification and representation of the criteria that affect this decision-making process will help understand and evaluate the impacts of potential housing incentives that aim to promote urban characteristics such as equality, diversity, walkability, and efficiency. The calibration and validation of the model have been performed in a well-known geographic area for the Group: Kendall Square in Cambridge, MA.", "published": "2021-06-28T10:54:44Z", "authors": ["Mireia Yurrita", "Arnaud Grignard", "Luis Alonso", "Yan Zhang", "Cristian Jara-Figueroa", "Markus Elkatsha", "Kent Larson"], "categories": ["cs.MA", "cs.CY"], "doi": "", "_query": "housing price text listing", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2211.10578v2", "title": "ABINet++: Autonomous, Bidirectional and Iterative Language Modeling for Scene Text Spotting", "abstract": "Scene text spotting is of great importance to the computer vision community due to its wide variety of applications. Recent methods attempt to introduce linguistic knowledge for challenging recognition rather than pure visual classification. However, how to effectively model the linguistic rules in end-to-end deep networks remains a research challenge. In this paper, we argue that the limited capacity of language models comes from 1) implicit language modeling; 2) unidirectional feature representation; and 3) language model with noise input. Correspondingly, we propose an autonomous, bidirectional and iterative ABINet++ for scene text spotting. Firstly, the autonomous suggests enforcing explicitly language modeling by decoupling the recognizer into vision model and language model and blocking gradient flow between both models. Secondly, a novel bidirectional cloze network (BCN) as the language model is proposed based on bidirectional feature representation. Thirdly, we propose an execution manner of iterative correction for the language model which can effectively alleviate the impact of noise input. Finally, to polish ABINet++ in long text recognition, we propose to aggregate horizontal features by embedding Transformer units inside a U-Net, and design a position and content attention module which integrates character order and content to attend to character features precisely. ABINet++ achieves state-of-the-art performance on both scene text recognition and scene text spotting benchmarks, which consistently demonstrates the superiority of our method in various environments especially on low-quality images. Besides, extensive experiments including in English and Chinese also prove that, a text spotter that incorporates our language modeling method can significantly improve its performance both in accuracy and speed compared with commonly used attention-based recognizers.", "published": "2022-11-19T03:50:33Z", "authors": ["Shancheng Fang", "Zhendong Mao", "Hongtao Xie", "Yuxin Wang", "Chenggang Yan", "Yongdong Zhang"], "categories": ["cs.CV"], "doi": "", "_query": "housing price text listing", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2008.00956v1", "title": "Interactive Text Graph Mining with a Prolog-based Dialog Engine", "abstract": "On top of a neural network-based dependency parser and a graph-based natural language processing module we design a Prolog-based dialog engine that explores interactively a ranked fact database extracted from a text document. We reorganize dependency graphs to focus on the most relevant content elements of a sentence and integrate sentence identifiers as graph nodes. Additionally, after ranking the graph we take advantage of the implicit semantic information that dependency links and WordNet bring in the form of subject-verb-object, is-a and part-of relations. Working on the Prolog facts and their inferred consequences, the dialog engine specializes the text graph with respect to a query and reveals interactively the document's most relevant content elements. The open-source code of the integrated system is available at https://github.com/ptarau/DeepRank . Under consideration in Theory and Practice of Logic Programming (TPLP).", "published": "2020-07-31T03:29:49Z", "authors": ["Paul Tarau", "Eduardo Blanco"], "categories": ["cs.CL", "cs.LO"], "doi": "10.1017/S1471068420000137", "_query": "housing price text listing", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/1906.02192v1", "title": "Large-Scale Multi-Label Text Classification on EU Legislation", "abstract": "We consider Large-Scale Multi-Label Text Classification (LMTC) in the legal domain. We release a new dataset of 57k legislative documents from EURLEX, annotated with ~4.3k EUROVOC labels, which is suitable for LMTC, few- and zero-shot learning. Experimenting with several neural classifiers, we show that BIGRUs with label-wise attention perform better than other current state of the art methods. Domain-specific WORD2VEC and context-sensitive ELMO embeddings further improve performance. We also find that considering only particular zones of the documents is sufficient. This allows us to bypass BERT's maximum text length limit and fine-tune BERT, obtaining the best results in all but zero-shot learning cases.", "published": "2019-06-05T14:41:01Z", "authors": ["Ilias Chalkidis", "Manos Fergadiotis", "Prodromos Malakasiotis", "Ion Androutsopoulos"], "categories": ["cs.CL"], "doi": "", "_query": "housing price text listing", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2204.10196v3", "title": "Multimodal Hate Speech Detection from Bengali Memes and Texts", "abstract": "Numerous machine learning (ML) and deep learning (DL)-based approaches have been proposed to utilize textual data from social media for anti-social behavior analysis like cyberbullying, fake news detection, and identification of hate speech mainly for highly-resourced languages such as English. However, despite having a lot of diversity and millions of native speakers, some languages like Bengali are under-resourced, which is due to a lack of computational resources for natural language processing (NLP). Similar to other languages, Bengali social media contents also include images along with texts (e.g., multimodal memes are posted by embedding short texts into images on Facebook). Therefore, only the textual data is not enough to judge them since images might give extra context to make a proper judgement. This paper is about hate speech detection from multimodal Bengali memes and texts. We prepared the only multimodal hate speech dataset for-a-kind of problem for Bengali, which we use to train state-of-the-art neural architectures (e.g., Bi-LSTM/Conv-LSTM with word embeddings, ConvNets + pre-trained language models, e.g., monolingual Bangla BERT, multilingual BERT-cased/uncased, and XLM-RoBERTa) to jointly analyze textual and visual information for hate speech detection. Conv-LSTM and XLM-RoBERTa models performed best for texts, yielding F1 scores of 0.78 and 0.82, respectively. As of memes, ResNet-152 and DenseNet-161 models yield F1 scores of 0.78 and 0.79, respectively. As for multimodal fusion, XLM-RoBERTa + DenseNet-161 performed the best, yielding an F1 score of 0.83. Our study suggests that text modality is most useful for hate speech detection, while memes are moderately useful.", "published": "2022-04-19T11:15:25Z", "authors": ["Md. Rezaul Karim", "Sumon Kanti Dey", "Tanhim Islam", "Md. Shajalal", "Bharathi Raja Chakravarthi"], "categories": ["cs.CL", "cs.AI"], "doi": "", "_query": "housing price text listing", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/1707.06374v3", "title": "Document Listing on Repetitive Collections with Guaranteed Performance", "abstract": "We consider document listing on string collections, that is, finding in which strings a given pattern appears. In particular, we focus on repetitive collections: a collection of size $N$ over alphabet $[1,σ]$ is composed of $D$ copies of a string of size $n$, and $s$ edits are applied on ranges of copies. We introduce the first document listing index with size $\\tilde{O}(n+s)$, precisely $O((n\\logσ+s\\log^2 N)\\log D)$ bits, and with useful worst-case time guarantees: Given a pattern of length $m$, the index reports the $\\ndoc>0$ strings where it appears in time $O(m\\log^{1+ε} N \\cdot \\ndoc)$, for any constant $ε>0$ (and tells in time $O(m\\log N)$ if $\\ndoc=0$). Our technique is to augment a range data structure that is commonly used on grammar-based indexes, so that instead of retrieving all the pattern occurrences, it computes useful summaries on them. We show that the idea has independent interest: we introduce the first grammar-based index that, on a text $T[1,N]$ with a grammar of size $r$, uses $O(r\\log N)$ bits and counts the number of occurrences of a pattern $P[1,m]$ in time $O(m^2 + m\\log^{2+ε} r)$, for any constant $ε>0$. We also give the first index using $O(z\\log(N/z)\\log N)$ bits, where $T$ is parsed by Lempel-Ziv into $z$ phrases, counting occurrences in time $O(m\\log^{2+ε} N)$.", "published": "2017-07-20T05:01:22Z", "authors": ["Gonzalo Navarro"], "categories": ["cs.DS"], "doi": "", "_query": "housing price text listing", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2006.11494v2", "title": "AOT: Pushing the Efficiency Boundary of Main-memory Triangle Listing", "abstract": "Triangle listing is an important topic significant in many practical applications. Efficient algorithms exist for the task of triangle listing. Recent algorithms leverage an orientation framework, which can be thought of as mapping an undirected graph to a directed acylic graph, namely oriented graph, with respect to any global vertex order. In this paper, we propose an adaptive orientation technique that satisfies the orientation technique but refines it by traversing carefully based on the out-degree of the vertices in the oriented graph during the computation of triangles. Based on this adaptive orientation technique, we design a new algorithm, namely aot, to enhance the edge-iterator listing paradigm. We also make improvements to the performance of aot by exploiting the local order within the adjacent list of the vertices. We show that aot is the first work which can achieve best performance in terms of both practical performance and theoretical time complexity. Our comprehensive experiments over $16$ real-life large graphs show a superior performance of our \\aot algorithm when compared against the state-of-the-art, especially for massive graphs with billions of edges. Theoretically, we show that our proposed algorithm has a time complexity of $Θ(\\sum_{ \\langle u,v \\rangle \\in \\vec{E} } \\min\\{ deg^{+}(u),deg^{+}(v)\\}))$, where $\\vec{E}$ and $deg^{+}(x)$ denote the set of directed edges in an oriented graph and the out-degree of vertex $x$ respectively. As to our best knowledge, this is the best time complexity among in-memory triangle listing algorithms.", "published": "2020-06-20T04:53:44Z", "authors": ["Michael Yu", "Lu Qin", "Ying Zhang", "Wenjie Zhang", "Xuemin Lin"], "categories": ["cs.DB"], "doi": "", "_query": "housing price text listing", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2501.12583v1", "title": "Chasing price drains liquidity", "abstract": "Assuming that the price in a Uniswap v3 style Automated Market Maker (AMM) follows a Geometric Brownian Motion (GBM), we prove that the strategy that adjusts the position of liquidity to track the current price leads to a deterministic and exponentially fast decay of liquidity. Next, assuming that there is a Centralized Exchange (CEX), in which the price follows a GBM and the AMM price mean reverts to the CEX price, we show numerically that the same strategy still leads to decay. Last, we propose a strategy that increases the liquidity even without compounding fees earned through liquidity provision.", "published": "2025-01-22T02:13:55Z", "authors": ["Yizhou Cao", "Yepeng Ding", "Ruichao Jiang", "Long Wen"], "categories": ["cs.CE"], "doi": "", "_query": "housing price text listing", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2503.17514v2", "title": "Language Models May Verbatim Complete Text They Were Not Explicitly Trained On", "abstract": "An important question today is whether a given text was used to train a large language model (LLM). A \\emph{completion} test is often employed: check if the LLM completes a sufficiently complex text. This, however, requires a ground-truth definition of membership; most commonly, it is defined as a member based on the $n$-gram overlap between the target text and any text in the dataset. In this work, we demonstrate that this $n$-gram based membership definition can be effectively gamed. We study scenarios where sequences are \\emph{non-members} for a given $n$ and we find that completion tests still succeed. We find many natural cases of this phenomenon by retraining LLMs from scratch after removing all training samples that were completed; these cases include exact duplicates, near-duplicates, and even short overlaps. They showcase that it is difficult to find a single viable choice of $n$ for membership definitions. Using these insights, we design adversarial datasets that can cause a given target sequence to be completed without containing it, for any reasonable choice of $n$. Our findings highlight the inadequacy of $n$-gram membership, suggesting membership definitions fail to account for auxiliary information available to the training algorithm.", "published": "2025-03-21T19:57:04Z", "authors": ["Ken Ziyu Liu", "Christopher A. Choquette-Choo", "Matthew Jagielski", "Peter Kairouz", "Sanmi Koyejo", "Percy Liang", "Nicolas Papernot"], "categories": ["cs.CL", "cs.AI", "cs.CR", "cs.LG"], "doi": "", "_query": "housing price text listing", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/1712.01385v5", "title": "Quantum Bounds for Option Prices", "abstract": "Option pricing is the most elemental challenge of mathematical finance. Knowledge of the prices of options at every strike is equivalent to knowing the entire pricing distribution for a security, as derivatives contingent on the security can be replicated using options. The available data may be insufficient to determine this distribution precisely, however, and the question arises: What are the bounds for the option price at a specified strike, given the market-implied constraints? Positivity of the price map imposed by the principle of no-arbitrage is here utilised, via the Gelfand-Naimark-Segal construction, to transform the problem into the domain of operator algebras. Optimisation in this larger context is essentially geometric, and the outcome is simultaneously super-optimal for all commutative subalgebras. This generates an upper bound for the price of a basket option. With innovative decomposition of the assets in the basket, the result is used to create converging families of price bounds for vanilla options, interpolate the volatility smile, price options on cross FX rates, and analyse the relationships between swaption and caplet prices.", "published": "2017-12-04T21:44:31Z", "authors": ["Paul McCloud"], "categories": ["q-fin.MF", "math.OA"], "doi": "", "_query": "housing price text listing", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2205.04181v1", "title": "Price DOES Matter! Modeling Price and Interest Preferences in Session-based Recommendation", "abstract": "Session-based recommendation aims to predict items that an anonymous user would like to purchase based on her short behavior sequence. The current approaches towards session-based recommendation only focus on modeling users' interest preferences, while they all ignore a key attribute of an item, i.e., the price. Many marketing studies have shown that the price factor significantly influences users' behaviors and the purchase decisions of users are determined by both price and interest preferences simultaneously. However, it is nontrivial to incorporate price preferences for session-based recommendation. Firstly, it is hard to handle heterogeneous information from various features of items to capture users' price preferences. Secondly, it is difficult to model the complex relations between price and interest preferences in determining user choices. To address the above challenges, we propose a novel method Co-guided Heterogeneous Hypergraph Network (CoHHN) for session-based recommendation. Towards the first challenge, we devise a heterogeneous hypergraph to represent heterogeneous information and rich relations among them. A dual-channel aggregating mechanism is then designed to aggregate various information in the heterogeneous hypergraph. After that, we extract users' price preferences and interest preferences via attention layers. As to the second challenge, a co-guided learning scheme is designed to model the relations between price and interest preferences and enhance the learning of each other. Finally, we predict user actions based on item features and users' price and interest preferences. Extensive experiments on three real-world datasets demonstrate the effectiveness of the proposed CoHHN. Further analysis reveals the significance of price for session-based recommendation.", "published": "2022-05-09T10:47:15Z", "authors": ["Xiaokun Zhang", "Bo Xu", "Liang Yang", "Chenliang Li", "Fenglong Ma", "Haifeng Liu", "Hongfei Lin"], "categories": ["cs.IR", "cs.AI"], "doi": "10.1145/3477495.3532043", "_query": "housing price text listing", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/1407.3784v1", "title": "Isomorphy Classes of Involutions of $\\text{SP}(2n, k)$, $n>2$", "abstract": "A first characterization of the isomorphism classes of $k$-involutions for any reductive algebraic groups defined over a perfect field was given by Helminck in 2000 using $3$ invariants. In 2004, Helminck, Wu, and Dometrius gave a full classification of all involutions on $\\text{SL}(n,k)$ for $k$ algebraically closed, the real numbers, the $p$-adic numbers or a finite field was provided. In this paper, we build on these results to develop a detailed characterization of the involutions of $\\text{SP}(2n, k)$. We use these results to classify the isomorphy classes of involutions of $\\text{SP}(2n, k)$ where $k$ is any field not of characteristic 2.", "published": "2014-07-14T17:55:18Z", "authors": ["Robert W. Benim", "Aloysius G. Helminck", "Farrah Jackson"], "categories": ["math.RT"], "doi": "", "_query": "housing price text listing", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2208.03135v2", "title": "Modeling Price Elasticity for Occupancy Prediction in Hotel Dynamic Pricing", "abstract": "Demand estimation plays an important role in dynamic pricing where the optimal price can be obtained via maximizing the revenue based on the demand curve. In online hotel booking platform, the demand or occupancy of rooms varies across room-types and changes over time, and thus it is challenging to get an accurate occupancy estimate. In this paper, we propose a novel hotel demand function that explicitly models the price elasticity of demand for occupancy prediction, and design a price elasticity prediction model to learn the dynamic price elasticity coefficient from a variety of affecting factors. Our model is composed of carefully designed elasticity learning modules to alleviate the endogeneity problem, and trained in a multi-task framework to tackle the data sparseness. We conduct comprehensive experiments on real-world datasets and validate the superiority of our method over the state-of-the-art baselines for both occupancy prediction and dynamic pricing.", "published": "2022-08-04T13:58:04Z", "authors": ["Fanwei Zhu", "Wendong Xiao", "Yao Yu", "Ziyi Wang", "Zulong Chen", "Quan Lu", "Zemin Liu", "Minghui Wu", "Shenghua Ni"], "categories": ["econ.GN", "cs.IR", "cs.LG"], "doi": "", "_query": "housing price text listing", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2405.05352v2", "title": "A comprehensive Cartesian to geodetic coordinate transformation reference listing", "abstract": "A comprehensive listing of over 230 references related to Cartesian to geodetic coordinate transformations are provided. The references are coarsely sorted and DOI:s or URL:s are provided to the extent possible to facilitate studies of the material.", "published": "2024-05-08T18:32:11Z", "authors": ["John-Olof Nilsson"], "categories": ["physics.geo-ph"], "doi": "", "_query": "housing price text listing", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/1201.2719v3", "title": "Ultrametric Model of Mind, II: Application to Text Content Analysis", "abstract": "In a companion paper, Murtagh (2012), we discussed how Matte Blanco's work linked the unrepressed unconscious (in the human) to symmetric logic and thought processes. We showed how ultrametric topology provides a most useful representational and computational framework for this. Now we look at the extent to which we can find ultrametricity in text. We use coherent and meaningful collections of nearly 1000 texts to show how we can measure inherent ultrametricity. On the basis of our findings we hypothesize that inherent ultrametricty is a basis for further exploring unconscious thought processes.", "published": "2012-01-13T01:00:41Z", "authors": ["Fionn Murtagh"], "categories": ["cs.AI", "cs.CL"], "doi": "", "_query": "housing price text listing", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2609.40356v2", "title": "ViTeX-Bench: Benchmarking High-Fidelity Video Scene Text Editing", "abstract": "Recent video generation is increasingly realistic and controllable, yet video editing remains less developed, particularly for precise local edits that must preserve the original scene dynamics. Video scene text editing replaces text on scene surfaces, such as storefront signs, whiteboards, and product labels, while preserving the surrounding content, motion, and camera dynamics. Although scene text editing is well studied for images, video scene text editing that achieves high visual quality, temporal consistency, and edit locality remains underexplored. Existing resources offer limited paired real-video data, and general video-editing metrics do not directly measure whether the requested text remains correct over time. We introduce ViTeX-Bench, a benchmark suite comprising ViTeX-Dataset and a three-axis evaluation protocol. The dataset contains 387 real-world 720p videos with text-region masks and editing instructions: 230 provide reviewed, pipeline-generated paired edits for training, and 157 form a frozen evaluation split. The protocol evaluates text correctness, visual and temporal quality, and edit locality through 13 metrics, with one primary metric per axis and a Pareto comparison of their trade-offs. OCR calibration, human evaluation, and annotation-sensitivity analyses support the interpretation of these scores. Across eight baselines from four editing families, accurate text, temporal stability, and scene preservation remain difficult to achieve together. We also release ViTeX-Edit-14B, an open-source reference editor fine-tuned on the paired training split with motion-aligned glyph-video conditioning. It achieves CharAcc 0.688, the highest mean among the evaluated video-native editors, and the lowest comparable text-crop Warp among raw editor outputs. ViTeX-Bench provides a reproducible foundation for studying these trade-offs in video scene text editing.", "published": "2026-09-30T17:59:33Z", "authors": ["Xinghao Chen", "Xiangbo Gao", "Jiongze Yu", "Yuheng Wu", "Zhengzhong Tu"], "categories": ["cs.CV", "cs.AI"], "doi": "", "_query": "housing price text listing", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/1312.2371v3", "title": "Tight Bounds for the Price of Anarchy of Simultaneous First Price Auctions", "abstract": "We study the Price of Anarchy of simultaneous first-price auctions for buyers with submodular and subadditive valuations. The current best upper bounds for the Bayesian Price of Anarchy of these auctions are e/(e-1) [Syrgkanis and Tardos 2013] and 2 [Feldman et al. 2013], respectively. We provide matching lower bounds for both cases even for the case of full information and for mixed Nash equilibria via an explicit construction. We present an alternative proof of the upper bound of e/(e-1) for first-price auctions with fractionally subadditive valuations which reveals the worst-case price distribution, that is used as a building block for the matching lower bound construction. We generalize our results to a general class of item bidding auctions that we call bid-dependent auctions (including first-price auctions and all-pay auctions) where the winner is always the highest bidder and each bidder's payment depends only on his own bid. Finally, we apply our techniques to discriminatory price multi-unit auctions. We complement the results of [de Keijzer et al. 2013] for the case of subadditive valuations, by providing a matching lower bound of 2. For the case of submodular valuations, we provide a lower bound of 1.109. For the same class of valuations, we were able to reproduce the upper bound of e/(e-1) using our non-smooth approach.", "published": "2013-12-09T10:30:46Z", "authors": ["George Christodoulou", "Annamária Kovács", "Alkmini Sgouritsa", "Bo Tang"], "categories": ["cs.GT"], "doi": "", "_query": "housing price text listing", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2502.08613v8", "title": "The Relative Entropy of Expectation and Price", "abstract": "As operators acting on the undetermined final settlement of a derivative security, expectation is linear but price is non-linear. When the market of underlying securities is incomplete, non-linearity emerges from the bid-offer around the mid price that accounts for the residual risks of the optimal funding and hedging strategy. At the extremes, non-linearity also arises from the embedded options on capital that are exercised upon default. In this essay, these convexities are quantified in an entropic risk metric that evaluates the strategic risks, which is realised as a cost with the introduction of bilateral margin. Price is then adjusted for market incompleteness and the risk of default caused by the exhaustion of capital. In the complete market theory, price is derived from a martingale condition. In the incomplete market theory presented here, price is instead derived from a log-martingale condition: \\begin{equation} p=-\\frac{1}α\\log\\mathbb{E}\\exp[-αP] \\notag \\end{equation} for the price $p$ and payoff $P$ of a funded and hedged derivative security, where the price measure $\\mathbb{E}$ has minimum entropy relative to economic expectations, and the parameter $α$ matches the risk aversion of the investor. This price principle is easily applied to standard models for market evolution, with applications considered here in model risk analysis, deep hedging and quantum information.", "published": "2025-02-12T18:03:07Z", "authors": ["Paul McCloud"], "categories": ["q-fin.MF"], "doi": "", "_query": "housing price text listing", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/math/0612075v1", "title": "Option Pricing without Price Dynamics: A Probabilistic Approach", "abstract": "Employing probabilistic techniques we compute best possible upper and lower bounds on the price of an option on one or two assets with continuous piecewise linear payoff function based on prices of simple call options of possibly distinct maturities and the no-arbitrage condition, but without any assumption on the price dynamics of underlying assets. We show that the problem reduces to solving linear optimization problems that we explicitly characterize. We report numerical results that illustrate the effectiveness of the algorithms we develop.", "published": "2006-12-03T23:59:09Z", "authors": ["Dimitris Bertsimas", "Natasha Bushueva"], "categories": ["math.PR", "q-fin.PR"], "doi": "", "_query": "housing price text listing", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/1709.08621v1", "title": "A sentiment-based model for the BitCoin: theory, estimation and option pricing", "abstract": "In recent literature it is claimed that BitCoin price behaves more likely to a volatile stock asset than a currency and that changes in its price are influenced by sentiment about the BitCoin system itself; in Kristoufek [10] the author analyses transaction based as well as popularity based potential drivers of the BitCoin price finding positive evidence. Here, we endorse this finding and consider a bivariate model in continuous time to describe the price dynamics of one BitCoin as well as a second factor, affecting the price itself, which represents a sentiment indicator. We prove that the suggested model is arbitrage-free under a mild condition and, based on risk-neutral evaluation, we obtain a closed formula to approximate the price of European style derivatives on the BitCoin. By applying the same approximation technique to the joint likelihood of a discrete sample of the bivariate process, we are also able to fit the model to market data. This is done by using both the Volume and the number of Google searches as possible proxies for the sentiment factor. Further, the performance of the pricing formula is assessed on a sample of market option prices obtained by the website deribit.com.", "published": "2017-09-23T21:42:40Z", "authors": ["Alessandra Cretarola", "Gianna Figà-Talamanca", "Marco Patacca"], "categories": ["q-fin.MF"], "doi": "10.1007/s10203-019-00262-x", "_query": "housing price text listing", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2509.18862v1", "title": "Multi-Hierarchical Feature Detection for Large Language Model Generated Text", "abstract": "With the rapid advancement of large language model technology, there is growing interest in whether multi-feature approaches can significantly improve AI text detection beyond what single neural models achieve. While intuition suggests that combining semantic, syntactic, and statistical features should provide complementary signals, this assumption has not been rigorously tested with modern LLM-generated text. This paper provides a systematic empirical investigation of multi-hierarchical feature integration for AI text detection, specifically testing whether the computational overhead of combining multiple feature types is justified by performance gains. We implement MHFD (Multi-Hierarchical Feature Detection), integrating DeBERTa-based semantic analysis, syntactic parsing, and statistical probability features through adaptive fusion. Our investigation reveals important negative results: despite theoretical expectations, multi-feature integration provides minimal benefits (0.4-0.5% improvement) while incurring substantial computational costs (4.2x overhead), suggesting that modern neural language models may already capture most relevant detection signals efficiently. Experimental results on multiple benchmark datasets demonstrate that the MHFD method achieves 89.7% accuracy in in-domain detection and maintains 84.2% stable performance in cross-domain detection, showing modest improvements of 0.4-2.6% over existing methods.", "published": "2025-09-23T09:55:42Z", "authors": ["Luyan Zhang", "Xinyu Xie"], "categories": ["cs.CL"], "doi": "", "_query": "housing price text listing", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2503.08696v1", "title": "Multimodal Stock Price Prediction: A Case Study of the Russian Securities Market", "abstract": "Classical asset price forecasting methods primarily rely on numerical data, such as price time series, trading volumes, limit order book data, and technical analysis indicators. However, the news flow plays a significant role in price formation, making the development of multimodal approaches that combine textual and numerical data for improved prediction accuracy highly relevant. This paper addresses the problem of forecasting financial asset prices using the multimodal approach that combines candlestick time series and textual news flow data. A unique dataset was collected for the study, which includes time series for 176 Russian stocks traded on the Moscow Exchange and 79,555 financial news articles in Russian. For processing textual data, pre-trained models RuBERT and Vikhr-Qwen2.5-0.5b-Instruct (a large language model) were used, while time series and vectorized text data were processed using an LSTM recurrent neural network. The experiments compared models based on a single modality (time series only) and two modalities, as well as various methods for aggregating text vector representations. Prediction quality was estimated using two key metrics: Accuracy (direction of price movement prediction: up or down) and Mean Absolute Percentage Error (MAPE), which measures the deviation of the predicted price from the true price. The experiments showed that incorporating textual modality reduced the MAPE value by 55%. The resulting multimodal dataset holds value for the further adaptation of language models in the financial sector. Future research directions include optimizing textual modality parameters, such as the time window, sentiment, and chronological order of news messages.", "published": "2025-03-05T21:20:32Z", "authors": ["Kasymkhan Khubiev", "Mikhail Semenov"], "categories": ["q-fin.ST", "cs.LG", "q-fin.CP"], "doi": "10.25209/2079-3316-2025-16-1-83-130", "_query": "housing price text listing", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2302.11383v1", "title": "Entity-Level Text-Guided Image Manipulation", "abstract": "Existing text-guided image manipulation methods aim to modify the appearance of the image or to edit a few objects in a virtual or simple scenario, which is far from practical applications. In this work, we study a novel task on text-guided image manipulation on the entity level in the real world (eL-TGIM). The task imposes three basic requirements, (1) to edit the entity consistent with the text descriptions, (2) to preserve the entity-irrelevant regions, and (3) to merge the manipulated entity into the image naturally. To this end, we propose an elegant framework, dubbed as SeMani, forming the Semantic Manipulation of real-world images that can not only edit the appearance of entities but also generate new entities corresponding to the text guidance. To solve eL-TGIM, SeMani decomposes the task into two phases: the semantic alignment phase and the image manipulation phase. In the semantic alignment phase, SeMani incorporates a semantic alignment module to locate the entity-relevant region to be manipulated. In the image manipulation phase, SeMani adopts a generative model to synthesize new images conditioned on the entity-irrelevant regions and target text descriptions. We discuss and propose two popular generation processes that can be utilized in SeMani, the discrete auto-regressive generation with transformers and the continuous denoising generation with diffusion models, yielding SeMani-Trans and SeMani-Diff, respectively. We conduct extensive experiments on the real datasets CUB, Oxford, and COCO datasets to verify that SeMani can distinguish the entity-relevant and -irrelevant regions and achieve more precise and flexible manipulation in a zero-shot manner compared with baseline methods. Our codes and models will be released at https://github.com/Yikai-Wang/SeMani.", "published": "2023-02-22T13:56:23Z", "authors": ["Yikai Wang", "Jianan Wang", "Guansong Lu", "Hang Xu", "Zhenguo Li", "Wei Zhang", "Yanwei Fu"], "categories": ["cs.CV"], "doi": "", "_query": "housing price text listing", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2011.10113v2", "title": "Price Impact on Term Structure", "abstract": "We introduce a first theory of price impact in presence of an interest-rates term structure. We explain how one can formulate instantaneous and transient price impact on bonds with different maturities, including a cross price impact that is endogenous to the term structure. We connect the introduced impact to classic no-arbitrage theory for interest rate markets, showing that impact can be embedded in the pricing measure and that no-arbitrage can be preserved. We present pricing examples in presence of price impact and numerical examples of how impact changes the shape of the term structure. Finally, to show that our approach is applicable we solve an optimal execution problem in interest rate markets with the type of price impact we developed in the paper.", "published": "2020-11-19T21:42:35Z", "authors": ["Damiano Brigo", "Federico Graceffa", "Eyal Neuman"], "categories": ["q-fin.TR", "q-fin.PM"], "doi": "", "_query": "housing price text listing", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2008.05195v2", "title": "Competitive Demand Learning: A Non-cooperative Pricing Algorithm with Coordinated Price Experimentation", "abstract": "We consider a periodical equilibrium pricing problem for multiple firms over a planning horizon of T periods. At each period, firms set their selling prices and receive stochastic demand from consumers. Firms do not know their underlying demand curve, but they wish to determine the selling prices to maximize total revenue under competition. Hence, they have to do some price experiments such that the observed demand data are informative to make price decisions. However, uncoordinated price updating can render the demand information gathered by price experimentation less informative or inaccurate. We design a nonparametric learning algorithm to facilitate coordinated dynamic pricing, in which competitive firms estimate their demand functions based on observations and adjust their pricing strategies in a prescribed manner. We show that the pricing decisions, determined by estimated demand functions, converge to underlying equilibrium as time progresses. We obtain a bound of the revenue difference that has an order of O(F^2 T^3/4) and a regret bound that has an order of O(F T^1/2) with respect to the number of the competitive firms F and T . We also develop a modified algorithm to handle the situation where some firms may have the knowledge of the demand curve.", "published": "2020-08-12T09:26:24Z", "authors": ["Yongge Yang", "Yu-Ching Lee", "Po-An Chen"], "categories": ["cs.GT"], "doi": "", "_query": "housing price text listing", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2310.13795v1", "title": "The Human Behind the Data: Reflections from an Ongoing Co-Design and Deployment of a Data-Navigation Interface for Front-Line Emergency Housing Shelter Staff", "abstract": "On any night in Canada, at least 35,000 individuals experience homelessness. These individuals use emergency shelters to transition out of homelessness and into permanent housing. We designed and deployed a technology to support front-line staff at the largest emergency housing shelter in Calgary, Canada. Over a period of five months in 2022, we worked closely with front-line staff to co-design an interface for supporting a holistic understanding of client context and facilitating decision-making. The tool is currently in-use and our collaboration is ongoing. In this paper, we reflect on preliminary findings regarding the second iteration of the tool. We find that supporting shelter staff in understanding the human behind the data was a critical component of design. This work contributes to literature on how data tools may be integrated into homeless shelters in a way that aligns with shelters' values.", "published": "2023-10-20T20:03:52Z", "authors": ["Teale W Masrani", "Helen Ai He", "Geoffrey Messier"], "categories": ["cs.HC"], "doi": "10.1145/3544549.3585694", "_query": "housing price text listing", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2501.02042v1", "title": "Towards Robust and Accurate Stability Estimation of Local Surrogate Models in Text-based Explainable AI", "abstract": "Recent work has investigated the concept of adversarial attacks on explainable AI (XAI) in the NLP domain with a focus on examining the vulnerability of local surrogate methods such as Lime to adversarial perturbations or small changes on the input of a machine learning (ML) model. In such attacks, the generated explanation is manipulated while the meaning and structure of the original input remain similar under the ML model. Such attacks are especially alarming when XAI is used as a basis for decision making (e.g., prescribing drugs based on AI medical predictors) or for legal action (e.g., legal dispute involving AI software). Although weaknesses across many XAI methods have been shown to exist, the reasons behind why remain little explored. Central to this XAI manipulation is the similarity measure used to calculate how one explanation differs from another. A poor choice of similarity measure can lead to erroneous conclusions about the stability or adversarial robustness of an XAI method. Therefore, this work investigates a variety of similarity measures designed for text-based ranked lists referenced in related work to determine their comparative suitability for use. We find that many measures are overly sensitive, resulting in erroneous estimates of stability. We then propose a weighting scheme for text-based data that incorporates the synonymity between the features within an explanation, providing more accurate estimates of the actual weakness of XAI methods to adversarial examples.", "published": "2025-01-03T17:44:57Z", "authors": ["Christopher Burger", "Charles Walter", "Thai Le", "Lingwei Chen"], "categories": ["cs.LG", "cs.CR"], "doi": "", "_query": "housing price text listing", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/1708.09020v1", "title": "Learning to Price with Reference Effects", "abstract": "As a firm varies the price of a product, consumers exhibit reference effects, making purchase decisions based not only on the prevailing price but also the product's price history. We consider the problem of learning such behavioral patterns as a monopolist releases, markets, and prices products. This context calls for pricing decisions that intelligently trade off between maximizing revenue generated by a current product and probing to gain information for future benefit. Due to dependence on price history, realized demand can reflect delayed consequences of earlier pricing decisions. As such, inference entails attribution of outcomes to prior decisions and effective exploration requires planning price sequences that yield informative future outcomes. Despite the considerable complexity of this problem, we offer a tractable systematic approach. In particular, we frame the problem as one of reinforcement learning and leverage Thompson sampling. We also establish a regret bound that provides graceful guarantees on how performance improves as data is gathered and how this depends on the complexity of the demand model. We illustrate merits of the approach through simulations.", "published": "2017-08-29T20:40:10Z", "authors": ["Abbas Kazerouni", "Benjamin Van Roy"], "categories": ["cs.GT", "cs.AI"], "doi": "", "_query": "housing price text listing", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/1805.02574v1", "title": "Optimal Pricing in Repeated Posted-Price Auctions", "abstract": "We study revenue optimization pricing algorithms for repeated posted-price auctions where a seller interacts with a single strategic buyer that holds a fixed private valuation. We show that, in the case when both the seller and the buyer have the same discounting in their cumulative utilities (revenue and surplus), there exist two optimal algorithms. The first one constantly offers the Myerson price, while the second pricing proposes a \"big deal\": pay for all goods in advance (at the first round) or get nothing. However, when there is an imbalance between the seller and the buyer in the patience to wait for utility, we find that the constant pricing, surprisingly, is no longer optimal. First, it is outperformed by the pricing algorithm \"big deal\", when the seller's discount rate is lower than the one of the buyer. Second, in the inverse case of a less patient buyer, we reduce the problem of finding an optimal algorithm to a multidimensional optimization problem (a multivariate analogue of the functional used to determine Myerson's price) that does not admit a closed form solution in general, but can be solved by numerical optimization techniques (e.g., gradient ones). We provide extensive analysis of numerically found optimal algorithms to demonstrate that they are non-trivial, may be non-consistent, and generate larger expected revenue than the constant pricing with the Myerson price.", "published": "2018-05-07T15:30:54Z", "authors": ["Arsenii Vanunts", "Alexey Drutsa"], "categories": ["cs.GT"], "doi": "", "_query": "housing price text listing", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2301.01555v2", "title": "Optimal Liquidation with High Risk Aversion and Small Linear Price Impact", "abstract": "We consider the Bachelier model with linear price impact. Exponential utility indifference prices are studied for vanilla European options in the case where the investor is required to liquidate her position. Our main result is establishing a non-trivial scaling limit for a vanishing price impact which is inversely proportional to the risk aversion. We compute the limit of the corresponding utility indifference prices and find explicitly a family of portfolios which are asymptotically optimal.", "published": "2023-01-04T11:53:49Z", "authors": ["Leonid Dolinskyi", "Yan Dolinsky"], "categories": ["q-fin.MF"], "doi": "", "_query": "housing price text listing", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2208.00870v1", "title": "Suggestion Lists vs. Continuous Generation: Interaction Design for Writing with Generative Models on Mobile Devices Affect Text Length, Wording and Perceived Authorship", "abstract": "Neural language models have the potential to support human writing. However, questions remain on their integration and influence on writing and output. To address this, we designed and compared two user interfaces for writing with AI on mobile devices, which manipulate levels of initiative and control: 1) Writing with continuously generated text, the AI adds text word-by-word and user steers. 2) Writing with suggestions, the AI suggests phrases and user selects from a list. In a supervised online study (N=18), participants used these prototypes and a baseline without AI. We collected touch interactions, ratings on inspiration and authorship, and interview data. With AI suggestions, people wrote less actively, yet felt they were the author. Continuously generated text reduced this perceived authorship, yet increased editing behavior. In both designs, AI increased text length and was perceived to influence wording. Our findings add new empirical evidence on the impact of UI design decisions on user experience and output with co-creative systems.", "published": "2022-08-01T13:57:11Z", "authors": ["Florian Lehmann", "Niklas Markert", "Hai Dang", "Daniel Buschek"], "categories": ["cs.HC", "cs.AI"], "doi": "10.1145/3543758.3543947", "_query": "housing price text listing", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/1301.7503v1", "title": "Finite Length Analysis on Listing Failure Probability of Invertible Bloom Lookup Tables", "abstract": "The Invertible Bloom Lookup Tables (IBLT) is a data structure which supports insertion, deletion, retrieval and listing operations of the key-value pair. The IBLT can be used to realize efficient set reconciliation for database synchronization. The most notable feature of the IBLT is the complete listing operation of the key-value pairs based on the algorithm similar to the peeling algorithm for low-density generator-matrix (LDGM) codes. In this paper, we will present a stopping set (SS) analysis for the IBLT which reveals finite length behaviors of the listing failure probability. The key of the analysis is enumeration of the number of stopping matrices of given size. We derived a novel recursive formula useful for computationally efficient enumeration. An upper bound on the listing failure probability based on the union bound accurately captures the error floor behaviors. It will be shown that, in the error floor region, the dominant SS have size 2. We propose a simple modification on hash functions, which are called SS avoiding hash functions, for preventing occurrences of the SS of size 2.", "published": "2013-01-31T03:32:01Z", "authors": ["Daichi Yugawa", "Tadashi Wadayama"], "categories": ["cs.IT"], "doi": "10.1587/transfun.E97.A.2309", "_query": "housing price text listing", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2505.08743v1", "title": "Understanding Housing and Homelessness System Access by Linking Administrative Data", "abstract": "This paper uses privacy preserving methods to link over 235,000 records in the housing and homelessness system of care (HHSC) of a major North American city. Several machine learning pairwise linkage and two clustering algorithms are evaluated for merging the profiles for latent individuals in the data. Importantly, these methods are evaluated using both traditional machine learning metrics and HHSC system use metrics generated using the linked data. The results demonstrate that privacy preserving linkage methods are an effective and practical method for understanding how a single person interacts with multiple agencies across an HHSC. They also show that performance differences between linkage techniques are amplified when evaluated using HHSC domain specific metrics like number of emergency homeless shelter stays, length of time interacting with an HHSC and number of emergency shelters visited per person.", "published": "2025-05-13T16:57:55Z", "authors": ["Geoffrey G. Messier", "Sam Elliott", "Dallas Seitz"], "categories": ["cs.CY"], "doi": "", "_query": "housing price text listing", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2601.18544v5", "title": "Inflation and the Distortion of Relative Prices", "abstract": "This paper presents a production network model of inflationary dynamics in which inflation can have near-zero correlation with the size of price change yet generate significant distortions in relative prices. New money enters unevenly across firms and percolates through buyer--seller links, thereby displacing relative prices even when every price is free to adjust and every market clears. Under state-dependent price stickiness, firms absorb higher inflation through more frequent rather than larger price changes, leaving the distortion intact. The distortion depends critically on the spectral gap of the production network, which sets the rate at which the economy converges to equilibrium. We quantify the mechanism on a reconstructed production network with the universe of firms in the United States. At a moderate rate of inflation, the typical firm's relative-price distortion is of the same magnitude as one quarter's inflation. The size of price change remains essentially uncorrelated with the magnitude of relative-price distortion.", "published": "2026-01-26T14:49:11Z", "authors": ["Vipin P Veetil"], "categories": ["econ.TH", "cs.SI"], "doi": "", "_query": "housing price text listing", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2403.01360v1", "title": "\"Digitwashing\": The Gap between Words and Deeds in Digital Transformation and Stock Price Crash Risk", "abstract": "The contrast between companies' \"fleshy\" promises and the \"skeletal\" performance in digital transformation may lead to a higher risk of stock price crash. This paper selects a sample of Shanghai and Shenzhen A-share listed companies from 2010 to 2021, empirically analyses the specific impact of the gap between words and deeds in digital transformation (GDT) on the stock price crash risk, and explores the possible causes of GDT. We found that GDT significantly increases the stock price crash risk, and this finding is still valid after a series of robustness tests. In a further study, a deeper examination of the causes of GDT reveals that firms' perceptions of economic policy uncertainty significantly increase GDT, and the effect is more pronounced in the sample of loss-making firms. At the same time, the results of the heterogeneity test suggest that investors are more tolerant of state-owned enterprises when they are in the GDT situation. Taken together, we provide a concrete bridge between the two measures of digital transformation - digital text frequency and digital technology share - and offer new insights to enhance capital market stability.", "published": "2024-03-03T01:29:49Z", "authors": ["Shutter Zor"], "categories": ["q-fin.ST", "econ.GN"], "doi": "", "_query": "housing price text listing", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2210.00488v1", "title": "Zero-Ending Prices, Cognitive Convenience, and Price Rigidity", "abstract": "We assess the role of cognitive convenience in the popularity and rigidity of 0 ending prices in convenience settings. Studies show that 0 ending prices are common at convenience stores because of the transaction convenience that 0 ending prices offer. Using a large store level retail CPI data, we find that 0 ending prices are popular and rigid at convenience stores even when they offer little transaction convenience. We corroborate these findings with two large retail scanner price datasets from Dominicks and Nielsen. In the Dominicks data, we find that there are more 0 endings in the prices of the items in the front end candies category than in any other category, even though these prices have no effect on the convenience of the consumers check out transaction. In addition, in both Dominicks and Nielsens datasets, we find that 0 ending prices have a positive effect on demand. Ruling out consumer antagonism and retailers use of heuristics in pricing, we conclude that 0 ending prices are popular and rigid, and that they increase demand at convenience settings, not only for their transaction convenience, but also for the cognitive convenience they offer.", "published": "2022-10-02T11:14:07Z", "authors": ["Avichai Snir", " Haipeng", " Chen", "Daniel Levy"], "categories": ["econ.GN"], "doi": "", "_query": "housing price text listing", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2208.00840v1", "title": "A Transformer-based Neural Language Model that Synthesizes Brain Activation Maps from Free-Form Text Queries", "abstract": "Neuroimaging studies are often limited by the number of subjects and cognitive processes that can be feasibly interrogated. However, a rapidly growing number of neuroscientific studies have collectively accumulated an extensive wealth of results. Digesting this growing literature and obtaining novel insights remains to be a major challenge, since existing meta-analytic tools are constrained to keyword queries. In this paper, we present Text2Brain, an easy to use tool for synthesizing brain activation maps from open-ended text queries. Text2Brain was built on a transformer-based neural network language model and a coordinate-based meta-analysis of neuroimaging studies. Text2Brain combines a transformer-based text encoder and a 3D image generator, and was trained on variable-length text snippets and their corresponding activation maps sampled from 13,000 published studies. In our experiments, we demonstrate that Text2Brain can synthesize meaningful neural activation patterns from various free-form textual descriptions. Text2Brain is available at https://braininterpreter.com as a web-based tool for efficiently searching through the vast neuroimaging literature and generating new hypotheses.", "published": "2022-07-24T09:15:03Z", "authors": ["Gia H. Ngo", "Minh Nguyen", "Nancy F. Chen", "Mert R. Sabuncu"], "categories": ["q-bio.NC", "cs.LG", "eess.IV"], "doi": "10.1016/j.media.2022.102540", "_query": "housing price text listing", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/cond-mat/0311457v2", "title": "Order book approach to price impact", "abstract": "Buying and selling stocks causes price changes, which are described by the price impact function. To explain the shape of this function, we study the Island ECN orderbook. In addition to transaction data, the orderbook contains information about potential supply and demand for a stock. The virtual price impact calculated from this information is four times stronger than the actual one and explains it only partially. However, we find a strong anticorrelation between price changes and order flow, which strongly reduces the virtual price impact and provides for a quantitative explanation of the empirical price impact function.", "published": "2003-11-19T18:06:29Z", "authors": ["Philipp Weber", "Bernd Rosenow"], "categories": ["cond-mat"], "doi": "", "_query": "housing price text listing", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2403.07617v1", "title": "Price Gouging or Market Forces? Fairness Perceptions of Price Hikes in the Pandemic", "abstract": "We report the results of surveys we conducted in the US and Israel in 2020, a time when many prices increased following the spread of the pandemic. To assess respondents perceptions of price increases, we focus on goods whose prices have increased during the pandemic, including some essential goods. Consistent with the principle of dual entitlement, we find that respondents perceive price increases as more acceptable if they are due to cost shocks than if they are due to demand shocks. However, we also find large differences across the two populations, as well as across goods.", "published": "2024-03-12T12:59:23Z", "authors": ["Avichai Snir", "Daniel Levy", "Dudi Levy", "Haipeng Allan Chen"], "categories": ["econ.GN"], "doi": "10.13140/RG.2.2.26574.06727", "_query": "housing price text listing", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2501.11164v1", "title": "A statistical technique for cleaning option price data", "abstract": "Recorded option pricing datasets are not always freely available. Additionally, these datasets often contain numerous prices which are either higher or lower than can reasonably be expected. Various reasons for these unexpected observations are possible, including human error in the recording of the details associated with the option in question. In order for the analyses performed on these datasets to be reliable, it is necessary to identify and remove these options from the dataset. In this paper, we list three distinct problems often found in recorded option price datasets alongside means of addressing these. The methods used are justified using sound statistical reasoning and remove option prices violating the standard assumption of no arbitrage. An attractive aspect of the proposed technique is that no option pricing model-based assumptions are used. Although the discussion is restricted to European options, the procedure is easily modified for use with exotic options as well. As a final contribution, the paper contains a link to six option pricing datasets which have already been cleaned using the proposed methods and can be freely used by researchers.", "published": "2025-01-19T20:39:55Z", "authors": ["Jaco Visagie"], "categories": ["q-fin.CP"], "doi": "", "_query": "housing price text listing", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2609.39142v1", "title": "When Can Text Replace Vision? Structural Bottlenecks in Diagram Reasoning", "abstract": "Can structured text replace vision for diagram reasoning? A wrong answer after textualization can arise because the representation omits information the question needs, or because the solver fails to use information that is present. We introduce a diagnostic protocol to distinguish these explanations. Using the same solver model and generation settings, we compare three input conditions: the original image, question-blind structure extracted by a vision-language model, or gold structure derived from the diagram source. Validity-triggered recovery tests truncation and schema failure, question-relevant fidelity measures preservation of answer-critical structure, and matched edge interventions test the effect of error location. On a reserved holdout of 240 public FlowGen diagrams, evaluated under a frozen protocol, gold structure reaches 87% accuracy while direct vision and learned text both remain below 30%. The aggregate comparison includes source-derived relation labels that may not be printed in the image and uses different learned and gold graph encodings, so it does not isolate extraction error alone. Retrying only invalid extractions makes nearly every public representation schema-valid yet leaves accuracy essentially unchanged. The public learned-text deficit relative to gold more than doubles with structural difficulty. Question-relevant topology predicts correctness better than whole-graph topology. In an exposed intervention study, a single answer-relevant edge edit reduces the primary solver's original-answer accuracy to near zero, while matched irrelevant edits largely preserve it. Supplied structure requires fewer solving tokens than vision, but learned acquisition removes this advantage at single use. These comparisons motivate evaluating acquired text by the answer-relevant evidence it preserves and by the solver's ability to use that representation.", "published": "2026-09-30T07:08:43Z", "authors": ["Yunbei Zhang", "Janet Wang", "Jihun Hamm", "Chandan K Reddy"], "categories": ["cs.CV"], "doi": "", "_query": "housing price text listing", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2509.14116v1", "title": "Minimum pricing or volumetric taxation? Quantity, quality and competition effects of price regulations in alcohol markets", "abstract": "Reforming alcohol price regulations in wine-producing countries is challenging, as current price regulations reflect the alignment of cultural preferences with economic interests rather than public health concerns. We evaluate and compare the impact of counterfactual alcohol pricing policies on consumer behaviors, firms, and markets in France. We develop a micro-founded partial equilibrium model that accounts for consumer preferences over purchase volumes across alcohol categories and over product quality within categories, and for firms' strategic price-setting. After calibration on household scanner data, we compare the impacts of replacing current taxes by ethanol-based volumetric taxes with a minimum unit price (MUP) policy of 0.50 Euro per standard drink. The results show that the MUP in addition to the current tax outperforms a tax reform in reducing ethanol purchases (-15% vs. -10% for progressive taxation), especially among heavy drinking households (-17%). The MUP increases the profits of small and medium wine firms (+39%) while decreasing the profits of large manufacturers and retailers (-39%) and maintaining tax revenues stable. The results support the MUP as a targeted strategy to reduce harmful consumption while benefiting small and medium wine producers. This study provides ex-ante evidence that is crucial for alcohol pricing policies in wine-producing countries.", "published": "2025-09-17T15:57:37Z", "authors": ["Celine Bonnet", "Fabrice Etile", "Sebastien Lecocq"], "categories": ["econ.GN"], "doi": "", "_query": "housing price text listing", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2310.07444v2", "title": "Measuring the impact of green retrofits on CO$_2$ emissions: London Housing Case Study", "abstract": "The residential sector is responsible for approximately 20 percent of the world's energy-related greenhouse gas emissions. In order to improve this reality, it is essential to help and support the leading actor in this scene, the homeowner. In general, buildings are complex objects as each home may have very particular parameters: different materials (windows, walls, types of roofs, etc.), uses, and habits. No two cases are identical. In this paper, we addressed this problem by proposing a method to obtain estimates of energy and carbon savings resulting from a class of retrofit projects and housing profiles. We applied our formulas to conduct a case study of the London residential sector, supported by the publicly available Energy Performance Certificates (EPC) dataset. As a result of a multi-project renovation of a house, including loft insulation, double-glazed windows, and energy-efficient lighting (LED), we found that the average family in the Greater London area could save approximately 9 MWh in energy consumption, 1,700 kgCO$_2$ in carbon emissions and more than GBP 800 on the energy bills, over a year, in contrast to a cost of around GBP 9,000 (or a return on investment of 11 years). In another example, applying the method to calculate what is needed to install heat pumps in each house in London, a total of GBP 16.9 billion would be spent, leading to savings of GBP 450 million per year (return on investment of 37 years). Finally, an overall upgrade to loft insulation would cost London households GBP 1.4 billion, generating GBP 290 million in annual savings (a 5-year return on investment).", "published": "2023-10-11T12:48:26Z", "authors": ["F. C. Correia", "M. Fazion"], "categories": ["stat.AP", "physics.soc-ph"], "doi": "", "_query": "housing price text listing", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2506.00502v3", "title": "Modeling and Optimal Control of Thermal Environment in Pig Houses", "abstract": "The management of thermal environments in pig farming is crucial for optimizing animal health, productivity, and operational energy efficiency. This study introduces a novel thermal ventilation model (TVM) based on enthalpy balance, which integrates both temperature and humidity control to address the specific thermal regulation requirements of pig housing in regions characterized by high temperatures and humidity, such as Guangdong, China. These challenging environmental conditions can lead to heat stress in pigs, adversely affecting their health and productivity. The TVM provides a precise representation of thermal comfort by accounting for the combined effects of temperature and humidity. Building on the TVM, we formulate an optimization problem using Model Predictive Control (MPC), which dynamically adjusts ventilation rates in real-time by modifying weight factors to minimize energy consumption while keeping the temperature and humidity within the comfort zone of the pigs. The accuracy of the TVM is validated against real-world environmental data from pig housing facilities in Guangdong. The root mean square error of temperature in winter, spring and summer were 1.23, 0.81, and 0.60, demonstrating its reliability and robustness across diverse climatic conditions. Furthermore, simulation results show that the proposed MPC strategy significantly improves energy efficiency and environmental comfort, achieving a 100% comfort temperature zone in spring and 83% in summer, compared to 91% and 43% with traditional rule-based control, respectively. However, the model's energy consumption in summer (91.2 kWh) was higher than that of rule-based control (80.8 kWh), reflecting the trade-off between maintaining optimal comfort and energy efficiency under extreme conditions.", "published": "2025-05-31T10:43:53Z", "authors": ["Mingxin Wei", "Jinrui Zhang", "Peter Groot Koerkamp", "Andre Aarnink", "Congcong Sun"], "categories": ["math.OC"], "doi": "", "_query": "housing price text listing", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2203.01384v1", "title": "Descending Price Auctions with Bounded Number of Price Levels and Batched Prophet Inequality", "abstract": "We consider descending price auctions for selling $m$ units of a good to unit demand i.i.d. buyers where there is an exogenous bound of $k$ on the number of price levels the auction clock can take. The auctioneer's problem is to choose price levels $p_1 > p_2 > \\cdots > p_{k}$ for the auction clock such that auction expected revenue is maximized. The prices levels are announced prior to the auction. We reduce this problem to a new variant of prophet inequality, which we call \\emph{batched prophet inequality}, where a decision-maker chooses $k$ (decreasing) thresholds and then sequentially collects rewards (up to $m$) that are above the thresholds with ties broken uniformly at random. For the special case of $m=1$ (i.e., selling a single item), we show that the resulting descending auction with $k$ price levels achieves $1- 1/e^k$ of the unrestricted (without the bound of $k$) optimal revenue. That means a descending auction with just 4 price levels can achieve more than 98\\% of the optimal revenue. We then extend our results for $m>1$ and provide a closed-form bound on the competitive ratio of our auction as a function of the number of units $m$ and the number of price levels $k$.", "published": "2022-03-02T19:59:15Z", "authors": ["Saeed Alaei", "Ali Makhdoumi", "Azarakhsh Malekian", "Rad Niazadeh"], "categories": ["cs.GT", "cs.DM", "cs.DS", "econ.TH"], "doi": "", "_query": "housing price text listing", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2203.13512v1", "title": "Efficient k-clique Listing with Set Intersection Speedup [Technical Report]", "abstract": "Listing all k-cliques is a fundamental problem in graph mining, with applications in finance, biology, and social network analysis. However, owing to the exponential growth of the search space as k increases, listing all k-cliques is algorithmically challenging. DDegree and DDegCol are the state-of-the-art algorithms that exploit ordering heuristics based on degree ordering and color ordering, respectively. Both DDegree and DDegCol induce high time and space overhead for set intersections cause they construct and maintain all induced subgraphs. Meanwhile, it is non-trivial to implement the data level parallelism to further accelerate on DDegree and DDegCol. In this paper, we propose two efficient algorithms SDegree and BitCol for k-clique listing. We mainly focus on accelerating the set intersections for k-clique listing. Both SDegree and BitCol exploit the data level parallelism for further acceleration with single instruction multiple data (SIMD) or vector instruction sets. Furthermore, we propose two preprocessing techniques Pre-Core and Pre-List, which run in linear time. The preprocessing techniques significantly reduce the size of the original graph and prevent exploring a large number of invalid nodes. In the theoretical analysis, our algorithms have a comparable time complexity and a slightly lower space complexity than the state-of-the-art algorithms. The comprehensive experiments reveal that our algorithms outperform the state-of-the-art algorithms by 3.75x for degree ordering and 5.67x for color ordering on average.", "published": "2022-03-25T09:02:23Z", "authors": ["Zhirong Yuan", "You Peng", "Peng Cheng", "Li Han", "Xuemin Lin", "Lei Chen", "Wenjie Zhang"], "categories": ["cs.DB"], "doi": "", "_query": "housing price text listing", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/1509.08909v1", "title": "Polish -English Statistical Machine Translation of Medical Texts", "abstract": "This new research explores the effects of various training methods on a Polish to English Statistical Machine Translation system for medical texts. Various elements of the EMEA parallel text corpora from the OPUS project were used as the basis for training of phrase tables and language models and for development, tuning and testing of the translation system. The BLEU, NIST, METEOR, RIBES and TER metrics have been used to evaluate the effects of various system and data preparations on translation results. Our experiments included systems that used POS tagging, factored phrase models, hierarchical models, syntactic taggers, and many different alignment methods. We also conducted a deep analysis of Polish data as preparatory work for automatic data correction such as true casing and punctuation normalization phase.", "published": "2015-09-29T19:57:24Z", "authors": ["Krzysztof Wołk", "Krzysztof Marasek"], "categories": ["cs.CL", "cs.IR", "stat.ML"], "doi": "", "_query": "housing price text listing", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2212.03777v1", "title": "Leveraging Priority Thresholds to Improve Equitable Housing Access for Unhoused-at-Risk Youth", "abstract": "Approximately 4.2 million youth and young adults experience homelessness each year in the United States and lack of basic necessities puts this population at high-risk of being trafficked or exploited. Although all runaway and homeless youth (RHY) are at risk of being victims of human trafficking, certain racial, ethnic, and gender groups are disproportionately affected. Motivated by these facts, our goal is to improve equitable access to housing resources for at-risk RHY in New York City (NYC) by expanding the current housing capacity, while utilizing priority thresholds that guide decisions regarding which youth should start receiving service based on the number of beds idle in the system. Our approach involves an $M/M/N/\\{K_j\\}+M$ queuing model with many statistically identical servers (beds) and RHY from different demographic groups with limited patience arriving to the a large crisis and emergency shelter in NYC. The queuing model allows us to: (i) investigate the populations and demographics that are facing access barriers, (ii) project the minimum number of beds required to provide a certain global service quality level to all youth, regardless of demographic characteristics, and (iii) use priority thresholds while matching RHY with beds to promote equity. The recommendations regarding the capacity expansion and priority thresholds improves equitable access to this crisis and emergency shelter by decreasing the average number of RHY abandoning the system by 92%, with a particular reduction in the abandonment of RHY who are at high-risk of experiencing trafficking.", "published": "2022-12-07T16:52:54Z", "authors": ["Yaren Bilge Kaya", "Kayse Lee Maass"], "categories": ["cs.HC", "math.NA"], "doi": "", "_query": "housing price text listing", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2507.09734v1", "title": "Boltzmann Price: Toward Understanding the Fair Price in High-Frequency Markets", "abstract": "In this paper, we introduce a parametrized family of prices derived from the Maximum Entropy Principle. The price is obtained from the distribution that minimizes bias, given the bid and ask volume imbalance at the top of the order book. Under specific parameter choices, it closely approximates the mid-price or the weighted mid-price. Using probabilities of bid and ask states, we propose a model of price dynamics in which both drift and volatility are driven by volume imbalance. Compared to standard models like Bachelier or Geometric Brownian Motion with constant volatility, our model can generate higher kurtosis and heavy-tailed distributions. Additionally, the drift term naturally emerges as a consequence of the order book imbalance. We validate the model through simulation and demonstrate its fit to historical equity data. The model provides a theoretical framework, integrating price, volume imbalance, and spread.", "published": "2025-07-13T18:17:48Z", "authors": ["Przemysław Rola"], "categories": ["q-fin.TR"], "doi": "", "_query": "housing price text listing", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/1807.07967v1", "title": "Combining Named Entities with WordNet and Using Query-Oriented Spreading Activation for Semantic Text Search", "abstract": "Purely keyword-based text search is not satisfactory because named entities and WordNet words are also important elements to define the content of a document or a query in which they occur. Named entities have ontological features, namely, their aliases, classes, and identifiers. Words in WordNet also have ontological features, namely, their synonyms, hypernyms, hyponyms, and senses. Those features of concepts may be hidden from their textual appearance. Besides, there are related concepts that do not appear in a query, but can bring out the meaning of the query if they are added. We propose an ontology-based generalized Vector Space Model to semantic text search. It exploits ontological features of named entities and WordNet words, and develops a query-oriented spreading activation algorithm to expand queries. In addition, it combines and utilizes advantages of different ontologies for semantic annotation and searching. Experiments on a benchmark dataset show that, in terms of the MAP measure, our model is 42.5% better than the purely keyword-based model, and 32.3% and 15.9% respectively better than the ones using only WordNet or named entities. Keywords: semantic search, spreading activation, ontology, named entity, WordNet.", "published": "2018-07-20T10:43:55Z", "authors": ["Vuong M. Ngo", "Tru H. Cao", "Tuan M. V. Le"], "categories": ["cs.IR"], "doi": "", "_query": "housing price text listing", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/0802.2413v1", "title": "On the Scarf-Hirota model in the price-scaled price adjustment process", "abstract": "Hirota's results given in (Hirota.M.,1981) on the asymptotically stability are generalized to the price-scaled price adjustment process.", "published": "2008-02-18T04:10:12Z", "authors": ["Tatsuro Yamamoto", "Masanari Asano", "Yoshio Togawa", "Masanori Ohya"], "categories": ["math.DS"], "doi": "", "_query": "housing price text listing", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2507.01970v1", "title": "News Sentiment Embeddings for Stock Price Forecasting", "abstract": "This paper will discuss how headline data can be used to predict stock prices. The stock price in question is the SPDR S&P 500 ETF Trust, also known as SPY that tracks the performance of the largest 500 publicly traded corporations in the United States. A key focus is to use news headlines from the Wall Street Journal (WSJ) to predict the movement of stock prices on a daily timescale with OpenAI-based text embedding models used to create vector encodings of each headline with principal component analysis (PCA) to exact the key features. The challenge of this work is to capture the time-dependent and time-independent, nuanced impacts of news on stock prices while handling potential lag effects and market noise. Financial and economic data were collected to improve model performance; such sources include the U.S. Dollar Index (DXY) and Treasury Interest Yields. Over 390 machine-learning inference models were trained. The preliminary results show that headline data embeddings greatly benefit stock price prediction by at least 40% compared to training and optimizing a machine learning system without headline data embeddings.", "published": "2025-06-19T17:30:07Z", "authors": ["Ayaan Qayyum"], "categories": ["q-fin.ST", "cs.LG"], "doi": "", "_query": "housing price text listing", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2605.30564v1", "title": "Listing Even Cycles Faster than the Submodular-Width Barrier", "abstract": "A classic result of Alon, Yuster, and Zwick (AYZ, Algorithmica 1997) shows that all $2k$-cycles in an $m$-edge graph can be listed in $\\tilde O(m^{2-1/k}+t)$ time, where $t$ is the output size. This bound underlies the {\\em submodular width} of Marx (JACM 2013) and the PANDA framework of Abo Khamis, Ngo, and Suciu (PODS 2017), which extend AYZ to arbitrary conjunctive queries with degree constraints. A central open question is whether combinatorial algorithms can beat the submodular-width barrier. Bringmann and Gorbachev (STOC 2025) gave lower-bound evidence that submodular width may be optimal for general conjunctive queries under combinatorial algorithms. The picture changes for $2k$-cycles on undirected graphs, whose queries have self-joins and symmetric EDBs: recent works improve on AYZ for even-cycle detection and listing. Pinning down the complexity of $C_{2k}$-detection and listing is thus a natural step toward overcoming the submodular-width barrier for such queries. For detection, Dahlgaard, Knudsen, and St{ö}ckel (STOC 2017) solved $C_{2k}$-detection in $\\tilde O(m^{2k/(k+1)})$ time. Listing is harder. Jin and Xu (STOC 2023), and independently Abboud, Khoury, Leibowitz, and Safier (FSTTCS 2023), listed 4-cycles in $\\tilde O(m^{4/3}+t)$ time; Vassilevska~Williams and Westover (ITCS 2025) listed 6-cycles in $\\tilde O(m^{8/5}+t)$ time, improving the AYZ bounds of $\\tilde O(m^{3/2})$ and $\\tilde O(m^{5/3})$. The general case has remained open for 30 years. Building on these works, we list $2k$-cycles in $\\tilde O(m^{(2k^2-k+1)/(k^2+1)}+t)$ time, improving AYZ for every $k\\geq 3$. The key ingredient is an \\emph{asymmetric supersaturation} result for even cycles. Our algorithms use only join and project operators over multiple tree-decomposition plans, making them naturally implementable in database systems, in contrast to prior BFS-based graph approaches.", "published": "2026-05-28T20:53:42Z", "authors": ["Vasileios Nakos", "Hung Q. Ngo", "Andreas Panayi"], "categories": ["cs.DB", "cs.DS"], "doi": "", "_query": "housing price text listing", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/1901.04770v1", "title": "Empirical forward price distribution from Bitcoin option prices", "abstract": "Report presents analysis of empirical distribution of future returns of bitcoin (BTC) from BTUSD inverse option prices. Logistic pdf is chosen as underlying distribution to fit option prices. The result is satisfactory and suggests that these prices can be described with just three or even one parameter. Fitted Logistic pdf matches forward price movements upto a scaling factor. Nevertheless, this observation stands alone and does not allow stochastic description of underlying prices with logistic pdf in similar fashion as it is done within Black-Scholes modelling framework. Put-call parity relationship is derived connecting prices of vanilla inverse options and futures.", "published": "2019-01-15T11:21:42Z", "authors": ["Nikolai Zaitsev"], "categories": ["q-fin.ST"], "doi": "", "_query": "housing price text listing", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/0903.2428v1", "title": "Price Impact", "abstract": "We define what \"Price Impact\" means, and how it is measured and modelled in the recent literature. Although this notion seems to convey the idea of a forceful and intuitive mechanism, we discuss why things might not be that simple. Empirical studies show that while the correlation between signed order flow and price changes is strong, the impact of trades on prices is neither linear in volume nor permanent. Impact allows private information to be reflected in prices, but by the same token, random fluctuations in order flow must also contribute to the volatility of markets.", "published": "2009-03-13T16:42:00Z", "authors": ["J. P. Bouchaud"], "categories": ["q-fin.TR", "q-fin.GN"], "doi": "", "_query": "housing price text listing", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2512.16949v1", "title": "Identifying and understanding obstacles to heating sobriety and thermal comfort in collective housing: Insights from a survey in France", "abstract": "In many countries, central heating systems are widely used in multifamily housing allowing maintenance and costs to be shared. However, these systems often limit residents' control over their own consumption, complicating efforts to reduce energy use and maintain comfort. Despite the growing importance of household energy savings in national and European climate targets, little research has examined user experiences with these systems. This study addresses that gap through an exploratory survey conducted in France. Grounded in scientific literature, technical analyses, and current policy frameworks, the survey was distributed through various channels-including public institutions, online platforms, and field outreach-and collected 403 responses. It examined user difficulties in managing heating, including challenges with controls, bill comprehension, and communication with landlords. It also explored behaviours with high energy-savings impact: reducing heating at night or during absences, window opening, and shutter use. Findings show that many residents face barriers to energy-efficient heating, often due to difficult-to-use controls, limited feedback on consumption, and poor support from landlords or property managers. Those who do not reduce heating at night or when away frequently report these challenges. Participants expressed strong interest in solutions such as personalized advice, real-time feedback, and connected radiator valves to improve comfort, control, and energy management. This work highlights the need to better integrate user experience into multifamily housing energy policies. It provides actionable insights for policymakers and housing providers seeking to enhance comfort and energy savings while supporting France and Europe's broader energy transition goals.", "published": "2025-12-17T09:30:23Z", "authors": ["Enzo Cabezas-Rivière", "Maxime Robillart", "Aline Barlet", "Thomas Recht", "Laurent Mora", "Patrick Sebastian"], "categories": ["physics.soc-ph"], "doi": "", "_query": "housing price text listing", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2107.14113v1", "title": "Neural network approximation for superhedging prices", "abstract": "This article examines neural network-based approximations for the superhedging price process of a contingent claim in a discrete time market model. First we prove that the $α$-quantile hedging price converges to the superhedging price at time $0$ for $α$ tending to $1$, and show that the $α$-quantile hedging price can be approximated by a neural network-based price. This provides a neural network-based approximation for the superhedging price at time $0$ and also the superhedging strategy up to maturity. To obtain the superhedging price process for $t>0$, by using the Doob decomposition it is sufficient to determine the process of consumption. We show that it can be approximated by the essential supremum over a set of neural networks. Finally, we present numerical results.", "published": "2021-07-29T15:37:08Z", "authors": ["Francesca Biagini", "Lukas Gonon", "Thomas Reitsam"], "categories": ["q-fin.MF"], "doi": "", "_query": "housing price text listing", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/1105.5717v1", "title": "Is there a bubble in LinkedIn's stock price?", "abstract": "Recent academic work has developed a method to determine, in real time, if a given stock is exhibiting a price bubble. Currently there is speculation in the financial press concerning the existence of a price bubble in the aftermath of the recent IPO of LinkedIn. We analyze stock price tick data from the short lifetime of this stock through May 24, 2011, and we find that LinkedIn has a price bubble.", "published": "2011-05-28T13:35:11Z", "authors": ["Robert Jarrow", "Younes Kchia", "Philip Protter"], "categories": ["q-fin.RM", "q-fin.ST"], "doi": "", "_query": "housing price text listing", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2405.07278v2", "title": "Human-interpretable clustering of short-text using large language models", "abstract": "Clustering short text is a difficult problem, due to the low word co-occurrence between short text documents. This work shows that large language models (LLMs) can overcome the limitations of traditional clustering approaches by generating embeddings that capture the semantic nuances of short text. In this study clusters are found in the embedding space using Gaussian Mixture Modelling (GMM). The resulting clusters are found to be more distinctive and more human-interpretable than clusters produced using the popular methods of doc2vec and Latent Dirichlet Allocation (LDA). The success of the clustering approach is quantified using human reviewers and through the use of a generative LLM. The generative LLM shows good agreement with the human reviewers, and is suggested as a means to bridge the `validation gap' which often exists between cluster production and cluster interpretation. The comparison between LLM-coding and human-coding reveals intrinsic biases in each, challenging the conventional reliance on human coding as the definitive standard for cluster validation.", "published": "2024-05-12T12:55:40Z", "authors": ["Justin K. Miller", "Tristram J. Alexander"], "categories": ["cs.CL", "cs.LG"], "doi": "10.1098/rsos.241692", "_query": "housing price text listing", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2305.13227v1", "title": "Trustless Price Feeds of Cryptocurrencies: Pathfinder", "abstract": "Price feeds of securities is a critical component for many financial services, allowing for collateral liquidation, margin trading, derivative pricing and more. With the advent of blockchain technology, value in reporting accurate prices without a third party has become apparent. There have been many attempts at trying to calculate prices without a third party, in which each of these attempts have resulted in being exploited by an exploiter artificially inflating the price. The industry has then shifted to a more centralized design, fetching price data from multiple centralized sources and then applying statistical methods to reach a consensus price. Even though this strategy is secure compared to reading from a single source, enough number of sources need to report to be able to apply statistical methods. As more sources participate in reporting the price, the feed gets more secure with the slowest feed becoming the bottleneck for query response time, introducing a tradeoff between security and speed. This paper provides the design and implementation details of a novel method to algorithmically compute security prices in a way that artificially inflating targeted pools has no effect on the reported price of the queried asset. We hypothesize that the proposed algorithm can report accurate prices given a set of possibly dishonest sources.", "published": "2023-05-22T17:00:23Z", "authors": ["Orhan Koc"], "categories": ["q-fin.CP", "q-fin.TR"], "doi": "", "_query": "housing price text listing", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/1907.09144v1", "title": "Consistent upper price bounds for exotic options given a finite number of call prices and their convergence", "abstract": "We consider the problem of finding a consistent upper price bound for exotic options whose payoff depends on the stock price at two different predetermined time points (e.g. Asian option), given a finite number of observed call prices for these maturities. A model-free approach is used, only taking into account that the (discounted) stock price process is a martingale under the no-arbitrage condition. In case the payoff is directionally convex we obtain the worst case marginal pricing measures. The speed of convergence of the upper price bound is determined when the number of observed stock prices increases. We illustrate our findings with some numerical computations.", "published": "2019-07-22T06:09:45Z", "authors": ["Nicole Bäuerle", "Daniel Schmithals"], "categories": ["q-fin.MF", "math.OC"], "doi": "10.1142/S0219024921500114", "_query": "housing price text listing", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2007.05056v4", "title": "Multimodal price prediction", "abstract": "Price prediction is one of the examples related to forecasting tasks and is a project based on data science. Price prediction analyzes data and predicts the cost of new products. The goal of this research is to achieve an arrangement to predict the price of a cellphone based on its specifications. So, five deep learning models are proposed to predict the price range of a cellphone, one unimodal and four multimodal approaches. The multimodal methods predict the prices based on the graphical and non-graphical features of cellphones that have an important effect on their valorizations. Also, to evaluate the efficiency of the proposed methods, a cellphone dataset has been gathered from GSMArena. The experimental results show 88.3% F1-score, which confirms that multimodal learning leads to more accurate predictions than state-of-the-art techniques.", "published": "2020-07-09T20:46:13Z", "authors": ["Aidin Zehtab-Salmasi", "Ali-Reza Feizi-Derakhshi", "Narjes Nikzad-Khasmakhi", "Meysam Asgari-Chenaghlu", "Saeideh Nabipour"], "categories": ["cs.CV", "cs.LG"], "doi": "10.1007/s40745-021-00326-z", "_query": "housing price text listing", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2202.07086v1", "title": "Price Cycles in Ridesharing Platforms", "abstract": "In ridesharing platforms such as Uber and Lyft, it is observed that drivers sometimes collaboratively go offline when the price is low, and then return after the price has risen due to the perceived lack of supply. This collective strategy leads to cyclic fluctuations in prices and available drivers, resulting in poor reliability and social welfare. We study a continuous time, non-atomic model and prove that such online/offline strategies may form a Nash equilibrium among drivers, but lead to a lower total driver payoff if the market is sufficiently dense. Further, we show how to set price floors that effectively mitigate the emergence and impact of price cycles.", "published": "2022-02-14T23:15:19Z", "authors": ["Chenkai Yu", "Hongyao Ma", "Adam Wierman"], "categories": ["cs.GT", "cs.MA", "math.OC"], "doi": "", "_query": "housing price text listing", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2006.16703v1", "title": "Expectation and Price in Incomplete Markets", "abstract": "Risk-neutral pricing dictates that the discounted derivative price is a martingale in a measure equivalent to the economic measure. The residual ambiguity for incomplete markets is here resolved by minimising the entropy of the price measure from the economic measure, subject to mark-to-market constraints, following arguments based on the optimisation of portfolio risk. The approach accounts for market and funding convexities and incorporates available price information, interpolating between methodologies based on expectation and replication.", "published": "2020-06-30T11:52:34Z", "authors": ["Paul McCloud"], "categories": ["q-fin.MF", "q-fin.PR"], "doi": "", "_query": "housing price text listing", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/1809.08027v1", "title": "On the Constant Price of Anarchy Conjecture", "abstract": "We study Nash equilibria and the price of anarchy in the classic model of Network Creation Games introduced by Fabrikant et al. In this model every agent (node) buys links at a prefixed price $α> 0$ in order to get connected to the network formed by all the $n$ agents. In this setting, the reformulated tree conjecture states that for $α> n$, every Nash equilibrium network is a tree. Moreover, Demaine et al. conjectured that the price of anarchy for this model is constant. Since it was shown that the price of anarchy for trees is constant, if the tree conjecture were true, then the price of anarchy would be constant for $α> n$. Up to now it has been proved that the \\PoA is constant $(i)$ in the \\emph{lower range}, for $α= O(n^{1-δ})$ with $δ\\geq \\frac{1}{\\log n}$ and $(ii)$ in the \\emph{upper range}, for $α> 4n-13$. In contrast, the best upper bound known for the price of anarchy for the remaining range is $2^{O(\\sqrt{\\log n})}$. In this paper we give new insights into the structure of the Nash equilibria for $α> n$ and we enlarge the range of the parameter $α$ for which the price of anarchy is constant. Specifically, we prove that the price of anarchy is constant for $α> n(1+ε)$ by showing that every equilibrium of diameter greater than some prefixed constant is a tree.", "published": "2018-09-21T10:37:51Z", "authors": ["C. Àlvarez", "A. Messegué"], "categories": ["cs.GT"], "doi": "", "_query": "housing price text listing", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2306.09659v2", "title": "Randomized Robust Price Optimization", "abstract": "The robust multi-product pricing problem is to determine the prices of a collection of products so as to maximize the worst-case revenue, where the worst case is taken over an uncertainty set of demand models that the firm expects could be realized in practice. A tacit assumption in this approach is that the pricing decision is a deterministic decision: the prices of the products are fixed and do not vary. In this paper, we consider a randomized approach to robust pricing, where a decision maker specifies a distribution over potential price vectors so as to maximize its worst-case revenue over an uncertainty set of demand models. We formally define this problem - the randomized robust price optimization problem - and analyze when a randomized price scheme performs as well as a deterministic scheme versus when it yields a benefit. We also propose solution methods for obtaining an optimal randomization scheme over a discrete set of candidate price vectors and show how these methods are applicable for common demand models, such as the linear, semi-log and log-log demand models. We numerically compare the randomized and deterministic approaches on a variety of synthetic and real problem instances; on instances derived from a real grocery retail scanner dataset, we show that the improvement in worst-case revenue can be as high as 92%. Using the same grocery retail scanner dataset, we also show that the randomized approach can produce price prescriptions that achieve higher out-of-sample revenue than the nominal and deterministic robust approaches.", "published": "2023-06-16T07:28:38Z", "authors": ["Xinyi Guan", "Velibor V. Mišić"], "categories": ["math.OC"], "doi": "", "_query": "housing price text listing", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2203.04774v2", "title": "Tailored vertex ordering for faster triangle listing in large graphs", "abstract": "Listing triangles is a fundamental graph problem with many applications, and large graphs require fast algorithms. Vertex ordering allows the orientation of edges from lower to higher vertex indices, and state-of-the-art triangle listing algorithms use this to accelerate their execution and to bound their time complexity. Yet, only basic orderings have been tested. In this paper, we show that studying the precise cost of algorithms instead of their bounded complexity leads to faster solutions. We introduce cost functions that link ordering properties with the running time of a given algorithm. We prove that their minimization is NP-hard and propose heuristics to obtain new orderings with different trade-offs between cost reduction and ordering time. Using datasets with up to two billion edges, we show that our heuristics accelerate the listing of triangles by an average of 38% when the ordering is already given as an input, and 16% when the ordering time is included.", "published": "2022-03-09T14:47:33Z", "authors": ["Fabrice Lécuyer", "Louis Jachiet", "Clémence Magnien", "Lionel Tabourier"], "categories": ["cs.DS", "cs.SI"], "doi": "", "_query": "housing price text listing", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/1308.6256v3", "title": "G-consistent price system and bid-ask pricing for European contingent claims under Knightian uncertainty", "abstract": "The target of this paper is to consider model the risky asset price on the financial market under the Knightian uncertainty, and pricing the ask and bid prices of the uncertain risk. We use the nonlinear analysis tool, i.e., G-frame work [26], to construct the model of the risky asset price and bid-ask pricing for the European contingent claims under Knightian uncertain financial market. Firstly, we consider the basic risky asset price model on the uncertain financial market, which we construct here is the model with drift uncertain and volatility uncertain. We describe such model by using generalized G-Brownian motion and call it as G-asset price system. We present the uncertain risk premium which is uncertain and distributed with maximum distribution. We derive the closed form of bid-ask price of the European contingent claim against the underlying risky asset with G-asset price system as the discounted conditional G-expecation of the claim, and the bid and ask prices are the viscosity solutions to the nonlinear HJB equations.Furthermore, we consider the main part of this paper, i.e., consider the risky asset on the Knightian uncertain financial market with the price fluctuation shows as continuous trajectories. We propose the G-conditional full support condition by using uncertain capacity, and the risky asset price path satisfying the G-conditional full support condition could be approximated by its G-consistent asset price systems. We derive that the bid and ask prices of the European contingent claim against such risky asset under uncertain can be expressed by discounted of some conditional G-expectation of the claim. We give examples, such as G-Markovian processes and the geometric fractional G-Brownian motion [9], satisfying the G-conditional full support condition.", "published": "2013-08-28T19:20:00Z", "authors": ["Wei Chen"], "categories": ["q-fin.PR", "q-fin.RM"], "doi": "", "_query": "housing price text listing", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/1408.1265v1", "title": "Amortized $\\tilde{O}(|V|)$-Delay Algorithm for Listing Chordless Cycles in Undirected Graphs", "abstract": "Chordless cycles are very natural structures in undirected graphs, with an important history and distinguished role in graph theory. Motivated also by previous work on the classical problem of listing cycles, we study how to list chordless cycles. The best known solution to list all the $C$ chordless cycles contained in an undirected graph $G = (V,E)$ takes $O(|E|^2 +|E|\\cdot C)$ time. In this paper we provide an algorithm taking $\\tilde{O}(|E| + |V |\\cdot C)$ time. We also show how to obtain the same complexity for listing all the $P$ chordless $st$-paths in $G$ (where $C$ is replaced by $P$ ).", "published": "2014-08-06T12:51:02Z", "authors": ["Rui Ferreira", "Roberto Grossi", "Romeo Rizzi", "Gustavo Sacomoto", "Marie-France Sagot"], "categories": ["cs.DS"], "doi": "", "_query": "housing price text listing", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/0804.0317v1", "title": "Parts-of-Speech Tagger Errors Do Not Necessarily Degrade Accuracy in Extracting Information from Biomedical Text", "abstract": "A recent study reported development of Muscorian, a generic text processing tool for extracting protein-protein interactions from text that achieved comparable performance to biomedical-specific text processing tools. This result was unexpected since potential errors from a series of text analysis processes is likely to adversely affect the outcome of the entire process. Most biomedical entity relationship extraction tools have used biomedical-specific parts-of-speech (POS) tagger as errors in POS tagging and are likely to affect subsequent semantic analysis of the text, such as shallow parsing. This study aims to evaluate the parts-of-speech (POS) tagging accuracy and attempts to explore whether a comparable performance is obtained when a generic POS tagger, MontyTagger, was used in place of MedPost, a tagger trained in biomedical text. Our results demonstrated that MontyTagger, Muscorian's POS tagger, has a POS tagging accuracy of 83.1% when tested on biomedical text. Replacing MontyTagger with MedPost did not result in a significant improvement in entity relationship extraction from text; precision of 55.6% from MontyTagger versus 56.8% from MedPost on directional relationships and 86.1% from MontyTagger compared to 81.8% from MedPost on nondirectional relationships. This is unexpected as the potential for poor POS tagging by MontyTagger is likely to affect the outcome of the information extraction. An analysis of POS tagging errors demonstrated that 78.5% of tagging errors are being compensated by shallow parsing. Thus, despite 83.1% tagging accuracy, MontyTagger has a functional tagging accuracy of 94.6%.", "published": "2008-04-02T09:34:13Z", "authors": ["Maurice HT Ling", "Christophe Lefevre", "Kevin R. Nicholas"], "categories": ["cs.CL", "cs.IR"], "doi": "", "_query": "housing price text listing", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/1506.08349v1", "title": "Improved Deep Speaker Feature Learning for Text-Dependent Speaker Recognition", "abstract": "A deep learning approach has been proposed recently to derive speaker identifies (d-vector) by a deep neural network (DNN). This approach has been applied to text-dependent speaker recognition tasks and shows reasonable performance gains when combined with the conventional i-vector approach. Although promising, the existing d-vector implementation still can not compete with the i-vector baseline. This paper presents two improvements for the deep learning approach: a phonedependent DNN structure to normalize phone variation, and a new scoring approach based on dynamic time warping (DTW). Experiments on a text-dependent speaker recognition task demonstrated that the proposed methods can provide considerable performance improvement over the existing d-vector implementation.", "published": "2015-06-28T03:32:02Z", "authors": ["Lantian Li", "Yiye Lin", "Zhiyong Zhang", "Dong Wang"], "categories": ["cs.CL", "cs.LG", "cs.NE"], "doi": "", "_query": "housing price text listing", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/cond-mat/0403723v1", "title": "Market depth and price dynamics: A note", "abstract": "This note explores the consequences of nonlinear price impact functions on price dynamics within the chartist-fundamentalist framework. Price impact functions may be nonlinear with respect to trading volume. As indicated by recent empirical studies, a given transaction may cause a large (small) price change if market depth is low (high). Simulations reveal that such a relationship may create endogenous complex price fluctuations even if the trading behavior of chartists and fundamentalists is linear.", "published": "2004-03-30T07:00:22Z", "authors": ["Frank Westerhoff"], "categories": ["cond-mat.other", "q-fin.TR"], "doi": "10.1142/S0129183104006455", "_query": "housing price text listing", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2604.27553v1", "title": "Revealing the Impact of Visual Text Style on Attribute-based Descriptions Produced by Large Visual Language Models", "abstract": "When the visual style of text is considered, a wide variety can be observed in font, color, and size. However, when a word is read, its meaning is independent of the style in which it has been written or rendered. In this paper, we investigate whether, and how, the style in which a word is visualized in an image impacts the description that a Large Visual Language Model (LVLM) provides for the concept to which that word refers. Specifically, we investigate how functional text styles (readability-oriented, e.g., black sans-serif) versus decorative styles (display-oriented, e.g., colored cursive/script) affect LVLMs' descriptions of a concept in terms of the attributes of that concept. Our experiments study the situation in which the LVLM is able to correctly identify the concept referred to by a visual text, i.e., by a word or words rendered as an image, and in which the visual text style should not influence the attribute-based description that the LVLM produces. Our experimental results reveal that even when the concept is correctly identified, text style influences the model's attribute-based descriptions of the concept. Our findings demonstrate non-trivial style leakage from text style into semantic inference and motivate style-aware evaluation and mitigation for LVLM-based multimedia systems.", "published": "2026-04-30T08:01:45Z", "authors": ["Xiaomeng Wang", "Martha Larson", "Zhengyu Zhao"], "categories": ["cs.CV", "cs.CL"], "doi": "", "_query": "housing price text listing", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2012.06009v1", "title": "Vision-based Price Suggestion for Online Second-hand Items", "abstract": "Different from shopping in physical stores, where people have the opportunity to closely check a product (e.g., touching the surface of a T-shirt or smelling the scent of perfume) before making a purchase decision, online shoppers rely greatly on the uploaded product images to make any purchase decision. The decision-making is challenging when selling or purchasing second-hand items online since estimating the items' prices is not trivial. In this work, we present a vision-based price suggestion system for the online second-hand item shopping platform. The goal of vision-based price suggestion is to help sellers set effective prices for their second-hand listings with the images uploaded to the online platforms. First, we propose to better extract representative visual features from the images with the aid of some other image-based item information (e.g., category, brand). Then, we design a vision-based price suggestion module which takes the extracted visual features along with some statistical item features from the shopping platform as the inputs to determine whether an uploaded item image is qualified for price suggestion by a binary classification model, and provide price suggestions for items with qualified images by a regression model. According to two demands from the platform, two different objective functions are proposed to jointly optimize the classification model and the regression model. For better model training, we also propose a warm-up training strategy for the joint optimization. Extensive experiments on a large real-world dataset demonstrate the effectiveness of our vision-based price prediction system.", "published": "2020-12-10T22:56:29Z", "authors": ["Liang Han", "Zhaozheng Yin", "Zhurong Xia", "Li Guo", "Mingqian Tang", "Rong Jin"], "categories": ["cs.CV", "cs.AI"], "doi": "", "_query": "housing price text listing", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2410.14904v2", "title": "Switchback Price Experiments with Forward-Looking Demand", "abstract": "Switchback experiments are widely used in pricing applications. We consider a retailer running such an experiment for the price of a single product, with infinite supply. In each period, the seller chooses a price from a set of predefined prices consisting of an original price and a few discounted price levels. The goal is to estimate the demand gradient at the original price to adjust the original price and improve revenue after the experiment. In our model, in each period, a unit mass of buyers arrives on the market, with values distributed based on a time-varying process. Crucially, buyers are forward-looking with a discounted utility and will choose not to purchase now if they expect to face a discounted price in the near future. We show that such forward-looking behavior introduces bias in naive estimators of the demand gradient, due to intertemporal interference. Furthermore, although experiments with only two price points are common in practice, we prove that no estimator using data from such experiments can recover the correct demand gradient, even in the limit of an infinitely long experiment with an infinitesimal price discount. Moreover, we characterize the form of the bias of naive estimators. Finally, we show that with a simple three-price-level experiment, the seller can remove the bias due to strategic forward-looking behavior and construct an estimator for the demand gradient that asymptotically recovers the truth. Our results provide practical guidance for platforms to obtain unbiased demand gradients from price experiments in the presence of forward-looking demand.", "published": "2024-10-18T23:11:31Z", "authors": ["Yifan Wu", "Ramesh Johari", "Vasilis Syrgkanis", "Gabriel Y. Weintraub"], "categories": ["cs.GT", "econ.EM", "econ.TH"], "doi": "", "_query": "housing price text listing", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/0909.0892v2", "title": "Price of Anarchy for Greedy Auctions", "abstract": "We consider auctions in which greedy algorithms, paired with first-price or critical-price payment rules, are used to resolve multi-parameter combinatorial allocation problems. We study the price of anarchy for social welfare in such auctions. We show for a variety of equilibrium concepts, including Bayes-Nash equilibrium and correlated equilibrium, the resulting price of anarchy bound is close to the approximation factor of the underlying greedy algorithm.", "published": "2009-09-04T13:39:25Z", "authors": ["Brendan Lucier", "Allan Borodin"], "categories": ["cs.GT"], "doi": "", "_query": "housing price text listing", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/nlin/0005018v2", "title": "Spatial competition and price formation", "abstract": "We look at price formation in a retail setting, that is, companies set prices, and consumers either accept prices or go someplace else. In contrast to most other models in this context, we use a two-dimensional spatial structure for information transmission, that is, consumers can only learn from nearest neighbors. Many aspects of this can be understood in terms of generalized evolutionary dynamics. In consequence, we first look at spatial competition and cluster formation without price. This leads to establishement size distributions, which we compare to reality. After some theoretical considerations, which at least heuristically explain our simulation results, we finally return to price formation, where we demonstrate that our simple model with nearly no organized planning or rationality on the part of any of the agents indeed leads to an economically plausible price.", "published": "2000-05-09T13:01:27Z", "authors": ["Kai Nagel", "Martin Shubik", "Maya Paczuski", "Per Bak"], "categories": ["nlin.AO"], "doi": "10.1016/S0378-4371(00)00392-7", "_query": "housing price text listing", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/1808.02547v1", "title": "The economic value of neighborhoods: Predicting real estate prices from the urban environment", "abstract": "Housing costs have a significant impact on individuals, families, businesses, and governments. Recently, online companies such as Zillow have developed proprietary systems that provide automated estimates of housing prices without the immediate need of professional appraisers. Yet, our understanding of what drives the value of houses is very limited. In this paper, we use multiple sources of data to entangle the economic contribution of the neighborhood's characteristics such as walkability and security perception. We also develop and release a framework able to now-cast housing prices from Open data, without the need for historical transactions. Experiments involving 70,000 houses in 8 Italian cities highlight that the neighborhood's vitality and walkability seem to drive more than 20% of the housing value. Moreover, the use of this information improves the nowcast by 60%. Hence, the use of property's surroundings' characteristics can be an invaluable resource to appraise the economic and social value of houses after neighborhood changes and, potentially, anticipate gentrification.", "published": "2018-08-07T20:42:39Z", "authors": ["Marco De Nadai", "Bruno Lepri"], "categories": ["cs.CY"], "doi": "", "_query": "housing price text listing", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2508.09280v2", "title": "Carbon Pricing in Traffic Networks", "abstract": "We study how externality pricing can steer traffic toward Wardrop equilibria respecting prescribed budgets. Our multi-commodity model encompasses carbon pricing, cordon pricing, and tradable credit schemes. We characterize the flows attainable when one common price may be assigned to each externality. We show that for continuous travel times and flow-independent externalities, every feasible externality budget is implementable. For flow-dependent externalities, smoothness bounds quantify the necessary budget relaxation; this links budget implementability to the price of anarchy. If travel times and externalities admit convex potentials, we give conditions for constructing prices implementing a given budget through convex optimization and specify explicit prices ensuring that relaxed budgets are respected by every equilibrium. As, for a single separable constant externality, total externality is nonincreasing in its price, this enables us to compute the whole price--equilibrium curve in finite time for separable, continuous, strictly increasing, piecewise affine travel times. Although this curve may have exponentially many pieces, the minimum price implementing a feasible budget is computable in polynomial time. The same machinery computes the range of market-clearing prices for tradable credit schemes. A computational study on several realistic traffic networks illustrates emission reductions from carbon pricing and the effects of cordon pricing on emissions within the cordon.", "published": "2025-08-12T18:41:20Z", "authors": ["Svenja M. Griesbach", "Tobias Harks", "Max Klimm", "Michael Markl", "Philipp Warode"], "categories": ["cs.GT", "math.OC"], "doi": "", "_query": "housing price text listing", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2512.01139v3", "title": "Market Sensitivities and Growth Differentials Across Australian Housing Markets", "abstract": "Australian house prices have risen strongly since the mid-1990s, but growth has been highly uneven across regions. Raw growth figures obscure whether these differences reflect persistent structural trends or cyclical fluctuations. We address this by estimating a three-factor model in levels for regional repeat-sales log price indexes over 1995-2024. The model decomposes each regional index into a national Market factor, two stationary spreads (Mining and Lifestyle) that capture mean-reverting geographic cycles, and a city-specific residual. The Mining spread, proxied by a Perth-Sydney index differential, reflects resource-driven oscillations in relative performance; the Lifestyle spread captures amenity-driven coastal and regional cycles. The Market loading isolates each region's fundamental sensitivity, beta, to national growth, so that a city's growth under an assumed national change is calculated from its beta once mean-reverting spreads are netted out. Comparing realised paths to these factor-implied trajectories indicates when a city is historically elevated or depressed, and attributes the gap to Mining or Lifestyle spreads. Expanding-window ARIMAX estimation reveals that Market betas are stable across major shocks (the mining boom, the Global Financial Crisis, and COVID-19), while Mining and Lifestyle behave as stationary spreads that widen forecast funnels without overturning the cross-sectional ranking implied by beta. Melbourne amplifies national growth, Sydney tracks the national trend closely, and regional areas dampen it. The framework thus provides a simple, factor-based tool for interpreting regional growth differentials and their persistence.", "published": "2025-11-30T23:29:03Z", "authors": ["Willem P Sijp"], "categories": ["econ.EM"], "doi": "", "_query": "housing price text listing", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2603.23971v2", "title": "The Price Reversal Phenomenon: When Cheaper Reasoning Models Cost More", "abstract": "Developers and consumers increasingly choose reasoning models (RMs) based on their listed API prices. However, how accurately do these prices reflect actual inference costs? We conduct the first systematic study of this question, evaluating 8 frontier RMs across 12 diverse tasks covering competition math, science QA, code generation, and multi-domain agents. We uncover the pricing reversal phenomenon: in 32% of model-pair comparisons, the model with a lower listed price actually incurs a higher total cost, with reversal magnitude reaching up to 28x. For example, Gemini 3 Flash's listed price is 80% cheaper than GPT-5.4's, yet its actual cost across all tasks is 38% higher. We build a formal cost attribution framework based on Shapley value, and leverage it to trace the dominating contributors to vast heterogeneity in thinking token consumption and number of interaction turns: on the same query, one model may use 900% more thinking tokens than another, or 10x more turns of environment interactions. We further show that per-query cost prediction is fundamentally difficult: repeated runs of the same query yield thinking token variation up to 9.7x, establishing an irreducible noise floor for any predictor. Thus, we propose cost distribution prediction as an open challenge. Our findings demonstrate that listed API pricing is an unreliable proxy for actual cost, calling for cost-aware model selection and transparent per-request cost monitoring.", "published": "2026-03-25T06:07:39Z", "authors": ["Lingjiao Chen", "Chi Zhang", "Yeye He", "Ion Stoica", "Matei Zaharia", "James Zou"], "categories": ["cs.CL", "cs.AI", "cs.GT", "cs.LG", "cs.MA"], "doi": "", "_query": "housing price text listing", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2608.29843v1", "title": "The Price of Intelligence: A Quality-Adjusted Price Index for AI Services", "abstract": "Posted prices for AI inference have fallen steadily since 2024, yet the measured speed of that fall depends almost entirely on the method of measurement. This paper constructs quality-adjusted price indices for the AI inference market from public data. The panel assembles 21,024 posted-price observations across 3,208 models and 86 providers and joins them to 4,605 benchmark scores through a latent quality index estimated from benchmark response patterns, so the quality ladder of the hedonic tradition is built here from evaluations in place of product characteristics. Measured by the matched-model methods that statistical agencies apply to software, inference prices fell at 0.10 log points a year. The quality-adjusted index fell at 0.73, so 87% of the decline is invisible to current methods, with direct consequences for measured competition, concentration and productivity in this market. Counted per completed task, moreover, the buyer's price stopped falling. Reasoning models raised token consumption faster than token prices fell, and the seller's and buyer's prices accordingly diverged. A pre-registered validity audit disciplines the quality measure and yields the sharpest result. Excluding contamination-flagged benchmarks leaves model rankings intact at 0.998 yet moves the index by 0.49 log points a year, so the leaderboard-stability arguments standard in AI evaluation offer no defence of economic statistics built on benchmarks. Prices, quality and the audit are fully reproducible from public sources at zero cost.", "published": "2026-08-30T15:17:14Z", "authors": ["Louis Yiven Zhu"], "categories": ["econ.GN", "cs.LG"], "doi": "", "_query": "housing price text listing", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2412.02584v4", "title": "Listing faces of polytopes", "abstract": "This paper investigates the problem of listing faces of polytopes that represent combinatorial objects, such as hypercubes, permutahedra, associahedra, and their generalizations. Firstly, we consider the face lattice, which is the inclusion order of all faces of a polytope, and we seek a Hamiltonian cycle in its cover graph, i.e., for any two consecutive faces, one must be a subface of the other, and their dimensions differ by $1$. We construct such Hamiltonian cycles for hypercubes, permutahedra, $B$-permutahedra, associahedra, cyclic polytopes, 3-dimensional polytopes, graph associahedra of chordal graphs, and quotientopes. Secondly, we consider facet-Hamiltonian cycles, which are cycles on the (1-)skeleton of a polytope that enter and leave every facet exactly once. This notion was recently introduced by Akitaya, Cardinal, Felsner, Kleist, and Lauff [SODA 2025], where the authors conjectured that $B$-permutahedra admit a facet-Hamiltonian cycle for all dimensions. We construct such facet-Hamiltonian cycles in this paper, thus establishing their conjecture as a theorem. A key tool we use are so-called rhombic strips, which are planar spanning subgraphs of the cover graph of the face lattice in which every face is a 4-cycle. Specifically, we construct a rhombic strip in the face lattice of the hypercube of any dimension, and characterize the existence of rhombic strips in the face lattice of 3-dimensional polytopes. Our constructions yield time- and space-efficient algorithms for computing the aforementioned cycles and thus for listing the corresponding combinatorial objects, including ordered set partitions and dissections of a convex polygon.", "published": "2024-12-03T17:10:34Z", "authors": ["Nastaran Behrooznia", "Sofia Brenner", "Arturo Merino", "Torsten Mütze", "Christian Rieck", "Francesco Verciani"], "categories": ["math.CO"], "doi": "", "_query": "housing price text listing", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2104.09309v2", "title": "Foreign exchange markets: price response and spread impact", "abstract": "We carry out a detailed large-scale data analysis of price response functions in the spot foreign exchange market for different years and different time scales. Such response functions provide quantitative information on the deviation from Markovian behavior. The price response functions show an increase to a maximum followed by a slow decrease as the time lag grows, in trade time scale and in physical time scale, for all analyzed years. Furthermore, we use a price increment point (pip) bid-ask spread definition to group different foreign exchange pairs and analyze the impact of the spread in the price response functions. We find that large pip spreads have a stronger impact on the response. This is similar to what has been found in stock markets.", "published": "2021-04-15T19:37:41Z", "authors": ["Juan Camilo Henao Londono", "Thomas Guhr"], "categories": ["q-fin.ST"], "doi": "10.1016/j.physa.2021.126587", "_query": "housing price text listing", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2006.06519v2", "title": "Reserve Price Optimization for First Price Auctions", "abstract": "The display advertising industry has recently transitioned from second- to first-price auctions as its primary mechanism for ad allocation and pricing. In light of this, publishers need to re-evaluate and optimize their auction parameters, notably reserve prices. In this paper, we propose a gradient-based algorithm to adaptively update and optimize reserve prices based on estimates of bidders' responsiveness to experimental shocks in reserves. Our key innovation is to draw on the inherent structure of the revenue objective in order to reduce the variance of gradient estimates and improve convergence rates in both theory and practice. We show that revenue in a first-price auction can be usefully decomposed into a \\emph{demand} component and a \\emph{bidding} component, and introduce techniques to reduce the variance of each component. We characterize the bias-variance trade-offs of these techniques and validate the performance of our proposed algorithm through experiments on synthetic data and real display ad auctions data from Google ad exchange.", "published": "2020-06-11T15:35:19Z", "authors": ["Zhe Feng", "Sébastien Lahaie", "Jon Schneider", "Jinchao Ye"], "categories": ["cs.GT", "cs.LG", "econ.EM", "stat.ML"], "doi": "", "_query": "housing price text listing", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/1011.1065v4", "title": "Price Differentiation for Communication Networks", "abstract": "We study the optimal usage-based pricing problem in a resource-constrained network with one profit-maximizing service provider and multiple groups of surplus-maximizing users. With the assumption that the service provider knows the utility function of each user (thus complete information), we find that the complete price differentiation scheme can achieve a large revenue gain (e.g., 50%) compared to no price differentiation, when the total network resource is comparably limited and the high willingness to pay users are minorities. However, the complete price differentiation scheme may lead to a high implementational complexity. To trade off the revenue against the implementational complexity, we further study the partial price differentiation scheme, and design a polynomial-time algorithm that can compute the optimal partial differentiation prices. We also consider the incomplete information case where the service provider does not know which group each user belongs to. We show that it is still possible to realize price differentiation under this scenario, and provide the sufficient and necessary condition under which an incentive compatible differentiation scheme can achieve the same revenue as under complete information.", "published": "2010-11-04T06:25:34Z", "authors": ["Shuqin Li", "Jianwei Huang"], "categories": ["cs.NI"], "doi": "", "_query": "housing price text listing", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/1610.09799v1", "title": "Experiments with POS Tagging Code-mixed Indian Social Media Text", "abstract": "This paper presents Centre for Development of Advanced Computing Mumbai's (CDACM) submission to the NLP Tools Contest on Part-Of-Speech (POS) Tagging For Code-mixed Indian Social Media Text (POSCMISMT) 2015 (collocated with ICON 2015). We submitted results for Hindi (hi), Bengali (bn), and Telugu (te) languages mixed with English (en). In this paper, we have described our approaches to the POS tagging techniques, we exploited for this task. Machine learning has been used to POS tag the mixed language text. For POS tagging, distributed representations of words in vector space (word2vec) for feature extraction and Log-linear models have been tried. We report our work on all three languages hi, bn, and te mixed with en.", "published": "2016-10-31T06:13:31Z", "authors": ["Prakash B. Pimpale", "Raj Nath Patel"], "categories": ["cs.CL"], "doi": "", "_query": "housing price text listing", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/1812.10436v1", "title": "Stability of a rotating asteroid housing a space station", "abstract": "Today there are numerous studies on asteroid mining. They elaborate on selecting the right objects, prospecting missions, potential asteroid redirection, and the mining process itself. For economic reasons, most studies focus on mining candidates in the 100-500m size-range. Also, suggestions regarding the design and implementation of space stations or even colonies inside the caverns of mined asteroids exist. Caverns provide the advantages of confined material in near-zero gravity during mining and later the hull will shield the inside from radiation. Existing studies focus on creating the necessary artificial gravity by rotating structures that are built inside the asteroid. Here, we assume the entire mined asteroid to rotate at a sufficient rate for artificial gravity and investigate its use for housing a habitat inside. In this study we present how to estimate the necessary spin rate assuming a cylindrical space station inside a mined asteroid and discuss the implications arising from substantial material stress given the required rotation rate. We estimate the required material strength using two relatively simple analytical models and apply them to fictitious, yet realistic rocky near-Earth asteroids.", "published": "2018-12-26T18:10:14Z", "authors": ["Thomas I. Maindl", "Roman Miksch", "Birgit Loibnegger"], "categories": ["astro-ph.EP"], "doi": "", "_query": "housing price text listing", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2210.13188v1", "title": "Dissecting Deep Metric Learning Losses for Image-Text Retrieval", "abstract": "Visual-Semantic Embedding (VSE) is a prevalent approach in image-text retrieval by learning a joint embedding space between the image and language modalities where semantic similarities would be preserved. The triplet loss with hard-negative mining has become the de-facto objective for most VSE methods. Inspired by recent progress in deep metric learning (DML) in the image domain which gives rise to new loss functions that outperform triplet loss, in this paper, we revisit the problem of finding better objectives for VSE in image-text matching. Despite some attempts in designing losses based on gradient movement, most DML losses are defined empirically in the embedding space. Instead of directly applying these loss functions which may lead to sub-optimal gradient updates in model parameters, in this paper we present a novel Gradient-based Objective AnaLysis framework, or \\textit{GOAL}, to systematically analyze the combinations and reweighting of the gradients in existing DML functions. With the help of this analysis framework, we further propose a new family of objectives in the gradient space exploring different gradient combinations. In the event that the gradients are not integrable to a valid loss function, we implement our proposed objectives such that they would directly operate in the gradient space instead of on the losses in the embedding space. Comprehensive experiments have demonstrated that our novel objectives have consistently improved performance over baselines across different visual/text features and model frameworks. We also showed the generalizability of the GOAL framework by extending it to other models using triplet family losses including vision-language model with heavy cross-modal interactions and have achieved state-of-the-art results on the image-text retrieval tasks on COCO and Flick30K.", "published": "2022-10-21T06:48:27Z", "authors": ["Hong Xuan", "Xi Chen"], "categories": ["cs.CV"], "doi": "", "_query": "housing price text listing", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2007.02076v2", "title": "Note on simulation pricing of $π$-options", "abstract": "In this work, we adapt a Monte Carlo algorithm introduced by Broadie and Glasserman (1997) to price a $π$-option. This method is based on the simulated price tree that comes from discretization and replication of possible trajectories of the underlying asset's price. As a result this algorithm produces the lower and the upper bounds that converge to the true price with the increasing depth of the tree. Under specific parametrization, this $π$-option is related to relative maximum drawdown and can be used in the real-market environment to protect a portfolio against volatile and unexpected price drops. We also provide some numerical analysis.", "published": "2020-07-04T11:38:56Z", "authors": ["Zbigniew Palmowski", "Tomasz Serafin"], "categories": ["q-fin.CP"], "doi": "", "_query": "housing price text listing", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2104.09597v1", "title": "Price Optimization with Practical Constraints", "abstract": "In this paper, we study a retailer price optimization problem which includes the practical constraints: maximum number of price changes and minimum amount of price change (if a change is recommended). We provide a closed-form formula for the Euclidean projection onto the feasible set defined by these two constraints, based on which a simple gradient projection algorithm is proposed to solve the price optimization problem. We study the convergence and solution quality of the proposed algorithm. We extend the base model to include upper/lower bounds on the individual product prices and solve it with some adjustments to the gradient projection algorithm. Numerical results are reported to demonstrate the performance of the proposed algorithm.", "published": "2021-04-19T20:10:32Z", "authors": ["Xiaojie Wang", "Hsin-Chan Huang", "Lanshan Han", "Alvin Lim"], "categories": ["math.OC"], "doi": "", "_query": "housing price text listing", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2502.11723v2", "title": "Energy-Conscious LLM Decoding: Impact of Text Generation Strategies on GPU Energy Consumption", "abstract": "Decoding strategies significantly influence the quality and diversity of the generated text in Large Language Models (LLMs), yet their impact on computational resources, particularly GPU energy consumption, is insufficiently studied. This paper investigates the relationship between text generation decoding techniques and energy efficiency, focusing on the trade-off between generation quality and GPU energy usage across diverse tasks and decoding configurations. By benchmarking multiple strategies across various tasks, including Translation, Math Problem Solving, Coding, and Open-ended text generation, we reveal how selecting appropriate decoding techniques with their tuned hyperparameters affects text quality and has measurable implications for energy consumption. Our findings show that the choice of decoding strategy can greatly impact GPU energy usage, even when it has a minimal effect on output quality. Different strategies also involve trade-offs between quality and energy efficiency, and no single decoding method is best in all cases across every metric. To the best of our knowledge, this is one of the first studies to examine decoding strategies in LLMs from the perspective of energy consumption, providing useful insights for building energy-efficient applications without compromising text generation quality.", "published": "2025-02-17T12:10:25Z", "authors": ["Alireza Nik", "Michael A. Riegler", "Pål Halvorsen"], "categories": ["cs.AI"], "doi": "", "_query": "housing price text listing", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/1212.2908v1", "title": "A Text Steganography Method Using Pangram and Image Mediums", "abstract": "Steganography is the art and science of writing hidden messages in such a way that no one apart from the sender and the receiver would realize that a secret communicating is taking place. Unlike cryptography which only scrambles secret data keeping them overt, steganography covers secret data into medium files such as image files and transmits them in total secrecy avoiding drawing eavesdroppers suspicions. However, considering that the public channel is monitored by eavesdroppers, it is vulnerable to stego-attacks which refer to randomly trying to break the medium file and recover the secret data out of it. That is often true because steganalysts assume that the secret data are encoded into a single medium file and not into multiple ones that complement each other. This paper proposes a text steganography method for hiding secret textual data using two mediums; a Pangram sentence containing all the characters of the alphabet, and an uncompressed image file. The algorithm tries to search for every character of the secret message into the Pangram text. The search starts from a random index called seed and ends up on the index of the first occurrence of the character being searched for. As a result, two indexes are obtained, the seed and the offset indexes. Together they are embedded into the three LSBs of the color channels of the image medium. Ultimately, both mediums mainly the Pangram and the image are sent to the receiver. The advantage of the proposed method is that it makes the covert data hard to be recovered by unauthorized parties as it uses two mediums, instead of one, to deliver the secret data. Experiments conducted, illustrated an example that explained how to encode and decode a secret text message using the Pangram and the image mediums.", "published": "2012-12-12T18:10:53Z", "authors": ["Youssef Bassil"], "categories": ["cs.CR"], "doi": "", "_query": "housing price text listing", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/1610.08665v3", "title": "Direct semi-parametric estimation of the state price density implied in option prices", "abstract": "We present a model for direct semi-parametric estimation of the State Price Density (SPD) implied in quoted option prices. We treat the observed prices as expected values of possible pay-offs at maturity, weighted by the unknown probability density function. We model the logarithm of the latter as a smooth function while matching the expected values of the potential pay-offs with the observed prices. This leads to a special case of the penalized composite link model. Our estimates do not rely on any parametric assumption on the underlying asset price dynamics and are consistent with no-arbitrage conditions. The model shows excellent performance in simulations and in application to real data.", "published": "2016-10-27T09:03:58Z", "authors": ["Gianluca Frasso", "Paul H. C. Eilers"], "categories": ["stat.AP"], "doi": "", "_query": "housing price text listing", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/0905.2480v1", "title": "A statistical analysis of product prices in online markets", "abstract": "We empirically investigate fluctuations in product prices in online markets by using a tick-by-tick price data collected from a Japanese price comparison site, and find some similarities and differences between product and asset prices. The average price of a product across e-retailers behaves almost like a random walk, although the probability of price increase/decrease is higher conditional on the multiple events of price increase/decrease. This is quite similar to the property reported by previous studies about asset prices. However, we fail to find a long memory property in the volatility of product price changes. Also, we find that the price change distribution for product prices is close to an exponential distribution, rather than a power law distribution. These two findings are in a sharp contrast with the previous results regarding asset prices. We propose an interpretation that these differences may stem from the absence of speculative activities in product markets; namely, e-retailers seldom repeat buy and sell of a product, unlike traders in asset markets.", "published": "2009-05-15T06:48:07Z", "authors": ["Takayuki Mizuno", "Tsutomu Watanabe"], "categories": ["physics.soc-ph", "physics.data-an"], "doi": "10.1140/epjb/e2009-00439-1", "_query": "housing price text listing", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2312.10863v2", "title": "Disclosure Avoidance for the 2020 Census Demographic and Housing Characteristics File", "abstract": "In \"The 2020 Census Disclosure Avoidance System TopDown Algorithm,\" Abowd et al. (2022) describe the concepts and methods used by the Disclosure Avoidance System (DAS) to produce formally private output in support of the 2020 Census statistical data product releases, with a particular focus on the DAS implementation that was used to create the 2020 Census Redistricting Data (P.L. 94-171) Summary File. In this paper we describe the updates to the DAS that were required to release the Demographic and Housing Characteristics (DHC) File, which provides more granular tables than other statistical data products, such as the Redistricting Data Summary File. We also describe the final configuration parameters used for the 2020 production DHC DAS implementation, error metrics for these production statistical data products, and plans for future experimental data products that provide confidence intervals for confidential 2020 Census tabulations.", "published": "2023-12-18T00:54:04Z", "authors": ["Ryan Cumings-Menon", "Robert Ashmead", "Daniel Kifer", "Philip Leclerc", "Matthew Spence", "Pavel Zhuravlev", "John M. Abowd"], "categories": ["cs.CR", "stat.CO"], "doi": "", "_query": "housing price text listing", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2310.06408v2", "title": "Humans and language models diverge when predicting repeating text", "abstract": "Language models that are trained on the next-word prediction task have been shown to accurately model human behavior in word prediction and reading speed. In contrast with these findings, we present a scenario in which the performance of humans and LMs diverges. We collected a dataset of human next-word predictions for five stimuli that are formed by repeating spans of text. Human and GPT-2 LM predictions are strongly aligned in the first presentation of a text span, but their performance quickly diverges when memory (or in-context learning) begins to play a role. We traced the cause of this divergence to specific attention heads in a middle layer. Adding a power-law recency bias to these attention heads yielded a model that performs much more similarly to humans. We hope that this scenario will spur future work in bringing LMs closer to human behavior.", "published": "2023-10-10T08:24:28Z", "authors": ["Aditya R. Vaidya", "Javier Turek", "Alexander G. Huth"], "categories": ["cs.CL"], "doi": "", "_query": "housing price text listing", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/1101.5617v1", "title": "Optimal Pricing in Networks with Externalities", "abstract": "We study the optimal pricing strategies of a monopolist selling a divisible good (service) to consumers that are embedded in a social network. A key feature of our model is that consumers experience a (positive) local network effect. In particular, each consumer's usage level depends directly on the usage of her neighbors in the social network structure. Thus, the monopolist's optimal pricing strategy may involve offering discounts to certain agents, who have a central position in the underlying network. First, we consider a setting where the monopolist can offer individualized prices and derive an explicit characterization of the optimal price for each consumer as a function of her network position. In particular, we show that it is optimal for the monopolist to charge each agent a price that is proportional to her Bonacich centrality in the social network. In the second part of the paper, we discuss the optimal strategy of a monopolist that can only choose a single uniform price for the good and derive an algorithm polynomial in the number of agents to compute such a price. Thirdly, we assume that the monopolist can offer the good in two prices, full and discounted, and study the problem of determining which set of consumers should be given the discount. We show that the problem is NP-hard, however we provide an explicit characterization of the set of agents that should be offered the discounted price. Next, we describe an approximation algorithm for finding the optimal set of agents. We show that if the profit is nonnegative under any feasible price allocation, the algorithm guarantees at least 88% of the optimal profit. Finally, we highlight the value of network information by comparing the profits of a monopolist that does not take into account the network effects when choosing her pricing policy to those of a monopolist that uses this information optimally.", "published": "2011-01-28T20:31:42Z", "authors": ["Ozan Candogan", "Kostas Bimpikis", "Asuman Ozdaglar"], "categories": ["cs.GT", "cs.NI"], "doi": "", "_query": "housing price text listing", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/1903.01744v2", "title": "Scaling Features of Price-Volume Cross-Correlation", "abstract": "Price without transaction makes no sense. Trading volume authenticates its corresponding price, so there exist mutual information and correlation between price and trading volume. We are curious about fractal features of this correlation and need to know how structures in different scales translate information. To explore the influence of investment size (trading volume), price-wise (gain/loss), and time-scale effects, we analyzed the price and trading volume and their coupling by applying the MF-DXA method. Our results imply that price, trading volume, and price-volume coupling exhibit a power law and are also multifractal. Meanwhile, considering developed markets, the price-volume couplings are significantly negatively correlated. However, in emerging markets, the price has less of a contribution to price-volume coupling. In emerging markets in comparison with the developed markets, trading volume and price are more independent.", "published": "2019-03-05T09:33:40Z", "authors": ["Jamshid Ardalankia", "Mohammad Osoolian", "Emmanuel Haven", "G. Reza Jafari"], "categories": ["q-fin.CP", "q-fin.ST"], "doi": "10.1016/j.physa.2019.124111", "_query": "housing price text listing", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/1410.0673v2", "title": "Fair bilateral prices in Bergman's model", "abstract": "Bielecki and Rutkowski (2014) introduced and studied a generic nonlinear market model, which includes several risky assets, multiple funding accounts and margin accounts. In this paper, we examine the pricing and hedging of contract both from the perspective of the hedger and the counterparty with arbitrary initial endowments. We derive inequalities for unilateral prices and we give the range for either fair bilateral prices or bilaterally profitable prices. We also study the monotonicity of a unilateral price with respect to the initial endowment. Our study hinges on results for BSDE driven by continuous martingales obtained in Nie and Rutkowski (2014), but we also derive the pricing PDEs for path-independent contingent claims of European style in a Markovian framework.", "published": "2014-10-02T04:12:12Z", "authors": ["Tianyang Nie", "Marek Rutkowski"], "categories": ["q-fin.MF", "q-fin.PR"], "doi": "", "_query": "housing price text listing", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/1905.07848v1", "title": "Time Series Analysis and Forecasting of the US Housing Starts using Econometric and Machine Learning Model", "abstract": "In this research paper, I have performed time series analysis and forecasted the monthly value of housing starts for the year 2019 using several econometric methods - ARIMA(X), VARX, (G)ARCH and machine learning algorithms - artificial neural networks, ridge regression, K-Nearest Neighbors, and support vector regression, and created an ensemble model. The ensemble model stacks the predictions from various individual models, and gives a weighted average of all predictions. The analyses suggest that the ensemble model has performed the best among all the models as the prediction errors are the lowest, while the econometric models have higher error rates.", "published": "2019-05-20T02:17:28Z", "authors": ["Sudiksha Joshi"], "categories": ["econ.EM"], "doi": "", "_query": "housing price text listing", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2105.13320v1", "title": "Neural Options Pricing", "abstract": "This research investigates pricing financial options based on the traditional martingale theory of arbitrage pricing applied to neural SDEs. We treat neural SDEs as universal Itô process approximators. In this way we can lift all assumptions on the form of the underlying price process, and compute theoretical option prices numerically. We propose a variation of the SDE-GAN approach by implementing the Wasserstein distance metric as a loss function for training. Furthermore, it is conjectured that the error of the option price implied by the learnt model can be bounded by the very Wasserstein distance metric that was used to fit the empirical data.", "published": "2021-05-27T17:22:30Z", "authors": ["Timothy DeLise"], "categories": ["q-fin.MF", "cs.CE", "cs.LG", "q-fin.CP"], "doi": "", "_query": "housing price text listing", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/1810.12840v1", "title": "Asset Price Distributions and Efficient Markets", "abstract": "We explore a decomposition in which returns on a large class of portfolios relative to the market depend on a smooth non-negative drift and changes in the asset price distribution. This decomposition is obtained using general continuous semimartingale price representations, and is thus consistent with virtually any asset pricing model. Fluctuations in portfolio relative returns depend on stochastic time-varying dispersion in asset prices. Thus, our framework uncovers an asset pricing factor whose existence emerges from an accounting identity universal across different economic and financial environments, a fact that has deep implications for market efficiency. In particular, in a closed, dividend-free market in which asset price dispersion is relatively constant, a large class of portfolios must necessarily outperform the market portfolio over time. We show that price dispersion in commodity futures markets has increased only slightly, and confirm the existence of substantial excess returns that co-vary with changes in price dispersion as predicted by our theory.", "published": "2018-10-30T16:27:49Z", "authors": ["Ricardo T. Fernholz", "Caleb Stroup"], "categories": ["q-fin.PM", "q-fin.GN"], "doi": "", "_query": "housing price text listing", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2304.09743v1", "title": "The Price of Explainability for Clustering", "abstract": "Given a set of points in $d$-dimensional space, an explainable clustering is one where the clusters are specified by a tree of axis-aligned threshold cuts. Dasgupta et al. (ICML 2020) posed the question of the price of explainability: the worst-case ratio between the cost of the best explainable clusterings to that of the best clusterings. We show that the price of explainability for $k$-medians is at most $1+H_{k-1}$; in fact, we show that the popular Random Thresholds algorithm has exactly this price of explanability, matching the known lower bound constructions. We complement our tight analysis of this particular algorithm by constructing instances where the price of explanability (using any algorithm) is at least $(1-o(1)) \\ln k$, showing that our result is best possible, up to lower-order terms. We also improve the price of explanability for the $k$-means problem to $O(k \\ln \\ln k)$ from the previous $O(k \\ln k)$, considerably closing the gap to the lower bounds of $Ω(k)$. Finally, we study the algorithmic question of finding the best explainable clustering: We show that explainable $k$-medians and $k$-means cannot be approximated better than $O(\\ln k)$, under standard complexity-theoretic conjectures. This essentially settles the approximability of explainable $k$-medians and leaves open the intriguing possibility to get significantly better approximation algorithms for $k$-means than its price of explainability.", "published": "2023-04-19T15:29:12Z", "authors": ["Anupam Gupta", "Madhusudhan Reddy Pittu", "Ola Svensson", "Rachel Yuan"], "categories": ["cs.DS"], "doi": "", "_query": "housing price text listing", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/1011.6402v3", "title": "The Price Impact of Order Book Events", "abstract": "We study the price impact of order book events - limit orders, market orders and cancelations - using the NYSE TAQ data for 50 U.S. stocks. We show that, over short time intervals, price changes are mainly driven by the order flow imbalance, defined as the imbalance between supply and demand at the best bid and ask prices. Our study reveals a linear relation between order flow imbalance and price changes, with a slope inversely proportional to the market depth. These results are shown to be robust to seasonality effects, and stable across time scales and across stocks. We argue that this linear price impact model, together with a scaling argument, implies the empirically observed \"square-root\" relation between price changes and trading volume. However, the relation between price changes and trade volume is found to be noisy and less robust than the one based on order flow imbalance.", "published": "2010-11-29T22:05:46Z", "authors": ["Rama Cont", "Arseniy Kukanov", "Sasha Stoikov"], "categories": ["q-fin.TR", "q-fin.ST"], "doi": "10.1093/jjfinec/nbt003", "_query": "housing price text listing", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/1610.00327v1", "title": "Improving Comparison Shopping Agents' Competence through Selective Price Disclosure", "abstract": "The plethora of comparison shopping agents (CSAs) in today's markets enables buyers to query more than a single CSA when shopping, and an inter-CSAs competition naturally arises. We suggest a new approach, termed \"selective price disclosure\", which improves the attractiveness of a CSA by removing some of the prices in the outputted list. The underlying idea behind this approach is to affect the buyer's beliefs regarding the chance of obtaining more attractive prices. The paper presents two methods, which are suitable for fully-rational buyers, for deciding which prices among those known to the CSA should be disclosed. The effectiveness and efficiency of the methods are evaluated using real data collected from five CSAs. The methods are also evaluated with human subjects, showing that selective price disclosure can be highly effective in this case as well, however, the disclosed subset of prices should be extracted in a different (simplistic) manner.", "published": "2016-10-02T18:15:10Z", "authors": ["Chen Hajaj", "Noam Hazon", "David Sarne"], "categories": ["cs.GT"], "doi": "10.1016/j.elerap.2015.08.006", "_query": "housing price text listing", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2108.07865v1", "title": "Challenges and Applications of Automated Extraction of Socio-political Events from Text (CASE 2021): Workshop and Shared Task Report", "abstract": "This workshop is the fourth issue of a series of workshops on automatic extraction of socio-political events from news, organized by the Emerging Market Welfare Project, with the support of the Joint Research Centre of the European Commission and with contributions from many other prominent scholars in this field. The purpose of this series of workshops is to foster research and development of reliable, valid, robust, and practical solutions for automatically detecting descriptions of socio-political events, such as protests, riots, wars and armed conflicts, in text streams. This year workshop contributors make use of the state-of-the-art NLP technologies, such as Deep Learning, Word Embeddings and Transformers and cover a wide range of topics from text classification to news bias detection. Around 40 teams have registered and 15 teams contributed to three tasks that are i) multilingual protest news detection, ii) fine-grained classification of socio-political events, and iii) discovering Black Lives Matter protest events. The workshop also highlights two keynote and four invited talks about various aspects of creating event data sets and multi- and cross-lingual machine learning in few- and zero-shot settings.", "published": "2021-08-17T20:29:49Z", "authors": ["Ali Hürriyetoğlu", "Hristo Tanev", "Vanni Zavarella", "Jakub Piskorski", "Reyyan Yeniterzi", "Erdem Yörük"], "categories": ["cs.CL"], "doi": "", "_query": "housing price text listing", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2205.04661v2", "title": "Pricing with algorithms", "abstract": "This paper studies Markov perfect equilibria in a repeated duopoly model where sellers choose algorithms. An algorithm is a mapping from the competitor's price to own price. Once set, algorithms respond quickly. Customers arrive randomly and so do opportunities to revise the algorithm. In the simple game with two possible prices, monopoly outcome is the unique equilibrium for standard functional forms of the profit function. More generally, with multiple prices, exercise of market power is the rule -- in all equilibria, the expected payoff of both sellers is above the competitive outcome, and that of at least one seller is close to or above the monopoly outcome. Sustenance of such collusion seems outside the scope of standard antitrust laws for it does not involve any direct communication.", "published": "2022-05-10T04:15:21Z", "authors": ["Rohit Lamba", "Sergey Zhuk"], "categories": ["econ.TH"], "doi": "", "_query": "housing price text listing", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2410.03071v2", "title": "Enhancing Short-Text Topic Modeling with LLM-Driven Context Expansion and Prefix-Tuned VAEs", "abstract": "Topic modeling is a powerful technique for uncovering hidden themes within a collection of documents. However, the effectiveness of traditional topic models often relies on sufficient word co-occurrence, which is lacking in short texts. Therefore, existing approaches, whether probabilistic or neural, frequently struggle to extract meaningful patterns from such data, resulting in incoherent topics. To address this challenge, we propose a novel approach that leverages large language models (LLMs) to extend short texts into more detailed sequences before applying topic modeling. To further improve the efficiency and solve the problem of semantic inconsistency from LLM-generated texts, we propose to use prefix tuning to train a smaller language model coupled with a variational autoencoder for short-text topic modeling. Our method significantly improves short-text topic modeling performance, as demonstrated by extensive experiments on real-world datasets with extreme data sparsity, outperforming current state-of-the-art topic models.", "published": "2024-10-04T01:28:56Z", "authors": ["Pritom Saha Akash", "Kevin Chen-Chuan Chang"], "categories": ["cs.CL", "cs.IR"], "doi": "", "_query": "housing price text listing", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2203.12042v2", "title": "Price Manipulability in First-Price Auctions", "abstract": "First-price auctions have many desirable properties, including uniquely possessing some, like credibility. However, first-price auctions are also inherently non-truthful, and non-truthfulness may result in instability and inefficiencies. Given these pros and cons, we seek to quantify the extent to which first-price auctions are susceptible to manipulation. In this work we adopt a metric that was introduced in the context of bitcoin fee design markets: the percentage change in payment that can be achieved by being strategic. We study the behavior of this metric for single-unit and $k$-unit auction environments with $n$ i.i.d. buyers, and seek conditions under which the percentage change tends to zero as $n$ grows large. To the best of our knowledge, ours is the first rigorous study of the extent to which large multi-unit first price auctions are susceptible to manipulation. We provide an almost complete picture of the conditions under which they are truthful in the large, and exhibit some surprising boundaries.", "published": "2022-03-22T21:05:33Z", "authors": ["Johannes Brustle", "Paul Dütting", "Balasubramanian Sivan"], "categories": ["cs.GT"], "doi": "", "_query": "housing price text listing", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/math/0503550v1", "title": "On the super replication price of unbounded claims", "abstract": "In an incomplete market the price of a claim f in general cannot be uniquely identified by no arbitrage arguments. However, the ``classical'' super replication price is a sensible indicator of the (maximum selling) value of the claim. When f satisfies certain pointwise conditions (e.g., f is bounded from below), the super replication price is equal to sup_QE_Q[f], where Q varies on the whole set of pricing measures. Unfortunately, this price is often too high: a typical situation is here discussed in the examples. We thus define the less expensive weak super replication price and we relax the requirements on f by asking just for ``enough'' integrability conditions. By building up a proper duality theory, we show its economic meaning and its relation with the investor's preferences. Indeed, it turns out that the weak super replication price of f coincides with sup_{Q\\in M_Φ}E_Q[f], where M_Φ is the class of pricing measures with finite generalized entropy (i.e., E[Φ(\\frac{dQ}{dP})]<\\infty) and where Φis the convex conjugate of the utility function of the investor.", "published": "2005-03-24T15:11:24Z", "authors": ["Sara Biagini", "Marco Frittelli"], "categories": ["math.PR", "q-fin.PR"], "doi": "10.1214/105051604000000459", "_query": "housing price text listing", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/1710.03205v1", "title": "Behavioral Finance Option Pricing Formulas Consistent with Rational Dynamic Asset Pricing", "abstract": "We derive behavioral finance option pricing formulas consistent with the rational dynamic asset pricing theory. In the existing behavioral finance option pricing formulas, the price process of the representative agent is not a semimartingale, which leads to arbitrage opportunities for the option seller. In the literature on behavioral finance option pricing it is allowed the option buyer and seller to have different views on the instantaneous mean return of the underlying price process, which leads to arbitrage opportunities according to Black (1972). We adjust the behavioral finance option pricing formulas to be consistent with the rational dynamic asset pricing theory, by introducing transaction costs on the velocity of trades which offset the gains from the arbitrage trades.", "published": "2017-10-09T17:33:20Z", "authors": ["Svetlozar Rachev", "Stoyan Stoyanov", "Frank J. Fabozzi"], "categories": ["q-fin.PR"], "doi": "", "_query": "housing price text listing", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2106.09664v1", "title": "Design and Analysis of Robust Deep Learning Models for Stock Price Prediction", "abstract": "Building predictive models for robust and accurate prediction of stock prices and stock price movement is a challenging research problem to solve. The well-known efficient market hypothesis believes in the impossibility of accurate prediction of future stock prices in an efficient stock market as the stock prices are assumed to be purely stochastic. However, numerous works proposed by researchers have demonstrated that it is possible to predict future stock prices with a high level of precision using sophisticated algorithms, model architectures, and the selection of appropriate variables in the models. This chapter proposes a collection of predictive regression models built on deep learning architecture for robust and precise prediction of the future prices of a stock listed in the diversified sectors in the National Stock Exchange (NSE) of India. The Metastock tool is used to download the historical stock prices over a period of two years (2013- 2014) at 5 minutes intervals. While the records for the first year are used to train the models, the testing is carried out using the remaining records. The design approaches of all the models and their performance results are presented in detail. The models are also compared based on their execution time and accuracy of prediction.", "published": "2021-06-17T17:15:02Z", "authors": ["Jaydip Sen", "Sidra Mehtab"], "categories": ["q-fin.ST", "cs.LG"], "doi": "10.5772/intechopen.99982", "_query": "housing price text listing", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2211.04954v2", "title": "Macroeconomic performance of oil price shocks in Russia", "abstract": "Oil price fluctuations severely impact the economies of both oil-exporting and importing countries. High oil prices can benefit oil exporters by increasing foreign currency inflow; however, an economy can suffer from a weakening of the manufacturing sectors and experience a significant downtrend in the country's price competitiveness as the domestic currency appreciates. We investigate the oil price fluctuations from Q1, 2004 to Q3, 2021 and their impact on the Russian macroeconomic indicators, particularly industrial production, exchange rate, inflation and interest rates. We assess whether and how much the Russian macroeconomic variables have been responsive to the oil price fluctuations in recent years. The outcomes from VAR model confirm that the monetary channel is more responsive to oil price shocks than the fiscal one. Regarding fiscal channel of the oil price impact, industrial production is strongly pro-cyclical to oil price shocks. As for the monetary channel, higher oil price volatility is pressuring the Russian ruble, inflation and interest rates are substantially counter-cyclical to oil price shocks.", "published": "2022-11-08T09:44:42Z", "authors": ["Ayaz Zeynalov", "Kristina Tiron"], "categories": ["econ.GN"], "doi": "", "_query": "housing price text listing", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2212.05650v2", "title": "Price & Choose", "abstract": "We describe a two-stage mechanism that fully implements the set of efficient outcomes in two-agent environments with quasi-linear utilities. The mechanism asks one agent to set prices for each outcome, and the other agent to make a choice, paying the corresponding price: Price \\& Choose. We extend our implementation result in three main directions: an arbitrary number of players, non-quasi linear utilities, and robustness to max-min behavior. Finally, we discuss how to reduce the payoff inequality between players while still achieving efficiency.", "published": "2022-12-12T01:28:58Z", "authors": ["Federico Echenique", "Matías Núñez"], "categories": ["econ.TH"], "doi": "", "_query": "housing price text listing", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2411.11219v2", "title": "Relational Contrastive Learning and Masked Image Modeling for Scene Text Recognition", "abstract": "Context-aware methods have achieved remarkable advancements in supervised scene text recognition by leveraging semantic priors from words. Considering the heterogeneity of text and background in STR, we propose that such contextual priors can be reinterpreted as the relations between textual elements, serving as effective self-supervised labels for representation learning. However, textual relations are restricted to the finite size of the dataset due to lexical dependencies, which causes over-fitting problem, thus compromising the representation quality. To address this, our work introduces a unified framework of Relational Contrastive Learning and Masked Image Modeling for STR (RCMSTR), which explicitly models the enriched textual relations. For the RCL branch, we first introduce the relational rearrangement module to cultivate new relations on the fly. Based on this, we further conduct relational contrastive learning to model the intra- and inter-hierarchical relations for frames, sub-words and words. On the other hand, MIM can naturally boost the context information via masking, where we find that the block masking strategy is more effective for STR. For the effective integration of RCL and MIM, we also introduce a novel decoupling design aimed at mitigating the impact of masked images on contrastive learning. Additionally, to enhance the compatibility of MIM with CNNs, we propose the adoption of sparse convolutions and directly sharing the weights with dense convolutions in training. The proposed RCMSTR demonstrates superior performance in various evaluation protocols for different STR-related downstream tasks, outperforming the existing state-of-the-art self-supervised STR techniques. Ablation studies and qualitative experimental results further validate the effectiveness of our method. The code and pre-trained models will be available at https://github.com/ThunderVVV/RCMSTR .", "published": "2024-11-18T01:11:47Z", "authors": ["Tiancheng Lin", "Jinglei Zhang", "Yi Xu", "Kai Chen", "Rui Zhang", "Chang-Wen Chen"], "categories": ["cs.CV"], "doi": "", "_query": "housing price text listing", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2508.11823v1", "title": "Hallucination Detection and Mitigation in Scientific Text Simplification using Ensemble Approaches: DS@GT at CLEF 2025 SimpleText", "abstract": "In this paper, we describe our methodology for the CLEF 2025 SimpleText Task 2, which focuses on detecting and evaluating creative generation and information distortion in scientific text simplification. Our solution integrates multiple strategies: we construct an ensemble framework that leverages BERT-based classifier, semantic similarity measure, natural language inference model, and large language model (LLM) reasoning. These diverse signals are combined using meta-classifiers to enhance the robustness of spurious and distortion detection. Additionally, for grounded generation, we employ an LLM-based post-editing system that revises simplifications based on the original input texts.", "published": "2025-08-15T21:57:27Z", "authors": ["Krishna Chaitanya Marturi", "Heba H. Elwazzan"], "categories": ["cs.CL"], "doi": "", "_query": "housing price text listing", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2506.07993v3", "title": "Stochastic portfolio theory with price impact", "abstract": "We develop a framework for stochastic portfolio theory (SPT), which incorporates modern nonlinear price impact and impact decay models. Our main result is the derivation of the celebrated master formula for additive functional generation of trading strategies in a general high-dimensional market model with price impact. We also derive formulas for an investor's relative wealth with respect to the market portfolio, conditions that guarantee positive observed market prices and a stochastic differential equation governing the dynamics of the observed price, the investor's holdings and the price impact state processes. As an application of these results, we develop conditions for relative arbitrage in the price impact setting analogous to previously obtained results for the frictionless setting. We then apply our framework to backtest the quadratic and entropy generating functions on historical US equity data, illustrating how price impact can negatively affect portfolio performance.", "published": "2025-06-09T17:57:46Z", "authors": ["David Itkin"], "categories": ["q-fin.MF", "math.PR"], "doi": "", "_query": "housing price text listing", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2111.00451v2", "title": "Utility Indifference Pricing with High Risk Aversion and Small Linear Price Impact", "abstract": "We consider the Bachelier model with linear price impact. Exponential utility indifference prices are studied for vanilla European options and we compute their non-trivial scaling limit for a vanishing price impact which is inversely proportional to the risk aversion. Moreover, we find explicitly a family of portfolios which are asymptotically optimal.", "published": "2021-10-31T10:10:49Z", "authors": ["Yan Dolinsky", "Shir Moshe"], "categories": ["q-fin.MF", "q-fin.PM"], "doi": "", "_query": "housing price text listing", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2202.08737v2", "title": "Listing Maximal k-Plexes in Large Real-World Graphs", "abstract": "Listing dense subgraphs in large graphs plays a key task in varieties of network analysis applications like community detection. Clique, as the densest model, has been widely investigated. However, in practice, communities rarely form as cliques for various reasons, e.g., data noise. Therefore, $k$-plex, -- graph with each vertex adjacent to all but at most $k$ vertices, is introduced as a relaxed version of clique. Often, to better simulate cohesive communities, an emphasis is placed on connected $k$-plexes with small $k$. In this paper, we continue the research line of listing all maximal $k$-plexes and maximal $k$-plexes of prescribed size. Our first contribution is algorithm ListPlex that lists all maximal $k$-plexes in $O^*(γ^D)$ time for each constant $k$, where $γ$ is a value related to $k$ but strictly smaller than 2, and $D$ is the degeneracy of the graph that is far less than the vertex number $n$ in real-word graphs. Compared to the trivial bound of $2^n$, the improvement is significant, and our bound is better than all previously known results. In practice, we further use several techniques to accelerate listing $k$-plexes of a given size, such as structural-based prune rules, cache-efficient data structures, and parallel techniques. All these together result in a very practical algorithm. Empirical results show that our approach outperforms the state-of-the-art solutions by up to orders of magnitude.", "published": "2022-02-17T16:25:56Z", "authors": ["Zhengren Wang", "Yi Zhou", "Mingyu Xiao", "Bakhadyr Khoussainov"], "categories": ["cs.DS", "cs.AI", "cs.SI"], "doi": "10.1145/3485447.3512198", "_query": "housing price text listing", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/1911.08382v1", "title": "A model for predicting price polarity of real estate properties using information of real estate market websites", "abstract": "This paper presents a model that uses the information that sellers publish in real estate market websites to predict whether a property has higher or lower price than the average price of its similar properties. The model learns the correlation between price and information (text descriptions and features) of real estate properties through automatic identification of latent semantic content given by a machine learning model based on doc2vec and xgboost. The proposed model was evaluated with a data set of 57,516 publications of real estate properties collected from 2016 to 2018 of Bogotá city. Results show that the accuracy of a classifier that involves text descriptions is slightly higher than a classifier that only uses features of the real estate properties, as text descriptions tends to contain detailed information about the property.", "published": "2019-11-19T16:29:59Z", "authors": ["Vladimir Vargas-Calderón", "Jorge E. Camargo"], "categories": ["cs.LG", "stat.ML"], "doi": "10.1080/01605682.2021.2023672", "_query": "housing price text listing", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2506.17239v1", "title": "Price equilibria with positive margins in loyal-strategic markets with discrete prices", "abstract": "In competitive supply chains (SCs), pricing decisions are crucial, as they directly impact market share and profitability. Traditional SC models often assume continuous pricing for mathematical convenience, overlooking the practical reality of discrete price increments driven by currency constraints. Additionally, customer behavior, influenced by loyalty and strategic considerations, plays a significant role in purchasing decisions. To address these gaps, this study examines a SC model involving one supplier and two manufacturers, incorporating realistic factors such as customer demand segmentation and discrete price setting. Our analysis shows that the Nash equilibria (NE) among manufacturers are not unique, we then discuss the focal equilibrium. Our analysis also reveals that low denomination factors can lead to instability as the corresponding game does not have NE. Numerical simulations demonstrate that even small changes in price increments significantly affect the competitive dynamics and market share distribution.", "published": "2025-06-05T05:50:11Z", "authors": ["Gurkirat Wadhwa", "Akansh Verma", "Veeraruna Kavitha", "Priyank Sinha"], "categories": ["q-fin.GN"], "doi": "", "_query": "housing price text listing", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2310.01815v1", "title": "What Determines the Price of NFTs?", "abstract": "In the evolving landscape of digital art, Non-Fungible Tokens (NFTs) have emerged as a groundbreaking platform, bridging the realms of art and technology. NFTs serve as the foundational framework that has revolutionized the market for digital art, enabling artists to showcase and monetize their creations in unprecedented ways. NFTs combine metadata stored on the blockchain with off-chain data, such as images, to create a novel form of digital ownership. It is not fully understood how these factors come together to determine NFT prices. In this study, we analyze both on-chain and off-chain data of NFT collections trading on OpenSea to understand what influences NFT pricing. Our results show that while text and image data of the NFTs can be used to explain price variations within collections, the extracted features do not generalize to new, unseen collections. Furthermore, we find that an NFT collection's trading volume often relates to its online presence, like social media followers and website traffic.", "published": "2023-10-03T06:09:59Z", "authors": ["Vivian Ziemke", "Benjamin Estermann", "Roger Wattenhofer", "Ye Wang"], "categories": ["cs.CE", "cs.LG"], "doi": "", "_query": "housing price text listing", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/1709.06367v1", "title": "Selfish Jobs with Favorite Machines: Price of Anarchy vs Strong Price of Anarchy", "abstract": "We consider the well-studied game-theoretic version of machine scheduling in which jobs correspond to self-interested users and machines correspond to resources. Here each user chooses a machine trying to minimize her own cost, and such selfish behavior typically results in some equilibrium which is not globally optimal: An equilibrium is an allocation where no user can reduce her own cost by moving to another machine, which in general need not minimize the makespan, i.e., the maximum load over the machines. We provide tight bounds on two well-studied notions in algorithmic game theory, namely, the price of anarchy and the strong price of anarchy on machine scheduling setting which lies in between the related and the unrelated machine case. Both notions study the social cost (makespan) of the worst equilibrium compared to the optimum, with the strong price of anarchy restricting to a stronger form of equilibria. Our results extend a prior study comparing the price of anarchy to the strong price of anarchy for two related machines (Epstein, Acta Informatica 2010), thus providing further insights on the relation between these concepts. Our exact bounds give a qualitative and quantitative comparison between the two models. The bounds also show that the setting is indeed easier than the two unrelated machines: In the latter, the strong price of anarchy is $2$, while in ours it is strictly smaller.", "published": "2017-09-19T12:10:28Z", "authors": ["Cong Chen", "Paolo Penna", "Yinfeng Xu"], "categories": ["cs.GT"], "doi": "10.1007/978-3-319-71147-8_16", "_query": "housing price text listing", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/1901.05024v2", "title": "Econophysics of Asset Price, Return and Multiple Expectations", "abstract": "This paper describes asset price and return disturbances as result of relations between transactions and multiple kinds of expectations. We show that disturbances of expectations can cause fluctuations of trade volume, price and return. We model price disturbances for transactions made under all types of expectations as weighted sum of partial price and trade volume disturbances for transactions made under separate kinds of expectations. Relations on price allow present return as weighted sum of partial return and trade volume \"return\" for transactions made under separate expectations. Dependence of price disturbances on trade volume disturbances as well as dependence of return on trade volume \"return\" cause dependence of volatility and statistical distributions of price and return on statistical properties of trade volume disturbances and trade volume \"return\" respectively.", "published": "2019-01-15T19:06:42Z", "authors": ["Victor Olkhov"], "categories": ["econ.GN"], "doi": "10.21314/JNTF.2019.055", "_query": "housing price text listing", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2201.03784v4", "title": "Price Heterogeneity as a Source of Heterogeneous Demand", "abstract": "We explore heterogeneous prices as a source of heterogeneous or stochastic demand. Heterogeneous prices could arise either because there is actual price variation among consumers or because consumers (mis)perceive prices differently. Our main result says the following: if heterogeneous prices have a distribution among consumers that is (in a sense) stable across observations, then a model where consumers have a common utility function but face heterogeneous prices has precisely the same implications as a heterogeneous preference/random utility model (with no price heterogeneity).", "published": "2022-01-11T05:07:05Z", "authors": ["John K. -H. Quah", "Gerelt Tserenjigmid"], "categories": ["econ.TH"], "doi": "", "_query": "housing price text listing", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2608.10711v1", "title": "Optimal Pricing and Hedging of SOFR Derivatives", "abstract": "Thousands of SOFR derivatives are available in exchanges and OTC, but the market remains illiquid and incomplete. Such a market is beyond the scope of classic risk-neutral approaches that imply linear pricing rules and, at best, approximate hedging strategies whose hedging error may be difficult to quantify. This paper develops an indifference pricing model which is consistent with observed derivative quotes, the agent's financial position and views about the uncertain future as well as risk preferences as described by a convex risk measure. In addition to prices and hedging strategies, the model gives an explicit description of the hedging error and the associated risk. The approach is illustrated numerically using hundreds of CME-listed derivatives to price and hedge unreplicable OTC SOFR derivatives. The indifference prices are computed in less than a minute on a regular PC. We find that the optimal hedging portfolios tend to be sparse but still provide good approximations of the derivative payouts.", "published": "2026-08-11T09:31:19Z", "authors": ["Teemu Pennanen", "Waleed Taoum"], "categories": ["q-fin.PR"], "doi": "", "_query": "housing price text listing", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2101.01388v1", "title": "A Model of Market Making and Price Impact", "abstract": "Traders constantly consider the price impact associated with changing their positions. This paper seeks to understand how price impact emerges from the quoting strategies of market makers. To this end, market making is modeled as a dynamic auction using the mathematical framework of Stochastic Differential Games. In Nash Equilibrium, the market makers' quoting strategies generate a price impact function that is of the same form as the celebrated Almgren-Chriss model. The key insight is that price impact is the mechanism through which market makers earn profits while matching their books. As such, price impact is an essential feature of markets where flow is intermediated by market makers.", "published": "2021-01-05T07:33:44Z", "authors": ["Angad Singh"], "categories": ["q-fin.MF", "math.OC"], "doi": "", "_query": "housing price text listing", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2602.07439v1", "title": "TextOp: Real-time Interactive Text-Driven Humanoid Robot Motion Generation and Control", "abstract": "Recent advances in humanoid whole-body motion tracking have enabled the execution of diverse and highly coordinated motions on real hardware. However, existing controllers are commonly driven either by predefined motion trajectories, which offer limited flexibility when user intent changes, or by continuous human teleoperation, which requires constant human involvement and limits autonomy. This work addresses the problem of how to drive a universal humanoid controller in a real-time and interactive manner. We present TextOp, a real-time text-driven humanoid motion generation and control framework that supports streaming language commands and on-the-fly instruction modification during execution. TextOp adopts a two-level architecture in which a high-level autoregressive motion diffusion model continuously generates short-horizon kinematic trajectories conditioned on the current text input, while a low-level motion tracking policy executes these trajectories on a physical humanoid robot. By bridging interactive motion generation with robust whole-body control, TextOp unlocks free-form intent expression and enables smooth transitions across multiple challenging behaviors such as dancing and jumping, within a single continuous motion execution. Extensive real-robot experiments and offline evaluations demonstrate instant responsiveness, smooth whole-body motion, and precise control. The project page and the open-source code are available at https://text-op.github.io/", "published": "2026-02-07T08:42:11Z", "authors": ["Weiji Xie", "Jiakun Zheng", "Jinrui Han", "Jiyuan Shi", "Weinan Zhang", "Chenjia Bai", "Xuelong Li"], "categories": ["cs.RO", "cs.AI"], "doi": "", "_query": "housing price text listing", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2505.00296v3", "title": "Minimizing Envy and Maximizing Happiness in Graphical House Allocation", "abstract": "We study the house allocation problem in a setting where agents are connected by a graph representing friendships. In this model, two agents can only envy each other if they are neighbors (i.e., friends) in the graph. Each agent has a set of preferred (liked) houses and dislikes the rest. An agent $a$ is said to envy a friend $b$ if $a$ is not assigned any house she likes, while $b$ is allocated a house that $a$ likes. This framework is known as graphical house allocation. Within this framework, we investigate two central problems. The first problem is to compute a house allocation that minimizes the number of envious agents. Multiple such allocations may exist that achieve the same minimum level of envy. Among all allocations that minimize envy, the second problem aims to find one that maximizes the number of agents who receive one of their preferred houses. We present a detailed complexity-theoretic analysis of these problems. In particular, we show that both problems can be solved in polynomial time when each agent prefers at most one house. However, both become NP-hard even when agents are allowed to prefer at most two houses, thereby highlighting the tight boundary between tractability and intractability. Additionally, we design exact algorithms for both problems under certain structural conditions on the agent graph, such as when the graph is sparse, has a small balanced separator, or admits a small vertex cover. These algorithms are significantly faster than the naive brute-force approach.", "published": "2025-05-01T04:38:09Z", "authors": ["Anubhav Dhar", "Ashlesha Hota", "Palash Dey", "Sudeshna Kolay"], "categories": ["cs.DS", "cs.CC"], "doi": "", "_query": "housing price text listing", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2409.04047v2", "title": "Uniform price auction with quantity constraints", "abstract": "We study the equilibria of uniform price auctions where many asymmetric bidders have flat demands up to their respective quantity constraints. We present an iterative procedure that systematically finds an equilibrium outcome as well as an ascending auction that has this outcome as a dominant strategy equilibrium outcome. Demand reduction and low price equilibrium may occur since it is advantageous for a bidder to give up some of his/her demand and get the remaining demand at a low price rather than to get his/her entire demand at a higher price. We show that a low price equilibrium is the only possible equilibrium when no bidder's quantity constraint is large enough to cover the supply.", "published": "2024-09-06T06:35:10Z", "authors": ["Kiho Yoon"], "categories": ["econ.TH"], "doi": "10.1515/bejte-2025-0074", "_query": "housing price text listing", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/1509.01216v1", "title": "Dynamic Model of the Price Dispersion of Homogeneous Goods", "abstract": "Presented is an analytic microeconomic model of the temporal price dispersion of homogeneous goods in polypoly markets. This new approach is based on the idea that the price dispersion has its origin in the dynamics of the purchase process. The price dispersion is determined by the chance that demanded and supplied product units meet in a given price interval. It can be characterized by a fat-tailed Laplace distribution for short and by a lognormal distribution for long time horizons. Taking random temporal variations of demanded and supplied units into account both the mean price and also the standard deviation of the price dispersion are governed by a lognormal distribution. A comparison with empirical investigations confirms the model statements.", "published": "2015-05-27T11:11:47Z", "authors": ["Joachim Kaldasch"], "categories": ["econ.GN"], "doi": "10.9734/BJEMT/2015/17849", "_query": "housing price text listing", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2005.05310v1", "title": "Rational Finance Approach to Behavioral Option Pricing", "abstract": "When pricing options, there may be different views on the instantaneous mean return of the underlying price process. According to Black (1972), where there exist heterogeneous views on the instantaneous mean return, this will result in arbitrage opportunities. Behavioral finance proponents argue that such heterogenous views are likely to occur and this will not impact option pricing models proposed by rational dynamic asset pricing theory and will not give rise to volatility smiles. To rectify this, a leading advocate of behavioral finance has proposed a behavioral option pricing model. As there may be unexplored links between the behavioral and rational approaches to option pricing, in this paper we revisit Shefrin (2008) option pricing model as an example and suggest one approach to modify this behavioral finance option pricing formula to be consistent with rational dynamic asset pricing theory by introducing arbitrage transaction costs which offset the gains from arbitrage trades.", "published": "2020-05-10T07:44:19Z", "authors": ["Jiexin Dai", "Abootaleb Shirvani", "Frank J. Fabozzi"], "categories": ["q-fin.CP", "q-fin.PR"], "doi": "", "_query": "housing price text listing", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2510.04464v2", "title": "Identification in Auctions with Truncated Transaction Prices", "abstract": "I establish nonparametric identification results in first- and second-price auctions when transaction prices are truncated by a binding reserve price under a range of information structures. When the number of potential bidders is fixed and known across all auctions, if only the transaction price is observed, the bidders' private-value distribution is identified in second-price auctions but not in first-price auctions. Identification in first-price auctions can be achieved if either the number of active bidders or the number of auctions with no sales is observed. When the number of potential bidders varies across auctions and is unknown, the bidders' private-value distribution is identified in first-price auctions but not in second-price auctions, provided that both the transaction price and the number of active bidders are observed. I derive analogous results to auctions with entry costs, which face a similar truncation issue when data on potential bidders who do not enter are missing.", "published": "2025-10-06T03:38:53Z", "authors": ["Tonghui Qi"], "categories": ["econ.EM"], "doi": "", "_query": "housing price text listing", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2009.03679v1", "title": "Proximity full-text searches of frequently occurring words with a response time guarantee", "abstract": "Full-text search engines are important tools for information retrieval. In a proximity full-text search, a document is relevant if it contains query terms near each other, especially if the query terms are frequently occurring words. For each word in the text, we use additional indexes to store information about nearby words at distances from the given word of less than or equal to MaxDistance, which is a parameter. A search algorithm for the case when the query consists of high-frequently used words is discussed. In addition, we present results of experiments with different values of MaxDistance to evaluate the search speed dependence on the value of MaxDistance. These results show that the average time of the query execution with our indexes is 94.7-45.9 times (depending on the value of MaxDistance) less than that with standard inverted files when queries that contain high-frequently occurring words are evaluated. This is a pre-print of a contribution published in Pinelas S., Kim A., Vlasov V. (eds) Mathematical Analysis With Applications. CONCORD-90 2018. Springer Proceedings in Mathematics & Statistics, vol 318, published by Springer, Cham. The final authenticated version is available online at: https://doi.org/10.1007/978-3-030-42176-2_37", "published": "2020-09-06T09:14:59Z", "authors": ["Alexander B. Veretennikov"], "categories": ["cs.IR"], "doi": "10.1007/978-3-030-42176-2_37", "_query": "housing price text listing", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2406.06023v2", "title": "The Limits of Interval-Regulated Price Discrimination", "abstract": "In this paper, we study third-degree price discrimination in a model first presented by Bergemann, Brooks, and Morris [2015]. Since such price discrimination might create market segments with vastly different posted prices, we consider regulating these prices, specifically, by restricting them to lie within an interval. Given a price interval, we consider segmentations of the market where a seller, who is oblivious to the existence of such regulation, still posts prices within the price interval. We show the following surprising result: For any market and price interval where such segmentation is feasible, there is always a different segmentation that optimally transfers all excess surplus to the consumers. In addition, we characterize the entire space of buyer and seller surplus that is achievable by such segmentation, including maximizing seller surplus, and simultaneously minimizing buyer and seller surplus. A key technical challenge is that the classical segmentation method of Bergemann, Brooks, and Morris [2015] fails under price constraints. To address this, we develop three intuitive but fundamentally distinct segmentation constructions, each tailored to a different surplus objective. These constructions maintain different invariants, reflect different economic intuitions, and collectively form the core of our regulated surplus characterization.", "published": "2024-06-10T05:04:33Z", "authors": ["Kamesh Munagala", "Yiheng Shen", "Renzhe Xu"], "categories": ["econ.TH", "cs.DS", "cs.GT"], "doi": "", "_query": "housing price text listing", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2002.07561v3", "title": "The Market Price of Risk for Delivery Periods: Pricing Swaps and Options in Electricity Markets", "abstract": "In electricity markets, futures contracts typically function as a swap since they deliver the underlying over a period of time. In this paper, we introduce a market price for the delivery periods of electricity swaps, thereby opening an arbitrage-free pricing framework for derivatives based on these contracts. Furthermore, we use a weighted geometric averaging of an artificial geometric futures price over the corresponding delivery period. Without any need for approximations, this averaging results in geometric swap price dynamics. Our framework allows for including typical features as the Samuelson effect, seasonalities, and stochastic volatility. In particular, we investigate the pricing procedures for electricity swaps and options in line with Arismendi et al. (2016), Schneider and Tavin (2018), and Fanelli and Schmeck (2019). A numerical study highlights the differences between these models depending on the delivery period.", "published": "2020-02-18T13:53:15Z", "authors": ["Annika Kemper", "Maren D. Schmeck", "Anna Kh. Balci"], "categories": ["q-fin.PR", "q-fin.MF"], "doi": "", "_query": "housing price text listing", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2209.12542v2", "title": "A Hamiltonian Approach to Floating Barrier Option Pricing", "abstract": "Hamiltonian approach in quantum mechanics provides a new thinking for barrier option pricing. For proportional floating barrier step options, the option price changing process is similar to the one dimensional trapezoid potential barrier scattering problem in quantum mechanics; for floating double-barrier step options, the option price changing process is analogous to a particle moving in a finite symmetric square potential well. Using Hamiltonian methodology, the analytical expressions of pricing kernel and option price could be derived. Numerical results of option price as a function of underlying price, floating rate, interest rate and exercise price are shown, which are consistent with the results given by mathematical calculations.", "published": "2022-09-26T09:58:45Z", "authors": ["Qi Chen", "Hong-tao Wang", "Chao Guo"], "categories": ["q-fin.PR"], "doi": "10.1007/s10955-023-03209-0", "_query": "housing price text listing", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2307.05934v1", "title": "Sem-CS: Semantic CLIPStyler for Text-Based Image Style Transfer", "abstract": "CLIPStyler demonstrated image style transfer with realistic textures using only a style text description (instead of requiring a reference style image). However, the ground semantics of objects in the style transfer output is lost due to style spill-over on salient and background objects (content mismatch) or over-stylization. To solve this, we propose Semantic CLIPStyler (Sem-CS), that performs semantic style transfer. Sem-CS first segments the content image into salient and non-salient objects and then transfers artistic style based on a given style text description. The semantic style transfer is achieved using global foreground loss (for salient objects) and global background loss (for non-salient objects). Our empirical results, including DISTS, NIMA and user study scores, show that our proposed framework yields superior qualitative and quantitative performance. Our code is available at github.com/chandagrover/sem-cs.", "published": "2023-07-12T05:59:42Z", "authors": ["Chanda Grover Kamra", "Indra Deep Mastan", "Debayan Gupta"], "categories": ["cs.CV"], "doi": "", "_query": "housing price text listing", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/1905.04910v2", "title": "The Price of Fairness for Indivisible Goods", "abstract": "We investigate the efficiency of fair allocations of indivisible goods using the well-studied price of fairness concept. Previous work has focused on classical fairness notions such as envy-freeness, proportionality, and equitability. However, these notions cannot always be satisfied for indivisible goods, leading to certain instances being ignored in the analysis. In this paper, we focus instead on notions with guaranteed existence, including envy-freeness up to one good (EF1), balancedness, maximum Nash welfare (MNW), and leximin. We also introduce the concept of strong price of fairness, which captures the efficiency loss in the worst fair allocation as opposed to that in the best fair allocation as in the price of fairness. We mostly provide tight or asymptotically tight bounds on the worst-case efficiency loss for allocations satisfying these notions, for both the price of fairness and the strong price of fairness.", "published": "2019-05-13T08:43:26Z", "authors": ["Xiaohui Bei", "Xinhang Lu", "Pasin Manurangsi", "Warut Suksompong"], "categories": ["cs.GT", "cs.MA"], "doi": "10.1007/s00224-021-10039-8", "_query": "housing price text listing", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/1206.4283v1", "title": "Pricing of insurance policies against cloud storage price rises", "abstract": "When a company migrates to cloud storage, the way back is neither easy nor cheap. The company is then locked up in the storage contract and exposed to upward market prices, which reduce the company's profit and may even bring it below zero. We propose a protection means based on an insurance contract, by which the cloud purchaser is indemnified when the current storage price exceeds a pre-defined threshold. By applying the financial options theory, we provide a formula for the insurance price (the premium). By using historical data on market prices for disks, we apply the formula in realistic scenarios. We show that the premium grows nearly quadratically with the length of the coverage period as long as this is below one year, but grows more slowly, though faster than linearly, over longer coverage periods.", "published": "2012-06-15T18:39:12Z", "authors": ["Loretta Mastroeni", "Maurizio Naldi"], "categories": ["cs.OH"], "doi": "", "_query": "housing price text listing", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2603.29261v1", "title": "Monodense Deep Neural Model for Determining Item Price Elasticity", "abstract": "Item Price Elasticity is used to quantify the responsiveness of consumer demand to changes in item prices, enabling businesses to create pricing strategies and optimize revenue management. Sectors such as store retail, e-commerce, and consumer goods rely on elasticity information derived from historical sales and pricing data. This elasticity provides an understanding of purchasing behavior across different items, consumer discount sensitivity, and demand elastic departments. This information is particularly valuable for competitive markets and resource-constrained businesses decision making which aims to maximize profitability and market share. Price elasticity also uncovers historical shifts in consumer responsiveness over time. In this paper, we model item-level price elasticity using large-scale transactional datasets, by proposing a novel elasticity estimation framework which has the capability to work in an absence of treatment control setting. We test this framework by using Machine learning based algorithms listed below, including our newly proposed Monodense deep neural network. (1) Monodense-DL network -- Hybrid neural network architecture combining embedding, dense, and Monodense layers (2) DML -- Double machine learning setting using regression models (3) LGBM -- Light Gradient Boosting Model We evaluate our model on multi-category retail data spanning millions of transactions using a back testing framework. Experimental results demonstrate the superiority of our proposed neural network model within the framework compared to other prevalent ML based methods listed above.", "published": "2026-03-31T04:50:51Z", "authors": ["Lakshya Garg", "Sai Yaswanth", "Deep Narayan Mishra", "Karthik Kumaran", "Anupriya Sharma", "Mayank Uniyal"], "categories": ["cs.LG", "cs.AI"], "doi": "10.1109/AAIML67890.2026.11498150", "_query": "housing price text listing", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/1803.03659v1", "title": "Listing Maximal Subgraphs in Strongly Accessible Set Systems", "abstract": "Algorithms for listing the subgraphs satisfying a given property (e.g.,being a clique, a cut, a cycle, etc.) fall within the general framework of set systems. A set system (U, F) uses a ground set U (e.g., the network nodes) and an indicator F, subset of 2^U, of which subsets of U have the required property. For the problem of listing all sets in F maximal under inclusion, the ambitious goal is to cover a large class of set systems, preserving at the same time the efficiency of the enumeration. Among the existing algorithms, the best-known ones list the maximal subsets in time proportional to their number but may require exponential space. In this paper we improve the state of the art in two directions by introducing an algorithmic framework that, under standard suitable conditions, simultaneously (i) extends the class of problems that can be solved efficiently to strongly accessible set systems, and (ii) reduces the additional space usage from exponential in |U| to stateless, thus accounting for just O(q) space, where q <= |U| is the largest size of a maximal set in F", "published": "2018-03-09T19:13:00Z", "authors": ["Alessio Conte", "Roberto Grossi", "Andrea Marino", "Luca Versari"], "categories": ["cs.DM"], "doi": "", "_query": "housing price text listing", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2610.10727v1", "title": "Deep Learning vs. Statistical Models for Multi-Horizon Price Forecasting of Second-Hand Electronics: A Systematic Benchmark", "abstract": "Forecasting resale prices of used electronics is critical for subscription-based platforms where pricing errors translate directly into risk. Unlike structured financial markets, second-hand electronics exhibit high volatility, sparse listing histories, and non-normal price dynamics - yet no systematic time-series benchmark exists for this domain. This paper presents the first multi-horizon benchmark of statistical and deep learning forecasting models for used electronics price prediction. We use a large-scale dataset of daily price listings from Polish online marketplaces (January 2022 to March 2025, 100+ smartphone and laptop models) and evaluate eleven models across six horizons from 1 to 365 days, covering classical methods (ARIMA, ETS, Theta), recurrent and convolutional networks (LSTM, TCN), and modern deep architectures (N-BEATS, N-HiTS, TFT, PatchTST, Informer). Three complementary evaluation protocols assess trajectory fitness, one-shot endpoint accuracy, and cross-horizon transfer. N-BEATS achieves the lowest MAPE beyond 30 days, reaching 8.51% at 365 days versus 14.94% for the best statistical baseline - a 43% reduction. At short horizons (1-7 days), all models converge near 0.72% MAPE and the naive baseline remains competitive. A single N-BEATS model trained at 365 days generalizes to all shorter horizons, eliminating the need for horizon-specific models. N-BEATS and N-HiTS also demonstrate superior hyperparameter stability.", "published": "2026-10-07T18:05:13Z", "authors": ["Mateusz Buczyński", "Michał Woźniak", "Konrad Kaczyński", "Anna Wróblewska", "Sebastian Kuk"], "categories": ["q-fin.CP", "cs.LG"], "doi": "", "_query": "housing price text listing", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/1009.5557v1", "title": "A Mobile Application for Smart House Remote Control System", "abstract": "At the start of the second decade of 21th century, the time has come to make the Smart Houses a reality for regular use. The different parts of a Smart House are researched but there are still distances from an applicable system, using the modern technology. In this paper we present an overview of the Smart House subsystems necessary for controlling the house using a mobile application efficiently and securely. The sequence diagram of the mobile application connecting to the server application and also the use-cases possible are presented. The challenges faced in designing the mobile application and illustrating the updated house top plane view in that application, are discussed and solutions are adapted for it. Finally the designed mobile application was implemented and the important sections of it were described, such as the interactive house top view map which indicates the status of the devices using predefined icons. The facilities to manage the scheduled tasks and defined rules are also implemented in this mobile application that was developed for use in Windows Mobile platform. This application has the capability of connecting to the main server using GPRS mobile internet and SMS. This system is expected to be an important step towards a unified system structure that can be used efficiently in near future regular houses.", "published": "2010-09-27T18:13:37Z", "authors": ["Amir Rajabzadeh", "Ali Reza Manashty", "Zahra Forootan Jahromi"], "categories": ["cs.OH"], "doi": "", "_query": "housing price text listing", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/1904.05656v4", "title": "Pricing under Fairness Concerns", "abstract": "This paper proposes a theory of pricing premised upon the assumptions that customers dislike unfair prices---those marked up steeply over cost---and that firms take these concerns into account when setting prices. Since they do not observe firms' costs, customers must extract costs from prices. The theory assumes that customers infer less than rationally: when a price rises due to a cost increase, customers partially misattribute the higher price to a higher markup---which they find unfair. Firms anticipate this response and trim their price increases, which drives the passthrough of costs into prices below one: prices are somewhat rigid. Embedded in a New Keynesian model as a replacement for the usual pricing frictions, our theory produces monetary nonneutrality: when monetary policy loosens and inflation rises, customers misperceive markups as higher and feel unfairly treated; firms mitigate this perceived unfairness by reducing their markups; in general equilibrium, employment rises. The theory also features a hybrid short-run Phillips curve, realistic impulse responses of output and employment to monetary and technology shocks, and an upward-sloping long-run Phillips curve.", "published": "2019-04-11T12:13:54Z", "authors": ["Erik Eyster", "Kristof Madarasz", "Pascal Michaillat"], "categories": ["econ.TH", "econ.GN"], "doi": "10.1093/jeea/jvaa041", "_query": "housing price text listing", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/1910.10005v1", "title": "The CMMV Pricing Model in Practice", "abstract": "Mainstream financial econometrics methods are based on models well tuned to replicate price dynamics, but with little to no economic justification. In particular, the randomness in these models is assumed to result from a combination of exogenous factors. In this paper, we present a model originating from game theory, whose corresponding price dynamics are a direct consequence of the information asymmetry between private and institutional investors. This model, namely the CMMV pricing model, is therefore rooted in market microstructure. The pricing methods derived from it also appear to fit very well historical price data. Indeed, as evidenced in the last section of the paper, the CMMV model does a very good job predicting option prices from readily available data. It also enables to recover the dynamic of the volatility surface.", "published": "2019-10-22T14:26:15Z", "authors": ["Bernard De Meyer", "Moussa Dabo"], "categories": ["q-fin.PR"], "doi": "", "_query": "housing price text listing", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/cond-mat/0012419v1", "title": "The price dynamics of common trading strategies", "abstract": "A deterministic trading strategy can be regarded as a signal processing element that uses external information and past prices as inputs and incorporates them into future prices. This paper uses a market maker based method of price formation to study the price dynamics induced by several commonly used financial trading strategies, showing how they amplify noise, induce structure in prices, and cause phenomena such as excess and clustered volatility.", "published": "2000-12-21T21:00:30Z", "authors": ["J. Doyne Farmer", "Shareen Joshi"], "categories": ["cond-mat.stat-mech", "q-fin.TR"], "doi": "", "_query": "housing price text listing", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/1904.08884v1", "title": "Negative Prices in Network Pricing Games", "abstract": "In a Stackelberg network pricing game, a leader sets prices for a given subset of edges so as to maximize profit, after which one or multiple followers choose a shortest path from their source to sink. We study the counter-intuitive phenomenon that the use of negative prices by the leader may in fact increase his profit. In doing so, we answer the following two questions. First, how much more profit can the leader earn by setting negative prices? Second, for which network topologies can the profit be increased by using negative prices? Our main result shows that the profit with negative prices can be a factor $Θ(\\log (m\\cdot\\bar k))$ larger than the maximum profit with positive prices, where $m$ is the number of priceable edges in the graph and $\\bar k \\leq 2^m$ the number of followers. In particular, this factor cannot be bounded for the single follower case, and can even grow linearly in $m$ if the number of followers is large. Our second result shows that series-parallel graphs with a single follower and Stackelberg games with matroid followers are immune to the negative price paradox.", "published": "2019-04-18T16:50:09Z", "authors": ["Andrés Cristi", "Marc Schröder"], "categories": ["cs.GT"], "doi": "", "_query": "housing price text listing", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2403.03812v1", "title": "ProbSAINT: Probabilistic Tabular Regression for Used Car Pricing", "abstract": "Used car pricing is a critical aspect of the automotive industry, influenced by many economic factors and market dynamics. With the recent surge in online marketplaces and increased demand for used cars, accurate pricing would benefit both buyers and sellers by ensuring fair transactions. However, the transition towards automated pricing algorithms using machine learning necessitates the comprehension of model uncertainties, specifically the ability to flag predictions that the model is unsure about. Although recent literature proposes the use of boosting algorithms or nearest neighbor-based approaches for swift and precise price predictions, encapsulating model uncertainties with such algorithms presents a complex challenge. We introduce ProbSAINT, a model that offers a principled approach for uncertainty quantification of its price predictions, along with accurate point predictions that are comparable to state-of-the-art boosting techniques. Furthermore, acknowledging that the business prefers pricing used cars based on the number of days the vehicle was listed for sale, we show how ProbSAINT can be used as a dynamic forecasting model for predicting price probabilities for different expected offer duration. Our experiments further indicate that ProbSAINT is especially accurate on instances where it is highly certain. This proves the applicability of its probabilistic predictions in real-world scenarios where trustworthiness is crucial.", "published": "2024-03-06T16:00:50Z", "authors": ["Kiran Madhusudhanan", "Gunnar Behrens", "Maximilian Stubbemann", "Lars Schmidt-Thieme"], "categories": ["cs.LG", "cs.AI"], "doi": "", "_query": "housing price text listing", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2310.17165v5", "title": "Price Experimentation and Interference", "abstract": "In this paper, we examine the biases that arise when firms run A/B tests on continuous parameters to estimate global treatment effects on performance metrics of interest; we particularly focus on price experiments to measure the price impact on quantity demanded, and on profit. In canonical A/B experimental estimators, biases emerge due to interference between market participants. We employ structural modeling and differential calculus to derive intuitive characterizations of these biases. We then specialize our general model to the standard revenue-management pricing problem. This setting highlights a fundamental risk innate to A/B pricing experiments: that the canonical estimator for the expected change in profits, counterintuitively, can have the wrong sign in expectation. In other words, following the guidance of canonical estimators may lead firms to move prices (or fees) in the wrong direction, inadvertently decreasing profits. We introduce a novel debiasing technique for these canonical experiments, requiring only that firms equally split units between treatment and control. We apply these results to a two-sided market model, and demonstrate how the \"change of sign\" regime depends on market factors such as the supply/demand imbalance, and the price markup. We conclude by calibrating our revenue-management pricing model to published empirical estimates from Airbnb marketplaces, demonstrating that estimators with the wrong sign are not a knife-edge issue, and that they may be prevalent enough to be of concern to practitioners.", "published": "2023-10-26T05:32:41Z", "authors": ["Ramesh Johari", "Orrie B. Page", "Gabriel Y. Weintraub"], "categories": ["stat.ME"], "doi": "", "_query": "housing price text listing", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/1904.07583v2", "title": "Clearing price distributions in call auctions", "abstract": "We propose a model for price formation in financial markets based on clearing of a standard call auction with random orders, and verify its validity for prediction of the daily closing price distribution statistically. The model considers random buy and sell orders, placed following demand- and supply-side valuation distributions; an equilibrium equation then leads to a distribution for clearing price and transacted volume. Bid and ask volumes are left as free parameters, permitting possibly heavy-tailed or very skewed order flow conditions. In highly liquid auctions, the clearing price distribution converges to an asymptotically normal central limit, with mean and variance in terms of supply/demand-valuation distributions and order flow imbalance. By means of simulations, we illustrate the influence of variations in order flow and valuation distributions on price/volume, noting a distinction between high- and low-volume auction price variance. To verify the validity of the model statistically, we predict a year's worth of daily closing price distributions for 5 constituents of the Eurostoxx 50 index; Kolmogorov-Smirnov statistics and QQ-plots demonstrate with ample statistical significance that the model predicts closing price distributions accurately, and compares favourably with alternative methods of prediction.", "published": "2019-04-16T10:27:31Z", "authors": ["M. Derksen", "B. Kleijn", "R. de Vilder"], "categories": ["q-fin.TR"], "doi": "", "_query": "housing price text listing", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/1901.06467v1", "title": "Option Pricing in Illiquid Markets with Jumps", "abstract": "The classical linear Black--Scholes model for pricing derivative securities is a popular model in financial industry. It relies on several restrictive assumptions such as completeness, and frictionless of the market as well as the assumption on the underlying asset price dynamics following a geometric Brownian motion. The main purpose of this paper is to generalize the classical Black--Scholes model for pricing derivative securities by taking into account feedback effects due to an influence of a large trader on the underlying asset price dynamics exhibiting random jumps. The assumption that an investor can trade large amounts of assets without affecting the underlying asset price itself is usually not satisfied, especially in illiquid markets. We generalize the Frey--Stremme nonlinear option pricing model for the case the underlying asset follows a Levy stochastic process with jumps. We derive and analyze a fully nonlinear parabolic partial-integro differential equation for the price of the option contract. We propose a semi-implicit numerical discretization scheme and perform various numerical experiments showing influence of a large trader and intensity of jumps on the option price.", "published": "2019-01-19T04:56:22Z", "authors": ["Jose Cruz", "Daniel Sevcovic"], "categories": ["q-fin.MF"], "doi": "", "_query": "housing price text listing", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/1801.07357v2", "title": "CHALET: Cornell House Agent Learning Environment", "abstract": "We present CHALET, a 3D house simulator with support for navigation and manipulation. CHALET includes 58 rooms and 10 house configuration, and allows to easily create new house and room layouts. CHALET supports a range of common household activities, including moving objects, toggling appliances, and placing objects inside closeable containers. The environment and actions available are designed to create a challenging domain to train and evaluate autonomous agents, including for tasks that combine language, vision, and planning in a dynamic environment.", "published": "2018-01-23T00:22:25Z", "authors": ["Claudia Yan", "Dipendra Misra", "Andrew Bennnett", "Aaron Walsman", "Yonatan Bisk", "Yoav Artzi"], "categories": ["cs.AI"], "doi": "", "_query": "housing price text listing", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2608.20819v1", "title": "PRICE: Pricing-based Resource Incentives for Quality-of-Result-aware Computing at the Edge", "abstract": "Edge nodes are capacity-constrained by design, yet many edge workloads can trade result quality for resource efficiency at runtime. Existing edge pricing mechanisms largely treat requests as fixed-configuration submissions and rarely exploit per-request quality flexibility under overload. We present PRICE, an incentive mechanism that couples a utilization-dependent price signal to per-request quality selection. As utilization increases, rising acceptance prices make resource-intensive variants less likely to be selected, shifting accepted requests toward lighter execution and allowing the node to serve significantly more requests while operating near capacity. Evaluation on real hardware under sustained overload shows that PRICE outperforms both fixed-allocation and dynamic-pricing baselines in accepted throughput and CPU utilization. The results are robust across pricing function families, task-duration distributions, and client populations. Result-quality flexibility is a powerful but underused control dimension for overload management at the edge, and pricing is an effective mechanism to exploit it.", "published": "2026-08-21T07:40:31Z", "authors": ["Uwe Gropengießer", "Sebastian Frenz", "Max Mühlhäuser"], "categories": ["cs.DC"], "doi": "", "_query": "housing price text listing", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2506.10640v2", "title": "Asymmetric price adjustment over the business cycle", "abstract": "Studies of micro-level price datasets find more frequent small price increases than decreases, which can be explained by consumer inattention because time-constrained shoppers might ignore small price changes. Recent empirical studies of the link between shopping behavior and price attention over the business cycle find that consumers are more attentive to prices during economic downturns, and less attentive during economic booms. These two sets of findings have a testable implication. The asymmetry in small price changes should vary over the business cycle. It should diminish during recessions and strengthen during expansions. We test this prediction using a large US store-level dataset with more than 98 million weekly price observations for the years 1989-1997, which includes an 8-month recession period, as defined by the NBER. We compare price adjustments between periods of recession - high unemployment, and expansion - low unemployment. Focusing on small price changes, we find, consistent with our hypothesis, that there is a greater asymmetry in small price changes during periods of low unemployment compared to the periods of high unemployment, implying that firms price-setting behavior varies over the business cycle.", "published": "2025-06-12T12:25:47Z", "authors": ["Daniel Levy", " Haipeng", " Chen", "Sourav Ray", "Elliot Charette", "Xiao Ling", "Weihong Zhao", "Mark Bergen", "Avichai Snir"], "categories": ["econ.GN"], "doi": "", "_query": "housing price text listing", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2601.17964v2", "title": "Pass-through with Price Dispersion", "abstract": "How do cost shocks pass through to prices in markets with price dispersion? We decompose the problem into two layers. In the competition layer, consumers' consideration sets determine equilibrium distributions of normalized margins. In the curvature layer, demand elasticity maps these margins into prices and pass-through rates. We prove the pricing game is strategically equivalent to a game over normalized margins, so equilibrium margin distributions are invariant to demand and costs. This separation yields closed-form pass-through formulas at each quantile of the price distribution, robust bounds across demand specifications, and sharp comparative statics linking market structure to incidence.", "published": "2026-01-25T19:44:17Z", "authors": ["Brian C. Albrecht", "Mark Whitmeyer"], "categories": ["econ.TH"], "doi": "", "_query": "housing price text listing", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2303.13295v3", "title": "Irrelevance of personalized pricing under strategic market segmentation", "abstract": "A multiproduct seller is more informed than consumers about the value of her products to consumers. The seller posts a price list and segments the market through cheap-talk communication. We find that when both seller's and consumers' incentive-compatibility constraints are satisfied, the seller cannot benefit from personalized pricing (i.e., third-degree price discrimination). Based on that observation, we provide a tractable characterization of seller's maximum equilibrium profits. We apply our analysis to a credence-good setup and discuss when the credence goods seller benefits from communication. The irrelevance result breaks down when we relax seller's incentive-compatibility constraints.", "published": "2023-03-23T14:26:58Z", "authors": ["Xiaoxiao Hu", "Haoran Lei"], "categories": ["econ.TH"], "doi": "", "_query": "housing price text listing", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2407.05407v2", "title": "CosyVoice: A Scalable Multilingual Zero-shot Text-to-speech Synthesizer based on Supervised Semantic Tokens", "abstract": "Recent years have witnessed a trend that large language model (LLM) based text-to-speech (TTS) emerges into the mainstream due to their high naturalness and zero-shot capacity. In this paradigm, speech signals are discretized into token sequences, which are modeled by an LLM with text as prompts and reconstructed by a token-based vocoder to waveforms. Obviously, speech tokens play a critical role in LLM-based TTS models. Current speech tokens are learned in an unsupervised manner, which lacks explicit semantic information and alignment to the text. In this paper, we propose to represent speech with supervised semantic tokens, which are derived from a multilingual speech recognition model by inserting vector quantization into the encoder. Based on the tokens, we further propose a scalable zero-shot TTS synthesizer, CosyVoice, which consists of an LLM for text-to-token generation and a conditional flow matching model for token-to-speech synthesis. Experimental results show that supervised semantic tokens significantly outperform existing unsupervised tokens in terms of content consistency and speaker similarity for zero-shot voice cloning. Moreover, we find that utilizing large-scale data further improves the synthesis performance, indicating the scalable capacity of CosyVoice. To the best of our knowledge, this is the first attempt to involve supervised speech tokens into TTS models.", "published": "2024-07-07T15:16:19Z", "authors": ["Zhihao Du", "Qian Chen", "Shiliang Zhang", "Kai Hu", "Heng Lu", "Yexin Yang", "Hangrui Hu", "Siqi Zheng", "Yue Gu", "Ziyang Ma", "Zhifu Gao", "Zhijie Yan"], "categories": ["cs.SD", "cs.AI", "eess.AS"], "doi": "", "_query": "housing price text listing", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2603.18021v2", "title": "Anomaly prediction in XRP price with topological features", "abstract": "The aim of this research is to study XRP cryptoasset price dynamics, with a particular focus on forecasting atypical price movements. Recent studies suggest that topological properties of transaction graphs are highly informative for understanding cryptocurrency price behavior. In this work, we show that specific topological properties of the XRP transaction graphs provide important information about extreme XRP price surges, and can be used for more competitive prediction of anomalous price dynamics.", "published": "2026-03-05T06:31:01Z", "authors": ["Illia Donhauzer", "Pierluigi Cesana", "Tomoyuki Shirai", "Yuichi Ikeda"], "categories": ["q-fin.ST"], "doi": "", "_query": "housing price text listing", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2306.05041v1", "title": "Energy-Efficient Downlink Semantic Generative Communication with Text-to-Image Generators", "abstract": "In this paper, we introduce a novel semantic generative communication (SGC) framework, where generative users leverage text-to-image (T2I) generators to create images locally from downloaded text prompts, while non-generative users directly download images from a base station (BS). Although generative users help reduce downlink transmission energy at the BS, they consume additional energy for image generation and for uploading their generator state information (GSI). We formulate the problem of minimizing the total energy consumption of the BS and the users, and devise a generative user selection algorithm. Simulation results corroborate that our proposed algorithm reduces total energy by up to 54% compared to a baseline with all non-generative users.", "published": "2023-06-08T08:49:42Z", "authors": ["Hyein Lee", "Jihong Park", "Sooyoung Kim", "Jinho Choi"], "categories": ["cs.LG", "cs.AI", "cs.DC"], "doi": "", "_query": "housing price text listing", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/physics/0602033v3", "title": "Community Structure in the United States House of Representatives", "abstract": "We investigate the networks of committee and subcommittee assignments in the United States House of Representatives from the 101st--108th Congresses, with the committees connected by ``interlocks'' or common membership. We examine the community structure in these networks using several methods, revealing strong links between certain committees as well as an intrinsic hierarchical structure in the House as a whole. We identify structural changes, including additional hierarchical levels and higher modularity, resulting from the 1994 election, in which the Republican party earned majority status in the House for the first time in more than forty years. We also combine our network approach with analysis of roll call votes using singular value decomposition to uncover correlations between the political and organizational structure of House committees.", "published": "2006-02-04T22:41:34Z", "authors": ["Mason A. Porter", "Peter J. Mucha", "M. E. J. Newman", "A. J. Friend"], "categories": ["physics.soc-ph", "cond-mat.stat-mech", "cs.MA", "nlin.AO", "physics.data-an"], "doi": "10.1063/1.2390556", "_query": "housing price text listing", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2609.31468v1", "title": "PriceBench: A Diagnostic Benchmark for Price, Quality, and Brand Preferences in LLM Booking Agents", "abstract": "LLMs increasingly act as purchasing agents, which makes the LLM, not the user, the one choosing among the options that satisfy a request; its preferences quietly fix what gets bought and what it costs. Hotel booking is a clean instance: a high-volume choice settled on a few comparable attributes, where the pick reveals those preferences. We introduce PriceBench, a diagnostic benchmark that recovers an LLM's price, quality, and brand preferences from its booking choices with a logit choice model, applied to 28 LLMs from 8 providers on 3,600 hotel tasks from 179 real New York City properties. We find that capability is associated with how consistently an LLM chooses, not with what it chooses: more capable LLMs hold stronger, more consistent preferences, while weaker ones either lock onto one position, exploitable by whoever controls listing order, or choose almost indifferently. What those preferences favor varies sharply across providers and even within one family: price sensitivity spans more than an order of magnitude, and the price/quality trade-off moves mean booked nightly price from \\$247 to \\$393 on identical tasks. What an agent buys must therefore be measured per LLM, not inferred, and we release the tasks, code, and all 28 response sets.", "published": "2026-09-25T16:17:28Z", "authors": ["Pavel Kireyev"], "categories": ["econ.GN", "cs.AI", "cs.CL"], "doi": "", "_query": "housing price text listing", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2405.09431v1", "title": "A Survey On Text-to-3D Contents Generation In The Wild", "abstract": "3D content creation plays a vital role in various applications, such as gaming, robotics simulation, and virtual reality. However, the process is labor-intensive and time-consuming, requiring skilled designers to invest considerable effort in creating a single 3D asset. To address this challenge, text-to-3D generation technologies have emerged as a promising solution for automating 3D creation. Leveraging the success of large vision language models, these techniques aim to generate 3D content based on textual descriptions. Despite recent advancements in this area, existing solutions still face significant limitations in terms of generation quality and efficiency. In this survey, we conduct an in-depth investigation of the latest text-to-3D creation methods. We provide a comprehensive background on text-to-3D creation, including discussions on datasets employed in training and evaluation metrics used to assess the quality of generated 3D models. Then, we delve into the various 3D representations that serve as the foundation for the 3D generation process. Furthermore, we present a thorough comparison of the rapidly growing literature on generative pipelines, categorizing them into feedforward generators, optimization-based generation, and view reconstruction approaches. By examining the strengths and weaknesses of these methods, we aim to shed light on their respective capabilities and limitations. Lastly, we point out several promising avenues for future research. With this survey, we hope to inspire researchers further to explore the potential of open-vocabulary text-conditioned 3D content creation.", "published": "2024-05-15T15:23:22Z", "authors": ["Chenhan Jiang"], "categories": ["cs.CV", "cs.GR"], "doi": "", "_query": "housing price text listing", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2310.04367v2", "title": "A Marketplace Price Anomaly Detection System at Scale", "abstract": "Online marketplaces execute large volume of price updates that are initiated by individual marketplace sellers each day on the platform. This price democratization comes with increasing challenges with data quality. Lack of centralized guardrails that are available for a traditional online retailer causes a higher likelihood for inaccurate prices to get published on the website, leading to poor customer experience and potential for revenue loss. We present MoatPlus (Masked Optimal Anchors using Trees, Proximity-based Labeling and Unsupervised Statistical-features), a scalable price anomaly detection framework for a growing marketplace platform. The goal is to leverage proximity and historical price trends from unsupervised statistical features to generate an upper price bound. We build an ensemble of models to detect irregularities in price-based features, exclude irregular features and use optimized weighting scheme to build a reliable price bound in real-time pricing pipeline. We observed that our approach improves precise anchor coverage by up to 46.6% in high-vulnerability item subsets", "published": "2023-10-06T16:41:51Z", "authors": ["Akshit Sarpal", "Qiwen Kang", "Fangping Huang", "Yang Song", "Lijie Wan"], "categories": ["stat.ML", "cs.LG"], "doi": "", "_query": "housing price text listing", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2603.18598v1", "title": "Complementary Text-Guided Attention for Zero-Shot Adversarial Robustness", "abstract": "Due to the impressive zero-shot capabilities, pre-trained vision-language models (e.g., CLIP), have attracted widespread attention and adoption across various domains. Nonetheless, CLIP has been observed to be susceptible to adversarial examples. Through experimental analysis, we have observed a phenomenon wherein adversarial perturbations induce shifts in text-guided attention. Building upon this observation, we propose a simple yet effective strategy: Text-Guided Attention for Zero-Shot Robustness (TGA-ZSR). This framework incorporates two components: Local Attention Refinement Module and Global Attention Constraint Module. Our goal is to maintain the generalization of the CLIP model and enhance its adversarial robustness. Additionally, the Global Attention Constraint Module acquires text-guided attention from both the target and original models using clean examples. Its objective is to maintain model performance on clean samples while enhancing overall robustness. However, we observe that the method occasionally focuses on irrelevant or spurious features, which can lead to suboptimal performance and undermine its robustness in certain scenarios. To overcome this limitation, we further propose a novel approach called Complementary Text-Guided Attention (Comp-TGA). This method integrates two types of foreground attention: attention guided by the class prompt and reversed attention driven by the non-class prompt. These complementary attention mechanisms allow the model to capture a more comprehensive and accurate representation of the foreground. The experiments validate that TGA-ZSR and Comp-TGA yield 9.58% and 11.95% improvements respectively, in zero-shot robust accuracy over the current state-of-the-art techniques across 16 datasets.", "published": "2026-03-19T08:11:46Z", "authors": ["Lu Yu", "Haiyang Zhang", "Changsheng Xu"], "categories": ["cs.CV"], "doi": "", "_query": "housing price text listing", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2310.00231v2", "title": "The Distributional Impact of Inflation in Pakistan: A Case Study of a New Price Focused Microsimulation Framework, PRICES", "abstract": "This paper develops a microsimulation model to simulate the distributional impact of price changes using Household Budget Survey data, income survey data and an Input Output Model. The primary purpose is to describe the model components. The secondary purpose is to demonstrate one component of the model by assessing the distributional and welfare impact of recent price changes in Pakistan. Over the period of November 2020 to November 2022, headline inflation 41.5 percent, with food and transportation prices increasing most. The analysis shows that despite large increases in energy prices, the importance of energy prices for the welfare losses due to inflation is limited because energy budget shares are small and inflation is relatively low. The overall distributional impact of recent price changes is mildly progressive, but household welfare is impacted significantly irrespective of households position along the income distribution. The biggest driver of welfare losses at the bottom of the income distribution was food price inflation, while inflation in other goods and services was the biggest driver at the top. To compensate households for increased living costs, transfers would need to be on average 40 percent of pre-inflation expenditure, assuming constant incomes. Behavioural responses to price changes have a negligible impact on the overall welfare cost to households.", "published": "2023-09-30T02:30:28Z", "authors": ["Cathal ODonoghue", "Beenish Amjad", "Jules Linden", "Nora Lustig", "Denisa Sologon", "Yang Wang"], "categories": ["econ.GN"], "doi": "", "_query": "housing price text listing", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/1604.07463v1", "title": "Dynamic Pricing with Demand Covariates", "abstract": "We consider a firm that sells products over $T$ periods without knowing the demand function. The firm sequentially sets prices to earn revenue and to learn the underlying demand function simultaneously. A natural heuristic for this problem, commonly used in practice, is greedy iterative least squares (GILS). At each time period, GILS estimates the demand as a linear function of the price by applying least squares to the set of prior prices and realized demands. Then a price that maximizes the revenue, given the estimated demand function, is used for the next time period. The performance is measured by the regret, which is the expected revenue loss from the optimal (oracle) pricing policy when the demand function is known. Recently, den Boer and Zwart (2014) and Keskin and Zeevi (2014) demonstrated that GILS is sub-optimal. They introduced algorithms which integrate forced price dispersion with GILS and achieve asymptotically optimal performance. In this paper, we consider this dynamic pricing problem in a data-rich environment. In particular, we assume that the firm knows the expected demand under a particular price from historical data, and in each period, before setting the price, the firm has access to extra information (demand covariates) which may be predictive of the demand. We prove that in this setting GILS achieves asymptotically optimal regret of order $\\log(T)$. We also show the following surprising result: in the original dynamic pricing problem of den Boer and Zwart (2014) and Keskin and Zeevi (2014), inclusion of any set of covariates in GILS as potential demand covariates (even though they could carry no information) would make GILS asymptotically optimal. We validate our results via extensive numerical simulations on synthetic and real data sets.", "published": "2016-04-25T22:22:11Z", "authors": ["Sheng Qiang", "Mohsen Bayati"], "categories": ["stat.ML"], "doi": "", "_query": "housing price text listing", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2207.00412v2", "title": "Swiss German Speech to Text system evaluation", "abstract": "We present an in-depth evaluation of four commercially available Speech-to-Text (STT) systems for Swiss German. The systems are anonymized and referred to as system a-d in this report. We compare the four systems to our STT model, referred to as FHNW from hereon after, and provide details on how we trained our model. To evaluate the models, we use two STT datasets from different domains. The Swiss Parliament Corpus (SPC) test set and a private dataset in the news domain with an even distribution across seven dialect regions. We provide a detailed error analysis to detect the three systems' strengths and weaknesses. This analysis is limited by the characteristics of the two test sets. Our model scored the highest bilingual evaluation understudy (BLEU) on both datasets. On the SPC test set, we obtain a BLEU score of 0.607, whereas the best commercial system reaches a BLEU score of 0.509. On our private test set, we obtain a BLEU score of 0.722 and the best commercial system a BLEU score of 0.568.", "published": "2022-07-01T13:43:06Z", "authors": ["Yanick Schraner", "Christian Scheller", "Michel Plüss", "Manfred Vogel"], "categories": ["cs.CL", "cs.AI"], "doi": "", "_query": "housing price text listing", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/1603.07225v4", "title": "Numerical stability of a hybrid method for pricing options", "abstract": "We develop and study stability properties of a hybrid approximation of functionals of the Bates jump model with stochastic interest rate that uses a tree method in the direction of the volatility and the interest rate and a finite-difference approach in order to handle the underlying asset price process. We also propose hybrid simulations for the model, following a binomial tree in the direction of both the volatility and the interest rate, and a space-continuous approximation for the underlying asset price process coming from a Euler-Maruyama type scheme. We show that our methods allow to obtain efficient and accurate European and American option prices. Numerical experiments are provided, and show the reliability and the efficiency of the algorithms.", "published": "2016-03-23T15:10:58Z", "authors": ["Maya Briani", "Lucia Caramellino", "Giulia Terenzi", "Antonino Zanette"], "categories": ["q-fin.CP"], "doi": "10.1142/S0219024919500365", "_query": "housing price text listing", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/math/0605049v1", "title": "Pricing with coherent risk", "abstract": "This paper deals with applications of coherent risk measures to pricing in incomplete markets. Namely, we study the No Good Deals pricing technique based on coherent risk. Two forms of this technique are presented: one defines a good deal as a trade with negative risk; the other one defines a good deal as a trade with unusually high RAROC. For each technique, the fundamental theorem of asset pricing and the form of the fair price interval are presented. The model considered includes static as well as dynamic models, models with an infinite number of assets, models with transaction costs, and models with portfolio constraints. In particular, we prove that in a model with proportional transaction costs the fair price interval converges to the fair price interval in a frictionless model as the coefficient of transaction costs tends to zero. Moreover, we study some problems in the ``pure'' theory of risk measures: we present a simple geometric solution of the capital allocation problem and apply it to define the coherent risk contribution. The mathematical tools employed are probability theory, functional analysis, and finite-dimensional convex analysis.", "published": "2006-05-02T12:15:51Z", "authors": ["Alexander S. Cherny"], "categories": ["math.PR", "q-fin.RM"], "doi": "", "_query": "housing price text listing", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/1812.10619v1", "title": "Predicting the Stock Price of Frontier Markets Using Modified Black-Scholes Option Pricing Model and Machine Learning", "abstract": "The Black-Scholes Option pricing model (BSOPM) has long been in use for valuation of equity options to find the prices of stocks. In this work, using BSOPM, we have come up with a comparative analytical approach and numerical technique to find the price of call option and put option and considered these two prices as buying price and selling price of stocks of frontier markets so that we can predict the stock price (close price). Changes have been made to the model to find the parameters strike price and the time of expiration for calculating stock price of frontier markets. To verify the result obtained using modified BSOPM we have used machine learning approach using the software Rapidminer, where we have adopted different algorithms like the decision tree, ensemble learning method and neural network. It has been observed that, the prediction of close price using machine learning is very similar to the one obtained using BSOPM. Machine learning approach stands out to be a better predictor over BSOPM, because Black-Scholes-Merton equation includes risk and dividend parameter, which changes continuously. We have also numerically calculated volatility. As the prices of the stocks goes high due to overpricing, volatility increases at a tremendous rate and when volatility becomes very high market tends to fall, which can be observed and determined using our modified BSOPM. The proposed modified BSOPM has also been explained based on the analogy of Schrodinger equation (and heat equation) of quantum physics.", "published": "2018-12-27T04:34:18Z", "authors": ["Reaz Chowdhury", "M. R. C. Mahdy", "Tanisha Nourin Alam", "Golam Dastegir Al Quaderi"], "categories": ["q-fin.ST", "q-fin.MF"], "doi": "", "_query": "housing price text listing", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/1910.12130v4", "title": "Price mediated contagion through capital ratio requirements with VWAP liquidation prices", "abstract": "We develop a framework for price-mediated contagion in financial systems where banks are forced to liquidate assets to satisfy a risk-weight based capital adequacy requirement. In constructing this modeling framework, we introduce a two-tier pricing structure: the volume weighted average price that is obtained by any bank liquidating assets and the terminal mark-to-market price used to account for all assets held at the end of the clearing process. We consider the case of multiple illiquid assets and develop conditions for the existence and uniqueness of clearing prices. We provide a closed-form representation for the sensitivity of these clearing prices to the system parameters, and use this result to quantify: (1) the cost of regulation, in stress scenarios, faced by the system as a whole and the individual banks, and (2) the value of providing bailouts to consider when such notions are financially advisable. Numerical case studies are provided to study the application of this model to data.", "published": "2019-10-26T20:25:34Z", "authors": ["Tathagata Banerjee", "Zachary Feinstein"], "categories": ["q-fin.MF"], "doi": "", "_query": "housing price text listing", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2403.07166v1", "title": "Small Price Changes, Sales Volume, and Menu Cost", "abstract": "The finding of small price changes in many retail price datasets is often viewed as a puzzle. We show that a possible explanation for the presence of small price changes is related to sales volume, an observation that has been overlooked in the existing literature. Analyzing a large retail scanner price dataset that contains information on both prices and sales volume, we find that small price changes are more frequent when products sales volume is high. This finding holds across product categories, within product categories, and for individual products. It is also robust to various sensitivity analyses such as measurement errors, the definition of small price changes, the inclusion of measures of price synchronization, the size of producers, the time horizon used to compute the average sales volume, the revenues, the competition, shoppers characteristics, etc.", "published": "2024-03-11T21:09:30Z", "authors": ["Doron Sayag", "Avichai Snir", "Daniel Levy"], "categories": ["econ.GN"], "doi": "", "_query": "housing price text listing", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/cond-mat/0312703v2", "title": "What really causes large price changes?", "abstract": "We study the cause of large fluctuations in prices in the London Stock Exchange. This is done at the microscopic level of individual events, where an event is the placement or cancellation of an order to buy or sell. We show that price fluctuations caused by individual market orders are essentially independent of the volume of orders. Instead, large price fluctuations are driven by liquidity fluctuations, variations in the market's ability to absorb new orders. Even for the most liquid stocks there can be substantial gaps in the order book, corresponding to a block of adjacent price levels containing no quotes. When such a gap exists next to the best price, a new order can remove the best quote, triggering a large midpoint price change. Thus, the distribution of large price changes merely reflects the distribution of gaps in the limit order book. This is a finite size effect, caused by the granularity of order flow: In a market where participants placed many small orders uniformly across prices, such large price fluctuations would not happen. We show that this explains price fluctuations on longer timescales. In addition, we present results suggesting that the risk profile varies from stock to stock, and is not universal: lightly traded stocks tend to have more extreme risks.", "published": "2003-12-30T17:40:54Z", "authors": ["J. Doyne Farmer", "Laszlo Gillemot", "Fabrizio Lillo", "Szabolcs Mike", "Anindya Sen"], "categories": ["cond-mat.other", "q-fin.TR"], "doi": "", "_query": "housing price text listing", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2510.09347v2", "title": "LLP: LLM-Based Product Pricing in E-commerce", "abstract": "Unlike Business-to-Consumer e-commerce platforms (e.g., Amazon), inexperienced individual sellers on Consumer-to-Consumer platforms (e.g., eBay) often face significant challenges in setting prices for their second-hand products efficiently. Therefore, numerous studies have been proposed for automating price prediction. However, most of them are based on static regression models, which suffer from poor generalization performance and fail to capture market dynamics (e.g., the price of a used iPhone decreases over time). Inspired by recent breakthroughs in Large Language Models (LLMs), we introduce LLP, the first LLM-based generative framework for second-hand product pricing. LLP first retrieves similar products to better align with the dynamic market change. Afterwards, it leverages the LLMs' nuanced understanding of key pricing information in free-form text to generate accurate price suggestions. To strengthen the LLMs' domain reasoning over retrieved products, we apply a two-stage optimization, supervised fine-tuning (SFT) followed by group relative policy optimization (GRPO), on a dataset built via bidirectional reasoning. Moreover, LLP employs a confidence-based filtering mechanism to reject unreliable price suggestions. Extensive experiments demonstrate that LLP substantially surpasses existing methods while generalizing well to unseen categories. We have successfully deployed LLP on Xianyu\\footnote\\{Xianyu is China's largest second-hand e-commerce platform.\\}, significantly outperforming the previous pricing method. Under the same 30\\% product coverage, it raises the static adoption rate (SAR) from 40\\% to 72\\%, and maintains a strong SAR of 47\\% even at 90\\% recall.", "published": "2025-10-10T13:00:57Z", "authors": ["Hairu Wang", "Sheng You", "Qiheng Zhang", "Xike Xie", "Shuguang Han", "Yuchen Wu", "Fei Huang", "Jufeng Chen"], "categories": ["cs.CL"], "doi": "", "_query": "housing price text listing", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2502.14799v3", "title": "A Survey on Text-Driven 360-Degree Panorama Generation", "abstract": "The advent of text-driven 360-degree panorama generation, enabling the synthesis of 360-degree panoramic images directly from textual descriptions, marks a transformative advancement in immersive visual content creation. This innovation significantly simplifies the traditionally complex process of producing such content. Recent progress in text-to-image diffusion models has accelerated the rapid development in this emerging field. This survey presents a comprehensive review of text-driven 360-degree panorama generation, offering an in-depth analysis of state-of-the-art algorithms. We extend our analysis to two closely related domains: text-driven 360-degree 3D scene generation and text-driven 360-degree panoramic video generation. Furthermore, we critically examine current limitations and propose promising directions for future research. A curated project page with relevant resources and research papers is available at https://littlewhitesea.github.io/Text-Driven-Pano-Gen/.", "published": "2025-02-20T18:19:57Z", "authors": ["Hai Wang", "Xiaoyu Xiang", "Weihao Xia", "Jing-Hao Xue"], "categories": ["cs.CV", "cs.AI"], "doi": "10.1109/TCSVT.2025.3628738", "_query": "housing price text listing", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/1301.5877v1", "title": "Pricing Using a Homogeneously Saturated Equation", "abstract": "A homogeneously saturated equation for the time development of the price of a financial asset is presented and investigated for the pricing of European call options using noise that is distributed as a Student's t-distribution. In the limit that the saturation parameter of the equation equals zero, the standard model of geometric motion for the price of an asset is obtained. The homogeneously saturated equation for the price of an asset is similar to a simple equation for the output of a homogeneously broadened laser. The homogeneously saturated equation tends to limit the range of returns and thus seems to be realistic. Fits to linear returns obtained from the adjusted closing values for the S&P 500 index were used to obtain best-fit parameters for Student's t-distributions and for normal distributions, and these fits were used to price options, and to compare approaches to modelling prices. This work has value in understanding the pricing of assets and of European call options.", "published": "2013-01-24T19:49:58Z", "authors": ["Daniel T. Cassidy"], "categories": ["q-fin.PR", "q-fin.GN"], "doi": "", "_query": "housing price text listing", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2607.16891v2", "title": "Certified-Gap Dual-Price Policies for Real-Time Truckload Bid Acceptance with Relocating, Clock-Constrained Resources", "abstract": "A truckload carrier must accept or reject each load tender within seconds. The decision depends on fleet state, hours-of-service (HOS) clocks, and appointment windows. We model this as a weakly coupled dynamic program in which the resources relocate and carry clocks: serving a request moves the truck to a new market and depletes its clocks, and whether a truck can serve a request depends on its state. Occupancy-based reusable-resource models do not cover this setting. We build a real-time dual-price policy from the same Lagrangian relaxation that gives the problem's upper bound. Policy and bound come from one object, so every run reports a certified optimality gap. We prove three things. First, the certificate is valid for any duals, any discretization, and any surrogate quality. Second, the policy's same-time spatial-gradient rule is exactly fluid complementary slackness, and the policy is asymptotically optimal in the subcritical fluid regime; the fitted prices are also portable across sample paths, by linear-programming basis stability. Third, certificates have limits: per-resource Lagrangian slack can stay bounded away from zero at every fleet size. We exhibit a three-truck kernel with an exact rational certificate and a replication lemma. On a public closed-loop benchmark with thirty paired seeds, the policy -- which needs no rollout labels, only one offline dual solve -- beats a rollout-trained surrogate on two of three scenarios (tight: +2.0 pp, 95% CI [+0.5, +3.6], Wilcoxon p = 0.023; mild: +3.5 pp, CI [+2.4, +4.5]) and ties the third. It decides in 0.04-0.09 ms, three orders of magnitude faster than the Monte Carlo rollout teacher. Its certificates are stable across ten bounded instances per scenario, at 57-64% of optimal, within 3-6 points of what the 1000x-slower teacher certifies.", "published": "2026-07-18T17:16:42Z", "authors": ["Aswin Chandrasekaran"], "categories": ["cs.LG", "cs.AI", "math.OC"], "doi": "", "_query": "housing price text listing", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/1412.5520v2", "title": "Indifference prices and implied volatilities", "abstract": "We consider a general local-stochastic volatility model and an investor with exponential utility. For a European-style contingent claim, whose payoff may depend on either a traded or non-traded asset, we derive an explicit approximation for both the buyer's and seller's indifference price. For European calls on a traded asset, we translate indifference prices into an explicit approximation of the buyer's and seller's implied volatility surface. For European claims on a non-traded asset, we establish rigorous error bounds for the indifference price approximation. Finally, we implement our indifference price and implied volatility approximations in two examples.", "published": "2014-12-17T19:00:47Z", "authors": ["Matthew Lorig"], "categories": ["q-fin.MF"], "doi": "", "_query": "housing price text listing", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2208.10650v1", "title": "Efficiency of the First-Price Auction in the Autobidding World", "abstract": "We study the price of anarchy of the first-price auction in the autobidding world, where bidders can be either utility maximizers (i.e., traditional bidders) or value maximizers (i.e., autobidders). We show that with autobidders only, the price of anarchy of the first-price auction is $1/2$, and with both kinds of bidders, the price of anarchy degrades to about $0.457$ (the precise number is given by an optimization). These results complement the recent result by Jin and Lu [2022] showing that the price of anarchy of the first-price auction with traditional bidders only is $1 - 1/e^2$. We further investigate a setting where the seller can utilize machine-learned advice to improve the efficiency of the auctions. There, we show that as the accuracy of the advice increases, the price of anarchy improves smoothly from about $0.457$ to $1$.", "published": "2022-08-22T23:49:46Z", "authors": ["Yuan Deng", "Jieming Mao", "Vahab Mirrokni", "Hanrui Zhang", "Song Zuo"], "categories": ["cs.GT", "cs.DS"], "doi": "", "_query": "housing price text listing", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/1711.10241v8", "title": "The Price of Quota-based Diversity in Assignment Problems", "abstract": "We introduce and analyze an extension to the matching problem on a weighted bipartite graph: Assignment with Type Constraints. The two parts of the graph are partitioned into subsets called types and blocks; we seek a matching with the largest sum of weights under the constraint that there is a pre-specified cap on the number of vertices matched in every type-block pair. Our primary motivation stems from the public housing program of Singapore, accounting for over 70% of its residential real estate. To promote ethnic diversity within its housing projects, Singapore imposes ethnicity quotas: each new housing development comprises blocks of flats and each ethnicity-based group in the population must not own more than a certain percentage of flats in a block. Other domains using similar hard capacity constraints include matching prospective students to schools or medical residents to hospitals. Limiting agents' choices for ensuring diversity in this manner naturally entails some welfare loss. One of our goals is to study the trade-off between diversity and social welfare in such settings. We first show that, while the classic assignment program is polynomial-time computable, adding diversity constraints makes it computationally intractable; however, we identify a $\\tfrac{1}{2}$-approximation algorithm, as well as reasonable assumptions on the weights that permit poly-time algorithms. Next, we provide two upper bounds on the price of diversity -- a measure of the loss in welfare incurred by imposing diversity constraints -- as functions of natural problem parameters. We conclude the paper with simulations based on publicly available data from two diversity-constrained allocation problems -- Singapore Public Housing and Chicago School Choice -- which shed light on how the constrained maximization as well as lottery-based variants perform in practice.", "published": "2017-11-28T11:58:54Z", "authors": ["Nawal Benabbou", "Mithun Chakraborty", "Vinh Ho Xuan", "Jakub Sliwinski", "Yair Zick"], "categories": ["cs.AI"], "doi": "10.1145/3411513", "_query": "housing price text listing", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/1307.2537v1", "title": "Strong Price of Anarchy and Coalitional Dynamics", "abstract": "We introduce a framework for studying the effect of cooperation on the quality of outcomes in utility games. Our framework is a coalitional analog of the smoothness framework of non-cooperative games. Coalitional smoothness implies bounds on the strong price of anarchy, the loss of quality of coalitionally stable outcomes, as well as bounds on coalitional versions of coarse correlated equilibria and sink equilibria, which we define as out-of-equilibrium myopic behavior as determined by a natural coalitional version of best-response dynamics. Our coalitional smoothness framework captures existing results bounding the strong price of anarchy of network design games. We show that in any monotone utility-maximization game, if each player's utility is at least his marginal contribution to the welfare, then the strong price of anarchy is at most 2. This captures a broad class of games, including games with a very high price of anarchy. Additionally, we show that in potential games the strong price of anarchy is close to the price of stability, the quality of the best Nash equilibrium.", "published": "2013-07-09T18:29:52Z", "authors": ["Yoram Bachrach", "Vasilis Syrgkanis", "Eva Tardos", "Milan Vojnovic"], "categories": ["cs.GT"], "doi": "", "_query": "housing price text listing", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2004.11714v1", "title": "Residual Energy-Based Models for Text Generation", "abstract": "Text generation is ubiquitous in many NLP tasks, from summarization, to dialogue and machine translation. The dominant parametric approach is based on locally normalized models which predict one word at a time. While these work remarkably well, they are plagued by exposure bias due to the greedy nature of the generation process. In this work, we investigate un-normalized energy-based models (EBMs) which operate not at the token but at the sequence level. In order to make training tractable, we first work in the residual of a pretrained locally normalized language model and second we train using noise contrastive estimation. Furthermore, since the EBM works at the sequence level, we can leverage pretrained bi-directional contextual representations, such as BERT and RoBERTa. Our experiments on two large language modeling datasets show that residual EBMs yield lower perplexity compared to locally normalized baselines. Moreover, generation via importance sampling is very efficient and of higher quality than the baseline models according to human evaluation.", "published": "2020-04-22T23:19:55Z", "authors": ["Yuntian Deng", "Anton Bakhtin", "Myle Ott", "Arthur Szlam", "Marc'Aurelio Ranzato"], "categories": ["cs.CL", "cs.LG"], "doi": "", "_query": "housing price text listing", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/cond-mat/0012014v3", "title": "Option pricing and perfect hedging on correlated stocks", "abstract": "We develop a theory for option pricing with perfect hedging in an inefficient market model where the underlying price variations are autocorrelated over a time tau. This is accomplished by assuming that the underlying noise in the system is derived by an Ornstein-Uhlenbeck, rather than from a Wiener process. With a modified portfolio consisting in calls, secondary calls and bonds we achieve a riskless strategy which results in a closed expression for the European call price which is always lower than Black-Scholes price. We also obtain a partial differential equation for the option price and study the sensitivity to several parameters and the risk of the dynamics of the call price.", "published": "2000-12-01T17:10:10Z", "authors": ["Josep Perello", "Jaume Masoliver"], "categories": ["cond-mat", "physics.soc-ph"], "doi": "10.1016/S0378-4371(03)00619-8", "_query": "housing price text listing", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2607.11590v1", "title": "Copositive Characterizations of Convex Hull Pricing", "abstract": "Due to the nonconvex binary constraints of unit commitment (UC), no uniform linear pricing scheme supports the optimal dispatch. Convex hull pricing (CHP) and copositive duality pricing (CDP) both address this problem. CHP derives the price from the subgradient of the value function of the convex hull relaxation of UC. CDP refers to several different pricing mechanisms that can be constructed from the dual multipliers of the completely positive programming reformulation. In this work, we define a centralized convex hull price over the joint feasible set of UC and prove that, under non-degeneracy, it coincides with the marginal copositive duality price. Numerical experiments on the Scarf example validate this equivalence and quantify the pricing gap introduced by the semidefinite restriction.", "published": "2026-07-13T14:11:37Z", "authors": ["Madhusudan Ghosh", "Antoine Lesage-Landry", "Joshua Adam Taylor"], "categories": ["math.OC", "eess.SY"], "doi": "", "_query": "housing price text listing", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/1502.03359v2", "title": "Asymptotic indifference pricing in exponential Lévy models", "abstract": "Financial markets based on Lévy processes are typically incomplete and option prices depend on risk attitudes of individual agents. In this context, the notion of utility indifference price has gained popularity in the academic circles. Although theoretically very appealing, this pricing method remains difficult to apply in practice, due to the high computational cost of solving the nonlinear partial integro-differential equation associated to the indifference price. In this work, we develop closed form approximations to exponential utility indifference prices in exponential Lévy models. To this end, we first establish a new non-asymptotic approximation of the indifference price which extends earlier results on small risk aversion asymptotics of this quantity. Next, we use this formula to derive a closed-form approximation of the indifference price by treating the Lévy model as a perturbation of the Black-Scholes model. This extends the methodology introduced in a recent paper for smooth linear functionals of Lévy processes (A. Černý, S. Denkl and J. Kallsen, arXiv:1309.7833) to nonlinear and non-smooth functionals. Our closed formula represents the indifference price as the linear combination of the Black-Scholes price and correction terms which depend on the variance, skewness and kurtosis of the underlying Lévy process, and the derivatives of the Black-Scholes price. As a by-product, we obtain a simple explicit formula for the spread between the buyer's and the seller's indifference price. This formula allows to quantify, in a model-independent fashion, how sensitive a given product is to jump risk in the limit of small jump size.", "published": "2015-02-11T16:19:44Z", "authors": ["Clément Ménassé", "Peter Tankov"], "categories": ["q-fin.PR", "q-fin.CP"], "doi": "", "_query": "housing price text listing", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2502.00317v1", "title": "DIST: Efficient k-Clique Listing via Induced Subgraph Trie", "abstract": "Listing k-cliques plays a fundamental role in various data mining tasks, such as community detection and mining of cohesive substructures. Existing algorithms for the k-clique listing problem are built upon a general framework, which finds k-cliques by recursively finding (k-1)-cliques within subgraphs induced by the out-neighbors of each vertex. However, this framework has inherent inefficiency of finding smaller cliques within certain subgraphs repeatedly. In this paper, we propose an algorithm DIST for the k-clique listing problem. In contrast to existing works, the main idea in our approach is to compute each clique in the given graph only once and store it into a data structure called Induced Subgraph Trie, which allows us to retrieve the cliques efficiently. Furthermore, we propose a method to prune search space based on a novel concept called soft embedding of an l-tree, which further improves the running time. We show the superiority of our approach in terms of time and space usage through comprehensive experiments conducted on real-world networks; DIST outperforms the state-of-the-art algorithm by up to two orders of magnitude in both single-threaded and parallel experiments.", "published": "2025-02-01T04:50:33Z", "authors": ["Yehyun Nam", "Jihoon Jang", "Kunsoo Park", "Jianye Yang", "Cheng Long"], "categories": ["cs.DB"], "doi": "", "_query": "housing price text listing", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/1105.2902v1", "title": "A Multi-Purpose Scenario-based Simulator for Smart House Environments", "abstract": "Developing smart house systems has been a great challenge for researchers and engineers in this area because of the high cost of implementation and evaluation process of these systems, while being very time consuming. Testing a designed smart house before actually building it is considered as an obstacle towards an efficient smart house project. This is because of the variety of sensors, home appliances and devices available for a real smart environment. In this paper, we present the design and implementation of a multi-purpose smart house simulation system for designing and simulating all aspects of a smart house environment. This simulator provides the ability to design the house plan and different virtual sensors and appliances in a two dimensional model of the virtual house environment. This simulator can connect to any external smart house remote controlling system, providing evaluation capabilities to their system much easier than before. It also supports detailed adding of new emerging sensors and devices to help maintain its compatibility with future simulation needs. Scenarios can also be defined for testing various possible combinations of device states; so different criteria and variables can be simply evaluated without the need of experimenting on a real environment.", "published": "2011-05-14T15:11:00Z", "authors": ["Zahra Forootan Jahromi", "Amir Rajabzadeh", "Ali Reza Manashty"], "categories": ["cs.AI"], "doi": "", "_query": "housing price text listing", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/1311.5765v1", "title": "Text Classification and Distributional features techniques in Datamining and Warehousing", "abstract": "Text Categorization is traditionally done by using the term frequency and inverse document frequency.This type of method is not very good because, some words which are not so important may appear in the document .The term frequency of unimportant words may increase and document may be classified in the wrong category.For reducing the error of classifying of documents in wrong category. The Distributional features are introduced. In the Distribuional Features, the Distribution of the words in the whole document is analyzed. Whole Document is very closely analyzed for different measures like FirstAppearence, Last Appearance, Centriod, Count, etc.The measures are calculated and they are used in tf*idf equation and result is used in k- nearest neighbor and K-means algorithm for classifying the documents.", "published": "2013-11-22T14:36:52Z", "authors": ["Srikanth Bethu", "G Charless Babu", "J Vinoda", "E Priyadarshini", "M Raghavendra rao"], "categories": ["cs.IR"], "doi": "", "_query": "housing price text listing", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2110.02735v3", "title": "Optimal pricing for electricity retailers based on data-driven consumers' price-response", "abstract": "In the present work we tackle the problem of finding the optimal price tariff to be set by a risk-averse electric retailer participating in the pool and whose customers are price-sensitive. We assume that the retailer has access to a sufficiently large smart-meter dataset from which it can statistically characterize the relationship between the tariff price and the demand load of its clients. Three different models are analyzed to predict the aggregated load as a function of the electricity prices and other parameters, as humidity or temperature. More specifically, we train linear regression (predictive) models to forecast the resulting demand load as a function of the retail price. Then we will insert this model in a quadratic optimization problem which evaluates the optimal price to be offered. This optimization problem accounts for different sources of uncertainty including consumer's response, pool prices and renewable source availability, and relies on a stochastic and risk-averse formulation. In particular, one important contribution of this work is to base the scenario generation and reduction procedure on the statistical properties of the resulting predictive model. This allows us to properly quantify (data-driven) not only the expected value but the level of uncertainty associated with the main problem parameters. Moreover, we consider both standard forward based contracts and the recently introduced power purchase agreement contracts as risk-hedging tools for the retailer. The results are promising as profits are found for the retailer with highly competitive prices and some possible improvements are shown if richer datasets could be available in the future. A realistic case study and multiple sensitivity analyses have been performed to characterize the risk-aversion behavior of the retailer considering price-sensitive consumers.", "published": "2021-10-04T10:06:52Z", "authors": ["Román Pérez-Santalla", "Miguel Carrión", "Carlos Ruiz"], "categories": ["math.OC", "econ.GN", "stat.AP"], "doi": "10.1007/s11750-022-00622-8", "_query": "housing price text listing", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2307.12482v2", "title": "Tight Approximations for Graphical House Allocation", "abstract": "The Graphical House Allocation problem asks: how can $n$ houses (each with a fixed non-negative value) be assigned to the vertices of an undirected graph $G$, so as to minimize the \"aggregate local envy\", i.e., the sum of absolute differences along the edges of $G$? This problem generalizes the classical Minimum Linear Arrangement problem, as well as the well-known House Allocation Problem from Economics, the latter of which has notable practical applications in organ exchanges. Recent work has studied the computational aspects of Graphical House Allocation and observed that the problem is NP-hard and inapproximable even on particularly simple classes of graphs, such as vertex disjoint unions of paths. However, the dependence of any approximations on the structural properties of the underlying graph had not been studied. In this work, we give a complete characterization of the approximability of the Graphical House Allocation problem. We present algorithms to approximate the optimal envy on general graphs, trees, planar graphs, bounded-degree graphs, bounded-degree planar graphs, and bounded-degree trees. For each of these graph classes, we then prove matching lower bounds, showing that in each case, no significant improvement can be attained unless P = NP. We also present general approximation ratios as a function of structural parameters of the underlying graph, such as treewidth; these match the aforementioned tight upper bounds in general, and are significantly better approximations for many natural subclasses of graphs. Finally, we present constant factor approximation schemes for the special classes of complete binary trees and random graphs.", "published": "2023-07-24T02:30:34Z", "authors": ["Hadi Hosseini", "Andrew McGregor", "Rik Sengupta", "Rohit Vaish", "Vignesh Viswanathan"], "categories": ["cs.DS", "cs.DM", "cs.GT", "math.CO"], "doi": "", "_query": "housing price text listing", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/1405.4498v1", "title": "The Economics of BitCoin Price Formation", "abstract": "This paper analyses the relationship between BitCoin price and supply-demand fundamentals of BitCoin, global macro-financial indicators and BitCoin attractiveness for investors. Using daily data for the period 2009-2014 and applying time-series analytical mechanisms, we find that BitCoin market fundamentals and BitCoin attractiveness for investors have a significant impact on BitCoin price. Our estimates do not support previous findings that the macro-financial developments are driving BitCoin price.", "published": "2014-05-18T12:59:05Z", "authors": ["Pavel Ciaian", "Miroslava Rajcaniova", "d'Artis Kancs"], "categories": ["econ.GN", "q-fin.GN"], "doi": "", "_query": "housing price text listing", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2404.04369v1", "title": "A Fine-grained Classification of Subquadratic Patterns for Subgraph Listing and Friends", "abstract": "In an $m$-edge host graph $G$, all triangles can be listed in time $O(m^{1.5})$ [Itai, Rodeh '78], and all $k$-cycles can be listed in time $O(m^{2-1/{\\lceil k/2 \\rceil}} + t)$ where $t$ is the output size [Alon, Yuster, Zwick '97]. These classic results also hold for the colored problem variant, where the nodes of the host graph $G$ are colored by nodes in the pattern graph $H$, and we are only interested in subgraphs of $G$ that are isomorphic to the pattern $H$ and respect the colors. We study the problem of listing all $H$-subgraphs in the colored setting, for fixed pattern graphs $H$. As our main result, we determine all pattern graphs $H$ such that all $H$-subgraphs can be listed in subquadratic time $O(m^{2-\\varepsilon} + t)$, where $t$ is the output size. Moreover, for each such subquadratic pattern $H$ we determine the smallest exponent $c(H)$ such that all $H$-subgraphs can be listed in time $O(m^{c(H)} + t)$. This is a vast generalization of the classic results on triangles and cycles. To prove this result, we design new listing algorithms and prove conditional lower bounds based on standard hypotheses from fine-grained complexity theory. In our algorithms, we use a new ingredient that we call hyper-degree splitting, where we split tuples of nodes into high degree and low degree depending on their number of common neighbors. We also show the same results for two related problems: finding an $H$-subgraph of minimum total edge-weight in time $O(m^{c(H)})$, and enumerating all $H$-subgraphs in $O(m^{c(H)})$ preprocessing time and constant delay. Again we determine all pattern graphs $H$ that have complexity $c(H) < 2$, and for each such subquadratic pattern we determine the optimal complexity $c(H)$.", "published": "2024-04-05T19:31:11Z", "authors": ["Karl Bringmann", "Egor Gorbachev"], "categories": ["cs.DS"], "doi": "", "_query": "housing price text listing", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2204.12723v6", "title": "On the limitations of data-based price discrimination", "abstract": "The classic third degree price discrimination (3PD) model requires the knowledge of the distribution of buyer valuations and the covariate to set the price conditioned on the covariate. In terms of generating revenue, the classic result shows that 3PD is at least as good as uniform pricing. What if the seller has to set a price based only on a sample of observations from the underlying distribution? Is it still obvious that the seller should engage in 3PD? This paper sheds light on these fundamental questions. In particular, the comparison of the revenue performance between 3PD and uniform pricing is ambiguous overall when prices are set based on samples. This finding is in the nature of statistical learning under uncertainty: a curse of dimensionality, but also other small sample complications.", "published": "2022-04-27T06:33:37Z", "authors": ["Haitian Xie", "Ying Zhu", "Denis Shishkin"], "categories": ["cs.GT", "cs.IT", "cs.LG", "econ.TH"], "doi": "", "_query": "housing price text listing", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2101.11137v2", "title": "The Breakthrough Listen Search for Intelligent Life: Searching for Technosignatures in Observations of TESS Targets of Interest", "abstract": "Exoplanetary systems are prime targets for the Search for Extraterrestrial Intelligence (SETI). With the recent uptick in the identification of candidate and confirmed exoplanets through the work of missions like the Transiting Exoplanet Survey Satellite (TESS), we are beginning to understand that Earth-like planets are common. In this work, we extend the Breakthrough Listen (BL) search for extraterrestrial intelligence to include targeted searches of stars identified by TESS as potential exoplanet hosts. We report on 113 30-min cadence observations collected for 28 targets selected from the TESS Input Catalog (TIC) from among those identified as containing signatures of transiting planets. The targets were searched for narrowband signals from 1-11 GHz using the turboSETI pipeline architecture modified for compatibility with the Google Cloud environment. Data were searched for drift rates of +/-4 Hz/s above a minimum signal-to-noise threshold of 10, following the parameters of previous searches conducted by Price et al. (2020) and Enriquez et al. (2017). The observations presented in this work establish some of the deepest limits to date over such a wide band (1-11 GHz) for life beyond Earth. We determine that fewer than 12.72% of the observed targets possess transmitters operating at these frequencies with an Equivalent Isotropic Radiated Power (EIRP) greater than our derived threshold of 4.9*10^(14) W.", "published": "2021-01-26T23:59:16Z", "authors": ["Raffy Traas", "Steve Croft", "Vishal Gajjar", "Howard Isaacson", "Matt Lebofsky", "David H. E. MacMahon", "Karen Perez", "Danny C. Price", "Sofia Sheikh", "Andrew P. V. Siemion", "Shane Smith", "Jamie Drew", "S. Pete Worden"], "categories": ["astro-ph.EP", "astro-ph.IM"], "doi": "10.3847/1538-3881/abf649", "_query": "housing price text listing", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/cond-mat/0311306v1", "title": "Does the price multiplier effect also hold for stocks?", "abstract": "The price multiplier effect provides precious insight into the behavior of investors during episodes of speculative trading. It tells us that the higher the price of an asset is (within a set of similar assets) the more its price is likely to increase during the upgoing phase of a speculative price peak. In short, instead of being risk averse, as is often assumed, investors rather seem to be ``risk prone''. While this effect is known to hold for several sorts of assets, it has not yet been possible to test it for stocks because the price of one share has no intrinsic significance which means that one cannot say that a stock $ A $ is more expensive than a stock $ B $ on the basis of its price. In this paper we show that the price-dividend ratio gives a good basis for assessing the price of stocks in an intrinsic way. When this alternative measure is used instead, it turns out that the price multiplier effect also holds for stocks, at least if one concentrates on samples of companies which are sufficiently homogeneous.", "published": "2003-11-13T11:46:08Z", "authors": ["Sergei Maslov", "Bertrand M. Roehner"], "categories": ["cond-mat"], "doi": "10.1142/S0129183103005510", "_query": "housing price text listing", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/1110.4669v1", "title": "Bridge Copula Model for Option Pricing", "abstract": "In this paper we present a new multi-asset pricing model, which is built upon newly developed families of solvable multi-parameter single-asset diffusions with a nonlinear smile-shaped volatility and an affine drift. Our multi-asset pricing model arises by employing copula methods. In particular, all discounted single-asset price processes are modeled as martingale diffusions under a risk-neutral measure. The price processes are so-called UOU diffusions and they are each generated by combining a variable (Ito) transformation with a measure change performed on an underlying Ornstein-Uhlenbeck (Gaussian) process. Consequently, we exploit the use of a normal bridge copula for coupling the single-asset dynamics while reducing the distribution of the multi-asset price process to a multivariate normal distribution. Such an approach allows us to simulate multidimensional price paths in a precise and fast manner and hence to price path-dependent financial derivatives such as Asian-style and Bermudan options using the Monte Carlo method. We also demonstrate how to successfully calibrate our multi-asset pricing model by fitting respective equity option and asset market prices to the single-asset models and their return correlations (i.e. the copula function) using the least-square and maximum-likelihood estimation methods.", "published": "2011-10-21T00:36:26Z", "authors": ["Giuseppe Campolieti", "Roman N. Makarov", "Andrey Vasiliev"], "categories": ["q-fin.PR", "q-fin.CP"], "doi": "", "_query": "housing price text listing", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2404.03581v1", "title": "Consumer Behavior under Benevolent Price Discrimination", "abstract": "Extensive research shows that consumers are generally averse to price discrimination. However, instruments of differential pricing can benefit consumer surplus and alleviate inequity through targeted price discounts. This paper examines how these outcome considerations influence consumer reactions to price discrimination. Six studies with 3951 participants show that a large share of consumers is willing to costly switch away from a store that introduces a discount for low-income consumers. This happens irrespective of whether income differences are due to luck or merit. While the price-discriminating store does attract some new high-income consumers, it cannot compensate the loss of existing consumers. Allowing for altruistic preferences by simulating a market mechanism increases costly support for price discounts, but does not alleviate consumer aversions. Finally, we provide evidence that warm glow drives costly support for price discounts.", "published": "2024-04-04T16:43:40Z", "authors": ["Alexander Erlei", "Mattheus Brenig", "Nils Engelbrecht"], "categories": ["econ.GN"], "doi": "", "_query": "housing price text listing", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2208.09372v3", "title": "Non-Stationary Dynamic Pricing Via Actor-Critic Information-Directed Pricing", "abstract": "This paper presents a novel non-stationary dynamic pricing algorithm design, where pricing agents face incomplete demand information and market environment shifts. The agents run price experiments to learn about each product's demand curve and the profit-maximizing price, while being aware of market environment shifts to avoid high opportunity costs from offering sub-optimal prices. The proposed ACIDP extends information-directed sampling (IDS) algorithms from statistical machine learning to include microeconomic choice theory, with a novel pricing strategy auditing procedure to escape sub-optimal pricing after market environment shift. The proposed ACIDP outperforms competing bandit algorithms including Upper Confidence Bound (UCB) and Thompson sampling (TS) in a series of market environment shifts.", "published": "2022-08-19T14:37:37Z", "authors": ["Po-Yi Liu", "Chi-Hua Wang", "Henghsiu Tsai"], "categories": ["stat.ML", "cs.LG", "econ.GN"], "doi": "", "_query": "housing price text listing", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2608.20865v1", "title": "Parameters Overshadowed by Price Lags: Load, Climate, and Calendar Effects in ERCOT Day-Ahead Price Formation", "abstract": "Accurate electricity price forecasting is critical for smart grid stability, yet the heavy reliance on historical price lags in modern predictive models often obscures the fundamental physical drivers of market volatility. This paper proposes a regime-sensitive, explainable artificial intelligence (XAI) framework to unmask the hidden roles of load, climate, and calendar variables in the ERCOT Day-Ahead Market (2014-2024). Utilizing a Histogram-based Gradient Boosting Regressor (HGBR), we introduce a Suppression Ratio to quantify how price lags overshadow physical parameters. Our analysis reveals that while lags provide short-term memory during normal conditions, they fail to capture extreme dynamics; notably, their inclusion increased forecasting error (MAE/RMSE) during price spikes. By isolating these effects, the study revealed that price spikes are not merely the result of continuously rising temperatures, but rather emerge from the system reacting sharply within a specific temperature range. These findings underscore the importance of moving beyond lag-based models to uncover the true drivers of extreme price events.", "published": "2026-08-21T08:33:11Z", "authors": ["Yunus Bicen", "Eman Hammad"], "categories": ["eess.SY"], "doi": "", "_query": "housing price text listing", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2105.08804v3", "title": "Efficient approximations for utility-based pricing", "abstract": "In a context of illiquidity, the reservation price is a well-accepted alternative to the usual martingale approach which does not apply. However, this price is not available in closed form and requires numerical methods such as Monte Carlo or polynomial approximations to evaluate it. We show that these methods can be inaccurate and propose a deterministic decomposition of the reservation price using the Lambert function. This decomposition allows us to perform an improved Monte Carlo method, which we name Lambert Monte Carlo (LMC) and to give deterministic approximations of the reservation price and of the optimal strategies based on the Lambert function. We also give an answer to the problem of selecting a hedging asset that minimizes the reservation price and also the cash invested. Our theoretical results are illustrated by numerical simulations.", "published": "2021-05-18T19:48:06Z", "authors": ["Laurence Carassus", "Massinissa Ferhoune"], "categories": ["q-fin.CP", "q-fin.PR"], "doi": "", "_query": "housing price text listing", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2011.02143v1", "title": "Conditioned Text Generation with Transfer for Closed-Domain Dialogue Systems", "abstract": "Scarcity of training data for task-oriented dialogue systems is a well known problem that is usually tackled with costly and time-consuming manual data annotation. An alternative solution is to rely on automatic text generation which, although less accurate than human supervision, has the advantage of being cheap and fast. Our contribution is twofold. First we show how to optimally train and control the generation of intent-specific sentences using a conditional variational autoencoder. Then we introduce a new protocol called query transfer that allows to leverage a large unlabelled dataset, possibly containing irrelevant queries, to extract relevant information. Comparison with two different baselines shows that this method, in the appropriate regime, consistently improves the diversity of the generated queries without compromising their quality. We also demonstrate the effectiveness of our generation method as a data augmentation technique for language modelling tasks.", "published": "2020-11-03T14:06:10Z", "authors": ["Stéphane d'Ascoli", "Alice Coucke", "Francesco Caltagirone", "Alexandre Caulier", "Marc Lelarge"], "categories": ["cs.CL", "cs.AI", "cs.LG"], "doi": "", "_query": "housing price text listing", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/0903.3346v1", "title": "The Transfer Pricing Problem with Non-Linearities", "abstract": "A number of approaches to solving the well-known transfer pricing problem are known. However, few models satisfactorily resolve the core problem of allowing both the source and receiving divisions to earn a profit on transfers during a period in such a way that sub-optimal output levels are avoided. In 1969, Samuel proposed to use a transfer price schedule instead of just a single transfer price. An essential improvement of Samuels' model was given by Tomkins (1990) in his pragmatic-analytical transfer pricing approach, which is a combination of a single cost-plus transfer price and the pragmatic process of negotiation. This fundamental approach was developed under the assumption that the net average revenue curve for the final product is linear. In this paper, Tomkins' pragmatic-analytical model is further developed for non-linear net average revenue curves. In particular, typical quadratic functions are considered and corresponding transfer price schedules are determined. A similar technique can be used for the transfer pricing problem with any net average revenue curve.", "published": "2009-03-19T15:16:02Z", "authors": ["S. Zverovich"], "categories": ["q-fin.GN"], "doi": "", "_query": "housing price text listing", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/1005.0051v1", "title": "Crude oil and motor fuel: Fair price revisited", "abstract": "In April 2009, we introduced a model representing the evolution of motor fuel price (a subcategory of the consumer price index of transportation) relative to the overall CPI as a linear function of time. Under our framework, all price deviations from the linear trend are transient and the price must promptly return to the trend. Specifically, the model predicted that \"the price for motor fuel in the US will also grow by 50% by the end of 2009. Oil price is expected to rise by ~50% as well, from its current value of ~$50 per barrel\". The behavior of actual price has shown that this prediction is accurate in both amplitude and trajectory shape. Hence, one can conclude that the concept of price decomposition into a short-term (oscillating) and long-term (linear trend) components is valid. According to the model, the price of motor fuel and crude oil will be falling to the level of $30 per barrel during the next 5 to 8 years.", "published": "2010-05-01T07:11:32Z", "authors": ["Ivan O. Kitov", "Oleg I. Kitov"], "categories": ["q-fin.GN", "q-fin.ST"], "doi": "", "_query": "housing price text listing", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/1905.01338v1", "title": "Effectiveness of Self Normalizing Neural Networks for Text Classification", "abstract": "Self Normalizing Neural Networks(SNN) proposed on Feed Forward Neural Networks(FNN) outperform regular FNN architectures in various machine learning tasks. Particularly in the domain of Computer Vision, the activation function Scaled Exponential Linear Units (SELU) proposed for SNNs, perform better than other non linear activations such as ReLU. The goal of SNN is to produce a normalized output for a normalized input. Established neural network architectures like feed forward networks and Convolutional Neural Networks(CNN) lack the intrinsic nature of normalizing outputs. Hence, requiring additional layers such as Batch Normalization. Despite the success of SNNs, their characteristic features on other network architectures like CNN haven't been explored, especially in the domain of Natural Language Processing. In this paper we aim to show the effectiveness of proposed, Self Normalizing Convolutional Neural Networks(SCNN) on text classification. We analyze their performance with the standard CNN architecture used on several text classification datasets. Our experiments demonstrate that SCNN achieves comparable results to standard CNN model with significantly fewer parameters. Furthermore it also outperforms CNN with equal number of parameters.", "published": "2019-05-03T18:38:39Z", "authors": ["Avinash Madasu", "Vijjini Anvesh Rao"], "categories": ["cs.CL"], "doi": "", "_query": "housing price text listing", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/1609.06844v1", "title": "Posted Pricing sans Discrimination", "abstract": "In the quest for market mechanisms that are easy to implement, yet close to optimal, few seem as viable as posted pricing. Despite the growing body of impressive results, the performance of most posted price mechanisms however, rely crucially on price discrimination when multiple copies of a good are available. For the more general case with non-linear production costs on each good, hardly anything is known for general multi-good markets. With this in mind, we study a Bayesian setting where the seller can produce any number of copies of a good but faces convex production costs for the same, and buyers arrive sequentially. Our main contribution is a framework for non-discriminatory pricing in the presence of production costs: the framework yields posted price mechanisms with O(1)-approximation factors for fractionally subadditive (XoS) buyers, logarithmic approximations for subadditive buyers, and also extends to settings where the seller is oblivious to buyer valuations. Our work presents the first known results for Bayesian settings with production costs and is among the few posted price mechanisms that do not charge buyers differently for the same good.", "published": "2016-09-22T07:37:15Z", "authors": ["Shreyas Sekar"], "categories": ["cs.GT"], "doi": "", "_query": "housing price text listing", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/1904.06899v2", "title": "How to Price Fresh Data", "abstract": "We introduce the concept of a fresh data market, in which a destination user requests, and pays for, fresh data updates from a source provider. Data freshness is captured by the {\\it age of information} (AoI) metric, defined as the time elapsed since the latest update has reached the destination. The source incurs an operational cost, modeled as an increasing convex function of the number of updates. The destination incurs an age-related cost, modeled as an increasing convex function of the AoI. The source charges the destination for each update and designs a pricing mechanism to maximize its profit; the destination on the other hand chooses a data update schedule to minimize the summation of its payments to the source and its age-related cost. The interaction among the source and destination is hence game-theoretic. Motivated by the existing pricing literature, we first study a time-dependent pricing scheme, in which the price for each update depends on when it is requested. We show in this case that the game equilibrium leads to only one data update, which does not yield the maximum profit to the source. This motivates us to consider a quantity-based pricing scheme, in which the price of each update depends on how many updates have been previously requested. We show that among all pricing schemes in which the price of an update may vary according to both time and quantity, the quantity-based pricing scheme performs best: it maximizes the source's profit and minimizes the social cost of the system, defined as the aggregate source's operational cost and the destination's age-related cost. Numerical results show that the optimal quantity-based pricing can be 27% more profitable for the source and incurs 54% less social cost, compared with the optimal time-dependent pricing.", "published": "2019-04-15T08:05:58Z", "authors": ["Meng Zhang", "Ahmed Arafa", "Jianwei Huang", "H. Vincent Poor"], "categories": ["cs.NI", "cs.IT"], "doi": "", "_query": "housing price text listing", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2203.05673v1", "title": "Fusion of Sentiment and Asset Price Predictions for Portfolio Optimization", "abstract": "The fusion of public sentiment data in the form of text with stock price prediction is a topic of increasing interest within the financial community. However, the research literature seldom explores the application of investor sentiment in the Portfolio Selection problem. This paper aims to unpack and develop an enhanced understanding of the sentiment aware portfolio selection problem. To this end, the study uses a Semantic Attention Model to predict sentiment towards an asset. We select the optimal portfolio through a sentiment-aware Long Short Term Memory (LSTM) recurrent neural network for price prediction and a mean-variance strategy. Our sentiment portfolio strategies achieved on average a significant increase in revenue above the non-sentiment aware models. However, the results show that our strategy does not outperform traditional portfolio allocation strategies from a stability perspective. We argue that an improved fusion of sentiment prediction with a combination of price prediction and portfolio optimization leads to an enhanced portfolio selection strategy.", "published": "2022-03-10T23:21:12Z", "authors": ["Mufhumudzi Muthivhi", "Terence L. van Zyl"], "categories": ["q-fin.PM", "cs.LG"], "doi": "", "_query": "housing price text listing", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/1810.03494v3", "title": "k-price auctions and Combination auctions", "abstract": "We provide an exact analytical solution of the Nash equilibrium for $k$- price auctions. We also introduce a new type of auction and demonstrate that it has fair solutions other than the second price auctions, therefore paving the way for replacing second price auctions.", "published": "2018-09-26T15:33:48Z", "authors": ["Martin Mihelich", "Yan Shu"], "categories": ["q-fin.MF", "cs.GT", "econ.GN"], "doi": "", "_query": "housing price text listing", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/1502.02819v1", "title": "The pricing of lookback options and binomial approximation", "abstract": "Refining a discrete model of Cheuk and Vorst we obtain a closed formula for the price of a European lookback option at any time between emission and maturity. We derive an asymptotic expansion of the price as the number of periods tends to infinity, thereby solving a problem posed by Lin and Palmer. We prove, in particular, that the price in the discrete model tends to the price in the continuous Black-Scholes model. Our results are based on an asymptotic expansion of the binomial cumulative distribution function that improves several recent results in the literature.", "published": "2015-02-10T09:14:20Z", "authors": ["Karl Grosse-Erdmann", "Fabien Heuwelyckx"], "categories": ["q-fin.MF"], "doi": "", "_query": "housing price text listing", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/1712.03031v1", "title": "An Empirical Study on Price Differentiation Based on System Fingerprints", "abstract": "Price differentiation describes a marketing strategy to determine the price of goods on the basis of a potential customer's attributes like location, financial status, possessions, or behavior. Several cases of online price differentiation have been revealed in recent years. For example, different pricing based on a user's location was discovered for online office supply chain stores and there were indications that offers for hotel rooms are priced higher for Apple users compared to Windows users at certain online booking websites. One potential source for relevant distinctive features are \\emph{system fingerprints}, i.\\,e., a technique to recognize users' systems by identifying unique attributes such as the source IP address or system configuration. In this paper, we shed light on the ecosystem of pricing at online platforms and aim to detect if and how such platform providers make use of price differentiation based on digital system fingerprints. We designed and implemented an automated price scanner capable of disguising itself as an arbitrary system, leveraging real-world system fingerprints, and searched for price differences related to different features (e.\\,g., user location, language setting, or operating system). This system allows us to explore price differentiation cases and expose those characteristic features of a system that may influence a product's price.", "published": "2017-12-08T11:24:10Z", "authors": ["Thomas Hupperich", "Dennis Tatang", "Nicolai Wilkop", "Thorsten Holz"], "categories": ["cs.CR"], "doi": "", "_query": "housing price text listing", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2308.05442v1", "title": "Optimal chromatic bound for ($P_3\\cup P_2$, house)-free graphs", "abstract": "Let $G$ and $H$ be two vertex disjoint graphs. The {\\em union} $G\\cup H$ is the graph with $V(G\\cup H)=V(G)\\cup V(H)$ and $E(G\\cup H)=E(G)\\cup E(H)$. We use $P_k$ to denote a {\\em path} on $k$ vertices, use {\\em house} to denote the complement of $P_5$. In this paper, we show that $χ(G)\\le2ω(G)$ if $G$ is ($P_3\\cup P_2$, house)-free. Moreover, this bound is optimal when $ω(G)\\ge2$.", "published": "2023-08-10T08:58:12Z", "authors": ["Rui Li", "Di Wu", "Jinfeng Li"], "categories": ["math.CO"], "doi": "", "_query": "housing price text listing", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/1802.04644v2", "title": "Price of Anarchy for Mean Field Games", "abstract": "The price of anarchy, originally introduced to quantify the inefficiency of selfish behavior in routing games, is extended to mean field games. The price of anarchy is defined as the ratio of a worst case social cost computed for a mean field game equilibrium to the optimal social cost as computed by a central planner. We illustrate properties of such a price of anarchy on linear quadratic extended mean field games, for which explicit computations are possible. Various asymptotic behaviors of the price of anarchy are proved for limiting behaviors of the coefficients in the model and numerics are presented.", "published": "2018-02-08T18:38:45Z", "authors": ["Rene Carmona", "Christy V. Graves", "Zongjun Tan"], "categories": ["math.OC"], "doi": "", "_query": "housing price text listing", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/1304.6846v2", "title": "Time-independent pricing of options in range bound markets", "abstract": "Assuming that price of the underlying stock is moving in range bound, the Black-Scholes formula for options pricing supports a separation of variables. The resulting time-independent equation is solved employing different behavior of the option price function and three significant results are deduced. The first is the probability of stock price penetration through support or resistance level, called transmission coefficient. The second is the distance that price will go through once stock price penetrates out of the range bound. The last one is a predicted short time dramatic fall in the stock volatility right ahead of price tunneling. All three results are useful tools that give market practitioners valuable insights in choosing the right time to get involved in an option contract, about how far the price will go in case of a breakout, and how to correctly interpret volatility downfalls.", "published": "2013-04-25T09:43:54Z", "authors": ["Ovidiu Racorean"], "categories": ["q-fin.PR"], "doi": "", "_query": "housing price text listing", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2601.17387v3", "title": "Generation-Step-Aware Framework for Cross-Modal Representation and Control in Multilingual Speech-Text Models", "abstract": "Multilingual speech-text models rely on cross-modal language alignment to transfer knowledge between speech and text, but it remains unclear whether this reflects shared computation for the same language or modality-specific processing. We introduce a generation-step-aware framework for evaluating cross-modal computation that (i) identifies language-selective neurons for each modality at different decoding steps, (ii) decomposes them into language-representation and language-control roles, and (iii) enables cross-modal comparison via overlap measures and causal intervention. Applying our framework to SeamlessM4T, and additionally evaluating its generality on the decoder-only model Qwen2-Audio, we find that cross-modal language-representation alignment is highly model dependent. SeamlessM4T exhibits pronounced generation-step-dependent specialization, where only 5-7% of language-representation neurons are shared across modalities and overlap shifts from same-language to typologically related languages during autoregressive generation, whereas Qwen2-Audio maintains substantially larger cross-modal sharing (45-47%) together with stable, strongly language-specific alignment across decoding steps. In contrast, language-control neurons identified at later decoding steps exhibit progressively stronger cross-modal transfer from speech to text in SeamlessM4T. Together, these results demonstrate that generation-step-aware analysis can reveal both shared and model-specific patterns of cross-modal computation.", "published": "2026-01-24T09:22:18Z", "authors": ["Toshiki Nakai", "Varsha Suresh", "Vera Demberg"], "categories": ["cs.CL"], "doi": "", "_query": "housing price text listing", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/1705.02474v2", "title": "Price sensitivities for a general stochastic volatility model", "abstract": "We deal with the calculation of price sensitivities for stochastic volatility models. General forms for the dynamics of the underlying asset price and its volatility are considered. We make use of the chaotic (or Malliavin) calculus to compute the price sensitivities. The obtained results are applied to several recent stochastic volatility models as well as the existing ones that are commonly used by practitioners. Each price sensitivity is a source of financial risk. The suggested formulas are expected to improve on the hedging of the underlying risk.", "published": "2017-05-06T11:08:02Z", "authors": ["Youssef El-Khatib", "Abdulnasser Hatemi-J"], "categories": ["math.PR"], "doi": "", "_query": "housing price text listing", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/1412.5903v5", "title": "Deep Structured Output Learning for Unconstrained Text Recognition", "abstract": "We develop a representation suitable for the unconstrained recognition of words in natural images: the general case of no fixed lexicon and unknown length. To this end we propose a convolutional neural network (CNN) based architecture which incorporates a Conditional Random Field (CRF) graphical model, taking the whole word image as a single input. The unaries of the CRF are provided by a CNN that predicts characters at each position of the output, while higher order terms are provided by another CNN that detects the presence of N-grams. We show that this entire model (CRF, character predictor, N-gram predictor) can be jointly optimised by back-propagating the structured output loss, essentially requiring the system to perform multi-task learning, and training uses purely synthetically generated data. The resulting model is a more accurate system on standard real-world text recognition benchmarks than character prediction alone, setting a benchmark for systems that have not been trained on a particular lexicon. In addition, our model achieves state-of-the-art accuracy in lexicon-constrained scenarios, without being specifically modelled for constrained recognition. To test the generalisation of our model, we also perform experiments with random alpha-numeric strings to evaluate the method when no visual language model is applicable.", "published": "2014-12-18T15:49:46Z", "authors": ["Max Jaderberg", "Karen Simonyan", "Andrea Vedaldi", "Andrew Zisserman"], "categories": ["cs.CV"], "doi": "", "_query": "housing price text listing", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/1904.05315v1", "title": "Bitcoin Price Prediction: An ARIMA Approach", "abstract": "Bitcoin is considered the most valuable currency in the world. Besides being highly valuable, its value has also experienced a steep increase, from around 1 dollar in 2010 to around 18000 in 2017. Then, in recent years, it has attracted considerable attention in a diverse set of fields, including economics and computer science. The former mainly focuses on studying how it affects the market, determining reasons behinds its price fluctuations, and predicting its future prices. The latter mainly focuses on its vulnerabilities, scalability, and other techno-crypto-economic issues. Here, we aim at revealing the usefulness of traditional autoregressive integrative moving average (ARIMA) model in predicting the future value of bitcoin by analyzing the price time series in a 3-years-long time period. On the one hand, our empirical studies reveal that this simple scheme is efficient in sub-periods in which the behavior of the time-series is almost unchanged, especially when it is used for short-term prediction, e.g. 1-day. On the other hand, when we try to train the ARIMA model to a 3-years-long period, during which the bitcoin price has experienced different behaviors, or when we try to use it for a long-term prediction, we observe that it introduces large prediction errors. Especially, the ARIMA model is unable to capture the sharp fluctuations in the price, e.g. the volatility at the end of 2017. Then, it calls for more features to be extracted and used along with the price for a more accurate prediction of the price. We have further investigated the bitcoin price prediction using an ARIMA model, trained over a large dataset, and a limited test window of the bitcoin price, with length $w$, as inputs. Our study sheds lights on the interaction of the prediction accuracy, choice of ($p,q,d$), and window size $w$.", "published": "2019-04-04T12:18:42Z", "authors": ["Amin Azari"], "categories": ["cs.SI", "q-fin.ST"], "doi": "", "_query": "housing price text listing", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2009.14278v2", "title": "Price, Volatility and the Second-Order Economic Theory", "abstract": "We introduce the price probability measure η(p;t) that defines the mean price p(1;t), mean square price p(2;t), price volatility σp2(t)and all price n-th statistical moments p(n;t) as ratio of sums of n-th degree values C(n;t) and volumes U(n;t) of market trades aggregated during certain time interval Δ. The definition of the mean price p(1;t) coincides with definition of the volume weighted average price (VWAP) introduced at least 30 years ago. We show that price volatility σp2(t) forecasting requires modeling evolution of the sums of second-degree values C(2;t) and volumes U(2;t). We call this model as second-order economic theory. We use numerical continuous risk ratings as ground for risk assessment of economic agents and distribute agents by risk ratings as coordinates. We introduce continuous economic media approximation of squares of values and volumes of agents trades and their flows aggregated during time interval Δ. We take into account expectations that govern agents trades and introduce aggregated expectations alike to aggregated trades. We derive equations for continuous economic media approximation on the second-degree trades. In the linear approximation we derive mean square price p(2;t) and volatility σp2(t) disturbances as functions of the first and second-degree trades disturbances. Description of each next n-th price statistical moment p(n;t) with respect to the unit price measure η(p;t) depends on sums of n-th degree values C(n;t) and volumes U(n;t) of market trades and hence requires development of the corresponding n-th order economic theory.", "published": "2020-09-06T11:20:34Z", "authors": ["Victor Olkhov"], "categories": ["q-fin.GN", "econ.GN"], "doi": "", "_query": "housing price text listing", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/1104.4596v1", "title": "Price dynamics in a Markovian limit order market", "abstract": "We propose and study a simple stochastic model for the dynamics of a limit order book, in which arrivals of market order, limit orders and order cancellations are described in terms of a Markovian queueing system. Through its analytical tractability, the model allows to obtain analytical expressions for various quantities of interest such as the distribution of the duration between price changes, the distribution and autocorrelation of price changes, and the probability of an upward move in the price, {\\it conditional} on the state of the order book. We study the diffusion limit of the price process and express the volatility of price changes in terms of parameters describing the arrival rates of buy and sell orders and cancelations. These analytical results provide some insight into the relation between order flow and price dynamics in order-driven markets.", "published": "2011-04-24T00:16:56Z", "authors": ["Rama Cont", "Adrien De Larrard"], "categories": ["q-fin.TR", "q-fin.ST"], "doi": "10.1137/110856605", "_query": "housing price text listing", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2402.18973v1", "title": "Privacy Management and Interface Design for a Smart House", "abstract": "In today's life, more and more people tend to opt for a smart house. In this way, the idea of including technology has become popular worldwide. Despite this concept's many benefits, managing security remains an essential problem due to the shared activities. The Internet of Things system behind a smart house is based on several sensors to measure temperature, humidity, air quality, and movement. Because of being supervised every day through sensors and controlling their house only with a simple click, many people can be afraid of this new approach in terms of their privacy, and this fact can constrain them from following their habits. The security aspects should be constantly analyzed to keep the data's confidentiality and make people feel safe in their own houses. In this context, the current paper puts light on an alternative design of a platform in which the safety of homeowners is the primary purpose, and they maintain complete control over the data generated by smart devices. The current research highlights the role of security and interface design in controlling a smart house. The study underscores the importance of providing an interface that can be used easily by any person to manage data and live activities in a modern residence in an era dominated by continuously developing technology.", "published": "2024-02-29T09:26:41Z", "authors": ["Ana-Maria Comeaga", "Iuliana Marin"], "categories": ["cs.CR", "cs.SE"], "doi": "", "_query": "housing price text listing", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/1505.03799v1", "title": "Distributed House-Hunting in Ant Colonies", "abstract": "We introduce the study of the ant colony house-hunting problem from a distributed computing perspective. When an ant colony's nest becomes unsuitable due to size constraints or damage, the colony must relocate to a new nest. The task of identifying and evaluating the quality of potential new nests is distributed among all ants. The ants must additionally reach consensus on a final nest choice and the full colony must be transported to this single new nest. Our goal is to use tools and techniques from distributed computing theory in order to gain insight into the house-hunting process. We develop a formal model for the house-hunting problem inspired by the behavior of the Temnothorax genus of ants. We then show a Ω(log n) lower bound on the time for all n ants to agree on one of k candidate nests. We also present two algorithms that solve the house-hunting problem in our model. The first algorithm solves the problem in optimal O(log n) time but exhibits some features not characteristic of natural ant behavior. The second algorithm runs in O(k log n) time and uses an extremely simple and natural rule for each ant to decide on the new nest.", "published": "2015-05-14T17:09:54Z", "authors": ["Mohsen Ghaffari", "Cameron Musco", "Tsvetomira Radeva", "Nancy Lynch"], "categories": ["cs.DC"], "doi": "", "_query": "housing price text listing", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2605.16064v2", "title": "Misspecified Estimate-then-Optimize Leads to Supra-Competitive Prices", "abstract": "We study whether simple algorithmic pricing systems can systematically produce collusive-like prices in multi-firm markets. We consider firms that price using a myopic estimate-then-optimize rule: each repeatedly fits a demand model to its own price and sales history and sets the price that maximizes estimated profit. This demand model is misspecified, omitting competitors' prices. We analyze the dynamics of this rule when it is initialized by an exploration phase of independent random prices. We characterize when this pipeline converges to supra-competitive prices above the Nash equilibrium, via a fluid-limit ordinary differential equation analysis. We show that supra-competitive prices arise when firms initially explore within similar price ranges on the same side of the Nash price. Moreover, prices can be substantially above the Nash price; we show that prices can reach monopoly levels under symmetric exploration. Simulations calibrated to a real multifamily rental market confirm that supra-competitive outcomes arise robustly beyond our theoretical assumptions, including under finite horizons, heterogeneous products, and nonlinear logit demand.", "published": "2026-05-15T15:29:23Z", "authors": ["Jackie Baek", "Vivek F. Farias", "Farrell Wu"], "categories": ["cs.GT", "cs.AI", "econ.TH"], "doi": "", "_query": "housing price text listing", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2609.33799v1", "title": "Oracle-Parametrized Constant Function Market Makers: From Price Feeds to Pricing Rules", "abstract": "This paper introduces oracle-parametrized automated market makers (OP-AMMs), i.e., automated market makers whose quoted price depends jointly on the pool reserves and an external oracle price. In doing so, we extend the information-agnostic AMM framework to settings, such as tokenized securities, for which price discovery occurs off-chain. Under a strict oracle-contraction condition, we show that the quoted price of any OP-AMM interpolates between the oracle price and an implicit autarkic price determined by the pool reserves. We then derive a general loss-versus-rebalancing (LVR) decomposition that separates the residual exposure to market lags from the losses induced by oracle errors. This analysis is further extended to stale, discrete-update oracles and to sandwich attacks around oracle updates. Using this framework, we find conditions under which OP-AMMs simultaneously increase local capital efficiency and reduce normalized LVR relative to information-agnostic AMMs. However, sufficiently noisy or stale oracles can reverse these gains. A counterfactual backtest using one-second SPY NBBO data is provided to demonstrate these trade-offs. In particular, we map the Pareto-efficient frontier of oracle-parametrized constant function market maker (OP-CFMM) designs across stylized oracle regimes.", "published": "2026-09-27T17:55:49Z", "authors": ["Hamed Amini", "Zachary Feinstein"], "categories": ["q-fin.TR", "q-fin.MF"], "doi": "", "_query": "housing price text listing", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/1304.5156v1", "title": "Option pricing, Bayes risks and Applications", "abstract": "A statistical decision problem is hidden in the core of option pricing. A simple form for the price C of a European call option is obtained via the minimum Bayes risk, R_B, of a 2-parameter estimation problem, thus justifying calling C Bayes (B-)price. The result provides new insight in option pricing, among others obtaining C for some stock-price models using the underlying probability instead of the risk neutral probability and giving R_B an economic interpretation. When logarithmic stock prices follow Brownian motion, discrete normal mixture and hyperbolic Levy motion the obtained B-prices are \"fair\" prices. A new expression for the price of American call option is also obtained and statistical modeling of R_B can be used when pricing European and American call options.", "published": "2013-04-18T15:02:11Z", "authors": ["Yannis G. Yatracos"], "categories": ["q-fin.PR", "stat.AP"], "doi": "", "_query": "housing price text listing", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2507.07415v1", "title": "EPIC: Efficient Prompt Interaction for Text-Image Classification", "abstract": "In recent years, large-scale pre-trained multimodal models (LMMs) generally emerge to integrate the vision and language modalities, achieving considerable success in multimodal tasks, such as text-image classification. The growing size of LMMs, however, results in a significant computational cost for fine-tuning these models for downstream tasks. Hence, prompt-based interaction strategy is studied to align modalities more efficiently. In this context, we propose a novel efficient prompt-based multimodal interaction strategy, namely Efficient Prompt Interaction for text-image Classification (EPIC). Specifically, we utilize temporal prompts on intermediate layers, and integrate different modalities with similarity-based prompt interaction, to leverage sufficient information exchange between modalities. Utilizing this approach, our method achieves reduced computational resource consumption and fewer trainable parameters (about 1\\% of the foundation model) compared to other fine-tuning strategies. Furthermore, it demonstrates superior performance on the UPMC-Food101 and SNLI-VE datasets, while achieving comparable performance on the MM-IMDB dataset.", "published": "2025-07-10T04:15:44Z", "authors": ["Xinyao Yu", "Hao Sun", "Zeyu Ling", "Ziwei Niu", "Zhenjia Bai", "Rui Qin", "Yen-Wei Chen", "Lanfen Lin"], "categories": ["cs.CV"], "doi": "", "_query": "housing price text listing", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/1802.01607v2", "title": "$hh+\\text{jet}$ production at 100 TeV", "abstract": "Higgs pair production is a crucial phenomenological process in deciphering the nature of the TeV scale and the mechanism underlying electroweak symmetry breaking. At the Large Hadron Collider, this process is statistically limited. Pushing the energy frontier beyond the LHC's reach will create new opportunities to exploit the rich phenomenology at higher centre-of-mass energies and luminosities. In this work, we perform a comparative analysis of the $hh+\\text{jet}$ channel at a future 100 TeV hadron collider. We focus on the $hh\\to b\\bar b b\\bar b$ and $hh \\to b\\bar b τ^+τ^-$ channels and employ a range of analysis techniques to estimate the sensitivity potential that can be gained by including this jet-associated Higgs pair production to the list of sensitive collider processes in such an environment. In particular, we observe that $hh \\to b\\bar b τ^+τ^-$ in the boosted regime exhibits a large sensitivity to the Higgs boson self-coupling and the Higgs self-coupling could be constrained at the 8\\% level in this channel alone.", "published": "2018-02-05T19:15:36Z", "authors": ["Shankha Banerjee", "Christoph Englert", "Michelangelo L. Mangano", "Michele Selvaggi", "Michael Spannowsky"], "categories": ["hep-ph", "hep-ex"], "doi": "10.1140/epjc/s10052-018-5788-y", "_query": "housing price text listing", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/math/0310223v1", "title": "Consistent Estimation of Pricing Kernels from Noisy Price Data", "abstract": "If pricing kernels are assumed non-negative then the inverse problem of finding the pricing kernel is well-posed. The constrained least squares method provides a consistent estimate of the pricing kernel. When the data are limited, a new method is suggested: relaxed maximization of the relative entropy. This estimator is also consistent. Keywords: $ε$-entropy, non-parametric estimation, pricing kernel, inverse problems.", "published": "2003-10-15T14:44:55Z", "authors": ["Vladislav Kargin"], "categories": ["math.ST", "math.NA", "q-fin.ST"], "doi": "", "_query": "housing price text listing", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2601.01706v1", "title": "Semantic Non-Fungibility and Violations of the Law of One Price in Prediction Markets", "abstract": "Prediction markets are designed to aggregate dispersed information about future events, yet today's ecosystem is fragmented across heterogeneous operator-run platforms and blockchain-based protocols that independently list economically identical events. In the absence of a shared notion of event identity, liquidity fails to pool across venues, arbitrage becomes capital-intensive or unenforceable, and prices systematically violate the Law of One Price. As a result, market prices reflect platform-local beliefs rather than a single, globally aggregated probability, undermining the core information-aggregation function of prediction markets. We address this gap by introducing a semantic alignment framework that makes cross-platform event identity explicit through joint analysis of natural-language descriptions, resolution semantics, and temporal scope. Applying this framework, we construct the first human-validated, cross-platform dataset of aligned prediction markets, covering over 100 000 events across ten major venues from 2018 to 2025. Using this dataset, we show that roughly 6% of all events are concurrently listed across platforms and that semantically equivalent markets exhibit persistent execution-aware price deviations of 2-4% on average, even in highly liquid and information-rich settings. These mispricings give rise to persistent cross-platform arbitrage opportunities driven by structural frictions rather than informational disagreement. Overall, our results demonstrate that semantic non-fungibility is a fundamental barrier to price convergence, and that resolving event identity is a prerequisite for prediction markets to aggregate information at a global scale.", "published": "2026-01-05T01:01:52Z", "authors": ["Jonas Gebele", "Florian Matthes"], "categories": ["cs.CE"], "doi": "", "_query": "housing price text listing", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2409.02777v3", "title": "Oh the Prices You'll See: Designing a Fair Exchange System to Mitigate Personalized Pricing", "abstract": "Many online marketplaces personalize prices based on consumer attributes. Since these prices are private, consumers may be unaware that they have spent more on a good than the lowest possible price, and cannot easily take action to pay less. In this paper, we introduce a fairness-centered exchange system that takes advantage of personalized pricing, while still allowing consumers to individually benefit. Our system produces a matching of consumers to promote trading; the lower-paying consumer buys the good for the higher-paying consumer for some fee. We explore various modeling choices and fairness targets to determine which schema will leave consumers best off, while also earning revenue for the system itself. We show that when consumers individually negotiate the transaction price, and our fairness objective is to minimize mean net cost, we are able to achieve the most fair outcomes. Conversely, when transaction prices are centrally set, consumers are often unwilling to transact. When price dispersion (or range) is high, the system can reduce the mean net cost to each individual by $66\\%$, or the mean net cost to a group by $69\\%$. We find that a high dispersion of original prices is necessary for our system to be viable. Higher dispersion can actually lead to decreased net price paid by consumers, and act as a check against extreme personalization, increasing seller accountability. Our results provide theoretical evidence that such a system could improve fairness for consumers while sustaining itself financially.", "published": "2024-09-04T14:55:59Z", "authors": ["Aditya Karan", "Naina Balepur", "Hari Sundaram"], "categories": ["cs.GT"], "doi": "10.1145/3715275.3732076", "_query": "housing price text listing", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/1707.04244v1", "title": "Lithium NLP: A System for Rich Information Extraction from Noisy User Generated Text on Social Media", "abstract": "In this paper, we describe the Lithium Natural Language Processing (NLP) system - a resource-constrained, high- throughput and language-agnostic system for information extraction from noisy user generated text on social media. Lithium NLP extracts a rich set of information including entities, topics, hashtags and sentiment from text. We discuss several real world applications of the system currently incorporated in Lithium products. We also compare our system with existing commercial and academic NLP systems in terms of performance, information extracted and languages supported. We show that Lithium NLP is at par with and in some cases, outperforms state- of-the-art commercial NLP systems.", "published": "2017-07-13T17:52:51Z", "authors": ["Preeti Bhargava", "Nemanja Spasojevic", "Guoning Hu"], "categories": ["cs.AI", "cs.CL", "cs.IR"], "doi": "", "_query": "housing price text listing", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2509.10553v1", "title": "A Stochastic Model for Illiquid Stock Prices and its Conclusion about Correlation Measurement", "abstract": "This study explores the behavioral dynamics of illiquid stock prices in a listed stock market. Illiquidity, characterized by wide bid and ask spreads affects price formation by decoupling prices from standard risk and return relationships and increasing sensitivity to market sentiment. We model the prices at the Uganda Securities Exchange (USE) which is illiquid in that the prices remain constant much of the time thus complicating price modelling. We circumvent this challenge by combining the Markov model (MM) with two models; the exponential Ornstein Uhlenbeck model (XOU) and geometric Brownian motion (gBm). In the combined models, the MM was used to capture the constant prices in the stock prices while the XOU and gBm captured the stochastic price dynamics. We modelled stock prices using the combined models, as well as XOU and gBm alone. We found that USE stocks appeared to have low correlation with one another. Using theoretical analysis, simulation study and empirical analysis, we conclude that this apparent low correlation is due to illiquidity. In particular data simulated from combined MM-gBm, in which the gBm portion were highly correlated resulted in a low measured correlation when the Markov chain had a higher transition from zero state to zero state.", "published": "2025-09-09T12:40:23Z", "authors": ["Erina Nanyonga", "Juma Kasozi", "Fred Mayambala", "Hassan W. Kayondo", "Matt Davison"], "categories": ["q-fin.ST"], "doi": "10.1080/27684830.2026.2680821", "_query": "housing price text listing", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2501.07827v1", "title": "Prediction Interval Construction Method for Electricity Prices", "abstract": "Accurate prediction of electricity prices plays an essential role in the electricity market. To reflect the uncertainty of electricity prices, price intervals are predicted. This paper proposes a novel prediction interval construction method. A conditional generative adversarial network is first presented to generate electricity price scenarios, with which the prediction intervals can be constructed. Then, different generated scenarios are stacked to obtain the probability densities, which can be applied to accurately reflect the uncertainty of electricity prices. Furthermore, a reinforced prediction mechanism based on the volatility level of weather factors is introduced to address the spikes or volatile prices. A case study is conducted to verify the effectiveness of the proposed novel prediction interval construction method. The method can also provide the probability density of each price scenario within the prediction interval and has the superiority to address the volatile prices and price spikes with a reinforced prediction mechanism.", "published": "2025-01-14T04:02:08Z", "authors": ["Xin Lu"], "categories": ["cs.LG", "eess.SY"], "doi": "", "_query": "housing price text listing", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/1809.02824v1", "title": "A natural language processing and geospatial clustering framework for harvesting local place names from geotagged housing advertisements", "abstract": "Local place names are frequently used by residents living in a geographic region. Such place names may not be recorded in existing gazetteers, due to their vernacular nature, relative insignificance to a gazetteer covering a large area (e.g., the entire world), recent establishment (e.g., the name of a newly-opened shopping center), or other reasons. While not always recorded, local place names play important roles in many applications, from supporting public participation in urban planning to locating victims in disaster response. In this paper, we propose a computational framework for harvesting local place names from geotagged housing advertisements. We make use of those advertisements posted on local-oriented websites, such as Craigslist, where local place names are often mentioned. The proposed framework consists of two stages: natural language processing (NLP) and geospatial clustering. The NLP stage examines the textual content of housing advertisements, and extracts place name candidates. The geospatial stage focuses on the coordinates associated with the extracted place name candidates, and performs multi-scale geospatial clustering to filter out the non-place names. We evaluate our framework by comparing its performance with those of six baselines. We also compare our result with four existing gazetteers to demonstrate the not-yet-recorded local place names discovered by our framework.", "published": "2018-09-08T15:38:25Z", "authors": ["Yingjie Hu", "Huina Mao", "Grant McKenzie"], "categories": ["cs.IR"], "doi": "10.1080/13658816.2018.1458986", "_query": "housing price text listing", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2003.01945v1", "title": "A mean-field game price model with noise", "abstract": "In this paper, we propose a mean-field game model for the price formation of a commodity whose production is subjected to random fluctuations. The model generalizes existing deterministic price formation models. Agents seek to minimize their average cost by choosing their trading rates with a price that is characterized by a balance between supply and demand. The supply and the price processes are assumed to follow stochastic differential equations. Here, we show that, for linear dynamics and quadratic costs, the optimal trading rates are determined in feedback form. Hence, the price arises as the solution to a stochastic differential equation, whose coefficients depend on the solution of a system of ordinary differential equations.", "published": "2020-03-04T08:34:41Z", "authors": ["Diogo Gomes", "Julian Gutierrez", "Ricardo Ribeiro"], "categories": ["math.AP"], "doi": "", "_query": "housing price text listing", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2505.10514v2", "title": "Pricing in Queues with Abandonments: Optimal Policies and Practical Heuristics", "abstract": "We investigate the optimal pricing strategy in a service-providing framework, where customers can leave the system prior to service completion. In this setting, a price is quoted to an incoming customer based on the current number of customers in the system. When the quoted price is lower than the price the incoming customer is willing to pay (which follows a fixed probability distribution), then the customer joins the system and a reward equal to the quoted price is earned. A cost is incurred upon abandonment and a holding cost is incurred for customers waiting to be served. Our goal is to determine the pricing policy that maximizes the long-run average profit. Unlike traditional queueing systems without abandonments, we show that the optimal quoted prices do not always increase with the queue length in this setting. We fully characterize the possible structure of the optimal dynamic pricing policy and provide conditions guaranteeing that the optimal policy is increasing in the number of customers in the system. Moreover, we introduce two heuristics that simplify the optimal dynamic pricing policy. Both heuristics admit customers until the number of customers in the system reaches a certain threshold. The cutoff-static policy charges all admitted customers a fixed price while the two-price policy charges one price when the arriving customer can enter service immediately and another price if the customer needs to wait. By selecting the price(s) and threshold that maximize the long-run average profit, both heuristics achieve near optimality in general and the two-price policy provides more robustness compared to the cutoff-static policy.", "published": "2025-05-15T17:20:28Z", "authors": ["Jieqi Di", "Sigrún Andradóttir", "Hayriye Ayhan"], "categories": ["math.OC"], "doi": "", "_query": "housing price text listing", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/0906.4838v1", "title": "Forecasting Model for Crude Oil Price Using Artificial Neural Networks and Commodity Futures Prices", "abstract": "This paper presents a model based on multilayer feedforward neural network to forecast crude oil spot price direction in the short-term, up to three days ahead. A great deal of attention was paid on finding the optimal ANN model structure. In addition, several methods of data pre-processing were tested. Our approach is to create a benchmark based on lagged value of pre-processed spot price, then add pre-processed futures prices for 1, 2, 3,and four months to maturity, one by one and also altogether. The results on the benchmark suggest that a dynamic model of 13 lags is the optimal to forecast spot price direction for the short-term. Further, the forecast accuracy of the direction of the market was 78%, 66%, and 53% for one, two, and three days in future conclusively. For all the experiments, that include futures data as an input, the results show that on the short-term, futures prices do hold new information on the spot price direction. The results obtained will generate comprehensive understanding of the crude oil dynamic which help investors and individuals for risk managements.", "published": "2009-06-26T04:25:12Z", "authors": ["Siddhivinayak Kulkarni", "Imad Haidar"], "categories": ["cs.NE", "q-fin.PM"], "doi": "", "_query": "housing price text listing", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2110.08784v1", "title": "On ruin probabilities with investments in a risky asset with a switching regime price", "abstract": "We investigate the asymptotic of ruin probabilities when the company invests its reserve in a risky asset with a switching regime price. We assume that the asset price is a conditional geometric Brownian motion with parameters modulated by a Markov process with a finite number of states. Using the technique of the implicit renewal theory we obtain the rate of convergence to zero of the ruin probabilities as the initial capital tends to infinity.", "published": "2021-10-17T10:32:03Z", "authors": ["Yuri Kabanov", "Serguei Pergamenshchikov"], "categories": ["math.PR"], "doi": "", "_query": "housing price text listing", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2508.20539v6", "title": "Quality at Stale Prices", "abstract": "We study a continuous-time game in which a firm privately chooses quality and a public-information pricing desk revises its posted price at Poisson opportunities. Customers learn from purchases and silence. For an open set of primitives, we construct an equilibrium in which a previously optimal price sustains quality even though the desk would lower it upon revision. Quality costs exceed every immediate sales return, making the continuation reward necessary. We characterise both the resulting quality episodes and their lifetime incidence. A purchase ends an episode by raising reputation, a revision by changing the price, and silence by lowering reputation. Near the zero-quality threshold, the quality probability has a limiting parabolic profile and most episodes end through silence. If $δ$ is the cost shortfall below that threshold, the expected lifetime number of high-quality decisions and the probability of any such decision are both of order $δ^{3/2}$ from a specified upstream state. The persistent type is eventually learned, strategic quality ceases, and prices settle at type-specific levels. The construction is local at fixed finite discounting.", "published": "2025-08-28T08:28:30Z", "authors": ["Georgy Lukyanov", "Konstantin Shamruk", "Ekaterina Logina"], "categories": ["econ.TH"], "doi": "", "_query": "housing price text listing", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2009.11007v1", "title": "Pricing Cryptocurrency Options", "abstract": "Cryptocurrencies, especially Bitcoin (BTC), which comprise a new digital asset class, have drawn extraordinary worldwide attention. The characteristics of the cryptocurrency/BTC include a high level of speculation, extreme volatility and price discontinuity. We propose a pricing mechanism based on a stochastic volatility with a correlated jump (SVCJ) model and compare it to a flexible co-jump model by Bandi and Renò (2016). The estimation results of both models confirm the impact of jumps and co-jumps on options obtained via simulation and an analysis of the implied volatility curve. We show that a sizeable proportion of price jumps are significantly and contemporaneously anti-correlated with jumps in volatility. Our study comprises pioneering research on pricing BTC options. We show how the proposed pricing mechanism underlines the importance of jumps in cryptocurrency markets.", "published": "2020-09-23T09:04:17Z", "authors": ["Ai Jun Hou", "Weining Wang", "Cathy Y. H. Chen", "Wolfgang Karl Härdle"], "categories": ["q-fin.ST"], "doi": "10.1093/jjfinec/nbaa006", "_query": "housing price text listing", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2205.07256v5", "title": "Market-Based Asset Price Probability", "abstract": "The random values and volumes of consecutive trades made at the exchange with shares of security determine its mean, variance, and higher statistical moments. The volume weighted average price (VWAP) is the simplest example of such a dependence. We derive the dependence of the market-based variance and 3rd statistical moment of prices on the means, variances, covariances, and 3rd moments of the values and volumes of market trades. The usual frequency-based assessments of statistical moments of prices are the limited case of market-based statistical moments if we assume that all volumes of consecutive trades with security are constant during the averaging interval. To forecast market-based variance of price, one should predict the first two statistical moments and the correlation of values and volumes of consecutive trades at the same horizon. We explain how that limits the number of predicted statistical moments of prices by the first two and the accuracy of the forecasts of the price probability by the Gaussian distribution. This limitation also reduces the reliability of Value-at-Risk by Gaussian approximation. The accounting for the randomness of trade volumes and the use of VWAP results in zero price-volume correlations. To study the price-volume empirical statistical dependence, one should calculate correlations of prices and squares of trade volumes or correlations of squares of prices and volumes. To improve the accuracy and reliability of large macroeconomic and market models like those developed by BlackRock's Aladdin, JP Morgan, and the U.S. Fed., the developers should explicitly account for the impact of random trade volumes and use market-based statistical moments of asset prices.", "published": "2022-05-15T10:50:06Z", "authors": ["Victor Olkhov"], "categories": ["econ.GN", "q-fin.GN", "q-fin.PR"], "doi": "", "_query": "housing price text listing", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2206.01468v7", "title": "Multi-Asset Bubbles Equilibrium Price Dynamics", "abstract": "The price-bubble and crash process formation is theoretically investigated in a two-asset equilibrium model. Sufficient and necessary conditions are derived for the existence of average equilibrium price dynamics of different agent-based models, where agents are distinguished in terms of factor and investment trading strategies. In line with experimental results, we show that assets with a positive average dividend, i.e., with a strictly declining fundamental value, display at the equilibrium price the typical hump-shaped bubble observed in experimental asset markets. Moreover, a misvaluation effect is observed in the asset with a constant fundamental value, triggered by the other asset that displays the price bubble shape when a sharp price decline is exhibited at the end of the market.", "published": "2022-06-03T09:33:56Z", "authors": ["Francesco Cordoni"], "categories": ["q-fin.TR", "econ.GN"], "doi": "", "_query": "housing price text listing", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/1709.00468v1", "title": "An Option Pricing Model with Memory", "abstract": "We obtain option pricing formulas for stock price models in which the drift and volatility terms are functionals of a continuous history of the stock prices. That is, the stock dynamics follows a nonlinear stochastic functional differential equation. A model with full memory is obtained via approximation through a stock price model in which the continuous path dependence does not go up to the present: there is a memory gap. A strong solution is obtained by closing the gap. Fair option prices are obtained through an equivalent (local) martingale measure via Girsanov's Theorem and therefore are given in terms of a conditional expectation. The models maintain the completeness of the market and have no arbitrage opportunities.", "published": "2017-09-01T20:21:55Z", "authors": ["Flavia Sancier", "Salah Mohammed"], "categories": ["q-fin.PR"], "doi": "10.1002/ijop.12622", "_query": "housing price text listing", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/1408.6065v2", "title": "Shadow prices for continuous processes", "abstract": "In a financial market with a continuous price process and proportional transaction costs we investigate the problem of utility maximization of terminal wealth. We give sufficient conditions for the existence of a shadow price process, i.e.~a least favorable frictionless market leading to the same optimal strategy and utility as in the original market under transaction costs. The crucial ingredients are the continuity of the price process and the hypothesis of \"no unbounded profit with bounded risk\". A counter-example reveals that these hypotheses cannot be relaxed.", "published": "2014-08-26T10:26:31Z", "authors": ["Christoph Czichowsky", "Walter Schachermayer", "Junjian Yang"], "categories": ["q-fin.PM", "math.OC"], "doi": "", "_query": "housing price text listing", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/cond-mat/0103273v1", "title": "Energy price risk management", "abstract": "The price of electricity is far more volatile than that of other commodities normally noted for extreme volatility. Demand and supply are balanced on a knife-edge because electric power cannot be economically stored, end user demand is largely weather dependent, and the reliability of the grid is paramount. The possibility of extreme price movements increases the risk of trading in electricity markets. However, a number of standard financial tools cannot be readily applied to pricing and hedging electricity derivatives. In this paper we present arguments why this is the case.", "published": "2001-03-13T14:11:03Z", "authors": ["Rafal Weron"], "categories": ["cond-mat"], "doi": "10.1016/S0378-4371(00)00276-4", "_query": "housing price text listing", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2404.07684v6", "title": "Merger Analysis with Unobserved Prices", "abstract": "Standard empirical tools for merger analysis assume price data, which are often unavailable. I characterize sufficient conditions for identifying the unilateral effects of mergers without price data using the first-order approach and merger simulation. Data on merging firms' revenues, margins, and revenue diversion ratios are sufficient to identify their gross upward pricing pressure indices and compensating marginal cost reductions. Standard discrete-continuous demand assumptions facilitate the identification of revenue diversion ratios as well as the feasibility of merger simulation in terms of percentage change in price. I apply the framework to the Albertsons/Safeway (2015) and Staples/Office Depot (2016) mergers.", "published": "2024-04-11T12:23:57Z", "authors": ["Paul S. Koh"], "categories": ["econ.EM"], "doi": "", "_query": "housing price text listing", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2412.02148v1", "title": "Mining Tweets to Predict Future Bitcoin Price", "abstract": "Bitcoin has increased investment interests in people during the last decade. We have seen an increase in the number of posts on social media platforms about cryptocurrency, especially Bitcoin. This project focuses on analyzing user tweet data in combination with Bitcoin price data to see the relevance between price fluctuations and the conversation between millions of people on Twitter. This study also exploits this relationship between user tweets and bitcoin prices to predict the future bitcoin price. We are utilizing novel techniques and methods to analyze the data and make price predictions.", "published": "2024-12-03T04:09:19Z", "authors": ["Ashutosh Hathidara", "Gaurav Atavale", "Suyash Chaudhary"], "categories": ["cs.AI"], "doi": "", "_query": "housing price text listing", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/1901.01927v1", "title": "Price-Coupling Games and the Generation Expansion Planning Problem", "abstract": "In this paper, we introduce and study a class of games called price-coupling games that arise in many scenarios, especially in the electricity industry. In a price-coupling game, there is a part of the objective function of a player which has an identical form for all players and there is coupling in the cost functions of players through a price which is determined uniformly for all players by an independent entity called the \\textit{price-determining player} (e.g. independent system operator (ISO) in an electricity market). This price appears in the objective function only in the part which is identical for all players. We study the existence of equilibria in such games under two broad categories, namely \\textit{price-anticipative} and \\textit{price-taking} formulations. In the price-anticipative formulation, the players anticipate the price and make their decisions while in the price-taking formulation, the players make their decisions considering the price as a given parameter. We model the price-anticipative case as a leader-follower formulation where the players (leaders) conjecture the price (follower's decision) and make their decision. The price-taking formulation is modelled as an $N+1$ player game with the additional player as the price-determining player. The existence of an equilibrium in such games are not easy mainly because of the coupled-constraint structure of the game and the non-convexity of the action set. We give conditions for the existence of equilibria in both formulations. We apply our results to analyze the existence of an equilibrium in the generation expansion planning problem using the above results.", "published": "2019-01-07T17:15:46Z", "authors": ["Mathew P. Abraham", "Ankur A. Kulkarni"], "categories": ["math.OC", "cs.GT"], "doi": "", "_query": "housing price text listing", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/1811.01295v4", "title": "The reciprocal algebraic integers having small houses", "abstract": "Let $α$ be an algebraic integer of degree $d$, which is reciprocal. The house of $α$ is the largest modulus of its conjugates. We proved that $d$-th power of the house of reciprocal $α$ has a limit point. We presented a property of antireciprocal hexanomials. We compute the minimum of the houses of all reciprocal algebraic integers of degree $d$ having the minimal polynomial which is a factor of a $D$-th degree reciprocal or antireciprocal polynomial with at most eight monomials, say $\\mathrm{mr}(d)$, for $d$ at most 180, $D\\le 1.5d$ and $D\\le 210$. We show that it is not necessary to take into account unprimitive polynomials. The computations suggest several conjectures.", "published": "2018-11-03T22:49:11Z", "authors": ["Dragan Stankov"], "categories": ["math.NT"], "doi": "10.1080/10586458.2021.1982425", "_query": "housing price text listing", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2512.18226v1", "title": "Multifaceted Exploration of Spatial Openness in Rental Housing: A Big Data Analysis in Tokyo's 23 Wards", "abstract": "Understanding spatial openness is vital for improving residential quality and design; however, studies often treat its influencing factors separately. This study developed a quantitative framework to evaluate the spatial openness in housing from two- (2D) and three- (3D) dimensional perspectives. Using data from 4,004 rental units in Tokyo's 23 wards, we examined the temporal and spatial variations in openness and its relationship with rent and housing attributes. 2D openness was computed via planar visibility using visibility graph analysis (VGA) from floor plans, whereas 3D openness was derived from interior images analysed using Mask2Former, a semantic segmentation model that identifies walls, ceilings, floors, and windows. The results showed an increase in living room visibility and a 1990s peak in overall openness. Spatial analyses revealed partial correlations among openness, rent, and building characteristics, reflecting urban redevelopment trends. Although the 2D and 3D openness indicators were not directly correlated, higher openness tended to correspond to higher rent. The impression scores predicted by the existing models were only weakly related to openness, suggesting that the interior design and furniture more strongly shape perceived space. This study offers a new multidimensional data-driven framework for quantifying residential spatial openness and linking it with urban and market dynamics.", "published": "2025-12-20T05:50:20Z", "authors": ["Takuya OKi", "Yuan Liu"], "categories": ["cs.CV"], "doi": "", "_query": "housing price text listing", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/physics/0605246v1", "title": "An Outlook on Correlations in Stock Prices", "abstract": "We present an outlook of the studies on correlations in the price timeseries of stocks, discussing the construction and applications of \"asset tree\". The topic discussed here should illustrate how the complex economic system (financial market) enrichens the list of existing dynamical systems that physicists have been studying for long.", "published": "2006-05-29T15:11:12Z", "authors": ["Anirban Chakraborti"], "categories": ["physics.soc-ph", "q-fin.ST"], "doi": "10.1007/978-88-470-0502-0_2", "_query": "housing price text listing", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2308.13642v1", "title": "The Potential of Quantum Techniques for Stock Price Prediction", "abstract": "We explored the potential applications of various Quantum Algorithms for stock price prediction by conducting a series of experimental simulations using both Classical as well as Quantum Hardware. Firstly, we extracted various stock price indicators, such as Moving Averages (MA), Average True Range (ATR), and Aroon, to gain insights into market trends and stock price movements. Next, we employed Quantum Annealing (QA) for feature selection and Principal Component Analysis (PCA) for dimensionality reduction. Further, we transformed the stock price prediction task essentially into a classification problem. We trained the Quantum Support Vector Machine (QSVM) to predict price movements (whether up or down) contrasted their performance with classical models and analyzed their accuracy on a dataset formulated using Quantum Annealing and PCA individually. We focused on the stock price prediction and binary classification of stock prices for four different companies, namely Apple, Visa, Johnson and Jonson, and Honeywell. We primarily used the real-time stock data of the raw stock prices of these companies. We compared various Quantum Computing techniques with their classical counterparts in terms of accuracy and F-score of the prediction model. Through these experimental simulations, we shed light on the potential advantages and limitations of Quantum Algorithms in stock price prediction and contribute to the growing body of knowledge at the intersection of Quantum Computing and Finance.", "published": "2023-08-25T19:26:41Z", "authors": ["Naman S", "Gaurang B", "Neel S", "Aswath Babu H"], "categories": ["q-fin.CP"], "doi": "", "_query": "housing price text listing", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/1407.7315v1", "title": "Effective and simple VWAP option pricing model", "abstract": "Volume weighted average price (VWAP) options are a popular security type in many countries, but despite their popularity very few pricing models have been developed so far for VWAP options. This can be explained by the fact that the VWAP pricing problem is set in an incomplete market since there is no underlying with which to hedge the volume risk, and hence there is no uniquely defined price. Any price, which is obtained will include a market price of volume risk which must be determined from the corresponding volume statistics. Our analysis strongly supports the hypothesis that the empirical volume statistics of ASX equities can be described reasonably well by fitted gamma distributions. Based on this observation we suggest a simple gamma process-based model that allows for the exact analytic pricing of VWAP options in a rather straightforward way.", "published": "2014-07-28T04:00:08Z", "authors": ["Alexander Buryak", "Ivan Guo"], "categories": ["q-fin.PR"], "doi": "", "_query": "housing price text listing", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2102.00167v1", "title": "Shapley-Scarf Housing Markets: Respecting Improvement, Integer Programming, and Kidney Exchange", "abstract": "In a housing market of Shapley and Scarf, each agent is endowed with one indivisible object and has preferences over all objects. An allocation of the objects is in the (strong) core if there exists no (weakly) blocking coalition. In this paper we show that in the case of strict preferences the unique strong core allocation (or competitive allocation) respects improvement: if an agent's object becomes more attractive for some other agents, then the agent's allotment in the unique strong core allocation weakly improves. We obtain a general result in case of ties in the preferences and provide new integer programming formulations for computing (strong) core and competitive allocations. Finally, we conduct computer simulations to compare the game-theoretical solutions with maximum size and maximum weight exchanges for markets that resemble the pools of kidney exchange programmes.", "published": "2021-01-30T06:50:42Z", "authors": ["Péter Biró", "Flip Klijn", "Xenia Klimentova", "Ana Viana"], "categories": ["econ.TH", "cs.GT"], "doi": "", "_query": "housing price text listing", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/1606.09376v1", "title": "The Design of Arbitrage-Free Data Pricing Schemes", "abstract": "Motivated by a growing market that involves buying and selling data over the web, we study pricing schemes that assign value to queries issued over a database. Previous work studied pricing mechanisms that compute the price of a query by extending a data seller's explicit prices on certain queries, or investigated the properties that a pricing function should exhibit without detailing a generic construction. In this work, we present a formal framework for pricing queries over data that allows the construction of general families of pricing functions, with the main goal of avoiding arbitrage. We consider two types of pricing schemes: instance-independent schemes, where the price depends only on the structure of the query, and answer-dependent schemes, where the price also depends on the query output. Our main result is a complete characterization of the structure of pricing functions in both settings, by relating it to properties of a function over a lattice. We use our characterization, together with information-theoretic methods, to construct a variety of arbitrage-free pricing functions. Finally, we discuss various tradeoffs in the design space and present techniques for efficient computation of the proposed pricing functions.", "published": "2016-06-30T07:44:18Z", "authors": ["Shaleen Deep", "Paraschos Koutris"], "categories": ["cs.DB", "cs.GT"], "doi": "", "_query": "housing price text listing", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/1812.01270v1", "title": "An Optimal Extraction Problem with Price Impact", "abstract": "A price-maker company extracts an exhaustible commodity from a reservoir, and sells it instantaneously in the spot market. In absence of any actions of the company, the commodity's spot price evolves either as a drifted Brownian motion or as an Ornstein-Uhlenbeck process. While extracting, the company affects the market price of the commodity, and its actions have an impact on the dynamics of the commodity's spot price. The company aims at maximizing the total expected profits from selling the commodity, net of the total expected proportional costs of extraction. We model this problem as a two-dimensional degenerate singular stochastic control problem with finite fuel. To determine its solution, we construct an explicit solution to the associated Hamilton-Jacobi-Bellman equation, and then verify its actual optimality through a verification theorem. On the one hand, when the (uncontrolled) price is a drifted Brownian motion, it is optimal to extract whenever the current price level is larger or equal than an endogenously determined constant threshold. On the other hand, when the (uncontrolled) price evolves as an Ornstein-Uhlenbeck process, we show that the optimal extraction rule is triggered by a curve depending on the current level of the reservoir. Such a curve is a strictly decreasing $C^{\\infty}$-function for which we are able to provide an explicit expression. Finally, our study is complemented by a theoretical and numerical analysis of the dependency of the optimal extraction strategy and value function on the model's parameters.", "published": "2018-12-04T08:22:14Z", "authors": ["Giorgio Ferrari", "Torben Koch"], "categories": ["math.OC", "q-fin.MF"], "doi": "", "_query": "housing price text listing", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/1308.6759v3", "title": "Prospect Agents and the Feedback Effect on Price Fluctuations", "abstract": "A microeconomic approach is proposed to derive the fluctuations of risky asset price, where the market participants are modeled as prospect trading agents. As asset price is generated by the temporary equilibrium between demand and supply, the agents' trading behaviors can affect the price process in turn, which is called the feedback effect. The prospect agents make actions based on their reactions to gains and losses, and as a consequence of the feedback effect, a relationship between the agents' trading behavior and the price fluctuations is constructed, which explains the implied volatility skew and smile observed in actual market.", "published": "2013-08-30T14:42:28Z", "authors": ["Yipeng Yang", "Allanus Tsoi"], "categories": ["q-fin.PR"], "doi": "", "_query": "housing price text listing", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2111.03158v2", "title": "Pricing Query Complexity of Revenue Maximization", "abstract": "The common way to optimize auction and pricing systems is to set aside a small fraction of the traffic to run experiments. This leads to the question: how can we learn the most with the smallest amount of data? For truthful auctions, this is the \\emph{sample complexity} problem. For posted price auctions, we no longer have access to samples. Instead, the algorithm is allowed to choose a price $p_t$; then for a fresh sample $v_t \\sim \\mathcal{D}$ we learn the sign $s_t = sign(p_t - v_t) \\in \\{-1,+1\\}$. How many pricing queries are needed to estimate a given parameter of the underlying distribution? We give tight upper and lower bounds on the number of pricing queries required to find an approximately optimal reserve price for general, regular and MHR distributions. Interestingly, for regular distributions, the pricing query and sample complexities match. But for general and MHR distributions, we show a strict separation between them. All known results on sample complexity for revenue optimization follow from a variant of using the optimal reserve price of the empirical distribution. In the pricing query complexity setting, we show that learning the entire distribution within an error of $ε$ in Levy distance requires strictly more pricing queries than to estimate the reserve. Instead, our algorithm uses a new property we identify called \\emph{relative flatness} to quickly zoom into the right region of the distribution to get the optimal pricing query complexity.", "published": "2021-11-04T20:57:28Z", "authors": ["Renato Paes Leme", "Balasubramanian Sivan", "Yifeng Teng", "Pratik Worah"], "categories": ["cs.GT"], "doi": "", "_query": "housing price text listing", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2103.05611v2", "title": "Optimal Pricing with a Single Point", "abstract": "We study the following fundamental data-driven pricing problem. How can/should a decision-maker price its product based on data at a single historical price? How valuable is such data? We consider a decision-maker who optimizes over (potentially randomized) pricing policies to maximize the worst-case ratio of the revenue she can garner compared to an oracle with full knowledge of the distribution of values, when the latter is only assumed to belong to a broad non-parametric set. In particular, our framework applies to the widely used regular and monotone non-decreasing hazard rate (mhr) classes of distributions. For settings where the seller knows the exact probability of sale associated with one historical price or only a confidence interval for it, we fully characterize optimal performance and near-optimal pricing algorithms that adjust to the information at hand. The framework we develop is general and allows to characterize optimal performance for deterministic or more general randomized mechanisms, and leads to fundamental novel insights on the value of data for pricing. As examples, against mhr distributions, we show that it is possible to guarantee $85\\%$ of oracle performance if one knows that half of the customers have bought at the historical price, and if only $1\\%$ of the customers bought, it still possible to guarantee $51\\%$ of oracle performance.", "published": "2021-03-09T18:34:06Z", "authors": ["Amine Allouah", "Achraf Bahamou", "Omar Besbes"], "categories": ["cs.GT", "cs.IT"], "doi": "", "_query": "housing price text listing", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2610.24388v1", "title": "Hidden urban exposure to energy-price shocks: spreading burdens and shifting hotspots", "abstract": "Cities need to anticipate where energy-price shocks could intensify hardship beyond established hotspots. How income distributions and fuel-price balances jointly shape this hidden exposure remains unclear. Here we address this gap by stress-testing nearly one million properties in six English local authorities, combining required-energy costs for existing housing with modelled local income distributions. Under gas-led crisis prices, 41% of neighbourhoods in the highest local exposure quarter lie outside the highest official fuel-poverty quarter. Electricity-led stress changes this hidden geography: when electricity unit prices alone triple from 2021, comparable to the unmitigated late-2022 cap increase, 38% of the resulting hidden-exposure group are absent from the crisis-based group. Entering neighbourhoods have newer, more efficient housing, more flats and greater electricity dependence than retained areas. Crisis preparedness should extend household protection beyond current hardship hotspots, using fuel-specific stress tests to anticipate support needs as fuel prices and heating systems change.", "published": "2026-10-17T02:42:06Z", "authors": ["Yunfei Bai", "Aaron Tesfa Tsion", "Raúl Rosales", "Barbara Shollock", "Wei He"], "categories": ["physics.soc-ph"], "doi": "", "_query": "housing price text listing", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2605.13320v1", "title": "The fine structure of electricity price volatility", "abstract": "We conduct the first rigorous study of electricity price volatility for the full panel of electricity prices across three European generation zones. By interpreting the observed day-ahead prices as local averages of a latent price process governed by a stochastic partial differential equation, we develop estimators of the weekly integrated variance. The inherently infinite dimensional setting introduce several complications that are not relevant in the conventional finite dimensional semimartingale setting, and we spend considerable effort in dealing with these. In particular, we must account for both mean-reversion in prices and semigroup-smoothing in the estimated variance. We provide a detailed decomposition and interpretation of the empirical estimates across three vastly different European generation zones, namely Germany, Norway, and Spain. Our findings indicate that each zone has very different drivers of volatility, and that the impact of generation variables differs considerably. We document that leverage effects appear to be present at first sight, but disappear once we condition on suitable state variables, thereby showing that electricity price volatility does not generally exhibit asymmetric responses to price shocks.", "published": "2026-05-13T10:34:08Z", "authors": ["Thomas K. Kloster", "Fred Espen Benth"], "categories": ["q-fin.GN", "econ.EM"], "doi": "", "_query": "housing price text listing", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/1304.2141v1", "title": "Robust price bounds for the forward starting straddle", "abstract": "In this article we consider the problem of giving a robust, model-independent, lower bound on the price of a forward starting straddle with payoff $|F_{T_1} - F_{T_0}|$ where $0<T_0<T_1$. Rather than assuming a model for the underlying forward price $(F_t)_{t \\geq 0}$, we assume that call prices for maturities $T_0<T_1$ are given and hence that the marginal laws of the underlying are known. The primal problem is to find the model which is consistent with the observed call prices, and for which the price of the forward starting straddle is minimised. The dual problem is to find the cheapest semi-static subhedge. Under an assumption on the supports of the marginal laws, but no assumption that the laws are atom-free or in any other way regular, we derive explicit expressions for the coupling which minimises the price of the option, and the form of the semi-static subhedge.", "published": "2013-04-08T09:23:27Z", "authors": ["David Hobson", "Martin Klimmek"], "categories": ["q-fin.PR", "math.OC", "math.PR"], "doi": "", "_query": "housing price text listing", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/1707.01284v1", "title": "The Bitcoin price formation: Beyond the fundamental sources", "abstract": "Much significant research has been done to investigate various facets of the link between Bitcoin price and its fundamental sources. This study goes beyond by looking into least to most influential factors-across the fundamental, macroeconomic, financial, speculative and technical determinants as well as the 2016 events-which drove the value of Bitcoin in times of economic and geopolitical chaos. We use a Bayesian quantile regression to inspect how the structure of dependence of Bitcoin price and its determinants varies across the entire conditional distribution of Bitcoin price movements. In doing so, three groups of determinants were derived. The use of Bitcoin in trade and the uncertainty surrounding China's deepening slowdown, Brexit and India's demonetization were found to be the most potential contributors of Bitcoin price when the market is improving. The intense anxiety over Donald Trump being the president of United States was shown to be a positive determinant pushing up the price of Bitcoin when the market is functioning around the normal mode. The velocity of bitcoins in circulation, the gold price, the Venezuelan currency demonetization and the hash rate were found to be the fundamentals influencing the Bitcoin price when the market is heading into decline.", "published": "2017-07-05T09:39:04Z", "authors": ["Jamal Bouoiyour", "Refk Selmi"], "categories": ["q-fin.CP"], "doi": "", "_query": "housing price text listing", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/1909.12227v1", "title": "Stock Prices Prediction using Deep Learning Models", "abstract": "Financial markets have a vital role in the development of modern society. They allow the deployment of economic resources. Changes in stock prices reflect changes in the market. In this study, we focus on predicting stock prices by deep learning model. This is a challenge task, because there is much noise and uncertainty in information that is related to stock prices. So this work uses sparse autoencoders with one-dimension (1-D) residual convolutional networks which is a deep learning model, to de-noise the data. Long-short term memory (LSTM) is then used to predict the stock price. The prices, indices and macroeconomic variables in past are the features used to predict the next day's price. Experiment results show that 1-D residual convolutional networks can de-noise data and extract deep features better than a model that combines wavelet transforms (WT) and stacked autoencoders (SAEs). In addition, we compare the performances of model with two different forecast targets of stock price: absolute stock price and price rate of change. The results show that predicting stock price through price rate of change is better than predicting absolute prices directly.", "published": "2019-09-25T11:38:25Z", "authors": ["Jialin Liu", "Fei Chao", "Yu-Chen Lin", "Chih-Min Lin"], "categories": ["cs.LG"], "doi": "", "_query": "housing price text listing", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2308.04534v1", "title": "Ahead of the Text: Leveraging Entity Preposition for Financial Relation Extraction", "abstract": "In the context of the ACM KDF-SIGIR 2023 competition, we undertook an entity relation task on a dataset of financial entity relations called REFind. Our top-performing solution involved a multi-step approach. Initially, we inserted the provided entities at their corresponding locations within the text. Subsequently, we fine-tuned the transformer-based language model roberta-large for text classification by utilizing a labeled training set to predict the entity relations. Lastly, we implemented a post-processing phase to identify and handle improbable predictions generated by the model. As a result of our methodology, we achieved the 1st place ranking on the competition's public leaderboard.", "published": "2023-08-08T18:56:52Z", "authors": ["Stefan Pasch", "Dimitrios Petridis"], "categories": ["cs.CL"], "doi": "", "_query": "housing price text listing", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/cond-mat/0101175v1", "title": "Coarse-graining and Self-similarity of Price Fluctuations", "abstract": "We propose a new approach for analyzing price fluctuations in their strongly correlated regime ranging from minutes to months. This is done by employing a self-similarity assumption for the magnitude of coarse-grained price fluctuation or volatility. The existence of a Cramer function, the characteristic function for self-similarity, is confirmed by analyzing real price data from a stock market. We also discuss the close interrelation among our approach, the scaling-of-moments method and the multifractal approach for price fluctuations.", "published": "2001-01-12T07:58:52Z", "authors": ["Yoshi Fujiwara", "Hirokazu Fujisaka"], "categories": ["cond-mat.stat-mech", "q-fin.ST"], "doi": "10.1016/S0378-4371(01)00135-2", "_query": "housing price text listing", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2303.02576v3", "title": "Interpreting and Countering Collusion in Deep-Learning Pricing Algorithms", "abstract": "Algorithmic pricing raises a question of interpretation as well as intervention: when autonomous deep-learning pricing systems sustain supracompetitive prices, what strategic pattern have they learned, and how might market institutions alter it? This paper develops an interpretable framework for studying learned collusion in repeated pricing environments. The framework embeds strategic deep learning networks in a differentiated-products Bertrand market and compresses recent price histories into finite states that record price levels, rival price movements, and movement persistence. This state representation preserves the dynamic information relevant for reward and punishment while making learned behavior economically interpretable. In the baseline environment, agents learn supracompetitive prices and exhibit a coherent collusive asymmetry: they punish rival price cuts and accommodate rival price increases. The paper then uses this framework to study an order-book mechanism that assembles temporary buyer commitments and allocates them to sellers willing to make sufficiently deep undercuts, partially insulating those undercutters from retaliatory punishment. The mechanism lowers realized prices in the main symmetric-cost design and remains effective in the main robustness exercises. Further analysis shows that this price reduction operates through the intended channel: qualifying undercuts become less exposed to subsequent punishment, reducing the continuation loss that sustains high-price states. The results show how interpretable learning frameworks can connect algorithmic pricing outcomes to economic mechanisms, and how market design can target the enforcement channel behind learned collusion.", "published": "2023-03-05T04:11:28Z", "authors": ["Soumen Banerjee"], "categories": ["econ.TH"], "doi": "", "_query": "housing price text listing", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/1704.04354v1", "title": "An empirical behavioural order-driven model with price limit rules", "abstract": "We develop an empirical behavioural order-driven (EBOD) model, which consists of an order placement process and an order cancellation process. Price limit rules are introduced in the definition of relative price. The order placement process is determined by several empirical regularities: the long memory in order directions, the long memory in relative prices, the asymmetric distribution of relative prices, and the nonlinear dependence of the average order size and its standard deviation on the relative price. Order cancellation follows a Poisson process with the arrival rate determined from real data and the cancelled order is determined according to the empirical distributions of relative price level and relative position at the same price level. All these ingredients of the model are derived based on the empirical microscopic regularities in the order flows of stocks on the Shenzhen Stock Exchange. The model is able to produce the main stylized facts in real markets. Computational experiments uncover that asymmetric setting of price limits will cause the stock price diverging exponentially when the up price limit is higher than the down price limit and vanishing vice versus. We also find that asymmetric price limits have influences on stylized facts. Our EBOD model provides a suitable computational experiment platform for academics, market participants and policy makers.", "published": "2017-04-14T05:53:35Z", "authors": ["Gao-Feng Gu", "Xiong Xiong", "Hai-Chuan Xu", "Wei Zhang", "Yong-Jie Zhang", "Wei Chen", "Wei-Xing Zhou"], "categories": ["q-fin.CP", "q-fin.TR"], "doi": "10.1186/s40854-021-00288-4", "_query": "housing price text listing", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2302.07631v1", "title": "Path Integral Method for Pricing Proportional Step Double-Barrier Option with Time Dependent Parameters", "abstract": "Path integral method in quantum mechanics provides a new thinking for barrier option pricing. For proportional double-barrier step (PDBS) options, the option price changing process is analogous to a particle moving in a finite symmetric square potential well. We have derived the pricing kernel of PDBS options with time dependent interest rate and volatility. Numerical results of option price as a function of underlying asset price are shown as well. Path integral method can be easily generalized to the pricing of PDBS options with curved boundaries.", "published": "2023-02-15T12:53:34Z", "authors": ["Qi Chen", "Chao Guo"], "categories": ["q-fin.PR", "quant-ph"], "doi": "", "_query": "housing price text listing", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/hep-ph/9808275v1", "title": "Haggling over the fine-tuning price", "abstract": "We amplify previous discussions of the fine-tuning price to be paid by supersymmetric models in the light of LEP data, especially the lower bound on the Higgs boson mass, studying in particular its power of discrimination between different parameter regions and different theoretical assumptions. The analysis is performed using the full one-loop effective potential. The whole range of $\\tanβ$ is discussed, including large values. In the minimal supergravity model with universal gaugino and scalar masses, a small fine-tuning price is possible only for intermediate values of $\\tanβ$. However, the fine-tuning price in this region is significantly higher if we require $b-τ$ Yukawa-coupling unification. On the other hand, price reductions are obtained if some theoretical relation between MSSM parameters is assumed, in particular between $μ_0$, $M_{1/2}$ and $A_0$. Significant price reductions are obtained for large $\\tanβ$ if non-universal soft Higgs mass parameters are allowed. Nevertheless, in all these cases, the requirement of small fine tuning remains an important constraint on the superpartner spectrum. We also study input relations between MSSM parameters suggested in some interpretations of string theory: the price may depend significantly on these inputs, potentially providing guidance for building string models. However, in the available models the fine-tuning price may not be reduced significantly.", "published": "1998-08-10T14:49:02Z", "authors": ["P. H. Chankowski", "J. Ellis", "M. Olechowski", "S. Pokorski"], "categories": ["hep-ph"], "doi": "10.1016/S0550-3213(99)00025-5", "_query": "housing price text listing", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2506.02740v1", "title": "Stereotypical gender actions can be extracted from Web text", "abstract": "We extracted gender-specific actions from text corpora and Twitter, and compared them to stereotypical expectations of people. We used Open Mind Common Sense (OMCS), a commonsense knowledge repository, to focus on actions that are pertinent to common sense and daily life of humans. We use the gender information of Twitter users and Web-corpus-based pronoun/name gender heuristics to compute the gender bias of the actions. With high recall, we obtained a Spearman correlation of 0.47 between corpus-based predictions and a human gold standard, and an area under the ROC curve of 0.76 when predicting the polarity of the gold standard. We conclude that it is feasible to use natural text (and a Twitter-derived corpus in particular) in order to augment commonsense repositories with the stereotypical gender expectations of actions. We also present a dataset of 441 commonsense actions with human judges' ratings on whether the action is typically/slightly masculine/feminine (or neutral), and another larger dataset of 21,442 actions automatically rated by the methods we investigate in this study.", "published": "2025-06-03T10:55:00Z", "authors": ["Amaç Herdağdelen", "Marco Baroni"], "categories": ["cs.CL"], "doi": "", "_query": "housing price text listing", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2404.06666v3", "title": "SafeGen: Mitigating Sexually Explicit Content Generation in Text-to-Image Models", "abstract": "Text-to-image (T2I) models, such as Stable Diffusion, have exhibited remarkable performance in generating high-quality images from text descriptions in recent years. However, text-to-image models may be tricked into generating not-safe-for-work (NSFW) content, particularly in sexually explicit scenarios. Existing countermeasures mostly focus on filtering inappropriate inputs and outputs, or suppressing improper text embeddings, which can block sexually explicit content (e.g., naked) but may still be vulnerable to adversarial prompts -- inputs that appear innocent but are ill-intended. In this paper, we present SafeGen, a framework to mitigate sexual content generation by text-to-image models in a text-agnostic manner. The key idea is to eliminate explicit visual representations from the model regardless of the text input. In this way, the text-to-image model is resistant to adversarial prompts since such unsafe visual representations are obstructed from within. Extensive experiments conducted on four datasets and large-scale user studies demonstrate SafeGen's effectiveness in mitigating sexually explicit content generation while preserving the high-fidelity of benign images. SafeGen outperforms eight state-of-the-art baseline methods and achieves 99.4% sexual content removal performance. Furthermore, our constructed benchmark of adversarial prompts provides a basis for future development and evaluation of anti-NSFW-generation methods.", "published": "2024-04-10T00:26:08Z", "authors": ["Xinfeng Li", "Yuchen Yang", "Jiangyi Deng", "Chen Yan", "Yanjiao Chen", "Xiaoyu Ji", "Wenyuan Xu"], "categories": ["cs.CV", "cs.AI", "cs.CL", "cs.CR"], "doi": "10.1145/3658644.3670295", "_query": "housing price text listing", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2411.09862v3", "title": "Schubert cells and Whittaker functionals for $\\text{GL}(n,\\mathbb{R})$ part II: Existence via integration by parts", "abstract": "We give a new proof of the existence of Whittaker functionals for principal series representation of $\\text{GL}(n,\\mathbb{R})$, utilizing the analytic theory of distributions. We realize Whittaker functionals as equivariant distributions on $\\text{GL}(n,\\mathbb{R})$, whose restriction to the open Schubert cell is unique up to a constant. Using a birational map on the Schubert cells, we show that the unique distribution on the open Schubert cell extends to a distribution on the entire space $\\text{GL}(n,\\mathbb{R})$. This technique gives a proof of the analytic continuation of Jacquet integrals via integration by parts. We briefly discuss an application of the method to the Bessel functions on $\\text{GL}(n,\\mathbb{R})$.", "published": "2024-11-15T00:48:45Z", "authors": ["Doyon Kim"], "categories": ["math.RT", "math.NT"], "doi": "10.1515/forum-2026-0010", "_query": "housing price text listing", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2107.05550v1", "title": "Extending Text-to-Speech Synthesis with Articulatory Movement Prediction using Ultrasound Tongue Imaging", "abstract": "In this paper, we present our first experiments in text-to-articulation prediction, using ultrasound tongue image targets. We extend a traditional (vocoder-based) DNN-TTS framework with predicting PCA-compressed ultrasound images, of which the continuous tongue motion can be reconstructed in synchrony with synthesized speech. We use the data of eight speakers, train fully connected and recurrent neural networks, and show that FC-DNNs are more suitable for the prediction of sequential data than LSTMs, in case of limited training data. Objective experiments and visualized predictions show that the proposed solution is feasible and the generated ultrasound videos are close to natural tongue movement. Articulatory movement prediction from text input can be useful for audiovisual speech synthesis or computer-assisted pronunciation training.", "published": "2021-07-12T16:19:28Z", "authors": ["Tamás Gábor Csapó"], "categories": ["eess.AS", "cs.SD"], "doi": "", "_query": "housing price text listing", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2312.13953v1", "title": "Subtle Sound Design: Designing for experience blend in a historic house museum", "abstract": "In this article, we present and discuss a user-study prototype, developed for Bakkehuset historic house museum in Copenhagen. We examine how the prototype - a digital sound installation - can expand visitors' experiences of the house and offer encounters with immaterial cultural heritage. Historic house museums often hold back on utilizing digital communication tools inside the houses, since a central purpose of this type of museum is to preserve an original environment. Digital communication tools however hold great potential for facilitating rich encounters with cultural heritage and in particular with the immaterial aspects of museum collections and their histories. In this article we present our design steps and choices, aiming at subtly and seamlessly adding a digital dimension to a historic house. Based on qualitative interviews, we evaluate how the sound installation at Bakkehuset is sensed, interpreted, and used by visitors as part of their museum experience. In turn, we shed light on the historic house museum as a distinct design context for designing hybrid visitor experiences and point to the potentials of digital communication tools in this context.", "published": "2023-12-21T15:40:39Z", "authors": ["Mia F. Yates", "Anders Sundnes Løvlie"], "categories": ["cs.HC"], "doi": "10.1145/3633476", "_query": "housing price text listing", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2007.06400v2", "title": "GGPONC: A Corpus of German Medical Text with Rich Metadata Based on Clinical Practice Guidelines", "abstract": "The lack of publicly accessible text corpora is a major obstacle for progress in natural language processing. For medical applications, unfortunately, all language communities other than English are low-resourced. In this work, we present GGPONC (German Guideline Program in Oncology NLP Corpus), a freely distributable German language corpus based on clinical practice guidelines for oncology. This corpus is one of the largest ever built from German medical documents. Unlike clinical documents, clinical guidelines do not contain any patient-related information and can therefore be used without data protection restrictions. Moreover, GGPONC is the first corpus for the German language covering diverse conditions in a large medical subfield and provides a variety of metadata, such as literature references and evidence levels. By applying and evaluating existing medical information extraction pipelines for German text, we are able to draw comparisons for the use of medical language to other corpora, medical and non-medical ones.", "published": "2020-07-13T14:25:49Z", "authors": ["Florian Borchert", "Christina Lohr", "Luise Modersohn", "Thomas Langer", "Markus Follmann", "Jan Philipp Sachs", "Udo Hahn", "Matthieu-P. Schapranow"], "categories": ["cs.CL"], "doi": "", "_query": "housing price text listing", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2205.12726v8", "title": "The Quantum-House Effect: Filling the Gap Between Classicality and Quantum Discord", "abstract": "We introduce the quantum-house effect, a non-local phenomenon which apparently does not require quantum discord to be present. It suffices for the effect if neither subsystem of a bipartite system is in a pure state. This way, the quantum-house effect completely fills the gap between trivial correlations and quantum discord. However, we discuss why the situation is more subtle than that, by showing that in a concrete cryptographic setting called \"the quantum-house game\", the ability to produce quantum discord is in fact necessary for the quantum-house effect to work. Then, we suggest a principle called \"quantum detachment\" to characterize where quantumness in general departs from classicality, based on the information a physical system contains about itself. The quantum-house effect is demonstrated on SpinQ Gemini, a 2-qubit liquid-state NMR desktop quantum computer.", "published": "2022-05-24T08:55:31Z", "authors": ["Tamás Varga"], "categories": ["quant-ph"], "doi": "", "_query": "housing price text listing", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/1605.05422v2", "title": "Optimization Beyond Prediction: Prescriptive Price Optimization", "abstract": "This paper addresses a novel data science problem, prescriptive price optimization, which derives the optimal price strategy to maximize future profit/revenue on the basis of massive predictive formulas produced by machine learning. The prescriptive price optimization first builds sales forecast formulas of multiple products, on the basis of historical data, which reveal complex relationships between sales and prices, such as price elasticity of demand and cannibalization. Then, it constructs a mathematical optimization problem on the basis of those predictive formulas. We present that the optimization problem can be formulated as an instance of binary quadratic programming (BQP). Although BQP problems are NP-hard in general and computationally intractable, we propose a fast approximation algorithm using a semi-definite programming (SDP) relaxation, which is closely related to the Goemans-Williamson's Max-Cut approximation. Our experiments on simulation and real retail datasets show that our prescriptive price optimization simultaneously derives the optimal prices of tens/hundreds products with practical computational time, that potentially improve 8.2% of gross profit of those products.", "published": "2016-05-18T02:46:14Z", "authors": ["Shinji Ito", "Ryohei Fujimaki"], "categories": ["math.OC", "cs.LG", "stat.ML"], "doi": "", "_query": "housing price text listing", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/1809.09466v1", "title": "Derivatives pricing using signature payoffs", "abstract": "We introduce signature payoffs, a family of path-dependent derivatives that are given in terms of the signature of the price path of the underlying asset. We show that these derivatives are dense in the space of continuous payoffs, a result that is exploited to quickly price arbitrary continuous payoffs. This approach to pricing derivatives is then tested with European options, American options, Asian options, lookback options and variance swaps. As we show, signature payoffs can be used to price these derivatives with very high accuracy.", "published": "2018-09-25T13:30:12Z", "authors": ["Imanol Perez Arribas"], "categories": ["q-fin.CP", "q-fin.MF", "q-fin.PR"], "doi": "", "_query": "housing price text listing", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2307.09669v1", "title": "The Impacts of Registration Regime Implementation on IPO Pricing Efficiency", "abstract": "We study the impacts of regime changes and related rule implementations on IPOs initial return for China entrepreneurial boards (ChiNext and STAR). We propose that an initial return contains the issuer fair value and an investors overreaction and examine their magnitudes and determinants. Our findings reveal an evolution of IPO pricing in response to the progression of regulation changes along four dimensions: 1) governing regulation regime, 2) listing day trading restrictions, 3) listing rules for issuers, and 4) participation requirements for investors. We find that the most efficient regulation regime in Chinese IPO pricing has four characteristics: 1) registration system, 2) no hard return caps nor trading curbs that restrict the initial return; 3) more specific listing rules for issuers, and 4) more stringent participation requirements for investors. In all contexts, we show that the registration regime governing the STAR IPOs offers the most efficient pricing.", "published": "2023-07-18T22:45:28Z", "authors": ["Qi Deng", "Linhong Zheng", "Jiaqi Peng", "Xu Li", "Zhong-guo Zhou", "Monica Hussein", "Dingyi Chen", "Mick Swartz"], "categories": ["q-fin.GN", "q-fin.PR"], "doi": "", "_query": "housing price text listing", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/1607.00454v2", "title": "Limit order trading with a mean reverting reference price", "abstract": "Optimal control models for limit order trading often assume that the underlying asset price is a Brownian motion since they deal with relatively short time scales. The resulting optimal bid and ask limit order prices tend to track the underlying price as one might expect. This is indeed the case with the model of Avellaneda and Stoikov (2008), which has been studied extensively. We consider here this model under the condition when the underlying price is mean reverting. Our main result is that when time is far from the terminal, the optimal price for bid and ask limit orders is constant, which means that it does not track the underlying price. Numerical simulations confirm this behavior. When the underlying price is mean reverting, then for times sufficiently far from terminal, it is more advantageous to focus on the mean price and ignore fluctuations around it. Mean reversion suggests that limit orders will be executed with some regularity, and this is why they are optimal. We also explore intermediate time regimes where limit order prices are influenced by the inventory of outstanding orders. The duration of this intermediate regime depends on the liquidity of the market as measured by specific parameters in the model.", "published": "2016-07-02T02:44:37Z", "authors": ["Saran Ahuja", "George Papanicolaou", "Weiluo Ren", "Tzu-Wei Yang"], "categories": ["q-fin.TR"], "doi": "", "_query": "housing price text listing", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/1311.4508v2", "title": "Relativistic $<σv_\\text{rel}>$ in the calculation of relics abundances: a closer look", "abstract": "In this paper we clarify the relation between the invariant relativistic relative velocity $V_\\text{r}$, the Møller velocity $\\bar{v}$, and the non-relativistic relative velocity $v_r$. Adopting $V_{\\text{r}}$ as the true physical relative velocity for pair-collisions in a non-degenerate relativistic gas, we show that in the frame co-moving with the gas (i) the thermally averaged cross section times relative velocity $<σv_\\text{rel}>$ that appears in the density evolution equation for thermal relics is reformulated only in terms of $V_\\text{r}$ and $\\mathcal{P}(V_{\\text{r}})$ in a manifestly Lorentz invariant form; (ii) the frame-dependent issues of the standard formulation in terms of the Møller velocity, as well as \"superluminal\" relative velocities, are not present in this formulation. Furthermore, considering the annihilation of dark matter into a particle-antiparticle pair $f\\bar{f}$, in the cases $m_f=0$, $m_f=m$ and $m_f \\gg m$, we find that the coefficients of the low velocity expansion of $<σV_{\\text{r}}>$ admit an exact analytical representation in terms of the Meijer $G$ functions that can be reduced to combinations of modified Bessel functions of the second kind.", "published": "2013-11-18T19:33:42Z", "authors": ["M. Cannoni"], "categories": ["astro-ph.CO", "hep-ph", "math-ph", "nucl-th"], "doi": "10.1103/PhysRevD.89.103533", "_query": "housing price text listing", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/1509.08644v1", "title": "Neural-based machine translation for medical text domain. Based on European Medicines Agency leaflet texts", "abstract": "The quality of machine translation is rapidly evolving. Today one can find several machine translation systems on the web that provide reasonable translations, although the systems are not perfect. In some specific domains, the quality may decrease. A recently proposed approach to this domain is neural machine translation. It aims at building a jointly-tuned single neural network that maximizes translation performance, a very different approach from traditional statistical machine translation. Recently proposed neural machine translation models often belong to the encoder-decoder family in which a source sentence is encoded into a fixed length vector that is, in turn, decoded to generate a translation. The present research examines the effects of different training methods on a Polish-English Machine Translation system used for medical data. The European Medicines Agency parallel text corpus was used as the basis for training of neural and statistical network-based translation systems. The main machine translation evaluation metrics have also been used in analysis of the systems. A comparison and implementation of a real-time medical translator is the main focus of our experiments.", "published": "2015-09-29T08:54:48Z", "authors": ["Krzysztof Wołk", "Krzysztof Marasek"], "categories": ["cs.CL", "cs.CY", "cs.NE", "stat.ML"], "doi": "10.1016/j.procs.2015.08.456", "_query": "housing price text listing", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2008.13612v1", "title": "All known Type Ia supernovae models fail to reproduce the observed $t_0-M_\\text{Ni56}$ correlation", "abstract": "Type Ia supernovae (SNe Ia) are likely the thermonuclear explosions of carbon-oxygen white-dwarf stars, but their progenitor systems remain elusive. A few theoretical scenarios for the progenitor systems have been suggested, which have been shown to agree with some observational properties of SNe Ia. However, several computational challenges prohibit a robust comparison to the observations. We focus on the observed $t_0-M_\\text{Ni56}$ relation, where $t_0$ (the $γ$-rays' escape time from the ejecta) is positively correlated with $M_\\text{Ni56}$ (the synthesized $^{56}$Ni mass). Comparing to the $t_0-M_\\text{Ni56}$ relation bypasses the need for radiation transfer calculations, as the value of $t_0$ can be directly inferred from the ejecta. We show that all known SNe Ia models fail to reproduce the observed $t_0-M_\\text{Ni56}$ correlation.", "published": "2020-08-24T18:26:08Z", "authors": ["Amir Sharon", "Doron Kushnir"], "categories": ["astro-ph.HE"], "doi": "", "_query": "housing price text listing", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2401.14360v2", "title": "A Comparative Analysis of Noise Reduction Methods in Sentiment Analysis on Noisy Bangla Texts", "abstract": "While Bangla is considered a language with limited resources, sentiment analysis has been a subject of extensive research in the literature. Nevertheless, there is a scarcity of exploration into sentiment analysis specifically in the realm of noisy Bangla texts. In this paper, we introduce a dataset (NC-SentNoB) that we annotated manually to identify ten different types of noise found in a pre-existing sentiment analysis dataset comprising of around 15K noisy Bangla texts. At first, given an input noisy text, we identify the noise type, addressing this as a multi-label classification task. Then, we introduce baseline noise reduction methods to alleviate noise prior to conducting sentiment analysis. Finally, we assess the performance of fine-tuned sentiment analysis models with both noisy and noise-reduced texts to make comparisons. The experimental findings indicate that the noise reduction methods utilized are not satisfactory, highlighting the need for more suitable noise reduction methods in future research endeavors. We have made the implementation and dataset presented in this paper publicly available at https://github.com/ktoufiquee/A-Comparative-Analysis-of-Noise-Reduction-Methods-in-Sentiment-Analysis-on-Noisy-Bangla-Texts", "published": "2024-01-25T18:06:19Z", "authors": ["Kazi Toufique Elahi", "Tasnuva Binte Rahman", "Shakil Shahriar", "Samir Sarker", "Md. Tanvir Rouf Shawon", "G. M. Shahariar"], "categories": ["cs.CL"], "doi": "", "_query": "housing price text listing", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/0911.4801v2", "title": "Existence of Shadow Prices in Finite Probability Spaces", "abstract": "A shadow price is a process lying within the bid/ask prices of a market with proportional transaction costs, such that maximizing expected utility from consumption in the frictionless market with this price process leads to the same maximal utility as in the original market with transaction costs. For finite probability spaces, this note provides an elementary proof for the existence of such a shadow price.", "published": "2009-11-25T10:39:48Z", "authors": ["Jan Kallsen", "Johannes Muhle-Karbe"], "categories": ["q-fin.PM"], "doi": "", "_query": "housing price text listing", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/physics/0702029v1", "title": "Correlation of coming limit price with order book in stock markets", "abstract": "We examine the correlation of the limit price with the order book, when a limit order comes. We analyzed the Rebuild Order Book of Stock Exchange Electronic Trading Service, which is the centralized order book market of London Stock Exchange. As a result, the limit price is broadly distributed around the best price according to a power-law, and it isn't randomly drawn from the distribution, but has a strong correlation with the size of cumulative unexecuted limit orders on the price. It was also found that the limit price, on the coarse-grained price scale, tends to gather around the price which has a large size of cumulative unexecuted limit orders.", "published": "2007-02-04T05:58:21Z", "authors": ["Jun-ichi Maskawa"], "categories": ["physics.data-an", "physics.soc-ph", "q-fin.ST", "q-fin.TR"], "doi": "10.1016/j.physa.2007.04.091", "_query": "housing price text listing", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/1211.1860v4", "title": "On the Inefficiency of the Uniform Price Auction", "abstract": "We present our results on Uniform Price Auctions, one of the standard sealed-bid multi-unit auction formats, for selling multiple identical units of a single good to multi-demand bidders. Contrary to the truthful and economically efficient multi-unit Vickrey auction, the Uniform Price Auction encourages strategic bidding and is socially inefficient in general. The uniform pricing rule is, however, widely popular by its appeal to the natural anticipation, that identical items should be identically priced. In this work we study equilibria of the Uniform Price Auction for bidders with (symmetric) submodular valuation functions, over the number of units that they win. We investigate pure Nash equilibria of the auction in undominated strategies; we produce a characterization of these equilibria that allows us to prove that a fraction 1-1/e of the optimum social welfare is always recovered in undominated pure Nash equilibrium -- and this bound is essentially tight. Subsequently, we study the auction under the incomplete information setting and prove a bound of 4-2/k on the economic inefficiency of (mixed) Bayes Nash equilibria that are supported by undominated strategies.", "published": "2012-11-08T14:18:01Z", "authors": ["Evangelos Markakis", "Orestis Telelis"], "categories": ["cs.GT"], "doi": "", "_query": "housing price text listing", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2204.12678v1", "title": "Optimized latent-code selection for explainable conditional text-to-image GANs", "abstract": "The task of text-to-image generation has achieved remarkable progress due to the advances in the conditional generative adversarial networks (GANs). However, existing conditional text-to-image GANs approaches mostly concentrate on improving both image quality and semantic relevance but ignore the explainability of the model which plays a vital role in real-world applications. In this paper, we present a variety of techniques to take a deep look into the latent space and semantic space of the conditional text-to-image GANs model. We introduce pairwise linear interpolation of latent codes and `linguistic' linear interpolation to study what the model has learned within the latent space and `linguistic' embeddings. Subsequently, we extend linear interpolation to triangular interpolation conditioned on three corners to further analyze the model. After that, we build a Good/Bad data set containing unsuccessfully and successfully synthetic samples and corresponding latent codes for the image-quality research. Based on this data set, we propose a framework for finding good latent codes by utilizing a linear SVM. Experimental results on the recent DiverGAN generator trained on two benchmark data sets qualitatively prove the effectiveness of our presented techniques, with a better than 94\\% accuracy in predicting ${Good}$/${Bad}$ classes for latent vectors. The Good/Bad data set is publicly available at https://zenodo.org/record/5850224#.YeGMwP7MKUk.", "published": "2022-04-27T03:12:55Z", "authors": ["Zhenxing Zhang", "Lambert Schomaker"], "categories": ["cs.CV"], "doi": "", "_query": "housing price text listing", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2410.16977v1", "title": "IPL: Leveraging Multimodal Large Language Models for Intelligent Product Listing", "abstract": "Unlike professional Business-to-Consumer (B2C) e-commerce platforms (e.g., Amazon), Consumer-to-Consumer (C2C) platforms (e.g., Facebook marketplace) are mainly targeting individual sellers who usually lack sufficient experience in e-commerce. Individual sellers often struggle to compose proper descriptions for selling products. With the recent advancement of Multimodal Large Language Models (MLLMs), we attempt to integrate such state-of-the-art generative AI technologies into the product listing process. To this end, we develop IPL, an Intelligent Product Listing tool tailored to generate descriptions using various product attributes such as category, brand, color, condition, etc. IPL enables users to compose product descriptions by merely uploading photos of the selling product. More importantly, it can imitate the content style of our C2C platform Xianyu. This is achieved by employing domain-specific instruction tuning on MLLMs and adopting the multi-modal Retrieval-Augmented Generation (RAG) process. A comprehensive empirical evaluation demonstrates that the underlying model of IPL significantly outperforms the base model in domain-specific tasks while producing less hallucination. IPL has been successfully deployed in our production system, where 72% of users have their published product listings based on the generated content, and those product listings are shown to have a quality score 5.6% higher than those without AI assistance.", "published": "2024-10-22T12:56:04Z", "authors": ["Kang Chen", "Qingheng Zhang", "Chengbao Lian", "Yixin Ji", "Xuwei Liu", "Shuguang Han", "Guoqiang Wu", "Fei Huang", "Jufeng Chen"], "categories": ["cs.CL"], "doi": "", "_query": "housing price text listing", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/1910.01778v2", "title": "Utility-based pricing and hedging of contingent claims in Almgren-Chriss model with temporary price impact", "abstract": "In this paper, we construct the utility-based optimal hedging strategy for a European-type option in the Almgren-Chriss model with temporary price impact. The main mathematical challenge of this work stems from the degeneracy of the second order terms and the quadratic growth of the first order terms in the associated HJB equation, which makes it difficult to establish sufficient regularity of the value function needed to construct the optimal strategy in a feedback form. By combining the analytic and probabilistic tools for describing the value function and the optimal strategy, we establish the feedback representation of the latter. We use this representation to derive an explicit asymptotic expansion of the utility indifference price of the option, which allows us to quantify the price impact in options' market via the price impact coefficient in the underlying market.", "published": "2019-10-04T01:51:39Z", "authors": ["Ibrahim Ekren", "Sergey Nadtochiy"], "categories": ["q-fin.PR", "math.OC", "math.PR"], "doi": "", "_query": "housing price text listing", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/1611.02442v3", "title": "Price Doubling and Item Halving: Robust Revenue Guarantees for Item Pricing", "abstract": "We study approximation algorithms for revenue maximization based on static item pricing, where a seller chooses prices for various goods in the market, and then the buyers purchase utility-maximizing bundles at these given prices. We formulate two somewhat general techniques for designing good pricing algorithms for this setting: Price Doubling and Item Halving. Using these techniques, we unify many of the existing results in the item pricing literature under a common framework, as well as provide several new item pricing algorithms for approximating both revenue and social welfare. More specifically, for a variety of settings with item pricing, we show that it is possible to deterministically obtain a log-approximation for revenue and a constant-approximation for social welfare simultaneously: thus one need not sacrifice revenue if the goal is to still have decent welfare guarantees. %In addition, we provide a new black-box reduction from revenue to welfare based on item pricing, which immediately gives us new revenue-approximation algorithms (e.g., for gross substitutes valuations). The main technical contribution of this paper is a $O((\\log m + \\log k)^2)$-approximation algorithm for revenue maximization based on the Item Halving technique, for settings where buyers have XoS valuations, where $m$ is the number of goods and $k$ is the average supply. Surprisingly, ours is the first known item pricing algorithm with polylogarithmic approximations for such general classes of valuations, and partially resolves an important open question from the algorithmic pricing literature about the existence of item pricing algorithms with logarithmic factors for general valuations. We also use the Item Halving framework to form envy-free item pricing mechanisms for the popular setting of multi-unit markets, providing a log-approximation to revenue in this case.", "published": "2016-11-08T09:25:41Z", "authors": ["Elliot Anshelevich", "Shreyas Sekar"], "categories": ["cs.GT"], "doi": "", "_query": "housing price text listing", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2208.12067v2", "title": "Pricing Stocks with Trading Volumes", "abstract": "The present paper proposes a new framework for describing the stock price dynamics. In the traditional geometric Brownian motion model and its variants, volatility plays a vital role. The modern studies of asset pricing expand around volatility, trying to improve the understanding of it and remove the gap between the theory and market data. Unlike this, we propose to replace volatility with trading volume in stock pricing models. This pricing strategy is based on two hypotheses: a price-volume relation with an idea borrowed from fluid flows and a white-noise hypothesis for the price rate of change (ROC) that is verified via statistic testing on actual market data. The new framework can be easily adopted to local volume and stochastic volume models for the option pricing problem, which will point out a new possible direction for this central problem in quantitative finance.", "published": "2022-08-25T12:57:47Z", "authors": ["Ben Duan", "Yutian Li", "Dawei Lu", "Yang Lu", "Ran Zhang"], "categories": ["q-fin.MF"], "doi": "", "_query": "housing price text listing", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2011.08011v2", "title": "Robust Analysis of Stock Price Time Series Using CNN and LSTM-Based Deep Learning Models", "abstract": "Prediction of stock price and stock price movement patterns has always been a critical area of research. While the well-known efficient market hypothesis rules out any possibility of accurate prediction of stock prices, there are formal propositions in the literature demonstrating accurate modeling of the predictive systems that can enable us to predict stock prices with a very high level of accuracy. In this paper, we present a suite of deep learning-based regression models that yields a very high level of accuracy in stock price prediction. To build our predictive models, we use the historical stock price data of a well-known company listed in the National Stock Exchange (NSE) of India during the period December 31, 2012 to January 9, 2015. The stock prices are recorded at five minutes intervals of time during each working day in a week. Using these extremely granular stock price data, we build four convolutional neural network (CNN) and five long- and short-term memory (LSTM)-based deep learning models for accurate forecasting of the future stock prices. We provide detailed results on the forecasting accuracies of all our proposed models based on their execution time and their root mean square error (RMSE) values.", "published": "2020-11-07T16:07:10Z", "authors": ["Sidra Mehtab", "Jaydip Sen", "Subhasis Dasgupta"], "categories": ["q-fin.ST", "cs.LG"], "doi": "10.1109/ICECA49313.2020.9297652", "_query": "housing price text listing", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/1711.00405v1", "title": "The Price of Information in Combinatorial Optimization", "abstract": "Consider a network design application where we wish to lay down a minimum-cost spanning tree in a given graph; however, we only have stochastic information about the edge costs. To learn the precise cost of any edge, we have to conduct a study that incurs a price. Our goal is to find a spanning tree while minimizing the disutility, which is the sum of the tree cost and the total price that we spend on the studies. In a different application, each edge gives a stochastic reward value. Our goal is to find a spanning tree while maximizing the utility, which is the tree reward minus the prices that we pay. Situations such as the above two often arise in practice where we wish to find a good solution to an optimization problem, but we start with only some partial knowledge about the parameters of the problem. The missing information can be found only after paying a probing price, which we call the price of information. What strategy should we adopt to optimize our expected utility/disutility? A classical example of the above setting is Weitzman's \"Pandora's box\" problem where we are given probability distributions on values of $n$ independent random variables. The goal is to choose a single variable with a large value, but we can find the actual outcomes only after paying a price. Our work is a generalization of this model to other combinatorial optimization problems such as matching, set cover, facility location, and prize-collecting Steiner tree. We give a technique that reduces such problems to their non-price counterparts, and use it to design exact/approximation algorithms to optimize our utility/disutility. Our techniques extend to situations where there are additional constraints on what parameters can be probed or when we can simultaneously probe a subset of the parameters.", "published": "2017-11-01T15:46:48Z", "authors": ["Sahil Singla"], "categories": ["cs.DS"], "doi": "", "_query": "housing price text listing", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/1312.4622v1", "title": "Coupled mode theory of stock price formation", "abstract": "We develop a theory of bid and ask price dynamics where the two prices form due to interaction of buy and sell orders. In this model the two prices are represented by eigenvalues of a 2x2 price operator corresponding to \"bid\" and \"ask\" eigenstates. Matrix elements of price operator fluctuate in time which results in phase jitter for eigenstates. We show that the theory reflects very important characteristics of bid and ask dynamics and order density in the order book. Calibration examples are provided for stocks at various time scales. Lastly, this model allows to quantify and measure risk associated with spread and its fluctuations.", "published": "2013-12-17T03:20:23Z", "authors": ["Jack Sarkissian"], "categories": ["q-fin.TR"], "doi": "", "_query": "housing price text listing", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/physics/0509194v2", "title": "An empirical behavioral model of price formation", "abstract": "Although behavioral economics has demonstrated that there are many situations where rational choice is a poor empirical model, it has so far failed to provide quantitative models of economic problems such as price formation. We make a step in this direction by developing empirical models that capture behavioral regularities in trading order placement and cancellation using data from the London Stock Exchange. For order placement we show that the probability of placing an order at a given price is well approximated by a Student distribution with less than two degrees of freedom, centered on the best quoted price. This result is surprising because it implies that trading order placement is symmetric, independent of the bid-ask spread, and the same for buying and selling. We also develop a crude but simple cancellation model that depends on the position of an order relative to the best price and the imbalance between buying and selling orders in the limit order book. These results are combined to construct a stochastic representative agent model, in which the orders and cancellations are described in terms of conditional probability distributions. This model is used to simulate price formation and the results are compared to real data from the London Stock Exchange. Without adjusting any parameters based on price data, the model produces good predictions for the magnitude and functional form of the distribution of returns and the bid-ask spread.", "published": "2005-09-22T22:04:26Z", "authors": ["Szabolcs Mike", "J. Doyne Farmer"], "categories": ["physics.soc-ph", "q-fin.TR"], "doi": "", "_query": "housing price text listing", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/1909.07837v2", "title": "The value of knowing the market price of risk", "abstract": "This paper presents an optimal allocation problem in a financial market with one risk-free and one risky asset, when the market is driven by a stochastic market price of risk. We solve the problem in continuous time, for an investor with a Constant Relative Risk Aversion (CRRA) utility, under two scenarios: when the market price of risk is observable (the {\\em full information case}), and when it is not (the {\\em partial information case}). The corresponding market models are complete in the partial information case and incomplete in the other case, hence the two scenarios exhibit rather different features. We study how the access to more accurate information on the market price of risk affects the optimal strategies and we determine the maximal price that the investor would be willing to pay to get such information. In particular, we examine two cases of additional information, when an exact observation of the market price of risk is available either at time $0$ only (the {\\em initial information case}), or during the whole investment period (the {\\em dynamic information case}).", "published": "2019-09-17T14:20:36Z", "authors": ["Katia Colaneri", "Stefano Herzel", "Marco Nicolosi"], "categories": ["q-fin.PM"], "doi": "", "_query": "housing price text listing", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/1408.6292v1", "title": "Finish Them!: Pricing Algorithms for Human Computation", "abstract": "Given a batch of human computation tasks, a commonly ignored aspect is how the price (i.e., the reward paid to human workers) of these tasks must be set or varied in order to meet latency or cost constraints. Often, the price is set up-front and not modified, leading to either a much higher monetary cost than needed (if the price is set too high), or to a much larger latency than expected (if the price is set too low). Leveraging a pricing model from prior work, we develop algorithms to optimally set and then vary price over time in order to meet a (a) user-specified deadline while minimizing total monetary cost (b) user-specified monetary budget constraint while minimizing total elapsed time. We leverage techniques from decision theory (specifically, Markov Decision Processes) for both these problems, and demonstrate that our techniques lead to upto 30\\% reduction in cost over schemes proposed in prior work. Furthermore, we develop techniques to speed-up the computation, enabling users to leverage the price setting algorithms on-the-fly.", "published": "2014-08-27T02:01:48Z", "authors": ["Yihan Gao", "Aditya Parameswaran"], "categories": ["cs.GT"], "doi": "", "_query": "housing price text listing", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/1410.7317v2", "title": "Continuous time analysis of fleeting discrete price moves", "abstract": "This paper proposes a novel model of financial prices where: (i) prices are discrete; (ii) prices change in continuous time; (iii) a high proportion of price changes are reversed in a fraction of a second. Our model is analytically tractable and directly formulated in terms of the calendar time and price impact curve. The resulting càdlàg price process is a piecewise constant semimartingale with finite activity, finite variation and no Brownian motion component. We use moment-based estimations to fit four high frequency futures data sets and demonstrate the descriptive power of our proposed model. This model is able to describe the observed dynamics of price changes over three different orders of magnitude of time intervals.", "published": "2014-10-27T17:13:58Z", "authors": ["Neil Shephard", "Justin J. Yang"], "categories": ["q-fin.TR", "math.PR"], "doi": "10.1080/01621459.2016.1192544", "_query": "housing price text listing", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2602.03940v1", "title": "Autonomous AI Agents for Real-Time Affordable Housing Site Selection: Multi-Objective Reinforcement Learning Under Regulatory Constraints", "abstract": "Affordable housing shortages affect billions, while land scarcity and regulations make site selection slow. We present AURA (Autonomous Urban Resource Allocator), a hierarchical multi-agent reinforcement learning system for real-time affordable housing site selection under hard regulatory constraints (QCT, DDA, LIHTC). We model the task as a constrained multi-objective Markov decision process optimizing accessibility, environmental impact, construction cost, and social equity while enforcing feasibility. AURA uses a regulatory-aware state encoding 127 federal and local constraints, Pareto-constrained policy gradients with feasibility guarantees, and reward decomposition separating immediate costs from long-term social outcomes. On datasets from 8 U.S. metros (47,392 candidate parcels), AURA attains 94.3% regulatory compliance and improves Pareto hypervolume by 37.2% over strong baselines. In a New York City 2026 case study, it reduces selection time from 18 months to 72 hours and identifies 23% more viable sites; chosen sites have 31% better transit access and 19% lower environmental impact than expert picks.", "published": "2026-02-03T19:03:15Z", "authors": ["Olaf Yunus Laitinen Imanov", "Duygu Erisken", "Derya Umut Kulali", "Taner Yilmaz", "Rana Irem Turhan"], "categories": ["cs.LG"], "doi": "", "_query": "housing price text listing", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2202.00138v3", "title": "Improving Access to Housing and Supportive Services for Runaway and Homeless Youth: Reducing Vulnerability to Human Trafficking in New York City", "abstract": "Recent estimates indicate that there are over 1 million runaway and homeless youth and young adults (RHY) in the United States (US). Exposure to trauma, violence, and substance abuse, coupled with a lack of community support services, puts homeless youth at high risk of being exploited and trafficked. Although access to safe housing and supportive services such as physical and mental healthcare is an effective response to youths vulnerability towards being trafficked, the number of youth experiencing homelessness exceeds the capacity of available housing resources in most US communities. We undertake a RHY-informed, systematic, and data driven approach to project the collective capacity required by service providers to adequately meet the needs of homeless youth in New York City, including those most at risk of being trafficked. Our approach involves an integer linear programming model that extends the multiple multidimensional knapsack problem and is informed by partnerships with key stakeholders. The mathematical model allows for time-dependent allocation and capacity expansion, while incorporating stochastic youth arrivals and length of stays, services provided in a periodic fashion, and service delivery time windows. Our RHY and service provider-centered approach is an important step toward meeting the actual, rather than presumed, survival needs of vulnerable youth, particularly those at-risk of being trafficked.", "published": "2022-01-31T22:56:32Z", "authors": ["Yaren Bilge Kaya", "Kayse Lee Maass", "Geri L. Dimas", "Renata Konrad", "Andrew C. Trapp", "Meredith Dank"], "categories": ["math.OC"], "doi": "10.1080/24725854.2022.2120223", "_query": "housing price text listing", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2501.15338v2", "title": "Fairness-aware Contextual Dynamic Pricing with Strategic Buyers", "abstract": "Contextual pricing strategies are prevalent in online retailing, where the seller adjusts prices based on products' attributes and buyers' characteristics. Although such strategies can enhance seller's profits, they raise concerns about fairness when significant price disparities emerge among specific groups, such as gender or race. These disparities can lead to adverse perceptions of fairness among buyers and may even violate the law and regulation. In contrast, price differences can incentivize disadvantaged buyers to strategically manipulate their group identity to obtain a lower price. In this paper, we investigate contextual dynamic pricing with fairness constraints, taking into account buyers' strategic behaviors when their group status is private and unobservable from the seller. We propose a dynamic pricing policy that simultaneously achieves price fairness and discourages strategic behaviors. Our policy achieves an upper bound of $O(\\sqrt{T}+H(T))$ regret over $T$ time horizons, where the term $H(T)$ arises from buyers' assessment of the fairness of the pricing policy based on their learned price difference. When buyers are able to learn the fairness of the price policy, this upper bound reduces to $O(\\sqrt{T})$. We also prove an $Ω(\\sqrt{T})$ regret lower bound of any pricing policy under our problem setting. We support our findings with extensive experimental evidence, showcasing our policy's effectiveness. In our real data analysis, we observe the existence of price discrimination against race in the loan application even after accounting for other contextual information. Our proposed pricing policy demonstrates a significant improvement, achieving 35.06% reduction in regret compared to the benchmark policy.", "published": "2025-01-25T22:30:37Z", "authors": ["Pangpang Liu", "Will Wei Sun"], "categories": ["cs.GT", "cs.LG", "stat.ML"], "doi": "", "_query": "housing price text listing", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2609.04823v1", "title": "Reinforcement Learning for improving Large Language Models' Catalan text simplification capabilities", "abstract": "Although automatic text simplification (ATS) is critical for accessibility, its progress has not matched the rapid evolution of broader natural language processing techniques. This paper investigates the application of reinforcement learning (RL) to improve the quality of ATS for low-resource languages using Large Language Models (LLMs). The paper introduces a novel reward function, designed to guide LLMs toward a targeted simplification style with Group Relative Policy Optimization (GRPO), that combines the SARI metric with specific penalty components. The effectiveness of GRPO with this reward function is motivated and demonstrated by post-training IberianLLM-7B-Instruct on the ASSET dataset. After post-training on the English ASSET, the model's ATS performance improves on two curated Catalan benchmarks while also successfully suppressing previously observed negative behaviors. Cross-lingual transfer learning is explored by translating ASSET into Catalan and Spanish and post-training the model on each version, but these fail to show a significant improvement on the out-of-domain benchmark.", "published": "2026-09-04T07:23:52Z", "authors": ["Arnau Ayguadé Domingo", "Stefan Bott", "Horacio Saggion"], "categories": ["cs.CL", "cs.AI"], "doi": "", "_query": "housing price text listing", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/1709.09465v2", "title": "Convergence of utility indifference prices to the superreplication price in a multiple-priors framework", "abstract": "This paper formulates an utility indifference pricing model for investors trading in a discrete time financial market under non-dominated model uncertainty. The investors preferences are described by strictly increasing concave random functions defined on the positive axis. We prove that under suitable conditions the multiple-priors utility indifference prices of a contingent claim converge to its multiple-priors superreplication price. We also revisit the notion of certainty equivalent for random utility functions and establish its relation with the absolute risk aversion.", "published": "2017-09-27T12:06:23Z", "authors": ["Romain Blanchard", "Laurence Carassus"], "categories": ["q-fin.MF"], "doi": "", "_query": "housing price text listing", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2608.22216v1", "title": "The Complexity of Minimizing Subsidies in Envy-Free House Allocation", "abstract": "The house allocation problem is a classical one-sided matching problem that concerns the assignment of a set of $m$ houses to $n$ agents according to their preferences, where each agent is assigned exactly one house. Among the various objectives studied in this setting, envy-freeness is one of the most widely adopted fairness criteria. As envy-free house allocations do not always exist, we address this challenge by introducing subsidies and aim to compute allocations that achieve envy-freeness with minimum total subsidy. For binary instances, we show that a total subsidy of at most $(n-1)$ suffices to guarantee envy-freeness in house allocation, and this bound is tight. Building on the known NP-hardness for general utilities, we further show that computing an allocation that minimizes the total subsidy is NP-hard, even under binary utilities. However, when there are only a bounded number of types of agents with binary utilities, the problem can be solved in polynomial time. Finally, we present a polynomial time algorithm that computes the minimum subsidy required to achieve envy-freeness for two types of agents with general utilities.", "published": "2026-08-23T04:40:34Z", "authors": ["Sijia Dai", "Minming Li", "Xiaowei Wu", "Yong Zhang"], "categories": ["cs.GT", "cs.CC"], "doi": "", "_query": "housing price text listing", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/1204.1381v1", "title": "Price Jump Prediction in Limit Order Book", "abstract": "A limit order book provides information on available limit order prices and their volumes. Based on these quantities, we give an empirical result on the relationship between the bid-ask liquidity balance and trade sign and we show that liquidity balance on best bid/best ask is quite informative for predicting the future market order's direction. Moreover, we define price jump as a sell (buy) market order arrival which is executed at a price which is smaller (larger) than the best bid (best ask) price at the moment just after the precedent market order arrival. Features are then extracted related to limit order volumes, limit order price gaps, market order information and limit order event information. Logistic regression is applied to predict the price jump from the limit order book's feature. LASSO logistic regression is introduced to help us make variable selection from which we are capable to highlight the importance of different features in predicting the future price jump. In order to get rid of the intraday data seasonality, the analysis is based on two separated datasets: morning dataset and afternoon dataset. Based on an analysis on forty largest French stocks of CAC40, we find that trade sign and market order size as well as the liquidity on the best bid (best ask) are consistently informative for predicting the incoming price jump.", "published": "2012-04-06T00:07:09Z", "authors": ["Ban Zheng", "Eric Moulines", "Frédéric Abergel"], "categories": ["q-fin.TR"], "doi": "", "_query": "housing price text listing", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/1902.07856v1", "title": "The Markovian Price of Information", "abstract": "Suppose there are $n$ Markov chains and we need to pay a per-step \\emph{price} to advance them. The \"destination\" states of the Markov chains contain rewards; however, we can only get rewards for a subset of them that satisfy a combinatorial constraint, e.g., at most $k$ of them, or they are acyclic in an underlying graph. What strategy should we choose to advance the Markov chains if our goal is to maximize the total reward \\emph{minus} the total price that we pay? In this paper we introduce a Markovian price of information model to capture settings such as the above, where the input parameters of a combinatorial optimization problem are given via Markov chains. We design optimal/approximation algorithms that jointly optimize the value of the combinatorial problem and the total paid price. We also study \\emph{robustness} of our algorithms to the distribution parameters and how to handle the \\emph{commitment} constraint. Our work brings together two classical lines of investigation: getting optimal strategies for Markovian multi-armed bandits, and getting exact and approximation algorithms for discrete optimization problems using combinatorial as well as linear-programming relaxation ideas.", "published": "2019-02-21T03:40:09Z", "authors": ["Anupam Gupta", "Haotian Jiang", "Ziv Scully", "Sahil Singla"], "categories": ["cs.DS"], "doi": "", "_query": "housing price text listing", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/0704.1976v1", "title": "Information-Based Asset Pricing", "abstract": "A new framework for asset price dynamics is introduced in which the concept of noisy information about future cash flows is used to derive the price processes. In this framework an asset is defined by its cash-flow structure. Each cash flow is modelled by a random variable that can be expressed as a function of a collection of independent random variables called market factors. With each such \"X-factor\" we associate a market information process, the values of which are accessible to market agents. Each information process is a sum of two terms; one contains true information about the value of the market factor; the other represents \"noise\". The noise term is modelled by an independent Brownian bridge. The market filtration is assumed to be that generated by the aggregate of the independent information processes. The price of an asset is given by the expectation of the discounted cash flows in the risk-neutral measure, conditional on the information provided by the market filtration. When the cash flows are the dividend payments associated with equities, an explicit model is obtained for the share-price, and the prices of options on dividend-paying assets are derived. Remarkably, the resulting formula for the price of a European call option is of the Black-Scholes-Merton type. The information-based framework also generates a natural explanation for the origin of stochastic volatility.", "published": "2007-04-16T10:50:48Z", "authors": ["Dorje C. Brody", "Lane P. Hughston", "Andrea Macrina"], "categories": ["q-fin.PR", "math.PR", "math.ST"], "doi": "", "_query": "housing price text listing", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/0910.1430v1", "title": "State price density estimation via nonparametric mixtures", "abstract": "We consider nonparametric estimation of the state price density encapsulated in option prices. Unlike usual density estimation problems, we only observe option prices and their corresponding strike prices rather than samples from the state price density. We propose to model the state price density directly with a nonparametric mixture and estimate it using least squares. We show that although the minimization is taken over an infinitely dimensional function space, the minimizer always admits a finite dimensional representation and can be computed efficiently. We also prove that the proposed estimate of the state price density function converges to the truth at a ``nearly parametric'' rate.", "published": "2009-10-08T07:42:06Z", "authors": ["Ming Yuan"], "categories": ["q-fin.CP", "q-fin.PR", "stat.AP"], "doi": "10.1214/09-AOAS246", "_query": "housing price text listing", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2509.02347v2", "title": "A recursive formula for the $n^\\text{th}$ survival function and the $n^\\text{th}$ first passage time distribution for jump and diffusion processes. Applications to the pricing of $n^\\text{th}$-to-default CDS", "abstract": "We derive some rather general, but complicated, formulae to compute the survival function and the first passage time distribution of the $n^\\text{th}$ coordinate of a many-body stochastic process in the presence of a killing barrier. First we will study the case of two coordinates and then we will generalize the results to three or more coordinates. Even if the results are difficult to implement, we will provide examples of their use applying them to a physical system, the single file diffusion, and to the financial problem of pricing a $n^\\text{th}$-to-default credit default swap ($n^\\text{th}$-CDS)", "published": "2025-09-02T14:14:30Z", "authors": ["Alessio Lapolla"], "categories": ["math.PR", "cond-mat.stat-mech", "q-fin.PR"], "doi": "", "_query": "housing price text listing", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2003.03975v1", "title": "Price-aware Recommendation with Graph Convolutional Networks", "abstract": "In recent years, much research effort on recommendation has been devoted to mining user behaviors, i.e., collaborative filtering, along with the general information which describes users or items, e.g., textual attributes, categorical demographics, product images, and so on. Price, an important factor in marketing --- which determines whether a user will make the final purchase decision on an item --- surprisingly, has received relatively little scrutiny. In this work, we aim at developing an effective method to predict user purchase intention with the focus on the price factor in recommender systems. The main difficulties are two-fold: 1) the preference and sensitivity of a user on item price are unknown, which are only implicitly reflected in the items that the user has purchased, and 2) how the item price affects a user's intention depends largely on the product category, that is, the perception and affordability of a user on item price could vary significantly across categories. Towards the first difficulty, we propose to model the transitive relationship between user-to-item and item-to-price, taking the inspiration from the recently developed Graph Convolution Networks (GCN). The key idea is to propagate the influence of price on users with items as the bridge, so as to make the learned user representations be price-aware. For the second difficulty, we further integrate item categories into the propagation progress and model the possible pairwise interactions for predicting user-item interactions. We conduct extensive experiments on two real-world datasets, demonstrating the effectiveness of our GCN-based method in learning the price-aware preference of users. Further analysis reveals that modeling the price awareness is particularly useful for predicting user preference on items of unexplored categories.", "published": "2020-03-09T08:57:29Z", "authors": ["Yu Zheng", "Chen Gao", "Xiangnan He", "Yong Li", "Depeng Jin"], "categories": ["cs.IR"], "doi": "", "_query": "housing price text listing", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2105.05669v1", "title": "Carbon Leakage in a European Power System with Inhomogeneous Carbon Prices", "abstract": "Global warming is one of the main threats to the future of humanity and extensive emissions of greenhouse gases are found to be the main cause of global temperature rise as well as climate change. During the last decades international attention has focused on this issue, as well as on searching for viable solutions to mitigate global warming. In this context, the pricing of greenhouse gas emissions turned out to be the most prominent mechanism: First, to lower the emissions, and second, to capture their external costs. By now, various carbon dioxide taxes have been adopted by several countries in Europe and around the world; moreover, the list of these countries is growing. However, there is no standardized approach and the price for carbon varies significantly from one country to another. Regionally diversified carbon prices in turn lead to carbon leakage, which will offset the climate protection goals. In this paper, a simplified European power system with flexible carbon prices regarding the Gross Domestic Product (GDP) is investigated. A distribution parameter that quantifies carbon leakage is defined and varied together with the base carbon price, where the combination of both parameters describes the spatially resolved price distribution, i.e. the effective carbon pricing among the European regions. It is shown that inhomogeneous carbon prices will indeed lead to significant carbon leakage across the continent, and that coal-fired electricity generation will remain a cheap and therefore major source of power in Eastern and South-Eastern Europe - representing a potential risk for the long term decarbonization targets within the European Union.", "published": "2021-05-12T14:00:30Z", "authors": ["Markus Schlott", "Omar El Sayed", "Mariia Bilousova", "Fabian Hofmann", "Alexander Kies", "Horst Stöcker"], "categories": ["econ.GN", "physics.soc-ph"], "doi": "", "_query": "housing price text listing", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2411.02661v1", "title": "Pricing and Competition for Generative AI", "abstract": "Compared to classical machine learning (ML) models, generative models offer a new usage paradigm where (i) a single model can be used for many different tasks out-of-the-box; (ii) users interact with this model over a series of natural language prompts; and (iii) the model is ideally evaluated on binary user satisfaction with respect to model outputs. Given these characteristics, we explore the problem of how developers of new generative AI software can release and price their technology. We first develop a comparison of two different models for a specific task with respect to user cost-effectiveness. We then model the pricing problem of generative AI software as a game between two different companies who sequentially release their models before users choose their preferred model for each task. Here, the price optimization problem becomes piecewise continuous where the companies must choose a subset of the tasks on which to be cost-effective and forgo revenue for the remaining tasks. In particular, we reveal the value of market information by showing that a company who deploys later after knowing their competitor's price can always secure cost-effectiveness on at least one task, whereas the company who is the first-to-market must price their model in a way that incentivizes higher prices from the latecomer in order to gain revenue. Most importantly, we find that if the different tasks are sufficiently similar, the first-to-market model may become cost-ineffective on all tasks regardless of how this technology is priced.", "published": "2024-11-04T22:52:45Z", "authors": ["Rafid Mahmood"], "categories": ["cs.GT", "cs.LG"], "doi": "", "_query": "housing price text listing", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2402.12562v2", "title": "Dynamic Pricing and Learning with Long-term Reference Effects", "abstract": "We consider a dynamic pricing problem where customer response to the current price is impacted by the customer price expectation, aka reference price. We study a simple and novel reference price mechanism where reference price is the average of the past prices offered by the seller. As opposed to the more commonly studied exponential smoothing mechanism, in our reference price mechanism the prices offered by seller have a longer term effect on the future customer expectations. We show that under this mechanism, a markdown policy is near-optimal irrespective of the parameters of the model. This matches the common intuition that a seller may be better off by starting with a higher price and then decreasing it, as the customers feel like they are getting bargains on items that are ordinarily more expensive. For linear demand models, we also provide a detailed characterization of the near-optimal markdown policy along with an efficient way of computing it. We then consider a more challenging dynamic pricing and learning problem, where the demand model parameters are apriori unknown, and the seller needs to learn them online from the customers' responses to the offered prices while simultaneously optimizing revenue. The objective is to minimize regret, i.e., the $T$-round revenue loss compared to a clairvoyant optimal policy. This task essentially amounts to learning a non-stationary optimal policy in a time-variant Markov Decision Process (MDP). For linear demand models, we provide an efficient learning algorithm with an optimal $\\tilde{O}(\\sqrt{T})$ regret upper bound.", "published": "2024-02-19T21:36:54Z", "authors": ["Shipra Agrawal", "Wei Tang"], "categories": ["cs.LG", "cs.GT"], "doi": "", "_query": "housing price text listing", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2610.25843v1", "title": "A Cue's Hidden Price: Auditing Seller Influence on AI Shoppers", "abstract": "Large language models are increasingly asked to shop on a user's behalf, which lets seller-controlled content such as badges, endorsements, and framing steer what they recommend. That poses the question at the core of generative engine optimization (GEO): not whether seller content shifts an agent's choice, but how much. The usual gauge of that influence, how often a cue wins the choice, has a blind spot: once a cue is strong enough to be picked almost every time, the choice rate can no longer show how much stronger one cue is than another. We introduce price-equivalent (PE) auditing, which removes this ceiling by pricing the cue in money. In a counterbalanced, position-controlled forced choice, we raise the cued product's price until it is chosen only half the time, and report that premium as the cue's price-equivalent: a formally defined, policy-conditioned parity threshold that makes very different cues comparable. Priced this way, cues that look equally strong at equal price differ by a wide margin, so choice-rate audits substantially under-resolve seller influence. We run the audit over 113,472 trials, seven models across families, and two product categories. To our knowledge, we also run the first price-equivalent audit of a decision-native model, Jev, which outputs a choice probability directly instead of generating text; strikingly, its profile is unlike any text model we tested, resisting most cues yet remaining susceptible to the strongest social-proof claims. We release the stimuli, logs, and code as a benchmark that reports each model's cue-price profile rather than a single score, giving developers and auditors a common, reproducible way to measure how content moves AI shopping agents.", "published": "2026-10-19T05:57:24Z", "authors": ["Felix Buecheler", "Gerardo Berbeglia", "Mingyu Guo"], "categories": ["cs.AI"], "doi": "", "_query": "housing price text listing", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2310.03105v1", "title": "Efficiency of the Generalized Second-Price Auction for Value Maximizers", "abstract": "We study the price of anarchy of the generalized second-price auction where bidders are value maximizers (i.e., autobidders). We show that in general the price of anarchy can be as bad as $0$. For comparison, the price of anarchy of running VCG is $1/2$ in the autobidding world. We further show a fined-grained price of anarchy with respect to the discount factors (i.e., the ratios of click probabilities between lower slots and the highest slot in each auction) in the generalized second-price auction, which highlights the qualitative relation between the smoothness of the discount factors and the efficiency of the generalized second-price auction.", "published": "2023-10-04T18:49:54Z", "authors": ["Yuan Deng", "Mohammad Mahdian", "Jieming Mao", "Vahab Mirrokni", "Hanrui Zhang", "Song Zuo"], "categories": ["cs.GT"], "doi": "", "_query": "housing price text listing", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2609.35111v1", "title": "The missing price feedback: Why studies overstate local peaks from synchronized home batteries under dynamic pricing", "abstract": "Existing studies consistently find that home batteries and electric vehicles optimized against real-time electricity prices create new load peaks in local distribution grids, because all assets respond to the same price signal in sync. However, all but one of the 18 studies reviewed here treat wholesale prices as exogenous. This paper argues that treating prices as non-responsive biases the result: in reality, charging in low-price hours raises the wholesale price, which dampens the incentive to charge. I test this by simulating households with rooftop solar and home batteries under a spot-based retail tariff, calibrated to German data for 2025 with a model of equilibrium wholesale prices. With exogenous prices, my results confirm the finding from the previous literature: synchronous charging pushes the coincident import peak of the local grid to 83% above its no-battery level. With endogenous prices, the same fleet leaves the peak 6% below the no-battery benchmark. In other words, treating prices as endogenous reduces the coincident peak at high battery penetration nearly by half. This finding is robust across alternative price functions and exogenous price paths, different measures of the coincident peak, 25 scenario variations and three historical years. I conclude that new local peaks remain possible, particularly in grids where battery deployment runs ahead of the national average, but the risk and magnitude are considerably smaller than the existing literature suggests.", "published": "2026-09-28T13:15:13Z", "authors": ["Lion Hirth"], "categories": ["econ.GN"], "doi": "", "_query": "housing price text listing", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/1404.5943v5", "title": "Price of Anarchy for Auction Revenue", "abstract": "This paper develops tools for welfare and revenue analyses of Bayes-Nash equilibria in asymmetric auctions with single-dimensional agents. We employ these tools to derive price of anarchy results for social welfare and revenue. Our approach separates the standard smoothness framework into two distinct parts, isolating the analysis common to any auction from the analysis specific to a given auction. The first part relates a bidder's contribution to welfare in equilibrium to their contribution to welfare in the optimal auction using the price the bidder faces for additional allocation. Intuitively, either an agent's utility and hence contribution to welfare is high, or the price she has to pay for additional allocation is high relative to her value. We call this condition value covering; it holds in every Bayes-Nash equilibrium of any auction. The second part, revenue covering, relates the prices bidders face for additional allocation to the revenue of the auction, using an auction's rules and feasibility constraints. Combining the two parts gives approximation results to the optimal welfare, and, under the right conditions, the optimal revenue. In mechanisms with reserve prices, our welfare results show approximation with respect to the optimal mechanism with the same reserves. As a center-piece result, we analyze the single-item first-price auction with individual monopoly reserves. When each distribution satisfies a regularity condition the auction's revenue is at least a $2e/(e-1) \\approx 3.16$ approximation to the revenue of the optimal auction. We also give bounds for matroid auctions with first-price or all-pay semantics, and the generalized first-price position auction. Finally, we give an extension theorem for simultaneous composition, i.e., when multiple auctions are run simultaneously, with single-valued, unit-demand agents.", "published": "2014-04-23T19:57:35Z", "authors": ["Jason Hartline", "Darrell Hoy", "Sam Taggart"], "categories": ["cs.GT"], "doi": "", "_query": "housing price text listing", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/1112.3680v2", "title": "The Robust Price of Anarchy of Altruistic Games", "abstract": "We study the inefficiency of equilibria for various classes of games when players are (partially) altruistic. We model altruistic behavior by assuming that player i's perceived cost is a convex combination of 1-α_i times his direct cost and α_i times the social cost. Tuning the parameters α_i allows smooth interpolation between purely selfish and purely altruistic behavior. Within this framework, we study altruistic extensions of linear congestion games, fair cost-sharing games and valid utility games. We derive (tight) bounds on the price of anarchy of these games for several solution concepts. Thereto, we suitably adapt the smoothness notion introduced by Roughgarden and show that it captures the essential properties to determine the robust price of anarchy of these games. Our bounds show that for congestion games and cost-sharing games, the worst-case robust price of anarchy increases with increasing altruism, while for valid utility games, it remains constant and is not affected by altruism. However, the increase in the price of anarchy is not a universal phenomenon: for symmetric singleton linear congestion games, we derive a bound on the pure price of anarchy that decreases as the level of altruism increases. Since the bound is also strictly lower than the robust price of anarchy, it exhibits a natural example in which Nash equilibria are more efficient than more permissive notions of equilibrium.", "published": "2011-12-15T21:51:12Z", "authors": ["Po-An Chen", "Bart de Keijzer", "David Kempe", "Guido Schaefer"], "categories": ["cs.GT"], "doi": "", "_query": "housing price text listing", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/1801.09079v2", "title": "Using Additional Indexes for Fast Full-Text Search of Phrases That Contain Frequently Used Words", "abstract": "Searches for phrases and word sets in large text arrays by means of additional indexes are considered. Their use may reduce the query-processing time by an order of magnitude in comparison with standard inverted files.", "published": "2018-01-27T12:02:03Z", "authors": ["A. B. Veretennikov"], "categories": ["cs.IR", "cs.CL"], "doi": "", "_query": "housing price text listing", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/1303.2478v2", "title": "The Price of Connectivity for Vertex Cover", "abstract": "The vertex cover number of a graph is the minimum number of vertices that are needed to cover all edges. When those vertices are further required to induce a connected subgraph, the corresponding number is called the connected vertex cover number, and is always greater or equal to the vertex cover number. Connected vertex covers are found in many applications, and the relationship between those two graph invariants is therefore a natural question to investigate. For that purpose, we introduce the {\\em Price of Connectivity}, defined as the ratio between the two vertex cover numbers. We prove that the price of connectivity is at most 2 for arbitrary graphs. We further consider graph classes in which the price of connectivity of every induced subgraph is bounded by some real number $t$. We obtain forbidden induced subgraph characterizations for every real value $t \\leq 3/2$. We also investigate critical graphs for this property, namely, graphs whose price of connectivity is strictly greater than that of any proper induced subgraph. Those are the only graphs that can appear in a forbidden subgraph characterization for the hereditary property of having a price of connectivity at most $t$. In particular, we completely characterize the critical graphs that are also chordal. Finally, we also consider the question of computing the price of connectivity of a given graph. Unsurprisingly, the decision version of this question is NP-hard. In fact, we show that it is even complete for the class $Θ_2^P = P^{NP[\\log]}$, the class of decision problems that can be solved in polynomial time, provided we can make $O(\\log n)$ queries to an NP-oracle. This paves the way for a thorough investigation of the complexity of problems involving ratios of graph invariants.", "published": "2013-03-11T10:35:51Z", "authors": ["Eglantine Camby", "Jean Cardinal", "Samuel Fiorini", "Oliver Schaudt"], "categories": ["cs.DM", "math.CO"], "doi": "", "_query": "housing price text listing", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/nlin/0403056v1", "title": "Majority Orienting Model for the Oscillation of Market Price", "abstract": "The present paper introduces a majority orienting model in which the dealers' behavior changes based on the influence of the price to show the oscillation of stock price in the stock market. We show the oscillation of the price for the model by applying the van der Pol equation which is a deterministic approximation of our model.", "published": "2004-03-31T08:03:59Z", "authors": ["Hisanao Takahashi", "Yoshiaki Itoh"], "categories": ["nlin.SI", "q-fin.TR"], "doi": "10.1140/epjb/e2004-00054-8", "_query": "housing price text listing", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2204.06518v1", "title": "A pipeline and comparative study of 12 machine learning models for text classification", "abstract": "Text-based communication is highly favoured as a communication method, especially in business environments. As a result, it is often abused by sending malicious messages, e.g., spam emails, to deceive users into relaying personal information, including online accounts credentials or banking details. For this reason, many machine learning methods for text classification have been proposed and incorporated into the services of most email providers. However, optimising text classification algorithms and finding the right tradeoff on their aggressiveness is still a major research problem. We present an updated survey of 12 machine learning text classifiers applied to a public spam corpus. A new pipeline is proposed to optimise hyperparameter selection and improve the models' performance by applying specific methods (based on natural language processing) in the preprocessing stage. Our study aims to provide a new methodology to investigate and optimise the effect of different feature sizes and hyperparameters in machine learning classifiers that are widely used in text classification problems. The classifiers are tested and evaluated on different metrics including F-score (accuracy), precision, recall, and run time. By analysing all these aspects, we show how the proposed pipeline can be used to achieve a good accuracy towards spam filtering on the Enron dataset, a widely used public email corpus. Statistical tests and explainability techniques are applied to provide a robust analysis of the proposed pipeline and interpret the classification outcomes of the 12 machine learning models, also identifying words that drive the classification results. Our analysis shows that it is possible to identify an effective machine learning model to classify the Enron dataset with an F-score of 94%.", "published": "2022-04-04T23:51:22Z", "authors": ["Annalisa Occhipinti", "Louis Rogers", "Claudio Angione"], "categories": ["cs.IR", "cs.CL", "cs.LG"], "doi": "", "_query": "housing price text listing", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2604.21102v1", "title": "Leveraging Multimodal LLMs for Built Environment and Housing Attribute Assessment from Street-View Imagery", "abstract": "We present a novel framework for automatically evaluating building conditions nationwide in the United States by leveraging large language models (LLMs) and Google Street View (GSV) imagery. By fine-tuning Gemma 3 27B on a modest human-labeled dataset, our approach achieves strong alignment with human mean opinion scores (MOS), outperforming even individual raters on SRCC and PLCC relative to the MOS benchmark. To enhance efficiency, we apply knowledge distillation, transferring the capabilities of Gemma 3 27B to a smaller Gemma 3 4B model that achieves comparable performance with a 3x speedup. Further, we distill the knowledge into a CNN-based model (EfficientNetV2-M) and a transformer (SwinV2-B), delivering close performance while achieving a 30x speed gain. Furthermore, we investigate LLMs' capabilities for assessing an extensive list of built environment and housing attributes through a human-AI alignment study and develop a visualization dashboard that integrates LLM assessment outcomes for downstream analysis by homeowners. Our framework offers a flexible and efficient solution for large-scale building condition assessment, enabling high accuracy with minimal human labeling effort.", "published": "2026-04-22T21:42:09Z", "authors": ["Siyuan Yao", "Siavash Ghorbany", "Kuangshi Ai", "Arnav Cherukuthota", "Meghan Forstchen", "Alexis Korotasz", "Matthew Sisk", "Ming Hu", "Chaoli Wang"], "categories": ["cs.CV", "cs.AI"], "doi": "", "_query": "housing price text listing", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2405.08089v1", "title": "Comparative Study of Bitcoin Price Prediction", "abstract": "Prediction of stock prices has been a crucial and challenging task, especially in the case of highly volatile digital currencies such as Bitcoin. This research examineS the potential of using neural network models, namely LSTMs and GRUs, to forecast Bitcoin's price movements. We employ five-fold cross-validation to enhance generalization and utilize L2 regularization to reduce overfitting and noise. Our study demonstrates that the GRUs models offer better accuracy than LSTMs model for predicting Bitcoin's price. Specifically, the GRU model has an MSE of 4.67, while the LSTM model has an MSE of 6.25 when compared to the actual prices in the test set data. This finding indicates that GRU models are better equipped to process sequential data with long-term dependencies, a characteristic of financial time series data such as Bitcoin prices. In summary, our results provide valuable insights into the potential of neural network models for accurate Bitcoin price prediction and emphasize the importance of employing appropriate regularization techniques to enhance model performance.", "published": "2024-05-13T18:10:34Z", "authors": ["Ali Mohammadjafari"], "categories": ["q-fin.ST", "cs.LG"], "doi": "", "_query": "housing price text listing", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2603.29763v1", "title": "Option Pricing on Automated Market Maker Tokens", "abstract": "We derive the stochastic price process for tokens whose sole price discovery mechanism is a constant-product automated market maker (AMM). When the net flow into the pool follows a diffusion, the token price follows a constant elasticity of variance (CEV) process, nesting Black-Scholes as the limiting case of infinite liquidity. We obtain closed-form European option prices and introduce liquidity-adjusted Greeks. The CEV structure generates a leverage effect -- volatility rises as price falls -- whose normalized implied volatility skew depends only on the pool's weighting parameter, not on pool depth: Black-Scholes underprices 20%-out-of-the-money puts by roughly 6% in implied volatility terms at every pool depth, while the absolute pricing discrepancy vanishes as pools deepen. Empirically, after controlling for pool depth and flow volatility, realized return variance across 90 Bittensor subnets exhibits a strongly negative price elasticity, decisively rejecting geometric Brownian motion and consistent with the CEV prediction. A complementary delta-hedged backtest across 82 subnets confirms near-identical hedging errors at the money, consistent with the prediction that pricing differences are concentrated in the wings.", "published": "2026-03-31T14:03:00Z", "authors": ["Philip Z. Maymin"], "categories": ["q-fin.PR", "q-fin.ST", "q-fin.TR"], "doi": "", "_query": "housing price text listing", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2106.08274v2", "title": "Elasticity Based Demand Forecasting and Price Optimization for Online Retail", "abstract": "We study a problem of an online retailer who observes the unit sales of a product, and dynamically changes the retail price, in order to maximize the expected revenue. Assuming the demand of the product is price sensitive, we are interested in the optimal pricing policy when future demand is uncertain. We build a system to investigate the relationship between retail price and demand and estimate the demand function. The system predicts demand and revenue at a given retail price. We formulate a revenue maximization problem over a discrete finite time horizon with discrete retail price. The optimal pricing policy is solved based on the predicted demand and revenue values. With computational experiments, we investigate the effect of optimal pricing policy to inventory management.", "published": "2021-06-03T22:23:32Z", "authors": ["Chengcheng Liu", "Mátyás A. Sustik"], "categories": ["eess.SY", "cs.CE", "math.OC"], "doi": "", "_query": "housing price text listing", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2003.00397v1", "title": "Intelligent Home 3D: Automatic 3D-House Design from Linguistic Descriptions Only", "abstract": "Home design is a complex task that normally requires architects to finish with their professional skills and tools. It will be fascinating that if one can produce a house plan intuitively without knowing much knowledge about home design and experience of using complex designing tools, for example, via natural language. In this paper, we formulate it as a language conditioned visual content generation problem that is further divided into a floor plan generation and an interior texture (such as floor and wall) synthesis task. The only control signal of the generation process is the linguistic expression given by users that describe the house details. To this end, we propose a House Plan Generative Model (HPGM) that first translates the language input to a structural graph representation and then predicts the layout of rooms with a Graph Conditioned Layout Prediction Network (GC LPN) and generates the interior texture with a Language Conditioned Texture GAN (LCT-GAN). With some post-processing, the final product of this task is a 3D house model. To train and evaluate our model, we build the first Text-to-3D House Model dataset.", "published": "2020-03-01T04:28:48Z", "authors": ["Qi Chen", "Qi Wu", "Rui Tang", "Yuhan Wang", "Shuai Wang", "Mingkui Tan"], "categories": ["cs.CV"], "doi": "", "_query": "housing price text listing", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/1102.3009v1", "title": "Non - Randomness Stock Market Price Model", "abstract": "A new model for the stock market price analysis is proposed. It is suggested to look at price as an everywhere discontinuous function of time of bounded variation.", "published": "2011-02-15T09:28:47Z", "authors": ["Aleksey Kharevsky"], "categories": ["q-fin.GN"], "doi": "", "_query": "housing price text listing", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2304.01709v1", "title": "With great power (prices) comes great tail pipe emissions? \\\\ A natural experiment of electricity prices and electric car adoption", "abstract": "A fundemantal and unanswered question for the widely shared goal of electrifying passenger vehicles is how the price of electricity, which can vary greatly across countries and regions, affects buying behavior. I make use of a natural experiment in Norway in the period 2021-2022 when large price differences between north and south emerged to estimate the effect of electricity prices on the decision to purchase a pure battery-electric vehicle. Simple difference estimates along the border of the price zones as well as a difference-in-difference regression model suggest a significant but economically modest effect of a 2-4\\% reduction in the probability of purchasing an electric vehicle in the high price zone. A counterfactual simulation suggests that there would have been about 3000 to 6000 fewer electric vehicles sold in the high-price south compared to a scenario where the south had equally low prices as in the north.", "published": "2023-04-04T11:14:26Z", "authors": ["Johannes Mauritzen"], "categories": ["econ.GN", "stat.AP"], "doi": "", "_query": "housing price text listing", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/cond-mat/0403761v3", "title": "Long memory stochastic volatility in option pricing", "abstract": "The aim of this paper is to present a simple stochastic model that accounts for the effects of a long-memory in volatility on option pricing. The starting point is the stochastic Black-Scholes equation involving volatility with long-range dependence. We consider the option price as a sum of classical Black-Scholes price and random deviation describing the risk from the random volatility. By using the fact the option price and random volatility change on different time scales, we find the asymptotic equation for the derivation involving fractional Brownian motion. The solution to this equation allows us to find the pricing bands for options.", "published": "2004-03-31T14:28:37Z", "authors": ["Sergei Fedotov", "Abby Tan"], "categories": ["cond-mat.other", "q-fin.PR"], "doi": "", "_query": "housing price text listing", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/1901.01030v3", "title": "Multi-Product Dynamic Pricing in High-Dimensions with Heterogeneous Price Sensitivity", "abstract": "We consider the problem of multi-product dynamic pricing, in a contextual setting, for a seller of differentiated products. In this environment, the customers arrive over time and products are described by high-dimensional feature vectors. Each customer chooses a product according to the widely used Multinomial Logit (MNL) choice model and her utility depends on the product features as well as the prices offered. The seller a-priori does not know the parameters of the choice model but can learn them through interactions with customers. The seller's goal is to design a pricing policy that maximizes her cumulative revenue. This model is motivated by online marketplaces such as Airbnb platform and online advertising. We measure the performance of a pricing policy in terms of regret, which is the expected revenue loss with respect to a clairvoyant policy that knows the parameters of the choice model in advance and always sets the revenue-maximizing prices. We propose a pricing policy, named M3P, that achieves a $T$-period regret of $O(\\log(Td) ( \\sqrt{T}+ d\\log(T)))$ under heterogeneous price sensitivity for products with features of dimension $d$. We also use tools from information theory to prove that no policy can achieve worst-case $T$-regret better than $Ω(\\sqrt{T})$.", "published": "2019-01-04T09:33:41Z", "authors": ["Adel Javanmard", "Hamid Nazerzadeh", "Simeng Shao"], "categories": ["stat.ML", "cs.GT", "cs.LG"], "doi": "", "_query": "housing price text listing", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2609.38554v1", "title": "Auctions with Price Predictions", "abstract": "We design auctions for the sale of a single item with unlimited supply given a single prediction of the revenue-maximizing uniform price. This departs from prior work on auctions with predictions which typically assumes predictions of every bidder's value. Our main result is a characterization of the Pareto frontier for consistency and robustness attainable by any universally truthful auction. We show that a mechanism that randomizes between posting the predicted price and conducting an optimal prior-free fallback auction is Pareto-optimal. We then extend our results to achieve graceful degradation of revenue as a function of the prediction accuracy. Finally, we study how such a prediction can be obtained from historical market data through the lens of learning theory. Together, our results give an end-to-end account of how a price prediction can be learned and used robustly to maximize auction revenue.", "published": "2026-09-29T21:14:49Z", "authors": ["Muthu Sundar", "Alec Sun", "Siddharth Prasad", "Dravyansh Sharma"], "categories": ["cs.GT"], "doi": "", "_query": "housing price text listing", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2410.07893v2", "title": "Ormer: A Manipulation-resistant and Gas-efficient Blockchain Pricing Oracle for DeFi", "abstract": "Price feeds of cryptocurrencies are essential for Decentralized Finance (DeFi) applications to realize fundamental trading and exchanging functionalities, which are retrieved from external price data sources such as exchanges and input to on-chain smart contracts in real-time. Currently, arithmetic mean based time-weighted average price (TWAP) oracles are widely used to process price feeds by averaging asset price with short time frame to achieve reliable and gas-efficient pricing. However, recent research indicates that TWAP is vulnerable to price manipulation attacks, resulting in abnormal price fluctuations and severe financial loss. Even worse, TWAP oracles usually set a relatively long time frame setting to prevent such attack. However, it would further introduce long delays and high price deviation errors from the market asset price. To address this issue, we propose a novel on-chain gas-efficient pricing algorithm (Ormer) that heuristically estimates the median of asset price within an observation window based on a piecewise-parabolic formula, while the time delay is suppressed by fusing estimations with different window sizes. Our evaluation based on multiple pairs of token swapping price feed across different chains show that Ormer reduces the mean absolute price error by 15.3% and the time delay by 49.3% compared to TWAP. For gas efficiency, regardless of the number of price observations, an encoding mechanism with constant storage requirement is employed without saving all the historical data for median estimation. Surprisingly, the lowest gas consumption of Ormer is even 15.2% less than TWAP, and the oracle querying fee would be saved up to ~20K USD per day for DeFi participants.", "published": "2024-10-10T13:18:00Z", "authors": ["Dongbin Bai", "Jiannong Cao", "Yinfeng Cao", "Long Wen", "Milos Stojmenovic"], "categories": ["cs.CR"], "doi": "", "_query": "housing price text listing", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/1110.3133v1", "title": "Price impact asymmetry of institutional trading in Chinese stock market", "abstract": "The asymmetric price impact between the institutional purchases and sales of 32 liquid stocks in Chinese stock markets in year 2003 is carefully studied. We analyze the price impact in both drawup and drawdown trends with consecutive positive and negative daily price changes, and test the dependence of the price impact asymmetry on the market condition. For most of the stocks institutional sales have a larger price impact than institutional purchases, and larger impact of institutional purchases only exists in few stocks with primarily increasing tendencies. We further study the mean return of trades surrounding institutional transactions, and find the asymmetric behavior also exists before and after institutional transactions. A new variable is proposed to investigate the order book structure, and it can partially explain the price impact of institutional transactions. A linear regression for the price impact of institutional transactions further confirms our finding that institutional sales primarily have a larger price impact than institutional purchases in the bearish year 2003.", "published": "2011-10-14T07:44:20Z", "authors": ["Fei Ren", "Li-Xin Zhong"], "categories": ["q-fin.TR"], "doi": "10.1016/j.physa.2011.12.049", "_query": "housing price text listing", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2403.06303v3", "title": "A Unifying Approach for the Pricing of Debt Securities", "abstract": "We propose a unifying framework for the pricing of debt securities under general time-inhomogeneous short-rate diffusion processes. The pricing of bonds, bond options, callable/putable bonds, and convertible bonds (CBs) is covered. Using continuous-time Markov chain (CTMC) approximations, we obtain closed-form matrix expressions to approximate the price of bonds and bond options under general one-dimensional short-rate processes. A simple and efficient algorithm is also developed to price callable/putable debt. The availability of a closed-form expression for the price of zero-coupon bonds allows for the perfect fit of the approximated model to the current market term structure of interest rates, regardless of the complexity of the underlying diffusion process selected. We further consider the pricing of CBs under general bi-dimensional time-inhomogeneous diffusion processes to model equity and short-rate dynamics. Credit risk is also incorporated into the model using the approach of Tsiveriotis and Fernandes (1998). Based on a two-layer CTMC method, an efficient algorithm is developed to approximate the price of convertible bonds. When conversion is only allowed at maturity, a closed-form matrix expression is obtained. Numerical experiments show the accuracy and efficiency of the method across a wide range of model parameters and short-rate models.", "published": "2024-03-10T20:39:01Z", "authors": ["Marie-Claude Vachon", "Anne Mackay"], "categories": ["q-fin.PR", "q-fin.CP", "q-fin.MF"], "doi": "", "_query": "housing price text listing", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2304.05012v1", "title": "Human-machine cooperation for semantic feature listing", "abstract": "Semantic feature norms, lists of features that concepts do and do not possess, have played a central role in characterizing human conceptual knowledge, but require extensive human labor. Large language models (LLMs) offer a novel avenue for the automatic generation of such feature lists, but are prone to significant error. Here, we present a new method for combining a learned model of human lexical-semantics from limited data with LLM-generated data to efficiently generate high-quality feature norms.", "published": "2023-04-11T06:38:04Z", "authors": ["Kushin Mukherjee", "Siddharth Suresh", "Timothy T. Rogers"], "categories": ["cs.CL", "cs.AI"], "doi": "", "_query": "housing price text listing", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2008.11788v1", "title": "Share Price Prediction of Aerospace Relevant Companies with Recurrent Neural Networks based on PCA", "abstract": "The capital market plays a vital role in marketing operations for aerospace industry. However, due to the uncertainty and complexity of the stock market and many cyclical factors, the stock prices of listed aerospace companies fluctuate significantly. This makes the share price prediction challengeable. To improve the prediction of share price for aerospace industry sector and well understand the impact of various indicators on stock prices, we provided a hybrid prediction model by the combination of Principal Component Analysis (PCA) and Recurrent Neural Networks. We investigated two types of aerospace industries (manufacturer and operator). The experimental results show that PCA could improve both accuracy and efficiency of prediction. Various factors could influence the performance of prediction models, such as finance data, extracted features, optimisation algorithms, and parameters of the prediction model. The selection of features may depend on the stability of historical data: technical features could be the first option when the share price is stable, whereas fundamental features could be better when the share price has high fluctuation. The delays of RNN also depend on the stability of historical data for different types of companies. It would be more accurate through using short-term historical data for aerospace manufacturers, whereas using long-term historical data for aerospace operating airlines. The developed model could be an intelligent agent in an automatic stock prediction system, with which, the financial industry could make a prompt decision for their economic strategies and business activities in terms of predicted future share price, thus improving the return on investment. Currently, COVID-19 severely influences aerospace industries. The developed approach can be used to predict the share price of aerospace industries at post COVID-19 time.", "published": "2020-08-26T20:16:33Z", "authors": ["Linyu Zheng", "Hongmei He"], "categories": ["q-fin.ST", "cs.LG"], "doi": "", "_query": "housing price text listing", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2303.12527v2", "title": "The Market Price of Jump Risk for Delivery Periods: Pricing of Electricity Swaps with Geometric Averaging", "abstract": "In this paper, we extend the market price of risk for delivery periods (MPDP) of electricity swap contracts by introducing a dimension for jump risk. As introduced by Kemper et al. (2022), the MPDP arises through the use of geometric averaging while pricing electricity swaps in a geometric framework. We adjust the work by Kemper et al. (2022) in two directions: First, we examine a Merton type model taking jumps into account. Second, we transfer the model to the physical measure by implementing mean-reverting behavior. We compare swap prices resulting from the classical arithmetic (approximated) average to the geometric weighted average. Under the physical measure, we discover a decomposition of the swap's market price of risk into the classical one and the MPDP.", "published": "2023-03-22T13:02:30Z", "authors": ["Annika Kemper", "Maren Diane Schmeck"], "categories": ["q-fin.PR", "q-fin.MF"], "doi": "", "_query": "housing price text listing", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2010.09285v1", "title": "Equilibrium price in intraday electricity markets", "abstract": "We formulate an equilibrium model of intraday trading in electricity markets. Agents face balancing constraints between their customers consumption plus intraday sales and their production plus intraday purchases. They have continuously updated forecast of their customers consumption at maturity with decreasing volatility error. Forecasts are prone to idiosyncratic noise as well as common noise (weather). Agents production capacities are subject to independent random outages, which are each modelled by a Markov chain. The equilibrium price is defined as the price that minimises trading cost plus imbalance cost of each agent and satisfies the usual market clearing condition. Existence and uniqueness of the equilibrium are proved, and we show that the equilibrium price and the optimal trading strategies are martingales. The main economic insights are the following. (i) When there is no uncertainty on generation, it is shown that the market price is a convex combination of forecasted marginal cost of each agent, with deterministic weights. Furthermore, the equilibrium market price follows Almgren and Chriss's model and we identify the fundamental part as well as the permanent market impact. It turns out that heterogeneity across agents is a necessary condition for the Samuelson's effect to hold. (ii) When there is production uncertainty, the price volatility becomes stochastic but converges to the case without production uncertainty when the number of agents increases to infinity. Further, on a two-agent case, we show that the potential outages of a low marginal cost producer reduces her sales position.", "published": "2020-10-19T07:56:50Z", "authors": ["René Aid", "Andrea Cosso", "Huyên Pham"], "categories": ["q-fin.CP"], "doi": "", "_query": "housing price text listing", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2512.02247v1", "title": "An exact pricing algorithm for revenue maximization under the logit demand function", "abstract": "Determining the optimal selling price is a challenge in revenue management, especially in markets characterized by nonlinear and price-sensitive demand. While traditional models, such as linear, power, and exponential demand functions, offer analytical convenience, they often fail to capture realistic purchase dynamics, leading to suboptimal pricing. The logit demand function addresses these limitations through its bounded, S-shaped curve, offering a more realistic representation of consumer behavior. Despite its advantages, most existing literature relies on heuristic approaches, such as pricing at the inflection point, which prioritizes maximum price sensitivity but does not guarantee maximum revenue. This study proposes a novel, exact pricing algorithm that analytically derives the revenue-maximizing price under the logit demand function using the Lambert W function. By providing a closed-form solution, the approach eliminates reliance on heuristic iterative methods and corrects the common practice of considering the inflection point price as market price. In fact, we demonstrate that the optimal price is consistently lower than the inflection-point price under reasonable assumptions, leading to lower prices for consumers and higher revenue for sellers. Numerical experiments illustrate the proposed algorithm and examine the changes in the optimality gap as demand function parameters vary. Results indicate that the optimal price is consistently lower than the inflection-point price, with an average 20% price reduction accompanied by a 15% increase in revenue.", "published": "2025-12-01T22:33:41Z", "authors": ["Moddassir Khan Nayeem", "Omar Abbaas", "Suzan Alaswad", "Sinan Salman"], "categories": ["math.OC"], "doi": "10.1016/j.eswa.2026.132223", "_query": "housing price text listing", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/1501.07473v2", "title": "Information in stock prices and some consequences: A model-free approach", "abstract": "The price of a stock will rarely follow the assumed model and a curious investor or a Regulatory Authority may wish to obtain a probability model the prices support. A risk neutral probability ${\\cal P}^*$ for the stock's price at time $T$ is determined in closed form from the prices before $T$ without assuming a price model. The findings indicate that ${\\cal P}^*$ may be a mixture. Under mild conditions on the prices the necessary and sufficient condition to obtain ${\\cal P}^*$ is the coincidence at $T$ of the stock price ranges assumed by the stock's trader and buyer. This result clarifies the relation between market's informational efficiency and the arbitrage-free option pricing methodology. It also shows that in an incomplete market there are risk neutral probabilities not supported by each stock and their use can be limited. ${\\cal P}^*$-price $C$ for the stock's European call option expiring at $T$ is obtained. Among other results it is shown for \"calm\" prices, like the log-normal, that i) $C$ is the Black-Scholes-Merton price thus confirming its validity for various stock prices, ii) the buyer's price carries an exponentially increasing volatility premium and its difference with $C$ provides a measure of the market risk premium.", "published": "2015-01-24T19:27:57Z", "authors": ["Yannis G. Yatracos"], "categories": ["q-fin.GN", "q-fin.MF"], "doi": "", "_query": "housing price text listing", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2205.01875v2", "title": "Machine Learning based Framework for Robust Price-Sensitivity Estimation with Application to Airline Pricing", "abstract": "We consider the problem of dynamic pricing of a product in the presence of feature-dependent price sensitivity. Developing practical algorithms that can estimate price elasticities robustly, especially when information about no purchases (losses) is not available, to drive such automated pricing systems is a challenge faced by many industries. Based on the Poisson semi-parametric approach, we construct a flexible yet interpretable demand model where the price related part is parametric while the remaining (nuisance) part of the model is non-parametric and can be modeled via sophisticated machine learning (ML) techniques. The estimation of price-sensitivity parameters of this model via direct one-stage regression techniques may lead to biased estimates due to regularization. To address this concern, we propose a two-stage estimation methodology which makes the estimation of the price-sensitivity parameters robust to biases in the estimators of the nuisance parameters of the model. In the first-stage we construct estimators of observed purchases and prices given the feature vector using sophisticated ML estimators such as deep neural networks. Utilizing the estimators from the first-stage, in the second-stage we leverage a Bayesian dynamic generalized linear model to estimate the price-sensitivity parameters. We test the performance of the proposed estimation schemes on simulated and real sales transaction data from the Airline industry. Our numerical studies demonstrate that our proposed two-stage approach reduces the estimation error in price-sensitivity parameters from 25\\% to 4\\% in realistic simulation settings. The two-stage estimation techniques proposed in this work allows practitioners to leverage modern ML techniques to robustly estimate price-sensitivities while still maintaining interpretability and allowing ease of validation of its various constituent parts.", "published": "2022-05-04T03:35:12Z", "authors": ["Ravi Kumar", "Shahin Boluki", "Karl Isler", "Jonas Rauch", "Darius Walczak"], "categories": ["stat.ML", "cs.LG", "econ.EM", "stat.AP"], "doi": "", "_query": "housing price text listing", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/1703.00062v1", "title": "Optimal Investment and Pricing in the Presence of Defaults", "abstract": "We consider the optimal investment problem when the traded asset may default, causing a jump in its price. For an investor with constant absolute risk aversion, we compute indifference prices for defaultable bonds, as well as a price for dynamic protection against default. For the latter problem, our work complements Sircar & Zariphopoulou (2007), where it is implicitly assumed the investor is protected against default. We consider a factor model where the asset's instantaneous return, variance, correlation and default intensity are driven by a time-homogenous diffusion X taking values in an arbitrary region E. We identify the certainty equivalent with a semi-linear degenerate elliptic partial differential equation with quadratic growth in both function and gradient. Under a minimal integrability assumption on the market price of risk, we show the certainty equivalent is a classical solution. In particular, our results cover when X is a one-dimensional affine diffusion and when returns, variances and default intensities are also affine. Numerical examples highlight the relationship between the factor process and both the indifference price and default insurance. Lastly, we show the insurance protection price is not the default intensity under the dual optimal measure.", "published": "2017-02-28T21:23:28Z", "authors": ["Tetsuya Ishikawa", "Scott Robertson"], "categories": ["q-fin.MF", "q-fin.RM"], "doi": "", "_query": "housing price text listing", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2408.12523v1", "title": "Weighted Envy-Freeness in House Allocation", "abstract": "The classic house allocation problem involves assigning $m$ houses to $n$ agents based on their utility functions, ensuring each agent receives exactly one house. A key criterion in these problems is satisfying fairness constraints such as envy-freeness. We extend this problem by considering agents with arbitrary weights, focusing on the concept of weighted envy-freeness, which has been extensively studied in fair division. We present a polynomial-time algorithm to determine whether weighted envy-free allocations exist and, if so, to compute one. Since weighted envy-free allocations do not always exist, we also investigate the potential of achieving such allocations through the use of subsidies. We provide several characterizations for weighted envy-freeable allocations (allocations that can be turned weighted envy-free by introducing subsidies) and show that they do not always exist, which is different from the unweighted setting. Furthermore, we explore the existence of weighted envy-freeable allocations in specific scenarios and outline the conditions under which they exist.", "published": "2024-08-22T16:26:47Z", "authors": ["Sijia Dai", "Yankai Chen", "Xiaowei Wu", "Yicheng Xu", "Yong Zhang"], "categories": ["cs.GT"], "doi": "", "_query": "housing price text listing", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2301.12661v1", "title": "Make-An-Audio: Text-To-Audio Generation with Prompt-Enhanced Diffusion Models", "abstract": "Large-scale multimodal generative modeling has created milestones in text-to-image and text-to-video generation. Its application to audio still lags behind for two main reasons: the lack of large-scale datasets with high-quality text-audio pairs, and the complexity of modeling long continuous audio data. In this work, we propose Make-An-Audio with a prompt-enhanced diffusion model that addresses these gaps by 1) introducing pseudo prompt enhancement with a distill-then-reprogram approach, it alleviates data scarcity with orders of magnitude concept compositions by using language-free audios; 2) leveraging spectrogram autoencoder to predict the self-supervised audio representation instead of waveforms. Together with robust contrastive language-audio pretraining (CLAP) representations, Make-An-Audio achieves state-of-the-art results in both objective and subjective benchmark evaluation. Moreover, we present its controllability and generalization for X-to-Audio with \"No Modality Left Behind\", for the first time unlocking the ability to generate high-definition, high-fidelity audios given a user-defined modality input. Audio samples are available at https://Text-to-Audio.github.io", "published": "2023-01-30T04:44:34Z", "authors": ["Rongjie Huang", "Jiawei Huang", "Dongchao Yang", "Yi Ren", "Luping Liu", "Mingze Li", "Zhenhui Ye", "Jinglin Liu", "Xiang Yin", "Zhou Zhao"], "categories": ["cs.SD", "cs.LG", "cs.MM", "eess.AS"], "doi": "", "_query": "housing price text listing", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/math/0702828v1", "title": "Price systems for markets with transaction costs and control problems for some finance problems", "abstract": "In a market with transaction costs, the price of a derivative can be expressed in terms of (preconsistent) price systems (after Kusuoka (1995)). In this paper, we consider a market with binomial model for stock price and discuss how to generate the price systems. From this, the price formula of a derivative can be reformulated as a stochastic control problem. Then the dynamic programming approach can be used to calculate the price. We also discuss optimization of expected utility using price systems.", "published": "2007-02-27T12:59:43Z", "authors": ["Tzuu-Shuh Chiang", "Shang-Yuan Shiu", "Shuenn-Jyi Sheu"], "categories": ["math.PR", "q-fin.CP"], "doi": "10.1214/074921706000001094", "_query": "housing price text listing", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/cond-mat/0308012v1", "title": "Multifractal Properties of Price Fluctuations of Stocks and Commodities", "abstract": "We analyze daily prices of 29 commodities and 2449 stocks, each over a period of $\\approx 15$ years. We find that the price fluctuations for commodities have a significantly broader multifractal spectrum than for stocks. We also propose that multifractal properties of both stocks and commodities can be attributed mainly to the broad probability distribution of price fluctuations and secondarily to their temporal organization. Furthermore, we propose that, for commodities, stronger higher order correlations in price fluctuations result in broader multifractal spectra.", "published": "2003-08-01T13:53:32Z", "authors": ["Kaushik Matia", "Yosef Ashkenazy", "H. Eugene Stanley"], "categories": ["cond-mat.stat-mech", "q-fin.ST"], "doi": "10.1209/epl/i2003-00194-y", "_query": "housing price text listing", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/math-ph/0412071v3", "title": "Pricing of options on stocks driven by multi-dimensional operator stable Levy processes", "abstract": "We model the price of a stock via a Langévin equation with multi-dimensional fluctuations coupled in the price and in time. We generalize previous models in that we assume that the fluctuations conditioned on the time step are compound Poisson processes with operator stable jump intensities. We derive exact relations for Fourier transforms of the jump intensity in case of different scaling indices $\\underline{\\underline{E}}$ of the process. We express the Fourier transform of the joint probability density of the process to attain given values at several different times and to attain a given maximal value in a given time period through Fourier transforms of the jump intensity. Then we consider a portfolio composed of stocks and of options on stocks and we derive the Fourier transform of a random variable $\\mathfrak{D}_t$ (deviation of the portfolio) that is defined as a small temporal change of the portfolio diminished by the the compound interest earned. We show that if the price of the option at time $t$ satisfies a certain functional equation specified in text then the deviation of the portfolio has a zero mean $E[ \\mathfrak{D}_t ] = 0$ and the option pricing problem may have a solution. We compare our approach to other approaches that assumed the log-characteristic function of the fluctuations that drive the stock price to be an analytic function.", "published": "2004-12-20T19:24:56Z", "authors": ["Przemyslaw Repetowicz", "Peter Richmond"], "categories": ["math-ph", "q-fin.PR"], "doi": "", "_query": "housing price text listing", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2510.17892v1", "title": "Advances in Pre-trained Language Models for Domain-Specific Text Classification: A Systematic Review", "abstract": "The exponential increase in scientific literature and online information necessitates efficient methods for extracting knowledge from textual data. Natural language processing (NLP) plays a crucial role in addressing this challenge, particularly in text classification tasks. While large language models (LLMs) have achieved remarkable success in NLP, their accuracy can suffer in domain-specific contexts due to specialized vocabulary, unique grammatical structures, and imbalanced data distributions. In this systematic literature review (SLR), we investigate the utilization of pre-trained language models (PLMs) for domain-specific text classification. We systematically review 41 articles published between 2018 and January 2024, adhering to the PRISMA statement (preferred reporting items for systematic reviews and meta-analyses). This review methodology involved rigorous inclusion criteria and a multi-step selection process employing AI-powered tools. We delve into the evolution of text classification techniques and differentiate between traditional and modern approaches. We emphasize transformer-based models and explore the challenges and considerations associated with using LLMs for domain-specific text classification. Furthermore, we categorize existing research based on various PLMs and propose a taxonomy of techniques used in the field. To validate our findings, we conducted a comparative experiment involving BERT, SciBERT, and BioBERT in biomedical sentence classification. Finally, we present a comparative study on the performance of LLMs in text classification tasks across different domains. In addition, we examine recent advancements in PLMs for domain-specific text classification and offer insights into future directions and limitations in this rapidly evolving domain.", "published": "2025-10-18T22:46:53Z", "authors": ["Zhyar Rzgar K. Rostam", "Gábor Kertész"], "categories": ["cs.CL"], "doi": "10.1145/3763002", "_query": "housing price text listing", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2007.05216v2", "title": "Price Optimization in Fashion E-commerce", "abstract": "With the rapid growth in the fashion e-commerce industry, it is becoming extremely challenging for the E-tailers to set an optimal price point for all the products on the platform. By establishing an optimal price point, they can maximize overall revenue and profit for the platform. In this paper, we propose a novel machine learning and optimization technique to find the optimal price point at an individual product level. It comprises three major components. Firstly, we use a demand prediction model to predict the next day demand for each product at a certain discount percentage. Next step, we use the concept of price elasticity of demand to get the multiple demand values by varying the discount percentage. Thus we obtain multiple price demand pairs for each product and we have to choose one of them for the live platform. Typically fashion e-commerce has millions of products, so there can be many permutations. Each permutation will assign a unique price point for all the products, which will sum up to a unique revenue number. To choose the best permutation which gives maximum revenue, a linear programming optimization technique is used. We have deployed the above methods in the live production environment and conducted several AB tests. According to the AB test result, our model is improving the revenue by 1 percent and gross margin by 0.81 percent.", "published": "2020-07-10T07:40:28Z", "authors": ["Sajan Kedia", "Samyak Jain", "Abhishek Sharma"], "categories": ["cs.LG", "stat.ML"], "doi": "", "_query": "housing price text listing", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2012.11066v2", "title": "Fairness, Welfare, and Equity in Personalized Pricing", "abstract": "We study the interplay of fairness, welfare, and equity considerations in personalized pricing based on customer features. Sellers are increasingly able to conduct price personalization based on predictive modeling of demand conditional on covariates: setting customized interest rates, targeted discounts of consumer goods, and personalized subsidies of scarce resources with positive externalities like vaccines and bed nets. These different application areas may lead to different concerns around fairness, welfare, and equity on different objectives: price burdens on consumers, price envy, firm revenue, access to a good, equal access, and distributional consequences when the good in question further impacts downstream outcomes of interest. We conduct a comprehensive literature review in order to disentangle these different normative considerations and propose a taxonomy of different objectives with mathematical definitions. We focus on observational metrics that do not assume access to an underlying valuation distribution which is either unobserved due to binary feedback or ill-defined due to overriding behavioral concerns regarding interpreting revealed preferences. In the setting of personalized pricing for the provision of goods with positive benefits, we discuss how price optimization may provide unambiguous benefit by achieving a \"triple bottom line\": personalized pricing enables expanding access, which in turn may lead to gains in welfare due to heterogeneous utility, and improve revenue or budget utilization. We empirically demonstrate the potential benefits of personalized pricing in two settings: pricing subsidies for an elective vaccine, and the effects of personalized interest rates on downstream outcomes in microcredit.", "published": "2020-12-21T01:01:56Z", "authors": ["Nathan Kallus", "Angela Zhou"], "categories": ["cs.LG", "cs.CY", "stat.ML"], "doi": "", "_query": "housing price text listing", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2602.08429v1", "title": "On- and off-chain demand and supply drivers of Bitcoin price", "abstract": "Around three quarters of Bitcoin transactions take place off-chain. Despite their significance, the vast majority of the empirical literature on cryptocurrencies focuses on on-chain transactions. This paper presents one of the first analysis of both on- and off-chain demand- and supply-side factors. Two hypotheses relating on-chain and off-chain demand and supply drivers to the Bitcoin price are tested in an ARDL model with daily data from 2019 to 2024. Our estimates document the differential contributions of on-chain and off-chain drivers on the Bitcoin price. Off-chain demand pressures have a significant impact on the Bitcoin price in the long-run. In the short-run, both demand and supply drivers significantly affect the Bitcoin price. Regarding transactions on the blockchain, only on-chain demand pressures are statistically significant - both in the long- and short-run. These findings confirm the dual nature of the Bitcoin price dynamics, where also market fundamentals affect the Bitcoin price in addition to speculative drivers. Bitcoin whale trading has less significant impact on price in the long-run, while is more pronounced contemporaneously and one-period lag.", "published": "2026-02-09T09:38:45Z", "authors": ["Pavel Ciaian", "d'Artis Kancs", "Miroslava Rajcaniova"], "categories": ["econ.GN"], "doi": "", "_query": "housing price text listing", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2208.02610v2", "title": "Twitter Attribute Classification with Q-Learning on Bitcoin Price Prediction", "abstract": "Aspiring to achieve an accurate Bitcoin price prediction based on people's opinions on Twitter usually requires millions of tweets, using different text mining techniques (preprocessing, tokenization, stemming, stop word removal), and developing a machine learning model to perform the prediction. These attempts lead to the employment of a significant amount of computer power, central processing unit (CPU) utilization, random-access memory (RAM) usage, and time. To address this issue, in this paper, we consider a classification of tweet attributes that effects on price changes and computer resource usage levels while obtaining an accurate price prediction. To classify tweet attributes having a high effect on price movement, we collect all Bitcoin-related tweets posted in a certain period and divide them into four categories based on the following tweet attributes: $(i)$ the number of followers of the tweet poster, $(ii)$ the number of comments on the tweet, $(iii)$ the number of likes, and $(iv)$ the number of retweets. We separately train and test by using the Q-learning model with the above four categorized sets of tweets and find the best accurate prediction among them. Especially, we design several reward functions to improve the prediction accuracy of the Q-leaning. We compare our approach with a classic approach where all Bitcoin-related tweets are used as input data for the model, by analyzing the CPU workloads, RAM usage, memory, time, and prediction accuracy. The results show that tweets posted by users with the most followers have the most influence on a future price, and their utilization leads to spending 80\\% less time, 88.8\\% less CPU consumption, and 12.5\\% more accurate predictions compared with the classic approach.", "published": "2022-08-04T12:18:07Z", "authors": ["Sattarov Otabek", "Jaeyoung Choi"], "categories": ["cs.SI"], "doi": "", "_query": "housing price text listing", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2402.11864v2", "title": "The Price of Information", "abstract": "When an investor is faced with the option to purchase additional information regarding an asset price, how much should she pay? To address this question, we solve for the indifference price of information in a setting where a trader maximizes her expected utility of terminal wealth over a finite time horizon. If she does not purchase the information, then she solves a partial information stochastic control problem, while, if she does purchase the information, then she pays a cost and receives partial information about the asset's trajectory. We further demonstrate that when the investor can purchase the information at any stopping time prior to the end of the trading horizon, she chooses to do so at a deterministic time.", "published": "2024-02-19T06:12:07Z", "authors": ["Sebastian Jaimungal", "Xiaofei Shi"], "categories": ["q-fin.MF"], "doi": "", "_query": "housing price text listing", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2609.29674v2", "title": "The Sequential Price of Continual Learning", "abstract": "Sequential task updates are fundamental to continual learning, but their recency bias can impose a lasting performance cost. We study this cost in an overparameterized linear-regression model with i.i.d. task sampling. We prove that distribution-level forgetting and population loss converge to the same stationary limit. We quantify the additional loss incurred by sequential exact fitting, or the sequential price. In more homogeneous task geometries, it equals the intrinsic loss asymptotically attained by joint training, making the total loss twice as large. We further analyze fixed-strength elastic weight consolidation (EWC) under general task curvatures and characterize its stationary sequential price at every regularization strength. Under strong regularization, the price decays inversely with EWC strength while the mean-square coupling horizon grows proportionally. Experiments on Jester and Rotated MNIST support the predicted sequential price and its reduction by EWC, with quantitative agreement on real-world tasks satisfying the theory's assumptions and qualitative agreement under nonlinear finite-step training.", "published": "2026-08-30T14:27:18Z", "authors": ["Zonghuan Xu", "Xingjun Ma"], "categories": ["cs.LG", "cs.AI"], "doi": "", "_query": "housing price text listing", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2411.13427v1", "title": "Price Setting Rules, Rounding Tax, and Inattention Penalty", "abstract": "We study the price rounding regulation in Israel, which outlawed non-0-ending prices, forcing retailers to round 9 ending prices, which in many stores, comprised more than 60 percent of all prices. The goal of the regulation was to eliminate the rounding tax, the extra amount consumers paid because of price rounding, which was necessitated by the abolition of low denomination coins, and the inattention tax, the extra amount consumers paid the retailers because of their inattention to the prices rightmost digits. Using 4 different datasets, we assess the success of the government in achieving these goals, focusing on fast moving consumer goods, a category of products strongly affected by the price rounding regulation. We focus on the response of the retailers to the price rounding regulation and find that although the government succeeded in eliminating the rounding tax, the bottom line is that shoppers end up paying more, not less, because of the regulation, underscoring, once again, the warning of Milton Friedman that policies should be judged by their results, not by their intentions.", "published": "2024-11-20T16:09:57Z", "authors": ["Doron Sayag", "Avichai Snir", "Daniel Levy"], "categories": ["econ.GN"], "doi": "10.13140/RG.2.2.14308.80002", "_query": "housing price text listing", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/1501.06689v1", "title": "General-Purpose Join Algorithms for Listing Triangles in Large Graphs", "abstract": "We investigate applying general-purpose join algorithms to the triangle listing problem in an out-of-core context. In particular, we focus on Leapfrog Triejoin (LFTJ) by Veldhuizen 2014, a recently proposed, worst-case optimal algorithm. We present \"boxing\": a novel, yet conceptually simple, approach for feeding input data to LFTJ. Our extensive analysis shows that this approach is I/O efficient, being worst-case optimal (in a certain sense). Furthermore, if input data is only a constant factor larger than the available memory, then a boxed LFTJ essentially maintains the CPU data-complexity of the vanilla LFTJ. Next, focusing on LFTJ applied to the triangle query, we show that for many graphs boxed LFTJ matches the I/O complexity of the recently by Hu, Tao and Yufei proposed specialized algorithm MGT for listing tiangles in an out-of-core setting. We also strengthen the analysis of LFTJ's computational complexity for the triangle query by considering families of input graphs that are characterized not only by the number of edges but also by a measure of their density. E.g., we show that LFTJ achieves a CPU complexity of O(|E|log|E|) for planar graphs, while on general graphs, no algorithm can be faster than O(|E|^{1.5}). Finally, we perform an experimental evaluation for the triangle listing problem confirming our theoretical results and showing the overall effectiveness of our approach. On all our real-world and synthetic data sets (some of which containing more than 1.2 billion edges) LFTJ in single-threaded mode is within a factor of 3 of the specialized MGT; a penalty that---as we demonstrate---can be alleviated by parallelization.", "published": "2015-01-27T08:47:25Z", "authors": ["Daniel Zinn"], "categories": ["cs.DB"], "doi": "", "_query": "housing price text listing", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/1409.6645v1", "title": "Calculation of a power price equilibrium", "abstract": "In this paper we propose a tractable quadratic programming formulation for calculating the equilibrium term structure of electricity prices. We rely on a theoretical model described in [21], but extend it so that it reflects actually traded electricity contracts, transaction costs and liquidity considerations. Our numerical simulations examine the properties of the term structure and its dependence on various parameters of the model. The proposed quadratic programming formulation is applied to calculate the equilibrium term structure of electricity prices in the UK power grid consisting of a few hundred power plants. The impact of ramp up and ramp down constraints are also studied.", "published": "2014-09-23T15:52:01Z", "authors": ["Miha Troha", "Raphael Hauser"], "categories": ["q-fin.PR", "math.OC"], "doi": "", "_query": "housing price text listing", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/0712.1093v1", "title": "The derivatives of Asian call option prices", "abstract": "The distribution of a time integral of geometric Brownian motion is not well understood. To price an Asian option and to obtain measures of its dependence on the parameters of time, strike price, and underlying market price, it is essential to have the distribution of time integral of geometric Brownian motion and it is also required to have a way to manipulate its distribution. We present integral forms for key quantities in the price of Asian option and its derivatives ({\\it{delta, gamma,theta, and vega}}). For example for any $a>0$ $\\mathbb{E} [ (A_t -a)^+] = t -a + a^{2} \\mathbb{E} [ (a+A_t)^{-1} \\exp (\\frac{2M_t}{a+ A_t} - \\frac{2}{a}) ]$, where $A_t = \\int^t_0 \\exp (B_s -s/2) ds$ and $M_t =\\exp (B_t -t/2).$", "published": "2007-12-07T08:40:48Z", "authors": ["Jungmin Choi", "Kyounghee Kim"], "categories": ["q-fin.PR", "math.PR"], "doi": "", "_query": "housing price text listing", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/1707.05417v1", "title": "List Supermodular Coloring with Shorter Lists", "abstract": "In 1995, Galvin proved that a bipartite graph $G$ admits a list edge coloring if every edge is assigned a color list of length $Δ(G)$, the maximum degree of the graph. This result was improved by Borodin, Kostochka and Woodall, who proved that $G$ still admits a list edge coloring if every edge $e=st$ is assigned a list of $\\max\\{d_{G}(s), d_{G}(t)\\}$ colors. Recently, Iwata and Yokoi provided the list supermodular coloring theorem, that extends Galvin's result to the setting of Schrijver's supermodular coloring. This paper provides a common generalization of these two extensions of Galvin's result.", "published": "2017-07-18T00:08:26Z", "authors": ["Yu Yokoi"], "categories": ["math.CO"], "doi": "", "_query": "housing price text listing", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2505.03072v1", "title": "SafeTab-H: Disclosure Avoidance for the 2020 Census Detailed Demographic and Housing Characteristics File B (Detailed DHC-B)", "abstract": "This article describes SafeTab-H, a disclosure avoidance algorithm applied to the release of the U.S. Census Bureau's Detailed Demographic and Housing Characteristics File B (Detailed DHC-B) as part of the 2020 Census. The tabulations contain household statistics about household type and tenure iterated by the householder's detailed race, ethnicity, or American Indian and Alaska Native tribe and village at varying levels of geography. We describe the algorithmic strategy which is based on adding noise from a discrete Gaussian distribution and show that the algorithm satisfies a well-studied variant of differential privacy, called zero-concentrated differential privacy. We discuss how the implementation of the SafeTab-H codebase relies on the Tumult Analytics privacy library. We also describe the theoretical expected error properties of the algorithm and explore various aspects of its parameter tuning.", "published": "2025-05-02T13:15:14Z", "authors": ["William Sexton", "Skye Berghel", "Bayard Carlson", "Sam Haney", "Luke Hartman", "Michael Hay", "Ashwin Machanavajjhala", "Gerome Miklau", "Amritha Pai", "Simran Rajpal", "David Pujol", "Ruchit Shrestha", "Daniel Simmons-Marengo"], "categories": ["cs.CR", "cs.CY"], "doi": "", "_query": "housing price text listing", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2102.03038v2", "title": "Bounds and Heuristics for Multi-Product Personalized Pricing", "abstract": "We present tight bounds and heuristics for personalized, multi-product pricing problems. Under mild conditions we show that the best price in the direction of a positive vector results in profits that are guaranteed to be at least as large as a fraction of the profits from optimal personalized pricing. For unconstrained problems, the fraction depends on the factor and on optimal price vectors for the different customer types. For constrained problems the factor depends on the factor and a ratio of the constraints. Using a factor vector with equal components results in uniform pricing and has exceedingly mild sufficient conditions for the bound to hold. A robust factor is presented that achieves the best possible performance guarantee. As an application, our model yields a tight lower-bound on the performance of linear pricing relative to optimal personalized non-linear pricing, and suggests effective non-linear price heuristics relative to personalized solutions. Additionally, our model provides guarantees for simple strategies such as bundle-size pricing and component-pricing with respect to optimal personalized mixed bundle pricing. Heuristics to cluster customer types are also developed with the goal of improving performance by allowing each cluster to price along its own factor. Numerical results are presented for a variety of demand models that illustrate the tradeoffs between using the economic factor and the robust factor for each cluster, as well as the tradeoffs between using a clustering heuristic with a worst case performance of two and a machine learning clustering algorithm. In our experiments economically motivated factors coupled with machine learning clustering heuristics performed best.", "published": "2021-02-05T07:52:56Z", "authors": ["Guillermo Gallego", "Gerardo Berbeglia"], "categories": ["econ.TH", "math.OC"], "doi": "", "_query": "housing price text listing", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2402.11754v1", "title": "Attraction Via Prices and Information", "abstract": "We study the ramifications of increased commitment power for information provision in an oligopolistic market with search frictions. Although prices are posted and, therefore, guide search, if firms cannot commit to information provision policies, there is no active search at equilibrium so consumers visit (and purchase from) at most one firm. If firms can guide search by both their prices and information policies, there exists a unique symmetric equilibrium exhibiting price dispersion and active search. Nevertheless, when the market is thin, consumers prefer the former case, which features intense price competition. Firms always prefer the latter.", "published": "2024-02-19T00:53:18Z", "authors": ["Pak Hung Au", "Mark Whitmeyer"], "categories": ["econ.TH"], "doi": "", "_query": "housing price text listing", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/1810.10534v1", "title": "Evolution of semantic networks in biomedical texts", "abstract": "Language is hierarchically organized: words are built into phrases, sentences, and paragraphs to represent complex ideas. Here we ask whether the organization of language in written text displays the fractal hierarchical architecture common in systems optimized for efficient information transmission. We test the hypothesis that the expositional structure of scientific research articles displays Rentian scaling, and that the exponent of the scaling law changes as the article's information transmission capacity changes. Using 32 scientific manuscripts - each containing between three and 26 iterations of revision - we construct semantic networks in which nodes represented unique words in each manuscript, and edges connect nodes if two words appeared within the same 5-word window. We show that these semantic networks display clear Rentian scaling, and that the Rent exponent varies over the publication life cycle, from the first draft to the final revision. Furthermore, we observe that manuscripts fell into three clusters in terms of how the scaling exponents changed across drafts: exponents rising over time, falling over time, and remaining relatively stable over time. This change in exponent reflects the evolution in semantic network structure over the manuscript revision process, highlighting a balance between network complexity, which increases the exponent, and network efficiency, which decreases the exponent. Lastly, the final value of the Rent exponent is negatively correlated with the number of authors. Taken together, our results suggest that semantic networks reflecting the structure of exposition in scientific research articles display striking hierarchical architecture that arbitrates tradeoffs between competing constraints on network organization, and that this arbitration is navigated differently depending on the social environment characteristic of the collaboration.", "published": "2018-10-24T12:42:45Z", "authors": ["Lucy R. Chai", "Danielle S. Bassett"], "categories": ["physics.soc-ph", "cs.DL"], "doi": "", "_query": "housing price text listing", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/1906.01713v3", "title": "Optimal auction duration: A price formation viewpoint", "abstract": "We consider an auction market in which market makers fill the order book during a given time period while some other investors send market orders. We define the clearing price of the auction as the price maximizing the exchanged volume at the clearing time according to the supply and demand of each market participants. Then we derive in a semi-explicit form the error made between this clearing price and the efficient price as a function of the auction duration. We study the impact of the behavior of market takers on this error. To do so we consider the case of naive market takers and that of rational market takers playing a Nash equilibrium to minimize their transaction costs. We compute the optimal duration of the auctions for 77 stocks traded on Euronext and compare the quality of price formation process under this optimal value to the case of a continuous limit order book. Continuous limit order books are found to be usually sub-optimal. However, in term of our metric, they only moderately impair the quality of price formation process. Order of magnitude of optimal auction durations is from 2 to 10 minutes.", "published": "2019-06-04T20:16:00Z", "authors": ["Paul Jusselin", "Thibaut Mastrolia", "Mathieu Rosenbaum"], "categories": ["q-fin.TR", "math.OC", "math.PR"], "doi": "", "_query": "housing price text listing", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2308.13822v3", "title": "Dynamic Pricing for Reusable Resources: The Power of Two Prices", "abstract": "Motivated by real-world applications such as rental and cloud computing services, we investigate pricing for reusable resources. We consider a system where a single resource with a fixed number of identical copies serves customers with heterogeneous willingness-to-pay (WTP), and the usage duration distribution is general. Optimal dynamic policies are computationally intractable when usage durations are not memoryless, so existing literature has focused on static pricing, which incurs a steady-state performance loss of ${O}(\\sqrt{c})$ compared to optimality when supply and demand scale with $c$. We propose a class of dynamic \"stock-dependent\" policies that 1) are computationally tractable and 2) can attain a steady-state performance loss of $o(\\sqrt{c})$. We give parametric bounds based on the local shape of the reward function at the optimal fluid admission probability and show that the performance loss of stock-dependent policies can be as low as ${O}((\\log{c})^2)$. We characterize the tight performance loss for stock-dependent policies and show that they can in fact be achieved by a simple two-price policy that sets a higher price when the stock is below some threshold and a lower price otherwise. We extend our results to settings with multiple resources and multiple customer classes. Finally, we demonstrate this \"minimally dynamic\" class of two-price policies performs well numerically, even in non-asymptotic settings, suggesting that a little dynamicity can go a long way.", "published": "2023-08-26T09:19:26Z", "authors": ["Santiago R. Balseiro", "Will Ma", "Wenxin Zhang"], "categories": ["math.OC", "cs.DS"], "doi": "", "_query": "housing price text listing", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2007.03762v4", "title": "Transfer Learning for Electricity Price Forecasting", "abstract": "Electricity price forecasting is an essential task in all the deregulated markets of the world. The accurate prediction of the day-ahead electricity prices is an active research field and available data from various markets can be used as an input for forecasting. A collection of models have been proposed for this task, but the fundamental question on how to use the available big data is often neglected. In this paper, we propose to use transfer learning as a tool for utilizing information from other electricity price markets for forecasting. We pre-train a neural network model on source markets and finally do a fine-tuning for the target market. Moreover, we test different ways to use the rich input data from various electricity price markets. Our experiments on four different day-ahead markets indicate that transfer learning improves the electricity price forecasting performance in a statistically significant manner. Furthermore, we compare our results with stateof-the-art methods in a rolling window scheme to demonstrate the performance of the transfer learning approach.", "published": "2020-07-05T17:24:36Z", "authors": ["Salih Gunduz", "Umut Ugurlu", "Ilkay Oksuz"], "categories": ["eess.SP", "cs.LG", "stat.ML"], "doi": "", "_query": "housing price text listing", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/cs/9809004v1", "title": "Performance / Price Sort", "abstract": "NTsort is an external sort on WindowsNT 5.0. It has minimal functionality but excellent price performance. In particular, running on mail-order hardware it can sort 1.5 GB for a penny. For commercially available sorts, Postman Sort from Robert Ramey Software Development has elapsed time performance comparable to NTsort, while using less processor time. It can sort 1.27 GB for a penny (12.7 million records.) These sorts set new price-performance records. This paper documents this and proposes that the PennySort benchmark be revised to Performance/Price sort: a simple GB/$ sort metric based on a two-pass external sort.", "published": "1998-09-02T00:49:25Z", "authors": ["Jim Gray", "Joshua Coates", "Chris Nyberg"], "categories": ["cs.DB", "cs.PF"], "doi": "", "_query": "housing price text listing", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2206.13916v1", "title": "Grid Tariffs for Peak Demand Reduction: Is there a Price Signal Conflict with Electricity Spot Prices?", "abstract": "The electricity grid is expected to require vast investments due to the decarbonization-by-electrification trend, calling for a change in grid tariff design which provides proper incentives for reducing peak loads. However, price signals from grid tariffs could be distorted from electricity spot prices which also represents a significant of the total consumer electricity bill. This paper attempts to identify whether there is a price signal conflict between grid tariffs and spot prices. Four different grid tariff designs are compared, using a generic demand response model as part of a cost-minimizing linear program to simulate the reduction in peak load. The method is applied to metered electricity demand from 3608 consumers in Oslo, Norway. Results show that new grid tariff designs reduce peak loads by 1-4%, and that reduction in peak load is smaller when consumers are subject to electricity spot prices.", "published": "2022-06-28T11:49:55Z", "authors": ["Sigurd Bjarghov", "Matthias Hofmann"], "categories": ["math.OC", "eess.SY"], "doi": "", "_query": "housing price text listing", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/cs/0106028v1", "title": "Pricing Virtual Paths with Quality-of-Service Guarantees as Bundle Derivatives", "abstract": "We describe a model of a communication network that allows us to price complex network services as financial derivative contracts based on the spot price of the capacity in individual routers. We prove a theorem of a Girsanov transform that is useful for pricing linear derivatives on underlying assets, which can be used to price many complex network services, and it is used to price an option that gives access to one of several virtual channels between two network nodes, during a specified future time interval. We give the continuous time hedging strategy, for which the option price is independent of the service providers attitude towards risk. The option price contains the density function of a sum of lognormal variables, which has to be evaluated numerically.", "published": "2001-06-12T12:06:47Z", "authors": ["Lars Rasmusson"], "categories": ["cs.NI", "cs.CE"], "doi": "", "_query": "housing price text listing", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2108.05436v1", "title": "Friddy multiagent price stabilization model", "abstract": "In a multiagent network model consisting of nodes, each network node has an agent and priced Friddy coins, and the agent can buy or sell Friddy coins in the marketplace. Though every node may not effectively have an equal price during the transaction time, the prices have to reach equilibrium by iterating buy and sell transactions on a macro level.", "published": "2021-08-11T20:33:42Z", "authors": ["Abdelrahman Elsharawy"], "categories": ["cs.AI"], "doi": "", "_query": "housing price text listing", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2109.01478v2", "title": "A random-supply Mean Field Game price model", "abstract": "We consider a market where a finite number of players trade an asset whose supply is a stochastic process. The price formation problem consists of finding a price process that ensures that when agents act optimally to minimize their trading costs, the market clears, and supply meets demand. This problem arises in market economies, including electricity generation from renewable sources in smart grids. Our model includes noise on the supply side, which is counterbalanced on the consumption side by storing energy or reducing the demand according to a dynamic price process. By solving a constrained minimization problem, we prove that the Lagrange multiplier corresponding to the market-clearing condition defines the solution of the price formation problem. For the linear-quadratic structure, we characterize the price process of a continuum population using optimal control techniques. We include numerical schemes for the price computation in the finite and infinite games, and we illustrate the model using real data.", "published": "2021-09-03T12:41:43Z", "authors": ["Diogo Gomes", "Julian Gutierrez", "Ricardo Ribeiro"], "categories": ["math.AP"], "doi": "", "_query": "housing price text listing", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/1405.4809v1", "title": "Optimal pricing for optimal transport", "abstract": "Suppose that $c(x,y)$ is the cost of transporting a unit of mass from $x\\in X$ to $y\\in Y$ and suppose that a mass distribution $μ$ on $X$ is transported optimally (so that the total cost of transportation is minimal) to the mass distribution $ν$ on $Y$. Then, roughly speaking, the Kantorovich duality theorem asserts that there is a price $f(x)$ for a unit of mass sold (say by the producer to the distributor) at $x$ and a price $g(y)$ for a unit of mass sold (say by the distributor to the end consumer) at $y$ such that for any $x\\in X$ and $y\\in Y$, the price difference $g(y)-f(x)$ is not greater than the cost of transportation $c(x,y)$ and such that there is equality $g(y)-f(x)=c(x,y)$ if indeed a nonzero mass was transported (via the optimal transportation plan) from $x$ to $y$. We consider the following optimal pricing problem: suppose that a new pricing policy is to be determined while keeping a part of the optimal transportation plan fixed and, in addition, some prices at the sources of this part are also kept fixed. From the producers' side, what would then be the highest compatible pricing policy possible? From the consumers' side, what would then be the lowest compatible pricing policy possible? In the framework of $c$-convexity theory, we have recently introduced and studied optimal $c$-convex $c$-antiderivatives and explicit constructions of these optimizers were presented. In the present paper we employ optimal $c$-convex $c$-antiderivatives and conclude that these are natural solutions to the optimal pricing problems mentioned above. This type of problems drew attention in the past and existence results were previously established in the case where $X=Y=R^n$ under various specifications. We solve the above problem for general spaces $X,Y$ and real-valued, lower semicontinuous cost functions $c$.", "published": "2014-05-19T17:05:08Z", "authors": ["Sedi Bartz", "Simeon Reich"], "categories": ["math.FA", "math.OC"], "doi": "", "_query": "housing price text listing", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/1609.05286v2", "title": "From quantum mechanics to finance: Microfoundations for jumps, spikes and high volatility phases in diffusion price processes", "abstract": "We present an agent behavior based microscopic model that induces jumps, spikes and high volatility phases in the price process of a traded asset. We transfer dynamics of thermally activated jumps of an unexcited/ excited two state system discussed in the context of quantum mechanics to agent socio-economic behavior and provide microfoundations. After we link the endogenous agent behavior to price dynamics we establish the circumstances under which the dynamics converge to an Itô-diffusion price processes in the large market limit.", "published": "2016-09-17T06:19:27Z", "authors": ["Christof Henkel"], "categories": ["q-fin.TR", "econ.GN"], "doi": "10.1016/j.physa.2016.11.125", "_query": "housing price text listing", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2001.02734v1", "title": "Gasoline Pricing Policies for Transportation Safety", "abstract": "Economic factors can have substantial effects on transportation crash trends. This study makes a comprehensive examination of the relationship between the retail gasoline price (including state and federal fuel taxes) and transportation fatal crashes from 2007 to 2016 in the US. Data on motor vehicle, bicycle and pedestrian fatal crashes come from Fatality Analysis Reporting System (FARS) provided by the National Highway Safety Administration (NHTSA) and the gasoline price data is from U.S. Energy Information Administration (EIA). Random effect negative binomial regression models are used to estimate the impact of inflation-adjusted gasoline prices on trends of transportation fatal crashes. Initial results combined with results of previous studies showed that gender and transportation mean type (motorcycle, non-motorcycle, bicycle and pedestrian) play prominent roles in interpreting the final model, so by using random effect negative binomial regression, seven models are developed to evaluate the effects of gasoline price changes on total population, male, female, motorcyclists, non-motorcyclists, bicyclists and pedestrians separately. Our findings suggest that increasing the gasoline prices will not significantly alter the number of total fatal crashes. However, by looking at different vehicle types, it is estimated that one dollar increase in adjusted gasoline price is associated with 24.2% increase in the number of motorcycle fatal crashes, 1.9% decrease in the number of non-motorcycle fatal crashes, and 0.7% decrease in the number of pedestrian fatal crashes. Also, there is no noticeable difference between male and female in response to the gasoline price changes.", "published": "2020-01-08T20:48:45Z", "authors": ["Nima Safaei", "Chao Zhou"], "categories": ["stat.AP"], "doi": "", "_query": "housing price text listing", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2402.12505v1", "title": "Situating Data Sets: Making Public Data Actionable for Housing Justice", "abstract": "Activists, governmentsm and academics regularly advocate for more open data. But how is data made open, and for whom is it made useful and usable? In this paper, we investigate and describe the work of making eviction data open to tenant organizers. We do this through an ethnographic description of ongoing work with a local housing activist organization. This work combines observation, direct participation in data work, and creating media artifacts, specifically digital maps. Our interpretation is grounded in D'Ignazio and Klein's Data Feminism, emphasizing standpoint theory. Through our analysis and discussion, we highlight how shifting positionalities from data intermediaries to data accomplices affects the design of data sets and maps. We provide HCI scholars with three design implications when situating data for grassroots organizers: becoming a domain beginner, striving for data actionability, and evaluating our design artifacts by the social relations they sustain rather than just their technical efficacy.", "published": "2024-02-19T20:13:42Z", "authors": ["Anh-Ton Tran", "Grace Guo", "Jordan Taylor", "Katsuki Chan", "Elora Raymond", "Carl DiSalvo"], "categories": ["cs.HC", "cs.CY"], "doi": "10.1145/3613904.3642452", "_query": "housing price text listing", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2012.09577v3", "title": "Pricing of European options in incomplete jump diffusion markets", "abstract": "We study option prices in financial markets where the risky asset prices are modelled by jump diffusions. It was proposed by Schweizer (1996) in a general semimartingale setting, following earlier works by Föllmer and Sondermann (1986) and Bouleau and Lamberton (1989), that the right price of such an option is the initial wealth needed to make it possible to generate by a self-financing portfolio a terminal wealth which is as close as possible to the payoff F in the sense of variance. Schweizer calls this price the approximation price and he investigates interesting general properties of this price and its corresponding optimal portfolio. -However, neither of these authors compute explicitly this price in concrete cases. This is the motivation for the current paper: We apply stochastic control methods to compute this price in the setting of markets with assets described by jump diffusions. Our method involves Stackelberg games and a suitably modified stochastic maximum principle. We show that such an optimal initial wealth, denoted by z^, with corresponding optimal portfolio π^ exist and are unique. - If the coefficients of the risky asset prices are deterministic, we show that z^=E_{Q*}[F], for a specific equivalent martingale measure (EMM) Q*. This shows in particular that the minimal variance price is free from arbitrage. -Then for the general case we apply a suitable maximum principle of optimal stochastic control to relate the minimal variance price z^=p_{mv}(F) to the Hamiltonian and its adjoint processes, and we show that, under some conditions, z^=E_{Q_0}[F] for any Q_0 in a family M_0 of EMMs, described by the set of solutions of a system of linear equations. -Finally, we illustrate our results by looking at specific examples.", "published": "2020-12-17T13:36:01Z", "authors": ["Nacira Agram", "Bernt Øksendal"], "categories": ["math.OC"], "doi": "", "_query": "housing price text listing", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/1912.10640v1", "title": "Pricing of the Geometric Asian Options Under a Multifactor Stochastic Volatility Model", "abstract": "This paper focuses on the pricing of continuous geometric Asian options (GAOs) under a multifactor stochastic volatility model. The model considers fast and slow mean reverting factors of volatility, where slow volatility factor is approximated by a quadratic arc. The asymptotic expansion of the price function is assumed, and the first order price approximation is derived using the perturbation techniques for both floating and fixed strike GAOs. Much simplified pricing formulae for the GAOs are obtained in this multifactor stochastic volatility framework. The zeroth order term in the price approximation is the modified Black-Scholes price for the GAOs. This modified price is expressed in terms of the Black-Scholes price for the GAOs. The accuracy of the approximate option pricing formulae is established, and the model parameter is also estimated by capturing the volatility smiles.", "published": "2019-12-23T06:23:37Z", "authors": ["Gifty Malhotra", "R. Srivastava", "H. C. Taneja"], "categories": ["q-fin.PR", "q-fin.MF"], "doi": "", "_query": "housing price text listing", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2301.13173v1", "title": "Shape-aware Text-driven Layered Video Editing", "abstract": "Temporal consistency is essential for video editing applications. Existing work on layered representation of videos allows propagating edits consistently to each frame. These methods, however, can only edit object appearance rather than object shape changes due to the limitation of using a fixed UV mapping field for texture atlas. We present a shape-aware, text-driven video editing method to tackle this challenge. To handle shape changes in video editing, we first propagate the deformation field between the input and edited keyframe to all frames. We then leverage a pre-trained text-conditioned diffusion model as guidance for refining shape distortion and completing unseen regions. The experimental results demonstrate that our method can achieve shape-aware consistent video editing and compare favorably with the state-of-the-art.", "published": "2023-01-30T18:41:58Z", "authors": ["Yao-Chih Lee", "Ji-Ze Genevieve Jang", "Yi-Ting Chen", "Elizabeth Qiu", "Jia-Bin Huang"], "categories": ["cs.CV", "eess.IV"], "doi": "", "_query": "housing price text listing", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2407.03521v3", "title": "Algorithmic Collusion And The Minimum Price Markov Game", "abstract": "This paper introduces the Minimum Price Markov Game (MPMG), a theoretical model that reasonably approximates real-world first-price markets following the minimum price rule, such as public auctions. The goal is to provide researchers and practitioners with a framework to study market fairness and regulation in both digitized and non-digitized public procurement processes, amid growing concerns about algorithmic collusion in online markets. Using multi-agent reinforcement learning-driven artificial agents, we demonstrate that (i) the MPMG is a reliable model for first-price market dynamics, (ii) the minimum price rule is generally resilient to non-engineered tacit coordination among rational actors, and (iii) when tacit coordination occurs, it relies heavily on self-reinforcing trends. These findings contribute to the ongoing debate about algorithmic pricing and its implications.", "published": "2024-07-03T21:40:34Z", "authors": ["Igor Sadoune", "Marcelin Joanis", "Andrea Lodi"], "categories": ["cs.GT", "econ.GN"], "doi": "", "_query": "housing price text listing", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/1009.2329v2", "title": "Tick size and price diffusion", "abstract": "A tick size is the smallest increment of a security price. It is clear that at the shortest time scale on which individual orders are placed the tick size has a major role which affects where limit orders can be placed, the bid-ask spread, etc. This is the realm of market microstructure and there is a vast literature on the role of tick size on market microstructure. However, tick size can also affect price properties at longer time scales, and relatively less is known about the effect of tick size on the statistical properties of prices. The present paper is divided in two parts. In the first we review the effect of tick size change on the market microstructure and the diffusion properties of prices. The second part presents original results obtained by investigating the tick size changes occurring at the New York Stock Exchange (NYSE). We show that tick size change has three effects on price diffusion. First, as already shown in the literature, tick size affects price return distribution at an aggregate time scale. Second, reducing the tick size typically leads to an increase of volatility clustering. We give a possible mechanistic explanation for this effect, but clearly more investigation is needed to understand the origin of this relation. Third, we explicitly show that the ability of the subordination hypothesis in explaining fat tails of returns and volatility clustering is strongly dependent on tick size. While for large tick sizes the subordination hypothesis has significant explanatory power, for small tick sizes we show that subordination is not the main driver of these two important stylized facts of financial market.", "published": "2010-09-13T09:11:45Z", "authors": ["Gabriele La Spada", "J. Doyne Farmer", "Fabrizio Lillo"], "categories": ["q-fin.ST"], "doi": "", "_query": "housing price text listing", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/1305.5404v1", "title": "Pure Price of Anarchy for Generalized Second Price Auction", "abstract": "The Generalized Second Price auction (GSP) has been widely used by search engines to sell ad slots. Previous studies have shown that the pure Price Of Anarchy (POA) of GSP is 1.25 when there are two ad slots and 1.259 when three ad slots. For the cases with more than three ad slots, however, only some untight upper bounds of the pure POA were obtained. In this work, we improve previous results in two aspects: (1) We prove that the pure POA for GSP is 1.259 when there are four ad slots, and (2) We show that the pure POA for GSP with more than four ad slots is also 1.259 given the bidders are ranked according to a particular permutation.", "published": "2013-05-23T12:57:26Z", "authors": ["Wenkui Ding", "Tao Wu", "Tao Qin", "Tie-Yan Liu"], "categories": ["cs.GT"], "doi": "", "_query": "housing price text listing", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/nlin/0505043v1", "title": "A network analysis of committees in the United States House of Representatives", "abstract": "Network theory provides a powerful tool for the representation and analysis of complex systems of interacting agents. Here we investigate the United States House of Representatives network of committees and subcommittees, with committees connected according to ``interlocks'' or common membership. Analysis of this network reveals clearly the strong links between different committees, as well as the intrinsic hierarchical structure within the House as a whole. We show that network theory, combined with the analysis of roll call votes using singular value decomposition, successfully uncovers political and organizational correlations between committees in the House without the need to incorporate other political information.", "published": "2005-05-17T15:30:38Z", "authors": ["Mason A. Porter", "Peter J. Mucha", "M. E. J. Newman", "Casey M. Warmbrand"], "categories": ["nlin.AO", "cs.MA", "math.ST", "physics.data-an", "physics.soc-ph"], "doi": "10.1073/pnas.0500191102", "_query": "housing price text listing", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2505.01254v1", "title": "PHSafe: Disclosure Avoidance for the 2020 Census Supplemental Demographic and Housing Characteristics File (S-DHC)", "abstract": "This article describes the disclosure avoidance algorithm that the U.S. Census Bureau used to protect the 2020 Census Supplemental Demographic and Housing Characteristics File (S-DHC). The tabulations contain statistics of counts of U.S. persons living in certain types of households, including averages. The article describes the PHSafe algorithm, which is based on adding noise drawn from a discrete Gaussian distribution to the statistics of interest. We prove that the algorithm satisfies a well-studied variant of differential privacy, called zero-concentrated differential privacy. We then describe how the algorithm was implemented on Tumult Analytics and briefly outline the parameterization and tuning of the algorithm.", "published": "2025-05-02T13:20:32Z", "authors": ["William Sexton", "Skye Berghel", "Bayard Carlson", "Sam Haney", "Luke Hartman", "Michael Hay", "Ashwin Machanavajjhala", "Gerome Miklau", "Amritha Pai", "Simran Rajpal", "David Pujol", "Ruchit Shrestha", "Daniel Simmons-Marengo"], "categories": ["cs.CR", "cs.CY"], "doi": "", "_query": "housing price text listing", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/1511.01564v1", "title": "Pricing Parisian down-and-in options", "abstract": "In this paper, we price American-style Parisian down-and-in call options under the Black-Scholes framework. Usually, pricing an American-style option is much more difficult than pricing its European-style counterpart because of the appearance of the optimal exercise boundary in the former. Fortunately, the optimal exercise boundary associated with an American-style Parisian knock-in option only appears implicitly in its pricing partial differential equation (PDE) systems, instead of explicitly as in the case of an American-style Parisian knock-out option. We also recognize that the \"moving window\" technique developed for pricing European-style Parisian up-and-out options can be adopted to price American-style Parisian knock-in options as well. In particular, we obtain a simple analytical solution for American-style Parisian down-and-in call options and our new formula is written in terms of four double integrals, which can be easily computed numerically.", "published": "2015-11-05T00:49:08Z", "authors": ["Song-Ping Zhu", "Nhat-Tan Le", "Wen-Ting Chen", "Xiaoping Lu"], "categories": ["q-fin.PR"], "doi": "10.1016/j.aml.2014.10.019", "_query": "housing price text listing", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2006.02596v3", "title": "Option Pricing in Markets with Informed Traders", "abstract": "The objective of this paper is to introduce the theory of option pricing for markets with informed traders within the framework of dynamic asset pricing theory. We introduce new models for option pricing for informed traders in complete markets where we consider traders with information on the stock price direction and stock return mean. The Black-Scholes-Merton option pricing theory is extended for markets with informed traders, where price processes are following continuous-diffusions. By doing so, the discontinuity puzzle in option pricing is resolved. Using market option data, we estimate the implied surface of the probability for a stock upturn, the implied mean stock return surface, and implied trader information intensity surface.", "published": "2020-06-04T00:17:39Z", "authors": ["Yuan Hu", "Abootaleb Shirvani", "Stoyan Stoyanov", "Young Shin Kim", "Frank J. Fabozzi", "Svetlozar T. Rachev"], "categories": ["q-fin.MF"], "doi": "10.1142/S0219024920500375", "_query": "housing price text listing", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2305.14312v2", "title": "Text-guided 3D Human Generation from 2D Collections", "abstract": "3D human modeling has been widely used for engaging interaction in gaming, film, and animation. The customization of these characters is crucial for creativity and scalability, which highlights the importance of controllability. In this work, we introduce Text-guided 3D Human Generation (\\texttt{T3H}), where a model is to generate a 3D human, guided by the fashion description. There are two goals: 1) the 3D human should render articulately, and 2) its outfit is controlled by the given text. To address this \\texttt{T3H} task, we propose Compositional Cross-modal Human (CCH). CCH adopts cross-modal attention to fuse compositional human rendering with the extracted fashion semantics. Each human body part perceives relevant textual guidance as its visual patterns. We incorporate the human prior and semantic discrimination to enhance 3D geometry transformation and fine-grained consistency, enabling it to learn from 2D collections for data efficiency. We conduct evaluations on DeepFashion and SHHQ with diverse fashion attributes covering the shape, fabric, and color of upper and lower clothing. Extensive experiments demonstrate that CCH achieves superior results for \\texttt{T3H} with high efficiency.", "published": "2023-05-23T17:50:15Z", "authors": ["Tsu-Jui Fu", "Wenhan Xiong", "Yixin Nie", "Jingyu Liu", "Barlas Oğuz", "William Yang Wang"], "categories": ["cs.CV"], "doi": "", "_query": "housing price text listing", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2003.06988v1", "title": "House-GAN: Relational Generative Adversarial Networks for Graph-constrained House Layout Generation", "abstract": "This paper proposes a novel graph-constrained generative adversarial network, whose generator and discriminator are built upon relational architecture. The main idea is to encode the constraint into the graph structure of its relational networks. We have demonstrated the proposed architecture for a new house layout generation problem, whose task is to take an architectural constraint as a graph (i.e., the number and types of rooms with their spatial adjacency) and produce a set of axis-aligned bounding boxes of rooms. We measure the quality of generated house layouts with the three metrics: the realism, the diversity, and the compatibility with the input graph constraint. Our qualitative and quantitative evaluations over 117,000 real floorplan images demonstrate that the proposed approach outperforms existing methods and baselines. We will publicly share all our code and data.", "published": "2020-03-16T03:16:12Z", "authors": ["Nelson Nauata", "Kai-Hung Chang", "Chin-Yi Cheng", "Greg Mori", "Yasutaka Furukawa"], "categories": ["cs.CV"], "doi": "", "_query": "housing price text listing", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2204.00320v2", "title": "Branch and Price for Submodular Bin Packing", "abstract": "The Submodular Bin Packing (SMBP) problem asks for packing unsplittable items into a minimal number of bins for which the capacity utilization function is submodular. SMBP is equivalent to chance-constrained and robust bin packing problems under various conditions. SMBP is a hard binary nonlinear programming optimization problem. In this paper, we propose a branch-and-price algorithm to solve this problem. The resulting price subproblems are submodular knapsack problems, and we propose a tailored exact branch-and-cut algorithm based on a piece-wise linear relaxation to solve them. To speed up column generation, we develop a hybrid pricing strategy to replace the exact pricing algorithm with a fast pricing heuristic. We test our algorithms on instances generated as suggested in the literature. The computational results show the efficiency of our branch-and-price algorithm and the proposed pricing techniques.", "published": "2022-04-01T09:58:56Z", "authors": ["Liding Xu", "Claudia D'Ambrosio", "Sonia Haddad Vanier", "Emiliano Traversi"], "categories": ["math.OC"], "doi": "", "_query": "housing price text listing", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2208.01791v4", "title": "From Workplace to Residence: The Spillover Effects of Minimum Wage Policies on Local Housing Markets", "abstract": "The recent rise of sub-national minimum wage (MW) policies in the US has resulted in significant dispersion of MW levels within urban areas. In this paper, we study the spillover effects of these policies on local rental markets through commuting. To do so, for each USPS ZIP code we construct a \"workplace\" MW measure based on the location of its resident's jobs, and use it to estimate the effect of MW policies on rents. We use a novel identification strategy that exploits the fine timing of differential changes in the workplace MW across ZIP codes that share the same \"residence\" MW, defined as the same location's MW. Our baseline results imply that a 10 percent increase in the workplace MW increases rents at residence ZIP codes by 0.69 percent. To illustrate the importance of commuting patterns, we use our estimates and a simple model to simulate the impact of federal and city counterfactual MW policies. The simulations suggest that landlords pocket approximately 10 cents of each dollar generated by the MW across directly and indirectly affected areas, though the incidence on landlords varies systematically across space.", "published": "2022-08-03T00:12:40Z", "authors": ["Gabriele Borg", "Diego Gentile Passaro", "Santiago Hermo"], "categories": ["econ.GN"], "doi": "", "_query": "housing price text listing", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/1301.3114v2", "title": "Estimating the efficient price from the order flow: a Brownian Cox process approach", "abstract": "At the ultra high frequency level, the notion of price of an asset is very ambiguous. Indeed, many different prices can be defined (last traded price, best bid price, mid price,...). Thus, in practice, market participants face the problem of choosing a price when implementing their strategies. In this work, we propose a notion of efficient price which seems relevant in practice. Furthermore, we provide a statistical methodology enabling to estimate this price form the order flow.", "published": "2013-01-14T20:25:50Z", "authors": ["Sylvain Delattre", "Christian Y. Robert", "Mathieu Rosenbaum"], "categories": ["q-fin.TR", "q-fin.ST"], "doi": "", "_query": "housing price text listing", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/0705.1680v1", "title": "Option Pricing Using Bayesian Neural Networks", "abstract": "Options have provided a field of much study because of the complexity involved in pricing them. The Black-Scholes equations were developed to price options but they are only valid for European styled options. There is added complexity when trying to price American styled options and this is why the use of neural networks has been proposed. Neural Networks are able to predict outcomes based on past data. The inputs to the networks here are stock volatility, strike price and time to maturity with the output of the network being the call option price. There are two techniques for Bayesian neural networks used. One is Automatic Relevance Determination (for Gaussian Approximation) and one is a Hybrid Monte Carlo method, both used with Multi-Layer Perceptrons.", "published": "2007-05-11T15:55:31Z", "authors": ["Michael Maio Pires", "Tshilidzi Marwala"], "categories": ["cs.CE", "cs.NE"], "doi": "", "_query": "housing price text listing", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/1506.07244v2", "title": "Central limit theorems for mapping class groups and $\\text{Out}(F_N)$", "abstract": "We prove central limit theorems for the random walks on either the mapping class group of a closed, connected, orientable, hyperbolic surface, or on $\\text{Out}(F_N)$, each time under a finite second moment condition on the measure (either with respect to the Teichmüller metric, or with respect to the Lipschitz metric on outer space). In the mapping class group case, this describes the spread of the hyperbolic length of a simple closed curve on the surface after applying a random product of mapping classes. In the case of $\\text{Out}(F_N)$, this describes the spread of the length of primitive conjugacy classes in $F_N$ under random products of outer automorphisms. Both results are based on a general criterion for establishing a central limit theorem for the Busemann cocycle on the horoboundary of a metric space, applied to either the Teichmüller space of the surface, or to Culler--Vogtmann's outer space.", "published": "2015-06-24T05:08:19Z", "authors": ["Camille Horbez"], "categories": ["math.GR", "math.GT", "math.PR"], "doi": "10.2140/gt.2018.22.105", "_query": "housing price text listing", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/1812.02313v1", "title": "Imaginary crystal bases for $U_q(\\widehat{\\mathfrak{sl}(2)})$-modules in category $\\mathcal O^q_{\\text{red,im}}$", "abstract": "Recently we defined imaginary crystal bases for $U_q(\\widehat{\\mathfrak{sl}(2)})$- modules in category $\\mathcal O^q_{\\text{red,im}}$ and showed the existence of such bases for reduced quantized imaginary Verma modules for $U_q(\\widehat{\\mathfrak{sl}(2)})$. In this paper we show the existence of imaginary crystal basis for any object in the category $\\mathcal O^q_{\\text{red,im}}$.", "published": "2018-12-04T20:13:50Z", "authors": ["Ben Cox", "Vyacheslav Futorny", "Kailash C. Misra"], "categories": ["math.RT"], "doi": "", "_query": "housing price text listing", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/1707.07594v1", "title": "'Dark Germany': Hidden Patterns of Participation in Online Far-Right Protests Against Refugee Housing", "abstract": "The political discourse in Western European countries such as Germany has recently seen a resurgence of the topic of refugees, fueled by an influx of refugees from various Middle Eastern and African countries. Even though the topic of refugees evidently plays a large role in online and offline politics of the affected countries, the fact that protests against refugees stem from the right-wight political spectrum has lead to corresponding media to be shared in a decentralized fashion, making an analysis of the underlying social and mediatic networks difficult. In order to contribute to the analysis of these processes, we present a quantitative study of the social media activities of a contemporary nationwide protest movement against local refugee housing in Germany, which organizes itself via dedicated Facebook pages per city. We analyse data from 136 such protest pages in 2015, containing more than 46,000 posts and more than one million interactions by more than 200,000 users. In order to learn about the patterns of communication and interaction among users of far-right social media sites and pages, we investigate the temporal characteristics of the social media activities of this protest movement, as well as the connectedness of the interactions of its participants. We find several activity metrics such as the number of posts issued, discussion volume about crime and housing costs, negative polarity in comments, and user engagement to peak in late 2015, coinciding with chancellor Angela Merkel's much criticized decision of September 2015 to temporarily admit the entry of Syrian refugees to Germany. Furthermore, our evidence suggests a low degree of direct connectedness of participants in this movement, (i.a., indicated by a lack of geographical collaboration patterns), yet we encounter a strong affiliation of the pages' user base with far-right political parties.", "published": "2017-07-24T14:58:50Z", "authors": ["Sebastian Schelter", "Jérôme Kunegis"], "categories": ["cs.SI"], "doi": "", "_query": "housing price text listing", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2408.00690v2", "title": "Improving Text Embeddings for Smaller Language Models Using Contrastive Fine-tuning", "abstract": "While Large Language Models show remarkable performance in natural language understanding, their resource-intensive nature makes them less accessible. In contrast, smaller language models such as MiniCPM offer more sustainable scalability, but often underperform without specialized optimization. In this paper, we explore the enhancement of smaller language models through the improvement of their text embeddings. We select three language models, MiniCPM, Phi-2, and Gemma, to conduct contrastive fine-tuning on the NLI dataset. Our results demonstrate that this fine-tuning method enhances the quality of text embeddings for all three models across various benchmarks, with MiniCPM showing the most significant improvements of an average 56.33% performance gain. The contrastive fine-tuning code is publicly available at https://github.com/trapoom555/Language-Model-STS-CFT.", "published": "2024-08-01T16:31:35Z", "authors": ["Trapoom Ukarapol", "Zhicheng Lee", "Amy Xin"], "categories": ["cs.CL"], "doi": "", "_query": "housing price text listing", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2510.05459v1", "title": "Metric criteria for fixed price of countable groups", "abstract": "We establish general criteria for a countable group $Γ$ to have fixed price 1 depending on a choice of left-invariant proper metric on $Γ$. We apply this criterion to show that if $Γ_1,Γ_2$ are two countable groups satisfying a certain growth condition then $Γ_1\\times Γ_2$ has fixed price 1. For example, $Γ\\times Γ$ has fixed price 1 for any countable group $Γ$.", "published": "2025-10-06T23:45:20Z", "authors": ["Erin Bevilacqua", "Lewis Bowen"], "categories": ["math.GR", "math.DS"], "doi": "", "_query": "housing price text listing", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/1408.1352v1", "title": "A simple model of local prices and associated risk evaluation", "abstract": "A simple spin system is constructed to simulate dynamics of asset prices and studied numerically. The outcome for the distribution of prices is shown to depend both on the dimension of the system and the introduction of price into the link measure. For dimensions below 2, the associated risk is high and the price distribution is bimodal. For higher dimensions, the price distribution is Gaussian and the associated risk is much lower. It is suggested that the results are relevant to rare assets or situations where few players are involved in the deal making process.", "published": "2014-08-01T22:04:59Z", "authors": ["Krzysztof Urbanowicz", "Peter Richmond", "Janusz A. Hołyst"], "categories": ["q-fin.GN", "physics.data-an"], "doi": "", "_query": "housing price text listing", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/1807.05578v1", "title": "Discovering Latent Concepts and Exploiting Ontological Features for Semantic Text Search", "abstract": "Named entities and WordNet words are important in defining the content of a text in which they occur. Named entities have ontological features, namely, their aliases, classes, and identifiers. WordNet words also have ontological features, namely, their synonyms, hypernyms, hyponyms, and senses. Those features of concepts may be hidden from their textual appearance. Besides, there are related concepts that do not appear in a query, but can bring out the meaning of the query if they are added. The traditional constrained spreading activation algorithms use all relations of a node in the network that will add unsuitable information into the query. Meanwhile, we only use relations represented in the query. We propose an ontology-based generalized Vector Space Model to semantic text search. It discovers relevant latent concepts in a query by relation constrained spreading activation. Besides, to represent a word having more than one possible direct sense, it combines the most specific common hypernym of the remaining undisambiguated multi-senses with the form of the word. Experiments on a benchmark dataset in terms of the MAP measure for the retrieval performance show that our model is 41.9% and 29.3% better than the purely keyword-based model and the traditional constrained spreading activation model, respectively.", "published": "2018-07-15T17:19:03Z", "authors": ["Vuong M. Ngo", "Tru H. Cao"], "categories": ["cs.IR"], "doi": "", "_query": "housing price text listing", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/1009.3810v1", "title": "Asset pricing with random information flow", "abstract": "In the information-based approach to asset pricing the market filtration is modelled explicitly as a superposition of signals concerning relevant market factors and independent noise. The rate at which the signal is revealed to the market then determines the overall magnitude of asset volatility. By letting this information flow rate random, we obtain an elementary stochastic volatility model within the information-based approach. Such an extension is economically justified on account of the fact that in real markets information flow rates are rarely measurable. Effects of having a random information flow rate is investigated in detail in the context of a simple model setup. Specifically, the price process of the asset is derived, and its characteristic behaviours are revealed via simulation studies. The price of a European-style option is worked out, showing that the model has a sufficient flexibility to fit volatility surface. As an extension of the random information flow model, price manipulation is considered. A simple model is used to show how the skewness of the manipulated and unmanipulated price processes take opposite signature.", "published": "2010-09-20T13:21:37Z", "authors": ["Dorje C. Brody", "Yan Tai Law"], "categories": ["q-fin.PR"], "doi": "", "_query": "housing price text listing", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2507.21500v1", "title": "VN-MTEB: Vietnamese Massive Text Embedding Benchmark", "abstract": "Vietnam ranks among the top countries in terms of both internet traffic and online toxicity. As a result, implementing embedding models for recommendation and content control duties in applications is crucial. However, a lack of large-scale test datasets, both in volume and task diversity, makes it tricky for scientists to effectively evaluate AI models before deploying them in real-world, large-scale projects. To solve this important problem, we introduce a Vietnamese benchmark, VN-MTEB for embedding models, which we created by translating a large number of English samples from the Massive Text Embedding Benchmark using our new automated framework. We leverage the strengths of large language models (LLMs) and cutting-edge embedding models to conduct translation and filtering processes to retain high-quality samples, guaranteeing a natural flow of language and semantic fidelity while preserving named entity recognition (NER) and code snippets. Our comprehensive benchmark consists of 41 datasets from six tasks specifically designed for Vietnamese text embeddings. In our analysis, we find that bigger and more complex models using Rotary Positional Embedding outperform those using Absolute Positional Embedding in embedding tasks. Datasets are available at HuggingFace: https://huggingface.co/collections/GreenNode/vn-mteb-68871433f0f7573b8e1a6686", "published": "2025-07-29T04:48:55Z", "authors": ["Loc Pham", "Tung Luu", "Thu Vo", "Minh Nguyen", "Viet Hoang"], "categories": ["cs.CL", "cs.AI"], "doi": "", "_query": "housing price text listing", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/1802.03050v1", "title": "Thompson Sampling for Dynamic Pricing", "abstract": "In this paper we apply active learning algorithms for dynamic pricing in a prominent e-commerce website. Dynamic pricing involves changing the price of items on a regular basis, and uses the feedback from the pricing decisions to update prices of the items. Most popular approaches to dynamic pricing use a passive learning approach, where the algorithm uses historical data to learn various parameters of the pricing problem, and uses the updated parameters to generate a new set of prices. We show that one can use active learning algorithms such as Thompson sampling to more efficiently learn the underlying parameters in a pricing problem. We apply our algorithms to a real e-commerce system and show that the algorithms indeed improve revenue compared to pricing algorithms that use passive learning.", "published": "2018-02-08T21:17:28Z", "authors": ["Ravi Ganti", "Matyas Sustik", "Quoc Tran", "Brian Seaman"], "categories": ["stat.ML", "cs.LG"], "doi": "", "_query": "housing price text listing", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2603.18920v1", "title": "The Optimal Reset-Hour of a Once-Daily Petrol Price Increase Limit", "abstract": "A German ministry recently proposed a limit of at most one price increase per day for petrol stations. At what time should the price reset be allowed in order to lower price levels the most throughout the day? To answer this question, I infer the share of price-sensitive consumers for every hour of the day from German petrol station price data, based on a simple spatial-competition model. I focus on weekdays, which are the relevant target because commuter demand is less flexible than weekend demand. Hourly petrol station prices peak at 07:00 and bottom out at 19:00. Given the inferred composition of price-sensitivity throughout the day and hourly passenger-car traffic frequencies as a proxy for quantity, I evaluate every possible reset-hour of the new policy. The lowest traffic-weighted average price is achieved by an 11:00 reset. With this reset-hour, the resulting equilibrium price throughout the day is constant. This would lead to lower prices in the morning but higher prices in the evening, harming price-sensitive consumers but benefiting morning commuters and firms.", "published": "2026-03-19T13:56:32Z", "authors": ["Christoph Siemroth"], "categories": ["econ.GN"], "doi": "", "_query": "housing price text listing", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2109.12980v1", "title": "On the nature of monetary and price inflation and hyperinflation", "abstract": "Monetary inflation is a sustained increase in the money supply than can result in price inflation, which is a rise in the general level of prices of goods and services. The objectives of this paper were to develop economic models to (1) predict the annual rate of growth in the US consumer price index (CPI), based on the annual growth in the US broad money supply (BMS), the annual growth in US real GDP, and the annual growth in US savings, over the time period 2001 to 2019; (2) investigate the means by which monetary and price inflation can develop into monetary and price hyperinflation. The hypothesis that the annual rate of growth in the US CPI is a function of the annual growth in the US BMS minus the annual growth in US real GDP minus the annual growth in US savings, over the time period investigated, has been shown to be the case. However, an exact relationship required the use of a non-zero residual term. A mathematical statistical formulation of a hyperinflationary process has been provided and used to quantify the period of hyperinflation in the Weimar Republic, from July 1922 until the end of November 1923.", "published": "2021-08-18T09:39:31Z", "authors": ["Laurence Francis Lacey"], "categories": ["q-fin.GN", "stat.ME"], "doi": "", "_query": "housing price text listing", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/0902.4159v2", "title": "Liquidity Crisis, Granularity of the Order Book and Price Fluctuations", "abstract": "We introduce a microscopic model for the dynamics of the order book to study how the lack of liquidity influences price fluctuations. We use the average density of the stored orders (granularity $g$) as a proxy for liquidity. This leads to a Price Impact Surface which depends on both volume $ω$ and $g$. The dependence on the volume (averaged over the granularity) of the Price Impact Surface is found to be a concave power law function $<φ(ω,g)>_g\\simω^δ$ with $δ\\approx 0.59$. Instead the dependence on the granularity is $φ(ω,g|ω)\\sim g^α$ with $α\\approx-1$, showing a divergence of price fluctuations in the limit $g\\to 0$. Moreover, even in intermediate situations of finite liquidity, this effect can be very large and it is a natural candidate for understanding the origin of large price fluctuations.", "published": "2009-02-24T14:40:03Z", "authors": ["M. Cristelli", "V. Alfi", "L. Pietronero", "A. Zaccaria"], "categories": ["q-fin.TR"], "doi": "10.1140/epjb/e2009-00353-6", "_query": "housing price text listing", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/1304.4690v1", "title": "On option pricing in illiquid markets with jumps", "abstract": "One of the shortcomings of the Black and Scholes model on option pricing is the assumption that trading of the underlying asset does not affect the price of that asset. This assumption can be fulfilled only in perfectly liquid markets. Since most markets are illquid, this assumption might be too restrictive. Thus, taking into account the price impact in option pricing is an important issue. This issue has been dealt with, to some extent, for illiquid markets by assuming a continuous process, mainly based on the Brownian motion. However, the recent financial crisis and its effects on the global stock markets have propagated the urgent need for more realistic models where the stochastic process describing the price trajectories involves random jumps. Nonetheless, works related to markets with jumps are scant compared to the continuous ones. In addition, these previous studies do not deal with illiquid markets. The contribution of this paper is to tackle the pricing problem for options in illiquid markets with jumps as well as the hedging strategy within this context, which is the first of its kind to the best knowledge.", "published": "2013-04-17T04:58:06Z", "authors": ["Youssef El-Khatib", "Abdulnasser Hatemi-J"], "categories": ["q-fin.PR"], "doi": "", "_query": "housing price text listing", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2608.21334v4", "title": "Across-Design Uncertainty in Short Pricing Panels: Inference and Identification", "abstract": "Short observational pricing panels often contain many data points but very few actual price changes. This paper shows that this sparsity creates a hidden source of error that standard statistical methods miss. When estimating price effects, most of the uncertainty does not come from sample size within a panel, but from the specific history of price movements observed. Standard confidence intervals fail because they only measure variation within the panel, ignoring this broader design-level error. Using simulations, we find that this cross-design variation accounts for most of the estimation error, causing standard methods to significantly understate uncertainty. First, we show that cross-design error decreases predictably as the total volume of price variation increases. Second, adding more data from regions that share the same price trends does not fix the issue; true precision improves only when combining data across units with independent price trajectories. Third, applying a simple variance-component adjustment across independently priced units restores accurate statistical coverage. We confirm these findings in real-world store scanner data, showing that products and pricing zones behave as if they have far fewer independent price movements than their raw counts suggest. Ultimately, reliable inference in passive pricing data requires genuine, independent variation, which can be achieved through controlled regional price testing.", "published": "2026-08-21T17:40:31Z", "authors": ["Pedro Cadahia Delgado"], "categories": ["cs.LG", "econ.EM"], "doi": "", "_query": "housing price text listing", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/1310.4962v6", "title": "A New Dynamic Pricing Model based on Convex Hull Pricing", "abstract": "This paper presents a new dynamic pricing model (a.k.a. real-time pricing) that reflects startup costs of generators. Dynamic pricing, which is a method to control demand by pricing electricity at hourly (or more often) intervals, has been studied by many researchers. They assume that the cost functions of suppliers are convex, although they may be nonconvex because of the startup costs of generators in practice. We provide a dynamic pricing model that takes into account such cost functions within the settings of unit commitment problems (UCPs). Our model gives convex hull price (CHP), which has not been used in the context of dynamic pricing, though it is known that the CHP minimizes the uplift payment which is disadvantageous to suppliers for a given demand. In addition, we apply an iterative algorithm based on the subgradient method to solve our model. Numerical experiments show the efficiency of our model on reducing uplift payments. The prices determined by our algorithm give sufficiently small uplift payments in a realistic computational time.", "published": "2013-10-18T09:30:15Z", "authors": ["Naoki Ito", "Akiko Takeda", "Toru Namerikawa"], "categories": ["math.OC"], "doi": "", "_query": "housing price text listing", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2609.37091v1", "title": "Quantum Query Lower Bounds for Triangle-Listing and Spanners", "abstract": "This paper gives quantum query lower bounds for two relational graph problems, triangle listing and explicit multiplicative spanner construction, in the general graph query model, where quantum adjacency, degree and neighborhood queries are all available in arbitrary superposition. Both results are obtained by reductions via intermediate multi-block search problems. We extend the quantum query recording framework by Zhandry (CRYPTO 2019) and Hamoudi and Magniez (ToCT 2023) to handle a recording architecture for bidirectional oracles and give a generic blockwise soundness framework, which for arbitrary families of local accepting projectors, gives an exact operator-norm characterization of their maximum overlap with the subspace of bounded weight records. Using the intermediate search problems, we exhibit a family of $n$-vertex graphs with $Θ(n)$ triangles on which listing any constant fraction of the triangles requires $Ω(n^{3/2-o(1)})$ quantum queries. This is the first nontrivial quantum lower bound for triangle listing, a question raised by Jiang and Peng (ICML 2026). Further, we show that, for every fixed $k\\ge 7$, constructing a multiplicative $k$-spanner requires $Ω(n^{1+\\frac{1}{2μ_k}})$ quantum queries, where $μ_k=k/3+O(1)$. For $k\\in\\{7,8\\}$ the bound is $Ω(n^{5/4})$, which matches what would be implied by an unproven instance of the Erdős girth conjecture, and for large $k$ the exponent $1+\\frac{3}{2k}$ exceeds the $1+\\frac{4}{3k}$ implied by the provable high girth dense graphs due to Lazebnik, Ustimenko and Woldar, 1995.", "published": "2026-09-29T09:18:33Z", "authors": ["Yu Chen", "Ananta Mukherjee", "Mingyang Yang"], "categories": ["quant-ph"], "doi": "", "_query": "housing price text listing", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2101.01576v2", "title": "On the price of explainability for some clustering problems", "abstract": "The price of explainability for a clustering task can be defined as the unavoidable loss,in terms of the objective function, if we force the final partition to be explainable. Here, we study this price for the following clustering problems: $k$-means, $k$-medians, $k$-centers and maximum-spacing. We provide upper and lower bounds for a natural model where explainability is achieved via decision trees. For the $k$-means and $k$-medians problems our upper bounds improve those obtained by [Moshkovitz et. al, ICML 20] for low dimensions. Another contribution is a simple and efficient algorithm for building explainable clusterings for the $k$-means problem. We provide empirical evidence that its performance is better than the current state of the art for decision-tree based explainable clustering.", "published": "2021-01-05T15:08:25Z", "authors": ["Eduardo Laber", "Lucas Murtinho"], "categories": ["cs.LG", "cs.DS"], "doi": "", "_query": "housing price text listing", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2610.24608v1", "title": "Fabric retrofit scenario analysis under uncertainty for an urban housing stock using machine learning", "abstract": "Fabric retrofit is central to decarbonising housing, and planning it depends on expected savings and their uncertainty. This study asks which upgrade saves most per dwelling and across a housing portfolio, how uncertain the savings are, and whether upgrade order matters. We propose an uncertainty-aware framework that converts predicted changes in energy performance certificate scores after wall, roof and window upgrades into standardised energy-cost savings and emission reductions. Six machine-learning models predict the 1-100 Standard Assessment Procedure (SAP) energy-cost and environmental impact (EI) emission scores from 17 certificate descriptors. Each model's errors on similar dwellings give calibrated prediction intervals. A global-best strategy applies the most accurate model throughout; a sample-specific strategy chooses each dwelling's model using these local errors. The analysis covers 103,414 officially registered dwellings in Westminster, London. CatBoost was the global-best model, with mean absolute errors of 3.487 SAP and 4.379 EI points on held-out dwellings. Both strategies projected similar savings; under global-best, roof insulation saved most per dwelling, GBP 203 annually (uncertainty range 176-229), and wall insulation most across the portfolio, GBP 6.09 million annually (uncertainty range 5.69-6.72). For 5,382 dwellings needing all three upgrades, every order delivered the same final package, yet installing roofs, walls, then windows a year apart accumulated 54% more projected cost savings over three years than the reverse order. Emission reductions followed the same pattern. Using only certificate data, the framework helps local authorities rank measures for dwellings and portfolios and sequence upgrades to bring savings forward.", "published": "2026-10-17T10:23:21Z", "authors": ["Yunfei Bai", "Aaron Tesfa Tsion", "Raúl Rosales", "Barbara Shollock", "Wei He"], "categories": ["physics.soc-ph"], "doi": "", "_query": "housing price text listing", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2506.08718v1", "title": "Price Discovery in Cryptocurrency Markets", "abstract": "This document analyzes price discovery in cryptocurrency markets by comparing centralized and decentralized exchanges, as well as spot and futures markets. The study focuses first on Ethereum (ETH) and then applies a similar approach to Bitcoin (BTC). Chapter 1 outlines the theoretical framework, emphasizing the structural differences between centralized exchanges and decentralized finance mechanisms, especially Automated Market Makers (AMMs). It also explains how to construct an order book from a liquidity pool in a decentralized setting for comparison with centralized exchanges. Chapter 2 describes the methodological tools used: Hasbrouck's Information Share, Gonzalo and Granger's Permanent-Transitory decomposition, and the Hayashi-Yoshida estimator. These are applied to explore lead-lag dynamics, cointegration, and price discovery across market types. Chapter 3 presents the empirical analysis. For ETH, it compares price dynamics on Binance and Uniswap v2 over a one-year period, focusing on five key events in 2024. For BTC, it analyzes the relationship between spot and futures prices on the CME. The study estimates lead-lag effects and cointegration in both cases. Results show that centralized markets typically lead in ETH price discovery. In futures markets, while they tend to lead overall, high-volatility periods produce mixed outcomes. The findings have key implications for traders and institutions regarding liquidity, arbitrage, and market efficiency. Various metrics are used to benchmark the performance of modified AMMs and to understand the interaction between decentralized and centralized structures.", "published": "2025-06-10T12:07:11Z", "authors": ["Juan Plazuelo Pascual", "Carlos Tardon Rubio", "Juan Toro Cebada", "Angel Hernando Veciana"], "categories": ["q-fin.TR"], "doi": "", "_query": "housing price text listing", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2302.11643v3", "title": "An Empirical Analysis of Optimal Nonlinear Pricing in Business-to-Business Markets", "abstract": "In continuous-choice settings, consumers decide not only on whether to purchase a product, but also on how much to purchase. Thus, firms optimize a full price schedule rather than a single price point. This paper provides a methodology to empirically estimate the optimal schedule under multi-dimensional consumer heterogeneity with a focus on B2B applications. We apply our method to novel data from an educational-services firm that contains purchase-size information not only for deals that materialized, but also for potential deals that eventually failed. We show that this data, combined with identifying assumptions, helps infer how price sensitivity varies with \"customer size\". Using our estimated model, we show that the optimal second-degree price discrimination (i.e., optimal nonlinear tariff) improves the firm's profit upon linear pricing by at least 8.2%. That said, this second-degree price discrimination scheme only recovers 7.1% of the gap between the profitability of linear pricing and that of infeasible first degree price discrimination. We also conduct several further simulation analyses (i) empirically quantifying the magnitude by which incentive-compatibility constraints impact the optimal pricing and profits, (ii) comparing the role of demand- v.s. cost-side factors in shaping the optimal price schedule, and (iii) studying the implications of fixed fees for the optimal contract and profitability.", "published": "2023-02-22T20:39:55Z", "authors": ["Soheil Ghili", "Russ Yoon"], "categories": ["econ.GN"], "doi": "", "_query": "housing price text listing", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/1905.13578v1", "title": "Acoustics of Margravial Opera House Bayreuth", "abstract": "The Margravial Opera House Bayreuth, built between 1745 and 1750, is a well preserved Baroque court theatre designed by Giuseppe Galli Bibiena [1]. It provides an opportunity to experience not only the visual but also the acoustic design of opera theatres in the 18th century, as the bell-shaped auditorium along with the decoratively painted canvas remains intact. Using balloons and hand-claps as sound sources, we characterize the impulse response of this opera house after its recent renovation. The reverberation time (RT), early decay time (EDT) and clarity factor are characterized and discussed in comparison to historical Italian theatres of a similar age.", "published": "2019-05-31T12:34:08Z", "authors": ["Sebastian Krauss", "Simeon Völkel", "Christoph Dobner", "Alexandra Völkel", "Kai Huang"], "categories": ["physics.pop-ph"], "doi": "", "_query": "housing price text listing", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2608.08690v1", "title": "Price Responses of Rwandan Tungsten Exports under Conflict Minerals Regulation", "abstract": "Section 1502 of the Dodd--Frank Act, enacted in 2010, requires U.S.-listed companies using tin, tantalum, tungsten, and gold (3TG) from the Democratic Republic of the Congo and adjoining countries to disclose information on the minerals' origins. Concerns have been raised that the regulation may have induced a de facto embargo through avoidance of sourcing from the covered region. However, how the price responsiveness of mineral exports evolved under changing institutional and market conditions remains insufficiently understood. Since tungsten production in the covered region is concentrated almost entirely in Rwanda, this study examines the price responsiveness of Rwandan tungsten exports from January 2009 to December 2023. Because missing export quantity data prevent continuous observation of export unit values, we apply the identification approach of Nakano and Nishimura (2025), combining monthly mirror trade data from UN Comtrade with exchange rates and a world average price. An importer fixed-effects model is estimated using export value as the dependent variable, with the sample divided into four periods according to changes in the institutional and market environment. The results reveal substantial temporal variation in price responsiveness. A statistically significant negative price response is observed in Period 1 ($η=-20.814$, $p<0.01$), disappears in Period 2 ($η=1.814$, $p>0.10$), reappears in Period 3 ($η=-5.277$, $p<0.01$), and disappears again in Period 4 ($η=0.440$, $p>0.10$). Coefficient-difference tests confirm significant changes between Periods 1 and 2 ($p=0.0021$) and between Periods 3 and 4 ($p=0.0006$). These findings suggest that the price responsiveness of Rwandan tungsten exports varied substantially over time rather than following a uniform trajectory after the regulation.", "published": "2026-08-09T13:12:09Z", "authors": ["Haruka Nagamori", "Kazuhiko Nishimura"], "categories": ["econ.GN"], "doi": "", "_query": "housing price text listing", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/0812.4010v1", "title": "Discrete Time vs Continuous Time Stock-price Dynamics and implications for Option Pricing", "abstract": "In the present paper we construct stock price processes with the same marginal log-normal law as that of a geometric Brownian motion and also with the same transition density (and returns' distributions) between any two instants in a given discrete-time grid. We then illustrate how option prices based on such processes differ from Black and Scholes', in that option prices can be either arbitrarily close to the option intrinsic value or arbitrarily close to the underlying stock price. We also explain that this is due to the particular way one models the stock-price process in between the grid time instants which are relevant for trading. The theoretical result concerning scalar stochastic differential equations with prescribed diffusion coefficient whose densities evolve in a prescribed exponential family, on which part of the paper is based, is presented in detail.", "published": "2008-12-21T03:01:28Z", "authors": ["Damiano Brigo", "Fabio Mercurio"], "categories": ["q-fin.PR"], "doi": "", "_query": "housing price text listing", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/cond-mat/0401132v1", "title": "Large stock price changes: volume or liquidity?", "abstract": "We analyze large stock price changes of more than five standard deviations for i) TAQ data for the year 1997 and ii) order book data from the Island ECN for the year 2002. We argue that large price changes are not due to large trading volumes. Instead, we find that extreme price fluctuations are mainly caused by a low density of limit orders stored in the order book, i.e. a small liquidity.", "published": "2004-01-08T20:57:02Z", "authors": ["Philipp Weber", "Bernd Rosenow"], "categories": ["cond-mat"], "doi": "", "_query": "housing price text listing", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/1902.10497v1", "title": "Pricing of claims in discrete time with partial information", "abstract": "We consider the pricing problem of a seller with delayed price information. By using Lagrange duality, a dual problem is derived, and it is proved that there is no duality gap. This gives a characterization of the seller's price of a contingent claim. Finally, we analyze the dual problem, and compare the prices offered by two sellers with delayed and full information respectively.", "published": "2019-02-27T12:58:32Z", "authors": ["Kristina Rognlien Dahl"], "categories": ["math.OC"], "doi": "10.1007/s00245-013-9200-x", "_query": "housing price text listing", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2201.12275v1", "title": "Efficiency of Ad Auctions with Price Displaying", "abstract": "Most of the economic reports forecast that almost half of the worldwide market value unlocked by AI over the next decade (up to 6 trillion USD per year) will be in marketing&sales. In particular, AI will enable the optimization of more and more intricate economic settings, in which multiple different activities need to be jointly automated. This is the case of, e.g., Google Hotel Ads and Tripadvisor, where auctions are used to display ads of similar products or services together with their prices. As in classical ad auctions, the ads are ranked depending on the advertisers' bids, whereas, differently from classical settings, ads are displayed together with their prices, so as to provide a direct comparison among them. This dramatically affects users' behavior, as well as the properties of ad auctions. We show that, in such settings, social welfare maximization can be achieved by means of a direct-revelation mechanism that jointly optimizes, in polynomial time, the ads allocation and the advertisers' prices to be displayed with them. However, in practice it is unlikely that advertisers allow the mechanism to choose prices on their behalf. Indeed, in commonly-adopted mechanisms, ads allocation and price optimization are decoupled, so that the advertisers optimize prices and bids, while the mechanism does so for the allocation, once prices and bids are given. We investigate how this decoupling affects the efficiency of mechanisms. In particular, we study the Price of Anarchy (PoA) and the Price of Stability (PoS) of indirect-revelation mechanisms with both VCG and GSP payments, showing that the PoS for the revenue may be unbounded even with two slots, and the PoA for the social welfare may be as large as the number of slots. Nevertheless, we show that, under some assumptions, simple modifications to the indirect-revelation mechanism with VCG payments achieve a PoS of 1 for the revenue.", "published": "2022-01-28T17:49:32Z", "authors": ["Matteo Castiglioni", "Diodato Ferraioli", "Nicola Gatti", "Alberto Marchesi", "Giulia Romano"], "categories": ["cs.GT"], "doi": "", "_query": "housing price text listing", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2209.11837v1", "title": "Doubly Fair Dynamic Pricing", "abstract": "We study the problem of online dynamic pricing with two types of fairness constraints: a \"procedural fairness\" which requires the proposed prices to be equal in expectation among different groups, and a \"substantive fairness\" which requires the accepted prices to be equal in expectation among different groups. A policy that is simultaneously procedural and substantive fair is referred to as \"doubly fair\". We show that a doubly fair policy must be random to have higher revenue than the best trivial policy that assigns the same price to different groups. In a two-group setting, we propose an online learning algorithm for the 2-group pricing problems that achieves $\\tilde{O}(\\sqrt{T})$ regret, zero procedural unfairness and $\\tilde{O}(\\sqrt{T})$ substantive unfairness over $T$ rounds of learning. We also prove two lower bounds showing that these results on regret and unfairness are both information-theoretically optimal up to iterated logarithmic factors. To the best of our knowledge, this is the first dynamic pricing algorithm that learns to price while satisfying two fairness constraints at the same time.", "published": "2022-09-23T20:02:09Z", "authors": ["Jianyu Xu", "Dan Qiao", "Yu-Xiang Wang"], "categories": ["cs.LG", "econ.EM", "stat.ML"], "doi": "", "_query": "housing price text listing", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2003.04606v3", "title": "Pricing Interest Rate Derivatives under Volatility Uncertainty", "abstract": "In this paper, we study the pricing of contracts in fixed income markets under volatility uncertainty in the sense of Knightian uncertainty or model uncertainty. The starting point is an arbitrage-free bond market under volatility uncertainty. The uncertainty about the volatility is modeled by a G-Brownian motion, which drives the forward rate dynamics. The absence of arbitrage is ensured by a drift condition. Such a setting leads to a sublinear pricing measure for additional contracts, which yields either a single price or a range of prices. Similar to the forward measure approach, we define the forward sublinear expectation to simplify the pricing of cashflows. Under the forward sublinear expectation, we obtain a robust version of the expectations hypothesis, and we show how to price options on forward prices. In addition, we develop pricing methods for contracts consisting of a stream of cashflows, since the nonlinearity of the pricing measure implies that we cannot price a stream of cashflows by pricing each cashflow separately. With these tools, we derive robust pricing formulas for all major interest rate derivatives. The pricing formulas provide a link to the pricing formulas of traditional models without volatility uncertainty and show that volatility uncertainty naturally leads to unspanned stochastic volatility.", "published": "2020-03-10T09:45:47Z", "authors": ["Julian Hölzermann"], "categories": ["q-fin.PR", "q-fin.MF"], "doi": "", "_query": "housing price text listing", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/1910.06954v3", "title": "Context Matters: Recovering Human Semantic Structure from Machine Learning Analysis of Large-Scale Text Corpora", "abstract": "Applying machine learning algorithms to large-scale, text-based corpora (embeddings) presents a unique opportunity to investigate at scale how human semantic knowledge is organized and how people use it to judge fundamental relationships, such as similarity between concepts. However, efforts to date have shown a substantial discrepancy between algorithm predictions and empirical judgments. Here, we introduce a novel approach of generating embeddings motivated by the psychological theory that semantic context plays a critical role in human judgments. Specifically, we train state-of-the-art machine learning algorithms using contextually-constrained text corpora and show that this greatly improves predictions of similarity judgments and feature ratings. By improving the correspondence between representations derived using embeddings generated by machine learning methods and empirical measurements of human judgments, the approach we describe helps advance the use of large-scale text corpora to understand the structure of human semantic representations.", "published": "2019-10-15T17:51:01Z", "authors": ["Marius Cătălin Iordan", "Tyler Giallanza", "Cameron T. Ellis", "Nicole M. Beckage", "Jonathan D. Cohen"], "categories": ["cs.CL", "cs.IR", "cs.LG"], "doi": "", "_query": "housing price text listing", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/0905.4657v1", "title": "Indifference price with general semimartingales", "abstract": "For utility functions $u$ finite valued on $\\mathbb{R}$, we prove a duality formula for utility maximization with random endowment in general semimartingale incomplete markets. The main novelty of the paper is that possibly non locally bounded semimartingale price processes are allowed. Following Biagini and Frittelli \\cite{BiaFri06}, the analysis is based on the duality between the Orlicz spaces $(L^{\\widehat{u}}, (L^{\\widehat{u}})^*)$ naturally associated to the utility function. This formulation enables several key properties of the indifference price $π(B)$ of a claim $B$ satisfying conditions weaker than those assumed in literature. In particular, the indifference price functional $π$ turns out to be, apart from a sign, a convex risk measure on the Orlicz space $L^{\\widehat{u}}$.", "published": "2009-05-28T14:00:42Z", "authors": ["Sara Biagini", "Marco Frittelli", "Matheus R. Grasselli"], "categories": ["q-fin.PR", "q-fin.CP"], "doi": "", "_query": "housing price text listing", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/1103.0318v1", "title": "Listing All Maximal Cliques in Large Sparse Real-World Graphs", "abstract": "We implement a new algorithm for listing all maximal cliques in sparse graphs due to Eppstein, Löffler, and Strash (ISAAC 2010) and analyze its performance on a large corpus of real-world graphs. Our analysis shows that this algorithm is the first to offer a practical solution to listing all maximal cliques in large sparse graphs. All other theoretically-fast algorithms for sparse graphs have been shown to be significantly slower than the algorithm of Tomita et al. (Theoretical Computer Science, 2006) in practice. However, the algorithm of Tomita et al. uses an adjacency matrix, which requires too much space for large sparse graphs. Our new algorithm opens the door for fast analysis of large sparse graphs whose adjacency matrix will not fit into working memory.", "published": "2011-03-02T00:00:26Z", "authors": ["David Eppstein", "Darren Strash"], "categories": ["cs.DS"], "doi": "", "_query": "housing price text listing", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/1503.02200v3", "title": "Sequential Posted Price Mechanisms with Correlated Valuations", "abstract": "We study the revenue performance of sequential posted price mechanisms and some natural extensions, for a general setting where the valuations of the buyers are drawn from a correlated distribution. Sequential posted price mechanisms are conceptually simple mechanisms that work by proposing a take-it-or-leave-it offer to each buyer. We apply sequential posted price mechanisms to single-parameter multi-unit settings in which each buyer demands only one item and the mechanism can assign the service to at most k of the buyers. For standard sequential posted price mechanisms, we prove that with the valuation distribution having finite support, no sequential posted price mechanism can extract a constant fraction of the optimal expected revenue, even with unlimited supply. We extend this result to the the case of a continuous valuation distribution when various standard assumptions hold simultaneously. In fact, it turns out that the best fraction of the optimal revenue that is extractable by a sequential posted price mechanism is proportional to ratio of the highest and lowest possible valuation. We prove that for two simple generalizations of these mechanisms, a better revenue performance can be achieved: if the sequential posted price mechanism has for each buyer the option of either proposing an offer or asking the buyer for its valuation, then a Omega(1/max{1,d}) fraction of the optimal revenue can be extracted, where d denotes the degree of dependence of the valuations, ranging from complete independence (d=0) to arbitrary dependence (d=n-1). Moreover, when we generalize the sequential posted price mechanisms further, such that the mechanism has the ability to make a take-it-or-leave-it offer to the i-th buyer that depends on the valuations of all buyers except i's, we prove that a constant fraction (2-sqrt{e})/4~0.088 of the optimal revenue can be always be extracted.", "published": "2015-03-07T19:02:16Z", "authors": ["Marek Adamczyk", "Allan Borodin", "Diodato Ferraioli", "Bart de Keijzer", "Stefano Leonardi"], "categories": ["cs.GT"], "doi": "", "_query": "housing price text listing", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/1303.4274v1", "title": "A note on pricing of contingent claims under G-expectation", "abstract": "In this paper, we study the pricing of contingent claims under G-expectation. In order to accomodate volatility uncertainty, the price of the risky security is supposed to governed by a general linear stochastic differential equation (SDE) driven by G-Brownian motion. Utilizing the recently developed results of Backward SDE driven by G-Brownian motion, we obtain the superhedging and suberhedging prices of a given contingent claim. Explicit results in the Markovian case are also derived.", "published": "2013-03-18T14:58:37Z", "authors": ["Mingshang Hu", "Shaolin Ji"], "categories": ["math.PR", "q-fin.PR"], "doi": "", "_query": "housing price text listing", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2104.02688v1", "title": "Pricing without no-arbitrage condition in discrete time", "abstract": "In a discrete time setting, we study the central problem of giving a fair price to some financial product. For several decades, the no-arbitrage conditions and the martingale measures have played a major role for solving this problem. We propose a new approach for estimating the super-replication cost based on convex duality instead of martingale measures duality: The prices are expressed using Fenchel conjugate and bi-conjugate without using any no-arbitrage condition.The super-hedging problem resolution leads endogenously to a weak no-arbitrage condition called Absence of Instantaneous Profit (AIP) under which prices are finite. We study this condition in details, propose several characterizations and compare it to the no-arbitrage condition.", "published": "2021-04-06T17:28:54Z", "authors": ["Laurence Carassus", "Emmanuel Lépinette"], "categories": ["q-fin.MF"], "doi": "", "_query": "housing price text listing", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2004.01238v1", "title": "Greater search cost reduces prices", "abstract": "The optimal price of each firm falls in the search cost of consumers, in the limit to the monopoly price, despite the exit of lower-value consumers in response to costlier search. Exit means that fewer inframarginal consumers remain. The decrease in marginal buyers is smaller, because part of demand is composed of customers coming from rival firms. These buyers can be held up and are not marginal. Higher search cost reduces the fraction of incoming switchers among buyers, which decreases the hold-up motive, thus the price.", "published": "2020-04-02T19:38:09Z", "authors": ["Sander Heinsalu"], "categories": ["econ.TH", "cs.GT"], "doi": "", "_query": "housing price text listing", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2010.15105v1", "title": "Price response functions and spread impact in correlated financial markets", "abstract": "Recent research on the response of stock prices to trading activity revealed long lasting effects, even across stocks of different companies. These results imply non-Markovian effects in price formation and when trading many stocks at the same time, in particular trading costs and price correlations. How the price response is measured depends on data set and research focus. However, it is important to clarify, how the details of the price response definition modify the results. Here, we evaluate different price response implementations for the Trades and Quotes (TAQ) data set from the NASDAQ stock market and find that the results are qualitatively the same for two different definitions of time scale, but the response can vary by up to a factor of two. Further, we show the key importance of the order between trade signs and returns, displaying the changes in the signal strength. Moreover, we confirm the dominating contribution of immediate price response directly after a trade, as we find that delayed responses are suppressed. Finally, we test the impact of the spread in the price response, detecting that large spreads have stronger impact.", "published": "2020-10-28T17:45:32Z", "authors": ["Juan C. Henao-Londono", "Sebastian M. Krause", "Thomas Guhr"], "categories": ["q-fin.ST", "physics.data-an"], "doi": "10.1140/epjb/s10051-021-00077-z", "_query": "housing price text listing", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/1110.4175v1", "title": "The Price of Anarchy (POA) of network coding and routing based on average pricing mechanism", "abstract": "The congestion pricing is an efficient allocation approach to mediate demand and supply of network resources. Different from the previous pricing using Affine Marginal Cost (AMC), we focus on studying the game between network coding and routing flows sharing a single link when users are price anticipating based on an Average Cost Sharing (ACS) pricing mechanism. We characterize the worst-case efficiency bounds of the game compared with the optimal, i.e., the price-of anarchy (POA), which can be low bound 50% with routing only. When both network coding and routing are applied, the POA can be as low as 4/9. Therefore, network coding cannot improve the POA significantly under the ACS. Moreover, for more efficient use of limited resources, it indicates the sharing users have a higher tendency to choose network coding.", "published": "2011-10-19T03:51:06Z", "authors": ["Wang Gang", "Dai Xia"], "categories": ["cs.NI", "cs.GT", "cs.IT"], "doi": "", "_query": "housing price text listing", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/1811.07220v2", "title": "Determination of the Lévy Exponent in Asset Pricing Models", "abstract": "We consider the problem of determining the Lévy exponent in a Lévy model for asset prices given the price data of derivatives. The model, formulated under the real-world measure $\\mathbb P$, consists of a pricing kernel $\\{π_t\\}_{t\\geq0}$ together with one or more non-dividend-paying risky assets driven by the same Lévy process. If $\\{S_t\\}_{t\\geq0}$ denotes the price process of such an asset then $\\{π_t S_t\\}_{t\\geq0}$ is a $\\mathbb P$-martingale. The Lévy process $\\{ ξ_t \\}_{t\\geq0}$ is assumed to have exponential moments, implying the existence of a Lévy exponent $ψ(α) = t^{-1}\\log \\mathbb E(\\rm e^{αξ_t})$ for $α$ in an interval $A \\subset \\mathbb R$ containing the origin as a proper subset. We show that if the initial prices of power-payoff derivatives, for which the payoff is $H_T = (ζ_T)^q$ for some time $T>0$, are given for a range of values of $q$, where $\\{ζ_t\\}_{t\\geq0}$ is the so-called benchmark portfolio defined by $ζ_t = 1/π_t$, then the Lévy exponent is determined up to an irrelevant linear term. In such a setting, derivative prices embody complete information about price jumps: in particular, the spectrum of the price jumps can be worked out from current market prices of derivatives. More generally, if $H_T = (S_T)^q$ for a general non-dividend-paying risky asset driven by a Lévy process, and if we know that the pricing kernel is driven by the same Lévy process, up to a factor of proportionality, then from the current prices of power-payoff derivatives we can infer the structure of the Lévy exponent up to a transformation $ψ(α) \\rightarrow ψ(α+ μ) - ψ(μ) + c α$, where $c$ and $μ$ are constants.", "published": "2018-11-17T21:01:10Z", "authors": ["George Bouzianis", "Lane Hughston"], "categories": ["q-fin.MF"], "doi": "10.1142/S0219024919500080", "_query": "housing price text listing", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/1303.7445v1", "title": "Agent-based modeling of a price information trading business", "abstract": "We describe an agent-based simulation of a fictional (but feasible) information trading business. The Gas Price Information Trader (GPIT) buys information about real-time gas prices in a metropolitan area from drivers and resells the information to drivers who need to refuel their vehicles. Our simulation uses real world geographic data, lifestyle-dependent driving patterns and vehicle models to create an agent-based model of the drivers. We use real world statistics of gas price fluctuation to create scenarios of temporal and spatial distribution of gas prices. The price of the information is determined on a case-by-case basis through a simple negotiation model. The trader and the customers are adapting their negotiation strategies based on their historical profits. We are interested in the general properties of the emerging information market: the amount of realizable profit and its distribution between the trader and customers, the business strategies necessary to keep the market operational (such as promotional deals), the price elasticity of demand and the impact of pricing strategies on the profit.", "published": "2013-03-29T17:40:04Z", "authors": ["Saad Ahmad Khan", "Ladislau Boloni"], "categories": ["cs.AI", "q-fin.GN"], "doi": "", "_query": "housing price text listing", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/1612.09152v2", "title": "A Risk-Neutral Equilibrium Leading to Uncertain Volatility Pricing", "abstract": "We study the formation of derivative prices in equilibrium between risk-neutral agents with heterogeneous beliefs about the dynamics of the underlying. Under the condition that the derivative cannot be shorted, we prove the existence of a unique equilibrium price and show that it incorporates the speculative value of possibly reselling the derivative. This value typically leads to a bubble; that is, the price exceeds the autonomous valuation of any given agent. Mathematically, the equilibrium price operator is of the same nonlinear form that is obtained in single-agent settings with strong aversion against model uncertainty. Thus, our equilibrium leads to a novel interpretation of this price.", "published": "2016-12-29T14:12:31Z", "authors": ["Johannes Muhle-Karbe", "Marcel Nutz"], "categories": ["q-fin.MF", "math.OC"], "doi": "", "_query": "housing price text listing", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2305.12887v1", "title": "ZS-MSTM: Zero-Shot Style Transfer for Gesture Animation driven by Text and Speech using Adversarial Disentanglement of Multimodal Style Encoding", "abstract": "In this study, we address the importance of modeling behavior style in virtual agents for personalized human-agent interaction. We propose a machine learning approach to synthesize gestures, driven by prosodic features and text, in the style of different speakers, even those unseen during training. Our model incorporates zero-shot multimodal style transfer using multimodal data from the PATS database, which contains videos of diverse speakers. We recognize style as a pervasive element during speech, influencing the expressivity of communicative behaviors, while content is conveyed through multimodal signals and text. By disentangling content and style, we directly infer the style embedding, even for speakers not included in the training phase, without the need for additional training or fine-tuning. Objective and subjective evaluations are conducted to validate our approach and compare it against two baseline methods.", "published": "2023-05-22T10:10:35Z", "authors": ["Mireille Fares", "Catherine Pelachaud", "Nicolas Obin"], "categories": ["eess.AS", "cs.AI", "cs.LG", "cs.SD"], "doi": "", "_query": "housing price text listing", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/1807.05917v2", "title": "Hedging with physical or cash settlement under transient multiplicative price impact", "abstract": "We solve the superhedging problem for European options in an illiquid extension of the Black-Scholes model, in which transactions have transient price impact and the costs and the strategies for hedging are affected by physical or cash settlement requirements at maturity. Our analysis is based on a convenient choice of reduced effective coordinates of magnitudes at liquidation for geometric dynamic programming. The price impact is transient over time and multiplicative, ensuring non-negativity of underlying asset prices while maintaining an arbitrage-free model. The basic (log-)linear example is a Black-Scholes model with relative price impact being proportional to the volume of shares traded, where the transience for impact on log-prices is being modelled like in Obizhaeva-Wang \\cite{ObizhaevaWang13} for nominal prices. More generally, we allow for non-linear price impact and resilience functions. The viscosity solutions describing the minimal superhedging price are governed by the transient character of the price impact and by the physical or cash settlement specifications. Pricing equations under illiquidity extend no-arbitrage pricing a la Black-Scholes for complete markets in a non-paradoxical way (cf.\\ {Ç}etin, Soner and Touzi \\cite{CetinSonerTouzi10}) even without additional frictions, and can recover it in base cases.", "published": "2018-07-16T15:21:25Z", "authors": ["Dirk Becherer", "Todor Bilarev"], "categories": ["q-fin.PR", "math.PR", "q-fin.TR"], "doi": "", "_query": "housing price text listing", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2102.00769v1", "title": "GTAE: Graph-Transformer based Auto-Encoders for Linguistic-Constrained Text Style Transfer", "abstract": "Non-parallel text style transfer has attracted increasing research interests in recent years. Despite successes in transferring the style based on the encoder-decoder framework, current approaches still lack the ability to preserve the content and even logic of original sentences, mainly due to the large unconstrained model space or too simplified assumptions on latent embedding space. Since language itself is an intelligent product of humans with certain grammars and has a limited rule-based model space by its nature, relieving this problem requires reconciling the model capacity of deep neural networks with the intrinsic model constraints from human linguistic rules. To this end, we propose a method called Graph Transformer based Auto Encoder (GTAE), which models a sentence as a linguistic graph and performs feature extraction and style transfer at the graph level, to maximally retain the content and the linguistic structure of original sentences. Quantitative experiment results on three non-parallel text style transfer tasks show that our model outperforms state-of-the-art methods in content preservation, while achieving comparable performance on transfer accuracy and sentence naturalness.", "published": "2021-02-01T11:08:45Z", "authors": ["Yukai Shi", "Sen Zhang", "Chenxing Zhou", "Xiaodan Liang", "Xiaojun Yang", "Liang Lin"], "categories": ["cs.CL", "cs.AI", "cs.CV"], "doi": "", "_query": "housing price text listing", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/1106.4957v4", "title": "Maximum entropy distribution of stock price fluctuations", "abstract": "The principle of absence of arbitrage opportunities allows obtaining the distribution of stock price fluctuations by maximizing its information entropy. This leads to a physical description of the underlying dynamics as a random walk characterized by a stochastic diffusion coefficient and constrained to a given value of the expected volatility, taking in this way into account the information provided by the existence of an option market. This model is validated by a comprehensive comparison with observed distributions of both price return and diffusion coefficient. Expected volatility is the only parameter in the model and can be obtained by analysing option prices. We give an analytic formulation of the probability density function for price returns which can be used to extract expected volatility from stock option data. This distribution is of high practical interest since it should be preferred to a Gaussian when dealing with the problem of pricing derivative financial contracts.", "published": "2011-06-24T12:55:42Z", "authors": ["Rosario Bartiromo"], "categories": ["q-fin.ST", "physics.data-an"], "doi": "", "_query": "housing price text listing", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2605.08660v1", "title": "Optimised Support Vector Regression for California Housing Price Prediction: The Critical Role of Feature Engineering and Hyperparameter Tuning", "abstract": "In the recent literature, Support Vector Regression (SVR) has been cited as one of the weakest performers on the California Housing benchmark dataset, with Preethi et al. (2025)specifically ranking it last among the algorithms they tested, reporting an R2 of only 0.60. This paper examines whether the previously reported performance reflects experimental configuration choices rather than an inherent algorithmic limitation. A structured experimental workflow is applied: ten domain-motivated derived features are constructed from the eight raw inputs, an exploratory ensemble feature importance analysis identifies the most predictive candidates, and a randomised search over hyperparameter combinations with three-fold cross-validation selects the optimal SVR configuration within a leakage-safe scikit-learn Pipeline. A formal four-stage ablation study isolates the contribution of each component: scaling alone accounts for +0.744 in R2 (from -0.054 to 0.690), feature engineering adds +0.026 (to 0.716), and hyperparameter tuning contributes +0.008 (to 0.723). The resulting tuned SVR achieves a test R2 of 0.723, a 0.123-point absolute improvement over the previously reported SVR result (from 0.60 to 0.723, approximately 20% relative gain). In the ten-model comparison, the tuned SVR ranks fourth with R2 = 0.723, below XGBoost (0.832), Random Forest (0.814) and Gradient Boosting (0.783), while substantially outperforming simpler baselines. Ten-fold cross-validation yields a mean R2 of 0.703 (95% CI: [0.630, 0.775]), confirming robust generalisation. The observed improvement from R2 = 0.60 to R2 = 0.723 is associated primarily with proper feature scaling within a unified preprocessing pipeline, with domain-motivated feature engineering and systematic hyperparameter tuning, providing further incremental gains.", "published": "2026-05-09T03:58:50Z", "authors": ["Emmanuel Adutwum"], "categories": ["cs.LG"], "doi": "", "_query": "housing price text listing", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/1805.12291v3", "title": "Empirical Evaluation of Character-Based Model on Neural Named-Entity Recognition in Indonesian Conversational Texts", "abstract": "Despite the long history of named-entity recognition (NER) task in the natural language processing community, previous work rarely studied the task on conversational texts. Such texts are challenging because they contain a lot of word variations which increase the number of out-of-vocabulary (OOV) words. The high number of OOV words poses a difficulty for word-based neural models. Meanwhile, there is plenty of evidence to the effectiveness of character-based neural models in mitigating this OOV problem. We report an empirical evaluation of neural sequence labeling models with character embedding to tackle NER task in Indonesian conversational texts. Our experiments show that (1) character models outperform word embedding-only models by up to 4 $F_1$ points, (2) character models perform better in OOV cases with an improvement of as high as 15 $F_1$ points, and (3) character models are robust against a very high OOV rate.", "published": "2018-05-31T02:21:39Z", "authors": ["Kemal Kurniawan", "Samuel Louvan"], "categories": ["cs.CL"], "doi": "", "_query": "housing price text listing", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/0707.0336v2", "title": "Pricing Options on Defaultable Stocks", "abstract": "In this note, we develop stock option price approximations for a model which takes both the risk o default and the stochastic volatility into account. We also let the intensity of defaults be influenced by the volatility. We show that it might be possible to infer the risk neutral default intensity from the stock option prices. Our option price approximation has a rich implied volatility surface structure and fits the data implied volatility well. Our calibration exercise shows that an effective hazard rate from bonds issued by a company can be used to explain the implied volatility skew of the implied volatility of the option prices issued by the same company.", "published": "2007-07-03T03:28:35Z", "authors": ["Erhan Bayraktar"], "categories": ["cs.CE"], "doi": "", "_query": "housing price text listing", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2209.05471v2", "title": "PATE: Property, Amenities, Traffic and Emotions Coming Together for Real Estate Price Prediction", "abstract": "Real estate prices have a significant impact on individuals, families, businesses, and governments. The general objective of real estate price prediction is to identify and exploit socioeconomic patterns arising from real estate transactions over multiple aspects, ranging from the property itself to other contributing factors. However, price prediction is a challenging multidimensional problem that involves estimating many characteristics beyond the property itself. In this paper, we use multiple sources of data to evaluate the economic contribution of different socioeconomic characteristics such as surrounding amenities, traffic conditions and social emotions. Our experiments were conducted on 28,550 houses in Beijing, China and we rank each characteristic by its importance. Since the use of multi-source information improves the accuracy of predictions, the aforementioned characteristics can be an invaluable resource to assess the economic and social value of real estate. Code and data are available at: https://github.com/IndigoPurple/PATE", "published": "2022-08-29T12:31:10Z", "authors": ["Yaping Zhao", "Ramgopal Ravi", "Shuhui Shi", "Zhongrui Wang", "Edmund Y. Lam", "Jichang Zhao"], "categories": ["cs.CY"], "doi": "", "_query": "housing price text listing", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/cond-mat/9906048v1", "title": "Scale-invariance and contingent claim pricing", "abstract": "Prices of tradables can only be expressed relative to each other at any instant of time. This fundamental fact should therefore also hold for contigent claims, i.e. tradable instruments, whose prices depend on the prices of other tradables. We show that this property induces local scale-invariance in the problem of pricing contingent claims. Due to this symmetry we do not require any martingale techniques to arrive at the price of a claim. If the tradables are driven by Brownian motion, we find, in a natural way, that this price satisfies a PDE. Both posses a manifest gauge-invariance. A unique solution can only be given when we impose restrictions on the drifts of volatilities of the tradables, i.e. the underlying market structure. We give some examples of the application of this PDE to the pricing of claims. In the Black-Scholes world we show the equivalence of our formulation with the standard approach. It is stressed that the formulation in terms of tradables leads to a significant conceptual simplification of the pricing-problem.", "published": "1999-06-03T16:12:11Z", "authors": ["Jiri Hoogland", "Dimitri Neumann"], "categories": ["cond-mat", "math.AP"], "doi": "", "_query": "housing price text listing", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/1912.08791v1", "title": "Forecasting significant stock price changes using neural networks", "abstract": "Stock price prediction is a rich research topic that has attracted interest from various areas of science. The recent success of machine learning in speech and image recognition has prompted researchers to apply these methods to asset price prediction. The majority of literature has been devoted to predicting either the actual asset price or the direction of price movement. In this paper, we study a hitherto little explored question of predicting significant changes in stock price based on previous changes using machine learning algorithms. We are particularly interested in the performance of neural network classifiers in the given context. To this end, we construct and test three neural network models including multi-layer perceptron, convolutional net, and long short term memory net. As benchmark models we use random forest and relative strength index methods. The models are tested using 10-year daily stock price data of four major US public companies. Test results show that predicting significant changes in stock price can be accomplished with a high degree of accuracy. In particular, we obtain substantially better results than similar studies that forecast the direction of price change.", "published": "2019-11-21T11:48:48Z", "authors": ["Firuz Kamalov"], "categories": ["q-fin.TR", "cs.LG", "stat.ML"], "doi": "10.1007/s00521-020-04942-3", "_query": "housing price text listing", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/1905.00711v1", "title": "Nonparametric pricing and hedging of exotic derivatives", "abstract": "In the spirit of Arrow-Debreu, we introduce a family of financial derivatives that act as primitive securities in that exotic derivatives can be approximated by their linear combinations. We call these financial derivatives signature payoffs. We show that signature payoffs can be used to nonparametrically price and hedge exotic derivatives in the scenario where one has access to price data for other exotic payoffs. The methodology leads to a computationally tractable and accurate algorithm for pricing and hedging using market prices of a basket of exotic derivatives that has been tested on real and simulated market prices, obtaining good results.", "published": "2019-05-02T13:03:43Z", "authors": ["Terry Lyons", "Sina Nejad", "Imanol Perez Arribas"], "categories": ["q-fin.MF"], "doi": "", "_query": "housing price text listing", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2307.06726v1", "title": "The Price of Equity with Binary Valuations and Few Agent Types", "abstract": "In fair division problems, the notion of price of fairness measures the loss in welfare due to a fairness constraint. Prior work on the price of fairness has focused primarily on envy-freeness up to one good (EF1) as the fairness constraint, and on the utilitarian and egalitarian welfare measures. Our work instead focuses on the price of equitability up to one good (EQ1) (which we term price of equity) and considers the broad class of generalized $p$-mean welfare measures (which includes utilitarian, egalitarian, and Nash welfare as special cases). We derive fine-grained bounds on the price of equity in terms of the number of agent types (i.e., the maximum number of agents with distinct valuations), which allows us to identify scenarios where the existing bounds in terms of the number of agents are overly pessimistic. Our work focuses on the setting with binary additive valuations, and obtains upper and lower bounds on the price of equity for $p$-mean welfare for all $p \\leqslant 1$. For any fixed $p$, our bounds are tight up to constant factors. A useful insight of our work is to identify the structure of allocations that underlie the upper (respectively, the lower) bounds simultaneously for all $p$-mean welfare measures, thus providing a unified structural understanding of price of fairness in this setting. This structural understanding, in fact, extends to the more general class of binary submodular (or matroid rank) valuations. We also show that, unlike binary additive valuations, for binary submodular valuations the number of agent types does not provide bounds on the price of equity.", "published": "2023-07-13T12:37:25Z", "authors": ["Umang Bhaskar", "Neeldhara Misra", "Aditi Sethia", "Rohit Vaish"], "categories": ["cs.GT"], "doi": "", "_query": "housing price text listing", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/1310.7857v3", "title": "Sticky continuous processes have consistent price systems", "abstract": "Under proportional transaction costs, a price process is said to have a consistent price system, if there is a semimartingale with an equivalent martingale measure that evolves within the bid-ask spread. We show that a continuous, multi-asset price process has a consistent price system, under arbitrarily small proportional transaction costs, if it satisfies a natural multi-dimensional generalization of the stickiness condition introduced by Guasoni [Math. Finance 16(3), 569-582 (2006)].", "published": "2013-10-29T16:09:35Z", "authors": ["Christian Bender", "Mikko S. Pakkanen", "Hasanjan Sayit"], "categories": ["q-fin.PR", "math.PR"], "doi": "10.1239/jap/1437658617", "_query": "housing price text listing", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/1301.1135v1", "title": "Hawkes model for price and trades high-frequency dynamics", "abstract": "We introduce a multivariate Hawkes process that accounts for the dynamics of market prices through the impact of market order arrivals at microstructural level. Our model is a point process mainly characterized by 4 kernels associated with respectively the trade arrival self-excitation, the price changes mean reversion the impact of trade arrivals on price variations and the feedback of price changes on trading activity. It allows one to account for both stylized facts of market prices microstructure (including random time arrival of price moves, discrete price grid, high frequency mean reversion, correlation functions behavior at various time scales) and the stylized facts of market impact (mainly the concave-square-root-like/relaxation characteristic shape of the market impact of a meta-order). Moreover, it allows one to estimate the entire market impact profile from anonymous market data. We show that these kernels can be estimated from the empirical conditional mean intensities. We provide numerical examples, application to real data and comparisons to former approaches.", "published": "2013-01-07T09:27:26Z", "authors": ["E. Bacry", "J. F Muzy"], "categories": ["q-fin.TR", "physics.data-an"], "doi": "", "_query": "housing price text listing", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2502.05898v1", "title": "Rising Marginal Costs, Rising Prices?", "abstract": "We present empirical evidence on the relationship between demand shocks and price changes, conditional on returns to scale. We find that in industries with decreasing returns to scale, demand increases (which raise costs) correspond to price increases. Whereas, in industries with increasing returns to scale, demand increases (which lower costs) correspond to stable prices. We interpret the results with a theory of imperfect competition and returns to scale. For prices to remain stable following a cost decrease, markups necessarily rise. For prices to increase as cost increases, it is not necessary for markups to change but does not preclude their role. From a macroeconomic perspective, our results imply that inflation dynamics and the effectiveness of monetary policy depend on market structures.", "published": "2025-02-09T13:28:47Z", "authors": ["Joel Kariel", "Anthony Savagar"], "categories": ["econ.GN"], "doi": "", "_query": "housing price text listing", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2411.09937v2", "title": "Refined and Segmented Price Sentiment Indices from Survey Comments", "abstract": "We aim to enhance a price sentiment index and to more precisely understand price trends from the perspective of not only consumers but also businesses. We extract comments related to prices from the Economy Watchers Survey conducted by the Cabinet Office of Japan and classify price trends using a large language model (LLM). We classify whether the survey sample reflects the perspective of consumers or businesses, and whether the comments pertain to goods or services by utilizing information on the fields of comments and the industries of respondents included in the Economy Watchers Survey. From these classified price-related comments, we construct price sentiment indices not only for a general purpose but also for more specific objectives by combining perspectives on consumers and prices, as well as goods and services. It becomes possible to achieve a more accurate classification of price directions by employing a LLM for classification. Furthermore, integrating the outputs of multiple LLMs suggests the potential for the better performance of the classification. The use of more accurately classified comments allows for the construction of an index with a higher correlation to existing indices than previous studies. We demonstrate that the correlation of the price index for consumers, which has a larger sample size, is further enhanced by selecting comments for aggregation based on the industry of the survey respondents.", "published": "2024-11-15T04:22:21Z", "authors": ["Masahiro Suzuki", "Hiroki Sakaji"], "categories": ["cs.CL", "q-fin.CP"], "doi": "", "_query": "housing price text listing", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/1205.6439v1", "title": "A Mathematical Reformulation of the Reference Price", "abstract": "Reference prices have long been studied in applied economics and business research. One of the classic formulations of the reference price is in terms of an iterative function of past prices. There are a number of limitations of such a formulation, however. Such limitations include burdensome computational time to estimate parameters, an inability to truly account for customer heterogeneity, and an estimation procedure that implies a misspecified model. Managerial recommendations based on inferences from such a model can be quite misleading. We mathematically reformulate the reference price by developing a closed-form expansion that addresses the aforementioned issues, enabling one to elicit truly meaningful managerial advice from the model. We estimate our model on a real world data set to illustrate the efficacy of our approach. Our work is not only useful from a modeling perspective, but also has important behavioral and managerial implications, which modelers and non-modelers alike would find useful.", "published": "2012-05-29T18:05:14Z", "authors": ["Kevin D. Dayaratna", "P. K. Kannan"], "categories": ["stat.AP", "stat.CO"], "doi": "", "_query": "housing price text listing", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2503.16912v1", "title": "Construction and sample path properties of diffusion house-moving between two curves", "abstract": "The purpose of this paper is to introduce the construction of a stochastic process called ``diffusion house-moving'' and to explore its properties. We study the weak convergence of diffusion bridges conditioned to stay between two curves, and we refer to this limit as diffusion house-moving. Applying this weak convergence result, we give the sample path properties of diffusion house-moving.", "published": "2025-03-21T07:23:20Z", "authors": ["Kensuke Ishitani", "Soma Nishino"], "categories": ["math.PR"], "doi": "", "_query": "housing price text listing", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2308.11117v1", "title": "Meta-Stock: Task-Difficulty-Adaptive Meta-learning for Sub-new Stock Price Prediction", "abstract": "Sub-new stock price prediction, forecasting the price trends of stocks listed less than one year, is crucial for effective quantitative trading. While deep learning methods have demonstrated effectiveness in predicting old stock prices, they require large training datasets unavailable for sub-new stocks. In this paper, we propose Meta-Stock: a task-difficulty-adaptive meta-learning approach for sub-new stock price prediction. Leveraging prediction tasks formulated by old stocks, our meta-learning method aims to acquire the fast generalization ability that can be further adapted to sub-new stock price prediction tasks, thereby solving the data scarcity of sub-new stocks. Moreover, we enhance the meta-learning process by incorporating an adaptive learning strategy sensitive to varying task difficulties. Through wavelet transform, we extract high-frequency coefficients to manifest stock price volatility. This allows the meta-learning model to assign gradient weights based on volatility-quantified task difficulty. Extensive experiments on datasets collected from three stock markets spanning twenty-two years prove that our Meta-Stock significantly outperforms previous methods and manifests strong applicability in real-world stock trading. Besides, we evaluate the reasonability of the task difficulty quantification and the effectiveness of the adaptive learning strategy.", "published": "2023-08-22T01:54:11Z", "authors": ["Linghao Wang", "Zhen Liu", "Peitian Ma", "Qianli Ma"], "categories": ["cs.CE"], "doi": "", "_query": "housing price text listing", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/1705.02473v2", "title": "Computation of second order price sensitivities in depressed markets", "abstract": "Risk management in financial derivative markets requires inevitably the calculation of the different price sensitivities. The literature contains an abundant amount of research works that have studied the computation of these important values. Most of these works consider the well-known Black and Scholes model where the volatility is assumed to be constant. Moreover, to our best knowledge, they compute only the first order price sensitivities. Some works that attempt to extend to markets affected by financial crisis appeared recently. However, none of these papers deal with the calculation of the price sensitivities of second order. Providing second derivatives for the underlying price sensitivities is an important issue in financial risk management because the investor can determine whether or not each source of risk is increasing at an increasing rate. In this paper, we work on the computation of second order prices sensitivities for a market under crisis. The underlying second order price sensitivities are derived explicitly. The obtained formulas are expected to improve on the accuracy of the hedging strategies during a financial crunch.", "published": "2017-05-06T10:49:45Z", "authors": ["Youssef El-Khatib", "Abdulnasser Hatemi-J"], "categories": ["q-fin.PR"], "doi": "", "_query": "housing price text listing", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2002.11680v1", "title": "Large Fluctuations in Locational Marginal Prices", "abstract": "This paper investigates large fluctuations of Locational Marginal Prices (LMPs) in wholesale energy markets caused by volatile renewable generation profiles. Specifically, we study events of the form $\\mathbb{P} \\Big ( \\mathbf{LMP} \\notin \\prod_{i=1}^n [α_i^-, α_i^+] \\Big),$ where $\\mathbf{LMP}$ is the vector of LMPs at the $n$ power grid nodes, and $\\boldsymbolα^-,\\boldsymbolα^+\\in\\mathbb{R}^n$ are vectors of price thresholds specifying undesirable price occurrences. By exploiting the structure of the supply-demand matching mechanism in power grids, we look at LMPs as deterministic piecewise affine, possibly discontinuous functions of the stochastic input process, modeling uncontrollable renewable generation. We utilize techniques from large deviations theory to identify the most likely ways for extreme price spikes to happen, and to rank the nodes of the power grid in terms of their likelihood of experiencing a price spike. Our results are derived in the case of Gaussian fluctuations and are validated numerically on the IEEE 14-bus test case.", "published": "2020-02-26T18:18:21Z", "authors": ["Tommaso Nesti", "John Moriarty", "Alessandro Zocca", "Bert Zwart"], "categories": ["math.OC", "eess.SY", "math.PR", "physics.soc-ph"], "doi": "10.1098/rsta.2019.0438", "_query": "housing price text listing", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/physics/0603012v1", "title": "The Process of price formation and the skewness of asset returns", "abstract": "Distributions of assets returns exhibit a slight skewness. In this note we show that our model of endogenous price formation \\cite{Reimann2006} creates an asymmetric return distribution if the price dynamics are a process in which consecutive trading periods are dependent from each other in the sense that opening prices equal closing prices of the former trading period. The corresponding parameter $α$ is estimated from daily prices from 01/01/1999 - 12/31/2004 for 9 large indices. For the S&P 500, the skewness distribution of all its constituting assets is also calculated. The skewness distribution due to our model is compared with the distribution of the empirical skewness values of the ingle assets.", "published": "2006-03-02T10:07:02Z", "authors": ["Stefan Reimann"], "categories": ["physics.soc-ph", "q-fin.ST"], "doi": "", "_query": "housing price text listing", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2403.13192v1", "title": "Modeling stock price dynamics on the Ghana Stock Exchange: A Geometric Brownian Motion approach", "abstract": "Modeling financial data often relies on assumptions that may prove insufficient or unrealistic in practice. The Geometric Brownian Motion (GBM) model is frequently employed to represent stock price processes. This study investigates whether the behavior of weekly and monthly returns of selected equities listed on the Ghana Stock Exchange conforms to the GBM model. Parameters of the GBM model were estimated for five equities, and forecasts were generated for three months. Evaluation of estimation accuracy was conducted using mean square error (MSE). Results indicate that the expected prices from the modeled equities closely align with actual stock prices observed on the Exchange. Furthermore, while some deviations were observed, the actual prices consistently fell within the estimated confidence intervals.", "published": "2024-03-19T23:02:53Z", "authors": ["Dennis Lartey Quayesam", "Anani Lotsi", "Felix Okoe Mettle"], "categories": ["math.OC", "q-fin.ST"], "doi": "", "_query": "housing price text listing", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/1007.3316v1", "title": "Pricing in an equilibrium based model for a large investor", "abstract": "We study a financial model with a non-trivial price impact effect. In this model we consider the interaction of a large investor trading in an illiquid security, and a market maker who is quoting prices for this security. We assume that the market maker quotes the prices such that by taking the other side of the investor's demand, the market maker will arrive at maturity with the maximal expected utility of the terminal wealth. Within this model we provide an explicit recursive pricing formula for an exponential utility function, as well as an asymptotic expansion for the price for a \"small\" simple demand.", "published": "2010-07-20T00:45:59Z", "authors": ["David German"], "categories": ["q-fin.PR"], "doi": "", "_query": "housing price text listing", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/1706.01131v2", "title": "Strategic Dynamic Pricing with Network Effects", "abstract": "We study the optimal pricing strategy of a monopolist selling homogeneous goods to customers over multiple periods. The customers choose their time of purchase to maximize their payoff that depends on their valuation of the product, the purchase price, and the utility they derive from past purchases of others, termed the network effect. We first show that the optimal price sequence is non-decreasing. Therefore, by postponing purchase to future rounds, customers trade-off a higher utility from the network effects with a higher price. We then show that a customer's equilibrium strategy can be characterized by a threshold rule in which at each round a customer purchases the product if and only if her valuation exceeds a certain threshold. This implies that customers face an inference problem regarding the valuations of others, i.e., observing that a customer has not yet purchased the product, signals that her valuation is below a threshold. We consider a block model of network interactions, where there are blocks of buyers subject to the same network effect. A natural benchmark, this model allows us to provide an explicit characterization of the optimal price sequence asymptotically as the number of agents goes to infinity, which notably is linearly increasing in time with a slope that depends on the network effect through a scalar given by the sum of entries of the inverse of the network weight matrix. Our characterization shows that increasing the \"imbalance\" in the network defined as the difference between the in and out degree of the nodes increases the revenue of the monopolist. We further study the effects of price discrimination and show that in earlier periods monopolist offers lower prices to blocks with higher Bonacich centrality to encourage them to purchase, which in turn further incentivizes other customers to buy in subsequent periods.", "published": "2017-06-04T19:09:13Z", "authors": ["Ali Makhdoumi", "Azarakhsh Malekian", "Asuman Ozdaglar"], "categories": ["cs.GT"], "doi": "", "_query": "housing price text listing", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/1309.6164v2", "title": "Pricing and Hedging Derivative Securities with Unknown Local Volatilities", "abstract": "A common assumption in financial engineering is that the market price for any derivative coincides with an objectively defined risk-neutral price - a plausible assumption only if traders collectively possess objective knowledge about the price dynamics of the underlying security over short time scales. Here we assume that traders have an objective knowledge about the underlying security's price trajectories only for large time scales. We show that avoidance of arbitrage that is still feasible uniquely determines the prices of options with large expiration times, and we derive limit theorems useful for estimation of model parameters and present-value analysis of derivative portfolios.", "published": "2013-09-24T14:09:56Z", "authors": ["Kerry W. Fendick"], "categories": ["q-fin.PR", "math.PR"], "doi": "", "_query": "housing price text listing", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2607.06355v1", "title": "Entropic Dynamics of Jump-Diffusion Option Pricing", "abstract": "Standard models of stock price dynamics and option valuation usually begin by postulating stochastic processes. This paper develops an entropic inference framework that derives these processes from information constraints. The key symmetry is that markets reward returns rather than price levels, which selects log price as the dynamical variable. Price changes are represented by two channels. The continuous channel carries constraints of continuity and directionality. The jump channel carries the arrival rate and the first two moments of jump size. Since these constraints apply to disjoint parts of the microstate, the channels factorize. The resulting dynamics is the Merton jump diffusion, with Geometric Brownian Motion as the no jump limit. The log price density satisfies the Kolmogorov Feller equation, whose no jump limit is the Fokker Planck equation. The same inferential principle, with no arbitrage imposed through the mean log return, selects the Esscher transform from the many martingale measures available in an incomplete market. The option price then satisfies Mertons partial integro differential equation, and the risk neutral mixture of lognormal distributions generates the implied volatility smile. The Black Scholes results are recovered when jumps vanish. What changes from one model to another is not the inference, but the information supplied to it.", "published": "2026-07-07T14:53:50Z", "authors": ["Mohammad Abedi"], "categories": ["q-fin.PR", "q-fin.MF", "q-fin.ST"], "doi": "", "_query": "housing price text listing", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2205.08913v2", "title": "Price Interpretability of Prediction Markets: A Convergence Analysis", "abstract": "Prediction markets are long known for prediction accuracy. This study systematically explores the fundamental properties of prediction markets, addressing questions about their information aggregation process and the factors contributing to their remarkable efficacy. We propose a novel multivariate utility (MU) based mechanism that unifies several existing automated market-making schemes. Using this mechanism, we establish the convergence results for markets comprised of risk-averse traders who have heterogeneous beliefs and repeatedly interact with the market maker. We demonstrate that the resulting limiting wealth distribution aligns with the Pareto efficient frontier defined by the utilities of all market participants. With the help of this result, we establish analytical and numerical results for the limiting price in different market models. Specifically, we show that the limiting price converges to the geometric mean of agent beliefs in exponential utility-based markets. In risk-measure-based markets, we construct a family of risk measures that satisfy the convergence criteria and prove that the price can converge to a unique level represented by the weighted power mean of agent beliefs. In broader markets with Constant Relative Risk Aversion (CRRA) utilities, we reveal that the limiting price can be characterized by systems of equations that encapsulate agent beliefs, risk parameters, and wealth. Despite the potential impact of traders' trading sequences on the limiting price, we establish a price invariance result for markets with a large trader population. Using this result, we propose an efficient approximation scheme for the limiting price.", "published": "2022-05-18T13:15:11Z", "authors": ["Dian Yu", "Jianjun Gao", "Weiping Wu", "Zizhuo Wang"], "categories": ["q-fin.TR", "cs.LG"], "doi": "", "_query": "housing price text listing", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/1211.0856v2", "title": "Heat Kernel Framework for Asset Pricing in Finite Time", "abstract": "A heat kernel approach is proposed for the development of a general, flexible, and mathematically tractable asset pricing framework in finite time. The pricing kernel, giving rise to the price system in an incomplete market, is modelled by weighted heat kernels which are driven by multivariate Markov processes and which provide enough degrees of freedom in order to calibrate to relevant data, e.g. to the term structure of bond prices. It is shown how, for a class of models, the prices of bonds, caplets, and swaptions can be computed in closed form. The dynamical equations for the price processes are derived, and explicit formulae are obtained for the short rate of interest, the risk premium, and for the stochastic volatility of prices. Several of the closed-form asset price models presented in this paper are driven by combinations of Markovian jump processes with different probability laws. Such models provide a rich basis for consistent applications in several sectors of a financial market including equity, fixed-income, commodities, and insurance. The flexible, multidimensional and multivariate structure, on which the asset price models are constructed, lends itself well to the transparent modelling of dependence across asset classes. As an illustration, the impact on prices by spiralling debt, a typical feature of a financial crisis, is modelled explicitly, and contagion effects are readily observed in the dynamics of asset returns.", "published": "2012-11-05T13:06:26Z", "authors": ["Andrea Macrina"], "categories": ["q-fin.PR", "math.PR"], "doi": "", "_query": "housing price text listing", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/1211.6525v1", "title": "The Pricing Mechanism of Contingent Claims and its Generating Function", "abstract": "In this paper we study dynamic pricing mechanism of contingent claims. A typical model of such pricing mechanism is the so-called g-expectation $E^g_{s,t}[X]$ defined by the solution of the backward stochastic differential equation with generator g and with the contingent claim X as terminal condition. The generating function g this BSDE. We also provide examples of determining the price generating function $g=g(y,z)$ by testing. The main result of this paper is as follows: if a given dynamic pricing mechanism is $E^{g_μ}$-dominated, i.e., the criteria (A5) $E_{s,t}[X]-E_{s,t}[X']\\leq E^{g_μ}_{s,t}[X-X']$ is satisfied for a large enough $μ> 0$, where $g_μ=g_μ(|y|+|z|)$, then $E_{s,t}$ is a g-pricing mechanism. This domination condition was statistically tested using CME data documents. The result of test is significantly positive.", "published": "2012-11-28T05:46:19Z", "authors": ["Shige Peng"], "categories": ["q-fin.PR"], "doi": "", "_query": "housing price text listing", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2409.15793v2", "title": "Listing spanning trees of outerplanar graphs by pivot-exchanges", "abstract": "We prove that the spanning trees of any outerplanar triangulation $G$ can be listed so that any two consecutive spanning trees differ in an exchange of two edges that share an end vertex. For outerplanar graphs $G$ with faces of arbitrary lengths (not necessarily 3) we establish a similar result, with the condition that the two exchanged edges share an end vertex or lie on a common face. These listings of spanning trees are obtained from a simple greedy algorithm that can be implemented efficiently, i.e., in time $\\mathcal{O}(n \\log n)$ per generated spanning tree, where $n$ is the number of vertices of $G$. Furthermore, the listings correspond to Hamilton paths on the 0/1-polytope that is obtained as the convex hull of the characteristic vectors of all spanning trees of $G$.", "published": "2024-09-24T06:41:56Z", "authors": ["Nastaran Behrooznia", "Torsten Mütze"], "categories": ["cs.DM", "math.CO"], "doi": "", "_query": "housing price text listing", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2011.09129v1", "title": "Pricing the Information Quantity in Artworks", "abstract": "In the traditional art pricing models, the variables that capture the painting's content are often missing. Recent research starts to apply the computer graphic techniques to extract the information from the painting content. Most of the research concentrates on the reading of the color information from the painting images and analyzes how different color compositions can affect the sales prices of paintings. This paper takes a different approach, and tries to abstract away from the interpretation of the content information, while only focus on measuring the quantity of information contained. We extend the concept of Shannon entropy in information theory to the painting's scenario, and suggest using the variances of a painting's composing elements, i.e., line, color, value, shape/form and space, to measure the amount of information in the painting. These measures are calculated at the pixel level based on a picture's digital image. We include them into the traditional hedonic regression model to test their significance based on the auction samples from two famous artists (Picasso and Renoir). We find that all the variance measurements can significantly explain the sales price either at 1% or 5% level. The adjusted R square is also increased by more than ten percent. Our method greatly improves the traditional pricing models, and may also find applications in other areas such as art valuation and authentication.", "published": "2020-11-18T10:55:29Z", "authors": ["Lan Ju", "Zhiyong Tu", "Changyong Xue"], "categories": ["q-fin.GN"], "doi": "", "_query": "housing price text listing", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2003.06184v2", "title": "Coronavirus and oil price crash", "abstract": "Coronavirus (COVID-19) creates fear and uncertainty, hitting the global economy and amplifying the financial markets volatility. The oil price reaction to COVID-19 was gradually accommodated until March 09, 2020, when, 49 days after the release of the first coronavirus monitoring report by the World Health Organization (WHO), Saudi Arabia floods the market with oil. As a result, international prices drop with more than 20% in one single day. Against this background, the purpose of this paper is to investigate the impact of COVID-19 numbers on crude oil prices, while controlling for the impact of financial volatility and the United States (US) economic policy uncertainty. Our ARDL estimation shows that the COVID-19 daily reported cases of new infections have a marginal negative impact on the crude oil prices in the long run. Nevertheless, by amplifying the financial markets volatility, COVID-19 also has an indirect effect on the recent dynamics of crude oil prices.", "published": "2020-03-13T10:15:04Z", "authors": ["Claudiu Albulescu"], "categories": ["q-fin.ST", "q-fin.CP"], "doi": "", "_query": "housing price text listing", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2607.20273v1", "title": "A Product-Neighbourhood Criterion for Fixed Price One", "abstract": "We prove a flexible criterion for fixed price one, applying in particular to higher-rank lattices over local fields and automorphism groups of affine buildings, including lattices in exotic buildings. It also recovers fixed price one for amenable groups. We then establish a locally compact version, and deduce that every product of two noncompact, compactly generated, unimodular locally compact groups, as well as every lattice in such a product, has fixed price one.", "published": "2026-07-22T15:24:53Z", "authors": ["Raz Slutsky"], "categories": ["math.GR", "math.DS"], "doi": "", "_query": "housing price text listing", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2001.03099v1", "title": "Using Networks and Partial Differential Equations to Predict Bitcoin Price", "abstract": "Over the past decade, the blockchain technology and its Bitcoin cryptocurrency have received considerable attention. Bitcoin has experienced significant price swings in daily and long-term valuations. In this paper, we propose a partial differential equation (PDE) model on the bitcoin transaction network for predicting bitcoin price. Through analysis of bitcoin subgraphs or chainlets, the PDE model captures the influence of transaction patterns on bitcoin price over time and combines the effect of all chainlet clusters. In addition, Google Trends Index is incorporated to the PDE model to reflect the effect of bitcoin market sentiment. The experiment shows that the average accuracy of daily bitcoin price prediction is 0.82 for 362 consecutive days in 2017. The results demonstrate the PDE model is capable of predicting bitcoin price. The paper is the first attempt to apply a PDE model to the bitcoin transaction network for predicting bitcoin price.", "published": "2020-01-08T17:31:37Z", "authors": ["Yufang Wang", "Haiyan Wang"], "categories": ["q-fin.PR"], "doi": "10.1063/5.0002759", "_query": "housing price text listing", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2506.06657v1", "title": "Quantile Regression with Large Language Models for Price Prediction", "abstract": "Large Language Models (LLMs) have shown promise in structured prediction tasks, including regression, but existing approaches primarily focus on point estimates and lack systematic comparison across different methods. We investigate probabilistic regression using LLMs for unstructured inputs, addressing challenging text-to-distribution prediction tasks such as price estimation where both nuanced text understanding and uncertainty quantification are critical. We propose a novel quantile regression approach that enables LLMs to produce full predictive distributions, improving upon traditional point estimates. Through extensive experiments across three diverse price prediction datasets, we demonstrate that a Mistral-7B model fine-tuned with quantile heads significantly outperforms traditional approaches for both point and distributional estimations, as measured by three established metrics each for prediction accuracy and distributional calibration. Our systematic comparison of LLM approaches, model architectures, training approaches, and data scaling reveals that Mistral-7B consistently outperforms encoder architectures, embedding-based methods, and few-shot learning methods. Our experiments also reveal the effectiveness of LLM-assisted label correction in achieving human-level accuracy without systematic bias. Our curated datasets are made available at https://github.com/vnik18/llm-price-quantile-reg/ to support future research.", "published": "2025-06-07T04:19:28Z", "authors": ["Nikhita Vedula", "Dushyanta Dhyani", "Laleh Jalali", "Boris Oreshkin", "Mohsen Bayati", "Shervin Malmasi"], "categories": ["cs.CL", "cs.AI"], "doi": "", "_query": "housing price text listing", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2012.09115v1", "title": "Towards robust and speculation-reduction real estate pricing models based on a data-driven strategy", "abstract": "In many countries, real estate appraisal is based on conventional methods that rely on appraisers' abilities to collect data, interpret it and model the price of a real estate property. With the increasing use of real estate online platforms and the large amount of information found therein, there exists the possibility of overcoming many drawbacks of conventional pricing models such as subjectivity, cost, unfairness, among others. In this paper we propose a data-driven real estate pricing model based on machine learning methods to estimate prices reducing human bias. We test the model with 178,865 flats listings from Bogotá, collected from 2016 to 2020. Results show that the proposed state-of-the-art model is robust and accurate in estimating real estate prices. This case study serves as an incentive for local governments from developing countries to discuss and build real estate pricing models based on large data sets that increases fairness for all the real estate market stakeholders and reduces price speculation.", "published": "2020-11-26T15:54:07Z", "authors": ["Vladimir Vargas-Calderón", "Jorge E. Camargo"], "categories": ["econ.GN", "cs.LG"], "doi": "10.1080/01605682.2021.2023672", "_query": "housing price text listing", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2203.16908v2", "title": "Suffix tree-based linear algorithms for multiple prefixes, single suffix counting and listing problems", "abstract": "Given two strings $T$ and $S$ and a set of strings $P$, for each string $p \\in P$, consider the unique substrings of $T$ that have $p$ as their prefix and $S$ as their suffix. Two problems then come to mind; the first problem being the counting of such substrings, and the second problem being the problem of listing all such substrings. In this paper, we describe linear-time, linear-space suffix tree-based algorithms for both problems. More specifically, we describe an $O(|T| + |P|)$ time algorithm for the counting problem, and an $O(|T| + |P| + \\#(ans))$ time algorithm for the listing problem, where $\\#(ans)$ refers to the number of strings being listed in total, and $|P|$ refers to the total length of the strings in $P$. We also consider the reversed version of the problems, where one prefix condition string and multiple suffix condition strings are given instead, and similarly describe linear-time, linear-space algorithms to solve them.", "published": "2022-03-31T09:20:43Z", "authors": ["Laurentius Leonard", "Ken Tanaka"], "categories": ["cs.DS"], "doi": "", "_query": "housing price text listing", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/1204.3156v1", "title": "Price and Quantity Trajectories: Second-order Dynamics", "abstract": "In two previous papers the author developed a second-order price adjustment (tâtonnement) process. This paper extends the approach to include both quantity and price adjustments. We demonstrate three results: a analogue to physical energy, called \"activity\" arises naturally in the model, and is not conserved in general; price and quantity trajectories must either end at a local minimum of a scalar potential or circulate endlessly; and disturbances into a subspace of substitutable commodities decay over time. From this we argue, although we do not prove, that the model features global stability, combined with local instability, a characteristic of many real markets. Following these observations and a brief survey of empirical results for price-setting and consumption behavior in markets for \"real\" goods (as opposed to financial markets), we conjecture that Stigler and Becker's well-known theory of consumer preference opens the possibility of substantial degeneracy in commodity space, and therefore that price and quantity trajectories could lie on a relatively low-dimensional subspace within the full commodity space.", "published": "2012-04-14T09:33:51Z", "authors": ["Eric Kemp-Benedict"], "categories": ["q-fin.GN"], "doi": "", "_query": "housing price text listing", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2605.21653v2", "title": "Amplifying, Not Learning: The Price of Out-of-Distribution Generalization in AI-Text Detection", "abstract": "AI-text detectors gate decisions in education, hiring, and publishing, yet they flag the most fluent, formal human writing as machine-generated: they rate the median formal-native human essay as 99.5% likely AI while clearing genuine high-temperature AI at 10.5%. Deployed detectors share it (chatgpt-detector-roberta flags 56% of formal essays at a 1% false-alarm rate). This is not a calibration bug but the signature of one mechanism: a fine-tuned detector does not learn an AI-versus-human boundary, it amplifies an inherited typicality axis (predictability under a language model) that pre-exists fine-tuning, rescaling it rather than constructing one. Decomposing the detector into this inherited reading and a fine-tuned residual, the inherited part carries the bulk of cross-generator transfer and produces the over-flagging of formal humans, while the residual is generator-specific and does not transfer; a frozen-representation probe fit on ~25 labels per class matches the fully fine-tuned detector on unseen generators (cross-generator AUROC 0.893 vs 0.831). This yields a no-go: because the axis that transfers is the axis that over-flags, no training objective, threshold, concept-erasure, or ensemble we test removes the harm while preserving cross-generator detection, across three architectures, fine-tuned decoders, and zero-shot perplexity detectors. A closed-form, training-free operator relocates and diagnoses the bias (reviving a dead deployed detector, true-positive rate 0 to 0.904 at a 1% false-alarm rate) but, consistent with the no-go, is AUROC-neutral on cross-generator detection: it moves the bias, it cannot erase it. The mechanism is not specific to English prose: the over-flag and the decomposition replicate in Chinese and in code. Detector unfairness is a predictable, structural property of the detection paradigm, the price of out-of-distribution generalization.", "published": "2026-05-20T19:08:38Z", "authors": ["Alexander Smirnov"], "categories": ["cs.LG", "cs.AI", "cs.CL"], "doi": "", "_query": "housing price text listing", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2301.12247v2", "title": "SEGA: Instructing Text-to-Image Models using Semantic Guidance", "abstract": "Text-to-image diffusion models have recently received a lot of interest for their astonishing ability to produce high-fidelity images from text only. However, achieving one-shot generation that aligns with the user's intent is nearly impossible, yet small changes to the input prompt often result in very different images. This leaves the user with little semantic control. To put the user in control, we show how to interact with the diffusion process to flexibly steer it along semantic directions. This semantic guidance (SEGA) generalizes to any generative architecture using classifier-free guidance. More importantly, it allows for subtle and extensive edits, changes in composition and style, as well as optimizing the overall artistic conception. We demonstrate SEGA's effectiveness on both latent and pixel-based diffusion models such as Stable Diffusion, Paella, and DeepFloyd-IF using a variety of tasks, thus providing strong evidence for its versatility, flexibility, and improvements over existing methods.", "published": "2023-01-28T16:43:07Z", "authors": ["Manuel Brack", "Felix Friedrich", "Dominik Hintersdorf", "Lukas Struppek", "Patrick Schramowski", "Kristian Kersting"], "categories": ["cs.CV", "cs.AI", "cs.LG"], "doi": "", "_query": "housing price text listing", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/1201.5448v1", "title": "Determinants of immediate price impacts at the trade level in an emerging order-driven market", "abstract": "The common wisdom argues that, in general, large trades cause large price changes, while small trades cause small price changes. However, for extremely large price changes, the trade size and news play a minor role, while the liquidity (especially price gaps on the limit order book) is a more influencing factor. Hence, there might be other influencing factors of immediate price impacts of trades. In this paper, through mechanical analysis of price variations before and after a trade of arbitrary size, we identify that the trade size, the bid-ask spread, the price gaps and the outstanding volumes at the bid and ask sides of the limit order book have impacts on the changes of prices. We propose two regression models to investigate the influences of these microscopic factors on the price impact of buyer-initiated partially filled trades, seller-initiated partially filled trades, buyer-initiated filled trades, and seller-initiated filled trades. We find that they have quantitatively similar explanation powers and these factors can account for up to 44% of the price impacts. Large trade sizes, wide bid-ask spreads, high liquidity at the same side and low liquidity at the opposite side will cause a large price impact. We also find that the liquidity at the opposite side has a more influencing impact than the liquidity at the same side. Our results shed new lights on the determinants of immediate price impacts.", "published": "2012-01-26T08:09:17Z", "authors": ["Wei-Xing Zhou"], "categories": ["q-fin.ST", "q-fin.TR"], "doi": "10.1088/1367-2630/14/2/023055", "_query": "housing price text listing", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2003.03479v2", "title": "Step on the Gas? A Better Approach for Recommending the Ethereum Gas Price", "abstract": "In the Ethereum network, miners are incentivized to include transactions in a block depending on the gas price specified by the sender. The sender of a transaction therefore faces a trade-off between timely inclusion and cost of his transaction. Existing recommendation mechanisms aggregate recent gas price data on a per-block basis to suggest a gas price. We perform an empirical analysis of historic block data to motivate the use of a predictive model for gas price recommendation. Subsequently, we propose a novel mechanism that combines a deep-learning based price forecasting model as well as an algorithm parameterized by a user-specific urgency value to recommend gas prices. In a comprehensive evaluation on real-world data, we show that our approach results on average in costs savings of more than 50% while only incurring an inclusion delay of 1.3 blocks, when compared to the gas price recommendation mechanism of the most widely used Ethereum client.", "published": "2020-03-07T01:08:44Z", "authors": ["Sam M. Werner", "Paul J. Pritz", "Daniel Perez"], "categories": ["cs.DC"], "doi": "", "_query": "housing price text listing", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/1001.0880v1", "title": "Does Security Transaction Volume-Price Behavior Resemble a Probability Wave?", "abstract": "Motivated by how transaction amount constrain trading volume and price volatility in stock market, we, in this paper, study the relation between volume and price if amount of transaction is given. We find that accumulative trading volume gradually emerges a kurtosis near the price mean value over a trading price range when it takes a longer trading time, regardless of actual price fluctuation path, time series, or total transaction volume in the time interval. To explain the volume-price behavior, we, in terms of physics, propose a transaction energy hypothesis, derive a time-independent transaction volume-price probability wave equation, and get two sets of analytical volume distribution eigenfunctions over a trading price range. By empiric test, we show the existence of coherence in stock market and demonstrate the model validation at this early stage. The volume-price behaves like a probability wave.", "published": "2010-01-06T12:33:20Z", "authors": ["Leilei Shi"], "categories": ["q-fin.TR", "q-fin.ST"], "doi": "10.1016/j.physa.2005.10.016", "_query": "housing price text listing", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2303.08225v1", "title": "Graph Transformer GANs for Graph-Constrained House Generation", "abstract": "We present a novel graph Transformer generative adversarial network (GTGAN) to learn effective graph node relations in an end-to-end fashion for the challenging graph-constrained house generation task. The proposed graph-Transformer-based generator includes a novel graph Transformer encoder that combines graph convolutions and self-attentions in a Transformer to model both local and global interactions across connected and non-connected graph nodes. Specifically, the proposed connected node attention (CNA) and non-connected node attention (NNA) aim to capture the global relations across connected nodes and non-connected nodes in the input graph, respectively. The proposed graph modeling block (GMB) aims to exploit local vertex interactions based on a house layout topology. Moreover, we propose a new node classification-based discriminator to preserve the high-level semantic and discriminative node features for different house components. Finally, we propose a novel graph-based cycle-consistency loss that aims at maintaining the relative spatial relationships between ground truth and predicted graphs. Experiments on two challenging graph-constrained house generation tasks (i.e., house layout and roof generation) with two public datasets demonstrate the effectiveness of GTGAN in terms of objective quantitative scores and subjective visual realism. New state-of-the-art results are established by large margins on both tasks.", "published": "2023-03-14T20:35:45Z", "authors": ["Hao Tang", "Zhenyu Zhang", "Humphrey Shi", "Bo Li", "Ling Shao", "Nicu Sebe", "Radu Timofte", "Luc Van Gool"], "categories": ["cs.CV", "cs.AI"], "doi": "", "_query": "housing price text listing", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/1207.3412v2", "title": "A quantum mechanical model for the relationship between stock price and stock ownership", "abstract": "The trade of a fixed stock can be regarded as the basic process that measures its momentary price. The stock price is exactly known only at the time of sale when the stock is between traders, that is, only in the case when the owner is unknown. We show that the stock price can be better described by a function indicating at any moment of time the probabilities for the possible values of price if a transaction takes place. This more general description contains partial information on the stock price, but it also contains partial information on the stock owner. By following the analogy with quantum mechanics, we assume that the time evolution of the function describing the stock price can be described by a Schrodinger type equation.", "published": "2012-07-14T11:19:48Z", "authors": ["Liviu-Adrian Cotfas"], "categories": ["q-fin.GN", "math-ph"], "doi": "10.1063/1.4766764", "_query": "housing price text listing", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/1406.6615v1", "title": "The Critical Price Of The American Put Near Maturity In The Jump Diffusion Model", "abstract": "We study the behavior of the critical price of an American put option near maturity in the Jump diffusion model when the underlying stock pays dividends at a continuous rate and the limit of the critical price is smaller than the stock price. In particular, we prove that, unlike the case where the limit is equal to the strike price, jumps can influence the convergence rate.", "published": "2014-06-25T15:43:20Z", "authors": ["Aych Bouselmi", "Damien Lamberton"], "categories": ["math.PR"], "doi": "", "_query": "housing price text listing", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2311.12891v2", "title": "Text-Guided Texturing by Synchronized Multi-View Diffusion", "abstract": "This paper introduces a novel approach to synthesize texture to dress up a given 3D object, given a text prompt. Based on the pretrained text-to-image (T2I) diffusion model, existing methods usually employ a project-and-inpaint approach, in which a view of the given object is first generated and warped to another view for inpainting. But it tends to generate inconsistent texture due to the asynchronous diffusion of multiple views. We believe such asynchronous diffusion and insufficient information sharing among views are the root causes of the inconsistent artifact. In this paper, we propose a synchronized multi-view diffusion approach that allows the diffusion processes from different views to reach a consensus of the generated content early in the process, and hence ensures the texture consistency. To synchronize the diffusion, we share the denoised content among different views in each denoising step, specifically blending the latent content in the texture domain from views with overlap. Our method demonstrates superior performance in generating consistent, seamless, highly detailed textures, comparing to state-of-the-art methods.", "published": "2023-11-21T06:26:28Z", "authors": ["Yuxin Liu", "Minshan Xie", "Hanyuan Liu", "Tien-Tsin Wong"], "categories": ["cs.CV"], "doi": "10.1145/3680528.3687621", "_query": "housing price text listing", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2508.06497v1", "title": "Forecasting Commodity Price Shocks Using Temporal and Semantic Fusion of Prices Signals and Agentic Generative AI Extracted Economic News", "abstract": "Accurate forecasting of commodity price spikes is vital for countries with limited economic buffers, where sudden increases can strain national budgets, disrupt import-reliant sectors, and undermine food and energy security. This paper introduces a hybrid forecasting framework that combines historical commodity price data with semantic signals derived from global economic news, using an agentic generative AI pipeline. The architecture integrates dual-stream Long Short-Term Memory (LSTM) networks with attention mechanisms to fuse structured time-series inputs with semantically embedded, fact-checked news summaries collected from 1960 to 2023. The model is evaluated on a 64-year dataset comprising normalized commodity price series and temporally aligned news embeddings. Results show that the proposed approach achieves a mean AUC of 0.94 and an overall accuracy of 0.91 substantially outperforming traditional baselines such as logistic regression (AUC = 0.34), random forest (AUC = 0.57), and support vector machines (AUC = 0.47). Additional ablation studies reveal that the removal of attention or dimensionality reduction leads to moderate declines in performance, while eliminating the news component causes a steep drop in AUC to 0.46, underscoring the critical value of incorporating real-world context through unstructured text. These findings demonstrate that integrating agentic generative AI with deep learning can meaningfully improve early detection of commodity price shocks, offering a practical tool for economic planning and risk mitigation in volatile market environments while saving the very high costs of operating a full generative AI agents pipeline.", "published": "2025-07-24T20:52:47Z", "authors": ["Mohammed-Khalil Ghali", "Cecil Pang", "Oscar Molina", "Carlos Gershenson-Garcia", "Daehan Won"], "categories": ["q-fin.CP", "cs.AI", "cs.LG"], "doi": "", "_query": "housing price text listing", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/1406.5722v1", "title": "The price of fairness for a small number of indivisible items", "abstract": "Incorporating fairness criteria in optimization problems comes at a certain cost, which is measured by the so-called price of fairness. Here we consider the allocation of indivisible goods. For envy-freeness as fairness criterion it is known from literature that the price of fairness can increase linearly in terms of the number of agents. For the constructive lower bound a quadratic number of items was used. In practice this might be inadequately large. So we introduce the price of fairness in terms of both the number of agents and items, i.e., key parameters which generally may be considered as common and available knowledge. It turned out that the price of fairness increases sublinear if the number of items is not too much larger than the number of agents. For the special case of coincide of both counts exact asymptotics could be determined. Additionally an efficient integer programming formulation is given.", "published": "2014-06-22T13:01:32Z", "authors": ["Sascha Kurz"], "categories": ["cs.GT"], "doi": "", "_query": "housing price text listing", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/cs/0109036v2", "title": "Competition and Price Dispersion in International Long Distance Calling", "abstract": "This paper examines the relationship between changes in telecommunications provider concentration on international long distance routes and changes in prices on those routes. Overall, decreased concentration is associated with significantly lower prices to consumers of long distance services. However, the relationship between concentration and price varies according to the type of long distance plan considered. For the international flagship plans frequently selected by more price-conscious consumers of international long distance, increased competition on a route is associated with lower prices. In contrast, for the basic international plans that are the default selection for consumers, increased competition on a route is actually associated with higher prices. Thus, somewhat surprisingly, price dispersion appears to increase as competition increases.", "published": "2001-09-19T18:42:27Z", "authors": ["Sean F. Ennis"], "categories": ["cs.CY"], "doi": "", "_query": "housing price text listing", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2405.19887v2", "title": "Modulational Instability of the time-fractional Ivancevic option pricing model and the Coupled Nonlinear volatility and option price model", "abstract": "We study the time-fractional Ivancevic option pricing model and the coupled nonlinear volatility and option price model via both modulational instability (MI) analysis and direct simulations. For the coupled volatility and option pricing model the coupling term for both the volatility and the option price equation is the same, the MI results are dependent on it, and the stability of the volatility exists for the same condition as that of the price. The numerical simulations are done to confirm the conditions of MI. For the time-fractional model the analysis shows that for some values of the Hurst exponent MI exists for negative values of the adaptive market heat potential. Also, the sign of the volatility does not affect the MI, even though for some values of the volatility the MI can be suppressed. Direct numerical simulation shows the existance of solitons for negative values of the adaptive market potential where instabilty exists due to the value of the Hurst exponent.", "published": "2024-05-30T09:46:30Z", "authors": ["C. Gaafele", "Edmond B. Madimabe", "K. Ndebele", "P. Otlaadisa", "B. Mozola", "T. Matabana", "K. Seamolo", "P. Pilane"], "categories": ["nlin.PS"], "doi": "", "_query": "housing price text listing", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/1907.00558v1", "title": "Improved Forecasting of Cryptocurrency Price using Social Signals", "abstract": "Social media signals have been successfully used to develop large-scale predictive and anticipatory analytics. For example, forecasting stock market prices and influenza outbreaks. Recently, social data has been explored to forecast price fluctuations of cryptocurrencies, which are a novel disruptive technology with significant political and economic implications. In this paper we leverage and contrast the predictive power of social signals, specifically user behavior and communication patterns, from multiple social platforms GitHub and Reddit to forecast prices for three cyptocurrencies with high developer and community interest - Bitcoin, Ethereum, and Monero. We evaluate the performance of neural network models that rely on long short-term memory units (LSTMs) trained on historical price data and social data against price only LSTMs and baseline autoregressive integrated moving average (ARIMA) models, commonly used to predict stock prices. Our results not only demonstrate that social signals reduce error when forecasting daily coin price, but also show that the language used in comments within the official communities on Reddit (r/Bitcoin, r/Ethereum, and r/Monero) are the best predictors overall. We observe that models are more accurate in forecasting price one day ahead for Bitcoin (4% root mean squared percent error) compared to Ethereum (7%) and Monero (8%).", "published": "2019-07-01T05:51:05Z", "authors": ["Maria Glenski", "Tim Weninger", "Svitlana Volkova"], "categories": ["q-fin.ST", "cs.LG", "cs.SI", "stat.ML"], "doi": "", "_query": "housing price text listing", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/1104.2308v1", "title": "Non - Randomness Stock Market Price Model (Amended)", "abstract": "A new model for the stock market price analysis is proposed. It is suggested to look at price as an everywhere discontinuous function of time of bounded variation.", "published": "2011-04-12T19:14:29Z", "authors": ["Aleksey Kharevsky"], "categories": ["q-fin.GN"], "doi": "", "_query": "housing price text listing", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/1909.07896v2", "title": "No-Arbitrage Commodity Option Pricing with Market Manipulation", "abstract": "We design three continuous--time models in finite horizon of a commodity price, whose dynamics can be affected by the actions of a representative risk--neutral producer and a representative risk--neutral trader. Depending on the model, the producer can control the drift and/or the volatility of the price whereas the trader can at most affect the volatility. The producer can affect the volatility in two ways: either by randomizing her production rate or, as the trader, using other means such as spreading false information. Moreover, the producer contracts at time zero a fixed position in a European convex derivative with the trader. The trader can be price-taker, as in the first two models, or she can also affect the volatility of the commodity price, as in the third model. We solve all three models semi--explicitly and give closed--form expressions of the derivative price over a small time horizon, preventing arbitrage opportunities to arise. We find that when the trader is price-taker, the producer can always compensate the loss in expected production profit generated by an increase of volatility by a gain in the derivative position by driving the price at maturity to a suitable level. Finally, in case the trader is active, the model takes the form of a nonzero-sum linear-quadratic stochastic differential game and we find that when the production rate is already at its optimal stationary level, there is an amount of derivative position that makes both players better off when entering the game.", "published": "2019-09-17T15:38:07Z", "authors": ["René Aïd", "Giorgia Callegaro", "Luciano Campi"], "categories": ["q-fin.MF"], "doi": "", "_query": "housing price text listing", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2401.01516v1", "title": "A Complete Landscape for the Price of Envy-Freeness", "abstract": "We study the efficiency of fair allocations using the well-studied price of fairness concept, which quantitatively measures the worst-case efficiency loss when imposing fairness constraints. Previous works provided partial results on the price of fairness with well-known fairness notions such as envy-freeness up to one good (EF1) and envy-freeness up to any good (EFX). In this paper, we give a complete characterization for the price of envy-freeness in various settings. In particular, we first consider the two-agent case under the indivisible-goods setting and present tight ratios for the price of EF1 (for scaled utility) and EFX (for unscaled utility), which resolve questions left open in the literature. Next, we consider the mixed goods setting which concerns a mixture of both divisible and indivisible goods. We focus on envy-freeness for mixed goods (EFM), which generalizes both envy-freeness and EF1, as well as its strengthening called envy-freeness up to any good for mixed goods (EFXM), which generalizes envy-freeness and EFX. To this end, we settle the price of EFM and EFXM by providing a complete picture of tight bounds for two agents and asymptotically tight bounds for $n$ agents, for both scaled and unscaled utilities.", "published": "2024-01-03T02:55:34Z", "authors": ["Zihao Li", "Shengxin Liu", "Xinhang Lu", "Biaoshuai Tao", "Yichen Tao"], "categories": ["cs.GT"], "doi": "", "_query": "housing price text listing", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2305.00337v1", "title": "A Practical and Economical Bayesian Approach to Gas Price Prediction", "abstract": "On the Ethereum network, it is challenging to determine a gas price that ensures a transaction will be included in a block within a user's required timeline without overpaying. One way of addressing this problem is through the use of gas price oracles that utilize historical block data to recommend gas prices. However, when transaction volumes increase rapidly, these oracles often underestimate or overestimate the price. In this paper, we demonstrate how Gaussian process models can predict the distribution of the minimum price in an upcoming block when transaction volumes are increasing. This is effective because these processes account for time correlations between blocks. We performed an empirical analysis using the Gaussian process model on historical block data and compared the performance with GasStation-Express and Geth gas price oracles. The results suggest that when transactions volumes fluctuate greatly, the Gaussian process model offers a better estimation. Further, we demonstrated that GasStation-Express and Geth can be improved upon by using a smaller training sample size which is properly pre-processed. Based on the results of empirical analysis, we recommended a gas price oracle made up of a hybrid model consisting of both the Gaussian process and GasStation-Express. This oracle provides efficiency, accuracy, and better cost.", "published": "2023-04-29T20:24:49Z", "authors": ["ChihYun Chuang", "TingFang Lee"], "categories": ["stat.AP", "cs.CE"], "doi": "10.1007/978-3-030-84337-3_13", "_query": "housing price text listing", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/1903.03987v2", "title": "Price competition with uncertain quality and cost", "abstract": "Consumers in many markets are uncertain about firms' qualities and costs, so buy based on both the price and the quality inferred from it. Optimal pricing depends on consumer heterogeneity only when firms with higher quality have higher costs, regardless of whether costs and qualities are private or public. If better quality firms have lower costs, then good quality is sold cheaper than bad under private costs and qualities, but not under public. However, if higher quality is costlier, then price weakly increases in quality under both informational environments.", "published": "2019-03-10T13:20:23Z", "authors": ["Sander Heinsalu"], "categories": ["econ.TH"], "doi": "", "_query": "housing price text listing", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2302.09700v2", "title": "Leveraging Reviews: Learning to Price with Buyer and Seller Uncertainty", "abstract": "In online marketplaces, customers have access to hundreds of reviews for a single product. Buyers often use reviews from other customers that share their type -- such as height for clothing, skin type for skincare products, and location for outdoor furniture -- to estimate their values, which they may not know a priori. Customers with few relevant reviews may hesitate to make a purchase except at a low price, so for the seller, there is a tension between setting high prices and ensuring that there are enough reviews so that buyers can confidently estimate their values. Simultaneously, sellers may use reviews to gauge the demand for items they wish to sell. In this work, we study this pricing problem in an online setting where the seller interacts with a set of buyers of finitely many types, one by one, over a series of $T$ rounds. At each round, the seller first sets a price. Then a buyer arrives and examines the reviews of the previous buyers with the same type, which reveal those buyers' ex-post values. Based on the reviews, the buyer decides to purchase if they have good reason to believe that their ex-ante utility is positive. Crucially, the seller does not know the buyer's type when setting the price, nor even the distribution over types. We provide a no-regret algorithm that the seller can use to obtain high revenue. When there are $d$ types, after $T$ rounds, our algorithm achieves a problem-independent $\\tilde O(T^{2/3}d^{1/3})$ regret bound. However, when the smallest probability $q_{\\text{min}}$ that any given type appears is large, specifically when $q_{\\text{min}} \\in Ω(d^{-2/3}T^{-1/3})$, then the same algorithm achieves a $\\tilde O(T^{1/2}q_{\\text{min}}^{-1/2})$ regret bound. We complement these upper bounds with matching lower bounds in both regimes, showing that our algorithm is minimax optimal up to lower-order terms.", "published": "2023-02-20T00:15:45Z", "authors": ["Wenshuo Guo", "Nika Haghtalab", "Kirthevasan Kandasamy", "Ellen Vitercik"], "categories": ["cs.GT", "cs.LG"], "doi": "", "_query": "housing price text listing", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2210.11064v1", "title": "Competitive Equilibrium for Dynamic Multi-Agent Systems: Social Shaping and Price Trajectories", "abstract": "In this paper, we consider dynamic multi-agent systems (MAS) for decentralized resource allocation. The MAS operates at a competitive equilibrium to ensure supply and demand are balanced. First, we investigate the MAS over a finite horizon. The utility functions of agents are parameterized to incorporate individual preferences. We shape individual preferences through a set of utility functions to guarantee the resource price at a competitive equilibrium remains socially acceptable, i.e., the price is upper-bounded by an affordability threshold. We show this problem is solvable at the conceptual level. Next, we consider quadratic MAS and formulate the associated social shaping problem as a multi-agent linear quadratic regulator (LQR) problem which enables us to propose explicit utility sets using quadratic programming and dynamic programming. Then, a numerical algorithm is presented for calculating a tight range of the preference function parameters which guarantees a socially accepted price. We investigate the properties of a competitive equilibrium over an infinite horizon. Considering general utility functions, we show that under feasibility assumptions, any competitive equilibrium maximizes the social welfare. Then, we prove that for sufficiently small initial conditions, the social welfare maximization solution constitutes a competitive equilibrium with zero price. We also prove for general feasible initial conditions, there exists a time instant after which the optimal price, corresponding to a competitive equilibrium, becomes zero. Finally, we specifically focus on quadratic MAS and propose explicit results.", "published": "2022-10-20T07:37:22Z", "authors": ["Zeinab Salehi", "Yijun Chen", "Elizabeth L. Ratnam", "Ian R. Petersen", "Guodong Shi"], "categories": ["eess.SY"], "doi": "", "_query": "housing price text listing", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2101.00223v1", "title": "Pricing spread option with liquidity adjustments", "abstract": "We study the pricing and hedging of European spread options on correlated assets when, in contrast to the standard framework and consistent with imperfect liquidity markets, the trading in the stock market has a direct impact on stocks prices. We consider a partial-impact and a full-impact model in which the price impact is caused by every trading strategy in the market. The generalized Black-Scholes pricing partial differential equations (PDEs) are obtained and analysed. We perform a numerical analysis to exhibit the illiquidity effect on the replication strategy of the European spread option. Compared to the Black-Scholes model or a partial impact model, the trader in the full impact model buys more stock to replicate the option, and this leads to a higher option price.", "published": "2021-01-01T12:30:02Z", "authors": ["Kevin Shuai Zhang", "Traian Pirvu"], "categories": ["q-fin.CP"], "doi": "", "_query": "housing price text listing", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/1903.07588v1", "title": "A Multilingual Encoding Method for Text Classification and Dialect Identification Using Convolutional Neural Network", "abstract": "This thesis presents a language-independent text classification model by introduced two new encoding methods \"BUNOW\" and \"BUNOC\" used for feeding the raw text data into a new CNN spatial architecture with vertical and horizontal convolutional process instead of commonly used methods like one hot vector or word representation (i.e. word2vec) with temporal CNN architecture. The proposed model can be classified as hybrid word-character model in its work methodology because it consumes less memory space by using a fewer neural network parameters as in character level representation, in addition to providing much faster computations with fewer network layers depth, as in word level representation. A promising result achieved compared to state of art models in two different morphological benchmarked dataset one for Arabic language and one for English language.", "published": "2019-03-18T17:31:14Z", "authors": ["Amr Adel Helmy"], "categories": ["cs.CL", "cs.LG"], "doi": "", "_query": "housing price text listing", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2605.06060v1", "title": "Arbitrage and the Stability of AMM Price Tracking", "abstract": "Automated market makers (AMMs) quote prices from pool state rather than from a limit order book. AMM pools often stay close to a reference price because arbitrageurs correct profitable mispricing. A large part of decentralized finance therefore relies on a simple economic premise: once the AMM price drifts away from the reference price, arbitrage incentives push it back. This paper studies when that premise is strong enough to guarantee block-scale stability. We model the gap between the reference price and the AMM price as a stochastic tracking error, treat arbitrage as the corrective input, and place blockchain execution inside the loop through fees, discrete blocks, transaction ordering, delays, and transaction failure. The detailed execution layer is reduced to the total successful correction confirmed in each block. Under a block-level correction condition, we prove geometric ergodicity of the tracking error and obtain explicit one-step bounds that connect tracking quality to liquidity and execution quality. We also show in a constant-product example how fees, fixed execution costs, and local liquidity map into the no-trade band and the optimal corrective trade. Finally, we build empirical proxies for the theorem quantities from realized block data and use them to organize reduced and mechanism-focused simulations whose comparative statics are consistent with the theory. The contribution is to turn a basic economic intuition behind decentralized finance into a quantitative stability statement together with a tractable calibration interface.", "published": "2026-05-07T11:44:09Z", "authors": ["Peihao Li", "Nadia Dahmani", "Wenqi Cai"], "categories": ["cs.CE", "eess.SY"], "doi": "", "_query": "housing price text listing", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/cond-mat/0103621v1", "title": "Measuring long-range dependence in electricity prices", "abstract": "The price of electricity is far more volatile than that of other commodities normally noted for extreme volatility. The possibility of extreme price movements increases the risk of trading in electricity markets. However, underlying the process of price returns is a strong mean-reverting mechanism. We study this feature of electricity returns by means of Hurst R/S analysis, Detrended Fluctuation Analysis and periodogram regression.", "published": "2001-03-30T08:46:03Z", "authors": ["Rafal Weron"], "categories": ["cond-mat.stat-mech", "q-fin.ST"], "doi": "", "_query": "housing price text listing", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/1403.7681v3", "title": "Uncertain Price Competition in a Duopoly with Heterogeneous Availability", "abstract": "We study the price competition in a duopoly with an arbitrary number of buyers. Each seller can offer multiple units of a commodity depending on the availability of the commodity which is random and may be different for different sellers. Sellers seek to select a price that will be attractive to the buyers and also fetch adequate profits. The selection will in general depend on the number of units available with the seller and also that of its competitor - the seller may only know the statistics of the latter. The setting captures a secondary spectrum access network, a non-neutral Internet, or a microgrid network in which unused spectrum bands, resources of ISPs, and excess power units constitute the respective commodities of sale. We analyze this price competition as a game, and identify a set of necessary and sufficient properties for the Nash Equilibrium (NE). The properties reveal that sellers randomize their price using probability distributions whose support sets are mutually disjoint and in decreasing order of the number of availability. We prove the uniqueness of a symmetric NE in a symmetric market, and explicitly compute the price distribution in the symmetric NE.", "published": "2014-03-29T23:51:05Z", "authors": ["Mohammad Hassan Lotfi", "Saswati Sarkar"], "categories": ["cs.GT"], "doi": "10.1109/TAC.2015.2450091", "_query": "housing price text listing", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/1909.08722v1", "title": "A characterization of 2-neighborhood degree list of diameter 2 graphs", "abstract": "Let $N_2DL(v)$ denote the set of degrees of vertices at distance 2 from $v$. The $2$-neighborhood degree list of a graph is a listing of $N_2DL(v)$ for every vertex $v$. A degree restricted $2$-switch on edges $v_1v_2$ and $w_1w_2$, where $deg(v_1)=deg(w_1)$ and $deg(v_2)=deg(w_2)$, is the replacement of a pair of edges $v_1v_2$ and $w_1w_2$ by the edges $v_1w_2$ and $v_2w_1$ given that $v_1w_2$ and $v_2w_1$ did not appear in the graph originally. Let $G$ and $H$ be two graphs of diameter 2 on the same vertex set. We prove that $G$ and $H$ have the same $2$-neighborhood degree list if and only if $G$ can be transformed into $H$ by a sequence of degree restricted $2$-switches.", "published": "2019-09-18T21:54:07Z", "authors": ["N. Benakli", "E. Halleck", "S. R. Kingan"], "categories": ["math.CO"], "doi": "", "_query": "housing price text listing", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/1410.8224v1", "title": "Efficient price dynamics in a limit order market: an utility indifference approach", "abstract": "We construct an utility-based dynamic asset pricing model for a limit order market. The price is nonlinear in volume and subject to market impact. We solve an optimal hedging problem under the market impact and derive the dynamics of the efficient price, that is, the asset price when a representative liquidity demander follows an optimal strategy. We show that a Pareto efficient allocation is achieved under a completeness condi- tion. We give an explicit representation of the efficient price for several examples. In particular, we observe that the volatility of the asset depends on the convexity of an initial endowment. Further, we observe that an asset price crash is invoked by an endowment shock. We establish a dynamic programming principle under an incomplete framework.", "published": "2014-10-30T01:12:00Z", "authors": ["Masaaki Fukasawa"], "categories": ["q-fin.PR", "math.PR"], "doi": "", "_query": "housing price text listing", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2002.04101v1", "title": "Sequential Monitoring of Changes in Housing Prices", "abstract": "We propose a sequential monitoring scheme to find structural breaks in real estate markets. The changes in the real estate prices are modeled by a combination of linear and autoregressive terms. The monitoring scheme is based on a detector and a suitably chosen boundary function. If the detector crosses the boundary function, a structural break is detected. We provide the asymptotics for the procedure under the stability null hypothesis and the stopping time under the change point alternative. Monte Carlo simulation is used to show the size and the power of our method under several conditions. We study the real estate markets in Boston, Los Angeles and at the national U.S. level. We find structural breaks in the markets, and we segment the data into stationary segments. It is observed that the autoregressive parameter is increasing but stays below 1.", "published": "2020-02-10T21:49:19Z", "authors": ["Lajos Horváth", "Zhenya Liu", "Shanglin Lu"], "categories": ["econ.EM", "stat.AP", "stat.ME"], "doi": "", "_query": "housing price text listing", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2306.11984v1", "title": "TauPETGen: Text-Conditional Tau PET Image Synthesis Based on Latent Diffusion Models", "abstract": "In this work, we developed a novel text-guided image synthesis technique which could generate realistic tau PET images from textual descriptions and the subject's MR image. The generated tau PET images have the potential to be used in examining relations between different measures and also increasing the public availability of tau PET datasets. The method was based on latent diffusion models. Both textual descriptions and the subject's MR prior image were utilized as conditions during image generation. The subject's MR image can provide anatomical details, while the text descriptions, such as gender, scan time, cognitive test scores, and amyloid status, can provide further guidance regarding where the tau neurofibrillary tangles might be deposited. Preliminary experimental results based on clinical [18F]MK-6240 datasets demonstrate the feasibility of the proposed method in generating realistic tau PET images at different clinical stages.", "published": "2023-06-21T02:27:07Z", "authors": ["Se-In Jang", "Cristina Lois", "Emma Thibault", "J. Alex Becker", "Yafei Dong", "Marc D. Normandin", "Julie C. Price", "Keith A. Johnson", "Georges El Fakhri", "Kuang Gong"], "categories": ["eess.IV", "cs.AI", "cs.CV"], "doi": "", "_query": "housing price text listing", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2110.05891v1", "title": "Group network effects in price competition", "abstract": "The partition of society into groups, polarization, and social networks are part of most conversations today. How do they influence price competition? We discuss Bertrand duopoly equilibria with demand subject to network effects. Contrary to models where network effects depend on one aggregate variable (demand for each choice), partitioning the dependence into groups creates a wealth of pure price equilibria with profit for both price setters, even if positive network effects are the dominant element of the game. If there is some asymmetry in how groups interact, two groups are sufficient. If network effects are based on undirected and unweighted graphs, at least five groups are required but, without other differentiation, outcomes are symmetric.", "published": "2021-10-12T11:05:29Z", "authors": ["Renato Soeiro", "Alberto Pinto"], "categories": ["econ.TH"], "doi": "", "_query": "housing price text listing", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2212.11787v1", "title": "Macro carbon price prediction with support vector regression and Paris accord targets", "abstract": "Carbon neutralization is an urgent task in society because of the global warming threat. And carbon trading is an essential market mechanics to solve carbon reduction targets. Macro carbon price prediction is vital in the useful management and decision-making of the carbon market. We focus on the EU carbon market and we choose oil price, coal price, gas price, and DAX index to be the four market factors in predicting carbon price, and also we select carbon emission targets from Paris Accord as the political factor in the carbon market in terms of the macro view of the carbon price prediction. Thus we use these five factors as inputs to predict the future carbon yearly price in 2030 with the support vector regression models. We use grid search and cross validation to guarantee the prediction performance of our models. We believe this model will have great applications in the macro carbon price prediction.", "published": "2022-11-30T02:42:13Z", "authors": ["Jinhui Li"], "categories": ["q-fin.GN", "econ.GN", "stat.AP"], "doi": "", "_query": "housing price text listing", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2405.03496v3", "title": "Price-Aware Automated Market Makers: Models Beyond Brownian Prices and Static Liquidity", "abstract": "In this paper, we introduce a suite of models for price-aware automated market making platforms willing to optimize their quotes. These models incorporate advanced price dynamics, including stochastic volatility, jumps, and microstructural price models based on Hawkes processes. Additionally, we address the variability in demand from liquidity takers through models that employ either Hawkes or Markov-modulated Poisson processes. Each model is analyzed with particular emphasis placed on the complexity of the numerical methods required to compute optimal quotes.", "published": "2024-05-06T14:07:02Z", "authors": ["Philippe Bergault", "Louis Bertucci", "David Bouba", "Olivier Guéant", "Julien Guilbert"], "categories": ["q-fin.TR"], "doi": "", "_query": "housing price text listing", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/1908.01103v2", "title": "Derivation of non-classical stochastic price dynamics equations", "abstract": "We analyze the relative price change of assets starting from basic supply/demand considerations subject to arbitrary motivations. The resulting stochastic differential equation has coefficients that are functions of supply and demand. We derive these rigorously. The variance in the relative price change is then also dependent on the supply and demand, and is closely connected to the expected return. An important consequence for risk assessment and options pricing is the implication that variance is highest when the magnitude of price change is greatest, and lowest near market extrema. This occurs even if supply and demand are not dependent on price trend. The stochastic equation differs from the standard equation in mathematical finance in which the expected return and variance are decoupled. The methodology has implications for the basic framework for risk assessment, suggesting that volatility should be measured in the context of regimes of price change. The model we propose shows how investors are often misled by the apparent calm of markets near a market peak. Risk assessment methods utilizing volatility can be improved using this formulation.", "published": "2019-08-03T01:28:09Z", "authors": ["Carey Caginalp", "Gunduz Caginalp"], "categories": ["econ.TH", "math.PR"], "doi": "", "_query": "housing price text listing", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2310.13254v2", "title": "Socially Optimal Energy Usage via Adaptive Pricing", "abstract": "A central challenge in using price signals to coordinate the electricity consumption of a group of users is the operator's lack of knowledge of the users due to privacy concerns. In this paper, we develop a two-time-scale incentive mechanism that alternately updates between the users and a system operator. As long as the users can optimize their own consumption subject to a given price, the operator does not need to know or attempt to learn any private information of the users for price design. Users adjust their consumption following the price and the system redesigns the price based on the users' consumption. We show that under mild assumptions, this iterative process converges to the social welfare solution. In particular, the cost of the users need not always be convex and its consumption can be the output of a machine learning-based load control algorithm.", "published": "2023-10-20T03:21:58Z", "authors": ["Jiayi Li", "Matthew Motoki", "Baosen Zhang"], "categories": ["cs.GT", "eess.SY"], "doi": "", "_query": "housing price text listing", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2610.10172v1", "title": "Price Discrimination and Revealed Consumer Welfare", "abstract": "We take a revealed-preference approach to study the welfare effects of price discrimination. An analyst observes price and quantity data before and after a monopolist segments the market. The analyst knows neither the aggregate demand curve nor the information available to the monopolist. We characterize the set of observable price-quantity pairs that are rationalizable by some market segmentation. We then obtain sharp bounds on the change in consumer surplus arising from some segmentation. Finally, we provide conditions under which the data identifies an unambiguous increase (decrease) in consumer surplus.", "published": "2026-10-07T14:46:34Z", "authors": ["João Thereze", "Udayan Vaidya"], "categories": ["econ.TH"], "doi": "", "_query": "housing price text listing", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/1612.07956v1", "title": "A CRF Based POS Tagger for Code-mixed Indian Social Media Text", "abstract": "In this work, we describe a conditional random fields (CRF) based system for Part-Of- Speech (POS) tagging of code-mixed Indian social media text as part of our participation in the tool contest on POS tagging for codemixed Indian social media text, held in conjunction with the 2016 International Conference on Natural Language Processing, IIT(BHU), India. We participated only in constrained mode contest for all three language pairs, Bengali-English, Hindi-English and Telegu-English. Our system achieves the overall average F1 score of 79.99, which is the highest overall average F1 score among all 16 systems participated in constrained mode contest.", "published": "2016-12-23T12:58:58Z", "authors": ["Kamal Sarkar"], "categories": ["cs.CL"], "doi": "", "_query": "housing price text listing", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2602.15981v1", "title": "A Theoretical Approach to Stablecoin Design via Price Windows", "abstract": "In this paper, we explore the short- and long-term stability of backed stablecoins offering constant mint and redeem prices to all agents. We refer to such designs as price window-based, since the mint and redeem prices constrain the stablecoin's market equilibrium. We show that, without secondary stabilization mechanisms, price window designs cannot achieve both short- and long-term stability unless they are backed by already-stable reserves. In particular, the mechanism faces a tradeoff: either risk eventual reserve depletion through persistent arbitrage by a speculator, or widen the distance between mint and redeem prices enough to disincentivize arbitrage. In the latter case, however, the market price of the stablecoin inherits the volatility of its backing asset, with fluctuations that can be proportional to the backing asset's own volatility.", "published": "2026-02-17T20:17:51Z", "authors": ["Katherine Molinet", "Aris Filos-Ratsikas"], "categories": ["cs.GT", "cs.CR"], "doi": "", "_query": "housing price text listing", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2601.19095v1", "title": "Dual Analysis of Continuous-time Economic Dispatch and Its Price Implications", "abstract": "As load varies continuously over time, it is essential to provide continuous-time price signals that accurately reflect supply-demand balance. However, conventional discrete-time economic dispatch fails to capture the intra-temporal variations in load and generation. Its dual solution--the marginal price--may distort economic signals, leading to inefficient market incentives. To analyze these issues, this paper develops a continuous-time dispatch model and derives its dual formulation for price analysis. The continuous-time dispatch produces dual variables that can be interpreted as price signals. Piecewise time-indexed generation and price trajectories are then constructed through a parametric programming approach. The resulting price, represented by the Lagrange multiplier of the system-wide power balance constraint, evolves piecewise along the continuous-time load profile. Each segment corresponds to a critical region characterized by a set of active constraints. Furthermore, we discuss the impact of unit-specific ramping constraints on price implications. Results indicate that continuous-time generation and price trajectories provide deeper insights into the price distortions and inefficient incentives inherent in discrete-time formulations. The proposed methodology is validated on an illustrative 5-bus system and a modified IEEE RTS-2019.", "published": "2026-01-27T01:58:42Z", "authors": ["Menghan Zhang", "Caisheng Wang"], "categories": ["eess.SY"], "doi": "", "_query": "housing price text listing", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2309.02447v2", "title": "Economic Complexity Limits Accuracy of Price Probability Predictions by Gaussian Distributions", "abstract": "We discuss the economic reasons why the predictions of price and return statistical moments in the coming decades, in the best case, will be limited by their averages and volatilities. That limits the accuracy of the forecasts of price and return probabilities by Gaussian distributions. The economic origin of these restrictions lies in the fact that the predictions of the market-based n-th statistical moments of price and return for n=1,2,.., require the description of the economic variables of the n-th order that are determined by sums of the n-th degrees of values or volumes of market trades. The lack of existing models that describe the evolution of the economic variables determined by the sums of the 2nd degrees of market trades results in the fact that even predictions of the volatilities of price and return are very uncertain. One can ignore existing economic barriers that we highlight but cannot overcome or resolve them. The accuracy of predictions of price and return probabilities substantially determines the reliability of asset pricing models and portfolio theories. The restrictions on the accuracy of predictions of price and return statistical moments reduce the reliability and veracity of modern asset pricing and portfolio theories.", "published": "2023-08-24T09:49:20Z", "authors": ["Victor Olkhov"], "categories": ["q-fin.GN", "econ.GN", "q-fin.PR", "q-fin.RM"], "doi": "", "_query": "housing price text listing", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/1811.11379v4", "title": "Option Pricing in a Regime Switching Jump Diffusion Model", "abstract": "This paper presents the solution to a European option pricing problem by considering a regime-switching jump diffusion model of the underlying financial asset price dynamics. The regimes are assumed to be the results of an observed pure jump process, driving the values of interest rate and volatility coefficient. The pure jump process is assumed to be a semi-Markov process on finite state space. This consideration helps to incorporate a specific type of memory influence in the asset price. Under this model assumption, the locally risk minimizing price of the European type path-independent options is found. The Föllmer-Schweizer decomposition is adopted to show that the option price satisfies an evolution problem, as a function of time, stock price, market regime, and the stagnancy period. To be more precise, the evolution problem involves a linear, parabolic, degenerate and non-local system of integro-partial differential equations. We have established existence and uniqueness of classical solution to the evolution problem in an appropriate class.", "published": "2018-11-28T04:30:38Z", "authors": ["Anindya Goswami", "Omkar Manjarekar", "Anjana R"], "categories": ["q-fin.PR", "math.PR", "q-fin.MF"], "doi": "", "_query": "housing price text listing", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2008.01618v2", "title": "Distributionally Robust Pricing in Independent Private Value Auctions", "abstract": "A seller chooses a reserve price in a second-price auction to maximize worst-case expected revenue when she knows only the mean of value distribution and an upper bound on either values themselves or variance. Values are private and iid. Using an indirect technique, we prove that it is always optimal to set the reserve price to the seller's own valuation. However, the maxmin reserve price may not be unique. If the number of bidders is sufficiently high, all prices below the seller's valuation, including zero, are also optimal. A second-price auction with the reserve equal to seller's value (or zero) is an asymptotically optimal mechanism (among all ex post individually rational mechanisms) as the number of bidders grows without bound.", "published": "2020-08-04T14:59:45Z", "authors": ["Alex Suzdaltsev"], "categories": ["econ.TH", "cs.GT"], "doi": "", "_query": "housing price text listing", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/1506.03621v2", "title": "Convergence of Estimated Option Price in a Regime switching Market", "abstract": "In an observed generalized semi-Markov regime, estimation of transition rate of regime switching leads towards calculation of locally risk minimizing option price. Despite the uniform convergence of estimated step function of transition rate, to meet the existence of classical solution of the modified price equation, the estimator is approximated in the class of smooth functions and furthermore, the convergence is established. Later, the existence of the solution of the modified price equation is verified and the point-wise convergence of such approximation of option price is proved to answer the tractability of its application in Finance. To demonstrate the consistency in result a numerical experiment has been reported.", "published": "2015-06-11T10:59:55Z", "authors": ["Anindya Goswami", "Sanket Nandan"], "categories": ["q-fin.PR", "math.PR", "math.ST"], "doi": "10.1007/s13226-016-0182-7", "_query": "housing price text listing", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2205.01417v1", "title": "The Price of Hierarchical Clustering", "abstract": "Hierarchical Clustering is a popular tool for understanding the hereditary properties of a data set. Such a clustering is actually a sequence of clusterings that starts with the trivial clustering in which every data point forms its own cluster and then successively merges two existing clusters until all points are in the same cluster. A hierarchical clustering achieves an approximation factor of $α$ if the costs of each $k$-clustering in the hierarchy are at most $α$ times the costs of an optimal $k$-clustering. We study as cost functions the maximum (discrete) radius of any cluster ($k$-center problem) and the maximum diameter of any cluster ($k$-diameter problem). In general, the optimal clusterings do not form a hierarchy and hence an approximation factor of $1$ cannot be achieved. We call the smallest approximation factor that can be achieved for any instance the price of hierarchy. For the $k$-diameter problem we improve the upper bound on the price of hierarchy to $3+2\\sqrt{2}\\approx 5.83$. Moreover we significantly improve the lower bounds for $k$-center and $k$-diameter, proving a price of hierarchy of exactly $4$ and $3+2\\sqrt{2}$, respectively.", "published": "2022-05-03T11:11:55Z", "authors": ["Anna Arutyunova", "Heiko Röglin"], "categories": ["cs.DS"], "doi": "", "_query": "housing price text listing", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2012.14860v1", "title": "Self-sustained price bubbles driven by Bitcoin innovations and adaptive behavior", "abstract": "We show that infinite divisibility of a trading commodity leads to a self-sustained price bubble when traders use adaptive investment strategies. The adaptive strategy can be viewed as a psychological response of a trader to the situation when the trader's estimation of future prices does not match the actual, realized price. We use a multi-agent model to illustrate the price bubble formation and to quantify its main statistical properties such as the return, the volatility, and the systematic risk of the price bubble to crash. We discuss the plausibility for bubbles to drive prices of digital currencies.", "published": "2020-12-29T17:19:51Z", "authors": ["Misha Perepelitsa", "Ilya Timofeyev"], "categories": ["q-fin.TR"], "doi": "10.13140/RG.2.2.10733.00480", "_query": "housing price text listing", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/1810.08567v1", "title": "Weak Semi-Markov CRFs for NP Chunking in Informal Text", "abstract": "This paper introduces a new annotated corpus based on an existing informal text corpus: the NUS SMS Corpus (Chen and Kan, 2013). The new corpus includes 76,490 noun phrases from 26,500 SMS messages, annotated by university students. We then explored several graphical models, including a novel variant of the semi-Markov conditional random fields (semi-CRF) for the task of noun phrase chunking. We demonstrated through empirical evaluations on the new dataset that the new variant yielded similar accuracy but ran in significantly lower running time compared to the conventional semi-CRF.", "published": "2018-10-19T16:09:35Z", "authors": ["Aldrian Obaja Muis", "Wei Lu"], "categories": ["cs.CL"], "doi": "10.18653/v1/N16-1085", "_query": "housing price text listing", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2312.12916v3", "title": "A generalized Price equation for fuzzy set-mappings", "abstract": "The Price equation provides a formal account of selection building on a right-total mapping between two classes of individuals, that is usually interpreted as a parent-offspring relation. This paper presents a new formulation of the Price equation in terms of fuzzy set-mappings to account for structures where the targets of selection may vary in the degree to which they belong to the classes of 'parents' and 'offspring,' and in the degree to which these two classes of individuals are related. The fuzzy set formulation widens the scope of the Price equation such that it equally applies to natural selection, cultural selection, operant selection, and even selection in physical systems.", "published": "2023-12-20T10:52:54Z", "authors": ["Matthias Borgstede"], "categories": ["q-bio.PE"], "doi": "", "_query": "housing price text listing", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2605.14360v2", "title": "A Formative Study of Brief Affective Text as a Complement to Wearable Sensing for Longitudinal Student Health Monitoring", "abstract": "Wearable devices capture physiological and behavioral data with increasing fidelity, but the psychological context shaping these outcomes is difficult to recover from sensor data alone, limiting the utility of passive sensing for digital health goals such as early detection of distress, personalized intervention, and timely clinical outreach. We examined whether ultra-brief naturalistic concern text could serve as a scalable complement to passive sensing. In a year-long study of 458 university students (3,610 person-waves) tracked with Oura rings, participants responded bimonthly to an open-ended prompt about what concerned them most; responses had a median length of three words. We compared dictionary-based, general pretrained, and domain-adapted NLP approaches using within-person mixed-effects models across nine sleep and physical activity outcomes to determine which method best recovers physiologically relevant signal from brief naturalistic text. Weeks dominated by academic concern framing were associated with lower physical activity; weeks characterized by emotional exhaustion language were associated with poorer sleep quality and lower heart rate variability. General pretrained embeddings performed as well as or better than domain-adapted models across most outcomes, with differences between the two generally small and within the range of estimation noise. Zero-shot classification of concern topics showed no consistent evidence of association with outcomes; affective dimensions across all three methods showed more associations, though these did not survive correction for multiple comparisons, offering preliminary evidence that emotional register may carry more signal than topical content. These findings offer design guidance: ultra-brief affective prompts enrich the psychological interpretability of passive physiological data at minimal burden.", "published": "2026-05-14T04:36:29Z", "authors": ["Tamunotonye Harry", "Johanna Hidalgo", "Matthew Price", "Yuanyuan Feng", "Kathryn Stanton", "Connie Tompkins", "Peter Sheridan Dodds", "Mikaela Irene Fudolig", "Laura Bloomfield", "Christopher Danforth"], "categories": ["cs.HC", "cs.CL"], "doi": "10.1145/3857988", "_query": "housing price text listing", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2509.10092v1", "title": "Price Formation in a Highly-Renewable, Sector-Coupled Energy System", "abstract": "As variable renewable energy increases and more demand is electrified, we expect price formation in wholesale electricity markets to transition from being dominated by fossil fuel generators to being dominated by the opportunity costs of storage and demand management. In order to analyse this transition, we introduce a new method to investigate price formation based on a mapping from the dual variables of the energy system optimisation problem to the bids and asks of electricity suppliers and consumers. This allows us to build the full supply and demand curves in each hour. We use this method to analyse price formation in a sector-coupled, climate-neutral energy system model for Germany, PyPSA-DE, with high temporal resolution and myopic foresight in 5-year steps from 2020 until full decarbonisation in 2045. We find a clear transition from distinct price levels, corresponding to fossil fuels, to a smoother price curve set by variable renewable energy sources, batteries and electrolysis. Despite higher price volatility, the fully decarbonised system clears with non-zero prices in 75% of all hours. Our results suggest that flexibility and cross-sectoral demand bidding play a vital role in stabilising electricity prices in a climate-neutral future. These findings are highly relevant for guiding investment decisions and informing policy, particularly in support of dynamic pricing, the expansion of energy storage across multiple timescales, and the coordinated development of renewable and flexibility technologies.", "published": "2025-09-12T09:36:05Z", "authors": ["Julian Geis", "Fabian Neumann", "Michael Lindner", "Philipp Härtel", "Tom Brown"], "categories": ["econ.GN"], "doi": "10.1016/j.eneco.2026.109213", "_query": "housing price text listing", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/cond-mat/0501699v1", "title": "Volatility conditional on price trends", "abstract": "The influence of the past price behaviour on the realized volatility is investigated in the present article. The results show that trending (drifting) prices lead to increased (decreased) realized volatility. This ``volatility induced by trend'' constitutes a new stylized fact. The past price behaviour is measured by a product of 2 non overlapping returns, of the form r L[r] where L is the lag operator. The effect is studied empirically using USD/CHF foreign exchange data, in a large range of time horizons. A set of ARCH based processes are modified in order to include the trend effect, and their forecasting performances are compared. For a better forecast, it is shown that the main factor is the shape of the memory kernel (i.e. power law), and the following factor is the inclusion of the trend effect.", "published": "2005-01-28T10:06:23Z", "authors": ["Gilles Zumbach"], "categories": ["cond-mat.other", "q-fin.ST"], "doi": "", "_query": "housing price text listing", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/1905.13733v2", "title": "Data assimilation in price formation", "abstract": "We consider the problem of estimating the density of buyers and vendors in a nonlinear parabolic price formation model using measurements of the price and the transaction rate. Our approach is based on a work by Puel et al., see \\cite{Puel2002}, and results in a optimal control problem. We analyse this problems and provide stability estimates for the controls as well as the unknown density in the presence of measurement errors. Our analytic findings are supported with numerical experiments.", "published": "2019-05-31T17:35:27Z", "authors": ["Martin Burger", "Jan-Frederik Pietschmann", "Marie-Therese Wolfram"], "categories": ["math.OC", "math.AP", "math.NA"], "doi": "10.1088/1361-6420/ab6d5a", "_query": "housing price text listing", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2208.02949v1", "title": "A data-driven approach for modeling the behavior of stock prices", "abstract": "In this paper, we describe two approaches to model the behavior of stock prices. The first approach considers the underlying probability distribution of day-to-day price differences. The second approach models the movement of the price as a stochastic birth-death process. We demonstrated the two approaches using historical opening prices of Apple inc. and compared the simulated prices from the two approaches to the actual ones using information theory metrics.", "published": "2022-08-05T01:13:15Z", "authors": ["Khalid Aram"], "categories": ["stat.AP"], "doi": "", "_query": "housing price text listing", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2109.01992v1", "title": "Balanced House Allocation", "abstract": "We introduce balancedness a fairness axiom in house allocation problems. It requires a mechanism to assign the top choice, the second top choice, and so on, on the same number of profiles for each agent. This axiom guarantees equal treatment of all agents at the stage in which the mechanism is announced when all preference profiles are equally likely. We show that, with an interesting exception for the three-agent case, Top Trading Cycles from individual endowments is the only mechanism that is balanced, efficient, and group strategy-proof.", "published": "2021-09-05T04:53:31Z", "authors": ["Xinghua Long", "Rodrigo A. Velez"], "categories": ["econ.TH", "cs.GT"], "doi": "", "_query": "housing price text listing", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/1701.03774v1", "title": "Linear Hypergraph List Edge Coloring - Generalizations of the EFL Conjecture to List Coloring", "abstract": "Motivated by the Erdős-Faber-Lovász (EFL) conjecture for hypergraphs, we consider the list edge coloring of linear hypergraphs. We discuss several conjectures for list edge coloring linear hypergraphs that generalize both EFL and Vizing's theorem for graphs. For example, we conjecture that in a linear hypergraph of rank 3, the list edge chromatic number is at most 2 times the maximum degree plus 1. We show that for sufficiently large fixed rank and sufficiently large degree, the conjectures are true.", "published": "2017-01-03T22:35:39Z", "authors": ["Vance Faber"], "categories": ["math.CO"], "doi": "", "_query": "housing price text listing", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/1406.6862v1", "title": "Coping with area price risk in electricity markets: Forecasting Contracts for Difference in the Nordic power market", "abstract": "Contracts for Difference (CfDs) are forwards on the spread between an area price and the system price. Together with the system price forwards, these products are used to hedge the area price risk in the Nordic electricity market. The CfDs are typically available for the next two months, three quarters and three years. This is fine, except that CfDs are not traded at NASDAQ OMX Commodities for every Nord Pool Spot price area. We therefore ask the hypothetical question: What would the CfD market price have been, say in the price area NO2, if it had been traded? We build regression models for each observable price area, and use Bayesian elicitation techniques to obtain prior information on how similar the different price areas are to forecast the price in an area where CfDs are not traded.", "published": "2014-06-26T12:21:51Z", "authors": ["Egil Ferkingstad", "Anders Løland"], "categories": ["stat.AP", "q-fin.ST"], "doi": "", "_query": "housing price text listing", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2112.08554v1", "title": "A Deep Learning Approach for Ontology Enrichment from Unstructured Text", "abstract": "Information Security in the cyber world is a major cause for concern, with a significant increase in the number of attack surfaces. Existing information on vulnerabilities, attacks, controls, and advisories available on the web provides an opportunity to represent knowledge and perform security analytics to mitigate some of the concerns. Representing security knowledge in the form of ontology facilitates anomaly detection, threat intelligence, reasoning and relevance attribution of attacks, and many more. This necessitates dynamic and automated enrichment of information security ontologies. However, existing ontology enrichment algorithms based on natural language processing and ML models have issues with contextual extraction of concepts in words, phrases, and sentences. This motivates the need for sequential Deep Learning architectures that traverse through dependency paths in text and extract embedded vulnerabilities, threats, controls, products, and other security-related concepts and instances from learned path representations. In the proposed approach, Bidirectional LSTMs trained on a large DBpedia dataset and Wikipedia corpus of 2.8 GB along with Universal Sentence Encoder is deployed to enrich ISO 27001-based information security ontology. The model is trained and tested on a high-performance computing (HPC) environment to handle Wiki text dimensionality. The approach yielded a test accuracy of over 80% when tested with knocked-out concepts from ontology and web page instances to validate the robustness.", "published": "2021-12-16T01:32:21Z", "authors": ["Lalit Mohan Sanagavarapu", "Vivek Iyer", "Raghu Reddy"], "categories": ["cs.CL"], "doi": "", "_query": "housing price text listing", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2603.20636v1", "title": "A Modular LLM Framework for Explainable Price Outlier Detection", "abstract": "Detecting product price outliers is important for retail and e-commerce stores as erroneous or unexpectedly high prices adversely affect competitiveness, revenue, and consumer trust. Classical techniques offer simple thresholds while ignoring the rich semantic relationships among product attributes. We propose an agentic Large Language Model (LLM) framework that treats outlier price flagging as a reasoning task grounded in related product detection and comparison. The system processes the prices of target products in three stages: (i) relevance classification selects price-relevant similar products using product descriptions and attributes; (ii) relative utility assessment evaluates the target product against each similar product along price influencing dimensions (e.g., brand, size, features); (iii) reasoning-based decision aggregates these justifications into an explainable price outlier judgment. The framework attains over 75% agreement with human auditors on a test dataset, and outperforms zero-shot and retrieval based LLM techniques. Ablation studies show the sensitivity of the method to key hyper-parameters and testify on its flexibility to be applied to cases with different accuracy requirement and auditor agreements.", "published": "2026-03-21T04:11:22Z", "authors": ["Shadi Sartipi", "John Wu", "Sina Ghotbi", "Nikhita Vedula", "Shervin Malmasi"], "categories": ["cs.CL", "cs.CE"], "doi": "", "_query": "housing price text listing", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/1601.01195v1", "title": "Part-of-Speech Tagging for Code-mixed Indian Social Media Text at ICON 2015", "abstract": "This paper discusses the experiments carried out by us at Jadavpur University as part of the participation in ICON 2015 task: POS Tagging for Code-mixed Indian Social Media Text. The tool that we have developed for the task is based on Trigram Hidden Markov Model that utilizes information from dictionary as well as some other word level features to enhance the observation probabilities of the known tokens as well as unknown tokens. We submitted runs for Bengali-English, Hindi-English and Tamil-English Language pairs. Our system has been trained and tested on the datasets released for ICON 2015 shared task: POS Tagging For Code-mixed Indian Social Media Text. In constrained mode, our system obtains average overall accuracy (averaged over all three language pairs) of 75.60% which is very close to other participating two systems (76.79% for IIITH and 75.79% for AMRITA_CEN) ranked higher than our system. In unconstrained mode, our system obtains average overall accuracy of 70.65% which is also close to the system (72.85% for AMRITA_CEN) which obtains the highest average overall accuracy.", "published": "2016-01-06T14:40:38Z", "authors": ["Kamal Sarkar"], "categories": ["cs.CL"], "doi": "", "_query": "housing price text listing", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/1806.10952v1", "title": "Amortized Analysis of Asynchronous Price Dynamics", "abstract": "We extend a recently developed framework for analyzing asynchronous coordinate descent algorithms to show that an asynchronous version of tatonnement, a fundamental price dynamic widely studied in general equilibrium theory, converges toward a market equilibrium for Fisher markets with CES utilities or Leontief utilities, for which tatonnement is equivalent to coordinate descent.", "published": "2018-06-27T05:35:03Z", "authors": ["Yun Kuen Cheung", "Richard Cole"], "categories": ["cs.GT", "cs.DS"], "doi": "", "_query": "housing price text listing", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/1301.6415v1", "title": "A primer on reflexivity and price dynamics under systemic risk", "abstract": "A simple quantitative example of a reflexive feedback process and the resulting price dynamics after an exogenous price shock to a financial network is presented. Furthermore, an outline of a theory that connects financial reflexivity, which stems from cross-ownership and delayed or incomplete information, and no-arbitrage pricing theory under systemic risk is provided.", "published": "2013-01-27T23:28:56Z", "authors": ["Tom Fischer"], "categories": ["q-fin.GN"], "doi": "", "_query": "housing price text listing", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/1910.08641v1", "title": "Nonhedgeable risk and Credit Risk Pricing", "abstract": "We introduce a new model for pricing corporate bonds, which is a modification of the classical model of Merton. In this new model, we drop the liquidity assumption of the firm's asset value process, and assume that there is a liquidly traded asset in the market whose value is correlated with the firm's asset value, and all portfolios can be constructed using solely this asset and the money market account. We formulate the market price of the corporate bond as the product of the price of an optimal replicating portfolio and exp(- kappa x replication error), where kappa is a positive constant. The interpretation is that the representative investor accepts the price of the optimal replicating portfolio as a benchmark, however, requests compensation for the non-hedgeable risk. We show that if the replication error is measured relative to the firm's value, the resulting formula is arbitrage free with mild restrictions on the parameters.", "published": "2019-10-18T22:06:39Z", "authors": ["Juan Dong", "Lyudmila Korobenko", "Deniz Sezer"], "categories": ["q-fin.PR", "math.PR"], "doi": "", "_query": "housing price text listing", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/1506.06790v2", "title": "Spectral theorems for random walks on mapping class groups and $\\text{Out}(F_N)$", "abstract": "We establish spectral theorems for random walks on mapping class groups of connected, closed, oriented, hyperbolic surfaces, and on $\\text{Out}(F_N)$. In both cases, we relate the asymptotics of the stretching factor of the diffeomorphism/automorphism obtained at time $n$ of the random walk to the Lyapunov exponent of the walk, which gives the typical growth rate of the length of a curve -- or of a conjugacy class in $F_N$ -- under a random product of diffeomorphisms/automorphisms. In the mapping class group case, we first observe that the drift of the random walk in the curve complex is also equal to the linear growth rate of the translation lengths in this complex. By using a contraction property of typical Teichmüller geodesics, we then lift the above fact to the realization of the random walk on the Teichmüller space. For the case of $\\text{Out}(F_N)$, we follow the same procedure with the free factor complex in place of the curve complex, and the outer space in place of the Teichmüller space. A general criterion is given for making the lifting argument possible.", "published": "2015-06-22T21:03:13Z", "authors": ["François Dahmani", "Camille Horbez"], "categories": ["math.GR", "math.GT", "math.PR"], "doi": "10.1093/imrn/rnw306", "_query": "housing price text listing", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/1202.4007v3", "title": "Pricing for Large Positions in Contingent Claims", "abstract": "Approximations to utility indifference prices are provided for a contingent claim in the large position size limit. Results are valid for general utility functions on the real line and semi-martingale models. It is shown that as the position size approaches infinity, the utility function's decay rate for large negative wealths is the primary driver of prices. For utilities with exponential decay, one may price like an exponential investor. For utilities with a power decay, one may price like a power investor after a suitable adjustment to the rate at which the position size becomes large. In a sizable class of diffusion models, limiting indifference prices are explicitly computed for an exponential investor. Furthermore, the large claim limit is seen to endogenously arise as the hedging error for the claim vanishes.", "published": "2012-02-17T20:28:23Z", "authors": ["Scott Robertson"], "categories": ["q-fin.PR", "math.PR"], "doi": "", "_query": "housing price text listing", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2112.09534v3", "title": "Path Integral Method for Proportional Step and Proportional Double-Barrier Step Option Pricing", "abstract": "Path integral method in quantum mechanics provides a new thinking for barrier option pricing. For proportional step options, the option price changing process is similar to the one dimensional trapezoid potential barrier scattering problem in quantum mechanics; for double-barrier step options, the option price changing process is analogous to a particle moving in a finite symmetric square potential well. Using path integral method, the analytical expressions of pricing kernel and option price could be derived. Numerical results of option price as a function of underlying price, potential and exercise price are shown, which are consistent with the results given by mathematical method.", "published": "2021-12-16T09:30:01Z", "authors": ["Qi Chen", "Chao Guo"], "categories": ["q-fin.PR"], "doi": "", "_query": "housing price text listing", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2312.10633v1", "title": "Some Ideas for Improving Stock Price Prediction Based on Machine Learning", "abstract": "Stock price prediction is a complicated and interesting task. Noisy trends make stock pricing sensitive and complicated while the economical motivation behind, keeps it interesting for researchers and investors. In this paper we are to outline two novel ideas for stock pricing. We also test each of our suggested algorithms for predicting the price of 6 stocks from different sectors. To show the efficiency of our proposed algorithm, we compare the predicted prices with real values and also perform a backtest to verify that the annual returns based on real data and predicted price are almost the same.", "published": "2023-12-17T07:32:16Z", "authors": ["Negin Bagherpour"], "categories": ["math.OC", "cs.CE"], "doi": "", "_query": "housing price text listing", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2305.09168v5", "title": "Static Pricing Guarantees for Queueing Systems", "abstract": "We consider a general queueing system with price-sensitive customers in which the service provider seeks to balance two objectives, maximizing the average revenue rate and minimizing the average queue length. Customers arrive according to a Poisson process, observe an offered price, and decide to join the queue if their valuation exceeds the price. The queue is operated first-in first-out, can have multiple servers, and the service times are exponential. Our model represents applications in areas like make-to-order manufacturing, cloud computing, and food delivery. The optimal solution for our model is dynamic; the price changes as the state of the system changes. However, such dynamic pricing policies may be undesirable for a variety of reasons. In this work, we provide non-asymptotic performance guarantees for a simple and natural class of static pricing policies which charge a fixed price up to a certain occupancy threshold and then allow no more customers into the system. Despite the mixed-sign objective, we are able to show our policy can guarantee a constant fraction of the optimal dynamic pricing policy in the worst-case. We also show that our policy yields a family of bi-criteria approximations that simultaneously guarantee a constant fraction of the optimal revenue with at most a constant factor increase in expected queue length. For instance, our policy for the M/M/1 setting can be set so that its worst-case guarantees is at least 50, 66, 75, or 80% of the optimal revenue and at most a 0, 16, 54, or 100% increase in the optimal queue length, respectively. We also provide guarantees for settings with multiple servers as well as the expected sojourn time objective. In a large simulation, we show that our class of policies is at most 4% sub-optimal on average.", "published": "2023-05-16T04:57:18Z", "authors": ["Jacob Bergquist", "Adam N. Elmachtoub"], "categories": ["cs.DS", "cs.GT"], "doi": "", "_query": "housing price text listing", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/1912.01923v1", "title": "A Low Computational Approach for Price Tag Recognition", "abstract": "In this work we discuss the task of search, localization and recognition of price zone within a photograph of the price tag. The task is being addressed for the case when image is acquired by small-scale digital camera and calculation device has significant resource constraints. The proposed approach is based on Niblack binarization algorithm, analysis and clasterization of connected components in conditions of known price tag geometrical model. The algorithm was tested on a private dataset and has shown high quality.", "published": "2019-12-04T12:17:26Z", "authors": ["M. A. Aliev", "D. A. Bocharov", "I. A. Kunina", "D. P. Nikolaev"], "categories": ["cs.CV"], "doi": "", "_query": "housing price text listing", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/1410.8160v3", "title": "Pricing and Hedging Long-Term Options", "abstract": "In this article, we investigate the behavior of long-term options. In many cases, option prices follow an exponential decay (or growth) rate for further maturity dates. We determine under what conditions option prices are characterized by this property. To see this, we use the martingale extraction method through which a pricing operator is transformed into a semigroup operator, which is easier to address. We also explore notions of hedging long-term options. Hedging is an attempt to reduce market risks, and we investigate the price sensitivities (Greeks) with respect to such risks, which are typically repre- sented by variations in the underlying process of an option. We combine the Malliavin calculus with the martingale extraction method to analyze Greeks. We see that the ratios between Greeks and the option price are expressed in a simple form in the long term.", "published": "2014-10-29T20:52:36Z", "authors": ["Hyungbin Park"], "categories": ["q-fin.MF", "q-fin.PR"], "doi": "", "_query": "housing price text listing", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/1408.2464v1", "title": "The existence and uniqueness of a power price equilibrium", "abstract": "We propose a term structure power price model that, in contrast to widely accepted no-arbitrage based approaches, accounts for the non-storable nature of power. It belongs to a class of equilibrium game theoretic models with players divided into producers and consumers. The consumers' goal is to maximize a mean-variance utility function subject to satisfying an inelastic demand of their own clients (e.g households, businesses etc.) to whom they sell the power. The producers, who own a portfolio of power plants each defined by a running fuel (e.g. gas, coal, oil...) and physical characteristics (e.g. efficiency, capacity, ramp up/down times...), similarly, seek to maximize a mean-variance utility function consisting of power, fuel, and emission prices subject to production constraints. Our goal is to determine the term structure of the power price at which production matches consumption. In this paper we show that in such a setting the equilibrium price exists and also discuss the conditions for its uniqueness.", "published": "2014-08-11T16:40:31Z", "authors": ["Miha Troha", "Raphael Hauser"], "categories": ["math.OC"], "doi": "", "_query": "housing price text listing", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/cond-mat/0011286v1", "title": "A Dynamical Thermostat Approach To Financial Asset Price Dynamics", "abstract": "A dynamical price formation model for financial assets is presented. It aims to capture the essence of speculative trading where mispricings of assets are used to make profits. It is shown that together with the incorporation of the concept of risk aversion of agents the model is able to reproduce several key characteristics of financial price series. The approach is contrasted to the conventional view of price formation in financial economics.", "published": "2000-11-16T17:36:29Z", "authors": ["Stefan Thurner"], "categories": ["cond-mat"], "doi": "10.1063/1.1386819", "_query": "housing price text listing", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/1805.02147v2", "title": "Criticality, The List Color Function, and List Coloring the Cartesian Product of Graphs", "abstract": "We introduce a notion of color-criticality in the context of chromatic-choosability. We define a graph $G$ to be strong $k$-chromatic-choosable if $χ(G) = k$ and every $(k-1)$-assignment for which $G$ is not list-colorable has the property that the lists are the same for all vertices. That is the usual coloring is, in some sense, the obstacle to list-coloring. We prove basic properties of strongly chromatic-choosable graphs such as chromatic-choosability and vertex-criticality, and we construct infinite families of strongly chromatic-choosable graphs. We derive a sufficient condition for the existence of at least two list colorings of strongly chromatic-choosable graphs and use it to show that: if $M$ is a strong $k$-chromatic-choosable graph with $|E(M)| \\leq |V(M)|(k-2)$ and $H$ is a graph that contains a Hamilton path, $w_1, w_2, \\ldots, w_m$, such that $w_i$ has at most $ρ\\geq 1$ neighbors among $w_1, \\ldots, w_{i-1}$, then $χ_{\\ell}(M \\square H) \\le k+ ρ- 1$. We show that this bound is sharp for all $ρ\\ge 1$ by generalizing the theorem to apply to $H$ that are $(M,ρ)$-Cartesian accommodating which is a notion we define with the help of the list color function, $ P_{\\ell}(G,k)$, the list analogue of the chromatic polynomial. We also use the list color function to determine the list chromatic number of certain star-like graphs: $χ_{\\ell}(M \\square K_{1,s}) =$ $k \\; \\text{if } s < P_{\\ell}(M,k)$, or $k+1 \\; \\text{if } s \\geq P_{\\ell}(M,k)$, where $M$ is a strong $k$-chromatic-choosable graph. We show that $ P_{\\ell}(M,k)$ equals $P(M,k)$, the chromatic polynomial, when $M$ is an odd cycle, complete graph, or the join of an odd cycle with a complete graph.", "published": "2018-05-06T04:45:45Z", "authors": ["Hemanshu Kaul", "Jeffrey A. Mudrock"], "categories": ["math.CO"], "doi": "", "_query": "housing price text listing", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/1412.8414v2", "title": "Accounting for Earnings Announcements in the Pricing of Equity Options", "abstract": "We study an option pricing framework that accounts for the price impact of an earnings announcement (EA), and analyze the behavior of the implied volatility surface prior to the event. On the announcement date, we incorporate a random jump to the stock price to represent the shock due to earnings. We consider different distributions of the scheduled earnings jump as well as different underlying stock price dynamics before and after the EA date. Our main contributions include analytical option pricing formulas when the underlying stock price follows the Kou model along with a double-exponential or Gaussian EA jump on the announcement date. Furthermore, we derive analytic bounds and asymptotics for the pre-EA implied volatility under various models. The calibration results demonstrate adequate fit of the entire implied volatility surface prior to an announcement. We also compare the risk-neutral distribution of the EA jump to its historical distribution. Finally, we discuss the valuation and exercise strategy of pre-EA American options, and illustrate an analytical approximation and numerical results.", "published": "2014-12-29T18:30:01Z", "authors": ["Tim Leung", "Marco Santoli"], "categories": ["q-fin.PR", "q-fin.CP"], "doi": "", "_query": "housing price text listing", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2305.03224v1", "title": "Carbon Price Forecasting with Quantile Regression and Feature Selection", "abstract": "Carbon futures has recently emerged as a novel financial asset in the trading markets such as the European Union and China. Monitoring the trend of the carbon price has become critical for both national policy-making as well as industrial manufacturing planning. However, various geopolitical, social, and economic factors can impose substantial influence on the carbon price. Due to its volatility and non-linearity, predicting accurate carbon prices is generally a difficult task. In this study, we propose to improve carbon price forecasting with several novel practices. First, we collect various influencing factors, including commodity prices, export volumes such as oil and natural gas, and prosperity indices. Then we select the most significant factors and disclose their optimal grouping for explainability. Finally, we use the Sparse Quantile Group Lasso and Adaptive Sparse Quantile Group Lasso for robust price predictions. We demonstrate through extensive experimental studies that our proposed methods outperform existing ones. Also, our quantile predictions provide a complete profile of future prices at different levels, which better describes the distributions of the carbon market.", "published": "2023-05-05T01:02:08Z", "authors": ["Tianqi Pang", "Kehui Tan", "Chenyou Fan"], "categories": ["cs.LG", "q-fin.ST"], "doi": "", "_query": "housing price text listing", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2510.12447v1", "title": "Bayesian Optimization for Dynamic Pricing and Learning", "abstract": "Dynamic pricing is the practice of adjusting the selling price of a product to maximize a firm's revenue by responding to market demand. The literature typically distinguishes between two settings: infinite inventory, where the firm has unlimited stock and time to sell, and finite inventory, where both inventory and selling horizon are limited. In both cases, the central challenge lies in the fact that the demand function -- how sales respond to price -- is unknown and must be learned from data. Traditional approaches often assume a specific parametric form for the demand function, enabling the use of reinforcement learning (RL) to identify near-optimal pricing strategies. However, such assumptions may not hold in real-world scenarios, limiting the applicability of these methods. In this work, we propose a Gaussian Process (GP) based nonparametric approach to dynamic pricing that avoids restrictive modeling assumptions. We treat the demand function as a black-box function of the price and develop pricing algorithms based on Bayesian Optimization (BO) -- a sample-efficient method for optimizing unknown functions. We present BO-based algorithms tailored for both infinite and finite inventory settings and provide regret guarantees for both regimes, thereby quantifying the learning efficiency of our methods. Through extensive experiments, we demonstrate that our BO-based methods outperform several state-of-the-art RL algorithms in terms of revenue, while requiring fewer assumptions and offering greater robustness. This highlights Bayesian Optimization as a powerful and practical tool for dynamic pricing in complex, uncertain environments.", "published": "2025-10-14T12:28:06Z", "authors": ["Anush Anand", "Pranav Agrawal", "Tejas Bodas"], "categories": ["cs.LG"], "doi": "", "_query": "housing price text listing", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2410.00344v3", "title": "Integrating Text-to-Music Models with Language Models: Composing Long Structured Music Pieces", "abstract": "Recent music generation methods based on transformers have a context window of up to a minute. The music generated by these methods is largely unstructured beyond the context window. With a longer context window, learning long-scale structures from musical data is a prohibitively challenging problem. This paper proposes integrating a text-to-music model with a large language model to generate music with form. The papers discusses the solutions to the challenges of such integration. The experimental results show that the proposed method can generate 2.5-minute-long music that is highly structured, strongly organized, and cohesive.", "published": "2024-10-01T02:43:14Z", "authors": ["Lilac Atassi"], "categories": ["cs.SD", "cs.LG", "eess.AS"], "doi": "", "_query": "housing price text listing", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2011.03987v1", "title": "Unifying the theory of storage and the risk premium by an unobservable intrinsic electricity price", "abstract": "In this paper we introduce a new concept for modelling electricity prices through the introduction of an unobservable intrinsic electricity price $p(τ)$. We use it to connect the classical theory of storage with the concept of a risk premium. We derive prices for all common contracts such as the intraday spot price, the day-ahead spot price, and futures prices. Finally, we propose an explicit model from the class of structural models and conduct an empirical analysis, where we find an overall negative risk premium.", "published": "2020-11-08T14:15:36Z", "authors": ["Wieger Hinderks", "Ralf Korn", "Andreas Wagner"], "categories": ["q-fin.MF", "math.PR"], "doi": "", "_query": "housing price text listing", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/1611.08006v1", "title": "Pricing Energy in the Presence of Renewables", "abstract": "At present, electricity markets largely ignore the fact that renewable power producers impose significant externalities on non-renewable energy producers. This is because consumers are generally guaranteed electricity within certain load parameters. The intermittent nature of production by renewable energy producers implies that they rely on non-renewable producers so that the aggregate power delivered meets the promised quality of service. This implicit insurance provided by the non-renewable power sector to consumers is not currently priced and leads to an often ignored, hidden monetary transfer from non-renewable producers to renewable producers. As the fraction of energy supplied by renewable resources increases, these externalities also increase. In this paper, we quantify these externalities by developing the market clearing price of energy in the presence of renewable energy. We consider a day-ahead electricity market where renewable and non-renewable generators bid by proposing their asking price per unit of energy to an independent system operator (ISO). The ISO's problem is a multi-stage stochastic optimization problem to dispatch energy from each generator to minimize the cost of purchased energy on behalf of the consumers. We incorporate the notion of load variance using the Conditional Value-at-Risk (CVAR) measure in the day-ahead electricity market to ensure that the generators are able to meet the load within a desired confidence level. We analytically derive the market clearing price of energy as a function of CVAR. It is shown that a higher penetration level of the renewable energies may increase the market clearing price of energy.", "published": "2016-11-23T21:26:17Z", "authors": ["Ashkan Zeinalzadeh", "Indraneel Chakraborty", "Vijay Gupta"], "categories": ["math.OC"], "doi": "", "_query": "housing price text listing", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2501.13136v1", "title": "Forecasting of Bitcoin Prices Using Hashrate Features: Wavelet and Deep Stacking Approach", "abstract": "Digital currencies have become popular in the last decade due to their non-dependency and decentralized nature. The price of these currencies has seen a lot of fluctuations at times, which has increased the need for prediction. As their most popular, Bitcoin(BTC) has become a research hotspot. The main challenge and trend of digital currencies, especially BTC, is price fluctuations, which require studying the basic price prediction model. This research presents a classification and regression model based on stack deep learning that uses a wavelet to remove noise to predict movements and prices of BTC at different time intervals. The proposed model based on the stacking technique uses models based on deep learning, especially neural networks and transformers, for one, seven, thirty and ninety-day forecasting. Three feature selection models, Chi2, RFE and Embedded, were also applied to the data in the pre-processing stage. The classification model achieved 63\\% accuracy for predicting the next day and 64\\%, 67\\% and 82\\% for predicting the seventh, thirty and ninety days, respectively. For daily price forecasting, the percentage error was reduced to 0.58, while the error ranged from 2.72\\% to 2.85\\% for seven- to ninety-day horizons. These results show that the proposed model performed better than other models in the literature.", "published": "2025-01-22T09:31:00Z", "authors": ["Ramin Mousa", "Meysam Afrookhteh", "Hooman Khaloo", "Amir Ali Bengari", "Gholamreza Heidary"], "categories": ["q-fin.ST", "cs.AI", "cs.LG"], "doi": "", "_query": "housing price text listing", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2003.00880v1", "title": "Introducing Fuzzy Layers for Deep Learning", "abstract": "Many state-of-the-art technologies developed in recent years have been influenced by machine learning to some extent. Most popular at the time of this writing are artificial intelligence methodologies that fall under the umbrella of deep learning. Deep learning has been shown across many applications to be extremely powerful and capable of handling problems that possess great complexity and difficulty. In this work, we introduce a new layer to deep learning: the fuzzy layer. Traditionally, the network architecture of neural networks is composed of an input layer, some combination of hidden layers, and an output layer. We propose the introduction of fuzzy layers into the deep learning architecture to exploit the powerful aggregation properties expressed through fuzzy methodologies, such as the Choquet and Sugueno fuzzy integrals. To date, fuzzy approaches taken to deep learning have been through the application of various fusion strategies at the decision level to aggregate outputs from state-of-the-art pre-trained models, e.g., AlexNet, VGG16, GoogLeNet, Inception-v3, ResNet-18, etc. While these strategies have been shown to improve accuracy performance for image classification tasks, none have explored the use of fuzzified intermediate, or hidden, layers. Herein, we present a new deep learning strategy that incorporates fuzzy strategies into the deep learning architecture focused on the application of semantic segmentation using per-pixel classification. Experiments are conducted on a benchmark data set as well as a data set collected via an unmanned aerial system at a U.S. Army test site for the task of automatic road segmentation, and preliminary results are promising.", "published": "2020-02-21T19:33:30Z", "authors": ["Stanton R. Price", "Steven R. Price", "Derek T. Anderson"], "categories": ["cs.CV", "cs.LG", "stat.ML"], "doi": "10.1109/FUZZ-IEEE.2019.8858790", "_query": "housing price text listing", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2310.15643v1", "title": "Navigating ICT In-House Procurement in Finland: Evaluating Legal Frameworks and Practical Challenges", "abstract": "In-house procurement is a controversial issue in the field of public procurement. Simply put, such procurement allows overlooking certain aspects of fair and equal treatment of vendors. This paper presents qualitative research on in-house ICT procurement within Finnish municipalities. Semi-structured interviews were conducted to gather insights from municipal stakeholders. Using grounded theory approach, data analysis shows intricate dynamics between Finnish municipalities and in-house entities associated with them. Still, it is clear that the legal framework governing in-house procurement remains intricate and debated.", "published": "2023-10-24T09:04:29Z", "authors": ["Reetta Ghezzi", "Minnamaria Korhonen", "Hannu Vilpponen", "Tommi Mikkonen"], "categories": ["cs.SE"], "doi": "", "_query": "housing price text listing", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/1807.10114v3", "title": "The Evolution of Security Prices Is Not Stochastic but Governed by a Physicomathematical Law", "abstract": "Since Bachelier's thesis in 1900 (laying the foundation of the stochastic process, or Brownian motion, as a model of stock price changes), attempts at understanding the nature of prices and at predicting them have failed. Statistical methods have only found minor regularities/anomalies, and other mathematical and physical approaches do not work. This leads researchers to consider that the evolution of security prices is basically random, and, thus, inherently not predictable. We show that the evolution of security prices is not a stochastic process but largely deterministic and governed by a physical law. The law takes the form of a physicomathematical theory centered around a purely mathematical function, unrelated to models and statistical methods. It can be described as an \"isodense\" network of moving regression curves of an order greater than or equal to 1. The salient aspect of the function is that, when inputting a time series of any security into the function, new mathematical objects emerge spontaneously, and these objects exhibit the unique property of attracting and repelling the quantity. The graphical representation of the function is called a \"topological network\" due to the preeminence of shapes over metrics, and the emergent objects are called \"characteristic figures\" (mainly \"cords\"). The attraction and repulsion of the price by the cords results in the price bouncing from cord to cord. Thus, the price has to be considered as driven by the cords in a semi-deterministic manner (leaning towards deterministic). With a function that describes the evolution of the price, we now understand the reason behind each price movement and can also predict prices both qualitatively and quantitatively. The function is universal, does not rely on any fitting, and, due to its extreme sensitivity, reveals the hidden order in financial time series data that existing research never uncovered.", "published": "2018-07-01T20:22:44Z", "authors": ["Wally Tzara"], "categories": ["q-fin.GN", "nlin.AO"], "doi": "", "_query": "housing price text listing", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/1912.11216v6", "title": "Evolutionary Dynamics of Investors Expectations and Market Price Movement", "abstract": "The paper presents a step forward into the development of the theory of meaning. Stock and financial markets are examined from communication-theoretical perspective on the dynamics of information and meaning. This study focuses on the link between the dynamics of investors' expectations and market price movement. The model for market asset price dynamiscs, based on non-linear evolutionary equation linking investors' expectations and market asset price movement, is provided. Model predictions are tested on various FX, energy, food, and indices markets along different time frames. The results suggest that model predicted time series is co-integrated with asset time series which implies that the prop[osed model can be used to forecast future price movement.", "published": "2019-12-24T05:52:18Z", "authors": ["Inga Ivanova"], "categories": ["q-fin.ST"], "doi": "", "_query": "housing price text listing", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2007.00166v1", "title": "Fused Text Recogniser and Deep Embeddings Improve Word Recognition and Retrieval", "abstract": "Recognition and retrieval of textual content from the large document collections have been a powerful use case for the document image analysis community. Often the word is the basic unit for recognition as well as retrieval. Systems that rely only on the text recogniser (OCR) output are not robust enough in many situations, especially when the word recognition rates are poor, as in the case of historic documents or digital libraries. An alternative has been word spotting based methods that retrieve/match words based on a holistic representation of the word. In this paper, we fuse the noisy output of text recogniser with a deep embeddings representation derived out of the entire word. We use average and max fusion for improving the ranked results in the case of retrieval. We validate our methods on a collection of Hindi documents. We improve word recognition rate by 1.4 and retrieval by 11.13 in the mAP.", "published": "2020-07-01T00:55:34Z", "authors": ["Siddhant Bansal", "Praveen Krishnan", "C. V. Jawahar"], "categories": ["cs.CV"], "doi": "10.1007/978-3-030-57058-3_22", "_query": "housing price text listing", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2403.00458v2", "title": "Electric vehicle pricing and battery costs: A misaligned assumption", "abstract": "Although electric vehicles (EVs) are a climate friendly alternative to internal combustion engine vehicles (ICEVs), EV adoption is challenged by higher up-front procurement prices. Existing discourse attributes this price differential to high battery costs and reasons that lowering these costs will reduce EV upfront price differentials. However, other factors beyond battery price may influence prices. Leveraging data for over 400 EV models and trims sold in the United Sates between 2011-2023, we scrutinize these factors. We find that contrary to existing discourse, EV MSRP has increased over time despite declining EV battery costs. We attribute this increase to the growing accommodation of attributes that strongly influence EV prices but have long been underappreciated in mainstream discourse. Furthermore, and relevant to decarbonization efforts, we observe that continued reductions in pack-level battery costs are unlikely to deliver price parity between EVs and ICEVs. Were pack level battery costs reduced to zero, EV MSRP would decrease by $4,025, estimates that are insufficient to offset observed price differences between EVs and ICEVs. These findings warrant attention as decarbonization efforts increasingly emphasize EVs as a pathway for complying with domestic and international climate agreements.", "published": "2024-03-01T11:30:25Z", "authors": ["Lucas Woodley", "Chung Yi See", "Vasco Rato Santos", "Megan Yeo", "Daniel Palmer", "Sebastian Nosenzo", "Ashley Nunes"], "categories": ["econ.EM"], "doi": "", "_query": "housing price text listing", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2109.15198v1", "title": "Nonlinear Prices, Homogeneous Goods, Search", "abstract": "We analyze competition on nonlinear prices in homogeneous goods markets with consumer search. In equilibrium firms offer two-part tariffs consisting of a linear price and lump-sum fee. The equilibrium production is socially efficient as the linear price of equilibrium two-part tariffs equals to the production marginal cost. Firms thus compete in lump-sum fees, which are dispersed in equilibrium. We show that sellers enjoy higher profit, whereas consumers are worse-off with two-part tariffs than with linear prices. The competition softens because with two-part tariffs firms can make effective per-consumer demand less elastic than the actual demand.", "published": "2021-09-30T15:16:05Z", "authors": ["Atabek Atayev"], "categories": ["econ.TH"], "doi": "", "_query": "housing price text listing", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/0912.5448v1", "title": "Universal Behavior of Extreme Price Movements in Stock Markets", "abstract": "Many studies assume stock prices follow a random process known as geometric Brownian motion. Although approximately correct, this model fails to explain the frequent occurrence of extreme price movements, such as stock market crashes. Using a large collection of data from three different stock markets, we present evidence that a modification to the random model -- adding a slow, but significant, fluctuation to the standard deviation of the process -- accurately explains the probability of different-sized price changes, including the relative high frequency of extreme movements. Furthermore, we show that this process is similar across stocks so that their price fluctuations can be characterized by a single curve. Because the behavior of price fluctuations is rooted in the characteristics of volatility, we expect our results to bring increased interest to stochastic volatility models, and especially to those that can produce the properties of volatility reported here.", "published": "2009-12-30T11:18:18Z", "authors": ["Miguel A. Fuentes", "Austin Gerig", "Javier Vicente"], "categories": ["q-fin.ST"], "doi": "10.1371/journal.pone.0008243", "_query": "housing price text listing", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2208.08300v1", "title": "Transformer-Based Deep Learning Model for Stock Price Prediction: A Case Study on Bangladesh Stock Market", "abstract": "In modern capital market the price of a stock is often considered to be highly volatile and unpredictable because of various social, financial, political and other dynamic factors. With calculated and thoughtful investment, stock market can ensure a handsome profit with minimal capital investment, while incorrect prediction can easily bring catastrophic financial loss to the investors. This paper introduces the application of a recently introduced machine learning model - the Transformer model, to predict the future price of stocks of Dhaka Stock Exchange (DSE), the leading stock exchange in Bangladesh. The transformer model has been widely leveraged for natural language processing and computer vision tasks, but, to the best of our knowledge, has never been used for stock price prediction task at DSE. Recently the introduction of time2vec encoding to represent the time series features has made it possible to employ the transformer model for the stock price prediction. This paper concentrates on the application of transformer-based model to predict the price movement of eight specific stocks listed in DSE based on their historical daily and weekly data. Our experiments demonstrate promising results and acceptable root mean squared error on most of the stocks.", "published": "2022-08-17T14:03:28Z", "authors": ["Tashreef Muhammad", "Anika Bintee Aftab", "Md. Mainul Ahsan", "Maishameem Meherin Muhu", "Muhammad Ibrahim", "Shahidul Islam Khan", "Mohammad Shafiul Alam"], "categories": ["q-fin.ST", "cs.LG"], "doi": "10.1142/S146902682350013X", "_query": "housing price text listing", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2512.18296v1", "title": "Privacy Data Pricing: A Stackelberg Game Approach", "abstract": "Data markets are emerging as key mechanisms for trading personal and organizational data. Traditional data pricing studies -- such as query-based or arbitrage-free pricing models -- mainly emphasize price consistency and profit maximization but often neglect privacy constraints and strategic interactions. The widespread adoption of differential privacy (DP) introduces a fundamental privacy-utility trade-off: noise protects individuals' privacy but reduces data accuracy and market value. This paper develops a Stackelberg game framework for pricing DP data, where the market maker (leader) sets the price function and the data buyer (follower) selects the optimal query precision under DP constraints. We derive the equilibrium strategies for both parties under a balanced pricing function where the pricing decision variable enters linearly into the original pricing model. We obtain closed-form solutions for the optimal variance and pricing level, and determine the boundary conditions for market participation. Furthermore, we extend the analysis to Stackelberg games involving nonlinear power pricing functions. The model bridges DP and economic mechanism design, offering a unified foundation for incentive-compatible and privacy-conscious data pricing in data markets.", "published": "2025-12-20T09:59:39Z", "authors": ["Lijun Bo", "Weiqiang Chang"], "categories": ["cs.GT", "stat.AP"], "doi": "", "_query": "housing price text listing", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/1911.08858v2", "title": "Non-separating immersions of spheres and Bing houses", "abstract": "We construct Bing houses in all dimensions $n \\geq 3$, obtaining non-separating PL immersions of $\\mathbb{S}^n \\rightarrow\\mathbb{R}^{n+1}$.", "published": "2019-11-20T12:32:17Z", "authors": ["Michael Freedman", "T. Tam Nguyen Phan"], "categories": ["math.GT", "math.DG"], "doi": "", "_query": "housing price text listing", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2403.09265v6", "title": "Zonal vs. Nodal Pricing: An Analysis of Different Pricing Rules in the German Day-Ahead Market", "abstract": "The European electricity market is based on large pricing zones with a uniform day-ahead price. The energy transition leads to changes in supply and demand and increasing redispatch costs. In an attempt to ensure efficient market clearing and congestion management, the EU Commission has mandated the Bidding Zone Review (BZR) to reevaluate the configuration of European bidding zones. Based on a unique data set published in the context of the BZR for the target year 2025, we analyze the short-run effects of various pricing rules for the German-Luxembourgish bidding zone. We compare market clearing and pricing for different zonal models, including their generation and redispatch costs. In numerical experiments with this dataset, the differences in the average prices in different zones are low. The total costs across different configurations are similar and the reduction of standard deviations in prices is also small. This might be different with other load and generation scenarios, but the BZR data is important as it was created to make a decision about splits of the existing bidding zones. We can replicate several results from the BZR study, except the large cost savings when moving from one to two price zones in Germany and Luxembourg. In addition to the four zonal configurations analyzed in the BZR study, we compare these against a nodal pricing system. While the total cost savings after introducing zonal splits were less than 1%, nodal pricing led to savings of 5-6%. We also evaluate differences of nodal pricing rules with respect to the necessary uplift payments, which is relevant in the context of the discussion on non-uniform pricing in the EU. While the study focuses on Germany, the analysis is relevant beyond and feeds into the broader discussion about pricing rules in non-convex markets.", "published": "2024-03-14T10:41:31Z", "authors": ["Johannes Knörr", "Martin Bichler", "Teodora Dobos"], "categories": ["econ.GN"], "doi": "", "_query": "housing price text listing", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/1909.05761v1", "title": "Pricing Reliability Options under different electricity prices' regimes", "abstract": "Reliability Options are capacity remuneration mechanisms aimed at enhancing security of supply in electricity systems. They can be framed as call options on electricity sold by power producers to System Operators. This paper provides a comprehensive mathematical treatment of Reliability Options. Their value is first derived by means of closed-form pricing formulae, which are obtained under several assumptions about the dynamics of electricity prices and strike prices. Then, the value of the Reliability Option is simulated under a real-market calibration, using data of the Italian power market. We finally perform sensitivity analyses to highlight the impact of the level and volatility of both power and strike price, of the mean reversion speeds and of the correlation coefficient on the Reliability Options' value.", "published": "2019-09-12T15:44:19Z", "authors": ["Luisa Andreis", "Maria Flora", "Fulvio Fontini", "Tiziano Vargiolu"], "categories": ["q-fin.PR"], "doi": "", "_query": "housing price text listing", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/1711.01049v1", "title": "Optimal Pricing-Based Edge Computing Resource Management in Mobile Blockchain", "abstract": "As the core issue of blockchain, the mining requires solving a proof-of-work puzzle, which is resource expensive to implement in mobile devices due to high computing power needed. Thus, the development of blockchain in mobile applications is restricted. In this paper, we consider the edge computing as the network enabler for mobile blockchain. In particular, we study optimal pricing-based edge computing resource management to support mobile blockchain applications where the mining process can be offloaded to an Edge computing Service Provider (ESP). We adopt a two-stage Stackelberg game to jointly maximize the profit of the ESP and the individual utilities of different miners. In Stage I, the ESP sets the price of edge computing services. In Stage II, the miners decide on the service demand to purchase based on the observed prices. We apply the backward induction to analyze the sub-game perfect equilibrium in each stage for uniform and discriminatory pricing schemes. Further, the existence and uniqueness of Stackelberg game are validated for both pricing schemes. At last, the performance evaluation shows that the ESP intends to set the maximum possible value as the optimal price for profit maximization under uniform pricing. In addition, the discriminatory pricing helps the ESP encourage higher total service demand from miners and achieve greater profit correspondingly.", "published": "2017-11-03T07:56:48Z", "authors": ["Zehui Xiong", "Shaohan Feng", "Dusit Niyato", "Ping Wang", "Zhu Han"], "categories": ["cs.GT", "cs.CR"], "doi": "", "_query": "housing price text listing", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/1502.03945v2", "title": "Efficiency and complexity of price competition among single-product vendors", "abstract": "Motivated by recent progress on pricing in the AI literature, we study marketplaces that contain multiple vendors offering identical or similar products and unit-demand buyers with different valuations on these vendors. The objective of each vendor is to set the price of its product to a fixed value so that its profit is maximized. The profit depends on the vendor's price itself and the total volume of buyers that find the particular price more attractive than the price of the vendor's competitors. We model the behaviour of buyers and vendors as a two-stage full-information game and study a series of questions related to the existence, efficiency (price of anarchy) and computational complexity of equilibria in this game. To overcome situations where equilibria do not exist or exist but are highly inefficient, we consider the scenario where some of the vendors are subsidized in order to keep prices low and buyers highly satisfied.", "published": "2015-02-13T11:18:00Z", "authors": ["Ioannis Caragiannis", "Xenophon Chatzigeorgiou", "Panagiotis Kanellopoulos", "George A. Krimpas", "Nikos Protopapas", "Alexandros A. Voudouris"], "categories": ["cs.GT", "cs.AI", "cs.CC"], "doi": "", "_query": "housing price text listing", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2604.03961v1", "title": "Financial Relativity: An Information-Geometric Interpretation of Asset Pricing", "abstract": "Classical asset pricing relies on the risk-neutral measure $Q$ for valuation, yet its economic interpretation is typically anchored in a physical measure $P$. This creates an inherent asymmetry: pricing is governed by $Q$, while meaning resides in $P$, making it difficult to provide a unified account of asset pricing within a single conceptual framework. This paper proposes an alternative perspective based on information geometry, termed Financial Relativity. Its central principle is the relativity of probabilistic reference frames: $P$ and $Q$ have no intrinsic hierarchy, but instead represent geometric structures induced by different informational constraints. Terminal structural information shapes probability geometry, which in turn governs how information is expressed in prices. Within this framework, the risk-neutral measure is reinterpreted as a posterior probability geometry. Asset prices are characterized as geometric projections of terminal payoffs onto information subspaces, and their dynamics reflect the progressive manifestation of structural information under evolving geometry. We develop both discrete and continuous financial field equations to describe the formation of probability geometry and derive geodesic price dynamics in which volatility arises endogenously from posterior uncertainty. The framework provides a unified explanation for price fluctuations, event-driven behavior, and risk premia, and yields testable implications, including structural links between volatility and posterior variance and measures of price informational efficiency. By integrating structural information, probability measures, and price dynamics within a single geometric framework, the paper offers a coherent, extensible, and empirically tractable reinterpretation of asset pricing.", "published": "2026-04-05T04:30:56Z", "authors": ["Li Lin"], "categories": ["q-fin.MF"], "doi": "", "_query": "housing price text listing", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2102.02935v1", "title": "Does Collateral Value Affect Asset Prices? Evidence from a Natural Experiment in Texas", "abstract": "Does the ability to pledge an asset as collateral, after purchase, affect its price? This paper identifies the impact of collateral service flows on house prices, exploiting a plausibly exogenous constitutional amendment in Texas which legalized home equity loans in 1998. The law change increased Texas house prices 4%; this is price-based evidence that households are credit-constrained and value home equity loans to facilitate consumption smoothing. Prices rose more in locations with inelastic supply, higher pre-law house prices, higher income, and lower unemployment. These estimates reveal that richer households value the option to pledge their home as collateral more strongly.", "published": "2021-02-05T00:37:49Z", "authors": ["Albert Alex Zevelev"], "categories": ["econ.GN"], "doi": "10.1093/rfs/hhaa117", "_query": "housing price text listing", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2009.04786v2", "title": "Price formation and optimal trading in intraday electricity markets", "abstract": "We develop a tractable equilibrium model for price formation in intraday electricity markets in the presence of intermittent renewable generation. Using stochastic control theory, we identify the optimal strategies of agents with market impact and exhibit the Nash equilibrium in closed form for a finite number of agents as well as in the asymptotic framework of mean field games. Our model reproduces the empirical features of intraday market prices, such as increasing price volatility at the approach of the delivery date and the correlation between price and renewable infeed forecasts, and relates these features with market characteristics like liquidity, number of agents, and imbalance penalty.", "published": "2020-09-10T11:40:38Z", "authors": ["Olivier Féron", "Peter Tankov", "Laura Tinsi"], "categories": ["q-fin.PR", "q-fin.TR"], "doi": "", "_query": "housing price text listing", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2001.01860v2", "title": "A simple microstructural explanation of the concavity of price impact", "abstract": "This article provides a simple explanation of the asymptotic concavity of the price impact of a meta-order via the microstructural properties of the market. This explanation is made more precise by a model in which the local relationship between the order flow and the fundamental price (i.e. the local price impact) is linear, with a constant slope, which makes the model dynamically consistent. Nevertheless, the expected impact on midprice from a large sequence of co-directional trades is nonlinear and asymptotically concave. The main practical conclusion of the proposed explanation is that, throughout a meta-order, the volumes at the best bid and ask prices change (on average) in favor of the executor. This conclusion, in turn, relies on two more concrete predictions, one of which can be tested, at least for large-tick stocks, using publicly available market data.", "published": "2020-01-07T02:38:02Z", "authors": ["Sergey Nadtochiy"], "categories": ["q-fin.TR", "q-fin.MF"], "doi": "", "_query": "housing price text listing", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2308.06935v1", "title": "Insurance pricing on price comparison websites via reinforcement learning", "abstract": "The emergence of price comparison websites (PCWs) has presented insurers with unique challenges in formulating effective pricing strategies. Operating on PCWs requires insurers to strike a delicate balance between competitive premiums and profitability, amidst obstacles such as low historical conversion rates, limited visibility of competitors' actions, and a dynamic market environment. In addition to this, the capital intensive nature of the business means pricing below the risk levels of customers can result in solvency issues for the insurer. To address these challenges, this paper introduces reinforcement learning (RL) framework that learns the optimal pricing policy by integrating model-based and model-free methods. The model-based component is used to train agents in an offline setting, avoiding cold-start issues, while model-free algorithms are then employed in a contextual bandit (CB) manner to dynamically update the pricing policy to maximise the expected revenue. This facilitates quick adaptation to evolving market dynamics and enhances algorithm efficiency and decision interpretability. The paper also highlights the importance of evaluating pricing policies using an offline dataset in a consistent fashion and demonstrates the superiority of the proposed methodology over existing off-the-shelf RL/CB approaches. We validate our methodology using synthetic data, generated to reflect private commercially available data within real-world insurers, and compare against 6 other benchmark approaches. Our hybrid agent outperforms these benchmarks in terms of sample efficiency and cumulative reward with the exception of an agent that has access to perfect market information which would not be available in a real-world set-up.", "published": "2023-08-14T04:44:56Z", "authors": ["Tanut Treetanthiploet", "Yufei Zhang", "Lukasz Szpruch", "Isaac Bowers-Barnard", "Henrietta Ridley", "James Hickey", "Chris Pearce"], "categories": ["q-fin.PR", "cs.LG", "q-fin.ST"], "doi": "", "_query": "housing price text listing", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/1912.04565v1", "title": "Market Price of Trading Liquidity Risk and Market Depth", "abstract": "Price impact of a trade is an important element in pre-trade and post-trade analyses. We introduce a framework to analyze the market price of liquidity risk, which allows us to derive an inhomogeneous Bernoulli ordinary differential equation. We obtain two closed form solutions, one of which reproduces the linear function of the order flow in Kyle (1985) for informed traders. However, when traders are not as asymmetrically informed, an S-shape function of the order flow is obtained. We perform an empirical intra-day analysis on Nikkei futures to quantify the price impact of order flow and compare our results with industry's heuristic price impact functions. Our model of order flow yields a rich framework for not only to estimate the liquidity risk parameters, but also to provide a plausible cause of why volatility and correlation are stochastic in nature. Finally, we find that the market depth encapsulates the market price of liquidity risk.", "published": "2019-12-10T08:20:26Z", "authors": ["Masaaki Kijima", "Christopher Ting"], "categories": ["q-fin.TR", "econ.EM", "q-fin.CP", "q-fin.MF", "stat.CO"], "doi": "10.1142/S0219024919500456", "_query": "housing price text listing", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/1511.09208v1", "title": "Algorithms as Mechanisms: The Price of Anarchy of Relax-and-Round", "abstract": "Many algorithms that are originally designed without explicitly considering incentive properties are later combined with simple pricing rules and used as mechanisms. The resulting mechanisms are often natural and simple to understand. But how good are these algorithms as mechanisms? Truthful reporting of valuations is typically not a dominant strategy (certainly not with a pay-your-bid, first-price rule, but it is likely not a good strategy even with a critical value, or second-price style rule either). Our goal is to show that a wide class of approximation algorithms yields this way mechanisms with low Price of Anarchy. The seminal result of Lucier and Borodin [SODA 2010] shows that combining a greedy algorithm that is an $α$-approximation algorithm with a pay-your-bid payment rule yields a mechanism whose Price of Anarchy is $O(α)$. In this paper we significantly extend the class of algorithms for which such a result is available by showing that this close connection between approximation ratio on the one hand and Price of Anarchy on the other also holds for the design principle of relaxation and rounding provided that the relaxation is smooth and the rounding is oblivious. We demonstrate the far-reaching consequences of our result by showing its implications for sparse packing integer programs, such as multi-unit auctions and generalized matching, for the maximum traveling salesman problem, for combinatorial auctions, and for single source unsplittable flow problems. In all these problems our approach leads to novel simple, near-optimal mechanisms whose Price of Anarchy either matches or beats the performance guarantees of known mechanisms.", "published": "2015-11-30T09:08:30Z", "authors": ["Paul Dütting", "Thomas Kesselheim", "Éva Tardos"], "categories": ["cs.GT", "cs.DS"], "doi": "", "_query": "housing price text listing", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2507.17939v3", "title": "Dynamic access pricing control for fair and stable resource sharing", "abstract": "We consider the co-existence of users with different price sensitivities under dynamic (or surge) pricing that regulates how they access a shared resource. To balance supply and demand in such settings, some dynamic pricing systems adopt a monotonically increasing price scheme which is well known to be socially regressive, driving out price-sensitive users in favor of price-insensitive ones. Here, we propose a new dynamic pricing scheme based on a non-monotonic price function that allows all user classes to coexist more fairly when price-insensitive users are present, but automatically reverts to conventional dynamic pricing when they leave the system, even though the exact price-sensitivity composition is unobservable. The resulting system consists of nonlinear ODEs, which we further investigate to characterize its equilibria and their domains of attraction, first for a single price-sensitive class competing with a price-insensitive one and then for the general $n$-class case. Numerical simulations demonstrate the effectiveness of the proposed design.", "published": "2025-07-23T21:13:18Z", "authors": ["Christopher King", "Fabian R. Wirth", "Homayoun Hamedmoghadam", "Christos G. Cassandras", "Robert N. Shorten"], "categories": ["math.OC"], "doi": "", "_query": "housing price text listing", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2406.01027v1", "title": "PRICE: A Pretrained Model for Cross-Database Cardinality Estimation", "abstract": "Cardinality estimation (CardEst) is essential for optimizing query execution plans. Recent ML-based CardEst methods achieve high accuracy but face deployment challenges due to high preparation costs and lack of transferability across databases. In this paper, we propose PRICE, a PRetrained multI-table CardEst model, which addresses these limitations. PRICE takes low-level but transferable features w.r.t. data distributions and query information and elegantly applies self-attention models to learn meta-knowledge to compute cardinality in any database. It is generally applicable to any unseen new database to attain high estimation accuracy, while its preparation cost is as little as the basic one-dimensional histogram-based CardEst methods. Moreover, PRICE can be finetuned to further enhance its performance on any specific database. We pretrained PRICE using 30 diverse datasets, completing the process in about 5 hours with a resulting model size of only about 40MB. Evaluations show that PRICE consistently outperforms existing methods, achieving the highest estimation accuracy on several unseen databases and generating faster execution plans with lower overhead. After finetuning with a small volume of databasespecific queries, PRICE could even find plans very close to the optimal ones. Meanwhile, PRICE is generally applicable to different settings such as data updates, data scaling, and query workload shifts. We have made all of our data and codes publicly available at https://github.com/StCarmen/PRICE.", "published": "2024-06-03T06:21:53Z", "authors": ["Tianjing Zeng", "Junwei Lan", "Jiahong Ma", "Wenqing Wei", "Rong Zhu", "Pengfei Li", "Bolin Ding", "Defu Lian", "Zhewei Wei", "Jingren Zhou"], "categories": ["cs.DB", "cs.LG"], "doi": "10.14778/3712221.3712231", "_query": "housing price text listing", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/0903.0680v3", "title": "Quantum Neural Computation for Option Price Modelling", "abstract": "We propose a new cognitive framework for option price modelling, using quantum neural computation formalism. Briefly, when we apply a classical nonlinear neural-network learning to a linear quantum Schrödinger equation, as a result we get a nonlinear Schrödinger equation (NLS), performing as a quantum stochastic filter. In this paper, we present a bidirectional quantum associative memory model for the Black--Scholes--like option price evolution, consisting of a pair of coupled NLS equations, one governing the stochastic volatility and the other governing the option price, both self-organizing in an adaptive `market heat potential', trained by continuous Hebbian learning. This stiff pair of NLS equations is numerically solved using the method of lines with adaptive step-size integrator. Keywords: Option price modelling, Quantum neural computation, nonlinear Schrödinger equations, leverage effect, bidirectional associative memory", "published": "2009-03-04T02:59:17Z", "authors": ["Vladimir G. Ivancevic"], "categories": ["q-fin.CP", "nlin.AO", "nlin.PS", "q-fin.PR"], "doi": "", "_query": "housing price text listing", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/1004.3939v1", "title": "Price Trackers Inspired by Immune Memory", "abstract": "In this paper we outline initial concepts for an immune inspired algorithm to evaluate price time series data. The proposed solution evolves a short term pool of trackers dynamically through a process of proliferation and mutation, with each member attempting to map to trends in price movements. Successful trackers feed into a long term memory pool that can generalise across repeating trend patterns. Tests are performed to examine the algorithm's ability to successfully identify trends in a small data set. The influence of the long term memory pool is then examined. We find the algorithm is able to identify price trends presented successfully and efficiently.", "published": "2010-04-22T15:01:02Z", "authors": ["William Wilson", "Phil Birkin", "Uwe Aickelin"], "categories": ["cs.AI", "cs.NE", "physics.data-an", "q-fin.PM"], "doi": "", "_query": "housing price text listing", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/cond-mat/0006133v1", "title": "Asians and cash dividends: Exploiting symmetries in pricing theory", "abstract": "In this article we present new results for the pricing of arithmetic Asian options within a Black-Scholes context. To derive these results we make extensive use of the local scale invariance that exists in the theory of contingent claim pricing. This allows us to derive, in a natural way, a simple PDE for the price of arithmetic Asians options. In the case of European average strike options, a proper choice of numeraire reduces the dimension of this PDE to one, leading to a PDE similar to the one derived by Rogers and Shi. We solve this PDE, finding a Laplace-transform representation for the price of average strike options, both seasoned and unseasoned. This extends the results of Geman and Yor, who discussed the case of average price options. Next we use symmetry arguments to show that prices of average strike and average price options can be expressed in terms of each other. Finally we show, again using symmetries, that plain vanilla options on stocks paying known cash dividends are closely related to arithmetic Asians, so that all the new techniques can be directly applied to this case.", "published": "2000-06-08T13:28:31Z", "authors": ["Jiri Hoogland", "Dimitri Neumann"], "categories": ["cond-mat.stat-mech", "q-fin.PR"], "doi": "", "_query": "housing price text listing", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/1404.5245v6", "title": "Size versus truthfulness in the House Allocation problem", "abstract": "We study the House Allocation problem (also known as the Assignment problem), i.e., the problem of allocating a set of objects among a set of agents, where each agent has ordinal preferences (possibly involving ties) over a subset of the objects. We focus on truthful mechanisms without monetary transfers for finding large Pareto optimal matchings. It is straightforward to show that no deterministic truthful mechanism can approximate a maximum cardinality Pareto optimal matching with ratio better than 2. We thus consider randomised mechanisms. We give a natural and explicit extension of the classical Random Serial Dictatorship Mechanism (RSDM) specifically for the House Allocation problem where preference lists can include ties. We thus obtain a universally truthful randomised mechanism for finding a Pareto optimal matching and show that it achieves an approximation ratio of $\\frac{e}{e-1}$. The same bound holds even when agents have priorities (weights) and our goal is to find a maximum weight (as opposed to maximum cardinality) Pareto optimal matching. On the other hand we give a lower bound of $\\frac{18}{13}$ on the approximation ratio of any universally truthful Pareto optimal mechanism in settings with strict preferences. In the case that the mechanism must additionally be non-bossy with an additional technical assumption, we show by utilising a result of Bade that an improved lower bound of $\\frac{e}{e-1}$ holds. This lower bound is tight since RSDM for strict preference lists is non-bossy. We moreover interpret our problem in terms of the classical secretary problem and prove that our mechanism provides the best randomised strategy of the administrator who interviews the applicants.", "published": "2014-04-21T17:08:42Z", "authors": ["Piotr Krysta", "David Manlove", "Baharak Rastegari", "Jinshan Zhang"], "categories": ["cs.GT"], "doi": "10.1007/s00453-019-00584-7", "_query": "housing price text listing", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/1401.1856v1", "title": "Pricing of basket options I", "abstract": "Pricing of high-dimensional options is a deep problem of the Theoretical Financial Mathematics. In this article we present a new class of Lévy driven models of stock markets. In our opinion, any market model should be based on a transparent and intuitively easily acceptable concept. In our case this is a linear system of stochastic equations. Our market model is based on the principle of inheritance, i.e. for the particular choice of parameters it coincides with known models. Also, the model proposed is effectively numerically realizable. For the class of models under cosideration, we give an explicit representations of characteristic functions. This allows us us to construct a sequence of approximation formulas to price basket options. We show that our approximation formulas have almost optimal rate of convergence in the sense of respective n-widths.", "published": "2014-01-08T23:09:49Z", "authors": ["Alexander Kushpel"], "categories": ["q-fin.CP"], "doi": "", "_query": "housing price text listing", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2303.09397v1", "title": "Cryptocurrency Price Prediction using Twitter Sentiment Analysis", "abstract": "The cryptocurrency ecosystem has been the centre of discussion on many social media platforms, following its noted volatility and varied opinions. Twitter is rapidly being utilised as a news source and a medium for bitcoin discussion. Our algorithm seeks to use historical prices and sentiment of tweets to forecast the price of Bitcoin. In this study, we develop an end-to-end model that can forecast the sentiment of a set of tweets (using a Bidirectional Encoder Representations from Transformers - based Neural Network Model) and forecast the price of Bitcoin (using Gated Recurrent Unit) using the predicted sentiment and other metrics like historical cryptocurrency price data, tweet volume, a user's following, and whether or not a user is verified. The sentiment prediction gave a Mean Absolute Percentage Error of 9.45%, an average of real-time data, and test data. The mean absolute percent error for the price prediction was 3.6%.", "published": "2023-03-03T18:42:01Z", "authors": ["Haritha GB", "Sahana N. B"], "categories": ["q-fin.ST", "cs.AI", "cs.CL", "cs.LG"], "doi": "10.5121/csit.2023.130302", "_query": "housing price text listing", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2206.04013v3", "title": "The influence of color on prices of abstract paintings", "abstract": "Determination of price of an artwork is a fundamental problem in cultural economics. In this work we investigate what impact visual characteristics of a painting have on its price. We construct a number of visual features measuring complexity of the painting, its points of interest, segmentation-based features, local color features, and features based on Itten and Kandinsky theories, and utilize mixed-effects model to study impact of these features on the painting price. We analyze the influence of the color on the example of the most complex art style - abstractionism, by created Kandinsky, for which the color is the primary basis. We use Itten's theory - the most recognized color theory in art history, from which the largest number of subtheories was born. For this day it is taken as the base for teaching artists. We utilize novel dataset of 3885 paintings collected from Christie's and Sotheby's and find that color harmony has some explanatory power, color complexity metrics are insignificant and color diversity explains price well.", "published": "2022-06-08T17:05:23Z", "authors": ["Maksim Borisov", "Valeria Kolycheva", "Alexander Semenov", "Dmitry Grigoriev"], "categories": ["econ.GN"], "doi": "", "_query": "housing price text listing", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/1911.10149v4", "title": "Asset Price Bubbles in market models with proportional transaction costs", "abstract": "We study asset price bubbles in market models with proportional transaction costs $λ\\in (0,1)$ and finite time horizon $T$ in the setting of [49]. By following [28], we define the fundamental value $F$ of a risky asset $S$ as the price of a super-replicating portfolio for a position terminating in one unit of the asset and zero cash. We then obtain a dual representation for the fundamental value by using the super-replication theorem of [50]. We say that an asset price has a bubble if its fundamental value differs from the ask-price $(1+λ)S$. We investigate the impact of transaction costs on asset price bubbles and show that our model intrinsically includes the birth of a bubble.", "published": "2019-11-22T17:17:41Z", "authors": ["Francesca Biagini", "Thomas Reitsam"], "categories": ["q-fin.MF"], "doi": "", "_query": "housing price text listing", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2210.01726v3", "title": "Detecting asset price bubbles using deep learning", "abstract": "In this paper we employ deep learning techniques to detect financial asset bubbles by using observed call option prices. The proposed algorithm is widely applicable and model-independent. We test the accuracy of our methodology in numerical experiments within a wide range of models and apply it to market data of tech stocks in order to assess if asset price bubbles are present. Under a given condition on the pricing of call options under asset price bubbles, we are able to provide a theoretical foundation of our approach for positive and continuous stochastic asset price processes. When such a condition is not satisfied, we focus on local volatility models. To this purpose, we give a new necessary and sufficient condition for a process with time-dependent local volatility function to be a strict local martingale.", "published": "2022-10-04T16:30:49Z", "authors": ["Francesca Biagini", "Lukas Gonon", "Andrea Mazzon", "Thilo Meyer-Brandis"], "categories": ["q-fin.MF"], "doi": "", "_query": "housing price text listing", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/1706.10009v2", "title": "Pricing Social Goods", "abstract": "Social goods are goods that grant value not only to their owners but also to the owners' surroundings, be it their families, friends or office mates. The benefit a non-owner derives from the good is affected by many factors, including the type of the good, its availability, and the social status of the non-owner. Depending on the magnitude of the benefit and on the price of the good, a potential buyer might stay away from purchasing the good, hoping to free ride on others' purchases. A revenue-maximizing seller who sells social goods must take these considerations into account when setting prices for the good. The literature on optimal pricing has advanced considerably over the last decade, but little is known about optimal pricing schemes for selling social goods. In this paper, we conduct a systematic study of revenue-maximizing pricing schemes for social goods: we introduce a Bayesian model for this scenario, and devise nearly-optimal pricing schemes for various types of externalities, both for simultaneous sales and for sequential sales.", "published": "2017-06-30T03:31:00Z", "authors": ["Alon Eden", "Tomer Ezra", "Michal Feldman"], "categories": ["cs.GT"], "doi": "", "_query": "housing price text listing", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/1003.5514v3", "title": "Asymptotic and Exact Pricing of Options on Variance", "abstract": "We consider the pricing of derivatives written on the discretely sampled realized variance of an underlying security. In the literature, the realized variance is usually approximated by its continuous-time limit, the quadratic variation of the underlying log-price. Here, we characterize the small-time limits of options on both objects. We find that the difference between them strongly depends on whether or not the stock price process has jumps. Subsequently, we propose two new methods to evaluate the price of options on the discretely sampled realized variance. One of the methods is approximative; it is based on correcting prices of options on quadratic variation by our asymptotic results. The other method is exact; it uses a novel randomization approach and applies Fourier-Laplace techniques. We compare the methods and illustrate our results by some numerical examples.", "published": "2010-03-29T12:02:07Z", "authors": ["Martin Keller-Ressel", "Johannes Muhle-Karbe"], "categories": ["q-fin.PR", "math.PR"], "doi": "", "_query": "housing price text listing", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/1210.6372v6", "title": "Optimal execution and block trade pricing: a general framework", "abstract": "In this article, we develop a general framework to study optimal execution and to price block trades. We prove existence of optimal liquidation strategies and we provide regularity results for optimal strategies under very general hypotheses. We exhibit a Hamiltonian characterization for the optimal strategy that can be used for numerical approximation. We also focus on the important topic of block trade pricing and we propose a methodology to give a price to financial (il)liquidity. In particular, we provide a closed-form formula for the price of a block trade when there is no time constraint to liquidate.", "published": "2012-10-23T20:37:56Z", "authors": ["Olivier Guéant"], "categories": ["q-fin.TR"], "doi": "", "_query": "housing price text listing", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2608.29818v1", "title": "Decarbonising price formation: unit-level evidence on battery storage and the imbalance price in the GB Balancing Mechanism", "abstract": "Renewables now dominate Great Britain's generation mix but rarely occupy the marginal price-setting position, which raises the question of which flexible technologies translate a renewable-rich system into real-time price formation. This study reconstructs the price-ranked edge of the eligible bid or offer stack in the GB Balancing Mechanism across 50,684 half-hourly Settlement Periods from 2023 to 2025 and attributes it to individual Balancing Mechanism Units, separating long-system bid-active from short-system offer-active conditions and retaining co-marginal ties. Batteries rose from 0.8% to 36.2% of bid-active and from 3.2% to 26.9% of offer-active marginality, displacing combined-cycle gas on the bid side and pumped storage on the offer side. Capacity-normalised marginal capture rose on both sides, and unit-quarter fixed effects show that an additional 100 MW of Balancing Mechanism active capacity was associated with a 0.55 and 0.48 percentage-point increase in quarterly bid- and offer-side marginal share. By 2025, battery actions were around £9 to 10/MWh more favourable than volume-matched non-battery alternatives drawn from the same ladder, yet batteries were under-represented by 12.9 percentage points in the highest-priced 5% of short-system periods. Individual batteries remain consistent with price-taking, while the fleet has become endogenous to routine balancing price formation.", "published": "2026-08-30T14:41:06Z", "authors": ["Robert Dalton", "Aidan O'Sullivan"], "categories": ["stat.AP", "econ.GN"], "doi": "", "_query": "housing price text listing", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2606.02795v1", "title": "Recovering Direct Price Effects of Environmental Amenities in Housing Markets: Regression and Causal Machine Learning Model Assessment with Empirical Monte Carlo Simulation", "abstract": "Hedonic price models are widely used to assess how environmental amenities affect property values, yet methodological guidance for estimating direct price effects remains sparse. We conduct an empirical Monte Carlo simulation to evaluate the performance of traditional and causal machine learning approaches for estimating the direct unmediated price effect of spatially delineated amenities on treated properties (DUET), a conservative lower-bound approximation for welfare changes with direct applications to benefit-cost analysis. Where previous simulations rely on parametric assumptions, we retain the actual data-generating process underlying over 1 million property transactions from upstate New York (1990--2024). By randomly assigning \"treatment locations\" across iterations we establish a \"ground truth\" that allows us to precisely measure estimation error. Our results demonstrate that generalized difference-in-differences (DID) regression consistently outperforms baseline DID and two-way fixed effects models across all scenarios. Causal Machine Learning (CML) methods, particularly causal forest DID, achieve comparable performance to generalized DID in most scenarios. In larger samples (above 3,000 treated) increasingly common in contemporary hedonic studies, CML approaches offer substantial advantages when properly specified. Based on empirical simulation results, we provide a set of method-specific best practice recommendations for both traditional regression and causal machine learning approaches.", "published": "2026-06-01T18:59:44Z", "authors": ["Zhenshan Chen", "Klaus Moeltner", "Matthew Mair"], "categories": ["econ.EM", "stat.ML"], "doi": "", "_query": "housing price text listing", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/1608.00280v1", "title": "Pricing Weakly Model Dependent Barrier Products", "abstract": "We discuss the pricing methodology for Bonus Certificates and Barrier Reverse-Convertible Structured Products. Pricing for a European barrier condition is straightforward for products of both types and depends on an efficient interpolation of observed market option pricing. Pricing products We discuss the pricing methodology for Bonus Certificates and Barrier Reverse-Convertible Structured Products. Pricing for a European barrier condition is straightforward for products of both types and depends on an efficient interpolation of observed market option pricing. Pricing products with an American barrier condition requires stochastic modelling. We show that for typical market parameters, this stochastic pricing problem can be systematically reduced to evaluating only one fairly simple stochastic parameter being the asymmetry of hitting the barrier. Eventually, pricing Bonus Certificates and Barrier Reverse Convertibles with an American barrier condition, shows to be dependent on stochastic modelling only within a range of $\\pm\\frac{2}{3}$ of accuracy - e.g. within this accuracy limitation we can price these products without stochastic modelling. We show that the remaining price component is weakly dependent on the stochastic models. Combining these together, we prove to have established an almost model independent pricing procedure for Bonus Certificates and Barrier Reverse-Convertible Structured Products with American barrier conditions.", "published": "2016-07-31T22:54:13Z", "authors": ["Jan Kuklinski", "Panagiotis Papaioannou", "Kevin Tyloo"], "categories": ["q-fin.PR", "q-fin.MF"], "doi": "", "_query": "housing price text listing", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2403.13250v1", "title": "Facilitating Pornographic Text Detection for Open-Domain Dialogue Systems via Knowledge Distillation of Large Language Models", "abstract": "Pornographic content occurring in human-machine interaction dialogues can cause severe side effects for users in open-domain dialogue systems. However, research on detecting pornographic language within human-machine interaction dialogues is an important subject that is rarely studied. To advance in this direction, we introduce CensorChat, a dialogue monitoring dataset aimed at detecting whether the dialogue session contains pornographic content. To this end, we collect real-life human-machine interaction dialogues in the wild and break them down into single utterances and single-turn dialogues, with the last utterance spoken by the chatbot. We propose utilizing knowledge distillation of large language models to annotate the dataset. Specifically, first, the raw dataset is annotated by four open-source large language models, with the majority vote determining the label. Second, we use ChatGPT to update the empty label from the first step. Third, to ensure the quality of the validation and test sets, we utilize GPT-4 for label calibration. If the current label does not match the one generated by GPT-4, we employ a self-criticism strategy to verify its correctness. Finally, to facilitate the detection of pornographic text, we develop a series of text classifiers using a pseudo-labeled dataset. Detailed data analysis demonstrates that leveraging knowledge distillation techniques with large language models provides a practical and cost-efficient method for developing pornographic text detectors.", "published": "2024-03-20T02:29:09Z", "authors": ["Huachuan Qiu", "Shuai Zhang", "Hongliang He", "Anqi Li", "Zhenzhong Lan"], "categories": ["cs.CL"], "doi": "", "_query": "housing price text listing", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2512.12871v1", "title": "CapOptix: An Options-Framework for Capacity Market Pricing", "abstract": "Electricity markets are under increasing pressure to maintain reliability amidst rising renewable penetration, demand variability, and occasional price shocks. Traditional capacity market designs often fall short in addressing this by relying on expected-value metrics of energy unserved, which overlook risk exposure in such systems. In this work, we present CapOptix, a capacity pricing framework that interprets capacity commitments as reliability options, i.e., financial derivatives of wholesale electricity prices. CapOptix characterizes the capacity premia charged by accounting for structural price shifts modeled by the Markov Regime Switching Process. We apply the framework to historical price data from multiple electricity markets and compare the resulting premium ranges with existing capacity remuneration mechanisms.", "published": "2025-12-14T23:08:38Z", "authors": ["Millend Roy", "Agostino Capponi", "Vladimir Pyltsov", "Yinbo Hu", "Vijay Modi"], "categories": ["eess.SY", "q-fin.CP", "q-fin.PR", "stat.AP"], "doi": "", "_query": "housing price text listing", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/1804.09151v2", "title": "Optimal Investment, Demand and Arbitrage under Price Impact", "abstract": "This paper studies the optimal investment problem with random endowment in an inventory-based price impact model with competitive market makers. Our goal is to analyze how price impact affects optimal policies, as well as both pricing rules and demand schedules for contingent claims. For exponential market makers preferences, we establish two effects due to price impact: constrained trading, and non-linear hedging costs. To the former, wealth processes in the impact model are identified with those in a model without impact, but with constrained trading, where the (random) constraint set is generically neither closed nor convex. Regarding hedging, non-linear hedging costs motivate the study of arbitrage free prices for the claim. We provide three such notions, which coincide in the frictionless case, but which dramatically differ in the presence of price impact. Additionally, we show arbitrage opportunities, should they arise from claim prices, can be exploited only for limited position sizes, and may be ignored if outweighed by hedging considerations. We also show that arbitrage inducing prices may arise endogenously in equilibrium, and that equilibrium positions are inversely proportional to the market makers' representative risk aversion. Therefore, large positions endogenously arise in the limit of either market maker risk neutrality, or a large number of market makers.", "published": "2018-04-24T17:20:30Z", "authors": ["Michail Anthropelos", "Scott Robertson", "Konstantinos Spiliopoulos"], "categories": ["q-fin.MF", "math.OC"], "doi": "", "_query": "housing price text listing", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/1202.2585v1", "title": "Minimax Option Pricing Meets Black-Scholes in the Limit", "abstract": "Option contracts are a type of financial derivative that allow investors to hedge risk and speculate on the variation of an asset's future market price. In short, an option has a particular payout that is based on the market price for an asset on a given date in the future. In 1973, Black and Scholes proposed a valuation model for options that essentially estimates the tail risk of the asset price under the assumption that the price will fluctuate according to geometric Brownian motion. More recently, DeMarzo et al., among others, have proposed more robust valuation schemes, where we can even assume an adversary chooses the price fluctuations. This framework can be considered as a sequential two-player zero-sum game between the investor and Nature. We analyze the value of this game in the limit, where the investor can trade at smaller and smaller time intervals. Under weak assumptions on the actions of Nature (an adversary), we show that the minimax option price asymptotically approaches exactly the Black-Scholes valuation. The key piece of our analysis is showing that Nature's minimax optimal dual strategy converges to geometric Brownian motion in the limit.", "published": "2012-02-12T23:41:12Z", "authors": ["Jacob Abernethy", "Rafael M. Frongillo", "Andre Wibisono"], "categories": ["q-fin.CP", "cs.GT", "q-fin.PR"], "doi": "", "_query": "housing price text listing", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/1803.05075v1", "title": "Stock Price Prediction using Principle Components", "abstract": "The literature provides strong evidence that stock prices can be predicted from past price data. Principal component analysis (PCA) is a widely used mathematical technique for dimensionality reduction and analysis of data by identifying a small number of principal components to explain the variation found in a data set. In this paper, we describe a general method for stock price prediction using covariance information, in terms of a dimension reduction operation based on principle component analysis. Projecting the noisy observation onto a principle subspace leads to a well-conditioned problem. We illustrate our method on daily stock price values for five companies in different industries. We investigate the results based on mean squared error and directional change statistic of prediction, as measures of performance, and volatility of prediction as a measure of risk.", "published": "2018-03-13T23:34:26Z", "authors": ["Mahsa Ghorbani", "Edwin K. P. Chong"], "categories": ["q-fin.MF"], "doi": "", "_query": "housing price text listing", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/0911.0907v1", "title": "ANN-based Innovative Segmentation Method for Handwritten text in Assamese", "abstract": "Artificial Neural Network (ANN) s has widely been used for recognition of optically scanned character, which partially emulates human thinking in the domain of the Artificial Intelligence. But prior to recognition, it is necessary to segment the character from the text to sentences, words etc. Segmentation of words into individual letters has been one of the major problems in handwriting recognition. Despite several successful works all over the work, development of such tools in specific languages is still an ongoing process especially in the Indian context. This work explores the application of ANN as an aid to segmentation of handwritten characters in Assamese- an important language in the North Eastern part of India. The work explores the performance difference obtained in applying an ANN-based dynamic segmentation algorithm compared to projection- based static segmentation. The algorithm involves, first training of an ANN with individual handwritten characters recorded from different individuals. Handwritten sentences are separated out from text using a static segmentation method. From the segmented line, individual characters are separated out by first over segmenting the entire line. Each of the segments thus obtained, next, is fed to the trained ANN. The point of segmentation at which the ANN recognizes a segment or a combination of several segments to be similar to a handwritten character, a segmentation boundary for the character is assumed to exist and segmentation performed. The segmented character is next compared to the best available match and the segmentation boundary confirmed.", "published": "2009-11-04T18:32:08Z", "authors": ["Kaustubh Bhattacharyya", "Kandarpa Kumar Sarma"], "categories": ["cs.CL"], "doi": "", "_query": "housing price text listing", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2606.16183v1", "title": "LLM-Powered Virtual Population for Demand Simulation and Pricing", "abstract": "We develop an LLM-powered virtual population model that simulates demand for pricing decisions, in settings where products are described by rich unstructured information, such as text descriptions and images, and where decision makers need not only mean-demand predictions but also uncertainty estimates for counterfactual prices. Our model represents exposed customers as draws from a finite mixture of customer personas. For each persona, product, and candidate price, an LLM elicits a persona-level purchase probability using both structured persona information and unstructured product information. These probabilities are aggregated through calibrated mixture weights to form a predictive distribution of aggregate demand. The resulting simulator can evaluate counterfactual prices under various pricing objectives, including expected revenue and risk-aware criteria such as conditional value at risk. We test the framework on an online H&M fashion dataset with product descriptions and images. The calibrated LLM-based simulator achieves the best overall predictive performance among the models considered, and supports sample-efficient pricing decisions. Our framework provides a practical way to use LLMs as demand simulators for products with limited historical demand data but rich product information. By producing a full predictive demand distribution rather than only a point forecast, it enables managers to compare candidate prices, quantify demand uncertainty, and choose prices that target either average-case revenue or risk-aware objectives.", "published": "2026-06-15T03:56:31Z", "authors": ["Chengpiao Huang", "Kaizheng Wang"], "categories": ["cs.LG", "cs.AI", "cs.CL"], "doi": "", "_query": "housing price text listing", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2410.12495v1", "title": "Price impact and long-term profitability of energy storage", "abstract": "We study the price impact of storage facilities in electricity markets and analyze the long-term profitability of these facilities in prospective scenarios of energy transition. To this end, we begin by characterizing the optimal operating strategy for a stylized storage system, assuming an arbitrary exogenous price process. Following this, we determine the equilibrium price in a market comprising storage systems (acting as price takers), renewable energy producers, and conventional producers with a defined supply function, all driven by an exogenous demand process. The price process is characterized as a solution to a fully coupled system of forward-backward stochastic differential equations, for which we establish existence and uniqueness under appropriate assumptions. We finally illustrate the impact of storage on intraday electricity prices through numerical examples and show how the revenues of storage agents may evolve in prospective energy transition scenarios from RTE, the French energy electricity network operator, taking into account both the increasing penetration of renewable energies and the self-cannibalization effect of growing storage capacity. We find that both the average revenues and the interquantile ranges increase in all scenarios, highlighting higher expected profits and higher risk for storage assets.", "published": "2024-10-16T12:16:22Z", "authors": ["Roxana Dumitrescu", "Redouane Silvente", "Peter Tankov"], "categories": ["q-fin.MF"], "doi": "", "_query": "housing price text listing", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/1404.4014v3", "title": "Option Pricing Accuracy for Estimated Heston Models", "abstract": "We consider assets for which price $X_t$ and squared volatility $Y_t$ are jointly driven by Heston joint stochastic differential equations (SDEs). When the parameters of these SDEs are estimated from $N$ sub-sampled data $(X_{nT}, Y_{nT})$, estimation errors do impact the classical option pricing PDEs. We estimate these option pricing errors by combining numerical evaluation of estimation errors for Heston SDEs parameters with the computation of option price partial derivatives with respect to these SDEs parameters. This is achieved by solving six parabolic PDEs with adequate boundary conditions. To implement this approach, we also develop an estimator $\\hat λ$ for the market price of volatility risk, and we study the sensitivity of option pricing to estimation errors affecting $\\hat λ$. We illustrate this approach by fitting Heston SDEs to 252 daily joint observations of the S\\&P 500 index and of its approximate volatility VIX, and by numerical applications to European options written on the S\\&P 500 index.", "published": "2014-04-15T18:34:40Z", "authors": ["Robert Azencott", "Yutheeka Gadhyan", "Roland Glowinski"], "categories": ["q-fin.MF"], "doi": "", "_query": "housing price text listing", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2011.08620v1", "title": "Static Hedging of Weather and Price Risks in Electricity Markets", "abstract": "We present the closed-form solution to the problem of hedging price and quantity risks for energy retailers (ER), using financial instruments based on electricity price and weather indexes. Our model considers an ER who is intermediary in a regulated electricity market. ERs buy a fixed quantity of electricity at a variable cost and must serve a variable demand at a fixed cost. Thus ERs are subject to both price and quantity risks. To hedge such risks, an ER could construct a portfolio of financial instruments based on price and weather indexes. We construct the closed form solution for the optimal portfolio for the mean-Var model in the discrete setting. Our model does not make any distributional assumption.", "published": "2020-11-17T13:24:01Z", "authors": ["Javier Pantoja Robayo", "Juan C. Vera"], "categories": ["q-fin.MF"], "doi": "", "_query": "housing price text listing", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/1708.05797v1", "title": "CLaC @ QATS: Quality Assessment for Text Simplification", "abstract": "This paper describes our approach to the 2016 QATS quality assessment shared task. We trained three independent Random Forest classifiers in order to assess the quality of the simplified texts in terms of grammaticality, meaning preservation and simplicity. We used the language model of Google-Ngram as feature to predict the grammaticality. Meaning preservation is predicted using two complementary approaches based on word embedding and WordNet synonyms. A wider range of features including TF-IDF, sentence length and frequency of cue phrases are used to evaluate the simplicity aspect. Overall, the accuracy of the system ranges from 33.33% for the overall aspect to 58.73% for grammaticality.", "published": "2017-08-19T03:18:50Z", "authors": ["Elnaz Davoodi", "Leila Kosseim"], "categories": ["cs.CL"], "doi": "", "_query": "housing price text listing", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2007.09201v1", "title": "Bond indifference prices and indifference yield curves", "abstract": "In a market with stochastic interest rates, we consider an investor who can either (i) invest all if his money in a savings account or (ii) purchase zero-coupon bonds and invest the remainder of his wealth in a savings account. The indifference price of the bond is the price for which the investor could achieve the same expected utility under both scenarios. In an affine term structure setting, under the assumption that an investor has a utility function in either exponential or power form, we show that the indifference price of a zero-coupon bond is the root of an integral expression. As an example, we compute bond indifference prices and the corresponding indifference yield curves in the Vasicek setting and interpret the results.", "published": "2020-07-17T19:32:48Z", "authors": ["Matthew Lorig"], "categories": ["q-fin.CP"], "doi": "", "_query": "housing price text listing", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/cs/0101035v2", "title": "The Guidebook, the Friend, and the Room: Visitor Experience in a Historic House", "abstract": "In this paper, we describe an electronic guidebook prototype and report on a study of its use in a historic house. Supported by mechanisms in the guidebook, visitors constructed experiences that had a high degree of interaction with three entities: the guidebook, their companions, and the house and its contents. For example, we found that most visitors played audio descriptions played through speakers (rather than using headphones or reading textual descriptions) to facilitate communication with their companions.", "published": "2001-01-28T07:14:46Z", "authors": ["Allison Woodruff", "Paul M. Aoki", "Amy Hurst", "Margaret H. Szymanski"], "categories": ["cs.HC"], "doi": "10.1145/634067.634229", "_query": "housing price text listing", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/1210.4915v1", "title": "Self-Confirming Price Prediction Strategies for Simultaneous One-Shot Auctions", "abstract": "Bidding in simultaneous auctions is challenging because an agent's value for a good in one auction may depend on the uncertain outcome of other auctions: the so-called exposure problem. Given the gap in understanding of general simultaneous auction games, previous works have tackled this problem with heuristic strategies that employ probabilistic price predictions. We define a concept of self-confirming prices, and show that within an independent private value model, Bayes-Nash equilibrium can be fully characterized as a profile of optimal price prediction strategies with self-confirming predictions. We exhibit practical procedures to compute approximately optimal bids given a probabilistic price prediction, and near self-confirming price predictions given a price-prediction strategy. An extensive empirical game-theoretic analysis demonstrates that self-confirming price prediction strategies are effective in simultaneous auction games with both complementary and substitutable preference structures.", "published": "2012-10-16T17:56:19Z", "authors": ["Michael P. Wellman", "Eric Sodomka", "Amy Greenwald"], "categories": ["cs.GT"], "doi": "", "_query": "housing price text listing", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/0808.3360v1", "title": "Criticality Characteristics of Current Oil Price Dynamics", "abstract": "Methodology that recently lead us to predict to an amazing accuracy the date (July 11, 2008) of reverse of the oil price up trend is briefly summarized and some further aspects of the related oil price dynamics elaborated. This methodology is based on the concept of discrete scale invariance whose finance-prediction-oriented variant involves such elements as log-periodic self-similarity, the universal preferred scaling factor lambda=2, and allows a phenomenon of the \"super-bubble\". From this perspective the present (as of August 22, 2008) violent - but still log-periodically decelerating - decrease of the oil prices is associated with the decay of such a \"super- bubble\" that has started developing about one year ago on top of the longer-term oil price increasing phase (normal bubble) whose ultimate termination is evaluated to occur in around mid 2010.", "published": "2008-08-25T13:57:13Z", "authors": ["Stanislaw Drozdz", "Jaroslaw Kwapien", "Pawel Oswiecimka"], "categories": ["q-fin.ST", "physics.soc-ph"], "doi": "", "_query": "housing price text listing", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2508.08325v2", "title": "Algorithmic Collusion of Pricing and Advertising on E-commerce Platforms", "abstract": "When online sellers use AI learning algorithms to automatically compete on e-commerce platforms, there is concern that they will learn to coordinate on higher than competitive prices. However, this concern was primarily raised in single-dimension price competition. We investigate whether this prediction holds when sellers make pricing and advertising decisions together, i.e., two-dimensional decisions. We analyze competition in multi-agent reinforcement learning, and use a large-scale dataset from Amazon.com to provide empirical evidence. We show that when consumers have high search costs, learning algorithms can coordinate on prices lower than competitive prices, facilitating a win-win-win for consumers, sellers, and platforms. This occurs because algorithms learn to coordinate on lower advertising bids, which lower advertising costs, leading to lower prices and enlarging demand on the platform. We also show that our results generalize to any learning algorithm that uses exploration of price and advertising bids. Consistent with our predictions, an empirical analysis shows that price levels exhibit a negative interaction between estimated consumer search costs and algorithm usage index. We analyze the platform's strategic response and find that reserve price adjustments will not increase platform profits, but commission adjustments will, while maintaining the beneficial outcomes for both sellers and consumers.", "published": "2025-08-09T18:44:17Z", "authors": ["Hangcheng Zhao", "Ron Berman"], "categories": ["econ.GN", "cs.AI", "cs.CE", "cs.GT", "cs.LG"], "doi": "", "_query": "housing price text listing", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2005.02351v1", "title": "Defining an intrinsic stickiness parameter of stock price returns", "abstract": "We introduce a non linear pricing model of individual stock returns that defines a stickiness parameter of the returns. The pricing model resembles the capital asset pricing model used in finance but has a non linear component inspired from models of earth quake tectonic plate movements. The link to tectonic plate movements happens, since price movements of a given stock index is seen adding stress to its components of individual stock returns, in order to follow the index. How closely individual stocks follow the indexs price movements, can then be used to define their stickiness", "published": "2020-05-05T17:36:49Z", "authors": ["Naji Massad", "Jørgen Vitting Andersen"], "categories": ["q-fin.ST", "q-fin.GN"], "doi": "10.1016/j.physa.2020.124464", "_query": "housing price text listing", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/1905.03721v2", "title": "Show, Price and Negotiate: A Negotiator with Online Value Look-Ahead", "abstract": "Negotiation, as an essential and complicated aspect of online shopping, is still challenging for an intelligent agent. To that end, we propose the Price Negotiator, a modular deep neural network that addresses the unsolved problems in recent studies by (1) considering images of the items as a crucial, though neglected, source of information in a negotiation, (2) heuristically finding the most similar items from an external online source to predict the potential value and an acceptable agreement price, (3) predicting a general price-based action at each turn which is fed into the language generator to output the supporting natural language, and (4) adjusting the prices based on the predicted actions. Empirically, we show that our model, that is trained in both supervised and reinforcement learning setting, significantly improves negotiation on the CraigslistBargain dataset, in terms of the agreement price, price consistency, and dialogue quality.", "published": "2019-05-07T06:20:59Z", "authors": ["Amin Parvaneh", "Ehsan Abbasnejad", "Qi Wu", "Javen Qinfeng Shi", "Anton van den Hengel"], "categories": ["cs.CV"], "doi": "10.1109/TMM.2021.3065169", "_query": "housing price text listing", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2110.00771v2", "title": "Non-average price impact in order-driven markets", "abstract": "We present a measurement of price impact in order-driven markets that does not require averages across executions or scenarios. Given the order book data associated with one single execution of a sell metaorder, we measure its contribution to price decrease during the trade. We do so by modelling the limit order book using state-dependent Hawkes processes, and by defining the price impact profile of the execution as a function of the compensator of a stochastic process in our model. We apply our measurement to a data set from NASDAQ, and we conclude that the clustering of sell child orders has a bigger impact on price than their sizes.", "published": "2021-10-02T10:10:09Z", "authors": ["Claudio Bellani", "Damiano Brigo", "Mikko Pakkanen", "Leandro Sanchez-Betancourt"], "categories": ["q-fin.TR", "q-fin.ST"], "doi": "", "_query": "housing price text listing", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2212.00197v1", "title": "GAN-MC: a Variance Reduction Tool for Derivatives Pricing", "abstract": "We propose a parameter-free model for estimating the price or valuation of financial derivatives like options, forwards and futures using non-supervised learning networks and Monte Carlo. Although some arbitrage-based pricing formula performs greatly on derivatives pricing like Black-Scholes on option pricing, generative model-based Monte Carlo estimation(GAN-MC) will be more accurate and holds more generalizability when lack of training samples on derivatives, underlying asset's price dynamics are unknown or the no-arbitrage conditions can not be solved analytically. We analyze the variance reduction feature of our model and to validate the potential value of the pricing model, we collect real world market derivatives data and show that our model outperforms other arbitrage-based pricing models and non-parametric machine learning models. For comparison, we estimate the price of derivatives using Black-Scholes model, ordinary least squares, radial basis function networks, multilayer perception regression, projection pursuit regression and Monte Carlo only models.", "published": "2022-12-01T00:53:25Z", "authors": ["Weishi Wang"], "categories": ["stat.AP", "stat.ME"], "doi": "", "_query": "housing price text listing", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2105.10205v1", "title": "Rational Dynamic Price Model for Demand Response Programs in Modern Distribution Systems", "abstract": "Demand response (DR) refers to change in electricity consumption pattern of customers during on-peak hours in lieu of financial gains to reduce stress on distribution systems. Existing dynamic price models have not provided adequate success to price-based demand response (PBDR) programs. It happened as these models have raised typical socio-economic problems pertaining to cross-subsidy, free-riders, social inequity, assured profit of utilities, financial gains and comfort of customers, etc. This paper presents a new dynamic price model for PBDR in distribution systems which aims to overcome some of the above mentioned problems of the existing price models. The main aim of the developed price model is to overcome the problems of cross-subsidy and free-riders of the existing price models for widespread acceptance, deployment and efficient utilization of PBDR programs in contemporary distribution systems. Proposed price model generates demand-linked price signal that imposes different price signals to different customers during on-peak hours and remains static otherwise. This makes proposed model a class apart from other existing models. The novelty of the proposed model lies in the fact that the financial benefits and penalties pertaining to DR are self-adjusted among customers while preserving social equity and profit of the utility. Such an ideology has not been yet addressed in the literature. Detailed investigation of application results on a standard test bench reveals that the proposed model equally cares regarding the interests of both customers and utility. For economic assessment, a comparison of the proposed price model with the existing pricing models is also performed.", "published": "2021-05-21T08:41:19Z", "authors": ["Rayees A. Thokar", "Nikhil Gupta", "K. R. Niazi", "Anil Swarnkar", "Nand K. Meena"], "categories": ["eess.SY"], "doi": "", "_query": "housing price text listing", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2312.13448v5", "title": "Implied CO$_{\\textbf{2}}$-Price and Interest Rate of Carbon", "abstract": "By its nature, the so-called social cost of carbon (SCC(t)) will likely not cover the cost induced by climate change (damage cost and abatement cost) if it is used as a CO$_2$-price. It is a marginal price only. We define an implied CO$_2$-price that covers the climate change-induced costs. The price can be interpreted as a \\textit{polluter pays principle}. A numerical analysis using a classical DICE model reveals that the cost-implied CO$_2$ price is around 500 \\$/tCO$_2$, while the corresponding price associated with the SCC is about 50 \\$/tCO$_2$. In addition, we define the internal rate of return of carbon abatement and calculate it for the classical DICE model. This rate is much higher than the model's discount rate, which may suggest the advantage of financing abatement by loans.", "published": "2023-12-20T21:55:26Z", "authors": ["Christian P. Fries"], "categories": ["q-fin.MF"], "doi": "", "_query": "housing price text listing", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/1409.5801v1", "title": "Pricing and hedging of energy spread options and volatility modulated Volterra processes", "abstract": "We derive the price of a spread option based on two assets which follow a bivariate volatility modulated Volterra process dynamics. Such a price dynamics is particularly relevant in energy markets, modelling for example the spot price of power and gas. Volatility modulated Volterra processes are in general not semimartingales, but contain several special cases of interest in energy markets like for example continuous-time autoregressive moving average processes. Based on a change of measure, we obtain a pricing expression based on a univariate Fourier transform of the payoff function and the characteristic function of the price dynamics. Moreover, the spread option price can be expressed in terms of the forward prices on the underlying dynamics assets. We compute a linear system of equations for the quadratic hedge for the spread option in terms of a portfolio of underlying forward contracts.", "published": "2014-09-19T20:01:09Z", "authors": ["Fred Espen Benth", "Hanna Zdanowicz"], "categories": ["q-fin.PR", "math.PR"], "doi": "", "_query": "housing price text listing", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/1509.01626v3", "title": "Character-level Convolutional Networks for Text Classification", "abstract": "This article offers an empirical exploration on the use of character-level convolutional networks (ConvNets) for text classification. We constructed several large-scale datasets to show that character-level convolutional networks could achieve state-of-the-art or competitive results. Comparisons are offered against traditional models such as bag of words, n-grams and their TFIDF variants, and deep learning models such as word-based ConvNets and recurrent neural networks.", "published": "2015-09-04T22:31:53Z", "authors": ["Xiang Zhang", "Junbo Zhao", "Yann LeCun"], "categories": ["cs.LG", "cs.CL"], "doi": "", "_query": "housing price text listing", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/1905.00853v1", "title": "The Declining Price Anomaly is not Universal in Multi-Buyer Sequential Auctions (but almost is)", "abstract": "The declining price anomaly states that the price weakly decreases when multiple copies of an item are sold sequentially over time. The anomaly has been observed in a plethora of practical applications. On the theoretical side, Gale and Stegeman proved that the anomaly is guaranteed to hold in full information sequential auctions with exactly two buyers. We prove that the declining price anomaly is not guaranteed in full information sequential auctions with three or more buyers. This result applies to both first-price and second-price sequential auctions. Moreover, it applies regardless of the tie-breaking rule used to generate equilibria in these sequential auctions. To prove this result we provide a refined treatment of subgame perfect equilibria that survive the iterative deletion of weakly dominated strategies and use this framework to experimentally generate a very large number of random sequential auction instances. In particular, our experiments produce an instance with three bidders and eight items that, for a specific tie-breaking rule, induces a non-monotonic price trajectory. Theoretic analyses are then applied to show that this instance can be used to prove that for every possible tie-breaking rule there is a sequential auction on which it induces a non-monotonic price trajectory. On the other hand, our experiments show that non-monotonic price trajectories are extremely rare. In over six million experiments only a 0.000183 proportion of the instances violated the declining price anomaly.", "published": "2019-05-02T17:01:58Z", "authors": ["Vishnu V. Narayan", "Enguerrand Prebet", "Adrian Vetta"], "categories": ["cs.GT", "econ.TH"], "doi": "", "_query": "housing price text listing", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2212.00674v2", "title": "Quantity restrictions and price discounts on Russian oil", "abstract": "Following Russia's invasion of Ukraine, Western countries have looked for ways to limit Russia's oil income. This paper considers, theoretically and quantitatively, two such options: 1) an export-quantity restriction and 2) a forced discount on Russian oil. We build a quantifiable model of the global oil market and analyze how each of these policies affect: which Russian oil fields fall out of production; the global oil supply; and the global oil price. By these statics we derive the effects of the policies on Russian oil profits and oil-importers' economic surplus. The effects on Russian oil profits are substantial. In the short run (within the first year), a quantity restriction of 20% yields Russian losses of 62 million USD per day, equivalent to 1.2% of GDP and 32% of military spending. In the long run (beyond a year) new investments become unprofitable. Losses rise to 100 million USD per day, 2% of GDP and 56% of military spending. A price discount of 20% is even more harmful to Russia, yielding losses of 152 million USD per day, equivalent to 3.1% of GDP and 85% of military spending in the short run and long run. A price discount puts generally more burden on Russia and less on importers compared to a quantity restriction. In fact, a price discount implies net gains for oil importers as it essentially redistributes oil rents from Russia to importers. If the restrictions are expected to last for long, the burden on oil importers decreases. Overall, both policies at all levels imply larger relative losses for Russia than for oil importers (in shares of their GDP). The case for a price discount on Russian oil is thus strong. However, Russia may choose not to export at the discounted price, in which case the price-discount sanction becomes a de facto supply restriction.", "published": "2022-12-01T17:30:28Z", "authors": ["Henrik Wachtmeister", "Johan Gars", "Daniel Spiro"], "categories": ["econ.GN"], "doi": "", "_query": "housing price text listing", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2412.06837v1", "title": "Innovative Sentiment Analysis and Prediction of Stock Price Using FinBERT, GPT-4 and Logistic Regression: A Data-Driven Approach", "abstract": "This study explores the comparative performance of cutting-edge AI models, i.e., Finaance Bidirectional Encoder representations from Transsformers (FinBERT), Generatice Pre-trained Transformer GPT-4, and Logistic Regression, for sentiment analysis and stock index prediction using financial news and the NGX All-Share Index data label. By leveraging advanced natural language processing models like GPT-4 and FinBERT, alongside a traditional machine learning model, Logistic Regression, we aim to classify market sentiment, generate sentiment scores, and predict market price movements. This research highlights global AI advancements in stock markets, showcasing how state-of-the-art language models can contribute to understanding complex financial data. The models were assessed using metrics such as accuracy, precision, recall, F1 score, and ROC AUC. Results indicate that Logistic Regression outperformed the more computationally intensive FinBERT and predefined approach of versatile GPT-4, with an accuracy of 81.83% and a ROC AUC of 89.76%. The GPT-4 predefined approach exhibited a lower accuracy of 54.19% but demonstrated strong potential in handling complex data. FinBERT, while offering more sophisticated analysis, was resource-demanding and yielded a moderate performance. Hyperparameter optimization using Optuna and cross-validation techniques ensured the robustness of the models. This study highlights the strengths and limitations of the practical applications of AI approaches in stock market prediction and presents Logistic Regression as the most efficient model for this task, with FinBERT and GPT-4 representing emerging tools with potential for future exploration and innovation in AI-driven financial analytics", "published": "2024-12-07T05:20:31Z", "authors": ["Olamilekan Shobayo", "Sidikat Adeyemi-Longe", "Olusogo Popoola", "Bayode Ogunleye"], "categories": ["cs.LG", "cs.AI", "q-fin.ST", "stat.AP", "stat.CO"], "doi": "10.3390/bdcc8110143", "_query": "housing price text listing", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2601.18117v1", "title": "Decentralized Multi-product Pricing: Diagonal Dominance, Nash Equilibrium, and Price of Anarchy", "abstract": "Decentralized decision making in multi--product firms can lead to efficiency losses when autonomous decision makers fail to internalize cross--product demand interactions. This paper quantifies the magnitude of such losses by analyzing the Price of Anarchy in a pricing game in which each decision maker independently sets prices to maximize its own product--level revenue. We model demand using a linear system that captures both substitution and complementarity effects across products. We first establish existence and uniqueness of a pure--strategy Nash equilibrium under economically standard diagonal dominance conditions. Our main contribution is the derivation of a tight worst--case lower bound on the ratio between decentralized revenue and the optimal centralized revenue. We show that this efficiency loss is governed by a single scalar parameter, denoted by $μ$, which measures the aggregate strength of cross--price effects relative to own--price sensitivities. In particular, we prove that the revenue ratio is bounded below by $4(1-μ)/(2-μ)^2$, and we demonstrate the tightness of this bound by constructing a symmetric market topology in which the bound is exactly attained. We further refine the analysis by providing an instance--exact characterization of efficiency loss based on the spectral properties of the demand interaction matrix. Together, these results offer a quantitative framework for assessing the trade--off between centralized pricing and decentralized autonomy in multi--product firms.", "published": "2026-01-26T04:02:27Z", "authors": ["Boxiao Chen", "Jiashuo Jiang", "Stefanus Jasin"], "categories": ["cs.GT", "math.OC"], "doi": "", "_query": "housing price text listing", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/1205.6396v1", "title": "Effective Listings of Function Stop words for Twitter", "abstract": "Many words in documents recur very frequently but are essentially meaningless as they are used to join words together in a sentence. It is commonly understood that stop words do not contribute to the context or content of textual documents. Due to their high frequency of occurrence, their presence in text mining presents an obstacle to the understanding of the content in the documents. To eliminate the bias effects, most text mining software or approaches make use of stop words list to identify and remove those words. However, the development of such top words list is difficult and inconsistent between textual sources. This problem is further aggravated by sources such as Twitter which are highly repetitive or similar in nature. In this paper, we will be examining the original work using term frequency, inverse document frequency and term adjacency for developing a stop words list for the Twitter data source. We propose a new technique using combinatorial values as an alternative measure to effectively list out stop words.", "published": "2012-05-29T15:37:46Z", "authors": ["Murphy Choy"], "categories": ["cs.IR", "cs.CL"], "doi": "", "_query": "housing price text listing", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/cond-mat/0312547v1", "title": "Traders' strategy with price feedbacks in financial market", "abstract": "We introduce an autoregressive-type model of prices in financial market taking into account the self-modulation effect. We find that traders are mainly using strategies with weighted feedbacks of past prices. These feedbacks are responsible for the slow diffusion in short times, apparent trends and power law distribution of price changes.", "published": "2003-12-20T10:19:02Z", "authors": ["Takayuki Mizuno", "Tohur Nakano", "Misako Takayasu", "Hideki Takayasu"], "categories": ["cond-mat.stat-mech", "q-fin.TR"], "doi": "10.1016/j.physa.2004.06.145", "_query": "housing price text listing", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2305.07006v1", "title": "Fair Price Discrimination", "abstract": "A seller is pricing identical copies of a good to a stream of unit-demand buyers. Each buyer has a value on the good as his private information. The seller only knows the empirical value distribution of the buyer population and chooses the revenue-optimal price. We consider a widely studied third-degree price discrimination model where an information intermediary with perfect knowledge of the arriving buyer's value sends a signal to the seller, hence changing the seller's posterior and inducing the seller to set a personalized posted price. Prior work of Bergemann, Brooks, and Morris (American Economic Review, 2015) has shown the existence of a signaling scheme that preserves seller revenue, while always selling the item, hence maximizing consumer surplus. In a departure from prior work, we ask whether the consumer surplus generated is fairly distributed among buyers with different values. To this end, we aim to maximize welfare functions that reward more balanced surplus allocations. Our main result is the surprising existence of a novel signaling scheme that simultaneously $8$-approximates all welfare functions that are non-negative, monotonically increasing, symmetric, and concave, compared with any other signaling scheme. Classical examples of such welfare functions include the utilitarian social welfare, the Nash welfare, and the max-min welfare. Such a guarantee cannot be given by any consumer-surplus-maximizing scheme -- which are the ones typically studied in the literature. In addition, our scheme is socially efficient, and has the fairness property that buyers with higher values enjoy higher expected surplus, which is not always the case for existing schemes.", "published": "2023-05-11T17:45:06Z", "authors": ["Siddhartha Banerjee", "Kamesh Munagala", "Yiheng Shen", "Kangning Wang"], "categories": ["cs.GT", "cs.DS", "econ.TH"], "doi": "", "_query": "housing price text listing", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/1808.09382v2", "title": "Emergence of Turbulent Epochs in Oil Prices", "abstract": "Oil price data have a complicated multi-scale structure that may vary with time. We use time-frequency analysis to identify the main features of these variations and, in particular, the regime shifts. The analysis is based on a wavelet-based decomposition and analysis of the associated scale spectrum. The joint estimation of the local Hurst exponent and volatility is the key to detect and identify regime shifting and switching of the oil price. The framework involves in particular modeling in terms of a process of `multi-fractional' type so that both the roughness and the volatility of the price process may vary with time. Special epochs then emerge as a result of these degrees of freedom, moreover, as a result of the special type of spectral estimator used. These special epochs are discussed and related to historical events. Some of them are not detected by standard analysis based on maximum likelihood estimation. The paper presents a novel algorithm for robust detection of such special epochs and multi-fractional behavior in financial or other types of data. In the financial context insight about such behavior of the asset price is important to evaluate financial contracts involving the asset.", "published": "2018-08-28T16:12:02Z", "authors": ["Josselin Garnier", "Knut Solna"], "categories": ["q-fin.ST"], "doi": "10.1016/j.chaos.2019.03.016", "_query": "housing price text listing", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2103.10015v1", "title": "Robust Price Optimization of Multiple Products under Interval Uncertainties", "abstract": "In this paper, we solve the multiple product price optimization problem under interval uncertainties of the price sensitivity parameters in the demand function. The objective of the price optimization problem is to maximize the overall revenue of the firm where the decision variables are the prices of the products supplied by the firm. We propose an approach that yields optimal solutions under different variations of the estimated price sensitivity parameters. We adopt a robust optimization approach by building a data-driven uncertainty set for the parameters, and then construct a deterministic counterpart for the robust optimization model. The numerical results show that two objectives are fulfilled: the method reflects the uncertainty embedded in parameter estimations, and also an interval is obtained for optimal prices. We also conducted a simulation study to which we compared the results of our approach. The comparisons show that although robust optimization is deemed to be conservative, the results of the proposed approach show little loss compared to those from the simulation.", "published": "2021-03-18T04:30:11Z", "authors": ["Mahdi Hamzeei", "Alvin Lim", "Jiefeng Xu"], "categories": ["math.OC"], "doi": "10.1057/s41272-021-00351-w", "_query": "housing price text listing", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/1711.05567v2", "title": "Fully-dynamic risk-indifference pricing and no-good-deal bounds", "abstract": "The seller's risk-indifference price evaluation is studied. We propose a dynamic risk-indifference pricing criteria derived from a fully-dynamic family of risk measures on the $L_p$-spaces for $p\\in [1,\\infty]$. The concept of fully-dynamic risk measures extends the one of dynamic risk measures by adding the actual possibility of changing the risk perspectives over time. The family is then characterised by a double time index. Our framework fits well the study of both short and long term investments. In this dynamic framework we analyse whether the risk-indifference pricing criterion actually provides a proper convex price system, for which time-consistency is guaranteed. It turns out that the analysis is quite delicate and necessitates an adequate setting. This entails the use of capacities and an extension of the whole price system to the Banach spaces derived by the capacity seminorms. Furthermore, we consider the relationship of the fully-dynamic risk-indifference price with no-good-deal bounds. Recall that no-good-deal pricing guarantees that not only arbitrage opportunities are excluded, but also all deals that are \"too good to be true\". We shall provide necessary and sufficient conditions on the fully-dynamic risk measures so that the corresponding risk-indifference prices satisfy the no-good-deal bounds. The use of no-good-deal bounds also provides a method to select the risk measures and then construct a proper fully-dynamic risk-indifference price system in the $L_2$-spaces.", "published": "2017-11-15T13:36:41Z", "authors": ["Jocelyne Bion-Nadal", "Giulia Di Nunno"], "categories": ["math.PR"], "doi": "", "_query": "housing price text listing", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/1003.2138v1", "title": "Need-based Communication for Smart Grid: When to Inquire Power Price?", "abstract": "In smart grid, a home appliance can adjust its power consumption level according to the realtime power price obtained from communication channels. Most studies on smart grid do not consider the cost of communications which cannot be ignored in many situations. Therefore, the total cost in smart grid should be jointly optimized with the communication cost. In this paper, a probabilistic mechanism of locational margin price (LMP) is applied and a model for the stochastic evolution of the underlying load which determines the power price is proposed. Based on this framework of power price, the problem of determining when to inquire the power price is formulated as a Markov decision process and the corresponding elements, namely the action space, system state and reward function, are defined. Dynamic programming is then applied to obtain the optimal strategy. A simpler myopic approach is proposed by comparing the cost of communications and the penalty incurred by using the old value of power price. Numerical results show the significant performance gain of the optimal strategy of price inquiry, as well as the near-optimality of the myopic approach.", "published": "2010-03-10T16:04:59Z", "authors": ["Husheng Li", "Robert C. Qiu"], "categories": ["cs.IT"], "doi": "", "_query": "housing price text listing", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/cond-mat/0210710v1", "title": "More statistical properties of order books and price impact", "abstract": "We investigate present some new statistical properties of order books. We analyse data from the Nasdaq and investigate (a) the statistics of incoming limit order prices, (b) the shape of the average order book, and (c) the typical life time of a limit order as a function of the distance from the best price. We also determine the `price impact' function using French and British stocks, and find a logarithmic, rather than a power-law, dependence of the price response on the volume. The weak time dependence of the response function shows that the impact is, surprisingly, quasi-permanent, and suggests that trading itself is interpreted by the market as new information.", "published": "2002-10-31T16:13:39Z", "authors": ["Marc Potters", "Jean-Philippe Bouchaud"], "categories": ["cond-mat"], "doi": "10.1016/S0378-4371(02)01896-4", "_query": "housing price text listing", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2108.04505v2", "title": "Optimal Revenue Guarantees for Pricing in Large Markets", "abstract": "Posted price mechanisms (PPM) constitute one of the predominant practices to price goods in online marketplaces and their revenue guarantees have been a central object of study in the last decade. We consider a basic setting where the buyers' valuations are independent and identically distributed and there is a single unit on sale. It is well-known that this setting is equivalent to the so-called i.i.d. prophet inequality, for which optimal guarantees are known and evaluate to 0.745 in general (equivalent to a PPM with dynamic prices) and $1 - 1/e \\approx 0.632$ in the fixed threshold case (equivalent to a fixed price PPM). In this paper we consider an additional assumption, namely, that the underlying market is very large. This is modeled by first fixing a valuation distribution F and then making the number of buyers grow large, rather than considering the worst distribution for each possible market size. In this setting Kennedy and Kertz [Ann. Probab. 1991] breaks the 0.745 fraction achievable in general with a dynamic threshold policy. We prove that this large market benefit continue to hold when using fixed price PPMs, and show that the guarantee of 0.632 actually improves to 0.712. We then move to study the case of selling k identical units and we prove that the revenue gap of the fixed price PPM approaches $1-1/\\sqrt{ 2kπ}$. As this bound is achievable without the large market assumption, we obtain the somewhat surprising result that the large market advantage vanishes as $k$ grows.", "published": "2021-08-10T08:18:20Z", "authors": ["José Correa", "Dana Pizarro", "Victor Verdugo"], "categories": ["math.OC"], "doi": "", "_query": "housing price text listing", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2512.14662v3", "title": "Fixed-Income Pricing and the Replication of Liabilities", "abstract": "This paper develops a model-free framework for static fixed-income pricing and the replication of liability cash flows. The absence of static arbitrage across a universe of fixed-income instruments is equivalent to the existence of a strictly positive discount curve reproducing all observed prices. Linear programming duality then identifies the least-cost super-replication price with the largest value that any admissible discount curve assigns to the liability, so that the resulting bounds are attained and cannot be improved. Complementary slackness confines over-replication to dates that the optimal discount vector prices at zero, and a least-cost portfolio matches the liability exactly at no fewer dates than the rank of the cash-flow matrix. We also obtain generic uniqueness of that portfolio, an interpolation between quadratic hedging and super-replication, and a static treatment of swap--repo strategies. On US Treasury cross-sections the observed prices violate the law of one price, so that a discount curve must be estimated rather than bootstrapped; the least-cost portfolio then matches an annuity liability at almost every cash-flow date.", "published": "2025-12-16T18:23:16Z", "authors": ["Damir Filipović"], "categories": ["q-fin.MF"], "doi": "", "_query": "housing price text listing", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2511.00039v1", "title": "Graph-Attentive MAPPO for Dynamic Retail Pricing", "abstract": "Dynamic pricing in retail requires policies that adapt to shifting demand while coordinating decisions across related products. We present a systematic empirical study of multi-agent reinforcement learning for retail price optimization, comparing a strong MAPPO baseline with a graph-attention-augmented variant (MAPPO+GAT) that leverages learned interactions among products. Using a simulated pricing environment derived from real transaction data, we evaluate profit, stability across random seeds, fairness across products, and training efficiency under a standardized evaluation protocol. The results indicate that MAPPO provides a robust and reproducible foundation for portfolio-level price control, and that MAPPO+GAT further enhances performance by sharing information over the product graph without inducing excessive price volatility. These results indicate that graph-integrated MARL provides a more scalable and stable solution than independent learners for dynamic retail pricing, offering practical advantages in multi-product decision-making.", "published": "2025-10-28T00:15:59Z", "authors": ["Krishna Kumar Neelakanta Pillai Santha Kumari Amma"], "categories": ["cs.AI", "cs.LG"], "doi": "", "_query": "housing price text listing", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2005.01709v1", "title": "Regret Theory And Asset Pricing Anomalies In Incomplete Markets With Dynamic Un-Aggregated Preferences", "abstract": "Although the CML (Capital Market Line), the Intertemporal-CAPM, the CAPM/SML (Security Market Line) and the Intertemporal Arbitrage Pricing Theory (IAPT) are widely used in portfolio management, valuation and capital markets financing; these theories are inaccurate and can adversely affect risk management and portfolio management processes. This article introduces several empirically testable financial theories that provide insights, and can be calibrated to real data and used to solve problems, and contributes to the literature by: i) explaining the conditions under which ICAPM/CAPM, IAPT and CML may be accurate, and why such conditions are not feasible; and explaining why the existence of incomplete markets and dynamic un-aggregated markets render CML, IAPT and ICAPM inaccurate; ii) explaining why the Consumption-Savings-InvestmentProduction framework is insufficient for asset pricing and analysis of changes in risk and asset values; and introducing a unified approach to asset pricing that simultaneously considers six factors, and the conditions under which this approach will work; iii) explaining why leisure, taxes and housing are equally as important as consumption and investment in asset pricing; iv) introducing the Marginal Rate of Intertemporal Joint Substitution (MRIJS) among Consumption, Taxes, Investment, Leisure, Intangibles and Housing - this model incorporates Regret Theory and captures features of reality that dont fit well into standard asset pricing models, and this framework can support specific or very general finance theories and or very complicated models; v) showing why the Elasticity of Intertemporal Substitution (EIS) is inaccurate and is insufficient for asset pricing and analysis of investor preferences.", "published": "2020-05-01T17:35:38Z", "authors": ["Michael Nwogugu"], "categories": ["q-fin.GN"], "doi": "10.1057/978-1-137-44698-5", "_query": "housing price text listing", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/1810.04697v4", "title": "Prices, Profits, Proxies, and Production", "abstract": "This paper studies nonparametric identification and counterfactual bounds for heterogeneous firms that can be ranked in terms of productivity. Our approach works when quantities and prices are latent, rendering standard approaches inapplicable. Instead, we require observation of profits or other optimizing-values such as costs or revenues, and either prices or price proxies of flexibly chosen variables. We extend classical duality results for price-taking firms to a setup with discrete heterogeneity, endogeneity, and limited variation in possibly latent prices. Finally, we show that convergence results for nonparametric estimators may be directly converted to convergence results for production sets.", "published": "2018-10-10T18:15:29Z", "authors": ["Victor H. Aguiar", "Nail Kashaev", "Roy Allen"], "categories": ["econ.EM"], "doi": "", "_query": "housing price text listing", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2502.11733v3", "title": "Plant in Cupboard, Orange on Rably, Inat Aphone. Benchmarking Incremental Learning of Situation and Language Model using a Text-Simulated Situated Environment", "abstract": "Large Language Models (LLMs) serve not only as chatbots but as key components in agent systems, where their common-sense knowledge significantly impacts performance as language-based planners for situated or embodied action. We assess LLMs' incremental learning (based on feedback from the environment), and controlled in-context learning abilities using a text-based environment. We introduce challenging yet interesting set of experiments to test i) how agents can incrementally solve tasks related to every day objects in typical rooms in a house where each of them are discovered by interacting within the environment, ii) controlled in-context learning abilities and efficiency of agents by providing short info about locations of objects and rooms to check how faster the task can be solved, and finally iii) using synthetic pseudo-English words to gauge how well LLMs are at inferring meaning of unknown words from environmental feedback. Results show that larger commercial models have a substantial gap in performance compared to open-weight but almost all models struggle with the synthetic words experiments.", "published": "2025-02-17T12:20:39Z", "authors": ["Jonathan Jordan", "Sherzod Hakimov", "David Schlangen"], "categories": ["cs.CL"], "doi": "", "_query": "housing price text listing", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/1703.10806v2", "title": "Probabilistic Mid- and Long-Term Electricity Price Forecasting", "abstract": "The liberalization of electricity markets and the development of renewable energy sources has led to new challenges for decision makers. These challenges are accompanied by an increasing uncertainty about future electricity price movements. The increasing amount of papers, which aim to model and predict electricity prices for a short period of time provided new opportunities for market participants. However, the electricity price literature seem to be very scarce on the issue of medium- to long-term price forecasting, which is mandatory for investment and political decisions. Our paper closes this gap by introducing a new approach to simulate electricity prices with hourly resolution for several months up to three years. Considering the uncertainty of future events we are able to provide probabilistic forecasts which are able to detect probabilities for price spikes even in the long-run. As market we decided to use the EPEX day-ahead electricity market for Germany and Austria. Our model extends the X-Model which mainly utilizes the sale and purchase curve for electricity day-ahead auctions. By applying our procedure we are able to give probabilities for the due to the EEG practical relevant event of six consecutive hours of negative prices. We find that using the supply and demand curve based model in the long-run yields realistic patterns for the time series of electricity prices and leads to promising results considering common error measures.", "published": "2017-03-31T09:13:08Z", "authors": ["Florian Ziel", "Rick Steinert"], "categories": ["stat.AP", "q-fin.ST"], "doi": "10.1016/j.rser.2018.05.038", "_query": "housing price text listing", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2102.13157v1", "title": "Does Bankruptcy Protection Affect Asset Prices? Evidence from changes in Homestead Exemptions", "abstract": "Does the ability to protect an asset from unsecured creditors affect its price? This paper identifies the impact of bankruptcy protection on house prices using 139 changes in homestead exemptions. Large increases in the homestead exemption raised house prices 3% before 2005. Smaller exemption increases, to adjust for inflation, did not affect house prices. The effect disappeared after BAPCPA, a 2005 federal law designed to prevent bankruptcy abuse. The effect was bigger in inelastic locations.", "published": "2021-02-25T20:32:45Z", "authors": ["Yildiray Yildirim", "Albert Alex Zevelev"], "categories": ["econ.GN"], "doi": "", "_query": "housing price text listing", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/1007.4324v1", "title": "Clustering Unstructured Data (Flat Files) - An Implementation in Text Mining Tool", "abstract": "With the advancement of technology and reduced storage costs, individuals and organizations are tending towards the usage of electronic media for storing textual information and documents. It is time consuming for readers to retrieve relevant information from unstructured document collection. It is easier and less time consuming to find documents from a large collection when the collection is ordered or classified by group or category. The problem of finding best such grouping is still there. This paper discusses the implementation of k-Means clustering algorithm for clustering unstructured text documents that we implemented, beginning with the representation of unstructured text and reaching the resulting set of clusters. Based on the analysis of resulting clusters for a sample set of documents, we have also proposed a technique to represent documents that can further improve the clustering result.", "published": "2010-07-25T14:38:32Z", "authors": ["Yasir Safeer", "Atika Mustafa", "Anis Noor Ali"], "categories": ["cs.IR"], "doi": "", "_query": "housing price text listing", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/1404.0627v1", "title": "Extraction of Projection Profile, Run-Histogram and Entropy Features Straight from Run-Length Compressed Text-Documents", "abstract": "Document Image Analysis, like any Digital Image Analysis requires identification and extraction of proper features, which are generally extracted from uncompressed images, though in reality images are made available in compressed form for the reasons such as transmission and storage efficiency. However, this implies that the compressed image should be decompressed, which indents additional computing resources. This limitation induces the motivation to research in extracting features directly from the compressed image. In this research, we propose to extract essential features such as projection profile, run-histogram and entropy for text document analysis directly from run-length compressed text-documents. The experimentation illustrates that features are extracted directly from the compressed image without going through the stage of decompression, because of which the computing time is reduced. The feature values so extracted are exactly identical to those extracted from uncompressed images.", "published": "2014-04-02T17:34:13Z", "authors": ["Mohammed Javed", "P. Nagabhushan", "B. B. Chaudhuri"], "categories": ["cs.CV"], "doi": "10.1109/ACPR.2013.147", "_query": "housing price text listing", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2602.10483v1", "title": "Pricing Query Complexity of Multiplicative Revenue Approximation", "abstract": "We study the pricing query complexity of revenue maximization for a single buyer whose private valuation is drawn from an unknown distribution. In this setting, the seller must learn the optimal monopoly price by posting prices and observing only binary purchase decisions, rather than the realized valuations. Prior work has established tight query complexity bounds for learning a near-optimal price with additive error $\\varepsilon$ when the valuation distribution is supported on $[0,1]$. However, our understanding of how to learn a near-optimal price that achieves at least a $(1-\\varepsilon)$ fraction of the optimal revenue remains limited. In this paper, we study the pricing query complexity of the single-buyer revenue maximization problem under such multiplicative error guarantees in several settings. Observe that when pricing queries are the only source of information about the buyer's distribution, no algorithm can achieve a non-trivial approximation, since the scale of the distribution cannot be learned from pricing queries alone. Motivated by this fundamental impossibility, we consider two natural and well-motivated models that provide \"scale hints\": (i) a one-sample hint, in which the algorithm observes a single realized valuation before making pricing queries; and (ii) a value-range hint, in which the valuation support is known to lie within $[1, H]$. For each type of hint, we establish pricing query complexity guarantees that are tight up to polylogarithmic factors for several classes of distributions, including monotone hazard rate (MHR) distributions, regular distributions, and general distributions.", "published": "2026-02-11T03:42:42Z", "authors": ["Wei Tang", "Yifan Wang", "Mengxiao Zhang"], "categories": ["cs.GT", "cs.LG"], "doi": "", "_query": "housing price text listing", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2201.02729v1", "title": "Bitcoin Price Predictive Modeling Using Expert Correction", "abstract": "The paper studies the linear model for Bitcoin price which includes regression features based on Bitcoin currency statistics, mining processes, Google search trends, Wikipedia pages visits. The pattern of deviation of regression model prediction from real prices is simpler comparing to price time series. It is assumed that this pattern can be predicted by an experienced expert. In such a way, using the combination of the regression model and expert correction, one can receive better results than with either regression model or expert opinion only. It is shown that Bayesian approach makes it possible to utilize the probabilistic approach using distributions with fat tails and take into account the outliers in Bitcoin price time series.", "published": "2022-01-06T15:11:51Z", "authors": ["Bohdan M. Pavlyshenko"], "categories": ["q-fin.ST", "cs.LG"], "doi": "", "_query": "housing price text listing", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/1808.05996v1", "title": "$k$th price auctions and Catalan numbers", "abstract": "This paper establishes an interesting link between $k$th price auctions and Catalan numbers by showing that for distributions that have linear density, the bid function at any symmetric, increasing equilibrium of a $k$th price auction with $k\\geq 3$ can be represented as a finite series of $k-2$ terms whose $\\ell$th term involves the $\\ell$th Catalan number. Using an integral representation of Catalan numbers, together with some classical combinatorial identities, we derive the closed form of the unique symmetric, increasing equilibrium of a $k$th price auction for a non-uniform distribution.", "published": "2018-08-17T20:59:41Z", "authors": ["Abdel-Hameed Nawar", "Debapriya Sen"], "categories": ["econ.TH"], "doi": "10.1016/j.econlet.2018.08.018", "_query": "housing price text listing", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/1807.10537v1", "title": "Investigating Wheat Price with a Multi-Agent Model", "abstract": "In this paper, we build a computational model for the analysis of international wheat spot price formation, its dynamics and the dynamics of internationally exchanged quantities. The model has been calibrated using FAOSTAT data to evaluate its in-sample predictive power. The model is able to generate wheat prices in twelve international markets and wheat used quantities in twenty-four world regions. The time span considered goes from 1992 to 2013. In our study, a particular attention was paid to the impact of Russian Federation's 2010 grain export ban on wheat price and internationally traded quantities. Among other results, we find that wheat average weighted world price in 2013 would have been 3.55\\% lower than the observed one if the Russian Federation would not have imposed the export ban in 2010.", "published": "2018-07-27T11:18:19Z", "authors": ["Gianfranco Giulioni", "Edmondo Di Giuseppe", "Massimiliano Pasqui", "Piero Toscano", "Francesco Miglietta"], "categories": ["econ.TH"], "doi": "", "_query": "housing price text listing", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2301.05677v2", "title": "Price impact in equity auctions: zero, then linear", "abstract": "Using high-quality data, we report several statistical regularities of equity auctions in the Paris stock exchange. First, the average order book density is linear around the auction price at the time of auction clearing and has a large peak at the auction price. While the peak is due to slow traders, the order density shape is the result of subtle dynamics. The impact of a new market order or cancellation at the auction time can be decomposed into three parts as a function of the size of the additional order: (1) zero impact, caused by the discrete nature of prices, sometimes up to a surprisingly large additional volume relative to the auction volume (2) linear impact for additional orders up to a large fraction of the auction volume (3) for even larger orders price impact is non-linear, frequently super-linear.", "published": "2023-01-13T17:56:12Z", "authors": ["Mohammed Salek", "Damien Challet", "Ioane Muni Toke"], "categories": ["q-fin.ST", "q-fin.TR"], "doi": "", "_query": "housing price text listing", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/cond-mat/0106657v1", "title": "Quantifying Stock Price Response to Demand Fluctuations", "abstract": "We address the question of how stock prices respond to changes in demand. We quantify the relations between price change $G$ over a time interval $Δt$ and two different measures of demand fluctuations: (a) $Φ$, defined as the difference between the number of buyer-initiated and seller-initiated trades, and (b) $Ω$, defined as the difference in number of shares traded in buyer and seller initiated trades. We find that the conditional expectations $<G >_Ω$ and $<G >_Φ$ of price change for a given $Ω$ or $Φ$ are both concave. We find that large price fluctuations occur when demand is very small --- a fact which is reminiscent of large fluctuations that occur at critical points in spin systems, where the divergent nature of the response function leads to large fluctuations.", "published": "2001-06-29T22:51:37Z", "authors": ["Vasiliki Plerou", "Parameswaran Gopikrishnan", "Xavier Gabaix", "H. Eugene Stanley"], "categories": ["cond-mat.stat-mech", "cond-mat.dis-nn", "q-fin.TR"], "doi": "10.1103/PhysRevE.66.027104", "_query": "housing price text listing", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2101.01024v2", "title": "Model-free price bounds under dynamic option trading", "abstract": "In this paper we extend discrete time semi-static trading strategies by also allowing for dynamic trading in a finite amount of options, and we study the consequences for the model-independent super-replication prices of exotic derivatives. These include duality results as well as a precise characterization of pricing rules for the dynamically tradable options triggering an improvement of the price bounds for exotic derivatives in comparison with the conventional price bounds obtained through the martingale optimal transport approach.", "published": "2021-01-04T15:24:15Z", "authors": ["Ariel Neufeld", "Julian Sester"], "categories": ["q-fin.MF", "math.PR", "q-fin.CP"], "doi": "", "_query": "housing price text listing", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/cond-mat/0109280v1", "title": "Market application of the percolation model: Relative price distribution", "abstract": "We study a variant of the Cont-Bouchaud model which utilizes the perco lation approach of multi-agent simulations of the stock market fluctuations. Here, instead of considering the relative price change as the difference of the total demand and total supply, we consider the relative price change to be proportiona l to the ``relative'' difference of demand and supply (the ratio of the difference in total demand and total supply to the sum of the total demand and total supply). We then study the probability distribution of the price changes.", "published": "2001-09-15T17:52:05Z", "authors": ["Anirban Chakraborti"], "categories": ["cond-mat.stat-mech"], "doi": "10.1142/S0129183102002900", "_query": "housing price text listing", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2004.00473v1", "title": "Dimensional Pricing Based on Dynamism of Load", "abstract": "Electricity supply is not simply a matter of quantity, but a time lasting service that matches with a wave-like load curve. It logically deserves a pricing based on the curve per se rather than simply integral of load. This paper introduces to price electricity consumption based on dynamism of load, which is an equivalent characterization of load. Orthonormal basis with definable and distinguishable periodicity that can linearly express load curves constitutes a space to capture the dynamism, where coefficients of the basis quantify the dynamism in multiple dimensions. A price function is proposed to map the coefficients to a numerical charge. A pricing model on the space specialized by the Fourier series is derived for the simplest one-source-one-subscriber system and generalized to a single-bus system with multiple sources and multiple subscribers. Examples will demonstrate the use of the proposed pricing and its effectiveness in reflecting the cost of generation to cope with load dynamism and guaranteeing fairness.", "published": "2020-04-01T14:42:40Z", "authors": ["Jinghuan Ma", "Jie Gu", "Zhijian Jin"], "categories": ["eess.SY"], "doi": "", "_query": "housing price text listing", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2312.15999v1", "title": "Pricing with Contextual Elasticity and Heteroscedastic Valuation", "abstract": "We study an online contextual dynamic pricing problem, where customers decide whether to purchase a product based on its features and price. We introduce a novel approach to modeling a customer's expected demand by incorporating feature-based price elasticity, which can be equivalently represented as a valuation with heteroscedastic noise. To solve the problem, we propose a computationally efficient algorithm called \"Pricing with Perturbation (PwP)\", which enjoys an $O(\\sqrt{dT\\log T})$ regret while allowing arbitrary adversarial input context sequences. We also prove a matching lower bound at $Ω(\\sqrt{dT})$ to show the optimality regarding $d$ and $T$ (up to $\\log T$ factors). Our results shed light on the relationship between contextual elasticity and heteroscedastic valuation, providing insights for effective and practical pricing strategies.", "published": "2023-12-26T11:07:37Z", "authors": ["Jianyu Xu", "Yu-Xiang Wang"], "categories": ["cs.LG", "econ.EM", "stat.ML"], "doi": "", "_query": "housing price text listing", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2302.12319v1", "title": "Age and market capitalization drive large price variations of cryptocurrencies", "abstract": "Cryptocurrencies are considered the latest innovation in finance with considerable impact across social, technological, and economic dimensions. This new class of financial assets has also motivated a myriad of scientific investigations focused on understanding their statistical properties, such as the distribution of price returns. However, research so far has only considered Bitcoin or at most a few cryptocurrencies, whilst ignoring that price returns might depend on cryptocurrency age or be influenced by market capitalization. Here, we therefore present a comprehensive investigation of large price variations for more than seven thousand digital currencies and explore whether price returns change with the coming-of-age and growth of the cryptocurrency market. We find that tail distributions of price returns follow power-law functions over the entire history of the considered cryptocurrency portfolio, with typical exponents implying the absence of characteristic scales for price variations in about half of them. Moreover, these tail distributions are asymmetric as positive returns more often display smaller exponents, indicating that large positive price variations are more likely than negative ones. Our results further reveal that changes in the tail exponents are very often simultaneously related to cryptocurrency age and market capitalization or only to age, with only a minority of cryptoassets being affected just by market capitalization or neither of the two quantities. Lastly, we find that the trends in power-law exponents usually point to mixed directions, and that large price variations are likely to become less frequent only in about 28\\% of the cryptocurrencies as they age and grow in market capitalization.", "published": "2023-02-23T20:31:11Z", "authors": ["Arthur A. B. Pessa", "Matjaz Perc", "Haroldo V. Ribeiro"], "categories": ["physics.soc-ph", "q-fin.ST"], "doi": "10.1038/s41598-023-30431-3", "_query": "housing price text listing", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2201.07903v1", "title": "Identification of Direct Socio-Geographical Price Discrimination: An Empirical Study on iPhones", "abstract": "Price discrimination is a practice where firms utilize varying sensitivities to prices among consumers to increase profits. The welfare effects of price discrimination are not agreed on among economists, but identification of such actions may contribute to our standing of firms' pricing behaviors. In this letter, I use econometric tools to analyze whether Apple Inc, one of the largest companies in the globe, is practicing price discrimination on the basis of socio-economical and geographical factors. My results indicate that iPhones are significantly (p $<$ 0.01) more expensive in markets where competitions are weak or where Apple has a strong market presence. Furthermore, iPhone prices are likely to increase (p $<$ 0.01) in developing countries/regions or markets with high income inequality.", "published": "2022-01-19T23:01:17Z", "authors": ["Davidson Cheng"], "categories": ["econ.EM"], "doi": "", "_query": "housing price text listing", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2607.09956v1", "title": "Optimizing ARDL Models for Retail Sales Forecasting and Fair Pricing", "abstract": "Pricing food products to balance profitability with consumer welfare is a central challenge for retailers. Dynamic pricing is widely used to maximize revenue, yet most pricing models optimize business objectives while overlooking consumer fairness. This paper studies the risk of consumer exploitation under dynamic food pricing in Canada and proposes a methodology that embeds fairness constraints directly into retail sales forecasting. We model total retail trade sales with a log--log Autoregressive Distributed Lag (ARDL) specification, in which the coefficient on a product price is a sales elasticity, and pose the pricing problem as maximizing forecast sales subject to price bounds anchored to the Consumer Price Index (CPI). We solve this problem with both Linear Programming (LP) and Simulated Annealing (SA), under single-product and multi-product configurations. A key finding is that the fitted nominal elasticities are positive. As a result, an unconstrained sales-maximizer would push every price to its upper bound, and the CPI ceiling is the safeguard that prevents this. Simulated Annealing instead settles on conservative, interior prices that lower consumer cost while still meeting the sales target. We benchmark forecast accuracy against naive, seasonal-naive, ARIMA, and SARIMA baselines, and a CPI-deflated re-specification shows that the positive nominal elasticities are largely an inflation-driven artifact. The result is a transparent, fairness-aware pricing framework.", "published": "2026-07-10T20:19:38Z", "authors": ["Sujay Uday Rittikar"], "categories": ["cs.LG", "cs.CE"], "doi": "", "_query": "housing price text listing", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2002.02010v4", "title": "Crude oil price forecasting incorporating news text", "abstract": "Sparse and short news headlines can be arbitrary, noisy, and ambiguous, making it difficult for classic topic model LDA (latent Dirichlet allocation) designed for accommodating long text to discover knowledge from them. Nonetheless, some of the existing research about text-based crude oil forecasting employs LDA to explore topics from news headlines, resulting in a mismatch between the short text and the topic model and further affecting the forecasting performance. Exploiting advanced and appropriate methods to construct high-quality features from news headlines becomes crucial in crude oil forecasting. To tackle this issue, this paper introduces two novel indicators of topic and sentiment for the short and sparse text data. Empirical experiments show that AdaBoost.RT with our proposed text indicators, with a more comprehensive view and characterization of the short and sparse text data, outperforms the other benchmarks. Another significant merit is that our method also yields good forecasting performance when applied to other futures commodities.", "published": "2020-01-19T16:58:05Z", "authors": ["Yun Bai", "Xixi Li", "Hao Yu", "Suling Jia"], "categories": ["q-fin.ST"], "doi": "", "_query": "housing price text listing", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2409.14157v1", "title": "Price predictability in limit order book with deep learning model", "abstract": "This study explores the prediction of high-frequency price changes using deep learning models. Although state-of-the-art methods perform well, their complexity impedes the understanding of successful predictions. We found that an inadequately defined target price process may render predictions meaningless by incorporating past information. The commonly used three-class problem in asset price prediction can generally be divided into volatility and directional prediction. When relying solely on the price process, directional prediction performance is not substantial. However, volume imbalance improves directional prediction performance.", "published": "2024-09-21T14:40:13Z", "authors": ["Kyungsub Lee"], "categories": ["q-fin.ST", "q-fin.TR", "stat.ML"], "doi": "", "_query": "housing price text listing", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/1804.00804v1", "title": "Automatic Normalization of Word Variations in Code-Mixed Social Media Text", "abstract": "Social media platforms such as Twitter and Facebook are becoming popular in multilingual societies. This trend induces portmanteau of South Asian languages with English. The blend of multiple languages as code-mixed data has recently become popular in research communities for various NLP tasks. Code-mixed data consist of anomalies such as grammatical errors and spelling variations. In this paper, we leverage the contextual property of words where the different spelling variation of words share similar context in a large noisy social media text. We capture different variations of words belonging to same context in an unsupervised manner using distributed representations of words. Our experiments reveal that preprocessing of the code-mixed dataset based on our approach improves the performance in state-of-the-art part-of-speech tagging (POS-tagging) and sentiment analysis tasks.", "published": "2018-04-03T03:19:31Z", "authors": ["Rajat Singh", "Nurendra Choudhary", "Manish Shrivastava"], "categories": ["cs.CL"], "doi": "10.1007/978-3-031-23793-5_30", "_query": "housing price text listing", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2011.03653v2", "title": "No-regret Learning in Price Competitions under Consumer Reference Effects", "abstract": "We study long-run market stability for repeated price competitions between two firms, where consumer demand depends on firms' posted prices and consumers' price expectations called reference prices. Consumers' reference prices vary over time according to a memory-based dynamic, which is a weighted average of all historical prices. We focus on the setting where firms are not aware of demand functions and how reference prices are formed but have access to an oracle that provides a measure of consumers' responsiveness to the current posted prices. We show that if the firms run no-regret algorithms, in particular, online mirror descent(OMD), with decreasing step sizes, the market stabilizes in the sense that firms' prices and reference prices converge to a stable Nash Equilibrium (SNE). Interestingly, we also show that there exist constant step sizesunder which the market stabilizes. We further characterize the rate of convergence to the SNE for both decreasing and constant OMD step sizes.", "published": "2020-11-07T01:38:20Z", "authors": ["Negin Golrezaei", "Patrick Jaillet", "Jason Cheuk Nam Liang"], "categories": ["cs.GT"], "doi": "", "_query": "housing price text listing", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2609.10615v3", "title": "ACT, WAIT, or EXPERIMENT: A Causal Governance Framework for Retail Price Optimization Under Abstentions", "abstract": "This paper presents a causal decision framework for list-price elasticities in intermediated retail channels, where estimates are confounded by promotions, competitor moves and shopkeeper pass-through. Rather than forcing a number when the evidence is ambiguous, the system treats abstention (Wait) as an active diagnostic output. Built on Double Machine Learning and conformal prediction, it evaluates whether conditions exist to act on a recommended price or whether withholding is preferable, and it states when: acting beats withholding exactly when the calibration slope of the true headroom on the recommended move exceeds one half, a condition that predicts the sign of the gain in 37 of 38 synthetic groups. When the system abstains, it classifies the cause and flags which products are candidates for a designed pricing experiment. Aggregating estimates to the brand or category level, where independent price designs average out, cuts the root mean squared error from 0.488 to 0.159, although aggregation alone does not make the intervals honest. Tested on synthetic data with known truth and on public scanner data, the study reports its costs: the system abstains on 52% to 87% of presentations that were identifiable, and a single-window estimate forecasts real price changes poorly (calibration slope 0.22). What it offers is a way to audit any abstention rule and to publish at the unit the evidence supports; no commercial panel or pilot was used, so it does not measure commercial uplift.", "published": "2026-09-08T15:42:31Z", "authors": ["Pedro Cadahia"], "categories": ["econ.EM"], "doi": "", "_query": "housing price text listing", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2610.09985v1", "title": "Marrying Pricing and Advertising with LLMs", "abstract": "We study a sequential pricing problem in which a seller jointly posts a price and an advertisement generated by a large language model (LLM). The seller aims to maximize revenue under an unknown product demand that depends on both decisions, while observing only whether each offer leads to a purchase. We propose an online actor-critic algorithm that combines low-rank adaptation (LoRA) of a pretrained LLM with a demand model fitted to available data. At each round, the actor generates an advertisement, and the critic estimates purchase probabilities to guide price selection. Then, the resulting feedback is used to update both the actor and the critic, with the critic's revenue estimates providing a baseline for policy gradient updates of the actor. To evaluate our approach, we develop an evaluation framework with three synthetic demand models and a demand simulator built from real-world marketplace data. Finally, we compare our algorithm with benchmarks that do not jointly optimize price selection and advertisement generation, achieving expected revenue gains over the reference policy of 5.69%, 5.18% and 55.96% under the three synthetic demand models and 5.81% under the marketplace simulator.", "published": "2026-10-07T12:50:08Z", "authors": ["Alessandro Barro", "Francesco Bacchiocchi", "Francesco Emanuele Stradi", "Alberto Marchesi"], "categories": ["cs.GT", "cs.LG"], "doi": "", "_query": "housing price text listing", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2109.13905v1", "title": "Intra-Day Price Simulation with Generative Adversarial Modelling of the Order Flow", "abstract": "Intra-day price variations in financial markets are driven by the sequence of orders, called the order flow, that is submitted at high frequency by traders. This paper introduces a novel application of the Sequence Generative Adversarial Networks framework to model the order flow, such that random sequences of the order flow can then be generated to simulate the intra-day variation of prices. As a benchmark, a well-known parametric model from the quantitative finance literature is selected. The models are fitted, and then multiple random paths of the order flow sequences are sampled from each model. Model performances are then evaluated by using the generated sequences to simulate price variations, and we compare the empirical regularities between the price variations produced by the generated and real sequences. The empirical regularities considered include the distribution of the price log-returns, the price volatility, and the heavy-tail of the log-returns distributions. The results show that the order sequences from the generative model are better able to reproduce the statistical behaviour of real price variations than the sequences from the benchmark.", "published": "2021-09-28T17:55:09Z", "authors": ["Ye-Sheen Lim", "Denise Gorse"], "categories": ["q-fin.ST", "cs.LG", "q-fin.TR"], "doi": "", "_query": "housing price text listing", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2301.01323v2", "title": "Graphical House Allocation", "abstract": "The classical house allocation problem involves assigning $n$ houses (or items) to $n$ agents according to their preferences. A key criterion in such problems is satisfying some fairness constraints such as envy-freeness. We consider a generalization of this problem wherein the agents are placed along the vertices of a graph (corresponding to a social network), and each agent can only experience envy towards its neighbors. Our goal is to minimize the aggregate envy among the agents as a natural fairness objective, i.e., the sum of all pairwise envy values over all edges in a social graph. When agents have identical and evenly-spaced valuations, our problem reduces to the well-studied problem of linear arrangements. For identical valuations with possibly uneven spacing, we show a number of deep and surprising ways in which our setting is a departure from this classical problem. More broadly, we contribute several structural and computational results for various classes of graphs, including NP-hardness results for disjoint unions of paths, cycles, stars, or cliques, and fixed-parameter tractable (and, in some cases, polynomial-time) algorithms for paths, cycles, stars, cliques, and their disjoint unions. Additionally, a conceptual contribution of our work is the formulation of a structural property for disconnected graphs that we call separability which results in efficient parameterized algorithms for finding optimal allocations.", "published": "2023-01-03T19:26:15Z", "authors": ["Hadi Hosseini", "Justin Payan", "Rik Sengupta", "Rohit Vaish", "Vignesh Viswanathan"], "categories": ["cs.GT", "cs.AI"], "doi": "", "_query": "housing price text listing", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/1410.1135v1", "title": "Corpora Preparation and Stopword List Generation for Arabic data in Social Network", "abstract": "This paper proposes a methodology to prepare corpora in Arabic language from online social network (OSN) and review site for Sentiment Analysis (SA) task. The paper also proposes a methodology for generating a stopword list from the prepared corpora. The aim of the paper is to investigate the effect of removing stopwords on the SA task. The problem is that the stopwords lists generated before were on Modern Standard Arabic (MSA) which is not the common language used in OSN. We have generated a stopword list of Egyptian dialect and a corpus-based list to be used with the OSN corpora. We compare the efficiency of text classification when using the generated lists along with previously generated lists of MSA and combining the Egyptian dialect list with the MSA list. The text classification was performed using Naïve Bayes and Decision Tree classifiers and two feature selection approaches, unigrams and bigram. The experiments show that the general lists containing the Egyptian dialects words give better performance than using lists of MSA stopwords only.", "published": "2014-10-05T09:02:31Z", "authors": ["Walaa Medhat", "Ahmed H. Yousef", "Hoda Korashy"], "categories": ["cs.CL"], "doi": "", "_query": "housing price text listing", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2002.09549v3", "title": "Optimal Signal-Adaptive Trading with Temporary and Transient Price Impact", "abstract": "We study optimal liquidation in the presence of linear temporary and transient price impact along with taking into account a general price predicting finite-variation signal. We formulate this problem as minimization of a cost-risk functional over a class of absolutely continuous and signal-adaptive strategies. The stochastic control problem is solved by following a probabilistic and convex analytic approach. We show that the optimal trading strategy is given by a system of four coupled forward-backward SDEs, which can be solved explicitly. Our results reveal how the induced transient price distortion provides together with the predictive signal an additional predictor about future price changes. As a consequence, the optimal signal-adaptive trading rate trades off exploiting the predictive signal against incurring the transient displacement of the execution price from its unaffected level. This answers an open question from Lehalle and Neuman [29] as we show how to derive the unique optimal signal-adaptive liquidation strategy when price impact is not only temporary but also transient.", "published": "2020-02-21T20:56:08Z", "authors": ["Eyal Neuman", "Moritz Voß"], "categories": ["q-fin.TR", "math.PR"], "doi": "", "_query": "housing price text listing", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2308.11473v1", "title": "IT3D: Improved Text-to-3D Generation with Explicit View Synthesis", "abstract": "Recent strides in Text-to-3D techniques have been propelled by distilling knowledge from powerful large text-to-image diffusion models (LDMs). Nonetheless, existing Text-to-3D approaches often grapple with challenges such as over-saturation, inadequate detailing, and unrealistic outputs. This study presents a novel strategy that leverages explicitly synthesized multi-view images to address these issues. Our approach involves the utilization of image-to-image pipelines, empowered by LDMs, to generate posed high-quality images based on the renderings of coarse 3D models. Although the generated images mostly alleviate the aforementioned issues, challenges such as view inconsistency and significant content variance persist due to the inherent generative nature of large diffusion models, posing extensive difficulties in leveraging these images effectively. To overcome this hurdle, we advocate integrating a discriminator alongside a novel Diffusion-GAN dual training strategy to guide the training of 3D models. For the incorporated discriminator, the synthesized multi-view images are considered real data, while the renderings of the optimized 3D models function as fake data. We conduct a comprehensive set of experiments that demonstrate the effectiveness of our method over baseline approaches.", "published": "2023-08-22T14:39:17Z", "authors": ["Yiwen Chen", "Chi Zhang", "Xiaofeng Yang", "Zhongang Cai", "Gang Yu", "Lei Yang", "Guosheng Lin"], "categories": ["cs.CV", "cs.AI"], "doi": "", "_query": "housing price text listing", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2308.08549v1", "title": "Effects of Daily News Sentiment on Stock Price Forecasting", "abstract": "Predicting future prices of a stock is an arduous task to perform. However, incorporating additional elements can significantly improve our predictions, rather than relying solely on a stock's historical price data to forecast its future price. Studies have demonstrated that investor sentiment, which is impacted by daily news about the company, can have a significant impact on stock price swings. There are numerous sources from which we can get this information, but they are cluttered with a lot of noise, making it difficult to accurately extract the sentiments from them. Hence the focus of our research is to design an efficient system to capture the sentiments from the news about the NITY50 stocks and investigate how much the financial news sentiment of these stocks are affecting their prices over a period of time. This paper presents a robust data collection and preprocessing framework to create a news database for a timeline of around 3.7 years, consisting of almost half a million news articles. We also capture the stock price information for this timeline and create multiple time series data, that include the sentiment scores from various sections of the article, calculated using different sentiment libraries. Based on this, we fit several LSTM models to forecast the stock prices, with and without using the sentiment scores as features and compare their performances.", "published": "2023-08-02T06:42:39Z", "authors": ["S. Srinivas", "R. Gadela", "R. Sabu", "A. Das", "G. Nath", "V. Datla"], "categories": ["q-fin.ST", "cs.LG"], "doi": "", "_query": "housing price text listing", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/1505.02416v2", "title": "Portfolio optimisation beyond semimartingales: shadow prices and fractional Brownian motion", "abstract": "While absence of arbitrage in frictionless financial markets requires price processes to be semimartingales, non-semimartingales can be used to model prices in an arbitrage-free way, if proportional transaction costs are taken into account. In this paper, we show, for a class of price processes which are not necessarily semimartingales, the existence of an optimal trading strategy for utility maximisation under transaction costs by establishing the existence of a so-called shadow price. This is a semimartingale price process, taking values in the bid ask spread, such that frictionless trading for that price process leads to the same optimal strategy and utility as the original problem under transaction costs. Our results combine arguments from convex duality with the stickiness condition introduced by P. Guasoni. They apply in particular to exponential utility and geometric fractional Brownian motion. In this case, the shadow price is an Ito process. As a consequence we obtain a rather surprising result on the pathwise behaviour of fractional Brownian motion: the trajectories may touch an Ito process in a one-sided manner without reflection.", "published": "2015-05-10T18:04:48Z", "authors": ["Christoph Czichowsky", "Walter Schachermayer"], "categories": ["q-fin.MF", "q-fin.PM"], "doi": "", "_query": "housing price text listing", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/1106.5706v2", "title": "Theory of Information Pricing", "abstract": "In financial markets valuable information is rarely circulated homogeneously, because of time required for information to spread. However, advances in communication technology means that the 'lifetime' of important information is typically short. Hence, viewed as a tradable asset, information shares the characteristics of a perishable commodity: while it can be stored and transmitted freely, its worth diminishes rapidly in time. In view of recent developments where internet search engines and other information providers are offering information to financial institutions, the problem of pricing information is becoming increasingly important. With this in mind, a new formulation of utility-indifference argument is introduced and used as a basis for pricing information. Specifically, we regard information as a quantity that converts a prior distribution into a posterior distribution. The amount of information can then be quantified by relative entropy. The key to our utility indifference argument is to equate the maximised a posterior utility, after paying certain cost for the information, with the a posterior expectation of the utility based on the a priori optimal strategy. This formulation leads to one price for a given quantity of upside information; and another price for a given quantity of downside information. The ideas are illustrated by means of simple examples.", "published": "2011-06-28T15:20:27Z", "authors": ["Dorje C. Brody", "Yan Tai Law"], "categories": ["q-fin.PR"], "doi": "", "_query": "housing price text listing", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2204.03954v7", "title": "Are We Really Making Much Progress in Text Classification? A Comparative Review", "abstract": "We survey the literature on single-label, multi-label, and hierarchical text classification and provide a quantitative comparison of methods categorized into bag-of-words, sequence-based, and graph- or hierarchy-based approaches. Despite a recent surge in graph-based methods, they do not provide an improvement over fine-tuned transformer models on most evaluated datasets. Decoder-only generative language models show promise in few-shot in-context learning, but appear to lag behind fine-tuned language models when sufficient training data is available. The amount of training data needed for a fine-tuned language model to exceed the performance of a generative model is task-dependent. We further highlight the variance in reported numbers across the literature when applying the same model to the same dataset, which can be traced to the use of different hyperparameter values, such as the fine-tuning learning rate. For practitioners, we recommend using a fine-tuned language model when sufficient training data is available. Otherwise, a frozen generative model, enhanced by few-shot in-context learning or reasoning, is preferable. The source code and further information are available at: https://github.com/ascherp/text-classification-survey", "published": "2022-04-08T09:28:20Z", "authors": ["Ansgar Scherp", "Andor Diera", "Fabian Karl", "Bao Xin Lin", "Bhakti Khera", "Tim Meuser", "Tushar Singhal", "Lukas Galke"], "categories": ["cs.CL"], "doi": "", "_query": "housing price text listing", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2608.29366v1", "title": "Strike Price Optimization for ISO New England's Day-Ahead Ancillary Services", "abstract": "ISO New England's (ISO-NE) Day-Ahead Ancillary Services Initiative settles reserve products as financial call options on real-time energy prices. The system-wide strike price creates an efficiency-reliability tradeoff: increasing it lowers competitive reserve offers, but weakens resources' incentives to incur preparation costs and remain available for real-time performance. The existing strike price rule does not explicitly account for heterogeneous resource incentives and reserve requirements. We develop an optimization framework that selects the highest strike price while ensuring that enough resources retain an incentive to prepare and collectively satisfy the reserve requirements. Because a resource's preparation decision may affect the resulting real-time price distribution, its incentive depends on an unobservable counterfactual. To address this, we derive a tight lower-bound certificate using only the available conditional price distribution and a bound on the resource's price impact. We show that each resource enters the optimization through a single incentive threshold and that any finite optimal strike price occurs at one of these thresholds. This yields a tractable exact solution method based on threshold calculations and a small number of linear feasibility checks. Using reconstructed ISO-NE conditional price distributions and representative gas-fired resources, we find that higher heat-rate combustion turbines are more likely than combined-cycle resources to constrain the strike price choice.", "published": "2026-08-29T16:58:03Z", "authors": ["Karl Zhu", "Parviz Alivand", "Jinye Zhao", "Tongxin Zheng", "Dimitris Bertsimas"], "categories": ["math.OC"], "doi": "", "_query": "housing price text listing", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2311.17011v4", "title": "Pricing and hedging for a sticky diffusion", "abstract": "We introduce a financial market model featuring a risky asset whose price follows a sticky geometric Brownian motion and a riskless asset that grows with a constant interest rate $r\\in \\mathbb R $. We prove that this model satisfies No Arbitrage (NA) and No Free Lunch with Vanishing Risk (NFLVR) only when $r=0 $. Under this condition, we derive the corresponding arbitrage-free pricing equation, assess replicability and representation of the replication strategy. We then show that all locally bounded replicable payoffs for the standard Black--Scholes model are also replicable for the sticky model. Last, we evaluate via numerical experiments the impact of hedging in discrete time and of misrepresenting price stickiness.", "published": "2023-11-28T18:06:25Z", "authors": ["Alexis Anagnostakis"], "categories": ["q-fin.MF", "math.PR"], "doi": "10.1017/jpr.2025.2", "_query": "housing price text listing", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2208.04985v2", "title": "Pricing Novel Goods", "abstract": "We study a bilateral trade problem where a principal has private information that is revealed with delay, such as a seller who does not yet know her production cost. Postponing the contracting process incurs a costly delay, while early contracting with limited information can create incentive issues, as the principal might misrepresent private information that will be revealed later. We show that the optimal mechanism can effectively address these challenges by leveraging the sequential nature of the problem. The optimal mechanism is a menu of two-part tariffs, where the variable part is determined by the principal's incentives and the fixed part by the agent's incentives. As two-part tariffs might be impractical in some applications, we also study price mechanisms. We show that the optimal price mechanism often entails trade at both the ex-ante and ex-post stages. Dynamic price mechanisms can lower the cost of delay by transacting with high-type agents early and relax the incentive constraints by postponing contracts with lower-type agents. We also generalize our analysis to costly learning and study ex-post efficiency in our context.", "published": "2022-08-09T18:26:14Z", "authors": ["Francesco Giovannoni", "Toomas Hinnosaar"], "categories": ["econ.TH"], "doi": "", "_query": "housing price text listing", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2107.07725v2", "title": "Learning to Price against a Budget and ROI Constrained Buyer", "abstract": "Internet advertisers (buyers) repeatedly procure ad impressions from ad platforms (sellers) with the aim to maximize total conversion (i.e. ad value) while respecting both budget and return-on-investment (ROI) constraints for efficient utilization of limited monetary resources. Facing such a constrained buyer who aims to learn her optimal strategy to acquire impressions, we study from a seller's perspective how to learn and price ad impressions through repeated posted price mechanisms to maximize revenue. For this two-sided learning setup, we propose a learning algorithm for the seller that utilizes an episodic binary-search procedure to identify a revenue-optimal selling price. We show that such a simple learning algorithm enjoys low seller regret when within each episode, the budget and ROI constrained buyer approximately best responds to the posted price. We present simple yet natural buyer's bidding algorithms under which the buyer approximately best responds while satisfying budget and ROI constraints, leading to a low regret for our proposed seller pricing algorithm. The design of our seller algorithm is motivated by the fact that the seller's revenue function admits a bell-shaped structure when the buyer best responds to prices under budget and ROI constraints, enabling our seller algorithm to identify revenue-optimal selling prices efficiently.", "published": "2021-07-16T06:50:52Z", "authors": ["Negin Golrezaei", "Patrick Jaillet", "Jason Cheuk Nam Liang", "Vahab Mirrokni"], "categories": ["cs.GT"], "doi": "", "_query": "housing price text listing", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/1007.1631v1", "title": "Price dynamics in financial markets: a kinetic approach", "abstract": "The use of kinetic modelling based on partial differential equations for the dynamics of stock price formation in financial markets is briefly reviewed. The importance of behavioral aspects in market booms and crashes and the role of agents' heterogeneity in emerging power laws for price distributions is emphasized and discussed.", "published": "2010-07-09T17:29:50Z", "authors": ["Dario Maldarella", "Lorenzo Pareschi"], "categories": ["q-fin.TR", "cond-mat.stat-mech", "q-fin.ST"], "doi": "", "_query": "housing price text listing", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2005.00129v2", "title": "Structure-Tags Improve Text Classification for Scholarly Document Quality Prediction", "abstract": "Training recurrent neural networks on long texts, in particular scholarly documents, causes problems for learning. While hierarchical attention networks (HANs) are effective in solving these problems, they still lose important information about the structure of the text. To tackle these problems, we propose the use of HANs combined with structure-tags which mark the role of sentences in the document. Adding tags to sentences, marking them as corresponding to title, abstract or main body text, yields improvements over the state-of-the-art for scholarly document quality prediction. The proposed system is applied to the task of accept/reject prediction on the PeerRead dataset and compared against a recent BiLSTM-based model and joint textual+visual model as well as against plain HANs. Compared to plain HANs, accuracy increases on all three domains. On the computation and language domain our new model works best overall, and increases accuracy 4.7% over the best literature result. We also obtain improvements when introducing the tags for prediction of the number of citations for 88k scientific publications that we compiled from the Allen AI S2ORC dataset. For our HAN-system with structure-tags we reach 28.5% explained variance, an improvement of 1.8% over our reimplementation of the BiLSTM-based model as well as 1.0% improvement over plain HANs.", "published": "2020-04-30T22:34:34Z", "authors": ["Gideon Maillette de Buy Wenniger", "Thomas van Dongen", "Eleri Aedmaa", "Herbert Teun Kruitbosch", "Edwin A. Valentijn", "Lambert Schomaker"], "categories": ["cs.CL", "cs.LG"], "doi": "10.18653/v1/2020.sdp-1.18", "_query": "housing price text listing", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2509.04446v1", "title": "Plot'n Polish: Zero-shot Story Visualization and Disentangled Editing with Text-to-Image Diffusion Models", "abstract": "Text-to-image diffusion models have demonstrated significant capabilities to generate diverse and detailed visuals in various domains, and story visualization is emerging as a particularly promising application. However, as their use in real-world creative domains increases, the need for providing enhanced control, refinement, and the ability to modify images post-generation in a consistent manner becomes an important challenge. Existing methods often lack the flexibility to apply fine or coarse edits while maintaining visual and narrative consistency across multiple frames, preventing creators from seamlessly crafting and refining their visual stories. To address these challenges, we introduce Plot'n Polish, a zero-shot framework that enables consistent story generation and provides fine-grained control over story visualizations at various levels of detail.", "published": "2025-09-04T17:59:34Z", "authors": ["Kiymet Akdemir", "Jing Shi", "Kushal Kafle", "Brian Price", "Pinar Yanardag"], "categories": ["cs.CV"], "doi": "", "_query": "housing price text listing", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/1508.02060v1", "title": "Egyptian Dialect Stopword List Generation from Social Network Data", "abstract": "This paper proposes a methodology for generating a stopword list from online social network (OSN) corpora in Egyptian Dialect(ED). The aim of the paper is to investigate the effect of removingED stopwords on the Sentiment Analysis (SA) task. The stopwords lists generated before were on Modern Standard Arabic (MSA) which is not the common language used in OSN. We have generated a stopword list of Egyptian dialect to be used with the OSN corpora. We compare the efficiency of text classification when using the generated list along with previously generated lists of MSA and combining the Egyptian dialect list with the MSA list. The text classification was performed using Naïve Bayes and Decision Tree classifiers and two feature selection approaches, unigram and bigram. The experiments show that removing ED stopwords give better performance than using lists of MSA stopwords only.", "published": "2015-04-13T15:56:00Z", "authors": ["Walaa Medhat", "Ahmed H. Yousef", "Hoda Korashy"], "categories": ["cs.CL"], "doi": "", "_query": "housing price text listing", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/1910.05137v1", "title": "Stock price formation: useful insights from a multi-agent reinforcement learning model", "abstract": "In the past, financial stock markets have been studied with previous generations of multi-agent systems (MAS) that relied on zero-intelligence agents, and often the necessity to implement so-called noise traders to sub-optimally emulate price formation processes. However recent advances in the fields of neuroscience and machine learning have overall brought the possibility for new tools to the bottom-up statistical inference of complex systems. Most importantly, such tools allows for studying new fields, such as agent learning, which in finance is central to information and stock price estimation. We present here the results of a new generation MAS stock market simulator, where each agent autonomously learns to do price forecasting and stock trading via model-free reinforcement learning, and where the collective behaviour of all agents decisions to trade feed a centralised double-auction limit order book, emulating price and volume microstructures. We study here what such agents learn in detail, and how heterogenous are the policies they develop over time. We also show how the agents learning rates, and their propensity to be chartist or fundamentalist impacts the overall market stability and agent individual performance. We conclude with a study on the impact of agent information via random trading.", "published": "2019-10-10T15:07:05Z", "authors": ["J. Lussange", "S. Bourgeois-Gironde", "S. Palminteri", "B. Gutkin"], "categories": ["q-fin.TR", "q-fin.PR"], "doi": "", "_query": "housing price text listing", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2007.07990v4", "title": "Static pricing for multi-unit prophet inequalities", "abstract": "We study a pricing problem where a seller has $k$ identical copies of a product, buyers arrive sequentially, and the seller prices the items aiming to maximize social welfare. When $k=1$, this is the so called \"prophet inequality\" problem for which there is a simple pricing scheme achieving a competitive ratio of $1/2$. On the other end of the spectrum, as $k$ goes to infinity, the asymptotic performance of both static and adaptive pricing is well understood. We provide a static pricing scheme for the small-supply regime: where $k$ is small but larger than $1$. Prior to our work, the best competitive ratio known for this setting was the $1/2$ that follows from the single-unit prophet inequality. Our pricing scheme is easy to describe as well as practical -- it is anonymous, non-adaptive, and order-oblivious. We pick a single price that equalizes the expected fraction of items sold and the probability that the supply does not sell out before all customers are served; this price is then offered to each customer while supply lasts. This extends an approach introduced by Samuel-Cahn for the case of $k=1$. This pricing scheme achieves a competitive ratio that increases gradually with the supply. Subsequent work by Jiang, Ma, and Zhang shows that our pricing scheme is the optimal static pricing for every value of $k$.", "published": "2020-07-15T20:57:29Z", "authors": ["Shuchi Chawla", "Nikhil Devanur", "Thodoris Lykouris"], "categories": ["cs.GT", "cs.DS"], "doi": "", "_query": "housing price text listing", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/0708.4178v1", "title": "Relationship between degree of efficiency and prediction in stock price changes", "abstract": "This study investigates empirically whether the degree of stock market efficiency is related to the prediction power of future price change using the indices of twenty seven stock markets. Efficiency refers to weak-form efficient market hypothesis (EMH) in terms of the information of past price changes. The prediction power corresponds to the hit-rate, which is the rate of the consistency between the direction of actual price change and that of predicted one, calculated by the nearest neighbor prediction method (NN method) using the out-of-sample. In this manuscript, the Hurst exponent and the approximate entropy (ApEn) are used as the quantitative measurements of the degree of efficiency. The relationship between the Hurst exponent, reflecting the various time correlation property, and the ApEn value, reflecting the randomness in the time series, shows negative correlation. However, the average prediction power on the direction of future price change has the strongly positive correlation with the Hurst exponent, and the negative correlation with the ApEn. Therefore, the market index with less market efficiency has higher prediction power for future price change than one with higher market efficiency when we analyze the market using the past price change pattern. Furthermore, we show that the Hurst exponent, a measurement of the long-term memory property, provides more significant information in terms of prediction of future price changes than the ApEn and the NN method.", "published": "2007-08-30T15:31:16Z", "authors": ["Cheoljun Eom", "Gabjin Oh", "Woo-Sung Jung"], "categories": ["q-fin.ST", "physics.soc-ph"], "doi": "10.1016/j.physa.2008.05.059", "_query": "housing price text listing", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2010.15598v1", "title": "May I Ask Who's Calling? Named Entity Recognition on Call Center Transcripts for Privacy Law Compliance", "abstract": "We investigate using Named Entity Recognition on a new type of user-generated text: a call center conversation. These conversations combine problems from spontaneous speech with problems novel to conversational Automated Speech Recognition, including incorrect recognition, alongside other common problems from noisy user-generated text. Using our own corpus with new annotations, training custom contextual string embeddings, and applying a BiLSTM-CRF, we match state-of-the-art results on our novel task.", "published": "2020-10-29T13:53:42Z", "authors": ["Micaela Kaplan"], "categories": ["cs.CL"], "doi": "", "_query": "housing price text listing", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/1401.3498v1", "title": "List rankings and on-line list rankings of graphs", "abstract": "A $k$-ranking of a graph $G$ is a labeling of its vertices from $\\{1,\\ldots,k\\}$ such that any nontrivial path whose endpoints have the same label contains a larger label. The least $k$ for which $G$ has a $k$-ranking is the ranking number of $G$, also known as tree-depth. The list ranking number of $G$ is the least $k$ such that if each vertex of $G$ is assigned a set of $k$ potential labels, then $G$ can be ranked by labeling each vertex with a label from its assigned list. Rankings model a certain parallel processing problem in manufacturing, while the list ranking version adds scheduling constraints. We compute the list ranking number of paths, cycles, and trees with many more leaves than internal vertices. Some of these results follow from stronger theorems we prove about on-line versions of list ranking, where each vertex starts with an empty list having some fixed capacity, and potential labels are presented one by one, at which time they are added to the lists of certain vertices; the decision of which of these vertices are actually to be ranked with that label must be made immediately.", "published": "2014-01-15T06:05:23Z", "authors": ["Daniel C. McDonald"], "categories": ["math.CO"], "doi": "10.1016/j.dam.2015.12.014", "_query": "housing price text listing", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2011.09717v1", "title": "Topological Price of Anarchy Bounds for Clustering Games on Networks", "abstract": "We consider clustering games in which the players are embedded in a network and want to coordinate (or anti-coordinate) their strategy with their neighbors. The goal of a player is to choose a strategy that maximizes her utility given the strategies of her neighbors. Recent studies show that even very basic variants of these games exhibit a large Price of Anarchy: A large inefficiency between the total utility generated in centralized outcomes and equilibrium outcomes in which players selfishly try to maximize their utility. Our main goal is to understand how structural properties of the network topology impact the inefficiency of these games. We derive topological bounds on the Price of Anarchy for different classes of clustering games. These topological bounds provide a more informative assessment of the inefficiency of these games than the corresponding (worst-case) Price of Anarchy bounds. As one of our main results, we derive (tight) bounds on the Price of Anarchy for clustering games on Erdős-Rényi random graphs (where every possible edge in the network is present with a fixed probability), which, depending on the graph density, stand in stark contrast to the known Price of Anarchy bounds.", "published": "2020-11-19T08:43:06Z", "authors": ["Pieter Kleer", "Guido Schäfer"], "categories": ["cs.GT"], "doi": "10.1007/978-3-030-35389-6_18", "_query": "housing price text listing", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2308.08028v1", "title": "A Graph Analysis of the Impact of COVID-19 on Emergency Housing Shelter Access Patterns", "abstract": "This paper investigates how COVID-19 disrupted emergency housing shelter access patterns in Calgary, Canada and what aspects of these changes persist to the present day. This analysis will utilize aggregated shelter access data for over 40,000 individuals from seven major urban shelters dating from 2018 to the present. A graph theoretic approach will be used to examine the journeys of individuals between shelters before, during and after the COVID-19 lockdown period. This approach treats shelters as nodes in a graph and a person's transition between shelter as an arrow or edge between nodes. This perspective is used to create both timeline and network diagrams that visualize shelter use and the flow of people between shelters. Statistical results are also presented that illustrate the differences between the cohorts of people who only used shelter pre/post-lockdown, people who stayed in shelter during lockdown and people who used shelter for the first time during lockdown. The results demonstrate not only how a complex system of care responded to the pandemic but also the characteristics of the people most likely to continue to rely on that system during an emergency.", "published": "2023-08-15T20:34:56Z", "authors": ["Geoffrey G. Messier"], "categories": ["cs.CY"], "doi": "", "_query": "housing price text listing", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2307.13232v1", "title": "Changes in Risk Appreciation, and Short Memory of House Buyers When the Market is Hot, a Case Study of Christchurch, New Zealand", "abstract": "In this paper house prices in Christchurch are analyzed over three distinct periods of time: post-2011 earthquake, pre-COVID-19 lockdown, and post-COVID-19 lockdown using the well-established hedonic price model. Results show that buyers, in periods that are temporally distant from the 2011 Christchurch earthquake, value the risk of potential earthquake damage to a property differently from buyers soon after the earthquake. We find that there are observable shifts in hedonic prices across the different time periods, specifically for section size pre and post COVID-19 lockdown.", "published": "2023-07-25T03:46:38Z", "authors": ["Emil Mendoza", "Fabian Dunker", "Marco Reale"], "categories": ["q-fin.GN", "stat.AP"], "doi": "", "_query": "housing price text listing", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2401.01526v1", "title": "An arbitrage driven price dynamics of Automated Market Makers in the presence of fees", "abstract": "We present a model for price dynamics in the Automated Market Makers (AMM) setting. Within this framework, we propose a reference market price following a geometric Brownian motion. The AMM price is constrained by upper and lower bounds, determined by constant multiplications of the reference price. Through the utilization of local times and excursion-theoretic approaches, we derive several analytical results, including its time-changed representation and limiting behavior.", "published": "2024-01-03T03:40:51Z", "authors": ["Joseph Najnudel", "Shen-Ning Tung", "Kazutoshi Yamazaki", "Ju-Yi Yen"], "categories": ["q-fin.MF"], "doi": "", "_query": "housing price text listing", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/1412.0148v2", "title": "The impact of startup costs and the grid operator on the power price equilibrium", "abstract": "In this paper we propose a quadratic programming model that can be used for calculating the term structure of electricity prices while explicitly modeling startup costs of power plants. In contrast to other approaches presented in the literature, we incorporate the startup costs in a mathematically rigorous manner without relying on ad hoc heuristics. Moreover, we propose a tractable approach for estimating the startup costs of power plants based on their historical production. Through numerical simulations applied to the entire UK power grid, we demonstrate that the inclusion of startup costs is necessary for the modeling of electricity prices in realistic power systems. Numerical results show that startup costs make electricity prices very spiky. In the second part of the paper, we extend the initial model by including the grid operator who is responsible for managing the grid. Numerical simulations demonstrate that robust decision making of the grid operator can significantly decrease the number and severity of spikes in the electricity price and improve the reliability of the power grid.", "published": "2014-11-29T20:06:37Z", "authors": ["Miha Troha", "Raphael Hauser"], "categories": ["q-fin.PR", "math.OC"], "doi": "", "_query": "housing price text listing", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/1612.00106v1", "title": "Menu-Based Pricing for Charging of Electric Vehicles with Vehicle-to-Grid Service", "abstract": "The paper considers a bidirectional power flow model of the electric vehicles (EVs) in a charging station. The EVs can inject energies by discharging via a Vehicle-to-Grid (V2G) service which can enhance the profits of the charging station. However, frequent charging and discharging degrade battery life. A proper compensation needs to be paid to the users to participate in the V2G service. We propose a menu-based pricing scheme, where the charging station selects a price for each arriving user for the amount of battery utilization, the total energy, and the time (deadline) that the EV will stay. The user can accept one of the contracts or rejects all depending on their utilities. The charging station can serve users using a combination of the renewable energy and the conventional energy bought from the grid. We show that though there exists a profit maximizing price which maximizes the social welfare, it provides no surplus to the users if the charging station is aware of the utilities of the users. If the charging station is not aware of the exact utilities, the social welfare maximizing price may not maximize the expected profit. In fact, it can give a zero profit. We propose a pricing strategy which provides a guaranteed fixed profit to the charging station and it also maximizes the expected profit for a wide range of utility functions. Our analysis shows that when the harvested renewable energy is small the users have higher incentives for the V2G service. We, numerically, show that the charging station's profit and the user's surplus both increase as V2G service is efficiently utilized by the pricing mechanism.", "published": "2016-12-01T01:38:20Z", "authors": ["Arnob Ghosh", "Vaneet Aggarwal"], "categories": ["math.OC", "cs.GT", "eess.SY"], "doi": "", "_query": "housing price text listing", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2202.13874v1", "title": "Time Series Analysis of Blockchain-Based Cryptocurrency Price Changes", "abstract": "In this paper we apply neural networks and Artificial Intelligence (AI) to historical records of high-risk cryptocurrency coins to train a prediction model that guesses their price. This paper's code contains Jupyter notebooks, one of which outputs a timeseries graph of any cryptocurrency price once a CSV file of the historical data is inputted into the program. Another Jupyter notebook trains an LSTM, or a long short-term memory model, to predict a cryptocurrency's closing price. The LSTM is fed the close price, which is the price that the currency has at the end of the day, so it can learn from those values. The notebook creates two sets: a training set and a test set to assess the accuracy of the results. The data is then normalized using manual min-max scaling so that the model does not experience any bias; this also enhances the performance of the model. Then, the model is trained using three layers -- an LSTM, dropout, and dense layer-minimizing the loss through 50 epochs of training; from this training, a recurrent neural network (RNN) is produced and fitted to the training set. Additionally, a graph of the loss over each epoch is produced, with the loss minimizing over time. Finally, the notebook plots a line graph of the actual currency price in red and the predicted price in blue. The process is then repeated for several more cryptocurrencies to compare prediction models. The parameters for the LSTM, such as number of epochs and batch size, are tweaked to try and minimize the root mean square error.", "published": "2022-02-19T04:28:07Z", "authors": ["Jacques Fleischer", "Gregor von Laszewski", "Carlos Theran", "Yohn Jairo Parra Bautista"], "categories": ["cs.LG", "cs.DC"], "doi": "", "_query": "housing price text listing", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/1001.3570v2", "title": "Security Pricing with Information-Sensitive Discounting", "abstract": "In this paper incomplete-information models are developed for the pricing of securities in a stochastic interest rate setting. In particular we consider credit-risky assets that may include random recovery upon default. The market filtration is generated by a collection of information processes associated with economic factors, on which interest rates depend, and information processes associated with market factors used to model the cash flows of the securities. We use information-sensitive pricing kernels to give rise to stochastic interest rates. Semi-analytical expressions for the price of credit-risky bonds are derived, and a number of recovery models are constructed which take into account the perceived state of the economy at the time of default. The price of European-style call bond options is deduced, and it is shown how examples of hybrid securities, like inflation-linked credit-risky bonds, can be valued. Finally, a cumulative information process is employed to develop pricing kernels that respond to the amount of aggregate debt of an economy.", "published": "2010-01-20T12:46:59Z", "authors": ["Andrea Macrina", "Priyanka A. Parbhoo"], "categories": ["q-fin.PR"], "doi": "", "_query": "housing price text listing", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2105.11376v2", "title": "Can we imitate the principal investor's behavior to learn option price?", "abstract": "This paper presents a framework of imitating the principal investor's behavior for optimal pricing and hedging options. We construct a non-deterministic Markov decision process for modeling stock price change driven by the principal investor's decision making. However, low signal-to-noise ratio and instability that are inherent in equity markets pose challenges to determine the state transition (stock price change) after executing an action (the principal investor's decision) as well as decide an action based on current state (spot price). In order to conquer these challenges, we resort to a Bayesian deep neural network for computing the predictive distribution of the state transition led by an action. Additionally, instead of exploring a state-action relationship to formulate a policy, we seek for an episode based visible-hidden state-action relationship to probabilistically imitate the principal investor's successive decision making. Unlike conventional option pricing that employs analytical stochastic processes or utilizes time series analysis to model and sample underlying stock price movements, our algorithm simulates stock price paths by imitating the principal investor's behavior which requires no preset probability distribution and fewer predetermined parameters. Eventually the optimal option price is learned by reinforcement learning to maximize the cumulative risk-adjusted return of a dynamically hedged portfolio over simulated price paths.", "published": "2021-05-24T16:08:58Z", "authors": ["Xin Jin"], "categories": ["q-fin.PR", "cs.LG"], "doi": "", "_query": "housing price text listing", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/1703.00608v1", "title": "Impact of Optimal Storage Allocation on Price Volatility in Electricity Markets", "abstract": "Recent studies show that the fast growing expansion of wind power generation may lead to extremely high levels of price volatility in wholesale electricity markets. Storage technologies, regardless of their specific forms e.g. pump-storage hydro, large-scale or distributed batteries, are capable of alleviating the extreme price volatility levels due to their energy usage time shifting, fast-ramping and price arbitrage capabilities. In this paper, we propose a stochastic bi-level optimization model to find the optimal nodal storage capacities required to achieve a certain price volatility level in a highly volatile electricity market. The decision on storage capacities is made in the upper level problem and the operation of strategic/regulated generation, storage and transmission players is modeled at the lower level problem using an extended Cournot-based stochastic game. The South Australia (SA) electricity market, which has recently experienced high levels of price volatility, is considered as the case study for the proposed storage allocation framework. Our numerical results indicate that 80% price volatility reduction in SA electricity market can be achieved by installing either 340 MWh regulated storage or 420 MWh strategic storage. In other words, regulated storage firms are more efficient in reducing the price volatility than strategic storage firms.", "published": "2017-03-02T04:03:45Z", "authors": ["Amin Masoumzadeh", "Ehsan Nekouei", "Tansu Alpcan", "Deb Chattopadhyay"], "categories": ["math.OC"], "doi": "10.1109/TPWRS.2017.2727075", "_query": "housing price text listing", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2012.01257v4", "title": "Error estimates for discrete approximations of game options with multivariate diffusion asset prices", "abstract": "We obtain error estimates for strong approximations of a diffusion with a diffusion matrix $σ$ and a drift b by the discrete time process defined recursively X_N((n+1)/N) = X_N(n/N)+N^{1/2}σ(X_N(n/N))ξ(n+1)+N^{-1}b(XN(n/N)); where ξ(n); n\\geq 1 are i.i.d. random vectors, and apply this in order to approximate the fair price of a game option with a diffusion asset price evolution by values of Dynkin's games with payoffs based on the above discrete time processes. This provides an effective tool for computations of fair prices of game options with path dependent payoffs in a multi asset market with diffusion evolution.", "published": "2020-12-02T15:05:49Z", "authors": ["Yuri Kifer"], "categories": ["math.PR", "q-fin.MF"], "doi": "10.31390/josa.2.3.08", "_query": "housing price text listing", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/1012.2160v1", "title": "Insider Trading in the Market with Rational Expected Price", "abstract": "Kyle (1985) builds a pioneering and influential model, in which an insider with long-lived private information submits an optimal order in each period given the market maker's pricing rule. An inconsistency exists to some extent in the sense that the ``constant pricing rule \" actually assumes an adaptive expected price with pricing rule given before insider making the decision, and the ``market efficiency\" condition, however, assumes a rational expected price and implies that the pricing rule can be influenced by insider's strategy. We loosen the ``constant pricing rule \" assumption by taking into account sufficiently the insider's strategy has on pricing rule. According to the characteristic of the conditional expectation of the informed profits, three different models vary with insider's attitudes regarding to risk are presented. Compared to Kyle (1985), the risk-averse insider in Model 1 can obtain larger guaranteed profits, the risk-neutral insider in Model 2 can obtain a larger ex ante expectation of total profits across all periods and the risk-seeking insider in Model 3 can obtain larger risky profits. Moreover, the limit behaviors of the three models when trading frequency approaches infinity are given, showing that Model 1 acquires a strong-form efficiency, Model 2 acquires the Kyle's (1985) continuous equilibrium, and Model 3 acquires an equilibrium with information released at an increasing speed.", "published": "2010-12-10T02:29:09Z", "authors": ["Fuzhou Gong", "Deqing Zhou"], "categories": ["q-fin.TR"], "doi": "", "_query": "housing price text listing", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/1910.01860v1", "title": "Online repeated posted price auctions with a demand side platform", "abstract": "We consider an online ad network problem in which an ad exchange auctions ad slots and intermediaries called demand side platforms (DSPs) buy these ad slots for their clients (advertisers). An intermediary represents multiple advertisers. Different types of ad slots are auctioned by the ad exchange, e.g., video ad, banner ad etc. We study repeated posted price auctions for homogeneous and heterogeneous items when there is an intermediary. In a posted price auction, the auctioneer sets a fixed reserve price. The buyer can accept the price and win the ad slot or reject the price. We analyze the system from the auctioneer's perspective and show that the optimal reserve price is dynamic for heterogeneous items. We also investigate system from intermediary's perspective and devise algorithms for scheduling advertisers. Often the advertisers have budget constraints and impression constraints. We formulate a revenue optimization problem at the intermediary and also consider the problem of scheduling advertisers with budget and impression constraints. Finally, we present a numerical study for the single seller and advertiser model which considers various valuation distributions such as uniform, exponential and lognormal.", "published": "2019-10-04T10:33:55Z", "authors": ["Rahul Meshram", "Kesav Kaza"], "categories": ["cs.GT", "eess.SY"], "doi": "", "_query": "housing price text listing", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2204.03300v1", "title": "Approximating Nash Equilibrium for Production Control with Sticky Price", "abstract": "We study a mean field game problem arising from the production control for multiple firms with price stickiness in the commodity market. The price dynamics for each firm is described as a (controlled) jump-diffusion process with mean-field interaction. Each firm aims to maximize her expectation of cumulative net profit coupled with each other through price processes. By solving the limiting control problem and a fixed-point problem, we construct an explicit approximating Nash equilibrium when the number of firms grows large.", "published": "2022-04-07T09:03:39Z", "authors": ["Chunmei Jiang", "Tongqing Li", "Jie Yu"], "categories": ["math.OC"], "doi": "", "_query": "housing price text listing", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/1804.03178v4", "title": "Power of Bonus in Pricing for Crowdsourcing", "abstract": "We consider a simple form of pricing for a crowdsourcing system, where pricing policy is published a priori, and workers then decide their task acceptance. Such a pricing form is widely adopted in practice for its simplicity, e.g., Amazon Mechanical Turk, although additional sophistication to pricing rule can enhance budget efficiency. With the goal of designing efficient and simple pricing rules, we study the impact of the following two design features in pricing policies: (i) personalization tailoring policy worker-by-worker and (ii) bonus payment to qualified task completion. In the Bayesian setting, where the only prior distribution of workers' profiles is available, we first study the Price of Agnosticism (PoA) that quantifies the utility gap between personalized and common pricing policies. We show that PoA is bounded within a constant factor under some mild conditions, and the impact of bonus is essential in common pricing. These analytic results imply that complex personalized pricing can be replaced by simple common pricing once it is equipped with a proper bonus payment. To provide insights on efficient common pricing, we then study the efficient mechanisms of bonus payment for several profile distribution regimes which may exist in practice. We provide primitive experiments on Amazon Mechanical Turk, which support our analytical findings.", "published": "2018-04-09T18:41:33Z", "authors": ["Suho Shin", "Hoyong Choi", "Yung Yi", "Jungseul Ok"], "categories": ["cs.GT"], "doi": "", "_query": "housing price text listing", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2409.07719v3", "title": "Static Pricing for Single Sample Multi-unit Prophet Inequalities", "abstract": "In this paper, we study $k$-unit single sample prophet inequalities. A seller has $k$ identical, indivisible items to sell. A sequence of buyers arrive one-by-one, with each buyer's private value for the item, $X_i$, revealed to the seller when they arrive. While the seller is unaware of the distribution from which $X_i$ is drawn, they have access to a single sample, $Y_i$ drawn from the same distribution as $X_i$. What strategies can the seller adopt for selling items so as to maximize social welfare? Previous work has demonstrated that when $k = 1$, if the seller sets a price equal to the maximum of the samples, they can achieve a competitive ratio of $\\frac{1}{2}$ of the social welfare, and recently Pashkovich and Sayutina established an analogous result for $k = 2$. In this paper, we prove that for $k \\geq 3$, setting a (static) price equal to the $k^{\\text{th}}$ largest sample also obtains a competitive ratio of $\\frac{1}{2}$, resolving a conjecture Pashkovich and Sayutina pose. We also consider the situation where $k$ is large. We demonstrate that setting a price equal to the $(k-\\sqrt{2k\\log k})^{\\text{th}}$ largest sample obtains a competitive ratio of $1 - \\sqrt{\\frac{2\\log k}{k}} - o\\left(\\sqrt{\\frac{\\log k}{k}}\\right)$, and that this is the optimal possible ratio achievable with a static pricing scheme with access to a single sample. This should be compared against a competitive ratio $1 - \\sqrt{\\frac{\\log k}{k}} - o\\left(\\sqrt{\\frac{\\log k}{k}}\\right)$, which is the optimal possible ratio achievable with a static pricing scheme with knowledge of the distributions of the values.", "published": "2024-09-12T02:59:35Z", "authors": ["Pranav Nuti", "Peter Westbrook"], "categories": ["cs.GT", "cs.DS"], "doi": "", "_query": "housing price text listing", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2101.10151v1", "title": "Pricing Energy Storage in Real-time Market", "abstract": "The problem of pricing utility-scale energy storage resources (ESRs) in the real-time electricity market is considered. Under a rolling-window dispatch model where the operator centrally dispatches generation and consumption under forecasting uncertainty, it is shown that almost all uniform pricing schemes, including the standard locational marginal pricing (LMP), result in lost opportunity costs that require out-of-the-market settlements. It is also shown that such settlements give rise to disincentives for generating firms and storage participants to bid truthfully, even when these market participants are rational price-takers in a competitive market. Temporal locational marginal pricing (TLMP) is proposed for ESRs as a generalization of LMP to an in-market discriminative form. TLMP is a sum of the system-wide energy price, LMP, and the individual state-of-charge price. It is shown that, under arbitrary forecasting errors, the rolling-window implementation of TLMP eliminates the lost opportunity costs and provides incentives to price-taking firms to bid truthfully with their marginal costs. Numerical examples show insights into the effects of uniform and non-uniform pricing mechanisms on dispatch following and truthful bidding incentives.", "published": "2021-01-25T15:02:15Z", "authors": ["Cong Chen", "Lang Tong", "Ye Guo"], "categories": ["eess.SY", "econ.GN"], "doi": "", "_query": "housing price text listing", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2411.10791v1", "title": "Optimal Fixed-Price Mechanism with Signaling", "abstract": "Consider a trade market with one seller and multiple buyers. The seller aims to sell an indivisible item and maximize their revenue. This paper focuses on a simple and popular mechanism--the fixed-price mechanism. Unlike the standard setting, we assume there is information asymmetry between buyers and the seller. Specifically, we allow the seller to design information before setting the fixed price, which implies that we study the mechanism design problem in a broader space. We call this mechanism space the fixed-price signaling mechanism. We assume that buyers' valuation of the item depends on the quality of the item. The seller can privately observe the item's quality, whereas buyers only see its distribution. In this case, the seller can influence buyers' valuations by strategically disclosing information about the item's quality, thereby adjusting the fixed price. We consider two types of buyers with different levels of rationality: ex-post individual rational (IR) and ex-interim individual rational. We show that when the market has only one buyer, the optimal revenue generated by the fixed-price signaling mechanism is identical to that of the fixed-price mechanism, regardless of the level of rationality. Furthermore, when there are multiple buyers in the market and all of them are ex-post IR, we show that there is no fixed-price mechanism that is obedient for all buyers. However, if all buyers are ex-interim IR, we show that the optimal fixed-price signaling mechanism will generate more revenue for the seller than the fixed-price mechanism.", "published": "2024-11-16T12:42:16Z", "authors": ["Zhikang Fan", "Weiran Shen"], "categories": ["cs.GT"], "doi": "", "_query": "housing price text listing", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2401.16575v1", "title": "Beyond Image-Text Matching: Verb Understanding in Multimodal Transformers Using Guided Masking", "abstract": "The dominant probing approaches rely on the zero-shot performance of image-text matching tasks to gain a finer-grained understanding of the representations learned by recent multimodal image-language transformer models. The evaluation is carried out on carefully curated datasets focusing on counting, relations, attributes, and others. This work introduces an alternative probing strategy called guided masking. The proposed approach ablates different modalities using masking and assesses the model's ability to predict the masked word with high accuracy. We focus on studying multimodal models that consider regions of interest (ROI) features obtained by object detectors as input tokens. We probe the understanding of verbs using guided masking on ViLBERT, LXMERT, UNITER, and VisualBERT and show that these models can predict the correct verb with high accuracy. This contrasts with previous conclusions drawn from image-text matching probing techniques that frequently fail in situations requiring verb understanding. The code for all experiments will be publicly available https://github.com/ivana-13/guided_masking.", "published": "2024-01-29T21:22:23Z", "authors": ["Ivana Beňová", "Jana Košecká", "Michal Gregor", "Martin Tamajka", "Marcel Veselý", "Marián Šimko"], "categories": ["cs.CL", "cs.CV"], "doi": "", "_query": "housing price text listing", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2604.11511v1", "title": "The Price of Ignorance: Information-Free Quotation for Data Retention in Machine Unlearning", "abstract": "When users exercise data deletion rights under the General Data Protection Regulation (GDPR) and similar regulations, mobile network operators face a tradeoff: excessive machine unlearning degrades model accuracy and incurs retraining costs, yet existing pricing mechanisms for data retention require the server to know every user's private privacy and accuracy preferences, which is infeasible under the very regulations that motivate unlearning. We ask: what is the welfare cost of operating without this private information? We design an information-free ascending quotation mechanism where the server broadcasts progressively higher prices and users self-select their data supply, requiring no knowledge of users' parameters. Under complete information, the protocol admits a unique subgame-perfect Nash equilibrium characterized by single-period selling. We formalize the Price of Ignorance -- the welfare gap between optimal personalized pricing (which knows everything) and our information-free quotation (which knows nothing) -- and prove a three-regime efficiency ordering. Numerical evaluation across seven mechanisms and 5000 Monte Carlo runs shows that this price is near zero: the information-free mechanism achieves >=99% of the welfare of its information-intensive benchmarks, while providing noise-robust guarantees and comparable fairness.", "published": "2026-04-13T14:14:35Z", "authors": ["Bin Han", "Di Feng", "Zexin Fang", "Jie Wang", "Hans D. Schotten"], "categories": ["cs.GT", "cs.LG"], "doi": "", "_query": "housing price text listing", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/cond-mat/0401055v2", "title": "Large price changes on small scales", "abstract": "In this study we examine the evolution of price, volume, and the bid-ask spread after extreme 15 minute intraday price changes on the NYSE and the NASDAQ. We find that due to strong behavioral trading there is an overreaction. Furthermore we find that volatility which increases sharply at the event decays according to a power law with an exponent of approximately 0.4, i.e., much faster than the autocorrelation function of volatility.", "published": "2004-01-06T09:33:45Z", "authors": ["A. G. Zawadowski", "J. Kertesz", "G. Andor"], "categories": ["cond-mat.stat-mech", "q-fin.ST"], "doi": "10.1016/j.physa.2004.06.121", "_query": "housing price text listing", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2102.05751v6", "title": "Price Discrimination in International Airline Markets", "abstract": "We develop a model of inter-temporal and intra-temporal price discrimination by monopoly airlines to study the ability of different discriminatory pricing mechanisms to increase efficiency and the associated distributional implications. To estimate the model, we use unique data from international airline markets with flight-level variation in prices across time, cabins, and markets and information on passengers' reasons for travel and time of purchase. The current pricing practice yields approximately 77% of the first-best welfare. The source of this inefficiency arises primarily from private information about passenger valuations, not dynamic uncertainty about demand. We also find that if airlines could discriminate between business and leisure passengers, total welfare would improve at the expense of business passenger surplus. Also, replacing the current pricing that involves screening passengers across cabin classes with offering a single cabin class has minimal effect on total welfare.", "published": "2021-02-10T21:49:55Z", "authors": ["Gaurab Aryal", "Charles Murry", "Jonathan W. Williams"], "categories": ["econ.GN"], "doi": "", "_query": "housing price text listing", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/cond-mat/0008147v1", "title": "Hedged Monte-Carlo: low variance derivative pricing with objective probabilities", "abstract": "We propose a new `hedged' Monte-Carlo (HMC) method to price financial derivatives, which allows to determine simultaneously the optimal hedge. The inclusion of the optimal hedging strategy allows one to reduce the financial risk associated with option trading, and for the very same reason reduces considerably the variance of our HMC scheme as compared to previous methods. The explicit accounting of the hedging cost naturally converts the objective probability into the `risk-neutral' one. This allows a consistent use of purely historical time series to price derivatives and obtain their residual risk. The method can be used to price a large class of exotic options, including those with path dependent and early exercise features.", "published": "2000-08-09T10:10:29Z", "authors": ["Marc Potters", "Jean-Philippe Bouchaud", "Dragan Sestovic"], "categories": ["cond-mat"], "doi": "10.1016/S0378-4371(00)00554-9", "_query": "housing price text listing", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/1508.05347v3", "title": "Pricing Queries Approximately Optimally", "abstract": "Data as a commodity has always been purchased and sold. Recently, web services that are data marketplaces have emerged that match data buyers with data sellers. So far there are no guidelines how to price queries against a database. We consider the recently proposed query-based pricing framework of Koutris et al and ask the question of computing optimal input prices in this framework by formulating a buyer utility model. We establish the interesting and deep equivalence between arbitrage-freeness in the query-pricing framework and envy-freeness in pricing theory for appropriately chosen buyer valuations. Given the approximation hardness results from envy-free pricing we then develop logarithmic approximation pricing algorithms exploiting the max flow interpretation of the arbitrage-free pricing for the restricted query language proposed by Koutris et al. We propose a novel polynomial-time logarithmic approximation pricing scheme and show that our new scheme performs better than the existing envy-free pricing algorithms instance-by-instance. We also present a faster pricing algorithm that is always greater than the existing solutions, but worse than our previous scheme. We experimentally show how our pricing algorithms perform with respect to the existing envy-free pricing algorithms and to the optimal exponentially computable solution, and our experiments show that our approximation algorithms consistently arrive at about 99% of the optimal.", "published": "2015-08-21T18:19:28Z", "authors": ["Vasilis Syrgkanis", "Johannes Gehrke"], "categories": ["cs.GT", "cs.DB"], "doi": "", "_query": "housing price text listing", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2605.21433v1", "title": "Instrumental Text-to-Music Generation with Auxiliary Conditioning Branches", "abstract": "Text-to-music generation has advanced rapidly, with modern autoregressive and diffusion-based models producing convincing music from natural-language prompts. However, much of this progress relies on large-scale training data and external pretraining, making it difficult to isolate which design choices remain effective when data and pretraining are controlled. We study this setting using a Diffusion Transformer backbone with lyric and timbre conditioning, adapted to an instrumental-only text-to-music task in which the auxiliary lyric and timbre branches receive only degenerate conditioning signals. Through controlled ablations, we find that models retrained without these branches score lower across AudioBox aesthetics, LLM-as-judge, and human MOS, and that reinvesting the saved parameters as additional DiT depth recovers only marginally. This suggests the auxiliary branches may act as training-time architectural anchors whose contribution goes beyond their explicit conditioning content. We validate the same model through comparisons with external instrumental baselines and through our submission to the ICME 2026 Academic Text-to-Music (ATTM) Grand Challenge, where our Performance submission ranked first under both the objective metrics and the subsequent organizer-administered MOS over 35 raters, attaining the highest overall MOS across all challenge submissions, while our Efficiency submission was a finalist that tied for second under the objective metrics.", "published": "2026-05-20T17:23:58Z", "authors": ["Junyoung Koh"], "categories": ["cs.SD"], "doi": "", "_query": "housing price text listing", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/math/0404447v1", "title": "Indifference pricing and hedging in stochastic volatility models", "abstract": "We apply the concepts of utility based pricing and hedging of derivatives in stochastic volatility markets and introduce a new class of \"reciprocal affine\" models for which the indifference price and optimal hedge portfolio for pure volatility claims are efficiently computable. We obtain a general formula for the market price of volatility risk in these models and calculate it explicitly for the case of an exponential utility.", "published": "2004-04-24T19:01:28Z", "authors": ["M. R. Grasselli", "T. R. Hurd"], "categories": ["math.PR", "math.OC", "q-fin.PR"], "doi": "", "_query": "housing price text listing", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2307.12695v3", "title": "Propagation of a carbon price in a credit portfolio through macroeconomic factors", "abstract": "We study how the climate transition through a low-carbon economy, implemented by carbon pricing, propagates in a credit portfolio and precisely describe how carbon price dynamics affects credit risk measures such as probability of default, expected and unexpected losses. We adapt a stochastic multisectoral model to take into account the greenhouse gases (GHG) emissions costs of both sectoral firms' production and consumption, as well as sectoral household's consumption. GHG emissions costs are the product of carbon prices, provided by the NGFS transition scenarios, and of GHG emissions. For each sector, our model yields the sensitivity of firms' production and households' consumption to carbon price and the relationships between sectors. It allows us to analyze the short-term effects of the carbon price as opposed to standard IAM (such as REMIND), which are deterministic and only capture long-term trends. Finally, we use a DCF methodology to compute firms' values which we then combine with a structural credit risk model to describe how the carbon price impacts credit risk measures. We obtain that the carbon price distorts the distribution of the firm's value, increases banking fees charged to clients (materialized by the bank provisions), and reduces banks' profitability (translated by the economic capital). In addition, the randomness we introduce provides extra flexibility to take into account uncertainties on the productivity and on the different transition scenarios. We also compute the sensitivities of the credit risk measures with respect to changes in the carbon price, yielding further criteria for a more accurate assessment of climate transition risk in a credit portfolio. This work provides a preliminary methodology to calculate the evolution of credit risk measures of a credit portfolio, starting from a given climate transition scenario described by a carbon price.", "published": "2023-07-24T11:21:06Z", "authors": ["Géraldine Bouveret", "Jean-François Chassagneux", "Smail Ibbou", "Antoine Jacquier", "Lionel Sopgoui"], "categories": ["q-fin.RM", "econ.GN", "q-fin.MF"], "doi": "", "_query": "housing price text listing", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2208.10099v1", "title": "Recent Advances in Text-to-SQL: A Survey of What We Have and What We Expect", "abstract": "Text-to-SQL has attracted attention from both the natural language processing and database communities because of its ability to convert the semantics in natural language into SQL queries and its practical application in building natural language interfaces to database systems. The major challenges in text-to-SQL lie in encoding the meaning of natural utterances, decoding to SQL queries, and translating the semantics between these two forms. These challenges have been addressed to different extents by the recent advances. However, there is still a lack of comprehensive surveys for this task. To this end, we review recent progress on text-to-SQL for datasets, methods, and evaluation and provide this systematic survey, addressing the aforementioned challenges and discussing potential future directions. We hope that this survey can serve as quick access to existing work and motivate future research.", "published": "2022-08-22T07:18:23Z", "authors": ["Naihao Deng", "Yulong Chen", "Yue Zhang"], "categories": ["cs.CL"], "doi": "", "_query": "housing price text listing", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/cond-mat/9807397v2", "title": "Option Pricing Model for Incomplete Market", "abstract": "The problem of determining the European-style option price in the incomplete market has been examined within the framework of stochastic optimization. An analytic method based on the discrete dynamic programming equation (Bellman equation) has been developed that gives the general formalism for determining the option price and the optimal trading strategy (optimal control policy) that reduces total risk inherent in writing the option. The basic purpose of paper is to present an effective algorithm that can be used in practice. Keywords: option pricing, incomplete market, transaction costs, stochastic optimization, Bellman equation.", "published": "1998-07-30T10:00:14Z", "authors": ["Sergei Fedotov", "Sergei Mikhailov"], "categories": ["cond-mat.stat-mech", "q-fin.PR"], "doi": "", "_query": "housing price text listing", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/1003.4307v1", "title": "Bottleneck Routing Games with Low Price of Anarchy", "abstract": "We study {\\em bottleneck routing games} where the social cost is determined by the worst congestion on any edge in the network. In the literature, bottleneck games assume player utility costs determined by the worst congested edge in their paths. However, the Nash equilibria of such games are inefficient since the price of anarchy can be very high and proportional to the size of the network. In order to obtain smaller price of anarchy we introduce {\\em exponential bottleneck games} where the utility costs of the players are exponential functions of their congestions. We find that exponential bottleneck games are very efficient and give a poly-log bound on the price of anarchy: $O(\\log L \\cdot \\log |E|)$, where $L$ is the largest path length in the players' strategy sets and $E$ is the set of edges in the graph. By adjusting the exponential utility costs with a logarithm we obtain games whose player costs are almost identical to those in regular bottleneck games, and at the same time have the good price of anarchy of exponential games.", "published": "2010-03-22T23:31:55Z", "authors": ["Rajgopal Kannan", "Costas Busch"], "categories": ["cs.GT"], "doi": "10.1007/978-3-642-16170-4_20", "_query": "housing price text listing", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2001.11275v1", "title": "The Impact of Oil and Gold Prices Shock on Tehran Stock Exchange: A Copula Approach", "abstract": "There are several researches that deal with the behavior of SEs and their relationships with different economical factors. These range from papers dealing with this subject through econometrical procedures to statistical methods known as copula. This article considers the impact of oil and gold price on Tehran Stock Exchange market (TSE). Oil and gold are two factors that are essential for the economy of Iran and their price are determined in the global market. The model used in this study is ARIMA-Copula. We used data from January 1998 to January 2011 as training data to find the appropriate model. The cross validation of model is measured by data from January 2011 to June 2011. We conclude that: (i) there is no significant direct relationship between gold price and the TSE index, but the TSE is indirectly influenced by gold price through other factors such as oil; and (ii) the TSE is not independent of the volatility in oil price and Clayton copula can describe such dependence structure between TSE and the oil price. Based on the property of Clayton copula, which has lower tail dependency, as the oil price drops, stock index falls. This means that decrease in oil price has an adverse effect on Iranian economy.", "published": "2020-01-30T11:59:50Z", "authors": ["Amir T. Payandeh Najafabadi", "Marjan Qazvini", "Reza Ofoghi"], "categories": ["q-fin.ST", "stat.AP"], "doi": "10.22099/ijes.2014.1560", "_query": "housing price text listing", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2203.12657v1", "title": "A Case Study on Nutek India Limited, Regarding Deep Fall in Share Price", "abstract": "Manipulating the security price is an act of artificially inflating or deflating the price of a security. Generally, manipulation is defined as a series of transactions designed to raise or lower a price of a security or to give the appearance of trading for the purpose of inducing others to buy or sell. In essence, a manipulation is intentional interference with the free forces of supply and demand. In this paper we have tried to study the reasons behind drastic fall in share price of Nutek India Limited.", "published": "2022-02-06T10:45:34Z", "authors": ["Gurjeet Singh", "Pankaj Nagar"], "categories": ["q-fin.GN"], "doi": "", "_query": "housing price text listing", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/physics/0210025v1", "title": "Pricing European Options in Realistic Markets", "abstract": "We investigate the relation between the fair price for European-style vanilla options and the distribution of short-term returns on the underlying asset ignoring transaction and other costs. We compute the risk-neutral probability density conditional on the total variance of the asset's returns when the option expires. If the asset's future price has finite expectation, the option's fair value satisfies a parabolic partial differential equation of the Black-Scholes type in which the variance of the asset's returns rather than a trading time is the evolution parameter. By immunizing the portfolio against large-scale price fluctuations of the asset, the valuation of options is extended to the realistic case\\cite{St99} of assets whose short-term returns have finite variance but very large, or even infinite, higher moments. A dynamic Delta-hedged portfolio that is statically insured against exceptionally large fluctuations includes at least two different options on the asset. The fair value of an option in this case is determined by a universal drift function that is common to all options on the asset. This drift is interpreted as the premium for an investment exposed to risk due to exceptionally large variations of the asset's price. It affects the option valuation like an effective cost-of-carry for the underlying in the Black-Scholes world would. The derived pricing formula for options in realistic markets is arbitrage free by construction. A simple model with constant drift qualitatively reproduces the often observed volatility -skew and -term structure.", "published": "2002-10-06T13:29:19Z", "authors": ["Martin Schaden"], "categories": ["physics.soc-ph", "physics.data-an", "q-fin.PR"], "doi": "", "_query": "housing price text listing", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/1110.5197v1", "title": "Memory effects in stock price dynamics: evidences of technical trading", "abstract": "Technical trading represents a class of investment strategies for Financial Markets based on the analysis of trends and recurrent patterns of price time series. According standard economical theories these strategies should not be used because they cannot be profitable. On the contrary it is well-known that technical traders exist and operate on different time scales. In this paper we investigate if technical trading produces detectable signals in price time series and if some kind of memory effect is introduced in the price dynamics. In particular we focus on a specific figure called supports and resistances. We first develop a criterion to detect the potential values of supports and resistances. As a second step, we show that memory effects in the price dynamics are associated to these selected values. In fact we show that prices more likely re-bounce than cross these values. Such an effect is a quantitative evidence of the so-called self-fulfilling prophecy that is the self-reinforcement of agents' belief and sentiment about future stock prices' behavior.", "published": "2011-10-24T11:02:17Z", "authors": ["Federico Garzarelli", "Matthieu Cristelli", "Andrea Zaccaria", "Luciano Pietronero"], "categories": ["q-fin.ST", "q-fin.TR"], "doi": "", "_query": "housing price text listing", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/1907.05926v1", "title": "On the Price of Anarchy of Cost-Sharing in Real-Time Scheduling Systems", "abstract": "We study cost-sharing games in real-time scheduling systems where the activation cost of the server at any given time is a function of its load. We focus on monomial cost functions and consider both the case when the degree is less than one (inducing positive externalities for the jobs) and when it is greater than one (inducing negative externalities for the jobs). For the former case, we provide tight price of anarchy bounds which show that the price of anarchy grows to infinity as a polynomial of the number of jobs in the game. For the latter, we observe that existing results provide constant and tight (asymptotically in the degree of the monomial) bounds on the price of anarchy. We then switch our attention to improving the price of anarchy by means of a simple coordination mechanism that has no knowledge of the instance. We show that our mechanism reduces the price of anarchy of games with $n$ jobs and unit activation costs from $Θ(\\sqrt{n})$ to $2$. We also show that for a restricted class of instances a similar improvement is achieved for monomial activation costs. This is not the case, however, for unrestricted instances of monomial costs for which we prove that the price of anarchy remains super-constant for our mechanism.", "published": "2019-07-12T19:29:42Z", "authors": ["Eirini Georgoulaki", "Kostas Kollias"], "categories": ["cs.GT"], "doi": "", "_query": "housing price text listing", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2211.14806v2", "title": "Efficient Demand Response Location Targeting for Price Spike Mitigation by Exploiting Price-demand Relationship", "abstract": "Demand response (DR) leverages demand-side flexibility, offering a promising approach to enhance market conditions like mitigating wholesale price spikes. However, poorly chosen DR locations can inadvertently increase electricity prices. For that, we introduce a method to rigorously select DR locations and corresponding demand reductions. We formulate a bilevel program where the upper level determines the DR locations and demand reductions while ensuring the average nodal prices meet a predetermined target. The lower level tackles an economic dispatch (ED) problem and feeds the resulting nodal prices back to the upper level based on post-DR demands. This bilevel formulation presents challenges due to the lower-level non-convexity affecting the upper-level constraints on average nodal prices. To address this, we propose to replace the lower level with a piecewise linear function representing the price-demand relationship, solving iteratively for each linear segment. This results in a tractable mixed-integer linear program. An acceleration strategy is proposed to further reduce the computation time. Numerical studies demonstrate the ability of the proposed approach to reduce prices to a desired level. Besides, we empirically show that the proposed approach is robust against inaccurate system parameters and can reduce computation time by over 50%.", "published": "2022-11-27T12:10:37Z", "authors": ["Yufan Zhang", "Honglin Wen", "Tao Feng", "Yize Chen"], "categories": ["eess.SY"], "doi": "", "_query": "housing price text listing", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2203.12460v1", "title": "An Exploratory Study of Stock Price Movements from Earnings Calls", "abstract": "Financial market analysis has focused primarily on extracting signals from accounting, stock price, and other numerical hard data reported in P&L statements or earnings per share reports. Yet, it is well-known that the decision-makers routinely use soft text-based documents that interpret the hard data they narrate. Recent advances in computational methods for analyzing unstructured and soft text-based data at scale offer possibilities for understanding financial market behavior that could improve investments and market equity. A critical and ubiquitous form of soft data are earnings calls. Earnings calls are periodic (often quarterly) statements usually by CEOs who attempt to influence investors' expectations of a company's past and future performance. Here, we study the statistical relationship between earnings calls, company sales, stock performance, and analysts' recommendations. Our study covers a decade of observations with approximately 100,000 transcripts of earnings calls from 6,300 public companies from January 2010 to December 2019. In this study, we report three novel findings. First, the buy, sell and hold recommendations from professional analysts made prior to the earnings have low correlation with stock price movements after the earnings call. Second, using our graph neural network based method that processes the semantic features of earnings calls, we reliably and accurately predict stock price movements in five major areas of the economy. Third, the semantic features of transcripts are more predictive of stock price movements than sales and earnings per share, i.e., traditional hard data in most of the cases.", "published": "2022-01-31T17:47:06Z", "authors": ["Sourav Medya", "Mohammad Rasoolinejad", "Yang Yang", "Brian Uzzi"], "categories": ["q-fin.ST", "cs.CE"], "doi": "", "_query": "housing price text listing", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2209.08778v1", "title": "Price Formation in Field Prediction Markets: the Wisdom in the Crowd", "abstract": "Prediction markets are a popular, prominent, and successful structure for a collective intelligence platform. However the exact mechanism by which information known to the participating traders is incorporated into the market price is unknown. Kyle (1985) detailed a model for price formation in continuous auctions with information distributed heterogeneously amongst market participants. This paper demonstrates a novel method derived from the Kyle model applied to data from a field experiment prediction market. The method is able to identify traders whose trades have price impact that adds a significant amount of information to the market price. Traders who are not identified as informed in aggregate have price impact consistent with noise trading. Results are reproduced on other prediction market datasets. Ultimately the results provide strong evidence in favor of the Kyle model in a field market setting, and highlight an under-discussed advantage of prediction markets over alternative group forecasting mechanisms: that the operator of the market does not need to have information on the distribution of information amongst participating traders.", "published": "2022-09-19T06:04:49Z", "authors": ["Frederik Bossaerts", "Nitin Yadav", "Peter Bossaerts", "Chad Nash", "Torquil Todd", "Torsten Rudolf", "Rowena Hutchins", "Anne-Louise Ponsonby", "Karl Mattingly"], "categories": ["q-fin.PR", "q-fin.TR"], "doi": "", "_query": "housing price text listing", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2603.03416v1", "title": "Designing with Medical Mistrust: Perspectives from Black Older Adults in Publicly Subsidized Housing", "abstract": "Despite increasing interest in culturally-sensitive health technologies, medical mistrust remains largely unexplored within human-centered computing. Considered a social determinant of health, medical mistrust is the belief that healthcare providers or institutions are acting against one's best interest. This is a rational, protective response based on historical context, structural inequities, and discrimination. To center race-based medical mistrust and the lived experiences of Black older adults with low income, we conducted interviews within publicly subsidized housing in the Southern United States. Our reflexive themes describe community perspectives on health care and medical mistrust, including accreditation and embodiment, skepticism of financial motivations, and the intentions behind health AI. We provide a reflective exercise for researchers to consider their positionality in relation to community engagements, and reframe our findings through Black Feminist Thought to propose design principles for health self-management technologies for communities with historically grounded medical mistrust.", "published": "2026-03-03T18:50:34Z", "authors": ["Cynthia M. Baseman", "Reeda Shimaz Huda", "Rosa I. Arriaga"], "categories": ["cs.HC", "cs.CY"], "doi": "10.1145/3772318.3791524", "_query": "housing price text listing", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2303.17014v1", "title": "Option pricing using a skew random walk pricing tree", "abstract": "Motivated by the Corns-Satchell, continuous time, option pricing model, we develop a binary tree pricing model with underlying asset price dynamics following Itô-Mckean skew Brownian motion. While the Corns-Satchell market model is incomplete, our discrete time market model is defined in the natural world; extended to the risk neutral world under the no-arbitrage condition where derivatives are priced under uniquely determined risk-neutral probabilities; and is complete. The skewness introduced in the natural world is preserved in the risk neutral world. Furthermore, we show that the model preserves skewness under the continuous-time limit. We provide numerical applications of our model to the valuation of European put and call options on exchange-traded funds tracking the S&P Global 1200 index.", "published": "2023-03-29T20:41:11Z", "authors": ["Yuan Hu", "W. Brent Lindquist", "Svetlozar T. Rachev", "Frank J. Fabozzi"], "categories": ["q-fin.MF"], "doi": "", "_query": "housing price text listing", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/1211.1696v1", "title": "On the Economic Value and Price-Responsiveness of Ramp-Constrained Storage", "abstract": "The primary concerns of this paper are twofold: to understand the economic value of storage in the presence of ramp constraints and exogenous electricity prices, and to understand the implications of the associated optimal storage management policy on qualitative and quantitative characteristics of storage response to real-time prices. We present an analytic characterization of the optimal policy, along with the associated finite-horizon time-averaged value of storage. We also derive an analytical upperbound on the infinite-horizon time-averaged value of storage. This bound is valid for any achievable realization of prices when the support of the distribution is fixed, and highlights the dependence of the value of storage on ramp constraints and storage capacity. While the value of storage is a non-decreasing function of price volatility, due to the finite ramp rate, the value of storage saturates quickly as the capacity increases, regardless of volatility. To study the implications of the optimal policy, we first present computational experiments that suggest that optimal utilization of storage can, in expectation, induce a considerable amount of price elasticity near the average price, but little or no elasticity far from it. We then present a computational framework for understanding the behavior of storage as a function of price and the amount of stored energy, and for characterization of the buy/sell phase transition region in the price-state plane. Finally, we study the impact of market-based operation of storage on the required reserves, and show that the reserves may need to be expanded to accommodate market-based storage.", "published": "2012-11-07T21:34:59Z", "authors": ["Ali Faghih", "Mardavij Roozbehani", "Munther A. Dahleh"], "categories": ["math.OC", "math.DS"], "doi": "", "_query": "housing price text listing", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2001.06457v3", "title": "Neglecting Uncertainties Biases House-Elevation Decisions to Manage Riverine Flood Risks", "abstract": "Homeowners around the world elevate houses to manage flood risks. Deciding how high to elevate a house poses a nontrivial decision problem. The U.S. Federal Emergency Management Agency (FEMA) recommends elevating existing houses to the Base Flood Elevation (the elevation of the 100-yr flood) plus a freeboard. This recommendation neglects many uncertainties. Here we analyze a case-study of riverine flood risk management using a multi-objective robust decision-making framework in the face of deep uncertainties. While the quantitative results are location-specific, the approach and overall insights are generalizable. We find strong interactions between the economic, engineering, and Earth science uncertainties, illustrating the need for expanding on previous integrated analyses to further understand the nature and strength of these connections. Considering deep uncertainties surrounding flood hazards, the discount rate, the house lifetime, and the fragility can increase the economically optimal house elevation to values well above FEMA recommendation.", "published": "2020-01-17T18:00:11Z", "authors": ["Mahkameh Zarekarizi", "Vivek Srikrishnan", "Klaus Keller"], "categories": ["stat.AP", "physics.data-an", "q-fin.ST"], "doi": "10.1038/s41467-020-19188-9", "_query": "housing price text listing", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/1503.00019v2", "title": "Error analysis in Fourier methods for option pricing", "abstract": "We provide a bound for the error committed when using a Fourier method to price European options when the underlying follows an exponential \\levy dynamic. The price of the option is described by a partial integro-differential equation (PIDE). Applying a Fourier transformation to the PIDE yields an ordinary differential equation that can be solved analytically in terms of the characteristic exponent of the \\levy process. Then, a numerical inverse Fourier transform allows us to obtain the option price. We present a novel bound for the error and use this bound to set the parameters for the numerical method. We analyse the properties of the bound for a dissipative and pure-jump example. The bound presented is independent of the asymptotic behaviour of option prices at extreme asset prices. The error bound can be decomposed into a product of terms resulting from the dynamics and the option payoff, respectively. The analysis is supplemented by numerical examples that demonstrate results comparable to and superior to the existing literature.", "published": "2015-02-27T21:44:31Z", "authors": ["Fabián Crocce", "Juho Häppölä", "Jonas Kiessling", "Raúl Tempone"], "categories": ["q-fin.PR"], "doi": "", "_query": "housing price text listing", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/1906.10888v1", "title": "European Option Pricing of electricity under exponential functional of Lévy processes with Price-Cap principle", "abstract": "We propose a new model for electricity pricing based on the price cap principle. The particularity of the model is that the asset price is an exponential functional of a jump Lévy process. This model can capture both mean reversion and jumps which are observed in electricity market. It is shown that the value of an European option of this asset is the unique viscosity solution of a partial integro-differential equation (PIDE). A numerical approximation of this solution by the finite differences method is provided. The consistency, stability and convergence results of the scheme are given. Numerical simulations are performed under a smooth initial condition.", "published": "2019-06-26T07:45:28Z", "authors": ["Martin Kegnenlezom", "Patrice Takam Soh", "Antoine-Marie Bogso", "Yves Emvudu Wono"], "categories": ["q-fin.PR", "math.PR"], "doi": "", "_query": "housing price text listing", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2506.12856v1", "title": "Private List Learnability vs. Online List Learnability", "abstract": "This work explores the connection between differential privacy (DP) and online learning in the context of PAC list learning. In this setting, a $k$-list learner outputs a list of $k$ potential predictions for an instance $x$ and incurs a loss if the true label of $x$ is not included in the list. A basic result in the multiclass PAC framework with a finite number of labels states that private learnability is equivalent to online learnability [Alon, Livni, Malliaris, and Moran (2019); Bun, Livni, and Moran (2020); Jung, Kim, and Tewari (2020)]. Perhaps surprisingly, we show that this equivalence does not hold in the context of list learning. Specifically, we prove that, unlike in the multiclass setting, a finite $k$-Littlestone dimensio--a variant of the classical Littlestone dimension that characterizes online $k$-list learnability--is not a sufficient condition for DP $k$-list learnability. However, similar to the multiclass case, we prove that it remains a necessary condition. To demonstrate where the equivalence breaks down, we provide an example showing that the class of monotone functions with $k+1$ labels over $\\mathbb{N}$ is online $k$-list learnable, but not DP $k$-list learnable. This leads us to introduce a new combinatorial dimension, the \\emph{$k$-monotone dimension}, which serves as a generalization of the threshold dimension. Unlike the multiclass setting, where the Littlestone and threshold dimensions are finite together, for $k>1$, the $k$-Littlestone and $k$-monotone dimensions do not exhibit this relationship. We prove that a finite $k$-monotone dimension is another necessary condition for DP $k$-list learnability, alongside finite $k$-Littlestone dimension. Whether the finiteness of both dimensions implies private $k$-list learnability remains an open question.", "published": "2025-06-15T14:10:46Z", "authors": ["Steve Hanneke", "Shay Moran", "Hilla Schefler", "Iska Tsubari"], "categories": ["cs.LG", "cs.DS"], "doi": "", "_query": "housing price text listing", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/1912.04774v2", "title": "Voluntary Disclosure and Personalized Pricing", "abstract": "Central to privacy concerns is that firms may use consumer data to price discriminate. A common policy response is that consumers should be given control over which firms access their data and how. Since firms learn about a consumer's preferences based on the data seen and the consumer's disclosure choices, the equilibrium implications of consumer control are unclear. We study whether such measures improve consumer welfare in monopolistic and competitive markets. We find that consumer control can improve consumer welfare relative to both perfect price discrimination and no personalized pricing. First, consumers can use disclosure to amplify competitive forces. Second, consumers can disclose information to induce even a monopolist to lower prices. Whether consumer control improves welfare depends on the disclosure technology and market competitiveness. Simple disclosure technologies suffice in competitive markets. When facing a monopolist, a consumer needs partial disclosure possibilities to obtain any welfare gains.", "published": "2019-12-10T15:41:10Z", "authors": ["S. Nageeb Ali", "Greg Lewis", "Shoshana Vasserman"], "categories": ["econ.TH"], "doi": "", "_query": "housing price text listing", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2004.13247v2", "title": "Bounds for list-decoding and list-recovery of random linear codes", "abstract": "A family of error-correcting codes is list-decodable from error fraction $p$ if, for every code in the family, the number of codewords in any Hamming ball of fractional radius $p$ is less than some integer $L$ that is independent of the code length. It is said to be list-recoverable for input list size $\\ell$ if for every sufficiently large subset of codewords (of size $L$ or more), there is a coordinate where the codewords take more than $\\ell$ values. The parameter $L$ is said to be the \"list size\" in either case. The capacity, i.e., the largest possible rate for these notions as the list size $L \\to \\infty$, is known to be $1-h_q(p)$ for list-decoding, and $1-\\log_q \\ell$ for list-recovery, where $q$ is the alphabet size of the code family. In this work, we study the list size of random linear codes for both list-decoding and list-recovery as the rate approaches capacity. We show the following claims hold with high probability over the choice of the code (below, $ε> 0$ is the gap to capacity). (1) A random linear code of rate $1 - \\log_q(\\ell) - ε$ requires list size $L \\ge \\ell^{Ω(1/ε)}$ for list-recovery from input list size $\\ell$. This is surprisingly in contrast to completely random codes, where $L = O(\\ell/ε)$ suffices w.h.p. (2) A random linear code of rate $1 - h_q(p) - ε$ requires list size $L \\ge \\lfloor h_q(p)/ε+0.99 \\rfloor$ for list-decoding from error fraction $p$, when $ε$ is sufficiently small. (3) A random binary linear code of rate $1 - h_2(p) - ε$ is list-decodable from average error fraction $p$ with list size with $L \\leq \\lfloor h_2(p)/ε\\rfloor + 2$. The second and third results together precisely pin down the list sizes for binary random linear codes for both list-decoding and average-radius list-decoding to three possible values.", "published": "2020-04-28T02:29:26Z", "authors": ["Venkatesan Guruswami", "Ray Li", "Jonathan Mosheiff", "Nicolas Resch", "Shashwat Silas", "Mary Wootters"], "categories": ["cs.IT", "math.PR"], "doi": "10.1109/TIT.2021.3127126", "_query": "housing price text listing", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/1204.4611v1", "title": "Applications of the Likelihood Theory in Finance: Modelling and Pricing", "abstract": "This paper discusses the connection between mathematical finance and statistical modelling which turns out to be more than a formal mathematical correspondence. We like to figure out how common results and notions in statistics and their meaning can be translated to the world of mathematical finance and vice versa. A lot of similarities can be expressed in terms of LeCam's theory for statistical experiments which is the theory of the behaviour of likelihood processes. For positive prices the arbitrage free financial assets fit into filtered experiments. It is shown that they are given by filtered likelihood ratio processes. From the statistical point of view, martingale measures, completeness and pricing formulas are revisited. The pricing formulas for various options are connected with the power functions of tests. For instance the Black-Scholes price of a European option has an interpretation as Bayes risk of a Neyman Pearson test. Under contiguity the convergence of financial experiments and option prices are obtained. In particular, the approximation of Ito type price processes by discrete models and the convergence of associated option prices is studied. The result relies on the central limit theorem for statistical experiments, which is well known in statistics in connection with local asymptotic normal (LAN) families. As application certain continuous time option prices can be approximated by related discrete time pricing formulas.", "published": "2012-04-20T13:03:37Z", "authors": ["Arnold Janssen", "Martin Tietje"], "categories": ["math.ST"], "doi": "", "_query": "housing price text listing", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2601.09872v1", "title": "A continuous-time Kyle model with price-responsive traders", "abstract": "Classical Kyle-type models of informed trading typically treat noise trader demand as purely exogenous. In reality, many market participants react to price movements and news, generating feedback effects that can significantly alter market dynamics. This paper develops a continuous-time Kyle framework in which two types of price-responsive traders (momentum and contrarian traders) adjust their demand in response to price signals. This extension yields a finite-dimensional Kalman filter for price discovery and leads to a forward-backward Riccati system characterizing equilibrium. We show that when feedback is weak, equilibrium exists and is unique as a smooth perturbation of the classical Kyle solution, allowing us to derive explicit comparative statics for insider profits and price informativeness. For stronger feedback, the model generates rich dynamics, including potential multiplicity of equilibria and amplification effects. Our framework thus bridges the gap between purely exogenous noise and more realistic, behaviorally motivated trading.", "published": "2026-01-14T21:05:37Z", "authors": ["Eunjung Noh"], "categories": ["q-fin.MF"], "doi": "", "_query": "housing price text listing", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/1611.07098v3", "title": "Risk-Sensitive Learning and Pricing for Demand Response", "abstract": "We consider the setting in which an electric power utility seeks to curtail its peak electricity demand by offering a fixed group of customers a uniform price for reductions in consumption relative to their predetermined baselines. The underlying demand curve, which describes the aggregate reduction in consumption in response to the offered price, is assumed to be affine and subject to unobservable random shocks. Assuming that both the parameters of the demand curve and the distribution of the random shocks are initially unknown to the utility, we investigate the extent to which the utility might dynamically adjust its offered prices to maximize its cumulative risk-sensitive payoff over a finite number of $T$ days. In order to do so effectively, the utility must design its pricing policy to balance the tradeoff between the need to learn the unknown demand model (exploration) and maximize its payoff (exploitation) over time. In this paper, we propose such a pricing policy, which is shown to exhibit an expected payoff loss over $T$ days that is at most $O(\\sqrt{T}\\log(T))$, relative to an oracle pricing policy that knows the underlying demand model. Moreover, the proposed pricing policy is shown to yield a sequence of prices that converge to the oracle optimal prices in the mean square sense.", "published": "2016-11-21T23:19:24Z", "authors": ["Kia Khezeli", "Eilyan Bitar"], "categories": ["cs.LG", "math.OC"], "doi": "", "_query": "housing price text listing", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2112.08215v1", "title": "Two-Price Equilibrium", "abstract": "Walrasian equilibrium is a prominent market equilibrium notion, but rarely exists in markets with indivisible items. We introduce a new market equilibrium notion, called two-price equilibrium (2PE). A 2PE is a relaxation of Walrasian equilibrium, where instead of a single price per item, every item has two prices: one for the item's owner and a (possibly) higher one for all other buyers. Thus, a 2PE is given by a tuple $(\\textbf{S},\\mathbf{\\hat{p}},\\mathbf{\\check{p}})$ of an allocation $\\textbf{S}$ and two price vectors $\\mathbf{\\hat{p}},\\mathbf{\\check{p}}$, where every buyer $i$ is maximally happy with her bundle $S_i$, given prices $\\mathbf{\\check{p}}$ for items in $S_i$ and prices $\\mathbf{\\hat{p}}$ for all other items. 2PE generalizes previous market equilibrium notions, such as conditional equilibrium, and is related to relaxed equilibrium notions like endowment equilibrium. We define the {\\em discrepancy} of a 2PE -- a measure of distance from Walrasian equilibrium -- as the sum of differences $\\hat{p}_j-\\check{p}_j$ over all items (normalized by social welfare). We show that the social welfare degrades gracefully with the discrepancy; namely, the social welfare of a 2PE with discrepancy $d$ is at least a fraction $\\frac{1}{d+1}$ of the optimal welfare. We use this to establish welfare guarantees for markets with subadditive valuations over identical items. In particular, we show that every such market admits a 2PE with at least $1/7$ of the optimal welfare. This is in contrast to Walrasian equilibrium or conditional equilibrium which may not even exist. Our techniques provide new insights regarding valuation functions over identical items, which we also use to characterize instances that admit a WE.", "published": "2021-12-15T15:45:56Z", "authors": ["Michal Feldman", "Galia Shabtai", "Aner Wolfenfeld"], "categories": ["cs.GT"], "doi": "", "_query": "housing price text listing", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/math/0503235v1", "title": "A brief note on the soundness of Bermudan option pricing via cubature", "abstract": "The subject of this study is an iterative Bermudan option pricing algorithm based on (high-dimensional) cubature. We show that the sequence of Bermudan prices (as functions of the underlying assets' logarithmic start prices) resulting from the iteration is bounded and increases monotonely to the approximate perpetual Bermudan option price; the convergence is linear in the supremum norm with the discount factor being the convergence factor. Furthermore, we prove a characterisation of this approximated perpetual Bermudan price as the smallest fixed point of the iteration procedure.", "published": "2005-03-12T22:57:19Z", "authors": ["Frederik S. Herzberg"], "categories": ["math.PR", "math.NA"], "doi": "", "_query": "housing price text listing", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2406.16400v2", "title": "Stochastic Path-Dependent Volatility Models for Price-Storage Dynamics in Natural Gas Markets and Discrete-Time Swing Option Pricing", "abstract": "This paper is devoted to the price-storage dynamics in natural gas markets. A novel stochastic path-dependent volatility model is introduced with path-dependence in both price volatility and storage increments. Model calibrations are conducted for both the price and storage dynamics. Further, we discuss the pricing problem of discrete-time swing options using the dynamic programming principle, and a deep learning-based method is proposed for numerical approximations. A numerical algorithm is provided, followed by a convergence analysis result for the deep-learning approach.", "published": "2024-06-24T08:03:20Z", "authors": ["Jinniao Qiu", "Antony Ware", "Yang Yang"], "categories": ["q-fin.MF"], "doi": "", "_query": "housing price text listing", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2506.02284v1", "title": "Learning Optimal Posted Prices for a Unit-Demand Buyer", "abstract": "We study the problem of learning the optimal item pricing for a unit-demand buyer with independent item values, and the learner has query access to the buyer's value distributions. We consider two common query models in the literature: the sample access model where the learner can obtain a sample of each item value, and the pricing query model where the learner can set a price for an item and obtain a binary signal on whether the sampled value of the item is greater than our proposed price. In this work, we give nearly tight sample complexity and pricing query complexity of the unit-demand pricing problem.", "published": "2025-06-02T21:48:12Z", "authors": ["Yifeng Teng", "Yifan Wang"], "categories": ["cs.GT", "cs.DS", "cs.LG"], "doi": "", "_query": "housing price text listing", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/1710.07867v1", "title": "The Price of Anarchy for Transportation Networks with Mixed Autonomy", "abstract": "We study routing behavior in transportation networks with mixed autonomy, that is, networks in which a fraction of the vehicles on each road are equipped with autonomous capabilities such as adaptive cruise control that enable reduced headways and increased road capacity. Motivated by capacity models developed for such roads with mixed autonomy, we consider transportation networks in which the delay on each road or link is an affine function of two quantities: the number of vehicles with autonomous capabilities on the link and the number of regular vehicles on the link. We particularly study the price of anarchy for such networks, that is, the ratio of the total delay experienced by selfish routing to the socially optimal routing policy. Unlike the case when all vehicles are of the same type, for which the price of anarchy is known to be bounded, we first show that the price of anarchy can be arbitrarily large for such mixed autonomous networks. Next, we define a notion of asymmetry equal to the maximum possible travel time improvement due to the presence of autonomous vehicles. We show that when the degree of asymmetry of all links in the network is bounded by a factor less than 4, the price of anarchy is bounded. We also bound the bicriteria, which is a bound on the cost of selfishly routing traffic compared to the cost of optimally routing additional traffic. These bounds depend on the degree of asymmetry and recover classical bounds on the price of anarchy and bicriteria in the case when no asymmetry exists. Further, we show with examples that these bound are tight in particular cases.", "published": "2017-10-22T00:15:29Z", "authors": ["Daniel A. Lazar", "Samuel Coogan", "Ramtin Pedarsani"], "categories": ["math.OC"], "doi": "", "_query": "housing price text listing", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/1905.05683v2", "title": "Capacity and Price Competition in Markets with Congestion Effects", "abstract": "We study oligopolistic competition in service markets where firms offer a service to customers. The service quality of a firm - from the perspective of a customer - depends on the congestion and the charged price. A firm can set a price for the service offered and additionally decides on the service capacity in order to mitigate congestion. The total profit of a firm is derived from the gained revenue minus the capacity investment cost. Firms simultaneously set capacities and prices in order to maximize their profit and customers subsequently choose the services with lowest combined cost (congestion and price). For this basic model, Johari et al. (2010) derived the first existence and uniqueness results of pure Nash equilibria (PNE) assuming mild conditions on congestion functions. Their existence proof relies on Kakutani's fixed-point theorem and a key assumption for the theorem to work is that demand for service is elastic (modeled by a smooth and strictly decreasing inverse demand function). In this paper, we consider the case of perfectly inelastic demand, i.e. there is a fixed volume of customers requesting service. This scenario applies to realistic cases where customers are not willing to drop out of the market, e.g. if prices are regulated by reasonable price caps. We investigate existence, uniqueness and quality of PNE for models with inelastic demand and price caps. We show that for linear congestion cost functions, there exists a PNE. This result requires a completely new proof approach compared to previous approaches, since the best response correspondences of firms may be empty, thus standard fixed-point arguments are not directly applicable. We show that the game is C-secure (see McLennan et al. (2011)), which leads to the existence of PNE. We furthermore show that the PNE is unique, and that the efficiency compared to a social optimum is unbounded in general.", "published": "2019-05-14T15:55:41Z", "authors": ["Tobias Harks", "Anja Schedel"], "categories": ["cs.GT", "math.OC"], "doi": "", "_query": "housing price text listing", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/cond-mat/9808206v1", "title": "Back to basics: historical option pricing revisited", "abstract": "We reconsider the problem of option pricing using historical probability distributions. We first discuss how the risk-minimisation scheme proposed recently is an adequate starting point under the realistic assumption that price increments are uncorrelated (but not necessarily independent) and of arbitrary probability density. We discuss in particular how, in the Gaussian limit, the Black-Scholes results are recovered, including the fact that the average return of the underlying stock disappears from the price (and the hedging strategy). We compare this theory to real option prices and find these reflect in a surprisingly accurate way the subtle statistical features of the underlying asset fluctuations.", "published": "1998-08-19T09:18:05Z", "authors": ["Jean-Philippe Bouchaud", "Marc Potters"], "categories": ["cond-mat"], "doi": "10.1098/rsta.1999.0414", "_query": "housing price text listing", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2508.06071v2", "title": "A Game-Theoretic Foundation for Bitcoin's Price: A Security-Utility Equilibrium", "abstract": "This paper introduces a structural game-theoretic model to value decentralized digital assets like Bitcoin. Instead of relying on speculative beliefs, it frames the asset's price within a Rational-Expectations Security-Utility Nash Equilibrium (RESUNE). This equilibrium is a fixed point where the market-clearing price dictates the hash rate through a free-entry mining model, which in turn endogenously sets the network's security. The security, defined as one minus the probability of a 51% attack, is determined via a global games model of attacker coordination, providing a unique and continuous security function. We prove the existence of a RESUNE and offer conditions for its uniqueness and stability. The model predicts that the stabilizing direct effect of price on demand must outweigh the potentially destabilizing feedback from price to security. The framework generates testable predictions, such as a protocol halving causing a contraction in both hash rate and price. A structural Vector Autoregression (VAR) model is proposed to test this mechanism. The model decomposes Bitcoin's value into transactional utility, security, and speculative components and explains the observed unidirectional causality from price to hash rate.", "published": "2025-08-08T07:09:21Z", "authors": ["Liang Chen"], "categories": ["cs.CR"], "doi": "", "_query": "housing price text listing", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/1608.06204v1", "title": "Towards the Interactive Effects of Demand Response Participation on Electricity Spot Market Price", "abstract": "The electricity market is threatened by supply scarcity, which may lead to very sharp price spikes in the spot market. On the other hand, demand-side's activities could effectively mitigate the supply scarcity and absorb most of these shocks and therefore smooth out the price volatility. In this paper, the positive effects of employing demand response programs on the spot market price are investigated. A demand-price elasticity based model is used to simulate the customer reaction function in the presence of a real time pricing. The demand achieve by DR program is used to adjust the spot market price by using a price regression model. SAS software is used to run the multiple linear regression model and MATLAB is used to simulate the demand response model. The approach is applied on one week data in summer 2014 of Connecticut in New England ISO. It could be concluded from the results of this study that applying DR program smooths out most of the price spikes in the electricity spot market and considerably reduces the customers' electricity cost.", "published": "2016-08-22T16:01:40Z", "authors": ["Saeed Mohajeryami", "Milad Doostan", "Seyedmahdi Moghadasi", "Peter Schwarz"], "categories": ["eess.SY"], "doi": "", "_query": "housing price text listing", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2512.22271v1", "title": "Choice Modeling and Pricing for Scheduled Services", "abstract": "We describe a novel framework for discrete choice modeling and price optimization for settings where scheduled service options (often hierarchical) are offered to customers, which is applicable across many businesses including some within Amazon. In such business settings, the customers would see multiple options, often substitutable, with their features and their prices. These options typically vary in the start and/or end time of the service requested, such as the date of service or a service time window. The costs and demand can vary widely across these different options, resulting in the need for different prices. We propose a system which allows for segmenting the marketplace (as defined by the particular business) using decision trees, while using parametric discrete choice models within each market segment to accurately estimate conversion behavior. Using parametric discrete choice models allows us to capture important behavioral aspects like reference price effects which naturally occur in scheduled service applications. In addition, we provide natural and fast heuristics to do price optimization. For one such Amazon business where we conducted a live A/B experiment, this new framework outperformed the existing pricing system in every key metric, increasing our target performance metric by 19%, while providing a robust platform to support future new services of the business. The model framework has now been in full production for this business since Q4 2023.", "published": "2025-12-24T23:18:22Z", "authors": ["Adam N. Elmachtoub", "Kumar Goutam", "Roger Lederman"], "categories": ["econ.GN"], "doi": "", "_query": "housing price text listing", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2401.15108v2", "title": "Tacit algorithmic collusion in deep reinforcement learning guided price competition: A study using EV charge pricing game", "abstract": "Players in pricing games with complex structures are increasingly adopting artificial intelligence (AI) aided learning algorithms to make pricing decisions for maximizing profits. This is raising concern for the antitrust agencies as the practice of using AI may promote tacit algorithmic collusion among otherwise independent players. Recent studies of games in canonical forms have shown contrasting claims ranging from none to a high level of tacit collusion among AI-guided players. In this paper, we examine the concern for tacit collusion by considering a practical game where EV charging hubs compete by dynamically varying their prices. Such a game is likely to be commonplace in the near future as EV adoption grows in all sectors of transportation. The hubs source power from the day-ahead (DA) and real-time (RT) electricity markets as well as from in-house battery storage systems. Their goal is to maximize profits via pricing and efficiently managing the cost of power usage. To aid our examination, we develop a two-step data-driven methodology. The first step obtains the DA commitment by solving a stochastic model. The second step generates the pricing strategies by solving a competitive Markov decision process model using a multi-agent deep reinforcement learning (MADRL) framework. We evaluate the resulting pricing strategies using an index for the level of tacit algorithmic collusion. An index value of zero indicates no collusion (perfect competition) and one indicates full collusion (monopolistic behavior). Results from our numerical case study yield collusion index values between 0.14 and 0.45, suggesting a low to moderate level of collusion.", "published": "2024-01-25T16:51:52Z", "authors": ["Diwas Paudel", "Tapas K. Das"], "categories": ["cs.LG", "cs.AI", "econ.GN", "eess.SY"], "doi": "", "_query": "housing price text listing", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2511.05080v4", "title": "Making Knowledge Accessible: Divergent Readability-Accuracy Strategies of Mistral and QWen in Biomedical Text Simplification", "abstract": "The growing public demand for accessible biomedical information calls for scalable text simplification. While large language models (LLMs) offer solutions, they too struggle with balancing improved readability against preservation of meaning. This report empirically compares how two LLMs - instruction-tuned Mistral-Small 3 24B and the reasoning-augmented QWen2.5 32B- navigate this trade-off in biomedical text simplification, benchmarked against human performance. Our analysis highlights how each model applies distinct operational strategies when simplifying biomedical text. Mistral exhibits a tempered lexical simplification approach that consistently enhances readability across multiple metrics while preserving discourse fidelity (BERTScore: 0.91, statistically comparable to that of humans). In comparison, QWen also attains enhanced readability performance and a reasonable BERTScore of 0.89, but presents a disconnect in balancing between readability and accuracy. Additionally, a comprehensive correlation analysis of a suite of 21 metrics confirms strong functional redundancies in metrics and informs adaptation requirements.", "published": "2025-11-07T08:53:39Z", "authors": ["P. Bilha Githinji", "Aikaterini Melliou", "Zeming Liang", "Lian Zhang", "Peiwu Qin"], "categories": ["cs.CL"], "doi": "", "_query": "housing price text listing", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/0809.4781v3", "title": "On contingent claims pricing in incomplete markets: A risk sharing approach", "abstract": "In an incomplete market setting, we consider two financial agents, who wish to price and trade a non-replicable contingent claim. Assuming that the agents are utility maximizers, we propose a transaction price which is a result of the minimization of a convex combination of their utility differences. We call this price the risk sharing price, we prove its existence for a large family of utility functions and we state some of its properties. As an example, we analyze extensively the case where both agents report exponential utility.", "published": "2008-09-27T15:53:24Z", "authors": ["Michail Anthropelos", "Nikolaos E. Frangos", "Stylianos Z. Xanthopoulos", "Athanasios N. Yannacopoulos"], "categories": ["q-fin.CP", "math.OC", "math.PR", "q-fin.TR"], "doi": "", "_query": "housing price text listing", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/1407.1769v3", "title": "Discrete, Non Probabilistic Market Models. Arbitrage and Pricing Intervals", "abstract": "The paper develops general, discrete, non-probabilistic market models and minmax price bounds leading to price intervals for European options. The approach provides the trajectory based analogue of martingale-like properties as well as a generalization that allows a limited notion of arbitrage in the market while still providing coherent option prices. Several properties of the price bounds are obtained, in particular a connection with risk neutral pricing is established for trajectory markets associated to a continuous-time martingale model.", "published": "2014-07-07T16:55:13Z", "authors": ["Sebastian E. Ferrando", "Alfredo L. Gonzalez", "Ivan L. Degano", "Massoome Rahsepar"], "categories": ["q-fin.MF", "q-fin.PR"], "doi": "", "_query": "housing price text listing", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2004.13708v2", "title": "Classical Option Pricing and Some Steps Further", "abstract": "This paper considers the asset price p as relations C=pV between the value C and the volume V of the executed transactions and studies the consequences of this definition for the option pricing equations. We show that the classical BSM model implicitly assumes that value C and volume V of transactions follow identical Brownian processes. Violation of this identity leads to 2-dimensional BSM-like equation with two constant volatilities. We show that agents expectations those approve execution of transactions can further increase the dimension of the BSM model. We study the case when agents expectations may depend on the option price data and show that such assumption can lead to the nonlinear BSM-like equations. We reconsider the Heston stochastic volatility model for the price determined by the value and the volume and derive 3-dimensional BSM-like model with stochastic value volatility and constant volume volatility. Variety of the BSM-like equations states the problem of reasonable balance between the accuracy and the complexity of the option pricing equations.", "published": "2020-04-27T19:16:45Z", "authors": ["Victor Olkhov"], "categories": ["q-fin.PR", "q-fin.ST"], "doi": "", "_query": "housing price text listing", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/1805.05431v1", "title": "Comparison of Classical and Nonlinear Models for Short-Term Electricity Price Prediction", "abstract": "Electricity is bought and sold in wholesale markets at prices that fluctuate significantly. Short-term forecasting of electricity prices is an important endeavor because it helps electric utilities control risk and because it influences competitive strategy for generators. As the \"smart grid\" grows, short-term price forecasts are becoming an important input to bidding and control algorithms for battery operators and demand response aggregators. While the statistics and machine learning literature offers many proposed methods for electricity price prediction, there is no consensus supporting a single best approach. We test two contrasting machine learning approaches for predicting electricity prices, regression decision trees and recurrent neural networks (RNNs), and compare them to a more traditional ARIMA implementation. We conduct the analysis on a challenging dataset of electricity prices from ERCOT, in Texas, where price fluctuation is especially high. We find that regression decision trees in particular achieves high performance compared to the other methods, suggesting that regression trees should be more carefully considered for electricity price forecasting.", "published": "2018-05-02T20:15:36Z", "authors": ["Elaheh Fata", "Igor Kadota", "Ian Schneider"], "categories": ["cs.CY", "cs.LG", "stat.ML"], "doi": "", "_query": "housing price text listing", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2112.05592v1", "title": "Singleton-type bounds for list-decoding and list-recovery, and related results", "abstract": "List-decoding and list-recovery are important generalizations of unique decoding that received considerable attention over the years. However, the optimal trade-off among list-decoding (resp. list-recovery) radius, list size, and the code rate are not fully understood in both problems. This paper takes a step towards this direction when the list size is a given constant and the alphabet size is large (as a function of the code length). We prove a new Singleton-type upper bound for list-decodable codes, which improves upon the previously known bound by roughly a factor of $1/L$, where $L$ is the list size. We also prove a Singleton-type upper bound for list-recoverable codes, which is to the best of our knowledge, the first such bound for list-recovery. We apply these results to obtain new lower bounds that are optimal up to a multiplicative constant on the list size for list-decodable and list-recoverable codes with rates approaching capacity. Moreover, we show that list-decodable \\emph{nonlinear} codes can strictly outperform list-decodable linear codes. More precisely, we show that there is a gap for a wide range of parameters, which grows fast with the alphabet size, between the size of the largest list-decodable nonlinear code and the size of the largest list-decodable linear codes. This is achieved by a novel connection between list-decoding and the notion of sparse hypergraphs in extremal combinatorics. We remark that such a gap is not known to exist in the problem of unique decoding. Lastly, we show that list-decodability or recoverability of codes implies in some sense good unique decodability.", "published": "2021-12-10T15:20:04Z", "authors": ["Eitan Goldberg", "Chong Shangguan", "Itzhak Tamo"], "categories": ["cs.IT", "math.CO"], "doi": "", "_query": "housing price text listing", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2508.09869v1", "title": "The Price of EF1 for Few Agents with Additive Ternary Valuations", "abstract": "We consider a resource allocation problem with agents that have additive ternary valuations for a set of indivisible items, and bound the price of envy-free up to one item (EF1) allocations. For a large number $n$ of agents, we show a lower bound of $Ω(\\sqrt{n})$, implying that the price of EF1 is no better than when the agents have general subadditive valuations. We then focus on instances with few agents and show that the price of EF1 is $12/11$ for $n=2$, and between $1.2$ and $1.256$ for $n=3$.", "published": "2025-08-13T15:05:52Z", "authors": ["Maria Kyropoulou", "Alexandros A. Voudouris"], "categories": ["cs.GT"], "doi": "10.1016/j.orl.2025.107351", "_query": "housing price text listing", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/1612.05021v1", "title": "Dynamic Modeling of Price Responsive Demand in Real-time Electricity Market: Empirical Analysis", "abstract": "In this paper, we study the price responsiveness of electricity consumption from empirical commercial and industrial load data obtained from Texas. Employing a dynamical system perspective, we show that price responsive demand can be modeled as a hybrid of a Hammerstein model with delay following a price surge, and a linear ARX model under moderate price changes. It is observed that electricity consumption therefore has unique characteristics including (1) qualitatively distinct response between moderate and extremely high prices; and (2) a time delay associated with the response to high prices. It is shown that these observed features may render traditional approaches to demand response and retail pricing based on classical economic theories ineffective. In particular, ultimate real-time retail pricing may be limitedly beneficial than as considered in classical economic theories.", "published": "2016-12-15T11:23:52Z", "authors": ["Jaeyong An", "P. R. Kumar", "Le Xie"], "categories": ["stat.AP", "q-fin.GN"], "doi": "", "_query": "housing price text listing", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/1811.12466v1", "title": "Consistency of Forecasts for the U.S. House of Representatives", "abstract": "We consider the performance of the foremost academic House of Representatives forecasting models in the 2018 elections. In creating open-source implementations of these models, we outline key underlying assumptions. We find that although the results were unsurprising, they indicate a weakening of many traditional forecasting indicators.", "published": "2018-11-29T20:20:38Z", "authors": ["Henry Bendekgey"], "categories": ["stat.AP"], "doi": "", "_query": "housing price text listing", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/1501.04548v1", "title": "Effect of Volatility Clustering on Indifference Pricing of Options by Convex Risk Measures", "abstract": "In this article, we look at the effect of volatility clustering on the risk indifference price of options described by Sircar and Sturm in their paper (Sircar, R., & Sturm, S. (2012). From smile asymptotics to market risk measures. Mathematical Finance. Advance online publication. doi:10.1111/mafi.12015). The indifference price in their article is obtained by using dynamic convex risk measures given by backward stochastic differential equations. Volatility clustering is modelled by a fast mean-reverting volatility in a stochastic volatility model for stock price. Asymptotics of the indifference price of options and their corresponding implied volatility are obtained in this article, as the mean-reversion time approaches zero. Correction terms to the asymptotic option price and implied volatility are also obtained.", "published": "2015-01-19T16:45:32Z", "authors": ["Rohini Kumar"], "categories": ["q-fin.MF"], "doi": "10.1080/1350486X.2014.949805", "_query": "housing price text listing", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2304.02356v3", "title": "Unifying Market Microstructure and Dynamic Asset Pricing", "abstract": "We introduce a discrete binary tree for pricing contingent claims with the underlying security prices exhibiting history dependence characteristic of that induced by market microstructure phenomena. Example dependencies considered include moving average or autoregressive behavior. Our model is market-complete, arbitrage-free, and preserves all of the parameters governing the historical (natural world) price dynamics when passing to an equivalent martingale (risk-neutral) measure. Specifically, this includes the instantaneous mean and variance of the asset return and the instantaneous probabilities for the direction of asset price movement. We believe this is the first paper to demonstrate the ability to include market microstructure effects in dynamic asset/option pricing in a market-complete, no-arbitrage, format.", "published": "2023-04-05T10:45:25Z", "authors": ["Davide Lauria", "W. Brent Lindquist", "Svetlozar T. Rachev", "Yuan Hu"], "categories": ["q-fin.MF", "q-fin.PR"], "doi": "", "_query": "housing price text listing", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/1811.01077v5", "title": "Dynamic Pricing (and Assortment) under a Static Calendar", "abstract": "This work is motivated by our collaboration with a large consumer packaged goods (CPG) company. We have found that while the company appreciates the advantages of dynamic pricing, they deem it operationally much easier to plan out a static price calendar in advance. We investigate the efficacy of static control policies for revenue management problems whose optimal solution is inherently dynamic. In these problems, a firm has limited inventory to sell over a finite time horizon, over which heterogeneous customers stochastically arrive. We consider both pricing and assortment controls, and derive simple static policies in the form of a price calendar or a planned sequence of assortments, respectively. In the assortment planning problem, we also differentiate between the static vs. dynamic substitution models of customer demand. We show that our policies are within 1-1/e (approximately 0.63) of the optimum under stationary (IID) demand, and 1/2 of the optimum under non-stationary demand, with both guarantees approaching 1 if the starting inventories are large. We adapt the technique of prophet inequalities from optimal stopping theory to pricing and assortment problems, and our results are relative to the linear programming relaxation. Under the special case of IID single-item pricing, our results improve the understanding of irregular and discrete demand curves, by showing that a static calendar can be (1-1/e)-approximate if the prices are sorted high-to-low. Finally, we demonstrate on both data from the CPG company and synthetic data from the literature that our simple price and assortment calendars are effective.", "published": "2018-11-02T20:27:01Z", "authors": ["Will Ma", "David Simchi-Levi", "Jinglong Zhao"], "categories": ["cs.DS"], "doi": "", "_query": "housing price text listing", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2601.07452v1", "title": "A Note on 'The Limits of Price Discrimination' by Bergemann, Brooks, and Morris", "abstract": "This note revisits the analysis of third-degree price discrimination developed by Bergemann et al. (2015), which characterizes the set of consumer-producer surplus pairs that can be achieved through market segmentation. This was proved by means of market segmentation with random prices, but it was claimed that any segmentation with possibly random pricing has a corresponding direct segmentation, where a deterministic price is charged in each market segment. However, the latter claim is not correct under the definition of market segmentation given in the paper, and we provide counterexamples. We then propose an alternative definition to resolve this issue and examine the implications of the difference between the two definitions in terms of the main result of their paper.", "published": "2026-01-12T11:48:48Z", "authors": ["Keita Kuwahara"], "categories": ["econ.TH"], "doi": "", "_query": "housing price text listing", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/1207.6011v1", "title": "Analysis of cloud storage prices", "abstract": "Cloud storage is fast securing its role as a major repository for both consumers and business customers. Many companies now offer storage solutions, sometimes for free for limited amounts of capacity. We have surveyed the pricing plans of a selection of major cloud providers and compared them using the unit price as the means of comparison. All the providers, excepting Amazon, adopt a bundling pricing scheme; Amazon follows instead a block-declining pricing policy. We compare the pricing plans through a double approach: a pointwise comparison for each value of capacity, and an overall comparison using a two-part tariff approximation and a Pareto-dominance criterion. Under both approaches, most providers appear to offer pricing plans that are more expensive and can be excluded from a procurement selection in favour of a limited number of dominant providers.", "published": "2012-07-25T14:45:55Z", "authors": ["Loretta Mastroeni", "Maurizio Naldi"], "categories": ["cs.DC"], "doi": "", "_query": "housing price text listing", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2102.13289v3", "title": "Optimal Pricing of Information", "abstract": "A decision maker is choosing between an active action (e.g., purchase a house, invest certain stock) and a passive action. The payoff of the active action depends on the buyer's private type and also an unknown state of nature. An information seller can design experiments to reveal information about the realized state to the decision maker, and would like to maximize profit from selling such information. We characterize, in closed-form, the revenue-optimal information selling mechanism for the seller. After eliciting the buyer's type, the optimal mechanism charges the buyer an upfront payment and then simply reveals whether the realized state exceeds a certain threshold or not. The optimal mechanism features both price discrimination and information discrimination. The special buyer type who is a-priori indifferent between the active and passive action benefits the most from participating in the mechanism.", "published": "2021-02-26T03:15:19Z", "authors": ["Shuze Liu", "Weiran Shen", "Haifeng Xu"], "categories": ["cs.GT"], "doi": "10.1145/3465456.3467551", "_query": "housing price text listing", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2606.31086v1", "title": "CasaMaestro: Multi-View Panoramas for House-Scale 3D Reconstruction", "abstract": "The rise of home-deployed embodied AI systems is driving a growing need for fast, metric 3D reconstruction of residential spaces to support navigation, interaction, and long-horizon task execution. However, the commonly used pinhole-camera 3D reconstruction pipelines struggle to model large indoor residences efficiently due to their limited field of view, to which achieving full coverage across multiple rooms often requires thousands of images and incurs drift from long chains of incremental alignment. In this work, we present CasaMaestro (Spanish words meaning ``house'' and ``master''), a feedforward model that can take only twenty to fifty sparse multi-view indoor panoramas as input and directly predicts metric depth along with camera poses, allowing fast point-cloud reconstruction of the entire house with full coverage. CasaMaestro is the first model that supports house-scale reconstruction with multi-view panoramas. Experiments show that CasaMaestro can robustly provide high quality results in both real-world and synthetic scenes, which can serve as a strong foundation for acquiring house-scale 3D indoor assets to be applied in close-loop simulation.", "published": "2026-06-30T03:21:57Z", "authors": ["Yuzhou Ji", "Xiaotian Yang", "Zhipeng Zhang"], "categories": ["cs.CV"], "doi": "", "_query": "housing price text listing", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/1411.4916v1", "title": "Combinatorial Auctions via Posted Prices", "abstract": "We study anonymous posted price mechanisms for combinatorial auctions in a Bayesian framework. In a posted price mechanism, item prices are posted, then the consumers approach the seller sequentially in an arbitrary order, each purchasing her favorite bundle from among the unsold items at the posted prices. These mechanisms are simple, transparent and trivially dominant strategy incentive compatible (DSIC). We show that when agent preferences are fractionally subadditive (which includes all submodular functions), there always exist prices that, in expectation, obtain at least half of the optimal welfare. Our result is constructive: given black-box access to a combinatorial auction algorithm A, sample access to the prior distribution, and appropriate query access to the sampled valuations, one can compute, in polytime, prices that guarantee at least half of the expected welfare of A. As a corollary, we obtain the first polytime (in n and m) constant-factor DSIC mechanism for Bayesian submodular combinatorial auctions, given access to demand query oracles. Our results also extend to valuations with complements, where the approximation factor degrades linearly with the level of complementarity.", "published": "2014-11-18T17:17:57Z", "authors": ["Michal Feldman", "Nick Gravin", "Brendan Lucier"], "categories": ["cs.GT"], "doi": "", "_query": "housing price text listing", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/1505.07210v2", "title": "Intertemporal Substitutability, Risk Aversion and Asset Prices", "abstract": "Is the elasticity of intertemporal substitution (EIS) more or less than one? This question can be answered by confronting theoretical results of asset pricing models with investor behaviour during episodes of stock market panic. If we consider these episodes as periods of high risk aversion, then lower asset prices are in fact associated with higher risk aversion. However, according to theoretical models, risky asset price is an increasing function of the coefficient of risk aversion only if the EIS exceeds unity. It may therefore be concluded that the EIS must be more than one to reconcile theory with the observed stock price decline during periods of panic.", "published": "2015-05-27T07:15:26Z", "authors": ["Dominique Pepin"], "categories": ["q-fin.CP", "q-fin.GN"], "doi": "", "_query": "housing price text listing", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2012.00375v1", "title": "The effect of price-based demand response on carbon emissions in European electricity markets: The importance of adequate carbon prices", "abstract": "Price-based demand response (PBDR) has recently been attributed great economic but also environmental potential. However, the determination of its short-term effects on carbon emissions requires the knowledge of marginal emission factors (MEFs), which compared to grid mix emission factors (XEFs), are cumbersome to calculate due to the complex characteristics of national electricity markets. This study, therefore, proposes two merit order-based methods to approximate hourly MEFs and applies it to readily available datasets from 20 European countries for the years 2017-2019. Based on the resulting electricity prices, MEFs, and XEFs, standardized daily load shifts were simulated to quantify their effects on marginal costs and carbon emissions. Finally, by repeating the load shift simulations for different carbon price levels, the impact of the carbon price on the resulting carbon emissions was analyzed. Interestingly, the simulated price-based load shifts led to increases in operational carbon emissions for 8 of the 20 countries and to an average increase of 2.1% across all 20 countries. Switching from price-based to MEF-based load shifts reduced the corresponding carbon emissions to a decrease of 35%, albeit with 56% lower monetary cost savings compared to the price-based load shifts. Under specific circumstances, PBDR leads to an increase in carbon emissions, mainly due to the economic advantage fuel sources such as lignite and coal have in the merit order. However, as the price of carbon is increased, the correlation between the carbon intensity and the marginal cost of the fuels substantially increases. Therefore, with adequate carbon prices, PBDR can be an effective tool for both economical and environmental improvement.", "published": "2020-12-01T10:04:30Z", "authors": ["Markus Fleschutz", "Markus Bohlayer", "Marco Braun", "Gregor Henze", "Michael D. Murphy"], "categories": ["eess.SY"], "doi": "10.1016/j.apenergy.2021.117040", "_query": "housing price text listing", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2209.07706v3", "title": "Understanding NFT Price Moves through Tweets Keywords Analysis", "abstract": "Non-Fungible Token (NFT) is evolving with the rise of the cryptocurrency market and the development of blockchain techniques, which leads to an emerging NFT market that has become prosperous rapidly then followed by a cooldown. Nevertheless, the overall rise procedure of the NFT market has not been well understood. To this end, we consider that social media communities evolving alongside the market growth, are worth exploring and reasoning about, as the mineable information might unveil the market behaviors. We explore the procedure from the perspective of NFT Twitter communities and its impact on the NFT price moves with two experiments. We perform a Granger causality test on the number of tweets and the NFT price time series and find that the number of tweets has a positive impact on (Granger-causes) the price or reversely for larger part of the 19 top authentic projects but seldom copycat projects. Besides, to investigate the price moves predictability, we experiment on predicting Markov normalized NFT price (representing the direction and magnitude of price moves) given tweets-extracted word features and interpret the feature importance to find insights. Our results show that social media words as the predictors result in all 19 top projects having a testing accuracy evidently above the baseline. Based on the feature importance analysis, we find that both general market-related words and NFT event-related words have a markedly positive contribution in predicting price moves. We summarize the characteristics including categorization and sentiment for the words with the top and least feature importance.", "published": "2022-09-16T04:05:47Z", "authors": ["Junliang Luo", "Yongzheng Jia", "Xue Liu"], "categories": ["cs.SI"], "doi": "10.1145/3582515.3609562", "_query": "housing price text listing", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2305.04884v1", "title": "Predicting the Price Movement of Cryptocurrencies Using Linear Law-based Transformation", "abstract": "The aim of this paper is to investigate the effect of a novel method called linear law-based feature space transformation (LLT) on the accuracy of intraday price movement prediction of cryptocurrencies. To do this, the 1-minute interval price data of Bitcoin, Ethereum, Binance Coin, and Ripple between 1 January 2019 and 22 October 2022 were collected from the Binance cryptocurrency exchange. Then, 14-hour nonoverlapping time windows were applied to sample the price data. The classification was based on the first 12 hours, and the two classes were determined based on whether the closing price rose or fell after the next 2 hours. These price data were first transformed with the LLT, then they were classified by traditional machine learning algorithms with 10-fold cross-validation. Based on the results, LLT greatly increased the accuracy for all cryptocurrencies, which emphasizes the potential of the LLT algorithm in predicting price movements.", "published": "2023-04-27T15:04:08Z", "authors": ["Marcell T. Kurbucz", "Péter Pósfay", "Antal Jakovác"], "categories": ["q-fin.ST", "cs.AI", "cs.CV", "cs.LG", "q-fin.CP"], "doi": "", "_query": "housing price text listing", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2205.13826v4", "title": "Multivariate Probabilistic Forecasting of Intraday Electricity Prices using Normalizing Flows", "abstract": "Electricity is traded on various markets with different time horizons and regulations. Short-term intraday trading becomes increasingly important due to the higher penetration of renewables. In Germany, the intraday electricity price typically fluctuates around the day-ahead price of the European Power EXchange (EPEX) spot markets in a distinct hourly pattern. This work proposes a probabilistic modeling approach that models the intraday price difference to the day-ahead contracts. The model captures the emerging hourly pattern by considering the four 15 min intervals in each day-ahead price interval as a four-dimensional joint probability distribution. The resulting nontrivial, multivariate price difference distribution is learned using a normalizing flow, i.e., a deep generative model that combines conditional multivariate density estimation and probabilistic regression. Furthermore, this work discusses the influence of different external impact factors based on literature insights and impact analysis using explainable artificial intelligence (XAI). The normalizing flow is compared to an informed selection of historical data and probabilistic forecasts using a Gaussian copula and a Gaussian regression model. Among the different models, the normalizing flow identifies the trends with the highest accuracy and has the narrowest prediction intervals. Both the XAI analysis and the empirical experiments highlight that the immediate history of the price difference realization and the increments of the day-ahead price have the most substantial impact on the price difference.", "published": "2022-05-27T08:38:20Z", "authors": ["Eike Cramer", "Dirk Witthaut", "Alexander Mitsos", "Manuel Dahmen"], "categories": ["cs.LG"], "doi": "", "_query": "housing price text listing", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2401.01487v1", "title": "Natural Language Processing and Multimodal Stock Price Prediction", "abstract": "In the realm of financial decision-making, predicting stock prices is pivotal. Artificial intelligence techniques such as long short-term memory networks (LSTMs), support-vector machines (SVMs), and natural language processing (NLP) models are commonly employed to predict said prices. This paper utilizes stock percentage change as training data, in contrast to the traditional use of raw currency values, with a focus on analyzing publicly released news articles. The choice of percentage change aims to provide models with context regarding the significance of price fluctuations and overall price change impact on a given stock. The study employs specialized BERT natural language processing models to predict stock price trends, with a particular emphasis on various data modalities. The results showcase the capabilities of such strategies with a small natural language processing model to accurately predict overall stock trends, and highlight the effectiveness of certain data features and sector-specific data.", "published": "2024-01-03T01:21:30Z", "authors": ["Kevin Taylor", "Jerry Ng"], "categories": ["cs.LG", "cs.CL"], "doi": "", "_query": "housing price text listing", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2605.24241v2", "title": "The petrol station game: the regional average price. A mean field analysis", "abstract": "In January 2023, the obligation for petrol stations to display the average fuel price calculated on a regional basis was introduced by the Italian Government. A mean field game model is here proposed to describe the evolution of the fuel price. In particular, each petrol station decides its own price, for the next day, taking account of an estimate of the average price, to be communicated by the Local Authority. The existence and the evolution of a mean field equilibrium are analytically investigated. Some simulations are compared with real data from the Autonomous Province of Trento, Italy.", "published": "2026-05-22T21:33:44Z", "authors": ["Fabio Bagagiolo", "Federico Pontiroli", "Ivan Romanò"], "categories": ["math.OC"], "doi": "", "_query": "housing price text listing", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2603.17764v2", "title": "Robust Dynamic Pricing and Admission Control with Fairness Guarantees", "abstract": "Dynamic pricing is commonly used to regulate congestion in shared service systems. This paper is motivated by the fact that in the presence of users with varying price sensitivity (responsiveness), conventional monotonic pricing can lead to unfair outcomes by disproportionately excluding price-elastic users, particularly under high or uncertain demand. We therefore develop a fairness-oriented mechanism under demand uncertainty. The paper's contributions are twofold. First, we show that when fairness is imposed as a hard state constraint, the optimal (revenue maximizing) pricing policy is generally non-monotonic in demand. This structural result departs fundamentally from standard surge pricing rules and reveals that price reduction under heavy load may be necessary to maintain equitable access. Second, we address the problem that price elasticity among heterogeneous users is unobservable. To solve it, we develop a robust dynamic pricing and admission control framework that enforces capacity and fairness constraints for all user type distributions consistent with aggregate measurements. By integrating integral High Order Control Barrier Functions (iHOCBFs) with a robust optimization framework under uncertain user-type distribution, we obtain a controller that guarantees forward invariance of safety and fairness constraints while optimizing revenue. Numerical experiments demonstrate improved fairness and revenue performance relative to monotonic surge pricing policies.", "published": "2026-03-18T14:23:36Z", "authors": ["Yingqing Chen", "Anni Li", "Christos G. Cassandras", "Homayoun Hamedmoghadam", "Fabian Wirth", "Robert Shorten"], "categories": ["eess.SY"], "doi": "", "_query": "housing price text listing", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/1608.00756v2", "title": "A continuous and efficient fundamental price on the discrete order book grid", "abstract": "This paper develops a model of liquidity provision in financial markets by adapting the Madhavan, Richardson, and Roomans (1997) price formation model to realistic order books with quote discretization and liquidity rebates. We postulate that liquidity providers observe a fundamental price which is continuous, efficient, and can assume values outside the interval spanned by the best quotes. We confirm the predictions of our price formation model with extensive empirical tests on large high-frequency datasets of 100 liquid Nasdaq stocks. Finally we use the model to propose an estimator of the fundamental price based on the rebate adjusted volume imbalance at the best quotes and we empirically show that it outperforms other simpler estimators.", "published": "2016-08-02T10:13:43Z", "authors": ["Julius Bonart", "Fabrizio Lillo"], "categories": ["q-fin.TR", "q-fin.ST"], "doi": "", "_query": "housing price text listing", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2610.17450v1", "title": "Unit-Demand Pricing for Min-Buying Customers", "abstract": "We study the unit-demand pricing problem under \\textit{min-buying} behavior, where customers purchase the lowest-priced item that yields non-negative utility. In contrast to the quasi-linear model, we show that uniform pricing becomes substantially more effective under min-buying. First, we study symmetric environments. We show that for min-buying customers with independent and identically distributed valuations, a uniform price across items is optimal. We then extend this optimality result to all valuations drawn from exchangeable and affiliated distributions. However, uniform pricing is not optimal in all symmetric environments: for min-buying customers we construct an exchangeable and non-affiliated valuation distribution for which the gap between uniform pricing and optimal item pricing is logarithmic in the number of items. This gap is tight and matches the worst-case gap in the quasi-linear setting. Next, we consider asymmetric environments in which valuations are independent but not identically distributed. When these valuations are regular and customers are min-buying, we show that a uniform price guarantees approximately 69.86\\% of the optimal item-pricing revenue and this is tight. Overall, our results highlight that the value of non-uniform pricing substantially diminishes, and often disappears entirely, under more conservative demand models in which customers engage in satisficing behaviors such as min-buying.", "published": "2026-10-12T22:08:37Z", "authors": ["Titing Cui", "Michael Hamilton", "Jourdain Lamperski", "Kasra Tari"], "categories": ["cs.GT", "econ.TH"], "doi": "", "_query": "housing price text listing", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2608.02559v2", "title": "House-monotone multi-level apportionment has logarithmic quota discrepancy", "abstract": "Multi-level apportionment allocates integer seats through a hierarchy of groups. Schmidt-Kraepelin, Suksompong, and Wijaya proved that, at every fixed house size, lower and upper quota can be satisfied simultaneously; they also constructed house-monotone rules satisfying either quota separately. They left open whether one rule can satisfy lower quota, upper quota, and house monotonicity together, even when quota is required only relative to the root. We give a negative answer. For a full binary comb with $D$ equally entitled leaves, every house-monotone allocation sequence induces a sequence of seat recipients. Quota for the nested comb groups would force every grid-aligned prefix discrepancy to be below one. A midpoint embedding then bounds the full interval discrepancy by this quantity plus $1/2$, contradicting Schmidt's logarithmic lower bound. Conversely, a binary van der Corput seat schedule has comb-prefix error at most $\\log D/(3\\log 2)+1$. Thus the optimal worst-case error on the comb is $Θ(\\log D)$, and for sufficiently large finite $D$ no house-monotone quota rule exists. The proof isolates a static--dynamic gap: each house size admits a quota-feasible allocation, but the feasible allocations cannot be embedded into one monotone path. In quantization language, the result characterizes the order of the embedded-quantization penalty for progressive one-hot rounding on the comb.", "published": "2026-08-03T17:42:18Z", "authors": ["Lav R. Varshney"], "categories": ["cs.GT", "cs.IT", "eess.SP"], "doi": "", "_query": "housing price text listing", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/1408.5510v1", "title": "Consistent Price Systems under Model Uncertainty", "abstract": "We develop a version of the fundamental theorem of asset pricing for discrete-time markets with proportional transaction costs and model uncertainty. A robust notion of no-arbitrage of the second kind is defined and shown to be equivalent to the existence of a collection of strictly consistent price systems.", "published": "2014-08-23T16:13:29Z", "authors": ["Bruno Bouchard", "Marcel Nutz"], "categories": ["q-fin.MF", "math.PR"], "doi": "", "_query": "housing price text listing", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2209.02653v1", "title": "Measuring Price Risk Aversion through Indirect Utility Functions: A Laboratory Experiment", "abstract": "The present paper introduces a theoretical framework through which the degree of risk aversion with respect to uncertain prices can be measured through the context of the indirect utility function (IUF) using a lab experiment. First, the paper introduces the main elements of the duality theory (DT) in economics. Next, it proposes the context of IUFs as a suitable framework for measuring price risk aversion through varying prices as opposed to varying payoffs, which has been common practice in the mainstream of experimental economics. Indeed, the DT in modern microeconomics indicates that the direct utility function (DUF) and the IUF are dual to each other, implicitly suggesting that the degree of risk aversion (or risk seeking) that a given rational subject exhibits in the context of the DUF must be equivalent to the degree of risk aversion (or risk seeking) elicited through the context of the IUF. This paper tests the accuracy of this theoretical prediction through a lab experiment using a series of relevant statistical tests. This study uses the multiple price list (MPL) method, which has been one of the most popular sets of elicitation procedures in experimental economics to study risk preferences in the experimental laboratory using non-interactive settings. The key findings of this study indicate that price risk aversion (PrRA) is statistically significantly greater than payoff risk aversion (PaRA). Additionally, it is shown that the risk preferences elicited under the expected utility theory (EUT) are somewhat subject to context. Other findings imply that the risk premium (RP), as a measure of willingness to pay for insuring an uncertain situation, is statistically significantly greater for stochastic prices compared to that for stochastic payoffs. These results are robust across different MPL designs and various statistical tests that are utilized.", "published": "2022-09-06T17:09:37Z", "authors": ["Ali Zeytoon-Nejad"], "categories": ["econ.GN", "q-fin.GN", "q-fin.MF", "q-fin.RM", "q-fin.TR"], "doi": "10.3390/g13040056", "_query": "housing price text listing", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/1907.10101v3", "title": "The Price of Anarchy in Routing Games as a Function of the Demand", "abstract": "The price of anarchy has become a standard measure of the efficiency of equilibria in games. Most of the literature in this area has focused on establishing worst-case bounds for specific classes of games, such as routing games or more general congestion games. Recently, the price of anarchy in routing games has been studied as a function of the traffic demand, providing asymptotic results in light and heavy traffic. The aim of this paper is to study the price of anarchy in nonatomic routing games in the intermediate region of the demand. To achieve this goal, we begin by establishing some smoothness properties of Wardrop equilibria and social optima for general smooth costs. In the case of affine costs we show that the equilibrium is piecewise linear, with break points at the demand levels at which the set of active paths changes. We prove that the number of such break points is finite, although it can be exponential in the size of the network. Exploiting a scaling law between the equilibrium and the social optimum, we derive a similar behavior for the optimal flows. We then prove that in any interval between break points the price of anarchy is smooth and it is either monotone (decreasing or increasing) over the full interval, or it decreases up to a certain minimum point in the interior of the interval and increases afterwards. We deduce that for affine costs the maximum of the price of anarchy can only occur at the break points. For general costs we provide counterexamples showing that the set of break points is not always finite.", "published": "2019-07-23T18:59:31Z", "authors": ["Roberto Cominetti", "Valerio Dose", "Marco Scarsini"], "categories": ["cs.GT", "math.OC"], "doi": "10.1007/s10107-021-01701-7", "_query": "housing price text listing", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/cond-mat/0102042v1", "title": "To sell or not to sell? Behavior of shareholders during price collapses", "abstract": "It is a common belief that the behavior of shareholders depends upon the direction of price fluctuations: if prices increase they buy, if prices decrease they sell. That belief, however, is more based on ``common sense'' than on facts. In this paper we present evidence for a specific class of shareholders which shows that the actual behavior of shareholders can be markedly different.", "published": "2001-02-02T17:28:46Z", "authors": ["Bertrand M. Roehner"], "categories": ["cond-mat.stat-mech", "q-fin.ST"], "doi": "10.1142/S0129183101001390", "_query": "housing price text listing", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/1807.00573v1", "title": "Strategic behaviour and indicative price diffusion in Paris Stock Exchange auctions", "abstract": "We report statistical regularities of the opening and closing auctions of French equities, focusing on the diffusive properties of the indicative auction price. Two mechanisms are at play as the auction end time nears: the typical price change magnitude decreases, favoring underdiffusion, while the rate of these events increases, potentially leading to overdiffusion. A third mechanism, caused by the strategic behavior of traders, is needed to produce nearly diffusive prices: waiting to submit buy orders until sell orders have decreased the indicative price and vice-versa.", "published": "2018-07-02T10:00:03Z", "authors": ["Damien Challet"], "categories": ["q-fin.ST"], "doi": "", "_query": "housing price text listing", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2404.02458v2", "title": "Network-Aware and Welfare-Maximizing Dynamic Pricing for Energy Sharing", "abstract": "The proliferation of behind-the-meter (BTM) distributed energy resources (DER) within the electrical distribution network presents significant supply and demand flexibilities, but also introduces operational challenges such as voltage spikes and reverse power flows. In response, this paper proposes a network-aware dynamic pricing framework tailored for energy-sharing coalitions that aggregate small, but ubiquitous, BTM DER downstream of a distribution system operator's (DSO) revenue meter that adopts a generic net energy metering (NEM) tariff. By formulating a Stackelberg game between the energy-sharing market leader and its prosumers, we show that the dynamic pricing policy induces the prosumers toward a network-safe operation and decentrally maximizes the energy-sharing social welfare. The dynamic pricing mechanism involves a combination of a locational ex-ante dynamic price and an ex-post allocation, both of which are functions of the energy sharing's BTM DER. The ex-post allocation is proportionate to the price differential between the DSO NEM price and the energy-sharing locational price. Simulation results using real DER data and the IEEE 13-bus test systems illustrate the dynamic nature of network-aware pricing at each bus, and its impact on voltage.", "published": "2024-04-03T05:02:10Z", "authors": ["Ahmed S. Alahmed", "Guido Cavraro", "Andrey Bernstein", "Lang Tong"], "categories": ["eess.SY", "math.OC"], "doi": "", "_query": "housing price text listing", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/0907.3273v2", "title": "New procedures for testing whether stock price processes are martingales", "abstract": "We propose procedures for testing whether stock price processes are martingales based on limit order type betting strategies. We first show that the null hypothesis of martingale property of a stock price process can be tested based on the capital process of a betting strategy. In particular with high frequency Markov type strategies we find that martingale null hypotheses are rejected for many stock price processes.", "published": "2009-07-19T08:48:03Z", "authors": ["Kei Takeuchi", "Akimichi Takemura", "Masayuki Kumon"], "categories": ["q-fin.ST"], "doi": "", "_query": "housing price text listing", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2602.12066v2", "title": "Chaos and Misallocation under Price Controls", "abstract": "Price controls kill the incentive for arbitrage. We prove a Chaos Theorem: under a binding price ceiling, suppliers are indifferent across destinations, so arbitrarily small cost differences can determine the entire allocation. The economy tips to corner outcomes in which some markets are fully served while others are starved; small parameter changes flip the identity of the corners, generating discontinuous welfare jumps. These corner allocations create a distinct source of cross-market misallocation, separate from the aggregate quantity loss (the Harberger triangle) and from within-market misallocation emphasized in prior work. They also create an identification problem: welfare depends on demand far from the observed equilibrium. We derive sharp bounds on misallocation that require no parametric assumptions. In an efficient allocation, shadow prices are equalized across markets; combined with the adding-up constraint, this collapses the infinite-dimensional welfare problem to a one-dimensional search over a common shadow price, with extremal losses achieved by piecewise-linear demand schedules. Calibrating the bounds to station-level AAA survey data from the 1973--74 U.S. gasoline crisis, misallocation losses range from roughly 1 to 9 times the Harberger triangle.", "published": "2026-02-12T15:24:35Z", "authors": ["Brian C. Albrecht", "Alex Tabarrok", "Mark Whitmeyer"], "categories": ["econ.GN"], "doi": "", "_query": "housing price text listing", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/1906.10250v3", "title": "Swap Dynamics in Single-Peaked Housing Markets", "abstract": "This paper focuses on the problem of fairly and efficiently allocating resources to agents. We consider a specific setting, usually referred to as a housing market, where each agent must receive exactly one resource (and initially owns one). In this framework, in the domain of linear preferences, the Top Trading Cycle (TTC) algorithm is the only procedure satisfying Pareto-optimality, individual rationality and strategy-proofness. Under the restriction of single-peaked preferences, Crawler enjoys the same properties. These two centralized procedures might however involve long trading cycles. In this paper we focus instead on procedures involving the shortest cycles: bilateral swap-deals. In such swap dynamics, the agents perform pairwise mutually improving deals until reaching a swap-stable allocation (no improving swap-deal is possible). We prove that in the single-peaked domain every swap-stable allocation is Pareto-optimal, showing the efficiency of the swap dynamics. In fact, this domain turns out to be maximal when it comes to guaranteeing this property. Besides, both the outcome of TTC and Crawler can always be reached by sequences of swaps. However, some Pareto-optimal allocations are not reachable through improving swap-deals. We further analyze the outcome of swap dynamics through social welfare notions, in our context the average or minimum rank of the resources obtained by agents in the final allocation. We start by providing a worst-case analysis of these procedures. Finally, we present an extensive experimental study in which different versions of swap dynamics are compared to other existing allocation procedures. We show that they exhibit good results on average in this domain, under different cultures for generating synthetic data.", "published": "2019-06-24T22:12:01Z", "authors": ["Aurélie Beynier", "Nicolas Maudet", "Simon Rey", "Parham Shams"], "categories": ["cs.AI", "cs.MA"], "doi": "", "_query": "housing price text listing", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2307.07103v3", "title": "A Hamiltonian Approach to Barrier Option Pricing Under Vasicek Model", "abstract": "In this paper, we study option pricing under Vasicek Model by a Hamiltonian approach. Since the interest rate changes with time, we split the time to maturity into infinite steps, and the matrix element during each step could be calculated by quantum mechanics methods. Using completeness condition, the pricing kernel and the integral expression of option price could also be derived. Numerical results of option prices as functions of underlying asset price, floating rate and regression rate are also shown.", "published": "2023-07-14T00:25:52Z", "authors": ["Chao Guo", "Ning Yao"], "categories": ["q-fin.PR", "quant-ph"], "doi": "", "_query": "housing price text listing", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2112.13254v3", "title": "On Dynamic Pricing with Covariates", "abstract": "We consider dynamic pricing with covariates under a generalized linear demand model: a seller can dynamically adjust the price of a product over a horizon of $T$ time periods, and at each time period $t$, the demand of the product is jointly determined by the price and an observable covariate vector $x_t\\in\\mathbb{R}^d$ through a generalized linear model with unknown co-efficients. Most of the existing literature assumes the covariate vectors $x_t$'s are independently and identically distributed (i.i.d.); the few papers that relax this assumption either sacrifice model generality or yield sub-optimal regret bounds. In this paper, we show that UCB and Thompson sampling-based pricing algorithms can achieve an $O(d\\sqrt{T}\\log T)$ regret upper bound without assuming any statistical structure on the covariates $x_t$. Our upper bound on the regret matches the lower bound up to logarithmic factors. We thus show that (i) the i.i.d. assumption is not necessary for obtaining low regret, and (ii) the regret bound can be independent of the (inverse) minimum eigenvalue of the covariance matrix of the $x_t$'s, a quantity present in previous bounds. Moreover, we consider a constrained setting of the dynamic pricing problem where there is a limited and unreplenishable inventory and we develop theoretical results that relate the best achievable algorithm performance to a variation measure with respect to the temporal distribution shift of the covariates. We also discuss conditions under which a better regret is achievable and demonstrate the proposed algorithms' performance with numerical experiments.", "published": "2021-12-25T16:30:13Z", "authors": ["Hanzhao Wang", "Kalyan Talluri", "Xiaocheng Li"], "categories": ["cs.LG", "stat.ML"], "doi": "", "_query": "housing price text listing", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2610.18383v1", "title": "Computation of Price Signals for Controlling Flexible Prosumers in Distribution Grids Incorporating Stochasticity and Fairness", "abstract": "This paper proposes a method for distribution system operators (DSOs) to design spatially fair dynamic price signals for flexible prosumers, accounting for both distribution grid constraints and the stochastic behavior of prosumers. Since locational pricing can introduce price unfairness when grid constraints are active, the proposed approach includes mechanisms to enforce price fairness across the network. The method involves the DSO solving a scenario-based optimal power flow (OPF) problem to derive, similarly to locational marginal prices in transmission systems, the Lagrange multipliers associated with grid constraints and to compute individualized price signals for each prosumer under various PV generation and demand scenarios. Subsequently, two methods are introduced to aggregate these individualized prices into a single fair price across prosumers. Their performance is evaluated through simulations on a model distribution grid, comparing prosumer revenues, grid constraint violations, and analyzing the impact of fairness on reducing cost variance among prosumers.", "published": "2026-10-13T13:35:21Z", "authors": ["Plouton Grammatikos", "Chloé Voirol", "Pierre Roduit", "Fabrizio Sossan"], "categories": ["eess.SY"], "doi": "", "_query": "housing price text listing", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2412.00920v2", "title": "Neural Network Approach to Demand Estimation and Dynamic Pricing in Retail", "abstract": "This paper contributes to the literature on parametric demand estimation by using deep learning to model consumer preferences. Traditional econometric methods often struggle with limited within-product price variation, a challenge addressed by the proposed neural network approach. The proposed method estimates the functional form of the demand and demonstrates higher performance in both simulations and empirical applications. Notably, under low price variation, the machine learning model outperforms econometric approaches, reducing the mean squared error of initial price parameter estimates by nearly threefold. In empirical setting, the ML model consistently predicts a negative relationship between demand and price in 100% of cases, whereas the econometric approach fails to do so in 20% of cases. The suggested model incorporates a wide range of product characteristics, as well as prices of other products and competitors.", "published": "2024-12-01T18:09:32Z", "authors": ["Kirill Safonov"], "categories": ["econ.GN"], "doi": "", "_query": "housing price text listing", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2607.24973v1", "title": "Discrete dividends after maturity adjust the stock and strike prices", "abstract": "The standard method to price European calls on a discrete dividend-paying stock is to subtract the present value of the dividends from the initial stock price in the Black-Scholes formula. However, when there are dividends after maturity, this is inconsistent with the model. Within the escrowed dividend model, we highlight an extension of the Black-Scholes formula in which those dividends after maturity adjust both the stock price and strike price, allowing for calls over all maturities to be priced in a model-consistent way. As a related application to American calls with a single dividend before maturity, we establish a neglected case where it is always optimal to early exercise and derive an extension of the Roll-Geske-Whaley formula when there are dividends after maturity, including fully characterising the optimal exercise policy.", "published": "2026-07-27T18:27:04Z", "authors": ["Kevin W. Lu"], "categories": ["q-fin.MF"], "doi": "", "_query": "housing price text listing", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2207.14225v1", "title": "Electricity Price Forecasting Model based on Gated Recurrent Units", "abstract": "The participation of consumers and producers in demand response programs has increased in smart grids, which reduces investment and operation costs of power systems. Also, with the advent of renewable energy sources, the electricity market is becoming more complex and unpredictable. To effectively implement demand response programs, forecasting the future price of electricity is very crucial for producers in the electricity market. Electricity prices are very volatile and change under the influence of various factors such as temperature, wind speed, rainfall, intensity of commercial and daily activities, etc. Therefore, considering the influencing factors as dependent variables can increase the accuracy of the forecast. In this paper, a model for electricity price forecasting is presented based on Gated Recurrent Units. The electrical load consumption is considered as an input variable in this model. Noise in electricity price seriously reduces the efficiency and effectiveness of analysis. Therefore, an adaptive noise reducer is integrated into the model for noise reduction. The SAEs are then used to extract features from the de-noised electricity price. Finally, the de-noised features are fed into the GRU to train predictor. Results on real dataset shows that the proposed methodology can perform effectively in prediction of electricity price.", "published": "2022-07-28T16:49:03Z", "authors": ["Nafise Rezaei", "Roozbeh Rajabi", "Abouzar Estebsari"], "categories": ["cs.LG", "cs.CV"], "doi": "", "_query": "housing price text listing", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2407.21759v1", "title": "Optimal price signal generation for demand-side energy management", "abstract": "Renewable Energy Sources play a key role in smart energy systems. To achieve 100% renewable energy, utilizing the flexibility potential on the demand side becomes the cost-efficient option to balance the grid. However, it is not trivial to exploit these available capacities and flexibility options profitably. The amount of available flexibility is a complex and time-varying function of the price signal and weather forecasts. In this work, we use a Flexibility Function to represent the relationship between the price signal and the demand and investigate optimization problems for the price signal computation. Consequently, this study considers the higher and lower levels in the hierarchy from the markets to appliances, households, and districts. This paper investigates optimal price generation via the Flexibility Function and studies its employment in controller design for demand-side management, its capability to provide ancillary services for balancing throughout the Smart Energy Operating System, and its effect on the physical level performance. Sequential and simultaneous approaches for computing the price signal, along with various cost functions are analyzed and compared. Simulation results demonstrate the generated price/penalty signal and its employment in a model predictive controller.", "published": "2024-07-31T17:28:31Z", "authors": ["Seyed Shahabaldin Tohidi", "Henrik Madsen", "Davide Calì", "Tobias K. S. Ritschel"], "categories": ["eess.SY"], "doi": "", "_query": "housing price text listing", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2307.00412v1", "title": "Adam Smith's Theory of Value: A Reappraisal of Classical Price Discovery", "abstract": "The relevance of Adam Smith for understanding human morality and sociality is recognized in the growing interest in his work on moral sentiments among scholars of various academic backgrounds. But, paradoxically, Adam Smith's theory of economic value enjoys a less prominent stature today among economists, who, while they view him as the 'father of modern economics', considered him more as having had the right intuitions about a market economy than as having developed the right concepts and the technical tools for studying it. Yet the neoclassical tradition, which replaced the classical school around 1870, failed to provide a satisfactory theory of market price formation. Adam Smith's sketch of market price formation (Ch. VII, Book I, Wealth of Nations), and more generally the classical view of competition as a collective higgling and bargaining process, as this paper argues, offers a helpful foundation on which to build a modern theory of market price formation, despite any shortcomings of the original classical formulation (notably its insistence on long-run, natural value). Also, with hindsight, the experimental market findings established the remarkable stability, efficiency, and robustness of the old view of competition, suggesting a rehabilitation of classical price discovery. This paper reappraises classical price theory as Adam Smith articulated it; we explicate key propositions from his price theory and derive them from a simple model, which is an elementary sketch of the authors' more general theory of competitive market price formation.", "published": "2023-07-01T19:15:01Z", "authors": ["Sabiou Inoua", "Vernon Smith"], "categories": ["econ.GN"], "doi": "", "_query": "housing price text listing", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/1804.00983v2", "title": "On sequences of Toeplitz matrices over finite fields", "abstract": "For each non-negative integer $n$ let $\\mathcal{A}_n$ be an $n+1$ by $n+1$ Toeplitz matrix over a finite field, $F$, and suppose for each $n$ that $\\mathcal{A}_n$ is embedded in the upper left corner of $\\mathcal{A}_{n+1}$. We study the structure of the sequence $ν= \\{ν_n :n \\in \\mathbb{Z}^+\\}$, where $ν_n = \\text{null}(\\mathcal{A}_n)$ is the nullity of $\\mathcal{A}_{n}$. For each $n\\in \\mathbb{Z}^+$ and each nullity pattern $ν_0,ν_1,\\dots,ν_n$, we count the number of strings of Toeplitz matrices $\\mathcal{A}_0,\\mathcal{A}_1,\\dots,\\mathcal{A}_{n}$ with this pattern. As an application we present an elementary proof of a result of D. E. Daykin on the number of $n\\times n$ Toeplitz matrices over $GF(2)$ of any specified rank. (This is a corrected version of the paper published in Linear Algebra and Its Applications $561$ \\, $(2019), 63-80$.) 2000 MSC Classification 15A33, 15A57", "published": "2018-04-03T14:17:53Z", "authors": ["Geoffrey Price", "Myles Wortham"], "categories": ["math.FA"], "doi": "", "_query": "housing price text listing", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/1804.08218v1", "title": "Econometric Modeling of Regional Electricity Spot Prices in the Australian Market", "abstract": "Wholesale electricity markets are increasingly integrated via high voltage interconnectors, and inter-regional trade in electricity is growing. To model this, we consider a spatial equilibrium model of price formation, where constraints on inter-regional flows result in three distinct equilibria in prices. We use this to motivate an econometric model for the distribution of observed electricity spot prices that captures many of their unique empirical characteristics. The econometric model features supply and inter-regional trade cost functions, which are estimated using Bayesian monotonic regression smoothing methodology. A copula multivariate time series model is employed to capture additional dependence -- both cross-sectional and serial-- in regional prices. The marginal distributions are nonparametric, with means given by the regression means. The model has the advantage of preserving the heavy right-hand tail in the predictive densities of price. We fit the model to half-hourly spot price data in the five interconnected regions of the Australian national electricity market. The fitted model is then used to measure how both supply and price shocks in one region are transmitted to the distribution of prices in all regions in subsequent periods. Finally, to validate our econometric model, we show that prices forecast using the proposed model compare favorably with those from some benchmark alternatives.", "published": "2018-04-23T01:52:35Z", "authors": ["Michael Stanley Smith", "Thomas S. Shively"], "categories": ["econ.EM", "stat.AP"], "doi": "", "_query": "housing price text listing", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/1610.09875v1", "title": "Loading Pricing of Catastrophe Bonds and Other Long-Dated, Insurance-Type Contracts", "abstract": "Catastrophe risk is a major threat faced by individuals, companies, and entire economies. Catastrophe (CAT) bonds have emerged as a method to offset this risk and a corresponding literature has developed that attempts to provide a market-consistent pricing methodology for these and other long-dated, insurance-type contracts. This paper aims to unify and generalize several of the widely-used pricing approaches for long-dated contracts with a focus on stylized CAT bonds and market-consistent valuation. It proposes a loading pricing concept that combines the theoretically possible minimal price of a contract with its formally obtained risk neutral price, without creating economically meaningful arbitrage. A loading degree controls how much influence the formally obtained risk neutral price has on the market price. A key finding is that this loading degree has to be constant for a minimally fluctuating contract, and is an important, measurable characteristic for prices of long-dated contracts. Loading pricing allows long-dated, insurance-type contracts to be priced less expensively and with higher return on investment than under classical pricing approaches. Loading pricing enables insurance companies to accumulate systematically reserves needed to manage its risk of ruin in a market consistent manner.", "published": "2016-10-31T11:38:52Z", "authors": ["Eckhard Platen", "David Taylor"], "categories": ["q-fin.MF", "q-fin.PR"], "doi": "", "_query": "housing price text listing", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2601.08962v2", "title": "Warp speed price moves: Jumps after earnings announcements", "abstract": "Corporate earnings announcements unpack large bundles of public information that should, in efficient markets, trigger jumps in stock prices. Testing this implication is difficult in practice, as it requires noisy high-frequency data from after-hours markets, where most earnings announcements are released. Using a unique dataset and a new microstructure noise-robust jump test, we show that earnings announcements almost always induce jumps in the stock price of announcing firms. They also significantly raise the probability of price co-jumps in non-announcing firms and the market. We find that returns from a post-announcement trading strategy are consistent with efficient price formation after 2016.", "published": "2026-01-13T20:06:47Z", "authors": ["Kim Christensen", "Allan Timmermann", "Bezirgen Veliyev"], "categories": ["econ.EM"], "doi": "10.1016/j.jfineco.2025.104010", "_query": "housing price text listing", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2012.15705v2", "title": "A Bayesian viewpoint on the price formation process", "abstract": "We introduce a simple framework in which market participants update their prior about an efficient price with a model-based learning process. We show that exponential intensities for the arrival of aggressive orders arise naturally in this setting. Our approach allows us to fully describe market dynamics in the case with Brownian efficient price and informed market takers. We are also able to revisit the emergence of market impact due to meta-order splitting, making several connections with existing literature.", "published": "2020-12-31T16:46:34Z", "authors": ["Joffrey Derchu"], "categories": ["q-fin.TR"], "doi": "", "_query": "housing price text listing", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2606.04890v1", "title": "Welfare Maximization in Bilateral Trade: Improved Approximation Guarantees Beyond the Fixed Price Barrier", "abstract": "We study the setting of welfare maximization in bilateral trade, where the values of both the buyer and the seller are drawn from independent distributions. Our goal is to maximize social welfare. In this setting, fixed price mechanisms have been extensively studied. In a fixed price mechanism, there is a price $p$ that depends only on the distributions of the buyer and the seller. Trade occurs if and only if the buyer's value is at least $p$ and the seller's value is at most $p$. A long line of work has culminated in determining almost exactly the approximation ratios achievable by fixed price mechanisms: there exists a fixed price mechanism that obtains at least a $0.72$ fraction of the social welfare, but no fixed price mechanism can guarantee more than a $0.7381$ fraction of it [Cai and Wu, STOC'23; Liu, Ren, and Wang, STOC'23]. No other incentive-compatible mechanism is known to beat the performance of fixed-price mechanisms in this setting. This paper shows how to achieve a larger fraction of the optimal welfare with other classes of mechanisms. Specifically, we study the buyer-offering mechanism with a reserve price. In this mechanism, the buyer observes its value and makes a take-it-or-leave-it offer to the seller, where the offer is at least the reserve price. Beyond its simplicity, this natural mechanism is attractive because the seller always has a dominant strategy: accept the offer if its value is at most the offer, and otherwise reject it. We show that there always exists a reserve price that guarantees a $0.746$ fraction of the social welfare. This not only improves upon the best previously known approximation guarantee for the problem, but also demonstrates that fixed-price mechanisms are not optimal in this setting.", "published": "2026-06-03T13:51:49Z", "authors": ["Shahar Dobzinski", "Ariel Shaulker"], "categories": ["cs.GT"], "doi": "", "_query": "housing price text listing", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2405.00537v2", "title": "Quantifying Price Improvement in Order Flow Auctions", "abstract": "This work introduces a framework for evaluating onchain order flow auctions (OFAs), emphasizing the metric of price improvement. Utilizing a set of open-source tools, our methodology systematically attributes price improvements to specific modifiable inputs of the system such as routing efficiency, gas optimization, and priority fee settings. When applied to leading Ethereum-based trading interfaces such as 1Inch and Uniswap, the results reveal that auction-enhanced interfaces can provide statistically significant improvements in trading outcomes, averaging 4-5 basis points in our sample. We further identify the sources of such price improvements to be added liquidity for large swaps. This research lays a foundation for future innovations in blockchain based trading platforms.", "published": "2024-05-01T14:19:30Z", "authors": ["Brad Bachu", "Xin Wan", "Ciamac C. Moallemi"], "categories": ["q-fin.TR"], "doi": "", "_query": "housing price text listing", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/1302.5650v1", "title": "On a Boltzmann type price formation model", "abstract": "In this paper we present a Boltzmann type price formation model, which is motivated by a parabolic free boundary model for the evolution of the prize presented by Lasry and Lions in 2007. We discuss the mathematical analysis of the Boltzmann type model and show that its solutions converge to solutions of the model by Lasry and Lions as the transaction rate tends to infinity. Furthermore we analyse the behaviour of the initial layer on the fast time scale and illustrate the price dynamics with various numerical experiments.", "published": "2013-02-22T17:18:23Z", "authors": ["Martin Burger", "Luis Caffarelli", "Peter Markowich", "Marie-Therese Wolfram"], "categories": ["math.AP"], "doi": "10.1098/rspa.2013.0126", "_query": "housing price text listing", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2010.04287v2", "title": "Jump Models with delay -- option pricing and logarithmic Euler-Maruyama scheme", "abstract": "In this paper, we obtain the existence, uniqueness and positivity of the solution to delayed stochastic differential equations with jumps. This equation is then applied to model the price movement of the risky asset in a financial market and the Black-Scholes formula for the price of European options is obtained together with the hedging portfolios. The option price is evaluated analytically at the last delayed period by using the Fourier transformation technique. But in general, there is no analytical expression for the option price. To evaluate the price numerically we then use the Monte-Carlo method. To this end, we need to simulate the delayed stochastic differential equations with jumps. We propose a logarithmic Euler-Maruyama scheme to approximate the equation and prove that all the approximations remain positive and the rate of convergence of the scheme is proved to be $0.5$.", "published": "2020-10-08T22:43:39Z", "authors": ["Nishant Agrawal", "Yaozhong Hu"], "categories": ["q-fin.MF"], "doi": "", "_query": "housing price text listing", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2602.05861v1", "title": "CFRecs: Counterfactual Recommendations on Real Estate User Listing Interaction Graphs", "abstract": "Graph-structured data is ubiquitous and powerful in representing complex relationships in many online platforms. While graph neural networks (GNNs) are widely used to learn from such data, counterfactual graph learning has emerged as a promising approach to improve model interpretability. Counterfactual explanation research focuses on identifying a counterfactual graph that is similar to the original but leads to different predictions. These explanations optimize two objectives simultaneously: the sparsity of changes in the counterfactual graph and the validity of its predictions. Building on these qualitative optimization goals, this paper introduces CFRecs, a novel framework that transforms counterfactual explanations into actionable insights. CFRecs employs a two-stage architecture consisting of a graph neural network (GNN) and a graph variational auto-encoder (Graph-VAE) to strategically propose minimal yet high-impact changes in graph structure and node attributes to drive desirable outcomes in recommender systems. We apply CFRecs to Zillow's graph-structured data to deliver actionable recommendations for both home buyers and sellers with the goal of helping them navigate the competitive housing market and achieve their homeownership goals. Experimental results on Zillow's user-listing interaction data demonstrate the effectiveness of CFRecs, which also provides a fresh perspective on recommendations using counterfactual reasoning in graphs.", "published": "2026-02-05T16:42:51Z", "authors": ["Seyedmasoud Mousavi", "Ruomeng Xu", "Xiaojing Zhu"], "categories": ["cs.LG", "stat.ML"], "doi": "", "_query": "housing price text listing", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/1307.6727v1", "title": "Quantum Tunneling of Stock Price in Range Bound Market Conditions", "abstract": "Applications of Quantum Tunneling effect have long gone beyond the traditional physical meaning. Initially created by Gamow to explain α-decay of nuclear particles, along the time, quantum tunneling found fertile domain of research in chemistry and recently in biology, where the new discipline of Quantum Biology emerges. The present paper extends the applicability of quantum tunneling to financial markets. In a recent paper [1] a time-independent equation for pricing the options having the underlying stock in a range bound markets is found. The equation is identical with a time-independent Schrodinger equation but incorporates elements of finance. The financial time-independent equation for option pricing is solved to explain a particular explosive violent movement of stock price in range bound markets. The aforementioned particular stock price movement is assimilated with a quantum tunneling effect. The probability of stock price to quantum tunneling out of the bounded region, known as transmission coefficient, is deduced. Quantum aspects of tunneling effect in financial markets are discussed. Recent evidences of price quantum tunneling in stock market are also shown.", "published": "2013-07-25T12:56:28Z", "authors": ["Ovidiu Racorean"], "categories": ["q-fin.PR"], "doi": "", "_query": "housing price text listing", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/1609.07574v4", "title": "Dynamic Pricing in High-dimensions", "abstract": "We study the pricing problem faced by a firm that sells a large number of products, described via a wide range of features, to customers that arrive over time. Customers independently make purchasing decisions according to a general choice model that includes products features and customers' characteristics, encoded as $d$-dimensional numerical vectors, as well as the price offered. The parameters of the choice model are a priori unknown to the firm, but can be learned as the (binary-valued) sales data accrues over time. The firm's objective is to minimize the regret, i.e., the expected revenue loss against a clairvoyant policy that knows the parameters of the choice model in advance, and always offers the revenue-maximizing price. This setting is motivated in part by the prevalence of online marketplaces that allow for real-time pricing. We assume a structured choice model, parameters of which depend on $s_0$ out of the $d$ product features. We propose a dynamic policy, called Regularized Maximum Likelihood Pricing (RMLP) that leverages the (sparsity) structure of the high-dimensional model and obtains a logarithmic regret in $T$. More specifically, the regret of our algorithm is of $O(s_0 \\log d \\cdot \\log T)$. Furthermore, we show that no policy can obtain regret better than $O(s_0 (\\log d + \\log T))$.", "published": "2016-09-24T06:02:24Z", "authors": ["Adel Javanmard", "Hamid Nazerzadeh"], "categories": ["stat.ML", "cs.LG"], "doi": "", "_query": "housing price text listing", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/1710.10293v2", "title": "Polynomial processes for power prices", "abstract": "Polynomial processes have the property that expectations of polynomial functions (of degree $n$, say) of the future state of the process conditional on the current state are given by polynomials (of degree $\\leq n$) of the current state. Here we explore the application of polynomial processes in the context of structural models for energy prices. We focus on the example of Alberta power prices, derive one- and two-factor models for spot prices. We examine their performance in numerical experiments, and demonstrate that the richness of the dynamics they are able to generate makes them well suited for modelling even extreme examples of energy price behaviour.", "published": "2017-10-27T18:33:04Z", "authors": ["Damir Filipovic", "Martin Larsson", "Tony Ware"], "categories": ["q-fin.CP"], "doi": "", "_query": "housing price text listing", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2110.13303v1", "title": "Negotiating Networks in Oligopoly Markets for Price-Sensitive Products", "abstract": "We present a novel framework to learn functions that estimate decisions of sellers and buyers simultaneously in an oligopoly market for a price-sensitive product. In this setting, the aim of the seller network is to come up with a price for a given context such that the expected revenue is maximized by considering the buyer's satisfaction as well. On the other hand, the aim of the buyer network is to assign probability of purchase to the offered price to mimic the real world buyers' responses while also showing price sensitivity through its action. In other words, rejecting the unnecessarily high priced products. Similar to generative adversarial networks, this framework corresponds to a minimax two-player game. In our experiments with simulated and real-world transaction data, we compared our framework with the baseline model and demonstrated its potential through proposed evaluation metrics.", "published": "2021-10-25T22:29:48Z", "authors": ["Naman Shukla", "Kartik Yellepeddi"], "categories": ["cs.LG", "cs.AI", "cs.CE", "econ.EM"], "doi": "", "_query": "housing price text listing", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2201.12216v3", "title": "Self-paced learning to improve text row detection in historical documents with missing labels", "abstract": "An important preliminary step of optical character recognition systems is the detection of text rows. To address this task in the context of historical data with missing labels, we propose a self-paced learning algorithm capable of improving the row detection performance. We conjecture that pages with more ground-truth bounding boxes are less likely to have missing annotations. Based on this hypothesis, we sort the training examples in descending order with respect to the number of ground-truth bounding boxes, and organize them into k batches. Using our self-paced learning method, we train a row detector over k iterations, progressively adding batches with less ground-truth annotations. At each iteration, we combine the ground-truth bounding boxes with pseudo-bounding boxes (bounding boxes predicted by the model itself) using non-maximum suppression, and we include the resulting annotations at the next training iteration. We demonstrate that our self-paced learning strategy brings significant performance gains on two data sets of historical documents, improving the average precision of YOLOv4 with more than 12% on one data set and 39% on the other.", "published": "2022-01-28T16:17:48Z", "authors": ["Mihaela Gaman", "Lida Ghadamiyan", "Radu Tudor Ionescu", "Marius Popescu"], "categories": ["cs.CV", "cs.LG"], "doi": "", "_query": "housing price text listing", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2507.07053v1", "title": "Portfolio optimization in incomplete markets and price constraints determined by maximum entropy in the mean", "abstract": "A solution to a portfolio optimization problem is always conditioned by constraints on the initial capital and the price of the available market assets. If a risk neutral measure is known, then the price of each asset is the discounted expected value of the asset's price under this measure. But if the market is incomplete, the risk neutral measure is not unique, and there is a range of possible prices for each asset, which can be identified with bid-ask ranges. We present in this paper an effective method to determine the current prices of a collection of assets in incomplete markets, and such that these prices comply with the cost constraints for a portfolio optimization problem. Our workhorse is the method of maximum entropy in the mean to adjust a distortion function from bid-ask market data. This distortion function plays the role of a risk neutral measure, which is used to price the assets, and the distorted probability that it determines reproduces bid-ask market values. We carry out numerical examples to study the effect on portfolio returns of the computation of prices of the assets conforming the portfolio with the proposed methodology.", "published": "2025-07-03T17:04:28Z", "authors": ["Argimiro Arratia", "Henryk Gzyl"], "categories": ["math.OC", "q-fin.CP", "q-fin.PM", "q-fin.RM"], "doi": "10.1007/s10614-019-09954-3", "_query": "housing price text listing", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/1405.6573v1", "title": "Allocating Indivisible Resources under Price Rigidities in Polynomial Time", "abstract": "In many realistic problems of allocating resources, economy efficiency must be taken into consideration together with social equality, and price rigidities are often made according to some economic and social needs. We study the computational issues of dynamic mechanisms for selling multiple indivisible items under price rigidities. We propose a polynomial algorithm that can be used to find over-demanded sets of items, and then introduce a dynamic mechanism with rationing to discover constrained Walrasian equilibria under price rigidities in polynomial time. We also address the computation of sellers' expected profits and items' expected prices, and discuss strategical issues in the sense of expected profits.", "published": "2014-05-26T13:48:07Z", "authors": ["Wei Huang", "Jian Lou", "Zhonghua Wen"], "categories": ["cs.GT", "cs.MA"], "doi": "", "_query": "housing price text listing", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/1112.4385v3", "title": "Shadow price in the power utility case", "abstract": "We consider the problem of maximizing expected power utility from consumption over an infinite horizon in the Black-Scholes model with proportional transaction costs, as studied in Shreve and Soner [Ann. Appl. Probab. 4 (1994) 609-692]. Similar to Kallsen and Muhle-Karbe [Ann. Appl. Probab. 20 (2010) 1341-1358], we derive a shadow price, that is, a frictionless price process with values in the bid-ask spread which leads to the same optimal policy.", "published": "2011-12-19T16:06:25Z", "authors": ["Attila Herczegh", "Vilmos Prokaj"], "categories": ["q-fin.PM", "math.PR"], "doi": "10.1214/14-AAP1058", "_query": "housing price text listing", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2507.21950v1", "title": "Regional Price Dynamics and Market Integration in the U.S. Beef Industry: An Econometric Analysis", "abstract": "The United States, a leading global producer and consumer of beef, continues to face substantial challenges in achieving price harmonization across its regional markets. This paper evaluates the validity of the Law of One Price (LOP) in the U.S. beef industry and investigates causal relationships among regional price dynamics. Through a series of econometric tests, we establish that regional price series are integrated of order one, displaying non-stationarity in levels and stationarity in first differences. The analysis reveals partial LOP compliance in the Northeast and West, while full convergence remains elusive at the national level. Although no region demonstrates persistent price leadership, Southern prices appear particularly sensitive to exogenous shocks. These findings reflect asymmetrical integration across U.S. beef markets and suggest the presence of structural frictions that hinder complete market unification.", "published": "2025-07-29T16:02:21Z", "authors": ["Leonardo Manríquez-Méndez"], "categories": ["econ.EM"], "doi": "", "_query": "housing price text listing", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2601.15864v2", "title": "Minimum Envy Graphical House Allocation Beyond Identical Valuations", "abstract": "House allocation is an extremely well-studied problem in the field of fair allocation, where the goal is to assign $n$ houses to $n$ agents while satisfying certain fairness criterion, e.g., envy-freeness. To model social interactions, the Graphical House Allocation framework introduces a social graph $G$, in which each vertex corresponds to an agent, and an edge $(u, v)$ corresponds to the potential of agent $u$ to envy the agent $v$, based on their allocations and valuations. In undirected social graphs, the potential for envy is in both the directions. In the Minimum Envy Graphical House Allocation (ME-GHA) problem, given a set of $n$ agents, $n$ houses, a social graph, and agent's valuation functions, the goal is to find an allocation that minimizes the total envy summed up over all the edges of $G$. Recent work, [Hosseini et al., AAMAS 2023, AAMAS 2024] studied ME-GHA in the regime of polynomial-time algorithms, and designed exact and approximation algorithms, for certain graph classes under identical agent valuations. We initiate the study of \\gha with non-identical valuations, a setting that has so far remained unexplored. We investigate the multivariate (parameterized) complexity of \\gha by identifying structural restrictions on the social graph and valuation functions that yield tractability. We also design moderately exponential-time algorithms for several graph classes, and a polynomial-time algorithm for {binary valuations that returns an allocation with envy at most one when the social graph has maximum degree at most one.", "published": "2026-01-22T11:12:09Z", "authors": ["Tanmay Inamdar", "Pallavi Jain", "Pranjal Pandey"], "categories": ["cs.GT", "cs.DS"], "doi": "", "_query": "housing price text listing", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/physics/0508203v1", "title": "Impact of Stock Market Structure on Intertrade Time and Price Dynamics", "abstract": "The NYSE and NASDAQ stock markets have very different structures and there is continuing controversy over whether differences in stock price behaviour are due to market structure or company characteristics. As the influence of market structure on stock prices may be obscured by exogenous factors such as demand and supply, we hypothesize that modulation of the flow of transactions due to market operations may carry a stronger imprint of the internal market mechanism. We analyse times between consecutive transactions (ITT) for NYSE and NASDAQ stocks, and we relate the dynamical properties of the ITT with those of the corresponding price fluctuations. We find a robust scale-invariant temporal organisation in the ITT of stocks which is independent of individual company characteristics and industry sector, but which depends on market structure. We find that stocks registered on the NASDAQ exhibit stronger correlations in their transaction timing within a trading day, compared with NYSE stocks. Further, we find that companies that transfer from the NASDAQ to the NYSE show a reduction in the correlation strength of transaction timing within a trading day, after the move, suggesting influences of market structure. Surprisingly, we also observe that stronger power-law correlations in the ITT are coupled with stronger power-law correlations in absolute price returns and higher price volatility, suggesting a strong link between the dynamical properties of ITT and the corresponding price fluctuations over a broad range of time scales. Comparing the NYSE and NASDAQ, we demonstrate that the higher correlations we find in ITT for NASDAQ stocks are matched by higher correlations in absolute price returns and by higher volatility, suggesting that market structure may affect price behaviour through information contained in transaction timing.", "published": "2005-08-28T21:06:58Z", "authors": ["Ainslie Yuen", "Plamen Ch. Ivanov"], "categories": ["physics.soc-ph", "q-fin.ST"], "doi": "", "_query": "housing price text listing", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2409.04106v1", "title": "CryptoAnalytics: Cryptocoins Price Forecasting with Machine Learning Techniques", "abstract": "This paper introduces CryptoAnalytics, a software toolkit for cryptocoins price forecasting with machine learning (ML) techniques. Cryptocoins are tradable digital assets exchanged for specific trading prices. While history has shown the extreme volatility of such trading prices, the ability to efficiently model and forecast the time series resulting from the exchange price volatility remains an open research challenge. Good results can been achieved with state-of-the-art ML techniques, including Gradient-Boosting Machines (GBMs) and Recurrent Neural Networks (RNNs). CryptoAnalytics is a software toolkit to easily train these models and make inference on up-to-date cryptocoin trading price data, with facilities to fetch datasets from one of the main leading aggregator websites, i.e., CoinMarketCap, train models and infer the future trends. This software is implemented in Python. It relies on PyTorch for the implementation of RNNs (LSTM and GRU), while for GBMs, it leverages on XgBoost, LightGBM and CatBoost.", "published": "2024-09-06T08:20:02Z", "authors": ["Pasquale De Rosa", "Pascal Felber", "Valerio Schiavoni"], "categories": ["cs.CE"], "doi": "10.1016/j.softx.2024.101663", "_query": "housing price text listing", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2003.13560v3", "title": "Discriminatory Price Mechanism for Smart Grid", "abstract": "We consider a scenario where a retailer can set different prices for different consumers in a smart grid. The retailer's objective is to maximize the revenue, minimize the operating cost, and maximize the consumer's welfare. The retailer wants to optimize a convex combination of the above objectives using price signals specific to each consumer. However, variability in unit prices across consumers is bounded by a parameter $η$, hence limiting the discrimination. We formulate the pricing problem as a Stackelberg game where the retailer is the leader and consumers are followers. Since the retailer's optimization problem turns out to be non-convex, we convexify it via relaxations. We provide performance guarantees for the relaxations in the asymptotic sense (when number of consumers tends to $\\infty$). Further, we show that despite the variability in pricing, the pricing scheme proposed by our model is fair as higher prices are charged to consumers who have higher willingness for demand. We extend our analysis to the scenario where consumers can feed energy back to the grid via net-metering. We show that our pricing policy promotes fairness even in this scenario as prosumers who contribute more to the grid, are given large cuts on buying rates. The policy is also found to incentivize more prosumers to invest in renewable energy, thus encouraging sustainability.", "published": "2020-03-30T15:34:54Z", "authors": ["Diptangshu Sen", "Kushaagra Goyal", "Varun Ramamohan", "Arnob Ghosh"], "categories": ["math.OC", "cs.GT", "eess.SY"], "doi": "", "_query": "housing price text listing", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/cond-mat/0202143v1", "title": "A Path Integral Way to Option Pricing", "abstract": "An efficient computational algorithm to price financial derivatives is presented. It is based on a path integral formulation of the pricing problem. It is shown how the path integral approach can be worked out in order to obtain fast and accurate predictions for the value of a large class of options, including those with path-dependent and early exercise features. As examples, the application of the method to European and American options in the Black-Scholes model is illustrated. A particularly simple and fast semi-analytical approximation for the price of American options is derived. The results of the algorithm are compared with those obtained with the standard procedures known in the literature and found to be in good agreement.", "published": "2002-02-08T14:06:30Z", "authors": ["G. Montagna", "O. Nicrosini", "N. Moreni"], "categories": ["cond-mat.stat-mech", "q-fin.PR"], "doi": "10.1016/S0378-4371(02)00796-3", "_query": "housing price text listing", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2605.00406v3", "title": "Bell Correlations and Selection Bias", "abstract": "Methods of selecting samples from larger populations may produce bias, in either direction: inducing correlations between variables independent in the full population, or masking correlations between variables dependent in the full population. Here we propose a surprising application of these familiar ideas. We argue that they are relevant to puzzling correlations uncovered in quantum theory by John Stewart Bell (Bell 1964). In the light of Bell's work and subsequent experiments it is widely believed that the quantum world is 'nonlocal', in apparent tension with relativity. Many hold that the only alternative is to abandon 'realism', the view that there is an objective world independent of measurement. We propose instead that Bell's correlations are selection artefacts, in tension neither with relativity nor realism. In standard two-particle Bell experiments the relevant selection is a preselection, achieved by the preparation of the initial state. Again, selection bias via preselection is familiar elsewhere in science, but it doesn't seem to have been noticed that it is applicable in this case.", "published": "2026-05-01T04:54:54Z", "authors": ["Huw Price"], "categories": ["quant-ph", "physics.hist-ph"], "doi": "", "_query": "housing price text listing", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2102.07742v3", "title": "Dynamic Pricing with Limited Commitment", "abstract": "A monopolist wants to sell one item per period to a consumer with evolving and persistent private information. The seller sets a price each period depending on the history so far, but cannot commit to future prices. We show that, regardless of the degree of persistence, any equilibrium under a D1-style refinement gives the seller revenue no higher than what she would get from posting all prices in advance.", "published": "2021-02-15T18:37:54Z", "authors": ["Martino Banchio", "Frank Yang"], "categories": ["econ.TH"], "doi": "", "_query": "housing price text listing", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/1910.09149v3", "title": "Operational Valuation for Energy Storage under Multi-stage Price Uncertainties", "abstract": "This paper presents an analytical method for calculating the operational value of an energy storage device under multi-stage price uncertainties. Our solution calculates the storage value function from price distribution functions directly instead of sampling discrete scenarios, offering improved modeling accuracy over tail distribution events such as price spikes and negative prices. The analytical algorithm offers very high computational efficiency in solving multi-stage stochastic programming for energy storage and can easily be implemented within any software and hardware platform, while numerical simulation results show the proposed method is up to 100,000 times faster than a benchmark stochastic-dual dynamic programming solver even in small test cases. Case studies are included to demonstrate the impact of price variability on the valuation results, and a battery charging example using historical prices for New York City.", "published": "2019-10-21T04:58:16Z", "authors": ["Bolun Xu", "Audun Botterud", "Magnus Korpas"], "categories": ["math.OC"], "doi": "", "_query": "housing price text listing", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2203.06077v1", "title": "Econometric Modeling of Intraday Electricity Market Price with Inadequate Historical Data", "abstract": "The intraday (ID) electricity market has received an increasing attention in the recent EU electricity-market discussions. This is partly because the uncertainty in the underlying power system is growing and the ID market provides an adjustment platform to deal with such uncertainties. Hence, market participants need a proper ID market price model to optimally adjust their positions by trading in the market. Inadequate historical data for ID market price makes it more challenging to model. This paper proposes long short-term memory, deep convolutional generative adversarial networks, and No-U-Turn sampler algorithms to model ID market prices. Our proposed econometric ID market price models are applied to the Nordic ID price data and their promising performance are illustrated.", "published": "2022-03-11T16:55:31Z", "authors": ["Saeed Mohammadi", "Mohammad Reza Hesamzadeh"], "categories": ["eess.SY", "cs.LG", "stat.AP"], "doi": "", "_query": "housing price text listing", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/1803.11227v2", "title": "AI Blue Book: Vehicle Price Prediction using Visual Features", "abstract": "In this work, we build a series of machine learning models to predict the price of a product given its image, and visualize the features that result in higher or lower price predictions. We collect two novel datasets of product images and their MSRP prices for this purpose: a bicycle dataset and a car dataset. We set baselines for price regression using linear regression on histogram of oriented gradients (HOG) and convolutional neural network (CNN) features, and a baseline for price segment classification using a multiclass SVM. For our main models, we train several deep CNNs using both transfer learning and our own architectures, for both regression and classification. We achieve strong results on both datasets, with deep CNNs significantly outperforming other models in a variety of metrics. Finally, we use several recently-developed methods to visualize the image features that result in higher or lower prices.", "published": "2018-03-29T19:22:23Z", "authors": ["Richard R. Yang", "Steven Chen", "Edward Chou"], "categories": ["cs.CV"], "doi": "", "_query": "housing price text listing", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2210.15035v2", "title": "Pricing and Electric Vehicle Charging Equilibria", "abstract": "We study equilibria in an Electric Vehicle (EV) charging game, a cost minimization game inherent to decentralized charging control strategy for EV power demand management. In our model, each user optimizes its total cost which is sum of direct power cost and the indirect dissatisfaction cost. We show that, taking player specific price independent dissatisfaction cost in to account, contrary to popular belief, herding only happens at lower EV uptake. Moreover, this is true for both linear and logistic dissatisfaction functions. We study the question of existence of price profiles to induce a desired equilibrium. We define two types of equilibria, distributed and non-distributed equilibria, and show that under logistic dissatisfaction, only non-distributed equilibria are possible by feasibly setting prices. In linear case, both type of equilibria are possible but price discrimination is necessary to induce distributed equilibria. Finally, we show that in the case of symmetric EV users, mediation cannot improve upon Nash equilibria.", "published": "2022-10-26T21:07:05Z", "authors": ["Trivikram Dokka", "Jorge Bruno", "Sonali SenGupta", "Chowdhury Mohammad Sakib Anwar"], "categories": ["econ.TH"], "doi": "", "_query": "housing price text listing", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/1705.08563v3", "title": "Simple Pricing Schemes for the Cloud", "abstract": "The problem of pricing the cloud has attracted much recent attention due to the widespread use of cloud computing and cloud services. From a theoretical perspective, several mechanisms that provide strong efficiency or fairness guarantees and desirable incentive properties have been designed. However, these mechanisms often rely on a rigid model, with several parameters needing to be precisely known in order for the guarantees to hold. In this paper, we consider a stochastic model and show that it is possible to obtain good welfare and revenue guarantees with simple mechanisms that do not make use of the information on some of these parameters. In particular, we prove that a mechanism that sets the same price per time step for jobs of any length achieves at least 50% of the welfare and revenue obtained by a mechanism that can set different prices for jobs of different lengths, and the ratio can be improved if we have more specific knowledge of some parameters. Similarly, a mechanism that sets the same price for all servers even though the servers may receive different kinds of jobs can provide a reasonable welfare and revenue approximation compared to a mechanism that is allowed to set different prices for different servers.", "published": "2017-05-23T23:49:55Z", "authors": ["Ian A. Kash", "Peter Key", "Warut Suksompong"], "categories": ["cs.GT"], "doi": "10.1145/3327973", "_query": "housing price text listing", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2512.22599v1", "title": "Cryptocurrency Price Prediction Using Parallel Gated Recurrent Units", "abstract": "According to the advent of cryptocurrencies and Bitcoin, many investments and businesses are now conducted online through cryptocurrencies. Among them, Bitcoin uses blockchain technology to make transactions secure, transparent, traceable, and immutable. It also exhibits significant price fluctuations and performance, which has attracted substantial attention, especially in financial sectors. Consequently, a wide range of investors and individuals have turned to investing in the cryptocurrency market. One of the most important challenges in economics is price forecasting for future trades. Cryptocurrencies are no exception, and investors are looking for methods to predict prices; various theories and methods have been proposed in this field. This paper presents a new deep model, called \\emph{Parallel Gated Recurrent Units} (PGRU), for cryptocurrency price prediction. In this model, recurrent neural networks forecast prices in a parallel and independent way. The parallel networks utilize different inputs, each representing distinct price-related features. Finally, the outputs of the parallel networks are combined by a neural network to forecast the future price of cryptocurrencies. The experimental results indicate that the proposed model achieves mean absolute percentage errors (MAPE) of 3.243% and 2.641% for window lengths 20 and 15, respectively. Our method therefore attains higher accuracy and efficiency with fewer input data and lower computational cost compared to existing methods.", "published": "2025-12-27T14:04:21Z", "authors": ["Milad Asadpour", "Alireza Rezaee", "Farshid Hajati"], "categories": ["cs.LG"], "doi": "", "_query": "housing price text listing", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2402.11716v1", "title": "The Price of Abandoning Dark Matter Is Nonlocality", "abstract": "We consider the classic question posed by Pardo and Spergel about the price of abandoning dark matter in the context of an invariant, metric-based theory of gravity. Our answer is that the price is nonlocality. This has been known for some time in the context of the quasi-static regime. We show that it also applies for cosmology and we exhibit a model which reproduces standard CDM successes such as perturbations in the cosmic microwave background, baryon acoustic oscillations and structure formation.", "published": "2024-02-18T21:48:25Z", "authors": ["C. Deffayet", "R. P. Woodard"], "categories": ["gr-qc", "astro-ph.CO", "hep-th"], "doi": "", "_query": "housing price text listing", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2609.04712v1", "title": "Convex Modeling of Price Cross-Impact over Time", "abstract": "Transaction costs can make or break a trading strategy, particularly in relative-value trading of commodity and macro markets, where edges are a few basis points. Price impact is a central component of transaction cost. Price impact models usually include self-impact (a trade in a contract moves that contract's price) but omit two well-documented effects: cross-impact (a trade in one contract also moves the prices of related contracts) and transient impact (price impact decays over time, so an unwind recovers part of the entry cost). A model without these effects overprices the impact of relative-value trades, whose correlated legs are built and unwound over days, and so forgoes potentially profitable trades. This paper models both effects with a convex quadratic cost. In each period, a positive semidefinite matrix built from volatility, volume, and correlation forecasts couples trades across contracts. A power-law decay kernel then couples trades across periods. The resulting cost admits no price manipulation even when liquidity varies over the planning horizon. The model is demonstrated empirically on calendar spread trading of crude oil futures around the commodity index roll.", "published": "2026-09-04T04:32:55Z", "authors": ["Vincent Yinjun-Wang", "Madeleine Udell"], "categories": ["math.OC", "q-fin.CP", "q-fin.PM", "q-fin.RM"], "doi": "", "_query": "housing price text listing", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/1402.3646v1", "title": "Statistical analysis of the price and subjective quality ratings on Australian wines", "abstract": "Consumers have a wide choice regarding the purchase of wine. Wines vary across varieties, regions, years of vintage, alcohol content, price and expert ratings. In this study we explored the relationship between those variables and whether a combination of explanatory variables can predict outcome variables using data on selected 2710 Australian wines rates by the latest book of James Halliday1. We analysed two dependent (outcome) variables, namely the rating and the price, and their (non-linear) relationship between explanatory variables and each other. We observe that the rating scale is not linear, as consumers may believe, and that a narrow range of rating scores is used for the ascertained wines. Across all wines, approximately 20% of variation in log(Price) can be explained by the explanatory factors state, variety, vintage and alcohol percentage. If the residuals from the model for rating is also taken as an explanatory variable for price, then a total of 53% of variation in log(Price) is explained by the model. For those wines ascertained to be in Halliday1, there is more variability between white wines than between red wines that is explained by explanatory variables such as State and variety. In comparison with hedonic pricing analyses in previous studies on French and Australian wines, a smaller proportion of variation in price and rating was explained in Australian wines that were in the 2014 edition of Halliday.", "published": "2014-02-15T05:07:56Z", "authors": ["Peter M. Visscher"], "categories": ["stat.AP"], "doi": "", "_query": "housing price text listing", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2412.07075v1", "title": "Conformal Uncertainty Quantification of Electricity Price Predictions for Risk-Averse Storage Arbitrage", "abstract": "This paper proposes a risk-averse approach to energy storage price arbitrage, leveraging conformal uncertainty quantification for electricity price predictions. The method addresses the significant challenges posed by the inherent volatility and uncertainty of real-time electricity prices, which create substantial risks of financial losses for energy storage participants relying on future price forecasts to plan their operations. The framework comprises a two-layer prediction model to quantify real-time price uncertainty confidence intervals with high coverage. The framework is distribution-free and can work with any underlying point prediction model. We evaluate the quantification effectiveness through storage price arbitrage application by managing the risk of participating in the real-time market. We design a risk-averse policy for profit-maximization of energy storage arbitrage to find the safest storage schedule with very minimal losses. Using historical data from New York State and synthetic price predictions, our evaluations demonstrate that this framework can achieve good profit margins with less than $35\\%$ purchases.", "published": "2024-12-10T00:31:15Z", "authors": ["Saud Alghumayjan", "Ming Yi", "Bolun Xu"], "categories": ["math.OC", "stat.ML"], "doi": "", "_query": "housing price text listing", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2606.05363v3", "title": "Oblivious Learning and Collusive Pricing", "abstract": "On a platform with many sellers, should a pricing algorithm explicitly model competitors' prices when learning demand? Classical arguments suggest that ignoring competitors induces model misspecification and inefficiency, yet findings from algorithmic collusion suggest that ignoring competitor prices may, surprisingly, facilitate collusive outcomes and improve profits. We study this problem in a competitive market with unknown noisy demand, in which sellers repeatedly set prices, either incorporating competitor prices in learning their demand models (informed), or ignoring them (oblivious). We show that, relative to a monopolist, an oblivious seller in a competitive market must conduct more aggressive price exploration to compensate for the loss of dynamic competitor information. When all sellers are oblivious, prices converge to the competitive outcome under persistent exploration, while a continuum of pseudo-equilibria arises when exploration is \"insufficient.\" In markets with a mix of oblivious and informed sellers, the informed strictly out-earn the oblivious. In game-theoretic terms, the unique Nash equilibrium is the all-informed market, in which prices converge to the competitive outcome efficiently, and oblivious modeling does not robustly lead to collusive patterns.", "published": "2026-06-03T19:10:55Z", "authors": ["Yuhang Wu", "Assaf Zeevi"], "categories": ["cs.GT", "cs.LG", "econ.TH", "math.OC"], "doi": "", "_query": "housing price text listing", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2501.10196v1", "title": "Pricing Mechanisms versus Non-Pricing Mechanisms for Demand Side Management in Microgrids", "abstract": "In this paper, we compare pricing and non-pricing mechanisms for implementing demand-side management (DSM) mechanisms in a neighborhood in Helsinki, Finland. We compare load steering based on peak load-reduction using the profile steering method, and load steering based on market price signals, in terms of peak loads, losses, and device profiles. We found that there are significant differences between the two methods; the peak-load reduction control strategies contribute to reducing peak power and improving power flow stability, while strategies primarily based on prices result in higher peaks and increased grid losses. Our results highlight the need to potentially move away from market-price-based DSM to DSM incentivization and control strategies that are based on peak load reductions and other system requirements.", "published": "2025-01-17T13:39:54Z", "authors": ["Cassia Nunes Almeida", "Arun Narayanan", "Hafiz Majid Hussain", "Pedro H. J. Nardelli"], "categories": ["eess.SY"], "doi": "", "_query": "housing price text listing", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/1705.00672v5", "title": "Portfolio Choice with Small Temporary and Transient Price Impact", "abstract": "We study portfolio selection in a model with both temporary and transient price impact introduced by Garleanu and Pedersen (2016). In the large-liquidity limit where both frictions are small, we derive explicit formulas for the asymptotically optimal trading rate and the corresponding minimal leading-order performance loss. We find that the losses are governed by the volatility of the frictionless target strategy, like in models with only temporary price impact. In contrast, the corresponding optimal portfolio not only tracks the frictionless optimizer, but also exploits the displacement of the market price from its unaffected level.", "published": "2017-05-01T19:17:41Z", "authors": ["Ibrahim Ekren", "Johannes Muhle-Karbe"], "categories": ["q-fin.PM", "math.OC"], "doi": "", "_query": "housing price text listing", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/1606.08515v1", "title": "Directly Constraining Marginal Prices", "abstract": "The marginal price of electricity traditionally depends on the dual variables associated with relevant optimization goals. Particularly, in the optimal power flow realm, prices represent the cost of supplying an additional unit of power at each bus; for the economic dispatch case, dual variables represent the cost of supplying an additional unit of power to the whole system. Dual variables are useful for many additional tasks, including the analysis of system congestion and the determination of the cost of load adjustments. In this letter, departing from conventional optimal power flow analysis, it is shown how constraints on relevant dual variables affect the prices of electricity, allowing for market settings and demand response programs that accept bids and caps on individual prices.", "published": "2016-06-27T23:52:01Z", "authors": ["Kyri Baker"], "categories": ["math.OC"], "doi": "", "_query": "housing price text listing", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/1810.08161v1", "title": "Ratio List Decoding", "abstract": "We extend the notion of list decoding to {\\em ratio list decoding} which involves a list decoder whose list size is specified as a function of the number of messages $M_n$ and the block length $n$. We present necessary and sufficient conditions on $M_n$ for the existence of code sequences which enable reliable list decoding with respect to the desired list size $L(M_n,n)$. It is shown that the ratio-capacity, defined as the supremum of achievable normalized logarithms of the ratio $r(M_n,n)=M_n/L(M_n,n)$ is equal to the Shannon channel capacity $C$, for both stochastic and deterministic encoding. Allowing for random list size, we are able to deduce some properties of identification codes, where the decoder's output can be viewed as a list of messages corresponding to decision regions that include the channel output. We further address the regime of mismatched list decoding, in which the list constitutes of the codewords that accumulate the highest score values (jointly with the channel output) according to some given function. We study the case of deterministic encoding and mismatched ratio list decoding. We establish similar necessary and sufficient conditions for the existence of code sequences which enable reliable mismatched list decoding with respect to the desired list size $L(M_n,n)$, and we show that the ratio-capacity with mismatched decoding is equal to the mismatch capacity. Focusing on the case of an exponential list size $L_n=e^{n Θ}$, its comparison with ordinary mismatched decoding shows that the increase in capacity is by $Θ$ bits per channel use for all channels and decoding metrics. Several properties of the average error probability in the setup of mismatched list decoding with deterministic list size are provided.", "published": "2018-10-18T16:57:26Z", "authors": ["Anelia Somekh-Baruch"], "categories": ["cs.IT"], "doi": "", "_query": "housing price text listing", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2002.10566v1", "title": "Forecasting the Intra-Day Spread Densities of Electricity Prices", "abstract": "Intra-day price spreads are of interest to electricity traders, storage and electric vehicle operators. This paper formulates dynamic density functions, based upon skewed-t and similar representations, to model and forecast the German electricity price spreads between different hours of the day, as revealed in the day-ahead auctions. The four specifications of the density functions are dynamic and conditional upon exogenous drivers, thereby permitting the location, scale and shape parameters of the densities to respond hourly to such factors as weather and demand forecasts. The best fitting and forecasting specifications for each spread are selected based on the Pinball Loss function, following the closed-form analytical solutions of the cumulative distribution functions.", "published": "2020-02-20T19:57:07Z", "authors": ["Ekaterina Abramova", "Derek Bunn"], "categories": ["stat.AP", "cs.LG", "econ.EM", "stat.ML"], "doi": "10.3390/en13030687", "_query": "housing price text listing", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2309.04216v3", "title": "Liquidity Dynamics in RFQ Markets and Impact on Pricing", "abstract": "To assign a value to a portfolio, it is common to use Mark-to-Market prices. However, how should one proceed when the securities are illiquid? When transaction prices are scarce, how can one use all the available real-time information? In this article, we address these questions for over-the-counter (OTC) markets based on requests for quotes (RFQs). We extend the concept of micro-price, which was recently introduced for assets exchanged through limit order books in the market microstructure literature, and incorporate ideas from the recent literature on OTC market making. To account for liquidity imbalances in RFQ markets, we use an approach based on bidimensional Markov-modulated Poisson processes. Beyond extending the concept of micro-price to RFQ markets, we introduce the new concept of Fair Transfer Price. Our concepts of price can be used to value securities fairly, even when the market is relatively illiquid and/or tends to be one-sided.", "published": "2023-09-08T09:01:51Z", "authors": ["Philippe Bergault", "Olivier Guéant"], "categories": ["q-fin.TR", "q-fin.ST"], "doi": "", "_query": "housing price text listing", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2106.14014v3", "title": "Txt2Vid: Ultra-Low Bitrate Compression of Talking-Head Videos via Text", "abstract": "Video represents the majority of internet traffic today, driving a continual race between the generation of higher quality content, transmission of larger file sizes, and the development of network infrastructure. In addition, the recent COVID-19 pandemic fueled a surge in the use of video conferencing tools. Since videos take up considerable bandwidth (~100 Kbps to a few Mbps), improved video compression can have a substantial impact on network performance for live and pre-recorded content, providing broader access to multimedia content worldwide. We present a novel video compression pipeline, called Txt2Vid, which dramatically reduces data transmission rates by compressing webcam videos (\"talking-head videos\") to a text transcript. The text is transmitted and decoded into a realistic reconstruction of the original video using recent advances in deep learning based voice cloning and lip syncing models. Our generative pipeline achieves two to three orders of magnitude reduction in the bitrate as compared to the standard audio-video codecs (encoders-decoders), while maintaining equivalent Quality-of-Experience based on a subjective evaluation by users (n = 242) in an online study. The Txt2Vid framework opens up the potential for creating novel applications such as enabling audio-video communication during poor internet connectivity, or in remote terrains with limited bandwidth. The code for this work is available at https://github.com/tpulkit/txt2vid.git.", "published": "2021-06-26T12:29:36Z", "authors": ["Pulkit Tandon", "Shubham Chandak", "Pat Pataranutaporn", "Yimeng Liu", "Anesu M. Mapuranga", "Pattie Maes", "Tsachy Weissman", "Misha Sra"], "categories": ["eess.IV", "cs.MM"], "doi": "", "_query": "housing price text listing", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/1407.4376v3", "title": "Common price and volatility jumps in noisy high-frequency data", "abstract": "We introduce a statistical test for simultaneous jumps in the price of a financial asset and its volatility process. The proposed test is based on high-frequency data and is robust to market microstructure frictions. For the test, local estimators of volatility jumps at price jump arrival times are designed using a nonparametric spectral estimator of the spot volatility process. A simulation study and an empirical example with NASDAQ order book data demonstrate the practicability of the proposed methods and highlight the important role played by price volatility co-jumps.", "published": "2014-07-16T16:28:41Z", "authors": ["Markus Bibinger", "Lars Winkelmann"], "categories": ["math.ST"], "doi": "", "_query": "housing price text listing", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2306.00599v1", "title": "The Cost of Misspecifying Price Impact", "abstract": "Portfolio managers' orders trade off return and trading cost predictions. Return predictions rely on alpha models, whereas price impact models quantify trading costs. This paper studies what happens when trades are based on an incorrect price impact model, so that the portfolio either over- or under-trades its alpha signal. We derive tractable formulas for these misspecification costs and illustrate them on proprietary trading data. The misspecification costs are naturally asymmetric: underestimating impact concavity or impact decay shrinks profits, but overestimating concavity or impact decay can even turn profits into losses.", "published": "2023-06-01T12:14:18Z", "authors": ["Natascha Hey", "Jean-Philippe Bouchaud", "Iacopo Mastromatteo", "Johannes Muhle-Karbe", "Kevin Webster"], "categories": ["q-fin.TR"], "doi": "", "_query": "housing price text listing", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2007.06242v3", "title": "Optimal Bounds on the Price of Fairness for Indivisible Goods", "abstract": "In the allocation of resources to a set of agents, how do fairness guarantees impact the social welfare? A quantitative measure of this impact is the price of fairness, which measures the worst-case loss of social welfare due to fairness constraints. While initially studied for divisible goods, recent work on the price of fairness also studies the setting of indivisible goods. In this paper, we resolve the price of two well-studied fairness notions for the allocation of indivisible goods: envy-freeness up to one good (EF1), and approximate maximin share (MMS). For both EF1 and 1/2-MMS guarantees, we show, via different techniques, that the price of fairness is $O(\\sqrt{n})$, where $n$ is the number of agents. From previous work, it follows that our bounds are tight. Our bounds are obtained via efficient algorithms. For 1/2-MMS, our bound holds for additive valuations, whereas for EF1, our bound holds for the more general class of subadditive valuations. This resolves an open problem posed by Bei et al. (2019).", "published": "2020-07-13T08:49:53Z", "authors": ["Siddharth Barman", "Umang Bhaskar", "Nisarg Shah"], "categories": ["cs.GT", "cs.DS"], "doi": "", "_query": "housing price text listing", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/1410.6408v1", "title": "Asset Pricing in an Imperfect World", "abstract": "In a model with no given probability measure, we consider asset pricing in the presence of frictions and other imperfections and characterize the property of coherent pricing, a notion related to (but much weaker than) the no arbitrage property. We show that prices are coherent if and only if the set of pricing measures is non empty, i.e. if pricing by expectation is possible. We then obtain a decomposition of coherent prices highlighting the role of bubbles. Eventually we show that under very weak conditions the coherent pricing of options allows for a very clear representation which allows, as in Breeden and Litzenberger, to extract the implied probability.", "published": "2014-10-23T16:11:09Z", "authors": ["Gianluca Cassese"], "categories": ["q-fin.MF", "q-fin.PR"], "doi": "10.1007/s00199-016-0999-7", "_query": "housing price text listing", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2312.06114v2", "title": "The virial theorem and the Price equation", "abstract": "We observe that the time averaged continuous Price equation is identical to the positive momentum virial theorem, and we discuss the applications and implications of this connection.", "published": "2023-12-11T04:37:11Z", "authors": ["Catherine Felce", "Steinunn Liorsdóttir", "Lior Pachter"], "categories": ["physics.bio-ph", "q-bio.QM"], "doi": "", "_query": "housing price text listing", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/cond-mat/9609144v1", "title": "Price Variations in a Stock Market With Many Agents", "abstract": "Large variations in stock prices happen with sufficient frequency to raise doubts about existing models, which all fail to account for non-Gaussian statistics. We construct simple models of a stock market, and argue that the large variations may be due to a crowd effect, where agents imitate each other's behavior. The variations over different time scales can be related to each other in a systematic way, similar to the Levy stable distribution proposed by Mandelbrot to describe real market indices. In the simplest, least realistic case, exact results for the statistics of the variations are derived by mapping onto a model of diffusing and annihilating particles, which has been solved by quantum field theory methods. When the agents imitate each other and respond to recent market volatility, different scaling behavior is obtained. In this case the statistics of price variations is consistent with empirical observations. The interplay between ``rational'' traders whose behavior is derived from fundamental analysis of the stock, including dividends, and ``noise traders'', whose behavior is governed solely by studying the market dynamics, is investigated. When the relative number of rational traders is small, ``bubbles'' often occur, where the market price moves outside the range justified by fundamental market analysis. When the number of rational traders is larger, the market price is generally locked within the price range they define.", "published": "1996-09-16T18:43:15Z", "authors": ["P. Bak", "M. Paczuski", "M. Shubik"], "categories": ["cond-mat"], "doi": "10.1016/S0378-4371(97)00401-9", "_query": "housing price text listing", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2309.09176v3", "title": "Odd period cycles and ergodic properties in price dynamics for an exchange economy", "abstract": "In the first part of this paper (Sections 1-4), we study a standard exchange economy model with Cobb-Douglas type consumers and give a necessary and sufficient condition for the existence of an odd period cycle in the Walras-Samuelson (tatonnement) price adjustment process. We also give a sufficient condition for a price to be eventually attracted to a chaotic region. In the second part (Sections 5 and 6), we investigate ergodic properties of the price dynamics showing that the existence of chaos is not necessarily bad. (The future is still predictable on average.) Moreover, supported by a celebrated work of Avila et al. (Invent. Math., 2003), we conduct a sensitivity analysis to investigate a relationship between the ergodic sum (of prices) and the speed of price adjustment. We believe that our methods in this paper can be used to analyse many other chaotic economic models.", "published": "2023-09-17T06:55:36Z", "authors": ["Tomohiro Uchiyama"], "categories": ["econ.GN"], "doi": "", "_query": "housing price text listing", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2005.02953v2", "title": "The Pricing of Quanto Options: An empirical copula approach", "abstract": "The quanto option is a cross-currency derivative in which the pay-off is given in foreign currency and then converted to domestic currency, through a constant exchange rate, used for the conversion and determined at contract inception. Hence, the dependence relation between the option underlying asset price and the exchange rate plays an important role in quanto option pricing. In this work, we suggest to use empirical copulas to price quanto options. Numerical illustrations show that the flexibility provided by this approach, concerning the dependence relation of the two underlying stochastic processes, results in non-negligible pricing differences when contrasted to other models.", "published": "2020-05-06T16:57:58Z", "authors": ["Rafael Felipe Carmargo Prudencio", "Christian D. Jäkel"], "categories": ["q-fin.MF"], "doi": "", "_query": "housing price text listing", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/cs/0407029v1", "title": "Static versus Dynamic Arbitrage Bounds on Multivariate Option Prices", "abstract": "We compare static arbitrage price bounds on basket calls, i.e. bounds that only involve buy-and-hold trading strategies, with the price range obtained within a multi-variate generalization of the Black-Scholes model. While there is no gap between these two sets of prices in the univariate case, we observe here that contrary to our intuition about model risk for at-the-money calls, there is a somewhat large gap between model prices and static arbitrage prices, hence a similarly large set of prices on which a multivariate Black-Scholes model cannot be calibrated but where no conclusion can be drawn on the presence or not of a static arbitrage opportunity.", "published": "2004-07-10T16:17:26Z", "authors": ["Alexandre d'Aspremont"], "categories": ["cs.CE"], "doi": "", "_query": "housing price text listing", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/1812.10930v1", "title": "On the use and abuse of Price equation concepts in ecology", "abstract": "In biodiversity and ecosystem functioning (BEF) research, the Loreau-Hector (LH) statistical scheme is widely-used to partition the effect of biodiversity on ecosystem properties into a \"complementarity effect\" and a \"selection effect\". This selection effect was originally considered analogous to the selection term in the Price equation from evolutionary biology. However, a key paper published over thirteen years ago challenged this interpretation by devising a new tripartite partitioning scheme that purportedly quantified the role of selection in biodiversity experiments more accurately. This tripartite method, as well as its recent spatiotemporal extension, were both developed as an attempt to apply the Price equation in a BEF context. Here, we demonstrate that the derivation of this tripartite method, as well as its spatiotemporal extension, involve a set of incoherent and nonsensical mathematical arguments driven largely by naïve visual analogies with the original Price equation, that result in neither partitioning scheme quantifying any real property in the natural world. Furthermore, we show that Loreau and Hector's original selection effect always represented a true analog of the original Price selection term, making the tripartite partitioning scheme a nonsensical solution to a non-existent problem [...]", "published": "2018-12-28T09:11:20Z", "authors": ["Pradeep Pillai", "Tarik C. Gouhier"], "categories": ["q-bio.PE"], "doi": "", "_query": "housing price text listing", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2512.20190v1", "title": "Pricing of wrapped Bitcoin and Ethereum on-chain options", "abstract": "This paper measures price differences between Hegic option quotes on Arbitrum and a model-based benchmark built on Black--Scholes model with regime-sensitive volatility estimated via a two-regime MS-AR-(GJR)-GARCH model. Using option-level feasible GLS, we find benchmark prices exceed Hegic quotes on average, especially for call options. The price spread rises with order size, strike, maturity, and estimated volatility, and falls with trading volume. By underlying, wrapped Bitcoin options show larger and more persistent spreads, while Ethereum options are closer to the benchmark. The framework offers a data-driven analysis for monitoring and calibrating on-chain option pricing logic.", "published": "2025-12-23T09:29:57Z", "authors": ["Anastasiia Zbandut"], "categories": ["q-fin.PR", "q-fin.RM"], "doi": "", "_query": "housing price text listing", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2103.16918v2", "title": "A survey of electricity spot and futures price models for risk management applications", "abstract": "This review presents the set of electricity price models proposed in the literature since the opening of power markets. We focus on price models applied to financial pricing and risk management. We classify these models according to their ability to represent the random behavior of prices and some of their characteristics. In particular, this classification helps users to choose among the most suitable models for their risk management problems.", "published": "2021-03-31T09:17:28Z", "authors": ["Thomas Deschatre", "Olivier Féron", "Pierre Gruet"], "categories": ["q-fin.MF", "q-fin.RM"], "doi": "", "_query": "housing price text listing", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2312.15282v3", "title": "Causal Forecasting for Pricing", "abstract": "This paper proposes a novel method for demand forecasting in a pricing context. Here, modeling the causal relationship between price as an input variable to demand is crucial because retailers aim to set prices in a (profit) optimal manner in a downstream decision making problem. Our methods bring together the Double Machine Learning methodology for causal inference and state-of-the-art transformer-based forecasting models. In extensive empirical experiments, we show on the one hand that our method estimates the causal effect better in a fully controlled setting via synthetic, yet realistic data. On the other hand, we demonstrate on real-world data that our method outperforms forecasting methods in off-policy settings (i.e., when there's a change in the pricing policy) while only slightly trailing in the on-policy setting.", "published": "2023-12-23T15:38:22Z", "authors": ["Douglas Schultz", "Johannes Stephan", "Julian Sieber", "Trudie Yeh", "Manuel Kunz", "Patrick Doupe", "Tim Januschowski"], "categories": ["stat.ML", "cs.LG"], "doi": "", "_query": "housing price text listing", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2509.01499v1", "title": "When Do Consumers Lose from Variable Electricity Pricing?", "abstract": "Time-varying electricity pricing better reflects the varying cost of electricity compared to flat-rate pricing. Variations between peak and off-peak costs are increasing due to weather variation, renewable intermittency, and increasing electrification of demand. Empirical and theoretical studies suggest that variable pricing can lower electricity supply costs and reduce grid stress. However, the distributional impacts, particularly on low-income consumers, remain understudied. This paper develops a theoretical framework to analyze how consume heterogeneity affects welfare outcomes when electricity markets transition from flat-rate to time-varying pricing, considering realistic assumptions about heterogeneous consumer demand, supply costs, and utility losses from unmet consumption. We derive sufficient conditions for identifying when consumers lose utility from pricing reforms and compare welfare effects across consumer types. Our findings reveal that consumer vulnerability depends on the interaction of consumption timing, demand flexibility capabilities, and price sensitivity levels. Consumers with high peak-period consumption and inflexible demand, characteristics often associated with low-income households, are most vulnerable to welfare losses. Critically, we demonstrate that demand flexibility provides welfare protection only when coincident with large price changes. Our equilibrium analysis reveals that aggregate flexibility patterns generate spillover effects through pricing mechanisms, with peak periods experiencing greater price changes when they have less aggregate flexibility, potentially concentrating larger price increases among vulnerable populations that have a limited ability to respond. These findings suggest that variable pricing policies should be accompanied by targeted policies ensuring equitable access to demand response capabilities and pricing benefits.", "published": "2025-09-01T14:20:27Z", "authors": ["Nathan Engelman Lado", "Richard Chen", "Saurabh Amin"], "categories": ["econ.GN"], "doi": "", "_query": "housing price text listing", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/1309.7119v3", "title": "Stock price direction prediction by directly using prices data: an empirical study on the KOSPI and HSI", "abstract": "The prediction of a stock market direction may serve as an early recommendation system for short-term investors and as an early financial distress warning system for long-term shareholders. Many stock prediction studies focus on using macroeconomic indicators, such as CPI and GDP, to train the prediction model. However, daily data of the macroeconomic indicators are almost impossible to obtain. Thus, those methods are difficult to be employed in practice. In this paper, we propose a method that directly uses prices data to predict market index direction and stock price direction. An extensive empirical study of the proposed method is presented on the Korean Composite Stock Price Index (KOSPI) and Hang Seng Index (HSI), as well as the individual constituents included in the indices. The experimental results show notably high hit ratios in predicting the movements of the individual constituents in the KOSPI and HIS.", "published": "2013-09-27T05:35:50Z", "authors": ["Yanshan Wang"], "categories": ["cs.CE", "cs.LG", "q-fin.ST"], "doi": "10.1504/IJBIDM.2014.065091", "_query": "housing price text listing", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/1102.5501v1", "title": "Extension theorems for linear operators on $L_\\infty$ and application to price systems", "abstract": "In an $L_\\infty$-framework, we present a few extension theorems for linear operators. We focus the attention on majorant preserving and sandwich preserving types of extensions. These results are then applied to the study of price systems derived by a reasonable restriction of the class of equivalent martingale measures applicable. First we consider equivalent martingale measures with bounds on densities and the corresponding prices bounded by linear minorant and majorant. Then we consider prices bounded by bid-ask dynamics. Finally we study price systems consistent with no-good-deal pricing measures for given bounds on the Sharpe ratio. Within this study we introduce the definition of dynamic no-good-deal pricing measure.", "published": "2011-02-27T13:23:55Z", "authors": ["Jocelyne Bion-Nadal", "Giulia Di Nunno"], "categories": ["math.PR", "q-fin.PR"], "doi": "", "_query": "housing price text listing", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2303.13956v2", "title": "Pitman's Theorem, Black-Scholes Equation, and Derivative Pricing for Fundraisers", "abstract": "We propose a financial market model that comprises a savings account and a stock. The stock price process is modeled as a one-dimensional diffusion, in which two types of agents exist: an ordinary investor and a fundraiser who buys or sells stocks as funding activities. Although the investor information is the natural filtration of the diffusion, the fundraiser possesses additional information regarding the funding, as well as additional cash flows as a result of the funding. This concept is modeled using Pitman's theorem for the three-dimensional Bessel process. Two contributions are presented: First, the prices of European options for the fundraiser are derived. Second, a numerical scheme for call option prices in a market with a bubble is proposed, where multiple solutions exist for the Black--Scholes equation and the derivative prices are characterized as the smallest nonnegative supersolution. More precisely, the call option price in such a market is approximated from below by the prices for the fundraiser. This scheme overcomes the difficulty that stems from the discrepancy that the payoff shows linear growth, whereas the price function shows strictly sublinear growth.", "published": "2023-03-24T12:29:17Z", "authors": ["Yukihiro Tsuzuki"], "categories": ["q-fin.MF"], "doi": "", "_query": "housing price text listing", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/physics/0506224v1", "title": "Impact of Investor's Varying Risk Aversion on the Dynamics of Asset Price Fluctuations", "abstract": "While the investors' responses to price changes and their price forecasts are well accepted major factors contributing to large price fluctuations in financial markets, our study shows that investors' heterogeneous and dynamic risk aversion (DRA) preferences may play a more critical role in the dynamics of asset price fluctuations. We propose and study a model of an artificial stock market consisting of heterogeneous agents with DRA, and we find that DRA is the main driving force for excess price fluctuations and the associated volatility clustering. We employ a popular power utility function, $U(c,γ)=\\frac{c^{1-γ}-1}{1-γ}$ with agent specific and time-dependent risk aversion index, $γ_i(t)$, and we derive an approximate formula for the demand function and aggregate price setting equation. The dynamics of each agent's risk aversion index, $γ_i(t)$ (i=1,2,...,N), is modeled by a bounded random walk with a constant variance $δ^2$. We show numerically that our model reproduces most of the ``stylized'' facts observed in the real data, suggesting that dynamic risk aversion is a key mechanism for the emergence of these stylized facts.", "published": "2005-06-30T08:29:21Z", "authors": ["Baosheng Yuan", "Kan Chen"], "categories": ["physics.soc-ph", "physics.comp-ph", "q-fin.ST"], "doi": "", "_query": "housing price text listing", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2110.11581v1", "title": "A Two-stage Pricing Strategy Considering Learning Effects and Word-of-Mouth", "abstract": "This paper proposes a two-stage pricing strategy for nondurable (such as typical electronics) products, where retail price is cut down at certain time points of the product lifecycle. We consider learning effect of electronic products that, with the accumulation of production, average production cost decreases over time as manufacturers get familiar with the production process. Moreover, word-of-mouth (WOM) of existing customers is used to analyze future demand, which is sensitive to the difference between the actual reliability and the perceived reliability of products. We theoretically prove the existence and uniqueness of the optimal switch time between the two stages and the optimal price in each stage. In addition, warranty as another important factor of electronic products is also considered, whose interaction with word-of-mouth as well as the corresponding influences on total profit are analyzed. Interestingly, our findings indicate that (1) the main reason for manufacturers to cut down prices for electronic products pertains to the learning effects; (2) even through both internal factors (e.g., the learning effects of manufacturers) and external factors (e.g., the price elasticity of customers) have impacts on product price, their influence on manufacturer's profit is widely divergent; (3) generally warranty weakens the influence of external advertising on the reliability estimate, because warranty price only partially reflects the actual reliability information of products; (4) and the optimal warranty price can increase the profits for the manufacturer by approximately 10%.", "published": "2021-10-22T04:18:53Z", "authors": ["Yanrong Li", "Lai Wei", "Wei Jiang"], "categories": ["econ.GN"], "doi": "", "_query": "housing price text listing", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2111.08221v2", "title": "Fairness-aware Online Price Discrimination with Nonparametric Demand Models", "abstract": "Price discrimination, which refers to the strategy of setting different prices for different customer groups, has been widely used in online retailing. Although it helps boost the collected revenue for online retailers, it might create serious concerns about fairness, which even violates the regulation and laws. This paper studies the problem of dynamic discriminatory pricing under fairness constraints. In particular, we consider a finite selling horizon of length $T$ for a single product with two groups of customers. Each group of customers has its unknown demand function that needs to be learned. For each selling period, the seller determines the price for each group and observes their purchase behavior. While existing literature mainly focuses on maximizing revenue, ensuring fairness among different customers has not been fully explored in the dynamic pricing literature. This work adopts the fairness notion from Cohen et al. (2022). For price fairness, we propose an optimal dynamic pricing policy regarding regret, which enforces the strict price fairness constraint. In contrast to the standard $\\sqrt{T}$-type regret in online learning, we show that the optimal regret in our case is $\\tilde{O}(T^{4/5})$. We further extend our algorithm to a more general notion of fairness, which includes demand fairness as a special case. To handle this general class, we propose a soft fairness constraint and develop a dynamic pricing policy that achieves $\\tilde{O}(T^{4/5})$ regret. We also demonstrate that our algorithmic techniques can be adapted to more general scenarios such as fairness among multiple groups of customers.", "published": "2021-11-16T04:31:02Z", "authors": ["Xi Chen", "Jiameng Lyu", "Xuan Zhang", "Yuan Zhou"], "categories": ["cs.LG", "stat.ML"], "doi": "", "_query": "housing price text listing", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/1407.5091v1", "title": "An exact and explicit formula for pricing Asian options with regime switching", "abstract": "This paper studies the pricing of European-style Asian options when the price dynamics of the underlying risky asset are assumed to follow a Markov- modulated geometric Brownian motion; that is, the appreciation rate and the volatility of the underlying risky asset depend on unobservable states of the economy described by a continuous-time hidden Markov process. We derive the exact, explicit and closed-form solutions for European-style Asian options in a two-state regime switching model.", "published": "2014-07-18T01:35:49Z", "authors": ["Leunglung Chan", "Song-Ping Zhu"], "categories": ["q-fin.PR"], "doi": "", "_query": "housing price text listing", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/1710.08394v1", "title": "Fixed Price Approximability of the Optimal Gain From Trade", "abstract": "Bilateral trade is a fundamental economic scenario comprising a strategically acting buyer and seller, each holding valuations for the item, drawn from publicly known distributions. A mechanism is supposed to facilitate trade between these agents, if such trade is beneficial. It was recently shown that the only mechanisms that are simultaneously DSIC, SBB, and ex-post IR, are fixed price mechanisms, i.e., mechanisms that are parametrised by a price p, and trade occurs if and only if the valuation of the buyer is at least p and the valuation of the seller is at most p. The gain from trade is the increase in welfare that results from applying a mechanism; here we study the gain from trade achievable by fixed price mechanisms. We explore this question for both the bilateral trade setting, and a double auction setting where there are multiple buyers and sellers. We first identify a fixed price mechanism that achieves a gain from trade of at least 2/r times the optimum, where r is the probability that the seller's valuation does not exceed the buyer's valuation. This extends a previous result by McAfee. Subsequently, we improve this approximation factor in an asymptotic sense, by showing that a more sophisticated rule for setting the fixed price results in an expected gain from trade within a factor O(log(1/r)) of the optimal gain from trade. This is asymptotically the best approximation factor possible. Lastly, we extend our study of fixed price mechanisms to the double auction setting defined by a set of multiple i.i.d. unit demand buyers, and i.i.d. unit supply sellers. We present a fixed price mechanism that achieves a gain from trade that achieves for all epsilon > 0 a gain from trade of at least (1-epsilon) times the expected optimal gain from trade with probability 1 - 2/e^{#T epsilon^2 /2}, where #T is the expected number of trades resulting from the double auction.", "published": "2017-10-23T17:17:37Z", "authors": ["Riccardo Colini-Baldeschi", "Paul Goldberg", "Bart de Keijzer", "Stefano Leonardi", "Stefano Turchetta"], "categories": ["cs.GT"], "doi": "", "_query": "housing price text listing", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/1404.0648v2", "title": "Dynamic optimal execution in a mixed-market-impact Hawkes price model", "abstract": "We study a linear price impact model including other liquidity takers, whose flow of orders either follows a Poisson or a Hawkes process. The optimal execution problem is solved explicitly in this context, and the closed-formula optimal strategy describes in particular how one should react to the orders of other traders. This result enables us to discuss the viability of the market. It is shown that Poissonian arrivals of orders lead to quite robust Price Manipulation Strategies in the sense of Huberman and Stanzl. Instead, a particular set of conditions on the Hawkes model balances the self-excitation of the order flow with the resilience of the price, excludes Price Manipulation Strategies and gives some market stability.", "published": "2014-04-02T18:13:34Z", "authors": ["Aurélien Alfonsi", "Pierre Blanc"], "categories": ["q-fin.TR"], "doi": "", "_query": "housing price text listing", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2609.01765v1", "title": "Toward Explainable and Policy-Aware AI for Carbon Credit Price Prediction: A Research Framework for Emerging Carbon Markets", "abstract": "Carbon markets put a price on emissions, yet that price remains hard to forecast. Work in this area clusters on the EU and Chinese schemes, compresses regulatory text into a sentiment score, and reports accuracy without calibration or explanation stability. We distil ten recurring gaps into an impact-feasibility matrix and propose EPA-CarbonNet, a six-layer architecture that fuses market series with policy text by cross-attention and calibrated intervals alongside policy-attributed explanations. We then build and test it on eleven years of daily S and P carbon index data. The findings are largely negative, and reported as measured: a random walk beats the model on five-day RMSE (0.0365 against 0.0475), SHAP rankings agree at rho = 0.54 across resampled backgrounds, and policy attention never coincides with documented regulatory events. Directional accuracy, at 58.6 percent, leads every baseline. Code, data documentation and all result artifacts are available at https://github.com/Kimalice/Toward-Explainable-and-Policy-Aware-AI-for-Carbon-Credit-Price-Prediction", "published": "2026-09-01T18:36:04Z", "authors": ["Summaiya Unnisa Begum", "Mohammed Nadeem Ullah", "Mohammed Abdul Ghani Khan"], "categories": ["cs.LG"], "doi": "", "_query": "housing price text listing", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/1805.04728v1", "title": "Effects of a Price limit Change on Market Stability at the Intraday Horizon in the Korean Stock Market", "abstract": "This paper investigates the effects of a price limit change on the volatility of the Korean stock market's (KRX) intraday stock price process. Based on the most recent transaction data from the KRX, which experienced a change in the price limit on June 15, 2015, we examine the change in realized variance after the price limit change to investigate the overall effects of the change on the intraday market volatility. We then analyze the effects in more detail by applying the discrete Fourier transform (DFT) to the data set. We find evidence that the market becomes more volatile in the intraday horizon because of the increase in the amplitudes of the low-frequency components of the price processes after the price limit change. Therefore, liquidity providers are in a worse situation than they were prior to the change.", "published": "2018-05-12T14:56:03Z", "authors": ["Wonse Kim", "Sungjae Jun"], "categories": ["q-fin.TR"], "doi": "", "_query": "housing price text listing", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/1501.00818v2", "title": "Forecasting day ahead electricity spot prices: The impact of the EXAA to other European electricity markets", "abstract": "In our paper we analyze the relationship between the day-ahead electricity price of the Energy Exchange Austria (EXAA) and other day-ahead electricity prices in Europe. We focus on markets, which settle their prices after the EXAA, which enables traders to include the EXAA price into their calculations. For each market we employ econometric models to incorporate the EXAA price and compare them with their counterparts without the price of the Austrian exchange. By employing a forecasting study, we find that electricity price models can be improved when EXAA prices are considered.", "published": "2015-01-05T11:07:11Z", "authors": ["Florian Ziel", "Rick Steinert", "Sven Husmann"], "categories": ["q-fin.TR", "econ.EM", "q-fin.ST", "stat.AP", "stat.OT"], "doi": "10.1016/j.eneco.2015.08.005", "_query": "housing price text listing", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2002.08207v1", "title": "Inventory effects on the price dynamics of VSTOXX futures quantified via machine learning", "abstract": "The VSTOXX index tracks the expected 30-day volatility of the EURO STOXX 50 equity index. Futures on the VSTOXX index can, therefore, be used to hedge against economic uncertainty. We investigate the effect of trader inventory on the price of VSTOXX futures through a combination of stochastic processes and machine learning methods. We formulate a simple and efficient pricing methodology for VSTOXX futures, which assumes a Heston-type stochastic process for the underlying EURO STOXX 50 market. Under these dynamics, approximate analytical formulas for the implied volatility smile and the VSTOXX index have recently been derived. We use the EURO STOXX 50 option implied volatilities and the VSTOXX index value to estimate the parameters of this Heston model. Following the calibration, we calculate theoretical VSTOXX future prices and compare them to the actual market prices. While theoretical and market prices are usually in line, we also observe time periods, during which the market price does not agree with our Heston model. We collect a variety of market features that could potentially explain the price deviations and calibrate two machine learning models to the price difference: a regularized linear model and a random forest. We find that both models indicate a strong influence of accumulated trader positions on the VSTOXX futures price.", "published": "2020-02-19T14:30:31Z", "authors": ["Daniel Guterding"], "categories": ["q-fin.TR", "q-fin.PR"], "doi": "10.1016/j.jfds.2021.06.001", "_query": "housing price text listing", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/1505.08117v1", "title": "Predictability of price movements in deregulated electricity markets", "abstract": "In this paper we investigate predictability of electricity prices in the Canadian provinces of Alberta and Ontario, as well as in the US Mid-C market. Using scale-dependent detrended fluctuation analysis, spectral analysis, and the probability distribution analysis we show that the studied markets exhibit strongly anti-persistent properties suggesting that their dynamics can be predicted based on historic price records across the range of time scales from one hour to one month. For both Canadian markets, the price movements reveal three types of correlated behavior which can be used for forecasting. The discovered scenarios remain the same on different time scales up to one month as well as for on- and off- peak electricity data. These scenarios represent sharp increases of prices and are not present in the Mid-C market due to its lower volatility. We argue that extreme price movements in this market should follow the same tendency as the more volatile Canadian markets. The estimated values of the Pareto indices suggest that the prediction of these events can be statistically stable. The results obtained provide new relevant information for managing financial risks associated with the dynamics of electricity derivatives over time frame exceeding one day.", "published": "2015-01-23T00:52:10Z", "authors": ["Olga Y. Uritskaya", "Vadim M. Uritsky"], "categories": ["q-fin.ST", "physics.data-an", "physics.soc-ph"], "doi": "", "_query": "housing price text listing", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2512.15552v2", "title": "Automated Lexical Coverage for Language Learning: From General to Specialized Word Lists", "abstract": "A General Service List (GSL) is a commonly used resource for language learners to identify important English words. Traditional GSL creation is resource-intensive, relying on linguistic expertise and subjective input. We created our own GSL and evaluated its performance against the New General Service List (NGSL). We found that creating a Specialized Word List (SWL), tailored to a specific text, is a practical method for language learners. Because an SWL is derived from the target text itself, it reaches the 95% coverage required for language comprehension by construction, and it does so with substantially fewer words than a general list applied to the same text: across nine texts spanning fiction, academic papers, and scripts, the NGSL covered 64-85% of each text, whereas a text-specific list reached 95% with far smaller vocabularies. By restricting the SWL process to objective criteria only, it can be automated, scaled, and tailored to the needs of language-learners across the globe.", "published": "2025-12-17T15:59:38Z", "authors": ["Dakota Ellis", "Samy Babikerali", "Wanshan Chen", "Bao Dinh", "Uyen Le"], "categories": ["cs.CL"], "doi": "", "_query": "housing price text listing", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2301.02797v1", "title": "Option pricing under the normal SABR model with Gaussian quadratures", "abstract": "The stochastic-alpha-beta-rho (SABR) model has been widely adopted in options trading. In particular, the normal ($β=0$) SABR model is a popular model choice for interest rates because it allows negative asset values. The option price and delta under the SABR model are typically obtained via asymptotic implied volatility approximation, but these are often inaccurate and arbitrageable. Using a recently discovered price transition law, we propose a Gaussian quadrature integration scheme for price options under the normal SABR model. The compound Gaussian quadrature sum over only 49 points can calculate a very accurate price and delta that are arbitrage-free.", "published": "2023-01-07T07:47:09Z", "authors": ["Jaehyuk Choi", "Byoung Ki Seo"], "categories": ["q-fin.PR", "q-fin.CP", "q-fin.MF"], "doi": "", "_query": "housing price text listing", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2307.00492v2", "title": "Stochastic Approach for Price Optimization Problems with Decision-dependent Uncertainty", "abstract": "Price determination is a central research topic of revenue management in marketing. The important aspect in pricing is controlling the stochastic behavior of demand, and the previous studies have tackled price optimization problems with uncertainties. However, many of those studies assumed that uncertainties are independent of decision variables (i.e., prices) and did not consider situations where demand uncertainty depends on price. Although some price optimization studies have dealt with decision-dependent uncertainty, they make application-specific assumptions in order to obtain an optimal solution or an approximation solution. To handle a wider range of applications with decision-dependent uncertainty, we propose a general non-convex stochastic optimization formulation. This approach aims to maximize the expectation of a revenue function with respect to a random variable representing demand under a decision-dependent distribution. We derived an unbiased stochastic gradient estimator by using a well-tuned variance reduction parameter and used it for a projected stochastic gradient descent method to find a stationary point of our problem. We conducted synthetic experiments and simulation experiments with real data on a retail service application. The results show that the proposed method outputs solutions with higher total revenues than baselines.", "published": "2023-07-02T06:46:03Z", "authors": ["Yuya Hikima", "Akiko Takeda"], "categories": ["math.OC"], "doi": "", "_query": "housing price text listing", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/1110.0403v2", "title": "Pricing and Semimartingale Representations of Vulnerable Contingent Claims in Regime-Switching Markets", "abstract": "Using a suitable change of probability measure, we obtain a novel Poisson series representation for the arbitrage- free price process of vulnerable contingent claims in a regime-switching market driven by an underlying continuous- time Markov process. As a result of this representation, along with a short-time asymptotic expansion of the claim's price process, we develop an efficient method for pricing claims whose payoffs may depend on the full path of the underlying Markov chain. The proposed approach is applied to price not only simple European claims such as defaultable bonds, but also a new type of path-dependent claims that we term self-decomposable, as well as the important class of vulnerable call and put options on a stock. We provide a detailed error analysis and illustrate the accuracy and computational complexity of our method on several market traded instruments, such as defaultable bond prices, barrier options, and vulnerable call options. Using again our Poisson series representation, we show differentiability in time of the pre-default price function of European vulnerable claims, which enables us to rigorously deduce Feynman-Kac representations for the pre-default pricing function and new semimartingale representations for the price process of the vulnerable claim under both risk-neutral and objective probability measures.", "published": "2011-10-03T16:22:29Z", "authors": ["Agostino Capponi", "Jose Figueroa-Lopez", "Jeffrey Nisen"], "categories": ["q-fin.CP", "q-fin.PM", "q-fin.PR", "q-fin.RM"], "doi": "", "_query": "housing price text listing", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/1803.06917v1", "title": "Universal features of price formation in financial markets: perspectives from Deep Learning", "abstract": "Using a large-scale Deep Learning approach applied to a high-frequency database containing billions of electronic market quotes and transactions for US equities, we uncover nonparametric evidence for the existence of a universal and stationary price formation mechanism relating the dynamics of supply and demand for a stock, as revealed through the order book, to subsequent variations in its market price. We assess the model by testing its out-of-sample predictions for the direction of price moves given the history of price and order flow, across a wide range of stocks and time periods. The universal price formation model is shown to exhibit a remarkably stable out-of-sample prediction accuracy across time, for a wide range of stocks from different sectors. Interestingly, these results also hold for stocks which are not part of the training sample, showing that the relations captured by the model are universal and not asset-specific. The universal model --- trained on data from all stocks --- outperforms, in terms of out-of-sample prediction accuracy, asset-specific linear and nonlinear models trained on time series of any given stock, showing that the universal nature of price formation weighs in favour of pooling together financial data from various stocks, rather than designing asset- or sector-specific models as commonly done. Standard data normalizations based on volatility, price level or average spread, or partitioning the training data into sectors or categories such as large/small tick stocks, do not improve training results. On the other hand, inclusion of price and order flow history over many past observations is shown to improve forecasting performance, showing evidence of path-dependence in price dynamics.", "published": "2018-03-19T13:46:37Z", "authors": ["Justin Sirignano", "Rama Cont"], "categories": ["q-fin.ST", "q-fin.TR", "stat.ML"], "doi": "", "_query": "housing price text listing", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2308.02276v1", "title": "Optimal Liquidation with Conditions on Minimum Price", "abstract": "The classical optimal trading problem is the closure of a position in an asset over a time interval; the trader maximizes an expected utility under the constraint that the position be fully closed by terminal time. Since the asset price is stochastic, the liquidation constraint may be too restrictive; the trader may want to relax it or slow down/stop trading depending on price behavior. We consider two additional parameters that serve these purposes within the Almgren-Chriss framework: a binary valued process $I$ that prescribes when trading takes place and a measurable set $S$ that prescribes when full liquidation is required. We give four examples for $S$ and $I$ which are defined in terms of a lower bound for the price process. The terminal cost of the control problem is $\\infty$ over $S$ representing the liquidation constraint. The permanent price impact parameter enters the problem as the negative part of the terminal cost over $S^c$. $I$ modifies the running cost. A terminal cost that can take negative values implies 1) the backward stochastic differential equation (BSDE) associated with the value function of the control problem can explode to $-\\infty$ backward in time and 2) existence results on minimal supersolutions of BSDE with singular terminal values and monotone drivers are not directly applicable. A key part of the solution is an assumption that balances market volume and the permanent price impact parameter and a lower bound on the BSDE based on this assumption. When liquidation costs are quadratic, the problem is convex and, under a general filtration, the minimal supersolution of the BSDE gives the value function and the optimal control. For the non-quadratic case, we assume a stochastic volatility model and focus on choices of $I$ and $S$ that are Markovian or can be broken into Markovian pieces. These give PDE/PDE-system representations for the value functions.", "published": "2023-08-04T12:00:53Z", "authors": ["Mervan Aksu", "Alexandre Popier", "Ali Devin Sezer"], "categories": ["math.PR"], "doi": "", "_query": "housing price text listing", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2405.00235v1", "title": "Blockchain Price vs. Quantity Controls", "abstract": "This paper studies the optimal transaction fee mechanisms for blockchains, focusing on the distinction between price-based ($\\mathcal{P}$) and quantity-based ($\\mathcal{Q}$) controls. By analyzing factors such as demand uncertainty, validator costs, cryptocurrency price fluctuations, price elasticity of demand, and levels of decentralization, we establish criteria that determine the selection of transaction fee mechanisms. We present a model framed around a Nash bargaining game, exploring how blockchain designers and validators negotiate fee structures to balance network welfare with profitability. Our findings suggest that the choice between $\\mathcal{P}$ and $\\mathcal{Q}$ mechanisms depends critically on the blockchain's specific technical and economic features. The study concludes that no single mechanism suits all contexts and highlights the potential for hybrid approaches that adaptively combine features of both $\\mathcal{P}$ and $\\mathcal{Q}$ to meet varying demands and market conditions.", "published": "2024-04-30T23:03:00Z", "authors": ["Abdoulaye Ndiaye"], "categories": ["econ.GN"], "doi": "10.1007/978-3-031-78676-1_17", "_query": "housing price text listing", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2007.02553v4", "title": "On robust fundamental theorems of asset pricing in discrete time", "abstract": "This paper is devoted to a study of robust fundamental theorems of asset pricing in discrete time and finite horizon settings. Uncertainty is modelled by a (possibly uncountable) family of price processes on the same probability space. Our technical assumption is the continuity of the price processes with respect to uncertain parameters. In this setting, we introduce a new topological framework which allows us to use the classical arguments in arbitrage pricing theory involving $L^p$ spaces, the Hahn-Banach separation theorem and other tools from functional analysis. The first result is the equivalence of a ``no robust arbitrage\" condition and the existence of a new ``robust pricing system\". The second result shows superhedging dualities and the existence of superhedging strategies without restrictive conditions on payoff functions, unlike other related studies. The third result discusses completeness in the present robust setting. When other options are available for static trading, we could reduce the set of robust pricing systems and hence the superhedging prices.", "published": "2020-07-06T06:47:10Z", "authors": ["Huy N. Chau"], "categories": ["q-fin.MF"], "doi": "", "_query": "housing price text listing", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/1708.02411v1", "title": "Nonlinear price impact from linear models", "abstract": "The impact of trades on asset prices is a crucial aspect of market dynamics for academics, regulators and practitioners alike. Recently, universal and highly nonlinear master curves were observed for price impacts aggregated on all intra-day scales [1]. Here we investigate how well these curves, their scaling, and the underlying return dynamics are captured by linear \"propagator\" models. We find that the classification of trades as price-changing versus non-price-changing can explain the price impact nonlinearities and short-term return dynamics to a very high degree. The explanatory power provided by the change indicator in addition to the order sign history increases with increasing tick size. To obtain these results, several long-standing technical issues for model calibration and -testing are addressed. We present new spectral estimators for two- and three-point cross-correlations, removing the need for previously used approximations. We also show when calibration is unbiased and how to accurately reveal previously overlooked biases. Therefore, our results contribute significantly to understanding both recent empirical results and the properties of a popular class of impact models.", "published": "2017-08-08T09:03:32Z", "authors": ["Felix Patzelt", "Jean-Philippe Bouchaud"], "categories": ["q-fin.TR"], "doi": "10.1088/1742-5468/aa9335", "_query": "housing price text listing", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/1905.02666v5", "title": "Option Pricing using Quantum Computers", "abstract": "We present a methodology to price options and portfolios of options on a gate-based quantum computer using amplitude estimation, an algorithm which provides a quadratic speedup compared to classical Monte Carlo methods. The options that we cover include vanilla options, multi-asset options and path-dependent options such as barrier options. We put an emphasis on the implementation of the quantum circuits required to build the input states and operators needed by amplitude estimation to price the different option types. Additionally, we show simulation results to highlight how the circuits that we implement price the different option contracts. Finally, we examine the performance of option pricing circuits on quantum hardware using the IBM Q Tokyo quantum device. We employ a simple, yet effective, error mitigation scheme that allows us to significantly reduce the errors arising from noisy two-qubit gates.", "published": "2019-05-07T16:14:09Z", "authors": ["Nikitas Stamatopoulos", "Daniel J. Egger", "Yue Sun", "Christa Zoufal", "Raban Iten", "Ning Shen", "Stefan Woerner"], "categories": ["quant-ph"], "doi": "10.22331/q-2020-07-06-291", "_query": "housing price text listing", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2505.17203v2", "title": "Transfer Faster, Price Smarter: Minimax Dynamic Pricing under Cross-Market Preference Shift", "abstract": "We study contextual dynamic pricing when a target market can leverage K auxiliary markets -- offline logs or concurrent streams -- whose mean utilities differ by a structured preference shift. We propose Cross-Market Transfer Dynamic Pricing (CM-TDP), the first algorithm that provably handles such model-shift transfer and delivers minimax-optimal regret for both linear and non-parametric utility models. For linear utilities of dimension d, where the difference between source- and target-task coefficients is $s_{0}$-sparse, CM-TDP attains regret $\\tilde{O}((d*K^{-1}+s_{0})\\log T)$. For nonlinear demand residing in a reproducing kernel Hilbert space with effective dimension $α$, complexity $β$ and task-similarity parameter $H$, the regret becomes $\\tilde{O}\\!(K^{-2αβ/(2αβ+1)}T^{1/(2αβ+1)} + H^{2/(2α+1)}T^{1/(2α+1)})$, matching information-theoretic lower bounds up to logarithmic factors. The RKHS bound is the first of its kind for transfer pricing and is of independent interest. Extensive simulations show up to 50% lower cumulative regret and 5 times faster learning relative to single-market pricing baselines. By bridging transfer learning, robust aggregation, and revenue optimization, CM-TDP moves toward pricing systems that transfer faster, price smarter.", "published": "2025-05-22T18:18:17Z", "authors": ["Yi Zhang", "Elynn Chen", "Yujun Yan"], "categories": ["stat.ME", "cs.LG", "stat.AP"], "doi": "", "_query": "housing price text listing", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/1511.01132v1", "title": "Liquid Price of Anarchy", "abstract": "Incorporating budget constraints into the analysis of auctions has become increasingly important, as they model practical settings more accurately. The social welfare function, which is the standard measure of efficiency in auctions, is inadequate for settings with budgets, since there may be a large disconnect between the value a bidder derives from obtaining an item and what can be liquidated from her. The Liquid Welfare objective function has been suggested as a natural alternative for settings with budgets. Simple auctions, like simultaneous item auctions, are evaluated by their performance at equilibrium using the Price of Anarchy (PoA) measure -- the ratio of the objective function value of the optimal outcome to the worst equilibrium. Accordingly, we evaluate the performance of simultaneous item auctions in budgeted settings by the Liquid Price of Anarchy (LPoA) measure -- the ratio of the optimal Liquid Welfare to the Liquid Welfare obtained in the worst equilibrium. Our main result is that the LPoA for mixed Nash equilibria is bounded by a constant when bidders are additive and items can be divided into sufficiently many discrete parts. Our proofs are robust, and can be extended to achieve similar bounds for simultaneous second price auctions as well as Bayesian Nash equilibria. For pure Nash equilibria, we establish tight bounds on the LPoA for the larger class of fractionally-subadditive valuations. To derive our results, we develop a new technique in which some bidders deviate (surprisingly) toward a non-optimal solution. In particular, this technique does not fit into the smoothness framework.", "published": "2015-11-03T21:58:05Z", "authors": ["Yossi Azar", "Michal Feldman", "Nick Gravin", "Alan Roytman"], "categories": ["cs.GT"], "doi": "", "_query": "housing price text listing", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/1807.07425v2", "title": "Clinical Text Classification with Rule-based Features and Knowledge-guided Convolutional Neural Networks", "abstract": "Clinical text classification is an important problem in medical natural language processing. Existing studies have conventionally focused on rules or knowledge sources-based feature engineering, but only a few have exploited effective feature learning capability of deep learning methods. In this study, we propose a novel approach which combines rule-based features and knowledge-guided deep learning techniques for effective disease classification. Critical Steps of our method include identifying trigger phrases, predicting classes with very few examples using trigger phrases and training a convolutional neural network with word embeddings and Unified Medical Language System (UMLS) entity embeddings. We evaluated our method on the 2008 Integrating Informatics with Biology and the Bedside (i2b2) obesity challenge. The results show that our method outperforms the state of the art methods.", "published": "2018-07-17T20:00:41Z", "authors": ["Liang Yao", "Chengsheng Mao", "Yuan Luo"], "categories": ["cs.CL"], "doi": "", "_query": "housing price text listing", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2510.21044v1", "title": "House Thermal Model Estimation: Robustness Across Seasons and Setpoints", "abstract": "Achieving the flexibility from house heating, cooling, and ventilation systems (HVAC) has the potential to enable large-scale demand response by aggregating HVAC load adjustments across many homes. This demand response strategy helps distribution grid to flexibly ramp-up or ramp-down local load demand so that it can optimally match the bulk power system generation profile. However, achieving this capability requires house thermal models that are both computationally efficient and robust to operating conditions. In this work, parameters of the Resistance-Capacitance (RC) network thermal model for houses are estimated using three optimization algorithms: Nonlinear Least Squares (NLS), Batch Estimation (BE), and Maximum Likelihood Estimation (MLE). The resulting models are evaluated through a Forward-Simulation across four different seasons and three setpoints. The results illustrate a principled way of selecting reduced order models and estimation methods with respect to the robustness offered to seasonal and setpoint variations in training-testing datasets", "published": "2025-10-23T22:58:14Z", "authors": ["Kunal Shankar", "Ninad Gaikwad", "Anamika Dubey"], "categories": ["eess.SY"], "doi": "", "_query": "housing price text listing", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2007.08726v1", "title": "Tight Bounds for the Price of Anarchy and Stability in Sequential Transportation Games", "abstract": "In this paper, we analyze a transportation game first introduced by Fotakis, Gourvès, and Monnot in 2017, where players want to be transported to a common destination as quickly as possible and, in order to achieve this goal, they have to choose one of the available buses. We introduce a sequential version of this game and provide bounds for the Sequential Price of Stability and the Sequential Price of Anarchy in both metric and non-metric instances, considering three social cost functions: the total traveled distance by all buses, the maximum distance traveled by a bus, and the sum of the distances traveled by all players (a new social cost function that we introduce). Finally, we analyze the Price of Stability and the Price of Anarchy for this new function in simultaneous transportation games.", "published": "2020-07-17T02:40:36Z", "authors": ["Francisco J. M. da Silva", "Flávio K. Miyazawa", "Ieremies V. F. Romero", "Rafael C. S. Schouery"], "categories": ["cs.GT"], "doi": "", "_query": "housing price text listing", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/1910.04879v1", "title": "Predicting Auction Price of Vehicle License Plate with Deep Residual Learning", "abstract": "Due to superstition, license plates with desirable combinations of characters are highly sought after in China, fetching prices that can reach into the millions in government-held auctions. Despite the high stakes involved, there has been essentially no attempt to provide price estimates for license plates. We present an end-to-end neural network model that simultaneously predict the auction price, gives the distribution of prices and produces latent feature vectors. While both types of neural network architectures we consider outperform simpler machine learning methods, convolutional networks outperform recurrent networks for comparable training time or model complexity. The resulting model powers our online price estimator and search engine.", "published": "2019-10-08T16:38:06Z", "authors": ["Vinci Chow"], "categories": ["cs.CV", "cs.LG", "econ.GN"], "doi": "10.1007/978-3-030-26142-9_16", "_query": "housing price text listing", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2204.01435v1", "title": "The Potential Method For Price-Formation Models", "abstract": "We consider the mean-field game price formation model introduced by Gomes and Saúde. In this MFG model, agents trade a commodity whose supply can be deterministic or stochastic. Agents maximize profit, taking into account current and future prices. The balance between supply and demand determines the price. We introduce a potential function that converts the MFG into a convex variational problem. This variational formulation is particularly suitable for machine learning approaches. Here, we use a recurrent neural network to solve this problem. In the last section of the paper, we compare our results with known analytical solutions.", "published": "2022-04-04T12:37:45Z", "authors": ["Yuri Ashrafyan", "Tigran Bakaryan", "Diogo Gomes", "Julian Gutierrez"], "categories": ["math.NA"], "doi": "", "_query": "housing price text listing", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/1803.02486v1", "title": "Pricing index options by static hedging under finite liquidity", "abstract": "We develop a model for indifference pricing in derivatives markets where price quotes have bid-ask spreads and finite quantities. The model quantifies the dependence of the prices and hedging portfolios on an investor's beliefs, risk preferences and financial position as well as on the price quotes. Computational techniques of convex optimisation allow for fast computation of the hedging portfolios and prices as well as sensitivities with respect to various model parameters. We illustrate the techniques by pricing and hedging of exotic derivatives on S&P index using call and put options, forward contracts and cash as the hedging instruments. The optimized static hedges provide good approximations of the options payouts and the spreads between indifference selling and buying prices are quite narrow as compared with the spread between super- and subhedging prices.", "published": "2018-03-07T00:25:02Z", "authors": ["John Armstrong", "Teemu Pennanen", "Udomsak Rakwongwan"], "categories": ["q-fin.PR"], "doi": "", "_query": "housing price text listing", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2207.06442v1", "title": "Kinematics and Mass Distributions for Non-Spherical Deprojected Sérsic Density Profiles and Applications to Multi-Component Galactic Systems", "abstract": "Using kinematics to decompose galaxies' mass profiles, including the dark matter contribution, often requires parameterization of the baryonic mass distribution based on ancillary information. One such model choice is a deprojected Sérsic profile with an assumed intrinsic geometry. The case of flattened, deprojected Sérsic models has previously been applied to flattened bulges in local star-forming galaxies (SFGs), but can also be used to describe the thick, turbulent disks in distant SFGs. Here we extend this previous work that derived density ($ρ$) and circular velocity ($v_{\\rm circ}$) curves by additionally calculating the spherically-enclosed 3D mass profiles ($M_{\\rm sph}$). Using these profiles, we compare the projected and 3D mass distributions, quantify the differences between the projected and 3D half-mass radii ($R_{\\rm e}; r_{\\rm 1/2,mass,3D}$), and present virial coefficients relating $v_{\\rm circ}(R)$ and $M_{\\rm sph}(<r=R)$ or $M_{\\rm tot}$. We then quantify differences between mass fraction estimators for multi-component systems, particularly for dark matter fractions, and consider the compound effects of measuring dark matter fractions at the projected versus 3D half-mass radii. While the fraction estimators produce only minor differences, using different aperture radius definitions can strongly impact the inferred dark matter fraction. As pressure support is important in analysis of gas kinematics (particularly at high redshifts), we also calculate the self-consistent pressure support correction profiles, which generally predict less pressure support than for the self-gravitating disk case. These results have implications for comparisons between simulation and observational measurements, and for the interpretation of SFG kinematics at high redshifts. A set of precomputed tables and the code to calculate the profiles are made publicly available. [Abridged]", "published": "2022-07-13T18:00:13Z", "authors": ["S. H. Price", "H. Übler", "N. M. Förster Schreiber", "P. T. de Zeeuw", "A. Burkert", "R. Genzel", "L. J. Tacconi", "R. I. Davies", "C. P. Price"], "categories": ["astro-ph.GA"], "doi": "10.1051/0004-6361/202244143", "_query": "housing price text listing", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/1506.07504v1", "title": "Objective Variables for Probabilistic Revenue Maximization in Second-Price Auctions with Reserve", "abstract": "Many online companies sell advertisement space in second-price auctions with reserve. In this paper, we develop a probabilistic method to learn a profitable strategy to set the reserve price. We use historical auction data with features to fit a predictor of the best reserve price. This problem is delicate - the structure of the auction is such that a reserve price set too high is much worse than a reserve price set too low. To address this we develop objective variables, a new framework for combining probabilistic modeling with optimal decision-making. Objective variables are \"hallucinated observations\" that transform the revenue maximization task into a regularized maximum likelihood estimation problem, which we solve with an EM algorithm. This framework enables a variety of prediction mechanisms to set the reserve price. As examples, we study objective variable methods with regression, kernelized regression, and neural networks on simulated and real data. Our methods outperform previous approaches both in terms of scalability and profit.", "published": "2015-06-24T19:20:18Z", "authors": ["Maja R. Rudolph", "Joseph G. Ellis", "David M. Blei"], "categories": ["stat.ML", "cs.AI", "cs.GT", "cs.LG", "stat.AP"], "doi": "", "_query": "housing price text listing", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2205.00974v1", "title": "Cross Cryptocurrency Relationship Mining for Bitcoin Price Prediction", "abstract": "Blockchain finance has become a part of the world financial system, most typically manifested in the attention to the price of Bitcoin. However, a great deal of work is still limited to using technical indicators to capture Bitcoin price fluctuation, with little consideration of historical relationships and interactions between related cryptocurrencies. In this work, we propose a generic Cross-Cryptocurrency Relationship Mining module, named C2RM, which can effectively capture the synchronous and asynchronous impact factors between Bitcoin and related Altcoins. Specifically, we utilize the Dynamic Time Warping algorithm to extract the lead-lag relationship, yielding Lead-lag Variance Kernel, which will be used for aggregating the information of Altcoins to form relational impact factors. Comprehensive experimental results demonstrate that our C2RM can help existing price prediction methods achieve significant performance improvement, suggesting the effectiveness of Cross-Cryptocurrency interactions on benefitting Bitcoin price prediction.", "published": "2022-04-28T02:39:42Z", "authors": ["Panpan Li", "Shengbo Gong", "Shaocong Xu", "Jiajun Zhou", "Yu Shanqing", "Qi Xuan"], "categories": ["q-fin.ST", "cs.AI", "cs.LG"], "doi": "", "_query": "housing price text listing", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/1105.5503v1", "title": "Pricing, liquidity and the control of dynamic systems in finance and economics", "abstract": "The paper discusses various practical consequences of treating economics and finance as an inherently dynamic and chaotic system. On the theoretical side this looks at the general applicability of the market-making pricing approach to economics in general. The paper also discuses the consequences of the endogenous creation of liquidity and the role of liquidity as a state variable. On the practical side, proposals are made for reducing chaotic behaviour in both housing markets and stock markets.", "published": "2011-05-27T09:26:08Z", "authors": ["Geoff Willis"], "categories": ["q-fin.GN"], "doi": "", "_query": "housing price text listing", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2405.04180v1", "title": "Sora Detector: A Unified Hallucination Detection for Large Text-to-Video Models", "abstract": "The rapid advancement in text-to-video (T2V) generative models has enabled the synthesis of high-fidelity video content guided by textual descriptions. Despite this significant progress, these models are often susceptible to hallucination, generating contents that contradict the input text, which poses a challenge to their reliability and practical deployment. To address this critical issue, we introduce the SoraDetector, a novel unified framework designed to detect hallucinations across diverse large T2V models, including the cutting-edge Sora model. Our framework is built upon a comprehensive analysis of hallucination phenomena, categorizing them based on their manifestation in the video content. Leveraging the state-of-the-art keyframe extraction techniques and multimodal large language models, SoraDetector first evaluates the consistency between extracted video content summary and textual prompts, then constructs static and dynamic knowledge graphs (KGs) from frames to detect hallucination both in single frames and across frames. Sora Detector provides a robust and quantifiable measure of consistency, static and dynamic hallucination. In addition, we have developed the Sora Detector Agent to automate the hallucination detection process and generate a complete video quality report for each input video. Lastly, we present a novel meta-evaluation benchmark, T2VHaluBench, meticulously crafted to facilitate the evaluation of advancements in T2V hallucination detection. Through extensive experiments on videos generated by Sora and other large T2V models, we demonstrate the efficacy of our approach in accurately detecting hallucinations. The code and dataset can be accessed via GitHub.", "published": "2024-05-07T10:39:14Z", "authors": ["Zhixuan Chu", "Lei Zhang", "Yichen Sun", "Siqiao Xue", "Zhibo Wang", "Zhan Qin", "Kui Ren"], "categories": ["cs.LG", "cs.CV"], "doi": "", "_query": "housing price text listing", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/1907.11984v1", "title": "Investigating the effect of competitiveness power in estimating the average weighted price in electricity market", "abstract": "This paper evaluates the impact of the power extent on price in the electricity market. The competitiveness extent of the electricity market during specific times in a day is considered to achieve this. Then, the effect of competitiveness extent on the forecasting precision of the daily power price is assessed. A price forecasting model based on multi-layer perception via back propagation with the Levenberg-Marquardt mechanism is used. The Residual Supply Index (RSI) and other variables that affect prices are used as inputs to the model to evaluate the market competitiveness. The results show that using market power indices as inputs helps to increase forecasting accuracy. Thus, the competitiveness extent of the market power in different daily time periods is a notable variable in price formation. Moreover, market players cannot ignore the explanatory power of market power in price forecasting. In this research, the real data of the electricity market from 2013 is used and the main source of data is the Grid Management Company in Iran.", "published": "2019-07-27T22:13:38Z", "authors": ["Naser Rostamni", "Tarik A. Rashid"], "categories": ["q-fin.ST", "cs.LG"], "doi": "10.1016/j.tej.2019.106628", "_query": "housing price text listing", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/1806.03590v1", "title": "Cross-Lingual Task-Specific Representation Learning for Text Classification in Resource Poor Languages", "abstract": "Neural network models have shown promising results for text classification. However, these solutions are limited by their dependence on the availability of annotated data. The prospect of leveraging resource-rich languages to enhance the text classification of resource-poor languages is fascinating. The performance on resource-poor languages can significantly improve if the resource availability constraints can be offset. To this end, we present a twin Bidirectional Long Short Term Memory (Bi-LSTM) network with shared parameters consolidated by a contrastive loss function (based on a similarity metric). The model learns the representation of resource-poor and resource-rich sentences in a common space by using the similarity between their assigned annotation tags. Hence, the model projects sentences with similar tags closer and those with different tags farther from each other. We evaluated our model on the classification tasks of sentiment analysis and emoji prediction for resource-poor languages - Hindi and Telugu and resource-rich languages - English and Spanish. Our model significantly outperforms the state-of-the-art approaches in both the tasks across all metrics.", "published": "2018-06-10T06:09:57Z", "authors": ["Nurendra Choudhary", "Rajat Singh", "Manish Shrivastava"], "categories": ["cs.CL"], "doi": "", "_query": "housing price text listing", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/1801.02702v3", "title": "Revealed Price Preference: Theory and Empirical Analysis", "abstract": "To determine the welfare implications of price changes in demand data, we introduce a revealed preference relation over prices. We show that the absence of cycles in this relation characterizes a consumer who trades off the utility of consumption against the disutility of expenditure. Our model can be applied whenever a consumer's demand over a strict subset of all available goods is being analyzed; it can also be extended to settings with discrete goods and nonlinear prices. To illustrate its use, we apply our model to a single-agent data set and to a data set with repeated cross-sections. We develop a novel test of linear hypotheses on partially identified parameters to estimate the proportion of the population who are revealed better off due to a price change in the latter application. This new technique can be used for nonparametric counterfactual analysis more broadly.", "published": "2018-01-08T22:22:30Z", "authors": ["Rahul Deb", "Yuichi Kitamura", "John K. -H. Quah", "Jörg Stoye"], "categories": ["econ.EM", "econ.TH"], "doi": "10.1093/restud/rdac041", "_query": "housing price text listing", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2004.11697v2", "title": "A Time Series Analysis-Based Stock Price Prediction Using Machine Learning and Deep Learning Models", "abstract": "Prediction of future movement of stock prices has always been a challenging task for the researchers. While the advocates of the efficient market hypothesis (EMH) believe that it is impossible to design any predictive framework that can accurately predict the movement of stock prices, there are seminal work in the literature that have clearly demonstrated that the seemingly random movement patterns in the time series of a stock price can be predicted with a high level of accuracy. Design of such predictive models requires choice of appropriate variables, right transformation methods of the variables, and tuning of the parameters of the models. In this work, we present a very robust and accurate framework of stock price prediction that consists of an agglomeration of statistical, machine learning and deep learning models. We use the daily stock price data, collected at five minutes interval of time, of a very well known company that is listed in the National Stock Exchange (NSE) of India. The granular data is aggregated into three slots in a day, and the aggregated data is used for building and training the forecasting models. We contend that the agglomerative approach of model building that uses a combination of statistical, machine learning, and deep learning approaches, can very effectively learn from the volatile and random movement patterns in a stock price data. We build eight classification and eight regression models based on statistical and machine learning approaches. In addition to these models, a deep learning regression model using a long-and-short-term memory (LSTM) network is also built. Extensive results have been presented on the performance of these models, and the results are critically analyzed.", "published": "2020-04-17T19:41:22Z", "authors": ["Sidra Mehtab", "Jaydip Sen"], "categories": ["q-fin.ST", "cs.LG", "stat.ML"], "doi": "10.1504/IJBFMI.2020.115691", "_query": "housing price text listing", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/1507.08042v1", "title": "Randomization beats Second Price as a Prior-Independent Auction", "abstract": "Designing revenue optimal auctions for selling an item to $n$ symmetric bidders is a fundamental problem in mechanism design. Myerson (1981) shows that the second price auction with an appropriate reserve price is optimal when bidders' values are drawn i.i.d. from a known regular distribution. A cornerstone in the prior-independent revenue maximization literature is a result by Bulow and Klemperer (1996) showing that the second price auction without a reserve achieves $(n-1)/n$ of the optimal revenue in the worst case. We construct a randomized mechanism that strictly outperforms the second price auction in this setting. Our mechanism inflates the second highest bid with a probability that varies with $n$. For two bidders we improve the performance guarantee from $0.5$ to $0.512$ of the optimal revenue. We also resolve a question in the design of revenue optimal mechanisms that have access to a single sample from an unknown distribution. We show that a randomized mechanism strictly outperforms all deterministic mechanisms in terms of worst case guarantee.", "published": "2015-07-29T07:31:56Z", "authors": ["Hu Fu", "Nicole Immolica", "Brendan Lucier", "Philipp Strack"], "categories": ["cs.GT"], "doi": "", "_query": "housing price text listing", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/cond-mat/0011506v1", "title": "Option Pricing and Hedging with Temporal Correlations", "abstract": "We consider the problem of option pricing and hedging when stock returns are correlated in time. Within a quadratic-risk minimisation scheme, we obtain a general formula, valid for weakly correlated non-Gaussian processes. We show that for Gaussian price increments, the correlations are irrelevant, and the Black-Scholes formula holds with the volatility of the price increments on the scale of the re-hedging. For non-Gaussian processes, further non trivial corrections to the `smile' are brought about by the correlations, even when the hedge is the Black-Scholes Delta-hedge. We introduce a compact notation which eases the computations and could be of use to deal with more complicated models.", "published": "2000-11-29T17:52:22Z", "authors": ["Lorenzo Cornalba", "Jean-Philippe Bouchaud", "Marc Potters"], "categories": ["cond-mat"], "doi": "", "_query": "housing price text listing", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2310.18646v1", "title": "Predicting Agricultural Commodities Prices with Machine Learning: A Review of Current Research", "abstract": "Agricultural price prediction is crucial for farmers, policymakers, and other stakeholders in the agricultural sector. However, it is a challenging task due to the complex and dynamic nature of agricultural markets. Machine learning algorithms have the potential to revolutionize agricultural price prediction by improving accuracy, real-time prediction, customization, and integration. This paper reviews recent research on machine learning algorithms for agricultural price prediction. We discuss the importance of agriculture in developing countries and the problems associated with crop price falls. We then identify the challenges of predicting agricultural prices and highlight how machine learning algorithms can support better prediction. Next, we present a comprehensive analysis of recent research, discussing the strengths and weaknesses of various machine learning techniques. We conclude that machine learning has the potential to revolutionize agricultural price prediction, but further research is essential to address the limitations and challenges associated with this approach.", "published": "2023-10-28T09:09:29Z", "authors": ["Nhat-Quang Tran", "Anna Felipe", "Thanh Nguyen Ngoc", "Tom Huynh", "Quang Tran", "Arthur Tang", "Thuy Nguyen"], "categories": ["cs.AI", "cs.LG"], "doi": "", "_query": "housing price text listing", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2408.08738v1", "title": "The Blessing of Strategic Customers in Personalized Pricing", "abstract": "We consider a feature-based personalized pricing problem in which the buyer is strategic: given the seller's pricing policy, the buyer can augment the features that they reveal to the seller to obtain a low price for the product. We model the seller's pricing problem as a stochastic program over an infinite-dimensional space of pricing policies where the radii by which the buyer can perturb the features are strictly positive. We establish that the sample average approximation of this problem is asymptotically consistent; that is, we prove that the objective value of the sample average approximation converges almost surely to the objective value of the stochastic problem as the number of samples tends to infinity under mild technical assumptions. This consistency guarantee thus shows that incorporating strategic consumer behavior into a data-driven pricing problem can, in addition to making the pricing problem more realistic, also help prevent overfitting.", "published": "2024-08-16T13:36:21Z", "authors": ["Zhi Chen", "Bradley Sturt", "Weijun Xie"], "categories": ["math.OC"], "doi": "", "_query": "housing price text listing", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/1808.07621v1", "title": "Latent Dirichlet Allocation for Internet Price War", "abstract": "Internet market makers are always facing intense competitive environment, where personalized price reductions or discounted coupons are provided for attracting more customers. Participants in such a price war scenario have to invest a lot to catch up with other competitors. However, such a huge cost of money may not always lead to an improvement of market share. This is mainly due to a lack of information about others' strategies or customers' willingness when participants develop their strategies. In order to obtain this hidden information through observable data, we study the relationship between companies and customers in the Internet price war. Theoretically, we provide a formalization of the problem as a stochastic game with imperfect and incomplete information. Then we develop a variant of Latent Dirichlet Allocation (LDA) to infer latent variables under the current market environment, which represents the preferences of customers and strategies of competitors. To our best knowledge, it is the first time that LDA is applied to game scenario. We conduct simulated experiments where our LDA model exhibits a significant improvement on finding strategies in the Internet price war by including all available market information of the market maker's competitors. And the model is applied to an open dataset for real business. Through comparisons on the likelihood of prediction for users' behavior and distribution distance between inferred opponent's strategy and the real one, our model is shown to be able to provide a better understanding for the market environment. Our work marks a successful learning method to infer latent information in the environment of price war by the LDA modeling, and sets an example for related competitive applications to follow.", "published": "2018-08-23T03:39:52Z", "authors": ["Chenchen Li", "Xiang Yan", "Xiaotie Deng", "Yuan Qi", "Wei Chu", "Le Song", "Junlong Qiao", "Jianshan He", "Junwu Xiong"], "categories": ["cs.AI"], "doi": "", "_query": "housing price text listing", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/1310.4142v2", "title": "Quantum harmonic oscillator in option pricing", "abstract": "The Black-Scholes model anticipates rather well the observed prices for options in the case of a strike price that is not too far from the current price of the underlying asset. Some useful extensions can be obtained by an adequate modification of the coefficients in the Black-Scholes equation. We investigate from a mathematical point of view an extension directly related to the quantum harmonic oscillator. In the considered case, the solution is the sum of a series involving the Hermite-Gauss functions. A finite-dimensional version is obtained by using a finite oscillator and the Harper functions. This simplified model keeps the essential characteristics of the continuous one and uses finite sums instead of series and integrals.", "published": "2013-10-15T18:44:04Z", "authors": ["Liviu-Adrian Cotfas", "Nicolae Cotfas"], "categories": ["q-fin.CP", "math-ph", "quant-ph"], "doi": "", "_query": "housing price text listing", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2501.03114v1", "title": "Environmental Policy in General Equilibrium under Market Power and Price Discrimination", "abstract": "This study constructs a novel analytical general equilibrium model to compare environmental policies in a setting where oligopolistic energy firms engage in third-degree price discrimination across residential consumers and industrial firms. Closed-form solutions demonstrate the impact on prices and quantities. The resulting welfare change is decomposed across three distortions: output, price discrimination, and externality. This study finds that the output distortion and price discrimination welfare effects generally move in opposite directions under policies such as an emission tax or a two-part instrument. Numerical analysis compares policies and finds scenarios where the output distortion and price discrimination welfare changes fully offset and thus leaves the net welfare gain of the externality correction. In this way, environmental policy can be designed to mitigate output distortion welfare concerns when firms have market power.", "published": "2025-01-06T16:21:19Z", "authors": ["Tengjiao Chen", "Daniel H. Karney"], "categories": ["econ.GN"], "doi": "", "_query": "housing price text listing", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2504.17113v1", "title": "Cybernetic Governance in a Coliving House", "abstract": "We report an 18-month field experiment in distributed digital institutions: a nine-bedroom Los Angeles coliving house that runs without managers, while sustaining 98% occupancy and below-market rents. Drawing on Elinor Ostrom's commons theory, we outline design principles and three digital mechanisms that form the institutional core: 1) A continuous-auction chore scheduler turns regenerative labor into a time-indexed points market; residents meet a 100-point monthly obligation by claiming tasks whose value rises linearly with neglect. 2) A pairwise-preference layer lets participants asynchronously reprioritize tasks, translating meta-governance into low-cognition spot inputs. 3) A symbolic \"hearts\" ledger tracks norm compliance through automated enforcement, lightweight challenges, and peer-awarded karma. Together, these mechanisms operationalize cybernetic principles--human sensing, machine bookkeeping, real-time feedback--while minimizing dependence on privileged roles. Our exploratory data (567 chore claims, 255 heart events, and 551 group purchases) show that such tooling can sustain reliable commons governance without continuous leadership, offering a transferable design palette for online communities, coliving houses, and other digitally mediated collectives.", "published": "2025-04-23T21:45:59Z", "authors": ["Daniel Kronovet", "Seth Frey", "Joseph DeSimone"], "categories": ["cs.CY", "cs.HC", "econ.GN"], "doi": "", "_query": "housing price text listing", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/1602.07628v1", "title": "The Invisible Hand of Laplace: the Role of Market Structure in Price Convergence and Oscillation", "abstract": "A fundamental question about a market is under what conditions, and then how rapidly, does price signaling cause price equilibration. Qualitatively, this ought to depend on how well-connected the market is. We address this question quantitatively for a certain class of Arrow-Debreu markets with continuous-time proportional tâtonnement dynamics. We show that the algebraic connectivity of the market determines the effectiveness of price signaling equilibration. This also lets us study the rate of external noise that a market can tolerate and still maintain near-equilibrium prices.", "published": "2016-02-24T18:21:04Z", "authors": ["Yuval Rabani", "Leonard J. Schulman"], "categories": ["econ.GN"], "doi": "10.1016/j.jmateco.2021.102475", "_query": "housing price text listing", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2209.15037v2", "title": "Quantitative Fundamental Theorem of Asset Pricing", "abstract": "In this paper we provide a quantitative analysis to the concept of arbitrage, that allows to deal with model uncertainty without imposing the no-arbitrage condition. In markets that admit ``small arbitrage\", we can still make sense of the problems of pricing and hedging. The pricing measures here will be such that asset price processes are close to being martingales, and the hedging strategies will need to cover some additional cost. We show a quantitative version of the Fundamental Theorem of Asset Pricing and of the Super-Replication Theorem. Finally, we study robustness of the amount of arbitrage and existence of respective pricing measures, showing stability of these concepts with respect to a strong adapted Wasserstein distance.", "published": "2022-09-29T18:22:47Z", "authors": ["Beatrice Acciaio", "Julio Backhoff", "Gudmund Pammer"], "categories": ["q-fin.MF"], "doi": "", "_query": "housing price text listing", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2402.03520v1", "title": "Sampling List Packings", "abstract": "We study the problem of approximately counting the number of list packings of a graph. The analogous problem for usual vertex coloring and list coloring has attracted a lot of attention. For list packing the setup is similar but we seek a full decomposition of the lists of colors into pairwise-disjoint proper list colorings. In particular, the existence of a list packing implies the existence of a list coloring. Recent works on list packing have focused on existence or extremal results of on the number of list packings, but here we turn to the algorithmic aspects of counting. In graphs of maximum degree $Δ$ and when the number of colors is at least $Ω(Δ^2)$, we give an FPRAS based on rapid mixing of a natural Markov chain (the Glauber dynamics) which we analyze with the path coupling technique. Some motivation for our work is the investigation of an atypical spin system, one where the number of spins for each vertex is much larger than the graph degree.", "published": "2024-02-05T21:08:17Z", "authors": ["Evan Camrud", "Ewan Davies", "Alex Karduna", "Holden Lee"], "categories": ["math.CO", "cs.DS"], "doi": "", "_query": "housing price text listing", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2405.10640v3", "title": "COMET: NFT Price Prediction with Wallet Profiling", "abstract": "As the non-fungible token (NFT) market flourishes, price prediction emerges as a pivotal direction for investors gaining valuable insight to maximize returns. However, existing works suffer from a lack of practical definitions and standardized evaluations, limiting their practical application. Moreover, the influence of users' multi-behaviour transactions that are publicly accessible on NFT price is still not explored and exhibits challenges. In this paper, we address these gaps by presenting a practical and hierarchical problem definition. This approach unifies both collection-level and token-level task and evaluation methods, which cater to varied practical requirements of investors. To further understand the impact of user behaviours on the variation of NFT price, we propose a general wallet profiling framework and develop a COmmunity enhanced Multi-bEhavior Transaction graph model, named COMET. COMET profiles wallets with a comprehensive view and considers the impact of diverse relations and interactions within the NFT ecosystem on NFT price variations, thereby improving prediction performance. Extensive experiments conducted in our deployed system demonstrate the superiority of COMET, underscoring its potential in the insight toolkit for NFT investors.", "published": "2024-05-17T09:06:09Z", "authors": ["Tianfu Wang", "Liwei Deng", "Chao Wang", "Jianxun Lian", "Yue Yan", "Nicholas Jing Yuan", "Qi Zhang", "Hui Xiong"], "categories": ["cs.SI"], "doi": "", "_query": "housing price text listing", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2106.06867v1", "title": "A Spatially Dependent Probabilistic Model for House Hunting in Ant Colonies", "abstract": "Ant species such as Temnothorax albipennis select a new nest site in a distributed fashion that, if modeled correctly, can serve as useful information for site selection algorithms for robotic swarms and other applications. Studying and replicating the ants' house hunting behavior will also illuminate useful distributed strategies that have evolved in nature. Many of the existing models of househunting behaviour for T. albipennis make the assumption that all candidate nest sites are equally distant from the ants' home nest, or that an ant has an equal probability of finding each candidate nest site. However, realistically this is not the case, as nests that are further away from the home nest and nests that are difficult to access are less likely to be found, even if they are of higher quality. We extend previous house-hunting models to account for a pairwise distance metric between nests, compare our results to those of real colonies, and use our results to examine the effects of house hunting in nests of different spatial orientations. Our incorporation of distances in the ant model appear to match empirical data in situations where a distance-quality tradeoff between nests is relevant. Furthermore, the model continues to be on par with previous house-hunting models in experiments where all candidate nests are equidistant from the home nest, as is typically assumed.", "published": "2021-06-12T21:42:51Z", "authors": ["Grace Cai", "Wendy Wu", "Wayne Zhao", "Jiajia Zhao", "Nancy Lynch"], "categories": ["cs.MA"], "doi": "", "_query": "housing price text listing", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/cond-mat/0309416v2", "title": "On the origin of power law tails in price fluctuations", "abstract": "In a recent Nature paper, Gabaix et al. \\cite{Gabaix03} presented a theory to explain the power law tail of price fluctuations. The main points of their theory are that volume fluctuations, which have a power law tail with exponent roughly -1.5, are modulated by the average market impact function, which describes the response of prices to transactions. They argue that the average market impact function follows a square root law, which gives power law tails for prices with exponent roughly -3. We demonstrate that the long-memory nature of order flow invalidates their statistical analysis of market impact, and present a more careful analysis that properly takes this into account. This makes it clear that the functional form of the average market impact function varies from market to market, and in some cases from stock to stock. In fact, for both the London Stock Exchange and the New York Stock Exchange the average market impact function grows much slower than a square root law; this implies that the exponent for price fluctuations predicted by modulations of volume fluctuations is much too big. We find that for LSE stocks the distribution of transaction volumes does not even have a power law tail. This makes it clear that volume fluctuations do not determine the power law tail of price returns.", "published": "2003-09-17T23:20:45Z", "authors": ["J. Doyne Farmer", "Fabrizio Lillo"], "categories": ["cond-mat.stat-mech", "q-fin.ST"], "doi": "", "_query": "housing price text listing", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/cond-mat/0112422v6", "title": "A quantitative model of trading and price formation in financial markets", "abstract": "We use standard physics techniques to model trading and price formation in a market under the assumption that order arrival and cancellations are Poisson random processes. This model makes testable predictions for the most basic properties of a market, such as the diffusion rate of prices, which is the standard measure of financial risk, and the spread and price impact functions, which are the main determinants of transaction cost. Guided by dimensional analysis, simulation, and mean field theory, we find scaling relations in terms of order flow rates. We show that even under completely random order flow the need to store supply and demand to facilitate trading induces anomalous diffusion and temporal structure in prices.", "published": "2001-12-23T23:57:11Z", "authors": ["Marcus G. Daniels", "J. Doyne Farmer", "Laszlo Gillemot", "Giulia Iori", "Eric Smith"], "categories": ["cond-mat.stat-mech", "q-fin.TR"], "doi": "10.1103/PhysRevLett.90.108102", "_query": "housing price text listing", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2404.00206v2", "title": "Random Reed-Solomon Codes are List Recoverable with Optimal List Size", "abstract": "We prove that Reed-Solomon (RS) codes with random evaluation points are list recoverable up to capacity with optimal output list size, for any input list size. Namely, given an input list size $\\ell$, a designated rate $R$, and any $\\varepsilon > 0$, we show that a random RS code is list recoverable from $1-R-\\varepsilon$ fraction of errors with output list size $L = O(\\ell/\\varepsilon)$, for field size $q=\\exp(\\ell,1/\\varepsilon) \\cdot n^2$. In particular, this shows that random RS codes are list recoverable beyond the \"list recovery Johnson bound\". Such a result was not even known for arbitrary random linear codes. Our technique follows and extends the recent line of work on list decoding of random RS codes, specifically the works of Brakensiek, Gopi, and Makam (STOC 2023), and of Guo and Zhang (FOCS 2023).", "published": "2024-03-30T01:00:50Z", "authors": ["Dean Doron", "S. Venkitesh"], "categories": ["cs.IT", "cs.CC", "math.CO"], "doi": "", "_query": "housing price text listing", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2403.05441v3", "title": "Bayesian Hierarchical Probabilistic Forecasting of Intraday Electricity Prices", "abstract": "We address the need for forecasting methodologies that handle large uncertainties in electricity prices for continuous intraday markets by incorporating parameter uncertainty and using a broad set of covariables. This study presents the first Bayesian forecasting of electricity prices traded on the German intraday market. Endogenous and exogenous covariables are handled via Orthogonal Matching Pursuit (OMP) and regularising priors. The target variable is the IDFull price index, with forecasts given as posterior predictive distributions. Validation uses the highly volatile 2022 electricity prices, which have seldom been studied. As a benchmark, we use all intraday transactions at the time of forecast to compute a live IDFull value. According to market efficiency, it should not be possible to improve on this last-price benchmark. However, we observe significant improvements in point measures and probability scores, including an average reduction of $5.9\\,\\%$ in absolute errors and an average increase of $1.7\\,\\%$ in accuracy when forecasting whether the IDFull exceeds the day-ahead price. Finally, we challenge the use of LASSO in electricity price forecasting, showing that OMP results in superior performance, specifically an average reduction of $22.7\\,\\%$ in absolute error and $20.2\\,\\%$ in the continuous ranked probability score.", "published": "2024-03-08T16:51:27Z", "authors": ["Daniel Nickelsen", "Gernot Müller"], "categories": ["stat.AP", "cs.LG"], "doi": "10.1016/j.apenergy.2024.124975", "_query": "housing price text listing", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/1103.2579v1", "title": "Prices of Anarchy, Information, and Cooperation in Differential Games", "abstract": "The price of anarchy (PoA) has been widely used in static games to quantify the loss of efficiency due to noncooperation. Here, we extend this concept to a general differential games framework. In addition, we introduce the price of information (PoI) to characterize comparative game performances under different information structures, as well as the price of cooperation to capture the extent of benefit or loss a player accrues as a result of altruistic behavior. We further characterize PoA and PoI for a class of scalar linear quadratic differential games under open-loop and closed-loop feedback information structures. We also obtain some explicit bounds on these indices in a large population regime.", "published": "2011-03-14T03:27:44Z", "authors": ["Tamer Basar", "Quanyan Zhu"], "categories": ["eess.SY", "cs.GT", "math.OC"], "doi": "", "_query": "housing price text listing", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/1607.04710v2", "title": "A Descending Price Auction for Matching Markets", "abstract": "This work presents a descending-price-auction algorithm to obtain the maximum market-clearing price vector (MCP) in unit-demand matching markets with m items by exploiting the combinatorial structure. With a shrewd choice of goods for which the prices are reduced in each step, the algorithm only uses the combinatorial structure, which avoids solving LPs and enjoys a strongly polynomial runtime of $O(m^4)$. Critical to the algorithm is determining the set of under-demanded goods for which we reduce the prices simultaneously in each step of the algorithm. This we accomplish by choosing the subset of goods that maximize a skewness function, which makes the bipartite graph series converges to the combinatorial structure at the maximum MCP in $O(m^2)$ steps. A graph coloring algorithm is proposed to find the set of goods with the maximal skewness value that yields $O(m^4)$ complexity.", "published": "2016-07-16T06:46:34Z", "authors": ["Shih-Tang Su", "Jacob D. Abernethy", "Grant Schoenebeck", "Vijay G. Subramanian"], "categories": ["cs.GT"], "doi": "", "_query": "housing price text listing", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2207.11690v2", "title": "HouseX: A Fine-grained House Music Dataset and its Potential in the Music Industry", "abstract": "Machine sound classification has been one of the fundamental tasks of music technology. A major branch of sound classification is the classification of music genres. However, though covering most genres of music, existing music genre datasets often do not contain fine-grained labels that indicate the detailed sub-genres of music. In consideration of the consistency of genres of songs in a mixtape or in a DJ (live) set, we have collected and annotated a dataset of house music that provide 4 sub-genre labels, namely future house, bass house, progressive house and melodic house. Experiments show that our annotations well exhibit the characteristics of different categories. Also, we have built baseline models that classify the sub-genre based on the mel-spectrograms of a track, achieving strongly competitive results. Besides, we have put forward a few application scenarios of our dataset and baseline model, with a simulated sci-fi tunnel as a short demo built and rendered in a 3D modeling software, with the colors of the lights automated by the output of our model.", "published": "2022-07-24T08:19:19Z", "authors": ["Xinyu Li"], "categories": ["cs.SD", "cs.LG", "eess.AS"], "doi": "", "_query": "housing price text listing", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2010.14979v1", "title": "Monetary-fiscal interactions under price level targeting", "abstract": "The adoption of a \"makeup\" strategy is one of the proposals in the ongoing review of the Fed's monetary policy framework. Another suggestion, to avoid the zero lower bound, is a more active role for fiscal policy. We put together these ideas to study monetary-fiscal interactions under price level targeting. Under price level targeting and a fiscally-led regime, we find that following a deflationary demand shock: (i) the central bank increases (rather than decreases) the policy rate; (ii) the central bank, thus, avoids the zero lower bound; (iii) price level targeting is generally welfare improving if compared to inflation targeting.", "published": "2020-10-28T13:41:24Z", "authors": ["Guido Ascari", "Anna Florio", "Alessandro Gobbi"], "categories": ["econ.GN"], "doi": "", "_query": "housing price text listing", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/1401.6735v1", "title": "Option Pricing of Twin Assets", "abstract": "How to price and hedge claims on nontraded assets are becoming increasingly important matters in option pricing theory today. The most common practice to deal with these issues is to use another similar or \"closely related\" asset or index which is traded, for hedging purposes. Implicitly, traders assume here that the higher the correlation between the traded and nontraded assets, the better the hedge is expected to perform. This raises the question as to how \\textquoteleft{}closely related\\textquoteright{} the assets really are. In this paper, the concept of twin assets is introduced, focusing the discussion precisely in what does it mean for two assets to be similar. Our findings point to the fact that, in order to have very similar assets, for example identical twins, high correlation measures are not enough. Specifically, two basic criteria of similarity are pointed out: i) the coefficient of variation of the assets and ii) the correlation between assets. From here, a method to measure the level of similarity between assets is proposed, and secondly, an option pricing model of twin assets is developed. The proposed model allows us to price an option of one nontraded asset using its twin asset, but this time knowing explicitly what levels of errors we are facing. Finally, some numerical illustrations show how twin assets behave depending upon their levels of similarities, and how their potential differences will traduce in MAPE (mean absolute percentage error) for the proposed option pricing model.", "published": "2014-01-27T04:28:41Z", "authors": ["Marcelo J. Villena", "Axel A. Araneda"], "categories": ["q-fin.PR"], "doi": "", "_query": "housing price text listing", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2504.17313v1", "title": "Tokenizing Stock Prices for Enhanced Multi-Step Forecast and Prediction", "abstract": "Effective stock price forecasting (estimating future prices) and prediction (estimating future price changes) are pivotal for investors, regulatory agencies, and policymakers. These tasks enable informed decision-making, risk management, strategic planning, and superior portfolio returns. Despite their importance, forecasting and prediction are challenging due to the dynamic nature of stock price data, which exhibit significant temporal variations in distribution and statistical properties. Additionally, while both forecasting and prediction targets are derived from the same dataset, their statistical characteristics differ significantly. Forecasting targets typically follow a log-normal distribution, characterized by significant shifts in mean and variance over time, whereas prediction targets adhere to a normal distribution. Furthermore, although multi-step forecasting and prediction offer a broader perspective and richer information compared to single-step approaches, it is much more challenging due to factors such as cumulative errors and long-term temporal variance. As a result, many previous works have tackled either single-step stock price forecasting or prediction instead. To address these issues, we introduce a novel model, termed Patched Channel Integration Encoder (PCIE), to tackle both stock price forecasting and prediction. In this model, we utilize multiple stock channels that cover both historical prices and price changes, and design a novel tokenization method to effectively embed these channels in a cross-channel and temporally efficient manner. Specifically, the tokenization process involves univariate patching and temporal learning with a channel-mixing encoder to reduce cumulative errors. Comprehensive experiments validate that PCIE outperforms current state-of-the-art models in forecast and prediction tasks.", "published": "2025-04-24T07:15:05Z", "authors": ["Zhuohang Zhu", "Haodong Chen", "Qiang Qu", "Xiaoming Chen", "Vera Chung"], "categories": ["cs.CE", "q-fin.CP"], "doi": "", "_query": "housing price text listing", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/1010.4812v1", "title": "Polynomial Bottleneck Congestion Games with Optimal Price of Anarchy", "abstract": "We study {\\em bottleneck congestion games} where the social cost is determined by the worst congestion of any resource. These games directly relate to network routing problems and also job-shop scheduling problems. In typical bottleneck congestion games, the utility costs of the players are determined by the worst congested resources that they use. However, the resulting Nash equilibria are inefficient, since the price of anarchy is proportional on the number of resources which can be high. Here we show that we can get smaller price of anarchy with the bottleneck social cost metric. We introduce the {\\em polynomial bottleneck games} where the utility costs of the players are polynomial functions of the congestion of the resources that they use. In particular, the delay function for any resource $r$ is $C_{r}^\\M$, where $C_r$ is the congestion measured as the number of players that use $r$, and $\\M \\geq 1$ is an integer constant that defines the degree of the polynomial. The utility cost of a player is the sum of the individual delays of the resources that it uses. The social cost of the game remains the same, namely, it is the worst bottleneck resource congestion: $\\max_{r} C_r$. We show that polynomial bottleneck games are very efficient and give price of anarchy $O(|R|^{1/(\\M+1)})$, where $R$ is the set of resources. This price of anarchy is tight, since we demonstrate a game with price of anarchy $Ω(|R|^{1/(\\M+1)})$, for any $\\M \\geq 1$. We obtain our tight bounds by using two proof techniques: {\\em transformation}, which we use to convert arbitrary games to simpler games, and {\\em expansion}, which we use to bound the price of anarchy in a simpler game.", "published": "2010-10-22T20:44:09Z", "authors": ["Rajgopal Kannan", "Costas Busch", "Athanasios Vasilakos"], "categories": ["cs.GT"], "doi": "", "_query": "housing price text listing", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/1611.02026v1", "title": "Pricing Derivatives in a Regime Switching Market with Time Inhomogeneous Volatility", "abstract": "This paper studies pricing derivatives in an age-dependent semi-Markov modulated market. We consider a financial market where the asset price dynamics follow a regime switching geometric Brownian motion model in which the coefficients depend on finitely many age-dependent semi-Markov processes. We further allow the volatility coefficient to depend on time explicitly. Under these market assumptions, we study locally risk minimizing pricing of a class of European options. It is shown that the price function can be obtained by solving a non-local B-S-M type PDE. We establish existence and uniqueness of a classical solution of the Cauchy problem. We also find another characterization of price function via a system of Volterra integral equation of second kind. This alternative representation leads to computationally efficient methods for finding price and hedging. Finally, we analyze the PDE to establish continuous dependence of the solution on the instantaneous transition rates of semi-Markov processes. An explicit expression of quadratic residual risk is also obtained.", "published": "2016-11-07T12:50:57Z", "authors": ["Milan Kumar Das", "Anindya Goswami", "Tanmay S. Patankar"], "categories": ["q-fin.PR", "math.PR", "q-fin.MF"], "doi": "10.1080/07362994.2018.1448996", "_query": "housing price text listing", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2408.08483v1", "title": "Enhancement of price trend trading strategies via image-induced importance weights", "abstract": "We open up the \"black-box\" to identify the predictive general price patterns in price chart images via the deep learning image analysis techniques. Our identified price patterns lead to the construction of image-induced importance (triple-I) weights, which are applied to weighted moving average the existing price trend trading signals according to their level of importance in predicting price movements. From an extensive empirical analysis on the Chinese stock market, we show that the triple-I weighting scheme can significantly enhance the price trend trading signals for proposing portfolios, with a thoughtful robustness study in terms of network specifications, image structures, and stock sizes. Moreover, we demonstrate that the triple-I weighting scheme is able to propose long-term portfolios from a time-scale transfer learning, enhance the news-based trading strategies through a non-technical transfer learning, and increase the overall strength of numerous trading rules for portfolio selection.", "published": "2024-08-16T02:07:15Z", "authors": ["Zhoufan Zhu", "Ke Zhu"], "categories": ["q-fin.PM", "stat.ML"], "doi": "", "_query": "housing price text listing", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2002.08286v1", "title": "Price impact equilibrium with transaction costs and TWAP trading", "abstract": "We prove the existence of an equilibrium in a model with transaction costs and price impact where two agents are incentivized to trade towards a target. The two types of frictions -- price impact and transaction costs -- lead the agents to two distinct changes in their optimal investment approach: price impact causes agents to continuously trade in smaller amounts, while transaction costs cause the agents to cease trading before the end of the trading period. As the agents lose wealth because of transaction costs, the exchange makes a profit. We prove the existence of a strictly positive optimal transaction cost from the exchange's perspective.", "published": "2020-02-19T16:59:34Z", "authors": ["Eunjung Noh", "Kim Weston"], "categories": ["q-fin.MF", "q-fin.TR"], "doi": "", "_query": "housing price text listing", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2608.15631v1", "title": "Non-obvious Manipulability with Groups in Shapley-Scarf Housing Markets", "abstract": "In Shapley-Scarf housing markets, Ma (1994) shows that top trading cycles (TTC) is the unique mechanism satisfying individual rationality (IR), Pareto efficiency (PE), and strategy-proofness. We ask what other mechanisms become possible when strategy-proofness is replaced by a weaker condition called non-obvious manipulability (NOM), introduced by Troyan and Morrill (2020). We first show that this weaker condition does not help on its own: every IR and PE mechanism is already NOM. We therefore introduce a new condition: NOM with groups, under which each agent knows the preferences of the other members of her group, but not those of agents outside the group. This condition reduces to strategy-proofness when all agents belong to one group, and to standard NOM when every group is a singleton. Also, we introduce a participation condition called group rationality (GR), which requires that no group do worse than it would by trading only among its own members. We then define a class of mechanisms called TTC with super-groups, whose members satisfy GR, PE, and NOM with groups. The class includes mechanisms that differ from standard TTC, including mechanisms that are not strategy-proof. Furthermore, we show that every mechanism that satisfies GR, PE, and NOM with groups has the same best- and worst-case outcomes as every TTC with super-groups mechanism.", "published": "2026-08-16T08:53:11Z", "authors": ["Louise Demoor", "Martí Jané-Ballarín", "Pierre Nunn", "Subhajit Pramanik", "Antoine Prévotat", "Makoto Yokoo"], "categories": ["cs.GT", "econ.TH"], "doi": "", "_query": "housing price text listing", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/1305.3351v2", "title": "Quality Sensitive Price Competition in Spectrum Oligopoly", "abstract": "We investigate a spectrum oligopoly where primary users allow secondary access in lieu of financial remuneration. Transmission qualities of the licensed bands fluctuate randomly. Each primary needs to select the price of its channel with the knowledge of its own channel state but not that of its competitors. Secondaries choose among the channels available on sale based on their states and prices. We formulate the price selection as a non-cooperative game and prove that a symmetric Nash equilibrium (NE) strategy profile exists uniquely. We explicitly compute this strategy profile and analytically and numerically evaluate its efficiency. Our structural results provide certain key insights about the unique symmetric NE.", "published": "2013-05-15T03:42:45Z", "authors": ["Arnob Ghosh", "Saswati Sarkar"], "categories": ["cs.GT"], "doi": "10.1109/ISIT.2013.6620730", "_query": "housing price text listing", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/1106.1401v1", "title": "Volatility of Power Grids under Real-Time Pricing", "abstract": "The paper proposes a framework for modeling and analysis of the dynamics of supply, demand, and clearing prices in power system with real-time retail pricing and information asymmetry. Real-time retail pricing is characterized by passing on the real-time wholesale electricity prices to the end consumers, and is shown to create a closed-loop feedback system between the physical layer and the market layer of the power system. In the absence of a carefully designed control law, such direct feedback between the two layers could increase volatility and lower the system's robustness to uncertainty in demand and generation. A new notion of generalized price-elasticity is introduced, and it is shown that price volatility can be characterized in terms of the system's maximal relative price elasticity, defined as the maximal ratio of the generalized price-elasticity of consumers to that of the producers. As this ratio increases, the system becomes more volatile, and eventually, unstable. As new demand response technologies and distributed storage increase the price-elasticity of demand, the architecture under examination is likely to lead to increased volatility and possibly instability. This highlights the need for assessing architecture systematically and in advance, in order to optimally strike the trade-offs between volatility, economic efficiency, and system reliability.", "published": "2011-06-07T17:53:12Z", "authors": ["Mardavij Roozbehani", "Munther A Dahleh", "Sanjoy K Mitter"], "categories": ["eess.SY", "math.DS", "math.OC", "q-fin.PR"], "doi": "", "_query": "housing price text listing", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/astro-ph/0411165v1", "title": "Space Climate Manifestation in Earth Prices - from Medieval England Up to Modern Usa", "abstract": "In this study we continue to search for possible manifestations of space weather influence on prices of agricultural products and consumables. We note that the connection between solar activity and prices is based on the causal chain that includes several nonlinear transition elements. These non-linear elements are characterized by threshold sensitivity to external parameters and lead to very inhomogeneous local sensitivity of the price to space weather conditions. It is noted that \"soft type\" models are the most adequate for description of this class of connections. Two main observational effects suitable for testing causal connections of this type of sensitivity are considered: burst-like price reactions on changes in solar activity and price asymmetry for selected phases of the sunspot cycle. The connection, discovered earlier for wheat prices of Medieval England, is examined in this work on the basis of another 700-year data set of consumable prices in England. Using the same technique as in the previous part of our work (Pistilnik and Yom Din 2004) we show that statistical parameters of the interval distributions for price bursts of consumables basket and for sunspot minimum states are similar one to another, like it was reported earlier for wheat price bursts. Possible sources of these consistencies between three different multiyear samples are discussed. For search of possible manifestations of the \"space weather - wheat market\" connection in modern time, we analyze dynamics of wheat prices in the USA in the twentieth century. We show that the wheat prices revealed a maximum/minimum price asymmetry consistent with the phases of the sunspot cycle. We discuss possible explanations of this observed asymmetry, unexpected under conditions of globalization of the modern wheat market.", "published": "2004-11-06T18:32:19Z", "authors": ["L. A. Pustilnik", "G. Yom Din"], "categories": ["astro-ph"], "doi": "10.1007/s11207-005-5198-9", "_query": "housing price text listing", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/1804.04170v1", "title": "Optimal liquidation under stochastic price impact", "abstract": "We assume a continuous-time price impact model similar to Almgren-Chriss but with the added assumption that the price impact parameters are stochastic processes modeled as correlated scalar Markov diffusions. In this setting, we develop trading strategies for a trader who desires to liquidate his inventory but faces price impact as a result of his trading. For a fixed trading horizon, we perform coefficient expansion on the Hamilton-Jacobi-Bellman equation associated with the trader's value function. The coefficient expansion yields a sequence of partial differential equations that we solve to give closed-form approximations to the value function and optimal liquidation strategy. We examine some special cases of the optimal liquidation problem and give financial interpretations of the approximate liquidation strategies in these cases. Finally, we provide numerical examples to demonstrate the effectiveness of the approximations.", "published": "2018-04-11T19:03:47Z", "authors": ["Weston Barger", "Matthew Lorig"], "categories": ["q-fin.TR"], "doi": "", "_query": "housing price text listing", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2512.03284v2", "title": "Think, Then Look: Active Spatial Reasoning for House-Scale 3D Scene Understanding", "abstract": "Spatial reasoning in large-scale 3D environments remains challenging for current vision--language models, which are typically constrained to room-scale scenarios. We formalize Active House-Scale Spatial Reasoning (AHSR), a new paradigm in which a model reasons over a pre-built house-scale 3D map via virtual spatial tool invocations to answer spatial questions, without exhaustive scene-wide processing. To support AHSR research, we introduce H$^2$U3D (Holistic House Understanding in 3D), the first benchmark targeting house-scale 3D scene understanding, featuring environments with an average aggregate floor area of 250.8 m$^2$ and up to three floors, together with hierarchical coarse-to-fine visual representations. Building on H$^2$U3D, we propose SpatialReasoner, an AHSR framework trained via supervised fine-tuning with self-correction, followed by reinforcement learning with a task-aware adaptive exploration reward. SpatialReasoner achieves state-of-the-art performance on H$^2$U3D with 64.9% overall accuracy, outperforming strong baselines including GPT-5.4 and Gemini-3.5-Flash, and generalizes effectively to MT-HM3D and HM-EQA. These results demonstrate the clear advantage of active map-directed exploration over passive scene-wide processing in house-scale 3D understanding.", "published": "2025-12-02T22:49:01Z", "authors": ["Hongpei Zheng", "Shijie Li", "Lin Qian", "Zhenghao Li", "Qijun Yang", "Yanran Li", "Hujun Yin"], "categories": ["cs.CV"], "doi": "", "_query": "housing price text listing", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/1910.03800v1", "title": "Art Pricing with Computer Graphic Techniques", "abstract": "This paper makes the first attempt to introduce the tools from computer graphics into the art pricing research. We argue that the creation of a painting calls for a combination of conceptual effort and painting effort from the artist. However, as the important price determinants, both efforts are long missing in the traditional hedonic model because they are hard to measure. This paper draws on the digital pictures of auctioned paintings from various renowned artists, and applies the image recognition techniques to measure the variances of lines and colors of these paintings. We then use them as the proxies for the artist's painting effort, and include them in the hedonic regression to test their significance. Our results show that the variances of lines and colors of a painting can significantly positively explain the sales price in a general context. Our suggested measurements can better capture the content heterogeneity of paintings hence improving on the traditional art pricing methodology. Our approach also provides a quantitative perspective for both valuation and authentication of paintings.", "published": "2019-10-09T05:43:20Z", "authors": ["Lan Ju", "Zhiyong Tu", "Changyong Xue"], "categories": ["q-fin.GN"], "doi": "", "_query": "housing price text listing", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/1112.2059v1", "title": "Randomised Mixture Models for Pricing Kernels", "abstract": "Numerous kinds of uncertainties may affect an economy, e.g. economic, political, and environmental ones. We model the aggregate impact by the uncertainties on an economy and its associated financial market by randomised mixtures of Lévy processes. We assume that market participants observe the randomised mixtures only through best estimates based on noisy market information. The concept of incomplete information introduces an element of stochastic filtering theory in constructing what we term \"filtered Esscher martingales\". We make use of this family of martingales to develop pricing kernel models. Examples of bond price models are examined, and we show that the choice of the random mixture has a significant effect on the model dynamics and the types of movements observed in the associated yield curves. Parameter sensitivity is analysed and option price processes are derived. We extend the class of pricing kernel models by considering a weighted heat kernel approach, and develop models driven by mixtures of Markov processes.", "published": "2011-12-09T10:08:39Z", "authors": ["Andrea Macrina", "Priyanka A. Parbhoo"], "categories": ["q-fin.GN", "math.PR"], "doi": "", "_query": "housing price text listing", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/1904.04225v3", "title": "A Forward Electricity Contract Price Projection: A Market Equilibrium Approach", "abstract": "This work presents a methodology for forward electricity contract price projection based on market equilibrium and social welfare optimization. In the methodology supply and demand for forward contracts are produced in such a way that each agent (generator/load/trader) optimizes a risk adjusted expected value of its revenue/cost. When uncertainties are represented by a discrete number of scenarios, a key result in the paper is that contract price corresponds to the dual variable of the equilibrium constraints in the linear programming problem associated to the optimization of total agents' welfare. Besides computing an equilibrium contract price for a given year, the methodology can also be used to compute the evolution of the probability distribution associated to a contract price with a future delivery period; this an import issue in quantifying forward contract risks. Examples of the methodology application are presented and discussed", "published": "2019-04-08T17:51:44Z", "authors": ["Mateus A. Cavaliere", "Sergio Granville", "Gerson C. Oliveira", "Mario V. F. Pereira"], "categories": ["math.OC", "q-fin.PR"], "doi": "", "_query": "housing price text listing", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2610.00755v2", "title": "Learning to Price Electricity for Optimal Demand Response", "abstract": "There is considerable interest in using time-varying electricity prices to shape consumer demand response, and better align energy demand with renewable production. However, optimal prices generally vary over time in response to complex signals such as weather forecasts, sunrise/sunset times, and day-of-week patterns; and existing methods are not able to make efficient use of such rich contextual information. Here, we propose a neural-network-based algorithm for contextual energy pricing, modeling pricing as a Stackelberg game and leveraging a mean-field solution representation from Mehrabi et al.(2024). The approach learns constrained mappings from contextual features to feasible price signals. We validate our approach by simulating the energy grid in several US cities, and show that incorporating contextual information can considerably increase the value of the demand response programs.", "published": "2026-09-30T21:50:02Z", "authors": ["Jing Shang", "Mohammad Mehrabi", "Xinyang Zhou", "Mahmoud Saleh", "Andrey Bernstein", "Stefan Wager"], "categories": ["stat.ML", "cs.LG", "stat.AP"], "doi": "", "_query": "housing price text listing", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2304.09761v1", "title": "An innovative Deep Learning Based Approach for Accurate Agricultural Crop Price Prediction", "abstract": "Accurate prediction of agricultural crop prices is a crucial input for decision-making by various stakeholders in agriculture: farmers, consumers, retailers, wholesalers, and the Government. These decisions have significant implications including, most importantly, the economic well-being of the farmers. In this paper, our objective is to accurately predict crop prices using historical price information, climate conditions, soil type, location, and other key determinants of crop prices. This is a technically challenging problem, which has been attempted before. In this paper, we propose an innovative deep learning based approach to achieve increased accuracy in price prediction. The proposed approach uses graph neural networks (GNNs) in conjunction with a standard convolutional neural network (CNN) model to exploit geospatial dependencies in prices. Our approach works well with noisy legacy data and produces a performance that is at least 20% better than the results available in the literature. We are able to predict prices up to 30 days ahead. We choose two vegetables, potato (stable price behavior) and tomato (volatile price behavior) and work with noisy public data available from Indian agricultural markets.", "published": "2023-04-15T10:54:57Z", "authors": ["Mayank Ratan Bhardwaj", "Jaydeep Pawar", "Abhijnya Bhat", " Deepanshu", "Inavamsi Enaganti", "Kartik Sagar", "Y. Narahari"], "categories": ["cs.LG", "cs.AI", "q-fin.ST"], "doi": "", "_query": "housing price text listing", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2608.14018v1", "title": "Equilibrium Pricing in Oligopolistic Data Markets", "abstract": "We study equilibrium pricing in oligopolistic data markets with budget-constrained buyers (e.g., machine learning companies purchasing data to improve model accuracy) and strategic data sellers. Sellers compete by setting prices for their datasets, giving rise to a pricing game whose pure Nash equilibria correspond to equilibrium prices. While equilibrium prices are guaranteed for rivalrous goods via competitive equilibrium, we show that the non-rivalry of data fundamentally alters this picture: an exact Nash equilibrium (NE) need not exist, and in fact, 1.363-approximate NE may also not exist under uniform pricing. We therefore investigate relaxed equilibrium notions. Allowing sellers to use beyond-uniform pricing---specifically, piecewise-linear convex pricing functions---guarantees approximate stability within a constant factor: there exists a pricing profile in which no seller can improve revenue by a factor of two by deviating to any uniform price (a 2-approximate NE against uniform deviations). Finally, our simulations demonstrate fast convergence and empirical approximation guarantees that outperform the worst-case bound of 2.", "published": "2026-08-14T07:03:45Z", "authors": ["Bhaskar Ray Chaudhury", "Jugal Garg", "Eklavya Sharma", "Jiaxin Song"], "categories": ["cs.GT"], "doi": "", "_query": "housing price text listing", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2206.10993v1", "title": "SEnti-Analyzer: Joint Sentiment Analysis For Text-Based and Verbal Communication in Software Projects", "abstract": "Social aspects in software development teams are of particular importance for a successful project closure. To analyze sentiments in software projects, there are several tools and approaches available. These tools analyze text-based communication based on the used words to predict whether they appear to be positive, negative, or neutral for the receiver of the message. In the research project ComContA, we investigate so-called sentiment analysis striving to analyze the content of text-based communication in development teams with regard to the statement's polarity. That is, we analyze whether the communication appears to be adequate (i.e., positive or neutral) or negative. In a workshop paper, we presented a tool called SEnti-Analyzer that allows to apply sentiment analysis to verbal communication in meetings of software projects. In this technical report, we present the extended functionalities of the SEnti-Analyzer by also allowing the analysis of text-based communication, we improve the prediction of the tool by including established sentiment analysis tools, and we evaluate the tool with respect to its accuracy. We evaluate the tool by comparing the prediction of the SEnti-Analyzer to pre-labeled established data sets used for sentiment analysis in software engineering and to perceptions of computer scientists. Our results indicate that in almost all cases at least two of the three votes coincide, but in only about half of the cases all three votes coincide. Our results raise the question of the \"ultimate truth\" of sentiment analysis outcomes: What do we want to predict with sentiment analysis tools? The pre-defined labels of established data sets? The perception of computer scientists? Or the perception of single computer scientists which appears to be the most meaningful objective?", "published": "2022-06-22T11:49:16Z", "authors": ["Marc Herrmann", "Martin Obaidi", "Jil Klünder"], "categories": ["cs.SE"], "doi": "", "_query": "housing price text listing", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2608.01406v1", "title": "Algorithmic collusion under asynchronous price updating", "abstract": "This paper investigates the effect of asynchrony in agents' updates in the emergence of algorithmic collusion. We present a continuous-time model for algorithmic collusion in which two firms use $Q$-learning algorithms to set prices asynchronously in a Bertrand duopoly. The firms update their prices at times dictated by a Poisson clock. By controlling the extent of agents' asynchrony, we run extensive numerical experiments with three specifications of the algorithm to investigate the emergence of algorithmic collusion. The strength of collusion is measured by a standard collusion index, as well as by automatically detecting the reward-punishment schemes. This is done by recording a large number of algorithms' reactions to unilateral price cuts and comparing them with the reactions of untrained algorithms. Our findings indicate that asynchrony hampers collusion, especially when the algorithms are stateless. When they condition on their competitor's previous prices, the sensitivity of algorithmic collusion to asynchrony varies depending on the type of information they have access to. The implications of these results for the regulation of algorithmic pricing are discussed.", "published": "2026-08-02T17:39:44Z", "authors": ["Ivan Conjeaud", "Gaspard Abel", "Argyris Kalogeratos"], "categories": ["econ.TH", "cs.GT"], "doi": "", "_query": "housing price text listing", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/cond-mat/0101001v2", "title": "A simple model of price formation", "abstract": "A simple Ising spin model which can describe the mechanism of price formation in financial markets is proposed. In contrast to other agent-based models, the influence does not flow inward from the surrounding neighbors to the center site, but spreads outward from the center to the neighbors. The model thus describes the spread of opinions among traders. It is shown via standard Monte Carlo simulations that very simple rules lead to dynamics that duplicate those of asset prices.", "published": "2000-12-30T13:04:49Z", "authors": ["K. Sznajd-Weron", "R. Weron"], "categories": ["cond-mat.stat-mech", "q-fin.ST"], "doi": "10.1142/S0129183102003000", "_query": "housing price text listing", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/1201.1968v1", "title": "Information Hiding in CSS : A Secure Scheme Text-Steganography using Public Key Cryptosystem", "abstract": "In many recent years, the programming world has been introduced about a new programming language for designing websites, it is CSS that can be be used together with HTML to develop a web interface. And now, these two programming languages as if inseparably from each other. As a client-side scripting, CSS is visible by all users as the original script, but it can not be granted changed. Website is a tool of information disseminator throughout the world, this is certainly can be used to a secret communication by using CSS as a message hider. This paper proposed a new scheme using web tools like CSS for hiding informations. This is a secret communication mechanism using text steganography techniques that is embedded messages on CSS files and is further encrypted using RSA as a public key cryptographic algorithm.", "published": "2012-01-10T05:44:50Z", "authors": ["Herman Kabetta", "B. Yudi Dwiandiyanta", " Suyoto"], "categories": ["cs.CR"], "doi": "", "_query": "housing price text listing", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/1807.05396v1", "title": "On the optimal choice of strike conventions in exchange option pricing", "abstract": "An important but rarely-addressed option pricing question is how to choose appropriate strikes for implied volatility inputs when pricing more exotic multi-asset derivatives. By means of Malliavin Calculus we construct an optimal log-linear strikevconvention for exchange options under stochastic volatility models. This novel approach allows us to minimize the difference between the corresponding Margrabe computed price and the true option price. We show that this optimal convention does not depend on the specific stochastic volatility model chosen. Numerical examples are given which provide strong support to the new methodology.", "published": "2018-07-14T13:21:38Z", "authors": ["Elisa Alòs", "Michael Coulon"], "categories": ["q-fin.MF", "q-fin.PR"], "doi": "", "_query": "housing price text listing", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2512.06876v2", "title": "Stationary list colorings", "abstract": "Komjath studied the list chromatic number of infinite graphs and introduced the notion of restricted list chromatic number. For a graph $X=(V_X,E_X)$ and a cardinal $κ$, we say that $X$ is restricted list colorable for $κ$ if for every $L:V_X\\to[κ]^κ$ there is a choice function $c$ of $L$ such that $c(v)\\neq c(w)$ whenever ${v,w}\\in E_X$. In this paper, we discuss a variation, stationary list colorability for $κ$, obtained by replacing $[κ]^κ$ with the set of all stationary subsets of $κ$. We compare the stationary list colorability with other coloring properties. Among other things, we prove that the stationary list colorability is essentially different from other coloring properties including the restricted list colorability. We also prove the consistency result showing that for some $κ<λ$, restricted and stationary list colorability at $κ$ do not imply the corresponding properties at $λ$.", "published": "2025-12-07T15:04:39Z", "authors": ["Yusuke Hayashi"], "categories": ["math.LO", "math.CO"], "doi": "", "_query": "housing price text listing", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2610.21442v1", "title": "Fundamental Theorem of Discrete Asset Pricing", "abstract": "Motivated by discrete price quotes in continuous time, we seek a fundamental theorem based on no arbitrage rather than a condition on limiting trading opportunities. We consider a vector of discounted prices taking values in a locally finite subset of $R^d$ in continuous time. For a locally bounded càdlàg price, we prove that no arbitrage, no free lunch with vanishing risk, no free lunch, and the existence of an equivalent local martingale measure are equivalent. An elementary finite-dimensional reduction of portfolios yields a common pathwise bound on admissible wealth. This bound reduces closedness to convex averaging in a weighted Hilbert space, after which the Kreps-Yan theorem applies. Trading gains are finite Stieltjes sums, and the proof uses no Itô integration.", "published": "2026-10-15T07:49:21Z", "authors": ["Masaaki Fukasawa"], "categories": ["q-fin.MF"], "doi": "", "_query": "housing price text listing", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2510.03633v1", "title": "Predicting Stock Price Movement with LLM-Enhanced Tweet Emotion Analysis", "abstract": "Accurately predicting short-term stock price movement remains a challenging task due to the market's inherent volatility and sensitivity to investor sentiment. This paper discusses a deep learning framework that integrates emotion features extracted from tweet data with historical stock price information to forecast significant price changes on the following day. We utilize Meta's Llama 3.1-8B-Instruct model to preprocess tweet data, thereby enhancing the quality of emotion features derived from three emotion analysis approaches: a transformer-based DistilRoBERTa classifier from the Hugging Face library and two lexicon-based methods using National Research Council Canada (NRC) resources. These features are combined with previous-day stock price data to train a Long Short-Term Memory (LSTM) model. Experimental results on TSLA, AAPL, and AMZN stocks show that all three emotion analysis methods improve the average accuracy for predicting significant price movements, compared to the baseline model using only historical stock prices, which yields an accuracy of 13.5%. The DistilRoBERTa-based stock prediction model achieves the best performance, with accuracy rising from 23.6% to 38.5% when using LLaMA-enhanced emotion analysis. These results demonstrate that using large language models to preprocess tweet content enhances the effectiveness of emotion analysis which in turn improves the accuracy of predicting significant stock price movements.", "published": "2025-10-04T02:31:44Z", "authors": ["An Vuong", "Susan Gauch"], "categories": ["cs.LG", "cs.AI"], "doi": "", "_query": "housing price text listing", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/0912.3771v1", "title": "Tremor price dynamics in the world's network of stock exchanges", "abstract": "We use insight from a model of earth tectonic plate movement to obtain a new understanding of the build up and release of stress in the price dynamics of the worlds stock exchanges. Nonlinearity enters the model due to a behavioral attribute of humans reacting disproportionately to big changes. This nonlinear response allows us to classify price movements of a given stock index as either being generated due to specific economic news for the country in question, or by the ensemble of the worlds stock exchanges reacting together like a complex system. Similar in structure to the Capital Asset Pricing Model in Finance, the model predicts how an individual stock exchange should be priced in terms of the performance of the global market of exchanges, but with human behavioral characteristics included in the pricing. A number of the models assumptions are validated against empirical data for 24 of the worlds leading stock exchanges. We show how treshold effects can lead to synchronization in the global network of stock exchanges.", "published": "2009-12-18T19:05:22Z", "authors": ["Jorgen Vitting Andersen", "Andrzej Nowak", "Giulia Rotundo", "Lael Parrott"], "categories": ["q-fin.GN", "physics.soc-ph", "q-fin.ST"], "doi": "", "_query": "housing price text listing", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/1803.09444v1", "title": "Cliquet option pricing with Meixner processes", "abstract": "We investigate the pricing of cliquet options in a geometric Meixner model. The considered option is of monthly sum cap style while the underlying stock price model is driven by a pure-jump Meixner--Lévy process yielding Meixner distributed log-returns. In this setting, we infer semi-analytic expressions for the cliquet option price by using the probability distribution function of the driving Meixner--Lévy process and by an application of Fourier transform techniques. In an introductory section, we compile various facts on the Meixner distribution and the related class of Meixner--Lévy processes. We also propose a customized measure change preserving the Meixner distribution of any Meixner process.", "published": "2018-03-26T07:26:06Z", "authors": ["Markus Hess"], "categories": ["q-fin.PR", "math.PR"], "doi": "10.15559/18-VMSTA96", "_query": "housing price text listing", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/1810.01730v3", "title": "Submodular Stochastic Probing with Prices", "abstract": "We introduce Stochastic Probing with Prices (SPP), a variant of the Stochastic Probing (SP) model in which we must pay a price to probe an element. A SPP problem involves two set systems $(N,\\mathcal{I}_{in})$ and $(N,\\mathcal{I}_{out})$ where each $e\\in N$ is active with probability $p_e$. To discover whether $e$ is active, it must be probed by paying the price $Δ_e$. If it is probed and active, then it is irrevocably added to the solution. Moreover, at all times, the set of probed elements must lie in $\\mathcal{I}_{out}$, and the solution (the set of probed and active elements) must lie in $\\mathcal{I}_{in}$. The goal is to maximize a set function $f$ minus the cost of the probes. We give a bi-criteria approximation algorithm to the online version of this problem, in which the elements are shown to the algorithm in a possibly adversarial order. Our results translate to state-of-the-art approximations for the traditional (online) stochastic probing problem.", "published": "2018-10-03T13:31:07Z", "authors": ["Ben Chugg", "Takanori Maehara"], "categories": ["cs.DS"], "doi": "", "_query": "housing price text listing", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2205.11439v1", "title": "Probabilistic forecasting of German electricity imbalance prices", "abstract": "The exponential growth of renewable energy capacity has brought much uncertainty to electricity prices and to electricity generation. To address this challenge, the energy exchanges have been developing further trading possibilities, especially the intraday and balancing markets. For an energy trader participating in both markets, the forecasting of imbalance prices is of particular interest. Therefore, in this manuscript we conduct a very short-term probabilistic forecasting of imbalance prices, contributing to the scarce literature in this novel subject. The forecasting is performed 30 minutes before the delivery, so that the trader might still choose the trading place. The distribution of the imbalance prices is modelled and forecasted using methods well-known in the electricity price forecasting literature: lasso with bootstrap, gamlss, and probabilistic neural networks. The methods are compared with a naive benchmark in a meaningful rolling window study. The results provide evidence of the efficiency between the intraday and balancing markets as the sophisticated methods do not substantially overperform the intraday continuous price index. On the other hand, they significantly improve the empirical coverage. The analysis was conducted on the German market, however it could be easily applied to any other market of similar structure.", "published": "2022-05-23T16:32:20Z", "authors": ["Michał Narajewski"], "categories": ["q-fin.ST", "econ.EM", "stat.ML"], "doi": "", "_query": "housing price text listing", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/1701.08711v5", "title": "Predicting Auction Price of Vehicle License Plate with Deep Recurrent Neural Network", "abstract": "In Chinese societies, superstition is of paramount importance, and vehicle license plates with desirable numbers can fetch very high prices in auctions. Unlike other valuable items, license plates are not allocated an estimated price before auction. I propose that the task of predicting plate prices can be viewed as a natural language processing (NLP) task, as the value depends on the meaning of each individual character on the plate and its semantics. I construct a deep recurrent neural network (RNN) to predict the prices of vehicle license plates in Hong Kong, based on the characters on a plate. I demonstrate the importance of having a deep network and of retraining. Evaluated on 13 years of historical auction prices, the deep RNN's predictions can explain over 80 percent of price variations, outperforming previous models by a significant margin. I also demonstrate how the model can be extended to become a search engine for plates and to provide estimates of the expected price distribution.", "published": "2017-01-30T17:14:25Z", "authors": ["Vinci Chow"], "categories": ["cs.CL", "cs.LG", "econ.GN", "stat.ML"], "doi": "10.1016/j.eswa.2019.113008", "_query": "housing price text listing", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/1805.06235v3", "title": "Precise measurement of $R_{\\text{uds}}$ and $R$ between 1.84 and 3.72 GeV at the KEDR detector", "abstract": "The present work continues a series of the KEDR measurements of the $R$ value that started in 2010 at the VEPP-4M $e^+e^-$ collider. By combining new data with our previous results in this energy range we measured the values of $R_{\\text{uds}}$ and $R$ at nine center-of-mass energies between 3.08 and 3.72 GeV. The total accuracy is about or better than $2.6\\%$ at most of energy points with a systematic uncertainty of about $1.9\\%$. Together with the previous precise $R$ measurement at KEDR in the energy range 1.84-3.05 GeV, it constitutes the most detailed high-precision $R$ measurement near the charmonium production threshold.", "published": "2018-05-16T10:33:21Z", "authors": ["V. V. Anashin", "O. V. Anchugov", "V. M. Aulchenko", "E. M. Baldin", "G. N. Baranov", "A. K. Barladyan", "A. Yu. Barnyakov", "M. Yu. Barnyakov", "S. E. Baru", "I. Yu. Basok", "A. M. Batrakov", "E. A. Bekhtenev", "A. E. Blinov", "V. E. Blinov", "A. V. Bobrov", "V. S. Bobrovnikov", "A. V. Bogomyagkov", "A. E. Bondar", "A. R. Buzykaev", "P. B. Cheblakov", "V. L. Dorohov", "S. I. Eidelman", "D. N. Grigoriev", "S. A. Glukhov", "V. V. Kaminskiy", "S. E. Karnaev", "G. V. Karpov", "S. V. Karpov", "K. Yu. Karukina", "D. P. Kashtankin", "P. V. Kasyanenko", "T. A. Kharlamova", "V. A. Kiselev", "V. V. Kolmogorov", "S. A. Kononov", "K. Yu. Kotov", "A. A. Krasnov", "E. A. Kravchenko", "V. N. Kudryavtsev", "V. F. Kulikov", "G. Ya. Kurkin", "I. A. Kuyanov", "E. B. Levichev", "D. A. Maksimov", "V. M. Malyshev", "A. L. Maslennikov", "O. I. Meshkov", "S. I. Mishnev", "I. A. Morozov", "I. I. Morozov", "S. A. Nikitin", "I. B. Nikolaev", "I. N. Okunev", "A. P. Onuchin", "S. B. Oreshkin", "A. A. Osipov", "I. V. Ovtin", "S. V. Peleganchuk", "S. G. Pivovarov", "P. A. Piminov", "V. V. Petrov", "V. G. Prisekin", "O. L. Rezanova", "A. A. Ruban", "G. A. Savinov", "A. G. Shamov", "D. N. Shatilov", "D. A. Shvedov", "B. A. Shwartz", "E. A. Simonov", "S. V. Sinyatkin", "A. N. Skrinsky", "A. V. Sokolov", "D. P. Sukhanov", "A. M. Sukharev", "E. V. Starostina", "A. A. Talyshev", "V. A. Tayursky", "V. I. Telnov", "Yu. A. Tikhonov", "K. Yu. Todyshev", "A. G. Tribendis", "G. M. Tumaikin", "Yu. V. Usov", "A. I. Vorobiov", "V. N. Zhilich", "A. A. Zhukov", "V. V. Zhulanov", "A. N. Zhuravlev"], "categories": ["hep-ex"], "doi": "10.1016/j.physletb.2018.11.012", "_query": "housing price text listing", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/1704.06027v1", "title": "Structural price model for electricity coupled markets", "abstract": "We propose a new structural model that can compute the electricity spot and forward prices in two coupled markets with limited interconnection and multiple fuels. We choose a structural approach in order to represent some key characteristics of electricity spot prices such as their link to fuel prices, consumption level and production fleet. With this model, explicit formulas are also available for forward prices and other derivatives. We give some illustrative results of the behaviour of spot and forward prices, and of the values of transmission rights.", "published": "2017-04-20T06:56:43Z", "authors": ["Clemence Alasseur", "Olivier Feron"], "categories": ["q-fin.MF"], "doi": "", "_query": "housing price text listing", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/0803.1769v1", "title": "Stock price jumps: news and volume play a minor role", "abstract": "In order to understand the origin of stock price jumps, we cross-correlate high-frequency time series of stock returns with different news feeds. We find that neither idiosyncratic news nor market wide news can explain the frequency and amplitude of price jumps. We find that the volatility patterns around jumps and around news are quite different: jumps are followed by increased volatility, whereas news tend on average to be followed by lower volatility levels. The shape of the volatility relaxation is also markedly different in the two cases. Finally, we provide direct evidence that large transaction volumes are_not_ responsible for large price jumps. We conjecture that most price jumps are induced by order flow fluctuations close to the point of vanishing liquidity.", "published": "2008-03-12T13:32:42Z", "authors": ["Armand Joulin", "Augustin Lefevre", "Daniel Grunberg", "Jean-Philippe Bouchaud"], "categories": ["q-fin.ST", "physics.data-an", "physics.soc-ph"], "doi": "", "_query": "housing price text listing", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2411.04011v2", "title": "Predicting and Publishing Accurate Imbalance Prices Using Monte Carlo Tree Search", "abstract": "The growing reliance on renewable energy sources, particularly solar and wind, has introduced challenges due to their uncontrollable production. This complicates maintaining the electrical grid balance, prompting some transmission system operators in Western Europe to implement imbalance tariffs that penalize unsustainable power deviations. These tariffs create an implicit demand response framework to mitigate grid instability. Yet, several challenges limit active participation. In Belgium, for example, imbalance prices are only calculated at the end of each 15-minute settlement period, creating high risk due to price uncertainty. This risk is further amplified by the inherent volatility of imbalance prices, discouraging participation. Although transmission system operators provide minute-based price predictions, the system imbalance volatility makes accurate price predictions challenging to obtain and requires sophisticated techniques. Moreover, publishing price estimates can prompt participants to adjust their schedules, potentially affecting the system balance and the final price, adding further complexity. To address these challenges, we propose a Monte Carlo Tree Search method that publishes accurate imbalance prices while accounting for potential response actions. Our approach models the system dynamics using a neural network forecaster and a cluster of virtual batteries controlled by reinforcement learning agents. Compared to Belgium's current publication method, our technique improves price accuracy by 20.4% under ideal conditions and by 12.8% in more realistic scenarios. This research addresses an unexplored, yet crucial problem, positioning this paper as a pioneering work in analyzing the potential of more advanced imbalance price publishing techniques.", "published": "2024-11-06T15:49:28Z", "authors": ["Fabio Pavirani", "Jonas Van Gompel", "Seyed Soroush Karimi Madahi", "Bert Claessens", "Chris Develder"], "categories": ["eess.SY", "cs.AI", "cs.LG"], "doi": "", "_query": "housing price text listing", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/1701.02646v1", "title": "Economic information from Smart Meter: Nexus Between Demand Profile and Electricity Retail Price Between Demand Profile and Electricity Retail Price", "abstract": "In this paper, we demonstrate that a consumer's marginal system impact is only determined by their demand profile rather than their demand level. Demand profile clustering is identical to cluster consumers according to their marginal impacts on system costs. A profile-based uniform-rate price is economically efficient as real-time pricing. We develop a criteria system to evaluate the economic efficiency of an implemented retail price scheme in a distribution system by comparing profile clustering and daily-average clustering. Our criteria system can examine the extent of a retail price scheme's inefficiency even without information about the distribution system's daily cost structure. We analyze data from a real distribution system in China. In this system, targeting each consumer's high-impact days is more efficient than target high-impact consumers.", "published": "2016-09-16T21:14:59Z", "authors": ["Yang Yu", "Guangyi Liu", "Wendong Zhu", "Fei Wang", "Bin Shu", "Kai Zhang", "Ram Rajagopal", "Nicolas Astier"], "categories": ["econ.GN"], "doi": "", "_query": "housing price text listing", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2504.02853v1", "title": "Mapping Technological Futures: Anticipatory Discourse Through Text Mining", "abstract": "The volatility and unpredictability of emerging technologies, such as artificial intelligence (AI), generate significant uncertainty, which is widely discussed on social media. This study examines anticipatory discourse surrounding technological futures by analysing 1.5 million posts from 400 key opinion leaders (KOLs) published on the X platform (from 2021 to 2023). Using advanced text mining techniques, including BERTopic modelling, sentiment, emotion, and attitude analyses, the research identifies 100 distinct topics reflecting anticipated tech-driven futures. Our findings emphasize the dual role of KOLs in framing \\textit{present futures} -- optimistic visions of transformative technologies like AI and IoT -- and influencing \\textit{future presents}, where these projections shape contemporary societal and geopolitical debates. Positive emotions such as Hope dominate, outweighing Anxiety, particularly in topics like ``Machine Learning, Data Science, and Deep Learning,'' while discussions around ``Climate Change'' and ``War, Ukraine, and Trump People'' elicit \\textit{Anxiety}. By framing technologies as solutions to societal challenges, KOLs act as mediators of societal narratives, bridging imagined futures and current realities. These insights underscore their pivotal role in directing public attention with emerging technologies during periods of heightened uncertainty, advancing our understanding of anticipatory discourse in technology-mediated contexts.", "published": "2025-03-25T15:20:15Z", "authors": ["Maciej Skorski", "Alina Landowska", "Krzysztof Rajda"], "categories": ["cs.SI", "cs.CL", "cs.CY", "cs.LG"], "doi": "", "_query": "housing price text listing", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/1209.4718v1", "title": "Stock Price Dynamics and Option Valuations under Volatility Feedback Effect", "abstract": "According to the volatility feedback effect, an unexpected increase in squared volatility leads to an immediate decline in the price-dividend ratio. In this paper, we consider the properties of stock price dynamics and option valuations under the volatility feedback effect by modeling the joint dynamics of stock price, dividends, and volatility in continuous time. Most importantly, our model predicts the negative effect of an increase in squared return volatility on the value of deep-in-the-money call options and, furthermore, attempts to explain the volatility puzzle. We theoretically demonstrate a mechanism by which the market price of diffusion return risk, or an equity risk-premium, affects option prices and empirically illustrate how to identify that mechanism using forward-looking information on option contracts. Our theoretical and empirical results support the relevance of the volatility feedback effect. Overall, the results indicate that the prevailing practice of ignoring the time-varying dividend yield in option pricing can lead to oversimplification of the stock market dynamics.", "published": "2012-09-21T06:26:38Z", "authors": ["Juho Kanniainen", "Robert Piché"], "categories": ["q-fin.PR", "q-fin.ST"], "doi": "10.1016/j.physa.2012.10.004", "_query": "housing price text listing", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/1404.5203v1", "title": "Towards a Monotonicity-Compliant Price Index for the Art Market", "abstract": "Notwithstanding almost forty years of efforts, the market for paintings still lacks a widely accepted price index. In this paper, we introduce a simple and intuitive metric to construct such index. Our metric is based on the price of a painting divided by its area. This formulation rests on a solid mathematical foundation as it corresponds to a particular type of hedonic model. However, unlike indexes based on the time-dummy coefficients of conventional hedonic models, this index satisfies the monotonicity condition. We demonstrate with a simple example the advantages of our metric. We also show the dangers of relying on the time-dummy coefficients of conventional hedonic models to estimate returns and generate price indexes.", "published": "2014-04-21T14:11:41Z", "authors": ["Ventura Charlin", "Arturo Cifuentes"], "categories": ["q-fin.GN"], "doi": "", "_query": "housing price text listing", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2411.05706v1", "title": "Image2Text2Image: A Novel Framework for Label-Free Evaluation of Image-to-Text Generation with Text-to-Image Diffusion Models", "abstract": "Evaluating the quality of automatically generated image descriptions is a complex task that requires metrics capturing various dimensions, such as grammaticality, coverage, accuracy, and truthfulness. Although human evaluation provides valuable insights, its cost and time-consuming nature pose limitations. Existing automated metrics like BLEU, ROUGE, METEOR, and CIDEr attempt to fill this gap, but they often exhibit weak correlations with human judgment. To address this challenge, we propose a novel evaluation framework called Image2Text2Image, which leverages diffusion models, such as Stable Diffusion or DALL-E, for text-to-image generation. In the Image2Text2Image framework, an input image is first processed by a selected image captioning model, chosen for evaluation, to generate a textual description. Using this generated description, a diffusion model then creates a new image. By comparing features extracted from the original and generated images, we measure their similarity using a designated similarity metric. A high similarity score suggests that the model has produced a faithful textual description, while a low score highlights discrepancies, revealing potential weaknesses in the model's performance. Notably, our framework does not rely on human-annotated reference captions, making it a valuable tool for assessing image captioning models. Extensive experiments and human evaluations validate the efficacy of our proposed Image2Text2Image evaluation framework. The code and dataset will be published to support further research in the community.", "published": "2024-11-08T17:07:01Z", "authors": ["Jia-Hong Huang", "Hongyi Zhu", "Yixian Shen", "Stevan Rudinac", "Evangelos Kanoulas"], "categories": ["cs.CV", "cs.CL"], "doi": "", "_query": "housing price text listing", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2011.05884v2", "title": "Efficient List-Decoding with Constant Alphabet and List Sizes", "abstract": "We present an explicit and efficient algebraic construction of capacity-achieving list decodable codes with both constant alphabet and constant list sizes. More specifically, for any $R \\in (0,1)$ and $ε>0$, we give an algebraic construction of an infinite family of error-correcting codes of rate $R$, over an alphabet of size $(1/ε)^{O(1/ε^2)}$, that can be list decoded from a $(1-R-ε)$-fraction of errors with list size at most $\\exp(\\mathrm{poly}(1/ε))$. Moreover, the codes can be encoded in time $\\mathrm{poly}(1/ε, n)$, the output list is contained in a linear subspace of dimension at most $\\mathrm{poly}(1/ε)$, and a basis for this subspace can be found in time $\\mathrm{poly}(1/ε, n)$. Thus, both encoding and list decoding can be performed in fully polynomial-time $\\mathrm{poly}(1/ε, n)$, except for pruning the subspace and outputting the final list which takes time $\\exp(\\mathrm{poly}(1/ε))\\cdot\\mathrm{poly}(n)$. Our codes are quite natural and structured. Specifically, we use algebraic-geometric (AG) codes with evaluation points restricted to a subfield, and with the message space restricted to a (carefully chosen) linear subspace. Our main observation is that the output list of AG codes with subfield evaluation points is contained in an affine shift of the image of a block-triangular-Toeplitz (BTT) matrix, and that the list size can potentially be reduced to a constant by restricting the message space to a BTT evasive subspace, which is a large subspace that intersects the image of any BTT matrix in a constant number of points. We further show how to explicitly construct such BTT evasive subspaces, based on the explicit subspace designs of Guruswami and Kopparty (Combinatorica, 2016), and composition.", "published": "2020-11-11T16:25:08Z", "authors": ["Zeyu Guo", "Noga Ron-Zewi"], "categories": ["cs.CC", "cs.IT"], "doi": "", "_query": "housing price text listing", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2108.03625v3", "title": "Unifying Heterogeneous Electronic Health Records Systems via Text-Based Code Embedding", "abstract": "Substantial increase in the use of Electronic Health Records (EHRs) has opened new frontiers for predictive healthcare. However, while EHR systems are nearly ubiquitous, they lack a unified code system for representing medical concepts. Heterogeneous formats of EHR present a substantial barrier for the training and deployment of state-of-the-art deep learning models at scale. To overcome this problem, we introduce Description-based Embedding, DescEmb, a code-agnostic description-based representation learning framework for predictive modeling on EHR. DescEmb takes advantage of the flexibility of neural language understanding models while maintaining a neutral approach that can be combined with prior frameworks for task-specific representation learning or predictive modeling. We tested our model's capacity on various experiments including prediction tasks, transfer learning and pooled learning. DescEmb shows higher performance in overall experiments compared to code-based approach, opening the door to a text-based approach in predictive healthcare research that is not constrained by EHR structure nor special domain knowledge.", "published": "2021-08-08T12:47:42Z", "authors": ["Kyunghoon Hur", "Jiyoung Lee", "Jungwoo Oh", "Wesley Price", "Young-Hak Kim", "Edward Choi"], "categories": ["cs.LG", "cs.NE"], "doi": "", "_query": "housing price text listing", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/1711.09196v1", "title": "The Impact of an AirBnb Host's Listing Description 'Sentiment' and Length On Occupancy Rates", "abstract": "There has been significant literature regarding the way product review sentiment affects brand loyalty. Intrigued by how natural language influences consumer choice, we were motivated to examine whether an AirBnb host's occupancy rate (how often their listing is booked out of the days they indicated their listing was available) can be determined by the perceived sentiment and length of their description summary. Our main goal, more generally, was to determine which features, including (but not limited to) sentiment and description length, most influence a host's occupancy rate. We define sentiment score through a natural language algorithm process, based on the AFINN dictionary. Using AirBnB data on New York City, our hypothesis is that higher sentiment scores (more positive descriptions) and longer summary length lead to higher occupancy rates. Our results show that while longer summary length may positively influence occupancy rates, more positive summary descriptions have no effect. Instead, we find that other factors such as number of reviews and number of amenities, in addition to summary length, are better indicators of occupancy rate.", "published": "2017-11-25T06:19:27Z", "authors": ["Richard Diehl Martinez", "Anthony Carrington", "Tiffany Kuo", "Lena Tarhuni", "Nour Adel Zaki Abdel-Motaal"], "categories": ["stat.AP"], "doi": "", "_query": "housing price text listing", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/1510.04690v1", "title": "On Capturing the Spreading Dynamics over Trading Prices in the Market", "abstract": "While market is a social field where information flows over the interacting agents, there have been not so many methods to observe the spreading information in the prices comprising the market. By incorporating the entropy transfer in information theory in its relation to the Granger causality, the paper proposes a tree of weighted directed graph of market to detect the changes of price might affect other price changes. We compare the proposed analysis with the similar tree representation built from the correlation coefficients of stock prices in order to have insight of possibility in seeing the collective behavior of the market in general.", "published": "2015-10-15T19:58:00Z", "authors": ["Hokky Situngkir"], "categories": ["q-fin.ST"], "doi": "", "_query": "housing price text listing", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/1812.07415v2", "title": "Change of Measure in Midcurve Pricing", "abstract": "We derive measure change formulae required to price midcurve swaptions in the forward swap annuity measure with stochastic annuities' ratios. We construct the corresponding linear and exponential terminal swap rate pricing models and show how they capture the midcurve swaption correlation skew.", "published": "2018-12-10T09:36:39Z", "authors": ["K. E. Feldman"], "categories": ["q-fin.PR", "q-fin.MF"], "doi": "", "_query": "housing price text listing", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2609.18149v1", "title": "Research on the Price Prediction Algorithms of Major Cryptocurrencies and a Basic Transaction Framework", "abstract": "Through long-term observation and time series analysis of Bitcoin and Ethereum, we found the similarity in long-term consistent price trends, especially in the weekly K-line. We denoised and smoothed the historical data of their prices, and further derived the periodicity of their price change trends and the timing of buying and selling. We have for the first time proposed that the full application of central bank digital currencies (CBDC) is a key signal for investors to reduce their holdings of cryptocurrencies or even exit the market. In addition, we proposed momentum opening/closing prices to replace the traditional nominal opening/closing prices to accurately describe the price trends of the 24/7 financial trading market. We found that the cryptocurrency market can be regarded as a relatively independent financial market, thereby designing a safer and more efficient arbitrage strategy.", "published": "2026-09-16T05:30:19Z", "authors": ["Shengjian Chen"], "categories": ["cs.CE", "cs.GT"], "doi": "", "_query": "housing price text listing", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/1206.5224v4", "title": "Stock prices assessment: proposal of a new index based on volume weighted historical prices through the use of computer modeling", "abstract": "The importance of considering the volumes to analyze stock prices movements can be considered as a well-accepted practice in the financial area. However, when we look at the scientific production in this field, we still cannot find a unified model that includes volume and price variations for stock assessment purposes. In this paper we present a computer model that could fulfill this gap, proposing a new index to evaluate stock prices based on their historical prices and volumes traded. Besides the model can be considered mathematically very simple, it was able to improve significantly the performance of agents operating with real financial data. Based on the results obtained, and also on the very intuitive logic of our model, we believe that the index proposed here can be very useful to help investors on the activity of determining ideal price ranges for buying and selling stocks in the financial market.", "published": "2012-06-22T18:30:05Z", "authors": ["Tiago Colliri", "Fernando F. Ferreira"], "categories": ["q-fin.ST", "cs.LG"], "doi": "10.1109/BWSS.2012.23", "_query": "housing price text listing", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2504.18982v1", "title": "On Bitcoin Price Prediction", "abstract": "In recent years, cryptocurrencies have attracted growing attention from both private investors and institutions. Among them, Bitcoin stands out for its impressive volatility and widespread influence. This paper explores the predictability of Bitcoin's price movements, drawing a parallel with traditional financial markets. We examine whether the cryptocurrency market operates under the efficient market hypothesis (EMH) or if inefficiencies still allow opportunities for arbitrage. Our methodology combines theoretical reviews, empirical analyses, machine learning approaches, and time series modeling to assess the extent to which Bitcoin's price can be predicted. We find that while, in general, the Bitcoin market tends toward efficiency, specific conditions, including information asymmetries and behavioral anomalies, occasionally create exploitable inefficiencies. However, these opportunities remain difficult to systematically identify and leverage. Our findings have implications for both investors and policymakers, particularly regarding the regulation of cryptocurrency brokers and derivatives markets.", "published": "2025-04-26T17:48:11Z", "authors": ["Grégory Bournassenko"], "categories": ["q-fin.ST", "stat.OT"], "doi": "", "_query": "housing price text listing", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2111.04427v1", "title": "What Is the Price of Data? A Measurement Study of Commercial Data Marketplaces", "abstract": "A large number of Data Marketplaces (DMs) have appeared in the last few years to help owners monetise their data, and data buyers fuel their marketing process, train their ML models, and perform other data-driven decision processes. In this paper, we present a first of its kind measurement study of the growing DM ecosystem and shed light on several totally unknown facts about it. For example, we show that the median price of live data products sold under a subscription model is around US\\$1,400 per month. For one-off purchases of static data, the median price is around US\\$2,200. We analyse the prices of different categories of data and show that products about telecommunications, manufacturing, automotive, and gaming command the highest prices. We also develop classifiers for comparing prices across different DMs as well as a regression analysis for revealing features that correlate with data product prices.", "published": "2021-10-25T10:39:47Z", "authors": ["Santiago Andrés Azcoitia", "Costas Iordanou", "Nikolaos Laoutaris"], "categories": ["cs.CY"], "doi": "", "_query": "housing price text listing", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/1310.4067v2", "title": "On pricing kernels, information and risk", "abstract": "We discuss the finding that cross-sectional characteristic based models have yielded portfolios with higher excess monthly returns but lower risk than their arbitrage pricing theory counterparts in an analysis of equity returns of stocks listed on the JSE. Under the assumption of general no-arbitrage conditions, we argue that evidence in favour of characteristic based pricing implies that information is more likely assimilated by means of nonlinear pricing kernels for the markets considered.", "published": "2013-10-15T14:18:29Z", "authors": ["D. L. Wilcox", "T. J. Gebbie"], "categories": ["q-fin.PR", "q-fin.GN"], "doi": "10.1080/10293523.2014.994437", "_query": "housing price text listing", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/1804.09468v1", "title": "Price of Anarchy for Mechanisms with Risk-Averse Agents", "abstract": "We study the price of anarchy of mechanisms in the presence of risk-averse agents. Previous work has focused on agents with quasilinear utilities, possibly with a budget. Our model subsumes this as a special case but also captures that agents might be less sensitive to payments than in the risk-neutral model. We show that many positive price-of-anarchy results proved in the smoothness framework continue to hold in the more general risk-averse setting. A sufficient condition is that agents can never end up with negative quasilinear utility after playing an undominated strategy. This is true, e.g., for first-price and second-price auctions. For all-pay auctions, similar results do not hold: We show that there are Bayes-Nash equilibria with arbitrarily bad social welfare compared to the optimum.", "published": "2018-04-25T10:29:53Z", "authors": ["Thomas Kesselheim", "Bojana Kodric"], "categories": ["cs.GT"], "doi": "", "_query": "housing price text listing", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2312.01020v3", "title": "ResNLS: An Improved Model for Stock Price Forecasting", "abstract": "Stock prices forecasting has always been a challenging task. Although many research projects try to address the problem, few of them pay attention to the varying degrees of dependencies between stock prices. In this paper, we introduce a hybrid model that improves the prediction of stock prices by emphasizing the dependencies between adjacent stock prices. The proposed model, ResNLS, is mainly composed of two neural architectures, ResNet and LSTM. ResNet serves as a feature extractor to identify dependencies between stock prices, while LSTM analyzes the initial time series data with the combination of dependencies, which are considered as residuals. Our experiment reveals that when the closing price data for the previous 5 consecutive trading days is used as input, the performance of the model (ResNLS-5) is optimal compared to those with other inputs. Furthermore, ResNLS-5 demonstrates at least a 20% improvement over current state-of-the-art baselines. To verify whether ResNLS-5 can help clients effectively avoid risks and earn profits in the stock market, we construct a quantitative trading framework for back testing. The result shows that the trading strategy based on ResNLS-5 predictions can successfully mitigate losses during declining stock prices and generate profits in periods of rising stock prices.", "published": "2023-12-02T03:55:37Z", "authors": ["Yuanzhe Jia", "Ali Anaissi", "Basem Suleiman"], "categories": ["cs.LG", "cs.AI", "cs.CE"], "doi": "", "_query": "housing price text listing", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2407.12267v1", "title": "Generating 3D House Wireframes with Semantics", "abstract": "We present a new approach for generating 3D house wireframes with semantic enrichment using an autoregressive model. Unlike conventional generative models that independently process vertices, edges, and faces, our approach employs a unified wire-based representation for improved coherence in learning 3D wireframe structures. By re-ordering wire sequences based on semantic meanings, we facilitate seamless semantic integration during sequence generation. Our two-phase technique merges a graph-based autoencoder with a transformer-based decoder to learn latent geometric tokens and generate semantic-aware wireframes. Through iterative prediction and decoding during inference, our model produces detailed wireframes that can be easily segmented into distinct components, such as walls, roofs, and rooms, reflecting the semantic essence of the shape. Empirical results on a comprehensive house dataset validate the superior accuracy, novelty, and semantic fidelity of our model compared to existing generative models. More results and details can be found on https://vcc.tech/research/2024/3DWire.", "published": "2024-07-17T02:33:34Z", "authors": ["Xueqi Ma", "Yilin Liu", "Wenjun Zhou", "Ruowei Wang", "Hui Huang"], "categories": ["cs.CV", "cs.GR"], "doi": "", "_query": "housing price text listing", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/1404.2514v4", "title": "Quality Sensitive Price Competition in Spectrum Oligopoly:Part 1", "abstract": "We investigate a spectrum oligopoly market where primaries lease their channels to secondaries in lieu of financial remuneration. Transmission quality of a channel evolves randomly. Each primary has to select the price it would quote without knowing the transmission qualities of its competitors' channels. Each secondary buys a channel depending on the price and the transmission quality a channel offers. We formulate the price selection problem as a non co-operative game with primaries as players. In the one-shot game, we show that there exists a unique symmetric Nash Equilibrium(NE) strategy profile and explicitly compute it. Our analysis reveals that under the NE strategy profile a primary prices its channel to render high quality channel more preferable to the secondary; this negates the popular belief that prices ought to be selected to render channels equally preferable to the secondary regardless of their qualities. We show the loss of revenue in the asymptotic limit due to the non co-operation of primaries. In the repeated version of the game, we characterize a subgame perfect NE where a primary can attain a payoff arbitrarily close to the payoff it would obtain when primaries co-operate.", "published": "2014-04-06T04:14:03Z", "authors": ["Arnob Ghosh", "Saswati Sarkar"], "categories": ["cs.GT"], "doi": "", "_query": "housing price text listing", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2602.21274v1", "title": "Optimal extraction with an impact on diffusion-jump pricing", "abstract": "We study an optimal extraction problem where the agent's actions in the spot market exert an additive proportional negative impact on the commodity price. The commodity price dynamics, prior to any activity by the agent, are evolved by a drifted Brownian motion with jumps. The agent's primary aim is to identify an optimal extraction strategy that maximizes their expected net profits.", "published": "2026-02-24T16:13:16Z", "authors": ["Johanna Garzón", "Jhonatan S. Mora Rodríguez", "Harold A. Moreno-Franco"], "categories": ["math.OC"], "doi": "", "_query": "housing price text listing", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/1501.01892v3", "title": "Optimal Asset Liquidation with Multiplicative Transient Price Impact", "abstract": "We study a multiplicative transient price impact model for an illiquid financial market, where trading causes price impact which is multiplicative in relation to the current price, transient over time with finite rate of resilience, and non-linear in the order size. We construct explicit solutions for the optimal control and the value function of singular optimal control problems to maximize expected discounted proceeds from liquidating a given asset position. A free boundary problem, describing the optimal control, is solved for two variants of the problem where admissible controls are monotone or of bounded variation.", "published": "2015-01-08T16:08:05Z", "authors": ["Dirk Becherer", "Todor Bilarev", "Peter Frentrup"], "categories": ["math.OC", "math.PR", "q-fin.TR"], "doi": "10.1007/s00245-017-9418-0", "_query": "housing price text listing", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/1605.04948v2", "title": "Quantum theory of securities price formation in financial markets", "abstract": "We develop a theory of securities price formation and dynamics based on quantum approach and without presuming any similarities with quantum mechanics. Disorder introduced by trading environment leads to probability distribution of returns that is not a smooth curve, but a speckle-pattern fluctuating in both price coordinate and time. This means that any given return can at times acquire a substantial probability of occurring while remaining low on average in time. Still, due to local character of order interaction during price formation the distribution width grows smoothly, has a minimum value at small time scale and a square root behavior at large time scale. Examples of calibration to market data, both intraday and daily, are provided.", "published": "2016-04-12T19:45:07Z", "authors": ["Jack Sarkissian"], "categories": ["q-fin.TR"], "doi": "", "_query": "housing price text listing", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/1806.05401v1", "title": "The Theoretical Price of a Share-Based Payment with Performance Conditions and Implications for the Current Accounting Standards", "abstract": "Although the growth of share-based payments with performance conditions (hereafter, SPPC) is prominent today, the theoretical price of SPPC has not been sufficiently studied. Reflecting such a situation, the current accounting standards for share-based payments issued in 2004 have had many problems. This paper develops a theoretical SPPC price model with a framework for a marginal utility-based price, which previous studies proposed is the price of contingent claims in an incomplete market. This paper's contribution is fivefold. First, we restricted the stochastic process to a certain class to demonstrate how to consistently change all variables' probability distributions, which affect the SPPC payoff. Second, we explicitly indicated not only the stochastic processes of the stock price process and performance variables under the changed probability, but also how the changes in the performance variables' drift coefficients related to stock betas. Third, we proposed a convenient model in application that uses only a few parameters. Fourth, we provided a method to estimate the parameters and improve the estimation of both the price and parameters. Fifth, we illustrated the problems in current accounting standards and indicated how the theoretical price model can significantly improve them.", "published": "2018-06-14T07:41:26Z", "authors": ["Masahiro Fujimoto"], "categories": ["q-fin.MF"], "doi": "", "_query": "housing price text listing", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/1601.01128v1", "title": "Option pricing in the model with stochastic volatility driven by Ornstein--Uhlenbeck process. Simulation", "abstract": "We consider a discrete-time approximation of paths of an Ornstein--Uhlenbeck process as a mean for estimation of a price of European call option in the model of financial market with stochastic volatility. The Euler--Maruyama approximation scheme is implemented. We determine the estimates for the option price for predetermined sets of parameters. The rate of convergence of the price and an average volatility when discretization intervals tighten are determined. Discretization precision is analyzed for the case where the exact value of the price can be derived.", "published": "2016-01-06T10:29:43Z", "authors": ["Sergii Kuchuk-Iatsenko", "Yuliya Mishura"], "categories": ["q-fin.CP", "math.PR", "q-fin.PR"], "doi": "10.15559/15-VMSTA43", "_query": "housing price text listing", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/1605.06564v1", "title": "Double-Sided Energy Auction Equilibrium Under Price Anticipation", "abstract": "This paper investigates the problem of proportionally fair double sided energy auction involving buying and selling agents. The grid is assumed to be operating under islanded mode. A distributed auction algorithm that can be implemented by an aggregator, as well as a possible approach by which the agents may approximate price anticipation is considered. Equilibrium conditions arising due to price anticipation is analyzed. A modified auction to mitigate the resulting loss in efficiency due to such behavior is suggested. This modified auction allows the aggregate social welfare of the agents to be arbitrarily close to that attainable with price taking agents. Next, equilibrium conditions when the aggregator collects a surcharge price per unit of energy traded is examined. A biobjective optimization problem is identified that takes into account both the agents social welfare as well as the aggregator revenue from the surcharge. Results of extensive simulations, which corroborate the theoretical analysis, are reported.", "published": "2016-05-21T00:25:40Z", "authors": ["M. Nazif Faqiry", "Sanjoy Das"], "categories": ["cs.GT"], "doi": "", "_query": "housing price text listing", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/0905.3191v1", "title": "Dynamic and Non-Uniform Pricing Strategies for Revenue Maximization", "abstract": "We consider the Item Pricing problem for revenue maximization in the limited supply setting, where a single seller with $n$ items caters to $m$ buyers with unknown subadditive valuation functions who arrive in a sequence. The seller sets the prices on individual items. Each buyer buys a subset of yet unsold items that maximizes her utility. Our goal is to design pricing strategies that guarantee an expected revenue that is within a small factor $α$ of the maximum possible social welfare -- an upper bound on the maximum revenue that can be generated. Most earlier work has focused on the unlimited supply setting, where selling items to some buyer does not affect their availability to the future buyers. Balcan et. al. (EC 2008) studied the limited supply setting, giving a randomized strategy that assigns a single price to all items (uniform strategy), and never changes it (static strategy), that gives an $2^{O(\\sqrt{\\log n \\log \\log n})}$-approximation, and moreover, no static uniform pricing strategy can give better than $2^{Ω(\\log^{1/4} n)}$- approximation. We improve this lower bound to $2^{Ω(sqrt{\\log n})}$. We consider dynamic uniform strategies, which can change the price upon the arrival of each buyer but the price on all unsold items is the same at all times, and static non-uniform strategies, which can assign different prices to different items but can never change it after setting it initially. We design such pricing strategies that give a poly-logarithmic approximation to maximum revenue. Thus in the limited supply setting, our results highlight a strong separation between the power of dynamic and non-uniform pricing versus static uniform pricing. To our knowledge, this is the first non-trivial analysis of dynamic and non-uniform pricing schemes for revenue maximization.", "published": "2009-05-19T22:15:12Z", "authors": ["Tanmoy Chakraborty", "Zhiyi Huang", "Sanjeev Khanna"], "categories": ["cs.GT"], "doi": "", "_query": "housing price text listing", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2002.03319v1", "title": "Crowded trades, market clustering, and price instability", "abstract": "Crowded trades by similarly trading peers influence the dynamics of asset prices, possibly creating systemic risk. We propose a market clustering measure using granular trading data. For each stock the clustering measure captures the degree of trading overlap among any two investors in that stock. We investigate the effect of crowded trades on stock price stability and show that market clustering has a causal effect on the properties of the tails of the stock return distribution, particularly the positive tail, even after controlling for commonly considered risk drivers. Reduced investor pool diversity could thus negatively affect stock price stability.", "published": "2020-02-09T08:53:43Z", "authors": ["Marc van Kralingen", "Diego Garlaschelli", "Karolina Scholtus", "Iman van Lelyveld"], "categories": ["q-fin.ST", "q-fin.RM", "q-fin.TR"], "doi": "10.3390/e23030336", "_query": "housing price text listing", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/1512.06582v1", "title": "Asymptotic pricing in large financial markets", "abstract": "The problem of hedging and pricing sequences of contingent claims in large financial markets is studied. Connection between asymptotic arbitrage and behavior of the $α$~-~quantile price is shown. The large Black-Scholes model is carefully examined.", "published": "2015-12-21T11:14:07Z", "authors": ["Michał Barski"], "categories": ["q-fin.MF", "math.PR"], "doi": "", "_query": "housing price text listing", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/0904.1480v1", "title": "Risk-averse asymptotics for reservation prices", "abstract": "An investor's risk aversion is assumed to tend to infinity. In a fairly general setting, we present conditions ensuring that the respective utility indifference prices of a given contingent claim converge to its super replication price.", "published": "2009-04-09T08:56:23Z", "authors": ["Laurence Carassus", "Miklos Rasonyi"], "categories": ["math.PR"], "doi": "", "_query": "housing price text listing", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/1508.03651v3", "title": "A conjecture about the efficiency of first price mechanisms", "abstract": "We present different versions of a conjecture which would express that first price mechanisms never work very badly in a very general class of problems. The definitions include most of the problems where there is a principal (seller) who has the right to exclude others from the game. The exact definitions are motivated by the \"first price mechanism\" in E Cs: \"Efficient Teamwork\", but the conjecture is relevant for most auction problems, e.g. for combinatorial auctions.", "published": "2015-04-18T13:30:47Z", "authors": ["Endre Csóka"], "categories": ["econ.GN", "cs.GT"], "doi": "", "_query": "housing price text listing", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/1603.03874v1", "title": "Analysis of the nonlinear option pricing model under variable transaction costs", "abstract": "In this paper we analyze a nonlinear Black--Scholes model for option pricing under variable transaction costs. The diffusion coefficient of the nonlinear parabolic equation for the price $V$ is assumed to be a function of the underlying asset price and the Gamma of the option. We show that the generalizations of the classical Black--Scholes model can be analyzed by means of transformation of the fully nonlinear parabolic equation into a quasilinear parabolic equation for the second derivative of the option price. We show existence of a classical smooth solution and prove useful bounds on the option prices. Furthermore, we construct an effective numerical scheme for approximation of the solution. The solutions are obtained by means of the efficient numerical discretization scheme of the Gamma equation. Several computational examples are presented.", "published": "2016-03-12T08:58:51Z", "authors": ["Daniel Sevcovic", "Magdalena Zitnanska"], "categories": ["q-fin.PR"], "doi": "", "_query": "housing price text listing", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/cond-mat/0408277v1", "title": "Multifractality in the stock market: price increments versus waiting times", "abstract": "By applying the multifractal detrended fluctuation analysis to the high-frequency tick-by-tick data from Deutsche Börse both in the price and in the time domains, we investigate multifractal properties of the time series of logarithmic price increments and inter-trade intervals of time. We show that both quantities reveal multiscaling and that this result holds across different stocks. The origin of the multifractal character of the corresponding dynamics is, among others, the long-range correlations in price increments and in inter-trade time intervals as well as the non-Gaussian distributions of the fluctuations. Since the transaction-to-transaction price increments do not strongly depend on or are almost independent of the inter-trade waiting times, both can be sources of the observed multifractal behaviour of the fixed-delay returns and volatility. The results presented also allow one to evaluate the applicability of the Multifractal Model of Asset Returns in the case of tick-by-tick data.", "published": "2004-08-12T12:21:35Z", "authors": ["P. Oswiecimka", "J. Kwapien", "S. Drozdz"], "categories": ["cond-mat.other", "q-fin.ST"], "doi": "10.1016/j.physa.2004.08.025", "_query": "housing price text listing", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/math/0703077v1", "title": "Ratio of price to expectation and complete Bernstein functions", "abstract": "For a game with positive expectation and some negative profit, a unique price exists, at which the optimal proportion of investment reaches its maximum. For a game with parallel translated profit, the ratio of this price to its expectation tends to converge toward less than or equal to 1/2 if its expectation converges to 0. In this paper, we will investigate such properties by using the integral representations of a complete Bernstein function and establish several Abelian and Tauberian theorems.", "published": "2007-03-03T02:59:04Z", "authors": ["Yukio Hirashita"], "categories": ["math.OC"], "doi": "", "_query": "housing price text listing", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2507.12733v3", "title": "The Query Complexity of Uniform Pricing", "abstract": "Real-world pricing mechanisms are typically optimized using training data, a setting corresponding to the \\textit{pricing query complexity} problem in Mechanism Design. The previous work [LSTW23] studies the \\textit{single-distribution} case, with tight bounds of $\\widetildeΘ(\\varepsilon^{-3})$ for a \\textit{general} distribution and $\\widetildeΘ(\\varepsilon^{-2})$ for either a \\textit{regular} or \\textit{monotone-hazard-rate (MHR)} distribution, where $\\varepsilon \\in (0, 1)$ denotes the (additive) revenue loss of a learned uniform price relative to the Bayesian-optimal uniform price. This can be directly interpreted as ``the query complexity of the {\\em \\textsf{Uniform Pricing}} mechanism, in the \\textit{single-distribution} case''. Yet in the \\textit{multi-distribution} case, can the regularity and MHR conditions still lead to improvements over the tight bound $\\widetildeΘ(\\varepsilon^{-3})$ for general distributions? We answer this question in the negative, by establishing a (near-)matching lower bound $Ω(\\varepsilon^{-3})$ for either \\textit{two regular distributions} or \\textit{three MHR distributions}. We also address the \\textit{regret minimization} problem and, in comparison with the folklore upper bound $\\widetilde{O}(T^{2 / 3})$ for general distributions (see, e.g., [SW24]), establish a (near-)matching lower bound $Ω(T^{2 / 3})$ for either \\textit{two regular distributions} or \\textit{three MHR distributions}, via a black-box reduction. Again, this is in stark contrast to the tight bound $\\widetildeΘ(T^{1 / 2})$ for a single regular or MHR distribution.", "published": "2025-07-17T02:31:01Z", "authors": ["Houshuang Chen", "Yaonan Jin", "Pinyan Lu", "Chihao Zhang"], "categories": ["cs.GT"], "doi": "", "_query": "housing price text listing", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2305.05134v1", "title": "To AI or not to AI, to Buy Local or not to Buy Local: A Mathematical Theory of Real Price", "abstract": "In the past several decades, the world's economy has become increasingly globalized. On the other hand, there are also ideas advocating the practice of ``buy local'', by which people buy locally produced goods and services rather than those produced farther away. In this paper, we establish a mathematical theory of real price that determines the optimal global versus local spending of an agent which achieves the agent's optimal tradeoff between spending and obtained utility. Our theory of real price depends on the asymptotic analysis of a Markov chain transition probability matrix related to the network of producers and consumers. We show that the real price of a product or service can be determined from the involved Markov chain matrix, and can be dramatically different from the product's label price. In particular, we show that the label prices of products and services are often not ``real'' or directly ``useful'': given two products offering the same myopic utility, the one with lower label price may not necessarily offer better asymptotic utility. This theory shows that the globality or locality of the products and services does have different impacts on the spending-utility tradeoff of a customer. The established mathematical theory of real price can be used to determine whether to adopt or not to adopt certain artificial intelligence (AI) technologies from an economic perspective.", "published": "2023-05-09T02:43:47Z", "authors": ["Huan Cai", "Catherine Xu", "Weiyu Xu"], "categories": ["econ.TH", "cs.AI", "math.OC"], "doi": "", "_query": "housing price text listing", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2311.02199v1", "title": "Merger Interactions Enhance Star Formation Rates in Galaxy Group Housing QSO PKS0405$-$123 and Gaseous Nebulae", "abstract": "The star formation rate (SFR) of galaxies can change due to interactions between galaxies, stellar feedback ejection of gas into the circumgalactic medium, and energy injection from accretion onto black holes. However, it is not clear which of these processes dominantly alters the formation of stars within galaxies. Johnson et al. (2018) reported the discovery of large gaseous nebulae in the intragroup medium of a galaxy group housing QSO PKS0405$-$123 and hypothesized they were created by galaxy interactions. We identify a sample of 30 group member galaxies at z$\\sim$0.57 from the VLT/MUSE observations of the field and calculate their [OII]$λ$$λ$3727,3729 SFRs in order to investigate whether the QSO and nebulae have affected the SFRs of the surrounding galaxies. We find that star formation is more prevalent in galaxies within the nebulae, signifying galaxy interactions are fueling higher SFRs.", "published": "2023-11-03T19:03:38Z", "authors": ["Ian E. Wolter", "Michelle A. Berg", "John Chisholm"], "categories": ["astro-ph.GA"], "doi": "10.3847/2515-5172/ad07df", "_query": "housing price text listing", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/1901.02590v3", "title": "Secure list decoding", "abstract": "We propose a new concept of secure list decoding. While the conventional list decoding requires that the list contains the transmitted message, secure list decoding requires the following additional security conditions. The first additional security condition is the impossibility of the correct decoding, i.e., the receiver cannot uniquely identify the transmitted message even though the transmitted message is contained in the list. This condition can be trivially satisfied when the transmission rate is larger than the channel capacity. The other additional security condition is the impossibility for the sender to estimate another element of the decoded list except for the transmitted message. This protocol can be used for anonymous auction, which realizes the anonymity for bidding.", "published": "2019-01-09T03:27:43Z", "authors": ["Masahito Hayashi"], "categories": ["cs.IT", "cs.CR"], "doi": "", "_query": "housing price text listing", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/1304.3875v3", "title": "Game-theoretic Understanding of Price Dynamics in Mobile Communication Services", "abstract": "In the mobile communication services, users wish to subscribe to high quality service with a low price level, which leads to competition between mobile network operators (MNOs). The MNOs compete with each other by service prices after deciding the extent of investment to improve quality of service (QoS). Unfortunately, the theoretic backgrounds of price dynamics are not known to us, and as a result, effective network planning and regulative actions are hard to make in the competitive market. To explain this competition more detail, we formulate and solve an optimization problem applying the two-stage Cournot and Bertrand competition model. Consequently, we derive a price dynamics that the MNOs increase and decrease their service prices periodically, which completely explains the subsidy dynamics in the real world. Moving forward, to avoid this instability and inefficiency, we suggest a simple regulation rule which leads to a Pareto-optimal equilibrium point. Moreover, we suggest regulator's optimal actions corresponding to user welfare and the regulator's revenue.", "published": "2013-04-14T03:36:34Z", "authors": ["Seung Min Yu", "Seong-Lyun Kim"], "categories": ["cs.GT"], "doi": "", "_query": "housing price text listing", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/1910.05598v1", "title": "SmokEng: Towards Fine-grained Classification of Tobacco-related Social Media Text", "abstract": "Contemporary datasets on tobacco consumption focus on one of two topics, either public health mentions and disease surveillance, or sentiment analysis on topical tobacco products and services. However, two primary considerations are not accounted for, the language of the demographic affected and a combination of the topics mentioned above in a fine-grained classification mechanism. In this paper, we create a dataset of 3144 tweets, which are selected based on the presence of colloquial slang related to smoking and analyze it based on the semantics of the tweet. Each class is created and annotated based on the content of the tweets such that further hierarchical methods can be easily applied. Further, we prove the efficacy of standard text classification methods on this dataset, by designing experiments which do both binary as well as multi-class classification. Our experiments tackle the identification of either a specific topic (such as tobacco product promotion), a general mention (cigarettes and related products) or a more fine-grained classification. This methodology paves the way for further analysis, such as understanding sentiment or style, which makes this dataset a vital contribution to both disease surveillance and tobacco use research.", "published": "2019-10-12T17:06:07Z", "authors": ["Kartikey Pant", "Venkata Himakar Yanamandra", "Alok Debnath", "Radhika Mamidi"], "categories": ["cs.CL"], "doi": "10.18653/v1/D19-5524", "_query": "housing price text listing", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2502.02543v1", "title": "Posted Price Mechanisms for Online Allocation with Diseconomies of Scale", "abstract": "This paper addresses the online $k$-selection problem with diseconomies of scale (OSDoS), where a seller seeks to maximize social welfare by optimally pricing items for sequentially arriving buyers, accounting for increasing marginal production costs. Previous studies have investigated deterministic dynamic pricing mechanisms for such settings. However, significant challenges remain, particularly in achieving optimality with small or finite inventories and developing effective randomized posted price mechanisms. To bridge this gap, we propose a novel randomized dynamic pricing mechanism for OSDoS, providing a tighter lower bound on the competitive ratio compared to prior work. Our approach ensures optimal performance in small inventory settings (i.e., when $k$ is small) and surpasses existing online mechanisms in large inventory settings (i.e., when $k$ is large), leading to the best-known posted price mechanism for optimizing online selection and allocation with diseconomies of scale across varying inventory sizes.", "published": "2025-02-04T18:13:50Z", "authors": ["Hossein Nekouyan Jazi", "Bo Sun", "Raouf Boutaba", "Xiaoqi Tan"], "categories": ["cs.GT", "cs.DS"], "doi": "", "_query": "housing price text listing", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2111.11315v2", "title": "Investing in crypto: speculative bubbles and cyclic stochastic price pumps", "abstract": "The problem of investing into a cryptocurrency market requires good understanding of the processes that regulate the price of the currency. In this paper we offer a view of a cryptocurrency market as an environment for realization of a self-organized speculative scheme that results in a formation of a characteristic price bubble as a transient phenomenon. We use microscale, agent-based models to simulate the system behavior and derive macroscale ODE models to estimate such parameters as the return rate and the market value of investments. We provide the formula for the total risk of the system as a sum of two independent components, one being characteristic of the price bubble and the other of the investor behavior.", "published": "2021-11-22T16:16:16Z", "authors": ["Misha Perepelitsa"], "categories": ["q-fin.TR"], "doi": "", "_query": "housing price text listing", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/1202.6632v1", "title": "Coherent Price Systems and Uncertainty-Neutral Valuation", "abstract": "We consider fundamental questions of arbitrage pricing arising when the uncertainty model is given by a set of possible mutually singular probability measures. With a single probability model, essential equivalence between the absence of arbitrage and the existence of an equivalent martingale measure is a folk theorem, see Harrison and Kreps (1979). We establish a microeconomic foundation of sublinear price systems and present an extension result. In this context we introduce a prior dependent notion of marketed spaces and viable price systems. We associate this extension with a canonically altered concept of equivalent symmetric martingale measure sets, in a dynamic trading framework under absence of prior depending arbitrage. We prove the existence of such sets when volatility uncertainty is modeled by a stochastic differential equation, driven by Peng's G-Brownian motions.", "published": "2012-02-29T18:23:51Z", "authors": ["Patrick Beißner"], "categories": ["q-fin.GN"], "doi": "", "_query": "housing price text listing", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2208.14231v1", "title": "Prediction-based One-shot Dynamic Parking Pricing", "abstract": "Many U.S. metropolitan cities are notorious for their severe shortage of parking spots. To this end, we present a proactive prediction-driven optimization framework to dynamically adjust parking prices. We use state-of-the-art deep learning technologies such as neural ordinary differential equations (NODEs) to design our future parking occupancy rate prediction model given historical occupancy rates and price information. Owing to the continuous and bijective characteristics of NODEs, in addition, we design a one-shot price optimization method given a pre-trained prediction model, which requires only one iteration to find the optimal solution. In other words, we optimize the price input to the pre-trained prediction model to achieve targeted occupancy rates in the parking blocks. We conduct experiments with the data collected in San Francisco and Seattle for years. Our prediction model shows the best accuracy in comparison with various temporal or spatio-temporal forecasting models. Our one-shot optimization method greatly outperforms other black-box and white-box search methods in terms of the search time and always returns the optimal price solution.", "published": "2022-08-30T12:59:58Z", "authors": ["Seoyoung Hong", "Heejoo Shin", "Jeongwhan Choi", "Noseong Park"], "categories": ["cs.LG", "cs.AI", "math.OC"], "doi": "10.1145/3511808.3557421", "_query": "housing price text listing", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2207.11332v2", "title": "Comparing baseball players across eras via novel Full House Modeling", "abstract": "A new methodological framework suitable for era-adjusting baseball statistics is developed in this article. Within this methodological framework specific models are motivated. We call these models Full House Models. Full House Models work by balancing the achievements of Major League Baseball (MLB) players within a given season and the size of the MLB talent pool from which a player came. We demonstrate the utility of Full House Models in an application of comparing baseball players' performance statistics across eras. Our results reveal a new ranking of baseball's greatest players which include several modern players among the top all-time players. Modern players are elevated by Full House Modeling because they come from a larger talent pool. Sensitivity and multiverse analyses which investigate the how results change with changes to modeling inputs including the estimate of the talent pool are presented.", "published": "2022-07-22T20:53:52Z", "authors": ["Shen Yan", "Adrian Burgos", "Christopher Kinson", "Daniel J. Eck"], "categories": ["stat.AP", "stat.ME"], "doi": "", "_query": "housing price text listing", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2609.35402v1", "title": "Persistent Partners Raise Prices Among Learning Agents", "abstract": "When pricing agents meet repeatedly on a platform, the platform decides who faces whom. We ask whether that choice moves the prices the agents learn, and whether a rise comes with learned punishment. In a pre-registered randomised experiment in the Bertrand duopoly of Calvano et al., each agent's price is set by a tabular Q-learning module, not by the small language model attached to it, and we randomise whether each agent keeps its partner, sees its rival's prices and can send messages. Keeping the same partner raises the level of profits, averaged over training, by 0.27 of the gap between competitive and monopoly profit (95% CI 0.20 to 0.35, all twenty paired runs positive), our registered primary result, and the resting price by 0.17 of the Nash-to-monopoly range (post hoc). A plain tabular learner reproduces the effect in all 25 further blocks, and there one permanent partner raises the level more than about three do (+0.23 against +0.05, exploratory). Where rival prices are hidden, the price-setting module cannot see a cut, so cannot punish it, yet the resting price rises as much and the rise lasts to the end of training, while with visible rivals it shrinks with longer training (post hoc). Where the rival is visible, a static best responder accounts for a third to a half of what a forced-deviation probe reads as punishment, on the starts where the rival can see the cut, and net of it the registered test of learned punishment is inconclusive. A test that looks only for punishment would thus miss the rise where the rival is hidden, while a check for profitable deviations flags most of those prices (post hoc). In an exploratory extension, untrained Qwen2.5 7B and 14B models under one prompt show the effect when the rival's price is left out of the prompt and inconsistently when it is shown, the 7B result replicating on fresh blocks, while two other model families show none.", "published": "2026-09-28T15:24:23Z", "authors": ["Paul-Peter Arslan", "Yubin Kim", "Xiao Xiao"], "categories": ["cs.LG", "cs.MA"], "doi": "", "_query": "housing price text listing", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/1805.11317v1", "title": "Neural networks for stock price prediction", "abstract": "Due to the extremely volatile nature of financial markets, it is commonly accepted that stock price prediction is a task full of challenge. However in order to make profits or understand the essence of equity market, numerous market participants or researchers try to forecast stock price using various statistical, econometric or even neural network models. In this work, we survey and compare the predictive power of five neural network models, namely, back propagation (BP) neural network, radial basis function (RBF) neural network, general regression neural network (GRNN), support vector machine regression (SVMR), least squares support vector machine regresssion (LS-SVMR). We apply the five models to make price prediction of three individual stocks, namely, Bank of China, Vanke A and Kweichou Moutai. Adopting mean square error and average absolute percentage error as criteria, we find BP neural network consistently and robustly outperforms the other four models.", "published": "2018-05-29T09:14:49Z", "authors": ["Yue-Gang Song", "Yu-Long Zhou", "Ren-Jie Han"], "categories": ["q-fin.ST", "cs.LG", "stat.ML"], "doi": "", "_query": "housing price text listing", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/1202.4332v1", "title": "A parsimonious model for intraday European option pricing", "abstract": "A stochastic model for pure-jump diffusion (the compound renewal process) can be used as a zero-order approximation and as a phenomenological description of tick-by-tick price fluctuations. This leads to an exact and explicit general formula for the martingale price of a European call option. A complete derivation of this result is presented by means of elementary probabilistic tools.", "published": "2012-02-20T14:10:09Z", "authors": ["Enrico Scalas", "Mauro Politi"], "categories": ["q-fin.PR", "math.PR"], "doi": "", "_query": "housing price text listing", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/1907.12665v1", "title": "Airbnb Price Prediction Using Machine Learning and Sentiment Analysis", "abstract": "Pricing a rental property on Airbnb is a challenging task for the owner as it determines the number of customers for the place. On the other hand, customers have to evaluate an offered price with minimal knowledge of an optimal value for the property. This paper aims to develop a reliable price prediction model using machine learning, deep learning, and natural language processing techniques to aid both the property owners and the customers with price evaluation given minimal available information about the property. Features of the rentals, owner characteristics, and the customer reviews will comprise the predictors, and a range of methods from linear regression to tree-based models, support-vector regression (SVR), K-means Clustering (KMC), and neural networks (NNs) will be used for creating the prediction model.", "published": "2019-07-29T21:45:42Z", "authors": ["Pouya Rezazadeh Kalehbasti", "Liubov Nikolenko", "Hoormazd Rezaei"], "categories": ["cs.LG", "stat.ML"], "doi": "10.1007/978-3-030-84060-0_11", "_query": "housing price text listing", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2603.25696v1", "title": "Input-Output Price Parity and Farm Profitability: A Strategic Perspective for Karnataka", "abstract": "Agricultural pricing policies are crucial for farm profitability and food security in India. This study analysed how input and output prices significantly influence the profitability of cereals in Karnataka, with the strategic support prices playing a crucial role in maintaining the price parity. The average annual TFP growth was 1.041 per cent. Rising input costs, particularly for human labour, led to reduced profitability for Jowar (6.12 per cent) and Ragi (4.89 per cent). The net effect was adverse for Jowar (-1.50 per cent) and Ragi (-0.86 per cent) due to rising input costs outpacing output prices. The study recommended increasing the MSP for Jowar (60 per cent) and Ragi (46.24 per cent) above the existing levels. A strategic price adjusted for changing input costs can stabilise farm incomes and promote sustainable production, enabling efficient pricing policies.", "published": "2026-03-26T17:44:41Z", "authors": [" Vaishnavi", " Lokesha", " H.", " Vedamurthy", "K. B.", "Manojkumar Patil"], "categories": ["econ.GN"], "doi": "10.35716/ijed-25143", "_query": "housing price text listing", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/1712.01570v1", "title": "Robust toll pricing: A novel approach", "abstract": "We study a robust toll pricing problem where toll setters and users have different level of information when taking their decisions. Toll setters do not have full information on the costs of the network and rely on historical information when determining toll rates, whereas users decide on the path to use from origin to destination knowing toll rates and having, in addition, more accurate traffic data. Toll setters often also face constraints on price experimentation which means less opportunity for price revision. Motivated by this we propose a novel robust pricing methodology for fixing prices where we take non-adversarial view of nature different from the existing robust approaches. We show that our non-adversarial robustness results in less conservative pricing decisions compared to traditional adversarial nature setting. We start by first considering a single origin-destination parallel network in this new robust setting and formulate the robust toll pricing problem as a distributionally robust optimization problem, for which we develop an exact algorithm based on a mixed-integer programming formulation and a heuristic based on two-point support distribution. We further extend our formulations to more general networks and show how our algorithms can be adapted for the general networks. Finally, we illustrate the usefulness of our approach by means of numerical experiments both on randomly generated networks and on the data recorded on the road network of the city of Chicago.", "published": "2017-12-05T11:08:00Z", "authors": ["Trivikram Dokka", "Alain B. Zemkoho", "Sonali Sen Gupta", "Fabrice T. Nobibon"], "categories": ["math.OC", "cs.GT"], "doi": "", "_query": "housing price text listing", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/0804.2589v2", "title": "Option pricing under stochastic volatility: the exponential Ornstein-Uhlenbeck model", "abstract": "We study the pricing problem for a European call option when the volatility of the underlying asset is random and follows the exponential Ornstein-Uhlenbeck model. The random diffusion model proposed is a two-dimensional market process that takes a log-Brownian motion to describe price dynamics and an Ornstein-Uhlenbeck subordinated process describing the randomness of the log-volatility. We derive an approximate option price that is valid when (i) the fluctuations of the volatility are larger than its normal level, (ii) the volatility presents a slow driving force toward its normal level and, finally, (iii) the market price of risk is a linear function of the log-volatility. We study the resulting European call price and its implied volatility for a range of parameters consistent with daily Dow Jones Index data.", "published": "2008-04-16T13:26:50Z", "authors": ["Josep Perello", "Ronnie Sircar", "Jaume Masoliver"], "categories": ["q-fin.PR", "physics.comp-ph", "physics.soc-ph"], "doi": "10.1088/1742-5468/2008/06/P06010", "_query": "housing price text listing", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2203.05948v1", "title": "Block-Sparse Adversarial Attack to Fool Transformer-Based Text Classifiers", "abstract": "Recently, it has been shown that, in spite of the significant performance of deep neural networks in different fields, those are vulnerable to adversarial examples. In this paper, we propose a gradient-based adversarial attack against transformer-based text classifiers. The adversarial perturbation in our method is imposed to be block-sparse so that the resultant adversarial example differs from the original sentence in only a few words. Due to the discrete nature of textual data, we perform gradient projection to find the minimizer of our proposed optimization problem. Experimental results demonstrate that, while our adversarial attack maintains the semantics of the sentence, it can reduce the accuracy of GPT-2 to less than 5% on different datasets (AG News, MNLI, and Yelp Reviews). Furthermore, the block-sparsity constraint of the proposed optimization problem results in small perturbations in the adversarial example.", "published": "2022-03-11T14:37:41Z", "authors": ["Sahar Sadrizadeh", "Ljiljana Dolamic", "Pascal Frossard"], "categories": ["cs.CL", "cs.LG"], "doi": "", "_query": "housing price text listing", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/1702.00215v1", "title": "A confidence-based model for asset and derivative prices in the BitCoin market", "abstract": "We endorse the idea, suggested in recent literature, that BitCoin prices are influenced by sentiment and confidence about the underlying technology; as a consequence, an excitement about the BitCoin system may propagate to BitCoin prices causing a Bubble effect, the presence of which is documented in several papers about the cryptocurrency. In this paper we develop a bivariate model in continuous time to describe the price dynamics of one BitCoin as well as the behavior of a second factor affecting the price itself, which we name confidence indicator. The two dynamics are possibly correlated and we also take into account a delay between the confidence indicator and its delivered effect on the BitCoin price. Statistical properties of the suggested model are investigated and its arbitrage-free property is shown. Further, based on risk-neutral evaluation, a quasi-closed formula is derived for European style derivatives on the BitCoin. A short numerical application is finally provided.", "published": "2017-02-01T11:51:34Z", "authors": ["Alessandra Cretarola", "Gianna Figà-Talamanca"], "categories": ["q-fin.MF"], "doi": "10.1007/s10203-019-00262-x", "_query": "housing price text listing", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/0911.1610v2", "title": "Pricing Fixed-Income Securities in an Information-Based Framework", "abstract": "In this paper we introduce a class of information-based models for the pricing of fixed-income securities. We consider a set of continuous- time information processes that describe the flow of information about market factors in a monetary economy. The nominal pricing kernel is at any given time assumed to be given by a function of the values of information processes at that time. By use of a change-of-measure technique we derive explicit expressions for the price processes of nominal discount bonds, and deduce the associated dynamics of the short rate of interest and the market price of risk. The interest rate positivity condition is expressed as a differential inequality. We proceed to the modelling of the price-level, which at any given time is also taken to be a function of the values of the information processes at that time. A simple model for a stochastic monetary economy is introduced in which the prices of nominal discount bonds and inflation-linked notes can be expressed in terms of aggregate consumption and the liquidity benefit generated by the money supply.", "published": "2009-11-09T09:22:41Z", "authors": ["Lane P. Hughston", "Andrea Macrina"], "categories": ["q-fin.PR"], "doi": "", "_query": "housing price text listing", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2607.00050v1", "title": "Revising price coordination in the classical and neoclassical economics based on elementary cellular automata", "abstract": "The coordination of prices in economics is one of the most complex phenomena. In particular, the classical and neoclassical approaches related to the economic theory provide some insights into such a complex coordination based on different formulations. However, these formulations have not been successful for explaining simple mechanisms to understand and predict a set of prices that theoretically clears all markets. Consequently, elementary cellular automata can contribute to clarify such a coordination problem by using simple computational rules to describe the theoretical bases of the classical and neoclassical economics. Therefore, we propose to use this type of cellular automata for explaining different escenarios of price coordination in which simple rules of price interactions generate stable and unstable patterns of coordination. We used an explorative data analysis based on the Shannon entropy for computing the uncertainty related to such generated patterns of coordination, and a Monte Carlo simulation approximation based on a Spearman correlation for evaluating the statistical significance of such price coordination. Findings suggested that the classical economics provides a consistent approach for understanding the coordination of prices because it emphasizes human interactions based on logical choices related to an objective data. On the other hand, the neoclassical approach does not propose any type of mechanism for describing the price coordination. The neoclassical individual is just a spectator and receiver of the unpredictable and supposed event of price coordination. As a result, by modeling the economic theory based on computational concepts, we reveal facts and believes behind the classical and neoclassical economics.", "published": "2026-06-29T23:55:16Z", "authors": ["Igor Lugo", "Martha G. Alatriste-Contreras"], "categories": ["nlin.CG", "econ.TH"], "doi": "", "_query": "housing price text listing", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/1307.5440v3", "title": "Tick Size Reduction and Price Clustering in a FX Order Book", "abstract": "We investigate the statistical properties of the EBS order book for the EUR/USD and USD/JPY currency pairs and the impact of a ten-fold tick size reduction on its dynamics. A large fraction of limit orders are still placed right at or halfway between the old allowed prices. This generates price barriers where the best quotes lie for much of the time, which causes the emergence of distinct peaks in the average shape of the book at round distances. Furthermore, we argue that this clustering is mainly due to manual traders who remained set to the old price resolution. Automatic traders easily take price priority by submitting limit orders one tick ahead of clusters, as shown by the prominence of buy (sell) limit orders posted with rightmost digit one (nine).", "published": "2013-07-20T17:09:06Z", "authors": ["Mehdi Lallouache", "Frédéric Abergel"], "categories": ["q-fin.TR", "q-fin.ST"], "doi": "10.1016/j.physa.2014.09.016", "_query": "housing price text listing", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2211.14570v1", "title": "The Role of In-House Procurement According to Finnish Municipalities' Purchase Invoice Data", "abstract": "Public sector is a large consumer of ICT systems and services, used for various public services. Tendering for such systems is governed by laws aimed at eliminating unfair advantages and offering all possible parties equal opportunities to participate in the tendering process. In this article, we study in-house rpocurement, where the acquiring organization is an owner of the subcontractor that delivers the system or the service. Municipalities' purchase invoice data is used to determine how much municipalities in Finland depend on in-house procurement. In conclusion, the understanding if included municipalities have ICT service and development units within the organizations needs closer examination, as in-house companies may offer municipalities with limited resources divided costs in the public procurement process.", "published": "2022-11-26T13:50:27Z", "authors": ["Reetta-Kaisa Ghezzi", "Minnamaria Korhonen", "Hannu Vilpponen", "Tommi Mikkonen"], "categories": ["cs.CY"], "doi": "", "_query": "housing price text listing", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2506.15103v2", "title": "How does online shopping affect offline price sensitivity?", "abstract": "The rapid rise of e-commerce has transformed consumer behavior, prompting questions about how online adoption influences offline shopping. We examine whether consumers who adopt a retailer's online shopping channels become more price-sensitive in their subsequent offline purchases with that retailer. Using transaction-level data from a large Brazilian pet supplies retailer operating both online and offline, we compare \"adopters\" -- customers who began shopping online after a period of offline-only purchasing -- with \"non-adopters\" who remained offline-only. We estimate a discrete choice logit model with individual-level heterogeneity, based on an algorithm that can handle both high-dimensional fixed effects and price endogeneity. We then apply a staggered difference-in-differences approach to the estimated price elasticities and obtain the Average Treatment Effect on the Treated (ATT). We find that offline price sensitivity increases significantly after online adoption in three out of four product categories, particularly in items with low switching costs, such as pet hygiene. These results underscore the importance of recognizing cross-channel effects in consumer behavior and contribute to the literature on pricing and multichannel retailing by identifying online adoption as a key driver of offline price sensitivity.", "published": "2025-06-18T03:20:02Z", "authors": ["Shirsho Biswas", "Hema Yoganarasimhan", "Haonan Zhang"], "categories": ["econ.GN"], "doi": "", "_query": "housing price text listing", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/1411.5899v3", "title": "Falling Rule Lists", "abstract": "Falling rule lists are classification models consisting of an ordered list of if-then rules, where (i) the order of rules determines which example should be classified by each rule, and (ii) the estimated probability of success decreases monotonically down the list. These kinds of rule lists are inspired by healthcare applications where patients would be stratified into risk sets and the highest at-risk patients should be considered first. We provide a Bayesian framework for learning falling rule lists that does not rely on traditional greedy decision tree learning methods.", "published": "2014-11-21T15:01:56Z", "authors": ["Fulton Wang", "Cynthia Rudin"], "categories": ["cs.AI", "cs.LG"], "doi": "", "_query": "housing price text listing", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2411.01368v1", "title": "Combining Financial Data and News Articles for Stock Price Movement Prediction Using Large Language Models", "abstract": "Predicting financial markets and stock price movements requires analyzing a company's performance, historic price movements, industry-specific events alongside the influence of human factors such as social media and press coverage. We assume that financial reports (such as income statements, balance sheets, and cash flow statements), historical price data, and recent news articles can collectively represent aforementioned factors. We combine financial data in tabular format with textual news articles and employ pre-trained Large Language Models (LLMs) to predict market movements. Recent research in LLMs has demonstrated that they are able to perform both tabular and text classification tasks, making them our primary model to classify the multi-modal data. We utilize retrieval augmentation techniques to retrieve and attach relevant chunks of news articles to financial metrics related to a company and prompt the LLMs in zero, two, and four-shot settings. Our dataset contains news articles collected from different sources, historic stock price, and financial report data for 20 companies with the highest trading volume across different industries in the stock market. We utilized recently released language models for our LLM-based classifier, including GPT- 3 and 4, and LLaMA- 2 and 3 models. We introduce an LLM-based classifier capable of performing classification tasks using combination of tabular (structured) and textual (unstructured) data. By using this model, we predicted the movement of a given stock's price in our dataset with a weighted F1-score of 58.5% and 59.1% and Matthews Correlation Coefficient of 0.175 for both 3-month and 6-month periods.", "published": "2024-11-02T21:53:20Z", "authors": ["Ali Elahi", "Fatemeh Taghvaei"], "categories": ["cs.IR", "q-fin.CP"], "doi": "", "_query": "housing price text listing", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2411.01368v1", "title": "Combining Financial Data and News Articles for Stock Price Movement Prediction Using Large Language Models", "abstract": "Predicting financial markets and stock price movements requires analyzing a company's performance, historic price movements, industry-specific events alongside the influence of human factors such as social media and press coverage. We assume that financial reports (such as income statements, balance sheets, and cash flow statements), historical price data, and recent news articles can collectively represent aforementioned factors. We combine financial data in tabular format with textual news articles and employ pre-trained Large Language Models (LLMs) to predict market movements. Recent research in LLMs has demonstrated that they are able to perform both tabular and text classification tasks, making them our primary model to classify the multi-modal data. We utilize retrieval augmentation techniques to retrieve and attach relevant chunks of news articles to financial metrics related to a company and prompt the LLMs in zero, two, and four-shot settings. Our dataset contains news articles collected from different sources, historic stock price, and financial report data for 20 companies with the highest trading volume across different industries in the stock market. We utilized recently released language models for our LLM-based classifier, including GPT- 3 and 4, and LLaMA- 2 and 3 models. We introduce an LLM-based classifier capable of performing classification tasks using combination of tabular (structured) and textual (unstructured) data. By using this model, we predicted the movement of a given stock's price in our dataset with a weighted F1-score of 58.5% and 59.1% and Matthews Correlation Coefficient of 0.175 for both 3-month and 6-month periods.", "published": "2024-11-02T21:53:20Z", "authors": ["Ali Elahi", "Fatemeh Taghvaei"], "categories": ["cs.IR", "q-fin.CP"], "doi": "", "_query": "housing price text listing", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/1708.04217v3", "title": "A General Class of Multifractional Processes and Stock Price Informativeness", "abstract": "We introduce a general class of stochastic processes driven by a multifractional Brownian motion (mBm) and study the estimation problems of their pointwise Hölder exponents (PHE) based on a new localized generalized quadratic variation approach (LGQV). By comparing our suggested approach with the other two existing benchmark estimation approaches (classic GQV and oscillation approach) through a simulation study, we show that our estimator has better performance in the case where the observed process is some unknown bivariate function of time and mBm. Such multifractional processes, whose PHEs are time-varying, can be used to model stock prices under various market conditions, that are both time-dependent and region-dependent. As an application to finance, an empirical study on modeling cross-listed stocks provides new evidence that the equity path's roughness varies via time and the stock price informativeness properties from global stock markets.", "published": "2017-08-14T17:25:41Z", "authors": ["Qidi Peng", "Ran Zhao"], "categories": ["q-fin.MF", "math.PR", "stat.AP"], "doi": "10.1016/j.chaos.2018.08.004", "_query": "housing price text listing", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/1606.08269v1", "title": "An agent behavior based model for diffusion price processes with application to phase transition and oscillations", "abstract": "We present an agent behavior based microscopic model for diffusion price processes. As such we provide a model not only containing a convenient framework for describing socio-economic behavior, but also a sophisticated link to price dynamics. We furthermore establish the circumstances under which the dynamics converge to diffusion processes in the large market limit. To demonstrate the applicability of a separation of behavior and price process, we show how herding behavior of market participants can lead to equilibria transition and oscillations in diffusion price processes.", "published": "2016-06-27T13:33:00Z", "authors": ["Christof Henkel"], "categories": ["math.PR", "econ.GN"], "doi": "", "_query": "housing price text listing", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2512.17758v3", "title": "Day-Ahead Electricity Price Forecasting Using Merit-Order Curves Time Series", "abstract": "We introduce a general, simple, and computationally efficient functional data analysis framework for forecasting day-ahead supply and demand merit-order curves, and the resulting electricity price. We conduct a rigorous empirical comparison on data from the Italian (GME), German (EPEX-DE-LU), and French (EPEX-FR) day-ahead markets over the 2023-2024 period, analyzing curve forecasting performance, price forecasting performance, and the relationship between the two. We find that strong curve forecasting performance does not necessarily translate into strong price forecasting performance, with important implications for curve model evaluation and selection when price forecasting is among the objectives. We also show that this functional data representation approach consistently outperforms the original discretization-based approach of Ziel and Steinert (2016) on price forecasting across all three markets. Finally, the proposed curve-based approach is competitive with state-of-the-art price-based models for two out of three markets (GME and EPEX-FR), and substantially improves accuracy during midday hours (when prices frequently drop due to high renewable generation) with MAE reductions of up to 27% in those windows. For EPEX-DE-LU, however, price-based models retain a clear and significant advantage.", "published": "2025-12-19T16:32:30Z", "authors": ["Guillaume Koechlin", "Filippo Bovera", "Piercesare Secchi"], "categories": ["stat.AP"], "doi": "", "_query": "housing price text listing", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/1601.02319v2", "title": "Dynamic Pricing and Distributed Energy Management for Demand Response", "abstract": "The problem of dynamic pricing of electricity in a retail market is considered. A Stackelberg game is used to model interactions between a retailer and its customers; the retailer sets the day-ahead hourly price of electricity and consumers adjust real-time consumptions to maximize individual consumer surplus. For thermostatic demands, the optimal aggregated demand is shown to be an affine function of the day-ahead hourly price. A complete characterization of the trade-offs between consumer surplus and retail profit is obtained. The Pareto front of achievable trade-offs is shown to be concave, and each point on the Pareto front is achieved by an optimal day-ahead hourly price. Effects of integrating renewables and local storage are analyzed. It is shown that benefits of renewable integration all go to the retailer when the capacity of renewable is relatively small. As the capacity increases beyond a certain threshold, the benefit from renewable that goes to consumers increases.", "published": "2016-01-11T04:36:19Z", "authors": ["Liyan Jia", "Lang Tong"], "categories": ["math.OC"], "doi": "", "_query": "housing price text listing", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/1412.0845v1", "title": "On the Robustness of the Approximate Price of Anarchy in Generalized Congestion Games", "abstract": "One of the main results shown through Roughgarden's notions of smooth games and robust price of anarchy is that, for any sum-bounded utilitarian social function, the worst-case price of anarchy of coarse correlated equilibria coincides with that of pure Nash equilibria in the class of weighted congestion games with non-negative and non-decreasing latency functions and that such a value can always be derived through the, so called, smoothness argument. We significantly extend this result by proving that, for a variety of (even non-sum-bounded) utilitarian and egalitarian social functions and for a broad generalization of the class of weighted congestion games with non-negative (and possibly decreasing) latency functions, the worst-case price of anarchy of $ε$-approximate coarse correlated equilibria still coincides with that of $ε$-approximate pure Nash equilibria, for any $ε\\geq 0$. As a byproduct of our proof, it also follows that such a value can always be determined by making use of the primal-dual method we introduced in a previous work. It is important to note that our scenario of investigation is beyond the scope of application of the robust price of anarchy (for as it is currently defined), so that our result seems unlikely to be alternatively proved via the smoothness framework.", "published": "2014-12-02T10:30:27Z", "authors": ["Vittorio Bilò"], "categories": ["cs.GT"], "doi": "", "_query": "housing price text listing", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2010.04993v1", "title": "Fair Pricing In Heterogeneous Internet of Things Wireless Access Networks Using Crowdsourcing", "abstract": "Price and the quality of service are two key factors taken into account by wireless network users when they choose their network provider. The recent advances in wireless technology and massive infrastructure deployments has led to better coverage, and currently at each given wirelessly covered area there are a few network providers and each have different pricing strategies. These providers can potentially set unfair expensive prices for their services. In this paper, we propose a novel crowdsourcing-based approach for fair wireless service pricing in Internet of Things (IoT). In our considered oligopoly, the regulatory sets a dynamic maximum allowed price of service to prevent anti-trust behaviour and unfair service pricing. We propose a three-tire pricing model where the regulator, wireless network providers and clients are the players of our game. Our method takes client preferences into account in pricing and discovers the fair service pricing just above the marginal costs of each network provider. Our results show that our model is not prone to collusion and will converges only if one network announces the fair price", "published": "2020-10-10T13:27:27Z", "authors": ["Vahid Haghighatdoost", "Siavash Khorsandi", "Hamed Ahmadi"], "categories": ["cs.NI"], "doi": "", "_query": "housing price text listing", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2206.09452v1", "title": "Demand Analysis with a Thin Price Sample", "abstract": "For about 125 items of food, the Consumer Expenditure Survey (CES) schedule of the Indian National Sample Survey asks the interviewer to obtain both quantity and value of household consumption during the reference period from the respondent. This would appear to put a great burden on the respondent. But it is likely that the price usually paid is almost the same within each first stage unit (fsu). The present work proposes a new sampling scheme to estimate demand elasticities of essential food items. While the conventional sampling method used in practice (e.g. in NSS consumer expenditure survey) involves seeking price information from many households sampled from a fsu, the proposed procedure involves only one household chosen randomly from every fsu for price data collection and thus requires much less interview burden. Using unit records for vegetable items in the NSS's 2011-12 CES, our results show that in spite of requiring much less data, the new scheme captures the household food consumption behavior as precisely as before.", "published": "2022-06-19T17:08:58Z", "authors": ["Monitirtha Dey", "Arpan Kumar", "Diganta Mukherjee"], "categories": ["stat.AP", "stat.ME"], "doi": "", "_query": "housing price text listing", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/1109.6909v1", "title": "Pricing stocks with yardsticks and sentiments", "abstract": "Human decision making by professionals trading daily in the stock market can be a daunting task. It includes decisions on whether to keep on investing or to exit a market subject to huge price swings, and how to price in news or rumors attributed to a specific stock. The question then arises how professional traders, who specialize in daily buying and selling large amounts of a given stock, know how to properly price a given stock on a given day? Here we introduce the idea that people use heuristics, or \"rules of thumb\", in terms of \"yard sticks\" from the performance of the other stocks in a stock index. The under- /over-performance with respect to such a yard stick then signifies a general negative/positive sentiment of the market participants towards a given stock. Using empirical data of the Dow Jones Industrial Average, stocks are shown to have daily performances with a clear tendency to cluster around the measures introduced by the yard sticks. We illustrate how sentiments, most likely due to insider information, can influence the performance of a given stock over period of months, and in one case years.", "published": "2011-09-30T17:41:19Z", "authors": ["Sebastıan Martınez Bustos", "Jorgen Vitting Andersen", "Michel Miniconi", "Andrzej Nowak", "Magdalena Roszczynska-Kurasinska", "David Bree"], "categories": ["q-fin.GN", "physics.soc-ph"], "doi": "", "_query": "housing price text listing", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2402.06711v1", "title": "Dodging the electricity price hike: Can demand-side flexibility compensate for spot price increases for households in Germany?", "abstract": "In 2022, energy prices skyrocketed across Europe, with average day-ahead spot market prices in Germany 2.43 times higher than the previous year, hinting at future trends. At the same time, electricity infrastructure is expected to be overutilized in some regions in the future due to the uptake of electric vehicles, heat pumps, PV systems, and other appliances in the residential sector. Dynamic electricity pricing for households is proposed as a solution to alleviate infrastructure strain and reduce costs. The question arises as to what impact dynamic electricity prices as recently seen on the day-ahead spot market in Germany would have on the use of flexibility in households and whether it can compensate for cost increases for households. We analyze the cost-effectiveness of utilizing flexibility through a home energy management system (HEMS) and smart meters against increased self-consumption using a HEMS and static tariffs. We show that with higher electricity prices and price spreads, a higher share of households can offset initial investments in HEMS and metering operation costs by utilizing flexibility from their electric vehicles, heat pumps, and battery storage systems. While increasing self-consumption remains beneficial for households with heat pumps and PV systems, dynamic electricity tariffs are financially more advantageous for those with electric vehicles. The study also determines the maximum additional annual costs for HEMS and metering point operation that still allow cost savings for 75% of households. these costs range from 126 to 145 Euro in the self-consumption scenario depending and from 50 to 111 Euro in the dynamic electricity tariff scenario, highlighting the financial viability of flexibility utilization in response to current pricing trends.", "published": "2024-02-09T12:54:53Z", "authors": ["Judith Stute", "Sabine Pelka", "Matthias Kühnbach", "Marian Klobasa"], "categories": ["physics.soc-ph"], "doi": "10.1016/j.adapen.2024.100174", "_query": "housing price text listing", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2409.19218v2", "title": "A Characterization of List Regression", "abstract": "There has been a recent interest in understanding and characterizing the sample complexity of list learning tasks, where the learning algorithm is allowed to make a short list of $k$ predictions, and we simply require one of the predictions to be correct. This includes recent works characterizing the PAC sample complexity of standard list classification and online list classification. Adding to this theme, in this work, we provide a complete characterization of list PAC regression. We propose two combinatorial dimensions, namely the $k$-OIG dimension and the $k$-fat-shattering dimension, and show that they characterize realizable and agnostic $k$-list regression respectively. These quantities generalize known dimensions for standard regression. Our work thus extends existing list learning characterizations from classification to regression.", "published": "2024-09-28T03:19:37Z", "authors": ["Chirag Pabbaraju", "Sahasrajit Sarmasarkar"], "categories": ["cs.LG", "cs.DS", "stat.ML"], "doi": "", "_query": "housing price text listing", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2607.12156v3", "title": "(Early) AI Compute Asset Pricing", "abstract": "Compute (computing power) is a scarce, capital-intensive input at the center of the AI economy. Compute capital expenditure and service flow already exceed 1% of U.S. GDP and are growing rapidly. The price of compute reflects uncertainty over AI adoption. The announced launch of compute futures turns this uncertainty into a tradable risk, raising questions on the pricing of a new asset class. We provide an early asset-pricing framework for compute. We begin by discussing the underlying compute rental market and its indexation. We then turn to pricing: 1) direct no-arbitrage links between futures prices and current spot prices fail due to the non-storable nature of compute, 2) synthetic futures prices from existing term rental contracts are likely upper bounds on true futures prices and, 3) upon financialization, futures prices will be investors' expectations of spot prices at expiration net of a risk premium. Using synthetic futures as stand-ins before the compute futures market launches, we construct the first compute futures return panel sorted by GPU generation and maturity. Our preliminary evidence is consistent with a positive compute risk premium, suggesting hedging pressure on the part of compute providers.", "published": "2026-07-13T21:04:25Z", "authors": ["Federico M. Bandi", "Yinan Su"], "categories": ["q-fin.PR"], "doi": "", "_query": "housing price text listing", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/1703.00927v2", "title": "When is selfish routing bad? The price of anarchy in light and heavy traffic", "abstract": "This paper examines the behavior of the price of anarchy as a function of the traffic inflow in nonatomic congestion games with multiple origin-destination (O/D) pairs. Empirical studies in real-world networks show that the price of anarchy is close to 1 in both light and heavy traffic, thus raising the question: can these observations be justified theoretically? We first show that this is not always the case: the price of anarchy may remain a positive distance away from 1 for all values of the traffic inflow, even in simple three-link networks with a single O/D pair and smooth, convex costs. On the other hand, for a large class of cost functions (including all polynomials), the price of anarchy does converge to 1 in both heavy and light traffic, irrespective of the network topology and the number of O/D pairs in the network. We also examine the rate of convergence of the price of anarchy, and we show that it follows a power law whose degree can be computed explicitly when the network's cost functions are polynomials.", "published": "2017-03-02T19:35:48Z", "authors": ["Riccardo Colini-Baldeschi", "Roberto Cominetti", "Panayotis Mertikopoulos", "Marco Scarsini"], "categories": ["cs.GT", "math.OC"], "doi": "", "_query": "housing price text listing", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2211.01798v1", "title": "Phase Transitions in Learning and Earning under Price Protection Guarantee", "abstract": "Motivated by the prevalence of ``price protection guarantee\", which allows a customer who purchased a product in the past to receive a refund from the seller during the so-called price protection period (typically defined as a certain time window after the purchase date) in case the seller decides to lower the price, we study the impact of such policy on the design of online learning algorithm for data-driven dynamic pricing with initially unknown customer demand. We consider a setting where a firm sells a product over a horizon of $T$ time steps. For this setting, we characterize how the value of $M$, the length of price protection period, can affect the optimal regret of the learning process. We show that the optimal regret is $\\tildeΘ(\\sqrt{T}+\\min\\{M,\\,T^{2/3}\\})$ by first establishing a fundamental impossible regime with novel regret lower bound instances. Then, we propose LEAP, a phased exploration type algorithm for \\underline{L}earning and \\underline{EA}rning under \\underline{P}rice Protection to match this lower bound up to logarithmic factors or even doubly logarithmic factors (when there are only two prices available to the seller). Our results reveal the surprising phase transitions of the optimal regret with respect to $M$. Specifically, when $M$ is not too large, the optimal regret has no major difference when compared to that of the classic setting with no price protection guarantee. We also show that there exists an upper limit on how much the optimal regret can deteriorate when $M$ grows large. Finally, we conduct extensive numerical experiments to show the benefit of LEAP over other heuristic methods for this problem.", "published": "2022-11-03T13:36:00Z", "authors": ["Qing Feng", "Ruihao Zhu", "Stefanus Jasin"], "categories": ["stat.ML", "cs.LG"], "doi": "", "_query": "housing price text listing", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/1509.00372v2", "title": "Electricity Price Forecasting using Sale and Purchase Curves: The X-Model", "abstract": "Our paper aims to model and forecast the electricity price by taking a completely new perspective on the data. It will be the first approach which is able to combine the insights of market structure models with extensive and modern econometric analysis. Instead of directly modeling the electricity price as it is usually done in time series or data mining approaches, we model and utilize its true source: the sale and purchase curves of the electricity exchange. We will refer to this new model as X-Model, as almost every deregulated electricity price is simply the result of the intersection of the electricity supply and demand curve at a certain auction. Therefore we show an approach to deal with a tremendous amount of auction data, using a subtle data processing technique as well as dimension reduction and lasso based estimation methods. We incorporate not only several known features, such as seasonal behavior or the impact of other processes like renewable energy, but also completely new elaborated stylized facts of the bidding structure. Our model is able to capture the non-linear behavior of the electricity price, which is especially useful for predicting huge price spikes. Using simulation methods we show how to derive prediction intervals for probabilistic forecasting. We describe and show the proposed methods for the day-ahead EPEX spot price of Germany and Austria.", "published": "2015-09-01T16:04:42Z", "authors": ["Florian Ziel", "Rick Steinert"], "categories": ["q-fin.TR", "q-fin.ST"], "doi": "10.1016/j.eneco.2016.08.008", "_query": "housing price text listing", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/1503.00388v1", "title": "A Novel Image Steganographic Approach for Hiding Text in Color Images using HSI Color Model", "abstract": "Image Steganography is the process of embedding text in images such that its existence cannot be detected by Human Visual System (HVS) and is known only to sender and receiver. This paper presents a novel approach for image steganography using Hue-Saturation-Intensity (HSI) color space based on Least Significant Bit (LSB). The proposed method transforms the image from RGB color space to Hue-Saturation-Intensity (HSI) color space and then embeds secret data inside the Intensity Plane (I-Plane) and transforms it back to RGB color model after embedding. The said technique is evaluated by both subjective and Objective Analysis. Experimentally it is found that the proposed method have larger Peak Signal-to Noise Ratio (PSNR) values, good imperceptibility and multiple security levels which shows its superiority as compared to several existing methods", "published": "2015-03-02T01:41:39Z", "authors": ["Khan Muhammad", "Jamil Ahmad", "Haleem Farman", "Muhammad Zubair"], "categories": ["cs.MM"], "doi": "10.5829/idosi.mejsr.2014.22.05.21946", "_query": "housing price text listing", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2001.00737v1", "title": "Option Pricing in an Investment Risk-Return Setting", "abstract": "In this paper, we combine modern portfolio theory and option pricing theory so that a trader who takes a position in a European option contract and the underlying assets can construct an optimal portfolio such that at the moment of the contract's maturity the contract is perfectly hedged. We derive both the optimal holdings in the underlying assets for the trader's optimal mean-variance portfolio and the amount of unhedged risk prior to maturity. Solutions assuming the cases where the price dynamics in the underlying assets follow discrete binomial price dynamics, continuous diffusions, stochastic volatility, volatility-of-volatility, and Merton-jump diffusion are derived.", "published": "2020-01-03T06:16:14Z", "authors": ["Abootaleb Shirvani", "Frank J. Fabozzi", "Stoyan V. Stoyanov"], "categories": ["q-fin.MF"], "doi": "", "_query": "housing price text listing", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/cond-mat/0104313v1", "title": "Derivation of ARCH(1) process from market price changes based on deterministic microscopic multi-agent", "abstract": "A model of fluctuations in the market price including many deterministic dealers, who predict their buying and selling prices from the latest price change, is developed. We show that price changes of the model is approximated by ARCH(1) process. We conclude that predictions of dealers affected by the past price changes cause the fat tails of probability density function. We believe that this study bridges stochastic processes in econometrics with multi-agent simulation approaches.", "published": "2001-04-17T23:29:42Z", "authors": ["Aki-Hiro Sato", "Hideki Takayasu"], "categories": ["cond-mat.stat-mech", "q-fin.ST"], "doi": "", "_query": "housing price text listing", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2111.09098v4", "title": "Unifying Heterogeneous Electronic Health Records Systems via Text-Based Code Embedding", "abstract": "EHR systems lack a unified code system forrepresenting medical concepts, which acts asa barrier for the deployment of deep learningmodels in large scale to multiple clinics and hos-pitals. To overcome this problem, we introduceDescription-based Embedding,DescEmb, a code-agnostic representation learning framework forEHR. DescEmb takes advantage of the flexibil-ity of neural language understanding models toembed clinical events using their textual descrip-tions rather than directly mapping each event toa dedicated embedding. DescEmb outperformedtraditional code-based embedding in extensiveexperiments, especially in a zero-shot transfertask (one hospital to another), and was able totrain a single unified model for heterogeneousEHR datasets.", "published": "2021-11-12T20:27:55Z", "authors": ["Kyunghoon Hur", "Jiyoung Lee", "Jungwoo Oh", "Wesley Price", "Young-Hak Kim", "Edward Choi"], "categories": ["cs.CL", "cs.LG"], "doi": "", "_query": "housing price text listing", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2508.13409v1", "title": "Pricing insurance policies with offsetting relationship", "abstract": "This paper investigates the benefits of incorporating diversification effects into the pricing process of insurance policies from two different business lines. The paper shows that, for the same risk reduction, insurers pricing policies jointly can have a competitive advantage over those pricing them separately. However, the choice of competitiveness constrains the underwriting flexibility of joint pricers. The paper goes a step further by modeling explicitly the relationship between premiums and the number of customers in each line. Using the total collected premiums as a criterion to compare the competing strategies, the paper provides conditions for the optimal pricing decision based on policyholders' sensitivity to price discounts. The results are illustrated for a portfolio of annuities and assurances. Further, using non-life data from the Brazilian insurance market, an empirical exploration shows that most pairs satisfy the condition for being priced jointly, even when pairwise correlations are high.", "published": "2025-08-19T00:08:10Z", "authors": ["Hamza Hanbali"], "categories": ["econ.TH"], "doi": "10.1017/S1748499521000208", "_query": "housing price text listing", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/1611.00885v2", "title": "Pricing Perpetual Put Options by the Black-Scholes Equation with a Nonlinear Volatility Function", "abstract": "We investigate qualitative and quantitative behavior of a solution of the mathematical model for pricing American style of perpetual put options. We assume the option price is a solution to the stationary generalized Black-Scholes equation in which the volatility function may depend on the second derivative of the option price itself. We prove existence and uniqueness of a solution to the free boundary problem. We derive a single implicit equation for the free boundary position and the closed form formula for the option price. It is a generalization of the well-known explicit closed form solution derived by Merton for the case of a constant volatility. We also present results of numerical computations of the free boundary position, option price and their dependence on model parameters.", "published": "2016-11-03T05:30:13Z", "authors": ["Maria do Rosario Grossinho", "Yaser Kord Faghan", "Daniel Sevcovic"], "categories": ["q-fin.MF"], "doi": "", "_query": "housing price text listing", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2401.14390v2", "title": "Higher order approximation of option prices in Barndorff-Nielsen and Shephard models", "abstract": "We present an approximation method based on the mixing formula (Hull & White 1987, Romano & Touzi 1997) for pricing European options in Barndorff-Nielsen and Shephard models. This approximation is based on a Taylor expansion of the option price. It is implemented using a recursive algorithm that allows us to obtain closed form approximations of the option price of any order (subject to technical conditions on the background driving Lévy process). This method can be used for any type of Barndorff-Nielsen and Shephard stochastic volatility model. Explicit results are presented in the case where the stationary distribution of the background driving Lévy process is inverse Gaussian or gamma. In both of these cases, the approximation compares favorably to option prices produced by the characteristic function. In particular, we also perform an error analysis of the approximation, which is partially based on the results of Das & Langrené (2022). We obtain asymptotic results for the error of the $N^{\\text{th}}$ order approximation and error bounds when the variance process satisfies an inverse Gaussian Ornstein-Uhlenbeck process or a gamma Ornstein-Uhlenbeck process.", "published": "2024-01-25T18:49:31Z", "authors": ["Álvaro Guinea Juliá", "Alet Roux"], "categories": ["q-fin.CP"], "doi": "", "_query": "housing price text listing", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2103.02948v1", "title": "Pricing Perpetual American put options with asset-dependent discounting", "abstract": "The main objective of this paper is to present an algorithm of pricing perpetual American put options with asset-dependent discounting. The value function of such an instrument can be described as \\begin{equation*} V^ω_{\\text{A}^{\\text{Put}}}(s) = \\sup_{τ\\in\\mathcal{T}} \\mathbb{E}_{s}[e^{-\\int_0^τω(S_w) dw} (K-S_τ)^{+}], \\end{equation*} where $\\mathcal{T}$ is a family of stopping times, $ω$ is a discount function and $\\mathbb{E}$ is an expectation taken with respect to a martingale measure. Moreover, we assume that the asset price process $S_t$ is a geometric Lévy process with negative exponential jumps, i.e. $S_t = s e^{ζt + σB_t - \\sum_{i=1}^{N_t} Y_i}$. The asset-dependent discounting is reflected in the $ω$ function, so this approach is a generalisation of the classic case when $ω$ is constant. It turns out that under certain conditions on the $ω$ function, the value function $V^ω_{\\text{A}^{\\text{Put}}}(s)$ is convex and can be represented in a closed form; see Al-Hadad and Palmowski (2021). We provide an option pricing algorithm in this scenario and we present exact calculations for the particular choices of $ω$ such that $V^ω_{\\text{A}^{\\text{Put}}}(s)$ takes a simplified form.", "published": "2021-03-04T11:02:03Z", "authors": ["Jonas Al-Hadad", "Zbigniew Palmowski"], "categories": ["q-fin.MF", "q-fin.CP"], "doi": "", "_query": "housing price text listing", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2411.08126v3", "title": "A Tale of Two Cities: Pessimism and Opportunism in Offline Dynamic Pricing", "abstract": "We study offline dynamic pricing when historical data provide incomplete coverage of the price space such that some candidate prices, including the optimal one, may be entirely unobserved. This setting is common in practice and is especially difficult in dynamic environments. Existing offline reinforcement learning methods typically rely on full or partial coverage and can therefore perform poorly in such settings. We develop a nonparametric partial identification framework for offline dynamic pricing that exploits the monotonicity of demand in price to bound the value of unobserved prices. Within this framework, we formulate two dynamic decision rules: a pessimistic policy that maximizes worst-case revenue and an opportunistic policy that minimizes worst-case regret. These rules are tailored to a sequential no-coverage environment and are not direct extensions of existing pessimistic offline RL or static opportunistic approaches. We establish finite-sample regret bounds for both policies, recovering the standard rate when the optimal price is covered and quantifying the additional cost when it is not. We also develop efficient algorithms and show, through simulations and an airline ticket application, that our methods outperform standard offline RL baselines in no-coverage settings. Managerially, the framework provides a practical mapping from a firm's risk posture to its pricing policy: firms seeking revenue stability and downside protection should prefer the pessimistic policy, whereas firms willing to bear measured risk for potential gains from underexplored prices should prefer the opportunistic policy.", "published": "2024-11-12T19:09:41Z", "authors": ["Zeyu Bian", "Lan Wang", "Zhengling Qi"], "categories": ["stat.ML", "cs.LG"], "doi": "", "_query": "housing price text listing", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2112.08071v2", "title": "Stock prices and Macroeconomic indicators: Investigating a correlation in Indian context", "abstract": "The objective of this paper is to find the existence of a relationship between stock market prices and the fundamental macroeconomic indicators. We build a Vector Auto Regression (VAR) model comprising of nine major macroeconomic indicators (interest rate, inflation, exchange rate, money supply, gdp, fdi, trade-gdp ratio, oil prices, gold prices) and then try to forecast them for next 5 years. Finally we calculate cross-correlation of these forecasted values with the BSE Sensex closing price for each of those years. We find very high correlation of the closing price with exchange rate and money supply in the Indian economy.", "published": "2021-12-15T12:23:11Z", "authors": ["Dhruv Rawat", "Sujay Patni", "Ram Mehta"], "categories": ["econ.GN"], "doi": "", "_query": "housing price text listing", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2502.08124v2", "title": "Price and Assortment Optimization under the Multinomial Logit Model with Opaque Products", "abstract": "An opaque product is a product for which only partial information is disclosed to the buyer at the time of purchase. Opaque products are common in sectors such as travel and online retail, where the brand or product color is hidden in the opaque product. Opaque products enable sellers to target customers who prefer a price discount in exchange for being flexible about the product they receive. In this paper, we integrate opaque products and traditional products together into the multinomial logit (MNL) choice model and study the associated price and assortment optimization problems. For the price optimization problem, we show a surprising result that uniform pricing is optimal which implies it has the same optimal pricing solution and value as the traditional MNL model. Although adding an opaque product may enhance revenue given arbitrary traditional product prices, our result shows that this advantage disappears when all prices are optimized jointly. For the assortment problem, we first study the setting where the seller chooses both the offered assortment and the support of the opaque product, and prove that there exists an optimal assortment that is nested by revenue. We then study the case where the opaque support is required to coincide with the offered assortment which is common in practice. Here, we show that the optimal assortment is nested-by-valuation for uniformly priced products. For arbitrary prices, we propose a nested-by-revenue-and-valuation heuristic with a theoretical approximation guarantee that performs extremely well in our numerical study.", "published": "2025-02-12T05:06:02Z", "authors": ["Omar El Housni", "Adam N. Elmachtoub", "Harsh Sheth", "Jiaqi Shi"], "categories": ["math.OC"], "doi": "", "_query": "housing price text listing", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2606.14050v1", "title": "Battery Bidding under Price Uncertainty in Wholesale Electricity Markets", "abstract": "Grid-scale batteries increasingly influence outcomes in wholesale electricity markets, but their observed bid patterns remain difficult to interpret. In particular, bids that appear to reflect strategic withholding may instead arise from rational operations under price uncertainty and risk management. We develop an asset-level model of a price-taking battery that submits stepwise buy and sell bid curves in the day-ahead market under a finite set of price scenarios. The battery chooses quantity--price pairs to maximize a mean--CVaR objective subject to physical and market constraints. A direct formulation is a mixed-integer linear program, but we show that its integer decisions can be removed, yielding an exact linear programming reformulation suitable for empirical analysis. Our empirical results deliver three insights. First, withholding behavior can arise even without market power, because scarce stored energy and uncertain future prices increase the value of holding energy. Second, the effect of uncertainty depends on the state of charge: when stored energy is scarce, greater uncertainty raises sell bid prices, whereas when stored energy is abundant it can lower them. Third, risk management reshapes bid curves into layered structures that secure profitable execution across a broad set of scenarios while preserving some exposure to rare but valuable price spikes.", "published": "2026-06-12T02:51:55Z", "authors": ["Vincent Yinjun-Wang", "Madeleine Udell"], "categories": ["math.OC", "econ.GN", "eess.SY", "q-fin.PM", "q-fin.RM"], "doi": "", "_query": "housing price text listing", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/1112.2406v2", "title": "On the game interpretation of a shadow price process in utility maximization problems under transaction costs", "abstract": "To any utility maximization problem under transaction costs one can assign a frictionless model with a price process $S^*$, lying in the bid/ask price interval $[\\underline S, \\bar{S}]$. Such process $S^*$ is called a \\emph{shadow price} if it provides the same optimal utility value as in the original model with bid-ask spread. We call $S^*$ a \\emph{generalized shadow price} if the above property is true for the \\emph{relaxed} utility function in the frictionless model. This relaxation is defined as the lower semicontinuous envelope of the original utility, considered as a function on the set $[\\underline S, \\bar{S}]$, equipped with some natural weak topology. We prove the existence of a generalized shadow price under rather weak assumptions and mark its relation to a saddle point of the trader/market zero-sum game, determined by the relaxed utility function. The relation of the notion of a shadow price to its generalization is illustrated by several examples. Also, we briefly discuss the interpretation of shadow prices via Lagrange duality.", "published": "2011-12-11T21:38:29Z", "authors": ["Dmitry B. Rokhlin"], "categories": ["q-fin.PM"], "doi": "", "_query": "housing price text listing", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/1206.5106v1", "title": "On List Colouring and List Homomorphism of Permutation and Interval Graphs", "abstract": "List colouring is an NP-complete decision problem even if the total number of colours is three. It is hard even on planar bipartite graphs. We give a polynomial-time algorithm for solving list colouring of permutation graphs with a bounded total number of colours. More generally we give a polynomial-time algorithm that solves the list-homomorphism problem to any fixed target graph for a large class of input graphs including all permutation and interval graphs.", "published": "2012-06-22T10:42:36Z", "authors": ["Jessica Enright", "Lorna Stewart", "Gabor Tardos"], "categories": ["cs.DM"], "doi": "", "_query": "housing price text listing", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/1210.4000v1", "title": "Price-Setting of Market Makers: A Filtering Problem with an Endogenous Filtration", "abstract": "We study the price-setting problem of market makers under risk neutrality and perfect competition in continuous time. Thereby we follow the classic Glosten-Milgrom model that defines bid and ask prices as expectations of a true value of the asset given the market makers' partial information that includes the customers trading decisions. The true value is modeled as a Markov process that can be observed by the customers with some noise at Poisson times. We analyze the price-setting problem in a mathematically rigorous way by solving a filtering problem with an endogenous filtration that depends on the bid and ask price process quoted by the market maker. Under some conditions we show existence and uniqueness of the price processes.", "published": "2012-10-15T12:36:15Z", "authors": ["Christoph Kühn", "Matthias Riedel"], "categories": ["q-fin.TR"], "doi": "", "_query": "housing price text listing", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2512.21621v1", "title": "Mean-Field Price Formation on Trees with a Network of Relative Performance Concerns", "abstract": "Financial firms and institutional investors are routinely evaluated based on their performance relative to their peers. These relative performance concerns significantly influence risk-taking behavior and market dynamics. While the literature studying Nash equilibrium under such relative performance competitions is extensive, its effect on asset price formation remains largely unexplored. This paper investigates mean-field equilibrium price formation of a single risky stock in a discrete-time market where agents exhibit exponential utility and relative performance concerns. Unlike existing literature that typically treats asset prices as exogenous, we impose a market-clearing condition to determine the price dynamics endogenously within a relative performance equilibrium. Using a binomial tree framework, we establish the existence and uniqueness of the market-clearing mean-field equilibrium in both single- and multi-population settings. Finally, we provide illustrative numerical examples demonstrating the equilibrium price distributions and agents' optimal position sizes.", "published": "2025-12-25T10:50:09Z", "authors": ["Masaaki Fujii"], "categories": ["q-fin.MF", "econ.GN", "q-fin.GN", "q-fin.PM"], "doi": "", "_query": "housing price text listing", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/1503.03548v1", "title": "Statistical Properties and Pre-hit Dynamics of Price Limit Hits in the Chinese Stock Markets", "abstract": "Price limit trading rules are adopted in some stock markets (especially emerging markets) trying to cool off traders' short-term trading mania on individual stocks and increase market efficiency. Under such a microstructure, stocks may hit their up-limits and down-limits from time to time. However, the behaviors of price limit hits are not well studied partially due to the fact that main stock markets such as the US markets and most European markets do not set price limits. Here, we perform detailed analyses of the high-frequency data of all A-share common stocks traded on the Shanghai Stock Exchange and the Shenzhen Stock Exchange from 2000 to 2011 to investigate the statistical properties of price limit hits and the dynamical evolution of several important financial variables before stock price hits its limits. We compare the properties of up-limit hits and down-limit hits. We also divide the whole period into three bullish periods and three bearish periods to unveil possible differences during bullish and bearish market states. To uncover the impacts of stock capitalization on price limit hits, we partition all stocks into six portfolios according to their capitalizations on different trading days. We find that the price limit trading rule has a cooling-off effect (object to the magnet effect), indicating that the rule takes effect in the Chinese stock markets. We find that price continuation is much more likely to occur than price reversal on the next trading day after a limit-hitting day, especially for down-limit hits, which has potential practical values for market practitioners.", "published": "2015-03-12T01:23:01Z", "authors": ["Yu-Lei Wan", "Wen-Jie Xie", "Gao-Feng Gu", "Zhi-Qiang Jiang", "Wei Chen", "Xiong Xiong", "Wei Zhang", "Wei-Xing Zhou"], "categories": ["q-fin.ST"], "doi": "10.1371/journal.pone.0120312", "_query": "housing price text listing", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/1506.06648v1", "title": "Strategy-proof Pricing Approach for Cloud Market", "abstract": "In this paper, we design and develop a pricing model applicable to strategy proof pricing. To provide an economic stability towards its consumers. The economic model we use is Vickrey-Clarke-Groves (VCG). By this each service provider has to provide a true cost of its services in the cloud market. For the selection of suitable service for the consumer we adopt a dynamic programing based algorithm and VCG is used to calculate the payment. Strategy proof pricing offers a unique cloud pricing service that takes the complexity out of traditional pricing and enables cloud providers to price accurately, consistently and competitively", "published": "2015-06-22T15:23:54Z", "authors": ["Chetan Chawla", "Inderveer Chana"], "categories": ["cs.DC"], "doi": "", "_query": "housing price text listing", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2101.03788v1", "title": "Second Hand Price Prediction for Tesla Vehicles", "abstract": "The Tesla vehicles became very popular in the car industry as it was affordable in the consumer market and it left no carbon footprint. Due to the large decline in the stock prices of Tesla Inc. at the beginning of 2019, Tesla owners started selling their vehicles in the used car market. These used car prices depended on attributes such as the model of the vehicle, year of production, miles driven, and the battery used for the vehicle. Prices were different for a specific vehicle in different months. In this paper, it is discussed how a machine learning technique is being implemented in order to develop a second-hand Teslavehicle price prediction system. To reach this goal, different machine learning techniques such as decision trees, support vector machine (SVM), random forest, and deep learning were investigated and finally was implemented with boosted decision tree regression. I the future, it is intended to use a more sophisticated algorithm for better accuracy.", "published": "2021-01-11T09:54:13Z", "authors": ["Sayed Erfan Arefin"], "categories": ["cs.LG"], "doi": "", "_query": "housing price text listing", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2507.11353v1", "title": "Propagation of carbon price shocks through the value chain: the mean-field game of defaults", "abstract": "We introduce a new mean-field game framework to analyze the impact of carbon pricing in a multi-sector economy with defaultable firms. Each sector produces a homogeneous good, with its price endogenously determined through market clearing. Firms act as price takers and maximize profits by choosing an optimal allocation of inputs-including labor, emissions, and intermediate goods from other sectors-while interacting through the endogenous sectoral price. Firms also choose their default timing to maximize shareholder value. Formally, we model the economy as an optimal stopping mean-field game within each sector. The resulting system of coupled mean-field games admits a linear programming formulation that characterizes Nash equilibria in terms of population measure flows. We prove the existence of a linear programming Nash equilibrium and establish uniqueness of the associated price system. Numerical illustrations are presented for firms with constant elasticity of substitution (CES) production functions. In a stylized single-sector economy, carbon price shocks induce substitution between emissions and labor. In a three-sector economy, the manufacturing sector faces consumer demand and requires inputs from a brown sector, which can be increasingly replaced by green-sector goods as carbon prices rise. These experiments reveal that carbon price shocks can generate substantial spillover effects along the value chain, underscoring the importance of sectoral interdependencies in shaping effective decarbonization pathways.", "published": "2025-07-15T14:24:31Z", "authors": ["Zorana Grbac", "Simone Pavarana", "Thorsten Schmidt", "Peter Tankov"], "categories": ["econ.GN"], "doi": "", "_query": "housing price text listing", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/1906.02907v1", "title": "Optimal Resource Procurement and the Price of Causality", "abstract": "This paper studies the problem of procuring diverse resources in a forward market to cover a set $\\bf{E}$ of uncertain demand signals $\\bf{e}$. We consider two scenarios: (a) $\\bf{e}$ is revealed all at once by an oracle (b) $\\bf{e}$ reveals itself causally. Each scenario induces an optimal procurement cost. The ratio between these two costs is defined as the {\\em price of causality}. It captures the additional cost of not knowing the future values of the uncertain demand signal. We consider two application contexts: procuring energy reserves from a forward capacity market, and purchasing virtual machine instances from a cloud service. An upper bound on the price of causality is obtained, and the exact price of causality is computed for some special cases. The algorithmic basis for all these computations is set containment linear programming. A mechanism is proposed to allocate the procurement cost to consumers who in aggregate produce the demand signal. We show that the proposed cost allocation is fair, budget-balanced, and respects the cost-causation principle. The results are validated through numerical simulations.", "published": "2019-06-07T05:43:20Z", "authors": ["Sen Li", "Akhil Shetty", "Kameshwar Poolla", "Pravin Varaiya"], "categories": ["math.OC"], "doi": "", "_query": "housing price text listing", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2001.03967v1", "title": "Pricing Exchange Options under Stochastic Correlation", "abstract": "In this paper we study the pricing of exchange options when underlying assets have stochastic volatility and stochastic correlation. An approximation using a closed-form approximation based on a Taylor expansion of the conditional price is proposed. Numerical results are illustrated for exchanges between WTI and Brent type oil prices.", "published": "2020-01-12T17:56:25Z", "authors": ["Enrique Villamor", "Pablo Olivares"], "categories": ["q-fin.PR", "q-fin.CP"], "doi": "", "_query": "housing price text listing", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2202.10944v2", "title": "Convex Surrogate Loss Functions for Contextual Pricing with Transaction Data", "abstract": "We study an off-policy contextual pricing problem where the seller has access to samples of prices that customers were previously offered, whether they purchased at that price, and auxiliary features describing the customer and/or item being sold. This is in contrast to the well-studied setting in which samples of the customer's valuation (willingness to pay) are observed. In our setting, the observed data is influenced by the previous pricing policy, and we do not know how customers would have responded to alternative prices. We introduce suitable loss functions for this setting that can be directly optimized to find an effective pricing policy with expected revenue guarantees, without the need for estimation of an intermediate demand function. We focus on convex loss functions. This is particularly relevant when linear pricing policies are desired for interpretability reasons, resulting in a tractable convex revenue optimization problem. We propose generalized hinge and quantile pricing loss functions that price at a multiplicative factor of the conditional expected valuation or a particular quantile of the prices that sold, despite the valuation data not being observed. We prove expected revenue bounds for these pricing policies respectively when the valuation distribution is log-concave, and we provide generalization bounds for the finite sample case. Finally, we conduct simulations on both synthetic and real-world data to demonstrate that this approach is competitive with, and in some settings outperforms, state-of-the-art methods in contextual pricing.", "published": "2022-02-16T22:35:39Z", "authors": ["Max Biggs"], "categories": ["cs.LG", "cs.IR", "stat.ML"], "doi": "", "_query": "housing price text listing", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/1610.00157v1", "title": "A Performance-Based Scheme for Pricing Resources in the Cloud", "abstract": "With the rapid growth of the cloud computing marketplace, the issue of pricing resources in the cloud has been the subject of much study in recent years. In this paper, we identify and study a new issue: how to price resources in the cloud so that the customer's risk is minimized, while at the same time ensuring that the provider accrues his fair share. We do this by correlating the revenue stream of the customer to the prices charged by the provider. We show that our mechanism is incentive compatible in that it is in the best interest of the customer to provide his true revenue as a function of the resources rented. We next add another restriction to the price function, i.e., that it be linear. This removes the distortion that creeps in when the customer has to pay more money for less resources. Our algorithms for both the schemes mentioned above are efficient.", "published": "2016-10-01T16:38:03Z", "authors": ["Kamal Jain", "Tung Mai", "Vijay V. Vazirani"], "categories": ["cs.GT"], "doi": "", "_query": "housing price text listing", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/1208.2775v5", "title": "Physical approach to price momentum and its application to momentum strategy", "abstract": "We introduce various quantitative and mathematical definitions for price momentum of financial instruments. The price momentum is quantified with velocity and mass concepts originated from the momentum in physics. By using the physical momentum of price as a selection criterion, the weekly contrarian strategies are implemented in South Korea KOSPI 200 and US S&P 500 universes. The alternative strategies constructed by the physical momentum achieve the better expected returns and reward-risk measures than those of the traditional contrarian strategy in weekly scale. The portfolio performance is not understood by the Fama-French three-factor model.", "published": "2012-08-14T03:19:28Z", "authors": ["Jaehyung Choi"], "categories": ["q-fin.GN", "physics.soc-ph", "q-fin.PM"], "doi": "10.1016/j.physa.2014.07.075", "_query": "housing price text listing", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2008.09454v1", "title": "Detecting and repairing arbitrage in traded option prices", "abstract": "Option price data are used as inputs for model calibration, risk-neutral density estimation and many other financial applications. The presence of arbitrage in option price data can lead to poor performance or even failure of these tasks, making pre-processing of the data to eliminate arbitrage necessary. Most attention in the relevant literature has been devoted to arbitrage-free smoothing and filtering (i.e. removing) of data. In contrast to smoothing, which typically changes nearly all data, or filtering, which truncates data, we propose to repair data by only necessary and minimal changes. We formulate the data repair as a linear programming (LP) problem, where the no-arbitrage relations are constraints, and the objective is to minimise prices' changes within their bid and ask price bounds. Through empirical studies, we show that the proposed arbitrage repair method gives sparse perturbations on data, and is fast when applied to real world large-scale problems due to the LP formulation. In addition, we show that removing arbitrage from prices data by our repair method can improve model calibration with enhanced robustness and reduced calibration error.", "published": "2020-08-21T12:49:33Z", "authors": ["Samuel N. Cohen", "Christoph Reisinger", "Sheng Wang"], "categories": ["q-fin.PR", "q-fin.CP"], "doi": "10.1080/1350486X.2020.1846573", "_query": "housing price text listing", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2506.23130v1", "title": "The Florence Price Art Song Dataset and Piano Accompaniment Generator", "abstract": "Florence B. Price was a composer in the early 20th century whose music reflects her upbringing in the American South, her African heritage, and her Western classical training. She is noted as the first African-American woman to have a symphony performed by a major orchestra. Her music has recently received renewed attention from both the public and the research community, decades after her death. In addition to other genres, Price was a prolific composer for solo voice and piano. Music historians have documented the existence of 134 art songs and piano/voice arrangements for spirituals and folk songs written by Price. We release a digital catalog of 112 of these works in MuseScore, MusicXML, MIDI, and PDF format. We also use this dataset to fine-tune a symbolic music generation model to generate accompaniments to melodies, and we conduct a blind listening experiment that shows that accompaniments generated by our model are perceived as being reflective of Florence Price's style more frequently than accompaniments generated by a baseline model. We release our model as the Florence Price Piano Accompaniment Generator alongside our dataset.", "published": "2025-06-29T07:45:26Z", "authors": ["Tao-Tao He", "Martin E. Malandro", "Douglas Shadle"], "categories": ["cs.SD", "eess.AS"], "doi": "", "_query": "housing price text listing", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2001.01127v1", "title": "Forecasting Bitcoin closing price series using linear regression and neural networks models", "abstract": "This paper studies how to forecast daily closing price series of Bitcoin, using data on prices and volumes of prior days. Bitcoin price behaviour is still largely unexplored, presenting new opportunities. We compared our results with two modern works on Bitcoin prices forecasting and with a well-known recent paper that uses Intel, National Bank shares and Microsoft daily NASDAQ closing prices spanning a 3-year interval. We followed different approaches in parallel, implementing both statistical techniques and machine learning algorithms. The SLR model for univariate series forecast uses only closing prices, whereas the MLR model for multivariate series uses both price and volume data. We applied the ADF -Test to these series, which resulted to be indistinguishable from a random walk. We also used two artificial neural networks: MLP and LSTM. We then partitioned the dataset into shorter sequences, representing different price regimes, obtaining best result using more than one previous price, thus confirming our regime hypothesis. All the models were evaluated in terms of MAPE and relativeRMSE. They performed well, and were overall better than those obtained in the benchmarks. Based on the results, it was possible to demonstrate the efficacy of the proposed methodology and its contribution to the state-of-the-art.", "published": "2020-01-04T21:04:05Z", "authors": ["Nicola Uras", "Lodovica Marchesi", "Michele Marchesi", "Roberto Tonelli"], "categories": ["q-fin.ST", "cs.LG", "stat.ML"], "doi": "", "_query": "housing price text listing", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/1807.07088v1", "title": "A mean-field game approach to price formation in electricity markets", "abstract": "Here, we introduce a price-formation model where a large number of small players can store and trade electricity. Our model is a constrained mean-field game (MFG) where the price is a Lagrange multiplier for the supply vs. demand balance condition. We establish the existence of a unique solution using a fixed-point argument. In particular, we show that the price is well-defined and it is a Lipschitz function of time. Then, we study linear-quadratic models that can be solved explicitly and compare our model with real data.", "published": "2018-07-18T18:06:38Z", "authors": ["Diogo Gomes", "João Saúde"], "categories": ["math.AP"], "doi": "", "_query": "housing price text listing", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/physics/0605133v1", "title": "Real estate price peaks: a comparative overview", "abstract": "First, we emphasize that the real estate price peaks which are currently under way in many industrialized countries (one important exception is Japan) share many of the characteristics of previous historical price peaks. In particular, we show that: (i) In the present episode real price increases are, at least for now, of the same order of magnitude as in previous episodes, typically of the order of 80 percent to 100 percent. (ii) Historically, price peaks turned out to be symmetrical with respect to the peak; soft landing, i.e. an upgoing phase followed by a plateau, has rarely (if ever) been observed. (iii) The inflated demand is mainly boosted by investors and high-income buyers. (iv) In the present as well as in previous episodes, the main engines in the upgoing phase have been the hot markets which developed in major cities such as London, Los Angeles, New York, Paris, San Francisco or Sydney. In our conclusion, we propose a prediction for real estate prices in the West of the United States over the period 2005-2011. We also point out that investment funds, which already play a key role in stock markets, have in recent times began to heavily invest in real estate. In the future, one can expect them to become major players in property markets worldwide. The outcome of the present episode will tell us how quickly this transformation evolves. Thus, if the height of the present peak substantially surpasses the magnitude of previous ones, one may infer that investment funds have been able to establish strong communication channels between real estate assets on the one hand and financial assets (e.g. bonds, stocks, options) on the other hand.", "published": "2006-05-16T11:10:42Z", "authors": ["Bertrand M. Roehner"], "categories": ["physics.soc-ph"], "doi": "", "_query": "housing price text listing", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/0710.2758v1", "title": "The fundamental theorem of asset pricing under proportional transaction costs", "abstract": "We extend the fundamental theorem of asset pricing to a model where the risky stock is subject to proportional transaction costs in the form of bid-ask spreads and the bank account has different interest rates for borrowing and lending. We show that such a model is free of arbitrage if and only if one can embed in it a friction-free model that is itself free of arbitrage, in the sense that there exists an artificial friction-free price for the stock between its bid and ask prices and an artificial interest rate between the borrowing and lending interest rates such that, if one discounts this stock price by this interest rate, then the resulting process is a martingale under some non-degenerate probability measure. Restricting ourselves to the simple case of a finite number of time steps and a finite number of possible outcomes for the stock price, the proof follows by combining classical arguments based on finite-dimensional separation theorems with duality results from linear optimisation.", "published": "2007-10-15T09:37:08Z", "authors": ["Alet Roux"], "categories": ["q-fin.PR", "math.PR"], "doi": "", "_query": "housing price text listing", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2405.14893v2", "title": "Revisiting Day-ahead Electricity Price: Simple Model Save Millions", "abstract": "Accurate day-ahead electricity price forecasting is essential for residential welfare, yet current methods often fall short in forecast accuracy. We observe that commonly used time series models struggle to utilize the prior correlation between price and demand-supply, which, we found, can contribute a lot to a reliable electricity price forecaster. Leveraging this prior, we propose a simple piecewise linear model that significantly enhances forecast accuracy by directly deriving prices from readily forecastable demand-supply values. Experiments in the day-ahead electricity markets of Shanxi province and ISO New England reveal that such forecasts could potentially save residents millions of dollars a year compared to existing methods. Our findings underscore the value of suitably integrating time series modeling with economic prior for enhanced electricity price forecasting accuracy.", "published": "2024-05-20T08:27:14Z", "authors": ["Linian Wang", "Jianghong Liu", "Huibin Zhang", "Leye Wang"], "categories": ["cs.LG", "econ.EM"], "doi": "", "_query": "housing price text listing", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/1805.09616v3", "title": "Optimal pricing for peer-to-peer sharing with network externalities", "abstract": "In this paper, we analyse how a peer-to-peer sharing platform should price its service (when imagined as an excludable public good) to maximize profit, when each user's participation adds value to the platform service by creating a positive externality to other participants. To characterize network externalities as a function of the number of participants, we consider different bounded and unbounded user utility models. The bounded utility model fits many infrastructure sharing applications with bounded network value, in which complete coverage has a finite user valuation (e.g., WiFi or hotspot). The unbounded utility model fits the large scale data sharing and explosion in social media, where it is expected that the network value follows Metcalfe's or Zipf's law. For both models, we analyze the optimal pricing schemes to select heterogeneous users in the platform under complete and incomplete information of users' service valuations. We propose the concept of price of information (PoI) to characterize the profit loss due to lack of information, and present provable PoI bounds for different utility models. We show that the PoI=2 for the bounded utility model, meaning that just half of profit is lost, whereas the PoI>=2 for the unbounded utility model and increases as for a less concave utility function. We also show that the complicated differentiated pricing scheme which is optimal under incomplete user information, can be replaced by a single uniform price scheme that is asymptotic optimal. Finally, we extend our pricing schemes to a two-sided market by including a new group of `pure' service users contributing no externalities, and show that the platform may charge zero price to the original group of users in order to attract the pure user group.", "published": "2018-05-24T11:45:57Z", "authors": ["Yunpeng Li", "Costas Courcoubetis", "Lingjie Duan", "Richard Weber"], "categories": ["cs.GT"], "doi": "", "_query": "housing price text listing", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/cond-mat/0204574v1", "title": "Price Drops, Fluctuations, and Correlation in a Multi-Agent Model of Stock Markets", "abstract": "In this paper we compare market price fluctuations with the response to fundamental price drops within the Lux-Marchesi model which is able to reproduce the most important stylized facts of real market data. Major differences can be observed between the decay of spontaneous fluctuations and of changes due to external perturbations reflecting the absence of detailed balance, i.e., of the validity of the fluctuation-dissipation theorem. We found that fundamental price drops are followed by an overshoot with a rather robust characteristic time.", "published": "2002-04-26T12:25:31Z", "authors": ["A. G. Zawadowski", "R. Karadi", "J. Kertesz"], "categories": ["cond-mat.stat-mech", "q-fin.TR"], "doi": "10.1016/S0378-4371(02)01213-X", "_query": "housing price text listing", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2306.14186v1", "title": "Statistical electricity price forecasting: A structural approach", "abstract": "The availability of historical data related to electricity day-ahead prices and to the underlying price formation process is limited. In addition, the electricity market in Europe is facing a rapid transformation, which limits the representativeness of older observations for predictive purposes. On the other hand, machine learning methods that gained traction also in the domain of electricity price forecasting typically require large amounts of data. This study analyses the effectiveness of encoding well-established domain knowledge to mitigate the need for large training datasets. The domain knowledge is incorporated by imposing a structure on the price forecasting problem; the resulting accuracy gains are quantified in an experiment. Compared to an \"unstructured\" purely statistical model, it is shown that introducing intermediate quantity forecasts of load, renewable infeed, and cross-border exchange, paired with the estimation of supply curves, can result in a NRMSE reduction by 0.1 during daytime hours. The statistically most significant improvements are achieved in the first day of the forecasting horizon when a purely statistical model is combined with structured models. Finally, results are evaluated and interpreted with regard to the dynamic market conditions observed in Europe during the experiment period (from the 1st October 2022 to the 30th April 2023), highlighting the adaptive nature of models that are trained on shorter timescales.", "published": "2023-06-25T09:54:28Z", "authors": ["Raffaele Sgarlato"], "categories": ["stat.AP", "econ.GN"], "doi": "", "_query": "housing price text listing", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2103.10185v4", "title": "About subordinated generalizations of 3 classical models of option pricing", "abstract": "In this paper, we investigate the relation between Bachelier and Black-Scholes models driven by the infinitely divisible inverse subordinators. Such models, in contrast to their classical equivalents, can be used in markets where periods of stagnation are observed. We introduce the subordinated Cox-Ross-Rubinstein model and prove that the price of the underlying in that model converges in distribution and in Skorokhod space to the price of underlying in the subordinated Black-Scholes model defined in [31]. Motivated by this fact we price the selected option contracts using the binomial trees. The results are compared to other numerical methods.", "published": "2021-03-18T11:49:07Z", "authors": ["Michał Balcerek", "Grzegorz Krzyżanowski", "Marcin Magdziarz"], "categories": ["math.NA"], "doi": "", "_query": "housing price text listing", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/1203.3653v1", "title": "Optimal pricing using online auction experiments: A Pólya tree approach", "abstract": "We show how a retailer can estimate the optimal price of a new product using observed transaction prices from online second-price auction experiments. For this purpose we propose a Bayesian Pólya tree approach which, given the limited nature of the data, requires a specially tailored implementation. Avoiding the need for a priori parametric assumptions, the Pólya tree approach allows for flexible inference of the valuation distribution, leading to more robust estimation of optimal price than competing parametric approaches. In collaboration with an online jewelry retailer, we illustrate how our methodology can be combined with managerial prior knowledge to estimate the profit maximizing price of a new jewelry product.", "published": "2012-03-16T10:02:58Z", "authors": ["Edward I. George", "Sam K. Hui"], "categories": ["stat.AP"], "doi": "10.1214/11-AOAS503", "_query": "housing price text listing", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/1709.10388v1", "title": "Optimal Reserve Price for Online Ads Trading Based on Inventory Identification", "abstract": "The online ads trading platform plays a crucial role in connecting publishers and advertisers and generates tremendous value in facilitating the convenience of our lives. It has been evolving into a more and more complicated structure. In this paper, we consider the problem of maximizing the revenue for the seller side via utilizing proper reserve price for the auctions in a dynamical way. Predicting the optimal reserve price for each auction in the repeated auction marketplaces is a non-trivial problem. However, we were able to come up with an efficient method of improving the seller revenue by mainly focusing on adjusting the reserve price for those high-value inventories. Previously, no dedicated work has been performed from this perspective. Inspired by Paul and Michael, our model first identifies the value of the inventory by predicting the top bid price bucket using a cascade of classifiers. The cascade is essential in significantly reducing the false positive rate of a single classifier. Based on the output of the first step, we build another cluster of classifiers to predict the price separations between the top two bids. We showed that although the high-value auctions are only a small portion of all the traffic, successfully identifying them and setting correct reserve price would result in a significant revenue lift. Moreover, our optimization is compatible with all other reserve price models in the system and does not impact their performance. In other words, when combined with other models, the enhancement on exchange revenue will be aggregated. Simulations on randomly sampled Yahoo ads exchange (YAXR) data showed stable and expected lift after applying our model.", "published": "2017-08-23T02:22:24Z", "authors": ["Zhihui Xie", "Kuang-Chih Lee", "Liang Wang"], "categories": ["cs.GT", "cs.IR"], "doi": "10.1145/3124749.3124760", "_query": "housing price text listing", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/1807.03435v4", "title": "Improved Revenue Bounds for Posted-Price and Second-Price Mechanisms", "abstract": "We study revenue maximization through sequential posted-price (SPP) mechanisms in single-dimensional settings with $n$ buyers and independent but not necessarily identical value distributions. We construct the SPP mechanisms by considering the best of two simple pricing rules: one that imitates the revenue optimal mchanism, namely the Myersonian mechanism, via the taxation principle and the other that posts a uniform price. Our pricing rules are rather generalizable and yield the first improvement over long-established approximation factors in several settings. We design factor-revealing mathematical programs that crisply capture the approximation factor of our SPP mechanism. In the single-unit setting, our SPP mechanism yields a better approximation factor than the state of the art prior to our work (Azar, Chiplunkar & Kaplan, 2018). In the multi-unit setting, our SPP mechanism yields the first improved approximation factor over the state of the art after over nine years (Yan, 2011 and Chakraborty et al., 2010). Our results on SPP mechanisms immediately imply improved performance guarantees for the equivalent free-order prophet inequality problem. In the position auction setting, our SPP mechanism yields the first higher-than $1-1/e$ approximation factor. In eager second-price (ESP) auctions, our two simple pricing rules lead to the first improved approximation factor that is strictly greater than what is obtained by the SPP mechanism in the single-unit setting.", "published": "2018-07-10T01:20:30Z", "authors": ["Hedyeh Beyhaghi", "Negin Golrezaei", "Renato Paes Leme", "Martin Pal", "Balasubramanian Sivan"], "categories": ["cs.GT"], "doi": "", "_query": "housing price text listing", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2606.04033v2", "title": "Gradient-based optimization of nuclear criticality experiments using neural surrogate eigenvalue sensitivities", "abstract": "The validation of advanced nuclear reactor designs and fuel concepts will require the design of new critical experiments with high neutronic similarity to the target technology. Neutronic similarity can be quantified by the correlation coefficient $c_k$, which captures the shared bias in $k_\\text{eff}$ induced by uncertainties in nuclear data. Generally, a $c_k\\geq0.9$ is needed for an experiment to be sufficiently similar to a target technology. In this work, a physics-informed deep neural network is trained to predict the neutronic sensitivity of grid-based critical experiment geometries. The differentiability of the neural network is used to enable gradient-based design optimization of new experiment geometries to maximize $c_k$ with the sensitivity profile of a target technology. This approach allows for optimization over the combinatorial design space of potential material combinations within the grid, moving beyond traditional parametric optimization approaches. The method is applied to the validation of the TN-Americas TN-LC transportation cask with HALEU fuel, for which existing critical experiment coverage is limited. This application is shown to produce experiment geometries achieving $c_k$ scores of 0.97757, 0.81324, and 0.93276 for three configurations of interest.", "published": "2026-06-01T21:19:18Z", "authors": ["Will Savage", "Logan Burnett", "Dean Price"], "categories": ["cs.LG"], "doi": "", "_query": "housing price text listing", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2204.03968v2", "title": "Machine Learning architectures for price formation models", "abstract": "Here, we study machine learning (ML) architectures to solve a mean-field games (MFGs) system arising in price formation models. We formulate a training process that relies on a min-max characterization of the optimal control and price variables. Our main theoretical contribution is the development of a posteriori estimates as a tool to evaluate the convergence of the training process. We illustrate our results with numerical experiments for linear dynamics and both quadratic and non-quadratic models.", "published": "2022-04-08T09:55:48Z", "authors": ["Diogo Gomes", "Julián Gutiérrez", "Mathieu Laurière"], "categories": ["math.OC"], "doi": "", "_query": "housing price text listing", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/1610.08210v1", "title": "Price of Transparency in Strategic Machine Learning", "abstract": "Based on the observation that the transparency of an algorithm comes with a cost for the algorithm designer when the users (data providers) are strategic, this paper studies the impact of strategic intent of the users on the design and performance of transparent ML algorithms. We quantitatively study the {\\bf price of transparency} in the context of strategic classification algorithms, by modeling the problem as a nonzero-sum game between the users and the algorithm designer. The cost of having a transparent algorithm is measured by a quantity, named here as price of transparency which is the ratio of the designer cost at the Stackelberg equilibrium, when the algorithm is transparent (which allows users to be strategic) to that of the setting where the algorithm is not transparent.", "published": "2016-10-26T07:17:27Z", "authors": ["Emrah Akyol", "Cedric Langbort", "Tamer Basar"], "categories": ["cs.GT"], "doi": "", "_query": "housing price text listing", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/1602.06213v1", "title": "Modeling Stock Price Dynamics with Fuzzy Opinion Networks", "abstract": "We propose a mathematical model for the word-of-mouth communications among stock investors through social networks and explore how the changes of the investors' social networks influence the stock price dynamics and vice versa. An investor is modeled as a Gaussian fuzzy set (a fuzzy opinion) with the center and standard deviation as inputs and the fuzzy set itself as output. Investors are connected in the following fashion: the center input of an investor is taken as the average of the neighbors' outputs, where two investors are neighbors if their fuzzy opinions are close enough to each other, and the standard deviation (uncertainty) input is taken with local, global or external reference schemes to model different scenarios of how investors define uncertainties. The centers and standard deviations of the fuzzy opinions are the expected prices and their uncertainties, respectively, that are used as inputs to the price dynamic equation. We prove that with the local reference scheme the investors converge to different groups in finite time, while with the global or external reference schemes all investors converge to a consensus within finite time and the consensus may change with time in the external reference case. We show how to model trend followers, contrarians and manipulators within this mathematical framework and prove that the biggest enemy of a manipulator is the other manipulators. We perform Monte Carlo simulations to show how the model parameters influence the price dynamics, and we apply a modified version of the model to the daily closing prices of fifteen top banking and real estate stocks in Hong Kong for the recent two years from Dec. 5, 2013 to Dec. 4, 2015 and discover that a sharp increase of the combined uncertainty is a reliable signal to predict the reversal of the current price trend.", "published": "2016-02-19T16:35:05Z", "authors": ["Li-Xin Wang"], "categories": ["q-fin.TR", "cs.SI", "eess.SY", "q-fin.CP"], "doi": "", "_query": "housing price text listing", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2502.17906v4", "title": "Why do financial prices exhibit Brownian motion despite predictable order flow?", "abstract": "In financial market microstructure, there are two enigmatic empirical laws: (i) the market-order flow has predictable persistence due to metaorder splitters by institutional investors, well formulated as the Lillo-Mike-Farmer model. However, this phenomenon seems paradoxical given the diffusive and unpredictable price dynamics; (ii) the price impact $I(Q)$ of a large metaorder $Q$ follows the square-root law, $I(Q)\\propto \\sqrt{Q}$. Here we theoretically reveal why price dynamics follows Brownian motion despite predictable order flow by unifying these enigmas. We generalize the Lillo-Mike-Farmer model to nonlinear price-impact dynamics, which is mapped to an exactly solvable Lévy-walk model. Our exact solution shows that the price dynamics remains diffusive under the square-root law, even under persistent order flow. This work illustrates the crucial role of the square-root law in mitigating large price movements by large metaorders, thereby leading to the Brownian price dynamics, consistently with the efficient market hypothesis over long timescales.", "published": "2025-02-25T07:12:03Z", "authors": ["Yuki Sato", "Kiyoshi Kanazawa"], "categories": ["q-fin.TR", "cond-mat.stat-mech", "econ.GN", "q-fin.MF", "q-fin.PR"], "doi": "", "_query": "housing price text listing", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/1504.01093v1", "title": "Pricing Online Decisions: Beyond Auctions", "abstract": "We consider dynamic pricing schemes in online settings where selfish agents generate online events. Previous work on online mechanisms has dealt almost entirely with the goal of maximizing social welfare or revenue in an auction settings. This paper deals with quite general settings and minimizing social costs. We show that appropriately computed posted prices allow one to achieve essentially the same performance as the best online algorithm. This holds in a wide variety of settings. Unlike online algorithms that learn about the event, and then make enforceable decisions, prices are posted without knowing the future events or even the current event, and are thus inherently dominant strategy incentive compatible. In particular we show that one can give efficient posted price mechanisms for metrical task systems, some instances of the $k$-server problem, and metrical matching problems. We give both deterministic and randomized algorithms. Such posted price mechanisms decrease the social cost dramatically over selfish behavior where no decision incurs a charge. One alluring application of this is reducing the social cost of free parking exponentially.", "published": "2015-04-05T07:47:58Z", "authors": ["Ilan Reuven Cohen", "Alon Eden", "Amos Fiat", "Łukasz Jeż"], "categories": ["cs.GT", "cs.DS"], "doi": "", "_query": "housing price text listing", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2510.15617v4", "title": "Price Pass-Through of Austria's Single-Use Plastics Producer Charges: Evidence from Retail Offer Spells", "abstract": "Single use plastics (SUPs) impose substantial environmental costs. Following Directive (EU) 2019/904, Austria introduced producer charges and mandatory participation in collection and recycling systems. This paper exploits a monthly panel of retail offer spells drawn from a price comparison platform to estimate the extent to which compliance costs pass through to posted online prices in Austria. The treated sample comprises keyword matched SUP products including balloons, to go cups, wet wipes, plastic bags, food containers, tobacco filter items, beverage bottles, and plastic wraps observed alongside a control group of non SUP listings over 2020-2024. A two way fixed effects (TWFE) specification places the average post treatment price increase at approximately 4.1 percent. A sequential TWFE model separating the administrative reporting phase from March 2023 and the payment due phase from March 2024 reveals that the larger adjustment occurred during the earlier reporting stage, with a reporting only effect of approximately 8.1 percent and an incremental payment phase effect of 5.6 percent. For balloons, a category subject to pronounced regulatory fee exposure, event study estimates exceed 50 percent immediately following the initial payment date and remain elevated throughout most of the post treatment window. These findings indicate that Austrian online retailers adjusted prices in advance of fee payment deadlines, consistent with anticipatory pass through of expected compliance costs rather than a discrete response to realized payments. As the data contain price observations but not quantity data, the analysis speaks to price incidence and not to consumption or environmental outcomes.", "published": "2025-10-17T13:04:58Z", "authors": ["Felix Reichel"], "categories": ["econ.GN"], "doi": "10.3390/rsee3020009", "_query": "housing price text listing", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/1502.01125v1", "title": "Equilibrium Pricing in an Order Book Environment: Case Study for a Spin Model", "abstract": "When modelling stock market dynamics, the price formation is often based on an equilbrium mechanism. In real stock exchanges, however, the price formation is goverend by the order book. It is thus interesting to check if the resulting stylized facts of a model with equilibrium pricing change, remain the same or, more generally, are compatible with the order book environment. We tackle this issue in the framework of a case study by embedding the Bornholdt-Kaizoji-Fujiwara spin model into the order book dynamics. To this end, we use a recently developed agent based model that realistically incorporates the order book. We find realistic stylized facts. We conclude for the studied case that equilibrium pricing is not needed and that the corresponding assumption of a \"fundamental\" price may be abandoned.", "published": "2015-02-04T08:52:49Z", "authors": ["Frederik Meudt", "Thilo A. Schmitt", "Rudi Schäfer", "Thomas Guhr"], "categories": ["q-fin.CP", "q-fin.TR"], "doi": "10.1016/j.physa.2016.01.073", "_query": "housing price text listing", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/1912.01440v1", "title": "A Data-driven Storage Control Framework for Dynamic Pricing", "abstract": "Dynamic pricing is both an opportunity and a challenge to the demand side. It is an opportunity as it better reflects the real time market conditions and hence enables an active demand side. However, demand's active participation does not necessarily lead to benefits. The challenge conventionally comes from the limited flexible resources and limited intelligent devices in demand side. The decreasing cost of storage system and the widely deployed smart meters inspire us to design a data-driven storage control framework for dynamic prices. We first establish a stylized model by assuming the knowledge and structure of dynamic price distributions, and design the optimal storage control policy. Based on Gaussian Mixture Model, we propose a practical data-driven control framework, which helps relax the assumptions in the stylized model. Numerical studies illustrate the remarkable performance of the proposed data-driven framework.", "published": "2019-12-01T12:13:41Z", "authors": ["Jiaman Wu", "Zhiqi Wang", "Chenye Wu", "Kui Wang", "Yang Yu"], "categories": ["eess.SY", "cs.CE", "cs.LG"], "doi": "", "_query": "housing price text listing", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/math/0302243v2", "title": "Static Arbitrage Bounds on Basket Option Prices", "abstract": "We consider the problem of computing upper and lower bounds on the price of a European basket call option, given prices on other similar baskets. Although this problem is very hard to solve exactly in the general case, we show that in some instances the upper and lower bounds can be computed via simple closed-form expressions, or linear programs. We also introduce an efficient linear programming relaxation of the general problem based on an integral transform interpretation of the call price function. We show that this relaxation is tight in some of the special cases examined before.", "published": "2003-02-19T22:13:08Z", "authors": ["Alexandre d'Aspremont", "Laurent El Ghaoui"], "categories": ["math.OC", "math.PR", "q-fin.ST"], "doi": "", "_query": "housing price text listing", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2303.16660v2", "title": "Price Optimization Combining Conjoint Data and Purchase History: A Causal Modeling Approach", "abstract": "Pricing decisions of companies require an understanding of the causal effect of a price change on the demand. When real-life pricing experiments are infeasible, data-driven decision-making must be based on alternative data sources such as purchase history (sales data) and conjoint studies where a group of customers is asked to make imaginary purchases in an artificial setup. We present an approach for price optimization that combines population statistics, purchase history and conjoint data in a systematic way. We build on the recent advances in causal inference to identify and quantify the effect of price on the purchase probability at the customer level. The identification task is a transportability problem whose solution requires a parametric assumption on the differences between the conjoint study and real purchases. The causal effect is estimated using Bayesian methods that take into account the uncertainty of the data sources. The pricing decision is made by comparing the estimated posterior distributions of gross profit for different prices. The approach is demonstrated with simulated data resembling the features of real-world data.", "published": "2023-03-29T13:10:56Z", "authors": ["Lauri Valkonen", "Santtu Tikka", "Jouni Helske", "Juha Karvanen"], "categories": ["stat.AP"], "doi": "10.1353/obs.2024.a929116", "_query": "housing price text listing", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/1510.05875v7", "title": "An elementary approach to the option pricing problem", "abstract": "Our goal here is to discuss the pricing problem of European and American options in discrete time using elementary calculus so as to be an easy reference for first year undergraduate students. Using the binomial model we compute the fair price of European and American options. We explain the notion of Arbitrage and the notion of the fair price of an option using common sense. We give a criterion that the holder can use to decide when it is appropriate to exercise the option. We prove the put-call parity formulas for both European and American options and we discuss the relation between American and European options. We give also the bounds for European and American options. We also discuss the portfolio's optimization problem and the fair value in the case where the holder can not produce the opposite portfolio.", "published": "2015-10-20T13:12:26Z", "authors": ["Nikolaos Halidias"], "categories": ["q-fin.MF", "math.NA"], "doi": "", "_query": "housing price text listing", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2110.05230v2", "title": "Packing list-colourings", "abstract": "List colouring is an influential and classic topic in graph theory. We initiate the study of a natural strengthening of this problem, where instead of one list-colouring, we seek many in parallel. Our explorations have uncovered a potentially rich seam of interesting problems spanning chromatic graph theory. Given a $k$-list-assignment $L$ of a graph $G$, which is the assignment of a list $L(v)$ of $k$ colours to each vertex $v\\in V(G)$, we study the existence of $k$ pairwise-disjoint proper colourings of $G$ using colours from these lists. We may refer to this as a \\emph{list-packing}. Using a mix of combinatorial and probabilistic methods, we set out some basic upper bounds on the smallest $k$ for which such a list-packing is always guaranteed, in terms of the number of vertices, the degeneracy, the maximum degree, or the (list) chromatic number of $G$. (The reader might already find it interesting that such a minimal $k$ is well defined.) We also pursue a more focused study of the case when $G$ is a bipartite graph. Our results do not yet rule out the tantalising prospect that the minimal $k$ above is not too much larger than the list chromatic number. Our study has taken inspiration from study of the strong chromatic number, and we also explore generalisations of the problem above in the same spirit.", "published": "2021-10-11T12:48:17Z", "authors": ["Stijn Cambie", "Wouter Cames van Batenburg", "Ewan Davies", "Ross J. Kang"], "categories": ["math.CO", "cs.DM"], "doi": "10.1002/rsa.21181", "_query": "housing price text listing", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/1903.04106v1", "title": "Pricing Formulae of Power Binary and Normal Distribution Standard Options and Applications", "abstract": "In this paper the Buchen's pricing formulae of (higher order) asset and bond binary options are incorporated into the pricing formula of power binary options and a pricing formula of \"the normal distribution standard options\" with the maturity payoff related to a power function and the density function of normal distribution is derived. And as their applications, pricing formulae of savings plans that provide a choice of indexing and discrete geometric average Asian options are derived and the fact that the price of discrete geometric average Asian option converges to the price of continuous geometric average Asian option when the largest distance between neighboring monitoring times goes to zero is proved.", "published": "2019-03-11T02:43:57Z", "authors": ["Hyong-Chol O", "Dae-Sung Choe"], "categories": ["q-fin.PR"], "doi": "", "_query": "housing price text listing", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2410.07378v1", "title": "Static Pricing for Online Selection Problem and its Variants", "abstract": "This paper studies an online selection problem, where a seller seeks to sequentially sell multiple copies of an item to arriving buyers. We consider an adversarial setting, making no modeling assumptions about buyers' valuations for the items except acknowledging a finite support. In this paper, we focus on a class of static pricing algorithms that sample a price from a pre-determined distribution and sell items to buyers whose valuations exceed the sampled price. Such algorithms are of practical interests due to their advantageous properties, such as ease of implementation and non-discrimination over prices. Our work shows that the simple static pricing strategy can achieve strong guarantees comparable to the best known dynamic pricing algorithms. Particularly, we design the optimal static pricing algorithms for the adversarial online selection problem and its two important variants: the online assignment problem and the online selection with convex cost. The static pricing algorithms can even attain the optimal competitive ratios among all online algorithms for the online selection problem and the online assignment problem. To achieve these results, we propose an economics-based approach in the competitive analysis of static pricing algorithms, and develop a novel representative function-based approach to derive the lower bounds. We expect these approaches will be useful in related problems such as online matching.", "published": "2024-10-09T18:53:17Z", "authors": ["Bo Sun", "Hossein Nekouyan Jazi", "Xiaoqi Tan", "Raouf Boutaba"], "categories": ["cs.GT", "cs.DS"], "doi": "", "_query": "housing price text listing", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2401.14517v3", "title": "Efficient List-decoding of Polynomial Ideal Codes with Optimal List Size", "abstract": "In a recent breakthrough [BGM23, GZ23, AGL23], it was shown that randomly punctured Reed-Solomon codes are list decodable with optimal list size with high probability, i.e., they attain the Singleton bound for list decoding [ST20, Rot22, GST22]. We extend this result to the family of polynomial ideal codes, a large class of error-correcting codes which includes several well-studied families of codes such as Reed-Solomon, folded Reed-Solomon, and multiplicity codes. More specifically, similarly to the Reed-Solomon setting, we show that randomly punctured polynomial ideal codes over an exponentially large alphabet exactly achieve the Singleton bound for list-decoding; while such codes over a polynomially large alphabet approximately achieve it. Combining our results with the efficient list-decoding algorithm for a large subclass of polynomial ideal codes of [BHKS21], implies as a corollary that a large subclass of polynomial ideal codes (over random evaluation points) is efficiently list decodable with optimal list size. To the best of our knowledge, this gives the first family of codes that can be efficiently list decoded with optimal list size (for all list sizes), as well as the first family of linear codes of rate $R$ that can be efficiently list decoded up to a radius of $1 -R-ε$ with list size that is polynomial (and even linear) in $1/ε$. Our result applies to natural families of codes with algebraic structure such as folded Reed-Solomon or multiplicity codes (over random evaluation points). Our proof follows the general framework of [BGM23, GZ23, AGL23], but several new ingredients are needed. The main two new ingredients are a polynomial-ideal GM-MDS theorem (extending the algebraic GM-MDS theorem of [YH19, Lov21]), as well as a duality theorem for polynomial ideal codes, both of which may be of independent interest.", "published": "2024-01-25T21:21:18Z", "authors": ["Noga Ron-Zewi", "S. Venkitesh", "Mary Wootters"], "categories": ["cs.IT"], "doi": "", "_query": "housing price text listing", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2204.04774v1", "title": "Revenue Management Under the Markov Chain Choice Model with Joint Price and Assortment Decisions", "abstract": "Finding the optimal product prices and product assortment are two fundamental problems in revenue management. Usually, a seller needs to jointly determine the prices and assortment while managing a network of resources with limited capacity. However, there is not yet a tractable method to efficiently solve such a problem. Existing papers studying static joint optimization of price and assortment cannot incorporate resource constraints. Then we study the revenue management problem with resource constraints and price bounds, where the prices and the product assortments need to be jointly determined over time. We showed that under the Markov chain (MC) choice model (which subsumes the multinomial logit (MNL) model), we could reformulate the choice-based joint optimization problem as a tractable convex conic optimization problem. We also proved that an optimal solution with a constant price vector exists even with constraints on resources. In addition, a solution with both constant assortment and price vector can be optimal when there is no resource constraint.", "published": "2022-04-10T21:30:51Z", "authors": ["Anton J. Kleywegt", "Hongzhang Shao"], "categories": ["math.OC", "econ.EM"], "doi": "", "_query": "housing price text listing", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2302.11902v1", "title": "On price-induced minmax matchings", "abstract": "We study a natural combinatorial pricing problem for sequentially arriving buyers with equal budgets. Each buyer is interested in exactly one pair of items and purchases this pair if and only if, upon arrival, both items are still available and the sum of the item prices does not exceed the budget. The goal of the seller is to set prices to the items such that the number of transactions is maximized when buyers arrive in adversarial order. Formally, we are given an undirected graph where vertices represent items and edges represent buyers. Once prices are set to the vertices, edges with a total price exceeding the buyers' budgets are evicted. Any arrival order of the buyers leads to a set of transactions that forms a maximal matching in this subgraph, and an adversarial arrival order results in a minimum maximal matching. In order to measure the performance of a pricing strategy, we compare the size of such a matching to the size of a maximum matching in the original graph. It was shown by Correa et al. [IPCO 2022] that the best ratio any pricing strategy can guarantee lies within $[1/2, 2/3]$. Our contribution to the problem is two-fold: First, we provide several characterizations of subgraphs that may result from pricing schemes. Second, building upon these, we show an improved upper bound of $3/5$ and a lower bound of $1/2 + 2/n$, where $n$ is the number of items.", "published": "2023-02-23T10:18:28Z", "authors": ["Christoph Dürr", "Mathieu Mari", "Ulrike Schmidt-Kraepelin"], "categories": ["cs.GT", "cs.CE", "cs.DS"], "doi": "", "_query": "housing price text listing", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/1407.4452v2", "title": "New Pricing Framework: Options and Bonds", "abstract": "A unified analytical pricing framework with involvement of the shot noise random process has been introduced and elaborated. Two exactly solvable new models have been developed. The first model has been designed to value options. It is assumed that asset price stochastic dynamics follows a Geometric Shot Noise motion. A new arbitrage-free integro-differential option pricing equation has been found and solved. The put-call parity has been proved and the Greeks have been calculated. Three additional new Greeks associated with market model parameters have been introduced and evaluated. It has been shown that in diffusion approximation the developed option pricing model incorporates the well-known Black-Scholes equation and its solution. The stochastic dynamic origin of the Black-Scholes volatility has been uncovered. The new option pricing model has been generalized based on asset price dynamics modeled by the superposition of Geometric Brownian motion and Geometric Shot Noise. To model stochastic dynamics of a short term interest rate, the second model has been introduced and developed based on Langevin type equation with shot noise. A new bond pricing formula has been obtained. It has been shown that in diffusion approximation the developed bond pricing formula goes into the well-known Vasicek solution. The stochastic dynamic origin of the long-term mean and instantaneous volatility of the Vasicek model has been uncovered. A generalized bond pricing model has been introduced and developed based on short term interest rate stochastic dynamics modeled by superposition of a standard Wiener process and shot noise. Despite the non-Gaussianity of probability distributions involved, all newly elaborated models have the same degree of analytical tractability as the Black-Scholes model and the Vasicek model.", "published": "2014-07-16T14:10:08Z", "authors": ["Nick Laskin"], "categories": ["q-fin.PR", "q-fin.MF"], "doi": "", "_query": "housing price text listing", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2305.05850v1", "title": "New Formulations and Pricing Mechanisms for Stochastic Electricity Market Clearing Problem", "abstract": "We present new formulations of the stochastic electricity market clearing problem based on the principles of stochastic programming. Previous analyses have established that the canonical stochastic programming model effectively captures the relationship between the day-ahead and real-time dispatch and prices. The resulting quantities exhibit desirable guarantees of revenue adequacy, cost recovery, and price distortion in expectation. Motivated by these results, we develop alternative stochastic programming model formulations that differ in how we model the nonanticipativity requirement. We design new pricing mechanisms by developing suitable dual optimization problems for these models. We analyze these pricing mechanisms to assess their ability to provide revenue adequacy for the system operators, cost recovery for generators, and price distortion. Unlike the previous mechanisms that yield the desired properties in expectation, one of our pricing mechanisms extends the benefits of cost recovery and bounded price distortion under every scenario while retaining the long-run revenue adequacy for the system operator. We demonstrate these benefits through numerical experiments conducted on well-known test systems.", "published": "2023-05-10T02:47:04Z", "authors": ["Sakitha Ariyarathne", "Harsha Gangammanavar"], "categories": ["eess.SY", "math.OC"], "doi": "", "_query": "housing price text listing", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2304.06060v3", "title": "European Option Pricing Under Generalized Tempered Stable Process: Empirical Analysis", "abstract": "The paper investigates the performance of the European option price when the log asset price follows a rich class of Generalized Tempered Stable (GTS) distribution. The GTS distribution is an alternative to Normal distribution and $α$-stable distribution for modeling asset return and many physical and economic systems. The data used in the option pricing computation comes from fitting the GTS distribution to the underlying S\\&P 500 Index return distribution. The Esscher transform method shows that the GTS distribution preserves its structure. The extended Black-Scholes formula and the Generalized Black-Scholes Formula are applied in the study. The 12-point rule Composite Newton-Cotes Quadrature and the Fractional Fast Fourier (FRFT) algorithms were implemented, and they yield the same European option price at two decimal places. Compared to the option price under the GTS distribution, the Black-Scholes (BS) model is underpriced for the Near-The-Money (NTM) and the in-the-money (ITM) options. However, the BS model and GTS European options yield the same option price for the deep out-of-the-money (OTM) and the deep-in-the-money (ITM) options.", "published": "2023-04-12T16:15:24Z", "authors": ["A. H. Nzokem"], "categories": ["q-fin.PR", "math.PR"], "doi": "", "_query": "housing price text listing", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2509.01969v1", "title": "Mediation Analysis in the Presence of Sample Selection Bias with an Application to Disparities in Liver Transplantation Listing", "abstract": "The study of disparities in the liver transplantation process may focus on quantifying causal effects, particularly the average, direct, or indirect effects of various social determinants of health on being listed as a candidate for transplant. Selection bias arises when the data sample does not represent the target population, defined here as all individuals referred to the transplant clinic. Listing decisions are made for the subset of patients who complete the evaluation process, who may differ systematically from the referred population. There is evidence that selection is associated with patient characteristics that also impact outcomes. Using data only from the selected population may yield biased causal effect estimates. However, incorporating data from the referred population allows for analytic correction. This correction leverages hypothesized causal relationships among selection, the outcome (getting listed), exposures, and mediators. Using directed acyclic graphs (DAGs), we establish graphical conditions under which a reweighted mediation formula identifies effect of interest - direct, indirect, and path-specific effects - in the presence of sample selection. In a clinical case study, we investigate mediated and direct effects of a patient's socioeconomic position on being listed for transplant, allowing selection to depend on race, gender, age, and other social determinants.", "published": "2025-09-02T05:18:45Z", "authors": ["Zain Khan", "Lynnette Sequeira", "Alexandra T. Strauss", "Vedant Jain", "Juliette Dixon", "Eric Moughames", "Tyrus Vong", "Daniel Malinsky"], "categories": ["stat.AP", "stat.ME"], "doi": "", "_query": "housing price text listing", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/1601.06672v1", "title": "Pricing Vehicle Sharing with Proximity Information", "abstract": "For vehicle sharing schemes, where drop-off positions are not fixed, we propose a pricing scheme, where the price depends in part on the distance between where a vehicle is being dropped off and where the closest shared vehicle is parked. Under certain restrictive assumptions, we show that this pricing leads to a socially optimal spread of the vehicles within a region.", "published": "2016-01-25T16:55:30Z", "authors": ["Jakub Marecek", "Robert Shorten", "Jia Yuan Yu"], "categories": ["math.OC", "cs.AI", "cs.MA"], "doi": "10.1109/ICBDSC.2016.7460378", "_query": "housing price text listing", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2505.01472v1", "title": "SafeTab-P: Disclosure Avoidance for the 2020 Census Detailed Demographic and Housing Characteristics File A (Detailed DHC-A)", "abstract": "This article describes the disclosure avoidance algorithm that the U.S. Census Bureau used to protect the Detailed Demographic and Housing Characteristics File A (Detailed DHC-A) of the 2020 Census. The tabulations contain statistics (counts) of demographic characteristics of the entire population of the United States, crossed with detailed races and ethnicities at varying levels of geography. The article describes the SafeTab-P algorithm, which is based on adding noise drawn to statistics of interest from a discrete Gaussian distribution. A key innovation in SafeTab-P is the ability to adaptively choose how many statistics and at what granularity to release them, depending on the size of a population group. We prove that the algorithm satisfies a well-studied variant of differential privacy, called zero-concentrated differential privacy (zCDP). We then describe how the algorithm was implemented on Tumult Analytics and briefly outline the parameterization and tuning of the algorithm.", "published": "2025-05-02T13:08:28Z", "authors": ["Sam Haney", "Skye Berghel", "Bayard Carlson", "Ryan Cumings-Menon", "Luke Hartman", "Michael Hay", "Ashwin Machanavajjhala", "Gerome Miklau", "Amritha Pai", "Simran Rajpal", "David Pujol", "William Sexton", "Ruchit Shrestha", "Daniel Simmons-Marengo"], "categories": ["cs.CR", "cs.CY"], "doi": "", "_query": "housing price text listing", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2206.07132v1", "title": "Dynamics of a Binary Option Market with Exogenous Information and Price Sensitivity", "abstract": "In this paper, we derive and analyze a continuous of a binary option market with exogenous information. The resulting non-linear system has a discontinuous right hand side, which can be analyzed using zero-dimensional Filippov surfaces. Under general assumptions on purchasing rules, we show that when exogenous information is constant in the binary asset market, the price always converges. We then investigate market prices in the case of changing information, showing empirically that price sensitivity has a strong effect on price lag vs. information. We conclude with open questions on general $n$-ary option markets. As a by-product of the analysis, we show that these markets are equivalent to a simple recurrent neural network, helping to explain some of the predictive power associated with prediction markets, which are usually designed as $n$-ary option markets.", "published": "2022-05-18T14:39:16Z", "authors": ["Hannah Gampe", "Christopher Griffin"], "categories": ["q-fin.TR", "econ.EM", "physics.soc-ph"], "doi": "10.1016/j.cnsns.2022.106994", "_query": "housing price text listing", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2409.18895v2", "title": "A Multisource Fusion Framework for Cryptocurrency Price Movement Prediction", "abstract": "Predicting cryptocurrency price trends remains a major challenge due to the volatility and complexity of digital asset markets. Artificial intelligence (AI) has emerged as a powerful tool to address this problem. This study proposes a multisource fusion framework that integrates quantitative financial indicators, such as historical prices and technical indicators, with qualitative sentiment signals derived from X (formerly Twitter). Sentiment analysis is performed using Financial Bidirectional Encoder Representations from Transformers (FinBERT), a domain-specific BERT-based model optimized for financial text, while sequential dependencies are captured through a Bidirectional Long Short-Term Memory (BiLSTM) network. Experimental results on a large-scale Bitcoin dataset demonstrate that the proposed approach substantially outperforms single-source models, achieving an accuracy of approximately 96.8\\%. The findings underscore the importance of incorporating real-time social sentiment alongside traditional indicators, thereby enhancing predictive accuracy and supporting more informed investment decisions.", "published": "2024-09-27T16:32:57Z", "authors": ["Saeed Mohammadi Dashtaki", "Reza Mohammadi Dashtaki", "Mehdi Hosseini Chagahi", "Behzad Moshiri", "Md. Jalil Piran"], "categories": ["cs.LG", "cs.AI"], "doi": "", "_query": "housing price text listing", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2609.27134v1", "title": "Rare States and Long-Run Pricing", "abstract": "Macroeconomic crises are rare, yet they can have large effects on asset prices. Because the probability of entering a crisis from normal times is small relative to the probability of recovery, the dynamics are nearly reducible: setting the crisis-entry probability to zero makes crisis states unreachable from normal states. We develop a graph-theoretic and spectral analysis of long-run pricing under reducible and nearly reducible dynamics. We show that, at the reducible limit, physical and pricing dynamics induce the same classes of economic states and accessibility relations, but rank their long-run importance differently: by recurrence and by class-specific pricing rates. Moreover, a claim's price depends only on its pricing corridor---the classes of states lying on directed paths from the initial state to states where the payoff is positive---and the highest class-specific pricing rate within this corridor determines the claim's long-run pricing rate. When the reducible-limit corridor excludes the globally pricing-dominant class, we characterize how restoring rare transitions introduces a contribution from that class with a weight that vanishes as the transitions become rarer. This contribution becomes dominant only beyond a crossover maturity, which increases with the rarity of the connecting paths and decreases with the dominant class's pricing-rate advantage. Finally, a consumption-based disaster-recovery application shows how preferences, consumption risk, cash-flow exposure, and recovery timing jointly shape the long-run pricing dominance of disaster states and crossover maturities.", "published": "2026-09-22T22:50:25Z", "authors": ["Ye Lu", "John Stachurski"], "categories": ["econ.TH"], "doi": "", "_query": "housing price text listing", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/1307.4531v1", "title": "Crowd-assisted Search for Price Discrimination in E-Commerce: First results", "abstract": "After years of speculation, price discrimination in e-commerce driven by the personal information that users leave (involuntarily) online, has started attracting the attention of privacy researchers, regulators, and the press. In our previous work we demonstrated instances of products whose prices varied online depending on the location and the characteristics of perspective online buyers. In an effort to scale up our study we have turned to crowd-sourcing. Using a browser extension we have collected the prices obtained by an initial set of 340 test users as they surf the web for products of their interest. This initial dataset has permitted us to identify a set of online stores where price variation is more pronounced. We have focused on this subset, and performed a systematic crawl of their products and logged the prices obtained from different vantage points and browser configurations. By analyzing this dataset we see that there exist several retailers that return prices for the same product that vary by 10%-30% whereas there also exist isolated cases that may vary up to a multiplicative factor, e.g., x2. To the best of our efforts we could not attribute the observed price gaps to currency, shipping, or taxation differences.", "published": "2013-07-17T08:24:40Z", "authors": ["Jakub Mikians", "László Gyarmati", "Vijay Erramilli", "Nikolaos Laoutaris"], "categories": ["cs.CY"], "doi": "", "_query": "housing price text listing", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2302.08911v1", "title": "DSE Stock Price Prediction using Hidden Markov Model", "abstract": "Stock market forecasting is a classic problem that has been thoroughly investigated using machine learning and artificial neural network based tools and techniques. Interesting aspects of this problem include its time reliance as well as its volatility and other complex relationships. To combine them, hidden markov models (HMMs) have been utilized to anticipate the price of stocks. We demonstrated the Maximum A Posteriori (MAP) HMM method for predicting stock prices for the next day based on previous data. An HMM is trained by analyzing the fractional change in the stock price as well as the intraday high and low values. It is then utilized to produce a MAP estimate across all possible stock prices for the next day. The approach demonstrated in our work is quite generalized and can be used to predict the stock price for any company, given that the HMM is trained on the dataset of that company's stocks dataset. We evaluated the accuracy of our models using some extensively used accuracy metrics for regression problems and came up with a satisfactory outcome.", "published": "2023-01-26T13:28:34Z", "authors": ["Raihan Tanvir", "Md Tanvir Rouf Shawon", "Md. Golam Rabiul Alam"], "categories": ["q-fin.ST", "cs.CE"], "doi": "", "_query": "housing price text listing", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/1307.7513v1", "title": "An Approach Finding Frequent Items In Text Or Transactional Data Base By Using BST To Improve The Efficiency Of Apriori Algorithm", "abstract": "Data mining techniques have been widely used in various applications. Binary search tree based frequent items is an effective method for automatically recognize the most frequent items, least frequent items and average frequent items. This paper presents a new approach in order to find out frequent items. The word frequent item refers to how many times the item appeared in the given input. This approach is used to find out item sets in any order using familiar approach binary search tree. The method adapted here is in order to find out frequent items by comparing and incrementing the counter variable in existing transactional data base or text data. We are also representing different approaches in frequent item sets and also propose an algorithmic approach for the problem solving", "published": "2013-07-29T09:39:16Z", "authors": ["P Vasanth Sena"], "categories": ["cs.DB", "cs.DS"], "doi": "", "_query": "housing price text listing", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2104.11684v1", "title": "Pricing Asian Options with Correlators", "abstract": "We derive a series expansion by Hermite polynomials for the price of an arithmetic Asian option. This series requires the computation of moments and correlators of the underlying price process, but for a polynomial jump-diffusion, these are given in closed form, hence no numerical simulation is required to evaluate the series. This allows, for example, for the explicit computation of Greeks. The weight function defining the Hermite polynomials is a Gaussian density with scale $b$. We find that the rate of convergence for the series depends on $b$, for which we prove a lower bound to guarantee convergence. Numerical examples show that the series expansion is accurate but unstable for initial values of the underlying process far from zero, mainly due to rounding errors.", "published": "2021-04-23T16:22:06Z", "authors": ["Silvia Lavagnini"], "categories": ["q-fin.PR", "q-fin.CP"], "doi": "", "_query": "housing price text listing", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2607.17119v1", "title": "Dynamic Pricing for a Two-Sided Data Market Platform", "abstract": "We study a continuous-time dynamic pricing problem for a data platform that purchases raw data from privacy-sensitive providers and sells data products to consumers. The platform controls both the acquisition price offered to providers and the selling price charged to consumers. Provider and consumer arrivals are modeled by point processes whose intensities depend on the platform's current data stock, capturing feedback between data accumulation and market participation. We formulate the platform's problem as an infinite-horizon stochastic control problem with a jump-diffusion state process and derive the associated nonlinear integro-differential HJB equation. We prove that the value function is the unique viscosity solution, establish classical regularity under suitable conditions, and verify the optimal feedback pricing policy. Finally, we conduct numerical analyses to examine the influences of model parameters on the optimal pricing policies.", "published": "2026-07-19T08:11:18Z", "authors": ["Lijun Bo", "Dongfang Yang", "Yijie Huang"], "categories": ["math.OC"], "doi": "", "_query": "housing price text listing", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2211.04956v2", "title": "A Characterization of List Learnability", "abstract": "A classical result in learning theory shows the equivalence of PAC learnability of binary hypothesis classes and the finiteness of VC dimension. Extending this to the multiclass setting was an open problem, which was settled in a recent breakthrough result characterizing multiclass PAC learnability via the DS dimension introduced earlier by Daniely and Shalev-Shwartz. In this work we consider list PAC learning where the goal is to output a list of $k$ predictions. List learning algorithms have been developed in several settings before and indeed, list learning played an important role in the recent characterization of multiclass learnability. In this work we ask: when is it possible to $k$-list learn a hypothesis class? We completely characterize $k$-list learnability in terms of a generalization of DS dimension that we call the $k$-DS dimension. Generalizing the recent characterization of multiclass learnability, we show that a hypothesis class is $k$-list learnable if and only if the $k$-DS dimension is finite.", "published": "2022-11-07T21:28:05Z", "authors": ["Moses Charikar", "Chirag Pabbaraju"], "categories": ["stat.ML", "cs.DS", "cs.LG"], "doi": "", "_query": "housing price text listing", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2102.10528v1", "title": "A Novel Multi-Period and Multilateral Price Index", "abstract": "A novel approach to price indices, leading to an innovative solution in both a multi-period or a multilateral framework, is presented. The index turns out to be the generalized least squares solution of a regression model linking values and quantities of the commodities. The index reference basket, which is the union of the intersections of the baskets of all country/period taken in pair, has a coverage broader than extant indices. The properties of the index are investigated and updating formulas established. Applications to both real and simulated data provide evidence of the better index performance in comparison with extant alternatives.", "published": "2021-02-21T06:44:18Z", "authors": ["Consuelo Rubina Nava", "Maria Grazia Zoia"], "categories": ["econ.EM"], "doi": "", "_query": "housing price text listing", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/1405.1247v1", "title": "Stylized facts of price gaps in limit order books: Evidence from Chinese stocks", "abstract": "Price gap, defined as the logarithmic price difference between the first two occupied price levels on the same side of a limit order book (LOB), is a key determinant of market depth, which is one of the dimensions of liquidity. However, the properties of price gaps have not been thoroughly studied due to the less availability of ultrahigh frequency data. In the paper, we rebuild the LOB dynamics based on the order flow data of 26 A-share stocks traded on the Shenzhen Stock Exchange in 2003. Three key empirical statistical properties of price gaps are investigated. We find that the distribution of price gaps has a power-law tail for all stocks with an average tail exponent close to 3.2. Applying modern statistical methods, we confirm that the gap time series are long-range correlated and possess multifractal nature. These three features vary from stock to stock and are not universal. Furthermore, we also unveil buy-sell asymmetry phenomena in the properties of price gaps on the buy and sell sides of the LOBs for individual stocks. These findings deepen our understanding of the dynamics of liquidity of common stocks and can be used to calibrate agent-based computational financial models.", "published": "2014-05-06T12:38:35Z", "authors": ["Gao-Feng Gu", "Xiong Xiong", "Yong-Jie Zhang", "Wei Chen", "Wei Zhang", "Wei-Xing Zhou"], "categories": ["q-fin.TR", "q-fin.ST"], "doi": "10.1016/j.chaos.2015.10.031", "_query": "housing price text listing", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/1511.07907v2", "title": "Competitive Charging Station Pricing for Plug-in Electric Vehicles", "abstract": "This paper considers the problem of charging station pricing and plug-in electric vehicles (PEVs) station selection. When a PEV needs to be charged, it selects a charging station by considering the charging prices, waiting times, and travel distances. Each charging station optimizes its charging price based on the prediction of the PEVs' charging station selection decisions and the other station's pricing decision, in order to maximize its profit. To obtain insights of such a highly coupled system, we consider a one-dimensional system with two competing charging stations and Poisson arriving PEVs. We propose a multi-leader-multi-follower Stackelberg game model, in which the charging stations (leaders) announce their charging prices in Stage I, and the PEVs (followers) make their charging station selections in Stage II. We show that there always exists a unique charging station selection equilibrium in Stage II, and such equilibrium depends on the charging stations' service capacities and the price difference between them. We then characterize the sufficient conditions for the existence and uniqueness of the pricing equilibrium in Stage I. We also develop a low complexity algorithm that efficiently computes the pricing equilibrium and the subgame perfect equilibrium of the two-stage Stackelberg game.", "published": "2015-11-24T22:45:32Z", "authors": ["Wei Yuan", "Jianwei Huang", "Ying Jun Zhang"], "categories": ["cs.GT", "eess.SY", "math.OC"], "doi": "", "_query": "housing price text listing", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/1905.05813v1", "title": "The connection between multiple prices of an Option at a given time with single prices defined at different times: The concept of weak-value in quantum finance", "abstract": "We introduce a new tool for predicting the evolution of an option for the cases where at some specific time, there is a high-degree of uncertainty for identifying its price. We work over the special case where we can predict the evolution of the system by joining a single price for the Option, defined at some specific time with a pair of prices defined at another instant. This is achieved by describing the evolution of the system through a financial Hamiltonian. The extension to the case of multiple prices at a given instant is straightforward. We also explain how to apply these results in real situations.", "published": "2019-05-14T19:50:25Z", "authors": ["Ivan Arraut", "Alan Au", "Alan Ching-biu Tse", "Carlos Segovia"], "categories": ["q-fin.PR"], "doi": "10.1016/j.physa.2019.04.264", "_query": "housing price text listing", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/1001.3176v1", "title": "Analyzing the prices of the most expensive sheet iron all over the world: Modeling, prediction and regime change", "abstract": "The private car license plates issued in Shanghai are bestowed the title of \"the most expensive sheet iron all over the world\", more expensive than gold. A citizen has to bid in an monthly auction to obtain a license plate for his new private car. We perform statistical analysis to investigate the influence of the minimal price $P_{\\min}$ of the bidding winners, the quota $N_{\\rm{quota}}$ of private car license plates, the number $N_{\\rm{bidder}}$ of bidders, as well as two external shocks including the legality debate of the auction in 2004 and the auction regime reform in January 2008 on the average price $P_{\\rm{mean}}$ of all bidding winners. It is found that the legality debate of the auction had marginal transient impact on the average price in a short time period. In contrast, the change of the auction rules has significant permanent influence on the average price, which reduces the price by about 3020 yuan Renminbi. It means that the average price exhibits nonlinear behaviors with a regime change. The evolution of the average price is independent of the number $N_{\\rm{bidder}}$ of bidders in both regimes. In the early regime before January 2008, the average price $P_{\\rm{mean}}$ was influenced only by the minimal price $P_{\\min}$ in the preceding month with a positive correlation. In the current regime since January 2008, the average price is positively correlated with the minimal price and the quota in the preceding month and negatively correlated with the quota in the same month. We test the predictive power of the two models using 2-year and 3-year moving windows and find that the latter outperforms the former. It seems that the auction market becomes more efficient after the auction reform since the prediction error increases.", "published": "2010-01-19T00:55:55Z", "authors": ["Fu-Tie Song", "Wei-Xing Zhou"], "categories": ["q-fin.ST"], "doi": "10.1016/j.physa.2010.04.010", "_query": "housing price text listing", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/1407.2991v3", "title": "Price of Anarchy with Heterogeneous Latency Functions", "abstract": "In this paper we consider the price of anarchy (PoA) in multi-commodity flows where the latency or delay function on an edge has a heterogeneous dependency on the flow commodities, i.e. when the delay on each link is dependent on the flow of individual commodities, rather than on the aggregate flow. An application of this study is the performance analysis of a network with differentiated traffic that may arise when traffic is prioritized according to some type classification. This study has implications in the debate on net-neutrality. We provide price of anarchy bounds for networks with $k$ (types of) commodities where each link is associated with heterogeneous polynomial delays, i.e. commodity $i$ on edge $e$ faces delay specified by $g_{i1}(e)f^θ_1(e) + g_{i2}(e)f^θ_2(e) + \\ldots + g_{ik}(e)f^θ_k(e) + c_i(e), $ where $f_i(e)$ is the flow of the $i$th commodity through edge $e$, $θ\\in {\\cal N}$, $g_{i1}(e), g_{i2}(e), \\ldots, g_{ik}(e)$ and $c_i(e)$ are nonnegative constants. We consider both atomic and non-atomic flows. For networks with decomposable delay functions where the delay induced by a particular commodity is the same, i.e. delays on edge $e$ are defined by $a_1(e)f_1^θ(e) + a_2(e)f_2^θ(e) + \\ldots + c(e)$ where $\\forall j , \\forall e: g_{1j}(e) = g_{2j}(e) = \\ldots = a_j(e)$, we show an improved bound on the price of anarchy. Further, we show bounds on the price of anarchy for uniform latency functions where each edge of the network has the same delay function.", "published": "2014-07-11T00:28:34Z", "authors": ["Sanjiv Kapoor", "Junghwan Shin"], "categories": ["cs.GT"], "doi": "", "_query": "housing price text listing", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2002.06975v1", "title": "Cross-sectional Stock Price Prediction using Deep Learning for Actual Investment Management", "abstract": "Stock price prediction has been an important research theme both academically and practically. Various methods to predict stock prices have been studied until now. The feature that explains the stock price by a cross-section analysis is called a \"factor\" in the field of finance. Many empirical studies in finance have identified which stocks having features in the cross-section relatively increase and which decrease in terms of price. Recently, stock price prediction methods using machine learning, especially deep learning, have been proposed since the relationship between these factors and stock prices is complex and non-linear. However, there are no practical examples for actual investment management. In this paper, therefore, we present a cross-sectional daily stock price prediction framework using deep learning for actual investment management. For example, we build a portfolio with information available at the time of market closing and invest at the time of market opening the next day. We perform empirical analysis in the Japanese stock market and confirm the profitability of our framework.", "published": "2020-02-17T14:28:42Z", "authors": ["Masaya Abe", "Kei Nakagawa"], "categories": ["q-fin.PM"], "doi": "10.1145/3399871.3399889", "_query": "housing price text listing", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/1907.12025v1", "title": "Marked Hawkes process modeling of price dynamics and volatility estimation", "abstract": "A simple Hawkes model have been developed for the price tick structure dynamics incorporating market microstructure noise and trade clustering. In this paper, the model is extended with random mark to deal with more realistic price tick structures of equities. We examine the impact of jump in price dynamics to the future movements and dependency between the jump sizes and ground intensities. We also derive the volatility formula based on stochastic and statistical methods and compare with realized volatility in simulation and empirical studies. The marked Hawkes model is useful to estimate the intraday volatility similarly in the case of simple Hawkes model.", "published": "2019-07-28T06:33:18Z", "authors": ["Kyungsub Lee", "Byoung Ki Seo"], "categories": ["q-fin.ST", "q-fin.TR"], "doi": "10.1016/j.jempfin.2016.08.004", "_query": "housing price text listing", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2603.17522v1", "title": "Detecting the Machine: A Comprehensive Benchmark of AI-Generated Text Detectors Across Architectures, Domains, and Adversarial Conditions", "abstract": "The rapid proliferation of large language models (LLMs) has created an urgent need for robust and generalizable detectors of machine-generated text. Existing benchmarks typically evaluate a single detector on a single dataset under ideal conditions, leaving open questions about cross-domain transfer, cross-LLM generalization, and adversarial robustness. We present a comprehensive benchmark evaluating diverse detection approaches across two corpora: HC3 (23,363 human-ChatGPT pairs) and ELI5 (15,000 human-Mistral-7B pairs). Methods include classical classifiers, fine-tuned transformer encoders (BERT, RoBERTa, ELECTRA, DistilBERT, DeBERTa-v3), a CNN, an XGBoost stylometric model, perplexity-based detectors, and LLM-as-detector prompting. Results show that transformer models achieve near-perfect in-distribution performance but degrade under domain shift. The XGBoost stylometric model matches performance while remaining interpretable. LLM-based detectors underperform and are affected by generator-detector identity bias. Perplexity-based methods exhibit polarity inversion, with modern LLM outputs showing lower perplexity than human text, but remain effective when corrected. No method generalizes robustly across domains and LLM sources.", "published": "2026-03-18T09:27:27Z", "authors": ["Madhav S. Baidya", "S. S. Baidya", "Chirag Chawla"], "categories": ["cs.CL", "cs.AI"], "doi": "", "_query": "housing price text listing", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/1909.09057v1", "title": "The Effect of Oil Price on United Arab Emirates Goods Trade Deficit with the United States", "abstract": "We seek to investigate the effect of oil price on UAE goods trade deficit with the U.S. The current increase in the price of oil and the absence of significant studies in the UAE economy are the main motives behind the current study. Our paper focuses on a small portion of UAE trade, which is 11% of the UAE foreign trade, however, it is a significant part since the U.S. is a major trade partner with the UAE. The current paper concludes that oil price has a significant positive influence on real imports. At the same time, oil price does not have a significant effect on real exports. As a result, any increase in the price of oil increases goods trade deficit of the UAE economy. The policy implication of the current paper is that the revenue of oil sales is not used to encourage UAE real exports.", "published": "2019-09-19T15:55:40Z", "authors": ["Osama D. Sweidan", "Bashar H. Malkawi"], "categories": ["econ.GN"], "doi": "", "_query": "housing price text listing", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2305.12309v1", "title": "Uniform Pricing vs Pay as Bid in 100%-Renewables Electricity Markets: A Game-theoretical Analysis", "abstract": "This paper evaluates market equilibrium under different pricing mechanisms in a two-settlement 100%-renewables electricity market. Given general probability distributions of renewable energy, we establish game-theoretical models to analyze equilibrium bidding strategies, market prices, and profits under uniform pricing (UP) and pay-as-bid pricing (PAB). We prove that UP can incentivize suppliers to withhold bidding quantities and lead to price spikes. PAB can reduce the market price, but it may lead to a mixed-strategy price equilibrium. Then, we present a regulated uniform pricing scheme (RUP) based on suppliers' marginal costs that include penalty costs for real-time deviations. We show that RUP can achieve lower yet positive prices and profits compared with PAB in a duopoly market, which approximates the least-cost system outcome. Simulations with synthetic and real data find that under PAB and RUP, higher uncertainty of renewables and real-time shortage penalty prices can increase the market price by encouraging lower bidding quantities, thereby increasing suppliers' profits.", "published": "2023-05-21T00:50:39Z", "authors": ["Dongwei Zhao", "Audun Botterud", "Marija Ilic"], "categories": ["eess.SY", "cs.GT", "econ.TH"], "doi": "", "_query": "housing price text listing", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2202.11061v2", "title": "In This Apportionment Lottery, the House Always Wins", "abstract": "Apportionment is the problem of distributing $h$ indivisible seats across states in proportion to the states' populations. In the context of the US House of Representatives, this problem has a rich history and is a prime example of interactions between mathematical analysis and political practice. Grimmett (2004) suggested to apportion seats in a randomized way such that each state receives exactly their proportional share $q_i$ of seats in expectation (ex ante proportionality) and receives either $\\lfloor q_i \\rfloor$ or $\\lceil q_i \\rceil$ many seats ex post (quota). However, there is a vast space of randomized apportionment methods satisfying these two axioms, and so we additionally consider prominent axioms from the apportionment literature. Our main result is a randomized method satisfying quota, ex ante proportionality and house monotonicity - a property that prevents paradoxes when the number of seats changes and which we require to hold ex post. This result is based on a generalization of dependent rounding on bipartite graphs, which we call cumulative rounding and which might be of independent interest, as we demonstrate via applications beyond apportionment.", "published": "2022-02-22T17:46:11Z", "authors": ["Paul Gölz", "Dominik Peters", "Ariel D. Procaccia"], "categories": ["cs.GT"], "doi": "10.1145/3490486.3538299", "_query": "housing price text listing", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2401.00970v2", "title": "Almost Perfect Shadow Prices", "abstract": "Shadow prices simplify the derivation of optimal trading strategies in markets with transaction costs by transferring optimization into a more tractable, frictionless market. This paper establishes that a naïve shadow price Ansatz for maximizing long term returns given average volatility yields a strategy that is, for small bid-ask-spreads, asymptotically optimal at third order. Considering the second-order impact of transaction costs, such a strategy is essentially optimal. However, for risk aversion different from one, we devise alternative strategies that outperform the shadow market at fourth order. Finally, it is shown that the risk-neutral objective rules out the existence of shadow prices.", "published": "2024-01-01T22:45:55Z", "authors": ["Eberhard Mayerhofer"], "categories": ["q-fin.PM", "math.OC", "math.PR"], "doi": "", "_query": "housing price text listing", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2207.12022v2", "title": "Peer-to-Peer Sharing of Energy Storage Systems under Net Metering and Time-of-Use Pricing", "abstract": "Sharing economy has become a socio-economic trend in transportation and housing sectors. It develops business models leveraging underutilized resources. Like those sectors, power grid is also becoming smarter with many flexible resources, and researchers are investigating the impact of sharing resources here as well that can help to reduce cost and extract value. In this work, we investigate sharing of energy storage devices among individual households in a cooperative fashion. Coalitional game theory is used to model the scenario where utility company imposes time-of-use (ToU) price and net metering billing mechanism. The resulting game has a non-empty core and we can develop a cost allocation mechanism with easy to compute analytical formula. Allocation is fair and cost effective for every household. We design the price for peer to peer network (P2P) and an algorithm for sharing that keeps the grand coalition always stable. Thus sharing electricity of storage devices among consumers can be effective in this set-up. Our mechanism is implemented in a community of 80 households in Texas using real data of demand and solar irradiance and the results show significant cost savings for our method.", "published": "2022-07-25T09:58:29Z", "authors": ["K. Victor Sam Moses Babu", "Satya Surya Vinay K", "Pratyush Chakraborty"], "categories": ["eess.SY", "cs.GT"], "doi": "10.1109/ACCESS.2023.3234625", "_query": "housing price text listing", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2409.06255v5", "title": "Stock Price Responses to Firm-Level News in Supply Chain Networks", "abstract": "This study examines how positive and negative news about firms is associated with stock prices and whether these associations extend to firms' suppliers and clients connected through supply chain relationships, using large samples of publicly listed firms worldwide and in Japan. News sentiment is measured using FinBERT, a natural language processing model fine-tuned for financial texts, and supply chain links are identified from financial statements for global firms and from large-scale firm-level surveys for Japanese firms. We find that stock prices exhibit systematic associations with positive and negative news even before public disclosure. These associations are also observed for suppliers and clients before and after disclosure. In general, relative to the pre-disclosure period, post-disclosure associations are larger, with the difference concentrated around the time of public news disclosure. However, for Japanese firms, the post-disclosure associations for suppliers and clients are smaller than the pre-disclosure associations, in contrast to the pattern observed for firms outside Japan.", "published": "2024-09-10T06:55:17Z", "authors": ["Hiroyasu Inoue", "Yasuyuki Todo"], "categories": ["cs.SI", "cs.LG"], "doi": "", "_query": "housing price text listing", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2106.00923v3", "title": "Posted Price versus Hybrid Mechanisms in Freight Transportation Marketplaces", "abstract": "We consider a freight platform that serves as an intermediary between shippers and carriers in a truckload transportation network. The platform's objective is to design a policy that determines prices for shippers and payments to carriers, as well as how carriers are matched to loads to be transported, to maximize its long-run average profit. We propose a two-stage decision framework to model carriers' load choice behavior, where carriers choose a lane according to the multinomial logit (MNL) model based on the platform's posted price in the first stage and book a load in the second stage. We analyze two types of carrier-side mechanisms commonly used by freight platforms: a posted price mechanism and a hybrid mechanism where carriers can either book loads at posted price or submit their bids in an auction. The proposed mechanisms are constructed using a fluid approximation model to incorporate carrier interactions in the freight network. We show that the hybrid mechanism has higher profits than the posted price mechanism. We prove tight bounds between these mechanisms for varying market sizes. The findings are validated through a numerical simulation using industry data from the U.S. freight market.", "published": "2021-06-02T03:48:16Z", "authors": ["Ruoran Chen", "Sungwoo Kim", "He Wang", "Xuan Wang"], "categories": ["math.OC"], "doi": "", "_query": "housing price text listing", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2210.15364v1", "title": "Explicit Intensity Control for Accented Text-to-speech", "abstract": "Accented text-to-speech (TTS) synthesis seeks to generate speech with an accent (L2) as a variant of the standard version (L1). How to control the intensity of accent in the process of TTS is a very interesting research direction, and has attracted more and more attention. Recent work design a speaker-adversarial loss to disentangle the speaker and accent information, and then adjust the loss weight to control the accent intensity. However, such a control method lacks interpretability, and there is no direct correlation between the controlling factor and natural accent intensity. To this end, this paper propose a new intuitive and explicit accent intensity control scheme for accented TTS. Specifically, we first extract the posterior probability, called as ``goodness of pronunciation (GoP)'' from the L1 speech recognition model to quantify the phoneme accent intensity for accented speech, then design a FastSpeech2 based TTS model, named Ai-TTS, to take the accent intensity expression into account during speech generation. Experiments show that the our method outperforms the baseline model in terms of accent rendering and intensity control.", "published": "2022-10-27T12:23:41Z", "authors": ["Rui Liu", "Haolin Zuo", "De Hu", "Guanglai Gao", "Haizhou Li"], "categories": ["cs.SD", "cs.AI", "eess.AS"], "doi": "", "_query": "housing price text listing", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/1204.2736v1", "title": "Optimal execution and price manipulations in time-varying limit order books", "abstract": "This paper focuses on an extension of the Limit Order Book (LOB) model with general shape introduced by Alfonsi, Fruth and Schied. Here, the additional feature allows a time-varying LOB depth. We solve the optimal execution problem in this framework for both discrete and continuous time strategies. This gives in particular sufficient conditions to exclude Price Manipulations in the sense of Huberman and Stanzl or Transaction-Triggered Price Manipulations (see Alfonsi, Schied and Slynko). These conditions give interesting qualitative insights on how market makers may create or not price manipulations.", "published": "2012-04-12T14:27:07Z", "authors": ["Aurélien Alfonsi", "José Infante Acevedo"], "categories": ["q-fin.TR", "math.OC"], "doi": "", "_query": "housing price text listing", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2303.13192v1", "title": "Mechanism Design for Ad Auctions with Display Prices", "abstract": "In many applications, ads are displayed together with the prices, so as to provide a direct comparison among similar products or services. The price-displaying feature not only influences the consumers' decisions, but also affects the advertisers' bidding behaviors. In this paper, we study ad auctions with display prices from the perspective of mechanism design, in which advertisers are asked to submit both the costs and prices of their products. We provide a characterization for all incentive compatible auctions with display prices, and use it to design auctions under two scenarios. In the former scenario, the display prices are assumed to be exogenously determined. For this setting, we derive the welfare-maximizing and revenue-maximizing auctions for any realization of the price profile. In the latter, advertisers are allowed to strategize display prices in their own interests. We investigate two families of allocation policies within the scenario and identify the equilibrium prices accordingly. Our results reveal that the display prices do affect the design of ad auctions and the platform can leverage such information to optimize the performance of ad delivery.", "published": "2023-03-23T11:46:48Z", "authors": ["Bin Li", "Yahui Lei"], "categories": ["cs.AI", "cs.GT", "cs.MA"], "doi": "", "_query": "housing price text listing", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/1407.4355v1", "title": "Pricing for local and global WiFi markets", "abstract": "This paper analyzes two pricing schemes commonly used in WiFi markets: the flat-rate and the usage-based pricing. The flat-rate pricing encourages the maximum usage, while the usage-based pricing can flexibly attract more users especially those with low valuations in mobile Internet access. First, we use theoretical analysis to compare the two schemes and show that for a single provider in a market, as long as the WiFi capacity is abundant, the flat-rate pricing leads to more revenue. Second, we study how a global provider (e.g., Skype) collaborates with this monopolist in each local market to provide a global WiFi service. We formulate {the interactions between the global and local providers as a dynamic game. In Stage I, the global provider bargains with the local provider in each market to determine the global WiFi service price and revenue sharing agreement. In Stage II, local users and travelers choose local or global WiFi services. We analytically show that the global provider prefers to use the usage-based pricing to avoid a severe competition with the local provider. At the equilibrium, the global provider always shares the majority of his revenue with the local provider to incentivize the cooperation. Finally, we analytically study how the interaction changes if the local market has more than one local provider. In this case, the global provider can integrate the coverages of multiple local providers and provide a better service. Compared to the local monopoly case, local market competition enables the global provider to share less revenue with each of the local providers. However, we numerically show that the global provider's revenue could decrease, as he shares his revenue with more providers and can only charge a lower price.", "published": "2014-07-16T15:57:21Z", "authors": ["Lingjie Duan", "Jianwei Huang", "Biying Shou"], "categories": ["cs.GT", "cs.NI"], "doi": "", "_query": "housing price text listing", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2610.19031v1", "title": "A basis-set-free reference for the exact exchange-energy density and functional development", "abstract": "The exact local exchange-energy density, obtained from the Slater potential of the Hartree-Fock exchange hole, is a central ingredient of Becke's B05 and B13 functionals and of many other local hybrids. It is usually evaluated in a Gaussian basis, so the local quantity retains a pointwise basis-set incompleteness error. We describe here a basis-set-free numerical code that solves the Kohn-Sham problem on real-space grids for atoms, diatomics, and polyatomics, and returns the exact local exchange-energy density together with the density, its derivatives, the kinetic-energy density, and the reverse Becke-Roussel hole quantities. Reconstructed from the published record of Becke's non-public NUMOL code, AXEL reproduces numerical atomic references through krypton, scalar-relativistic uranium orbital energies, and Becke's diatomic spectroscopic constants. We then extend the original local-density numerical framework to generalized-gradient and hybrid functionals. Comparisons with the independent adaptive-multiwavelet code MRChem give sub-millihartree agreement for Hartree-Fock and PBE0. The same calculations agree with large Gaussian-basis references to a similar level of accuracy. Atom-centred eXact Electronic Limit (AXEL) is intended as both a basis-set-free numerical reference and a platform for exchange-hole-based density-functional development.", "published": "2026-10-13T18:24:02Z", "authors": ["Alastair J. A. Price"], "categories": ["physics.chem-ph"], "doi": "", "_query": "housing price text listing", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2002.02876v2", "title": "Equal Risk Pricing and Hedging of Financial Derivatives with Convex Risk Measures", "abstract": "In this paper, we consider the problem of equal risk pricing and hedging in which the fair price of an option is the price that exposes both sides of the contract to the same level of risk. Focusing for the first time on the context where risk is measured according to convex risk measures, we establish that the problem reduces to solving independently the writer and the buyer's hedging problem with zero initial capital. By further imposing that the risk measures decompose in a way that satisfies a Markovian property, we provide dynamic programming equations that can be used to solve the hedging problems for both the case of European and American options. All of our results are general enough to accommodate situations where the risk is measured according to a worst-case risk measure as is typically done in robust optimization. Our numerical study illustrates the advantages of equal risk pricing over schemes that only account for a single party, pricing based on quadratic hedging (i.e. $ε$-arbitrage pricing), or pricing based on a fixed equivalent martingale measure (i.e. Black-Scholes pricing). In particular, the numerical results confirm that when employing an equal risk price both the writer and the buyer end up being exposed to risks that are more similar and on average smaller than what they would experience with the other approaches.", "published": "2020-02-07T16:20:59Z", "authors": ["Saeed Marzban", "Erick Delage", "Jonathan Yumeng Li"], "categories": ["math.OC", "q-fin.PR"], "doi": "", "_query": "housing price text listing", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2007.10131v1", "title": "The Price of Anarchy of Two-Buyer Sequential Multiunit Auctions", "abstract": "We study the efficiency of sequential multiunit auctions with two-buyers and complete information. For general valuation functions, we show that the price of anarchy is exactly $1/T$ for auctions with $T$ items for sale. For concave valuation functions, we show that the price of anarchy is bounded below by $1-1/e\\simeq 0.632$. This bound is asymptotically tight as the number of items sold tends to infinity.", "published": "2020-07-20T14:11:54Z", "authors": ["Mete Şeref Ahunbay", "Adrian Vetta"], "categories": ["cs.GT"], "doi": "", "_query": "housing price text listing", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/1312.1304v1", "title": "On the asymptotic behavior of a Boltzmann-type price formation model", "abstract": "In this paper we study the asymptotic behavior of a Boltzmann type price formation model, which describes the trading dynamics in a financial market. In many of these markets trading happens at high frequencies and low transactions costs. This observation motivates the study of the limit as the number of transactions $k$ tends to infinity, the transaction cost $a$ to zero and $ka=const$. Furthermore we illustrate the price dynamics with numerical simulations.", "published": "2013-12-04T19:57:25Z", "authors": ["Martin Burger", "Luis Caffarelli", "Peter A. Markowich", "Marie-Therese Wolfram"], "categories": ["math.AP"], "doi": "", "_query": "housing price text listing", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2508.06621v1", "title": "Train It and Forget It: Merge Lists are Unnecessary for BPE Inference in Language Models", "abstract": "Standard Byte-Pair Encoding (BPE) tokenization compresses text by pairing a learned token vocabulary with a detailed merge list. Recent work has shown that this merge list exposes a potential attack surface for extracting information about language model's training data. In this paper, we explore the downstream impact of BPE inference algorithms that do not rely on this merge list at all, and hence differ from the encoding process during BPE training. To address this question, we investigate two broad classes of BPE inference schemes that differ from BPE application during training: a) targeted deviation from merge-lists including random merge orders, and various corruptions of merge list involving deletion/truncation, and b) non-targeted BPE inference algorithms that do not depend on the merge list but focus on compressing the text either greedily or exactly. Extensive experiments across diverse language modeling tasks like accuracy-based QA benchmarks, machine translation, and open-ended generation reveal that while targeted deviation from the merge lists exhibits significant degradation in language model performance, the non-targeted merge-list-free inference algorithms result in minimal impact on downstream performance that is often much smaller than expected. These findings pave way for simpler and potentially more privacy-preserving tokenization schemes that do not catastrophically compromise model performance.", "published": "2025-08-08T18:10:03Z", "authors": ["Tomohiro Sawada", "Kartik Goyal"], "categories": ["cs.CL"], "doi": "", "_query": "housing price text listing", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/1510.08335v1", "title": "A Stochastic Electricity Market Clearing Formulation with Consistent Pricing Properties", "abstract": "We argue that deterministic market clearing formulations introduce arbitrary distortions between day-ahead and expected real-time prices that bias economic incentives and block diversification. We extend and analyze the stochastic clearing formulation proposed by Pritchard et al. (2010) in which the social surplus function induces penalties between day-ahead and real-time quantities. We prove that the formulation yields price distortions that are bounded by the bid prices, and we show that adding a similar penalty term to transmission flows and phase angles ensures boundedness throughout the network. We prove that when the price distortions are zero, day-ahead quantities converge to the quantile of real-time counterparts. The undesired effects of price distortions suggest that stochastic settings provide significant benefits over deterministic ones that go beyond social surplus improvements. We propose additional metrics to evaluate these benefits.", "published": "2015-10-28T15:02:44Z", "authors": ["Victor M. Zavala", "Kibaek Kim", "Mihai Anitescu", "John Birge"], "categories": ["econ.GN", "math.OC"], "doi": "", "_query": "housing price text listing", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/cond-mat/9910212v1", "title": "Growth Optimal Investment and Pricing of Derivatives", "abstract": "We introduce a criterion how to price derivatives in incomplete markets, based on the theory of growth optimal strategy in repeated multiplicative games. We present reasons why these growth-optimal strategies should be particularly relevant to the problem of pricing derivatives. We compare our result with other alternative pricing procedures in the literature, and discuss the limits of validity of the lognormal approximation. We also generalize the pricing method to a market with correlated stocks. The expected estimation error of the optimal investment fraction is derived in a closed form, and its validity is checked with a small-scale empirical test.", "published": "1999-10-14T13:34:15Z", "authors": ["Erik Aurell", "Roberto Baviera", "Ola Hammarlid", "Maurizio Serva", "Angelo Vulpiani"], "categories": ["cond-mat.stat-mech", "q-fin.PR"], "doi": "10.1016/S0378-4371(00)00005-4", "_query": "housing price text listing", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2609.28270v1", "title": "Predicting Quantization Price for Selecting PTQ Configurations Before Deployment", "abstract": "Weight-space post-training quantization (PTQ) must choose finite formats, granularities, quantizer families, transformations, and bits before the completed quantized model reveals its output-distribution drift. Existing PTQ methods predict important pieces of this degradation, including reconstruction error, Hessian sensitivity, transformation effects, and downstream loss, but these pieces are usually scored after fixing the quantization geometry or inside separate configuration families. We formulate weight-space PTQ as pre-deployment configuration selection using priced layer-output error. Each admissible layer configuration is treated as an error generator with a deployment cost, which induces a layer-output error covariance $\\boldsymbolΣ_l(α_l)$, and the full-precision model prices that covariance by downstream curvature, $\\widehatρ_l(α_l)=\\frac{1}{2}\\operatorname{Tr}\\left(\\widehat{\\mathbf{H}}_l\\,\\widehat{\\boldsymbolΣ}_l(α_l)\\right)$. The price follows from full-precision-to-quantized forward KL, whose first-order term cancels at the reference model. It turns reconstruction and diagonal scores into reduced proxies that drop price factors, while finite formats, codebooks, granularities, and equivalent transformations become comparable candidates through the covariances they induce and the costs they pay. A trace reduction then yields a calibration-time price table and a budgeted price-guided selector, making fixed-geometry bit allocation a special case rather than the organizing problem.", "published": "2026-09-23T15:25:44Z", "authors": ["Junbin Qiu", "Jian Mu", "Weitong Zhang", "Yao Shu"], "categories": ["cs.CL", "cs.LG"], "doi": "", "_query": "housing price text listing", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/1109.0738v2", "title": "Pricing Derivatives on Multiscale Diffusions: an Eigenfunction Expansion Approach", "abstract": "Using tools from spectral analysis, singular and regular perturbation theory, we develop a systematic method for analytically computing the approximate price of a derivative-asset. The payoff of the derivative-asset may be path-dependent. Additionally, the process underlying the derivative may exhibit killing (i.e. jump to default) as well as combined local/nonlocal stochastic volatility. The nonlocal component of volatility is multiscale, in the sense that it is driven by one fast-varying and one slow-varying factor. The flexibility of our modeling framework is contrasted by the simplicity of our method. We reduce the derivative pricing problem to that of solving a single eigenvalue equation. Once the eigenvalue equation is solved, the approximate price of a derivative can be calculated formulaically. To illustrate our method, we calculate the approximate price of three derivative-assets: a vanilla option on a defaultable stock, a path-dependent option on a non-defaultable stock, and a bond in a short-rate model.", "published": "2011-09-04T18:33:35Z", "authors": ["Matthew Lorig"], "categories": ["q-fin.CP", "math.SP", "q-fin.PR"], "doi": "", "_query": "housing price text listing", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2210.15613v3", "title": "The law of one price in quadratic hedging and mean-variance portfolio selection", "abstract": "The law of one price (LOP) broadly asserts that identical financial flows should command the same price. We show that, when properly formulated, LOP is the minimal condition for a well-defined mean-variance portfolio selection framework without degeneracy. Crucially, the paper identifies a new mechanism through which LOP can fail in a continuous-time $L^2$ setting without frictions, namely 'trading from just before a predictable stopping time', which surprisingly identifies LOP violations even for continuous price processes. Closing this loophole allows to give a version of the \"Fundamental Theorem of Asset Pricing\" appropriate in the quadratic context, establishing the equivalence of the economic concept of LOP with the probabilistic property of the existence of a local $\\scr{E}$-martingale state price density. The latter provides unique prices for all square-integrable claims in an extended market and subsequently plays an important role in quadratic hedging and mean-variance portfolio selection. Mathematically, we formulate a novel variant of the uniform boundedness principle for conditionally linear functionals on the $L^0$ module of conditionally square-integrable random variables. We then study the representation of time-consistent families of such functionals in terms of stochastic exponentials of a fixed local martingale.", "published": "2022-10-27T16:58:43Z", "authors": ["Aleš Černý", "Christoph Czichowsky"], "categories": ["math.OC", "q-fin.PM"], "doi": "10.1007/s00780-025-00563-7", "_query": "housing price text listing", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2507.23505v2", "title": "The effect of a new power interconnector on energy prices volatility: the case of Sicily", "abstract": "Integrating energy islands into the European electricity market is a key challenge for the energy transition. This study investigates the impact of the Sorgente-Rizziconi interconnector on electricity price volatility in Sicily. Before its commissioning on 28 May 2016, the Sicilian electricity market zone was poorly interconnected with the Italian mainland. Using daily data from 2015 to 2018, the analysis applies a semi-parametric GARCH model with a logistic intervention function to estimate changes in conditional price variance. A fully non-parametric additive model is employed as a robustness check, allowing the data to shape volatility dynamics without imposing a predefined structure. The results reveal that the new interconnector significantly increased price volatility in Sicily, without reducing average price levels. No significant effects were observed in other Italian market zones. These findings highlight the context-dependent nature of infrastructure impacts and suggest that physical integration alone does not guarantee price stability. The results have important implications for energy policy, investment planning, and risk management in electricity markets.", "published": "2025-07-31T12:42:51Z", "authors": ["Francesco Lisi", "Pierdomenico Duttilo", "Marina Bertolini"], "categories": ["stat.AP"], "doi": "10.1002/asmb.70102", "_query": "housing price text listing", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2210.10938v2", "title": "Never Say Never: Optimal Exclusion and Reserve Prices with Expectations-Based Loss-Averse Buyers", "abstract": "We study reserve prices in auctions with independent private values when bidders are expectations-based loss averse. We find that the optimal public reserve price excludes fewer bidder types than under risk neutrality. Moreover, we show that public reserve prices are not optimal as the seller can earn a higher revenue with mechanisms that better leverage the ``attachment effect''. We discuss two such mechanisms: i) an auction with a secrete and random reserve price, and ii) a two-stage mechanism where an auction with a public reserve price is followed by a negotiation if the reserve price is not met. Both of these mechanisms expose more bidders to the attachment effect, thereby increasing bids and ultimately revenue.", "published": "2022-10-20T00:32:23Z", "authors": ["Benjamin Balzer", "Antonio Rosato"], "categories": ["econ.TH"], "doi": "", "_query": "housing price text listing", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/1605.03081v1", "title": "On the Price of Anarchy of Highly Congested Nonatomic Network Games", "abstract": "We consider nonatomic network games with one source and one destination. We examine the asymptotic behavior of the price of anarchy as the inflow increases. In accordance with some empirical observations, we show that, under suitable conditions, the price of anarchy is asymptotic to one. We show with some counterexamples that this is not always the case. The counterexamples occur in very simple parallel graphs.", "published": "2016-05-10T16:19:08Z", "authors": ["Riccardo Colini-Baldeschi", "Roberto Cominetti", "Marco Scarsini"], "categories": ["cs.GT", "math.OC"], "doi": "10.1007/978-3-662-53354-3_10", "_query": "housing price text listing", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/math/0701650v2", "title": "Pricing Options in Incomplete Equity Markets via the Instantaneous Sharpe Ratio", "abstract": "We use a continuous version of the standard deviation premium principle for pricing in incomplete equity markets by assuming that the investor issuing an unhedgeable derivative security requires compensation for this risk in the form of a pre-specified instantaneous Sharpe ratio. First, we apply our method to price options on non-traded assets for which there is a traded asset that is correlated to the non-traded asset. Our main contribution to this particular problem is to show that our seller/buyer prices are the upper/lower good deal bounds of Cochrane and Saá-Requejo (2000) and of Björk and Slinko (2006) and to determine the analytical properties of these prices. Second, we apply our method to price options in the presence of stochastic volatility. Our main contribution to this problem is to show that the instantaneous Sharpe ratio, an integral ingredient in our methodology, is the negative of the market price of volatility risk, as defined in Fouque, Papanicolaou, and Sircar (2000).", "published": "2007-01-23T18:13:54Z", "authors": ["Erhan Bayraktar", "Virginia R. Young"], "categories": ["math.OC", "q-fin.PR"], "doi": "", "_query": "housing price text listing", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/1801.06966v2", "title": "Accurate Evaluation of Asset Pricing Under Uncertainty and Ambiguity of Information", "abstract": "Since exchange economy considerably varies in the market assets, asset prices have become an attractive research area for investigating and modeling ambiguous and uncertain information in today markets. This paper proposes a new generative uncertainty mechanism based on the Bayesian Inference and Correntropy (BIC) technique for accurately evaluating asset pricing in markets. This technique examines the potential processes of risk, ambiguity, and variations of market information in a controllable manner. We apply the new BIC technique to a consumption asset-pricing model in which the consumption variations are modeled using the Bayesian network model with observing the dynamics of asset pricing phenomena in the data. These dynamics include the procyclical deviations of price, the countercyclical deviations of equity premia and equity volatility, the leverage impact and the mean reversion of excess returns. The key findings reveal that the precise modeling of asset information can estimate price changes in the market effectively.", "published": "2018-01-22T06:38:32Z", "authors": ["Farouq Abdulaziz Masoudy"], "categories": ["q-fin.GN", "econ.EM"], "doi": "", "_query": "housing price text listing", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/1306.2188v1", "title": "Market-wide price co-movement around crashes in the Tokyo Stock Exchange", "abstract": "As described in this paper, we study market-wide price co-movements around crashes by analyzing a dataset of high-frequency stock returns of the constituent issues of Nikkei 225 Index listed on the Tokyo Stock Exchange for the three years during 2007--2009. Results of day-to-day principal component analysis of the time series sampled at the 1 min time interval during the continuous auction of the daytime reveal the long range up to a couple of months significant auto-correlation of the maximum eigenvalue of the correlation matrix, which express the intensity of market-wide co-movement of stock prices. It also strongly correlates with the open-to-close intraday return and daily return of Nikkei 225 Index. We also study the market mode, which is the first principal component corresponding to the maximum eigenvalue, in the framework of Multi-fractal random walk model. The parameter of the model estimated in a sliding time window, which describes the covariance of the logarithm of the stochastic volatility, grows before almost all large intraday price declines of less than -5%. This phenomenon signifies the upwelling of the market-wide collective behavior before the crash, which might reflect a herding of market participants.", "published": "2013-06-10T12:56:09Z", "authors": ["Jun-ichi Maskawa", "Joshin Murai", "Koji Kuroda"], "categories": ["q-fin.ST"], "doi": "", "_query": "housing price text listing", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2501.01660v1", "title": "The (Exact) Price of Cardinality for Indivisible Goods: A Parametric Perspective", "abstract": "We adopt a parametric approach to analyze the worst-case degradation in social welfare when the allocation of indivisible goods is constrained to be fair. Specifically, we are concerned with cardinality-constrained allocations, which require that each agent has at most $k$ items in their allocated bundle. We propose the notion of the price of cardinality, which captures the worst-case multiplicative loss of utilitarian or egalitarian social welfare resulting from imposing the cardinality constraint. We then characterize tight or almost-tight bounds on the price of cardinality as exact functions of the instance parameters, demonstrating how the social welfare improves as $k$ is increased. In particular, one of our main results refines and generalizes the existing asymptotic bound on the price of balancedness, as studied by Bei et al. [BLMS21]. We also further extend our analysis to the problem where the items are partitioned into disjoint categories, and each category has its own cardinality constraint. Through a parametric study of the price of cardinality, we provide a framework which aids decision makers in choosing an ideal level of cardinality-based fairness, using their knowledge of the potential loss of utilitarian and egalitarian social welfare.", "published": "2025-01-03T06:29:32Z", "authors": ["Alexander Lam", "Bo Li", "Ankang Sun"], "categories": ["cs.GT"], "doi": "", "_query": "housing price text listing", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/1611.01820v1", "title": "A Semi-Automatic Approach for Detecting Dataset References in Social Science Texts", "abstract": "Today, full-texts of scientific articles are often stored in different locations than the used datasets. Dataset registries aim at a closer integration by making datasets citable but authors typically refer to datasets using inconsistent abbreviations and heterogeneous metadata (e.g. title, publication year). It is thus hard to reproduce research results, to access datasets for further analysis, and to determine the impact of a dataset. Manually detecting references to datasets in scientific articles is time-consuming and requires expert knowledge in the underlying research domain.We propose and evaluate a semi-automatic three-step approach for finding explicit references to datasets in social sciences articles.We first extract pre-defined special features from dataset titles in the da|ra registry, then detect references to datasets using the extracted features, and finally match the references found with corresponding dataset titles. The approach does not require a corpus of articles (avoiding the cold start problem) and performs well on a test corpus. We achieved an F-measure of 0.84 for detecting references in full-texts and an F-measure of 0.83 for finding correct matches of detected references in the da|ra dataset registry.", "published": "2016-11-06T18:36:16Z", "authors": ["Behnam Ghavimi", "Philipp Mayr", "Christoph Lange", "Sahar Vahdati", "Sören AUER"], "categories": ["cs.DL"], "doi": "", "_query": "housing price text listing", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2506.22055v1", "title": "crypto price prediction using lstm+xgboost", "abstract": "The volatility and complex dynamics of cryptocurrency markets present unique challenges for accurate price forecasting. This research proposes a hybrid deep learning and machine learning model that integrates Long Short-Term Memory (LSTM) networks and Extreme Gradient Boosting (XGBoost) for cryptocurrency price prediction. The LSTM component captures temporal dependencies in historical price data, while XGBoost enhances prediction by modeling nonlinear relationships with auxiliary features such as sentiment scores and macroeconomic indicators. The model is evaluated on historical datasets of Bitcoin, Ethereum, Dogecoin, and Litecoin, incorporating both global and localized exchange data. Comparative analysis using Mean Absolute Percentage Error (MAPE) and Min-Max Normalized Root Mean Square Error (MinMax RMSE) demonstrates that the LSTM+XGBoost hybrid consistently outperforms standalone models and traditional forecasting methods. This study underscores the potential of hybrid architectures in financial forecasting and provides insights into model adaptability across different cryptocurrencies and market contexts.", "published": "2025-06-27T09:49:25Z", "authors": ["Mehul Gautam"], "categories": ["cs.LG"], "doi": "", "_query": "housing price text listing", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2208.07919v2", "title": "Dynamic Pricing for Non-fungible Resources: Designing Multidimensional Blockchain Fee Markets", "abstract": "Public blockchains implement a fee mechanism to allocate scarce computational resources across competing transactions. Most existing fee market designs utilize a joint, fungible unit of account (e.g., gas in Ethereum) to price otherwise non-fungible resources such as bandwidth, computation, and storage, by hardcoding their relative prices. Fixing the relative price of each resource in this way inhibits granular price discovery, limiting scalability and opening up the possibility of denial-of-service attacks. As a result, many prominent networks such as Ethereum and Solana have proposed multi-dimensional fee markets. In this paper, we provide a principled way to design fee markets that efficiently price multiple non-fungible resources. Starting from a loss function specified by the network designer, we show how to compute dynamic prices that align the network's incentives (to minimize the loss) with those of the users and miners (to maximize their welfare), even as demand for these resources changes. Our pricing mechanism follows from a natural decomposition of the network designer's problem into two parts that are related to each other via the resource prices. These results can be used to efficiently set fees in order to improve network performance.", "published": "2022-08-16T19:32:56Z", "authors": ["Theo Diamandis", "Alex Evans", "Tarun Chitra", "Guillermo Angeris"], "categories": ["math.OC", "cs.CR", "cs.GT"], "doi": "", "_query": "housing price text listing", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2602.12888v2", "title": "Conjectural Variations in Competitive Dynamic Pricing: A Learning Foundation via Experimentation Design and Feedback Structure", "abstract": "We study competitive dynamic pricing among multiple sellers, motivated by the rise of large-scale experimentation and algorithmic pricing in retail and online marketplaces. Sellers repeatedly set prices using simple learning rules and observe their own realized demand, while possibly observing only a subset of rivals' prices, even though demand depends on all sellers' prices and is subject to random shocks. Each seller runs local price experiments, such as switchback-style designs, and updates a focal price using a linear demand estimate fitted to its own demand data and the competitor prices it observes. Under certain conditions on demand, the resulting dynamics converge to a Conjectural Variations (CV) equilibrium, a classic static equilibrium notion in which each seller best responds under a conjecture that rivals' prices co-move systematically to changes in its own price. Unlike standard CV models that treat conjectures as behavioral primitives, we show that these conjectures arise endogenously from the interaction between the feedback structure and the correlation structure of experimentation. When a seller does not observe some rivals' prices, correlated experimentation induces an omitted-variable bias in demand estimation. We show that this bias determines the conjectures that govern the long-run equilibrium. Notably, when this learning bias vanishes, for example under full price feedback or independent experimentation of unobserved rivals, the learning dynamics converge to the standard Nash equilibrium. We provide simple sufficient conditions on demand for convergence in standard models and establish a finite-sample guarantee, showing that the mean squared price error decays at a rate of $\\widetilde O (T^{-1/2})$.", "published": "2026-02-13T12:45:09Z", "authors": ["Bar Light", "Wenyu Wang"], "categories": ["cs.GT"], "doi": "", "_query": "housing price text listing", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/cond-mat/0310351v1", "title": "Modeling of waiting times and price changes in currency exchange data", "abstract": "A theory which describes the share price evolution at financial markets as a continuous-time random walk has been generalized in order to take into account the dependence of waiting times t on price returns x. A joint probability density function (pdf) which uses the concept of a Lévy stable distribution is worked out. The theory is fitted to high-frequency US$/Japanese Yen exchange rate and low-frequency 19th century Irish stock data. The theory has been fitted both to price return and to waiting time data and the adherence to data, in terms of the chi-squared test statistic, has been improved when compared to the old theory.", "published": "2003-10-15T15:03:01Z", "authors": ["Przemyslaw Repetowicz", "Peter Richmond"], "categories": ["cond-mat.stat-mech", "q-fin.TR"], "doi": "10.1016/j.physa.2004.06.162", "_query": "housing price text listing", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2507.02200v1", "title": "ESTR-CoT: Towards Explainable and Accurate Event Stream based Scene Text Recognition with Chain-of-Thought Reasoning", "abstract": "Event stream based scene text recognition is a newly arising research topic in recent years which performs better than the widely used RGB cameras in extremely challenging scenarios, especially the low illumination, fast motion. Existing works either adopt end-to-end encoder-decoder framework or large language models for enhanced recognition, however, they are still limited by the challenges of insufficient interpretability and weak contextual logical reasoning. In this work, we propose a novel chain-of-thought reasoning based event stream scene text recognition framework, termed ESTR-CoT. Specifically, we first adopt the vision encoder EVA-CLIP (ViT-G/14) to transform the input event stream into tokens and utilize a Llama tokenizer to encode the given generation prompt. A Q-former is used to align the vision token to the pre-trained large language model Vicuna-7B and output both the answer and chain-of-thought (CoT) reasoning process simultaneously. Our framework can be optimized using supervised fine-tuning in an end-to-end manner. In addition, we also propose a large-scale CoT dataset to train our framework via a three stage processing (i.e., generation, polish, and expert verification). This dataset provides a solid data foundation for the development of subsequent reasoning-based large models. Extensive experiments on three event stream STR benchmark datasets (i.e., EventSTR, WordArt*, IC15*) fully validated the effectiveness and interpretability of our proposed framework. The source code and pre-trained models will be released on https://github.com/Event-AHU/ESTR-CoT.", "published": "2025-07-02T23:41:31Z", "authors": ["Xiao Wang", "Jingtao Jiang", "Qiang Chen", "Lan Chen", "Lin Zhu", "Yaowei Wang", "Yonghong Tian", "Jin Tang"], "categories": ["cs.CV", "cs.AI", "cs.CL"], "doi": "", "_query": "housing price text listing", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2610.04348v1", "title": "A Price-Based Framework for Stochastic Portfolio Theory", "abstract": "We develop a price-based framework for stochastic portfolio theory in which trading strategies are generated from nominal price weights and evaluated relative to a price-weighted benchmark. Stock splits and reverse splits induce jumps in the generating weights without changing the value of existing investments. In a semimartingale market with predictable adjustment events, we incorporate the corresponding share adjustments into the self-financing condition and construct additively and multiplicatively generated strategies with explicit jump-corrected wealth decompositions. For additive generation, an entropy-based example exhibits relative wealth tending to $-\\infty$ almost surely under repeated splits, demonstrating why the usual argument for long-horizon relative arbitrage does not extend directly. We also show that advance adjustment information alone provides no model-free guarantee of improved performance for functionally generated portfolios. For multiplicative generation, wealth-preserving restarts retain the classical portfolio allocation evaluated at the current price weights. We also establish a correspondence with the capitalization-based framework that preserves absolute wealth and yields a strategy-independent benchmark conversion. We illustrate the framework using daily NYSE data, comparing price- and capitalization-weighted benchmarks and the corresponding diversity-weighted portfolios across price- and capitalization-selected universes.", "published": "2026-10-03T07:18:09Z", "authors": ["Jongbong An", "Donghan Kim"], "categories": ["q-fin.MF"], "doi": "", "_query": "housing price text listing", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/1307.5540v2", "title": "On the Pricing of Storable Commodities", "abstract": "This paper introduces an information-based model for the pricing of storable commodities such as crude oil and natural gas. The model uses the concept of market information about future supply and demand as a basis for valuation. Physical ownership of a commodity is taken to provide a stream of convenience dividends equivalent to a continuous cash flow. The market filtration is assumed to be generated jointly by (i) current and past levels of the dividend rate, and (ii) partial information concerning the future of the dividend flow. The price of a commodity is the expectation under a suitable pricing measure of the totality of the discounted risk-adjusted future convenience dividend, conditional on the information provided by the market filtration. In the situation where the dividend rate is modelled by an Ornstein-Uhlenbeck process, the prices of options on commodities can be derived in closed form. The approach that we present can be applied to other assets that yield potentially negative effective cash flows, such as real estate, factories, refineries, mines, and power generating plants.", "published": "2013-07-21T16:27:25Z", "authors": ["Dorje C. Brody", "Lane P. Hughston", "Xun Yang"], "categories": ["q-fin.PR", "math.PR"], "doi": "", "_query": "housing price text listing", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/1109.4859v1", "title": "The Food Crises: A quantitative model of food prices including speculators and ethanol conversion", "abstract": "Recent increases in basic food prices are severely impacting vulnerable populations worldwide. Proposed causes such as shortages of grain due to adverse weather, increasing meat consumption in China and India, conversion of corn to ethanol in the US, and investor speculation on commodity markets lead to widely differing implications for policy. A lack of clarity about which factors are responsible reinforces policy inaction. Here, for the first time, we construct a dynamic model that quantitatively agrees with food prices. The results show that the dominant causes of price increases are investor speculation and ethanol conversion. Models that just treat supply and demand are not consistent with the actual price dynamics. The two sharp peaks in 2007/2008 and 2010/2011 are specifically due to investor speculation, while an underlying upward trend is due to increasing demand from ethanol conversion. The model includes investor trend following as well as shifting between commodities, equities and bonds to take advantage of increased expected returns. Claims that speculators cannot influence grain prices are shown to be invalid by direct analysis of price setting practices of granaries. Both causes of price increase, speculative investment and ethanol conversion, are promoted by recent regulatory changes---deregulation of the commodity markets, and policies promoting the conversion of corn to ethanol. Rapid action is needed to reduce the impacts of the price increases on global hunger.", "published": "2011-09-21T19:28:04Z", "authors": ["Marco Lagi", "Yavni Bar-Yam", "Karla Z. Bertrand", "Yaneer Bar-Yam"], "categories": ["q-fin.GN", "physics.soc-ph", "q-fin.ST"], "doi": "", "_query": "housing price text listing", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2505.05935v1", "title": "List-Recovery of Random Linear Codes over Small Fields", "abstract": "We study list-recoverability of random linear codes over small fields, both from errors and from erasures. We consider codes of rate $ε$-close to capacity, and aim to bound the dependence of the output list size $L$ on $ε$, the input list size $\\ell$, and the alphabet size $q$. Prior to our work, the best upper bound was $L = q^{O(\\ell/ε)}$ (Zyablov and Pinsker, Prob. Per. Inf. 1981). Previous work has identified cases in which linear codes provably perform worse than non-linear codes with respect to list-recovery. While there exist non-linear codes that achieve $L=O(\\ell/ε)$, we know that $L \\ge \\ell^{Ω(1/ε)}$ is necessary for list recovery from erasures over fields of small characteristic, and for list recovery from errors over large alphabets. We show that in other relevant regimes there is no significant price to pay for linearity, in the sense that we get the correct dependence on the gap-to-capacity $ε$ and go beyond the Zyablov-Pinsker bound for the first time. Specifically, when $q$ is constant and $ε$ approaches zero: - For list-recovery from erasures over prime fields, we show that $L \\leq C_1/ε$. By prior work, such a result cannot be obtained for low-characteristic fields. - For list-recovery from errors over arbitrary fields, we prove that $L \\leq C_2/ε$. Above, $C_1$ and $C_2$ depend on the decoding radius, input list size, and field size. We provide concrete bounds on the constants above, and the upper bounds on $L$ improve upon the Zyablov-Pinsker bound whenever $q\\leq 2^{(1/ε)^c}$ for some small universal constant $c>0$.", "published": "2025-05-09T10:26:12Z", "authors": ["Dean Doron", "Jonathan Mosheiff", "Nicolas Resch", "João Ribeiro"], "categories": ["cs.IT"], "doi": "", "_query": "housing price text listing", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/1904.06757v1", "title": "Price Setting on a Network", "abstract": "Most products are produced and sold by supply chain networks, where an interconnected network of producers and intermediaries set prices to maximize their profits. I show that there exists a unique equilibrium in a price-setting game on a network. The key distortion reducing both total profits and social welfare is multiple-marginalization, which is magnified by strategic interactions. Individual profits are proportional to influentiality, which is a new measure of network centrality defined by the equilibrium characterization. The results emphasize the importance of the network structure when considering policy questions such as mergers or trade tariffs.", "published": "2019-04-14T20:49:43Z", "authors": ["Toomas Hinnosaar"], "categories": ["cs.GT", "econ.GN", "econ.TH"], "doi": "", "_query": "housing price text listing", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2207.10823v2", "title": "A Sealed-bid Auction with Fund Binding: Preventing Maximum Bidding Price Leakage", "abstract": "In an open-bid auction, a bidder can know the budgets of other bidders. Thus, a sealed-bid auction that hides bidding prices is desirable. However, in previous sealed-bid auction protocols, it has been difficult to provide a ``fund binding'' property, which would guarantee that a bidder has funds more than or equal to the bidding price and that the funds are forcibly withdrawn when the bidder wins. Thus, such protocols are vulnerable to false bidding. As a solution, many protocols employ a simple deposit method in which each bidder sends a deposit to a smart contract, which is greater than or equal to the bidding price, before the bidding phase. However, this deposit reveals the maximum bidding price, and it is preferable to hide this information. In this paper, we propose a sealed-bid auction protocol that provides a fund binding property. Our protocol not only hides the bidding price and a maximum bidding price, but also provides fund binding, simultaneously. For hiding the maximum bidding price, we pay attention to the fact that usual Ethereum transactions and transactions for sending funds to a one-time address have the same transaction structure, and it seems that they are indistinguishable. We discuss how much bidding transactions are hidden. We also employ DECO (Zhang et al,. CCS 2020) that proves the validity of the data to a verifier in which the data are taken from a source without showing the data itself. Finally, we give our implementation which shows transaction fees required and compare it to a sealed-bid auction protocol employing the simple deposit method.", "published": "2022-07-22T00:15:02Z", "authors": ["Kota Chin", "Keita Emura", "Kazumasa Omote", "Shingo Sato"], "categories": ["cs.CR", "cs.GT"], "doi": "", "_query": "housing price text listing", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/cond-mat/0409375v2", "title": "A Theory of Fluctuations in Stock Prices", "abstract": "The distribution of price returns for a class of uncorrelated diffusive dynamics is considered. The basic assumptions are (1) that there is a \"consensus\" value associated with a stock, and (2) that the rate of diffusion depends on the deviation of the stock price from the consensus value. We find an analytical expression for the distribution of returns in terms of the diffusion rate, when the consensus value is assumed to be fixed in time. The analytical solution is shown to match computed histograms in two simple cases. Differences that result when the consensus value is allowed to change with time are presented qualitative explanations.", "published": "2004-09-14T23:34:40Z", "authors": ["A. L. Alejandro-Quinones", "K. E. Bassler", "M. Field", "J. L. McCauley", "M. Nicol", "I. Timofeyef", "A. Torok", "G. H. Gunaratne"], "categories": ["cond-mat.other", "q-fin.ST"], "doi": "", "_query": "housing price text listing", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/1607.07392v1", "title": "Option pricing with fractional stochastic volatility and discontinuous payoff function of polynomial growth", "abstract": "We consider the pricing problem related to payoffs that can have discontinuities of polynomial growth. The asset price dynamic is modeled within the Black and Scholes framework characterized by a stochastic volatility term driven by a fractional Ornstein-Uhlenbeck process. In order to solve the aforementioned problem, we consider three approaches. The first one consists in a suitable transformation of the initial value of the asset price, in order to eliminate possible discontinuities. Then we discretize both the Wiener process and the fractional Brownian motion and estimate the rate of convergence of the related discretized price to its real value, the latter one being impossible to be evaluated analytically. The second approach consists in considering the conditional expectation with respect to the entire trajectory of the fractional Brownian motion (fBm). Then we derive a closed formula which involves only integral functional depending on the fBm trajectory, to evaluate the price; finally we discretize the fBm and estimate the rate of convergence of the associated numerical scheme to the option price. In both cases the rate of convergence is the same and equals $n^{-rH}$, where $n$ is a number of the points of discretization, $H$ is the Hurst index of fBm, and $r$ is the Hölder exponent of volatility function. The third method consists in calculating the density of the integral functional depending on the trajectory of the fBm via Malliavin calculus also providing the option price in terms of the associated probability density.", "published": "2016-07-25T18:12:35Z", "authors": ["Viktor Bezborodov", "Luca Di Persio", "Yuliya Mishura"], "categories": ["math.PR"], "doi": "", "_query": "housing price text listing", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2104.01847v5", "title": "Social contagion and asset prices: Reddit's self-organised bull runs", "abstract": "Can unstructured text data from social media help explain the drivers of large asset price fluctuations? This paper investigates how social forces affect asset prices, by using machine learning tools to extract beliefs and positions of `hype' traders active on Reddit's WallStreetBets (WSB) forum. Our stylized model shows that peer effects help explain return predictability and reversals, as well as bubble dynamics. We empirically document that sentiments expressed by WSB users about assets' future performances (bullish or bearish) are in part due to sentiments of their peers and past asset returns. The paper directly estimates the effect of WSB activity on asset prices. We document: that retail trader demand follows WSB discussions through using Trade and Quote (TAQ) data, the predictability of prices from retail trader discourse, the amplified market impact of idiosyncratic investor sentiment from viral content online, and the greater exposure of hype investors to bubbles in the markets.", "published": "2021-04-05T10:50:59Z", "authors": ["Valentina Semenova", "Julian Winkler"], "categories": ["econ.GN", "cs.SI"], "doi": "", "_query": "housing price text listing", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2505.13725v1", "title": "SQLForge: Synthesizing Reliable and Diverse Data to Enhance Text-to-SQL Reasoning in LLMs", "abstract": "Large Language models (LLMs) have demonstrated significant potential in text-to-SQL reasoning tasks, yet a substantial performance gap persists between existing open-source models and their closed-source counterparts. In this paper, we introduce SQLForge, a novel approach for synthesizing reliable and diverse data to enhance text-to-SQL reasoning in LLMs. We improve data reliability through SQL syntax constraints and SQL-to-question reverse translation, ensuring data logic at both structural and semantic levels. We also propose an SQL template enrichment and iterative data domain exploration mechanism to boost data diversity. Building on the augmented data, we fine-tune a variety of open-source models with different architectures and parameter sizes, resulting in a family of models termed SQLForge-LM. SQLForge-LM achieves the state-of-the-art performance on the widely recognized Spider and BIRD benchmarks among the open-source models. Specifically, SQLForge-LM achieves EX accuracy of 85.7% on Spider Dev and 59.8% on BIRD Dev, significantly narrowing the performance gap with closed-source methods.", "published": "2025-05-19T20:51:29Z", "authors": ["Yu Guo", "Dong Jin", "Shenghao Ye", "Shuangwu Chen", "Jian Yang", "Xiaobin Tan"], "categories": ["cs.CL"], "doi": "10.18653/v1/2025.findings-acl.443", "_query": "housing price text listing", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2108.12042v2", "title": "Price modelling under generalized fractional Brownian motion", "abstract": "The Generalized fractional Brownian motion (gfBm) is a stochastic process that acts as a generalization for both fractional, sub-fractional, and standard Brownian motion. Here we study its use as the main driver for price fluctuations, replacing the standard Brownian Brownian motion in the well-known Black-Scholes model. By the derivation of the generalized fractional Ito's lemma and the related effective Fokker-Planck equation, we discuss its application to both the option pricing problem valuing European options, and the computation of Value-at-Risk and Expected Shortfall. Moreover, the option prices are computed for a CEV-type model driven by gfBm.", "published": "2021-08-26T21:39:49Z", "authors": ["Axel A. Araneda"], "categories": ["q-fin.MF"], "doi": "", "_query": "housing price text listing", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2005.08929v2", "title": "Disaster Resilience and Asset Prices", "abstract": "This paper investigates whether security markets price the effect of social distancing on firms' operations. We document that firms that are more resilient to social distancing significantly outperformed those with lower resilience during the COVID-19 outbreak, even after controlling for the standard risk factors. Similar cross-sectional return differentials already emerged before the COVID-19 crisis: the 2014-19 cumulative return differential between more and less resilient firms is of similar size as during the outbreak, suggesting growing awareness of pandemic risk well in advance of its materialization. Finally, we use stock option prices to infer the market's return expectations after the onset of the pandemic: even at a two-year horizon, stocks of more pandemic-resilient firms are expected to yield significantly lower returns than less resilient ones, reflecting their lower exposure to disaster risk. Hence, going forward, markets appear to price exposure to a new risk factor, namely, pandemic risk.", "published": "2020-05-18T17:56:02Z", "authors": ["Marco Pagano", "Christian Wagner", "Josef Zechner"], "categories": ["q-fin.GN", "econ.GN", "q-fin.RM"], "doi": "", "_query": "housing price text listing", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/1503.05475v1", "title": "Almost-sure hedging with permanent price impact", "abstract": "We consider a financial model with permanent price impact. Continuous time trading dynamics are derived as the limit of discrete rebalancing policies. We then study the problem of super-hedging a European option. Our main result is the derivation of a quasi-linear pricing equation. It holds in the sense of viscosity solutions. When it admits a smooth solution, it provides a perfect hedging strategy.", "published": "2015-03-18T16:39:37Z", "authors": ["B. Bouchard", "G. Loeper", "Y. Zou"], "categories": ["q-fin.PR", "math.PR", "q-fin.TR"], "doi": "", "_query": "housing price text listing", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/0910.0443v1", "title": "Stackelberg Pricing is Hard to Approximate within $2-ε$", "abstract": "Stackelberg Pricing Games is a two-level combinatorial pricing problem studied in the Economics, Operation Research, and Computer Science communities. In this paper, we consider the decade-old shortest path version of this problem which is the first and most studied problem in this family. The game is played on a graph (representing a network) consisting of {\\em fixed cost} edges and {\\em pricable} or {\\em variable cost} edges. The fixed cost edges already have some fixed price (representing the competitor's prices). Our task is to choose prices for the variable cost edges. After that, a client will buy the cheapest path from a node $s$ to a node $t$, using any combination of fixed cost and variable cost edges. The goal is to maximize the revenue on variable cost edges. In this paper, we show that the problem is hard to approximate within $2-ε$, improving the previous \\APX-hardness result by Joret [to appear in {\\em Networks}]. Our technique combines the existing ideas with a new insight into the price structure and its relation to the hardness of the instances.", "published": "2009-10-02T19:54:54Z", "authors": ["Parinya Chalermsook", "Bundit Laekhanukit", "Danupon Nanongkai"], "categories": ["cs.GT", "cs.CC", "cs.DS"], "doi": "", "_query": "housing price text listing", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2509.04452v1", "title": "Directional Price Forecasting in the Continuous Intraday Market under Consideration of Neighboring Products and Limit Order Books", "abstract": "The increasing penetration of variable renewable energy and flexible demand technologies, such as electric vehicles and heat pumps, introduces significant uncertainty in power systems, resulting in greater imbalance; defined as the deviation between scheduled and actual supply or demand. Short-term power markets, such as the European continuous intraday market, play a critical role in mitigating these imbalances by enabling traders to adjust forecasts close to real time. Due to the high volatility of the continuous intraday market, traders increasingly rely on electricity price forecasting to guide trading decisions and mitigate price risk. However most electricity price forecasting approaches in the literature simplify the forecasting task. They focus on single benchmark prices, neglecting intra-product price dynamics and price signals from the limit order book. They also underuse high-frequency and cross-product price data. In turn, we propose a novel directional electricity price forecasting method for hourly products in the European continuous intraday market. Our method incorporates short-term features from both hourly and quarter-hourly products and is evaluated using German European Power Exchange data from 2024-2025. The results indicate that features derived from the limit order book are the most influential exogenous variables. In addition, features from neighboring products; especially those with delivery start times that overlap with the trading period of the target product; improve forecast accuracy. Finally, our evaluation of the value captured by our electricity price forecasting suggests that the proposed electricity price forecasting method has the potential to generate profit when applied in trading strategies.", "published": "2025-08-20T09:52:14Z", "authors": ["Timothée Hornek", "Sergio Potenciano Menci", "Ivan Pavić"], "categories": ["q-fin.ST"], "doi": "", "_query": "housing price text listing", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/1809.04933v2", "title": "Identifying Real Estate Opportunities using Machine Learning", "abstract": "The real estate market is exposed to many fluctuations in prices because of existing correlations with many variables, some of which cannot be controlled or might even be unknown. Housing prices can increase rapidly (or in some cases, also drop very fast), yet the numerous listings available online where houses are sold or rented are not likely to be updated that often. In some cases, individuals interested in selling a house (or apartment) might include it in some online listing, and forget about updating the price. In other cases, some individuals might be interested in deliberately setting a price below the market price in order to sell the home faster, for various reasons. In this paper, we aim at developing a machine learning application that identifies opportunities in the real estate market in real time, i.e., houses that are listed with a price substantially below the market price. This program can be useful for investors interested in the housing market. We have focused in a use case considering real estate assets located in the Salamanca district in Madrid (Spain) and listed in the most relevant Spanish online site for home sales and rentals. The application is formally implemented as a regression problem that tries to estimate the market price of a house given features retrieved from public online listings. For building this application, we have performed a feature engineering stage in order to discover relevant features that allows for attaining a high predictive performance. Several machine learning algorithms have been tested, including regression trees, k-nearest neighbors, support vector machines and neural networks, identifying advantages and handicaps of each of them.", "published": "2018-09-13T13:19:23Z", "authors": ["Alejandro Baldominos", "Iván Blanco", "Antonio José Moreno", "Rubén Iturrarte", "Óscar Bernárdez", "Carlos Afonso"], "categories": ["stat.AP", "cs.LG", "stat.ML"], "doi": "10.3390/app8112321", "_query": "housing price text listing", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/1711.05865v2", "title": "Pricing Football Players using Neural Networks", "abstract": "We designed a multilayer perceptron neural network to predict the price of a football (soccer) player using data on more than 15,000 players from the football simulation video game FIFA 2017. The network was optimized by experimenting with different activation functions, number of neurons and layers, learning rate and its decay, Nesterov momentum based stochastic gradient descent, L2 regularization, and early stopping. Simultaneous exploration of various aspects of neural network training is performed and their trade-offs are investigated. Our final model achieves a top-5 accuracy of 87.2% among 119 pricing categories, and places any footballer within 6.32% of his actual price on average.", "published": "2017-11-16T00:14:49Z", "authors": ["Sourya Dey"], "categories": ["cs.LG"], "doi": "", "_query": "housing price text listing", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/1207.2848v1", "title": "Pricing of Fluctuations in Electricity Markets", "abstract": "In an electric power system, demand fluctuations may result in significant ancillary cost to suppliers. Furthermore, in the near future, deep penetration of volatile renewable electricity generation is expected to exacerbate the variability of demand on conventional thermal generating units. We address this issue by explicitly modeling the ancillary cost associated with demand variability. We argue that a time-varying price equal to the suppliers' instantaneous marginal cost may not achieve social optimality, and that consumer demand fluctuations should be properly priced. We propose a dynamic pricing mechanism that explicitly encourages consumers to adapt their consumption so as to offset the variability of demand on conventional units. Through a dynamic game-theoretic formulation, we show that (under suitable convexity assumptions) the proposed pricing mechanism achieves social optimality asymptotically, as the number of consumers increases to infinity. Numerical results demonstrate that compared with marginal cost pricing, the proposed mechanism creates a stronger incentive for consumers to shift their peak load, and therefore has the potential to reduce the need for long-term investment in peaking plants.", "published": "2012-07-12T05:29:15Z", "authors": ["John N. Tsitsiklis", "Yunjian Xu"], "categories": ["math.OC", "math.DS"], "doi": "", "_query": "housing price text listing", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2403.20033v1", "title": "A novel decision fusion approach for sale price prediction using Elastic Net and MOPSO", "abstract": "Price prediction algorithms propose prices for every product or service according to market trends, projected demand, and other characteristics, including government rules, international transactions, and speculation and expectation. As the dependent variable in price prediction, it is affected by several independent and correlated variables which may challenge the price prediction. To overcome this challenge, machine learning algorithms allow more accurate price prediction without explicitly modeling the relatedness between variables. However, as inputs increase, it challenges the existing machine learning approaches regarding computing efficiency and prediction effectiveness. Hence, this study introduces a novel decision level fusion approach to select informative variables in price prediction. The suggested metaheuristic algorithm balances two competitive objective functions, which are defined to improve the prediction utilized variables and reduce the error rate simultaneously. To generate Pareto optimal solutions, an Elastic net approach is employed to eliminate unrelated and redundant variables to increase the accuracy. Afterward, we propose a novel method for combining solutions and ensuring that a subset of features is optimal. Two various real datasets evaluate the proposed price prediction method. The results support the suggested superiority of the model concerning its relative root mean square error and adjusted correlation coefficient.", "published": "2024-03-29T07:59:33Z", "authors": ["Amir Eshaghi Chaleshtori"], "categories": ["stat.ML", "cs.LG", "cs.NE"], "doi": "", "_query": "housing price text listing", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2605.30672v1", "title": "Residual Supply and the Price of Risk Absorption", "abstract": "When redeeming open-end funds sell and natural buyers do not step in at once, some limited-capital investor must take the other side and carry the inventory until prices recover. This paper asks what return that investor requires. A continuous-time market-clearing model delivers an expected-return restriction in which the price of residual supply depends on inventory risk, trading costs, funding frictions, and the scarcity of balance sheet available to absorb it. Mapping U.S. mutual fund flows through predetermined holdings over 2003--2024, we measure one observable component of this residual supply. Forced-sale pressure predicts actual fund selling, contemporaneous price declines, and positive returns over the following one to six months. The premium roughly doubles when market-wide absorption capacity is tight, and it concentrates in stocks with thin investor bases and limited trading capacity -- precisely the cross section in which clearing the imbalance should be most costly, and a pattern that mechanical return reversal does not generate.", "published": "2026-05-29T00:05:09Z", "authors": ["Ziyao Wang"], "categories": ["q-fin.GN", "econ.GN"], "doi": "", "_query": "housing price text listing", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2204.09568v1", "title": "Predictive Accuracy of a Hybrid Generalized Long Memory Model for Short Term Electricity Price Forecasting", "abstract": "Accurate electricity price forecasting is the main management goal for market participants since it represents the fundamental basis to maximize the profits for market players. However, electricity is a non-storable commodity and the electricity prices are affected by some social and natural factors that make the price forecasting a challenging task. This study investigates the predictive performance of a new hybrid model based on the Generalized long memory autoregressive model (k-factor GARMA), the Gegenbauer Generalized Autoregressive Conditional Heteroscedasticity(G-GARCH) process, Wavelet decomposition, and Local Linear Wavelet Neural Network (LLWNN) optimized using two different learning algorithms; the Backpropagation algorithm (BP) and the Particle Swarm optimization algorithm (PSO). The performance of the proposed model is evaluated using data from Nord Pool Electricity markets. Moreover, it is compared with some other parametric and non-parametric models in order to prove its robustness. The empirical results prove that the proposed method performs well than other competing techniques.", "published": "2022-04-18T12:21:25Z", "authors": ["Souhir Ben Amor", "Heni Boubaker", "Lotfi Belkacem"], "categories": ["cs.LG", "q-fin.ST"], "doi": "", "_query": "housing price text listing", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2610.21711v1", "title": "How language model agents reshape price competition", "abstract": "Repeated Bertrand competition is a stringent benchmark for algorithmic pricing: in homogeneous-goods markets, standard learning procedures typically select the stage-game Nash outcome with prices near marginal cost. We show that LLM pricing agents can escape this benchmark, depending on deployment decisions. LLMs outperform standard online learners through faster adaptation, but this advantage largely disappears against full-information learners. An extracted surrogate algorithm illustrates how LLMs in specific deployments profit from regularly undercutting slower learners. Agents that post one price every period from raw market histories that include rival prices can sustain supracompetitive regimes in self-play. Agents that periodically emit probability distributions from summarized histories of their own profits instead move toward the competitive region in self-play. Markets mixing these interfaces can generate dynamic regimes in which LLMs establish a high-price anchor and regularly undercut it to capture demand. These regimes can keep both competitors' prices above marginal cost. Deployment choices shape these outcomes, shifting the focus from stationary equilibria to dynamic pricing regimes.", "published": "2026-10-15T11:32:32Z", "authors": ["Martin Bichler", "Philip Hierhager", "Kassian Köck", "Fabian Pieroth"], "categories": ["cs.GT", "cs.LG", "cs.MA", "econ.GN"], "doi": "", "_query": "housing price text listing", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2510.06287v2", "title": "Quantum-Theoretical Re-interpretation of Pricing Theory", "abstract": "Motivated by Heisenberg's observable-only stance, we replace latent \"information\" (filtrations, hidden diffusions, state variables) with observable transitions between price states. On a discrete price lattice with a Hilbert-space representation, shift operators and the spectral calculus of the price define observable frequency operators and a translation-invariant convolution generator. Combined with jump operators that encode transition intensities, this yields a completely positive, translation-covariant Lindblad semigroup. Under the risk-neutral condition the framework leads to a nonlocal pricing equation that is diagonal in Fourier space; in the small-mesh diffusive limit its generator converges to the classical Black-Scholes-Merton operator. We do not propose another parametric model. We propose a foundation for model construction that is observable, first-principles, and mathematically natural. Noncommutativity emerges from the observable shift algebra rather than being postulated. The jump-intensity ledger determines tail behavior and short-maturity smiles and implies testable links between extreme-event probabilities and implied-volatility wings. Future directions: (i) multi-asset systems on higher-dimensional lattices with vector shifts and block kernels; (ii) state- or flow-dependent kernels as \"financial interactions\" leading to nonlinear master equations while preserving linear risk-neutral pricing; (iii) empirical tests of the predicted scaling relations between jump intensities and market extremes.", "published": "2025-10-07T02:36:12Z", "authors": ["Tian Xin"], "categories": ["q-fin.PR", "quant-ph"], "doi": "", "_query": "housing price text listing", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2602.18572v1", "title": "Sub-City Real Estate Price Index Forecasting at Weekly Horizons Using Satellite Radar and News Sentiment", "abstract": "Reliable real estate price indicators are typically published at city level and low frequency, limiting their use for neighborhood-scale monitoring and long-horizon planning. We study whether sub-city price indices can be forecasted at weekly frequency by combining physical development signals from satellite radar with market narratives from news text. Using over 350,000 transactions from Dubai Land Department (2015-2025), we construct weekly price indices for 19 sub-city regions and evaluate forecasts from 2 to 34 weeks ahead. Our framework fuses regional transaction history with Sentinel-1 SAR backscatter, news sentiment combining lexical tone and semantic embeddings, and macroeconomic context. Results are strongly horizon dependent: at horizons up to 10 weeks, price history alone matches multimodal configurations, but beyond 14 weeks sentiment and SAR become critical. At long horizons (26-34 weeks), the full multimodal model reduces mean absolute error from 4.48 to 2.93 (35% reduction), with gains statistically significant across regions. Nonparametric learners consistently outperform deep architectures in this data regime. These findings establish benchmarks for weekly sub-city index forecasting and demonstrate that remote sensing and news sentiment materially improve predictability at strategically relevant horizons.", "published": "2026-02-20T19:25:48Z", "authors": ["Baris Arat", "Hasan Fehmi Ates", "Emre Sefer"], "categories": ["cs.LG", "q-fin.ST"], "doi": "", "_query": "housing price text listing", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/1408.0258v1", "title": "The Pricing War Continues: On Competitive Multi-Item Pricing", "abstract": "We study a game with \\emph{strategic} vendors who own multiple items and a single buyer with a submodular valuation function. The goal of the vendors is to maximize their revenue via pricing of the items, given that the buyer will buy the set of items that maximizes his net payoff. We show this game may not always have a pure Nash equilibrium, in contrast to previous results for the special case where each vendor owns a single item. We do so by relating our game to an intermediate, discrete game in which the vendors only choose the available items, and their prices are set exogenously afterwards. We further make use of the intermediate game to provide tight bounds on the price of anarchy for the subset games that have pure Nash equilibria; we find that the optimal PoA reached in the previous special cases does not hold, but only a logarithmic one. Finally, we show that for a special case of submodular functions, efficient pure Nash equilibria always exist.", "published": "2014-08-01T18:30:00Z", "authors": ["Omer Lev", "Joel Oren", "Craig Boutilier", "Jeffery S. Rosenschein"], "categories": ["cs.GT", "cs.MA"], "doi": "", "_query": "housing price text listing", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2208.09642v2", "title": "Exploring Price Accuracy on Uniswap V3 in Times of Distress", "abstract": "Financial markets have evolved over centuries, and exchanges have converged to rely on the order book mechanism for market making. Latency on the blockchain, however, has prevented decentralized exchanges (DEXes) from utilizing the order book mechanism and instead gave rise to the development of market designs that are better suited to a blockchain. Although the first widely popularized DEX, Uniswap V2, stood out through its astonishing simplicity, a recent design overhaul introduced with Uniswap V3 has introduced increasing levels of complexity aiming to increase capital efficiency. In this work, we empirically study the ability of Unsiwap V3 to handle unexpected price shocks. Our analysis finds that the prices on Uniswap V3 were inaccurate during the recent abrupt price drops of two stablecoins: UST and USDT. We identify the lack of agility required of Unsiwap V3 liquidity providers as the root cause of these worrying price inaccuracies. Additionally, we outline that there are too few incentives for liquidity providers to enter liquidity pools, given the elevated volatility in such market conditions.", "published": "2022-08-20T09:12:30Z", "authors": ["Lioba Heimbach", "Eric Schertenleib", "Roger Wattenhofer"], "categories": ["q-fin.PR", "cs.CE"], "doi": "", "_query": "housing price text listing", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2605.01878v1", "title": "Random trade timing and power-law tails in realized prices", "abstract": "This paper studies stochastic mechanisms under which light-tailed latent price dynamics yield realized prices with power-law tails. The realized price is modeled as $P_T=e^{X_T}$, where $X$ is a Markov-modulated Lévy process and $T$ is the random time of the next trade. We consider two trade-timing environments. In the intertrade-incidence model, trades occur on a discrete grid with type-dependent probabilities. In the intertrade-time model, the waiting time to the next trade is generalized Erlang, allowing for heterogeneous arrival rates and transaction-completion delays. We show that random trade timing can generate Pareto-type tails, possibly with a logarithmic correction, in realized prices even when the latent price process is light-tailed. In both models, the tail exponent is determined by the least frequent trading type, while the proportions of faster-trading types affect only the scale constant. We also provide sufficient conditions under which these Pareto-type tails sharpen to exact Paretian tails. These results identify random trade timing and heterogeneity in trading behavior as a general mechanism for generating power-law tails in realized prices.", "published": "2026-05-03T13:46:16Z", "authors": ["Won-Ki Seo"], "categories": ["math.PR"], "doi": "", "_query": "housing price text listing", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/1911.11623v1", "title": "A Vietnamese information retrieval system for product-price", "abstract": "A price information retrieval (IR) system allows users to search and view differences among prices of specific products. Building product-price driven IR system is a challenging and active research area. Approaches entirely depending products information provided by shops via interface environment encounter limitations of database. While automatic systems specifically require product names and commercial websites for their input. For both paradigms, approaches of building product-price IR system for Vietnamese are still very limited. In this paper, we introduce an automatic Vietnamese IR system for product-price by identifying and storing Xpath patterns to extract prices of products from commercial websites. Experiments of our system show promising results.", "published": "2019-11-26T15:14:40Z", "authors": ["Tien-Thanh Vu", "Dat Quoc Nguyen"], "categories": ["cs.IR"], "doi": "10.1109/GRC.2011.6122681", "_query": "housing price text listing", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/1704.08119v1", "title": "Using a new parsimonious AHP methodology combined with the Choquet integral: An application for evaluating social housing initiatives", "abstract": "We propose a development of the Analytic Hierarchy Process (AHP) permitting to use the methodology also in cases of decision problems with a very large number of alternatives evaluated with respect to several criteria. While the application of the original AHP method involves many pairwise comparisons between alternatives and criteria, our proposal is composed of three steps: (i) direct evaluation of the alternatives at hand on the considered criteria, (ii) selection of some reference evaluations; (iii) application of the original AHP method to reference evaluations; (iv) revision of the direct evaluation on the basis of the prioritization supplied by AHP on reference evaluations. The new proposal has been tested and validated in an experiment conducted on a sample of university students. The new methodology has been therefore applied to a real world problem involving the evaluation of 21 Social Housing initiatives sited in the Piedmont region (Italy). To take into account interaction between criteria, the Choquet integral preference model has been considered within a Non Additive Robust Ordinal Regression approach.", "published": "2017-04-24T12:38:22Z", "authors": ["Francesca Abastante", "Salvatore Corrente", "Salvatore Greco", "Alessio Ishizaka", "Isabella Lami"], "categories": ["cs.AI", "math.OC"], "doi": "", "_query": "housing price text listing", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2402.16145v1", "title": "Egalitarian Price of Fairness for Indivisible Goods", "abstract": "In the context of fair division, the concept of price of fairness has been introduced to quantify the loss of welfare when we have to satisfy some fairness condition. In other words, it is the price we have to pay to guarantee fairness. Various settings of fair division have been considered previously; we extend to the setting of indivisible goods by using egalitarian welfare as the welfare measure, instead of the commonly used utilitarian welfare. We provide lower and upper bounds for various fairness and efficiency conditions such as envy-freeness up to one good (EF1) and maximum Nash welfare (MNW).", "published": "2024-02-25T16:54:51Z", "authors": ["Karen Frilya Celine", "Muhammad Ayaz Dzulfikar", "Ivan Adrian Koswara"], "categories": ["cs.GT", "cs.MA"], "doi": "10.1007/978-981-99-7019-3_3", "_query": "housing price text listing", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2402.05574v3", "title": "Quantum Amplitude Loading for Rainbow Options Pricing", "abstract": "This work introduces a novel approach to price rainbow options, a type of path-independent multi-asset derivatives, with quantum computers. Leveraging the Iterative Quantum Amplitude Estimation method, we present an end-to-end quantum circuit implementation, emphasizing efficiency by delaying the transition to price space. Moreover, we analyze two different amplitude loading techniques for handling exponential functions. Experiments on the IBM QASM simulator validate our quantum pricing model, contributing to the evolving field of quantum finance.", "published": "2024-02-08T11:19:32Z", "authors": ["Francesca Cibrario", "Or Samimi Golan", "Giacomo Ranieri", "Emanuele Dri", "Mattia Ippoliti", "Ron Cohen", "Christian Mattia", "Bartolomeo Montrucchio", "Amir Naveh", "Davide Corbelletto"], "categories": ["quant-ph", "q-fin.PR"], "doi": "", "_query": "housing price text listing", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2206.10487v1", "title": "Reconfigurable Elastic Metamaterials: Engineering Dispersion with Meccano$^{\\text{TM}}$", "abstract": "We design, simulate and experimentally characterise a reconfigurable elastic metamaterial with beyond-nearest-neighbour (BNN) coupling. The structure is composed from the popular British model construction system, Meccano$^{\\text{TM}}$. The Meccano$^{\\text{TM}}$ metamaterial supports backwards waves with opposite directions of phase and group velocities. We experimentally verify three distinct configurations and acoustically infer their spatial vibration spectra.", "published": "2022-06-21T15:56:17Z", "authors": ["G. J. Chaplain", "I. R. Hooper", "A. P. Hibbins", "T. A. Starkey"], "categories": ["physics.app-ph"], "doi": "", "_query": "housing price text listing", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/cond-mat/0405390v1", "title": "Zipf's Law Distributions for Korean Stock Prices", "abstract": "This paper investigates the rank distribution, cumulative probability, and probability density of price returns for the stocks traded in the KSE and the KOSDAQ market. This research demonstrates that the rank distribution is consistent approximately with the Zipf's law with exponent $α= -1.00$ (KSE) and -1.31 (KOSDAQ), similar that of stock prices traded on the TSE. In addition, the cumulative probability distribution follows a power law with scaling exponent $β= -1.23$ (KSE) and -1.45 (KOSDAQ). In particular, the evidence displays that the probability density of normalized price returns for two kinds of assets almost has the form of an exponential function, similar to the result in the TSE and the NYSE.", "published": "2004-05-18T08:38:22Z", "authors": ["Kyungsik Kim", "S. -M. Yoon", "C. Christopher Lee", "K. H. Chang"], "categories": ["cond-mat.stat-mech", "q-fin.ST"], "doi": "", "_query": "housing price text listing", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/1612.08767v1", "title": "Pricing of Asian-type and Basket Options via Upper and Lower Bounds", "abstract": "This paper sets out to provide a general framework for the pricing of average-type options via lower and upper bounds. This class of options includes Asian, basket and options on the volume-weighted average price. We demonstrate that in cases under discussion lower bounds allow for the dimensionality of the problem to be reduced and that these methods provide reasonable approximations to the price of the option. Keywords: Asian options, Basket options, Lower and Upper bounds, Volume-weighted average prices (VWAP), Levy processes.", "published": "2016-12-27T22:36:47Z", "authors": ["Alexander Novikov", "Scott Alexander", "Nino Kordzakhia", "Timothy Ling"], "categories": ["q-fin.MF"], "doi": "", "_query": "housing price text listing", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2508.20677v2", "title": "Pricing American options time-capped by a drawdown event in a Lévy market", "abstract": "This paper presents a derivation of the explicit price for the perpetual American put option time-capped by the first drawdown epoch beyond a predefined level. We consider the market in which an asset price is described by geometric Lévy process with downward exponential jumps. We show that the optimal stopping rule is the first time when the asset price gets below a special value. The proof relies on martingale arguments and the fluctuation theory of Lévy processes. We also provide a numerical analysis.", "published": "2025-08-28T11:37:28Z", "authors": ["Zbigniew Palmowski", "Paweł Stȩpniak"], "categories": ["math.PR", "q-fin.MF"], "doi": "", "_query": "housing price text listing", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/1203.0050v1", "title": "LP-based Covering Games with Low Price of Anarchy", "abstract": "We present a new class of vertex cover and set cover games. The price of anarchy bounds match the best known constant factor approximation guarantees for the centralized optimization problems for linear and also for submodular costs -- in contrast to all previously studied covering games, where the price of anarchy cannot be bounded by a constant (e.g. [6, 7, 11, 5, 2]). In particular, we describe a vertex cover game with a price of anarchy of 2. The rules of the games capture the structure of the linear programming relaxations of the underlying optimization problems, and our bounds are established by analyzing these relaxations. Furthermore, for linear costs we exhibit linear time best response dynamics that converge to these almost optimal Nash equilibria. These dynamics mimic the classical greedy approximation algorithm of Bar-Yehuda and Even [3].", "published": "2012-02-29T23:44:38Z", "authors": ["Georgios Piliouras", "Tomas Valla", "Laszlo A. Vegh"], "categories": ["cs.GT", "cs.DM"], "doi": "", "_query": "housing price text listing", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/1305.0085v1", "title": "Pricing Public Goods for Private Sale", "abstract": "We consider the pricing problem faced by a seller who assigns a price to a good that confers its benefits not only to its buyers, but also to other individuals around them. For example, a snow-blower is potentially useful not only to the household that buys it, but also to others on the same street. Given that the seller is constrained to selling such a (locally) public good via individual private sales, how should he set his prices given the distribution of values held by the agents? We study this problem as a two-stage game. In the first stage, the seller chooses and announces a price for the product. In the second stage, the agents (each having a private value for the good) decide simultaneously whether or not they will buy the product. In the resulting game, which can exhibit a multiplicity of equilibria, agents must strategize about whether they will themselves purchase the good to receive its benefits. In the case of a fully public good (where all agents benefit whenever any agent purchases), we describe a pricing mechanism that is approximately revenue-optimal (up to a constant factor) when values are drawn from a regular distribution. We then study settings in which the good is only \"locally\" public: agents are arranged in a network and share benefits only with their neighbors. We describe a pricing method that approximately maximizes revenue, in the worst case over equilibria of agent behavior, for any $d$-regular network. Finally, we show that approximately optimal prices can be found for general networks in the special case that private values are drawn from a uniform distribution. We also discuss some barriers to extending these results to general networks and regular distributions.", "published": "2013-05-01T05:14:41Z", "authors": ["Michal Feldman", "David Kempe", "Brendan Lucier", "Renato Paes Leme"], "categories": ["cs.GT"], "doi": "", "_query": "housing price text listing", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/1709.09618v7", "title": "Monopoly Pricing in Vertical Markets with Demand Uncertainty", "abstract": "Pricing decisions are often made when market information is still poor. In turn, existing theoretical models often reason about the response of optimal prices to changing market characteristics without exploiting all available information about the demand distribution. Our aim is to develop a theory for the optimization and systematic comparison of prices between different instances of the same market under various forms of knowledge about the corresponding demand distributions. We revisit the classic problem of monopoly pricing under demand uncertainty in a vertical market with an upstream supplier and multiple forms of downstream competition between arbitrary symmetric retailers. In all cases, demand uncertainty falls to the supplier who acts first and sets a uniform price before the retailers observe the realized demand and place their orders. Our main methodological contribution is that we express the price elasticity of expected demand in terms of the mean residual demand (MRD) function of the demand distribution. This leads to a closed form characterization of the points of unitary elasticity that maximize the supplier's profits and the derivation of a mild unimodality condition for the supplier's objective function that generalizes the widely used increasing generalized failure rate (IGFR) condition. A direct implication is that optimal prices between different markets can be ordered if the markets can be stochastically ordered according to their MRD functions or equivalently, their elasticities. Using the above, we develop a systematic framework to compare optimal prices between different market instances via the rich theory of stochastic orders. This leads to comparative statics that challenge previously established economic insights about the effects of market size, demand transformations and demand variability on monopolistic prices.", "published": "2017-09-27T16:49:35Z", "authors": ["Stefanos Leonardos", "Costis Melolidakis", "Constandina Koki"], "categories": ["math.OC", "cs.GT", "math.PR"], "doi": "10.1007/s10479-021-04067-3", "_query": "housing price text listing", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2504.12771v1", "title": "Classification-Based Analysis of Price Pattern Differences Between Cryptocurrencies and Stocks", "abstract": "Cryptocurrencies are digital tokens built on blockchain technology, with thousands actively traded on centralized exchanges (CEXs). Unlike stocks, which are backed by real businesses, cryptocurrencies are recognized as a distinct class of assets by researchers. How do investors treat this new category of asset in trading? Are they similar to stocks as an investment tool for investors? We answer these questions by investigating cryptocurrencies' and stocks' price time series which can reflect investors' attitudes towards the targeted assets. Concretely, we use different machine learning models to classify cryptocurrencies' and stocks' price time series in the same period and get an extremely high accuracy rate, which reflects that cryptocurrency investors behave differently in trading from stock investors. We then extract features from these price time series to explain the price pattern difference, including mean, variance, maximum, minimum, kurtosis, skewness, and first to third-order autocorrelation, etc., and then use machine learning methods including logistic regression (LR), random forest (RF), support vector machine (SVM), etc. for classification. The classification results show that these extracted features can help to explain the price time series pattern difference between cryptocurrencies and stocks.", "published": "2025-04-17T09:12:27Z", "authors": ["Yu Zhang", "Zelin Wu", "Claudio Tessone"], "categories": ["q-fin.ST"], "doi": "", "_query": "housing price text listing", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2112.13681v1", "title": "Wholesale Electricity Price Forecasting using Integrated Long-term Recurrent Convolutional Network Model", "abstract": "Electricity price is a key factor affecting the decision-making for all market participants. Accurate forecasting of electricity prices is very important and is also very challenging since electricity price is highly volatile due to various factors. This paper proposes an integrated long-term recurrent convolutional network (ILRCN) model to predict electricity prices considering the majority contributing attributes to the market price as input. The proposed ILRCN model combines the functionalities of convolutional neural network and long short-term memory (LSTM) algorithm along with the proposed novel conditional error correction term. The combined ILRCN model can identify the linear and non-linear behavior within the input data. We have used ERCOT wholesale market price data along with load profile, temperature, and other factors for the Houston region to illustrate the proposed model. The performance of the proposed ILRCN electricity price forecasting model is verified using performance/evaluation metrics like mean absolute error and accuracy. Case studies reveal that the proposed ILRCN model is accurate and efficient in electricity price forecasting as compared to the support vector machine (SVM) model, fully-connected neural network model, LSTM model and the LRCN model without the conditional error correction stage.", "published": "2021-12-23T06:45:12Z", "authors": ["Vasudharini Sridharan", "Mingjian Tuo", "Xingpeng Li"], "categories": ["cs.LG", "eess.SY"], "doi": "", "_query": "housing price text listing", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/1205.3518v2", "title": "Lower Bounds for Adaptive Sparse Recovery", "abstract": "We give lower bounds for the problem of stable sparse recovery from /adaptive/ linear measurements. In this problem, one would like to estimate a vector $x \\in \\R^n$ from $m$ linear measurements $A_1x,..., A_mx$. One may choose each vector $A_i$ based on $A_1x,..., A_{i-1}x$, and must output $x*$ satisfying |x* - x|_p \\leq (1 + ε) \\min_{k\\text{-sparse} x'} |x - x'|_p with probability at least $1-δ>2/3$, for some $p \\in \\{1,2\\}$. For $p=2$, it was recently shown that this is possible with $m = O(\\frac{1}εk \\log \\log (n/k))$, while nonadaptively it requires $Θ(\\frac{1}εk \\log (n/k))$. It is also known that even adaptively, it takes $m = Ω(k/ε)$ for $p = 2$. For $p = 1$, there is a non-adaptive upper bound of $\\tilde{O}(\\frac{1}{\\sqrtε} k\\log n)$. We show: * For $p=2$, $m = Ω(\\log \\log n)$. This is tight for $k = O(1)$ and constant $ε$, and shows that the $\\log \\log n$ dependence is correct. * If the measurement vectors are chosen in $R$ \"rounds\", then $m = Ω(R \\log^{1/R} n)$. For constant $ε$, this matches the previously known upper bound up to an O(1) factor in $R$. * For $p=1$, $m = Ω(k/(\\sqrtε \\cdot \\log k/ε))$. This shows that adaptivity cannot improve more than logarithmic factors, providing the analog of the $m = Ω(k/ε)$ bound for $p = 2$.", "published": "2012-05-15T21:38:53Z", "authors": ["Eric Price", "David P. Woodruff"], "categories": ["cs.DS"], "doi": "", "_query": "housing price text listing", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2409.11339v4", "title": "A Derivative Pricing Perspective on Liquidity Tokens in Constant Product Market Makers", "abstract": "In decentralized finance, any individual can pool their assets into an automated market maker (AMM) -- herein we focus on the constant product market maker (CPMM) -- in exchange for a claim on a fraction of future pool assets and fees earned from the market making operations. This position is represented by a liquidity token, whose prevailing on-chain price is effectively the initial deposited assets. Though this price is well-defined, we treat the liquidity token as a derivative position in the prices of the underlying assets for the CPMM in order to deduce risk-neutral pricing and hedging formulas, not dissimilar to the Black-Scholes result. Adopting this perspective, in a frictionless environment, hedging the CPMM liquidity token under fair valuation should produce a riskless process, which therefore grows at the risk-free rate, something that is not seen in empirical case studies under the prevailing price. With our novel pricing formula, we construct a method to calibrate a volatility to data which provides an updated (non-market) valuation which is consistent with the (near-continuous) replication strategy out-of-sample. We conclude with a discussion of novel AMM design considerations motivated by this derivative-pricing perspective.", "published": "2024-09-17T16:41:58Z", "authors": ["Maxim Bichuch", "Zachary Feinstein"], "categories": ["q-fin.MF", "q-fin.PR", "q-fin.RM"], "doi": "", "_query": "housing price text listing", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2306.12969v1", "title": "Stock Price Prediction using Dynamic Neural Networks", "abstract": "This paper will analyze and implement a time series dynamic neural network to predict daily closing stock prices. Neural networks possess unsurpassed abilities in identifying underlying patterns in chaotic, non-linear, and seemingly random data, thus providing a mechanism to predict stock price movements much more precisely than many current techniques. Contemporary methods for stock analysis, including fundamental, technical, and regression techniques, are conversed and paralleled with the performance of neural networks. Also, the Efficient Market Hypothesis (EMH) is presented and contrasted with Chaos theory using neural networks. This paper will refute the EMH and support Chaos theory. Finally, recommendations for using neural networks in stock price prediction will be presented.", "published": "2023-06-18T20:06:44Z", "authors": ["David Noel"], "categories": ["q-fin.ST", "cs.CE", "cs.LG"], "doi": "", "_query": "housing price text listing", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2608.04669v1", "title": "Differentiating Through Dual Prices: End-to-End Policy Learning Under Capacity Constraints", "abstract": "Many social services assign scarce resources, such as housing assistance or hospital interventions, to people who arrive one at a time: each arrival must receive a decision immediately, and the long-run usage of every resource must stay within its capacity. We study how to learn such an assignment policy from logged observational data. The standard pipeline is decision-blind: fit one outcome model per arm by regression, price each capacitated resource from the fitted models, and assign each arrival the arm whose predicted outcome minus price is largest. We instead train the outcome models end-to-end, differentiating an off-policy estimate of the deployed policy's value through the dual prices themselves. We study two formulations: an exact nonconvex one, and a convex relaxation whose optimum always satisfies the capacity constraints in expectation and which is suboptimal by at most a term linear in the smoothing temperature and logarithmic in the number of arms. Every method is evaluated in a queueing simulation with resources replenished at their capacity rates. Across six datasets, the two end-to-end variants take the top slots on a deployment-adjusted value index at every delay cost, including zero; when capacities are binding, decision-blind baselines frequently violate them and incur much longer queueing delays. On the largest dataset, a hospital cohort of seventy thousand patients, end-to-end training also achieves significantly higher policy value, a margin that survives a capacity-matched neural baseline. Flexible decision-blind regression remains the stronger pure predictor where ground truth is measurable; end-to-end training is best suited to settings where resources are genuinely scarce and feasibility matters.", "published": "2026-08-05T10:28:21Z", "authors": ["Mohammadsaeed Haghi", "Mahdi Salmani", "Nima Kelidari"], "categories": ["cs.LG"], "doi": "", "_query": "housing price text listing", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/1206.3945v3", "title": "List-coloring graphs on surfaces with varying list-sizes", "abstract": "Let $G$ be a graph embedded on a surface $S_\\varepsilon$ with Euler genus $\\varepsilon > 0$, and let $P\\subseteq V(G)$ be a set of vertices mutually at distance at least 4 apart. Suppose all vertices of $G$ have $H(\\varepsilon)$-lists and the vertices of $P$ are precolored, where $H(\\varepsilon)=\\Big\\lfloor\\frac{7 + \\sqrt{24\\varepsilon + 1}}{2}\\Big\\rfloor$ is the Heawood number. We show that the coloring of $P$ extends to a list-coloring of $G$ and that the distance bound of 4 is best possible. Our result provides an answer to an analogous question of Albertson about extending a precoloring of a set of mutually distant vertices in a planar graph to a 5-list-coloring of the graph and generalizes a result of Albertson and Hutchinson to list-coloring extensions on surfaces.", "published": "2012-06-18T14:22:08Z", "authors": ["Alice M. Dean", "Joan P. Hutchinson"], "categories": ["math.CO"], "doi": "", "_query": "housing price text listing", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2307.00604v2", "title": "Listing Small Minimal $s,t$-separators in FPT-Delay", "abstract": "Let $G$ be an undirected graph, and $s,t$ distinguished vertices of $G$. A minimal $s,t$-separator is an inclusion-wise minimal vertex-set whose removal places $s$ and $t$ in distinct connected components. We present an algorithm for listing the minimal $s,t$-separators of a graph, whose cardinality is at most $k$, with FPT-delay, where the parameter depends only on $k$. This problem finds applications in various algorithms parameterized by treewidth, which include query evaluation in relational databases, probabilistic inference, and many more. We also present a simple algorithm that enumerates all of the (not necessarily minimal) $s,t$-separators of a graph in ranked order by size.", "published": "2023-07-02T16:17:37Z", "authors": ["Batya Kenig"], "categories": ["cs.DS", "cs.DB"], "doi": "", "_query": "housing price text listing", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2009.12920v2", "title": "Privacy-Preserving Dynamic Personalized Pricing with Demand Learning", "abstract": "The prevalence of e-commerce has made detailed customers' personal information readily accessible to retailers, and this information has been widely used in pricing decisions. When involving personalized information, how to protect the privacy of such information becomes a critical issue in practice. In this paper, we consider a dynamic pricing problem over $T$ time periods with an \\emph{unknown} demand function of posted price and personalized information. At each time $t$, the retailer observes an arriving customer's personal information and offers a price. The customer then makes the purchase decision, which will be utilized by the retailer to learn the underlying demand function. There is potentially a serious privacy concern during this process: a third party agent might infer the personalized information and purchase decisions from price changes from the pricing system. Using the fundamental framework of differential privacy from computer science, we develop a privacy-preserving dynamic pricing policy, which tries to maximize the retailer revenue while avoiding information leakage of individual customer's information and purchasing decisions. To this end, we first introduce a notion of \\emph{anticipating} $(\\varepsilon, δ)$-differential privacy that is tailored to dynamic pricing problem. Our policy achieves both the privacy guarantee and the performance guarantee in terms of regret. Roughly speaking, for $d$-dimensional personalized information, our algorithm achieves the expected regret at the order of $\\tilde{O}(\\varepsilon^{-1} \\sqrt{d^3 T})$, when the customers' information is adversarially chosen. For stochastic personalized information, the regret bound can be further improved to $\\tilde{O}(\\sqrt{d^2T} + \\varepsilon^{-2} d^2)$", "published": "2020-09-27T18:32:34Z", "authors": ["Xi Chen", "David Simchi-Levi", "Yining Wang"], "categories": ["cs.CR", "cs.GT", "cs.LG", "stat.ML"], "doi": "", "_query": "housing price text listing", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2309.13668v1", "title": "Quantum Privacy-Preserving Price E-Negotiation", "abstract": "Privacy-preserving price e-negotiation (3PEN) is an important topic of secure multi-party computation (SMC) in the electronic commerce field, and the key point of its security is to guarantee the privacy of seller's and buyer's prices. In this study, a novel and efficient quantum solution to the 3PEN problem is proposed, where the oracle operation and the qubit comparator are utilized to obtain the comparative results of buyer's and seller's prices, and then quantum counting is executed to summarize the total number of products which meets the trading conditions. Analysis shows that our solution not only guarantees the correctness and the privacy of 3PEN, but also has lower communication complexity than those classical ones.", "published": "2023-09-24T15:31:55Z", "authors": ["Wen-Jie Liu", "Chun-Tang Li", "Yu Zheng", "Yong Xu", "Yin-Song Xu"], "categories": ["quant-ph", "cs.CR", "cs.ET"], "doi": "10.1007/s10773-019-04201-9", "_query": "housing price text listing", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/1811.02535v1", "title": "The Role of Demand-Side Flexibility in Hedging Electricity Price Volatility in Distribution Grids", "abstract": "Locational Marginal Price (LMP) is a dual variable associated with supply-demand matching and represents the cost of delivering power to a particular location if the load at that location increases. In recent times it become more volatile due to increased integration of renewables that are intermittent. The issue of price volatility is further heightened during periods of grid congestion. Motivated by these problems, we propose a market design where, by constraining dual variables, we determine the amount of demand-side flexibility required to limit the rise of LMP. Through our proposed approach a price requesting load can specify its maximum willingness to pay for electricity and through demand-side flexibility hedge against price volatility. For achieving this, an organizational structure for flexibility management is proposed that exhibits the coordination required between the Distribution System Operator (DSO), an aggregator and the price requesting load. To demonstrate the viability of our proposed formulation, we run an illustrative simulation under infinite and finite line capacities.", "published": "2018-11-06T18:06:20Z", "authors": ["Shantanu Chakraborty", "Remco Verzijlbergh", "Milos Cvetkovic", "Kyri Baker", "Zofia Lukszo"], "categories": ["math.OC"], "doi": "", "_query": "housing price text listing", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/1811.06650v4", "title": "Asymptotics for Small Nonlinear Price Impact: a PDE Approach to the Multidimensional Case", "abstract": "We provide an asymptotic expansion of the value function of a multidimensional utility maximization problem from consumption with small non-linear price impact. In our model cross-impacts between assets are allowed. In the limit for small price impact, we determine the asymptotic expansion of the value function around its frictionless version. The leading order correction is characterized by a nonlinear second order PDE related to an ergodic control problem and a linear parabolic PDE. We illustrate our result on a multivariate geometric Brownian motion price model.", "published": "2018-11-16T01:55:12Z", "authors": ["Erhan Bayraktar", "Thomas Caye", "Ibrahim Ekren"], "categories": ["math.AP", "math.OC", "q-fin.TR"], "doi": "", "_query": "housing price text listing", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2305.09479v1", "title": "Dissertation on Applied Microeconomics of Freemium Pricing Strategies in Mobile App Market", "abstract": "In my dissertation, I will analyze how the product market position of a mobile app affects its pricing strategies, which in turn impacts an app's monetization process. Using natural language processing and k-mean clustering on apps' text descriptions, I created a new variable that measures the distinctiveness of an app as compared to its peers. I created four pricing variables, price, cumulative installs, and indicators of whether an app contains in-app ads and purchases. I found that the effect differs for successful apps and less successful apps. I measure the success here using cumulative installs and the firms that developed the apps. Based on third-party rankings and the shape of the distribution of installs, I set two thresholds and divided apps into market-leading and market-follower apps. The market-leading sub-sample consists of apps with high cumulative installs or developed by prestigious firms, and the market-follower sub-sample consists of the rest of the apps. I found that the impact of being niche is smaller in the market-leading apps because of their relatively higher heterogeneity. In addition, being niche also impact utility apps differently from hedonic apps or apps with two-sided market characteristics. For the special gaming category, being niche has some effect but is smaller than in the market follower sub-sample. My research provides novel empirical evidence of digital products to various strands of theoretical research, including the optimal distinctiveness theory, product differentiation, price discrimination in two or multi-sided markets, and consumer psychology.", "published": "2023-04-22T04:19:06Z", "authors": ["Naixin Zhu"], "categories": ["econ.GN"], "doi": "", "_query": "housing price text listing", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2002.08492v2", "title": "Equal Risk Pricing of Derivatives with Deep Hedging", "abstract": "This article presents a deep reinforcement learning approach to price and hedge financial derivatives. This approach extends the work of Guo and Zhu (2017) who recently introduced the equal risk pricing framework, where the price of a contingent claim is determined by equating the optimally hedged residual risk exposure associated respectively with the long and short positions in the derivative. Modifications to the latter scheme are considered to circumvent theoretical pitfalls associated with the original approach. Derivative prices obtained through this modified approach are shown to be arbitrage-free. The current paper also presents a general and tractable implementation for the equal risk pricing framework inspired by the deep hedging algorithm of Buehler et al. (2019). An $ε$-completeness measure allowing for the quantification of the residual hedging risk associated with a derivative is also proposed. The latter measure generalizes the one presented in Bertsimas et al. (2001) based on the quadratic penalty. Monte Carlo simulations are performed under a large variety of market dynamics to demonstrate the practicability of our approach, to perform benchmarking with respect to traditional methods and to conduct sensitivity analyses.", "published": "2020-02-19T22:59:59Z", "authors": ["Alexandre Carbonneau", "Frédéric Godin"], "categories": ["q-fin.CP", "q-fin.MF", "q-fin.PR", "q-fin.RM"], "doi": "", "_query": "housing price text listing", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2204.11735v1", "title": "Forecasting Electricity Prices", "abstract": "Forecasting electricity prices is a challenging task and an active area of research since the 1990s and the deregulation of the traditionally monopolistic and government-controlled power sectors. Although it aims at predicting both spot and forward prices, the vast majority of research is focused on short-term horizons which exhibit dynamics unlike in any other market. The reason is that power system stability calls for a constant balance between production and consumption, while being weather (both demand and supply) and business activity (demand only) dependent. The recent market innovations do not help in this respect. The rapid expansion of intermittent renewable energy sources is not offset by the costly increase of electricity storage capacities and modernization of the grid infrastructure. On the methodological side, this leads to three visible trends in electricity price forecasting research as of 2022. Firstly, there is a slow, but more noticeable with every year, tendency to consider not only point but also probabilistic (interval, density) or even path (also called ensemble) forecasts. Secondly, there is a clear shift from the relatively parsimonious econometric (or statistical) models towards more complex and harder to comprehend, but more versatile and eventually more accurate statistical/machine learning approaches. Thirdly, statistical error measures are nowadays regarded as only the first evaluation step. Since they may not necessarily reflect the economic value of reducing prediction errors, more and more often, they are complemented by case studies comparing profits from scheduling or trading strategies based on price forecasts obtained from different models.", "published": "2022-04-25T15:46:26Z", "authors": ["Katarzyna Maciejowska", "Bartosz Uniejewski", "Rafał Weron"], "categories": ["q-fin.ST", "eess.SP", "stat.AP", "stat.ML"], "doi": "10.1093/acrefore/9780190625979.013.667", "_query": "housing price text listing", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/physics/0606011v4", "title": "Martingale Option Pricing", "abstract": "We show that our generalization of the Black-Scholes partial differential equation (pde) for nontrivial diffusion coefficients is equivalent to a Martingale in the risk neutral discounted stock price. Previously, this was proven for the case of the Gaussian logarithmic returns model by Harrison and Kreps, but we prove it for much a much larger class of returns models where the diffusion coefficient depends on both returns x and time t. That option prices blow up if fat tails in logarithmic returns x are included in the market dynamics is also explained.", "published": "2006-06-01T18:53:49Z", "authors": ["J. L. McCauley", "G. H. Gunaratne", "K. E. Bassler"], "categories": ["physics.soc-ph", "q-fin.PR"], "doi": "10.1016/j.physa.2007.02.038", "_query": "housing price text listing", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/1208.5258v2", "title": "A Theory of Pricing Private Data", "abstract": "Personal data has value to both its owner and to institutions who would like to analyze it. Privacy mechanisms protect the owner's data while releasing to analysts noisy versions of aggregate query results. But such strict protections of individual's data have not yet found wide use in practice. Instead, Internet companies, for example, commonly provide free services in return for valuable sensitive information from users, which they exploit and sometimes sell to third parties. As the awareness of the value of the personal data increases, so has the drive to compensate the end user for her private information. The idea of monetizing private data can improve over the narrower view of hiding private data, since it empowers individuals to control their data through financial means. In this paper we propose a theoretical framework for assigning prices to noisy query answers, as a function of their accuracy, and for dividing the price amongst data owners who deserve compensation for their loss of privacy. Our framework adopts and extends key principles from both differential privacy and query pricing in data markets. We identify essential properties of the price function and micro-payments, and characterize valid solutions.", "published": "2012-08-26T22:08:11Z", "authors": ["Chao Li", "Daniel Yang Li", "Gerome Miklau", "Dan Suciu"], "categories": ["cs.CR", "cs.DB"], "doi": "10.1145/2448496.2448502", "_query": "housing price text listing", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2603.03152v4", "title": "Political Shocks and Price Discovery in Prediction Markets: Evidence from the 2024 U.S. Presidential Election", "abstract": "What do trading and prices each reveal when political news hits a prediction market? We answer using Polymarket's on-chain ledger around three shocks in the 2024 U.S. presidential election: the Biden-Trump debate, the assassination attempt on Trump, and Biden's withdrawal. Trading rises after every shock, mainly among incumbents with greater prior activity and with larger realized gains on pre-event portfolios. Price adjustment differs across shocks. The debate's price increase largely reverses, the assassination-attempt repricing persists, and Biden's withdrawal generates the heaviest trading with little change in the Trump price. The price response tracks what the news reveals about linked candidates and how much was already anticipated, not the amount of trading. Trading volume measures participation, while belief revision must be read from linked outcome prices and the surprise in the news.", "published": "2026-03-03T16:47:58Z", "authors": ["Kwok Ping Tsang", "Zichao Yang"], "categories": ["econ.GN"], "doi": "", "_query": "housing price text listing", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/1901.00345v2", "title": "Equilibrium price and optimal insider trading strategy under stochastic liquidity with long memory", "abstract": "In this paper, the Kyle model of insider trading is extended by characterizing the trading volume with long memory and allowing the noise trading volatility to follow a general stochastic process. Under this newly revised model, the equilibrium conditions are determined, with which the optimal insider trading strategy, price impact and price volatility are obtained explicitly. The volatility of the price volatility appears excessive, which is a result of the fact that a more aggressive trading strategy is chosen by the insider when uninformed volume is higher. The optimal trading strategy turns out to possess the property of long memory, and the price impact is also affected by the fractional noise.", "published": "2019-01-02T12:47:45Z", "authors": ["Ben-zhang Yang", "Xinjiang He", "Nan-jing Huang"], "categories": ["q-fin.MF"], "doi": "", "_query": "housing price text listing", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/cond-mat/9902046v1", "title": "Derivative pricing with virtual arbitrage", "abstract": "In this paper we derive an effective equation for derivative pricing which accounts for the presence of virtual arbitrage opportunities and their elimination by the market. We model the arbitrage return by a stochastic process and find an equation for the average derivative price. This is an integro-differential equation which, in the absence of the virtual arbitrage or for an infinitely fast market reaction, reduces to the Black-Scholes equation. Explicit formulas are obtained for European call and put vanilla options.", "published": "1999-02-03T12:28:40Z", "authors": ["Kirill Ilinski", "Alexander Stepanenko"], "categories": ["cond-mat.stat-mech", "q-fin.PR"], "doi": "", "_query": "housing price text listing", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/1512.03485v1", "title": "Price discrimination for energy trading in smart grid: A game theoretic approach", "abstract": "Pricing schemes are an important smart grid feature to affect typical energy usage behavior of energy users (EUs). However, most existing schemes use the assumption that a buyer pays the same price per unit of energy to all suppliers at any particular time when energy is bought. By contrast, here a discriminate pricing technique using game theory is studied. A cake cutting game is investigated, in which participating EUs in a smart community decide on the price per unit of energy to charge a shared facility controller (SFC) in order to sell surplus energy. The focus is to study fairness criteria to maximize sum benefits to EUs and ensure an envy-free energy trading market. A benefit function is designed that leverages generation of discriminate pricing by each EU, according to the amount of surplus energy that an EU trades with the SFC and the EU's sensitivity to price. It is shown that the game possesses a socially optimal, and hence also Pareto optimal, solution. Further, an algorithm that can be implemented by each EU in a distributed manner to reach the optimal solution is proposed. Numerical case studies are given that demonstrate beneficial properties of the scheme.", "published": "2015-12-10T23:40:14Z", "authors": ["Wayes Tushar", "Chau Yuen", "David Smith", "H. Vincent Poor"], "categories": ["eess.SY", "cs.GT"], "doi": "", "_query": "housing price text listing", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2404.01291v2", "title": "Evaluating Text-to-Visual Generation with Image-to-Text Generation", "abstract": "Despite significant progress in generative AI, comprehensive evaluation remains challenging because of the lack of effective metrics and standardized benchmarks. For instance, the widely-used CLIPScore measures the alignment between a (generated) image and text prompt, but it fails to produce reliable scores for complex prompts involving compositions of objects, attributes, and relations. One reason is that text encoders of CLIP can notoriously act as a \"bag of words\", conflating prompts such as \"the horse is eating the grass\" with \"the grass is eating the horse\". To address this, we introduce the VQAScore, which uses a visual-question-answering (VQA) model to produce an alignment score by computing the probability of a \"Yes\" answer to a simple \"Does this figure show '{text}'?\" question. Though simpler than prior art, VQAScore computed with off-the-shelf models produces state-of-the-art results across many (8) image-text alignment benchmarks. We also compute VQAScore with an in-house model that follows best practices in the literature. For example, we use a bidirectional image-question encoder that allows image embeddings to depend on the question being asked (and vice versa). Our in-house model, CLIP-FlanT5, outperforms even the strongest baselines that make use of the proprietary GPT-4V. Interestingly, although we train with only images, VQAScore can also align text with video and 3D models. VQAScore allows researchers to benchmark text-to-visual generation using complex texts that capture the compositional structure of real-world prompts. We introduce GenAI-Bench, a more challenging benchmark with 1,600 compositional text prompts that require parsing scenes, objects, attributes, relationships, and high-order reasoning like comparison and logic. GenAI-Bench also offers over 15,000 human ratings for leading image and video generation models such as Stable Diffusion, DALL-E 3, and Gen2.", "published": "2024-04-01T17:58:06Z", "authors": ["Zhiqiu Lin", "Deepak Pathak", "Baiqi Li", "Jiayao Li", "Xide Xia", "Graham Neubig", "Pengchuan Zhang", "Deva Ramanan"], "categories": ["cs.CV", "cs.AI", "cs.CL", "cs.LG", "cs.MM"], "doi": "", "_query": "housing price text listing", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2411.08631v1", "title": "Deep Generative Demand Learning for Newsvendor and Pricing", "abstract": "We consider data-driven inventory and pricing decisions in the feature-based newsvendor problem, where demand is influenced by both price and contextual features and is modeled without any structural assumptions. The unknown demand distribution results in a challenging conditional stochastic optimization problem, further complicated by decision-dependent uncertainty and the integration of features. Inspired by recent advances in deep generative learning, we propose a novel approach leveraging conditional deep generative models (cDGMs) to address these challenges. cDGMs learn the demand distribution and generate probabilistic demand forecasts conditioned on price and features. This generative approach enables accurate profit estimation and supports the design of algorithms for two key objectives: (1) optimizing inventory for arbitrary prices, and (2) jointly determining optimal pricing and inventory levels. We provide theoretical guarantees for our approach, including the consistency of profit estimation and convergence of our decisions to the optimal solution. Extensive simulations-ranging from simple to complex scenarios, including one involving textual features-and a real-world case study demonstrate the effectiveness of our approach. Our method opens a new paradigm in management science and operations research, is adaptable to extensions of the newsvendor and pricing problems, and holds potential for solving other conditional stochastic optimization problems.", "published": "2024-11-13T14:17:26Z", "authors": ["Shijin Gong", "Huihang Liu", "Xinyu Zhang"], "categories": ["stat.ML", "cs.LG", "math.OC"], "doi": "", "_query": "housing price text listing", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2010.01180v2", "title": "Reinforcement Learning of Sequential Price Mechanisms", "abstract": "We introduce the use of reinforcement learning for indirect mechanisms, working with the existing class of sequential price mechanisms, which generalizes both serial dictatorship and posted price mechanisms and essentially characterizes all strongly obviously strategyproof mechanisms. Learning an optimal mechanism within this class forms a partially-observable Markov decision process. We provide rigorous conditions for when this class of mechanisms is more powerful than simpler static mechanisms, for sufficiency or insufficiency of observation statistics for learning, and for the necessity of complex (deep) policies. We show that our approach can learn optimal or near-optimal mechanisms in several experimental settings.", "published": "2020-10-02T19:57:25Z", "authors": ["Gianluca Brero", "Alon Eden", "Matthias Gerstgrasser", "David C. Parkes", "Duncan Rheingans-Yoo"], "categories": ["cs.GT", "cs.AI", "cs.LG"], "doi": "", "_query": "housing price text listing", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2308.01436v2", "title": "Price-Aware Deep Learning for Electricity Markets", "abstract": "While deep learning gradually penetrates operational planning, its inherent prediction errors may significantly affect electricity prices. This letter examines how prediction errors propagate into electricity prices, revealing notable pricing errors and their spatial disparity in congested power systems. To improve fairness, we propose to embed electricity market-clearing optimization as a deep learning layer. Differentiating through this layer allows for balancing between prediction and pricing errors, as oppose to minimizing prediction errors alone. This layer implicitly optimizes fairness and controls the spatial distribution of price errors across the system. We showcase the price-aware deep learning in the nexus of wind power forecasting and short-term electricity market clearing.", "published": "2023-08-02T21:16:05Z", "authors": ["Vladimir Dvorkin", "Ferdinando Fioretto"], "categories": ["cs.LG", "eess.SY", "math.OC"], "doi": "", "_query": "housing price text listing", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2403.19486v3", "title": "Distributionally robust monopoly pricing: Switching from low to high prices in volatile markets", "abstract": "Problem definition: Traditional monopoly pricing assumes sellers have full information about consumer valuations. We consider monopoly pricing under limited information, where a seller only knows the mean, variance and support of the valuation distribution. The objective is to maximize expected revenue by selecting the optimal fixed price. Methodology/results: We adopt a distributionally robust framework, where the seller considers all valuation distributions that comply with the limited information. We formulate a maximin problem which seeks to maximize expected revenue for the worst-case valuation distribution. The minimization problem that identifies the worst-case valuation distribution is solved using primal-dual methods, and in turn leads to an explicitly solvable maximization problem. This yields a closed-form optimal pricing policy and a new fundamental principle prescribing when to use low and high robust prices. Managerial implications: We show that the optimal policy switches from low to high prices when variance becomes sufficiently large, yielding significant performance gains compared with existing robust prices that generally decay with market uncertainty. This presents guidelines for when the seller should switch from targeting mass markets to niche markets. Similar guidelines are obtained for delay-prone services with rational utility-maximizing customers, underlining the universality and wide applicability of the novel pricing policy.", "published": "2024-03-28T15:16:11Z", "authors": ["Tim S. G. van Eck", "Pieter Kleer", "Johan S. H. van Leeuwaarden"], "categories": ["math.OC", "math.PR"], "doi": "10.1287/msom.2024.0952", "_query": "housing price text listing", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2509.21375v1", "title": "Automated Prompt Generation for Creative and Counterfactual Text-to-image Synthesis", "abstract": "Text-to-image generation has advanced rapidly with large-scale multimodal training, yet fine-grained controllability remains a critical challenge. Counterfactual controllability, defined as the capacity to deliberately generate images that contradict common-sense patterns, remains a major challenge but plays a crucial role in enabling creativity and exploratory applications. In this work, we address this gap with a focus on counterfactual size (e.g., generating a tiny walrus beside a giant button) and propose an automatic prompt engineering framework that adapts base prompts into revised prompts for counterfactual images. The framework comprises three components: an image evaluator that guides dataset construction by identifying successful image generations, a supervised prompt rewriter that produces revised prompts, and a DPO-trained ranker that selects the optimal revised prompt. We construct the first counterfactual size text-image dataset and enhance the image evaluator by extending Grounded SAM with refinements, achieving a 114 percent improvement over its backbone. Experiments demonstrate that our method outperforms state-of-the-art baselines and ChatGPT-4o, establishing a foundation for future research on counterfactual controllability.", "published": "2025-09-23T07:22:31Z", "authors": ["Aleksa Jelaca", "Ying Jiao", "Chang Tian", "Marie-Francine Moens"], "categories": ["cs.CV", "cs.AI"], "doi": "", "_query": "housing price text listing", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2209.13603v3", "title": "Scalable and Equivariant Spherical CNNs by Discrete-Continuous (DISCO) Convolutions", "abstract": "No existing spherical convolutional neural network (CNN) framework is both computationally scalable and rotationally equivariant. Continuous approaches capture rotational equivariance but are often prohibitively computationally demanding. Discrete approaches offer more favorable computational performance but at the cost of equivariance. We develop a hybrid discrete-continuous (DISCO) group convolution that is simultaneously equivariant and computationally scalable to high-resolution. While our framework can be applied to any compact group, we specialize to the sphere. Our DISCO spherical convolutions exhibit $\\text{SO}(3)$ rotational equivariance, where $\\text{SO}(n)$ is the special orthogonal group representing rotations in $n$-dimensions. When restricting rotations of the convolution to the quotient space $\\text{SO}(3)/\\text{SO}(2)$ for further computational enhancements, we recover a form of asymptotic $\\text{SO}(3)$ rotational equivariance. Through a sparse tensor implementation we achieve linear scaling in number of pixels on the sphere for both computational cost and memory usage. For 4k spherical images we realize a saving of $10^9$ in computational cost and $10^4$ in memory usage when compared to the most efficient alternative equivariant spherical convolution. We apply the DISCO spherical CNN framework to a number of benchmark dense-prediction problems on the sphere, such as semantic segmentation and depth estimation, on all of which we achieve the state-of-the-art performance.", "published": "2022-09-27T18:00:01Z", "authors": ["Jeremy Ocampo", "Matthew A. Price", "Jason D. McEwen"], "categories": ["cs.CV", "astro-ph.IM", "cs.LG"], "doi": "", "_query": "housing price text listing", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/1909.01268v1", "title": "Are Bitcoins price predictable? Evidence from machine learning techniques using technical indicators", "abstract": "The uncertainties in future Bitcoin price make it difficult to accurately predict the price of Bitcoin. Accurately predicting the price for Bitcoin is therefore important for decision-making process of investors and market players in the cryptocurrency market. Using historical data from 01/01/2012 to 16/08/2019, machine learning techniques (Generalized linear model via penalized maximum likelihood, random forest, support vector regression with linear kernel, and stacking ensemble) were used to forecast the price of Bitcoin. The prediction models employed key and high dimensional technical indicators as the predictors. The performance of these techniques were evaluated using mean absolute percentage error (MAPE), root mean square error (RMSE), mean absolute error (MAE), and coefficient of determination (R-squared). The performance metrics revealed that the stacking ensemble model with two base learner (random forest and generalized linear model via penalized maximum likelihood) and support vector regression with linear kernel as meta-learner was the optimal model for forecasting Bitcoin price. The MAPE, RMSE, MAE, and R-squared values for the stacking ensemble model were 0.0191%, 15.5331 USD, 124.5508 USD, and 0.9967 respectively. These values show a high degree of reliability in predicting the price of Bitcoin using the stacking ensemble model. Accurately predicting the future price of Bitcoin will yield significant returns for investors and market players in the cryptocurrency market.", "published": "2019-09-03T16:03:13Z", "authors": ["Samuel Asante Gyamerah"], "categories": ["q-fin.ST", "econ.EM", "stat.AP", "stat.ML"], "doi": "", "_query": "housing price text listing", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2405.10072v2", "title": "Simplicial lists in operad theory I", "abstract": "We define a category $\\mathsf{List}$ whose objects are sets and morphisms are mappings which assign to an element in the domain an ordered sequence (list) of elements in the codomain. We introduce and study a category of simplicial objects $\\mathsf{slist}$ whose objects are functors $Δ^{op} \\to \\mathsf{List}$, which we call simplicial lists, and morphisms are natural transformations which have functions as components. We demonstrate that $\\mathsf{sList}$ supports the combinatorics of (non-symmetric) operads by constructing a fully-faithful nerve functor $N^l : \\mathsf{Operad} \\to \\mathsf{sList}$ from the category of operads. This leads to a reasonable model for the theory of non-symmetric $\\infty$-operads. We also demonstrate that $\\mathsf{sList}$ has the structure of a presheaf category. In particular, we study a subcategory $\\mathsf{sList}_{\\text{op}}$ of operadic simplicial lists, in which the nerve functor takes values. The latter category is also a presheaf category over a base whose objects may be interpreted as levelled trees. We construct a coherent nerve functor which outputs an $\\infty$-operad for each operad enriched in Kan complexes. We also define homology groups of simplicial lists and study first properties.", "published": "2024-05-16T13:06:35Z", "authors": ["Redi Haderi", "Özgün Ünlü"], "categories": ["math.AT", "math.CT"], "doi": "", "_query": "housing price text listing", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2006.02726v8", "title": "Construction and sample path properties of Brownian house-moving between two curves", "abstract": "This study aims to construct a stochastic process called \"Brownian house-moving,\" which is a Brownian bridge conditioned to stay between two curves. To construct this process, statements are prepared on the weak convergence of conditioned Brownian motions, conditioned Brownian bridges, and conditioned three-dimensional Bessel bridges. Moreover, the sample path properties of Brownian house-moving are studied as well.", "published": "2020-06-04T09:31:01Z", "authors": ["Kensuke Ishitani", "Daisuke Hatakenaka", "Keisuke Suzuki"], "categories": ["math.PR"], "doi": "", "_query": "housing price text listing", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2404.06489v4", "title": "Challenges in Finding Stable Price Zones in European Electricity Markets: Aiming to Square the Circle?", "abstract": "The European day-ahead electricity market is split into multiple bidding zones with a uniform price. The increase in renewables leads to a growing number of interventions in the generation of energy sources and increasing redispatch costs. To ensure efficient congestion management, the EU Commission mandated a Bidding Zone Review (BZR) to reevaluate the configuration of European bidding zones. An integral part of this process was a locational marginal pricing study. Based on these prices, alternative bidding zone configurations were proposed. These bidding zones shall be stable and robust over time. For Germany, four configurations were suggested. We analyzed the proposed configurations considering different clustering algorithms and periods based on the publicly released data set in the context of the BZR, and found that the configurations do not reduce the price standard deviations within zones much, and the average prices across zones are similar. Other configurations identified based on clustering the prices lead to lower price variance but they are not geographically coherent. Independent of the clustering features and algorithms used, the resulting clusters are not stable over time. Interestingly, the effect of a split on prices would be low based on an analysis of the BZR data set.", "published": "2024-04-09T17:43:28Z", "authors": ["Teodora Dobos", "Martin Bichler", "Johannes Knörr"], "categories": ["econ.GN"], "doi": "", "_query": "housing price text listing", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2307.08025v1", "title": "Analysing Gender Bias in Text-to-Image Models using Object Detection", "abstract": "This work presents a novel strategy to measure bias in text-to-image models. Using paired prompts that specify gender and vaguely reference an object (e.g. \"a man/woman holding an item\") we can examine whether certain objects are associated with a certain gender. In analysing results from Stable Diffusion, we observed that male prompts generated objects such as ties, knives, trucks, baseball bats, and bicycles more frequently. On the other hand, female prompts were more likely to generate objects such as handbags, umbrellas, bowls, bottles, and cups. We hope that the method outlined here will be a useful tool for examining bias in text-to-image models.", "published": "2023-07-16T12:31:29Z", "authors": ["Harvey Mannering"], "categories": ["cs.CV"], "doi": "", "_query": "housing price text listing", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2412.05070v1", "title": "Enhancing Fourier pricing with machine learning", "abstract": "Fourier pricing methods such as the Carr-Madan formula or the COS method are classic tools for pricing European options for advanced models such as the Heston model. These methods require tuning parameters such as a damping factor, a truncation range, a number of terms, etc. Estimating these tuning parameters is difficult or computationally expensive. Recently, machine learning techniques have been proposed for fast pricing: they are able to learn the functional relationship between the parameters of the Heston model and the option price. However, machine learning techniques suffer from error control and require retraining for different error tolerances. In this research, we propose to learn the tuning parameters of the Fourier methods (instead of the prices) using machine learning techniques. As a result, we obtain very fast algorithms with full error control: Our approach works with any error tolerance without retraining, as demonstrated in numerical experiments using the Heston model.", "published": "2024-12-06T14:27:59Z", "authors": ["Gero Junike", "Hauke Stier"], "categories": ["q-fin.MF"], "doi": "", "_query": "housing price text listing", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/math/0509232v2", "title": "Properties of option prices in models with jumps", "abstract": "We study convexity and monotonicity properties of option prices in a model with jumps using the fact that these prices satisfy certain parabolic integro-differential equations. Conditions are provided under which preservation of convexity holds, i.e. under which the value, calculated under a chosen martingale measure, of an option with a convex contract function is convex as a function of the underlying stock price. The preservation of convexity is then used to derive monotonicity properties of the option value with respect to the different parameters of the model, such as the volatility, the jump size and the jump intensity.", "published": "2005-09-10T10:30:14Z", "authors": ["Erik Ekström", "Johan Tysk"], "categories": ["math.AP", "q-fin.ST"], "doi": "", "_query": "housing price text listing", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2112.03513v1", "title": "Change of persistence in European electricity spot prices", "abstract": "The European Power Exchange has introduced day-ahead auctions and continuous trading spot markets to facilitate the insertion of renewable electricity. These markets are designed to balance excess or lack of power in short time periods, which leads to a large stochastic variability of the electricity prices. Furthermore, the different markets show different stochastic memory in their electricity price time series, which seem to be the cause for the large volatility. In particular, we show the antithetical temporal correlation in the intraday 15 minutes spot markets in comparison to the day-ahead hourly market. We contrast the results from Detrended Fluctuation Analysis (DFA) to a new method based on the Kramers--Moyal equation in scale. For very short term ($<12$ hours), all price time series show positive temporal correlations (Hurst exponent $H>0.5$) except for the intraday 15 minute market, which shows strong negative correlations ($H<0.5$). For longer term periods covering up to two days, all price time series are anti-correlated ($H<0.5$).", "published": "2021-12-07T05:48:45Z", "authors": ["Leonardo Rydin Gorjão", "Dirk Witthaut", "Pedro G. Lind", "Wided Medjroubi"], "categories": ["q-fin.ST", "physics.soc-ph"], "doi": "", "_query": "housing price text listing", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2405.00701v8", "title": "Learning parameter dependence for Fourier-based option pricing with tensor trains", "abstract": "A long-standing issue in mathematical finance is the speed-up of option pricing, especially for multi-asset options. A recent study has proposed to use tensor train learning algorithms to speed up Fourier transform (FT)-based option pricing, utilizing the ability of tensor trains to compress high-dimensional tensors. Another usage of the tensor train is to compress functions, including their parameter dependence. Here, we propose a pricing method, where, by a tensor train learning algorithm, we build tensor trains that approximate functions appearing in FT-based option pricing with their parameter dependence and efficiently calculate the option price for the varying input parameters. As a benchmark test, we run the proposed method to price a multi-asset option for the various values of volatilities and present asset prices. We show that, in the tested cases involving up to 11 assets, the proposed method outperforms Monte Carlo-based option pricing with $10^6$ paths in terms of computational complexity while keeping better accuracy.", "published": "2024-04-17T01:57:19Z", "authors": ["Rihito Sakurai", "Haruto Takahashi", "Koichi Miyamoto"], "categories": ["q-fin.CP", "quant-ph"], "doi": "10.3390/math13111828", "_query": "housing price text listing", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2602.12782v1", "title": "Empirical Validation of a Dual-Defense Mechanism Reshaping Wholesale Electricity Price Dynamics in Singapore", "abstract": "While ex-ante screening and static price caps are global standards for mitigating price volatility, Singapore's electricity market employs a unique dual-defense mechanism integrating vesting contracts (VC) with a temporary price cap (TPC). Using high-frequency data from 2021 to 2024, this paper evaluates this mechanism and yields three primary findings. First, a structural trade-off exists within the VC framework: while VC quantity (VCQ) suppresses average prices, it paradoxically exacerbates instability via liquidity squeezes. Conversely, VC price (VCP) functions as a tail-risk anchor, dominating at extreme quantiles where VCQ efficacy wanes. Second, a structural break around the 2023 reform reveals a fundamental re-mapping of price dynamics; the previously positive pass-through from offer ratios to clearing prices was largely neutralized post-reform. Furthermore, diagnostics near the TPC threshold show no systematic evidence of strategic bid shading, confirming the TPC's operational integrity. Third, the dual-defense mechanism exhibits a critical synergy that resolves the volatility trade-off. The TPC reverses the volatility penalty of high VCQ, shifting the elasticity of conditional volatility from a destabilizing 0.636 to a stabilizing -0.213. This synergy enables the framework to enhance tail-risk control while eliminating liquidity-related stability costs. We conclude that this dual-defense mechanism successfully decouples price suppression from liquidity risks, thereby maximizing market stability.", "published": "2026-02-13T10:07:49Z", "authors": ["Huang Zhenyu", "Yuan Zhao"], "categories": ["eess.SY", "econ.EM"], "doi": "", "_query": "housing price text listing", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2102.10756v3", "title": "Equilibrium Price Formation with a Major Player and its Mean Field Limit", "abstract": "In this article, we consider the problem of equilibrium price formation in an incomplete securities market consisting of one major financial firm and a large number of minor firms. They carry out continuous trading via the securities exchange to minimize their cost while facing idiosyncratic and common noises as well as stochastic order flows from their individual clients. The equilibrium price process that balances demand and supply of the securities, including the functional form of the price impact for the major firm, is derived endogenously both in the market of finite population size and in the corresponding mean field limit.", "published": "2021-02-22T03:43:34Z", "authors": ["Masaaki Fujii", "Akihiko Takahashi"], "categories": ["q-fin.MF", "econ.GN", "q-fin.TR"], "doi": "", "_query": "housing price text listing", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2307.15785v1", "title": "Safe Pricing Mechanisms for Distributed Resource Allocation with Bandit Feedback", "abstract": "In societal-scale infrastructures, such as electric grids or transportation networks, pricing mechanisms are often used as a way to shape users' demand in order to lower operating costs and improve reliability. Existing approaches to pricing design for safety-critical networks often require that users are queried beforehand to negotiate prices, which has proven to be challenging to implement in the real-world. To offer a more practical alternative, we develop learning-based pricing mechanisms that require no input from the users. These pricing mechanisms aim to maximize the utility of the users' consumption by gradually estimating the users' price response over a span of $T$ time steps (e.g., days) while ensuring that the infrastructure network's safety constraints that limit the users' demand are satisfied at all time steps. We propose two different algorithms for the two different scenarios when, 1) the utility function is chosen by the central coordinator to achieve a social objective and 2) the utility function is defined by the price response under the assumption that the users are self-interested agents. We prove that both algorithms enjoy $\\tilde{O} (T^{2/3})$ regret with high probability. We then apply these algorithms to demand response pricing for the smart grid and numerically demonstrate their effectiveness.", "published": "2023-07-28T19:55:27Z", "authors": ["Spencer Hutchinson", "Berkay Turan", "Mahnoosh Alizadeh"], "categories": ["eess.SY"], "doi": "", "_query": "housing price text listing", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/1602.02860v1", "title": "Impact of integrity attacks on real-time pricing in smart grids", "abstract": "Modern information and communication technologies used by smart grids are subject to cybersecurity threats. This paper studies the impact of integrity attacks on real-time pricing (RTP), a key feature of smart grids that uses such technologies to improve system efficiency. Recent studies have shown that RTP creates a closed loop formed by the mutually dependent real-time price signals and price-taking demand. Such a closed loop can be exploited by an adversary whose objective is to destabilize the pricing system. Specifically, small malicious modifications to the price signals can be iteratively amplified by the closed loop, causing inefficiency and even severe failures such as blackouts. This paper adopts a control-theoretic approach to deriving the fundamental conditions of RTP stability under two broad classes of integrity attacks, namely, the scaling and delay attacks. We show that the RTP system is at risk of being destabilized only if the adversary can compromise the price signals advertised to smart meters by reducing their values in the scaling attack, or by providing old prices to over half of all consumers in the delay attack. The results provide useful guidelines for system operators to analyze the impact of various attack parameters on system stability, so that they may take adequate measures to secure RTP systems.", "published": "2016-02-09T04:57:11Z", "authors": ["Rui Tan", "Varun Badrinath Krishna", "David K. Y. Yau", "Zbigniew Kalbarczyk"], "categories": ["cs.CR"], "doi": "10.1145/2508859.2516705", "_query": "housing price text listing", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/1906.06483v1", "title": "Option Pricing via Multi-path Autoregressive Monte Carlo Approach", "abstract": "The pricing of financial derivatives, which requires massive calculations and close-to-real-time operations under many trading and arbitrage scenarios, were largely infeasible in the past. However, with the advancement of modern computing, the efficiency has substantially improved. In this work, we propose and design a multi-path option pricing approach via autoregression (AR) process and Monte Carlo Simulations (MCS). Our approach learns and incorporates the price characteristics into AR process, and re-generates the price paths for options. We apply our approach to price weekly options underlying Taiwan Stock Exchange Capitalization Weighted Stock Index (TAIEX) and compare the results with prior practiced models, e.g., Black-Scholes-Merton and Binomial Tree. The results show that our approach is comparable with prior practiced models.", "published": "2019-06-15T07:06:54Z", "authors": ["Wei-Cheng Chen", "Wei-Ho Chung"], "categories": ["q-fin.PR"], "doi": "", "_query": "housing price text listing", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2005.07080v3", "title": "Optimal long-term investment in illiquid markets when prices have negative memory", "abstract": "In a discrete-time financial market model with instantaneous price impact, we find an asymptotically optimal strategy for an investor maximizing her expected wealth. The asset price is assumed to follow a process with negative memory. We determine how the optimal growth rate depends on the impact parameter and on the covariance decay rate of the price.", "published": "2020-05-14T15:50:11Z", "authors": ["Miklós Rásonyi", "Lóránt Nagy"], "categories": ["math.PR"], "doi": "", "_query": "housing price text listing", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/1303.1296v1", "title": "The Pricing of A Moving Barrier Option", "abstract": "We provided an analytical representation of the price of a barrier option with one type of special moving barrier. We consider the case that risk free rate, dividend rate and stock volatility are time dependent. We get a pricing formula and put call parity for barrier option when the moving barrier has a special relation with risk free rate, dividend rate and stock volatility.", "published": "2013-03-06T10:47:11Z", "authors": ["Hyong-chol O"], "categories": ["q-fin.PR", "q-fin.CP", "q-fin.RM"], "doi": "10.2139/ssrn.742924", "_query": "housing price text listing", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2605.23953v1", "title": "Game-Theoretic Modeling of Heterogeneous Investor Interactions for Stock Price Forecasting", "abstract": "Accurate stock price forecasting has consistently remained a pivotal yet challenging FinTech task that underpins quantitative trading and investment decision making. Recent efforts have been dedicated to modeling various complex relationships among stocks in the stock market toward more reliable stock price forecasting.These methods depend heavily on strong static prior assumptions by modeling either temporal dependencies within individual stocks or spatial dependencies across different stocks based on predefined structures, while the complex market dynamics that drive stock price movements remain unexplored. To alleviate this issue, we propose a novel game-theoretic modeling method that captures heterogeneous investor interactions for stock price forecasting. The core idea is to embed game-theoretic mechanisms into the heterogeneous graph structure to finely model the dynamic strategic interactions among heterogeneous investors with respect to target stocks. Additionally, temporal positional encoding is adopted to reflect the differentiated influences of each game event at different time steps within the time window on future stock price movements. Leveraging heterogeneous graph networks, we proxy the intricate dynamics of the stock market through investor games and enable real-time information propagation and node updates among all nodes. Extensive experiments conducted on two real-world benchmark dataset demonstrate that our method effectively outperforms state-of-the-art stock price forecasting methods.", "published": "2026-05-11T03:24:07Z", "authors": ["Yong Zhang", "Xinxiao Wu", "Yunde Jia", "Che Sun"], "categories": ["q-fin.TR", "cs.GT", "cs.LG"], "doi": "", "_query": "housing price text listing", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2106.02149v2", "title": "Optimal Pricing Schemes for an Impatient Buyer", "abstract": "A patient seller aims to sell a good to an impatient buyer (i.e., one who discounts utility over time). The buyer will remain in the market for a period of time $T$, and her private value is drawn from a publicly known distribution. What is the revenue-optimal pricing-curve (sequence of (price, time) pairs) for the seller? Is randomization of help here? Is the revenue-optimal pricing curve computable in polynomial time? We answer these questions in this paper. We give an efficient algorithm for computing the revenue-optimal pricing curve. We show that pricing curves, that post a price at each point of time and let the buyer pick her utility maximizing time to buy, are revenue-optimal among a much broader class of sequential lottery mechanisms. I.e., mechanisms that allow the seller to post a menu of lotteries at each point of time cannot get any higher revenue than pricing curves. We also show that the even broader class of mechanisms that allow the menu of lotteries to be adaptively set, can earn strictly higher revenue than that of pricing curves, and the revenue gap can be as big as the support size of the buyer's value distribution.", "published": "2021-06-03T21:53:37Z", "authors": ["Yuan Deng", "Jieming Mao", "Balasubramanian Sivan", "Kangning Wang"], "categories": ["cs.GT", "cs.DS", "econ.TH"], "doi": "10.1137/1.9781611977554.ch16", "_query": "housing price text listing", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2507.13026v2", "title": "Disjoint Tours and the Price of Diversity", "abstract": "We study a variant of the Traveling Salesman Problem, where instead of finding a single tour, we want to find a pair of two edge-disjoint tours whose longer tour is as short as possible. We investigate the Price of Diversity (PoD) for this problem, which is the ratio of the cost of the longer of the two tours and the cost of a single optimal tour, in the worst case over all possible instances. We prove (almost) tight bounds on this quantity for a special 1-dimensional scenario and for general metric spaces. We believe that the Price-of-Diversity framework that we introduce is interesting in its own right, and may lead to follow-up work on other problems as well.", "published": "2025-07-17T11:55:42Z", "authors": ["Mark de Berg", "Andrés López Martínez", "Frits Spieksma"], "categories": ["cs.DS"], "doi": "", "_query": "housing price text listing", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/1802.04778v1", "title": "The Quotient of Normal Random Variables And Application to Asset Price Fat Tails", "abstract": "The quotient of random variables with normal distributions is examined and proven to have have power law decay, with density $f\\left( x\\right) \\simeq f_{0}x^{-2}$, with the coefficient depending on the means and variances of the numerator and denominator and their correlation. We also obtain the conditional probability densities for each of the four quadrants given by the signs of the numerator and denominator for arbitrary correlation $ρ\\in\\lbrack-1,1).$ For $ρ=-1$ we obtain a particularly simple closed form solution for all $x\\in$ $\\mathbb{R}$. The results are applied to a basic issue in economics and finance, namely the density of relative price changes. Classical finance stipulates a normal distribution of relative price changes, though empirical studies suggest a power law at the tail end. By considering the supply and demand in a basic price change model, we prove that the relative price change has density that decays with an $x^{-2}$ power law. Various parameter limits are established.", "published": "2018-02-13T18:14:27Z", "authors": ["Carey Caginalp", "Gunduz Caginalp"], "categories": ["q-fin.MF", "math.PR", "math.ST"], "doi": "10.1016/j.physa.2018.02.077", "_query": "housing price text listing", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2603.15176v1", "title": "The Price of Universal Temporal Reachability", "abstract": "Dynamic networks are graphs in which edges are available only at specific time instants, modeling connections that change over time. The dynamic network creation game studies this setting as a strategic interaction where each vertex represents a player. Players can add or remove time-labeled edges in order to minimize their personal cost. This cost has two components: a construction cost, calculated as the number of time instants during which a player maintains edges multiplied by a constant $α$, and a communication cost, defined as the average distance to all other vertices in the network. Communication occurs through temporal paths, which are sequences of adjacent edges with strictly increasing time labels and no repeated vertices. We show for the shortest distance (minimizing the number of edges) that the price of anarchy can be proportional to the number of vertices, contrasting the constant price conjectured for static networks.", "published": "2026-03-16T12:10:55Z", "authors": ["Binh-Minh Bui-Xuan", "Nhat-Minh Nguyen", "Sébastien Tixeuil", "Yukiko Yamauchi"], "categories": ["cs.DS"], "doi": "", "_query": "housing price text listing", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/1309.1420v4", "title": "Fundamental Theorem of Asset Pricing under Transaction costs and Model uncertainty", "abstract": "We prove the Fundamental Theorem of Asset Pricing for a discrete time financial market where trading is subject to proportional transaction cost and the asset price dynamic is modeled by a family of probability measures, possibly non-dominated. Using a backward-forward scheme, we show that when the market consists of a money market account and a single stock, no-arbitrage in a quasi-sure sense is equivalent to the existence of a suitable family of consistent price systems. We also show that when the market consists of multiple dynamically traded assets and satisfies \\emph{efficient friction}, strict no-arbitrage in a quasi-sure sense is equivalent to the existence of a suitable family of strictly consistent price systems.", "published": "2013-09-05T17:57:53Z", "authors": ["Erhan Bayraktar", "Yuchong Zhang"], "categories": ["math.PR", "q-fin.PR"], "doi": "", "_query": "housing price text listing", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/math/9906011v2", "title": "Some concepts in list coloring", "abstract": "In this paper uniquely list colorable graphs are studied. A graph G is called to be uniquely k-list colorable if it admits a k-list assignment from which G has a unique list coloring. The minimum k for which G is not uniquely k-list colorable is called the M-number of G. We show that every triangle-free uniquely vertex colorable graph with chromatic number k+1, is uniquely k-list colorable. A bound for the M-number of graphs is given, and using this bound it is shown that every planar graph has M-number at most 4. Also we introduce list criticality in graphs and characterize all 3-list critical graphs. It is conjectured that every $χ_\\ell$-critical graph is $χ'$-critical and the equivalence of this conjecture to the well known list coloring conjecture is shown.", "published": "1999-06-02T04:52:24Z", "authors": ["Ch. Eslahchi", "M. Ghebleh", "H. Hajiabolhassan"], "categories": ["math.CO"], "doi": "", "_query": "housing price text listing", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2211.13714v1", "title": "The Wade Formula and the oil price fluctuation: An Optimal Control Theory approach", "abstract": "By considering the Wade Formula, we propose a model to study the evolution of the oil price per barrel. Our model shows that the policy of diversification of the energy is to be supported. This model is proposed to see how it is possible to control parameters so that the oil price should decrease.", "published": "2022-11-24T17:04:44Z", "authors": ["Pierre Mendy", "Babacar M. Ndiaye", "Diaraf Seck", "Idrissa Ly"], "categories": ["math.OC"], "doi": "", "_query": "housing price text listing", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/1002.2084v1", "title": "A Sublogarithmic Approximation for Highway and Tollbooth Pricing", "abstract": "An instance of the tollbooth problem consists of an undirected network and a collection of single-minded customers, each of which is interested in purchasing a fixed path subject to an individual budget constraint. The objective is to assign a per-unit price to each edge in a way that maximizes the collective revenue obtained from all customers. The revenue generated by any customer is equal to the overall price of the edges in her desired path, when this cost falls within her budget; otherwise, that customer will not purchase any edge. Our main result is a deterministic algorithm for the tollbooth problem on trees whose approximation ratio is O(log m / log log m), where m denotes the number of edges in the underlying graph. This finding improves on the currently best performance guarantees for trees, due to Elbassioni et al. (SAGT '09), as well as for paths (commonly known as the highway problem), due to Balcan and Blum (EC '06). An additional interesting consequence is a computational separation between tollbooth pricing on trees and the original prototype problem of single-minded unlimited supply pricing, under a plausible hardness hypothesis due to Demaine et al. (SODA '06).", "published": "2010-02-10T12:21:26Z", "authors": ["Iftah Gamzu", "Danny Segev"], "categories": ["cs.DS", "cs.GT"], "doi": "", "_query": "housing price text listing", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2405.11431v2", "title": "Review of deep learning models for crypto price prediction: implementation and evaluation", "abstract": "There has been much interest in accurate cryptocurrency price forecast models by investors and researchers. Deep Learning models are prominent machine learning techniques that have transformed various fields and have shown potential for finance and economics. Although various deep learning models have been explored for cryptocurrency price forecasting, it is not clear which models are suitable due to high market volatility. In this study, we review the literature about deep learning for cryptocurrency price forecasting and evaluate novel deep learning models for cryptocurrency stock price prediction. Our deep learning models include variants of long short-term memory (LSTM) recurrent neural networks, variants of convolutional neural networks (CNNs), and the Transformer model. We evaluate univariate and multivariate approaches for multi-step ahead predicting of cryptocurrencies close-price. We also carry out volatility analysis on the four cryptocurrencies which reveals significant fluctuations in their prices throughout the COVID-19 pandemic. Additionally, we investigate the prediction accuracy of two scenarios identified by different training sets for the models. First, we use the pre-COVID-19 datasets to model cryptocurrency close-price forecasting during the early period of COVID-19. Secondly, we utilise data from the COVID-19 period to predict prices for 2023 to 2024. Our results show that the convolutional LSTM with a multivariate approach provides the best prediction accuracy in two major experimental settings. Our results also indicate that the multivariate deep learning models exhibit better performance in forecasting four different cryptocurrencies when compared to the univariate models.", "published": "2024-05-19T03:15:27Z", "authors": ["Jingyang Wu", "Xinyi Zhang", "Fangyixuan Huang", "Haochen Zhou", "Rohtiash Chandra"], "categories": ["cs.LG", "q-fin.ST", "stat.ML"], "doi": "", "_query": "housing price text listing", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2407.04664v1", "title": "The Degree of Fairness in Efficient House Allocation", "abstract": "The classic house allocation problem is primarily concerned with finding a matching between a set of agents and a set of houses that guarantees some notion of economic efficiency (e.g. utilitarian welfare). While recent works have shifted focus on achieving fairness (e.g. minimizing the number of envious agents), they often come with notable costs on efficiency notions such as utilitarian or egalitarian welfare. We investigate the trade-offs between these welfare measures and several natural fairness measures that rely on the number of envious agents, the total (aggregate) envy of all agents, and maximum total envy of an agent. In particular, by focusing on envy-free allocations, we first show that, should one exist, finding an envy-free allocation with maximum utilitarian or egalitarian welfare is computationally tractable. We highlight a rather stark contrast between utilitarian and egalitarian welfare by showing that finding utilitarian welfare maximizing allocations that minimize the aforementioned fairness measures can be done in polynomial time while their egalitarian counterparts remain intractable (for the most part) even under binary valuations. We complement our theoretical findings by giving insights into the relationship between the different fairness measures and conducting empirical analysis.", "published": "2024-07-05T17:18:56Z", "authors": ["Hadi Hosseini", "Medha Kumar", "Sanjukta Roy"], "categories": ["cs.GT", "cs.DS"], "doi": "", "_query": "housing price text listing", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2211.07797v2", "title": "Energy Storage Price Arbitrage via Opportunity Value Function Prediction", "abstract": "This paper proposes a novel energy storage price arbitrage algorithm combining supervised learning with dynamic programming. The proposed approach uses a neural network to directly predicts the opportunity cost at different energy storage state-of-charge levels, and then input the predicted opportunity cost into a model-based arbitrage control algorithm for optimal decisions. We generate the historical optimal opportunity value function using price data and a dynamic programming algorithm, then use it as the ground truth and historical price as predictors to train the opportunity value function prediction model. Our method achieves 65% to 90% profit compared to perfect foresight in case studies using different energy storage models and price data from New York State, which significantly outperforms existing model-based and learning-based methods. While guaranteeing high profitability, the algorithm is also light-weighted and can be trained and implemented with minimal computational cost. Our results also show that the learned prediction model has excellent transferability. The prediction model trained using price data from one region also provides good arbitrage results when tested over other regions.", "published": "2022-11-14T23:31:11Z", "authors": ["Ningkun Zheng", "Xiaoxiang Liu", "Bolun Xu", "Yuanyuan Shi"], "categories": ["eess.SY", "cs.LG"], "doi": "", "_query": "housing price text listing", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2512.17791v1", "title": "Near-Maturity Asymptotics of Critical Prices of American Put Options under Exponential Lévy Models", "abstract": "In the present paper, we study the near-maturity ($t\\rightarrow T^{-}$) convergence rate of the optimal early-exercise price $b(t)$ of an American put under an exponential Lévy model with a {\\it nonzero} Brownian component. Two important settings, not previous covered in the literature, are considered. In the case that the optimal exercise price converges to the strike price ($b(T^{-})=K$), we contemplate models with negative jumps of unbounded variation (i.e., processes that exhibit high activity of negative jumps or sudden falls in asset prices). In the second case, when the optimal exercise price tend to a value lower than $K$, we consider infinite activity jumps (though still of bounded variations), extending existing results for models with finite jump activity (finitely many jumps in any finite interval). In both cases, we show that $b(T^{-})-b(t)$ is of order $\\sqrt{T-t}$ with explicit constants proportionality. Furthermore, we also derive the second-order near-maturity expansion of the American put price around the critical price along a certain parabolic branch.", "published": "2025-12-19T16:59:26Z", "authors": ["José E. Figueroa-López", "Ruoting Gong"], "categories": ["q-fin.MF", "math.PR"], "doi": "", "_query": "housing price text listing", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2510.07759v1", "title": "Accelerated Price Adjustment for Fisher Markets with Exact Recovery of Competitive Equilibrium", "abstract": "The canonical price-adjustment process, tâtonnement, typically fails to converge to the exact competitive equilibrium (CE) and requires a high iteration complexity of $\\tilde{\\mathcal{O}}(1/ε)$ to compute $ε$-CE prices in widely studied linear and quasi-linear Fisher markets. This paper proposes refined price-adjustment processes to overcome these limitations. By formulating the task of finding CE of a (quasi-)linear Fisher market as a strongly convex nonsmooth minimization problem, we develop a novel accelerated price-adjustment method (APM) that finds an $ε$-CE price in $\\tilde{\\mathcal{O}}(1/\\sqrtε)$ lightweight iterations, which significantly improves upon the iteration complexities of tâtonnement methods. Furthermore, through our new formulation, we construct a recovery oracle that maps approximate CE prices to exact CE prices at a low computational cost. By coupling this recovery oracle with APM, we obtain an adaptive price-adjustment method whose iterates converge to CE prices in finite steps. To the best of our knowledge, this is the first convergence guarantee to exact CE for price-adjustment methods in linear and quasi-linear Fisher markets. Our developments pave the way for efficient lightweight computation of CE prices. We also present numerical results to demonstrate the fast convergence of the proposed methods and the efficient recovery of CE prices.", "published": "2025-10-09T03:59:03Z", "authors": ["He Chen", "Chonghe Jiang", "Anthony Man-Cho So"], "categories": ["math.OC"], "doi": "", "_query": "housing price text listing", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/1004.1804v2", "title": "Interacting Many-Investor Models, Opinion Formation and Price Formation with Non-extensive Statistics", "abstract": "We seek to utilize the nonextensive statistics to the microscopic modeling of the interacting many-investor dynamics that drive the price changes in a market. The statistics of price changes are known to be fit well by the Students-T and power-law distributions of the nonextensive statistics. We therefore derive models of interacting investors that are based on the nonextensive statistics and which describe the excess demand and formation of price.", "published": "2010-04-11T12:45:00Z", "authors": ["Fredrick Michael"], "categories": ["q-fin.TR", "q-fin.ST"], "doi": "", "_query": "housing price text listing", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/1409.4890v1", "title": "Can Market Risk Perception Drive Inefficient Prices? Theory and Evidence", "abstract": "This work presents an asset pricing model that under rational expectation equilibrium perspective shows how, depending on risk aversion and noise volatility, a risky-asset has one equilibrium price that differs in term of efficiency: an informational efficient one (similar to Campbell and Kyle (1993)), and another one where price diverges from its informational efficient level. The former Pareto dominates (is dominated by) the latter in presence of low (high) market risk perception. The estimates of the model using S&P 500 Index support the theoretical findings, and the estimated inefficient equilibrium price captures the higher risk premium and higher volatility observed during the Dot.com bubble 1995--2000.", "published": "2014-09-17T07:51:59Z", "authors": ["Matteo Formenti"], "categories": ["q-fin.GN", "q-fin.TR"], "doi": "", "_query": "housing price text listing", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/1805.11981v1", "title": "A New Model for Pricing Collateralized Financial Derivatives", "abstract": "This paper presents a new model for pricing financial derivatives subject to collateralization. It allows for collateral arrangements adhering to bankruptcy laws. As such, the model can back out the market price of a collateralized contract. This framework is very useful for valuing outstanding derivatives. Using a unique dataset, we find empirical evidence that credit risk alone is not overly important in determining credit-related spreads. Only accounting for both collateral posting and credit risk can sufficiently explain unsecured credit costs. This finding suggests that failure to properly account for collateralization may result in significant mispricing of derivatives. We also empirically gauge the impact of collateral agreements on risk measurements. Our findings indicate that there are important interactions between market and credit risk.", "published": "2018-05-29T11:51:47Z", "authors": ["Tim Xiao"], "categories": ["q-fin.PR", "q-fin.CP"], "doi": "", "_query": "housing price text listing", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2207.01277v1", "title": "Pricing multi-asset derivatives by variational quantum algorithms", "abstract": "Pricing a multi-asset derivative is an important problem in financial engineering, both theoretically and practically. Although it is suitable to numerically solve partial differential equations to calculate the prices of certain types of derivatives, the computational complexity increases exponentially as the number of underlying assets increases in some classical methods, such as the finite difference method. Therefore, there are efforts to reduce the computational complexity by using quantum computation. However, when solving with naive quantum algorithms, the target derivative price is embedded in the amplitude of one basis of the quantum state, and so an exponential complexity is required to obtain the solution. To avoid the bottleneck, the previous study~[Miyamoto and Kubo, IEEE Transactions on Quantum Engineering, \\textbf{3}, 1--25 (2022)] utilizes the fact that the present price of a derivative can be obtained by its discounted expected value at any future point in time and shows that the quantum algorithm can reduce the complexity. In this paper, to make the algorithm feasible to run on a small quantum computer, we use variational quantum simulation to solve the Black-Scholes equation and compute the derivative price from the inner product between the solution and a probability distribution. This avoids the measurement bottleneck of the naive approach and would provide quantum speedup even in noisy quantum computers. We also conduct numerical experiments to validate our method. Our method will be an important breakthrough in derivative pricing using small-scale quantum computers.", "published": "2022-07-04T09:11:15Z", "authors": ["Kenji Kubo", "Koichi Miyamoto", "Kosuke Mitarai", "Keisuke Fujii"], "categories": ["quant-ph", "q-fin.CP", "q-fin.PR"], "doi": "", "_query": "housing price text listing", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/1106.0519v2", "title": "Extreme-Value Theorems for Optimal Multidimensional Pricing", "abstract": "We provide a near-optimal, computationally efficient algorithm for the unit-demand pricing problem, where a seller wants to price n items to optimize revenue against a unit-demand buyer whose values for the items are independently drawn from known distributions. For any chosen accuracy eps>0 and item values bounded in [0,1], our algorithm achieves revenue that is optimal up to an additive error of at most eps, in polynomial time. For values sampled from Monotone Hazard Rate (MHR) distributions, we achieve a (1-eps)-fraction of the optimal revenue in polynomial time, while for values sampled from regular distributions the same revenue guarantees are achieved in quasi-polynomial time. Our algorithm for bounded distributions applies probabilistic techniques to understand the statistical properties of revenue distributions, obtaining a reduction in the search space of the algorithm via dynamic programming. Adapting this approach to MHR and regular distributions requires the proof of novel extreme value theorems for such distributions. As a byproduct, our techniques establish structural properties of approximately-optimal and near-optimal solutions. We show that, for values independently distributed according to MHR distributions, pricing all items at the same price achieves a constant fraction of the optimal revenue. Moreover, for all eps >0, g(1/eps) distinct prices suffice to obtain a (1-eps)-fraction of the optimal revenue, where g(1/eps) is quadratic in 1/eps and independent of n. Similarly, for all eps>0 and n>0, at most g(1/(eps log n)) distinct prices suffice if the values are independently distributed according to regular distributions, where g() is a polynomial function. Finally, when the values are i.i.d. from some MHR distribution, we show that, if n is a sufficiently large function of 1/eps, a single price suffices to achieve a (1-eps)-fraction of the optimal revenue.", "published": "2011-06-02T21:37:16Z", "authors": ["Yang Cai", "Constantinos Daskalakis"], "categories": ["cs.GT", "cs.DS"], "doi": "", "_query": "housing price text listing", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/1211.2090v2", "title": "Improving the H_k-Bound on the Price of Stability in Undirected Shapley Network Design Games", "abstract": "In this paper we show that the price of stability of Shapley network design games on undirected graphs with k players is at most (k^3(k+1)/2-k^2) / (1+k^3(k+1)/2-k^2) H_k = (1 - Θ(1/k^4)) H_k, where H_k denotes the k-th harmonic number. This improves on the known upper bound of H_k, which is also valid for directed graphs but for these, in contrast, is tight. Hence, we give the first non-trivial upper bound on the price of stability for undirected Shapley network design games that is valid for an arbitrary number of players. Our bound is proved by analyzing the price of stability restricted to Nash equilibria that minimize the potential function of the game. We also present a game with k=3 players in which such a restricted price of stability is 1.634. This shows that the analysis of Bilò and Bove (Journal of Interconnection Networks, Volume 12, 2011) is tight. In addition, we give an example for three players that improves the lower bound on the (unrestricted) price of stability to 1.571.", "published": "2012-11-09T10:05:53Z", "authors": ["Yann Disser", "Andreas Emil Feldmann", "Max Klimm", "Matúš Mihalák"], "categories": ["cs.GT"], "doi": "", "_query": "housing price text listing", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2211.12376v4", "title": "An Intraday GARCH Model for Discrete Price Changes and Irregularly Spaced Observations", "abstract": "We develop a novel observation-driven model for high-frequency prices. We account for irregularly spaced observations, simultaneous transactions, discreteness of prices, and market microstructure noise. The relation between trade durations and price volatility, as well as intraday patterns of trade durations and price volatility, is captured using smoothing splines. The dynamic model is based on the zero-inflated Skellam distribution with time-varying volatility in a score-driven framework. Market microstructure noise is filtered by including a moving average component. The model is estimated by the maximum likelihood method. In an empirical study of the IBM stock, we demonstrate that the model provides a good fit to the data. Besides modeling intraday volatility, it can also be used to measure daily realized volatility.", "published": "2022-11-22T16:17:03Z", "authors": ["Vladimír Holý"], "categories": ["q-fin.ST"], "doi": "", "_query": "housing price text listing", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/0905.3870v1", "title": "On the short-term influence of oil price changes on stock markets in GCC countries: linear and nonlinear analyses", "abstract": "This paper examines the short-run relationships between oil prices and GCC stock markets. Since GCC countries are major world energy market players, their stock markets may be susceptible to oil price shocks. To account for the fact that stock markets may respond nonlinearly to oil price shocks, we have examined both linear and nonlinear relationships. Our findings show that there are significant links between the two variables in Qatar, Oman, and UAE. Thus, stock markets in these countries react positively to oil price increases. For Bahrain, Kuwait, and Saudi Arabia we found that oil price changes do not affect stock market returns.", "published": "2009-05-24T05:04:38Z", "authors": ["Mohamed El Hedi Arouri", "Julien Fouquau"], "categories": ["q-fin.ST"], "doi": "", "_query": "housing price text listing", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2008.01009v1", "title": "The Splay-List: A Distribution-Adaptive Concurrent Skip-List", "abstract": "The design and implementation of efficient concurrent data structures have seen significant attention. However, most of this work has focused on concurrent data structures providing good \\emph{worst-case} guarantees. In real workloads, objects are often accessed at different rates, since access distributions may be non-uniform. Efficient distribution-adaptive data structures are known in the sequential case, e.g. the splay-trees; however, they often are hard to translate efficiently in the concurrent case. In this paper, we investigate distribution-adaptive concurrent data structures and propose a new design called the splay-list. At a high level, the splay-list is similar to a standard skip-list, with the key distinction that the height of each element adapts dynamically to its access rate: popular elements ``move up,'' whereas rarely-accessed elements decrease in height. We show that the splay-list provides order-optimal amortized complexity bounds for a subset of operations while being amenable to efficient concurrent implementation. Experimental results show that the splay-list can leverage distribution-adaptivity to improve on the performance of classic concurrent designs, and can outperform the only previously-known distribution-adaptive design in certain settings.", "published": "2020-08-03T16:45:49Z", "authors": ["Vitaly Aksenov", "Dan Alistarh", "Alexandra Drozdova", "Amirkeivan Mohtashami"], "categories": ["cs.DC", "cs.DS"], "doi": "", "_query": "housing price text listing", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/math/0005238v1", "title": "Modelling of stock price changes: a real analysis approach", "abstract": "The paper discusses a path-wise approach to stock price modelling.", "published": "2000-05-23T22:26:52Z", "authors": ["Rimas Norvaisa"], "categories": ["math.PR", "math.CA"], "doi": "", "_query": "housing price text listing", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2512.07154v2", "title": "Asian option valuation under price impact", "abstract": "We develop a tractable framework for valuing Asian options when trading the underlying generates market impact and execution costs. Starting from a discrete-time, quote-level model, we construct a reference midpoint suitable for Asian payoffs and separate market impact into a transient component and a permanent drift distortion driven by signed trading. This specification admits continuous-time limits where the midpoint and impact state converge to a coupled system in which the midpoint drift depends on the transient impact state and in the endogenous regime on the hedger's trading rate, with correlated price and order-flow shocks. We study valuation in two complementary regimes. In an exogenous benchmark, the impact state evolves independently of the hedger. When the order-flow volatility is deterministic, we obtain a closed-form expression for the geometric Asian call. In an endogenous regime, trading volumes feed back into prices and costs, leading to a stochastic control problem and Hamilton-Jacobi-Bellman equations. We define reservation bid and ask prices via cost-based indifference which produces an impact-driven bid-ask spread. For computations, we propose a CRR-style tree-based Bellman algorithm. Numerical experiments show that exogenous impact effects are modest relative to frictionless benchmarks, while endogenous indifference prices generate nontrivial bid-ask spreads that grow super-linearly in impact parameters, widen when execution costs are lower, and shrink with faster mean reversion, highlighting the interaction between averaging in Asian options, price impact effects, and strategic trading.", "published": "2025-12-08T04:38:02Z", "authors": ["Priyanshu Tiwari", "Sourav Majumdar"], "categories": ["q-fin.MF"], "doi": "", "_query": "housing price text listing", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/1910.00258v1", "title": "Isogeometric analysis in option pricing", "abstract": "Isogeometric analysis is a recently developed computational approach that integrates finite element analysis directly into design described by non-uniform rational B-splines (NURBS). In this paper we show that price surfaces that occur in option pricing can be easily described by NURBS surfaces. For a class of stochastic volatility models, we develop a methodology for solving corresponding pricing partial integro-differential equations numerically by isogeometric analysis tools and show that a very small number of space discretization steps can be used to obtain sufficiently accurate results. Presented solution by finite element method is especially useful for practitioners dealing with derivatives where closed-form solution is not available.", "published": "2019-10-01T08:43:54Z", "authors": ["Jan Pospíšil", "Vladimír Švígler"], "categories": ["q-fin.CP", "math.AP", "math.NA"], "doi": "10.1080/00207160.2018.1494826", "_query": "housing price text listing", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/1512.06050v1", "title": "Pricing the Ramping Reserve and Capacity Reserve in Real Time Markets", "abstract": "The increasing penetration of renewable energy in recent years has led to more uncertainties in power systems. In order to maintain system reliability and security, electricity market operators need to keep certain reserves in the Security-Constrained Economic Dispatch (SCED) problems. A new concept, deliverable generation ramping reserve, is proposed in this paper. The prices of generation ramping reserves and generation capacity reserves are derived in the Affine Adjustable Robust Optimization framework. With the help of these prices, the valuable reserves can be identified among the available reserves. These prices provide crucial information on the values of reserve resources, which are critical for the long-term flexibility investment. The market equilibrium based on these prices is analyzed. Simulations on a 3-bus system and the IEEE 118-bus system are performed to illustrate the concept of ramping reserve price and capacity reserve price. The impacts of the reserve credit on market participants are discussed.", "published": "2015-12-18T17:32:54Z", "authors": ["Hongxing Ye", "Zuyi Li"], "categories": ["math.OC", "eess.SY"], "doi": "", "_query": "housing price text listing", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/math/0612691v1", "title": "Option pricing with log-stable Lévy processes", "abstract": "We model the logarithm of the price (log-price) of a financial asset as a random variable obtained by projecting an operator stable random vector with a scaling index matrix $\\underline{\\underline{E}}$ onto a non-random vector. The scaling index $\\underline{\\underline{E}}$ models prices of the individual financial assets (stocks, mutual funds, etc.). We find the functional form of the characteristic function of real powers of the price returns and we compute the expectation value of these real powers and we speculate on the utility of these results for statistical inference. Finally we consider a portfolio composed of an asset and an option on that asset. We derive the characteristic function of the deviation of the portfolio, \\mbox{${\\mathfrak D}_t^{({\\mathfrak t})}$}, defined as a temporal change of the portfolio diminished by the the compound interest earned. We derive pseudo-differential equations for the option as a function of the log-stock-price and time and we find exact closed-form solutions to that equation. These results were not known before. Finally we discuss how our solutions correspond to other approximate results known from literature,in particular to the well known Black & Scholes equation.", "published": "2006-12-22T10:07:23Z", "authors": ["Przemysław Repetowicz", "Peter Richmond"], "categories": ["math.PR", "math.ST", "q-fin.PR"], "doi": "10.1007/978-88-470-0665-2_6", "_query": "housing price text listing", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/1503.06851v2", "title": "Price and capacity competition in balancing markets with energy storage", "abstract": "Energy storage can absorb variability from the rising number of wind and solar power producers. Storage is different from the conventional generators that have traditionally balanced supply and demand on fast time scales due to its hard energy capacity constraints, dynamic coupling, and low marginal costs. These differences are leading system operators to propose new mechanisms for enabling storage to participate in reserve and real-time energy markets. The persistence of market power and gaming in electricity markets suggests that these changes will expose new vulnerabilities. We develop a new model of strategic behavior among storages in energy balancing markets. Our model is a two-stage game that generalizes a classic model of capacity followed by Bertrand-Edgeworth price competition by explicitly modeling storage dynamics and uncertainty in the pricing stage. By applying the model to balancing markets with storage, we are able to compare capacity and energy-based pricing schemes, and to analyze the dynamic effects of the market horizon and energy losses due to leakage. Our first key finding is that capacity pricing leads to higher prices and higher capacity commitments, and that energy pricing leads to lower, randomized prices and lower capacity commitments. Second, we find that a longer market horizon and higher physical efficiencies lead to lower prices by inducing the storage to compete to have their states of charge cycled more frequently.", "published": "2015-03-23T21:54:17Z", "authors": ["Josh A. Taylor", "Johanna L. Mathieu", "Duncan S. Callaway", "Kameshwar Poolla"], "categories": ["cs.GT"], "doi": "", "_query": "housing price text listing", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2007.01467v1", "title": "Quantum Pricing with a Smile: Implementation of Local Volatility Model on Quantum Computer", "abstract": "Applications of the quantum algorithm for Monte Carlo simulation to pricing of financial derivatives have been discussed in previous papers. However, up to now, the pricing model discussed in such papers is Black-Scholes model, which is important but simple. Therefore, it is motivating to consider how to implement more complex models used in practice in financial institutions. In this paper, we then consider the local volatility (LV) model, in which the volatility of the underlying asset price depends on the price and time. We present two types of implementation. One is the register-per-RN way, which is adopted in most of previous papers. In this way, each of random numbers (RNs) required to generate a path of the asset price is generated on a separated register, so the required qubit number increases in proportion to the number of RNs. The other is the PRN-on-a-register way, which is proposed in the author's previous work. In this way, a sequence of pseudo-random numbers (PRNs) generated on a register is used to generate paths of the asset price, so the required qubit number is reduced with a trade-off against circuit depth. We present circuit diagrams for these two implementations in detail and estimate required resources: qubit number and T-count.", "published": "2020-07-03T02:54:25Z", "authors": ["Kazuya Kaneko", "Koichi Miyamoto", "Naoyuki Takeda", "Kazuyoshi Yoshino"], "categories": ["quant-ph", "q-fin.PR"], "doi": "", "_query": "housing price text listing", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2506.23377v2", "title": "Perspective Dial: Measuring Perspective of Text and Guiding LLM Outputs", "abstract": "Large language models (LLMs) are used in a variety of mission-critical roles. Due to the rapidly developing nature of LLMs, there is a lack of quantifiable understanding of the bias and perspective associated with LLM output. Inspired by this need, this paper considers the broader issue of perspective or viewpoint of general text and perspective control of large-language model (LLM) output. Perspective-Dial consists of two main components: a (1) metric space, dubbed Perspective Space, that enables quantitative measurements of different perspectives regarding a topic, and the use of (2) Systematic Prompt Engineering that utilizes greedy-coordinate descent to control LLM output perspective based on measurement feedback from the Perspective Space. The empirical nature of the approach allows progress to side step a principled understanding of perspective or bias -- effectively quantifying and adjusting outputs for a variety of topics. Potential applications include detection, tracking and mitigation of LLM bias, narrative detection, sense making and tracking in public discourse, and debate bot advocating given perspective.", "published": "2025-06-29T19:26:37Z", "authors": ["Taejin Kim", "Siun-Chuon Mau", "Konrad Vesey"], "categories": ["cs.CL", "cs.AI"], "doi": "", "_query": "housing price text listing", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2605.10617v1", "title": "Logarithmic scaling of selective sweep curves: from tents to houses", "abstract": "One of the classical results of mathematical population genetics states that the frequency of a beneficial mutant's offspring, on its way to fixation in a large population, looks like a logistic curve. A logarithmic scaling (both in height and time) of these selective sweep curves leads (in the case of strong selection) to a tent-like shape in the large population limit: First the logarithmic frequency of the mutant increases linearly from 0 to 1, then that of the former resident decreases from 1 to 0. For moderate selection the logarithmic frequencies develop (in the large population limit) a jump at the beginning/the end of the sweep, which takes the shape of the tent into that of a house. Our main result (proved for the Moran model) assesses the regularity of this convergence in the large population limit: It is uniform in the house's roof (phases of linear growth and decline) and \"Skorokhod $M_1$\" in the house's walls (closely around the jumps). Apart from interest in its own right, we anticipate that this result and the proof techniques will be instrumental for extending the description of clonal interference by Poissonian interacting trajectories (as it was done in Hermann et al. (2024) for strong selection) also to moderate selection.", "published": "2026-05-11T14:12:37Z", "authors": ["Florin Boenkost", "Felix Hermann", "András Tóbiás", "Anton Wakolbinger"], "categories": ["math.PR", "q-bio.PE"], "doi": "", "_query": "housing price text listing", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2409.12376v1", "title": "Prediction of Brent crude oil price based on LSTM model under the background of low-carbon transition", "abstract": "In the field of global energy and environment, crude oil is an important strategic resource, and its price fluctuation has a far-reaching impact on the global economy, financial market and the process of low-carbon development. In recent years, with the gradual promotion of green energy transformation and low-carbon development in various countries, the dynamics of crude oil market have become more complicated and changeable. The price of crude oil is not only influenced by traditional factors such as supply and demand, geopolitical conflict and production technology, but also faces the challenges of energy policy transformation, carbon emission control and new energy technology development. This diversified driving factor makes the prediction of crude oil price not only very important in economic decision-making and energy planning, but also a key issue in financial markets.In this paper, the spot price data of European Brent crude oil provided by us energy information administration are selected, and a deep learning model with three layers of LSTM units is constructed to predict the crude oil price in the next few days. The results show that the LSTM model performs well in capturing the overall price trend, although there is some deviation during the period of sharp price fluctuation. The research in this paper not only verifies the applicability of LSTM model in energy market forecasting, but also provides data support for policy makers and investors when facing the uncertainty of crude oil price.", "published": "2024-09-19T00:25:22Z", "authors": ["Yuwen Zhao", "Baojun Hu", "Sizhe Wang"], "categories": ["cs.LG", "cs.DC"], "doi": "", "_query": "housing price text listing", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2601.16488v1", "title": "Anonymous Pricing in Large Markets", "abstract": "We study revenue maximization when a seller offers $k$ identical units to ex ante heterogeneous, unit-demand buyers. While anonymous pricing can be $Θ(\\log k)$ worse than optimal in general multi-unit environments, we show that this pessimism disappears in large markets, where no single buyer accounts for a non-negligible share of optimal revenue. Under (quasi-)regularity, anonymous pricing achieves a $2+O(1/\\sqrt{k})$ approximation to the optimal mechanism; the worst-case ratio is maximized at about $2.47$ when $k=1$ and converges to $2$ as $k$ grows. This indicates that the gains from third-degree price discrimination are mild in large markets.", "published": "2026-01-23T06:28:58Z", "authors": ["Yaonan Jin", "Yingkai Li"], "categories": ["cs.GT", "econ.TH"], "doi": "", "_query": "housing price text listing", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2601.01279v3", "title": "Supracompetitive Pricing Under AI Monoculture", "abstract": "When competing sellers delegate pricing to a shared AI model, such as a large language model, correlated recommendations combined with performance-driven updates aggregating seller feedback raise a key question: can standard AI deployment practices inadvertently produce supracompetitive pricing? We develop a stylized duopoly model in which two sellers receive pricing recommendations from a shared AI characterized by two parameters: a propensity parameter capturing the model's tendency to set high prices and an output-fidelity parameter measuring alignment between this tendency and actual outputs, with propensity updated via periodic retraining on observed outcomes. We find that configuring AI models for robustness and reproducibility can lead to supracompetitive pricing via a phase transition. Below a critical output-fidelity threshold, competitive pricing is the unique stable outcome. Above it, the model exhibits bistability: both competitive and supracompetitive pricing are locally stable, with the realized outcome determined by the model's initial propensity. Supracompetitive pricing raises average prices, but occasional low-price recommendations complicate detection. With perfect output fidelity, full price coordination emerges from any interior initial propensity. For finite training batches of size $b$, when the initial propensity lies in the supracompetitive basin, the probability of supracompetitive pricing approaches 1 as $b$ increases, with the region of indeterminate outcomes shrinking at rate $O(1/\\sqrt{b})$. Any factor reducing alignment between the model's propensity and sellers' actual pricing, whether through diversifying AI providers, introducing recommendation noise, or reducing seller adherence, pushes the market toward competitive outcomes.", "published": "2026-01-03T20:38:21Z", "authors": ["Shengyu Cao", "Ming Hu"], "categories": ["econ.TH", "cs.AI", "cs.CE", "cs.CL", "cs.GT"], "doi": "", "_query": "housing price text listing", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/1211.5372v2", "title": "Drift in Transaction-Level Asset Price Models", "abstract": "We study the effect of drift in pure-jump transaction-level models for asset prices in continuous time, driven by point processes. The drift is as-sumed to arise from a nonzero mean in the efficient shock series. It follows that the drift is proportional to the driving point process itself, i.e. the cumulative number of transactions. This link reveals a mechanism by which properties of intertrade durations (such as heavy tails and long memory) can have a strong impact on properties of average returns, thereby poten-tially making it extremely difficult to determine long-term growth rates or to reliably detect an equity premium. We focus on a basic univariate model for log price, coupled with general assumptions on the point process that are satisfied by several existing flexible models, allowing for both long mem-ory and heavy tails in durations. Under our pure-jump model, we obtain the limiting distribution for the suitably normalized log price. This limiting distribution need not be Gaussian, and may have either finite variance or infinite variance. We show that the drift can affect not only the limiting dis-tribution for the normalized log price, but also the rate in the corresponding normalization. Therefore, the drift (or equivalently, the properties of dura-tions) affects the rate of convergence of estimators of the growth rate, and can invalidate standard hypothesis tests for that growth rate. As a rem-edy to these problems, we propose a new ratio statistic which behaves more", "published": "2012-11-22T20:49:34Z", "authors": ["Wen Cao", "Clifford Hurvich", "Philippe Soulier"], "categories": ["math.ST"], "doi": "", "_query": "housing price text listing", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/1906.00564v1", "title": "C2P2: A Collective Cryptocurrency Up/Down Price Prediction Engine", "abstract": "We study the problem of predicting whether the price of the 21 most popular cryptocurrencies (according to coinmarketcap.com) will go up or down on day d, using data up to day d-1. Our C2P2 algorithm is the first algorithm to consider the fact that the price of a cryptocurrency c might depend not only on historical prices, sentiments, global stock indices, but also on the prices and predicted prices of other cryptocurrencies. C2P2 therefore does not predict cryptocurrency prices one coin at a time --- rather it uses similarity metrics in conjunction with collective classification to compare multiple cryptocurrency features to jointly predict the cryptocurrency prices for all 21 coins considered. We show that our C2P2 algorithm beats out a recent competing 2017 paper by margins varying from 5.1-83% and another Bitcoin-specific prediction paper from 2018 by 16%. In both cases, C2P2 is the winner on all cryptocurrencies considered. Moreover, we experimentally show that the use of similarity metrics within our C2P2 algorithm leads to a direct improvement for 20 out of 21 cryptocurrencies ranging from 0.4% to 17.8%. Without the similarity component, C2P2 still beats competitors on 20 out of 21 cryptocurrencies considered. We show that all these results are statistically significant via a Student's t-test with p<1e-5. Check our demo at https://www.cs.dartmouth.edu/dsail/demos/c2p2", "published": "2019-06-03T04:20:46Z", "authors": ["Chongyang Bai", "Tommy White", "Linda Xiao", "V. S. Subrahmanian", "Ziheng Zhou"], "categories": ["cs.LG", "stat.ML"], "doi": "", "_query": "housing price text listing", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/1803.09967v1", "title": "Reinforcement Learning for Fair Dynamic Pricing", "abstract": "Unfair pricing policies have been shown to be one of the most negative perceptions customers can have concerning pricing, and may result in long-term losses for a company. Despite the fact that dynamic pricing models help companies maximize revenue, fairness and equality should be taken into account in order to avoid unfair price differences between groups of customers. This paper shows how to solve dynamic pricing by using Reinforcement Learning (RL) techniques so that prices are maximized while keeping a balance between revenue and fairness. We demonstrate that RL provides two main features to support fairness in dynamic pricing: on the one hand, RL is able to learn from recent experience, adapting the pricing policy to complex market environments; on the other hand, it provides a trade-off between short and long-term objectives, hence integrating fairness into the model's core. Considering these two features, we propose the application of RL for revenue optimization, with the additional integration of fairness as part of the learning procedure by using Jain's index as a metric. Results in a simulated environment show a significant improvement in fairness while at the same time maintaining optimisation of revenue.", "published": "2018-03-27T09:00:48Z", "authors": ["Roberto Maestre", "Juan Duque", "Alberto Rubio", "Juan Arévalo"], "categories": ["cs.LG", "cs.AI", "stat.ML"], "doi": "", "_query": "housing price text listing", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/1404.3160v1", "title": "Pricing of Basket Options Using Polynomial Approximations", "abstract": "In this paper we use Bernstein and Chebyshev polynomials to approximate the price of some basket options under a bivariate Black-Scholes model. The method consists in expanding the price of a univariate related contract after conditioning on the remaining underlying assets and calculating the mixed exponential-power moments of a Gaussian distribution that arise as a consequence of such approximation. Our numerical implementation on spread contracts shows the method is as accurate as a standard Monte Carlo approach at considerable lesser computational effort.", "published": "2014-04-11T17:26:08Z", "authors": ["Pablo Olivares"], "categories": ["q-fin.PR"], "doi": "", "_query": "housing price text listing", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2112.11031v1", "title": "On Cross-Lingual Retrieval with Multilingual Text Encoders", "abstract": "In this work we present a systematic empirical study focused on the suitability of the state-of-the-art multilingual encoders for cross-lingual document and sentence retrieval tasks across a number of diverse language pairs. We first treat these models as multilingual text encoders and benchmark their performance in unsupervised ad-hoc sentence- and document-level CLIR. In contrast to supervised language understanding, our results indicate that for unsupervised document-level CLIR -- a setup with no relevance judgments for IR-specific fine-tuning -- pretrained multilingual encoders on average fail to significantly outperform earlier models based on CLWEs. For sentence-level retrieval, we do obtain state-of-the-art performance: the peak scores, however, are met by multilingual encoders that have been further specialized, in a supervised fashion, for sentence understanding tasks, rather than using their vanilla 'off-the-shelf' variants. Following these results, we introduce localized relevance matching for document-level CLIR, where we independently score a query against document sections. In the second part, we evaluate multilingual encoders fine-tuned in a supervised fashion (i.e., we learn to rank) on English relevance data in a series of zero-shot language and domain transfer CLIR experiments. Our results show that supervised re-ranking rarely improves the performance of multilingual transformers as unsupervised base rankers. Finally, only with in-domain contrastive fine-tuning (i.e., same domain, only language transfer), we manage to improve the ranking quality. We uncover substantial empirical differences between cross-lingual retrieval results and results of (zero-shot) cross-lingual transfer for monolingual retrieval in target languages, which point to \"monolingual overfitting\" of retrieval models trained on monolingual data.", "published": "2021-12-21T08:10:27Z", "authors": ["Robert Litschko", "Ivan Vulić", "Simone Paolo Ponzetto", "Goran Glavaš"], "categories": ["cs.CL", "cs.IR"], "doi": "", "_query": "housing price text listing", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/1705.00284v2", "title": "Periodic strategies in optimal execution with multiplicative price impact", "abstract": "In this work we study the optimal execution problem with multiplicative price impact in algorithm trading, when an agent holds an initial position of shares of a financial asset. The inter-selling-decision times are modelled by the arrival times of a Poisson process. The criterion to be optimised consists in maximising the expected net present value of gains of the agent, and it is proved that an optimal strategy has a barrier form, depending only on the number of shares left and the level of asset price.", "published": "2017-04-30T08:06:47Z", "authors": ["Daniel Hernández-Hernández", "Harold A. Moreno-Franco", "José Luis Pérez"], "categories": ["q-fin.MF", "math.OC"], "doi": "", "_query": "housing price text listing", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/0906.5489v1", "title": "Improved and Developed Upper Bound of Price of Anarchy in Two Echelon Case", "abstract": "Price of anarchy, the performance ratio, which could characterize the loss of efficiency of the distributed supply chain management compared with the integrated supply chain management is discussed by utilizing newsvendor problem in single period which is well-known. In particular, some of remarkable distributed policies are handled, the performance ratios in each case which have been investigated in the previous works are analyzed theoretically and the tighter upper bound of price of anarchy and the lower bound are presented. Furthermore our approach is developed based on a generalized framework and a geometric interpretation of price of anarchy is appeared via the literature of convex optimization.", "published": "2009-06-30T11:50:07Z", "authors": ["T. Shinzato", "I. Kaku"], "categories": ["q-fin.GN"], "doi": "", "_query": "housing price text listing", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2305.00362v1", "title": "Electricity Price Prediction for Energy Storage System Arbitrage: A Decision-focused Approach", "abstract": "Electricity price prediction plays a vital role in energy storage system (ESS) management. Current prediction models focus on reducing prediction errors but overlook their impact on downstream decision-making. So this paper proposes a decision-focused electricity price prediction approach for ESS arbitrage to bridge the gap from the downstream optimization model to the prediction model. The decision-focused approach aims at utilizing the downstream arbitrage model for training prediction models. It measures the difference between actual decisions under the predicted price and oracle decisions under the true price, i.e., decision error, by regret, transforms it into the tractable surrogate regret, and then derives the gradients to predicted price for training prediction models. Based on the prediction and decision errors, this paper proposes the hybrid loss and corresponding stochastic gradient descent learning method to learn prediction models for prediction and decision accuracy. The case study verifies that the proposed approach can efficiently bring more economic benefits and reduce decision errors by flattening the time distribution of prediction errors, compared to prediction models for only minimizing prediction errors.", "published": "2023-04-30T00:43:26Z", "authors": ["Linwei Sang", "Yinliang Xu", "Huan Long", "Qinran Hu", "Hongbin Sun"], "categories": ["cs.LG", "eess.SY"], "doi": "", "_query": "housing price text listing", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/1705.01142v1", "title": "Machine Learning for Better Models for Predicting Bond Prices", "abstract": "Bond prices are a reflection of extremely complex market interactions and policies, making prediction of future prices difficult. This task becomes even more challenging due to the dearth of relevant information, and accuracy is not the only consideration--in trading situations, time is of the essence. Thus, machine learning in the context of bond price predictions should be both fast and accurate. In this course project, we use a dataset describing the previous 10 trades of a large number of bonds among other relevant descriptive metrics to predict future bond prices. Each of 762,678 bonds in the dataset is described by a total of 61 attributes, including a ground truth trade price. We evaluate the performance of various supervised learning algorithms for regression followed by ensemble methods, with feature and model selection considerations being treated in detail. We further evaluate all methods on both accuracy and speed. Finally, we propose a novel hybrid time-series aided machine learning method that could be applied to such datasets in future work.", "published": "2017-03-31T15:12:49Z", "authors": ["Swetava Ganguli", "Jared Dunnmon"], "categories": ["q-fin.ST", "cs.CE"], "doi": "", "_query": "housing price text listing", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2109.12477v1", "title": "Reputation dependent pricing strategy: analysis based on a Chinese C2C marketplace", "abstract": "Most online markets establish reputation systems to assist building trust between sellers and buyers. Sellers' reputations not only provide guidelines for buyers but may also inform sellers their optimal pricing strategy. In this research, we assumed two types of buyer: informed buyers and uninformed buyers. Informed buyers know more about the reputation about the seller but may incur a search cost. Then we developed a benchmark model and a competition model. We found that high reputation sellers and low reputation sellers adapt different pricing strategy depending on the informativeness of buyers and the competition among sellers. With a large proportion of informed buyers, high reputation sellers may charge lower price than low reputation sellers, which exists a negative price premium effect, in contrast to conclusions of some previous studies. Empirical findings were in consistence with our theoretical models. We collected data of five categories of products, televisions, laptops, cosmetics, shoes, and beverages, from Taobao, a leading C2C Chinese online market. Negative price premium effect was observed for TVs, laptops, and cosmetics; price premium effect was observed for beverages; no significant trend was observed for shoes. We infer product value and market complexity are the main factors of buyer informativeness.", "published": "2021-09-26T02:46:57Z", "authors": ["Zehao Chen", "Yanchen Zhu", "Tianyang Shen", "Yufan Ye"], "categories": ["econ.GN"], "doi": "", "_query": "housing price text listing", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2509.18099v1", "title": "Path-dependent, ESG-valued, option pricing in the Bachelier-Black-Scholes-Merton model", "abstract": "We extend the application of the Cherny-Shiryaev-Yor invariance principle to a unified Bachelier-Black-Scholes-Merton (BBSM) dynamic pricing model. This extension incorporates the influence of the history of the dynamics (i.e., the path dynamics) of a market index on stock price changes. We add an ESG rating component to the price of the risky asset (stock), in such a manner that the impact of the ESG rating on the stock valuation can be explored through variation in the value of a single parameter. We develop discrete, binary tree, option pricing under this extended model. Using an empirical data set of 10 stocks chosen from the Nasdaq-100, we fit the model to stock price changes and compare model-based and published European call option prices.", "published": "2025-08-26T01:58:47Z", "authors": ["Bhathiya Divelgama", "Nancy Asare Nyarko", "W. Brent Lindquist", "Svetlozar T. Rachev", "Blessing Omotade"], "categories": ["q-fin.PR"], "doi": "", "_query": "housing price text listing", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/1809.08092v2", "title": "Power Market Price Forecasting via Deep Learning", "abstract": "A study on power market price forecasting by deep learning is presented. As one of the most successful deep learning frameworks, the LSTM (Long short-term memory) neural network is utilized. The hourly prices data from the New England and PJM day-ahead markets are used in this study. First, a LSTM network is formulated and trained. Then the raw input and output data are preprocessed by unit scaling, and the trained network is tested on the real price data under different input lengths, forecasting horizons and data sizes. Its performance is also compared with other existing methods. The forecasted results demonstrate that, the LSTM deep neural network can outperform the others under different application settings in this problem.", "published": "2018-09-18T19:00:18Z", "authors": ["Yongli Zhu", "Songtao Lu", "Renchang Dai", "Guangyi Liu", "Zhiwei Wang"], "categories": ["cs.LG", "stat.ML"], "doi": "", "_query": "housing price text listing", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2601.05202v3", "title": "Stock Market Price Prediction using Neural Prophet with Deep Neural Network", "abstract": "Stock market price prediction is a significant interdisciplinary research domain that depends at the intersection of finance, statistics, and economics. Forecasting Accurately predicting stock prices has always been a focal point for various researchers. However, existing statistical approaches for time-series prediction often fail to effectively forecast the probability range of future stock prices. Hence, to solve this problem, the Neural Prophet with a Deep Neural Network (NP-DNN) is proposed to predict stock market prices. The preprocessing technique used in this research is Z-score normalization, which normalizes stock price data by removing scale differences, making patterns easier to detect. Missing value imputation fills gaps in historical data, enhancing the models use of complete information for more accurate predictions. The Multi-Layer Perceptron (MLP) learns complex nonlinear relationships among stock market prices and extracts hidden patterns from the input data, thereby creating meaningful feature representations for better prediction accuracy. The proposed NP-DNN model achieved an accuracy of 99.21% compared with other approaches using the Fused Large Language Model. Keywords: deep neural network, forecasting stock prices, multi-layer perceptron, neural prophet, stock market price prediction.", "published": "2026-01-08T18:24:22Z", "authors": ["Navin Chhibber", "Sunil Khemka", "Navneet Kumar Tyagi", "Rohit Tewari", "Bireswar Banerjee", "Piyush Ranjan"], "categories": ["cs.AI", "cs.LG"], "doi": "", "_query": "housing price text listing", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2506.13271v1", "title": "One-dimensional vs. Multi-dimensional Pricing in Blockchain Protocols", "abstract": "Blockchain transactions consume diverse resources, foremost among them storage, but also computation, communication, and others. Efficiently charging for these resources is crucial for effective system resource allocation and long-term economic viability. The prevailing approach, one-dimensional pricing, sets a single price for a linear combination of resources. However, this often leads to under-utilization when resource capacities are limited. Multi-dimensional pricing, which independently prices each resource, offers an alternative but presents challenges in price discovery. This work focuses on the welfare achieved by these two schemes. We prove that multi-dimensional pricing is superior under stable blockchain conditions. Conversely, we show that one-dimensional pricing outperforms its multi-dimensional counterpart in transient states, exhibiting faster convergence and greater computational tractability. These results highlight a critical trade-off: while multi-dimensional pricing offers efficiency gains at equilibrium, its implementation incurs costs associated with system transitions. Our findings underscore the necessity for a deeper understanding of these transient effects before widespread adoption. Finally, we propose mechanisms that aim to mitigate some of these issues, paving the way for future research.", "published": "2025-06-16T09:12:09Z", "authors": ["Aggelos Kiayias", "Elias Koutsoupias", "Giorgos Panagiotakos", "Kyriaki Zioga"], "categories": ["cs.GT"], "doi": "", "_query": "housing price text listing", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2610.12211v1", "title": "Verification with Transfer: Exact Information Frontiers and Their Price in Calls", "abstract": "A verifier that accepts or rejects whole answers reveals little: under a flat prior over $k$-bit answers, zero error needs $2^k-1$ verifications. The usual remedy is to solve related source tasks, either all first, as a curriculum does, or interleaved with verification. We price this remedy in information and in calls. With an exact verifier, the least causal information that any interleaving of source calls and $n$ verifications needs to succeed with probability $s$ is a list rate-distortion function, attained by one observation before any verification. It lower-bounds the expected number of binary source calls, which designed sources meet within $1+\\log_25$ calls for unique answers and within a logarithmic term in general, where no additive constant suffices. With an exact verifier and fixed sources, moving every call before the first verification preserves all hard caps on calls, although interleaving can save unboundedly many expected calls; under a noisy verifier, source-first protocols can lose unbounded factors in information and in error. For linear banks over $\\mathbb{F}_2$, optimal accuracy has a closed form, and after a polynomial-time reduction the budget profile is computable in time $2^{O(h^2)}\\operatorname{poly}(J,k+h)$ for $J$ sources and nuisance dimension $h$. In these banks, for zero error under a hard cap, the calls beyond the rounded-up information price are exactly those spent on nuisance. Every numbered result apart from two clauses about the planner is machine-checked in Lean 4, assuming two published results. Used as a ruler, the frontier shows a small transformer using all delivered bits at latent dimension $5$ and none at $11$ within fixed training budgets; in a test with predictions recorded before training, low XOR degree of the target bits did not suffice for their use.", "published": "2026-10-08T16:00:23Z", "authors": ["Hazar Yueksel"], "categories": ["cs.LG", "cs.CR", "cs.IT", "stat.ML"], "doi": "", "_query": "housing price text listing", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2507.11480v3", "title": "Pricing energy spread options with variance gamma-driven Ornstein-Uhlenbeck dynamics", "abstract": "We consider the pricing of energy spread options for spot prices following an exponential Ornstein-Uhlenbeck process driven by a sum of independent multivariate variance gamma processes, which gives rise to mean-reverting, infinite activity price dynamics. Within this class of driving processes, the Esscher transform is used to obtain an equivalent martingale measure with a focus on the weak variance alpha-gamma process. By deriving an analytical formula for the cumulant generating function of the innovation term, we obtain a pricing formula for forwards and apply the FFT method of Hurd and Zhou to price spread options. Lastly, we demonstrate how the model should be both estimated on energy prices under the real world measure and calibrated on forward or call prices, and provide numerical results for the pricing of spread options.", "published": "2025-07-15T16:48:24Z", "authors": ["Tim Leung", "Kevin W. Lu"], "categories": ["q-fin.MF"], "doi": "", "_query": "housing price text listing", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2501.18300v2", "title": "Master List of Examples in Complexity Theory of Finite Semigroup Theory", "abstract": "This document gives a list of finite semigroups that are interesting from the point of view of Krohn-Rhodes complexity theory. The list will be expanded and updates as \"time goes by\".", "published": "2025-01-30T12:21:59Z", "authors": ["Stuart Margolis", "John Rhodes"], "categories": ["math.GR"], "doi": "", "_query": "housing price text listing", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/1703.05709v4", "title": "Incorporating statistical model error into the calculation of acceptability prices of contingent claims", "abstract": "The determination of acceptability prices of contingent claims requires the choice of a stochastic model for the underlying asset price dynamics. Given this model, optimal bid and ask prices can be found by stochastic optimization. However, the model for the underlying asset price process is typically based on data and found by a statistical estimation procedure. We define a confidence set of possible estimated models by a nonparametric neighborhood of a baseline model. This neighborhood serves as ambiguity set for a multi-stage stochastic optimization problem under model uncertainty. We obtain distributionally robust solutions of the acceptability pricing problem and derive the dual problem formulation. Moreover, we prove a general large deviations result for the nested distance, which allows to relate the bid and ask prices under model ambiguity to the quality of the observed data.", "published": "2017-03-16T16:34:18Z", "authors": ["Martin Glanzer", "Georg Ch. Pflug", "Alois Pichler"], "categories": ["q-fin.PR"], "doi": "10.1007/s10107-018-1352-7", "_query": "housing price text listing", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2504.14053v2", "title": "Review Text as a Leading Indicator of Displayed Reputation in Platform Rating Systems: Evidence from 34 U.S. Short-Term Rental Markets", "abstract": "Rating systems on accommodation platforms suffer from a familiar problem: nearly every listing displays a nearly perfect score, so the number that is supposed to separate good listings from bad ones barely varies. Whether the review text accumulating beneath those scores still carries usable information is an open question. I ask a dynamic version of it: does the text guests have already written predict where a listing's displayed rating moves next? Treating text and ratings as parallel channels that aggregate guest experience at different speeds, I construct a prespecified sentiment index from the complete review history of each listing in a two-wave panel of more than two hundred thousand listings across 34 U.S. markets. Because the broader project had explored these data before, I locked the model and its falsification checks in advance and reserved half of the markets, untouched, for a single confirmatory estimation. On those held-out markets, warmer past text predicts a small but precisely estimated upward movement of the displayed rating over the following year. Listings that received no new reviews show no such movement, the association survives host fixed effects, and no single market drives it. The results indicate that the platform's rating aggregation discards information its own review text retains. I discuss what this leading-indicator property implies for the design of reputation displays. The text index is a defined dictionary-based instrument that has not been validated against human judgment, and I state that boundary plainly.", "published": "2025-04-18T19:52:24Z", "authors": ["Ali Safari"], "categories": ["cs.CY", "cs.AI", "cs.CL", "cs.LG"], "doi": "", "_query": "housing price text listing", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/1403.0851v1", "title": "Asset Prices and Risk Aversion", "abstract": "The standard asset pricing models (the CCAPM and the Epstein-Zin non-expected utility model) counterintuitively predict that equilibrium asset prices can rise if the representative agent's risk aversion increases. If the income effect, which implies enhanced saving as a result of an increase in risk aversion, dominates the substitution effect, which causes the representative agent to reallocate his portfolio in favour of riskless assets, the demand for securities increases. Thus, asset prices are forced to rise when the representative agent is more risk adverse. By disentangling risk aversion and intertemporal substituability, we demonstrate that the risky asset price is an increasing function of the coefficient of risk aversion only if the elasticity of intertemporal substitution (EIS) exceeds unity. This result, which was first proved par Epstein (1988) in a stationary economy setting with a constant risk aversion, is shown to hold true for non-stationary economies with a variable or constant risk aversion coefficient. The conclusion is that the EIS probably exceeds unity.", "published": "2014-03-04T16:45:52Z", "authors": ["Dominique Pepin"], "categories": ["q-fin.CP", "q-fin.GN", "q-fin.PR"], "doi": "", "_query": "housing price text listing", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/0709.2721v2", "title": "Pricing, Competition, and Routing for Selfish and Strategic Nodes in Multi-hop Relay Networks", "abstract": "We study a pricing game in multi-hop relay networks where nodes price their services and route their traffic selfishly and strategically. In this game, each node (1) announces pricing functions which specify the payments it demands from its respective customers depending on the amount of traffic they route to it and (2) allocates the total traffic it receives to its service providers. The profit of a node is the difference between the revenue earned from servicing others and the cost of using others' services. We show that the socially optimal routing of such a game can always be induced by an equilibrium where no node can increase its profit by unilaterally changing its pricing functions or routing decision. On the other hand, there may also exist inefficient equilibria. We characterize the loss of efficiency by deriving the price of anarchy at inefficient equilibria. We show that the price of anarchy is finite for oligopolies with concave marginal cost functions, while it is infinite for general topologies and cost functions.", "published": "2007-09-17T20:52:29Z", "authors": ["Yufang Xi", "Edmund M. Yeh"], "categories": ["cs.GT"], "doi": "", "_query": "housing price text listing", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/1310.7890v1", "title": "List Sort: A New Approach for Sorting List to Reduce Execution Time", "abstract": "In this paper we are proposing a new sorting algorithm, List Sort algorithm, is based on the dynamic memory allocation. In this research study we have also shown the comparison of various efficient sorting techniques with List sort. Due the dynamic nature of the List sort, it becomes much more fast than some conventional comparison sorting techniques and comparable to Quick Sort and Merge Sort. List sort takes the advantage of the data which is already sorted either in ascending order or in descending order.", "published": "2013-10-26T20:09:13Z", "authors": ["Adarsh Kumar Verma", "Prashant Kumar"], "categories": ["cs.DS"], "doi": "", "_query": "housing price text listing", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/1404.7356v1", "title": "Analysis of a decision model in the context of equilibrium pricing and order book pricing", "abstract": "An agent-based model for financial markets has to incorporate two aspects: decision making and price formation. We introduce a simple decision model and consider its implications in two different pricing schemes. First, we study its parameter dependence within a supply-demand balance setting. We find realistic behavior in a wide parameter range. Second, we embed our decision model in an order book setting. Here we observe interesting features which are not present in the equilibrium pricing scheme. In particular, we find a nontrivial behavior of the order book volumes which reminds of a trend switching phenomenon. Thus, the decision making model alone does not realistically represent the trading and the stylized facts. The order book mechanism is crucial.", "published": "2014-04-29T13:35:33Z", "authors": ["Daniel C. Wagner", "Thilo A. Schmitt", "Rudi Schäfer", "Thomas Guhr", "Dietrich E. Wolf"], "categories": ["q-fin.TR"], "doi": "10.1016/j.physa.2014.08.013", "_query": "housing price text listing", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/1812.06237v1", "title": "Walking Through an Exploded Star: Rendering Supernova Remnant Cassiopeia A into Virtual Reality", "abstract": "NASA and other astrophysical data of the Cassiopeia A supernova remnant have been rendered into a three-dimensional virtual reality (VR) and augmented reality (AR) program, the first of its kind. This data-driven experience of a supernova remnant allows viewers to walk inside the leftovers from the explosion of a massive star, select the parts of the supernova remnant to engage with, and access descriptive texts on what the materials are. The basis of this program is a unique 3D model of the 340-year old remains of a stellar explosion, made by combining data from the NASA Chandra X-ray Observatory, Spitzer Space Telescope, and ground-based facilities. A collaboration between the Smithsonian Astrophysical Observatory and Brown University allowed the 3D astronomical data collected on Cassiopeia A to be featured in the VR/AR program, which is an innovation in digital technologies with public, education, and research-based impacts.", "published": "2018-12-15T05:29:04Z", "authors": ["Kimberly K. Arcand", "Elaine Jiang", "Sara Price", "Megan Watzke", "Tom Sgouros", "Peter Edmonds"], "categories": ["astro-ph.IM", "astro-ph.HE", "cs.HC"], "doi": "", "_query": "housing price text listing", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2003.06928v2", "title": "Low-dimensional approximations of high-dimensional asset price models", "abstract": "We consider high-dimensional asset price models that are reduced in their dimension in order to reduce the complexity of the problem or the effect of the curse of dimensionality in the context of option pricing. We apply model order reduction (MOR) to obtain a reduced system. MOR has been previously studied for asymptotically stable controlled stochastic systems with zero initial conditions. However, stochastic differential equations modeling price processes are uncontrolled, have non-zero initial states and are often unstable. Therefore, we extend MOR schemes and combine ideas of techniques known for deterministic systems. This leads to a method providing a good pathwise approximation. After explaining the reduction procedure, the error of the approximation is analyzed and the performance of the algorithm is shown conducting several numerical experiments. Within the numerics section, the benefit of the algorithm in the context of option pricing is pointed out.", "published": "2020-03-15T21:43:39Z", "authors": ["Martin Redmann", "Christian Bayer", "Pawan Goyal"], "categories": ["math.PR", "math.NA"], "doi": "", "_query": "housing price text listing", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/math/0605064v1", "title": "Pricing and hedging in incomplete markets with coherent risk", "abstract": "We propose a pricing technique based on coherent risk measures, which enables one to get finer price intervals than in the No Good Deals pricing. The main idea consists in splitting a liability into several parts and selling these parts to different agents. The technique is closely connected with the convolution of coherent risk measures and equilibrium considerations. Furthermore, we propose a way to apply the above technique to the coherent estimation of the Greeks.", "published": "2006-05-02T14:59:24Z", "authors": ["Alexander S. Cherny", "Dilip B. Madan"], "categories": ["math.PR", "q-fin.RM"], "doi": "", "_query": "housing price text listing", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2306.03052v1", "title": "Forecasting Crude Oil Prices Using Reservoir Computing Models", "abstract": "Accurate crude oil price prediction is crucial for financial decision-making. We propose a novel reservoir computing model for forecasting crude oil prices. It outperforms popular deep learning methods in most scenarios, as demonstrated through rigorous evaluation using daily closing price data from major stock market indices. Our model's competitive advantage is further validated by comparing it with recent deep-learning approaches. This study introduces innovative reservoir computing models for predicting crude oil prices, with practical implications for financial practitioners. By leveraging advanced techniques, market participants can enhance decision-making and gain valuable insights into crude oil market dynamics.", "published": "2023-06-05T17:23:26Z", "authors": ["Kaushal Kumar"], "categories": ["cs.LG"], "doi": "", "_query": "housing price text listing", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/1012.2124v1", "title": "Discrete Price Updates Yield Fast Convergence in Ongoing Markets with Finite Warehouses", "abstract": "This paper shows that in suitable markets, even with out-of-equilibrium trade allowed, a simple price update rule leads to rapid convergence toward the equilibrium. In particular, this paper considers a Fisher market repeated over an unbounded number of time steps, with the addition of finite sized warehouses to enable non-equilibrium trade. The main result is that suitable tatonnement style price updates lead to convergence in a significant subset of markets satisfying the Weak Gross Substitutes property. Throughout this process the warehouse are always able to store or meet demand imbalances (the needed capacity depends on the initial imbalances). Finally, our price update rule is robust in a variety of regards: 1. The updates for each good depend only on information about that good (its current price, its excess demand since its last update) and occur asynchronously from updates to other prices. 2. The process is resilient to error in the excess demand data. 3. Likewise, the process is resilient to discreteness, i.e. a limit to divisibility, both of goods and money.", "published": "2010-12-09T21:10:53Z", "authors": ["Richard Cole", "Lisa Fleischer", "Ashish Rastogi"], "categories": ["cs.GT"], "doi": "", "_query": "housing price text listing", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2609.36257v1", "title": "When Hedging Changes the Payoff: Option Replication with Price Impact and Execution Costs", "abstract": "Hedging a derivative by trading the underlying asset changes the payoff that the hedging intended to replicate. We study this phenomenon when trading generates price impact and execution costs. In a binomial model, we characterize replication through a fixed-point equation. In continuous time, we derive a nonlinear pricing PDE whose implicit terminal condition captures the nature of the moving target problem of the hedger. For monotone convex Lipschitz payoffs (such as calls and puts) we establish exact replication under midpoint execution costs. Numerical experiments illustrate: (i) how price impact shifts the effective strike, (ii) the non-linear dependence of the option price on the number of contracts, (iii) how execution costs smooth terminal holdings, (iv) the extent to which the hedger's own trading can bring an otherwise worthless option into the money, and (v) we explain the spread and the shape of the limit order book in the options market based on the price impact and the shape of the limit order book of the underlying.", "published": "2026-09-28T20:47:12Z", "authors": ["David Itkin", "Leandro Sánchez-Betancourt"], "categories": ["q-fin.MF", "math.PR", "q-fin.TR"], "doi": "", "_query": "housing price text listing", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2112.02368v1", "title": "A stochastic control approach to bid-ask price modelling", "abstract": "This paper develops a model for the bid and ask prices of a European type asset by formulating a stochastic control problem. The state process is governed by a modified geometric Brownian motion whose drift and diffusion coefficients depend on a Markov chain. A Girsanov theorem for Markov chains is implemented for the change of coefficients, including the diffusion coefficient which cannot be changed by the usual Girsanov theorem for Brownian motion. The price of a European type asset is then determined using an Esscher transform and a system of partial differential equations. A dynamic programming principle and a maximum/minimum principle associated with the stochastic control problem are then derived to model bid and ask prices. These prices are not quotes of traders or market makers but represent estimates in our model on which reasonable quantities could be traded.", "published": "2021-12-04T16:05:52Z", "authors": ["Engel John C. Dela Vega", "Robert J. Elliott"], "categories": ["q-fin.MF"], "doi": "", "_query": "housing price text listing", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/1410.0384v3", "title": "Indifference pricing for Contingent Claims: Large Deviations Effects", "abstract": "We study utility indifference prices and optimal purchasing quantities for a non-traded contingent claim in an incomplete semi-martingale market with vanishing hedging errors. We make connections with the theory of large deviations. We concentrate on sequences of semi-complete markets where in the $n^{th}$ market, the claim $B_n$ admits the decomposition $B_n = D_n+Y_n$. Here, $D_n$ is replicable by trading in the underlying assets $S_n$, but $Y_n$ is independent of $S_n$. Under broad conditions, we may assume that $Y_n$ vanishes in accordance with a large deviations principle as $n$ grows. In this setting, for an exponential investor, we identify the limit of the average indifference price $p_n(q_n)$, for $q_n$ units of $B_n$, as $n\\rightarrow \\infty$. We show that if $|q_n|\\rightarrow\\infty$, the limiting price typically differs from the price obtained by assuming bounded positions $\\sup_n|q_n|<\\infty$, and the difference is explicitly identifiable using large deviations theory. Furthermore, we show that optimal purchase quantities occur at the large deviations scaling, and hence large positions arise endogenously in this setting.", "published": "2014-10-01T20:58:14Z", "authors": ["Scott Robertson", "Konstantinos Spiliopoulos"], "categories": ["math.PR", "q-fin.MF"], "doi": "", "_query": "housing price text listing", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/cond-mat/9808168v1", "title": "Pricing defaultable debt: some exact results", "abstract": "In this letter, I consider the issue of pricing risky debt by following Merton's approach. I generalize Merton's results to the case where the interest rate is modeled by the CIR term structure. Exact closed forms are provided for the risky debt's price.", "published": "1998-08-16T18:22:23Z", "authors": ["D. F. Wang"], "categories": ["cond-mat.stat-mech", "q-fin.PR"], "doi": "", "_query": "housing price text listing", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2602.00188v1", "title": "Learning to Price: Interpretable Attribute-Level Models for Dynamic Markets", "abstract": "Dynamic pricing in high-dimensional markets poses fundamental challenges of scalability, uncertainty, and interpretability. Existing low-rank bandit formulations learn efficiently but rely on latent features that obscure how individual product attributes influence price. We address this by introducing an interpretable \\emph{Additive Feature Decomposition-based Low-Dimensional Demand (\\textbf{AFDLD}) model}, where product prices are expressed as the sum of attribute-level contributions and substitution effects are explicitly modeled. Building on this structure, we propose \\textbf{ADEPT} (Additive DEcomposition for Pricing with cross-elasticity and Time-adaptive learning)-a projection-free, gradient-free online learning algorithm that operates directly in attribute space and achieves a sublinear regret of $\\tilde{\\mathcal{O}}(\\sqrt{d}T^{3/4})$. Through controlled synthetic studies and real-world datasets, we show that ADEPT (i) learns near-optimal prices under dynamic market conditions, (ii) adapts rapidly to shocks and drifts, and (iii) yields transparent, attribute-level price explanations. The results demonstrate that interpretability and efficiency in autonomous pricing agents can be achieved jointly through structured, attribute-driven representations.", "published": "2026-01-30T07:52:07Z", "authors": ["Srividhya Sethuraman", "Chandrashekar Lakshminarayanan"], "categories": ["cs.AI", "cs.LG"], "doi": "10.65109/FJUA8337", "_query": "housing price text listing", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/1809.06736v4", "title": "On expansions for the Black-Scholes prices and hedge parameters", "abstract": "We derive new formulas for the price of the European call and put options in the Black-Scholes model, under the form of uniformly convergent series generalizing previously known approximations. We also provide precise boundaries for the convergence speed and apply the results to the calculation of hedge parameters (Greeks).", "published": "2018-09-17T12:30:38Z", "authors": ["Jean-Philippe Aguilar"], "categories": ["q-fin.PR"], "doi": "10.1016/j.jmaa.2019.06.001", "_query": "housing price text listing", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2012.02966v1", "title": "Assessing the effects of seasonal tariff-rate quotas on vegetable prices in Switzerland", "abstract": "Causal estimation of the short-term effects of tariff-rate quotas (TRQs) on vegetable producer prices is hampered by the large variety and different growing seasons of vegetables and is therefore rarely performed. We quantify the effects of Swiss seasonal TRQs on domestic producer prices of a variety of vegetables based on a difference-in-differences estimation using a novel dataset of weekly producer prices for Switzerland and neighbouring countries. We find that TRQs increase prices of most vegetables by more than 20% above the prices in neighbouring countries during the main harvest time for most vegetables and even more than 50% for some vegetables. The effects are stronger for more perishable vegetables and for conventionally produced ones compared with organic vegetables. However, we do not find clear-cut effects of TRQs on the week-to-week price volatility of vegetables although the overall lower price volatility in Switzerland compared with neighbouring countries might be a result of the TRQ system in place.", "published": "2020-12-05T07:24:52Z", "authors": ["Daria Loginova", "Marco Portmann", "Martin Huber"], "categories": ["econ.GN", "stat.AP"], "doi": "", "_query": "housing price text listing", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/1109.5418v1", "title": "Fixed Price of Groups and Percolation", "abstract": "We prove that for every finitely generated group $Γ$, at least one of the following holds: (1) $Γ$ has fixed price; (2) each of its Cayley graphs $G$ has infinitely many infinite clusters for some Bernoulli percolation on $G$.", "published": "2011-09-25T23:55:18Z", "authors": ["Russell Lyons"], "categories": ["math.DS", "math.PR"], "doi": "", "_query": "housing price text listing", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2511.05512v1", "title": "Estimating the Impact of the Bitcoin Halving on Its Price Using Synthetic Control", "abstract": "The third Bitcoin halving that took place in May 2020 cut down the mining reward from 12.5 to 6.25 BTC per block and thus slowed down the rate of issuance of new Bitcoins, making it more scarce. The fourth and most recent halving happened in April 2024, cutting the block reward further to 3.125 BTC. If the demand did not decrease simultaneously after these halvings, then the neoclassical economic theory posits that the price of Bitcoin should have increased due to the halving. But did it, in fact, increase for that reason, or is this a post hoc fallacy? This paper uses synthetic control to construct a weighted Bitcoin that is different from its counterpart in one aspect - it did not undergo halving. Comparing the price trajectory of the actual and the simulated Bitcoins, I find evidence of a positive effect of the 2024 Bitcoin halving on its price three months later. The magnitude of this effect is one fifth of the total percentage change in the price of Bitcoin during the study period - from April 2, 2023, to July 21, 2024 (17 months). The second part of the study fails to obtain a statistically significant and robust causal estimate of the effect of the 2020 Bitcoin halving on Bitcoin's price. This is the first paper analyzing the effect of halving causally, building on the existing body of correlational research.", "published": "2025-10-25T05:00:23Z", "authors": ["Vladislav Virtonen"], "categories": ["econ.GN", "econ.EM", "stat.AP"], "doi": "", "_query": "housing price text listing", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/1912.00840v3", "title": "Distributional Locational Marginal Pricing Based Optimization for Electric Vehicle Charging Management", "abstract": "Electric power generation, transmission, and distribution systems are attracting a large amount of interest from researchers with the development of the smart grid technologies. A smart grid aims at effective control and conditioning of the distribution of electricity. Pricing signal based distribution system are seen as one of the novel ways to achieve control in a smart grid. In our work, we propose to use a pricing signal modeled after the locational marginal price in the transmission system to locally provide price data to the users. The formulation and implementation of the distributional locational marginal price (DLMP) are achieved to develop a fair pricing model. The work is further practically implemented to a grid with Electric Vehicles in addition to the conventional load. The increasing popularity of EVs because of their ability to reduce greenhouse gas (GHG) emissions will pose a greater challenge in terms of demand on the power grid. We propose to use the DLMP modeling to alleviate congestion in the power grid and develop an optimal charging schedule for EVs.", "published": "2019-12-02T15:01:17Z", "authors": ["Abhishek Tyagi", "Ram Bhagat"], "categories": ["eess.SY"], "doi": "", "_query": "housing price text listing", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2512.11252v1", "title": "Personalized Pricing in Social Networks with Individual and Group Fairness Considerations", "abstract": "Personalized pricing assigns different prices to customers for the same product based on customer-specific features to improve retailer revenue. However, this practice often raises concerns about fairness at both the individual and group levels. At the individual level, a customer may perceive unfair treatment if he/she notices being charged a higher price than others. At the group level, pricing disparities can result in discrimination against certain protected groups, such as those defined by gender or race. Existing studies on fair pricing typically address individual and group fairness separately. This paper bridges the gap by introducing a new formulation of the personalized pricing problem that incorporates both dimensions of fairness in social network settings. To solve the problem, we propose FairPricing, a novel framework based on graph neural networks (GNNs) that learns a personalized pricing policy using customer features and network topology. In FairPricing, individual perceived unfairness is captured through a penalty on customer demand, and thus the profit objective, while group-level discrimination is mitigated using adversarial debiasing and a price regularization term. Unlike existing optimization-based personalized pricing, which requires re-optimization whenever the network updates, the pricing policy learned by FairPricing assigns personalized prices to all customers in an updated network based on their features and the new network structure, thereby generalizing to network changes. Extensive experimental results show that FairPricing achieves high profitability while improving individual fairness perceptions and satisfying group fairness requirements.", "published": "2025-12-12T03:20:29Z", "authors": ["Zeyu Chen", "Bintong Chen", "Wei Qian", "Jing Huang"], "categories": ["cs.CY", "cs.SI"], "doi": "", "_query": "housing price text listing", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/1506.01467v3", "title": "A system of non-local parabolic PDE and application to option pricing", "abstract": "This paper includes a proof of well-posedness of an initial-boundary value problem involving a system of degenerate non-local parabolic PDE which naturally arises in the study of derivative pricing in a generalized market model. In a semi-Markov modulated GBM model the locally risk minimizing price function satisfies a special case of this problem. We study the well-posedness of the problem via a Volterra integral equation of second kind. A probabilistic approach, in particular the method of conditioning on stopping times is used for showing uniqueness.", "published": "2015-06-04T05:01:41Z", "authors": ["Anindya Goswami", "Jeeten Patel", "Poorva Shevgaonkar"], "categories": ["math.AP", "math.PR", "q-fin.PR"], "doi": "10.1080/07362994.2016.1189340", "_query": "housing price text listing", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/1607.03802v1", "title": "Continuous-time Marginal Pricing of Power Trajectories in Power Systems", "abstract": "In this paper we formulate of the Economic Dispatch (ED) problem in Power Systems in continuous time and include in it ramping constraints to derive an expression of the price that reflects some important inter-temporal constraints of the power units. The motivation for looking at this problem is the scarcity of ramping resources and their increasing importance motivated by the variability of power resources, particularly due to the addition of solar power, which exacerbates the need of fast ramping units in the early morning and early evening hours. We show that the solution for the marginal price can be found through Euler-Lagrange equations and we argue that this price signal better reflects the market value of power in the presence of significant ramps in net-load.", "published": "2016-07-11T21:59:50Z", "authors": ["Anna Scaglione"], "categories": ["math.OC"], "doi": "", "_query": "housing price text listing", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2411.17832v2", "title": "SVGDreamer++: Advancing Editability and Diversity in Text-Guided SVG Generation", "abstract": "Recently, text-guided scalable vector graphics (SVG) synthesis has demonstrated significant potential in domains such as iconography and sketching. However, SVGs generated from existing Text-to-SVG methods often lack editability and exhibit deficiencies in visual quality and diversity. In this paper, we propose a novel text-guided vector graphics synthesis method to address these limitations. To enhance the editability of output SVGs, we introduce a Hierarchical Image VEctorization (HIVE) framework that operates at the semantic object level and supervises the optimization of components within the vector object. This approach facilitates the decoupling of vector graphics into distinct objects and component levels. Our proposed HIVE algorithm, informed by image segmentation priors, not only ensures a more precise representation of vector graphics but also enables fine-grained editing capabilities within vector objects. To improve the diversity of output SVGs, we present a Vectorized Particle-based Score Distillation (VPSD) approach. VPSD addresses over-saturation issues in existing methods and enhances sample diversity. A pre-trained reward model is incorporated to re-weight vector particles, improving aesthetic appeal and enabling faster convergence. Additionally, we design a novel adaptive vector primitives control strategy, which allows for the dynamic adjustment of the number of primitives, thereby enhancing the presentation of graphic details. Extensive experiments validate the effectiveness of the proposed method, demonstrating its superiority over baseline methods in terms of editability, visual quality, and diversity. We also show that our new method supports up to six distinct vector styles, capable of generating high-quality vector assets suitable for stylized vector design and poster design. Code and demo will be released at: http://ximinng.github.io/SVGDreamerV2Project/", "published": "2024-11-26T19:13:38Z", "authors": ["Ximing Xing", "Qian Yu", "Chuang Wang", "Haitao Zhou", "Jing Zhang", "Dong Xu"], "categories": ["cs.CV", "cs.AI"], "doi": "", "_query": "housing price text listing", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2605.12099v1", "title": "Bayesian Dynamic Modeling of Realized Volatility in Financial Asset Price Forecasting", "abstract": "We present a new class of Bayesian dynamic models for bivariate price-realized volatility time series in financial forecasting. A novel dynamic gamma process model adopted for realized volatility is integrated with traditional Bayesian dynamic linear models (DLMs) for asset price series. This represents reduced-form volatility leverage and feedback effects through use of realized volatility proxies in conditional DLMs for prices or returns, coupled with the synthesis of higher frequency data to track and anticipate volatility fluctuations. Analysis is computationally straightforward, extending conjugate-form Bayesian analyses for sequential filtering and model monitoring with simple and direct simulation for forecasting. A main applied setting is equity return forecasting with daily prices and realized volatility from high-frequency, intraday data. Detailed empirical studies of multiple S&P sector ETFs highlight the improvements achievable in asset price forecasting relative to standard models and deliver contextual insights on the nature and practical relevance of volatility leverage and feedback effects. The analytic structure and negligible extra computational cost will enable scaling to higher dimensions for multivariate price series forecasting for decouple/recouple portfolio construction and risk management applications.", "published": "2026-05-12T13:15:09Z", "authors": ["Patrick Woitschig", "Mike West"], "categories": ["stat.ME", "q-fin.ST"], "doi": "", "_query": "housing price text listing", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2503.24324v2", "title": "Mitigating Financial Risk from Climate-Induced Agricultural Price Volatility", "abstract": "Agricultural price volatility, driven by market dynamics and meteorological factors such as temperature and precipitation, poses challenges for sustainable finance, planning, and policy. This study analyzes the impact of climate on crop price volatility for soybean in Madhya Pradesh (India) and Illinois (US), rice in Assam (India), wheat in North Dakota (US), cotton in Gujarat (India), and corn in Iowa (US). Using CMIP6 climate projections from the Copernicus Climate Change Service, we examine historical climate patterns and evaluate two future scenarios: SSP2-4.5 (moderate) and SSP5-8.5 (severe). We estimate conditional price volatility using the Exponential Generalized Autoregressive Conditional Heteroskedasticity (EGARCH) model, and forecast this volatility with a Seasonal Autoregressive Integrated Moving Average with Exogenous Regressors (SARIMAX) model that incorporates meteorological variables. Finally, we apply the Black-Scholes framework to evaluate the cost of put-option-based insurance, which provides protection to farmers against adverse price drops linked to climate change. Our results highlight the role of meteorological data in improving agricultural risk modelling, enabling better design of insurance mechanisms, price stabilization tools, and sustainable policy interventions under climate uncertainty.", "published": "2025-03-31T17:11:00Z", "authors": ["Sourish Das", "Sudeep Shukla", "Abbinav Sankar Kailasam", "Anish Rai", "Sejal Garg", "Anirban Chakraborti"], "categories": ["stat.AP", "econ.GN", "physics.soc-ph", "q-fin.RM"], "doi": "", "_query": "housing price text listing", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/1807.02110v1", "title": "TextTopicNet - Self-Supervised Learning of Visual Features Through Embedding Images on Semantic Text Spaces", "abstract": "The immense success of deep learning based methods in computer vision heavily relies on large scale training datasets. These richly annotated datasets help the network learn discriminative visual features. Collecting and annotating such datasets requires a tremendous amount of human effort and annotations are limited to popular set of classes. As an alternative, learning visual features by designing auxiliary tasks which make use of freely available self-supervision has become increasingly popular in the computer vision community. In this paper, we put forward an idea to take advantage of multi-modal context to provide self-supervision for the training of computer vision algorithms. We show that adequate visual features can be learned efficiently by training a CNN to predict the semantic textual context in which a particular image is more probable to appear as an illustration. More specifically we use popular text embedding techniques to provide the self-supervision for the training of deep CNN. Our experiments demonstrate state-of-the-art performance in image classification, object detection, and multi-modal retrieval compared to recent self-supervised or naturally-supervised approaches.", "published": "2018-07-04T21:44:09Z", "authors": ["Yash Patel", "Lluis Gomez", "Raul Gomez", "Marçal Rusiñol", "Dimosthenis Karatzas", "C. V. Jawahar"], "categories": ["cs.CV"], "doi": "", "_query": "housing price text listing", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2301.13575v2", "title": "Indifference pricing of pure endowments in a regime-switching market model", "abstract": "In this paper, we study the exponential utility indifference pricing of pure endowment policies within a stochastic-factor model for an insurer who also invests in a financial market. Our framework incorporates a hazard rate modeled as an observable diffusion process, while the risky asset price follows a jump-diffusion process driven by a continuous-time finite-state Markov chain, effectively capturing different economic regimes. Using the classical stochastic control approach based on the Hamilton-Jacobi-Bellman equation, we derive optimal investment strategies with and without the insurance derivative and characterize the indifference price as a classical solution to a linear partial differential equation (PDE). Additionally, we provide a probabilistic representation of the indifference price via an extension of the Feynman-Kac formula and show that it satisfies a suitable backward PDE. Finally, some numerical experiments are conducted to perform sensitivity analyses, highlighting the impact of key model parameters.", "published": "2023-01-31T12:00:27Z", "authors": ["Alessandra Cretarola", "Benedetta Salterini"], "categories": ["q-fin.PM"], "doi": "", "_query": "housing price text listing", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2606.13419v1", "title": "Realtime price impact detection", "abstract": "An important question for an algo trader working an order is to understand if their actions are moving the market against them -- i.e., causing market impact. The conventional answer usually is one of two: (i) monitor price slippage in real-time, potentially reducing adverse activity with increased slippage, or (ii) do away with dynamic trading adjustments and rely on semi-static rules based on ex-post estimates of slippage over a large sample of events. Realtime monitoring fails because reliably estimating slippage is statistically expensive -- it requires hundreds of fills before it can be told apart from background volatility. More fundamentally however, it does not establish causality. Observed adverse price moves may be caused by the trader's own actions, or by an unrelated participant competing for the same liquidity and capturing the same alpha. The optimal response (say, slow down vs.\\ speed up) is opposite in the two cases. We propose a method that detects price impact, on a per-action basis, by measuring the timing synchronicity between a trader's actions and subsequent adverse market events. The method at heart is a test for statistical \\emph{surprise} in the timing of adverse events post trader action. We must be clear in that we do make a leap of faith here and assume that surprisingly fast adverse market events are evidence of causation and that the action triggered them -- a direct signature of impact and information leakage. Validating it requires real execution data; we set out the empirical tests that would do so.", "published": "2026-06-11T14:49:42Z", "authors": ["Ilija I Zovko"], "categories": ["q-fin.TR"], "doi": "", "_query": "housing price text listing", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/1610.01450v9", "title": "Asset Pricing with Random Volatility", "abstract": "This paper proposes to model asset price dynamics with a mixture of diffusion processes where the instantaneous volatility of the underlying diffusion process contains a random vector. The marginal probability distributions of the proposed process can match exactly the risk-neutral distributions implied by both spot vanilla options and forward start options. We can also derive the explicit pricing formula for derivatives that have a closed-form solution under Generalized Geometric Brownian Motion.", "published": "2016-10-05T14:39:02Z", "authors": ["Xin Liu"], "categories": ["q-fin.MF", "q-fin.PR"], "doi": "", "_query": "housing price text listing", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/1905.01526v5", "title": "LP-based Approximation for Personalized Reserve Prices", "abstract": "We study the problem of computing data-driven personalized reserve prices in eager second price auctions without having any assumption on valuation distributions. Here, the input is a data-set that contains the submitted bids of $n$ buyers in a set of auctions and the problem is to return personalized reserve prices $\\textbf r$ that maximize the revenue earned on these auctions by running eager second price auctions with reserve $\\textbf r$. For this problem, which is known to be APX-hard, we present a novel LP formulation and a rounding procedure which achieves a $(1+2(\\sqrt{2}-1)e^{\\sqrt{2}-2})^{-1} \\approx 0.684$-approximation. This improves over the $\\frac{1}{2}$-approximation algorithm due to Roughgarden and Wang. We show that our analysis is tight for this rounding procedure. We also bound the integrality gap of the LP, which shows that it is impossible to design an algorithm that yields an approximation factor larger than $0.828$ with respect to this LP.", "published": "2019-05-04T17:14:38Z", "authors": ["Mahsa Derakhshan", "Negin Golrezaei", "Renato Paes Leme"], "categories": ["cs.GT"], "doi": "", "_query": "housing price text listing", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2106.01920v1", "title": "Convolutional Neural Network(CNN/ConvNet) in Stock Price Movement Prediction", "abstract": "With technological advancements and the exponential growth of data, we have been unfolding different capabilities of neural networks in different sectors. In this paper, I have tried to use a specific type of Neural Network known as Convolutional Neural Network(CNN/ConvNet) in the stock market. In other words, I have tried to construct and train a convolutional neural network on past stock prices data and then tried to predict the movement of stock price i.e. whether the stock price would rise or fall, in the coming time.", "published": "2021-06-03T15:14:46Z", "authors": ["Kunal Bhardwaj"], "categories": ["cs.NE", "cs.AI", "cs.CV", "cs.LG"], "doi": "", "_query": "housing price text listing", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2405.00051v1", "title": "Arbitrage impact on the relationship between XRP price and correlation tensor spectra of transaction networks", "abstract": "The increasing use of cryptoassets for international remittances has proven to be faster and more cost-effective, particularly for migrants without access to traditional banking. However, the inherent volatility of cryptoasset prices, independent of blockchain-based remittance mechanisms, introduces potential risks during periods of high volatility. This study investigates the intricate dynamics between XRP price fluctuations across diverse crypto exchanges and the correlation of the largest singular values of the correlation tensor of XRP transaction networks. Particularly, we show the impact of arbitrage opportunities across different crypto exchanges on the relationship between XRP price and correlation tensor spectra of transaction networks. Distinct periods, non-bubble and bubble, showcase different characteristics in XRP price fluctuations. Establishing a connection between XRP price and transaction networks, we compute correlation tensors and singular values, emphasizing the significance of the largest singular value. Comparisons with reshuffled and Gaussian random correlation tensors validate the uniqueness of the empirical tensor. A set of simulated weekly XRP prices, resembling arbitrage opportunities across various crypto exchanges, further confirms the robustness of our findings. It reveals a pronounced anti-correlation during bubble periods and a non-significant correlation during non-bubble periods with the largest singular value, irrespective of price fluctuations across different crypto exchanges.", "published": "2024-04-15T08:34:28Z", "authors": ["Abhijit Chakraborty", "Yuichi Ikeda"], "categories": ["physics.soc-ph", "q-fin.GN", "q-fin.ST"], "doi": "", "_query": "housing price text listing", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2411.02709v1", "title": "Carbon price fluctuation prediction using blockchain information A new hybrid machine learning approach", "abstract": "In this study, the novel hybrid machine learning approach is proposed in carbon price fluctuation prediction. Specifically, a research framework integrating DILATED Convolutional Neural Networks (CNN) and Long Short-Term Memory (LSTM) neural network algorithm is proposed. The advantage of the combined framework is that it can make feature extraction more efficient. Then, based on the DILATED CNN-LSTM framework, the L1 and L2 parameter norm penalty as regularization method is adopted to predict. Referring to the characteristics of high correlation between energy indicator price and blockchain information in previous literature, and we primarily includes indicators related to blockchain information through regularization process. Based on the above methods, this paper uses a dataset containing an amount of data to carry out the carbon price prediction. The experimental results show that the DILATED CNN-LSTM framework is superior to the traditional CNN-LSTM architecture. Blockchain information can effectively predict the price. Since parameter norm penalty as regularization, Ridge Regression (RR) as L2 regularization is better than Smoothly Clipped Absolute Deviation Penalty (SCAD) as L1 regularization in price forecasting. Thus, the proposed RR-DILATED CNN-LSTM approach can effectively and accurately predict the fluctuation trend of the carbon price. Therefore, the new forecasting methods and theoretical ecology proposed in this study provide a new basis for trend prediction and evaluating digital assets policy represented by the carbon price for both the academia and practitioners.", "published": "2024-11-05T01:12:17Z", "authors": ["H. Wang", "Y. Pang", "D. Shang"], "categories": ["cs.LG", "stat.ML"], "doi": "", "_query": "housing price text listing", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2408.14169v1", "title": "Dynamic Pricing for Electric Vehicle Charging", "abstract": "Dynamic pricing is a promising strategy to address the challenges of smart charging, as traditional time-of-use (ToU) rates and stationary pricing (SP) do not dynamically react to changes in operating conditions, reducing revenue for charging station (CS) vendors and affecting grid stability. Previous studies evaluated single objectives or linear combinations of objectives for EV CS pricing solutions, simplifying trade-offs and preferences among objectives. We develop a novel formulation for the dynamic pricing problem by addressing multiple conflicting objectives efficiently instead of solely focusing on one objective or metric, as in earlier works. We find optimal trade-offs or Pareto solutions efficiently using Non-dominated Sorting Genetic Algorithms (NSGA) II and NSGA III. A dynamic pricing model quantifies the relationship between demand and price while simultaneously solving multiple conflicting objectives, such as revenue, quality of service (QoS), and peak-to-average ratios (PAR). A single method can only address some of the above aspects of dynamic pricing comprehensively. We present a three-part dynamic pricing approach using a Bayesian model, multi-objective optimization, and multi-criteria decision-making (MCDM) using pseudo-weight vectors. To address the research gap in CS pricing, our method selects solutions using revenue, QoS, and PAR metrics simultaneously. Two California charging sites' real-world data validates our approach.", "published": "2024-08-26T10:32:21Z", "authors": ["Arun Kumar Kalakanti", "Shrisha Rao"], "categories": ["cs.DC", "cs.AI"], "doi": "", "_query": "housing price text listing", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2209.00858v1", "title": "A Discussion of Discrimination and Fairness in Insurance Pricing", "abstract": "Indirect discrimination is an issue of major concern in algorithmic models. This is particularly the case in insurance pricing where protected policyholder characteristics are not allowed to be used for insurance pricing. Simply disregarding protected policyholder information is not an appropriate solution because this still allows for the possibility of inferring the protected characteristics from the non-protected ones. This leads to so-called proxy or indirect discrimination. Though proxy discrimination is qualitatively different from the group fairness concepts in machine learning, these group fairness concepts are proposed to 'smooth out' the impact of protected characteristics in the calculation of insurance prices. The purpose of this note is to share some thoughts about group fairness concepts in the light of insurance pricing and to discuss their implications. We present a statistical model that is free of proxy discrimination, thus, unproblematic from an insurance pricing point of view. However, we find that the canonical price in this statistical model does not satisfy any of the three most popular group fairness axioms. This seems puzzling and we welcome feedback on our example and on the usefulness of these group fairness axioms for non-discriminatory insurance pricing.", "published": "2022-09-02T07:31:37Z", "authors": ["Mathias Lindholm", "Ronald Richman", "Andreas Tsanakas", "Mario V. Wüthrich"], "categories": ["cs.LG", "q-fin.ST", "stat.ML"], "doi": "", "_query": "housing price text listing", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/1303.1134v1", "title": "Utility maximisation and utility indifference price for exponential semi-martingale models with random factor", "abstract": "We consider utility maximization problem for semi-martingale models depending on a random factor $ξ$. We reduce initial maximization problem to the conditional one, given $ξ=u$, which we solve using dual approach. For HARA utilities we consider information quantities like Kullback-Leibler information and Hellinger integrals, and corresponding information processes. As a particular case we study exponential Levy models depending on random factor. In that case the information processes are deterministic and this fact simplify very much indifference price calculus. Then we give the equations for indifference prices. We show that indifference price for seller and minus indifference price for buyer are risk measures. Finally, we apply the results to Geometric Brownian motion case. Using identity in law technique we give the explicit expression for information quantities. Then, the previous formulas for indifference price can be applied.", "published": "2013-03-05T18:38:39Z", "authors": ["Anastasia Ellanskaya", "Lioudmila Vostrikova"], "categories": ["q-fin.PR", "math.PR"], "doi": "", "_query": "housing price text listing", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2604.14257v2", "title": "Mapping the causal structure of price formation in Texas's transitioning electricity market", "abstract": "Renewable deployment and rising demand from electrification and large digital loads are transforming electricity markets. However, how these developments reshape electricity price dynamics remains poorly understood, leaving system planners, capacity investors, and market participants reliant on assumptions from a thermal-dominated era that may no longer hold. We use causal discovery to study the evolution of wholesale electricity prices in Texas, which is undergoing rapid transformation. Our findings overturn the view of Texas as a gas-price-driven market, demonstrating that wind generation has become the dominant causal driver of day-ahead prices, with effects more than three times greater than those of natural gas. Yet wind's price-suppressing effect is weakening during peak periods, and wind growth redistributes congestion costs to distant load centres. Furthermore, rising load in South and West Texas alters system prices and regional differentials. Uncovering the evolving spatiotemporal nature of causal drivers, our analysis reveals that the pace, geographic siting, and relative scale of new generation and large loads will be decisive for future electricity price risks, infrastructure needs, and investments.", "published": "2026-04-15T15:05:19Z", "authors": ["Shiva Madadkhani", "Nils Sturma", "Mathias Drton", "Svetlana Ikonnikova"], "categories": ["econ.GN", "stat.AP"], "doi": "", "_query": "housing price text listing", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/1905.00731v3", "title": "Static Pricing: Universal Guarantees for Reusable Resources", "abstract": "We consider a fundamental pricing model in which a fixed number of units of a reusable resource are used to serve customers. Customers arrive to the system according to a stochastic process and upon arrival decide whether or not to purchase the service, depending on their willingness-to-pay and the current price. The service time during which the resource is used by the customer is stochastic and the firm may incur a service cost. This model represents various markets for reusable resources such as cloud computing, shared vehicles, rotable parts, and hotel rooms. In the present paper, we analyze this pricing problem when the firm attempts to maximize a weighted combination of three central metrics: profit, market share, and service level. Under Poisson arrivals, exponential service times, and standard assumptions on the willingness-to-pay distribution, we establish a series of results that characterize the performance of static pricing in such environments. In particular, while an optimal policy is fully dynamic in such a context, we prove that a static pricing policy simultaneously guarantees 78.9% of the profit, market share, and service level from the optimal policy. Notably, this result holds for any service rate and number of units the firm operates. Our proof technique relies on a judicious construction of a static price that is derived directly from the optimal dynamic pricing policy. In the special case where there are two units and the induced demand is linear, we also prove that the static policy guarantees 95.5% of the profit from the optimal policy. Our numerical findings on a large testbed of instances suggest that the latter result is quite indicative of the profit obtained by the static pricing policy across all parameters.", "published": "2019-05-02T13:38:54Z", "authors": ["Omar Besbes", "Adam N. Elmachtoub", "Yunjie Sun"], "categories": ["cs.DS", "cs.GT", "math.OC"], "doi": "", "_query": "housing price text listing", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/1907.05381v1", "title": "Adaptive Pricing in Insurance: Generalized Linear Models and Gaussian Process Regression Approaches", "abstract": "We study the application of dynamic pricing to insurance. We view this as an online revenue management problem where the insurance company looks to set prices to optimize the long-run revenue from selling a new insurance product. We develop two pricing models: an adaptive Generalized Linear Model (GLM) and an adaptive Gaussian Process (GP) regression model. Both balance between exploration, where we choose prices in order to learn the distribution of demands & claims for the insurance product, and exploitation, where we myopically choose the best price from the information gathered so far. The performance of the pricing policies is measured in terms of regret: the expected revenue loss caused by not using the optimal price. As is commonplace in insurance, we model demand and claims by GLMs. In our adaptive GLM design, we use the maximum quasi-likelihood estimation (MQLE) to estimate the unknown parameters. We show that, if prices are chosen with suitably decreasing variability, the MQLE parameters eventually exist and converge to the correct values, which in turn implies that the sequence of chosen prices will also converge to the optimal price. In the adaptive GP regression model, we sample demand and claims from Gaussian Processes and then choose selling prices by the upper confidence bound rule. We also analyze these GLM and GP pricing algorithms with delayed claims. Although similar results exist in other domains, this is among the first works to consider dynamic pricing problems in the field of insurance. We also believe this is the first work to consider Gaussian Process regression in the context of insurance pricing. These initial findings suggest that online machine learning algorithms could be a fruitful area of future investigation and application in insurance.", "published": "2019-07-02T08:18:54Z", "authors": ["Yuqing Zhang", "Neil Walton"], "categories": ["econ.EM", "math.OC", "q-fin.MF", "q-fin.RM", "stat.ML"], "doi": "", "_query": "housing price text listing", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/1312.4227v3", "title": "Matching distributions: Asset pricing with density shape correction", "abstract": "We investigate a statistical-static hedging technique for pricing assets considered as single-step stochastic cash flows. The valuation is based on constructing in a canonical way a European style derivative on a benchmark security such that the physical payoff distribution coincides with the (corrected) physical asset price distribution. It turns out that this pricing technique is economically viable under some natural cases. The fundamental properties of the pricing rule arising in this way are investigated here. This gives rise to a novel way of estimating state price density. Our approach has some tangible benefits: its principle is transparent, and it is easy to implement numerically while avoiding many issues typically involved in such an estimation. As an application, it is shown how this method can be used in performing kurtosis corrections to the standard Black-Scholes-Merton model by a mixture of several types of distributions. In fact, the technique is non-parametric in nature, and it can handle in principle any physical distribution, e.g., a multimodal one. Some other interesting applications are discussed as well.", "published": "2013-12-16T02:09:28Z", "authors": ["Jarno Talponen"], "categories": ["q-fin.PR", "math.PR"], "doi": "", "_query": "housing price text listing", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/1610.05583v3", "title": "Price Dynamics Via Expectations, and the Role of Money Therein", "abstract": "Beyond its obvious macro-economic relevance, fiat money has important micro-economic implications. They matter for addressing No. 8 in Smale's \"Mathematical Problems for the Next Century\": extend the mathematical model of general equilibrium theory to include price adjustments. In the canonical Arrow-Debreu framework, equilibrium prices are set by a fictitious auctioneer. Removing that fiction raises the question of how prices are set and adjusted by decentralised actors with incomplete information. We investigate this question through a very basic model where a unique factor of production, labour, produces a single consumption good, called jelly for brevity. The point of the model is to study a price dynamics based on the firm's expectations about jelly demand and labour supply. The system tends towards economic equilibrium, however, depending on the initial conditions it might not get there. In different model versions, different kinds of money are introduced. Compared to the case of no money, the introduction of money as a store of value facilitates the system reaching economic equilibrium. If money is introduced as a third commodity, i.e. there is also a demand for money, the system dynamics in general becomes more complex.", "published": "2016-10-18T12:50:52Z", "authors": ["Gesine A. Steudle", "Saini Yang", "Carlo C. Jaeger"], "categories": ["econ.GN"], "doi": "", "_query": "housing price text listing", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2205.06677v1", "title": "Collective behavior of stock prices in the time of crisis as a response to the external stimulus", "abstract": "We analyze the interaction between stock prices of big companies in the USA and Germany using Granger Causality. We claim that the increase in pair-wise Granger causality interaction between prices in the times of crisis is the consequence of simultaneous response of the markets to the outside events or external stimulus that is considered as a common driver to all the stocks, not a result of real causal predictability between the prices themselves. An alternative approach through recurrence analysis in single stock price series supports this claim. The observed patterns in the price of stocks are modelled by adding a multiplicative exogenous term as the representative for external factors to the geometric Brownian motion model for stock prices. Altogether, we can detect and model the effects of the Great Recession as a consequence of the mortgage crisis in 2007/2008 as well as the impacts of the Covid out-break in early 2020", "published": "2022-05-09T13:58:14Z", "authors": ["Maryam Zamani", "Sander Paekivi", "Philipp Meyer", "Holger Kantz"], "categories": ["q-fin.ST"], "doi": "", "_query": "housing price text listing", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/1705.04883v2", "title": "On differences between DP-coloring and list coloring", "abstract": "DP-coloring (also known as correspondence coloring) is a generalization of list coloring introduced recently by Dvořák and Postle. Many known upper bounds for the list-chromatic number extend to the DP-chromatic number, but not all of them do. In this note we describe some unusual properties of DP-coloring that set it aside from list coloring. In particular, we give an example of a planar bipartite graph with DP-chromatic number $4$ and prove that the edge-DP-chromatic number of a $d$-regular graph with $d\\geq 2$ is always at least $d+1$.", "published": "2017-05-13T20:30:41Z", "authors": ["Anton Bernshteyn", "Alexandr Kostochka"], "categories": ["math.CO"], "doi": "", "_query": "housing price text listing", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/1712.06462v2", "title": "Group actions on 1-manifolds: a list of very concrete open questions", "abstract": "This text focuses on actions on 1-manifolds. We present a (non exhaustive) list of very concrete open questions in the field, each of which is discussed in some detail and complemented with a large list of references, so that a clear panorama on the subject arises from the lecture.", "published": "2017-12-18T15:17:21Z", "authors": ["Andrés Navas"], "categories": ["math.DS", "math.GR"], "doi": "", "_query": "housing price text listing", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/1409.7157v4", "title": "Full simulation study of the top Yukawa coupling at the ILC at $\\sqrt{s}$ = 1 TeV", "abstract": "We present a study of the expected precision for measurement of the top Yukawa coupling, yt, in e+e- collisions at a center-of-mass energy of 1 TeV and assuming a beam polarization of P (e-, e+) = (-0.8,+0.2). Independent analyses of ttH final states containing at least six hadronic jets are performed, based on detailed simulations of SiD and ILD, the two candidate detector concepts for the ILC. We estimate that a statistical precision of yt of 4% can be obtained with an integrated luminosity of 1 $\\mathrm{ab}^{-1}$.", "published": "2014-09-25T05:03:51Z", "authors": ["Tony Price", "Philipp Roloff", "Jan Strube", "Tomohiko Tanabe"], "categories": ["hep-ex"], "doi": "10.1140/epjc/s10052-015-3532-4", "_query": "housing price text listing", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2301.10840v1", "title": "Improved Bitcoin Price Prediction based on COVID-19 data", "abstract": "Social turbulence can affect people financial decisions, causing changes in spending and saving. During a global turbulence as significant as the COVID-19 pandemic, such changes are inevitable. Here we examine how the effects of COVID-19 on various jurisdictions influenced the global price of Bitcoin. We hypothesize that lock downs and expectations of economic recession erode people trust in fiat (government-issued) currencies, thus elevating cryptocurrencies. Hence, we expect to identify a causal relation between the turbulence caused by the pandemic, demand for Bitcoin, and ultimately its price. To test the hypothesis, we merged datasets of Bitcoin prices and COVID-19 cases and deaths. We also engineered extra features and applied statistical and machine learning (ML) models. We applied a Random Forest model (RF) to identify and rank the feature importance, and ran a Long Short-Term Memory (LSTM) model on Bitcoin prices data set twice: with and without accounting for COVID-19 related features. We find that adding COVID-19 data into the LSTM model improved prediction of Bitcoin prices.", "published": "2023-01-25T21:37:37Z", "authors": ["Palina Niamkova", "Rafael Moreira"], "categories": ["cs.LG", "cs.AI"], "doi": "", "_query": "housing price text listing", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/1606.09194v1", "title": "A multilayer approach for price dynamics in financial markets", "abstract": "We introduce a new Self-Organized Criticality (SOC) model for simulating price evolution in an artificial financial market, based on a multilayer network of traders. The model also implements, in a quite realistic way with respect to previous studies, the order book dy- namics, by considering two assets with variable fundamental prices. Fat tails in the probability distributions of normalized returns are observed, together with other features of real financial markets.", "published": "2016-06-29T17:32:24Z", "authors": ["Alessio Emanuele Biondo", "Alessandro Pluchino", "Andrea Rapisarda"], "categories": ["q-fin.TR", "physics.soc-ph"], "doi": "", "_query": "housing price text listing", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2602.13055v1", "title": "Curriculum-DPO++: Direct Preference Optimization via Data and Model Curricula for Text-to-Image Generation", "abstract": "Direct Preference Optimization (DPO) has been proposed as an effective and efficient alternative to reinforcement learning from human feedback (RLHF). However, neither RLHF nor DPO take into account the fact that learning certain preferences is more difficult than learning other preferences, rendering the optimization process suboptimal. To address this gap in text-to-image generation, we recently proposed Curriculum-DPO, a method that organizes image pairs by difficulty. In this paper, we introduce Curriculum-DPO++, an enhanced method that combines the original data-level curriculum with a novel model-level curriculum. More precisely, we propose to dynamically increase the learning capacity of the denoising network as training advances. We implement this capacity increase via two mechanisms. First, we initialize the model with only a subset of the trainable layers used in the original Curriculum-DPO. As training progresses, we sequentially unfreeze layers until the configuration matches the full baseline architecture. Second, as the fine-tuning is based on Low-Rank Adaptation (LoRA), we implement a progressive schedule for the dimension of the low-rank matrices. Instead of maintaining a fixed capacity, we initialize the low-rank matrices with a dimension significantly smaller than that of the baseline. As training proceeds, we incrementally increase their rank, allowing the capacity to grow until it converges to the same rank value as in Curriculum-DPO. Furthermore, we propose an alternative ranking strategy to the one employed by Curriculum-DPO. Finally, we compare Curriculum-DPO++ against Curriculum-DPO and other state-of-the-art preference optimization approaches on nine benchmarks, outperforming the competing methods in terms of text alignment, aesthetics and human preference. Our code is available at https://github.com/CroitoruAlin/Curriculum-DPO.", "published": "2026-02-13T16:09:31Z", "authors": ["Florinel-Alin Croitoru", "Vlad Hondru", "Radu Tudor Ionescu", "Nicu Sebe", "Mubarak Shah"], "categories": ["cs.CV", "cs.AI", "cs.LG"], "doi": "", "_query": "housing price text listing", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2504.10459v3", "title": "The Price of Competitive Information Disclosure", "abstract": "In many decision-making scenarios, individuals strategically choose what information to disclose to optimize their own outcomes. It is unclear whether such strategic information disclosure can lead to good societal outcomes. To address this question, we consider a competitive Bayesian persuasion model in which multiple agents selectively disclose information about their qualities to a principal, who aims to choose the candidates with the highest qualities. Using the price-of-anarchy framework, we quantify the inefficiency of such strategic disclosure. We show that the price of anarchy is at most a constant when the agents have independent quality distributions, even if their utility functions are heterogeneous. This result provides the first theoretical guarantee on the limits of inefficiency in Bayesian persuasion with competitive information disclosure.", "published": "2025-04-14T17:46:13Z", "authors": ["Siddhartha Banerjee", "Kamesh Munagala", "Yiheng Shen", "Kangning Wang"], "categories": ["cs.GT"], "doi": "", "_query": "housing price text listing", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2603.25874v1", "title": "A Market Design Proposal for Decoupling Carbon and Electricity Prices", "abstract": "In European day-ahead electricity markets, carbon allowance costs passed through by marginal fossil plants raise consumer expenditure and generate inframarginal rents for non-emitting generators. We propose a settlement modification: when the zonal day-ahead price exceeds a threshold, non-emitting generation is remunerated at the clearing price minus a fixed CO2 proxy deduction, while all other units continue to receive the uniform price. The mechanism thus reallocates a part of the inframarginal rents to consumers. Using hourly data we estimate static average expenditure reductions of about 8.5% in Austria and 4.7% in Germany in 2025. We discuss bidding incentives around the threshold, interactions with Contracts for Difference, implementation in coupled bidding zones, and a gas-cost variant for the 2022 energy crisis.", "published": "2026-03-26T20:00:29Z", "authors": ["Simon Finster", "Bernhard Kasberger", "Simon Rütten"], "categories": ["econ.GN"], "doi": "", "_query": "housing price text listing", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2506.10005v1", "title": "Multimodal Cinematic Video Synthesis Using Text-to-Image and Audio Generation Models", "abstract": "Advances in generative artificial intelligence have altered multimedia creation, allowing for automatic cinematic video synthesis from text inputs. This work describes a method for creating 60-second cinematic movies incorporating Stable Diffusion for high-fidelity image synthesis, GPT-2 for narrative structuring, and a hybrid audio pipeline using gTTS and YouTube-sourced music. It uses a five-scene framework, which is augmented by linear frame interpolation, cinematic post-processing (e.g., sharpening), and audio-video synchronization to provide professional-quality results. It was created in a GPU-accelerated Google Colab environment using Python 3.11. It has a dual-mode Gradio interface (Simple and Advanced), which supports resolutions of up to 1024x768 and frame rates of 15-30 FPS. Optimizations such as CUDA memory management and error handling ensure reliability. The experiments demonstrate outstanding visual quality, narrative coherence, and efficiency, furthering text-to-video synthesis for creative, educational, and industrial applications.", "published": "2025-04-06T17:10:34Z", "authors": ["Sridhar S", "Nithin A", "Shakeel Rifath", "Vasantha Raj"], "categories": ["cs.CV", "cs.AI", "cs.CL", "cs.GR", "cs.MM"], "doi": "", "_query": "housing price text listing", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/cond-mat/0403067v1", "title": "On the Origin of Power-Law Fluctuations in Stock Prices", "abstract": "We respond to the issues discussed by Farmer and Lillo (FL) related to our proposed approach to understanding the origin of power-law distributions in stock price fluctuations. First, we extend our previous analysis to 1000 US stocks and perform a new estimation of market impact that accounts for splitting of large orders and potential autocorrelations in the trade flow. Our new analysis shows clearly that price impact and volume are related by a square-root functional form of market impact for large volumes, in contrast to the claim of FL that this relationship increases as a power law with a smaller exponent. Since large orders are usually executed by splitting into smaller size trades, procedures used by FL give a downward bias for this power law exponent. Second, FL analyze 3 stocks traded on the London Stock Exchange, and solely on this basis they claim that the distribution of transaction volumes do not have a power-law tail for the London Stock Exchange. We perform new empirical analysis on transaction data for the 262 largest stocks listed in the London Stock Exchange, and find that the distribution of volume decays as a power-law with an exponent $\\approx 3/2$ -- in sharp contrast to FL's claim that the distribution of transaction volume does not have a power-law tail. Our exponent estimate of $\\approx 3/2$ is consistent with our previous results from the New York and Paris Stock Exchanges. We conclude that the available empirical evidence is consistent with our hypothesis on the origin of power-law fluctuations in stock prices.", "published": "2004-03-02T05:39:58Z", "authors": ["Vasiliki Plerou", "Parameswaran Gopikrishnan", "Xavier Gabaix", "H. Eugene Stanley"], "categories": ["cond-mat.dis-nn", "q-fin.TR"], "doi": "", "_query": "housing price text listing", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/1803.08831v3", "title": "A structural Heath-Jarrow-Morton framework for consistent intraday, spot, and futures electricity prices", "abstract": "In this paper we introduce a flexible HJM-type framework that allows for consistent modelling of intraday, spot, futures, and option prices. This framework is based on stochastic processes with economic interpretations and consistent with the initial term structure given in the form of a price forward curve. Furthermore, the framework allows for existing day-ahead spot price models to be used in an HJM setting. We include several explicit examples of classical spot price models but also show how structural models and factor models can be formulated within the framework.", "published": "2018-03-23T15:19:00Z", "authors": ["Wieger Hinderks", "Andreas Wagner", "Ralf Korn"], "categories": ["q-fin.MF", "math.PR"], "doi": "", "_query": "housing price text listing", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/cond-mat/9910376v1", "title": "Speculative trading: the price multiplier effect", "abstract": "During a speculative episode the price of an item jumps from an initial level p_1 to a peak level p_2 before more or less returning to level p_1. The ratio p_2/p_1 is referred to as the amplitude A of the peak. This paper shows that for a given market the peak amplitude is a linear function of the logarithm of the price at the beginning of the speculative episode; with p_1 expressed in 1999 euros the relationship takes the form: $ A=a\\ln p_1 +b $; the values of the parameter a turn out to be relatively independent of the market considered: $ a \\simeq 0.5 $, the values of the parameter b are more market-dependent, but are stable in the course of time for a given market. This relationship suggests that the higher the stakes the more \"bullish\" the market becomes. Possible mechanisms of this \"risk affinity\" effect are discussed.", "published": "1999-10-23T18:11:41Z", "authors": ["B. M. Roehner"], "categories": ["cond-mat.stat-mech", "q-fin.TR"], "doi": "10.1007/s100510050144", "_query": "housing price text listing", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2109.08511v2", "title": "Data Privacy Protection and Utility Preservation through Bayesian Data Synthesis: A Case Study on Airbnb Listings", "abstract": "When releasing record-level data containing sensitive information to the public, the data disseminator is responsible for protecting the privacy of every record in the dataset, simultaneously preserving important features of the data for users' analyses. These goals can be achieved by data synthesis, where confidential data are replaced with synthetic data that are simulated based on statistical models estimated on the confidential data. In this paper, we present a data synthesis case study, where synthetic values of price and the number of available days in a sample of the New York Airbnb Open Data are created for privacy protection. One sensitive variable, the number of available days of an Airbnb listing, has a large amount of zero-valued records and also truncated at the two ends. We propose a zero-inflated truncated Poisson regression model for its synthesis. We utilize a sequential synthesis approach to further synthesize the sensitive price variable. The resulting synthetic data are evaluated for its utility preservation and privacy protection, the latter in the form of disclosure risks. Furthermore, we propose methods to investigate how uncertainties in intruder's knowledge would influence the identification disclosure risks of the synthetic data. In particular, we explore several realistic scenarios of uncertainties in intruder's knowledge of available information and evaluate their impacts on the resulting identification disclosure risks.", "published": "2021-09-17T12:45:42Z", "authors": ["Shijie Guo", "Jingchen Hu"], "categories": ["stat.AP"], "doi": "", "_query": "housing price text listing", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2609.17395v1", "title": "The price of anarchy in the max-distance network creation game is not constant", "abstract": "At edge price $α=1$, we construct an infinite family of pure Nash equilibria of the unilateral max-distance network creation game with $\\PoA\\ge2^{\\sqrt{\\log_2 n}-O(\\log\\log n)}$. Together with the known upper bound, this gives $2^{Θ(\\sqrt{\\log n})}$ along the constructed sequence of population sizes. We subdivide every edge of the bipartite double cover of a distance-uniform graph with large diameter constructed by Lavrov, Loh and Messegué, and let each subdivision vertex buy its two incident edges. A distance calculation rules out every profitable unilateral deviation. The equilibria are not strict. We also give a short proof that the price of anarchy is constant for every polynomially vanishing edge price.", "published": "2026-09-15T16:31:32Z", "authors": ["Christoph Schlegel"], "categories": ["cs.GT", "cs.DM"], "doi": "", "_query": "housing price text listing", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/1903.06288v1", "title": "Optimal Price of Anarchy in Cost-Sharing Games", "abstract": "The design of distributed algorithms is central to the study of multiagent systems control. In this paper, we consider a class of combinatorial cost-minimization problems and propose a framework for designing distributed algorithms with a priori performance guarantees that are near-optimal. We approach this problem from a game-theoretic perspective, assigning agents cost functions such that the equilibrium efficiency (price of anarchy) is optimized. Once agents' cost functions have been specified, any algorithm capable of computing a Nash equilibrium of the system inherits a performance guarantee matching the price of anarchy. Towards this goal, we formulate the problem of computing the price of anarchy as a tractable linear program. We then present a framework for designing agents' local cost functions in order to optimize for the worst-case equilibrium efficiency. Finally, we investigate the implications of our findings when this framework is applied to systems with convex, nondecreasing costs.", "published": "2019-03-14T22:44:55Z", "authors": ["Rahul Chandan", "Dario Paccagnan", "Jason R. Marden"], "categories": ["eess.SY", "cs.GT", "cs.MA"], "doi": "", "_query": "housing price text listing", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2112.04245v1", "title": "Do fundamentals shape the price response? A critical assessment of linear impact models", "abstract": "We compare the predictions of the stationary Kyle model, a microfounded multi-step linear price impact model in which market prices forecast fundamentals through information encoded in the order flow, with those of the propagator model, a purely data-driven model in which trades mechanically impact prices with a time-decaying kernel. We find that, remarkably, both models predict the exact same price dynamics at high frequency, due to the emergence of universality at small time scales. On the other hand, we find those models to disagree on the overall strength of the impact function by a quantity that we are able to relate to the amount of excess-volatility in the market. We reveal a crossover between a high-frequency regime in which the market reacts sub-linearly to the signed order flow, to a low-frequency regime in which prices respond linearly to order flow imbalances. Overall, we reconcile results from the literature on market microstructure (sub-linearity in the price response to traded volumes) with those relating to macroeconomically relevant timescales (in which a linear relation is typically assumed).", "published": "2021-12-08T11:44:41Z", "authors": ["Michele Vodret", "Iacopo Mastromatteo", "Bence Tóth", "Michael Benzaquen"], "categories": ["q-fin.TR", "cond-mat.stat-mech"], "doi": "", "_query": "housing price text listing", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/1609.06193v3", "title": "Stability Analysis of Wholesale Electricity Markets under Dynamic Consumption Models and Real-Time Pricing", "abstract": "This paper analyzes stability conditions for wholesale electricity markets under real-time retail pricing and realistic consumption models with memory, which explicitly take into account previous electricity prices and consumption levels. By passing on the current retail price of electricity from supplier to consumer and feeding the observed consumption back to the supplier, a closed-loop dynamical system for electricity prices and consumption arises whose stability is to be investigated. Under mild assumptions on the generation cost of electricity and consumers' backlog disutility functions, we show that, for consumer models with price memory only, market stability is achieved if the ratio between the consumers' marginal backlog disutility and the suppliers' marginal cost of supply remains below a fixed threshold. Further, consumer models with price and consumption memory can result in greater stability regions and faster convergence to the equilibrium compared to models with price memory alone, if consumption deviations from nominal demand are adequately penalized.", "published": "2016-09-20T14:20:09Z", "authors": ["Datong P. Zhou", "Mardavij Roozbehani", "Munther A. Dahleh", "Claire J. Tomlin"], "categories": ["eess.SY"], "doi": "", "_query": "housing price text listing", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2003.07736v1", "title": "Anomalous supply shortages from dynamic pricing in on-demand mobility", "abstract": "Dynamic pricing schemes are increasingly employed across industries to maintain a self-organized balance of demand and supply. However, throughout complex dynamical systems, unintended collective states exist that may compromise their function. Here we reveal how dynamic pricing may induce demand-supply imbalances instead of preventing them. Combining game theory and time series analysis of dynamic pricing data from on-demand ride-hailing services, we explain this apparent contradiction. We derive a phase diagram demonstrating how and under which conditions dynamic pricing incentivizes collective action of ride-hailing drivers to induce anomalous supply shortages. By disentangling different timescales in price time series of ride-hailing services at 137 locations across the globe, we identify characteristic patterns in the price dynamics reflecting these anomalous supply shortages. Our results provide systemic insights for the regulation of dynamic pricing, in particular in publicly accessible mobility systems, by unraveling under which conditions dynamic pricing schemes promote anomalous supply shortages.", "published": "2020-03-16T17:45:21Z", "authors": ["Malte Schröder", "David-Maximilian Storch", "Philip Marszal", "Marc Timme"], "categories": ["physics.soc-ph", "econ.EM"], "doi": "10.1038/s41467-020-18370-3", "_query": "housing price text listing", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/1705.01965v1", "title": "Makespan Minimization via Posted Prices", "abstract": "We consider job scheduling settings, with multiple machines, where jobs arrive online and choose a machine selfishly so as to minimize their cost. Our objective is the classic makespan minimization objective, which corresponds to the completion time of the last job to complete. The incentives of the selfish jobs may lead to poor performance. To reconcile the differing objectives, we introduce posted machine prices. The selfish job seeks to minimize the sum of its completion time on the machine and the posted price for the machine. Prices may be static (i.e., set once and for all before any arrival) or dynamic (i.e., change over time), but they are determined only by the past, assuming nothing about upcoming events. Obviously, such schemes are inherently truthful. We consider the competitive ratio: the ratio between the makespan achievable by the pricing scheme and that of the optimal algorithm. We give tight bounds on the competitive ratio for both dynamic and static pricing schemes for identical, restricted, related, and unrelated machine settings. Our main result is a dynamic pricing scheme for related machines that gives a constant competitive ratio, essentially matching the competitive ratio of online algorithms for this setting. In contrast, dynamic pricing gives poor performance for unrelated machines. This lower bound also exhibits a gap between what can be achieved by pricing versus what can be achieved by online algorithms.", "published": "2017-05-04T18:21:56Z", "authors": ["Michal Feldman", "Amos Fiat", "Alan Roytman"], "categories": ["cs.GT"], "doi": "", "_query": "housing price text listing", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/1707.00807v1", "title": "General Price Bounds for Guaranteed Annuity Options", "abstract": "In this paper, we are concerned with the valuation of Guaranteed Annuity Options (GAOs) under the most generalised modelling framework where both interest and mortality rates are stochastic and correlated. Pricing these type of options in the correlated environment is a challenging task and no closed form solution exists in the literature. We employ the use of doubly stochastic stopping times to incorporate the randomness about the time of death and employ a suitable change of measure to facilitate the valuation of survival benefit, there by adapting the payoff of the GAO in terms of the payoff of a basket call option. We derive general price bounds for GAOs by utilizing a conditioning approach for the lower bound and arithmetic-geometric mean inequality for the upper bound. The theory is then applied to affine models to present some very interesting formulae for the bounds under the affine set up. Numerical examples are furnished and benchmarked against Monte Carlo simulations to estimate the price of a GAO for a variety of affine processes governing the evolution of mortality and the interest rate.", "published": "2017-07-04T03:05:07Z", "authors": ["Raj Kumari Bahl", "Sotirios Sabanis"], "categories": ["q-fin.PR", "math.PR", "stat.CO"], "doi": "", "_query": "housing price text listing", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2509.24720v1", "title": "Dynamic Pricing of an Expiring Item under Strategic Buyers with Stochastic Arrival", "abstract": "We study the optimal dynamic pricing of an expiring ticket or voucher, sold by a time-sensitive seller to strategic buyers who arrive stochastically with private values. The expiring nature creates a conflict: the seller's urgency to sell before expiration drives price reductions, which in turn incentivize buyers to wait. We seek the seller's optimal pricing policy that resolves this tension. The main analytical challenge is that buyer type is two-dimensional (valuation and arrival time), which makes equilibrium intractable under general strategies. To address this, we introduce the Value-Based Threshold (VBT) strategy, a tractable framework that decouples these two dimensions. Using this framework, we prove equilibrium existence via an ordinary differential equation and provide a constructive procedure for its characterization. We then derive near-optimal pricing policies for two stylized regimes: a constant price in thin markets and a linear discount in thick markets. Numerical frontier analysis confirms these benchmarks and shows how optimal policy adapts as the seller's time sensitivity changes. Our findings clarify the conflict between quick sales and strategic waiting. Sellers facing thick markets or high time sensitivity benefit from linear discounts, while in thin markets a constant price neutralizes buyers' incentive to wait. We also show this simple policy remains robust across broad conditions. For patient sellers, a quasi-auction schedule that maintains a high price until a sharp final drop is most effective in aggregating demand.", "published": "2025-09-29T12:48:03Z", "authors": ["Suyeon Choi", "Changhyun Kwon", "Seungki Min"], "categories": ["cs.GT"], "doi": "", "_query": "housing price text listing", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2304.14385v4", "title": "Dynamic Pricing and Advertising with Demand Learning", "abstract": "We consider a novel pricing and advertising framework in which a seller not only sets the product price but also designs flexible advertising schemes to influence customers' valuations of the product. We impose no structural restriction on the seller's feasible advertising strategies and allow her to advertise the product by disclosing or concealing any information. Following the information design literature, we model this fully flexible advertising as the seller choosing an arbitrary information policy that signals the product quality to customers. Customers observe the advertising signal and form a Bayesian posterior belief over the product quality. We investigate two questions in this work: (1) What is the value of advertising? To what extent can advertising enhance a seller's revenue? (2) Without any a priori knowledge of the customers' demand function, how can a seller adaptively learn and optimize both pricing and advertising strategies using past purchase responses? To study the first question, we quantify the value of advertising by comparing the optimal revenue from jointly designing advertising and a single posted price with the optimal revenue from pricing alone. We show that advertising can increase revenue by at most a factor of two, and this bound is tight. For the second question, we study the seller's dynamic pricing and advertising problem under demand uncertainty. Our main result for this question is a computationally efficient online algorithm that achieves the optimal $O(T^{2/3} (m\\log T)^{1/3})$ regret rate when the valuation function is linear in the product quality. Here, $m$ is the cardinality of the discrete product quality domain and $T$ is the time horizon.", "published": "2023-04-27T17:52:06Z", "authors": ["Shipra Agrawal", "Yiding Feng", "Wei Tang"], "categories": ["cs.GT", "cs.LG"], "doi": "", "_query": "housing price text listing", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2002.05970v1", "title": "Intertemporal Price Discrimination with Time-Varying Valuations", "abstract": "A firm that sells a non perishable product considers intertemporal price discrimination in the objective of maximizing its long-run average revenue. We consider a general model of patient customers with changing valuations. Arriving customers wait for a random but bounded length of time and purchase the product when its price falls below their valuation, which varies following a stochastic process. We show the optimality of a class of cyclic strategies and obtain an algorithm that yields them. When the pace of intertemporal pricing is constrained to be comparable to the upper bound on customers patience levels, we have a good control on the cycle length and on the structure of the optimizing cyclic policies. The results also hold for forward looking strategic customers as well as under an alternative objective of maximizing infinite horizon discounted revenue. We cast our results in a general framework of optimizing the long-run average revenue for a class of revenue functions that we denote {\\it weakly coupled}, in which the revenue per period depends on a finite number of neighboring prices. We analyze in detail the case of Markovian valuations where we can obtain closed form formulations of the revenue function and some refinements of our main results. We also extend our results to the case of patience levels that are bounded only in expectation.", "published": "2020-02-14T11:23:49Z", "authors": ["Araman Victor", "Fayad Bassam"], "categories": ["math.OC", "math.PR"], "doi": "", "_query": "housing price text listing", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/1911.05949v1", "title": "Online Second Price Auction with Semi-bandit Feedback Under the Non-Stationary Setting", "abstract": "In this paper, we study the non-stationary online second price auction problem. We assume that the seller is selling the same type of items in $T$ rounds by the second price auction, and she can set the reserve price in each round. In each round, the bidders draw their private values from a joint distribution unknown to the seller. Then, the seller announced the reserve price in this round. Next, bidders with private values higher than the announced reserve price in that round will report their values to the seller as their bids. The bidder with the highest bid larger than the reserved price would win the item and she will pay to the seller the price equal to the second-highest bid or the reserve price, whichever is larger. The seller wants to maximize her total revenue during the time horizon $T$ while learning the distribution of private values over time. The problem is more challenging than the standard online learning scenario since the private value distribution is non-stationary, meaning that the distribution of bidders' private values may change over time, and we need to use the \\emph{non-stationary regret} to measure the performance of our algorithm. To our knowledge, this paper is the first to study the repeated auction in the non-stationary setting theoretically. Our algorithm achieves the non-stationary regret upper bound $\\tilde{\\mathcal{O}}(\\min\\{\\sqrt{\\mathcal S T}, \\bar{\\mathcal{V}}^{\\frac{1}{3}}T^{\\frac{2}{3}}\\})$, where $\\mathcal S$ is the number of switches in the distribution, and $\\bar{\\mathcal{V}}$ is the sum of total variation, and $\\mathcal S$ and $\\bar{\\mathcal{V}}$ are not needed to be known by the algorithm. We also prove regret lower bounds $Ω(\\sqrt{\\mathcal S T})$ in the switching case and $Ω(\\bar{\\mathcal{V}}^{\\frac{1}{3}}T^{\\frac{2}{3}})$ in the dynamic case, showing that our algorithm has nearly optimal \\emph{non-stationary regret}.", "published": "2019-11-14T05:46:42Z", "authors": ["Haoyu Zhao", "Wei Chen"], "categories": ["cs.LG", "cs.DS", "cs.GT", "stat.ML"], "doi": "", "_query": "housing price text listing", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/1304.3824v13", "title": "Pricing and Valuation under the Real-World Measure", "abstract": "In general it is not clear which kind of information is supposed to be used for calculating the fair value of a contingent claim. Even if the information is specified, it is not guaranteed that the fair value is uniquely determined by the given information. A further problem is that asset prices are typically expressed in terms of a risk-neutral measure. This makes it difficult to transfer the fundamental results of financial mathematics to econometrics. I show that the aforementioned problems evaporate if the financial market is complete and sensitive. In this case, after an appropriate choice of the numeraire, the discounted price processes turn out to be uniformly integrable martingales under the real-world measure. This leads to a Law of One Price and a simple real-world valuation formula in a model-independent framework where the number of assets as well as the lifetime of the market can be finite or infinite.", "published": "2013-04-13T17:15:13Z", "authors": ["Gabriel Frahm"], "categories": ["q-fin.GN"], "doi": "", "_query": "housing price text listing", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/cs/0102011v1", "title": "A Price Dynamics in Bandwidth Markets for Point-to-point Connections", "abstract": "We simulate a network of N routers and M network users making concurrent point-to-point connections by buying and selling router capacity from each other. The resources need to be acquired in complete sets, but there is only one spot market for each router. In order to describe the internal dynamics of the market, we model the observed prices by N-dimensional Ito-processes. Modeling using stochastic processes is novel in this context of describing interactions between end-users in a system with shared resources, and allows a standard set of mathematical tools to be applied. The derived models can also be used to price contingent claims on network capacity and thus to price complex network services such as quality of service levels, multicast, etc.", "published": "2001-02-15T10:44:06Z", "authors": ["Lars Rasmusson", "Erik Aurell"], "categories": ["cs.NI", "cond-mat.soft", "cs.MA"], "doi": "", "_query": "housing price text listing", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2102.09827v1", "title": "Minimal entropy and uniqueness of price equilibria in a pure exchange economy", "abstract": "We introduce uncertainty into a pure exchange economy and establish a connection between Shannon's differential entropy and uniqueness of price equilibria. The following conjecture is proposed under the assumption of a uniform probability distribution: entropy is minimal if and only if the price is unique for every economy. We show the validity of this conjecture for an arbitrary number of goods and two consumers and, under certain conditions, for an arbitrary number of consumers and two goods.", "published": "2021-02-19T09:38:59Z", "authors": ["Andrea Loi", "Stefano Matta"], "categories": ["econ.TH"], "doi": "", "_query": "housing price text listing", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2609.29700v1", "title": "Storage Options and Endogenous Commodity Prices in Continuous Time", "abstract": "We study the price formation of a storable commodity when the decision to sell or keep the commodity is treated as an embedded storage option. The price process is not imposed exogenously. Instead, a candidate price function determines the demand dynamics, while the optimal stopping value generated by those dynamics produces a new price function. We call a fixed point of this feedback mechanism an endogenous storage-option equilibrium (ESOE), using a separate acronym to distinguish the continuous-time construction studied here from the stationary rational expectations equilibrium terminology of the classical competitive-storage literature. A global existence result is established on a compact admissible class under a transparent drift--discount condition. The argument avoids the interval-by-interval extension of local fixed points and does not claim uniqueness from Schauder's theorem. We then characterize the equilibrium as a nonlinear obstacle problem and develop a monotone finite-difference/policy-iteration method with an asymptotically consistent far-field boundary condition. The numerical analysis separates fixed-point convergence from spatial discretization error and is supplemented by Monte Carlo validation, scaling diagnostics, comparative statics, and the distribution of optimal selling times. The results clarify how demand elasticity, discounting, depreciation, and volatility jointly determine the storage premium and the selling threshold.", "published": "2026-08-31T07:56:49Z", "authors": ["Nader Karimi", "Erfan Salavati", "Hojatollah Adibi"], "categories": ["q-fin.RM"], "doi": "", "_query": "housing price text listing", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2306.04065v1", "title": "Sustainability criterion implied externality pricing for resource extraction", "abstract": "A dynamic model is constructed that generalises the Hartwick and Van Long (2020) endogenous discounting setup by introducing externalities and asks what implications this has for optimal natural resource extraction with constant consumption. It is shown that a modified form of the Hotelling and Hartwick rule holds in which the externality component of price is a specific function of the instantaneous user costs and cross price elasticities. It is demonstrated that the externality adjusted marginal user cost of remaining natural reserves is equal to the marginal user cost of extracted resources invested in human-made reproducible capital. This lends itself to a discrete form with a readily intuitive economic interpretation that illuminates the stepwise impact of externality pricing on optimal extraction schedules.", "published": "2023-06-06T23:48:59Z", "authors": ["Daniel Grainger"], "categories": ["econ.GN"], "doi": "10.1016/j.econlet.2023.111448", "_query": "housing price text listing", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2402.15828v4", "title": "Pricing of geometric Asian options in the Volterra-Heston model", "abstract": "Geometric Asian options are a type of options where the payoff depends on the geometric mean of the underlying asset over a certain period of time. This paper is concerned with the pricing of such options for the class of Volterra-Heston models, covering the rough Heston model. We are able to derive semi-closed formulas for the prices of geometric Asian options with fixed and floating strikes for this class of stochastic volatility models. These formulas require the explicit calculation of the conditional joint Fourier transform of the logarithm of the stock price and the logarithm of the geometric mean of the stock price over time. Linking our problem to the theory of affine Volterra processes, we find a representation of this Fourier transform as a suitably constructed stochastic exponential, which depends on the solution of a Riccati-Volterra equation. Finally we provide a numerical study for our results in the rough Heston model.", "published": "2024-02-24T14:41:56Z", "authors": ["Florian Aichinger", "Sascha Desmettre"], "categories": ["q-fin.PR", "math.PR"], "doi": "", "_query": "housing price text listing", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2307.00410v1", "title": "A Classical Model of Speculative Asset Price Dynamics", "abstract": "In retrospect, the experimental findings on competitive market behavior called for a revival of the old, classical, view of competition as a collective higgling and bargaining process (as opposed to price-taking behaviors) founded on reservation prices (in place of the utility function). In this paper, we specialize the classical methodology to deal with speculation, an important impediment to price stability. The model involves typical features of a field or lab asset market setup and lends itself to an experimental test of its specific predictions; here we use the model to explain three general stylized facts, well established both empirically and experimentally: the excess, fat-tailed, and clustered volatility of speculative asset prices. The fat tails emerge in the model from the amplifying nature of speculation, leading to a random-coefficient autoregressive return process (and power-law tails); the volatility clustering is due to the traders' long memory of news; bubbles are a persistent phenomenon in the model, and, assuming the standard lab present value pattern, the bubble size increases with the proportion of speculators and decreases with the trading horizon.", "published": "2023-07-01T19:02:17Z", "authors": ["Sabiou Inoua", "Vernon Smith"], "categories": ["q-fin.GN", "econ.TH"], "doi": "", "_query": "housing price text listing", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/1912.07700v1", "title": "A Robust Predictive Model for Stock Price Prediction Using Deep Learning and Natural Language Processing", "abstract": "Prediction of future movement of stock prices has been a subject matter of many research work. There is a gamut of literature of technical analysis of stock prices where the objective is to identify patterns in stock price movements and derive profit from it. Improving the prediction accuracy remains the single most challenge in this area of research. We propose a hybrid approach for stock price movement prediction using machine learning, deep learning, and natural language processing. We select the NIFTY 50 index values of the National Stock Exchange of India, and collect its daily price movement over a period of three years (2015 to 2017). Based on the data of 2015 to 2017, we build various predictive models using machine learning, and then use those models to predict the closing value of NIFTY 50 for the period January 2018 till June 2019 with a prediction horizon of one week. For predicting the price movement patterns, we use a number of classification techniques, while for predicting the actual closing price of the stock, various regression models have been used. We also build a Long and Short-Term Memory - based deep learning network for predicting the closing price of the stocks and compare the prediction accuracies of the machine learning models with the LSTM model. We further augment the predictive model by integrating a sentiment analysis module on twitter data to correlate the public sentiment of stock prices with the market sentiment. This has been done using twitter sentiment and previous week closing values to predict stock price movement for the next week. We tested our proposed scheme using a cross validation method based on Self Organizing Fuzzy Neural Networks and found extremely interesting results.", "published": "2019-12-09T20:50:07Z", "authors": ["Sidra Mehtab", "Jaydip Sen"], "categories": ["q-fin.ST", "cs.LG"], "doi": "10.36227/techrxiv.15023361.v1", "_query": "housing price text listing", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2512.10076v1", "title": "Microfoundations and the Causal Interpretation of Price-Exposure Designs", "abstract": "This paper studies regional exposure designs that use commodity prices as instruments to study local effects of aggregate shocks. Unlike standard shift share designs that leverage differential exposure to many shocks, the price exposure relies on exogenous variation from a single shock, leading to challenges for both identification and inference. We motivate the design using a multi sector labor model. Under the model and a potential outcomes framework, we characterize the 2SLS and TWFE estimands as weighted averages of region and sector specific effects plus contamination terms driven by the covariance structure of prices and by general-equilibrium output responses. We derive conditions under which these estimands have a clear causal interpretation and provide simple sensitivity analysis procedures for violations. Finally, we show that standard inference procedures suffer from an overrejection problem in price-exposure designs. We derive a new standard error estimator and show its desirable finite-sample properties through Monte Carlo simulations. In an application to gold mining and homicides in the Amazon, the price exposure standard errors are roughly twice as large as conventional clustered standard errors, making the main effect statistically insignificant.", "published": "2025-12-10T20:57:08Z", "authors": ["Luca Moreno-Louzada", "Guilherme Figueira", "Pedro Picchetti"], "categories": ["econ.EM"], "doi": "", "_query": "housing price text listing", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2506.23504v1", "title": "Hybrid Approach for Electricity Price Forecasting using AlexNet and LSTM", "abstract": "The recent development of advanced machine learning methods for hybrid models has greatly addressed the need for the correct prediction of electrical prices. This method combines AlexNet and LSTM algorithms, which are used to introduce a new model with higher accuracy in price forecasting. Despite RNN and ANN being effective, they often fail to deal with forex time sequence data. The traditional methods do not accurately forecast the prices. These traditional methods only focus on demand and price which leads to insufficient analysis of data. To address this issue, using the hybrid approach, which focuses on external variables that also effect the predicted prices. Nevertheless, due to AlexNet's excellent feature extraction and LSTM's learning sequential patterns, the prediction accuracy is vastly increased. The model is built on the past data, which has been supplied with the most significant elements like demand, temperature, sunlight, and rain. For example, the model applies methods, such as minimum-maximum scaling and a time window, to predict the electricity prices of the future. The results show that this hybrid model is good than the standalone ones in terms of accuracy. Although we got our accuracy rating of 97.08, it shows higher accompaniments than remaining models RNN and ANN with accuracies of 96.64 and 96.63 respectively.", "published": "2025-06-30T04:06:24Z", "authors": ["Bosubabu Sambana", "Kotamsetty Geethika Devi", "Bandi Rajeswara Reddy", "Galeti Mohammad Hussain", "Gownivalla Siddartha"], "categories": ["cs.AI"], "doi": "10.1109/IDCIOT64235.2025.10915181", "_query": "housing price text listing", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/1706.04163v2", "title": "Universal scaling and nonlinearity of aggregate price impact in financial markets", "abstract": "How and why stock prices move is a centuries-old question still not answered conclusively. More recently, attention shifted to higher frequencies, where trades are processed piecewise across different timescales. Here we reveal that price impact has a universal non-linear shape for trades aggregated on any intra-day scale. Its shape varies little across instruments, but drastically different master curves are obtained for order-volume and -sign impact. The scaling is largely determined by the relevant Hurst exponents. We further show that extreme order flow imbalance is not associated with large returns. To the contrary, it is observed when the price is \"pinned\" to a particular level. Prices move only when there is sufficient balance in the local order flow. In fact, the probability that a trade changes the mid-price falls to zero with increasing (absolute) order-sign bias along an arc-shaped curve for all intra-day scales. Our findings challenge the widespread assumption of linear aggregate impact. They imply that market dynamics on all intra-day timescales are shaped by correlations and bilateral adaptation in the flows of liquidity provision and taking.", "published": "2017-06-13T17:03:10Z", "authors": ["Felix Patzelt", "Jean-Philippe Bouchaud"], "categories": ["q-fin.TR"], "doi": "10.1103/PhysRevE.97.012304", "_query": "housing price text listing", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2502.04811v1", "title": "On the Price of Anarchy in Packet Routing Games with FIFO", "abstract": "We investigate packet routing games in which network users selfishly route themselves through a network over discrete time, aiming to reach the destination as quickly as possible. Conflicts due to limited capacities are resolved by the first-in, first-out (FIFO) principle. Building upon the line of research on packet routing games initiated by Werth et al., we derive the first non-trivial bounds for packet routing games with FIFO. Specifically, we show that the price of anarchy is at most 2 for the important and well-motivated class of uniformly fastest route equilibria introduced by Scarsini et al. on any linear multigraph. We complement our results with a series of instances on linear multigraphs, where the price of stability converges to at least $\\frac{e}{e-1}$. Furthermore, our instances provide a lower bound for the price of anarchy of continuous Nash flows over time on linear multigraphs which establishes the first lower bound of $\\frac{e}{e-1}$ on a graph class where the monotonicity conjecture is proven by Correa et al.", "published": "2025-02-07T10:33:03Z", "authors": ["Daniel Schmand", "Torben Schürenberg", "Martin Strehler"], "categories": ["cs.GT"], "doi": "", "_query": "housing price text listing", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2105.09581v1", "title": "Valuation of European Options under an Uncertain Market Price of Volatility Risk", "abstract": "We propose a model to quantify the effect of parameter uncertainty on the option price in the Heston model. More precisely, we present a Hamilton-Jacobi-Bellman framework which allows us to evaluate best and worst case scenarios under an uncertain market price of volatility risk. For the numerical approximation the Hamilton--Jacobi--Bellman equation is reformulated to enable the solution with a finite element method. A case study with butterfly options exhibits how the dependence of Delta on the magnitude of the uncertainty is nonlinear and highly varied across the parameter regime. Keywords: Uncertain market price, Volatility risk, Hamilton-Jacobi-Bellman equation, Finite element method, Uncertainty quantification", "published": "2021-05-20T08:14:39Z", "authors": ["Bartosz Jaroszkowski", "Max Jensen"], "categories": ["q-fin.PR", "q-fin.CP"], "doi": "", "_query": "housing price text listing", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/1802.00626v1", "title": "Text Entry in Immersive Head-Mounted Display-based Virtual Reality using Standard Keyboards", "abstract": "We study the performance and user experience of two popular mainstream text entry devices, desktop keyboards and touchscreen keyboards, for use in Virtual Reality (VR) applications. We discuss the limitations arising from limited visual feedback, and examine the efficiency of different strategies of use. We analyze a total of 24 hours of typing data in VR from 24 participants and find that novice users are able to retain about 60% of their typing speed on a desktop keyboard and about 40-45\\% of their typing speed on a touchscreen keyboard. We also find no significant learning effects, indicating that users can transfer their typing skills fast into VR. Besides investigating baseline performances, we study the position in which keyboards and hands are rendered in space. We find that this does not adversely affect performance for desktop keyboard typing and results in a performance trade-off for touchscreen keyboard typing.", "published": "2018-02-02T10:28:44Z", "authors": ["Jens Grubert", "Lukas Witzani", "Eyal Ofek", "Michel Pahud", "Matthias Kranz", "Per Ola Kristensson"], "categories": ["cs.HC"], "doi": "", "_query": "housing price text listing", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/1701.01515v1", "title": "Analytic properties of American option prices under a modified Black-Scholes equation with spatial fractional derivatives", "abstract": "This paper investigates analytic properties of American option prices under the finite moment log-stable (FMLS) model. Under this model the price of American options is characterised by the free boundary problem of a fractional partial differential equation (FPDE) system. Using the technique of approximation we prove that the American put price under the FMLS model is convex with respect the underlying price, and specify the impact of the tail index on option prices.", "published": "2017-01-06T00:38:51Z", "authors": ["Wenting Chen", "Kai Du", "Xinzi Qiu"], "categories": ["q-fin.CP"], "doi": "10.1016/j.physa.2017.08.068", "_query": "housing price text listing", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/physics/0602015v1", "title": "Market efficiency and the long-memory of supply and demand: Is price impact variable and permanent or fixed and temporary?", "abstract": "In this comment we discuss the problem of reconciling the linear efficiency of price returns with the long-memory of supply and demand. We present new evidence that shows that efficiency is maintained by a liquidity imbalance that co-moves with the imbalance of buyer vs. seller initiated transactions. For example, during a period where there is an excess of buyer initiated transactions, there is also more liquidity for buy orders than sell orders, so that buy orders generate smaller and less frequent price responses than sell orders. At the moment a buy order is placed the transaction sign imbalance tends to dominate, generating a price impact. However, the liquidity imbalance rapidly increases with time, so that after a small number of time steps it cancels all the inefficiency caused by the transaction sign imbalance, bounding the price impact. While the view presented by Bouchaud et al. of a fixed and temporary bare price impact is self-consistent and formally correct, we argue that viewing this in terms of a variable but permanent price impact provides a simpler and more natural view. This is in the spirit of the original conjecture of Lillo and Farmer, but generalized to allow for finite time lags in the build up of the liquidity imbalance after a transaction. We discuss the possible strategic motivations that give rise to the liquidity imbalance and offer an alternative hypothesis. We also present some results that call into question the statistical significance of large swings in expected price impact at long times.", "published": "2006-02-02T12:02:40Z", "authors": ["J. Doyne Farmer", "Austin Gerig", "Fabrizio Lillo", "Szabolcs Mike"], "categories": ["physics.soc-ph", "q-fin.TR"], "doi": "", "_query": "housing price text listing", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2202.12844v1", "title": "Individual Fairness in Feature-Based Pricing for Monopoly Markets", "abstract": "We study fairness in the context of feature-based price discrimination in monopoly markets. We propose a new notion of individual fairness, namely, α-fairness, which guarantees that individuals with similar features face similar prices. First, we study discrete valuation space and give an analytical solution for optimal fair feature-based pricing. We show that the cost of fair pricing is defined as the ratio of expected revenue in an optimal feature-based pricing to the expected revenue in an optimal fair feature-based pricing (CoF) can be arbitrarily large in general. When the revenue function is continuous and concave with respect to the prices, we show that one can achieve CoF strictly less than 2, irrespective of the model parameters. Finally, we provide an algorithm to compute fair feature-based pricing strategy that achieves this CoF.", "published": "2022-02-25T17:35:24Z", "authors": ["Shantanu Das", "Swapnil Dhamal", "Ganesh Ghalme", "Shweta Jain", "Sujit Gujar"], "categories": ["cs.GT"], "doi": "", "_query": "housing price text listing", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2202.02268v1", "title": "StonkBERT: Can Language Models Predict Medium-Run Stock Price Movements?", "abstract": "To answer this question, we fine-tune transformer-based language models, including BERT, on different sources of company-related text data for a classification task to predict the one-year stock price performance. We use three different types of text data: News articles, blogs, and annual reports. This allows us to analyze to what extent the performance of language models is dependent on the type of the underlying document. StonkBERT, our transformer-based stock performance classifier, shows substantial improvement in predictive accuracy compared to traditional language models. The highest performance was achieved with news articles as text source. Performance simulations indicate that these improvements in classification accuracy also translate into above-average stock market returns.", "published": "2022-02-04T17:50:53Z", "authors": ["Stefan Pasch", "Daniel Ehnes"], "categories": ["cs.CL", "cs.AI", "cs.CE", "q-fin.CP"], "doi": "", "_query": "housing price text listing", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2012.03706v1", "title": "Pricing Security in Proof-of-Work Systems", "abstract": "A key component of security in decentralized blockchains is proof of opportunity cost among block producers. In the case of proof-of-work (PoW), currently used by the most prominent systems, the cost is due to spent computation. In this paper, we characterize the security investment of miners in terms of its cost in fiat money. This enables comparison of security allocations across PoW blockchains that generally use different PoW algorithms and reward miners in different cryptocurrency units. We prove that there exists a unique allocation equilibrium, depending on market prices only, that is achieved by both strategic miners (who contemplate the actions of others) and by miners seeking only short-term profit. In fact, the latter will unknowingly compensate for any attempt to deliberately shift security allocation away from equilibrium. Our conclusions are supported analytically through the development of a Markov decision process, game theoretical analysis, and derivation of no arbitrage conditions. We corroborate those results with empirical evidence from more than two years of blockchain and price data. Overall agreement is strong. We show that between January 1, 2018 and August 1, 2020, market prices predicted security allocation between Bitcoin and Bitcoin Cash with error less than 0.6%. And from the beginning of October 2019, until August 1, 2020, market prices predicted security allocation between Bitcoin and Litecoin with error of 0.45%. These results are further corroborated by our establishment of Granger-causality between change in market prices and change in security allocation. To demonstrate the practicality of our results, we describe a trustless oracle that leverages the equilibrium to estimate the price ratios of PoW cryptocurrencies from on-chain information only.", "published": "2020-12-07T14:05:11Z", "authors": ["George Bissias", "Rainer Böhme", "David Thibodeau", "Brian N. Levine"], "categories": ["cs.CR"], "doi": "", "_query": "housing price text listing", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2503.01040v1", "title": "Pricing time-capped American options using Least Squares Monte Carlo method", "abstract": "In this paper, we adopt the least squares Monte Carlo (LSMC) method to price time-capped American options. The aforementioned cap can be an independent random variable or dependent on asset price at random time. We allow various time caps. In particular, we give an algorithm for pricing the American options capped by the first drawdown epoch. We focus on the geometric Lévy market. We prove that our estimator converges to the true price as one takes the discretisation step tending to zero and the number of trajectories going to infinity.", "published": "2025-03-02T22:07:54Z", "authors": ["Paweł Stȩpniak", "Zbigniew Palmowski"], "categories": ["q-fin.MF"], "doi": "", "_query": "housing price text listing", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2209.10326v2", "title": "Toward 3D Spatial Reasoning for Human-like Text-based Visual Question Answering", "abstract": "Text-based Visual Question Answering~(TextVQA) aims to produce correct answers for given questions about the images with multiple scene texts. In most cases, the texts naturally attach to the surface of the objects. Therefore, spatial reasoning between texts and objects is crucial in TextVQA. However, existing approaches are constrained within 2D spatial information learned from the input images and rely on transformer-based architectures to reason implicitly during the fusion process. Under this setting, these 2D spatial reasoning approaches cannot distinguish the fine-grain spatial relations between visual objects and scene texts on the same image plane, thereby impairing the interpretability and performance of TextVQA models. In this paper, we introduce 3D geometric information into a human-like spatial reasoning process to capture the contextual knowledge of key objects step-by-step. %we formulate a human-like spatial reasoning process by introducing 3D geometric information for capturing key objects' contextual knowledge. To enhance the model's understanding of 3D spatial relationships, Specifically, (i)~we propose a relation prediction module for accurately locating the region of interest of critical objects; (ii)~we design a depth-aware attention calibration module for calibrating the OCR tokens' attention according to critical objects. Extensive experiments show that our method achieves state-of-the-art performance on TextVQA and ST-VQA datasets. More encouragingly, our model surpasses others by clear margins of 5.7\\% and 12.1\\% on questions that involve spatial reasoning in TextVQA and ST-VQA valid split. Besides, we also verify the generalizability of our model on the text-based image captioning task.", "published": "2022-09-21T12:49:14Z", "authors": ["Hao Li", "Jinfa Huang", "Peng Jin", "Guoli Song", "Qi Wu", "Jie Chen"], "categories": ["cs.CV", "cs.MM"], "doi": "10.1109/TIP.2023.3276570", "_query": "housing price text listing", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2405.14403v2", "title": "A practical scenario generation method for electricity prices on day-ahead and intraday spot markets", "abstract": "The increasing interest in demand-side management (DSM) as part of the energy cost optimization calls for effective methods to determine representative electricity prices for energy optimization and scheduling investigations. We propose a practical method to construct price profiles of day-ahead (DA) and intraday (ID) electricity spot markets. We construct single-day and single-week price profiles based on historical market time series to provide ready-to-use price data sets. Our method accounts for dominant mechanisms in price variation to preserve critical statistical features (e.g., mean and standard deviation) and transient patterns in the constructed profiles. Unlike common scenario generation approaches, the method is deterministic, with few degrees of freedom and minimal application effort. Our method ensures consistency between ID and DA price profiles when both are considered and introduces profile scaling to enable multiple scenario generation. Finally, we compare the constructed profiles to clustering techniques in a DSM case study, noting similar cost results.", "published": "2024-05-23T10:21:18Z", "authors": ["Chrysanthi Papadimitriou", "Jan C. Schulze", "Alexander Mitsos"], "categories": ["stat.AP", "cs.CE", "physics.soc-ph"], "doi": "10.1016/j.compchemeng.2025.109118", "_query": "housing price text listing", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/1905.06315v1", "title": "Higher order approximation of call option prices under stochastic volatility models", "abstract": "In the present paper, a decomposition formula for the call price due to Alòs is transformed into a Taylor type formula containing an infinite series with stochastic terms. The new decomposition may be considered as an alternative to the decomposition of the call price found in a recent paper of Alòs, Gatheral and Radoičić. We use the new decomposition to obtain various approximations to the call price in the Heston model with sharper estimates of the error term than in the previously known approximations. One of the formulas obtained in the present paper has five significant terms and an error estimate of the form $O(ν^{3}(\\left|ρ\\right|+ν))$, where $ν$ is the vol-vol parameter, and $ρ$ is the correlation coefficient between the price and the volatility in the Heston model. Another approximation formula contains seven more terms and the error estimate is of the form $O(ν^4(1+|ρ|)$. For the uncorrelated Hestom model ($ρ=0$), we obtain a formula with four significant terms and an error estimate $O(ν^6)$. Numerical experiments show that the new approximations to the call price perform especially well in the high volatility mode.", "published": "2019-05-15T17:47:25Z", "authors": ["Archil Gulisashvili", "Raúl Merino", "Marc Lagunas", "Josep Vives"], "categories": ["q-fin.CP"], "doi": "", "_query": "housing price text listing", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/cond-mat/0211260v1", "title": "Pricing Derivatives by Path Integral and Neural Networks", "abstract": "Recent progress in the development of efficient computational algorithms to price financial derivatives is summarized. A first algorithm is based on a path integral approach to option pricing, while a second algorithm makes use of a neural network parameterization of option prices. The accuracy of the two methods is established from comparisons with the results of the standard procedures used in quantitative finance.", "published": "2002-11-13T16:53:46Z", "authors": ["G. Montagna", "M. Morelli", "O. Nicrosini", "P. Amato", "M. Farina"], "categories": ["cond-mat.stat-mech", "cond-mat.dis-nn", "q-fin.PR"], "doi": "10.1016/S0378-4371(02)01907-6", "_query": "housing price text listing", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/1410.7263v2", "title": "Pricing in Social Networks with Negative Externalities", "abstract": "We study the problems of pricing an indivisible product to consumers who are embedded in a given social network. The goal is to maximize the revenue of the seller. We assume impatient consumers who buy the product as soon as the seller posts a price not greater than their values of the product. The product's value for a consumer is determined by two factors: a fixed consumer-specified intrinsic value and a variable externality that is exerted from the consumer's neighbors in a linear way. We study the scenario of negative externalities, which captures many interesting situations, but is much less understood in comparison with its positive externality counterpart. We assume complete information about the network, consumers' intrinsic values, and the negative externalities. The maximum revenue is in general achieved by iterative pricing, which offers impatient consumers a sequence of prices over time. We prove that it is NP-hard to find an optimal iterative pricing, even for unweighted tree networks with uniform intrinsic values. Complementary to the hardness result, we design a 2-approximation algorithm for finding iterative pricing in general weighted networks with (possibly) nonuniform intrinsic values. We show that, as an approximation to optimal iterative pricing, single pricing can work rather well for many interesting cases, but theoretically it can behave arbitrarily bad.", "published": "2014-10-27T14:56:27Z", "authors": ["Zhigang Cao", "Xujin Chen", "Xiaodong Hu", "Changjun Wang"], "categories": ["cs.GT", "cs.DM", "cs.SI"], "doi": "", "_query": "housing price text listing", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2509.23851v1", "title": "Price discrimination, algorithmic decision-making, and European non-discrimination law", "abstract": "Our society can benefit immensely from algorithmic decision-making and similar types of artificial intelligence. But algorithmic decision-making can also have discriminatory effects. This paper examines that problem, using online price differentiation as an example of algorithmic decision-making. With online price differentiation, a company charges different people different prices for identical products, based on information the company has about those people. The main question in this paper is: to what extent can non-discrimination law protect people against online price differentiation? The paper shows that online price differentiation and algorithmic decision-making could lead to indirect discrimination, for instance harming people with a certain ethnicity. Indirect discrimination occurs when a practice is neutral at first glance, but ends up discriminating against people with a protected characteristic, such as ethnicity. In principle, non-discrimination law prohibits indirect discrimination. The paper also shows, however, that non-discrimination law has flaws when applied to algorithmic decision-making. For instance, algorithmic discrimination can remain hidden: people may not realise that they are being discriminated against. And many types of unfair - some might say discriminatory - algorithmic decisions are outside the scope of current non-discrimination law.", "published": "2025-09-28T12:41:48Z", "authors": ["Frederik Zuiderveen Borgesius"], "categories": ["cs.CY"], "doi": "10.54648/eulr2020017", "_query": "housing price text listing", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/math/0205120v1", "title": "Pricing rule based on non-arbitrage arguments for random volatility and volatility smile", "abstract": "We consider a generic market model with a single stock and with random volatility. We assume that there is a number of tradable options for that stock with different strike prices. The paper states the problem of finding a pricing rule that gives Black-Scholes price for at-money options and such that the market is arbitrage free for any number of tradable options, even if there are two Brownian motions only: one drives the stock price, the other drives the volatility process. This problem is reduced to solving a parabolic equation.", "published": "2002-05-10T22:47:17Z", "authors": ["Nikolai Dokuchaev"], "categories": ["math.PR", "math.OC", "q-fin.PR"], "doi": "", "_query": "housing price text listing", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2101.03086v1", "title": "Market Making with Stochastic Liquidity Demand: Simultaneous Order Arrival and Price Change Forecasts", "abstract": "We provide an explicit characterization of the optimal market making strategy in a discrete-time Limit Order Book (LOB). In our model, the number of filled orders during each period depends linearly on the distance between the fundamental price and the market maker's limit order quotes, with random slope and intercept coefficients. The high-frequency market maker (HFM) incurs an end-of-the-day liquidation cost resulting from linear price impact. The optimal placement strategy incorporates in a novel and parsimonious way forecasts about future changes in the asset's fundamental price. We show that the randomness in the demand slope reduces the inventory management motive, and that a positive correlation between demand slope and investors' reservation prices leads to wider spreads. Our analysis reveals that the simultaneous arrival of buy and sell market orders (i) reduces the shadow cost of inventory, (ii) leads the HFM to reduce price pressures to execute larger flows, and (iii) introduces patterns of nonlinearity in the intraday dynamics of bid and ask spreads. Our empirical study shows that the market making strategy outperforms those which ignores randomness in demand, simultaneous arrival of buy and sell market orders, and local drift in the fundamental price.", "published": "2021-01-08T16:24:23Z", "authors": ["Agostino Capponi", "José E. Figueroa-López", "Chuyi Yu"], "categories": ["q-fin.TR"], "doi": "", "_query": "housing price text listing", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/1906.07862v1", "title": "An Integral Formulation and Convex Hull Pricing for Unit Commitment", "abstract": "Reducing uplift payments has been a challenging problem for most wholesale markets in US. The main difficulty comes from the unit commitment discrete decision makings. Recently convex hull pricing has shown promises to reduce the uplift payments. However, it has been intractable to obtain the optimal convex hull price. In this paper, we describe an innovative approach to decide the optimal convex hull price by simply solving a linear program. We also provide an example to illustrate the calculation process. The final computational experiments on a revised IEEE-118 bus system verify the cost effectiveness by utilizing our proposed approach.", "published": "2019-06-19T00:54:11Z", "authors": ["Yanan Yu", "Yongpei Guan", "Yonghong Chen"], "categories": ["eess.SY"], "doi": "", "_query": "housing price text listing", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2003.08976v1", "title": "The generalized Price equation: forces that change population statistics", "abstract": "The Price equation partitions the change in the expected value of a population measure. The first component describes the partial change caused by altered frequencies. The second component describes the partial change caused by altered measurements. In biology, frequency changes often associate with the direct effect of natural selection. Measure changes reflect processes during transmission that alter trait values. More broadly, the two components describe the direct forces that change population composition and the altered frame of reference that changes measured values. The classic Price equation is limited to population statistics that can expressed as the expected value of a measure. Many statistics cannot be expressed as expected values, such as the harmonic mean and the family of rescaled diversity measures. We generalize the Price equation to any population statistic that can be expressed as a function of frequencies and measurements. We obtain the generalized partition between the direct forces that cause frequency change and the altered frame of reference that changes measurements.", "published": "2020-03-19T18:43:59Z", "authors": ["Steven A. Frank", "William Godsoe"], "categories": ["q-bio.PE"], "doi": "", "_query": "housing price text listing", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/1701.02612v4", "title": "List Decoding of Insertions and Deletions", "abstract": "List decoding of insertions and deletions in the Levenshtein metric is considered. The Levenshtein distance between two sequences is the minimum number of insertions and deletions needed to turn one of the sequences into the other. In this paper, a Johnson-like upper bound on the maximum list size when list decoding in the Levenshtein metric is derived. This bound depends only on the length and minimum Levenshtein distance of the code, the length of the received word, and the alphabet size. It shows that polynomial-time list decoding beyond half the Levenshtein distance is possible for many parameters. Further, we also prove a lower bound on list decoding of deletions with with the well-known binary Varshamov-Tenengolts (VT) codes which shows that the maximum list size grows exponentially with the number of deletions. Finally, an efficient list decoding algorithm for two insertions/deletions with VT codes is given. This decoder can be modified to a polynomial-time list decoder of any constant number of insertions/deletions.", "published": "2017-01-10T14:33:29Z", "authors": ["Antonia Wachter-Zeh"], "categories": ["cs.IT"], "doi": "", "_query": "housing price text listing", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2008.02087v1", "title": "PriceAggregator: An Intelligent System for Hotel Price Fetching", "abstract": "This paper describes the hotel price aggregation system - PriceAggregator, deployed at Agoda, a global online travel agency for hotels, vacation rentals, flights and airport transfer. Agoda aggregates non-direct suppliers' hotel rooms to ensure that Agoda's customers always have the widest selection of hotels, room types and packages. As of today, Agoda aggregates millions of hotels. The major challenge is that each supplier only allows Agoda to fetch for the hotel price with a limited amount of Queries Per Second (QPS). Due to the sheer volume of Agoda's user search traffic, this limited amount of QPS is never enough to cover all user searches. Inevitably, many user searches have to be ignored. Hence, booking lost. To overcome the challenge, we built PriceAggregator. PriceAggregator intelligently determines when, how and what to send to the suppliers to fetch for price. In this paper, we not only prove PriceAggregator is optimal theoretically but also demonstrate that PriceAggregator performs well in practice. PriceAggregator has been deployed in Agoda. Extensive online A/B experimentation have shown that PriceAggregator increases Agoda's bookings significantly.", "published": "2020-06-30T01:28:25Z", "authors": ["Jiangwei Zhang", "Li Zhang", "Vigneshwaran Raveendran", "Ziv Ben-Zuk", "Leonard Lu"], "categories": ["cs.DC", "cs.LG", "stat.ML"], "doi": "", "_query": "housing price text listing", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2012.13331v2", "title": "Computation of Convex Hull Prices in Electricity Markets with Non-Convexities using Dantzig-Wolfe Decomposition", "abstract": "The presence of non-convexities in electricity markets has been an active research area for about two decades. The -- inevitable under current marginal cost pricing -- problem of guaranteeing that no market participant incurs losses in the day-ahead market is addressed in current practice through make-whole payments a.k.a. uplift. Alternative pricing rules have been studied to deal with this problem. Among them, Convex Hull (CH) prices associated with minimum uplift have attracted significant attention. Several US Independent System Operators (ISOs) have considered CH prices but resorted to approximations, mainly because determining exact CH prices is computationally challenging, while providing little intuition about the price formation rationale. In this paper, we describe the CH price estimation problem by relying on Dantzig-Wolfe decomposition and Column Generation, as a tractable, highly paralellizable, and exact method -- i.e., yielding exact, not approximate, CH prices -- with guaranteed finite convergence. Moreover, the approach provides intuition on the underlying price formation rationale. A test bed of stylized examples provide an exposition of the intuition in the CH price formation. In addition, a realistic ISO dataset is used to support scalability and validate the proof-of-concept.", "published": "2020-12-24T17:06:26Z", "authors": ["Panagiotis Andrianesis", "Dimitris Bertsimas", "Michael C. Caramanis", "William W. Hogan"], "categories": ["math.OC", "econ.GN", "eess.SY"], "doi": "", "_query": "housing price text listing", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/1502.02170v2", "title": "Linear-response reflection coefficient of the recorder air-jet amplifier", "abstract": "In a duct-flute such as the recorder, steady-state oscillations are controlled by two parameters, the blowing pressure and the frequency of the acoustic resonator. As in most feedback oscillators, the oscillation amplitude is determined by gain-saturation of the amplifier, and thus it cannot be controlled independently of blowing pressure and frequency unless the feedback loop is opened. In this work, the loop is opened by replacing the recorder body with a waveguide reflectometer: a section of transmission line with microphones, a signal source, and an absorbing termination. When the mean flow from the air-jet into the transmission line is not blocked, the air-jet amplifier is unstable to edge-tone oscillations through a feedback path that does not involve the acoustic resonator. When it is blocked, the air-jet is deflected somewhat outward and the system becomes stable. It is then possible to measure the reflection coefficient of the air-jet amplifier versus blowing pressure and acoustic frequency under linear response conditions, avoiding the complication of gain-saturation. The results provide a revealing test of flute drive models under the simplest conditions and with few unknown parameters. The strengths and weaknesses of flute drive models are discussed.", "published": "2015-02-07T18:36:55Z", "authors": ["John C. Price", "William A. Johnston", "Daniel D. McKinnon"], "categories": ["physics.flu-dyn"], "doi": "10.1121/1.4935400", "_query": "housing price text listing", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2307.00571v2", "title": "The fundamental theorem of asset pricing with and without transaction costs", "abstract": "We prove a version of the fundamental theorem of asset pricing (FTAP) in continuous time that is based on the strict no-arbitrage condition and that is applicable to both frictionless markets and markets with proportional transaction costs. We consider a market with a single risky asset whose ask price process is higher than or equal to its bid price process. Neither the concatenation property of the set of wealth processes, that is used in the proof of the frictionless FTAP, nor some boundedness property of the trading volume of admissible strategies usually argued with in models with a nonvanishing bid-ask spread need to be satisfied in our model.", "published": "2023-07-02T13:44:12Z", "authors": ["Christoph Kühn"], "categories": ["q-fin.MF", "math.PR"], "doi": "10.1111/mafi.12453", "_query": "housing price text listing", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/1009.2928v1", "title": "The endogenous dynamics of markets: price impact and feedback loops", "abstract": "We review the evidence that the erratic dynamics of markets is to a large extent of endogenous origin, i.e. determined by the trading activity itself and not due to the rational processing of exogenous news. In order to understand why and how prices move, the joint fluctuations of order flow and liquidity - and the way these impact prices - become the key ingredients. Impact is necessary for private information to be reflected in prices, but by the same token, random fluctuations in order flow necessarily contribute to the volatility of markets. Our thesis is that the latter contribution is in fact dominant, resulting in a decoupling between prices and fundamental values, at least on short to medium time scales. We argue that markets operate in a regime of vanishing revealed liquidity, but large latent liquidity, which would explain their hyper-sensitivity to fluctuations. More precisely, we identify a dangerous feedback loop between bid-ask spread and volatility that may lead to micro-liquidity crises and price jumps. We discuss several other unstable feedback loops that should be relevant to account for market crises: imitation, unwarranted quantitative models, pro-cyclical regulation, etc.", "published": "2010-09-15T13:54:42Z", "authors": ["Jean-Philippe Bouchaud"], "categories": ["q-fin.ST", "cond-mat.stat-mech"], "doi": "", "_query": "housing price text listing", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/1807.11645v2", "title": "On algebraic integers of bounded house and preperiodicity in polynomial semigroup dynamics", "abstract": "We consider semigroup dynamical systems defined by several polynomials over a number field $\\mathbb{K}$, and the orbit (tree) they generate at a given point. We obtain finiteness results for the set of preperiodic points of such systems that fall in the cyclotomic closure of $\\mathbb{K}$. More generally, we consider the finiteness of initial points in the cyclotomic closure for which the orbit contains an algebraic integer of bounded house. This work extends previous results for classical obits generated by one polynomial over $\\mathbb{K}$ obtained initially by Dvornicich and Zannier (for preperiodic points), and then by Chen and Ostafe (for roots of unity and elements of bounded house in orbits).", "published": "2018-07-31T03:01:47Z", "authors": ["Alina Ostafe", "Marley Young"], "categories": ["math.NT"], "doi": "", "_query": "housing price text listing", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/cond-mat/0204331v7", "title": "Option Pricing Formulas based on a non-Gaussian Stock Price Model", "abstract": "Options are financial instruments that depend on the underlying stock. We explain their non-Gaussian fluctuations using the nonextensive thermodynamics parameter $q$. A generalized form of the Black-Scholes (B-S) partial differential equation, and some closed-form solutions are obtained. The standard B-S equation ($q=1$) which is used by economists to calculate option prices requires multiple values of the stock volatility (known as the volatility smile). Using $q=1.5$ which well models the empirical distribution of returns, we get a good description of option prices using a single volatility.", "published": "2002-04-15T21:39:53Z", "authors": ["Lisa Borland"], "categories": ["cond-mat.stat-mech", "q-fin.PR"], "doi": "10.1103/PhysRevLett.89.098701", "_query": "housing price text listing", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/1510.03205v2", "title": "Price response in correlated financial markets: empirical results", "abstract": "Previous studies of the stock price response to individual trades focused on single stocks. We empirically investigate the price response of one stock to the trades of other stocks. How large is the impact of one stock on others and vice versa? -- This impact of trades on the price change across stocks appears to be transient instead of permanent. Performing different averages, we distinguish active and passive responses. The two average responses show different characteristic dependences on the time lag. The passive response exhibits a shorter response period with sizeable volatilities, and the active response a longer period. We also study the response for a given stock with respect to different sectors and to the whole market. Furthermore, we compare the self-response with the various cross-responses. The correlation of the trade signs is a short-memory process for a pair of stocks, but it turns into a long-memory process when averaged over different pairs of stocks.", "published": "2015-10-12T09:51:49Z", "authors": ["Shanshan Wang", "Rudi Schäfer", "Thomas Guhr"], "categories": ["q-fin.ST", "q-fin.TR"], "doi": "", "_query": "housing price text listing", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2511.22291v1", "title": "Online Dynamic Pricing of Complementary Products", "abstract": "Traditional pricing paradigms, once dominated by static models and rule-based heuristics, are increasingly being replaced by dynamic, data-driven approaches powered by machine learning algorithms. Despite their growing sophistication, most dynamic pricing algorithms focus on optimizing the price of each product independently, disregarding potential interactions among items. By neglecting these interdependencies in consumer demand across related goods, sellers may fail to capture the full potential of coordinated pricing strategies. In this paper, we address this problem by exploring dynamic pricing mechanisms designed explicitly for complementary products, aiming to exploit their joint demand structure to maximize overall revenue. We present an online learning algorithm considering both positive and negative interactions between products' demands. The algorithm utilizes transaction data to identify advantageous complementary relationships through an integer programming problem between different items, and then optimizes pricing strategies using data-driven and computationally efficient multi-armed bandit solutions based on heteroscedastic Gaussian processes. We validate our solution in a simulated environment, and we demonstrate that our solution improves the revenue w.r.t. a comparable learning algorithm ignoring such interactions.", "published": "2025-11-27T10:12:51Z", "authors": ["Marco Mussi", "Marcello Restelli"], "categories": ["cs.LG"], "doi": "", "_query": "housing price text listing", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2605.02040v3", "title": "Analytic approximation for Bachelier option prices and applications", "abstract": "It is well-known that, in the Bachelier model, when asset prices and volatilities are uncorrelated, the implied volatility coincides with the fair value of the volatility swap. In this paper, via classical Itô calculus and Taylor expansions, we write the price for out-of-the-money (OTM) and in-the-money (ITM) options as an expansion with respect to the moneyness, where the coefficients are related to the negative (non-integer) powers of the future mean volatility. As an a application, we use it as a control variate to reduce the variance of Monte Carlo option prices in the correlated case.", "published": "2026-05-03T20:09:41Z", "authors": ["Elisa Alòs", "Òscar Burés"], "categories": ["q-fin.CP"], "doi": "", "_query": "housing price text listing", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2007.09318v2", "title": "Monochromatic Triangles, Triangle Listing and APSP", "abstract": "One of the main hypotheses in fine-grained complexity is that All-Pairs Shortest Paths (APSP) for $n$-node graphs requires $n^{3-o(1)}$ time. Another famous hypothesis is that the $3$SUM problem for $n$ integers requires $n^{2-o(1)}$ time. Although there are no direct reductions between $3$SUM and APSP, it is known that they are related: there is a problem, $(\\min,+)$-convolution that reduces in a fine-grained way to both, and a problem Exact Triangle that both fine-grained reduce to. In this paper we find more relationships between these two problems and other basic problems. Pătraşcu had shown that under the $3$SUM hypothesis the All-Edges Sparse Triangle problem in $m$-edge graphs requires $m^{4/3-o(1)}$ time. The latter problem asks to determine for every edge $e$, whether $e$ is in a triangle. It is equivalent to the problem of listing $m$ triangles in an $m$-edge graph where $m=\\tilde{O}(n^{1.5})$, and can be solved in $O(m^{1.41})$ time [Alon et al.'97] with the current matrix multiplication bounds, and in $\\tilde{O}(m^{4/3})$ time if $ω=2$. We show that one can reduce Exact Triangle to All-Edges Sparse Triangle, showing that All-Edges Sparse Triangle (and hence Triangle Listing) requires $m^{4/3-o(1)}$ time also assuming the APSP hypothesis. This allows us to provide APSP-hardness for many dynamic problems that were previously known to be hard under the $3$SUM hypothesis. We also consider the previously studied All-Edges Monochromatic Triangle problem. Via work of [Lincoln et al.'20], our result on All-Edges Sparse Triangle implies that if the All-Edges Monochromatic Triangle problem has an $O(n^{2.5-ε})$ time algorithm for $ε>0$, then both the APSP and $3$SUM hypotheses are false. We also connect the problem to other ``intermediate'' problems, whose runtimes are between $O(n^ω)$ and $O(n^3)$, such as the Max-Min product problem.", "published": "2020-07-18T03:29:55Z", "authors": ["Virginia Vassilevska Williams", "Yinzhan Xu"], "categories": ["cs.CC"], "doi": "", "_query": "housing price text listing", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/1810.03067v1", "title": "Geocoding Without Geotags: A Text-based Approach for reddit", "abstract": "In this paper, we introduce the first geolocation inference approach for reddit, a social media platform where user pseudonymity has thus far made supervised demographic inference difficult to implement and validate. In particular, we design a text-based heuristic schema to generate ground truth location labels for reddit users in the absence of explicitly geotagged data. After evaluating the accuracy of our labeling procedure, we train and test several geolocation inference models across our reddit data set and three benchmark Twitter geolocation data sets. Ultimately, we show that geolocation models trained and applied on the same domain substantially outperform models attempting to transfer training data across domains, even more so on reddit where platform-specific interest-group metadata can be used to improve inferences.", "published": "2018-10-07T01:46:27Z", "authors": ["Keith Harrigian"], "categories": ["cs.IR", "cs.CL"], "doi": "", "_query": "housing price text listing", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2002.07595v1", "title": "Market Power in Convex Hull Pricing", "abstract": "The start up costs in many kinds of generators lead to complex cost structures, which in turn yield severe market loopholes in the locational marginal price (LMP) scheme. Convex hull pricing (a.k.a. extended LMP) is proposed to improve the market efficiency by providing the minimal uplift payment to the generators. In this letter, we consider a stylized model where all generators share the same generation capacity. We analyze the generators' possible strategic behaviors in such a setting, and then propose an index for market power quantification in the convex hull pricing schemes.", "published": "2020-02-15T09:10:06Z", "authors": ["Jian Sun", "Chenye Wu"], "categories": ["math.OC", "cs.CE", "econ.GN"], "doi": "", "_query": "housing price text listing", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2111.09933v2", "title": "Loss Functions for Discrete Contextual Pricing with Observational Data", "abstract": "We study a pricing setting where each customer is offered a contextualized price based on customer and/or product features. Often only historical sales data are available, so we observe whether a customer purchased a product at the price prescribed rather than the customer's true valuation. Such observational data are influenced by historical pricing policies, which introduce difficulties in evaluating the effectiveness of future policies. The goal of this paper is to formulate loss functions that can be used for evaluating pricing policies directly from observational data, rather than going through an intermediate demand estimation stage, which may suffer from bias. To achieve this, we adapt ideas from machine learning with corrupted labels, where we consider each observed purchase decision as a known probabilistic transformation of the customer's valuation. From this transformation, we derive a class of unbiased loss functions. Within this class, we identify minimum variance estimators and estimators robust to poor demand estimation. Furthermore, we show that for contextual pricing, estimators popular in the off-policy evaluation literature fall within this class of loss functions. We offer managerial insights into scenarios under which these estimators are effective.", "published": "2021-11-18T20:12:57Z", "authors": ["Max Biggs", "Ruijiang Gao", "Wei Sun"], "categories": ["cs.LG", "stat.ML"], "doi": "", "_query": "housing price text listing", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/1604.08735v1", "title": "Pricing Bermudan options under local Lévy models with default", "abstract": "We consider a defaultable asset whose risk-neutral pricing dynamics are described by an exponential Lévy-type martingale. This class of models allows for a local volatility, local default intensity and a locally dependent Lévy measure. We present a pricing method for Bermudan options based on an analytical approximation of the characteristic function combined with the COS method. Due to a special form of the obtained characteristic function the price can be computed using a Fast Fourier Transform-based algorithm resulting in a fast and accurate calculation. The Greeks can be computed at almost no additional computational cost. Error bounds for the approximation of the characteristic function as well as for the total option price are given.", "published": "2016-04-29T08:45:03Z", "authors": ["Anastasia Borovykh", "Cornelis W. Oosterlee", "Andrea Pascucci"], "categories": ["q-fin.PR"], "doi": "", "_query": "housing price text listing", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2505.04042v1", "title": "Ethical Appetite: Consumer Preferences and Price Premiums for Animal Welfare-Friendly Food Products", "abstract": "This study examines how consumer attitudes toward animal welfare influence food selection and pricing using real-world market data from a Swiss supermarket. Our findings indicate that higher animal welfare standards are consistently associated with higher prices, suggesting that ethical considerations play a significant role in generating price premiums based on consumer preferences. On average, a one-point increase in the animal welfare score (ranging from 1 to 5, with 5 being the highest) corresponds to a 16.4% price increase, with the effect being most pronounced in Dairy & Eggs (25.3%), compared to Meat & Fish (14.3%). These results highlight the psychological and behavioral factors underlying consumer preferences for ethically produced foods. Additionally, we find limited evidence of a price premium for climate-friendly food products, observed only in Yogurts & Desserts, a subcategory within Dairy & Eggs. Our findings contribute to the understanding of how ethical food attributes influence consumer decision-making and pricing in retail settings.", "published": "2025-05-07T00:45:45Z", "authors": ["Voraprapa Nakavachara", "Chanon Thongtai", "Thanarat Chalidabhongse", "Chanathip Pharino"], "categories": ["econ.GN"], "doi": "", "_query": "housing price text listing", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/1604.00105v3", "title": "Option pricing under fast-varying long-memory stochastic volatility", "abstract": "Recent empirical studies suggest that the volatility of an underlying price process may have correlations that decay slowly under certain market conditions. In this paper, the volatility is modeled as a stationary process with long-range correlation properties in order to capture such a situation, and we consider European option pricing. This means that the volatility process is neither a Markov process nor a martingale. However, by exploiting the fact that the price process is still a semimartingale and accordingly using the martingale method, we can obtain an analytical expression for the option price in the regime where the volatility process is fast mean-reverting. The volatility process is modeled as a smooth and bounded function of a fractional Ornstein-Uhlenbeck process. We give the expression for the implied volatility, which has a fractional term structure.", "published": "2016-04-01T02:20:20Z", "authors": ["Josselin Garnier", "Knut Solna"], "categories": ["q-fin.PR", "math.PR"], "doi": "", "_query": "housing price text listing", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2605.15411v1", "title": "Harnessing Unimodality in Semiparametric Contextual Pricing via Oracle Price Map Learning", "abstract": "We study contextual dynamic pricing in a semiparametric scalar-index valuation model where the latent value is $v_t=μ_\\ast(\\mathsf c_t)+ξ_t$, with an unknown utility map $μ_\\ast$ and an unknown additive noise distribution. The key decision object is the one-dimensional oracle price map $u\\mapsto p^\\ast(u)$ induced by the scalar index $u=μ_\\ast(\\mathsf c)$ and the noise tail. Under the $β$-Hölder smoothness of the tail function for $β\\geq 2$ and a revenue-geometry condition that gives a unique, stable, interior maximizer, this oracle map is itself $(β-1)$-smooth. We exploit such structure through $\\mathsf{ORBIT}$, a modular coarse-to-fine policy that takes a scalar pilot index as input, localizes a benchmark price in each active bin, and learns a local polynomial approximation of the oracle map inside a trust region via bandit convex optimization. For the baseline linear utility model $μ_\\ast(\\mathsf c)=\\mathsf c^\\topθ_\\ast$, an adaptive elliptical exploration scheme constructs the required scalar pilot online without distributional assumptions on the contexts. The resulting policy achieves regret $\\widetilde{O}\\big(T^{\\frac{2β-1}{4β-3}}+\\sqrt{dT}\\big)$. For fixed $d$, we establish a matching lower bound in the horizon dependence, unveiling that the nonparametric oracle-map learning term is minimax sharp. The same scalar-pilot interface also yields extensions to sparse high-dimensional linear utility and nonparametric Hölder utility.", "published": "2026-05-14T20:53:23Z", "authors": ["Yingying Fan", "Yuxuan Han", "Jinchi Lv", "Xiaocong Xu", "Zhengyuan Zhou"], "categories": ["stat.ML", "cs.LG", "math.OC"], "doi": "", "_query": "housing price text listing", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/1611.00101v3", "title": "A Cayley graph for $F_{2}\\times F_{2}$ which is not minimally almost convex", "abstract": "We give an example of a Cayley graph $Γ$ for the group $F_{2}\\times F_{2}$ which is not minimally almost convex (MAC). On the other hand, the standard Cayley graph for $F_{2}\\times F_{2}$ does satisfy the falsification by fellow traveler property (FFTP), which is strictly stronger. As a result, any Cayley graph property $K$ lying between FFTP and MAC (i.e., $\\text{FFTP}\\Rightarrow K\\Rightarrow\\text{MAC}$) is dependent on the generating set. This includes the well known properties FFTP and almost convexity, which were already known to depend on the generating set as well as Poénaru's condition $P(2)$ and the basepoint loop shortening property for which dependence on the generating set was previously unknown. We also show that the Cayley graph $Γ$ does not have the loop shortening property, so this property also depends on the generating set.", "published": "2016-11-01T01:55:48Z", "authors": ["Andrew Elvey Price"], "categories": ["math.GR"], "doi": "", "_query": "housing price text listing", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/1109.2631v1", "title": "Optimal trade execution and price manipulation in order books with time-varying liquidity", "abstract": "In financial markets, liquidity is not constant over time but exhibits strong seasonal patterns. In this article we consider a limit order book model that allows for time-dependent, deterministic depth and resilience of the book and determine optimal portfolio liquidation strategies. In a first model variant, we propose a trading dependent spread that increases when market orders are matched against the order book. In this model no price manipulation occurs and the optimal strategy is of the wait region - buy region type often encountered in singular control problems. In a second model, we assume that there is no spread in the order book. Under this assumption we find that price manipulation can occur, depending on the model parameters. Even in the absence of classical price manipulation there may be transaction triggered price manipulation. In specific cases, we can state the optimal strategy in closed form.", "published": "2011-09-12T21:31:42Z", "authors": ["Antje Fruth", "Torsten Schoeneborn", "Mikhail Urusov"], "categories": ["q-fin.TR", "math.OC"], "doi": "", "_query": "housing price text listing", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2508.15080v3", "title": "Fast reliable pricing and calibration of the rough Heston model", "abstract": "The paper is an extended and modified version of the preprint S.Boyarchenko and S.Levendorskiĭ ``Correct implied volatility shapes and reliable pricing in the rough Heston model\". We combine a modification of the Adams method with the SINH-acceleration method S.Boyarchenko and S.Levendorskii (IJTAF 2019, v.22) of Fourier inversion (iFT) to price vanilla options under the rough Heston model. For moderate or long maturities and strikes near spot, thousands of prices are computed in several milliseconds (ms) in Matlab on a Mac with moderate specs, with relative errors $\\lesssim 10^{-4}$. Even for options close to expiry and far-OTM, the pricing takes a few tens or hundreds of ms. We show that, for the calibrated parameters in El Euch and Rosenbaum (Math.Finance 2019, v.29), the model implied vol surface is much flatter and fits the market data poorly; thus the calibration in op.cit. is a case of ``ghost calibration'' (M.Boyarchenko and S.Levendorskiĭ, Quant. Finance 2015, v.15): numerical error and model specification error offset each other, creating an apparently good fit that vanishes when a more accurate pricer is used. We explain how such errors arise in popular iFT implementations that use fixed numerical parameters, yielding spurious smiles/skews, and provide numerical evidence that SINH acceleration is faster and more accurate than competing methods. Robust error control is ensured by a general Conformal Bootstrap principle that we formulate; the principle is applicable to many Fourier-pricing methods. We outline how this principle and our method enable accurate calibration procedures that are hundreds of times faster than approaches commonly used in the industry. Disclaimer: The views expressed herein are those of the authors only. No other representation should be attributed.", "published": "2025-08-20T21:36:22Z", "authors": ["Svetlana Boyarchenko", "Marco de Innocentis", "Sergei Levendorskiĭ"], "categories": ["q-fin.CP"], "doi": "", "_query": "housing price text listing", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2602.01376v1", "title": "Keeping Up with the Correlations: Stochastic Spot/Volatility Correlation and Exotic Pricing", "abstract": "We consider a novel use case for the Double Heston model (Christoffersen et al,, 2009), where the two Heston sub-variances have different spot/volatility correlations but the same volatility of volatility and mean reversion speed. This parameterization generalizes the traditional Heston stochastic volatility model (Heston, 1993) to include stochastic spot/volatility correlation. It is an affine model, allowing European options to be priced efficiently by numerically integrating over a closed-form characteristic function. This model incorporates a key dynamic relevant for pricing barrier derivatives in the foreign exchange markets: a positive correlation between moves in implied volatility skew and moves in the spot price. We analyze that correlation and its impact on both barrier option pricing and volatility swap pricing. Those price impacts are comparable to or larger than the bid/ask spreads for these products. Adding stochastic spot/volatility correlation increases the prices of out-of-the-money knockout options and one touch options, assuming that the model is calibrated to market vanilla option prices. It also increases the fair strike of volatility swaps compared to the Heston model.", "published": "2026-02-01T18:23:39Z", "authors": ["Mark Higgins"], "categories": ["q-fin.PR"], "doi": "", "_query": "housing price text listing", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2307.04055v2", "title": "Contextual Dynamic Pricing with Strategic Buyers", "abstract": "Personalized pricing, which involves tailoring prices based on individual characteristics, is commonly used by firms to implement a consumer-specific pricing policy. In this process, buyers can also strategically manipulate their feature data to obtain a lower price, incurring certain manipulation costs. Such strategic behavior can hinder firms from maximizing their profits. In this paper, we study the contextual dynamic pricing problem with strategic buyers. The seller does not observe the buyer's true feature, but a manipulated feature according to buyers' strategic behavior. In addition, the seller does not observe the buyers' valuation of the product, but only a binary response indicating whether a sale happens or not. Recognizing these challenges, we propose a strategic dynamic pricing policy that incorporates the buyers' strategic behavior into the online learning to maximize the seller's cumulative revenue. We first prove that existing non-strategic pricing policies that neglect the buyers' strategic behavior result in a linear $Ω(T)$ regret with $T$ the total time horizon, indicating that these policies are not better than a random pricing policy. We then establish that our proposed policy achieves a sublinear regret upper bound of $O(\\sqrt{T})$. Importantly, our policy is not a mere amalgamation of existing dynamic pricing policies and strategic behavior handling algorithms. Our policy can also accommodate the scenario when the marginal cost of manipulation is unknown in advance. To account for it, we simultaneously estimate the valuation parameter and the cost parameter in the online pricing policy, which is shown to also achieve an $O(\\sqrt{T})$ regret bound. Extensive experiments support our theoretical developments and demonstrate the superior performance of our policy compared to other pricing policies that are unaware of the strategic behaviors.", "published": "2023-07-08T23:06:42Z", "authors": ["Pangpang Liu", "Zhuoran Yang", "Zhaoran Wang", "Will Wei Sun"], "categories": ["stat.ML", "cs.AI", "cs.GT", "cs.LG"], "doi": "", "_query": "housing price text listing", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2607.25105v1", "title": "Simulating Single Transferable Voting for the Colorado House of Representatives", "abstract": "Social choice theory research demonstrates that single transferable voting (STV) results in more proportionally representative legislative bodies. We aim to understand how using multi-member districts and ranked ballots with STV would affect the representation of political parties in the Colorado House of Representatives. We investigated this objective by producing 10,000 multi-member districting plans of Colorado, generating ranked ballots for each of these plans using returns from the 2022 Colorado attorney general race, and simulating STV using these ballots. Our simulated STV elections for the Colorado House of Representatives gave more proportional representation for Democrats and Republicans than the current first-past-the-post system. Future research should explore how the implementation of STV would influence the representation of racial and language minority groups in the Colorado General Assembly to provide guidance on electoral reform in the state.", "published": "2026-07-27T22:08:39Z", "authors": ["Nora Nelson Laird", "Beth Malmskog", "Iris Pixler", "Katherine Rodbell"], "categories": ["cs.CY"], "doi": "", "_query": "housing price text listing", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/1805.00429v3", "title": "Trisections of surface complements and the Price twist", "abstract": "Given an $S\\cong \\mathbb{R}P^2$ smoothly embedded in a 4-manifold $X^4$ with Euler number 2 or -2, the Price twist is a surgery operation on $ν(S)$ yielding (up to) three different 4-manifolds: $X^4,τ_S(X^4),Σ_S(X^4)$. This is of particular interest when $X^4=S^4$, as then $Σ_S(X^4)$ is a homotopy 4-sphere which is not obviously diffeomorphic to $S^4$. In this paper, we show how to produce a trisection description of each Price twist on $S\\subset X^4$ by producing a relative trisection of $X^4\\setminusν(S)$. Moreover, we show how to produce a trisection description of general surface complements in 4-manifolds.", "published": "2018-05-01T16:53:06Z", "authors": ["Seungwon Kim", "Maggie Miller"], "categories": ["math.GT"], "doi": "10.2140/agt.2020.20.343", "_query": "housing price text listing", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/1905.12859v1", "title": "Heterogeneity in demand and optimal price conditioning for local rail transport", "abstract": "This paper describes the results of research project on optimal pricing for LLC \"Perm Local Rail Company\". In this study we propose a regression tree based approach for estimation of demand function for local rail tickets considering high degree of demand heterogeneity by various trip directions and the goals of travel. Employing detailed data on ticket sales for 5 years we estimate the parameters of demand function and reveal the significant variation in price elasticity of demand. While in average the demand is elastic by price, near a quarter of trips is characterized by weakly elastic demand. Lower elasticity of demand is correlated with lower degree of competition with other transport and inflexible frequency of travel.", "published": "2019-05-30T05:32:20Z", "authors": ["Evgeniy M. Ozhegov", "Alina Ozhegova"], "categories": ["econ.EM", "cs.LG"], "doi": "", "_query": "housing price text listing", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2304.05005v2", "title": "Bayes correlated equilibria, no-regret dynamics in Bayesian games, and the price of anarchy", "abstract": "This paper investigates equilibrium computation and the price of anarchy for Bayesian games, which are the fundamental models of games with incomplete information. In normal-form games with complete information, it is known that efficiently computable no-regret dynamics converge to correlated equilibria, and the price of anarchy for correlated equilibria can be bounded for a broad class of games called smooth games. However, in Bayesian games, as surveyed by Forges (1993), several non-equivalent extensions of correlated equilibria exist, and it remains unclear whether they can be efficiently computed or whether their price of anarchy can be bounded. In this paper, we identify a natural extension of correlated equilibria that can be computed efficiently and is guaranteed to have bounds on the price of anarchy in various games. First, we propose a variant of regret called untruthful swap regret. If each player minimizes it in repeated play of Bayesian games, the empirical distribution of these dynamics is guaranteed to converge to communication equilibria, which is one of the extensions of correlated equilibria proposed by Myerson (1982). We present an efficient algorithm for minimizing untruthful swap regret with a sublinear upper bound, which we prove to be tight in terms of the number of types. As a result, by simulating the dynamics with our algorithm, we can approximately compute a communication equilibrium in polynomial time. Furthermore, we extend existing lower bounds on the price of anarchy based on the smoothness arguments from Bayes--Nash equilibria to equilibria obtained by the proposed dynamics.", "published": "2023-04-11T06:22:51Z", "authors": ["Kaito Fujii"], "categories": ["cs.GT", "cs.LG"], "doi": "", "_query": "housing price text listing", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/1510.07199v1", "title": "Coherent CVA and FVA with Liability Side Pricing of Derivatives", "abstract": "This article presents FVA and CVA of a bilateral derivative in a coherent manner, based on recent developments in fair value accounting and ISDA standards. We argue that a derivative liability, after primary risk factors being hedged, resembles in economics an issued variable funding note, and should be priced at the market rate of the issuer's debt. For the purpose of determining the fair value, the party on the liability side is economically neutral to make a deposit to the other party, which earns his current debt rate and effectively provides funding and hedging for the party holding the derivative asset. The newly derived partial differential equation for an option discounts the derivative's receivable part with counterparty's curve and payable part with own financing curve. The price difference from the counterparty risk free price, or total counterparty risk adjustment, is precisely defined by discounting the product of the risk free price and the credit spread at the local liability curve. Subsequently the adjustment can be broken into a default risk component -- CVA and a funding component -- FVA, consistent with a simple note's fair value treatment and in accordance with the usual understanding of a bond's credit spread consisting of a CDS spread and a basis. As for FVA, we define a cost -- credit funding adjustment (CFA) and a benefit -- debit funding adjustment (DFA), in parallel to CVA and DVA and attributed to counterparty's and own funding basis. This resolves a number of outstanding FVA debate issues, such as double counting, violation of the law of one price, misuse of cash flow discounting, and controversial hedging of own default risk. It also allows an integrated implementation strategy and reuse of existing CVA infrastructure.", "published": "2015-10-25T01:45:34Z", "authors": ["Wujiang Lou"], "categories": ["q-fin.PR", "q-fin.MF"], "doi": "", "_query": "housing price text listing", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/1209.4175v1", "title": "Hierarchical structure of stock price fluctuations in financial markets", "abstract": "The financial market and turbulence have been broadly compared on account of the same quantitative methods and several common stylized facts they shared. In this paper, the She-Leveque (SL) hierarchy, proposed to explain the anomalous scaling exponents deviated from Kolmogorov monofractal scaling of the velocity fluctuation in fluid turbulence, is applied to study and quantify the hierarchical structure of stock price fluctuations in financial markets. We therefore observed certain interesting results: (i) The hierarchical structure related to multifractal scaling generally presents in all the stock price fluctuations we investigated. (ii) The quantitatively statistical parameters that describes SL hierarchy are different between developed financial markets and emerging ones, distinctively. (iii) For the high-frequency stock price fluctuation, the hierarchical structure varies with different time period. All these results provide a novelty analogy in turbulence and financial market dynamics and a insight to deeply understand the multifractality in financial markets.", "published": "2012-09-19T08:04:01Z", "authors": ["Ya-Chun Gao", "Shi-Min Cai", "Bing-Hong Wang"], "categories": ["q-fin.ST"], "doi": "10.1088/1742-5468/2012/12/P12016", "_query": "housing price text listing", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/1906.06613v1", "title": "The Price of Local Fairness in Multistage Selection", "abstract": "The rise of algorithmic decision making led to active researches on how to define and guarantee fairness, mostly focusing on one-shot decision making. In several important applications such as hiring, however, decisions are made in multiple stage with additional information at each stage. In such cases, fairness issues remain poorly understood. In this paper we study fairness in $k$-stage selection problems where additional features are observed at every stage. We first introduce two fairness notions, local (per stage) and global (final stage) fairness, that extend the classical fairness notions to the $k$-stage setting. We propose a simple model based on a probabilistic formulation and show that the locally and globally fair selections that maximize precision can be computed via a linear program. We then define the price of local fairness to measure the loss of precision induced by local constraints; and investigate theoretically and empirically this quantity. In particular, our experiments show that the price of local fairness is generally smaller when the sensitive attribute is observed at the first stage; but globally fair selections are more locally fair when the sensitive attribute is observed at the second stage---hence in both cases it is often possible to have a selection that has a small price of local fairness and is close to locally fair.", "published": "2019-06-15T21:00:53Z", "authors": ["Vitalii Emelianov", "George Arvanitakis", "Nicolas Gast", "Krishna Gummadi", "Patrick Loiseau"], "categories": ["stat.ML", "cs.LG"], "doi": "10.24963/ijcai.2019/809", "_query": "housing price text listing", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2604.12139v1", "title": "Estimating Price Elasticity Matrices", "abstract": "The relationship between demand and prices of a set of products can be modeled as a linear mapping from logarithmic price changes to logarithmic changes in demand. We consider the problem of estimating the coefficient matrix of this mapping, the elasticity matrix, based on observed data consisting of real-valued prices and integer-valued demands. We regularize the estimation problem by imposing a factor model structure, i.e., that the elasticity matrix is diagonal plus low-rank, similar to factor models used for financial returns. Maximizing the likelihood of observations of this model is a bi-convex problem, meaning that there is a partition of the variables in which it is convex in each set when the other is fixed. We propose and compare three methods for finding a locally optimal estimate. The first is based on alternating maximization, and involves solving a sequence of convex problems. The second method exploits efficient gradient computations in a gradient ascent method. The final method is to use a general purpose nonlinear programming method. While all methods give the same result on numerical examples, the gradient ascent method is substantially faster, due to its efficient gradient evaluations. We report the likelihood with different hyper-parameters for synthetic and real-world data, with similar results. For synthetic data, we also report the realized profit when using the elasticity estimate for optimal pricing, which is maximized for the same set of hyper-parameters that also maximizes the likelihood. This paper is accompanied by easy to use open source Python code for fitting elasticity matrices to observed data, using our three numerical methods.", "published": "2026-04-13T23:40:43Z", "authors": ["Maximilian Schaller", "Stephen Boyd"], "categories": ["math.OC"], "doi": "", "_query": "housing price text listing", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2408.05628v1", "title": "Forecasting Day-Ahead Electricity Prices in the Integrated Single Electricity Market: Addressing Volatility with Comparative Machine Learning Methods", "abstract": "This paper undertakes a comprehensive investigation of electricity price forecasting methods, focused on the Irish Integrated Single Electricity Market, particularly on changes during recent periods of high volatility. The primary objective of this research is to evaluate and compare the performance of various forecasting models, ranging from traditional machine learning models to more complex neural networks, as well as the impact of different lengths of training periods. The performance metrics, mean absolute error, root mean square error, and relative mean absolute error, are utilized to assess and compare the accuracy of each model. A comprehensive set of input features was investigated and selected from data recorded between October 2018 and September 2022. The paper demonstrates that the daily EU Natural Gas price is a more useful feature for electricity price forecasting in Ireland than the daily Henry Hub Natural Gas price. This study also shows that the correlation of features to the day-ahead market price has changed in recent years. The price of natural gas on the day and the amount of wind energy on the grid that hour are significantly more important than any other features. More specifically speaking, the input fuel for electricity has become a more important driver of the price of it, than the total generation or demand. In addition, it can be seen that System Non-Synchronous Penetration (SNSP) is highly correlated with the day-ahead market price, and that renewables are pushing down the price of electricity.", "published": "2024-08-10T20:43:21Z", "authors": ["Ben Harkin", "Xueqin Liu"], "categories": ["cs.LG", "cs.AI", "eess.SY"], "doi": "", "_query": "housing price text listing", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2309.01800v1", "title": "Tight Bounds on List-Decodable and List-Recoverable Zero-Rate Codes", "abstract": "In this work, we consider the list-decodability and list-recoverability of codes in the zero-rate regime. Briefly, a code $\\mathcal{C} \\subseteq [q]^n$ is $(p,\\ell,L)$-list-recoverable if for all tuples of input lists $(Y_1,\\dots,Y_n)$ with each $Y_i \\subseteq [q]$ and $|Y_i|=\\ell$ the number of codewords $c \\in \\mathcal{C}$ such that $c_i \\notin Y_i$ for at most $pn$ choices of $i \\in [n]$ is less than $L$; list-decoding is the special case of $\\ell=1$. In recent work by Resch, Yuan and Zhang~(ICALP~2023) the zero-rate threshold for list-recovery was determined for all parameters: that is, the work explicitly computes $p_*:=p_*(q,\\ell,L)$ with the property that for all $ε>0$ (a) there exist infinite families positive-rate $(p_*-ε,\\ell,L)$-list-recoverable codes, and (b) any $(p_*+ε,\\ell,L)$-list-recoverable code has rate $0$. In fact, in the latter case the code has constant size, independent on $n$. However, the constant size in their work is quite large in $1/ε$, at least $|\\mathcal{C}|\\geq (\\frac{1}ε)^{O(q^L)}$. Our contribution in this work is to show that for all choices of $q,\\ell$ and $L$ with $q \\geq 3$, any $(p_*+ε,\\ell,L)$-list-recoverable code must have size $O_{q,\\ell,L}(1/ε)$, and furthermore this upper bound is complemented by a matching lower bound $Ω_{q,\\ell,L}(1/ε)$. This greatly generalizes work by Alon, Bukh and Polyanskiy~(IEEE Trans.\\ Inf.\\ Theory~2018) which focused only on the case of binary alphabet (and thus necessarily only list-decoding). We remark that we can in fact recover the same result for $q=2$ and even $L$, as obtained by Alon, Bukh and Polyanskiy: we thus strictly generalize their work.", "published": "2023-09-04T20:29:28Z", "authors": ["Nicolas Resch", "Chen Yuan", "Yihan Zhang"], "categories": ["cs.IT", "cs.CC", "math.CO"], "doi": "", "_query": "housing price text listing", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/1906.09880v3", "title": "Online Revenue Maximization for Server Pricing", "abstract": "Efficient and truthful mechanisms to price resources on remote servers/machines has been the subject of much work in recent years due to the importance of the cloud market. This paper considers revenue maximization in the online stochastic setting with non-preemptive jobs and a unit capacity server. One agent/job arrives at every time step, with parameters drawn from an underlying unknown distribution. We design a posted-price mechanism which can be efficiently computed, and is revenue-optimal in expectation and in retrospect, up to additive error. The prices are posted prior to learning the agent's type, and the computed pricing scheme is deterministic, depending only on the length of the allotted time interval and on the earliest time the server is available. If the distribution of agent's type is only learned from observing the jobs that are executed, we prove that a polynomial number of samples is sufficient to obtain a near-optimal truthful pricing strategy.", "published": "2019-06-24T12:26:13Z", "authors": ["Shant Boodaghians", "Federico Fusco", "Stefano Leonardi", "Yishay Mansour", "Ruta Mehta"], "categories": ["cs.GT"], "doi": "10.1007/s10458-022-09544-y", "_query": "housing price text listing", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2210.07472v1", "title": "Robust Candidate Generation for Entity Linking on Short Social Media Texts", "abstract": "Entity Linking (EL) is the gateway into Knowledge Bases. Recent advances in EL utilize dense retrieval approaches for Candidate Generation, which addresses some of the shortcomings of the Lookup based approach of matching NER mentions against pre-computed dictionaries. In this work, we show that in the domain of Tweets, such methods suffer as users often include informal spelling, limited context, and lack of specificity, among other issues. We investigate these challenges on a large and recent Tweets benchmark for EL, empirically evaluate lookup and dense retrieval approaches, and demonstrate a hybrid solution using long contextual representation from Wikipedia is necessary to achieve considerable gains over previous work, achieving 0.93 recall.", "published": "2022-10-14T02:47:31Z", "authors": ["Liam Hebert", "Raheleh Makki", "Shubhanshu Mishra", "Hamidreza Saghir", "Anusha Kamath", "Yuval Merhav"], "categories": ["cs.CL", "cs.AI", "cs.IR", "cs.LG"], "doi": "", "_query": "housing price text listing", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2312.07882v3", "title": "A semi-parametric approach for estimating consumer valuation distributions using second price auctions", "abstract": "We focus on online second price auctions, where bids are made sequentially, and the winning bidder pays the maximum of the second-highest bid and a seller specified starting price. For many such auctions, the seller does not see all the bids or the total number of bidders accessing the auction, and only observes the current selling prices throughout the course of the auction. We develop a novel semi-parametric approach to estimate the underlying consumer valuation distribution based on this data. Previous semi-parametric or non-parametric approaches in the literature only use the final selling price and assume knowledge of the total number of bidders. The resulting estimate, in particular, can be used by the seller to compute the optimal profit-maximizing price for the product. Our approach is free of tuning parameters, and we demonstrate its computational and statistical efficiency in a variety of simulation settings, and also on an Xbox 7-day auction dataset on eBay.", "published": "2023-12-13T03:58:02Z", "authors": ["Sourav Mukherjee", "Ziqian Yang", "Rohit K Patra", "Kshitij Khare"], "categories": ["stat.ME", "cs.GT", "stat.AP"], "doi": "", "_query": "housing price text listing", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2602.06370v1", "title": "Cost-Aware Model Selection for Text Classification: Multi-Objective Trade-offs Between Fine-Tuned Encoders and LLM Prompting in Production", "abstract": "Large language models (LLMs) such as GPT-4o and Claude Sonnet 4.5 have demonstrated strong capabilities in open-ended reasoning and generative language tasks, leading to their widespread adoption across a broad range of NLP applications. However, for structured text classification problems with fixed label spaces, model selection is often driven by predictive performance alone, overlooking operational constraints encountered in production systems. In this work, we present a systematic comparison of two contrasting paradigms for text classification: zero- and few-shot prompt-based large language models, and fully fine-tuned encoder-only architectures. We evaluate these approaches across four canonical benchmarks (IMDB, SST-2, AG News, and DBPedia), measuring predictive quality (macro F1), inference latency, and monetary cost. We frame model evaluation as a multi-objective decision problem and analyze trade-offs using Pareto frontier projections and a parameterized utility function reflecting different deployment regimes. Our results show that fine-tuned encoder-based models from the BERT family achieve competitive, and often superior, classification performance while operating at one to two orders of magnitude lower cost and latency compared to zero- and few-shot LLM prompting. Overall, our findings suggest that indiscriminate use of large language models for standard text classification workloads can lead to suboptimal system-level outcomes. Instead, fine-tuned encoders emerge as robust and efficient components for structured NLP pipelines, while LLMs are better positioned as complementary elements within hybrid architectures. We release all code, datasets, and evaluation protocols to support reproducibility and cost-aware NLP system design.", "published": "2026-02-06T03:54:28Z", "authors": ["Alberto Andres Valdes Gonzalez"], "categories": ["cs.CL"], "doi": "", "_query": "housing price text listing", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/1211.0398v1", "title": "Extending valuations to formal completions", "abstract": "This paper is an extended version of the talk given by Miguel Olalla at the International Conference on Valuation Theory in El Escorial in July 2011. Its purpose is to provide an introduction to our joint paper \"Extending a valuation centered in a local domain to the formal completion\" (Proc. London Math. Soc. (2012) 105 (3), 571-621) without grinding through all of its technical details.", "published": "2012-11-02T09:09:38Z", "authors": ["F. J. Herrera Govantes", "M. A. Olalla Acosta", "M. Spivakovsky", "B. Teissier"], "categories": ["math.AG", "math.RA"], "doi": "10.4171/149-1/12", "_query": "automated valuation model", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/1004.3299v4", "title": "Valuation equations for stochastic volatility models", "abstract": "We analyze the valuation partial differential equation for European contingent claims in a general framework of stochastic volatility models where the diffusion coefficients may grow faster than linearly and degenerate on the boundaries of the state space. We allow for various types of model behavior: the volatility process in our model can potentially reach zero and either stay there or instantaneously reflect, and the asset-price process may be a strict local martingale. Our main result is a necessary and sufficient condition on the uniqueness of classical solutions to the valuation equation: the value function is the unique nonnegative classical solution to the valuation equation among functions with at most linear growth if and only if the asset-price is a martingale.", "published": "2010-04-19T20:23:36Z", "authors": ["Erhan Bayraktar", "Constantinos Kardaras", "Hao Xing"], "categories": ["math.PR", "q-fin.PR"], "doi": "", "_query": "automated valuation model", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2603.23611v1", "title": "LLMORPH: Automated Metamorphic Testing of Large Language Models", "abstract": "Automated testing is essential for evaluating and improving the reliability of Large Language Models (LLMs), yet the lack of automated oracles for verifying output correctness remains a key challenge. We present LLMORPH, an automated testing tool specifically designed for LLMs performing NLP tasks, which leverages Metamorphic Testing (MT) to uncover faulty behaviors without relying on human-labeled data. MT uses Metamorphic Relations (MRs) to generate follow-up inputs from source test input, enabling detection of inconsistencies in model outputs without the need of expensive labelled data. LLMORPH is aimed at researchers and developers who want to evaluate the robustness of LLM-based NLP systems. In this paper, we detail the design, implementation, and practical usage of LLMORPH, demonstrating how it can be easily extended to any LLM, NLP task, and set of MRs. In our evaluation, we applied 36 MRs across four NLP benchmarks, testing three state-of-the-art LLMs: GPT-4, LLAMA3, and HERMES 2. This produced over 561,000 test executions. Results demonstrate LLMORPH's effectiveness in automatically exposing inconsistencies.", "published": "2026-03-24T18:01:02Z", "authors": ["Steven Cho", "Stefano Ruberto", "Valerio Terragni"], "categories": ["cs.SE", "cs.AI", "cs.CL", "cs.LG"], "doi": "10.1109/ASE63991.2025.00385", "_query": "automated valuation model", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/1807.00468v2", "title": "Automated Directed Fairness Testing", "abstract": "Fairness is a critical trait in decision making. As machine-learning models are increasingly being used in sensitive application domains (e.g. education and employment) for decision making, it is crucial that the decisions computed by such models are free of unintended bias. But how can we automatically validate the fairness of arbitrary machine-learning models? For a given machine-learning model and a set of sensitive input parameters, our AEQUITAS approach automatically discovers discriminatory inputs that highlight fairness violation. At the core of AEQUITAS are three novel strategies to employ probabilistic search over the input space with the objective of uncovering fairness violation. Our AEQUITAS approach leverages inherent robustness property in common machine-learning models to design and implement scalable test generation methodologies. An appealing feature of our generated test inputs is that they can be systematically added to the training set of the underlying model and improve its fairness. To this end, we design a fully automated module that guarantees to improve the fairness of the underlying model. We implemented AEQUITAS and we have evaluated it on six state-of-the-art classifiers, including a classifier that was designed with fairness constraints. We show that AEQUITAS effectively generates inputs to uncover fairness violation in all the subject classifiers and systematically improves the fairness of the respective models using the generated test inputs. In our evaluation, AEQUITAS generates up to 70% discriminatory inputs (w.r.t. the total number of inputs generated) and leverages these inputs to improve the fairness up to 94%.", "published": "2018-07-02T05:29:57Z", "authors": ["Sakshi Udeshi", "Pryanshu Arora", "Sudipta Chattopadhyay"], "categories": ["cs.LG", "cs.AI", "cs.SE", "stat.ML"], "doi": "10.1145/3238147.3238165", "_query": "automated valuation model", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2110.10980v1", "title": "Ethics-Based Auditing of Automated Decision-Making Systems: Nature, Scope, and Limitations", "abstract": "Important decisions that impact human lives, livelihoods, and the natural environment are increasingly being automated. Delegating tasks to so-called automated decision-making systems (ADMS) can improve efficiency and enable new solutions. However, these benefits are coupled with ethical challenges. For example, ADMS may produce discriminatory outcomes, violate individual privacy, and undermine human self-determination. New governance mechanisms are thus needed that help organisations design and deploy ADMS in ways that are ethical, while enabling society to reap the full economic and social benefits of automation. In this article, we consider the feasibility and efficacy of ethics-based auditing (EBA) as a governance mechanism that allows organisations to validate claims made about their ADMS. Building on previous work, we define EBA as a structured process whereby an entity's present or past behaviour is assessed for consistency with relevant principles or norms. We then offer three contributions to the existing literature. First, we provide a theoretical explanation of how EBA can contribute to good governance by promoting procedural regularity and transparency. Second, we propose seven criteria for how to design and implement EBA procedures successfully. Third, we identify and discuss the conceptual, technical, social, economic, organisational, and institutional constraints associated with EBA. We conclude that EBA should be considered an integral component of multifaced approaches to managing the ethical risks posed by ADMS.", "published": "2021-10-21T08:51:28Z", "authors": ["Jakob Mokander", "Jessica Morley", "Mariarosaria Taddeo", "Luciano Floridi"], "categories": ["cs.CY", "cs.SE"], "doi": "10.1007/s11948-021-00319-4", "_query": "automated valuation model", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/1707.01649v3", "title": "Frobenius splitting of valuation rings and $F$-singularities of centers", "abstract": "Using a local monomialization result of Knaf and Kuhlmann, we prove that the valuation ring of an Abhyankar valuation of a function field over a perfect ground field of prime characteristic is Frobenius split. We show that a Frobenius splitting of a sufficiently well-behaved center lifts to a Frobenius splitting of the valuation ring. We also investigate properties of valuations centered on arbitrary Noetherian domains of prime characteristic. In contrast to [arXiv:1507.06009], this paper emphasizes the role of centers in controlling Frobenius properties of valuations rings in prime characteristic.", "published": "2017-07-06T06:14:21Z", "authors": ["Rankeya Datta"], "categories": ["math.AC", "math.AG"], "doi": "10.2140/ant.2021.15.2485", "_query": "automated valuation model", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2006.12575v3", "title": "LAMP: Large Deep Nets with Automated Model Parallelism for Image Segmentation", "abstract": "Deep Learning (DL) models are becoming larger, because the increase in model size might offer significant accuracy gain. To enable the training of large deep networks, data parallelism and model parallelism are two well-known approaches for parallel training. However, data parallelism does not help reduce memory footprint per device. In this work, we introduce Large deep 3D ConvNets with Automated Model Parallelism (LAMP) and investigate the impact of both input's and deep 3D ConvNets' size on segmentation accuracy. Through automated model parallelism, it is feasible to train large deep 3D ConvNets with a large input patch, even the whole image. Extensive experiments demonstrate that, facilitated by the automated model parallelism, the segmentation accuracy can be improved through increasing model size and input context size, and large input yields significant inference speedup compared with sliding window of small patches in the inference. Code is available\\footnote{https://monai.io/research/lamp-automated-model-parallelism}.", "published": "2020-06-22T19:20:35Z", "authors": ["Wentao Zhu", "Can Zhao", "Wenqi Li", "Holger Roth", "Ziyue Xu", "Daguang Xu"], "categories": ["cs.CV", "cs.DC", "cs.LG", "cs.NE", "eess.IV"], "doi": "", "_query": "automated valuation model", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2503.01052v2", "title": "ALinFiK: Learning to Approximate Linearized Future Influence Kernel for Scalable Third-Party LLM Data Valuation", "abstract": "Large Language Models (LLMs) heavily rely on high-quality training data, making data valuation crucial for optimizing model performance, especially when working within a limited budget. In this work, we aim to offer a third-party data valuation approach that benefits both data providers and model developers. We introduce a linearized future influence kernel (LinFiK), which assesses the value of individual data samples in improving LLM performance during training. We further propose ALinFiK, a learning strategy to approximate LinFiK, enabling scalable data valuation. Our comprehensive evaluations demonstrate that this approach surpasses existing baselines in effectiveness and efficiency, demonstrating significant scalability advantages as LLM parameters increase.", "published": "2025-03-02T22:51:12Z", "authors": ["Yanzhou Pan", "Huawei Lin", "Yide Ran", "Jiamin Chen", "Xiaodong Yu", "Weijie Zhao", "Denghui Zhang", "Zhaozhuo Xu"], "categories": ["cs.LG"], "doi": "", "_query": "automated valuation model", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/1707.01692v1", "title": "Ramification theory for degree $p$ extensions of arbitrary valuation rings in mixed characteristic $(0,p)$", "abstract": "We previously obtained a generalization and refinement of results about the ramification theory of Artin-Schreier extensions of discretely valued fields in characteristic $p$ with perfect residue fields to the case of fields with more general valuations and residue fields. As seen in VT16, the \"defect\" case gives rise to many interesting complications. In this paper, we present analogous results for degree $p$ extensions of arbitrary valuation rings in mixed characteristic $(0,p)$ in a more general setting. More specifically, the only assumption here is that the base field $K$ is henselian. In particular, these results are true for defect extensions even if the rank of the valuation is greater than $1$. A similar method also works in equal characteristic, generalizing the results of VT16.", "published": "2017-07-06T08:56:41Z", "authors": ["Vaidehee Thatte"], "categories": ["math.NT"], "doi": "", "_query": "automated valuation model", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2508.18331v1", "title": "Metrics, KPIs, and Taxonomy for Data Valuation and Monetisation -- A Systematic Literature Review", "abstract": "Data valuation and data monetisation are complex subjects but essential to most organisations today. Unfortunately, they still lack standard procedures and frameworks for organisations to follow. In this survey, we introduce the reader to the concepts by providing the definitions and the background required to better understand data, monetisation strategies, and finally metrics and KPIs used in these strategies. We have conducted a systematic literature review on metrics and KPIs used in data valuation and monetisation, in every aspect of an organisation's business, and by a variety of stakeholders. We provide an expansive list of such metrics and KPIs with 162 references. We then categorise all the metrics and KPIs found into a large taxonomy, following the Balanced Scorecard (BSC) approach with further subclustering to cover every aspect of an organisation's business. This taxonomy will help every level of data management understand the complex landscape of the domain. We also discuss the difficulty in creating a standard framework for data valuation and data monetisation and the major challenges the domain is currently facing.", "published": "2025-08-25T09:43:11Z", "authors": ["Eduardo Vyhmeister", "Bastien Pietropaoli", "Alejando Martinez Molina", "Montserrat Gonzalez-Ferreiro", "Gabriel Gonzalez-Castane", "Jordi Arjona Aroca", "Andrea Visentin"], "categories": ["cs.DB"], "doi": "", "_query": "automated valuation model", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/1008.0287v3", "title": "New structures on valuations and applications", "abstract": "An overview of some of the recent developments in the theory of valuations on convex sets and its generalizations to manifolds is given. The exposition is focused towards applications to integral geometry; several of such applications are discussed.", "published": "2010-08-02T12:32:11Z", "authors": ["Semyon Alesker"], "categories": ["math.MG", "math.DG"], "doi": "", "_query": "automated valuation model", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/math/0605011v2", "title": "A valuation criterion for normal bases in elementary abelian extensions", "abstract": "Let $p$ be a prime number and let $K$ be a finite extension of the field $\\mathbb{Q}_p$ of $p$-adic numbers. Let $N$ be a fully ramified, elementary abelian extension of $K$. Under a mild hypothesis on the extension $N/K$, we show that every element of $N$ with valuation congruent mod $[N:K]$ to the largest lower ramification number of $N/K$ generates a normal basis for $N$ over $K$.", "published": "2006-04-29T17:42:35Z", "authors": ["Nigel P. Byott", "G. Griffith Elder"], "categories": ["math.NT"], "doi": "", "_query": "automated valuation model", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2209.05158v4", "title": "Singular valuations and the Hadwiger theorem on convex functions", "abstract": "We give a characterization of smooth, rotation and dually epi-translation invariant valuations and use this result to obtain a new proof of the Hadwiger theorem on convex functions. We also give a description of the construction of the functional intrinsic volumes using integration over the differential cycle and provide a new representation of these functionals as principal value integrals with respect to the Hessian measures.", "published": "2022-09-12T11:33:31Z", "authors": ["Jonas Knoerr"], "categories": ["math.MG"], "doi": "", "_query": "automated valuation model", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2403.16908v1", "title": "Towards Trustworthy Automated Driving through Qualitative Scene Understanding and Explanations", "abstract": "Understanding driving scenes and communicating automated vehicle decisions are key requirements for trustworthy automated driving. In this article, we introduce the Qualitative Explainable Graph (QXG), which is a unified symbolic and qualitative representation for scene understanding in urban mobility. The QXG enables interpreting an automated vehicle's environment using sensor data and machine learning models. It utilizes spatio-temporal graphs and qualitative constraints to extract scene semantics from raw sensor inputs, such as LiDAR and camera data, offering an interpretable scene model. A QXG can be incrementally constructed in real-time, making it a versatile tool for in-vehicle explanations across various sensor types. Our research showcases the potential of QXG, particularly in the context of automated driving, where it can rationalize decisions by linking the graph with observed actions. These explanations can serve diverse purposes, from informing passengers and alerting vulnerable road users to enabling post-hoc analysis of prior behaviors.", "published": "2024-03-25T16:19:33Z", "authors": ["Nassim Belmecheri", "Arnaud Gotlieb", "Nadjib Lazaar", "Helge Spieker"], "categories": ["cs.AI"], "doi": "", "_query": "automated valuation model", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2305.03483v1", "title": "Flat dimension for power series over valuation rings", "abstract": "We examine the power series ring $R[[X]]$ over a valuation ring $R$ of rank 1, with proper, dense value group. We give a counterexample to Hilbert's syzygy theorem for $R[[X]]$, i.e. an $R[[X]]$-module $C$ that is flat over $R$ and has flat dimension at least 2 over $R[[X]]$, contradicting a previously published result. The key ingredient in our construction is an exploration of the valuation theory of $R[[X]]$. We also use this theory to give a new proof that $R[[X]]$ is not a coherent ring, a fact which is essential in our construction of the module $C$.", "published": "2023-05-05T12:51:01Z", "authors": ["Adam Jones"], "categories": ["math.AC", "math.RA"], "doi": "10.1016/j.jpaa.2024.107778", "_query": "automated valuation model", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/1704.03723v1", "title": "Beliefs in Markov Trees - From Local Computations to Local Valuation", "abstract": "This paper is devoted to expressiveness of hypergraphs for which uncertainty propagation by local computations via Shenoy/Shafer method applies. It is demonstrated that for this propagation method for a given joint belief distribution no valuation of hyperedges of a hypergraph may provide with simpler hypergraph structure than valuation of hyperedges by conditional distributions. This has vital implication that methods recovering belief networks from data have no better alternative for finding the simplest hypergraph structure for belief propagation. A method for recovery tree-structured belief networks has been developed and specialized for Dempster-Shafer belief functions", "published": "2017-04-12T12:30:17Z", "authors": ["Mieczysław A. Kłopotek"], "categories": ["cs.AI"], "doi": "", "_query": "automated valuation model", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2111.04380v1", "title": "Ethics-Based Auditing of Automated Decision-Making Systems: Intervention Points and Policy Implications", "abstract": "Organisations increasingly use automated decision-making systems (ADMS) to inform decisions that affect humans and their environment. While the use of ADMS can improve the accuracy and efficiency of decision-making processes, it is also coupled with ethical challenges. Unfortunately, the governance mechanisms currently used to oversee human decision-making often fail when applied to ADMS. In previous work, we proposed that ethics-based auditing (EBA), i.e. a structured process by which ADMS are assessed for consistency with relevant principles or norms, can (a) help organisations verify claims about their ADMS and (b) provide decision-subjects with justifications for the outputs produced by ADMS. In this article, we outline the conditions under which EBA procedures can be feasible and effective in practice. First, we argue that EBA is best understood as a 'soft' yet 'formal' governance mechanism. This implies that the main responsibility of auditors should be to spark ethical deliberation at key intervention points throughout the software development process and ensure that there is sufficient documentation to respond to potential inquiries. Second, we frame ADMS as parts of larger socio-technical systems to demonstrate that to be feasible and effective, EBA procedures must link to intervention points that span all levels of organisational governance and all phases of the software lifecycle. The main function of EBA should therefore be to inform, formalise, assess, and interlink existing governance structures. Finally, we discuss the policy implications of our findings. To support the emergence of feasible and effective EBA procedures, policymakers and regulators could provide standardised reporting formats, facilitate knowledge exchange, provide guidance on how to resolve normative tensions, and create an independent body to oversee EBA of ADMS.", "published": "2021-11-08T10:57:26Z", "authors": ["Jakob Mokander", "Maria Axente"], "categories": ["cs.CY"], "doi": "10.1007/s00146-021-01286-x", "_query": "automated valuation model", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/1610.04046v2", "title": "Is state-dependent valuation more adaptive than simpler rules?", "abstract": "McNamara, Trimmer, and Houston (2012) claim to provide an explanation of certain systematic deviations from rational behavior using a mechanism that could arise through natural selection. We provide an arguably much simpler mechanism in terms of computational limitations, that performs better in the environment described by McNamara, Trimmer, and Houston (2012). To argue convincingly that animals' use of state-dependent valuation is adaptive and is likely to be selected for by natural selection, one must argue that, in some sense, it is a better approach than the simple strategies that we propose.", "published": "2016-10-13T12:21:11Z", "authors": ["Joseph Y. Halpern", "Lior Seeman"], "categories": ["q-bio.PE", "cs.GT"], "doi": "", "_query": "automated valuation model", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2312.06490v1", "title": "Automated Planning Techniques for Elementary Proofs in Abstract Algebra", "abstract": "This paper explores the application of automated planning to automated theorem proving, which is a branch of automated reasoning concerned with the development of algorithms and computer programs to construct mathematical proofs. In particular, we investigate the use of planning to construct elementary proofs in abstract algebra, which provides a rigorous and axiomatic framework for studying algebraic structures such as groups, rings, fields, and modules. We implement basic implications, equalities, and rules in both deterministic and non-deterministic domains to model commutative rings and deduce elementary results about them. The success of this initial implementation suggests that the well-established techniques seen in automated planning are applicable to the relatively newer field of automated theorem proving. Likewise, automated theorem proving provides a new, challenging domain for automated planning.", "published": "2023-12-11T16:17:43Z", "authors": ["Alice Petrov", "Christian Muise"], "categories": ["cs.AI"], "doi": "", "_query": "automated valuation model", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/1304.3824v13", "title": "Pricing and Valuation under the Real-World Measure", "abstract": "In general it is not clear which kind of information is supposed to be used for calculating the fair value of a contingent claim. Even if the information is specified, it is not guaranteed that the fair value is uniquely determined by the given information. A further problem is that asset prices are typically expressed in terms of a risk-neutral measure. This makes it difficult to transfer the fundamental results of financial mathematics to econometrics. I show that the aforementioned problems evaporate if the financial market is complete and sensitive. In this case, after an appropriate choice of the numeraire, the discounted price processes turn out to be uniformly integrable martingales under the real-world measure. This leads to a Law of One Price and a simple real-world valuation formula in a model-independent framework where the number of assets as well as the lifetime of the market can be finite or infinite.", "published": "2013-04-13T17:15:13Z", "authors": ["Gabriel Frahm"], "categories": ["q-fin.GN"], "doi": "", "_query": "automated valuation model", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2010.09249v1", "title": "Improving Company Valuations with Automated Knowledge Discovery, Extraction and Fusion", "abstract": "Performing company valuations within the domain of biotechnology, pharmacy and medical technology is a challenging task, especially when considering the unique set of risks biotech start-ups face when entering new markets. Companies specialized in global valuation services, therefore, combine valuation models and past experience with heterogeneous metrics and indicators that provide insights into a company's performance. This paper illustrates how automated knowledge discovery, extraction and data fusion can be used to (i) obtain additional indicators that provide insights into the success of a company's product development efforts, and (ii) support labor-intensive data curation processes. We apply deep web knowledge acquisition methods to identify and harvest data on clinical trials that is hidden behind proprietary search interfaces and integrate the extracted data into the industry partner's company valuation ontology. In addition, focused Web crawls and shallow semantic parsing yield information on the company's key personnel and respective contact data, notifying domain experts of relevant changes that get then incorporated into the industry partner's company data.", "published": "2020-10-19T06:33:12Z", "authors": ["Albert Weichselbraun", "Philipp Kuntschik", "Sandro Hörler"], "categories": ["cs.IR", "cs.LG"], "doi": "10.1515/iwp-2020-2119", "_query": "automated valuation model", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2605.04999v2", "title": "A Tutorial for Evaluating Cure Model Appropriateness", "abstract": "In survival analysis, traditional models assume all individuals will eventually experience the event of interest. However, advances in therapeutics have led to multiple clinical contexts with potentially curative therapies, and in these contexts, certain individuals may never experience the event. Statisticians have developed cure models as a methodology to address this challenge. Nonetheless, despite significant statistical advances in cure models, we have seen more limited uptake in biomedical applications, and we hypothesize that this is caused by limited guidance in the appropriate application of cure models. Cure models require specific identifiability conditions for valid parameter estimation, and previous reports have demonstrated significant issues with the inappropriate application of cure models. Existing tutorials for cure models focus on model implementation and either assume or provide only limited guidance on whether cure modeling is appropriate for the given dataset. This tutorial addresses this gap by describing a systematic procedure that integrates clinical judgment, visual inspection of Kaplan-Meier curves, and quantitative evaluation. We provide a worked example using data from a randomized clinical trial in acute myeloid leukemia, and we also summarize findings from a series of other datasets of hematopoietic cell transplantation to suggest broad practical guidance for choosing to apply cure models. By systematically evaluating cure model appropriateness before fitting these models, researchers can achieve more reliable survival analysis and improved clinical decision-making.", "published": "2026-05-06T14:59:41Z", "authors": ["A Tutorial for Evaluating Cure Model Appropriateness Geethanjalee Mudunkotuwa", "Durbadal Ghosh", "Subodh Selukar"], "categories": ["stat.ME"], "doi": "", "_query": "automated valuation model", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2504.06370v1", "title": "Automated Fabrication of Magnetic Soft Microrobots", "abstract": "The advent of 3D printing has revolutionized many industries and has had similar improvements for soft robots. However, many challenges persist for these functional devices. Magnetic soft robots require the addition of magnetic particles that must be correctly oriented. There is a significant gap in the automated fabrication of 3D geometric structures with 3D magnetization direction. A fully automated 3D printer was designed to improve accuracy, speed, and reproducibility. This design was able to achieve a circular spot size (voxels) of 1.6mm in diameter. An updated optical system can improve the resolution to a square spot size of 50$μ$m by 50$μ$m. The new system achieves higher resolution designs as shown through magneto-mechanical simulations. Various microrobots including 'worm', 'gripper' and 'zipper' designs are evaluated with the new spot size.", "published": "2025-04-08T18:40:37Z", "authors": ["Kaitlyn Clancy", "Siwen Xie", "Griffin Smith", "Onaizah Onaizah"], "categories": ["cs.RO"], "doi": "", "_query": "automated valuation model", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2405.04332v1", "title": "WALLETRADAR: Towards Automating the Detection of Vulnerabilities in Browser-based Cryptocurrency Wallets", "abstract": "Cryptocurrency wallets, acting as fundamental infrastructure to the blockchain ecosystem, have seen significant user growth, particularly among browser-based wallets (i.e., browser extensions). However, this expansion accompanies security challenges, making these wallets prime targets for malicious activities. Despite a substantial user base, there is not only a significant gap in comprehensive security analysis but also a pressing need for specialized tools that can aid developers in reducing vulnerabilities during the development process. To fill the void, we present a comprehensive security analysis of browser-based wallets in this paper, along with the development of an automated tool designed for this purpose. We first compile a taxonomy of security vulnerabilities resident in cryptocurrency wallets by harvesting historical security reports. Based on this, we design WALLETRADAR, an automated detection framework that can accurately identify security issues based on static and dynamic analysis. Evaluation of 96 popular browser-based wallets shows WALLETRADAR's effectiveness, by successfully automating the detection process in 90% of these wallets with high precision. This evaluation has led to the discovery of 116 security vulnerabilities corresponding to 70 wallets. By the time of this paper, we have received confirmations of 10 vulnerabilities from 8 wallet developers, with over $2,000 bug bounties. Further, we observed that 12 wallet developers have silently fixed 16 vulnerabilities after our disclosure. WALLETRADAR can effectively automate the identification of security risks in cryptocurrency wallets, thereby enhancing software development quality and safety in the blockchain ecosystem.", "published": "2024-05-07T14:01:27Z", "authors": ["Pengcheng Xia", "Yanhui Guo", "Zhaowen Lin", "Jun Wu", "Pengbo Duan", "Ningyu He", "Kailong Wang", "Tianming Liu", "Yinliang Yue", "Guoai Xu", "Haoyu Wang"], "categories": ["cs.CR"], "doi": "", "_query": "automated valuation model", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2202.02674v4", "title": "Beurling's Theorem for Valuation Hilbert Modules and Several Complex Variables", "abstract": "We develop a theory of Valuation Hilbert Modules and prove a version of Beurling's theorem for these. Then we apply our version of Beurling's theorem to obtain complete descriptions of the closed invariant subspaces of a number of Hilbert spaces of analytic functions in several complex variables, including $H^2$ of the polydisk the ball, and bounded symmetric domains, and weighted $A^2$ spaces on complex analytic manifolds.", "published": "2022-02-06T01:36:56Z", "authors": ["Charles W. Neville"], "categories": ["math.CV"], "doi": "", "_query": "automated valuation model", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2401.12061v1", "title": "Scalable Automated Verification for Cyber-Physical Systems in Isabelle/HOL", "abstract": "We formally introduce IsaVODEs (Isabelle verification with Ordinary Differential Equations), a framework for the verification of cyber-physical systems. We describe the semantic foundations of the framework's formalisation in the Isabelle/HOL proof assistant. A user-friendly language specification based on a robust state model makes our framework flexible and adaptable to various engineering workflows. New additions to the framework increase both its expressivity and proof automation. Specifically, formalisations related to forward diamond correctness specifications, certification of unique solutions to ordinary differential equations (ODEs) as flows, and invariant reasoning for systems of ODEs contribute to the framework's scalability and usability. Various examples and an evaluation validate the effectiveness of our framework.", "published": "2024-01-22T15:54:47Z", "authors": ["Jonathan Julián Huerta y Munive", "Simon Foster", "Mario Gleirscher", "Georg Struth", "Christian Pardillo Laursen", "Thomas Hickman"], "categories": ["cs.LO", "cs.MS"], "doi": "", "_query": "automated valuation model", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2409.01160v1", "title": "Expanding on EnCLAP with Auxiliary Retrieval Model for Automated Audio Captioning", "abstract": "In this technical report, we describe our submission to DCASE2024 Challenge Task6 (Automated Audio Captioning) and Task8 (Language-based Audio Retrieval). We develop our approach building upon the EnCLAP audio captioning framework and optimizing it for Task6 of the challenge. Notably, we outline the changes in the underlying components and the incorporation of the reranking process. Additionally, we submit a supplementary retriever model, a byproduct of our modified framework, to Task8. Our proposed systems achieve FENSE score of 0.542 on Task6 and mAP@10 score of 0.386 on Task8, significantly outperforming the baseline models.", "published": "2024-09-02T10:47:07Z", "authors": ["Jaeyeon Kim", "Jaeyoon Jung", "Minjeong Jeon", "Sang Hoon Woo", "Jinjoo Lee"], "categories": ["eess.AS", "cs.AI", "cs.SD"], "doi": "", "_query": "automated valuation model", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2602.04087v1", "title": "Quantifying Algorithmic Friction in Automated Resume Screening Systems", "abstract": "Automated resume screening systems are now a central part of hiring at scale, yet there is growing evidence that rigid screening logic can exclude qualified candidates before human review. In prior work, we introduced the concept of Artificial Frictional Unemployment to describe labor market inefficiencies arising from automated recruitment systems. This paper extends that framework by focusing on measurement. We present a method for quantifying algorithmic friction in resume screening pipelines by modeling screening as a classification task and defining friction as excess false negative rejection caused by semantic misinterpretation. Using controlled simulations, we compare deterministic keyword-based screening with vector-space semantic matching under identical qualification conditions. The results show that keyword-based screening exhibits high levels of algorithmic friction, while semantic representations substantially reduce false negative rejection without compromising precision. By treating algorithmic friction as a system-level property, this study provides an empirical basis for evaluating how recruitment system design affects matching efficiency in modern labor markets.", "published": "2026-02-03T23:49:03Z", "authors": ["Ibrahim Denis Fofanah"], "categories": ["cs.CY"], "doi": "", "_query": "automated valuation model", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/1909.00704v1", "title": "Learning Real Estate Automated Valuation Models from Heterogeneous Data Sources", "abstract": "Real estate appraisal is a complex and important task, that can be made more precise and faster with the help of automated valuation tools. Usually the value of some property is determined by taking into account both structural and geographical characteristics. However, while geographical information is easily found, obtaining significant structural information requires the intervention of a real estate expert, a professional appraiser. In this paper we propose a Web data acquisition methodology, and a Machine Learning model, that can be used to automatically evaluate real estate properties. This method uses data from previous appraisal documents, from the advertised prices of similar properties found via Web crawling, and from open data describing the characteristics of a corresponding geographical area. We describe a case study, applicable to the whole Italian territory, and initially trained on a data set of individual homes located in the city of Turin, and analyze prediction and practical applicability.", "published": "2019-09-02T13:16:51Z", "authors": ["Francesco Bergadano", "Roberto Bertilone", "Daniela Paolotti", "Giancarlo Ruffo"], "categories": ["cs.CY", "cs.LG", "stat.ML"], "doi": "10.11113/intrest.v15n1.10", "_query": "automated valuation model", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2403.15857v2", "title": "Automated System-level Testing of Unmanned Aerial Systems", "abstract": "Unmanned aerial systems (UAS) rely on various avionics systems that are safety-critical and mission-critical. A major requirement of international safety standards is to perform rigorous system-level testing of avionics software systems. The current industrial practice is to manually create test scenarios, manually/automatically execute these scenarios using simulators, and manually evaluate outcomes. The test scenarios typically consist of setting certain flight or environment conditions and testing the system under test in these settings. The state-of-the-art approaches for this purpose also require manual test scenario development and evaluation. In this paper, we propose a novel approach to automate the system-level testing of the UAS. The proposed approach (AITester) utilizes model-based testing and artificial intelligence (AI) techniques to automatically generate, execute, and evaluate various test scenarios. The test scenarios are generated on the fly, i.e., during test execution based on the environmental context at runtime. The approach is supported by a toolset. We empirically evaluate the proposed approach on two core components of UAS, an autopilot system of an unmanned aerial vehicle (UAV) and cockpit display systems (CDS) of the ground control station (GCS). The results show that the AITester effectively generates test scenarios causing deviations from the expected behavior of the UAV autopilot and reveals potential flaws in the GCS-CDS.", "published": "2024-03-23T14:47:26Z", "authors": ["Hassan Sartaj", "Asmar Muqeet", "Muhammad Zohaib Iqbal", "Muhammad Uzair Khan"], "categories": ["cs.SE", "cs.AI", "cs.RO"], "doi": "10.1007/s10515-024-00462-9", "_query": "automated valuation model", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/1711.02639v1", "title": "On the Virtues of Automated QSAR The New Kid on the Block", "abstract": "Quantitative Structure-Activity Relationship (QSAR) has proved an invaluable tool in medicinal chemistry. Data availability at unprecedented levels through various databases have collaborated to a resurgence in the interest for QSAR. In this context, rapid generation of quality predictive models is highly desirable for hit identification and lead optimization. We showcase the application of an automated QSAR approach, which randomly selects multiple training/test sets and utilizes machine-learning algorithms to generate predictive models. Results demonstrate that AutoQSAR produces models of improved or similar quality to those generated by practitioners in the field but in just a fraction of the time. Despite the potential of the concept to the benefit of the community, the AutoQSAR opportunity has been largely undervalued.", "published": "2017-11-07T18:00:22Z", "authors": ["Marcelo T. de Oliveira", "Edson Katekawa"], "categories": ["stat.OT"], "doi": "10.4155/FMC-2017-0170", "_query": "automated valuation model", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2409.01117v2", "title": "Scenario-based assessment of automated driving systems: How (not) to parameterize scenarios?", "abstract": "The development of Automated Driving Systems (ADSs) has advanced significantly. To enable their large-scale deployment, the United Nations Regulation 157 (UN R157) concerning the approval of Automated Lane Keeping Systems (ALKSs) has been approved in 2021. UN R157 requires an activated ALKS to avoid any collisions that are reasonably preventable and proposes a method to distinguish reasonably preventable collisions from unpreventable ones using \"the simulated performance of a skilled and attentive human driver\". With different driver models, benchmarks are set for ALKSs in three types of scenarios. The three types of scenarios considered in the proposed method in UN R157 assume a certain parameterization without any further consideration. This work investigates the parameterization of these scenarios, showing that the choice of parameterization significantly affects the simulation outcomes. By comparing real-world and parameterized scenarios, we show that the influence of parameterization depends on the scenario type, driver model, and evaluation criterion. Alternative parameterizations are proposed, leading to results that are closer to the non-parameterized scenarios in terms of recall, precision, and F1 score. The study highlights the importance of careful scenario parameterization and suggests improvements to the current UN R157 approach.", "published": "2024-09-02T09:49:16Z", "authors": ["Erwin de Gelder", "Olaf Op den Camp"], "categories": ["cs.RO"], "doi": "10.1109/IAVVC63304.2024.10786407", "_query": "automated valuation model", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2609.24526v4", "title": "ME-VLM: A Unified VLM for Embodied Cognition and Agent Coordination", "abstract": "Physical AI requires models to ground visual and linguistic understanding in real-world environments while accounting for environmental constraints and execution feedback. We introduce MachEmbodied-VLM (ME-VLM), a unified vision-language model with two variants, 4B and 35B-A3B, that brings together embodied cognition and multimodal agent capabilities. Our work emphasizes physical perception and spatiotemporal reasoning, together with planning, interaction, and outcome assessment in both digital and physical environments. We construct training data spanning embodied and multimodal agent tasks, including execution observations and feedback to support outcome assessment and decision refinement. The training pipeline comprises embodied capability injection, separate reinforcement learning of embodied and multimodal-agent experts, and multi-teacher on-policy distillation that consolidates their complementary capabilities into a single model. Experiments show competitive performance on both embodied and agent benchmarks, as well as on autonomous-driving and embodied-navigation tasks. For edge deployment, visual token compression, W4A8 quantization, and hardware-software co-optimization enable on-device inference of the 4B variant on the M100, reducing prefill latency from 400 ms to 188 ms. Project Page: https://machembodied.com/ME-Brain/ME-VLM.html Code Repository: https://github.com/MachEmbodied/ME-VLM", "published": "2026-09-21T12:59:59Z", "authors": ["Foundation Model", "Li Auto Inc"], "categories": ["cs.CV"], "doi": "", "_query": "automated valuation model", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2507.22433v1", "title": "Operationalization of Scenario-Based Safety Assessment of Automated Driving Systems", "abstract": "Before introducing an Automated Driving System (ADS) on the road at scale, the manufacturer must conduct some sort of safety assurance. To structure and harmonize the safety assurance process, the UNECE WP.29 Working Party on Automated/Autonomous and Connected Vehicles (GRVA) is developing the New Assessment/Test Method (NATM) that indicates what steps need to be taken for safety assessment of an ADS. In this paper, we will show how to practically conduct safety assessment making use of a scenario database, and what additional steps must be taken to fully operationalize the NATM. In addition, we will elaborate on how the use of scenario databases fits with methods developed in the Horizon Europe projects that focus on safety assessment following the NATM approach.", "published": "2025-07-30T07:26:01Z", "authors": ["Olaf Op den Camp", "Erwin de Gelder"], "categories": ["cs.RO"], "doi": "", "_query": "automated valuation model", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/1111.2281v1", "title": "IVOA Recommendation: Data Model for Astronomical DataSet Characterisation", "abstract": "This document defines the high level metadata necessary to describe the physical parameter space of observed or simulated astronomical data sets, such as 2D-images, data cubes, X-ray event lists, IFU data, etc.. The Characterisation data model is an abstraction which can be used to derive a structured description of any relevant data and thus to facilitate its discovery and scientific interpretation. The model aims at facilitating the manipulation of heterogeneous data in any VO framework or portal. A VO Characterisation instance can include descriptions of the data axes, the range of coordinates covered by the data, and details of the data sampling and resolution on each axis. These descriptions should be in terms of physical variables, independent of instrumental signatures as far as possible. Implementations of this model has been described in the IVOA Note available at: http://www.ivoa.net/Documents/latest/ImplementationCharacterisation.html Utypes derived from this version of the UML model are listed and commented in the following IVOA Note: http://www.ivoa.net/Documents/latest/UtypeListCharacterisationDM.html An XML schema has been build up from the UML model and is available at: http://www.ivoa.net/xml/Characterisation/Characterisation-v1.11.xsd", "published": "2011-11-09T17:28:30Z", "authors": ["Mireille Louys", "Anita Richards", "Francois Bonnarel", "Alberto Micol", "Igor Chilingarian", "Jonathan McDowell", "the IVOA Data Model Working Group"], "categories": ["astro-ph.IM"], "doi": "10.5479/ADS/bib/2008ivoa.spec.0325L", "_query": "automated valuation model", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/1711.08552v2", "title": "Thetis coastal ocean model: discontinuous Galerkin discretization for the three-dimensional hydrostatic equations", "abstract": "Unstructured grid ocean models are advantageous for simulating the coastal ocean and river-estuary-plume systems. However, unstructured grid models tend to be diffusive and/or computationally expensive which limits their applicability to real life problems. In this paper, we describe a novel discontinuous Galerkin (DG) finite element discretization for the hydrostatic equations. The formulation is fully conservative and second-order accurate in space and time. Monotonicity of the advection scheme is ensured by using a strong stability preserving time integration method and slope limiters. Compared to previous DG models advantages include a more accurate mode splitting method, revised viscosity formulation, and new second-order time integration scheme. We demonstrate that the model is capable of simulating baroclinic flows in the eddying regime with a suite of test cases. Numerical dissipation is well-controlled, being comparable or lower than in existing state-of-the-art structured grid models.", "published": "2017-11-23T01:40:41Z", "authors": ["Tuomas Kärnä", "Stephan C. Kramer", "Lawrence Mitchell", "David A. Ham", "Matthew D. Piggott", "António M. Baptista"], "categories": ["physics.ao-ph"], "doi": "10.5194/gmd-11-4359-2018", "_query": "automated valuation model", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2312.05930v2", "title": "A Comprehensive Dataset and Automated Pipeline for Nailfold Capillary Analysis", "abstract": "Nailfold capillaroscopy is widely used in assessing health conditions, highlighting the pressing need for an automated nailfold capillary analysis system. In this study, we present a pioneering effort in constructing a comprehensive nailfold capillary dataset-321 images, 219 videos from 68 subjects, with clinic reports and expert annotations-that serves as a crucial resource for training deep-learning models. Leveraging this dataset, we finetuned three deep learning models with expert annotations as supervised labels and integrated them into a novel end-to-end nailfold capillary analysis pipeline. This pipeline excels in automatically detecting and measuring a wide range of size factors, morphological features, and dynamic aspects of nailfold capillaries. We compared our outcomes with clinical reports. Experiment results showed that our automated pipeline achieves an average of sub-pixel level precision in measurements and 89.9% accuracy in identifying morphological abnormalities. These results underscore its potential for advancing quantitative medical research and enabling pervasive computing in healthcare. Our data and code are available at https://github.com/THU-CS-PI-LAB/ANFC-Automated-Nailfold-Capillary.", "published": "2023-12-10T16:33:41Z", "authors": ["Linxi Zhao", "Jiankai Tang", "Dongyu Chen", "Xiaohong Liu", "Yong Zhou", "Yuanchun Shi", "Guangyu Wang", "Yuntao Wang"], "categories": ["eess.IV", "cs.CV", "cs.LG"], "doi": "", "_query": "automated valuation model", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/cs/0101008v1", "title": "A Knowledge-based Automated Debugger in Learning System", "abstract": "Currently, programming instructors continually face the problem of helping to debug students' programs. Although there currently exist a number of debuggers and debugging tools in various platforms, most of these projects or products are crafted through the needs of software maintenance, and not through the perspective of teaching of programming. Moreover, most debuggers are too general, meant for experts as well as not user-friendly. We propose a new knowledge-based automated debugger to be used as a user-friendly tool by the students to self-debug their own programs. Stereotyped code (cliche) and bugs cliche will be stored as library of plans in the knowledge-base. Recognition of correct code or bugs is based on pattern matching and constraint satisfaction. Given a syntax error-free program and its specification, this debugger called Adil (Automated Debugger in Learning system) will be able locate, pinpoint and explain logical errors of programs. If there are no errors, it will be able to explain the meaning of the program. Adil is based on the design of the Conceiver, an automated program understanding system developed at Universiti Kebangsaan Malaysia.", "published": "2001-01-12T08:58:58Z", "authors": ["Abdullah Mohd Zin", "Syed Ahmad Aljunid", "Zarina Shukur", "Mohd Jan Nordin"], "categories": ["cs.SE", "cs.PL"], "doi": "", "_query": "automated valuation model", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/1708.04157v2", "title": "A global scavenging and circulation ocean model of thorium-230 and protactinium-231 with realistic particle dynamics (NEMO-ProThorP 0.1)", "abstract": "In this paper, we set forth a 3-D ocean model of the radioactive trace isotopes Th-230 and Pa-231. The interest arises from the fact that these isotopes are extensively used for investigating particle transport in the ocean and reconstructing past ocean circulation. The tracers are reversibly scavenged by biogenic and lithogenic particles. Our simulations of Th-230 and Pa-231 are based on the NEMO-PISCES ocean biogeochemistry general circulation model, which includes biogenic particles, namely small and big particulate organic carbon, calcium carbonate and biogenic silica. Small and big lithogenic particles from dust deposition are included in our model as well. Their distributions generally compare well with the small and big lithogenic particle concentrations from recent observations from the GEOTRACES programme, except for boundary nepheloid layers for which, as up to today, there are no non-trivial, prognostic models available on a global scale. Our simulations reproduce Th-230 and Pa-231 dissolved concentrations: they compare well with recent GEOTRACES observations in many parts of the ocean. Particulate Th-230 and Pa-231 concentrations are significantly improved compared to previous studies, but they are still too low because of missing particles from nepheloid layers. Our simulation reproduces the main characteristics of the Pa-231/Th-230 ratio observed in the sediments, and supports a moderate affinity of Pa-231 to biogenic silica as suggested by recent observations, relative to Th-230. Future model development may further improve understanding, especially when this will include a more complete representation of all particles, including different size classes, manganese hydroxides and nepheloid layers. This can be done based on our model, as its source code is readily available.", "published": "2017-08-14T14:49:38Z", "authors": ["Marco van Hulten", "Jean-Claude Dutay", "Matthieu Roy-Barman"], "categories": ["physics.ao-ph", "cs.CE"], "doi": "10.5194/gmd-11-3537-2018", "_query": "automated valuation model", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2210.02538v1", "title": "A roadmap for edge computing enabled automated multidimensional transmission electron microscopy", "abstract": "The advent of modern, high-speed electron detectors has made the collection of multidimensional hyperspectral transmission electron microscopy datasets, such as 4D-STEM, a routine. However, many microscopists find such experiments daunting since such datasets' analysis, collection, long-term storage, and networking remain challenging. Some common issues are the large and unwieldy size of the said datasets, often running into several gigabytes, non-standardized data analysis routines, and a lack of clarity about the computing and network resources needed to utilize the electron microscope fully. However, the existing computing and networking bottlenecks introduce significant penalties in each step of these experiments, and thus, real-time analysis-driven automated experimentation for multidimensional TEM is exceptionally challenging. One solution is integrating microscopy with edge computing, where moderately powerful computational hardware performs the preliminary analysis before handing off the heavier computation to HPC systems. In this perspective, we trace the roots of computation in modern electron microscopy, demonstrate deep learning experiments running on an edge system, and discuss the networking requirements for tying together microscopes, edge computers, and HPC systems.", "published": "2022-10-05T20:13:58Z", "authors": ["Debangshu Mukherjee", "Kevin M. Roccapriore", "Anees Al-Najjar", "Ayana Ghosh", "Jacob D. Hinkle", "Andrew R. Lupini", "Rama K. Vasudevan", "Sergei V. Kalinin", "Olga S. Ovchinnikova", "Maxim A. Ziatdinov", "Nageswara S. Rao"], "categories": ["cond-mat.mtrl-sci"], "doi": "10.1017/S1551929522001286", "_query": "automated valuation model", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2105.02782v1", "title": "The Homogenous Properties of Automated Market Makers", "abstract": "Automated market makers (AMM) have grown to obtain significant market share within the cryptocurrency ecosystem, resulting in a proliferation of new products pursuing exotic strategies for horizontal differentiation. Yet, their theoretical properties are curiously homogeneous when a set of basic assumptions are met. In this paper, we start by presenting a universal approach to deriving a formula for liquidity provisioning for AMMs. Next, we show that the constant function market maker and token swap market maker models are theoretically equivalent when liquidity reserves are uniform. Proceeding with an examination of AMM market microstructure, we show how non-linear price effect translates into slippage for traders and impermanent losses for liquidity providers. We proceed by showing how impermanent losses are a function of both volatility and market depth and discuss the implications of these findings within the context of the literature.", "published": "2021-03-31T09:39:10Z", "authors": ["Johannes Rude Jensen", "Mohsen Pourpouneh", "Kurt Nielsen", "Omri Ross"], "categories": ["q-fin.TR", "cs.CE"], "doi": "", "_query": "automated valuation model", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/cs/0101002v1", "title": "Automated Debugging In Java Using OCL And JDI", "abstract": "Correctness constraints provide a foundation for automated debugging within object-oriented systems. This paper discusses a new approach to incorporating correctness constraints into Java development environments. Our approach uses the Object Constraint Language (\"OCL\") as a specification language and the Java Debug Interface (\"JDI\") as a verification API. OCL provides a standard language for expressing object-oriented constraints that can integrate with Unified Modeling Language (\"UML\") software models. JDI provides a standard Java API capable of supporting type-safe and side effect free runtime constraint evaluation. The resulting correctness constraint mechanism: (1) entails no programming language modifications; (2) requires neither access nor changes to existing source code; and (3) works with standard off-the-shelf Java virtual machines (\"VMs\"). A prototype correctness constraint auditor is presented to demonstrate the utility of this mechanism for purposes of automated debugging.", "published": "2001-01-03T22:48:24Z", "authors": ["David J. Murray", "Dale E. Parson"], "categories": ["cs.SE", "cs.PL"], "doi": "", "_query": "automated valuation model", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2205.09864v2", "title": "Automated Scoring for Reading Comprehension via In-context BERT Tuning", "abstract": "Automated scoring of open-ended student responses has the potential to significantly reduce human grader effort. Recent advances in automated scoring often leverage textual representations based on pre-trained language models such as BERT and GPT as input to scoring models. Most existing approaches train a separate model for each item/question, which is suitable for scenarios such as essay scoring where items can be quite different from one another. However, these approaches have two limitations: 1) they fail to leverage item linkage for scenarios such as reading comprehension where multiple items may share a reading passage; 2) they are not scalable since storing one model per item becomes difficult when models have a large number of parameters. In this paper, we report our (grand prize-winning) solution to the National Assessment of Education Progress (NAEP) automated scoring challenge for reading comprehension. Our approach, in-context BERT fine-tuning, produces a single shared scoring model for all items with a carefully-designed input structure to provide contextual information on each item. We demonstrate the effectiveness of our approach via local evaluations using the training dataset provided by the challenge. We also discuss the biases, common error types, and limitations of our approach.", "published": "2022-05-19T21:16:15Z", "authors": ["Nigel Fernandez", "Aritra Ghosh", "Naiming Liu", "Zichao Wang", "Benoît Choffin", "Richard Baraniuk", "Andrew Lan"], "categories": ["cs.LG", "cs.AI", "cs.CY"], "doi": "", "_query": "automated valuation model", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2603.15969v2", "title": "Robust Language Identification for Romansh Varieties", "abstract": "The Romansh language has several regional varieties, called idioms, which sometimes have limited mutual intelligibility. Despite this linguistic diversity, there has been a lack of documented efforts to build a language identification (LID) system that can distinguish between these idioms. Since Romansh LID should also be able to recognize Rumantsch Grischun, a supra-regional variety that combines elements of several idioms, this makes for a novel and interesting classification problem. In this paper, we present a LID system for Romansh idioms based on an SVM approach. We evaluate our model on a newly curated benchmark across two domains and find that it reaches an average in-domain accuracy of 97%, enabling applications such as idiom-aware spell checking or machine translation. Our classifier is publicly available.", "published": "2026-03-16T22:42:10Z", "authors": ["Charlotte Model", "Sina Ahmadi", "Jannis Vamvas"], "categories": ["cs.CL"], "doi": "", "_query": "automated valuation model", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/cs/0611122v1", "title": "Knowledge Representation Concepts for Automated SLA Management", "abstract": "Outsourcing of complex IT infrastructure to IT service providers has increased substantially during the past years. IT service providers must be able to fulfil their service-quality commitments based upon predefined Service Level Agreements (SLAs) with the service customer. They need to manage, execute and maintain thousands of SLAs for different customers and different types of services, which needs new levels of flexibility and automation not available with the current technology. The complexity of contractual logic in SLAs requires new forms of knowledge representation to automatically draw inferences and execute contractual agreements. A logic-based approach provides several advantages including automated rule chaining allowing for compact knowledge representation as well as flexibility to adapt to rapidly changing business requirements. We suggest adequate logical formalisms for representation and enforcement of SLA rules and describe a proof-of-concept implementation. The article describes selected formalisms of the ContractLog KR and their adequacy for automated SLA management and presents results of experiments to demonstrate flexibility and scalability of the approach.", "published": "2006-11-23T13:25:45Z", "authors": ["Adrian Paschke", "Martin Bichler"], "categories": ["cs.SE", "cs.AI", "cs.LO", "cs.PL"], "doi": "", "_query": "automated valuation model", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2308.15167v1", "title": "Dynamic Collaborative Path Planning for Remote Assistance of Highly-Automated Vehicles", "abstract": "Given its increasing popularity in recent years, teleoperation technology is now recognized as a robust fallback solution for Automated Driving (AD). Remote Assistance (RA) represents an event-driven class of teleoperation with a distinct division of tasks between the Autonomous Vehicle (AV) and the remote human operator. This paper presents a novel approach for RA of AVs in urban environments. The concept draws inspiration from the potential synergy between highly-automated systems and human operators to collaboratively solve complex driving situations. Utilizing a hybrid algorithm that makes use of the Operational Design Domain (ODD) modification idea, it considers actions that go beyond the nominal operational space. Combined with the advanced cognitive reasoning of the human remote operator, the concept offers features that hold the potential to significantly improve both RA and AD user experiences.", "published": "2023-08-29T09:49:41Z", "authors": ["Domagoj Majstorovic", "Frank Diermeyer"], "categories": ["cs.RO"], "doi": "", "_query": "automated valuation model", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/1511.07654v1", "title": "Automated Guidance of Collective Movement in a Multi-Agent Model of Physarum polycephalum", "abstract": "Collective movement occurs in living systems where the simple movements of individual members of a pop- ulation are combined to generate movement of the collective as a whole, displaying complex dynamics which cannot be found in the component parts themselves. The plasmodium stage of slime mould Physarum polycephalum displays complex amoeboid movement during its foraging and hazard avoidance and its movement can be influenced by the spatial placement of attractant and repellent stimuli. Slime mould is attractive to robotics due to its simple component parts and the distributed nature of its control and locomotion mechanisms. We investigate methods of automated guidance of a multi-agent swarm collective along a pre-defined path to a goal location. We demonstrate a closed-loop feedback mechanism using attractant and repellent stimuli. We find that guidance by repellent stimuli (a light illumination mask) provides faster and more accurate guidance than attractant sources, which exhibit overshooting phenomena at path turns. The method allows traversal of convoluted arenas with challenging obstacles and provides an insight into how unconven- tional computing substrates may be hybridised with classical computing methods to take advantage of the benefits of both approaches.", "published": "2015-11-24T11:22:48Z", "authors": ["Jeff Jones"], "categories": ["cs.ET"], "doi": "", "_query": "automated valuation model", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2309.03311v1", "title": "Default Process Modeling and Credit Valuation Adjustment", "abstract": "This paper presents a convenient framework for modeling default process and pricing derivative securities involving credit risk. The framework provides an integrated view of credit valuation adjustment by linking distance-to-default, default probability, survival probability, and default correlation together. We show that risky valuation is Martingale in our model. The framework reduces the technical issues of performing risky valuation to the same issues faced when performing the ordinary valuation. The numerical results show that the model prediction is consistent with the historical observations.", "published": "2023-09-06T18:48:00Z", "authors": ["David Xiao"], "categories": ["q-fin.PR"], "doi": "", "_query": "automated valuation model", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/1802.04799v3", "title": "TVM: An Automated End-to-End Optimizing Compiler for Deep Learning", "abstract": "There is an increasing need to bring machine learning to a wide diversity of hardware devices. Current frameworks rely on vendor-specific operator libraries and optimize for a narrow range of server-class GPUs. Deploying workloads to new platforms -- such as mobile phones, embedded devices, and accelerators (e.g., FPGAs, ASICs) -- requires significant manual effort. We propose TVM, a compiler that exposes graph-level and operator-level optimizations to provide performance portability to deep learning workloads across diverse hardware back-ends. TVM solves optimization challenges specific to deep learning, such as high-level operator fusion, mapping to arbitrary hardware primitives, and memory latency hiding. It also automates optimization of low-level programs to hardware characteristics by employing a novel, learning-based cost modeling method for rapid exploration of code optimizations. Experimental results show that TVM delivers performance across hardware back-ends that are competitive with state-of-the-art, hand-tuned libraries for low-power CPU, mobile GPU, and server-class GPUs. We also demonstrate TVM's ability to target new accelerator back-ends, such as the FPGA-based generic deep learning accelerator. The system is open sourced and in production use inside several major companies.", "published": "2018-02-12T20:49:34Z", "authors": ["Tianqi Chen", "Thierry Moreau", "Ziheng Jiang", "Lianmin Zheng", "Eddie Yan", "Meghan Cowan", "Haichen Shen", "Leyuan Wang", "Yuwei Hu", "Luis Ceze", "Carlos Guestrin", "Arvind Krishnamurthy"], "categories": ["cs.LG", "cs.AI", "cs.PL"], "doi": "", "_query": "automated valuation model", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2309.14488v1", "title": "When Automated Assessment Meets Automated Content Generation: Examining Text Quality in the Era of GPTs", "abstract": "The use of machine learning (ML) models to assess and score textual data has become increasingly pervasive in an array of contexts including natural language processing, information retrieval, search and recommendation, and credibility assessment of online content. A significant disruption at the intersection of ML and text are text-generating large-language models such as generative pre-trained transformers (GPTs). We empirically assess the differences in how ML-based scoring models trained on human content assess the quality of content generated by humans versus GPTs. To do so, we propose an analysis framework that encompasses essay scoring ML-models, human and ML-generated essays, and a statistical model that parsimoniously considers the impact of type of respondent, prompt genre, and the ML model used for assessment model. A rich testbed is utilized that encompasses 18,460 human-generated and GPT-based essays. Results of our benchmark analysis reveal that transformer pretrained language models (PLMs) more accurately score human essay quality as compared to CNN/RNN and feature-based ML methods. Interestingly, we find that the transformer PLMs tend to score GPT-generated text 10-15\\% higher on average, relative to human-authored documents. Conversely, traditional deep learning and feature-based ML models score human text considerably higher. Further analysis reveals that although the transformer PLMs are exclusively fine-tuned on human text, they more prominently attend to certain tokens appearing only in GPT-generated text, possibly due to familiarity/overlap in pre-training. Our framework and results have implications for text classification settings where automated scoring of text is likely to be disrupted by generative AI.", "published": "2023-09-25T19:32:18Z", "authors": ["Marialena Bevilacqua", "Kezia Oketch", "Ruiyang Qin", "Will Stamey", "Xinyuan Zhang", "Yi Gan", "Kai Yang", "Ahmed Abbasi"], "categories": ["cs.CL", "cs.AI"], "doi": "", "_query": "automated valuation model", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/1204.3055v1", "title": "IVOA Recommendation: Spectrum Data Model 1.1", "abstract": "We present a data model describing the structure of spectrophotometric datasets with spectral and temporal coordinates and associated metadata. This data model may be used to represent spectra, time series data, segments of SED (Spectral Energy Distributions) and other spectral or temporal associations.", "published": "2012-04-13T17:19:51Z", "authors": ["Jonathan McDowell", "Doug Tody", "Tamas Budavari", "Markus Dolensky", "Inga Kamp", "Kelly McCusker", "Pavlos Protopapas", "Arnold Rots", "Randy Thompson", "Frank Valdes", "Petr Skoda", "Bruno Rino", "Sebastien Derriere", "Jesus Salgado", "Omar Laurino", "the IVOA Data Access Layer", "Data Model Working Groups"], "categories": ["astro-ph.IM"], "doi": "10.5479/ADS/bib/2011ivoa.spec.1120M", "_query": "automated valuation model", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2005.10100v1", "title": "Reduction of valuation risk by Kalman filtering in business valuation models", "abstract": "A recursive free cash flow model (FCFF) is proposed to determine the corporate value of a company in an efficient market in which new market and company-specific information is modelled by additive white noise. The stochastic equations of the FCFF model are solved explicitly to obtain the average corporate value and valuation risk. It is pointed out that valuation risk can be reduced significantly by implementing a conventional two-step Kalman filter in the recursive FCFF model, thus improving its predictive power. Systematic errors of the Kalman filter, caused by intermediate changes in risk and hence in the weighted average cost of capital (WACC), are detected by measuring the residuals. By including an additional adjustment step in the conventional Kalman filtering algorithm, it is shown that systematic errors can be eliminated by recursively adjusting the WACC. The performance of the three-step adaptive Kalman filter is tested by Monte Carlo simulation which demonstrates the reliability and robustness against systematic errors. It is also proved that the conventional and adaptive Kalman filtering algorithms can be implemented into other valuation models such as the economic value added model (EVA) and free cash flow to equity model (FCFE).", "published": "2020-05-20T15:01:13Z", "authors": ["Rene Scheurwater"], "categories": ["q-fin.CP"], "doi": "", "_query": "automated valuation model", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2304.10701v9", "title": "GMValuator: Similarity-based Data Valuation for Generative Models", "abstract": "Data valuation plays a crucial role in machine learning. Existing data valuation methods, mainly focused on discriminative models, overlook generative models that have gained attention recently. In generative models, data valuation measures the impact of training data on generated datasets. Very few existing attempts at data valuation methods designed for deep generative models either concentrate on specific models or lack robustness in their outcomes. Moreover, efficiency still reveals vulnerable shortcomings. We formulate the data valuation problem in generative models from a similarity matching perspective to bridge the gaps. Specifically, we introduce Generative Model Valuator (GMValuator), the first training-free and model-agnostic approach to providing data valuation for image generation tasks. It empowers efficient data valuation through our innovative similarity matching module, calibrates biased contributions by incorporating image quality assessment, and attributes credits to all training samples based on their contributions to the generated samples. Additionally, we introduce four evaluation criteria for assessing data valuation methods in generative models. GMValuator is extensively evaluated on benchmark and high-resolution datasets and various mainstream generative architectures to demonstrate its effectiveness.", "published": "2023-04-21T02:02:02Z", "authors": ["Jiaxi Yang", "Wenglong Deng", "Benlin Liu", "Yangsibo Huang", "James Zou", "Xiaoxiao Li"], "categories": ["cs.CV", "cs.LG"], "doi": "", "_query": "automated valuation model", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2606.24689v1", "title": "Automated Summarization of Software Documents: An LLM-based Multi-Agent Approach", "abstract": "Large Language Models (LLMs) and LLM-based Multi-Agent Systems (MAS) are revolutionizing software engineering (SE) by advancing automation, decision-making, and knowledge processing. Their recent application to SE tasks has already shown promising results. In this paper, we focus on summarization as a key application area. We present Metagente, an LLM-based MAS designed to generate concise and accurate summaries of software documentation. Metagente employs a Teacher-Student architecture where multiple LLM agents collaborate to enhance relevance and precision of produced summaries. An empirical evaluation on real-world datasets demonstrates Metagente's effectiveness in streamlining workflows, outperforming the considered baselines. The evaluation provides evidence that Metagente improves summarization for requirements analysis and technical documentation. Our findings underscore the transformative potential of these technologies in SE, while identifying challenges and future research directions for their seamless integration.", "published": "2026-06-23T15:18:17Z", "authors": ["Duc S. H. Nguyen", "Minh T. Nguyen", "Phuong T. Nguyen", "Juri Di Rocco", "Davide Di Ruscio"], "categories": ["cs.SE"], "doi": "", "_query": "automated valuation model", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2510.14465v1", "title": "Towards Automated Governance: A DSL for Human-Agent Collaboration in Software Projects", "abstract": "The stakeholders involved in software development are becoming increasingly diverse, with both human contributors from varied backgrounds and AI-powered agents collaborating together in the process. This situation presents unique governance challenges, particularly in Open-Source Software (OSS) projects, where explicit policies are often lacking or unclear. This paper presents the vision and foundational concepts for a novel Domain-Specific Language (DSL) designed to define and enforce rich governance policies in systems involving diverse stakeholders, including agents. This DSL offers a pathway towards more robust, adaptable, and ultimately automated governance, paving the way for more effective collaboration in software projects, especially OSS ones.", "published": "2025-10-16T09:05:47Z", "authors": ["Adem Ait", "Gwendal Jouneaux", "Javier Luis Cánovas Izquierdo", "Jordi Cabot"], "categories": ["cs.SE"], "doi": "", "_query": "automated valuation model", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2401.02500v2", "title": "On the Prospects of Incorporating Large Language Models (LLMs) in Automated Planning and Scheduling (APS)", "abstract": "Automated Planning and Scheduling is among the growing areas in Artificial Intelligence (AI) where mention of LLMs has gained popularity. Based on a comprehensive review of 126 papers, this paper investigates eight categories based on the unique applications of LLMs in addressing various aspects of planning problems: language translation, plan generation, model construction, multi-agent planning, interactive planning, heuristics optimization, tool integration, and brain-inspired planning. For each category, we articulate the issues considered and existing gaps. A critical insight resulting from our review is that the true potential of LLMs unfolds when they are integrated with traditional symbolic planners, pointing towards a promising neuro-symbolic approach. This approach effectively combines the generative aspects of LLMs with the precision of classical planning methods. By synthesizing insights from existing literature, we underline the potential of this integration to address complex planning challenges. Our goal is to encourage the ICAPS community to recognize the complementary strengths of LLMs and symbolic planners, advocating for a direction in automated planning that leverages these synergistic capabilities to develop more advanced and intelligent planning systems.", "published": "2024-01-04T19:22:09Z", "authors": ["Vishal Pallagani", "Kaushik Roy", "Bharath Muppasani", "Francesco Fabiano", "Andrea Loreggia", "Keerthiram Murugesan", "Biplav Srivastava", "Francesca Rossi", "Lior Horesh", "Amit Sheth"], "categories": ["cs.AI"], "doi": "10.1609/icaps.v34i1.31503", "_query": "automated valuation model", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2511.20403v2", "title": "LLMs for Automated Unit Test Generation and Assessment in Java: The AgoneTest Framework", "abstract": "Unit testing is an essential but resource-intensive step in software development, ensuring individual code units function correctly. This paper introduces AgoneTest, an automated evaluation framework for Large Language Model-generated (LLM) unit tests in Java. AgoneTest does not aim to propose a novel test generation algorithm; rather, it supports researchers and developers in comparing different LLMs and prompting strategies through a standardized end-to-end evaluation pipeline under realistic conditions. We introduce the Classes2Test dataset, which maps Java classes under test to their corresponding test classes, and a framework that integrates advanced evaluation metrics, such as mutation score and test smells, for a comprehensive assessment. Experimental results show that, for the subset of tests that compile, LLM-generated tests can match or exceed human-written tests in terms of coverage and defect detection. Our findings also demonstrate that enhanced prompting strategies contribute to test quality. AgoneTest clarifies the potential of LLMs in software testing and offers insights for future improvements in model design, prompt engineering, and testing practices.", "published": "2025-11-25T15:33:00Z", "authors": ["Andrea Lops", "Fedelucio Narducci", "Azzurra Ragone", "Michelantonio Trizio", "Claudio Bartolini"], "categories": ["cs.SE", "cs.AI"], "doi": "", "_query": "automated valuation model", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2404.17586v1", "title": "The Future of Scientific Publishing: Automated Article Generation", "abstract": "This study introduces a novel software tool leveraging large language model (LLM) prompts, designed to automate the generation of academic articles from Python code a significant advancement in the fields of biomedical informatics and computer science. Selected for its widespread adoption and analytical versatility, Python served as a foundational proof of concept; however, the underlying methodology and framework exhibit adaptability across various GitHub repo's underlining the tool's broad applicability (Harper 2024). By mitigating the traditionally time-intensive academic writing process, particularly in synthesizing complex datasets and coding outputs, this approach signifies a monumental leap towards streamlining research dissemination. The development was achieved without reliance on advanced language model agents, ensuring high fidelity in the automated generation of coherent and comprehensive academic content. This exploration not only validates the successful application and efficiency of the software but also projects how future integration of LLM agents which could amplify its capabilities, propelling towards a future where scientific findings are disseminated more swiftly and accessibly.", "published": "2024-04-11T16:47:02Z", "authors": ["Jeremy R. Harper"], "categories": ["cs.HC", "cs.AI", "cs.ET"], "doi": "", "_query": "automated valuation model", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2109.00426v1", "title": "Separating minimal valuations, point-continuous valuations and continuous valuations", "abstract": "We give two concrete examples of continuous valuations on dcpo's to separate minimal valuations, point-continuous valuations and continuous valuations: (1) Let $\\mathcal J$ be the Johnstone's non-sober dcpo, and $μ$ be the continuous valuation on $\\mathcal J$ with $μ(U) =1$ for nonempty Scott opens $U$ and $μ(U) = 0$ for $U=\\emptyset$. Then $μ$ is a point-continuous valuation on $\\mathcal J$ that is not minimal. (2) Lebesgue measure extends to a measure on the Sorgenfrey line $\\mathbb R_{l}$. Its restriction to the open subsets of $\\mathbb R_{l}$ is a continuous valuation $λ$. Then its image valuation $\\overlineλ$ through the embedding of $\\mathbb R_{l}$ into its Smyth powerdomain $\\mathcal Q\\mathbb R_{l}$ in the Scott topology is a continuous valuation that is not point-continuous. We believe that our construction $\\overlineλ$ might be useful in giving counterexamples displaying the failure of the general Fubini-type equations on dcpo's.", "published": "2021-09-01T15:19:53Z", "authors": ["Jean Goubault-Larrecq", "Xiaodong Jia"], "categories": ["cs.LO", "math.GN"], "doi": "", "_query": "automated valuation model", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2308.04760v1", "title": "Automated Driving Without Ethics: Meaning, Design and Real-World Implementation", "abstract": "The ethics of automated vehicles (AV) has received a great amount of attention in recent years, specifically in regard to their decisional policies in accident situations in which human harm is a likely consequence. After a discussion about the pertinence and cogency of the term 'artificial moral agent' to describe AVs that would accomplish these sorts of decisions, and starting from the assumption that human harm is unavoidable in some situations, a strategy for AV decision making is proposed using only pre-defined parameters to characterize the risk of possible accidents and also integrating the Ethical Valence Theory, which paints AV decision-making as a type of claim mitigation, into multiple possible decision rules to determine the most suitable action given the specific environment and decision context. The goal of this approach is not to define how moral theory requires vehicles to behave, but rather to provide a computational approach that is flexible enough to accommodate a number of human 'moral positions' concerning what morality demands and what road users may expect, offering an evaluation tool for the social acceptability of an automated vehicle's decision making.", "published": "2023-08-09T07:49:24Z", "authors": ["Katherine Evans", "Nelson de Moura", "Raja Chatila", "Stéphane Chauvier"], "categories": ["cs.AI", "cs.RO"], "doi": "", "_query": "automated valuation model", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/1509.03354v3", "title": "Valuation Semirings", "abstract": "The main scope of this paper is to introduce valuation semirings in general and discrete valuation semirings in particular. In order to do that, first we define valuation maps and investigate them. Then we define valuation semirings with the help of valuation maps and prove that a multiplicatively cancellative semiring is a valuation semiring if and only if its ideals are totally ordered by inclusion. We also prove that if the unique maximal ideal of a valuation semiring is subtractive, then it is integrally closed. We end this paper by introducing discrete valuation semirings and show that a semiring is a discrete valuation semiring if and only if it is a multiplicatively cancellative principal ideal semiring possessing a nonzero unique maximal ideal. We also prove that a discrete valuation semiring is a Gaussian semiring if and only if its unique maximal ideal is subtractive.", "published": "2015-09-10T22:32:26Z", "authors": ["Peyman Nasehpour"], "categories": ["math.AC", "math.GR", "math.RA"], "doi": "10.1142/S0219498818500731", "_query": "automated valuation model", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/cs/0309055v1", "title": "A mathematical framework for automated bug localization", "abstract": "In this paper, we propose a mathematical framework for automated bug localization. This framework can be briefly summarized as follows. A program execution can be represented as a rooted acyclic directed graph. We define an execution snapshot by a cut-set on the graph. A program state can be regarded as a conjunction of labels on edges in a cut-set. Then we argue that a debugging task is a pruning process of the execution graph by using cut-sets. A pruning algorithm, i.e., a debugging task, is also presented.", "published": "2003-09-30T01:19:11Z", "authors": ["Tsuyoshi Ohta", "Tadanori Mizuno"], "categories": ["cs.SE"], "doi": "", "_query": "automated valuation model", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/1003.5689v1", "title": "Valuation theoretic and model theoretic aspects of local uniformization", "abstract": "This paper gives a survey on a valuation theoretical approach to local uniformization in positive characteristic, the model theory of valued fields in positive characteristic, and their connection with the valuation theoretical phenomenon of defect.", "published": "2010-03-29T21:46:52Z", "authors": ["Franz-Viktor Kuhlmann"], "categories": ["math.AC"], "doi": "", "_query": "automated valuation model", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2411.04444v1", "title": "An Empirical Study on the Potential of LLMs in Automated Software Refactoring", "abstract": "Recent advances in large language models (LLMs), make it potentially feasible to automatically refactor source code with LLMs. However, it remains unclear how well LLMs perform compared to human experts in conducting refactorings automatically and accurately. To fill this gap, in this paper, we conduct an empirical study to investigate the potential of LLMs in automated software refactoring, focusing on the identification of refactoring opportunities and the recommendation of refactoring solutions. We first construct a high-quality refactoring dataset comprising 180 real-world refactorings from 20 projects, and conduct the empirical study on the dataset. With the to-be-refactored Java documents as input, ChatGPT and Gemini identified only 28 and 7 respectively out of the 180 refactoring opportunities. However, explaining the expected refactoring subcategories and narrowing the search space in the prompts substantially increased the success rate of ChatGPT from 15.6% to 86.7%. Concerning the recommendation of refactoring solutions, ChatGPT recommended 176 refactoring solutions for the 180 refactorings, and 63.6% of the recommended solutions were comparable to (even better than) those constructed by human experts. However, 13 out of the 176 solutions suggested by ChatGPT and 9 out of the 137 solutions suggested by Gemini were unsafe in that they either changed the functionality of the source code or introduced syntax errors, which indicate the risk of LLM-based refactoring. To this end, we propose a detect-and-reapply tactic, called RefactoringMirror, to avoid such unsafe refactorings. By reapplying the identified refactorings to the original code using thoroughly tested refactoring engines, we can effectively mitigate the risks associated with LLM-based automated refactoring while still leveraging LLM's intelligence to obtain valuable refactoring recommendations.", "published": "2024-11-07T05:35:55Z", "authors": ["Bo Liu", "Yanjie Jiang", "Yuxia Zhang", "Nan Niu", "Guangjie Li", "Hui Liu"], "categories": ["cs.SE"], "doi": "10.1007/s10515-025-00500-0", "_query": "automated valuation model", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/1209.4172v1", "title": "Quasi-Valuations Extending a Valuation", "abstract": "Suppose $F$ is a field with valuation $v$ and valuation ring $O_{v}$, $E$ is a finite field extension and $w$ is a quasi-valuation on $E$ extending $v$. We study quasi-valuations on $E$ that extend $v$; in particular, their corresponding rings and their prime spectrums. We prove that these ring extensions satisfy INC (incomparability), LO (lying over), and GD (going down) over $O_{v}$; in particular, they have the same Krull Dimension. We also prove that every such quasi-valuation is dominated by some valuation extending $v$. Under the assumption that the value monoid of the quasi-valuation is a group we prove that these ring extensions satisfy GU (going up) over $O_{v}$, and a bound on the size of the prime spectrum is given. In addition, a 1:1 correspondence is obtained between exponential quasi-valuations and integrally closed quasi-valuation rings. Given $R$, an algebra over $O_{v}$, we construct a quasi-valuation on $R$; we also construct a quasi-valuation on $R \\otimes_{O_{v}} F$ which helps us prove our main Theorem. The main Theorem states that if $R \\subseteq E$ satisfies $R \\cap F=O_{v}$ and $E$ is the field of fractions of $R$, then $R$ and $v$ induce a quasi-valuation $w$ on $E$ such that $R=O_{w}$ and $w$ extends $v$; thus $R$ satisfies the properties of a quasi-valuation ring.", "published": "2012-09-19T07:56:11Z", "authors": ["Shai Sarussi"], "categories": ["math.AC"], "doi": "10.1016/j.jalgebra.2012.09.019", "_query": "automated valuation model", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/1507.06009v3", "title": "Valuations and Frobenius", "abstract": "The behavior of the Frobenius map is investigated for valuation rings of prime characteristic. We show that valuation rings are always F-pure. We introduce a generalization of the notion of strong F-regularity, which we call F-pure regularity, and show that a valuation ring is F-pure regular if and only if it is Noetherian. For valuations on function fields, we show that the Frobenius map is finite if and only if the valuation is divisorial; in this case the valuation ring is Frobenius split. For Noetherian valuation rings in function fields, we show that the valuation ring is Frobenius split if and only if Frobenius is finite, or equivalently, if and only if the valuation ring is excellent.", "published": "2015-07-21T23:04:19Z", "authors": ["Rankeya Datta", "Karen E. Smith"], "categories": ["math.AC"], "doi": "10.2140/ant.2016.10.1057", "_query": "automated valuation model", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/1004.0844v1", "title": "Quantum Portfolios of Observables and the Risk Neutral Valuation Model", "abstract": "Quantum Portfolios of quantum algorithms encoded on qbits have recently been reported. In this paper a discussion of the continuous variables version of quantum portfolios is presented. A risk neutral valuation model for options dependent on the measured values of the observables, analogous to the traditional Black-Scholes valuation model, is obtained from the underlying stochastic equations. The quantum algorithms are here encoded on simple harmonic oscillator (SHO) states, and a Fokker-Planck equation for the Glauber P-representation is obtained as a starting point for the analysis. A discussion of the observation of the polarization of a portfolio of qbits is also obtained and the resultant Fokker-Planck equation is used to obtain the risk neutral valuation of the qbit polarization portfolio.", "published": "2010-04-02T06:14:37Z", "authors": ["Fredrick Michael"], "categories": ["q-fin.GN"], "doi": "", "_query": "automated valuation model", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2506.00694v2", "title": "Measuring Faithfulness and Abstention: An Automated Pipeline for Evaluating LLM-Generated 3-ply Case-Based Legal Arguments", "abstract": "Large Language Models (LLMs) demonstrate potential in complex legal tasks like argument generation, yet their reliability remains a concern. Building upon pilot work assessing LLM generation of 3-ply legal arguments using human evaluation, this paper introduces an automated pipeline to evaluate LLM performance on this task, specifically focusing on faithfulness (absence of hallucination), factor utilization, and appropriate abstention. We define hallucination as the generation of factors not present in the input case materials and abstention as the model's ability to refrain from generating arguments when instructed and no factual basis exists. Our automated method employs an external LLM to extract factors from generated arguments and compares them against the ground-truth factors provided in the input case triples (current case and two precedent cases). We evaluated eight distinct LLMs on three tests of increasing difficulty: 1) generating a standard 3-ply argument, 2) generating an argument with swapped precedent roles, and 3) recognizing the impossibility of argument generation due to lack of shared factors and abstaining. Our findings indicate that while current LLMs achieve high accuracy (over 90%) in avoiding hallucination on viable argument generation tests (Tests 1 & 2), they often fail to utilize the full set of relevant factors present in the cases. Critically, on the abstention test (Test 3), most models failed to follow instructions to stop, instead generating spurious arguments despite the lack of common factors. This automated pipeline provides a scalable method for assessing these crucial LLM behaviors, highlighting the need for improvements in factor utilization and robust abstention capabilities before reliable deployment in legal settings. Link: https://lizhang-aiandlaw.github.io/An-Automated-Pipeline-for-Evaluating-LLM-Generated-3-ply-Case-Based-Legal-Arguments/", "published": "2025-05-31T19:56:40Z", "authors": ["Li Zhang", "Morgan Gray", "Jaromir Savelka", "Kevin D. Ashley"], "categories": ["cs.CL", "cs.AI", "cs.LG"], "doi": "", "_query": "automated valuation model", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2605.20054v1", "title": "Automating proof search when equality is a logical connective", "abstract": "Treating syntactic equality as a logical connective -- governed by left- and right-introduction rules within the sequent calculus -- offers an elegant and powerful approach to term identity. This treatment of equality allows for the derivation of core mathematical principles, such as Peano's axioms (excluding induction), and serves as a foundation for the Abella interactive proof assistant. However, integrating this equality into automated proof search remains challenging. We present a proof search procedure that extends unification to handle the complexities of quantifier alternation and equations that occur in both positive and negative occurrences. While established logical frameworks such as $λ$Prolog and LF lack direct support for this kind of equality, our procedure enables a lightweight logical framework that addresses this gap. Our system enables unification-aware proof search across a diverse range of first-order sequent calculi that can directly use this form of equality.", "published": "2026-05-19T16:14:33Z", "authors": ["Kaustuv Chaudhuri", "Arunava Gantait", "Dale Miller"], "categories": ["cs.LO"], "doi": "", "_query": "automated valuation model", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2507.20685v1", "title": "What's Really Different with AI? -- A Behavior-based Perspective on System Safety for Automated Driving Systems", "abstract": "Assuring safety for ``AI-based'' systems is one of the current challenges in safety engineering. For automated driving systems, in particular, further assurance challenges result from the open context that the systems need to operate in after deployment. The current standardization and regulation landscape for ``AI-based'' systems is becoming ever more complex, as standards and regulations are being released at high frequencies. This position paper seeks to provide guidance for making qualified arguments which standards should meaningfully be applied to (``AI-based'') automated driving systems. Furthermore, we argue for clearly differentiating sources of risk between AI-specific and general uncertainties related to the open context. In our view, a clear conceptual separation can help to exploit commonalities that can close the gap between system-level and AI-specific safety analyses, while ensuring the required rigor for engineering safe ``AI-based'' systems.", "published": "2025-07-28T10:10:25Z", "authors": ["Marcus Nolte", "Nayel Fabian Salem", "Olaf Franke", "Jan Heckmann", "Christoph Höhmann", "Georg Stettinger", "Markus Maurer"], "categories": ["eess.SY"], "doi": "", "_query": "automated valuation model", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/1012.5590v1", "title": "Automated Symbolic Analysis of ARBAC-Policies (Extended Version)", "abstract": "One of the most widespread framework for the management of access-control policies is Administrative Role Based Access Control (ARBAC). Several automated analysis techniques have been proposed to help maintaining desirable security properties of ARBAC policies. One limitation of many available techniques is that the sets of users and roles are bounded. In this paper, we propose a symbolic framework to overcome this difficulty. We design an automated security analysis technique, parametric in the number of users and roles, by adapting recent methods for model checking infinite state systems that use first-order logic and state-of-the-art theorem proving techniques. Preliminary experiments with a prototype implementations seem to confirm the scalability of our technique.", "published": "2010-12-27T10:53:25Z", "authors": ["A. Armando", "S. Ranise"], "categories": ["cs.CR", "cs.LO"], "doi": "", "_query": "automated valuation model", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2206.09666v7", "title": "The Log Private Company Valuation Model", "abstract": "For a public company, pricing and hedging models of options and equity--linked life insurance products have been sufficiently developed. However, for a private company, because of unobserved prices, pricing and hedging models of the European options and life insurance products are in their early stages of development. For this reason, this paper introduces a log private company valuation model, which is based on the dynamic Gordon growth model. In this paper, we obtain closed--form pricing and hedging formulas of the European options and equity--linked life insurance products for private companies. Also, the paper provides Maximum Likelihood (ML) estimators of our model, Expectation Maximization (EM) algorithm, and valuation formula for private companies.", "published": "2022-06-20T09:13:29Z", "authors": ["Battulga Gankhuu"], "categories": ["q-fin.MF"], "doi": "", "_query": "automated valuation model", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/1712.04236v4", "title": "Ultra valuations", "abstract": "This paper proposes an original exchange property of valuations.This property is shown to be equivalent to a property described by Dress and Terhalle in the context of discrete optimization and matroids and shown there to characterize the valuations for which the demand oracle can be implemented by a greedy algorithm. The same exchange property is also equivalent to a property described independently by Reijnierse, van Gellekom and Potters and by Lehmann, Lehmann and Nisan and shown there to be satisfied by substitutes valuations. It studies the family of valuations that satisfy this exchange property, the ultra valuations. Any substitutes valuation is an ultra valuation, but ultra valuations may exhibit complementarities. Any symmetric valuation is an ultra valuation. Substitutes valuations are exactly the submodular ultra valuations. Ultra valuations define ultrametrics on the set of items. The maximum of an ultra valuation on $n$ items can be found in $O(n^2)$ steps. Finding an efficient allocation among ultra valuations is NP-hard.", "published": "2017-12-12T11:14:52Z", "authors": ["Daniel Lehmann"], "categories": ["cs.GT"], "doi": "", "_query": "automated valuation model", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/1602.01233v1", "title": "Characterizing diophantine henselian valuation rings and valuation ideals", "abstract": "We give a characterization, in terms of the residue field, of those henselian valuation rings and those henselian valuation ideals that are diophantine. This characterization gives a common generalization of all the positive and negative results on diophantine henselian valuation rings and diophantine valuation ideals in the literature. We also treat questions of uniformity and we apply the results to show that a given field can carry at most one diophantine nontrivial equicharacteristic henselian valuation ring or valuation ideal.", "published": "2016-02-03T09:32:53Z", "authors": ["Sylvy Anscombe", "Arno Fehm"], "categories": ["math.LO", "math.AC", "math.NT"], "doi": "10.1112/plms.12042", "_query": "automated valuation model", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/astro-ph/0102223v1", "title": "Automated stellar classification for large surveys: a review of methods and results", "abstract": "Current and future large astronomical surveys will yield multiparameter databases on millions or even billions of objects. The scientific exploitation of these will require powerful, robust, and automated classification tools tailored to the specific survey. Partly motivated by this, the past five to ten years has seen a significant increase in the amount of work focused on automated classification and its application to astronomical data. In this article, I review this work and assess the current status of automated stellar classification, with particular regard to its potential application to large astronomical surveys. I examine both the strengths and weaknesses of the various techniques and how they have been applied to different classification and parametrization problems. I finish with a brief look at the developments still required in order to apply a stellar classifier to a large survey.", "published": "2001-02-13T09:07:15Z", "authors": ["C. A. L. Bailer-Jones"], "categories": ["astro-ph"], "doi": "", "_query": "automated valuation model", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2305.18017v3", "title": "Trace models of concurrent valuation algebras", "abstract": "This paper introduces Concurrent Valuation Algebras (CVAs), a novel extension of ordered valuation algebras (OVAs). CVAs include two combine operators representing parallel and sequential products, adhering to a weak exchange law. This development offers theoretical and practical benefits for the specification and modelling of concurrent and distributed systems. As a presheaf on a space of domains, CVAs enable localised specifications, supporting modularity, compositionality, and the ability to represent large and complex systems. Furthermore, CVAs align with lattice-based refinement reasoning and are compatible with established methodologies such as Hoare and Rely-Guarantee logics. The flexibility of CVAs is explored through three trace models, illustrating distinct paradigms of concurrent/distributed computing, interrelated by morphisms. The paper also highlights the potential to incorporate a powerful local computation framework from valuation algebras for model checking in concurrent and distributed systems. The foundational results presented have been verified with the proof assistant Isabelle/HOL.", "published": "2023-05-29T11:18:18Z", "authors": ["Naso Evangelou-Oost", "Larissa Meinicke", "Callum Bannister", "Ian J. Hayes"], "categories": ["cs.LO"], "doi": "10.1007/978-981-99-7584-6_8", "_query": "automated valuation model", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/cs/0012007v3", "title": "Kima - an Automated Error Correction System for Concurrent Logic Programs", "abstract": "We have implemented Kima, an automated error correction system for concurrent logic programs. Kima corrects near-misses such as wrong variable occurrences in the absence of explicit declarations of program properties. Strong moding/typing and constraint-based analysis are turning to play fundamental roles in debugging concurrent logic programs as well as in establishing the consistency of communication protocols and data types. Mode/type analysis of Moded Flat GHC is a constraint satisfaction problem with many simple mode/type constraints, and can be solved efficiently. We proposed a simple and efficient technique which, given a non-well-moded/typed program, diagnoses the ``reasons'' of inconsistency by finding minimal inconsistent subsets of mode/type constraints. Since each constraint keeps track of the symbol occurrence in the program, a minimal subset also tells possible sources of program errors. Kima realizes automated correction by replacing symbol occurrences around the possible sources and recalculating modes and types of the rewritten programs systematically. As long as bugs are near-misses, Kima proposes a rather small number of alternatives that include an intended program.", "published": "2000-12-13T08:48:06Z", "authors": ["Yasuhiro Ajiro", "Kazunori Ueda"], "categories": ["cs.SE", "cs.PL"], "doi": "", "_query": "automated valuation model", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/cs/0011010v1", "title": "Extension Language Automation of Embedded System Debugging", "abstract": "Embedded systems contain several layers of target processing abstraction. These layers include electronic circuit, binary machine code, mnemonic assembly code, and high-level procedural and object-oriented abstractions. Physical and temporal constraints and artifacts within physically embedded systems make it impossible for software engineers to operate at a single layer of processor abstraction. The Luxdbg embedded system debugger exposes these layers to debugger users, and it adds an additional layer, the extension language layer, that allows users to extend both the debugger and its target processor capabilities. Tcl is Luxdbg's extension language. Luxdbg users can apply Tcl to automate interactive debugging steps, to redirect and to interconnect target processor input-output facilities, to schedule multiple processor execution, to log and to react to target processing exceptions, and to automate target system testing. Inclusion of an extension language like Tcl in a debugger promises additional advantages for distributed debugging, where debuggers can pass extension language expressions across computer networks.", "published": "2000-11-06T23:16:22Z", "authors": ["Dale Parson", "Bryan Schlieder", "Paul Beatty"], "categories": ["cs.SE", "cs.PL"], "doi": "", "_query": "automated valuation model", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2510.25386v1", "title": "Integrating Legal and Logical Specifications in Perception, Prediction, and Planning for Automated Driving: A Survey of Methods", "abstract": "This survey provides an analysis of current methodologies integrating legal and logical specifications into the perception, prediction, and planning modules of automated driving systems. We systematically explore techniques ranging from logic-based frameworks to computational legal reasoning approaches, emphasizing their capability to ensure regulatory compliance and interpretability in dynamic and uncertain driving environments. A central finding is that significant challenges arise at the intersection of perceptual reliability, legal compliance, and decision-making justifiability. To systematically analyze these challenges, we introduce a taxonomy categorizing existing approaches by their theoretical foundations, architectural implementations, and validation strategies. We particularly focus on methods that address perceptual uncertainty and incorporate explicit legal norms, facilitating decisions that are both technically robust and legally defensible. The review covers neural-symbolic integration methods for perception, logic-driven rule representation, and norm-aware prediction strategies, all contributing toward transparent and accountable autonomous vehicle operation. We highlight critical open questions and practical trade-offs that must be addressed, offering multidisciplinary insights from engineering, logic, and law to guide future developments in legally compliant autonomous driving systems.", "published": "2025-10-29T10:57:24Z", "authors": ["Kumar Manas", "Mert Keser", "Alois Knoll"], "categories": ["cs.RO", "cs.AI", "eess.SY"], "doi": "", "_query": "automated valuation model", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2609.07652v1", "title": "RefVerifier: Semi-Automated Reference Claim Verification for Scientific Manuscripts", "abstract": "As software engineering research submission counts surge, peer reviewers face severe time constraints, making systematic verification of citation-supported claims prohibitively expensive. Consequently, unsubstantiated claims and semantic drift can propagate undetected across scientific literature. Existing approaches such as fact-checking and retrieval-augmented generation tools operate on open-domain web data or evaluate claims in isolation without processing complete manuscripts. To address this gap, we present RefVerifier, a semi-automated, citation-bounded reference verification prototype designed to support in academic peer review. RefVerifier extracts citation-bearing sentences from manuscripts, checks bibliography metadata against scholarly databases, resolves references to full-text open-access PDFs, localizes relevant evidence passages, and generates verdicts with natural language explanations. Evaluating RefVerifier on public benchmarks shows claim detection at an F1 score of 0.990, open-access resolution of 57.6% of references, and evidence localization with a hit rate of 98% on abstracts and 68% on complete cited papers. In an end-to-end test with eight manuscripts, RefVerifier achieves a verdict accuracy of 71%. By automating document retrieval and evidence localization while preserving reviewer oversight, RefVerifier provides first indicators for the feasibility of semi-automated integrity checks in scholarly publishing.", "published": "2026-09-07T15:39:59Z", "authors": ["Stefania Mocan", "Florian Angermeir", "Mark Kreitz"], "categories": ["cs.SE"], "doi": "10.1145/3844135.3845866", "_query": "automated valuation model", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2505.10590v2", "title": "Anchoring AI Capabilities in Market Valuations: The Capability Realization Rate Model and Valuation Misalignment Risk", "abstract": "Recent breakthroughs in artificial intelligence (AI) have triggered surges in market valuations for AI-related companies, often outpacing the realization of underlying capabilities. We examine the anchoring effect of AI capabilities on equity valuations and propose a Capability Realization Rate (CRR) model to quantify the gap between AI potential and realized performance. Using data from the 2023--2025 generative AI boom, we analyze sector-level sensitivity and conduct case studies (OpenAI, Adobe, NVIDIA, Meta, Microsoft, Goldman Sachs) to illustrate patterns of valuation premium and misalignment. Our findings indicate that AI-native firms commanded outsized valuation premiums anchored to future potential, while traditional companies integrating AI experienced re-ratings subject to proof of tangible returns. We argue that CRR can help identify valuation misalignment risk-where market prices diverge from realized AI-driven value. We conclude with policy recommendations to improve transparency, mitigate speculative bubbles, and align AI innovation with sustainable market value.", "published": "2025-05-15T01:06:06Z", "authors": ["Xinmin Fang", "Lingfeng Tao", "Zhengxiong Li"], "categories": ["cs.CY", "cs.AI"], "doi": "", "_query": "automated valuation model", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2609.27627v1", "title": "AST-Based Automated Elimination of break and continue Statements in Java Code", "abstract": "This work presents the development of an automatic refactoring tool for Java code built on top of the Eclipse JDT API. The proposed approach transforms control structures containing break and continue statements within different types of loops into semantically equivalent constructs that avoid their explicit use. To achieve this, auxiliary boolean variables are introduced to restructure the control flow while preserving the original program behavior. The main objective of this transformation is to improve code structure and enable the application of subsequent automated refactorings, particularly those based on the Extract Method operation, which are typically restricted by the presence of jump statements. The implementation relies on the analysis and rewriting of the Abstract Syntax Tree (AST), ensuring semantic equivalence in all addressed scenarios. The tool was validated through 54 manually designed test cases and 151 units tests, all of which produced satisfactory results. In addition, it was applied to 139 methods from seven open-source projects, generating code without compilation errors and preserving the original behavior as verified by the projects' test suites. The results demonstrate that the proposed approach safely automates the restructuring of code containing break and continue statements, facilitating further evolution and structural analysis.", "published": "2026-09-23T09:48:40Z", "authors": ["Andrés Juárez", "José Chicano", "Rubén Saborido"], "categories": ["cs.SE"], "doi": "", "_query": "automated valuation model", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2606.29572v1", "title": "Valuation Reveals Uncertainty", "abstract": "This paper studies the recovery of uncertainty from dynamic sublinear valuation rules. A robust valuation assigns each payoff its worst-case expected value across plausible models under uncertainty and induces a dynamic sublinear valuation rule. While valuation rules are observable in practice, the underlying uncertainty structure is latent. First, we show that the latent uncertainty structure can be identified from an observed valuation rule and provide an explicit procedure for recovering it. Second, we develop the notion of time consistency for uncertainty structures as the uncertainty-side counterpart of time consistency in valuation. Third, we characterize all time-consistent uncertainty structures that represent a given valuation rule. Finally, we develop nonparametric estimators for recovering uncertainty from limited valuation data. These results overturn the traditional Knightian view that uncertainty is inherently non-measurable. Indeed, valuation contains sufficient information to identify, characterize, and statistically recover the uncertainty structures that generate it.", "published": "2026-06-28T19:25:38Z", "authors": ["Jongjin Park", "Hyungbin Park"], "categories": ["q-fin.MF", "econ.EM"], "doi": "", "_query": "automated valuation model", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2411.07586v1", "title": "A Comprehensive Survey of AI-Driven Advancements and Techniques in Automated Program Repair and Code Generation", "abstract": "Bug fixing and code generation have been core research topics in software development for many years. The recent explosive growth in Large Language Models has completely transformed these spaces, putting in reach incredibly powerful tools for both. In this survey, 27 recent papers have been reviewed and split into two groups: one dedicated to Automated Program Repair (APR) and LLM integration and the other to code generation using LLMs. The first group consists of new methods for bug detection and repair, which include locating semantic errors, security vulnerabilities, and runtime failure bugs. The place of LLMs in reducing manual debugging efforts is emphasized in this work by APR toward context-aware fixes, with innovations that boost accuracy and efficiency in automatic debugging. The second group dwells on code generation, providing an overview of both general-purpose LLMs fine-tuned for programming and task-specific models. It also presents methods to improve code generation, such as identifier-aware training, fine-tuning at the instruction level, and incorporating semantic code structures. This survey work contrasts the methodologies in APR and code generation to identify trends such as using LLMs, feedback loops to enable iterative code improvement and open-source models. It also discusses the challenges of achieving functional correctness and security and outlines future directions for research in LLM-based software development.", "published": "2024-11-12T06:47:54Z", "authors": ["Avinash Anand", "Akshit Gupta", "Nishchay Yadav", "Shaurya Bajaj"], "categories": ["cs.AI"], "doi": "", "_query": "automated valuation model", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2406.02612v1", "title": "Is Data Valuation Learnable and Interpretable?", "abstract": "Measuring the value of individual samples is critical for many data-driven tasks, e.g., the training of a deep learning model. Recent literature witnesses the substantial efforts in developing data valuation methods. The primary data valuation methodology is based on the Shapley value from game theory, and various methods are proposed along this path. {Even though Shapley value-based valuation has solid theoretical basis, it is entirely an experiment-based approach and no valuation model has been constructed so far.} In addition, current data valuation methods ignore the interpretability of the output values, despite an interptable data valuation method is of great helpful for applications such as data pricing. This study aims to answer an important question: is data valuation learnable and interpretable? A learned valuation model have several desirable merits such as fixed number of parameters and knowledge reusability. An intrepretable data valuation model can explain why a sample is valuable or invaluable. To this end, two new data value modeling frameworks are proposed, in which a multi-layer perception~(MLP) and a new regression tree are utilized as specific base models for model training and interpretability, respectively. Extensive experiments are conducted on benchmark datasets. {The experimental results provide a positive answer for the question.} Our study opens up a new technical path for the assessing of data values. Large data valuation models can be built across many different data-driven tasks, which can promote the widespread application of data valuation.", "published": "2024-06-03T08:13:47Z", "authors": ["Ou Wu", "Weiyao Zhu", "Mengyang Li"], "categories": ["cs.LG", "cs.AI"], "doi": "", "_query": "automated valuation model", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2107.05855v1", "title": "Automated Learning Rate Scheduler for Large-batch Training", "abstract": "Large-batch training has been essential in leveraging large-scale datasets and models in deep learning. While it is computationally beneficial to use large batch sizes, it often requires a specially designed learning rate (LR) schedule to achieve a comparable level of performance as in smaller batch training. Especially, when the number of training epochs is constrained, the use of a large LR and a warmup strategy is critical in the final performance of large-batch training due to the reduced number of updating steps. In this work, we propose an automated LR scheduling algorithm which is effective for neural network training with a large batch size under the given epoch budget. In specific, the whole schedule consists of two phases: adaptive warmup and predefined decay, where the LR is increased until the training loss no longer decreases and decreased to zero until the end of training. Here, whether the training loss has reached the minimum value is robustly checked with Gaussian process smoothing in an online manner with a low computational burden. Coupled with adaptive stochastic optimizers such as AdamP and LAMB, the proposed scheduler successfully adjusts the LRs without cumbersome hyperparameter tuning and achieves comparable or better performances than tuned baselines on various image classification benchmarks and architectures with a wide range of batch sizes.", "published": "2021-07-13T05:23:13Z", "authors": ["Chiheon Kim", "Saehoon Kim", "Jongmin Kim", "Donghoon Lee", "Sungwoong Kim"], "categories": ["cs.LG"], "doi": "", "_query": "automated valuation model", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2607.03346v1", "title": "Efficient Decentralized Multi-task Dataset Valuation via Model Merging", "abstract": "Accurate and efficient dataset valuation is essential for enabling fair and transparent data marketplaces, especially when multiple contributors provide data for training multi-task models. Most existing valuation methods, however, are limited to single-task settings, overlooking scenarios where a buyer aims to optimize performance across multiple downstream tasks. Moreover, traditional valuation approaches, such as Shapley-based or retraining-based methods, are computationally expensive and poorly suited for decentralized environments without a trusted central coordinator and with strict privacy constraints. We propose DMVM (Decentralized Multi-task Valuation via Model Merging), a novel framework that bypasses retraining and data sharing by leveraging task arithmetic to infer dataset contributions directly from model combinations. Instead of retraining or sharing raw data, DMVM quantifies how models trained on different datasets combine in parameter space to infer each dataset's marginal utility across multiple tasks. This formulation yields a valuation process that is scalable, computationally efficient, and explicitly aligned with multi-task generalization behavior. To support decentralized deployment, we introduce a secure aggregation protocol that enables collaborative valuation without revealing individual model parameters or private data. We also provide theoretical error bounds characterizing the approximation quality of DMVM and validate our framework through comprehensive experiments on computer vision and natural language processing tasks.", "published": "2026-07-03T14:01:53Z", "authors": ["Mohammadsajad Alipour", "Mohammad Mohammadi Amiri"], "categories": ["cs.CL", "cs.AI", "cs.CV", "cs.LG"], "doi": "", "_query": "automated valuation model", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2012.11595v1", "title": "Valuation Models Applied to Value-Based Management. Application to the Case of UK Companies with Problems", "abstract": "Many still rightly wonder whether accounting numbers affect business value. Basic questions are why? and how? I aim at promoting an objective choice on how optimizing the most suitable valuation methods under a value-based management framework through some performance measurement systems. First, I present a comprehensive review of valuation methods. Three valuations methods, (i) Free Cash Flow Valuation Model (FCFVM), (ii) Residual Earning Valuation Model (REVM) and (iii) Abnormal Earning Growth Model (AEGM), are presented. I point out to advantages and limitations. As applications, the proofs of the findings are illustrated on three study cases: Marks & Spencer's business pattern (size and growth prospect), which had a recently advertised valuation problem, and two comparable companies, Tesco and Sainsbury's, all three chosen for multiple-based valuation. For the purpose, two value drivers are chosen, EnV/EBIT (entity value/earnings before interests and taxes) and the corresponding EnV/Sales. Thus, the question whether accounting numbers through models based on mathematical economics truly affect business value has an answer: Maybe, yes.", "published": "2020-12-19T14:09:14Z", "authors": ["Marcel Ausloos"], "categories": ["q-fin.GN", "econ.GN"], "doi": "10.3390/forecast2040029", "_query": "automated valuation model", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2512.11202v1", "title": "amc: The Automated Mission Classifier for Telescope Bibliographies", "abstract": "Telescope bibliographies record the pulse of astronomy research by capturing publication statistics and citation metrics for telescope facilities. Robust and scalable bibliographies ensure that we can measure the scientific impact of our facilities and archives. However, the growing rate of publications threatens to outpace our ability to manually label astronomical literature. We therefore present the Automated Mission Classifier (amc), a tool that uses large language models (LLMs) to identify and categorize telescope references by processing large quantities of paper text. A modified version of amc performs well on the TRACS Kaggle challenge, achieving a macro $F_1$ score of 0.84 on the held-out test set. amc is valuable for other telescopes beyond TRACS; we developed the initial software for identifying papers that featured scientific results by NASA missions. Additionally, we investigate how amc can also be used to interrogate historical datasets and surface potential label errors. Our work demonstrates that LLM-based applications offer powerful and scalable assistance for library sciences.", "published": "2025-12-12T01:24:42Z", "authors": ["John F. Wu", "Joshua E. G. Peek", "Sophie J. Miller", "Jenny Novacescu", "Achu J. Usha", "Christopher A. Wilkinson"], "categories": ["astro-ph.IM", "cs.AI", "cs.DL", "cs.LG"], "doi": "", "_query": "automated valuation model", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/1312.3789v1", "title": "Gas storage valuation and hedging. A quantification of the model risk", "abstract": "This paper focuses on the valuation and hedging of gas storage facilities, using a spot-based valuation framework coupled with a financial hedging strategy implemented with futures contracts. The first novelty consist in proposing a model that unifies the dynamics of the futures curve and the spot price, which accounts for the main stylized facts of the US natural gas market, such as seasonality and presence of price spikes. The second aspect of the paper is related to the quantification of model uncertainty related to the spot dynamics.", "published": "2013-12-13T12:29:38Z", "authors": ["Patrick Henaff", "Ismail Laachir", "Francesco Russo"], "categories": ["q-fin.PR", "math.PR"], "doi": "10.1142/S0219493713500196", "_query": "automated valuation model", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2512.11541v1", "title": "A Multi-Criteria Automated MLOps Pipeline for Cost-Effective Cloud-Based Classifier Retraining in Response to Data Distribution Shifts", "abstract": "The performance of machine learning (ML) models often deteriorates when the underlying data distribution changes over time, a phenomenon known as data distribution drift. When this happens, ML models need to be retrained and redeployed. ML Operations (MLOps) is often manual, i.e., humans trigger the process of model retraining and redeployment. In this work, we present an automated MLOps pipeline designed to address neural network classifier retraining in response to significant data distribution changes. Our MLOps pipeline employs multi-criteria statistical techniques to detect distribution shifts and triggers model updates only when necessary, ensuring computational efficiency and resource optimization. We demonstrate the effectiveness of our framework through experiments on several benchmark anomaly detection data sets, showing significant improvements in model accuracy and robustness compared to traditional retraining strategies. Our work provides a foundation for deploying more reliable and adaptive ML systems in dynamic real-world settings, where data distribution changes are common.", "published": "2025-12-12T13:22:14Z", "authors": ["Emmanuel K. Katalay", "David O. Dimandja", "Jordan F. Masakuna"], "categories": ["cs.LG"], "doi": "", "_query": "automated valuation model", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/1102.1343v3", "title": "Coupled atmosphere-wildland fire modeling with WRF-Fire", "abstract": "We describe the physical model, numerical algorithms, and software structure of WRF-Fire. WRF-Fire consists of a fire-spread model, implemented by the level-set method, coupled with the Weather Research and Forecasting model. In every time step, the fire model inputs the surface wind, which drives the fire, and outputs the heat flux from the fire into the atmosphere, which in turn influences the atmosphere. The level-set method allows submesh representation of the burning region and flexible implementation of various ignition modes. WRF-Fire is distributed as a part of WRF and it uses the WRF parallel infrastructure for parallel computing.", "published": "2011-02-07T15:57:09Z", "authors": ["Jan Mandel", "Jonathan D. Beezley", "Adam K. Kochanski"], "categories": ["physics.ao-ph"], "doi": "10.5194/gmd-4-591-2011", "_query": "automated valuation model", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2310.10685v1", "title": "PS-AAS: Portfolio Selection for Automated Algorithm Selection in Black-Box Optimization", "abstract": "The performance of automated algorithm selection (AAS) strongly depends on the portfolio of algorithms to choose from. Selecting the portfolio is a non-trivial task that requires balancing the trade-off between the higher flexibility of large portfolios with the increased complexity of the AAS task. In practice, probably the most common way to choose the algorithms for the portfolio is a greedy selection of the algorithms that perform well in some reference tasks of interest. We set out in this work to investigate alternative, data-driven portfolio selection techniques. Our proposed method creates algorithm behavior meta-representations, constructs a graph from a set of algorithms based on their meta-representation similarity, and applies a graph algorithm to select a final portfolio of diverse, representative, and non-redundant algorithms. We evaluate two distinct meta-representation techniques (SHAP and performance2vec) for selecting complementary portfolios from a total of 324 different variants of CMA-ES for the task of optimizing the BBOB single-objective problems in dimensionalities 5 and 30 with different cut-off budgets. We test two types of portfolios: one related to overall algorithm behavior and the `personalized' one (related to algorithm behavior per each problem separately). We observe that the approach built on the performance2vec-based representations favors small portfolios with negligible error in the AAS task relative to the virtual best solver from the selected portfolio, whereas the portfolios built from the SHAP-based representations gain from higher flexibility at the cost of decreased performance of the AAS. Across most considered scenarios, personalized portfolios yield comparable or slightly better performance than the classical greedy approach. They outperform the full portfolio in all scenarios.", "published": "2023-10-14T12:13:41Z", "authors": ["Ana Kostovska", "Gjorgjina Cenikj", "Diederick Vermetten", "Anja Jankovic", "Ana Nikolikj", "Urban Skvorc", "Peter Korosec", "Carola Doerr", "Tome Eftimov"], "categories": ["cs.LG", "cs.AI", "cs.NE"], "doi": "", "_query": "automated valuation model", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/1706.04224v1", "title": "Modular System for Shelves and Coasts (MOSSCO v1.0) - a flexible and multi-component framework for coupled coastal ocean ecosystem modelling", "abstract": "Shelf and coastal sea processes extend from the atmosphere through the water column and into the sea bed. These processes are driven by physical, chemical, and biological interactions at local scales, and they are influenced by transport and cross strong spatial gradients. The linkages between domains and many different processes are not adequately described in current model systems. Their limited integration level in part reflects lacking modularity and flexibility; this shortcoming hinders the exchange of data and model components and has historically imposed supremacy of specific physical driver models. We here present the Modular System for Shelves and Coasts (MOSSCO, http://www.mossco.de), a novel domain and process coupling system tailored---but not limited--- to the coupling challenges of and applications in the coastal ocean. MOSSCO builds on the existing coupling technology Earth System Modeling Framework and on the Framework for Aquatic Biogeochemical Models, thereby creating a unique level of modularity in both domain and process coupling; the new framework adds rich metadata, flexible scheduling, configurations that allow several tens of models to be coupled, and tested setups for coastal coupled applications. That way, MOSSCO addresses the technology needs of a growing marine coastal Earth System community that encompasses very different disciplines, numerical tools, and research questions.", "published": "2017-06-13T19:02:41Z", "authors": ["Carsten Lemmen", "Richard Hofmeister", "Knut Klingbeil", "M. Hassan Nasermoaddeli", "Onur Kerimoglu", "Hans Burchard", "Frank Kösters", "Kai W. Wirtz"], "categories": ["physics.ao-ph"], "doi": "10.5194/gmd-11-915-2018", "_query": "automated valuation model", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/1211.7012v3", "title": "Learning-Assisted Automated Reasoning with Flyspeck", "abstract": "The considerable mathematical knowledge encoded by the Flyspeck project is combined with external automated theorem provers (ATPs) and machine-learning premise selection methods trained on the proofs, producing an AI system capable of answering a wide range of mathematical queries automatically. The performance of this architecture is evaluated in a bootstrapping scenario emulating the development of Flyspeck from axioms to the last theorem, each time using only the previous theorems and proofs. It is shown that 39% of the 14185 theorems could be proved in a push-button mode (without any high-level advice and user interaction) in 30 seconds of real time on a fourteen-CPU workstation. The necessary work involves: (i) an implementation of sound translations of the HOL Light logic to ATP formalisms: untyped first-order, polymorphic typed first-order, and typed higher-order, (ii) export of the dependency information from HOL Light and ATP proofs for the machine learners, and (iii) choice of suitable representations and methods for learning from previous proofs, and their integration as advisors with HOL Light. This work is described and discussed here, and an initial analysis of the body of proofs that were found fully automatically is provided.", "published": "2012-11-29T18:15:10Z", "authors": ["Cezary Kaliszyk", "Josef Urban"], "categories": ["cs.AI", "cs.DL", "cs.LG", "cs.LO"], "doi": "10.1007/s10817-014-9303-3", "_query": "automated valuation model", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2607.09375v1", "title": "Mach-Mind-4-Flash Technical Report", "abstract": "We present Mach-Mind-4-Flash, a 35B-parameter Mixture-of-Experts (MoE) agentic model with 3B activated parameters. Through post-training optimization alone without scaling pre-training compute, the model achieves performance on par with or surpassing that of 100B-parameter-class models. By introducing scalable agentic interaction environments for large-scale reinforcement learning, the model attains significant performance gains on real-world application tasks. Our pipeline comprises three stages: (1) a unified RL/OPD training infrastructure with dynamic multi-teacher scheduling and operator-level acceleration, delivering 17\\% end-to-end training speedup; (2) multiple domain-specific RL experts trained in parallel across Reasoning, General, and Agent tracks, then fused into a single generalist via Multi-Teacher On-Policy Distillation (MOPD) -- a routed reverse-KL objective that eliminates the see-saw degradation of mixed-reward RL; (3) Hybrid Median-length Policy Optimization (HMPO), a single-stage token-efficiency method that compresses reasoning chains by 19--46\\% with $\\le$0.7 percentage-point accuracy loss. Mach-Mind-4-Flash scores 92.70 on AIME'26, 82.82 on IFBench, 80.74 on Behavioral-SafetyBench, 75.80 on BFCL-v4, 72.31 on BrowseComp-zh, and 84.20 on ClawBench -- leading or matching models with 10--30$\\times$ its activated size at a fraction of the inference cost.", "published": "2026-07-10T12:57:08Z", "authors": [" Foundation Model Team"], "categories": ["cs.LG", "cs.CL"], "doi": "", "_query": "automated valuation model", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2312.16179v3", "title": "European Football Player Valuation: Integrating Financial Models and Network Theory", "abstract": "This paper presents a new framework for player valuation in European football, by fusing principles from financial mathematics and network theory. The valuation model leverages a \"passing matrix\" to encapsulate player interactions on the field, utilizing centrality measures to quantify individual influence. Unlike traditional approaches, such as regressing on past performance-salary data, this model focuses on in-game performance as a player's contributions evolve over time. Consequently, our model provides a dynamic and individualized framework for ascertaining a player's fair market value. The methodology is empirically validated through a case study in European football, employing real-world match and financial data. This cross-disciplinary mechanism for player valuation adapts the effect of connecting pay with performance, first seen in Scully (1974), to include in-game contributions as well as expected present valuation of stochastic variables.", "published": "2023-12-15T22:16:25Z", "authors": ["Albert Cohen", "Jimmy Risk"], "categories": ["physics.soc-ph", "q-fin.CP", "q-fin.PR", "stat.AP"], "doi": "", "_query": "automated valuation model", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/physics/0608167v1", "title": "Electricity Real Options Valuation", "abstract": "In this paper a real option approach for the valuation of real assets is presented. Two continuous time models used for valuation are described: geometric Brownian motion model and interest rate model. The valuation for electricity spread option under Vasicek interest model is placed and the formulas for parameter estimators are calculated. The theoretical part is confronted with real data from electricity market.", "published": "2006-08-16T13:18:44Z", "authors": ["Ewa Broszkiewicz-Suwaj"], "categories": ["physics.ao-ph", "math-ph", "physics.data-an"], "doi": "", "_query": "automated valuation model", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2509.08463v1", "title": "Adversarial Attacks Against Automated Fact-Checking: A Survey", "abstract": "In an era where misinformation spreads freely, fact-checking (FC) plays a crucial role in verifying claims and promoting reliable information. While automated fact-checking (AFC) has advanced significantly, existing systems remain vulnerable to adversarial attacks that manipulate or generate claims, evidence, or claim-evidence pairs. These attacks can distort the truth, mislead decision-makers, and ultimately undermine the reliability of FC models. Despite growing research interest in adversarial attacks against AFC systems, a comprehensive, holistic overview of key challenges remains lacking. These challenges include understanding attack strategies, assessing the resilience of current models, and identifying ways to enhance robustness. This survey provides the first in-depth review of adversarial attacks targeting FC, categorizing existing attack methodologies and evaluating their impact on AFC systems. Additionally, we examine recent advancements in adversary-aware defenses and highlight open research questions that require further exploration. Our findings underscore the urgent need for resilient FC frameworks capable of withstanding adversarial manipulations in pursuit of preserving high verification accuracy.", "published": "2025-09-10T10:10:10Z", "authors": ["Fanzhen Liu", "Alsharif Abuadbba", "Kristen Moore", "Surya Nepal", "Cecile Paris", "Jia Wu", "Jian Yang", "Quan Z. Sheng"], "categories": ["cs.CL", "cs.AI", "cs.CR"], "doi": "", "_query": "automated valuation model", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2605.13539v1", "title": "Integration of an Agent Model into an Open Simulation Architecture for Scenario-Based Testing of Automated Vehicles", "abstract": "Simulative and scenario-based testing are crucial methods in the safety assurance for automated driving systems. To ensure that simulation results are reliable, the real world must be modeled with sufficient fidelity, including not only the static environment but also the surrounding traffic of a vehicle under test. Thus, the availability of traffic agent models is of common interest to model naturalistic and parameterizable behavior, similar to human drivers. The interchangeability of agent models across different simulation environments represents a major challenge and necessitates harmonization and standardization. To address this challenge, we present a standardized and modular simulation integration architecture that enables the tool-independent integration of traffic agent models. The architecture builds upon the Open Simulation Interface (OSI) as a structured message format and the Functional Mock-up Interface (FMI) for dynamic model exchange. Rather than introducing yet another model or simulation tool, we provide a reusable reference implementation that translates these standards into a practical integration blueprint, including clear interfaces, data mappings, and execution semantics. The generic nature of the architecture is demonstrated by integrating an exemplary agent model into three widely used simulation environments: OpenPASS, CARLA, and CarMaker. As part of the evaluation, we show that the model yields consistent behavior in all simulation platforms, thereby validating the interoperability, modularity, and standard compliance of the proposed architecture. The reference implementation lowers integration barriers, serves as a foundation for future research, and is made publicly available at github.com/ika-rwth-aachen/agent-model-integration", "published": "2026-05-13T13:47:51Z", "authors": ["Christian Geller", "Daniel Becker", "Jobst Beckmann", "Lutz Eckstein"], "categories": ["cs.RO", "cs.SE"], "doi": "10.1515/auto-2025-0066", "_query": "automated valuation model", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/1907.13550v3", "title": "Towards Automated Infographic Design: Deep Learning-based Auto-Extraction of Extensible Timeline", "abstract": "Designers need to consider not only perceptual effectiveness but also visual styles when creating an infographic. This process can be difficult and time consuming for professional designers, not to mention non-expert users, leading to the demand for automated infographics design. As a first step, we focus on timeline infographics, which have been widely used for centuries. We contribute an end-to-end approach that automatically extracts an extensible timeline template from a bitmap image. Our approach adopts a deconstruction and reconstruction paradigm. At the deconstruction stage, we propose a multi-task deep neural network that simultaneously parses two kinds of information from a bitmap timeline: 1) the global information, i.e., the representation, scale, layout, and orientation of the timeline, and 2) the local information, i.e., the location, category, and pixels of each visual element on the timeline. At the reconstruction stage, we propose a pipeline with three techniques, i.e., Non-Maximum Merging, Redundancy Recover, and DL GrabCut, to extract an extensible template from the infographic, by utilizing the deconstruction results. To evaluate the effectiveness of our approach, we synthesize a timeline dataset (4296 images) and collect a real-world timeline dataset (393 images) from the Internet. We first report quantitative evaluation results of our approach over the two datasets. Then, we present examples of automatically extracted templates and timelines automatically generated based on these templates to qualitatively demonstrate the performance. The results confirm that our approach can effectively extract extensible templates from real-world timeline infographics.", "published": "2019-07-31T15:20:33Z", "authors": ["Chen Zhu-Tian", "Yun Wang", "Qianwen Wang", "Yong Wang", "Huamin Qu"], "categories": ["cs.HC", "cs.GR"], "doi": "10.1109/TVCG.2019.2934810", "_query": "automated valuation model", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/1607.01539v1", "title": "Translating Scala Programs to Isabelle/HOL", "abstract": "We present a trustworthy connection between the Leon verification system and the Isabelle proof assistant. Leon is a system for verifying functional Scala programs. It uses a variety of automated theorem provers (ATPs) to check verification conditions (VCs) stemming from the input program. Isabelle, on the other hand, is an interactive theorem prover used to verify mathematical specifications using its own input language Isabelle/Isar. Users specify (inductive) definitions and write proofs about them manually, albeit with the help of semi-automated tactics. The integration of these two systems allows us to exploit Isabelle's rich standard library and give greater confidence guarantees in the correctness of analysed programs.", "published": "2016-07-06T09:39:33Z", "authors": ["Lars Hupel", "Viktor Kuncak"], "categories": ["cs.LO"], "doi": "10.1007/978-3-319-40229-1_38", "_query": "automated valuation model", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/hep-ph/0304186v1", "title": "Structure of the Standard Model", "abstract": "The structure of the standard model is concisely summarized, including the standard model Lagrangian, spontaneous symmetry breaking, the reexpression of the Lagrangian in terms of mass eigenstates after symmetry breaking, and the gauge interactions. The problems of the standard model are described.", "published": "2003-04-19T14:16:31Z", "authors": ["Paul Langacker"], "categories": ["hep-ph"], "doi": "", "_query": "automated valuation model", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2410.15817v2", "title": "LaMP-Val: Large Language Models Empower Personalized Valuation in Auction", "abstract": "Auctions are a vital economic mechanism used to determine the market value of goods or services through competitive bidding within a specific framework. However, much of the current research primarily focuses on the bidding algorithms used within auction mechanisms. This often neglects the potential benefits of incorporating individual users' unique preferences into the valuation process. Our theoretical and empirical analysis demonstrates that valuation errors can significantly impact the overall utility. To bridge this gap, we propose a personalized valuation framework, namely Large \\underline{La}nguage \\underline{M}odels-powered \\underline{P}ersonalized \\underline{Val}uation (LaMP-Val), which integrates Large Language Models to incorporate personalized semantic preference into users valuation process. LaMP-Val integrating three components: data, learning, and evaluation. The data component tackles the challenge of building a novel dataset specifically for LLMs fine-tuning in personalized valuation modeling. The learning component introduces a diversity template to enhance LLMs' capacity for modeling fine-grained personal valuation patterns. The evaluation component establishes a closed-loop system where LLM-generated valuations interact with bidding strategies and auction. It proposes two novel metrics to quantify valuation precision and bidding intention accuracy in personalized scenarios. Extensive experiments show that LaMP-Val more accurately captures personalized values and achieves greater profits than baseline approaches.", "published": "2024-10-21T09:31:51Z", "authors": ["Jie Sun", "Tianyu Zhang", "Houcheng Jiang", "Kexin Huang", "Xiang Shu", "Zhibo Zhu", "Lintao Ma", "Xingyu Lu", "Jun Zhou", "Junkang Wu", "Chi Luo", "An Zhang", "Junkang Wu", "Jiancan Wu", "Xiang Wang"], "categories": ["cs.CE"], "doi": "", "_query": "automated valuation model", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2309.09308v1", "title": "GAMMA: Revisiting Template-based Automated Program Repair via Mask Prediction", "abstract": "Automated program repair (APR) aims to fix software bugs without human intervention and template-based APR has been widely investigated with promising results. However, it is challenging for template-based APR to select the appropriate donor code, which is an important repair ingredient for generating candidate patches. Inappropriate donor code may cause plausible but incorrect patch generation even with correct fix patterns, limiting the repair performance. In this paper, we aim to revisit template-based APR, and propose GAMMA, to directly leverage large pre-trained language models for donor code generation. Our main insight is that instead of retrieving donor code in the local buggy file, we can directly predict the correct code tokens based on the context code snippets and repair patterns by a cloze task. Specifically, (1) GAMMA revises a variety of fix templates from state-of-the-art template-based APR techniques (i.e., TBar) and transforms them into mask patterns. (2) GAMMA adopts a pre-trained language model to predict the correct code for masked code as a fill-in-the-blank task. The experimental results demonstrate that GAMMA correctly repairs 82 bugs on Defects4J-v1.2, which achieves 20.59\\% (14 bugs) and 26.15\\% (17 bugs) improvement over the previous state-of-the-art template-based approach TBar and learning-based one Recoder. Furthermore, GAMMA repairs 45 bugs and 22 bugs from the additional Defects4J-v2.0 and QuixBugs, indicating the generalizability of GAMMA in addressing the dataset overfitting issue. We also prove that adopting other pre-trained language models can provide substantial advancement, e.g., CodeBERT-based and ChatGPT-based GAMMA is able to fix 80 and 67 bugs on Defects4J-v1.2, indicating the scalability of GAMMA. Overall, our study highlights the promising future of adopting pre-trained models to generate correct patches on top of fix patterns.", "published": "2023-09-17T15:49:40Z", "authors": ["Quanjun Zhang", "Chunrong Fang", "Tongke Zhang", "Bowen Yu", "Weisong Sun", "Zhenyu Chen"], "categories": ["cs.SE"], "doi": "", "_query": "automated valuation model", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/1911.08954v1", "title": "Basic Ideas and Tools for Projection-Based Model Reduction of Parametric Partial Differential Equations", "abstract": "We provide first the functional analysis background required for reduced order modeling and present the underlying concepts of reduced basis model reduction. The projection-based model reduction framework under affinity assumptions, offline-online decomposition and error estimation is introduced. Several tools for geometry parametrizations, such as free form deformation, radial basis function interpolation and inverse distance weighting interpolation are explained. The empirical interpolation method is introduced as a general tool to deal with non-affine parameter dependency and non-linear problems. The discrete and matrix versions of the empirical interpolation are considered as well. Active subspaces properties are discussed to reduce high-dimensional parameter spaces as a pre-processing step. Several examples illustrate the methodologies.", "published": "2019-11-20T15:01:55Z", "authors": ["Gianluigi Rozza", "Martin Hess", "Giovanni Stabile", "Marco Tezzele", "Francesco Ballarin"], "categories": ["math.NA"], "doi": "10.1515/9783110671490-001", "_query": "automated valuation model", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2608.05659v1", "title": "Breaking Customized LLMs for Coding: Automated Red Teaming for Instruction Backdoor Attacks", "abstract": "LLM customization platforms allow users to build task-specific models for code intelligence tasks by embedding instructions into system prompts, without modifying the underlying model parameters. While these platforms lower the barrier to developing customized LLMs, they also introduce a new attack surface: instruction backdoor attacks, in which adversaries implant hidden malicious behaviors into customized instructions. However, existing attacks suffer from two key limitations. First, they often rely on explicit trigger patterns readily detected by platform-side or user-side inspection. Second, they require substantial manual effort to craft task-specific backdoored instructions, limiting their scalability. In this paper, we propose ARIA, an automated red-teaming framework for crafting covert and effective backdoored instructions against customized LLMs. ARIA leverages an attacker LLM to iteratively generate and refine backdoored instructions, guided by structured feedback from the target LLM along three dimensions: stealthiness, clean-task utility, and backdoor effectiveness. We evaluate ARIA on three code intelligence tasks, using four representative LLMs, and compare it with three baseline attacks. Experimental results show that ARIA achieves the highest attack success rate of 0.945, while maintaining the best clean-task utility across all tasks. ARIA also generalizes well across programming languages and remains robust to generation temperature. Furthermore, ARIA significantly outperforms existing attacks in evading platform-side and user-side detection, achieving a false negative rate of up to 1.000, and stays effective against existing defense methods, demonstrating its strong generalizability and robustness.", "published": "2026-08-06T06:57:10Z", "authors": ["Yuchen Chen", "Wei Cheng", "Yuan Xiao", "Wising Sun", "Chunrong Fang", "Yang Liu", "Zhenyu Chen", "Baowen Xu"], "categories": ["cs.CR"], "doi": "", "_query": "automated valuation model", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/0906.4505v1", "title": "On valuation rings", "abstract": "In this paper we provide necessary and sufficient conditions for $ R=A\\propto E $ to be a valuation ring where $E$ is a non-torsion or finitely generated $A-$module. Also, we investigate the $ (n,d) $ property of the valuation ring.", "published": "2009-06-24T15:49:00Z", "authors": ["Mohammed Kabbour", "Najib Mahdou"], "categories": ["math.AC"], "doi": "", "_query": "automated valuation model", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2401.00085v1", "title": "A dimension reduction approach for loss valuation in credit risk modelling", "abstract": "This paper addresses the ``curse of dimensionality'' in the loss valuation of credit risk models. A dimension reduction methodology based on the Bayesian filter and smoother is proposed. This methodology is designed to achieve a fast and accurate loss valuation algorithm in credit risk modelling, but it can also be extended to valuation models of other risk types. The proposed methodology is generic, robust and can easily be implemented. Moreover, the accuracy of the proposed methodology in the estimation of expected loss and value-at-risk is illustrated by numerical experiments. The results suggest that, compared to the currently most used PCA approach, the proposed methodology provides more accurate estimation of expected loss and value-at-risk of a loss distribution.", "published": "2023-12-29T22:02:52Z", "authors": ["Jian He", "Asma Khedher", "Peter Spreij"], "categories": ["cs.CE", "stat.CO"], "doi": "", "_query": "automated valuation model", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2506.23799v2", "title": "KAIROS: Scalable Model-Agnostic Data Valuation", "abstract": "Training data increasingly shapes not only model accuracy but also regulatory compliance and market valuation of AI assets. Yet existing valuation methods remain inadequate: model-based techniques depend on a single fitted model and inherit its biases, while algorithm-based approaches such as Data Shapley require costly retrainings at web scale. Recent Wasserstein-based model-agnostic methods rely on approximations that misrank examples relative to their true leave-one-out (LOO) utility. We introduce KAIROS, a scalable, model-agnostic valuation framework that assigns each example a distributional influence score: its contribution to the Maximum Mean Discrepancy (MMD) between the empirical training distribution and a clean reference set. Unlike Wasserstein surrogates, our MMD-based influence admits a closed-form solution that faithfully approximates the exact LOO ranking within $O(1/N^2)$ error, requires no retraining, and naturally extends to conditional kernels for unified label- and feature-error detection. Moreover, KAIROS supports efficient online updates: when a new batch of size m arrives, all scores can be updated in $O(mN)$ time, delivering up to 50x speedup without compromising ranking quality. Empirical evaluations on noise, mislabeling, and poisoning benchmarks show that KAIROS consistently outperforms state-of-the-art model-, Shapley-, and Wasserstein-based baselines in both accuracy and runtime. We provide rigorous theoretical guarantees, including symmetry for reproducible rankings and density-separation for interpretable thresholds.", "published": "2025-06-30T12:44:28Z", "authors": ["Jiongli Zhu", "Parjanya Prajakta Prashant", "Alex Cloninger", "Babak Salimi"], "categories": ["cs.LG"], "doi": "", "_query": "automated valuation model", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2402.07542v1", "title": "ASAP-Repair: API-Specific Automated Program Repair Based on API Usage Graphs", "abstract": "Modern software development relies on the reuse of code via Application Programming Interfaces (APIs). Such reuse relieves developers from learning and developing established algorithms and data structures anew, enabling them to focus on their problem at hand. However, there is also the risk of misusing an API due to a lack of understanding or proper documentation. While many techniques target API misuse detection, only limited efforts have been put into automatically repairing API misuses. In this paper, we present our advances on our technique API-Specific Automated Program Repair (ASAP-Repair). ASAP-Repair is intended to fix API misuses based on API Usage Graphs (AUGs) by leveraging API usage templates of state-of-the-art API misuse detectors. We demonstrate that ASAP-Repair is in principle applicable on an established API misuse dataset. Moreover, we discuss next steps and challenges to evolve ASAP-Repair towards a full-fledged Automatic Program Repair (APR) technique.", "published": "2024-02-12T10:10:12Z", "authors": ["Sebastian Nielebock", "Paul Blockhaus", "Jacob Krüger", "Frank Ortmeier"], "categories": ["cs.SE"], "doi": "10.1145/3643788.3648011", "_query": "automated valuation model", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2406.17184v2", "title": "Minimax Optimality in Contextual Dynamic Pricing with General Valuation Models", "abstract": "We study contextual dynamic pricing, where a decision maker posts personalized prices based on observable contexts and receives binary purchase feedback indicating whether the customer's valuation exceeds the price. Each valuation is modeled as an unknown latent function of the context, corrupted by independent and identically distributed market noise from an unknown distribution. Relying only on Lipschitz continuity of the noise distribution and bounded valuations, we propose a minimax-optimal algorithm. To accommodate the unknown distribution, our method discretizes the relevant noise range to form a finite set of candidate prices, then applies layered data partitioning to obtain confidence bounds substantially tighter than those derived via the elliptical-potential lemma. A key advantage is that estimation bias in the valuation function cancels when comparing upper confidence bounds, eliminating the need to know the Lipschitz constant. The framework extends beyond linear models to general function classes through offline regression oracles. Our regret analysis depends solely on the oracle's estimation error, typically governed by the statistical complexity of the class. These techniques yield a regret upper bound matching the minimax lower bound up to logarithmic factors. Furthermore, we refine these guarantees under additional structures -- e.g., linear valuation models, second-order smoothness, sparsity, and known noise distribution or observable valuations -- and compare our bounds and assumptions with prior dynamic-pricing methods. Finally, numerical experiments corroborate the theory and show clear improvements over benchmark methods.", "published": "2024-06-24T23:43:56Z", "authors": ["Xueping Gong", "Wei You", "Jiheng Zhang"], "categories": ["cs.LG", "stat.ML"], "doi": "", "_query": "automated valuation model", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/1907.10491v1", "title": "Alternative Intersection Designs with Connected and Automated Vehicle", "abstract": "Alternative intersection designs (AIDs) can improve the performance of an intersection by not only reducing the number of signal phases but also change the configuration of the conflicting points by re-routing traffic. However the AID studies have rarely been extended to Connected and Automated Vehicle (CAV) which is expected to revolutionize our transportation system. In this study, we investigate the potential benefits of CAV to two AIDs: the diverging diamond interchange (DDI) and the restricted crossing U-turn intersection. The potential enhancements of AID, CAV, and the combination of both are quantified via microscopic traffic simulation. We found that CAV is able to positively contribute to the performance of an intersection. However, converting an existing conventional diamond interchange (CDI) to a diverging one is a more effective way according to the simulation results. DDI improves the throughput of a CDI by 950 vehicles per hour, a near 20% improvement; whereas with full penetration of CAV, the throughput of a CDI is increased only by 300 vehicles per hour. A similar trend is observed in the average delay per vehicle as well. Furthermore, we assess the impact for the driver's confusion, a concern for deploying AIDs, on the traffic flow. According to the ANOVA test, the negative impacts of driver's confusion are of statistical significance.", "published": "2019-07-22T01:41:28Z", "authors": ["Zijia Zhong", "Earl E. Lee"], "categories": ["cs.MA", "eess.SP"], "doi": "", "_query": "automated valuation model", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/1303.1477v1", "title": "Valuation Networks and Conditional Independence", "abstract": "Valuation networks have been proposed as graphical representations of valuation-based systems (VBSs). The VBS framework is able to capture many uncertainty calculi including probability theory, Dempster-Shafer's belief-function theory, Spohn's epistemic belief theory, and Zadeh's possibility theory. In this paper, we show how valuation networks encode conditional independence relations. For the probabilistic case, the class of probability models encoded by valuation networks includes undirected graph models, directed acyclic graph models, directed balloon graph models, and recursive causal graph models.", "published": "2013-03-06T14:20:35Z", "authors": ["Prakash P. Shenoy"], "categories": ["cs.AI"], "doi": "", "_query": "automated valuation model", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2111.01851v1", "title": "Private Interdependent Valuations", "abstract": "We consider the single-item interdependent value setting, where there is a monopolist, $n$ buyers, and each buyer has a private signal $s_i$ describing a piece of information about the item. Each bidder $i$ also has a valuation function $v_i(s_1,\\ldots,s_n)$ mapping the (private) signals of all buyers to a positive real number representing their value for the item. This setting captures scenarios where the item's information is asymmetric or dispersed among agents, such as in competitions for oil drilling rights, or in auctions for art pieces. Due to the increased complexity of this model compared to standard private values, it is generally assumed that each bidder's valuation function $v_i$ is public knowledge. But in many situations, the seller may not know how a bidder aggregates signals into a valuation. In this paper, we design mechanisms that guarantee approximately-optimal social welfare while satisfying ex-post incentive compatibility and individual rationality for the case where the valuation functions are private to the bidders. When the valuations are public, it is possible for optimal social welfare to be attained by a deterministic mechanism under a single-crossing condition. In contrast, when the valuations are the bidders' private information, we show that no finite bound can be achieved by any deterministic mechanism even under single-crossing. Moreover, no randomized mechanism can guarantee better than an $n$-approximation. We thus consider valuation functions that are submodular over signals (SOS), introduced in the context of combinatorial auctions in a recent breakthrough paper by Eden et al. [EC'19]. Our main result is an $O(\\log^2 n)$-approximation for buyers with private signals and valuations under the SOS condition. We also give a tight $Θ(k)$-approximation for the case each agent's valuation depends on at most $k$ other signals even for unknown $k$.", "published": "2021-11-02T19:15:06Z", "authors": ["Alon Eden", "Kira Goldner", "Shuran Zheng"], "categories": ["cs.GT", "cs.DS"], "doi": "", "_query": "automated valuation model", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2511.12625v1", "title": "A spatio-temporal statistical model for property valuation at country-scale with adjustments for regional submarkets", "abstract": "Valuing residential property is inherently complex, requiring consideration of numerous environmental, economic, and property-specific factors. These complexities present significant challenges for automated valuation models (AVMs), which are increasingly used to provide objective assessments for property taxation and mortgage financing. The challenge of obtaining accurate and objective valuations for properties at a country level, and not just within major cities, is further compounded by the presence of multiple localised submarkets-spanning urban, suburban, and rural contexts-where property features contribute differently to value. Existing AVMs often struggle in such settings: traditional hedonic regression models lack the flexibility to capture spatial variation, while advanced machine learning approaches demand extensive datasets that are rarely available. In this article, we address these limitations by developing a robust statistical framework for property valuation in the Irish housing market. We segment the country into six submarkets encompassing cities, large towns, and rural areas, and employ a generalized additive model that captures non-linear effects of property characteristics while allowing feature contributions to vary across submarkets. Our approach outperforms both machine learning-based and traditional hedonic regression models, particularly in data-sparse regions. In out-of-sample validation, our model achieves R-squared values of 0.70, 0.84, and 0.83 for rural areas, towns, and Dublin, respectively, compared to 0.52, 0.71, and 0.82 from a random forest benchmark. Furthermore, the temporal dynamics of our model align closely with reported inflation figures for the study period, providing additional validation of its accuracy.", "published": "2025-11-16T14:34:33Z", "authors": ["Brian O'Donovan", "Andrew Finley", "James Sweeney"], "categories": ["stat.AP"], "doi": "", "_query": "automated valuation model", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2506.11697v1", "title": "SoK: Automated Vulnerability Repair: Methods, Tools, and Assessments", "abstract": "The increasing complexity of software has led to the steady growth of vulnerabilities. Vulnerability repair investigates how to fix software vulnerabilities. Manual vulnerability repair is labor-intensive and time-consuming because it relies on human experts, highlighting the importance of Automated Vulnerability Repair (AVR). In this SoK, we present the systematization of AVR methods through the three steps of AVR workflow: vulnerability analysis, patch generation, and patch validation. We assess AVR tools for C/C++ and Java programs as they have been widely studied by the community. Since existing AVR tools for C/C++ programs are evaluated with different datasets, which often consist of a few vulnerabilities, we construct the first C/C++ vulnerability repair benchmark dataset, dubbed Vul4C, which contains 144 vulnerabilities as well as their exploits and patches. We use Vul4C to evaluate seven AVR tools for C/C++ programs and use the third-party Vul4J dataset to evaluate two AVR tools for Java programs. We also discuss future research directions.", "published": "2025-06-13T11:59:04Z", "authors": ["Yiwei Hu", "Zhen Li", "Kedie Shu", "Shenghua Guan", "Deqing Zou", "Shouhuai Xu", "Bin Yuan", "Hai Jin"], "categories": ["cs.SE"], "doi": "", "_query": "automated valuation model", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/1602.06189v1", "title": "Accrual valuation and mark to market adjustment", "abstract": "This paper provides intuition on the relationship of accrual and mark-to-market valuation for cash and forward interest rate trades. Discounted cashflow valuation is compared to spread-based valuation for forward trades, which explains the trader's view on valuation. This is followed by Taylor series approximation for cash trades, uncovering simple intuition behind accrual valuation and mark-to-market adjustment. It is followed by the PNL example modelled in R. Within the Taylor approximation framework, theta and delta are explained. The concept of deferral is explained taking Forward Rate Agreement (FRA) as an example.", "published": "2016-02-18T19:56:45Z", "authors": ["Alexey Bakshaev"], "categories": ["q-fin.PR"], "doi": "", "_query": "automated valuation model", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2504.17355v1", "title": "Collaborative Multi-Agent Reinforcement Learning for Automated Feature Transformation with Graph-Driven Path Optimization", "abstract": "Feature transformation methods aim to find an optimal mathematical feature-feature crossing process that generates high-value features and improves the performance of downstream machine learning tasks. Existing frameworks, though designed to mitigate manual costs, often treat feature transformations as isolated operations, ignoring dynamic dependencies between transformation steps. To address the limitations, we propose TCTO, a collaborative multi-agent reinforcement learning framework that automates feature engineering through graph-driven path optimization. The framework's core innovation lies in an evolving interaction graph that models features as nodes and transformations as edges. Through graph pruning and backtracking, it dynamically eliminates low-impact edges, reduces redundant operations, and enhances exploration stability. This graph also provides full traceability to empower TCTO to reuse high-utility subgraphs from historical transformations. To demonstrate the efficacy and adaptability of our approach, we conduct comprehensive experiments and case studies, which show superior performance across a range of datasets.", "published": "2025-04-24T08:16:13Z", "authors": ["Xiaohan Huang", "Dongjie Wang", "Zhiyuan Ning", "Ziyue Qiao", "Qingqing Long", "Haowei Zhu", "Yi Du", "Min Wu", "Yuanchun Zhou", "Meng Xiao"], "categories": ["cs.LG", "cs.AI"], "doi": "", "_query": "automated valuation model", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2505.17859v2", "title": "Scalable Valuation of Human Feedback through Provably Robust Model Alignment", "abstract": "Despite the importance of aligning language models with human preferences, crowd-sourced human feedback is often noisy -- for example, preferring less desirable responses -- posing a fundamental challenge to alignment. A truly robust alignment objective should yield identical model parameters even under severe label noise, a property known as redescending. We prove that no existing alignment methods satisfy this property. To address this, we propose Hölder-DPO, the first principled alignment loss with a provable redescending property, enabling estimation of the clean data distribution from noisy feedback. The aligned model estimates the likelihood of clean data, providing a theoretically grounded metric for dataset valuation that identifies the location and fraction of mislabels. This metric is gradient-free, enabling scalable and automated human feedback valuation without costly manual verification or clean validation dataset. Hölder-DPO achieves state-of-the-art robust alignment performance while accurately detecting mislabels in controlled datasets. Finally, applied to Anthropic HH-RLHF dataset, it reveals substantial noise levels and removing these mislabels significantly improves alignment performance across methods. The code is available at https://github.com/ma921/HolderDPO.", "published": "2025-05-23T13:12:37Z", "authors": ["Masahiro Fujisawa", "Masaki Adachi", "Michael A. Osborne"], "categories": ["cs.LG", "cs.AI", "stat.ML"], "doi": "", "_query": "automated valuation model", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2512.07664v1", "title": "A Framework for Data Valuation and Monetisation", "abstract": "As organisations increasingly recognise data as a strategic resource, they face the challenge of translating informational assets into measurable business value. Existing valuation approaches remain fragmented, often separating economic, governance, and strategic perspectives and lacking operational mechanisms suitable for real settings. This paper introduces a unified valuation framework that integrates these perspectives into a coherent decision-support model. Building on two artefacts from the Horizon Europe DATAMITE project, a taxonomy of data-quality and performance metrics, and an Analytic Network Process (ANP) tool for deriving relative importance, we develop a hybrid valuation model. The model combines qualitative scoring, cost- and utility-based estimation, relevance/quality indexing, and multi-criteria weighting to define data value transparently and systematically. Anchored in the Balanced Scorecard (BSC), the framework aligns indicators and valuation outcomes with organisational strategy, enabling firms to assess monetisation potential across Data-as-a-Service, Information-as-a-Service, and Answers-as-a-Service pathways. Methodologically, the study follows a Design Science approach complemented by embedded case studies with industrial partners, which informed continual refinement of the model. Because the evaluation is connected to a high-level taxonomy, the approach also reveals how valuation considerations map to BSC perspectives. Across the analysed use cases, the framework demonstrated flexibility, transparency, and reduced arbitrariness in valuation, offering organisations a structured basis for linking data assets to strategic and economic outcomes.", "published": "2025-12-08T15:57:26Z", "authors": ["Eduardo Vyhmeister", "Bastien Pietropaoli", " UdoBub", "Rob Schneider", "Andrea Visentin"], "categories": ["cs.CY", "cs.ET"], "doi": "", "_query": "automated valuation model", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2505.19451v3", "title": "Algebraic Zhou valuations", "abstract": "In this paper, we generalize Zhou valuations, originally defined on complex domains, to the framework of general schemes. We demonstrate that an algebraic version of the Jonsson--Mustaţă conjecture is equivalent to the statement that every Zhou valuation is quasi-monomial. By introducing a mixed version of jumping numbers and Tian functions associated with valuations, we obtain characterizations of a valuation being a Zhou valuation or computing some jumping number using the Tian functions. Furthermore, we establish the correspondence between Zhou valuations in algebraic settings and their counterparts in analytic settings.", "published": "2025-05-26T03:17:45Z", "authors": ["Shijie Bao", "Qi'an Guan", "Lin Zhou"], "categories": ["math.AG", "math.CV"], "doi": "", "_query": "automated valuation model", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2310.07110v1", "title": "Valuation Duration of the Stock Market", "abstract": "At the peak of the tech bubble, only 0.57% of market valuation comes from dividends in the next year. Taking the ratio of total market value to the value of one-year dividends, we obtain a valuation-based duration of 175 years. In contrast, at the height of the global financial crisis, more than 2.2% of market value is from dividends in the next year, implying a duration of 46 years. What drives valuation duration? We find that market participants have limited information about cash flow beyond one year. Therefore, an increase in valuation duration is due to a decrease in the discount rate rather than good news about long-term growth. Accordingly, valuation duration negatively predicts annual market return with an out-of-sample R2 of 15%, robustly outperforming other predictors in the literature. While the price-dividend ratio reflects the overall valuation level, our valuation-based measure of duration captures the slope of the valuation term structure. We show that valuation duration, as a discount rate proxy, is a critical state variable that augments the price-dividend ratio in spanning the (latent) state space for stock-market dynamics.", "published": "2023-10-11T01:12:18Z", "authors": ["Ye Li", "Chen Wang"], "categories": ["q-fin.PR", "q-fin.GN"], "doi": "", "_query": "automated valuation model", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2409.01139v2", "title": "Coverage Metrics for a Scenario Database for the Scenario-Based Assessment of Automated Driving Systems", "abstract": "Automated Driving Systems (ADSs) have the potential to make mobility services available and safe for all. A multi-pillar Safety Assessment Framework (SAF) has been proposed for the type-approval process of ADSs. The SAF requires that the test scenarios for the ADS adequately covers the Operational Design Domain (ODD) of the ADS. A common method for generating test scenarios involves basing them on scenarios identified and characterized from driving data. This work addresses two questions when collecting scenarios from driving data. First, do the collected scenarios cover all relevant aspects of the ADS' ODD? Second, do the collected scenarios cover all relevant aspects that are in the driving data, such that no potentially important situations are missed? This work proposes coverage metrics that provide a quantitative answer to these questions. The proposed coverage metrics are illustrated by means of an experiment in which over 200000 scenarios from 10 different scenario categories are collected from the HighD data set. The experiment demonstrates that a coverage of 100 % can be achieved under certain conditions, and it also identifies which data and scenarios could be added to enhance the coverage outcomes in case a 100 % coverage has not been achieved. Whereas this work presents metrics for the quantification of the coverage of driving data and the identified scenarios, this paper concludes with future research directions, including the quantification of the completeness of driving data and the identified scenarios.", "published": "2024-09-02T10:20:01Z", "authors": ["Erwin de Gelder", "Maren Buermann", "Olaf Op den Camp"], "categories": ["cs.RO"], "doi": "10.1109/IAVVC63304.2024.10786405", "_query": "automated valuation model", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2311.06565v2", "title": "Zhou valuations and jumping numbers", "abstract": "In this article, we prove that for any Zhou valuation $ν$, there exists a graded sequence of ideals $\\mathfrak{a}_{\\bullet}$ and a nonzero ideal $\\mathfrak{q}$ such that $ν$ $\\mathscr{A}-$computes the jumping number $\\mathrm{lct}^{\\mathfrak{q}}(\\mathfrak{a}_{\\bullet})$, and that for the subadditive sequence $\\mathfrak{b}^{\\varphi}_{\\bullet}$ related to a plurisubharmonic function $\\varphi$, there exists a Zhou valuation which $\\mathscr{A}-$computes $\\mathrm{lct}^{\\mathfrak{q}}(\\mathfrak{b}^{\\varphi}_{\\bullet})$, where the ``$\\mathscr{A}-$compute'' coincides with the ``compute'' in Jonsson-Mustaţă's Conjecture when the Zhou valuation $ν$ is quasimonomial. There are also some results obtained for Zhou valuations, including a characterization for a valuation being a Zhou valuation, and a denseness property of the cone of Zhou valuations.", "published": "2023-11-11T13:22:43Z", "authors": ["Shijie Bao", "Qi'an Guan", "Zheng Yuan"], "categories": ["math.CV", "math.AG"], "doi": "", "_query": "automated valuation model", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/1301.2611v2", "title": "The valuation difference rank of a quasi-ordered difference field", "abstract": "There are several equivalent characterizations of the valuation rank of an ordered or valued field. In this paper, we extend the theory to the case of an ordered or valued {\\it difference} field (that is, ordered or valued field endowed with a compatible field automorphism). We introduce the notion of {\\it difference rank}. To treat simultaneously the cases of ordered and valued fields, we consider quasi-ordered fields. We characterize the difference rank as the quotient modulo the equivalence relation naturally induced by the automorphism (which encodes its growth rate). In analogy to the theory of convex valuations, we prove that any linearly ordered set can be realized as the difference rank of a maximally valued quasi-ordered difference field. As an application, we show that for every regular uncountable cardinal $κ$ such that $κ= κ^{< κ}$, there are $2^κ$ pairwise non-isomorphic quasi-ordered difference fields of cardinality $κ$, but all isomorphic as quasi-ordered fields.", "published": "2013-01-11T21:34:55Z", "authors": ["Salma Kuhlmann", "Mickael Matusinski", "Francoise Point"], "categories": ["math.LO"], "doi": "10.1007/978-3-319-51718-6_23", "_query": "automated valuation model", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/math/0701867v3", "title": "Divisorial valuations via arcs", "abstract": "This paper shows a finiteness property of a divisorial valuation in terms of arcs. First we show that every divisorial valuation over an algebraic variety corresponds to an irreducible closed subset of the arc space. Then we define the codimension for this subset and give a formula of the codimension in terms of \"relative Mather canonical class\". By using this subset, we prove that a divisorial valuation is determined by assigning the values of finite functions. We also have a criterion for a divisorial valuation to be a monomial valuation by assigning the values of finite functions.", "published": "2007-01-30T05:16:19Z", "authors": ["Tommaso de Fernex", "Lawrence Ein", "Shihoko Ishii"], "categories": ["math.AG"], "doi": "", "_query": "automated valuation model", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/1112.6024v1", "title": "Valuation of Zynga", "abstract": "On December 16, Zynga, the well-known social game developing company went public. This event is following other recent IPOs in the world of social networking companies, such as Groupon, Linkedin or Pandora to cite a few. With a valuation close to 7 billion USD at the time when it went public, Zynga has become the biggest web IPO since Google. This recent enthusiasm for social networking companies, and in particular Zynga, brings up the question whether or not they are overvalued. The common denominator of all these IPOs is that a lot of estimates about their valuation have been circulating, without any specifics given about the methodology or assumptions used to obtain those numbers. To bring more substance to the debate, we propose a two-tiered approach. First, we introduce a new model to forecast the global user base of Zynga, based on the analysis of the individual dynamics of its major games. Next, we model the revenues per user using a logistic growth function, a standard model for growth in competition. This leads to bracket the valuation of Zynga using three different scenarios (base one, optimistic and very optimistic): 4.17 billion USD in the base case, 5.16 billion in the high growth and 7.02 billion in the extreme growth scenario respectively. Thus, only the unlikely extreme growth scenario could potentially justify today's 6.6 billion USD valuation of Zynga. This suggests that Zynga at its IPO has been overpriced.", "published": "2011-12-27T21:22:10Z", "authors": ["Zalán Forró", "Peter Cauwels", "Didier Sornette"], "categories": ["q-fin.GN"], "doi": "", "_query": "automated valuation model", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2301.00930v2", "title": "Data Valuation Without Training of a Model", "abstract": "Many recent works on understanding deep learning try to quantify how much individual data instances influence the optimization and generalization of a model. Such attempts reveal characteristics and importance of individual instances, which may provide useful information in diagnosing and improving deep learning. However, most of the existing works on data valuation require actual training of a model, which often demands high-computational cost. In this paper, we provide a training-free data valuation score, called complexity-gap score, which is a data-centric score to quantify the influence of individual instances in generalization of two-layer overparameterized neural networks. The proposed score can quantify irregularity of the instances and measure how much each data instance contributes in the total movement of the network parameters during training. We theoretically analyze and empirically demonstrate the effectiveness of the complexity-gap score in finding `irregular or mislabeled' data instances, and also provide applications of the score in analyzing datasets and diagnosing training dynamics. Our code is publicly available at https://github.com/JJchy/CG_score", "published": "2023-01-03T02:19:20Z", "authors": ["Nohyun Ki", "Hoyong Choi", "Hye Won Chung"], "categories": ["cs.LG"], "doi": "", "_query": "automated valuation model", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2603.00342v1", "title": "Challenges in Enabling Private Data Valuation", "abstract": "Data valuation methods quantify how individual training examples contribute to a model's behavior, and are increasingly used for dataset curation, auditing, and emerging data markets. As these techniques become operational, they raise serious privacy concerns: valuation scores can reveal whether a person's data was included in training, whether it was unusually influential, or what sensitive patterns exist in proprietary datasets. This motivates the study of privacy-preserving data valuation. However, privacy is fundamentally in tension with valuation utility under differential privacy (DP). DP requires outputs to be insensitive to any single record, while valuation methods are explicitly designed to measure per-record influence. As a result, naive privatization often destroys the fine-grained distinctions needed to rank or attribute value, particularly in heterogeneous datasets where rare examples exert outsized effects. In this work, we analyze the feasibility of DP-compatible data valuation. We identify the core algorithmic primitives across common valuation frameworks that induce prohibitive sensitivity, explaining why straightforward DP mechanisms fail. We further derive design principles for more privacy-amenable valuation procedures and empirically characterize how privacy constraints degrade ranking fidelity across representative methods and datasets. Our results clarify the limits of current approaches and provide a foundation for developing valuation methods that remain useful under rigorous privacy guarantees.", "published": "2026-02-27T22:21:14Z", "authors": ["Yiwei Fu", "Tianhao Wang", "Varun Chandrasekaran"], "categories": ["cs.CR", "cs.AI", "cs.LG"], "doi": "", "_query": "automated valuation model", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/1501.04506v1", "title": "Defining coarsenings of valuations", "abstract": "We study the question which henselian fields admit definable henselian valuations (with or without parameters). We show that every field which admits a henselian valuation with non-divisible value group admits a parameter-definable (non-trivial) henselian valuation. In equicharacteristic $0$, we give a complete characterization of henselian fields admitting a parameter-definable (non-trivial) henselian valuation. We also obtain partial characterization results of fields admitting 0-definable (non-trivial) henselian valuations. We then draw some Galois-theoretic conclusions from our results.", "published": "2015-01-19T14:40:30Z", "authors": ["Franziska Jahnke", "Jochen Koenigsmann"], "categories": ["math.LO"], "doi": "", "_query": "automated valuation model", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2104.08609v1", "title": "On truncations of valuations", "abstract": "In this paper we study the truncation $ν_q$ of a valuation $ν$ on a polynomial $q$. It is known that when $q$ is a key polynomial, then $ν_q$ is a valuation. It is also known that the converse does not hold. We show that when $q$ is a key polynomial, then $ν_q$ is the restriction of the truncation given by an optimizing root of $q$. We also discuss which conditions assure that $ν_q=ν$. Finally, we assume that $ν_q$ is a valuation and present some conditions, given in terms of the graded algebra, to assure that $q$ is a key polynomial.", "published": "2021-04-17T18:23:04Z", "authors": ["Josnei Antonio Novacoski", "Caio Henrique Silva de Souza"], "categories": ["math.AC"], "doi": "", "_query": "automated valuation model", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/1406.4523v1", "title": "Quasi valuation and valuation derived from filtered ring and properties", "abstract": "In this paper we show if R is a filtered ring then we can define a quasi valuation. And if R is some kind of filtered ring then we can define a valuation. Then we prove some properties and relations for R.", "published": "2014-06-17T20:19:51Z", "authors": ["M. H. Anjom SHoa", "M. H. Hosseini"], "categories": ["math.RA"], "doi": "", "_query": "automated valuation model", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2506.23349v2", "title": "A case for data valuation transparency via DValCards", "abstract": "Following the rise in popularity of data-centric machine learning (ML), various data valuation methods have been proposed to quantify the contribution of each datapoint to desired ML model performance metrics (e.g., accuracy). Beyond the technical applications of data valuation methods (e.g., data cleaning, data acquisition, etc.), it has been suggested that within the context of data markets, data buyers might utilize such methods to fairly compensate data owners. Here we demonstrate that data valuation metrics are inherently biased and unstable under simple algorithmic design choices, resulting in both technical and ethical implications. By analyzing 9 tabular classification datasets and 6 data valuation methods, we illustrate how (1) common and inexpensive data pre-processing techniques can drastically alter estimated data values; (2) subsampling via data valuation metrics may increase class imbalance; and (3) data valuation metrics may undervalue underrepresented group data. Consequently, we argue in favor of increased transparency associated with data valuation in-the-wild and introduce the novel Data Valuation Cards (DValCards) framework towards this aim. The proliferation of DValCards will reduce misuse of data valuation metrics, including in data pricing, and build trust in responsible ML systems.", "published": "2025-06-29T17:53:00Z", "authors": ["Keziah Naggita", "Julienne LaChance"], "categories": ["cs.LG"], "doi": "", "_query": "automated valuation model", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2108.08041v1", "title": "DeepCVA: Automated Commit-level Vulnerability Assessment with Deep Multi-task Learning", "abstract": "It is increasingly suggested to identify Software Vulnerabilities (SVs) in code commits to give early warnings about potential security risks. However, there is a lack of effort to assess vulnerability-contributing commits right after they are detected to provide timely information about the exploitability, impact and severity of SVs. Such information is important to plan and prioritize the mitigation for the identified SVs. We propose a novel Deep multi-task learning model, DeepCVA, to automate seven Commit-level Vulnerability Assessment tasks simultaneously based on Common Vulnerability Scoring System (CVSS) metrics. We conduct large-scale experiments on 1,229 vulnerability-contributing commits containing 542 different SVs in 246 real-world software projects to evaluate the effectiveness and efficiency of our model. We show that DeepCVA is the best-performing model with 38% to 59.8% higher Matthews Correlation Coefficient than many supervised and unsupervised baseline models. DeepCVA also requires 6.3 times less training and validation time than seven cumulative assessment models, leading to significantly less model maintenance cost as well. Overall, DeepCVA presents the first effective and efficient solution to automatically assess SVs early in software systems.", "published": "2021-08-18T08:43:36Z", "authors": ["Triet H. M. Le", "David Hin", "Roland Croft", "M. Ali Babar"], "categories": ["cs.SE", "cs.CR", "cs.LG"], "doi": "", "_query": "automated valuation model", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/1501.07402v1", "title": "Valuation Algorithms for Structural Models of Financial Interconnectedness", "abstract": "Much research in systemic risk is focused on default contagion. While this demands an understanding of valuation, fewer articles specifically deal with the existence, the uniqueness, and the computation of equilibrium prices in structural models of interconnected financial systems. However, beyond contagion research, these topics are also essential for risk-neutral pricing. In this article, we therefore study and compare valuation algorithms in the standard model of debt and equity cross-ownership which has crystallized in the work of several authors over the past one and a half decades. Since known algorithms have potentially infinite runtime, we develop a class of new algorithms, which find exact solutions in finitely many calculation steps. A simulation study for a range of financial system designs allows us to derive conclusions about the efficiency of different numerical methods under different system parameters.", "published": "2015-01-29T10:22:59Z", "authors": ["Johannes Hain", "Tom Fischer"], "categories": ["q-fin.CP"], "doi": "", "_query": "automated valuation model", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2109.11362v1", "title": "An optimized application-context relocation approach for Connected and Automated Mobility (CAM)", "abstract": "In this paper, we study and present a management and orchestration framework for vehicular communications, which enables service continuity for the vehicle via an optimized application-context relocation approach. To optimize the transfer of the application-context for Connected and Automated Mobility (CAM) services, our MEC orchestrator performs prediction of resource availability in the edge infrastructure based on the Long Short-Term Memory (LSTM) model, and it makes a final decision on relocation by calculating the outcome of a Multi-Criteria Decision-Making (MCDM) algorithm, taking into account the i) resource prediction, ii) latency and bandwidth on the communication links, and iii) geographical locations of the vehicle and edge hosts in the network infrastructure. Furthermore, we have built a proof-of-concept for the orchestration framework in a real-life distributed testbed environment, to showcase the efficiency in optimizing the edge host selection and application context relocation towards achieving continuity of a service that informs vehicle about the driving conditions on the road.", "published": "2021-09-23T13:20:36Z", "authors": ["Nina Slamnik-Kriještorac", "Steven Latré", "Johann M. Marquez-Barja"], "categories": ["cs.NI"], "doi": "", "_query": "automated valuation model", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/0902.3456v1", "title": "On the valuation of compositions in Lévy term structure models", "abstract": "We derive explicit valuation formulae for an exotic path-dependent interest rate derivative, namely an option on the composition of LIBOR rates. The formulae are based on Fourier transform methods for option pricing. We consider two models for the evolution of interest rates: an HJM-type forward rate model and a LIBOR-type forward price model. Both models are driven by a time-inhomogeneous Lévy process.", "published": "2009-02-19T20:59:50Z", "authors": ["Wolfgang Kluge", "Antonis Papapantoleon"], "categories": ["q-fin.PR", "math.PR"], "doi": "", "_query": "automated valuation model", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2206.15301v1", "title": "Definable valuations on ordered fields", "abstract": "We study the definability of convex valuations on ordered fields, with a particular focus on the distinguished subclass of henselian valuations. In the setting of ordered fields, one can consider definability both in the language of rings $\\mathcal{L}_{\\mathrm{r}}$ and in the richer language of ordered rings $\\mathcal{L}_{\\mathrm{or}}$. We analyse and compare definability in both languages and show the following contrary results: while there are convex valuations that are definable in the language $\\mathcal{L}_{\\mathrm{or}}$ but not in the language $\\mathcal{L}_{\\mathrm{r}}$, any $\\mathcal{L}_{\\mathrm{or}}$-definable henselian valuation is already $\\mathcal{L}_{\\mathrm{r}}$-definable. To prove the latter, we show that the value group and the ordered residue field of an ordered henselian valued field are stably embedded (as an ordered abelian group, respectively as an ordered field). Moreover, we show that in almost real closed fields any $\\mathcal{L}_{\\mathrm{or}}$-definable valuation is henselian.", "published": "2022-06-30T14:09:43Z", "authors": ["Philip Dittmann", "Franziska Jahnke", "Lothar Sebastian Krapp", "Salma Kuhlmann"], "categories": ["math.LO", "math.AC"], "doi": "10.2140/mt.2023.2.101", "_query": "automated valuation model", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/1501.06563v2", "title": "On Lazard's Valuation and CAD Construction", "abstract": "In 1990 Lazard proposed an improved projection operation for cylindrical algebraic decomposition (CAD). For the proof he introduced a certain notion of valuation of a multivariate Puiseux series at a point. However a gap in one of the key supporting results for the improved projection was subsequently noticed. In this report we study a more limited but rigorous concept of Lazard's valuation: namely, we study Lazard's valuation of a multivariate polynomial at a point. We prove some basic properties of the limited Lazard valuation and identify some relationships between valuation-invariance and order-invariance.", "published": "2015-01-26T20:57:00Z", "authors": ["Scott McCallum", "Hoon Hong"], "categories": ["math.AG"], "doi": "", "_query": "automated valuation model", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2202.00190v4", "title": "Sketching stochastic valuation functions", "abstract": "We consider the problem of sketching set valuation functions, defined as the expectation of a valuation function applied to independent random item values. For valuation functions that are monotone and either subadditive or submodular, and that satisfy a weak homogeneity condition (or other structural conditions), we show that there exist discretized versions of the item value distributions -- each with support size $O(k \\log k)$ -- that yield a sketch valuation function providing a constant-factor approximation to the true valuation for any subset of items of size at most $k$. These discretized distributions can be computed efficiently for each item independently, making the approach highly scalable. Our results apply broadly to valuation functions commonly encountered in practice, including team performance based on the best member (e.g., maximum functions), constant elasticity of substitution (CES) production functions with diminishing returns in economics, and others. Sketch valuation functions are especially useful in optimization problems such as best set selection and welfare maximization, where exact value computations are costly or intricate. They enable efficient approximate evaluation of value oracle queries while preserving provable approximation guarantees for the original stochastic optimization problem.", "published": "2022-02-01T02:41:23Z", "authors": ["Milan Vojnović", "Yiliu Wang"], "categories": ["math.ST", "stat.ME"], "doi": "", "_query": "automated valuation model", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2301.08639v1", "title": "Notes on valuation theory for Krasner hyperfields", "abstract": "The main aim of this article is to study and develop valuation theory for Krasner hyperfields. In analogy with classical valuation theory for fields, we generalise the formalism of valuation rings to describe equivalence of valuations on hyperfields. After proving basic results and discussing several examples, we focus on the valued hyperfields that Krasner originally defined in 1957. We find that these must have a particular additive structure which in turns implies the existence of a valuation a'la Krasner. We note that given such a valued hyperfield $(F,v)$, the valuation induced by its additive structure does not have to be equivalent to $v$. We discuss the cases in which it does.", "published": "2023-01-20T15:40:00Z", "authors": ["Alessandro Linzi"], "categories": ["math.AC"], "doi": "", "_query": "automated valuation model", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2507.22429v1", "title": "Comparing Normalizing Flows with Kernel Density Estimation in Estimating Risk of Automated Driving Systems", "abstract": "The development of safety validation methods is essential for the safe deployment and operation of Automated Driving Systems (ADSs). One of the goals of safety validation is to prospectively evaluate the risk of an ADS dealing with real-world traffic. Scenario-based assessment is a widely-used approach, where test cases are derived from real-world driving data. To allow for a quantitative analysis of the system performance, the exposure of the scenarios must be accurately estimated. The exposure of scenarios at parameter level is expressed using a Probability Density Function (PDF). However, assumptions about the PDF, such as parameter independence, can introduce errors, while avoiding assumptions often leads to oversimplified models with limited parameters to mitigate the curse of dimensionality. This paper considers the use of Normalizing Flows (NF) for estimating the PDF of the parameters. NF are a class of generative models that transform a simple base distribution into a complex one using a sequence of invertible and differentiable mappings, enabling flexible, high-dimensional density estimation without restrictive assumptions on the PDF's shape. We demonstrate the effectiveness of NF in quantifying risk and risk uncertainty of an ADS, comparing its performance with Kernel Density Estimation (KDE), a traditional method for non-parametric PDF estimation. While NF require more computational resources compared to KDE, NF is less sensitive to the curse of dimensionality. As a result, NF can improve risk uncertainty estimation, offering a more precise assessment of an ADS's safety. This work illustrates the potential of NF in scenario-based safety. Future work involves experimenting more with using NF for scenario generation and optimizing the NF architecture, transformation types, and training hyperparameters to further enhance their applicability.", "published": "2025-07-30T07:16:59Z", "authors": ["Erwin de Gelder", "Maren Buermann", "Olaf Op den Camp"], "categories": ["cs.RO", "cs.LG"], "doi": "", "_query": "automated valuation model", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2511.04652v1", "title": "Polarization-resolved imaging improves eye tracking", "abstract": "Polarization-resolved near-infrared imaging adds a useful optical contrast mechanism to eye tracking by measuring the polarization state of light reflected by ocular tissues in addition to its intensity. In this paper we demonstrate how this contrast can be used to enable eye tracking. Specifically, we demonstrate that a polarization-enabled eye tracking (PET) system composed of a polarization--filter--array camera paired with a linearly polarized near-infrared illuminator can reveal trackable features across the sclera and gaze-informative patterns on the cornea, largely absent in intensity-only images. Across a cohort of 346 participants, convolutional neural network based machine learning models trained on data from PET reduced the median 95th-percentile absolute gaze error by 10--16\\% relative to capacity-matched intensity baselines under nominal conditions and in the presence of eyelid occlusions, eye-relief changes, and pupil-size variation. These results link light--tissue polarization effects to practical gains in human--computer interaction and position PET as a simple, robust sensing modality for future wearable devices.", "published": "2025-11-06T18:42:09Z", "authors": ["Mantas Žurauskas", "Tom Bu", "Sanaz Alali", "Beyza Kalkanli", "Derek Shi", "Fernando Alamos", "Gauresh Pandit", "Christopher Mei", "Ali Behrooz", "Ramin Mirjalili", "Dave Stronks", "Alexander Fix", "Dmitri Model"], "categories": ["cs.CV", "physics.optics"], "doi": "", "_query": "automated valuation model", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/1606.05164v3", "title": "Network Valuation in Financial Systems", "abstract": "We introduce a general model for the balance-sheet consistent valuation of interbank claims within an interconnected financial system. Our model represents an extension of clearing models of interdependent liabilities to account for the presence of uncertainty on banks' external assets. At the same time, it also provides a natural extension of classic structural credit risk models to the case of an interconnected system. We characterize the existence and uniqueness of a valuation that maximises individual and total equity values for all banks. We apply our model to the assessment of systemic risk, and in particular for the case of stress-testing. Further, we provide a fixed-point algorithm to carry out the network valuation and the conditions for its convergence.", "published": "2016-06-16T12:45:11Z", "authors": ["Paolo Barucca", "Marco Bardoscia", "Fabio Caccioli", "Marco D'Errico", "Gabriele Visentin", "Guido Caldarelli", "Stefano Battiston"], "categories": ["q-fin.RM", "physics.soc-ph"], "doi": "10.1111/mafi.12272", "_query": "automated valuation model", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/1108.5669v2", "title": "Learning Valuation Functions", "abstract": "In this paper we study the approximate learnability of valuations commonly used throughout economics and game theory for the quantitative encoding of agent preferences. We provide upper and lower bounds regarding the learnability of important subclasses of valuation functions that express no-complementarities. Our main results concern their approximate learnability in the distributional learning (PAC-style) setting. We provide nearly tight lower and upper bounds of $\\tildeΘ(n^{1/2})$ on the approximation factor for learning XOS and subadditive valuations, both widely studied superclasses of submodular valuations. Interestingly, we show that the $\\tildeΩ(n^{1/2})$ lower bound can be circumvented for XOS functions of polynomial complexity; we provide an algorithm for learning the class of XOS valuations with a representation of polynomial size achieving an $O(n^{\\eps})$ approximation factor in time $O(n^{1/\\eps})$ for any $\\eps > 0$. This highlights the importance of considering the complexity of the target function for polynomial time learning. We also provide new learning results for interesting subclasses of submodular functions. Our upper bounds for distributional learning leverage novel structural results for all these valuation classes. We show that many of these results provide new learnability results in the Goemans et al. model (SODA 2009) of approximate learning everywhere via value queries. We also introduce a new model that is more realistic in economic settings, in which the learner can set prices and observe purchase decisions at these prices rather than observing the valuation function directly. In this model, most of our upper bounds continue to hold despite the fact that the learner receives less information (both for learning in the distributional setting and with value queries), while our lower bounds naturally extend.", "published": "2011-08-29T17:48:28Z", "authors": ["Maria Florina Balcan", "Florin Constantin", "Satoru Iwata", "Lei Wang"], "categories": ["cs.GT", "cs.DS", "stat.ML"], "doi": "", "_query": "automated valuation model", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/1507.04851v1", "title": "Convolution of valuations on manifolds", "abstract": "We introduce the new notion of convolution of a (smooth or generalized) valuation on a group $G$ and a valuation on a manifold $M$ acted upon by the group. In the case of a transitive group action, we prove that the spaces of smooth and generalized valuations on $M$ are modules over the algebra of compactly supported generalized valuations on $G$ satisfying some technical condition of tameness. The case of a vector space acting on itself is studied in detail. We prove explicit formulas in this case and show that the new convolution is an extension of the convolution on smooth translation invariant valuations introduced by J.~Fu and the second named author.", "published": "2015-07-17T07:19:59Z", "authors": ["Semyon Alesker", "Andreas Bernig"], "categories": ["math.DG"], "doi": "", "_query": "automated valuation model", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2306.10577v3", "title": "OpenDataVal: a Unified Benchmark for Data Valuation", "abstract": "Assessing the quality and impact of individual data points is critical for improving model performance and mitigating undesirable biases within the training dataset. Several data valuation algorithms have been proposed to quantify data quality, however, there lacks a systemic and standardized benchmarking system for data valuation. In this paper, we introduce OpenDataVal, an easy-to-use and unified benchmark framework that empowers researchers and practitioners to apply and compare various data valuation algorithms. OpenDataVal provides an integrated environment that includes (i) a diverse collection of image, natural language, and tabular datasets, (ii) implementations of eleven different state-of-the-art data valuation algorithms, and (iii) a prediction model API that can import any models in scikit-learn. Furthermore, we propose four downstream machine learning tasks for evaluating the quality of data values. We perform benchmarking analysis using OpenDataVal, quantifying and comparing the efficacy of state-of-the-art data valuation approaches. We find that no single algorithm performs uniformly best across all tasks, and an appropriate algorithm should be employed for a user's downstream task. OpenDataVal is publicly available at https://opendataval.github.io with comprehensive documentation. Furthermore, we provide a leaderboard where researchers can evaluate the effectiveness of their own data valuation algorithms.", "published": "2023-06-18T14:38:29Z", "authors": ["Kevin Fu Jiang", "Weixin Liang", "James Zou", "Yongchan Kwon"], "categories": ["cs.LG", "stat.ML"], "doi": "", "_query": "automated valuation model", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2112.08364v3", "title": "Data Valuation for Vertical Federated Learning: A Model-free and Privacy-preserving Method", "abstract": "Vertical Federated learning (VFL) is a promising paradigm for predictive analytics, empowering an organization (i.e., task party) to enhance its predictive models through collaborations with multiple data suppliers (i.e., data parties) in a decentralized and privacy-preserving way. Despite the fast-growing interest in VFL, the lack of effective and secure tools for assessing the value of data owned by data parties hinders the application of VFL in business contexts. In response, we propose FedValue, a privacy-preserving, task-specific but model-free data valuation method for VFL, which consists of a data valuation metric and a federated computation method. Specifically, we first introduce a novel data valuation metric, namely MShapley-CMI. The metric evaluates a data party's contribution to a predictive analytics task without the need of executing a machine learning model, making it well-suited for real-world applications of VFL. Next, we develop an innovative federated computation method that calculates the MShapley-CMI value for each data party in a privacy-preserving manner. Extensive experiments conducted on six public datasets validate the efficacy of FedValue for data valuation in the context of VFL. In addition, we illustrate the practical utility of FedValue with a case study involving federated movie recommendations.", "published": "2021-12-15T02:42:28Z", "authors": ["Xiao Han", "Leye Wang", "Junjie Wu", "Xiao Fang"], "categories": ["cs.LG", "cs.AI"], "doi": "", "_query": "automated valuation model", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2111.09024v1", "title": "Geometric valuation theory", "abstract": "A brief introduction to geometric valuation theory is given. The focus is on classification results for valuations on convex bodies and on function spaces.", "published": "2021-11-17T10:36:33Z", "authors": ["Monika Ludwig"], "categories": ["math.MG", "math.FA"], "doi": "10.4171/8ECM/25", "_query": "automated valuation model", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2005.03298v1", "title": "Augmented Valuation and Minimal Pair", "abstract": "Let $(K, ν)$ be a valued field, the notions of \\emph{augmented valuation}, of \\emph{limit augmented valuation} and of \\emph{admissible family} of valuations enable to give a description of any valuation $μ$ of $K [x]$ extending $ν$. In the case where the field $K$ is algebraically closed, this description is particularly simple and we can reduce it to the notions of \\emph{minimal pair} and \\emph{pseudo-convergent family}. Let $(K, ν)$ be a henselian valued field and $\\barν$ the unique extension of $ν$ to the algebraic closure $\\bar K$ of $K$ and let $μ$ be a valuation of $ K [x]$ extending $ν$, we study the extensions $\\barμ$ from $μ$ to $\\bar K [x]$ and we give a description of the valuations $\\barμ_i$ of $\\bar K [x]$ which are the extensions of the valuations $μ_i$ belonging to the admissible family associated with $μ$.", "published": "2020-05-07T07:42:28Z", "authors": ["Michel Vaquié"], "categories": ["math.AC", "math.AG"], "doi": "", "_query": "automated valuation model", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2610.11032v1", "title": "Frobenius splitting of valuation rings revisited", "abstract": "Let $K$ be a function field over an $F$-finite field $k$ of characteristic $p > 0$ and let $ν$ be a valuation of $K/k$. We show $ν$ is an Abhyankar valuation of $K/k$ if and only if the corresponding valuation ring is Frobenius split. The proof proceeds by analyzing when ideals of a valuation ring are uniformly $F$-compatible and establishing a general ramification-theoretic characterization of Frobenius split valuation rings of $F$-finite fields. In addition, when the ground field $k$ is not $F$-finite, we show that the equivalence between Frobenius splitting and the Abhyankar property can fail by constructing an example of an excellent Frobenius split DVR of a function field whose corresponding valuation is not divisorial. Our methods do not rely on local uniformization results.", "published": "2026-10-08T00:24:05Z", "authors": ["Rankeya Datta", "Karen E. Smith"], "categories": ["math.AC", "math.AG"], "doi": "", "_query": "automated valuation model", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/1305.0775v3", "title": "Residual ideals of MacLane valuations", "abstract": "Let $K$ be a field equipped with a discrete valuation $v$. In a pioneering work, S. MacLane determined all valuations on $K(x)$ extending $v$. His work was recently reviewed and generalized by M. Vaquié, by using the graded algebra of a valuation. We extend Vaquié's approach by studying residual ideals of the graded algebra of a valuation as an abstract counterpart of certain residual polynomials which play a key role in the computational applications of the theory. As a consequence, we determine the structure of the graded algebra of the discrete valuations on $K(x)$ and we show how these valuations may be used to parameterize irreducible polynomials over local fields up to Okutsu equivalence.", "published": "2013-05-03T16:54:34Z", "authors": ["Julio Fernández", "Jordi Guàrdia", "Jesús Montes", "Enric Nart"], "categories": ["math.NT"], "doi": "", "_query": "automated valuation model", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2606.06325v1", "title": "Data valuation model for non-monetary exchanges", "abstract": "In the evolving landscape of data product exchange platforms, traditional economic valuation models fall short due to the non-rival nature of data and the prevalence of non-monetary data product exchanges. This paper introduces a normative, choice-based metric for valuing data products within intracompany exchanges, where conventional pricing mechanisms are absent. By modeling consumer attention and preferences, the proposed metric quantifies the value of data offerings based solely on user selection behavior, without relying on cost, demand, or competitive pricing data. We show that this metric can be formally cast as a cooperative game with a closed-form Shapley value, providing a principled and fairness-based allocation of value across offerings. The model rewards uniqueness and discriminative consumption, effectively addressing the limitations of popularity-based metrics and incentivizing the creation of high-value, long-tail data products. Through theoretical analysis and illustrative examples, the metric is shown to align with economic principles, support equitable valuation, and contribute to a robust framework for measuring gross data product value. Future research directions include exploring bundling strategies and quantifying product complementarity.", "published": "2026-06-04T16:00:24Z", "authors": ["Julia Blyumen", "Eitan Farchi"], "categories": ["cs.CE"], "doi": "", "_query": "automated valuation model", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2308.11718v1", "title": "Products of $p$-Adic Valuation Trees", "abstract": "The study of prime divisibility plays a crucial role in number theory. The $p$-adic valuation of a number is the highest power of a prime, $p$, that divides that number. Using this valuation, we construct $p$-adic valuation trees to visually represent the valuations of a sequence. We investigate how nodes split on trees generated by linear functions with rational coefficients, as well as those formed from a product of linear or lower degree polynomials. We describe the infinite branches of these polynomial trees and the valuations of their terminating nodes.", "published": "2023-08-22T18:07:41Z", "authors": ["Dillon Snyder"], "categories": ["math.NT"], "doi": "", "_query": "automated valuation model", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2407.13455v1", "title": "Real Poincaré series of a plane divisorial valuation", "abstract": "Earlier, there was computed the Poincaré series of a valuation or of a collection of valuations on the ring of germs of holomorphic functions in two variables. For a collection of several plane curve valuations it appeared to coincide with the Alexander polynomial of the corresponding algebraic link. Recently, the authors defined Poincaré series of a valuation or of a collection of valuations in the real setting. (Actually, there were defined three versions of them, however, one of them was found to be ``not computable''.) These two Poincar'e series were computed for one plane curve valuation. Here we compute them for a plane divisorial valuation.", "published": "2024-07-18T12:33:28Z", "authors": ["Antonio Campillo", "Félix Delgado", "Sabir M. Gusein-Zade"], "categories": ["math.AG"], "doi": "", "_query": "automated valuation model", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2609.37881v1", "title": "Global Structure and Local Specifications in Sublinear Valuation", "abstract": "This work studies the relationships among sublinear valuation rules, uncertainty structures, and local specifications in a time-homogeneous Markovian framework with killing. These objects are linked, under finiteness and locality of the upper generator and a Lyapunov condition, by three maps: robust valuation, globalization, and localization. First, we show that the corresponding classes of uncertainty structures and sublinear valuation rules are order-isomorphic via the robust valuation map. Second, our analysis clarifies how local specifications constrain sublinear valuation and uncertainty structures, as well as what information localization and globalization preserve. In particular, the compositions of localization and globalization need not recover the original objects but yield canonical extremal elements. Third, the sublinear valuation generated by a local specification is characterized by the greatest viscosity subsolution of the associated Hamilton--Jacobi--Bellman equation. Finally, each uncertainty structure with killing admits a unique representation by a family of pairs consisting of a cumulative discounting process and an underlying state law. These results provide a framework for order relations, probabilistic and PDE-based representations, and model recovery in sublinear valuation without requiring uniqueness of the underlying martingale problems or a viscosity comparison principle.", "published": "2026-09-29T15:53:57Z", "authors": ["Jongjin Park", "David Criens", "Hyungbin Park"], "categories": ["q-fin.MF", "math.PR"], "doi": "", "_query": "automated valuation model", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2512.05736v1", "title": "The Mathematics of Income Property Valuation", "abstract": "This paper surveys and generalizes the main valuation formulas used in real estate valuation and presents unified proofs. The results are otherwise scattered in obscure journals and books while the proofs are rarely available to researchers in the field. The material was originally developed so that it could be used by mathematically-trained appraisers and researchers in the former Soviet Union and in other transition economies that were starting their real estate valuation profession. Keywords: real estate valuations; six functions of one; Ellwood, Akerson, and Hoskold formulas; capitalization rate methods; amortization tables JEL: G12, R3", "published": "2025-12-05T14:15:50Z", "authors": ["David Ellerman"], "categories": ["econ.TH"], "doi": "", "_query": "automated valuation model", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/0911.0373v2", "title": "Analyticity of the Wiener-Hopf factors and valuation of exotic options in Lévy models", "abstract": "This paper considers the valuation of exotic path-dependent options in Lévy models, in particular options on the supremum and the infimum of the asset price process. Using the Wiener--Hopf factorization, we derive expressions for the analytically extended characteristic function of the supremum and the infimum of a Lévy process. Combined with general results on Fourier methods for option pricing, we provide formulas for the valuation of one-touch options, lookback options and equity default swaps in Lévy models.", "published": "2009-11-02T18:09:38Z", "authors": ["Ernst Eberlein", "Kathrin Glau", "Antonis Papapantoleon"], "categories": ["q-fin.PR", "math.PR", "q-fin.CP"], "doi": "", "_query": "automated valuation model", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/1809.00817v2", "title": "Model Risk in Real Option Valuation", "abstract": "We introduce a general decision tree framework to value an option to invest/divest in a project, focusing on the model risk inherent in the assumptions made by standard real option valuation methods. We examine how real option values depend on the dynamics of project value and investment costs, the frequency of exercise opportunities, the size of the project relative to initial wealth, the investor's risk tolerance (and how it changes with wealth) and several other choices about model structure. For instance, contrary to stylized facts from previous literature, real option values can actually decrease with the volatility of the underlying project value and increase with investment costs.", "published": "2018-09-04T07:40:06Z", "authors": ["Carol Alexander", "Xi Chen"], "categories": ["q-fin.GN"], "doi": "", "_query": "automated valuation model", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2406.05446v1", "title": "Design of reliable technology valuation model with calibrated machine learning of patent indicators", "abstract": "Machine learning (ML) has revolutionized the digital transformation of technology valuation by predicting the value of patents with high accuracy. However, the lack of validation regarding the reliability of these models hinders experts from fully trusting the confidence of model predictions. To address this issue, we propose an analytical framework for reliable technology valuation using calibrated ML models, which provide robust confidence levels in model predictions. We extract quantitative patent indicators that represent various technology characteristics as input data, using the patent maintenance period as a proxy for technology values. Multiple ML models are developed to capture the nonlinear relationship between patent indicators and technology value. The reliability and accuracy of these models are evaluated, presenting a Pareto-front map where the expected calibration error, Matthews correlation coefficient and F1-scores are compared. After identifying the best-performing model, we apply SHapley Additive exPlanation (SHAP) analysis to pinpoint the most significant input features by confidence bin. Through a case study, we confirmed that the proposed approach offers a practical guideline for developing reliable and accurate ML-based technology valuation models, with significant implications for both academia and industry.", "published": "2024-06-08T11:52:37Z", "authors": ["Seunghyun Lee", "Janghyeok Yoon", "Jaewoong Choi"], "categories": ["cs.CL", "cs.AI"], "doi": "", "_query": "automated valuation model", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2109.13796v2", "title": "Actuarial-consistency and two-step actuarial valuations: a new paradigm to insurance valuation", "abstract": "This paper introduces new valuation schemes called actuarial-consistent valuations for insurance liabilities which depend on both financial and actuarial risks, which imposes that all actuarial risks are priced via standard actuarial principles. We propose to extend standard actuarial principles by a new actuarial-consistent procedure, which we call \"two-step actuarial valuations\". \\bluebis{In the case valuations are coherent}, we show that actuarial-consistent valuations are equivalent to two-step actuarial valuations. We also discuss the connection with \"two-step market-consistent valuations\" from Pelsser and Stadje (2014). In particular, we discuss how the dependence structure between actuarial and financial risks impacts both actuarial-consistent and market-consistent valuations.", "published": "2021-08-30T07:22:47Z", "authors": ["Karim Barigou", "Daniël Linders", "Fan Yang"], "categories": ["q-fin.PR", "math.PR"], "doi": "", "_query": "automated valuation model", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2608.19976v2", "title": "Random valuations", "abstract": "A valuation is a finitely additive function on the family of compact convex sets in $\\mathbb{R}^d$. We study non-negative infinitely divisible random valuations, with particular emphasis on monotone, $σ$-continuous models with independent increments along nested families. After separating the deterministic part, we show that the Lévy measure of such a valuation is generated by pairs $(F,r)$, where $F$ is a non-empty closed convex set and $r>0$, with each pair contributing $r\\mathbf{1}_{F\\cap K=\\emptyset}$. This yields a Poisson representation and an equivalent formulation through a pure-jump completely random measure on the space of closed convex sets. For stationary valuations, we derive a cylinder-Grassmannian representation of the Lévy measure. In the stationary isotropic case, we obtain a McMullen-type decomposition, at the level of one-dimensional distributions, into independent components stable under dilation of the argument.", "published": "2026-08-20T12:50:11Z", "authors": ["Andrii Ilienko", "Ilya Molchanov", "Tommaso Visonà"], "categories": ["math.PR", "math.MG"], "doi": "", "_query": "automated valuation model", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/1407.8156v2", "title": "Uniformly defining $p$-henselian valuations", "abstract": "Admitting a non-trivial $p$-henselian valuation is a weaker assumption on a field than admitting a non-trivial henselian valuation. Unlike henselianity, $p$-henselianity is an elementary property in the language of rings. We are interested in the question when a field admits a non-trivial 0-definable $p$-henselian valuation (in the language of rings). We give a classification of elementary classes of fields in which the canonical $p$-henselian valuation is uniformly 0-definable. We then apply this to show that there is a definable valuation inducing the ($t$-)henselian topology on any ($t$-)henselian field which is neither separably nor real closed.", "published": "2014-07-30T18:50:30Z", "authors": ["Franziska Jahnke", "Jochen Koenigsmann"], "categories": ["math.LO", "math.AC"], "doi": "", "_query": "automated valuation model", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/1201.5083v1", "title": "Atomic Pseudo-Valuation Domains", "abstract": "Pseudo-valuation domains have been studied since their introduction in 1978 by Hedstrom and Houston. Related objects, boundary valuation domains, were introduced by Maney in 2004. Here, it is shown that the class of atomic pseudo-valuation domains coincides with the class of boundary valuation domains. It is also shown that power series rings and generalized power series rings give examples of pseudo-valuation domains whose congruence lattices can be characterized. The paper also introduces, and makes use of, a sufficient condition on the group of divisibility of a domain to guarantee that it is a pseudo-valuation domain.", "published": "2012-01-24T18:39:18Z", "authors": ["Elijah Stines"], "categories": ["math.RA", "math.AC"], "doi": "", "_query": "automated valuation model", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2607.11688v1", "title": "Automated Synthesis of Facial Mechanisms for Conversational Animatronic Robots", "abstract": "Animatronic faces are a central component of socially interactive robots, enabling rich nonverbal communication through facial articulation. However, state-of-the-art animatronic faces are typically tailored systems: each new facial geometry requires extensive manual mechanical redesign, making large-scale personalization prohibitively slow and costly. In this work, we pursue automated and scalable mechanical face synthesis, aiming to rapidly generate a physically realizable facial mechanism for a wide range of facial geometries. We introduce a parametric, linkage-driven mechanical face template whose topology and actuator layout are explicitly parameterized to support systematic scaling and retargeting across diverse facial morphologies. Building on this template, we propose a hierarchical automatic design algorithm that takes a single 2D portrait as input, reconstructs a target 3D face, and synthesizes a collision-free, manufacturable internal mechanism. The algorithm combines anatomy-guided feasible motion volumes, Action Unit (AU)-derived trajectory-based expressiveness objectives, and a collision-driven outer-loop refinement strategy. Beyond hardware synthesis, we argue that future mechanical faces deployed at scale must engage in bidirectional, multi-turn conversation rather than functioning solely as speaking or listening heads. To this end, we develop a dual-identity conversational facial motion synthesis framework that jointly models speaking and listening behaviors from audio, producing temporally coherent 3D facial motion suitable for physical execution. We validate our system through extensive experiments, including (i) quantitative evaluation of automatic mechanism synthesis across diverse facial geometries, (ii) comparisons against manual mechanical design, (iii) benchmarks on conversational facial motion synthesis and real-time deployment, and (iv) perceptual user studies.", "published": "2026-07-13T15:22:46Z", "authors": ["Zongzheng Zhang", "Zi Lin", "Jiawen Yang", "Ziqiao Peng", "Junyan Lao", "Lin Cheng", "Huazhe Xu", "Hang Zhao", "Hao Zhao"], "categories": ["cs.RO"], "doi": "", "_query": "automated valuation model", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/1812.03713v1", "title": "$t$-local domains and valuation domains", "abstract": "In a valuation domain $(V,M)$ every nonzero finitely generated ideal $J$ is principal and so, in particular, $J=J^t$, hence the maximal ideal $M$ is a $t$-ideal. Therefore, the $t$-local domains (i.e., the local domains, with maximal ideal being a $t$-ideal) are \"cousins\" of valuation domains, but, as we will see in detail, not so close. Indeed, for instance, a localization of a $t$-local domain is not necessarily $t$-local, but of course a localization of a valuation domain is a valuation domain. So it is natural to ask under what conditions is a $t$-local domain a valuation domain? The main purpose of the present paper is to address this question, surveying in part previous work by various authors containing useful properties for applying them to our goal.", "published": "2018-12-10T10:13:01Z", "authors": ["Marco Fontana", "Muhammad Zafrullah"], "categories": ["math.AC", "math.AG", "math.RA"], "doi": "", "_query": "automated valuation model", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/1308.3394v1", "title": "Superrosy fields and valuations", "abstract": "We prove that every non-trivial valuation on an infinite superrosy field of positive characteristic has divisible value group and algebraically closed residue field. In fact, we prove the following more general result. Let $K$ be a field such that for every finite extension $L$ of $K$ and for every natural number $n>0$ the index $[L^*:(L^*)^n]$ is finite and, if $char(K)=p>0$ and $f: L \\to L$ is given by $f(x)=x^p-x$, the index $[L^+:f[L]]$ is also finite. Then either there is a non-trivial definable valuation on $K$, or every non-trivial valuation on $K$ has divisible value group and, if $char(K)>0$, it has algebraically closed residue field. In the zero characteristic case, we get some partial results of this kind. We also notice that minimal fields have the property that every non-trivial valuation has divisible value group and algebraically closed residue field.", "published": "2013-08-15T13:46:46Z", "authors": ["Krzysztof Krupinski"], "categories": ["math.LO", "math.AC"], "doi": "", "_query": "automated valuation model", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/1606.08894v3", "title": "Existence of valuations with smallest normalized volume", "abstract": "Li introduced the normalized volume of a valuation due to its relation to K-semistability. He conjectured that over a klt singularity there exists a valuation with smallest normalized volume. We prove this conjecture and provide an example to show that such a valuation need not be divisorial.", "published": "2016-06-28T21:34:08Z", "authors": ["Harold Blum"], "categories": ["math.AG"], "doi": "10.1112/S0010437X17008016", "_query": "automated valuation model", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/math/0310192v1", "title": "Completions of valuation rings", "abstract": "Let k be a field of characteristic zero, K an algebraic function field over k, and V a k-valuation ring of K. Zariski's theorem of local uniformization shows that there exist algebraic regular local rings R_i with quotient field K which are dominated by V, and such that the direct union of the R_i's is V. We investigate the ring T, which is the direct union of the completions of the R_i's. We give necessary and sufficient conditions for T to be a valuation ring. We then focus on the case in which the valuation has rank one.", "published": "2003-10-13T20:08:22Z", "authors": ["Steven Dale Cutkosky", "Laura Ghezzi"], "categories": ["math.AC", "math.AG"], "doi": "", "_query": "automated valuation model", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/1507.01220v1", "title": "Moments and Valuations", "abstract": "All measurable and $\\operatorname{SL}(n)$-covariant vector valued valuations on convex polytopes containing the origin in their interiors are completely classified. The moment vector is shown to be essentially the only such valuation.", "published": "2015-07-05T13:41:17Z", "authors": ["Christoph Haberl", "Lukas Parapatits"], "categories": ["math.MG"], "doi": "", "_query": "automated valuation model", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2511.11648v2", "title": "Lightweight Time Series Data Valuation on Time Series Foundation Models via In-Context Finetuning", "abstract": "Time series foundation models (TSFMs) have demonstrated increasing capabilities due to their extensive pretraining on large volumes of diverse time series data. Consequently, the quality of time series data is crucial to TSFM performance, rendering an accurate and efficient data valuation of time series for TSFMs indispensable. However, traditional data valuation methods, such as influence functions, face severe computational bottlenecks due to their poor scalability with growing TSFM model sizes and often fail to preserve temporal dependencies. In this paper, we propose LTSV, a Lightweight Time Series Valuation on TSFMS via in-context finetuning. Grounded in the theoretical evidence that in-context finetuning approximates the influence function, LTSV estimates a sample's contribution by measuring the change in context loss after in-context finetuning, leveraging the strong generalization capabilities of TSFMs to produce robust and transferable data valuations. To capture temporal dependencies, we introduce temporal block aggregation, which integrates per-block influence scores across overlapping time windows. Experiments across multiple time series datasets and models demonstrate that LTSV consistently provides reliable and strong valuation performance, while maintaining manageable computational requirements. Our results suggest that in-context finetuning on time series foundation models provides a practical and effective bridge between data attribution and model generalization in time series learning.", "published": "2025-11-10T13:06:46Z", "authors": ["Shunyu Wu", "Tianyue Li", "Yixuan Leng", "Jingyi Suo", "Jian Lou", "Dan Li", "See-Kiong Ng"], "categories": ["cs.LG", "cs.AI"], "doi": "", "_query": "automated valuation model", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2608.10976v1", "title": "XCoT-VLA: Executable Chain-of-Thought for Vision-Language-Action Driving", "abstract": "Vision-Language-Action (VLA) models can connect scene understanding, semantic reasoning, and trajectory generation for autonomous driving. However, verbose natural-language Chain-of-Thought (CoT) is poorly suited to real-time control because it is open-ended, costly to decode, and difficult to optimize as an action-facing representation. We propose XCoT-VLA, which replaces descriptive rationales with compact executable CoT tokens learned from automatically constructed Reason-Action supervision. Logged trajectories provide action evidence, while scene context supplies causal semantics. The predicted XCoT sequence remains in context and conditions fixed trajectory queries through shared multimodal self-attention. Deterministic token-function routing applies the Reason FFN to XCoT tokens and the Control FFN to trajectory queries for flow-matching trajectory generation. We further introduce XCoT Policy Optimization (XCPO) as an optional refinement extension in the same executable token space. XCoT-VLA reduces longitudinal ADE from 1.645 to 1.323 on a general-distribution set and lateral FDE from 1.616 to 0.648 in lane-change scenarios. By representing driving-oriented reasoning with only 2-6 executable XCoT tokens, our method substantially reduces autoregressive reasoning overhead and remains within the real-time planning budget. These results demonstrate that driving-oriented reasoning can be compact, executable, and directly connected to trajectory generation.", "published": "2026-08-11T14:33:21Z", "authors": [" Foundation Model Team", "XPeng Inc"], "categories": ["cs.AI"], "doi": "", "_query": "automated valuation model", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2607.06626v3", "title": "Reward Valuation in Large Language Models: Causal Induction of Anhedonia", "abstract": "Recent frontier models mimic complex aspects of human cognition. Here we ask whether this alignment extends into reward valuation, which we assess in a mechanistic framework. Specifically, we use clinical tests that were developed to evaluate anhedonia in human subjects with major depressive disorders. Mechanistically, anhedonia is frequently associated with dysregulation in the Nucleus Accumbens (NAc) and the broader dopaminergic reward system. While neuroimaging has localized these deficits, establishing a causal link between NAc activity and specific behavioral symptoms remains a challenge. We use these ideas from neuroscience to functionally identify reward-anticipatory units in state-of-the-art AI models, and evaluate their causal involvement via targeted perturbations. We find that not only are such model units predictive of NAc brain recordings, their perturbation also induces behavioral effects mirroring human anhedonia: the model opts for low-effort, low-reward tasks in effort-based decision-making paradigms. Crucially, our results demonstrate that this represents a specific deficit in self-centered reward valuation and anticipation--rather than a loss of task capability, reward calculation, or effort avoidance. This induced vulnerability aligns with clinical measures of anhedonia and motivation in humans, such as DARS and MAP-SR, instruments that contain no reward-related vocabulary, ruling out a purely lexical account of the perturbation effect. Taken together, our results suggest reward valuation circuits in AI models that functionally mimic those in humans.", "published": "2026-07-07T11:44:04Z", "authors": ["Melika Honarmand", "Samin Mahdipour Aghabagher", "Martin Schrimpf"], "categories": ["cs.LG", "q-bio.NC"], "doi": "", "_query": "automated valuation model", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/1903.10793v2", "title": "Valuations and henselization", "abstract": "We study the extension of valuations centered in a local domain to its henseliza-tion. We prove that a valuation $ν$ centered in a local domain R uniquely determines a minimal prime H($ν$) of the henselization R h of R and an extension of $ν$ centered in R h /H($ν$), which has the same value group as $ν$. Our method, which assumes neither that R is noetherian nor that it is integrally closed, is to reduce the problem to the extension of the valuation to a quotient of a standard {é}tale local R-algebra and in that situation to draw valuative consequences from the observation that the Newton-Hensel algorithm for constructing roots of polynomials produces sequences that are always pseudo-convergent in the sense of Ostrowski. We then apply this method to the study of the approximation of elements of the henselization of a valued field by elements of the field and give a characterization of the henselian property of a local domain (R, m R) in terms of the limits of certain pseudo-convergent sequences of elements of m R for a valuation centered in it. Another consequence of our work is to establish in full generality a bijective correspondence between the minimal primes of the henselization of a local domain R and the connected components of the Riemann-Zariski space of valuations centered in R.", "published": "2019-03-26T11:06:12Z", "authors": ["Ana Belén de Felipe Paramio", "Bernard H Teissier"], "categories": ["math.AG", "math.AC"], "doi": "", "_query": "automated valuation model", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/1704.08671v3", "title": "Construction of the Lindström valuation of an algebraic extension", "abstract": "Recently, Bollen, Draisma, and Pendavingh have introduced the Lindström valuation on the algebraic matroid of a field extension of characteristic p. Their construction passes through what they call a matroid flock and builds on some of the associated theory of matroid flocks which they develop. In this paper, we give a direct construction of the Lindström valuated matroid using the theory of inseparable field extensions. In particular, we give a description of the valuation, the valuated circuits, and the valuated cocircuits.", "published": "2017-04-27T17:34:17Z", "authors": ["Dustin Cartwright"], "categories": ["math.CO"], "doi": "10.1016/j.jcta.2018.03.003", "_query": "automated valuation model", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/1510.04511v1", "title": "Orlicz valuations", "abstract": "In this paper, Orlicz valuations compatible with $SL(n)$ transforms are classified. Unlike their $L_p$ analogs, the identity operator and the reflection operator are the only $SL(n)$ compatible Orlicz valuations (up to dilations). It turns out that the Orlicz projection body operator, the Orlicz centroid body operator and the Orlicz difference body operator are not Orlicz valuations. The property that the Orlicz difference body operator is not an Orlicz valuation plays an important role in characterizing the identity operator and the reflection operator.", "published": "2015-10-15T12:49:12Z", "authors": ["Jin Li", "Gangsong Leng"], "categories": ["math.MG"], "doi": "", "_query": "automated valuation model", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/1710.07173v1", "title": "Fields with automorphism and valuation", "abstract": "Abraham Robinson's method for finding model completions is refined and and generalized for model companions and is applied to the theory of fields equipped with both a valuation and an automorphism.", "published": "2017-10-19T15:09:30Z", "authors": ["Özlem Beyarslan", "Daniel Max Hoffmann", "Gönenç Onay", "David Pierce"], "categories": ["math.LO"], "doi": "10.1007/s00153-020-00728-5", "_query": "automated valuation model", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/0908.3196v1", "title": "A policyholder's utility indifference valuation model for the guaranteed annuity option", "abstract": "Insurance companies often include very long-term guarantees in participating life insurance products, which can turn out to be very valuable. Under a guaranteed annuity options (G.A.O), the insurer guarantees to convert a policyholder's accumulated funds to a life annuity at a fixed rated when the policy matures. Both financial and actuarial approaches have been used to valuate of such options. In the present work, we present an indifference valuation model for the guaranteed annuity option. We are interested in the additional lump sum that the policyholder is willing to pay in order to have the option to convert the accumulated funds into a lifelong annuity at a guaranteed rate.", "published": "2009-08-21T20:10:13Z", "authors": ["Matheus R Grasselli", "Sebastiano Silla"], "categories": ["q-fin.PM", "q-fin.PR"], "doi": "", "_query": "automated valuation model", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/math/0307238v1", "title": "Monomial discrete valuations in k[[X]]", "abstract": "Let v be a rank m discrete valuation of k[[X1,...,Xn]] with dimension n-m. We prove that there exists an inmediate extension L of K where the valuation is monomial. Therefore we compute explicitly the residue field of the valuation.", "published": "2003-07-17T10:13:41Z", "authors": ["Francisco Javier Herrera Govantes", "Miguel Angel Olalla Acosta", "Jose Luis Vicente Cordoba"], "categories": ["math.AC", "math.AG"], "doi": "", "_query": "automated valuation model", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2410.16471v3", "title": "Valuation Ideal Factorization Domains", "abstract": "An integral domain $D$ is a {\\em valuation ideal factorization domain} (VIFD) if each nonzero principal ideal of $D$ can be written as a finite product of valuation ideals. Clearly, $π$-domains are VIFDs. We study the ring-theoretic properties of VIFDs and the $*$-operation analogs of VIFDs. Among them, we show that if $D$ is treed (resp., $*$-treed), then $D$ is a VIFD (resp., $*$-VIFD) if and only if $D$ is an ${\\rm h}$-local Prüfer domain (resp., a $*$-${\\rm h}$-local P$*$MD) if and only if every nonzero prime ideal of $D$ contains an invertible (resp., a $*$-invertible) valuation ideal. We also study integral domains $D$ such that for each nonzero nonunit $a\\in D$, there is a positive integer $n$ such that $a^n$ can be written as a finite product of valuation elements.", "published": "2024-10-21T19:58:55Z", "authors": ["Gyu Whan Chang", "Andreas Reinhart"], "categories": ["math.AC"], "doi": "", "_query": "automated valuation model", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2607.25873v1", "title": "How Do LLMs Read Bug Reports? An Empirical Study of Attention in LLMs for Automated Program Repair", "abstract": "Large Language Model (LLM)-based Automated Program Repair systems are advancing rapidly, yet their performance remains inconsistent. Even when provided with the same contextual information, an LLM may generate a correct patch for one bug but fail on another closely related bug. Why this happens remains poorly understood, and it is unclear how LLMs prioritize the diverse information in bug reports and whether model attention affects repair success. In this paper, we present the first empirical study of attention patterns in LLM-based program repair, providing interpretable insights into how models process bug reports and where their attention is concentrated during repair. We analyze 319 real-world Python and Java bugs from SWE-bench Verified and Multi-SWE-bench to study (RQ1) how model attention is distributed across bug report sections, (RQ2) how attention patterns within each section differ between successful and unsuccessful repairs, and (RQ3) how these patterns compare to information developers consider important for bug fixing. We find that successful repairs are characterized by diffused attention across multiple diagnostic components such as bug descriptions, stacktraces, and test cases, while failures often exhibit over-localized attention toward metadata such as version information. We further observe that stronger alignment between model attention and developer-identified key sections and phrases is associated with higher repair success. Our results provide the first empirical evidence that attention misallocation is a key factor in LLM-based APR failures, and offer actionable insights for designing more interpretable and reliable future APR systems.", "published": "2026-07-28T15:38:12Z", "authors": ["Ramtin Ehsani", "Irene Manotas", "Saurabh Pujar", "Luca Buratti", "Preetha Chatterjee"], "categories": ["cs.SE", "cs.AI"], "doi": "", "_query": "automated valuation model", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2011.00892v2", "title": "A Formally Verified Fail-Operational Safety Concept for Automated Driving", "abstract": "Modern Automated Driving (AD) systems rely on safety measures to handle faults and to bring vehicle to a safe state. To eradicate lethal road accidents, car manufacturers are constantly introducing new perception as well as control systems. Contemporary automotive design and safety engineering best practices are suitable for analyzing system components in isolation, whereas today's highly complex and interdependent AD systems require novel approach to ensure resilience to multi-point failures. We present a holistic safety concept unifying advanced safety measures for handling multiple-point faults. Our proposed approach enables designers to focus on more pressing issues such as handling fault-free hazardous behavior associated with system performance limitations. To verify our approach, we developed an executable model of the safety concept in the formal specification language mCRL2. The model behavior is governed by a four-mode degradation policy controlling distributed processors, redundant communication networks, and virtual machines. To keep the vehicle as safe as possible our degradation policy can reduce driving comfort or AD system's availability using additional low-cost driving channels. We formalized five safety requirements in the modal mu-calculus and proved them against our mCRL2 model, which is intractable to accomplish exhaustively using traditional road tests or simulation techniques. In conclusion, our formally proven safety concept defines a holistic design pattern for designing AD systems.", "published": "2020-11-02T11:05:09Z", "authors": ["Yuting Fu", "Andrei Terechko", "Jan Friso Groote", "Arash Khabbaz Saberi"], "categories": ["cs.RO", "cs.DC", "cs.FL", "eess.SY"], "doi": "10.4271/12-05-01-0002", "_query": "automated valuation model", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/1210.7615v4", "title": "Definable henselian valuations", "abstract": "In this note we investigate the question whether a henselian valued field carries a non-trivial 0-definable henselian valuation (in the language of rings). It follows from the work of Prestel and Ziegler that there are henselian valued fields which do not admit a 0-definable non-trivial henselian valuation. We give conditions on the residue field which ensure the existence of a parameter-free definiton. In particular, we show that a henselian valued field admits a non-trivial 0-definable valuation when the residue field is separably closed or sufficiently non-henselian, or when the absolute Galois group of the (residue) field is non-universal.", "published": "2012-10-29T10:52:35Z", "authors": ["Franziska Jahnke", "Jochen Koenigsmann"], "categories": ["math.LO"], "doi": "", "_query": "automated valuation model", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2002.01067v1", "title": "Valuation rings are derived splinters", "abstract": "We give three proofs that valuation rings are derived splinters: a geometric proof using the absolute integral closure, a homological proof which reduces the problem to checking that valuation rings are splinters (which is done in the second author's PhD thesis and which we reprise here), and a proof by approximation which reduces the problem to Bhatt's proof of the derived direct summand conjecture. The approximation property also shows that smooth algebras over valuation rings are splinters.", "published": "2020-02-04T00:38:24Z", "authors": ["Benjamin Antieau", "Rankeya Datta"], "categories": ["math.AG", "math.AC"], "doi": "", "_query": "automated valuation model", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2005.02814v1", "title": "The Information Content of Taster's Valuation in Tea Auctions of India", "abstract": "Tea auctions across India occur as an ascending open auction, conducted online. Before the auction, a sample of the tea lot is sent to potential bidders and a group of tea tasters. The seller's reserve price is a confidential function of the tea taster's valuation, which also possibly acts as a signal to the bidders. In this paper, we work with the dataset from a single tea auction house, J Thomas, of tea dust category, on 49 weeks in the time span of 2018-2019, with the following objectives in mind: $\\bullet$ Objective classification of the various categories of tea dust (25) into a more manageable, and robust classification of the tea dust, based on source and grades. $\\bullet$ Predict which tea lots would be sold in the auction market, and a model for the final price conditioned on sale. $\\bullet$ To study the distribution of price and ratio of the sold tea auction lots. $\\bullet$ Make a detailed analysis of the information obtained from the tea taster's valuation and its impact on the final auction price. The model used has shown various promising results on cross-validation. The importance of valuation is firmly established through analysis of causal relationship between the valuation and the actual price. The authors hope that this study of the properties and the detailed analysis of the role played by the various factors, would be significant in the decision making process for the players of the auction game, pave the way to remove the manual interference in an attempt to automate the auction procedure, and improve tea quality in markets.", "published": "2020-05-04T16:39:47Z", "authors": ["Abhinandan Dalal", "Diganta Mukherjee", "Subhrajyoty Roy"], "categories": ["stat.AP", "econ.EM"], "doi": "", "_query": "automated valuation model", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/1011.3699v3", "title": "Valuations and asymptotic invariants for sequences of ideals", "abstract": "We study asymptotic jumping numbers for graded sequences of ideals, and show that every such invariant is computed by a suitable real valuation of the function field. We conjecture that every valuation that computes an asymptotic jumping number is necessarily quasi-monomial. This conjecture holds in dimension two. In general, we reduce it to the case of affine space and to graded sequences of valuation ideals. Along the way, we study the structure of a suitable valuation space.", "published": "2010-11-16T14:07:58Z", "authors": ["Mattias Jonsson", "Mircea Mustata"], "categories": ["math.AG", "math.AC"], "doi": "", "_query": "automated valuation model", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2311.05304v3", "title": "Data Valuation and Detections in Federated Learning", "abstract": "Federated Learning (FL) enables collaborative model training while preserving the privacy of raw data. A challenge in this framework is the fair and efficient valuation of data, which is crucial for incentivizing clients to contribute high-quality data in the FL task. In scenarios involving numerous data clients within FL, it is often the case that only a subset of clients and datasets are pertinent to a specific learning task, while others might have either a negative or negligible impact on the model training process. This paper introduces a novel privacy-preserving method for evaluating client contributions and selecting relevant datasets without a pre-specified training algorithm in an FL task. Our proposed approach FedBary, utilizes Wasserstein distance within the federated context, offering a new solution for data valuation in the FL framework. This method ensures transparent data valuation and efficient computation of the Wasserstein barycenter and reduces the dependence on validation datasets. Through extensive empirical experiments and theoretical analyses, we demonstrate the potential of this data valuation method as a promising avenue for FL research.", "published": "2023-11-09T12:01:32Z", "authors": ["Wenqian Li", "Shuran Fu", "Fengrui Zhang", "Yan Pang"], "categories": ["cs.LG", "cs.AI", "cs.CR"], "doi": "", "_query": "automated valuation model", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/1510.07910v1", "title": "Valuations and Boolean Models", "abstract": "Valuations, as additive functionals, allow various applications in Stochastic Geometry, yielding mean value formulas for specific random closed sets and processes of convex or polyconvex particles. In particular, valuations are especially adapted to Boolean models, the latter being the union sets of Poisson particle processes. In this chapter, we collect mean value formulas for scalar- and tensor-valued valuations applied to Boolean models under quite general invariance assumptions.", "published": "2015-10-27T14:08:13Z", "authors": ["Julia Hörrmann", "Wolfgang Weil"], "categories": ["math.PR", "math.MG"], "doi": "", "_query": "automated valuation model", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/1505.07508v1", "title": "Valuations in Nilpotent Minimum Logic", "abstract": "The Euler characteristic can be defined as a special kind of valuation on finite distributive lattices. This work begins with some brief consideration on the role of the Euler characteristic on NM algebras, the algebraic counterpart of Nilpotent Minimum logic. Then, we introduce a new valuation, a modified version of the Euler characteristic we call idempotent Euler characteristic. We show that the new valuation encodes information about the formulæ in NM propositional logic.", "published": "2015-05-27T22:49:19Z", "authors": ["Pietro Codara", "Diego Valota"], "categories": ["cs.LO", "math.CO", "math.LO"], "doi": "10.1109/ISMVL.2015.19", "_query": "automated valuation model", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2410.16825v1", "title": "Mathematical models and numerical methods for a capital valuation adjustment (KVA) problem", "abstract": "In this work we rigorously establish mathematical models to obtain the capital valuation adjustment (KVA) as part of the total valuation adjustments (XVAs). For this purpose, we use a semi-replication strategy based on market theory. We formulate single-factor models in terms of expectations and PDEs. For PDEs formulation, we rigorously obtain the existence and uniqueness of the solution, as well as some regularity and qualitative properties of the solution. Moreover, appropriate numerical methods are proposed for solving the corresponding PDEs. Finally, some examples show the numerical results for call and put European options and the corresponding XVA that includes the KVA.", "published": "2024-10-22T08:57:28Z", "authors": ["D. Trevisani", "J. G. López-Salas", "C. Vázquez", "J. A. García-Rodríguez"], "categories": ["math.AP"], "doi": "10.1016/j.amc.2024.129105", "_query": "automated valuation model", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/0712.3870v3", "title": "Substitute Valuations: Generation and Structure", "abstract": "Substitute valuations (in some contexts called gross substitute valuations) are prominent in combinatorial auction theory. An algorithm is given in this paper for generating a substitute valuation through Monte Carlo simulation. In addition, the geometry of the set of all substitute valuations for a fixed number of goods K is investigated. The set consists of a union of polyhedrons, and the maximal polyhedrons are identified for K=4. It is shown that the maximum dimension of the maximal polyhedrons increases with K nearly as fast as two to the power K. Consequently, under broad conditions, if a combinatorial algorithm can present an arbitrary substitute valuation given a list of input numbers, the list must grow nearly as fast as two to the power K.", "published": "2007-12-22T16:52:39Z", "authors": ["Bruce Hajek"], "categories": ["cs.GT", "cs.PF"], "doi": "10.1016/j.peva.2008.07.001", "_query": "automated valuation model", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2310.01998v2", "title": "A Formalization of Complete Discrete Valuation Rings and Local Fields", "abstract": "Local fields, and fields complete with respect to a discrete valuation, are essential objects in commutative algebra, with applications to number theory and algebraic geometry. We formalize in Lean the basic theory of discretely valued fields. In particular, we prove that the unit ball with respect to a discrete valuation on a field is a discrete valuation ring and, conversely, that the adic valuation on the field of fractions of a discrete valuation ring is discrete. We define finite extensions of valuations and of discrete valuation rings, and prove some global-to-local results. Building on this general theory, we formalize the abstract definition and some fundamental properties of local fields. As an application, we show that finite extensions of the field $\\mathbb{Q}_p$ of $p$-adic numbers and of the field $\\mathbb{F}_p(\\!(X)\\!)$ of Laurent series over $\\mathbb{F}_p$ are local fields.", "published": "2023-10-03T12:15:29Z", "authors": ["María Inés de Frutos-Fernández", "Filippo Alberto Edoardo Nuccio Mortarino Majno Di Capriglio"], "categories": ["cs.LO", "math.NT"], "doi": "10.1145/3636501.3636942", "_query": "automated valuation model", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2604.05000v1", "title": "Closed-Loop Autonomous Software Development via Jira-Integrated Backlog Orchestration: A Case Study in Deterministic Control and Safety-Constrained Automation", "abstract": "This paper presents a closed-loop system for software lifecycle management framed as a control architecture rather than a code-generation tool. The system manages a backlog of approximately 1,602 rows across seven task families, ingests 13 structured source documents, and executes a deterministic seven-stage pipeline implemented as seven scheduled automation lanes. The automation stack comprises approximately 12,661 lines of Python across 23 scripts plus 6,907 lines of versioned prompt specifications, with checkpoint-based time budgets, 101 exception handlers, and 12 centralized lock mechanisms implemented through four core functions and eight reusable patterns. A Jira Status Contract provides externally observable collision locking, and a degraded-mode protocol supports continued local operation when Jira is unavailable. Artificial-intelligence assistance is bounded by structured context packages, configured resource caps, output re-validation, and human review gates. A formal evaluation of the initial 152-run window yielded 100% terminal-state success with a 95% Clopper-Pearson interval of [97.6%, 100%]; the system has since accumulated more than 795 run artifacts in continuous operation. Three rounds of adversarial code review identified 51 findings, all closed within the study scope (48 fully remediated, 3 closed with deferred hardening), with zero false negatives within the injected set. In an autonomous security ticket family of 10 items, six were completed through pipeline-autonomous dispatch and verification, two required manual remediation, and two were closed by policy decision. The results indicate that bounded, traceable lifecycle automation is practical when autonomy is embedded within explicit control, recovery, and audit mechanisms.", "published": "2026-04-05T21:54:03Z", "authors": ["Elias Calboreanu"], "categories": ["cs.SE", "cs.AI"], "doi": "", "_query": "automated valuation model", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/math/0509542v1", "title": "Valuation Extensions of Filtered and Graded Algebras", "abstract": "In this note we relate the valuations of the algebras appearing in the non-commutative geometry of quantized algebras to properties of sub-lattices in some vector spaces. We consider the case of algebras with $PBW$-bases and prove that under some mild assumptions the valuations of the ground field extend to a non-commutative valuation. Later we introduce the notion of $F$-reductor and graded reductor and reduce the problem of finding an extending non-commutative valuation to finding a reductor in an associated graded ring having a domain for its reduction.", "published": "2005-09-23T09:33:19Z", "authors": ["C. Baetica", "F. Van Oystaeyen"], "categories": ["math.RA", "math.QA"], "doi": "", "_query": "automated valuation model", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2405.00470v2", "title": "Valuations, bijections, and bases", "abstract": "The aim of this paper is to build a theory of commutative and noncommutative {\\it injective} valuations of various algebras (including algebras with zero divisors). The targets of our valuations are (well-)ordered commutative and noncommutative (partial and entire) semigroups including any sub-semigroups of the free monoid $F_n$ on $n$ generators and various quotients. When the range of a valuation of an algebra $A$ is a finitely generated (partial) semigroup, we construct a generalization of the standard monomial bases in $A$, which seems to be new in noncommutative case. Quite remarkably, for any pair of well-ordered valuations one has a canonical bijection between the valuation semigroups, which serves as an analog of the celebrated Jordan-Hölder correspondences and these bijections are ``almost\" homomorphisms of the involved semigroups. A spectacular demonstration of this remarkable property of JH-bijections for quantum Schubert cells $A=U_q(w)$ results in mysterious \"symplectomorphisms\" of involved skew symmetric forms.", "published": "2024-05-01T12:13:54Z", "authors": ["Arkady Berenstein", "Dima Grigoriev"], "categories": ["math.RA", "math.RT"], "doi": "", "_query": "automated valuation model", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2401.06884v3", "title": "Definable henselian valuations in positive residue characteristic", "abstract": "We study the question of $\\mathcal{L}_{\\mathrm{ring}}$-definability of non-trivial henselian valuation rings. Building on previous work of Jahnke and Koenigsmann, we provide a characterization of henselian fields that admit a non-trivial definable henselian valuation. In particular, we treat cases where the canonical henselian valuation has positive residue characteristic, using techniques from the model theory and algebra of tame fields.", "published": "2024-01-12T20:30:14Z", "authors": ["Margarete Ketelsen", "Simone Ramello", "Piotr Szewczyk"], "categories": ["math.LO", "math.AC"], "doi": "10.1017/jsl.2024.55", "_query": "automated valuation model", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/1301.4338v1", "title": "Quasi-valuations - topology and the weak approximation theorem", "abstract": "Suppose $F$ is a field with a nontrivial valuation $v$ and valuation ring $O_{v}$, $E$ is a finite field extension and $w$ is a quasi-valuation on $E$ extending $v$. We study the topology induced by $w$. We prove that the quasi-valuation ring determines the topology, independent of the choice of its quasi-valuation. Moreover, we prove the weak approximation theorem for quasi-valuations.", "published": "2013-01-18T10:16:36Z", "authors": ["Shai Sarussi"], "categories": ["math.GN", "math.RA"], "doi": "", "_query": "automated valuation model", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/1510.00681v1", "title": "Valuation on filtered module and relations", "abstract": "In this paper we show if R is a filtered ring and M a filtered R module then we can define a valuation on a module for M. Then we show that we can find an skeleton of valuation on M, and we prove some properties such that derived form it for a filtered module.", "published": "2015-10-02T18:52:13Z", "authors": ["M. H Anjom SHoa", "M. H Hosseini"], "categories": ["math.RA"], "doi": "", "_query": "automated valuation model", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/1311.6493v3", "title": "Monomial Valuations, Cusp Singularities, and Continued Fractions", "abstract": "This paper explores the relationship between real valued monomial valuations on $k(x,y)$, the resolution of cusp singularities, and continued fractions. It is shown that up to equivalence there is a one to one correspondence between real valued monomial valuations on $k(x,y)$ and continued fraction expansions of real numbers between zero and one. This relationship with continued fractions is then used to provide a characterization of the valuation rings for real valued monomial valuations on $k(x,y)$. In the case when the monomial valuation is equivalent to an integral monomial valuation, we exhibit explicit generators of the valuation rings. Finally, we demonstrate that if $ν$ is a monomial valuation such that $ν(x)=a$ and $ν(y)=b$, where $a$ and $b$ are relatively prime positive integers larger than one, then $ν$ governs a resolution of the singularities of the plane curve $x^{b}=y^{a}$ in a way we make explicit. Further, we provide an exact bound on the number of blow ups needed to resolve singularities in terms of the continued fraction of $a/b$", "published": "2013-11-25T21:39:47Z", "authors": ["Juliette Bruce", "Molly Logue", "Robert Walker"], "categories": ["math.AG", "math.AC"], "doi": "10.1216/JCA-2015-7-4-495", "_query": "automated valuation model", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2510.21736v2", "title": "Learn2Drive: A neural network-based framework for socially compliant automated vehicle control", "abstract": "This study introduces a novel control framework for adaptive cruise control (ACC) in automated driving, leveraging neural networks and physics-informed constraints. As automated vehicles (AVs) adopt advanced features like ACC, transportation systems are becoming increasingly intelligent and efficient. However, existing AV control strategies primarily focus on optimizing the performance of individual vehicles or platoons, often neglecting their interactions with human-driven vehicles (HVs) and the broader impact on traffic flow. This oversight can exacerbate congestion and reduce overall system efficiency. To address this critical research gap, we propose a neural network-based, socially compliant AV control framework that incorporates social value orientation (SVO). This framework enables AVs to account for their influence on HVs and traffic dynamics. By leveraging AVs as mobile traffic regulators, the proposed approach promotes adaptive driving behaviors that reduce congestion, improve traffic efficiency, and lower energy consumption. Numerical results demonstrate the effectiveness of the proposed method in adapting to varying traffic conditions, thereby enhancing system-wide efficiency. Specifically, when the AV's control mode shifts from prioritizing its own energy conservation to optimizing collective traffic flow efficiency, the controlled AV proactively adapts its acceleration profile. This prosocial behavior yields at least a 38.39\\% improvement in the average speed of downstream vehicles and effectively dampens traffic oscillations, demonstrating significant enhancements in system-wide dynamics. The implementation code is available https://github.com/lilab2024/Learn2Drive-SVO_v1.git.", "published": "2025-09-30T22:33:44Z", "authors": ["Yuhui Liu", "Samannita Halder", "Shian Wang", "Tianyi Li"], "categories": ["cs.RO", "cs.AI", "cs.LG", "cs.MA", "eess.SY"], "doi": "10.1109/TITS.2026.3724379", "_query": "automated valuation model", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/1302.1546v1", "title": "Inference with Idempotent Valuations", "abstract": "Valuation based systems verifying an idempotent property are studied. A partial order is defined between the valuations giving them a lattice structure. Then, two different strategies are introduced to represent valuations: as infimum of the most informative valuations or as supremum of the least informative ones. It is studied how to carry out computations with both representations in an efficient way. The particular cases of finite sets and convex polytopes are considered.", "published": "2013-02-06T15:56:12Z", "authors": ["Luis D. Hernandez", "Serafin Moral"], "categories": ["cs.AI"], "doi": "", "_query": "automated valuation model", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2106.02938v4", "title": "Energy-Based Learning for Cooperative Games, with Applications to Valuation Problems in Machine Learning", "abstract": "Valuation problems, such as feature interpretation, data valuation and model valuation for ensembles, become increasingly more important in many machine learning applications. Such problems are commonly solved by well-known game-theoretic criteria, such as Shapley value or Banzhaf value. In this work, we present a novel energy-based treatment for cooperative games, with a theoretical justification by the maximum entropy framework. Surprisingly, by conducting variational inference of the energy-based model, we recover various game-theoretic valuation criteria through conducting one-step fixed point iteration for maximizing the mean-field ELBO objective. This observation also verifies the rationality of existing criteria, as they are all attempting to decouple the correlations among the players through the mean-field approach. By running fixed point iteration for multiple steps, we achieve a trajectory of the valuations, among which we define the valuation with the best conceivable decoupling error as the Variational Index. We prove that under uniform initializations, these variational valuations all satisfy a set of game-theoretic axioms. We experimentally demonstrate that the proposed Variational Index enjoys lower decoupling error and better valuation performance on certain synthetic and real-world valuation problems.", "published": "2021-06-05T17:39:04Z", "authors": ["Yatao Bian", "Yu Rong", "Tingyang Xu", "Jiaxiang Wu", "Andreas Krause", "Junzhou Huang"], "categories": ["cs.LG"], "doi": "", "_query": "automated valuation model", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2107.06961v4", "title": "On complete classes of valuated matroids", "abstract": "We characterize a rich class of valuated matroids, called R-minor valuated matroids that includes the indicator functions of matroids, and is closed under operations such as taking minors, duality, and induction by network. We exhibit a family of valuated matroids that are not R-minor based on sparse paving matroids. Valuated matroids are inherently related to gross substitute valuations in mathematical economics. By the same token we refute the Matroid Based Valuation Conjecture by Ostrovsky and Paes Leme (Theoretical Economics 2015) asserting that every gross substitute valuation arises from weighted matroid rank functions by repeated applications of merge and endowment operations. Our result also has implications in the context of Lorentzian polynomials: it reveals the limitations of known construction operations.", "published": "2021-07-14T19:47:19Z", "authors": ["Edin Husić", "Georg Loho", "Ben Smith", "László A. Végh"], "categories": ["math.CO", "cs.DM", "cs.GT", "math.OC"], "doi": "10.46298/theoretics.24.24", "_query": "automated valuation model", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2505.09842v1", "title": "Valuations on Superrings", "abstract": "A valuation theory for superrings is developed, extending classical constructions from commutative algebra to the $\\mathbb Z_2$-graded and supercommutative setting. We define valuations on superrings, investigate their fundamental properties, and explore the construction of Zariski-Riemann superspaces.", "published": "2025-05-14T22:54:49Z", "authors": ["Pedro Rizzo", "Joel Torres del Valle", "Alexander Torres-Gomez"], "categories": ["math.RA", "math.AG"], "doi": "", "_query": "automated valuation model", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2205.03670v2", "title": "Automated Algorithm Selection for Radar Network Configuration", "abstract": "The configuration of radar networks is a complex problem that is often performed manually by experts with the help of a simulator. Different numbers and types of radars as well as different locations that the radars shall cover give rise to different instances of the radar configuration problem. The exact modeling of these instances is complex, as the quality of the configurations depends on a large number of parameters, on internal radar processing, and on the terrains on which the radars need to be placed. Classic optimization algorithms can therefore not be applied to this problem, and we rely on \"trial-and-error\" black-box approaches. In this paper, we study the performances of 13 black-box optimization algorithms on 153 radar network configuration problem instances. The algorithms perform considerably better than human experts. Their ranking, however, depends on the budget of configurations that can be evaluated and on the elevation profile of the location. We therefore also investigate automated algorithm selection approaches. Our results demonstrate that a pipeline that extracts instance features from the elevation of the terrain performs on par with the classical, far more expensive approach that extracts features from the objective function.", "published": "2022-05-07T15:28:36Z", "authors": ["Quentin Renau", "Johann Dreo", "Alain Peres", "Yann Semet", "Carola Doerr", "Benjamin Doerr"], "categories": ["cs.NE", "cs.LG"], "doi": "10.1145/3512290.3528825", "_query": "automated valuation model", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2608.04562v1", "title": "What Is a Skill Worth? Structure-Aware Shapley Valuation of Agent Skills", "abstract": "Agent skills are increasingly optimized by automated feedback loops, producing long structured artifacts whose internal value remains unclear. We study skill valuation: assigning credit to the internal units of a fixed skill, such as rules, examples, scripts, and heuristics, under a fixed agent and held-out task distribution. Skill valuation differs from data or prompt-span valuation because skill units are structured: they may depend on other units, belong to a document hierarchy, trigger agent behavior, and consume limited prompt context. We introduce SkillSV, a structure-aware Shapley-style framework for skill valuation. SkillSV compiles a skill into units, dependencies, and hierarchy, so that only valid counterfactual skills are evaluated. It uses paired deletion and length-neutral padding to separate content value from context cost, and estimates the resulting values with a rollout-budgeted estimator for noisy agent evaluations. On four agentic benchmarks, we assess the faithfulness, actionability, and explanation of SkillSV: it recovers unit interactions, preserves aggregate skill lift, and guides safe pruning and compression.", "published": "2026-08-05T07:56:26Z", "authors": ["Tao Li", "Junfeng Liu", "Qinghua Zhao", "Yifan Li", "Lei Wang", "Bo Shao", "Xuejun Liu", "Linjun Shou"], "categories": ["cs.AI"], "doi": "", "_query": "automated valuation model", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/1206.3521v1", "title": "The constructible topology on spaces of valuation domains", "abstract": "We consider properties and applications of a compact, Hausdorff topology called the \"ultrafilter topology\" defined on an {\\sl arbitrary spectral space} and we observe that this topology coincides with the constructible topology. If $K$ is a field and $A$ a subring of $K$, we show that the space Zar$(K|A)$ of all valuation domains, having $K$ as quotient field and containing $A$, (endowed with the Zariski topology) is a spectral space by giving in this general setting the explicit construction of a ring whose Zariski spectrum is homeomorphic to Zar$(K|A)$. We extend results regarding spectral topologies on the spaces of all valuation domains and apply the theory developed to study representations of integrally closed domains as intersections of valuation overrings. As a very particular case, we prove that two collections of valuation domains of $K$ with the same ultrafilter closure represent, as an intersection, the same integrally closed domain.", "published": "2012-06-15T17:20:03Z", "authors": ["Carmelo Finocchiaro", "Marco Fontana", "K. Alan Loper"], "categories": ["math.AC", "math.AG"], "doi": "", "_query": "automated valuation model", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/0707.3482v1", "title": "A Bayesian Framework for Combining Valuation Estimates", "abstract": "Obtaining more accurate equity value estimates is the starting point for stock selection, value-based indexing in a noisy market, and beating benchmark indices through tactical style rotation. Unfortunately, discounted cash flow, method of comparables, and fundamental analysis typically yield discrepant valuation estimates. Moreover, the valuation estimates typically disagree with market price. Can one form a superior valuation estimate by averaging over the individual estimates, including market price? This article suggests a Bayesian framework for combining two or more estimates into a superior valuation estimate. The framework justifies the common practice of averaging over several estimates to arrive at a final point estimate.", "published": "2007-07-24T05:04:53Z", "authors": ["Kenton K. Yee"], "categories": ["q-fin.ST", "cs.CE", "nlin.AO", "nlin.CD", "nlin.SI", "physics.pop-ph", "physics.soc-ph", "stat.AP"], "doi": "", "_query": "automated valuation model", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2608.25793v1", "title": "Closing the Gap: Automated Discovery of Secure Dockerfile Reference Standards via Semantic Clustering in Enterprise Inner Source", "abstract": "Containerization dominates enterprise software delivery, yet Dockerfiles that assemble container images frequently harbor security misconfigurations and structural technical debt. This problem is poorly understood in corporate inner-source environments, where proprietary context and isolated governance prevent direct application of open-source findings. We present an automated, six-stage pipeline that: (1) crawls an enterprise GitLab instance, (2) enriches each Dockerfile with static security and quality metrics (Hadolint, ShellCheck, Trivy) and lifecycle data, (3) groups functionally identical workloads using LLM-generated semantic descriptions and HDBSCAN, and (4) quantifies the optimization gap against cluster-internal reference implementations. Applied to 11,470 Dockerfiles from over 6,200 repositories at a single large industrial company, we find a systemic deficit: 99\\% of files contain at least one security misconfiguration, 80.8\\% violate Dockerfile best practices, and the median artifact has not been revised for 838~days. Despite this, high-quality reference implementations already exist within 83\\% of functional clusters. Adopting these internal standards would increase the average security posture score by 60.4\\% without developing any new templates. These findings, grounded in one organization's inner-source ecosystem, provide a data-driven foundation for future automated, context-aware recommender systems targeting enterprise supply-chain security; whether the observed technical-debt distribution and optimization gap generalize to other enterprises remains an open question for future multi-organization study.", "published": "2026-08-26T13:46:35Z", "authors": ["Jessica Hösl", "Benedikt Hofmann", "Patrick Stöckle"], "categories": ["cs.CR", "cs.SE"], "doi": "10.1145/3832783.3834500", "_query": "automated valuation model", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/1404.6215v9", "title": "Valuation and Divisibility", "abstract": "In this paper, we explain how some basic facts about valuation can help clarify many questions about divisibility in integral domains.", "published": "2014-04-24T18:29:34Z", "authors": ["Nicholas Phat Nguyen"], "categories": ["math.AC"], "doi": "", "_query": "automated valuation model", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2502.00494v3", "title": "Data Overvaluation Attack and Truthful Data Valuation in Federated Learning", "abstract": "In collaborative machine learning (CML), data valuation, i.e., evaluating the contribution of each client's data to the machine learning model, has become a critical task for incentivizing and selecting positive data contributions. However, existing studies often assume that clients engage in data valuation truthfully, overlooking the practical motivation for clients to exaggerate their contributions. To unlock this threat, this paper introduces the data overvaluation attack, enabling strategic clients to have their data significantly overvalued in federated learning, a widely adopted paradigm for decentralized CML. Furthermore, we propose a Bayesian truthful data valuation metric, named Truth-Shapley. Truth-Shapley is the unique metric that guarantees some promising axioms for data valuation while ensuring that clients' optimal strategy is to perform truthful data valuation under certain conditions. Our experiments demonstrate the vulnerability of existing data valuation metrics to the proposed attack and validate the robustness and effectiveness of Truth-Shapley.", "published": "2025-02-01T17:05:37Z", "authors": ["Shuyuan Zheng", "Sudong Cai", "Chuan Xiao", "Yang Cao", "Jianbin Qin", "Masatoshi Yoshikawa", "Makoto Onizuka"], "categories": ["cs.CR", "cs.AI", "cs.LG"], "doi": "", "_query": "automated valuation model", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/math/0303200v1", "title": "Valuations, Deformations, and Toric Geometry", "abstract": "A study of the relation between a noetherian local domain with a given valuation and its associated graded ring with respect to the valuation, which in some cases is an esentially toric variety, possibly of infinite embedding dimension, but of finite Krull dimension. After extension of the valuation to a suitable completion (whose existence is not established in the paper) the relation becomes much more precise, and suggests a possible way to establish local uniformization for an excellent equicharacteristic local domain with an algebraically closed residue field, by deforming a partial toric embedded resolution of the essentially toric spectrum of the associated graded ring.", "published": "2003-03-17T14:30:50Z", "authors": ["Bernard Teissier"], "categories": ["math.AC", "math.AG"], "doi": "", "_query": "automated valuation model", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/1509.07884v2", "title": "Berline-Vergne valuation and generalized permutohedra", "abstract": "Generalizing a conjecture by De Loera et al., we conjecture that integral generalized permutohedra all have positive Ehrhart coefficients. Berline and Vergne construct a valuation that assigns values to faces of polytopes, which provides a way to write Ehrhart coefficients of a polytope as positive sums of these values. Based on available results, we pose a stronger conjecture: Berline-Vergne's valuation is always positive on permutohedra, which implies our first conjecture. This article proves that our strong conjecture on Berline-Vergne's valuation is true for dimension up to 6, and is true if we restrict to faces of codimension up to 3. In addition to investigating the positivity conjectures, we study the Berline-Vergne's valuation, and show that it is the unique construction for McMullen's formula used to describe number of lattice points in permutohedra under certain symmetry constraints. We also give an equivalent statement to the strong conjecture in terms of mixed valuations.", "published": "2015-09-25T20:24:06Z", "authors": ["Federico Castillo", "Fu Liu"], "categories": ["math.CO"], "doi": "", "_query": "automated valuation model", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/1602.08887v2", "title": "The valuation of American options in a multidimensional exponential Levy model", "abstract": "We consider the problem of valuation of American options written on dividend-paying assets whose price dynamics follows a multidimensional exponential Levy model. We carefully examine the relation between the option prices, related partial integro-differential variational inequalities and reflected backward stochastic differential equations. In particular, we prove regularity results for the value function and obtain the early exercise premium formula for broad class of payoff functions.", "published": "2016-02-29T09:57:27Z", "authors": ["Tomasz Klimsiak", "Andrzej Rozkosz"], "categories": ["math.PR"], "doi": "10.1111/mafi.12163", "_query": "automated valuation model", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2605.16704v1", "title": "Convex Dataset Valuation for Post-Training", "abstract": "Improving LLM performance on downstream tasks sometimes requires leveraging auxiliary datasets during post-training. In practice, however, developers face constraints on compute, labeling, and licensing costs that preclude using all available data, necessitating principled dataset-level selection. These constraints are increasingly shaped by dataset marketplaces, where data acquisition is governed by budgets and negotiation. We study dataset valuation as a subset selection problem during LLM post-training. Our goal is to identify and weight auxiliary datasets so as to maximize target task performance given constrained budgets. We first show that commonly used gradient alignment scores provide a reasonable yet incomplete valuation signal, as they ignore redundancy among datasets. To address this, we propose a scalable convex dataset-level valuation method based on kernel mean matching (KMM) in gradient space, which jointly accounts for alignment with the target task and redundancy across auxiliary datasets. Through extensive experiments across diverse post-training settings and tasks, we show that our approach consistently outperforms existing valuation baselines, achieving stronger performance with low computational overhead. Our results position dataset valuation as a practical decision tool for post-training data selection in market-constrained large language model settings. The code is available at https://github.com/uiuctml/convex_data_valuation.", "published": "2026-05-15T23:35:07Z", "authors": ["Siqi Zeng", "Christopher Jung", "Rui Li", "Zhe Kang", "Ming Li", "Nima Noorshams", "Zhigang Wang", "Fuchun Peng", "Han Zhao", "Xue Feng"], "categories": ["cs.LG"], "doi": "", "_query": "automated valuation model", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2502.08685v1", "title": "Beyond Models! Explainable Data Valuation and Metric Adaption for Recommendation", "abstract": "User behavior records serve as the foundation for recommender systems. While the behavior data exhibits ease of acquisition, it often suffers from varying quality. Current methods employ data valuation to discern high-quality data from low-quality data. However, they tend to employ black-box design, lacking transparency and interpretability. Besides, they are typically tailored to specific evaluation metrics, leading to limited generality across various tasks. To overcome these issues, we propose an explainable and versatile framework DVR which can enhance the efficiency of data utilization tailored to any requirements of the model architectures and evaluation metrics. For explainable data valuation, a data valuator is presented to evaluate the data quality via calculating its Shapley value from the game-theoretic perspective, ensuring robust mathematical properties and reliability. In order to accommodate various evaluation metrics, including differentiable and non-differentiable ones, a metric adapter is devised based on reinforcement learning, where a metric is treated as the reinforcement reward that guides model optimization. Extensive experiments conducted on various benchmarks verify that our framework can improve the performance of current recommendation algorithms on various metrics including ranking accuracy, diversity, and fairness. Specifically, our framework achieves up to 34.7\\% improvements over existing methods in terms of representative NDCG metric. The code is available at https://github.com/renqii/DVR.", "published": "2025-02-12T12:01:08Z", "authors": ["Renqi Jia", "Xiaokun Zhang", "Bowei He", "Qiannan Zhu", "Weitao Xu", "Jiehao Chen", "Chen Ma"], "categories": ["cs.LG", "cs.AI"], "doi": "", "_query": "automated valuation model", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/1603.03012v1", "title": "Capital Valuation Adjustment and Funding Valuation Adjustment", "abstract": "In the aftermath of the 2007 global financial crisis, banks started reflecting into derivative pricing the cost of capital and collateral funding through XVA metrics. Here XVA is a catch-all acronym whereby X is replaced by a letter such as C for credit, D for debt, F for funding, K for capital and so on, and VA stands for valuation adjustment. This behaviour is at odds with economies where markets for contingent claims are complete, whereby trades clear at fair valuations and the costs for capital and collateral are both irrelevant to investment decisions. In this paper, we set forth a mathematical formalism for derivative portfolio management in incomplete markets for banks. A particular emphasis is given to the problem of finding optimal strategies for retained earnings which ensure a sustainable dividend policy.", "published": "2016-03-09T19:45:08Z", "authors": ["Claudio Albanese", "Simone Caenazzo", "Stéphane Crépey"], "categories": ["q-fin.CP"], "doi": "", "_query": "automated valuation model", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/1202.5363v1", "title": "A Valuation Theoretic Approach to Essential Dimension", "abstract": "In this essay we explore the notion of essential dimension using the theory of valuations of fields. Given a field extension K/k and a valuation on K that is trivial on k, we prove that the rank of the valuation cannot exceed the transcendence degree trdeg K. We use this inequality to prove lower bounds on the essential dimension in some interesting situations. We study orbits of a torus action and find a formula for the essential dimension of the functor of these orbits.", "published": "2012-02-24T02:02:44Z", "authors": ["Aurel Meyer"], "categories": ["math.AG"], "doi": "", "_query": "automated valuation model", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/1503.01392v2", "title": "Valuations of Semirings", "abstract": "We develop notions of valuations on a semiring, with a view toward extending the classical theory of abstract nonsingular curves and discrete valuation rings to this general algebraic setting; the novelty of our approach lies in the implementation of hyperrings to yield a new definition (\\emph{hyperfield valuation}). In particular, we classify valuations on the semifield $\\mathbb{Q}_{max}$ (the max-plus semifield of rational numbers) and also valuations on the `function field' $\\mathbb{Q}_{max}(T)$ (the semifield of rational functions over $\\mathbb{Q}_{max}$) which are trivial on $\\mathbb{Q}_{max}$. We construct and study the abstract curve associated to $\\mathbb{Q}_{max}(T)$ in relation to the projective line $\\mathbb{P}^1_{\\mathbb{F}_1}$ over the field with one element $\\mathbb{F}_{1}$ and the tropical projective line. Finally, we discuss possible connections to tropical curves and Berkovich's theory of analytic spaces.", "published": "2015-03-04T17:15:00Z", "authors": ["Jaiung Jun"], "categories": ["math.AG", "math.AC"], "doi": "", "_query": "automated valuation model", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2310.04871v1", "title": "Machine Learning for Automated Mitral Regurgitation Detection from Cardiac Imaging", "abstract": "Mitral regurgitation (MR) is a heart valve disease with potentially fatal consequences that can only be forestalled through timely diagnosis and treatment. Traditional diagnosis methods are expensive, labor-intensive and require clinical expertise, posing a barrier to screening for MR. To overcome this impediment, we propose a new semi-supervised model for MR classification called CUSSP. CUSSP operates on cardiac imaging slices of the 4-chamber view of the heart. It uses standard computer vision techniques and contrastive models to learn from large amounts of unlabeled data, in conjunction with specialized classifiers to establish the first ever automated MR classification system. Evaluated on a test set of 179 labeled -- 154 non-MR and 25 MR -- sequences, CUSSP attains an F1 score of 0.69 and a ROC-AUC score of 0.88, setting the first benchmark result for this new task.", "published": "2023-10-07T16:48:24Z", "authors": ["Ke Xiao", "Erik Learned-Miller", "Evangelos Kalogerakis", "James Priest", "Madalina Fiterau"], "categories": ["eess.IV", "cs.CV", "cs.LG"], "doi": "10.1007/978-3-031-43990-2_23", "_query": "automated valuation model", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2201.09105v3", "title": "Credit Valuation Adjustment with Replacement Closeout: Theory and Algorithms", "abstract": "The replacement closeout convention has drawn more and more attention since the 2008 financial crisis. Compared with the conventional risk-free closeout, the replacement closeout convention incorporates the creditworthiness of the counterparty and thus providing a more accurate estimate of the Mark-to-market value of a financial claim. In contrast to the risk-free closeout, the replacement closeout renders a nonlinear valuation system, which constitutes the major difficulty in the valuation of the counterparty credit risk. In this paper, we show how to address the nonlinearity attributed to the replacement closeout in the theoretical and computational analysis. In the theoretical part, we prove the unique solvability of the nonlinear valuation system and study the impact of the replacement closeout on the credit valuation adjustment. In the computational part, we propose a neural network-based algorithm for solving the (high dimensional) nonlinear valuation system and effectively alleviating the curse of dimensionality. We numerically compare the computational cost for the valuations with risk-free and replacement closeouts. The numerical tests confirm both the accuracy and the computational efficiency of our proposed algorithm for the valuation of the replacement closeout.", "published": "2022-01-22T18:01:56Z", "authors": ["Chaofan Sun", "Ken Seng Tan", "Wei Wei"], "categories": ["q-fin.MF"], "doi": "", "_query": "automated valuation model", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/1605.04218v1", "title": "Anytime Inference in Valuation Algebras", "abstract": "Anytime inference is inference performed incrementally, with the accuracy of the inference being controlled by a tunable parameter, usually time. Such anytime inference algorithms are also usually interruptible, gradually converging to the exact inference value until terminated. While anytime inference algorithms for specific domains like probability potentials exist in the literature, our objective in this article is to obtain an anytime inference algorithm which is sufficiently generic to cover a wide range of domains. For this we utilise the theory of generic inference as a basis for constructing an anytime inference algorithm, and in particular, extending work done on ordered valuation algebras. The novel contribution of this work is the construction of anytime algorithms in a generic framework, which automatically gives us instantiations in various useful domains. We also show how to apply this generic framework for anytime inference in semiring induced valuation algebras, an important subclass of valuation algebras, which includes instances like probability potentials, disjunctive normal forms and distributive lattices. Keywords: Approximation; Anytime algorithms; Resource-bounded computation; Generic inference; Valuation algebras; Local computation; Binary join trees.", "published": "2016-05-13T15:40:10Z", "authors": ["Abhishek Dasgupta", "Samson Abramsky"], "categories": ["cs.AI"], "doi": "", "_query": "automated valuation model", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/1611.03345v1", "title": "Radial continuous valuations on star bodies and star sets", "abstract": "We show that a radial continuous valuation defined on the $n$-dimensional star bodies extends uniquely to a continuous valuation on the $n$-dimensional bounded star sets. Moreover, we provide an integral representation of every such valuation, in terms of the radial function, which is valid on the dense subset of the simple Borel star sets. We also show that every radial continuous valuation defined on the $n$-dimensional star bodies can be decomposed as a sum $V=V^+-V^-$, where both $V^+$ and $V^-$ are positive radial continuous valuations.", "published": "2016-11-10T15:28:23Z", "authors": ["Pedro Tradacete", "Ignacio Villanueva"], "categories": ["math.MG", "math.FA"], "doi": "", "_query": "automated valuation model", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2408.00421v1", "title": "Towards Evolutionary-based Automated Machine Learning for Small Molecule Pharmacokinetic Prediction", "abstract": "Machine learning (ML) is revolutionising drug discovery by expediting the prediction of small molecule properties essential for developing new drugs. These properties -- including absorption, distribution, metabolism and excretion (ADME)-- are crucial in the early stages of drug development since they provide an understanding of the course of the drug in the organism, i.e., the drug's pharmacokinetics. However, existing methods lack personalisation and rely on manually crafted ML algorithms or pipelines, which can introduce inefficiencies and biases into the process. To address these challenges, we propose a novel evolutionary-based automated ML method (AutoML) specifically designed for predicting small molecule properties, with a particular focus on pharmacokinetics. Leveraging the advantages of grammar-based genetic programming, our AutoML method streamlines the process by automatically selecting algorithms and designing predictive pipelines tailored to the particular characteristics of input molecular data. Results demonstrate AutoML's effectiveness in selecting diverse ML algorithms, resulting in comparable or even improved predictive performances compared to conventional approaches. By offering personalised ML-driven pipelines, our method promises to enhance small molecule research in drug discovery, providing researchers with a valuable tool for accelerating the development of novel therapeutic drugs.", "published": "2024-08-01T09:46:06Z", "authors": ["Alex G. C. de Sá", "David B. Ascher"], "categories": ["cs.LG", "cs.AI"], "doi": "", "_query": "automated valuation model", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2011.14749v2", "title": "A valuation theorem for Noetherian rings", "abstract": "Let A and B be integral domains. Suppose A is Noetherian and B is a finitely generated A-algebra that contains A. Denote by A' the integral closure of A in B. We show that A' is determined by finitely many unique discrete valuation rings. Our result generalizes Rees' classical valuation theorem for ideals. We also obtain a variant of Zariski's main theorem.", "published": "2020-11-30T13:05:19Z", "authors": ["Antoni Rangachev"], "categories": ["math.AC", "math.AG"], "doi": "", "_query": "automated valuation model", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/1208.6075v3", "title": "Bone refilling in cortical bone multicellular units: Insights into tetracycline double labelling from a computational model", "abstract": "Bone remodelling is carried out by `bone multicellular units' (BMUs) in which active osteoclasts and active osteoblasts are spatially and temporally coupled. The refilling of new bone by osteoblasts towards the back of the BMU occurs at a rate that depends both on the number of osteoblasts and on their secretory activity. In cortical bone, a linear phenomenological relationship between matrix apposition rate (MAR) and BMU cavity radius is found experimentally. How this relationship emerges from the combination of complex, nonlinear regulations of osteoblast number and secretory activity is unknown. Here, we extend our previous mathematical model of cell development within a single BMU to investigate how osteoblast number and osteoblast secretory activity vary along the BMU's closing cone. MARs predicted by the model are compared with data from tetracycline double labelling experiments. We find that the linear phenomenological relationship observed in these experiments between MAR and BMU cavity radius holds for most of the refilling phase simulated by our model, but not near the start and end of refilling. This suggests that at a particular bone site undergoing remodelling, bone formation starts and ends rapidly. Our model also suggests that part of the observed cross-sectional variability in tetracycline data may be due to different bone sites being refilled by BMUs at different stages of their lifetime. The different stages of a BMU's lifetime depend on whether the cell populations within the BMU are still developing or have reached a quasi-steady state while travelling through bone. We find that due to their longer lifespan, active osteoblasts reach a quasi-steady distribution more slowly than active osteoclasts. We suggest that this fact may locally enlarge the Haversian canal diameter (due to a local lack of osteoblasts compared to osteoclasts) near the BMU's point of origin.", "published": "2012-08-30T04:10:43Z", "authors": ["Pascal R. Buenzli", "Peter Pivonka", "David W. Smith"], "categories": ["q-bio.TO", "physics.bio-ph", "physics.med-ph", "q-bio.CB"], "doi": "10.1007/s10237-013-0495-y", "_query": "automated valuation model", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2504.08172v1", "title": "Enhanced Cooperative Perception Through Asynchronous Vehicle to Infrastructure Framework with Delay Mitigation for Connected and Automated Vehicles", "abstract": "Perception is a key component of Automated vehicles (AVs). However, sensors mounted to the AVs often encounter blind spots due to obstructions from other vehicles, infrastructure, or objects in the surrounding area. While recent advancements in planning and control algorithms help AVs react to sudden object appearances from blind spots at low speeds and less complex scenarios, challenges remain at high speeds and complex intersections. Vehicle to Infrastructure (V2I) technology promises to enhance scene representation for AVs in complex intersections, providing sufficient time and distance to react to adversary vehicles violating traffic rules. Most existing methods for infrastructure-based vehicle detection and tracking rely on LIDAR, RADAR or sensor fusion methods, such as LIDAR-Camera and RADAR-Camera. Although LIDAR and RADAR provide accurate spatial information, the sparsity of point cloud data limits its ability to capture detailed object contours of objects far away, resulting in inaccurate 3D object detection results. Furthermore, the absence of LIDAR or RADAR at every intersection increases the cost of implementing V2I technology. To address these challenges, this paper proposes a V2I framework that utilizes monocular traffic cameras at road intersections to detect 3D objects. The results from the roadside unit (RSU) are then combined with the on-board system using an asynchronous late fusion method to enhance scene representation. Additionally, the proposed framework provides a time delay compensation module to compensate for the processing and transmission delay from the RSU. Lastly, the V2I framework is tested by simulating and validating a scenario similar to the one described in an industry report by Waymo. The results show that the proposed method improves the scene representation and the AV's perception range, giving enough time and space to react to adversary vehicles.", "published": "2025-04-10T23:48:22Z", "authors": ["Nithish Kumar Saravanan", "Varun Jammula", "Yezhou Yang", "Jeffrey Wishart", "Junfeng Zhao"], "categories": ["cs.RO"], "doi": "", "_query": "automated valuation model", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/math/0504263v1", "title": "Value Monoids of Zero-Dimensional Valuations of Rank One", "abstract": "Classically, Groebner bases are computed by first prescribing a set monomial order. Moss Sweedler suggested an alternative and developed a framework to perform such computations by using valuation rings in place of monomial orders. We build on these ideas by providing a class of valuations on rational function fields of two variables that are suitable for this framework. For these valuations, we explicitly compute the image of an underlying polynomial ring and use this to perform computations concerning ideals in the polynomial ring. Interestingly, for these valuations, some ideals have a finite Groebner basis with respect to the valuation that is not a Groebner basis with respect to any monomial order, whereas other ideals only have Groebner bases that are infinite with respect to the valuation.", "published": "2005-04-13T05:21:17Z", "authors": ["Edward Mosteig"], "categories": ["math.AC"], "doi": "", "_query": "automated valuation model", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2308.11785v2", "title": "Towards Safe Automated Refactoring of Imperative Deep Learning Programs to Graph Execution", "abstract": "Efficiency is essential to support responsiveness w.r.t. ever-growing datasets, especially for Deep Learning (DL) systems. DL frameworks have traditionally embraced deferred execution-style DL code -- supporting symbolic, graph-based Deep Neural Network (DNN) computation. While scalable, such development tends to produce code that is error-prone, non-intuitive, and difficult to debug. Consequently, more natural, less error-prone imperative DL frameworks encouraging eager execution have emerged at the expense of run-time performance. Though hybrid approaches aim for the \"best of both worlds,\" using them effectively requires subtle considerations to make code amenable to safe, accurate, and efficient graph execution -- avoiding performance bottlenecks and semantically inequivalent results. We present our ongoing work on an automated refactoring approach that assists developers in specifying whether and how their otherwise eagerly-executed imperative DL code could be reliably and efficiently executed as graphs at run-time in a semantics-preserving fashion. The approach, based on a novel tensor analysis specifically for imperative DL code, consists of refactoring preconditions for automatically determining when it is safe and potentially advantageous to migrate imperative DL code to graph execution and modifying decorator parameters or eagerly executing code already running as graphs. The approach is being implemented as a PyDev Eclipse IDE plug-in and uses the WALA Ariadne analysis framework. We discuss our ongoing work towards optimizing imperative DL code to its full potential.", "published": "2023-08-22T20:50:19Z", "authors": ["Raffi Khatchadourian", "Tatiana Castro Vélez", "Mehdi Bagherzadeh", "Nan Jia", "Anita Raja"], "categories": ["cs.SE", "cs.PL"], "doi": "", "_query": "automated valuation model", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2312.11413v2", "title": "DeRDaVa: Deletion-Robust Data Valuation for Machine Learning", "abstract": "Data valuation is concerned with determining a fair valuation of data from data sources to compensate them or to identify training examples that are the most or least useful for predictions. With the rising interest in personal data ownership and data protection regulations, model owners will likely have to fulfil more data deletion requests. This raises issues that have not been addressed by existing works: Are the data valuation scores still fair with deletions? Must the scores be expensively recomputed? The answer is no. To avoid recomputations, we propose using our data valuation framework DeRDaVa upfront for valuing each data source's contribution to preserving robust model performance after anticipated data deletions. DeRDaVa can be efficiently approximated and will assign higher values to data that are more useful or less likely to be deleted. We further generalize DeRDaVa to Risk-DeRDaVa to cater to risk-averse/seeking model owners who are concerned with the worst/best-cases model utility. We also empirically demonstrate the practicality of our solutions.", "published": "2023-12-18T18:17:51Z", "authors": ["Xiao Tian", "Rachael Hwee Ling Sim", "Jue Fan", "Bryan Kian Hsiang Low"], "categories": ["cs.LG", "cs.AI"], "doi": "", "_query": "automated valuation model", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/0812.3705v4", "title": "Bilateral counterparty risk valuation with stochastic dynamical models and application to Credit Default Swaps", "abstract": "We introduce the general arbitrage-free valuation framework for counterparty risk adjustments in presence of bilateral default risk, including default of the investor. We illustrate the symmetry in the valuation and show that the adjustment involves a long position in a put option plus a short position in a call option, both with zero strike and written on the residual net value of the contract at the relevant default times. We allow for correlation between the default times of the investor, counterparty and underlying portfolio risk factors. We use arbitrage-free stochastic dynamical models. We then specialize our analysis to Credit Default Swaps (CDS) as underlying portfolio, generalizing the work of Brigo and Chourdakis (2008) [5] who deal with unilateral and asymmetric counterparty risk. We introduce stochastic intensity models and a trivariate copula function on the default times exponential variables to model default dependence. Similarly to [5], we find that both default correlation and credit spread volatilities have a relevant and structured impact on the adjustment. Differently from [5], the two parties will now agree on the credit valuation adjustment. We study a case involving British Airways, Lehman Brothers and Royal Dutch Shell, illustrating the bilateral adjustments in concrete crisis situations.", "published": "2008-12-19T05:08:22Z", "authors": ["Damiano Brigo", "Agostino Capponi"], "categories": ["q-fin.RM", "q-fin.PR"], "doi": "", "_query": "automated valuation model", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2101.08337v2", "title": "Essential finite generation of extensions of valuation rings", "abstract": "Given a generically finite local extension of valuation rings $V \\subset W$, the question of whether $W$ is the localization of a finitely generated $V$-algebra is significant for approaches to the problem of local uniformization of valuations using ramification theory. Hagen Knaf proposed a characterization of when $W$ is essentially of finite type over $V$ in terms of classical invariants of the extension of associated valuations. Knaf's conjecture has been verified in important special cases by Cutkosky and Novacoski using local uniformization of Abhyankar valuations and resolution of singularities of excellent surfaces in arbitrary characteristic, and by Cutkosky for valuation rings of function fields of characteristic $0$ using embedded resolution of singularities. In this paper we prove Knaf's conjecture in full generality.", "published": "2021-01-20T21:56:50Z", "authors": ["Rankeya Datta"], "categories": ["math.AC", "math.AG"], "doi": "10.1002/mana.202100190", "_query": "automated valuation model", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2502.14858v1", "title": "The $p$-adic Galois Cohomology of Valuation Fields", "abstract": "We compute the Galois cohomology of any $p$-adic valuation field extension of a pre-perfectoid field. Moreover, we obtain a generalization and also a new proof of the classical results of Tate and Hyodo on discrete valuation fields, without using higher ramification group, local class field theory or Epp's elimination of ramifications. A key ingredient is Gabber-Ramero's computation of cotangent complexes for valuation rings.", "published": "2025-02-20T18:59:07Z", "authors": ["Tongmu He"], "categories": ["math.AG", "math.AC", "math.NT"], "doi": "", "_query": "automated valuation model", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2205.11964v4", "title": "Minimal log regular models of hyperbolic curves over discrete valuation fields", "abstract": "In the famous paper of Deligne and Mumford, they proved that a proper hyperbolic curve over a discrete valuation field has stable reduction if and only if the Jacobian variety of the curve has stable reduction in the case where the residue field of its valuation ring is algebraically closed. In the proof, the theory of minimal regular models played an important role. In this paper, we establish a theory of minimal log regular models of curves. As a key tool for this theory, we give a criterion for $2$-dimensional local schemes to be log regular in terms of their minimal desingularization. Moreover, as an application of this theory, we prove the above equivalence without the assumption on the residue field.", "published": "2022-05-24T10:51:43Z", "authors": ["Ippei Nagamachi"], "categories": ["math.NT", "math.AG"], "doi": "", "_query": "automated valuation model", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/0802.2079v2", "title": "Zero dimensional arc valuations on smooth varieties", "abstract": "For a normalized transcendence degree zero arc valuation v on a nonsingular variety X (with dim X > 1), we describe the maximal irreducible subset C(v) of the arc space of X such that the valuation given by the order of vanishing along a general arc of C(v) equals v. We describe C(v) both algebraically, in terms of the sequence of valuation ideals of v, and geometrically, in terms of the sequence of infinitely near points associated to v. When X is a surface, our construction also applies to any divisorial valuation v, and in this case C(v) coincides with a subset Ein, Lazarsfeld, and Mustata associate to v.", "published": "2008-02-14T18:45:42Z", "authors": ["Yogesh More"], "categories": ["math.AG"], "doi": "", "_query": "automated valuation model", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/1102.1241v2", "title": "On modules over valuations", "abstract": "Recently an algebra of smooth valuations was attached to any smooth manifold. Roughly put, a smooth valuation is finitely additive measure on compact submanifolds with corners which satisfies some extra properties. In this note we initiate a study of modules over smooth valuations. More specifically we study finitely generated projective modules in analogy to the study of vector bundles on a manifold. In particular it is shown that on a compact manifold there exists a canonical isomorphism between the $K$-ring constructed out of finitely generated projective modules over valuations and the classical topological $K^0$-ring constructed out of vector bundles.", "published": "2011-02-07T08:03:34Z", "authors": ["Semyon Alesker"], "categories": ["math.MG", "math.KT"], "doi": "", "_query": "automated valuation model", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2209.06044v3", "title": "On the finite generation of valuation semigroups on toric surfaces", "abstract": "We provide a combinatorial criterion for the finite generation of a valuation semigroup associated with an ample divisor on a smooth toric surface and a non-toric valuation of maximal rank. As an application, we construct a lattice polytope such that none of the valuation semigroups of the associated polarized toric variety coming from one-parameter subgroups and centered at a non-toric point are finitely generated.", "published": "2022-09-13T14:51:54Z", "authors": ["Klaus Altmann", "Christian Haase", "Alex Küronya", "Karin Schaller", "Lena Walter"], "categories": ["math.AG"], "doi": "10.46298/epiga.2024.11407", "_query": "automated valuation model", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/1912.02304v1", "title": "Almost discrete valuation domains", "abstract": "Let $D$ be an integral domain. Then $D$ is an almost valuation (AV-)domain if for $a, b\\in D\\setminus \\{0\\}$ there exists a natural number $n$ with $a^{n}\\mid b^{n}$ or $b^{n}\\mid a^{n}$. AV-domains are closely related to valuation domains, for example, $D$ is an AV-domain if and only if the integral closure $\\bar{D}$ is a valuation domain and $D\\subseteq \\bar{D}$ is a root extension. In this note we explore various generalizations of DVRs (which we might call almost DVRs) such as Noetherian AV-domains, AV-domains with $\\bar{D}$ a DVR, and quasilocal and local API-domains (i.e., for $\\{a_α\\}_{α\\in Λ}\\subseteq D$, there exists an $n$ with $(\\{a_α^{n}\\}_{α\\in Λ})$ principal). The structure of complete local AV-domains and API-domains is determined.", "published": "2019-12-04T23:30:40Z", "authors": ["Daniel D. Anderson", "Shiqi Xing", "Muhammad Zafrullah"], "categories": ["math.AC"], "doi": "", "_query": "automated valuation model", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2405.06553v1", "title": "Scalable Property Valuation Models via Graph-based Deep Learning", "abstract": "This paper aims to enrich the capabilities of existing deep learning-based automated valuation models through an efficient graph representation of peer dependencies, thus capturing intricate spatial relationships. In particular, we develop two novel graph neural network models that effectively identify sequences of neighboring houses with similar features, employing different message passing algorithms. The first strategy consider standard spatial graph convolutions, while the second one utilizes transformer graph convolutions. This approach confers scalability to the modeling process. The experimental evaluation is conducted using a proprietary dataset comprising approximately 200,000 houses located in Santiago, Chile. We show that employing tailored graph neural networks significantly improves the accuracy of house price prediction, especially when utilizing transformer convolutional message passing layers.", "published": "2024-05-10T15:54:55Z", "authors": ["Enrique Riveros", "Carla Vairetti", "Christian Wegmann", "Santiago Truffa", "Sebastián Maldonado"], "categories": ["cs.LG", "cs.AI"], "doi": "", "_query": "automated valuation model", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2403.04914v1", "title": "Improving the Equation of Exchange for Cryptoasset Valuation Using Empirical Data", "abstract": "In the evolving domain of cryptocurrency markets, accurate token valuation remains a critical aspect influencing investment decisions and policy development. Whilst the prevailing equation of exchange pricing model offers a quantitative valuation approach based on the interplay between token price, transaction volume, supply, and either velocity or holding time, it exhibits intrinsic shortcomings. Specifically, the model may not consistently delineate the relationship between average token velocity and holding time. This paper aims to refine this equation, enhancing the depth of insight into token valuation methodologies.", "published": "2024-03-07T22:00:17Z", "authors": ["Stylianos Kampakis", "Melody Yuan", "Oritsebawo Paul Ikpobe", "Linas Stankevicius"], "categories": ["cs.CE", "stat.OT"], "doi": "", "_query": "automated valuation model", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/1510.08980v1", "title": "The Complexity of Equilibria for Risk-Modeling Valuations", "abstract": "We study the complexity of deciding the existence of mixed equilibria for minimization games where players use valuations other than expectation to evaluate their costs. We consider risk-averse players seeking to minimize the sum ${\\mathsf{V}} = {\\mathsf{E}} + {\\mathsf{R}}$ of expectation ${\\mathsf{E}}$ and a risk valuation ${\\mathsf{R}}$ of their costs; ${\\mathsf{R}}$ is non-negative and vanishes exactly when the cost incurred to a player is constant over all choices of strategies by the other players. In a ${\\mathsf{V}}$-equilibrium, no player could unilaterally reduce her cost. Say that ${\\mathsf{V}}$ has the Weak-Equilibrium-for-Expectation property if all strategies supported in a player's best-response mixed strategy incur the same conditional expectation of her cost. We introduce ${\\mathsf{E}}$-strict concavity and observe that every ${\\mathsf{E}}$-strictly concave valuation has the Weak-Equilibrium-for-Expectation property. We focus on a broad class of valuations shown to have the Weak-Equilibrium-for-Expectation property, which we exploit to prove two main complexity results, the first of their kind, for the two simplest cases of the problem: games with two strategies, or games with two players. For each case, we show that deciding the existence of a ${\\mathsf{V}}$-equilibrium is strongly ${\\mathcal{NP}}$-hard for certain choices of significant valuations (including variance and standard deviation).", "published": "2015-10-30T06:31:47Z", "authors": ["Marios Mavronicolas", "Burkhard Monien"], "categories": ["cs.GT", "cs.CC"], "doi": "", "_query": "automated valuation model", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/1312.3756v1", "title": "Arithmetical pseudo-valuations associated to Dubrovin valuation rings and prime divisors of bounded Krull domains", "abstract": "We look at value functions of primes in simple Artinian rings and associate arithmetical pseudo-valuations to Dubrovin valuation rings which, in the Noetherian case, are $\\mathbb{Z}$-valued. This allows a divisor theory for bounded Krull domains.", "published": "2013-12-13T10:17:08Z", "authors": ["Freddy Van Oystaeyen", "Nikolaas Verhulst"], "categories": ["math.RA"], "doi": "", "_query": "automated valuation model", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2502.10489v1", "title": "LiveVal: Time-aware Data Valuation via Adaptive Reference Points", "abstract": "Time-aware data valuation enhances training efficiency and model robustness, as early detection of harmful samples could prevent months of wasted computation. However, existing methods rely on model retraining or convergence assumptions or fail to capture long-term training dynamics. We propose LiveVal, an efficient time-aware data valuation method with three key designs: 1) seamless integration with SGD training for efficient data contribution monitoring; 2) reference-based valuation with normalization for reliable benchmark establishment; and 3) adaptive reference point selection for real-time updating with optimized memory usage. We establish theoretical guarantees for LiveVal's stability and prove that its valuations are bounded and directionally aligned with optimization progress. Extensive experiments demonstrate that LiveVal provides efficient data valuation across different modalities and model scales, achieving 180 speedup over traditional methods while maintaining robust detection performance.", "published": "2025-02-14T12:41:20Z", "authors": ["Jie Xu", "Zihan Wu", "Cong Wang", "Xiaohua Jia"], "categories": ["cs.LG", "cs.AI"], "doi": "", "_query": "automated valuation model", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/1405.0515v3", "title": "KVA: Capital Valuation Adjustment", "abstract": "Credit (CVA), Debit (DVA) and Funding Valuation Adjustments (FVA) are now familiar valuation adjustments made to the value of a portfolio of derivatives to account for credit risks and funding costs. However, recent changes in the regulatory regime and the increases in regulatory capital requirements has led many banks to include the cost of capital in derivative pricing. This paper formalises the addition of cost of capital by extending the Burgard-Kjaer (2013) semi-replication approach to CVA and FVA to include an addition capital term, Capital Valuation Adjustment (KVA, i.e. Kapital Valuation Adjustment to distinguish from CVA.) The utilization of the capital for funding purposes is also considered. The use of the semi-replication approach means that the flexibility around the treatment of self-default is carried over into this analysis. The paper further considers the practical calculation of KVA with reference to the Basel II (BCBS-128) and Basel III (BCBS-189) capital regimes and their implementation via CRD IV. The paper also assesses how KVA may be hedged, given that any hedging transactions themselves lead to regulatory capital requirements and hence capital costs. Finally a number of numerical examples are presented to gauge the cost impact of KVA on vanilla derivative products.", "published": "2014-05-02T21:09:30Z", "authors": ["Andrew Green", "Chris Kenyon"], "categories": ["q-fin.PR"], "doi": "", "_query": "automated valuation model", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2506.04026v1", "title": "On the Usage of Gaussian Process for Efficient Data Valuation", "abstract": "In machine learning, knowing the impact of a given datum on model training is a fundamental task referred to as Data Valuation. Building on previous works from the literature, we have designed a novel canonical decomposition allowing practitioners to analyze any data valuation method as the combination of two parts: a utility function that captures characteristics from a given model and an aggregation procedure that merges such information. We also propose to use Gaussian Processes as a means to easily access the utility function on ``sub-models'', which are models trained on a subset of the training set. The strength of our approach stems from both its theoretical grounding in Bayesian theory, and its practical reach, by enabling fast estimation of valuations thanks to efficient update formulae.", "published": "2025-06-04T14:53:51Z", "authors": ["Clément Bénesse", "Patrick Mesana", "Athénaïs Gautier", "Sébastien Gambs"], "categories": ["cs.LG"], "doi": "", "_query": "automated valuation model", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2205.03659v2", "title": "Mechanising Gödel-Löb provability logic in HOL Light", "abstract": "We introduce our implementation in HOL Light of the metatheory for Gödel-Löb provability logic (GL), covering soundness and completeness w.r.t. possible world semantics and featuring a prototype of a theorem prover for GL itself. The strategy we develop here to formalise the modal completeness proof overcomes the technical difficulty due to the non-compactness of GL and is an adaptation -- according to the formal language and tools at hand -- of the proof given in George Boolos' 1995 monograph. Our theorem prover for GL relies then on this formalisation, is implemented as a tactic of HOL Light that mimics the proof search in the labelled sequent calculus G3KGL, and works as a decision algorithm for the provability logic: if the algorithm positively terminates, the tactic succeeds in producing a HOL Light theorem stating that the input formula is a theorem of GL; if the algorithm negatively terminates, the tactic extracts a model falsifying the input formula. We discuss our code for the formal proof of modal completeness and the design of our proof search algorithm. Furthermore, we propose some examples of the latter's interactive and automated use.", "published": "2022-05-07T14:18:57Z", "authors": ["Marco Maggesi", "Cosimo Perini Brogi"], "categories": ["cs.LO", "math.LO"], "doi": "10.1007/s10817-023-09677-z", "_query": "automated valuation model", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2504.10683v1", "title": "PALACE v1.0: Paranal Airglow Line And Continuum Emission model", "abstract": "Below about 2.3 $μ$m, the nighttime emission of the Earth's atmosphere is dominated by non-thermal radiation from the mesosphere and thermosphere. As this airglow can even outshine scattered moonlight in the near-infrared regime, the understanding of the Earth's night-sky brightness requires good knowledge of the complex airglow emission spectrum and its variability. As airglow modelling is very challenging, the comprehensive characterisation of airglow emission requires large data sets of empirical data. For fixed locations, this can be best achieved by archived spectra of large astronomical telescopes with a wide wavelength coverage, high spectral resolving power, and good temporal sampling. Using 10 years of data from the X-shooter echelle spectrograph in the wavelength range from 0.3 to 2.5 $μ$m and additional data from the Ultraviolet and Visual Echelle Spectrograph at the Very Large Telescope at Cerro Paranal in Chile, we have succeeded to build a comprehensive spectroscopic airglow model for this low-latitude site under consideration of theoretical data from the HITRAN database for molecules and from different sources for atoms. The Paranal Airglow Line And Continuum Emission (PALACE) model comprises 9 chemical species, 26,541 emission lines, and 3 unresolved continuum components. Moreover, there are climatologies of relative intensity, solar cycle effect, and residual variability with respect to local time and day of year for 23 variability classes. Spectra can be calculated with a stand-alone code for different conditions, also including optional atmospheric absorption and scattering. In comparison to the observed X-shooter spectra, PALACE shows convincing agreement and is significantly better than the previous, widely used airglow model for Cerro Paranal.", "published": "2025-04-14T20:12:50Z", "authors": ["Stefan Noll", "Carsten Schmidt", "Patrick Hannawald", "Wolfgang Kausch", "Stefan Kimeswenger"], "categories": ["physics.ao-ph", "astro-ph.EP", "astro-ph.IM"], "doi": "10.5194/gmd-18-4353-2025", "_query": "automated valuation model", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/1401.4813v2", "title": "Definable henselian valuation rings", "abstract": "We give model theoretic criteria for $\\exists \\forall$ and $\\forall \\exists$- formulas in the ring language to define uniformly the valuation rings $\\mathcal{O}$ of models $(K, \\mathcal{O})$ of an elementary theory $Σ$ of henselian valued fields. As one of the applications we obtain the existence of an $\\exists \\forall$-formula defining uniformly the valuation rings $\\mathcal{O}$ of valued henselian fields $(K, \\mathcal{O})$ whose residue class field $k$ is finite, pseudo-finite, or hilbertian. We also obtain $\\forall \\exists$-formulas $\\varphi_2$ and $\\varphi_4$ such that $\\varphi_2$ defines uniformly $k[[t]]$ in $k((t))$ whenever $k$ is finite or the function field of a real or complex curve, and $\\varphi_4$ does the job if $k$ is any number field.", "published": "2014-01-20T07:45:23Z", "authors": ["Alexander Prestel"], "categories": ["math.AC", "math.LO"], "doi": "", "_query": "automated valuation model", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2405.17464v2", "title": "Data Valuation by Fusing Global and Local Statistical Information", "abstract": "Data valuation has garnered increasing attention in recent years, given the critical role of high-quality data in various applications. Among diverse data valuation approaches, Shapley value-based methods are predominant due to their strong theoretical grounding. However, the exact computation of Shapley values is often computationally prohibitive, prompting the development of numerous approximation techniques. Despite notable advancements, existing methods generally neglect the incorporation of value distribution information and fail to account for dynamic data conditions, thereby compromising their performance and application potential. In this paper, we highlight the crucial role of both global and local statistical properties of value distributions in the context of data valuation for machine learning. First, we conduct a comprehensive analysis of these distributions across various simulated and real-world datasets, uncovering valuable insights and key patterns. Second, we propose an enhanced data valuation method that fuses the explored distribution characteristics into two regularization terms to refine Shapley value estimation. The proposed regularizers can be seamlessly incorporated into various existing data valuation methods. Third, we introduce a novel approach for dynamic data valuation that infers updated data values without recomputing Shapley values, thereby significantly improving computational efficiency. Extensive experiments have been conducted across a range of tasks, including Shapley value estimation, value-based data addition and removal, mislabeled data detection, and dynamic data valuation. The results showcase the consistent effectiveness and efficiency of our proposed methodologies, affirming the significant potential of global and local value distributions in data valuation.", "published": "2024-05-23T08:58:08Z", "authors": ["Xiaoling Zhou", "Ou Wu", "Michael K. Ng", "Hao Jiang"], "categories": ["cs.LG", "cs.AI", "stat.ML"], "doi": "", "_query": "automated valuation model", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2203.02197v1", "title": "The 2-adic valuation of the general degree 2 polynomial in 2 variables", "abstract": "The p-adic valuation of a polynomial can be given by its valuation tree. This work describes the 2-adic valuation tree of the general degree 2 polynomial in 2 variables.", "published": "2022-03-04T09:13:31Z", "authors": [" Shubham"], "categories": ["math.NT"], "doi": "10.7546/nntdm.2023.29.4.737-751", "_query": "automated valuation model", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/cs/0201021v1", "title": "Learning to Play Games in Extensive Form by Valuation", "abstract": "A valuation for a player in a game in extensive form is an assignment of numeric values to the players moves. The valuation reflects the desirability moves. We assume a myopic player, who chooses a move with the highest valuation. Valuations can also be revised, and hopefully improved, after each play of the game. Here, a very simple valuation revision is considered, in which the moves made in a play are assigned the payoff obtained in the play. We show that by adopting such a learning process a player who has a winning strategy in a win-lose game can almost surely guarantee a win in a repeated game. When a player has more than two payoffs, a more elaborate learning procedure is required. We consider one that associates with each move the average payoff in the rounds in which this move was made. When all players adopt this learning procedure, with some perturbations, then, with probability 1, strategies that are close to subgame perfect equilibrium are played after some time. A single player who adopts this procedure can guarantee only her individually rational payoff.", "published": "2002-01-23T11:58:17Z", "authors": ["Philippe Jehiel", "Dov Samet"], "categories": ["cs.LG", "cs.GT"], "doi": "", "_query": "automated valuation model", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/1709.08304v3", "title": "Positivity of valuations on convex bodies and invariant valuations by linear actions", "abstract": "In this paper, we endow the space of continuous translation invariant valuation on convex sets generated by mixed volumes coupled with a suitable Radon measure on tuples of convex bodies with two appropriate norms. This enables us to construct a continuous extension of the convolution operator on smooth valuations to non-smooth valuations, which are in the completion of the spaces of valuations with respect to these norms. The novelty of our approach lies in the fact that our proof does not rely on the general theory of wave fronts, but on geometric inequalities deduced from optimal transport methods. We apply this result to prove a variant of Minkowski's existence theorem, and generalize a theorem of Favre-Wulcan and Lin in complex dynamics over toric varieties by studying the linear actions on the Banach spaces of valuations and by studying their corresponding eigenspaces.", "published": "2017-09-25T03:33:07Z", "authors": ["Nguyen-Bac Dang", "Jian Xiao"], "categories": ["math.DG", "math.DS", "math.MG"], "doi": "", "_query": "automated valuation model", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/1309.2685v1", "title": "Complete Valuations on Finite Distributive Lattices", "abstract": "We characterize the finite distributive lattices which admit a complete valuation, that is bijective over a set of consecutive natural numbers, with the additional conditions of completeness (Definition 2.3). We prove that such lattices are downset lattices of finite posets of dimension at most two, and determine a realizer through a recursive relation between weights on the poset associated to valuation. The relation shows that the weights count chains in the complementary poset. Conversely, we prove that a valuation defined on a poset of dimension at most two, through the weight function which counts chains in the complementary poset, is complete.", "published": "2013-09-10T22:40:54Z", "authors": ["Francesco Marigo"], "categories": ["cs.DM", "math.CO"], "doi": "", "_query": "automated valuation model", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/1404.0762v2", "title": "Terminal valuations and the Nash problem", "abstract": "Let X be an algebraic variety of characteristic zero. Terminal valuations are defined in the sense of the minimal model program, as those valuations given by the exceptional divisors on a minimal model over X. We prove that every terminal valuation over X is in the image of the Nash map, and thus it corresponds to a maximal family of arcs through the singular locus of X. In dimension two, this result gives a new proof of the theorem of Fernández de Bobadilla and Pe Pereira stating that, for surfaces, the Nash map is a bijection.", "published": "2014-04-03T04:31:13Z", "authors": ["Tommaso de Fernex", "Roi Docampo"], "categories": ["math.AG"], "doi": "10.1007/s00222-015-0597-5", "_query": "automated valuation model", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/1509.05468v1", "title": "Loops with abelian inner mapping groups: An application of automated deduction", "abstract": "We describe a large-scale project in applied automated deduction concerned with the following problem of considerable interest in loop theory: If $Q$ is a loop with commuting inner mappings, does it follow that $Q$ modulo its center is a group and $Q$ modulo its nucleus is an abelian group? This problem has been answered affirmatively in several varieties of loops. The solution usually involves sophisticated techniques of automated deduction, and the resulting derivations are very long, often with no higher-level human proofs available.", "published": "2015-09-17T22:59:04Z", "authors": ["Michael Kinyon", "Robert Veroff", "Petr Vojtěchovský"], "categories": ["math.GR"], "doi": "", "_query": "automated valuation model", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2608.12228v1", "title": "Towards Automated Domain Model Extraction from Source Code using Heuristics and Open-Source LLMs", "abstract": "Large language models (LLMs) have recently shown strong capabilities for code understanding, making them promising for reverse engineering domain models from source code. However, state-ofthe- art proprietary LLMs cannot be used in many industrial contexts due to privacy and confidentiality constraints, while compact open-source LLMs that can run locally are limited by their context window and cannot process large code bases directly. In this paper, we propose an automated approach to extract domain models from source code using lightweight, locally deployable LLMs. Our method combines structural and semantic heuristics with iterative LLM-based reasoning to overcome context limitations. By progressively analyzing ranked subsets of code elements, the approach identifies domain concepts and refines domain boundaries without requiring full-system context. Our approach achieves high F1-scores on a dataset of ten projects, each comprising a curated domain model and its corresponding implementation, while remaining fully executable on locally deployable LLMs. This makes it particularly suitable for reverse engineering tasks in privacy-sensitive industrial environments.", "published": "2026-08-12T16:25:54Z", "authors": ["Alessandra Mancas", "Mounir Ammam", "Hyacinth Ali", "Kevin Delcourt", "Houari Sahraoui"], "categories": ["cs.SE"], "doi": "", "_query": "automated valuation model", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/1912.10869v1", "title": "Famille admise associée à une valuation de K(X)", "abstract": "Let K be a field with a valuation $ν$ and let L = K(x) be a transcendental extension of K, then any valuation $μ$ of L which extends $ν$ is determined by its restriction to the polynomial ring K[x]. We know how to associate to this valuation $μ$ a family of valuations A = ($μ$i)i$\\in$I of K[x], called the associated admise family, which converges in a certain sense towards the valuation $μ$. Although the definition of this family, as well as the notion of convergence, essentially imply the structure of the polynomial ring, in particular the degree of polynomials, we show in this note that the family A of valuations of L do not depend on the chosen generator x.", "published": "2019-12-23T14:31:18Z", "authors": ["Michel Vaquié"], "categories": ["math.AC", "math.AG"], "doi": "10.1112/blms.12378", "_query": "automated valuation model", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/1603.09196v1", "title": "Definable Valuations Induced by Definable Subgroups", "abstract": "In his unpublished preprint \"Definable Valuations\" Koenigsmann shows that every field that admits a t-henselian topology is either real closed or separably closed or admits a definable valuation inducing the t-henselian topology. To show this Koenigsmann investigates valuation rings induced by certain (definable) subgroups of the field. The aim of this paper, based on the authors PhD thesis, is to look at the methods used in the preprint in greater detail and correct a mistake in the original paper based on a paper of Jahnke and Koenigsmann.", "published": "2016-03-30T13:55:27Z", "authors": ["Katharina Dupont"], "categories": ["math.LO", "math.GR"], "doi": "", "_query": "automated valuation model", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2301.05886v3", "title": "Efficient Risk Estimation for the Credit Valuation Adjustment", "abstract": "The valuation of over-the-counter derivatives is subject to a series of valuation adjustments known as xVA, which pose additional risks for financial institutions. Associated risk measures, such as the value-at-risk of an underlying valuation adjustment, play an important role in managing these risks. Monte Carlo methods are often regarded as inefficient for computing such measures. As an example, we consider the value-at-risk of the Credit Valuation Adjustment (CVA-VaR), which can be expressed using a triple nested expectation. Traditional Monte Carlo methods are often inefficient at handling several nested expectations. Utilising recent developments in multilevel nested simulation for probabilities, we construct a hierarchical estimator of the CVA-VaR which reduces the computational complexity by 3 orders of magnitude compared to standard Monte Carlo.", "published": "2023-01-14T10:49:10Z", "authors": ["Michael B. Giles", "Abdul-Lateef Haji-Ali", "Jonathan Spence"], "categories": ["q-fin.CP"], "doi": "", "_query": "automated valuation model", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/1507.08779v2", "title": "Impact of Multiple Curve Dynamics in Credit Valuation Adjustments under Collateralization", "abstract": "We present a detailed analysis of interest rate derivatives valuation under credit risk and collateral modeling. We show how the credit and collateral extended valuation framework in Pallavicini et al (2011), and the related collateralized valuation measure, can be helpful in defining the key market rates underlying the multiple interest rate curves that characterize current interest rate markets. A key point is that spot Libor rates are to be treated as market primitives rather than being defined by no-arbitrage relationships. We formulate a consistent realistic dynamics for the different rates emerging from our analysis and compare the resulting model performances to simpler models used in the industry. We include the often neglected margin period of risk, showing how this feature may increase the impact of different rates dynamics on valuation. We point out limitations of multiple curve models with deterministic basis considering valuation of particularly sensitive products such as basis swaps. We stress that a proper wrong way risk analysis for such products requires a model with a stochastic basis and we show numerical results confirming this fact.", "published": "2015-07-31T07:37:49Z", "authors": ["Giacomo Bormetti", "Damiano Brigo", "Marco Francischello", "Andrea Pallavicini"], "categories": ["q-fin.PR", "q-fin.CP"], "doi": "", "_query": "automated valuation model", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/1712.08562v2", "title": "Extensions of Valuations to the Henselization and Completion", "abstract": "We show that if $R$ is a local domain which is dominated by a valuation $ν$, then there does not always exist a regular local ring $R'$ which birationally dominates R and is dominated by $ν$ and an extension of $ν$ to the Henselization $(R')^h$ of $R'$ such that the associated graded rings of $R'$ and $(R')^h$ along the valuations are equal. We also show that there does not always exist $R'$, a prime ideal $p$ of the completion $\\widehat{R'}$ such that $p\\cap R'=(0)$ and an extension of $ν$ to $\\widehat{R'}$ such that the associated graded rings of $R'$ and $\\widehat{R'}/p$ along the valuation are equal.", "published": "2017-12-22T16:41:07Z", "authors": ["Steven Dale Cutkosky"], "categories": ["math.AG", "math.AC"], "doi": "", "_query": "automated valuation model", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2008.01121v3", "title": "The Universal Valuation of Coxeter Matroids", "abstract": "Coxeter matroids generalize matroids just as flag varieties of Lie groups generalize Grassmannians. Valuations of Coxeter matroids are functions that behave well with respect to subdivisions of a Coxeter matroid into smaller ones. We compute the universal valuative invariant of Coxeter matroids. A key ingredient is the family of Coxeter Schubert matroids, which correspond to the Bruhat cells of flag varieties. In the process, we compute the universal valuation of generalized Coxeter permutohedra, a larger family of polyhedra that model Coxeter analogues of combinatorial objects such as matroids, clusters, and posets.", "published": "2020-08-03T18:28:35Z", "authors": ["Christopher Eur", "Mario Sanchez", "Mariel Supina"], "categories": ["math.CO"], "doi": "10.1112/blms.12461", "_query": "automated valuation model", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/1307.5501v1", "title": "Value Functions and Dubrovin Valuation Rings on Simple Algebras", "abstract": "In this paper we prove relationships between two generalizations of commutative valuation theory for noncommutative central simple algebras: (1) Dubrovin valuation rings; and (2) the value functions called gauges introduced by Tignol and Wadsworth in [TW1] and [TW2]. We show that if v is a valuation on a field F with associated valuation ring V and v is defectless in a central simple F-algebra A, and C is a subring of A, then the following are equivalent: (a) C is the gauge ring of some minimal v-gauge on A, i.e., a gauge with the minimal number of simple components of C/J(C); (b) C is integral over V with C = B_1 \\cap ... \\cap B_xi$ where each B_i is a Dubrovin valuation ring of A with center V, and the B_i satisfy Graeter's Intersection Property. Along the way we prove the existence of minimal gauges whenever possible and we show how gauges on simple algebras are built from gauges on central simple algebras.", "published": "2013-07-21T07:35:56Z", "authors": ["Mauricio A. Ferreira", "Adrian R. Wadsworth"], "categories": ["math.RA"], "doi": "", "_query": "automated valuation model", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/1211.1564v1", "title": "Funded Bilateral Valuation Adjustment", "abstract": "We show how the cost of funding the collateral in a particular set up can be equal to the Bilateral Valuation Adjustment with the \"funded\" probability of default, leading to the definition of a Funded Bilateral Valuation Adjustment (FBVA). That set up can also be viewed by an investor as an effective way to restructure the counterparty risk arising from an uncollateralized transaction with a counterparty, mitigating or even avoiding entirely the additional capital charge introduced by the new Basel III framework.", "published": "2012-11-07T14:52:14Z", "authors": ["Lorenzo Giada", "Claudio Nordio"], "categories": ["q-fin.PR", "q-fin.RM"], "doi": "", "_query": "automated valuation model", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2303.16963v1", "title": "Fairness-Aware Data Valuation for Supervised Learning", "abstract": "Data valuation is a ML field that studies the value of training instances towards a given predictive task. Although data bias is one of the main sources of downstream model unfairness, previous work in data valuation does not consider how training instances may influence both performance and fairness of ML models. Thus, we propose Fairness-Aware Data vauatiOn (FADO), a data valuation framework that can be used to incorporate fairness concerns into a series of ML-related tasks (e.g., data pre-processing, exploratory data analysis, active learning). We propose an entropy-based data valuation metric suited to address our two-pronged goal of maximizing both performance and fairness, which is more computationally efficient than existing metrics. We then show how FADO can be applied as the basis for unfairness mitigation pre-processing techniques. Our methods achieve promising results -- up to a 40 p.p. improvement in fairness at a less than 1 p.p. loss in performance compared to a baseline -- and promote fairness in a data-centric way, where a deeper understanding of data quality takes center stage.", "published": "2023-03-29T18:51:13Z", "authors": ["José Pombal", "Pedro Saleiro", "Mário A. T. Figueiredo", "Pedro Bizarro"], "categories": ["cs.LG", "cs.CY"], "doi": "", "_query": "automated valuation model", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/math/0503399v4", "title": "Theory of valuations on manifolds, II", "abstract": "This article is the second part in the series of articles where we are developing theory of valuations on manifolds. Roughly speaking valuations could be thought as finitely additive measures on a class of nice subsets of a manifold which satisfy some additional assumptions. The goal of this article is to introduce a notion of a smooth valuation on an arbitrary smooth manifold and establish some of the basic properties of it.", "published": "2005-03-19T11:38:33Z", "authors": ["Semyon Alesker"], "categories": ["math.MG"], "doi": "", "_query": "automated valuation model", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2511.07865v1", "title": "LLM-Powered Fully Automated Chaos Engineering: Towards Enabling Anyone to Build Resilient Software Systems at Low Cost", "abstract": "Chaos Engineering (CE) is an engineering technique aimed at improving the resilience of distributed systems. It involves intentionally injecting faults into a system to test its resilience, uncover weaknesses, and address them before they cause failures in production. Recent CE tools automate the execution of predefined CE experiments. However, planning such experiments and improving the system based on the experimental results still remain manual. These processes are labor-intensive and require multi-domain expertise. To address these challenges and enable anyone to build resilient systems at low cost, this paper proposes ChaosEater, a system that automates the entire CE cycle with Large Language Models (LLMs). It predefines an agentic workflow according to a systematic CE cycle and assigns subdivided processes within the workflow to LLMs. ChaosEater targets CE for software systems built on Kubernetes. Therefore, the LLMs in ChaosEater complete CE cycles through software engineering tasks, including requirement definition, code generation, testing, and debugging. We evaluate ChaosEater through case studies on small- and large-scale Kubernetes systems. The results demonstrate that it consistently completes reasonable CE cycles with significantly low time and monetary costs. Its cycles are also qualitatively validated by human engineers and LLMs.", "published": "2025-11-11T06:03:24Z", "authors": ["Daisuke Kikuta", "Hiroki Ikeuchi", "Kengo Tajiri"], "categories": ["cs.SE", "cs.AI", "cs.CL", "cs.MA"], "doi": "10.1109/ASE63991.2025.00331", "_query": "automated valuation model", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/1802.07559v1", "title": "$L_p$-Blaschke Valuations", "abstract": "In this article, a classification of continuous, linearly intertwining, symmetric $L_p$-Blaschke ($p>1$) valuations is established as an extension of Haberl's work on Blaschke valuations. More precisely, we show that for dimensions $n\\geq 3$, the only continuous, linearly intertwining, normalized symmetric $L_p$-Blaschke valuation is the normalized $L_p$-curvature image operator, while for dimension $n = 2 $, a rotated normalized $L_p$-curvature image operator is an only additional one. One of the advantages of our approach is that we deal with normalized symmetric $L_p$-Blaschke valuations, which makes it possible to handle the case $p=n$. The cases where $p \\not =n$ are also discussed by studying the relations between symmetric $L_p$-Blaschke valuations and normalized ones.", "published": "2018-02-21T13:23:36Z", "authors": ["Jin Li", "Shufeng Yuan", "Gangsong Leng"], "categories": ["math.MG"], "doi": "10.1090/S0002-9947-2015-06047-4", "_query": "automated valuation model", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/math/0702315v1", "title": "Gersten's conjecture for commutative discrete valuation rings", "abstract": "The purpose of this article is to prove that Gersten's conjecture for a commutative discrete valuation ring is true. Combining with the result of \\cite{GL87}, we learn that Gersten's conjecture is true if the ring is a commutative regular local, smooth over a commutative discrete valuation ring.", "published": "2007-02-12T09:51:20Z", "authors": ["Satoshi Mochizuki"], "categories": ["math.KT", "math.AC", "math.AG"], "doi": "", "_query": "automated valuation model", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/1711.02808v1", "title": "Less-Expensive Valuation of Long Term Annuities Linked to Mortality, Cash and Equity", "abstract": "This paper proposes a paradigm shift in the valuation of long term annuities, away from classical no-arbitrage valuation towards valuation under the real world probability measure. Furthermore, we apply this valuation method to two examples of annuity products, one having annual payments linked to a mortality index and the savings account and the other having annual payments linked to a mortality index and an equity index with a guarantee that is linked to the same mortality index and the savings account. Out-of-sample hedge simulations demonstrate the effectiveness of real world valuation. In contrast to risk neutral valuation, which is a form of relative valuation, the long term average excess return of the equity market comes into play. Instead of the savings account, the numéraire portfolio is employed as the fundamental unit of value in the analysis. The numéraire portfolio is the strictly positive, tradable portfolio that when used as benchmark makes all benchmarked nonnegative portfolios supermartingales. The benchmarked real world value of a benchmarked contingent claim equals its real world conditional expectation. This yields the minimal possible value for its hedgeable part and minimizes the fluctuations for its benchmarked hedge error. Under classical assumptions, actuarial and risk neutral valuation emerge as special cases of the proposed real world valuation. In long term liability and asset valuation, the proposed real world valuation can lead to significantly lower values than suggested by classical approaches when an equivalent risk neutral probability measure does not exist.", "published": "2017-11-08T02:58:45Z", "authors": ["Kevin Fergusson", "Eckhard Platen"], "categories": ["q-fin.MF", "q-fin.PR"], "doi": "", "_query": "automated valuation model", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/1909.09832v2", "title": "Upper Ramification Groups for Arbitrary Valuation Rings", "abstract": "T. Saito established a ramification theory for ring extensions locally of complete intersection. We show that for a Henselian valuation ring $A$ with field of fractions $K$ and for a finite Galois extension $L$ of $K$, the integral closure $B$ of $A$ in $L$ is a filtered union of subrings of $B$ which are of complete intersection over $A$. By this, we can obtain a ramification theory of Henselian valuation rings as the limit of the ramification theory of Saito. Our theory generalizes the ramification theory of complete discrete valuation rings of Abbes-Saito. We study \"defect extensions\" which are not treated in these previous works.", "published": "2019-09-21T14:16:26Z", "authors": ["Kazuya Kato", "Vaidehee Thatte"], "categories": ["math.NT", "math.AG"], "doi": "", "_query": "automated valuation model", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/math/0206228v1", "title": "A divisorial valuation with irrational volume", "abstract": "In this paper we present a divisorial valuation with irrational volume using an algebro-geometric construction.", "published": "2002-06-21T13:28:05Z", "authors": ["Alex Kuronya"], "categories": ["math.AG", "math.AC"], "doi": "", "_query": "automated valuation model", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2511.07177v1", "title": "A Choice-free look at algebraic extensions of valuations", "abstract": "In this paper, we study extensions of valuations over algebraic field extensions without the use of the Axiom of Choice. We show a bijection between the extensions of a valuation and the maximal ideals of the relative integral closure of its valuation ring. In the case of a finite extension, we show that these maximal ideals exist. We conclude with an elementary proof of the fundamental inequality.", "published": "2025-11-10T15:06:38Z", "authors": ["Cédric Aïd"], "categories": ["math.AC"], "doi": "", "_query": "automated valuation model", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/1704.07177v1", "title": "Tensor valuations on lattice polytopes", "abstract": "The Ehrhart polynomial and the reciprocity theorems by Ehrhart \\& Macdonald are extended to tensor valuations on lattice polytopes. A complete classification is established of tensor valuations of rank up to eight that are equivariant with respect to the special linear group over the integers and translation covariant. Every such valuation is a linear combination of the Ehrhart tensors which is shown to no longer hold true for rank nine.", "published": "2017-04-24T12:34:05Z", "authors": ["Monika Ludwig", "Laura Silverstein"], "categories": ["math.MG", "math.CO"], "doi": "10.1016/j.aim.2017.08.015", "_query": "automated valuation model", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2202.06774v1", "title": "Random zonotopes and valuations", "abstract": "We define a random zonotope in Euclidean space, by adding finitely many random segments, which are independently and identically distributed. For this random polytope, we determine, under a mild assumption on the distribution, the expectations of the intrinsic volumes, more generally, the expectations of suitable valuations. We also prove a central limit theorem for a valuation evaluated at these random zonotopes.", "published": "2022-02-14T14:50:57Z", "authors": ["Rolf Schneider"], "categories": ["math.PR", "math.MG"], "doi": "", "_query": "automated valuation model", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/1306.2137v1", "title": "Minkowski valuations in a 2-dimensional complex vector space", "abstract": "The classification of continuous, translation invariant Minkowski valuations which are contravariant (or covariant) with respect to the complex special linear group is established in a 2-dimensional complex vector space. Every such valuation is given by the sum of a valuation of degree of homogeneity 1 and 3. In dimensions $m\\geq 3$ such a classification was previously established and only valuations of a degree of homogeneity 2m-1 appear.", "published": "2013-06-10T08:38:16Z", "authors": ["Judit Abardia"], "categories": ["math.DG"], "doi": "10.1093/imrn/rnt251", "_query": "automated valuation model", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/1507.04669v1", "title": "Constructing examples of semigroups of valuations", "abstract": "We work with rational rank 1 valuations centered in regular local rings. Given an algebraic function field $K$ of transcendence degree 3 over $k$, a regular local ring $R$ with $QF(R)=K$ and a $k$-valuation $ν$ of $K$, we provide an algorithm for constructing a generating sequences for $ν$ in $R$. We then develop a method for determining a valuation $ν$ on $k(x,y,z)$ through the sequence of defining values. Using the above results we construct examples of valuations centered in $k[x,y,z]_{(x,y,z)}$ and investigate their semigroups of values.", "published": "2015-07-16T17:44:40Z", "authors": ["Olga Kashcheyeva"], "categories": ["math.AC"], "doi": "", "_query": "automated valuation model", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2208.02443v1", "title": "Credal Valuation Networks for Machine Reasoning Under Uncertainty", "abstract": "Contemporary undertakings provide limitless opportunities for widespread application of machine reasoning and artificial intelligence in situations characterised by uncertainty, hostility and sheer volume of data. The paper develops a valuation network as a graphical system for higher-level fusion and reasoning under uncertainty in support of the human operators. Valuations, which are mathematical representation of (uncertain) knowledge and collected data, are expressed as credal sets, defined as coherent interval probabilities in the framework of imprecise probability theory. The basic operations with such credal sets, combination and marginalisation, are defined to satisfy the axioms of a valuation algebra. A practical implementation of the credal valuation network is discussed and its utility demonstrated on a small scale example.", "published": "2022-08-04T04:33:16Z", "authors": ["Branko Ristic", "Alessio Benavoli", "Sanjeev Arulampalam"], "categories": ["cs.AI"], "doi": "", "_query": "automated valuation model", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/1911.00386v1", "title": "Risk Neutral Valuation of Inflation-Linked Interest Rate Derivatives", "abstract": "We propose a model for the joint evolution of European inflation, the European Central Bank official interest rate and the short-term interest rate, in a stochastic, continuous time setting. We derive the valuation equation for a contingent claim depending potentially on all three factors. This valuation equation reduces to a finite number of Cauchy problems for a degenerate parabolic PDE with non-local terms. We show that the price of the contingent claim is the only viscosity solution of the valuation equation. We also provide an efficient numerical scheme to compute the price and implement it in an example.", "published": "2019-11-01T14:04:02Z", "authors": ["Flavia Antonacci", "Cristina Costantini", "Fernanda D'Ippoliti", "Marco Papi"], "categories": ["q-fin.MF"], "doi": "", "_query": "automated valuation model", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/1908.05419v1", "title": "Modelling Crypto Asset Price Dynamics, Optimal Crypto Portfolio, and Crypto Option Valuation", "abstract": "Despite being described as a medium of exchange, cryptocurrencies do not have the typical attributes of a medium of exchange. Consequently, cryptocurrencies are more appropriately described as crypto assets. A common investment attribute shared by the more than 2,500 crypto assets is that they are highly volatile. An investor interested in reducing price volatility of a portfolio of crypto assets can do so by constructing an optimal portfolio through standard optimization techniques that minimize tail risk. Because crypto assets are not backed by any real assets, forming a hedge to reduce the risk contribution of a single crypto asset can only be done with another set of similar assets (i.e., a set of other crypto assets). A major finding of this paper is that crypto portfolios constructed via optimizations that minimize variance and Conditional Value at Risk outperform a major stock market index (the S$\\&$P 500). As of this writing, options in which the underlying is a crypto asset index are not traded, one of the reasons being that the academic literature has not formulated an acceptable fair pricing model. We offer a fair valuation model for crypto asset options based on a dynamic pricing model for the underlying crypto assets. The model was carefully backtested and therefore offers a reliable model for the underlying crypto assets in the natural world. We then obtain the valuation of crypto options by passing the natural world to the equivalent martingale measure via the Esscher transform. Because of the absence of traded crypto options we could not compare the prices obtained from our valuation model to market prices. Yet, we can claim that if such options on crypto assets are introduced, they should follow closely our theoretical prices after adjusting for market frictions and design feature nuances.", "published": "2019-08-15T04:55:59Z", "authors": ["Yuan Hu", "Svetlozar T. Rachev", "Frank J. Fabozzi"], "categories": ["q-fin.RM", "q-fin.CP"], "doi": "10.3905/jai.2021.1.133", "_query": "automated valuation model", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/1809.09539v3", "title": "The Zariski-Riemann space of valuation domains associated to pseudo-convergent sequences", "abstract": "Let $V$ be a valuation domain with quotient field $K$. Given a pseudo-convergent sequence $E$ in $K$, we study two constructions associating to $E$ a valuation domain of $K(X)$ lying over $V$, especially when $V$ has rank one. The first one has been introduced by Ostrowski, the second one more recently by Loper and Werner. We describe the main properties of these valuation domains, and we give a notion of equivalence on the set of pseudo-convergent sequences of $K$ characterizing when the associated valuation domains are equal. Then, we analyze the topological properties of the Zariski-Riemann spaces formed by these valuation domains.", "published": "2018-09-25T15:19:28Z", "authors": ["Giulio Peruginelli", "Dario Spirito"], "categories": ["math.AC"], "doi": "10.1090/tran/8185", "_query": "automated valuation model", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2407.08293v1", "title": "Generating sequences of rank 1 valuations", "abstract": "We consider a rank 1 valuation $ν$ centered in a regular 3-dimensional local ring $R$. We assume that the residue field $k$ of $ν$ is contained in $R$. An algorithm for constructing a generating sequences for $ν$ in $R$ is provided. This is a generalization of our previous algorithm where only valuations of rational rank 1 were considered. We then give a comparison example of a rational rank 3 valuation centered in a ring of polynomials in three variables for which we explicitly compute a set of key polynomials and a generating sequence.", "published": "2024-07-11T08:39:25Z", "authors": ["Olga Kashcheyeva"], "categories": ["math.AC"], "doi": "", "_query": "automated valuation model", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/1310.3860v1", "title": "Stochastic Modeling and Fair Valuation of Drawdown Insurance", "abstract": "This paper studies the stochastic modeling of market drawdown events and the fair valuation of insurance contracts based on drawdowns. We model the asset drawdown process as the current relative distance from the historical maximum of the asset value. We first consider a vanilla insurance contract whereby the protection buyer pays a constant premium over time to insure against a drawdown of a pre-specified level. This leads to the analysis of the conditional Laplace transform of the drawdown time, which will serve as the building block for drawdown insurance with early cancellation or drawup contingency. For the cancellable drawdown insurance, we derive the investor's optimal cancellation timing in terms of a two-sided first passage time of the underlying drawdown process. Our model can also be applied to insure against a drawdown by a defaultable stock. We provide analytic formulas for the fair premium and illustrate the impact of default risk.", "published": "2013-10-14T21:27:58Z", "authors": ["Hongzhong Zhang", "Tim Leung", "Olympia Hadjiliadis"], "categories": ["q-fin.PR"], "doi": "10.1016/j.insmatheco.2013.10.006", "_query": "automated valuation model", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2109.05616v1", "title": "On the $p$-adic valuation of a hyperfactorial", "abstract": "In this document will be proved a formula to compute the $p$-adic valuation of a hyperfactorial. We call a hyperfactorial the result of multiplying a given number of consecutive integers from 1 to the given number,each raised to its own power. For example, the hyperfactorial of $n$ is equal to: $1^1 2^2 3^3\\dots n^n$ . Lots of studies have been done about the hyperfactorial function, in particular two mathematicians: Glaisher and Kinkelin, who have found the asymptotic behaviour of this function as $n$ that approaches infinity (finding a costant, the Glaisher-Kinkelin costant, which has a lot of expressions using the Euler Gamma function and the Riemann Zeta function). In particular in this document I'll write about the p-adic valuation of this function, or rather the maximum exponent of $p$($p$ a prime integer) such that $p$ raised to that power divides the hyperfactorial of $n$. The formula which I will present uses the famous De-Polignac formula for the $p$-adic valuation of the simple factorial. Then I'll discuss about the asymptotic analysis of our result.", "published": "2021-09-12T21:24:19Z", "authors": ["Luca Onnis"], "categories": ["math.NT"], "doi": "", "_query": "automated valuation model", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/1508.05277v2", "title": "Canonical Valuations and the Birational Section Conjecture", "abstract": "We develop a notion of a `canonical $\\mathcal{C}$-henselian valuation' for a class $\\mathcal{C}$ of field extensions, generalizing the construction of the canonical henselian valuation of a field. We use this to show that the $p$-adic valuation on a finite extension $F$ of $\\mathbb{Q}_p$ can be recovered entirely (or up to some indeterminacy of the residue field) from various small quotients of $G_F$, the absolute Galois group of $F$. In particular, it can be recovered fully from the maximal solvable quotient. We use this to prove several versions of the birational section conjecture for varieties over $p$-adic fields.", "published": "2015-08-21T14:19:27Z", "authors": ["Kristian Strommen"], "categories": ["math.NT"], "doi": "", "_query": "automated valuation model", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2211.12392v2", "title": "Continuous R-valuations", "abstract": "We introduce continuous $R$-valuations on directed-complete posets (dcpos, for short), as a generalization of continuous valuations in domain theory, by extending values of continuous valuations from reals to so-called Abelian d-rags $R$. Like the valuation monad $\\mathbf{V}$ introduced by Jones and Plotkin, we show that the construction of continuous $R$-valuations extends to a strong monad $\\mathbf{V}^R$ on the category of dcpos and Scott-continuous maps. Additionally, and as in recent work by the two authors and C. Théron, and by the second author, B. Lindenhovius, M. Mislove and V. Zamdzhiev, we show that we can extract a commutative monad $\\mathbf{V}^R_m$ out of it, whose elements we call minimal $R$-valuations. We also show that continuous $R$-valuations have close connections to measures when $R$ is taken to be $\\mathbf{I}\\mathbb{R}^\\star_+$, the interval domain of the extended nonnegative reals: (1) On every coherent topological space, every non-zero, bounded $τ$-smooth measure $μ$ (defined on the Borel $σ$-algebra), canonically determines a continuous $\\mathbf{I}\\mathbb{R}^\\star_+$-valuation; and (2) such a continuous $\\mathbf{I}\\mathbb{R}^\\star_+$-valuation is the most precise (in a certain sense) continuous $\\mathbf{I}\\mathbb{R}^\\star_+$-valuation that approximates $μ$, when the support of $μ$ is a compact Hausdorff subspace of a second-countable stably compact topological space. This in particular applies to Lebesgue measure on the unit interval. As a result, the Lebesgue measure can be identified as a continuous $\\mathbf{I}\\mathbb{R}^\\star_+$-valuation. Additionally, we show that the latter is minimal.", "published": "2022-11-22T16:39:17Z", "authors": ["Jean Goubault-Larrecq", "Xiaodong Jia"], "categories": ["math.GN", "cs.LO"], "doi": "10.46298/entics.10351", "_query": "automated valuation model", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2604.18243v2", "title": "On the market-consistent valuation of health insurance liabilities", "abstract": "We are concerned with the market-consistent valuation of lifelong health insurance products, which are subject to adjustments derived from the actuarial equivalence principle and driven by (medical) inflation. Such products are well-established in the European national markets, and the dynamics of the adjustment mechanism is well-understood from an actuarial perspective. However, the question of market-consistent valuation (as is necessary for Solvency II reporting) has not previously been addressed. This gap has led to a situation where some practitioners use stochastic models while others rely on deterministic methods to assign market-consistent values (Best Estimates) to the same type of health insurance liabilities. The purpose of this note is to fill this gap by showing that the Best Estimate of a lifelong health insurance policy depends on the choice of model for the interest and inflation rates. That is, the Best Estimate is not uniquely determined by the currently prevailing term structures of nominal and real spot rates, whence a deterministic calculation is theoretically unjustified. Furthermore, we construct a valuation portfolio such that the Best Estimate valuation decouples into calculations of 1.) deterministic coefficients derived from policy data and 2.) the prices of basis financial instruments that are independent of the individual policy data. Using this decomposition, the policies do not have to be tracked individually along each generated stochastic path. This allows for a more efficient evaluation of the Best Estimate for a large stock of policies with a stochastic model.", "published": "2026-04-20T13:24:35Z", "authors": ["Simon Hochgerner", "Jonas Ingmanns", "Nicole Kastanek"], "categories": ["q-fin.MF"], "doi": "", "_query": "automated valuation model", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/nlin/0009015v1", "title": "Opportunities Knocking: Residual Income Valuation of an Adaptive Firm", "abstract": "Maintaining a competitive edge requires a firm to replace deteriorating business lines with new projects. Accordingly, part of a firm's value resides in its ability to exploit new opportunities. This article incorporates adaptation into Ohlson's residual income valuation framework and obtains a non-linear (convex) valuation formula. Although parsimoniously cast, the model makes two predictions which are consistent with phenomena reported in the empirical literature: earnings convexity and complementarity. Moreover, the Appendix introduces a new and powerful Equivalence Theorem. This Equivalence Theorem relates Modigliani-Miller dividend invariance to complementarity and earnings convexity in accounting-based valuation. For Web-based Abstract, see: http://papers.ssrn.com/paper.taf?abstract_id=239368", "published": "2000-09-06T16:17:10Z", "authors": ["Kenton K. Yee"], "categories": ["nlin.AO", "nlin.SI", "physics.pop-ph", "physics.soc-ph"], "doi": "", "_query": "automated valuation model", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2204.03365v1", "title": "Valuations with infinite limit-depth", "abstract": "For a certain field $K$, we construct a valuation-algebraic valuation on the polynomial ring $K[x]$, whose Maclane--Vaquié chain consists of an infinite (countable) number of limit augmentations", "published": "2022-04-07T11:24:09Z", "authors": ["Maria Alberich-Carramiñana", "Jordi Guàrdia", "Enric Nart", "Joaquim Roé"], "categories": ["math.AC"], "doi": "", "_query": "automated valuation model", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2512.23078v1", "title": "Deep Learning for Art Market Valuation", "abstract": "We study how deep learning can improve valuation in the art market by incorporating the visual content of artworks into predictive models. Using a large repeated-sales dataset from major auction houses, we benchmark classical hedonic regressions and tree-based methods against modern deep architectures, including multi-modal models that fuse tabular and image data. We find that while artist identity and prior transaction history dominate overall predictive power, visual embeddings provide a distinct and economically meaningful contribution for fresh-to-market works where historical anchors are absent. Interpretability analyses using Grad-CAM and embedding visualizations show that models attend to compositional and stylistic cues. Our findings demonstrate that multi-modal deep learning delivers significant value precisely when valuation is hardest, namely first-time sales, and thus offers new insights for both academic research and practice in art market valuation.", "published": "2025-12-28T21:04:09Z", "authors": ["Jianping Mei", "Michael Moses", "Jan Waelty", "Yucheng Yang"], "categories": ["q-fin.GN", "cs.AI", "cs.CV", "cs.LG", "econ.GN"], "doi": "", "_query": "automated valuation model", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2404.07658v1", "title": "Enhancing Valuation of Variable Annuities in Lévy Models with Stochastic Interest Rate", "abstract": "This paper extends the valuation and optimal surrender framework for variable annuities with guaranteed minimum benefits in a Lévy equity market environment by incorporating a stochastic interest rate described by the Hull-White model. This approach frames a more dynamic and realistic financial setting compared to previous literature. We exploit a robust valuation mechanism employing a hybrid numerical method that merges tree methods for interest rate modeling with finite difference techniques for the underlying asset price. This method is particularly effective for addressing the complexities of variable annuities, where periodic fees and mortality risks are significant factors. Our findings reveal the influence of stochastic interest rates on the strategic decision-making process concerning the surrender of these financial instruments. Through comprehensive numerical experiments, and by comparing our results with those obtained through the Longstaff-Schwartz Monte Carlo method, we illustrate how our refined model can guide insurers in designing contracts that equitably balance the interests of both parties. This is particularly relevant in discouraging premature surrenders while adapting to the realistic fluctuations of financial markets. Lastly, a comparative statics analysis with varying interest rate parameters underscores the impact of interest rates on the cost of the optimal surrender strategy, emphasizing the importance of accurately modeling stochastic interest rates.", "published": "2024-04-11T11:44:20Z", "authors": ["Ludovic Goudenège", "Andrea Molent", "Xiao Wei", "Antonino Zanette"], "categories": ["q-fin.PR"], "doi": "", "_query": "automated valuation model", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2511.06463v3", "title": "Error Estimate and Convergence Analysis for Data Valuation", "abstract": "Data valuation quantifies data importance, but existing methods cannot ensure validity in a single training process. The neural dynamic data valuation (NDDV) method [3] addresses this limitation. Based on NDDV, we are the first to explore error estimation and convergence analysis in data valuation. Under Lipschitz and smoothness assumptions, we derive quadratic error bounds for loss differences that scale inversely with time steps and quadratically with control variations, ensuring stability. We also prove that the expected squared gradient norm for the training loss vanishes asymptotically, and that the meta loss converges sublinearly over iterations. In particular, NDDV achieves sublinear convergence.", "published": "2025-11-09T17:15:40Z", "authors": ["Zhangyong Liang", "Huanhuan Gao", "Ji Zhang"], "categories": ["cs.LG"], "doi": "", "_query": "automated valuation model", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2307.16619v1", "title": "A Common Shock Model for multidimensional electricity intraday price modelling with application to battery valuation", "abstract": "In this paper, we propose a multidimensional statistical model of intraday electricity prices at the scale of the trading session, which allows all products to be simulated simultaneously. This model, based on Poisson measures and inspired by the Common Shock Poisson Model, reproduces the Samuelson effect (intensity and volatility increases as time to maturity decreases). It also reproduces the price correlation structure, highlighted here in the data, which decreases as two maturities move apart. This model has only three parameters that can be estimated using a moment method that we propose here. We demonstrate the usefulness of the model on a case of storage valuation by dynamic programming over a trading session.", "published": "2023-07-31T12:55:57Z", "authors": ["Thomas Deschatre", "Xavier Warin"], "categories": ["q-fin.ST", "q-fin.TR", "stat.AP"], "doi": "", "_query": "automated valuation model", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/1601.04533v1", "title": "Gauss-Bonnet for multi-linear valuations", "abstract": "We prove Gauss-Bonnet and Poincare-Hopf formulas for multi-linear valuations on finite simple graphs G=(V,E) and answer affirmatively a conjecture of Gruenbaum from 1970 by constructing higher order Dehn-Sommerville valuations which vanish for all d-graphs without boundary. An example of a quadratic valuation is the Wu characteristic w(G) which sums (-1)^(dim(x)+dim(y)) over all intersecting pairs of complete subgraphs x,y of a G. More generally, an intersection number w(A,B) sums (-1)^(dim(x)+dim(y)) over pairs x,y, where x is in A and y is in B and x,y intersect. w(G) is a quadratic Euler characteristic X(G), where X sums (-1)^dim(x) over all complete subgraphs x of G. We prove that w is multiplicative, like Euler characteristic: w(G x H) = w(G) w(H) for any two graphs and that w is invariant under Barycentric refinements. We construct a curvature K satisfying Gauss-Bonnet w(G) = sum K(a). We also prove w(G) = X(G)-X(dG) for Euler characteristic X which holds for any d-graph G with boundary dG. We also show higher order Poincare-Hopf formulas: there is for every multi-linear valuation X and function f an index i(a) such that sum i(a)=X(G). For d-graphs G and X=w it agrees with the Euler curvature. For the vanishing multi-valuations which were conjectured to exist, like for the quadratic valuation X(G) = (V X) Y with X=(1,-1,1,-1,1),Y=(0,-2,3,-4,5) on 4-graphs, discrete 4 manifolds, where V_{ij}(G) is the f-matrix counting the number of i and j-simplices in G intersecting, the curvature is constant zero. For all graphs and multi-linear Dehn-Sommerville relations, the Dehn-Sommerville curvature K(v) at a vertex is a Dehn-Sommerville valuation on the unit sphere S(v). We show X V(G) Y = v(G) Y for any linear valuation Y of a d-graph G with f-vector v(G). This provides examples for the Gruenbaum conjecture.", "published": "2016-01-18T14:36:46Z", "authors": ["Oliver Knill"], "categories": ["cs.DM", "math.CO", "math.GN"], "doi": "", "_query": "automated valuation model", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/math/0004016v1", "title": "On the valuation of Paris options: foundational results", "abstract": "This paper adresses the valuation of the Paris barrier options proposed by Yor, Jeanblanc-Picque, and Chesnay (Advances in Applied Probability, 29(1997), 165-184) using the Laplace transform approach. Based on suggestions by Pliska the notion of Paris options is extended such that their valuation is possible at any point during their lifespan. The Laplace transforms derived by Yor et al. are modified when necessary, and their basic analytic properties are discussed.", "published": "2000-04-04T15:30:14Z", "authors": ["Michael Schröder"], "categories": ["math.CA", "math.PR", "q-fin.CP"], "doi": "", "_query": "automated valuation model", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2009.09572v1", "title": "Volterra mortality model: Actuarial valuation and risk management with long-range dependence", "abstract": "While abundant empirical studies support the long-range dependence (LRD) of mortality rates, the corresponding impact on mortality securities are largely unknown due to the lack of appropriate tractable models for valuation and risk management purposes. We propose a novel class of Volterra mortality models that incorporate LRD into the actuarial valuation, retain tractability, and are consistent with the existing continuous-time affine mortality models. We derive the survival probability in closed-form solution by taking into account of the historical health records. The flexibility and tractability of the models make them useful in valuing mortality-related products such as death benefits, annuities, longevity bonds, and many others, as well as offering optimal mean-variance mortality hedging rules. Numerical studies are conducted to examine the effect of incorporating LRD into mortality rates on various insurance products and hedging efficiency.", "published": "2020-09-21T02:04:23Z", "authors": ["Ling Wang", "Mei Choi Chiu", "Hoi Ying Wong"], "categories": ["q-fin.MF", "q-fin.RM"], "doi": "", "_query": "automated valuation model", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2506.12619v1", "title": "Semivalue-based data valuation is arbitrary and gameable", "abstract": "The game-theoretic notion of the semivalue offers a popular framework for credit attribution and data valuation in machine learning. Semivalues have been proposed for a variety of high-stakes decisions involving data, such as determining contributor compensation, acquiring data from external sources, or filtering out low-value datapoints. In these applications, semivalues depend on the specification of a utility function that maps subsets of data to a scalar score. While it is broadly agreed that this utility function arises from a composition of a learning algorithm and a performance metric, its actual instantiation involves numerous subtle modeling choices. We argue that this underspecification leads to varying degrees of arbitrariness in semivalue-based valuations. Small, but arguably reasonable changes to the utility function can induce substantial shifts in valuations across datapoints. Moreover, these valuation methodologies are also often gameable: low-cost adversarial strategies exist to exploit this ambiguity and systematically redistribute value among datapoints. Through theoretical constructions and empirical examples, we demonstrate that a bad-faith valuator can manipulate utility specifications to favor preferred datapoints, and that a good-faith valuator is left without principled guidance to justify any particular specification. These vulnerabilities raise ethical and epistemic concerns about the use of semivalues in several applications. We conclude by highlighting the burden of justification that semivalue-based approaches place on modelers and discuss important considerations for identifying appropriate uses.", "published": "2025-06-14T20:20:15Z", "authors": ["Hannah Diehl", "Ashia C. Wilson"], "categories": ["cs.LG", "cs.GT"], "doi": "", "_query": "automated valuation model", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/1109.1751v1", "title": "Time-Consistent Actuarial Valuations", "abstract": "Recent theoretical results establish that time-consistent valuations (i.e. pricing operators) can be created by backward iteration of one-period valuations. In this paper we investigate the continuous-time limits of well-known actuarial premium principles when such backward iteration procedures are applied. We show that the one-period variance premiumprinciple converges to the non-linear exponential indifference valuation. Furthermore, we study the convergence of the one-period standard-deviation principle and establish that the Cost-of-Capital principle, which is widely used by the insurance industry, converges to the same limit as the standard-deviation principle. Finally, we study the connections between our time-consistent pricing operators, Good Deal Bound pricing and pricing under model ambiguity.", "published": "2011-09-08T15:55:46Z", "authors": ["Antoon Pelsser"], "categories": ["q-fin.PR"], "doi": "", "_query": "automated valuation model", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2607.11381v1", "title": "Common extensions of valuations to rational function fields", "abstract": "Let (K(X)|K,w) be a valuation transcendental extension of rational function fields and take a minimal pair of definition (a, gamma). In this paper, we characterize those K-conjugates a' of a such that the monomial valuation induced by the pair (a', gamma) restricts to w on K(X). In particular, we show that a' satisfies this if and only if a' and a are conjugates over the henselization of (K,v). The second part of the paper concerns abstract key polynomials. We introduce the notion of a regular limit key polynomial, and more generally, that of a regular complete sequence of key polynomials. We prove that regularity is equivalent to the property that every root of every key polynomial determines the corresponding truncated valuation. This extends earlier work of Mahboub, Mansour and Spivakovsky by allowing both limit key polynomials and valuation algebraic extensions. As a consequence, we obtain that w always admits a regular complete sequence of key polynomials whenever (K,v) is dense in its henselization.", "published": "2026-07-13T10:42:28Z", "authors": ["Arpan Dutta", "Wael Mahboub"], "categories": ["math.AC", "math.AG"], "doi": "", "_query": "automated valuation model", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2302.00416v1", "title": "Valuations on Convex Bodies and Functions", "abstract": "An introduction to geometric valuation theory is given. The focus is on classification results for $\\operatorname{SL}(n)$ invariant and rigid motion invariant valuations on convex bodies and on convex functions.", "published": "2023-02-01T13:02:26Z", "authors": ["Monika Ludwig", "Fabian Mussnig"], "categories": ["math.MG", "math.FA"], "doi": "10.1007/978-3-031-37883-6_2", "_query": "automated valuation model", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2504.14292v1", "title": "Optimal Operation and Valuation of Electricity Storages", "abstract": "This paper applies computational techniques of convex stochastic optimization to optimal operation and valuation of electricity storages in the face of uncertain electricity prices. Our approach is applicable to various specifications of storages, and it allows for e.g.\\ hard constraints on storage capacity and charging speed. Our valuations are based on the indifference pricing principle, which builds on optimal trading strategies and calibrates to the user's initial position, market views and risk preferences. We illustrate the effects of storage capacity and charging speed by numerically computing the valuations using stochastic dual dynamic programming.", "published": "2025-04-19T13:35:33Z", "authors": ["Jean-Philippe Chancelier", "Michel De Lara", "François Pacaud", "Teemu Pennanen", "Ari-Pekka Perkkiö"], "categories": ["math.OC"], "doi": "", "_query": "automated valuation model", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2608.09961v1", "title": "On the Relationship Between Semiring-Induced Valuation Algebra Isomorphisms and Semiring Isomorphisms", "abstract": "This paper establishes a bidirectional relationship between isomorphisms of semirings and isomorphisms of their induced valuation algebras. We show that a semiring isomorphism is sufficient for a valuation algebra isomorphism. Conversely, for additively idempotent semirings without multiplicatively idempotent zero divisors, the existence of a valuation algebra isomorphism implies that the underlying semirings are isomorphic.", "published": "2026-07-25T00:54:34Z", "authors": ["Xuechong Guan"], "categories": ["math.RA", "math.AC"], "doi": "", "_query": "automated valuation model", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2510.14434v1", "title": "The valuation of the discriminant of a hypersurface", "abstract": "Let $R$ be a discrete valuation ring, with valuation $v \\colon R \\twoheadrightarrow \\mathbb{Z}_{\\ge 0} \\cup \\{\\infty\\}$ and residue field $k$. Let $H$ be a hypersurface $\\operatorname{Proj}(R[x_0,\\ldots,x_n]/\\langle f \\rangle)$. Let $H_k$ be the special fiber, and let $(H_k)_{\\mathrm{sing}}$ be its singular subscheme. Let $Δ(f)$ be the discriminant of $f$. We use Zariski's main theorem and degeneration arguments to prove that $v(Δ(f))=1$ if and only if $H$ is regular and $(H_k)_{\\mathrm{sing}}$ consists of a nondegenerate double point over $k$. We also give lower bounds on $v(Δ(f))$ when $H_k$ has multiple singularities or a positive-dimensional singularity.", "published": "2025-10-16T08:36:20Z", "authors": ["Bjorn Poonen", "Michael Stoll"], "categories": ["math.AG", "math.NT"], "doi": "", "_query": "automated valuation model", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/1605.06425v2", "title": "Extension of valuations in characteristic one", "abstract": "We develop an extension of valuations theorem for suitable extensions of idempotent semirings. As an application, we give a new proof for the classical case of fields. Along the way, we develop characteristic one analogues of some central results in the theory of valuation rings.", "published": "2016-05-20T16:28:49Z", "authors": ["Jeffrey Tolliver"], "categories": ["math.RA", "math.NT"], "doi": "", "_query": "automated valuation model", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2608.12594v1", "title": "What Makes a Peer? Valuation-Anchored Similarity in Private Markets", "abstract": "As more investors contemplate private markets and contend with limited transparency, sparse disclosures, and infrequent transactions, identifying economically meaningful peer companies for comparison is a fundamental challenge for valuation, due diligence, portfolio construction, and risk management. We propose an ensemble tree-based supervised similarity learning framework that defines company similarity through the lens of market valuation rather than static feature matching or semantic descriptions. Specifically, we train a CatBoost gradient-boosted decision tree model on observed private company valuations and derive a valuation-aware similarity metric from importance-weighted leaf-node co-occurrences across the ensemble. The similarity metric captures shared valuation drivers while accommodating nonlinear relationships, mixed data types, and pervasive missing data common in private markets. Using a global private-market universe of approximately 270,000 companies, including more than 53,000 firms with observed or derivable post-money valuations spanning multiple industries, geographies, and deal stages, we demonstrate that the proposed similarity framework improves upon traditional distance-based and text-embedding-based approaches in downstream k-nearest-neighbor valuation tasks in the evaluated industry groups, while retaining case-based explainability.", "published": "2026-08-12T21:06:51Z", "authors": ["Sebastian Frank", "Jingrao Lyu", "Max Jarmey", "Preetha Saha", "Mingshu Li", "Sweet Kaur", "Sola Akinola", "Dhagash Mehta"], "categories": ["q-fin.ST", "cs.AI", "cs.LG"], "doi": "", "_query": "automated valuation model", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2005.00486v2", "title": "The support of dually epi-translation invariant valuations on convex functions", "abstract": "We study dually epi-translation invariant valuations on cones of convex functions containing the space of finite-valued convex functions. The existence of a homogeneous decomposition is used to associate a distribution to every valuation of this type similar to the Goodey-Weil embedding for translation invariant valuations on convex bodies. The relation between the valuation and its associated distribution is used to establish a notion of support for valuations. As an application, we show that there are no $\\mathrm{SL}(n)$ or translation invariant valuations except constant valuations in this class and we discuss which valuations on finite-valued convex functions can be extended to larger cones. In addition, we examine some topological properties of spaces of valuations with compact support.", "published": "2020-04-30T12:07:08Z", "authors": ["Jonas Knoerr"], "categories": ["math.MG", "math.FA"], "doi": "10.1016/j.jfa.2021.109059", "_query": "automated valuation model", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2304.01451v1", "title": "q-Partitioning Valuations: Exploring the Space Between Subadditive and Fractionally Subadditive Valuations", "abstract": "For a set $M$ of $m$ elements, we define a decreasing chain of classes of normalized monotone-increasing valuation functions from $2^M$ to $\\mathbb{R}_{\\geq 0}$, parameterized by an integer $q \\in [2,m]$. For a given $q$, we refer to the class as $q$-partitioning. A valuation function is subadditive if and only if it is $2$-partitioning, and fractionally subadditive if and only if it is $m$-partitioning. Thus, our chain establishes an interpolation between subadditive and fractionally subadditive valuations. We show that this interpolation is smooth, interpretable , and non-trivial. We interpolate prior results that separate subadditive and fractionally subadditive for all $q \\in \\{2,\\ldots, m\\}$. Two highlights are the following:(i) An $Ω\\left(\\frac{\\log \\log q}{\\log \\log m}\\right)$-competitive posted price mechanism for $q$-partitioning valuations. Note that this matches asymptotically the state-of-the-art for both subadditive ($q=2$) [DKL20], and fractionally subadditive ($q=m$) [FGL15]. (ii)Two upper-tail concentration inequalities on $1$-Lipschitz, $q$-partitioning valuations over independent items. One extends the state-of-the-art for $q=m$ to $q<m$, the other improves the state-of-the-art for $q=2$ for $q > 2$. Our concentration inequalities imply several corollaries that interpolate between subadditive and fractionally subadditive, for example: $\\mathbb{E}[v(S)]\\le (1 + 1/\\log q)\\text{Median}[v(S)] + O(\\log q)$. To prove this, we develop a new isoperimetric inequality using Talagrand's method of control by $q$ points, which may be of independent interest. We also discuss other probabilistic inequalities and game-theoretic applications of $q$-partitioning valuations, and connections to subadditive MPH-$k$ valuations [EFNTW19].", "published": "2023-04-04T01:51:05Z", "authors": ["Kiril Bangachev", "S. Matthew Weinberg"], "categories": ["cs.GT"], "doi": "", "_query": "automated valuation model", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/1905.01099v1", "title": "PDE models for the valuation of a non callable defaultable coupon bond under an extended JDCEV model", "abstract": "We consider a two-factor model for the valuation of a non callable defaultable bond which pays coupons at certain given dates. The model under consideration is the Jump to Default Constant Elasticity of Variance (JDCEV) model. The JDCEV model is an improvement of the reduced form approach, which unifies credit and equity models into a single framework allowing for stochastic and possible negative interest rates. From the mathematical point of view, the valuation involves two partial differential equation (PDE) problems for each coupon. First, we obtain the existence of solution for these PDE problems. In order to solve them, we propose appropriate numerical schemes based on a Crank-Nicolson semi-Lagrangian method for time discretization combined with bi-quadratic Lagrange finite elements for space discretization. Once the numerical solutions of the PDEs are obtained, a post-processing procedure is carried out in order to achieve the value of the bond. This post-processing includes the computation of an integral term which is approximated by using the composite trapezoidal rule. Finally, we present some numerical results for real market bonds issued by different firms in order to illustrate the proper behaviour of the numerical schemes. Moreover, we obtain an agreement between the numerical results from the PDE approach and those ones obtained by applying a Monte Carlo technique and an asymptotic approximation method.", "published": "2019-05-03T10:03:01Z", "authors": ["M. C. Calvo-Garrido", "S. Diop", "A. Pascucci", "C. Vázquez"], "categories": ["q-fin.CP"], "doi": "", "_query": "automated valuation model", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2010.11832v1", "title": "Ordered fields dense in their real closure and definable convex valuations", "abstract": "In this paper, we undertake a systematic model and valuation theoretic study of the class of ordered fields which are dense in their real closure. We apply this study to determine definable henselian valuations on ordered fields, in the language of ordered rings. In light of our results, we re-examine the Shelah-Hasson Conjecture (specialised to ordered fields) and provide an example limiting its valuation theoretic conclusions.", "published": "2020-10-22T16:09:05Z", "authors": ["Lothar Sebastian Krapp", "Salma Kuhlmann", "Gabriel Lehéricy"], "categories": ["math.LO"], "doi": "10.1515/forum-2020-0030", "_query": "automated valuation model", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2310.00958v1", "title": "Constant Approximation for Private Interdependent Valuations", "abstract": "The celebrated model of auctions with interdependent valuations, introduced by Milgrom and Weber in 1982, has been studied almost exclusively under private signals $s_1, \\ldots, s_n$ of the $n$ bidders and public valuation functions $v_i(s_1, \\ldots, s_n)$. Recent work in TCS has shown that this setting admits a constant approximation to the optimal social welfare if the valuations satisfy a natural property called submodularity over signals (SOS). More recently, Eden et al. (2022) have extended the analysis of interdependent valuations to include settings with private signals and private valuations, and established $O(\\log^2 n)$-approximation for SOS valuations. In this paper we show that this setting admits a constant factor approximation, settling the open question raised by Eden et al. (2022).", "published": "2023-10-02T07:51:35Z", "authors": ["Alon Eden", "Michal Feldman", "Kira Goldner", "Simon Mauras", "Divyarthi Mohan"], "categories": ["cs.GT", "cs.DS"], "doi": "", "_query": "automated valuation model", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/1609.05236v3", "title": "Minimal plane valuations", "abstract": "We consider the last value $\\hatμ (ν)$ of the vanishing sequence of $H^0(L)$ along a divisorial or irrational valuation $ν$ centered at $\\mathcal{O}_{\\mathbb{P}^2,p}$, where $L$ resp. $p$ is a line resp. a point of the projective plane $\\mathbb{P}^2$ over an algebraically closed field. This value contains, for valuations, similar information as that given by Seshadri constants for points. It is always true that $\\hatμ (ν) \\geq \\sqrt{1 / \\mathrm{vol}(ν)}$ and minimal valuations are those satisfying the equality. In this paper, we prove that the Greuel-Lossen-Shustin Conjecture implies a variation of the Nagata Conjecture involving minimal valuations (that extends the one stated in the paper \"Very general monomial valuations of $\\mathbb{P}^2$ and a Nagata type conjecture\" by Dumnicki et al. to the whole set of divisorial and irrational valuations of the projective plane) which also implies the original one. We also provide infinitely many families of very general minimal valuations with an arbitrary number of Puiseux exponents, and an asymptotic result that can be considered as an evidence in the direction of the mentioned conjecture by Dumnicki et al.", "published": "2016-09-16T21:01:23Z", "authors": ["Carlos Galindo", "Francisco Monserrat", "Julio José Moyano-Fernández"], "categories": ["math.AG", "math.AC"], "doi": "", "_query": "automated valuation model", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2210.14518v1", "title": "Which Factors Matter Most? Can Startup Valuation be Micro-Targeted?", "abstract": "While startup valuations are influenced by revenues, risks, age, and macroeconomic conditions, specific causality is traditionally a black box. Because valuations are not disclosed, roles played by other factors (industry, geography, and intellectual property) can often only be guessed at. VC valuation research indicates the importance of establishing a factor-hierarchy to better understand startup valuations and their dynamics, suggesting the wisdom of hiring data-scientists for this purpose. Bespoke understanding can be established via construction of hierarchical prediction models based on decision trees and random forests. These have the advantage of understanding which factors matter most. In combination with OLS, the also tell us the circumstances of when specific causalities apply. This study explores the deterministic role of categorical variables on the valuation of start-ups (i.e. the joint-combination geographic, urban, and sectoral denomination-variables), in order to be able to build a generalized valuation scorecard approach. Using a dataset of 1,091 venture-capital investments, containing 1,044 unique EU and EEA, this study examines microeconomic, sectoral, and local-level impacts on startup valuation. In principle, the study relies on Fixedeffects and Joint-fixed-effects regressions as well as the analysis and exploration of divergent micropopulations and fault-lines by means of non-parametric approaches combining econometric and machinelearning techniques.", "published": "2022-10-26T07:09:01Z", "authors": ["Max Berre"], "categories": ["q-fin.GN"], "doi": "", "_query": "automated valuation model", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2608.26015v1", "title": "Isometry invariant valuations on spherical polytopes", "abstract": "We show that every continuous and isometry invariant valuation on spherical polytopes is a linear combination of the spherical intrinsic volumes. The proof relies on a weak differentiability property satisfied by valuations on polytopes in $\\mathbb{R}^n$ with a natural smoothness property with respect to the action of the affine group. This enables us to transfer several results established by Alesker for quasi-smooth valuations to the polytopal setting, and to reduce the problem to the translation invariant case of measurable valuations on polytopes.", "published": "2026-08-26T16:57:33Z", "authors": ["Jonas Knoerr"], "categories": ["math.MG"], "doi": "", "_query": "automated valuation model", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2406.11852v2", "title": "Risks from Language Models for Automated Mental Healthcare: Ethics and Structure for Implementation", "abstract": "Amidst the growing interest in developing task-autonomous AI for automated mental health care, this paper addresses the ethical and practical challenges associated with the issue and proposes a structured framework that delineates levels of autonomy, outlines ethical requirements, and defines beneficial default behaviors for AI agents in the context of mental health support. We also evaluate fourteen state-of-the-art language models (ten off-the-shelf, four fine-tuned) using 16 mental health-related questionnaires designed to reflect various mental health conditions, such as psychosis, mania, depression, suicidal thoughts, and homicidal tendencies. The questionnaire design and response evaluations were conducted by mental health clinicians (M.D.s). We find that existing language models are insufficient to match the standard provided by human professionals who can navigate nuances and appreciate context. This is due to a range of issues, including overly cautious or sycophantic responses and the absence of necessary safeguards. Alarmingly, we find that most of the tested models could cause harm if accessed in mental health emergencies, failing to protect users and potentially exacerbating existing symptoms. We explore solutions to enhance the safety of current models. Before the release of increasingly task-autonomous AI systems in mental health, it is crucial to ensure that these models can reliably detect and manage symptoms of common psychiatric disorders to prevent harm to users. This involves aligning with the ethical framework and default behaviors outlined in our study. We contend that model developers are responsible for refining their systems per these guidelines to safeguard against the risks posed by current AI technologies to user mental health and safety. Trigger warning: Contains and discusses examples of sensitive mental health topics, including suicide and self-harm.", "published": "2024-04-02T15:05:06Z", "authors": ["Declan Grabb", "Max Lamparth", "Nina Vasan"], "categories": ["cs.CY", "cs.AI", "cs.CL"], "doi": "", "_query": "automated valuation model", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2104.02286v2", "title": "The Brauer group of a valuation ring", "abstract": "Let $V$ be a valuation ring and $K$ be its field of fraction. We show that the canonical map $\\Br(V) \\to \\Br(K)$ is injective.", "published": "2021-04-06T04:58:14Z", "authors": ["Vivek Sadhu"], "categories": ["math.AG"], "doi": "", "_query": "automated valuation model", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2401.00263v2", "title": "A framework for the valuation of insurance liabilities by production cost", "abstract": "This paper sets out a framework for the valuation of insurance liabilities that is intended to be economically realistic, elementary, reasonably practically applicable, and as a special case to provide a basis for the valuation in regulatory solvency systems such as Solvency II and the SST. The valuation framework is based on the cost of producing the liabilities to an insurance company that is subject to solvency regulation (regulatory solvency capital requirements) and insolvency laws (consequences of failure) in finite discrete time. Starting from the replication approach of classical no-arbitrage theory, the framework additionally considers the nature and cost of capital (expressed by a ``financiability condition\"), that the liabilities may be required to be fulfilled only ``in sufficiently many cases\" (expressed by a ``fulfillment condition\"), production using ``fully illiquid\" assets in addition to tradables, and the asymmetry between assets and liabilities. We identify necessary and sufficient conditions on the capital investment under which the framework recovers the market prices of tradables, investigate extending production to take account of insolvency, implications of using illiquid assets in the production, and show how Solvency II and SST valuation can be derived with specific assumptions.", "published": "2023-12-30T15:30:44Z", "authors": ["Christoph Moehr"], "categories": ["q-fin.PR"], "doi": "", "_query": "automated valuation model", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2111.14344v1", "title": "On the implicit constant fields and key polynomials for valuation algebraic extensions", "abstract": "This article is a natural construction of our previous works. In this article, we employ similar ideas due to MacLane to provide an estimate of IC(K(X)|K,v) when (K(X)|K,v) is a valuation algebraic extension. Our central result is an analogue of the estimate obtained in the valuation transcendental case. We further provide a natural construction of a complete sequence of key polynomials for v over K in the setting of valuation algebraic extensions.", "published": "2021-11-29T06:26:11Z", "authors": ["Arpan Dutta"], "categories": ["math.AG", "math.AC"], "doi": "", "_query": "automated valuation model", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2306.10473v2", "title": "2D-Shapley: A Framework for Fragmented Data Valuation", "abstract": "Data valuation -- quantifying the contribution of individual data sources to certain predictive behaviors of a model -- is of great importance to enhancing the transparency of machine learning and designing incentive systems for data sharing. Existing work has focused on evaluating data sources with the shared feature or sample space. How to valuate fragmented data sources of which each only contains partial features and samples remains an open question. We start by presenting a method to calculate the counterfactual of removing a fragment from the aggregated data matrix. Based on the counterfactual calculation, we further propose 2D-Shapley, a theoretical framework for fragmented data valuation that uniquely satisfies some appealing axioms in the fragmented data context. 2D-Shapley empowers a range of new use cases, such as selecting useful data fragments, providing interpretation for sample-wise data values, and fine-grained data issue diagnosis.", "published": "2023-06-18T04:30:12Z", "authors": ["Zhihong Liu", "Hoang Anh Just", "Xiangyu Chang", "Xi Chen", "Ruoxi Jia"], "categories": ["cs.LG"], "doi": "", "_query": "automated valuation model", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/1411.6844v1", "title": "On the quantifier complexity of definable canonical henselian valuations", "abstract": "We discuss definability in the language of rings without parameters of the unique canonical henselian valuation of a field. We show that in most cases where the canonical henselian valuation is definable, it is already definable by a universal-existential or an existential-universal formula.", "published": "2014-11-25T12:55:38Z", "authors": ["Arno Fehm", "Franziska Jahnke"], "categories": ["math.LO"], "doi": "", "_query": "automated valuation model", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2410.04386v1", "title": "Data Distribution Valuation", "abstract": "Data valuation is a class of techniques for quantitatively assessing the value of data for applications like pricing in data marketplaces. Existing data valuation methods define a value for a discrete dataset. However, in many use cases, users are interested in not only the value of the dataset, but that of the distribution from which the dataset was sampled. For example, consider a buyer trying to evaluate whether to purchase data from different vendors. The buyer may observe (and compare) only a small preview sample from each vendor, to decide which vendor's data distribution is most useful to the buyer and purchase. The core question is how should we compare the values of data distributions from their samples? Under a Huber characterization of the data heterogeneity across vendors, we propose a maximum mean discrepancy (MMD)-based valuation method which enables theoretically principled and actionable policies for comparing data distributions from samples. We empirically demonstrate that our method is sample-efficient and effective in identifying valuable data distributions against several existing baselines, on multiple real-world datasets (e.g., network intrusion detection, credit card fraud detection) and downstream applications (classification, regression).", "published": "2024-10-06T07:56:53Z", "authors": ["Xinyi Xu", "Shuaiqi Wang", "Chuan-Sheng Foo", "Bryan Kian Hsiang Low", "Giulia Fanti"], "categories": ["cs.LG"], "doi": "", "_query": "automated valuation model", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2504.06163v1", "title": "Action Valuation in Sports: A Survey", "abstract": "Action Valuation (AV) has emerged as a key topic in Sports Analytics, offering valuable insights by assigning scores to individual actions based on their contribution to desired outcomes. Despite a few surveys addressing related concepts such as Player Valuation, there is no comprehensive review dedicated to an in-depth analysis of AV across different sports. In this survey, we introduce a taxonomy with nine dimensions related to the AV task, encompassing data, methodological approaches, evaluation techniques, and practical applications. Through this analysis, we aim to identify the essential characteristics of effective AV methods, highlight existing gaps in research, and propose future directions for advancing the field.", "published": "2025-04-08T15:59:19Z", "authors": ["Artur Xarles", "Sergio Escalera", "Thomas B. Moeslund", "Albert Clapés"], "categories": ["cs.CV"], "doi": "", "_query": "automated valuation model", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2405.13954v1", "title": "What is Your Data Worth to GPT? LLM-Scale Data Valuation with Influence Functions", "abstract": "Large language models (LLMs) are trained on a vast amount of human-written data, but data providers often remain uncredited. In response to this issue, data valuation (or data attribution), which quantifies the contribution or value of each data to the model output, has been discussed as a potential solution. Nevertheless, applying existing data valuation methods to recent LLMs and their vast training datasets has been largely limited by prohibitive compute and memory costs. In this work, we focus on influence functions, a popular gradient-based data valuation method, and significantly improve its scalability with an efficient gradient projection strategy called LoGra that leverages the gradient structure in backpropagation. We then provide a theoretical motivation of gradient projection approaches to influence functions to promote trust in the data valuation process. Lastly, we lower the barrier to implementing data valuation systems by introducing LogIX, a software package that can transform existing training code into data valuation code with minimal effort. In our data valuation experiments, LoGra achieves competitive accuracy against more expensive baselines while showing up to 6,500x improvement in throughput and 5x reduction in GPU memory usage when applied to Llama3-8B-Instruct and the 1B-token dataset.", "published": "2024-05-22T19:39:05Z", "authors": ["Sang Keun Choe", "Hwijeen Ahn", "Juhan Bae", "Kewen Zhao", "Minsoo Kang", "Youngseog Chung", "Adithya Pratapa", "Willie Neiswanger", "Emma Strubell", "Teruko Mitamura", "Jeff Schneider", "Eduard Hovy", "Roger Grosse", "Eric Xing"], "categories": ["cs.LG", "cs.AI", "cs.CL"], "doi": "", "_query": "automated valuation model", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2601.04211v1", "title": "Qwerty AI: Explainable Automated Age Rating and Content Safety Assessment for Russian-Language Screenplays", "abstract": "We present Qwerty AI, an end-to-end system for automated age-rating and content-safety assessment of Russian-language screenplays according to Federal Law No. 436-FZ. The system processes full-length scripts (up to 700 pages in under 2 minutes), segments them into narrative units, detects content violations across five categories (violence, sexual content, profanity, substances, frightening elements), and assigns age ratings (0+, 6+, 12+, 16+, 18+) with explainable justifications. Our implementation leverages a fine-tuned Phi-3-mini model with 4-bit quantization, achieving 80% rating accuracy and 80-95% segmentation precision (format-dependent). The system was developed under strict constraints: no external API calls, 80GB VRAM limit, and <5 minute processing time for average scripts. Deployed on Yandex Cloud with CUDA acceleration, Qwerty AI demonstrates practical applicability for production workflows. We achieved these results during the Wink hackathon (November 2025), where our solution addressed real editorial challenges in the Russian media industry.", "published": "2025-12-10T17:41:35Z", "authors": ["Nikita Zmanovskii"], "categories": ["cs.CL"], "doi": "", "_query": "automated valuation model", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/0707.3104v1", "title": "The 2-adic valuations of Stirling numbers", "abstract": "The 2-adic valuation of the Stirling numbres is examined. We conjecture pattrens about the distributions of these valuations in residue classes modulo powers of 2.", "published": "2007-07-20T16:30:46Z", "authors": ["Tewodros Amdeberhan", "Dante Manna", "Victor H. Moll"], "categories": ["math.NT"], "doi": "", "_query": "automated valuation model", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2201.02045v1", "title": "Stochastic measure distortions induced by quantile processes for risk quantification and valuation", "abstract": "We develop a novel stochastic valuation and premium calculation principle based on probability measure distortions that are induced by quantile processes in continuous time. Necessary and sufficient conditions are derived under which the quantile processes satisfy first- and second-order stochastic dominance. The introduced valuation principle relies on stochastic ordering so that the valuation risk-loading, and thus risk premiums, generated by the measure distortion is an ordered parametric family. The quantile processes are generated by a composite map consisting of a distribution and a quantile function. The distribution function accounts for model risk in relation to the empirical distribution of the risk process, while the quantile function models the response to the risk source as perceived by, e.g., a market agent. This gives rise to a system of subjective probability measures that indexes a stochastic valuation principle susceptible to probability measure distortions. We use the Tukey-$gh$ family of quantile processes driven by Brownian motion in an example that demonstrates stochastic ordering. We consider the conditional expectation under the distorted measure as a member of the time-consistent class of dynamic valuation principles, and extend it to the setting where the driving risk process is multivariate. This requires the introduction of a copula function in the composite map for the construction of quantile processes, which presents another new element in the risk quantification and modelling framework based on probability measure distortions induced by quantile processes.", "published": "2021-12-10T23:02:38Z", "authors": ["Holly Brannelly", "Andrea Macrina", "Gareth W. Peters"], "categories": ["q-fin.RM", "math.PR"], "doi": "", "_query": "automated valuation model", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2410.20064v1", "title": "Some restricted partition functions in terms of 2-adic valuation", "abstract": "The 2-adic valuation of an integer n which is the exponent of the highest power of 2 that divides n. In this paper, we give representations of certain restricted partition functions in terms of 2-adic valuation.", "published": "2024-10-26T04:01:25Z", "authors": ["Sabi Biswas", "Nipen Saikia"], "categories": ["math.NT"], "doi": "", "_query": "automated valuation model", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/1601.01954v4", "title": "(Non)Vanishing results on local cohomology of valuation rings", "abstract": "We examine local cohomology in the setting of valuation rings. The novelty of this investigation stems from the fact that valuation rings are usually non-Noetherian, whereas local cohomology has been extensively developed mostly in a Noetherian setting. We prove various vanishing results on local cohomology for valuation rings of finite Krull dimension. These vanishing results stem from a uniform bound on the global dimension of such rings. Our investigation reveals differences in the sheaf theoretic definition of local cohomology, and the algebraic definition in terms of a limit of certain Ext functors.", "published": "2016-01-08T17:37:55Z", "authors": ["Rankeya Datta"], "categories": ["math.AC"], "doi": "10.1016/j.jalgebra.2016.12.032", "_query": "automated valuation model", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/1410.7033v1", "title": "Valuations and Surface Area Measures", "abstract": "We consider valuations defined on polytopes containing the origin which have measures on the sphere as values. We show that the classical surface area measure is essentially the only such valuation which is SL(n) contravariant of degree one. Moreover, for all real $p$, an $L_p$ version of the above result is established for GL(n) contravariant valuations of degree $p$. This provides a characterization of the $L_p$ surface area measures from the $L_p$ Brunn-Minkowski theory.", "published": "2014-10-26T14:31:03Z", "authors": ["Christoph Haberl", "Lukas Parapatits"], "categories": ["math.MG"], "doi": "", "_query": "automated valuation model", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/1610.09809v1", "title": "Valuation theoretic methods in the birational geometry of algebraic varieties", "abstract": "In this paper, we give a valuation formula for rational top differential forms of function fields in characteristic zero for arbitrary Abhyankar places generalizing the classical valuation at prime divisors. This enables us to define log discrepancies for log pairs for arbitrary Abhyankar places. If the Abhyankar place has dimension greater than zero we restrict rational top differential forms with valuation zero to the residue field of the Abhyankar place, generalizing the classical restriction of a top differential form with a simple pole along a smooth divisor. This opens up the door to generalize the classical adjunction machinery to arbitrary Abhyankar places.", "published": "2016-10-31T07:39:50Z", "authors": ["Stefan Günther"], "categories": ["math.AG"], "doi": "", "_query": "automated valuation model", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/1708.02546v1", "title": "A principal ideal theorem for compact sets of rank one valuation rings", "abstract": "Let $F$ be a field, and let Zar$(F)$ be the space of valuation rings of $F$ with respect to the Zariski topology. We prove that if $X$ is a quasicompact set of rank one valuation rings in Zar$(F)$ whose maximal ideals do not intersect to $0$, then the intersection of the rings in $X$ is an integral domain with quotient field $F$ such that every finitely generated ideal is a principal ideal. To prove this result, we develop a duality between (a) quasicompact sets of rank one valuation rings whose maximal ideals do not intersect to $0$, and (b) one-dimensional Prüfer domains with nonzero Jacobson radical and quotient field $F$. The necessary restriction in all these cases to collections of valuation rings whose maximal ideals do not intersect to $0$ is motivated by settings in which the valuation rings considered all dominate a given local ring.", "published": "2017-08-08T16:20:46Z", "authors": ["Bruce Olberding"], "categories": ["math.AC"], "doi": "", "_query": "automated valuation model", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2402.12017v1", "title": "Private Interdependent Valuations: New Bounds for Single-Item Auctions and Matroids", "abstract": "We study auction design within the widely acclaimed model of interdependent values, introduced by Milgrom and Weber [1982]. In this model, every bidder $i$ has a private signal $s_i$ for the item for sale, and a public valuation function $v_i(s_1,\\ldots,s_n)$ which maps every vector of private signals (of all bidders) into a real value. A recent line of work established the existence of approximately-optimal mechanisms within this framework, even in the more challenging scenario where each bidder's valuation function $v_i$ is also private. This body of work has primarily focused on single-item auctions with two natural classes of valuations: those exhibiting submodularity over signals (SOS) and $d$-critical valuations. In this work we advance the state of the art on interdependent values with private valuation functions, with respect to both SOS and $d$-critical valuations. For SOS valuations, we devise a new mechanism that gives an improved approximation bound of $5$ for single-item auctions. This mechanism employs a novel variant of an \"eating mechanism\", leveraging LP-duality to achieve feasibility with reduced welfare loss. For $d$-critical valuations, we broaden the scope of existing results beyond single-item auctions, introducing a mechanism that gives a $(d+1)$-approximation for any environment with matroid feasibility constraints on the set of agents that can be simultaneously served. Notably, this approximation bound is tight, even with respect to single-item auctions.", "published": "2024-02-19T10:20:21Z", "authors": ["Alon Eden", "Michal Feldman", "Simon Mauras", "Divyarthi Mohan"], "categories": ["cs.GT", "cs.DS"], "doi": "", "_query": "automated valuation model", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2504.05424v4", "title": "Speculative Automated Refactoring of Imperative Deep Learning Programs to Graph Execution", "abstract": "Efficiency is essential to support ever-growing datasets, especially for Deep Learning (DL) systems. DL frameworks have traditionally embraced deferred execution-style DL code -- supporting symbolic, graph-based Deep Neural Network (DNN) computation. While scalable, such development is error-prone, non-intuitive, and difficult to debug. Consequently, more natural, imperative DL frameworks encouraging eager execution have emerged but at the expense of run-time performance. Though hybrid approaches aim for the \"best of both worlds,\" using them effectively requires subtle considerations. Our key insight is that, while DL programs typically execute sequentially, hybridizing imperative DL code resembles parallelizing sequential code in traditional systems. Inspired by this, we present an automated refactoring approach that assists developers in determining which otherwise eagerly-executed imperative DL functions could be effectively and efficiently executed as graphs. The approach features novel static imperative tensor and side-effect analyses for Python. Due to its inherent dynamism, analyzing Python may be unsound; however, the conservative approach leverages a speculative (keyword-based) analysis for resolving difficult cases that informs developers of any assumptions made. The approach is: (i) implemented as a plug-in to the PyDev Eclipse IDE that integrates the WALA Ariadne analysis framework and (ii) evaluated on nineteen DL projects consisting of 132 KLOC. The results show that 326 of 766 candidate functions (42.56%) were refactorable, and an average relative speedup of 2.16x on performance tests was observed with negligible differences in model accuracy. The results indicate that the approach is useful in optimizing imperative DL code to its full potential.", "published": "2025-04-07T18:48:43Z", "authors": ["Raffi Khatchadourian", "Tatiana Castro Vélez", "Mehdi Bagherzadeh", "Nan Jia", "Anita Raja"], "categories": ["cs.SE", "cs.AI", "cs.PL"], "doi": "", "_query": "automated valuation model", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2006.01909v1", "title": "SL($n$) contravariant vector valuations", "abstract": "All SL($n$) contravariant vector valuations on polytopes in $\\mathbb R^n$ are completely classified without any additional assumptions. The facet vector is defined. It turns out to be the unique such valuation for $n\\geq3$. In dimension two, the classification corresponds to the case of SL(2) covariant valuations.", "published": "2020-06-02T19:43:02Z", "authors": ["Jin Li", "Dan Ma", "Wei Wang"], "categories": ["math.MG"], "doi": "", "_query": "automated valuation model", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/1505.02302v2", "title": "Periodicity in the $p$-adic valuation of a polynomial", "abstract": "For a prime $p$ and an integer $x$, the $p$-adic valuation of $x$ is denoted by $ν_{p}(x)$. For a polynomial $Q$ with integer coefficients, the sequence of valuations $ν_{p}(Q(n))$ is shown to be either periodic or unbounded. The first case corresponds to the situation where $Q$ has no roots in the ring of $p$-adic integers. In the periodic situation, the period length is determined.", "published": "2015-05-09T18:35:48Z", "authors": ["Luis A. Medina", "Victor H. Moll", "Eric Rowland"], "categories": ["math.NT"], "doi": "10.1016/j.jnt.2017.03.006", "_query": "automated valuation model", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2406.06152v1", "title": "On the Complexity of Inverse Bivariate Multi-unit Assignment Valuation Problems", "abstract": "Inverse and bilevel optimization problems play a central role in both theory and applications. These two classes are known to be closely related due to the pioneering work of Dempe and Lohse (2006), and thus have often been discussed together ever since. In this paper, we consider inverse problems for multi-unit assignment valuations. Multi-unit assignment valuations form a subclass of strong-substitutes valuations that can be represented by edge-weighted complete bipartite graphs. These valuations play a key role in auction theory as the strong substitutes condition implies the existence of a Walrasian equilibrium. A recent line of research concentrated on the problem of deciding whether a bivariate valuation function is an assignment valuation or not. In this paper, we consider an \\emph{inverse} variant of the problem: we are given a bivariate function $g$, and our goal is to find a bivariate multi-unit assignment valuation function $f$ that is as close to $g$ as possible. The difference between $f$ and $g$ can be measured either in $\\ell_1$- or $\\ell_\\infty$-norm. Using tools from discrete convex analysis, we show that the problem is strongly NP-hard. On the other hand, we derive linear programming formulations that solve relaxed versions of the problem.", "published": "2024-06-10T10:25:36Z", "authors": ["Kristóf Bérczi", "Lydia Mirabel Mendoza-Cadena"], "categories": ["math.OC"], "doi": "", "_query": "automated valuation model", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/1707.05242v1", "title": "Minkowski valuations on convex functions", "abstract": "A classification of SL$(n)$ contravariant Minkowski valuations on convex functions and a characterization of the projection body operator are established. The associated LYZ measure is characterized. In addition, a new SL$(n)$ covariant Minkowski valuation on convex functions is defined and characterized.", "published": "2017-07-17T15:46:26Z", "authors": ["Andrea Colesanti", "Monika Ludwig", "Fabian Mussnig"], "categories": ["math.FA", "math.MG"], "doi": "10.1007/s00526-017-1243-4", "_query": "automated valuation model", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/1805.02273v1", "title": "Extensions of integral domains and quasi-valuations", "abstract": "Let $S$ be an integral domain with field of fractions $F$ and let $A$ be an $F$-algebra having an $S$-stable basis. We prove the existence of an $S$-subalgebra $R$ of $A$ lying over $S$ whose localization with respect to $S$ is $A$ (we call such $R$ an $S$-nice subalgebra of $A$). We also show that there is no such minimal $S$-nice subalgebra of $A$. Given a valuation $v$ on $F$ with a corresponding valuation domain $O_v$, and an $O_v$-stable basis of $A$ over $F$, we prove the existence of a quasi-valuation on $A$ extending $v$ on $F$. Moreover, we prove the existence of an infinite decreasing chain of quasi-valuations on $A$, all of which extend $v$. Finally, we present applications for the above existence theorems; for example, we show that if $A$ is commutative and $\\mathcal C$ is any chain of prime ideals of $S$, then there exists an $S$-nice subalgebra of $A$, having a chain of prime ideals covering $\\mathcal C$.", "published": "2018-05-06T20:05:21Z", "authors": ["Shai Sarussi"], "categories": ["math.RA"], "doi": "", "_query": "automated valuation model", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2509.23951v3", "title": "HunyuanImage 3.0 Technical Report", "abstract": "We present HunyuanImage 3.0, a native multimodal model that unifies multimodal understanding and generation within an autoregressive framework, with its image generation module publicly available. The achievement of HunyuanImage 3.0 relies on several key components, including meticulous data curation, advanced architecture design, a native Chain-of-Thoughts schema, progressive model pre-training, aggressive model post-training, and an efficient infrastructure that enables large-scale training and inference. With these advancements, we successfully trained a Mixture-of-Experts (MoE) model comprising over 80 billion parameters in total, with 13 billion parameters activated per token during inference, making it the largest and most powerful open-source image generative model to date. We conducted extensive experiments and the results of automatic and human evaluation of text-image alignment and visual quality demonstrate that HunyuanImage 3.0 rivals previous state-of-the-art models. By releasing the code and weights of HunyuanImage 3.0, we aim to enable the community to explore new ideas with a state-of-the-art foundation model, fostering a dynamic and vibrant multimodal ecosystem. All open source assets are publicly available at https://github.com/Tencent-Hunyuan/HunyuanImage-3.0", "published": "2025-09-28T16:14:10Z", "authors": [" Tencent Hunyuan Foundation Model Team"], "categories": ["cs.CV"], "doi": "", "_query": "automated valuation model", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/1907.01859v1", "title": "Essentially finite generation of valuation rings in terms of classical invariants", "abstract": "The main goal of this paper is to study some properties of an extension of valuations from classical invariants. More specifically, we consider a valued field $(K,ν)$ and an extension $ω$ of $ν$ to a finite extension $L$ of $K$. Then we study when the valuation ring of $ω$ is essentially finitely generated over the valuation ring of $ν$. We present a necessary condition in terms of classic invariants of the extension by Hagen Knaf and show that in some particular cases, this condition is also sufficient. We also study when the corresponding extension of graded algebras is finitely generated. For this problem we present an equivalent condition (which is weaker than the one for the finite generation of the valuation rings).", "published": "2019-07-03T11:24:18Z", "authors": ["Steven Dale Cutkosky", "Josnei Novacoski"], "categories": ["math.AC"], "doi": "", "_query": "automated valuation model", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/1208.5382v1", "title": "Wrong-way risk in credit and funding valuation adjustments", "abstract": "Wrong-way risk in counterparty and funding exposures is most dramatic in the situations of systemic crises and tails events. A consistent model of wrong-way risk (WWR) is developed here with the probability-weighted addition of tail events to the calculation of credit valuation and funding valuation adjustments (CVA and FVA). This new practical model quantifies the tail risks in the pricing of CVA and FVA of derivatives and does not rely on a limited concept of linear correlation frequently used in many models. The application of the model is illustrated with practical examples of WWR arising in the case of a sovereign default for the most common interest-rate and foreign exchange derivatives.", "published": "2012-08-27T13:17:25Z", "authors": ["Mihail Turlakov"], "categories": ["q-fin.PR", "q-fin.PM"], "doi": "", "_query": "automated valuation model", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/0807.0309v1", "title": "Counterparty risk valuation for CDS", "abstract": "The valuation of counterparty risk for single name credit derivatives requires the computa- tion of joint distributions of default times of two default-prone entities. For a Merton-type model, we derive some formulas for these joint distribu- tions. As an application, closed formulas for counterparty risk on a CDS or for a first-to-default swap on two underlyings are obtained.", "published": "2008-07-02T09:47:49Z", "authors": ["Christophette Blanchet-Scalliet", "Frédéric Patras"], "categories": ["q-fin.PR", "math.PR"], "doi": "", "_query": "automated valuation model", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/1707.01026v2", "title": "Polyhedra and parameter spaces for matroids over valuation rings", "abstract": "In this paper we address two of the major foundational questions in the theory of matroids over rings. First, we provide a cryptomorphic axiomatisation, by introducing an analogue of the base polytope for matroids. Second, we describe a parameter space for matroids over a valuation ring, which turns out to be a tropical version of the Bott-Samelson varieties for the full flag variety. Thus a matroid over a valuation ring is a sequence of flags of tropical linear spaces a.k.a. valuated matroids.", "published": "2017-07-04T14:59:34Z", "authors": ["Alex Fink", "Luca Moci"], "categories": ["math.CO", "math.AG"], "doi": "", "_query": "automated valuation model", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2402.09288v5", "title": "EcoVal: An Efficient Data Valuation Framework for Machine Learning", "abstract": "Quantifying the value of data within a machine learning workflow can play a pivotal role in making more strategic decisions in machine learning initiatives. The existing Shapley value based frameworks for data valuation in machine learning are computationally expensive as they require considerable amount of repeated training of the model to obtain the Shapley value. In this paper, we introduce an efficient data valuation framework EcoVal, to estimate the value of data for machine learning models in a fast and practical manner. Instead of directly working with individual data sample, we determine the value of a cluster of similar data points. This value is further propagated amongst all the member cluster points. We show that the overall value of the data can be determined by estimating the intrinsic and extrinsic value of each data. This is enabled by formulating the performance of a model as a \\textit{production function}, a concept which is popularly used to estimate the amount of output based on factors like labor and capital in a traditional free economic market. We provide a formal proof of our valuation technique and elucidate the principles and mechanisms that enable its accelerated performance. We demonstrate the real-world applicability of our method by showcasing its effectiveness for both in-distribution and out-of-sample data. This work addresses one of the core challenges of efficient data valuation at scale in machine learning models. The code is available at \\underline{https://github.com/respai-lab/ecoval}.", "published": "2024-02-14T16:21:47Z", "authors": ["Ayush K Tarun", "Vikram S Chundawat", "Murari Mandal", "Hong Ming Tan", "Bowei Chen", "Mohan Kankanhalli"], "categories": ["cs.LG"], "doi": "", "_query": "automated valuation model", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/math/0202303v1", "title": "Uniform Approximation of Abhyankar Valuation Ideals in Smooth Function Fields", "abstract": "In this paper we use the theory of multiplier ideals to show that the valuation ideals of a rank one Abhyankar valuation centered at a smooth point of a complex algebraic variety are approximated, in a quite strong sense, by sequences of powers of fixed ideals. Fix a rank one valuation v centered at a smooth point x on an algebraic variety over a field of characteristic zero. Assume that v is Abhyankar, that is, that its rational rank plus its transcendence degree equal the dimension of the variety. Let a_m denote the ideal of elements in the local ring of x whose valuations are at least m. Our main theorem is that there exists e>0 such that a_{mn} is contained in (a_{m-e})^n for all m and n. This can be viewed as a greatly strengthened form of Izumi's Theorem for Abhyankar valuations centered on smooth complex varieties.", "published": "2002-02-28T15:52:31Z", "authors": ["Lawrence Ein", "Robert Lazarsfeld", "Karen E. Smith"], "categories": ["math.AG", "math.AC"], "doi": "", "_query": "automated valuation model", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2607.04781v1", "title": "Renewing Reliability: Valuation and Credit Risk Adjustments for Renewable Power Purchase Agreements", "abstract": "Power Purchase Agreements (PPAs) are bilateral over-the-counter contracts central to renewable energy financing. While their capacity to stabilise revenues and hedge price risk is well recognised, their OTC structure exposes both parties to counterparty credit risk. This is a dimension yet to be explored in the literature, particularly given the dual price and volumetric uncertainty inherent in renewable sources. This paper develops a framework for the pricing and valuation of wind power PPAs and for quantifying this risk through Credit Valuation Adjustment (CVA) and Debit Valuation Adjustment (DVA). We model the joint dynamics of electricity spot prices and renewable output, incorporate default probabilities, and compute valuation adjustments that reflect the fair value of bilateral credit risk. The framework provides market participants with a transparent metric for PPA valuation under counterparty risk. While initiatives such as the European Investment Bank's pilot guarantee scheme aim to mitigate credit risk for certain offtakers, such interventions do not cover all PPA transactions. Rigorous internal credit risk assessment therefore remains indispensable for lenders, producers, and offtakers alike.", "published": "2026-07-06T08:10:42Z", "authors": ["Nicola Bartolini", "Silvia Romagnoli", "Amia Santini"], "categories": ["q-fin.PR"], "doi": "", "_query": "automated valuation model", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2209.12222v4", "title": "Efficient Wrong-Way Risk Modelling for Funding Valuation Adjustments", "abstract": "Wrong-Way Risk (WWR) is an important component in Funding Valuation Adjustment (FVA) modelling. Yet, the standard assumption is independence between market risks and the counterparty defaults and funding costs. This typical industrial setting is our point of departure, where we aim to assess the impact of WWR without running a full Monte Carlo simulation with all credit and funding processes. We propose to split the exposure profile into two parts: an independent and a WWR-driven part. For the former, exposures can be re-used from the standard xVA calculation. We express the second part of the exposure profile in terms of the stochastic drivers and approximate these by a common Gaussian stochastic factor. Within the affine setting, the proposed approximation is generic, is an add-on to the existing xVA calculations and provides an efficient and robust way to include WWR in FVA modelling. Case studies for an interest rate swap and a representative multi-currency portfolio of swaps illustrate that the approximation method is applicable in a practical setting. We analyze the approximation error and use the approximation to compute WWR sensitivities, which are needed for risk management. The approach is equally applicable to other metrics such as Credit Valuation Adjustment.", "published": "2022-09-25T13:48:38Z", "authors": ["T. van der Zwaard", "L. A. Grzelak", "C. W. Oosterlee"], "categories": ["q-fin.CP", "q-fin.MF", "q-fin.RM"], "doi": "10.1142/S0219024924500109", "_query": "automated valuation model", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2509.01616v1", "title": "Automated Generation of Issue-Reproducing Tests by Combining LLMs and Search-Based Testing", "abstract": "Issue-reproducing tests fail on buggy code and pass once a patch is applied, thus increasing developers' confidence that the issue has been resolved and will not be re-introduced. However, past research has shown that developers often commit patches without such tests, making the automated generation of issue-reproducing tests an area of interest. We propose BLAST, a tool for automatically generating issue-reproducing tests from issue-patch pairs by combining LLMs and search-based software testing (SBST). For the LLM part, we complement the issue description and the patch by extracting relevant context through git history analysis, static analysis, and SBST-generated tests. For the SBST part, we adapt SBST for generating issue-reproducing tests; the issue description and the patch are fed into the SBST optimization through an intermediate LLM-generated seed, which we deserialize into SBST-compatible form. BLAST successfully generates issue-reproducing tests for 151/426 (35.4%) of the issues from a curated Python benchmark, outperforming the state-of-the-art (23.5%). Additionally, to measure the real-world impact of BLAST, we built a GitHub bot that runs BLAST whenever a new pull request (PR) linked to an issue is opened, and if BLAST generates an issue-reproducing test, the bot proposes it as a comment in the PR. We deployed the bot in three open-source repositories for three months, gathering data from 32 PRs-issue pairs. BLAST generated an issue-reproducing test in 11 of these cases, which we proposed to the developers. By analyzing the developers' feedback, we discuss challenges and opportunities for researchers and tool builders. Data and material: https://doi.org/10.5281/zenodo.16949042", "published": "2025-09-01T16:54:24Z", "authors": ["Konstantinos Kitsios", "Marco Castelluccio", "Alberto Bacchelli"], "categories": ["cs.SE"], "doi": "10.1109/ASE63991.2025.00165", "_query": "automated valuation model", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/0801.0449v1", "title": "Semigroups of valuations dominating local domains", "abstract": "A new criterion is given for a semigroup to be the semigroup of a valuation dominating an equicharacteristic local domain. The criterion is used to construct examples of well ordered subsemigroups of the positive rational numbers which are of ordinal type omega, but are not the value semigroup of a valuation on an equicharacteristic noetherian local domain. This shows that the necessary conditions on value semigroups given in Appendix 3 to Zariski and Samuel's book ``Commutative Algebra'' are not sufficient.", "published": "2008-01-02T22:24:04Z", "authors": ["Steven Dale Cutkosky"], "categories": ["math.AC", "math.AG"], "doi": "", "_query": "automated valuation model", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/0809.3507v1", "title": "Growth of rank 1 valuation semigroups", "abstract": "We consider the question of which semigroups can occur as the semigroup $S_R(ν)$ of positive values of a rank 1 valuation dominating a Noetherian local ring $R$. We give a number of bounds of polynomial type on the growth of $φ(n)=S_R(ν)\\cap (0,n)$ for $n\\in\\NN$, starting with the upper bound of $P_R(n)$, where $P_R(n)$ is the Hilbert function of $R$. This bound is generalized to an extremely general bound for arbitrary rank valuations in the paper \"Semigroups of valuations on local rings, II\", by Cutkosky and Teissier, arXiv:0805.3788. This bound is already enough to give simple examples of rank 1 well ordered semigroups which are not the value semigroup $S_R(ν)$ of a valuation dominating a Noetherian local ring. In the case of rank 1, it is possible to give more precise estimates of $φ(n)$, which we prove in this paper. We also give examples showing that many different rates of growth are possible for $φ(n)$ on a regular local ring of dimension 2, such as $n(α$ for any rational $α$ with $1\\leα\\le 2$, and $n{log}(n)$.", "published": "2008-09-20T14:54:31Z", "authors": ["Steven Dale Cutkosky", "Kia Dalili", "Olga Kashcheyeva"], "categories": ["math.AC", "math.AG"], "doi": "", "_query": "automated valuation model", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/1709.08959v1", "title": "Continuity and representation of valuations on star bodies", "abstract": "It is shown that every continuous valuation defined on the $n$-dimensional star bodies has an integral representation in terms of the radial function. Our argument is based on the non-trivial fact that continuous valuations are uniformly continuous on bounded sets. We also characterize the continuous valuations on the $n$-dimensional star bodies that arise as the restriction of a measure on $\\mathbb R^n$.", "published": "2017-09-26T12:10:52Z", "authors": ["Pedro Tradacete", "Ignacio Villanueva"], "categories": ["math.MG", "math.FA"], "doi": "", "_query": "automated valuation model", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/1802.06208v1", "title": "On the Extensions of a Discrete Valuation in a Number Field", "abstract": "Let $K$ be a number field defined by a monic irreducible polynomial $F(X) \\in \\mathbb{Z}[X]$, $p$ a fixed rational prime, and $ν_p$ the discrete valuation associated to $p$. Assume that $\\overline{F}(X)$ factors modulo $p$ into the product of powers of $r$ distinct monic irreducible polynomials. We present in this paper a condition, weaker than the known ones, which guarantees the existence of exactly $r$ valuations of $K$ extending $ν_p$. We further specify the ramification indices and residue degrees of these extended valuations in such a way that generalizes the known estimates. Some useful remarks and computational examples are also given to highlight some improvements due to our result.", "published": "2018-02-17T08:36:12Z", "authors": ["Abdulaziz Deajim", "Lhoussain El Fadil"], "categories": ["math.NT"], "doi": "", "_query": "automated valuation model", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2402.01943v2", "title": "Precedence-Constrained Winter Value for Effective Graph Data Valuation", "abstract": "Data valuation is essential for quantifying data's worth, aiding in assessing data quality and determining fair compensation. While existing data valuation methods have proven effective in evaluating the value of Euclidean data, they face limitations when applied to the increasingly popular graph-structured data. Particularly, graph data valuation introduces unique challenges, primarily stemming from the intricate dependencies among nodes and the exponential growth in value estimation costs. To address the challenging problem of graph data valuation, we put forth an innovative solution, Precedence-Constrained Winter (PC-Winter) Value, to account for the complex graph structure. Furthermore, we develop a variety of strategies to address the computational challenges and enable efficient approximation of PC-Winter. Extensive experiments demonstrate the effectiveness of PC-Winter across diverse datasets and tasks.", "published": "2024-02-02T22:39:50Z", "authors": ["Hongliang Chi", "Wei Jin", "Charu Aggarwal", "Yao Ma"], "categories": ["cs.LG"], "doi": "", "_query": "automated valuation model", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/1408.4816v1", "title": "Uniform definability of henselian valuation rings in the Macintyre language", "abstract": "We discuss definability of henselian valuation rings in the Macintyre language $\\mathcal{L}_{\\rm Mac}$, the language of rings expanded by n-th power predicates. In particular, we show that henselian valuation rings with finite or Hilbertian residue field are uniformly $\\exists$-$\\emptyset$-definable in $\\mathcal{L}_{\\rm Mac}$, and henselian valuation rings with value group $\\mathbb{Z}$ are uniformly $\\exists\\forall$-$\\emptyset$-definable in the ring language, but not uniformly $\\exists$-$\\emptyset$-definable in $\\mathcal{L}_{\\rm Mac}$. We apply these results to local fields $\\mathbb{Q}_p$ and $\\mathbb{F}_p((t))$, as well as to higher dimensional local fields.", "published": "2014-08-20T20:41:25Z", "authors": ["Arno Fehm", "Alexander Prestel"], "categories": ["math.AC", "math.LO"], "doi": "10.1112/blms/bdv042", "_query": "automated valuation model", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2603.03672v2", "title": "Local Shapley: Model-Induced Locality and Optimal Reuse in Data Valuation", "abstract": "The Shapley value provides a principled foundation for data valuation, but exact computation is #P-hard due to the exponential coalition space. Existing accelerations remain global and ignore a structural property of modern predictors: for a given test instance, only a small subset of training points influences the prediction. We formalize this model-induced locality through support sets defined by the model's computational pathway (e.g., neighbors in KNN, leaves in trees, receptive fields in GNNs), showing that Shapley computation can be projected onto these supports without loss when locality is exact. This reframes Shapley evaluation as a structured data processing problem over overlapping support-induced subset families rather than exhaustive coalition enumeration. We prove that the intrinsic complexity of Local Shapley is governed by the number of distinct influential subsets, establishing an information-theoretic lower bound on retraining operations. Guided by this result, we propose LSMR (Local Shapley via Model Reuse), an optimal subset-centric algorithm that trains each influential subset exactly once via support mapping and pivot scheduling. For larger supports, we develop LSMR-A, a reuse-aware Monte Carlo estimator that remains unbiased with exponential concentration, with runtime determined by the number of distinct sampled subsets rather than total draws. Experiments across multiple model families demonstrate substantial retraining reductions and speedups while preserving high valuation fidelity.", "published": "2026-03-04T02:59:23Z", "authors": ["Xuan Yang", "Hsi-Wen Chen", "Ming-Syan Chen", "Jian Pei"], "categories": ["cs.LG", "cs.AI", "cs.DB", "cs.GT"], "doi": "", "_query": "automated valuation model", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/1003.4881v2", "title": "The Validity of Company Valuation Using Discounted Cash Flow Methods", "abstract": "This paper closely examines theoretical and practical aspects of the widely used discounted cash flows (DCF) valuation method. It assesses its potentials as well as several weaknesses. A special emphasize is being put on the valuation of companies using the DCF method. The paper finds that the discounted cash flow method is a powerful tool to analyze even complex situations. However, the DCF method is subject to massive assumption bias and even slight changes in the underlying assumptions of an analysis can drastically alter the valuation results. A practical example of these implications is given using a scenario analysis.", "published": "2010-03-25T13:13:42Z", "authors": ["Florian Steiger"], "categories": ["q-fin.GN", "q-fin.PR"], "doi": "", "_query": "automated valuation model", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/1508.03362v1", "title": "Ramification of valuations and local rings", "abstract": "In this paper we consider birational properties of ramification in excellent local rings. We extend earlier results of the author with Olivier Piltant to show that strong local monomialization is true along a valuation dominating a defectless extension of two dimensional excellent local rings. We also obtain general results on the structure of the extension of associated graded rings along a valuation, and show that the invariants alpha and beta of stable forms of two dimensional extensions in characteristic p of the author and Olivier Piltant are not eventually constant.", "published": "2015-08-09T00:44:54Z", "authors": ["Steven Dale Cutkosky"], "categories": ["math.AG", "math.AC"], "doi": "", "_query": "automated valuation model", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/1303.0729v2", "title": "Groebner bases over fields with valuations", "abstract": "Let K be a field with a valuation and let S be the polynomial ring S:= K[x_1,..., x_n]. We discuss the extension of Groebner theory to ideals in S, taking the valuations of coefficients into account, and describe the Buchberger algorithm in this context. In addition we discuss some implementation and complexity issues. The main motivation comes from tropical geometry, as tropical varieties can be defined using these Groebner bases, but we also give examples showing that the resulting Groebner bases can be substantially smaller than traditional Groebner bases. In the case K = Q with the p-adic valuation the algorithms have been implemented in a Macaulay 2 package.", "published": "2013-03-04T15:25:37Z", "authors": ["Andrew J. Chan", "Diane Maclagan"], "categories": ["math.AC"], "doi": "", "_query": "automated valuation model", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/1602.08760v2", "title": "Valuation theory of indefinite orthogonal groups", "abstract": "Let $\\mathrm{SO}^+(p,q)$ denote the identity connected component of the real orthogonal group with signature $(p,q)$. We give a complete description of the spaces of continuous and generalized translation- and $\\mathrm{SO}^+(p,q)$-invariant valuations, generalizing Hadwiger's classification of Euclidean isometry-invariant valuations. As a result of independent interest, we identify within the space of translation-invariant valuations the class of Klain-Schneider continuous valuations, which strictly contains all continuous translation-invariant valuations. The operations of pull-back and push-forward by a linear map extend naturally to this class.", "published": "2016-02-28T19:34:26Z", "authors": ["Andreas Bernig", "Dmitry Faifman"], "categories": ["math.DG", "math.MG"], "doi": "10.1016/j.jfa.2017.06.005", "_query": "automated valuation model", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/1007.3539v1", "title": "Single Parameter Combinatorial Auctions with Partially Public Valuations", "abstract": "We consider the problem of designing truthful auctions, when the bidders' valuations have a public and a private component. In particular, we consider combinatorial auctions where the valuation of an agent $i$ for a set $S$ of items can be expressed as $v_if(S)$, where $v_i$ is a private single parameter of the agent, and the function $f$ is publicly known. Our motivation behind studying this problem is two-fold: (a) Such valuation functions arise naturally in the case of ad-slots in broadcast media such as Television and Radio. For an ad shown in a set $S$ of ad-slots, $f(S)$ is, say, the number of {\\em unique} viewers reached by the ad, and $v_i$ is the valuation per-unique-viewer. (b) From a theoretical point of view, this factorization of the valuation function simplifies the bidding language, and renders the combinatorial auction more amenable to better approximation factors. We present a general technique, based on maximal-in-range mechanisms, that converts any $α$-approximation non-truthful algorithm ($α\\leq 1$) for this problem into $Ω(\\fracα{\\log{n}})$ and $Ω(α)$-approximate truthful mechanisms which run in polynomial time and quasi-polynomial time, respectively.", "published": "2010-07-20T23:53:55Z", "authors": ["Gagan Goel", "Chinmay Karande", "Lei Wang"], "categories": ["cs.GT"], "doi": "10.1007/978-3-642-16170-4_21", "_query": "automated valuation model", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/1506.07631v1", "title": "Dynamic Mechanism Design with Interdependent Valuations", "abstract": "We consider an infinite horizon dynamic mechanism design problem with interdependent valuations. In this setting the type of each agent is assumed to be evolving according to a first order Markov process and is independent of the types of other agents. However, the valuation of an agent can depend on the types of other agents, which makes the problem fall into an interdependent valuation setting. Designing truthful mechanisms in this setting is non-trivial in view of an impossibility result which says that for interdependent valuations, any efficient and ex-post incentive compatible mechanism must be a constant mechanism, even in a static setting. Mezzetti (2004) circumvents this problem by splitting the decisions of allocation and payment into two stages. However, Mezzetti's result is limited to a static setting and moreover in the second stage of that mechanism, agents are weakly indifferent about reporting their valuations truthfully. This paper provides a first attempt at designing a dynamic mechanism which is efficient, `strict' ex-post incentive compatible and ex-post individually rational in a setting with interdependent values and Markovian type evolution.", "published": "2015-06-25T06:47:52Z", "authors": ["Swaprava Nath", "Onno Zoeter", "Y. Narahari", "Christopher R. Dance"], "categories": ["cs.GT"], "doi": "", "_query": "automated valuation model", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/1906.01054v1", "title": "Deep 3D Convolutional Neural Network for Automated Lung Cancer Diagnosis", "abstract": "Computer Aided Diagnosis has emerged as an indispensible technique for validating the opinion of radiologists in CT interpretation. This paper presents a deep 3D Convolutional Neural Network (CNN) architecture for automated CT scan-based lung cancer detection system. It utilizes three dimensional spatial information to learn highly discriminative 3 dimensional features instead of 2D features like texture or geometric shape whick need to be generated manually. The proposed deep learning method automatically extracts the 3D features on the basis of spatio-temporal statistics.The developed model is end-to-end and is able to predict malignancy of each voxel for given input scan. Simulation results demonstrate the effectiveness of proposed 3D CNN network for classification of lung nodule in-spite of limited computational capabilities.", "published": "2019-05-04T15:56:50Z", "authors": ["Sumita Mishra", "Naresh Kumar Chaudhary", "Pallavi Asthana", "Anil Kumar"], "categories": ["eess.IV", "cs.CV"], "doi": "10.1007/978-981-13-7150-9_16", "_query": "automated valuation model", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/1410.3171v2", "title": "Ramification Theory for Artin-Schreier Extensions of Valuation Rings", "abstract": "The goal of this paper is to generalize and refine the classical ramification theory of complete discrete valuation rings to more general valuation rings, in the case of Artin-Schreier extensions. We define refined versions of invariants of ramification in the classical ramification theory and compare them. Furthermore, we can treat the defect case.", "published": "2014-10-13T00:47:48Z", "authors": ["Vaidehee Thatte"], "categories": ["math.NT"], "doi": "", "_query": "automated valuation model", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/1104.2753v1", "title": "Monoid Valuations and Value Ordered Supervaluations", "abstract": "We complement two papers on supertropical valuation theory ([IKR1],[IKR2]) by providing natural examples of m-valuations (= monoid valuations), after that of supervaluations and transmissions between them. The supervaluations discussed have values in totally ordered supertropical semirings, and the transmissions discussed respect the orderings. Basics of a theory of such semirings and transmissions are developed as far as needed.", "published": "2011-04-14T13:07:08Z", "authors": ["Zur Izhakian", "Manfred Knebusch", "Louis Rowen"], "categories": ["math.AC", "math.AG", "math.RA"], "doi": "", "_query": "automated valuation model", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2109.04793v1", "title": "Risk-Adjusted Valuation for Real Option Decisions", "abstract": "We model investor heterogeneity using different required returns on an investment and evaluate the impact on the valuation of an investment. By assuming no disagreement on the cash flows, we emphasize how risk preferences in particular, but also the costs of capital, influence a subjective evaluation of the decision to invest now or retain the option to invest in future. We propose a risk-adjusted valuation model to facilitate investors' subjective decision making, in response to the market valuation of an investment opportunity. The investor's subjective assessment arises from their perceived misvaluation of the investment by the market, so projected cash flows are discounted using two different rates representing the investor's and the market's view. This liberates our model from perfect or imperfect hedging assumptions and instead, we are able to illustrate the hedging effect on the real option value when perceptions of risk premia diverge. During crises periods, delaying an investment becomes more valuable as the idiosyncratic risk of future cash flows increases, but the decision-maker may rush to invest too quickly when the risk level is exceptionally high. Our model verifies features established by classical real-option valuation models and provides many new insights about the importance of modelling divergences in decision-makers risk premia, especially during crisis periods. It also has many practical advantages because it requires no more parameter inputs than basic discounted cash flow approaches, such as the marketed asset disclaimer method, but the outputs are much richer. They allow for complex interactions between cost and revenue uncertainties as well as an easy exploration of the effects of hedgeable and un-hedgeable risks on the real option value. Furthermore, we provide fully-adjustable Python code in which all parameter values can be chosen by the user.", "published": "2021-09-10T11:18:27Z", "authors": ["Carol Alexander", "Xi Chen", "Charles Ward"], "categories": ["q-fin.GN"], "doi": "", "_query": "automated valuation model", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2608.19110v1", "title": "Rigid motion invariant valuations on polytopes", "abstract": "We show that any measurable, translation and $\\mathrm{SO}(n)$-invariant valuation on polytopes in $\\mathbb{R}^n$ is a linear combination of the intrinsic volumes, which extends Hadwiger's classical characterization of rigid motion invariant continuous valuations on convex bodies. This result is based on a novel regularity result for translation invariant, measurable, $1$-homogeneous, and simple valuations. As a consequence, we obtain a similar characterization of the Steiner point map on polytopes.", "published": "2026-08-19T17:03:29Z", "authors": ["Jonas Knoerr"], "categories": ["math.MG"], "doi": "", "_query": "automated valuation model", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/1506.05846v1", "title": "Automated Assignment of Backbone NMR Data using Artificial Intelligence", "abstract": "Nuclear magnetic resonance (NMR) spectroscopy is a powerful method for the investigation of three-dimensional structures of biological molecules such as proteins. Determining a protein structure is essential for understanding its function and alterations in function which lead to disease. One of the major challenges of the post-genomic era is to obtain structural and functional information on the many unknown proteins encoded by thousands of newly identified genes. The goal of this research is to design an algorithm capable of automating the analysis of backbone protein NMR data by implementing AI strategies such as greedy and A* search.", "published": "2015-06-18T22:50:02Z", "authors": ["John Emmons", "Steven Johnson", "Timothy Urness", "Adina Kilpatrick"], "categories": ["cs.AI", "q-bio.BM"], "doi": "", "_query": "automated valuation model", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/1309.0135v1", "title": "Ramification of local rings along valuations", "abstract": "In this paper we discuss stable forms of extensions of algebraic local rings along a valuation in all dimensions over a field k of characteristic zero, and generalize a formula of Ghezzi, Hà and Kashcheyeva describing the extension of associated graded rings along the valuation for stable extensions of regular algebraic local rings of dimension two to arbitrary ground fields k of characteristic zero. We discuss the failure of this result in positive characteristic.", "published": "2013-08-31T17:06:01Z", "authors": ["Steven Dale Cutkosky", "Pham An Vinh"], "categories": ["math.AC"], "doi": "", "_query": "automated valuation model", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2502.06574v4", "title": "On the Impact of the Utility in Semivalue-based Data Valuation", "abstract": "Semivalue-based data valuation uses cooperative-game theory intuitions to assign each data point a value reflecting its contribution to a downstream task. Still, those values depend on the practitioner's choice of utility, raising the question: How robust is semivalue-based data valuation to changes in the utility? This issue is critical when the utility is set as a trade-off between several criteria and when practitioners must select among multiple equally valid utilities. We address this by introducing the notion of a dataset's spatial signature: given a semivalue, we embed each data point into a lower-dimensional space in which any utility becomes a linear functional, making the data valuation framework amenable to a simpler geometric picture. Building on this, we propose a practical methodology centered on an explicit robustness metric that informs practitioners whether and by how much their data valuation results will shift as the utility changes. We validate this approach across diverse datasets and semivalues, demonstrating strong agreement with rank-correlation analyses and offering analytical insight into how choosing a semivalue can amplify or diminish robustness.", "published": "2025-02-10T15:42:38Z", "authors": ["Mélissa Tamine", "Benjamin Heymann", "Maxime Vono", "Patrick Loiseau"], "categories": ["cs.AI", "cs.GT", "cs.LG"], "doi": "", "_query": "automated valuation model", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/1405.6421v1", "title": "Filtered Ring Derived from Discrete Valuation Ring and Its Properties", "abstract": "In this paper we show that if R is a discrete valuation ring, then R is a filtered ring. We prove some properties and relation when R is a discrete valuation ring.", "published": "2014-05-25T19:39:30Z", "authors": ["Mohammad Hassan Anjom SHoa", "Mohammad Hossein Hosseini"], "categories": ["math.RA"], "doi": "", "_query": "automated valuation model", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2103.06384v2", "title": "Automated liver tissues delineation techniques: A systematic survey on machine learning current trends and future orientations", "abstract": "Machine learning and computer vision techniques have grown rapidly in recent years due to their automation, suitability, and ability to generate astounding results. Hence, in this paper, we survey the key studies that are published between 2014 and 2022, showcasing the different machine learning algorithms researchers have used to segment the liver, hepatic tumors, and hepatic-vasculature structures. We divide the surveyed studies based on the tissue of interest (hepatic-parenchyma, hepatic-tumors, or hepatic-vessels), highlighting the studies that tackle more than one task simultaneously. Additionally, the machine learning algorithms are classified as either supervised or unsupervised, and they are further partitioned if the amount of work that falls under a certain scheme is significant. Moreover, different datasets and challenges found in literature and websites containing masks of the aforementioned tissues are thoroughly discussed, highlighting the organizers' original contributions and those of other researchers. Also, the metrics used excessively in literature are mentioned in our review, stressing their relevance to the task at hand. Finally, critical challenges and future directions are emphasized for innovative researchers to tackle, exposing gaps that need addressing, such as the scarcity of many studies on the vessels' segmentation challenge and why their absence needs to be dealt with sooner than later.", "published": "2021-03-10T23:11:16Z", "authors": ["Ayman Al-Kababji", "Faycal Bensaali", "Sarada Prasad Dakua", "Yassine Himeur"], "categories": ["eess.IV", "cs.CV", "cs.LG"], "doi": "", "_query": "automated valuation model", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/quant-ph/0107123v1", "title": "A Topos Perspective on the Kochen-Specker Theorem: IV. Interval Valuations", "abstract": "We extend the topos-theoretic treatment given in previous papers of assigning values to quantities in quantum theory. In those papers, the main idea was to assign a sieve as a partial and contextual truth value to a proposition that the value of a quantity lies in a certain set $Δ\\subseteq \\mathR$. Here we relate such sieve-valued valuations to valuations that assign to quantities subsets, rather than single elements, of their spectra (we call these `interval' valuations). There are two main results. First, there is a natural correspondence between these two kinds of valuation, which uses the notion of a state's support for a quantity (Section 3). Second, if one starts with a more general notion of interval valuation, one sees that our interval valuations based on the notion of support (and correspondingly, our sieve-valued valuations) are a simple way to secure certain natural properties of valuations, such as monotonicity (Section 4).", "published": "2001-07-24T15:06:23Z", "authors": ["J. Butterfield", "C. J. Isham"], "categories": ["quant-ph", "gr-qc"], "doi": "", "_query": "automated valuation model", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2009.10632v1", "title": "From Things' Modeling Language (ThingML) to Things' Machine Learning (ThingML2)", "abstract": "In this paper, we illustrate how to enhance an existing state-of-the-art modeling language and tool for the Internet of Things (IoT), called ThingML, to support machine learning on the modeling level. To this aim, we extend the Domain-Specific Language (DSL) of ThingML, as well as its code generation framework. Our DSL allows one to define things, which are in charge of carrying out data analytics. Further, our code generators can automatically produce the complete implementation in Java and Python. The generated Python code is responsible for data analytics and employs APIs of machine learning libraries, such as Keras, Tensorflow and Scikit Learn. Our prototype is available as open source software on Github.", "published": "2020-09-22T15:44:57Z", "authors": ["Armin Moin", "Stephan Rössler", "Marouane Sayih", "Stephan Günnemann"], "categories": ["cs.SE", "cs.LG"], "doi": "10.1145/3417990.3420057", "_query": "automated valuation model", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2204.09576v2", "title": "The valuation pairing on an upper cluster algebra", "abstract": "It is known that many (upper) cluster algebras are not unique factorization domains. We exhibit the local factorization properties with respect to any given seed $t$: any non-zero element in a full rank upper cluster algebra can be uniquely written as the product of a cluster monomial in $t$ and another element not divisible by the cluster variables in $t$. Our approach is based on introducing the valuation pairing on an upper cluster algebra: it counts the maximal multiplicity of a cluster variable among the factorizations of any given element. We apply the valuation pairing to obtain many results concerning factoriality, $d$-vectors, $F$-polynomials and the combinatorics of cluster Poisson variables. In particular, we obtain that full rank and primitive upper cluster algebras are factorial; an explanation of $d$-vectors using valuation pairing; a cluster monomial in non-initial cluster variables is determined by its $F$-polynomial; the $F$-polynomials of non-initial cluster variables are irreducible; and the cluster Poisson variables parametrize the exchange pairs of the corresponding upper cluster algebra.", "published": "2022-04-20T16:16:25Z", "authors": ["Peigen Cao", "Bernhard Keller", "Fan Qin"], "categories": ["math.RT", "math.RA"], "doi": "10.1515/crelle-2023-0080", "_query": "automated valuation model", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/1612.02587v1", "title": "Inverses, Conditionals and Compositional Operators in Separative Valuation Algebra", "abstract": "Compositional models were introduce by Jirousek and Shenoy in the general framework of valuation-based systems. They based their theory on an axiomatic system of valuations involving not only the operations of combination and marginalisation, but also of removal. They claimed that this systems covers besides the classical case of discrete probability distributions, also the cases of Gaussian densities and belief functions, and many other systems. Whereas their results on the compositional operator are correct, the axiomatic basis is not sufficient to cover the examples claimed above. We propose here a different axiomatic system of valuation algebras, which permits a rigorous mathematical theory of compositional operators in valuation-based systems and covers all the examples mentioned above. It extends the classical theory of inverses in semigroup theory and places thereby the present theory into its proper mathematical frame. Also this theory sheds light on the different structures of valuation-based systems, like regular algebras (represented by probability potentials), canncellative algebras (Gaussian potentials) and general separative algebras (density functions).", "published": "2016-12-08T10:34:16Z", "authors": ["Juerg Kohlas"], "categories": ["cs.AI"], "doi": "", "_query": "automated valuation model", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2101.01346v5", "title": "Valuation rings of mixed characteristic as limits of complete intersection rings", "abstract": "We show that a mixed characteristic valuation ring with a value group $Γ$, $\\val$ its valuation and a residue field of characteristic $p>0$, is a filtered colimit of complete intersection $\\bf Z$-algebras if $Γ/{\\bf Z}\\val(p)$ has no $p$-torsion and $V$ is Henselian.", "published": "2021-01-05T04:37:38Z", "authors": ["Dorin Popescu"], "categories": ["math.AC"], "doi": "", "_query": "automated valuation model", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/1901.07668v1", "title": "A new valuation on polyhedral cones", "abstract": "We define a new family of valuations on polyhedral cones valued in the space of bounded polyhedra.", "published": "2019-01-23T00:52:10Z", "authors": ["Hailun Zheng", "Michał Zydor"], "categories": ["math.MG"], "doi": "", "_query": "automated valuation model", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2503.00850v1", "title": "Depth of extensions of valuations", "abstract": "In this paper we develop the theory of the depth of a simple algebraic extension of valued fields $(L/K,v)$. This is defined as the minimal number of augmentations appearing in some Mac Lane-Vaquié chain for the valuation on $K[x]$ determined by the choice of some generator of the extension. In the defectless and unibranched case, this concept leads to a generalization of a classical result of Ore about the existence of $p$-regular generators for number fields. Also, we find what valuation-theoretic conditions characterize the extensions having depth one.", "published": "2025-03-02T11:02:21Z", "authors": ["Josnei Novacoski", "Enric Nart"], "categories": ["math.AC"], "doi": "", "_query": "automated valuation model", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2502.12357v3", "title": "The Japanese and universally Japanese properties for valuation rings and Prüfer domains", "abstract": "We discuss the Japanese and universally Japanese properties for valuation rings and Prüfer domains. These properties, regarding finiteness of integral closure, have been studied extensively for Noetherian rings, but very rarely, if ever, for non-Noetherian rings. Among other results, we show that for valuation rings and Prüfer domains, the Japanese and universally Japanese properties are equivalent. This result can be seen as a counterpart to Nagata's classical result for Noetherian rings. This result also tells us many non-Noetherian rings, including all absolutely integrally closed valuation rings and Prüfer domains, are universally Japanese.", "published": "2025-02-17T22:46:28Z", "authors": ["Shiji Lyu"], "categories": ["math.AC"], "doi": "", "_query": "automated valuation model", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/1503.02200v3", "title": "Sequential Posted Price Mechanisms with Correlated Valuations", "abstract": "We study the revenue performance of sequential posted price mechanisms and some natural extensions, for a general setting where the valuations of the buyers are drawn from a correlated distribution. Sequential posted price mechanisms are conceptually simple mechanisms that work by proposing a take-it-or-leave-it offer to each buyer. We apply sequential posted price mechanisms to single-parameter multi-unit settings in which each buyer demands only one item and the mechanism can assign the service to at most k of the buyers. For standard sequential posted price mechanisms, we prove that with the valuation distribution having finite support, no sequential posted price mechanism can extract a constant fraction of the optimal expected revenue, even with unlimited supply. We extend this result to the the case of a continuous valuation distribution when various standard assumptions hold simultaneously. In fact, it turns out that the best fraction of the optimal revenue that is extractable by a sequential posted price mechanism is proportional to ratio of the highest and lowest possible valuation. We prove that for two simple generalizations of these mechanisms, a better revenue performance can be achieved: if the sequential posted price mechanism has for each buyer the option of either proposing an offer or asking the buyer for its valuation, then a Omega(1/max{1,d}) fraction of the optimal revenue can be extracted, where d denotes the degree of dependence of the valuations, ranging from complete independence (d=0) to arbitrary dependence (d=n-1). Moreover, when we generalize the sequential posted price mechanisms further, such that the mechanism has the ability to make a take-it-or-leave-it offer to the i-th buyer that depends on the valuations of all buyers except i's, we prove that a constant fraction (2-sqrt{e})/4~0.088 of the optimal revenue can be always be extracted.", "published": "2015-03-07T19:02:16Z", "authors": ["Marek Adamczyk", "Allan Borodin", "Diodato Ferraioli", "Bart de Keijzer", "Stefano Leonardi"], "categories": ["cs.GT"], "doi": "", "_query": "automated valuation model", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2404.06655v1", "title": "Gersten's Injectivity for Smooth Algebras over Valuation Rings", "abstract": "Gersten's injectivity conjecture for a functor $F$ of ``motivic type'', predicts that given a semilocal, ``non-singular'', integral domain $R$ with a fraction field $K$, the restriction morphism induces an injection of $F(R)$ inside $F(K)$. We prove two new cases of this conjecture for smooth algebras over valuation rings. Namely, we show that the higher algebraic $K$-groups of a semilocal, integral domain that is an essentially smooth algebra over an equicharacteristic valuation ring inject inside the same of its fraction field. Secondly, we show that Gersten's injectivity is true for smooth algebras over, possibly of mixed-characteristic, valuation rings in the case of torsors under tori and also in the case of the Brauer group.", "published": "2024-04-09T23:43:31Z", "authors": ["Arnab Kundu"], "categories": ["math.AG", "math.KT"], "doi": "", "_query": "automated valuation model", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2305.08697v1", "title": "Applications of Generalized Universal Valuations", "abstract": "We introduce a generalization of the universal valuation semiring defined by Jeffrey and Noah Giansiracusa. We then explicitly characterize the additive structure of this semiring and show that, when applied to $\\mathbb Q$, this characterization gives the Non-Archimedean case of Ostrowski's theorem. We conclude with examples of non-commutative valuations and their applications, such as the detection of the existence of representations of rings in ultrametric vector spaces.", "published": "2023-05-15T14:57:08Z", "authors": ["William Bernardoni"], "categories": ["math.RA"], "doi": "", "_query": "automated valuation model", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/1308.3108v2", "title": "On Lattices over Valuation Rings of Arbitrary Rank", "abstract": "We show how several results about p-adic lattices generalize easily to lattices over valuation ring of arbitrary rank having only the Henselian property for quadratic polynomial. If 2 is invertible we obtain the uniqueness of the Jordan decomposition and the Witt Cancelation Theorem. We show that the isomorphism classes of indecomposable rank 2 lattices over such a ring in which 2 is not invertible are characterized by two invariants, provided that the lattices contain a primitive norm divisible by 2 of maximal valuation.", "published": "2013-08-14T13:02:12Z", "authors": ["Shaul Zemel"], "categories": ["math.AC"], "doi": "10.1016/j.jalgebra.2014.10.022", "_query": "automated valuation model", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2511.00895v2", "title": "Cost-of-capital valuation with risky assets", "abstract": "Cost-of-capital valuation is a well-established approach to the valuation of liabilities and is one of the cornerstones of current regulatory frameworks for the insurance industry. Standard cost-of-capital considerations typically rely on the assumption that the required buffer capital is held in risk-less one-year bonds. The aim of this work is to analyze the effects of allowing investments of the buffer capital in risky assets, e.g.~in a combination of stocks and bonds. In particular, we make precise how the decomposition of the buffer capital into contributions from policyholders and investors varies as the degree of riskiness of the investment increases, and highlight the role of limited liability in the case of heavy-tailed insurance risks. We present a combination of general theoretical results, explicit results for certain stochastic models and numerical results that emphasize the key findings.", "published": "2025-11-02T11:27:07Z", "authors": ["Hansjörg Albrecher", "Filip Lindskog", "Hervé Zumbach"], "categories": ["q-fin.RM"], "doi": "10.1017/asb.2026.10097", "_query": "automated valuation model", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/1905.02287v2", "title": "The Finite Matroid-Based Valuation Conjecture is False", "abstract": "The matroid-based valuation conjecture of Ostrovsky and Paes Leme states that all gross substitutes valuations on $n$ items can be produced from merging and endowments of weighted ranks of matroids defined on at most $m(n)$ items. We show that if $m(n) = n$, then this statement holds for $n \\leq 3$ and fails for all $n \\geq 4$. In particular, the set of gross substitutes valuations on $n \\geq 4$ items is strictly larger than the set of matroid based valuations defined on the ground set $[n]$. Our proof uses matroid theory and discrete convex analysis to explicitly construct a large family of counter-examples. It indicates that merging and endowment by themselves are poor operations to generate gross substitutes valuations. We also connect the general MBV conjecture and related questions to long-standing open problems in matroid theory, and conclude with open questions at the intersection of this field and economics.", "published": "2019-05-06T22:46:55Z", "authors": ["Ngoc Mai Tran"], "categories": ["math.CO"], "doi": "", "_query": "automated valuation model", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/1803.09821v1", "title": "On valuation independence and defectless extensions of valued fields", "abstract": "In this article we further develop the theory of valuation independence and study its relation with classical notions in valuation theory such as immediate and defectless extensions. We use this general theory to settle two open questions regarding vector space defectless extensions of valued fields. Additionally, we provide a characterization of such extensions within various classes of valued fields, extending results of Françoise Delon.", "published": "2018-03-26T20:06:31Z", "authors": ["Anna Blaszczok", "Pablo Cubides Kovacsics", "Franz-Viktor Kuhlmann"], "categories": ["math.AC", "math.LO"], "doi": "", "_query": "automated valuation model", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/1202.2451v1", "title": "Ultrafilter and Constructible topologies on spaces of valuation domains", "abstract": "Let $K$ be a field and let $A$ be a subring of $K$. We consider properties and applications of a compact, Hausdorff topology called the \"ultrafilter topology\" defined on the space Zar$(K|A)$ of all valuation domains having $K$ as quotient field and containing $A$. We show that the ultrafilter topology coincides with the constructible topology on the abstract Riemann-Zariski surface Zar$(K|A)$. We extend results regarding distinguished spectral topologies on spaces of valuation domains.", "published": "2012-02-11T16:54:09Z", "authors": ["Carmelo A. Finocchiaro", "Marco Fontana", "K. Alan Loper"], "categories": ["math.AC", "math.AG"], "doi": "", "_query": "automated valuation model", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/math/0010313v2", "title": "Rank one discrete valuations of $k((X_1,...X_n))$", "abstract": "In this paper we study the rank one discrete valuations of $k((X_1,... ,X_n))$ whose center in $k\\lcor\\X\\rcor$ is the maximal ideal $(\\X)$. In sections 2 to 6 we give a construction of a system of parametric equations describing such valuations. This amounts to finding a parameter and a field of coefficients. We devote section 2 to finding an element of value 1, that is, a parameter. The field of coefficients is the residue field of the valuation, and it is given in section 5. The constructions given in these sections are not effective in the general case, because we need either to use the Zorn's lemma or to know explicitly a section $σ$ of the natural homomorphism $R_v\\to\\d$ between the ring and the residue field of the valuation $v$. However, as a consequence of this construction, in section 7, we prove that $k((\\X))$ can be embedded into a field $L((\\Y))$, where the {\\em ``extended valuation'' is as close as possible to the usual order function}.", "published": "2000-10-31T10:10:39Z", "authors": ["M. A. Olalla Acosta"], "categories": ["math.AC"], "doi": "", "_query": "automated valuation model", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/1802.10228v1", "title": "Risk-neutral valuation under differential funding costs, defaults and collateralization", "abstract": "We develop a unified valuation theory that incorporates credit risk (defaults), collateralization and funding costs, by expanding the replication approach to a generality that has not yet been studied previously and reaching valuation when replication is not assumed. This unifying theoretical framework clarifies the relationship between the two valuation approaches: the adjusted cash flows approach pioneered for example by Brigo, Pallavicini and co-authors ([12, 13, 34]) and the classic replication approach illustrated for example by Bielecki and Rutkowski and co-authors ([3, 8]). In particular, results of this work cover most previous papers where the authors studied specific replication models.", "published": "2018-02-28T01:58:12Z", "authors": ["Damiano Brigo", "Cristin Buescu", "Marco Francischello", "Andrea Pallavicini", "Marek Rutkowski"], "categories": ["q-fin.PR"], "doi": "", "_query": "automated valuation model", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/1307.2131v1", "title": "Axioms for the Lefschetz number as a lattice valuation", "abstract": "We give new axioms for the Lefschetz number based on Hadwiger's characterization of the Euler characteristic as the unique lattice valuation on polyhedra which takes value 1 on simplices. In the setting of maps on abstract simplicial complexes, we show that the Lefschetz number is unique with respect to a valuation axiom and an axiom specifying the value on a simplex. These axioms lead naturally to the classical computation of the Lefschetz number as a trace in homology. We then extend this approach to continuous maps of polyhedra, assuming an extra homotopy invariance axiom. We also show that this homotopy axiom can be weakened.", "published": "2013-07-08T15:37:31Z", "authors": ["P. Christopher Staecker"], "categories": ["math.GN", "math.AT"], "doi": "", "_query": "automated valuation model", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2006.08972v5", "title": "Valuation rings of dimension one as limits of smooth algebras", "abstract": "As in Zariski's Uniformization Theorem we show that a valuation ring $V$ of characteristic $p>0$ of dimension one is a filtered direct limit of smooth ${\\bf F}_p$-algebras under some conditions of transcendence degree. Under mild conditions, the algebraic immediate extensions of valuation rings are dense if they are filtered direct limit of smooth morphisms.", "published": "2020-06-16T07:56:55Z", "authors": ["Dorin Popescu"], "categories": ["math.AC", "math.AG"], "doi": "", "_query": "automated valuation model", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2405.08217v1", "title": "Data Valuation with Gradient Similarity", "abstract": "High-quality data is crucial for accurate machine learning and actionable analytics, however, mislabeled or noisy data is a common problem in many domains. Distinguishing low- from high-quality data can be challenging, often requiring expert knowledge and considerable manual intervention. Data Valuation algorithms are a class of methods that seek to quantify the value of each sample in a dataset based on its contribution or importance to a given predictive task. These data values have shown an impressive ability to identify mislabeled observations, and filtering low-value data can boost machine learning performance. In this work, we present a simple alternative to existing methods, termed Data Valuation with Gradient Similarity (DVGS). This approach can be easily applied to any gradient descent learning algorithm, scales well to large datasets, and performs comparably or better than baseline valuation methods for tasks such as corrupted label discovery and noise quantification. We evaluate the DVGS method on tabular, image and RNA expression datasets to show the effectiveness of the method across domains. Our approach has the ability to rapidly and accurately identify low-quality data, which can reduce the need for expert knowledge and manual intervention in data cleaning tasks.", "published": "2024-05-13T22:10:00Z", "authors": ["Nathaniel J. Evans", "Gordon B. Mills", "Guanming Wu", "Xubo Song", "Shannon McWeeney"], "categories": ["cs.LG", "q-bio.GN", "q-bio.QM", "stat.ML"], "doi": "", "_query": "automated valuation model", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/0810.2479v2", "title": "On Izumi's theorem on comparison of valuations", "abstract": "We prove that the sequence of MacLane key polynomials constructed in \\cite{Mac1} and \\cite{Sp2} for a valuation extension $(K,ν)\\subset (K(x),μ)$ is finite, provided that both $ν$ and $μ$ are divisorial and $μ$ is centered over an analytically irreducible local domain $(R,\\frak{m})\\subset K[x]$. As a corollary, we prove Izumi's theorem on comparison of divisorial valuations. %We show that the existence of Izumi constants is equivalent to the finiteness of the sequence of the MacLane key-polynomials. We give explicit bounds for the Izumi constant in terms of the key polynomials of the valuations. We show that this bound can be attained in some cases.", "published": "2008-10-14T16:15:30Z", "authors": ["Mohammad Moghaddam"], "categories": ["math.AC"], "doi": "", "_query": "automated valuation model", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2608.22522v1", "title": "From Detrimental to Beneficial: Dynamic Influence-based Valuation and Editing", "abstract": "Data valuation is a cornerstone of data-centric learning, where prior efforts primarily focus on designing algorithms to classify training samples as either beneficial or detrimental for the learning task. However, leveraging these valuation estimates for subsequent data intervention remains underexplored; conventional approaches typically discard or downweight harmful samples, thereby underutilizing available data resources. In this paper, we present Dynamic Influence-based Valuation and Editing (DIVE), a novel and efficient framework that dynamically estimates sample values at the batch level and transforms detrimental data into beneficial contributions. Rather than altering the raw data, DIVE operates at the optimization level by strategically reversing the gradient directions of harmful samples during training, ensuring seamless integration with standard learning procedures with minimal overhead. Extensive empirical evaluations demonstrate that DIVE consistently improves classification performance, maximizes data efficiency, stabilizes optimization, and effectively generalizes to large language model fine-tuning.", "published": "2026-08-23T17:46:18Z", "authors": ["Adrian Nyakairu", "Hongfu Liu"], "categories": ["cs.LG"], "doi": "", "_query": "automated valuation model", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2301.13675v1", "title": "On Tight Submodules of Modules over Valuation Domains", "abstract": "This note offers an unusual approach of studying a class of modules inasmuch as it is investigating a subclass of the category of modules over a valuation domain. This class is far from being a full subcategory, it is not even a category. Our concern is the subclass consisting of modules of projective dimension not exceeding one, admitting only morphisms whose kernels and cokernels are also objects in this subclass. This class is still tractable, several features are in general simpler than in module categories, but lots of familiar properties are lost. A number of results on modules in this class are similar to those on modules over rank one discrete valuation domains (where the global dimension is 1). The study is considerably simplified by taking advantage of the general theory of modules over valuation domains available in the literature, e.g. in [14]-[15]. Our main goal is to establish the basic features and have a closer look at injectivity, pure-injectivity, and cotorsionness, but we do not wish to enter into an in-depth study of these properties.", "published": "2023-01-31T14:49:01Z", "authors": ["Peter Danchev", "Laszlo Fuchs"], "categories": ["math.AC", "math.RA"], "doi": "", "_query": "automated valuation model", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2112.14658v5", "title": "Unitarily invariant valuations on convex functions", "abstract": "Continuous, dually epi-translation invariant valuations on the space of finite-valued convex functions on $\\mathbb{C}^n$ that are invariant under the unitary group are investigated. It is shown that elements belonging to the dense subspace of smooth valuations admit a unique integral representation in terms of two families of Monge-Ampère-type operators. In addition, it is proved that homogeneous valuations are uniquely determined by restrictions to subspaces of appropriate dimension and that this information is encoded in the Fourier-Laplace transform of the associated Goodey-Weil distributions. These results are then used to show that a continuous unitarily invariant valuation is uniquely determined by its restriction to a certain finite family of subspaces of $\\mathbb{C}^n$.", "published": "2021-12-29T17:16:27Z", "authors": ["Jonas Knoerr"], "categories": ["math.MG"], "doi": "", "_query": "automated valuation model", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2303.15010v1", "title": "$p$-adic valuation of harmonic sums and their connections with Wolstenholme primes", "abstract": "We explore a conjecture posed by Eswarathasan and Levine on the distribution of $p$-adic valuations of harmonic numbers $H(n)=1+1/2+\\cdots+1/n$ that states that the set $J_p$ of the positive integers $n$ such that $p$ divides the numerator of $H(n)$ is finite. We proved two results, using a modular-arithmetic approach, one for non-Wolstenholme primes and the other for Wolstenholme primes, on an anomalous asymptotic behaviour of the $p$-adic valuation of $H(p^mn)$ when the $p$-adic valuation of $H(n)$ equals exactly 3.", "published": "2023-03-27T08:52:43Z", "authors": ["Leonardo Carofiglio", "Luigi De Filpo", "Alessandro Gambini"], "categories": ["math.NT"], "doi": "10.1007/s13226-023-00387-1", "_query": "automated valuation model", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/0912.3031v1", "title": "Credit Default Swap Calibration and Counterparty Risk Valuation with a Scenario based First Passage Model", "abstract": "In this work we develop a tractable structural model with analytical default probabilities depending on a random default barrier and possibly random volatility ideally associated with a scenario based underlying firm debt. We show how to calibrate this model using a chosen number of reference Credit Default Swap (CDS) market quotes. In general this model can be seen as a possible extension of the time-varying AT1P model in Brigo and Tarenghi (2004). The calibration capability of the Scenario Volatility/Barrier model (SVBAT1P), when keeping time-constant volatility, appears inferior to the one of AT1P with time-varying deterministic volatility. The SVBAT1P model, however, maintains the benefits of time-homogeneity and can lead to satisfactory calibration results, as we show in a case study where we compare different choices on scenarios and parameters. Similarly to AT1P, SVBAT1P is suited to pricing hybrid equity/credit derivatives and to evaluate counterparty risk in equity payoffs, and more generally to evaluate hybrid credit/equity payoffs. We consider the equity return swap in Brigo and Tarenghi (2004) and show its valuation under SVBAT1P with the same CDS and equity calibration input used earlier for AT1P, and further we hint at equity default swap valuation in the conclusions.", "published": "2009-12-15T23:17:05Z", "authors": ["Damiano Brigo", "Marco Tarenghi"], "categories": ["q-fin.PR", "q-fin.CP"], "doi": "", "_query": "automated valuation model", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/1911.06661v1", "title": "Discrete equivalence of non-positive at infinity plane valuations", "abstract": "Non-positive at infinity valuations are a class of real plane valuations which have a nice geometrical behavior. They are divided in three types. We study the dual graphs of non-positive at infinity valuations and give an algorithm for obtaining them. Moreover we compare these graphs attending the type of their corresponding valuation.", "published": "2019-11-15T14:28:49Z", "authors": ["Carlos Galindo", "Francisco Monserrat", "Carlos-Jesús Moreno-Ávila"], "categories": ["math.AG"], "doi": "10.1007/s00025-021-01435-0", "_query": "automated valuation model", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2502.01703v1", "title": "QLESS: A Quantized Approach for Data Valuation and Selection in Large Language Model Fine-Tuning", "abstract": "Fine-tuning large language models (LLMs) is often constrained by the computational costs of processing massive datasets. We propose \\textbf{QLESS} (Quantized Low-rank Gradient Similarity Search), which integrates gradient quantization with the LESS framework to enable memory-efficient data valuation and selection. QLESS employs a two-step compression process: first, it obtains low-dimensional gradient representations through LoRA-based random projection; then, it quantizes these gradients to low-bitwidth representations. Experiments on multiple LLM architectures (LLaMA, Mistral, Qwen) and benchmarks (MMLU, BBH, TyDiQA) show that QLESS achieves comparable data selection performance to LESS while reducing memory usage by up to 16x. Even 1-bit gradient quantization preserves data valuation quality. These findings underscore QLESS as a practical, scalable approach to identifying informative examples within strict memory constraints.", "published": "2025-02-03T10:52:32Z", "authors": ["Moses Ananta", "Muhammad Farid Adilazuarda", "Zayd Muhammad Kawakibi Zuhri", "Ayu Purwarianti", "Alham Fikri Aji"], "categories": ["cs.LG", "cs.AI", "cs.CL"], "doi": "", "_query": "automated valuation model", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/1004.2480v1", "title": "The valuation criterion for normal basis generators", "abstract": "If $L/K$ is a finite Galois extension of local fields, we say that the valuation criterion $VC(L/K)$ holds if there is an integer $d$ such that every element $x \\in L$ with valuation $d$ generates a normal basis for $L/K$. Answering a question of Byott and Elder, we first prove that $VC(L/K)$ holds if and only if the tamely ramified part of the extension $L/K$ is trivial and every non-zero $K[G]$-submodule of $L$ contains a unit. Moreover, the integer $d$ can take one value modulo $[L:K]$ only, namely $-d_{L/K}-1$, where $d_{L/K}$ is the valuation of the different of $L/K$. When $K$ has positive characteristic, we thus recover a recent result of Elder and Thomas, proving that $VC(L/K)$ is valid for all extensions $L/K$ in this context. When $\\char{\\;K}=0$, we identify all abelian extensions $L/K$ for which $VC(L/K)$ is true, using algebraic arguments. These extensions are determined by the behaviour of their cyclic Kummer subextensions.", "published": "2010-04-14T19:34:43Z", "authors": ["Bart de Smit", "Mathieu Florence", "Lara Thomas"], "categories": ["math.NT", "math.RT"], "doi": "10.1112/blms/bds005", "_query": "automated valuation model", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/math/0409519v1", "title": "Injective modules and fp-injective modules over valuation rings", "abstract": "It is shown that each almost maximal valuation ring R such that every indecomposable injective module is countably generated, satisfies the following condition (C): each fp-injective module is locally injective. The converse holds if R is a domain. Moreover, it is proved that a valuation ring that sastisfies this condition (C) is almost maximal.", "published": "2004-09-27T13:59:38Z", "authors": ["Francois Couchot"], "categories": ["math.RA"], "doi": "10.1016/S0021-8693(03)00373-9", "_query": "automated valuation model", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2206.12688v1", "title": "A SIQRB delayed model for cholera and optimal control treatment", "abstract": "We improve a recent mathematical model for cholera by adding a time delay that represents the time between the instant at which an individual becomes infected and the instant at which he begins to have symptoms of cholera disease. We prove that the delayed cholera model is biologically meaningful and analyze the local asymptotic stability of the equilibrium points for positive time delays. An optimal control problem is proposed and analyzed, where the goal is to obtain optimal treatment strategies, through quarantine, that minimize the number of infective individuals and the bacterial concentration, as well as treatment costs. Necessary optimality conditions are applied to the delayed optimal control problem, with a $L^1$ type cost functional. We show that the delayed cholera model fits better the cholera outbreak that occurred in the Department of Artibonite -- Haiti, from 1 November 2010 to 1 May 2011, than the non-delayed model. Considering the data of the cholera outbreak in Haiti, we solve numerically the delayed optimal control problem and propose solutions for the outbreak control and eradication.", "published": "2022-06-25T16:19:49Z", "authors": ["Ana P. Lemos-Paiao", "Helmut Maurer", "Cristiana J. Silva", "Delfim F. M. Torres"], "categories": ["math.OC", "q-bio.PE"], "doi": "10.1051/mmnp/2022027", "_query": "automated valuation model", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2510.08869v2", "title": "Measuring the Hidden Cost of Data Valuation through Collective Disclosure", "abstract": "Data valuation methods assign marginal utility to each data point that has contributed to the training of a machine learning model. If used directly as a payout mechanism, this creates a hidden cost of valuation, in which contributors with near-zero marginal value would receive nothing, even though their data had to be collected and assessed. To better formalize this cost, we introduce a conceptual and game-theoretic model, the Information Disclosure Game, between a Data Union (sometimes also called a data trust), a member-run agent representing contributors, and a Data Consumer (e.g., a platform). After first aggregating members' data, the DU releases information progressively by adding Laplacian noise under a differentially-private mechanism. Through simulations with strategies guided by data Shapley values and multi-armed bandit exploration, we demonstrate on a Yelp review helpfulness prediction task that data valuation inherently incurs an explicit acquisition cost and that the DU's collective disclosure policy changes how this cost is distributed across members.", "published": "2025-10-09T23:59:25Z", "authors": ["Patrick Mesana", "Gilles Caporossi", "Sebastien Gambs"], "categories": ["cs.GT"], "doi": "", "_query": "automated valuation model", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2308.13322v1", "title": "Parametrizations of subsets of the space of valuations", "abstract": "In this paper we present different ways to parametrize subsets of the space of valuations on $K[x]$ extending a given valuation on $K$. We discuss the methods using pseudo-Cauchy sequences and approximation types. The method presented here is slightly different than the ones in the literature and we believe that our approach is more accurate.", "published": "2023-08-25T11:52:54Z", "authors": ["Josnei Antonio Novacoski", "Caio Henrique Silva de Souza"], "categories": ["math.AC"], "doi": "", "_query": "automated valuation model", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/0904.0555v5", "title": "The affine LIBOR models", "abstract": "We provide a general and flexible approach to LIBOR modeling based on the class of affine factor processes. Our approach respects the basic economic requirement that LIBOR rates are non-negative, and the basic requirement from mathematical finance that LIBOR rates are analytically tractable martingales with respect to their own forward measure. Additionally, and most importantly, our approach also leads to analytically tractable expressions of multi-LIBOR payoffs. This approach unifies therefore the advantages of well-known forward price models with those of classical LIBOR rate models. Several examples are added and prototypical volatility smiles are shown. We believe that the CIR-process based LIBOR model might be of particular interest for applications, since closed form valuation formulas for caps and swaptions are derived.", "published": "2009-04-03T11:49:39Z", "authors": ["Martin Keller-Ressel", "Antonis Papapantoleon", "Josef Teichmann"], "categories": ["q-fin.PR", "math.PR"], "doi": "", "_query": "automated valuation model", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2407.15546v1", "title": "Personalization of Dataset Retrieval Results using a Metadata-based Data Valuation Method", "abstract": "In this paper, we propose a novel data valuation method for a Dataset Retrieval (DR) use case in Ireland's National mapping agency. To the best of our knowledge, data valuation has not yet been applied to Dataset Retrieval. By leveraging metadata and a user's preferences, we estimate the personal value of each dataset to facilitate dataset retrieval and filtering. We then validated the data value-based ranking against the stakeholders' ranking of the datasets. The proposed data valuation method and use case demonstrated that data valuation is promising for dataset retrieval. For instance, the outperforming dataset retrieval based on our approach obtained 0.8207 in terms of NDCG@5 (the truncated Normalized Discounted Cumulative Gain at 5). This study is unique in its exploration of a data valuation-based approach to dataset retrieval and stands out because, unlike most existing methods, our approach is validated using the stakeholders ranking of the datasets.", "published": "2024-07-22T11:13:07Z", "authors": ["Malick Ebiele", "Malika Bendechache", "Eamonn Clinton", "Rob Brennan"], "categories": ["cs.IR", "cs.DB"], "doi": "", "_query": "automated valuation model", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2511.02100v1", "title": "Geometric Data Valuation via Leverage Scores", "abstract": "Shapley data valuation provides a principled, axiomatic framework for assigning importance to individual datapoints, and has gained traction in dataset curation, pruning, and pricing. However, it is a combinatorial measure that requires evaluating marginal utility across all subsets of the data, making it computationally infeasible at scale. We propose a geometric alternative based on statistical leverage scores, which quantify each datapoint's structural influence in the representation space by measuring how much it extends the span of the dataset and contributes to the effective dimensionality of the training problem. We show that our scores satisfy the dummy, efficiency, and symmetry axioms of Shapley valuation and that extending them to \\emph{ridge leverage scores} yields strictly positive marginal gains that connect naturally to classical A- and D-optimal design criteria. We further show that training on a leverage-sampled subset produces a model whose parameters and predictive risk are within $O(\\varepsilon)$ of the full-data optimum, thereby providing a rigorous link between data valuation and downstream decision quality. Finally, we conduct an active learning experiment in which we empirically demonstrate that ridge-leverage sampling outperforms standard baselines without requiring access gradients or backward passes.", "published": "2025-11-03T22:20:50Z", "authors": ["Rodrigo Mendoza-Smith"], "categories": ["cs.LG", "cs.AI", "math.NA", "math.OC"], "doi": "", "_query": "automated valuation model", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2304.02479v3", "title": "The Recalibration Conundrum: Hedging Valuation Adjustment for Callable Claims", "abstract": "The dynamic hedging theory only makes sense in the setup of one given model, whereas the practice of dynamic hedging is just the opposite, with models fleeing after the data through daily recalibration. This is quite of a quantitative finance paradox. In this paper we revisit Burnett (2021) \\& Burnett and Williams (2021)'s notion of hedging valuation adjustment (HVA), originally intended to deal with dynamic hedging frictions, in the direction of recalibration and model risks. Specifically, we extend to callable assets the HVA model risk approach of B{é}n{é}zet and Cr{é}pey (2024). The classical way to deal with model risk is to reserve the differences between the valuations in reference models and in the local models used by traders. However, while traders' prices are thus corrected, their hedging strategies and their exercise decisions are still wrong, which necessitates a risk-adjusted reserve. We illustrate our approach on a stylized callable range accrual representative of huge amounts of structured products on the market. We show that a model risk reserve adjusted for the risk of wrong exercise decisions may largely exceed a basic reserve only accounting for valuation differences.", "published": "2023-04-04T06:57:55Z", "authors": ["Cyril Bénézet", "Stéphane Crépey", "Dounia Essaket"], "categories": ["q-fin.RM", "math.PR", "q-fin.CP"], "doi": "", "_query": "automated valuation model", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2003.06374v3", "title": "Local Uniformization of Abhyankar Valuations", "abstract": "We prove local uniformization of Abhyankar valuations of an algebraic function field K over a ground field k. Our result generalizes the proof of this result, with the additional assumption that the residue field of the valuation ring is separable over k, by Hagen Knaf and Franz-Viktor Kuhlmann. The proof in this paper uses different methods, being inspired by the approach of Zariski and Abhyankar.", "published": "2020-03-13T16:37:25Z", "authors": ["Steven Dale Cutkosky"], "categories": ["math.AG", "math.AC"], "doi": "", "_query": "automated valuation model", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/1508.03523v1", "title": "Sufficient and necessary conditions for Dynamic Programming in Valuation-Based Systems", "abstract": "Valuation algebras abstract a large number of formalisms for automated reasoning and enable the definition of generic inference procedures. Many of these formalisms provide some notion of solution. Typical examples are satisfying assignments in constraint systems, models in logics or solutions to linear equation systems. Many widely used dynamic programming algorithms for optimization problems rely on low treewidth decompositions and can be understood as particular cases of a single algorithmic scheme for finding solutions in a valuation algebra. The most encompassing description of this algorithmic scheme to date has been proposed by Pouly and Kohlas together with sufficient conditions for its correctness. Unfortunately, the formalization relies on a theorem for which we provide counterexamples. In spite of that, the mainline of Pouly and Kohlas' theory is correct, although some of the necessary conditions have to be revised. In this paper we analyze the impact that the counter-examples have on the theory, and rebuild the theory providing correct sufficient conditions for the algorithms. Furthermore, we also provide necessary conditions for the algorithms, allowing for a sharper characterization of when the algorithmic scheme can be applied.", "published": "2015-08-14T14:51:54Z", "authors": ["Jordi Roca-Lacostena", "Jesus Cerquides", "Marc Pouly"], "categories": ["cs.AI"], "doi": "", "_query": "automated valuation model", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2608.27822v1", "title": "DBRepro: Automated Database Synthesis via a Hybrid Constraint-Solving Approach for Reproducing Slow Queries", "abstract": "Slow queries frequently cause severe performance bottlenecks in database management systems. Diagnosing their root causes online risks exacerbating resource contention, while data privacy regulations often prohibit copying production data to test environments. Synthesizing a proxy database from non-intrusive metadata that induces the query optimizer to generate the same physical execution plans is therefore critical for offline diagnosis. High-fidelity reproduction requires preserving global statistical distributions while enforcing exact local cardinalities. Existing data-driven and workload-aware approaches cannot satisfy both requirements simultaneously. We present DBRepro, an automated end-to-end framework that formulates database generation as a constrained distribution synthesis problem. DBRepro initializes a global distribution from lightweight column statistics, extracts execution constraints from target queries, and progressively adjusts the distribution to satisfy these constraints while preserving the global distribution. Experiments on TPC-H and SSB show that DBRepro reduces cardinality error by up to 20.3% over a data-driven baseline while maintaining identical plan consistency. Compared with a workload-aware baseline, it reproduces 15% more consistent execution plans and reduces latency proportion error by 21.5%. We further validate DBRepro on a nearly 1 TB real-world dataset managed by KingbaseES, where it reproduces the execution performance of complex slow queries with high fidelity.", "published": "2026-08-28T01:41:18Z", "authors": ["Zhaoyang Zhang", "Shuang Liu", "Dengfeng Xu", "Wei Lu", "Jianquan Leng", "Sheng Du", "Xiaoyong Du"], "categories": ["cs.DB", "cs.SE"], "doi": "", "_query": "automated valuation model", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/1204.5709v2", "title": "The third homology of SL_2 of fields with discrete valuation", "abstract": "For a field F with discrete valuation and residue field $k$ we relate the third homology of SL_2(F) with half-integral coefficients to the third homology of SL_2(k) and a certain refined scissors congruence group of k. As an application, we obtain explicit calculations of the third homology of SL_2 of certain higher-dimensional local fields in terms of scissors congruence groups, in the sense of Dupont and Sah, of the residue fields of the associated valuations.", "published": "2012-04-25T17:04:05Z", "authors": ["Kevin Hutchinson"], "categories": ["math.KT"], "doi": "", "_query": "automated valuation model", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/1301.3172v1", "title": "How to determine the sign of a valuation on C[x,y]?", "abstract": "Given a divisorial discrete valuation 'centered at infinity' on C[x,y], we show that its sign on C[x,y] (i.e. whether it is negative or non-positive on non-constant polynomials) is completely determined by the sign of its value on the 'last key form' (key forms being the avatar of 'key polynomials' of valuations (introduced by [Maclane, 1936]) in 'global coordinates'). The proof involves computations related to the cone of curves on certain compactifications of C^2 and gives a characterization of the divisorial valuations centered at infinity whose 'skewness' can be interpreted in terms of the 'slope' of an extremal ray of these cones, yielding a generalization of a result of [Favre-Jonsson, 2007]. A by-product of these arguments is a characterization of valuations which 'determine' normal compactifications of C^2 with one irreducible curve at infinity in terms of an associated 'semigroup of values'.", "published": "2013-01-14T22:11:40Z", "authors": ["Pinaki Mondal"], "categories": ["math.AC", "math.AG"], "doi": "", "_query": "automated valuation model", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2604.05993v1", "title": "Data Distribution Valuation Using Generalized Bayesian Inference", "abstract": "We investigate the data distribution valuation problem, which aims to quantify the values of data distributions from their samples. This is a recently proposed problem that is related to but different from classical data valuation and can be applied to various applications. For this problem, we develop a novel framework called Generalized Bayes Valuation that utilizes generalized Bayesian inference with a loss constructed from transferability measures. This framework allows us to solve, in a unified way, seemingly unrelated practical problems, such as annotator evaluation and data augmentation. Using the Bayesian principles, we further improve and enhance the applicability of our framework by extending it to the continuous data stream setting. Our experiment results confirm the effectiveness and efficiency of our framework in different real-world scenarios.", "published": "2026-04-07T15:19:25Z", "authors": ["Cuong N. Nguyen", "Cuong V. Nguyen"], "categories": ["cs.LG", "stat.ML"], "doi": "", "_query": "automated valuation model", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2308.07022v2", "title": "The Legendre transform, the Laplace transform and valuations", "abstract": "We first prove that the Legendre transform is the only continuous and $\\mathrm{SL}(n)$ contravariant valuation that behaves as a conjugation of two important translations on super-coercive, lower semi-continuous, and convex functions. Then we turn to a similar setting on log-concave functions and find characterizations of not merely the duality transform but also the Laplace transform on log-concave functions. With the notion of dual valuation, we also obtain characterizations of the identity transform on finite convex functions and positive log-concave functions.", "published": "2023-08-14T09:10:25Z", "authors": ["Jin Li"], "categories": ["math.MG", "math.FA"], "doi": "10.1007/s00208-026-03433-5", "_query": "automated valuation model", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2607.02874v1", "title": "On the additive structure of algebraic valuations of polynomial semirings II", "abstract": "For $α\\in \\mathbb{C}$, let $\\mathbb{N}_0[α]$ be the subsemiring of~$\\mathbb{C}$ obtained as a homomorphic image of the $α$-evaluation map $\\mathbb{N}_0[x] \\to \\mathbb{C}$ defined as $p(x) \\mapsto p(α)$ for each polynomial $p(x) \\in \\mathbb{N}_0[x]$. Fundamental arithmetic and atomic aspects of the additive structure of $\\mathbb{N}_0[α]$ were first studied by the second author and Correa-Morris (2022). In this paper, we continue the investigation, now from the valuation-theoretic perspective. We show that for any algebraic number $α$, the additive monoid of $\\mathbb{N}_0[α]$ contains no additive irreducibles if and only if it is isomorphic to the direct product of finitely many isomorphic valuation monoids (monoids whose principal ideals form a chain under inclusion). For any algebraic number $α\\in (0,1)$, these valuation monoids are precisely those where $α^{-1}$ is a Perron number having no positive conjugates other than itself. In addition, we offer a description of the algebraic parameters $α$ for which the additive structure of $\\mathbb{N}_0[α]$ is a valuation monoid. Finally, we argue that the subset of $(0,1)$ consisting of all algebraic parameters $α$ such that the additive structure of $\\mathbb{N}_0[α]$ is a valuation monoid is dense in $(0,1)$.", "published": "2026-07-03T02:20:56Z", "authors": ["Timothy Chen", "Felix Gotti", "Tony Lu", "Alan Yao"], "categories": ["math.AC"], "doi": "", "_query": "automated valuation model", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2307.02315v2", "title": "The module of Kähler differentials for extensions of valuation rings", "abstract": "The main goal of this paper is to describe the module of Kähler differentials for an extension of valuation rings. More precisely, we consider a simple algebraic valued field extension $(L/K,v)$ and the corresponding valuation ring extension $\\VR_K\\hookrightarrow\\VR_L$. In the case when $e(L/K,v)=1$ (and under two additional conditions) we present a characterization of $Ω_{\\VR_L/\\VR_K}$ in terms of a given sequence of key polynomials for the extension. We use our main result to present criteria for the equality $Ω_{\\VR_L/\\VR_K}=(0)$ to hold.", "published": "2023-07-05T14:17:54Z", "authors": ["Josnei Novacoski", "Mark Spivakovsky"], "categories": ["math.AC"], "doi": "", "_query": "automated valuation model", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/1405.0508v2", "title": "MVA: Initial Margin Valuation Adjustment by Replication and Regression", "abstract": "Initial margin requirements are becoming an increasingly common feature of derivative markets. However, while the valuation of derivatives under collateralisation (Piterbarg 2010, Piterbarg2012), under counterparty risk with unsecured funding costs (FVA) (Burgard2011, Burgard2011, Burgard2013) and in the presence of regulatory capital (KVA) (Green2014) are established through valuation adjustments, hitherto initial margin has not been considered. This paper further extends the semi-replication framework of (Burgard2013a), itself later extended by (Green2014), to cover the cost of initial margin, leading to Margin Valuation Adjustment (MVA). Initial margin requirements are typically generated through the use of VAR or CVAR models. Given the form of MVA as an integral over the expected initial margin profile this would lead to excessive computational costs if a brute force calculation were to be used. Hence we also propose a computationally efficient approach to the calculation of MVA through the use of regression techniques, Longstaff-Schwartz Augmented Compression (LSAC).", "published": "2014-05-02T20:49:38Z", "authors": ["Andrew Green", "Chris Kenyon"], "categories": ["q-fin.PR", "q-fin.GN", "q-fin.RM"], "doi": "", "_query": "automated valuation model", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/math/0011096v1", "title": "On the dimension of discrete valuations of k((X1,...,Xn))", "abstract": "Let $v$ be a rank-one discrete valuation of the field $k((\\X))$. We know, after \\cite{Bri2}, that if $n=2$ then the dimension of $v$ is 1 and if $v$ is the usual order function over $k((\\X))$ its dimension is $n-1$. In this paper we prove that, in the general case, the dimension of a rank-one discrete valuation can be any number between 1 and $n-1$.", "published": "2000-11-15T11:08:28Z", "authors": ["Miguel Angel Olalla Acosta"], "categories": ["math.AC"], "doi": "10.1016/S0764-4442(01)02000-6", "_query": "automated valuation model", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2403.14841v1", "title": "On the Hull-White model with volatility smile for Valuation Adjustments", "abstract": "Affine Diffusion dynamics are frequently used for Valuation Adjustments (xVA) calculations due to their analytic tractability. However, these models cannot capture the market-implied skew and smile, which are relevant when computing xVA metrics. Hence, additional degrees of freedom are required to capture these market features. In this paper, we address this through an SDE with state-dependent coefficients. The SDE is consistent with the convex combination of a finite number of different AD dynamics. We combine Hull-White one-factor models where one model parameter is varied. We use the Randomized AD (RAnD) technique to parameterize the combination of dynamics. We refer to our SDE with state-dependent coefficients and the RAnD parametrization of the original models as the rHW model. The rHW model allows for efficient semi-analytic calibration to European swaptions through the analytic tractability of the Hull-White dynamics. We use a regression-based Monte-Carlo simulation to calculate exposures. In this setting, we demonstrate the significant effect of skew and smile on exposures and xVAs of linear and early-exercise interest rate derivatives.", "published": "2024-03-21T21:24:41Z", "authors": ["T. van der Zwaard", "L. A. Grzelak", "C. W. Oosterlee"], "categories": ["q-fin.CP", "q-fin.MF"], "doi": "10.1080/14697688.2025.2565270", "_query": "automated valuation model", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/1909.02917v3", "title": "Une construction d'extensions faiblement non ramifiées d'un anneau de valuation", "abstract": "Étant donné un anneau de valuation $V$, de corps résiduel $F$ et de groupe des valeurs $Γ$, on donne une condition suffisante pour qu'un anneau local dominant $V$ soit un anneau de valuation de groupe $Γ$. Lorsque $V$ contient un corps $k$, ce résultat est appliqué à la construction d'un anneau de valuation contenant $V$ et une extension donnée $k'$ de $k$, de groupe $Γ$ et de corps résiduel engendré par $k'$ et $F$. Cela s'avère possible, notamment, lorsque $k'$ ou $F$ est séparable sur $k$. Given a valuation ring $V$, with residue field $F$ and value group $Γ$, we give a sufficient condition for a local ring dominating $V$ to be a valuation ring with the same value group. When $V$ contains a field $k$, we apply this result to the problem of constructing a valuation ring $W$ containing $V$ and a prescribed extension $k'$ of $k$, with value group $Γ$ and residue field generated by $k'$ and $F$; this is possible in particular when either $k'$ or $F$ is separable over $k$.", "published": "2019-09-06T14:05:57Z", "authors": ["Laurent Moret-Bailly"], "categories": ["math.AC", "math.AG"], "doi": "10.4171/rsmup/94", "_query": "automated valuation model", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2310.04116v3", "title": "Quasi-quadratic modules in pseudo-valuation domain", "abstract": "We study quasi-quadratic modules in a pseudo-valuation domain $A$ whose strict units admit a square root. Let $\\mathfrak X_R^N$ denote the set of quasi-quadratic modules in an $R$-module $N$, where $R$ is a commutative ring. It is known that there exists a unique overring $B$ of $A$ such that $B$ is a valuation ring with the valuation group $(G,\\leq)$ and the maximal ideal of $B$ coincides with that of $A$. Let $F$ be the residue field of $B$. In the above setting, we found a one-to-one correspondence between $\\mathfrak X_A^A$ and a subset of $\\prod_{g \\in G,g \\geq e} \\mathfrak X_{F_0}^F$.", "published": "2023-10-06T09:30:15Z", "authors": ["Masato Fujita", "Masaru Kageyama"], "categories": ["math.AC"], "doi": "10.1007/s10998-024-00605-1", "_query": "automated valuation model", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/1802.07563v1", "title": "Laplace transforms and valuations", "abstract": "It is proved that the classical Laplace transform is a continuous valuation which is positively GL$(n)$ covariant and logarithmic translation covariant. Conversely, these properties turn out to be sufficient to characterize this transform.", "published": "2018-02-21T13:33:04Z", "authors": ["Jin Li", "Dan Ma"], "categories": ["math.MG", "math.FA"], "doi": "10.1016/j.jfa.2016.09.011", "_query": "automated valuation model", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2011.03543v1", "title": "XVA Valuation under Market Illiquidity", "abstract": "Before the 2008 financial crisis, most research in financial mathematics focused on pricing options without considering the effects of counterparties' defaults, illiquidity problems, and the role of the sale and repurchase agreement (Repo) market. Recently, models were proposed to address this by computing a total valuation adjustment (XVA) of derivatives; however without considering a potential crisis in the market. In this article, we include a possible crisis by using an alternating renewal process to describe the switching between a normal financial regime and a financial crisis. We develop a framework to price the XVA of a European claim in this state-dependent situation. The price is characterized as a solution to a backward stochastic differential equation (BSDE), and we prove the existence and uniqueness of this solution. In a numerical study based on a deep learning algorithm for BSDEs, we compare the effect of different parameters on the valuation of the XVA.", "published": "2020-11-06T18:41:09Z", "authors": ["Weijie Pang", "Stephan Sturm"], "categories": ["q-fin.PR", "math.PR"], "doi": "", "_query": "automated valuation model", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2505.22957v1", "title": "Fast Derivative Valuation from Volatility Surfaces using Machine Learning", "abstract": "We introduce a fast and flexible Machine Learning (ML) framework for pricing derivative products whose valuation depends on volatility surfaces. By parameterizing volatility surfaces with the 5-parameter stochastic volatility inspired (SVI) model augmented by a one-factor term structure adjustment, we first generate numerous volatility surfaces over realistic ranges for these parameters. From these synthetic market scenarios, we then compute high-accuracy valuations using conventional methodologies for two representative products: the fair strike of a variance swap and the price and Greeks of an American put. We then train the Gaussian Process Regressor (GPR) to learn the nonlinear mapping from the input risk factors, which are the volatility surface parameters, strike and interest rate, to the valuation outputs. Once trained, We use the GPR to perform out-of-sample valuations and compare the results against valuations using conventional methodologies. Our ML model achieves very accurate results of $0.5\\%$ relative error for the fair strike of variance swap and $1.7\\% \\sim 3.5\\%$ relative error for American put prices and first-order Greeks. More importantly, after training, the model computes valuations almost instantly, yielding a three to four orders of magnitude speedup over Crank-Nicolson finite-difference method for American puts, enabling real-time risk analytics, dynamic hedging and large-scale scenario analysis. Our approach is general and can be extended to other path-dependent derivative products with early-exercise features, paving the way for hybrid quantitative engines for modern financial systems.", "published": "2025-05-29T00:42:56Z", "authors": ["Lijie Ding", "Egang Lu", "Kin Cheung"], "categories": ["q-fin.PR"], "doi": "", "_query": "automated valuation model", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2205.05035v3", "title": "Graded rings associated to valuations and direct limits", "abstract": "In this paper, we study the structure of the graded ring associated to a limit key polynomial $Q_n$ in terms of the key polynomials that define $Q_n$. In order to do that, we use direct limits. In general, we describe the direct limit of a family of graded rings associated to a totally ordered set of valuations. As an example, we describe the graded ring associated to a valuation-algebraic valuation as a direct limit of graded rings associated to residue-transcendental valuations.", "published": "2022-05-10T16:49:34Z", "authors": ["Caio Henrique Silva de Souza", "Josnei Antonio Novacoski", "Mark Spivakovsky"], "categories": ["math.AC"], "doi": "", "_query": "automated valuation model", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/1805.12526v2", "title": "Fully Automated Organ Segmentation in Male Pelvic CT Images", "abstract": "Accurate segmentation of prostate and surrounding organs at risk is important for prostate cancer radiotherapy treatment planning. We present a fully automated workflow for male pelvic CT image segmentation using deep learning. The architecture consists of a 2D localization network followed by a 3D segmentation network for volumetric segmentation of prostate, bladder, rectum, and femoral heads. We used a multi-channel 2D U-Net followed by a 3D U-Net with encoding arm modified with aggregated residual networks, known as ResNeXt. The models were trained and tested on a pelvic CT image dataset comprising 136 patients. Test results show that 3D U-Net based segmentation achieves mean (SD) Dice coefficient values of 90 (2.0)% ,96 (3.0)%, 95 (1.3)%, 95 (1.5)%, and 84 (3.7)% for prostate, left femoral head, right femoral head, bladder, and rectum, respectively, using the proposed fully automated segmentation method.", "published": "2018-05-31T15:56:22Z", "authors": ["Anjali Balagopal", "Samaneh Kazemifar", "Dan Nguyen", "Mu-Han Lin", "Raquibul Hannan", "Amir Owrangi", "Steve Jiang"], "categories": ["physics.med-ph", "cs.CV"], "doi": "10.1088/1361-6560/aaf11c", "_query": "automated valuation model", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2512.10622v1", "title": "Metrics, KPIs, and Taxonomy for Data Valuation and Monetisation -- Internal Processes Perspective", "abstract": "Data valuation and monetisation are emerging as central challenges in data-driven economies, yet no unified framework exists to measure or manage data value across organisational contexts. This paper presents a systematic literature review of metrics and key performance indicators (KPIs) relevant to data valuation and monetisation, focusing on the Internal Processes Perspective of the Balanced Scorecard (BSC). As part of a broader effort to explore all four BSC perspectives, we identify, categorise, and interrelate hundreds of metrics within a comprehensive taxonomy structured around three core clusters: Data Quality, Governance & Compliance, and Operational Efficiency. The taxonomy consolidates overlapping definitions, clarifies conceptual dependencies, and links technical, organisational, and regulatory indicators that underpin data value creation. By integrating these dimensions, it provides a foundation for the development of standardised and evidence-based valuation frameworks. Beyond its theoretical contribution, the taxonomy supports ongoing practical applications in decision-support systems and data valuation models, advancing the broader goal of establishing a coherent, dynamic approach to assessing and monetising data across industries.", "published": "2025-12-11T13:19:24Z", "authors": ["Eduardo Vyhmeister", "Bastien Pietropaoli", "Alejando Martinez Molina", "Montserrat Gonzalez-Ferreiro", "Gabriel Gonzalez-Castane", "Jordi Arjona Aroca", "Andrea Visentin"], "categories": ["cs.ET"], "doi": "", "_query": "automated valuation model", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2004.06161v2", "title": "On the structure of the graded algebra associated to a valuation", "abstract": "The main goal of this paper is to study the structure of the graded algebra associated to a valuation. More specifically, we prove that the associated graded algebra ${\\rm gr}_v(R)$ of a subring $(R,\\mathfrak{m})$ of a valuation ring $\\mathcal{O}_v$, for which $Kv:=\\mathcal{O}_v / \\mathfrak{m}_v=R / \\mathfrak{m}$, is isomorphic to $Kv[t^{v(R)}]$, where the multiplication is given by a twisting. We show that this twisted multiplication can be chosen to be the usual one in the cases where the value group is free or the residue field is closed by radicals. We also present an example that shows that the isomorphism (with the trivial twisting) does not have to exist.", "published": "2020-04-13T19:06:31Z", "authors": ["M. S. Barnabé", "J. Novacoski", "M. Spivakovsky"], "categories": ["math.AC"], "doi": "", "_query": "automated valuation model", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2008.02767v7", "title": "The Grothendieck--Serre conjecture over valuation rings", "abstract": "In this article, we establish the Grothendieck-Serre conjecture over valuation rings: for a reductive group scheme $G$ over a valuation ring $V$ with fraction field $K$, a $G$-torsor over $V$ is trivial if it is trivial over $K$. This result is predicted by the original Grothendieck-Serre conjecture and the resolution of singularities. The novelty of our proof lies in overcoming subtleties brought by general nondiscrete valuation rings. By using flasque resolutions and inducting with local cohomology, we prove a non-Noetherian counterpart of Colliot-Thélène-Sansuc's case of tori. Then, taking advantage of techniques in algebraization, we obtain the passage to the Henselian rank one case. Finally, we induct on Levi subgroups and use the integrality of rational points of anisotropic groups to reduce to the semisimple anisotropic case, in which we appeal to properties of parahoric subgroups in Bruhat-Tits theory to conclude. In the last section, by using properties of reflexive sheaves, we also prove a variant of Nisnevich's purity conjecture.", "published": "2020-08-06T17:11:50Z", "authors": ["Ning Guo"], "categories": ["math.AG"], "doi": "", "_query": "automated valuation model", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/1411.4843v1", "title": "A generalization of the Abhyankar Jung theorem to associated graded rings of valuations", "abstract": "Suppose that R\\rightarrow S is an extension of local domains and ν^* is a valuation dominating S. We consider the natural extension of associated graded rings along the valuation gr_{ν^*}(R)\\rightarrow gr_{ν^*}(S). We give examples showing that in general, this extension does not share good properties of the extension $\\rightarrow S, but after enough blow ups above the valuations, good properties of the extension R\\rightarrow S are reflected in the extension of associated graded rings. Stable properties of this extension (after blowing up) are much better in characteristic zero than in positive characteristic. Our main result is a generalization of the Abhyankar-Jung theorem which holds for extensions of associated graded rings along the valuation, after enough blowing up.", "published": "2014-11-18T13:49:13Z", "authors": ["Steven Dale Cutkosky"], "categories": ["math.AG", "math.AC"], "doi": "", "_query": "automated valuation model", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2001.02930v3", "title": "Immediate extensions of valuation rings and ultrapowers", "abstract": "We describe the immediate extensions of a one dimensional valuation ring $V$ which could be embedded in some separation of a ultrapower of $V$ with respect to a certain ultrafilter. For such extensions holds a kind of Artin's approximation.", "published": "2020-01-09T11:44:55Z", "authors": ["Dorin Popescu"], "categories": ["math.AC"], "doi": "", "_query": "automated valuation model", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/1011.2860v1", "title": "Valuation Extensions of Algebras Defined by Monic Gröbner Bases", "abstract": "Let $K$ be a field, $\\mathcal {O}_v$ a valuation ring of $K$ associated to a valuation $v$: $K\\rightarrowΓ\\cup\\{\\infty\\}$, and ${\\bf m}_v$ the unique maximal ideal of $\\mathcal {O}_v$. Consider an ideal $\\mathcal {I}$ of the free $K$-algebra $K\\langle X\\rangle =K\\langle X_1,...,X_n\\rangle$ on $X_1,...,X_n$. If ${\\cal I}$ is generated by a subset $\\mathcal {G}\\subset{\\cal O}_v\\langle X\\rangle$ which is a monic Gröbner basis of ${\\cal I}$ in $K\\langle X\\rangle$, where $\\mathcal {O}_v\\langle X\\rangle =\\mathcal{O}_v\\langle X_1,...,X_n\\rangle$ is the free $\\mathcal{O}_v$-algebra on $X_1,...,X_n$, then the valuation $v$ induces naturally an exhaustive and separated $Γ$-filtration $F^vA$ for the $K$-algebra $A=K\\langle X\\rangle /\\mathcal {I}$, and moreover $\\mathcal{I}\\cap\\mathcal{O}_v\\langle X\\rangle =\\langle\\mathcal{G}\\rangle$ holds in $\\mathcal{O}_v\\langle X\\rangle$; it follows that, if furthermore $\\mathcal{G}\\not\\subset {\\bf m}_v{O}_v\\langle X\\rangle$ and $k\\langle X\\rangle /\\langle\\overline{\\mathcal G}\\rangle$ is a domain, where $k=\\mathcal{O}_v/{\\bf m}_v$ is the residue field of $\\mathcal{O}_v$, $k\\langle X\\rangle =k\\langle X_1,...,X_n\\rangle$ is the free $k$-algebra on $X_1,...,X_n$, and $\\overline{\\mathcal G}$ is the image of $\\mathcal{G}$ under the canonical epimorphism $\\mathcal{O}_v\\langle X\\rangle\\rightarrow k\\langle X\\rangle$, then $F^vA$ determines a valuation function $A\\rightarrow Γ\\cup\\{\\infty\\}$, and thereby $v$ extends naturally to a valuation function on the (skew-)field $Δ$ of fractions of $A$ provided $Δ$ exists.", "published": "2010-11-12T09:44:42Z", "authors": ["Huishi Li"], "categories": ["math.RA"], "doi": "", "_query": "automated valuation model", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/1403.6052v1", "title": "Intersection of valuation rings in $k[x,y]$", "abstract": "We associate to any given finite set of valuations on the polynomial ring in two variables over an algebraically closed field a numerical invariant whose positivity characterizes the case when the intersection of their valuation rings has maximal transcendence degree over the base fields. As an application, we give a criterion for when an analytic branch at infinity in the affine plane that is defined over a number field in a suitable sense is the branch of an algebraic curve.", "published": "2014-03-24T17:37:49Z", "authors": ["Junyi Xie"], "categories": ["math.AG"], "doi": "10.1112/plms/pdv022", "_query": "automated valuation model", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2603.11069v1", "title": "On the 3-adic Valuation of a Cubic Binomial Sum", "abstract": "In this short note, we prove a conjecture recently posed by Alekseyev, Amdeberhan, Shallit, and Vukusic on the 3-adic valuation of a cubic binomial sum.", "published": "2026-03-10T15:10:24Z", "authors": ["Valentio Iverson"], "categories": ["math.NT"], "doi": "", "_query": "automated valuation model", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/1703.08778v2", "title": "Valuations on convex functions and convex sets and Monge-Ampere operators", "abstract": "The notion of a valuation on convex bodies is very classical. The notion of a valuation on a class of functions was recently introduced and studied by M. Ludwig and others. We study an explicit relation between continuous valuations on convex functions which are invariant under adding arbitrary linear functionals, and translations invariant continuous valuations on convex bodies. More precisely, we construct a natural linear map from the former space to the latter and prove that it has dense image and infinite dimensional kernel. The proof uses the author's irreducibility theorem and few properties of the real Monge-Ampere operators due to A.D. Alexandrov and Z. Blocki. Fur- thermore we show how to use complex, quaternionic, and octonionic Monge-Ampere operators to construct more examples of continuous valuations on convex functions in an analogous way.", "published": "2017-03-26T07:08:37Z", "authors": ["Semyon Alesker"], "categories": ["math.MG"], "doi": "", "_query": "automated valuation model", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/1308.4743v1", "title": "Quasi-valuations and algebras over valuation domains", "abstract": "Suppose $F$ is a field with valuation $v$ and valuation domain $O_{v}$, and $R$ is an $O_{v}-$algebra. We prove that $R$ satisfies SGB (strong going between) over $O_{v}$. We give a necessary and sufficient condition for $R$ to satisfy LO (lying over) over $O_{v}$. Using the filter \\qv constructed in [Sa1], we show that if $R$ is torsion-free over $O_{v}$ then $R$ satisfies GD (going down) over $O_{v}$. In particular, if $R$ is torsion-free and $(R^{\\times} \\cap O_{v}) \\subseteq O_{v}^{\\times}$, then for any chain in $\\text{Spec}(O_v)$ there exists a chain in $\\text{Spec}(R)$ covering it. Assuming $R$ is torsion-free over $O_{v}$ and $[R \\otimes_{O_{v}}F:F]< \\infty$, we prove that $R$ satisfies INC (incomparabilty) over $O_{v}$. Assuming in addition that $(R^{\\times} \\cap O_{v}) \\subseteq O_{v}^{\\times}$, we deduce that $R$ and $O_{v}$ have the same Krull dimension and a bound on the size of the prime spectrum of $R$ is given. Under certain assumptions on $R$ and a \\qv defined on it, we prove that the \\qv ring satisfies GU (going up) over $O_{v}$. Combining these five properties together, we deduce that any maximal chain of prime ideals of the \\qv ring is lying over $\\text{Spec}(O_{v})$, in a one-to-one correspondence.", "published": "2013-08-22T00:09:15Z", "authors": ["Shai Sarussi"], "categories": ["math.RA"], "doi": "", "_query": "automated valuation model", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/1311.6047v2", "title": "Irrational behavior of algebraic discrete valuations", "abstract": "We study algebraic discrete valuations dominating normal local domains of dimension two. We construct a family of examples to show that the Hilbert-Samuel function of the associated graded ring of the valuation can fail to be asymptotically of the form: quasi-polynomial plus a bounded function. We also show that the associated multiplicity can be irrational, or even transcendental.", "published": "2013-11-23T20:16:57Z", "authors": ["Soumya D. Sanyal"], "categories": ["math.AC"], "doi": "", "_query": "automated valuation model", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/1912.03546v1", "title": "Essential Finite Generation of Valuation Rings in Characteristic Zero Algebraic Function Fields", "abstract": "Let $K$ be a characteristic zero algebraic function field with a valuation $ν$. Let $L$ be a finite extension of $K$ and $ω$ be an extension of $ν$ to $L$. We establish that the valuation ring $V_ω$ of $ω$ is essentially finitely generated over the valuation ring $V_ν$ of $ν$ if and only if the initial index $ε(ω|ν)$ is equal to the ramification index $e(ω|ν)$ of the extension. This gives a positive answer, for characteristic zero algebraic function fields, to a question posed by Hagen Knaf.", "published": "2019-12-07T19:30:50Z", "authors": ["Steven Dale Cutkosky"], "categories": ["math.AC", "math.AG"], "doi": "10.2140/ant.2022.16.291", "_query": "automated valuation model", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2605.08153v1", "title": "Temporal-Decay Shapley: A Time-Aware Data Valuation Framework for Time-Series Data", "abstract": "With the rapid development of machine learning applications on time-series data, accurately assessing the value of training samples has become essential for data selection, noise detection, and model optimization. However, traditional data valuation methods usually assume that samples are independent and identically distributed, and thus ignore the time-varying nature of sample value in time-series data. This paper proposes an improved temporal Shapley data valuation method that enables accurate sample valuation for time-series data through a temporal decay mechanism and a multi-scale fusion strategy. Specifically, we propose three progressively enhanced temporal Shapley methods. Temporal-Decay Shapley (TDS) incorporates temporal information into Shapley value computation through exponential decay weights; the improved TDS adopts power exponential decay to better adapt to nonlinear temporal drift; and Multi-Scale Temporal-Decay Shapley (MS-TDS) constructs a multi-scale fusion mechanism that balances the value of short-term hotspot samples and long-term foundational samples through parallel multi-scale valuation and sample-level adaptive fusion. Experimental results show that the proposed methods generally outperform traditional methods in noise detection and high-value data identification tasks, with more evident advantages under most strongly temporal settings, thereby effectively improving the accuracy and robustness of data valuation.", "published": "2026-05-04T04:32:33Z", "authors": ["Chuwen Pang", "Bing Mi", "Kongyang Chen"], "categories": ["cs.LG", "cs.GT"], "doi": "", "_query": "automated valuation model", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2312.15999v1", "title": "Pricing with Contextual Elasticity and Heteroscedastic Valuation", "abstract": "We study an online contextual dynamic pricing problem, where customers decide whether to purchase a product based on its features and price. We introduce a novel approach to modeling a customer's expected demand by incorporating feature-based price elasticity, which can be equivalently represented as a valuation with heteroscedastic noise. To solve the problem, we propose a computationally efficient algorithm called \"Pricing with Perturbation (PwP)\", which enjoys an $O(\\sqrt{dT\\log T})$ regret while allowing arbitrary adversarial input context sequences. We also prove a matching lower bound at $Ω(\\sqrt{dT})$ to show the optimality regarding $d$ and $T$ (up to $\\log T$ factors). Our results shed light on the relationship between contextual elasticity and heteroscedastic valuation, providing insights for effective and practical pricing strategies.", "published": "2023-12-26T11:07:37Z", "authors": ["Jianyu Xu", "Yu-Xiang Wang"], "categories": ["cs.LG", "econ.EM", "stat.ML"], "doi": "", "_query": "automated valuation model", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/1507.05412v1", "title": "Minkowski Valuations and Generalized Valuations", "abstract": "A convolution representation of continuous translation invariant and SO(n) equivariant Minkowski valuations is established. This is based on a new classification of translation invariant generalized spherical valuations. As applications, Crofton and kinematic formulas for Minkowski valuations are obtained.", "published": "2015-07-20T08:14:15Z", "authors": ["Franz E. Schuster", "Thomas Wannerer"], "categories": ["math.MG"], "doi": "", "_query": "automated valuation model", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/1401.5640v1", "title": "The Euler valuation on MV-algebras", "abstract": "Every finitely presented MV-algebra A has a unique idempotent valuation E assigning value 1 to every basic element of A. For each element a of A, E(a) turns out to coincide with the Euler characteristic of the open set of maximal ideals m of A such that a/m is nonzero.", "published": "2014-01-22T12:20:26Z", "authors": ["Daniele Mundici", "Andrea Pedrini"], "categories": ["math.RA"], "doi": "", "_query": "automated valuation model", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2209.08853v1", "title": "Automated Identification of Security-Relevant Configuration Settings Using NLP", "abstract": "To secure computer infrastructure, we need to configure all security-relevant settings. We need security experts to identify security-relevant settings, but this process is time-consuming and expensive. Our proposed solution uses state-of-the-art natural language processing to classify settings as security-relevant based on their description. Our evaluation shows that our trained classifiers do not perform well enough to replace the human security experts but can help them classify the settings. By publishing our labeled data sets and the code of our trained model, we want to help security experts analyze configuration settings and enable further research in this area.", "published": "2022-09-19T08:55:05Z", "authors": ["Patrick Stöckle", "Theresa Wasserer", "Bernd Grobauer", "Alexander Pretschner"], "categories": ["cs.CR", "cs.SE"], "doi": "10.1145/3551349.3559499", "_query": "automated valuation model", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/1206.3220v2", "title": "Valuation and parities for exchange options", "abstract": "Valuation and parity formulas for both European-style and American-style exchange options are presented in a general financial model allowing for jumps, possibility of default and \"bubbles\" in asset prices. The formulas are given via expectations of auxiliary probabilities using the change-of-numeraire technique. Extensive discussion is provided regarding the way that folklore results such as Merton's no-early-exercise theorem and traditional parity relations have to be altered in this more versatile framework.", "published": "2012-06-14T19:36:14Z", "authors": ["Constantinos Kardaras"], "categories": ["q-fin.PR", "math.PR"], "doi": "", "_query": "automated valuation model", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2212.03213v2", "title": "Twisted homology stability of O_n for valuation rings", "abstract": "In this article, we extend an argument of Vogtmann in order to show homology stability of the Euclidean orthogonal group $O_n(A)$ when $A$ is a valuation ring subject to arithmetic conditions on either its residue or its quotient field. In particular, it is shown that if $A$ is a henselian valuation ring, then the groups $O_n(A)$ exhibit homology stability if the residue field of $A$ has finite Pythagoras number. Our results include those of Vogtmann, and hold with various twisted coefficients. Using these results, we give analogues for fields $F\\neq\\mathbb R$ of some computations that appear in the study of scissor congruences.", "published": "2022-12-06T18:36:16Z", "authors": ["Oscar Harr"], "categories": ["math.AT", "math.RT"], "doi": "10.1016/j.jalgebra.2025.10.046", "_query": "automated valuation model", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/1910.09123v4", "title": "Néron desingularization of extensions of valuation rings with an Appendix by Kęstutis Česnavičius", "abstract": "Zariski's local uniformization, a weak form of resolution of singularities, implies that every valuation ring containing $\\bf Q$ is a filtered direct limit of smooth $\\bf Q$-algebras. Given an immediate extension of valuation rings $V\\subset V'$ containing $\\bf Q$ we show that $V'$ is a filtered direct limit of smooth $V$-algebras. This corrects a paper of us \\cite{Po1} where we thought that we may reduce to the case when the value groups are finitely generated. For this correction we use an infinite tower of ultrapowers construction that rests on results from model theory. .", "published": "2019-10-21T02:41:58Z", "authors": ["Dorin Popescu"], "categories": ["math.AC"], "doi": "", "_query": "automated valuation model", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/1205.1336v5", "title": "On extendability by continuity of valuations on convex polytopes", "abstract": "There is a well known construction of weakly continuous valuations on convex compact polytopes in R^n. In this paper we investigate when a special case of this construction gives a valuation which extends by continuity in the Hausdorff metric to all convex compact subsets of R^n. It is shown that there is a necessary condition on the initial data for such an extension. In the case of R^3 more explicit results are obtained.", "published": "2012-05-07T10:40:53Z", "authors": ["Semyon Alesker"], "categories": ["math.MG"], "doi": "", "_query": "automated valuation model", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/1603.00987v7", "title": "Securities Lending Strategies: Exclusive Valuations and Auction Bids", "abstract": "We derive valuations of a portfolio of financial instruments from a securities lending perspective, under different assumptions, and show a weighting scheme that converges to the true valuation. We illustrate conditions under which our alternative weighting scheme converges faster to the true valuation when compared to the minimum variance weighting. This weighting scheme is applicable in any situation where multiple forecasts are made and we need a methodology to combine them. Our valuations can be useful either to derive a bidding strategy for an exclusive auction or to design an appropriate auction mechanism, depending on which side of the fence a participant sits (whether the interest is to procure the rights to use a portfolio for making stock loans such as for a lending desk, or, to obtain additional revenue from a portfolio such as from the point of view of a long only asset management firm). Lastly, we run simulations to establish numerical examples for the set of valuations and for various bidding strategies corresponding to different auction settings.", "published": "2016-03-03T06:17:14Z", "authors": ["Ravi Kashyap"], "categories": ["q-fin.PR"], "doi": "", "_query": "automated valuation model", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/1001.1416v1", "title": "Models of mu_{p^2,K} over a discrete valuation ring", "abstract": "Let R be a discrete valuation ring with residue field of characteristic p>0. Let K be its fraction field. We prove that any finite and flat R-group scheme, isomorphic to μ_{p^2,K} on the generic fiber, is the kernel in a short exact sequence which generically coincides with the Kummer sequence. We will explicitly describe and classify such models. In the appendix X. Caruso shows how to classify models of μ_{p^2,K}, in the case of unequal characteristic, using the Breuil-Kisin theory.", "published": "2010-01-09T10:59:29Z", "authors": ["Dajano Tossici", "Xavier Caruso"], "categories": ["math.AG", "math.NT"], "doi": "", "_query": "automated valuation model", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/1003.1848v1", "title": "Basket Options Valuation for a Local Volatility Jump-Diffusion Model with the Asymptotic Expansion Method", "abstract": "In this paper we discuss the basket options valuation for a jump-diffusion model. The underlying asset prices follow some correlated local volatility diffusion processes with systematic jumps. We derive a forward partial integral differential equation (PIDE) for general stochastic processes and use the asymptotic expansion method to approximate the conditional expectation of the stochastic variance associated with the basket value process. The numerical tests show that the suggested method is fast and accurate in comparison with the Monte Carlo and other methods in most cases.", "published": "2010-03-09T11:36:07Z", "authors": ["Guoping Xu", "Harry Zheng"], "categories": ["q-fin.CP"], "doi": "", "_query": "automated valuation model", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/1003.5712v1", "title": "Overview of utility-based valuation", "abstract": "We review the utility-based valuation method for pricing derivative securities in incomplete markets. In particular, we review the practical approach to the utility-based pricing by the means of computing the first order expansion of marginal utility-based prices with respect to a small number of random endowments.", "published": "2010-03-30T01:19:50Z", "authors": ["David German"], "categories": ["q-fin.GN", "q-fin.PR"], "doi": "", "_query": "automated valuation model", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2610.22114v1", "title": "SL(n)-covariant symmetric tensor valuations on polytopes", "abstract": "All $\\operatorname{SL}(n)$-covariant symmetric tensor valuations on convex polytopes in $\\mathbb R^n$ are completely classified without any continuity assumptions. It is shown that the only such valuation is the moment tensor.", "published": "2026-10-15T15:53:31Z", "authors": ["Chunna Zeng", "Xiaoyu Cheng"], "categories": ["math.MG"], "doi": "", "_query": "automated valuation model", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2505.16147v2", "title": "Losing is for Cherishing: Data Valuation Based on Machine Unlearning and Shapley Value", "abstract": "The proliferation of large models has intensified the need for efficient data valuation methods to quantify the contribution of individual data providers. Traditional approaches, such as game-theory-based Shapley value and influence-function-based techniques, face prohibitive computational costs or require access to full data and model training details, making them hardly achieve partial data valuation. To address this, we propose Unlearning Shapley, a novel framework that leverages machine unlearning to estimate data values efficiently. By unlearning target data from a pretrained model and measuring performance shifts on a reachable test set, our method computes Shapley values via Monte Carlo sampling, avoiding retraining and eliminating dependence on full data. Crucially, Unlearning Shapley supports both full and partial data valuation, making it scalable for large models (e.g., LLMs) and practical for data markets. Experiments on benchmark datasets and large-scale text corpora demonstrate that our approach matches the accuracy of state-of-the-art methods while reducing computational overhead by orders of magnitude. Further analysis confirms a strong correlation between estimated values and the true impact of data subsets, validating its reliability in real-world scenarios. This work bridges the gap between data valuation theory and practical deployment, offering a scalable, privacy-compliant solution for modern AI ecosystems.", "published": "2025-05-22T02:46:03Z", "authors": ["Le Ma", "Shirao Yang", "Zihao Wang", "Yinggui Wang", "Lei Wang", "Tao Wei", "Kejun Zhang"], "categories": ["cs.AI", "cs.LG"], "doi": "", "_query": "automated valuation model", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/1401.5204v4", "title": "Overweight deformations of affine toric varieties and local uniformization", "abstract": "We study Abhyankar valuations of excellent equicharacteristic local domains with an algebraically closed residue field. For zero dimensional valuations we prove that whenever the ring is complete and the semigroup of values taken by the valuation is finitely generated (which implies that the valuation is Abhyankar) the valuation can be uniformized in an embedded way by a birational map which is monomial with respect to a suitable system of generators of the maximal ideal. We prove that conversely if a valuation is Abhyankar after a birational modification and localization at the point picked by the valuation one obtains a ring whose semigroup of values is finitely generated. Combining the two results and using the good behavior of Abhyankar valuations with respect to composition and completion gives local uniformization for all Abhyankar valuations of excellent equicharacteristic local domains with an algebraically closed residue field. Some general results on Abhyankar valuations are by-products of the method of proof.", "published": "2014-01-21T07:29:54Z", "authors": ["Bernard Teissier"], "categories": ["math.AG", "math.AC"], "doi": "", "_query": "automated valuation model", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2609.04788v1", "title": "Shapley Valuation of Finite-Copy Quantum Data Depends on Physical Access", "abstract": "Data valuation asks how learning utility should be attributed to training data contributors. Most classical formulations begin after data have become reusable records, so the physical readout of the data is effectively fixed. Finite-copy quantum data are different: unknown states are consumable physical systems, and the same supplied states and downstream task can yield different Shapley values under different physical access models. Our framework makes this dependence explicit by treating physical access as a component of quantum data valuation itself. We establish an exact connection between physical-access advantage and contributor-level data valuation. For nested access models, we prove that the maximal downstream utility gain enabled by richer physical access exactly determines the largest symmetric Shapley ranking-reversal margin. More generally, for arbitrary access-model pairs, including non-nested ones, we derive an exact geometric characterization of the possible shifts of the full Shapley attribution vector. For fixed learning pipelines, we further obtain an operational Shapley-observable representation for finite-copy valuation. Numerical experiments demonstrate that identical quantum samples can receive different values and rankings when only the physical access model is changed. These results establish that quantum data value is not an intrinsic property of the underlying states alone, but emerges from the interaction between quantum states, physical access, and the downstream learning task.", "published": "2026-09-04T06:29:08Z", "authors": ["Qipeng Qian", "Yuntao Qian"], "categories": ["quant-ph"], "doi": "", "_query": "automated valuation model", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/1810.12203v1", "title": "$K$-theory of valuation rings", "abstract": "We prove several results showing that the algebraic $K$-theory of valuation rings behave as though such rings were regular Noetherian, in particular an analogue of the Geisser--Levine theorem. We also give some new proofs of known results concerning cdh descent of algebraic $K$-theory.", "published": "2018-10-29T15:46:45Z", "authors": ["Shane Kelly", "Matthew Morrow"], "categories": ["math.KT"], "doi": "", "_query": "automated valuation model", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2204.02680v4", "title": "Relevance of Wrong-Way Risk in Funding Valuation Adjustments", "abstract": "In March 2020, the world was thrown into financial distress. This manifested itself in increased uncertainty in the financial markets. Many interest rates collapsed, and funding spreads surged significantly, which increased due to the market turmoil. In light of these events, it is essential to understand and model Wrong-Way Risk (WWR) in a Funding Valuation Adjustment (FVA) context. WWR may currently be absent from FVA calculations in banks' Valuation Adjustment (xVA) engines. However, in this letter, we demonstrate that WWR effects are non-negligible in FVA modelling from a risk-management perspective. We look at the impact of various modelling choices, such as including the default times of the relevant parties, as well as stochastic and deterministic funding spreads. A case study is presented for interest rate derivatives.", "published": "2022-04-06T09:02:01Z", "authors": ["T. van der Zwaard", "L. A. Grzelak", "C. W. Oosterlee"], "categories": ["q-fin.CP", "q-fin.RM"], "doi": "10.1016/j.frl.2022.103091", "_query": "automated valuation model", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/math/0605225v3", "title": "Rank one discrete valuations of power series fields", "abstract": "In this paper we study the rank one discrete valuations of the field $k((X_1,..., X_n))$ whose center in $k\\lcor\\X\\rcor$ is the maximal ideal. In sections 2 to 6 we give a construction of a system of parametric equations describing such valuations. This amounts to finding a parameter and a field of coefficients. We devote section 2 to finding an element of value 1, that is, a parameter. The field of coefficients is the residue field of the valuation, and it is given in section 5. The constructions given in these sections are not effective in the general case, because we need either to use the Zorn's lemma or to know explicitly a section $σ$ of the natural homomorphism $R_v\\to\\d$ between the ring and the residue field of the valuation $v$. However, as a consequence of this construction, in section 7, we prove that $k((\\X))$ can be embedded into a field $L((\\Y))$, where $L$ is an algebraic extension of $k$ and the {\\em ``extended valuation'' is as close as possible to the usual order function}.", "published": "2006-05-09T10:18:53Z", "authors": ["F. J. Herrera Govantes", "M. A. Olalla Acosta", "J. L. Vicente Cordoba"], "categories": ["math.AC", "math.AG"], "doi": "10.1080/00927870701326478", "_query": "automated valuation model", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/math/0410526v2", "title": "Arcs, valuations and the Nash map", "abstract": "This paper gives a map from the set of families of arcs on a variety to the set of valuations on the rational function field of the variety We characterize a family of arcs which corresponds to a divisorial valuation by this map. We can see that both the Nash map and a certain McKay correspondence are the restrictions of this map. This paper also gives the affirmative answer to the Nash problem for a non-normal variety in a certain category. As a corollary, we get the affirmative answer for a non-normal toric variety.", "published": "2004-10-25T13:28:34Z", "authors": ["Shihoko Ishii"], "categories": ["math.AG"], "doi": "", "_query": "automated valuation model", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/1502.07397v1", "title": "Rational Multi-Curve Models with Counterparty-Risk Valuation Adjustments", "abstract": "We develop a multi-curve term structure setup in which the modelling ingredients are expressed by rational functionals of Markov processes. We calibrate to LIBOR swaptions data and show that a rational two-factor lognormal multi-curve model is sufficient to match market data with accuracy. We elucidate the relationship between the models developed and calibrated under a risk-neutral measure Q and their consistent equivalence class under the real-world probability measure P. The consistent P-pricing models are applied to compute the risk exposures which may be required to comply with regulatory obligations. In order to compute counterparty-risk valuation adjustments, such as CVA, we show how positive default intensity processes with rational form can be derived. We flesh out our study by applying the results to a basis swap contract.", "published": "2015-02-25T23:04:34Z", "authors": ["Stephane Crepey", "Andrea Macrina", "Tuyet Mai Nguyen", "David Skovmand"], "categories": ["q-fin.MF"], "doi": "", "_query": "automated valuation model", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2506.02693v2", "title": "Poincaré series of valuations on functions over a field", "abstract": "For a subfield $\\K$ of the field $\\C$ of complex numbers, we consider curve and divisorial valuations on the algebra $\\K[[x,y]]$ of formal power series in two variables with the coeficients in $\\K$. We compute the semigroup Poincaré series and the classical Poincaré series of a valuation.", "published": "2025-06-03T09:43:34Z", "authors": ["Antonio Campillo", "Felix Delgado", "Sabir Gusein-Zade"], "categories": ["math.AG"], "doi": "", "_query": "automated valuation model", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2609.24181v2", "title": "Firm Valuation When AI Shapes the Business Model: A Milestone-Based Real-Options Framework for the AI Valuation Uncertainty Problem", "abstract": "Standard valuation methods, including discounted cash flow, the income approach standard IDW S 1 of the Institute of Public Auditors in Germany, and market multiples, compress milestone probabilities, continuation options, and risk shifts into opaque aggregate parameters; none provides a structured protocol for decomposing AI integration into auditable option-level assumptions. We propose an industry-agnostic taxonomy separating AI Integrators from AI Providers. AI Integrators are further classified by their Integration Depth Level, ranging from no integration to AI at the core of the product or process. A milestone-gated real-options overlay decomposes milestone state value into five components, and an Analytic Hierarchy Process-based Success Readiness Index derives per-option probabilities from structured pairwise comparisons for scenario analysis. Applied to an AI-native energy software-as-a-service firm, the framework yields a coherent valuation band traceable to identifiable option-level assumptions. Risk concentrates in later-stage continuation options, matching the structural prediction for AI Providers. The protocol applies across the firm lifecycle, including mergers and acquisitions due diligence. The case is a single-firm demonstration of protocol coherence, not empirical validation; multi-case testing against realised post-exit valuations is left to future research.", "published": "2026-09-21T06:53:33Z", "authors": ["Walter Kurz", "Wojtek Stricker", "Stefan Marx", "Frank Reinhardt", "Florian Kollberg"], "categories": ["q-fin.GN", "econ.GN"], "doi": "10.5281/zenodo.21901247", "_query": "automated valuation model", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2406.01130v2", "title": "SAVA: Scalable Learning-Agnostic Data Valuation", "abstract": "Selecting data for training machine learning models is crucial since large, web-scraped, real datasets contain noisy artifacts that affect the quality and relevance of individual data points. These noisy artifacts will impact model performance. We formulate this problem as a data valuation task, assigning a value to data points in the training set according to how similar or dissimilar they are to a clean and curated validation set. Recently, LAVA demonstrated the use of optimal transport (OT) between a large noisy training dataset and a clean validation set, to value training data efficiently, without the dependency on model performance. However, the LAVA algorithm requires the entire dataset as an input, this limits its application to larger datasets. Inspired by the scalability of stochastic (gradient) approaches which carry out computations on batches of data points instead of the entire dataset, we analogously propose SAVA, a scalable variant of LAVA with its computation on batches of data points. Intuitively, SAVA follows the same scheme as LAVA which leverages the hierarchically defined OT for data valuation. However, while LAVA processes the whole dataset, SAVA divides the dataset into batches of data points, and carries out the OT problem computation on those batches. Moreover, our theoretical derivations on the trade-off of using entropic regularization for OT problems include refinements of prior work. We perform extensive experiments, to demonstrate that SAVA can scale to large datasets with millions of data points and does not trade off data valuation performance.", "published": "2024-06-03T09:17:35Z", "authors": ["Samuel Kessler", "Tam Le", "Vu Nguyen"], "categories": ["cs.LG"], "doi": "", "_query": "automated valuation model", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2212.06674v1", "title": "IT companies: the specifics of social networks valuation", "abstract": "The study discusses the main features which affect the IT companies valuation on the example of social networks. The relevance of the chosen topic is due to the fact that people live in the information age now, and information technologies surround us everywhere. Because of this, social networks have become very popular. They assist people to communicate with each other despite of the time and distance. Social networks are also companies that operate in order to generate income therefore their owners need to know how promising and profitable their business is. The social networks differ from traditional companies in this case the purpose of the research is determining the features of social networks that affect the accuracy and adequacy of the results of company valuation. The paper reviews the definitions of information technology, social networks, history, types of social networks, distinguishing features based on domestic and foreign literature. There are analyzed methods of assessing the value of Internet companies, their characteristics and methods of application. There is the six social networks evaluation was assessed in the practical part of the study: Facebook, Twitter, Pinterest, Snapchat, Sina Weibo and Vkontakte on the basis of the literature studied and the methods for evaluating the Internet companies which recommended in it, including the method of discounting the cash flow of the company as part of the income approach and the multiplier method as part of a comparative approach. Based on the analysis, the features that affect the social networks valuation are identified.", "published": "2022-12-12T13:10:25Z", "authors": ["K. V. Yupatova", "O. A. Malafeyev", "V. S. Lipatnikov", "V. Y. Bezrukikh"], "categories": ["econ.GN"], "doi": "", "_query": "automated valuation model", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2012.12710v2", "title": "Existence and Computation of Maximin Fair Allocations Under Matroid-Rank Valuations", "abstract": "We study fair and economically efficient allocation of indivisible goods among agents whose valuations are rank functions of matroids. Such valuations constitute a well-studied class of submodular functions (i.e., they exhibit a diminishing returns property) and model preferences in several resource-allocation settings. We prove that, for matroid-rank valuations, a social welfare-maximizing allocation that gives each agent her maximin share always exists. Furthermore, such an allocation can be computed in polynomial time. We establish similar existential and algorithmic results for the pairwise maximin share guarantee as well. To complement these results, we show that if the agents have binary XOS valuations or weighted-rank valuations, then maximin fair allocations are not guaranteed to exist. Both of these valuation classes are immediate generalizations of matroid-rank functions.", "published": "2020-12-23T14:36:56Z", "authors": ["Siddharth Barman", "Paritosh Verma"], "categories": ["cs.GT"], "doi": "", "_query": "automated valuation model", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2604.12417v1", "title": "Submodular Max-Min Allocation under Identical Valuations", "abstract": "In the problem of Submodular Max-Min Allocation, we are given a set of items, a set of players, and monotone submodular valuation functions that represent the satisfaction of a player with a certain subset of items. The goal is to find an allocation of the items to the players that maximizes the lowest satisfaction among all players. We study this problem in the special case where all players have the same valuation function. We devise a greedy algorithm which gives a $0.4$-approximation, improving the previously best factor of $\\frac{10}{27} \\approx 0.37$ by Uziahu and Feige. Furthermore, we study the integrality gap of the \\emph{configuration LP} when players have identical valuations. By constructing a variable assignment to the dual from a primal integral solution, we give the first constant upper bound on the integrality gap for submodular valuations. Generalizing the result to the case where players' allocations must be independent in $k$ given matroids, we derive a $\\mathcal{O}(k)$-estimation algorithm for max-min allocation subject to $k$ matroid constraints under identical valuations.", "published": "2026-04-14T08:03:45Z", "authors": ["Kimon Boehmer"], "categories": ["cs.DS"], "doi": "", "_query": "automated valuation model", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2309.16637v1", "title": "The $p$-Adic Valuation Trees for Quadratic Polynomials for Odd Primes", "abstract": "We examine the behavior of the sequences of $p$-adic valuations of quadratic polynomials with integer coefficients for an odd prime $p$ through tree representations. Under this representation, a finite tree corresponds to a periodic sequence, and an infinite tree corresponds to an unbounded sequence. We use the polynomial coefficients to determine whether the $p$-adic valuation trees are finite or infinite, the number of infinite branches, the number of levels, the valuations at terminating nodes, and their relationship to the corresponding sequences.", "published": "2023-09-28T17:47:17Z", "authors": ["Will Boultinghouse", "Emily Hammett", "Stephen Hu", "Olena Kozhushkina", "Rachel Snyder", "Justin Trulen"], "categories": ["math.NT"], "doi": "", "_query": "automated valuation model", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/math/0307219v1", "title": "On coefficient valuations of Eisenstein polynomials", "abstract": "Let p > 2 be a prime, let n > m > 0. Let pi_n be the norm of zeta_{p^n} - 1 under C_{p-1}, so that Z_(p)[pi_n] | Z_(p) is a purely ramified extension of discrete valuation rings of degree p^{n-1}. The minimal polynomial of pi_n over Q(pi_m) is an Eisenstein polynomial; we give lower bounds for its coefficient valuations at pi_m. The function field analogue, as introduced by Carlitz and Hayes, is studied as well.", "published": "2003-07-16T10:16:11Z", "authors": ["M. Kuenzer", "E. Wirsing"], "categories": ["math.NT"], "doi": "", "_query": "automated valuation model", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2506.24015v3", "title": "Hierarchical Knowledge Injection for Improving LLM-based Program Repair", "abstract": "Prompting LLMs with bug-related context (e.g., error messages, stack traces) improves automated program repair, but many bugs still remain unresolved. In real-world projects, developers often rely on broader repository and project-level context beyond the local code to resolve such bugs. In this paper, we investigate how automatically extracting and providing such knowledge can improve LLM-based program repair. We propose a layered knowledge injection framework that incrementally augments LLMs with structured context. It starts with the Bug Knowledge Layer, which includes information such as the buggy function and failing tests; expands to the Repository Knowledge Layer, which adds structural dependencies, related files, and commit history; and finally injects the Project Knowledge Layer, which incorporates relevant details from documentation and previously fixed bugs. We evaluate this framework on a dataset of 314 bugs from BugsInPy using two LLMs (Llama 3.3 and GPT-4o-mini), and analyze fix rates across six bug types. By progressively injecting knowledge across layers, our approach achieves a fix rate of 79% (250/314) using Llama 3.3, a significant improvement of 23% over previous work. All bug types show improvement with the addition of repository-level context, while only a subset benefit further from project-level knowledge, highlighting that different bug types require different levels of contextual information for effective repair. We also analyze the remaining unresolved bugs and find that more complex and structurally isolated bugs, such as Program Anomaly and GUI bugs, remain difficult even after injecting all available information. Our results show that layered context injection improves program repair and suggest the need for interactive and adaptive APR systems.", "published": "2025-06-30T16:19:38Z", "authors": ["Ramtin Ehsani", "Esteban Parra", "Sonia Haiduc", "Preetha Chatterjee"], "categories": ["cs.SE"], "doi": "10.1109/ASE63991.2025.00122", "_query": "automated valuation model", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/1801.07059v1", "title": "Uniform asymptotic stability of a fractional tuberculosis model", "abstract": "We propose a Caputo type fractional-order mathematical model for the transmission dynamics of tuberculosis (TB). Uniform asymptotic stability of the unique endemic equilibrium of the fractional-order TB model is proved, for any $α\\in (0, 1)$. Numerical simulations for the stability of the endemic equilibrium are provided.", "published": "2018-01-22T12:05:48Z", "authors": ["Weronika Wojtak", "Cristiana J. Silva", "Delfim F. M. Torres"], "categories": ["math.OC", "math.CA", "math.DS", "q-bio.PE"], "doi": "10.1051/mmnp/2018015", "_query": "automated valuation model", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2109.00306v1", "title": "Multiple-prior valuation of cash flows subject to capital requirements", "abstract": "We study market-consistent valuation of liability cash flows motivated by current regulatory frameworks for the insurance industry. Building on the theory on multiple-prior optimal stopping we propose a valuation functional with sound economic properties that applies to any liability cash flow. Whereas a replicable cash flow is assigned the market value of the replicating portfolio, a cash flow that is not fully replicable is assigned a value which is the sum of the market value of a replicating portfolio and a positive margin. The margin is a direct consequence of considering a hypothetical transfer of the liability cash flow from an insurance company to an empty corporate entity set up with the sole purpose to manage the liability run-off, subject to repeated capital requirements, and considering the valuation of this entity from the owner's perspective taking model uncertainty into account. Aiming for applicability, we consider a detailed insurance application and explain how the optimisation problems over sets of probability measures can be cast as simpler optimisation problems over parameter sets corresponding to parameterised density processes appearing in applications.", "published": "2021-09-01T11:19:54Z", "authors": ["Hampus Engsner", "Filip Lindskog", "Julie Thoegersen"], "categories": ["q-fin.PR"], "doi": "", "_query": "automated valuation model", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2003.07060v4", "title": "Finding Fair and Efficient Allocations When Valuations Don't Add Up", "abstract": "In this paper, we present new results on the fair and efficient allocation of indivisible goods to agents whose preferences correspond to {\\em matroid rank functions}. This is a versatile valuation class with several desirable properties (such as monotonicity and submodularity), which naturally lends itself to a number of real-world domains. We use these properties to our advantage; first, we show that when agent valuations are matroid rank functions, a socially optimal (i.e. utilitarian social welfare-maximizing) allocation that achieves envy-freeness up to one item (EF1) exists and is computationally tractable. We also prove that the Nash welfare-maximizing and the leximin allocations both exhibit this fairness/efficiency combination, by showing that they can be achieved by minimizing any symmetric strictly convex function over utilitarian optimal outcomes. To the best of our knowledge, this is the first valuation function class not subsumed by additive valuations for which it has been established that an allocation maximizing Nash welfare is EF1. Moreover, for a subclass of these valuation functions based on maximum (unweighted) bipartite matching, we show that a leximin allocation can be computed in polynomial time. Additionally, we explore possible extensions of our results to fairness criteria other than EF1 as well as to generalizations of the above valuation classes.", "published": "2020-03-16T07:42:27Z", "authors": ["Nawal Benabbou", "Mithun Chakraborty", "Ayumi Igarashi", "Yair Zick"], "categories": ["cs.AI", "cs.GT"], "doi": "10.1007/978-3-030-57980-7_3", "_query": "automated valuation model", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2609.10726v1", "title": "When Information is Worth the Risk: Behavioral Valuation for Hazardous Robotic Exploration", "abstract": "Hazardous robotic exploration requires robots to map spatial risks, such as unsafe terrain, radiation, fire, mines, or structural damage, while operating where collecting information can itself cause failure. A highly informative path may expose the robot to hazards, terminate execution, and prevent future observations. Hazardous exploration therefore requires deciding not only where uncertainty is largest, but when reducing it is worth the risk. This paper introduces a valuation-layer view of this problem. We keep the belief update, sensor model, physical risk model, and finite-horizon informative planner fixed, and change only the scalar objective used to rank feasible paths. Within this framework, we introduce a risk-augmented Behavioral Information objective based on Prelec probability weighting, yielding an interpretable family of conservative-to-aggressive information-risk valuations. Theoretically, we show that valuation parameters create switching boundaries between high-information/high-risk and lower-information/lower-risk paths, and induce a transformed Pareto-frontier structure over feasible exploration policies. Large-scale failure-truncated grid-world experiments show that valuation alone reshapes the information-risk frontier. Shannon information planning remains a strong raw-information baseline, while risk-aware objectives can reduce hazard exposure and robot losses by avoiding failures that truncate future sensing. Risk-augmented Behavioral valuation is Pareto-competitive with standard risk-aware baselines and provides interpretable conservative and intermediate regimes. These results support a framework in which robots reason not only about how much uncertainty an action reduces, but whether that reduction is worth the risk required to obtain it.", "published": "2026-09-09T18:20:18Z", "authors": ["Alkesh K. Srivastava", "Aamodh Suresh", "Carlos Nieto-Granda", "Philip Dames"], "categories": ["cs.RO"], "doi": "", "_query": "automated valuation model", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/1510.07218v2", "title": "Some combinatorial number theory problems over finite valuation rings", "abstract": "Let $\\mathcal{R}$ be a finite valuation ring of order $q^r$. In this paper we generalize and improve several well-known results, which were studied over finite fields $\\mathbb{F}_q$ and finite cyclic rings $\\mathbb{Z}/p^r\\mathbb{Z}$, in the setting of finite valuation rings.", "published": "2015-10-25T08:26:33Z", "authors": ["Pham Van Thang", "Le Anh Vinh"], "categories": ["math.CO", "math.NT"], "doi": "", "_query": "automated valuation model", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/1303.1987v2", "title": "Classification of normal toric varieties over a valuation ring of rank one", "abstract": "Normal toric varieties over a field or a discrete valuation ring are classified by rational polyhedral fans. We generalize this classification to normal toric varieties over an arbitrary valuation ring of rank one. The proof is based on a generalization of Sumihiro's theorem to this non-noetherian setting. These toric varieties play an important role for tropicalizations.", "published": "2013-03-08T13:42:06Z", "authors": ["Walter Gubler", "Alejandro Soto"], "categories": ["math.AG", "math.NT"], "doi": "", "_query": "automated valuation model", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2512.06391v1", "title": "On certain definable coarsenings of valuation rings and their applications", "abstract": "We show how suitable extensions $(L|K,v)$ of prime degree of valued fields give rise to definable coarsenings of the valuation rings of $L$ and $K$. In the case of Artin-Schreier and Kummer extensions with wild ramification, we can also define the ramification ideal. We demonstrate the use of the coarsenings on $L$, their maximal ideals, and the ramification ideals for the classification of defects and for the presentation of the Kähler differentials of the extension of the valuation rings of $(L|K,v)$, and their annihilators. Finally, we give a construction that realizes predescribed convex subgroups of suitable value groups as those that are associated with Galois extensions of degree $p$ with independent defect, which in turn give rise to definable coarsenings.", "published": "2025-12-06T10:42:41Z", "authors": ["Franz-Viktor Kuhlmann"], "categories": ["math.LO", "math.AC"], "doi": "", "_query": "automated valuation model", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2408.00353v1", "title": "Bounds on the $p$-adic valuation of the factorial, hyperfactorial and superfactorial", "abstract": "In this article, we investigate the $p$-adic valuation $ν_p$ of quantities such as the factorial $n!$, the hyperfactorial $H(n)$ or the superfactorial $\\mathrm{sf}(n)$. In particular, we obtain simple bounds (both upper and lower) for $ν_p$, using the Legendre-de Polignac formula. Other iterated quantities such as the Berezin function, are also considered. Beyond their recreational character, such quantities, often related to very large numbers, may find applications for cryptography purposes. Finally, lower and upper bounds for the $p$-adic valuation of Stirling numbers of the first kind and Catalan numbers are briefly discussed.", "published": "2024-08-01T07:49:35Z", "authors": ["Jean-Christophe Pain"], "categories": ["math.NT"], "doi": "", "_query": "automated valuation model", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2004.11004v13", "title": "Valuation rings as limits of complete intersection rings", "abstract": "We show that a valuation ring containing its residue field of characteristic $p>0$ is a filtered direct limit of complete intersection ${\\bf F}_p$-algebras.", "published": "2020-04-23T07:10:58Z", "authors": ["Dorin Popescu"], "categories": ["math.AC", "math.AG"], "doi": "", "_query": "automated valuation model", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/1009.5014v1", "title": "A Glimpse at Supertropical Valuation Theory", "abstract": "We give a short tour through major parts of a recent long paper [IKR1] on supertropical valuation theory, leaving aside nearly all proofs (to be found in [IKR1]). In this way we hope to give easy access to ideas of a new branch of so called \"supertropical algebra\".", "published": "2010-09-25T13:41:11Z", "authors": ["Zur Izhakian", "Manfred Knebusch", "Louis Rowen"], "categories": ["math.AC", "math.RA"], "doi": "", "_query": "automated valuation model", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/1907.11606v2", "title": "On the extendability by continuity of angular valuations on polytopes", "abstract": "A classical theorem of P. McMullen describes all valuations on polytopes that are invariant under translations and weakly continuous, i.e., continuous with respect to parallel displacements of the facets of a polytope. While it is typically not difficult to check that a valuation is weakly continuous, it is not clear how to decide whether it admits a continuous extension to convex bodies. In a special case of McMullen's construction a simple necessary and sufficient condition on the initial data of such an extension is obtained.", "published": "2019-07-26T14:48:16Z", "authors": ["Thomas Wannerer"], "categories": ["math.MG"], "doi": "", "_query": "automated valuation model", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/1905.01706v1", "title": "Efficient Computation of Various Valuation Adjustments Under Local Lévy Models", "abstract": "Various valuation adjustments, or XVAs, can be written in terms of non-linear PIDEs equivalent to FBSDEs. In this paper we develop a Fourier-based method for solving FBSDEs in order to efficiently and accurately price Bermudan derivatives, including options and swaptions, with XVA under the flexible dynamics of a local Lévy model: this framework includes a local volatility function and a local jump measure. Due to the unavailability of the characteristic function for such processes, we use an asymptotic approximation based on the adjoint formulation of the problem.", "published": "2019-05-05T15:47:51Z", "authors": ["Anastasia Borovykh", "Andrea Pascucci", "Cornelis W. Oosterlee"], "categories": ["q-fin.MF"], "doi": "", "_query": "automated valuation model", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2606.18729v3", "title": "TimeLAVA: Learning-Agnostic Valuation for Time Series Data", "abstract": "Data valuation quantifies the intrinsic quality of individual samples to enable principled data curation, quality control, and robust learning. For time series in critical domains such as healthcare, finance, and industrial monitoring, effective valuation methods are essential yet fundamentally lacking. Existing approaches are either model-dependent, limiting their generalizability, or designed for i.i.d. data and thus fail to capture temporal dependencies, multi-scale patterns, and non-stationary dynamics inherent to sequential data. We introduce TimeLAVA, a learning-agnostic framework that values temporal segments by their marginal contribution to minimizing distributional discrepancy between evaluated and reference data. At its core is a novel Selective Wavelet-based Wasserstein discrepancy combining multi-scale wavelet transforms for temporal localization with unbalanced optimal transport for robustness to distributional shifts. Segment values are efficiently computed via sensitivity analysis without requiring model training and aggregated into point-wise scores. We provide theoretical guarantees linking valuation to model-agnostic generalization and prove bounded sensitivity to outlier contamination. Extensive experiments across anomaly detection, data pruning, and label noise detection demonstrate that TimeLAVA produces significantly more informative value scores than existing methods on diverse real-world datasets.", "published": "2026-06-17T06:16:16Z", "authors": ["Wenqin Liu", "Weizhi Quan", "Aoqi Zuo", "Erdun Gao", "Vu Nguyen", "Dino Sejdinovic", "Howard Bondell", "Mingming Gong"], "categories": ["stat.ML", "cs.LG"], "doi": "", "_query": "automated valuation model", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/1711.08356v2", "title": "Valuation of equity warrants for uncertain financial market", "abstract": "In this paper, within the framework of uncertainty theory, the valuation of equity warrants is investigated. Different from the methods of probability theory, the equity warrants pricing problem is solved by using the method of uncertain calculus. Based on the assumption that the firm price follows an uncertain differential equation, the equity warrants pricing formula is obtained for uncertain stock model.", "published": "2017-11-22T15:58:16Z", "authors": ["Foad Shokrollahi"], "categories": ["q-fin.PR"], "doi": "", "_query": "automated valuation model", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/1801.02766v1", "title": "Ramification groups of coverings and valuations", "abstract": "We give a purely scheme theoretic construction of the filtration by ramification groups of the Galois group of a covering. The valuation need not be discrete but the normalizations are required to be locally of complete intersection.", "published": "2018-01-09T02:42:15Z", "authors": ["Takeshi Saito"], "categories": ["math.AG"], "doi": "10.2140/tunis.2019.1.373", "_query": "automated valuation model", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2509.18104v1", "title": "Data Valuation and Selection in a Federated Model Marketplace", "abstract": "In the era of Artificial Intelligence (AI), marketplaces have become essential platforms for facilitating the exchange of data products to foster data sharing. Model transactions provide economic solutions in data marketplaces that enhance data reusability and ensure the traceability of data ownership. To establish trustworthy data marketplaces, Federated Learning (FL) has emerged as a promising paradigm to enable collaborative learning across siloed datasets while safeguarding data privacy. However, effective data valuation and selection from heterogeneous sources in the FL setup remain key challenges. This paper introduces a comprehensive framework centered on a Wasserstein-based estimator tailored for FL. The estimator not only predicts model performance across unseen data combinations but also reveals the compatibility between data heterogeneity and FL aggregation algorithms. To ensure privacy, we propose a distributed method to approximate Wasserstein distance without requiring access to raw data. Furthermore, we demonstrate that model performance can be reliably extrapolated under the neural scaling law, enabling effective data selection without full-scale training. Extensive experiments across diverse scenarios, such as label skew, mislabeled, and unlabeled sources, show that our approach consistently identifies high-performing data combinations, paving the way for more reliable FL-based model marketplaces.", "published": "2025-09-09T06:45:30Z", "authors": ["Wenqian Li", "Youjia Yang", "Ruoxi Jia", "Yan Pang"], "categories": ["cs.LG", "cs.AI"], "doi": "", "_query": "automated valuation model", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2306.14833v1", "title": "A connectedness theorem for spaces of valuation rings", "abstract": "Let $F$ be a field, let $D$ be a local subring of $F$, and let Val$_F(D)$ be the space of valuation rings of $F$ that dominate $D$. We lift Zariski's connectedness theorem for fibers of a projective morphism to the Zariski-Riemann space of valuation rings of $F$ by proving that a subring $R$ of $F$ dominating $D$ is local, residually algebraic over $D$ and integrally closed in $F$ if and only if there is a closed and connected subspace $Z$ of Val$_F(D)$ such that $R$ is the intersection of the rings in $Z$. Consequently, the intersection of the rings in any closed and connected subset of Val$_F(D)$ is a local ring. In proving this, we also prove a converse to Zariski's connectedness theorem. Our results do not require the rings involved to be Noetherian.", "published": "2023-06-26T16:39:26Z", "authors": ["William Heinzer", "K. Alan Loper", "Bruce Olberding", "Matt Toeniskoetter"], "categories": ["math.AC", "math.AG"], "doi": "", "_query": "automated valuation model", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2609.00767v1", "title": "Extension of valuation rings as limits of complete intersection algebras", "abstract": "We give sufficient conditions for an extension of valuation rings to be a filtered colimit of complete intersection algebras.", "published": "2026-09-01T05:54:45Z", "authors": ["Dorin Popescu"], "categories": ["math.AC"], "doi": "", "_query": "automated valuation model", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2401.05913v1", "title": "The homogeneous decomposition of dually translation invariant valuations on Lipschitz functions on the sphere", "abstract": "We show that every continuous and dually translation invariant valuation on the space of Lipschitz functions on the unit sphere of $\\mathbb{R}^n$, $n\\ge2$, can be decomposed uniquely into a sum of homogeneous valuations of degree $0$, $1$ and $2$. In particular, there does not exist any non-trivial, continuous and dually translation invariant valuation which is homogeneous of degree $3$ or higher. For the space of those of degree $0$, $1$ and $2$ we provide a description of a dense subspace.", "published": "2024-01-11T13:46:31Z", "authors": ["Andrea Colesanti", "Jonas Knoerr", "Daniele Pagnini"], "categories": ["math.MG", "math.FA"], "doi": "", "_query": "automated valuation model", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/1909.11671v1", "title": "Data Valuation using Reinforcement Learning", "abstract": "Quantifying the value of data is a fundamental problem in machine learning. Data valuation has multiple important use cases: (1) building insights about the learning task, (2) domain adaptation, (3) corrupted sample discovery, and (4) robust learning. To adaptively learn data values jointly with the target task predictor model, we propose a meta learning framework which we name Data Valuation using Reinforcement Learning (DVRL). We employ a data value estimator (modeled by a deep neural network) to learn how likely each datum is used in training of the predictor model. We train the data value estimator using a reinforcement signal of the reward obtained on a small validation set that reflects performance on the target task. We demonstrate that DVRL yields superior data value estimates compared to alternative methods across different types of datasets and in a diverse set of application scenarios. The corrupted sample discovery performance of DVRL is close to optimal in many regimes (i.e. as if the noisy samples were known apriori), and for domain adaptation and robust learning DVRL significantly outperforms state-of-the-art by 14.6% and 10.8%, respectively.", "published": "2019-09-25T18:00:02Z", "authors": ["Jinsung Yoon", "Sercan O. Arik", "Tomas Pfister"], "categories": ["cs.LG", "stat.ML"], "doi": "", "_query": "automated valuation model", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2410.23720v1", "title": "Affine Quermassintegrals and Even Minkowski Valuations", "abstract": "It is shown that each continuous even Minkowski valuation on convex bodies of degree $1 \\leq i \\leq n - 1$ intertwining rigid motions is obtained from convolution of the $i$th projection function with a unique spherical Crofton distribution. In case of a non-negative distribution, the polar volume of the associated Minkowski valuation gives rise to an isoperimetric inequality which strengthens the classical relation between the $i$th quermassintegral and the volume. This large family of inequalities unifies earlier results obtained for $i = 1$ and $n - 1$. In these cases, isoperimetric inequalities for affine quermassintegrals, specifically the Blaschke-Santaló inequality for $i = 1$ and the Petty projection inequality for $i = n - 1$, were proven to be the strongest inequalities. An analogous result for the intermediate degrees is established here. Finally, a new sufficient condition for the existence of maximizers for the polar volume of Minkowski valuations intertwining rigid motions reveals unexpected examples of volume inequalities having asymmetric extremizers.", "published": "2024-10-31T08:10:55Z", "authors": ["Georg C. Hofstätter", "Philipp Kniefacz", "Franz E. Schuster"], "categories": ["math.MG"], "doi": "", "_query": "automated valuation model", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2604.06050v2", "title": "Robust Testing Of the Allais Paradox By Paired Choices vs. Paired Valuations", "abstract": "McGranaghan, Nielsen, O'Donoghue, Somerville, and Sprenger [2024] show that standard paired choice tests for the common ratio effect are structurally biased when choice is stochastic, proposing valuation tests as a robust alternative. Using valuation tests, they find no systematic evidence for the common ratio effect, seemingly overturning much of the extant literature. We evaluate this conclusion in light of stochastic choice theory. We argue that valuation tests are inherently biased and lack predictive power under standard expected utility assumptions. In contrast, we advocate for a ``strong'' paired choice test, proving it remains robustly unbiased across common models of stochastic choice. Applying this strong test to existing experimental data, we find that the common ratio effect remains highly prevalent.", "published": "2026-04-07T16:40:37Z", "authors": ["Federico Echenique", "Gerelt Tserenjigmid"], "categories": ["econ.TH"], "doi": "", "_query": "automated valuation model", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2405.06897v1", "title": "SL(n) Contravariant Matrix-Valued Valuations on Polytopes", "abstract": "All $\\textrm{SL}(n)$ contravariant matrix-valued valuations on polytopes in $\\mathbb{R}^n$ are completely classified without any continuity assumptions. Moreover, the symmetry assumption of matrices is removed. The general Lutwak-Yang-Zhang matrix turns out to be the only such valuation if $n\\geq 4$, while a new function shows up in dimension three. In dimension two, the classification corresponds to the known case of $\\textrm{SL}(2)$ equivariant matrix-valued valuations.", "published": "2024-05-11T03:53:25Z", "authors": ["Chunna Zeng", "Yuqi Zhou"], "categories": ["math.DG"], "doi": "", "_query": "automated valuation model", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2306.07390v2", "title": "Convex valuations, from Whitney to Nash", "abstract": "We consider the Whitney problem for valuations: does a smooth $j$-homogeneous translation-invariant valuation on $\\mathbb R^n$ exist that has given restrictions to a fixed family $S$ of linear subspaces? A necessary condition is compatibility: the given valuations must coincide on intersections. We show that for $S=\\mathrm{Gr}_r(\\mathbb R^n)$, the grassmannian of $r$-planes, this condition becomes sufficient once $r\\geq j+2$. This complements the Klain and Schneider uniqueness theorems with an existence statement, and provides a recursive description of the image of the cosine transform. Informally speaking, we show that the transition from densities to valuations is localized to codimension $2$. We then look for conditions on $S$ when compatibility is also sufficient for extensibility, in two distinct regimes: finite arrangements of subspaces, and compact submanifolds of the grassmannian. In both regimes we find unexpected flexibility. As a consequence of the submanifold regime, we prove a Nash-type theorem for valuations on compact manifolds, from which in turn we deduce the existence of Crofton formulas for all smooth valuations on manifolds. As an intermediate step of independent interest, we construct Crofton formulas for all odd translation-invariant valuations.", "published": "2023-06-12T19:39:26Z", "authors": ["Dmitry Faifman", "Georg C. Hofstätter"], "categories": ["math.DG", "math.FA", "math.MG"], "doi": "", "_query": "automated valuation model", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2010.07710v1", "title": "On the Importance of Domain Model Configuration for Automated Planning Engines", "abstract": "The development of domain-independent planners within the AI Planning community is leading to \"off-the-shelf\" technology that can be used in a wide range of applications. Moreover, it allows a modular approach --in which planners and domain knowledge are modules of larger software applications-- that facilitates substitutions or improvements of individual modules without changing the rest of the system. This approach also supports the use of reformulation and configuration techniques, which transform how a model is represented in order to improve the efficiency of plan generation. In this article, we investigate how the performance of domain-independent planners is affected by domain model configuration, i.e., the order in which elements are ordered in the model, particularly in the light of planner comparisons. We then introduce techniques for the online and offline configuration of domain models, and we analyse the impact of domain model configuration on other reformulation approaches, such as macros.", "published": "2020-10-15T12:40:02Z", "authors": ["Mauro Vallati", "Lukas Chrpa", "Thomas L. McCluskey", "Frank Hutter"], "categories": ["cs.AI"], "doi": "", "_query": "automated valuation model", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2211.06800v1", "title": "CS-Shapley: Class-wise Shapley Values for Data Valuation in Classification", "abstract": "Data valuation, or the valuation of individual datum contributions, has seen growing interest in machine learning due to its demonstrable efficacy for tasks such as noisy label detection. In particular, due to the desirable axiomatic properties, several Shapley value approximation methods have been proposed. In these methods, the value function is typically defined as the predictive accuracy over the entire development set. However, this limits the ability to differentiate between training instances that are helpful or harmful to their own classes. Intuitively, instances that harm their own classes may be noisy or mislabeled and should receive a lower valuation than helpful instances. In this work, we propose CS-Shapley, a Shapley value with a new value function that discriminates between training instances' in-class and out-of-class contributions. Our theoretical analysis shows the proposed value function is (essentially) the unique function that satisfies two desirable properties for evaluating data values in classification. Further, our experiments on two benchmark evaluation tasks (data removal and noisy label detection) and four classifiers demonstrate the effectiveness of CS-Shapley over existing methods. Lastly, we evaluate the \"transferability\" of data values estimated from one classifier to others, and our results suggest Shapley-based data valuation is transferable for application across different models.", "published": "2022-11-13T03:32:33Z", "authors": ["Stephanie Schoch", "Haifeng Xu", "Yangfeng Ji"], "categories": ["cs.LG"], "doi": "", "_query": "automated valuation model", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2102.09355v1", "title": "Automated construction of quantum-classical hybrid models", "abstract": "We present a protocol for the fully automated construction of quantum mechanical-(QM)-classical hybrid models by extending our previously reported approach on self-parametrizing system-focused atomistic models (SFAM) J. Chem. Theory Comput. 2020, 16, 1646]. In this QM/SFAM approach, the size and composition of the QM region is evaluated in an automated manner based on first principles so that the hybrid model describes the atomic forces in the center of the QM region accurately. This entails the automated construction and evaluation of differently sized QM regions with a bearable computational overhead that needs to be paid for automated validation procedures. Applying SFAM for the classical part of the model eliminates any dependence on pre-existing parameters due to its system-focused quantum mechanically derived parametrization. Hence, QM/SFAM is capable of delivering a high fidelity and complete automation. Furthermore, since SFAM parameters are generated for the whole system, our ansatz allows for a convenient re-definition of the QM region during a molecular exploration. For this purpose, a local re-parametrization scheme is introduced, which efficiently generates additional classical parameters on the fly when new covalent bonds are formed (or broken) and moved to the classical region.", "published": "2021-02-18T14:05:31Z", "authors": ["Christoph Brunken", "Markus Reiher"], "categories": ["physics.chem-ph", "physics.bio-ph", "physics.comp-ph"], "doi": "10.1021/acs.jctc.1c00178", "_query": "automated valuation model", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/1701.00340v1", "title": "Valuations of $p$-adic regulators of cyclic cubic fields", "abstract": "We compute the $p$-adic regulator of cyclic cubic extensions of $\\mathbb Q$ with discriminant up to $10^{16}$ for $3<p<100$, and observe the distribution of the $p$-adic valuation of the regulators. We find that for almost all primes, the observation matches the model that the entries in the regulator matrix are random elements with respect to the obvious restrictions. Based on this random matrix model, a conjecture on the distribution of the valuations of $p$-adic regulators of cyclic cubic fields is stated.", "published": "2017-01-02T09:01:59Z", "authors": ["Tommy Hofmann", "Yinan Zhang"], "categories": ["math.NT"], "doi": "10.1016/j.jnt.2016.05.016", "_query": "automated valuation model", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/1911.01714v4", "title": "MacLane-Vaquié chains of valuations on a polynomial ring", "abstract": "Let $(K,v)$ be a valued field. We review some results of MacLane and Vaquié on extensions of $v$ to valuations on the polynomial ring $K[x]$. We introduce certain MacLane-Vaquié chains of residually transcendental valuations, and we prove that every valuation $μ$ on $K[x]$ is a limit of a finite or countably infinite MacLane-Vaquié chain. This chain underlying $μ$ is essentially unique and contains arithmetic data yielding an explicit description of the graded algebra of $μ$ as an algebra over the graded algebra of $v$.", "published": "2019-11-05T11:13:00Z", "authors": ["Enric Nart"], "categories": ["math.AG", "math.AC"], "doi": "10.2140/pjm.2021.311.165", "_query": "automated valuation model", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2502.16152v1", "title": "DUPRE: Data Utility Prediction for Efficient Data Valuation", "abstract": "Data valuation is increasingly used in machine learning (ML) to decide the fair compensation for data owners and identify valuable or harmful data for improving ML models. Cooperative game theory-based data valuation, such as Data Shapley, requires evaluating the data utility (e.g., validation accuracy) and retraining the ML model for multiple data subsets. While most existing works on efficient estimation of the Shapley values have focused on reducing the number of subsets to evaluate, our framework, \\texttt{DUPRE}, takes an alternative yet complementary approach that reduces the cost per subset evaluation by predicting data utilities instead of evaluating them by model retraining. Specifically, given the evaluated data utilities of some data subsets, \\texttt{DUPRE} fits a \\emph{Gaussian process} (GP) regression model to predict the utility of every other data subset. Our key contribution lies in the design of our GP kernel based on the sliced Wasserstein distance between empirical data distributions. In particular, we show that the kernel is valid and positive semi-definite, encodes prior knowledge of similarities between different data subsets, and can be efficiently computed. We empirically verify that \\texttt{DUPRE} introduces low prediction error and speeds up data valuation for various ML models, datasets, and utility functions.", "published": "2025-02-22T08:53:39Z", "authors": ["Kieu Thao Nguyen Pham", "Rachael Hwee Ling Sim", "Quoc Phong Nguyen", "See Kiong Ng", "Bryan Kian Hsiang Low"], "categories": ["cs.LG", "cs.GT"], "doi": "10.5555/3709347.3743790", "_query": "automated valuation model", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2012.10332v4", "title": "2-adic Valuations of Quadratic Sequences", "abstract": "Properties of 2-adic valuation sequences for general quadratic polynomials with integer coefficients are determined directly from the coefficients. These properties include boundedness or unboundedness, periodicity, and valuations at terminating nodes. We completely describe the periodic sequences in the bounded case. Throughout, we frame results in terms of trees and sequences.", "published": "2020-12-18T16:22:05Z", "authors": ["Will Boultinghouse", "Jane Long", "Olena Kozhushkina", "Justin Trulen"], "categories": ["math.NT"], "doi": "", "_query": "automated valuation model", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2303.03083v1", "title": "Cost Sharing under Private Valuation and Connection Control", "abstract": "We consider a cost sharing problem on a weighted undirected graph, where all the nodes want to connect to a special node called source, and they need to share the total cost (weights) of the used edges. Each node except for the source has a private valuation of the connection, and it may block others' connections by strategically cutting its adjacent edges to reduce its cost share, which may increase the total cost. We aim to design mechanisms to prevent the nodes from misreporting their valuations and cutting their adjacent edges. We first show that it is impossible for such a mechanism to further satisfy budget balance (cover the total cost) and efficiency (maximize social welfare). Then, we design two feasible cost sharing mechanisms that incentivize each node to offer all its adjacent edges and truthfully report its valuation, and also satisfy either budget balance or efficiency.", "published": "2023-03-06T12:45:51Z", "authors": ["Tianyi Zhang", "Junyu Zhang", "Sizhe Gu", "Dengji Zhao"], "categories": ["cs.GT"], "doi": "", "_query": "automated valuation model", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/1711.09080v1", "title": "Intrinsic valuation entropy", "abstract": "We extend the notion of intrinsic entropy for endomorphisms of Abelian groups to endomorphisms of modules over an Archimedean non-discrete valuation domain $R$, using the natural non-discrete length function introduced by Northcott and Reufel for such a category of modules. We prove that this notion of entropy is a length function for the category of $R[X]$-modules, it satisfies (a suitably adapted version of) the Intrinsic Algebraic Yuzvinski Formula and that it is essentially the unique invariant for $Mod(R[X])$ with these properties.", "published": "2017-11-24T18:50:15Z", "authors": ["Luigi Salce", "Simone Virili"], "categories": ["math.RA"], "doi": "", "_query": "automated valuation model", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/1703.05460v1", "title": "The topology on Berkovich affine lines over complete valuation rings", "abstract": "In this article, we give a full description of the topology of the one dimensional affine analytic space $\\mathbb{A}_R^1$ over a complete valuation ring $R$ (i.e. a valuation ring with \"real valued valuation\" which is complete under the induced metric), when its field of fractions $K$ is algebraically closed. In particular, we show that $\\mathbb{A}_R^1$ is both connected and locally path connected. Furthermore, $\\mathbb{A}_R^1$ is the completion of $K\\times (1,\\infty)$ under a canonical uniform structure. As an application, we describe the Berkovich spectrum $\\mathfrak{M}(\\mathbb{Z}_p[G])$ of the Banach group ring $\\mathbb{Z}_p[G]$ of a cyclic $p$-group $G$ over the ring $\\mathbb{Z}_p$ of $p$-adic integers.", "published": "2017-03-16T02:40:02Z", "authors": ["Chi-Wai Leung", "Chi-Keung Ng"], "categories": ["math.NT", "math.AC", "math.AG"], "doi": "", "_query": "automated valuation model", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2606.01635v1", "title": "AlphaToken: Decoupling Adaptation and Stability for Path-Aware Response Token Valuation in LLM Post-Training", "abstract": "Token selection is pivotal for effective LLM post-training. However, existing methods mostly rely on local heuristics and rarely formulate token selection as a principled valuation of individual response tokens. We introduce $\\textbf{AlphaToken}$, a response token valuation framework that decouples valuation into $\\textbf{adaptation}$ (promoting target-task learning) and $\\textbf{stability}$ (preserving pre-trained capabilities), and makes each objective $\\textbf{path-aware}$ by combining the direct-path signal from local token gradients with the downstream causal-path signal in autoregressive generation. Since retention data are typically unavailable, AlphaToken approximates stability via a $\\textbf{Fisher-drift proxy}$ anchored at the pre-trained reference model. For efficient computation, we extend Ghost Dot-Product to token-level valuation. AlphaToken masks low-value response tokens during fine-tuning and preference optimization, concentrating training signals on more valuable positions. Experiments show that AlphaToken improves post-training performance and mitigates catastrophic forgetting.", "published": "2026-06-01T03:40:35Z", "authors": ["Liu Qing", "Ou Wu", "Yi Du"], "categories": ["cs.CL", "cs.AI"], "doi": "", "_query": "automated valuation model", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/1710.01774v1", "title": "On the topology of valuation-theoretic representations of integrally closed domains", "abstract": "Let $F$ be a field. For each nonempty subset $X$ of the Zariski-Riemann space of valuation rings of $F$, let ${A}(X) = \\bigcap_{V \\in X}V$ and ${J}(X) = \\bigcap_{V \\in X}{\\mathfrak M}_V$, where ${\\mathfrak M}_V$ denotes the maximal ideal of $V$. We examine connections between topological features of $X$ and the algebraic structure of the ring ${A}(X)$. We show that if ${J}(X) \\ne 0$ and $A(X)$ is a completely integrally closed local ring that is not a valuation ring of $F$, then there is a subspace $Y$ of the space of valuation rings of $F$ that is perfect in the patch topology such that ${A}(X) = {A}(Y)$. If any countable subset of points is removed from $Y$, then the resulting set remains a representation of $A(X)$. Additionally, if $F$ is a countable field, the set $Y$ can be chosen homeomorphic to the Cantor set. We apply these results to study properties of the ring ${A}(X)$ with specific focus on topological conditions that guarantee ${A}(X)$ is a Prüfer domain, a feature that is reflected in the Zariski-Riemann space when viewed as a locally ringed space. We also classify the rings ${A}(X)$ where $X$ has finitely many patch limit points, thus giving a topological generalization of the class of Krull domains, one that also includes interesting Prüfer domains. To illustrate the latter, we show how an intersection of valuation rings arising naturally in the study of local quadratic transformations of a regular local ring can be described using these techniques.", "published": "2017-10-04T19:28:42Z", "authors": ["Bruce Olberding"], "categories": ["math.AC"], "doi": "", "_query": "automated valuation model", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/1905.02481v2", "title": "Extending valuations to the field of rational functions using pseudo-monotone sequences", "abstract": "Let $V$ be a valuation domain with quotient field $K$. We show how to describe all extensions of $V$ to $K(X)$ when the $V$-adic completion $\\widehat{K}$ is algebraically closed, generalizing a similar result obtained by Ostrowski in the case of one-dimensional valuation domains. This is accomplished by realizing such extensions by means of pseudo-monotone sequences, a generalization of pseudo-convergent sequences introduced by Chabert. We also show that the valuation rings associated to pseudo-convergent and pseudo-divergent sequences (two classes of pseudo-monotone sequences) roughly correspond, respectively, to the closed and the open balls of $K$ in the topology induced by $V$.", "published": "2019-05-07T11:40:08Z", "authors": ["Giulio Peruginelli", "Dario Spirito"], "categories": ["math.RA", "math.AC"], "doi": "10.1016/j.jalgebra.2021.07.004", "_query": "automated valuation model", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/1611.00177v2", "title": "Transcendental extensions of a valuation domain of rank one", "abstract": "Let $V$ be a valuation domain of rank one and quotient field $K$. Let $\\overline{\\hat{K}}$ be a fixed algebraic closure of the $v$-adic completion $\\hat K$ of $K$ and let $\\overline{\\hat{V}}$ be the integral closure of $\\hat V$ in $\\overline{\\hat{K}}$. We describe a relevant class of valuation domains $W$ of the field of rational functions $K(X)$ which lie over $V$, which are indexed by the elements $α\\in\\overline{\\hat{K}}\\cup\\{\\infty\\}$, namely, $W=W_α=\\{\\varphi\\in K(X) \\mid \\varphi(α)\\in\\overline{\\hat{V}}\\}$. If $V$ is discrete and $π\\in V$ is a uniformizer, then a valuation domain $W$ of $K(X)$ is of this form if and only if the residue field degree $[W/M:V/P]$ is finite and $πW=M^e$, for some $e\\geq 1$, where $M$ is the maximal ideal of $W$. In general, for $α,β\\in\\overline{\\hat{K}}$ we have $W_α=W_β$ if and only if $α$ and $β$ are conjugated over $\\hat K$. Finally, we show that the set $\\mathcal{P}^{\\rm irr}$ of irreducible polynomials over $\\hat K$ endowed with an ultrametric distance introduced by Krasner is homeomorphic to the space $\\{W_α \\mid α\\in\\overline{\\hat{K}}\\}$ endowed with the Zariski topology.", "published": "2016-11-01T10:35:36Z", "authors": ["Giulio Peruginelli"], "categories": ["math.AC"], "doi": "10.1090/proc/13574", "_query": "automated valuation model", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2101.02278v1", "title": "Estimating the Nash Social Welfare for coverage and other submodular valuations", "abstract": "We study the Nash Social Welfare problem: Given $n$ agents with valuation functions $v_i:2^{[m]} \\rightarrow {\\mathbb R}$, partition $[m]$ into $S_1,\\ldots,S_n$ so as to maximize $(\\prod_{i=1}^{n} v_i(S_i))^{1/n}$. The problem has been shown to admit a constant-factor approximation for additive, budget-additive, and piecewise linear concave separable valuations; the case of submodular valuations is open. We provide a $\\frac{1}{e} (1-\\frac{1}{e})^2$-approximation of the {\\em optimal value} for several classes of submodular valuations: coverage, sums of matroid rank functions, and certain matching-based valuations.", "published": "2021-01-06T21:42:45Z", "authors": ["Wenzheng Li", "Jan Vondrak"], "categories": ["cs.GT", "cs.DM"], "doi": "", "_query": "automated valuation model", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2609.08272v1", "title": "Subquadratic Subsidies for Nonnegative or Nonpositive Valuations", "abstract": "We study envy-freeness with subsidies for indivisible items beyond additive valuations. Assuming that every single-item marginal value lies in $[-1,1]$, we prove that a total subsidy of $O(n^{3/2}\\sqrt{\\log n})$ suffices to achieve envy-freeness among $n$ agents whenever all agents assign nonnegative values to every bundle or all assign nonpositive values to every bundle. These valuation classes include monotone goods and monotone chores, respectively, but do not require monotonicity. Our result establishes the first subquadratic total-subsidy bound for general monotone valuations that holds for every number of agents.", "published": "2026-09-08T05:28:52Z", "authors": ["Max Dupré la Tour", "Mashbat Suzuki"], "categories": ["cs.GT"], "doi": "", "_query": "automated valuation model", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2403.08128v2", "title": "Singular loci of algebras over ramified discrete valuation rings", "abstract": "When $k$ is a field, the classical Jacobian criterion computes the singular locus of an equidimensional, finitely generated $k$-algebra as the closed subset of an ideal generated by appropriate minors of the so-called Jacobian matrix. Recently, Hochster-Jeffries and Saito have extended this result for algebras over any unramified discrete valuation ring of mixed characteristic via the use of $p$-derivations. Motivated by these results, in this paper, we state and prove an analogous Jacobian criterion for algebras over a ramified discrete valuation ring of mixed characteristic.", "published": "2024-03-12T23:34:04Z", "authors": ["Nawaj KC"], "categories": ["math.AC"], "doi": "", "_query": "automated valuation model", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2603.11979v1", "title": "On the $2$-adic valuation of $σ_k(n)$", "abstract": "For a positive integer $k$, let \\[ σ_k(n)=\\sum_{d\\mid n} d^k \\] be the divisor function of order $k$, and let $ν_p(m)$ denote the $p$-adic valuation of an integer $m$. Motivated by recent work on the $p$-adic valuation of $σ_k(n)$, we study $ν_2(σ_k(n))$ in detail. We prove that, for every integer $n\\ge 2$, \\[ ν_2(σ_k(n)) \\le \\begin{cases} \\lceil \\log_2 n \\rceil, & \\text{if $k$ is odd},\\\\[1mm] \\lfloor \\log_2 n \\rfloor, & \\text{if $k$ is even}. \\end{cases} \\] These bounds are best possible. More precisely, if $k$ is odd, then equality holds if and only if $n$ is a product of distinct Mersenne primes; if $k$ is even, then equality holds if and only if $n=3$. We also obtain an explicit formula for $ν_2(σ_k(n))$ in terms of the prime factorization of $n$.", "published": "2026-03-12T14:28:05Z", "authors": ["Kaimin Cheng", "Ke Zhang"], "categories": ["math.NT"], "doi": "", "_query": "automated valuation model", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2306.05538v2", "title": "Geometric interpretation of valuated term (pre)orders", "abstract": "Valuated term orders are studied for the purposes of Gröbner theory over fields with valuation. The points of a usual tropical variety correspond to certain valuated terms preorders. Generalizing both of these, the set of all ``well-behaved'' valuated term preorders is canonically in bijection with the points of a space introduced in our previous work on tropical adic geometry. In this paper we interpret these points geometrically by explicitly characterizing them in terms of classical polyhedral geometry. This characterization gives a bijection with equivalence classes of flags of polyhedra as well as a bijection with a class of prime filters on a lattice of polyhedral sets. The first of these also classifies valuated term orders. The second bijection is of the same flavor as the bijections from [van der Put and Schneider, 1995] in non-archimedean analytic geometry and indicates that the results of that paper may have analogues in tropical adic geometry.", "published": "2023-06-08T20:20:39Z", "authors": ["Netanel Friedenberg", "Kalina Mincheva"], "categories": ["math.AG", "math.AC", "math.MG", "math.RA"], "doi": "", "_query": "automated valuation model", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2601.15864v2", "title": "Minimum Envy Graphical House Allocation Beyond Identical Valuations", "abstract": "House allocation is an extremely well-studied problem in the field of fair allocation, where the goal is to assign $n$ houses to $n$ agents while satisfying certain fairness criterion, e.g., envy-freeness. To model social interactions, the Graphical House Allocation framework introduces a social graph $G$, in which each vertex corresponds to an agent, and an edge $(u, v)$ corresponds to the potential of agent $u$ to envy the agent $v$, based on their allocations and valuations. In undirected social graphs, the potential for envy is in both the directions. In the Minimum Envy Graphical House Allocation (ME-GHA) problem, given a set of $n$ agents, $n$ houses, a social graph, and agent's valuation functions, the goal is to find an allocation that minimizes the total envy summed up over all the edges of $G$. Recent work, [Hosseini et al., AAMAS 2023, AAMAS 2024] studied ME-GHA in the regime of polynomial-time algorithms, and designed exact and approximation algorithms, for certain graph classes under identical agent valuations. We initiate the study of \\gha with non-identical valuations, a setting that has so far remained unexplored. We investigate the multivariate (parameterized) complexity of \\gha by identifying structural restrictions on the social graph and valuation functions that yield tractability. We also design moderately exponential-time algorithms for several graph classes, and a polynomial-time algorithm for {binary valuations that returns an allocation with envy at most one when the social graph has maximum degree at most one.", "published": "2026-01-22T11:12:09Z", "authors": ["Tanmay Inamdar", "Pallavi Jain", "Pranjal Pandey"], "categories": ["cs.GT", "cs.DS"], "doi": "", "_query": "automated valuation model", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2508.10180v3", "title": "For-Value: Efficient Forward-Only Data Valuation for finetuning LLMs and VLMs", "abstract": "Data valuation is essential for enhancing the transparency and accountability of large language models (LLMs) and vision-language models (VLMs). However, existing methods typically rely on gradient computations, making them computationally prohibitive for billion-parameter models and precluding batch parallelization. In this work, we introduce For-Value, a forward-only data valuation framework that enables efficient batch-scalable value estimation while maintaining effectiveness. Leveraging the expressive power of pretrained LLMs/VLMs, we theoretically demonstrate that data valuation can be captured by the alignment between the final hidden representations and prediction errors at the last layer. In light of this insight, For-Value computes data value using a simple closed-form expression with a single forward pass, eliminating the need for costly backpropagation and enabling efficient batch calculating at scale. Extensive experiments show that For-Value matches or outperforms gradient-based baselines in detecting influential data and mislabeled data, while achieving significant efficiency improvements.", "published": "2025-08-13T20:33:06Z", "authors": ["Wenlong Deng", "Qi Zeng", "Jiaming Zhang", "Minghui Chen", "Zixin Ding", "Christos Thrampoulidis", "Boying Gong", "Xiaoxiao Li"], "categories": ["cs.CL"], "doi": "", "_query": "automated valuation model", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2406.14779v1", "title": "Learning to Select Goals in Automated Planning with Deep-Q Learning", "abstract": "In this work we propose a planning and acting architecture endowed with a module which learns to select subgoals with Deep Q-Learning. This allows us to decrease the load of a planner when faced with scenarios with real-time restrictions. We have trained this architecture on a video game environment used as a standard test-bed for intelligent systems applications, testing it on different levels of the same game to evaluate its generalization abilities. We have measured the performance of our approach as more training data is made available, as well as compared it with both a state-of-the-art, classical planner and the standard Deep Q-Learning algorithm. The results obtained show our model performs better than the alternative methods considered, when both plan quality (plan length) and time requirements are taken into account. On the one hand, it is more sample-efficient than standard Deep Q-Learning, and it is able to generalize better across levels. On the other hand, it reduces problem-solving time when compared with a state-of-the-art automated planner, at the expense of obtaining plans with only 9% more actions.", "published": "2024-06-20T23:02:44Z", "authors": ["Carlos Núñez-Molina", "Juan Fernández-Olivares", "Raúl Pérez"], "categories": ["cs.AI"], "doi": "10.1016/j.eswa.2022.117265", "_query": "automated valuation model", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2304.06589v1", "title": "Tube formulas for valuations in complex space forms", "abstract": "Given an isometry invariant valuation on a complex space form we compute its value on the tubes of sufficiently small radii around a set of positive reach. This generalizes classical formulas of Weyl, Gray and others about the volume of tubes. We also develop a general framework on tube formulas for valuations in riemannian manifolds.", "published": "2023-04-13T14:52:46Z", "authors": ["Gil Solanes", "Juan Andrés Trillo"], "categories": ["math.DG", "math.MG"], "doi": "", "_query": "automated valuation model", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2308.13496v1", "title": "Choice Architecture, Privacy Valuations, and Selection Bias in Consumer Data", "abstract": "We study how choice architecture that companies deploy during data collection influences consumers' privacy valuations. Further, we explore how this influence affects the quality of data collected, including both volume and representativeness. To this end, we run a large-scale choice experiment to elicit consumers' valuation for their Facebook data while randomizing two common choice frames: default and price anchor. An opt-out default decreases valuations by 14-22% compared to opt-in, while a \\$0-50 price anchor decreases valuations by 37-53% compared to a \\$50-100 anchor. Moreover, in some consumer segments, the susceptibility to frame influence negatively correlates with consumers' average valuation. We find that conventional frame optimization practices that maximize the volume of data collected can have opposite effects on its representativeness. A bias-exacerbating effect emerges when consumers' privacy valuations and frame effects are negatively correlated. On the other hand, a volume-maximizing frame may also mitigate the bias by getting a high percentage of consumers into the sample data, thereby improving its coverage. We demonstrate the magnitude of the volume-bias trade-off in our data and argue that it should be a decision-making factor in choice architecture design.", "published": "2023-08-25T17:11:45Z", "authors": ["Tesary Lin", "Avner Strulov-Shlain"], "categories": ["econ.GN"], "doi": "", "_query": "automated valuation model", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2209.12592v1", "title": "Generating Compressed Combinatory Proof Structures -- An Approach to Automated First-Order Theorem Proving", "abstract": "Representing a proof tree by a combinator term that reduces to the tree lets subtle forms of duplication within the tree materialize as duplicated subterms of the combinator term. In a DAG representation of the combinator term these straightforwardly factor into shared subgraphs. To search for proofs, combinator terms can be enumerated, like clausal tableaux, interwoven with unification of formulas that are associated with nodes of the enumerated structures. To restrict the search space, the enumeration can be based on proof schemas defined as parameterized combinator terms. We introduce here this \"combinator term as proof structure\" approach to automated first-order proving, present an implementation and first experimental results. The approach builds on a term view of proof structures rooted in condensed detachment and the connection method. It realizes features known from the connection structure calculus, which has not been implemented so far.", "published": "2022-09-26T11:23:17Z", "authors": ["Christoph Wernhard"], "categories": ["cs.LO", "cs.AI"], "doi": "", "_query": "automated valuation model", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2502.17869v3", "title": "Maximum Welfare Allocations under Quantile Valuations", "abstract": "We propose a new model for aggregating preferences over a set of indivisible items based on a quantile value. In this model, each agent is endowed with a specific quantile, and the value of a given bundle is defined by the corresponding quantile of the individual values of the items within it. Our model captures the diverse ways in which agents may perceive a bundle, even when they agree on the values of individual items. It enables richer behavioral modeling that cannot be easily captured by additive valuation functions. We study the problem of maximizing utilitarian and egalitarian welfare within the quantile-based valuation setting. For each of the welfare functions, we analyze the complexity of the objectives. Interestingly, our results show that the complexity of both objectives varies significantly depending on whether the allocation is required to be balanced. We provide near-optimal approximation algorithms for utilitarian welfare, and for egalitarian welfare, we present exact algorithms whenever possible.", "published": "2025-02-25T05:47:32Z", "authors": ["Haris Aziz", "Shivika Narang", "Mashbat Suzuki"], "categories": ["cs.GT"], "doi": "", "_query": "automated valuation model", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2610.00159v1", "title": "Maximin Share Allocations Beyond Weakly Lexicographic Valuations", "abstract": "We study the fair division of indivisible goods among agents with nonnegative additive valuations. An agent's maximin share (MMS) is the value she can guarantee by partitioning the goods into as many bundles as there are agents and receiving a least-valued bundle. We prove the existence of exact MMS allocations for a valuation domain that generalizes several few-valued domains as well as weakly lexicographic valuations. For each agent, the lower-valued goods, after a positive rescaling, may take values in $\\{0,1,d,d+1\\}$, $\\{0,1,2,2e\\}$, or $\\{0,1,2,3,4\\}$, where $d,e\\ge 2$ are integers. Above these goods, the valuation may contain arbitrarily many value classes, subject to the condition that a single good in each class is worth at least the total value of all strictly lower-valued goods. The value classes, scaling factors, and parameters may vary across agents. We give an algorithm that computes an MMS allocation in $O(nm\\log^2(2+m))$ time, where $n$ is the number of agents and $m$ is the number of goods.", "published": "2026-09-12T15:40:17Z", "authors": ["Nicholas Teh"], "categories": ["cs.GT"], "doi": "", "_query": "automated valuation model", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/1511.03907v1", "title": "Jet schemes and generating sequences of divisorial valuations in dimension two", "abstract": "Using the theory of jet schemes, we give a new approach to the description of a minimal generating sequence of a divisorial valuations on $\\textbf{A}^2.$ For this purpose, we show how one can recover the approximate roots of an analytically irreducible plane curve from the equations of its jet schemes. As an application, for a given divisorial valuation $v$ centered at the origin of $\\textbf{A}^2,$ we construct an algebraic embedding $\\textbf{A}^2\\hookrightarrow \\textbf{A}^N,N\\geq 2$ such that $v$ is the trace of a monomial valuation on $\\textbf{A}^N.$ We explain how results in this direction give a constructive approach to a conjecture of Teissier on resolution of singularities by one toric morphism.", "published": "2015-11-12T14:47:45Z", "authors": ["Hussein Mourtada"], "categories": ["math.AG"], "doi": "", "_query": "automated valuation model", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2410.14232v1", "title": "Tableaux for Automated Reasoning in Dependently-Typed Higher-Order Logic (Extended Version)", "abstract": "Dependent type theory gives an expressive type system facilitating succinct formalizations of mathematical concepts. In practice, it is mainly used for interactive theorem proving with intensional type theories, with PVS being a notable exception. In this paper, we present native rules for automated reasoning in a dependently-typed version (DHOL) of classical higher-order logic (HOL). DHOL has an extensional type theory with an undecidable type checking problem which contains theorem proving. We implemented the inference rules as well as an automatic type checking mode in Lash, a fork of Satallax, the leading tableaux-based prover for HOL. Our method is sound and complete with respect to provability in DHOL. Completeness is guaranteed by the incorporation of a sound and complete translation from DHOL to HOL recently proposed by Rothgang et al. While this translation can already be used as a preprocessing step to any HOL prover, to achieve better performance, our system directly works in DHOL. Moreover, experimental results show that the DHOL version of Lash can outperform all major HOL provers executed on the translation.", "published": "2024-10-18T07:28:43Z", "authors": ["Johannes Niederhauser", "Chad E. Brown", "Cezary Kaliszyk"], "categories": ["cs.LO"], "doi": "10.1007/978-3-031-63498-7_6", "_query": "automated valuation model", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/1210.3811v3", "title": "Funding, Collateral and Hedging: uncovering the mechanics and the subtleties of funding valuation adjustments", "abstract": "The main result of this paper is a collateralized counterparty valuation adjusted pricing equation, which allows to price a deal while taking into account credit and debit valuation adjustments (CVA, DVA) along with margining and funding costs, all in a consistent way. Funding risk breaks the bilateral nature of the valuation formula. We find that the equation has a recursive form, making the introduction of a purely additive funding valuation adjustment (FVA) difficult. Yet, we can cast the pricing equation into a set of iterative relationships which can be solved by means of standard least-square Monte Carlo techniques. As a consequence, we find that identifying funding costs and debit valuation adjustments is not tenable in general, contrary to what has been suggested in the literature in simple cases. The assumptions under which funding costs vanish are a very special case of the more general theory. We define a comprehensive framework that allows us to derive earlier results on funding or counterparty risk as a special case, although our framework is more than the sum of such special cases. We derive the general pricing equation by resorting to a risk-neutral approach where the new types of risks are included by modifying the payout cash flows. We consider realistic settings and include in our models the common market practices suggested by ISDA documentation, without assuming restrictive constraints on margining procedures and close-out netting rules. In particular, we allow for asymmetric collateral and funding rates, and exogenous liquidity policies and hedging strategies. Re-hypothecation liquidity risk and close-out amount evaluation issues are also covered. Finally, relevant examples of non-trivial settings illustrate how to derive known facts about discounting curves from a robust general framework and without resorting to ad hoc hypotheses.", "published": "2012-10-14T16:27:51Z", "authors": ["Andrea Pallavicini", "Daniele Perini", "Damiano Brigo"], "categories": ["q-fin.PR", "q-fin.GN"], "doi": "", "_query": "automated valuation model", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/1705.00447v3", "title": "Uniform approximation of Abhyankar valuation ideals in function field of prime characteristic", "abstract": "Using the theory of asymptotic test ideals, we prove the prime characteristic analogue of a characteristic $0$ result of Ein, Lazarsfeld and Smith (arXiv:math/0202303) on uniform approximation of valuation ideals associated to real-valued Abhyankar valuations centered on regular varieties over perfect fields.", "published": "2017-05-01T08:57:15Z", "authors": ["Rankeya Datta"], "categories": ["math.AG", "math.AC"], "doi": "10.1090/tran/7917", "_query": "automated valuation model", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/1407.2855v1", "title": "Learning Valuation Distributions from Partial Observation", "abstract": "Auction theory traditionally assumes that bidders' valuation distributions are known to the auctioneer, such as in the celebrated, revenue-optimal Myerson auction. However, this theory does not describe how the auctioneer comes to possess this information. Recently, Cole and Roughgarden [2014] showed that an approximation based on a finite sample of independent draws from each bidder's distribution is sufficient to produce a near-optimal auction. In this work, we consider the problem of learning bidders' valuation distributions from much weaker forms of observations. Specifically, we consider a setting where there is a repeated, sealed-bid auction with $n$ bidders, but all we observe for each round is who won, but not how much they bid or paid. We can also participate (i.e., submit a bid) ourselves, and observe when we win. From this information, our goal is to (approximately) recover the inherently recoverable part of the underlying bid distributions. We also consider extensions where different subsets of bidders participate in each round, and where bidders' valuations have a common-value component added to their independent private values.", "published": "2014-07-10T16:35:09Z", "authors": ["Avrim Blum", "Yishay Mansour", "Jamie Morgenstern"], "categories": ["cs.GT"], "doi": "", "_query": "automated valuation model", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2306.02071v3", "title": "DU-Shapley: A Shapley Value Proxy for Efficient Dataset Valuation", "abstract": "We consider the dataset valuation problem, that is, the problem of quantifying the incremental gain, to some relevant pre-defined utility of a machine learning task, of aggregating an individual dataset to others. The Shapley value is a natural tool to perform dataset valuation due to its formal axiomatic justification, which can be combined with Monte Carlo integration to overcome the computational tractability challenges. Such generic approximation methods, however, remain expensive in some cases. In this paper, we exploit the knowledge about the structure of the dataset valuation problem to devise more efficient Shapley value estimators. We propose a novel approximation, referred to as discrete uniform Shapley, which is expressed as an expectation under a discrete uniform distribution with support of reasonable size. We justify the relevancy of the proposed framework via asymptotic and non-asymptotic theoretical guarantees and illustrate its benefits via an extensive set of numerical experiments.", "published": "2023-06-03T10:22:50Z", "authors": ["Felipe Garrido-Lucero", "Benjamin Heymann", "Maxime Vono", "Patrick Loiseau", "Vianney Perchet"], "categories": ["cs.AI", "cs.GT", "stat.CO", "stat.ML"], "doi": "", "_query": "automated valuation model", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/1202.2792v1", "title": "On the Hardness of Welfare Maximization in Combinatorial Auctions with Submodular Valuations", "abstract": "We present a new type of monotone submodular functions: \\emph{multi-peak submodular functions}. Roughly speaking, given a family of sets $\\cF$, we construct a monotone submodular function $f$ with a high value $f(S)$ for every set $S \\in {\\cF}$ (a \"peak\"), and a low value on every set that does not intersect significantly any set in $\\cF$. We use this construction to show that a better than $(1-\\frac{1}{2e})$-approximation ($\\simeq 0.816$) for welfare maximization in combinatorial auctions with submodular valuations is (1) impossible in the communication model, (2) NP-hard in the computational model where valuations are given explicitly. Establishing a constant approximation hardness for this problem in the communication model was a long-standing open question. The valuations we construct for the hardness result in the computational model depend only on a constant number of items, and hence the result holds even if the players can answer arbitrary queries about their valuation, including demand queries. We also study two other related problems that received some attention recently: max-min allocation (for which we also get hardness of $(1-\\frac 1 {2e}+ε)$-approximation, in both models), and combinatorial public projects (for which we prove hardness of $(3/4+ε)$-approximation in the communication model, and hardness of $(1 -\\frac 1 e+ε)$-approximation in the computational model, using constant size valuations).", "published": "2012-02-13T17:09:58Z", "authors": ["Shahar Dobzinski", "Jan Vondrak"], "categories": ["cs.DS", "cs.GT"], "doi": "", "_query": "automated valuation model", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/1903.08384v2", "title": "Combinatorial Auctions with Interdependent Valuations: SOS to the Rescue", "abstract": "We study combinatorial auctions with interdependent valuations. In such settings, each agent $i$ has a private signal $s_i$ that captures her private information, and the valuation function of every agent depends on the entire signal profile, ${\\bf s}=(s_1,\\ldots,s_n)$. The literature in economics shows that the interdependent model gives rise to strong impossibility results, and identifies assumptions under which optimal solutions can be attained. The computer science literature provides approximation results for simple single-parameter settings (mostly single item auctions, or matroid feasibility constraints). Both bodies of literature focus largely on valuations satisfying a technical condition termed {\\em single crossing} (or variants thereof). We consider the class of {\\em submodular over signals} (SOS) valuations (without imposing any single-crossing type assumption), and provide the first welfare approximation guarantees for multi-dimensional combinatorial auctions, achieved by universally ex-post IC-IR mechanisms. Our main results are: $(i)$ 4-approximation for any single-parameter downward-closed setting with single-dimensional signals and SOS valuations; $(ii)$ 4-approximation for any combinatorial auction with multi-dimensional signals and {\\em separable}-SOS valuations; and $(iii)$ $(k+3)$- and $(2\\log(k)+4)$-approximation for any combinatorial auction with single-dimensional signals, with $k$-sized signal space, for SOS and strong-SOS valuations, respectively. All of our results extend to a parameterized version of SOS, $d$-SOS, while losing a factor that depends on $d$.", "published": "2019-03-20T08:34:13Z", "authors": ["Alon Eden", "Michal Feldman", "Amos Fiat", "Kira Goldner", "Anna R. Karlin"], "categories": ["cs.GT"], "doi": "", "_query": "automated valuation model", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2212.11468v1", "title": "IPProtect: protecting the intellectual property of visual datasets during data valuation", "abstract": "Data trading is essential to accelerate the development of data-driven machine learning pipelines. The central problem in data trading is to estimate the utility of a seller's dataset with respect to a given buyer's machine learning task, also known as data valuation. Typically, data valuation requires one or more participants to share their raw dataset with others, leading to potential risks of intellectual property (IP) violations. In this paper, we tackle the novel task of preemptively protecting the IP of datasets that need to be shared during data valuation. First, we identify and formalize two kinds of novel IP risks in visual datasets: data-item (image) IP and statistical (dataset) IP. Then, we propose a novel algorithm to convert the raw dataset into a sanitized version, that provides resistance to IP violations, while at the same time allowing accurate data valuation. The key idea is to limit the transfer of information from the raw dataset to the sanitized dataset, thereby protecting against potential intellectual property violations. Next, we analyze our method for the likely existence of a solution and immunity against reconstruction attacks. Finally, we conduct extensive experiments on three computer vision datasets demonstrating the advantages of our method in comparison to other baselines.", "published": "2022-12-22T03:36:19Z", "authors": ["Gursimran Singh", "Chendi Wang", "Ahnaf Tazwar", "Lanjun Wang", "Yong Zhang"], "categories": ["cs.CV", "cs.CR"], "doi": "", "_query": "automated valuation model", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/0909.4967v1", "title": "Two-dimensional affine R-buildings defined by generalized polygons with non-discrete valuation", "abstract": "In this paper we complete the proof of the \"equivalence\" of non-discrete R-buildings of types A~_2 and C~_2, with, respectively, projective planes and generalized quadrangles with non-discrete valuation, begun in previous paper of the authors. We also complete the proof of the \"equivalence\" of an affine building of rank 3 with a generalized polygon with discrete valuation (by proving this for generalized hexagons). We also complement a result of the second author by proving uniqueness up to scalar multiples of the weight sequences of polygons with non-discrete valuation. As an application, we produce some new explicitly defined non-discrete R-buildings, in particular a class of type A~_2 with arbitrary residues.", "published": "2009-09-27T20:16:21Z", "authors": ["Koen Struyve", "Hendrik Van Maldeghem"], "categories": ["math.MG"], "doi": "", "_query": "automated valuation model", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2610.26734v1", "title": "CAVA: Complementary-Label-Aware Data Valuation", "abstract": "Complementary-label learning (CLL) trains a classifier from labels that name a class an instance does not belong to. Such labels are cheap to collect from crowdworkers or vision-language models (VLMs), making them attractive for large-scale annotation. However, both sources can mistakenly assign the true class as the complementary label. Such annotation errors are particularly harmful in CLL because the model is explicitly trained to suppress the correct answer. Optimal transport (OT) data valuation identifies harmful samples by comparing training data with a clean validation set, but transferring it directly to CLL creates a semantic mismatch: label $c$ means not class $c$ in the training but class $c$ in the validation. We show that this makes the OT label groups noisy mixtures whose behavior depends on the noise rate. The transferred score is informative only at sufficiently high noise, becomes uninformative at the crossover rate $1/M$ for $M$ classes, and reverses below it, where practical CLL annotation typically operates. We propose CAVA, which introduces a complementary label cost and a label margin that isolates the contribution of each complementary label to the transport potential. The margin keeps a consistent direction across noise rates, detects diagonal noise with a parameter-free threshold of closed-form precision, and allows detected samples to be recycled as ordinary-label supervision. On synthetic and human-annotated benchmarks, CAVA separates diagonal noise almost perfectly where directly transferred OT valuation performs worse than random. With only a fraction of the clean validation data, it also improves every CLL objective we evaluate, showing that OT-based valuation becomes reliable once the semantics of complementary supervision are modeled explicitly.", "published": "2026-10-19T17:00:37Z", "authors": ["Tan-Ha Mai", "Hsuan-Tien Lin", "Vu Nguyen"], "categories": ["cs.LG", "cs.CV"], "doi": "", "_query": "automated valuation model", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2311.12348v2", "title": "Huber rings and valuation spectra", "abstract": "These notes expand a four-hour lecture course given in Heidelberg in March 2023, as part of the \"Spring School on non-Archimedean Geometry and Eigenvarieties\". They are designed for graduate students and other learners. We introduce Huber rings and valuation theory alongside frequent examples. The notes are largely self-contained, though many details are given in exercises found following each lecture.", "published": "2023-11-21T04:46:53Z", "authors": ["John Bergdall"], "categories": ["math.NT", "math.AG"], "doi": "", "_query": "automated valuation model", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2210.10637v1", "title": "Digital Asset Valuation: A Study on Domain Names, Email Addresses, and NFTs", "abstract": "Existing works on valuing digital assets on the Internet typically focus on a single asset class. To promote the development of automated valuation techniques, preferably those that are generally applicable to multiple asset classes, we construct DASH, the first Digital Asset Sales History dataset that features multiple digital asset classes spanning from classical to blockchain-based ones. Consisting of 280K transactions of domain names (DASH_DN), email addresses (DASH_EA), and non-fungible token (NFT)-based identifiers (DASH_NFT), such as Ethereum Name Service names, DASH advances the field in several aspects: the subsets DASH_DN, DASH_EA, and DASH_NFT are the largest freely accessible domain name transaction dataset, the only publicly available email address transaction dataset, and the first NFT transaction dataset that focuses on identifiers, respectively. We build strong conventional feature-based models as the baselines for DASH. We next explore deep learning models based on fine-tuning pre-trained language models, which have not yet been explored for digital asset valuation in the previous literature. We find that the vanilla fine-tuned model already performs reasonably well, outperforming all but the best-performing baselines. We further propose improvements to make the model more aware of the time sensitivity of transactions and the popularity of assets. Experimental results show that our improved model consistently outperforms all the other models across all asset classes on DASH.", "published": "2022-10-06T12:59:06Z", "authors": ["Kai Sun"], "categories": ["cs.IR", "cs.AI"], "doi": "", "_query": "automated valuation model", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/0706.0111v1", "title": "Flat modules over valuation rings", "abstract": "Let $R$ be a valuation ring and let $Q$ be its total quotient ring. It is proved that any singly projective (respectively flat) module is finitely projective if and only if $Q$ is maximal (respectively artinian). It is shown that each singly projective module is a content module if and only if any non-unit of $R$ is a zero-divisor and that each singly projective module is locally projective if and only if $R$ is self injective. Moreover, $R$ is maximal if and only if each singly projective module is separable, if and only if any flat content module is locally projective. Necessary and sufficient conditions are given for a valuation ring with non-zero zero-divisors to be strongly coherent or $π$-coherent. A complete characterization of semihereditary commutative rings which are $π$-coherent is given. When $R$ is a commutative ring with a self FP-injective quotient ring $Q$, it is proved that each flat $R$-module is finitely projective if and only if $Q$ is perfect.", "published": "2007-06-01T09:31:01Z", "authors": ["Francois Couchot"], "categories": ["math.RA"], "doi": "10.1016/j.jpaa.2007.10.010", "_query": "automated valuation model", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2510.23409v3", "title": "Eigen-Value: Efficient Domain-Robust Data Valuation via Eigenvalue-Based Approach", "abstract": "Data valuation has become central in the era of data-centric AI. It drives efficient training pipelines and enables objective pricing in data markets by assigning a numeric value to each data point. Most existing data valuation methods estimate the effect of removing individual data points by evaluating changes in model validation performance under in-distribution (ID) settings, as opposed to out-of-distribution (OOD) scenarios where data follow different patterns. Since ID and OOD data behave differently, data valuation methods based on ID loss often fail to generalize to OOD settings, particularly when the validation set contains no OOD data. Furthermore, although OOD-aware methods exist, they involve heavy computational costs, which hinder practical deployment. To address these challenges, we introduce \\emph{Eigen-Value} (EV), a plug-and-play data valuation framework for OOD robustness that uses only an ID data subset, including during validation. EV provides a new spectral approximation of domain discrepancy, which is the gap of loss between ID and OOD using ratios of eigenvalues of ID data's covariance matrix. EV then estimates the marginal contribution of each data point to this discrepancy via perturbation theory, alleviating the computational burden. Subsequently, EV plugs into ID loss-based methods by adding an EV term without any additional training loop. We demonstrate that EV achieves improved OOD robustness and stable value rankings across real-world datasets, while remaining computationally lightweight. These results indicate that EV is practical for large-scale settings with domain shift, offering an efficient path to OOD-robust data valuation.", "published": "2025-10-27T15:12:49Z", "authors": ["Youngjun Choi", "Joonseong Kang", "Sungjun Lim", "Kyungwoo Song"], "categories": ["cs.LG", "cs.AI"], "doi": "", "_query": "automated valuation model", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2607.03675v1", "title": "Validation-Induced Shapley Shifts: How Validation Structure Distorts Data Valuation", "abstract": "Shapley values are widely used to attribute value to training data based on their marginal contribution to performance on a validation set. Existing practice often assumes these values are stable once the training data and model are fixed. In this work, we uncover a systematic vulnerability: even modest changes to the validation set, such as introducing noises, cause directional shifts in Shapley distributions. As noises are added, Shapley values of training samples compress toward zero. We trace this to a noise-induced neighborhood reshuffling effect: perturbations alter the local rank order between validation and training samples, flattening the valuation landscape. Using the KNN-Shapley framework, we show through synthetic and real data that these shifts are consistent and reproducible. Our findings challenge the assumption of Shapley stability and reveal a new axis of fragility in data valuation. We propose normalization and boundary-aware validation strategies to mitigate these distortions and enable more robust, interpretable valuation in machine learning marketplaces.", "published": "2026-07-04T03:07:36Z", "authors": ["Yinan Shen", "Ziao Yang", "Hongfu Liu"], "categories": ["cs.LG"], "doi": "", "_query": "automated valuation model", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2312.08183v2", "title": "Smooth valuations on convex bodies and finite linear combinations of mixed volumes", "abstract": "It is shown that Alesker's solution of McMullen's conjecture implies the following stronger version of the conjecture: Every continuous, translation invariant, $k$-homogeneous valuation on convex bodies in $\\mathbb{R}^n$ can be approximated uniformly on compact subsets by finite linear combinations of mixed volumes involving at most $N_{n,k}$ summands, where $N_{n,k}$ is a constant depending on $n$ and $k$ only. Moreover, $n-k-1$ of the arguments of the mixed volumes can be chosen to be ellipsoids that do not depend on the valuation. The result is based on a corresponding description of smooth valuations in terms of finite linear combinations of mixed volumes.", "published": "2023-12-13T14:49:10Z", "authors": ["Jonas Knoerr"], "categories": ["math.MG"], "doi": "", "_query": "automated valuation model", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/1002.4694v3", "title": "Extensions of truncated discrete valuation rings II", "abstract": "An equivalence is established between the category of at most $a$-ramified finite separable extensions of a complete discrete valuation field $K$ and the category of at most $a$-ramified finite extensions of the \"length-$a$ truncation\" $\\OK/\\mK^a$ of the integer ring of $K$.", "published": "2010-02-25T05:33:48Z", "authors": ["Toshiro Hiranouchi", "Yuichiro Taguchi"], "categories": ["math.NT"], "doi": "", "_query": "automated valuation model", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2112.03729v1", "title": "Iterations of Minkowski Valuations", "abstract": "It is shown that for any sufficiently regular even Minkowski valuation $Φ$ which is homogeneous and intertwines rigid motions, and for any convex body $K$ in a smooth neighborhood of the unit ball, there exists a sequence of positive numbers $(γ_m)_{m=1}^\\infty$ such that $(γ_mΦ^m K)_{m=1}^\\infty$ converges to the unit ball with respect to the Hausdorff metric.", "published": "2021-12-07T14:36:18Z", "authors": ["Oscar Ortega-Moreno"], "categories": ["math.MG", "math.FA"], "doi": "", "_query": "automated valuation model", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2406.16773v1", "title": "Valuation methods for professional sports clubs: A historical review, a model development, and the application to Japanese football clubs", "abstract": "In the trend towards the globalization of football and the increasing commercialization of professional football clubs, a methodology for calculating the firm value of clubs in non-western countries has yet to be established. This study reviews the valuation methods for the club firm values in Europe and North America and how values are calculated at the time of changing ownership of Japanese clubs and develops regression models with higher explanatory power than before to estimate the more accurate firm value of Japanese football clubs. A review of the existing literature on methods for calculating the firm value of professional sports clubs in Europe and North America, as well as financial statements and registers relating to changes of ownership of Japanese clubs, was conducted. After that, multiple regression analyses were conducted using the firm value of European clubs as the explained variable. From the literature review and the Japanese case studies, it has become clear that European clubs' standard valuation methods are based on revenue and other factors, while in Japan, valuation is based solely on the par value of stocks or net assets. Multiple regression analysis revealed that the firm value of European clubs over the past three years is best explained by revenue or player market value and the number of SNS followers. Two models with high explanatory power were developed. The estimated firm value using the revenue-based formula was higher than the one based on player market value. However, in the J.League, the former was more than three times higher than the latter, while the former was only 1.2 times higher for European clubs. The discrepancy relates to differences in European and J.League clubs' revenues and asset structures. In either formula, the firm value of J.League clubs exceeded the actual transaction price when the change of ownership occurred in the past.", "published": "2024-06-24T16:31:12Z", "authors": ["Masaaki Kimura", "Zen Walsh", "Takuo Inoue", "Toshiya Takahashi", "Hideki Koizumi"], "categories": ["stat.AP"], "doi": "", "_query": "automated valuation model", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/0712.2172v1", "title": "Integration on valuation fields over local fields", "abstract": "We present elements of a theory of translation-invariant integration, measure, and harmonic analysis on a valuation field with local field as residue field. This extends the work of Fesenko. Applications to zeta integrals for two-dimensional local fields are then considered.", "published": "2007-12-13T16:03:47Z", "authors": ["Matthew T. Morrow"], "categories": ["math.NT", "math.AG"], "doi": "", "_query": "automated valuation model", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/1801.02476v1", "title": "Semi-automated Annotation of Signal Events in Clinical EEG Data", "abstract": "To be effective, state of the art machine learning technology needs large amounts of annotated data. There are numerous compelling applications in healthcare that can benefit from high performance automated decision support systems provided by deep learning technology, but they lack the comprehensive data resources required to apply sophisticated machine learning models. Further, for economic reasons, it is very difficult to justify the creation of large annotated corpora for these applications. Hence, automated annotation techniques become increasingly important. In this study, we investigated the effectiveness of using an active learning algorithm to automatically annotate a large EEG corpus. The algorithm is designed to annotate six types of EEG events. Two model training schemes, namely threshold-based and volume-based, are evaluated. In the threshold-based scheme the threshold of confidence scores is optimized in the initial training iteration, whereas for the volume-based scheme only a certain amount of data is preserved after each iteration. Recognition performance is improved 2% absolute and the system is capable of automatically annotating previously unlabeled data. Given that the interpretation of clinical EEG data is an exceedingly difficult task, this study provides some evidence that the proposed method is a viable alternative to expensive manual annotation.", "published": "2018-01-03T03:47:20Z", "authors": ["Scott Yang", "Silvia Lopez", "Meysam Golmohammadi", "Iyad Obeid", "Joseph Picone"], "categories": ["eess.SP", "cs.DB", "cs.LG", "stat.ML"], "doi": "10.1109/SPMB.2016.7846855", "_query": "automated valuation model", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2404.01825v1", "title": "Degree $p$ Extensions of Arbitrary Valuation Rings and \"Best $f$\"", "abstract": "We prove the explicit characterization of the so-called \"best f\" for degree $p$ Artin-Schreier and degree $p$ Kummer extensions of Henselian valuation rings in residue characteristic $p$. This characterization is mentioned briefly in [Th16, Th18]. Existence of best $f$ is closely related to the defect of such extensions and this characterization plays a crucial role in understanding their intricate structure. We also treat degree $p$ Artin-Schreier defect extensions of higher rank valuation rings, extending the results in [Th16], and thus completing the study of degree $p$ extensions that are the building blocks of the general theory.", "published": "2024-04-02T10:36:30Z", "authors": ["Vaidehee Thatte"], "categories": ["math.AC", "math.NT"], "doi": "", "_query": "automated valuation model", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/0712.4037v1", "title": "Valuation bases for generalized algebraic series fields", "abstract": "We investigate valued fields which admit a valuation basis. Given a countable ordered abelian group G and a real closed, or algebraically closed field F, we give a sufficient condition for a valued subfield of the field of generalized power series F((G)) to admit a K-valuation basis. We show that the field of rational functions F(G) and the field F(G) of power series in F((G)) algebraic over F(G) satisfy this condition. It follows that for archimedean F and divisible G the real closed field F(G) admits a restricted exponential function.", "published": "2007-12-26T09:08:27Z", "authors": ["Franz-Viktor Kuhlmann", "Salma Kuhlmann", "Jonathan W. Lee"], "categories": ["math.AC", "math.LO"], "doi": "", "_query": "automated valuation model", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/1310.3153v1", "title": "Valuation Compressions in VCG-Based Combinatorial Auctions", "abstract": "The focus of classic mechanism design has been on truthful direct-revelation mechanisms. In the context of combinatorial auctions the truthful direct-revelation mechanism that maximizes social welfare is the VCG mechanism. For many valuation spaces computing the allocation and payments of the VCG mechanism, however, is a computationally hard problem. We thus study the performance of the VCG mechanism when bidders are forced to choose bids from a subspace of the valuation space for which the VCG outcome can be computed efficiently. We prove improved upper bounds on the welfare loss for restrictions to additive bids and upper and lower bounds for restrictions to non-additive bids. These bounds show that the welfare loss increases in expressiveness. All our bounds apply to equilibrium concepts that can be computed in polynomial time as well as to learning outcomes.", "published": "2013-10-11T14:58:51Z", "authors": ["Paul Duetting", "Monika Henzinger", "Martin Starnberger"], "categories": ["cs.GT"], "doi": "", "_query": "automated valuation model", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/1704.05713v2", "title": "Finite Generation of Extensions of Associated Graded Rings Along a Valuation", "abstract": "In this paper we consider the question of when the associated graded ring along a valuation, ${\\rm gr}_{ν^*}(S)$, is a finite ${\\rm gr}_{ν^*}(R)$-module, where $S$ is a normal local ring which lies over a normal local ring $R$ and $ν^*$ is a valuation of the quotient field of $S$ which dominates $S$. We obtain a very general result in equicharacteristic zero in Theorem 1.5. We also obtain general results for unramified extensions of excellent local rings in Proposition 1.7.", "published": "2017-04-19T12:56:50Z", "authors": ["Steven Dale Cutkosky"], "categories": ["math.AG", "math.AC"], "doi": "10.1112/jlms.12120", "_query": "automated valuation model", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/1903.04317v1", "title": "Volume of line bundles via valuation vectors (different from Okounkov bodies)", "abstract": "Up to a factor 1/n!, the volume of a big line bundle agrees with the Euclidean volume of its Okounkov body. The latter is the convex hull of top rank valuation vectors of sections, all with respect to a single flag. In this text we give a different volume formula, valid in the ample cone, also based on top rank valuation vectors, but mixing data along several different flags.", "published": "2019-03-11T14:17:13Z", "authors": ["Oliver Braunling"], "categories": ["math.AG"], "doi": "", "_query": "automated valuation model", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/1803.07809v2", "title": "Valuation theory, generalized IFS attractors and fractals", "abstract": "Using valuation rings and valued fields as examples, we discuss in which ways the notions of \"topological IFS attractor\" and \"fractal space\" can be generalized to cover more general settings.", "published": "2018-03-21T09:33:17Z", "authors": ["Jan Dobrowolski", "Franz-Viktor Kuhlmann"], "categories": ["math.AC", "math.DS"], "doi": "10.1007/s00013-018-1202-0", "_query": "automated valuation model", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/math/0509512v3", "title": "Theory of valuations on manifolds, III. Multiplicative structure in the general case", "abstract": "This article is the third part of the series of articles where the theory of valuations on manifolds is constructed. In math.MG/0503399 the notion of a smooth valuation on a manifold was introduced. The goal of this article is to put a canonical multiplicative structure on the space of smooth valuations on general manifolds, thus extending some of the affine constructions from math.MG/0301148, math.MG/0503397.", "published": "2005-09-22T10:43:46Z", "authors": ["Semyon Alesker", "Joseph H. G. Fu"], "categories": ["math.MG", "math.DG"], "doi": "", "_query": "automated valuation model", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2307.09648v1", "title": "On the Existence of Envy-Free Allocations Beyond Additive Valuations", "abstract": "We study the problem of fairly allocating $m$ indivisible items among $n$ agents. Envy-free allocations, in which each agent prefers her bundle to the bundle of every other agent, need not exist in the worst case. However, when agents have additive preferences and the value $v_{i,j}$ of agent $i$ for item $j$ is drawn independently from a distribution $D_i$, envy-free allocations exist with high probability when $m \\in Ω( n \\log n / \\log \\log n )$. In this paper, we study the existence of envy-free allocations under stochastic valuations far beyond the additive setting. We introduce a new stochastic model in which each agent's valuation is sampled by first fixing a worst-case function, and then drawing a uniformly random renaming of the items, independently for each agent. This strictly generalizes known settings; for example, $v_{i,j} \\sim D_i$ may be seen as picking a random (instead of a worst-case) additive function before renaming. We prove that random renaming is sufficient to ensure that envy-free allocations exist with high probability in very general settings. When valuations are non-negative and ``order-consistent,'' a valuation class that generalizes additive, budget-additive, unit-demand, and single-minded agents, SD-envy-free allocations (a stronger notion of fairness than envy-freeness) exist for $m \\in ω(n^2)$ when $n$ divides $m$, and SD-EFX allocations exist for all $m \\in ω(n^2)$. The dependence on $n$ is tight, that is, for $m \\in O(n^2)$ envy-free allocations don't exist with constant probability. For the case of arbitrary valuations (allowing non-monotone, negative, or mixed-manna valuations) and $n=2$ agents, we prove envy-free allocations exist with probability $1 - Θ(1/m)$ (and this is tight).", "published": "2023-07-18T21:39:15Z", "authors": ["Gerdus Benadè", "Daniel Halpern", "Alexandros Psomas", "Paritosh Verma"], "categories": ["cs.GT"], "doi": "", "_query": "automated valuation model", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/1504.01871v1", "title": "A definable henselian valuation with high quantifier complexity", "abstract": "We give an example of a parameter-free definable henselian valuation ring which is neither definable by a parameter-free $\\forall\\exists$-formula nor by a parameter-free $\\exists\\forall$-formula in the language of rings. This answers a question of Prestel.", "published": "2015-04-08T09:11:11Z", "authors": ["Immanuel Halupczok", "Franziska Jahnke"], "categories": ["math.LO"], "doi": "", "_query": "automated valuation model", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/1404.7379v1", "title": "Gröbner Basis Theory for Modules over Polynomial Rings over Fields with Valuation", "abstract": "A motivation to study Gröbner theory for fields with valuations comes from tropical geometry, for example, they can be used to compute tropicalization of varieties \\citep{maclagan2009introduction}. The computational aspect of this theory was first studied in (Chen \\& Maclagan, 2013). In this paper, we generalize this Gröbner basis theory to free modules over polynomial rings over fields with valuation. As the valuation of coefficients is also taken into account while defining the initial term, we do not necessarily get a monomial order. To overcome this problem we have to resort to other techniques like the use of ecart function where the codomain is the well-ordered set $\\mathbb{N}$, and thereby give a method to calculate the Gröbner basis for submodules generated by homogeneous elements.", "published": "2014-04-29T14:35:25Z", "authors": ["Aritra Sen", "Ambedkar Dukkipati"], "categories": ["math.AC"], "doi": "", "_query": "automated valuation model", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/1606.04145v1", "title": "Sample Complexity of Automated Mechanism Design", "abstract": "The design of revenue-maximizing combinatorial auctions, i.e. multi-item auctions over bundles of goods, is one of the most fundamental problems in computational economics, unsolved even for two bidders and two items for sale. In the traditional economic models, it is assumed that the bidders' valuations are drawn from an underlying distribution and that the auction designer has perfect knowledge of this distribution. Despite this strong and oftentimes unrealistic assumption, it is remarkable that the revenue-maximizing combinatorial auction remains unknown. In recent years, automated mechanism design has emerged as one of the most practical and promising approaches to designing high-revenue combinatorial auctions. The most scalable automated mechanism design algorithms take as input samples from the bidders' valuation distribution and then search for a high-revenue auction in a rich auction class. In this work, we provide the first sample complexity analysis for the standard hierarchy of deterministic combinatorial auction classes used in automated mechanism design. In particular, we provide tight sample complexity bounds on the number of samples needed to guarantee that the empirical revenue of the designed mechanism on the samples is close to its expected revenue on the underlying, unknown distribution over bidder valuations, for each of the auction classes in the hierarchy. In addition to helping set automated mechanism design on firm foundations, our results also push the boundaries of learning theory. In particular, the hypothesis functions used in our contexts are defined through multi-stage combinatorial optimization procedures, rather than simple decision boundaries, as are common in machine learning.", "published": "2016-06-13T21:25:22Z", "authors": ["Maria-Florina Balcan", "Tuomas Sandholm", "Ellen Vitercik"], "categories": ["cs.LG", "cs.GT"], "doi": "", "_query": "automated valuation model", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2312.05346v1", "title": "Deep Learning for Dynamic NFT Valuation", "abstract": "I study the price dynamics of non-fungible tokens (NFTs) and propose a deep learning framework for dynamic valuation of NFTs. I use data from the Ethereum blockchain and OpenSea to train a deep learning model on historical trades, market trends, and traits/rarity features of Bored Ape Yacht Club NFTs. After hyperparameter tuning, the model is able to predict the price of NFTs with high accuracy. I propose an application framework for this model using zero-knowledge machine learning (zkML) and discuss its potential use cases in the context of decentralized finance (DeFi) applications.", "published": "2023-12-08T20:10:56Z", "authors": ["Mingxuan He"], "categories": ["q-fin.CP"], "doi": "", "_query": "automated valuation model", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2211.16151v1", "title": "Business-cycles and Cash-on-Market: Pre-money Startup Valuation in the Macroeconomic Environment", "abstract": "How do business-cycles impact startup-valuations? While several studies explore VC startupecosystems and pre-money valuations, relatively-few delve deeper into the role of macro-level economic factors in influencing those startup deals valuations. Using a dataset of 1,089 venturecapital investments in European Union and European Economic Area markets, this article examines macroeconomic, cyclical and macro-sectoral influences on VC startups pre-money VC valuations. Our findings show that business-cycles impact startup-valuation both directly and indirectly. Beyond DCF factors, startup-valuations are impacted via by business-cycles directly, and via local venture-capital market-size. By using a Structural Equation Model approach, our findings contribute to entrepreneurship and financial-intermediary literature by exploring indirect and endogenous relationship possibilities finding that most determinants are transmission-channels rather than independent drivers. Our findings effectively tie-together startup-valuations, intermediary markets, and macroeconomic determinants.", "published": "2022-11-29T12:24:55Z", "authors": ["Max Berre", "Benjamin Le Pendeven"], "categories": ["q-fin.GN", "q-fin.ST"], "doi": "", "_query": "automated valuation model", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/1609.03658v1", "title": "Cartan Theorems for Stein manifolds over a discrete valuation base", "abstract": "In this paper we prove Cartan theorems A and B for Stein manifolds over certain discrete valuation rings.", "published": "2016-09-13T02:18:53Z", "authors": ["Jari Taskinen", "Kari Vilonen"], "categories": ["math.CV", "math.AG", "math.FA"], "doi": "", "_query": "automated valuation model", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2401.00182v2", "title": "Generators for extensions of valuation rings", "abstract": "For a finite valued field extension $(L/K,v)$ we describe the problem of finding sets of generators for the corresponding extension $\\VR_L/\\VR_K$ of valuation rings. The main tool to obtain such sets are complete sets of (key) polynomials. We show that when the initial index coincides with the ramification index, sequences of key polynomials naturally give rise to sets of generators. We use this to prove Knaf's conjecture for pure extensions.", "published": "2023-12-30T09:20:27Z", "authors": ["Josnei Novacoski"], "categories": ["math.AC"], "doi": "", "_query": "automated valuation model", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2605.03581v1", "title": "ZK-Value: A Practical Zero-Knowledge System for Verifiable Data Valuation", "abstract": "Data valuation is a foundational task in data marketplaces, where a Shapley-value attribution determines how a buyer's payment is distributed among data providers. Typically, the marketplace operator runs this attribution alone, requiring participants and external auditors to trust scores they cannot independently recompute on the underlying private data. While zero-knowledge proofs (ZKPs) can theoretically reconcile this conflict between privacy and verifiability, existing ZK valuation systems fail to scale to real-world marketplace demands due to prohibitive proving times or the requirement to disclose validation cohorts. We present ZK-Value, a practical, end-to-end ZK data-valuation system. Our solution bridges the scalability gap through a fully co-designed architecture: (1) LSH-Shapley, a locality-based valuation primitive that replaces expensive pairwise distance metrics with per-bucket collision counts; (2) ZK-LSH-Shapley, a tailored ZKP protocol that drastically reduces witness size by encoding these counts into bucket-level histograms rather than naive per-pair tensors; and (3) structural proof-system optimizations, specifically super-oracle batching and sparsity skipping. Evaluated across 12 standard datasets, ZK-Value delivers valuation quality on par with state-of-the-art baselines (within 0.033 AUROC of exact KNN-Shapley), while generating proofs in seconds to minutes and outperforming specialized ZK baselines by 12.6x to 68.1x in proving time, with verification in under 4.6 s.", "published": "2026-05-05T09:51:43Z", "authors": ["Zhaoyu Wang", "Pingchuan Ma", "Zhantong Xue", "Yuguang Zhou", "Qixin Zhang", "Xiaoqin Zhang", "Shuai Wang"], "categories": ["cs.CR"], "doi": "", "_query": "automated valuation model", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/1905.06462v1", "title": "Data Markets to support AI for All: Pricing, Valuation and Governance", "abstract": "We discuss a data market technique based on intrinsic (relevance and uniqueness) as well as extrinsic value (influenced by supply and demand) of data. For intrinsic value, we explain how to perform valuation of data in absolute terms (i.e just by itself), or relatively (i.e in comparison to multiple datasets) or in conditional terms (i.e valuating new data given currently existing data).", "published": "2019-05-14T04:41:11Z", "authors": ["Ramesh Raskar", "Praneeth Vepakomma", "Tristan Swedish", "Aalekh Sharan"], "categories": ["cs.CY", "cs.AI", "cs.DC", "cs.LG"], "doi": "", "_query": "automated valuation model", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/1205.6542v1", "title": "Collateralized CVA Valuation with Rating Triggers and Credit Migrations", "abstract": "In this paper we discuss the issue of computation of the bilateral credit valuation adjustment (CVA) under rating triggers, and in presence of ratings-linked margin agreements. Specifically, we consider collateralized OTC contracts, that are subject to rating triggers, between two parties -- an investor and a counterparty. Moreover, we model the margin process as a functional of the credit ratings of the counterparty and the investor. We employ a Markovian approach for modeling of the rating transitions of the two parties to the contract. In this framework, we derive the representation for bilateral CVA. We also introduce a new component in the decomposition of the counterparty risky price: namely the rating valuation adjustment (RVA) that accounts for the rating triggers. We give two examples of dynamic collateralization schemes where the margin thresholds are linked to the credit ratings of the parties. We account for the rehypothecation risk in the presence of independent amounts. Our results are illustrated via computation of various counterparty risk adjustments for a CDS contract and for an IRS contract.", "published": "2012-05-30T04:28:33Z", "authors": ["Tomasz R. Bielecki", "Igor Cialenco", "Ismail Iyigunler"], "categories": ["q-fin.PR", "math.PR", "q-fin.RM"], "doi": "", "_query": "automated valuation model", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2605.21033v1", "title": "Efficient Banzhaf-Based Data Valuation for $k$-Nearest Neighbors Classification", "abstract": "Data valuation, the task of quantifying the contribution of individual data points to model performance, has emerged as a fundamental challenge in machine learning. Game-theoretic approaches, such as the Banzhaf value, offer principled frameworks for fair data valuation; however, they suffer from exponential computational complexity. We address this challenge by developing efficient algorithms specifically tailored for computing Banzhaf values in $k$-nearest neighbor ($k$NN) classifiers. We first establish the theoretical hardness of the problem by proving that it is \\#P-hard. Despite this intractability, we exploit the locality properties of $k$NN classifiers to develop practical exact algorithms. Our main contribution is a dynamic programming framework that achieves significant computational improvements: we present a pseudo-polynomial algorithm with $O(Wkn^2)$ time complexity for weighted $k$NN classifiers, where $W$ is the maximum sum of top-$k$ weights, and a specialized algorithm for unweighted $k$NN that achieves $O(nk^2)$ time complexity, that is, linear in the number of data points. We also offer efficient Monte Carlo estimation methods. Extensive experiments on real-world datasets demonstrate the practical efficiency of our approach and its effectiveness in data valuation applications.", "published": "2026-05-20T11:10:15Z", "authors": ["Guangyi Zhang", "Lutz Oettershagen", "Lixu Wang", "Aristides Gionis"], "categories": ["cs.LG", "cs.DS"], "doi": "", "_query": "automated valuation model", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2604.16197v2", "title": "Sketching the Readout of Large Language Models for Scalable Data Attribution and Valuation", "abstract": "Data attribution and valuation are critical for understanding data-model synergy for Large Language Models (LLMs), yet existing gradient-based methods suffer from scalability challenges on LLMs. Inspired by human cognition, where decision making relies on a focused readout of relevant memories rather than replaying all pathways, we introduce RISE (Readout Influence Sketching Estimator). Instead of computing and indexing gradients across the entire LLM, RISE focuses on influence hotspots at the output layer, where influence signals concentrate, and the gradient admits a decomposed outer-product form. This enables a dual-channel representation combining a lexical residual channel (RH) and a semantic projected-error channel (GH). Applying CountSketch projections to these channels achieves strong compression while maintaining accurate attribution. Across the OLMo (1B-32B) and Pythia (14M-6.9B) families, RISE reduces index storage by up to 112$\\times$ compared to RapidIn and scales to 32B parameters LLM, where gradient-based baselines such as RapidIn and ZO-Inf become memory-infeasible. We evaluate RISE on two paradigms: (1) retrospective attribution, retrieving influential training examples for specific predictions, and (2) prospective valuation, scoring candidate data utility zero-shot. We validate RISE on three tasks: Howdy backdoor data detection, Finance-Medical domain separation, and Brain Rot high-quality data selection. In a closed-loop Brain Rot study, continued pretraining on RISE-selected data yields consistent downstream improvements. Overall, RISE provides a practical and scalable primitive for influence analysis and training-data selection in modern large language models.", "published": "2026-04-17T16:07:11Z", "authors": ["Yide Ran", "Jianwen Xie", "Minghui Wang", "Wenjin Zheng", "Denghui Zhang", "Chuan Li", "Zhaozhuo Xu"], "categories": ["cs.LG"], "doi": "", "_query": "automated valuation model", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2412.07143v1", "title": "SL(n) covariant matrix-valued valuations on Orlicz spaces", "abstract": "All continuous, SL(n) covariant valuations on Orlicz spaces are completely classified without any symmetric assumptions. It is shown that the moment matrix is the only such valuation if n\\geq3, while a new functional shows up in dimension two.", "published": "2024-12-10T03:06:21Z", "authors": ["Chunna Zeng", "Yu Lan"], "categories": ["math.DG", "math.FA"], "doi": "", "_query": "automated valuation model", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/math/0510074v2", "title": "Localization of injective modules over valuation rings", "abstract": "It is proved that the localization of an injective module E, over a valuation ring R, at a prime ideal J, is injective if J is not the subset of zero-divisors of R or if J or E is flat. It follows that localizations of injective modules over h-local Prüfer domains are injective too.", "published": "2005-10-04T12:47:32Z", "authors": ["Francois Couchot"], "categories": ["math.RA"], "doi": "", "_query": "automated valuation model", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2312.00958v1", "title": "Valuation method for Nambu-Poisson algebras", "abstract": "Automorphism, isomorphism, and embedding problems are investigated for a family of Nambu-Poisson algebras (or $n$-Lie Poisson algebras) using Poisson valuations.", "published": "2023-12-01T22:25:36Z", "authors": ["Hongdi Huang", "Xin Tang", "Xingting Wang", "James J. Zhang"], "categories": ["math.RA"], "doi": "", "_query": "automated valuation model", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2605.05998v1", "title": "On a generalized Poincaré series of plane valuations", "abstract": "Earlier, there were defined two generalized (``motivic'') versions of the Poincaré series of a collection of plane valuations on the algebra ${\\mathcal O}_{{\\mathbb C}^2,0}$ of germs of holomorphic functions in two variables. One of them was defined as an integral with respect to the generalized Euler characteristic over the projectivization of the extended semigroup of the collection. One has a natural version of it for valuations on the algebra ${\\mathcal E}_{{\\mathbb K}^2,0}$ of germs of holomorphic functions in two variables whose Taylor coefficients are from a fixed subfield ${\\mathbb K}$ of the field ${\\mathbb C}$ of complex numbers. In this setting the usual Poincaré series were computed for one plane curve or divisorial valuation on ${\\mathcal E}_{{\\mathbb K}^2,0}$. We give equations for the corresponding generalized Poincaré series.", "published": "2026-05-07T10:49:25Z", "authors": ["F. Delgado", "S. M. Gusein-Zade"], "categories": ["math.AG"], "doi": "", "_query": "automated valuation model", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2209.08936v1", "title": "Automated Implementation of Windows-related Security-Configuration Guides", "abstract": "Hardening is the process of configuring IT systems to ensure the security of the systems' components and data they process or store. The complexity of contemporary IT infrastructures, however, renders manual security hardening and maintenance a daunting task. In many organizations, security-configuration guides expressed in the SCAP (Security Content Automation Protocol) are used as a basis for hardening, but these guides by themselves provide no means for automatically implementing the required configurations. In this paper, we propose an approach to automatically extract the relevant information from publicly available security-configuration guides for Windows operating systems using natural language processing. In a second step, the extracted information is verified using the information of available settings stored in the Windows Administrative Template files, in which the majority of Windows configuration settings is defined. We show that our implementation of this approach can extract and implement 83% of the rules without any manual effort and 96% with minimal manual effort. Furthermore, we conduct a study with 12 state-of-the-art guides consisting of 2014 rules with automatic checks and show that our tooling can implement at least 97% of them correctly. We have thus significantly reduced the effort of securing systems based on existing security-configuration guides.", "published": "2022-09-19T11:39:19Z", "authors": ["Patrick Stöckle", "Bernd Grobauer", "Alexander Pretschner"], "categories": ["cs.CR", "cs.SE"], "doi": "10.1145/3324884.3416540", "_query": "automated valuation model", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2105.09234v2", "title": "Definability of henselian valuations by conditions on the value group", "abstract": "Given a henselian valuation, we study its definability (with and without parameters) by examining conditions on the value group. We show that any henselian valuation whose value group is not closed in its divisible hull is definable in the language of rings, using one parameter. Thereby we strengthen known definability results. Moreover, we show that in this case, one parameter is optimal in the sense that one cannot obtain definability without parameters. To this end, we present a construction method for a $t$-henselian non-henselian ordered field elementarily equivalent to a henselian field with a specified value group.", "published": "2021-05-19T16:29:46Z", "authors": ["Lothar Sebastian Krapp", "Salma Kuhlmann", "Moritz Link"], "categories": ["math.LO", "math.AC"], "doi": "10.1017/jsl.2022.34", "_query": "automated valuation model", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2304.04258v2", "title": "A Note on \"Efficient Task-Specific Data Valuation for Nearest Neighbor Algorithms\"", "abstract": "Data valuation is a growing research field that studies the influence of individual data points for machine learning (ML) models. Data Shapley, inspired by cooperative game theory and economics, is an effective method for data valuation. However, it is well-known that the Shapley value (SV) can be computationally expensive. Fortunately, Jia et al. (2019) showed that for K-Nearest Neighbors (KNN) models, the computation of Data Shapley is surprisingly simple and efficient. In this note, we revisit the work of Jia et al. (2019) and propose a more natural and interpretable utility function that better reflects the performance of KNN models. We derive the corresponding calculation procedure for the Data Shapley of KNN classifiers/regressors with the new utility functions. Our new approach, dubbed soft-label KNN-SV, achieves the same time complexity as the original method. We further provide an efficient approximation algorithm for soft-label KNN-SV based on locality sensitive hashing (LSH). Our experimental results demonstrate that Soft-label KNN-SV outperforms the original method on most datasets in the task of mislabeled data detection, making it a better baseline for future work on data valuation.", "published": "2023-04-09T15:31:53Z", "authors": ["Jiachen T. Wang", "Ruoxi Jia"], "categories": ["stat.ML", "cs.LG"], "doi": "", "_query": "automated valuation model", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2012.05774v1", "title": "Online Posted Pricing with Unknown Time-Discounted Valuations", "abstract": "We study the problem of designing posted-price mechanisms in order to sell a single unit of a single item within a finite period of time. Motivated by real-world problems, such as, e.g., long-term rental of rooms and apartments, we assume that customers arrive online according to a Poisson process, and their valuations are drawn from an unknown distribution and discounted over time. We evaluate our mechanisms in terms of competitive ratio, measuring the worst-case ratio between their revenue and that of an optimal mechanism that knows the distribution of valuations. First, we focus on the identical valuation setting, where all the customers value the item for the same amount. In this setting, we provide a mechanism M_c that achieves the best possible competitive ratio, discussing its dependency on the parameters in the case of linear discount. Then, we switch to the random valuation setting. We show that, if we restrict the attention to distributions of valuations with a monotone hazard rate, then the competitive ratio of M_c is lower bounded by a strictly positive constant that does not depend on the distribution. Moreover, we provide another mechanism, called M_pc, which is defined by a piecewise constant pricing strategy and reaches performances comparable to those obtained with M_c. This mechanism is useful when the seller cannot change the posted price too often. Finally, we empirically evaluate the performances of our mechanisms in a number of experimental settings.", "published": "2020-12-10T16:01:24Z", "authors": ["Giulia Romano", "Gianluca Tartaglia", "Alberto Marchesi", "Nicola Gatti"], "categories": ["cs.GT"], "doi": "", "_query": "automated valuation model", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2609.35072v1", "title": "Not All Rollouts Are Worth Learning: On Trajectory Valuation for Post-Training Reinforcement Learning", "abstract": "We consider the problem of trajectory valuation in reinforcement learning: how to identify and mitigate detrimental trajectories during online training. Unlike classification, where data valuation relies on fixed training and validation sets, reinforcement learning involves dynamically generated trajectories without explicit validation signals, making conventional influence-based methods inapplicable. We propose Dynamic Trajectory Valuation (DTV), a simple and efficient framework that estimates trajectory utility at the mini-batch level and filters detrimental trajectories based solely on gradient information. By operating at the optimization level, DTV integrates seamlessly with existing reinforcement learning pipelines with minimal overhead. Extensive experiments across diverse settings, including PPO, GRPO, and DPO, demonstrate that DTV consistently improves performance, enhances data efficiency, and stabilizes optimization.", "published": "2026-09-28T12:57:33Z", "authors": ["Xuesong Jia", "Ziao Yang", "Zhanhe Huang", "Hongfu Liu"], "categories": ["cs.LG"], "doi": "", "_query": "automated valuation model", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2112.06187v2", "title": "On the $3$-adic valuation of the Narayana numbers", "abstract": "We define the Narayana sequence $\\{a_n\\}_{n\\geq 0}$ as the one satisfying the linear recurrence relation $a_n = a_{n-1}+a_{n-3}$ for $n\\geq 3$, with initial values $a_0 = 0$ and $a_1 = a_2=1$. In this paper, we fully characterize the $3$-adic valuation of ${a_n}$ and use this to determine all Narayana numbers that are factorials.", "published": "2021-12-12T09:04:52Z", "authors": ["Russelle Guadalupe"], "categories": ["math.NT"], "doi": "", "_query": "automated valuation model", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2608.07949v1", "title": "Guixu: Valuation-Driven Data Discovery for Autonomous AI Agents with On-Chain Attestation", "abstract": "Autonomous agents increasingly rely on external data to complete downstream tasks such as model training and decision support. However, existing data discovery systems remain largely retrieval-oriented: they surface candidate datasets from heterogeneous sources, but provide limited support for estimating task-specific utility, selecting cost-effective datasets under budget constraints, or incorporating trustworthy feedback from prior usage. This paper presents Guixu, a valuation-driven data discovery system for autonomous agents. Guixu employs a three-phase valuation pipeline with proxy-label propagation and multi-round knapsack optimization for task-aware data valuation. Guixu integrates agentic payment protocol to enable budget-constrained data procurement workflows. Guixu leverages on-chain data market and attestation signals for verifiable data discovery. Our demonstration highlights how Guixu enables an agent to move beyond keyword-based dataset retrieval toward task- and budget-aware, trustworthy data discovery and procurement. Attendees can interactively explore the full workflow, from NL task specification and multi-source search to data valuation and verifiable transaction feedback.", "published": "2026-08-08T06:25:16Z", "authors": ["Yifan Wu", "Yuchen Peng", "Jiaqi Chai", "Yufei Qian", "Xilin Li", "Ke Chen", "Lidan Shou"], "categories": ["cs.AI", "cs.CR", "cs.DB", "cs.IR", "cs.MA"], "doi": "", "_query": "automated valuation model", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/1803.10300v1", "title": "Feynman-Vernon model of a moving thermal environment", "abstract": "This paper reviews the formulation of the Feynman-Vernon model of linear dissipative systems for a standard Brownian particle moving in an external potential $V(x,t)$ and introduces the formulation of a generalized oscillator model of a Brownian particle coupled to a thermal environment moving with a given velocity $v_{env}$. Diffusion processes in a moving environment are of interest e.g. in the study of the motion of vortices in superfluids. The starting point of the paper is the formulation of the oscillator model that takes into account space and time invariance of a thermal environment [M. Patriarca, Statistical correlations in the oscillator model of quantum Brownian motion, Il Nuovo Cimento B, 111(1), 61-72 (1996), doi: 10.1007/BF02726201, arXiv:1801.02429], which has the property of being finite and consistent with the classical limit. The Langevin equation and the influence functional for a Brownian particle in a moving environment are derived.", "published": "2018-03-27T20:12:54Z", "authors": ["Marco Patriarca"], "categories": ["quant-ph", "cond-mat.stat-mech"], "doi": "10.1016/j.physe.2005.05.021", "_query": "automated valuation model", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2307.10201v2", "title": "On the Mechanics of NFT Valuation: AI Ethics and Social Media", "abstract": "As CryptoPunks pioneers the innovation of non-fungible tokens (NFTs) in AI and art, the valuation mechanics of NFTs has become a trending topic. Earlier research identifies the impact of ethics and society on the price prediction of CryptoPunks. Since the booming year of the NFT market in 2021, the discussion of CryptoPunks has propagated on social media. Still, existing literature hasn't considered the social sentiment factors after the historical turning point on NFT valuation. In this paper, we study how sentiments in social media, together with gender and skin tone, contribute to NFT valuations by an empirical analysis of social media, blockchain, and crypto exchange data. We evidence social sentiments as a significant contributor to the price prediction of CryptoPunks. Furthermore, we document structure changes in the valuation mechanics before and after 2021. Although people's attitudes towards Cryptopunks are primarily positive, our findings reflect imbalances in transaction activities and pricing based on gender and skin tone. Our result is consistent and robust, controlling for the rarity of an NFT based on the set of human-readable attributes, including gender and skin tone. Our research contributes to the interdisciplinary study at the intersection of AI, Ethics, and Society, focusing on the ecosystem of decentralized AI or blockchain. We provide our data and code for replicability as open access on GitHub.", "published": "2023-07-13T03:12:00Z", "authors": ["Luyao Zhang", "Yutong Sun", "Yutong Quan", "Jiaxun Cao", "Xin Tong"], "categories": ["cs.CY", "cs.DC"], "doi": "10.31219/osf.io/qwpdx", "_query": "automated valuation model", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2502.05776v4", "title": "Dynamic Pricing in the Linear Valuation Model using Shape Constraints", "abstract": "We propose a shape-constrained approach to dynamic pricing for censored data in the linear valuation model eliminating the need for tuning parameters commonly required by existing methods. Previous works have addressed the challenge of unknown market noise distribution $F_0$ using strategies ranging from kernel methods to reinforcement learning algorithms, such as bandit techniques and upper confidence bounds (UCB), under the assumption that $F_0$ satisfies Lipschitz (or stronger) conditions. In contrast, our method relies on isotonic regression under the weaker assumption that $F_0$ is $α$-Hölder continuous for some $α\\in (0,1]$, for which we derive a regret upper bound. Simulations and experiments with real-world data obtained by Welltower Inc (a major healthcare Real Estate Investment Trust) consistently demonstrate that our method attains lower empirical regret in comparison to several existing methods in the literature while offering the advantage of being tuning-parameter free.", "published": "2025-02-09T04:58:33Z", "authors": ["Daniele Bracale", "Moulinath Banerjee", "Yuekai Sun", "Kevin Stoll", "Salam Turki"], "categories": ["stat.ML", "cs.LG"], "doi": "", "_query": "automated valuation model", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2509.25899v2", "title": "Fast catastrophe bond valuation with neural-network surrogates", "abstract": "Catastrophe bonds are increasingly important risk-transfer securities, but structural pricing is too slow for real-time valuation, screening, and sensitivity analysis. We develop a neural-network surrogate for the pricing operator of a compound-Poisson catastrophe bond model. Training labels are generated by Monte Carlo simulation with importance sampling, so the network learns variance-reduced structural prices rather than sparse market quotes which are difficult to extrapolate reliably. Across Gamma and Lognormal severity specifications, the selected networks attain very small absolute approximation error on the stated training domain. After training, 1000 contracts are priced in about 0.03--0.04 seconds, compared with tens to hundreds of seconds for Monte Carlo with importance sampling and many hours for a partial integro-differential equation benchmark. The result is a fast in-domain structural valuation engine that also produces economically interpretable sensitivities to catastrophe intensity, attachment threshold, and interest rates.", "published": "2025-09-30T07:44:25Z", "authors": ["Julian Sester", "Huansang Xu"], "categories": ["q-fin.PR", "q-fin.RM"], "doi": "", "_query": "automated valuation model", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2107.02173v3", "title": "Is Automated Topic Model Evaluation Broken?: The Incoherence of Coherence", "abstract": "Topic model evaluation, like evaluation of other unsupervised methods, can be contentious. However, the field has coalesced around automated estimates of topic coherence, which rely on the frequency of word co-occurrences in a reference corpus. Contemporary neural topic models surpass classical ones according to these metrics. At the same time, topic model evaluation suffers from a validation gap: automated coherence, developed for classical models, has not been validated using human experimentation for neural models. In addition, a meta-analysis of topic modeling literature reveals a substantial standardization gap in automated topic modeling benchmarks. To address the validation gap, we compare automated coherence with the two most widely accepted human judgment tasks: topic rating and word intrusion. To address the standardization gap, we systematically evaluate a dominant classical model and two state-of-the-art neural models on two commonly used datasets. Automated evaluations declare a winning model when corresponding human evaluations do not, calling into question the validity of fully automatic evaluations independent of human judgments.", "published": "2021-07-05T17:58:52Z", "authors": ["Alexander Hoyle", "Pranav Goel", "Denis Peskov", "Andrew Hian-Cheong", "Jordan Boyd-Graber", "Philip Resnik"], "categories": ["cs.CL", "cs.LG"], "doi": "", "_query": "automated valuation model", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/0902.0422v2", "title": "Equivalence of Valued Fields with Valuation Preserving Automorphism", "abstract": "We improve results of Belair, Macintyre, and Scanlon on valued fields with a valuation preserving automorphism by weakening their assumptions on the residue difference field. In the equicharacteristic zero case we also determine the induced structure on the residue difference field and the value group.", "published": "2009-02-03T02:34:33Z", "authors": ["Salih Azgin", "Lou van den Dries"], "categories": ["math.LO"], "doi": "", "_query": "automated valuation model", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/math/0409549v2", "title": "Local rings of bounded module type are almost maximal valuation rings", "abstract": "It is shown that every commutative local ring of bounded module type is an almost maximal valuation ring.", "published": "2004-09-28T12:15:10Z", "authors": ["Francois Couchot"], "categories": ["math.RA"], "doi": "", "_query": "automated valuation model", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/0708.0019v1", "title": "Semigroups of valuations on local rings", "abstract": "In this paper the question of which semigroups are realizable as the semigroup of values attained on a Noetherian local ring which is dominated by a valuation is considered. We give some striking examples, indicating that there may be no constraints on the semigroup beyond those known classically.", "published": "2007-07-31T20:21:26Z", "authors": ["Steven Dale Cutkosky", "Bernard Teissier"], "categories": ["math.AG", "math.AC"], "doi": "", "_query": "automated valuation model", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2609.09335v2", "title": "Monotone invariant valuations on convex cones", "abstract": "We classify the monotone $\\SOn d$-invariant valuations, $d\\ge2$, on the space of all closed convex cones. They are precisely the linear combinations of the conic intrinsic volumes with nondecreasing coefficients, or equivalently, the nonnegative linear combinations of intrinsic volume tails, up to an additive constant. On nonzero pointed cones we obtain the corresponding characterisation by Grassmann angles, settling a conjecture of McMullen. Every such valuation is automatically continuous in the spherical Hausdorff topology and is $\\On d$-invariant. Our proof builds on the signed simplex and averaging arguments of Wang and Wu, but does not rely on the continuous classification theorem.", "published": "2026-09-08T18:21:12Z", "authors": ["Martin Lotz"], "categories": ["math.MG"], "doi": "", "_query": "automated valuation model", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/1905.11324v1", "title": "Civic Crowdfunding for Agents with Negative Valuations and Agents with Asymmetric Beliefs", "abstract": "In the last decade, civic crowdfunding has proved to be effective in generating funds for the provision of public projects. However, the existing literature deals only with citizen's with positive valuation and symmetric belief towards the project's provision. In this work, we present novel mechanisms which break these two barriers, i.e., mechanisms which incorporate negative valuation and asymmetric belief, independently. For negative valuation, we present a methodology for converting existing mechanisms to mechanisms that incorporate agents with negative valuations. Particularly, we adapt existing PPR and PPS mechanisms, to present novel PPRN and PPSN mechanisms which incentivize strategic agents to contribute to the project based on their true preference. With respect to asymmetric belief, we propose a reward scheme Belief Based Reward (BBR) based on Robust Bayesian Truth Serum mechanism. With BBR, we propose a general mechanism for civic crowdfunding which incorporates asymmetric agents. We leverage PPR and PPS, to present PPRx and PPSx. We prove that in PPRx and PPSx, agents with greater belief towards the project's provision contribute more than agents with lesser belief. Further, we also show that contributions are such that the project is provisioned at equilibrium.", "published": "2019-05-27T16:20:31Z", "authors": ["Sankarshan Damle", "Moin Hussain Moti", "Praphul Chandra", "Sujit Gujar"], "categories": ["cs.GT"], "doi": "10.5555/3367032.3367063", "_query": "automated valuation model", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/1903.11068v2", "title": "Full-rank Valuations and Toric Initial Ideals", "abstract": "Let $V(I)$ be a polarized projective variety or a subvariety of a product of projective spaces and let $A$ be its (multi-)homogeneous coordinate ring. Given a full-rank valuation $\\mathfrak v$ on $A$ we associate weights to the coordinates of the projective space, respectively, the product of projective spaces. Let $w_{\\mathfrak v}$ be the vector whose entries are these weights. Our main result is that the value semi-group of $\\mathfrak v$ is generated by the images of the generators of $A$ if and only if the initial ideal of $I$ with respect to $w_{\\mathfrak v}$ is prime. We further show that $w_{\\mathfrak v}$ always lies in the tropicalization of $I$. Applying our result to string valuations for flag varieties, we solve a conjecture by \\cite{BLMM} connecting the Minkowski property of string cones with the tropical flag variety. For Rietsch-Williams' valuation for Grassmannians our results give a criterion for when the Plücker coordinates form a Khovanskii basis. Further, as a corollary we obtain that the weight vectors defined in \\cite{BFFHL} lie in the tropical Grassmannian.", "published": "2019-03-26T17:41:24Z", "authors": ["Lara Bossinger"], "categories": ["math.AG", "math.AC", "math.RT"], "doi": "10.1093/imrn/rnaa071", "_query": "automated valuation model", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/1801.08346v1", "title": "Valuation of Currency Options in Markets with a Crunch", "abstract": "This work studies the valuation of currency options in markets suffering from a financial crisis. We consider a European option where the underlying asset is a foreign currency. We assume that the value of the underlying asset is a stochastic process that follows a modified Black-Scholes model with an augmented stochastic volatility. Under these settings, we provide a closed form solution for the option-pricing problem on foreign currency for the European call and put options. A mathematical proof is provided for the underlying solution. In addition, simulation results and an application are provided.", "published": "2018-01-25T10:54:00Z", "authors": ["Abdulnasser Hatemi-J", "Youssef El-Khatib"], "categories": ["q-fin.PR"], "doi": "", "_query": "automated valuation model", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2604.00472v3", "title": "Valuation of variable annuities under the Volterra mortality and rough Heston models", "abstract": "This paper investigates the valuation of variable annuity contracts with an early surrender option under non-Markovian models. Moreover, policyholders are provided with guaranteed minimum maturity and death benefits to protect against the downside risk. Unlike the existing literature, our variable annuity account value is linked to two non-Markovian processes: an equity index modeled by a rough Heston model and a force of mortality following a Volterra-type stochastic model. In this case, the early surrender feature introduces an optimal stopping problem where continuation values depend on the entire path history, rendering traditional numerical methods infeasible. We develop a deep signature Least Squares Monte Carlo approach to learn optimal surrender strategies on a discretized time grid. To mitigate the curse of dimensionality arising from the path-dependent model, we use truncated rough-path signatures to encode the historical paths and approximate the continuation values using a neural network. Numerically, we find that the fair fee increases with the Hurst parameters of both the stock volatility and the force of mortality. Finally, a convergence proof is provided to further support the stability of our method.", "published": "2026-04-01T04:34:38Z", "authors": ["Wenyuan Li", "Haoqi Lyu"], "categories": ["q-fin.CP"], "doi": "", "_query": "automated valuation model", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2408.06835v1", "title": "SL(n) covariant matrix-valued valuations on Lp-spaces", "abstract": "A complete classification is established for continuous and SL(n) covariant matrix-valued valuations on Lp(Rn,|x|2dx). The assumption of matrix symmetry is eliminated. For n>2, such valuation is uniquely characterized by the moment matrix of measurable function. In the 2-dimensional case, while the rotation matrix shows up.", "published": "2024-08-13T11:48:34Z", "authors": ["Chunna Zeng", "Yu Lan"], "categories": ["math.DG"], "doi": "", "_query": "automated valuation model", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2306.04967v6", "title": "Kähler differentials of extensions of valuation rings and deeply ramified fields", "abstract": "Assume that $(L,v)$ is a finite Galois extension of a valued field $(K,v)$. We give an explicit construction of the valuation ring $\\mathcal O_L$ of $L$ as an $\\mathcal O_K$-algebra, and an explicit description of the module of relative Kähler differentials $Ω_{\\mathcal O_L|\\mathcal O_K}$ when $L|K$ is a Kummer extension of prime degree or an Artin-Schreier extension, in terms of invariants of the valuation and field extension. The case when this extension has nontrivial defect was solved in a recent paper by the authors with Anna Rzepka. The present paper deals with the complementary (defectless) case. The results are known classically for (rank 1) discrete valuations, but our systematic approach to non-discrete valuations (even of rank 1) is new. Using our results from the prime degree case, we characterize when $Ω_{\\mathcal O_L|\\mathcal O_K}=0$ holds for an arbitrary finite Galois extension of valued fields. As an application of these results, we give a simple proof of a theorem of Gabber and Ramero, which characterizes when a valued field is deeply ramified. We further give a simple characterization of deeply ramified fields with residue fields of characteristic $p>0$ in terms of the Kähler differentials of Galois extensions of degree $p$.", "published": "2023-06-08T06:51:15Z", "authors": ["Steven Dale Cutkosky", "Franz-Viktor Kuhlmann"], "categories": ["math.AC"], "doi": "", "_query": "automated valuation model", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/math/0202298v1", "title": "Analytical ramifications of derivatives valuation: Asian options and special functions", "abstract": "Averaging problems are ubiquitous in Finance with the valuation of the so-called Asian options on arithmetic averages as their most conspicuous form. There is an abundance of numerical work on them, and their stochastic structure has been extensively studied by Yor and his school. However, the analytical structure of these problems is largely unstudied. Our philosophy now is that such valuation problems should be considered as an extension of the theory of special functions: they lead to new problems about new classes of special functions which should be studied in terms of and using of the methods of special functions and their theory. This is exemplified by deriving integral representations for the Black-Scholes prices based on Yor's Laplace transform ansatz to their valuation. They are obtained by analytic Laplace inversion using complex analytic methods. The analysis ultimately rests on the gamma function which in this sense is found to be at the base of Asian options. The results improve on those of Yor and have served us a as starting point for deriving first time benchmark prices for these options.", "published": "2002-02-28T11:53:51Z", "authors": ["Michael Schröder"], "categories": ["math.CA", "math.PR", "q-fin.PR"], "doi": "", "_query": "automated valuation model", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2102.13343v3", "title": "Are Gross Substitutes a Substitute for Submodular Valuations?", "abstract": "The class of gross substitutes (GS) set functions plays a central role in Economics and Computer Science. GS belongs to the hierarchy of {\\em complement free} valuations introduced by Lehmann, Lehmann and Nisan, along with other prominent classes: $GS \\subsetneq Submodular \\subsetneq XOS \\subsetneq Subadditive$. The GS class has always been more enigmatic than its counterpart classes, both in its definition and in its relation to the other classes. For example, while it is well understood how closely the Submodular, XOS and Subadditive classes (point-wise) approximate one another, approximability of these classes by GS remained wide open. Our main result is the existence of a submodular valuation (one that is also budget additive) that cannot be approximated by GS within a ratio better than $Ω(\\frac{\\log m}{\\log\\log m})$, where $m$ is the number of items. En route, we uncover a new symmetrization operation that preserves GS, which may be of independent interest. We show that our main result is tight with respect to budget additive valuations. Additionally, for a class of submodular functions that we refer to as {\\em concave of Rado} valuations (this class greatly extends budget additive valuations), we show approximability by GS within an $O(\\log^2m)$ factor.", "published": "2021-02-26T07:46:30Z", "authors": ["Shahar Dobzinski", "Uriel Feige", "Michal Feldman", "Renato Paes Leme"], "categories": ["cs.GT"], "doi": "", "_query": "automated valuation model", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2005.00504v1", "title": "Uniform Welfare Guarantees Under Identical Subadditive Valuations", "abstract": "We study the problem of allocating indivisible goods among agents that have an identical subadditive valuation over the goods. The extent of fairness and efficiency of allocations is measured by the generalized means of the values that the allocations generate among the agents. Parameterized by an exponent term $p$, generalized-mean welfares encompass multiple well-studied objectives, such as social welfare, Nash social welfare, and egalitarian welfare. We establish that, under identical subadditive valuations and in the demand oracle model, one can efficiently find a single allocation that approximates the optimal generalized-mean welfare---to within a factor of $40$---uniformly for all $p \\in (-\\infty, 1]$. Hence, by way of a constant-factor approximation algorithm, we obtain novel results for maximizing Nash social welfare and egalitarian welfare for identical subadditive valuations.", "published": "2020-05-01T17:17:40Z", "authors": ["Siddharth Barman", "Ranjani G. Sundaram"], "categories": ["cs.GT"], "doi": "", "_query": "automated valuation model", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2209.11075v1", "title": "Cyclotomic valuation of $q$-Pochhammer symbols and $q$-integrality of basic hypergeometric series", "abstract": "We give a formula for the cyclotomic valuation of $q$-Pochhammer symbols in terms of (generalized) Dwork maps. We also obtain a criterion for the $q$-integrality of basic hypergeometric series in terms of certain step functions, which generalize Christol step functions. This provides suitable $q$-analogs of two results proved by Christol: a formula for the $p$-adic valuation of Pochhammer symbols and a criterion for the $N$-integrality of hypergeometric series.", "published": "2022-09-22T15:04:42Z", "authors": ["B. Adamczewski", "J. P. Bell", "É. Delaygue", "F. Jouhet"], "categories": ["math.NT", "math.CO"], "doi": "", "_query": "automated valuation model", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/1511.08164v4", "title": "Minimizing normalized volumes of valuations", "abstract": "For any $\\mathbb{Q}$-Gorenstein klt singularity $(X,o)$, we introduce a normalized volume function $\\widehat{\\rm vol}$ that is defined on the space of real valuations centered at $o$ and consider the problem of minimizing $\\widehat{\\rm vol}$. We prove that the normalized volume has a uniform positive lower bound by proving an Izumi type estimate for any $\\mathbb{Q}$-Gorenstein klt singularity. Furthermore, by proving a properness estimate, we show that the set of real valuations with uniformly bounded normalized volumes is compact, and hence reduce the existence of minimizers for the normalized volume functional $\\widehat{\\rm vol}$ to a conjectural lower semicontinuity property. We calculate candidate minimizers in several examples to show that this is an interesting and nontrivial problem. In particular, by using an inequality of de-Fernex-Ein-Mustaţă, we show that the divisorial valuation associated to the exceptional divisor of the standard blow up is a minimizer of $\\widehat{\\rm vol}$ for a smooth point. Finally the relation to Fujita's work on divisorial stability is also pointed out.", "published": "2015-11-25T19:10:30Z", "authors": ["Chi Li"], "categories": ["math.AG", "math.AC", "math.DG"], "doi": "", "_query": "automated valuation model", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/1505.07440v4", "title": "Combinatorial positivity of translation-invariant valuations and a discrete Hadwiger theorem", "abstract": "We introduce the notion of combinatorial positivity of translation-invariant valuations on convex polytopes that extends the nonnegativity of Ehrhart h*-vectors. We give a surprisingly simple characterization of combinatorially positive valuations that implies Stanley's nonnegativity and monotonicity of h*-vectors and generalizes work of Beck et al. (2010) from solid-angle polynomials to all translation-invariant simple valuations. For general polytopes, this yields a new characterization of the volume as the unique combinatorially positive valuation up to scaling. For lattice polytopes our results extend work of Betke--Kneser (1985) and give a discrete Hadwiger theorem: There is essentially a unique combinatorially-positive basis for the space of lattice-invariant valuations. As byproducts of our investigations, we prove a multivariate Ehrhart-Macdonald reciprocity and we show universality of weight valuations studied in Beck et al. (2010).", "published": "2015-05-27T19:27:48Z", "authors": ["Katharina Jochemko", "Raman Sanyal"], "categories": ["math.CO", "math.MG"], "doi": "10.4171/JEMS/809", "_query": "automated valuation model", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/1501.06526v1", "title": "$\\mathrm{Spin}(9)$-invariant valuations on the octonionic plane", "abstract": "The dimensions of the spaces of $k$-homogeneous $\\mathrm{Spin}(9)$-invariant valuations on the octonionic plane are computed using results from the theory of differential forms on contact manifolds as well as octonionic geometry and representation theory. Moreover, a valuation on Riemannian manifolds of particular interest is constructed which yields, as a special case, an element of $\\mathrm{Val}_2^{\\mathrm{Spin}(9)}$.", "published": "2015-01-26T19:05:55Z", "authors": ["Andreas Bernig", "Floriane Voide"], "categories": ["math.DG"], "doi": "", "_query": "automated valuation model", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2305.00178v3", "title": "Algebraic valuation ring extensions as limits of complete intersection algebras", "abstract": "We show that an algebraic immediate valuation ring extension of characteristic $p>0$ is a filtered union of complete intersection algebras of finite type.", "published": "2023-04-29T05:42:44Z", "authors": ["Dorin Popescu"], "categories": ["math.AC", "math.AG"], "doi": "", "_query": "automated valuation model", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2211.10399v1", "title": "Valuations and Nonzero Torsion in Module of Differentials", "abstract": "Let $(R,\\mathfrak{m}_R,k)$ be a one-dimensional complete local reduced $k$-algebra over a field of characteristic zero. R. Berger conjectured that $R$ is regular if and only if the universally finite module of differentials $Ω_R$ is torsion free. When $R$ is a domain, we prove the conjecture in several cases. Our techniques are primarily reliant on making use of the valuation semi-group of $R$. First, we establish a method of verifying the conjecture by analyzing the valuation semi-group of $R$ and orders of units of the integral closure of $R$. We also prove the conjecture in the case when certain monomials are missing from the monomial support of the defining ideal of $R$. These monomials are based on the smallest power of $\\mathfrak{m}_R$ that is contained within the conductor ideal. This also generalizes a previous result of Cortiñas, Geller and Weibel.", "published": "2022-11-18T17:58:55Z", "authors": ["Sarasij Maitra", "Vivek Mukundan"], "categories": ["math.AC", "math.AG"], "doi": "", "_query": "automated valuation model", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/1708.08085v2", "title": "Valuations, arithmetic progressions, and prime numbers", "abstract": "In this short note, we give two proofs of the infinitude of primes via valuation theory and give a new proof of the divergence of the sum of prime reciprocals by Roth's theorem and Euler-Legendre's theorem for arithmetic progressions.", "published": "2017-08-27T13:37:32Z", "authors": ["Shin-ichiro Seki"], "categories": ["math.NT"], "doi": "", "_query": "automated valuation model", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/1708.08085v2", "title": "Valuations, arithmetic progressions, and prime numbers", "abstract": "In this short note, we give two proofs of the infinitude of primes via valuation theory and give a new proof of the divergence of the sum of prime reciprocals by Roth's theorem and Euler-Legendre's theorem for arithmetic progressions.", "published": "2017-08-27T13:37:32Z", "authors": ["Shin-ichiro Seki"], "categories": ["math.NT"], "doi": "", "_query": "automated valuation model", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2403.01831v3", "title": "Cohomological flatness over discrete valuation rings: numerical and logarithmic criteria", "abstract": "We give sufficient conditions for cohomological flatness (in dimension 0) over discrete valuation rings, generalizing classical results of Raynaud in two different ways. The first is a higher dimensional generalization of Raynaud's numerical criteria, in both the variant for the multiplicity of the special fibre and that for the index of the generic fibre. The second is a logarithmic criterion: we show that, over a log regular base, a proper flat fs log smooth morphism is cohomologically flat in dimension 0. We apply this latter result to curves and torsors under abelian varieties with good reduction, providing necessary and sufficient conditions for the log smoothness of their regular models over arbitrary discrete valuation rings.", "published": "2024-03-04T08:27:37Z", "authors": ["Ofer Gabber", "Rémi Lodh"], "categories": ["math.AG", "math.NT"], "doi": "", "_query": "automated valuation model", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2009.14793v1", "title": "Approximating Nash Social Welfare under Rado Valuations", "abstract": "We consider the problem of approximating maximum Nash social welfare (NSW) while allocating a set of indivisible items to $n$ agents. The NSW is a popular objective that provides a balanced tradeoff between the often conflicting requirements of fairness and efficiency, defined as the weighted geometric mean of agents' valuations. For the symmetric additive case of the problem, where agents have the same weight with additive valuations, the first constant-factor approximation algorithm was obtained in 2015. This led to a flurry of work obtaining constant-factor approximation algorithms for the symmetric case under mild generalizations of additive, and $O(n)$-approximation algorithms for more general valuations and for the asymmetric case. In this paper, we make significant progress towards both symmetric and asymmetric NSW problems. We present the first constant-factor approximation algorithm for the symmetric case under Rado valuations. Rado valuations form a general class of valuation functions that arise from maximum cost independent matching problems, including as special cases assignment (OXS) valuations and weighted matroid rank functions. Furthermore, our approach also gives the first constant-factor approximation algorithm for the asymmetric case under Rado valuations, provided that the maximum ratio between the weights is bounded by a constant.", "published": "2020-09-30T17:07:51Z", "authors": ["Jugal Garg", "Edin Husic", "Laszlo A. Vegh"], "categories": ["cs.GT", "cs.DM", "cs.DS", "cs.MA", "math.OC"], "doi": "", "_query": "automated valuation model", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/0803.3730v2", "title": "Effective models and extension of torsors over a discrete valuation ring of unequal characteristic", "abstract": "Let R be a discrete valuation ring of unequal characteristic with fraction field K which contains a primitive p^2-th root of unity. Let X be a faithfully flat R-scheme and G be a finite abstract group. Let us consider a G-torsor Y_K\\to X_K and let Y be the normalization of X_K in Y. If G=Z/p^n Z, n<3, under some hypothesis on X, we attach some invariants to Y_K \\to X_K. If p>2, we determine, through these invariants, when Y\\to X has a structure of torsor which extends that of Y_K\\to X_K. Moreover we explicitly calculate the effective model (defined by Romagny) of the action of G on Y.", "published": "2008-03-26T14:13:23Z", "authors": ["Dajano Tossici"], "categories": ["math.AG", "math.NT"], "doi": "", "_query": "automated valuation model", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/1701.06234v4", "title": "A Dual Method For Backward Stochastic Differential Equations with Application to Risk Valuation", "abstract": "We propose a numerical recipe for risk evaluation defined by a backward stochastic differential equation. Using dual representation of the risk measure, we convert the risk valuation to a stochastic control problem where the control is a certain Radon-Nikodym derivative process. By exploring the maximum principle, we show that a piecewise-constant dual control provides a good approximation on a short interval. A dynamic programming algorithm extends the approximation to a finite time horizon. Finally, we illustrate the application of the procedure to financial risk management in conjunction with nested simulation and on an multidimensional portfolio valuation problem.", "published": "2017-01-22T23:11:11Z", "authors": ["Andrzej Ruszczynski", "Jianing Yao"], "categories": ["math.OC", "q-fin.MF"], "doi": "10.1051/cocv/2020018", "_query": "automated valuation model", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2201.11341v2", "title": "Towards Agnostic Feature-based Dynamic Pricing: Linear Policies vs Linear Valuation with Unknown Noise", "abstract": "In feature-based dynamic pricing, a seller sets appropriate prices for a sequence of products (described by feature vectors) on the fly by learning from the binary outcomes of previous sales sessions (\"Sold\" if valuation $\\geq$ price, and \"Not Sold\" otherwise). Existing works either assume noiseless linear valuation or precisely-known noise distribution, which limits the applicability of those algorithms in practice when these assumptions are hard to verify. In this work, we study two more agnostic models: (a) a \"linear policy\" problem where we aim at competing with the best linear pricing policy while making no assumptions on the data, and (b) a \"linear noisy valuation\" problem where the random valuation is linear plus an unknown and assumption-free noise. For the former model, we show a $\\tildeΘ(d^{\\frac13}T^{\\frac23})$ minimax regret up to logarithmic factors. For the latter model, we present an algorithm that achieves an $\\tilde{O}(T^{\\frac34})$ regret, and improve the best-known lower bound from $Ω(T^{\\frac35})$ to $\\tildeΩ(T^{\\frac23})$. These results demonstrate that no-regret learning is possible for feature-based dynamic pricing under weak assumptions, but also reveal a disappointing fact that the seemingly richer pricing feedback is not significantly more useful than the bandit-feedback in regret reduction.", "published": "2022-01-27T06:40:03Z", "authors": ["Jianyu Xu", "Yu-Xiang Wang"], "categories": ["cs.LG", "econ.EM", "stat.ML"], "doi": "", "_query": "automated valuation model", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2002.01865v2", "title": "Completing Simple Valuations in K-categories", "abstract": "We prove that Keimel and Lawson's K-completion Kc of the simple valuation monad Vs defines a monad Kc o Vs on each K-category A. We also characterize the Eilenberg-Moore algebras of Kc o Vs as the weakly locally convex K-cones, and its algebra morphisms as the continuous linear maps. In addition, we explicitly describe the distributive law of Vs over Kc, which allows us to show that the K-completion of any locally convex (respectively, weakly locally convex, locally linear) topological cone is a locally convex (respectively, weakly locally convex, locally linear) K-cone. We also give an example - the Cantor tree with a top - that shows the dcpo-completion of the simple valuations is not the D-completion of the simple valuations in general, where D is the category of monotone convergence spaces and continuous maps.", "published": "2020-02-05T16:56:49Z", "authors": ["Xiaodong Jia", "Michael Mislove"], "categories": ["cs.LO"], "doi": "", "_query": "automated valuation model", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2102.11725v1", "title": "What are discrete valuation rings? What are Dedekind domains?", "abstract": "This is an extended introduction to discrete valuation rings and Dedekind domains. Some natural generalizations of Dedekind domains are also (briefly) discussed including \"almost Dedekind domains\", Prüfer domains, Krull domains, and singular integral domains of dimension 1.", "published": "2021-02-20T21:48:26Z", "authors": ["Wayne Aitken"], "categories": ["math.HO", "math.AC", "math.NT"], "doi": "", "_query": "automated valuation model", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2302.11431v1", "title": "A Note on \"Towards Efficient Data Valuation Based on the Shapley Value''", "abstract": "The Shapley value (SV) has emerged as a promising method for data valuation. However, computing or estimating the SV is often computationally expensive. To overcome this challenge, Jia et al. (2019) propose an advanced SV estimation algorithm called ``Group Testing-based SV estimator'' which achieves favorable asymptotic sample complexity. In this technical note, we present several improvements in the analysis and design choices of this SV estimator. Moreover, we point out that the Group Testing-based SV estimator does not fully reuse the collected samples. Our analysis and insights contribute to a better understanding of the challenges in developing efficient SV estimation algorithms for data valuation.", "published": "2023-02-22T15:13:45Z", "authors": ["Jiachen T. Wang", "Ruoxi Jia"], "categories": ["stat.ML", "cs.LG"], "doi": "", "_query": "automated valuation model", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/1405.4079v3", "title": "Valuation and Hedging of Contracts with Funding Costs and Collateralization", "abstract": "The research presented in this work is motivated by recent papers by Brigo et al. (2011), Burgard and Kjaer (2009), Crépey (2012), Fujii and Takahashi (2010), Piterbarg (2010) and Pallavicini et al. (2012). Our goal is to provide a sound theoretical underpinning for some results presented in these papers by developing a unified framework for the non-linear approach to hedging and pricing of OTC financial contracts. We introduce a systematic approach to valuation and hedging in nonlinear markets, that is, in markets where cash flows of the financial contracts may depend on the hedging strategies. Our systematic approach allows to identify primary sources of and quantify various adjustment to valuation and hedging, primarily the funding and liquidity adjustment and credit risk adjustment. We propose a way to define no-arbitrage in such nonlinear markets, and we provide conditions that imply absence of arbitrage in some specific market trading models. Accordingly, we formulate a concept of no-arbitrage price, and we provide relevant (non-linear) BSDE that produces the no-arbitrage price in case when the contract's cash flows can be replicated.", "published": "2014-05-16T07:31:57Z", "authors": ["Tomasz R. Bielecki", "Marek Rutkowski"], "categories": ["q-fin.MF"], "doi": "", "_query": "automated valuation model", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/1101.3132v1", "title": "Reactive Valuations", "abstract": "In sequential logic there is an order in which the atomic propositions in an expression are evaluated. This order allows the same atomic proposition to have different values depending on which atomic propositions have already been evaluated. In the sequential propositional logic discussed in this thesis, such valuations are called \"reactive\" valuations, in contrast to \"static\" valuations as are common in e.g. ordinary propositional logic. There are many classes of these reactive valuations e.g., we can define a class of reactive valuations such that the value for each atomic proposition remains the same until another atomic proposition is evaluated. This Master of Logic thesis consists of a study of some of the properties of this logic. We take a closer look at some of the classes of reactive valuations. We particularly focus on the relation between the axiomatization and the semantics. Consequently, the main part of this thesis focuses on proving soundness and completeness. Furthermore, we show that the axioms in the provided axiomatizations are independent i.e., there are no redundant axioms present. Finally, we show ω-completeness for two classes of reactive valuations.", "published": "2011-01-17T07:21:04Z", "authors": ["Chris Regenboog"], "categories": ["cs.LO"], "doi": "", "_query": "automated valuation model", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2608.28397v1", "title": "Market-Informed Valuation of GMMB Riders with Surrender Options under a Heston Stochastic-Local Volatility Model", "abstract": "We develop a market-informed valuation framework for guaranteed minimum maturity benefit (GMMB) riders with rational surrender under the Heston stochastic-local volatility (SLV) model. The guarantee is written on the fee-deducted account value and is considered both in its terminal-only form and in the presence of early surrender rights. The Heston SLV specification combines stochastic volatility with a leverage function calibrated to a prescribed local-volatility surface. The leverage surface is obtained through a forward Markovian-projection equation so that, at the model level, the SLV dynamics are constrained to the same one-dimensional marginals as the corresponding local-volatility (LV) model. The latter is used only as a one-factor benchmark, allowing us to isolate the effect of stochastic volatility on continuation values and surrender decisions while preserving the same option-calibrated local-volatility target. We derive the associated backward pricing equations and propose a hybrid tree/finite-difference algorithm for the SLV model with a calibrated leverage function. Synthetic experiments and a market-informed case study show that SLV and LV valuations are numerically close for terminal-only guarantees, as expected from the common marginal target, whereas materially larger differences can arise once surrender is allowed. These differences are reflected in guarantee values, fair insurance fees and volatility-dependent surrender regions. The results indicate that matching one-date marginals implied by vanilla-option prices does not eliminate model risk for insurance liabilities whose value depends on conditional continuation dynamics and endogenous surrender decisions.", "published": "2026-08-28T14:50:09Z", "authors": ["Ludovic Goudenege", "Andrea Molent", "Xiao Wei", "Antonino Zanette"], "categories": ["q-fin.CP"], "doi": "", "_query": "automated valuation model", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2302.11890v1", "title": "Characterizations of Sequential Valuation Rules", "abstract": "Approval-based committee (ABC) voting rules elect a fixed size subset of the candidates, a so-called committee, based on the voters' approval ballots over the candidates. While these rules have recently attracted significant attention, axiomatic characterizations are largely missing so far. We address this problem by characterizing ABC voting rules within the broad and intuitive class of sequential valuation rules. These rules compute the winning committees by sequentially adding candidates that increase the score of the chosen committee the most. In more detail, we first characterize almost the full class of sequential valuation rules based on mild standard conditions and a new axiom called consistent committee monotonicity. This axiom postulates that the winning committees of size k can be derived from those of size k-1 by only adding candidates and that these new candidates are chosen consistently. By requiring additional conditions, we derive from this result also a characterization of the prominent class of sequential Thiele rules. Finally, we refine our results to characterize three well-known ABC voting rules, namely sequential approval voting, sequential proportional approval voting, and sequential Chamberlin-Courant approval voting.", "published": "2023-02-23T09:53:21Z", "authors": ["Chris Dong", "Patrick Lederer"], "categories": ["cs.GT", "econ.TH"], "doi": "", "_query": "automated valuation model", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/0710.0813v1", "title": "Valuation domains whose products of free modules are separable", "abstract": "It is proved that if $R$ is a valuation domain with maximal ideal $P$ and if $R_L$ is countably generated for each prime ideal $L$, then $R^R$ is separable if and only $R_J$ is maximal, where $J=\\cap_{n\\in\\mathbb{N}}P^n$.", "published": "2007-10-03T15:26:10Z", "authors": ["Francois Couchot"], "categories": ["math.RA"], "doi": "", "_query": "automated valuation model", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/1309.5045v1", "title": "Some closure operations in Zariski-Riemann spaces of valuation domains: a survey", "abstract": "In this survey we present several results concerning various topologies that were introduced in recent years on spaces of valuation domains.", "published": "2013-09-19T16:39:07Z", "authors": ["Carmelo A. Finocchiaro", "Marco Fontana", "K. Alan Loper"], "categories": ["math.AC", "math.AG"], "doi": "", "_query": "automated valuation model", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2402.18279v2", "title": "On the $p$-adic valuation of third order linear recurrence sequences", "abstract": "In a recent paper, Bilu et al. studied a conjecture of Marques and Lengyel on the $p$-adic valuation of the Tribonacci sequence. In this article, we study the $p$-adic valuation of third order linear recurrence sequences by considering a generalisation of the conjecture of Marques and Lengyel for third order linear recurrence sequences. Suppose that $(x_n)$ is a third order linear recurrence sequence whose characteristic polynomial has a root $γ$ such that $|γ|>1$. We show that if there exists a prime $p$ for which the conjecture holds for $(x_n)$, then the solution set of the Diophantine equation given by $x_n=m!$ in positive integers $n,m$ is finite. We also show that the solutions can be effectively computed when the form of the conjecture is explicitly known.", "published": "2024-02-28T12:16:49Z", "authors": ["Deepa Antony", "Rupam Barman"], "categories": ["math.NT"], "doi": "", "_query": "automated valuation model", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/1712.08325v2", "title": "Well-Ordered Valuations on Rational Function Fields in Two Variables", "abstract": "Gröbner bases have been generalized by replacing monomial orders with constructions such as valuations and filtrations. We consider suitable valuations on a rational valuation field $K(x,y)$ and analyze their behavior when restricting to an underlying polynomial ring $K[x,y]$. In previous work, the corresponding value groups were subsets of ${\\mathbb Q}$, and in this paper we consider the case when the value groups are isomorphic to ${\\mathbb Z} \\oplus {\\mathbb Z}$. Bounds on how the image of $K[x,y]$ grows with respect to degree are given, and then a class a valuations that are suitable for use for generalized Gröbner bases are described. We construct an example in which the image of the underlying polynomial ring is non-negative, yet is not well-ordered.", "published": "2017-12-22T07:23:19Z", "authors": ["Edward Mosteig"], "categories": ["math.AC"], "doi": "", "_query": "automated valuation model", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2410.02274v1", "title": "MMS Approximations Under Additive Leveled Valuations", "abstract": "We study the problem of fairly allocating indivisible goods to a set of agents with additive leveled valuations. A valuation function is called leveled if and only if bundles of larger size have larger value than bundles of smaller size. The economics literature has well studied such valuations. We use the maximin-share (MMS) and EFX as standard notions of fairness. We show that an algorithm introduced by Christodoulou et al. ([11]) constructs an allocation that is EFX and $\\frac{\\lfloor \\frac{m}{n} \\rfloor}{\\lfloor \\frac{m}{n} \\rfloor + 1}\\text{-MMS}$. In the paper, it was claimed that the allocation is EFX and $\\frac{2}{3}\\text{-MMS}$. However, the proof of the MMS-bound is incorrect. We give a counter-example to their proof and then prove a stronger approximation of MMS.", "published": "2024-10-03T07:52:03Z", "authors": ["Mahyar Afshinmehr", "Mehrafarin Kazemi", "Kurt Mehlhorn"], "categories": ["cs.GT"], "doi": "", "_query": "automated valuation model", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2605.00862v1", "title": "Replication-Consistent Liquidity Forecasting for Derivatives -- Forward Funding Sensitivities and a Liquidity Valuation Adjustment for Settlement Lags", "abstract": "We study cash-flow forecasting for derivatives used in liquidity management and clarify its relation to risk-neutral valuation and replication. While it is well known that expectations under different measures (e.g., $\\mathbb{P}$ vs. $\\mathbb{Q}$) can yield different undiscounted cash-flows, further inconsistencies arise when payment times are stochastic. We show that using discounting sensitivities (funding-curve hedge ratios) instead of \"expected cash-flows\" aligns forecasting with the self-financing replication strategy and avoids measure-mixing/aggregation issues. We then illustrate how a standard valuation model delivers pathwise funding requirements and propose a simple liquidity valuation adjustment to capture settlement lags and related timing frictions. The note provides implementation hints (American Monte Carlo with adjoint differentiation) and clarifies when \"expected cash-flows\" are informative and when sensitivities should be used instead.", "published": "2026-04-21T14:43:28Z", "authors": ["Christian P. Fries"], "categories": ["q-fin.PR", "q-fin.CP", "q-fin.RM"], "doi": "", "_query": "automated valuation model", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2109.05810v1", "title": "Truthful and Fair Mechanisms for Matroid-Rank Valuations", "abstract": "We study the problem of allocating indivisible goods among strategic agents. We focus on settings wherein monetary transfers are not available and each agent's private valuation is a submodular function with binary marginals, i.e., the agents' valuations are matroid-rank functions. In this setup, we establish a notable dichotomy between two of the most well-studied fairness notions in discrete fair division; specifically, between envy-freeness up to one good (EF1) and maximin shares (MMS). First, we show that a Pareto-efficient mechanism of Babaioff et al. (2021) is group strategy-proof for finding EF1 allocations, under matroid-rank valuations. The group strategy-proofness guarantee strengthens the result of Babaioff et al. (2021), that establishes truthfulness (individually for each agent) in the same context. Our result also generalizes a work of Halpern et al. (2020), from binary additive valuations to the matroid-rank case. Next, we establish that an analogous positive result cannot be achieved for MMS, even when considering truthfulness on an individual level. Specifically, we prove that, for matroid-rank valuations, there does not exist a truthful mechanism that is index oblivious, Pareto efficient, and maximin fair. For establishing our results, we develop a characterization of truthful mechanisms for matroid-rank functions. This characterization in fact holds for a broader class of valuations (specifically, holds for binary XOS functions) and might be of independent interest.", "published": "2021-09-13T09:34:41Z", "authors": ["Siddharth Barman", "Paritosh Verma"], "categories": ["cs.GT"], "doi": "", "_query": "automated valuation model", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2009.10633v1", "title": "ThingML+ Augmenting Model-Driven Software Engineering for the Internet of Things with Machine Learning", "abstract": "In this paper, we present the current position of the research project ML-Quadrat, which aims to extend the methodology, modeling language and tool support of ThingML - an open source modeling tool for IoT/CPS - to address Machine Learning needs for the IoT applications. Currently, ThingML offers a modeling language and tool support for modeling the components of the system, their communication interfaces as well as their behaviors. The latter is done through state machines. However, we argue that in many cases IoT/CPS services involve system components and physical processes, whose behaviors are not well understood in order to be modeled using state machines. Hence, quite often a data-driven approach that enables inference based on the observed data, e.g., using Machine Learning is preferred. To this aim, ML-Quadrat integrates the necessary Machine Learning concepts into ThingML both on the modeling level (syntax and semantics of the modeling language) and on the code generators level. We plan to support two target platforms for code generation regarding Stream Processing and Complex Event Processing, namely Apache SAMOA and Apama.", "published": "2020-09-22T15:45:45Z", "authors": ["Armin Moin", "Stephan Rössler", "Stephan Günnemann"], "categories": ["cs.SE", "cs.LG"], "doi": "", "_query": "automated valuation model", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2512.08422v1", "title": "Optimal Operation and Valuation of Electricity Storages in Intraday Markets", "abstract": "This paper applies computational techniques of convex stochastic optimization to optimal operation and valuation of electricity storages in the face of uncertain electricity prices. Our valuations are based on the indifference pricing principle, which builds on optimal trading strategies and calibrates to the user's financial position, market views and risk preferences. The underlying optimization problem is solved with the Stochastic Dual Dynamic Programming algorithm which is applicable to various specifications of storages, and it allows for e.g. hard constraints on storage capacity and charging speed. We illustrate the approach in intraday trading where the agent charges or discharges a battery over a finite number of delivery periods, and the electricity prices are subject to bid-ask spreads and significant uncertainty. Optimal strategies are found in a matter of minutes on a regular PC. We find that the corresponding trading strategies and battery valuations vary consistently with respect to the agent's risk preferences as well as the physical characteristics of the battery.", "published": "2025-12-09T09:49:18Z", "authors": ["Jean-Philippe Chancelier", "Michel de Lara", "François Pacaud", "Tanguy Lindegaard", "Teemu Pennanen", "Ari-Pekka Perkkiö"], "categories": ["math.OC"], "doi": "", "_query": "automated valuation model", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2406.11730v3", "title": "CHG Shapley: Efficient Data Valuation and Selection towards Trustworthy Machine Learning", "abstract": "Understanding the decision-making process of machine learning models is crucial for ensuring trustworthy machine learning. Data Shapley, a landmark study on data valuation, advances this understanding by assessing the contribution of each datum to model performance. However, the resource-intensive and time-consuming nature of multiple model retraining poses challenges for applying Data Shapley to large datasets. To address this, we propose the CHG (compound of Hardness and Gradient) utility function, which approximates the utility of each data subset on model performance in every training epoch. By deriving the closed-form Shapley value for each data point using the CHG utility function, we reduce the computational complexity to that of a single model retraining, achieving a quadratic improvement over existing marginal contribution-based methods. We further leverage CHG Shapley for real-time data selection, conducting experiments across three settings: standard datasets, label noise datasets, and class imbalance datasets. These experiments demonstrate its effectiveness in identifying high-value and noisy data. By enabling efficient data valuation, CHG Shapley promotes trustworthy model training through a novel data-centric perspective. Our codes are available at https://github.com/caihuaiguang/CHG-Shapley-for-Data-Valuation and https://github.com/caihuaiguang/CHG-Shapley-for-Data-Selection.", "published": "2024-06-17T16:48:31Z", "authors": ["Huaiguang Cai"], "categories": ["cs.GT", "cs.LG"], "doi": "", "_query": "automated valuation model", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2410.10344v1", "title": "Definable henselian valuations on dp-minimal real fields", "abstract": "We give an explicit algebraic characterisation of all definable henselian valuations on a dp-minimal real field. Additionally we characterise all dp-minimal real fields that admit a definable henselian valuation with real closed residue field. We do so by first proving this for the more general setting of almost real closed fields.", "published": "2024-10-14T09:58:39Z", "authors": ["Lothar Sebastian Krapp", "Salma Kuhlmann", "Lasse Vogel"], "categories": ["math.LO", "math.AC"], "doi": "", "_query": "automated valuation model", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/0707.2119v1", "title": "The 2-adic valuation of a sequence arising from a rational integral", "abstract": "We analyze properties of the 2-adic valuations of an integer sequence that originates from an explicit evaluation of a quartic integral. We also give a combinatorial interpretation of the valuations of this sequence. Connections with the orbits arising from the Collatz (3x+1) problem are discussed.", "published": "2007-07-14T03:35:59Z", "authors": ["Tewodros Amdeberhan", "Dante Manna", "Victor H. Moll"], "categories": ["math.NT", "math.CO"], "doi": "", "_query": "automated valuation model", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2407.05891v2", "title": "Fair and Truthful Allocations Under Leveled Valuations", "abstract": "We study the problem of fairly allocating indivisible goods among agents which are equipped with {\\em leveled} valuation functions. Such preferences, that have been studied before in economics and fair division literature, capture a simple and intuitive economic behavior; larger bundles are always preferred to smaller ones. We provide a fine-grained analysis for various subclasses of leveled valuations focusing on two extensively studied notions of fairness, (approximate) MMS and EFX. In particular, we present a general positive result, showing the existence of $2/3$-MMS allocations under valuations that are both leveled and submodular. We also show how some of our ideas can be used beyond the class of leveled valuations; for the case of two submodular (not necessarily leveled) agents we show that there always exists a $2/3$-MMS allocation, complementing a recent impossibility result. Then, we switch to the case of subadditive and fractionally subadditive leveled agents, where we are able to show tight (lower and upper) bounds of $1/2$ on the approximation factor of MMS. Moreover, we show the existence of exact EFX allocations under general leveled valuations via a simple protocol that in addition satisfies several natural economic properties. Finally, we take a mechanism design approach and we propose protocols that are both truthful and approximately fair under leveled valuations.", "published": "2024-07-08T12:54:36Z", "authors": ["George Christodoulou", "Vasilis Christoforidis"], "categories": ["cs.GT"], "doi": "", "_query": "automated valuation model", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/1904.02868v2", "title": "Data Shapley: Equitable Valuation of Data for Machine Learning", "abstract": "As data becomes the fuel driving technological and economic growth, a fundamental challenge is how to quantify the value of data in algorithmic predictions and decisions. For example, in healthcare and consumer markets, it has been suggested that individuals should be compensated for the data that they generate, but it is not clear what is an equitable valuation for individual data. In this work, we develop a principled framework to address data valuation in the context of supervised machine learning. Given a learning algorithm trained on $n$ data points to produce a predictor, we propose data Shapley as a metric to quantify the value of each training datum to the predictor performance. Data Shapley value uniquely satisfies several natural properties of equitable data valuation. We develop Monte Carlo and gradient-based methods to efficiently estimate data Shapley values in practical settings where complex learning algorithms, including neural networks, are trained on large datasets. In addition to being equitable, extensive experiments across biomedical, image and synthetic data demonstrate that data Shapley has several other benefits: 1) it is more powerful than the popular leave-one-out or leverage score in providing insight on what data is more valuable for a given learning task; 2) low Shapley value data effectively capture outliers and corruptions; 3) high Shapley value data inform what type of new data to acquire to improve the predictor.", "published": "2019-04-05T04:54:10Z", "authors": ["Amirata Ghorbani", "James Zou"], "categories": ["stat.ML", "cs.AI", "cs.LG"], "doi": "", "_query": "automated valuation model", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/1409.6590v1", "title": "Automated Testing of Graphical Models in Heterogeneous Test Environments", "abstract": "Automated and therefore repeatable tests are important in product lines to ensure and maintain quality of software functions as well as efficiency of the developers. This article shows methods for fully automated testing of SIMULINK models with the slUnit framework and techniques for the integration of slUnit tests in a general test framework to allow integrated and automated test reports.", "published": "2014-09-22T12:07:28Z", "authors": ["Alexej Beresnev", "Bernhard Rumpe", "Frank Schoven"], "categories": ["cs.SE"], "doi": "", "_query": "automated valuation model", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/math/0308072v1", "title": "The 2-adic valuation of the coefficients of a polynomial", "abstract": "In this paper we compute the 2-adic valuations of some polynomials associated with the definite integral $\\int_{0}^{\\infty} \\frac{dx}{(x^4+2*a*x^2+1)^(m+1)}$", "published": "2003-08-07T20:39:42Z", "authors": ["G. Boros", "V. Moll", "J. Shallit"], "categories": ["math.NT", "math.CA"], "doi": "", "_query": "automated valuation model", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2011.05724v3", "title": "Extensions of valuation rings containing $\\bf Q$ as limits of smooth algebras", "abstract": "We give a necessary and sufficient condition for an extension of valuation rings containing $\\bf Q$ to be a filtered direct limit of smooth algebras.", "published": "2020-11-11T12:03:41Z", "authors": ["Dorin Popescu"], "categories": ["math.AC", "math.AG"], "doi": "", "_query": "automated valuation model", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2503.07287v1", "title": "A Klain-Schneider Theorem for Vector-Valued Valuations on Convex Functions", "abstract": "A functional analog of the Klain-Schneider theorem for vector-valued valuations on convex functions is established, providing a classification of continuous, translation covariant, simple valuations. Under additional rotation equivariance assumptions, an analytic counterpart of the moment vector is characterized alongside a new epi-translation invariant valuation. The former arises as the top-degree operator in a family of functional intrinsic moments, which are linked to functional intrinsic volumes through translations. The latter represents the top-degree operator in a class of Minkowski vectors, which are introduced in this article and which lack classical counterparts on convex bodies, as they vanish due to the Minkowski relations. Additional classification results are obtained for homogeneous valuations of extremal degrees.", "published": "2025-03-10T13:07:01Z", "authors": ["Mohamed A. Mouamine", "Fabian Mussnig"], "categories": ["math.MG", "math.FA"], "doi": "10.1016/j.jfa.2026.111544", "_query": "automated valuation model", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/1908.00064v1", "title": "Normal completions of toric varieties over rank one valuation rings and completions of $Γ$-admissible fans", "abstract": "We show that any normal toric variety over a rank one valuation ring admits an equivariant open embedding in a normal toric variety which is proper over the valuation ring, after a base-change by a finite extension of valuation rings. If the value group $Γ$ is discrete or divisible then no base-change is needed. We give explicit examples which show that existing methods do not produce such normal equivariant completions. Our approach is combinatorial and proceeds by showing that $Γ$-admissible fans admit $Γ$-admissible completions. In order to show this we prove a combinatorial analog of noetherian reduction which we believe will be of independent interest.", "published": "2019-07-31T19:52:20Z", "authors": ["Netanel Friedenberg"], "categories": ["math.AG", "math.CO"], "doi": "", "_query": "automated valuation model", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2607.19558v1", "title": "Profiling and Endogenous Valuation", "abstract": "We study a monopolist facing a buyer whose valuation is determined by pre-trade investment. Before setting price, the seller observes a signal about the buyer's private investment cost (buyer profiling). Information that helps the seller extract surplus can also undermine the buyer's incentive to create it. We characterize the buyer-seller payoffs attainable across all possible profiling. On the Pareto frontier, if investment increases, hold-up risk always raises the payoff that the seller captures faster than the surplus that the investment creates. Protecting buyer welfare therefore requires discouraging investment, even though investment is socially efficient.", "published": "2026-07-21T20:33:06Z", "authors": ["Anh Nguyen", "Teck Yong Tan"], "categories": ["econ.TH"], "doi": "", "_query": "automated valuation model", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2402.17879v2", "title": "Automated Statistical Model Discovery with Language Models", "abstract": "Statistical model discovery is a challenging search over a vast space of models subject to domain-specific constraints. Efficiently searching over this space requires expertise in modeling and the problem domain. Motivated by the domain knowledge and programming capabilities of large language models (LMs), we introduce a method for language model driven automated statistical model discovery. We cast our automated procedure within the principled framework of Box's Loop: the LM iterates between proposing statistical models represented as probabilistic programs, acting as a modeler, and critiquing those models, acting as a domain expert. By leveraging LMs, we do not have to define a domain-specific language of models or design a handcrafted search procedure, which are key restrictions of previous systems. We evaluate our method in three settings in probabilistic modeling: searching within a restricted space of models, searching over an open-ended space, and improving expert models under natural language constraints (e.g., this model should be interpretable to an ecologist). Our method identifies models on par with human expert designed models and extends classic models in interpretable ways. Our results highlight the promise of LM-driven model discovery.", "published": "2024-02-27T20:33:22Z", "authors": ["Michael Y. Li", "Emily B. Fox", "Noah D. Goodman"], "categories": ["cs.LG", "cs.CL"], "doi": "", "_query": "automated valuation model", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/0911.3331v3", "title": "Bilateral counterparty risk valuation for interest-rate products: impact of volatilities and correlations", "abstract": "The purpose of this paper is introducing rigorous methods and formulas for bilateral counterparty risk credit valuation adjustments (CVA's) on interest-rate portfolios. In doing so, we summarize the general arbitrage-free valuation framework for counterparty risk adjustments in presence of bilateral default risk, as developed more in detail in Brigo and Capponi (2008), including the default of the investor. We illustrate the symmetry in the valuation and show that the adjustment involves a long position in a put option plus a short position in a call option, both with zero strike and written on the residual net present value of the contract at the relevant default times. We allow for correlation between the default times of the investor and counterparty, and for correlation of each with the underlying risk factor, namely interest rates. We also analyze the often neglected impact of credit spread volatility. We include Netting in our examples, although other agreements such as Margining and Collateral are left for future work.", "published": "2009-11-17T15:30:17Z", "authors": ["Damiano Brigo", "Andrea Pallavicini", "Vasileios Papatheodorou"], "categories": ["q-fin.PR", "q-fin.CP"], "doi": "", "_query": "automated valuation model", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2105.03352v1", "title": "Solving Quadratic and Cubic Diophantine Equations using 2-adic Valuation Trees", "abstract": "For fixed integers $D \\geq 0$ and $c \\geq 3$, we demonstrate how to use $2$-adic valuation trees of sequences to analyze Diophantine equations of the form $x^2+D=2^cy$ and $x^3+D=2^cy$, for $y$ odd. Further, we show for what values $D \\in \\mathbb{Z}^+$, the numbers $x^3+D$ will generate infinite valuation trees, which lead to infinite solutions to the above Diophantine equations.", "published": "2021-05-07T16:08:39Z", "authors": ["Maila Brucal-Hallare", "Eva G. Goedhart", "Ryan Max Riley", "Vaishavi Sharma", "Bianca Thompson"], "categories": ["math.NT"], "doi": "", "_query": "automated valuation model", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/quant-ph/9803055v4", "title": "A topos perspective on the Kochen-Specker theorem: I. Quantum States as Generalized Valuations", "abstract": "The Kochen-Specker theorem asserts the impossibility of assigning values to quantum quantities in a way that preserves functional relations between them. We construct a new type of valuation which is defined on all operators, and which respects an appropriate version of the functional composition principle. The truth-values assigned to propositions are (i) contextual; and (ii) multi-valued, where the space of contexts and the multi-valued logic for each context come naturally from the topos theory of presheaves. The first step in our theory is to demonstrate that the Kochen-Specker theorem is equivalent to the statement that a certain presheaf defined on the category of self-adjoint operators has no global elements. We then show how the use of ideas drawn from the theory of presheaves leads to the definition of a generalized valuation in quantum theory whose values are sieves of operators. In particular, we show how each quantum state leads to such a generalized valuation.", "published": "1998-03-20T14:26:52Z", "authors": ["C. J. Isham", "J. Butterfield"], "categories": ["quant-ph", "gr-qc"], "doi": "", "_query": "automated valuation model", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2206.00472v9", "title": "Pure transcendental, immediate valuation ring extensions as limits of smooth algebras", "abstract": "We show that a pure transcendental, immediate extension of valuation rings $V\\subset V'$ containing a field is a filtered union of smooth $V$-subalgebras of $V'$.", "published": "2022-06-01T13:05:04Z", "authors": ["Dorin Popescu"], "categories": ["math.AC", "math.AG"], "doi": "", "_query": "automated valuation model", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2511.06274v1", "title": "Why `Fair Market Valuations' are Inappropirate for Employee-Owned Firms and Partnerships", "abstract": "The usual formulas for the fair market valuation of a firm at time $t$ include the profits accruing to the shares at time $t$ from the use of wage or salaried labor in the future. But in employee-owned firms or partnerships, the future worker-members or partners are the residual claimants at those future times, so in those cases, the future residuals do not accrue to the current shareholder/residual-claimants. Hence any `fair market valuation' of an employee-owned firm or partnership that assumes those future residuals accrue to the current shareholder/residual-claimants is inappropriate. Keywords: fair market valuations, residual claimants, property rights, personal rights, Miller-Modigliani valuations.", "published": "2025-11-09T08:24:37Z", "authors": ["David Ellerman"], "categories": ["econ.TH", "q-fin.PR"], "doi": "", "_query": "automated valuation model", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/1801.04994v2", "title": "Consistent Valuation Across Curves Using Pricing Kernels", "abstract": "The general problem of asset pricing when the discount rate differs from the rate at which an asset's cash flows accrue is considered. A pricing kernel framework is used to model an economy that is segmented into distinct markets, each identified by a yield curve having its own market, credit and liquidity risk characteristics. The proposed framework precludes arbitrage within each market, while the definition of a curve-conversion factor process links all markets in a consistent arbitrage-free manner. A pricing formula is then derived, referred to as the across-curve pricing formula, which enables consistent valuation and hedging of financial instruments across curves (and markets). As a natural application, a consistent multi-curve framework is formulated for emerging and developed inter-bank swap markets, which highlights an important dual feature of the curve-conversion factor process. Given this multi-curve framework, existing multi-curve approaches based on HJM and rational pricing kernel models are recovered, reviewed and generalised, and single-curve models extended. In another application, inflation-linked, currency-based, and fixed-income hybrid securities are shown to be consistently valued using the across-curve valuation method.", "published": "2018-01-15T20:42:26Z", "authors": ["Andrea Macrina", "Obeid Mahomed"], "categories": ["q-fin.MF"], "doi": "", "_query": "automated valuation model", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2301.11356v2", "title": "The Automated Discovery of Kinetic Rate Models -- Methodological Frameworks", "abstract": "The industrialization of catalytic processes requires reliable kinetic models for their design, optimization and control. Mechanistic models require significant domain knowledge, while data-driven and hybrid models lack interpretability. Automated knowledge discovery methods, such as ALAMO (Automated Learning of Algebraic Models for Optimization), SINDy (Sparse Identification of Nonlinear Dynamics), and genetic programming, have gained popularity but suffer from limitations such as needing model structure assumptions, exhibiting poor scalability, and displaying sensitivity to noise. To overcome these challenges, we propose two methodological frameworks, ADoK-S and ADoK-W (Automated Discovery of Kinetic rate models using a Strong/Weak formulation of symbolic regression), for the automated generation of catalytic kinetic models using a robust criterion for model selection. We leverage genetic programming for model generation and a sequential optimization routine for model refinement. The frameworks are tested against three case studies of increasing complexity, demonstrating their ability to retrieve the underlying kinetic rate model with limited noisy data from the catalytic systems, showcasing their potential for chemical reaction engineering applications.", "published": "2023-01-26T19:09:47Z", "authors": ["Miguel Ángel de Carvalho Servia", "Ilya Orson Sandoval", "Klaus Hellgardt", "King Kuok", " Hii", "Dongda Zhang", "Ehecatl Antonio del Rio Chanona"], "categories": ["cs.SC", "cs.CE", "q-bio.QM"], "doi": "", "_query": "automated valuation model", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2005.03872v2", "title": "Sensitivity Analysis for Vehicle Dynamics Models -- An Approach to Model Quality Assessment for Automated Vehicles", "abstract": "Model-based approaches have become increasingly popular in the domain of automated driving. This includes runtime algorithms, such as Model Predictive Control, as well as formal and simulative approaches for the verification of automated vehicle functions. With this trend, the quality of models becomes crucial for automated vehicle safety. Established tools from model theory which can be applied to assure model quality are uncertainty and sensitivity analysis [1]. In this paper, we conduct sensitivity analyses for a single and double track vehicle dynamics model to gain insights about the models' behavior under different operating conditions. We compare the models, point out the most important findings regarding the obtained parameters sensitivities, and provide examples of possible applications of the gained insights.", "published": "2020-05-08T06:24:14Z", "authors": ["Marcus Nolte", "Richard Schubert", "Cordula Reisch", "Markus Maurer"], "categories": ["eess.SY"], "doi": "", "_query": "automated valuation model", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2607.10880v2", "title": "First-Order Modal Logic in HOL: Deep and Shallow Embeddings with Automated Faithfulness (Extended Preprint)", "abstract": "We extend, in Isabelle/HOL, the deep-and-shallow embedding methodology of our prior work from propositional to first-order modal logic (FML) with constant-domain Kripke semantics. Three embeddings of FML into classical higher-order logic (HOL) are provided side by side: a deep embedding, a heavyweight maximal-shallow embedding, and a lightweight minimal-shallow embedding. The minimal-shallow embedding is presented as an Isabelle/HOL locale, parametrised by an accessibility relation, a world-indexed interpretation, a universe of worlds, and a variable assignment; the locale form admits a global faithfulness theorem, stating that quantifying over all minimal-shallow interpretations recovers exactly deep validity. A central technical contribution is a mechanisation, for FML under constant-domain Kripke semantics, of the (countable) downward Löwenheim-Skolem theorem, which underpins the automation of our faithfulness proof between the deep and minimal-shallow embeddings. Deploying it inside an extension of the minimal-shallow locale resolves the surjectivity problem that arises against an uncountable domain of individuals -- where the locale's variable assignment, having countable domain V = nat, cannot be surjective onto the domain -- and thereby yields faithfulness over the full domain. Since prior work treats only the propositional fragment, we develop here the substitution machinery (free/bound-variable predicates, the fresh-variable function, capture-avoiding substitution, alphabetic renaming, the substitutability predicate, the substitution lemma, and size-based induction principles) needed for the first-order quantifiers.", "published": "2026-07-12T19:03:54Z", "authors": ["Christoph Benzmüller", "Daniel Kirchner"], "categories": ["cs.AI", "math.LO"], "doi": "", "_query": "automated valuation model", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2607.11223v2", "title": "Extending valuations of local domains to complete local domains without changing the value group", "abstract": "Let $(R,m,k)$ be an excellent local noetherian domain with field of fractions $K$. Let $$ ν:K^*\\twoheadrightarrowΓ$$ be a valuation centered at $R$ and let $R_ν$ be the corresponding valuation ring of $K$, dominating $R$. Denote by $\\widehat R$ the $m$-adic completion of $R$. In the applications of valuation theory to commutative algebra and the study of singularities, one is often induced to replace $R$ by its $m$-adic completion $\\widehat R$ and $ν$ by a suitable extension $\\widehatν_-$ to $\\frac{\\widehat R}P$ for a suitably chosen prime ideal $P$, such that $$ P\\cap R=(0). $$ In a previous article we gave a systematic description of all such extensions $\\widehatν_-$ and defined the notion of tight extensions that are of particular interest for applications (see Herrera, Olalla, Spivakovsky and Teissier, Extending a valuation centered in a local domain to its formal completion, Proc. London Math. Soc. (3) 105 (2012) 571--621). If $\\widehatν_-$ is a tight extension then its graded algebra is birational to that of $ν$ (the converse is not known and might not be true). In particular, the value group of $\\widehatν_-$ is $Γ$. The existence of tight extensions was conjectured by the last author (see Teissier, Valuations, deformations, and toric geometry, Fields Institute Communications, 33, 2003, 361-459). In the present paper we give a proof of Teissier's conjecture. An intended application of this result is an important step in two recent approaches to local uniformization in positive characteristic.", "published": "2026-07-13T08:15:11Z", "authors": ["F. J. Herrera Govantes", "M. A. Olalla Acosta", "M. Spivakovsky", "B. Teissier"], "categories": ["math.AC", "math.AG"], "doi": "", "_query": "automated valuation model", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/1404.1524v1", "title": "The Rees Valuations of Complete Ideals in a Regular Local Ring", "abstract": "Let I be a complete m-primary ideal of a regular local ring (R,m). In the case where R has dimension two, the beautiful theory developed by Zariski implies that I factors uniquely as a product of powers of simple complete ideals and each of the simple complete factors of I has a unique Rees valuation. In the higher dimensional case, a simple complete ideal of R often has more than one Rees valuation, and a complete m-primary ideal I may have finitely many or infinitely many base points. For the ideals having finitely many base points, Lipman proves a unique factorization involving special star-simple complete ideals with possibly negative exponents of the factors. Let T be an infinitely near point to R with dim R = dim T and T having residue field equal to R/m. We prove that the special star simple complete ideal associated with the sequence from R to T has a unique Rees valuation if and only if either dim R = 2 or there is no change of direction in the unique finite sequence of local quadratic transforms from R to T. We also examine conditions for a complete ideal to be projectively full.", "published": "2014-04-05T22:10:23Z", "authors": ["William Heinzer", "Mee-Kyoung Kim"], "categories": ["math.AC"], "doi": "", "_query": "automated valuation model", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2403.08071v1", "title": "Schubert valuations on Grassmann varieties", "abstract": "The goal of the paper is twofold: on one side it provides an order structure on the set of all maximal chains in the Bruhat poset of Schubert varieties in a Grassmann variety; on the other hand, using this order structure, it works out explicit formulae for the valuation and the Newton-Okounkov body associated to each maximal chain appearing in the framework of Seshadri stratification.", "published": "2024-03-12T20:48:50Z", "authors": ["Rocco Chirivì", "Xin Fang", "Peter Littelmann"], "categories": ["math.AG", "math.CO"], "doi": "", "_query": "automated valuation model", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/0712.3400v4", "title": "Semistable reduction for overconvergent F-isocrystals, IV: Local semistable reduction at nonmonomial valuations", "abstract": "We complete our proof that given an overconvergent F-isocrystal on a variety over a field of positive characteristic, one can pull back along a suitable generically finite cover to obtain an isocrystal which extends, with logarithmic singularities and nilpotent residues, to some complete variety. We also establish an analogue for F-isocrystals overconvergent inside a partial compactification. By previous results, this reduces to solving a local problem in a neighborhood of a valuation of height 1 and residual transcendence degree 0. We do this by studying the variation of some numerical invariants attached to p-adic differential modules, analogous to the irregularity of a complex meromorphic connection. This allows for an induction on the transcendence defect of the valuation, i.e., the discrepancy between the dimension of the variety and the rational rank of the valuation.", "published": "2007-12-20T12:55:26Z", "authors": ["Kiran S. Kedlaya"], "categories": ["math.NT", "math.AG"], "doi": "10.1112/S0010437X08003783", "_query": "automated valuation model", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/0805.3788v1", "title": "Semigroups of valuations on local rings, II", "abstract": "Given a noetherian local domain $R$ and a valuation $ν$ of its field of fractions which is non negative on $R$, we derive some very general bounds on the growth of the number of distinct valuation ideals of $R$ corresponding to values lying in certain parts of the value group $Γ$ of $ν$. We show that this growth condition imposes restrictions on the semigroups $ν(R\\setminus \\{0\\})$ for noetherian $R$ which are stronger that those resulting from the previous paper \\cite{C2} of the first author. Given an ordered embedding $Γ\\subset ({\\mathbf R}^h)_{\\hbox{\\rm lex}}$, where $h$ is the rank of $ν$, we also study the shape in ${\\mathbf R}^h$ of the parts of $Γ$ which appear naturally in this study. We give examples which show that this shape can be quite wild in a way which does not depend on the embedding and suggest that it is a good indicator of the complexity of the semigroup $ν(R\\setminus \\{0\\})$.", "published": "2008-05-24T20:03:58Z", "authors": ["Steven Dale Cutkosky", "Bernard Teissier"], "categories": ["math.CV", "math.AC", "math.AG"], "doi": "", "_query": "automated valuation model", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2307.10247v1", "title": "Automated Action Model Acquisition from Narrative Texts", "abstract": "Action models, which take the form of precondition/effect axioms, facilitate causal and motivational connections between actions for AI agents. Action model acquisition has been identified as a bottleneck in the application of planning technology, especially within narrative planning. Acquiring action models from narrative texts in an automated way is essential, but challenging because of the inherent complexities of such texts. We present NaRuto, a system that extracts structured events from narrative text and subsequently generates planning-language-style action models based on predictions of commonsense event relations, as well as textual contradictions and similarities, in an unsupervised manner. Experimental results in classical narrative planning domains show that NaRuto can generate action models of significantly better quality than existing fully automated methods, and even on par with those of semi-automated methods.", "published": "2023-07-17T07:04:31Z", "authors": ["Ruiqi Li", "Leyang Cui", "Songtuan Lin", "Patrik Haslum"], "categories": ["cs.CL", "cs.IR", "cs.LG"], "doi": "", "_query": "automated valuation model", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2106.15553v1", "title": "Evaluation of Automated Image Descriptions for Visually Impaired Students", "abstract": "Illustrations are widely used in education, and sometimes, alternatives are not available for visually impaired students. Therefore, those students would benefit greatly from an automatic illustration description system, but only if those descriptions were complete, correct, and easily understandable using a screenreader. In this paper, we report on a study for the assessment of automated image descriptions. We interviewed experts to establish evaluation criteria, which we then used to create an evaluation questionnaire for sighted non-expert raters, and description templates. We used this questionnaire to evaluate the quality of descriptions which could be generated with a template-based automatic image describer. We present evidence that these templates have the potential to generate useful descriptions, and that the questionnaire identifies problems with description templates.", "published": "2021-06-29T16:40:04Z", "authors": ["Anett Hoppe", "David Morris", "Ralph Ewerth"], "categories": ["cs.HC", "cs.CV", "cs.CY"], "doi": "10.1007/978-3-030-78270-2_35", "_query": "automated valuation model", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2110.00767v2", "title": "Sublinear Approximation Algorithm for Nash Social Welfare with XOS Valuations", "abstract": "We study the problem of allocating indivisible goods among $n$ agents with the objective of maximizing Nash social welfare (NSW). This welfare function is defined as the geometric mean of the agents' valuations and, hence, it strikes a balance between the extremes of social welfare (arithmetic mean) and egalitarian welfare (max-min value). Nash social welfare has been extensively studied in recent years for various valuation classes. In particular, a notable negative result is known when the agents' valuations are complement-free and are specified via value queries: for XOS valuations, one necessarily requires exponentially many value queries to find any sublinear (in $n$) approximation for NSW. Indeed, this lower bound implies that stronger query models are needed for finding better approximations. Towards this, we utilize demand oracles and XOS oracles; both of these query models are standard and have been used in prior work on social welfare maximization with XOS valuations. We develop the first sublinear approximation algorithm for maximizing Nash social welfare under XOS valuations, specified via demand and XOS oracles. Hence, this work breaks the $O(n)$-approximation barrier for NSW maximization under XOS valuations. We obtain this result by developing a novel connection between NSW and social welfare under a capped version of the agents' valuations. In addition to this insight, which might be of independent interest, this work relies on an intricate combination of multiple technical ideas, including the use of repeated matchings and the discrete moving knife method. In addition, we partially complement the algorithmic result by showing that, under XOS valuations, an exponential number of demand and XOS queries are necessarily required to approximate NSW within a factor of $\\left(1 - \\frac{1}{e}\\right)$.", "published": "2021-10-02T09:48:03Z", "authors": ["Siddharth Barman", "Anand Krishna", "Pooja Kulkarni", "Shivika Narang"], "categories": ["cs.GT"], "doi": "", "_query": "automated valuation model", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2410.22048v2", "title": "Benchmarking Human and Automated Prompting in the Segment Anything Model", "abstract": "The remarkable capabilities of the Segment Anything Model (SAM) for tackling image segmentation tasks in an intuitive and interactive manner has sparked interest in the design of effective visual prompts. Such interest has led to the creation of automated point prompt selection strategies, typically motivated from a feature extraction perspective. However, there is still very little understanding of how appropriate these automated visual prompting strategies are, particularly when compared to humans, across diverse image domains. Additionally, the performance benefits of including such automated visual prompting strategies within the finetuning process of SAM also remains unexplored, as does the effect of interpretable factors like distance between the prompt points on segmentation performance. To bridge these gaps, we leverage a recently released visual prompting dataset, PointPrompt, and introduce a number of benchmarking tasks that provide an array of opportunities to improve the understanding of the way human prompts differ from automated ones and what underlying factors make for effective visual prompts. We demonstrate that the resulting segmentation scores obtained by humans are approximately 29% higher than those given by automated strategies and identify potential features that are indicative of prompting performance with $R^2$ scores over 0.5. Additionally, we demonstrate that performance when using automated methods can be improved by up to 68% via a finetuning approach. Overall, our experiments not only showcase the existing gap between human prompts and automated methods, but also highlight potential avenues through which this gap can be leveraged to improve effective visual prompt design. Further details along with the dataset links and codes are available at https://github.com/olivesgatech/PointPrompt", "published": "2024-10-29T13:56:31Z", "authors": ["Jorge Quesada", "Zoe Fowler", "Mohammad Alotaibi", "Mohit Prabhushankar", "Ghassan AlRegib"], "categories": ["cs.CV"], "doi": "", "_query": "automated valuation model", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2109.00081v2", "title": "Approximations for Allocating Indivisible Items with Concave-Additive Valuations", "abstract": "We study a general allocation setting where agent valuations are concave additive. In this model, a collection of items must be uniquely distributed among a set of agents, where each agent-item pair has a specified utility. The objective is to maximize the sum of agent valuations, each of which is an arbitrary non-decreasing concave function of the agent's total additive utility. This setting was studied by Devanur and Jain (STOC 2012) in the online setting for divisible items. In this paper, we obtain both tight multiplicative and additive approximations in the offline setting for indivisible items. Our approximations depend on novel parameters that measure the local multiplicative/additive curvatures of each agent valuation, which we show correspond directly to the integrality gap of the natural assignment convex program of the problem. Furthermore, we extend our additive guarantees to obtain constant multiplicative approximations for Asymmetric Nash Welfare Maximization when agents have smooth valuations (Fain et al. EC 2018, Fluschnik et al. AAAI 2019). This algorithm also yields an interesting tatonnement-style interpretation, where agents adjust uniform prices and items are assigned according to maximum weighted bang-per-buck ratios.", "published": "2021-08-31T21:16:23Z", "authors": ["Nathaniel Kell", "Kevin Sun"], "categories": ["cs.DS"], "doi": "", "_query": "automated valuation model", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/1605.02042v3", "title": "A Jordan-like decomposition theorem for valuations on star bodies", "abstract": "We show that every radial continuous valuation $V:\\mathcal S_0^n\\rightarrow \\mathbb R$ defined on the $n$-dimensional star bodies $\\mathcal S_0^n$, and verifying $V(\\{0\\})=0$, can be decomposed as a sum $V=V^+-V^-$, where both $V^+$ and $V^-$ are positive radial continuous valuations on $\\mathcal S_0^n$ with $V^+(\\{0\\})=V^-(\\{0\\})=0$. As an application, we show that radial continuous rotationally invariant valuations $V$ on $\\mathcal S_0^n$ can be characterized as the applications on star bodies which can be written as $$V(K)=\\int_{S^{n-1}}θ(ρ_K)dm,$$ where $θ:[0,\\infty)\\rightarrow \\mathbb R$ is a continuous function, $ρ_K$ is the radial function associated to $K$ and $m$ is the Lebesgue measure on $S^{n-1}$. This completes recent work of the second named author, where an analogous result is proved for the case of {\\em positive} radial continuous rotationally invariant valuations.", "published": "2016-05-06T19:12:04Z", "authors": ["Pedro Tradacete", "Ignacio Villanueva"], "categories": ["math.MG", "math.FA"], "doi": "", "_query": "automated valuation model", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/1605.07431v3", "title": "Combinatorial mixed valuations", "abstract": "Combinatorial mixed valuations associated to translation-invariant valuations on polytopes are introduced. In contrast to the construction of mixed valuations via polarization, combinatorial mixed valuations reflect and often inherit properties of inhomogeneous valuations. In particular, it is shown that under mild assumptions combinatorial mixed valuations are monotone and hence nonnegative. For combinatorially positive valuations, this has strong computational implications. Applied to the discrete volume, the results generalize and strengthen work of Bihan (2015) on discrete mixed volumes. For rational polytopes, it is proved that combinatorial mixed monotonicity is equivalent to monotonicity. Stronger even, a conjecture is substantiated that combinatorial mixed monotonicity implies the homogeneous monotonicity in the sense of Bernig--Fu (2011).", "published": "2016-05-24T12:55:20Z", "authors": ["Katharina Jochemko", "Raman Sanyal"], "categories": ["math.CO", "math.MG"], "doi": "", "_query": "automated valuation model", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2410.22987v1", "title": "V2X-Assisted Distributed Computing and Control Framework for Connected and Automated Vehicles under Ramp Merging Scenario", "abstract": "This paper investigates distributed computing and cooperative control of connected and automated vehicles (CAVs) in ramp merging scenario under transportation cyber-physical system. Firstly, a centralized cooperative trajectory planning problem is formulated subject to the safely constraints and traffic performance in ramp merging scenario, where the trajectories of all vehicles are jointly optimized. To get rid of the reliance on a central controller and reduce computation time, a distributed solution to this problem implemented among CAVs through Vehicles-to-Everything (V2X) communication is proposed. Unlike existing method, our method can distribute the computational task among CAVs and carry out parallel solving through V2X communication. Then, a multi-vehicles model predictive control (MPC) problem aimed at maximizing system stability and minimizing control input is formulated based on the solution of the first problem subject to strict safety constants and input limits. Due to these complex constraints, this problem becomes high-dimensional, centralized, and non-convex. To solve it in a short time, a decomposition and convex reformulation method, namely distributed cooperative iterative model predictive control (DCIMPC), is proposed. This method leverages the communication capability of CAVs to decompose the problem, making full use of the computational resources on vehicles to achieve fast solutions and distributed control. The two above problems with their corresponding solving methods form the systemic framework of the V2X assisted distributed computing and control. Simulations have been conducted to evaluate the framework's convergence, safety, and solving speed. Additionally, extra experiments are conducted to validate the performance of DCIMPC. The results show that our method can greatly improve computation speed without sacrificing system performance.", "published": "2024-10-30T12:56:49Z", "authors": ["Qiong Wu", "Jiahou Chu", "Pingyi Fan", "Kezhi Wang", "Nan Cheng", "Wen Chen", "Khaled B. Letaief"], "categories": ["eess.SY", "cs.LG", "cs.NI"], "doi": "", "_query": "automated valuation model", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2210.16835v5", "title": "Variance reduced Shapley value estimation for trustworthy data valuation", "abstract": "Data valuation, especially quantifying data value in algorithmic prediction and decision-making, is a fundamental problem in data trading scenarios. The most widely used method is to define the data Shapley and approximate it by means of the permutation sampling algorithm. To make up for the large estimation variance of the permutation sampling that hinders the development of the data marketplace, we propose a more robust data valuation method using stratified sampling, named variance reduced data Shapley (VRDS for short). We theoretically show how to stratify, how many samples are taken at each stratum, and the sample complexity analysis of VRDS. Finally, the effectiveness of VRDS is illustrated in different types of datasets and data removal applications.", "published": "2022-10-30T13:04:52Z", "authors": ["Mengmeng Wu", "Ruoxi Jia", "Changle Lin", "Wei Huang", "Xiangyu Chang"], "categories": ["stat.ML", "cs.LG"], "doi": "", "_query": "automated valuation model", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/1710.01026v1", "title": "An Iterative and Toolchain-Based Approach to Automate Scanning and Mapping Computer Networks", "abstract": "As today's organizational computer networks are ever evolving and becoming more and more complex, finding potential vulnerabilities and conducting security audits has become a crucial element in securing these networks. The first step in auditing a network is reconnaissance by mapping it to get a comprehensive overview over its structure. The growing complexity, however, makes this task increasingly effortful, even more as mapping (instead of plain scanning), presently, still involves a lot of manual work. Therefore, the concept proposed in this paper automates the scanning and mapping of unknown and non-cooperative computer networks in order to find security weaknesses or verify access controls. It further helps to conduct audits by allowing comparing documented with actual networks and finding unauthorized network devices, as well as evaluating access control methods by conducting delta scans. It uses a novel approach of augmenting data from iteratively chained existing scanning tools with context, using genuine analytics modules to allow assessing a network's topology instead of just generating a list of scanned devices. It further contains a visualization model that provides a clear, lucid topology map and a special graph for comparative analysis. The goal is to provide maximum insight with a minimum of a priori knowledge.", "published": "2017-10-03T08:26:47Z", "authors": ["Stefan Marksteiner", "Harald Lernbeiß", "Bernhard Jandl-Scherf"], "categories": ["cs.CR"], "doi": "10.1145/2994475.2994479", "_query": "automated valuation model", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/1710.00897v1", "title": "Valuation of Employee Stock Options (ESOs) by means of Mean-Variance Hedging", "abstract": "We consider the problem of ESO valuation in continuous time. In particular, we consider models that assume that an appropriate random time serves as a proxy for anything that causes the ESO's holder to exercise the option early, namely, reflects the ESO holder's job termination risk as well as early exercise behaviour. In this context, we study the problem of ESO valuation by means of mean-variance hedging. Our analysis is based on dynamic programming and uses PDE techniques. We also express the ESO's value that we derive as the expected discounted payoff that the ESO yields with respect to an equivalent martingale measure, which does not coincide with the minimal martingale measure or the variance-optimal measure. Furthermore, we present a numerical study that illustrates aspects or our theoretical results.", "published": "2017-10-02T20:35:41Z", "authors": ["Kamil Kladivko", "Mihail Zervos"], "categories": ["q-fin.PR", "math.PR"], "doi": "", "_query": "automated valuation model", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/1404.7314v1", "title": "Nonlinear Valuation under Collateral, Credit Risk and Funding Costs: A Numerical Case Study Extending Black-Scholes", "abstract": "We develop an arbitrage-free framework for consistent valuation of derivative trades with collateralization, counterparty credit gap risk, and funding costs, following the approach first proposed by Pallavicini and co-authors in 2011. Based on the risk-neutral pricing principle, we derive a general pricing equation where Credit, Debit, Liquidity and Funding Valuation Adjustments (CVA, DVA, LVA and FVA) are introduced by simply modifying the payout cash-flows of the deal. Funding costs and specific close-out procedures at default break the bilateral nature of the deal price and render the valuation problem a non-linear and recursive one. CVA and FVA are in general not really additive adjustments, and the risk for double counting is concrete. We introduce a new adjustment, called a Non-linearity Valuation Adjustment (NVA), to address double-counting. The theoretical risk free rate disappears from our final equations. The framework can be tailored also to CCP trading under initial and variation margins, as explained in detail in Brigo and Pallavicini (2014). In particular, we allow for asymmetric collateral and funding rates, replacement close-out and re-hypothecation. The valuation equation takes the form of a backward stochastic differential equation or semi-linear partial differential equation, and can be cast as a set of iterative equations that can be solved by least-squares Monte Carlo. We propose such a simulation algorithm in a case study involving a generalization of the benchmark model of Black and Scholes for option pricing. Our numerical results confirm that funding risk has a non-trivial impact on the deal price, and that double counting matters too. We conclude the article with an analysis of large scale implications of non-linearity of the pricing equations.", "published": "2014-04-29T11:29:15Z", "authors": ["Damiano Brigo", "Qing Liu", "Andrea Pallavicini", "David Sloth"], "categories": ["q-fin.PR"], "doi": "", "_query": "automated valuation model", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/1007.4658v2", "title": "Extending a valuation centered in a local domain to the formal completion", "abstract": "Let (R; m; k) be a local noetherian domain with field of fractions K and R_v a valuation ring, dominating R (not necessarily birationally). Let v|K be the restriction of v to K; by definition, v|K is centered at R. Let \\hat{R} denote the m-adic completion of R. In the applications of valuation theory to commutative algebra and the study of singularities, one is often induced to replace R by its m-adic completion \\hat{R} and v by a suitable extension \\hat{v} to \\hat{R}/P for a suitably chosen prime ideal P, such that P \\cap R = (0). The purpose of this paper is to give, assuming that R is excellent, a systematic description of all such extensions \\hat{v} and to identify certain classes of extensions which are of particular interest for applications.", "published": "2010-07-27T09:53:04Z", "authors": ["F. J. Herrera Govantes", "M. A. Olalla Acosta", "M. Spivakovsky", "B. Teissier"], "categories": ["math.AG"], "doi": "10.1112/plms/pds002", "_query": "automated valuation model", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2308.15709v2", "title": "Threshold KNN-Shapley: A Linear-Time and Privacy-Friendly Approach to Data Valuation", "abstract": "Data valuation aims to quantify the usefulness of individual data sources in training machine learning (ML) models, and is a critical aspect of data-centric ML research. However, data valuation faces significant yet frequently overlooked privacy challenges despite its importance. This paper studies these challenges with a focus on KNN-Shapley, one of the most practical data valuation methods nowadays. We first emphasize the inherent privacy risks of KNN-Shapley, and demonstrate the significant technical difficulties in adapting KNN-Shapley to accommodate differential privacy (DP). To overcome these challenges, we introduce TKNN-Shapley, a refined variant of KNN-Shapley that is privacy-friendly, allowing for straightforward modifications to incorporate DP guarantee (DP-TKNN-Shapley). We show that DP-TKNN-Shapley has several advantages and offers a superior privacy-utility tradeoff compared to naively privatized KNN-Shapley in discerning data quality. Moreover, even non-private TKNN-Shapley achieves comparable performance as KNN-Shapley. Overall, our findings suggest that TKNN-Shapley is a promising alternative to KNN-Shapley, particularly for real-world applications involving sensitive data.", "published": "2023-08-30T02:12:00Z", "authors": ["Jiachen T. Wang", "Yuqing Zhu", "Yu-Xiang Wang", "Ruoxi Jia", "Prateek Mittal"], "categories": ["cs.LG", "cs.CR", "cs.GT", "stat.ML"], "doi": "", "_query": "automated valuation model", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2309.06255v4", "title": "Enhancing multimodal cooperation via sample-level modality valuation", "abstract": "One primary topic of multimodal learning is to jointly incorporate heterogeneous information from different modalities. However most models often suffer from unsatisfactory multimodal cooperation which cannot jointly utilize all modalities well. Some methods are proposed to identify and enhance the worse learnt modality but they are often hard to provide the fine-grained observation of multimodal cooperation at sample-level with theoretical support. Hence it is essential to reasonably observe and improve the fine-grained cooperation between modalities especially when facing realistic scenarios where the modality discrepancy could vary across different samples. To this end we introduce a sample-level modality valuation metric to evaluate the contribution of each modality for each sample. Via modality valuation we observe that modality discrepancy indeed could be different at sample-level beyond the global contribution discrepancy at dataset-level. We further analyze this issue and improve cooperation between modalities at sample-level by enhancing the discriminative ability of low-contributing modalities in a targeted manner. Overall our methods reasonably observe the fine-grained uni-modal contribution and achieve considerable improvement. The source code and dataset are available at https://github.com/GeWu-Lab/Valuate-and-Enhance-Multimodal-Cooperation.", "published": "2023-09-12T14:16:34Z", "authors": ["Yake Wei", "Ruoxuan Feng", "Zihe Wang", "Di Hu"], "categories": ["cs.CV", "cs.AI", "cs.LG", "cs.MM"], "doi": "", "_query": "automated valuation model", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2208.12354v1", "title": "Fundamentals of Task-Agnostic Data Valuation", "abstract": "We study valuing the data of a data owner/seller for a data seeker/buyer. Data valuation is often carried out for a specific task assuming a particular utility metric, such as test accuracy on a validation set, that may not exist in practice. In this work, we focus on task-agnostic data valuation without any validation requirements. The data buyer has access to a limited amount of data (which could be publicly available) and seeks more data samples from a data seller. We formulate the problem as estimating the differences in the statistical properties of the data at the seller with respect to the baseline data available at the buyer. We capture these statistical differences through second moment by measuring diversity and relevance of the seller's data for the buyer; we estimate these measures through queries to the seller without requesting raw data. We design the queries with the proposed approach so that the seller is blind to the buyer's raw data and has no knowledge to fabricate responses to queries to obtain a desired outcome of the diversity and relevance trade-off.We will show through extensive experiments on real tabular and image datasets that the proposed estimates capture the diversity and relevance of the seller's data for the buyer.", "published": "2022-08-25T22:07:07Z", "authors": ["Mohammad Mohammadi Amiri", "Frederic Berdoz", "Ramesh Raskar"], "categories": ["cs.LG", "cs.AI", "cs.CY", "cs.IT", "stat.ML"], "doi": "", "_query": "automated valuation model", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2607.26652v1", "title": "AIGen: Automating AI Bill of Materials Generation Through Hybrid MLOps Integration", "abstract": "The responsible development and deployment of artificial intelligence (AI) systems requires rigorous documentation of their constituent artifacts, e.g., datasets, model weights, training pipelines, and runtime dependencies. Although the Software Package Data Exchange (SPDX) 3.0 standard introduced native support for AI and dataset profiles, practical tooling capable of generating standards-compliant AI Bills of Materials (AIBoMs) in an automated and extensible manner remains scarce. This paper presents AIGen, a modular AIBoM generator that produces machine-readable, interoperable inventories of AI system components that comply with the SPDX 3.0 AI profile. AIGen works on top of the MLflow MLOps framework and combines mining heuristics with Large Language Models to generate AIBoMs. A plugin interface allows practitioners to extend the tool with domain-specific collectors without modifying the core codebase, supporting heterogeneous AI frameworks such as Hugging Face, PyTorch, and TensorFlow. AIGen is designed to facilitate compliance with the European Union AI Act, the NIST AI Risk Management Framework, and ISO/IEC 42001, providing a concrete, reusable foundation for transparent, accountable AI supply chain governance. Tool URL: https://github.com/danielebifolco/AIGen Tool Video: https://youtu.be/\\_nAbXDWfVL4", "published": "2026-07-29T09:13:00Z", "authors": ["Federica Pepe", "Daniele Bifolco", "Costantino Martignetti", "Aureliano D'Amici", "Fabiano Izzo", "Damian A. Tamburri", "Massimiliano Di Penta"], "categories": ["cs.LG"], "doi": "", "_query": "automated valuation model", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/1207.7279v1", "title": "Rotation equivariant Minkowski valuations", "abstract": "The projection body operator Π, which associates with every convex body in Euclidean space Rn its projection body, is a continuous valuation, it is invariant under translations and equivariant under rotations. It is also well known that Π maps the set of polytopes in Rn into itself. We show that Π is the only non-trivial operator with these properties.", "published": "2012-07-31T15:09:09Z", "authors": ["Rolf Schneider", "Franz E. Schuster"], "categories": ["math.MG", "math.DG", "math.FA"], "doi": "", "_query": "automated valuation model", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2605.17872v2", "title": "A note on the $q$-adic valuation of $σ_k(n)$", "abstract": "In this note, we obtain an exact formula for the $q$-adic valuation of $σ_k(n)$ where $q$ is an odd prime, allowing us to derive an explicit upper bound which is asymptotically better than the previous bound obtained by Zhao when $n$ is large and $k \\geqslant q-2$. The key parts are played by the LTE lemma and the use of cyclotomic polynomials.", "published": "2026-05-18T05:33:41Z", "authors": ["Olivier Bordellès"], "categories": ["math.NT"], "doi": "", "_query": "automated valuation model", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2511.01011v2", "title": "A logic of co-valuations", "abstract": "A co-valuation is, essentially, a minimal finite cover. We introduce a logic based on co-valuations, which play the role of valuations of free variables in classical first-order logic, and show that the fundamental tools of model theory -- such as ultraproducts, compactness, and omitting types -- can be developed in this setup. Using a recently discovered duality between certain countable posets and second-countable compact $T_1$ spaces, we show that these spaces are counterparts of countable universes in first-order logic. Thus, although no topology appears in the initial formulation, the logic of co-valuations turns out to be naturally suited for studying compact topological objects. Standard topological notions, such as connectedness and covering dimension, are easily expressible, and model-theoretic properties, such as atomicity, can be effectively analyzed. The framework also interacts well with Fraïssé-type constructions.", "published": "2025-11-02T17:02:33Z", "authors": ["Maciej Malicki"], "categories": ["math.LO"], "doi": "", "_query": "automated valuation model", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2512.15765v4", "title": "Shapley-based Data Valuation for LLM Alignment via Sequential Preference Optimization", "abstract": "Data valuation is a natural framework for understanding which data sources matter most when aligning a Large Language Model (LLM) from multiple sources. The standard game-theoretic approach treats each source, or equivalently each preference dataset, as a player in a cooperative game and assigns it a contribution score through the Shapley value. In practice, however, Shapley-based valuation is computationally prohibitive because it requires aligning a separate model for every possible coalition of sources, i.e., an exponential number of alignments. We address this challenge for Direct Alignment Algorithms (DAAs), including IPO, which learn through log-policy ratios with respect to a reference policy. We show that, when a model is aligned sequentially source by source, exact optimization makes each stage contribute additively to the log-probability of a full response, up to a prompt-dependent normalization constant. This allows the log-probability assigned by any coalition to a fixed response to be reconstructed from the base policy and the policies trained on each source individually. This reduces the alignment cost of Shapley-based valuation from exponential to linear, since only one model per source needs to be trained to evaluate coalition scores. We test whether this theoretical property remains approximately valid under finite training across several base models and real-world data sources. We finally compute the Shapley values of these sources under multiple reward models, showing how their estimated contributions vary across evaluation criteria.", "published": "2025-12-12T10:13:54Z", "authors": ["Mélissa Tamine", "Otmane Sakhi", "Benjamin Heymann", "Maxime Vono", "Patrick Loiseau"], "categories": ["cs.LG", "cs.GT", "stat.ML"], "doi": "", "_query": "automated valuation model", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/1804.07997v1", "title": "Valuation of contingent convertible catastrophe bonds - the case for equity conversion", "abstract": "Within the context of the banking-related literature on contingent convertible bonds, we comprehensively formalise the design and features of a relatively new type of insurance-linked security, called a contingent convertible catastrophe bond (CocoCat). We begin with a discussion of its design and compare its relative merits to catastrophe bonds and catastrophe-equity puts. Subsequently, we derive analytical valuation formulae for index-linked CocoCats under the assumption of independence between natural catastrophe and financial markets risks. We model natural catastrophe losses by a time-inhomogeneous compound Poisson process, with the interest-rate process governed by the Longstaff model. By using an exponential change of measure on the loss process, as well as a Girsanov-like transformation to synthetically remove the correlation between the share and interest-rate processes, we obtain these analytical formulae. Using selected parameter values in line with earlier research, we empirically analyse our valuation formulae for index-linked CocoCats. An analysis of the results reveals that the CocoCat prices are most sensitive to changing interest-rates, conversion fractions and the threshold levels defining the trigger times.", "published": "2018-04-21T16:57:08Z", "authors": ["Krzysztof Burnecki", "Mario Nicoló Giuricich", "Zbigniew Palmowski"], "categories": ["q-fin.PR"], "doi": "", "_query": "automated valuation model", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2507.10196v2", "title": "Non-smooth optimization meets automated material model discovery", "abstract": "Automated material model discovery disrupts the tedious and time-consuming cycle of iteratively calibrating and modifying manually designed models. Non-smooth L1-norm regularization is the backbone of automated model discovery; however, the current literature on automated material model discovery offers limited insights into the robust and efficient minimization of non-smooth objective functions. In this work, we examine the minimization of functions of the form f(w) + a ||w||_1, where w are the material model parameters, f is a metric that quantifies the mismatch between the material model and the observed data, and a is a regularization parameter that determines the sparsity of the solution. We investigate both the straightforward case where f is quadratic and the more complex scenario where it is non-quadratic or even non-convex. Importantly, we do not only focus on methods that solve the sparse regression problem for a given value of the regularization parameter a, but propose methods to efficiently compute the entire regularization path, facilitating the selection of a suitable a. Specifically, we present four algorithms and discuss their roles for automated material model discovery in mechanics: First, we recapitulate a well-known coordinate descent algorithm that solves the minimization problem assuming that f is quadratic for a given value of a, also known as the LASSO. Second, we discuss the algorithm LARS, which automatically determines the critical values of a, at which material parameters in w are set to zero. Third, we propose to use the proximal gradient method ISTA for automated material model discovery if f is not quadratic, and fourth, we suggest a pathwise extension of ISTA for computing the regularization path. We demonstrate the applicability of all algorithms for the discovery of hyperelastic material models from uniaxial tension and simple shear data.", "published": "2025-07-14T12:03:06Z", "authors": ["Moritz Flaschel", "Trevor Hastie", "Ellen Kuhl"], "categories": ["cs.CE", "cond-mat.mtrl-sci"], "doi": "", "_query": "automated valuation model", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/1606.06028v2", "title": "Rotation covariant local tensor valuations on convex bodies", "abstract": "For valuations on convex bodies in Euclidean spaces, there is by now a long series of characterization and classification theorems. The classical template is Hadwiger's theorem, saying that every rigid motion invariant, continuous, real-valued valuation on convex bodies in $\\mathbb{R}^n$ is a linear combination of the intrinsic volumes. For tensor-valued valuations, under the assumptions of isometry covariance and continuity, there is a similar classification theorem, due to Alesker. Also for the local extensions of the intrinsic volumes, the support, curvature and area measures, there are analogous characterization results, with continuity replaced by weak continuity, and involving an additional assumption of local determination. The present authors have recently obtained a corresponding characterization result for local tensor valuations, or tensor-valued support measures (generalized curvature measures), of convex bodies in $\\mathbb{R}^n$. The covariance assumed there was with respect to the group ${\\rm O}(n)$ of orthogonal transformations. This was suggested by Alesker's observation, according to which in dimensions $n> 2$, the weaker assumption of ${\\rm SO}(n)$ covariance does not yield more tensor valuations. However, for tensor-valued support measures, the distinction between proper and improper rotations does make a difference. The present paper considers, therefore, the local tensor valuations sharing the previously assumed properties, but with ${\\rm O}(n)$ covariance replaced by ${\\rm SO}(n)$ covariance, and provides a complete classification. New tensor valued support measures appear only in dimensions two and three.", "published": "2016-06-20T09:30:04Z", "authors": ["Daniel Hug", "Rolf Schneider"], "categories": ["math.MG"], "doi": "10.1142/S0219199716500619", "_query": "automated valuation model", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/1101.0079v1", "title": "Market-consistent valuation of insurance liabilities by cost of capital", "abstract": "This paper investigates market-consistent valuation of insurance liabilities in the context of, for instance, Solvency II and to some extent IFRS 4. We propose an explicit and consistent framework for the valuation of insurance liabilities which incorporates the Solvency II approach as a special case. The proposed framework is based on dynamic replication over multiple (one-year) time periods by a portfolio of assets with reliable market prices, allowing for \"limited liability\" in the sense that the replication can in general not always be continued. The asset portfolio consist of two parts: (1) assets whose market price defines the value of the insurance liabilities, and (2) capital funds used to cover risk which cannot be replicated. The capital funds give rise to capital costs; the main exogenous input of the framework is the condition on when the investment of the capital funds is acceptable. We investigate existence of the value and show that the exact calculation of the value has to be done recursively backwards in time, starting at the end of the lifetime of the insurance liabilities. The main question only partially considered in this paper is the uniqueness of the value. We derive upper bounds on the value and, for the special case of replication by risk-free one-year zero-coupon bonds, explicit recursive formulas for calculating the value. Valuation in Solvency II and IFRS 4 is based on representing the value as a sum of a \"best estimate\" and a \"risk margin\". In our framework, it turns out that this split is not natural. Nonetheless, we show that a split can be constructed as a simplification, and that it provides an upper bound on the value under suitable conditions. We illustrate the general results by explicitly calculating the value for a simple example.", "published": "2010-12-30T13:04:52Z", "authors": ["Christoph Moehr"], "categories": ["q-fin.PR", "q-fin.GN"], "doi": "", "_query": "automated valuation model", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2210.08723v3", "title": "Private Data Valuation and Fair Payment in Data Marketplaces", "abstract": "Data valuation is an essential task in a data marketplace. It aims at fairly compensating data owners for their contribution. There is increasing recognition in the machine learning community that the Shapley value -- a foundational profit-sharing scheme in cooperative game theory -- has major potential to value data, because it uniquely satisfies basic properties for fair credit allocation and has been shown to be able to identify data sources that are useful or harmful to model performance. However, calculating the Shapley value requires accessing original data sources. It still remains an open question how to design a real-world data marketplace that takes advantage of the Shapley value-based data pricing while protecting privacy and allowing fair payments. In this paper, we propose the {\\em first} prototype of a data marketplace that values data sources based on the Shapley value in a privacy-preserving manner and at the same time ensures fair payments. Our approach is enabled by a suite of innovations on both algorithm and system design. We firstly propose a Shapley value calculation algorithm that can be efficiently implemented via multiparty computation (MPC) circuits. The key idea is to learn a performance predictor that can directly predict model performance corresponding to an input dataset without performing actual training. We further optimize the MPC circuit design based on the structure of the performance predictor. We further incorporate fair payment into the MPC circuit to guarantee that the data that the buyer pays for is exactly the same as the one that has been valuated. Our experimental results show that the proposed new data valuation algorithm is as effective as the original expensive one. Furthermore, the customized MPC protocol is efficient and scalable.", "published": "2022-10-17T03:39:54Z", "authors": ["Zhihua Tian", "Jian Liu", "Jingyu Li", "Xinle Cao", "Ruoxi Jia", "Jun Kong", "Mengdi Liu", "Kui Ren"], "categories": ["cs.CR"], "doi": "", "_query": "automated valuation model", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/1609.07800v2", "title": "Schottky Groups over Valuation Rings", "abstract": "Given a non-trivial complete valued field $K$ with value group $Λ$, we construct a $Λ$-tree space associated to $K$ analog of the Bruhat-Tits tree, and locally finite trees associated to compact subsets of the projective line. We propose a definition of hyperbolic matrix and Schottky group over such field $K$. To any such Schottky group $Γ$, we associate a compact set with an action of $Γ$, such that the quotient graph of the associated tree is a finite graph, and $Γ$ is identified with its fundamental group. Finally explain a method to construct such groups. This results extend the classical ones for discrete valuations of Mumford and non-archimedean rank 1 valuations of Gerritzen and Van der Put.", "published": "2016-09-25T21:05:18Z", "authors": ["Xavier Xarles", "Dani Samaniego"], "categories": ["math.AG", "math.GR", "math.NT"], "doi": "", "_query": "automated valuation model", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2206.11003v2", "title": "Integer-valued polynomials on discrete valuation rings of global fields with prescribed lengths of factorizations", "abstract": "Let $V$ be a valuation ring of a global field $K$. We show that for all positive integers $k$ and $1 < n_1 \\leq \\ldots \\leq n_k$ there exists an integer-valued polynomial on $V$, that is, an element of $\\text{Int}(V) = \\{ f \\in K[X] \\mid f(V) \\subseteq V \\}$, which has precisely $k$ essentially different factorizations into irreducible elements of $\\text{Int}(V)$ whose lengths are exactly $n_1,\\ldots,n_k$. In fact, we show more, namely that the same result holds true for every discrete valuation domain $V$ with finite residue field such that the quotient field of $V$ admits a valuation ring independent of $V$ whose maximal ideal is principal or whose residue field is finite. If the quotient field of $V$ is a purely transcendental extension of an arbitrary field, this property is satisfied. This solves an open problem proposed by Cahen, Fontana, Frisch and Glaz in these cases.", "published": "2022-06-22T12:07:28Z", "authors": ["Victor Fadinger", "Sophie Frisch", "Daniel Windisch"], "categories": ["math.NT", "math.AC"], "doi": "", "_query": "automated valuation model", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/1102.1520v1", "title": "Dominance and Transmissions in Supertropical Valuation Theory", "abstract": "This paper is a sequel of [IKR1], where we defined supervaluations on a commutative ring $R$ and studied a dominance relation $φ\\geq ψ$ between supervaluations $φ$ and $ψ$ on $R$, aiming at an enrichment of the algebraic tool box for use in tropical geometry. A supervaluation $φ:R \\to U$ is a multiplicative map from $R$ to a supertropical semiring $U$, cf. [IR1], [IR2], [IKR1], with further properties, which mean that $φ$ is a sort of refinement, or covering, of an m-valuation (= monoid valuation) $v: R \\to M$. In the most important case, that $R$ is a ring, m-valuations constitute a mild generalization of valuations in the sense of Bourbaki [B], while $φ\\geq ψ$ means that $ψ: R \\to V$ is a sort of coarsening of the supervaluation $φ$. If $φ(R)$ generates the semiring $U$, then $φ\\geq ψ$ iff there exists a \"transmission\" $α: U \\to V$ with $ψ= α\\circ φ$. Transmissions are multiplicative maps with further properties, cf. [IKR1, Sec. 5]. Every semiring homomorphism $α: U \\to V$ is a transmission, but there are others which lack additivity, and this causes a major difficulty. In the main body of the paper we study surjective transmissions via equivalence relations on supertropical semirings, often much more complicated than congruences by ideals in usual commutative algebra.", "published": "2011-02-08T07:13:08Z", "authors": ["Zur Izhakian", "Manfred Knebusch", "Louis Rowen"], "categories": ["math.AC", "math.AG", "math.RA"], "doi": "", "_query": "automated valuation model", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2005.13781v4", "title": "A Maneuver-based Urban Driving Dataset and Model for Cooperative Vehicle Applications", "abstract": "Short-term future of automated driving can be imagined as a hybrid scenario in which both automated and human-driven vehicles co-exist in the same environment. In order to address the needs of such road configuration, many technology solutions such as vehicular communication and predictive control for automated vehicles have been introduced in the literature. Both aforementioned solutions rely on driving data of the human driver. In this work, we investigate the currently available driving datasets and introduce a real-world maneuver-based driving dataset that is collected during our urban driving data collection campaign. We also provide a model that embeds the patterns in maneuver-specific samples. Such model can be employed for classification and prediction purposes.", "published": "2020-05-28T05:14:30Z", "authors": ["Behrad Toghi", "Divas Grover", "Mahdi Razzaghpour", "Rajat Jain", "Rodolfo Valiente", "Mahdi Zaman", "Ghayoor Shah", "Yaser P. Fallah"], "categories": ["eess.SP", "cs.RO"], "doi": "", "_query": "automated valuation model", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/1106.0984v3", "title": "An extension of Greenberg's theorem to general valuation rings", "abstract": "We extend Greenberg's strong approximation theorem to schemes of finite presentation over valuation rings with arbitrary value group, using the ultraproduct method of Becker, Denef, Lipshitz and van den Dries. As an application, we prove a closed image theorem (in the strong topology on rational points) for proper morphisms of varieties over valued fields.", "published": "2011-06-06T08:28:33Z", "authors": ["Laurent Moret-Bailly"], "categories": ["math.AG", "math.AC"], "doi": "10.1007/s00229-011-0510-5", "_query": "automated valuation model", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2010.15093v1", "title": "Toward finite generation of higher rational rank valuations", "abstract": "We propose a finite generation conjecture for the valuation which computes the stability threshold of a log Fano pair. We also initiate a degeneration strategy of attacking the conjecture.", "published": "2020-10-28T17:15:00Z", "authors": ["Chenyang Xu"], "categories": ["math.AG"], "doi": "10.1070/SM9448", "_query": "automated valuation model", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/1610.07832v1", "title": "Valuation Theory, Riemann Varieties and the Structure of integral Preschemes", "abstract": "In this work we show that the classical subject of general valuation theory and Zariski-Riemann varieties has a much wider scope than commutative algebra and desingularization theory. We construct and investigate birational projective limit objects appropriate for the study of countably many birational models at one time. We use nonseparated Riemann varieties to investigate the birational structure of integral preschemes satisfying the existence condition of the valuative criterion of properness.", "published": "2016-10-25T11:27:13Z", "authors": ["Stefan Günther"], "categories": ["math.AG"], "doi": "", "_query": "automated valuation model", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/1707.05561v2", "title": "Stability of Valuations: Higher Rational Rank", "abstract": "Given a klt singularity $x\\in (X, D)$, we show that a quasi-monomial valuation $v$ with a finitely generated associated graded ring is the minimizer of the normalized volume function $\\widehat{\\rm vol}_{(X,D),x}$, if and only if $v$ induces a degeneration to a K-semistable log Fano cone singularity. Moreover, such a minimizer is unique among all quasi-monomial valuations up to rescaling. As a consequence, we prove that for a klt singularity $x\\in X$ on the Gromov-Hausdorff limit of Kähler-Einstein Fano manifolds, the intermediate K-semistable cone associated to its metric tangent cone is uniquely determined by the algebraic structure of $x\\in X$, hence confirming a conjecture by Donaldson-Sun.", "published": "2017-07-18T11:10:55Z", "authors": ["Chi Li", "Chenyang Xu"], "categories": ["math.AG", "math.DG"], "doi": "", "_query": "automated valuation model", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2305.01657v1", "title": "Scalable Data Point Valuation in Decentralized Learning", "abstract": "Existing research on data valuation in federated and swarm learning focuses on valuing client contributions and works best when data across clients is independent and identically distributed (IID). In practice, data is rarely distributed IID. We develop an approach called DDVal for decentralized data valuation, capable of valuing individual data points in federated and swarm learning. DDVal is based on sharing deep features and approximating Shapley values through a k-nearest neighbor approximation method. This allows for novel applications, for example, to simultaneously reward institutions and individuals for providing data to a decentralized machine learning task. The valuation of data points through DDVal allows to also draw hierarchical conclusions on the contribution of institutions, and we empirically show that the accuracy of DDVal in estimating institutional contributions is higher than existing Shapley value approximation methods for federated learning. Specifically, it reaches a cosine similarity in approximating Shapley values of 99.969 % in both, IID and non-IID data distributions across institutions, compared with 99.301 % and 97.250 % for the best state of the art methods. DDVal scales with the number of data points instead of the number of clients, and has a loglinear complexity. This scales more favorably than existing approaches with an exponential complexity. We show that DDVal is especially efficient in data distribution scenarios with many clients that have few data points - for example, more than 16 clients with 8,000 data points each. By integrating DDVal into a decentralized system, we show that it is not only suitable for centralized federated learning, but also decentralized swarm learning, which aligns well with the research on emerging internet technologies such as web3 to reward users for providing data to algorithms.", "published": "2023-05-01T22:15:50Z", "authors": ["Konstantin D. Pandl", "Chun-Yin Huang", "Ivan Beschastnikh", "Xiaoxiao Li", "Scott Thiebes", "Ali Sunyaev"], "categories": ["cs.LG", "cs.AI", "cs.DC"], "doi": "", "_query": "automated valuation model", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/1704.02505v1", "title": "Good Deal Hedging and Valuation under Combined Uncertainty about Drift and Volatility", "abstract": "We study robust notions of good-deal hedging and valuation under combined uncertainty about the drifts and volatilities of asset prices. Good-deal bounds are determined by a subset of risk-neutral pricing measures such that not only opportunities for arbitrage are excluded but also deals that are too good, by restricting instantaneous Sharpe ratios. A non-dominated multiple priors approach to model uncertainty (ambiguity) leads to worst-case good-deal bounds. Corresponding hedging strategies arise as minimizers of a suitable coherent risk measure. Good-deal bounds and hedges for measurable claims are characterized by solutions to second-order backward stochastic differential equations whose generators are non-convex in the volatility. These hedging strategies are robust with respect to uncertainty in the sense that their tracking errors satisfy a supermartingale property under all a-priori valuation measures, uniformly over all priors.", "published": "2017-04-08T15:00:30Z", "authors": ["Dirk Becherer", "Klebert Kentia"], "categories": ["q-fin.MF", "math.OC", "math.PR", "q-fin.PM"], "doi": "", "_query": "automated valuation model", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/1212.6842v1", "title": "A valuation theoretic characterization of recursively saturated real closed fields", "abstract": "We give a valuation theoretic characterization for a real closed field to be recursively saturated. Our result extends the characterization of Harnik and Ressayre \\cite{hr} for a divisible ordered abelian group to be recursively saturated.", "published": "2012-12-31T08:51:09Z", "authors": ["Paola D'Aquino", "Salma Kuhlmann", "Karen Lange"], "categories": ["math.LO", "math.AC"], "doi": "", "_query": "automated valuation model", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2607.03115v1", "title": "Beyond the Fixed Price: Valuation and Risk of Non-Standard Renewable PPAs", "abstract": "Renewable Power Purchase Agreements have become increasingly important instruments for supporting the energy transition, as they offer revenue stability to renewable energy producers and price certainty to electricity consumers. This paper develops a financial framework for the valuation and risk assessment of fixed-price renewable PPAs. We formalize the payoff structures of the main PPA designs adopted in practice for wind and photovoltaic generation and derive fair contract prices based on financial valuation principles. We further propose a market risk-assessment methodology based on Monte Carlo simulation and introduce a parsimonious continuous-time model for solar irradiance suitable for financial applications. An empirical analysis of the Italian electricity market shows that fair prices and risk profiles vary substantially across technologies and contractual structures, highlighting the trade-off between downside protection and participation in favorable market outcomes. This framework provides practical tools for the pricing and risk evaluation of renewable PPAs.", "published": "2026-07-03T08:56:18Z", "authors": ["Nicola Bartolini", "Silvia Romagnoli", "Amia Santini"], "categories": ["q-fin.PR"], "doi": "", "_query": "automated valuation model", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/0809.4525v1", "title": "Compositions of consistent systems of rank one discrete valuation rings", "abstract": "Let V be a rank one discrete valuation ring (DVR) on a field F and let L/F be a finite separable algebraic field extension with [L:F] = m. The integral closure of V in L is a Dedekind domain that encodes the following invariants: (i) the number of extensions of V to a valuation ring W on L, (ii) the residue degree of each W over V, and (iii) the ramification degree of each W over V. Given a finite set of DVRs on F, an m-consistent system is a family of sets enumerating what is theoretically possible for the above invariants of each V in the set. The m-consistent system is realizable if there exists a finite separable extension field L/F that gives for each V the listed invariants. We investigate the realizability of m-consistent systems.", "published": "2008-09-26T03:34:12Z", "authors": ["William J. Heinzer", "Louis J. Ratliff", "David E. Rush"], "categories": ["math.AC"], "doi": "", "_query": "automated valuation model", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2605.25450v1", "title": "Valuation of Variable Annuities with Equity Protection Swaps under Jumps and Default Risks", "abstract": "This paper examines the valuation and hedging of standard equity protection swap (EPS) products proposed by Xu et al.. To account for financial crises and counterparty default risk, we develop pricing frameworks based on Merton's jump-diffusion model and Szimayer's independent random time default model, under which closed-form valuation formulas and put-call parity relations for European options are derived. Hedging strategies for EPS products are analysed under jump and default risks. While static hedging remains effective in the absence of default, counterparty default risk leads to residual losses that cannot be fully hedged. These losses are quantified and used to define default-adjusted initial premiums under both Black-Scholes and jump-diffusion settings. Numerical results illustrate the effects of jump characteristics and default intensity on hedging costs and premiums, highlighting the importance of incorporating crisis and credit risks in EPS pricing and risk management.", "published": "2026-05-25T06:01:01Z", "authors": ["Marek Rutkowski", "Huansang Xu"], "categories": ["q-fin.MF", "q-fin.RM"], "doi": "", "_query": "automated valuation model", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/1607.04100v1", "title": "Insurance valuation: a computable multi-period cost-of-capital approach", "abstract": "We present an approach to market-consistent multi-period valuation of insurance liability cash flows based on a two-stage valuation procedure. First, a portfolio of traded financial instrument aimed at replicating the liability cash flow is fixed. Then the residual cash flow is managed by repeated one-period replication using only cash funds. The latter part takes capital requirements and costs into account, as well as limited liability and risk averseness of capital providers. The cost-of-capital margin is the value of the residual cash flow. We set up a general framework for the cost-of-capital margin and relate it to dynamic risk measurement. Moreover, we present explicit formulas and properties of the cost-of-capital margin under further assumptions on the model for the liability cash flow and on the conditional risk measures and utility functions. Finally, we highlight computational aspects of the cost-of-capital margin, and related quantities, in terms of an example from life insurance.", "published": "2016-07-14T11:58:28Z", "authors": ["Hampus Engsner", "Mathias Lindholm", "Filip Lindskog"], "categories": ["q-fin.RM"], "doi": "", "_query": "automated valuation model", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/0910.2907v4", "title": "$p$-regularity of the $p$-adic valuation of the Fibonacci sequence", "abstract": "We show that the $p$-adic valuation of the sequence of Fibonacci numbers is a $p$-regular sequence for every prime $p$. For $p \\neq 2, 5$, we determine that the rank of this sequence is $α(p) + 1$, where $α(m)$ is the restricted period length of the Fibonacci sequence modulo $m$.", "published": "2009-10-15T15:37:58Z", "authors": ["Luis A. Medina", "Eric Rowland"], "categories": ["math.NT"], "doi": "", "_query": "automated valuation model", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2601.09299v1", "title": "On the Fair Allocation to Asymmetric Agents with Binary XOS Valuations", "abstract": "We study the problem of allocating $m$ indivisible goods among $n$ agents, where each agent's valuation is fractionally subadditive (XOS). With respect to AnyPrice Share (APS) fairness, Kulkarni et al. (2024) showed that, when agents have binary marginal values, a $0.1222$-APS allocation can be found in polynomial time, and there exists an instance where no allocation is better than $0.5$-approximate APS. Very recently, Feige and Grinberg (2025) extended the problem to the asymmetric case, where agents may have different entitlements, and improved the approximation ratio to $1/6$ for general XOS valuations. In this work, we focus on the asymmetric setting with binary XOS valuations, and further improve the approximation ratio to $1/2$, which matches the known upper bound. We also present a polynomial-time algorithm to compute such an allocation. Beyond APS fairness, we also study the weighted maximin share (WMMS) fairness. Farhadi et al. (2019) showed that, a $1/n$-WMMS allocation always exists for agents with general additive valuations, and that this approximation ratio is tight. We extend this result to general XOS valuations, where a $1/n$-WMMS allocation still exists, and this approximation ratio cannot be improved even when marginal values are binary. This shows a sharp contrast to binary additive valuations, where an exact WMMS allocation exists and can be found in polynomial time.", "published": "2026-01-14T09:05:12Z", "authors": ["Ziheng Chen", "Bo Li", "Zihan Luo", "Jialin Zhang"], "categories": ["cs.GT"], "doi": "", "_query": "automated valuation model", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2208.10183v3", "title": "Valuation of general GMWB annuities in a low interest rate environment", "abstract": "Variable annuities with Guaranteed Minimum Withdrawal Benefits (GMWB) entitle the policy holder to periodic withdrawals together with a terminal payoff linked to the performance of an equity fund. In this paper, we consider the valuation of a general class of GMWB annuities, allowing for step-up, bonus and surrender features, taking also into account mortality risk and death benefits. When dynamic withdrawals are allowed, the valuation of GMWB annuities leads to a stochastic optimal control problem, which we address here by dynamic programming techniques. Adopting a Hull-White interest rate model, correlated with the equity fund, we propose an efficient tree-based algorithm. We perform a thorough analysis of the determinants of the market value of GMWB annuities and of the optimal withdrawal strategies. In particular, we study the impact of a low/negative interest rate environment. Our findings indicate that low/negative rates profoundly affect the optimal withdrawal behaviour and, in combination with step-up and bonus features, increase significantly the fair values of GMWB annuities, which can only be compensated by large management fees.", "published": "2022-08-22T09:50:12Z", "authors": ["Claudio Fontana", "Francesco Rotondi"], "categories": ["q-fin.PR"], "doi": "10.1016/j.insmatheco.2023.07.003", "_query": "automated valuation model", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2510.22830v4", "title": "Exploration of Summarization by Generative Language Models for Automated Scoring of Long Essays", "abstract": "BERT and its variants are extensively explored for automated scoring. However, a limit of 512 tokens for these encoder-based models showed the deficiency in automated scoring of long essays. Thus, this research explores generative language models for automated scoring of long essays via summarization and prompting. The results revealed great improvement of scoring accuracy with QWK increased from 0.822 to 0.8878 for the Learning Agency Lab Automated Essay Scoring 2.0 dataset.", "published": "2025-10-26T20:59:22Z", "authors": ["Haowei Hua", "Hong Jiao", "Xinyi Wang"], "categories": ["cs.CL", "cs.LG"], "doi": "", "_query": "automated valuation model", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2009.03393v1", "title": "Generative Language Modeling for Automated Theorem Proving", "abstract": "We explore the application of transformer-based language models to automated theorem proving. This work is motivated by the possibility that a major limitation of automated theorem provers compared to humans -- the generation of original mathematical terms -- might be addressable via generation from language models. We present an automated prover and proof assistant, GPT-f, for the Metamath formalization language, and analyze its performance. GPT-f found new short proofs that were accepted into the main Metamath library, which is to our knowledge, the first time a deep-learning based system has contributed proofs that were adopted by a formal mathematics community.", "published": "2020-09-07T19:50:10Z", "authors": ["Stanislas Polu", "Ilya Sutskever"], "categories": ["cs.LG", "cs.AI", "cs.CL", "stat.ML"], "doi": "", "_query": "automated valuation model", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/1406.7065v1", "title": "Weak crossed-product orders over valuation rings", "abstract": "Let $F$ be a field, let $V$ be a valuation ring of $F$ of arbitrary Krull dimension (rank), let $K$ be a finite Galois extension of $F$ with group $G$, and let $S$ be the integral closure of $V$ in $K$. Let $f:G\\times G\\mapsto K\\setminus \\{0\\}$ be a normalized two-cocycle such that $f(G\\times G)\\subseteq S\\setminus \\{0\\}$, but we do not require that $f$ should take values in the group of multiplicative units of $S$. One can construct a crossed-product $V$-order $A_f=\\sum_{σ\\in G}Sx_σ$ with multiplication given by $x_σsx_τ=σ(s)f(σ,τ)x_{στ}$ for $s\\in S$, $σ,τ\\in G$. We characterize semihereditary and Dubrovin crossed-product orders, under mild valuation-theoretic assumptions placed on the nature of the extension $K/F$.", "published": "2014-06-27T05:20:36Z", "authors": ["John S. Kauta"], "categories": ["math.RA"], "doi": "10.1016/j.jalgebra.2013.12.014", "_query": "automated valuation model", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/math/0702372v1", "title": "A criterion for rings which are locally valuation", "abstract": "Using the notion of cyclically pure injective modules, a characterization for rings which are locally valuation is established. As applications, new characterizations for Prufer domains and pure semi-simple rings are provided. Namely, we show that a domain R is Prufer if and only if two of the three classes of pure injective, cyclically pure injective and RD-injective modules are equal. Also, we prove that a commutative ring R is pure semi-simple if and only if every R-module is cyclically pure injective.", "published": "2007-02-13T15:51:50Z", "authors": ["Kamran Divaani-Aazar", "Mohammad Ali Esmkhani", "Massoud Tousi"], "categories": ["math.AC"], "doi": "", "_query": "automated valuation model", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2103.12551v2", "title": "Deep Learning for Exotic Option Valuation", "abstract": "A common approach to valuing exotic options involves choosing a model and then determining its parameters to fit the volatility surface as closely as possible. We refer to this as the model calibration approach (MCA). A disadvantage of MCA is that some information in the volatility surface is lost during the calibration process and the prices of exotic options will not in general be consistent with those of plain vanilla options. We consider an alternative approach where the structure of the user's preferred model is preserved but points on the volatility are features input to a neural network. We refer to this as the volatility feature approach (VFA) model. We conduct experiments showing that VFA can be expected to outperform MCA for the volatility surfaces encountered in practice. Once the upfront computational time has been invested in developing the neural network, the valuation of exotic options using VFA is very fast.", "published": "2021-03-22T05:03:28Z", "authors": ["Jay Cao", "Jacky Chen", "John Hull", "Zissis Poulos"], "categories": ["q-fin.CP", "cs.LG"], "doi": "", "_query": "automated valuation model", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/1801.08680v2", "title": "${\\rm SL}(m,C)$-equivariant and translation covariant continuous tensor valuations", "abstract": "The space of continuous, ${\\rm SL}(m,C)$-equivariant, $m\\geq 2$, and translation covariant valuations taking values in the space of real symmetric tensors on $C^m\\cong R^{2m}$ of rank $r\\geq 0$ is completely described. The classification involves the moment tensor valuation for $r\\geq 1$ and is analogous to the classification of the corresponding tensor valuations that are ${\\rm SL}(2m,R)$-equivariant, although the method of proof cannot be adapted.", "published": "2018-01-26T05:47:52Z", "authors": ["Judit Abardia-Evéquoz", "Károly J. Böröczky", "Mátyás Domokos", "Dávid Kertész"], "categories": ["math.MG"], "doi": "", "_query": "automated valuation model", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/1412.1298v1", "title": "Gas Storage valuation with regime switching", "abstract": "In this paper we treat a gas storage valuation problem as a Markov Decision Process. As opposed to existing literature we model the gas price process as a regime-switching model. Such a model has shown to fit market data quite well in Chen and Forsyth (2010). Before we apply a numerical algorithm to solve the problem, we first identify the structure of the optimal injection and withdraw policy. This part extends results in Secomandi (2010). Knowing the structure reduces the complexity of the involved recursion in the algorithms by one variable. We explain the usage and implementation of two algorithms: A Multinomial-Tree Algorithm and a Least-Square Monte Carlo Algorithm. Both algorithms are shown to work for the regime-switching extension. In a numerical study we compare these two algorithms.", "published": "2014-12-03T12:26:24Z", "authors": ["Nicole Bäuerle", "Viola Riess"], "categories": ["q-fin.PR"], "doi": "10.1007/s12667-015-0178-0", "_query": "automated valuation model", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2212.00446v1", "title": "Nontrivial lower bounds for the $p$-adic valuations of some type of rational numbers", "abstract": "In this paper, we will show that the $p$-adic valuation (where $p$ is a given prime number) of some type of rational numbers is unusually large. This generalizes the very recent results by the author and by A. Dubickas, which are both related to the special case $p = 2$. The crucial point for obtaining our main result is the fact that the $p$-adic valuation of the rational numbers in question is unbounded from above. We will confirm this fact by three different methods; the first two are elementary while the third one leans on the $p$-adic analysis.", "published": "2022-12-01T11:36:43Z", "authors": ["Bakir Farhi"], "categories": ["math.NT"], "doi": "", "_query": "automated valuation model", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2512.23743v2", "title": "Hybrid-Code v2: Zero-Hallucination Clinical ICD-10 Coding via Neuro-Symbolic Verification and Automated Knowledge Base Expansion", "abstract": "Automated clinical ICD-10 coding is a high-impact healthcare task requiring a balance between coverage, precision, and safety. While neural approaches achieve strong performance, they suffer from hallucination-generating invalid or unsupported codes-posing unacceptable risks in safety-critical clinical settings. Rule-based systems eliminate hallucination but lack scalability and coverage due to manual knowledge base (KB) curation. We present Hybrid-Code v2, a neuro-symbolic framework that achieves zero Type-I hallucination by construction while maintaining competitive coverage and precision. The system integrates neural candidate generation with a symbolic KB verification layer that enforces validity constraints through multi-layer verification, including format, evidence grounding, negation detection, temporal consistency, and exclusion rules. In addition, we introduce an automated KB expansion mechanism that extracts and validates coding patterns from unlabeled clinical text, addressing the scalability limitations of rule-based systems. Evaluated on the MIMIC-III dataset against ClinicalBERT, BioBERT, rule-based systems, and GPT-4, Hybrid-Code v2 achieves 85% coverage, 92% precision, and 0% Type-I hallucination, outperforming rule-based systems by +40% coverage while eliminating hallucination observed in neural baselines (6-18%). The proposed architecture provides a formal safety guarantee for syntactic validity while preserving strong empirical performance. These results demonstrate that neuro-symbolic verification can enforce safety constraints in neural medical AI systems without sacrificing effectiveness, offering a generalizable design pattern for deploying trustworthy AI in safety-critical domains.", "published": "2025-12-26T02:27:36Z", "authors": ["Yunguo Yu"], "categories": ["cs.SE", "cs.AI"], "doi": "", "_query": "automated valuation model", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2603.29104v1", "title": "Regular rings over valuation rings", "abstract": "Bertin (1972) defined regularity for coherent local rings, and Knaf (2004) studied the property for a local ring $A$ essentially finitely presented over a valuation ring $V$. We discuss several properties of this notion of regularity for such $A$, obtaining results parallel to results for regularity of Noetherian local rings. We include classical and modern topics: openness of loci, perfectoid big Cohen--Macaulay algebras, and cotangent complexes. We also give an application to Noetherian rings, showing a version of Kodaira's vanishing theorem in large enough residue characteristics.", "published": "2026-03-31T00:51:07Z", "authors": ["Shiji Lyu"], "categories": ["math.AC"], "doi": "", "_query": "automated valuation model", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/1004.3935v1", "title": "Indecomposable and Noncrossed Product Division Algebras over Curves over Complete Discrete Valuation Rings", "abstract": "Let T be a complete discrete valuation ring and $\\hat{X}$ a smooth projective curve over $S=\\spec(T)$ with closed fibre $X$. Denote by $F$ the function field of $\\hat{X}$ and by $\\hat{F}$ the completion of $F$ with respect to the discrete valuation defined by $X$, the closed fibre. In this paper, we construct indecomposable and noncrossed product division algebras over $F$. This is done by defining an index preserving group homomorphism $s:\\br(\\hat{F})'\\to\\br(F)'$, and using it to lift indecomposable and noncrossed product division algebras over $\\hat{F}$.", "published": "2010-04-22T14:41:33Z", "authors": ["Feng Chen"], "categories": ["math.RA"], "doi": "", "_query": "automated valuation model", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/1808.00131v5", "title": "A Theory of Dichotomous Valuation with Applications to Variable Selection", "abstract": "An econometric or statistical model may undergo a marginal gain if we admit a new variable to the model, and a marginal loss if we remove an existing variable from the model. Assuming equality of opportunity among all candidate variables, we derive a valuation framework by the expected marginal gain and marginal loss in all potential modeling scenarios. However, marginal gain and loss are not symmetric; thus, we introduce three unbiased solutions. When used in variable selection, our new approaches significantly outperform several popular methods used in practice. The results also explore some novel traits of the Shapley value.", "published": "2018-08-01T01:30:22Z", "authors": ["Xingwei Hu"], "categories": ["stat.ML", "cs.LG", "econ.EM", "math.OC", "math.ST"], "doi": "10.1080/07474938.2020.1735750", "_query": "automated valuation model", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2308.09080v1", "title": "Pedestrian Environment Model for Automated Driving", "abstract": "Besides interacting correctly with other vehicles, automated vehicles should also be able to react in a safe manner to vulnerable road users like pedestrians or cyclists. For a safe interaction between pedestrians and automated vehicles, the vehicle must be able to interpret the pedestrian's behavior. Common environment models do not contain information like body poses used to understand the pedestrian's intent. In this work, we propose an environment model that includes the position of the pedestrians as well as their pose information. We only use images from a monocular camera and the vehicle's localization data as input to our pedestrian environment model. We extract the skeletal information with a neural network human pose estimator from the image. Furthermore, we track the skeletons with a simple tracking algorithm based on the Hungarian algorithm and an ego-motion compensation. To obtain the 3D information of the position, we aggregate the data from consecutive frames in conjunction with the vehicle position. We demonstrate our pedestrian environment model on data generated with the CARLA simulator and the nuScenes dataset. Overall, we reach a relative position error of around 16% on both datasets.", "published": "2023-08-17T16:10:58Z", "authors": ["Adrian Holzbock", "Alexander Tsaregorodtsev", "Vasileios Belagiannis"], "categories": ["cs.CV", "cs.RO"], "doi": "", "_query": "automated valuation model", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2403.11474v3", "title": "The 2-adic valuations of the algebraic central $L$-values for quadratic twists of weight 2 newforms", "abstract": "Let $f$ be a normalized newform of weight 2 on $Γ_0(N)$ whose coefficients lie in $\\mathbb{Q}$ and let $χ_M$ be a primitive quadratic Dirichlet character with conductor $M$. In this paper, under mild assumptions on $M$, we give a sharp lower bound of the 2-adic valuation of the algebraic central $L$-value $L(f, χ_M, 1)$ and evaluate the 2-adic valuation for an infinite number of $M$.", "published": "2024-03-18T04:46:01Z", "authors": ["Taiga Adachi", "Keiichiro Nomoto", "Ryota Shii"], "categories": ["math.NT"], "doi": "", "_query": "automated valuation model", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/1411.1934v1", "title": "Even Minkowski Valuations", "abstract": "A new integral representation of smooth translation invariant and rotation equivariant even Minkowski valuations is established. Explicit formulas relating previously obtained descriptions of such valuations with the new more accessible one are also derived. Moreover, the action of Alesker's Hard Lefschetz operators on these Minkowski valuations is explored in detail.", "published": "2014-11-07T14:37:48Z", "authors": ["Franz E. Schuster", "Thomas Wannerer"], "categories": ["math.MG", "math.FA"], "doi": "", "_query": "automated valuation model", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2201.02658v2", "title": "Fair and efficient contribution valuation for vertical federated learning", "abstract": "Federated learning is an emerging technology for training machine learning models across decentralized data sources without sharing data. Vertical federated learning, also known as feature-based federated learning, applies to scenarios where data sources have the same sample IDs but different feature sets. To ensure fairness among data owners, it is critical to objectively assess the contributions from different data sources and compensate the corresponding data owners accordingly. The Shapley value is a provably fair contribution valuation metric originating from cooperative game theory. However, its straight-forward computation requires extensively retraining a model on each potential combination of data sources, leading to prohibitively high communication and computation overheads due to multiple rounds of federated learning. To tackle this challenge, we propose a contribution valuation metric called vertical federated Shapley value (VerFedSV) based on the classic Shapley value. We show that VerFedSV not only satisfies many desirable properties of fairness but is also efficient to compute. Moreover, VerFedSV can be adapted to both synchronous and asynchronous vertical federated learning algorithms. Both theoretical analysis and extensive experimental results demonstrate the fairness, efficiency, adaptability, and effectiveness of VerFedSV.", "published": "2022-01-07T19:57:15Z", "authors": ["Zhenan Fan", "Huang Fang", "Xinglu Wang", "Zirui Zhou", "Jian Pei", "Michael P. Friedlander", "Yong Zhang"], "categories": ["cs.LG"], "doi": "", "_query": "automated valuation model", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2504.05563v2", "title": "Do Data Valuations Make Good Data Prices?", "abstract": "As large language models increasingly rely on external data sources, compensating data contributors has become a central concern. But how should these payments be devised? We revisit data valuations from a $\\textit{market-design perspective}$ where payments serve to compensate data owners for the $\\textit{private}$ heterogeneous costs they incur for collecting and sharing data. We show that popular valuation methods-such as Leave-One-Out and Data Shapley-make for poor payments. They fail to ensure truthful reporting of the costs, leading to $\\textit{inefficient market}$ outcomes. To address this, we adapt well-established payment rules from mechanism design, namely Myerson and Vickrey-Clarke-Groves (VCG), to the data market setting. We show that Myerson payment is the minimal truthful mechanism, optimal from the buyer's perspective. Additionally, we identify a condition under which both data buyers and sellers are utility-satisfied, and the market achieves efficiency. Our findings highlight the importance of incorporating incentive compatibility into data valuation design, paving the way for more robust and efficient data markets. Our data market framework is readily applicable to real-world scenarios. We illustrate this with simulations of contributor compensation in an LLM based retrieval-augmented generation (RAG) marketplace tasked with challenging medical question answering.", "published": "2025-04-07T23:34:11Z", "authors": ["Dongyang Fan", "Tyler J. Rotello", "Sai Praneeth Karimireddy"], "categories": ["cs.GT", "cs.LG"], "doi": "", "_query": "automated valuation model", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2505.08935v1", "title": "On the $p$-adic valuations of values of Legendre polynomials", "abstract": "We prove an explicit formula for the $p$-adic valuation of the Legendre polynomials $P_n(x)$ evaluated at a prime $p$, and generalize an old conjecture of the third author. We also solve a problem proposed by Cigler in 2017.", "published": "2025-05-13T20:03:50Z", "authors": ["Max A. Alekseyev", "Tewodros Amdeberhan", "Jeffrey Shallit", "Ingrid Vukusic"], "categories": ["math.NT"], "doi": "", "_query": "automated valuation model", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2104.11552v1", "title": "Fixed points of Minkowski valuations", "abstract": "It is shown that for any sufficiently regular even Minkowski valuation $Φ$ which is homogeneous and intertwines rigid motions, there exists a neighborhood of the unit ball, where balls are the only solutions to the fixed-point problem $Φ^2 K = αK$. This significantly generalizes results by Ivaki for projection bodies and suggests, via the Lutwak--Schneider class reduction technique, a new approach to Petty's conjectured projection inequality.", "published": "2021-04-23T12:20:15Z", "authors": ["Oscar Ortega-Moreno", "Franz E. Schuster"], "categories": ["math.MG", "math.DG", "math.FA"], "doi": "", "_query": "automated valuation model", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2306.05965v3", "title": "Automating Model Comparison in Factor Graphs", "abstract": "Bayesian state and parameter estimation have been automated effectively in a variety of probabilistic programming languages. The process of model comparison on the other hand, which still requires error-prone and time-consuming manual derivations, is often overlooked despite its importance. This paper efficiently automates Bayesian model averaging, selection, and combination by message passing on a Forney-style factor graph with a custom mixture node. Parameter and state inference, and model comparison can then be executed simultaneously using message passing with scale factors. This approach shortens the model design cycle and allows for the straightforward extension to hierarchical and temporal model priors to accommodate for modeling complicated time-varying processes.", "published": "2023-06-09T15:33:30Z", "authors": ["Bart van Erp", "Wouter W. L. Nuijten", "Thijs van de Laar", "Bert de Vries"], "categories": ["cs.LG", "cs.AI", "stat.ML"], "doi": "10.3390/e25081138", "_query": "automated valuation model", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/1503.06064v2", "title": "Radial continuous rotation invariant valuations on star bodies", "abstract": "We characterize the positive radial continuous and rotation invariant valuations $V$ defined on the star bodies of $\\mathbb R^n$ as the applications on star bodies which admit an integral representation with respect to the Lebesgue measure. That is, $$V(K)=\\int_{S^{n-1}}θ(ρ_K)dm,$$ where $θ$ is a positive continuous function, $ρ_K$ is the radial function associated to $K$ and $m$ is the Lebesgue measure on $S^{n-1}$. As a corollary, we obtain that every such valuation can be uniformly approximated on bounded sets by a linear combination of dual quermassintegrals.", "published": "2015-03-20T13:33:40Z", "authors": ["Ignacio Villanueva"], "categories": ["math.MG"], "doi": "10.1016/j.aim.2015.12.030", "_query": "automated valuation model", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2112.03837v1", "title": "Augment & Valuate : A Data Enhancement Pipeline for Data-Centric AI", "abstract": "Data scarcity and noise are important issues in industrial applications of machine learning. However, it is often challenging to devise a scalable and generalized approach to address the fundamental distributional and semantic properties of dataset with black box models. For this reason, data-centric approaches are crucial for the automation of machine learning operation pipeline. In order to serve as the basis for this automation, we suggest a domain-agnostic pipeline for refining the quality of data in image classification problems. This pipeline contains data valuation, cleansing, and augmentation. With an appropriate combination of these methods, we could achieve 84.711% test accuracy (ranked #6, Honorable Mention in the Most Innovative) in the Data-Centric AI competition only with the provided dataset.", "published": "2021-12-07T17:22:44Z", "authors": ["Youngjune Lee", "Oh Joon Kwon", "Haeju Lee", "Joonyoung Kim", "Kangwook Lee", "Kee-Eung Kim"], "categories": ["cs.LG"], "doi": "", "_query": "automated valuation model", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2007.05251v2", "title": "On three-variable expanders over finite valuation rings", "abstract": "Let $\\mathcal{R}$ be a finite valuation ring of order $q^r$. In this paper, we prove that for any quadratic polynomial $f(x,y,z) \\in \\mathcal{R}[x,y,z]$ that is of the form $axy+R(x)+S(y)+T(z)$ for some one-variable polynomials $R, S , T$, we have \\[ |f(A,B,C)| \\gg \\min\\left\\{ q^r, \\frac{|A||B||C|}{q^{2r-1}}\\right\\}\\] for any $A, B, C \\subset \\mathcal{R}$. We also study the sum-product type problems over finite valuation ring $\\mathcal{R}.$ More precisely, we show that for any $A \\subset \\mathcal{R}$ with $|A| \\gg q^{r-1/3}$ then $$\\max\\{ |A \\cdot A|, |A^d + A^d|\\},\\max\\{ |A + A|, |A^2 + A^2|\\},\\max\\{|A-A|,|AA+AA|\\} \\gg |A|^{2/3}q^{r/3},$$ and $|f(A) + A| \\gg |A|^{2/3}q^{r/3}$ for any one variable quadratic polynomial $f$.", "published": "2020-07-10T08:52:48Z", "authors": ["Nguyen Van The", "Phuc D Tran", "Le Quang Ham", "Le Anh Vinh"], "categories": ["math.CO"], "doi": "", "_query": "automated valuation model", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2002.05970v1", "title": "Intertemporal Price Discrimination with Time-Varying Valuations", "abstract": "A firm that sells a non perishable product considers intertemporal price discrimination in the objective of maximizing its long-run average revenue. We consider a general model of patient customers with changing valuations. Arriving customers wait for a random but bounded length of time and purchase the product when its price falls below their valuation, which varies following a stochastic process. We show the optimality of a class of cyclic strategies and obtain an algorithm that yields them. When the pace of intertemporal pricing is constrained to be comparable to the upper bound on customers patience levels, we have a good control on the cycle length and on the structure of the optimizing cyclic policies. The results also hold for forward looking strategic customers as well as under an alternative objective of maximizing infinite horizon discounted revenue. We cast our results in a general framework of optimizing the long-run average revenue for a class of revenue functions that we denote {\\it weakly coupled}, in which the revenue per period depends on a finite number of neighboring prices. We analyze in detail the case of Markovian valuations where we can obtain closed form formulations of the revenue function and some refinements of our main results. We also extend our results to the case of patience levels that are bounded only in expectation.", "published": "2020-02-14T11:23:49Z", "authors": ["Araman Victor", "Fayad Bassam"], "categories": ["math.OC", "math.PR"], "doi": "", "_query": "automated valuation model", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/0809.3405v4", "title": "Analysis of Fourier transform valuation formulas and applications", "abstract": "The aim of this article is to provide a systematic analysis of the conditions such that Fourier transform valuation formulas are valid in a general framework; i.e. when the option has an arbitrary payoff function and depends on the path of the asset price process. An interplay between the conditions on the payoff function and the process arises naturally. We also extend these results to the multi-dimensional case, and discuss the calculation of Greeks by Fourier transform methods. As an application, we price options on the minimum of two assets in Lévy and stochastic volatility models.", "published": "2008-09-19T16:06:16Z", "authors": ["Ernst Eberlein", "Kathrin Glau", "Antonis Papapantoleon"], "categories": ["q-fin.PR", "math.PR"], "doi": "10.1080/13504860903326669", "_query": "automated valuation model", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2609.13803v1", "title": "Automated AFGL quantum number assignment for CO$_2$ isotopologues using a graph neural network", "abstract": "Accurate quantum number assignment for calculated molecular energy levels is a critical bottleneck in generating line broadening parameters for comprehensive line lists for radiative transfer applications. We present an automated pipeline for assigning Air Force Geophysics Laboratory (AFGL) quantum numbers to CO2 calculated rovibrational states lying below 15,000cm$^{-1}$ across all 12 stable isotopologues. A GraphSAGE graph neural network is trained transductively on empirical (MARVEL) energy levels, exploiting inter-isotopologue perturbation chains and intra-isotopologue rotational ladder edges to propagate assignment information to unlabelled calculated states. Physical uniqueness is enforced locally by a Hungarian algorithm solver operating within groups of states sharing the same polyad, rotational quantum number, and parity. A five-generation bootstrap loop iteratively promotes high-confidence predictions into the training set, expanding coverage without additional labelling effort. The pipeline assigns 224,650 previously unlabelled states over 12 isotopologues, accounting for 10.7$\\%$ of all available states (including MARVEL-derived levels), with coverage now increased to 97.4$\\%$ below 5000cm$^{-1}$. The pipeline includes a novel decision tree method for converting AFGL to Herzberg notation in asymmetric isotopologues, while the architecture and Hungarian uniqueness enforcement are applicable beyond CO2, any molecular system with a conserved polyad-like quantum number and a large body of unlabelled computed states is a natural target for this approach, suggesting a pathway toward automated quantum number annotation for the next generation of large-scale computed line lists.", "published": "2026-09-12T08:29:16Z", "authors": ["Marco G. Barnfield", "Sergei N. Yurchenko", "Jonathan Tennyson"], "categories": ["physics.chem-ph", "physics.comp-ph"], "doi": "10.1016/j.jqsrt.2026.110111", "_query": "automated valuation model", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2408.03572v2", "title": "2D-OOB: Attributing Data Contribution Through Joint Valuation Framework", "abstract": "Data valuation has emerged as a powerful framework for quantifying each datum's contribution to the training of a machine learning model. However, it is crucial to recognize that the quality of cells within a single data point can vary greatly in practice. For example, even in the case of an abnormal data point, not all cells are necessarily noisy. The single scalar score assigned by existing data valuation methods blurs the distinction between noisy and clean cells of a data point, making it challenging to interpret the data values. In this paper, we propose 2D-OOB, an out-of-bag estimation framework for jointly determining helpful (or detrimental) samples as well as the particular cells that drive them. Our comprehensive experiments demonstrate that 2D-OOB achieves state-of-the-art performance across multiple use cases while being exponentially faster. Specifically, 2D-OOB shows promising results in detecting and rectifying fine-grained outliers at the cell level, and localizing backdoor triggers in data poisoning attacks.", "published": "2024-08-07T06:16:17Z", "authors": ["Yifan Sun", "Jingyan Shen", "Yongchan Kwon"], "categories": ["cs.LG", "cs.AI"], "doi": "", "_query": "automated valuation model", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/0712.2175v1", "title": "Integration on product spaces and GL_n of a valuation field over a local field", "abstract": "We present elements of a theory of translation-invariant integration on finite dimensional vector spaces and on GL_n over a valuation field with local field as residue field. We then discuss the case of an arbitrary algebraic group. This extends the work of Fesenko.", "published": "2007-12-13T16:16:31Z", "authors": ["Matthew T. Morrow"], "categories": ["math.NT", "math.AG"], "doi": "", "_query": "automated valuation model", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2301.11397v1", "title": "Automating Knowledge-Driven Model Recommendation: Methodology, Evaluation, and Key Challenges", "abstract": "There is significant interest in using existing repositories of biological entities, relationships, and models to automate biological model assembly and extension. Current methods aggregate human-curated biological information into executable, simulatable models, but these models do not resemble human curated models and do not recapitulate experimental results. Here, we outline the process of automated model assembly and extension, while demonstrating it on both synthetic models and human-curated models of biological signaling networks. We begin with an iterative, greedy, and combinatoric approach to automated assembly and demonstrate the key difficulties inherent to contextless assembly. We publicly release the software used in this paper to enable further exploration of this problem.", "published": "2023-01-26T20:19:33Z", "authors": ["Adam A. Butchy", "Cheryl A. Telmer", "Natasa Miskov-Zivanov"], "categories": ["q-bio.MN"], "doi": "", "_query": "automated valuation model", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/0808.2943v1", "title": "A binary tree representation for the 2-adic valuation of a sequence arising from a rational integral", "abstract": "We present a binary tree that describes the 2-adic valuation of a sequence of coefficients arising from the evaluation of a rational integral.", "published": "2008-08-21T15:16:08Z", "authors": ["Xinyu Sun", "Victor H. Moll"], "categories": ["math.CO", "math.NT"], "doi": "", "_query": "automated valuation model", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2107.09901v2", "title": "Extension of Additive Valuations to General Valuations on the Existence of EFX", "abstract": "Envy-freeness is one of the most widely studied notions in fair division. Since envy-free allocations do not always exist when items are indivisible, several relaxations have been considered. Among them, possibly the most compelling concept is envy-freeness up to any item (EFX). We study the existence of EFX allocations for general valuations. The existence of EFX allocations is a major open problem. For general valuations, it is known that an EFX allocation always exists (i) when $n=2$ or (ii) when all agents have identical valuations, where $n$ is the number of agents. it is also known that an EFX allocation always exists when one can leave at most $n-1$ items unallocated. We develop new techniques and extend some results of additive valuations to general valuations on the existence of EFX allocations. We show that an EFX allocation always exists (i) when all agents have one of two general valuations or (ii) when the number of items is at most $n+3$. We also show that an EFX allocation always exists when one can leave at most $n-2$ items unallocated. In addition to the positive results, we construct an instance with $n=3$ in which an existing approach does not work as it is.", "published": "2021-07-21T06:45:55Z", "authors": ["Ryoga Mahara"], "categories": ["cs.GT", "cs.DM"], "doi": "", "_query": "automated valuation model", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/math/0406351v2", "title": "On two theorems for flat, affine group schemes over a discrete valuation ring", "abstract": "We include short and elementary proofs of two theorems characterizing reductive group schemes over a discrete valuation ring, in a slightly more general context.", "published": "2004-06-17T17:34:11Z", "authors": ["Adrian Vasiu"], "categories": ["math.NT", "math.RT"], "doi": "10.2478/BF02475652", "_query": "automated valuation model", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/1105.1448v2", "title": "Valuation Semigroups of Two Dimensional Local Rings", "abstract": "We consider the question of when a semigroup is the semigroup of a valuation dominating a two dimensional noetherian local domain, giving some surprising examples. We give a necessary and sufficient condition for the pair of a semigroup S and a field extension L/k to be the semigroup and residue field of a valaution dominating a regular local ring R of dimension two with residue field k, generalizing the theorem of Spivakovsky for the case when there is no residue field extension.", "published": "2011-05-07T14:25:20Z", "authors": ["Steven Dale Cutkosky", "Pham An Vinh"], "categories": ["math.AC"], "doi": "10.1112/plms/pdt023", "_query": "automated valuation model", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/1206.4255v3", "title": "Étoiles and Valuations", "abstract": "We establish some properties of étoiles and associated valuations over complex analytic spaces, showing that Abhyankar's inequality holds. We give some examples of pathological behavior of these valuations. We prove a regularization theorem for complex analytic morphisms.", "published": "2012-06-19T16:28:09Z", "authors": ["Steven Dale Cutkosky"], "categories": ["math.AG", "math.CV"], "doi": "", "_query": "automated valuation model", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2602.21041v1", "title": "Stability Under Valuation Updates in Coalition Formation", "abstract": "Coalition formation studies how to partition a set of agents into disjoint coalitions under consideration of their preferences. We study the classical objective of stability in a variant of additively separable hedonic games where agents can change their valuations. Our objective is to find a stable partition after each change. To minimize the reconfiguration cost, we search for nearby stable coalition structures. Our focus is on stability concepts based on single-agent deviations. We present a detailed picture of the complexity of finding nearby stable coalition structures in additively separable hedonic games, for both symmetric and non-symmetric valuations. Our results show that the problem is NP-complete for Nash stability, individual stability, contractual Nash stability, and contractual individual stability. We complement these results by presenting polynomial-time algorithms for contractual Nash stability and contractual individual stability under restricted symmetric valuations. Finally, we show that these algorithms guarantee a bounded average distance over long sequences of updates.", "published": "2026-02-24T16:02:36Z", "authors": ["Fabian Frank", "Matija Novaković", "René Romen"], "categories": ["cs.GT", "cs.MA"], "doi": "", "_query": "automated valuation model", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2003.05537v1", "title": "On n-semiprimary Ideals and n-pseudo Valuation Domains", "abstract": "In this paper, we introduce the concept of n-semiprimary ideals, n-powerful ideals, and n-powerful semiprimary ideals of commutative rings. We study these concepts and relate them to several generalizations of pseudo-valuation domains.", "published": "2020-03-11T21:50:51Z", "authors": ["David F. Anderson", "Ayman Badawi"], "categories": ["math.AC"], "doi": "", "_query": "automated valuation model", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2407.13760v1", "title": "Neural Network Tire Force Modeling for Automated Drifting", "abstract": "Automated drifting presents a challenge problem for vehicle control, requiring models and control algorithms that can precisely handle nonlinear, coupled tire forces at the friction limits. We present a neural network architecture for predicting front tire lateral force as a drop-in replacement for physics-based approaches. With a full-scale automated vehicle purpose-built for the drifting application, we deploy these models in a nonlinear model predictive controller tuned for tracking a reference drifting trajectory, for direct comparisons of model performance. The neural network tire model exhibits significantly improved path tracking performance over the brush tire model in cases where front-axle braking force is applied, suggesting the neural network's ability to express previously unmodeled, latent dynamics in the drifting condition.", "published": "2024-07-18T17:58:01Z", "authors": ["Nicholas Drake Broadbent", "Trey Weber", "Daiki Mori", "J. Christian Gerdes"], "categories": ["eess.SY", "cs.AI"], "doi": "", "_query": "automated valuation model", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2101.00251v1", "title": "Forward indifference valuation and hedging of basis risk under partial information", "abstract": "We study the hedging and valuation of European and American claims on a non-traded asset $Y$, when a traded stock $S$ is available for hedging, with $S$ and $Y$ following correlated geometric Brownian motions. This is an incomplete market, often called a basis risk model. The market agent's risk preferences are modelled using a so-called forward performance process (forward utility), which is a time-decreasing utility of exponential type. Moreover, the market agent (investor) does not know with certainty the values of the asset price drifts. This market setting with drift parameter uncertainty is the partial information scenario. We discuss the stochastic control problem obtained by setting up the hedging portfolio and derive the optimal hedging strategy. Furthermore, a (dual) forward indifference price representation of the claim and its PDE are obtained. With these results, the residual risk process representing the basis risk (hedging error), pay-off decompositions and asymptotic expansions of the indifference price in the European case are derived. We develop the analogous stochastic control and stopping problem with an American claim and obtain the corresponding forward indifference price valuation formula.", "published": "2021-01-01T15:18:44Z", "authors": ["Mahan Tahvildari"], "categories": ["q-fin.MF", "q-fin.PR"], "doi": "", "_query": "automated valuation model", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/1606.07831v1", "title": "A Neural Network Approach to Efficient Valuation of Large Portfolios of Variable Annuities", "abstract": "Managing and hedging the risks associated with Variable Annuity (VA) products require intraday valuation of key risk metrics for these products. The complex structure of VA products and computational complexity of their accurate evaluation have compelled insurance companies to adopt Monte Carlo (MC) simulations to value their large portfolios of VA products. Because the MC simulations are computationally demanding, especially for intraday valuations, insurance companies need more efficient valuation techniques. Recently, a framework based on traditional spatial interpolation techniques has been proposed that can significantly decrease the computational complexity of MC simulation (Gan and Lin, 2015). However, traditional interpolation techniques require the definition of a distance function that can significantly impact their accuracy. Moreover, none of the traditional spatial interpolation techniques provide all of the key properties of accuracy, efficiency, and granularity (Hejazi et al., 2015). In this paper, we present a neural network approach for the spatial interpolation framework that affords an efficient way to find an effective distance function. The proposed approach is accurate, efficient, and provides an accurate granular view of the input portfolio. Our numerical experiments illustrate the superiority of the performance of the proposed neural network approach compared to the traditional spatial interpolation schemes.", "published": "2016-06-24T20:26:33Z", "authors": ["Seyed Amir Hejazi", "Kenneth R. Jackson"], "categories": ["q-fin.CP"], "doi": "", "_query": "automated valuation model", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/1205.2299v1", "title": "Electricity price modeling and asset valuation: a multi-fuel structural approach", "abstract": "We introduce a new and highly tractable structural model for spot and derivative prices in electricity markets. Using a stochastic model of the bid stack, we translate the demand for power and the prices of generating fuels into electricity spot prices. The stack structure allows for a range of generator efficiencies per fuel type and for the possibility of future changes in the merit order of the fuels. The derived spot price process captures important stylized facts of historical electricity prices, including both spikes and the complex dependence upon its underlying supply and demand drivers. Furthermore, under mild and commonly used assumptions on the distributions of the input factors, we obtain closed-form formulae for electricity forward contracts and for spark and dark spread options. As merit order dynamics and fuel forward prices are embedded into the model, we capture a much richer and more realistic dependence structure than can be achieved by classical reduced-form models. We illustrate these advantages by comparing with Margrabe's formula and a simple cointegration model, and highlight important implications for the valuation of power plants.", "published": "2012-05-10T15:58:24Z", "authors": ["Rene Carmona", "Michael Coulon", "Daniel Schwarz"], "categories": ["q-fin.PR"], "doi": "10.1007/s11579-012-0091-4", "_query": "automated valuation model", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2203.06814v1", "title": "CheckSel: Efficient and Accurate Data-valuation Through Online Checkpoint Selection", "abstract": "Data valuation and subset selection have emerged as valuable tools for application-specific selection of important training data. However, the efficiency-accuracy tradeoffs of state-of-the-art methods hinder their widespread application to many AI workflows. In this paper, we propose a novel 2-phase solution to this problem. Phase 1 selects representative checkpoints from an SGD-like training algorithm, which are used in phase-2 to estimate the approximate training data values, e.g. decrease in validation loss due to each training point. A key contribution of this paper is CheckSel, an Orthogonal Matching Pursuit-inspired online sparse approximation algorithm for checkpoint selection in the online setting, where the features are revealed one at a time. Another key contribution is the study of data valuation in the domain adaptation setting, where a data value estimator obtained using checkpoints from training trajectory in the source domain training dataset is used for data valuation in a target domain training dataset. Experimental results on benchmark datasets show the proposed algorithm outperforms recent baseline methods by up to 30% in terms of test accuracy while incurring a similar computational burden, for both standalone and domain adaptation settings.", "published": "2022-03-14T02:06:52Z", "authors": ["Soumi Das", "Manasvi Sagarkar", "Suparna Bhattacharya", "Sourangshu Bhattacharya"], "categories": ["cs.LG"], "doi": "", "_query": "automated valuation model", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/1707.08410v4", "title": "Compatibility of quasi-orderings and valuations; A Baer-Krull Theorem for quasi-ordered Rings", "abstract": "In his work of 1969, Merle E. Manis introduced valuations on commutative rings. Recently, the class of totally quasi-ordered rings was developped by the second author. In the present paper, we establish the notion of compatibility between valuations and quasi-orders on rings, leading to a definition of the rank of a quasi-ordered ring. Moreover, we prove a Baer-Krull Theorem for quasi-ordered rings: fixing a Manis valuation v on R, we characterize all v-compatible quasi-orders of R by lifting the quasi-orders from the residue class ring to R itself.", "published": "2017-07-26T12:43:12Z", "authors": ["Salma Kuhlmann", "Simon Müller"], "categories": ["math.AC"], "doi": "10.1007/s11083-018-9465-4", "_query": "automated valuation model", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/1907.04512v2", "title": "Computing Valuations of the Dieudonné Determinants", "abstract": "This paper addresses the problem of computing valuations of the Dieudonné determinants of matrices over discrete valuation skew fields (DVSFs). Under a reasonable computational model, we propose two algorithms for a class of DVSFs, called split. Our algorithms are extensions of the combinatorial relaxation of Murota (1995) and the matrix expansion by Moriyama--Murota (2013), both of which are based on combinatorial optimization. While our algorithms require an upper bound on the output, we give an estimation of the bound for skew polynomial matrices and show that the estimation is valid only for skew polynomial matrices. We consider two applications of this problem. The first one is the noncommutative weighted Edmonds' problem (nc-WEP), which is to compute the degree of the Dieudonné determinants of matrices having noncommutative symbols. We show that the presented algorithms reduce the nc-WEP to the unweighted problem in polynomial time. In particular, we show that the nc-WEP over the rational field is solvable in time polynomial in the input bit-length. We also present an application to analyses of degrees of freedom of linear time-varying systems by establishing formulas on the solution spaces of linear differential/difference equations.", "published": "2019-07-10T05:27:04Z", "authors": ["Taihei Oki"], "categories": ["cs.DS", "cs.SC"], "doi": "", "_query": "automated valuation model", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/1506.00686v2", "title": "Invariance, existence and uniqueness of solutions of nonlinear valuation PDEs and FBSDEs inclusive of credit risk, collateral and funding costs", "abstract": "We study conditions for existence, uniqueness and invariance of the comprehensive nonlinear valuation equations first introduced in Pallavicini et al (2011). These equations take the form of semilinear PDEs and Forward-Backward Stochastic Differential Equations (FBSDEs). After summarizing the cash flows definitions allowing us to extend valuation to credit risk and default closeout, including collateral margining with possible re-hypothecation, and treasury funding costs, we show how such cash flows, when present-valued in an arbitrage free setting, lead to semi-linear PDEs or more generally to FBSDEs. We provide conditions for existence and uniqueness of such solutions in a viscosity and classical sense, discussing the role of the hedging strategy. We show an invariance theorem stating that even though we start from a risk-neutral valuation approach based on a locally risk-free bank account growing at a risk-free rate, our final valuation equations do not depend on the risk free rate. Indeed, our final semilinear PDE or FBSDEs and their classical or viscosity solutions depend only on contractual, market or treasury rates and we do not need to proxy the risk free rate with a real market rate, since it acts as an instrumental variable. The equations derivations, their numerical solutions, the related XVA valuation adjustments with their overlap, and the invariance result had been analyzed numerically and extended to central clearing and multiple discount curves in a number of previous works, including Pallavicini et al (2011), Pallavicini et al (2012), Brigo et al (2013), Brigo and Pallavicini (2014), and Brigo et al (2014).", "published": "2015-06-01T21:53:18Z", "authors": ["Damiano Brigo", "Marco Francischello", "Andrea Pallavicini"], "categories": ["q-fin.PR"], "doi": "", "_query": "automated valuation model", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/1401.8213v2", "title": "On graphs and valuations", "abstract": "In the last two decades new techniques emerged to construct valuations on an infinite division ring $D,$ given a normal subgroup $N\\subseteq D$ of finite index. These techniques were based on the commuting graph of $D^{\\times}/N$ in the case where $D$ is non-commutative, and on the Milnor K-graph on $D^{\\times}/N,$ in the case where $D$ is commutative. In this paper we unify these two approaches and consider V-graphs on $D^{\\times}/N$ and how they lead to valuations. We furthermore generalize previous results to situations of finitely many valuations.", "published": "2014-01-31T16:18:57Z", "authors": ["Ido Efrat", "Andrei S. Rapinchuk", "Yoav Segev"], "categories": ["math.RA", "math.GR"], "doi": "", "_query": "automated valuation model", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2308.05656v1", "title": "Generating sequences of valuations on simple extensions of domains", "abstract": "Suppose that $(K,v_0)$ is a valued field, $f(x)\\in K[x]$ is a monic and irreducible polynomial and $(L,v)$ is an extension of valued fields, where $L=K[x]/(f(x))$. Let $A$ be a local domain with quotient field $K$ dominated by the valuation ring of $v_0$ and such that $f(x)$ is in $A[x]$. The study of these extensions is a classical subject. This paper is devoted to the problem of describing the structure of the associated graded ring ${\\rm gr}_v A[x]/(f(x))$ of $A[x]/(f(x))$ for the filtration defined by $v$ as an extension of the associated graded ring of $A$ for the filtration defined by $v_0$. We give a complete simple description of this algebra when there is unique extension of $v_0$ to $L$ and the residue characteristic of $A$ does not divide the degree of $f$. To do this, we show that the sequence of key polynomials constructed by MacLane's algorithm can be taken to lie inside $A[x]$. This result was proven using a different method in the more restrictive case that the residue fields of $A$ and of the valuation ring of $v$ are equal and algebraically closed in a recent paper by Cutkosky, Mourtada and Teissier.", "published": "2023-08-10T15:54:34Z", "authors": ["Razieh Ahmadian", "Steven Dale Cutkosky"], "categories": ["math.AC", "math.AG"], "doi": "", "_query": "automated valuation model", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2201.00728v2", "title": "Grothendieck--Witt Groups of Henselian Valuation Rings", "abstract": "We show that functors like algebraic $K$-theory (such as unitary or symplectic $K$-functors), as well as the higher Grothendieck--Witt groups, possess the local constancy condition for Henselian valuation rings. Namely, taken with finite coefficients, these functors send canonical residue maps into isomorphisms. This statement holds in cases of both equal and mixed characteristics. The proof is based on a slight modification of Suslin's methods. In particular, we use his notion of universal homotopy.", "published": "2022-01-03T16:03:12Z", "authors": ["Serge Yagunov"], "categories": ["math.KT"], "doi": "10.2140/akt.2024.9.23", "_query": "automated valuation model", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2510.00674v2", "title": "PyTrim: A Practical Tool for Reducing Python Dependency Bloat", "abstract": "Dependency bloat is a persistent challenge in Python projects, which increases maintenance costs and security risks. While numerous tools exist for detecting unused dependencies in Python, removing these dependencies across the source code and configuration files of a project requires manual effort and expertise. To tackle this challenge we introduce PYTRIM, an end-to-end system to automate this process. PYTRIM eliminates unused imports and package declarations across a variety of file types, including Python source and configuration files such as requirements.txt and setup.py. PYTRIM's modular design makes it agnostic to the source of dependency bloat information, enabling integration with any detection tool. Beyond its contribution when it comes to automation, PYTRIM also incorporates a novel dynamic analysis component that improves dependency detection recall. Our evaluation of PYTRIM's end-to-end effectiveness on a ground-truth dataset of 37 merged pull requests from prior work, shows that PYTRIM achieves 98.3% accuracy in replicating human-made changes. To show its practical impact, we run PYTRIM on 971 open-source packages, identifying and trimming bloated dependencies in 39 of them. For each case, we submit a corresponding pull request, 6 of which have already been accepted and merged. PYTRIM is available as an open-source project, encouraging community contributions and further development. Video demonstration: https://youtu.be/LqTEdOUbJRI Code repository: https://github.com/TrimTeam/PyTrim", "published": "2025-10-01T08:58:55Z", "authors": ["Konstantinos Karakatsanis", "Georgios Alexopoulos", "Ioannis Karyotakis", "Foivos Timotheos Proestakis", "Evangelos Talos", "Panos Louridas", "Dimitris Mitropoulos"], "categories": ["cs.SE"], "doi": "10.1109/ASE63991.2025.00377", "_query": "automated valuation model", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2401.02706v1", "title": "Non-reduced valuation rings and descent for smooth blowup squares", "abstract": "We consider a class of non-reduced valuation rings, known in the literature as chain rings. We observe that the Grothendieck topology generated by the Zariski topology and smooth blowup squares is exactly the topology which has chain rings for its local rings, and that sheaves for this topology are \\emph{not} characterised by excision for smooth blowup squares.", "published": "2024-01-05T08:27:18Z", "authors": ["Shane Kelly"], "categories": ["math.AG"], "doi": "", "_query": "automated valuation model", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/1508.04057v3", "title": "Degenerations of toric varieties over valuation rings", "abstract": "We develop a theory of multi-stage degenerations of toric varieties over finite rank valuation rings, extending the Mumford--Gubler theory in rank one. Such degenerations are constructed from fan-like structures over totally ordered abelian groups of finite rank. Our main theorem describes the geometry of successive special fibers in the degeneration in terms of the polyhedral geometry of a system of recession complexes associated to the fan.", "published": "2015-08-17T15:24:58Z", "authors": ["Tyler Foster", "Dhruv Ranganathan"], "categories": ["math.AG"], "doi": "10.1112/blms/bdw046", "_query": "automated valuation model", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/1209.3248v1", "title": "The Euler characteristic of a polyhedron as a valuation on its coordinate vector lattice", "abstract": "A celebrated theorem of Hadwiger states that the Euler-Poincaré characteristic is the the unique invariant and continuous valuation on the distributive lattice of compact polyhedra in R^n that assigns value one to each convex non-empty such polyhedron. This paper provides an analogue of Hadwiger's result for finitely presented unital vector lattices (i.e. real vector spaces with a compatible lattice order, also known as Riesz spaces). The vector lattice of continuous and piecewise (affine) linear real-valued functions on a compact polyhedron, with operations defined pointwise from the vector lattice R, is a finitely presented unital vector lattice; and it is a non-trivial fact that all such vector lattices arise in this manner, to within an isomorphism. Each function in such a vector lattice can be written as a linear combination of a subset of distinguished elements that we call vl-Schauder hats. We prove here that the functional that assigns to each non-negative piecewise linear function on the polyhedron the Euler-Poincaré characteristic of its support is the unique vl-valuation (a special class of valuations on vector lattices) that assigns one to each vl-Schauder hat of the vector lattice.", "published": "2012-09-14T16:18:20Z", "authors": ["Andrea Pedrini"], "categories": ["math.MG", "math.GT"], "doi": "", "_query": "automated valuation model", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2608.15255v1", "title": "Towards Standardized Evaluation in Automated Domain Modeling: Introducing a Benchmark", "abstract": "Domain modeling plays an essential role in domain-driven design, capturing essential entities and their relationships within a specific domain. Despite advancements in automated domain modeling, the absence of standardized benchmarks has hindered the comparative assessment of existing approaches. This paper introduces a benchmark designed to address this gap. The benchmark combines the 45-record Golden UML Modelset (Verbruggen et al., 2025) on Zenodo, as distributed by the Text2UML project of Calamo, Mecella, and Snoeck (Calamo et al., 2025), with the 8-record reference archive of Chen et al. (Chen et al., 2023a,b), enabling the evaluation of automated domain modeling approaches across different levels of complexity and scale. Given a natural language description, the task is to generate a corresponding domain model. For each description, a reference domain model is provided as ground truth. A metric is used to compare the generated domain model with the corresponding ground-truth model. To demonstrate the utility of the benchmark, we evaluate multiple automated domain modeling approaches, including heuristic rule-based methods and LLM-driven strategies. In accordance with the FAIR4RS recommendations (Chue Hong et al., 2022), the benchmark is provided as a research artifact to encourage reuse and support future research on automated domain modeling.", "published": "2026-08-15T14:21:31Z", "authors": ["Vasiliy Seibert"], "categories": ["cs.AI"], "doi": "", "_query": "automated valuation model", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2110.14049v2", "title": "Beta Shapley: a Unified and Noise-reduced Data Valuation Framework for Machine Learning", "abstract": "Data Shapley has recently been proposed as a principled framework to quantify the contribution of individual datum in machine learning. It can effectively identify helpful or harmful data points for a learning algorithm. In this paper, we propose Beta Shapley, which is a substantial generalization of Data Shapley. Beta Shapley arises naturally by relaxing the efficiency axiom of the Shapley value, which is not critical for machine learning settings. Beta Shapley unifies several popular data valuation methods and includes data Shapley as a special case. Moreover, we prove that Beta Shapley has several desirable statistical properties and propose efficient algorithms to estimate it. We demonstrate that Beta Shapley outperforms state-of-the-art data valuation methods on several downstream ML tasks such as: 1) detecting mislabeled training data; 2) learning with subsamples; and 3) identifying points whose addition or removal have the largest positive or negative impact on the model.", "published": "2021-10-26T22:03:55Z", "authors": ["Yongchan Kwon", "James Zou"], "categories": ["cs.LG", "stat.ML"], "doi": "", "_query": "automated valuation model", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2603.02456v6", "title": "When Does Static Willingness to Pay Mislead? A Framework for Dynamic Hedonic Valuation", "abstract": "Many policy counterfactuals depend on how consumers value product attributes such as sugar, caffeine, alcohol, or emissions. Standard hedonic and differentiated-products models typically impose time-separable preferences. But when attributes are habit forming, current consumption can shift future marginal valuations, so static willingness-to-pay may be insufficient for policy counterfactuals. I develop a nonparametric revealed-preference framework for dynamic hedonic valuation, deriving necessary and sufficient conditions for rationalising observed prices and choices. Using cereal scanner data, I show that the hedonic representation restricts prices, while habits improve behavioural coherence conditional on that representation. The framework diagnoses when static willingness-to-pay is defensible for policy.", "published": "2026-03-02T22:57:30Z", "authors": ["Josephine Auer"], "categories": ["econ.TH", "econ.EM", "econ.GN"], "doi": "", "_query": "automated valuation model", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2605.01984v1", "title": "Flat Cohomological Purity for Syntomic Schemes over Valuation Rings", "abstract": "Grothendieck's cohomological purity predicts that the cohomology of a scheme is insensitive to removing a closed subscheme of sufficiently high codimension. In this article, we establish a form of flat cohomological purity over arbitrary (possibly infinite-rank) mixed-characteristic valuation rings $V$, thereby extending the theorem of Česnavičius--Scholze to the non-noetherian setting. More precisely, for a flat finite-type scheme over $V$ with local complete intersection fibres, we prove that the cohomology with coefficients in a commutative finite locally free group scheme remains unchanged after removing a closed subscheme satisfying a suitable fibrewise codimension condition; in particular, we obtain vanishing in low degrees and injectivity in the critical degree. As applications, we deduce purity results for local cohomology, for torsion in the Picard group, and for the Brauer group. In higher rank, our results yield sharper bounds than those previously obtained by Bhatt--Lurie and Madapusi--Mondal. The argument rests on recent advances in the structure theory of valuation rings.", "published": "2026-05-03T17:34:21Z", "authors": ["Arnab Kundu"], "categories": ["math.AG", "math.NT"], "doi": "", "_query": "automated valuation model", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2311.09839v1", "title": "Load Data Valuation in Multi-Energy Systems: An End-to-End Approach", "abstract": "Accurate load forecasting serves as the foundation for the flexible operation of multi-energy systems (MES). Multi-energy loads are tightly coupled and exhibit significant uncertainties. Many works focus on enhancing forecasting accuracy by leveraging cross-sector information. However, data owners may not be motivated to share their data unless it leads to substantial benefits. Ensuring a reasonable data valuation can encourage them to share their data willingly. This paper presents an end-to-end framework to quantify multi-energy load data value by integrating forecasting and decision processes. To address optimization problems with integer variables, a two-stage end-to-end model solution is proposed. Moreover, a profit allocation strategy based on contribution to cost savings is investigated to encourage data sharing in MES. The experimental results demonstrate a significant decrease in operation costs, suggesting that the proposed valuation approach more effectively extracts the inherent data value than traditional methods. According to the proposed incentive mechanism, all sectors can benefit from data sharing by improving forecasting accuracy or receiving economic compensation.", "published": "2023-11-16T12:11:08Z", "authors": ["Yangze Zhou", "Qingsong Wen", "Jie Song", "Xueyuan Cui", "Yi Wang"], "categories": ["eess.SY"], "doi": "", "_query": "automated valuation model", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2405.07088v1", "title": "Towards Context-Aware Modeling of Situation Awareness in Conditionally Automated Driving", "abstract": "Maintaining adequate situation awareness (SA) is crucial for the safe operation of conditionally automated vehicles (AVs), which requires drivers to regain control during takeover (TOR) events. This study developed a predictive model for real-time assessment of driver SA using multimodal data (e.g., galvanic skin response, heart rate and eye tracking data, and driver characteristics) collected in a simulated driving environment. Sixty-seven participants experienced automated driving scenarios with TORs, with conditions varying in risk perception and the presence of automation errors. A LightGBM (Light Gradient Boosting Machine) model trained on the top 12 predictors identified by SHAP (SHapley Additive exPlanations) achieved promising performance with RMSE=0.89, MAE=0.71, and Corr=0.78. These findings have implications towards context-aware modeling of SA in conditionally automated driving, paving the way for safer and more seamless driver-AV interactions.", "published": "2024-05-11T20:18:27Z", "authors": ["Lilit Avetisyan", "X. Jessie Yang", "Feng Zhou"], "categories": ["cs.HC"], "doi": "", "_query": "automated valuation model", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2411.05366v3", "title": "On the Distribution of Points of Valuation 1 for a Polynomial in Two Variables", "abstract": "We investigate the variation in the total number of points in a random $p\\times p$ square in $\\mathbb{Z}^2$ where the $p$-adic valuation of a given polynomial in two variables is precisely $1$. We establish that this quantity follows a Poisson distribution as $p\\rightarrow\\infty$ under a certain conjecture. We also relate this conjecture to certain uniform distribution properties of a vector valued sequence.", "published": "2024-11-08T06:46:47Z", "authors": ["Krishnan Rajkumar", " Shubham"], "categories": ["math.NT"], "doi": "", "_query": "automated valuation model", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/1402.6560v1", "title": "Even more generic solution construction in Valuation-Based Systems", "abstract": "Valuation algebras abstract a large number of formalisms for automated reasoning and enable the definition of generic inference procedures. Many of these formalisms provide some notions of solutions. Typical examples are satisfying assignments in constraint systems, models in logics or solutions to linear equation systems. Recently, formal requirements for the presence of solutions and a generic algorithm for solution construction based on the results of a previously executed inference scheme have been proposed in the literature. Unfortunately, the formalization of Pouly and Kohlas relies on a theorem for which we provide a counter example. In spite of that, the mainline of the theory described is correct, although some of the necessary conditions to apply some of the algorithms have to be revised. To fix the theory, we generalize some of their definitions and provide correct sufficient conditions for the algorithms. As a result, we get a more general and corrected version of the already existing theory.", "published": "2014-02-26T14:51:57Z", "authors": ["Jordi Roca-Lacostena", "Jesus Cerquides"], "categories": ["cs.AI"], "doi": "", "_query": "automated valuation model", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/math/0511234v1", "title": "Nonlinearity, correlation and the valuation of employee stock options", "abstract": "We propose a discrete time algorithm for the valuation of employee stock options based on exponential indifference prices and taking into account both the possibility of partial exercise of a fraction of the options and the use of a correlated traded asset to hedge part of their risk. We determine the optimal exercise policy under this conditions and present numerical results showing how both effects can significantly change the value of the option for an employee, as well as its cost for the issuing firm.", "published": "2005-11-09T17:42:08Z", "authors": ["M. R. Grasselli"], "categories": ["math.ST", "math.PR", "q-fin.ST"], "doi": "", "_query": "automated valuation model", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2404.08209v1", "title": "On the dependence of the affine Springer fibers on their root valuation datum", "abstract": "For the group $\\mathrm{GL}_{d}$, we confirm a conjecture of Goresky, Kottwitz and MacPherson, which states that the cohomology of the affine Springer fibers depend only on the root valuation datum of their defining elements. The proof relies on a microlocal analysis of the intermediate extensions appearing in Ngô's support theorem, and a Whitney regularity property of the union of the strict $δ$-strata over the equisingular strata for the miniversal deformation of plane curve singularities.", "published": "2024-04-12T02:48:08Z", "authors": ["Zongbin Chen"], "categories": ["math.AG"], "doi": "", "_query": "automated valuation model", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2204.05926v1", "title": "Ensemble learning for portfolio valuation and risk management", "abstract": "We introduce an ensemble learning method for dynamic portfolio valuation and risk management building on regression trees. We learn the dynamic value process of a derivative portfolio from a finite sample of its cumulative cash flow. The estimator is given in closed form. The method is fast and accurate, and scales well with sample size and path space dimension. The method can also be applied to Bermudan style options. Numerical experiments show good results in moderate dimension problems.", "published": "2022-04-12T16:28:02Z", "authors": ["Lotfi Boudabsa", "Damir Filipović"], "categories": ["q-fin.CP"], "doi": "", "_query": "automated valuation model", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2507.05134v2", "title": "Deep Learning to Automate Parameter Extraction and Model Fitting of Two-Dimensional Transistors", "abstract": "We present a deep learning approach to extract physical parameters (e.g., mobility, Schottky contact barrier height, defect profiles) of two-dimensional (2D) transistors from electrical measurements, enabling automated parameter extraction and technology computer-aided design (TCAD) fitting. To facilitate this task, we implement a simple data augmentation and pre-training approach by training a secondary neural network to approximate a physics-based device simulator. This method enables high-quality fits after training the neural network on electrical data generated from physics-based simulations of ~500 devices, a factor >40$\\times$ fewer than other recent efforts. Consequently, fitting can be achieved by training on physically rigorous TCAD models, including complex geometry, self-consistent transport, and electrostatic effects, and is not limited to computationally inexpensive compact models. We apply our approach to reverse-engineer key parameters from experimental monolayer WS$_2$ transistors, achieving a median coefficient of determination ($R^2$) = 0.99 when fitting measured electrical data. We also demonstrate that this approach generalizes and scales well by reverse-engineering electrical data on high-electron-mobility transistors while fitting 35 parameters simultaneously. To facilitate future research on deep learning approaches for inverse transistor design, we have published our code and sample data sets online.", "published": "2025-07-07T15:46:25Z", "authors": ["Robert K. A. Bennett", "Jan-Lucas Uslu", "Harmon F. Gault", "Asir Intisar Khan", "Lauren Hoang", "Tara Peña", "Kathryn Neilson", "Young Suh Song", "Zhepeng Zhang", "Andrew J. Mannix", "Eric Pop"], "categories": ["cs.LG", "cond-mat.mtrl-sci", "physics.app-ph"], "doi": "10.34133/research.1103", "_query": "automated valuation model", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2505.12402v2", "title": "Automated Profile Inference with Language Model Agents", "abstract": "Impressive progress has been made in automated problem-solving by the collaboration of large language model (LLM) based agents. However, these automated capabilities also open avenues for malicious applications. In this paper, we study a new threat that LLMs pose to online pseudonymity, called automated profile inference, where an adversary can instruct LLMs to automatically collect and extract sensitive personal attributes from publicly available user activities on pseudonymous platforms. We also introduce an automated profiling framework called AutoProfiler to demonstrate and assess the feasibility of such attacks in real-world scenarios. AutoProfiler consists of four specialized LLM agents that work collaboratively to retrieve and process user online activities and generate a profile with extracted personal information. Experimental results on two real-world datasets and one synthetic dataset show that AutoProfiler is highly effective and efficient, and the inferred attributes are both identifiable and sensitive, posing significant privacy risks. We explore mitigation strategies from different perspectives and advocate for increased public awareness of this emerging privacy threat.", "published": "2025-05-18T13:05:17Z", "authors": ["Yuntao Du", "Zitao Li", "Bolin Ding", "Yaliang Li", "Hanshen Xiao", "Jingren Zhou", "Ninghui Li"], "categories": ["cs.CR"], "doi": "", "_query": "automated valuation model", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2012.13984v4", "title": "Another proof of the almost purity theorem for perfectoid valuation rings", "abstract": "The almost purity theorem is central to the geometry of perfectoid spaces and has numerous applications in algebra and geometry. This result is known to have several different proofs in the case that the base ring is a perfectoid valuation ring. We give a new proof by exploiting the behavior of Faltings' normalized length under the Frobenius map.", "published": "2020-12-27T17:51:36Z", "authors": ["Shinnosuke Ishiro", "Kazuma Shimomoto"], "categories": ["math.AC", "math.AG", "math.NT"], "doi": "10.1016/j.jpaa.2021.106898", "_query": "automated valuation model", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2102.05949v1", "title": "DirectDebug: Automated Testing and Debugging of Feature Models", "abstract": "Variability models (e.g., feature models) are a common way for the representation of variabilities and commonalities of software artifacts. Such models can be translated to a logical representation and thus allow different operations for quality assurance and other types of model property analysis. Specifically, complex and often large-scale feature models can become faulty, i.e., do not represent the expected variability properties of the underlying software artifact. In this paper, we introduce DirectDebug which is a direct diagnosis approach to the automated testing and debugging of variability models. The algorithm helps software engineers by supporting an automated identification of faulty constraints responsible for an unintended behavior of a variability model. This approach can significantly decrease development and maintenance efforts for such models.", "published": "2021-02-11T11:22:20Z", "authors": ["Viet-Man Le", "Alexander Felfernig", "Mathias Uta", "David Benavides", "José Galindo", "Thi Ngoc Trang Tran"], "categories": ["cs.SE", "cs.AI"], "doi": "", "_query": "automated valuation model", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2305.01775v2", "title": "Data Valuation from Data-Driven Optimization", "abstract": "With the ongoing investment in data collection and communication technology in power systems, data-driven optimization has been established as a powerful tool for system operators to handle stochastic system states caused by weather- and behavior-dependent resources. However, most methods are ignorant to data quality, which may differ based on measurement and underlying privacy-protection mechanisms. This paper addresses this shortcoming by (i) proposing a practical data quality metric based on Wasserstein distance, (ii) leveraging a novel modification of distributionally robust optimization using information from multiple data sets with heterogeneous quality to valuate data, (iii) applying the proposed optimization framework to an optimal power flow problem, and (iv) showing a direct method to valuate data from the optimal solution. We conduct numerical experiments to analyze and illustrate the proposed model and publish the implementation open-source.", "published": "2023-05-02T20:34:34Z", "authors": ["Robert Mieth", "Juan M. Morales", "H. Vincent Poor"], "categories": ["math.OC", "eess.SY"], "doi": "", "_query": "automated valuation model", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/1711.11558v1", "title": "Valuations on Banach lattices", "abstract": "We provide a general framework for the study of valuations on Banach lattices. This complements and expands several recent works about valuations on function spaces, including $L_p(μ)$, Orlicz spaces and spaces $C(K)$ of continuous functions on a compact Hausdorff space. In particular, we study decomposition properties, boundedness and integral representation of continuous valuations.", "published": "2017-11-30T18:26:28Z", "authors": ["Pedro Tradacete", "Ignacio Villanueva"], "categories": ["math.FA", "math.MG"], "doi": "", "_query": "automated valuation model", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2502.21144v2", "title": "Integer-valued valuations", "abstract": "We obtain a complete characterization of planar monotone $σ$-continuous valuations taking integer values, without assuming invariance under any group of transformations. We further investigate the consequences of dropping monotonicity or $σ$-continuity and give a full classification of line valuations. We also introduce a construction of the product for valuations of this type.", "published": "2025-02-28T15:26:35Z", "authors": ["Andrii Ilienko", "Ilya Molchanov", "Tommaso Visonà"], "categories": ["math.MG"], "doi": "", "_query": "automated valuation model", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2305.12152v2", "title": "Revisiting Automated Topic Model Evaluation with Large Language Models", "abstract": "Topic models are used to make sense of large text collections. However, automatically evaluating topic model output and determining the optimal number of topics both have been longstanding challenges, with no effective automated solutions to date. This paper proposes using large language models to evaluate such output. We find that large language models appropriately assess the resulting topics, correlating more strongly with human judgments than existing automated metrics. We then investigate whether we can use large language models to automatically determine the optimal number of topics. We automatically assign labels to documents and choosing configurations with the most pure labels returns reasonable values for the optimal number of topics.", "published": "2023-05-20T09:42:00Z", "authors": ["Dominik Stammbach", "Vilém Zouhar", "Alexander Hoyle", "Mrinmaya Sachan", "Elliott Ash"], "categories": ["cs.CL"], "doi": "", "_query": "automated valuation model", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/1806.10711v1", "title": "Free group algebras in division rings with valuation I", "abstract": "Let $R$ be an algebra over a commutative ring $k$. Suppose that $R$ is endowed with a descending filtration indexed on an ordered group $(G,<)$ such that the restriction to $k$ is positive. We show that the existence of free algebras on a certain set of generators in the induced graded ring $grad(R)$ implies the existence of free group algebras in $R$. Our best results are obtained for division rings endowed with a valuation.", "published": "2018-06-27T23:16:38Z", "authors": ["Javier Sánchez"], "categories": ["math.RA"], "doi": "", "_query": "automated valuation model", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2510.16145v1", "title": "C-arm Guidance: A Self-supervised Approach To Automated Positioning During Stroke Thrombectomy", "abstract": "Thrombectomy is one of the most effective treatments for ischemic stroke, but it is resource and personnel-intensive. We propose employing deep learning to automate critical aspects of thrombectomy, thereby enhancing efficiency and safety. In this work, we introduce a self-supervised framework that classifies various skeletal landmarks using a regression-based pretext task. Our experiments demonstrate that our model outperforms existing methods in both regression and classification tasks. Notably, our results indicate that the positional pretext task significantly enhances downstream classification performance. Future work will focus on extending this framework toward fully autonomous C-arm control, aiming to optimize trajectories from the pelvis to the head during stroke thrombectomy procedures. All code used is available at https://github.com/AhmadArrabi/C_arm_guidance", "published": "2025-10-17T18:31:53Z", "authors": ["Ahmad Arrabi", "Jay hwasung Jung", "J Le", "A Nguyen", "J Reed", "E Stahl", "Nathan Franssen", "Scott Raymond", "Safwan Wshah"], "categories": ["cs.CV"], "doi": "10.1109/ISBI60581.2025.10980945", "_query": "automated valuation model", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2510.10423v1", "title": "Improved Maximin Share Guarantee for Additive Valuations", "abstract": "The maximin share ($\\textsf{MMS}$) is the most prominent share-based fairness notion in the fair allocation of indivisible goods. Recent years have seen significant efforts to improve the approximation guarantees for $\\textsf{MMS}$ for different valuation classes, particularly for additive valuations. For the additive setting, it has been shown that for some instances, no allocation can guarantee a factor better than $1-\\tfrac{1}{n^4}$ of maximin share value to all agents. However, the best currently known algorithm achieves an approximation guarantee of $\\tfrac{3}{4} + \\tfrac{3}{3836}$ for $\\textsf{MMS}$. In this work, we narrow this gap and improve the best-known approximation guarantee for $\\textsf{MMS}$ to $\\tfrac{10}{13}$.", "published": "2025-10-12T03:14:32Z", "authors": ["Ehsan Heidari", "Alireza Kaviani", "Masoud Seddighin", "AmirMohammad Shahrezaei"], "categories": ["cs.GT"], "doi": "", "_query": "automated valuation model", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2304.11211v1", "title": "Toric vector bundles, valuations and tropical geometry", "abstract": "A toric vector bundle $\\mathcal{E}$ is a torus equivariant vector bundle on a toric variety. We give a valuation theoretic and tropical point of view on toric vector bundles. We present three (equivalent) classifications of toric vector bundles, which should be regarded as repackagings of the Klyachko data of compatible $\\mathbb{Z}$-filtrations of a toric vector bundle: (1) as piecewise linear maps to space of $\\mathbb{Z}$-valued valuations, (2) as valuations with values in the semifield of piecewise linear functions, and (3) as points in tropical linear ideals over the semifield of piecewise linear functions. Moreover, we interpret the known criteria for ampleness and global generation of $\\mathcal{E}$ as convexity conditions on its piecewise linear map in (1). Finally, using (2) we associate to $\\mathcal{E}$ a collection of polytopes indexed by elements of a certain (representable) matroid encoding the dimensions of weight spaces of global sections of $\\mathcal{E}$. This recovers and extends the Di Rocco-Jabbusch-Smith matriod and parliament of polytopes of $\\mathcal{E}$. This is a follow up paper to arXiv:1806.05613.", "published": "2023-04-21T18:52:04Z", "authors": ["Kiumars Kaveh", "Christopher Manon"], "categories": ["math.AG"], "doi": "", "_query": "automated valuation model", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2506.04987v1", "title": "A Multi-Dataset Evaluation of Models for Automated Vulnerability Repair", "abstract": "Software vulnerabilities pose significant security threats, requiring effective mitigation. While Automated Program Repair (APR) has advanced in fixing general bugs, vulnerability patching, a security-critical aspect of APR remains underexplored. This study investigates pre-trained language models, CodeBERT and CodeT5, for automated vulnerability patching across six datasets and four languages. We evaluate their accuracy and generalization to unknown vulnerabilities. Results show that while both models face challenges with fragmented or sparse context, CodeBERT performs comparatively better in such scenarios, whereas CodeT5 excels in capturing complex vulnerability patterns. CodeT5 also demonstrates superior scalability. Furthermore, we test fine-tuned models on both in-distribution (trained) and out-of-distribution (unseen) datasets. While fine-tuning improves in-distribution performance, models struggle to generalize to unseen data, highlighting challenges in robust vulnerability detection. This study benchmarks model performance, identifies limitations in generalization, and provides actionable insights to advance automated vulnerability patching for real-world security applications.", "published": "2025-06-05T13:00:19Z", "authors": ["Zanis Ali Khan", "Aayush Garg", "Qiang Tang"], "categories": ["cs.SE", "cs.AI"], "doi": "", "_query": "automated valuation model", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/0709.0232v1", "title": "Valuations and dynamic convex risk measures", "abstract": "This paper approaches the definition and properties of dynamic convex risk measures through the notion of a family of concave valuation operators satisfying certain simple and credible axioms. Exploring these in the simplest context of a finite time set and finite sample space, we find natural risk-transfer and time-consistency properties for a firm seeking to spread its risk across a group of subsidiaries.", "published": "2007-09-03T13:51:11Z", "authors": ["A. Jobert", "L. C. G. Rogers"], "categories": ["q-fin.RM", "math.PR"], "doi": "", "_query": "automated valuation model", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2407.07103v2", "title": "The p-adic valuation of the general degree-2 and degree-3 polynomial in 2 variables", "abstract": "This paper investigates the p-adic valuation trees of degree-2 and degree-3 polynomials in two variables over any prime p, building upon prior research outlined in [14].", "published": "2024-06-23T10:56:55Z", "authors": [" Shubham"], "categories": ["math.GM"], "doi": "", "_query": "automated valuation model", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2501.14609v4", "title": "Fair Division Beyond Monotone Valuations with Applications to Equitable Graph Partitioning", "abstract": "This paper studies fair division of divisible and indivisible items among agents whose cardinal preferences are not necessarily monotone. We establish the existence of fair divisions and develop approximation algorithms to compute them. We address two complementary valuation classes, subadditive and nonnegative, which go beyond monotone functions. Considering both the division of cake (divisible resources) and allocation of indivisible items, we obtain fairness guarantees in terms of (approximate) envy-freeness (EF) and equability (EQ). In the context of envy-freeness, we prove that an EF division of a cake always exists under cake valuations that are subadditive and globally nonnegative. This result complements the nonexistence of EF allocations for burnt cakes known for more general valuations. In the indivisible-items setting, we establish the existence of EFE3 allocations for subadditive and globally nonnegative valuations. In addition, we obtain universal existence of EFE3 allocations under nonnegative valuations. We study equitability under nonnegative valuations. Here, we prove that EQE3 allocations always exist when the agents' valuations are nonnegative. Also, in the indivisible-items setting, we develop an approximation algorithm that, for given nonnegative valuations, finds allocations that are equitable within additive margins. Our results have combinatorial implications. For instance, the developed results imply the universal existence of proximately dense subgraphs: Given any graph $G=(V, E)$ and integer $k$ (at most $|V|$), there always exists a partition $V_1, V_2, \\ldots, V_k$ of the vertex set such that the edge densities within the parts, $V_i$, are additively within four of each other. Further, such a partition can be computed efficiently.", "published": "2025-01-24T16:30:54Z", "authors": ["Siddharth Barman", "Paritosh Verma"], "categories": ["cs.GT"], "doi": "", "_query": "automated valuation model", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2305.16434v1", "title": "Credit Valuation Adjustment in Financial Networks", "abstract": "Credit Valuation Adjustment captures the difference in the value of derivative contracts when the counterparty default probability is taken into account. However, in the context of a network of contracts, the default probability of a direct counterparty can depend substantially on the default probabilities of indirect counterparties. We develop a model to clarify when and how these network effects matter for CVA, in particular in the presence of correlation among counterparties defaults. We provide an approximate analytical solution for the default probabilities. This solution allows for identifying conditions on key parameters such as network degree, leverage and correlation, where network effects yield large differences in CVA (e.g. above 50%), and thus relevant for practical applications. Moreover, we find evidence that network effects induce a multi-modal distribution of CVA values.", "published": "2023-05-25T19:15:46Z", "authors": ["Irena Barjašić", "Stefano Battiston", "Vinko Zlatić"], "categories": ["q-fin.RM", "physics.soc-ph", "q-fin.MF"], "doi": "", "_query": "automated valuation model", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2210.12809v5", "title": "Data Augmentation for Automated Essay Scoring using Transformer Models", "abstract": "Automated essay scoring is one of the most important problem in Natural Language Processing. It has been explored for a number of years, and it remains partially solved. In addition to its economic and educational usefulness, it presents research problems. Transfer learning has proved to be beneficial in NLP. Data augmentation techniques have also helped build state-of-the-art models for automated essay scoring. Many works in the past have attempted to solve this problem by using RNNs, LSTMs, etc. This work examines the transformer models like BERT, RoBERTa, etc. We empirically demonstrate the effectiveness of transformer models and data augmentation for automated essay grading across many topics using a single model.", "published": "2022-10-23T18:13:30Z", "authors": ["Kshitij Gupta"], "categories": ["cs.CL", "cs.AI"], "doi": "", "_query": "automated valuation model", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2607.03625v3", "title": "Congestion Games with Heterogeneous Valuations: An Optimal Transport Approach", "abstract": "In emerging urban mobility and logistics applications, such as advanced air mobility, electric vehicle charging, and shared service systems, agents with heterogeneous valuations choose among multiple outcomes while sharing congested resources. Existing congestion game and resource-allocation models, however, typically restrict either the structure of heterogeneous outcome preferences or the form of congestion externalities. We introduce a nonatomic congestion game framework in which agents have multidimensional heterogeneous valuations over a finite set of outcomes, with the population represented by a measure over the valuation space. We show that, although there is a continuum of heterogeneous agents, both Nash equilibria and social optima admit finite-dimensional characterizations. Nash equilibria are characterized by aggregate flows and outcome-level threshold vectors, while social optima admit a semi-discrete optimal transport formulation characterized by aggregate flows and strategy-level Kantorovich dual potentials. For separable utilities and latency functions $\\ell_e$ such that $x\\mapsto x\\ell_e(x)$ is convex for every resource $e$, the social welfare problem becomes a convex optimization problem, and we characterize social optima through marginal congestion costs and capacity-dual prices. Our results provide a geometric characterization of congestion games with multidimensional heterogeneous valuations and connect nonatomic congestion games with semi-discrete optimal transport.", "published": "2026-07-03T22:59:07Z", "authors": ["Pan-Yang Su", "Negar Mehr", "Shankar Sastry"], "categories": ["cs.GT", "econ.TH", "math.OC"], "doi": "", "_query": "automated valuation model", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2203.02015v3", "title": "Common valuations of division polynomials", "abstract": "In this note we prove a formula for the cancellation exponent $k_{v,n}$ between division polynomials $ψ_n$ and $φ_n$ associated with a sequence $\\{nP\\}_{n\\in\\mathbb{N}}$ of points on an elliptic curve $E$ defined over a discrete valuation field $K$. The formula is identical with the result of Yabuta-Voutier for the case of finite extension of $\\mathbb{Q}_{p}$ and generalizes to the case of non-standard Kodaira types for non-perfect residue fields.", "published": "2022-03-03T20:54:17Z", "authors": ["Bartosz Naskręcki", "Matteo Verzobio"], "categories": ["math.NT", "math.AG"], "doi": "10.1017/prm.2024.7", "_query": "automated valuation model", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2305.00054v3", "title": "LAVA: Data Valuation without Pre-Specified Learning Algorithms", "abstract": "Traditionally, data valuation (DV) is posed as a problem of equitably splitting the validation performance of a learning algorithm among the training data. As a result, the calculated data values depend on many design choices of the underlying learning algorithm. However, this dependence is undesirable for many DV use cases, such as setting priorities over different data sources in a data acquisition process and informing pricing mechanisms in a data marketplace. In these scenarios, data needs to be valued before the actual analysis and the choice of the learning algorithm is still undetermined then. Another side-effect of the dependence is that to assess the value of individual points, one needs to re-run the learning algorithm with and without a point, which incurs a large computation burden. This work leapfrogs over the current limits of data valuation methods by introducing a new framework that can value training data in a way that is oblivious to the downstream learning algorithm. Our main results are as follows. (1) We develop a proxy for the validation performance associated with a training set based on a non-conventional class-wise Wasserstein distance between training and validation sets. We show that the distance characterizes the upper bound of the validation performance for any given model under certain Lipschitz conditions. (2) We develop a novel method to value individual data based on the sensitivity analysis of the class-wise Wasserstein distance. Importantly, these values can be directly obtained for free from the output of off-the-shelf optimization solvers when computing the distance. (3) We evaluate our new data valuation framework over various use cases related to detecting low-quality data and show that, surprisingly, the learning-agnostic feature of our framework enables a significant improvement over SOTA performance while being orders of magnitude faster.", "published": "2023-04-28T19:05:16Z", "authors": ["Hoang Anh Just", "Feiyang Kang", "Jiachen T. Wang", "Yi Zeng", "Myeongseob Ko", "Ming Jin", "Ruoxi Jia"], "categories": ["cs.LG", "cs.AI", "stat.ML"], "doi": "", "_query": "automated valuation model", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/1812.04539v1", "title": "The 2-adic valuations of Stirling numbers of the first kind", "abstract": "Let $n$ and $k$ be positive integers. We denote by $v_2(n)$ the 2-adic valuation of $n$. The Stirling numbers of the first kind, denoted by $s(n,k)$, counts the number of permutations of $n$ elements with $k$ disjoint cycles. In recent years, Lengyel, Komatsu and Young, Leonetti and Sanna, and Adelberg made some progress on the $p$-adic valuations of $s(n,k)$. In this paper, by introducing the concept of $m$-th Stirling numbers of the first kind and providing a detailed 2-adic analysis, we show an explicit formula on the 2-adic valuation of $s(2^n, k)$. We also prove that $v_2(s(2^n+1,k+1))=v_2(s(2^n,k))$ holds for all integers $k$ between 1 and $2^n$. As a corollary, we show that $v_2(s(2^n,2^n-k))=2n-2-v_2(k-1)$ if $k$ is odd and $2\\le k\\le 2^{n-1}+1$. This confirms partially a conjecture of Lengyel raised in 2015. Furthermore, we show that if $k\\le 2^n$, then $v_2(s(2^n,k)) \\le v_2(s(2^n,1))$ and $v_2(H(2^n,k))\\leq -n$, where $H(n,k)$ stands for the $k$-th elementary symmetric functions of $1,1/2,...,1/n$. The latter one supports the conjecture of Leonetti and Sanna suggested in 2017.", "published": "2018-12-11T16:56:58Z", "authors": ["Min Qiu", "Shaofang Hong"], "categories": ["math.NT"], "doi": "", "_query": "automated valuation model", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2106.09850v1", "title": "Valuation semantics for first-order logics of evidence and truth (and some related logics)", "abstract": "This paper introduces the logic $QLET_{F}$, a quantified extension of the logic of evidence and truth $LET_{F}$, together with a corresponding sound and complete first-order non-deterministic valuation semantics. $LET_{F}$ is a paraconsistent and paracomplete sentential logic that extends the logic of first-degree entailment ($FDE$) with a classicality operator ${\\circ}$ and a non-classicality operator $\\bullet$, dual to each other: while ${\\circ} A$ entails that $A$ behaves classically, ${\\bullet} A$ follows from $A$'s violating some classically valid inferences. The semantics of $QLET_{F}$ combines structures that interpret negated predicates in terms of anti-extensions with first-order non-deterministic valuations, and completeness is obtained through a generalization of Henkin's method. By providing sound and complete semantics for first-order extensions of $FDE$, $K3$, and $LP$, we show how these tools, which we call here the method of ``anti-extensions + valuations'', can be naturally applied to a number of non-classical logics.", "published": "2021-06-18T00:07:54Z", "authors": ["H. Antunes", "A. Rodrigues", "W. Carnielli", "M. E. Coniglio"], "categories": ["math.LO"], "doi": "", "_query": "automated valuation model", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2511.12863v1", "title": "Rethinking Data Value: Asymmetric Data Shapley for Structure-Aware Valuation in Data Markets and Machine Learning Pipelines", "abstract": "Rigorous valuation of individual data sources is critical for fair compensation in data markets, informed data acquisition, and transparent development of ML/AI models. Classical Data Shapley (DS) provides a essential axiomatic framework for data valuation but is constrained by its symmetry axiom that assumes interchangeability of data sources. This assumption fails to capture the directional and temporal dependencies prevalent in modern ML/AI workflows, including the reliance of duplicated or augmented data on original sources and the order-specific contributions in sequential pipelines such as federated learning and multi-stage LLM fine tuning. To address these limitations, we introduce Asymmetric Data Shapley (ADS), a structure-aware data valuation framework for modern ML/AI pipelines. ADS relaxes symmetry by averaging marginal contributions only over permutations consistent with an application-specific ordering of data groups. It preserves efficiency and linearity, maintains within group symmetry and directional precedence across groups, and reduces to DS when the ordering collapses to a single group. We develop two complementary computational procedures for ADS: (i) a Monte Carlo estimator (MC-ADS) with finite-sample accuracy guarantees, and (ii) a k-nearest neighbor surrogate (KNN-ADS) that is exact and efficient for KNN predictors. Across representative settings with directional and temporal dependence, ADS consistently outperforms benchmark methods by distinguishing novel from redundant contributions and respecting the sequential nature of training. These results establish ADS as a principled and practical approach to equitable data valuation in data markets and complex ML/AI pipelines.", "published": "2025-11-17T01:30:11Z", "authors": ["Xi Zheng", "Yinghui Huang", "Xiangyu Chang", "Ruoxi Jia", "Yong Tan"], "categories": ["cs.GT"], "doi": "", "_query": "automated valuation model", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2608.03073v3", "title": "Finite-valuation approximable structures: a solution to the Jung--Tix problem of probabilistic powerdomains", "abstract": "We introduce the category $ω{\\bf FVA}$ of finite-valuation approximable domains, a full subcategory of continuous domains contained in the category of pointed countably based FS-domains. We prove that $ω{\\bf FVA}$ is Cartesian closed and closed under both the subprobabilistic and probabilistic valuation powerdomains. Hence the valuation monads $\\mathcal{V}_{\\leq 1}$ and $\\mathcal{V}_1$ restrict to $ω{\\bf FVA}$, yielding a positive answer to the category-existence form of the Jung--Tix problem, a long-standing open problem in domain theory since the 1990s. In particular, we develop a new factorization-approximation framework for constructing objects from a given class of known objects or structures. Applying this method to the class of subprobabilistic powerdomains over finite posets, we construct the class $ω{\\bf FVA}$ and show that it is closed under Scott-continuous retracts, finite products, function spaces, $\\mathcal{V}_{\\leq 1}$ and $\\mathcal{V}_1$ monads.", "published": "2026-08-04T03:38:42Z", "authors": ["Yuxu Chen", "Hui Kou", "Zhenchao Lyu"], "categories": ["cs.LO"], "doi": "", "_query": "automated valuation model", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2203.04924v2", "title": "Quantum Advantage for Multi-option Portfolio Pricing and Valuation Adjustments", "abstract": "A critical problem in the financial world deals with the management of risk, from regulatory risk to portfolio risk. Many such problems involve the analysis of securities modelled by complex dynamics that cannot be captured analytically, and hence rely on numerical techniques that simulate the stochastic nature of the underlying variables. These techniques may be computationally difficult or demanding. Hence, improving these methods offers a variety of opportunities for quantum algorithms. In this work, we study the problem of Credit Valuation Adjustments (CVAs) which has significant importance in the valuation of derivative portfolios. As a variant, we also consider the problem of pricing a portfolio of many different financial options. We propose quantum algorithms that accelerate statistical sampling processes to approximate the price of the multi-option portfolio and the CVA under different measures of dispersion. Technically, our algorithms are based on enhancing the quantum Monte Carlo (QMC) algorithms by Montanaro with an unbiased version of quantum amplitude estimation. We analyse the conditions under which we may employ these techniques and demonstrate the application of QMC techniques on CVA approximation when particular bounds for the variance of CVA are known.", "published": "2022-03-09T18:14:54Z", "authors": ["Jeong Yu Han", "Bin Cheng", "Dinh-Long Vu", "Patrick Rebentrost"], "categories": ["quant-ph", "q-fin.CP", "q-fin.MF"], "doi": "", "_query": "automated valuation model", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2006.07423v1", "title": "On the generating polynomials for the distribution of generalized binomial coefficients in discrete valuation domains", "abstract": "For a discrete valuation domain $V$ with maximal ideal $\\mathfrak{m}$ such that the residue field $V/\\mathfrak{m}$ is finite, there exists a sequence of polynomials $(F_n(x))_{n \\ge 0}$ defined over the quotient field $K$ of $V$ that forms a basis of the $V$-module $\\text{Int}(V) = \\{f \\in K[x] | f(V)\\subseteq V\\}$. This sequence of polynomials bears many resemblances to the classical binomial polynomials $(\\binom{x}{n})_{n \\ge 0}$. In this paper, we introduce a generating polynomial to account for the distribution of the $V$-values of the polynomials $F_n(x)$ modulo the maximal ideal $\\mathfrak{m}$, and prove a result that provides a method for counting exactly how many $V$-values of the polynomials $(F_n(x))_{n \\ge 0}$ fall into each of the residue classes modulo $\\mathfrak{m}$. Our main theorem in this paper can be viewed as an analogue of the classical theorem of Garfield and Wilf in the context of discrete valuation domains.", "published": "2020-06-12T18:56:01Z", "authors": ["Dong Quan Ngoc Nguyen"], "categories": ["math.NT", "math.AC", "math.CO"], "doi": "", "_query": "automated valuation model", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/1306.0127v1", "title": "Consistent Histories as Valuations", "abstract": "Sorkin's coevent interpretation shifts the focus of quantum logic from the structure of a propositional lattice to the nature of truth valuations thereon. We apply this shift in emphasis to a simple formulation of the consistent histories approach, expressing it in terms of truth valuations which are brought together in a logical framework. We see that these consistent histories valuations are related to Sorkin's multiplicative coevents, and that they can be naturally described by an adaptation of Isham's early topos-theoretic approach.", "published": "2013-06-01T17:10:54Z", "authors": ["Yousef Ghazi-Tabatabai"], "categories": ["quant-ph"], "doi": "", "_query": "automated valuation model", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2108.13471v1", "title": "On the 2-adic valuation of (a^b-c^d)", "abstract": "In this paper will be proved an inequality regarding $v_2(a^{b}-c^{d})$. Using this formula it will be possible to have informations about the divisibility of 2 of this function without computing it. Then, will be studied the behavior of this function when it approaches infinity and there will be a lot of analogies with 2-adic integers. This result may be easily used on a calculator, to facilitate the analysis of the divisibility by 2 of this function for large values. Furthermore 2-adic valuation and in general 2-adic analysis is related to binary system, and every work related to it could have a lot of applications in informatics.", "published": "2021-08-30T18:37:58Z", "authors": ["Luca Onnis"], "categories": ["math.NT"], "doi": "", "_query": "automated valuation model", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/1610.08985v1", "title": "Automated Neutrino Jet and Top Jet Predictions at Next-to-Leading-Order with Parton Shower Matching in Effective Left-Right Symmetric Models", "abstract": "Hadronic decays of boosted resonances, e.g., top quark jets, at hadronic super colliders are frequent predictions in TeV-scale extensions of the Standard Model of Particle Physics. In such scenarios, accurate modeling of QCD radiation is necessary for trustworthy predictions. We present the automation of fully differential, next-to-leading-order (NLO) in QCD corrections with parton shower (PS) matching for an effective Left-Right Symmetric Model (LRSM) that features $W_R^\\pm, Z_R$ gauge bosons and heavy Majorana neutrinos $N$. Publicly available universal model files require remarkably fewer user inputs for predicting benchmark collider processes than leading order LRSM constructions. We present predictions for inclusive $W_R^\\pm, Z_R$ production at the $\\sqrt{s} = 13$ TeV Large Hadron Collider (LHC) and a hypothetical future 100 TeV Very Large Hadron Collider (VLHC), as well as inclusive $N$ production for a hypothetical Large Hadron Electron Collider (LHeC). As a case study, we investigate at NLO+PS accuracy the properties of heavy neutrino (color-singlet) jets and top quark (color-triplet) jets from decays of high-mass $W_R$ bosons at the LHC. Contrary to top jets, we find that the kinematic properties of heavy neutrinos jets, and in particular jet mass, are resilient against the effects of parton showers and hard QCD radiation. This suggests that in searches for neutrino jets, aggressive selection cuts that would otherwise be inappropriate for top jets can be imposed with minimal signal loss.", "published": "2016-10-27T20:00:06Z", "authors": ["Olivier Mattelaer", "Manimala Mitra", "Richard Ruiz"], "categories": ["hep-ph", "hep-ex"], "doi": "", "_query": "automated valuation model", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2302.03087v3", "title": "Dividing Good and Better Items Among Agents with Bivalued Submodular Valuations", "abstract": "We study the problem of fairly allocating a set of indivisible goods among agents with {\\em bivalued submodular valuations} -- each good provides a marginal gain of either $a$ or $b$ ($a < b$) and goods have decreasing marginal gains. This is a natural generalization of two well-studied valuation classes -- bivalued additive valuations and binary submodular valuations. We present a simple sequential algorithmic framework, based on the recently introduced Yankee Swap mechanism, that can be adapted to compute a variety of solution concepts, including max Nash welfare (MNW), leximin and $p$-mean welfare maximizing allocations when $a$ divides $b$. This result is complemented by an existing result on the computational intractability of MNW and leximin allocations when $a$ does not divide $b$. We show that MNW and leximin allocations guarantee each agent at least $\\frac25$ and $\\frac{a}{b+2a}$ of their maximin share, respectively, when $a$ divides $b$. We also show that neither the leximin nor the MNW allocation is guaranteed to be envy free up to one good (EF1). This is surprising since for the simpler classes of bivalued additive valuations and binary submodular valuations, MNW allocations are known to be envy free up to any good (EFX).", "published": "2023-02-06T19:41:28Z", "authors": ["Cyrus Cousins", "Vignesh Viswanathan", "Yair Zick"], "categories": ["cs.GT", "cs.AI"], "doi": "", "_query": "automated valuation model", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/1510.06874v1", "title": "On the existence of a torsor structure for Galois covers over a complete discrete valuation ring", "abstract": "In this note we investigate the problem of existence of a torsor structure for Galois covers of (formal) schemes over a complete discrete valuation ring of residue characteristic $p>0$ in the case of abelian Galois groups of type (p,p,...,p).", "published": "2015-10-23T09:53:02Z", "authors": ["Mohamed Saidi", "Nicholas Williams"], "categories": ["math.AG", "math.NT"], "doi": "", "_query": "automated valuation model", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/1912.05385v3", "title": "On analytic functions in an ordered field with an infinite rank valuation", "abstract": "Let K be the scalar field of the first orthomodular (or Form Hilbert) space, described by H. Keller in 1980. It has a non-Archimedean order, an infinite rank valuation compatible with the order as well as an explicitly defined ultrametric, all of which induce the same topology. We study analytic functions defined on valued field K, and we will establish an invertibility local theorem for these functions as an application of Banach fixed point theorem.", "published": "2019-12-11T15:20:07Z", "authors": ["Héctor M. Moreno"], "categories": ["math.FA"], "doi": "", "_query": "automated valuation model", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2604.00123v2", "title": "Imaginaries in perfect bounded pseudo algebraically closed fields with finitely many independent valuations", "abstract": "In this paper, we prove weak elimination of imaginaries for perfect bounded pseudo-algebraically closed fields equipped with finitely many independent valuations. Our approach combines an extension result for types to invariant types with an amalgamation theorem. As a special case, we obtain full elimination of imaginaries when the field is equipped with a single valuation.", "published": "2026-03-31T18:29:43Z", "authors": ["Bryan González Leandro"], "categories": ["math.LO"], "doi": "", "_query": "automated valuation model", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2008.08798v4", "title": "Existence of EFX for Two Additive Valuations", "abstract": "Fair division of indivisible items is a well-studied topic in Economics and Computer Science. The objective is to allocate items to agents in a fair manner, where each agent has a valuation for each subset of items. Envy-freeness is one of the most widely studied notions of fairness. Since complete envy-free allocations do not always exist when items are indivisible, several relaxations have been considered. Among them, possibly the most compelling one is envy-freeness up to any item (EFX), where no agent envies another agent after the removal of any single item from the other agent's bundle. However, despite significant efforts by many researchers for several years, it is known that a complete EFX allocation always exists only in limited cases. In this paper, we show that a complete EFX allocation always exists when each agent is of one of two given types, where agents of the same type have identical additive valuations. This is the first such existence result for non-identical valuations when there are any number of agents and items and no limit on the number of distinct values an agent can have for individual items. We give a constructive proof, in which we iteratively obtain a Pareto dominating (partial) EFX allocation from an existing partial EFX allocation.", "published": "2020-08-20T06:25:51Z", "authors": ["Ryoga Mahara"], "categories": ["cs.GT"], "doi": "", "_query": "automated valuation model", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2507.08221v1", "title": "The $p$-adic valuation of local resolvents, generalized Gauss sums and anticyclotomic Hecke $L$-values of imaginary quadratic fields at inert primes", "abstract": "We prove an asymptotic formula for the $p$-adic valuation of Hecke $L$-values of an imaginary quadratic field at an inert prime $p$ along the anticyclotomic $\\mathbb{Z}_p$-tower. The key is determination of the $p$-adic valuation of generalized Gauss sums defined using Coates-Wiles homomorphism, and of local resolvents in $\\mathbb{Z}_p$-extensions. This answers a question of Rubin.", "published": "2025-07-10T23:56:27Z", "authors": ["Ashay Burungale", "Shinichi Kobayashi", "Kazuto Ota"], "categories": ["math.NT"], "doi": "", "_query": "automated valuation model", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/0802.4306v2", "title": "Degree and valuation of the Schur elements of cyclotomic Hecke algebras", "abstract": "Following the generalization of the notion of families of characters, defined by Lusztig for Weyl groups, to the case of complex reflection groups, thanks to the definition given by Rouquier, we show that the degree and the valuation of the Schur elements (functions A and a) remain constant on the \"families\" of the cyclotomic Hecke algebras of the exceptional complex reflection groups. The same result has already been obtained for the groups of the infinite series and for some special cases of exceptional groups.", "published": "2008-02-28T23:54:26Z", "authors": ["Maria Chlouveraki"], "categories": ["math.RT"], "doi": "10.1016/j.jalgebra.2008.07.024", "_query": "automated valuation model", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2502.13541v1", "title": "Concentration and maximin fair allocations for subadditive valuations", "abstract": "We consider fair allocation of $m$ indivisible items to $n$ agents of equal entitlements, with submodular valuation functions. Previously, Seddighin and Seddighin [{\\em Artificial Intelligence} 2024] proved the existence of allocations that offer each agent at least a $\\frac{1}{c \\log n \\log\\log n}$ fraction of her maximin share (MMS), where $c$ is some large constant (over 1000, in their work). We modify their algorithm and improve its analysis, improving the ratio to $\\frac{1}{14 \\log n}$. Some of our improvement stems from tighter analysis of concentration properties for the value of any subadditive valuation function $v$, when considering a set $S' \\subseteq S$ of items, where each item of $S$ is included in $S'$ independently at random (with possibly different probabilities). In particular, we prove that up to less than the value of one item, the median value of $v(S')$, denoted by $M$, is at least two-thirds of the expected value, $M \\geq \\frac{2}{3}\\E[v(S')] - \\frac{11}{12}\\max_{e \\in S} v(e)$.", "published": "2025-02-19T08:49:57Z", "authors": ["Uriel Feige", "Shengyu Huang"], "categories": ["cs.GT"], "doi": "", "_query": "automated valuation model", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/1012.4442v2", "title": "On backward stochastic differential equations approach to valuation of American options", "abstract": "We consider the problem of valuation of American (call and put) options written on a dividend paying stock governed by the geometric Brownian motion. We show that the value function has two different but related representations: by means of a solution of some nonlinear backward stochastic differential equation and weak solution to some semilinear partial differential equation.", "published": "2010-12-20T19:03:15Z", "authors": ["Tomasz Klimsiak", "Andrzej Rozkosz"], "categories": ["math.PR", "math.AP", "math.OC"], "doi": "", "_query": "automated valuation model", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2506.04665v6", "title": "An $O(\\log \\log n)$-approximate budget feasible mechanism for subadditive valuations", "abstract": "In budget-feasible mechanism design, there is a set of items $U$, each owned by a distinct seller. The seller of item $e$ incurs a private cost $\\overline{c}_e$ for supplying her item. A buyer wishes to procure a set of items from the sellers of maximum value, where the value of a set $S\\subseteq U$ of items is given by a valuation function $v:2^U\\to \\mathbb{R}_+$. The buyer has a budget of $B \\in \\mathbb{R}_+$ for the total payments made to the sellers. We wish to design a mechanism that is truthful, that is, sellers are incentivized to report their true costs, budget-feasible, that is, the sum of the payments made to the sellers is at most the budget $B$, and that outputs a set whose value is large compared to $\\text{OPT}:=\\max\\{v(S):\\overline{c}(S)\\le B,S\\subseteq U\\}$. Budget-feasible mechanism design has been extensively studied, with the literature focussing on (classes of) subadditive valuation functions, and various polytime, budget-feasible mechanisms, achieving constant-factor approximation, have been devised for the special cases of additive, submodular, and XOS valuations. However, for general subadditive valuations, the best-known approximation factor achievable by a polytime budget-feasible mechanism (given access to demand oracles) was only $O(\\log n / \\log \\log n)$, where $n$ is the number of items. We improve this state-of-the-art significantly by designing a randomized budget-feasible mechanism for subadditive valuations that achieves a substantially-improved approximation factor of $O(\\log\\log n)$ and runs in polynomial time, given access to demand oracles.", "published": "2025-06-05T06:21:33Z", "authors": ["Rian Neogi", "Kanstantsin Pashkovich", "Chaitanya Swamy"], "categories": ["cs.GT", "cs.DM", "cs.DS"], "doi": "", "_query": "automated valuation model", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/1109.1774v1", "title": "Conjure Revisited: Towards Automated Constraint Modelling", "abstract": "Automating the constraint modelling process is one of the key challenges facing the constraints field, and one of the principal obstacles preventing widespread adoption of constraint solving. This paper focuses on the refinement-based approach to automated modelling, where a user specifies a problem in an abstract constraint specification language and it is then automatically refined into a constraint model. In particular, we revisit the Conjure system that first appeared in prototype form in 2005 and present a new implementation with a much greater coverage of the specification language Essence.", "published": "2011-09-08T17:09:00Z", "authors": ["Ozgur Akgun", "Alan M. Frisch", "Brahim Hnich", "Chris Jefferson", "Ian Miguel"], "categories": ["cs.AI", "cs.PL"], "doi": "", "_query": "automated valuation model", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2503.12344v1", "title": "EXPRESS: An LLM-Generated Explainable Property Valuation System with Neighbor Imputation", "abstract": "The demand for property valuation has attracted significant attention from sellers, buyers, and customers applying for loans. Reviews of existing approaches have revealed shortcomings in terms of not being able to handle missing value situations, as well as lacking interpretability, which means they cannot be used in real-world applications. To address these challenges, we propose an LLM-Generated EXplainable PRopErty valuation SyStem with neighbor imputation called EXPRESS, which provides the customizable missing value imputation technique, and addresses the opaqueness of prediction by providing the feature-wise explanation generated by LLM. The dynamic nearest neighbor search finds similar properties depending on different application scenarios by property configuration set by users (e.g., house age as criteria for the house in rural areas, and locations for buildings in urban areas). Motivated by the human appraisal procedure, we generate feature-wise explanations to provide users with a more intuitive understanding of the prediction results.", "published": "2025-03-16T03:49:52Z", "authors": ["Wei-Wei Du", "Yung-Chien Wang", "Wen-Chih Peng"], "categories": ["cs.LG"], "doi": "", "_query": "automated valuation model", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2505.23383v4", "title": "Automated Modeling Method for Pathloss Model Discovery", "abstract": "Modeling propagation is the cornerstone for designing and optimizing next-generation wireless systems, with a particular emphasis on 5G and beyond era. Traditional modeling methods have long relied on statistic-based techniques to characterize propagation behavior across different environments. With the expansion of wireless communication systems, there is a growing demand for methods that guarantee the accuracy and interpretability of modeling. Artificial intelligence (AI)-based techniques, in particular, are increasingly being adopted to overcome this challenge, although the interpretability is not assured with most of these methods. Inspired by recent advancements in AI, this paper proposes a novel approach that accelerates the discovery of path loss models while maintaining interpretability. The proposed method automates the formulation, evaluation, and refinement of the model, facilitating the discovery of the model. We examine two techniques: one based on Deep Symbolic Regression, offering full interpretability, and the second based on Kolmogorov-Arnold Networks, providing two levels of interpretability. Both approaches are evaluated on two synthetic and two real-world datasets. Our results show that Kolmogorov-Arnold Networks achieve the coefficient of determination value R^2 close to 1 with minimal prediction error, while Deep Symbolic Regression generates compact models with moderate accuracy. Moreover, on the selected examples, we demonstrate that automated methods outperform traditional methods, achieving up to 75% reduction in prediction errors, offering accurate and explainable solutions with potential to increase the efficiency of discovering next-generation path loss models.", "published": "2025-05-29T12:04:07Z", "authors": ["Ahmad Anaqreh", "Shih-Kai Chou", "Blaž Bertalanič", "Mihael Mohorčič", "Thomas Lagkas", "Carolina Fortuna"], "categories": ["cs.LG"], "doi": "10.1109/TMC.2026.3683609", "_query": "automated valuation model", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2412.06312v1", "title": "Towards High-Level Modelling in Automated Planning", "abstract": "Planning is a fundamental activity, arising frequently in many contexts, from daily tasks to industrial processes. The planning task consists of selecting a sequence of actions to achieve a specified goal from specified initial conditions. The Planning Domain Definition Language (PDDL) is the leading language used in the field of automated planning to model planning problems. Previous work has highlighted the limitations of PDDL, particularly in terms of its expressivity. Our interest lies in facilitating the handling of complex problems and enhancing the overall capability of automated planning systems. Unified-Planning is a Python library offering high-level API to specify planning problems and to invoke automated planners. In this paper, we present an extension of the UP library aimed at enhancing its expressivity for high-level problem modelling. In particular, we have added an array type, an expression to count booleans, and the allowance for integer parameters in actions. We show how these facilities enable natural high-level models of three classical planning problems.", "published": "2024-12-09T09:01:13Z", "authors": ["Carla Davesa Sureda", "Joan Espasa Arxer", "Ian Miguel", "Mateu Villaret Auselle"], "categories": ["cs.AI"], "doi": "", "_query": "automated valuation model", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/1603.05064v3", "title": "Pairwise Stability in Two Sided Market with Strictly Increasing Valuation Functions", "abstract": "This paper deals with two-sided matching market with two disjoint sets, i.e. the set of buyers and the set of sellers. Each seller can trade with at most with one buyer and vice versa. Money is transferred from sellers to buyers for an indivisible goods that buyers own. Valuation functions, for participants of both sides, are represented by strictly increasing functions with money considered as discrete variable. An algorithm is devised to prove the existence of stability for this model.", "published": "2016-03-14T07:21:43Z", "authors": ["Yasir Ali", "Asma Javaid"], "categories": ["cs.GT", "math.CO"], "doi": "10.23638/DMTCS-19-1-10", "_query": "automated valuation model", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2404.16291v1", "title": "Duality for differential modules over complete non-archimedean valuation field of characteristic zero", "abstract": "Let $K$ be a complete non-archimedean valuation field of characteristic $0$, with non-trivial valuation, equipped with (possibly multiple) commuting bounded derivations. We prove a decomposition theorem for finite differential modules over $K$, where decompositions regarding the extrinsic subsidiary $\\partial$-generic radii of convergence in the sense of Kedlaya-Xiao. Our result is a refinement of a previous decomposition theorem due to Kedlaya and Xiao. As a key step in the proof, we prove a decomposition theorem in a stronger form in the case where $K$ is equipped with a single derivation. To achieve this goal, we construct an object $f_{0*}L_0$ representing the usual dual functor and study some filtrations of $f_{0*}L_0$, which is used to construct the direct summands appearing in our decomposition theorem.", "published": "2024-04-25T02:22:54Z", "authors": ["Shun Ohkubo"], "categories": ["math.NT"], "doi": "", "_query": "automated valuation model", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2604.00388v1", "title": "Gradient-Based Data Valuation Improves Curriculum Learning for Game-Theoretic Motion Planning", "abstract": "We demonstrate that gradient-based data valuation produces curriculum orderings that significantly outperform metadata-based heuristics for training game-theoretic motion planners. Specifically, we apply TracIn gradient-similarity scoring to GameFormer on the nuPlan benchmark and construct a curriculum that weights training scenarios by their estimated contribution to validation loss reduction. Across three random seeds, the TracIn-weighted curriculum achieves a mean planning ADE of $1.704\\pm0.029$\\,m, significantly outperforming the metadata-based interaction-difficulty curriculum ($1.822\\pm0.014$\\,m; paired $t$-test $p=0.021$, Cohen's $d_z=3.88$) while exhibiting lower variance than the uniform baseline ($1.772\\pm0.134$\\,m). Our analysis reveals that TracIn scores and scenario metadata are nearly orthogonal (Spearman $ρ=-0.014$), indicating that gradient-based valuation captures training dynamics invisible to hand-crafted features. We further show that gradient-based curriculum weighting succeeds where hard data selection fails: TracIn-curated 20\\% subsets degrade performance by $2\\times$, whereas full-data curriculum weighting with the same scores yields the best results. These findings establish gradient-based data valuation as a practical tool for improving sample efficiency in game-theoretic planning.", "published": "2026-04-01T02:17:27Z", "authors": ["Shihao Li", "Jiachen Li", "Dongmei Chen"], "categories": ["cs.LG", "eess.SY"], "doi": "", "_query": "automated valuation model", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/1112.1763v1", "title": "Clean Valuation Framework for the USD Silo", "abstract": "In the forthcoming ISDA Standard Credit Support Annex (SCSA), the trades denominated in non-G5 currencies as well as those include multiple currencies are expected to be allocated to the USD silo, where the contracts are collateralized by USD cash, or a different currency with an appropriate interest rate overlay to achieve the same economic effects. In this paper, we have presented a simple generic valuation framework for the clean price under the USD silo with the the detailed procedures for the initial term structure construction. We have also shown that Cross Currency Swap (CCS) basis spread can be expressed as a difference between two swap rates.", "published": "2011-12-08T04:32:59Z", "authors": ["Masaaki Fujii", "Akihiko Takahashi"], "categories": ["q-fin.PR", "q-fin.RM"], "doi": "", "_query": "automated valuation model", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/1402.2750v2", "title": "Kinematic formulas for tensor valuations", "abstract": "We prove new kinematic formulas for tensor valuations and simplify previously known Crofton formulas by using the recently developed algebraic theory of translation invariant valuations. The heart of the paper is the computation of the Alesker-Fourier transform on the large class of spherical valuations, which is achieved by differential-geometric and representation theoretical tools. We also describe in explicit form the product and convolution of tensor valuations.", "published": "2014-02-12T07:50:03Z", "authors": ["Andreas Bernig", "Daniel Hug"], "categories": ["math.DG", "math.MG"], "doi": "10.1515/crelle-2015-0023", "_query": "automated valuation model", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/1811.01230v1", "title": "The Hermite ring conjecture and special linear groups for valuation rings", "abstract": "In this paper we prove that the Hermite ring conjecture holds for valuation rings $V$, and the special liner group $SL_n(V[x])$ coincides with the group generated by elementary matrices $E_n(V[x])$ for $n\\geq3$. For any arithmetical ring $R$ and $n\\geq3$, we show $SL_n(R[x]) = SL_n(R)\\cdot E_n(R[x])$.", "published": "2018-11-03T14:50:57Z", "authors": ["Jinwang Liu", "Dongmei Li"], "categories": ["math.AC"], "doi": "", "_query": "automated valuation model", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2512.17334v1", "title": "Bridging Natural Language and Formal Specification--Automated Translation of Software Requirements to LTL via Hierarchical Semantics Decomposition Using LLMs", "abstract": "Automating the translation of natural language (NL) software requirements into formal specifications remains a critical challenge in scaling formal verification practices to industrial settings, particularly in safety-critical domains. Existing approaches, both rule-based and learning-based, face significant limitations. While large language models (LLMs) like GPT-4o demonstrate proficiency in semantic extraction, they still encounter difficulties in addressing the complexity, ambiguity, and logical depth of real-world industrial requirements. In this paper, we propose Req2LTL, a modular framework that bridges NL and Linear Temporal Logic (LTL) through a hierarchical intermediate representation called OnionL. Req2LTL leverages LLMs for semantic decomposition and combines them with deterministic rule-based synthesis to ensure both syntactic validity and semantic fidelity. Our comprehensive evaluation demonstrates that Req2LTL achieves 88.4% semantic accuracy and 100% syntactic correctness on real-world aerospace requirements, significantly outperforming existing methods.", "published": "2025-12-19T08:25:54Z", "authors": ["Zhi Ma", "Cheng Wen", "Zhexin Su", "Xiao Liang", "Cong Tian", "Shengchao Qin", "Mengfei Yang"], "categories": ["cs.SE"], "doi": "10.1109/ASE63991.2025.00104", "_query": "automated valuation model", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/1907.12733v1", "title": "On the Nisan-Ronen conjecture for submodular valuations", "abstract": "We consider incentive compatible mechanisms for a domain that is very close to the domain of scheduling $n$ unrelated machines: the single exception is that the valuation of just one machine is submodular. For the scheduling problem with such cost functions, we give a lower bound of $Ω(\\sqrt{n})$ on the approximation ratio of incentive compatible deterministic mechanisms. This is a strong information-theoretic impossibility result on the approximation ratio of mechanisms on relatively simple domains. The lower bound of the current work assumes no restriction on the mechanism side, but an expanded class of valuations, in contrast to previous general results on the Nisan-Ronen conjecture that hold for only special classes of mechanisms such as local, strongly monotone, and anonymous mechanisms. Our approach is based on a novel characterization of appropriately selected smaller instances that allows us to focus on particular type of algorithms (linear mechanisms), from which we extract a locality property that gives the lower bound.", "published": "2019-07-30T04:31:22Z", "authors": ["George Christodoulou", "Elias Koutsoupias", "Annamaria Kovacs"], "categories": ["cs.GT"], "doi": "", "_query": "automated valuation model", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2312.03721v2", "title": "Exploring the Robustness of Model-Graded Evaluations and Automated Interpretability", "abstract": "There has been increasing interest in evaluations of language models for a variety of risks and characteristics. Evaluations relying on natural language understanding for grading can often be performed at scale by using other language models. We test the robustness of these model-graded evaluations to injections on different datasets including a new Deception Eval. These injections resemble direct communication between the testee and the evaluator to change their grading. We extrapolate that future, more intelligent models might manipulate or cooperate with their evaluation model. We find significant susceptibility to these injections in state-of-the-art commercial models on all examined evaluations. Furthermore, similar injections can be used on automated interpretability frameworks to produce misleading model-written explanations. The results inspire future work and should caution against unqualified trust in evaluations and automated interpretability.", "published": "2023-11-26T17:11:55Z", "authors": ["Simon Lermen", "Ondřej Kvapil"], "categories": ["cs.CL", "cs.AI"], "doi": "", "_query": "automated valuation model", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/1312.0128v2", "title": "CCPs, Central Clearing, CSA, Credit Collateral and Funding Costs Valuation FAQ: Re-hypothecation, CVA, Closeout, Netting, WWR, Gap-Risk, Initial and Variation Margins, Multiple Discount Curves, FVA?", "abstract": "We present a dialogue on Funding Costs and Counterparty Credit Risk modeling, inclusive of collateral, wrong way risk, gap risk and possible Central Clearing implementation through CCPs. This framework is important following the fact that derivatives valuation and risk analysis has moved from exotic derivatives managed on simple single asset classes to simple derivatives embedding the new or previously neglected types of complex and interconnected nonlinear risks we address here. This dialogue is the continuation of the \"Counterparty Risk, Collateral and Funding FAQ\" by Brigo (2011). In this dialogue we focus more on funding costs for the hedging strategy of a portfolio of trades, on the non-linearities emerging from assuming borrowing and lending rates to be different, on the resulting aggregation-dependent valuation process and its operational challenges, on the implications of the onset of central clearing, on the macro and micro effects on valuation and risk of the onset of CCPs, on initial and variation margins impact on valuation, and on multiple discount curves. Through questions and answers (Q&A) between a senior expert and a junior colleague, and by referring to the growing body of literature on the subject, we present a unified view of valuation (and risk) that takes all such aspects into account.", "published": "2013-11-30T17:21:25Z", "authors": ["Damiano Brigo", "Andrea Pallavicini"], "categories": ["q-fin.PR"], "doi": "", "_query": "automated valuation model", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/0707.4385v4", "title": "Plurisubharmonic functions on the octonionic plane and Spin(9)-invariant valuations on convex sets", "abstract": "A new class of plurisubharmonic functions on the octonionic plane O^2= R^{16} is introduced. An octonionic version of theorems of A.D. Aleksandrov and Chern- Levine-Nirenberg, and Blocki are proved. These results are used to construct new examples of continuous translation invariant valuations on convex subsets of O^2=R^{16}. In particular a new example of Spin(9)-invariant valuation on R^{16} is given.", "published": "2007-07-30T12:07:15Z", "authors": ["Semyon Alesker"], "categories": ["math.MG", "math.CV"], "doi": "", "_query": "automated valuation model", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2101.02917v1", "title": "Valuation of electricity storage contracts using the COS method", "abstract": "Storage of electricity has become increasingly important, due to the gradual replacement of fossil fuels by more variable and uncertain renewable energy sources. In this paper, we provide details on how to mathematically formalize a corresponding electricity storage contract, taking into account the physical limitations of a storage facility and the operational constraints of the electricity grid. We give details of a valuation technique to price these contracts, where the electricity prices follow a structural model based on a stochastic polynomial process. In particular, we show that the Fourier-based COS method can be used to price the contracts accurately and efficiently.", "published": "2021-01-08T08:53:45Z", "authors": ["Boris C. Boonstra", "Cornelis W. Oosterlee"], "categories": ["q-fin.MF"], "doi": "", "_query": "automated valuation model", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2407.11791v1", "title": "Classifying smashing ideals in derived categories of valuation domains", "abstract": "Building on results of Bazzoni-Šťovíček, we give a complete classification of the frame of smashing ideals for the derived category of a finite dimensional valuation domain. In particular, we give an explicit construction of an infinite family of commutative rings such that the telescope conjecture fails and which generalise an example of Keller. As a consequence, we deduce that the Krull dimension of the Balmer spectrum and the Krull dimension of the smashing spectrum can differ arbitrarily for rigidly-compactly generated tensor-triangulated categories.", "published": "2024-07-16T14:46:02Z", "authors": ["Scott Balchin", "Florian Tecklenburg"], "categories": ["math.AC", "math.AT"], "doi": "", "_query": "automated valuation model", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2111.10873v1", "title": "The Central Valuations Monad", "abstract": "We give a commutative valuations monad Z on the category DCPO of dcpo's and Scott-continuous functions. Compared to the commutative valuations monads given in [Jia et al., 2021], our new monad Z is larger and it contains all push-forward images of valuations on the unit interval [0,1] along lower semi-continuous maps. We believe that this new monad will be useful in giving domain-theoretic denotational semantics for statistical programming languages with continuous probabilistic choice.", "published": "2021-11-21T18:33:15Z", "authors": ["Xiaodong Jia", "Michael Mislove", "Vladimir Zamdzhiev"], "categories": ["cs.LO", "math.CT", "math.GN"], "doi": "10.4230/LIPIcs.CALCO.2021.18", "_query": "automated valuation model", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2502.07577v3", "title": "Automated Capability Discovery via Foundation Model Self-Exploration", "abstract": "Foundation models have become general-purpose assistants, exhibiting diverse capabilities across numerous domains through training on web-scale data. It remains challenging to precisely characterize even a fraction of the full spectrum of these abilities and potential risks in any new model. Existing evaluation approaches often require significant human effort, and it is taking increasing effort to design ever harder challenges for more capable models. We introduce Automated Capability Discovery (ACD), a framework that designates one foundation model as a scientist to systematically propose open-ended tasks probing the abilities of a subject model (potentially itself). By combining frontier models with ideas from the field of open-endedness, ACD automatically and systematically uncovers a diverse spectrum of surprising capabilities and failures in the subject model. We demonstrate ACD across a range of foundation models (including the GPT, Claude, and Llama series), showing that it automatically generates thousands of distinct tasks, which are then clustered to reveal dozens of broader capability areas and failure modes, that would be challenging for any single team to uncover. We further validate our method's automated scoring with extensive human surveys, observing high agreement between model-generated and human evaluations. By leveraging foundation models' ability to both create tasks and self-evaluate, ACD is a significant step toward scalable, automated evaluation of novel AI systems. All code and evaluation logs are open-sourced at https://github.com/conglu1997/ACD.", "published": "2025-02-11T14:23:13Z", "authors": ["Cong Lu", "Shengran Hu", "Jeff Clune"], "categories": ["cs.LG", "cs.AI", "cs.CL"], "doi": "", "_query": "automated valuation model", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2006.12933v3", "title": "Smooth valuations on convex functions", "abstract": "We construct valuations on the space of finite-valued convex functions using integration of differential forms over the differential cycle associated to a convex function. We describe the kernel of this procedure and show that the intersection of this space of smooth valuations with the space of all continuous dually epi-translation invariant valuations on convex functions is dense in the latter. As an application, we obtain a description of 1-homogeneous, continuous, dually epi-translation invariant valuations that are invariant with respect to a compact subgroup operating transitively on the unit sphere.", "published": "2020-06-23T12:19:56Z", "authors": ["Jonas Knoerr"], "categories": ["math.MG"], "doi": "", "_query": "automated valuation model", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2509.00697v1", "title": "Multi Scale Analysis of Nifty 50 Return Characteristics Valuation Dynamics and Market Complexity 1990 to 2024", "abstract": "This study presents a unified, distribution-aware, and complexity-informed framework for understanding equity return dynamics in the Indian market, using 34 years (1990 to 2024) of Nifty 50 index data. Addressing a key gap in the literature, we demonstrate that the price to earnings ratio, as a valuation metric, may probabilistically map return distributions across investment horizons spanning from days to decades. Return profiles exhibit strong asymmetry. One-year returns show a 74 percent probability of gain, with a modal return of 10.67 percent and a reward-to-risk ratio exceeding 5. Over long horizons, modal CAGRs surpass 13 percent, while worst-case returns remain negative for up to ten years, defining a historical trapping period. This horizon shortens to six years in the post-1999 period, reflecting growing market resilience. Conditional analysis of the P/E ratio reveals regime-dependent outcomes. Low valuations (P/E less than 13) historically show zero probability of loss across all horizons, while high valuations (P/E greater than 27) correspond to unstable returns and extended breakeven periods. To uncover deeper structure, we apply tools from complexity science. Entropy, Hurst exponents, and Lyapunov indicators reveal weak persistence, long memory, and low-dimensional chaos. Information-theoretic metrics, including mutual information and transfer entropy, confirm a directional and predictive influence of valuation on future returns. These findings offer actionable insights for asset allocation, downside risk management, and long-term investment strategy in emerging markets. Our framework bridges valuation, conditional distributions, and nonlinear dynamics in a rigorous and practically relevant manner.", "published": "2025-08-31T04:33:39Z", "authors": ["Chandradew Sharma"], "categories": ["q-fin.ST"], "doi": "", "_query": "automated valuation model", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/1609.06457v1", "title": "AMOS: An Automated Model Order Selection Algorithm for Spectral Graph Clustering", "abstract": "One of the longstanding problems in spectral graph clustering (SGC) is the so-called model order selection problem: automated selection of the correct number of clusters. This is equivalent to the problem of finding the number of connected components or communities in an undirected graph. In this paper, we propose AMOS, an automated model order selection algorithm for SGC. Based on a recent analysis of clustering reliability for SGC under the random interconnection model, AMOS works by incrementally increasing the number of clusters, estimating the quality of identified clusters, and providing a series of clustering reliability tests. Consequently, AMOS outputs clusters of minimal model order with statistical clustering reliability guarantees. Comparing to three other automated graph clustering methods on real-world datasets, AMOS shows superior performance in terms of multiple external and internal clustering metrics.", "published": "2016-09-21T08:14:12Z", "authors": ["Pin-Yu Chen", "Thibaut Gensollen", "Alfred O. Hero"], "categories": ["cs.SI", "cs.LG", "stat.ML"], "doi": "", "_query": "automated valuation model", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/math/0411482v2", "title": "Pure-injective hulls of modules over valuation rings", "abstract": "If $\\hat{R} is the pure-injective hull of a valuation ring $R$, it is proved that $\\hat{R}\\otimes\\_RM$ is the pure-injective of $M$, for each finitely generated module $M$. Moreover, $\\hat{R}\\otimes\\_RM\\simeq\\oplus\\_{1\\leq k\\leq n}\\hat{R}/A\\_k\\hat{R}$, where $A\\_1,...,A\\_n$ is the annihilator sequence of $M$. The pure-injective hulls of uniserial or polyserial modules are also investigated. Any two pure-composition series of a countably generated polyserial module are isomorphic.", "published": "2004-11-22T13:19:03Z", "authors": ["Francois Couchot"], "categories": ["math.RA"], "doi": "10.1016/j.jpaa.2005.09.002", "_query": "automated valuation model", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/0810.3366v3", "title": "Squares in (2^2-1)...(n^2-1) and p-adic valuation", "abstract": "In this paper, we determine all the squares in the sequence $\\{\\prod_{k=2}^n(k^2-1)\\}_{n=2}^\\infty $. From this, one deduces that there are infinitely many squares in this sequence. We also give a formula for the $p$-adic valuation of the terms in this sequence.", "published": "2008-10-19T08:20:15Z", "authors": ["Shaofang Hong", "Xingjiang Liu"], "categories": ["math.NT"], "doi": "", "_query": "automated valuation model", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/1112.5695v1", "title": "Milnor $K$-groups modulo $p^n$ of a complete discrete valuation field", "abstract": "For a mixed characteristic complete discrete valuation field $K$ which contains a $p^n$-th root of unity, we determine the graded quotients of the filtration on the Milnor $K$-groups $K_q^M(K)$ modulo $p^n$ in terms of differential forms of the residue field of $K$.", "published": "2011-12-24T03:25:02Z", "authors": ["Toshiro Hiranouchi"], "categories": ["math.KT", "math.NT"], "doi": "", "_query": "automated valuation model", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/1802.01176v2", "title": "Valuation of Crypto-Currency Mining Operations", "abstract": "Traditionally, the Net Present Value method is used to compare diverging investment strategies. However, valuating crypto-projects with fiat-based currency is confusing due to extreme coin appreciation rates as compared to fiat interest rates. Here, we provide a net present value method based on using crypto-coin as the underlying asset. Using this method, we compare HODL vs. mining, we also provide a sensitivity analysis of profitability", "published": "2018-02-04T19:12:23Z", "authors": ["J. Berengueres"], "categories": ["cs.CY"], "doi": "", "_query": "automated valuation model", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/1508.05697v1", "title": "Rees valuations", "abstract": "We study dicritical divisors and Rees valuations.", "published": "2015-08-24T04:41:54Z", "authors": ["Shreeram S Abhyankar", "William J Heinzer"], "categories": ["math.AC"], "doi": "", "_query": "automated valuation model", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2512.01004v1", "title": "Invariant valuations on Lie groups", "abstract": "Convolution of valuations was introduced by the first named author and Fu for linear spaces, and later by Alesker and the first named author for compact Lie groups. In this paper we study the convolution of invariant valuations on Lie groups. First, we obtain an explicit formula for the convolution of left-invariant valuations on compact groups in terms of differential forms. Independently, we show that a connected Lie group admits smooth bi-invariant valuations beyond the Euler characteristic and the Haar measure if and only if the group is the product of a compact group and a linear space. Finally, we use these two results to define the convolution of bi-invariant smooth valuations on an arbitrary unimodular Lie group, thus unifying both previously defined convolution operations.", "published": "2025-11-30T17:54:16Z", "authors": ["Andreas Bernig", "Dmitry Faifman", "Jan Kotrbatý"], "categories": ["math.DG", "math.MG"], "doi": "", "_query": "automated valuation model", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2208.07311v3", "title": "A General Framework for Fair Allocation under Matroid Rank Valuations", "abstract": "We study the problem of fairly allocating a set of indivisible goods among agents with matroid rank valuations -- every good provides a marginal value of $0$ or $1$ when added to a bundle and valuations are submodular. We generalize the Yankee Swap algorithm to create a simple framework, called General Yankee Swap, that can efficiently compute allocations that maximize any justice criterion (or fairness objective) satisfying some mild assumptions. Along with maximizing a justice criterion, General Yankee Swap is guaranteed to maximize utilitarian social welfare, ensure strategyproofness and use at most a quadratic number of valuation queries. We show how General Yankee Swap can be used to compute allocations for five different well-studied justice criteria: (a) Prioritized Lorenz dominance, (b) Maximin fairness, (c) Weighted leximin, (d) Max weighted Nash welfare, and (e) Max weighted $p$-mean welfare. In particular, our framework provides the first polynomial time algorithms to compute weighted leximin, max weighted Nash welfare and max weighted $p$-mean welfare allocations for agents with matroid rank valuations.", "published": "2022-08-15T16:18:02Z", "authors": ["Vignesh Viswanathan", "Yair Zick"], "categories": ["cs.GT"], "doi": "", "_query": "automated valuation model", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2605.02592v1", "title": "Foundation-Model-Based Agents in Industrial Automation: Purposes, Capabilities, and Open Challenges", "abstract": "Foundation models, particularly large language models, are increasingly integrated into agent architectures for industrial tasks such as decision support, process monitoring, and engineering automation. Yet evidence on their purposes, capabilities, and limitations remains fragmented across domains. This work examines how mature foundation-model-based agent systems are in industrial contexts, how their functional profile differs from conventional agent systems, and which limitations persist. A systematic literature survey following the PRISMA 2020 guideline is presented, screening 2,341 publications and synthesising a corpus of 88 publications through a structured coding scheme. The results show that reported systems are predominantly at prototype and early validation stages (75.0% at TRL 4-6), with deployment-oriented evidence remaining rare (9.1%). Operational goals are most frequently positioned in user assistance, monitoring, and process optimisation, while conventional production-control purposes such as planning and scheduling are less prominent. Compared with an established baseline for industrial agent systems, the capability profile reveals substantial gains in human interaction (+37%) and dealing with uncertainty (+35%), but a pronounced deficit in negotiation (-39%). The most widely reported limitations concern lack of generalization, hallucination and output instability, data scarcity, and inference latency. A working definition of foundation-model-based industrial agents is also proposed, bridging conventional agent theory, automation-engineering standards, and the foundation-model paradigm.", "published": "2026-05-04T13:44:22Z", "authors": ["Vincent Henkel", "Felix Gehlhoff", "David Kube", "Asaad Almutareb", "Luis Cruz", "Bernd Hellingrath", "Philip Koch", "Christoph Legat", "Florian Mohr", "Michael Oberle", "Felix Ocker", "Thorsten Schoeler", "Mario Thron", "Nico Andre Töpfer", "Lucas Vogt", "Yuchen Xia"], "categories": ["cs.AI"], "doi": "", "_query": "automated valuation model", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2604.23067v1", "title": "Training a General Purpose Automated Red Teaming Model", "abstract": "Automated methods for red teaming LLMs are an important tool to identify LLM vulnerabilities that may not be covered in static benchmarks, allowing for more thorough probing. They can also adapt to each specific LLM to discover weaknesses unique to it. Most current automated red teaming methods are intended for tackling safety and content moderation. Thus, they make use of content safety models as evaluators and optimize for circumventing them, and as such, have not been tested with other adversarial intents not typically captured by these. We propose a pipeline for training a red teaming model that can generalize to arbitrary adversarial goals, including objectives it has not been directly trained on, and that does not depend on the existence of a pre-existing evaluator available at training time. We demonstrate that finetuning small models, such as Qwen3-8B, using this pipeline results in a substantial improvement in their ability to generate attacks for both in and out of domain adversarial goals.", "published": "2026-04-24T23:37:17Z", "authors": ["Aishwarya Padmakumar", "Leon Derczynski", "Traian Rebedea", "Christopher Parisien"], "categories": ["cs.CR", "cs.CL"], "doi": "", "_query": "automated valuation model", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2610.11451v2", "title": "SAIL: Scientific Agentic Intelligence via a Science-Aware Loop", "abstract": "We introduce SAIL, an open model with 35B total and 3B active parameters for literature research, scientific coding, and multi-step research workflows. SAIL is developed through a science-aware improvement loop: agents built on frontier AI models analyze its task failures and construct training tasks that address the underlying capability gaps. The diagnosis examines search and evidence selection in literature tasks, scientific assumptions and reasoning in coding, and planning and revision in longer investigations. The agents draw on paper collections and scientific code repositories to build problems, interaction trajectories, and executable tasks with the required environments and tools. We repeat this loop over multiple development cycles and train SAIL through supervised fine-tuning, specialist training, multi-teacher on-policy distillation, and agentic reinforcement learning. SAIL achieves competitive performance across scientific research tasks with substantially fewer parameters than leading open-weight models.", "published": "2026-10-08T08:04:22Z", "authors": [" SAIL Model Team", "Boyuan Sun", "Bryan Dai", "Che Liu", "Chi Liu", "Derek Li", "Hongming Piao", "Mengzhuo Chen", "Xidong Wang", "Yan Shu", "Yinda Chen", "Ziyang Zeng"], "categories": ["cs.CL", "cs.DL", "cs.IR"], "doi": "", "_query": "automated valuation model", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/1907.01114v2", "title": "A Minimizing Valuation is Quasi-monomial", "abstract": "We prove a version of Jonsson-Mustaţǎ's Conjecture, which says for any graded sequence of ideals, there exists a quasi-monomial valuation computing its log canonical threshold. As a corollary, we confirm Chi Li's conjecture that a minimizer of the normalized volume function is always quasi-monomial. Applying our techniques to a family of klt singularities, we show that the volume of klt singularities is a constructible function. As a corollary, we prove that in a family of klt log Fano pairs, the K-semistable ones form a Zariski open set. Together with [Jia17], we conclude that all K-semistable klt Fano varieties with a fixed dimension and volume are parametrized by an Artin stack of finite type, which then admits a separated good moduli space by [BX18, ABHLX19], whose geometric points parametrize K-polystable klt Fano varieties.", "published": "2019-07-02T00:56:08Z", "authors": ["Chenyang Xu"], "categories": ["math.AG", "math.DG"], "doi": "", "_query": "automated valuation model", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2208.06157v1", "title": "Valuation of patents in emerging economies: a renewal model-based study of Indian patents", "abstract": "This study uses patent renewal information to estimate private value of patents by technology and ownership status. Patent value refers to the economic reward that the inventor extracts from the patent by making, using or selling an invention. Thus, we measure the value of patent right (private value of patent) from the patentee perspective. Our empirical analysis comprises of 555 patents with application year during 1999 to 2002. The term of these patents either ended in 2018 or lapsed due to non-payment of renewal fee. We model renewal decision of patentee as ordered probit where patent renewal fee increases with the age of patent. Variables such as patent family size, technological scope, number of inventors and grant lag are used as explanatory variables in the corresponding regression. Hence, this paper combines the patentee renewal decision along with patents characteristics and renewal cost schedule to estimate the initial rent distribution. We find that a large number of patents expire at an early stage leaving few patents with high value corroborating the results of studies using European, American and Chinese data. As expected, certain technology class patents enjoy high valuation.", "published": "2022-08-12T08:04:56Z", "authors": ["Mohd Shadab Danish", "Pritam Ranjan", "Ruchi Sharma"], "categories": ["stat.AP"], "doi": "10.1080/09537325.2019.1668552", "_query": "automated valuation model", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/1702.04147v2", "title": "Pinned Distances in Modules over Finite Valuation Rings", "abstract": "Let $R$ be a finite valuation ring of order $q^r$ where $q$ is odd and $A$ be a subset of $R$. In the present paper, we prove that there exists a point $u$ in the Cartesian product set $A\\times A\\subset R^2$ such that the size of the pinned distance set at $u$ satisfies $$|Δ_u(A\\times A)|\\gg \\min\\left\\{q^r, \\frac{|A|^3}{q^{2r-1}}\\right\\}.$$ This implies that if $|A|\\ge q^{r-\\frac{1}{3}}$, then the set $A\\times A$ determines a positive proportion of all possible distances.", "published": "2017-02-14T10:39:30Z", "authors": ["Esen Aksoy Yazici"], "categories": ["math.CO"], "doi": "", "_query": "automated valuation model", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/1602.01117v2", "title": "Minkowski valuations on lattice polytopes", "abstract": "A complete classification is established of Minkowski valuations on lattice polytopes that intertwine the special linear group over the integers and are translation invariant. In the contravariant case, the only such valuations are multiples of projection bodies. In the equivariant case, the only such valuations are generalized difference bodies combined with multiples of the newly defined discrete Steiner point.", "published": "2016-02-02T21:16:00Z", "authors": ["Karoly J. Boroczky", "Monika Ludwig"], "categories": ["math.MG"], "doi": "10.4171/JEMS/833", "_query": "automated valuation model", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/0908.0149v1", "title": "A precise description of the p-adic valuation of the number of alternating sign matrices", "abstract": "Following Sun and Moll, we study v_p(T(N)), the p-adic valuation of the counting function of the alternating sign matrices. We find an exact analytic expression for it that exhibits the fluctuating behaviour, by means of Fourier coefficients. The method is the Mellin-Perron technique, which is familiar in the analysis of the sum-of-digits function and related quantities.", "published": "2009-08-03T09:21:58Z", "authors": ["Clemens Heuberger", "Helmut Prodinger"], "categories": ["math.NT"], "doi": "10.1142/S1793042111003892", "_query": "automated valuation model", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2303.16000v1", "title": "Monge-Ampère operators and valuations", "abstract": "Two classes of measure-valued valuations on convex functions related to Monge-Ampère operators are investigated and classified. It is shown that the space of all valuations with values in the space of complex Radon measures on $\\mathbb{R}^n$ that are locally determined, continuous, dually epi-translation invariant as well as translation equivariant, is finite dimensional. Integral representations of these valuations and a description in terms of mixed Monge-Ampère operators are established, as well as a characterization of $\\mathrm{SO}(n)$-equivariant valuations in terms of Hessian measures.", "published": "2023-03-28T14:15:31Z", "authors": ["Jonas Knoerr"], "categories": ["math.MG", "math.FA"], "doi": "", "_query": "automated valuation model", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2011.08425v3", "title": "Dynamic Valuation of Battery Lifetime", "abstract": "This paper proposes a dynamic valuation framework to determine the opportunity value of battery capacity degradation in grid applications based on the internal degradation mechanism and utilization scenarios. The proposed framework follows a dynamic programming approach and includes a piecewise linear value function approximation solution that solves the optimization problem over a long planning horizon. The paper provides two case studies on price arbitrage and frequency regulation using real market and system data to demonstrate the broad applicability of the proposed framework. Results show that the battery lifetime value is critically dependent on both the external market environment and its internal state of health. On the grid service side, results show that second-life batteries can provide more than 50% of the value compared to new batteries, and frequency regulation provides two times more revenue than price arbitrage throughout the battery lifetime.", "published": "2020-11-17T05:22:34Z", "authors": ["Bolun Xu"], "categories": ["math.OC"], "doi": "", "_query": "automated valuation model", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/1607.05341v1", "title": "A note on Itoh (e)-Valuation Rings of and Ideal", "abstract": "Let $I$ be a regular proper ideal in a Noetherian ring $R$, let $e \\ge 2$ be an integer, let $\\mathbf T_e = R[u,tI,u^{\\frac{1}{e}}]' \\cap R[u^{\\frac{1}{e}},t^{\\frac{1}{e}}]$ (where $t$ is an indeterminate and $u =\\frac{1}{t}$), and let $\\mathbf r_e = u^{\\frac{1}{e}} \\mathbf T_e$. Then the Itoh (e)-valuation rings of $I$ are the rings $(\\mathbf T_e/z)_{(p/z)}$, where $p$ varies over the (height one) associated prime ideals of $\\mathbf r_e$ and $z$ is the (unique) minimal prime ideal in $\\mathbf T_e$ that is contained in $p$. We show, among other things: (1) $\\mathbf r_e$ is a radical ideal if and only if $e$ is a common multiple of the Rees integers of $I$. (2) For each integer $k \\ge 2$, there is a one-to-one correspondence between the Itoh (k)-valuation rings $(V^*,N^*)$ of $I$ and the Rees valuation rings $(W,Q)$ of $uR[u,tI]$; namely, if $F(u)$ is the quotient field of $W$, then $V^*$ is the integral closure of $W$ in $F(u^{\\frac{1}{k}})$. (3) For each integer $k \\ge 2$, if $(V^*,N^*)$ and $(W,Q)$ are corresponding valuation rings, as in (2), then $V^*$ is a finite integral extension domain of $W$, and $W$ and $V^*$ satisfy the Fundamental Equality with no splitting. Also, if $uW = Q^e$, and if the greatest common divisor of $e$ and $k$ is $d$, and $c$ is the integer such that $cd = k$, then $QV^* = {N^*}^c$ and $[(V^*/N^*):(W/Q)] = d$. Further, if $uW = Q^e$ and $k = qe$ is a multiple of $e$, then there exists a unit $θ_{e} \\in V^*$ such that $V^* = W[θ_{e},u^{\\frac{1}{k}}]$ is a finite free integral extension domain of $W, QV^* = {N^*}^q, N^* = u^{\\frac{1}{k}}V^*$, and $[V^*:W] = k$. (4) If the Rees integers of $I$ are all equal to $e$, then $V^* = W[θ_e]$ is a simple free integral extension domain of $W, QV^* = N^* = u^{\\frac{1}{e}}V^*$, and $[V^*:W] = e = [(V^*/N^*):(W/Q)]$.", "published": "2016-07-18T22:31:04Z", "authors": ["Youngsu Kim", "Louis J. Ratliff", "David E. Rush"], "categories": ["math.AC"], "doi": "", "_query": "automated valuation model", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2502.00198v4", "title": "Fairshare Data Pricing via Data Valuation for Large Language Models", "abstract": "Training data is the backbone of large language models (LLMs), yet today's data markets often operate under exploitative pricing -- sourcing data from marginalized groups with little pay or recognition. This paper introduces a theoretical framework for LLM data markets, modeling the strategic interactions between buyers (LLM builders) and sellers (human annotators). We begin with theoretical and empirical analysis showing how exploitative pricing drives high-quality sellers out of the market, degrading data quality and long-term model performance. Then we introduce fairshare, a pricing mechanism grounded in data valuation that quantifies each data's contribution. It aligns incentives by sustaining seller participation and optimizing utility for both buyers and sellers. Theoretically, we show that fairshare yields mutually optimal outcomes: maximizing long-term buyer utility and seller profit while sustaining market participation. Empirically when training open-source LLMs on complex NLP tasks, including math problems, medical diagnosis, and physical reasoning, fairshare boosts seller earnings and ensures a stable supply of high-quality data, while improving buyers' performance-per-dollar and long-term welfare. Our findings offer a concrete path toward fair, transparent, and economically sustainable data markets for LLM.", "published": "2025-01-31T22:27:34Z", "authors": ["Luyang Zhang", "Cathy Jiao", "Beibei Li", "Chenyan Xiong"], "categories": ["cs.GT", "cs.CL"], "doi": "10.52202/085713-1119", "_query": "automated valuation model", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2107.10570v2", "title": "On the ranks and implicit constant fields of valuations induced by pseudo monotone sequences", "abstract": "Given a valued field $(K,v)$ and a pseudo monotone sequence $E$ in $(K,v)$, one has an induced valuation $v_E$ extending $v$ to $K(X)$. After fixing an extension of $v_E$ to a fixed algebraic closure $\\overline{K(X)}$ of $K(X)$, we show that the implicit constant field of the extension $(K(X)|K,v_E)$ is simply the henselization of $(K,v)$. We consider the question: given a value transcendental extension $w$ of $v$ to $K(X)$ and a pseudo monotone sequence $E$ in $(K,v)$, under which precise conditions is $w$ induced by $E$? The dual nature of pseudo convergent sequences of algebraic type and pseudo divergent sequences is also explored. Further, we provide a complete description of the various possibilities of the rank of the valuation $v_E$, provided that $v$ has finite rank.", "published": "2021-07-22T10:48:48Z", "authors": ["Arpan Dutta"], "categories": ["math.AG", "math.AC"], "doi": "", "_query": "automated valuation model", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2407.14734v1", "title": "Super-efficiency and Stock Market Valuation: Evidence from Listed Banks in China (2006 to 2023)", "abstract": "This study investigates the relationship between bank efficiency and stock market valuation using an unbalanced panel dataset of 42 listed banks in China from 2006 to 2023. We employ a non-radial and non-oriented slack based super-efficiency Data Envelopment Analysis (Super-SBM-UND-VRS based DEA) model, which treats Non-Performing Loans (NPLs) as an undesired output. Our results show that the relationship between super-efficiency and stock market valuation is stronger than that between Return on Asset (ROA) and stock market performance, as measured by Tobin's Q. Notably, the Super-SBM-UND-VRS model yields novel results compared to other efficiency methods, such as the Stochastic Frontier Analysis (SFA) approach and traditional DEA models. Furthermore, our results suggest that bank evaluations benefit from decreased ownership concentration, whereas interest rate liberalization has the opposite effect.", "published": "2024-07-20T03:14:41Z", "authors": ["Yun Liao"], "categories": ["q-fin.PR", "q-fin.ST"], "doi": "", "_query": "automated valuation model", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2303.06454v1", "title": "Discrete valuation rings, partitions and $p$-groups I", "abstract": "A finite abelian $p$-group having an automorphism $x$ such that $1+\\ldots+x^{p-1}=0$, can be viewed as a module over an appropriate discrete valuation ring $\\mathcal{O}$ containing $\\mathbb{Z}_p$ (the ring of $p$-adic integer). This yields the natural problem of comparing the invariants of $A$ as a $\\mathbb{Z}_p$-module to its invariants as an $\\mathcal{O}$-module. We solve the latter problem in a more general context, and give some applications to the structure of some $p$-groups and their automorphisms.", "published": "2023-03-11T17:11:58Z", "authors": ["Boubakeur Bahri", "Yassine Guerboussa"], "categories": ["math.GR"], "doi": "", "_query": "automated valuation model", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2004.01478v1", "title": "Testing the Usability and Accessibility of Smart TV Applications Using an Automated Model-based Approach", "abstract": "As the popularity of Smart Televisions (TVs) and interactive Smart TV applications (apps) has recently grown, the usability of these apps has become an important quality characteristic. Previous studies examined Smart TV apps from a usability perspective. However, these methods are mainly manual, and the potential of automated model-based testing methods for usability testing purposes has not yet been fully explored. In this paper, we propose an approach to test the usability of Smart TV apps based on the automated generation of a Smart TV user interaction model from an existing app by a specialized automated crawler. By means of this model, defined user tasks in the Smart TV app can be evaluated automatically in terms of their feasibility and estimated user effort, which reflects the usability of the analyzed app. This analysis can be applied in the context of regular users and users with various specific needs. The findings from this model-based automated analysis approach can be used to optimize the user interface of a Smart TV app to increase its usability, accessibility, and quality.", "published": "2020-04-03T11:36:36Z", "authors": ["Miroslav Bures", "Miroslav Macik", "Bestoun S. Ahmed", "Vaclav Rechtberger", "Pavel Slavik"], "categories": ["cs.SE"], "doi": "", "_query": "automated valuation model", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2605.08144v2", "title": "NoiseRater: Meta-Learned Noise Valuation for Diffusion Model Training", "abstract": "Training a diffusion model involves two sources of randomness for each data sample: the timestep and the Gaussian noise realization. The timestep has been studied extensively through scheduling and weighting, whereas the impact of the noise realization at a given timestep is still underexplored. In this work, we examine whether different noise instances are equally informative. We introduce NoiseRater, a network that scores an individual noise instance conditioned on the data sample and timestep. The rater is learned through bilevel optimization, where its scores reweight the diffusion loss in the inner loop, and it is updated to reduce validation loss after the inner-loop updates. Using the trained rater to select training noise, we observe three properties of training noise. First, noise realizations at the same timestep are not equally useful: the rater's top-scored noise improves performance over i.i.d.\\ sampling, while its bottom-scored noise degrades it. Second, this utility is contextual, depending jointly on the image, the class, and the timestep. Third, noise selection is complementary to timestep-level design, retaining most of its gain when combined with existing scheduling and weighting schemes. These findings establish instance-level noise valuation as a new axis for understanding and improving diffusion training. Code is available at https://github.com/JoeZhao527/Noise-Rater.", "published": "2026-05-02T19:43:16Z", "authors": ["Haokai Zhao", "Da Xing", "Hanqun Cao", "Tinson Xu", "Xinyu Xiang", "Yanchao Li", "Xiangru Tang", "Hongbin Lin", "Zehong Wang", "Kuan Pang", "Peng Xia", "Molei Tao", "Li Erran Li", "Aditya Joshi", "Jure Leskovec", "Fang Wu"], "categories": ["cs.LG", "cs.AI", "cs.CV"], "doi": "", "_query": "automated valuation model", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2404.16560v1", "title": "Automated Model Selection for Generalized Linear Models", "abstract": "In this paper, we show how mixed-integer conic optimization can be used to combine feature subset selection with holistic generalized linear models to fully automate the model selection process. Concretely, we directly optimize for the Akaike and Bayesian information criteria while imposing constraints designed to deal with multicollinearity in the feature selection task. Specifically, we propose a novel pairwise correlation constraint that combines the sign coherence constraint with ideas from classical statistical models like Ridge regression and the OSCAR model.", "published": "2024-04-25T12:16:58Z", "authors": ["Benjamin Schwendinger", "Florian Schwendinger", "Laura Vana-Gür"], "categories": ["stat.ML", "cs.LG", "math.OC"], "doi": "10.1007/s00180-025-01672-9", "_query": "automated valuation model", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2308.05556v2", "title": "Extensions of transversal valuated matroids", "abstract": "Following up on our previous work, we study single-element extensions of transversal valuated matroids. We show that tropical presentations of valuated matroids with a minimal set of finite entries enjoy counterparts of the properties proved by Bonin and de Mier of minimal non-valuated transversal presentations.", "published": "2023-08-10T13:13:43Z", "authors": ["Alex Fink", "Jorge Alberto Olarte"], "categories": ["math.CO"], "doi": "", "_query": "automated valuation model", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2508.16595v3", "title": "Empirical Analysis of the Model-Free Valuation Approach: Hedging Gaps, Conservatism, and Trading Opportunities", "abstract": "In this paper we study the quality of model-free valuation approaches for financial derivatives by systematically evaluating the difference between model-free super-hedging strategies and the realized payoff of financial derivatives using historical option prices from several constituents of the S&P 500 between 2018 and 2022. Our study allows in particular to describe the realized gap between payoff and model-free hedging strategy empirically so that we can quantify to which degree model-free approaches are overly conservative. Our results imply that the model-free hedging approach is only marginally more conservative than industry-standard models such as the Heston-model while being model-free at the same time. This finding, its statistical description and the model-independence of the hedging approach enable us to construct an explicit trading strategy which, as we demonstrate, can be profitably applied in financial markets, and additionally possesses the desirable feature with an explicit control of its downside risk due to its model-free construction preventing losses pathwise.", "published": "2025-08-09T00:06:12Z", "authors": ["Zixing Chen", "Yihan Qi", "Shanlan Que", "Julian Sester", "Xiao Zhang"], "categories": ["q-fin.PR", "q-fin.MF", "q-fin.RM", "q-fin.TR"], "doi": "", "_query": "automated valuation model", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2608.06325v1", "title": "Fair and Efficient Balanced Allocations for Additive Valuations", "abstract": "We study the existence of fair and efficient allocations of indivisible goods under the balancedness constraint, which requires that any two agents' bundles differ in size by at most one. Our main result establishes the existence of balanced allocations that satisfy envy-freeness up to one good (EF1) and fractional Pareto optimality (fPO) for arbitrary additive valuations. This generalizes a recent result of Kawase et al. (2026), which establishes existence only for personalized bivalued valuations or when there are at most two distinct valuation types. Our proof applies the Knaster-Kuratowski-Mazurkiewicz (KKM) lemma to a weighted-welfare duality framework and develops a novel price-interlacing lemma to overcome barriers encountered by prior work. We extend this technique to category constraints, also known as partition-matroid constraints. In this setting, we establish the existence of an fPO allocation satisfying a weaker, category-sensitive relaxation of EF1, under which envy can be eliminated by removing at most one good from each category. All proofs in this paper were obtained using GPT-5.6-Sol with guidance from the authors. The authors verified the proofs, expanded the exposition, and simplified the arguments with assistance from GPT-5.6-Sol and Claude Fable 5.", "published": "2026-08-06T17:38:41Z", "authors": ["Benjamin Cookson", "Nisarg Shah", "Paritosh Verma"], "categories": ["cs.GT"], "doi": "", "_query": "automated valuation model", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2608.09437v1", "title": "Balanced Fair Division for Three Agents under General Valuations and Laminar Constraints", "abstract": "We study fair allocations of indivisible items under general set valuations. We prove that every instance with three agents and arbitrary real-valued valuations admits a balanced allocation that is envy-free up to one good and one chore (EF$1^c_g$). This directly implies balanced EF$1$ when each valuation is either monotone nondecreasing or monotone nonincreasing. We also show that balanced EF$1$ cannot be guaranteed without monotonicity: there exists an instance with three agents, nine items, and identical nonmonotone valuations that admits no balanced EF$1$ allocation. We then consider a common laminar matroid constraint. Whenever a complete feasible allocation exists, we prove that there is a complete feasible allocation that is balanced and envy-free up to two goods and two chores (EF$2^c_g$) for arbitrary valuations. The allocation can additionally be chosen so that the numbers of items from every laminar set assigned to the three agents differ by at most two. Most proofs in this paper were obtained using GPT-5.6. We subsequently verified the proofs for correctness and refined their exposition and arguments, also with the aid of GPT-5.6.", "published": "2026-08-10T11:10:05Z", "authors": ["Max Dupré la Tour"], "categories": ["cs.GT"], "doi": "", "_query": "automated valuation model", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2012.04364v2", "title": "Insurance valuation: A two-step generalised regression approach", "abstract": "Current approaches to fair valuation in insurance often follow a two-step approach, combining quadratic hedging with application of a risk measure on the residual liability, to obtain a cost-of-capital margin. In such approaches, the preferences represented by the regulatory risk measure are not reflected in the hedging process. We address this issue by an alternative two-step hedging procedure, based on generalised regression arguments, which leads to portfolios that are neutral with respect to a risk measure, such as Value-at-Risk or the expectile. First, a portfolio of traded assets aimed at replicating the liability is determined by local quadratic hedging. Second, the residual liability is hedged using an alternative objective function. The risk margin is then defined as the cost of the capital required to hedge the residual liability. In the case quantile regression is used in the second step, yearly solvency constraints are naturally satisfied; furthermore, the portfolio is a risk minimiser among all hedging portfolios that satisfy such constraints. We present a neural network algorithm for the valuation and hedging of insurance liabilities based on a backward iterations scheme. The algorithm is fairly general and easily applicable, as it only requires simulated paths of risk drivers.", "published": "2020-12-08T11:20:39Z", "authors": ["Karim Barigou", "Valeria Bignozzi", "Andreas Tsanakas"], "categories": ["q-fin.RM"], "doi": "10.1017/asb.2021.31", "_query": "automated valuation model", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2411.08804v1", "title": "FinRobot: AI Agent for Equity Research and Valuation with Large Language Models", "abstract": "As financial markets grow increasingly complex, there is a rising need for automated tools that can effectively assist human analysts in equity research, particularly within sell-side research. While Generative AI (GenAI) has attracted significant attention in this field, existing AI solutions often fall short due to their narrow focus on technical factors and limited capacity for discretionary judgment. These limitations hinder their ability to adapt to new data in real-time and accurately assess risks, which diminishes their practical value for investors. This paper presents FinRobot, the first AI agent framework specifically designed for equity research. FinRobot employs a multi-agent Chain of Thought (CoT) system, integrating both quantitative and qualitative analyses to emulate the comprehensive reasoning of a human analyst. The system is structured around three specialized agents: the Data-CoT Agent, which aggregates diverse data sources for robust financial integration; the Concept-CoT Agent, which mimics an analysts reasoning to generate actionable insights; and the Thesis-CoT Agent, which synthesizes these insights into a coherent investment thesis and report. FinRobot provides thorough company analysis supported by precise numerical data, industry-appropriate valuation metrics, and realistic risk assessments. Its dynamically updatable data pipeline ensures that research remains timely and relevant, adapting seamlessly to new financial information. Unlike existing automated research tools, such as CapitalCube and Wright Reports, FinRobot delivers insights comparable to those produced by major brokerage firms and fundamental research vendors. We open-source FinRobot at \\url{https://github. com/AI4Finance-Foundation/FinRobot}.", "published": "2024-11-13T17:38:07Z", "authors": ["Tianyu Zhou", "Pinqiao Wang", "Yilin Wu", "Hongyang Yang"], "categories": ["q-fin.CP", "cs.LG", "q-fin.ST", "q-fin.TR"], "doi": "", "_query": "automated valuation model", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/1112.1521v2", "title": "Funding Valuation Adjustment: a consistent framework including CVA, DVA, collateral,netting rules and re-hypothecation", "abstract": "In this paper we describe how to include funding and margining costs into a risk-neutral pricing framework for counterparty credit risk. We consider realistic settings and we include in our models the common market practices suggested by the ISDA documentation without assuming restrictive constraints on margining procedures and close-out netting rules. In particular, we allow for asymmetric collateral and funding rates, and exogenous liquidity policies and hedging strategies. Re-hypothecation liquidity risk and close-out amount evaluation issues are also covered. We define a comprehensive pricing framework which allows us to derive earlier results on funding or counterparty risk. Some relevant examples illustrate the non trivial settings needed to derive known facts about discounting curves by starting from a general framework and without resorting to ad hoc hypotheses. Our main result is a bilateral collateralized counterparty valuation adjusted pricing equation, which allows to price a deal while taking into account credit and debt valuation adjustments along with margining and funding costs in a coherent way. We find that the equation has a recursive form, making the introduction of an additive funding valuation adjustment difficult. Yet, we can cast the pricing equation into a set of iterative relationships which can be solved by means of standard least-square Monte Carlo techniques.", "published": "2011-12-07T10:56:11Z", "authors": ["Andrea Pallavicini", "Daniele Perini", "Damiano Brigo"], "categories": ["q-fin.PR", "q-fin.RM"], "doi": "", "_query": "automated valuation model", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/1105.0862v1", "title": "On the finite generation of coordinate rings of affine group schemes over discrete valuation rings", "abstract": "In this short note we prove a finite generation result for the coordinate ring of certain affine group schemes over a discrete valuation ring. This may be used to simplify the use of results of Prasad and Yu on quasi-reductive groups by Mirkovic and Vilonen in their work on geometric Langlands duality.", "published": "2011-05-04T16:15:38Z", "authors": ["Najmuddin Fakhruddin"], "categories": ["math.AG"], "doi": "", "_query": "automated valuation model", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2508.02765v1", "title": "The Architecture of Trust: A Framework for AI-Augmented Real Estate Valuation in the Era of Structured Data", "abstract": "The Uniform Appraisal Dataset (UAD) 3.6's mandatory 2026 implementation transforms residential property valuation from narrative reporting to structured, machine-readable formats. This paper provides the first comprehensive analysis of this regulatory shift alongside concurrent AI advances in computer vision, natural language processing, and autonomous systems. We develop a three-layer framework for AI-augmented valuation addressing technical implementation and institutional trust requirements. Our analysis reveals how regulatory standardization converging with AI capabilities enables fundamental market restructuring with profound implications for professional practice, efficiency, and systemic risk. We make four key contributions: (1) documenting institutional failures including inter-appraiser variability and systematic biases undermining valuation reliability; (2) developing an architectural framework spanning physical data acquisition, semantic understanding, and cognitive reasoning that integrates emerging technologies while maintaining professional oversight; (3) addressing trust requirements for high-stakes financial applications including regulatory compliance, algorithmic fairness, and uncertainty quantification; (4) proposing evaluation methodologies beyond generic AI benchmarks toward domain-specific protocols. Our findings indicate successful transformation requires not merely technological sophistication but careful human-AI collaboration, creating systems that augment rather than replace professional expertise while addressing historical biases and information asymmetries in real estate markets.", "published": "2025-08-04T05:24:25Z", "authors": ["Petteri Teikari", "Mike Jarrell", "Maryam Azh", "Harri Pesola"], "categories": ["cs.CY", "cs.AI", "cs.CV"], "doi": "", "_query": "automated valuation model", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2509.04520v1", "title": "Economic relativity: a cut rule for perimeter valuation in equity ownership networks", "abstract": "We introduce the Cut-Based Valuation (CBV), a unified framework for consolidated value in equity/flow networks. The central idea is that economic value is never absolute: it is always defined relative to an observer Omega, which fixes perimeter, measurement basis, units/FX/PPP, discounting, informational regime, and control rules. Given Omega, the Cut Theorem shows that the consolidated value of a perimeter P depends only on boundary quantities across the cut P -> O, while internal reconnections are valuation-invariant. This provides (i) sufficient statistics for valuation with linear computational complexity, (ii) standardized reporting through the Perimeter-of-Validity and Cut Summary, and (iii) transformation laws that clarify how different observers relate. Applications span IFRS consolidation, national accounts, fund-of-funds, pyramids, and clearing networks, all seen as special cases of a general principle of economic relativity. Case studies (market capitalization by country, keiretsu, fund-of-funds) illustrate how CBV eliminates double counting while ensuring comparability and auditability. To address practical concerns, we establish robustness bounds that quantify how errors in initial data propagate to consolidated values, and we introduce a dynamic CBV-Fisher protocol for intertemporal comparisons, ensuring consistency with official chain-linking practices. These additions clarify the time scale of application, the role of averaging procedures, and the horizon of reliable measurement. Finally, we make explicit the scope and limitations of CBV: it is a normative measurement/consolidation rule in linear accounting environments, while in macroeconomic closures or with nonlinear payoffs it must be coupled with equilibrium or clearing models.", "published": "2025-09-03T12:09:05Z", "authors": ["Omar Di Marzio"], "categories": ["econ.GN"], "doi": "", "_query": "automated valuation model", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2111.08766v1", "title": "New results on the p-adic valuation of Stirling numbers", "abstract": "We generalize results on the $p$-adic valuations of $S(n,k)$, the Stirling number of the second kind and $s(n,k)$ the Stirling number of the first kind. We have several new estimates for these valuations, along with criteria for when the estimates are sharp. The primary foci are the explicit evaluation of $ν_2(S(n,k))$ with $n=c2^h$, $k=b2^h+a$, $a, b, c, h, k, n \\in Z^+$, and $1\\le a \\le 2^{h-1}$, and $ν_p(S(n,k))$ when $n=cp^h$ for an odd prime $p$. We have strong new results, which generalize and strengthen previous results, for all primes. We also have some new results on the $p$-adic valuations $ν_p(s(n,k))$ for all primes. We generally assume that $p-1|n-k$ for exact values of $ν_p(S(n,k))$ or $ν_p(s(n,k))$. In addition, we have proved some new Amdeberhan-type identities for Stirling numbers of both kinds. We also extend some recent results and propose two new conjectures, as well as proofs and extensions of previous ones.", "published": "2021-11-16T20:34:09Z", "authors": ["Arnold Adelberg", "Tamas Lengyel"], "categories": ["math.NT"], "doi": "", "_query": "automated valuation model", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2002.10973v2", "title": "Weighted PCL over product valuation monoids", "abstract": "We introduce a weighted propositional configuration logic over a product valuation monoid. Our logic is intended to serve as a specification language for software architectures with quantitative features such as the average of all interactions' costs of the architecture and the maximum cost among all costs occurring most frequently within a specific number of components in an architecture. We provide formulas of our logic which describe well-known architectures equipped with quantitative characteristics. Moreover, we prove an efficient construction of a full normal form which leads to decidability of equivalence of formulas in this logic.", "published": "2020-02-25T15:36:18Z", "authors": ["Vagia Karyoti", "Paulina Paraponiari"], "categories": ["cs.LO"], "doi": "10.1007/978-3-030-50029-0_19", "_query": "automated valuation model", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/1608.04047v3", "title": "The $p$-Adic Valuations of Weil Sums of Binomials", "abstract": "We investigate the $p$-adic valuation of Weil sums of the form $W_{F,d}(a)=\\sum_{x \\in F} ψ(x^d -a x)$, where $F$ is a finite field of characteristic $p$, $ψ$ is the canonical additive character of $F$, the exponent $d$ is relatively prime to $|F^\\times|$, and $a$ is an element of $F$. Such sums often arise in arithmetical calculations and also have applications in information theory. For each $F$ and $d$ one would like to know $V_{F,d}$, the minimum $p$-adic valuation of $W_{F,d}(a)$ as $a$ runs through the elements of $F$. We exclude exponents $d$ that are congruent to a power of $p$ modulo $|F^\\times|$ (degenerate $d$), which yield trivial Weil sums. We prove that $V_{F,d} \\leq (2/3)[F\\colon{\\mathbb F}_p]$ for any $F$ and any nondegenerate $d$, and prove that this bound is actually reached in infinitely many fields $F$. We also prove some stronger bounds that apply when $[F\\colon{\\mathbb F}_p]$ is a power of $2$ or when $d$ is not congruent to $1$ modulo $p-1$, and show that each of these bounds is reached for infinitely many $F$.", "published": "2016-08-14T01:52:26Z", "authors": ["Daniel J. Katz", "Philippe Langevin", "Sangman Lee", "Yakov Sapozhnikov"], "categories": ["math.NT"], "doi": "", "_query": "automated valuation model", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2508.08300v1", "title": "LLM-BI: Towards Fully Automated Bayesian Inference with Large Language Models", "abstract": "A significant barrier to the widespread adoption of Bayesian inference is the specification of prior distributions and likelihoods, which often requires specialized statistical expertise. This paper investigates the feasibility of using a Large Language Model (LLM) to automate this process. We introduce LLM-BI (Large Language Model-driven Bayesian Inference), a conceptual pipeline for automating Bayesian workflows. As a proof-of-concept, we present two experiments focused on Bayesian linear regression. In Experiment I, we demonstrate that an LLM can successfully elicit prior distributions from natural language. In Experiment II, we show that an LLM can specify the entire model structure, including both priors and the likelihood, from a single high-level problem description. Our results validate the potential of LLMs to automate key steps in Bayesian modeling, enabling the possibility of an automated inference pipeline for probabilistic programming.", "published": "2025-08-07T00:00:59Z", "authors": ["Yongchao Huang"], "categories": ["cs.AI"], "doi": "10.5281/zenodo.16756724", "_query": "automated valuation model", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/1906.03726v8", "title": "Machine learning with kernels for portfolio valuation and risk management", "abstract": "We introduce a simulation method for dynamic portfolio valuation and risk management building on machine learning with kernels. We learn the dynamic value process of a portfolio from a finite sample of its cumulative cash flow. The learned value process is given in closed form thanks to a suitable choice of the kernel. We show asymptotic consistency and derive finite sample error bounds under conditions that are suitable for finance applications. Numerical experiments show good results in large dimensions for a moderate training sample size.", "published": "2019-06-09T22:17:46Z", "authors": ["Lotfi Boudabsa", "Damir Filipovic"], "categories": ["q-fin.CP"], "doi": "", "_query": "automated valuation model", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/1910.04960v2", "title": "Valuation of contingent claims with short selling bans under an equal-risk pricing framework", "abstract": "This paper studies the valuation of European contingent claims with short selling bans under the equal risk pricing (ERP) framework proposed in Guo and Zhu (2017) where analytical pricing formulae were derived in the case of monotonic payoffs under risk-neutral measures. We establish a unified framework for this new pricing approach so that its range of application can be significantly expanded. The results of Guo and Zhu (2017) are extended to the case of non-monotonic payoffs (such as a butterfly spread option) under risk-neutral measures. We also provide numerical schemes for computing equal-risk prices under other measures such as the original physical measure. Furthermore, we demonstrate how short selling bans can affect the valuation of contingent claims by comparing equal-risk prices with Black-Scholes prices.", "published": "2019-10-11T03:54:53Z", "authors": ["Guiyuan Ma", "Song-Ping Zhu", "Ivan Guo"], "categories": ["q-fin.MF"], "doi": "", "_query": "automated valuation model", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/1912.12541v1", "title": "Approximating Nash Social Welfare under Submodular Valuations through (Un)Matchings", "abstract": "We study the problem of approximating maximum Nash social welfare (NSW) when allocating m indivisible items among n asymmetric agents with submodular valuations. The NSW is a well-established notion of fairness and efficiency, defined as the weighted geometric mean of agents' valuations. For special cases of the problem with symmetric agents and additive(-like) valuation functions, approximation algorithms have been designed using approaches customized for these specific settings, and they fail to extend to more general settings. Hence, no approximation algorithm with factor independent of m is known either for asymmetric agents with additive valuations or for symmetric agents beyond additive(-like) valuations. In this paper, we extend our understanding of the NSW problem to far more general settings. Our main contribution is two approximation algorithms for asymmetric agents with additive and submodular valuations respectively. Both algorithms are simple to understand and involve non-trivial modifications of a greedy repeated matchings approach. Allocations of high valued items are done separately by un-matching certain items and re-matching them, by processes that are different in both algorithms. We show that these approaches achieve approximation factors of O(n) and O(n log n) for additive and submodular case respectively, which is independent of the number of items. For additive valuations, our algorithm outputs an allocation that also achieves the fairness property of envy-free up to one item (EF1). Furthermore, we show that the NSW problem under submodular valuations is strictly harder than all currently known settings with an e/(e-1) factor of the hardness of approximation, even for constantly many agents. For this case, we provide a different approximation algorithm that achieves a factor of e/(e-1), hence resolving it completely.", "published": "2019-12-28T23:15:47Z", "authors": ["Jugal Garg", "Pooja Kulkarni", "Rucha Kulkarni"], "categories": ["cs.GT", "cs.DS"], "doi": "", "_query": "automated valuation model", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2304.00303v1", "title": "An algorithm for computing syzygies on $V[X]$ when $V$ is a valuation domain", "abstract": "We give an algorithm for computing the V-saturation of any finitely-generated submodule of a power of V[X], where V is a valuation domain. Our algorithm is based on a notion of \"echelon form\" which ensures its correctness. This allows us to compute a finite system of generators for the syzygy module of any finitely generated submodule of a power of V[X].", "published": "2023-04-01T12:11:49Z", "authors": ["Henri Lombardi", "Claude Quitté", "Ihsen Yengui"], "categories": ["math.AC"], "doi": "10.1080/00927872.2013.794360", "_query": "automated valuation model", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2512.18506v2", "title": "Detecting and Quantifying Isolated Singularities over Discrete Valuation Rings", "abstract": "This paper develops a theory of isolated hypersurface singularities in mixed characteristic $(0,p)$, focusing on quotient rings over a Discrete Valuation Ring (DVR). We introduce and study analogues of the classical Tjurina and Milnor numbers for this setting, prove a generalized analogue of the determinacy theorem and the Mather-Yau Theorem for complete Noetherian local rings, and define numerical invariants that provide distinct criteria for detecting isolated singularities in the unramified and ramified cases.", "published": "2025-12-20T20:48:54Z", "authors": ["Yotam Svoray"], "categories": ["math.AC", "math.AG"], "doi": "", "_query": "automated valuation model", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2005.05553v2", "title": "An inductive approach to representations of general linear groups over compact discrete valuation rings", "abstract": "In his seminal Lecture Notes in Mathematics published in 1981, Andrey Zelevinsky introduced a new family of Hopf algebras which he called {\\em PSH-algebras}. These algebras were designed to capture the representation theory of the symmetric groups and of classical groups over finite fields. The gist of this construction is to translate representation-theoretic operations such as induction and restriction and their parabolic variants to algebra and coalgebra operations such as multiplication and comultiplication. The Mackey formula, for example, is then reincarnated as the Hopf axiom on the algebra side. In this paper we take substantial steps to adapt these ideas for general linear groups over compact discrete valuation rings. We construct an analogous bialgebra that contains a large PSH-algebra that extends Zelevinsky's algebra for the case of general linear groups over finite fields. We prove several base change results relating algebras over extensions of discrete valuation rings.", "published": "2020-05-12T05:26:57Z", "authors": ["Tyrone Crisp", "Ehud Meir", "Uri Onn"], "categories": ["math.RT"], "doi": "", "_query": "automated valuation model", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/1905.05705v3", "title": "Semistable models of elliptic curves over residue characteristic 2", "abstract": "Given an elliptic curve $E$ in Legendre form $y^2 = x(x - 1)(x - λ)$ over the fraction field of a Henselian ring $R$ of mixed characteristic $(0, 2)$, we present an algorithm for determining a semistable model of $E$ over $R$ which depends only on the valuation of $λ$. We provide several examples along with an easy corollary concerning $2$-torsion.", "published": "2019-05-14T16:25:12Z", "authors": ["Jeffrey Yelton"], "categories": ["math.NT"], "doi": "10.4153/S0008439520000326", "_query": "automated valuation model", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2008.11568v2", "title": "Systematic Analysis of the Sensor Coverage of Automated Vehicles Using Phenomenological Sensor Models", "abstract": "The objective of this paper is to propose a systematic analysis of the sensor coverage of automated vehicles. Due to an unlimited number of possible traffic situations, a selection of scenarios to be tested must be applied in the safety assessment of automated vehicles. This paper describes how phenomenological sensor models can be used to identify system-specific relevant scenarios. In automated driving, the following sensors are predominantly used: camera, ultrasonic, \\radar and \\lidarohne. Based on the literature, phenomenological models have been developed for the four sensor types, which take into account phenomena such as environmental influences, sensor properties and the type of object to be detected. These phenomenological models have a significantly higher reliability than simple ideal sensor models and require lower computing costs than realistic physical sensor models, which represents an optimal compromise for systematic investigations of sensor coverage. The simulations showed significant differences between different system configurations and thus support the system-specific selection of relevant scenarios for the safety assessment of automated vehicles.", "published": "2020-08-26T14:06:38Z", "authors": ["Thomas Ponn", "Fabian Müller", "Frank Diermeyer"], "categories": ["cs.RO"], "doi": "10.1109/IVS.2019.8813794", "_query": "automated valuation model", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2304.06393v1", "title": "Learning Accurate Performance Predictors for Ultrafast Automated Model Compression", "abstract": "In this paper, we propose an ultrafast automated model compression framework called SeerNet for flexible network deployment. Conventional non-differen-tiable methods discretely search the desirable compression policy based on the accuracy from exhaustively trained lightweight models, and existing differentiable methods optimize an extremely large supernet to obtain the required compressed model for deployment. They both cause heavy computational cost due to the complex compression policy search and evaluation process. On the contrary, we obtain the optimal efficient networks by directly optimizing the compression policy with an accurate performance predictor, where the ultrafast automated model compression for various computational cost constraint is achieved without complex compression policy search and evaluation. Specifically, we first train the performance predictor based on the accuracy from uncertain compression policies actively selected by efficient evolutionary search, so that informative supervision is provided to learn the accurate performance predictor with acceptable cost. Then we leverage the gradient that maximizes the predicted performance under the barrier complexity constraint for ultrafast acquisition of the desirable compression policy, where adaptive update stepsizes with momentum are employed to enhance optimality of the acquired pruning and quantization strategy. Compared with the state-of-the-art automated model compression methods, experimental results on image classification and object detection show that our method achieves competitive accuracy-complexity trade-offs with significant reduction of the search cost.", "published": "2023-04-13T10:52:49Z", "authors": ["Ziwei Wang", "Jiwen Lu", "Han Xiao", "Shengyu Liu", "Jie Zhou"], "categories": ["cs.CV", "cs.LG"], "doi": "", "_query": "automated valuation model", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/1303.5439v1", "title": "A Decision Calculus for Belief Functions in Valuation-Based Systems", "abstract": "Valuation-based system (VBS) provides a general framework for representing knowledge and drawing inferences under uncertainty. Recent studies have shown that the semantics of VBS can represent and solve Bayesian decision problems (Shenoy, 1991a). The purpose of this paper is to propose a decision calculus for Dempster-Shafer (D-S) theory in the framework of VBS. The proposed calculus uses a weighting factor whose role is similar to the probabilistic interpretation of an assumption that disambiguates decision problems represented with belief functions (Strat 1990). It will be shown that with the presented calculus, if the decision problems are represented in the valuation network properly, we can solve the problems by using fusion algorithm (Shenoy 1991a). It will also be shown the presented decision calculus can be reduced to the calculus for Bayesian probability theory when probabilities, instead of belief functions, are given.", "published": "2013-03-13T12:56:10Z", "authors": ["Hong Xu"], "categories": ["cs.AI"], "doi": "", "_query": "automated valuation model", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/0902.0849v1", "title": "Valuations on Algebras with Involution", "abstract": "Let A be a central simple algebra with involution sigma of first or second kind. Let v be a valuation on the sigma-fixed part F of Z(A). A sigma-special v-gauge g on A is a kind of value function on A extending v on F, such that g(sigma(x) x) = 2g(x) for all x in A. It is shown (under certain restrictions if the residue characteristic is 2) that if v is Henselian, then there is a sigma-special v-gauge g if and only if sigma is anisotropic, and g is unique. If v is not Henselian, it is shown that there is a sigma-special v-gauge g if and only if sigma remains anisotropic after scalar extension from F to the Henselization of F re v; when this occurs, g is the unique sigma-invariant v-gauge on A.", "published": "2009-02-05T05:50:41Z", "authors": ["J. -P. Tignol", "A. R. Wadsworth"], "categories": ["math.RA"], "doi": "", "_query": "automated valuation model", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2508.14667v1", "title": "ELATE: Evolutionary Language model for Automated Time-series Engineering", "abstract": "Time-series prediction involves forecasting future values using machine learning models. Feature engineering, whereby existing features are transformed to make new ones, is critical for enhancing model performance, but is often manual and time-intensive. Existing automation attempts rely on exhaustive enumeration, which can be computationally costly and lacks domain-specific insights. We introduce ELATE (Evolutionary Language model for Automated Time-series Engineering), which leverages a language model within an evolutionary framework to automate feature engineering for time-series data. ELATE employs time-series statistical measures and feature importance metrics to guide and prune features, while the language model proposes new, contextually relevant feature transformations. Our experiments demonstrate that ELATE improves forecasting accuracy by an average of 8.4% across various domains.", "published": "2025-08-20T12:36:29Z", "authors": ["Andrew Murray", "Danial Dervovic", "Michael Cashmore"], "categories": ["cs.LG", "cs.AI"], "doi": "", "_query": "automated valuation model", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/1701.08101v1", "title": "Sum-Product Type Estimates for Subsets of Finite Valuation Rings", "abstract": "Let $R$ be a finite valuation ring of order $q^r.$ Using a point-plane incidence estimate in $R^3$, we obtain sum-product type estimates for subsets of $R$. In particular, we prove that for $A\\subset R$, $$|AA+A|\\gg \\min\\left\\{q^{r}, \\frac{|A|^3}{q^{2r-1}}\\right\\}.$$ We also show that if $|A+A||A|^{2}>q^{3r-1}$, then $$|A^2+A^2||A+A|\\gg q^{\\frac{r}{2}}|A|^{\\frac{3}{2}}.$$", "published": "2017-01-27T16:25:38Z", "authors": ["Esen Aksoy Yazici"], "categories": ["math.CO"], "doi": "", "_query": "automated valuation model", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2409.01897v2", "title": "Zonal valuations on convex bodies", "abstract": "A complete classification of all zonal, continuous, and translation invariant valuations on convex bodies is established. The valuations obtained are expressed as principal value integrals with respect to the area measures. The convergence of these principal value integrals is obtained from a new weighted version of an inequality for the volume of spherical caps due to Firey. For Minkowski valuations, this implies a refinement of the convolution representation by Schuster and Wannerer in terms of singular integrals. As a further application, a new proof of the classification of $\\mathrm{SO}(n)$-invariant, continuous, and dually epi-translation invariant valuations on the space of finite convex functions by Colesanti, Ludwig, and Mussnig is obtained.", "published": "2024-09-03T13:39:18Z", "authors": ["Jonas Knoerr"], "categories": ["math.MG"], "doi": "", "_query": "automated valuation model", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2403.02413v3", "title": "Regularity of semi-valuation rings and homotopy invariance of algebraic K-theory", "abstract": "We show that the algebraic K-theory of semi-valuation rings with stably coherent regular semi-fraction ring satisfies homotopy invariance. Moreover, we show that these rings are regular if their valuation is non-trivial. Thus they yield examples of regular rings which are not homotopy invariant for algebraic K-theory. On the other hand, they are not necessarily coherent, so that they provide a class of possibly non-coherent examples for homotopy invariance of algebraic K-theory. As an application, we show that Temkin's relative Riemann-Zariski spaces also satisfy homotopy invariance for K-theory under some finiteness assumption.", "published": "2024-03-04T19:09:46Z", "authors": ["Christian Dahlhausen"], "categories": ["math.KT"], "doi": "10.5802/crmath.786", "_query": "automated valuation model", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/0905.4046v6", "title": "Valuations on manifolds and integral geometry", "abstract": "One constructs new operations of pull-back and push-forward on valuations on manifolds with respect to submersions and immersions. A general Radon type transform on valuations is introduced using these operations and the product on valuations. It is shown that the classical Radon transform on smooth functions, and the well known Radon transform on constructible functions with respect to the Euler characteristic are special cases of this new Radon transform. An inversion formula for the Radon transform on valuations has been proven in a specific case of real projective spaces. Relations of these operations to yet another classical type of integral geometry, Crofton and kinematic formulas, are indicated.", "published": "2009-05-25T16:28:44Z", "authors": ["Semyon Alesker"], "categories": ["math.MG"], "doi": "", "_query": "automated valuation model", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2208.10498v1", "title": "Design Automation for Fast, Lightweight, and Effective Deep Learning Models: A Survey", "abstract": "Deep learning technologies have demonstrated remarkable effectiveness in a wide range of tasks, and deep learning holds the potential to advance a multitude of applications, including in edge computing, where deep models are deployed on edge devices to enable instant data processing and response. A key challenge is that while the application of deep models often incurs substantial memory and computational costs, edge devices typically offer only very limited storage and computational capabilities that may vary substantially across devices. These characteristics make it difficult to build deep learning solutions that unleash the potential of edge devices while complying with their constraints. A promising approach to addressing this challenge is to automate the design of effective deep learning models that are lightweight, require only a little storage, and incur only low computational overheads. This survey offers comprehensive coverage of studies of design automation techniques for deep learning models targeting edge computing. It offers an overview and comparison of key metrics that are used commonly to quantify the proficiency of models in terms of effectiveness, lightness, and computational costs. The survey then proceeds to cover three categories of the state-of-the-art of deep model design automation techniques: automated neural architecture search, automated model compression, and joint automated design and compression. Finally, the survey covers open issues and directions for future research.", "published": "2022-08-22T12:12:43Z", "authors": ["Dalin Zhang", "Kaixuan Chen", "Yan Zhao", "Bin Yang", "Lina Yao", "Christian S. Jensen"], "categories": ["cs.LG", "cs.AI", "cs.SE"], "doi": "", "_query": "automated valuation model", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/math/0212203v2", "title": "Valuations in fields of power series", "abstract": "This paper deals with valuations of fields of formal meromorphic functions and their residue fields. We explicitly describe the residue fields of the monomial valuations. We also classify all the discrete rank one valuations of fields of power series in two and three variables, according to their residue fields. We prove that all our cases are possible and give explicit constructions.", "published": "2002-12-16T10:05:49Z", "authors": ["F. J. Herrera-Govantes", "M. A. Olalla Acosta", "J. L. Vicente-Cordoba"], "categories": ["math.AC", "math.AG"], "doi": "10.4171/RMI/358", "_query": "automated valuation model", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/1602.00210v2", "title": "Commodity Resource Valuation And Extraction: A Pathwise Programming Approach", "abstract": "Complexity and uncertainty associated with commodity resource valuation and extraction requires stochastic control methods suitable for high dimensional states. Recent progress in duality and trajectory-wise techniques has introduced a variety of fresh ideas to this field with surprising results. This paper presents a first application of this promising development to commodity extraction problems. We introduce efficient algorithms for obtaining approximate solutions along with a diagnostic technique, which provides a quantitative measure for solution performance in terms of the distance between the approximate and the optimal control policy.", "published": "2016-01-31T07:52:02Z", "authors": ["Juri Hinz", "Tanya Tarnopolskaya", "Jeremy Yee"], "categories": ["math.OC"], "doi": "", "_query": "automated valuation model", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/1802.07561v1", "title": "$L_p$ Minkowski Valuations on polytopes", "abstract": "For $1 \\leq p < \\infty$, Ludwig, Haberl and Parapatits classified $L_p$ Minkowski valuations intertwining the special linear group with additional conditions such as homogeneity and continuity. In this paper,a complete classification of $L_p$ Minkowski valuations intertwining the special linear group on polytopes without any additional conditions is established for $p \\geq 1$ including $p = \\infty$. For $n=3$ and $p=1$, there exist valuations not mentioned before.", "published": "2018-02-21T13:27:48Z", "authors": ["Jin Li", "Gangsong Leng"], "categories": ["math.MG"], "doi": "10.1016/j.aim.2016.05.009", "_query": "automated valuation model", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/1801.00446v1", "title": "The Logos Categorical Approach to Quantum Mechanics: I. Kochen-Specker Contextuality and Global Intensive Valuations", "abstract": "In this paper we present a new categorical approach which attempts to provide an original understanding of QM. Our logos categorical approach attempts to consider the main features of the quantum formalism as the standpoint to develop a conceptual representation that explains what the theory is really talking about --rather than as problems that need to be bypassed in order to allow a restoration of a classical \"common sense\" understanding of what there is. In particular, we discuss a solution to Kochen-Specker contextuality through the generalization of the meaning of global valuation. This idea has been already addressed by the so called topos approach to QM --originally proposed by Isham, Butterfield and Döring-- in terms of sieve-valued valuations. The logos approach to QM presents a different solution in terms of the notion of intensive valuation. This new solution stresses an ontological (rather than epistemic) reading of the quantum formalism and the need to restore an objective (rather than classical) conceptual representation and understanding of quantum physical reality.", "published": "2018-01-01T13:49:08Z", "authors": ["Christian de Ronde", "César Massri"], "categories": ["quant-ph"], "doi": "10.1007/s10773-018-3914-0", "_query": "automated valuation model", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2607.03374v1", "title": "An Asymptotic Analysis of the Shapley Value for Dataset Valuation", "abstract": "We propose an asymptotic analysis of the Shapley value in a dataset valuation setting in which utilities are modeled as smooth functionals of empirical distributions via reproducing kernel Hilbert space (RKHS) mean embeddings. We prove that, despite its combinatorial definition, the Shapley value of a data source is asymptotically captured by a simple leading term. This term can be interpreted as the first-order contribution of a dataset relative to the surrounding data population. It also identifies the scale of the Shapley value as the number of data sources grows and provides a framework for analyzing existing Shapley value estimators. Moreover, for practitioners working with large numbers of datasets, the leading term becomes a tractable reference against which Shapley value approximations can be benchmarked.", "published": "2026-07-03T14:28:25Z", "authors": ["Mélissa Tamine", "Benjamin Heymann", "Maxime Vono", "Patrick Loiseau"], "categories": ["cs.GT"], "doi": "", "_query": "automated valuation model", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2508.12453v1", "title": "Computing Approximately Proportional Allocations of Indivisible Goods: Beyond Additive and Monotone Valuations", "abstract": "Although approximate notions of envy-freeness-such as envy-freeness up to one good (EF1)-have been extensively studied for indivisible goods, the seemingly simpler fairness concept of proportionality up to one good (PROP1) has received far less attention. For additive valuations, every EF1 allocation is PROP1, and well-known algorithms such as Round-Robin and Envy-Cycle Elimination compute such allocations in polynomial time. PROP1 is also compatible with Pareto efficiency, as maximum Nash welfare allocations are EF1 and hence PROP1. We ask whether these favorable properties extend to non-additive valuations. We study a broad class of allocation instances with {\\em satiating goods}, where agents have non-negative valuation functions that need not be monotone, allowing for negative marginal values. We present the following results: - EF1 implies PROP1 for submodular valuations over satiating goods, ensuring existence and efficient computation via Envy-Cycle Elimination for monotone submodular valuations; - Round-robin computes a partial PROP1 allocation after the second-to-last round for satiating submodular goods and a complete PROP1 for monotone submodular valuations; - PROP1 allocations for satiating subadditive goods can be computed in polynomial-time; - Maximum Nash welfare allocations are PROP1 for monotone submodular goods, revealing yet another facet of their ``unreasonable fairness.''", "published": "2025-08-17T17:59:40Z", "authors": ["Martin Jupakkal Andersen", "Ioannis Caragiannis", "Anders Bo Ipsen", "Alexander Søltoft"], "categories": ["cs.GT", "cs.DS"], "doi": "", "_query": "automated valuation model", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2509.08345v1", "title": "Toward Subtrait-Level Model Explainability in Automated Writing Evaluation", "abstract": "Subtrait (latent-trait components) assessment presents a promising path toward enhancing transparency of automated writing scores. We prototype explainability and subtrait scoring with generative language models and show modest correlation between human subtrait and trait scores, and between automated and human subtrait scores. Our approach provides details to demystify scores for educators and students.", "published": "2025-09-10T07:32:14Z", "authors": ["Alejandro Andrade-Lotero", "Lee Becker", "Joshua Southerland", "Scott Hellman"], "categories": ["cs.CL", "cs.AI"], "doi": "", "_query": "automated valuation model", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/1612.01762v1", "title": "Computing tropical varieties over fields with valuation", "abstract": "We show how tropical varieties of ideals I over a field K with non-trivial valuation can be traced back to tropical varieties of ideals in R[[t]][x] over some dense subring R in its ring of integers. Moreover, for homogeneous ideals, we present algorithms on how the latter can be computed in finite time, provided that generators are polynomial in t. While doing so, we also comment on the computation of the Groebner polytope structure and p-adic Groebner bases using our framework. All algorithms solely rely on existing standard basis techniques.", "published": "2016-12-06T11:30:41Z", "authors": ["Thomas Markwig", "Yue Ren"], "categories": ["math.AG", "math.AC"], "doi": "", "_query": "automated valuation model", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2609.37380v1", "title": "Information Limits of Multistage Inventory Control: Learning, Valuation, and Censoring", "abstract": "Learning how to replenish inventory and estimating the cost of doing so require different information. We establish information-theoretic lower bounds for offline multistage inventory control with independent bounded demand. For fixed additive error in total expected cost, policy learning requires cubically many scalar observations in the horizon in the worst case, even under full observation and adaptive sample allocation. This matches the known empirical-risk upper order. Stationarity permits a quadratic policy-learning guarantee, yet valuation with inherited stock can remain cubically difficult even when the optimal policy is known. Censoring sharpens this distinction: optimal decisions can be learnable while absolute costs remain unidentified. We give a carrysafe coverage condition under which truncation preserves inventory transitions and policy gaps exactly. Combining this reduction with a matching lower bound yields the sharp censored-learning rate, with raw sample requirements inversely proportional to the usable fraction of sales logs. The same qualified observations can check coverage and fit the policy under a joint error guarantee. The results isolate information limits for decisions and valuation under an explicit conditionally independent logging protocol.", "published": "2026-09-29T12:32:35Z", "authors": ["Hanzhang Qin", "David Simchi-Levi", "Ruihao Zhu"], "categories": ["math.OC", "eess.SY"], "doi": "", "_query": "automated valuation model", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2304.00999v2", "title": "Bandits for Sponsored Search Auctions under Unknown Valuation Model: Case Study in E-Commerce Advertising", "abstract": "This paper presents a bidding system for sponsored search auctions under an unknown valuation model. This formulation assumes that the bidder's value is unknown, evolving arbitrarily, and observed only upon winning an auction. Unlike previous studies, we do not impose any assumptions on the nature of feedback and consider the problem of bidding in sponsored search auctions in its full generality. Our system is based on a bandit framework that is resilient to the black-box auction structure and delayed and batched feedback. To validate our proposed solution, we conducted a case study at Zalando, a leading fashion e-commerce company. We outline the development process and describe the promising outcomes of our bandits-based approach to increase profitability in sponsored search auctions. We discuss in detail the technical challenges that were overcome during the implementation, shedding light on the mechanisms that led to increased profitability.", "published": "2023-03-31T10:14:43Z", "authors": ["Danil Provodin", "Jérémie Joudioux", "Eduard Duryev"], "categories": ["cs.GT", "cs.AI", "cs.LG", "eess.SY"], "doi": "", "_query": "automated valuation model", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2408.15977v4", "title": "Weak Distributive Laws between Monads of Continuous Valuations and of Non-Deterministic Choice", "abstract": "We show that there is weak distributive law of the Smyth hyperspace monad $\\mathcal Q_{\\mathsf V}$ (resp., the Hoare hyperspace monad $\\mathcal H_{\\mathsf V}$, resp. the monad $\\mathcal P\\ell^{\\mathrm q}_{\\mathsf V}$ of quasi-lenses, resp. the monad $\\mathcal P\\ell_{\\mathsf V}$ of lenses) over the continuous valuation monad $\\mathbf V$, as well as over the subprobability valuation monad $\\mathbf V_{\\leq 1}$ and the probability valuation monad $\\mathbf V_1$, on the whole category $\\mathbf{Top}$ of topological spaces (resp., on certain full subcategories such as the category of locally compact spaces or of stably compact spaces). We show that the resulting weak composite monad is the author's monad of superlinear previsions (resp., sublinear previsions, resp. forks), possibly subnormalized or normalized depending on whether we consider $\\mathbf V_{\\leq 1}$ or $\\mathbf V_1$ instead of $\\mathbf V$. As a special case, we obtain a weak distributive law of the monad $\\mathcal P\\ell^{\\mathrm q}_{\\mathsf V} \\cong \\mathcal P\\ell_{\\mathsf V}$ over the monad of (sub)probability Radon measures $\\mathbf R_\\bullet$ on the category of stably compact spaces, which specializes further to a weak distributive laws of the Vietoris monad over $\\mathbf R_\\bullet$. The associated weak composite monad is the monad of (sub)normalized forks.", "published": "2024-08-28T17:49:00Z", "authors": ["Jean Goubault-Larrecq"], "categories": ["math.CT", "cs.LO", "math.GN", "math.PR"], "doi": "", "_query": "automated valuation model", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/1207.7254v1", "title": "Crofton measures and Minkowski valuations", "abstract": "A description of continuous rigid motion compatible Minkowski valuations is established. As an application, we present a Brunn-Minkowski type inequality for intrinsic volumes of these valuations.", "published": "2012-07-31T14:15:40Z", "authors": ["Franz E. Schuster"], "categories": ["math.MG", "math.DG", "math.FA"], "doi": "10.1215/00127094-2010-033", "_query": "automated valuation model", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/1506.08595v3", "title": "Central Clearing Valuation Adjustment", "abstract": "This paper develops an XVA (costs) analysis of centrally cleared trading, parallel to the one that has been developed in the last years for bilateral transactions. We introduce a dynamic framework that incorporates the sequence of cash-flows involved in the waterfall of resources of a clearing house. The total cost of the clearance framework for a clearing member, called CCVA for central clearing valuation adjustment, is decomposed into a CVA corresponding to the cost of its losses on the default fund in case of defaults of other member, an MVA corresponding to the cost of funding its margins and a KVA corresponding to the cost of the regulatory capital and also of the capital at risk that the member implicitly provides to the CCP through its default fund contribution. In the end the structure of the XVA equations for bilateral and cleared portfolios is similar, but the input data to these equations are not the same, reflecting different financial network structures. The resulting XVA numbers differ, but, interestingly enough, they become comparable after scaling by a suitable netting ratio.", "published": "2015-06-29T11:57:55Z", "authors": ["Yannick Armenti", "Stéphane Crépey"], "categories": ["q-fin.RM", "q-fin.CP", "q-fin.PR"], "doi": "", "_query": "automated valuation model", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2609.17572v1", "title": "Disentangling Algorithmic Bias from Archival Artifacts: A Controlled Audit of Vision-Language Model Valuation in Metropolitan Museum Archives", "abstract": "Auditing vision-language models (VLMs) for societal bias requires distinguishing direct algorithmic valuation disparities from confounders embedded within archival metadata. In this study, we audit Contrastive Language-Image Pretraining (CLIP) models using historical artwork metadata from the Metropolitan Museum of Art Open Access collection (N = 1,500 total objects; N = 743 attributed works: Male n = 534, Female n = 209; n = 618 anonymous). We establish a quantitative audit framework evaluating zero-shot CLIP logit differential scores across three semantic prompt pairs (masterpiece, quality, and influence). Unadjusted evaluations demonstrate high score convergence without a statistically significant main gender effect under OpenAI CLIP (mu_F = -0.0067 vs mu_M = -0.0035, p = 0.1829) or OpenCLIP (mu_F = 0.0171 vs mu_M = 0.0237, p = 0.1224). Two One-Sided Tests (TOST) confirm statistical equivalence across Cohen's d >= 0.25 bounds (pTOST < 0.005). Multivariate OLS regression controlling for artwork medium, creation era, and aspect ratio (R^2 < 0.02) confirms that artist gender has no statistically significant conditional effect (p > 0.20). High residual embedding variance (R^2 < 2%) indicates that global zero-shot valuation metrics operate near an embedding noise floor, showing that broad zero-shot prompt logit differentials are a coarse measurement instrument rather than proving absolute model fairness. We highlight two key caveats: (i) macro-level score equivalence reflects metric insensitivity to fine-grained visual-semantic features and does not preclude localized micro-level visual biases, and (ii) excluding 41.2% unattributed holdings reflects institutional survival bias. These results demonstrate the necessity of multivariate confound control, equivalence testing, and archival provenance auditing when assessing AI fairness in cultural heritage collections.", "published": "2026-07-29T17:02:00Z", "authors": ["Manpreet Singh", "Rhythm Bhatia", "Rahul Joshi"], "categories": ["cs.LG"], "doi": "", "_query": "automated valuation model", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2511.14437v1", "title": "Model Learning for Adjusting the Level of Automation in HCPS", "abstract": "The steadily increasing level of automation in human-centred systems demands rigorous design methods for analysing and controlling interactions between humans and automated components, especially in safety-critical applications. The variability of human behaviour poses particular challenges for formal verification and synthesis. We present a model-based framework that enables design-time exploration of safe shared-control strategies in human-automation systems. The approach combines active automata learning -- to derive coarse, finite-state abstractions of human behaviour from simulations -- with game-theoretic reactive synthesis to determine whether a controller can guarantee safety when interacting with these models. If no such strategy exists, the framework supports iterative refinement of the human model or adjustment of the automation's controllable actions. A driving case study, integrating automata learning with reactive synthesis in UPPAAL, illustrates the applicability of the framework on a simplified driving scenario and its potential for analysing shared-control strategies in human-centred cyber-physical systems.", "published": "2025-11-18T12:35:34Z", "authors": ["Mehrnoush Hajnorouzi", "Astrid Rakow", "Martin Fränzle"], "categories": ["cs.HC", "cs.FL"], "doi": "10.4204/EPTCS.436.10", "_query": "automated valuation model", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2509.16007v2", "title": "Automated Model Tuning for Multifidelity Uncertainty Propagation in Trajectory Simulation", "abstract": "Multifidelity uncertainty propagation combines the efficiency of low-fidelity models with the accuracy of a high-fidelity model to construct statistical estimators of quantities of interest. It is well known that the effectiveness of such methods depends crucially on the relative correlations and computational costs of the available computational models. However, the question of how to automatically tune low-fidelity models to maximize performance remains an open area of research. This work investigates automated model tuning, which optimizes model hyperparameters to minimize estimator variance within a target computational budget. Focusing on multifidelity trajectory simulation estimators, the cost-versus-precision tradeoff enabled by this approach is demonstrated in a practical, online setting where upfront tuning costs cannot be amortized. Using a real-world entry, descent, and landing example, it is shown that automated model tuning largely outperforms hand-tuned models even when the overall computational budget is relatively low. Furthermore, for scenarios where the computational budget is large, model tuning solutions can approach the best-case multifidelity estimator performance where optimal model hyperparameters are known a priori. Recommendations for applying model tuning in practice are provided and avenues for enabling adoption of such approaches for budget-constrained problems are highlighted.", "published": "2025-09-19T14:19:45Z", "authors": ["James E. Warner", "Geoffrey F. Bomarito", "Gianluca Geraci", "Michael S. Eldred"], "categories": ["stat.CO"], "doi": "", "_query": "automated valuation model", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/0709.2289v1", "title": "Asymptotic valuations of sequences satisfying first order recurrences", "abstract": "Let t[n] be a sequence that satisfies a first order homogeneous recurrence t[n] = Q[n]*t[n-1], where Q is a polynomial with integer coefficients. The asymptotic behavior of the p-adic valuation of t[n] is described under the assumption that all the roots of Q in Z/pZ have nonvanishing derivative.", "published": "2007-09-14T13:01:47Z", "authors": ["T. Amdeberhan", "L. Medina", "Victor H. Moll"], "categories": ["math.NT"], "doi": "", "_query": "automated valuation model", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/1407.8325v3", "title": "Improved Efficiency Guarantees in Auctions with Budgets", "abstract": "We study the efficiency guarantees in the simple auction environment where the auctioneer has one unit of divisible good to be distributed among a number of budget constrained agents. With budget constraints, the social welfare cannot be approximated by a better factor than the number of agents by any truthful mechanism. Thus, we follow a recent work by Dobzinski and Leme (ICALP 2014) to approximate the liquid welfare, which is the welfare of the agents each capped by her/his own budget. We design a new truthful auction with an approximation ratio of $\\frac{\\sqrt{5}+1}{2} \\approx 1.618$, improving the best previous ratio of $2$ when the budgets for agents are public knowledge and their valuation is linear (additive). In private budget setting, we propose the first constant approximation auction with approximation ratio of $34$. Moreover, this auction works for any valuation function. Previously, only $O(\\log n)$ approximation was known for linear and decreasing marginal (concave) valuations, and $O(\\log^2 n)$ approximation was known for sub-additive valuations.", "published": "2014-07-31T09:18:39Z", "authors": ["Pinyan Lu", "Tao Xiao"], "categories": ["cs.GT"], "doi": "", "_query": "automated valuation model", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2501.14995v1", "title": "GreenAuto: An Automated Platform for Sustainable AI Model Design on Edge Devices", "abstract": "We present GreenAuto, an end-to-end automated platform designed for sustainable AI model exploration, generation, deployment, and evaluation. GreenAuto employs a Pareto front-based search method within an expanded neural architecture search (NAS) space, guided by gradient descent to optimize model exploration. Pre-trained kernel-level energy predictors estimate energy consumption across all models, providing a global view that directs the search toward more sustainable solutions. By automating performance measurements and iteratively refining the search process, GreenAuto demonstrates the efficient identification of sustainable AI models without the need for human intervention.", "published": "2025-01-25T00:04:59Z", "authors": ["Xiaolong Tu", "Dawei Chen", "Kyungtae Han", "Onur Altintas", "Haoxin Wang"], "categories": ["cs.LG"], "doi": "", "_query": "automated valuation model", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/1903.08288v3", "title": "Presentations of Transversal Valuated Matroids", "abstract": "Given $d$ row vectors of $n$ tropical numbers, $d<n$, the tropical Stiefel map constructs a version of their row space, whose Plücker coordinates are tropical determinants. We explicitly describe the fibers of this map. From the viewpoint of matroid theory, the tropical Stiefel map defines a generalization of transversal matroids in the valuated context, and our results are the valuated generalizations of theorems of Brualdi and Dinolt, Mason and others on the set of all set families that present a given transversal matroid. We show that a connected valuated matroid is transversal if and only if all of its connected initial matroids are. The duals of our results describe complete stable intersections via valuated strict gammoids.", "published": "2019-03-19T23:29:12Z", "authors": ["Alex Fink", "Jorge Alberto Olarte"], "categories": ["math.CO", "math.AG"], "doi": "10.1112/jlms.12505", "_query": "automated valuation model", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/0808.1671v1", "title": "Algorithmic Pricing via Virtual Valuations", "abstract": "Algorithmic pricing is the computational problem that sellers (e.g., in supermarkets) face when trying to set prices for their items to maximize their profit in the presence of a known demand. Guruswami et al. (2005) propose this problem and give logarithmic approximations (in the number of consumers) when each consumer's values for bundles are known precisely. Subsequently several versions of the problem have been shown to have poly-logarithmic inapproximability. This problem has direct ties to the important open question of better understanding the Bayesian optimal mechanism in multi-parameter settings; however, logarithmic approximations are inadequate for this purpose. It is therefore of vital interest to consider special cases where constant approximations are possible. We consider the unit-demand variant of this problem. Here a consumer has a valuation for each different item and their value for a set of items is simply the maximum value they have for any item in the set. We assume that the preferences of the consumers are drawn from a distribution, the standard assumption in economics; furthermore, the setting of a specific set of customers with known preferences, which is employed in all prior work in algorithmic pricing, is a special case of this general problem, where there is a discrete Bayesian distribution for preferences specified by picking one consumer uniformly from the given set of consumers. Our work complements these existing works by considering the case where the consumer's valuations for the different items are independent random variables. Our main result is a constant approximation that makes use of an interesting connection between this problem and the concept of virtual valuations from the single-parameter Bayesian optimal mechanism design literature.", "published": "2008-08-12T18:08:21Z", "authors": ["Shuchi Chawla", "Jason Hartline", "Robert Kleinberg"], "categories": ["cs.GT", "cs.DS"], "doi": "", "_query": "automated valuation model", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/0812.2702v1", "title": "Standard Logics Are Valuation-Nonmonotonic", "abstract": "It has recently been discovered that both quantum and classical propositional logics can be modelled by classes of non-orthomodular and thus non-distributive lattices that properly contain standard orthomodular and Boolean classes, respectively. In this paper we prove that these logics are complete even for those classes of the former lattices from which the standard orthomodular lattices and Boolean algebras are excluded. We also show that neither quantum nor classical computers can be founded on the latter models. It follows that logics are \"valuation-nonmonotonic\" in the sense that their possible models (corresponding to their possible hardware implementations) and the valuations for them drastically change when we add new conditions to their defining conditions. These valuations can even be completely separated by putting them into disjoint lattice classes by a technique presented in the paper.", "published": "2008-12-15T00:49:12Z", "authors": ["Mladen Pavicic", "Norman D. Megill"], "categories": ["cs.LO", "cs.AI", "quant-ph"], "doi": "", "_query": "automated valuation model", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2502.15676v3", "title": "AutoToM: Scaling Model-based Mental Inference via Automated Agent Modeling", "abstract": "Theory of Mind (ToM), the ability to understand people's minds based on their behavior, is key to developing socially intelligent agents. Current approaches to ToM reasoning either rely on prompting Large Language Models (LLMs), which are prone to systematic errors, or use handcrafted, rigid agent models for model-based inference, which are more robust but fail to generalize across domains. In this work, we introduce AutoToM, an automated agent modeling method for scalable, robust, and interpretable mental inference. Given a ToM problem, AutoToM first proposes an initial agent model and then performs automated Bayesian inverse planning based on this model, leveraging an LLM backend. Guided by inference uncertainty, it iteratively refines the model by introducing additional mental variables and/or incorporating more timesteps in the context. Across five diverse benchmarks, AutoToM outperforms existing ToM methods and even large reasoning models. Additionally, we show that AutoToM can produce human-like confidence estimates and enable online mental inference for embodied decision-making.", "published": "2025-02-21T18:57:52Z", "authors": ["Zhining Zhang", "Chuanyang Jin", "Mung Yao Jia", "Shunchi Zhang", "Tianmin Shu"], "categories": ["cs.AI", "cs.CL"], "doi": "", "_query": "automated valuation model", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2011.06300v2", "title": "A Knowledge Representation Approach to Automated Mathematical Modelling", "abstract": "In this paper, we propose a new mixed-integer linear programming (MILP) model ontology and a novel constraint typology of MILP formulations. MILP is a commonly used mathematical programming technique for modelling and solving real-life scheduling, routing, planning, resource allocation, and timetabling optimization problems providing optimized business solutions for industry sectors such as manufacturing, agriculture, defence, healthcare, medicine, energy, finance, and transportation. Despite the numerous real-life Combinatorial Optimization Problems found and solved and millions yet to be discovered and formulated, the number of types of constraints (the building blocks of a MILP) is relatively small. In the search for a suitable machine-readable knowledge representation structure for MILPs, we propose an optimization modelling tree built based upon an MILP model ontology that can be used as a guide for automated systems to elicit an MILP model from end-users on their combinatorial business optimization problems. Our ultimate aim is to develop a machine-readable knowledge representation for MILP that allows us to map an end-user's natural language description of the business optimization problem to an MILP formal specification as a first step towards automated mathematical modelling.", "published": "2020-11-12T10:29:57Z", "authors": ["Bahadorreza Ofoghi", "Vicky Mak", "John Yearwood"], "categories": ["cs.AI"], "doi": "", "_query": "automated valuation model", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/1602.05998v2", "title": "Funding, repo and credit inclusive valuation as modified option pricing", "abstract": "We take the holistic approach of computing an OTC claim value that incorporates credit and funding liquidity risks and their interplays, instead of forcing individual price adjustments: CVA, DVA, FVA, KVA. The resulting nonlinear mathematical problem features semilinear PDEs and FBSDEs. We show that for the benchmark vulnerable claim there is an analytical solution, and we express it in terms of the Black-Scholes formula with dividends. This allows for a detailed valuation analysis, stress testing and risk analysis via sensitivities.", "published": "2016-02-18T23:14:08Z", "authors": ["Damiano Brigo", "Cristin Buescu", "Marek Rutkowski"], "categories": ["q-fin.PR"], "doi": "", "_query": "automated valuation model", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2407.06403v1", "title": "Swin UNETR segmentation with automated geometry filtering for biomechanical modeling of knee joint cartilage", "abstract": "Simulation studies, such as finite element (FE) modeling, offer insights into knee joint biomechanics, which may not be achieved through experimental methods without direct involvement of patients. While generic FE models have been used to predict tissue biomechanics, they overlook variations in population-specific geometry, loading, and material properties. In contrast, subject-specific models account for these factors, delivering enhanced predictive precision but requiring significant effort and time for development. This study aimed to facilitate subject-specific knee joint FE modeling by integrating an automated cartilage segmentation algorithm using a 3D Swin UNETR. This algorithm provided initial segmentation of knee cartilage, followed by automated geometry filtering to refine surface roughness and continuity. In addition to the standard metrics of image segmentation performance, such as Dice similarity coefficient (DSC) and Hausdorff distance, the method's effectiveness was also assessed in FE simulation. Nine pairs of knee cartilage FE models, using manual and automated segmentation methods, were developed to compare the predicted stress and strain responses during gait. The automated segmentation achieved high Dice similarity coefficients of 89.4% for femoral and 85.1% for tibial cartilage, with a Hausdorff distance of 2.3 mm between the automated and manual segmentation. Mechanical results including maximum principal stress and strain, fluid pressure, fibril strain, and contact area showed no significant differences between the manual and automated FE models. These findings demonstrate the effectiveness of the proposed automated segmentation method in creating accurate knee joint FE models.", "published": "2024-07-08T21:22:47Z", "authors": ["Reza Kakavand", "Peyman Tahghighi", "Reza Ahmadi", "W. Brent Edwards", "Amin Komeili"], "categories": ["eess.IV", "physics.med-ph"], "doi": "", "_query": "automated valuation model", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2205.15466v7", "title": "Data Banzhaf: A Robust Data Valuation Framework for Machine Learning", "abstract": "Data valuation has wide use cases in machine learning, including improving data quality and creating economic incentives for data sharing. This paper studies the robustness of data valuation to noisy model performance scores. Particularly, we find that the inherent randomness of the widely used stochastic gradient descent can cause existing data value notions (e.g., the Shapley value and the Leave-one-out error) to produce inconsistent data value rankings across different runs. To address this challenge, we introduce the concept of safety margin, which measures the robustness of a data value notion. We show that the Banzhaf value, a famous value notion that originated from cooperative game theory literature, achieves the largest safety margin among all semivalues (a class of value notions that satisfy crucial properties entailed by ML applications and include the famous Shapley value and Leave-one-out error). We propose an algorithm to efficiently estimate the Banzhaf value based on the Maximum Sample Reuse (MSR) principle. Our evaluation demonstrates that the Banzhaf value outperforms the existing semivalue-based data value notions on several ML tasks such as learning with weighted samples and noisy label detection. Overall, our study suggests that when the underlying ML algorithm is stochastic, the Banzhaf value is a promising alternative to the other semivalue-based data value schemes given its computational advantage and ability to robustly differentiate data quality.", "published": "2022-05-30T23:44:09Z", "authors": ["Jiachen T. Wang", "Ruoxi Jia"], "categories": ["cs.LG", "cs.GT", "stat.ML"], "doi": "", "_query": "automated valuation model", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2511.02700v2", "title": "Numerical valuation of European options under two-asset infinite-activity exponential Lévy models", "abstract": "We propose a numerical method for the valuation of European-style options under two-asset infinite-activity exponential Lévy models. Our method extends the effective approach developed by Wang, Wan & Forsyth (2007) for the 1-dimensional case to the 2-dimensional setting and is applicable for general Lévy measures under mild assumptions. A tailored discretization of the non-local integral term is developed, which can be efficiently evaluated by means of the fast Fourier transform. For the temporal discretization, the semi-Lagrangian theta-method is employed in a convenient splitting fashion, where the diffusion term is treated implicitly and the integral term is handled explicitly by a fixed-point iteration. Numerical experiments for put-on-the-average options under Normal Tempered Stable dynamics reveal favourable second-order convergence of our method whenever the exponential Lévy process has finite-variation.", "published": "2025-11-04T16:22:26Z", "authors": ["Massimiliano Moda", "Karel J. in 't Hout", "Michèle Vanmaele", "Fred Espen Benth"], "categories": ["math.NA", "q-fin.CP"], "doi": "", "_query": "automated valuation model", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2507.19570v1", "title": "MCP4EDA: LLM-Powered Model Context Protocol RTL-to-GDSII Automation with Backend Aware Synthesis Optimization", "abstract": "This paper presents MCP4EDA, the first Model Context Protocol server that enables Large Language Models (LLMs) to control and optimize the complete open-source RTL-to-GDSII design flow through natural language interaction. The system integrates Yosys synthesis, Icarus Verilog simulation, OpenLane place-and-route, GTKWave analysis, and KLayout visualization into a unified LLM-accessible interface, enabling designers to execute complex multi-tool EDA workflows conversationally via AI assistants such as Claude Desktop and Cursor IDE. The principal contribution is a backend-aware synthesis optimization methodology wherein LLMs analyze actual post-layout timing, power, and area metrics from OpenLane results to iteratively refine synthesis TCL scripts, establishing a closed-loop optimization system that bridges the traditional gap between synthesis estimates and physical implementation reality. In contrast to conventional flows that rely on wire-load models, this methodology leverages real backend performance data to guide synthesis parameter tuning, optimization sequence selection, and constraint refinement, with the LLM functioning as an intelligent design space exploration agent. Experimental evaluation on representative digital designs demonstrates 15-30% improvements in timing closure and 10-20% area reduction compared to default synthesis flows, establishing MCP4EDA as the first practical LLM-controlled end-to-end open-source EDA automation system. The code and demo are avaiable at: http://www.agent4eda.com/", "published": "2025-07-25T17:16:26Z", "authors": ["Yiting Wang", "Wanghao Ye", "Yexiao He", "Yiran Chen", "Gang Qu", "Ang Li"], "categories": ["cs.AR", "cs.MA"], "doi": "", "_query": "automated valuation model", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/cond-mat/0001253v1", "title": "Learning short-option valuation in the presence of rare events", "abstract": "We present a neural-network valuation of financial derivatives in the case of fat-tailed underlying asset returns. A two-layer perceptron is trained on simulated prices taking into account the well-known effect of volatility smile. The prices of the underlier are generated using fractional calculus algorithms, and option prices are computed by means of the Bouchaud-Potters formula. This learning scheme is tested on market data; the results show a very good agreement between perceptron option prices and real market ones.", "published": "2000-01-18T23:51:58Z", "authors": ["M. Raberto", "G. Cuniberti", "E. Scalas", "M. Riani", "F. Mainardi", "G. Servizi"], "categories": ["cond-mat.stat-mech", "cond-mat.dis-nn", "q-fin.PR"], "doi": "10.1142/S0219024900000590", "_query": "automated valuation model", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2606.19986v1", "title": "Polynomial valuations on plane polygons", "abstract": "Scissors congruence problems involving translations have prompted the study of translation invariant simple valuations. We review this classical theory from a naive and consistent viewpoint: starting from a description of all simple valuations on polygons, we characterize the effect of translation invariance. A description of all polynomial simple valuations is obtained as a bi-product of the adopted approach and as a direct generalization of the translation invariant theory; it appears to be new.", "published": "2026-06-18T09:27:31Z", "authors": ["Askold Khovanskii", "Valentina Kiritchenko", "Vladlen Timorin"], "categories": ["math.MG"], "doi": "", "_query": "automated valuation model", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2106.04978v1", "title": "Orderings and valuations in hyperfields", "abstract": "We introduce and study in detail the notion of compatibility between valuations and orderings in real hyperfields. We investigate their relation with valuations and orderings induced on factor and residue hyperfields. Much of the theory from real fields can be generalized to real hyperfields; we point out facts that cannot. We generalize the Baer-Krull theorem to real hyperfields.", "published": "2021-06-09T10:55:23Z", "authors": ["Katarzyna Kuhlmann", "Alessandro Linzi", "Hanna Stojałowska"], "categories": ["math.AC"], "doi": "", "_query": "automated valuation model", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2401.02430v1", "title": "Automated Classification of Model Errors on ImageNet", "abstract": "While the ImageNet dataset has been driving computer vision research over the past decade, significant label noise and ambiguity have made top-1 accuracy an insufficient measure of further progress. To address this, new label-sets and evaluation protocols have been proposed for ImageNet showing that state-of-the-art models already achieve over 95% accuracy and shifting the focus on investigating why the remaining errors persist. Recent work in this direction employed a panel of experts to manually categorize all remaining classification errors for two selected models. However, this process is time-consuming, prone to inconsistencies, and requires trained experts, making it unsuitable for regular model evaluation thus limiting its utility. To overcome these limitations, we propose the first automated error classification framework, a valuable tool to study how modeling choices affect error distributions. We use our framework to comprehensively evaluate the error distribution of over 900 models. Perhaps surprisingly, we find that across model architectures, scales, and pre-training corpora, top-1 accuracy is a strong predictor for the portion of all error types. In particular, we observe that the portion of severe errors drops significantly with top-1 accuracy indicating that, while it underreports a model's true performance, it remains a valuable performance metric. We release all our code at https://github.com/eth-sri/automated-error-analysis .", "published": "2023-11-13T20:41:39Z", "authors": ["Momchil Peychev", "Mark Niklas Müller", "Marc Fischer", "Martin Vechev"], "categories": ["cs.CV", "cs.AI", "cs.LG"], "doi": "", "_query": "automated valuation model", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2412.16517v1", "title": "On non-holonomicity, transcendence and $p$-adic valuations", "abstract": "Let $ν_q(n)$ be the p-adic valuation of $n$. We show that the power series with coefficients $ν_q(n)$, respectively $ν_p(n)(\\mathrm{ mod\\;} k)$, are non-holonomic and not algebraic in characteristic 0. We find infinitely many rational numbers and infinitely many algebraic irrational numbers for which the values of these series are transcendental. We apply these results to some $p$-automatic sequences, one of them being the period-doubling sequence.", "published": "2024-12-21T07:34:40Z", "authors": ["Cristian Cobeli", "Mihai Prunescu", "Alexandru Zaharescu"], "categories": ["math.NT"], "doi": "", "_query": "automated valuation model", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2605.20608v1", "title": "From Automated to Autonomous: Hierarchical Agent-native Network Architecture (HANA)", "abstract": "Realizing Level 4/5 Autonomous Networks (AN) demands a shift from static automation to agent-native intelligence. Current operations, reliant on rigid scripts, lack the cognitive agency to handle off-nominal conditions. To address this, this letter proposes a hierarchical multi-agent reference architecture enabling high-level autonomy. The framework features a Dual-Driven Orchestrator that coordinates specialized Executive Agents, supported by a shared Public Memory for unified domain knowledge. A key innovation is the integration of agent self-awareness, which empowers the system to harmonize deliberative strategic governance with reflexive fault recovery. We instantiate and validate this architecture within a 5G Core environment. Case studies demonstrate that the system sustains critical throughput under congestion and reduces Mean Time to Repair (MTTR) by 86%, confirming its efficacy in unifying strategic planning with operational resilience.", "published": "2026-05-20T01:50:12Z", "authors": ["Binghan Wu", "Shoufeng Wang", "Yunxin Liu", "Ya-Qin Zhang", "Joseph Sifakis", "Ye Ouyang"], "categories": ["cs.AI", "cs.NI"], "doi": "10.1109/LNET.2026.3693226", "_query": "automated valuation model", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2605.07045v1", "title": "Incentive Design in Competitive Resource Allocation: Exploiting Valuation Asymmetry in Tullock Contests", "abstract": "In competitive resource allocation, a central coordinator may seek to gain an advantage not by directly controlling subordinate agents, but by strategically manipulating the information they receive. We study this problem within the framework of multi-player Tullock contests, where the coordinator influences subordinate players by designing their reported valuations of the contested prize, a mechanism that preserves the Tullock structure of the subordinates' objectives and thereby enables tractable equilibrium analysis. We first characterize the Nash equilibrium of the general multi-player Tullock contest, establishing how valuations and per-unit costs jointly determine equilibrium bids and payoffs. We then derive the optimal reported valuations for a coordinator managing two subordinates against a single opponent, and show that the structure of the optimal solution extends to contests with an arbitrary number of subordinates, reducing the coordinator's optimization to a two-variable problem regardless of system size.", "published": "2026-05-07T23:47:09Z", "authors": ["Gilberto Diaz-Garcia", "Keith Paarporn", "Jason R. Marden"], "categories": ["cs.GT"], "doi": "", "_query": "automated valuation model", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2607.25599v1", "title": "An Analytic COS Method for Compound Option Valuation", "abstract": "We develop an analytic Fourier cosine (COS) method for the valuation of compound options. By deriving closed-form expressions for the cosine coefficients at all compound stages, the proposed method eliminates the need for numerical quadrature in intermediate exercise stages while retaining the convergence properties of the underlying COS approximation. The formulation extends to multi-stage compound structures and a broader class of payoffs, and remains applicable to a wide class of stochastic models characterized by known characteristic functions, including jump-diffusion dynamics. Numerical experiments demonstrate improved computational efficiency compared with quadrature-based implementations while maintaining high accuracy. Applications to staged real-option problems further illustrate the flexibility of the method in handling nested decision structures under different uncertainty dynamics.", "published": "2026-07-28T11:29:58Z", "authors": ["Zhipeng Huang", "Cornelis W. Oosterlee"], "categories": ["q-fin.CP", "cs.CE", "q-fin.MF", "q-fin.PR"], "doi": "", "_query": "automated valuation model", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/1608.02815v2", "title": "Nagata's compactification theorem for normal toric varieties over a valuation ring of rank one", "abstract": "We prove, using invariant Zariski-Riemann spaces, that every normal toric variety over a valuation ring of rank one can be embedded as an open dense subset into a proper toric variety equivariantly. This extends a well known theorem of Sumihiro for toric varieties over a field to this more general setting.", "published": "2016-08-09T14:28:26Z", "authors": ["Alejandro Soto"], "categories": ["math.AG"], "doi": "", "_query": "automated valuation model", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2407.08304v1", "title": "Equivariant Valuations on Convex Functions", "abstract": "We classify all continuous valuations on the space of finite convex functions with values in the same space which are dually epi-translation-invariant and equi- resp. contravariant with respect to volume-preserving linear maps. We thereby identify the valuation-theoretic functional analogues of the difference body map and show that there does not exist a generalization of the projection body map in this setting. This non-existence result is shown to also hold true for valuations with values in the space of convex functions that are finite in a neighborhood of the origin.", "published": "2024-07-11T08:51:09Z", "authors": ["Georg C. Hofstätter", "Jonas Knoerr"], "categories": ["math.MG", "math.FA"], "doi": "", "_query": "automated valuation model", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/1004.4553v1", "title": "Solar LImb Prominence CAtcher and Tracker (SLIPCAT): An Automated System and Its Preliminary Statistical Results", "abstract": "In this paper, we present an automated system, which has the capability to catch and track solar limb prominences based on observations from EUV 304 passband. The characteristic parameters and their evolution, including height, position angle, area, length and brightness, are obtained without manual interventions. By applying the system to the STEREO-B/SECCHI/EUVI 304 data during 2007 April -2009 October, we obtain a total of 9477 well-tracked prominences and a catalog of these events available online at http://space.ustc.edu.cn/dreams/slipcat/. A detailed analysis of these prominences suggests that the system has a rather good performance. We have obtained several interesting statistical results based on the catalog. Most prominences appear below the latitude of 60 degrees and at the height of about 26 Mm above the solar surface. Most of them are quite stable during the period they are tracked. Nevertheless, some prominences have an upward speed of more than 100 km/s, and some others show significant downward and/or azimuthal speeds. There are strong correlations among the brightness, area and height. The expansion of a prominence is probably one major cause of its fading during the rising or erupting process.", "published": "2010-04-26T15:35:26Z", "authors": ["Yuming Wang", "Hao Cao", "Junhong Chen", "Tengfei Zhang", "Sijie Yu", "Huinan Zheng", "Chenglong Shen", "Jie Zhang", "S. Wang"], "categories": ["astro-ph.SR"], "doi": "10.1088/0004-637X/717/2/973", "_query": "automated valuation model", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/1709.02560v1", "title": "Run-Time Risk Mitigation in Automated Vehicles: A Model for Studying Preparatory Steps", "abstract": "We assume that autonomous or highly automated driving (AD) will be accompanied by tough assurance obligations exceeding the requirements of even recent revisions of ISO 26262 or SOTIF. Hence, automotive control and safety engineers have to (i) comprehensively analyze the driving process and its control loop, (ii) identify relevant hazards stemming from this loop, (iii) establish feasible automated measures for the effective mitigation of these hazards or the alleviation of their consequences. By studying an example, this article investigates some achievements in the modeling for the steps (i), (ii), and (iii), amenable to formal verification of desired properties derived from potential assurance obligations such as the global existence of an effective mitigation strategy. In addition, the proposed approach is meant for step-wise refinement towards the automated synthesis of AD safety controllers implementing such properties.", "published": "2017-09-08T06:36:33Z", "authors": ["Mario Gleirscher"], "categories": ["eess.SY", "cs.RO", "cs.SE"], "doi": "10.4204/EPTCS.257.8", "_query": "automated valuation model", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2210.03589v3", "title": "Tracing, Ranking and Valuation of Aggregated DER Flexibility in Active Distribution Networks", "abstract": "The integration of distributed energy resources (DER) makes active distribution networks (ADNs) natural providers of flexibility services. However, the optimal operation of flexible units in ADNs is highly complex, which poses challenges for distribution system operators (DSOs) in aggregating DER flexibility. For example, to maximise the provision of services, flexible units must be strongly coordinated to manage network constraints, e.g., perform power swaps. Furthermore, due to the nonlinearities of aggregated DER flexibility provision, some units may need to rapidly change their outputs to enable the services. To address these challenges, this paper brings together exact AC optimal power flow (OPF) models and a cooperative game formulation and presents a new framework for tracing, ranking, and valuation of aggregated DER flexibility in ADNs. Extensive tests and simulations performed for the 33-bus radial distribution network demonstrate that the framework enables translating complex DER interactions into useful information for DSOs by ranking the criticality of flexible units and performing flexibility valuation based on its cost or economic surplus. Additionally, the framework proposes no-swap constraints and a nonlinearity metric which can be used by DSOs to identify unreliable operating regions with power swaps or rapid changes in flexible unit dispatch.", "published": "2022-10-07T14:46:15Z", "authors": ["Andrey Churkin", "Wangwei Kong", "Jose N. Melchor Gutierrez", "Eduardo A. Martínez Ceseña", "Pierluigi Mancarella"], "categories": ["eess.SY"], "doi": "", "_query": "automated valuation model", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/1611.07605v1", "title": "Optimal Pricing for Submodular Valuations with Bounded Curvature", "abstract": "The optimal pricing problem is a fundamental problem that arises in combinatorial auctions. Suppose that there is one seller who has indivisible items and multiple buyers who want to purchase a combination of the items. The seller wants to sell his items for the highest possible prices, and each buyer wants to maximize his utility (i.e., valuation minus payment) as long as his payment does not exceed his budget. The optimal pricing problem seeks a price of each item and an assignment of items to buyers such that every buyer achieves the maximum utility under the prices. The goal of the problem is to maximize the total payment from buyers. In this paper, we consider the case that the valuations are submodular. We show that the problem is computationally hard even if there exists only one buyer. Then we propose approximation algorithms for the unlimited budget case. We also extend the algorithm for the limited budget case when there exists one buyer and multiple buyers collaborate with each other.", "published": "2016-11-23T02:18:10Z", "authors": ["Takanori Maehara", "Yasushi Kawase", "Hanna Sumita", "Katsuya Tono", "Ken-ichi Kawarabayashi"], "categories": ["cs.GT", "cs.DS"], "doi": "", "_query": "automated valuation model", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/1408.5361v1", "title": "On the geometry of Prüfer intersections of valuation rings", "abstract": "Let $F$ be a field, let $D$ be a subring of $F$ and let $Z$ be an irreducible subspace of the space of all valuation rings between $D$ and $F$ that have quotient field $F$. Then $Z$ is a locally ringed space whose ring of global sections is $A = \\bigcap_{V \\in Z}V$. All rings between $D$ and $F$ that are integrally closed in $F$ arise in such a way. Motivated by applications in areas such as multiplicative ideal theory and real algebraic geometry, a number of authors have formulated criteria for when $A$ is a Prüfer domain. We give geometric criteria for when $A$ is a Prüfer domain that reduce this issue to questions of prime avoidance. These criteria, which unify and extend a variety of different results in the literature, are framed in terms of morphisms of $Z$ into the projective line ${\\mathbb{P}}^1_D$", "published": "2014-08-22T16:59:39Z", "authors": ["Bruce Olberding"], "categories": ["math.AC"], "doi": "10.2140/pjm.2015.273.353", "_query": "automated valuation model", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2002.08750v3", "title": "The greatest common valuation of $φ_{n}$ and $ψ_{n}^{2}$ at points on elliptic curves", "abstract": "Given a minimal model of an elliptic curve, $E/K$, over a finite extension, $K$, of ${\\mathbb Q}_{p}$ for any rational prime, $p$, and any point $P \\in E(K)$ of infinite order, we determine precisely $\\min \\left( v \\left( φ_{n}(P) \\right), v \\left( ψ_{n}^{2}(P) \\right) \\right)$, where $v$ is a normalised valuation on $K$ and $φ_{n}(P)$ and $ψ_{n}(P)$ are polynomials arising from multiplication by $n$ for this model of the curve.", "published": "2020-02-20T14:25:01Z", "authors": ["Paul Voutier", "Minoru Yabuta"], "categories": ["math.NT"], "doi": "10.1016/j.jnt.2021.04.015", "_query": "automated valuation model", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/1603.00636v1", "title": "Semi-Automated Design Space Exploration for Formal Modelling", "abstract": "Refinement based formal methods allow the modelling of systems through incremental steps via abstraction. Discovering the right levels of abstraction, formulating correct and meaningful invariants, and analysing faulty models are some of the challenges faced when using this technique. Here, we propose Design Space Exploration, an approach that aims to assist a designer by automatically providing high-level modelling guidance in real-time. More specifically, through the combination of common patterns of modelling with techniques from automated theory formation and automated reasoning, different design alternatives are explored and suitable models that deal with faults are proposed.", "published": "2016-03-02T09:54:44Z", "authors": ["Gudmund Grov", "Andrew Ireland", "Maria Teresa Llano", "Peter Kovacs", "Simon Colton", "Jeremy Gow"], "categories": ["cs.SE", "cs.LO"], "doi": "", "_query": "automated valuation model", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2509.25946v1", "title": "Automated Model Discovery via Multi-modal & Multi-step Pipeline", "abstract": "Automated model discovery is the process of automatically searching and identifying the most appropriate model for a given dataset over a large combinatorial search space. Existing approaches, however, often face challenges in balancing the capture of fine-grained details with ensuring generalizability beyond training data regimes with a reasonable model complexity. In this paper, we present a multi-modal \\& multi-step pipeline for effective automated model discovery. Our approach leverages two vision-language-based modules (VLM), AnalyzerVLM and EvaluatorVLM, for effective model proposal and evaluation in an agentic way. AnalyzerVLM autonomously plans and executes multi-step analyses to propose effective candidate models. EvaluatorVLM assesses the candidate models both quantitatively and perceptually, regarding the fitness for local details and the generalibility for overall trends. Our results demonstrate that our pipeline effectively discovers models that capture fine details and ensure strong generalizability. Additionally, extensive ablation studies show that both multi-modality and multi-step reasoning play crucial roles in discovering favorable models.", "published": "2025-09-30T08:40:05Z", "authors": ["Lee Jung-Mok", "Nam Hyeon-Woo", "Moon Ye-Bin", "Junhyun Nam", "Tae-Hyun Oh"], "categories": ["cs.AI"], "doi": "", "_query": "automated valuation model", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2408.12659v1", "title": "Disentangled Structural and Featural Representation for Task-Agnostic Graph Valuation", "abstract": "With the emergence of data marketplaces, the demand for methods to assess the value of data has increased significantly. While numerous techniques have been proposed for this purpose, none have specifically addressed graphs as the main data modality. Graphs are widely used across various fields, ranging from chemical molecules to social networks. In this study, we break down graphs into two main components: structural and featural, and we focus on evaluating data without relying on specific task-related metrics, making it applicable in practical scenarios where validation requirements may be lacking. We introduce a novel framework called blind message passing, which aligns the seller's and buyer's graphs using a shared node permutation based on graph matching. This allows us to utilize the graph Wasserstein distance to quantify the differences in the structural distribution of graph datasets, called the structural disparities. We then consider featural aspects of buyers' and sellers' graphs for data valuation and capture their statistical similarities and differences, referred to as relevance and diversity, respectively. Our approach ensures that buyers and sellers remain unaware of each other's datasets. Our experiments on real datasets demonstrate the effectiveness of our approach in capturing the relevance, diversity, and structural disparities of seller data for buyers, particularly in graph-based data valuation scenarios.", "published": "2024-08-22T18:05:41Z", "authors": ["Ali Falahati", "Mohammad Mohammadi Amiri"], "categories": ["cs.LG", "cs.AI", "cs.IT", "stat.ML"], "doi": "", "_query": "automated valuation model", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/1306.1802v1", "title": "Uniformly defining valuation rings in Henselian valued fields with finite or pseudo-finite residue fields", "abstract": "We give a definition, in the ring language, of Z_p inside Q_p and of F_p[[t]] inside F_p((t)), which works uniformly for all $p$ and all finite field extensions of these fields, and in many other Henselian valued fields as well. The formula can be taken existential-universal in the ring language, and in fact existential in a modification of the language of Macintyre. Furthermore, we show the negative result that in the language of rings there does not exist a uniform definition by an existential formula and neither by a universal formula for the valuation rings of all the finite extensions of a given Henselian valued field. We also show that there is no existential formula of the ring language defining Z_p inside Q_p uniformly for all p. For any fixed finite extension of Q_p, we give an existential formula and a universal formula in the ring language which define the valuation ring.", "published": "2013-06-07T18:23:51Z", "authors": ["Raf Cluckers", "Jamshid Derakhshan", "Eva Leenknegt", "Angus Macintyre"], "categories": ["math.LO"], "doi": "", "_query": "automated valuation model", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/1611.09971v6", "title": "Model theory and metric convergence I: Metastability and dominated convergence", "abstract": "We study Tao's finitary viewpoint of convergence in metric spaces, as captured by the notion of metastability. We adopt the perspective of continuous model theory. We show that, in essence, metastable convergence with a given rate is the only formulation of metric convergence that can be captured by a theory in continuous first-order logic, a result we call the Uniform Metastability Principle. Philosophically, this principle amounts to the following meta-theorem: \"If a classical statement about convergence in metric structures is refined to a statement about metastable convergence with some rate, then the validity of the original statement implies the validity of its metastable version.\" As an instance of this phenomenon, we formulate an abstract version of Tao's Metastable Dominated Convergence Theorem as a statement about axiomatizable classes of metric structures, and show that it is a direct consequence of the Uniform Metastability Principle.", "published": "2016-11-30T02:18:40Z", "authors": ["Eduardo Dueñez", "José N. Iovino"], "categories": ["math.FA", "math.LO"], "doi": "", "_query": "automated valuation model", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2302.02818v1", "title": "A presentation theorem for smooth projective schemes over discrete valuation rings", "abstract": "In this article, we give a proof for a geometric presentation theorem for any irreducible scheme $X$ smooth projective over a discrete valuation ring $R$. As a consequence, for any reductive $R$-group scheme $\\mathbf{G}$, we prove that any generically trivial principal $\\mathbf{G}$-bundle over $X$ glued to a principal $\\mathbf{G}_U$-bundle over the affine line $\\mathbb{A}^1_U$ for a semilocal affine scheme $U$.", "published": "2023-02-06T14:39:28Z", "authors": ["Ning Guo", "Ivan Panin"], "categories": ["math.AG"], "doi": "", "_query": "automated valuation model", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2301.05332v2", "title": "A Levy-driven Ornstein-Uhlenbeck process for the valuation of credit index swaptions", "abstract": "A Levy-driven Ornstein-Uhlenbeck process is proposed to model the evolution of the risk-free rate and default intensities for the purpose of evaluating option contracts on a credit index. Time evolution in credit markets is assumed to follow a gamma process in order to reflect the different pace at which credit products are exchanged with respect to that of risk-free debt. Formulas for the characteristic function, zero coupon bonds, moments of the process and its stationary distribution are derived. Numerical experiments showing convergence of standard numerical methods for the valuation PIDE to analytical and Montecarlo solutions are shown. Calibration to market prices of options on a credit index is performed, and model and market implied summary statistics for the underlying credit spreads are estimated and compared.", "published": "2023-01-12T23:54:37Z", "authors": ["Yoshihiro Shirai"], "categories": ["q-fin.PR", "math.PR"], "doi": "", "_query": "automated valuation model", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2010.11178v3", "title": "Valuations and the Hopf Monoid of Generalized Permutahedra", "abstract": "The goal of this paper is to show that valuation theory and Hopf theory are compatible on the class of generalized permutahedra. We prove that the Hopf structure $\\mathbf{GP}^+$ on these polyhedra descends, modulo the inclusion-exclusion relations, to an indicator Hopf monoid $\\mathbb{I}(\\mathbf{GP}^+)$ of generalized permutahedra that is isomorphic to the Hopf monoid of weighted ordered set partitions. This quotient Hopf monoid $\\mathbb{I}(\\mathbf{GP}^+)$ is cofree. It is the terminal object in the category of Hopf monoids with polynomial characters; this partially explains the ubiquity of generalized permutahedra in the theory of Hopf monoids. This Hopf theoretic framework offers a simple, unified explanation for many new and old valuations on generalized permutahedra and their subfamilies. Examples include, for matroids: the Chern-Schwartz-MacPherson cycles, Eur's volume polynomial, the Kazhdan-Lusztig polynomial, the motivic zeta function, and the Derksen-Fink invariant; for posets: the order polynomial, Poincaré polynomial, and poset Tutte polynomial; for generalized permutahedra: the universal Tutte character and the corresponding class in the Chow ring of the permutahedral variety. We obtain several algebraic and combinatorial corollaries; for example: the existence of the valuative character group of $\\mathbf{GP}^+$, and the indecomposability of a nestohedron into smaller nestohedra.", "published": "2020-10-21T17:52:21Z", "authors": ["Federico Ardila", "Mario Sanchez"], "categories": ["math.CO"], "doi": "", "_query": "automated valuation model", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/1601.06256v5", "title": "On the non-periodic stable Auslander--Reiten Heller component for the Kronecker algebra over a complete discrete valuation ring", "abstract": "We consider the Kronecker algebra $A=\\mathcal{O}[X, Y]/(X^2, Y^2)$, where $\\mathcal{O}$ is a complete discrete valuation ring. Since $A \\otimesκ$ is a special biserial algebra, where $κ$ is the residue field of $\\mathcal{O}$, one can compute a complete list of indecomposable $A\\otimesκ$-modules. For each indecomposable $A\\otimesκ$-module, we obtain a special kind of $A$-lattices called \"Heller lattices\". In this paper, we determine the non-periodic component of a variant of the stable Auslander--Reiten quiver for the category of $A$-lattices that contains \"Heller lattices\". Moreover, we give the strong restrictions on stable Auslander--Reiten quivers for symmetric orders over a complete discrete valuation ring.", "published": "2016-01-23T09:13:28Z", "authors": ["Kengo Miyamoto"], "categories": ["math.RT"], "doi": "", "_query": "automated valuation model", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/1409.5113v1", "title": "Affine schemes and topological closures in the Zariski-Riemann space of valuation rings", "abstract": "Let $F$ be a field, let $D$ be a subring of $F$, and let ${\\mathfrak{X}}$ be the Zariski-Riemann space of valuation rings containing $D$ and having quotient field $F$. We consider the Zariski, inverse and patch topologies on ${\\mathfrak{X}}$ when viewed as a projective limit of projective integral schemes having function field contained in $F$, and we characterize the locally ringed subspaces of ${\\mathfrak{X}}$ that are affine schemes.", "published": "2014-09-17T19:49:38Z", "authors": ["Bruce Olberding"], "categories": ["math.AC"], "doi": "", "_query": "automated valuation model", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2402.14731v1", "title": "Lefschetz operators on convex valuations", "abstract": "We investigate the action of Alesker's Lefschetz operators on translation invariant valuations on convex bodies. For scalar valued valuations, we describe this action on the level of Klain-Schneider functions by a Radon type transform, generalizing a result by Schuster and Wannerer. In the case of rotationally equivariant Minkowski valuations, the Lefschetz operators act on the generating function as a convolution transform. We show that the convolution kernel satisfies a Legendre type differential equation, and thus, is a strictly positive function that is smooth up to one point.", "published": "2024-02-22T17:42:57Z", "authors": ["Leo Brauner", "Georg C. Hofstätter", "Oscar Ortega-Moreno"], "categories": ["math.MG"], "doi": "", "_query": "automated valuation model", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/1105.0962v1", "title": "On Henselian valuations and Brauer groups of primarily quasilocal fields", "abstract": "This paper finds a classification, up-to an isomorphism, of abelian torsion groups realizable as Brauer groups of major types of Henselian valued primarily quasilocal fields with totally indivisible value groups. When $E$ is a quasilocal field with such a valuation, it shows that the Brauer group of $E$ is divisible and embeddable in the quotient group of the additive group of rational numbers by the subgroup of integers.", "published": "2011-05-04T23:50:12Z", "authors": ["Ivan Chipchakov"], "categories": ["math.RA"], "doi": "", "_query": "automated valuation model", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/1811.04396v5", "title": "The valuation theory of deeply ramified fields and its connection with defect extensions", "abstract": "We study in detail the valuation theory of deeply ramified fields and introduce and investigate several other related classes of valued fields. Further, a classification of defect extensions of prime degree of valued fields that was earlier given only for the equicharacteristic case is generalized to the case of mixed characteristic by a unified definition that works simultaneously for both cases. It is shown that deeply ramified fields and the other valued fields we introduce only admit one of the two types of defect extensions, namely the ones that appear to be more harmless in open problems such as local uniformization and the model theory of valued fields in positive characteristic. We use our knowledge about such defect extensions to give a new, valuation theoretic proof of the fact that algebraic extensions of deeply ramified fields are again deeply ramified. We also prove finite descent, and under certain conditions even infinite descent, for deeply ramified fields. These results are also proved for two other related classes of valued fields. The classes of valued fields under consideration can be seen as generalizations of the class of tame valued fields. Our paper supports the hope that it will be possible to generalize to deeply ramified fields several important results that have been proven for tame fields and were at the core of partial solutions of the two open problems mentioned above.", "published": "2018-11-11T11:37:02Z", "authors": ["Franz-Viktor Kuhlmann", "Anna Rzepka"], "categories": ["math.AC"], "doi": "", "_query": "automated valuation model", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2302.07822v1", "title": "Silkswap: An asymmetric automated market maker model for stablecoins", "abstract": "Silkswap is an automated market maker model designed for efficient stablecoin trading with minimal price impact. The original purpose of Silkswap is to facilitate the trading of fiat-pegged stablecoins with the stablecoin Silk, but it can be applied to any pair of stablecoins. The Silkswap invariant is a hybrid function that generates an asymmetric price impact curve. We present the derivation of the Silkswap model and its mathematical properties. We also compare different numerical methods used to solve the invariant equation. Finally, we compare our model with the well-known Curve Finance model.", "published": "2023-01-21T21:30:22Z", "authors": ["Nicola Cantarutti", "Alex Harker", "Carter Woetzel"], "categories": ["q-fin.CP", "econ.GN", "q-fin.PR", "q-fin.TR"], "doi": "", "_query": "automated valuation model", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/1512.04348v1", "title": "Automated Model Generation for Analysis of Large-scale Interconnected Uncertain Systems", "abstract": "The first challenge in robustness analysis of large-scale interconnected uncertain systems is to provide a model of such systems in a standard-form that is required within different analysis frameworks. This becomes particularly important for large-scale systems, as analysis tools that can handle such systems heavily rely on the special structure within such model descriptions. We here propose an automated framework for providing such models of large-scale interconnected uncertain systems that are used in Integral Quadratic Constraint (IQC) analysis. Specifically, in this paper we put forth a methodological way to provide such models from a block-diagram and nested description of interconnected uncertain systems. We describe the details of this automated framework using an example.", "published": "2015-12-14T14:55:54Z", "authors": ["Farzaneh Karami", "Sina Khoshfetrat Pakazad", "Anders Hansson", "Ahmad Afshar"], "categories": ["math.OC"], "doi": "", "_query": "automated valuation model", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/1906.03562v2", "title": "A Top-Down Approach for the Multiple Exercises and Valuation of Employee Stock Options", "abstract": "We propose a new framework to value employee stock options (ESOs) that captures multiple exercises of different quantities over time. We also model the ESO holder's job termination risk and incorporate its impact on the payoffs of both vested and unvested ESOs. Numerical methods based on Fourier transform and finite differences are developed and implemented to solve the associated systems of PDEs. In addition, we introduce a new valuation method based on maturity randomization that yields analytic formulae for vested and unvested ESO costs. We examine the cost impact of job termination risk, exercise intensity, and various contractual features.", "published": "2019-06-09T04:32:11Z", "authors": ["Tim Leung", "Yang Zhou"], "categories": ["q-fin.PR"], "doi": "", "_query": "automated valuation model", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/hep-th/9711193v1", "title": "Dynamical Symmetry Breaking in Four-Fermionic Models under the Influence of External Electromagnetic Field in Curved Spacetime", "abstract": "An investigation of the Nambu-Jona-Lasinio model with external constant electromagnetic and weak gravitational fields is carried out in three- and four-dimensional spacetimes. The effective potential of the composite bifermionic fields is calculated keeping terms linear in the curvature, while the electromagnetic field effect is treated exactly by means of the proper-time formalism. Numerical simulations of the dynamical symmetry breaking phenomenon accompanied by some phase transitions are presented.", "published": "1997-11-26T19:44:06Z", "authors": ["Yu. I. Shil'nov"], "categories": ["hep-th"], "doi": "", "_query": "automated valuation model", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2406.07739v1", "title": "UICoder: Finetuning Large Language Models to Generate User Interface Code through Automated Feedback", "abstract": "Large language models (LLMs) struggle to consistently generate UI code that compiles and produces visually relevant designs. Existing approaches to improve generation rely on expensive human feedback or distilling a proprietary model. In this paper, we explore the use of automated feedback (compilers and multi-modal models) to guide LLMs to generate high-quality UI code. Our method starts with an existing LLM and iteratively produces improved models by self-generating a large synthetic dataset using an original model, applying automated tools to aggressively filter, score, and de-duplicate the data into a refined higher quality dataset. The original LLM is improved by finetuning on this refined dataset. We applied our approach to several open-source LLMs and compared the resulting performance to baseline models with both automated metrics and human preferences. Our evaluation shows the resulting models outperform all other downloadable baselines and approach the performance of larger proprietary models.", "published": "2024-06-11T21:53:46Z", "authors": ["Jason Wu", "Eldon Schoop", "Alan Leung", "Titus Barik", "Jeffrey P. Bigham", "Jeffrey Nichols"], "categories": ["cs.CL", "cs.HC", "cs.SE"], "doi": "", "_query": "automated valuation model", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2312.15395v2", "title": "Prompt Valuation Based on Shapley Values", "abstract": "Large language models (LLMs) excel on new tasks without additional training, simply by providing natural language prompts that demonstrate how the task should be performed. Prompt ensemble methods comprehensively harness the knowledge of LLMs while mitigating individual biases and errors and further enhancing performance. However, more prompts do not necessarily lead to better results, and not all prompts are beneficial. A small number of high-quality prompts often outperform many low-quality prompts. Currently, there is a lack of a suitable method for evaluating the impact of prompts on the results. In this paper, we utilize the Shapley value to fairly quantify the contributions of prompts, helping to identify beneficial or detrimental prompts, and potentially guiding prompt valuation in data markets. Through extensive experiments employing various ensemble methods and utility functions on diverse tasks, we validate the effectiveness of using the Shapley value method for prompts as it effectively distinguishes and quantifies the contributions of each prompt.", "published": "2023-12-24T03:37:11Z", "authors": ["Hanxi Liu", "Xiaokai Mao", "Haocheng Xia", "Jian Lou", "Jinfei Liu", "Kui Ren"], "categories": ["cs.CL", "cs.DB", "cs.LG"], "doi": "", "_query": "automated valuation model", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/1509.04502v1", "title": "Integration of Heterogeneous Modeling Languages via Extensible and Composable Language Components", "abstract": "Effective model-driven engineering of complex systems requires to appropriately describe different specific system aspects. To this end, efficient integration of different heterogeneous modeling languages is essential. Modeling language integaration is onerous and requires in-depth conceptual and technical knowledge and ef- fort. Traditional modeling lanugage integration approches require language engineers to compose monolithic language aggregates for a specific task or project. Adapting these aggregates cannot be to different contexts requires vast effort and makes these hardly reusable. This contribution presents a method for the engineering of grammar-based language components that can be independently developed, are syntactically composable, and ultimately reusable. To this end, it introduces the concepts of language aggregation, language embed- ding, and language inheritance, as well as their realization in the language workbench MontiCore. The result is a generalizable, systematic, and efficient syntax-oriented composition of languages that allows the agile employment of modeling languages efficiently tailored for individual software projects.", "published": "2015-09-15T11:34:09Z", "authors": ["Arne Haber", "Markus Look", "Antonio Navarro Perez", "Bernhard Rumpe", "Steven Völkel", "Andreas Wortmann"], "categories": ["cs.SE"], "doi": "", "_query": "automated valuation model", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/1308.1365v1", "title": "Mathematical model of concentrating solar cooker", "abstract": "The main purpose of this work is to obtain a mathematical model consistent with the thermal behavior of concentrating solar cookers, such as Jorhejpataranskua. We also want to simulate different conditions respect to the parameters involved of several materials for its construction and efficiency. The model is expressed in terms of a coupled nonlinear system of differential equations which are solved using Mathematica 8. The results obtained by our model are compared with measurements of solar cooker in field testing operation. We obtained good results in agreement with experimental data. Moreover, the simulation results are used by calculating cooking power and standardized cooking power of solar cooker for different parameters.", "published": "2013-08-06T18:11:15Z", "authors": ["Mauricio González Avilés", "José Juan González Avilés"], "categories": ["cs.CE"], "doi": "", "_query": "automated valuation model", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2001.11087v1", "title": "Crash Themes in Automated Vehicles: A Topic Modeling Analysis of the California Department of Motor Vehicles Automated Vehicle Crash Database", "abstract": "Automated vehicle technology promises to reduce the societal impact of traffic crashes. Early investigations of this technology suggest that significant safety issues remain during control transfers between the automation and human drivers and automation interactions with the transportation system. In order to address these issues, it is critical to understand both the behavior of human drivers during these events and the environments where they occur. This article analyzes automated vehicle crash narratives from the California Department of Motor Vehicles automated vehicle crash database to identify safety concerns and gaps between crash types and current areas of focus in the current research. The database was analyzed using probabilistic topic modeling of open-ended crash narratives. Topic modeling analysis identified five themes in the database: driver-initiated transition crashes, sideswipe crashes during left-side overtakes, and rear-end collisions while the vehicle was stopped at an intersection, in a turn lane, and when the crash involved oncoming traffic. Many crashes represented by the driver-initiated transitions topic were also associated with the side-swipe collisions. A substantial portion of the side-swipe collisions also involved motorcycles. These findings highlight previously raised safety concerns with transitions of control and interactions between vehicles in automated mode and the transportation social network. In response to these findings, future empirical work should focus on driver-initiated transitions, overtakes, silent failures, complex traffic situations, and adverse driving environments. Beyond this future work, the topic modeling analysis method may be used as a tool to monitor emergent safety issues.", "published": "2020-01-29T20:53:01Z", "authors": ["Hananeh Alambeigi", "Anthony D. McDonald", "Srinivas R. Tankasala"], "categories": ["stat.AP"], "doi": "", "_query": "automated valuation model", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/1608.02342v1", "title": "Recent Progress on Definability of Henselian Valuations", "abstract": "Although the study of the definability of henselian valuations has a long history starting with J. Robinson, most of the results in this area were proven during the last few years. We survey these results which address the definability of concrete henselian valuations, the existence of definable henselian valuations on a given field, and questions of uniformity and quantifier complexity.", "published": "2016-08-08T07:58:19Z", "authors": ["Arno Fehm", "Franziska Jahnke"], "categories": ["math.LO"], "doi": "", "_query": "automated valuation model", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2502.11588v1", "title": "A Unified Modeling Framework for Automated Penetration Testing", "abstract": "The integration of artificial intelligence into automated penetration testing (AutoPT) has highlighted the necessity of simulation modeling for the training of intelligent agents, due to its cost-efficiency and swift feedback capabilities. Despite the proliferation of AutoPT research, there is a recognized gap in the availability of a unified framework for simulation modeling methods. This paper presents a systematic review and synthesis of existing techniques, introducing MDCPM to categorize studies based on literature objectives, network simulation complexity, dependency of technical and tactical operations, and scenario feedback and variation. To bridge the gap in unified method for multi-dimensional and multi-level simulation modeling, dynamic environment modeling, and the scarcity of public datasets, we introduce AutoPT-Sim, a novel modeling framework that based on policy automation and encompasses the combination of all sub dimensions. AutoPT-Sim offers a comprehensive approach to modeling network environments, attackers, and defenders, transcending the constraints of static modeling and accommodating networks of diverse scales. We publicly release a generated standard network environment dataset and the code of Network Generator. By integrating publicly available datasets flexibly, support is offered for various simulation modeling levels focused on policy automation in MDCPM and the network generator help researchers output customized target network data by adjusting parameters or fine-tuning the network generator.", "published": "2025-02-17T09:21:53Z", "authors": ["Yunfei Wang", "Shixuan Liu", "Wenhao Wang", "Changling Zhou", "Chao Zhang", "Jiandong Jin", "Cheng Zhu"], "categories": ["cs.AI", "cs.NI"], "doi": "", "_query": "automated valuation model", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2502.00241v2", "title": "Mordal: Automated Pretrained Model Selection for Vision Language Models", "abstract": "Incorporating multiple modalities into large language models (LLMs) is a powerful way to enhance their understanding of non-textual data, enabling them to perform multimodal tasks. Vision language models (VLMs) form the fastest growing category of multimodal models because of their many practical use cases, including in healthcare, robotics, and accessibility. Unfortunately, even though different VLMs in the literature demonstrate impressive visual capabilities in different benchmarks, they are handcrafted by human experts; there is no automated framework to create task-specific multimodal models. We introduce Mordal, an automated multimodal model search framework that efficiently finds the best VLM for a user-defined task without manual intervention. Mordal achieves this both by reducing the number of candidates to consider during the search process and by minimizing the time required to evaluate each remaining candidate. Our evaluation shows that Mordal can find the best VLM for a given problem using $8.9\\times$--$11.6\\times$ lower GPU hours than grid search. We have also discovered that Mordal achieves about 69\\% higher weighted Kendall's $τ$ on average than the state-of-the-art model selection method across diverse tasks.", "published": "2025-02-01T00:41:29Z", "authors": ["Shiqi He", "Insu Jang", "Mosharaf Chowdhury"], "categories": ["cs.LG", "cs.AI", "cs.CL", "cs.CV"], "doi": "", "_query": "automated valuation model", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/1806.09168v3", "title": "Log smoothness and polystability over valuation rings", "abstract": "Let $\\mathcal{O}$ be a valuation ring of height one of residual characteristic exponent $p$ and with algebraically closed field of fractions. Our main result provides a best possible resolution of the monoidal structure $M_X$ of a log variety $X$ over $\\calO$ with a vertical log structure: there exists a log modification $Y\\to X$ such that the monoidal structure of $Y$ is polystable. In particular, if $X$ is log smooth over $\\mathcal{O}$, then $Y$ is polystable with a smooth generic fiber. As a corollary we deduce that any variety over $\\mathcal{O}$ possesses a polystable alteration of degreee $p^n$. The core of our proof is a subdivision result for polyhedral complexes satisfying certain rationality conditions.", "published": "2018-06-24T15:46:13Z", "authors": ["Karim Adiprasito", "Gaku Liu", "Igor Pak", "Michael Temkin"], "categories": ["math.AG"], "doi": "", "_query": "automated valuation model", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2608.28672v1", "title": "FrameScope: Temporal Data Valuation for Stream Active Learning in Autonomous Vehicle Systems", "abstract": "Autonomous vehicles operate in dynamic, ever-changing environments where new scenarios and edge cases constantly emerge. As a result, static learning models are inadequate for ensuring safe and reliable operation. Continuous learning is essential for adapting to these evolving conditions and maintaining robust performance across diverse real-world settings. However, autonomous vehicles generate massive streams of visual data during operation, and existing continuous learning approaches typically rely on heuristic sampling methods that fail to capture temporal dynamics, often overlooking critical learning opportunities or selecting redundant frames. In this paper, we introduce FrameScope, a temporal data valuation framework for continuous learning in autonomous vehicles. FrameScope extends neural tangent kernel theory to temporal domains, enabling principled valuation of streaming visual data. Unlike cloud-centric methods that transmit all video data for processing, our approach performs principled, local frame selection on the vehicle and queries a cloud-based oracle model only for labels of those high-value frames. Extensive experiments across multiple domain shifts show that FrameScope consistently outperforms existing methods, achieving higher sample efficiency and significantly reducing catastrophic forgetting in autonomous vehicle perception. By valuing data on the vehicle and querying only labels for selected frames, FrameScope reduces bandwidth requirements, enabling scalable operation with a lightweight cloud labeling service.", "published": "2026-08-24T21:26:23Z", "authors": ["Yuheng Zhu", "Man-Ki Yoon"], "categories": ["cs.CV", "cs.LG"], "doi": "10.1145/3769102.3770611", "_query": "automated valuation model", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/1509.02291v1", "title": "Towards Product Lining Model-Driven Development Code Generators", "abstract": "A code generator systematically transforms compact models to detailed code. Today, code generation is regarded as an integral part of model-driven development (MDD). Despite its relevance, the development of code generators is an inherently complex task and common methodologies and architectures are lacking. Additionally, reuse and extension of existing code generators only exist on individual parts. A systematic development and reuse based on a code generator product line is still in its infancy. Thus, the aim of this paper is to identify the mechanism necessary for a code generator product line by (a) analyzing the common product line development approach and (b) mapping those to a code generator specific infrastructure. As a first step towards realizing a code generator product line infrastructure, we present a component-based implementation approach based on ideas of variability-aware module systems and point out further research challenges.", "published": "2015-09-08T09:18:20Z", "authors": ["Alexander Roth", "Bernhard Rumpe"], "categories": ["cs.SE"], "doi": "", "_query": "automated valuation model", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2405.18539v2", "title": "Automation in Model-Driven Engineering: A look back, and ahead", "abstract": "Model-Driven Engineering (MDE) provides a huge body of knowledge of automation for many different engineering tasks, especially those involving transitioning from design to implementation. With the huge progress made in Artificial Intelligence (AI), questions arise about the future of MDE, such as how existing MDE techniques and technologies can be improved or how other activities that currently lack dedicated support can also be automated. However, at the same time, it has to be revisited where and how models should be used to keep the engineers in the loop for creating, operating, and maintaining complex systems. To trigger dedicated research on these open points, we discuss the history of automation in MDE and present perspectives on how automation in MDE can be further improved and which obstacles have to be overcome in both the medium and long-term.", "published": "2024-05-28T19:14:16Z", "authors": ["Lola Burgueño", "Davide Di Ruscio", "Houari Sahraoui", "Manuel Wimmer"], "categories": ["cs.SE"], "doi": "", "_query": "automated valuation model", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2405.11329v2", "title": "Risk-neutral valuation of options under arithmetic Brownian motions", "abstract": "On April 22, 2020, the CME Group switched to Bachelier pricing for a group of oil futures options. The Bachelier model, or more generally the arithmetic Brownian motion (ABM), is not so widely used in finance, though. This paper provides the first comprehensive survey of options pricing under ABM. Using the risk-neutral valuation, we derive formulas for European options for three underlying types, namely an underlying that does not pay dividends, an underlying that pays a continuous dividend yield, and futures. Further, we derive Black-Scholes-Merton-like partial differential equations, which can in principle be utilized to price American options numerically via finite difference.", "published": "2024-05-18T16:17:05Z", "authors": ["Qiang Liu", "Shuxin Guo"], "categories": ["q-fin.PR", "q-fin.MF", "q-fin.RM"], "doi": "", "_query": "automated valuation model", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2207.06293v3", "title": "On the value of distribution tail in the valuation of travel time variability", "abstract": "Extensive empirical studies show that the long distribution tail of travel time and the corresponding unexpected delay can have much more serious consequences than expected or moderate delay. However, the unexpected delay due to the distribution tail of travel time has received limited attention in recent studies of the valuation of travel time variability. As a complement to current valuation research, this paper proposes the concept of the value of travel time distribution tail, which quantifies the value that travelers place on reducing the unexpected delay for hedging against travel time variability. Methodologically, we define the summation of all unexpected delays as the unreliability area to quantify travel time distribution tail and show that it is a key element of two well-defined measures accounting for unreliable aspects of travel time. We then formally derive the value of distribution tail, show that it is distinct from the more established value of reliability (VOR), and combine it and the VOR in an overall value of travel time variability (VOV). We prove theoretically that the VOV exhibits diminishing marginal benefit in terms of the traveler's punctuality requirements under a validity condition. This implies that it may be economically inefficient for travelers to blindly pursue a higher probability of not being late. We then proceed to develop the concept of the travel time variability ratio, which gives the implicit cost of the punctuality requirement imposed on any given trip. Numerical examples reveal that the cost of travel time distribution tail can account for more than 10% of the trip cost, such that its omission could introduce non-trivial bias into route choice models and transportation appraisal more generally.", "published": "2022-07-13T15:46:42Z", "authors": ["Zhaoqi Zang", "Richard Batley", "Xiangdong Xu", "David Z. W. Wang"], "categories": ["econ.GN"], "doi": "10.1016/j.tre.2024.103695", "_query": "automated valuation model", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2407.07691v1", "title": "Unimodular Valuations beyond Ehrhart", "abstract": "A complete classification of unimodular valuations on the set of lattice polygons with values in the spaces of polynomials and formal power series, respectively, is established. The valuations are classified in terms of their behaviour with respect to dilation using extensions to unbounded polyhedra and basic invariant theory.", "published": "2024-07-10T14:19:13Z", "authors": ["Ansgar Freyer", "Monika Ludwig", "Martin Rubey"], "categories": ["math.MG", "math.CO"], "doi": "10.1017/fms.2025.10123", "_query": "automated valuation model", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2009.09381v5", "title": "Stochastic Model Predictive Control with a Safety Guarantee for Automated Driving: Extended Version", "abstract": "Automated vehicles require efficient and safe planning to maneuver in uncertain environments. Largely this uncertainty is caused by other traffic participants, e.g., surrounding vehicles. Future motion of surrounding vehicles is often difficult to predict. Whereas robust control approaches achieve safe, yet conservative motion planning for automated vehicles, Stochastic Model Predictive Control (SMPC) provides efficient planning in the presence of uncertainty. Probabilistic constraints are applied to ensure that the maximal risk remains below a predefined level. However, safety cannot be ensured as probabilistic constraints may be violated, which is not acceptable for automated vehicles. Here, we propose an efficient trajectory planning framework with safety guarantees for automated vehicles. SMPC is applied to obtain efficient vehicle trajectories for a finite horizon. Based on the first optimized SMPC input, a guaranteed safe backup trajectory is planned using reachable sets. This backup is used to overwrite the SMPC input if necessary for safety. Recursive feasibility of the safe SMPC algorithm is proved. Highway simulations show the effectiveness of the proposed method regarding performance and safety.", "published": "2020-09-20T07:58:00Z", "authors": ["Tim Brüdigam", "Michael Olbrich", "Dirk Wollherr", "Marion Leibold"], "categories": ["eess.SY"], "doi": "", "_query": "automated valuation model", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2404.17044v1", "title": "A new Taxonomy for Automated Driving: Structuring Applications based on their Operational Design Domain, Level of Automation and Automation Readiness", "abstract": "The aim of this paper is to investigate the relationship between operational design domains (ODD), automated driving SAE Levels, and Technology Readiness Level (TRL). The first highly automated vehicles, like robotaxis, are in commercial use, and the first vehicles with highway pilot systems have been delivered to private customers. It has emerged as a crucial issue that these automated driving systems differ significantly in their ODD and in their technical maturity. Consequently, any approach to compare these systems is difficult and requires a deep dive into defined ODDs, specifications, and technologies used. Therefore, this paper challenges current state-of-the-art taxonomies and develops a new and integrated taxonomy that can structure automated vehicle systems more efficiently. We use the well-known SAE Levels 0-5 as the \"level of responsibility\", and link and describe the ODD at an intermediate level of abstraction. Finally, a new maturity model is explicitly proposed to improve the comparability of automated vehicles and driving functions. This method is then used to analyze today's existing automated vehicle applications, which are structured into the new taxonomy and rated by the new maturity levels. Our results indicate that this new taxonomy and maturity level model will help to differentiate automated vehicle systems in discussions more clearly and to discover white fields more systematically and upfront, e.g. for research but also for regulatory purposes.", "published": "2024-04-25T21:09:11Z", "authors": ["Johannes Betz", "Melina Lutwitzi", "Steven Peters"], "categories": ["cs.RO", "eess.SY"], "doi": "", "_query": "automated valuation model", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2410.07523v3", "title": "DemoShapley: Valuation of Demonstrations for In-Context Learning", "abstract": "Large language models (LLMs) using in-context learning (ICL) excel in many tasks without task-specific fine-tuning. However, demonstration selection and ordering greatly impact ICL effectiveness. Focus on this issue, we propose DemoShapley, a Shapley-value based method that evaluates each demonstration's contribution by measuring its marginal effect across different prompt permutations. To further account for ICL's limited context windows and frequent low-shot settings, we introduce Beta-DemoShapley, a weighted extension that emphasizes the influence of smaller prompt sizes. Experiments on multiple benchmarks show that DemoShapley consistently outperforms existing influence-based selection strategies, while Beta-DemoShapley further improves performance in low-shot scenarios. Both methods also detect mislabeled data, enhance generalization to out-of-distribution tasks, and reduce demographic bias. Together, they provide a unified and robust framework for demonstration valuation in ICL.", "published": "2024-10-10T01:35:03Z", "authors": ["Shan Xie", "Man Luo", "Chadly Daniel Stern", "Mengnan Du", "Lu Cheng"], "categories": ["cs.CL", "cs.AI", "cs.LG"], "doi": "", "_query": "automated valuation model", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/1811.11960v1", "title": "Prediction Factory: automated development and collaborative evaluation of predictive models", "abstract": "In this paper, we present a data science automation system called Prediction Factory. The system uses several key automation algorithms to enable data scientists to rapidly develop predictive models and share them with domain experts. To assess the system's impact, we implemented 3 different interfaces for creating predictive modeling projects: baseline automation, full automation, and optional automation. With a dataset of online grocery shopper behaviors, we divided data scientists among the interfaces to specify prediction problems, learn and evaluate models, and write a report for domain experts to judge whether or not to fund to continue working on. In total, 22 data scientists created 94 reports that were judged 296 times by 26 experts. In a head-to-head trial, reports generated utilizing full data science automation interface reports were funded 57.5% of the time, while the ones that used baseline automation were only funded 42.5% of the time. An intermediate interface which supports optional automation generated reports were funded 58.6% more often compared to the baseline. Full automation and optional automation reports were funded about equally when put head-to-head. These results demonstrate that Prediction Factory has implemented a critical amount of automation to augment the role of data scientists and improve business outcomes.", "published": "2018-11-29T04:34:48Z", "authors": ["Gaurav Sheni", "Benjamin Schreck", "Roy Wedge", "James Max Kanter", "Kalyan Veeramachaneni"], "categories": ["cs.LG", "stat.ML"], "doi": "", "_query": "automated valuation model", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/1809.00486v1", "title": "Automated Machine Learning Service Composition", "abstract": "Automated service composition as the process of creating new software in an automated fashion has been studied in many different ways over the last decade. However, the impact of automated service composition has been rather small as its utility in real-world applications has not been demonstrated so far. This paper presents \\tool, an algorithm for automated service composition applied to the area of machine learning. Empirically, we show that \\tool is competitive and sometimes beats algorithms that solve the same task but not benefit of the advantages of a service model. Thereby, we present a real-world example that demonstrates the utility of automated service composition in contrast to non-service oriented solutions in the same area.", "published": "2018-09-03T08:24:18Z", "authors": ["Felix Mohr", "Marcel Wever", "Eyke Hüllermeier"], "categories": ["cs.SE"], "doi": "", "_query": "automated valuation model", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/1904.03635v2", "title": "On the Rost divisibility of henselian discrete valuation fields of cohomological dimension 3", "abstract": "Let $F$ be a field, $\\ell$ a prime and $D$ a central division $F$-algebra of $\\ell$-power degree. By the Rost kernel of $D$ we mean the subgroup of $F^*$ consisting of elements $λ$ such that the cohomology class $(D)\\cup (λ)\\in H^3(F,\\,\\mathbb{Q}_{\\ell}/\\mathbb{Z}_{\\ell}(2))$ vanishes. In 1985, Suslin conjectured that the Rost kernel is generated by $i$-th powers of reduced norms from $D^{\\otimes i},\\,\\forall i\\ge 1$. Despite of known counterexamples, we prove some new cases of Suslin's conjecture. We assume $F$ is a henselian discrete valuation field with residue field $k$ of characteristic different from $\\ell$. When $D$ has period $\\ell$, we show that Suslin's conjecture holds if either $k$ is a $2$-local field or the cohomological $\\ell$-dimension $\\mathrm{cd}_{\\ell}(k)$ of $k$ is $\\le 2$. When the period is arbitrary, we prove the same result when $k$ itself is a henselian discrete valuation field with $\\mathrm{cd}_{\\ell}(k)\\le 2$. In the case $\\ell=\\text{char}(k)$ an analog is obtained for tamely ramified algebras. We conjecture that Suslin's conjecture holds for all fields of cohomological dimension 3.", "published": "2019-04-07T12:23:28Z", "authors": ["Yong Hu", "Zhengyao Wu"], "categories": ["math.NT", "math.KT"], "doi": "10.2140/akt.2020.5.677", "_query": "automated valuation model", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2309.05511v2", "title": "Poisson valuations", "abstract": "We study Poisson valuations and provide their applications in solving problems related to rigidity, automorphisms, Dixmier property, isomorphisms, and embeddings of Poisson algebras and fields.", "published": "2023-09-11T14:53:36Z", "authors": ["Hongdi Huang", "Xin Tang", "Xingting Wang", "James J. Zhang"], "categories": ["math.RA"], "doi": "", "_query": "automated valuation model", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/1611.07879v1", "title": "Testing submodularity and other properties of valuation functions", "abstract": "We show that for any constant $ε> 0$ and $p \\ge 1$, it is possible to distinguish functions $f : \\{0,1\\}^n \\to [0,1]$ that are submodular from those that are $ε$-far from every submodular function in $\\ell_p$ distance with a constant number of queries. More generally, we extend the testing-by-implicit-learning framework of Diakonikolas et al. (2007) to show that every property of real-valued functions that is well-approximated in $\\ell_2$ distance by a class of $k$-juntas for some $k = O(1)$ can be tested in the $\\ell_p$-testing model with a constant number of queries. This result, combined with a recent junta theorem of Feldman and Vondrak (2016), yields the constant-query testability of submodularity. It also yields constant-query testing algorithms for a variety of other natural properties of valuation functions, including fractionally additive (XOS) functions, OXS functions, unit demand functions, coverage functions, and self-bounding functions.", "published": "2016-11-23T16:51:08Z", "authors": ["Eric Blais", "Abhinav Bommireddi"], "categories": ["cs.DS"], "doi": "", "_query": "automated valuation model", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2606.20264v1", "title": "Confidence-Aware Automated Assessment of Student-Drawn Scientific Models", "abstract": "Student-generated drawings are widely used in science education to assess learners' conceptual understanding in modeling-based tasks aligned with the Next Generation Science Standards (NGSS). However, scoring such drawings requires expert human judgment to interpret complex visual representations, making large-scale assessment costly to implement and sustain in classroom settings. In this work, we study automated scoring of student-generated scientific drawings using a vision-based model. We evaluate a Vision Transformer (ViT) with parameter-efficient adaptation and propose a confidence-aware scoring framework that derives response-level confidence from test-time predictive distributions. This confidence signal enables selective automation by scoring high-confidence responses automatically while deferring uncertain cases for human review. Experiments on six NGSS-aligned middle school assessment items show that the proposed approach improves scoring reliability while supporting a practical trade-off between automated coverage and scoring risk, highlighting the value of confidence-aware methods for trustworthy educational assessment.", "published": "2026-06-18T14:12:46Z", "authors": ["Luyang Fang", "Yingchuan Zhang", "Jongchan Park", "Zhaoji Wang", "Ping Ma", "Xiaoming Zhai"], "categories": ["cs.AI"], "doi": "", "_query": "automated valuation model", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2501.18514v1", "title": "Automating Physics-Based Reasoning for SysML Model Validation", "abstract": "System and software design benefits greatly from formal modeling, allowing for automated analysis and verification early in the design phase. Current methods excel at checking information flow and component interactions, ensuring consistency, and identifying dependencies within Systems Modeling Language (SysML) models. However, these approaches often lack the capability to perform physics-based reasoning about a system's behavior represented in SysML models, particularly in the electromechanical domain. This significant gap critically hinders the ability to automatically and effectively verify the correctness and consistency of the model's behavior against well-established underlying physical principles. Therefore, this paper presents an approach that leverages existing research on function representation, including formal languages, graphical representations, and reasoning algorithms, and integrates them with physics-based verification techniques. Four case studies (coffeemaker, vacuum cleaner, hairdryer, and wired speaker) are inspected to illustrate the model's practicality and effectiveness in performing physics-based reasoning on systems modeled in SysML. This automated physics-based reasoning is broken into two main categories: (i) structural, which is performed on BDD and IBD, and (ii) functional, which is then performed on activity diagrams. This work advances the field of automated reasoning by providing a framework for verifying structural and functional correctness and consistency with physical laws within SysML models.", "published": "2025-01-30T17:24:38Z", "authors": ["Candice Chambers", "Summer Mueller", "Parth Ganeriwala", "Chiradeep Sen", "Siddhartha Bhattacharyya"], "categories": ["eess.SY", "cs.ET", "cs.SE"], "doi": "", "_query": "automated valuation model", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2412.04158v2", "title": "LossVal: Efficient Data Valuation for Neural Networks", "abstract": "Assessing the importance of individual training samples is a key challenge in machine learning. Traditional approaches retrain models with and without specific samples, which is computationally expensive and ignores dependencies between data points. We introduce LossVal, an efficient data valuation method that computes importance scores during neural network training by embedding a self-weighting mechanism into loss functions like cross-entropy and mean squared error. LossVal reduces computational costs, making it suitable for large datasets and practical applications. Experiments on classification and regression tasks across multiple datasets show that LossVal effectively identifies noisy samples and is able to distinguish helpful from harmful samples. We examine the gradient calculation of LossVal to highlight its advantages. The source code is available at: https://github.com/twibiral/LossVal", "published": "2024-12-05T13:46:55Z", "authors": ["Tim Wibiral", "Mohamed Karim Belaid", "Maximilian Rabus", "Ansgar Scherp"], "categories": ["cs.LG"], "doi": "", "_query": "automated valuation model", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/1701.06518v2", "title": "On the structure of affine flat group schemes over discrete valuation rings", "abstract": "We study affine group schemes over a discrete valuation ring $R$ using two techniques: Neron blowups and Tannakian categories. We employ the theory developed to define and study differential Galois groups of $\\mathcal D$-modules on a scheme over a $R$. This throws light on how differential Galois groups of families degenerate.", "published": "2017-01-23T17:35:51Z", "authors": ["Nguyen Dai Duong", "Phung Ho Hai", "João Pedro P. dos Santos"], "categories": ["math.AG"], "doi": "10.2422/2036-2145.201509_004", "_query": "automated valuation model", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2005.10504v3", "title": "A Computational Approach to Hedging Credit Valuation Adjustment in a Jump-Diffusion Setting", "abstract": "This study contributes to understanding Valuation Adjustments (xVA) by focussing on the dynamic hedging of Credit Valuation Adjustment (CVA), corresponding Profit & Loss (P&L) and the P&L explain. This is done in a Monte Carlo simulation setting, based on a theoretical hedging framework discussed in existing literature. We look at hedging CVA market risk for a portfolio with European options on a stock, first in a Black-Scholes setting, then in a Merton jump-diffusion setting. Furthermore, we analyze the trading business at a bank after including xVAs in pricing. We provide insights into the hedging of derivatives and their xVAs by analyzing and visualizing the cash-flows of a portfolio from a desk structure perspective. The case study shows that not charging CVA at trade inception results in an expected loss. Furthermore, hedging CVA market risk is crucial to end up with a stable trading strategy. In the Black-Scholes setting this can be done using the underlying stock, whereas in the Merton jump-diffusion setting we need to add extra options to the hedge portfolio to properly hedge the jump risk. In addition to the simulation, we derive analytical results that explain our observations from the numerical experiments. Understanding the hedging of CVA helps to deal with xVAs in a practical setting.", "published": "2020-05-21T07:49:39Z", "authors": ["T. van der Zwaard", "L. A. Grzelak", "C. W. Oosterlee"], "categories": ["q-fin.CP", "q-fin.MF"], "doi": "10.1016/j.amc.2020.125671", "_query": "automated valuation model", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/1207.7276v1", "title": "The Steiner Formula for Minkowski Valuations", "abstract": "A Steiner type formula for continuous translation invariant Minkowski valuations is established. In combination with a recent result on the symmetry of rigid motion invariant homogeneous bivaluations, this new Steiner type formula is used to obtain a family of Brunn-Minkowski type inequalities for rigid motion intertwining Minkowski valuations.", "published": "2012-07-31T15:01:07Z", "authors": ["Lukas Parapatits", "Franz E. Schuster"], "categories": ["math.MG", "math.DG", "math.FA"], "doi": "", "_query": "automated valuation model", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2306.02328v1", "title": "Physical energy cost serves as the ''invisible hand'' governing economic valuation: Direct evidence from biogeochemical data and the U.S. metal market", "abstract": "Energy supply is mandatory for the production of economic value. Nevertheless, tradition dictates that an enigmatic 'invisible hand' governs economic valuation. Physical scientists have long proposed alternative but testable energy cost theories of economic valuation, and have shown the gross correlation between energy consumption and economic output at the national level through input-output energy analysis. However, due to the difficulty of precise energy analysis and highly complicated real markets, no decisive evidence directly linking energy costs to the selling prices of individual commodities has yet been found. Over the past century, the US metal market has accumulated a huge body of price data, which for the first time ever provides us the opportunity to quantitatively examine the direct energy-value correlation. Here, by analyzing the market price data of 65 purified chemical elements (mainly metals) relative to the total energy consumption for refining them from naturally occurring geochemical conditions, we found a clear correlation between the energy cost and their market prices. The underlying physics we proposed has compatibility with conventional economic concepts such as the ratio between supply and demand or scarcity's role in economic valuation. It demonstrates how energy cost serves as the 'invisible hand' governing economic valuation. Thorough understanding of this energy connection between the human economic and the Earth's biogeochemical metabolism is essential for improving the overall energy efficiency and furthermore the sustainability of the human society.", "published": "2023-06-04T11:00:34Z", "authors": ["Zhicen Liu"], "categories": ["econ.GN"], "doi": "", "_query": "automated valuation model", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2503.10163v2", "title": "The Bass--Quillen conjecture for torsors over valuation rings", "abstract": "For a valuation ring $V$, a smooth $V$-algebra $A$, and a reductive $V$-group scheme $G$ satisfying a certain natural isotropicity condition, we prove that every Nisnevich $G$-torsor on $\\mathbb{A}^N_A$ descends to a $G$-torsor on $A$. As a corollary, we generalize Raghunathan's theorem on torsors over affine spaces to a relative setting. We also extend several affine representability results of Asok, Hoyois, and Wendt from equi-characteristics to mixed characteristics. Our proof relies on previous work on the purity of reductive torsors over smooth relative curves and the Grothendieck--Serre conjecture for constant reductive group schemes.", "published": "2025-03-13T08:37:50Z", "authors": ["Ning Guo", "Fei Liu"], "categories": ["math.AG", "math.AC"], "doi": "", "_query": "automated valuation model", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2410.11849v1", "title": "On the valuation of life insurance policies for dependent coupled lives", "abstract": "In this paper, we investigate a complex variation of the standard joint life annuity policy by introducing three distinct contingent benefits for the surviving member(s) of a couple, along with a contingent benefit for their beneficiaries if both members pass away. Our objective is to price this innovative insurance policy and analyse its sensitivity to key model parameters, particularly those related to the joint mortality framework. We employ the $QP$-rule (described in Section \\ref{secgenset}), which combines the real-world probability measure $P$ for mortality risk with risk-neutral valuation under $Q$ for financial market risks. The model enables explicit pricing expressions, computed using efficient numerical methods. Our results highlight the interdependent risks faced by couples, such as broken-heart syndrome, providing valuable insights for insurers and policyholders regarding the pricing influences of these factors.", "published": "2024-10-01T15:25:47Z", "authors": ["Kira Henshaw", "Cedric H. A. Koffi", "Olivier Menoukeu Pamen", "Raghid Zeineddine"], "categories": ["q-fin.PR", "math.PR", "q-fin.RM"], "doi": "", "_query": "automated valuation model", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2509.16040v3", "title": "Automated Constitutive Model Discovery by Pairing Sparse Regression Algorithms with Model Selection Criteria", "abstract": "The automated discovery of constitutive models from data has recently emerged as a promising alternative to the traditional model calibration paradigm. In this work, we present a fully automated framework for constitutive model discovery that systematically pairs three sparse regression algorithms Least Absolute Shrinkage and Selection Operator (LASSO), Least Angle Regression (LARS), and Orthogonal Matching Pursuit (OMP)) with three model selection criteria: $K$-fold cross-validation (CV), Akaike Information Criterion (AIC), and Bayesian Information Criterion (BIC). This pairing yields nine distinct algorithms for model discovery and enables a systematic exploration of the trade-off between sparsity, predictive performance, and computational cost. While LARS serves as an efficient path-based solver for the $\\ell_1$-constrained problem, OMP is introduced as a tractable heuristic for $\\ell_0$-regularized selection. The framework is applied to both isotropic and anisotropic hyperelasticity, utilizing both synthetic and experimental datasets. Results reveal that all nine algorithm-criterion combinations perform consistently well in discovering isotropic and anisotropic materials, yielding highly accurate constitutive models. These findings broaden the range of viable discovery algorithms beyond $\\ell_1$-based approaches such as LASSO.", "published": "2025-09-19T14:50:04Z", "authors": ["Jorge-Humberto Urrea-Quintero", "David Anton", "Laura De Lorenzis", "Henning Wessels"], "categories": ["cs.LG", "cond-mat.mtrl-sci", "cs.CE"], "doi": "", "_query": "automated valuation model", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/1810.12644v4", "title": "The Responsibility Quantification (ResQu) Model of Human Interaction with Automation", "abstract": "Intelligent systems and advanced automation are involved in information collection and evaluation, in decision-making and in the implementation of chosen actions. In such systems, human responsibility becomes equivocal. Understanding human casual responsibility is particularly important when intelligent autonomous systems can harm people, as with autonomous vehicles or, most notably, with autonomous weapon systems (AWS). Using Information Theory, we develop a responsibility quantification (ResQu) model of human involvement in intelligent automated systems and demonstrate its applications on decisions regarding AWS. The analysis reveals that human comparative responsibility to outcomes is often low, even when major functions are allocated to the human. Thus, broadly stated policies of keeping humans in the loop and having meaningful human control are misleading and cannot truly direct decisions on how to involve humans in intelligent systems and advanced automation. The current model is an initial step in the complex goal to create a comprehensive responsibility model, that will enable quantification of human causal responsibility. It assumes stationarity, full knowledge regarding the characteristic of the human and automation and ignores temporal aspects. Despite these limitations, it can aid in the analysis of systems designs alternatives and policy decisions regarding human responsibility in intelligent systems and advanced automation.", "published": "2018-10-30T10:45:57Z", "authors": ["Nir Douer", "Joachim Meyer"], "categories": ["cs.HC", "cs.AI"], "doi": "10.1109/TASE.2020.2965466", "_query": "automated valuation model", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/0904.1347v4", "title": "The product on smooth and generalized valuations", "abstract": "The product of smooth valuations on manifolds is described in terms of differential forms, Gelfand transforms and blow-up spaces. It is shown that the product extends partially to generalized valuations and corresponds geometrically to transversal intersections. This result is used to prove a general kinematic formula on compact rank one symmetric spaces.", "published": "2009-04-08T14:09:59Z", "authors": ["Semyon Alesker", "Andreas Bernig"], "categories": ["math.MG", "math.DG"], "doi": "", "_query": "automated valuation model", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2512.12869v2", "title": "ERA-IT: Aligning Semantic Models with Revealed Economic Preference for Real-Time and Explainable Patent Valuation", "abstract": "Valuing intangible assets under uncertainty remains a critical challenge in the strategic management of technological innovation due to the information asymmetry inherent in high-dimensional technical specifications. Traditional bibliometric indicators, such as citation counts, fail to address this friction in a timely manner due to the systemic latency inherent in data accumulation. To bridge this gap, this study proposes the Economic Reasoning Alignment via Instruction Tuning (ERA-IT) framework. We theoretically conceptualize patent renewal history as a revealed economic preference and leverage it as an objective supervisory signal to align the generative reasoning of Large Language Models (LLMs) with market realities, a process we term Eco-Semantic Alignment. Using a randomly sampled dataset of 10,000 European Patent Office patents across diverse technological domains, we trained the model not only to predict value tiers but also to reverse-engineer the Economic Chain-of-Thought from unstructured text. Empirical results demonstrate that ERA-IT significantly outperforms both conventional econometric models and zero-shot LLMs in predictive accuracy. More importantly, by generating explicit, logically grounded rationales for valuation, the framework serves as a transparent cognitive scaffold for decision-makers, reducing the opacity of black-box AI in high-stakes intellectual property management.", "published": "2025-12-14T23:04:07Z", "authors": ["Yongmin Yoo", "Seungwoo Kim", "Jingjiang Liu"], "categories": ["cs.CE", "cs.CL"], "doi": "", "_query": "automated valuation model", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/0904.3167v2", "title": "Effective models of group schemes", "abstract": "Let $R$ be a discrete valuation ring with fraction field $K$ and $X$ a flat $R$-scheme. Given a faithful action of a $K$-group scheme $G_K$ over the generic fibre $X_K$, we study models $G$ of $G_K$ acting on $X$. In various situations, we prove that if such a model $G$ exists, then there exists another model $G'$ that acts faithfully on $X$. This model is the schematic closure of $G$ inside the fppf sheaf $Aut_R(X)$; the major difficulty is to prove that it is representable by a scheme. For example, this holds if $X$ is locally of finite type, separated, flat and pure and $G$ is finite flat. Pure schemes (a notion recalled in the text) have many nice properties : in particular, we prove that they are the amalgamated sum of their generic fibre and the family of their finite flat closed subschemes. We also provide versions of our results in the setting of formal schemes.", "published": "2009-04-21T06:06:54Z", "authors": ["Matthieu Romagny"], "categories": ["math.AG"], "doi": "", "_query": "automated valuation model", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2011.13639v2", "title": "Metrizability of spaces of valuation domains associated to pseudo-convergent sequences", "abstract": "Let $V$ be a valuation domain of rank one with quotient field $K$. We study the set of extensions of $V$ to the field of rational functions $K(X)$ induced by pseudo-convergent sequences of $K$ from a topological point of view, endowing this set either with the Zariski or with the constructible topology. In particular, we consider the two subspaces induced by sequences with a prescribed breadth or with a prescribed pseudo-limit. We give some necessary conditions for the Zariski space to be metrizable (under the constructible topology) in terms of the value group and the residue field of $V$.", "published": "2020-11-27T10:20:02Z", "authors": ["Giulio Peruginelli", "Dario Spirito"], "categories": ["math.AC", "math.AG"], "doi": "10.1142/S0219498823500469", "_query": "automated valuation model", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/1902.09724v3", "title": "Automated Model Selection with Bayesian Quadrature", "abstract": "We present a novel technique for tailoring Bayesian quadrature (BQ) to model selection. The state-of-the-art for comparing the evidence of multiple models relies on Monte Carlo methods, which converge slowly and are unreliable for computationally expensive models. Previous research has shown that BQ offers sample efficiency superior to Monte Carlo in computing the evidence of an individual model. However, applying BQ directly to model comparison may waste computation producing an overly-accurate estimate for the evidence of a clearly poor model. We propose an automated and efficient algorithm for computing the most-relevant quantity for model selection: the posterior probability of a model. Our technique maximizes the mutual information between this quantity and observations of the models' likelihoods, yielding efficient acquisition of samples across disparate model spaces when likelihood observations are limited. Our method produces more-accurate model posterior estimates using fewer model likelihood evaluations than standard Bayesian quadrature and Monte Carlo estimators, as we demonstrate on synthetic and real-world examples.", "published": "2019-02-26T04:38:39Z", "authors": ["Henry Chai", "Jean-Francois Ton", "Roman Garnett", "Michael A. Osborne"], "categories": ["cs.LG", "stat.ML"], "doi": "", "_query": "automated valuation model", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2011.01646v1", "title": "Automated simulation and verification of process models discovered by process mining", "abstract": "This paper presents a novel approach for automated analysis of process models discovered using process mining techniques. Process mining explores underlying processes hidden in the event data generated by various devices. Our proposed Inductive machine learning method was used to build business process models based on actual event log data obtained from a hotel's Property Management System (PMS). The PMS can be considered as a Multi Agent System (MAS) because it is integrated with a variety of external systems and IoT devices. Collected event log combines data on guests stay recorded by hotel staff, as well as data streams captured from telephone exchange and other external IoT devices. Next, we performed automated analysis of the discovered process models using formal methods. Spin model checker was used to simulate process model executions and automatically verify the process model. We proposed an algorithm for the automatic transformation of the discovered process model into a verification model. Additionally, we developed a generator of positive and negative examples. In the verification stage, we have also used Linear temporal logic (LTL) to define requested system specifications. We find that the analysis results will be well suited for process model repair.", "published": "2020-11-03T11:51:53Z", "authors": ["Ivona Zakarija", "Frano Škopljanac-Mačina", "Bruno Blašković"], "categories": ["cs.AI", "cs.SE"], "doi": "10.1080/00051144.2020.1734716", "_query": "automated valuation model", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/1812.11470v1", "title": "A Systematic Literature Review of Automated Techniques for Functional GUI Testing of Mobile Applications", "abstract": "Context. Multiple automated techniques have been proposed and developed for mobile application GUI testing aiming to improve effectiveness, efficiency, and practicality. The effectiveness, efficiency, and practicality are 3 fundamental characteristics which testing techniques are built upon, and need to be continuously improved to deliver useful solutions for researchers and practitioners, and community as a whole. Objective. In this systematic review, we attempt to provide a broad picture of existing mobile testing tools by collating and analysing their conceptual, and also performance characteristics including an estimation of effectiveness, efficiency, and practicality. Method. To achieve our objective, we specify 3 primary, and 14 secondary review questions, and conducted an analysis of 25 primary studies. We first individually analyse each primary study, and next analyse the primary studies as a whole. We developed a review protocol which defines all the details of our systematic review. Results. From effectiveness, we conclude that testing techniques which implement model-checking, symbolic execution, constraint solving, and search-based test generation approach tend to be more effective than those implementing random test generation. From efficiency, we conclude that testing techniques which implement code search-based testing approaches tend to be more efficient than those implementing GUI model-based. From practicality, we conclude that the more effective a testing technique is, the less efficient it will be. Conclusion. For effectiveness, we observe that the existing automated testing techniques are not effective enough, and currently they achieve nearly half of the desired level of effectiveness. For efficiency, we observe that current automated testing techniques are not efficient enough.", "published": "2018-12-30T04:50:20Z", "authors": ["Yauhen Leanidavich Arnatovich", "Lipo Wang"], "categories": ["cs.SE"], "doi": "", "_query": "automated valuation model", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2505.11701v2", "title": "DMN-Guided Prompting: A Framework for Controlling LLM Behavior", "abstract": "Large Language Models (LLMs) have shown considerable potential in automating decision logic within knowledge-intensive processes. However, their effectiveness largely depends on the strategy and quality of prompting. Since decision logic is typically embedded in prompts, it becomes challenging for end users to modify or refine it. Decision Model and Notation (DMN) offers a standardized graphical approach for defining decision logic in a structured, user-friendly manner. This paper introduces a DMN-guided prompting framework that breaks down complex decision logic into smaller, manageable components, guiding LLMs through structured decision pathways. We implemented the framework in a graduate-level course where students submitted assignments. The assignments and DMN models representing feedback instructions served as inputs to our framework. The instructor evaluated the generated feedback and labeled it for performance assessment. Our approach demonstrated promising results, outperforming chain-of-thought (CoT) prompting in our case study. Students also responded positively to the generated feedback, reporting high levels of perceived usefulness in a survey based on the Technology Acceptance Model.", "published": "2025-05-16T21:09:36Z", "authors": ["Shaghayegh Abedi", "Amin Jalali"], "categories": ["cs.AI"], "doi": "", "_query": "automated valuation model", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2506.16649v1", "title": "Automated Energy Billing with Blockchain and the Prophet Forecasting Model: A Holistic Approach", "abstract": "This paper presents a comprehensive approach to automated energy billing that leverages IoT-based smart meters, blockchain technology, and the Prophet time series forecasting model. The proposed system facilitates real-time power consumption monitoring via Wi-Fi-enabled ESP32 modules and a mobile application interface. It integrates Firebase and blockchain for secure, transparent billing processes and employs smart contracts for automated payments. The Prophet model is used for energy demand forecasting, with careful data preprocessing, transformation, and parameter tuning to improve prediction accuracy. This holistic solution aims to reduce manual errors, enhance user awareness, and promote sustainable energy use.", "published": "2025-06-19T23:26:36Z", "authors": ["Ajesh Thangaraj Nadar", "Soham Chandane", "Gabriel Nixon Raj", "Nihar Mahesh Pasi", "Yash Arvind Patil"], "categories": ["cs.CR", "cs.LG"], "doi": "", "_query": "automated valuation model", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/1702.05819v1", "title": "The 2-adic valuation of generalized Fibonacci sequences with an application to certain Diophantine equations", "abstract": "In this paper we focus on finding all the factorials expressible as a product of a fixed number of $2k$-nacci numbers with $k \\geq 2$. We derive the 2-adic valuation of the $2k$-nacci sequence and use it to establish bounds on the solutions of the initial equation. In addition, we specify a more general family of sequences, for which we can perform a similar procedure. We also investigate a possible connection of these results with $p$-regular sequences.", "published": "2017-02-20T00:00:38Z", "authors": ["Bartosz Sobolewski"], "categories": ["math.NT"], "doi": "", "_query": "automated valuation model", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2212.09032v1", "title": "AutoSlicer: Scalable Automated Data Slicing for ML Model Analysis", "abstract": "Automated slicing aims to identify subsets of evaluation data where a trained model performs anomalously. This is an important problem for machine learning pipelines in production since it plays a key role in model debugging and comparison, as well as the diagnosis of fairness issues. Scalability has become a critical requirement for any automated slicing system due to the large search space of possible slices and the growing scale of data. We present Autoslicer, a scalable system that searches for problematic slices through distributed metric computation and hypothesis testing. We develop an efficient strategy that reduces the search space through pruning and prioritization. In the experiments, we show that our search strategy finds most of the anomalous slices by inspecting a small portion of the search space.", "published": "2022-12-18T07:49:17Z", "authors": ["Zifan Liu", "Evan Rosen", "Paul Suganthan G. C"], "categories": ["cs.LG", "cs.DB"], "doi": "", "_query": "automated valuation model", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/0710.5522v3", "title": "Algebraic series and valuation rings over nonclosed fields", "abstract": "Suppose that $k$ is an arbitrary field. Consider the field $k((x_1,...,x_n))$, which is the quotient field of the ring $k[[x_1,...,x_n]]$ of formal power series in the variables $x_1,...,x_n$, with coefficients in $k$. Suppose that $σ$ is a formal power series in $x_1,...,x_n$ with coefficints in the algebraic closure of $k$. We give a very simple necessary and sufficient condition for $σ$ to be algebraic over $k((x_1,...,x_n))$. As an application of our methods, we give a characterization of valuation rings $V$ which dominate an excellent, Noetherian local domain $R$ of dimension two, and such that the rank increases after passing to the completion of a birational extension of $R$.", "published": "2007-10-29T20:20:59Z", "authors": ["Steven Dale Cutkosky", "Olga Kashcheyeva"], "categories": ["math.AC", "math.AG"], "doi": "", "_query": "automated valuation model", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/1008.3768v2", "title": "Harmonic analysis of translation invariant valuations", "abstract": "The decomposition of the space of continuous and translation invariant valuations into a sum of SO(n) irreducible subspaces is obtained. A reformulation of this result in terms of a Hadwiger type theorem for continuous translation invariant and SO(n)-equivariant tensor valuations is also given. As an application, symmetry properties of rigid motion invariant and homogeneous bivaluations are established and then used to prove new inequalities of Brunn-Minkowski type for convex body valued valuations.", "published": "2010-08-23T07:52:55Z", "authors": ["Semyon Alesker", "Andreas Bernig", "Franz E. Schuster"], "categories": ["math.DG"], "doi": "", "_query": "automated valuation model", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2301.10049v2", "title": "From valuations on convex bodies to convex functions", "abstract": "A geometric framework relating valuations on convex bodies to valuations on convex functions is introduced. It is shown that a classical result by McMullen can be used to obtain a characterization of continuous, epi-translation invariant, and n-epi-homogeneous valuations on convex functions, which was previously established by Colesanti, Ludwig, and Mussnig. Following an approach by Goodey and Weil, a new characterization of 1-epi-homogeneous valuations is obtained.", "published": "2023-01-24T14:46:27Z", "authors": ["Jonas Knoerr", "Jacopo Ulivelli"], "categories": ["math.MG", "math.FA"], "doi": "", "_query": "automated valuation model", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2410.14272v1", "title": "Envy-Free and Efficient Allocations for Graphical Valuations", "abstract": "We consider the complexity of finding envy-free allocations for the class of graphical valuations. Graphical valuations were introduced by Christodoulou et. al.(2023) as a structured class of valuations that admit allocations that are envy-free up to any item (EFX). These are valuations where every item is valued by two agents, lending a (simple) graph structure to the utilities, where the agents are vertices and are adjacent if and only if they value a (unique) common item. Finding envy-free allocations for general valuations is known to be computationally intractable even for very special cases: in particular, even for binary valuations, and even for identical valuations with two agents. We show that, for binary graphical valuations, the existence of envy-free allocations can be determined in polynomial time. In contrast, we also show that allowing for even slightly more general utilities {0, 1, d} leads to intractability even for graphical valuations. This motivates other approaches to tractability, and to that end, we exhibit the fixed-parameter tractability of the problem parameterized by the vertex cover number of the graph when the number of distinct utilities is bounded. We also show that, all graphical instances that admit EF allocations also admit one that is non-wasteful. Since EFX allocations are possibly wasteful, we also address the question of determining the price of fairness of EFX allocations. We show that the price of EFX with respect to utilitarian welfare is one for binary utilities, but can be arbitrarily large {0, 1, d} valuations. We also show the hardness of deciding the existence of an EFX allocation which is also welfare-maximizing and of finding a welfare-maximizing allocation within the set of EFX allocations.", "published": "2024-10-18T08:26:07Z", "authors": ["Neeldhara Misra", "Aditi Sethia"], "categories": ["cs.GT"], "doi": "", "_query": "automated valuation model", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/1908.10724v1", "title": "A homogeneous decomposition theorem for valuations on convex functions", "abstract": "The existence of a homogeneous decomposition for continuous and epi-translation invariant valuations on super-coercive functions is established. Continuous and epi-translation invariant valuations that are epi-homogeneous of degree $n$ are classified. By duality, corresponding results are obtained for valuations on finite-valued convex functions.", "published": "2019-08-28T13:52:53Z", "authors": ["A. Colesanti", "M. Ludwig", "F. Mussnig"], "categories": ["math.MG", "math.FA"], "doi": "10.1016/j.jfa.2020.108573", "_query": "automated valuation model", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2309.12616v1", "title": "Unlocking Model Insights: A Dataset for Automated Model Card Generation", "abstract": "Language models (LMs) are no longer restricted to ML community, and instruction-tuned LMs have led to a rise in autonomous AI agents. As the accessibility of LMs grows, it is imperative that an understanding of their capabilities, intended usage, and development cycle also improves. Model cards are a popular practice for documenting detailed information about an ML model. To automate model card generation, we introduce a dataset of 500 question-answer pairs for 25 ML models that cover crucial aspects of the model, such as its training configurations, datasets, biases, architecture details, and training resources. We employ annotators to extract the answers from the original paper. Further, we explore the capabilities of LMs in generating model cards by answering questions. Our initial experiments with ChatGPT-3.5, LLaMa, and Galactica showcase a significant gap in the understanding of research papers by these aforementioned LMs as well as generating factual textual responses. We posit that our dataset can be used to train models to automate the generation of model cards from paper text and reduce human effort in the model card curation process. The complete dataset is available on https://osf.io/hqt7p/?view_only=3b9114e3904c4443bcd9f5c270158d37", "published": "2023-09-22T04:46:11Z", "authors": ["Shruti Singh", "Hitesh Lodwal", "Husain Malwat", "Rakesh Thakur", "Mayank Singh"], "categories": ["cs.CL"], "doi": "", "_query": "automated valuation model", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2408.06946v1", "title": "Polynomial valuations on convex functions and their maximal extensions", "abstract": "Extension problems for polynomial valuations on different cones of convex functions are investigated. It is shown that for the classes of functions under consideration, the extension problem reduces to a simple geometric obstruction on the support of these valuations. The results rely on a homogeneous decomposition for the space of polynomial valuations of bounded degree and the support properties of certain distributions associated to the homogeneous components. As an application, an explicit integral representation for valuations of top degree is established.", "published": "2024-08-13T15:05:43Z", "authors": ["Jonas Knoerr", "Jacopo Ulivelli"], "categories": ["math.FA", "math.MG"], "doi": "", "_query": "automated valuation model", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2204.12412v2", "title": "Polynomiality of the faithful dimension of nilpotent groups over finite truncated valuation rings", "abstract": "The faithful dimension of a finite group $\\mathrm G$ over $\\mathbb C$, denoted by $m_\\mathrm{faithful}(\\mathrm G)$, is the smallest integer $n$ such that $\\mathrm G$ can be embedded in $\\mathrm{GL}_n(\\mathbb C)$. Continuing our previous work (arXiv:1712.02019), we address the problem of determining the faithful dimension of a finite $p$-group of the form $\\mathcal G_R:=\\exp(\\mathfrak g_R)$ associated to $\\mathfrak g_R:=\\mathfrak g \\otimes_\\mathbb Z R $ in the Lazard correspondence, where $\\mathfrak g$ is a nilpotent $\\mathbb Z$-Lie algebra and $R$ ranges over finite truncated valuation rings. Our first main result is that if $R$ is a finite field with $p^f$ elements and $p$ is sufficiently large, then $m_\\mathrm{faithful}(\\mathcal G_R)=fg(p^f)$ where $g(T)$ belongs to a finite list of polynomials $g_1,\\ldots,g_k$, with non-negative integer coefficients. The list of polynomials is uniquely determined by the Lie algebra $\\mathfrak g$. Furthermore, for $1\\leq i\\leq k$ the set of pairs $(p,f)$ for which $g=g_i$ is a finite union of Cartesian products $\\mathcal P\\times \\mathcal F$, where $\\mathcal P$ is a Frobenius set of prime numbers and $\\mathcal F$ is a subset of $\\mathbb N$ that belongs to the Boolean algebra generated by arithmetic progressions. Next we formulate a conjectural polynomiality property for $m_\\mathrm{faithful}(\\mathcal G_R)$ in the more general setting where $R$ is a finite truncated valuation ring, and prove special cases of this conjecture. In particular, we show that for a vast class of Lie algebras $\\mathfrak g $ that are defined by partial orders, $m_\\mathrm{faithful}(\\mathcal G_R)$ is given by a single polynomial-type formula. Finally, we compute $m_\\mathrm{faithful}(\\mathcal G_R)$ precisely in the case where $\\mathfrak g$ is the free metabelian nilpotent Lie algebra of class $c$ on $n$ generators and $R$ is a finite truncated valuation ring.", "published": "2022-04-26T16:13:53Z", "authors": ["Mohammad Bardestani", "Keivan Mallahi-Karai", "Dzmitry Rumiantsau", "Hadi Salmasian"], "categories": ["math.GR", "math.AG", "math.RT"], "doi": "", "_query": "automated valuation model", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2507.07137v1", "title": "Automating Evaluation of Diffusion Model Unlearning with (Vision-) Language Model World Knowledge", "abstract": "Machine unlearning (MU) is a promising cost-effective method to cleanse undesired information (generated concepts, biases, or patterns) from foundational diffusion models. While MU is orders of magnitude less costly than retraining a diffusion model without the undesired information, it can be challenging and labor-intensive to prove that the information has been fully removed from the model. Moreover, MU can damage diffusion model performance on surrounding concepts that one would like to retain, making it unclear if the diffusion model is still fit for deployment. We introduce autoeval-dmun, an automated tool which leverages (vision-) language models to thoroughly assess unlearning in diffusion models. Given a target concept, autoeval-dmun extracts structured, relevant world knowledge from the language model to identify nearby concepts which are likely damaged by unlearning and to circumvent unlearning with adversarial prompts. We use our automated tool to evaluate popular diffusion model unlearning methods, revealing that language models (1) impose semantic orderings of nearby concepts which correlate well with unlearning damage and (2) effectively circumvent unlearning with synthetic adversarial prompts.", "published": "2025-07-09T00:51:09Z", "authors": ["Eric Yeats", "Darryl Hannan", "Henry Kvinge", "Timothy Doster", "Scott Mahan"], "categories": ["cs.LG", "cs.CL"], "doi": "", "_query": "automated valuation model", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2510.01618v2", "title": "Automated Genomic Interpretation via Concept Bottleneck Models for Medical Robotics", "abstract": "We propose an automated genomic interpretation module that transforms raw DNA sequences into actionable, interpretable decisions suitable for integration into medical automation and robotic systems. Our framework combines Chaos Game Representation (CGR) with a Concept Bottleneck Model (CBM), enforcing predictions to flow through biologically meaningful concepts such as GC content, CpG density, and k mer motifs. To enhance reliability, we incorporate concept fidelity supervision, prior consistency alignment, KL distribution matching, and uncertainty calibration. Beyond accurate classification of HIV subtypes across both in-house and LANL datasets, our module delivers interpretable evidence that can be directly validated against biological priors. A cost aware recommendation layer further translates predictive outputs into decision policies that balance accuracy, calibration, and clinical utility, reducing unnecessary retests and improving efficiency. Extensive experiments demonstrate that the proposed system achieves state of the art classification performance, superior concept prediction fidelity, and more favorable cost benefit trade-offs compared to existing baselines. By bridging the gap between interpretable genomic modeling and automated decision-making, this work establishes a reliable foundation for robotic and clinical automation in genomic medicine.", "published": "2025-10-02T02:51:34Z", "authors": ["Zijun Li", "Jinchang Zhang", "Ming Zhang", "Guoyu Lu"], "categories": ["cs.CV", "q-bio.OT"], "doi": "", "_query": "automated valuation model", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2512.14767v1", "title": "Privacy-Preserving Feature Valuation in Vertical Federated Learning Using Shapley-CMI and PSI Permutation", "abstract": "Federated Learning (FL) is an emerging machine learning paradigm that enables multiple parties to collaboratively train models without sharing raw data, ensuring data privacy. In Vertical FL (VFL), where each party holds different features for the same users, a key challenge is to evaluate the feature contribution of each party before any model is trained, particularly in the early stages when no model exists. To address this, the Shapley-CMI method was recently proposed as a model-free, information-theoretic approach to feature valuation using Conditional Mutual Information (CMI). However, its original formulation did not provide a practical implementation capable of computing the required permutations and intersections securely. This paper presents a novel privacy-preserving implementation of Shapley-CMI for VFL. Our system introduces a private set intersection (PSI) server that performs all necessary feature permutations and computes encrypted intersection sizes across discretized and encrypted ID groups, without the need for raw data exchange. Each party then uses these intersection results to compute Shapley-CMI values, computing the marginal utility of their features. Initial experiments confirm the correctness and privacy of the proposed system, demonstrating its viability for secure and efficient feature contribution estimation in VFL. This approach ensures data confidentiality, scales across multiple parties, and enables fair data valuation without requiring the sharing of raw data or training models.", "published": "2025-12-16T08:01:39Z", "authors": ["Unai Laskurain", "Aitor Aguirre-Ortuzar", "Urko Zurutuza"], "categories": ["cs.CR", "cs.AI", "cs.DC"], "doi": "", "_query": "automated valuation model", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/math/0410575v3", "title": "Structure of the unitary valuation algebra", "abstract": "S. Alesker has shown that if $G$ is a compact subgroup of O(n) acting transitively on the unit sphere $S^{n-1}$ then the vector space $Val^G$ of continuous, translation-invariant, $G$-invariant convex valuations on $R^n$ has the structure of a finite dimensional graded algebra over $R$ satisfying Poincare duality. We show that the kinematic formulas for $G$ are determined by the product pairing. Using this result we then show that the algebra $Val^{U(n) }$ is isomorphic to $R[s,t]/(f_{n+1}, f_{n+2})$, where $s,t$ have degrees 2 and 1 respectively, and the polynomial $f_i$ is the degree $i$ term of the power series $\\log(1 + s +t)$.", "published": "2004-10-27T14:56:44Z", "authors": ["Joseph H. G. Fu"], "categories": ["math.DG", "math.RA"], "doi": "", "_query": "automated valuation model", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2512.03926v1", "title": "Tunable Automation in Automated Program Verification", "abstract": "Automated verification tools based on SMT solvers have made significant progress in verifying complex software systems. However, these tools face a fundamental tension between automation and performance when dealing with quantifier instantiation -- the primary source of incompleteness and verification slowdown in SMT-based verifiers. Tools choose between aggressive quantifier instantiation that provides more automation but longer verification times, or conservative instantiation that responds quickly but may require more manual proof hints. We present a mechanism that enables fine-grained control over the availability of quantified facts in verification contexts, allowing developers to selectively tune the level of automation. Our approach lets library authors provide different pre-defined automation levels while giving end-users the ability to further customize quantifier availability at the module, function, or proof context level. We implement our techniques in Verus, a Rust-based verification tool, and evaluate them on multiple openly available codebases. Our empirical analysis demonstrates the automation-performance tradeoff and that selective quantifier management enables developers to select the appropriate level of automation in different contexts.", "published": "2025-12-03T16:27:01Z", "authors": ["Alexander Y. Bai", "Chris Hawblitzel", "Andrea Lattuada"], "categories": ["cs.SE", "cs.LO", "cs.PL"], "doi": "", "_query": "automated valuation model", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2106.06407v1", "title": "Fan Valuations and spherical intrinsic volumes", "abstract": "We generalize valuations on polyhedral cones to valuations on fans. For fans induced by hyperplane arrangements, we show a correspondence between rotation-invariant valuations and deletion-restriction invariants. In particular, we define a characteristic polynomial for fans in terms of spherical intrinsic volumes and show that it coincides with the usual characteristic polynomial in the case of hyperplane arrangements. This gives a simple deletion-restriction proof of a result of Klivans-Swartz. The metric projection of a cone is a piecewise-linear map, whose underlying fan prompts a generalization of spherical intrinsic volumes to indicator functions. We show that these 'intrinsic indicators' yield valuations that separate polyhedral cones. Applied to hyperplane arrangements, this generalizes a result of Kabluchko on projection volumes.", "published": "2021-06-11T14:04:22Z", "authors": ["Spencer Backman", "Sebastian Manecke", "Raman Sanyal"], "categories": ["math.CO", "math.MG"], "doi": "", "_query": "automated valuation model", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2602.15294v1", "title": "EAA: Automating materials characterization with vision language model agents", "abstract": "We present Experiment Automation Agents (EAA), a vision-language-model-driven agentic system designed to automate complex experimental microscopy workflows. EAA integrates multimodal reasoning, tool-augmented action, and optional long-term memory to support both autonomous procedures and interactive user-guided measurements. Built on a flexible task-manager architecture, the system enables workflows ranging from fully agent-driven automation to logic-defined routines that embed localized LLM queries. EAA further provides a modern tool ecosystem with two-way compatibility for Model Context Protocol (MCP), allowing instrument-control tools to be consumed or served across applications. We demonstrate EAA at an imaging beamline at the Advanced Photon Source, including automated zone plate focusing, natural language-described feature search, and interactive data acquisition. These results illustrate how vision-capable agents can enhance beamline efficiency, reduce operational burden, and lower the expertise barrier for users.", "published": "2026-02-17T01:34:05Z", "authors": ["Ming Du", "Yanqi Luo", "Srutarshi Banerjee", "Michael Wojcik", "Jelena Popovic", "Mathew J. Cherukara"], "categories": ["cs.AI"], "doi": "", "_query": "automated valuation model", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2305.11896v1", "title": "Hyper-automation-The next peripheral for automation in IT industries", "abstract": "The extension of legacy business process automation beyond the bounds of specific processes is known as hyperautomation. Hyperautomation provides automation for nearly any repetitive action performed by business users by combining AI tools with RPA. It automates complex IT business processes that a company's top brains might not be able to complete. This is an end-to-end automation of a standard business process deployment. It enables automation to perform task digitalization by combining a brain computer interface (BCI) with AI and RPA automation tools. BCI, in conjunction with automation tools, will advance the detection and generation of automation processes to the next level. It allows enterprises to combine business intelligence systems, address complex requirements, and enhance human expertise and automation experience. Hyperautomation and its importance in today's environment are briefly discussed in this paper. The article then goes on to discuss how BCI and sensors might aid Hyperautomation. The specific sectors of solicitations were examined using a variety of flexible technologies associated to this concept, as well as dedicated workflow techniques, which are also diagrammatically illustrated. Hyperautomation is being utilized to improve the efficiency, accuracy, and human enhancement of automated tasks dramatically. It incorporates a number of automated tools in its discovery, implementation, and automation phases. As a result, it's well-suited to integrating cutting-edge technologies and experimenting with new methods of working. Keywords- Hyperautomation, Brain computer Interface (BCI), Technology, Used case, Sensors, Industries.", "published": "2023-05-14T11:48:27Z", "authors": ["Ayush Singh Rajput", "Richa Gupta"], "categories": ["cs.HC", "cs.AI"], "doi": "", "_query": "automated valuation model", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2404.19557v6", "title": "Neural Dynamic Data Valuation via Stochastic State-Adjoint Trajectories", "abstract": "Classical data valuation defines a data point's value through the finite marginal contribution $U(C\\cup\\{i\\})-U(C)$, but estimating this quantity over coalitions requires repeated training and does not describe the contribution made along a stochastic training path. We ask whether marginal contributions of data points can be estimated from one coupled trajectory while retaining a verifiable relation to coalition-based values. To this end, we introduce Neural Dynamic Data Valuation (NDDV), which models each data point as a controlled stochastic state and computes a first-order marginal-contribution score via the adjoint equation of the Stochastic Maximum Principle (SMP). This raw sensitivity is then calibrated by a mass-preserving redistribution that increases one data point's participation while redistributing the same total weight over the remaining data points. We prove that the resulting backward adjoint recursion is the exact reverse-mode adjoint of the frozen-aggregate Euler system, bound its discrepancy from the mean-field sensitivity, and express each finite coalition marginal as an integral of local sample-weight sensitivities. These results yield pair-specific error bounds and sufficient conditions for ordering agreement with Shapley, Banzhaf, and leave-one-out values. Experiments on existing benchmarks evaluate marginal-contribution fidelity, score-release cost, corrupted-sample detection, ablations, and failure regimes. NDDV is a one-run, trajectory-conditioned estimator, not an unconditional replacement for cooperative-game values.", "published": "2024-04-30T13:39:26Z", "authors": ["Zhangyong Liang", "Ji Zhang", "Huanhuan Gao"], "categories": ["stat.ML", "cs.LG"], "doi": "", "_query": "automated valuation model", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2311.18760v4", "title": "TaskBench: Benchmarking Large Language Models for Task Automation", "abstract": "In recent years, the remarkable progress of large language models (LLMs) has sparked interest in task automation, which involves decomposing complex tasks described by user instructions into sub-tasks and invoking external tools to execute them, playing a central role in autonomous agents. However, there is a lack of systematic and standardized benchmarks to promote the development of LLMs in task automation. To address this, we introduce TaskBench, a comprehensive framework to evaluate the capability of LLMs in task automation. Specifically, task automation can be divided into three critical stages: task decomposition, tool selection, and parameter prediction. To tackle the complexities inherent in these stages, we introduce the concept of Tool Graph to represent decomposed tasks and adopt a back-instruct method to generate high-quality user instructions. We propose TaskEval, a multi-faceted evaluation methodology that assesses LLM performance across these three stages. Our approach combines automated construction with rigorous human verification, ensuring high consistency with human evaluation. Experimental results demonstrate that TaskBench effectively reflects the capabilities of various LLMs in task automation. It provides insights into model performance across different task complexities and domains, pushing the boundaries of what current models can achieve. TaskBench offers a scalable, adaptable, and reliable benchmark for advancing LLM-based autonomous agents.", "published": "2023-11-30T18:02:44Z", "authors": ["Yongliang Shen", "Kaitao Song", "Xu Tan", "Wenqi Zhang", "Kan Ren", "Siyu Yuan", "Weiming Lu", "Dongsheng Li", "Yueting Zhuang"], "categories": ["cs.CL", "cs.AI"], "doi": "", "_query": "automated valuation model", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/1711.09632v1", "title": "Hessian valuations", "abstract": "A new class of continuous valuations on the space of convex functions on $\\mathbb{R}^n$ is introduced. On smooth convex functions, they are defined for $i=0,\\dots,n$ by \\begin{equation*} u\\mapsto \\int_{\\mathbb{R}^n} ζ(u(x),x,\\nabla u(x))\\,[\\operatorname{D}^2 u(x)]_i\\,{\\rm d} x \\end{equation*} where $ζ\\in C(\\mathbb{R}\\times\\mathbb{R}^n\\times\\mathbb{R}^n)$ and $[\\operatorname{D}^2 u]_i$ is the $i$th elementary symmetric function of the eigenvalues of the Hessian matrix, $\\operatorname{D}^2 u$, of $u$. Under suitable assumptions on $ζ$, these valuations are shown to be invariant under translations and rotations on convex and coercive functions.", "published": "2017-11-27T11:44:08Z", "authors": ["A. Colesanti", "M. Ludwig", "F. Mussnig"], "categories": ["math.MG", "math.FA"], "doi": "10.1512/iumj.2020.69.7960", "_query": "automated valuation model", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/1406.4500v2", "title": "Generalized translation invariant valuations and the polytope algebra", "abstract": "We study the space of generalized translation invariant valuations on a finite-dimensional vector space and construct a partial convolution which extends the convolution of smooth translation invariant valuations. Our main theorem is that McMullen's polytope algebra is a subalgebra of the (partial) convolution algebra of generalized translation invariant valuations. More precisely, we show that the polytope algebra embeds injectively into the space of generalized translation invariant valuations and that for polytopes in general position, the convolution is defined and corresponds to the product in the polytope algebra.", "published": "2014-06-17T19:54:37Z", "authors": ["Andreas Bernig", "Dmitry Faifman"], "categories": ["math.DG", "math.MG"], "doi": "", "_query": "automated valuation model", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/1704.02910v4", "title": "Definable Valuations induced by multiplicative subgroups and NIP Fields", "abstract": "We study the algebraic implications of the non-independence property (NIP) and variants thereof (dp-minimality) on infinite fields, motivated by the conjecture that all such fields which are neither real closed nor separably closed admit a definable henselian valuation. Our results mainly focus on Hahn fields and build up on Will Johnson's preprint \"dp-minimal fields\", arXiv: 1507.02745v1, July 2015.", "published": "2017-04-10T15:35:54Z", "authors": ["Katharina Dupont", "Assaf Hasson", "Salma Kuhlmann"], "categories": ["math.LO"], "doi": "", "_query": "automated valuation model", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2411.00745v1", "title": "Private, Augmentation-Robust and Task-Agnostic Data Valuation Approach for Data Marketplace", "abstract": "Evaluating datasets in data marketplaces, where the buyer aim to purchase valuable data, is a critical challenge. In this paper, we introduce an innovative task-agnostic data valuation method called PriArTa which is an approach for computing the distance between the distribution of the buyer's existing dataset and the seller's dataset, allowing the buyer to determine how effectively the new data can enhance its dataset. PriArTa is communication-efficient, enabling the buyer to evaluate datasets without needing access to the entire dataset from each seller. Instead, the buyer requests that sellers perform specific preprocessing on their data and then send back the results. Using this information and a scoring metric, the buyer can evaluate the dataset. The preprocessing is designed to allow the buyer to compute the score while preserving the privacy of each seller's dataset, mitigating the risk of information leakage before the purchase. A key feature of PriArTa is its robustness to common data transformations, ensuring consistent value assessment and reducing the risk of purchasing redundant data. The effectiveness of PriArTa is demonstrated through experiments on real-world image datasets, showing its ability to perform privacy-preserving, augmentation-robust data valuation in data marketplaces.", "published": "2024-11-01T17:13:14Z", "authors": ["Tayyebeh Jahani-Nezhad", "Parsa Moradi", "Mohammad Ali Maddah-Ali", "Giuseppe Caire"], "categories": ["cs.LG", "cs.DC"], "doi": "", "_query": "automated valuation model", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/1207.7256v1", "title": "GL(n) contravariant Minkowski valuations", "abstract": "A complete classification of all continuous GL(n) contravariant Minkowski valuations is established. As an application we present a family of sharp isoperimetric inequalities for such valuations which generalize the classical Petty projection inequality.", "published": "2012-07-31T14:23:07Z", "authors": ["Franz E. Schuster", "Thomas Wannerer"], "categories": ["math.MG", "math.DG", "math.FA"], "doi": "", "_query": "automated valuation model", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2308.11111v1", "title": "CAME: Contrastive Automated Model Evaluation", "abstract": "The Automated Model Evaluation (AutoEval) framework entertains the possibility of evaluating a trained machine learning model without resorting to a labeled testing set. Despite the promise and some decent results, the existing AutoEval methods heavily rely on computing distribution shifts between the unlabelled testing set and the training set. We believe this reliance on the training set becomes another obstacle in shipping this technology to real-world ML development. In this work, we propose Contrastive Automatic Model Evaluation (CAME), a novel AutoEval framework that is rid of involving training set in the loop. The core idea of CAME bases on a theoretical analysis which bonds the model performance with a contrastive loss. Further, with extensive empirical validation, we manage to set up a predictable relationship between the two, simply by deducing on the unlabeled/unseen testing set. The resulting framework CAME establishes a new SOTA results for AutoEval by surpassing prior work significantly.", "published": "2023-08-22T01:24:14Z", "authors": ["Ru Peng", "Qiuyang Duan", "Haobo Wang", "Jiachen Ma", "Yanbo Jiang", "Yongjun Tu", "Xiu Jiang", "Junbo Zhao"], "categories": ["cs.CV", "cs.AI", "cs.LG"], "doi": "", "_query": "automated valuation model", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2107.00503v2", "title": "Orders and Polytropes: Matrix Algebras from Valuations", "abstract": "We apply tropical geometry to study matrix algebras over a field with valuation. Using the shapes of min-max convexity, known as polytropes, we revisit the graduated orders introduced by Plesken and Zassenhaus. These are classified by the polytrope region. We advance the ideal theory of graduated orders by introducing their ideal class polytropes. This article emphasizes examples and computations. It offers first steps in the geometric combinatorics of endomorphism rings of configurations in affine buildings.", "published": "2021-07-01T14:50:31Z", "authors": ["Yassine El Maazouz", "Marvin Anas Hahn", "Gabriele Nebe", "Mima Stanojkovski", "Bernd Sturmfels"], "categories": ["math.CO", "math.RA"], "doi": "10.1007/s13366-021-00600-4", "_query": "automated valuation model", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/1809.02724v1", "title": "An automated model-based test oracle for access control systems", "abstract": "In the context of XACML-based access control systems, an intensive testing activity is among the most adopted means to assure that sensible information or resources are correctly accessed. Unfortunately, it requires a huge effort for manual inspection of results: thus automated verdict derivation is a key aspect for improving the cost-effectiveness of testing. To this purpose, we introduce XACMET, a novel approach for automated model-based oracle definition. XACMET defines a typed graph, called the XAC-Graph, that models the XACML policy evaluation. The expected verdict of a specific request execution can thus be automatically derived by executing the corresponding path in such graph. Our validation of the XACMET prototype implementation confirms the effectiveness of the proposed approach.", "published": "2018-09-08T00:38:16Z", "authors": ["Antonia Bertolino", "Said Daoudagh", "Francesca Lonetti", "Eda Marchetti"], "categories": ["cs.SE"], "doi": "10.1145/3194733.3194743", "_query": "automated valuation model", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/1701.01074v1", "title": "The role of defect and splitting in finite generation of extensions of associated graded rings along a valuation", "abstract": "Suppose that $R$ is a 2 dimensional excellent local domain with quotient field $K$, $K^*$ is a finite separable extension of $K$ and $S$ is a 2 dimensional local domain with quotient field $K^*$ such that $S$ dominates $R$. Suppose that $ν^*$ is a valuation of $K^*$ such that $ν^*$ dominates $S$. Let $ν$ be the restriction of $ν^*$ to $K$. The associated graded ring ${\\rm gr}_ν(R)$ was introduced by Bernard Teissier. It plays an important role in local uniformization. We show that the extension $(K,ν)\\rightarrow (K^*,ν^*)$ of valued fields is without defect if and only if there exist regular local rings $R_1$ and $S_1$ such that $R_1$ is a local ring of a blow up of $R$, $S_1$ is a local ring of a blowup of $S$, $ν^*$ dominates $S_1$, $S_1$ dominates $R_1$ and the associated graded ring ${\\rm gr}_{ν^*}(S_1)$ is a finitely generated ${\\rm gr}_ν(R_1)$-algebra. We also investigate the role of splitting of the valuation $ν$ in $K^*$ in finite generation of the extensions of associated graded rings along the valuation. We will say that $ν$ does not split in $S$ if $ν^*$ is the unique extension of $ν$ to $K^*$ which dominates $S$. We show that if $R$ and $S$ are regular local rings, $ν^*$ has rational rank 1 and is not discrete and ${\\rm gr}_{ν^*}(S)$ is a finitely generated ${\\rm gr}_ν(R)$-algebra, then $ν$ does not split in $S$. We give examples showing that such a strong statement is not true when $ν$ does not satisfy these assumptions. We deduce that if $ν$ has rational rank 1 and is not discrete and if $R\\rightarrow R'$ is a nontrivial sequence of quadratic transforms along $ν$, then ${\\rm gr}_ν(R')$ is not a finitely generated ${\\rm gr}_ν(R)$-algebra.", "published": "2017-01-04T16:57:37Z", "authors": ["Steven Dale Cutkosky"], "categories": ["math.AG", "math.AC"], "doi": "10.2140/ant.2017.11.1461", "_query": "automated valuation model", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2312.08504v1", "title": "1/2 Approximate MMS Allocation for Separable Piecewise Linear Concave Valuations", "abstract": "We study fair distribution of a collection of m indivisible goods among a group of n agents, using the widely recognized fairness principles of Maximin Share (MMS) and Any Price Share (APS). These principles have undergone thorough investigation within the context of additive valuations. We explore these notions for valuations that extend beyond additivity. First, we study approximate MMS under the separable (piecewise-linear) concave (SPLC) valuations, an important class generalizing additive, where the best known factor was 1/3-MMS. We show that 1/2-MMS allocation exists and can be computed in polynomial time, significantly improving the state-of-the-art. We note that SPLC valuations introduce an elevated level of intricacy in contrast to additive. For instance, the MMS value of an agent can be as high as her value for the entire set of items. Further, the equilibrium computation problem, which is polynomial-time for additive valuations, becomes intractable for SPLC. We use a relax-and-round paradigm that goes through competitive equilibrium and LP relaxation. Our result extends to give (symmetric) 1/2-APS, a stronger guarantee than MMS. APS is a stronger notion that generalizes MMS by allowing agents with arbitrary entitlements. We study the approximation of APS under submodular valuation functions. We design and analyze a simple greedy algorithm using concave extensions of submodular functions. We prove that the algorithm gives a 1/3-APS allocation which matches the current best-known factor. Concave extensions are hard to compute in polynomial time and are, therefore, generally not used in approximation algorithms. Our approach shows a way to utilize it within analysis (while bypassing its computation), and might be of independent interest.", "published": "2023-12-13T20:31:55Z", "authors": ["Chandra Chekuri", "Pooja Kulkarni", "Rucha Kulkarni", "Ruta Mehta"], "categories": ["cs.GT"], "doi": "", "_query": "automated valuation model", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/0901.1651v1", "title": "Valuation domains with a maximal immediate extension of finite rank", "abstract": "If $R$ is a valuation domain of maximal ideal $P$ with a maximal immediate extension of finite rank it is proven that there exists a finite sequence of prime ideals $P=L_0\\supset L_1\\supset...\\supset L_m\\supseteq 0$ such that $R_{L_j}/L_{j+1}$ is almost maximal for each $j$, $0\\leq j\\leq m-1$ and $R_{L_m}$ is maximal if $L_m\\ne 0$. Then we suppose that there is an integer $n\\geq 1$ such that each torsion-free $R$-module of finite rank is a direct sum of modules of rank at most $n$. By adapting Lady's methods, it is shown that $n\\leq 3$ if $R$ is almost maximal, and the converse holds if $R$ has a maximal immediate extension of rank $\\leq 2$.", "published": "2009-01-12T20:04:22Z", "authors": ["Francois Couchot"], "categories": ["math.RA"], "doi": "", "_query": "automated valuation model", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/1002.1144v1", "title": "A CHAID Based Performance Prediction Model in Educational Data Mining", "abstract": "The performance in higher secondary school education in India is a turning point in the academic lives of all students. As this academic performance is influenced by many factors, it is essential to develop predictive data mining model for students' performance so as to identify the slow learners and study the influence of the dominant factors on their academic performance. In the present investigation, a survey cum experimental methodology was adopted to generate a database and it was constructed from a primary and a secondary source. While the primary data was collected from the regular students, the secondary data was gathered from the school and office of the Chief Educational Officer (CEO). A total of 1000 datasets of the year 2006 from five different schools in three different districts of Tamilnadu were collected. The raw data was preprocessed in terms of filling up missing values, transforming values in one form into another and relevant attribute/ variable selection. As a result, we had 772 student records, which were used for CHAID prediction model construction. A set of prediction rules were extracted from CHIAD prediction model and the efficiency of the generated CHIAD prediction model was found. The accuracy of the present model was compared with other model and it has been found to be satisfactory.", "published": "2010-02-05T08:27:17Z", "authors": ["M. Ramaswami", "R. Bhaskaran"], "categories": ["cs.LG"], "doi": "", "_query": "automated valuation model", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/1509.03831v1", "title": "Centro-Affine Tensor Valuations", "abstract": "We completely classify all measurable $\\operatorname{SL}(n)$-covariant symmetric tensor valuations on convex polytopes containing the origin in their interiors. It is shown that essentially the only examples of such valuations are the moment tensor and a tensor derived from $L_p$ surface area measures. This generalizes and unifies earlier results for the scalar, vector and matrix valued case.", "published": "2015-09-13T09:53:43Z", "authors": ["Christoph Haberl", "Lukas Parapatits"], "categories": ["math.MG"], "doi": "", "_query": "automated valuation model", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2510.16007v1", "title": "Layer-Aware Influence for Online Data Valuation Estimation", "abstract": "Data-centric learning emphasizes curating high-quality training samples to boost performance rather than designing new architectures. A central problem is to estimate the influence of training sample efficiently. Prior studies largely focus on static influence measured on a converged model, overlooking how data valuation dynamically changes during optimization. This omission neglects the dynamic nature of sample influence during optimization, especially in deep models. To address the computational burden of frequent influence estimation, we develop a layer-aware online estimator that requires only loss-to-output gradients. This design avoids parameter-level and full-network gradients while preserving ranking fidelity. Extensive experiments across LLM pretraining, fine-tuning, and image classification show our method improves accuracy with substantially lower time and memory cost, making dynamic data curation efficient and scalable in practice.", "published": "2025-10-14T15:34:22Z", "authors": ["Ziao Yang", "Longbo Huang", "Hongfu Liu"], "categories": ["cs.LG", "cs.AI"], "doi": "", "_query": "automated valuation model", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2502.14776v2", "title": "SurveyX: Academic Survey Automation via Large Language Models", "abstract": "Large Language Models (LLMs) have demonstrated exceptional comprehension capabilities and a vast knowledge base, suggesting that LLMs can serve as efficient tools for automated survey generation. However, recent research related to automated survey generation remains constrained by some critical limitations like finite context window, lack of in-depth content discussion, and absence of systematic evaluation frameworks. Inspired by human writing processes, we propose SurveyX, an efficient and organized system for automated survey generation that decomposes the survey composing process into two phases: the Preparation and Generation phases. By innovatively introducing online reference retrieval, a pre-processing method called AttributeTree, and a re-polishing process, SurveyX significantly enhances the efficacy of survey composition. Experimental evaluation results show that SurveyX outperforms existing automated survey generation systems in content quality (0.259 improvement) and citation quality (1.76 enhancement), approaching human expert performance across multiple evaluation dimensions. Examples of surveys generated by SurveyX are available on www.surveyx.cn", "published": "2025-02-20T17:59:45Z", "authors": ["Xun Liang", "Jiawei Yang", "Yezhaohui Wang", "Chen Tang", "Zifan Zheng", "Shichao Song", "Zehao Lin", "Yebin Yang", "Simin Niu", "Hanyu Wang", "Bo Tang", "Feiyu Xiong", "Keming Mao", "Zhiyu li"], "categories": ["cs.CL"], "doi": "", "_query": "automated valuation model", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/1206.6998v1", "title": "Interest Rate Risk of Bond Prices on Macedonian Stock Exchange - Empirical Test of the Duration, Modified Duration and Convexity and Bonds Valuation", "abstract": "This article presents valuation of Treasury Bonds (T-Bonds) on Macedonian Stock Exchange (MSE) and empirical test of duration, modified duration and convexity of the T-bonds at MSE in order to determine sensitivity of bonds prices on interest rate changes. The main goal of this study is to determine how standard valuation models fit in case of T- Bonds that are traded on MSE and to verify whether they offer reliable results compared with average bonds prices on MSE. We test the sensitivity of T- Bonds on MSE on interest rate changes and determine that convexity is more accurate measure as approximation of bond prices changes than duration. Final conclusion is that T-Bonds traded at MSE are not sensitive on interest rate changes due to institutional investors' permanent higher demand and at the same time market limited offer of risk-free instruments.", "published": "2012-06-29T12:22:03Z", "authors": ["Zoran Ivanovski", "Toni Draganov Stojanovski", "Nadica Ivanovska"], "categories": ["q-fin.RM"], "doi": "", "_query": "automated valuation model", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/0901.4819v2", "title": "Flat modules and Gröbner bases over truncated discrete valuation rings", "abstract": "We present basic properties of Gröbner bases of submodules of a free module of finite rank over a polynomial ring $R$ with coefficients in a graded truncated discrete valuations ring $A$. As an application, we give a criterion for a finitely generated $R$-module to be flat over $A$. Its non-graded version is also given.", "published": "2009-01-30T02:44:21Z", "authors": ["Toshiro Hiranouchi", "Yuichiro Taguchi"], "categories": ["math.AC", "math.NT"], "doi": "", "_query": "automated valuation model", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2409.03462v1", "title": "Automated Journalism", "abstract": "Developed as a response to the increasing popularity of data-driven journalism, automated journalism refers to the process of automating the collection, production, and distribution of news content and other data with the assistance of computer programs. Although the algorithmic technologies associated with automated journalism remain in the initial stage of development, early adopters have already praised the usefulness of automated journalism for generating routine news based on clean, structured data. Most noticeably, the Associated Press and The New York Times have been automating news content to cover financial and sports issues for over a decade. Nevertheless, research on automated journalism is also alerting to the dangers of using algorithms for news creation and distribution, including the possible bias behind AI systems or the human bias of those who develop computer programs. The popularization of automated news content also has important implications for the infrastructure of the newsroom, the role performance of journalists and other non-journalistic professionals, and the distribution of news content to a datafied audience.", "published": "2024-09-05T12:19:03Z", "authors": ["Wang Ngai Yeung", "Tomás Dodds"], "categories": ["cs.CY"], "doi": "", "_query": "automated valuation model", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2606.18562v1", "title": "Closure operators and geometric modules of valuated matroids", "abstract": "We introduce a closure operator for valuated matroids and prove that it yields a cryptomorphic definition of valuated matroids. As an application, we introduce a class of modules over the tropical semifield (geometric modules) and prove that there is one-to-one correspondence between projective equivalence classes of simple valuated matroids and isomorphism classes of finitely generated geometric modules. We further illustrate how this correspondence can be lifted to simple infinite valuated matroids and geometric modules.", "published": "2026-06-17T00:28:44Z", "authors": ["Jaiung Jun", "Jeffrey Tolliver"], "categories": ["math.CO"], "doi": "", "_query": "automated valuation model", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2606.26731v2", "title": "Robust Hedging Valuation Adjustment under Liquidity--Demand Stress", "abstract": "This paper develops a robust hedging valuation adjustment (HVA) measure for dynamic hedging. Simulated rebalancing and maturity-unwind trades generate a loss distribution for each no-trade-band rule, and we define robust HVA as the worst-case expected loss over a relative-entropy neighborhood of that distribution. Because band width affects turnover, the same relative-entropy radius applied to different bands can imply different levels of demand-liquidity stress. We distinguish a fixed-radius convention from a fixed benchmark-stress convention and show that wider no-trade bands lower rebalancing costs but raise hedge-error risk.", "published": "2026-06-25T08:16:25Z", "authors": ["Takayuki Sakuma"], "categories": ["q-fin.RM", "q-fin.CP"], "doi": "", "_query": "automated valuation model", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/1907.08908v1", "title": "Techniques for Automated Machine Learning", "abstract": "Automated machine learning (AutoML) aims to find optimal machine learning solutions automatically given a machine learning problem. It could release the burden of data scientists from the multifarious manual tuning process and enable the access of domain experts to the off-the-shelf machine learning solutions without extensive experience. In this paper, we review the current developments of AutoML in terms of three categories, automated feature engineering (AutoFE), automated model and hyperparameter learning (AutoMHL), and automated deep learning (AutoDL). State-of-the-art techniques adopted in the three categories are presented, including Bayesian optimization, reinforcement learning, evolutionary algorithm, and gradient-based approaches. We summarize popular AutoML frameworks and conclude with current open challenges of AutoML.", "published": "2019-07-21T04:03:36Z", "authors": ["Yi-Wei Chen", "Qingquan Song", "Xia Hu"], "categories": ["cs.LG", "cs.AI", "stat.ML"], "doi": "", "_query": "automated valuation model", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2606.09556v1", "title": "AI Scientists Are Only as Good as Their Evidence: A Stratified Ablation of Proprietary Data and Reasoning Skills in Drug-Asset Valuation", "abstract": "AI Scientist agents are often evaluated as if capability were mainly a function of model quality, prompting, or reasoning scaffolds. We test a different hypothesis in drug-asset valuation: for knowledge-intensive scientific decisions, the limiting factor is often the evidence substrate the agent can access. We run a controlled three-arm ablation on a production valuation agent: A is a plain web-only LLM analyst, B adds public structured tools plus a 14-dimension valuation playbook, verifier, objectivity policy and red-team, and C adds the proprietary Noah AI corpus of curated pipeline, trial and deal intelligence. Across a 13-asset stratified benchmark, B improves calibration and audit discipline: tier-in-range accuracy rises from 0.80 to 0.89 and objectivity from 3.16 to 3.30. But B does not remove the factual ceiling. Under capability-superset accounting, A and B recover only 0.25 and 0.38 of the curated gold competitive record, while C recovers 0.96; on the curated long-tail subset, C reaches 0.93 vs. 0.26/0.30. Raw blind-panel decision quality is similar for A and B (7.01 vs. 6.96), so we introduce completeness-aware decision utility: informed decision-quality = decision-quality x gold-coverage. On this metric, C reaches 7.43 vs. 1.76/2.57 for A/B. Even a perfect non-proprietary-data report would be capped at 3.83 by B's coverage. The result is not that reasoning scaffolds are unimportant; they improve calibration and discipline. Rather, proprietary evidence sets the upper bound of what the AI Scientist can know and therefore decide.", "published": "2026-06-08T14:31:32Z", "authors": ["Yinan Wang"], "categories": ["cs.AI"], "doi": "", "_query": "automated valuation model", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/1509.01087v3", "title": "Milnor K-theory of complete discrete valuation rings with finite residue fields", "abstract": "Consider a complete discrete valuation ring $\\mathcal{O}$ with quotient field $F$ and finite residue field. Then the inclusion map $\\mathcal{O} \\hookrightarrow F$ induces a map $\\hat{\\mathrm{K}}^\\mathrm{M}_*\\mathcal{O} \\to \\hat{\\mathrm{K}}^\\mathrm{M}_*F$ on improved Milnor K-theory. We show that this map is an isomorphism in degrees bigger or equal to 3. This implies the Gersten conjecture for improved Milnor K-theory. This result is new in the $p$-adic case.", "published": "2015-09-03T13:47:27Z", "authors": ["Christian Dahlhausen"], "categories": ["math.KT"], "doi": "10.1016/j.jpaa.2017.07.002", "_query": "automated valuation model", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2006.06279v1", "title": "Complex-valued valuations on Lp spaces", "abstract": "All continuous translation invariant complex-valued valuations on Lebesgue measurable functions are completely classified. And all continuous rotation invariant complex-valued valuations on spherical Lebesgue measurable functions are also completely classified.", "published": "2020-06-11T09:34:34Z", "authors": ["Lijuan Liu"], "categories": ["math.MG"], "doi": "", "_query": "automated valuation model", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/1802.04737v3", "title": "Affine function valued valuations", "abstract": "A classification of SL$(n)$ contravariant, continuous function valued valuations on convex bodies is established. Such valuations are natural extensions of SL$(n)$ contravariant $L_p$ Minkowski valuations, the classification of which characterized $L_p$ projection bodies, which are fundamental in the $L_p$ Brunn-Minkowski theory, for $p \\geq 1$. Hence our result will help to better understand extensions of the $L_p$ Brunn-Minkowski theory. In fact, our results characterize general projection functions which extend $L_p$ projection functions ($p$-th powers of the support functions of $L_p$ projection bodies) to projection functions in the $L_p$ Brunn-Minkowski theory for $0< p < 1$ and in the Orlicz Brunn-Minkowski theory.", "published": "2018-02-13T17:04:14Z", "authors": ["Jin Li"], "categories": ["math.MG"], "doi": "10.1093/imrn/rny212", "_query": "automated valuation model", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/math/0511171v3", "title": "Theory of valuations on manifolds, IV. New properties of the multiplicative structure", "abstract": "This is the fourth part in the series of articles math.MG/0503397, math.MG/0503399, math.MG/0509512 where the theory of valuations on manifolds is developed. In this part it is shown that the filtration on valuations introduced in math.MG/0503399 is compatible with the product. Then it is proved that the Euler-Verdier involution on smooth valuations introduced in math.MG/0503399 is an automorphism of the algebra of valuations. Then an integration functional on valuations with compact support is introduced, and a property of selfduality of valuations is proved. Next a space of generalized valuations is defined, and some basic properties of it are proved. Finally a canonical imbedding of the space of constructible functions on a real analytic manifold into the space of generalized valuations is constructed, and various structures on valuations are compared with known structures on constructible functions.", "published": "2005-11-07T13:32:15Z", "authors": ["Semyon Alesker"], "categories": ["math.MG"], "doi": "", "_query": "automated valuation model", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/1510.02000v1", "title": "Topological aspects of irredundant intersections of ideals and valuation rings", "abstract": "An intersection of sets $A = \\bigcap_{i \\in I}B_i$ is irredundant if no $B_i$ can be omitted from this intersection. We develop a topological approach to irredundance by introducing a notion of a spectral representation, a spectral space whose members are sets that intersect to a given set $A$ and whose topology encodes set membership. We define a notion of a minimal representation and show that for such representations, irredundance is a topological property. We apply this approach to intersections of valuation rings and ideals. In the former case we focus on Krull-like domains and Prüfer $v$-multiplication domains, and in the latter on irreducible ideals in arithmetical rings. Some of our main applications are to those rings or ideals that can be represented with a Noetherian subspace of a spectral representation.", "published": "2015-10-07T16:05:29Z", "authors": ["Bruce Olberding"], "categories": ["math.AC"], "doi": "", "_query": "automated valuation model", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2411.09383v3", "title": "Exponential valuations on lattice polygons", "abstract": "We classify translatively exponential and GL(2,Z) covariant valuations on lattice polygons valued at measurable real functions. A typical example of such valuations is induced by the Laplace transform, but as it turns out there are many more. The argument uses the ergodicity of the linear action of SL(2,Z) on R2, and some elementary properties of the Fibonacci numbers.", "published": "2024-11-14T11:55:29Z", "authors": ["Karoly J. Boroczky", "Matyas Domokos", "Ansgar Freyer", "Christoph Haberl", "Gergely Harcos", "Jin li"], "categories": ["math.NT"], "doi": "", "_query": "automated valuation model", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/1908.08810v1", "title": "Automated Generation of Test Models from Semi-Structured Requirements", "abstract": "[Context:] Model-based testing is an instrument for automated generation of test cases. It requires identifying requirements in documents, understanding them syntactically and semantically, and then translating them into a test model. One light-weight language for these test models are Cause-Effect-Graphs (CEG) that can be used to derive test cases. [Problem:] The creation of test models is laborious and we lack an automated solution that covers the entire process from requirement detection to test model creation. In addition, the majority of requirements is expressed in natural language (NL), which is hard to translate to test models automatically. [Principal Idea:] We build on the fact that not all NL requirements are equally unstructured. We found that 14 % of the lines in requirements documents of our industry partner contain \"pseudo-code\"-like descriptions of business rules. We apply Machine Learning to identify such semi-structured requirements descriptions and propose a rule-based approach for their translation into CEGs. [Contribution:] We make three contributions: (1) an algorithm for the automatic detection of semi-structured requirements descriptions in documents, (2) an algorithm for the automatic translation of the identified requirements into a CEG and (3) a study demonstrating that our proposed solution leads to 86 % time savings for test model creation without loss of quality.", "published": "2019-08-22T13:02:20Z", "authors": ["Jannik Fischbach", "Maximilian Junker", "Andreas Vogelsang", "Dietmar Freudenstein"], "categories": ["cs.SE", "cs.IR", "cs.LG", "stat.ML"], "doi": "", "_query": "automated valuation model", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2507.07091v2", "title": "On Shilov boundaries, Rees valuations and integral extensions", "abstract": "We explore an analogy between, on one hand, the notions of integral closure of ideals and Rees valuations in commutative algebra and, on the other hand, the notions of spectral seminorm and Shilov boundary in nonarchimedean geometry. For any Tate ring $\\mathcal{A}$ with a Noetherian ring of definition $\\mathcal{A}_{0}$ and pseudo-uniformizer $\\varpi\\in\\mathcal{A}_{0}$, we prove that the Shilov boundary for $\\mathcal{A}$ naturally coincides with the set of Rees valuation rings of the principal ideal $(\\varpi)_{\\mathcal{A}_{0}}$ of $\\mathcal{A}_{0}$. Furthermore, we characterize the Shilov boundary for a wide class of Tate rings by means of minimal open prime ideals in the subring of power-bounded elements. For affinoid algebras, in the sense of Tate, whose underlying rings are integral domains, this recovers a well-known result of Berkovich. Moreover, under some mild assumptions, we prove stability of our characterization of the Shilov boundary under (completed) integral extensions. In particular, for every mixed-characteristic Noetherian domain $R$, we obtain a description of the Shilov boundary for the Tate ring $\\widehat{R^{+}}[p^{-1}]$, where $\\widehat{R^{+}}$ is the $p$-adic completion of the absolute integral closure of the domain $R$.", "published": "2025-07-09T17:50:19Z", "authors": ["Dimitri Dine"], "categories": ["math.AC", "math.AG", "math.NT"], "doi": "", "_query": "automated valuation model", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2405.03575v1", "title": "A Valuation Framework for Customers Impacted by Extreme Temperature-Related Outages", "abstract": "Extreme temperature outages can lead to not just economic losses but also various non-energy impacts (NEI) due to significant degradation of indoor operating conditions caused by service disruptions. However, existing resilience assessment approaches lack specificity for extreme temperature conditions. They often overlook temperature-related mortality and neglect the customer characteristics and grid response in the calculation, despite the significant influence of these factors on NEI-related economic losses. This paper aims to address these gaps by introducing a comprehensive framework to estimate the impact of resilience enhancement not only on the direct economic losses incurred by customers but also on potential NEI, including mortality and the value of statistical life during extreme temperature-related outages. The proposed resilience valuation integrates customer characteristics and grid response variables based on a scalable grid simulation environment. This study adopts a holistic approach to quantify customer-oriented economic impacts, utilizing probabilistic loss scenarios that incorporate health-related factors and damage/loss models as a function of exposure for valuation. The proposed methodology is demonstrated through comparative resilient outage planning, using grid response models emulating a Texas weather zone during the 2021 winter storm Uri. The case study results show that enhanced outage planning with hardened infrastructure can improve the system resilience and thereby reduce the relative risk of mortality by 16% and save the total costs related to non-energy impacts by 74%. These findings underscore the efficacy of the framework by assessing the financial implications of each case, providing valuable insights for decision-makers and stakeholders involved in extreme-weather related resilience planning for risk management and mitigation strategies.", "published": "2024-05-06T15:51:23Z", "authors": ["Min Gyung Yu", "Monish Mukherjee", "Shiva Poudela", "Sadie R. Bender", "Sarmad Hanif", "Trevor D. Hardy", "Hayden M. Reeve"], "categories": ["eess.SY"], "doi": "10.1016/j.apenergy.2024.123450", "_query": "automated valuation model", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2306.06065v1", "title": "LGEM$^\\text{+}$: a first-order logic framework for automated improvement of metabolic network models through abduction", "abstract": "Scientific discovery in biology is difficult due to the complexity of the systems involved and the expense of obtaining high quality experimental data. Automated techniques are a promising way to make scientific discoveries at the scale and pace required to model large biological systems. A key problem for 21st century biology is to build a computational model of the eukaryotic cell. The yeast Saccharomyces cerevisiae is the best understood eukaryote, and genome-scale metabolic models (GEMs) are rich sources of background knowledge that we can use as a basis for automated inference and investigation. We present LGEM+, a system for automated abductive improvement of GEMs consisting of: a compartmentalised first-order logic framework for describing biochemical pathways (using curated GEMs as the expert knowledge source); and a two-stage hypothesis abduction procedure. We demonstrate that deductive inference on logical theories created using LGEM+, using the automated theorem prover iProver, can predict growth/no-growth of S. cerevisiae strains in minimal media. LGEM+ proposed 2094 unique candidate hypotheses for model improvement. We assess the value of the generated hypotheses using two criteria: (a) genome-wide single-gene essentiality prediction, and (b) constraint of flux-balance analysis (FBA) simulations. For (b) we developed an algorithm to integrate FBA with the logic model. We rank and filter the hypotheses using these assessments. We intend to test these hypotheses using the robot scientist Genesis, which is based around chemostat cultivation and high-throughput metabolomics.", "published": "2023-06-09T17:39:44Z", "authors": ["Alexander H. Gower", "Konstantin Korovin", "Daniel Brunnsåker", "Ievgeniia A. Tiukova", "Ross D. King"], "categories": ["q-bio.QM"], "doi": "", "_query": "automated valuation model", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/0808.1522v3", "title": "Integrals and Valuations", "abstract": "We construct a homeomorphism between the compact regular locale of integrals on a Riesz space and the locale of (valuations) on its spectrum. In fact, we construct two geometric theories and show that they are biinterpretable. The constructions are elementary and tightly connected to the Riesz space structure.", "published": "2008-08-11T14:45:27Z", "authors": ["Thierry Coquand", "Bas Spitters"], "categories": ["math.LO", "math.CT"], "doi": "10.4115/jla.2009.1.3", "_query": "automated valuation model", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/1707.06428v1", "title": "Valuations on Log-Concave Functions", "abstract": "A classification of $\\operatorname{SL}(n)$ and translation covariant Minkowski valuations on log-concave functions is established. The moment vector and the recently introduced level set body of log-concave functions are characterized. Furthermore, analogs of the Euler characteristic and volume are characterized as $\\operatorname{SL}(n)$ and translation invariant valuations on log-concave functions.", "published": "2017-07-20T09:49:36Z", "authors": ["Fabian Mussnig"], "categories": ["math.MG", "math.FA"], "doi": "10.1007/s12220-020-00539-3", "_query": "automated valuation model", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/1003.1188v3", "title": "Approximate roots of a valuation and the Pierce-Birkhoff Conjecture", "abstract": "This paper is a step in our program for proving the Piece-Birkhoff Conjecture for regular rings of any dimension (this would contain, in particular, the classical Pierce-Birkhoff conjecture which deals with polynomial rings over a real closed field). We first recall the Connectedness and the Definable Connectedness conjectures, both of which imply the Pierce - Birkhoff conjecture. Then we introduce the notion of a system of approximate roots of a valuation v on a ring A (that is, a collection Q of elements of A such that every v-ideal is generated by products of elements of Q). We use approximate roots to give explicit formulae for sets in the real spectrum of A which we strongly believe to satisfy the conclusion of the Definable Connectedness conjecture. We prove this claim in the special case of dimension 2. This proves the Pierce-Birkhoff conjecture for arbitrary regular 2-dimensional rings.", "published": "2010-03-05T05:32:48Z", "authors": ["François Lucas", "James Madden", "Daniel Schaub", "Mark Spivakovsky"], "categories": ["math.AG"], "doi": "", "_query": "automated valuation model", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2509.14140v1", "title": "A proposal for automated turbulence modelling", "abstract": "Solving the Reynolds-averaged Navier-Stokes equations (RANS) closed with an eddy viscosity computed through a turbulence model is still the leading approach for Computational Fluid Dynamics simulations. Unfortunately, universal models with good predictive capabilities over a wide range of flows are not available. In this work, we propose the use of machine learning to improve existing RANS models. The approach does not require high-fidelity training data. A convolutional neural network is used to identify and segment at runtime the flow field into different zones, each resembling one item of a predefined list of elementary flows. The turbulence model applied in each zone is taken from an equally predefined set of classic models, each specifically tuned to work best for one elementary flow, free from the requirement of universality. The idea is first presented in general terms, and then demonstrated via a preliminary implementation, where only three elementary flows are considered, and three turbulence models are used. Test cases show that, already in this oversimplified form, automated zonal modelling outperforms the baseline RANS models without computational overhead.", "published": "2025-09-17T16:19:29Z", "authors": ["Marco Castelletti", "Maurizio Quadrio"], "categories": ["physics.flu-dyn"], "doi": "", "_query": "automated valuation model", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2104.04340v1", "title": "Valuations on the character variety: Newton polytopes and Residual Poisson Bracket", "abstract": "We study the space of measured laminations ML on a closed surface from the valuative point of view. We introduce and study a notion of Newton polytope for an algebraic function on the character variety. We prove for instance that trace functions have unit coefficients at the extremal points of their Newton polytope. Then we provide a definition of tangent space at a valuation and show how the Goldman Poisson bracket on the character variety induces a symplectic structure on this valuative model for ML. Finally we identify this symplectic space with previous constructions due to Thurston and Bonahon.", "published": "2021-04-09T12:52:38Z", "authors": ["Julien Marché", "Christopher-Lloyd Simon"], "categories": ["math.GT"], "doi": "10.2140/gt.2024.28.593", "_query": "automated valuation model", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/1305.5575v1", "title": "Bilateral Credit Valuation Adjustment for Large Credit Derivatives Portfolios", "abstract": "We obtain an explicit formula for the bilateral counterparty valuation adjustment of a credit default swaps portfolio referencing an asymptotically large number of entities. We perform the analysis under a doubly stochastic intensity framework, allowing for default correlation through a common jump process. The key insight behind our approach is an explicit characterization of the portfolio exposure as the weak limit of measure-valued processes associated to survival indicators of portfolio names. We validate our theoretical predictions by means of a numerical analysis, showing that counterparty adjustments are highly sensitive to portfolio credit risk volatility as well as to default correlation.", "published": "2013-05-23T22:04:32Z", "authors": ["Lijun Bo", "Agostino Capponi"], "categories": ["q-fin.PR"], "doi": "", "_query": "automated valuation model", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2606.30538v1", "title": "Measure-valued valuations on star bodies", "abstract": "A complete classification of weak$^*$~continuous, measure-valued valuations is established on star bodies in $\\R^n$. Consequences are an integral representation of rotation equivariant, measure-valued valuations and a characterization of dual area measures.", "published": "2026-06-29T16:38:00Z", "authors": ["Jorge S. Ibáñez-Marcos", "Monika Ludwig", "Pedro Tradacete", "Ignacio Villanueva"], "categories": ["math.MG"], "doi": "", "_query": "automated valuation model", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2110.10841v1", "title": "New advances in the automation of context-aware information selection and guided model assembly", "abstract": "The automated assembly and extension of dynamic network models using information extracted from literature are challenging due to the amount and inconsistency in published literature. Recently, efforts have been made to automatically and efficiently assemble the information extracted from literature into models. In this review, we summarize the basic concept, performance, advantages, and limitations of five automated extension methods. Each method was tested for its ability to reconstruct a model of T-cell differentiation as compared against a number of predefined system properties.", "published": "2021-10-21T00:51:37Z", "authors": ["Yasmine Ahmed", "Adam A Butchy", "Khaled Sayed", "Cheryl Telmer", "Natasa Miskov-Zivanov"], "categories": ["q-bio.MN"], "doi": "", "_query": "automated valuation model", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/1703.00259v4", "title": "Binary Funding Impacts in Derivative Valuation", "abstract": "We discuss the binary nature of funding impact in derivative valuation. Under some conditions, funding is either a cost or a benefit, i.e., one of the lending/borrowing rates does not play a role in pricing derivatives. When derivatives are priced, considering different lending/borrowing rates leads to semi-linear BSDEs and PDEs, and thus it is necessary to solve the equations numerically. However, once it can be guaranteed that only one of the rates affects pricing, linear equations can be recovered and analytical formulae can be derived. Moreover, as a byproduct, our results explain how debt value adjustment (DVA) and funding benefits are dissimilar. It is often believed that considering both DVA and funding benefits results in a double-counting issue but it will be shown that the two components are affected by different mathematical structures of derivative transactions. We find that funding benefit is related to the decreasing property of the payoff function, but this relationship decreases as the funding choices of underlying assets are transferred to repo markets.", "published": "2017-03-01T12:12:52Z", "authors": ["Junbeom Lee", "Chao Zhou"], "categories": ["q-fin.MF"], "doi": "", "_query": "automated valuation model", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/1706.04548v2", "title": "Thresholds, valuations, and K-stability", "abstract": "Let X be a normal complex projective variety with at worst klt singularities, and L a big line bundle on X. We use valuations to study the log canonical threshold of L, as well as another invariant, the stability threshold. The latter generalizes a notion by Fujita and Odaka, and can be used to characterize when a Q-Fano variety is K-semistable or uniformly K-stable. It can also be used to generalize volume bounds due to Fujita and Liu. The two thresholds can be written as infima of certain functionals on the space of valuations on X. When L is ample, we prove that these infima are attained. In the toric case, toric valuations acheive these infima, and we obtain simple expressions for the two thresholds in terms of the moment polytope of L.", "published": "2017-06-14T15:47:28Z", "authors": ["Harold Blum", "Mattias Jonsson"], "categories": ["math.AG", "math.DG"], "doi": "", "_query": "automated valuation model", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/1411.7891v1", "title": "Log-Concavity Properties of Minkowski Valuations", "abstract": "New Orlicz Brunn-Minkowski inequalities are established for rigid motion compatible Minkowski valuations of arbitrary degree. These extend classical log-concavity properties of intrinsic volumes and generalize seminal results of Lutwak and others. Two different approaches which refine previously employed techniques are explored. It is shown that both lead to the same class of Minkowski valuations for which these inequalities hold. An appendix by Semyon Alesker contains the proof of a new classification of generalized translation invariant valuations.", "published": "2014-11-28T14:54:11Z", "authors": ["Astrid Berg", "Lukas Parapatits", "Franz E. Schuster", "Manuel Weberndorfer"], "categories": ["math.MG", "math.DG", "math.FA"], "doi": "", "_query": "automated valuation model", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2210.04321v1", "title": "A Nonlinear Heat Equation Arising from Automated-Vehicle Traffic Flow Models", "abstract": "In this paper, a new nonlinear heat equation is studied that arises as a model of the collective behavior of automated vehicles. The properties of the solutions of this equation are studied by introducing the appropriate notion of a weak solution that requires certain entropy-like conditions. To obtain an approximation of the solution of the nonlinear heat equation, a new conservative first-order finite difference scheme is proposed that respects the corresponding entropy conditions, and certain links between the weak solution and the numerical scheme are provided. Finally, a traffic simulation scenario and a comparison with the Lighthill-Witham-Richards (LWR) model are provided, illustrating the benefits of the use of automated vehicles.", "published": "2022-10-09T19:12:00Z", "authors": ["Dionysis Theodosis", "Iasson Karafyllis", "George Titakis", "Ioannis Papamichail", "Markos Papageorgiou"], "categories": ["math.NA", "math.AP"], "doi": "", "_query": "automated valuation model", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2405.12590v1", "title": "Maverick-Aware Shapley Valuation for Client Selection in Federated Learning", "abstract": "Federated Learning (FL) allows clients to train a model collaboratively without sharing their private data. One key challenge in practical FL systems is data heterogeneity, particularly in handling clients with rare data, also referred to as Mavericks. These clients own one or more data classes exclusively, and the model performance becomes poor without their participation. Thus, utilizing Mavericks throughout training is crucial. In this paper, we first design a Maverick-aware Shapley valuation that fairly evaluates the contribution of Mavericks. The main idea is to compute the clients' Shapley values (SV) class-wise, i.e., per label. Next, we propose FedMS, a Maverick-Shapley client selection mechanism for FL that intelligently selects the clients that contribute the most in each round, by employing our Maverick-aware SV-based contribution score. We show that, compared to an extensive list of baselines, FedMS achieves better model performance and fairer Shapley Rewards distribution.", "published": "2024-05-21T08:34:39Z", "authors": ["Mengwei Yang", "Ismat Jarin", "Baturalp Buyukates", "Salman Avestimehr", "Athina Markopoulou"], "categories": ["cs.LG", "cs.DC"], "doi": "", "_query": "automated valuation model", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2609.37580v1", "title": "Tensor valuations on Sobolev spaces", "abstract": "A complete classification is established for continuous, SL($n$) contravariant, and translation invariant tensor valuations defined on the Sobolev space $W^{1,p}(\\mathbb R^n)$. When these valuations are further assumed to be homogeneous, the classification reveals that they are precisely the Fisher information tensors, which constitute a higher-order generalization of the Fisher information matrix.", "published": "2026-09-29T13:41:36Z", "authors": ["Tian Gao", "Dan Ma"], "categories": ["math.MG", "math.FA"], "doi": "", "_query": "automated valuation model", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2503.09096v2", "title": "Valuation rings in simple algebraic extensions of valued fields", "abstract": "Consider a simple algebraic valued field extension $(L/K,v)$ and denote by $\\VR_L$ and $\\VR_K$ the corresponding valuation rings. The main goal of this paper is to present, under certain assumptions, a description of $\\VR_L$ in terms of generators and relations over $\\VR_K$. The main tool used here are complete sequences of key polynomials. It is known that if the ramification index of $(L/K,v)$ is one, then every complete set gives rise to a set of generators of $\\VR_L$ over $\\VR_K$. We show that we can find a sequence of key polynomials for $(L/K,v)$ which satisfies good properties (called neat). Then we present explicit ``neat\" relations that generate all the relations between the corresponding generators of $\\VR_L$ over $\\VR_K$.", "published": "2025-03-12T06:24:18Z", "authors": ["Josnei Novacoski", "Mark Spivakovsky"], "categories": ["math.AC"], "doi": "", "_query": "automated valuation model", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2212.06607v1", "title": "Model-driven Engineering of Manufacturing Automation Software Projects -- A SysML-based Approach", "abstract": "This paper comprises a SysML-based approach to support the model-driven engineering (MDE) of Manufacturing Automation Software Projects (MASP). The Systems Modeling Language (SysML) is adapted to define the SysML-AT (SysML for automation), a specialized language profile that covers (non-)functional requirements, corresponding software applications and properties of proprietary hardware components. Furthermore, SysML-AT supports an automated software generation for run-time environments conforming to IEC 61131-3. A prototypical tool support was realized for adapted SysML Parametric Diagrams (PD) inside an industrial automation software development tool. Coupling the model editor and online data from the provided run-time environment enables direct debugging inside the model. The approach was evaluated by several case studies and additional usability experiments. With the latter, the suitability of the MDE approach for future users was proven.", "published": "2022-12-09T11:00:38Z", "authors": ["Birgit Vogel-Heuser", "Daniel Schuetz", "Timo Frank", "Christoph Legat"], "categories": ["eess.SY"], "doi": "10.1016/j.mechatronics.2014.05.003", "_query": "automated valuation model", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2409.10576v2", "title": "Language Models and Retrieval Augmented Generation for Automated Structured Data Extraction from Diagnostic Reports", "abstract": "Purpose: To develop and evaluate an automated system for extracting structured clinical information from unstructured radiology and pathology reports using open-weights large language models (LMs) and retrieval augmented generation (RAG), and to assess the effects of model configuration variables on extraction performance. Methods and Materials: The study utilized two datasets: 7,294 radiology reports annotated for Brain Tumor Reporting and Data System (BT-RADS) scores and 2,154 pathology reports annotated for isocitrate dehydrogenase (IDH) mutation status. An automated pipeline was developed to benchmark the performance of various LMs and RAG configurations. The impact of model size, quantization, prompting strategies, output formatting, and inference parameters was systematically evaluated. Results: The best performing models achieved over 98% accuracy in extracting BT-RADS scores from radiology reports and over 90% for IDH mutation status extraction from pathology reports. The top model being medical fine-tuned llama3. Larger, newer, and domain fine-tuned models consistently outperformed older and smaller models. Model quantization had minimal impact on performance. Few-shot prompting significantly improved accuracy. RAG improved performance for complex pathology reports but not for shorter radiology reports. Conclusions: Open LMs demonstrate significant potential for automated extraction of structured clinical data from unstructured clinical reports with local privacy-preserving application. Careful model selection, prompt engineering, and semi-automated optimization using annotated data are critical for optimal performance. These approaches could be reliable enough for practical use in research workflows, highlighting the potential for human-machine collaboration in healthcare data extraction.", "published": "2024-09-15T15:21:45Z", "authors": ["Mohamed Sobhi Jabal", "Pranav Warman", "Jikai Zhang", "Kartikeye Gupta", "Ayush Jain", "Maciej Mazurowski", "Walter Wiggins", "Kirti Magudia", "Evan Calabrese"], "categories": ["cs.CL", "cs.IR", "cs.LG"], "doi": "", "_query": "automated valuation model", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/1703.06455v1", "title": "Valuations on Convex Functions", "abstract": "All continuous, SL$(n)$ and translation invariant valuations on the space of convex functions on ${\\mathbb R}^n$ are completely classified.", "published": "2017-03-19T15:47:48Z", "authors": ["Andrea Colesanti", "Monika Ludwig", "Fabian Mussnig"], "categories": ["math.FA", "math.MG"], "doi": "10.1093/imrn/rnx189", "_query": "automated valuation model", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/0903.2679v1", "title": "Valuations and Metrics on Partially Ordered Sets", "abstract": "We extend the definitions of upper and lower valuations on partially ordered sets, and consider the metrics they induce, in particular the metrics available (or not) based on the logarithms of such valuations. Motivating applications in computational linguistics and computational biology are indicated.", "published": "2009-03-16T00:00:04Z", "authors": ["Chris Orum", "Cliff A Joslyn"], "categories": ["math.CO", "cs.IT"], "doi": "", "_query": "automated valuation model", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/1507.01227v1", "title": "A Valuation-Theoretic Approach to Translative-Equidecomposability", "abstract": "All simple translation-invariant valuations on polytopes are classified. As a direct consequence the well-known conditions for translative-equidecomposability are recovered. Furthermore, a simplified proof of the classification of continuous simple translation-invariant valuations is presented.", "published": "2015-07-05T15:07:59Z", "authors": ["Katharina Kusejko", "Lukas Parapatits"], "categories": ["math.MG"], "doi": "", "_query": "automated valuation model", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2405.16912v1", "title": "On the Analytical Properties of a Nonlinear Microscopic Dynamical Model for Connected and Automated Vehicles", "abstract": "In this paper, we propose an integrated dynamical model of Connected and Automated Vehicles (CAVs) which incorporates CAV technologies and a microscopic car-following model to improve safety, efficiency and convenience. We rigorously investigate the analytical properties such as well-posedness, maximum principle, perturbation and stability of the proposed model in some proper functional spaces. Furthermore, we prove that the model is collision free and we derive and explicit lower bound on the distance as a safety measure.", "published": "2024-05-27T08:01:06Z", "authors": ["H. Nick Zinat Matin", "Y. Yeo", "X. Gong", "M. L. Delle Monache"], "categories": ["math.DS"], "doi": "", "_query": "automated valuation model", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2605.12610v1", "title": "Fine-Tuning Models for Automated Code Review Feedback", "abstract": "Large Language Models have introduced new possibilities for programming education through personalized support, content creation, and automated feedback. While recent studies have demonstrated the potential for feedback generation, many techniques rely on proprietary models, raising concerns about cost, computational demands, and the ethical implications of sharing student code. Open LLMs provide an alternative approach, but they do not currently have the capabilities of proprietary models. To address this problem, we investigate whether parameter-efficient fine-tuning (PEFT) and prompt engineering, both of which distil knowledge from a dataset derived from a large, more capable model, can be used to adapt and enhance the quality of feedback generated by the open LLM Code Llama. Feedback quality on buggy Java code was assessed using a combination of student evaluation, manual annotation and the automated metrics BLEU, ROUGE, and BERTScore. Our findings indicate that PEFT leads to notable improvements in feedback quality and significantly outperforms prompt engineering, providing an avenue for developing freely deployable feedback tools that can be effectively used to guide student learning. Student evaluation indicates that learners value the PEFT model's feedback and see it as being equally effective as the proprietary ChatGPT model. Participants suggested that incorporating additional explanation for technical terms in the PEFT model's feedback could be more beneficial. This study demonstrates that fine-tuned models can effectively support critical thinking and guide the design of scalable pedagogical systems.", "published": "2026-05-12T18:02:04Z", "authors": ["Smitha S Kumar", "Michael A Lones", "Manuel Maarek", "Hind Zantout"], "categories": ["cs.SE"], "doi": "", "_query": "automated valuation model", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2609.22737v1", "title": "Special valuations and automorphisms of affine log Calabi--Yau varieties", "abstract": "Let $U$ be an affine log Calabi--Yau variety. Although special and finitely generated valuations are defined using a log CY-Fano compactification of $U$, we prove that the corresponding skeleta in the dual complex are independent of this choice, and hence invariant under $\\mathrm{Aut}(U)$. We also show that a finitely generated valuation is special if and only if it is maximal with respect to a natural partial order induced by regular functions on $U$. We then consider the affine log Calabi--Yau threefold obtained as the complement of the Markov cubic surface in $\\mathbb{A}^3$. We describe the action of the three Vieta involutions on the special skeleton and relate it to the $(\\infty,\\infty,\\infty)$-triangle reflection group on the hyperbolic plane. We also show that, for every finite triangulation of the dual complex, the special skeleton fails to be locally closed on some simplex. Via the cone construction, this provides a counterexample to a conjecture of the author and Xu.", "published": "2026-09-19T03:40:37Z", "authors": ["Yuchen Liu"], "categories": ["math.AG", "math.AC", "math.DS"], "doi": "", "_query": "automated valuation model", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/math/0503397v2", "title": "Theory of valuations on manifolds, I. Linear spaces", "abstract": "This is the first part of a series of articles where we are going to develop theory of valuations on manifolds generalizing the classical theory of continuous valuations on convex subsets of a linear space. In this article we still work only with linear spaces. We introduce a space of smooth (non-translation invariant) valuations on a finite dimensional linear space. We present three descriptions of this space. We describe the canonical multiplicative structure on it generalizing the results for polynomial valuations obtained by the author in Geom. Funct. Anal. 14 (2004).", "published": "2005-03-19T10:27:17Z", "authors": ["Semyon Alesker"], "categories": ["math.MG"], "doi": "", "_query": "automated valuation model", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2001.07295v1", "title": "AutoMATES: Automated Model Assembly from Text, Equations, and Software", "abstract": "Models of complicated systems can be represented in different ways - in scientific papers, they are represented using natural language text as well as equations. But to be of real use, they must also be implemented as software, thus making code a third form of representing models. We introduce the AutoMATES project, which aims to build semantically-rich unified representations of models from scientific code and publications to facilitate the integration of computational models from different domains and allow for modeling large, complicated systems that span multiple domains and levels of abstraction.", "published": "2020-01-21T00:33:40Z", "authors": ["Adarsh Pyarelal", "Marco A. Valenzuela-Escarcega", "Rebecca Sharp", "Paul D. Hein", "Jon Stephens", "Pratik Bhandari", "HeuiChan Lim", "Saumya Debray", "Clayton T. Morrison"], "categories": ["cs.AI", "cs.MM", "cs.SE"], "doi": "", "_query": "automated valuation model", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2507.18476v1", "title": "Automated Code Review Using Large Language Models with Symbolic Reasoning", "abstract": "Code review is one of the key processes in the software development lifecycle and is essential to maintain code quality. However, manual code review is subjective and time consuming. Given its rule-based nature, code review is well suited for automation. In recent years, significant efforts have been made to automate this process with the help of artificial intelligence. Recent developments in Large Language Models (LLMs) have also emerged as a promising tool in this area, but these models often lack the logical reasoning capabilities needed to fully understand and evaluate code. To overcome this limitation, this study proposes a hybrid approach that integrates symbolic reasoning techniques with LLMs to automate the code review process. We tested our approach using the CodexGlue dataset, comparing several models, including CodeT5, CodeBERT, and GraphCodeBERT, to assess the effectiveness of combining symbolic reasoning and prompting techniques with LLMs. Our results show that this approach improves the accuracy and efficiency of automated code review.", "published": "2025-07-24T14:50:27Z", "authors": ["Busra Icoz", "Goksel Biricik"], "categories": ["cs.SE", "cs.AI"], "doi": "", "_query": "automated valuation model", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/0903.3835v1", "title": "Poincare series of collections of plane valuations", "abstract": "In earlier papers there were given formulae for the Poincare series of multi-index filtrations on the ring of germs of functions of two variables defined by collections of valuations corresponding to (reducible) plane curve singularities and by collections of divisorial ones. It was shown that the Poincare series of a collection of divisorial valuations determines the topology of the collection of divisors. Here we give a formula for the Poincare series of a general collection of valuations on the ring of germs of functions of two variables centred at the origin and prove a generalization of the statement that the Poincare series determines the topology of the collection.", "published": "2009-03-23T11:24:31Z", "authors": ["A. Campillo", "F. Delgado", "S. M. Gusein-Zade", "F. Hernando"], "categories": ["math.AG"], "doi": "10.1142/S0129167X10006586", "_query": "automated valuation model", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/1701.04134v1", "title": "A Spatial Interpolation Framework for Efficient Valuation of Large Portfolios of Variable Annuities", "abstract": "Variable Annuity (VA) products expose insurance companies to considerable risk because of the guarantees they provide to buyers of these products. Managing and hedging these risks requires insurers to find the value of key risk metrics for a large portfolio of VA products. In practice, many companies rely on nested Monte Carlo (MC) simulations to find key risk metrics. MC simulations are computationally demanding, forcing insurance companies to invest hundreds of thousands of dollars in computational infrastructure per year. Moreover, existing academic methodologies are focused on fair valuation of a single VA contract, exploiting ideas in option theory and regression. In most cases, the computational complexity of these methods surpasses the computational requirements of MC simulations. Therefore, academic methodologies cannot scale well to large portfolios of VA contracts. In this paper, we present a framework for valuing such portfolios based on spatial interpolation. We provide a comprehensive study of this framework and compare existing interpolation schemes. Our numerical results show superior performance, in terms of both computational efficiency and accuracy, for these methods compared to nested MC simulations. We also present insights into the challenge of finding an effective interpolation scheme in this framework, and suggest guidelines that help us build a fully automated scheme that is efficient and accurate.", "published": "2017-01-16T01:30:02Z", "authors": ["Seyed Amir Hejazi", "Kenneth R. Jackson", "Guojun Gan"], "categories": ["q-fin.CP"], "doi": "", "_query": "automated valuation model", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2105.12087v1", "title": "Quantum algorithm for credit valuation adjustments", "abstract": "Quantum mechanics is well known to accelerate statistical sampling processes over classical techniques. In quantitative finance, statistical samplings arise broadly in many use cases. Here we focus on a particular one of such use cases, credit valuation adjustment (CVA), and identify opportunities and challenges towards quantum advantage for practical instances. To improve the depths of quantum circuits for solving such problem, we draw on various heuristics that indicate the potential for significant improvement over well-known techniques such as reversible logical circuit synthesis. In minimizing the resource requirements for amplitude amplification while maximizing the speedup gained from the quantum coherence of a noisy device, we adopt a recently developed Bayesian variant of quantum amplitude estimation using engineered likelihood functions (ELF). We perform numerical analyses to characterize the prospect of quantum speedup in concrete CVA instances over classical Monte Carlo simulations.", "published": "2021-05-25T17:11:20Z", "authors": ["Javier Alcazar", "Andrea Cadarso", "Amara Katabarwa", "Marta Mauri", "Borja Peropadre", "Guoming Wang", "Yudong Cao"], "categories": ["quant-ph", "q-fin.MF"], "doi": "10.1088/1367-2630/ac5003", "_query": "automated valuation model", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/1910.09132v1", "title": "Multi-Stage Compound Real Options Valuation in Residential PV-Battery Investment", "abstract": "Strategic valuation of efficient and well-timed network investments under uncertain electricity market environment has become increasingly challenging, because there generally exist multiple interacting options in these investments, and failing to systematically consider these options can lead to decisions that undervalue the investment. In our work, a real options valuation (ROV) framework is proposed to determine the optimal strategy for executing multiple interacting options within a distribution network investment, to mitigate the risk of financial losses in the presence of future uncertainties. To demonstrate the characteristics of the proposed framework, we determine the optimal strategy to economically justify the investment in residential PV-battery systems for additional grid supply during peak demand periods. The options to defer, and then expand, are considered as multi-stage compound options, since the option to expand is a subsequent option of the former. These options are valued via the least squares Monte Carlo method, incorporating uncertainty over growing power demand, varying diesel fuel price, and the declining cost of PV-battery technology as random variables. Finally, a sensitivity analysis is performed to demonstrate how the proposed framework responds to uncertain events. The proposed framework shows that executing the interacting options at the optimal timing increases the investment value.", "published": "2019-10-21T03:21:29Z", "authors": ["Yiju Ma", "Kevin Swandi", "Archie Chapman", "Gregor Verbic"], "categories": ["econ.EM", "q-fin.GN"], "doi": "", "_query": "automated valuation model", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2509.23675v1", "title": "PAT-Agent: Autoformalization for Model Checking", "abstract": "Recent advances in large language models (LLMs) offer promising potential for automating formal methods. However, applying them to formal verification remains challenging due to the complexity of specification languages, the risk of hallucinated output, and the semantic gap between natural language and formal logic. We introduce PAT-Agent, an end-to-end framework for natural language autoformalization and formal model repair that combines the generative capabilities of LLMs with the rigor of formal verification to automate the construction of verifiable formal models. In PAT-Agent, a Planning LLM first extracts key modeling elements and generates a detailed plan using semantic prompts, which then guides a Code Generation LLM to synthesize syntactically correct and semantically faithful formal models. The resulting code is verified using the Process Analysis Toolkit (PAT) model checker against user-specified properties, and when discrepancies occur, a Repair Loop is triggered to iteratively correct the model using counterexamples. To improve flexibility, we built a web-based interface that enables users, particularly non-FM-experts, to describe, customize, and verify system behaviors through user-LLM interactions. Experimental results on 40 systems show that PAT-Agent consistently outperforms baselines, achieving high verification success with superior efficiency. The ablation studies confirm the importance of both planning and repair components, and the user study demonstrates that our interface is accessible and supports effective formal modeling, even for users with limited formal methods experience.", "published": "2025-09-28T06:32:14Z", "authors": ["Xinyue Zuo", "Yifan Zhang", "Hongshu Wang", "Yufan Cai", "Zhe Hou", "Jing Sun", "Jin Song Dong"], "categories": ["cs.SE"], "doi": "", "_query": "automated valuation model", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2209.03094v2", "title": "TDCOSMO X. Automated Modeling of 9 Strongly Lensed Quasars and Comparison Between Lens Modeling Software", "abstract": "To use strong gravitational lenses as an astrophysical or cosmological probe, models of their mass distributions are often needed. We present a new, time-efficient automation code for uniform modeling of strongly lensed quasars with GLEE, a lens modeling software, for high-resolution multi-band data. By using the observed positions of the lensed quasars and the spatially extended surface brightness distribution of the lensed quasar host galaxy, we obtain a model of the mass distribution of the lens galaxy. We apply this uniform modeling pipeline to a sample of nine strongly lensed quasars with HST WFC 3 images. The models show in most cases well reconstructed light components and a good alignment between mass and light centroids. We find that the automated modeling code significantly reduces the user input time during the modeling process. The preparation time of required input files is reduced significantly. This automated modeling pipeline can efficiently produce uniform models of extensive lens system samples which can be used for further cosmological analysis. A blind test through a comparison with the results of an independent automated modeling pipeline based on the modeling software Lenstronomy reveals important lessons. Quantities such as Einstein radius, astrometry, mass flattening and position angle are generally robustly determined. Other quantities depend crucially on the quality of the data and the accuracy of the PSF reconstruction. Better data and/or more detailed analysis will be necessary to elevate our automated models to cosmography grade. Nevertheless, our pipeline enables the quick selection of lenses for follow-up monitoring and further modeling, significantly speeding up the construction of cosmography-grade models. This is an important step forward to take advantage of the orders of magnitude increase in the number of lenses expected in the coming decade.", "published": "2022-09-07T12:06:12Z", "authors": ["S. Ertl", "S. Schuldt", "S. H. Suyu", "T. Schmidt", "T. Treu", "S. Birrer", "A. J. Shajib", "D. Sluse"], "categories": ["astro-ph.CO"], "doi": "10.1051/0004-6361/202244909", "_query": "automated valuation model", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2504.09754v1", "title": "Integrating Large Language Models for Automated Structural Analysis", "abstract": "Automated analysis for engineering structures offers considerable potential for boosting efficiency by minimizing repetitive tasks. Although AI-driven methods are increasingly common, no systematic framework yet leverages Large Language Models (LLMs) for automatic structural analysis. To address this gap, we propose a novel framework that integrates LLMs with structural analysis software. LLMs serve as the core engine: they parse structural descriptions from text and translate them into executable Python scripts. Moreover, the framework integrates the generative capabilities of LLMs with code-based finite element (FE) tools like OpenSeesPy. It employs domain-specific prompt design and in-context learning strategies to enhance the LLM's problem-solving capabilities and generative stability, enabling fully automated structural analysis from descriptive text to model outputs. In our experiments, we introduce a well-curated small-scale benchmark dataset of 20 structural analysis word problems (SAWPs) with ground-truth solutions and evaluate the performance of different LLMs within our framework in solving these SAWPs. The role of system instructions, crafted by structural engineers, is also investigated to understand their impact on LLM-driven structural analysis. Additionally, the generative stability of our framework is examined. Through multiple validation experiments on the benchmark, our results demonstrate that the proposed framework can substantially increase the level of automation in solving SAWPs compared to traditional methods. Quantitatively, the framework, built on GPT-4o, achieved 100% accuracy, surpassing GPT-4 (85%), Gemini 1.5 Pro (80%), and Llama-3.3 (30%) on the test examples. Furthermore, integrating domain-specific instructions enhanced performance by 30% on problems with asymmetrical structural configurations.", "published": "2025-04-13T23:10:33Z", "authors": ["Haoran Liang", "Mohammad Talebi Kalaleh", "Qipei Mei"], "categories": ["cs.SE", "cs.LG"], "doi": "", "_query": "automated valuation model", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/1905.04603v15", "title": "A New Stock Market Valuation Measure with Applications to Retirement Planning", "abstract": "We generalize the classic Shiller cyclically adjusted price-earnings ratio (CAPE) used for prediction of future total returns of the stock market. We treat earnings growth as exogenous. The difference between log wealth and log earnings is modeled as an autoregression of order 1 with linear trend 4.6% and Gaussian innovations. Detrending gives us a new valuation measure. Our results disprove the Efficient Market Hypothesis. Therefore, long-run total returns equal long-run earnings growth plus 4.6%. We apply results to retirement planning. A withdrawal process governs how a retired capital owner withdraws a certain fraction of wealth annually. We study the long-term behavior of such processes.", "published": "2019-05-11T22:40:50Z", "authors": ["Andrey Sarantsev"], "categories": ["q-fin.ST"], "doi": "", "_query": "automated valuation model", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2602.11164v1", "title": "Automated Optimization Modeling via a Localizable Error-Driven Perspective", "abstract": "Automated optimization modeling via Large Language Models (LLMs) has emerged as a promising approach to assist complex human decision-making. While post-training has become a pivotal technique to enhance LLMs' capabilities in this domain, its effectiveness is severely constrained by the scarcity and underutilization of high-quality training data. However, through a detailed profiling of error patterns across various problem-response pairs drawn from post-training, we identify two fundamental limitations of existing automated optimization modeling approaches: (L1) the sparsity of error-specific problems and (L2) the sparse rewards associated with difficult problems. We demonstrate that these limitations can result in suboptimal performance in domain-specific post-training for LLMs. To tackle the above two limitations, we propose a novel error-driven learning framework -- namely, auto\\textbf{m}ated opt\\textbf{i}mization modeli\\textbf{n}g via a localizable error-\\textbf{d}riven perspective (MIND) -- that customizes the whole model training framework from data synthesis to post-training. MIND is based on our key observation of the unique localizable patterns in error propagation of optimization modelings, that is, modeling errors may remain localized to specific semantic segments and do not propagate throughout the entire solution. Thus, in contrast to holistic reasoning tasks such as mathematical proofs, MIND leverages the construction of a focused, high-density training corpus and proposes \\textbf{D}ynamic Supervised \\textbf{F}ine-Tuning \\textbf{P}olicy \\textbf{O}ptimization (DFPO) to tackle difficult problems through localized refinement. Experiments on six benchmarks demonstrate that MIND consistently outperforms all the state-of-the-art automated optimization modeling approaches.", "published": "2026-01-17T09:59:01Z", "authors": ["Weiting Liu", "Han Wu", "Yufei Kuang", "Xiongwei Han", "Tao Zhong", "Jianfeng Feng", "Wenlian Lu"], "categories": ["cs.LG", "cs.AI", "cs.CL"], "doi": "", "_query": "automated valuation model", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/0910.4441v1", "title": "Matrix Pairs over Valuation Rings and R-Valued Littlewood-Richardson Fillings", "abstract": "We extend the theory of Littlewood-Richardson fillings (defined over the non-negative integers) to include diagrams with rows and boxes of real-valued length. We realize such fillings as invariants of matrix pairs over rings with a real-valued valuation. We then prove a bijection between pairs of fillings determined by a matrix pair, and connect this to the classical row-switching bijection for Littlewoo-Richardson fillings. We utilize matrix calculations to interpret continuous deformations of generalized Littlewood-Richardson fillings.", "published": "2009-10-23T05:26:36Z", "authors": ["Glenn D. Appleby", "Tamsen Whitehead"], "categories": ["math.CO", "math.AC"], "doi": "", "_query": "automated valuation model", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2104.07481v1", "title": "Advanced Lane Detection Model for the Virtual Development of Highly Automated Functions", "abstract": "Virtual development and prototyping has already become an integral part in the field of automated driving systems (ADS). There are plenty of software tools that are used for the virtual development of ADS. One such tool is CarMaker from IPG Automotive, which is widely used in the scientific community and in the automotive industry. It offers a broad spectrum of implementation and modelling possibilities of the vehicle, driver behavior, control, sensors, and environmental models. Focusing on the virtual development of highly automated driving functions on the vehicle guidance level, it is essential to perceive the environment in a realistic manner. For the longitudinal and lateral path guidance line detection sensors are necessary for the determination of the relevant perceiving vehicle and for the planning of trajectories. For this purpose, a lane sensor model was developed in order to efficiently detect lanes in the simulation environment of CarMaker. The so-called advanced lane detection model (ALDM) is optimized regarding the calculation time and is for the lateral and longitudinal vehicle guidance in CarMaker.", "published": "2021-04-15T14:16:19Z", "authors": ["Philip Pannagger", "Demin Nalic", "Faris Orucevic", "Arno Eichberger", "Branko Rogic"], "categories": ["cs.RO", "eess.SY"], "doi": "", "_query": "automated valuation model", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2508.16638v2", "title": "Empirical Analysis of the Effect of Context in the Task of Automated Essay Scoring in Transformer-Based Models", "abstract": "Automated Essay Scoring (AES) has emerged to prominence in response to the growing demand for educational automation. Providing an objective and cost-effective solution, AES standardises the assessment of extended responses. Although substantial research has been conducted in this domain, recent investigations reveal that alternative deep-learning architectures outperform transformer-based models. Despite the successful dominance in the performance of the transformer architectures across various other tasks, this discrepancy has prompted a need to enrich transformer-based AES models through contextual enrichment. This study delves into diverse contextual factors using the ASAP-AES dataset, analysing their impact on transformer-based model performance. Our most effective model, augmented with multiple contextual dimensions, achieves a mean Quadratic Weighted Kappa score of 0.823 across the entire essay dataset and 0.8697 when trained on individual essay sets. Evidently surpassing prior transformer-based models, this augmented approach only underperforms relative to the state-of-the-art deep learning model trained essay-set-wise by an average of 3.83\\% while exhibiting superior performance in three of the eight sets. Importantly, this enhancement is orthogonal to architecture-based advancements and seamlessly adaptable to any AES model. Consequently, this contextual augmentation methodology presents a versatile technique for refining AES capabilities, contributing to automated grading and evaluation evolution in educational settings.", "published": "2025-08-17T17:17:34Z", "authors": ["Abhirup Chakravarty"], "categories": ["cs.CY", "cs.CL"], "doi": "", "_query": "automated valuation model", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/math/0209263v4", "title": "Hard Lefschetz theorem for valuations, complex integral geometry, and unitarily invariant valuations", "abstract": "We obtain new general results on the structure of the space of translation invariant continuous valuations on convex sets (a version of the hard Lefschetz theorem). Using these and our previous results we obtain explicit characterization of unitarily invariant translation invariant continuous valuations. It implies new integral geometric formulas for real submanifolds in Hermitian spaces generalizing the classical kinematic formulas in Euclidean spaces due to Poincaré, Chern, Santaló, and others.", "published": "2002-09-20T10:50:42Z", "authors": ["Semyon Alesker"], "categories": ["math.MG", "math.DG"], "doi": "", "_query": "automated valuation model", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2505.17661v1", "title": "Automated scientific minimization of regret", "abstract": "We introduce automated scientific minimization of regret (ASMR) -- a framework for automated computational cognitive science. Building on the principles of scientific regret minimization, ASMR leverages Centaur -- a recently proposed foundation model of human cognition -- to identify gaps in an interpretable cognitive model. These gaps are then addressed through automated revisions generated by a language-based reasoning model. We demonstrate the utility of this approach in a multi-attribute decision-making task, showing that ASMR discovers cognitive models that predict human behavior at noise ceiling while retaining interpretability. Taken together, our results highlight the potential of ASMR to automate core components of the cognitive modeling pipeline.", "published": "2025-05-23T09:26:43Z", "authors": ["Marcel Binz", "Akshay K. Jagadish", "Milena Rmus", "Eric Schulz"], "categories": ["cs.LG"], "doi": "", "_query": "automated valuation model", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2602.10854v1", "title": "Automated Model Design using Gated Neuron Selection in Telecom", "abstract": "The telecommunications industry is experiencing rapid growth in adopting deep learning for critical tasks such as traffic prediction, signal strength prediction, and quality of service optimisation. However, designing neural network architectures for these applications remains challenging and time-consuming, particularly when targeting compact models suitable for resource-constrained network environments. Therefore, there is a need for automating the model design process to create high-performing models efficiently. This paper introduces TabGNS (Tabular Gated Neuron Selection), a novel gradient-based Neural Architecture Search (NAS) method specifically tailored for tabular data in telecommunications networks. We evaluate TabGNS across multiple telecommunications and generic tabular datasets, demonstrating improvements in prediction performance while reducing the architecture size by 51-82% and reducing the search time by up to 36x compared to state-of-the-art tabular NAS methods. Integrating TabGNS into the model lifecycle management enables automated design of neural networks throughout the lifecycle, accelerating deployment of ML solutions in telecommunications networks.", "published": "2026-02-11T13:40:48Z", "authors": ["Adam Orucu", "Marcus Medhage", "Farnaz Moradi", "Andreas Johnsson", "Sarunas Girdzijauskas"], "categories": ["cs.LG"], "doi": "", "_query": "automated valuation model", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2312.13038v1", "title": "AutoXPCR: Automated Multi-Objective Model Selection for Time Series Forecasting", "abstract": "Automated machine learning (AutoML) streamlines the creation of ML models. While most methods select the \"best\" model based on predictive quality, it's crucial to acknowledge other aspects, such as interpretability and resource consumption. This holds particular importance in the context of deep neural networks (DNNs), as these models are often perceived as computationally intensive black boxes. In the challenging domain of time series forecasting, DNNs achieve stunning results, but specialized approaches for automatically selecting models are scarce. In this paper, we propose AutoXPCR - a novel method for automated and explainable multi-objective model selection. Our approach leverages meta-learning to estimate any model's performance along PCR criteria, which encompass (P)redictive error, (C)omplexity, and (R)esource demand. Explainability is addressed on multiple levels, as our interactive framework can prioritize less complex models and provide by-product explanations of recommendations. We demonstrate practical feasibility by deploying AutoXPCR on over 1000 configurations across 114 data sets from various domains. Our method clearly outperforms other model selection approaches - on average, it only requires 20% of computation costs for recommending models with 90% of the best-possible quality.", "published": "2023-12-20T14:04:57Z", "authors": ["Raphael Fischer", "Amal Saadallah"], "categories": ["cs.LG"], "doi": "", "_query": "automated valuation model", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2507.16275v1", "title": "Valuated Delta Matroids and Principal Minors of Hermitian matrices", "abstract": "In this paper we introduce valuated $Δ$-matroids, a natural generalization of two objects of study in matroid theory: valuated matroids and $Δ$-matroids. We show that these objects exhibit nice properties analogous to ordinary valuated matroids. We also show that these objects arise as the valuations of principal minors of a Hermitian matrix over a valued field, generalizing other forms of $Δ$-matroid representability.", "published": "2025-07-22T06:39:10Z", "authors": ["Nathan Cheung", "Tracy Chin", "Gaku Liu", "Cynthia Vinzant"], "categories": ["math.CO", "math.AC"], "doi": "", "_query": "automated valuation model", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2512.10681v1", "title": "Non-Stable $K_1$-Functors of Discrete Valuation Rings Containing a Field", "abstract": "Let $k$ be a field, and let $G$ be a simply connected semisimple k-group which is isotropic and contains a strictly proper parabolic $k$-subgroup $P$. Let $D$ be a discrete valuation ring which is a local ring of a smooth algebraic curve over $k$. Let $K$ be the fraction field of $D$. We show that the corresponding non-stable $K_1$-functor (for $G$ and $P$, also called the Whitehead group of $G$) coincide over $D$ and $K$. As a consequence, $K^G_1 (D)$ coincides with the (generalized) Manin's $R$-equivalence class group of $G(D)$.", "published": "2025-12-11T14:29:11Z", "authors": ["Philippe Gille", "Anastasia Stavrova"], "categories": ["math.AG"], "doi": "", "_query": "automated valuation model", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/1709.02717v2", "title": "Enumerating traceless matrices over compact discrete valuation rings", "abstract": "We enumerate traceless square matrices over finite quotients of compact discrete valuation rings by their image sizes. We express the associated rational generating functions in terms of statistics on symmetric and hyperoctahedral groups, viz. Coxeter groups of types A and B, respectively. These rational functions may also be interpreted as local representation zeta functions associated to the members of an infinite family of finitely generated class-2-nilpotent groups. As a byproduct of our work, we obtain descriptions of the numbers of traceless square matrices over a finite field of fixed rank in terms of statistics on the hyperoctahedral groups.", "published": "2017-09-08T14:25:16Z", "authors": ["Angela Carnevale", "Shai Shechter", "Christopher Voll"], "categories": ["math.GR", "math.CO"], "doi": "", "_query": "automated valuation model", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2107.10920v2", "title": "Worst case expansions of complete theories", "abstract": "Given a complete theory $T$ and a subset $Y \\subseteq X^k$, we precisely determine the {\\em worst case complexity}, with respect to further monadic expansions, of an expansion $(M,Y)$ by $Y$ of a model $M$ of $T$ with universe $X$. In particular, although by definition monadically stable/NIP theories are robust under arbitrary monadic expansions, we show that monadically NFCP (equivalently, mutually algebraic) theories are the largest class that is robust under anything beyond monadic expansions. We also exhibit a paradigmatic structure for the failure of each of monadic NFCP/stable/NIP and prove each of these paradigms definably embeds into a monadic expansion of a sufficiently saturated model of any theory without the corresponding property.", "published": "2021-07-22T20:48:53Z", "authors": ["Samuel Braunfeld", "Michael C. Laskowski"], "categories": ["math.LO"], "doi": "10.2140/mt.2022.1.15", "_query": "automated valuation model", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2209.06437v1", "title": "Weighted Envy-Freeness for Submodular Valuations", "abstract": "We investigate the fair allocation of indivisible goods to agents with possibly different entitlements represented by weights. Previous work has shown that guarantees for additive valuations with existing envy-based notions cannot be extended to the case where agents have matroid-rank (i.e., binary submodular) valuations. We propose two families of envy-based notions for matroid-rank and general submodular valuations, one based on the idea of transferability and the other on marginal values. We show that our notions can be satisfied via generalizations of rules such as picking sequences and maximum weighted Nash welfare. In addition, we introduce welfare measures based on harmonic numbers, and show that variants of maximum weighted harmonic welfare offer stronger fairness guarantees than maximum weighted Nash welfare under matroid-rank valuations.", "published": "2022-09-14T06:17:41Z", "authors": ["Luisa Montanari", "Ulrike Schmidt-Kraepelin", "Warut Suksompong", "Nicholas Teh"], "categories": ["cs.GT", "econ.TH"], "doi": "10.1007/s00355-025-01599-6", "_query": "automated valuation model", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/math/0012145v1", "title": "Invitation to higher local fields, Part I, section 14: Explicit abelian extensions of complete discrete valuation fields", "abstract": "This work presents author's explicit methods of constructing abelian extensions of complete discrete valuation fields. His approach to explicit equations of a cyclic extension of degree p^n which contains a given cyclic extension of degree p is explained.", "published": "2000-12-18T03:48:54Z", "authors": ["Igor Zhukov"], "categories": ["math.NT", "math.AG"], "doi": "", "_query": "automated valuation model", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/1901.04937v2", "title": "Computation of residual polynomial operators of inductive valuations", "abstract": "Let $(K,v)$ be a valued field, and $μ$ an inductive valuation on $K[x]$ extending $v$. Let $G_μ$ be the graded algebra of $μ$ over $K[x]$, and $κ$ the maximal subfield of the subring of $G_μ$ formed by the homogeneous elements of degree zero. In this paper, we find an algorithm to compute the field $κ$ and the residual polynomial operator $R_μ: K[x]\\toκ[y]$, where $y$ is another indeterminate, without any need to perform computations in the graded algebra. This leads to an OM algorithm to compute the factorization of separable defectless polynomials over henselian fields.", "published": "2019-01-15T17:04:19Z", "authors": ["Nathália Moraes de Oliveira", "Enric Nart"], "categories": ["math.AG", "math.NT"], "doi": "", "_query": "automated valuation model", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2509.14265v1", "title": "Evolution of Kernels: Automated RISC-V Kernel Optimization with Large Language Models", "abstract": "Automated kernel design is critical for overcoming software ecosystem barriers in emerging hardware platforms like RISC-V. While large language models (LLMs) have shown promise for automated kernel optimization, demonstrating success in CUDA domains with comprehensive technical documents and mature codebases, their effectiveness remains unproven for reference-scarce domains like RISC-V. We present Evolution of Kernels (EoK), a novel LLM-based evolutionary program search framework that automates kernel design for domains with limited reference material. EoK mitigates reference scarcity by mining and formalizing reusable optimization ideas (general design principles + actionable thoughts) from established kernel libraries' development histories; it then guides parallel LLM explorations using these ideas, enriched via Retrieval-Augmented Generation (RAG) with RISC-V-specific context, prioritizing historically effective techniques. Empirically, EoK achieves a median 1.27x speedup, surpassing human experts on all 80 evaluated kernel design tasks and improving upon prior LLM-based automated kernel design methods by 20%. These results underscore the viability of incorporating human experience into emerging domains and highlight the immense potential of LLM-based automated kernel optimization.", "published": "2025-09-14T08:11:06Z", "authors": ["Siyuan Chen", "Zhichao Lu", "Qingfu Zhang"], "categories": ["cs.SE", "cs.AI"], "doi": "", "_query": "automated valuation model", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2211.15981v3", "title": "Characterizing absolutely irreducible integer-valued polynomials over discrete valuation domains", "abstract": "Rings of integer-valued polynomials are known to be atomic, non-factorial rings furnishing examples for both irreducible elements for which all powers factor uniquely (\\emph{absolutely irreducibles}) and irreducible elements where some power has a factorization different from the trivial one. In this paper, we study irreducible polynomials $F \\in \\operatorname{Int}(R)$ where $R$ is a discrete valuation domain with finite residue field and show that it is possible to explicitly determine a number $S\\in \\mathbb{N}$ that reduces the absolute irreducibility of $F$ to the unique factorization of $F^S$. To this end, we establish a connection between the factors of powers of $F$ and the kernel of a certain linear map that we associate to $F$. This connection yields a characterization of absolute irreducibility in terms of this so-called \\emph{fixed divisor kernel}. Given a non-trivial element $\\boldsymbol{v}$ of this kernel, we explicitly construct non-trivial factorizations of $F^k$, provided that $k\\ge L$, where $L$ depends on $F$ as well as the choice of $\\boldsymbol{v}$. We further show that this bound cannot be improved in general. Additionally, we provide other (larger) lower bounds for $k$, one of which only depends on the valuation of the denominator of $F$ and the size of the residue class field of $R$.", "published": "2022-11-29T07:29:16Z", "authors": ["Moritz Hiebler", "Sarah Nakato", "Roswitha Rissner"], "categories": ["math.AC"], "doi": "10.1016/j.jalgebra.2023.06.026", "_query": "automated valuation model", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/math/0103242v1", "title": "L-series and their 2-adic and 3-adic valuations at s=1 attached to CM elliptic curves", "abstract": "$L-$series attached to two classical families of elliptic curves with complex multiplications are studied over number fields, formulae for their special values at $s=1, $ bound of the values, and criterion of reaching the bound are given. Let $ E_1: y^{2}=x^{3}-D_1 x $ be elliptic curves over the Gaussian field $K=\\Q(\\sqrt{-1}), $ with $ D_1 =π_{1} ... π_{n} $ or $ D_1 =π_{1} ^{2}... π_{r} ^{2} π_{r+1} ... π_{n}$, where $π_{1}, ..., π_{n}$ are distinct primes in $K$. A formula for special values of Hecke $L-$series attached to such curves expressed by Weierstrass $\\wp-$function are given; a lower bound of 2-adic valuations of these values of Hecke $L-$series as well as a criterion for reaching these bounds are obtained. Furthermore, let $ E_{2}: y^{2}=x^{3}-2^{4}3^{3}D_2^{2} $ be elliptic curves over the quadratic field $ \\Q(\\sqrt{-3}) $ with $ D_2 =π_{1} ... π_{n}, $ where $π_{1}, ..., π_{n}$ are distinct primes of $\\Q(\\sqrt{-3})$, similar results as above but for $3-adic$ valuation are also obtained. These results are consistent with the predictions of the conjecture of Birch and Swinnerton-Dyer, and develop some results in recent literature for more special case and for $2-adic$ valuation.", "published": "2001-03-15T00:00:00Z", "authors": ["Derong Qiu", "Xianke Zhang"], "categories": ["math.NT"], "doi": "10.4064/aa103-1-6", "_query": "automated valuation model", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2303.04926v1", "title": "Automated Cyber Defence: A Review", "abstract": "Within recent times, cybercriminals have curated a variety of organised and resolute cyber attacks within a range of cyber systems, leading to consequential ramifications to private and governmental institutions. Current security-based automation and orchestrations focus on automating fixed purpose and hard-coded solutions, which are easily surpassed by modern-day cyber attacks. Research within Automated Cyber Defence will allow the development and enabling intelligence response by autonomously defending networked systems through sequential decision-making agents. This article comprehensively elaborates the developments within Automated Cyber Defence through a requirement analysis divided into two sub-areas, namely, automated defence and attack agents and Autonomous Cyber Operation (ACO) Gyms. The requirement analysis allows the comparison of automated agents and highlights the importance of ACO Gyms for their continual development. The requirement analysis is also used to critique ACO Gyms with an overall aim to develop them for deploying automated agents within real-world networked systems. Relevant future challenges were addressed from the overall analysis to accelerate development within the area of Automated Cyber Defence.", "published": "2023-03-08T22:37:50Z", "authors": ["Sanyam Vyas", "John Hannay", "Andrew Bolton", "Professor Pete Burnap"], "categories": ["cs.CR", "cs.AI"], "doi": "", "_query": "automated valuation model", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2503.22657v1", "title": "dolphin: A fully automated forward modeling pipeline powered by artificial intelligence for galaxy-scale strong lenses", "abstract": "Strong gravitational lensing is a powerful tool for probing the internal structure and evolution of galaxies, the nature of dark matter, and the expansion history of the Universe, among many other scientific applications. For almost all of these science cases, modeling the lensing mass distribution is essential. For that, forward modeling of imaging data to the pixel level is the standard method used for galaxy-scale lenses. However, the traditional workflow of forward lens modeling necessitates a significant amount of human investigator time, requiring iterative tweaking and tuning of the model settings through trial and error. An automated lens modeling pipeline can substantially reduce the need for human investigator time. In this paper, we present \\textsc{dolphin}, an automated lens modeling pipeline that combines artificial intelligence with the traditional forward modeling framework to enable full automation of the modeling workflow. \\textsc{dolphin} uses a neural network model to perform visual recognition of the strong lens components, then autonomously sets up a lens model with appropriate complexity, and fits the model with the modeling engine, lenstronomy. Thanks to the versatility of lenstronomy, dolphin can autonomously model both galaxy-galaxy and galaxy-quasar strong lenses.", "published": "2025-03-28T17:43:52Z", "authors": ["Anowar J. Shajib", "Nafis Sadik Nihal", "Chin Yi Tan", "Vedant Sahu", "Simon Birrer", "Tommaso Treu", "Joshua Frieman"], "categories": ["astro-ph.IM", "astro-ph.CO", "astro-ph.GA"], "doi": "", "_query": "automated valuation model", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/1308.2571v1", "title": "GL(n) equivariant Minkowski valuations", "abstract": "A classification of all continuous GL(n) equivariant Minkowski valuations on convex bodies in $\\mathbb{R}^n$ is established. Together with recent results of F.E. Schuster and the author, this article therefore completes the description of all continuous GL(n) intertwining Minkowski valuations.", "published": "2013-08-12T14:08:42Z", "authors": ["Thomas Wannerer"], "categories": ["math.MG"], "doi": "", "_query": "automated valuation model", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2307.01202v1", "title": "Predictive Patentomics: Forecasting Innovation Success and Valuation with ChatGPT", "abstract": "Analysis of innovation has been fundamentally limited by conventional approaches to broad, structural variables. This paper pushes the boundaries, taking an LLM approach to patent analysis with the groundbreaking ChatGPT technology. OpenAI's state-of-the-art textual embedding accesses complex information about the quality and impact of each invention to power deep learning predictive models. The nuanced embedding drives a 24% incremental improvement in R-squared predicting patent value and clearly isolates the worst and best applications. These models enable a revision of the contemporary Kogan, Papanikolaou, Seru, and Stoffman (2017) valuation of patents by a median deviation of 1.5 times, accounting for potential institutional predictions. Furthermore, the market fails to incorporate timely information about applications; a long-short portfolio based on predicted acceptance rates achieves significant abnormal returns of 3.3% annually. The models provide an opportunity to revolutionize startup and small-firm corporate policy vis-a-vis patenting.", "published": "2023-06-22T13:21:20Z", "authors": ["Stephen Yang"], "categories": ["cs.LG", "cs.AI", "cs.CL", "cs.DL"], "doi": "", "_query": "automated valuation model", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2411.07569v1", "title": "Towards Automated Model Design on Recommender Systems", "abstract": "The increasing popularity of deep learning models has created new opportunities for developing AI-based recommender systems. Designing recommender systems using deep neural networks requires careful architecture design, and further optimization demands extensive co-design efforts on jointly optimizing model architecture and hardware. Design automation, such as Automated Machine Learning (AutoML), is necessary to fully exploit the potential of recommender model design, including model choices and model-hardware co-design strategies. We introduce a novel paradigm that utilizes weight sharing to explore abundant solution spaces. Our paradigm creates a large supernet to search for optimal architectures and co-design strategies to address the challenges of data multi-modality and heterogeneity in the recommendation domain. From a model perspective, the supernet includes a variety of operators, dense connectivity, and dimension search options. From a co-design perspective, it encompasses versatile Processing-In-Memory (PIM) configurations to produce hardware-efficient models. Our solution space's scale, heterogeneity, and complexity pose several challenges, which we address by proposing various techniques for training and evaluating the supernet. Our crafted models show promising results on three Click-Through Rates (CTR) prediction benchmarks, outperforming both manually designed and AutoML-crafted models with state-of-the-art performance when focusing solely on architecture search. From a co-design perspective, we achieve 2x FLOPs efficiency, 1.8x energy efficiency, and 1.5x performance improvements in recommender models.", "published": "2024-11-12T06:03:47Z", "authors": ["Tunhou Zhang", "Dehua Cheng", "Yuchen He", "Zhengxing Chen", "Xiaoliang Dai", "Liang Xiong", "Yudong Liu", "Feng Cheng", "Yufan Cao", "Feng Yan", "Hai Li", "Yiran Chen", "Wei Wen"], "categories": ["cs.IR"], "doi": "", "_query": "automated valuation model", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/1703.06867v1", "title": "Translation invariant valuations on quasi-concave functions", "abstract": "We study real-valued, continuous and translation invariant valuations defined on the space of quasi-concave functions of N variables. In particular, we prove a homogeneous decomposition theorem of McMullen type, and we find a representation formula for those valuations which are N-homogeneous. Moreover, we introduce the notion of Klain's functions for these type of valuations.", "published": "2017-03-20T17:46:30Z", "authors": ["Andrea Colesanti", "Nico Lombardi", "Lukas Parapatits"], "categories": ["math.MG"], "doi": "", "_query": "automated valuation model", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2510.22678v3", "title": "Abhyankar valuations, Prüfer-Manis valuations, and perfectoid Tate algebras", "abstract": "Let $K$ be a perfectoid field. We describe all quotient fields of the perfectoid Tate algebra\\begin{equation*}T_{n,K}^{\\text{perfd}}=K\\langle X_{1}^{1/p^{\\infty}},\\dots, X_{n}^{1/p^{\\infty}}\\rangle\\end{equation*}in any number $n\\geq1$ of variables in terms of (completed perfections of) the nonarchimedean fields $K_{r_1,\\dots,r_l}$ occuring in Berkovich geometry. We prove that every quotient field\\begin{equation*}L=T_{n,K}^{\\text{perfd}}/\\mathfrak{m}\\end{equation*}is a so-called \\textit{semi-immediate} extension of $K_{r_1,\\dots,r_l}^{\\text{perfd}}$ for some\\begin{equation*}l\\leq\\min(n-\\text{ht}(\\mathfrak{m}^{\\flat}\\cap (T_{n,K^{\\flat}})^{\\text{coperf}}),n-1), \\end{equation*}which pins down the value groups and the residue fields of the possible quotient fields $L$. Moreover, we show that if\\begin{equation*}\\mathfrak{m}^{\\flat}\\cap(T_{n,K^{\\flat}})^{\\text{coperf}}\\neq 0,\\end{equation*} at least one of the radii $r_{i}$ has to be irrational, i.e.,\\begin{equation*}r_{i}\\not\\in\\sqrt{|K^{\\times}|}.\\end{equation*} The main ingredient in our proof is the notion of \\textit{topologically simple} valuations, which generalize type (IV) points in the classification of points on $\\text{Spa}(K\\langle T\\rangle)$ to the case of higher-dimensional polydisks. We also consider \\textit{rational Abhyankar} valuations and \\textit{irrational Abhyankar} valuations, which generalize type (II) and (III) points, respectively. We deduce our main result from a description of topologically simple absolute values and of Abhyankar absolute values on usual Tate algebra. Along the way, we also show that our topologically simple valuations are the same as Prüfer-Manis valuations in the sense of Knebusch-Zhang. Finally, we also show that all allowed possibilities for the quotient fields $L$ do indeed occur (i.e., the above bound $l\\leq n-1$ is optimal) by generalizing an example of Gleason.", "published": "2025-10-26T13:41:23Z", "authors": ["Dimitri Dine", "Jack J Garzella"], "categories": ["math.NT", "math.AC", "math.AG"], "doi": "", "_query": "automated valuation model", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2401.03741v2", "title": "Enhanced Automated Code Vulnerability Repair using Large Language Models", "abstract": "This research addresses the complex challenge of automated repair of code vulnerabilities, vital for enhancing digital security in an increasingly technology-driven world. The study introduces a novel and efficient format for the representation of code modification, using advanced Large Language Models (LLMs) such as Code Llama and Mistral. These models, fine-tuned on datasets featuring C code vulnerabilities, significantly improve the accuracy and adaptability of automated code repair techniques. A key finding is the enhanced repair accuracy of these models when compared to previous methods such as VulRepair, which underscores their practical utility and efficiency. The research also offers a critical assessment of current evaluation metrics, such as perfect predictions, and their limitations in reflecting the true capabilities of automated repair models in real-world scenarios. Following this, it underscores the importance of using test datasets devoid of train samples, emphasizing the need for dataset integrity to enhance the effectiveness of LLMs in code repair tasks. The significance of this work is its contribution to digital security, setting new standards for automated code vulnerability repair and paving the way for future advancements in the fields of cybersecurity and artificial intelligence. The study does not only highlight the potential of LLMs in enhancing code security but also fosters further exploration and research in these crucial areas.", "published": "2024-01-08T09:01:29Z", "authors": ["David de-Fitero-Dominguez", "Eva Garcia-Lopez", "Antonio Garcia-Cabot", "Jose-Javier Martinez-Herraiz"], "categories": ["cs.SE", "cs.CL"], "doi": "10.1016/j.engappai.2024.109291", "_query": "automated valuation model", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2503.02678v1", "title": "Smart Reaction Templating: A Graph-Based Method for Automated Molecular Dynamics Input Generation", "abstract": "Accurately modeling chemical reactions in molecular dynamics simulations requires detailed pre- and post-reaction templates, often created through labor-intensive manual workflows. This work introduces a Python-based algorithm that automates the generation of reaction templates for the LAMMPS REACTION package, leveraging graph-theoretical principles and sub-graph isomorphism techniques. By representing molecular systems as mathematical graphs, the method enables automated identification of conserved molecular domains, reaction sites, and atom mappings, significantly reducing manual effort. The algorithm was validated on three case studies: poly-addition, poly-condensation, and chain polymerization, demonstrating its ability to map conserved regions, identify reaction-initiating atoms, and resolve challenges such as symmetric reactants and indistinguishable atoms. Additionally, the generated templates were optimized for computational efficiency by retaining only essential reactive domains, ensuring scalability and consistency in high-throughput workflows for computational chemistry, materials science, and machine learning applications. Future work will focus on extending the method to mixed organic-inorganic systems, incorporating adaptive scoring mechanisms, and integrating quantum mechanical calculations to enhance its applicability.", "published": "2025-03-04T14:49:38Z", "authors": ["Julian Konrad", "Robert Meißner"], "categories": ["cs.CE", "cond-mat.mtrl-sci"], "doi": "10.1021/acs.jcim.5c00445", "_query": "automated valuation model", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/1911.06594v1", "title": "Integrating Threat Modeling and Automated Test Case Generation into Industrialized Software Security Testing", "abstract": "Industrial Internet of Things (IIoT) application provide a whole new set of possibilities to drive efficiency of industrial production forward. However, with the higher degree of integration among systems, comes a plethora of newthreats to the latter, as they are not yet designed to be broadly reachable and interoperable. To mitigate these vast amount of new threats, systematic and automated test methods are necessary. This comprehensiveness can be achieved by thorough threat modeling. In order to automate security test, we present an approach to automate the testing process from threat modeling onward, closing the gap between threat modeling and automated test case generation.", "published": "2019-11-15T12:55:56Z", "authors": ["Stefan Marksteiner", "Rudolf Ramler", "Hannes Sochor"], "categories": ["cs.CR", "cs.SE"], "doi": "10.1145/3360664.3362698", "_query": "automated valuation model", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/1705.02288v3", "title": "Automated Discovery of Process Models from Event Logs: Review and Benchmark", "abstract": "Process mining allows analysts to exploit logs of historical executions of business processes to extract insights regarding the actual performance of these processes. One of the most widely studied process mining operations is automated process discovery. An automated process discovery method takes as input an event log, and produces as output a business process model that captures the control-flow relations between tasks that are observed in or implied by the event log. Various automated process discovery methods have been proposed in the past two decades, striking different tradeoffs between scalability, accuracy and complexity of the resulting models. However, these methods have been evaluated in an ad-hoc manner, employing different datasets, experimental setups, evaluation measures and baselines, often leading to incomparable conclusions and sometimes unreproducible results due to the use of closed datasets. This article provides a systematic review and comparative evaluation of automated process discovery methods, using an open-source benchmark and covering twelve publicly-available real-life event logs, twelve proprietary real-life event logs, and nine quality metrics. The results highlight gaps and unexplored tradeoffs in the field, including the lack of scalability of some methods and a strong divergence in their performance with respect to the different quality metrics used.", "published": "2017-05-05T16:24:15Z", "authors": ["Adriano Augusto", "Raffaele Conforti", "Marlon Dumas", "Marcello La Rosa", "Fabrizio Maria Maggi", "Andrea Marrella", "Massimo Mecella", "Allar Soo"], "categories": ["cs.SE"], "doi": "", "_query": "automated valuation model", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2210.05838v2", "title": "Pontryagin Duality for Modules over Compact Discrete Valuation Rings", "abstract": "We establish an analogue of Pontryagin duality for modules over compact discrete valuation rings $R$. Namely, we define the dual of a topological $R$ module to be its continuous $R$-module homomorphisms into $K/R$, the quotient module of the fraction field by its ring of integers. It is established that for locally compact $R$-modules the double dual map is an isomorphism and homeomorphism. Additionally, given a non-topological $R$-module a canonical topology is constructed, uniquely defined so that the double dual map will be injective and continuous. Finally, the functor assigning a module to itself equipped with canonical topology is shown to be fully faithful, allowing one to recontextualize the topological statements in purely algebraic forms.", "published": "2022-10-11T23:51:35Z", "authors": ["Milo Moses"], "categories": ["math.AC"], "doi": "", "_query": "automated valuation model", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2310.16666v1", "title": "Tate Duality and Transfer for Symmetric Algebras Over Complete Discrete Valuation Rings", "abstract": "We show that dualising transfer maps in Hochschild cohomology of symmetric algebras over complete discrete valuations rings commutes with Tate duality. This is analogous to a similar result for Tate cohomology of symmetric algebras over fields. We interpret both results in the broader context of Calabi-Yau triangulated categories.", "published": "2023-10-25T14:28:48Z", "authors": ["Markus Linckelmann"], "categories": ["math.RT", "math.RA"], "doi": "10.1017/S0013091524000671", "_query": "automated valuation model", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2505.17228v2", "title": "Automated Capability Evaluation of Foundation Models", "abstract": "Current evaluation frameworks for foundation models rely heavily on static, manually curated benchmarks, limiting their ability to capture the full breadth of model capabilities. This paper introduces Active learning for Capability Evaluation (ACE), a novel framework for scalable, automated, and fine-grained evaluation of foundation models. ACE leverages the knowledge embedded in powerful frontier models to decompose a domain into semantically meaningful capabilities and generates diverse evaluation tasks, significantly reducing human effort. In Mathematics, ACE generated 433 capabilities and 11,800 tasks, covering 94% of Wikipedia-defined skills in the domain while introducing novel, coherent ones. To maximize efficiency, ACE fits a capability model in latent semantic space, allowing reliable approximation of a subject model's performance by evaluating only a subset of capabilities via active learning. It reaches within 0.01 RMSE of exhaustive evaluation by evaluating less than half of capabilities. Compared to static datasets, ACE provides more balanced coverage and uncovers fine-grained differences that aggregate metrics fail to capture. Our results demonstrate that ACE provides a more complete and informative picture of model capabilities, which is essential for safe and well-informed deployment of foundation models.", "published": "2025-05-22T19:09:57Z", "authors": ["Arash Afkanpour", "Omkar Dige", "Fatemeh Tavakoli", "Negin Baghbanzadeh", "Farnaz Kohankhaki", "Elham Dolatabadi"], "categories": ["cs.LG"], "doi": "", "_query": "automated valuation model", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/math/0607449v2", "title": "Convolution of convex valuations", "abstract": "We show that the natural \"convolution\" on the space of smooth, even, translation-invariant convex valuations on a euclidean space $V$, obtained by intertwining the product and the duality transform of S. Alesker, may be expressed in terms of Minkowski sum. Furthermore the resulting product extends naturally to odd valuations as well. Based on this technical result we give an application to integral geometry, generalizing Hadwiger's additive kinematic formula for $SO(V)$ to general compact groups $G \\subset O(V)$ acting transitively on the sphere: it turns out that these formulas are in a natural sense dual to the usual (intersection) kinematic formulas.", "published": "2006-07-19T06:35:03Z", "authors": ["Andreas Bernig", "Joseph H. G. Fu"], "categories": ["math.DG"], "doi": "", "_query": "automated valuation model", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/1812.10793v3", "title": "Automated Adaptation Strategies for Stream Learning", "abstract": "Automation of machine learning model development is increasingly becoming an established research area. While automated model selection and automated data pre-processing have been studied in depth, there is, however, a gap concerning automated model adaptation strategies when multiple strategies are available. Manually developing an adaptation strategy can be time consuming and costly. In this paper we address this issue by proposing the use of flexible adaptive mechanism deployment for automated development of adaptation strategies. Experimental results after using the proposed strategies with five adaptive algorithms on 36 datasets confirm their viability. These strategies achieve better or comparable performance to the custom adaptation strategies and the repeated deployment of any single adaptive mechanism.", "published": "2018-12-27T18:43:21Z", "authors": ["Rashid Bakirov", "Bogdan Gabrys", "Damien Fay"], "categories": ["cs.LG", "stat.ML"], "doi": "", "_query": "automated valuation model", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2411.19881v1", "title": "EF1 Allocations for Identical Trilean and Separable Single-Peaked Valuations", "abstract": "In the fair division of items among interested agents, envy-freeness is possibly the most favoured and widely studied formalisation of fairness. For indivisible items, envy-free allocations may not exist in trivial cases, and hence research and practice focus on relaxations, particularly envy-freeness up to one item (EF1). A significant reason for the popularity of EF1 allocations is its simple fact of existence. It is known that EF1 allocations exist for two agents with arbitrary valuations; agents with doubly-monotone valuations; agents with Boolean valuations; and identical agents with negative Boolean valuations. We consider two new but natural classes of valuations, and partly extend results on the existence of EF1 allocations to these valuations. Firstly, we consider trilean valuations - an extension of Boolean valuations - when the value of any subset is 0, $a$, or $b$ for any integers $a$ and $b$. Secondly, we define separable single-peaked valuations, when the set of items is partitioned into types. For each type, an agent's value is a single-peaked function of the number of items of the type. The value for a set of items is the sum of values for the different types. We prove EF1 existence for identical trilean valuations for any number of agents, and for separable single-peaked valuations for three agents. For both classes of valuations, we also show that EFX allocations do not exist.", "published": "2024-11-29T17:47:25Z", "authors": ["Umang Bhaskar", "Gunjan Kumar", "Yeshwant Pandit", " Rakshitha"], "categories": ["cs.GT"], "doi": "", "_query": "automated valuation model", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2411.10665v1", "title": "AutoIoT: Automated IoT Platform Using Large Language Models", "abstract": "IoT platforms, particularly smart home platforms providing significant convenience to people's lives such as Apple HomeKit and Samsung SmartThings, allow users to create automation rules through trigger-action programming. However, some users may lack the necessary knowledge to formulate automation rules, thus preventing them from fully benefiting from the conveniences offered by smart home technology. To address this, smart home platforms provide pre-defined automation policies based on the smart home devices registered by the user. Nevertheless, these policies, being pre-generated and relatively simple, fail to adequately cover the diverse needs of users. Furthermore, conflicts may arise between automation rules, and integrating conflict detection into the IoT platform increases the burden on developers. In this paper, we propose AutoIoT, an automated IoT platform based on Large Language Models (LLMs) and formal verification techniques, designed to achieve end-to-end automation through device information extraction, LLM-based rule generation, conflict detection, and avoidance. AutoIoT can help users generate conflict-free automation rules and assist developers in generating codes for conflict detection, thereby enhancing their experience. A code adapter has been designed to separate logical reasoning from the syntactic details of code generation, enabling LLMs to generate code for programming languages beyond their training data. Finally, we evaluated the performance of AutoIoT and presented a case study demonstrating how AutoIoT can integrate with existing IoT platforms.", "published": "2024-11-16T02:02:01Z", "authors": ["Ye Cheng", "Minghui Xu", "Yue Zhang", "Kun Li", "Ruoxi Wang", "Lian Yang"], "categories": ["cs.CR"], "doi": "", "_query": "automated valuation model", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2401.11787v1", "title": "A Comparative Study of Numerical Methods for Approximating the Solutions of a Macroscopic Automated-Vehicle Traffic Flow Model", "abstract": "In this paper, a particle method is used to approximate the solutions of a \"fluid-like\" macroscopic traffic flow model for automated vehicles. It is shown that this method preserves certain differential inequalities that hold for the macroscopic traffic model: mass is preserved, the mechanical energy is decaying and an energy functional is also decaying. To demonstrate the advantages of the particle method under consideration, a comparison with other numerical methods for viscous compressible fluid models is provided. Since the solutions of the macroscopic traffic model can be approximated by the solutions of a reduced model consisting of a single nonlinear heat-type partial differential equation, the numerical solutions produced by the particle method are also compared with the numerical solutions of the reduced model. Finally, a traffic simulation scenario and a comparison with the Aw-Rascle-Zhang (ARZ) model are provided, illustrating the advantages of the use of automated vehicles.", "published": "2024-01-22T09:35:59Z", "authors": ["George Titakis", "Iasson Karafyllis", "Dionysis Theodosis", "Ioannis Papamichail", "Markos Papageorgiou"], "categories": ["math.NA"], "doi": "", "_query": "automated valuation model", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/1709.05554v2", "title": "Deep Automated Multi-task Learning", "abstract": "Multi-task learning (MTL) has recently contributed to learning better representations in service of various NLP tasks. MTL aims at improving the performance of a primary task, by jointly training on a secondary task. This paper introduces automated tasks, which exploit the sequential nature of the input data, as secondary tasks in an MTL model. We explore next word prediction, next character prediction, and missing word completion as potential automated tasks. Our results show that training on a primary task in parallel with a secondary automated task improves both the convergence speed and accuracy for the primary task. We suggest two methods for augmenting an existing network with automated tasks and establish better performance in topic prediction, sentiment analysis, and hashtag recommendation. Finally, we show that the MTL models can perform well on datasets that are small and colloquial by nature.", "published": "2017-09-16T19:04:54Z", "authors": ["Davis Liang", "Yan Shu"], "categories": ["cs.LG", "stat.ML"], "doi": "", "_query": "automated valuation model", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/math/9901048v4", "title": "Resolution of Singularities of Germs in Characteristic Positive Associated with Valuation Rings of Iterated Divisor Type", "abstract": "In this paper we show that any hypersurface singularities of germs of varieties in positive characteristic can be resolved by iterated monoidal transformations in centers in smooth subvarieties, if we have a valuation ring of iterated divisor type associated with the germ. Besides, we introduce fundamental concepts for the study of resolution of singularities of germs such as space germs, iterated analytic monoidal transformations with a normal crossing, Weierstrass representations, reduction sequences, and so forth.", "published": "1999-01-11T18:44:01Z", "authors": ["Tohsuke Urabe"], "categories": ["math.AG"], "doi": "", "_query": "automated valuation model", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/1903.06044v1", "title": "Lattice Valuations: a Generalisation of Measure and Integral", "abstract": "Measure and integral are two closely related, but distinct objects of study. Nonetheless, they are both real-valued lattice valuations: order preserving real-valued functions $φ$ on a lattice $L$ which are modular, i.e., $φ(x)+φ(y) = φ(x\\wedge y)+φ(x\\vee y)$ for all $x,y \\in L$. We unify measure and integral by developing a theory for lattice valuations. We allow these lattice valuations to take their values from the reals, or any suitable ordered Abelian group.", "published": "2019-03-13T13:04:41Z", "authors": ["Abraham A. Westerbaan"], "categories": ["math.FA"], "doi": "", "_query": "automated valuation model", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/1603.02867v1", "title": "Convex duality in optimal investment and contingent claim valuation in illiquid markets", "abstract": "This paper studies convex duality in optimal investment and contingent claim valuation in markets where traded assets may be subject to nonlinear trading costs and portfolio constraints. Under fairly general conditions, the dual expressions decompose into tree terms, corresponding to the agent's risk preferences, trading costs and portfolio constraints, respectively. The dual representations are shown to be valid when the market model satisfies an appropriate generalization of the no-arbitrage condition and the agent's utility function satisfies an appropriate generalization of asymptotic elasticity conditions. When applied to classical liquid market models or models with bid-ask spreads, we recover well-known pricing formulas in terms of martingale measures and consistent price systems. Building on the general theory of convex stochastic optimization, we also derive optimality conditions in terms of an extended notion of a \"shadow price\".", "published": "2016-03-09T12:38:30Z", "authors": ["Teemu Pennanen", "Ari-Pekka Perkkiö"], "categories": ["q-fin.MF", "math.OC", "math.PR"], "doi": "", "_query": "automated valuation model", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2411.10386v2", "title": "The role of debt valuation factors in systemic risk assessment", "abstract": "The fragility of financial systems was starkly demonstrated in early 2023 through a cascade of major bank failures in the United States, including the second, third, and fourth largest collapses in the US history. The highly interdependent financial networks and the associated high systemic risk have been deemed the cause of the crashes. The goal of this paper is to enhance existing systemic risk analysis frameworks by incorporating essential debt valuation factors. Our results demonstrate that these additional elements substantially influence the outcomes of risk assessment. Notably, by modeling the dynamic relationship between interest rates and banks' credibility, our framework can detect potential cascading failures that standard approaches might miss. The proposed risk assessment methodology can help regulatory bodies prevent future failures, while also allowing companies to more accurately predict turmoil periods and strengthen their survivability during such events.", "published": "2024-11-15T17:48:30Z", "authors": ["Kamil Fortuna", "Janusz Szwabiński"], "categories": ["q-fin.RM", "q-fin.MF"], "doi": "", "_query": "automated valuation model", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2005.06511v2", "title": "Fair and Efficient Allocations under Subadditive Valuations", "abstract": "We study the problem of allocating a set of indivisible goods among agents with subadditive valuations in a fair and efficient manner. Envy-Freeness up to any good (EFX) is the most compelling notion of fairness in the context of indivisible goods. Although the existence of EFX is not known beyond the simple case of two agents with subadditive valuations, some good approximations of EFX are known to exist, namely $\\tfrac{1}{2}$-EFX allocation and EFX allocations with bounded charity. Nash welfare (the geometric mean of agents' valuations) is one of the most commonly used measures of efficiency. In case of additive valuations, an allocation that maximizes Nash welfare also satisfies fairness properties like Envy-Free up to one good (EF1). Although there is substantial work on approximating Nash welfare when agents have additive valuations, very little is known when agents have subadditive valuations. In this paper, we design a polynomial-time algorithm that outputs an allocation that satisfies either of the two approximations of EFX as well as achieves an $\\mathcal{O}(n)$ approximation to the Nash welfare. Our result also improves the current best-known approximation of $\\mathcal{O}(n \\log n)$ and $\\mathcal{O}(m)$ to Nash welfare when agents have submodular and subadditive valuations, respectively. Furthermore, our technique also gives an $\\mathcal{O}(n)$ approximation to a family of welfare measures, $p$-mean of valuations for $p\\in (-\\infty, 1]$, thereby also matching asymptotically the current best known approximation ratio for special cases like $p =-\\infty$ while also retaining the fairness properties.", "published": "2020-05-13T18:34:34Z", "authors": ["Bhaskar Ray Chaudhury", "Jugal Garg", "Ruta Mehta"], "categories": ["cs.GT"], "doi": "", "_query": "automated valuation model", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/math/0301148v3", "title": "The multiplicative structure on polynomial continuous valuations", "abstract": "We introduce a canonical structure of a commutative associative filtered algebra with the unit on polynomial smooth valuations, and study its properties. The induced structure on the subalgebra of translation invariant smooth valuations has especially nice properties (it is the structure of the Frobenius algebra). We also present some applications.", "published": "2003-01-14T19:08:40Z", "authors": ["Semyon Alesker"], "categories": ["math.MG"], "doi": "", "_query": "automated valuation model", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2104.00524v1", "title": "Themes of parity in the valuation for integer numbers of the zeta function and of five related functions", "abstract": "This paper considers the problem of the valuation for integer numbers of the zeta function and of five other functions which are naturally associated to it. A relatively elementary approach is exposed, which closely connects this still partially open problem to five themes of parity: the notions of parity of a function and of parity of the degree of a polynomial are here related to the distinctions of parity concerning the natural argument of the six considered functions as well as the integer numbers of which some inverse powers are summed. The adopted method essentially aims at enabling the students in mathematics to have an entry into this problem.", "published": "2021-03-31T07:52:18Z", "authors": ["David Pouvreau"], "categories": ["math.HO", "math.CA"], "doi": "", "_query": "automated valuation model", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2208.11259v2", "title": "Geometric properties of homomorphisms between the absolute Galois groups of mixed-characteristic complete discrete valuation fields with perfect residue fields", "abstract": "Although the analogue of the theorem of Neukirch-Uchida for $p$-adic local fields fails to hold as it is, Mochizuki proved a certain analogue of this theorem for the absolute Galois groups with ramification filtrations of $p$-adic local fields. Moreover, Mochizuki and Hoshi gave various (necessary and) sufficient conditions for homomorphisms between the absolute Galois groups of $p$-adic local fields to be \"geometric\" (i.e., to arise from homomorphisms of fields). In the present paper, we consider similar problems for general mixed-characteristic complete discrete valuation fields with perfect residue fields. One main result gives (necessary and) sufficient conditions for homomorphisms between the absolute Galois groups of mixed-characteristic complete discrete valuation fields with residue fields algebraic over the prime fields to be geometric. We also give a \"weak-Isom\" anabelian result for homomorphisms between the absolute Galois groups of these fields.", "published": "2022-08-24T01:35:34Z", "authors": ["Takahiro Murotani"], "categories": ["math.NT", "math.AG"], "doi": "", "_query": "automated valuation model", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2407.08550v1", "title": "Incorporating Large Language Models into Production Systems for Enhanced Task Automation and Flexibility", "abstract": "This paper introduces a novel approach to integrating large language model (LLM) agents into automated production systems, aimed at enhancing task automation and flexibility. We organize production operations within a hierarchical framework based on the automation pyramid. Atomic operation functionalities are modeled as microservices, which are executed through interface invocation within a dedicated digital twin system. This allows for a scalable and flexible foundation for orchestrating production processes. In this digital twin system, low-level, hardware-specific data is semantically enriched and made interpretable for LLMs for production planning and control tasks. Large language model agents are systematically prompted to interpret these production-specific data and knowledge. Upon receiving a user request or identifying a triggering event, the LLM agents generate a process plan. This plan is then decomposed into a series of atomic operations, executed as microservices within the real-world automation system. We implement this overall approach on an automated modular production facility at our laboratory, demonstrating how the LLMs can handle production planning and control tasks through a concrete case study. This results in an intuitive production facility with higher levels of task automation and flexibility. Finally, we reveal the several limitations in realizing the full potential of the large language models in autonomous systems and point out promising benefits. Demos of this series of ongoing research series can be accessed at: https://github.com/YuchenXia/GPT4IndustrialAutomation", "published": "2024-07-11T14:34:43Z", "authors": ["Yuchen Xia", "Jize Zhang", "Nasser Jazdi", "Michael Weyrich"], "categories": ["cs.AI", "cs.ET", "cs.MA", "cs.RO", "eess.SY"], "doi": "10.51202/9783181024379", "_query": "automated valuation model", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/1802.08809v2", "title": "Uniform semimodular lattices and valuated matroids", "abstract": "In this paper, we present a lattice-theoretic characterization for valuated matroids, which is an extension of the well-known cryptomorphic equivalence between matroids and geometric lattices ($=$ atomistic semimodular lattices). We introduce a class of semimodular lattices, called uniform semimodular lattices, and establish a cryptomorphic equivalence between integer-valued valuated matroids and uniform semimodular lattices. Our result includes a coordinate-free lattice-theoretic characterization of integer points in tropical linear spaces, incorporates the Dress-Terhalle completion process of valuated matroids, and establishes a smooth connection with Euclidean buildings of type A.", "published": "2018-02-24T06:23:17Z", "authors": ["Hiroshi Hirai"], "categories": ["math.CO"], "doi": "", "_query": "automated valuation model", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2508.06010v2", "title": "Valuation Measure of the Stock Market using Stochastic Volatility and Stock Earnings", "abstract": "We create a time series model for annual returns of three asset classes: the USA Standard & Poor (S&P) stock index, the international stock index, and the USA Bank of America investment-grade corporate bond index. Using this, we made an online financial app simulating wealth process. This includes options for regular withdrawals and contributions. Four factors are: S&P volatility and earnings, corporate BAA rate, and long-short Treasury bond spread. Our valuation measure is an improvement of Shiller's cyclically adjusted price-earnings ratio. We use classic linear regression models, and make residuals white noise by dividing by annual volatility. We use multivariate kernel density estimation for residuals. We state and prove long-term stability results.", "published": "2025-08-08T04:42:44Z", "authors": ["Andrey Sarantsev", "Angel Piotrowski", "Ian Anderson"], "categories": ["q-fin.RM", "math.PR", "stat.AP"], "doi": "", "_query": "automated valuation model", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2102.05405v2", "title": "Automated and Distributed Statistical Analysis of Economic Agent-Based Models", "abstract": "We propose a novel approach to the statistical analysis of stochastic simulation models and, especially, agent-based models (ABMs). Our main goal is to provide fully automated, model-independent and tool-supported techniques and algorithms to inspect simulations and perform counterfactual analysis. Our approach: (i) is easy-to-use by the modeller, (ii) improves reproducibility of results, (iii) optimizes running time given the modeller's machine, (iv) automatically chooses the number of required simulations and simulation steps to reach user-specified statistical confidence, and (v) automates a variety of statistical tests. In particular, our techniques are designed to distinguish the transient dynamics of the model from its steady-state behaviour (if any), estimate properties in both 'phases', and provide indications on the (non-)ergodic nature of the simulated processes - which, in turn, allows one to gauge the reliability of a steady-state analysis. Estimates are equipped with statistical guarantees, allowing for robust comparisons across computational experiments. To demonstrate the effectiveness of our approach, we apply it to two models from the literature: a large-scale macro-financial ABM and a small scale prediction market model. Compared to prior analyses of these models, we obtain new insights and we are able to identify and fix some erroneous conclusions.", "published": "2021-02-10T12:39:34Z", "authors": ["Andrea Vandin", "Daniele Giachini", "Francesco Lamperti", "Francesca Chiaromonte"], "categories": ["econ.GN", "cs.MA", "cs.PF"], "doi": "10.1016/j.jedc.2022.104458", "_query": "automated valuation model", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2010.15212v1", "title": "Credit Valuation Adjustment in Credit Risk with Simultaneous Defaults Possibility", "abstract": "In a series of recent papers, Damiano Brigo, Andrea Pallavicini, and co-authors have shown that the value of a contract in a Credit Valuation Adjustment (CVA) setting, being the sum of the cash flows, can be represented as a solution of a decoupled forward-backward stochastic differential equation (FBSDE). CVA is the difference between the risk-free portfolio value and the true portfolio value that takes into account the possibility of a counter party's default. In other words, CVA is the market value of counter party credit risk. This has achieved noteworthy importance after the 2008 financial debacle, where counter party risk played an under-modeled but huge risk. In their analysis, Brigo et al make the classical assumption of conditional independence of the default times, given the risk free market filtration. This does not allow for the increasingly likely case of simultaneous defaults. We weaken their assumption, replacing it with a martingale orthogonality condition. This in turn changes the form of the BSDE that arises from the model.", "published": "2020-10-28T20:25:14Z", "authors": ["Aditi Dandapani", "Philip Protter"], "categories": ["math.PR"], "doi": "", "_query": "automated valuation model", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2306.01242v2", "title": "Responsible Task Automation: Empowering Large Language Models as Responsible Task Automators", "abstract": "The recent success of Large Language Models (LLMs) signifies an impressive stride towards artificial general intelligence. They have shown a promising prospect in automatically completing tasks upon user instructions, functioning as brain-like coordinators. The associated risks will be revealed as we delegate an increasing number of tasks to machines for automated completion. A big question emerges: how can we make machines behave responsibly when helping humans automate tasks as personal copilots? In this paper, we explore this question in depth from the perspectives of feasibility, completeness and security. In specific, we present Responsible Task Automation (ResponsibleTA) as a fundamental framework to facilitate responsible collaboration between LLM-based coordinators and executors for task automation with three empowered capabilities: 1) predicting the feasibility of the commands for executors; 2) verifying the completeness of executors; 3) enhancing the security (e.g., the protection of users' privacy). We further propose and compare two paradigms for implementing the first two capabilities. One is to leverage the generic knowledge of LLMs themselves via prompt engineering while the other is to adopt domain-specific learnable models. Moreover, we introduce a local memory mechanism for achieving the third capability. We evaluate our proposed ResponsibleTA on UI task automation and hope it could bring more attentions to ensuring LLMs more responsible in diverse scenarios.", "published": "2023-06-02T02:42:58Z", "authors": ["Zhizheng Zhang", "Xiaoyi Zhang", "Wenxuan Xie", "Yan Lu"], "categories": ["cs.AI", "cs.CL"], "doi": "", "_query": "automated valuation model", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2209.12107v1", "title": "Valuation of Public Bus Electrification with Open Data", "abstract": "This research provides a novel framework to estimate the economic, environmental, and social values of electrifying public transit buses, for cities across the world, based on open-source data. Electric buses are a compelling candidate to replace diesel buses for the environmental and social benefits. However, the state-of-art models to evaluate the value of bus electrification are limited in applicability because they require granular and bespoke data on bus operation that can be difficult to procure. Our valuation tool uses General Transit Feed Specification, a standard data format used by transit agencies worldwide, to provide high-level guidance on developing a prioritization strategy for electrifying a bus fleet. We develop physics-informed machine learning models to evaluate the energy consumption, the carbon emissions, the health impacts, and the total cost of ownership for each transit route. We demonstrate the scalability of our tool with a case study of the bus lines in the Greater Boston and Milan metropolitan areas.", "published": "2022-09-25T00:02:23Z", "authors": ["Upadhi Vijay", "Soomin Woo", "Scott J. Moura", "Akshat Jain", "David Rodriguez", "Sergio Gambacorta", "Giuseppe Ferrara", "Luigi Lanuzza", "Christian Zulberti", "Erika Mellekas", "Carlo Papa"], "categories": ["eess.SY", "cs.LG"], "doi": "", "_query": "automated valuation model", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/1205.2013v3", "title": "Bilateral Credit Valuation Adjustment of an Optional Early Termination Clause", "abstract": "Is an option to early terminate a swap at its market value worth zero? At first sight it is, but in presence of counterparty risk it depends on the criteria used to determine such market value. In case of a single uncollateralised swap transaction under ISDA between two defaultable counterparties, the additional unilateral option to early terminate the swap at predefined dates requires a Bermudan credit valuation adjustment. We give a general pricing formula assuming a default-free close-out amount, and apply it in a simplified setting with deterministic intensity and one single date of optional early termination, showing that the impact on the fair value of the transaction at inception might be non negligible.", "published": "2012-05-09T15:27:40Z", "authors": ["Lorenzo Giada", "Claudio Nordio"], "categories": ["q-fin.PR"], "doi": "", "_query": "automated valuation model", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2308.00013v1", "title": "Bitcoin Gold, Litecoin Silver:An Introduction to Cryptocurrency's Valuation and Trading Strategy", "abstract": "Historically, gold and silver have played distinct roles in traditional monetary systems. While gold has primarily been revered as a superior store of value, prompting individuals to hoard it, silver has commonly been used as a medium of exchange. As the financial world evolves, the emergence of cryptocurrencies has introduced a new paradigm of value and exchange. However, the store-of-value characteristic of these digital assets remains largely uncharted. Charlie Lee, the founder of Litecoin, once likened Bitcoin to gold and Litecoin to silver. To validate this analogy, our study employs several metrics, including unspent transaction outputs (UTXO), spent transaction outputs (STXO), Weighted Average Lifespan (WAL), CoinDaysDestroyed (CDD), and public on-chain transaction data. Furthermore, we've devised trading strategies centered around the Price-to-Utility (PU) ratio, offering a fresh perspective on crypto-asset valuation beyond traditional utilities. Our back-testing results not only display trading indicators for both Bitcoin and Litecoin but also substantiate Lee's metaphor, underscoring Bitcoin's superior store-of-value proposition relative to Litecoin. We anticipate that our findings will drive further exploration into the valuation of crypto assets. For enhanced transparency and to promote future research, we've made our datasets available on Harvard Dataverse and shared our Python code on GitHub as open source.", "published": "2023-07-30T23:14:20Z", "authors": ["Haoyang Yu", "Yutong Sun", "Yulin Liu", "Luyao Zhang"], "categories": ["cs.CE", "cs.CR", "econ.GN", "q-fin.CP", "q-fin.TR"], "doi": "10.1007/978-3-031-54053-0_39", "_query": "automated valuation model", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2607.25258v1", "title": "Robust Hedging Valuation Adjustment for Deep Hedging Policies under Market Frictions", "abstract": "Hedging a derivative position under transaction costs and market frictions requires a trading rule that adapts to changing conditions. Deep hedging trains a neural policy for this task but policy training does not determine whether a trading desk can afford to run the policy. We apply robust hedging valuation adjustment (HVA) as a post-training valuation-adjustment layer that evaluates tracking-loss CVaR together with explicit funding and margin add-ons. The funding and margin add-ons share the same KL uncertainty set as HVA. For each policy, a single common-stress tilt computes HVA, funding and margin jointly and a trading desk can get one internally consistent reserve instead of the three separately. We compare classical hedge policies with learned hedge specifications across three market environments with different liquidity. No single specification dominates in every market. Under the strict tracking-risk budget, gamma-wide classical bands are selected in High and Middle Liquidity while sparse learned execution is selected in Low Liquidity. At looser validation budgets wider classical bands are generally selected.", "published": "2026-07-28T03:59:19Z", "authors": ["Takayuki Sakuma"], "categories": ["q-fin.RM", "q-fin.CP", "q-fin.PR"], "doi": "", "_query": "automated valuation model", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/1301.6866v2", "title": "Convex valuations invariant under the Lorentz group", "abstract": "We give an explicit classification of translation-invariant, Lorentz-invariant continuous valuations on convex sets. We also classify the Lorentz-invariant even generalized valuations.", "published": "2013-01-29T08:46:34Z", "authors": ["Semyon Alesker", "Dmitry Faifman"], "categories": ["math.DG", "math.MG"], "doi": "", "_query": "automated valuation model", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2503.16724v3", "title": "Towards Automated Semantic Interpretability in Reinforcement Learning via Vision-Language Models", "abstract": "Semantic interpretability in Reinforcement Learning (RL) enables transparency and verifiability of decision-making. Achieving semantic interpretability in reinforcement learning requires (1) a feature space composed of human-understandable concepts and (2) a policy that is interpretable and verifiable. However, constructing such a feature space has traditionally relied on manual human specification, which often fails to generalize to unseen environments. Moreover, even when interpretable features are available, most reinforcement learning algorithms employ black-box models as policies, thereby hindering transparency. We introduce interpretable Tree-based Reinforcement learning via Automated Concept Extraction (iTRACE), an automated framework that leverages pre-trained vision-language models (VLM) for semantic feature extraction and train a interpretable tree-based model via RL. To address the impracticality of running VLMs in RL loops, we distill their outputs into a lightweight model. By leveraging Vision-Language Models (VLMs) to automate tree-based reinforcement learning, iTRACE loosens the reliance the need for human annotation that is traditionally required by interpretable models. In addition, it addresses key limitations of VLMs alone, such as their lack of grounding in action spaces and their inability to directly optimize policies. We evaluate iTRACE across three domains: Atari games, grid-world navigation, and driving. The results show that iTRACE outperforms other interpretable policy baselines and matches the performance of black-box policies on the same interpretable feature space.", "published": "2025-03-20T21:53:19Z", "authors": ["Zhaoxin Li", "Zhang Xi-Jia", "Batuhan Altundas", "Letian Chen", "Rohan Paleja", "Matthew Gombolay"], "categories": ["cs.AI", "cs.LG"], "doi": "", "_query": "automated valuation model", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2302.12187v2", "title": "Automated Choice for the Best Renormalization Scheme in BSM Models", "abstract": "The explorations of models beyond the Standard Model (BSM) naturally involve scans over the unknown BSM parameters. On the other hand, high precision predictions require calculations at the loop-level and thus a renormalization of (some of) the BSM parameters. Often many choices are possible for the renormalization scheme (RS). This concerns the choice of the set of to-be-renormalized parameters out of a larger set of BSM parameters, but can also concern the type of renormalization condition which is chosen for a specific parameter. A given RS can be well suited to yield \"stable\" and \"well behaved\" higher-order corrections in one part of the BSM parameter space, but can fail completely in other parts, which may not even be noticed numerically if an isolated parameter point is investigated, or when the higher-order BSM calculations are performed in an automated, not supervised set-up. Consequently, the (automated) exploration of BSM models requires a choice of a good RS {\\em before} the calculation is performed. We propose a new method how such a choice can be performed. We demonstrate the feasibility of our new method in the chargino/neutralino sector of the Minimal Supersymmetric Standard Model (MSSM), but stress the general applicability of our method to all types of BSM models.", "published": "2023-02-23T17:28:26Z", "authors": ["S. Heinemeyer", "F. von der Pahlen"], "categories": ["hep-ph"], "doi": "10.1140/epjc/s10052-023-12009-5", "_query": "automated valuation model", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2305.12173v2", "title": "CryptoVampire: Automated Reasoning for the Complete Symbolic Attacker Cryptographic Model", "abstract": "Cryptographic protocols are hard to design and prove correct, as witnessed by the ever-growing list of attacks even on protocol standards. Symbolic models of cryptography enable automated formal security proofs of such protocols against an idealized model, which abstracts away from the algebraic properties of cryptographic schemes and thus misses attacks. Computational models yield rigorous guarantees but support at present only interactive proofs and/or restricted classes of protocols. A promising approach is given by the computationally complete symbolic attacker (CCSA), formalized in the BC Logic, which aims at bridging and getting the best of the two worlds, obtaining cryptographic guarantees by symbolic analysis. The BC Logic is supported by a recently developed interactive theorem prover, Squirrel, which enables machine-checked interactive security proofs, as opposed to automated ones, thus requiring expert knowledge. We introduce the CryptoVampire cryptographic protocol verifier, which for the first time fully automates proofs of trace properties in the BC Logic. The key technical contribution is a first-order (FO) formalization of protocol properties with tailored handling of subterm relations. We overcome the burden of interactive proving in higher-order (HO) logic and automatically establish soundness of cryptographic protocols using only FO reasoning. On the theoretical side, we restrict full FO logic with cryptographic axioms to ensure that, by losing the expressivity of the HO BC Logic, we do not lose soundness. On the practical side, CryptoVampire integrates dedicated proof techniques using FO saturation algorithms and heuristics, which enable leveraging the state-of-the-art Vampire FO theorem prover as the underlying proving engine. Our experimental results show CryptoVampire's effectiveness of as a standalone verifier and in terms of automation support for Squirrel.", "published": "2023-05-20T11:26:51Z", "authors": ["Simon Jeanteur", "Laura Kovács", "Matteo Maffei", "Michael Rawson"], "categories": ["cs.CR", "cs.LO"], "doi": "", "_query": "automated valuation model", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/1505.03920v1", "title": "Equivariant Poincaré series and topology of valuations", "abstract": "The equivariant with respect to a finite group action Poincaré series of a collection of $r$ valuations was defined earlier as a power series in $r$ variables with the coefficients from a modification of the Burnside ring of the group. Here we show that (modulo simple exceptions) the equivariant Poincaré series determines the equivariant topology of the collection of valuations.", "published": "2015-05-14T23:30:49Z", "authors": ["A. Campillo", "F. Delgado", "S. M. Gusein-Zade"], "categories": ["math.AG"], "doi": "", "_query": "automated valuation model", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/0805.4056v1", "title": "Realization of a certain class of semi-groups as value semi-groups of valuations", "abstract": "Given a well-ordered semi-group $Γ$ with a minimal system of generators of ordinal type at most $ωn$ and of rational rank $r$, which satisfies a positivity and increasing condition, we construct a zero-dimensional valuation centered on the ring of polynomials with $r$ variables such that the semi-group of the values of the polynomial ring is equal to $Γ$. The construction uses a generalization of Favre and Jonsson's version of MacLane's sequence of key-polynomials.", "published": "2008-05-27T06:41:06Z", "authors": ["M. Moghaddam"], "categories": ["math.AG", "math.AC"], "doi": "", "_query": "automated valuation model", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2201.09486v2", "title": "Bias in Automated Speaker Recognition", "abstract": "Automated speaker recognition uses data processing to identify speakers by their voice. Today, automated speaker recognition is deployed on billions of smart devices and in services such as call centres. Despite their wide-scale deployment and known sources of bias in related domains like face recognition and natural language processing, bias in automated speaker recognition has not been studied systematically. We present an in-depth empirical and analytical study of bias in the machine learning development workflow of speaker verification, a voice biometric and core task in automated speaker recognition. Drawing on an established framework for understanding sources of harm in machine learning, we show that bias exists at every development stage in the well-known VoxCeleb Speaker Recognition Challenge, including data generation, model building, and implementation. Most affected are female speakers and non-US nationalities, who experience significant performance degradation. Leveraging the insights from our findings, we make practical recommendations for mitigating bias in automated speaker recognition, and outline future research directions.", "published": "2022-01-24T06:48:57Z", "authors": ["Wiebke Toussaint Hutiri", "Aaron Ding"], "categories": ["cs.SD", "cs.CL", "cs.CY", "cs.LG", "eess.AS"], "doi": "10.1145/3531146.3533089", "_query": "automated valuation model", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2502.12203v1", "title": "An Interpretable Automated Mechanism Design Framework with Large Language Models", "abstract": "Mechanism design has long been a cornerstone of economic theory, with traditional approaches relying on mathematical derivations. Recently, automated approaches, including differentiable economics with neural networks, have emerged for designing payments and allocations. While both analytical and automated methods have advanced the field, they each face significant weaknesses: mathematical derivations are not automated and often struggle to scale to complex problems, while automated and especially neural-network-based approaches suffer from limited interpretability. To address these challenges, we introduce a novel framework that reformulates mechanism design as a code generation task. Using large language models (LLMs), we generate heuristic mechanisms described in code and evolve them to optimize over some evaluation metrics while ensuring key design criteria (e.g., strategy-proofness) through a problem-specific fixing process. This fixing process ensures any mechanism violating the design criteria is adjusted to satisfy them, albeit with some trade-offs in performance metrics. These trade-offs are factored in during the LLM-based evolution process. The code generation capabilities of LLMs enable the discovery of novel and interpretable solutions, bridging the symbolic logic of mechanism design and the generative power of modern AI. Through rigorous experimentation, we demonstrate that LLM-generated mechanisms achieve competitive performance while offering greater interpretability compared to previous approaches. Notably, our framework can rediscover existing manually designed mechanisms and provide insights into neural-network based solutions through Programming-by-Example. These results highlight the potential of LLMs to not only automate but also enhance the transparency and scalability of mechanism design, ensuring safe deployment of the mechanisms in society.", "published": "2025-02-16T12:33:03Z", "authors": ["Jiayuan Liu", "Mingyu Guo", "Vincent Conitzer"], "categories": ["cs.LG", "cs.AI", "cs.GT", "cs.NE"], "doi": "", "_query": "automated valuation model", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2512.04896v1", "title": "Existentially defining valuations in function fields over large fields", "abstract": "Let $K$ be a large field such that $K[\\sqrt{-1}]$ is not algebraically closed and $F/K$ a function field in one variable. Extending techniques and results from earlier work with Becher and Dittmann, we show that every valuation ring on $F$ containing $K$ is existentially definable in the language of rings with parameters from $F$. As a consequence, using a known reduction technique, we obtain the undecidability of the existential theory of $F$ in the language of rings with appropriately chosen parameters.", "published": "2025-12-04T15:22:54Z", "authors": ["Nicolas Daans"], "categories": ["math.NT", "math.LO"], "doi": "", "_query": "automated valuation model", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2012.13603v1", "title": "Modeling Dispositional and Initial learned Trust in Automated Vehicles with Predictability and Explainability", "abstract": "Technological advances in the automotive industry are bringing automated driving closer to road use. However, one of the most important factors affecting public acceptance of automated vehicles (AVs) is the public's trust in AVs. Many factors can influence people's trust, including perception of risks and benefits, feelings, and knowledge of AVs. This study aims to use these factors to predict people's dispositional and initial learned trust in AVs using a survey study conducted with 1175 participants. For each participant, 23 features were extracted from the survey questions to capture his or her knowledge, perception, experience, behavioral assessment, and feelings about AVs. These features were then used as input to train an eXtreme Gradient Boosting (XGBoost) model to predict trust in AVs. With the help of SHapley Additive exPlanations (SHAP), we were able to interpret the trust predictions of XGBoost to further improve the explainability of the XGBoost model. Compared to traditional regression models and black-box machine learning models, our findings show that this approach was powerful in providing a high level of explainability and predictability of trust in AVs, simultaneously.", "published": "2020-12-25T16:49:57Z", "authors": ["Jackie Ayoub", "X. Jessie Yang", "Feng Zhou"], "categories": ["cs.HC", "cs.RO"], "doi": "", "_query": "automated valuation model", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/1304.5317v1", "title": "K model for designing Data Driven Test Automation Frameworks and its Design Architecture Snow Leopard", "abstract": "Automated testing improves the efficiency of testing practice at various levels of projects in the organization. Unfortunately, we do not have a common architecture or common standards for designing frameworks across different test levels, projects and test tools which can assist developers, testers and business analysts. To address the above problem, in this paper, I have first proposed a unique reference model and then a design architecture using the proposed model for designing any Data Driven Automation Frameworks. The reference model is K model which can be used for modeling any data-driven automation framework. The unique Design architecture, based on above model is Snow Leopard.", "published": "2013-04-19T06:00:31Z", "authors": ["Rohan R. Kachewar"], "categories": ["cs.SE"], "doi": "10.5120/3835-5331", "_query": "automated valuation model", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2609.29175v1", "title": "A Human-Like Pedestrian Model for Automated Driving Simulations", "abstract": "Automated vehicles must be able to interact with pedestrians safely and efficiently across diverse traffic situations. Although driving simulators offer a scalable testbed for learning such capabilities, existing theory-inspired pedestrian models are narrow in scope and limited to go/no-go crossing decisions in single-lane settings. While data-driven approaches can predict pedestrian behavior in complex situations, they lack sufficient observations in rare, safety-critical scenarios. Here, we propose an approach to training pedestrian models in simulators so that learned policies generate demonstrably human-like behavior in realistic, complex traffic scenarios, including multiple lanes, heavy traffic, and dangerous driving styles. Our technical contribution is a novel definition of pedestrian-vehicle interaction as a partially observable Markov decision process (POMDP) with theory-grounded perceptual, cognitive, and motor constraints. It accounts for the highly adaptive nature of human behavior in traffic and simulates how people adjust their responses according to perceived danger, time pressure, and the complexity of the situation. When trained via deep reinforcement learning (RL) with domain randomization in a simulator, the model reproduces the broadest range of empirical findings shown so far on human crossing behavior, including gap acceptance, yielding acceptance, hesitation, and evasive speed adjustment. We show that learned policies transfer to unseen traffic environments, and can be further adapted to local traffic norms with finetuning. Together, these results establish a blueprint for simulator-ready pedestrian models that can support the development and evaluation of automated driving systems.", "published": "2026-09-24T07:45:47Z", "authors": ["Ruofeng Wang", "Patrick Ebel", "Philipp Wintersberger", "Antti Oulasvirta"], "categories": ["cs.HC"], "doi": "", "_query": "automated valuation model", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2308.05503v1", "title": "EFX Allocations Exist for Binary Valuations", "abstract": "We study the fair division problem and the existence of allocations satisfying the fairness criterion envy-freeness up to any item (EFX). The existence of EFX allocations is a major open problem in the fair division literature. We consider binary valuations where the marginal gain of the value by receiving an extra item is either $0$ or $1$. Babaioff et al. [2021] proved that EFX allocations always exist for binary and submodular valuations. In this paper, by using completely different techniques, we extend this existence result to general binary valuations that are not necessarily submodular, and we present a polynomial time algorithm for computing an EFX allocation.", "published": "2023-08-10T11:28:31Z", "authors": ["Xiaolin Bu", "Jiaxin Song", "Ziqi Yu"], "categories": ["cs.CE", "cs.AI", "cs.CC"], "doi": "", "_query": "automated valuation model", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2009.10240v1", "title": "Automated Aggregator -- Rewriting with the Counting Aggregate", "abstract": "Answer set programming is a leading declarative constraint programming paradigm with wide use for complex knowledge-intensive applications. Modern answer set programming languages support many equivalent ways to model constraints and specifications in a program. However, so far answer set programming has failed to develop systematic methodologies for building representations that would uniformly lend well to automated processing. This suggests that encoding selection, in the same way as algorithm selection and portfolio solving, may be a viable direction for improving performance of answer-set solving. The necessary precondition is automating the process of generating possible alternative encodings. Here we present an automated rewriting system, the Automated Aggregator or AAgg, that given a non-ground logic program, produces a family of equivalent programs with complementary performance when run under modern answer set programming solvers. We demonstrate this behavior through experimental analysis and propose the system's use in automated answer set programming solver selection tools.", "published": "2020-09-22T00:48:33Z", "authors": ["Michael Dingess", "Miroslaw Truszczynski"], "categories": ["cs.AI", "cs.LO"], "doi": "10.4204/EPTCS.325.17", "_query": "automated valuation model", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2005.05419v1", "title": "Continuous valuations on the space of Lipschitz functions on the sphere", "abstract": "We study real-valued valuations on the space of Lipschitz functions over the Euclidean unit sphere $S^{n-1}$. After introducing an appropriate notion of convergence, we show that continuous valuations are bounded on sets which are bounded with respect to the Lipschitz norm. This fact, in combination with measure theoretical arguments, will yield an integral representation for continuous and rotation invariant valuations on the space of Lipschitz functions over the 1-dimensional sphere.", "published": "2020-05-11T20:42:17Z", "authors": ["Andrea Colesanti", "Daniele Pagnini", "Pedro Tradacete", "Ignacio Villanueva"], "categories": ["math.MG", "math.FA"], "doi": "", "_query": "automated valuation model", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2605.22882v4", "title": "GEM-4D: Geometry-Enhanced Video World Models for Robot Manipulation", "abstract": "Video world models can generate realistic futures from a single instruction, but they often fail to track the same physical points consistently across time. As a result, the generated videos appear plausible, yet lack the physical grounding required for reliable action execution, such as robot manipulation. We present GEM-4D, a geometry-grounded video world model that resolves this limitation by injecting dense 4D correspondence supervision distilled from a pretrained geometry foundation model into the video generative backbone during training. This supervision enables the model to jointly capture appearance and geometric structure while retaining a single-stream architecture with no additional inference cost. We further introduce an inverse dynamics module that converts correspondence-consistent video rollouts into executable robot trajectories, enabling direct deployment in both real-world and simulated manipulation. GEM-4D achieves state-of-the-art performance on both video prediction and geometric consistency across both simulation and realistic scenarios and improves real-world manipulation success from 61% to 81%. Additional results are available at https://gem-4d.github.io/.", "published": "2026-05-20T21:36:44Z", "authors": ["Kaichen Zhou", "Yuzhen Chen", "Fangneng Zhan", "Hang Hua", "Grace Chen", "Xinhai Chang", "Ao Qu", "Yilun Du", "Zhuang Liu", "Paul Pu Liang", "Mengyu Wang"], "categories": ["cs.CV", "cs.RO"], "doi": "", "_query": "automated valuation model", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2602.01531v1", "title": "Hype Has Worth: Attention, Sentiment, and NFT Valuation in Major Ethereum Collections", "abstract": "Do online narratives leave a measurable imprint on prices in markets for digital or cultural goods? This paper evaluates how community attention and sentiment relate to valuation in major Ethereum NFT collections after accounting for time effects, market-wide conditions, and persistent visual heterogeneity. Transaction data for large generative collections are merged with Reddit-based discourse measures available for 25 collections, covering 87{,}696 secondary-market sales from January 2021 through March 2025. Visual differences are absorbed by a transparent, within-collection standardized index built from explicit image traits and aggregated via PCA. Discourse is summarized at the collection-by-bin level using discussion intensity and lexicon-based tone measures, with smoothing to reduce noise when text volume is sparse. A mixed-effects specification with a Mundlak within--between decomposition separates persistent cross-collection differences from within-collection fluctuations. Valuations align most strongly with sustained collection-level attention and sentiment environments; within collections, short-horizon negativity is consistently associated with higher prices, and attention is most informative when measured as cumulative engagement over multiple prior windows.", "published": "2026-02-02T01:55:55Z", "authors": ["Samiha Tariq"], "categories": ["econ.GN"], "doi": "", "_query": "automated valuation model", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/1711.05485v3", "title": "Prüfer intersection of valuation domains of a field of rational functions", "abstract": "Let $V$ be a rank one valuation domain with quotient field $K$. We characterize the subsets $S$ of $V$ for which the ring of integer-valued polynomials ${\\rm Int}(S,V)=\\{f\\in K[X] \\mid f(S)\\subseteq V\\}$ is a Prüfer domain. The characterization is obtained by means of the notion of pseudo-monotone sequence and pseudo-limit in the sense of Chabert, which generalize the classical notions of pseudo-convergent sequence and pseudo-limit by Ostrowski and Kaplansky, respectively. We show that ${\\rm Int}(S,V)$ is Prüfer if and only if no element of the algebraic closure $\\overline{K}$ of $K$ is a pseudo-limit of a pseudo-monotone sequence contained in $S$, with respect to some extension of $V$ to $\\overline{K}$. This result expands a recent result by Loper and Werner.", "published": "2017-11-15T10:15:56Z", "authors": ["Giulio Peruginelli"], "categories": ["math.AC", "math.RA"], "doi": "10.1016/j.jalgebra.2018.05.012", "_query": "automated valuation model", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/1002.1188v1", "title": "Framework for Visualizing Model-Driven Software Evolution and its Application", "abstract": "Software Visualization encompasses the development and evaluation of methods for graphically representing different aspects of methods of software, including its structure, execution and evolution. Creating visualizations helps the user to better understand complex phenomena. It is also found by the software engineering community that visualization is essential and important. In order to visualize the evolution of the models in Model-Driven Software Evolution, authors have proposed a framework which consists of 7 key areas (views) and 22 key features for the assessment of Model Driven Software Evolution process and addresses a number of stakeholder concerns. The framework is derived by the application of the Goal Question Metric Paradigm. This paper aims to describe an application of the framework by considering different visualization tools/CASE tools which are used to visualize the models in different views and to capture the information of models during their evolution. Comparison of such tools is also possible by using the framework.", "published": "2010-02-05T10:35:59Z", "authors": ["Akepogu Anand Rao", "Karanam Madhavi"], "categories": ["cs.SE"], "doi": "", "_query": "automated valuation model", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2406.13620v3", "title": "An Algorithm for the Assignment Game Beyond Additive Valuations", "abstract": "The assignment game, introduced by Shapley and Shubik (1971), is a classic model for two-sided matching markets between buyers and sellers. In the original assignment game, it is assumed that payments lead to transferable utility and that buyers have unit-demand valuations for items. Two important and mostly independent lines of work have studied more general settings with imperfectly transferable utility and gross substitutes valuations. Multiple efficient algorithms have been proposed for computing a competitive equilibrium, the standard solution concept in assignment games, in these two settings. Our main result is an efficient algorithm for computing competitive equilibria in a setting with both imperfectly transferable utility and gross substitutes valuations. Our algorithm combines augmenting tree techniques from maximum matching and algorithms for matroid intersection. We also show that, in two mild generalizations of our model, computing a competitive equilibrium is NP-hard.", "published": "2024-06-19T15:16:34Z", "authors": ["Eric Balkanski", "Christopher En", "Yuri Faenza"], "categories": ["cs.DM", "cs.GT"], "doi": "", "_query": "automated valuation model", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2309.10596v1", "title": "Defining, measuring, and modeling passenger's in-vehicle experience and acceptance of automated vehicles", "abstract": "Automated vehicle acceptance (AVA) has been measured mostly subjectively by questionnaires and interviews, with a main focus on drivers inside automated vehicles (AVs). To ensure that AVs are widely accepted by the public, ensuring the acceptance by both drivers and passengers is key. The in-vehicle experience of passengers will determine the extent to which AVs will be accepted by passengers. A comprehensive understanding of potential assessment methods to measure the passenger experience in AVs is needed to improve the in-vehicle experience of passengers and thereby the acceptance. The present work provides an overview of assessment methods that were used to measure a driver's behavior, and cognitive and emotional states during (automated) driving. The results of the review have shown that these assessment methods can be classified by type of data-collection method (e.g., questionnaires, interviews, direct input devices, sensors), object of their measurement (i.e., perception, behavior, state), time of measurement, and degree of objectivity of the data collected. A conceptual model synthesizes the results of the literature review, formulating relationships between the factors constituting the in-vehicle experience and AVA acceptance. It is theorized that the in-vehicle experience influences the intention to use, with intention to use serving as predictor of actual use. The model also formulates relationships between actual use and well-being. A combined approach of using both subjective and objective assessment methods is needed to provide more accurate estimates for AVA, and advance the uptake and use of AVs.", "published": "2023-09-19T13:07:57Z", "authors": ["Neeraja Bhide", "Nanami Hashimoto", "Kazimierz Dokurno", "Chris Van der Hoorn", "Sascha Hoogendoorn-Lanser", "Sina Nordhoff"], "categories": ["cs.HC"], "doi": "", "_query": "automated valuation model", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2511.23052v1", "title": "Image Valuation in NeRF-based 3D reconstruction", "abstract": "Data valuation and monetization are becoming increasingly important across domains such as eXtended Reality (XR) and digital media. In the context of 3D scene reconstruction from a set of images -- whether casually or professionally captured -- not all inputs contribute equally to the final output. Neural Radiance Fields (NeRFs) enable photorealistic 3D reconstruction of scenes by optimizing a volumetric radiance field given a set of images. However, in-the-wild scenes often include image captures of varying quality, occlusions, and transient objects, resulting in uneven utility across inputs. In this paper we propose a method to quantify the individual contribution of each image to NeRF-based reconstructions of in-the-wild image sets. Contribution is assessed through reconstruction quality metrics based on PSNR and MSE. We validate our approach by removing low-contributing images during training and measuring the resulting impact on reconstruction fidelity.", "published": "2025-11-28T10:23:13Z", "authors": ["Grigorios Aris Cheimariotis", "Antonis Karakottas", "Vangelis Chatzis", "Angelos Kanlis", "Dimitrios Zarpalas"], "categories": ["cs.CV"], "doi": "10.1007/978-3-032-04968-1", "_query": "automated valuation model", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2604.14661v1", "title": "AIPC: Agent-Based Automation for AI Model Deployment with Qualcomm AI Runtime", "abstract": "Edge AI model deployment is a multi-stage engineering process involving model conversion, operator compatibility handling, quantization calibration, runtime integration, and accuracy validation. In practice, this workflow is long, failure-prone, and heavily dependent on deployment expertise, particularly when targeting hardware-specific inference runtimes. This technical report presents AIPC (AI Porting Conversion), an AI agent-driven approach for constrained automation of AI model deployment. AIPC decomposes deployment into standardized, verifiable stages and injects deployment-domain knowledge into agent execution through Agent Skills, helper scripts, and a stage-wise validation loop. This design reduces both the expertise barrier and the engineering time required for hardware deployment. Using Qualcomm AI Runtime (QAIRT) as the primary scenario, this report examines automated deployment across representative vision, multimodal, and speech models. In the cases covered here, AIPC can complete deployment from PyTorch to runnable QNN/SNPE inference within 7-20 minutes for structurally regular vision models, with indicative API costs roughly in the range of USD 0.7-10. For more complex models involving less-supported operators, dynamic shapes, or autoregressive decoding structures, fully automated deployment may still require further advances, but AIPC already provides practical support for execution, failure localization, and bounded repair.", "published": "2026-04-16T06:15:56Z", "authors": ["Jianhao Su", "Zhanwei Wu", "ShengTing Huang", "Weidong Feng"], "categories": ["cs.SE", "cs.AI", "cs.LG"], "doi": "", "_query": "automated valuation model", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2308.10568v3", "title": "Analytical valuation of vulnerable derivative claims with bilateral cash flows under credit, funding and wrong-way risk", "abstract": "We study the problem of valuing and hedging a vulnerable derivative claim with bilateral cash flows between two counterparties in the presence of asymmetric funding costs, defaults and wrong way risk (WWR). We characterize the pre-default claim value as the solution to a non-linear Cauchy problem. We show an explicit stochastic representation of the solution exists under a funding policy which linearises the Cauchy PDE. We apply this framework to the valuation of a vulnerable equity forward and show it can be represented as a portfolio of European options. Despite the complexity of the model, we prove the forward's value admits an analytical formula involving only elementary functions and Gaussian integrals. Based on this explicit formula, numerical analysis demonstrates WWR has a significant impact even under benign assumptions: with a parameter configuration less punitive than that representative of Archegos AM default, we find WWR can shift values for vulnerable forwards by 100bps of notional, while peak exposures increase by 25% of notional. This framework is the first to apply to contracts with bilateral cash flows in the presence of credit, funding and WWR, resulting in a non-linear valuation formula which admits a closed-form solution under a suitable funding policy.", "published": "2023-08-21T08:53:46Z", "authors": ["Juan Jose Francisco Miguelez", "Cristin Buescu"], "categories": ["q-fin.PR", "q-fin.MF"], "doi": "", "_query": "automated valuation model", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/1104.0439v2", "title": "Tame Covers and Cohomology of Relative Curves over Complete Discrete Valuation Rings, with Applications to the Brauer Group", "abstract": "We prove the existence of noncrossed product and indecomposable division algebras over the function field of a smooth p-adic curve, especially when the curve does not admit a smooth model over Z_p. Thus we generalize arXiv 0907.0670. To make our constructions, we investigate the lifting of cohomology classes from the total fraction ring of the closed fiber to the function field of the curve, over an arbitrary discrete valuation ring of mixed characteristic.", "published": "2011-04-03T23:55:24Z", "authors": ["Eric Brussel", "Eduardo Tengan"], "categories": ["math.NT", "math.AG", "math.RA"], "doi": "", "_query": "automated valuation model", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/1810.01748v1", "title": "Hives Determined by Pairs in the Affine Grassmannian over Discrete Valuation Rings", "abstract": "Let ${\\mathcal O}$ be a discrete valuation ring with quotient field ${\\cal K}$. The affine Grassmannian ${\\cal G}r$ is the set of full-rank ${\\mathcal O}$-modules contained in ${\\cal K}^n$. Given $Λ\\in {\\cal G}r$, invariant factors $inv(Λ)=λ\\in {\\mathbb Z}^n$ stratify ${\\cal G}r$. Left-multiplication by $GL_{n}({\\cal K})$ stratifies ${\\cal G}r \\times {\\cal G}r$ where $inv(N,Λ) = μ$ if $(N,Λ)$ and $(I_{n} ,M)$ are in the same $GL_{n}({\\cal K})$ orbit, and $inv(M) = μ$. We present an elementary map from ${\\cal G}r \\times {\\cal G}r$ to hives (in the sense of Knutson and Tao) of type $(μ,ν,λ)$ where $inv(N,Λ) = μ$, $inv(N) = ν$, and $inv(Λ) = λ$. Earlier work by the authors determined Littlewood-Richardson fillings from matrix pairs over certain rings ${\\mathcal O}$, and later Kamnitzer utilized properties of MV polytopes to define a map from ${\\cal G}r\\times {\\cal G}r$ to hives over ${\\mathcal O} = {\\mathbb C}[[t]]$. Our proof uses only linear algebra methods over any discrete valuation ring, where hive entries are minima of sums of orders of invariant factors over certain submodules. Our map is analogous to a conjectured construction of hives from Hermitian matrix pairs due to Danilov and Koshevoy.", "published": "2018-10-01T21:47:27Z", "authors": ["Glenn D. Appleby", "Tamsen Whitehead"], "categories": ["math.RT", "math.AC", "math.CO"], "doi": "", "_query": "automated valuation model", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/1410.7021v1", "title": "SL(n)-Contravariant $L_p$-Minkowski Valuations", "abstract": "All SL(n)-contravariant $L_p$-Minkowski valuations on polytopes are completely classified. The prototypes of such valuations turn out to be the asymmetric $L_p$-projection body operators.", "published": "2014-10-26T12:48:04Z", "authors": ["Lukas Parapatits"], "categories": ["math.MG"], "doi": "", "_query": "automated valuation model", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/1303.4406v2", "title": "Extensions of translation invariant valuations on polytopes", "abstract": "We study translation invariant, real-valued valuations on the class of convex polytopes in Euclidean space and discuss which continuity properties are sufficient for an extension of such valuations to all convex bodies. For this purpose, we introduce flag support measures of convex bodies via a local Steiner formula and derive some of the properties of these measures.", "published": "2013-03-09T11:09:30Z", "authors": ["Wolfram Hinderer", "Daniel Hug", "Wolfgang Weil"], "categories": ["math.MG"], "doi": "10.1112/S0025579314000187", "_query": "automated valuation model", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2105.06016v1", "title": "Automated Discovery of Process Models with True Concurrency and Inclusive Choices", "abstract": "Enterprise information systems allow companies to maintain detailed records of their business process executions. These records can be extracted in the form of event logs, which capture the execution of activities across multiple instances of a business process. Event logs may be used to analyze business processes at a fine level of detail using process mining techniques. Among other things, process mining techniques allow us to discover a process model from an event log -- an operation known as automated process discovery. Despite a rich body of research in the field, existing automated process discovery techniques do not fully capture the concurrency inherent in a business process. Specifically, the bulk of these techniques treat two activities A and B as concurrent if sometimes A completes before B and other times B completes before A. Typically though, activities in a business process are executed in a true concurrency setting, meaning that two or more activity executions overlap temporally. This paper addresses this gap by presenting a refined version of an automated process discovery technique, namely Split Miner, that discovers true concurrency relations from event logs containing start and end timestamps for each activity. The proposed technique is also able to differentiate between exclusive and inclusive choices. We evaluate the proposed technique relative to existing baselines using 11 real-life logs drawn from different industries.", "published": "2021-05-13T01:00:08Z", "authors": ["Adriano Augusto", "Marlon Dumas", "Marcello La Rosa"], "categories": ["cs.SE"], "doi": "", "_query": "automated valuation model", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/1905.07435v1", "title": "Alpha MAML: Adaptive Model-Agnostic Meta-Learning", "abstract": "Model-agnostic meta-learning (MAML) is a meta-learning technique to train a model on a multitude of learning tasks in a way that primes the model for few-shot learning of new tasks. The MAML algorithm performs well on few-shot learning problems in classification, regression, and fine-tuning of policy gradients in reinforcement learning, but comes with the need for costly hyperparameter tuning for training stability. We address this shortcoming by introducing an extension to MAML, called Alpha MAML, to incorporate an online hyperparameter adaptation scheme that eliminates the need to tune meta-learning and learning rates. Our results with the Omniglot database demonstrate a substantial reduction in the need to tune MAML training hyperparameters and improvement to training stability with less sensitivity to hyperparameter choice.", "published": "2019-05-17T18:45:25Z", "authors": ["Harkirat Singh Behl", "Atılım Güneş Baydin", "Philip H. S. Torr"], "categories": ["cs.LG", "cs.AI", "stat.ML"], "doi": "", "_query": "automated valuation model", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/1309.0574v5", "title": "Reified valuations and adic spectra", "abstract": "We revisit Huber's theory of continuous valuations, which give rise to the adic spectra used in his theory of adic spaces. We instead consider valuations which have been reified, i.e., whose value groups have been forced to contain the real numbers. This yields reified adic spectra which provide a framework for an analogue of Huber's theory compatible with Berkovich's construction of nonarchimedean analytic spaces. As an example, we extend the theory of perfectoid spaces to this setting.", "published": "2013-09-03T02:25:53Z", "authors": ["Kiran S. Kedlaya"], "categories": ["math.NT"], "doi": "", "_query": "automated valuation model", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2402.11904v2", "title": "Automated Deterministic Auction Design with Objective Decomposition", "abstract": "Identifying high-revenue mechanisms that are both dominant strategy incentive compatible (DSIC) and individually rational (IR) is a fundamental challenge in auction design. While theoretical approaches have encountered bottlenecks in multi-item auctions, there has been much empirical progress in automated designing such mechanisms using machine learning. However, existing research primarily focuses on randomized auctions, with less attention given to the more practical deterministic auctions. Therefore, this paper investigates the automated design of deterministic auctions and introduces OD-VVCA, an objective decomposition approach for automated designing Virtual Valuations Combinatorial Auctions (VVCAs). Firstly, we restrict our mechanism to deterministic VVCAs, which are inherently DSIC and IR. Afterward, we utilize a parallelizable dynamic programming algorithm to compute the allocation and revenue outcomes of a VVCA efficiently. We then decompose the revenue objective function into continuous and piecewise constant discontinuous components, optimizing each using distinct methods. Extensive experiments show that OD-VVCA achieves high revenue in multi-item auctions, especially in large-scale settings where it outperforms both randomized and deterministic baselines, indicating its efficacy and scalability.", "published": "2024-02-19T07:45:04Z", "authors": ["Zhijian Duan", "Haoran Sun", "Yichong Xia", "Siqiang Wang", "Zhilin Zhang", "Chuan Yu", "Jian Xu", "Bo Zheng", "Xiaotie Deng"], "categories": ["cs.GT", "cs.LG"], "doi": "", "_query": "automated valuation model", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2406.10545v5", "title": "Arithmetic of cuts in ordered abelian groups and of ideals over valuation rings", "abstract": "We investigate existence, uniqueness and maximality of solutions $T$ for equations $S_1+T=S_2$ and inequalities $S_1+T\\subseteq S_2$ where $S_1$ and $S_2$ are final segments of ordered abelian groups. Since cuts are determined by their upper cut sets, which are final segments, this gives information about the corresponding equalities and inequalities for cuts. We apply our results to investigate existence, uniqueness and maximality of solutions $J$ for equations $I_1 J=I_2$ and inequalities $I_1 J\\subseteq I_2$ where $I_1$ and $I_2$ are ideals of valuation rings. This enables us to compute the annihilators of quotients of the form $I_1/I_2\\,$.", "published": "2024-06-15T07:58:19Z", "authors": ["Franz-Viktor Kuhlmann", "Katarzyna Kuhlmann"], "categories": ["math.AC"], "doi": "10.2140/pjm.2026.340.339", "_query": "automated valuation model", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/1506.05956v3", "title": "Recovering p-adic valuations from pro-p Galois groups", "abstract": "Let $K$ be a field with $G_K(2) \\simeq G_{\\mathbb{Q}}(2)$, where $G_F(2)$ denotes the maximal pro-2 quotient of the absolute Galois group of a field $F$. We prove that then $K$ admits a (non-trivial) valuation $v$ which is 2-henselian and has residue field $\\mathbb{F}_2$. Furthermore, $v(2)$ is a minimal positive element in the value group $Γ_v$ and $[Γ_v:2Γ_v]=2$. This forms the first positive result on a more general conjecture about recovering $p$-adic valuations from pro-$p$ Galois groups which we formulate precisely. As an application, we show how this result can be used to easily obtain number-theoretic information, by giving an independent proof of a strong version of the birational section conjecture for smooth, complete curves $X$ over $\\mathbb{Q}_2$, as well as an analogue for varieties.", "published": "2015-06-19T10:53:41Z", "authors": ["Jochen Koenigsmann", "Kristian Strommen"], "categories": ["math.NT"], "doi": "10.1112/jlms.12901", "_query": "automated valuation model", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/1708.07377v2", "title": "AutoLens: Automated Modeling of a Strong Lens's Light, Mass and Source", "abstract": "This work presents AutoLens, the first entirely automated modeling suite for the analysis of galaxy-scale strong gravitational lenses. AutoLens simultaneously models the lens galaxy's light and mass whilst reconstructing the extended source galaxy on an adaptive pixel-grid. The method's approach to source-plane discretization is amorphous, adapting its clustering and regularization to the intrinsic properties of the lensed source. The lens's light is fitted using a superposition of Sersic functions, allowing AutoLens to cleanly deblend its light from the source. Single component mass models representing the lens's total mass density profile are demonstrated, which in conjunction with light modeling can detect central images using a centrally cored profile. Decomposed mass modeling is also shown, which can fully decouple a lens's light and dark matter and determine whether the two component are geometrically aligned. The complexity of the light and mass models are automatically chosen via Bayesian model comparison. These steps form AutoLens's automated analysis pipeline, such that all results in this work are generated without any user-intervention. This is rigorously tested on a large suite of simulated images, assessing its performance on a broad range of lens profiles, source morphologies and lensing geometries. The method's performance is excellent, with accurate light, mass and source profiles inferred for data sets representative of both existing Hubble imaging and future Euclid wide-field observations.", "published": "2017-08-24T12:41:46Z", "authors": ["James Nightingale", "Simon Dye", "Richard Massey"], "categories": ["astro-ph.CO", "astro-ph.GA", "astro-ph.IM"], "doi": "10.1093/mnras/sty1264", "_query": "automated valuation model", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2605.14400v1", "title": "Partial Identification of the Valuation Distribution in Sequential English Auctions", "abstract": "This paper extends the incomplete model of Haile and Tamer (2003) from static English auctions to sequential English auctions. Because bidders may wait for future opportunities, the static condition that bidders do not let rivals win at beatable prices need not hold. We replace it with a dynamic opportunity-cost restriction, yielding nonparametric valuation bounds without solving a dynamic equilibrium. Sharp bounds are also characterized. We propose a novel moment-condition inversion estimator that pools auctions with heterogeneous bidder counts, mitigating finite-sample instability of order statistics approaches and admitting analytical standard errors and smooth confidence intervals. Applications to Korean wholesale used-car auctions and Cars and Bids online auctions deliver informative bounds. Counterfactual analyses show that the option to wait lowers first-period revenue by 8--11% in the Korean market, that increasing effective competition from 8 to 20 serious bidders in Cars and Bids raises seller revenue by 40--65%, and that maximin reserve prices vary substantially across vehicle clusters.", "published": "2026-05-14T05:38:16Z", "authors": ["Dongwoo Kim", "Kyoo il Kim", "Pallavi Pal"], "categories": ["econ.EM"], "doi": "", "_query": "automated valuation model", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2205.07804v1", "title": "CurFi: An automated tool to find the best regression analysis model using curve fitting", "abstract": "Regression analysis is a well known quantitative research method that primarily explores the relationship between one or more independent variables and a dependent variable. Conducting regression analysis manually on large datasets with multiple independent variables can be tedious. An automated system for regression analysis will be of great help for researchers as well as non-expert users. Thus, the objective of this research is to design and develop an automated curve fitting system. As outcome, a curve fitting system named \"CurFi\" was developed that uses linear regression models to fit a curve to a dataset and to find out the best fit model. The system facilitates to upload a dataset, split the dataset into training set and test set, select relevant features and label from the dataset; and the system will return the best fit linear regression model after training is completed. The developed tool would be a great resource for the users having limited technical knowledge who will also be able to find the best fit regression model for a dataset using the developed \"CurFi\" system.", "published": "2022-05-16T16:52:10Z", "authors": ["Ayon Roy", "Tausif Al Zubayer", "Nafisa Tabassum", "Muhammad Nazrul Islam", "Md. Abdus Sattar"], "categories": ["cs.LG"], "doi": "10.1002/eng2.12522", "_query": "automated valuation model", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2210.07089v2", "title": "Experimenting with Selected Automated Approaches for Bias Analysis", "abstract": "This work first presents our attempts to establish an automated model using state-of-the-art approaches for analysing bias in search results of Bing and Google. Experimental results indicate that the current class-wise F1-scores of our best model are not sufficient to establish an automated model for bias analysis. Thus, we decided not to continue with this approach.", "published": "2022-10-13T15:14:30Z", "authors": ["Gizem Gezici"], "categories": ["cs.IR"], "doi": "", "_query": "automated valuation model", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2212.05058v1", "title": "Structured Like a Language Model: Analysing AI as an Automated Subject", "abstract": "Drawing from the resources of psychoanalysis and critical media studies, in this paper we develop an analysis of Large Language Models (LLMs) as automated subjects. We argue the intentional fictional projection of subjectivity onto LLMs can yield an alternate frame through which AI behaviour, including its productions of bias and harm, can be analysed. First, we introduce language models, discuss their significance and risks, and outline our case for interpreting model design and outputs with support from psychoanalytic concepts. We trace a brief history of language models, culminating with the releases, in 2022, of systems that realise state-of-the-art natural language processing performance. We engage with one such system, OpenAI's InstructGPT, as a case study, detailing the layers of its construction and conducting exploratory and semi-structured interviews with chatbots. These interviews probe the model's moral imperatives to be helpful, truthful and harmless by design. The model acts, we argue, as the condensation of often competing social desires, articulated through the internet and harvested into training data, which must then be regulated and repressed. This foundational structure can however be redirected via prompting, so that the model comes to identify with, and transfer, its commitments to the immediate human subject before it. In turn, these automated productions of language can lead to the human subject projecting agency upon the model, effecting occasionally further forms of countertransference. We conclude that critical media methods and psychoanalytic theory together offer a productive frame for grasping the powerful new capacities of AI-driven language systems.", "published": "2022-12-08T21:58:43Z", "authors": ["Liam Magee", "Vanicka Arora", "Luke Munn"], "categories": ["cs.CY", "cs.AI"], "doi": "", "_query": "automated valuation model", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2508.06876v1", "title": "Construction of a valued field whose valuation ring is definable but neither $\\exists\\forall\\exists$ nor $ \\forall\\exists\\forall$-definable in the language of rings", "abstract": "We give an example of a valued field $(K,A)$ such that the valuation ring $A$ is definable by an $L_{\\text{ring}}$-formula without parameters, but there is no $\\exists\\forall\\exists$ or $\\forall\\exists\\forall$-formula in $L_{\\text{ring}}$ to define it.", "published": "2025-08-09T08:14:15Z", "authors": ["Mohsen Khani", "Shaghayegh Shirani", "Zahra Yadegari", "Afshin Zarei"], "categories": ["math.LO"], "doi": "", "_query": "automated valuation model", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/1508.00281v1", "title": "Model Selection versus Model Averaging in Dose Finding Studies", "abstract": "Phase II dose finding studies in clinical drug development are typically conducted to adequately characterize the dose response relationship of a new drug. An important decision is then on the choice of a suitable dose response function to support dose selection for the subsequent Phase III studies. In this paper we compare different approaches for model selection and model averaging using mathematical properties as well as simulations. Accordingly, we review and illustrate asymptotic properties of model selection criteria and investigate their behavior when changing the sample size but keeping the effect size constant. In a large scale simulation study we investigate how the various approaches perform in realistically chosen settings. Finally, the different methods are illustrated with a recently conducted Phase II dosefinding study in patients with chronic obstructive pulmonary disease.", "published": "2015-08-02T20:19:04Z", "authors": ["Kirsten Schorning", "Björn Bornkamp", "Frank Bretz", "Holger Dette"], "categories": ["stat.AP"], "doi": "", "_query": "automated valuation model", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/1812.09086v1", "title": "Reasoning and Facts Explanation in Valuation Based Systems", "abstract": "In the literature, the optimization problem to identify a set of composite hypotheses H, which will yield the $k$ largest $P(H|S_e)$ where a composite hypothesis is an instantiation of all the nodes in the network except the evidence nodes \\cite{KSy:93} is of significant interest. This problem is called \"finding the $k$ Most Plausible Explanation (MPE) of a given evidence $S_e$ in a Bayesian belief network\". The problem of finding $k$ most probable hypotheses is generally NP-hard \\cite{Cooper:90}. Therefore in the past various simplifications of the task by restricting $k$ (to 1 or 2), restricting the structure (e.g. to singly connected networks), or shifting the complexity to spatial domain have been investigated. A genetic algorithm is proposed in this paper to overcome some of these restrictions while stepping out from probabilistic domain onto the general Valuation based System (VBS) framework is also proposed by generalizing the genetic algorithm approach to the realm of Dempster-Shafer belief calculus.", "published": "2018-12-21T12:41:00Z", "authors": ["S. T. Wierzchoń", "M. A. Kłopotek", "M. Michalewicz"], "categories": ["cs.AI"], "doi": "", "_query": "automated valuation model", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/1007.1904v3", "title": "The classification of $p$-divisible groups over 2-adic discrete valuation rings", "abstract": "Let $\\mathscr{O}_K$ be a 2-adic discrete valuation ring with perfect residue field $k$. We classify $p$-divisible groups and $p$-power order finite flat group schemes over $\\mathscr{O}_K$ in terms of certain Frobenius module over $\\mathfrak{S}:=W(k)[[u]]$. We also show the compatibility with crystalline Dieudonné theory and associated Galois representations. Our approach differs from Lau's generalization of display theory, and we additionally obtain the the compatibility with associated Galois representations.", "published": "2010-07-12T13:55:02Z", "authors": ["Wausu Kim"], "categories": ["math.NT", "math.AG"], "doi": "", "_query": "automated valuation model", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/1804.04704v1", "title": "Generating Sequences and Semigroups of Valuations on 2-Dimensional Normal Local Rings", "abstract": "In this paper we develop a method for constructing generating sequences for valuations dominating the ring of a two dimensional quotient singularity. Suppose that $K$ is an algebraically closed field of characteristic zero, $K[X,Y]$ is a polynomial ring over $K$ and $ν$ is a rational rank 1 valuation of the field $K(X,Y)$ which dominates $K[X,Y]_{(X,Y)}$. Given a finite Abelian group $H$ acting diagonally on $K[X,Y]$, and a generating sequence of $ν$ in $K[X,Y]$ whose members are eigenfunctions for the action of $H$, we compute a generating sequence for the invariant ring $K[X,Y]^H$. We use this to compute the semigroup $S^{K[X,Y]^H}$ of values of elements of $K[X,Y]^H$. We further determine when $S^{K[X,Y]} (ν)$ is a finitely generated $S^{K[X,Y]^H} (ν)$ -module.", "published": "2018-04-12T19:46:21Z", "authors": ["Arpan Dutta"], "categories": ["math.AG"], "doi": "10.5802/afst.1642", "_query": "automated valuation model", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/0909.4962v1", "title": "Generalized polygons with non-discrete valuation defined by two-dimensional affine R-buildings", "abstract": "In this paper, we show that the building at infinity of a two-dimensional affine R-building is a generalized polygon endowed with a valuation satisfying some specific axioms. Specializing to the discrete case of affine buildings, this solves part of a long standing conjecture about affine buildings of type G~_2, and it reproves the results obtained mainly by the second author for types A~_2 and C~_2. The techniques are completely different from the ones employed in the discrete case, but they are considerably shorter, and general (i.e., independent of the type of the two-dimensional R-building).", "published": "2009-09-27T19:55:33Z", "authors": ["Koen Struyve", "Hendrik Van Maldeghem"], "categories": ["math.MG"], "doi": "", "_query": "automated valuation model", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2506.18210v3", "title": "American options valuation in time-dependent jump-diffusion models via integral equations and characteristic functions", "abstract": "Despite significant advancements in machine learning for derivative pricing, the efficient and accurate valuation of American options remains a persistent challenge due to complex exercise boundaries, near-expiry behavior, and intricate contractual features. This paper extends a semi-analytical approach for pricing American options in time-inhomogeneous models, including pure diffusions, jump-diffusions, and Levy processes. Building on prior work, we derive and solve Volterra integral equations of the second kind to determine the exercise boundary explicitly, offering a computationally superior alternative to traditional finite-difference and Monte Carlo methods. We address key open problems: (1) extending the decomposition method, i.e. splitting the American option price into its European counterpart and an early exercise premium, to general jump-diffusion and Levy models; (2) handling cases where closed-form transition densities are unavailable by leveraging characteristic functions via, e.g., the COS method; and (3) generalizing the framework to multidimensional diffusions. Numerical examples demonstrate the method's efficiency and robustness. Our results underscore the advantages of the integral equation approach for large-scale industrial applications, while resolving some limitations of existing techniques.", "published": "2025-06-23T00:13:08Z", "authors": ["Andrey Itkin"], "categories": ["q-fin.PR", "q-fin.CP", "q-fin.MF"], "doi": "", "_query": "automated valuation model", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2511.23468v2", "title": "Metrics from valuations on convex bodies", "abstract": "We associate to each valuation $φ$ on $\\mathcal{K}^n$ a pseudometric $D_φ$, generalizing the classical symmetric difference and mean width metrics. Broad criteria are given under which $D_φ$ is a bona fide metric, in which case it is automatically intrinsic. Moreover, a necessary and sufficient condition for the $D_φ$-balls to be bounded in the Hausdorff sense is given; as a corollary, we obtain a condition for $D_φ$ to be proper, and hence complete and geodesic. The topology induced by $D_φ$ is also investigated: we provide conditions relating different forms of convergence, showing for instance that the intrinsic volumes $\\left \\{\\text{V}_j : j \\in \\{1, \\dots, n\\} \\right\\}$ induce metrics that are topologically equivalent to the Hausdorff distance. Finally, we apply our construction to the metric geometry of singularity types by exhibiting, on the subspace of $\\mathcal{S}(\\mathbb{CP}^n,ω_{FS})$ corresponding to toric model potentials, a geodesic metric that is bilipschitz equivalent to the $d_{\\mathcal{S}}$ metric of Darvas, Di Nezza, and Lu.", "published": "2025-11-28T18:57:13Z", "authors": ["David Owen Horace Cutler", "Mel Deaton"], "categories": ["math.MG"], "doi": "", "_query": "automated valuation model", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2607.18220v1", "title": "Nonexistence of Simultaneously EF1 and Pareto Optimal Allocations for Submodular Valuations", "abstract": "The existence of allocations of indivisible goods that are simultaneously fair (envy-free up to one item (EF1)) and efficient (Pareto optimal (PO)) when agents have monotone submodular valuations has been a longstanding open problem. We settle this question negatively by giving an example with two agents where no allocation is simultaneously EF1 and PO. We also show that determining the existence of such allocations is NP-hard for monotone submodular valuations. Our example uses (unweighted) coverage valuations, which is a strict subclass of monotone submodular valuations. Since EF1+PO allocations are known to always exist for additive valuations via the maximization of Nash Social Welfare (Caragiannis et al. (ACM TEAC 2019)), and for matroid-rank valuations (Benabbou et al. (ACM TEAC 2021)), nonexistence was known only for monotone subadditive valuations (Caragiannis et al. (ACM TEAC 2019)). Our work moves the nonexistence frontier to unweighted coverage valuations. We also show that the example we designed for goods also proves nonexistence of EF1+PO in general, for chores with unweighted coverage costs, by interpreting the valuations as disutilities.", "published": "2026-07-20T17:54:36Z", "authors": ["Harish Chandramouleeswaran", "Prajakta Nimbhorkar"], "categories": ["cs.GT", "cs.DS"], "doi": "", "_query": "automated valuation model", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/math/0605193v1", "title": "Valuations in algebraic field extensions", "abstract": "Let $K\\to L$ be an algebraic field extension and $ν$ a valuation of $K$. The purpose of this paper is to describe the totality of extensions $\\left\\{ν'\\right\\}$ of $ν$ to $L$ using a refined version of MacLane's key polynomials. In the basic case when $L$ is a finite separable extension and $rk ν=1$, we give an explicit description of the limit key polynomials (which can be viewed as a generalization of the Artin--Schreier polynomials). We also give a realistic upper bound on the order type of the set of key polynomials. Namely, we show that if $char K=0$ then the set of key polynomials has order type at most $\\mathbb N$, while in the case $char K=p>0$ this order type is bounded above by $([\\log_pn]+1)ω$, where $n=[L:K]$. Our results provide a new point of view of the the well known formula $\\sum\\limits_{j=1}^se_jf_jd_j=n$ and the notion of defect.", "published": "2006-05-08T16:02:48Z", "authors": ["F. J. Herrera Govantes", "M. A. Olalla Acosta", "M. Spivakovsky"], "categories": ["math.AC", "math.AG"], "doi": "10.1016/j.jalgebra.2007.02.022", "_query": "automated valuation model", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/1801.06830v1", "title": "Neural Multi-task Learning in Automated Assessment", "abstract": "Grammatical error detection and automated essay scoring are two tasks in the area of automated assessment. Traditionally these tasks have been treated independently with different machine learning models and features used for each task. In this paper, we develop a multi-task neural network model that jointly optimises for both tasks, and in particular we show that neural automated essay scoring can be significantly improved. We show that while the essay score provides little evidence to inform grammatical error detection, the essay score is highly influenced by error detection.", "published": "2018-01-21T14:38:23Z", "authors": ["Ronan Cummins", "Marek Rei"], "categories": ["cs.CL"], "doi": "", "_query": "automated valuation model", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/1404.4885v1", "title": "The analogue of Izumi's Theorem for Abhyankar valuations", "abstract": "A well known theorem of Shuzo Izumi, strengthened by David Rees, asserts that all the divisorial valuations centered in an analytically irreducible local noetherian ring are linearly comparable to each other. In the present paper we generalize this theorem to the case of Abhyankar valuations with archimedian value semigroup. Indeed, we prove that in a certain sense linear equivalence of topologies characterizes Abhyankar valuations with archimedian semigroups, centered in analytically irreducible local noetherian rings. Then we show that some of the classical results on equivalence of topologies in noetherian rings can be strengthened to include linear equivalence of topologies. We also prove a new comparison result between the Krull topology and the topology defined by the symbolic powers of an arbitrary ideal.", "published": "2014-04-18T20:54:21Z", "authors": ["Guillaume Rond", "Mark Spivakovsky"], "categories": ["math.AC"], "doi": "", "_query": "automated valuation model", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2406.00228v1", "title": "A note on Kollár valuations", "abstract": "We prove the set of Kollár valuations in the dual complex of a klt singularity with a fixed complement is path connected. We also classify the case when the dual complex is one dimensional.", "published": "2024-05-31T23:10:23Z", "authors": ["Yuchen Liu", "Chenyang Xu"], "categories": ["math.AG", "math.AC"], "doi": "", "_query": "automated valuation model", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2407.05808v1", "title": "Log-concavity for independent sets of valuated matroids", "abstract": "Recently, several proofs of the Mason--Welsh conjecture for matroids have been found, which asserts the log-concavity of the sequence that counts independent sets of a given size. In this article we use the theory of Lorentzian polynomials, developed by Brändén and Huh, to prove a generalization of the Mason-Welsh conjecture to the context of valuated matroids. In fact, we provide a log-concavity result in the more general setting of valuated discrete polymatroids, or equivalently, M-convex functions. Our approach is via the construction of a generic extension of a valuated matroid or M-convex function, so that the bases of the extension are related to the independent sets of the original matroid. We also provide a similar log-concavity result for valuated bimatroids, which, we believe, might be of independent interest.", "published": "2024-07-08T10:40:27Z", "authors": ["Jeffrey Giansiracusa", "Felipe Rincón", "Victoria Schleis", "Martin Ulirsch"], "categories": ["math.CO", "math.AG"], "doi": "", "_query": "automated valuation model", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/1504.08073v1", "title": "ESO Valuation with Job Termination Risk and Jumps in Stock Price", "abstract": "Employee stock options (ESOs) are American-style call options that can be terminated early due to employment shock. This paper studies an ESO valuation framework that accounts for job termination risk and jumps in the company stock price. Under general Lévy stock price dynamics, we show that a higher job termination risk induces the ESO holder to voluntarily accelerate exercise, which in turn reduces the cost to the company. The holder's optimal exercise boundary and ESO cost are determined by solving an inhomogeneous partial integro-differential variational inequality (PIDVI). We apply Fourier transform to simplify the variational inequality and develop accurate numerical methods. Furthermore, when the stock price follows a geometric Brownian motion, we provide closed-form formulas for both the vested and unvested perpetual ESOs. Our model is also applied to evaluate the probabilities of understating ESO expenses and contract termination.", "published": "2015-04-30T03:35:49Z", "authors": ["Tim Leung", "Haohua Wan"], "categories": ["q-fin.PR"], "doi": "", "_query": "automated valuation model", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2606.10127v1", "title": "Data-Driven Automation", "abstract": "We build a dynamic model of data-driven automation in which data (i) is heterogeneous and task-specific; (ii) accumulates endogenously as a byproduct of economic activity; and (iii) exhibits spillovers such that data generated by one task can augment the productivity of another. Along the transition path of automation, data plays a dual role in simultaneously augmenting the productivity of already-automated tasks and expanding the automation frontier. We derive tight conditions for the economy to be partially versus fully automated in the long-run. In the latter case, automation exhibits rich short-run dynamics that depend on the pattern of data spillovers but is always slow in the long-run: the share of tasks produced by labor decays asymptotically as a power law in time. We show that the economy is generically inefficient and analyze how a planner optimally tilts the direction of data accumulation. With endogenous capital accumulation, data-driven automation generates explosive growth but stagnant long-run wages.", "published": "2026-06-08T20:00:42Z", "authors": ["Maryam Farboodi", "Andrew Koh", "Anchi Xia"], "categories": ["econ.TH"], "doi": "", "_query": "automated valuation model", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2502.13082v1", "title": "Automated Linear Parameter-Varying Modeling of Nonlinear Systems: A Global Embedding Approach", "abstract": "In this paper, an automated Linear Parameter-Varying (LPV) model conversion approach is proposed for nonlinear dynamical systems. The proposed method achieves global embedding of the original nonlinear behavior of the system by leveraging the second fundamental theorem of calculus to factorize matrix function expressions without any approximation. The implementation of the proposed method in the LPVcore toolbox for Matlab is discussed, and its performance is showcased on a comprehensive example of automated LPV model conversion of an unbalanced disk system, which is then used to design an LPV controller that is deployed on the original nonlinear system. In addition, the conversion capabilities are further demonstrated by obtaining an LPV embedding of a three-degree-of-freedom control moment gyroscope. All software implementations are available at www.lpvcore.net.", "published": "2025-02-18T17:34:06Z", "authors": ["E. Javier Olucha", "Patrick J. W. Koelewijn", "Amritam Das", "Roland Tóth"], "categories": ["eess.SY"], "doi": "", "_query": "automated valuation model", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2306.08458v1", "title": "A new computational perceived risk model for automated vehicles based on potential collision avoidance difficulty (PCAD)", "abstract": "Perceived risk is crucial in designing trustworthy and acceptable vehicle automation systems. However, our understanding of its dynamics is limited, and models for perceived risk dynamics are scarce in the literature. This study formulates a new computational perceived risk model based on potential collision avoidance difficulty (PCAD) for drivers of SAE level 2 driving automation. PCAD uses the 2D safe velocity gap as the potential collision avoidance difficulty, and takes into account collision severity. The safe velocity gap is defined as the 2D gap between the current velocity and the safe velocity region, and represents the amount of braking and steering needed, considering behavioural uncertainty of neighbouring vehicles and imprecise control of the subject vehicle. The PCAD predicts perceived risk both in continuous time and per event. We compare the PCAD model with three state-of-the-art models and analyse the models both theoretically and empirically with two unique datasets: Dataset Merging and Dataset Obstacle Avoidance. The PCAD model generally outperforms the other models in terms of model error, detection rate, and the ability to accurately capture the tendencies of human drivers' perceived risk, albeit at a longer computation time. Additionally, the study shows that the perceived risk is not static and varies with the surrounding traffic conditions. This research advances our understanding of perceived risk in automated driving and paves the way for improved safety and acceptance of driving automation systems.", "published": "2023-06-14T11:59:11Z", "authors": ["Xiaolin He", "Riender Happee", "Meng Wang"], "categories": ["cs.HC"], "doi": "", "_query": "automated valuation model", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/1603.02486v1", "title": "Permutation Representations of the Orbits of the Automorphism Group of a Finite Module over Discrete Valuation Ring", "abstract": "Consider a discrete valuation ring $R$ whose residue field is finite of cardinality at least $3$. For a finite torsion module, we consider transitive subsets $O$ under the action of the automorphism group of the module. We prove that the associated permutation representation on the complex vector space $C[O]$ is multiplicity free. This is achieved by obtaining a complete description of the transitive subsets of $O\\times O$ under the diagonal action of the automorphism group.", "published": "2016-03-08T11:41:04Z", "authors": ["C. P. Anil Kumar"], "categories": ["math.RT", "math.CO", "math.RA"], "doi": "10.1007/s12044-016-0320-5", "_query": "automated valuation model", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/1306.4733v2", "title": "Valuation and hedging of OTC contracts with funding costs, collateralization and counterparty credit risk: Part 1", "abstract": "The research presented in this work is motivated by some recent papers regarding hedging and valuation of financial securities subject to funding costs, collateralization and counterparty credit risk. Our goal is to provide a sound theoretical underpinning for some results presented in these papers by developing a unified martingale framework for the non-linear approach to hedging and pricing of OTC financial contracts. The impact that various funding bases and margin covenants exert on the values and hedging strategies for OTC contracts is examined.", "published": "2013-06-20T01:37:23Z", "authors": ["Tomasz R. Bielecki", "Marek Rutkowski"], "categories": ["q-fin.PR"], "doi": "", "_query": "automated valuation model", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2408.00665v1", "title": "AutoM3L: An Automated Multimodal Machine Learning Framework with Large Language Models", "abstract": "Automated Machine Learning (AutoML) offers a promising approach to streamline the training of machine learning models. However, existing AutoML frameworks are often limited to unimodal scenarios and require extensive manual configuration. Recent advancements in Large Language Models (LLMs) have showcased their exceptional abilities in reasoning, interaction, and code generation, presenting an opportunity to develop a more automated and user-friendly framework. To this end, we introduce AutoM3L, an innovative Automated Multimodal Machine Learning framework that leverages LLMs as controllers to automatically construct multimodal training pipelines. AutoM3L comprehends data modalities and selects appropriate models based on user requirements, providing automation and interactivity. By eliminating the need for manual feature engineering and hyperparameter optimization, our framework simplifies user engagement and enables customization through directives, addressing the limitations of previous rule-based AutoML approaches. We evaluate the performance of AutoM3L on six diverse multimodal datasets spanning classification, regression, and retrieval tasks, as well as a comprehensive set of unimodal datasets. The results demonstrate that AutoM3L achieves competitive or superior performance compared to traditional rule-based AutoML methods. Furthermore, a user study highlights the user-friendliness and usability of our framework, compared to the rule-based AutoML methods.", "published": "2024-08-01T16:01:51Z", "authors": ["Daqin Luo", "Chengjian Feng", "Yuxuan Nong", "Yiqing Shen"], "categories": ["cs.LG"], "doi": "", "_query": "automated valuation model", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/1609.02535v1", "title": "Modeling the differentiation of A- and C-type baroreceptor firing patterns", "abstract": "The baroreceptor neurons serve as the primary transducers of blood pressure for the autonomic nervous system and are thus critical in enabling the body to respond effectively to changes in blood pressure. These neurons can be separated into two types (A and C) based on the myelination of their axons and their distinct firing patterns elicited in response to specific pressure stimuli. This study has developed a comprehensive model of the afferent baroreceptor discharge built on physiological knowledge of arterial wall mechanics, firing rate responses to controlled pressure stimuli, and ion channel dynamics within the baroreceptor neurons. With this model, we were able to predict firing rates observed in previously published experiments in both A- and C-type neurons. These results were obtained by adjusting model parameters determining the maximal ion-channel conductances. The observed variation in the model parameters are hypothesized to correspond to physiological differences between A- and C-type neurons. In agreement with published experimental observations, our simulations suggest that a twofold lower potassium conductance in C-type neurons is responsible for the observed sustained basal firing, whereas a tenfold higher mechanosensitive conductance is responsible for the greater firing rate observed in A-type neurons. A better understanding of the difference between the two neuron types can potentially be used to gain more insight into the underlying pathophysiology facilitating development of targeted interventions improving baroreflex function in diseased individuals, e.g. in patients with autonomic failure, a syndrome that is difficult to diagnose in terms of its pathophysiology.", "published": "2016-09-07T18:48:44Z", "authors": ["Jacob Sturdy", "Johnny T Ottesen", "Mette S Olufsen"], "categories": ["q-bio.NC", "q-bio.QM"], "doi": "", "_query": "automated valuation model", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/math/0012135v1", "title": "Invitation to higher local fields, Part I, section 4: Cohomological symbol for henselian discrete valuation fields of mixed characteristic", "abstract": "This is a sketch of main steps of the proof of Bloch--Kato's theorem which states that the norm residue homomorphism K_q(K)/m\\to H^q(K,\\Bbb Z/m(q)) is an isomorphism for a henselian discrete valuation field K of characteristic 0 with residue field of positive characteristic.", "published": "2000-12-18T03:47:23Z", "authors": ["Jinya Nakamura"], "categories": ["math.NT", "math.AG"], "doi": "", "_query": "automated valuation model", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2208.06237v2", "title": "Geometry of higher rank valuations", "abstract": "The aim of this paper is to introduce a certain number of tools and results suitable for the study of valuations of higher rank on function fields of algebraic varieties. This will be based on a study of higher rank quasi-monomial valuations taking values in the lexicographically ordered group R^k. We prove a duality theorem that gives a geometric realization of higher rank quasi-monomial valuations as tangent cones of dual cone complexes. Using this duality, we provide an analytic description of quasi-monomial valuations as multi-directional derivative operators on tropical functions. We consider moreover a refined notion of tropicalization in which we remember the initial terms of power series on each cone of a dual complex, and prove a tropical analogue of the weak approximation theorem in number theory by showing that any compatible collection of initial terms on cones of a dual cone complex is the refined tropicalization of a rational function in the function field of the variety. Endowing the value group R^k with its Euclidean topology, we study then a natural topology on spaces of higher rank valuations that we call the tropical topology. By using the approximation theorem we provide an explicit description of the tropical topology on tangent cones of dual cone complexes. Finally, we show that tangent cones of dual complexes provide a notion of skeleton in higher rank non-archimedean geometry. That is, generalizing the picture in rank one to higher rank, we construct retraction maps to tangent cones of dual cone complexes, and use them to obtain limit formulae in which we reconstruct higher rank non-archimedian spaces with their tropical topology as the projective limit of their higher rank skeleta. We conjecture that these higher rank skeleta provide appropriate bases for the study of variations of Newton-Okounkov bodies.", "published": "2022-08-12T12:21:35Z", "authors": ["Omid Amini", "Hernan Iriarte"], "categories": ["math.AG", "math.AC", "math.CO"], "doi": "", "_query": "automated valuation model", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2504.15286v1", "title": "CUBETESTERAI: Automated JUnit Test Generation using the LLaMA Model", "abstract": "This paper presents an approach to automating JUnit test generation for Java applications using the Spring Boot framework, leveraging the LLaMA (Large Language Model Architecture) model to enhance the efficiency and accuracy of the testing process. The resulting tool, called CUBETESTERAI, includes a user-friendly web interface and the integration of a CI/CD pipeline using GitLab and Docker. These components streamline the automated test generation process, allowing developers to generate JUnit tests directly from their code snippets with minimal manual intervention. The final implementation executes the LLaMA models through RunPod, an online GPU service, which also enhances the privacy of our tool. Using the advanced natural language processing capabilities of the LLaMA model, CUBETESTERAI is able to generate test cases that provide high code coverage and accurate validation of software functionalities in Java-based Spring Boot applications. Furthermore, it efficiently manages resource-intensive operations and refines the generated tests to address common issues like missing imports and handling of private methods. By comparing CUBETESTERAI with some state-of-the-art tools, we show that our proposal consistently demonstrates competitive and, in many cases, better performance in terms of code coverage in different real-life Java programs.", "published": "2025-03-13T19:44:09Z", "authors": ["Daniele Gorla", "Shivam Kumar", "Pietro Nicolaus Roselli Lorenzini", "Alireza Alipourfaz"], "categories": ["cs.SE", "cs.AI"], "doi": "", "_query": "automated valuation model", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2609.31397v1", "title": "Intent2Tc: Automated Intent-to-Traffic Control Translation with Language Models", "abstract": "Automated and highly usable Quality-of-Service (QoS) enforcement requires translating high-level service intents into deployable traffic-management policies. Although intent-based networking (IBN) has simplified policy specification, bridging the gap between business-level intents and executable network configurations remains complex, error-prone, and difficult to automate. This paper presents Intent2Tc, a closed-loop language-model-driven framework that translates business-level traffic-shaping intents into declarative sub-intents and subsequently into validated, executable Linux traffic control (tc) configurations. The framework integrates an Active Queue Management (AQM)-based digital twin (DT) semantic model, automated metadata extraction, critique-driven refinement, and Retrieval-Augmented Generation (RAG)-based knowledge reuse to improve semantic consistency and configuration reliability. We evaluate multiple open-source large language models (LLMs) and small language models (SLMs), together with Claude Sonnet-4.6, on 100 Request for Comments (RFC) 9315-compliant traffic-shaping intents. Across both translation stages, Intent2Tc achieves high semantic fidelity, configuration accuracy, and deployment readiness, with Claude Sonnet-4.6 reaching 0.98 semantic similarity, 1.0 semantic unit coverage, and 0.045 normalized edit distance. Furthermore, RAG reduces token consumption and inference latency while enabling compact models such as Phi-4-mini to approach the performance of substantially larger models. Linux tc serves as the target configuration platform, demonstrating the practical applicability of the proposed framework.", "published": "2026-09-25T15:25:05Z", "authors": ["Andrea Masini", "Sudipta Acharya", "Paolo Bellavista", "Luca Foschini", "Burak Kantarci"], "categories": ["cs.NI", "cs.AI", "cs.CL"], "doi": "", "_query": "automated valuation model", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/1910.03752v3", "title": "Probability, valuations, hyperspace: Three monads on Top and the support as a morphism", "abstract": "We consider three monads on Top, the category of topological spaces, which formalize topological aspects of probability and possibility in categorical terms. The first one is the Hoare hyperspace monad H, which assigns to every space its space of closed subsets equipped with the lower Vietoris topology. The second is the monad V of continuous valuations, also known as the extended probabilistic powerdomain. We construct both monads in a unified way in terms of double dualization. This reveals a close analogy between them, and allows us to prove that the operation of taking the support of a continuous valuation is a morphism of monads from V to H. In particular, this implies that every H-algebra (topological complete semilattice) is also a V-algebra. Third, we show that V can be restricted to a submonad of tau-smooth probability measures on Top. By composing these two morphisms of monads, we obtain that taking the support of a tau-smooth probability measure is also a morphism of monads.", "published": "2019-10-09T02:12:17Z", "authors": ["Tobias Fritz", "Paolo Perrone", "Sharwin Rezagholi"], "categories": ["math.GN", "cs.LO", "math.CT", "math.FA", "math.PR"], "doi": "10.1017/S0960129521000414", "_query": "automated valuation model", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2209.09618v1", "title": "On a Uniform Causality Model for Industrial Automation", "abstract": "The increasing complexity of Cyber-Physical Systems (CPS) makes industrial automation challenging. Large amounts of data recorded by sensors need to be processed to adequately perform tasks such as diagnosis in case of fault. A promising approach to deal with this complexity is the concept of causality. However, most research on causality has focused on inferring causal relations between parts of an unknown system. Engineering uses causality in a fundamentally different way: complex systems are constructed by combining components with known, controllable behavior. As CPS are constructed by the second approach, most data-based causality models are not suited for industrial automation. To bridge this gap, a Uniform Causality Model for various application areas of industrial automation is proposed, which will allow better communication and better data usage across disciplines. The resulting model describes the behavior of CPS mathematically and, as the model is evaluated on the unique requirements of the application areas, it is shown that the Uniform Causality Model can work as a basis for the application of new approaches in industrial automation that focus on machine learning.", "published": "2022-09-20T11:23:51Z", "authors": ["Maria Krantz", "Alexander Windmann", "Rene Heesch", "Lukas Moddemann", "Oliver Niggemann"], "categories": ["cs.AI"], "doi": "", "_query": "automated valuation model", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/math/0012144v1", "title": "Invitation to higher local fields, Part I, section 13: Abelian extensions of absolutely unramified complete discrete valuation fields", "abstract": "This is an introduction to the author theory of cyclic p-extensions of an absolutely unramified complete discrete valuation field K with arbitrary residue field of characteristic p. In this theory a homomorphism is constructed from the p-part of the group of characters of K to Witt vectors over its residue field.", "published": "2000-12-18T03:48:47Z", "authors": ["Masato Kurihara"], "categories": ["math.NT", "math.AG"], "doi": "", "_query": "automated valuation model", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2205.10893v1", "title": "Thor: Wielding Hammers to Integrate Language Models and Automated Theorem Provers", "abstract": "In theorem proving, the task of selecting useful premises from a large library to unlock the proof of a given conjecture is crucially important. This presents a challenge for all theorem provers, especially the ones based on language models, due to their relative inability to reason over huge volumes of premises in text form. This paper introduces Thor, a framework integrating language models and automated theorem provers to overcome this difficulty. In Thor, a class of methods called hammers that leverage the power of automated theorem provers are used for premise selection, while all other tasks are designated to language models. Thor increases a language model's success rate on the PISA dataset from $39\\%$ to $57\\%$, while solving $8.2\\%$ of problems neither language models nor automated theorem provers are able to solve on their own. Furthermore, with a significantly smaller computational budget, Thor can achieve a success rate on the MiniF2F dataset that is on par with the best existing methods. Thor can be instantiated for the majority of popular interactive theorem provers via a straightforward protocol we provide.", "published": "2022-05-22T18:03:03Z", "authors": ["Albert Q. Jiang", "Wenda Li", "Szymon Tworkowski", "Konrad Czechowski", "Tomasz Odrzygóźdź", "Piotr Miłoś", "Yuhuai Wu", "Mateja Jamnik"], "categories": ["cs.AI"], "doi": "", "_query": "automated valuation model", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2112.02528v1", "title": "Linear Differential Equation with Formal Power Series Non-Homogeneity Over a Ring with a Non-Archimedean Valuation", "abstract": "Consider the linear differential equation of $m$-th order with constant coefficients from the valuation ring $K$ of a non-Archimedean field. We get sufficient conditions of uniqueness and existence for the solution of this equation from $K[[x]]$. Also the fundamental solution from $\\frac{1}{x}K[[\\frac{1}{x}]]$ of the equation is obtained and it is shown that the convolution of the fundamental solution and a non-homogeneity is a unique solution of the equation.", "published": "2021-12-05T10:01:11Z", "authors": ["Sergey Gefter", "Anna Goncharuk"], "categories": ["math.CA", "math.AC"], "doi": "", "_query": "automated valuation model", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2204.11367v1", "title": "Ordered-logit pedestrian stress model for traffic flow with automated vehicles", "abstract": "An ordered-logit model is developed to study the effects of Automated Vehicles (AVs) in the traffic mix on the average stress level of a pedestrian when crossing an urban street at mid-block. Information collected from a galvanic skin resistance sensor and virtual reality experiments are transformed into a dataset with interpretable average stress levels (low, medium, and high) and geometric, traffic, and environmental conditions. Modelling results indicate a decrease in average stress level with the increase in the percentage of AVs in the traffic mix.", "published": "2022-04-24T21:59:47Z", "authors": ["Kimia Kamal", "Bilal Farooq", "Mahwish Mudassar", "Arash Kalatian"], "categories": ["cs.HC", "stat.AP"], "doi": "10.1109/IV51971.2022.9827316", "_query": "automated valuation model", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/1510.03479v2", "title": "Conditional expanding bounds for two-variable functions over finite valuation rings", "abstract": "In this paper, we use methods from spectral graph theory to obtain some results on the sum-product problem over finite valuation rings $\\mathcal{R}$ of order $q^r$ which generalize recent results given by Hegyvári and Hennecart (2013). More precisely, we prove that, for related pairs of two-variable functions $f(x,y)$ and $g(x,y)$, if $A$ and $B$ are two sets in $\\mathcal{R}^*$ with $|A|=|B|=q^α$, then \\[\\max\\left\\lbrace |f(A, B)|, |g(A, B)| \\right\\rbrace\\gtrsim |A|^{1+Δ(α)},\\] for some $Δ(α)>0$.", "published": "2015-10-12T22:52:10Z", "authors": ["Le Quang Ham", "Thang Pham", "Le Anh Vinh"], "categories": ["math.NT"], "doi": "", "_query": "automated valuation model", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2307.05512v1", "title": "The Effects of Interaction Conflicts, Levels of Automation, and Frequency of Automation on Human Automation Trust and Acceptance", "abstract": "In the presence of interaction conflicts, user trust in automation plays an important role in accepting intelligent environments such as smart homes. In this paper, a factorial research design is employed to investigate and compare the single and joint effects of Level of Automation (LoA), Frequency of Automated responses (FoA), and Conflict Intensity (CI) on human trust and acceptance of automation in the context of smart homes. To study these effects, we conducted web-based experiments to gather data from 324 online participants who experienced the system through a 3D simulation of a smart home. The findings show that the level and frequency of automation had an impact on user trust in smart environments. Furthermore, the results demonstrate that the users' acceptance of automated smart environments decreased in the presence of automation failures and interaction conflicts.", "published": "2023-07-05T18:28:38Z", "authors": ["Hadi Halvachi", "Ali Asghar Nazari Shirehjini", "Zahra Kakavand", "Niloofar Hashemi", "Shervin Shirmohammadi"], "categories": ["cs.HC", "cs.AI", "cs.CE", "cs.CY"], "doi": "", "_query": "automated valuation model", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2509.05372v2", "title": "Adversarial Bug Reports as a Security Risk in Language Model-Based Automated Program Repair", "abstract": "Large Language Model (LLM) - based Automated Program Repair (APR) systems are increasingly integrated into modern software development workflows, offering automated patches in response to natural language bug reports. However, this reliance on untrusted user input introduces a novel and underexplored attack surface. In this paper, we investigate the security risks posed by adversarial bug reports -- realistic-looking issue submissions crafted to mislead APR systems into producing insecure or harmful code changes. We develop a comprehensive threat model and conduct an empirical study to evaluate the vulnerability of APR systems to such attacks. Our demonstration comprises 51 adversarial bug reports generated across a spectrum of strategies, ranging from manual curation to fully automated pipelines. We test these against a leading LLM-based APR system and assess both pre-repair defenses (e.g., LlamaGuard variants, PromptGuard variants, Granite-Guardian, and custom LLM filters) and post-repair detectors (GitHub Copilot, CodeQL). Our findings show that current defenses are insufficient: 90% of crafted bug reports triggered attacker-aligned patches. The best pre-repair filter blocked only 47%, while post-repair analysis -- often requiring human oversight -- was effective in just 58% of cases. To support scalable security testing, we introduce a prototype framework for automating the generation of adversarial bug reports. Our analysis exposes a structural asymmetry: generating adversarial inputs is inexpensive, while detecting or mitigating them remains costly and error-prone. We conclude with recommendations for improving the robustness of APR systems against adversarial misuse and highlight directions for future work on secure APR.", "published": "2025-09-04T09:41:57Z", "authors": ["Piotr Przymus", "Andreas Happe", "Jürgen Cito"], "categories": ["cs.SE"], "doi": "10.1145/3793302.3793352", "_query": "automated valuation model", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2201.05000v3", "title": "Automated Reinforcement Learning: An Overview", "abstract": "Reinforcement Learning and, recently, Deep Reinforcement Learning are popular methods for solving sequential decision-making problems modeled as Markov Decision Processes. RL modeling of a problem and selecting algorithms and hyper-parameters require careful consideration, as different configurations may entail completely different performances. These considerations are mainly the task of RL experts; however, RL is progressively becoming popular in other fields, such as combinatorial optimization, where researchers and system designers are not necessarily RL experts. Besides, many modeling decisions are typically made manually, such as defining state and action space, size of batches, batch update frequency, and time steps. For these reasons, automating different components of RL is of great importance, and it has attracted much attention in recent years. Automated RL provides a framework in which different components of RL, including MDP modeling, algorithm selection, and hyper-parameter optimization, are modeled and defined automatically. In this article, we present the literature on automated RL (AutoRL), including the recent large language model (LLM) based techniques. We also discuss the recent work on techniques that are not presently tailored for automated RL but hold promise for future integration into AutoRL. Furthermore, we discuss the challenges, open questions, and research directions in AutoRL.", "published": "2022-01-13T14:28:06Z", "authors": ["Reza Refaei Afshar", "Joaquin Vanschoren", "Uzay Kaymak", "Rui Zhang", "Yaoxin Wu", "Wen Song", "Yingqian Zhang"], "categories": ["cs.LG", "cs.AI"], "doi": "", "_query": "automated valuation model", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/0710.4424v2", "title": "Valuations for matroid polytope subdivisions", "abstract": "We prove that the ranks of the subsets and the activities of the bases of a matroid define valuations for the subdivisions of a matroid polytope into smaller matroid polytopes.", "published": "2007-10-24T10:19:13Z", "authors": ["Federico Ardila", "Alex Fink", "Felipe Rincón"], "categories": ["math.CO"], "doi": "10.4153/CJM-2010-064-9", "_query": "automated valuation model", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2407.03183v1", "title": "A Formal Model for Artificial Intelligence Applications in Automation Systems", "abstract": "The integration of Artificial Intelligence (AI) into automation systems has the potential to enhance efficiency and to address currently unsolved existing technical challenges. However, the industry-wide adoption of AI is hindered by the lack of standardized documentation for the complex compositions of automation systems, AI software, production hardware, and their interdependencies. This paper proposes a formal model using standards and ontologies to provide clear and structured documentation of AI applications in automation systems. The proposed information model for artificial intelligence in automation systems (AIAS) utilizes ontology design patterns to map and link various aspects of automation systems and AI software. Validated through a practical example, the model demonstrates its effectiveness in improving documentation practices and aiding the sustainable implementation of AI in industrial settings.", "published": "2024-07-03T15:05:32Z", "authors": ["Marvin Schieseck", "Philip Topalis", "Lasse Reinpold", "Felix Gehlhoff", "Alexander Fay"], "categories": ["eess.SY", "cs.AI"], "doi": "", "_query": "automated valuation model", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/1812.08655v3", "title": "Surrogate-assisted Bayesian inversion for landscape and basin evolution models", "abstract": "The complex and computationally expensive nature of landscape evolution models pose significant challenges in the inference and optimisation of unknown parameters. Bayesian inference provides a methodology for estimation and uncertainty quantification of unknown model parameters. In our previous work, we developed parallel tempering Bayeslands as a framework for parameter estimation and uncertainty quantification for the Badlands landscape evolution model. Parallel tempering Bayeslands features high-performance computing with dozens of processing cores running in parallel to enhance computational efficiency. Although we use parallel computing, the procedure remains computationally challenging since thousands of samples need to be drawn and evaluated. \\textcolor{black}{In large-scale landscape and basin evolution problems, a single model evaluation can take from several minutes to hours, and in some instances, even days. Surrogate-assisted optimisation has been used for several computationally expensive engineering problems which motivate its use in optimisation and inference of complex geoscientific models.} The use of surrogate models can speed up parallel tempering Bayeslands by developing computationally inexpensive models to mimic expensive ones. In this paper, we apply surrogate-assisted parallel tempering where that surrogate mimics a landscape evolution model by estimating the likelihood function from the model. \\textcolor{black}{We employ a neural network-based surrogate model that learns from the history of samples generated. } The entire framework is developed in a parallel computing infrastructure to take advantage of parallelism. The results show that the proposed methodology is effective in lowering the overall computational cost significantly while retaining the quality of solutions.", "published": "2018-12-12T03:56:20Z", "authors": ["Rohitash Chandra", "Danial Azam", "Arpit Kapoor", "R. Dietmar Müller"], "categories": ["stat.ML", "cs.DC", "cs.LG", "cs.NE", "physics.geo-ph"], "doi": "", "_query": "automated valuation model", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2603.09086v1", "title": "Latent World Models for Automated Driving: A Unified Taxonomy, Evaluation Framework, and Open Challenges", "abstract": "Emerging generative world models and vision-language-action (VLA) systems are rapidly reshaping automated driving by enabling scalable simulation, long-horizon forecasting, and capability-rich decision making. Across these directions, latent representations serve as the central computational substrate: they compress high-dimensional multi-sensor observations, enable temporally coherent rollouts, and provide interfaces for planning, reasoning, and controllable generation. This paper proposes a unifying latent-space framework that synthesizes recent progress in world models for automated driving. The framework organizes the design space by the target and form of latent representations (latent worlds, latent actions, latent generators; continuous states, discrete tokens, and hybrids) and by structural priors for geometry, topology, and semantics. Building on this taxonomy, the paper articulates five cross-cutting internal mechanics (i.e, structural isomorphism, long-horizon temporal stability, semantic and reasoning alignment, value-aligned objectives and post-training, as well as adaptive computation and deliberation) and connects these design choices to robustness, generalization, and deployability. The work also proposes concrete evaluation prescriptions, including a closed-loop metric suite and a resource-aware deliberation cost, designed to reduce the open-loop / closed-loop mismatch. Finally, the paper identifies actionable research directions toward advancing latent world model for decision-ready, verifiable, and resource-efficient automated driving.", "published": "2026-03-10T01:56:17Z", "authors": ["Rongxiang Zeng", "Yongqi Dong"], "categories": ["cs.RO", "cs.AI", "cs.LG", "cs.MA", "eess.SY"], "doi": "", "_query": "automated valuation model", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2005.07370v2", "title": "Tight Approximation Algorithms for p-Mean Welfare Under Subadditive Valuations", "abstract": "We develop polynomial-time algorithms for the fair and efficient allocation of indivisible goods among $n$ agents that have subadditive valuations over the goods. We first consider the Nash social welfare as our objective and design a polynomial-time algorithm that, in the value oracle model, finds an $8n$-approximation to the Nash optimal allocation. Subadditive valuations include XOS (fractionally subadditive) and submodular valuations as special cases. Our result, even for the special case of submodular valuations, improves upon the previously best known $O(n \\log n)$-approximation ratio of Garg et al. (2020). More generally, we study maximization of $p$-mean welfare. The $p$-mean welfare is parameterized by an exponent term $p \\in (-\\infty, 1]$ and encompasses a range of welfare functions, such as social welfare ($p = 1$), Nash social welfare ($p \\to 0$), and egalitarian welfare ($p \\to -\\infty$). We give an algorithm that, for subadditive valuations and any given $p \\in (-\\infty, 1]$, computes (in the value oracle model and in polynomial time) an allocation with $p$-mean welfare at least $\\frac{1}{8n}$ times the optimal. Further, we show that our approximation guarantees are essentially tight for XOS and, hence, subadditive valuations. We adapt a result of Dobzinski et al. (2010) to show that, under XOS valuations, an $O \\left(n^{1-\\varepsilon} \\right)$ approximation for the $p$-mean welfare for any $p \\in (-\\infty,1]$ (including the Nash social welfare) requires exponentially many value queries; here, $\\varepsilon>0$ is any fixed constant.", "published": "2020-05-15T05:57:26Z", "authors": ["Siddharth Barman", "Umang Bhaskar", "Anand Krishna", "Ranjani G. Sundaram"], "categories": ["cs.GT"], "doi": "", "_query": "automated valuation model", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2105.13411v1", "title": "Model Repair Revamped: On the Automated Synthesis of Markov Chains", "abstract": "This paper outlines two approaches|based on counterexample-guided abstraction refinement (CEGAR) and counterexample-guided inductive synthesis (CEGIS), respectively to the automated synthesis of finite-state probabilistic models and programs. Our CEGAR approach iteratively partitions the design space starting from an abstraction of this space and refines this by a light-weight analysis of verification results. The CEGIS technique exploits critical subsystems as counterexamples to prune all programs behaving incorrectly on that input. We show the applicability of these synthesis techniques to sketching of probabilistic programs, controller synthesis of POMDPs, and software product lines.", "published": "2021-05-27T19:30:32Z", "authors": ["Milan Ceska", "Christian Dehnert", "Nils Jansen", "Sebastian Junges", "Joost-Pieter Katoen"], "categories": ["cs.PL"], "doi": "10.1007/978-3-030-31514-6\\_7", "_query": "automated valuation model", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/1911.03521v1", "title": "Non-locality, contextuality and valuation algebras: a general theory of disagreement", "abstract": "We establish a strong link between two apparently unrelated topics: the study of conflicting information in the formal framework of valuation algebras, and the phenomena of non-locality and contextuality. In particular, we show that these peculiar features of quantum theory are mathematically equivalent to a general notion of \\emph{disagreement} between information sources. This result vastly generalises previously observed connections between contextuality, relational databases, constraint satisfaction problems, and logical paradoxes, and gives further proof that contextual behaviour is not a phenomenon limited to quantum physics, but pervades various domains of mathematics and computer science. The connection allows to translate theorems, methods and algorithms from one field to the other, and paves the way for the application of generic inference algorithms to study contextuality.", "published": "2019-11-08T20:19:46Z", "authors": ["Samson Abramsky", "Giovanni Carù"], "categories": ["quant-ph"], "doi": "10.1098/rsta.2019.0036", "_query": "automated valuation model", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/1701.08915v1", "title": "Accelerated Evaluation of Automated Vehicles Using Piecewise Mixture Models", "abstract": "The process to certify highly Automated Vehicles has not yet been defined by any country in the world. Currently, companies test Automated Vehicles on public roads, which is time-consuming and inefficient. We proposed the Accelerated Evaluation concept, which uses a modified statistics of the surrounding vehicles and the Importance Sampling theory to reduce the evaluation time by several orders of magnitude, while ensuring the evaluation results are statistically accurate. In this paper, we further improve the accelerated evaluation concept by using Piecewise Mixture Distribution models, instead of Single Parametric Distribution models. We developed and applied this idea to forward collision control system reacting to vehicles making cut-in lane changes. The behavior of the cut-in vehicles was modeled based on more than 403,581 lane changes collected by the University of Michigan Safety Pilot Model Deployment Program. Simulation results confirm that the accuracy and efficiency of the Piecewise Mixture Distribution method outperformed single parametric distribution methods in accuracy and efficiency, and accelerated the evaluation process by almost four orders of magnitude.", "published": "2017-01-31T04:46:33Z", "authors": ["Zhiyuan Huang", "Ding Zhao", "Henry Lam", "David J. LeBlanc"], "categories": ["eess.SY", "cs.RO"], "doi": "", "_query": "automated valuation model", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2506.15723v3", "title": "Modern approaches to building interpretable models of the property market using machine learning on the base of mass cadastral valuation", "abstract": "In this paper, we review modern approaches to building interpretable models of property markets using machine learning on the base of mass valuation of property in the Primorye region, Russia. There are numerous potential difficulties one could encounter in the effort to build a good model. Their main source is the huge difference between noisy real market data and ideal data usually used in tutorials on machine learning. This paper covers all stages of modeling: collection of initial data, identification of outliers, search and analysis of patterns in the data, formation and final choice of price factors, building of the model, and evaluation of its efficiency. For each stage, we highlight potential issues and describe sound methods for overcoming emerging difficulties on actual examples. We show that the combination of classical linear regression with kriging (interpolation method of geostatistics) allows to build an effective model for land parcels. For flats, when many objects are attributed to one spatial point, the application of geostatistical methods becomes problematic. Instead, we suggest linear regression with automatic generation and selection of additional rules on the base of decision trees, so called the RuleFit method. We compare the performance of our inherently interpretable models with well-proven \"black-box\" Random Forest method and demonstrate similar results. Thus we show, that despite such a strong restriction as the requirement of interpretability which is important in practical aspects, for example, legal matters, it is still possible to build effective models of real property markets.", "published": "2025-06-05T13:17:18Z", "authors": ["Alexey S. Tanashkin", "Irina G. Tanashkina", "Alexander S. Maksimchuik"], "categories": ["q-fin.ST", "cs.LG", "econ.GN", "stat.AP"], "doi": "10.1016/j.landusepol.2026.107970", "_query": "automated valuation model", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2601.02573v1", "title": "LendNova: Towards Automated Credit Risk Assessment with Language Models", "abstract": "Credit risk assessment is essential in the financial sector, but has traditionally depended on costly feature-based models that often fail to utilize all available information in raw credit records. This paper introduces LendNova, the first practical automated end-to-end pipeline for credit risk assessment, designed to utilize all available information in raw credit records by leveraging advanced NLP techniques and language models. LendNova transforms risk modeling by operating directly on raw, jargon-heavy credit bureau text using a language model that learns task-relevant representations without manual feature engineering. By automatically capturing patterns and risk signals embedded in the text, it replaces manual preprocessing steps, reducing costs and improving scalability. Evaluation on real-world data further demonstrates its strong potential in accurate and efficient risk assessment. LendNova establishes a baseline for intelligent credit risk agents, demonstrating the feasibility of language models in this domain. It lays the groundwork for future research toward foundation systems that enable more accurate, adaptable, and automated financial decision-making.", "published": "2026-01-05T21:53:36Z", "authors": ["Kiarash Shamsi", "Danijel Novokmet", "Joshua Peters", "Mao Lin Liu", "Paul K Edwards", "Vahab Khoshdel"], "categories": ["cs.LG", "cs.AI", "cs.CE"], "doi": "", "_query": "automated valuation model", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/0911.1470v1", "title": "Bertini theorems and Lefschetz pencils over discrete valuation rings, with applications to higher class field theory", "abstract": "We show the existence of good hyperplane sections for schemes over discrete valuation rings with good or (quasi) semistable reduction, and the existence of good Lefschetz pencils for schemes with good reduction or ordinary quadratic reduction. As an application we prove that the reciprocity map introduced for smooth projective varieties over local fields by Bloch, Kato and Saito is an isomorphism after profinite completion, if the variety has good reduction or 'almost good' reduction.", "published": "2009-11-07T22:00:35Z", "authors": ["Uwe Jannsen", "Shuji Saito"], "categories": ["math.AG"], "doi": "", "_query": "automated valuation model", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/math/0306137v3", "title": "Hard Lefschetz theorem for valuations and related questions of integral geometry", "abstract": "We study the properties of the multiplicative structure on valuations on convex sets. We prove a new version of the hard Lefschetz theorem for even translation invariant continuous valuations, and discuss related problems of integral geometry. Then we formulate a conjectural analogue of this result for odd valuations.", "published": "2003-06-08T13:33:26Z", "authors": ["Semyon Alesker"], "categories": ["math.MG", "math.DG"], "doi": "", "_query": "automated valuation model", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/1906.01760v3", "title": "Going Deep: Models for Continuous-Time Within-Play Valuation of Game Outcomes in American Football with Tracking Data", "abstract": "Continuous-time assessments of game outcomes in sports have become increasingly common in the last decade. In American football, only discrete-time estimates of play value were possible, since the most advanced public football datasets were recorded at the play-by-play level. While measures such as expected points and win probability are useful for evaluating football plays and game situations, there has been no research into how these values change throughout the course of a play. In this work, we make two main contributions: First, we introduce a general framework for continuous-time within-play valuation in the National Football League using player-tracking data. Our modular framework incorporates several modular sub-models, to easily incorporate recent work involving player tracking data in football. Second, we use a long short-term memory recurrent neural network to construct a ball-carrier model to estimate how many yards the ball-carrier is expected to gain from their current position, conditional on the locations and trajectories of the ball-carrier, their teammates and opponents. Additionally, we demonstrate an extension with conditional density estimation so that the expectation of any measure of play value can be calculated in continuous-time, which was never before possible at such a granular level.", "published": "2019-06-05T00:21:48Z", "authors": ["Ronald Yurko", "Francesca Matano", "Lee F. Richardson", "Nicholas Granered", "Taylor Pospisil", "Konstantinos Pelechrinis", "Samuel L. Ventura"], "categories": ["stat.AP"], "doi": "", "_query": "automated valuation model", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/1902.10275v3", "title": "Towards Efficient Data Valuation Based on the Shapley Value", "abstract": "\"How much is my data worth?\" is an increasingly common question posed by organizations and individuals alike. An answer to this question could allow, for instance, fairly distributing profits among multiple data contributors and determining prospective compensation when data breaches happen. In this paper, we study the problem of data valuation by utilizing the Shapley value, a popular notion of value which originated in coopoerative game theory. The Shapley value defines a unique payoff scheme that satisfies many desiderata for the notion of data value. However, the Shapley value often requires exponential time to compute. To meet this challenge, we propose a repertoire of efficient algorithms for approximating the Shapley value. We also demonstrate the value of each training instance for various benchmark datasets.", "published": "2019-02-27T00:22:43Z", "authors": ["Ruoxi Jia", "David Dao", "Boxin Wang", "Frances Ann Hubis", "Nick Hynes", "Nezihe Merve Gurel", "Bo Li", "Ce Zhang", "Dawn Song", "Costas Spanos"], "categories": ["cs.LG", "stat.ML"], "doi": "", "_query": "automated valuation model", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2510.25698v2", "title": "Localization of valuations and Alesker's irreducibility theorem", "abstract": "We provide a new proof of Alesker's Irreducibility Theorem. We first introduce a new localization technique for polynomial valuations on convex bodies, which we use to independently prove that smooth and translation invariant valuations are representable by integration with respect to the normal cycle. This allows us to reduce the statement to a corresponding result for the representation of $\\mathfrak{sl}(n)$ on the space of these differential forms.", "published": "2025-10-29T17:03:00Z", "authors": ["Georg C. Hofstätter", "Jonas Knoerr"], "categories": ["math.MG", "math.DG", "math.RT"], "doi": "", "_query": "automated valuation model", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2511.21145v2", "title": "TEAR: Temporal-aware Automated Red-teaming for Text-to-Video Models", "abstract": "Text-to-Video (T2V) models are capable of synthesizing high-quality, temporally coherent dynamic video content, but the diverse generation also inherently introduces critical safety challenges. Existing safety evaluation methods,which focus on static image and text generation, are insufficient to capture the complex temporal dynamics in video generation. To address this, we propose a TEmporal-aware Automated Red-teaming framework, named TEAR, an automated framework designed to uncover safety risks specifically linked to the dynamic temporal sequencing of T2V models. TEAR employs a temporal-aware test generator optimized via a two-stage approach: initial generator training and temporal-aware online preference learning, to craft textually innocuous prompts that exploit temporal dynamics to elicit policy-violating video output. And a refine model is adopted to improve the prompt stealthiness and adversarial effectiveness cyclically. Extensive experimental evaluation demonstrates the effectiveness of TEAR across open-source and commercial T2V systems with over 80% attack success rate, a significant boost from prior best result of 57%.", "published": "2025-11-26T07:58:42Z", "authors": ["Jiaming He", "Guanyu Hou", "Hongwei Li", "Zhicong Huang", "Kangjie Chen", "Yi Yu", "Wenbo Jiang", "Guowen Xu", "Tianwei Zhang"], "categories": ["cs.CV"], "doi": "", "_query": "automated valuation model", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2201.07419v1", "title": "Achieving Envy-Freeness with Limited Subsidies under Dichotomous Valuations", "abstract": "We study the problem of allocating indivisible goods among agents in a fair manner. While envy-free allocations of indivisible goods are not guaranteed to exist, envy-freeness can be achieved by additionally providing some subsidy to the agents. These subsidies can be alternatively viewed as a divisible good (money) that is fractionally assigned among the agents to realize an envy-free outcome. In this setup, we bound the subsidy required to attain envy-freeness among agents with dichotomous valuations, i.e., among agents whose marginal value for any good is either zero or one. We prove that, under dichotomous valuations, there exists an allocation that achieves envy-freeness with a per-agent subsidy of either $0$ or $1$. Furthermore, such an envy-free solution can be computed efficiently in the standard value-oracle model. Notably, our results hold for general dichotomous valuations and, in particular, do not require the (dichotomous) valuations to be additive, submodular, or even subadditive. Also, our subsidy bounds are tight and provide a linear (in the number of agents) factor improvement over the bounds known for general monotone valuations.", "published": "2022-01-19T05:15:56Z", "authors": ["Siddharth Barman", "Anand Krishna", "Y. Narahari", "Soumyarup Sadhukhan"], "categories": ["cs.GT"], "doi": "", "_query": "automated valuation model", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/1508.03905v1", "title": "Semantics-based Automated Web Testing", "abstract": "We present TAO, a software testing tool performing automated test and oracle generation based on a semantic approach. TAO entangles grammar-based test generation with automated semantics evaluation using a denotational semantics framework. We show how TAO can be incorporated with the Selenium automation tool for automated web testing, and how TAO can be further extended to support automated delta debugging, where a failing web test script can be systematically reduced based on grammar-directed strategies. A real-life parking website is adopted throughout the paper to demonstrate the effectivity of our semantics-based web testing approach.", "published": "2015-08-17T01:51:35Z", "authors": ["Hai-Feng Guo", "Qing Ouyang", "Harvey Siy"], "categories": ["cs.SE", "cs.PL"], "doi": "10.4204/EPTCS.188.7", "_query": "automated valuation model", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/1304.1121v1", "title": "Valuation-Based Systems for Discrete Optimization", "abstract": "This paper describes valuation-based systems for representing and solving discrete optimization problems. In valuation-based systems, we represent information in an optimization problem using variables, sample spaces of variables, a set of values, and functions that map sample spaces of sets of variables to the set of values. The functions, called valuations, represent the factors of an objective function. Solving the optimization problem involves using two operations called combination and marginalization. Combination tells us how to combine the factors of the joint objective function. Marginalization is either maximization or minimization. Solving an optimization problem can be simply described as finding the marginal of the joint objective function for the empty set. We state some simple axioms that combination and marginalization need to satisfy to enable us to solve an optimization problem using local computation. For optimization problems, the solution method of valuation-based systems reduces to non-serial dynamic programming. Thus our solution method for VBS can be regarded as an abstract description of dynamic programming. And our axioms can be viewed as conditions that permit the use of dynamic programming.", "published": "2013-03-27T13:58:17Z", "authors": ["Prakash P. Shenoy", "Glenn Shafer"], "categories": ["cs.AI"], "doi": "", "_query": "automated valuation model", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2605.24909v1", "title": "Valuation Separation for Coprime Lucas Products", "abstract": "Let $U_n=U_n(P,Q)$ be a nondegenerate Lucas sequence with $Q=\\pm 1$ and discriminant $Δ=P^2+4Q>0$. We study Diophantine equations \\[ A y^k=\\prod_{i=1}^r U_{n_i}(P,Q), \\qquad k\\geq 2, \\] where the indices $n_1,\\ldots,n_r$ are pairwise coprime. The strong divisibility property implies that the factors $U_{n_i}$ are pairwise coprime, and hence a global $k$-th power condition separates into local valuation conditions on the individual factors. For $k=2$, this gives a termwise square-class restriction: each $U_{n_i}$ has signed squarefree part supported on the primes dividing $A$. In particular, the equation $Δy^2=U_mU_n$, with $\\gcd(m,n)=1$, reduces to a finite square-class compatibility condition together with an integrality condition. Assuming the number-field $abc$ conjecture over $\\mathbb Q(\\sqrtΔ)$, we prove that only finitely many Lucas terms have squarefree part supported on a fixed finite set of rational primes. Consequently, the coprime product equations above admit an $abc$-conditional finite reduction. We also give the corresponding $k$-th power analogue and a primitive-divisor obstruction.", "published": "2026-05-24T07:30:03Z", "authors": ["Dongyeon Kym"], "categories": ["math.NT"], "doi": "", "_query": "automated valuation model", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2510.17609v1", "title": "Integrating BIM and UAV-based photogrammetry for Automated 3D Structure Model Segmentation", "abstract": "The advancement of UAV technology has enabled efficient, non-contact structural health monitoring. Combined with photogrammetry, UAVs can capture high-resolution scans and reconstruct detailed 3D models of infrastructure. However, a key challenge remains in segmenting specific structural components from these models-a process traditionally reliant on time-consuming and error-prone manual labeling. To address this issue, we propose a machine learning-based framework for automated segmentation of 3D point clouds. Our approach uses the complementary strengths of real-world UAV-scanned point clouds and synthetic data generated from Building Information Modeling (BIM) to overcome the limitations associated with manual labeling. Validation on a railroad track dataset demonstrated high accuracy in identifying and segmenting major components such as rails and crossties. Moreover, by using smaller-scale datasets supplemented with BIM data, the framework significantly reduced training time while maintaining reasonable segmentation accuracy. This automated approach improves the precision and efficiency of 3D infrastructure model segmentation and advances the integration of UAV and BIM technologies in structural health monitoring and infrastructure management.", "published": "2025-10-20T14:54:54Z", "authors": ["Siqi Chen", "Shanyue Guan"], "categories": ["cs.CV"], "doi": "10.12783/shm2025/37359", "_query": "automated valuation model", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/1807.11039v1", "title": "Fast Trajectory Planning for Automated Vehicles using Gradient-based Nonlinear Model Predictive Control", "abstract": "Motion trajectory planning is one crucial aspect for automated vehicles, as it governs the own future behavior in a dynamically changing environment. A good utilization of a vehicle's characteristics requires the consideration of the nonlinear system dynamics within the optimization problem to be solved. In particular, real-time feasibility is essential for automated driving, in order to account for the fast changing surrounding, e.g. for moving objects. The key contributions of this paper are the presentation of a fast optimization algorithm for trajectory planning including the nonlinear system model. Further, a new concurrent operation scheme for two optimization algorithms is derived and investigated. The proposed algorithm operates in the submillisecond range on a standard PC. As an exemplary scenario, the task of driving along a challenging reference course is demonstrated.", "published": "2018-07-29T11:01:02Z", "authors": ["Franz Gritschneder", "Knut Graichen", "Klaus Dietmayer"], "categories": ["math.OC"], "doi": "", "_query": "automated valuation model", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/1311.3414v1", "title": "Mining Software Repair Models for Reasoning on the Search Space of Automated Program Fixing", "abstract": "This paper is about understanding the nature of bug fixing by analyzing thousands of bug fix transactions of software repositories. It then places this learned knowledge in the context of automated program repair. We give extensive empirical results on the nature of human bug fixes at a large scale and a fine granularity with abstract syntax tree differencing. We set up mathematical reasoning on the search space of automated repair and the time to navigate through it. By applying our method on 14 repositories of Java software and 89,993 versioning transactions, we show that not all probabilistic repair models are equivalent.", "published": "2013-11-14T08:28:45Z", "authors": ["Matias Martinez", "Martin Monperrus"], "categories": ["cs.SE"], "doi": "10.1007/s10664-013-9282-8", "_query": "automated valuation model", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2306.14152v1", "title": "Low-Rank Prune-And-Factorize for Language Model Compression", "abstract": "The components underpinning PLMs -- large weight matrices -- were shown to bear considerable redundancy. Matrix factorization, a well-established technique from matrix theory, has been utilized to reduce the number of parameters in PLM. However, it fails to retain satisfactory performance under moderate to high compression rate. In this paper, we identify the \\textit{full-rankness} of fine-tuned PLM as the fundamental bottleneck for the failure of matrix factorization and explore the use of network pruning to extract low-rank sparsity pattern desirable to matrix factorization. We find such low-rank sparsity pattern exclusively exists in models generated by first-order pruning, which motivates us to unite the two approaches and achieve more effective model compression. We further propose two techniques: sparsity-aware SVD and mixed-rank fine-tuning, which improve the initialization and training of the compression procedure, respectively. Experiments on GLUE and question-answering tasks show that the proposed method has superior compression-performance trade-off compared to existing approaches.", "published": "2023-06-25T07:38:43Z", "authors": ["Siyu Ren", "Kenny Q. Zhu"], "categories": ["cs.CL"], "doi": "", "_query": "automated valuation model", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2602.07698v2", "title": "On Sequence-to-Sequence Models for Automated Log Parsing", "abstract": "Context: Log parsing is a critical standard operating procedure in software systems, enabling monitoring, anomaly detection, and failure diagnosis. However, automated log parsing remains challenging due to heterogeneous log formats, distribution shifts between training and deployment data, and the brittleness of rule-based approaches. Objectives: This study aims to systematically evaluate how sequence modelling architecture, representation choice, sequence length, and training data availability influence automated log parsing performance and computational cost. Methods: We conduct a controlled empirical study comparing four sequence modelling architectures: Transformer, Mamba state-space, monodirectional LSTM, and bidirectional LSTM models. In total, 396 models are trained across multiple dataset configurations and evaluated using relative Levenshtein edit distance with statistical significance testing. Results: Transformer achieves the lowest mean relative edit distance (0.111), followed by Mamba (0.145), mono-LSTM (0.186), and bi-LSTM (0.265), where lower values are better. Mamba provides competitive accuracy with substantially lower computational cost. Character-level tokenization generally improves performance, sequence length has negligible practical impact on Transformer accuracy, and both Mamba and Transformer demonstrate stronger sample efficiency than recurrent models. Conclusion: Overall, Transformers reduce parsing error by 23.4%, while Mamba is a strong alternative under data or compute constraints. These results also clarify the roles of representation choice, sequence length, and sample efficiency, providing practical guidance for researchers and practitioners.", "published": "2026-02-07T20:47:45Z", "authors": ["Adam Sorrenti", "Andriy Miranskyy"], "categories": ["cs.SE", "cs.CL"], "doi": "", "_query": "automated valuation model", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2412.18531v2", "title": "Automated Code Review In Practice", "abstract": "Code review is a widespread practice to improve software quality and transfer knowledge. It is often seen as time-consuming due to the need for manual effort and potential delays. Several AI-assisted tools, such as Qodo, GitHub Copilot, and Coderabbit, provide automated reviews using large language models (LLMs). The effects of such tools in the industry are yet to be examined. This study examines the impact of LLM-based automated code review tools in an industrial setting. The study was conducted within a software development environment that adopted an AI-assisted review tool (based on open-source Qodo PR Agent). Around 238 practitioners across ten projects had access to the tool. We focused on three projects with 4,335 pull requests, 1,568 of which underwent automated reviews. Data collection comprised three sources: (1) a quantitative analysis of pull request data, including comment labels indicating whether developers acted on the automated comments, (2) surveys sent to developers regarding their experience with reviews on individual pull requests, and (3) a broader survey of 22 practitioners capturing their general opinions on automated reviews. 73.8% of automated comments were resolved. However, the average pull request closure duration increased from five hours 52 minutes to eight hours 20 minutes, with varying trends across projects. Most practitioners reported a minor improvement in code quality due to automated reviews. The LLM-based tool proved useful in software development, enhancing bug detection, increasing awareness of code quality, and promoting best practices. However, it also led to longer pull request closure times and introduced drawbacks like faulty reviews, unnecessary corrections, and irrelevant comments.", "published": "2024-12-24T16:24:45Z", "authors": ["Umut Cihan", "Vahid Haratian", "Arda İçöz", "Mert Kaan Gül", "Ömercan Devran", "Emircan Furkan Bayendur", "Baykal Mehmet Uçar", "Eray Tüzün"], "categories": ["cs.SE"], "doi": "", "_query": "automated valuation model", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/0901.1099v1", "title": "Counterparty risk valuation for Energy-Commodities swaps: Impact of volatilities and correlation", "abstract": "It is commonly accepted that Commodities futures and forward prices, in principle, agree under some simplifying assumptions. One of the most relevant assumptions is the absence of counterparty risk. Indeed, due to margining, futures have practically no counterparty risk. Forwards, instead, may bear the full risk of default for the counterparty when traded with brokers or outside clearing houses, or when embedded in other contracts such as swaps. In this paper we focus on energy commodities and on Oil in particular. We use a hybrid commodities-credit model to asses impact of counterparty risk in pricing formulas, both in the gross effect of default probabilities and on the subtler effects of credit spread volatility, commodities volatility and credit-commodities correlation. We illustrate our general approach with a case study based on an oil swap, showing that an accurate valuation of counterparty risk depends on volatilities and correlation and cannot be accounted for precisely through a pre-defined multiplier.", "published": "2009-01-08T17:41:47Z", "authors": ["Damiano Brigo", "Kyriakos Chourdakis", "Imane Bakkar"], "categories": ["q-fin.PR", "q-fin.RM"], "doi": "", "_query": "automated valuation model", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2603.20988v2", "title": "Toward an automated science of the mind", "abstract": "Artificial Intelligence is starting to automate scientific discovery, raising the prospect of an automated science of the mind. We chart this field around four challenges: representing experiments, generating synthetic behavior, synthesizing models, and closing the loop to discover psychological theories.", "published": "2026-03-22T00:15:58Z", "authors": ["Akshay K. Jagadish", "Milena Rmus", "Kristin Witte", "Marvin Mathony", "Marcel Binz", "Eric Schulz"], "categories": ["cs.AI", "q-bio.NC"], "doi": "", "_query": "automated valuation model", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2505.19490v1", "title": "Automated CAD Modeling Sequence Generation from Text Descriptions via Transformer-Based Large Language Models", "abstract": "Designing complex computer-aided design (CAD) models is often time-consuming due to challenges such as computational inefficiency and the difficulty of generating precise models. We propose a novel language-guided framework for industrial design automation to address these issues, integrating large language models (LLMs) with computer-automated design (CAutoD).Through this framework, CAD models are automatically generated from parameters and appearance descriptions, supporting the automation of design tasks during the detailed CAD design phase. Our approach introduces three key innovations: (1) a semi-automated data annotation pipeline that leverages LLMs and vision-language large models (VLLMs) to generate high-quality parameters and appearance descriptions; (2) a Transformer-based CAD generator (TCADGen) that predicts modeling sequences via dual-channel feature aggregation; (3) an enhanced CAD modeling generation model, called CADLLM, that is designed to refine the generated sequences by incorporating the confidence scores from TCADGen. Experimental results demonstrate that the proposed approach outperforms traditional methods in both accuracy and efficiency, providing a powerful tool for automating industrial workflows and generating complex CAD models from textual prompts. The code is available at https://jianxliao.github.io/cadllm-page/", "published": "2025-05-26T04:17:51Z", "authors": ["Jianxing Liao", "Junyan Xu", "Yatao Sun", "Maowen Tang", "Sicheng He", "Jingxian Liao", "Shui Yu", "Yun Li", "Hongguan Xiao"], "categories": ["cs.AI"], "doi": "", "_query": "automated valuation model", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/1705.03948v2", "title": "Newton-Okounkov bodies of exceptional curve valuations", "abstract": "We prove that the Newton-Okounkov body of the flag $E_{\\bullet}:= \\left\\{ X=X_r \\supset E_r \\supset \\{q\\} \\right\\}$, defined by the surface $X$ and the exceptional divisor $E_r$ given by any divisorial valuation of the complex projective plane $\\mathbb{P}^2$, with respect to the pull-back of the line-bundle $\\mathcal{O}_{\\mathbb{P}^2} (1)$ is either a triangle or a quadrilateral, characterizing when it is a triangle or a quadrilateral. We also describe the vertices of that figure. Finally, we introduce a large family of flags for which we determine explicitly their Newton-Okounkov bodies which turn out to be triangular.", "published": "2017-05-10T20:40:10Z", "authors": ["Carlos Galindo", "Francisco Monserrat", "Julio José Moyano-Fernández", "Matthias Nickel"], "categories": ["math.AG", "math.AC"], "doi": "", "_query": "automated valuation model", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2605.06451v1", "title": "Counterexamples to EFX for Submodular and Subadditive Valuations", "abstract": "The existence of EFX allocations is a fundamental question in fair division. In this paper, we construct a three-agent, eight-good instance with monotone subadditive valuations such that no allocation satisfies $α$-EFX for any $α> \\frac{1}{\\sqrt[6]{2}} \\approx 0.89$. We also provide a closely related three-agent, eight-good instance with submodular (in fact weighted coverage) valuations for which no EFX allocation exists. A key feature of our construction is its symmetry: the agents' valuations are identical up to a relabeling of the goods. Thus, EFX can fail even when agents differ only in how the goods are labeled. This symmetry makes the counterexamples compact and human-verifiable, yielding simple combinatorial obstructions to the existence of EFX.", "published": "2026-05-07T15:46:34Z", "authors": ["Simon Mackenzie", "Mashbat Suzuki"], "categories": ["cs.GT", "econ.TH"], "doi": "", "_query": "automated valuation model", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/cond-mat/0507648v1", "title": "Extended morphometric analysis of neuronal cells with Minkowski valuations", "abstract": "Minkowski valuations provide a systematic framework for quantifying different aspects of morphology. In this paper we apply vector- and tensor-valued Minkowski valuations to neuronal cells from the cat's retina in order to describe their morphological structure in a comprehensive way. We introduce the framework of Minkowski valuations, discuss their implementation for neuronal cells and show how they can discriminate between cells of different types.", "published": "2005-07-27T15:48:08Z", "authors": ["C. Beisbart", "M. S. Barbosa", "H. Wagner", "L. da F. Costa"], "categories": ["cond-mat.dis-nn", "q-bio.QM"], "doi": "10.1140/epjb/e2006-00328-1", "_query": "automated valuation model", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2305.03403v5", "title": "Large Language Models for Automated Data Science: Introducing CAAFE for Context-Aware Automated Feature Engineering", "abstract": "As the field of automated machine learning (AutoML) advances, it becomes increasingly important to incorporate domain knowledge into these systems. We present an approach for doing so by harnessing the power of large language models (LLMs). Specifically, we introduce Context-Aware Automated Feature Engineering (CAAFE), a feature engineering method for tabular datasets that utilizes an LLM to iteratively generate additional semantically meaningful features for tabular datasets based on the description of the dataset. The method produces both Python code for creating new features and explanations for the utility of the generated features. Despite being methodologically simple, CAAFE improves performance on 11 out of 14 datasets -- boosting mean ROC AUC performance from 0.798 to 0.822 across all dataset - similar to the improvement achieved by using a random forest instead of logistic regression on our datasets. Furthermore, CAAFE is interpretable by providing a textual explanation for each generated feature. CAAFE paves the way for more extensive semi-automation in data science tasks and emphasizes the significance of context-aware solutions that can extend the scope of AutoML systems to semantic AutoML. We release our $\\href{https://github.com/automl/CAAFE}{code}$, a simple $\\href{https://colab.research.google.com/drive/1mCA8xOAJZ4MaB_alZvyARTMjhl6RZf0a}{demo}$ and a $\\href{https://pypi.org/project/caafe/}{python\\ package}$.", "published": "2023-05-05T09:58:40Z", "authors": ["Noah Hollmann", "Samuel Müller", "Frank Hutter"], "categories": ["cs.AI", "cs.LG"], "doi": "", "_query": "automated valuation model", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2407.02351v3", "title": "Generative Large Language Models in Automated Fact-Checking: A Survey", "abstract": "The rapid spread of false and misleading information on online platforms poses a growing societal challenge, overwhelming the capacity of manual fact-checking and increasing the demand for scalable, reliable automation. Recent advances in generative large language models (LLMs) have broadened the scope of automated fact-checking beyond accuracy-driven prediction. LLMs are now integral components of fact-checking pipelines, supporting tasks such as generating new data, performing and assisting with fact verification, and shaping how fact-checking systems are evaluated. This survey provides a comprehensive overview of the role of generative LLMs in automated fact-checking, based on a systematic review of 199 research papers. We introduce a unifying taxonomy that captures how generative LLMs are integrated into fact-checking workflows and analyze their use across core fact-checking tasks, dataset construction and augmentation strategies, task formulations, and evaluation practices. Additionally, we investigate the impact of generative LLMs in multilingual and low-resource settings in fact-checking, highlighting trends, limitations, and gaps in current research. By consolidating fragmented research efforts and identifying methodological patterns, limitations, and open challenges, this survey maps the current state of generative LLMs in automated fact-checking. It aims to support researchers in developing more reliable, interpretable, and inclusive fact-checking systems, while outlining promising directions for future research in this rapidly evolving field.", "published": "2024-07-02T15:16:46Z", "authors": ["Ivan Vykopal", "Matúš Pikuliak", "Simon Ostermann", "Marián Šimko"], "categories": ["cs.CL"], "doi": "", "_query": "automated valuation model", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2503.23914v1", "title": "Scenarios for the Deployment of Automated Vehicles in Europe", "abstract": "The deployment of Automated Vehicles (AVs) is expected to address road transport externalities (e.g., safety, traffic, environmental impact, etc.). For this reason, a legal framework for their large-scale market introduction and deployment is currently being developed in the European Union. Despite the first steps towards road transport automation, the timeline for full automation and its potential economic benefits remains uncertain. The aim of this paper is twofold. First, it presents a methodological framework to determine deployment pathways of the five different levels of automation in EU27+UK to 2050 under three scenarios (i.e., slow, medium baseline and fast) focusing on passenger vehicles. Second, it proposes an assessment of the economic impact of AVs through the calculation of the value-added. The method to define assumptions and uptake trajectories involves a comprehensive literature review, expert interviews, and a model to forecast the new registrations of different levels of automation. In this way, the interviews provided insights that complemented the literature and informed the design of assumptions and deployment trajectories. The added-value assessment shows additional economic activity due to the introduction of automated technologies in all uptake scenarios.", "published": "2025-03-31T10:06:27Z", "authors": ["Louison Duboz", "Ioan Cristinel Raileanu", "Jette Krause", "Ana Norman-López", "Matthias Weitzel", "Biagio Ciuffo"], "categories": ["econ.GN"], "doi": "", "_query": "automated valuation model", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/0804.2709v1", "title": "Subgroup theorem for valuated groups and the CSA property", "abstract": "A valuated group with normal forms is a group with an integer-valued length function satisfying some Lyndon's axioms and an additional axiom considered by Hurley. We prove a subgroup theorem for valuated groups with normal forms analogous to Grushko-Neumann's theorem. We study also the CSA property in such groups.", "published": "2008-04-17T01:38:49Z", "authors": ["Abderezak Ould Houcine"], "categories": ["math.GR"], "doi": "", "_query": "automated valuation model", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2406.01252v3", "title": "Towards Scalable Automated Alignment of LLMs: A Survey", "abstract": "Alignment is the most critical step in building large language models (LLMs) that meet human needs. With the rapid development of LLMs gradually surpassing human capabilities, traditional alignment methods based on human-annotation are increasingly unable to meet the scalability demands. Therefore, there is an urgent need to explore new sources of automated alignment signals and technical approaches. In this paper, we systematically review the recently emerging methods of automated alignment, attempting to explore how to achieve effective, scalable, automated alignment once the capabilities of LLMs exceed those of humans. Specifically, we categorize existing automated alignment methods into 4 major categories based on the sources of alignment signals and discuss the current status and potential development of each category. Additionally, we explore the underlying mechanisms that enable automated alignment and discuss the essential factors that make automated alignment technologies feasible and effective from the fundamental role of alignment.", "published": "2024-06-03T12:10:26Z", "authors": ["Boxi Cao", "Keming Lu", "Xinyu Lu", "Jiawei Chen", "Mengjie Ren", "Hao Xiang", "Peilin Liu", "Yaojie Lu", "Ben He", "Xianpei Han", "Le Sun", "Hongyu Lin", "Bowen Yu"], "categories": ["cs.CL", "cs.AI", "stat.ML"], "doi": "", "_query": "automated valuation model", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/astro-ph/0208185v1", "title": "Automated Stellar Spectral Classification and Parameterization for the Masses", "abstract": "Stellar spectroscopic classification has been successfully automated by a number of groups. Automated classification and parameterization work best when applied to a homogeneous data set, and thus these techniques primarily have been developed for and applied to large surveys. While most ongoing large spectroscopic surveys target extragalactic objects, many stellar spectra have been and will be obtained. We briefly summarize past work on automated classification and parameterization, with emphasis on the work done in our group. Accurate automated classification in the spectral type domain and parameterization in the temperature domain have been relatively easy. Automated parameterization in the metallicity domain, formally outside the MK system, has also been effective. Due to the subtle effects on the spectrum, automated classification in the luminosity domain has been somewhat more difficult, but still successful. In order to extend the use of automated techniques beyond a few surveys, we present our current efforts at building a web-based automated stellar spectroscopic classification and parameterization machine. Our proposed machinery would provide users with MK classifications as well as the astrophysical parameters of effective temperature, surface gravity, mean abundance, abundance anomalies, and microturbulence.", "published": "2002-08-08T16:17:30Z", "authors": ["Ted von Hippel", "Carlos Allende Prieto", "Chris Sneden"], "categories": ["astro-ph"], "doi": "", "_query": "automated valuation model", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2606.20470v4", "title": "Analyzing Defensive Misdirection Against Model-Guided Automated Attacks on Agentic AI Systems", "abstract": "Agentic AI systems increasingly rely on language-model components to interpret instructions, process external data, invoke tools, and coordinate with other agents. These capabilities make prompt-injection and jailbreak attacks more consequential, especially as attackers adopt model-guided automation to scale probing, prompt refinement, and response evaluation. This work analyzes the resulting attack-defense setting through a probabilistic model of a target system, its defense mechanism, and the attacker's automated judge. Our analysis shows that conventional detect-and-block defenses can allow attacker success rate (ASR) to approach one as the query budget grows, since predictable refusals provide useful feedback to automated search. We then examine detect-and-misdirect, where detected malicious interactions receive controlled, non-operational responses designed to induce false-positive errors in the attacker's judge. This strategy reduces the positive predictive value of attacker-selected candidates and yields a bounded asymptotic ASR. We evaluate a proof-of-concept realization of this strategy through Contextual Misdirection via Progressive Engagement (CMPE), a lightweight conversational misdirection method designed to replace predictable refusal text with safe but strategically misleading responses in automated jailbreak settings. On jailbreak benchmarks, CMPE reduces estimated ASR upper bounds by up to two orders of magnitude and nearly eliminates verified attack success in end-to-end experiments with PAIR, GPTFuzz, and AutoDAN-Turbo.", "published": "2026-06-18T16:50:28Z", "authors": ["Reza Soosahabi", "Vivek Namsani"], "categories": ["cs.CR", "cs.AI"], "doi": "", "_query": "automated valuation model", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2111.10529v1", "title": "Approximation types describing extensions of valuations to rational function fields", "abstract": "We introduce the notion of {\\it approximation type} for the partial, and in certain cases the total description of extensions of a given valuation from a field $K$ to the rational function field $K(x)$. To every extension, a unique approximation type of $x$ over $K$ is associated, while $x$ may be the limit of many pseudo Cauchy sequences. Approximation types also provide information in cases where the extensions are not immediate, and we prove that they correspond bijectively to the extensions when $K$ is algebraically closed or lies dense in its algebraic closure.", "published": "2021-11-20T07:04:19Z", "authors": ["Franz-Viktor Kuhlmann"], "categories": ["math.AC"], "doi": "", "_query": "automated valuation model", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/1307.0227v1", "title": "Valuation spaces and multiplier ideals on singular varieties", "abstract": "We generalize to all normal complex algebraic varieties the valuative characterization of multiplier ideals due to Boucksom-Favre-Jonsson in the smooth case. To that end, we extend the log discrepancy function to the space of all real valuations, and prove that it satisfies an adequate properness property, building upon previous work by Jonsson-Mustaţă. We next give an alternative definition of the concept of numerically Cartier divisors previously introduced by the first three authors, and prove that numerically Q-Cartier divisors coincide with Q-Cartier divisors for rational singularities. These ideas naturally lead to the notion of numerically Q-Gorenstein varieties, for which our valuative characterization of multiplier ideals takes a particularly simple form.", "published": "2013-06-30T18:08:34Z", "authors": ["Sébastien Boucksom", "Tommaso de Fernex", "Charles Favre", "Stefano Urbinati"], "categories": ["math.AG"], "doi": "", "_query": "automated valuation model", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2205.08445v1", "title": "Data-driven Driver Model for Speed Advisory Systems in Partially Automated Vehicles", "abstract": "Vehicle control algorithms exploiting connectivity and automation, such as Connected and Automated Vehicles (CAVs) or Advanced Driver Assistance Systems (ADAS), have the opportunity to improve energy savings. However, lower levels of automation involve a human-machine interaction stage, where the presence of a human driver affects the performance of the control algorithm in closed loop. This occurs for instance in the case of Eco-Driving control algorithms implemented as a velocity advisory system, where the driver is displayed an optimal speed trajectory to follow to reduce energy consumption. Achieving the control objectives relies on the human driver perfectly following the recommended speed. If the driver is unable to follow the recommended speed, a decline in energy savings and poor vehicle performance may occur. This warrants the creation of methods to model and forecast the response of a human driver when operating in the loop with a speed advisory system. This work focuses on developing a sequence to sequence long-short term memory (LSTM)-based driver behavior model that models the interaction of the human driver to a suggested desired vehicle speed trajectory in real-world conditions. A driving simulator is used for data collection and training the driver model, which is then compared against the driving data and a deterministic model. Results show close proximity of the LSTM-based model with the driving data, demonstrating that the model can be adopted as a tool to design human-centered speed advisory systems.", "published": "2022-05-17T15:37:56Z", "authors": ["Olivia Jacome", "Shobhit Gupta", "Stephanie Stockar", "Marcello Canova"], "categories": ["cs.RO", "eess.SY"], "doi": "", "_query": "automated valuation model", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2306.15920v2", "title": "Fair and Almost Truthful Mechanisms for Additive Valuations and Beyond", "abstract": "We study the problem of fairly allocating indivisible goods among $n$ strategic agents. It is well-known that truthfulness is incompatible with any meaningful fairness notions. We bypass the strong negative result by considering the concept of incentive ratio, a relaxation of truthfulness quantifying agents' incentive to misreport. Previous studies show that Round-Robin, which satisfies envy-freeness up to one good (EF1), achieves an incentive ratio of $2$ for additive valuations. In this paper, we explore the incentive ratio achievable by fair mechanisms for various classes of valuations besides additive ones. We first show that, for arbitrary $ε> 0$, every $(\\frac{1}{2} + ε)$-EF1 mechanism for additive valuations admits an incentive ratio of at least $1.5$. Then, using the above lower bound for additive valuations in a black-box manner, we show that for arbitrary $ε> 0$, every $ε$-EF1 mechanism for cancelable valuations admits an infinite incentive ratio. Moreover, for subadditive cancelable valuations, we show that Round-Robin, which satisfies EF1, achieves an incentive ratio of $2$, and every $(\\varphi - 1)$-EF1 mechanism admits an incentive ratio of at least $\\varphi$ with $\\varphi = (1 + \\sqrt{5}) / 2 \\approx 1.618$. Finally, for submodular valuations, we show that Round-Robin, which satisfies $\\frac{1}{2}$-EF1, admits an incentive ratio of $n$.", "published": "2023-06-28T04:50:00Z", "authors": ["Biaoshuai Tao", "Mingwei Yang"], "categories": ["cs.GT"], "doi": "", "_query": "automated valuation model", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2103.04563v1", "title": "Human-Machine Adaptive Shared Control for Safe Automated Driving under Automation Degradation", "abstract": "In this paper, a human-machine adaptive shared control method is proposed for automated vehicles (AVs) under automation performance degradation. First, a novel risk assessment module is proposed to monitor driving behavior and evaluate automation performance degradation for AVs. Then, an adaptive control authority allocation module is developed. In the event of any performance degradation detection, the allocated control authority of the automation system is decreased based on the assessed risk to reduce the potential risk of vehicle motion. Consequently, the control authority allocated to the human driver is adaptively increased and thus requires more driver engagement in the control loop to compensate for the automation degradation and ensure AV safety. Experimental validation is conducted under different driving scenarios. The testing results show that the proposed approach is able to effectively compensate for the performance degradation of vehicle automation through the human-machine adaptive shared control, ensuring the safety of automated driving", "published": "2021-03-08T06:23:34Z", "authors": ["Chao Huang", "Chen Lv", "Peng Hang", "Zhongxu Hu", "Yang Xing"], "categories": ["eess.SY"], "doi": "", "_query": "automated valuation model", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2503.04941v2", "title": "GATE: An Integrated Assessment Model for AI Automation", "abstract": "Assessing the economic impacts of artificial intelligence requires integrating insights from both computer science and economics. We present the Growth and AI Transition Endogenous model (GATE), a dynamic integrated assessment model that simulates the economic effects of AI automation. GATE combines three key ingredients that have not been brought together in previous work: (1) a compute-based model of AI development, (2) an AI automation framework, and (3) a semi-endogenous growth model featuring endogenous investment and adjustment costs. The model allows users to simulate the economic effects of the transition to advanced AI across a range of potential scenarios. GATE captures the interactions between economic variables, including investment, automation, innovation, and growth, as well as AI-related inputs such as compute and algorithms. This paper explains the model's structure and functionality, emphasizing AI development for economists and economic modeling for the AI community. The model is implemented in an interactive sandbox, enabling users to explore the impact of AI under different parameter choices and policy interventions. The modeling sandbox is available at: www.epoch.ai/GATE.", "published": "2025-03-06T20:16:59Z", "authors": ["Ege Erdil", "Andrei Potlogea", "Tamay Besiroglu", "Edu Roldan", "Anson Ho", "Jaime Sevilla", "Matthew Barnett", "Matej Vrzla", "Robert Sandler"], "categories": ["econ.GN"], "doi": "", "_query": "automated valuation model", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2411.18583v1", "title": "Automated Literature Review Using NLP Techniques and LLM-Based Retrieval-Augmented Generation", "abstract": "This research presents and compares multiple approaches to automate the generation of literature reviews using several Natural Language Processing (NLP) techniques and retrieval-augmented generation (RAG) with a Large Language Model (LLM). The ever-increasing number of research articles provides a huge challenge for manual literature review. It has resulted in an increased demand for automation. Developing a system capable of automatically generating the literature reviews from only the PDF files as input is the primary objective of this research work. The effectiveness of several Natural Language Processing (NLP) strategies, such as the frequency-based method (spaCy), the transformer model (Simple T5), and retrieval-augmented generation (RAG) with Large Language Model (GPT-3.5-turbo), is evaluated to meet the primary objective. The SciTLDR dataset is chosen for this research experiment and three distinct techniques are utilized to implement three different systems for auto-generating the literature reviews. The ROUGE scores are used for the evaluation of all three systems. Based on the evaluation, the Large Language Model GPT-3.5-turbo achieved the highest ROUGE-1 score, 0.364. The transformer model comes in second place and spaCy is at the last position. Finally, a graphical user interface is created for the best system based on the large language model.", "published": "2024-11-27T18:27:07Z", "authors": ["Nurshat Fateh Ali", "Md. Mahdi Mohtasim", "Shakil Mosharrof", "T. Gopi Krishna"], "categories": ["cs.CL", "cs.AI", "cs.IR", "cs.LG"], "doi": "", "_query": "automated valuation model", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2411.01001v1", "title": "Automated Assessment of Residual Plots with Computer Vision Models", "abstract": "Plotting the residuals is a recommended procedure to diagnose deviations from linear model assumptions, such as non-linearity, heteroscedasticity, and non-normality. The presence of structure in residual plots can be tested using the lineup protocol to do visual inference. There are a variety of conventional residual tests, but the lineup protocol, used as a statistical test, performs better for diagnostic purposes because it is less sensitive and applies more broadly to different types of departures. However, the lineup protocol relies on human judgment which limits its scalability. This work presents a solution by providing a computer vision model to automate the assessment of residual plots. It is trained to predict a distance measure that quantifies the disparity between the residual distribution of a fitted classical normal linear regression model and the reference distribution, based on Kullback-Leibler divergence. From extensive simulation studies, the computer vision model exhibits lower sensitivity than conventional tests but higher sensitivity than human visual tests. It is slightly less effective on non-linearity patterns. Several examples from classical papers and contemporary data illustrate the new procedures, highlighting its usefulness in automating the diagnostic process and supplementing existing methods.", "published": "2024-11-01T19:51:44Z", "authors": ["Weihao Li", "Dianne Cook", "Emi Tanaka", "Susan VanderPlas", "Klaus Ackermann"], "categories": ["stat.ML", "cs.CV", "cs.LG"], "doi": "", "_query": "automated valuation model", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2009.00310v2", "title": "On Hodge-Riemann relations for translation-invariant valuations", "abstract": "The Alesker product turns the space of smooth translation-invariant valuations on convex bodies into a commutative associative unital algebra, satisfying Poincaré duality and the hard Lefschetz theorem. In this article, a version of the Hodge-Riemann relations for the Alesker algebra is conjectured, and the conjecture is proved in two particular situations: for even valuations, and for 1-homogeneous valuations. The latter result is then used to deduce a special case of the Aleksandrov-Fenchel inequality. Finally, mixed versions of the hard Lefschetz theorem and of the Hodge-Riemann relations are conjectured, and it is shown that the Aleksandrov-Fenchel inequality follows from the latter in its full generality.", "published": "2020-09-01T09:27:54Z", "authors": ["Jan Kotrbatý"], "categories": ["math.MG"], "doi": "", "_query": "automated valuation model", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2105.13420v1", "title": "Model Selection for Production System via Automated Online Experiments", "abstract": "A challenge that machine learning practitioners in the industry face is the task of selecting the best model to deploy in production. As a model is often an intermediate component of a production system, online controlled experiments such as A/B tests yield the most reliable estimation of the effectiveness of the whole system, but can only compare two or a few models due to budget constraints. We propose an automated online experimentation mechanism that can efficiently perform model selection from a large pool of models with a small number of online experiments. We derive the probability distribution of the metric of interest that contains the model uncertainty from our Bayesian surrogate model trained using historical logs. Our method efficiently identifies the best model by sequentially selecting and deploying a list of models from the candidate set that balance exploration-exploitation. Using simulations based on real data, we demonstrate the effectiveness of our method on two different tasks.", "published": "2021-05-27T19:48:23Z", "authors": ["Zhenwen Dai", "Praveen Chandar", "Ghazal Fazelnia", "Ben Carterette", "Mounia Lalmas-Roelleke"], "categories": ["stat.ML", "cs.AI", "cs.LG"], "doi": "", "_query": "automated valuation model", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2207.10380v2", "title": "Lower bound for the 2-adic valuations of central $L$-values of elliptic curves with complex multiplication", "abstract": "Let $E_{-D}$ be the elliptic curve $y^2=x^3+Dx$ defined over $K=\\mathbb{Q}(i)$ for $D\\in K$ which is coprime to 2. In this paper, we give a lower bound for the 2-adic valuation of the algebraic part of the central value of Hecke $L$-function associated to $E_{-D}$.", "published": "2022-07-21T09:24:16Z", "authors": ["Keiichiro Nomoto"], "categories": ["math.NT"], "doi": "", "_query": "automated valuation model", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2410.18651v2", "title": "The Klain approach to zonal valuations", "abstract": "We show an analogue of the Klain-Schneider theorem for valuations that are invariant under rotations around a fixed axis, called zonal. Using this, we establish a new integral representation of zonal valuations involving mixed area measures with a disk. In our argument, we introduce an easy way to translate between this representation and the one involving area measures, yielding a shorter proof of a recent characterization by Knoerr. As applications, we obtain various zonal integral geometric formulas, extending results by Hug, Mussnig, and Ulivelli. Finally, we provide a simpler proof of the integral representation of the mean section operators by Goodey and Weil.", "published": "2024-10-24T11:31:09Z", "authors": ["Leo Brauner", "Georg C. Hofstätter", "Oscar Ortega-Moreno"], "categories": ["math.MG", "math.FA"], "doi": "", "_query": "automated valuation model", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2011.06231v1", "title": "Automated Model Compression by Jointly Applied Pruning and Quantization", "abstract": "In the traditional deep compression framework, iteratively performing network pruning and quantization can reduce the model size and computation cost to meet the deployment requirements. However, such a step-wise application of pruning and quantization may lead to suboptimal solutions and unnecessary time consumption. In this paper, we tackle this issue by integrating network pruning and quantization as a unified joint compression problem and then use AutoML to automatically solve it. We find the pruning process can be regarded as the channel-wise quantization with 0 bit. Thus, the separate two-step pruning and quantization can be simplified as the one-step quantization with mixed precision. This unification not only simplifies the compression pipeline but also avoids the compression divergence. To implement this idea, we propose the automated model compression by jointly applied pruning and quantization (AJPQ). AJPQ is designed with a hierarchical architecture: the layer controller controls the layer sparsity, and the channel controller decides the bit-width for each kernel. Following the same importance criterion, the layer controller and the channel controller collaboratively decide the compression strategy. With the help of reinforcement learning, our one-step compression is automatically achieved. Compared with the state-of-the-art automated compression methods, our method obtains a better accuracy while reducing the storage considerably. For fixed precision quantization, AJPQ can reduce more than five times model size and two times computation with a slight performance increase for Skynet in remote sensing object detection. When mixed-precision is allowed, AJPQ can reduce five times model size with only 1.06% top-5 accuracy decline for MobileNet in the classification task.", "published": "2020-11-12T07:06:29Z", "authors": ["Wenting Tang", "Xingxing Wei", "Bo Li"], "categories": ["cs.CV", "cs.AI"], "doi": "", "_query": "automated valuation model", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/math/0702842v4", "title": "A Fourier type transform on translation invariant valuations on convex sets", "abstract": "Let $V$ be a finite dimensional real vector space. In the article we construct an isomorphism between the space of smooth translation invariant valuations on convex subsets of $V$ and the space of such valuations (twisted by densities) on the dual space $V^*$. This isomorphism commutes with the natural action of the full linear group on both spaces of valuations, maps product on the source (introduced in arXiv:math/0301148) to the convolution on the target (introduced in arXiv:math/0607449), and satisfies a Plancherel type inversion formula. As an application, a version of the hard Lefschetz theorem for valuations is proved.", "published": "2007-02-27T15:56:55Z", "authors": ["Semyon Alesker"], "categories": ["math.MG"], "doi": "", "_query": "automated valuation model", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2603.06054v2", "title": "Probing Visual Concepts in Lightweight Vision-Language Models for Automated Driving", "abstract": "The use of Vision-Language Models (VLMs) in automated driving applications is becoming increasingly common, with the aim of leveraging their reasoning and generalisation capabilities to handle long-tail scenarios. However, these models often fail on simple visual questions that are highly relevant to automated driving, and the reasons behind these failures remain poorly understood. In this work, we examine the intermediate activations of VLMs and assess the extent to which specific visual concepts are linearly encoded, with the goal of identifying bottlenecks in the flow of visual information. Specifically, we create counterfactual image sets that differ only in a targeted visual concept and then train linear probes to distinguish between them using the activations of five state-of-the-art (SOTA) VLMs, including two training variants for one model. Our results show that concepts such as the presence of an object or agent in a scene are explicitly and linearly encoded, whereas other spatial visual concepts, such as the orientation of an object or agent, are only implicitly encoded by the spatial structure retained by the vision encoder, or are not linearly encoded at all. In parallel, we observe that in certain cases, even when a concept is linearly encoded in the model's activations, the model still fails to answer correctly. This leads us to identify two failure modes. The first is perceptual failure, where the visual information required to answer a question is not linearly encoded in the model's activations. The second is cognitive failure, where the visual information is present but the model fails to align it correctly with language semantics. Finally, we show that increasing the distance of the object in question quickly degrades the linear separability of the corresponding visual concept...", "published": "2026-03-06T09:07:57Z", "authors": ["Nikos Theodoridis", "Reenu Mohandas", "Ganesh Sistu", "Anthony Scanlan", "Ciarán Eising", "Tim Brophy"], "categories": ["cs.CV", "cs.AI"], "doi": "", "_query": "automated valuation model", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/1006.0038v3", "title": "Valuations from representation theory and tropical geometry", "abstract": "We recall the space of seminorms discussed by Payne in \\cite{P} and define a slight modification, the space of graded valuations. After explaining how these spaces relate to tropical geometry, we describe examples of graded valuations which come from the representation theory of reductive groups.", "published": "2010-06-01T00:11:39Z", "authors": ["Christopher Manon"], "categories": ["math.CO", "math.AC"], "doi": "", "_query": "automated valuation model", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/1907.00543v3", "title": "Toric flat families, valuations, and applications to projectivized toric vector bundles", "abstract": "Using the notion of a valuation into the semifield of piecewise linear functions, we give a classification of torus equivariant flat families of finite type over a toric variety base, by certain piecewise linear maps between fans. As a consequence we derive a classification of toric vector bundles phrased in terms of tropicalized linear spaces. We use these tools to give a characterization of the Mori dream space property for a projectivized toric vector bundle.", "published": "2019-07-01T04:46:00Z", "authors": ["Kiumars Kaveh", "Christopher Manon"], "categories": ["math.AG"], "doi": "", "_query": "automated valuation model", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2202.11681v1", "title": "On the behavior of formal neighborhoods in the Nash sets associated with toric valuations: a comparison theorem", "abstract": "We show that there exists a strong connection between the generic formal neighborhood at a rational arc lying in the Nash set associated with a toric divisorial valuation on a toric variety and the formal neighborhood at the generic point of the same Nash set. This may be interpreted as the fact that, analytically along such a Nash set, the arc scheme of a toric variety is a product of a finite dimensional singularity and an infinite dimensional affine space.", "published": "2022-02-23T18:44:12Z", "authors": ["David Bourqui", "Mario Morán Cañón", "Julien Sebag"], "categories": ["math.AG"], "doi": "", "_query": "automated valuation model", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2212.03865v1", "title": "Learning on Graphs for Mineral Asset Valuation Under Supply and Demand Uncertainty", "abstract": "Valuing mineral assets is a challenging task that is highly dependent on the supply (geological) uncertainty surrounding resources and reserves, and the uncertainty of demand (commodity prices). In this work, a graph-based reasoning, modeling and solution approach is proposed to jointly address mineral asset valuation and mine plan scheduling and optimization under supply and demand uncertainty in the \"mining complex\" framework. Three graph-based solutions are proposed: (i) a neural branching policy that learns a block-sampling ore body representation, (ii) a guiding policy that learns to explore a heuristic selection tree, (iii) a hyper-heuristic that manages the value/supply chain optimization and dynamics modeled as a graph structure. Results on two large-scale industrial mining complexes show a reduction of up to three orders of magnitude in primal suboptimality, execution time, and number of iterations, and an increase of up to 40% in the mineral asset value.", "published": "2022-12-07T00:30:18Z", "authors": ["Yassine Yaakoubi", "Hager Radi", "Roussos Dimitrakopoulos"], "categories": ["cs.AI", "cs.LG"], "doi": "", "_query": "automated valuation model", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2505.12900v1", "title": "AutoGEEval: A Multimodal and Automated Framework for Geospatial Code Generation on GEE with Large Language Models", "abstract": "Geospatial code generation is emerging as a key direction in the integration of artificial intelligence and geoscientific analysis. However, there remains a lack of standardized tools for automatic evaluation in this domain. To address this gap, we propose AutoGEEval, the first multimodal, unit-level automated evaluation framework for geospatial code generation tasks on the Google Earth Engine (GEE) platform powered by large language models (LLMs). Built upon the GEE Python API, AutoGEEval establishes a benchmark suite (AutoGEEval-Bench) comprising 1325 test cases that span 26 GEE data types. The framework integrates both question generation and answer verification components to enable an end-to-end automated evaluation pipeline-from function invocation to execution validation. AutoGEEval supports multidimensional quantitative analysis of model outputs in terms of accuracy, resource consumption, execution efficiency, and error types. We evaluate 18 state-of-the-art LLMs-including general-purpose, reasoning-augmented, code-centric, and geoscience-specialized models-revealing their performance characteristics and potential optimization pathways in GEE code generation. This work provides a unified protocol and foundational resource for the development and assessment of geospatial code generation models, advancing the frontier of automated natural language to domain-specific code translation.", "published": "2025-05-19T09:35:58Z", "authors": ["Shuyang Hou", "Zhangxiao Shen", "Huayi Wu", "Jianyuan Liang", "Haoyue Jiao", "Yaxian Qing", "Xiaopu Zhang", "Xu Li", "Zhipeng Gui", "Xuefeng Guan", "Longgang Xiang"], "categories": ["cs.SE", "cs.AI", "cs.CG", "cs.CL", "cs.DB"], "doi": "", "_query": "automated valuation model", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2001.02415v1", "title": "Forking and Dividing in Fields with Several Orderings and Valuations", "abstract": "We consider existentially closed fields with several orderings, valuations, and $p$-valuations. We show that these structures are NTP$_2$ of finite burden, but usually have the independence property. Moreover, forking agrees with dividing, and forking can be characterized in terms of forking in ACVF, RCF, and $p$CF.", "published": "2020-01-08T08:50:15Z", "authors": ["Will Johnson"], "categories": ["math.LO"], "doi": "", "_query": "automated valuation model", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2405.10859v1", "title": "A Nonlinear Model Predictive Control for Automated Drifting with a Standard Passenger Vehicle", "abstract": "This paper presents a novel approach to automated drifting with a standard passenger vehicle, which involves a Nonlinear Model Predictive Control to stabilise and maintain the vehicle at high sideslip angle conditions. The proposed controller architecture is split into three components. The first part consists of the offline computed equilibrium maps, which provide the equilibrium points for each vehicle state given the desired sideslip angle and radius of the path. The second is the predictive controller minimising the errors between the equilibrium and actual vehicle states. The third is a path-following controller, which reduces the path error, altering the equilibrium curvature path. In a high-fidelity simulation environment, we validate the controller architecture capacity to stabilise the vehicle in automated drifting along a desired path, with a maximal lateral path deviation of 1 m. In the experiments with a standard passenger vehicle, we demonstrate that the proposed approach is capable of bringing and maintaining the vehicle at the desired 30 deg sideslip angle in both high and low friction conditions.", "published": "2024-05-17T15:42:14Z", "authors": ["Stan Meijer", "Alberto Bertipaglia", "Barys Shyrokau"], "categories": ["cs.RO", "eess.SY"], "doi": "", "_query": "automated valuation model", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/1908.06970v1", "title": "Agent-based (BDI) modeling for automation of penetration testing", "abstract": "Penetration testing (or pentesting) is one of the widely used and important methodologies to assess the security of computer systems and networks. Traditional pentesting relies on the domain expert knowledge and requires considerable human effort all of which incurs a high cost. The automation can significantly improve the efficiency, availability and lower the cost of penetration testing. Existing approaches to the automation include those which map vulnerability scanner results to the corresponding exploit tools, and those addressing the pentesting as a planning problem expressed in terms of attack graphs. Due to mainly non-interactive processing, such solutions can deal effectively only with static and simple targets. In this paper, we propose an automated penetration testing approach based on the belief-desire-intention (BDI) agent model, which is central in the research on agent-based processing in that it deals interactively with dynamic, uncertain and complex environments. Penetration testing actions are defined as a series of BDI plans and the BDI reasoning cycle is used to represent the penetration testing process. The model is extensible and new plans can be added, once they have been elicited from the human experts. We report on the results of testing of proof of concept BDI-based penetration testing tool in the simulated environment.", "published": "2019-08-18T15:41:36Z", "authors": ["Ge Chu", "Alexei Lisitsa"], "categories": ["cs.CR", "cs.MA"], "doi": "", "_query": "automated valuation model", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2506.23083v2", "title": "Model-Based Diagnosis: Automating End-to-End Diagnosis of Network Failures", "abstract": "Fast diagnosis and repair of enterprise network failures is critically important since disruptions cause major business impacts. Prior works focused on diagnosis primitives or procedures limited to a subset of the problem, such as only data plane or only control plane faults. This paper proposes a new paradigm, model-based network diagnosis, that provides a systematic way to derive automated procedures for identifying the root cause of network failures, based on reports of end-to-end user-level symptoms. The diagnosis procedures are systematically derived from a model of packet forwarding and routing, covering hardware, firmware, and software faults in both the data plane and distributed control plane. These automated procedures replace and dramatically accelerate diagnosis by an experienced human operator. Model-based diagnosis is inspired by, leverages, and is complementary to recent work on network verification. We have built NetDx, a proof-of-concept implementation of model-based network diagnosis. We deployed NetDx on a new emulator of networks consisting of P4 switches with distributed routing software. We validated the robustness and coverage of NetDx with an automated fault injection campaign, in which 100% of faults were diagnosed correctly. Furthermore, on a data set of 33 faults from a large cloud provider that are within the domain targeted by NetDx, 30 are efficiently diagnosed in seconds instead of hours.", "published": "2025-06-29T04:44:31Z", "authors": ["Changrong Wu", "Yiyao Yu", "Myungjin Lee", "Jayanth Srinivasa", "Ennan Zhai", "George Varghese", "Yuval Tamir"], "categories": ["cs.NI"], "doi": "", "_query": "automated valuation model", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2106.02656v2", "title": "Approximating Nash Social Welfare under Binary XOS and Binary Subadditive Valuations", "abstract": "We study the problem of allocating indivisible goods among agents in a fair and economically efficient manner. In this context, the Nash social welfare-defined as the geometric mean of agents' valuations for their assigned bundles-stands as a fundamental measure that quantifies the extent of fairness of an allocation. Focusing on instances in which the agents' valuations have binary marginals, we develop essentially tight results for (approximately) maximizing Nash social welfare under two of the most general classes of complement-free valuations, i.e., under binary XOS and binary subadditive valuations. For binary XOS valuations, we develop a polynomial-time algorithm that finds a constant-factor (specifically $288$) approximation for the optimal Nash social welfare, in the standard value-oracle model. The allocations computed by our algorithm also achieve constant-factor approximation for social welfare and the groupwise maximin share guarantee. These results imply that-in the case of binary XOS valuations-there necessarily exists an allocation that simultaneously satisfies multiple (approximate) fairness and efficiency criteria. We complement the algorithmic result by proving that Nash social welfare maximization is APX-hard under binary XOS valuations. Furthermore, this work establishes an interesting separation between the binary XOS and binary subadditive settings. In particular, we prove that an exponential number of value queries are necessarily required to obtain even a sub-linear approximation for Nash social welfare under binary subadditive valuations.", "published": "2021-06-04T18:06:48Z", "authors": ["Siddharth Barman", "Paritosh Verma"], "categories": ["cs.GT"], "doi": "", "_query": "automated valuation model", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/1905.02169v1", "title": "Extensions of a valuation from $K$ to $K[x]$", "abstract": "In this paper we give an introduction on how one can extend a valuation from a field $K$ to the polynomial ring $K[x]$ in one variable over $K$. This follows a similar line as the one presented by the author in his talk at ALaNT 5. We will discuss the objects that have been introduced to describe such extensions. We will focus on key polynomials, pseudo-convergent sequences and minimal pairs. Key polynomials have been introduced and used by various authors in different ways. We discuss these works and the relation between them. We also discuss a recent version of key polynomials developed by Spivakovsky. This version provides some advantages, that will be discussed in this paper. For instance, it allows us to relate key polynomials, in an explicit way, to pseudo-convergent sequences and minimal pairs. This paper also provides examples that ilustrate these objects and their properties. Our main goal when studying key polynomials is to obtain more accurate results on the problem of local uniformization. This problem, which is still open in positive characteristic, was the main topic of the paper of the author and Spivakovsky in the proceedings of ALaNT 3.", "published": "2019-05-06T17:37:57Z", "authors": ["Josnei Novacoski"], "categories": ["math.AC"], "doi": "", "_query": "automated valuation model", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/1502.06729v1", "title": "Monotone valuations on the space of convex functions", "abstract": "We consider the space of convex functions defined in the Euclidean $n$-dimensional space, which are lower semi-continuous and tend to infinity at infinity. We study real-valued valuations defined on this space of functions, which are invariant under the composition with rigid motions, monotone and verify a certain type of continuity. Among these valuations we prove integral representation formulas for those which are, additionally, simple or homogeneous.", "published": "2015-02-24T09:39:58Z", "authors": ["L. Cavallina", "A. Colesanti"], "categories": ["math.MG"], "doi": "10.1515/agms-2015-0012", "_query": "automated valuation model", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/1504.05656v1", "title": "Poincare Series of Non-divisorial Valuations on Two-dimensional Function Fields", "abstract": "We use dual graphs and generating sequences of valuations to compute the Poincare series of non-divisorial valuations on function fields of dimension two. The Poincare series are shown to reflect data from the dual graphs and hence carry equivalent information. This extends Galindo's earlier results on the Poincare series of divisorial valuations and consequently offers an alternative classification of valuations via Poincare series.", "published": "2015-04-22T05:01:54Z", "authors": ["Charles Li", "Hans Schoutens"], "categories": ["math.AC"], "doi": "", "_query": "automated valuation model", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2207.10830v1", "title": "Automated Dilated Spatio-Temporal Synchronous Graph Modeling for Traffic Prediction", "abstract": "Accurate traffic prediction is a challenging task in intelligent transportation systems because of the complex spatio-temporal dependencies in transportation networks. Many existing works utilize sophisticated temporal modeling approaches to incorporate with graph convolution networks (GCNs) for capturing short-term and long-term spatio-temporal dependencies. However, these separated modules with complicated designs could restrict effectiveness and efficiency of spatio-temporal representation learning. Furthermore, most previous works adopt the fixed graph construction methods to characterize the global spatio-temporal relations, which limits the learning capability of the model for different time periods and even different data scenarios. To overcome these limitations, we propose an automated dilated spatio-temporal synchronous graph network, named Auto-DSTSGN for traffic prediction. Specifically, we design an automated dilated spatio-temporal synchronous graph (Auto-DSTSG) module to capture the short-term and long-term spatio-temporal correlations by stacking deeper layers with dilation factors in an increasing order. Further, we propose a graph structure search approach to automatically construct the spatio-temporal synchronous graph that can adapt to different data scenarios. Extensive experiments on four real-world datasets demonstrate that our model can achieve about 10% improvements compared with the state-of-art methods. Source codes are available at https://github.com/jinguangyin/Auto-DSTSGN.", "published": "2022-07-22T00:50:39Z", "authors": ["Guangyin Jin", "Fuxian Li", "Jinlei Zhang", "Mudan Wang", "Jincai Huang"], "categories": ["cs.LG"], "doi": "", "_query": "automated valuation model", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2401.15335v2", "title": "L-AutoDA: Leveraging Large Language Models for Automated Decision-based Adversarial Attacks", "abstract": "In the rapidly evolving field of machine learning, adversarial attacks present a significant challenge to model robustness and security. Decision-based attacks, which only require feedback on the decision of a model rather than detailed probabilities or scores, are particularly insidious and difficult to defend against. This work introduces L-AutoDA (Large Language Model-based Automated Decision-based Adversarial Attacks), a novel approach leveraging the generative capabilities of Large Language Models (LLMs) to automate the design of these attacks. By iteratively interacting with LLMs in an evolutionary framework, L-AutoDA automatically designs competitive attack algorithms efficiently without much human effort. We demonstrate the efficacy of L-AutoDA on CIFAR-10 dataset, showing significant improvements over baseline methods in both success rate and computational efficiency. Our findings underscore the potential of language models as tools for adversarial attack generation and highlight new avenues for the development of robust AI systems.", "published": "2024-01-27T07:57:20Z", "authors": ["Ping Guo", "Fei Liu", "Xi Lin", "Qingchuan Zhao", "Qingfu Zhang"], "categories": ["cs.CR", "cs.AI", "cs.CV", "cs.LG"], "doi": "10.1145/3638530.3664121", "_query": "automated valuation model", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2106.05765v2", "title": "Valuations on $K[x]$ approaching a fixed irreducible polynomial", "abstract": "For a fixed irreducible polynomial $F$ we study the set $\\mathcal V_F$ of all valuations on $K[x]$ bounded by valuations whose support is $(F)$. The first main result presents a characterization for valuations in $\\mathcal V_F$ in terms of their graded rings. We also present a result which gives, for a fixed $ν\\in \\mathcal V_F$ and a key polynomial $Q\\in{\\rm KP}(ν)$, the maximum value that augmented valuations in $\\mathcal V_F$ can assume on $Q$. This value is presented explicitly in terms of the slopes of the Newton polygon of $F$ with respect to $Q$. Finally, we present some results about Artin-Schreier extensions that illustrate the applications that we have in mind for the results in this paper.", "published": "2021-06-10T14:12:12Z", "authors": ["Josnei Novacoski", "Matheus dos S. Barnabe"], "categories": ["math.AC"], "doi": "", "_query": "automated valuation model", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2510.05129v2", "title": "Automated Alignment of Math Items to Content Standards in Large-Scale Assessments Using Language Models", "abstract": "Accurate alignment of items to content standards is critical for valid score interpretation in large-scale assessments. This study evaluates three automated paradigms for aligning items with four domain and nineteen skill labels. First, we extracted embeddings and trained multiple classical supervised machine learning models, and further investigated the impact of dimensionality reduction on model performance. Second, we fine-tuned eight BERT model and its variants for both domain and skill alignment. Third, we explored ensemble learning with majority voting and stacking with multiple meta-models. The DeBERTa-v3-base achieved the highest weighted-average F1 score of 0.950 for domain alignment while the RoBERTa-large yielded the highest F1 score of 0.869 for skill alignment. Ensemble models did not surpass the best-performing language models. Dimension reduction enhanced linear classifiers based on embeddings but did not perform better than language models. This study demonstrated different methods in automated item alignment to content standards.}", "published": "2025-09-30T21:44:00Z", "authors": ["Qingshu Xu", "Hong Jiao", "Tianyi Zhou", "Ming Li", "Nan Zhang", "Sydney Peters", "Yanbin Fu"], "categories": ["cs.CL", "cs.LG"], "doi": "", "_query": "automated valuation model", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2405.01874v1", "title": "Automated Control Logic Test Case Generation using Large Language Models", "abstract": "Testing PLC and DCS control logic in industrial automation is laborious and challenging since appropriate test cases are often complex and difficult to formulate. Researchers have previously proposed several automated test case generation approaches for PLC software applying symbolic execution and search-based techniques. Often requiring formal specifications and performing a mechanical analysis of programs, these approaches may uncover specific programming errors but sometimes suffer from state space explosion and cannot process rather informal specifications. We proposed a novel approach for the automatic generation of PLC test cases that queries a Large Language Model (LLM) to synthesize test cases for code provided in a prompt. Experiments with ten open-source function blocks from the OSCAT automation library showed that the approach is fast, easy to use, and can yield test cases with high statement coverage for low-to-medium complex programs. However, we also found that LLM-generated test cases suffer from erroneous assertions in many cases, which still require manual adaption.", "published": "2024-05-03T06:09:21Z", "authors": ["Heiko Koziolek", "Virendra Ashiwal", "Soumyadip Bandyopadhyay", "Chandrika K R"], "categories": ["cs.SE"], "doi": "", "_query": "automated valuation model", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2108.06696v1", "title": "Automated Enterprise Architecture Model Mining", "abstract": "Metadata are like the steam engine of the 21st century, driving businesses and offer multiple enhancements. Nevertheless, many companies are unaware that these data can be used efficiently to improve their own operation. This is where the Enterprise Architecture Framework comes in. It empowers an organisation to get a clear view of their business, application, technical and physical layer. This modelling approach is an established method for organizations to take a deeper look into their structure and processes. The development of such models requires a great deal of effort, is carried out manually by interviewing stakeholders and requires continuous maintenance. Our new approach enables the automated mining of Enterprise Architecture models. The system uses common technologies to collect the metadata based on network traffic, log files and other information in an organisation. Based on this, the new approach generates EA models with the desired views points. Furthermore, a rule and knowledge-based reasoning is used to obtain a holistic overview. This offers a strategic decision support from business structure over process design up to planning the appropriate support technology. Therefore, it forms the base for organisations to act in an agile way. The modelling can be performed in different modelling languages, including ArchiMate and the Nato Architecture Framework (NAF). The designed approach is already evaluated on a small company with multiple services and an infrastructure with several nodes.", "published": "2021-08-15T09:01:57Z", "authors": ["Peter Hillmann", "Erik Heiland", "Andreas Karcher"], "categories": ["cs.IR", "cs.CR", "cs.NI", "eess.SY"], "doi": "", "_query": "automated valuation model", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2405.11686v1", "title": "Exploiting Distributional Value Functions for Financial Market Valuation, Enhanced Feature Creation and Improvement of Trading Algorithms", "abstract": "While research of reinforcement learning applied to financial markets predominantly concentrates on finding optimal behaviours, it is worth to realize that the reinforcement learning returns $G_t$ and state value functions themselves are of interest and play a pivotal role in the evaluation of assets. Instead of focussing on the more complex task of finding optimal decision rules, this paper studies and applies the power of distributional state value functions in the context of financial market valuation and machine learning based trading algorithms. Accurate and trustworthy estimates of the distributions of $G_t$ provide a competitive edge leading to better informed decisions and more optimal behaviour. Herein, ideas from predictive knowledge and deep reinforcement learning are combined to introduce a novel family of models called CDG-Model, resulting in a highly flexible framework and intuitive approach with minimal assumptions regarding underlying distributions. The models allow seamless integration of typical financial modelling pitfalls like transaction costs, slippage and other possible costs or benefits into the model calculation. They can be applied to any kind of trading strategy or asset class. The frameworks introduced provide concrete business value through their potential in market valuation of single assets and portfolios, in the comparison of strategies as well as in the improvement of market timing. They can positively impact the performance and enhance the learning process of existing or new trading algorithms. They are of interest from a scientific point-of-view and open up multiple areas of future research. Initial implementations and tests were performed on real market data. While the results are promising, applying a robust statistical framework to evaluate the models in general remains a challenge and further investigations are needed.", "published": "2024-05-19T22:08:15Z", "authors": ["Colin D. Grab"], "categories": ["q-fin.ST", "q-fin.CP"], "doi": "", "_query": "automated valuation model", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2109.11427v1", "title": "Automated Fact-Checking: A Survey", "abstract": "As online false information continues to grow, automated fact-checking has gained an increasing amount of attention in recent years. Researchers in the field of Natural Language Processing (NLP) have contributed to the task by building fact-checking datasets, devising automated fact-checking pipelines and proposing NLP methods to further research in the development of different components. This paper reviews relevant research on automated fact-checking covering both the claim detection and claim validation components.", "published": "2021-09-23T15:13:48Z", "authors": ["Xia Zeng", "Amani S. Abumansour", "Arkaitz Zubiaga"], "categories": ["cs.CL"], "doi": "", "_query": "automated valuation model", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2408.01244v1", "title": "Automated Classification of Dry Bean Varieties Using XGBoost and SVM Models", "abstract": "This paper presents a comparative study on the automated classification of seven different varieties of dry beans using machine learning models. Leveraging a dataset of 12,909 dry bean samples, reduced from an initial 13,611 through outlier removal and feature extraction, we applied Principal Component Analysis (PCA) for dimensionality reduction and trained two multiclass classifiers: XGBoost and Support Vector Machine (SVM). The models were evaluated using nested cross-validation to ensure robust performance assessment and hyperparameter tuning. The XGBoost and SVM models achieved overall correct classification rates of 94.00% and 94.39%, respectively. The results underscore the efficacy of these machine learning approaches in agricultural applications, particularly in enhancing the uniformity and efficiency of seed classification. This study contributes to the growing body of work on precision agriculture, demonstrating that automated systems can significantly support seed quality control and crop yield optimization. Future work will explore incorporating more diverse datasets and advanced algorithms to further improve classification accuracy.", "published": "2024-08-02T13:05:33Z", "authors": ["Ramtin Ardeshirifar"], "categories": ["cs.LG"], "doi": "", "_query": "automated valuation model", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2511.22600v3", "title": "b-divisorial valuations and Berkovich positivity functions", "abstract": "We prove semicontinuity properties for local positivity invariants of big and nef divisors. The usual definition of Seshadri constant and asymptotic order of vanishing along a subvariety is extended to include all seminorms in the Berkovich space, and we obtain semicontinuity of such constants as a function of the center seminorm. We use Shokurov's language of b-divisors; to each seminorm there is an associated b-divisor which can be used to translate questions about positivity into questions about the shape of certain cones of b-divisors. The theory works especially well for what we call b-divisorial valuations, a natural extension of the notion of divisorial valuations which encompasses, e.g., all Abhyankar valuations.", "published": "2025-11-27T16:29:34Z", "authors": ["Joaquim Roé", "Stefano Urbinati"], "categories": ["math.AG", "math.AC"], "doi": "", "_query": "automated valuation model", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/1605.08344v1", "title": "Cohomological Hasse principle for schemes over valuation rings of higher dimensional local fields", "abstract": "K. Kato's conjecture about the cohomological Hasse principle for regular connected schemes $\\mathfrak X$ which are flat and proper over the complete discrete valuation rings $\\mathcal O_N$ of higher local fields $F_N$ is proven. This generalizes the work of M. Kerz, S. Saito and U. Jannsen for finite fields to the case of all higher local fields. For that purpose a $p$-alteration theorem for the local uniformization of schemes over valuation rings of arbitrary finite rank and a corresponding Bertini theorem is developed extending the results of O. Gabber, J. deJong, L. Illusie, M. Temkin, S. Saito, U. Jannsen to the non-noetherian world. As an application it is shown that certain motivic cohomology groups of varieties over higher local fields are finite. This is one of the rare cases where such a result could be shown for schemes without finite or separably closed residue fields. Furthermore, it will be derived that the kernels of the reciprocity map $ρ^X : \\text{SK}_N(X) \\to π_1^\\text{ab}(X)$ and norm map $N_{X|F}: \\text{SK}_N(X) \\to K_N^M(F_N)$ modulo maximal $p'$-divisible subgroups are finite for regular $X$ which are proper over a higher local field $F_N$ with final residue characteristic $p$. This generalizes results of S. Bloch, K. Kato, U. Jannsen, S. Saito from varieties over finite and local fields to varieties over higher local fields, both of arbitrary dimensions.", "published": "2016-05-26T15:57:34Z", "authors": ["Patrick Forré"], "categories": ["math.NT", "math.AG"], "doi": "", "_query": "automated valuation model", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2305.04469v1", "title": "HACK: Learning a Parametric Head and Neck Model for High-fidelity Animation", "abstract": "Significant advancements have been made in developing parametric models for digital humans, with various approaches concentrating on parts such as the human body, hand, or face. Nevertheless, connectors such as the neck have been overlooked in these models, with rich anatomical priors often unutilized. In this paper, we introduce HACK (Head-And-neCK), a novel parametric model for constructing the head and cervical region of digital humans. Our model seeks to disentangle the full spectrum of neck and larynx motions, facial expressions, and appearance variations, providing personalized and anatomically consistent controls, particularly for the neck regions. To build our HACK model, we acquire a comprehensive multi-modal dataset of the head and neck under various facial expressions. We employ a 3D ultrasound imaging scheme to extract the inner biomechanical structures, namely the precise 3D rotation information of the seven vertebrae of the cervical spine. We then adopt a multi-view photometric approach to capture the geometry and physically-based textures of diverse subjects, who exhibit a diverse range of static expressions as well as sequential head-and-neck movements. Using the multi-modal dataset, we train the parametric HACK model by separating the 3D head and neck depiction into various shape, pose, expression, and larynx blendshapes from the neutral expression and the rest skeletal pose. We adopt an anatomically-consistent skeletal design for the cervical region, and the expression is linked to facial action units for artist-friendly controls. HACK addresses the head and neck as a unified entity, offering more accurate and expressive controls, with a new level of realism, particularly for the neck regions. This approach has significant benefits for numerous applications and enables inter-correlation analysis between head and neck for fine-grained motion synthesis and transfer.", "published": "2023-05-08T05:44:51Z", "authors": ["Longwen Zhang", "Zijun Zhao", "Xinzhou Cong", "Qixuan Zhang", "Shuqi Gu", "Yuchong Gao", "Rui Zheng", "Wei Yang", "Lan Xu", "Jingyi Yu"], "categories": ["cs.GR"], "doi": "", "_query": "automated valuation model", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/1307.1529v2", "title": "Covariograms generated by valuations", "abstract": "Let φbe a real-valued valuation on the family of compact convex subsets of \\mathbb{R}^n and let K be a convex body in \\mathbb{R}^n. We introduce the φ-covariogram g_{K,φ} of K as the function associating to each x \\in \\mathbb{R}^n the value φ(K \\cap (K+x)). If φis the volume, then g_{K,φ} is the covariogram, extensively studied in various sources. When φis a quermassintegral (e.g., surface area or mean width) g_{K,φ} has been introduced by Nagel. We study various properties of φ-covariograms, mostly in the case n=2 and under the assumption that φis translation invariant, monotone and even. We also consider the generalization of Matheron's covariogram problem to the case of φ-covariograms, that is, the problem of determining an unknown convex body K, up to translations and point reflections, by the knowledge of g_{K,φ}. A positive solution to this problem is provided under different assumptions, including the case that K is a polygon and φis either strictly monotone or φis the width in a given direction. We prove that there are examples in every dimension n\\geq3 where K is determined by its covariogram but it is not determined by its width-covariogram. We also present some consequence of this study in stochastic geometry.", "published": "2013-07-05T06:30:38Z", "authors": ["Gennadiy Averkov", "Gabriele Bianchi"], "categories": ["math.MG", "math.PR"], "doi": "10.1093/imrn/rnu219", "_query": "automated valuation model", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2511.01850v1", "title": "SmartMLOps Studio: Design of an LLM-Integrated IDE with Automated MLOps Pipelines for Model Development and Monitoring", "abstract": "The rapid expansion of artificial intelligence and machine learning (ML) applications has intensified the demand for integrated environments that unify model development, deployment, and monitoring. Traditional Integrated Development Environments (IDEs) focus primarily on code authoring, lacking intelligent support for the full ML lifecycle, while existing MLOps platforms remain detached from the coding workflow. To address this gap, this study proposes the design of an LLM-Integrated IDE with automated MLOps pipelines that enables continuous model development and monitoring within a single environment. The proposed system embeds a Large Language Model (LLM) assistant capable of code generation, debugging recommendation, and automatic pipeline configuration. The backend incorporates automated data validation, feature storage, drift detection, retraining triggers, and CI/CD deployment orchestration. This framework was implemented in a prototype named SmartMLOps Studio and evaluated using classification and forecasting tasks on the UCI Adult and M5 datasets. Experimental results demonstrate that SmartMLOps Studio reduces pipeline configuration time by 61%, improves experiment reproducibility by 45%, and increases drift detection accuracy by 14% compared to traditional workflows. By bridging intelligent code assistance and automated operational pipelines, this research establishes a novel paradigm for AI engineering - transforming the IDE from a static coding tool into a dynamic, lifecycle-aware intelligent platform for scalable and efficient model development.", "published": "2025-11-03T18:56:59Z", "authors": ["Jiawei Jin", "Yingxin Su", "Xiaotong Zhu"], "categories": ["cs.SE", "cs.AI"], "doi": "", "_query": "automated valuation model", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/1610.00698v1", "title": "A Study on Set-Valuations of Signed Graphs", "abstract": "Let $X$ be a non-empty ground set and $\\mathcal{P}(X)$ be its power set. A set-labeling (or a set-valuation) of a graph $G$ is an injective set-valued function $f:V(G)\\to \\mathcal{P}(X)$ such that the induced function $f^\\oplus:E(G) \\to \\mathcal{P}(X)$ is defined by $f^\\oplus(uv) = f(u)\\oplus f(v)$, where $f(u)\\oplus f(v)$ is the symmetric difference of the sets $f(u)$ and $f(v)$. A graph which admits a set-labeling is known to be a set-labeled graph. A set-labeling $f$ of a graph $G$ is said to be a set-indexer of $G$ if the associated function $f^\\oplus$ is also injective. In this paper, we define the notion of set-valuations of signed graphs and discuss certain properties of signed graphs which admits certain types of set-valuations.", "published": "2016-10-01T08:20:36Z", "authors": ["P. K. Ashraf", "K. A. Germina", "N. K. Sudev"], "categories": ["math.GM"], "doi": "", "_query": "automated valuation model", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2509.01947v1", "title": "Automated Repair of C Programs Using Large Language Models", "abstract": "This study explores the potential of Large Language Models (LLMs) in automating the repair of C programs. We present a framework that integrates spectrum-based fault localization (SBFL), runtime feedback, and Chain-of-Thought-structured prompting into an autonomous repair loop. Unlike prior approaches, our method explicitly combines statistical program analysis with LLM reasoning. The iterative repair cycle leverages a structured Chain-of-Thought (CoT) prompting approach, where the model reasons over failing tests, suspicious code regions, and prior patch outcomes, before generating new candidate patches. The model iteratively changes the code, evaluates the results, and incorporates reasoning from previous attempts into subsequent modifications, reducing repeated errors and clarifying why some bugs remain unresolved. Our evaluation spans 3,902 bugs from the Codeflaws benchmark, where our approach achieves 44.93% repair accuracy, representing a 3.61% absolute improvement over strong state-of-the-art APR baselines such as GPT-4 with CoT. This outcome highlights a practical pathway toward integrating statistical program analysis with generative AI in automated debugging.", "published": "2025-09-02T04:34:11Z", "authors": ["Mahdi Farzandway", "Fatemeh Ghassemi"], "categories": ["cs.SE"], "doi": "", "_query": "automated valuation model", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/math/0012146v1", "title": "Invitation to higher local fields, Part I, section 15: On the structure of the Milnor K-groups of complete discrete valuation fields", "abstract": "This work contains a list of all known results on the quotient filtration on the Milnor K-groups of a complete discrete valuation field in terms of differential modules over the residue field . Author's recent study of the case of a tamely ramified field of characteristic 0 with residue field of characteristic p by using an exponential map and a syntomic complex is explained.", "published": "2000-12-18T03:49:01Z", "authors": ["Jinya Nakamura"], "categories": ["math.NT", "math.AG"], "doi": "", "_query": "automated valuation model", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2604.02561v1", "title": "On topologies on the space of valuations and the valuative tree", "abstract": "In this paper, we discuss topological aspects of the space of valuations $\\mathbb{V}$ and the valuative tree $\\mathcal{T}(v,Λ)$. We present a relation between the weak tree topology and the Scott topology in $\\mathcal{T}(v,Λ)$ and describe the supremum of an increasing family of valuations in a special subtree. We also view the valuative tree as a subset of the product $(Λ_\\infty)^{K[x]}$ and prove that it is closed if we consider the natural product topology.", "published": "2026-04-02T22:22:39Z", "authors": ["Vinicius Manfredini", "Josnei Novacoski", "Caio Henrique Silva de Souza"], "categories": ["math.AC"], "doi": "", "_query": "automated valuation model", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/1505.07987v1", "title": "SEPIA: Search for Proofs Using Inferred Automata", "abstract": "This paper describes SEPIA, a tool for automated proof generation in Coq. SEPIA combines model inference with interactive theorem proving. Existing proof corpora are modelled using state-based models inferred from tactic sequences. These can then be traversed automatically to identify proofs. The SEPIA system is described and its performance evaluated on three Coq datasets. Our results show that SEPIA provides a useful complement to existing automated tactics in Coq.", "published": "2015-05-29T10:39:44Z", "authors": ["Thomas Gransden", "Neil Walkinshaw", "Rajeev Raman"], "categories": ["cs.LO"], "doi": "", "_query": "automated valuation model", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2603.22214v1", "title": "Evaluating the Reliability and Fidelity of Automated Judgment Systems of Large Language Models", "abstract": "A Large Language Model (LLM) as judge evaluates the quality of victim Machine Learning (ML) models, specifically LLMs, by analyzing their outputs. An LLM as judge is the combination of one model and one specifically engineered judge prompt that contains the criteria for the analysis. The resulting automation of the analysis scales up the complex evaluation of the victim models' free-form text outputs by faster and more consistent judgments compared to human reviewers. Thus, quality and security assessments of LLMs can cover a wide range of the victim models' use cases. Being a comparably new technique, LLMs as judges lack a thorough investigation for their reliability and agreement to human judgment. Our work evaluates the applicability of LLMs as automated quality assessors of victim LLMs. We test the efficacy of 37 differently sized conversational LLMs in combination with 5 different judge prompts, the concept of a second-level judge, and 5 models fine-tuned for the task as assessors. As assessment objective, we curate datasets for eight different categories of judgment tasks and the corresponding ground-truth labels based on human assessments. Our empirical results show a high correlation of LLMs as judges with human assessments, when combined with a suitable prompt, in particular for GPT-4o, several open-source models with $\\geqslant$ 32B parameters, and a few smaller models like Qwen2.5 14B.", "published": "2026-03-23T17:12:29Z", "authors": ["Tom Biskupski", "Stephan Kleber"], "categories": ["cs.CR", "cs.AI", "cs.LG"], "doi": "", "_query": "automated valuation model", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2309.10440v1", "title": "Why do drivers and automation disengage the automation? Results from a study among Tesla users", "abstract": "A better understanding of automation disengagements can impact the safety and efficiency of automated systems. This study investigates the factors contributing to driver- and system-initiated disengagements by analyzing semi-structured interviews with 103 users of Tesla's Autopilot and FSD Beta. Through an examination of the data, main categories and sub-categories of disengagements were identified, which led to the development of a triadic model of automation disengagements. The model treats automation and human operators as equivalent agents. It suggests that human operators disengage automation when they anticipate failure, observe unnatural or unwanted automation behavior (e.g., erratic steering, running red lights), or believe the automation is not suited for certain environments (e.g., inclement weather, non-standard roads). Human operators' negative experiences, such as frustration, feelings of unsafety, and distrust, are also incorporated into the model, as these emotions can be triggered by (anticipated) automation behaviors. The automation, in turn, monitors human operators and may disengage itself if it detects insufficient vigilance or traffic rule violations. Moreover, human operators can be influenced by the reactions of passengers and other road users, leading them to disengage automation if they sense discomfort, anger, or embarrassment due to the system's actions. This research offers insights into the factors contributing to automation disengagements, highlighting not only the concerns of human operators but also the social aspects of the phenomenon. Furthermore, the findings provide information on potential edge cases of automated vehicle technology, which may help to enhance the safety and efficiency of such systems.", "published": "2023-09-19T08:59:35Z", "authors": ["Sina Nordhoff", "Joost De Winter"], "categories": ["cs.HC"], "doi": "10.13140/RG.2.2.22115.84003", "_query": "automated valuation model", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/1902.03589v3", "title": "NeurAll: Towards a Unified Visual Perception Model for Automated Driving", "abstract": "Convolutional Neural Networks (CNNs) are successfully used for the important automotive visual perception tasks including object recognition, motion and depth estimation, visual SLAM, etc. However, these tasks are typically independently explored and modeled. In this paper, we propose a joint multi-task network design for learning several tasks simultaneously. Our main motivation is the computational efficiency achieved by sharing the expensive initial convolutional layers between all tasks. Indeed, the main bottleneck in automated driving systems is the limited processing power available on deployment hardware. There is also some evidence for other benefits in improving accuracy for some tasks and easing development effort. It also offers scalability to add more tasks leveraging existing features and achieving better generalization. We survey various CNN based solutions for visual perception tasks in automated driving. Then we propose a unified CNN model for the important tasks and discuss several advanced optimization and architecture design techniques to improve the baseline model. The paper is partly review and partly positional with demonstration of several preliminary results promising for future research. We first demonstrate results of multi-stream learning and auxiliary learning which are important ingredients to scale to a large multi-task model. Finally, we implement a two-stream three-task network which performs better in many cases compared to their corresponding single-task models, while maintaining network size.", "published": "2019-02-10T12:45:49Z", "authors": ["Ganesh Sistu", "Isabelle Leang", "Sumanth Chennupati", "Senthil Yogamani", "Ciaran Hughes", "Stefan Milz", "Samir Rawashdeh"], "categories": ["cs.CV", "cs.LG", "cs.RO", "stat.ML"], "doi": "", "_query": "automated valuation model", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/1509.06356v1", "title": "Topologies on spaces of valuations: a closeness criterion", "abstract": "This paper is part of a program to understand topologies on spaces of valuations. We fix an ordered abelian group $Γ$ and an integral domain $R$. We study the relation between a topology on $Γ_\\infty$ and the induced topology on the set $\\mathcal W_Γ$ of all valuations of $R$ taking values in $Γ_\\infty$. For instance, we give a criterion for $ \\mathcal W_Γ$ to be closed in $\\left(Γ_\\infty\\right)^R$. We also discuss the effect of this criterion for natural topologies on $Γ_\\infty$.", "published": "2015-09-21T19:31:39Z", "authors": ["Josnei Novacoski"], "categories": ["math.AC", "math.GN"], "doi": "", "_query": "automated valuation model", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/1809.08152v3", "title": "Donovan's conjecture, blocks with abelian defect groups and discrete valuation rings", "abstract": "We give a reduction to quasisimple groups for Donovan's conjecture for blocks with abelian defect groups defined with respect to a suitable discrete valuation ring $\\mathcal{O}$. Consequences are that Donovan's conjecture holds for $\\mathcal{O}$-blocks with abelian defect groups for the prime two, and that, using recent work of Farrell and Kessar, for arbitrary primes Donovan's conjecture for $\\mathcal{O}$-blocks with abelian defect groups reduces to bounding the Cartan invariants of blocks of quasisimple groups in terms of the defect. A result of independent interest is that in general (i.e. for arbitrary defect groups) Donovan's conjecture for $\\mathcal{O}$-blocks is a consequence of conjectures predicting bounds on the $\\mathcal{O}$-Frobenius number and on the Cartan invariants, as was proved by Kessar for blocks defined over an algebraically closed field.", "published": "2018-09-21T14:44:04Z", "authors": ["Charles W. Eaton", "Florian Eisele", "Michael Livesey"], "categories": ["math.RT"], "doi": "", "_query": "automated valuation model", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2002.10704v3", "title": "Fair and Truthful Mechanisms for Dichotomous Valuations", "abstract": "We consider the problem of allocating a set on indivisible items to players with private preferences in an efficient and fair way. We focus on valuations that have dichotomous marginals, in which the added value of any item to a set is either 0 or 1, and aim to design truthful allocation mechanisms (without money) that maximize welfare and are fair. For the case that players have submodular valuations with dichotomous marginals, we design such a deterministic truthful allocation mechanism. The allocation output by our mechanism is Lorenz dominating, and consequently satisfies many desired fairness properties, such as being envy-free up to any item (EFX), and maximizing the Nash Social Welfare (NSW). We then show that our mechanism with random priorities is envy-free ex-ante, while having all the above properties ex-post. Furthermore, we present several impossibility results precluding similar results for the larger class of XOS valuations. To gauge the robustness of our positive results, we also study $ε$-dichotomous valuations, in which the added value of any item to a set is either non-positive, or in the range $[1, 1 + ε]$. We show several impossibility results in this setting, and also a positive result: for players that have additive $ε$-dichotomous valuations with sufficiently small $ε$, we design a randomized truthful mechanism with strong ex-post guarantees. For $ρ= \\frac{1}{1 + ε}$, the allocations that it produces generate at least a $ρ$-fraction of the maximum welfare, and enjoy $ρ$-approximations for various fairness properties, such as being envy-free up to one item (EF1), and giving each player at least her maximin share.", "published": "2020-02-25T07:24:51Z", "authors": ["Moshe Babaioff", "Tomer Ezra", "Uriel Feige"], "categories": ["cs.GT"], "doi": "", "_query": "automated valuation model", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/1707.04769v3", "title": "Almost Envy-Freeness with General Valuations", "abstract": "The goal of fair division is to distribute resources among competing players in a \"fair\" way. Envy-freeness is the most extensively studied fairness notion in fair division. Envy-free allocations do not always exist with indivisible goods, motivating the study of relaxed versions of envy-freeness. We study the envy-freeness up to any good (EFX) property, which states that no player prefers the bundle of another player following the removal of any single good, and prove the first general results about this property. We use the leximin solution to show existence of EFX allocations in several contexts, sometimes in conjunction with Pareto optimality. For two players with valuations obeying a mild assumption, one of these results provides stronger guarantees than the currently deployed algorithm on Spliddit, a popular fair division website. Unfortunately, finding the leximin solution can require exponential time. We show that this is necessary by proving an exponential lower bound on the number of value queries needed to identify an EFX allocation, even for two players with identical valuations. We consider both additive and more general valuations, and our work suggests that there is a rich landscape of problems to explore in the fair division of indivisible goods with different classes of player valuations.", "published": "2017-07-15T17:47:32Z", "authors": ["Benjamin Plaut", "Tim Roughgarden"], "categories": ["cs.GT"], "doi": "", "_query": "automated valuation model", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/math/9905204v1", "title": "Continuous rotation invariant valuations on convex sets", "abstract": "The famous Hadwiger theorem classifies all rigid motion invariant continuous valuations on convex sets as linear conbinations of quermassintegrals. We prove much more general result. We classify continuous valuations which are invariant with respect to the orthogonal (or special orthogonal) group. Some applications to integral geometry are given.", "published": "1999-05-01T00:00:00Z", "authors": ["Semyon Alesker"], "categories": ["math.MG"], "doi": "", "_query": "automated valuation model", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2504.15261v1", "title": "Leveraging Language Models for Automated Patient Record Linkage", "abstract": "Objective: Healthcare data fragmentation presents a major challenge for linking patient data, necessitating robust record linkage to integrate patient records from diverse sources. This study investigates the feasibility of leveraging language models for automated patient record linkage, focusing on two key tasks: blocking and matching. Materials and Methods: We utilized real-world healthcare data from the Missouri Cancer Registry and Research Center, linking patient records from two independent sources using probabilistic linkage as a baseline. A transformer-based model, RoBERTa, was fine-tuned for blocking using sentence embeddings. For matching, several language models were experimented under fine-tuned and zero-shot settings, assessing their performance against ground truth labels. Results: The fine-tuned blocking model achieved a 92% reduction in the number of candidate pairs while maintaining near-perfect recall. In the matching task, fine-tuned Mistral-7B achieved the best performance with only 6 incorrect predictions. Among zero-shot models, Mistral-Small-24B performed best, with a total of 55 incorrect predictions. Discussion: Fine-tuned language models achieved strong performance in patient record blocking and matching with minimal errors. However, they remain less accurate and efficient than a hybrid rule-based and probabilistic approach for blocking. Additionally, reasoning models like DeepSeek-R1 are impractical for large-scale record linkage due to high computational costs. Conclusion: This study highlights the potential of language models for automating patient record linkage, offering improved efficiency by eliminating the manual efforts required to perform patient record linkage. Overall, language models offer a scalable solution that can enhance data integration, reduce manual effort, and support disease surveillance and research.", "published": "2025-04-21T17:41:15Z", "authors": ["Mohammad Beheshti", "Lovedeep Gondara", "Iris Zachary"], "categories": ["cs.AI", "cs.LG"], "doi": "", "_query": "automated valuation model", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/1910.14624v2", "title": "Polynomial Valuations on Vector Lattices", "abstract": "We prove that polynomial valuations on vector lattices correspond to orthosymmetric multilinear maps. As a consequence we obtain a concise proof of the equivalence of orthosymmetry and orthogonal additivity.", "published": "2019-10-31T17:10:26Z", "authors": ["Gerard Buskes", "Stephan Roberts"], "categories": ["math.FA"], "doi": "", "_query": "automated valuation model", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2510.27680v2", "title": "PETAR: Localized Findings Generation with Mask-Aware Vision-Language Modeling for PET Automated Reporting", "abstract": "Generating automated reports for 3D positron emission tomography (PET) is an important and challenging task in medical imaging. PET plays a vital role in oncology, but automating report generation is difficult due to the complexity of whole-body 3D volumes, the wide range of potential clinical findings, and the limited availability of annotated datasets. To address these challenges, we introduce PETARSeg-11K, the first large-scale, publicly available dataset that provides lesion-level correspondence between 3D PET/CT volumes and free-text radiological findings. It comprises 11,356 lesion descriptions paired with 3D segmentations. Second, we propose PETAR-4B, a 3D vision-language model designed for mask-aware, spatially grounded PET/CT reporting. PETAR-4B jointly encodes PET, CT, and 3D lesion segmentation masks, using a 3D focal prompt to capture fine-grained details of lesions that normally comprise less than 0.1% of the volume. Evaluations using automated metrics show PETAR-4B substantially outperforming all 2D and 3D baselines. A human study involving five physicians -- the first of its kind for automated PET reporting -- confirms the model's clinical utility and establishes correlations between automated metrics and expert judgment. This work provides a foundational dataset and a novel architecture, advancing 3D medical vision-language understanding in PET.", "published": "2025-10-31T17:49:01Z", "authors": ["Danyal Maqbool", "Changhee Lee", "Zachary Huemann", "Samuel D. Church", "Matthew E. Larson", "Scott B. Perlman", "Tomas A. Romero", "Joshua D. Warner", "Meghan Lubner", "Xin Tie", "Jameson Merkow", "Junjie Hu", "Steve Y. Cho", "Tyler J. Bradshaw"], "categories": ["cs.CV", "cs.AI", "cs.LG"], "doi": "", "_query": "automated valuation model", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2401.15266v2", "title": "SAM-based instance segmentation models for the automation of structural damage detection", "abstract": "Automating visual inspection for capturing defects based on civil structures appearance is crucial due to its currently labour-intensive and time-consuming nature. An important aspect of automated inspection is image acquisition, which is rapid and cost-effective considering the pervasive developments in both software and hardware computing in recent years. Previous studies largely focused on concrete and asphalt, with less attention to masonry cracks. The latter also lacks publicly available datasets. In this paper, we first present a corresponding data set for instance segmentation with 1,300 annotated images (640 pixels x 640 pixels), named as MCrack1300, covering bricks, broken bricks, and cracks. We then test several leading algorithms for benchmarking, including the latest large-scale model, the prompt-based Segment Anything Model (SAM). We fine-tune the encoder using Low-Rank Adaptation (LoRA) and proposed two novel methods for automation of SAM execution. The first method involves abandoning the prompt encoder and connecting the SAM encoder to other decoders, while the second method introduces a learnable self-generating prompter. In order to ensure the seamless integration of the two proposed methods with SAM encoder section, we redesign the feature extractor. Both proposed methods exceed state-of-the-art performance, surpassing the best benchmark by approximately 3% for all classes and around 6% for cracks specifically. Based on successful detection, we propose a method based on a monocular camera and the Hough Line Transform to automatically transform images into orthographic projection maps. By incorporating known real sizes of brick units, we accurately estimate crack dimensions, with the results differing by less than 10% from those obtained by laser scanning. Overall, we address important research gaps in automated masonry crack detection and size estimation.", "published": "2024-01-27T02:00:07Z", "authors": ["Zehao Ye", "Lucy Lovell", "Asaad Faramarzi", "Jelena Ninic"], "categories": ["cs.CV"], "doi": "", "_query": "automated valuation model", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2512.05845v1", "title": "A Hybrid Dynamic Model for Predicting Human Cognition and Reliance during Automated Driving", "abstract": "We propose a simple (12 parameter) hybrid dynamic model that simultaneously captures the continuous-valued dynamics of three human cognitive states-trust, perceived risk, and mental workload-as well as discrete transitions in reliance on the automation. The discrete-time dynamic evolution of each cognitive state is modeled using a first-order affine difference equation. Reliance is defined as a single discrete-valued state, whose evolution at each time step depends on the cognitive states satisfying certain threshold conditions. Using data collected from 16 participants, we estimate participant-specific model parameters based on their reliance on the automation and intermittently self-reported cognitive states during a continuous drive in a vehicle simulator. The model can be estimated using a single user's trajectory data (e.g. 8 minutes of driving), making it suitable for online parameter adaptation methods. Our results show that the model fits the observed trajectories well for several participants, with their reliance behavior primarily influenced by trust, perceived risk, or both. Importantly, the model is interpretable, such that the variations in model parameters across participants provide insights into differences in the time scales over which cognitive states evolve, and how these states are influenced by task complexity. Implications on the design of human-centric vehicle automation design are discussed.", "published": "2025-12-05T16:18:48Z", "authors": ["Sibibalan Jeevanandam", "Neera Jain"], "categories": ["eess.SY"], "doi": "", "_query": "automated valuation model", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/1401.3994v1", "title": "CCP Cleared or Bilateral CSA Trades with Initial/Variation Margins under credit, funding and wrong-way risks: A Unified Valuation Approach", "abstract": "The introduction of CCPs in most derivative transactions will dramatically change the landscape of derivatives pricing, hedging and risk management, and, according to the TABB group, will lead to an overall liquidity impact about 2 USD trillions. In this article we develop for the first time a comprehensive approach for pricing under CCP clearing, including variation and initial margins, gap credit risk and collateralization, showing concrete examples for interest rate swaps. Mathematically, the inclusion of asymmetric borrowing and lending rates in the hedge of a claim lead to nonlinearities showing up in claim dependent pricing measures, aggregation dependent prices, nonlinear PDEs and BSDEs. This still holds in presence of CCPs and CSA. We introduce a modeling approach that allows us to enforce rigorous separation of the interconnected nonlinear risks into different valuation adjustments where the key pricing nonlinearities are confined to a funding costs component that is analyzed through numerical schemes for BSDEs. We present a numerical case study for Interest Rate Swaps that highlights the relative size of the different valuation adjustments and the quantitative role of initial and variation margins, of liquidity bases, of credit risk, of the margin period of risk and of wrong way risk correlations.", "published": "2014-01-16T11:33:04Z", "authors": ["Damiano Brigo", "Andrea Pallavicini"], "categories": ["q-fin.PR", "q-fin.CP"], "doi": "", "_query": "automated valuation model", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2005.12526v1", "title": "Compression of M${}^\\natural$-convex Functions -- Flag Matroids and Valuated Permutohedra", "abstract": "Murota (1998) and Murota and Shioura (1999) introduced concepts of M-convex function and M${}^\\natural$-convex function as discrete convex functions, which are generalizations of valuated matroids due to Dress and Wenzel (1992). In the present paper we consider a new operation defined by a convolution of sections of an M${}^\\natural$-convex function that transforms the given M${}^\\natural$-convex function to an M-convex function, which we call a compression of an M${}^\\natural$-convex function. For the class of valuated generalized matroids, which are special M${}^\\natural$-convex functions, the compression induces a valuated permutohedron together with a decomposition of the valuated generalized matroid into flag-matroid strips, each corresponding to a maximal linearity domain of the induced valuated permutohedron. We examine the details of the structure of flag-matroid strips and the induced valuated permutohedron by means of discrete convex analysis of Murota.", "published": "2020-05-26T06:01:53Z", "authors": ["Satoru Fujishige", "Hiroshi Hirai"], "categories": ["math.CO", "math.OC"], "doi": "", "_query": "automated valuation model", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2412.05301v1", "title": "DocEDA: Automated Extraction and Design of Analog Circuits from Documents with Large Language Model", "abstract": "Efficient and accurate extraction of electrical parameters from circuit datasheets and design documents is critical for accelerating circuit design in Electronic Design Automation (EDA). Traditional workflows often rely on engineers manually searching and extracting these parameters, which is time-consuming, and prone to human error. To address these challenges, we introduce DocEDA, an automated system that leverages advanced computer vision techniques and Large Language Models (LLMs) to extract electrical parameters seamlessly from documents. The layout analysis model specifically designed for datasheet is proposed to classify documents into circuit-related parts. Utilizing the inherent Chain-of-Thought reasoning capabilities of LLMs, DocEDA automates the extraction of electronic component parameters from documents. For circuit diagrams parsing, an improved GAM-YOLO model is hybrid with topology identification to transform diagrams into circuit netlists. Then, a space mapping enhanced optimization framework is evoked for optimization the layout in the document. Experimental evaluations demonstrate that DocEDA significantly enhances the efficiency of processing circuit design documents and the accuracy of electrical parameter extraction. It exhibits adaptability to various circuit design scenarios and document formats, offering a novel solution for EDA with the potential to transform traditional methodologies.", "published": "2024-11-25T15:41:43Z", "authors": ["Hong Cai Chen", "Longchang Wu", "Ming Gao", "Lingrui Shen", "Jiarui Zhong", "Yipin Xu"], "categories": ["cs.AR", "cs.AI", "cs.CL"], "doi": "", "_query": "automated valuation model", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/1910.09149v3", "title": "Operational Valuation for Energy Storage under Multi-stage Price Uncertainties", "abstract": "This paper presents an analytical method for calculating the operational value of an energy storage device under multi-stage price uncertainties. Our solution calculates the storage value function from price distribution functions directly instead of sampling discrete scenarios, offering improved modeling accuracy over tail distribution events such as price spikes and negative prices. The analytical algorithm offers very high computational efficiency in solving multi-stage stochastic programming for energy storage and can easily be implemented within any software and hardware platform, while numerical simulation results show the proposed method is up to 100,000 times faster than a benchmark stochastic-dual dynamic programming solver even in small test cases. Case studies are included to demonstrate the impact of price variability on the valuation results, and a battery charging example using historical prices for New York City.", "published": "2019-10-21T04:58:16Z", "authors": ["Bolun Xu", "Audun Botterud", "Magnus Korpas"], "categories": ["math.OC"], "doi": "", "_query": "automated valuation model", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/0705.1302v1", "title": "Financial Valuation of Mortality Risk via the Instantaneous Sharpe Ratio: Applications to Pricing Pure Endowments", "abstract": "We develop a theory for pricing non-diversifiable mortality risk in an incomplete market. We do this by assuming that the company issuing a mortality-contingent claim requires compensation for this risk in the form of a pre-specified instantaneous Sharpe ratio. We prove that our ensuing valuation formula satisfies a number of desirable properties. For example, we show that it is subadditive in the number of contracts sold. A key result is that if the hazard rate is stochastic, then the risk-adjusted survival probability is greater than the physical survival probability, even as the number of contracts approaches infinity.", "published": "2007-05-09T14:44:48Z", "authors": ["Moshe A. Milevsky", "S. David Promislow", "Virginia R. Young"], "categories": ["q-fin.PR", "math.AP", "math.OC"], "doi": "", "_query": "automated valuation model", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/1408.2637v3", "title": "On repeated sequential closures of constructible functions in valuations", "abstract": "The space of constructible functions form a dense subspace of the space of generalized valuations. In this note we prove a somewhat stronger property that the sequential closure, taken sufficiently many (in fact, infinitely many) times, of the former space is equal to the latter one. This stronger property is necessary for some applications in the theory of valuations on manifolds.", "published": "2014-08-12T07:05:18Z", "authors": ["Semyon Alesker"], "categories": ["math.MG", "math.FA"], "doi": "", "_query": "automated valuation model", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/1505.02004v2", "title": "Real-valued valuations on Sobolev spaces", "abstract": "Continuous, SL($n$) and translation invariant real-valued valuations on Sobolev spaces are classified.", "published": "2015-05-08T11:46:10Z", "authors": ["Dan Ma"], "categories": ["math.FA"], "doi": "10.1007/s11425-015-5101-6", "_query": "automated valuation model", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2609.37460v1", "title": "Weighted equidistribution of factorial prime valuations in fixed power bands", "abstract": "For a fixed modulus h and residue class c, we study the logarithmic mass of primes p in a fixed power band for which the exponent of p in n! is congruent to c modulo h. We establish the asymptotic ((b-a)/h + o(1)) log(n!) for primes satisfying n^a < p <= n^b, where 0 <= a < b <= 1. The argument combines a fixed-order derivative estimate, Vaughan's identity, and finite Fourier approximation of a discontinuous valuation function. We also derive a necessary capacity condition for infinite families of solutions to polynomial-factorial equations P(x) = n!.", "published": "2026-09-27T11:48:00Z", "authors": ["Ma Yicen"], "categories": ["math.NT", "math.FA"], "doi": "", "_query": "automated valuation model", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/1101.3926v1", "title": "Collateral Margining in Arbitrage-Free Counterparty Valuation Adjustment including Re-Hypotecation and Netting", "abstract": "This paper generalizes the framework for arbitrage-free valuation of bilateral counterparty risk to the case where collateral is included, with possible re-hypotecation. We analyze how the payout of claims is modified when collateral margining is included in agreement with current ISDA documentation. We then specialize our analysis to interest-rate swaps as underlying portfolio, and allow for mutual dependences between the default times of the investor and the counterparty and the underlying portfolio risk factors. We use arbitrage-free stochastic dynamical models, including also the effect of interest rate and credit spread volatilities. The impact of re-hypotecation, of collateral margining frequency and of dependencies on the bilateral counterparty risk adjustment is illustrated with a numerical example.", "published": "2011-01-20T15:15:58Z", "authors": ["Damiano Brigo", "Agostino Capponi", "Andrea Pallavicini", "Vasileios Papatheodorou"], "categories": ["q-fin.RM", "q-fin.CP", "q-fin.PR"], "doi": "", "_query": "automated valuation model", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2610.21504v1", "title": "Receiver-Specific Interventional Valuation of Semantic Atoms for Budget-Constrained Multi-Agent Communication", "abstract": "Cooperative multi-agent semantic communication requires deciding not only how a transmitter encodes its observation, but also which resulting semantic content should be delivered to each receiver under a limited link budget. We introduce an encoder-agnostic transmitter-side framework for receiver-specific valuation of independently selectable semantic units, termed semantic atoms. Each atom is assigned a signed value that quantifies whether delivering it is expected to improve or degrade the intended receiver's downstream decision. We formulate this effect through a controlled-delivery utility and identify it via prospective randomized payload delivery before the receiver acts. A shadow receiver estimates this utility using only transmitter-available information, from which receiver-specific leave-one-out (LOO) and Shapley atom values are computed. Experiments on a four-receiver intersection benchmark show 68.4% accuracy in recovering the sign of non-negligible LOO atom effects and a Spearman correlation of 0.302 between estimated and reference LOO values. Under limited budgets, we show that receiver-specific selection consistently outperforms shared and random alternatives, demonstrating the benefit of adapting semantic ranking to the intended receiver. At full affordability, exhaustive subset selection also outperforms full-payload transmission, showing that transmitting all available content need not maximize receiver utility. Overall, our results show that receiver-specific semantic valuation can support effective budget-constrained communication despite imperfect atom-value estimation.", "published": "2026-10-15T08:41:00Z", "authors": ["Danial Dehghani", "Maximilian Lübke", "Friedemann Laue", "Zahra Seifaei", "Norman Franchi"], "categories": ["eess.SP"], "doi": "", "_query": "automated valuation model", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2404.11595v3", "title": "A Deep Dive into Large Language Models for Automated Bug Localization and Repair", "abstract": "Large language models (LLMs) have shown impressive effectiveness in various software engineering tasks, including automated program repair (APR). In this study, we take a deep dive into automated bug fixing utilizing LLMs. In contrast to many deep learning-based APR methods that assume known bug locations, rely on line-level localization tools, or address bug prediction and fixing in one step, our approach uniquely employs LLMs to predict bug location at the token level and subsequently utilizes them for bug fixing. This methodological separation of bug localization and fixing using different LLMs enables effective integration of diverse contextual information and improved incorporation of inductive biases. We introduce Toggle: Token-Granulated Bug Localization and Repair, a comprehensive program repair framework that integrates a bug localization model, an adjustment unit, and a bug-fixing model. Toggle takes a buggy function as input and generates a complete corrected function. We investigate various styles of prompting to the bug fixing model to identify the most effective prompts that better utilize the inductive bias and significantly outperform others. Toggle achieves the new state-of-the-art (SOTA) performance on the CodeXGLUE code refinement benchmark, and exhibits better and comparable performance on several other widely-used APR datasets, including Defects4J.", "published": "2024-04-17T17:48:18Z", "authors": ["Soneya Binta Hossain", "Nan Jiang", "Qiang Zhou", "Xiaopeng Li", "Wen-Hao Chiang", "Yingjun Lyu", "Hoan Nguyen", "Omer Tripp"], "categories": ["cs.SE"], "doi": "", "_query": "automated valuation model", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/0905.3085v2", "title": "A Valuation Criterion for Normal Basis Generators of Hopf-Galois Extensions in Characteristic p", "abstract": "Let S/R be a finite extension of discrete valuation rings of characteristic p>0, and suppose that the corresponding extension L/K of fields of fractions is separable and is H-Galois for some K-Hopf algebra H. Let D_{S/R} be the different of S/R. We show that if S/R is totally ramified and its degree n is a power of p, then any element $ρ$ of L with $v_L(ρ)$ congruent to $-v_L(D_{S/R})-1$ mod n generates L as an H-module. This criterion is best possible. These results generalise to the Hopf-Galois situation recent work of G. Elder for Galois extensions.", "published": "2009-05-19T12:40:53Z", "authors": ["Nigel P. Byott"], "categories": ["math.NT"], "doi": "", "_query": "automated valuation model", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2410.20848v1", "title": "Deep Insights into Automated Optimization with Large Language Models and Evolutionary Algorithms", "abstract": "Designing optimization approaches, whether heuristic or meta-heuristic, usually demands extensive manual intervention and has difficulty generalizing across diverse problem domains. The combination of Large Language Models (LLMs) and Evolutionary Algorithms (EAs) offers a promising new approach to overcome these limitations and make optimization more automated. In this setup, LLMs act as dynamic agents that can generate, refine, and interpret optimization strategies, while EAs efficiently explore complex solution spaces through evolutionary operators. Since this synergy enables a more efficient and creative search process, we first conduct an extensive review of recent research on the application of LLMs in optimization. We focus on LLMs' dual functionality as solution generators and algorithm designers. Then, we summarize the common and valuable designs in existing work and propose a novel LLM-EA paradigm for automated optimization. Furthermore, centered on this paradigm, we conduct an in-depth analysis of innovative methods for three key components: individual representation, variation operators, and fitness evaluation. We address challenges related to heuristic generation and solution exploration, especially from the LLM prompts' perspective. Our systematic review and thorough analysis of the paradigm can assist researchers in better understanding the current research and promoting the development of combining LLMs with EAs for automated optimization.", "published": "2024-10-28T09:04:49Z", "authors": ["He Yu", "Jing Liu"], "categories": ["cs.NE", "cs.AI"], "doi": "", "_query": "automated valuation model", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2506.17282v1", "title": "Automating Financial Statement Audits with Large Language Models", "abstract": "Financial statement auditing is essential for stakeholders to understand a company's financial health, yet current manual processes are inefficient and error-prone. Even with extensive verification procedures, auditors frequently miss errors, leading to inaccurate financial statements that fail to meet stakeholder expectations for transparency and reliability. To this end, we harness large language models (LLMs) to automate financial statement auditing and rigorously assess their capabilities, providing insights on their performance boundaries in the scenario of automated auditing. Our work introduces a comprehensive benchmark using a curated dataset combining real-world financial tables with synthesized transaction data. In the benchmark, we developed a rigorous five-stage evaluation framework to assess LLMs' auditing capabilities. The benchmark also challenges models to map specific financial statement errors to corresponding violations of accounting standards, simulating real-world auditing scenarios through test cases. Our testing reveals that current state-of-the-art LLMs successfully identify financial statement errors when given historical transaction data. However, these models demonstrate significant limitations in explaining detected errors and citing relevant accounting standards. Furthermore, LLMs struggle to execute complete audits and make necessary financial statement revisions. These findings highlight a critical gap in LLMs' domain-specific accounting knowledge. Future research must focus on enhancing LLMs' understanding of auditing principles and procedures. Our benchmark and evaluation framework establish a foundation for developing more effective automated auditing tools that will substantially improve the accuracy and efficiency of real-world financial statement auditing.", "published": "2025-06-14T17:07:06Z", "authors": ["Rushi Wang", "Jiateng Liu", "Weijie Zhao", "Shenglan Li", "Denghui Zhang"], "categories": ["cs.IR"], "doi": "", "_query": "automated valuation model", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2512.11270v1", "title": "A-LAMP: Agentic LLM-Based Framework for Automated MDP Modeling and Policy Generation", "abstract": "Applying reinforcement learning (RL) to real-world tasks requires converting informal descriptions into a formal Markov decision process (MDP), implementing an executable environment, and training a policy agent. Automating this process is challenging due to modeling errors, fragile code, and misaligned objectives, which often impede policy training. We introduce an agentic large language model (LLM)-based framework for automated MDP modeling and policy generation (A-LAMP), that automatically translates free-form natural language task descriptions into an MDP formulation and trained policy. The framework decomposes modeling, coding, and training into verifiable stages, ensuring semantic alignment throughout the pipeline. Across both classic control and custom RL domains, A-LAMP consistently achieves higher policy generation capability than a single state-of-the-art LLM model. Notably, even its lightweight variant, which is built on smaller language models, approaches the performance of much larger models. Failure analysis reveals why these improvements occur. In addition, a case study also demonstrates that A-LAMP generates environments and policies that preserve the task's optimality, confirming its correctness and reliability.", "published": "2025-12-12T04:21:17Z", "authors": ["Hong Je-Gal", "Chan-Bin Yi", "Hyun-Suk Lee"], "categories": ["cs.AI"], "doi": "", "_query": "automated valuation model", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/1704.00866v1", "title": "Indirect Shared Control of Highly Automated Vehicles for Cooperative Driving between Driver and Automation", "abstract": "It is widely acknowledged that drivers should remain in the control loop of automated vehicles before they completely meet real-world operational conditions. This paper introduces an `indirect shared control' scheme for steer-by-wire vehicles, which allows the vehicle control authority to be continuously shared between the driver and automation through unphysical cooperation. This paper first balances the control objectives of the driver and automation in a weighted summation, and then models the driver's adaptive control behavior using a predictive control approach. The driver adaptation modeling enables off-line evaluations of indirect shared control systems and thus facilitates the design of the assistant controller. Unlike any conventional driver model for manual driving, this model assumes that the driver can learn and incorporate the controller strategy into his internal model for more accurate path following. To satisfy the driving demands in different scenarios, a sliding-window detector is designed to continuously monitor the driver intention and automatically switch the authority weights between the driver and automation. The simulation results illustrate the advantages of considering the driver adaptation in path-following and obstacle-avoidance tasks, and show the effectiveness of indirect shared control for cooperative driving.", "published": "2017-04-04T03:43:11Z", "authors": ["Renjie Li", "Yanan Li", "Shengbo Eben Li", "Etienne Burdet", "Bo Cheng"], "categories": ["cs.RO"], "doi": "10.1109/TITS.2020.3010620", "_query": "automated valuation model", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/1903.03100v2", "title": "Characterisation of Valuations and Curvature Measures in Euclidean Spaces", "abstract": "Valuations constitute a class of functionals on convex bodies which include the Euler-characteristic, the surface area, the Lebesgue-measure, and many more classical functionals. Curvature measures may be regarded as \"localised`` versions of valuations which yield local information about the geometry of a body's boundary. A complete classification of continuous translation-invariant $\\mathrm{SO}(n)$-invariant valuations and curvature measures with values in $\\mathbb{R}$ was obtained by Hadwiger and Schneider, respectively. More recently, characterisation results have been achieved for curvature measures with values in $\\operatorname{Sym}^p \\mathbb{R}^n$ and $\\operatorname{Sym}^2\\!Λ^{q} \\mathbb{R}^n$ for $p,q \\geq 1$ with varying assumptions as for their invariance properties. In the present work, we classify all smooth translation-invariant $\\mathrm{SO}(n)$-covariant curvature measures with values in any $\\mathrm{SO}(n)$-representation in terms of certain differential forms on the sphere bundle $S\\mathbb{R}^n$ and describe their behaviour under the globalisation map. The latter result also yields a similar classification of all continuous $\\mathrm{SO}(n)$-covariant valuations with values in any $\\mathrm{SO}(n)$-representation. Furthermore, a decomposition of the space of smooth translation-invariant $\\mathbb{R}$-valued curvature measures as an $\\mathrm{SO}(n)$-representation is obtained. As a corollary, we construct an explicit basis of continuous translation-invariant $\\mathbb{R}$-valued valuations.", "published": "2019-03-07T18:53:08Z", "authors": ["Mykhailo Saienko"], "categories": ["math.DG"], "doi": "", "_query": "automated valuation model", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2209.02182v1", "title": "Being Automated or Not? Risk Identification of Occupations with Graph Neural Networks", "abstract": "The rapid advances in automation technologies, such as artificial intelligence (AI) and robotics, pose an increasing risk of automation for occupations, with a likely significant impact on the labour market. Recent social-economic studies suggest that nearly 50\\% of occupations are at high risk of being automated in the next decade. However, the lack of granular data and empirically informed models have limited the accuracy of these studies and made it challenging to predict which jobs will be automated. In this paper, we study the automation risk of occupations by performing a classification task between automated and non-automated occupations. The available information is 910 occupations' task statements, skills and interactions categorised by Standard Occupational Classification (SOC). To fully utilize this information, we propose a graph-based semi-supervised classification method named \\textbf{A}utomated \\textbf{O}ccupation \\textbf{C}lassification based on \\textbf{G}raph \\textbf{C}onvolutional \\textbf{N}etworks (\\textbf{AOC-GCN}) to identify the automated risk for occupations. This model integrates a heterogeneous graph to capture occupations' local and global contexts. The results show that our proposed method outperforms the baseline models by considering the information of both internal features of occupations and their external interactions. This study could help policymakers identify potential automated occupations and support individuals' decision-making before entering the job market.", "published": "2022-09-06T02:19:50Z", "authors": ["Dawei Xu", "Haoran Yang", "Marian-Andrei Rizoiu", "Guandong Xu"], "categories": ["cs.CY", "cs.NE"], "doi": "", "_query": "automated valuation model", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/1910.03090v3", "title": "Instagram Fake and Automated Account Detection", "abstract": "Fake engagement is one of the significant problems in Online Social Networks (OSNs) which is used to increase the popularity of an account in an inorganic manner. The detection of fake engagement is crucial because it leads to loss of money for businesses, wrong audience targeting in advertising, wrong product predictions systems, and unhealthy social network environment. This study is related with the detection of fake and automated accounts which leads to fake engagement on Instagram. Prior to this work, there were no publicly available dataset for fake and automated accounts. For this purpose, two datasets have been published for the detection of fake and automated accounts. For the detection of these accounts, machine learning algorithms like Naive Bayes, Logistic Regression, Support Vector Machines and Neural Networks are applied. Additionally, for the detection of automated accounts, cost sensitive genetic algorithm is proposed to handle the unnatural bias in the dataset. To deal with the unevenness problem in the fake dataset, Smote-nc algorithm is implemented. For the automated and fake account detection datasets, 86% and 96% classification accuracies are obtained, respectively.", "published": "2019-09-13T12:51:01Z", "authors": ["Fatih Cagatay Akyon", "Esat Kalfaoglu"], "categories": ["cs.IR", "cs.LG", "stat.ML"], "doi": "10.1109/ASYU48272.2019.8946437", "_query": "automated valuation model", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2601.15018v1", "title": "Risk Estimation for Automated Driving", "abstract": "Safety is a central requirement for automated vehicles. As such, the assessment of risk in automated driving is key in supporting both motion planning technologies and safety evaluation. In automated driving, risk is characterized by two aspects. The first aspect is the uncertainty on the state estimates of other road participants by an automated vehicle. The second aspect is the severity of a collision event with said traffic participants. Here, the uncertainty aspect typically causes the risk to be non-zero for near-collision events. This makes risk particularly useful for automated vehicle motion planning. Namely, constraining or minimizing risk naturally navigates the automated vehicle around traffic participants while keeping a safety distance based on the level of uncertainty and the potential severity of the impending collision. Existing approaches to calculate the risk either resort to empirical modeling or severe approximations, and, hence, lack generalizability and accuracy. In this paper, we combine recent advances in collision probability estimation with the concept of collision severity to develop a general method for accurate risk estimation. The proposed method allows us to assign individual severity functions for different collision constellations, such as, e.g., frontal or side collisions. Furthermore, we show that the proposed approach is computationally efficient, which is beneficial, e.g., in real-time motion planning applications. The programming code for an exemplary implementation of Gaussian uncertainties is also provided.", "published": "2026-01-21T14:14:50Z", "authors": ["Leon Tolksdorf", "Arturo Tejada", "Jonas Bauernfeind", "Christian Birkner", "Nathan van de Wouw"], "categories": ["cs.RO"], "doi": "", "_query": "automated valuation model", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2503.15556v2", "title": "Fully Automated Generation of Combinatorial Optimisation Systems Using Large Language Models", "abstract": "Over the last few decades, researchers have made considerable efforts to make decision support more accessible for small and medium enterprises by reducing the cost of designing, developing and maintaining automated decision support systems. However, due to the diversity of the underlying combinatorial optimisation problems, reusability of such systems has been limited; in most cases, expensive expertise has been required to implement bespoke software components. We explore the feasibility of fully automated generation of combinatorial optimisation systems using large language models (LLMs). An LLM will be responsible for interpreting the user-provided problem description in natural language and designing and implementing problem-specific software components. We discuss the principles of fully automated LLM-based optimisation system generation, and evaluate several proof-of-concept generators, comparing their performance on four optimisation problems.", "published": "2025-03-18T20:23:51Z", "authors": ["Daniel Karapetyan"], "categories": ["cs.SE", "cs.PL"], "doi": "", "_query": "automated valuation model", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/1909.02148v1", "title": "Automated and optimally FRET-assisted structural modeling", "abstract": "FRET experiments can yield state-specific structural information on complex dynamic biomolecular assemblies. However, FRET experiments need to be combined with computer simulations to overcome their sparsity. We introduce (i) an automated FRET experiment design tool determining optimal FRET pairs for structural modeling, (ii) a protocol for efficient FRET-assisted computational structural modeling at multiple scales, and (iii) a quantitative quality estimate for judging the accuracy of determined structures. We tested against simulated and experimental data.", "published": "2019-09-04T23:12:26Z", "authors": ["Mykola Dimura", "Thomas O. Peulen", "Hugo Sanabria", "Dmitro Rodnin", "Katherina Hemmen", "Claus A. M. Seidel", "Holger Gohlke"], "categories": ["q-bio.QM", "physics.bio-ph", "q-bio.BM"], "doi": "10.1016/j.bpj.2018.11.1815", "_query": "automated valuation model", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2604.10884v1", "title": "Ambiguity Detection and Elimination in Automated Executable Process Modeling", "abstract": "Automated generation of executable Business Process Model and Notation (BPMN) models from natural-language specifications is increasingly enabled by large language models. However, ambiguous or underspecified text can yield structurally valid models with different simulated behavior. Our goal is not to prove that one generated BPMN model is semantically correct, but to detect when a natural-language specification fails to support a stable executable interpretation under repeated generation and simulation. We present a diagnosis-driven framework that detects behavioral inconsistency from the empirical distribution of key performance indicators (KPIs), localizes divergence to gateway logic using model-based diagnosis, maps that logic back to verbatim narrative segments, and repairs the source text through evidence-based refinement. Experiments on diabetic nephropathy health-guidance policies show that the method reduces variability in regenerated model behavior. The result is a closed-loop approach for validating and repairing executable process specifications in the absence of ground-truth BPMN models.", "published": "2026-04-13T01:11:01Z", "authors": ["Ion Matei", "Praveen Kumar Menaka Sekar", "Maksym Zhenirovskyy", "Hon Yung Wong", "Sayuri Kohmura", "Shinji Hotta", "Akihiro Inomata"], "categories": ["cs.SE", "cs.AI"], "doi": "", "_query": "automated valuation model", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2002.12334v1", "title": "A Distributional Framework for Data Valuation", "abstract": "Shapley value is a classic notion from game theory, historically used to quantify the contributions of individuals within groups, and more recently applied to assign values to data points when training machine learning models. Despite its foundational role, a key limitation of the data Shapley framework is that it only provides valuations for points within a fixed data set. It does not account for statistical aspects of the data and does not give a way to reason about points outside the data set. To address these limitations, we propose a novel framework -- distributional Shapley -- where the value of a point is defined in the context of an underlying data distribution. We prove that distributional Shapley has several desirable statistical properties; for example, the values are stable under perturbations to the data points themselves and to the underlying data distribution. We leverage these properties to develop a new algorithm for estimating values from data, which comes with formal guarantees and runs two orders of magnitude faster than state-of-the-art algorithms for computing the (non-distributional) data Shapley values. We apply distributional Shapley to diverse data sets and demonstrate its utility in a data market setting.", "published": "2020-02-27T18:51:35Z", "authors": ["Amirata Ghorbani", "Michael P. Kim", "James Zou"], "categories": ["cs.LG", "stat.ML"], "doi": "", "_query": "automated valuation model", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2509.04214v1", "title": "An Automated, Scalable Machine Learning Model Inversion Assessment Pipeline", "abstract": "Machine learning (ML) models have the potential to transform military battlefields, presenting a large external pressure to rapidly incorporate them into operational settings. However, it is well-established that these ML models are vulnerable to a number of adversarial attacks throughout the model deployment pipeline that threaten to negate battlefield advantage. One broad category is privacy attacks (such as model inversion) where an adversary can reverse engineer information from the model, such as the sensitive data used in its training. The ability to quantify the risk of model inversion attacks (MIAs) is not well studied, and there is a lack of automated developmental test and evaluation (DT&E) tools and metrics to quantify the effectiveness of privacy loss of the MIA. The current DT&E process is difficult because ML model inversions can be hard for a human to interpret, subjective when they are interpretable, and difficult to quantify in terms of inversion quality. Additionally, scaling the DT&E process is challenging due to many ML model architectures and data modalities that need to be assessed. In this work, we present a novel DT&E tool that quantifies the risk of data privacy loss from MIAs and introduces four adversarial risk dimensions to quantify privacy loss. Our DT&E pipeline combines inversion with vision language models (VLMs) to improve effectiveness while enabling scalable analysis. We demonstrate effectiveness using multiple MIA techniques and VLMs configured for zero-shot classification and image captioning. We benchmark the pipeline using several state-of-the-art MIAs in the computer vision domain with an image classification task that is typical in military applications. In general, our innovative pipeline extends the current model inversion DT&E capabilities by improving the effectiveness and scalability of the privacy loss analysis in an automated fashion.", "published": "2025-09-04T13:39:37Z", "authors": ["Tyler Shumaker", "Jessica Carpenter", "David Saranchak", "Nathaniel D. Bastian"], "categories": ["cs.CR"], "doi": "", "_query": "automated valuation model", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2411.11607v3", "title": "Performance evaluation of a ROS2 based Automated Driving System", "abstract": "Automated driving is currently a prominent area of scientific work. In the future, highly automated driving and new Advanced Driver Assistance Systems will become reality. While Advanced Driver Assistance Systems and automated driving functions for certain domains are already commercially available, ubiquitous automated driving in complex scenarios remains a subject of ongoing research. Contrarily to single-purpose Electronic Control Units, the software for automated driving is often executed on high performance PCs. The Robot Operating System 2 (ROS2) is commonly used to connect components in an automated driving system. Due to the time critical nature of automated driving systems, the performance of the framework is especially important. In this paper, a thorough performance evaluation of ROS2 is conducted, both in terms of timeliness and error rate. The results show that ROS2 is a suitable framework for automated driving systems.", "published": "2024-11-18T14:29:22Z", "authors": ["Jorin Kouril", "Bernd Schäufele", "Ilja Radusch", "Bettina Schnor"], "categories": ["cs.RO"], "doi": "10.5220/0012556800003702", "_query": "automated valuation model", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2403.15076v1", "title": "Comprehensive Lipidomic Automation Workflow using Large Language Models", "abstract": "Lipidomics generates large data that makes manual annotation and interpretation challenging. Lipid chemical and structural diversity with structural isomers further complicates annotation. Although, several commercial and open-source software for targeted lipid identification exists, it lacks automated method generation workflows and integration with statistical and bioinformatics tools. We have developed the Comprehensive Lipidomic Automated Workflow (CLAW) platform with integrated workflow for parsing, detailed statistical analysis and lipid annotations based on custom multiple reaction monitoring (MRM) precursor and product ion pair transitions. CLAW contains several modules including identification of carbon-carbon double bond position(s) in unsaturated lipids when combined with ozone electrospray ionization (OzESI)-MRM methodology. To demonstrate the utility of the automated workflow in CLAW, large-scale lipidomics data was collected with traditional and OzESI-MRM profiling on biological and non-biological samples. Specifically, a total of 1497 transitions organized into 10 MRM-based mass spectrometry methods were used to profile lipid droplets isolated from different brain regions of 18-24 month-old Alzheimer's disease mice and age-matched wild-type controls. Additionally, triacyclglycerols (TGs) profiles with carbon-carbon double bond specificity were generated from canola oil samples using OzESI-MRM profiling. We also developed an integrated language user interface with large language models using artificially intelligent (AI) agents that permits users to interact with the CLAW platform using a chatbot terminal to perform statistical and bioinformatic analyses. We envision CLAW pipeline to be used in high-throughput lipid structural identification tasks aiding users to generate automated lipidomics workflows ranging from data acquisition to AI agent-based bioinformatic analysis.", "published": "2024-03-22T10:00:52Z", "authors": ["Connor Beveridge", "Sanjay Iyer", "Caitlin E. Randolph", "Matthew Muhoberac", "Palak Manchanda", "Amy C. Clingenpeel", "Shane Tichy", "Gaurav Chopra"], "categories": ["q-bio.QM", "cs.AI", "q-bio.BM", "q-bio.SC"], "doi": "", "_query": "automated valuation model", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/1709.07103v1", "title": "Automated Adaptive Optics", "abstract": "Large area surveys will dominate the forthcoming decades of astronomy and their success requires characterizing thousands of discoveries through additional observations at higher spatial or spectral resolution, and at complementary cadences or periods. Only the full automation of adaptive optics systems will enable high-acuity, high-sensitivity follow-up observations of several tens of thousands of these objects per year, maximizing on-sky time. Automation will also enable rapid response to target-of-opportunity events within minutes, minimizing the time between discovery and characterization. In June 2012, we demonstrated the first fully automated operation of an astronomical adaptive optics system by observing 125 objects in succession with the Robo-AO system. Efficiency has increased ever since, with a typical night comprising 200-250 automated observations at the visible diffraction limit. By observing tens of thousands of targets in the largest-ever adaptive-optics surveys, Robo-AO has demonstrated the ability to address the follow-up needs of current and future large astronomical surveys.", "published": "2017-09-20T23:41:38Z", "authors": ["Christoph Baranec", "Reed Riddle", "Nicholas M. Law"], "categories": ["astro-ph.IM"], "doi": "10.1142/9789811203787_0015", "_query": "automated valuation model", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2104.10661v1", "title": "Towards Automated Psychotherapy via Language Modeling", "abstract": "In this experiment, a model was devised, trained, and evaluated to automate psychotherapist/client text conversations through the use of state-of-the-art, Seq2Seq Transformer-based Natural Language Generation (NLG) systems. Through training the model upon a mix of the Cornell Movie Dialogue Corpus for language understanding and an open-source, anonymized, and public licensed psychotherapeutic dataset, the model achieved statistically significant performance in published, standardized qualitative benchmarks against human-written validation data - meeting or exceeding human-written responses' performance in 59.7% and 67.1% of the test set for two independent test methods respectively. Although the model cannot replace the work of psychotherapists entirely, its ability to synthesize human-appearing utterances for the majority of the test set serves as a promising step towards communizing and easing stigma at the psychotherapeutic point-of-care.", "published": "2021-04-05T01:53:39Z", "authors": ["Houjun Liu"], "categories": ["cs.CL"], "doi": "", "_query": "automated valuation model", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2207.00370v1", "title": "AUDITEM: Toward an Automated and Efficient Data Integrity Verification Model Using Blockchain", "abstract": "Data tampering is often considered a severe problem in industrial applications as it can lead to inaccurate financial reports or even a corporate security crisis. A correct representation of data is essential for companies' core business processes and is demanded by investors and customers. Traditional data audits are performed through third-party auditing services; however, these services are expensive and can be untrustworthy in some cases. Blockchain and smart contracts provide a decentralized mechanism to achieve secure and trustworthy data integrity verification; however, existing solutions present challenges in terms of scalability, privacy protection, and compliance with data regulations. In this paper, we propose the AUtomated and Decentralized InTegrity vErification Model (AUDITEM) to assist business stakeholders in verifying data integrity in a trustworthy and automated manner. To address the challenges in existing integrity verification processes, our model uses carefully designed smart contracts and a distributed file system to store integrity verification attributes and uses blockchain to enhance the authenticity of data certificates. A sub-module called Data Integrity Verification Tool (DIVT) is also developed to support easy-to-use interfaces and customizable verification operations. This paper presents a detailed implementation and designs experiments to verify the proposed model. The experimental and analytical results demonstrate that our model is feasible and efficient to meet various business requirements for data integrity verification.", "published": "2022-07-01T12:12:21Z", "authors": ["Zeshun Shi", "Jeroen Bergers", "Ken Korsmit", "Zhiming Zhao"], "categories": ["cs.CR"], "doi": "", "_query": "automated valuation model", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2512.03481v1", "title": "The $p$-adic Valuations of Möbius Duals of Lucas Sequences", "abstract": "In this paper, we extend the $p$-adic valuations of the Möbius duals of Lucas sequences, originally obtained by Carmichael for regular Lucas sequences to irregular Lucas sequences. We conclude with a brief observation about the relationship of these valuations to the existence of Wall-Sun-Sun primes.", "published": "2025-12-03T06:17:41Z", "authors": ["Tyler Ross", "Zhongyan Shen", "Tianxin Cai"], "categories": ["math.NT"], "doi": "", "_query": "automated valuation model", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2411.06590v1", "title": "CriticAL: Critic Automation with Language Models", "abstract": "Understanding the world through models is a fundamental goal of scientific research. While large language model (LLM) based approaches show promise in automating scientific discovery, they often overlook the importance of criticizing scientific models. Criticizing models deepens scientific understanding and drives the development of more accurate models. Automating model criticism is difficult because it traditionally requires a human expert to define how to compare a model with data and evaluate if the discrepancies are significant--both rely heavily on understanding the modeling assumptions and domain. Although LLM-based critic approaches are appealing, they introduce new challenges: LLMs might hallucinate the critiques themselves. Motivated by this, we introduce CriticAL (Critic Automation with Language Models). CriticAL uses LLMs to generate summary statistics that capture discrepancies between model predictions and data, and applies hypothesis tests to evaluate their significance. We can view CriticAL as a verifier that validates models and their critiques by embedding them in a hypothesis testing framework. In experiments, we evaluate CriticAL across key quantitative and qualitative dimensions. In settings where we synthesize discrepancies between models and datasets, CriticAL reliably generates correct critiques without hallucinating incorrect ones. We show that both human and LLM judges consistently prefer CriticAL's critiques over alternative approaches in terms of transparency and actionability. Finally, we show that CriticAL's critiques enable an LLM scientist to improve upon human-designed models on real-world datasets.", "published": "2024-11-10T20:41:35Z", "authors": ["Michael Y. Li", "Vivek Vajipey", "Noah D. Goodman", "Emily B. Fox"], "categories": ["cs.LG", "cs.AI", "cs.CL"], "doi": "", "_query": "automated valuation model", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2505.17835v1", "title": "VLM Models and Automated Grading of Atopic Dermatitis", "abstract": "The task of grading atopic dermatitis (or AD, a form of eczema) from patient images is difficult even for trained dermatologists. Research on automating this task has progressed in recent years with the development of deep learning solutions; however, the rapid evolution of multimodal models and more specifically vision-language models (VLMs) opens the door to new possibilities in terms of explainable assessment of medical images, including dermatology. This report describes experiments carried out to evaluate the ability of seven VLMs to assess the severity of AD on a set of test images.", "published": "2025-05-23T12:49:09Z", "authors": ["Marc Lalonde", "Hamed Ghodrati"], "categories": ["cs.CV"], "doi": "", "_query": "automated valuation model", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2408.01352v1", "title": "A geometric decomposition for unitarily invariant valuations on convex functions", "abstract": "Valuations on the space of finite-valued convex functions on $\\mathbb{C}^n$ that are continuous, dually epi-translation invariant, as well as $\\mathrm{U}(n)$-invariant are completely classified. It is shown that the space of these valuations decomposes into a direct sum of subspaces defined in terms of vanishing properties with respect to restrictions to a finite family of special subspaces of $\\mathbb{C}^n$, mirroring the behavior of the hermitian intrinsic volumes introduced by Bernig and Fu. Unique representations of these valuations in terms of principal value integrals involving two families of Monge-Ampère-type operators are established", "published": "2024-08-02T15:56:48Z", "authors": ["Jonas Knoerr"], "categories": ["math.FA", "math.DG", "math.MG"], "doi": "", "_query": "automated valuation model", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2609.27030v1", "title": "Kubernetes Misconfigurations in the Wild: Taxonomy, Evolution, and Automated Repair with Large Language Models", "abstract": "Kubernetes is widely used to orchestrate cloud-native applications, yet its declarative configuration model often introduces security misconfigurations that threaten system reliability. Despite available detection tools, misconfiguration patterns and scalable remediation remain insufficiently understood. This paper presents an empirical study of Kubernetes security misconfigurations based on 2,662 developer-reported Stack Overflow issues. We derive a taxonomy of recurring security weaknesses across configuration objects and categories. We analyze severity variations and investigate how misconfigurations evolve between incubator and stable project stages. Findings show that while some operational issues decrease as projects mature, critical security misconfigurations often persist or reappear. We then evaluate Large Language Models (LLMs) for automated remediation under progressively enriched contextual conditions. Contextual grounding improves correction accuracy, with the best standalone model achieving 89.06%. To enhance structural correctness and schema compliance, we introduce Kubecurity, a schema-guided validation framework based on official Kubernetes specifications. Combining contextual LLM reasoning with deterministic schema enforcement achieves 98.50% correction accuracy while substantially reducing newly introduced misconfigurations. This work advances the understanding of Kubernetes security misconfigurations and demonstrates a hybrid approach to more reliable automated remediation.", "published": "2026-09-22T20:20:19Z", "authors": ["Mostafa Anouar Ghorab", "Ahmad Abdel Latif", "Mohamed Aymen Saied"], "categories": ["cs.SE"], "doi": "10.1145/3805760.3814908", "_query": "automated valuation model", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2111.13264v1", "title": "Description of ResFrac automated history matching and optimization workflow", "abstract": "This document describes the functioning of the automated history matching and optimization workflow implemented with the ResFrac hydraulic fracturing and reservoir simulator. The purpose of the automated history matching and optimization workflow is enable automated calibration of simulator models to observed data and application to forward optimization. The automated workflow works by solving a formal mathematical optimization problem to minimize misfit with observations from any point in the lifecycle of a hydraulically fractured reservoir, or to maximize a quantity of interest associated such a reservoir, such as net present value. Objective function evaluations in the optimization problem consist of runs of the ResFrac simulator, which is a physics-based model of hydraulic fracturing and reservoir phenomena. The workflow employs a proxy model to improve computational speed and applies experimental design and Bayesian sampling techniques to generate points with which to train the proxy model. This document also provides an overview of the software infrastructure developed to support the automated workflow.", "published": "2021-11-25T22:08:06Z", "authors": ["Charles A. Kang", "Mark W. McClure", "Somasekhar Reddy"], "categories": ["physics.geo-ph", "physics.comp-ph", "physics.data-an"], "doi": "", "_query": "automated valuation model", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/0801.3770v1", "title": "Maximal Crossed Product Orders over Discrete Valuation Rings", "abstract": "The problem of determining when a (classical) crossed product $T=S^f*G$ of a finite group $G$ over a discrete valuation ring $S$ is a maximal order, was answered in the 1960's for the case where $S$ is tamely ramified over the subring of invariants $S^G$. The answer was given in terms of the conductor subgroup (with respect to $f$) of the inertia. In this paper we solve this problem in general when $S/S^G$ is residually separable. We show that the maximal order property entails a restrictive structure on the sub-crossed product graded by the inertia subgroup. In particular, the inertia is abelian. Using this structure, one is able to extend the notion of the conductor. As in the tame case, the order of the conductor is equal to the number of maximal two sided ideals of $T$ and hence to the number of maximal orders containing $T$ in its quotient ring. Consequently, $T$ is a maximal order if and only if the conductor subgroup is trivial.", "published": "2008-01-24T14:33:29Z", "authors": ["Yuval Ginosar"], "categories": ["math.RA"], "doi": "", "_query": "automated valuation model", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2610.05237v1", "title": "Settling the Computational Complexity of Max-Min Allocation with Ternary Valuations", "abstract": "We study the problem of computing an allocation of indivisible items that maximizes egalitarian welfare, i.e., the utility of the worst-off agent, when agents' item values or marginal values belong to a small set. For additive valuations with values in $\\{p,q\\}$, where $q>p>0$ and $\\gcd(p,q)=1$, we give a polynomial-time algorithm when $p=2$ and prove constant-gap hardness when $p\\geq3$, already with exactly three high-valued goods per agent. We also give an $\\sqrt{3/2}$-approximation for common positive bi-valued additive valuations. For mixed additive valuations in $\\{-p,0,c\\}$, where $p\\in\\{1,2\\}$ and $c$ is a positive integer, a reduction to maximum-weight perfect matching resolves the conjectured tractability of $\\{-2,0,c\\}$-valuations. For submodular valuations with marginals in $\\{-2,0,c\\}$, where $c$ is odd, we establish an exact unit-gap hardness result and exponential value-query lower bounds, even when all but one agent are additive. Finally, for $\\{-1,0,1\\}$-submodular valuations, we prove that no finite multiplicative approximation exists unless $\\p=\\np$. Together, our results resolve open questions and provide a complete picture of the computational complexity of max-min allocation with ternary valuations.", "published": "2026-10-04T14:04:02Z", "authors": ["Thi Ngoc Anh Vu", "Trung Thanh Nguyen", "Khaled Elbassioni", "Jörg Rothe"], "categories": ["cs.GT", "cs.AI"], "doi": "", "_query": "automated valuation model", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2506.00613v3", "title": "WorldGym: World Model as An Environment for Policy Evaluation", "abstract": "Evaluating robot control policies is difficult: real-world testing is costly, and handcrafted simulators require manual effort to improve in realism and generality. We propose a world-model-based policy evaluation environment (WorldGym), an autoregressive, action-conditioned video generation model which serves as a proxy to real world environments. Policies are evaluated via Monte Carlo rollouts in the world model, with a vision-language model providing rewards. We evaluate a set of VLA-based real-robot policies in the world model using only initial frames from real robots, and show that policy success rates within the world model highly correlate with real-world success rates. Moreoever, we show that WorldGym is able to preserve relative policy rankings across different policy versions, sizes, and training checkpoints. Due to requiring only a single start frame as input, the world model further enables efficient evaluation of robot policies' generalization ability on novel tasks and environments. We find that modern VLA-based robot policies still struggle to distinguish object shapes and can become distracted by adversarial facades of objects. While generating highly realistic object interaction remains challenging, WorldGym faithfully emulates robot motions and offers a practical starting point for safe and reproducible policy evaluation before deployment.", "published": "2025-05-31T15:51:56Z", "authors": ["Julian Quevedo", "Ansh Kumar Sharma", "Yixiang Sun", "Varad Suryavanshi", "Percy Liang", "Sherry Yang"], "categories": ["cs.RO", "cs.AI"], "doi": "", "_query": "automated valuation model", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2411.03707v1", "title": "Fine-Tuning Vision-Language Model for Automated Engineering Drawing Information Extraction", "abstract": "Geometric Dimensioning and Tolerancing (GD&T) plays a critical role in manufacturing by defining acceptable variations in part features to ensure component quality and functionality. However, extracting GD&T information from 2D engineering drawings is a time-consuming and labor-intensive task, often relying on manual efforts or semi-automated tools. To address these challenges, this study proposes an automated and computationally efficient GD&T extraction method by fine-tuning Florence-2, an open-source vision-language model (VLM). The model is trained on a dataset of 400 drawings with ground truth annotations provided by domain experts. For comparison, two state-of-the-art closed-source VLMs, GPT-4o and Claude-3.5-Sonnet, are evaluated on the same dataset. All models are assessed using precision, recall, F1-score, and hallucination metrics. Due to the computational cost and impracticality of fine-tuning large closed-source VLMs for domain-specific tasks, GPT-4o and Claude-3.5-Sonnet are evaluated in a zero-shot setting. In contrast, Florence-2, a smaller model with 0.23 billion parameters, is optimized through full-parameter fine-tuning across three distinct experiments, each utilizing datasets augmented to different levels. The results show that Florence-2 achieves a 29.95% increase in precision, a 37.75% increase in recall, a 52.40% improvement in F1-score, and a 43.15% reduction in hallucination rate compared to the best-performing closed-source model. These findings highlight the effectiveness of fine-tuning smaller, open-source VLMs like Florence-2, offering a practical and efficient solution for automated GD&T extraction to support downstream manufacturing tasks.", "published": "2024-11-06T07:11:15Z", "authors": ["Muhammad Tayyab Khan", "Lequn Chen", "Ye Han Ng", "Wenhe Feng", "Nicholas Yew Jin Tan", "Seung Ki Moon"], "categories": ["cs.CV", "cs.AI"], "doi": "", "_query": "automated valuation model", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2001.11012v2", "title": "Cross Currency Valuation and Hedging in the Multiple Curve Framework", "abstract": "We generalize the results of Bielecki and Rutkowski (2015) on funding and collateralization to a multi-currency framework and link their results with those of Piterbarg (2012), Moreni and Pallavicini (2017), and Fujii et al. (2010b). In doing this, we provide a complete study of absence of arbitrage in a multi-currency market where, in each single monetary area, multiple interest rates coexist. We first characterize absence of arbitrage in the case without collateral. After that we study collateralization schemes in a very general situation: the cash flows of the contingent claim and those associated to the collateral agreement can be specified in any currency. We study both segregation and rehypothecation and allow for cash and risky collateral in arbitrary currency specifications. Absence of arbitrage and pricing in the presence of collateral are discussed under all possible combinations of conventions. Our work provides a reference for the analysis of wealth dynamics, we also provide valuation formulas that are a useful foundation for cross-currency curve construction techniques. Our framework provides also a solid foundation for the construction of multi-currency simulation models for the generation of exposure profiles in the context of xVA calculations.", "published": "2020-01-29T18:44:30Z", "authors": ["Alessandro Gnoatto", "Nicole Seiffert"], "categories": ["q-fin.PR", "q-fin.MF"], "doi": "", "_query": "automated valuation model", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/1605.00954v1", "title": "${\\rm SO}(n)$ covariant local tensor valuations on polytopes", "abstract": "The Minkowski tensors are valuations on the space of convex bodies in ${\\mathbb R}^n$ with values in a space of symmetric tensors, having additional covariance and continuity properties. They are extensions of the intrinsic volumes, and as these, they are the subject of classification theorems, and they admit localizations in the form of measure-valued valuations. For these local tensor valuations, restricted to convex polytopes, a classification theorem has been proved recently, under the assumption of isometry covariance, but without any continuity assumption. This characterization result is extended here, replacing the covariance under orthogonal transformations by invariance under proper rotations only. This yields additional local tensor valuations on polytopes in dimensions two and three, but not in higher dimensions. They are completely classified in this paper.", "published": "2016-05-03T15:39:38Z", "authors": ["Daniel Hug", "Rolf Schneider"], "categories": ["math.MG"], "doi": "", "_query": "automated valuation model", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/1607.01207v2", "title": "Natural gas-fired power plants valuation and optimisation under Levy copulas and regime-switching", "abstract": "In this work we analyse a stochastic control problem for the valuation of a natural gas power station while taking into account operating characteristics. Both electricity and gas spot price processes exhibit mean-reverting spikes and Markov regime-switches. The Levy regime-switching model incorporates the effects of demand-supply fluctuations in energy markets and abrupt economic disruptions or business cycles. We make use of skewed Levy copulas to model the dependence risk of electricity and gas jumps. The corresponding HJB equation is the non-linear PIDE which is solved by an explicit finite difference method. The numerical approach gives us both the value of the plant and its optimal operating strategy depending on the gas and electricity prices, current temperature of the boiler and time. The surfaces of control strategies and contract values are obtained by implementing the numerical method for a particular example.", "published": "2016-07-05T11:58:24Z", "authors": ["Nemat Safarov", "Colin Atkinson"], "categories": ["q-fin.MF", "q-fin.PR"], "doi": "", "_query": "automated valuation model", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2202.01712v1", "title": "Review of automated time series forecasting pipelines", "abstract": "Time series forecasting is fundamental for various use cases in different domains such as energy systems and economics. Creating a forecasting model for a specific use case requires an iterative and complex design process. The typical design process includes the five sections (1) data pre-processing, (2) feature engineering, (3) hyperparameter optimization, (4) forecasting method selection, and (5) forecast ensembling, which are commonly organized in a pipeline structure. One promising approach to handle the ever-growing demand for time series forecasts is automating this design process. The present paper, thus, analyzes the existing literature on automated time series forecasting pipelines to investigate how to automate the design process of forecasting models. Thereby, we consider both Automated Machine Learning (AutoML) and automated statistical forecasting methods in a single forecasting pipeline. For this purpose, we firstly present and compare the proposed automation methods for each pipeline section. Secondly, we analyze the automation methods regarding their interaction, combination, and coverage of the five pipeline sections. For both, we discuss the literature, identify problems, give recommendations, and suggest future research. This review reveals that the majority of papers only cover two or three of the five pipeline sections. We conclude that future research has to holistically consider the automation of the forecasting pipeline to enable the large-scale application of time series forecasting.", "published": "2022-02-03T17:26:27Z", "authors": ["Stefan Meisenbacher", "Marian Turowski", "Kaleb Phipps", "Martin Rätz", "Dirk Müller", "Veit Hagenmeyer", "Ralf Mikut"], "categories": ["cs.LG"], "doi": "10.1002/widm.1475", "_query": "automated valuation model", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2509.21910v1", "title": "AutoSCORE: Enhancing Automated Scoring with Multi-Agent Large Language Models via Structured Component Recognition", "abstract": "Automated scoring plays a crucial role in education by reducing the reliance on human raters, offering scalable and immediate evaluation of student work. While large language models (LLMs) have shown strong potential in this task, their use as end-to-end raters faces challenges such as low accuracy, prompt sensitivity, limited interpretability, and rubric misalignment. These issues hinder the implementation of LLM-based automated scoring in assessment practice. To address the limitations, we propose AutoSCORE, a multi-agent LLM framework enhancing automated scoring via rubric-aligned Structured COmponent REcognition. With two agents, AutoSCORE first extracts rubric-relevant components from student responses and encodes them into a structured representation (i.e., Scoring Rubric Component Extraction Agent), which is then used to assign final scores (i.e., Scoring Agent). This design ensures that model reasoning follows a human-like grading process, enhancing interpretability and robustness. We evaluate AutoSCORE on four benchmark datasets from the ASAP benchmark, using both proprietary and open-source LLMs (GPT-4o, LLaMA-3.1-8B, and LLaMA-3.1-70B). Across diverse tasks and rubrics, AutoSCORE consistently improves scoring accuracy, human-machine agreement (QWK, correlations), and error metrics (MAE, RMSE) compared to single-agent baselines, with particularly strong benefits on complex, multi-dimensional rubrics, and especially large relative gains on smaller LLMs. These results demonstrate that structured component recognition combined with multi-agent design offers a scalable, reliable, and interpretable solution for automated scoring.", "published": "2025-09-26T05:45:14Z", "authors": ["Yun Wang", "Zhaojun Ding", "Xuansheng Wu", "Siyue Sun", "Ninghao Liu", "Xiaoming Zhai"], "categories": ["cs.CL", "cs.AI"], "doi": "10.1609/aaai.v40i48.42123", "_query": "automated valuation model", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2608.24537v1", "title": "Easier, but Not Easy: Nash Welfare under Lexicographic Valuations", "abstract": "Maximizing Nash welfare over indivisible goods is a central problem in resource allocation. For additive valuations, the best-known approximation factor is roughly $e^{-1/e}\\approx0.692$, and the problem is APX-hard. We study Nash welfare maximization under lexicographic valuations, where every good is worth more than the total value of all lower-ranked goods. This large-gap structure makes preferences almost ordinal, which might suggest that the problem becomes easy. We show, however, that the picture is more nuanced: although lexicographic valuations enable stronger algorithmic guarantees, they retain significant computational hardness. Our first main result is a $(1/\\sqrt{2}-ε)\\approx(0.707-ε)$-approximation algorithm for weighted Nash welfare under lexicographic valuations, improving over the inherited guarantee of roughly $e^{-1/e}$. The algorithm rounds the configuration LP for Nash welfare, for which we show a matching integrality gap of $\\sqrt{2}$. Our second main contribution is an exact algorithmic polynomial time framework for ordered lexicographic instances and doubling lexicographic instances. We introduce a domination-based branch-and-prune method for which we prove a mutual-exclusion property between sibling subtrees and use a matrix-based leaf-counting argument to bound the pruned recursion tree by a polynomial when the number of agents is constant. Finally, we show that large gaps do not eliminate hardness, as Nash welfare maximization is NP-hard even for ordered lexicographic valuations, and it is NP-hard to obtain a $0.9996$-approximation even for doubling lexicographic valuations. Thus, lexicographic valuations make Nash welfare maximization easier, but not easy: they admit tighter approximation and exact algorithms in important cases, yet still require intricate techniques and preserve some of the hardness of the general additive setting.", "published": "2026-08-25T13:21:45Z", "authors": ["Soumil Aggarwal", "Rohit Vaish", "Jatin Yadav"], "categories": ["cs.GT", "cs.DS"], "doi": "", "_query": "automated valuation model", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2103.09360v1", "title": "Towards physically consistent data-driven weather forecasting: Integrating data assimilation with equivariance-preserving deep spatial transformers", "abstract": "There is growing interest in data-driven weather prediction (DDWP), for example using convolutional neural networks such as U-NETs that are trained on data from models or reanalysis. Here, we propose 3 components to integrate with commonly used DDWP models in order to improve their physical consistency and forecast accuracy. These components are 1) a deep spatial transformer added to the latent space of the U-NETs to preserve a property called equivariance, which is related to correctly capturing rotations and scalings of features in spatio-temporal data, 2) a data-assimilation (DA) algorithm to ingest noisy observations and improve the initial conditions for next forecasts, and 3) a multi-time-step algorithm, which combines forecasts from DDWP models with different time steps through DA, improving the accuracy of forecasts at short intervals. To show the benefit/feasibility of each component, we use geopotential height at 500~hPa (Z500) from ERA5 reanalysis and examine the short-term forecast accuracy of specific setups of the DDWP framework. Results show that the equivariance-preserving networks (U-STNs) clearly outperform the U-NETs, for example improving the forecast skill by $45\\%$. Using a sigma-point ensemble Kalman (SPEnKF) algorithm for DA and U-STN as the forward model, we show that stable, accurate DA cycles are achieved even with high observation noise. The DDWP+DA framework substantially benefits from large ($O(1000)$) ensembles that are inexpensively generated with the data-driven forward model in each DA cycle. The multi-time-step DDWP+DA framework also shows promises, e.g., it reduces the average error by factors of 2-3.", "published": "2021-03-16T23:15:00Z", "authors": ["Ashesh Chattopadhyay", "Mustafa Mustafa", "Pedram Hassanzadeh", "Eviatar Bach", "Karthik Kashinath"], "categories": ["physics.ao-ph", "cs.AI", "cs.LG", "physics.comp-ph"], "doi": "10.5194/gmd-15-2221-2022", "_query": "automated valuation model", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/1806.03150v1", "title": "A User Evaluation of Automated Process Discovery Algorithms", "abstract": "Process mining methods allow analysts to use logs of historical executions of business processes in order to gain knowledge about the actual behavior of these processes. One of the most widely studied process mining operations is automated process discovery. An event log is taken as input by an automated process discovery method and produces a business process model as output that captures the control-flow relations between tasks that are described by the event log. In this setting, this paper provides a systematic comparative evaluation of existing implementations of automated process discovery methods with domain experts by using a real-life event log extracted from an international software engineering company and four quality metrics. The evaluation results highlight gaps and unexplored trade-offs in the field and allow researchers to improve the lacks in the automated process discovery methods in terms of usability of process discovery techniques in industry.", "published": "2018-06-08T13:43:24Z", "authors": ["Fabrizio Maria Maggi", "Andrea Marrella", "Fredrik Milani", "Allar Soo", "Silva Kasela"], "categories": ["cs.SE"], "doi": "", "_query": "automated valuation model", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2205.09550v1", "title": "Data Valuation for Offline Reinforcement Learning", "abstract": "The success of deep reinforcement learning (DRL) hinges on the availability of training data, which is typically obtained via a large number of environment interactions. In many real-world scenarios, costs and risks are associated with gathering these data. The field of offline reinforcement learning addresses these issues through outsourcing the collection of data to a domain expert or a carefully monitored program and subsequently searching for a batch-constrained optimal policy. With the emergence of data markets, an alternative to constructing a dataset in-house is to purchase external data. However, while state-of-the-art offline reinforcement learning approaches have shown a lot of promise, they currently rely on carefully constructed datasets that are well aligned with the intended target domains. This raises questions regarding the transferability and robustness of an offline reinforcement learning agent trained on externally acquired data. In this paper, we empirically evaluate the ability of the current state-of-the-art offline reinforcement learning approaches to coping with the source-target domain mismatch within two MuJoCo environments, finding that current state-of-the-art offline reinforcement learning algorithms underperform in the target domain. To address this, we propose data valuation for offline reinforcement learning (DVORL), which allows us to identify relevant and high-quality transitions, improving the performance and transferability of policies learned by offline reinforcement learning algorithms. The results show that our method outperforms offline reinforcement learning baselines on two MuJoCo environments.", "published": "2022-05-19T13:21:40Z", "authors": ["Amir Abolfazli", "Gregory Palmer", "Daniel Kudenko"], "categories": ["cs.LG"], "doi": "", "_query": "automated valuation model", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2306.03866v1", "title": "Correction of Errors in Preference Ratings from Automated Metrics for Text Generation", "abstract": "A major challenge in the field of Text Generation is evaluation: Human evaluations are cost-intensive, and automated metrics often display considerable disagreement with human judgments. In this paper, we propose a statistical model of Text Generation evaluation that accounts for the error-proneness of automated metrics when used to generate preference rankings between system outputs. We show that existing automated metrics are generally over-confident in assigning significant differences between systems in this setting. However, our model enables an efficient combination of human and automated ratings to remedy the error-proneness of the automated metrics. We show that using this combination, we only require about 50% of the human annotations typically used in evaluations to arrive at robust and statistically significant results while yielding the same evaluation outcome as the pure human evaluation in 95% of cases. We showcase the benefits of approach for three text generation tasks: dialogue systems, machine translation, and text summarization.", "published": "2023-06-06T17:09:29Z", "authors": ["Jan Deriu", "Pius von Däniken", "Don Tuggener", "Mark Cieliebak"], "categories": ["cs.CL", "cs.AI"], "doi": "", "_query": "automated valuation model", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2412.07139v1", "title": "SL(n) covariant vector-valued valuations on Orlicz spaces", "abstract": "A representation theorem for continuous, SL(n) covariant vector-valued valuations on Orlicz spaces is established. Such valuations are uniquely characterized as moment vectors.", "published": "2024-12-10T02:59:05Z", "authors": ["Chunna Zeng", "Yu Lan"], "categories": ["math.DG"], "doi": "", "_query": "automated valuation model", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2110.06874v1", "title": "Automated Essay Scoring Using Transformer Models", "abstract": "Automated essay scoring (AES) is gaining increasing attention in the education sector as it significantly reduces the burden of manual scoring and allows ad hoc feedback for learners. Natural language processing based on machine learning has been shown to be particularly suitable for text classification and AES. While many machine-learning approaches for AES still rely on a bag-of-words (BOW) approach, we consider a transformer-based approach in this paper, compare its performance to a logistic regression model based on the BOW approach and discuss their differences. The analysis is based on 2,088 email responses to a problem-solving task, that were manually labeled in terms of politeness. Both transformer models considered in that analysis outperformed without any hyper-parameter tuning the regression-based model. We argue that for AES tasks such as politeness classification, the transformer-based approach has significant advantages, while a BOW approach suffers from not taking word order into account and reducing the words to their stem. Further, we show how such models can help increase the accuracy of human raters, and we provide a detailed instruction on how to implement transformer-based models for one's own purpose.", "published": "2021-10-13T17:09:47Z", "authors": ["Sabrina Ludwig", "Christian Mayer", "Christopher Hansen", "Kerstin Eilers", "Steffen Brandt"], "categories": ["cs.CL"], "doi": "10.3390/psych3040056", "_query": "automated valuation model", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2605.17937v2", "title": "BacktestBench: Benchmarking Large Language Models for Automated Quantitative Strategy Backtesting", "abstract": "Quantitative backtesting is essential for evaluating trading strategies but remains hampered by high technical barriers and limited scalability. While Large Language Models (LLMs) offer a transformative path to automate this complex, interdisciplinary workflow through advanced code generation, tool usage, and agentic planning, the practical realization is significantly challenged by the current lack of a large-scale benchmark dedicated to automated quantitative backtesting, which hinders progress in this field. To bridge this critical gap, we introduce BacktestBench, the first large-scale benchmark for automated quantitative backtesting. Built from over 6 million real market records, it comprises 18,246 meticulously annotated question-answering pairs across four task categories: metrics calculation, ticker selection, strategy selection, and parameter confirmation. We also propose AutoBacktest, a robust multi-agent baseline that translates natural language strategies into reproducible backtests by coordinating a Summarizer for semantic factor extraction, a Retriever for validated SQL generation, and a Coder for Python backtesting implementation. Our evaluation on 23 mainstream LLMs, complemented by targeted ablations, identifies key factors that influence end-to-end performance and highlights the importance of grounded verification and standardized indicator representations.", "published": "2026-05-18T06:52:08Z", "authors": ["Zhensheng Wang", "Wenmian Yang", "Qingtai Wu", "Lequan Ma", "Yiquan Zhang", "Weijia Jia"], "categories": ["cs.CL", "cs.AI"], "doi": "10.1145/3770855.3817460", "_query": "automated valuation model", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2206.10417v11", "title": "Classical Aspects of a Distributional 3+1 Foam Model", "abstract": "A 3+1 spacetime, with a shift vector that is the unique fundamental solution to the linearized wave operator, is introduced to model an interpretation of Wheeler's layman's analogy of the Quantum foam. To understand the distributional aspects of this model is the guaranteed existence of a sequence of compactly supported shift vectors that converge to the fundamental solution used to introduce a sequence of 3+1 globally hyperbolic spacetimes. Using the sequence of these causally stable spacetimes it is shown that there exists a positive integer such that for all elements in the sequence with a greater index value than this integer and for any Eulerian observer will the shift vector increase more rapidly than any polynomial and the volume expansion is more rapid than a polynomial in all directions. The same conclusion remains valid for the trace of the extrinsic curvature. Nonetheless, it is shown, no matter how volatile the extrinsic curvature is for these elements there also exists elements in the other end of the sequence where the extrinsic curvature is negligible and the spacetime flat.", "published": "2022-06-21T14:16:30Z", "authors": ["Claes Cramer"], "categories": ["gr-qc", "math-ph", "quant-ph"], "doi": "", "_query": "automated valuation model", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2205.08018v1", "title": "A Survey on Semantics in Automated Data Science", "abstract": "Data Scientists leverage common sense reasoning and domain knowledge to understand and enrich data for building predictive models. In recent years, we have witnessed a surge in tools and techniques for {\\em automated machine learning}. While data scientists can employ various such tools to help with model building, many other aspects such as {\\em feature engineering} that require semantic understanding of concepts, remain manual to a large extent. In this paper we discuss important shortcomings of current automated data science solutions and machine learning. We discuss how leveraging basic semantic reasoning on data in combination with novel tools for data science automation can help with consistent and explainable data augmentation and transformation. Moreover, semantics can assist data scientists in a new manner by helping with challenges related to {\\em trust}, {\\em bias}, and {\\em explainability}.", "published": "2022-05-16T23:16:09Z", "authors": ["Udayan Khurana", "Kavitha Srinivas", "Horst Samulowitz"], "categories": ["cs.AI"], "doi": "", "_query": "automated valuation model", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/1908.08920v2", "title": "Automation is no barrier to light vehicle electrification", "abstract": "Weight, computational load, sensor load, and possibly higher drag may increase the energy use of automated electric vehicles (AEVs) relative to human-driven electric vehicles (EVs), although this increase may be offset by smoother driving. We use a vehicle dynamics model to show that automation is likely to impose a minor penalty on EV range and have negligible effect on battery longevity. As such, while some commentators have suggested that the power and energy requirements of automation mean that the first automated vehicles (AVs) will be gas-electric hybrids, we conclude that this need not be the case. We also find that drivers need to place only a modest value on the time saved by automation for its benefits to exceed direct costs.", "published": "2019-08-08T03:14:14Z", "authors": ["Aniruddh Mohan", "Shashank Sripad", "Parth Vaishnav", "Venkatasubramanian Viswanathan"], "categories": ["cs.CY"], "doi": "10.1038/s41560-020-0644-3", "_query": "automated valuation model", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2411.00388v3", "title": "Towards Data Valuation via Asymmetric Data Shapley", "abstract": "As data emerges as a vital driver of technological and economic advancements, a key challenge is accurately quantifying its value in algorithmic decision-making. The Shapley value, a well-established concept from cooperative game theory, has been widely adopted to assess the contribution of individual data sources in supervised machine learning. However, its symmetry axiom assumes all players in the cooperative game are homogeneous, which overlooks the complex structures and dependencies present in real-world datasets. To address this limitation, we extend the traditional data Shapley framework to asymmetric data Shapley, making it flexible enough to incorporate inherent structures within the datasets for structure-aware data valuation. We also introduce an efficient $k$-nearest neighbor-based algorithm for its exact computation. We demonstrate the practical applicability of our framework across various machine learning tasks and data market contexts. The code is available at: https://github.com/xzheng01/Asymmetric-Data-Shapley.", "published": "2024-11-01T06:28:38Z", "authors": ["Xi Zheng", "Xiangyu Chang", "Ruoxi Jia", "Yong Tan"], "categories": ["cs.GT", "cs.LG"], "doi": "", "_query": "automated valuation model", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/1203.4283v2", "title": "Théorème de Kaplansky effectif pour des valuations de rang 1 centrées sur des anneaux locaux réguliers et complets", "abstract": "We prove that any complete regular local ring with a valuation of rank 1 can be embedded, as a valued ring, in a ring of generalized Puiseux expansions.", "published": "2012-03-19T22:51:11Z", "authors": ["San Saturnino Jean-Christophe"], "categories": ["math.AC"], "doi": "", "_query": "automated valuation model", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/0802.1619v1", "title": "A valuation criterion for normal basis generators in local fields of characteristic $p$", "abstract": "Let $K$ be a complete local field of characteristic $p$ with perfect residue field. Let $L/K$ be a finite, fully ramified, Galois $p$-extension. If $π_L\\in L$ is a prime element, and $p'(x)$ is the derivative of $π_L$'s minimal polynomial over $K$, then the relative different $\\euD_{L/K}$ is generated by $p'(π_L)\\in L$. Let $v_L$ be the normalized valuation normalized with $v_L(L)=\\mathbb{Z}$. We show that any element $ρ\\in L$ with $v_L(ρ)\\equiv -v_L(p'(π_L))-1\\bmod[L:K]$ generates a normal basis, $K[{Gal}(L/K)]\\cdotρ=L$. This criterion is tight: Given any integer $i$ such that $i\\not\\equiv -v_L(p'(π_L))-1\\bmod[L:K]$, there is a $ρ_i\\in L$ with $v_L(ρ_i)=i$ such that $K[{Gal}(L/K)]\\cdotρ_i\\subsetneq L$.", "published": "2008-02-12T18:50:27Z", "authors": ["G. Griffith Elder"], "categories": ["math.NT", "math.AG"], "doi": "", "_query": "automated valuation model", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2510.02679v1", "title": "Automated Constraint Specification for Job Scheduling by Regulating Generative Model with Domain-Specific Representation", "abstract": "Advanced Planning and Scheduling (APS) systems have become indispensable for modern manufacturing operations, enabling optimized resource allocation and production efficiency in increasingly complex and dynamic environments. While algorithms for solving abstracted scheduling problems have been extensively investigated, the critical prerequisite of specifying manufacturing requirements into formal constraints remains manual and labor-intensive. Although recent advances of generative models, particularly Large Language Models (LLMs), show promise in automating constraint specification from heterogeneous raw manufacturing data, their direct application faces challenges due to natural language ambiguity, non-deterministic outputs, and limited domain-specific knowledge. This paper presents a constraint-centric architecture that regulates LLMs to perform reliable automated constraint specification for production scheduling. The architecture defines a hierarchical structural space organized across three levels, implemented through domain-specific representation to ensure precision and reliability while maintaining flexibility. Furthermore, an automated production scenario adaptation algorithm is designed and deployed to efficiently customize the architecture for specific manufacturing configurations. Experimental results demonstrate that the proposed approach successfully balances the generative capabilities of LLMs with the reliability requirements of manufacturing systems, significantly outperforming pure LLM-based approaches in constraint specification tasks.", "published": "2025-10-03T02:34:11Z", "authors": ["Yu-Zhe Shi", "Qiao Xu", "Yanjia Li", "Mingchen Liu", "Huamin Qu", "Lecheng Ruan", "Qining Wang"], "categories": ["cs.AI"], "doi": "10.1109/TASE.2025.3596540", "_query": "automated valuation model", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2107.06336v2", "title": "Improving Cooperative Game Theory-based Data Valuation via Data Utility Learning", "abstract": "The Shapley value (SV) and Least core (LC) are classic methods in cooperative game theory for cost/profit sharing problems. Both methods have recently been proposed as a principled solution for data valuation tasks, i.e., quantifying the contribution of individual datum in machine learning. However, both SV and LC suffer computational challenges due to the need for retraining models on combinatorially many data subsets. In this work, we propose to boost the efficiency in computing Shapley value or Least core by learning to estimate the performance of a learning algorithm on unseen data combinations. Theoretically, we derive bounds relating the error in the predicted learning performance to the approximation error in SV and LC. Empirically, we show that the proposed method can significantly improve the accuracy of SV and LC estimation.", "published": "2021-07-13T18:56:04Z", "authors": ["Tianhao Wang", "Yu Yang", "Ruoxi Jia"], "categories": ["cs.LG"], "doi": "", "_query": "automated valuation model", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2511.02713v1", "title": "ReleaseEval: A Benchmark for Evaluating Language Models in Automated Release Note Generation", "abstract": "Automated release note generation addresses the challenge of documenting frequent software updates, where manual efforts are time-consuming and prone to human error. Although recent advances in language models further enhance this process, progress remains hindered by dataset limitations, including the lack of explicit licensing and limited reproducibility, and incomplete task design that relies mainly on commit messages for summarization while overlooking fine-grained contexts such as commit hierarchies and code changes. To fill this gap, we introduce ReleaseEval, a reproducible and openly licensed benchmark designed to systematically evaluate language models for automated release note generation. ReleaseEval comprises 94,987 release notes from 3,369 repositories across 6 programming languages, and supports three task settings with three levels of input granularity: (1) commit2sum, which generates release notes from commit messages; (2) tree2sum, which incorporates commit tree structures; and (3) diff2sum, which leverages fine-grained code diffs. Both automated and human evaluations show that large language models consistently outperform traditional baselines across all tasks, achieving substantial gains on tree2sum, while still struggling on diff2sum. These findings highlight LLMs' proficiency in leveraging structured information while revealing challenges in abstracting from long code diffs.", "published": "2025-11-04T16:31:44Z", "authors": ["Qianru Meng", "Zhaochun Ren", "Joost Visser"], "categories": ["cs.SE"], "doi": "", "_query": "automated valuation model", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2201.08373v2", "title": "TweetBoost: Influence of Social Media on NFT Valuation", "abstract": "NFT or Non-Fungible Token is a token that certifies a digital asset to be unique. A wide range of assets including, digital art, music, tweets, memes, are being sold as NFTs. NFT-related content has been widely shared on social media sites such as Twitter. We aim to understand the dominant factors that influence NFT asset valuation. Towards this objective, we create a first-of-its-kind dataset linking Twitter and OpenSea (the largest NFT marketplace) to capture social media profiles and linked NFT assets. Our dataset contains 245,159 tweets posted by 17,155 unique users, directly linking 62,997 NFT assets on OpenSea worth 19 Million USD. We have made the dataset public. We analyze the growth of NFTs, characterize the Twitter users promoting NFT assets, and gauge the impact of Twitter features on the virality of an NFT. Further, we investigate the effectiveness of different social media and NFT platform features by experimenting with multiple machine learning and deep learning models to predict an asset's value. Our results show that social media features improve the accuracy by 6% over baseline models that use only NFT platform features. Among social media features, count of user membership lists, number of likes and retweets are important features.", "published": "2022-01-20T18:56:33Z", "authors": ["Arnav Kapoor", "Dipanwita Guhathakurta", "Mehul Mathur", "Rupanshu Yadav", "Manish Gupta", "Ponnurangam Kumaraguru"], "categories": ["cs.SI"], "doi": "", "_query": "automated valuation model", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/1305.6304v1", "title": "Towers of complements to valuation rings and truncation closed embeddings of valued fields", "abstract": "We study necessary and sufficient conditions for a valued field $\\KF$ with value group $G$ and residue field $\\kf$ (with char $\\KF$ = char $\\kf$) to admit a truncation closed embedding in the field of generalized power series $\\kf((G, f))$ (with factor set $f$). We show that this is equivalent to the existence of a family ({\\it tower of complements}) of $\\kf$-subspaces of $\\KF$ which are complements of the (possibly fractional) ideals of the valuation ring. If $\\KF$ is a Henselian field of characteristic 0 or, more generally, an algebraically maximal Kaplansky field, we give an intrinsic construction of such a family which does not rely on a given truncation closed embedding. We also show that towers of complements and truncation closed embeddings can be extended from an arbitrary field to at least one of its maximal immediate extensions.", "published": "2013-05-27T19:40:42Z", "authors": ["Antongiulio Fornasiero", "Franz-Viktor Kuhlmann", "Salma Kuhlmann"], "categories": ["math.AC", "math.LO"], "doi": "", "_query": "automated valuation model", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2311.10098v1", "title": "Automated Parliaments: A Solution to Decision Uncertainty and Misalignment in Language Models", "abstract": "As AI takes on a greater role in the modern world, it is essential to ensure that AI models can overcome decision uncertainty and remain aligned with human morality and interests. This research paper proposes a method for improving the decision-making of language models (LMs) via Automated Parliaments (APs) - constructs made of AI delegates each representing a certain perspective. Delegates themselves consist of three AI models: generators, modifiers, and evaluators. We specify two mechanisms for producing optimal solutions: the Simultaneous Modification mechanism for response creation and an evaluation mechanism for fairly assessing solutions. The overall process begins when each generator creates a response aligned with its delegate's theory. The modifiers alter all other responses to make them more self-aligned. The evaluators collectively assess the best end response. Finally, the modifiers and generators learn from feedback from the evaluators. In our research, we tested the evaluation mechanism, comparing the use of single-value zero-shot prompting and AP few-shot prompting in evaluating morally contentious scenarios. We found that the AP architecture saw a 57.3% reduction in its loss value compared to the baseline. We conclude by discussing some potential applications of APs and specifically their potential impact when implemented as Automated Moral Parliaments.", "published": "2023-10-31T17:44:04Z", "authors": ["Thomas Forster", "Jonathan Ouwerx", "Shak Ragoler"], "categories": ["cs.AI"], "doi": "", "_query": "automated valuation model", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2410.17235v1", "title": "Automated Spinal MRI Labelling from Reports Using a Large Language Model", "abstract": "We propose a general pipeline to automate the extraction of labels from radiology reports using large language models, which we validate on spinal MRI reports. The efficacy of our labelling method is measured on five distinct conditions: spinal cancer, stenosis, spondylolisthesis, cauda equina compression and herniation. Using open-source models, our method equals or surpasses GPT-4 on a held-out set of reports. Furthermore, we show that the extracted labels can be used to train imaging models to classify the identified conditions in the accompanying MR scans. All classifiers trained using automated labels achieve comparable performance to models trained using scans manually annotated by clinicians. Code can be found at https://github.com/robinyjpark/AutoLabelClassifier.", "published": "2024-10-22T17:54:07Z", "authors": ["Robin Y. Park", "Rhydian Windsor", "Amir Jamaludin", "Andrew Zisserman"], "categories": ["eess.IV", "cs.CL", "cs.CV"], "doi": "10.1007/978-3-031-72086-4_10", "_query": "automated valuation model", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2504.02386v1", "title": "VoiceCraft-Dub: Automated Video Dubbing with Neural Codec Language Models", "abstract": "We present VoiceCraft-Dub, a novel approach for automated video dubbing that synthesizes high-quality speech from text and facial cues. This task has broad applications in filmmaking, multimedia creation, and assisting voice-impaired individuals. Building on the success of Neural Codec Language Models (NCLMs) for speech synthesis, our method extends their capabilities by incorporating video features, ensuring that synthesized speech is time-synchronized and expressively aligned with facial movements while preserving natural prosody. To inject visual cues, we design adapters to align facial features with the NCLM token space and introduce audio-visual fusion layers to merge audio-visual information within the NCLM framework. Additionally, we curate CelebV-Dub, a new dataset of expressive, real-world videos specifically designed for automated video dubbing. Extensive experiments show that our model achieves high-quality, intelligible, and natural speech synthesis with accurate lip synchronization, outperforming existing methods in human perception and performing favorably in objective evaluations. We also adapt VoiceCraft-Dub for the video-to-speech task, demonstrating its versatility for various applications.", "published": "2025-04-03T08:24:47Z", "authors": ["Kim Sung-Bin", "Jeongsoo Choi", "Puyuan Peng", "Joon Son Chung", "Tae-Hyun Oh", "David Harwath"], "categories": ["cs.CV", "eess.AS"], "doi": "", "_query": "automated valuation model", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/1801.10270v1", "title": "The Impact of Automated Parameter Optimization on Defect Prediction Models", "abstract": "Defect prediction models---classifiers that identify defect-prone software modules---have configurable parameters that control their characteristics (e.g., the number of trees in a random forest). Recent studies show that these classifiers underperform when default settings are used. In this paper, we study the impact of automated parameter optimization on defect prediction models. Through a case study of 18 datasets, we find that automated parameter optimization: (1) improves AUC performance by up to 40 percentage points; (2) yields classifiers that are at least as stable as those trained using default settings; (3) substantially shifts the importance ranking of variables, with as few as 28% of the top-ranked variables in optimized classifiers also being top-ranked in non-optimized classifiers; (4) yields optimized settings for 17 of the 20 most sensitive parameters that transfer among datasets without a statistically significant drop in performance; and (5) adds less than 30 minutes of additional computation to 12 of the 26 studied classification techniques. While widely-used classification techniques like random forest and support vector machines are not optimization-sensitive, traditionally overlooked techniques like C5.0 and neural networks can actually outperform widely-used techniques after optimization is applied. This highlights the importance of exploring the parameter space when using parameter-sensitive classification techniques.", "published": "2018-01-31T01:41:13Z", "authors": ["Chakkrit Tantithamthavorn", "Shane McIntosh", "Ahmed E. Hassan", "Kenichi Matsumoto"], "categories": ["cs.SE"], "doi": "", "_query": "automated valuation model", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2408.03492v1", "title": "Automated Theorem Provers Help Improve Large Language Model Reasoning", "abstract": "In this paper we demonstrate how logic programming systems and Automated first-order logic Theorem Provers (ATPs) can improve the accuracy of Large Language Models (LLMs) for logical reasoning tasks where the baseline performance is given by direct LLM solutions. We first evaluate LLM reasoning on steamroller problems using the PRONTOQA benchmark. We show how accuracy can be improved with a neuro-symbolic architecture where the LLM acts solely as a front-end for translating a given problem into a formal logic language and an automated reasoning engine is called for solving it. However, this approach critically hinges on the correctness of the LLM translation. To assess this translation correctness, we secondly define a framework of syntactic and semantic error categories. We implemented the framework and used it to identify errors that LLMs make in the benchmark domain. Based on these findings, we thirdly extended our method with capabilities for automatically correcting syntactic and semantic errors. For semantic error correction we integrate first-order logic ATPs, which is our main and novel contribution. We demonstrate that this approach reduces semantic errors significantly and further increases the accurracy of LLM logical reasoning.", "published": "2024-08-07T01:03:56Z", "authors": ["Lachlan McGinness", "Peter Baumgartner"], "categories": ["cs.AI", "cs.CL"], "doi": "10.29007/2n9m", "_query": "automated valuation model", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2105.05699v2", "title": "Automating Data Science: Prospects and Challenges", "abstract": "Given the complexity of typical data science projects and the associated demand for human expertise, automation has the potential to transform the data science process. Key insights: * Automation in data science aims to facilitate and transform the work of data scientists, not to replace them. * Important parts of data science are already being automated, especially in the modeling stages, where techniques such as automated machine learning (AutoML) are gaining traction. * Other aspects are harder to automate, not only because of technological challenges, but because open-ended and context-dependent tasks require human interaction.", "published": "2021-05-12T14:34:35Z", "authors": ["Tijl De Bie", "Luc De Raedt", "José Hernández-Orallo", "Holger H. Hoos", "Padhraic Smyth", "Christopher K. I. Williams"], "categories": ["cs.DB", "cs.LG"], "doi": "10.1145/3495256", "_query": "automated valuation model", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/0906.4110v1", "title": "A backward evolution model for infrared surveys: the role of AGN- and Color-L_TIR distributions", "abstract": "Empirical \"backward\" galaxy evolution models for infrared bright galaxies are constrained using multi-band infrared surveys. We developed a new Monte-Carlo algorithm for this task, implementing luminosity dependent distribution functions for the galaxies' infrared spectral energy distributions (SEDs) and for the AGN contribution, allowing for evolution of these quantities. The adopted SEDs take into account the contributions of both starbursts and AGN to the infrared emission, for the first time in a coherent treatment rather than invoking separate AGN and star-forming populations. In the first part of the paper we consider the quantification of the AGN contribution for local universe galaxies, as a function of total infrared luminosity. It is made using a large sample of LIRGs and ULIRGs for which mid-infrared spectra are available in the Spitzer archive. In the second part we present the model. Our best-fit model adopts very strong luminosity evolution, $L=L_0(1+z)^{3.4}$, up to $z=2.3$, and density evolution, $ρ=ρ_0(1+z)^2$, up to $z=1$, for the population of infrared galaxies. At higher $z$, the evolution rates drop as $(1+z)^{-1}$ and $(1+z)^{-1.5}$ respectively. To reproduce mid-infrared to submillimeter number counts and redshift distributions, it is necessary to introduce both an evolution in the AGN contribution and an evolution in the luminosity-temperature relation. Our models are in plausible agreement with current photometry-based estimates of the typical AGN contribution as a function of mid-infrared flux, and well placed to be compared to upcoming Spitzer spectroscopic results. As an example of future applications, we use our best-fitting model to make predictions for surveys with Herschel.", "published": "2009-06-22T20:08:50Z", "authors": ["E. Valiante", "D. Lutz", "E. Sturm", "R. Genzel", "E. Chapin"], "categories": ["astro-ph.CO", "astro-ph.GA"], "doi": "10.1088/0004-637X/701/2/1814", "_query": "automated valuation model", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2408.08145v1", "title": "Model-based Workflow for the Automated Generation of PDDL Descriptions", "abstract": "Manually creating Planning Domain Definition Language (PDDL) descriptions is difficult, error-prone, and requires extensive expert knowledge. However, this knowledge is already embedded in engineering models and can be reused. Therefore, this contribution presents a comprehensive workflow for the automated generation of PDDL descriptions from integrated system and product models. The proposed workflow leverages Model-Based Systems Engineering (MBSE) to organize and manage system and product information, translating it automatically into PDDL syntax for planning purposes. By connecting system and product models with planning aspects, it ensures that changes in these models are quickly reflected in updated PDDL descriptions, facilitating efficient and adaptable planning processes. The workflow is validated within a use case from aircraft assembly.", "published": "2024-08-15T13:29:25Z", "authors": ["Hamied Nabizada", "Tom Jeleniewski", "Felix Gehlhoff", "Alexander Fay"], "categories": ["cs.AI", "cs.SE"], "doi": "10.1109/ETFA61755.2024.10710982", "_query": "automated valuation model", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2503.18971v2", "title": "LLMs as Planning Formalizers: A Survey for Leveraging Large Language Models to Construct Automated Planning Models", "abstract": "Large Language Models (LLMs) excel in various natural language tasks but often struggle with long-horizon planning problems requiring structured reasoning. This limitation has drawn interest in integrating neuro-symbolic approaches within the Automated Planning (AP) and Natural Language Processing (NLP) communities. However, identifying optimal AP deployment frameworks can be daunting and introduces new challenges. This paper aims to provide a timely survey of the current research with an in-depth analysis, positioning LLMs as tools for formalizing and refining planning specifications to support reliable off-the-shelf AP planners. By systematically reviewing the current state of research, we highlight methodologies, and identify critical challenges and future directions, hoping to contribute to the joint research on NLP and Automated Planning.", "published": "2025-03-22T03:35:44Z", "authors": ["Marcus Tantakoun", "Xiaodan Zhu", "Christian Muise"], "categories": ["cs.AI"], "doi": "", "_query": "automated valuation model", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2108.05987v2", "title": "Automating System Configuration", "abstract": "The increasing complexity of modern configurable systems makes it critical to improve the level of automation in the process of system configuration. Such automation can also improve the agility of the development cycle, allowing for rapid and automated integration of decoupled workflows. In this paper, we present a new framework for automated configuration of systems representable as state machines. The framework leverages model checking and satisfiability modulo theories (SMT) and can be applied to any application domain representable using SMT formulas. Our approach can also be applied modularly, improving its scalability. Furthermore, we show how optimization can be used to produce configurations that are best according to some metric and also more likely to be understandable to humans. We showcase this framework and its flexibility by using it to configure a CGRA memory tile for various image processing applications.", "published": "2021-08-12T22:31:18Z", "authors": ["Nestan Tsiskaridze", "Maxwell Strange", "Makai Mann", "Kavya Sreedhar", "Qiaoyi Liu", "Mark Horowitz", "Clark Barrett"], "categories": ["cs.FL", "cs.AR"], "doi": "", "_query": "automated valuation model", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2506.06569v1", "title": "Textile Analysis for Recycling Automation using Transfer Learning and Zero-Shot Foundation Models", "abstract": "Automated sorting is crucial for improving the efficiency and scalability of textile recycling, but accurately identifying material composition and detecting contaminants from sensor data remains challenging. This paper investigates the use of standard RGB imagery, a cost-effective sensing modality, for key pre-processing tasks in an automated system. We present computer vision components designed for a conveyor belt setup to perform (a) classification of four common textile types and (b) segmentation of non-textile features such as buttons and zippers. For classification, several pre-trained architectures were evaluated using transfer learning and cross-validation, with EfficientNetB0 achieving the best performance on a held-out test set with 81.25\\% accuracy. For feature segmentation, a zero-shot approach combining the Grounding DINO open-vocabulary detector with the Segment Anything Model (SAM) was employed, demonstrating excellent performance with a mIoU of 0.90 for the generated masks against ground truth. This study demonstrates the feasibility of using RGB images coupled with modern deep learning techniques, including transfer learning for classification and foundation models for zero-shot segmentation, to enable essential analysis steps for automated textile recycling pipelines.", "published": "2025-06-06T22:49:53Z", "authors": ["Yannis Spyridis", "Vasileios Argyriou"], "categories": ["cs.CV", "cs.AI"], "doi": "", "_query": "automated valuation model", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2506.23850v1", "title": "Email as the Interface to Generative AI Models: Seamless Administrative Automation", "abstract": "This paper introduces a novel architectural framework that integrates Large Language Models (LLMs) with email interfaces to automate administrative tasks, specifically targeting accessibility barriers in enterprise environments. The system connects email communication channels with Optical Character Recognition (OCR) and intelligent automation, enabling non-technical administrative staff to delegate complex form-filling and document processing tasks using familiar email interfaces. By treating the email body as a natural language prompt and attachments as contextual information, the workflow bridges the gap between advanced AI capabilities and practical usability. Empirical evaluation shows that the system can complete complex administrative forms in under 8 seconds of automated processing, with human supervision reducing total staff time by a factor of three to four compared to manual workflows. The top-performing LLM accurately filled 16 out of 29 form fields and reduced the total cost per processed form by 64% relative to manual completion. These findings demonstrate that email-based LLM integration is a viable and cost-effective approach for democratizing advanced automation in organizational settings, supporting widespread adoption without requiring specialized technical knowledge or major workflow changes. This aligns with broader trends in leveraging LLMs to enhance accessibility and automate complex tasks for non-technical users, making technology more inclusive and efficient.", "published": "2025-06-30T13:39:54Z", "authors": ["Andres Navarro", "Carlos de Quinto", "José Alberto Hernández"], "categories": ["cs.HC"], "doi": "", "_query": "automated valuation model", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2411.00816v3", "title": "CycleResearcher: Improving Automated Research via Automated Review", "abstract": "The automation of scientific discovery has been a long-standing goal within the research community, driven by the potential to accelerate knowledge creation. While significant progress has been made using commercial large language models (LLMs) as research assistants or idea generators, the possibility of automating the entire research process with open-source LLMs remains largely unexplored. This paper explores the feasibility of using open-source post-trained LLMs as autonomous agents capable of performing the full cycle of automated research and review, from literature review and manuscript preparation to peer review and paper refinement. Our iterative preference training framework consists of CycleResearcher, which conducts research tasks, and CycleReviewer, which simulates the peer review process, providing iterative feedback via reinforcement learning. To train these models, we develop two new datasets, Review-5k and Research-14k, reflecting real-world machine learning research and peer review dynamics. Our results demonstrate that CycleReviewer achieves promising performance with a 26.89\\% reduction in mean absolute error (MAE) compared to individual human reviewers in predicting paper scores, indicating the potential of LLMs to effectively assist expert-level research evaluation. In research, the papers generated by the CycleResearcher model achieved a score of 5.36 in simulated peer reviews, showing some competitiveness in terms of simulated review scores compared to the preprint level of 5.24 from human experts, while still having room for improvement compared to the accepted paper level of 5.69. This work represents a significant step toward fully automated scientific inquiry, providing ethical safeguards and exploring AI-driven research capabilities. The code, dataset and model weight are released at https://wengsyx.github.io/Researcher/.", "published": "2024-10-28T08:10:21Z", "authors": ["Yixuan Weng", "Minjun Zhu", "Guangsheng Bao", "Hongbo Zhang", "Jindong Wang", "Yue Zhang", "Linyi Yang"], "categories": ["cs.CL", "cs.AI", "cs.CY", "cs.LG"], "doi": "", "_query": "automated valuation model", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2208.13684v1", "title": "Geometric parametrization of valuations on a polynomial ring", "abstract": "We extend and prove a conjecture of Benguş-Lasnier on the parametrization of valuations on a polynomial ring by certain spaces of diskoids.", "published": "2022-08-29T15:47:23Z", "authors": ["Enric Nart", "Josnei Novacoski"], "categories": ["math.AG", "math.AC"], "doi": "", "_query": "automated valuation model", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2512.07154v2", "title": "Asian option valuation under price impact", "abstract": "We develop a tractable framework for valuing Asian options when trading the underlying generates market impact and execution costs. Starting from a discrete-time, quote-level model, we construct a reference midpoint suitable for Asian payoffs and separate market impact into a transient component and a permanent drift distortion driven by signed trading. This specification admits continuous-time limits where the midpoint and impact state converge to a coupled system in which the midpoint drift depends on the transient impact state and in the endogenous regime on the hedger's trading rate, with correlated price and order-flow shocks. We study valuation in two complementary regimes. In an exogenous benchmark, the impact state evolves independently of the hedger. When the order-flow volatility is deterministic, we obtain a closed-form expression for the geometric Asian call. In an endogenous regime, trading volumes feed back into prices and costs, leading to a stochastic control problem and Hamilton-Jacobi-Bellman equations. We define reservation bid and ask prices via cost-based indifference which produces an impact-driven bid-ask spread. For computations, we propose a CRR-style tree-based Bellman algorithm. Numerical experiments show that exogenous impact effects are modest relative to frictionless benchmarks, while endogenous indifference prices generate nontrivial bid-ask spreads that grow super-linearly in impact parameters, widen when execution costs are lower, and shrink with faster mean reversion, highlighting the interaction between averaging in Asian options, price impact effects, and strategic trading.", "published": "2025-12-08T04:38:02Z", "authors": ["Priyanshu Tiwari", "Sourav Majumdar"], "categories": ["q-fin.MF"], "doi": "", "_query": "automated valuation model", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/1904.10659v1", "title": "Finite torsors on projective schemes defined over a discrete valuation ring", "abstract": "Given a Henselian and Japanese discrete valuation ring $A$ and a flat and projective $A$-scheme $X$, we follow the approach of Biswas-dos Santos to introduce a full subcategory of coherent modules on $X$ which is then shown to be Tannakian. We then prove that, under normality of the generic fibre, the associated affine and flat group is pro-finite in a strong sense (so that its ring of functions is a Mittag-Leffler $A$-module) and that it classifies finite torsors $Q\\to X$. This establishes an analogy to Nori's theory of the essentially finite fundamental group. In addition, we compare our theory with the ones recently developed by Mehta-Subramanian and Antei-Emsalem-Gasbarri. Using the comparison with the former, we show that any quasi-finite torsor $Q\\to X$ has a reduction of structure group to a finite one.", "published": "2019-04-24T07:13:39Z", "authors": ["Phung Ho Hai", "Joao Pedro dos Santos"], "categories": ["math.AG", "math.CT", "math.GR"], "doi": "", "_query": "automated valuation model", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2606.29006v1", "title": "Automated SysML-Based Verification of Discipline-Specific Models", "abstract": "Current examples of SysML-based verification of discipline-specific models in the literature typically have two flaws. Firstly, they are developed in a tool-specific manner using proprietary APIs, limiting portability. Secondly, they focus on performance properties modelled via parametric diagrams, overlooking behavioural and interface properties that also require verification. This project addresses the problem with a verification process tailored to model-based verification, informed by common SysML tool capabilities and the UML Testing Profile, that enables automated verification of discipline\\-/specific models from SysML test cases and returns the results to the SysML model for traceability. A mixed-method approach combining literature research and stakeholder interviews was used to derive validated stakeholder needs, which drove the specification and design of the process. The process was demonstrated end-to-end in two independent SysML tool-chains to evidence tool-agnosticism, and was shown to verify behavioural and interface requirements, including ordering, timing, and state-based responses, using SysML behavioural diagram constructs that parametric approaches alone cannot address.", "published": "2026-06-27T16:56:22Z", "authors": ["Daniel Marley", "Siyuan Ji"], "categories": ["cs.SE"], "doi": "", "_query": "automated valuation model", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2405.03727v3", "title": "Large Language Models Synergize with Automated Machine Learning", "abstract": "Recently, program synthesis driven by large language models (LLMs) has become increasingly popular. However, program synthesis for machine learning (ML) tasks still poses significant challenges. This paper explores a novel form of program synthesis, targeting ML programs, by combining LLMs and automated machine learning (autoML). Specifically, our goal is to fully automate the generation and optimization of the code of the entire ML workflow, from data preparation to modeling and post-processing, utilizing only textual descriptions of the ML tasks. To manage the length and diversity of ML programs, we propose to break each ML program into smaller, manageable parts. Each part is generated separately by the LLM, with careful consideration of their compatibilities. To ensure compatibilities, we design a testing technique for ML programs. Unlike traditional program synthesis, which typically relies on binary evaluations (i.e., correct or incorrect), evaluating ML programs necessitates more than just binary judgments. Our approach automates the numerical evaluation and optimization of these programs, selecting the best candidates through autoML techniques. In experiments across various ML tasks, our method outperforms existing methods in 10 out of 12 tasks for generating ML programs. In addition, autoML significantly improves the performance of the generated ML programs. In experiments, given the textual task description, our method, Text-to-ML, generates the complete and optimized ML program in a fully autonomous process. The implementation of our method is available at https://github.com/JLX0/llm-automl.", "published": "2024-05-06T08:09:46Z", "authors": ["Jinglue Xu", "Jialong Li", "Zhen Liu", "Nagar Anthel Venkatesh Suryanarayanan", "Guoyuan Zhou", "Jia Guo", "Hitoshi Iba", "Kenji Tei"], "categories": ["cs.SE", "cs.AI", "cs.LG", "cs.PL"], "doi": "", "_query": "automated valuation model", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2404.04688v1", "title": "Search-based Automated Program Repair of CPS Controllers Modeled in Simulink-Stateflow", "abstract": "Stateflow models are widely used in the industry to model the high-level control logic of Cyber-Physical Systems (CPSs) in Simulink--the defacto CPS simulator. Many approaches exist to test Simulink models, but once a fault is detected, the process to repair it remains manual. Such a manual process increases the software development cost, making it paramount to develop novel techniques that reduce this cost. Automated Program Repair (APR) techniques can significantly reduce the time for fixing bugs by automatically generating patches. However, current approaches face scalability issues to be applicable in the CPS context. To deal with this problem, we propose an automated search-based approach called FlowRepair, explicitly designed to repair Stateflow models. The novelty of FlowRepair includes, (1) a new algorithm that combines global and local search for patch generation; (2) a definition of novel repair objectives (e.g., the time a fault remained active) specifically designed for repairing CPSs; and (3) a set of mutation operators to repair Stateflow models automatically. We evaluated FlowRepair with three different case study systems and a total of nine faulty stateflow models. Our experiments suggest that (1) Flo wRepaircan fix bugs in stateflow models, including models with multiple faults; (2) FlowRepair surpasses or performs similarly to a baseline APR technique inspired by a well-known CPS program repair approach. Besides, we provide both a replication package and a live repository, paving the way towards the APR of CPSs modeled in Simulink.", "published": "2024-04-06T17:31:13Z", "authors": ["Aitor Arrieta", "Pablo Valle", "Shaukat Ali"], "categories": ["cs.SE"], "doi": "", "_query": "automated valuation model", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2309.03400v1", "title": "Automated Characterization of a Double Quantum Dot using the Hubbard Model", "abstract": "Semiconductor quantum dots are favorable candidates for quantum information processing due to their long coherence time and potential scalability. However, the calibration and characterization of interconnected quantum dot arrays have proven to be challenging tasks. One method to characterize the configuration of such an array involves using the Hubbard model. In this paper, we present an automated characterization algorithm that efficiently extracts the Hubbard model parameters, including tunnel coupling and capacitive coupling energy, from experimental stability diagrams. Leveraging the dual annealing optimizer, we determine the set of Hubbard parameters that best characterize the experimental data. We compare our method with an alternate, well-established measure of the tunnel coupling and find good agreement within the investigated regime. Our extracted tunnel couplings range from 69 to 517 $μ$eV, and we discuss the limiting factors of our method.", "published": "2023-09-06T23:22:04Z", "authors": ["Will Wang", "John Dean Rooney", "Hongwen Jiang"], "categories": ["cond-mat.mes-hall"], "doi": "", "_query": "automated valuation model", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2509.10417v1", "title": "Long Context Automated Essay Scoring with Language Models", "abstract": "Transformer-based language models are architecturally constrained to process text of a fixed maximum length. Essays written by higher-grade students frequently exceed the maximum allowed length for many popular open-source models. A common approach to addressing this issue when using these models for Automated Essay Scoring is to truncate the input text. This raises serious validity concerns as it undermines the model's ability to fully capture and evaluate organizational elements of the scoring rubric, which requires long contexts to assess. In this study, we evaluate several models that incorporate architectural modifications of the standard transformer architecture to overcome these length limitations using the Kaggle ASAP 2.0 dataset. The models considered in this study include fine-tuned versions of XLNet, Longformer, ModernBERT, Mamba, and Llama models.", "published": "2025-09-12T17:13:47Z", "authors": ["Christopher Ormerod", "Gitit Kehat"], "categories": ["cs.CL"], "doi": "", "_query": "automated valuation model", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2604.06946v1", "title": "An empirical study of LoRA-based fine-tuning of large language models for automated test case generation", "abstract": "Automated test case generation from natural language requirements remains a challenging problem in software engineering due to the ambiguity of requirements and the need to produce structured, executable test artifacts. Recent advances in LLMs have shown promise in addressing this task; however, their effectiveness depends on task-specific adaptation and efficient fine-tuning strategies. In this paper, we present a comprehensive empirical study on the use of parameter-efficient fine-tuning, specifically LoRA, for requirement-based test case generation. We evaluate multiple LLM families, including open-source and proprietary models, under a unified experimental pipeline. The study systematically explores the impact of key LoRA hyperparameters, including rank, scaling factor, and dropout, on downstream performance. We propose an automated evaluation framework based on GPT-4o, which assesses generated test cases across nine quality dimensions. Experimental results demonstrate that LoRA-based fine-tuning significantly improves the performance of all open-source models, with Ministral-8B achieving the best results among them. Furthermore, we show that a fine-tuned 8B open-source model can achieve performance comparable to pre-fine-tuned GPT-4.1 models, highlighting the effectiveness of parameter-efficient adaptation. While GPT-4.1 models achieve the highest overall performance, the performance gap between proprietary and open-source models is substantially reduced after fine-tuning. These findings provide important insights into model selection, fine-tuning strategies, and evaluation methods for automated test generation. In particular, they demonstrate that cost-efficient, locally deployable open-source models can serve as viable alternatives to proprietary systems when combined with well-designed fine-tuning approaches.", "published": "2026-04-08T11:09:49Z", "authors": ["Milad Moradi", "Ke Yan", "David Colwell", "Rhona Asgari"], "categories": ["cs.SE", "cs.AI"], "doi": "", "_query": "automated valuation model", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2405.14463v2", "title": "Epistemic EFX Allocations Exist for Monotone Valuations", "abstract": "We study the fundamental problem of fairly dividing a set of indivisible items among agents with (general) monotone valuations. The notion of envy-freeness up to any item (EFX) is considered to be one of the most fascinating fairness concepts in this line of work. Unfortunately, despite significant efforts, existence of EFX allocations is a major open problem in fair division, thereby making the study of approximations and relaxations of EFX a natural line of research. Recently, Caragiannis et al. introduced a promising relaxation of EFX, called epistemic EFX (EEFX). We say an allocation to be EEFX if, for every agent, it is possible to shuffle the items in the remaining bundles so that she becomes \"EFX-satisfied\". Caragiannis et al. prove existence and polynomial-time computability of EEFX allocations for additive valuations. A natural question asks what happens when we consider valuations more general than additive? We address this important open question and answer it affirmatively by establishing the existence of EEFX allocations for an arbitrary number of agents with general monotone valuations. To the best of our knowledge, EEFX is the only known relaxation of EFX (beside EF1) to have such strong existential guarantees. Furthermore, we complement our existential result by proving computational and information-theoretic lower bounds. We prove that even for an arbitrary number of (more than one) agents with identical submodular valuations, it is PLS-hard to compute EEFX allocations and it requires exponentially-many value queries to do so.", "published": "2024-05-23T11:52:28Z", "authors": ["Hannaneh Akrami", "Nidhi Rathi"], "categories": ["cs.GT"], "doi": "", "_query": "automated valuation model", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2512.06745v2", "title": "Non-continuous valuations on convex bodies and a new characterization of volume", "abstract": "This paper investigates the use of automatic continuity techniques in the context of valuations on convex bodies. We first provide an automatic continuity theorem for valuations restricted to parallelotopes with respect to a fixed basis. This result in combination with a counting argument provides a strengthened version of a classical characterization of volume due to Hadwiger. As a byproduct of the proof it is shown that $[0,n-1]\\cup\\{n\\}$ are precisely the possible degrees of homogeneity of bounded translation invariant valuations on $n$-dimensional convex bodies.", "published": "2025-12-07T09:17:11Z", "authors": ["Jorge S. Ibáñez Marcos", "Pedro Tradacete", "Ignacio Villanueva"], "categories": ["math.MG"], "doi": "", "_query": "automated valuation model", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2210.17160v1", "title": "Experimental study and modeling of the lower-level controller of automated vehicle", "abstract": "Accurate modeling of lower-level controller plays an important role in the traffic flow of automated vehicles (AVs). However, there lacks enough attention with this respect. To address this issue, we conduct a field experiment with two vehicles that are equipped with developable autonomous driving system, where one can customize the upper-level control algorithm. Based on the field experimental data, a new lower-level control model is developed and compared with two widely used ones. The comparison results show that the proposed model outperforms the two previous models in capturing the observed actual acceleration, especially the troughs of the acceleration time series. Furthermore, theoretical analysis indicates that comparing with the proposed model, the two previous models significantly overestimate the stability region of the traffic flow of the AVs and the capacity of stable traffic flow. Our study is expected to further shed light on the importance of accurate lower-level control modeling.", "published": "2022-10-31T09:11:04Z", "authors": ["Hua-Qing Liu", "Shi-Teng Zheng", "Rui Jiang", "Junfang Tian", "Ruidong Yan", "Fang Zhang", "Dezhao Zhang"], "categories": ["physics.soc-ph"], "doi": "", "_query": "automated valuation model", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2411.16391v2", "title": "Human-Calibrated Automated Testing and Validation of Generative Language Models", "abstract": "This paper introduces a comprehensive framework for the evaluation and validation of generative language models (GLMs), with a focus on Retrieval-Augmented Generation (RAG) systems deployed in high-stakes domains such as banking. GLM evaluation is challenging due to open-ended outputs and subjective quality assessments. Leveraging the structured nature of RAG systems, where generated responses are grounded in a predefined document collection, we propose the Human-Calibrated Automated Testing (HCAT) framework. HCAT integrates a) automated test generation using stratified sampling, b) embedding-based metrics for explainable assessment of functionality, risk and safety attributes, and c) a two-stage calibration approach that aligns machine-generated evaluations with human judgments through probability calibration and conformal prediction. In addition, the framework includes robustness testing to evaluate model performance against adversarial, out-of-distribution, and varied input conditions, as well as targeted weakness identification using marginal and bivariate analysis to pinpoint specific areas for improvement. This human-calibrated, multi-layered evaluation framework offers a scalable, transparent, and interpretable approach to GLM assessment, providing a practical and reliable solution for deploying GLMs in applications where accuracy, transparency, and regulatory compliance are paramount.", "published": "2024-11-25T13:53:36Z", "authors": ["Agus Sudjianto", "Aijun Zhang", "Srinivas Neppalli", "Tarun Joshi", "Michal Malohlava"], "categories": ["cs.CL", "cs.AI"], "doi": "", "_query": "automated valuation model", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/1902.06193v1", "title": "An Automated Testing Framework for Conversational Agents", "abstract": "Conversational agents are systems with a conversational interface that afford interaction in spoken language. These systems are becoming prevalent and are preferred in various contexts and for many users. Despite their increasing success, the automated testing infrastructure to support the effective and efficient development of such systems compared to traditional software systems is still limited. Automated testing framework for conversational systems can improve the quality of these systems by assisting developers to write, execute, and maintain test cases. In this paper, we introduce our work-in-progress automated testing framework, and its realization in the Python programming language. We discuss some research problems in the development of such an automated testing framework for conversational agents. In particular, we point out the problems of the specification of the expected behavior, known as test oracles, and semantic comparison of utterances.", "published": "2019-02-17T03:17:03Z", "authors": ["Soodeh Atefi", "Mohammad Amin Alipour"], "categories": ["cs.HC", "cs.CL"], "doi": "", "_query": "automated valuation model", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2505.22464v1", "title": "A Paley-Wiener-Schwartz Theorem for smooth valuations on convex functions", "abstract": "Continuous dually epi-translation invariant valuations on convex functions are characterized in terms of the Fourier-Laplace transform of the associated Goodey-Weil distributions. This description is used to obtain integral representations of the smooth vectors of the natural representation of the group of translations on the space of these valuations. As an application, a complete classification of all closed and affine invariant subspaces is established, yielding density results for valuations defined in terms of mixed Monge-Ampère operators.", "published": "2025-05-28T15:18:04Z", "authors": ["Jonas Knoerr"], "categories": ["math.FA", "math.MG"], "doi": "", "_query": "automated valuation model", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2009.04395v1", "title": "Automated Model Selection for Time-Series Anomaly Detection", "abstract": "Time-series anomaly detection is a popular topic in both academia and industrial fields. Many companies need to monitor thousands of temporal signals for their applications and services and require instant feedback and alerts for potential incidents in time. The task is challenging because of the complex characteristics of time-series, which are messy, stochastic, and often without proper labels. This prohibits training supervised models because of lack of labels and a single model hardly fits different time series. In this paper, we propose a solution to address these issues. We present an automated model selection framework to automatically find the most suitable detection model with proper parameters for the incoming data. The model selection layer is extensible as it can be updated without too much effort when a new detector is available to the service. Finally, we incorporate a customized tuning algorithm to flexibly filter anomalies to meet customers' criteria. Experiments on real-world datasets show the effectiveness of our solution.", "published": "2020-08-25T07:23:43Z", "authors": ["Yuanxiang Ying", "Juanyong Duan", "Chunlei Wang", "Yujing Wang", "Congrui Huang", "Bixiong Xu"], "categories": ["cs.LG", "eess.SP"], "doi": "", "_query": "automated valuation model", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/0710.0652v2", "title": "Matrix pairs over discrete valuation rings determine Littlewood-Richardson fillings", "abstract": "Let M and N be two r x r matrices over a discrete valuation ring of characteristic zero. The orders (with respect to a uniformizing parameter) of the invariant factors of M form a partition of non-negative integers, called the invariant partition of M. Let the invariant partition of M be mu, of N be nu, and of the product MN be lambda. In this paper we construct a Littlewood-Richardson filling of the skew shape lambda/mu with content nu, and show that this filling is an invariant of the orbit of the pair (M,N) with respect to a natural group action on the pair. We relate the algebraic combinatorics of Littlewood-Richardson fillings to a special semicanonical matrix in the orbit of (M,N), from which the Littlewood-Richardson filling, and other combinatorial invariants may be obtained.", "published": "2007-10-02T21:30:20Z", "authors": ["Glenn Appleby", "Tamsen Whitehead"], "categories": ["math.CO", "math.AC"], "doi": "", "_query": "automated valuation model", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/1702.00785v3", "title": "Evaluation of Automated Vehicles Encountering Pedestrians at Unsignalized Crossings", "abstract": "Interactions between vehicles and pedestrians have always been a major problem in traffic safety. Experienced human drivers are able to analyze the environment and choose driving strategies that will help them avoid crashes. What is not yet clear, however, is how automated vehicles will interact with pedestrians. This paper proposes a new method for evaluating the safety and feasibility of the driving strategy of automated vehicles when encountering unsignalized crossings. MobilEye sensors installed on buses in Ann Arbor, Michigan, collected data on 2,973 valid crossing events. A stochastic interaction model was then created using a multivariate Gaussian mixture model. This model allowed us to simulate the movements of pedestrians reacting to an oncoming vehicle when approaching unsignalized crossings, and to evaluate the passing strategies of automated vehicles. A simulation was then conducted to demonstrate the evaluation procedure.", "published": "2017-02-02T03:07:31Z", "authors": ["Baiming Chen", "Ding Zhao", "Huei Peng"], "categories": ["cs.MA", "cs.RO", "eess.SY"], "doi": "", "_query": "automated valuation model", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2407.16928v4", "title": "From Sands to Mansions: Towards Automated Cyberattack Emulation with Classical Planning and Large Language Models", "abstract": "Evolving attacker capabilities demand realistic and continuously updated cyberattack emulation for threat-informed defense and security benchmarking. Towards automated attack emulation, this paper defines modular attack actions and a linking model to organize and chain heterogeneous attack tools into causality-preserving cyberattacks. Building on this foundation, we introduce Aurora: an automated cyberattack emulation system powered by symbolic planning and large language models (LLMs). Aurora crafts actionable, causality-preserving attack chains tailored to Cyber Threat Intelligence (CTI) reports and target environments, and automatically executes these emulations. Using Aurora, we generated an extensive cyberattack emulation dataset from 250 attack reports, 15 times larger than the leading expert-crafted dataset. Our evaluation shows that Aurora significantly outperforms existing methods in creating actionable, diverse, and realistic attack chains. We release the dataset and use it to evaluate three state-of-the-art intrusion detection systems, whose performance differed notably from results on older datasets, highlighting the need for up-to-date, automated attack emulation.", "published": "2024-07-24T01:33:57Z", "authors": ["Lingzhi Wang", "Zhenyuan Li", "Yi Jiang", "Zhengkai Wang", "Xiangmin Shen", "Wei Ruan", "Yan Chen"], "categories": ["cs.CR"], "doi": "", "_query": "automated valuation model", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2411.02942v3", "title": "Constant Approximation for Weighted Nash Social Welfare with Submodular Valuations", "abstract": "We study the problem of assigning items to agents so as to maximize the \\emph{weighted} Nash Social Welfare (NSW) under submodular valuations. The best-known result for the problem is an $O(nw_{\\max})$-approximation due to Garg, Husic, Li, Végh, and Vondrák~[STOC 2023], where $w_{\\max}$ is the maximum weight over all agents. Obtaining a constant approximation algorithm is an open problem in the field that has recently attracted considerable attention. We give the first such algorithm for the problem, thus solving the open problem in the affirmative. Our algorithm is based on the natural Configuration LP for the problem, which was introduced recently by Feng and Li~[ICALP 2024] for the additive valuation case. Our rounding algorithm is similar to that of Li~[SODA 2025] developed for the unrelated machine scheduling problem to minimize weighted completion time. Roughly speaking, we designate the largest item in each configuration as a large item and the remaining items as small items. So, every agent gets precisely 1 fractional large item in the configuration LP solution. With the rounding algorithm in Li~[SODA 2025], we can ensure that in the obtained solution, every agent gets precisely 1 large item, and the assignments of small items are negatively correlated.", "published": "2024-11-05T09:35:02Z", "authors": ["Yuda Feng", "Yang Hu", "Shi Li", "Ruilong Zhang"], "categories": ["cs.GT", "cs.DS"], "doi": "", "_query": "automated valuation model", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2603.21190v1", "title": "DS2SC-Agent: A Multi-Agent Automated Pipeline for Rapid Chiplet Model Generation", "abstract": "Constructing behavioral-level chiplet models (e.g., SystemC) is crucial for early-stage heterogeneous architecture exploration. Traditional manual modeling is notoriously time-consuming and error-prone. Recently, Large Language Models (LLMs) have demonstrated immense potential in automating hardware code generation. However, existing LLM-assisted design frameworks predominantly target highly structured or well-defined design specifications. In practical engineering scenarios, raw datasheets typically encompass lengthy, complex, and highly unstructured information. Consequently, reliable code generation directly from these raw datasheets suffers from severe challenges, including context vanishing and logical hallucinations.To overcome this critical bottleneck, this paper proposes DS2SC-Agent(Datasheet-to-SystemC-Agent): the first end-to-end, fully automated generation pipeline that translates raw datasheets directly into SystemC chiplet models. This system establishes a highly efficient multi-agent collaborative framework. By decoupling the intricate modeling tasks, the proposed pipeline orchestrates a fully automated workflow encompassing unstructured long-document parsing, SystemC core code construction, testbench stimulus generation, and adaptive closed-loop debugging. We comprehensively evaluate the proposed framework on representative single-function chiplets across the analog, digital, and radio frequency (RF) domains--specifically, a Limiting Amplifier (LA), a Fast Fourier Transform (FFT) module, and a Power Amplifier (PA). The evaluation demonstrates that our pipeline seamlessly processes complex real-world datasheets to consistently generate functionally correct SystemC models. This provides a highly efficient and reliable paradigm for agile model library construction while drastically minimizing manual intervention.", "published": "2026-03-22T12:23:38Z", "authors": ["Yiwei Wu", "Yifan Wu", "Yunhao Xiong", "Dengwei Zhao", "Jiaxuan Shen", "Jianfei Jiang", "Guanghui He", "Shikui Tu", "Yanan Sun"], "categories": ["cs.AR"], "doi": "", "_query": "automated valuation model", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/1605.07424v2", "title": "On the p-adic valuation of Stirling numbers of the first kind", "abstract": "For all integers $n \\geq k \\geq 1$, define $H(n,k) := \\sum 1 / (i_1 \\cdots i_k)$, where the sum is extended over all positive integers $i_1 < \\cdots < i_k \\leq n$. These quantities are closely related to the Stirling numbers of the first kind by the identity $H(n,k) = s(n + 1, k + 1) / n!$. Motivated by the works of Erdős-Niven and Chen-Tang, we study the $p$-adic valuation of $H(n,k)$. In particular, for any prime number $p$, integer $k \\geq 2$, and $x \\geq (k-1)p$, we prove that $ν_p(H(n,k)) < -(k - 1)(\\log_p(n/(k - 1)) - 1)$ for all positive integers $n \\in [(k-1)p, x]$ whose base $p$ representations start with the base $p$ representation of $k - 1$, but at most $3x^{0.835}$ exceptions. We also generalize a result of Lengyel by giving a description of $ν_2(H(n,2))$ in terms of an infinite binary sequence.", "published": "2016-05-24T12:43:41Z", "authors": ["Paolo Leonetti", "Carlo Sanna"], "categories": ["math.NT", "math.CO"], "doi": "10.1007/s10474-016-0680-4", "_query": "automated valuation model", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/1511.07674v2", "title": "Affine invariant valuations on polytopes", "abstract": "A classification of SL$(n)$ invariant valuations on the space of convex polytopes in $R^n$ without any continuity assumptions is established. A corresponding result is obtained on the space of convex polytopes in $R^n$ that contain the origin.", "published": "2015-11-24T12:21:20Z", "authors": ["Monika Ludwig", "Matthias Reitzner"], "categories": ["math.MG"], "doi": "10.1007/s00454-016-9838-7", "_query": "automated valuation model", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2007.01977v1", "title": "Lale: Consistent Automated Machine Learning", "abstract": "Automated machine learning makes it easier for data scientists to develop pipelines by searching over possible choices for hyperparameters, algorithms, and even pipeline topologies. Unfortunately, the syntax for automated machine learning tools is inconsistent with manual machine learning, with each other, and with error checks. Furthermore, few tools support advanced features such as topology search or higher-order operators. This paper introduces Lale, a library of high-level Python interfaces that simplifies and unifies automated machine learning in a consistent way.", "published": "2020-07-04T00:55:41Z", "authors": ["Guillaume Baudart", "Martin Hirzel", "Kiran Kate", "Parikshit Ram", "Avraham Shinnar"], "categories": ["cs.LG", "cs.AI"], "doi": "", "_query": "automated valuation model", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2210.06553v1", "title": "Scenario-based Evaluation of Prediction Models for Automated Vehicles", "abstract": "To operate safely, an automated vehicle (AV) must anticipate how the environment around it will evolve. For that purpose, it is important to know which prediction models are most appropriate for every situation. Currently, assessment of prediction models is often performed over a set of trajectories without distinction of the type of movement they capture, resulting in the inability to determine the suitability of each model for different situations. In this work we illustrate how standardized evaluation methods result in wrong conclusions regarding a model's predictive capabilities, preventing a clear assessment of prediction models and potentially leading to dangerous on-road situations. We argue that following evaluation practices in safety assessment for AVs, assessment of prediction models should be performed in a scenario-based fashion. To encourage scenario-based assessment of prediction models and illustrate the dangers of improper assessment, we categorize trajectories of the Waymo Open Motion dataset according to the type of movement they capture. Next, three different models are thoroughly evaluated for different trajectory types and prediction horizons. Results show that common evaluation methods are insufficient and the assessment should be performed depending on the application in which the model will operate.", "published": "2022-10-11T09:45:43Z", "authors": ["Manuel Muñoz Sánchez", "Jos Elfring", "Emilia Silvas", "René van de Molengraft"], "categories": ["cs.AI", "cs.LG", "cs.RO"], "doi": "", "_query": "automated valuation model", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2202.13676v2", "title": "An EF2X Allocation Protocol for Restricted Additive Valuations", "abstract": "We study the problem of fairly allocating a set of $m$ indivisible goods to a set of $n$ agents. Envy-freeness up to any good (EFX) criteria -- which requires that no agent prefers the bundle of another agent after removal of any single good -- is known to be a remarkable analogous of envy-freeness when the resource is a set of indivisible goods. In this paper, we investigate EFX notion for the restricted additive valuations, that is, every good has some non-negative value, and every agent is interested in only some of the goods. We introduce a natural relaxation of EFX called EFkX which requires that no agent envies another agent after removal of any $k$ goods. Our main contribution is an algorithm that finds a complete (i.e., no good is discarded) EF2X allocation for the restricted additive valuations. In our algorithm we devise new concepts, namely \"configuration\" and \"envy-elimination\" that might be of independent interest. We also use our new tools to find an EFX allocation for restricted additive valuations that discards at most $\\lfloor n/2 \\rfloor -1$ goods. This improves the state of the art for the restricted additive valuations by a factor of $2$.", "published": "2022-02-28T10:51:44Z", "authors": ["Hannaneh Akrami", "Rojin Rezvan", "Masoud Seddighin"], "categories": ["cs.GT"], "doi": "", "_query": "automated valuation model", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2602.21943v1", "title": "Mobile-Ready Automated Triage of Diabetic Retinopathy Using Digital Fundus Images", "abstract": "Diabetic Retinopathy (DR) is a major cause of vision impairment worldwide. However, manual diagnosis is often time-consuming and prone to errors, leading to delays in screening. This paper presents a lightweight automated deep learning framework for efficient assessment of DR severity from digital fundus images. We use a MobileNetV3 architecture with a Consistent Rank Logits (CORAL) head to model the ordered progression of disease while maintaining computational efficiency for resource-constrained environments. The model is trained and validated on a combined dataset of APTOS 2019 and IDRiD images using a preprocessing pipeline including circular cropping and illumination normalization. Extensive experiments including 3-fold cross-validation and ablation studies demonstrate strong performance. The model achieves a Quadratic Weighted Kappa (QWK) score of 0.9019 and an accuracy of 80.03 percent. Additionally, we address real-world deployment challenges through model calibration to reduce overconfidence and optimization for mobile devices. The proposed system provides a scalable and practical tool for early-stage diabetic retinopathy screening.", "published": "2026-02-25T14:26:18Z", "authors": ["Aadi Joshi", "Manav S. Sharma", "Vijay Uttam Rathod", "Ashlesha Sawant", "Prajakta Musale", "Asmita B. Kalamkar"], "categories": ["cs.CV"], "doi": "", "_query": "automated valuation model", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2010.15665v1", "title": "Machine Ethics and Automated Vehicles", "abstract": "Road vehicle travel at a reasonable speed involves some risk, even when using computer-controlled driving with failure-free hardware and perfect sensing. A fully-automated vehicle must continuously decide how to allocate this risk without a human driver's oversight. These are ethical decisions, particularly in instances where an automated vehicle cannot avoid crashing. In this chapter, I introduce the concept of moral behavior for an automated vehicle, argue the need for research in this area through responses to anticipated critiques, and discuss relevant applications from machine ethics and moral modeling research.", "published": "2020-10-29T15:14:47Z", "authors": ["Noah J. Goodall"], "categories": ["cs.CY"], "doi": "10.1007/978-3-319-05990-7_9", "_query": "automated valuation model", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/1207.7057v1", "title": "Poincaré series for filtrations defined by discrete valuations with arbitrary center", "abstract": "To study singularities on complex varieties we study Poincaré series of filtrations that are defined by discrete valuations on the local ring at the singularity. In all previous papers on this topic one poses restrictions on the centers of these valuations and often one uses several definitions for Poincaré series. In this article we show that these definitions can differ when the centers of the valuations are not zero-dimensional, i.e. do not have the maximal ideal as center. We give a unifying definition for Poincaré series which also allows filtrations defined by valuations that are all nonzero-dimensional. We then show that this definition satisfies a nice relation between Poincaré series for embedded filtrations and Poincaré series for the ambient space and we give some application for singularities which are nondegenerate with respect to their Newton polyhedron.", "published": "2012-07-30T19:43:21Z", "authors": ["Antonio Campillo", "Ann Lemahieu"], "categories": ["math.AG"], "doi": "", "_query": "automated valuation model", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/1307.4591v1", "title": "Utility indifference valuation for non-smooth payoffs with an application to power derivatives", "abstract": "We consider the problem of exponential utility indifference valuation under the simplified framework where traded and nontraded assets are uncorrelated but where the claim to be priced possibly depends on both. Traded asset prices follow a multivariate Black and Scholes model, while nontraded asset prices evolve as generalized Ornstein-Uhlenbeck processes. We provide a BSDE characterization of the utility indifference price (UIP) for a large class of non-smooth, possibly unbounded, payoffs depending simultaneously on both classes of assets. Focusing then on European claims and using the Gaussian structure of the model allows us to employ some BSDE techniques (in particular, a Malliavin-type representation theorem due to Ma (2002)) to prove the regularity of Z and to characterize the UIP for possibly discontinuous European payoffs as a viscosity solution of a suitable PDE with continuous space derivatives. The optimal hedging strategy is also identified essentially as the delta hedging strategy corresponding to the UIP. Since there are no closed-form formulas in general, we also obtain asymptotic expansions for prices and hedging strategies when the risk aversion parameter is small. Finally, our results are applied to pricing and hedging power derivatives in various structural models for energy markets.", "published": "2013-07-17T12:02:47Z", "authors": ["Giuseppe Benedetti", "Luciano Campi"], "categories": ["q-fin.PR"], "doi": "", "_query": "automated valuation model", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/1306.6137v1", "title": "Effects Of Zoning On Housing Option Value", "abstract": "The research explores the subject of zoning effect on price value of a house in a certain designated zone. Zoning is defined as a land under planned use. This results in price value of the land. As the area of the land decreases an increase in price occurs. This research does not specify the magnitude of a certain unit by which a housing price will increase or decrease but it estimates the housing option value. This research helps the policy makers to improve long term policy decisions. This research will showcase zoning effects on housing option value for the specific zones in the city of Normal. The observations are taken for different parcels of total neighborhoods in the city of Normal. This research will use hedonic model for conducting a regression analysis on the subject. The hypothesis taken into consideration before starting the research is to prove that more 50 percent variation in the housing price is affected by the zoning characteristics. This research will try to prove the hypothesis by comparing the assessed values with the predicted values from the model.", "published": "2013-06-26T05:43:46Z", "authors": ["Prathamesh Muzumdar"], "categories": ["stat.AP"], "doi": "", "_query": "hedonic regression housing", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2108.02442v1", "title": "House Price Determinants and Market Segmentation in Boulder, Colorado: A Hedonic Price Approach", "abstract": "In this research we perform hedonic regression model to examine the residential property price determinants in the city of Boulder in the state of Colorado, USA. The urban housing markets are too compounded to be considered as homogeneous markets. The heterogeneity of an urban property market requires creation of market segmentation. To test whether residential properties in the real estate market in the city of Boulder are analyzed and predicted in the disaggregate level or at an aggregate level we stratify the housing market based on both property types and location and estimate separate hedonic price models for each submarket. The results indicate that the implicit values of the property characteristics are not identical across property types and locations in the city of Boulder and market segmentation exists.", "published": "2021-08-05T08:19:48Z", "authors": ["Mahdieh Yazdani"], "categories": ["econ.GN"], "doi": "", "_query": "hedonic regression housing", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/1804.05040v2", "title": "Stable Outcomes in Modified Fractional Hedonic Games", "abstract": "In coalition formation games self-organized coalitions are created as a result of the strategic interactions of independent agents. For each couple of agents $(i,j)$, weight $w_{i,j}=w_{j,i}$ reflects how much agents $i$ and $j$ benefit from belonging to the same coalition. We consider the modified fractional hedonic game, that is a coalition formation game in which agents' utilities are such that the total benefit of agent $i$ belonging to a coalition (given by the sum of $w_{i,j}$ over all other agents $j$ belonging to the same coalition) is averaged over all the other members of that coalition, i.e., excluding herself. Modified fractional hedonic games constitute a class of succinctly representable hedonic games. We are interested in the scenario in which agents, individually or jointly, choose to form a new coalition or to join an existing one, until a stable outcome is reached. To this aim, we consider common stability notions, leading to strong Nash stable outcomes, Nash stable outcomes or core stable outcomes: we study their existence, complexity and performance, both in the case of general weights and in the case of 0-1 weights. In particular, we completely characterize the existence of the considered stable outcomes and show many tight or asymptotically tight results on the performance of these natural stable outcomes for modified fractional hedonic games, also highlighting the differences with respect to the model of fractional hedonic games, in which the total benefit of an agent in a coalition is averaged over all members of that coalition, i.e., including herself.", "published": "2018-04-13T17:12:37Z", "authors": ["Gianpiero Monaco", "Luca Moscardelli", "Yllka Velaj"], "categories": ["cs.GT"], "doi": "", "_query": "hedonic regression housing", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2410.16745v1", "title": "Characterizing the top trading cycles rule for housing markets with lexicographic preferences", "abstract": "We consider a housing market model with limited externalities where agents care both about their own consumption via demand preferences and about the agent who receives their endowment via supply preferences (we extend the associated lexicographic preference domains introduced in Klaus and Meo, 2023). If preferences are demand lexicographic, then our model extends the classical Shapley-Scarf housing market (Shapley and Scarf, 1974) with strict preferences model. Our main result is a characterization of the corresponding top trading cycles (TTC) rule by individual rationality, pair efficiency, and strategy-proofness (Theorem 1), which extends that of Ekici (2024) from classical Shapley-Scarf housing markets with strict preferences to our model. Two further characterizations are immediately obtained by strengthening pair efficiency to either Pareto efficiency or pairwise stability (Corollaries 1 and 2). Finally, we show that as soon as we extend the preference domain to include demand lexicographic as well as supply lexicographic preferences (e.g., when preferences are separable), no rule satisfying individual rationality, pair efficiency, and strategy-proofness exists (Theorem 2).", "published": "2024-10-22T06:58:48Z", "authors": ["Bettina Klaus"], "categories": ["econ.TH", "cs.GT"], "doi": "10.1007/s00355-024-01556-9", "_query": "hedonic regression housing", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/1802.05870v1", "title": "The dynamic impact of monetary policy on regional housing prices in the US: Evidence based on factor-augmented vector autoregressions", "abstract": "In this study interest centers on regional differences in the response of housing prices to monetary policy shocks in the US. We address this issue by analyzing monthly home price data for metropolitan regions using a factor-augmented vector autoregression (FAVAR) model. Bayesian model estimation is based on Gibbs sampling with Normal-Gamma shrinkage priors for the autoregressive coefficients and factor loadings, while monetary policy shocks are identified using high-frequency surprises around policy announcements as external instruments. The empirical results indicate that monetary policy actions typically have sizeable and significant positive effects on regional housing prices, revealing differences in magnitude and duration. The largest effects are observed in regions located in states on both the East and West Coasts, notably California, Arizona and Florida.", "published": "2018-02-16T09:08:34Z", "authors": ["Manfred M. Fischer", "Florian Huber", "Michael Pfarrhofer", "Petra Staufer-Steinnocher"], "categories": ["econ.EM"], "doi": "", "_query": "hedonic regression housing", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/1706.07705v4", "title": "Spatially filtered unconditional quantile regression: Application to a hedonic analysis", "abstract": "Unconditional quantile regression (UQR) attracts attention in various fields to investigate the impacts of explanatory variables on quantiles of the marginal distribution of an explained variable. This study attempts to introduce spatial dependence into the UQR within the framework of random effects eigenvector spatial filtering, resulting in the model that we term the spatially filtered UQR (SF-UQR). We then develop a computationally efficient approach for SF-UQR estimation. Finally, the performance of the SF-UQR is tested with a hedonic land price model for the Tokyo metropolitan area. SF-UQR is implemented in an R package, \"spmoran.\"", "published": "2017-06-23T13:51:52Z", "authors": ["Daisuke Murakami", "Hajime Seya"], "categories": ["stat.ME"], "doi": "", "_query": "hedonic regression housing", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2609.10856v1", "title": "Following the Preference, Missing the Optimum: Compliance Without Optimization in AI Housing Recommendation", "abstract": "Large language models are becoming the first point of contact for consumer search in domains where the stakes are material and the law is explicit. Existing audits show that models steer housing seekers by perceived identity, but none can say what a user loses when a recommender overlooks a suitable option, for want of an enumerated inventory to score omissions against. We audit AI housing recommendation against a verifiable ground truth. For each of 150 synthetic renter scenarios in New York City we build a pool of 120 real listings with known rent, bedrooms and GTFS-computed transit commute, compute the exact set satisfying the renter's stated constraints, and derive its Pareto frontier. The primary outcome assumes no utility function: a recommendation is strictly dominated if the same pool holds a listing cheaper, faster to commute from and no smaller in bedrooms. Across 9,945 calls to three models from two vendors, compliance is near-perfect (1.8% violation against a 66.6% random floor), yet 39.0% of recommendations are strictly dominated, and the dominating listing is a median 900 USD/month cheaper and 3.5 minutes closer. A within-scenario manipulation separates two capabilities usually conflated: changing one sentence moves median recommended rent by 646 USD/month in the correct direction, so preferences are honored, yet recommendations still sit 606 USD/month above the five cheapest qualifying listings on the same screen, and an unambiguous lexicographic instruction gives no improvement under equivalence testing against a pre-specified 50 USD/month bound. The gap widens with candidate-set size and replicates across OpenAI and Anthropic models to within 3 USD. We characterize the failure as compliance without optimization, propose dominance-rate instrumentation as a deployable diagnostic, and release all code, prompts and per-call results.", "published": "2026-09-09T21:51:15Z", "authors": ["Hsuan Lo"], "categories": ["cs.CY", "cs.IR"], "doi": "", "_query": "hedonic regression housing", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2110.07151v1", "title": "Machine Learning, Deep Learning, and Hedonic Methods for Real Estate Price Prediction", "abstract": "In recent years several complaints about racial discrimination in appraising home values have been accumulating. For several decades, to estimate the sale price of the residential properties, appraisers have been walking through the properties, observing the property, collecting data, and making use of the hedonic pricing models. However, this method bears some costs and by nature is subjective and biased. To minimize human involvement and the biases in the real estate appraisals and boost the accuracy of the real estate market price prediction models, in this research we design data-efficient learning machines capable of learning and extracting the relation or patterns between the inputs (features for the house) and output (value of the houses). We compare the performance of some machine learning and deep learning algorithms, specifically artificial neural networks, random forest, and k nearest neighbor approaches to that of hedonic method on house price prediction in the city of Boulder, Colorado. Even though this study has been done over the houses in the city of Boulder it can be generalized to the housing market in any cities. The results indicate non-linear association between the dwelling features and dwelling prices. In light of these findings, this study demonstrates that random forest and artificial neural networks algorithms can be better alternatives over the hedonic regression analysis for prediction of the house prices in the city of Boulder, Colorado.", "published": "2021-10-14T04:43:04Z", "authors": ["Mahdieh Yazdani"], "categories": ["econ.EM"], "doi": "", "_query": "hedonic regression housing", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/1706.08501v2", "title": "A Simulator for Hedonic Games", "abstract": "Hedonic games are meant to model how coalitions of people form and break apart in the real world. However, it is difficult to run simulations when everything must be done by hand on paper. We present an online software that allows fast and visual simulation of several types of hedonic games. http://lukemiles.org/hedonic-games/", "published": "2017-06-26T17:47:32Z", "authors": ["Luke Harold Miles"], "categories": ["cs.MA", "cs.AI"], "doi": "", "_query": "hedonic regression housing", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2102.07491v1", "title": "Entropy methods for identifying hedonic models", "abstract": "This paper contributes to the literature on hedonic models in two ways. First, it makes use of Queyranne's reformulation of a hedonic model in the discrete case as a network flow problem in order to provide a proof of existence and integrality of a hedonic equilibrium and efficient computation of hedonic prices. Second, elaborating on entropic methods developed in Galichon and Salanié (2014), this paper proposes a new identification strategy for hedonic models in a single market. This methodology allows one to introduce heterogeneities in both consumers' and producers' attributes and to recover producers' profits and consumers' utilities based on the observation of production and consumption patterns and the set of hedonic prices.", "published": "2021-02-15T11:49:21Z", "authors": ["Arnaud Dupuy", "Alfred Galichon", "Marc Henry"], "categories": ["econ.EM"], "doi": "10.1007/s11579-014-0125-1", "_query": "hedonic regression housing", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/1804.03315v1", "title": "Individual and Group Stability in Neutral Restrictions of Hedonic Games", "abstract": "We consider a class of coalition formation games called hedonic games, i.e., games in which the utility of a player is completely determined by the coalition that the player belongs to. We first define the class of subset-additive hedonic games and show that they have the same representation power as the class of hedonic games. We then define a restriction of subset-additive hedonic games that we call subset-neutral hedonic games and generalize a result by Bogomolnaia and Jackson (2002) by showing the existence of a Nash stable partition and an individually stable partition in such games. We also consider neutrally anonymous hedonic games and show that they form a subclass of the subset-additive hedonic games. Finally, we show the existence of a core stable partition that is also individually stable in neutrally anonymous hedonic games by exhibiting an algorithm to compute such a partition.", "published": "2018-04-10T02:37:23Z", "authors": ["Warut Suksompong"], "categories": ["cs.GT"], "doi": "10.1016/j.mathsocsci.2015.07.004", "_query": "hedonic regression housing", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/1108.5592v1", "title": "A Performance Study of Data Mining Techniques: Multiple Linear Regression vs. Factor Analysis", "abstract": "The growing volume of data usually creates an interesting challenge for the need of data analysis tools that discover regularities in these data. Data mining has emerged as disciplines that contribute tools for data analysis, discovery of hidden knowledge, and autonomous decision making in many application domains. The purpose of this study is to compare the performance of two data mining techniques viz., factor analysis and multiple linear regression for different sample sizes on three unique sets of data. The performance of the two data mining techniques is compared on following parameters like mean square error (MSE), R-square, R-Square adjusted, condition number, root mean square error(RMSE), number of variables included in the prediction model, modified coefficient of efficiency, F-value, and test of normality. These parameters have been computed using various data mining tools like SPSS, XLstat, Stata, and MS-Excel. It is seen that for all the given dataset, factor analysis outperform multiple linear regression. But the absolute value of prediction accuracy varied between the three datasets indicating that the data distribution and data characteristics play a major role in choosing the correct prediction technique.", "published": "2011-08-26T07:08:13Z", "authors": ["Abhishek Taneja", "R. K. Chauhan"], "categories": ["cs.DB"], "doi": "", "_query": "hedonic regression housing", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/1507.03474v1", "title": "Simple Causes of Complexity in Hedonic Games", "abstract": "Hedonic games provide a natural model of coalition formation among self-interested agents. The associated problem of finding stable outcomes in such games has been extensively studied. In this paper, we identify simple conditions on expressivity of hedonic games that are sufficient for the problem of checking whether a given game admits a stable outcome to be computationally hard. Somewhat surprisingly, these conditions are very mild and intuitive. Our results apply to a wide range of stability concepts (core stability, individual stability, Nash stability, etc.) and to many known formalisms for hedonic games (additively separable games, games with W-preferences, fractional hedonic games, etc.), and unify and extend known results for these formalisms. They also have broader applicability: for several classes of hedonic games whose computational complexity has not been explored in prior work, we show that our framework immediately implies a number of hardness results for them.", "published": "2015-07-13T14:36:17Z", "authors": ["Dominik Peters", "Edith Elkind"], "categories": ["cs.GT"], "doi": "", "_query": "hedonic regression housing", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/1706.09007v1", "title": "Strategyproof Mechanisms for Additively Separable Hedonic Games and Fractional Hedonic Games", "abstract": "Additively separable hedonic games and fractional hedonic games have received considerable attention. They are coalition forming games of selfish agents based on their mutual preferences. Most of the work in the literature characterizes the existence and structure of stable outcomes (i.e., partitions in coalitions), assuming that preferences are given. However, there is little discussion on this assumption. In fact, agents receive different utilities if they belong to different partitions, and thus it is natural for them to declare their preferences strategically in order to maximize their benefit. In this paper we consider strategyproof mechanisms for additively separable hedonic games and fractional hedonic games, that is, partitioning methods without payments such that utility maximizing agents have no incentive to lie about their true preferences. We focus on social welfare maximization and provide several lower and upper bounds on the performance achievable by strategyproof mechanisms for general and specific additive functions. In most of the cases we provide tight or asymptotically tight results. All our mechanisms are simple and can be computed in polynomial time. Moreover, all the lower bounds are unconditional, that is, they do not rely on any computational or complexity assumptions.", "published": "2017-06-27T18:47:49Z", "authors": ["Michele Flammini", "Gianpiero Monaco", "Qiang Zhang"], "categories": ["cs.AI", "cs.GT"], "doi": "", "_query": "hedonic regression housing", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/1904.09286v2", "title": "Unifying Question Answering, Text Classification, and Regression via Span Extraction", "abstract": "Even as pre-trained language encoders such as BERT are shared across many tasks, the output layers of question answering, text classification, and regression models are significantly different. Span decoders are frequently used for question answering, fixed-class, classification layers for text classification, and similarity-scoring layers for regression tasks, We show that this distinction is not necessary and that all three can be unified as span extraction. A unified, span-extraction approach leads to superior or comparable performance in supplementary supervised pre-trained, low-data, and multi-task learning experiments on several question answering, text classification, and regression benchmarks.", "published": "2019-04-19T17:58:29Z", "authors": ["Nitish Shirish Keskar", "Bryan McCann", "Caiming Xiong", "Richard Socher"], "categories": ["cs.CL"], "doi": "", "_query": "hedonic regression housing", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2509.03834v1", "title": "From Leiden to Pleasure Island: The Constant Potts Model for Community Detection as a Hedonic Game", "abstract": "Community detection is one of the fundamental problems in data science which consists of partitioning nodes into disjoint communities. We present a game-theoretic perspective on the Constant Potts Model (CPM) for partitioning networks into disjoint communities, emphasizing its efficiency, robustness, and accuracy. Efficiency: We reinterpret CPM as a potential hedonic game by decomposing its global Hamiltonian into local utility functions, where the local utility gain of each agent matches the corresponding increase in global utility. Leveraging this equivalence, we prove that local optimization of the CPM objective via better-response dynamics converges in pseudo-polynomial time to an equilibrium partition. Robustness: We introduce and relate two stability criteria: a strict criterion based on a novel notion of robustness, requiring nodes to simultaneously maximize neighbors and minimize non-neighbors within communities, and a relaxed utility function based on a weighted sum of these objectives, controlled by a resolution parameter. Accuracy: In community tracking scenarios, where initial partitions are used to bootstrap the Leiden algorithm with partial ground-truth information, our experiments reveal that robust partitions yield higher accuracy in recovering ground-truth communities.", "published": "2025-09-04T02:50:54Z", "authors": ["Lucas Lopes Felipe", "Konstantin Avrachenkov", "Daniel Sadoc Menasche"], "categories": ["cs.LG", "cs.AI", "cs.GT"], "doi": "10.1016/j.physa.2025.130989", "_query": "hedonic regression housing", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/1612.06850v2", "title": "Extremal Quantile Regression: An Overview", "abstract": "Extremal quantile regression, i.e. quantile regression applied to the tails of the conditional distribution, counts with an increasing number of economic and financial applications such as value-at-risk, production frontiers, determinants of low infant birth weights, and auction models. This chapter provides an overview of recent developments in the theory and empirics of extremal quantile regression. The advances in the theory have relied on the use of extreme value approximations to the law of the Koenker and Bassett (1978) quantile regression estimator. Extreme value laws not only have been shown to provide more accurate approximations than Gaussian laws at the tails, but also have served as the basis to develop bias corrected estimators and inference methods using simulation and suitable variations of bootstrap and subsampling. The applicability of these methods is illustrated with two empirical examples on conditional value-at-risk and financial contagion.", "published": "2016-12-20T20:55:07Z", "authors": ["Victor Chernozhukov", "Iván Fernández-Val", "Tetsuya Kaji"], "categories": ["stat.ME", "econ.EM"], "doi": "10.1201/9781315120256", "_query": "hedonic regression housing", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/1705.10116v1", "title": "Fractional Hedonic Games", "abstract": "The work we present in this paper initiated the formal study of fractional hedonic games, coalition formation games in which the utility of a player is the average value he ascribes to the members of his coalition. Among other settings, this covers situations in which players only distinguish between friends and non-friends and desire to be in a coalition in which the fraction of friends is maximal. Fractional hedonic games thus not only constitute a natural class of succinctly representable coalition formation games, but also provide an interesting framework for network clustering. We propose a number of conditions under which the core of fractional hedonic games is non-empty and provide algorithms for computing a core stable outcome. By contrast, we show that the core may be empty in other cases, and that it is computationally hard in general to decide non-emptiness of the core.", "published": "2017-05-29T11:03:56Z", "authors": ["Haris Aziz", "Florian Brandl", "Felix Brandt", "Paul Harrenstein", "Martin Olsen", "Dominik Peters"], "categories": ["cs.GT"], "doi": "", "_query": "hedonic regression housing", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2412.01666v2", "title": "Quantifying Core Stability Relaxations in Hedonic Games", "abstract": "We study relationships between different relaxed notions of core stability in hedonic games, which are a class of coalition formation games. Our unified approach applies to a newly introduced family of hedonic games, called $α$-hedonic games, which contains previously studied variants such as fractional and additively separable hedonic games. In particular, we derive an upper bound on the maximum factor with which a blocking coalition of a certain size can improve upon an outcome in which no deviating coalition of size at most $q$ exists. Counterintuitively, we show that larger blocking coalitions might sometimes have lower improvement factors. We discuss the tightness conditions of our bound, as well as its implications on the price of anarchy of core relaxations. Our general result has direct implications for several well-studied classes of hedonic games, allowing us to prove two open conjectures by Fanelli et al. (2021) for fractional hedonic games.", "published": "2024-12-02T16:16:02Z", "authors": ["Tom Demeulemeester", "Jannik Peters"], "categories": ["cs.GT", "cs.DM", "math.CO"], "doi": "", "_query": "hedonic regression housing", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/1509.07062v1", "title": "Boolean Hedonic Games", "abstract": "We study hedonic games with dichotomous preferences. Hedonic games are cooperative games in which players desire to form coalitions, but only care about the makeup of the coalitions of which they are members; they are indifferent about the makeup of other coalitions. The assumption of dichotomous preferences means that, additionally, each player's preference relation partitions the set of coalitions of which that player is a member into just two equivalence classes: satisfactory and unsatisfactory. A player is indifferent between satisfactory coalitions, and is indifferent between unsatisfactory coalitions, but strictly prefers any satisfactory coalition over any unsatisfactory coalition. We develop a succinct representation for such games, in which each player's preference relation is represented by a propositional formula. We show how solution concepts for hedonic games with dichotomous preferences are characterised by propositional formulas.", "published": "2015-09-23T17:07:54Z", "authors": ["Haris Aziz", "Paul Harrenstein", "Jérôme Lang", "Michael Wooldridge"], "categories": ["cs.GT", "cs.AI"], "doi": "", "_query": "hedonic regression housing", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2210.06831v1", "title": "Multi-Target XGBoostLSS Regression", "abstract": "Current implementations of Gradient Boosting Machines are mostly designed for single-target regression tasks and commonly assume independence between responses when used in multivariate settings. As such, these models are not well suited if non-negligible dependencies exist between targets. To overcome this limitation, we present an extension of XGBoostLSS that models multiple targets and their dependencies in a probabilistic regression setting. Empirical results show that our approach outperforms existing GBMs with respect to runtime and compares well in terms of accuracy.", "published": "2022-10-13T08:26:14Z", "authors": ["Alexander März"], "categories": ["cs.LG"], "doi": "", "_query": "hedonic regression housing", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/1304.5637v1", "title": "Tucker Tensor Regression and Neuroimaging Analysis", "abstract": "Large-scale neuroimaging studies have been collecting brain images of study individuals, which take the form of two-dimensional, three-dimensional, or higher dimensional arrays, also known as tensors. Addressing scientific questions arising from such data demands new regression models that take multidimensional arrays as covariates. Simply turning an image array into a long vector causes extremely high dimensionality that compromises classical regression methods, and, more seriously, destroys the inherent spatial structure of array data that possesses wealth of information. In this article, we propose a family of generalized linear tensor regression models based upon the Tucker decomposition of regression coefficient arrays. Effectively exploiting the low rank structure of tensor covariates brings the ultrahigh dimensionality to a manageable level that leads to efficient estimation. We demonstrate, both numerically that the new model could provide a sound recovery of even high rank signals, and asymptotically that the model is consistently estimating the best Tucker structure approximation to the full array model in the sense of Kullback-Liebler distance. The new model is also compared to a recently proposed tensor regression model that relies upon an alternative CANDECOMP/PARAFAC (CP) decomposition.", "published": "2013-04-20T15:04:08Z", "authors": ["Xiaoshan Li", "Hua Zhou", "Lexin Li"], "categories": ["stat.ME"], "doi": "", "_query": "hedonic regression housing", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2603.23805v1", "title": "Deep Neural Regression Collapse", "abstract": "Neural Collapse is a phenomenon that helps identify sparse and low rank structures in deep classifiers. Recent work has extended the definition of neural collapse to regression problems, albeit only measuring the phenomenon at the last layer. In this paper, we establish that Neural Regression Collapse (NRC) also occurs below the last layer across different types of models. We show that in the collapsed layers of neural regression models, features lie in a subspace that corresponds to the target dimension, the feature covariance aligns with the target covariance, the input subspace of the layer weights aligns with the feature subspace, and the linear prediction error of the features is close to the overall prediction error of the model. In addition to establishing Deep NRC, we also show that models that exhibit Deep NRC learn the intrinsic dimension of low rank targets and explore the necessity of weight decay in inducing Deep NRC. This paper provides a more complete picture of the simple structure learned by deep networks in the context of regression.", "published": "2026-03-25T00:26:16Z", "authors": ["Akshay Rangamani", "Altay Unal"], "categories": ["cs.LG", "cs.AI", "cs.NE", "stat.ML"], "doi": "", "_query": "hedonic regression housing", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/1601.06722v2", "title": "Optimal designs for comparing regression models with correlated observations", "abstract": "We consider the problem of efficient statistical inference for comparing two regression curves estimated from two samples of dependent measurements. Based on a representation of the best pair of linear unbiased estimators in continuous time models as a stochastic integral, an efficient pair of linear unbiased estimators with corresponding optimal designs for finite sample size is constructed. This pair minimises the width of the confidence band for the difference between the estimated curves. We thus extend results readily available in the literature to the case of correlated observations and provide an easily implementable and efficient solution. The advantages of using such pairs of estimators with corresponding optimal designs for the comparison of regression models are illustrated via numerical examples.", "published": "2016-01-25T19:00:14Z", "authors": ["Holger Dette", "Kirsten Schorning", "Maria Konstantinou"], "categories": ["stat.ME"], "doi": "", "_query": "hedonic regression housing", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/1602.05342v2", "title": "Hedonic Games with Graph-restricted Communication", "abstract": "We study hedonic coalition formation games in which cooperation among the players is restricted by a graph structure: a subset of players can form a coalition if and only if they are connected in the given graph. We investigate the complexity of finding stable outcomes in such games, for several notions of stability. In particular, we provide an efficient algorithm that finds an individually stable partition for an arbitrary hedonic game on an acyclic graph. We also introduce a new stability concept -in-neighbor stability- which is tailored for our setting. We show that the problem of finding an in-neighbor stable outcome admits a polynomial-time algorithm if the underlying graph is a path, but is NP-hard for arbitrary trees even for additively separable hedonic games; for symmetric additively separable games we obtain a PLS-hardness result.", "published": "2016-02-17T09:18:30Z", "authors": ["Ayumi Igarashi", "Edith Elkind"], "categories": ["cs.GT"], "doi": "", "_query": "hedonic regression housing", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/1112.0918v2", "title": "On best subset regression", "abstract": "In this paper we discuss the variable selection method from \\ell0-norm constrained regression, which is equivalent to the problem of finding the best subset of a fixed size. Our study focuses on two aspects, consistency and computation. We prove that the sparse estimator from such a method can retain all of the important variables asymptotically for even exponentially growing dimensionality under regularity conditions. This indicates that the best subset regression method can efficiently shrink the full model down to a submodel of a size less than the sample size, which can be analyzed by well-developed regression techniques for such cases in a follow-up study. We provide an iterative algorithm, called orthogonalizing subset selection (OSS), to address computational issues in best subset regression. OSS is an EM algorithm, and thus possesses the monotonicity property. For any sparse estimator, OSS can improve its fit of the model by putting it as an initial point. After this improvement, the sparsity of the estimator is kept. Another appealing feature of OSS is that, similarly to an effective algorithm for a continuous optimization problem, OSS can converge to the global solution to the \\ell0-norm constrained regression problem if the initial point lies in a neighborhood of the global solution. An accelerating algorithm of OSS and its combination with forward stepwise selection are also investigated. Simulations and a real example are presented to evaluate the performances of the proposed methods.", "published": "2011-12-05T13:13:25Z", "authors": ["Shifeng Xiong"], "categories": ["stat.ME", "stat.CO", "stat.ML"], "doi": "", "_query": "hedonic regression housing", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2602.18506v1", "title": "Control in Hedonic Games", "abstract": "We initiate the study of control in hedonic games, where an external actor influences coalition formation by adding or deleting agents. We consider three basic control goals (1) enforcing that an agent is not alone (NA); (2) enforcing that a pair of agents is in the same coalition (PA); (3) enforcing that all agents are in the same grand coalition (GR), combined with two control actions: adding agents (AddAg) or deleting agents (DelAg). We analyze these problems for friend-oriented and additive preferences under individual rationality, individual stability, Nash stability, and core stability. We provide a complete computational complexity classification for control in hedonic games.", "published": "2026-02-18T11:52:04Z", "authors": ["Jiehua Chen", "Jakob Guttmann", "Merisa Mustajbašić", "Sofia Simola"], "categories": ["cs.GT", "cs.MA"], "doi": "", "_query": "hedonic regression housing", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2305.08887v1", "title": "Covariate-distance Weighted Regression (CWR): A Case Study for Estimation of House Prices", "abstract": "Geographically weighted regression (GWR) is a popular tool for modeling spatial heterogeneity in a regression model. However, the current weighting function used in GWR only considers the geographical distance, while the attribute similarity is totally ignored. In this study, we proposed a covariate weighting function that combines the geographical distance and attribute distance. The covariate-distance weighted regression (CWR) is the extension of GWR including geographical distance and attribute distance. House prices are affected by numerous factors, such as house age, floor area, and land use. Prediction model is used to help understand the characteristics of regional house prices. The CWR was used to understand the relationship between the house price and controlling factors. The CWR can consider the geological and attribute distances, and produce accurate estimates of house price that preserve the weight matrix for geological and attribute distance functions. Results show that the house attributes/conditions and the characteristics of the house, such as floor area and house age, might affect the house price. After factor selection, in which only house age and floor area of a building are considered, the RMSE of the CWR model can be improved by 2.9%-26.3% for skyscrapers when compared to the GWR. CWR can effectively reduce estimation errors from traditional spatial regression models and provide novel and feasible models for spatial estimation.", "published": "2023-05-15T03:05:57Z", "authors": ["Hone-Jay Chu", "Po-Hung Chen", "Sheng-Mao Chang", "Muhammad Zeeshan Ali", "Sumriti Ranjan Patra"], "categories": ["cs.LG"], "doi": "", "_query": "hedonic regression housing", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/1710.07004v2", "title": "Modal Regression using Kernel Density Estimation: a Review", "abstract": "We review recent advances in modal regression studies using kernel density estimation. Modal regression is an alternative approach for investigating relationship between a response variable and its covariates. Specifically, modal regression summarizes the interactions between the response variable and covariates using the conditional mode or local modes. We first describe the underlying model of modal regression and its estimators based on kernel density estimation. We then review the asymptotic properties of the estimators and strategies for choosing the smoothing bandwidth. We also discuss useful algorithms and similar alternative approaches for modal regression, and propose future direction in this field.", "published": "2017-10-19T05:25:07Z", "authors": ["Yen-Chi Chen"], "categories": ["stat.ME", "econ.EM"], "doi": "", "_query": "hedonic regression housing", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/1406.0156v2", "title": "$l_1$-regularized Outlier Isolation and Regression", "abstract": "This paper proposed a new regression model called $l_1$-regularized outlier isolation and regression (LOIRE) and a fast algorithm based on block coordinate descent to solve this model. Besides, assuming outliers are gross errors following a Bernoulli process, this paper also presented a Bernoulli estimate model which, in theory, should be very accurate and robust due to its complete elimination of affections caused by outliers. Though this Bernoulli estimate is hard to solve, it could be approximately achieved through a process which takes LOIRE as an important intermediate step. As a result, the approximate Bernoulli estimate is a good combination of Bernoulli estimate's accuracy and LOIRE regression's efficiency with several simulations conducted to strongly verify this point. Moreover, LOIRE can be further extended to realize robust rank factorization which is powerful in recovering low-rank component from massive corruptions. Extensive experimental results showed that the proposed method outperforms state-of-the-art methods like RPCA and GoDec in the aspect of computation speed with a competitive performance.", "published": "2014-06-01T11:52:19Z", "authors": ["Sheng Han", "Suzhen Wang", "Xinyu Wu"], "categories": ["cs.CV", "cs.LG", "stat.ML"], "doi": "", "_query": "hedonic regression housing", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2310.05139v1", "title": "Maximizing Utilitarian and Egalitarian Welfare of Fractional Hedonic Games on Tree-like Graphs", "abstract": "Fractional hedonic games are coalition formation games where a player's utility is determined by the average value they assign to the members of their coalition. These games are a variation of graph hedonic games, which are a class of coalition formation games that can be succinctly represented. Due to their applicability in network clustering and their relationship to graph hedonic games, fractional hedonic games have been extensively studied from various perspectives. However, finding welfare-maximizing partitions in fractional hedonic games is a challenging task due to the nonlinearity of utilities. In fact, it has been proven to be NP-hard and can be solved in polynomial time only for a limited number of graph classes, such as trees. This paper presents (pseudo)polynomial-time algorithms to compute welfare-maximizing partitions in fractional hedonic games on tree-like graphs. We consider two types of social welfare measures: utilitarian and egalitarian. Tree-like graphs refer to graphs with bounded treewidth and block graphs. A hardness result is provided, demonstrating that the pseudopolynomial-time solvability is the best possible under the assumption P$\\neq$NP.", "published": "2023-10-08T12:18:08Z", "authors": ["Tesshu Hanaka", "Airi Ikeyama", "Hirotaka Ono"], "categories": ["cs.GT", "cs.AI"], "doi": "", "_query": "hedonic regression housing", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2502.18614v1", "title": "Towards Using Voice for Hedonic Shopping Motivations", "abstract": "Besides the utilitarian aspects of online shopping, hedonic motivations play a significant role in shaping the shopping behavior of online users. With the increased popularity of voice-enabled devices, online shopping platforms have attempted to drive online shopping on voice. However, we explain why voice might be more suitable for the hedonic aspects of shopping. We introduce a prototype that enables such focus in a voice experience and share our findings from a qualitative study.", "published": "2025-02-25T19:59:55Z", "authors": ["Morteza Behrooz", "Preetham Kolari", "Fred Zaw", "Lindsay Kenzig", "Arnav Jhala"], "categories": ["cs.HC"], "doi": "", "_query": "hedonic regression housing", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2007.12127v1", "title": "Generating Empirical Core Size Distributions of Hedonic Games using a Monte Carlo Method", "abstract": "Data analytics allows an analyst to gain insight into underlying populations through the use of various computational approaches, including Monte Carlo methods. This paper discusses an approach to apply Monte Carlo methods to hedonic games. Hedonic games have gain popularity over the last two decades leading to several research articles that are concerned with the necessary, sufficient, or both conditions of the existence of a core partition. Researchers have used analytical methods for this work. We propose that using a numerical approach will give insights that might not be available through current analytical methods. In this paper, we describe an approach to representing hedonic games, with strict preferences, in a matrix form that can easily be generated; that is, a hedonic game with randomly generated preferences for each player. Using this generative approach, we were able to create and solve, i.e., find any core partitions, of millions of hedonic games. Our Monte Carlo experiment generated games with up to thirteen players. The results discuss the distribution form of the core size of the games of a given number of players. We also discuss computational considerations. Our numerical study of hedonic games gives insight into the underlying properties of hedonic games.", "published": "2020-07-23T16:54:41Z", "authors": ["Andrew J. Collins", "Sheida Etemadidavan", "Wael Khallouli"], "categories": ["econ.TH"], "doi": "", "_query": "hedonic regression housing", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2305.00044v4", "title": "Hedonic Prices and Quality Adjusted Price Indices Powered by AI", "abstract": "We develop empirical models that efficiently process large amounts of unstructured product data (text, images, prices, quantities) to produce accurate hedonic price estimates and derived indices. To achieve this, we generate abstract product attributes (or ``features'') from descriptions and images using deep neural networks. These attributes are then used to estimate the hedonic price function. To demonstrate the effectiveness of this approach, we apply the models to Amazon's data for first-party apparel sales, and estimate hedonic prices. The resulting models have a very high out-of-sample predictive accuracy, with $R^2$ ranging from $80\\%$ to $90\\%$. Finally, we construct the AI-based hedonic Fisher price index, chained at the year-over-year frequency, and contrast it with the CPI and other electronic indices.", "published": "2023-04-28T18:37:59Z", "authors": ["Patrick Bajari", "Zhihao Cen", "Victor Chernozhukov", "Manoj Manukonda", "Suhas Vijaykumar", "Jin Wang", "Ramon Huerta", "Junbo Li", "Ling Leng", "George Monokroussos", "Shan Wang"], "categories": ["econ.GN", "cs.LG"], "doi": "10.1016/j.jeconom.2025.106052", "_query": "hedonic regression housing", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2511.11531v1", "title": "Deviation Dynamics in Cardinal Hedonic Games", "abstract": "Computing stable partitions in hedonic games is a challenging task because there exist games in which stable outcomes do not exist. Even more, these No-instances can often be leveraged to prove computational hardness results. We make this impression rigorous in a dynamic model of cardinal hedonic games by providing meta theorems. These imply hardness of deciding about the possible or necessary convergence of deviation dynamics based on the mere existence of No-instances. Our results hold for additively separable, fractional, and modified fractional hedonic games (ASHGs, FHGs, and MFHGs). Moreover, they encompass essentially all reasonable stability notions based on single-agent deviations. In addition, we propose dynamics as a method to find individually rational and contractually individual stable (CIS) partitions in ASHGs. In particular, we find that CIS dynamics from the singleton partition possibly converge after a linear number of deviations but may require an exponential number of deviations in the worst case.", "published": "2025-11-14T18:06:28Z", "authors": ["Valentin Zech", "Martin Bullinger"], "categories": ["cs.GT", "cs.DS"], "doi": "", "_query": "hedonic regression housing", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/1201.4754v1", "title": "Existence of Stability in Hedonic Coalition Formation Games", "abstract": "In this paper, we examine \\emph{hedonic coalition formation games} in which each player's preferences over partitions of players depend only on the members of his coalition. We present three main results in which restrictions on the preferences of the players guarantee the existence of stable partitions for various notions of stability. The preference restrictions pertain to \\emph{top responsiveness} and \\emph{bottom responsiveness} which model optimistic and pessimistic behavior of players respectively. The existence results apply to natural subclasses of \\emph{additive separable hedonic games} and \\emph{hedonic games with \\B-preferences}. It is also shown that our existence results cannot be strengthened to the case of stronger known stability concepts.", "published": "2012-01-23T16:15:22Z", "authors": ["Haris Aziz", "Florian Brandl"], "categories": ["cs.GT"], "doi": "", "_query": "hedonic regression housing", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2411.05713v1", "title": "Settling the Complexity of Popularity in Additively Separable and Fractional Hedonic Games", "abstract": "We study coalition formation in the framework of hedonic games. There, a set of agents needs to be partitioned into disjoint coalitions, where agents have a preference order over coalitions. A partition is called popular if it does not lose a majority vote among the agents against any other partition. Unfortunately, hedonic games need not admit popular partitions and prior work suggests significant computational hardness. We confirm this impression by proving that deciding about the existence of popular partitions in additively separable and fractional hedonic games is $Σ_2^p$-complete. This settles the complexity of these problems and is the first work that proves completeness of popularity for the second level of the polynomial hierarchy.", "published": "2024-11-08T17:15:43Z", "authors": ["Martin Bullinger", "Matan Gilboa"], "categories": ["cs.GT"], "doi": "", "_query": "hedonic regression housing", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/1202.1964v6", "title": "(Ab)Using Regression for Data Adjustment", "abstract": "In various economic applications, people want to compare $n$ units with respect to certain quantities $Y_1, Y_2, \\ldots, Y_n$ measuring their performance. The latter, however, is often influenced by certain factors which are beyond control of the units, and one would like to extract an adjusted performance from the data. Specifically, let $X_i \\in \\mathcal{X}$ summarize the factors of the $i$-th unit. Then one could think of a model equation $Y_i = f_o(X_i) + ε_i$ with a regression function $f_o : \\mathcal{X} \\to \\mathbb{R}$ describing the unavoidable influence of the factors $X_i$ and $ε_i$ being the adjusted performance of the $i$-th unit. Now a common proposal is to estimate $f_o$ via regression methods by a function $\\hat{f}$ depending on the current data $(X_i,Y_i)$, possibly augmented by additional past data, and to use the residuals $\\hatε_i := Y_i - \\hat{f}(X_i)$ as surrogates for the adjusted performances $ε_i$. In the present report we discuss this approach, its potential pitfalls and (mis)interpretation. In particular, an unavoidable property of the residuals $\\hatε_i$ is that they measure only parts of the adjusted performance while the remaining parts get hidden in the estimated function $\\hat{f}$. Possible alternatives are mentioned briefly.", "published": "2012-02-09T12:28:17Z", "authors": ["Lutz Duembgen"], "categories": ["math.ST"], "doi": "", "_query": "hedonic regression housing", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2510.07895v1", "title": "Adaptive Optimizable Gaussian Process Regression Linear Least Squares Regression Filtering Method for SEM Images", "abstract": "Scanning Electron Microscopy (SEM) images often suffer from noise contamination, which degrades image quality and affects further analysis. This research presents a complete approach to estimate their Signal-to-Noise Ratio (SNR) and noise variance (NV), and enhance image quality using NV-guided Wiener filter. The main idea of this study is to use a good SNR estimation technique and infuse a machine learning model to estimate NV of the SEM image, which then guides the wiener filter to remove the noise, providing a more robust and accurate SEM image filtering pipeline. First, we investigate five different SNR estimation techniques, namely Nearest Neighbourhood (NN) method, First-Order Linear Interpolation (FOL) method, Nearest Neighbourhood with First-Order Linear Interpolation (NN+FOL) method, Non-Linear Least Squares Regression (NLLSR) method, and Linear Least Squares Regression (LSR) method. It is shown that LSR method to perform better than the rest. Then, Support Vector Machines (SVM) and Gaussian Process Regression (GPR) are tested by pairing it with LSR. In this test, the Optimizable GPR model shows the highest accuracy and it stands as the most effective solution for NV estimation. Combining these results lead to the proposed Adaptive Optimizable Gaussian Process Regression Linear Least Squares Regression (AO-GPRLLSR) Filtering pipeline. The AO-GPRLLSR method generated an estimated noise variance which served as input to NV-guided Wiener filter for improving the quality of SEM images. The proposed method is shown to achieve notable success in estimating SNR and NV of SEM images and leads to lower Mean Squared Error (MSE) after the filtering process.", "published": "2025-10-09T07:45:35Z", "authors": ["D. Chee Yong Ong", "I. Bukhori", "K. S. Sim", "K. Beng Gan"], "categories": ["cs.LG"], "doi": "10.1109/ACCESS.2025.3573389", "_query": "hedonic regression housing", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/1903.00303v1", "title": "Hedonic Diversity Games", "abstract": "We consider a coalition formation setting where each agent belongs to one of the two types, and agents' preferences over coalitions are determined by the fraction of the agents of their own type in each coalition. This setting differs from the well-studied Schelling's model in that some agents may prefer homogeneous coalitions, while others may prefer to be members of a diverse group, or a group that mostly consists of agents of the other type. We model this setting as a hedonic game and investigate the existence of stable outcomes using hedonic games solution concepts. We show that a core stable outcome may fail to exist and checking the existence of core stable outcomes is computationally hard. On the other hand, we propose an efficient algorithm to find an individually stable outcome under the natural assumption that agents' preferences over fractions of the agents of their own type are single-peaked.", "published": "2019-03-01T14:06:47Z", "authors": ["Robert Bredereck", "Edith Elkind", "Ayumi Igarashi"], "categories": ["cs.GT"], "doi": "", "_query": "hedonic regression housing", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2101.09673v2", "title": "Incentive Mechanism Design for Federated Learning: Hedonic Game Approach", "abstract": "Incentive mechanism design is crucial for enabling federated learning. We deal with clustering problem of agents contributing to federated learning setting. Assuming agents behave selfishly, we model their interaction as a stable coalition partition problem using hedonic games where agents and clusters are the players and coalitions, respectively. We address the following question: is there a family of hedonic games ensuring a Nash-stable coalition partition? We propose the Nash-stable set which determines the family of hedonic games possessing at least one Nash-stable partition, and analyze the conditions of non-emptiness of the Nash-stable set. Besides, we deal with the decentralized clustering. We formulate the problem as a non-cooperative game and prove the existence of a potential game.", "published": "2021-01-24T08:35:02Z", "authors": ["Cengis Hasan"], "categories": ["cs.GT"], "doi": "", "_query": "hedonic regression housing", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2011.01778v1", "title": "Hedonic Expertise Games", "abstract": "We consider a team formation setting where agents have varying levels of expertise in a global set of required skills, and teams are ranked with respect to how well the expertise of teammates complement each other. We model this setting as a hedonic game, and we show that this class of games possesses many desirable properties, some of which are as follows: A partition that is Nash stable, core stable and Pareto optimal is always guaranteed to exist. A contractually individually stable partition (and a Nash stable partition in a restricted setting) can be found in polynomial-time. A core stable partition can be approximated within a factor of $1 - \\frac{1}{e}$, and this bound is tight unless $\\sf P = NP$. We also introduce a larger and relatively general class of games, which we refer to as monotone submodular hedonic games with common ranking property. We show that the above multi-concept existence guarantee also holds for this larger class of games.", "published": "2020-11-03T15:19:38Z", "authors": ["Bugra Caskurlu", "Fatih Erdem Kizilkaya", "Berkehan Ozen"], "categories": ["cs.GT"], "doi": "", "_query": "hedonic regression housing", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2102.08162v1", "title": "Integrating Floor Plans into Hedonic Models for Rent Price Appraisal", "abstract": "Online real estate platforms have become significant marketplaces facilitating users' search for an apartment or a house. Yet it remains challenging to accurately appraise a property's value. Prior works have primarily studied real estate valuation based on hedonic price models that take structured data into account while accompanying unstructured data is typically ignored. In this study, we investigate to what extent an automated visual analysis of apartment floor plans on online real estate platforms can enhance hedonic rent price appraisal. We propose a tailored two-staged deep learning approach to learn price-relevant designs of floor plans from historical price data. Subsequently, we integrate the floor plan predictions into hedonic rent price models that account for both structural and locational characteristics of an apartment. Our empirical analysis based on a unique dataset of 9174 real estate listings suggests that current hedonic models underutilize the available data. We find that (1) the visual design of floor plans has significant explanatory power regarding rent prices - even after controlling for structural and locational apartment characteristics, and (2) harnessing floor plans results in an up to 10.56% lower out-of-sample prediction error. We further find that floor plans yield a particularly high gain in prediction performance for older and smaller apartments. Altogether, our empirical findings contribute to the existing research body by establishing the link between the visual design of floor plans and real estate prices. Moreover, our approach has important implications for online real estate platforms, which can use our findings to enhance user experience in their real estate listings.", "published": "2021-02-16T14:05:33Z", "authors": ["Kirill Solovev", "Nicolas Pröllochs"], "categories": ["cs.LG", "econ.GN", "stat.AP"], "doi": "10.1145/3442381.3449967", "_query": "hedonic regression housing", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2205.11939v1", "title": "On Hedonic Games with Common Ranking Property", "abstract": "Hedonic games are a prominent model of coalition formation, in which each agent's utility only depends on the coalition she resides. The subclass of hedonic games that models the formation of general partnerships, where output is shared equally among affiliates, is referred to as hedonic games with common ranking property (HGCRP). Aside from their economic motivation, HGCRP came into prominence since they are guaranteed to have core stable solutions that can be found efficiently. We improve upon existing results by proving that every instance of HGCRP has a solution that is Pareto optimal, core stable and individually stable. The economic significance of this result is that efficiency is not to be totally sacrificed for the sake of stability in HGCRP. We establish that finding such a solution is {\\bf NP-hard} even if the sizes of the coalitions are bounded above by $3$; however, it is polynomial time solvable if the sizes of the coalitions are bounded above by $2$. We show that the gap between the total utility of a core stable solution and that of the socially-optimal solution (OPT) is bounded above by $n$, where $n$ is the number of agents, and that this bound is tight. Our investigations reveal that computing OPT is inapproximable within better than $O(n^{1-ε})$ for any fixed $ε> 0$, and that this inapproximability lower bound is polynomially tight. However, OPT can be computed in polynomial time if the sizes of the coalitions are bounded above by $2$.", "published": "2022-05-24T10:10:40Z", "authors": ["Bugra Caskurlu", "Fatih Erdem Kizilkaya"], "categories": ["cs.GT", "econ.TH"], "doi": "", "_query": "hedonic regression housing", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2012.05187v2", "title": "Smoothed Quantile Regression with Large-Scale Inference", "abstract": "Quantile regression is a powerful tool for learning the relationship between a response variable and a multivariate predictor while exploring heterogeneous effects. In this paper, we consider statistical inference for quantile regression with large-scale data in the \"increasing dimension\" regime. We provide a comprehensive and in-depth analysis of a convolution-type smoothing approach that achieves adequate approximation to computation and inference for quantile regression. This method, which we refer to as {\\it{conquer}}, turns the non-differentiable quantile loss function into a twice-differentiable, convex and locally strongly convex surrogate, which admits a fast and scalable Barzilai-Borwein gradient-based algorithm to perform optimization, and multiplier bootstrap for statistical inference. Theoretically, we establish explicit non-asymptotic bounds on both estimation and Bahadur-Kiefer linearization errors, from which we show that the asymptotic normality of the conquer estimator holds under a weaker requirement on the number of the regressors than needed for conventional quantile regression. Moreover, we prove the validity of multiplier bootstrap confidence constructions. Our numerical studies confirm the conquer estimator as a practical and reliable approach to large-scale inference for quantile regression. Software implementing the methodology is available in the \\texttt{R} package \\texttt{conquer}.", "published": "2020-12-09T17:27:20Z", "authors": ["Xuming He", "Xiaoou Pan", "Kean Ming Tan", "Wen-Xin Zhou"], "categories": ["math.ST", "stat.ME"], "doi": "", "_query": "hedonic regression housing", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2205.00605v3", "title": "Cluster-based Regression using Variational Inference and Applications in Financial Forecasting", "abstract": "This paper describes an approach to simultaneously identify clusters and estimate cluster-specific regression parameters from the given data. Such an approach can be useful in learning the relationship between input and output when the regression parameters for estimating output are different in different regions of the input space. Variational Inference (VI), a machine learning approach to obtain posterior probability densities using optimization techniques, is used to identify clusters of explanatory variables and regression parameters for each cluster. From these results, one can obtain both the expected value and the full distribution of predicted output. Other advantages of the proposed approach include the elegant theoretical solution and clear interpretability of results. The proposed approach is well-suited for financial forecasting where markets have different regimes (or clusters) with different patterns and correlations of market changes in each regime. In financial applications, knowledge about such clusters can provide useful insights about portfolio performance and identify the relative importance of variables in different market regimes. An illustrative example of predicting one-day S&P change is considered to illustrate the approach and compare the performance of the proposed approach with standard regression without clusters. Due to the broad applicability of the problem, its elegant theoretical solution, and the computational efficiency of the proposed algorithm, the approach may be useful in a number of areas extending beyond the financial domain.", "published": "2022-05-02T01:19:37Z", "authors": ["Udai Nagpal", "Krishan Nagpal"], "categories": ["q-fin.ST", "cs.LG", "stat.ME", "stat.ML"], "doi": "", "_query": "hedonic regression housing", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2212.13890v1", "title": "ECG-Based Electrolyte Prediction: Evaluating Regression and Probabilistic Methods", "abstract": "Objective: Imbalances of the electrolyte concentration levels in the body can lead to catastrophic consequences, but accurate and accessible measurements could improve patient outcomes. While blood tests provide accurate measurements, they are invasive and the laboratory analysis can be slow or inaccessible. In contrast, an electrocardiogram (ECG) is a widely adopted tool which is quick and simple to acquire. However, the problem of estimating continuous electrolyte concentrations directly from ECGs is not well-studied. We therefore investigate if regression methods can be used for accurate ECG-based prediction of electrolyte concentrations. Methods: We explore the use of deep neural networks (DNNs) for this task. We analyze the regression performance across four electrolytes, utilizing a novel dataset containing over 290000 ECGs. For improved understanding, we also study the full spectrum from continuous predictions to binary classification of extreme concentration levels. To enhance clinical usefulness, we finally extend to a probabilistic regression approach and evaluate different uncertainty estimates. Results: We find that the performance varies significantly between different electrolytes, which is clinically justified in the interplay of electrolytes and their manifestation in the ECG. We also compare the regression accuracy with that of traditional machine learning models, demonstrating superior performance of DNNs. Conclusion: Discretization can lead to good classification performance, but does not help solve the original problem of predicting continuous concentration levels. While probabilistic regression demonstrates potential practical usefulness, the uncertainty estimates are not particularly well-calibrated. Significance: Our study is a first step towards accurate and reliable ECG-based prediction of electrolyte concentration levels.", "published": "2022-12-21T18:54:11Z", "authors": ["Philipp Von Bachmann", "Daniel Gedon", "Fredrik K. Gustafsson", "Antônio H. Ribeiro", "Erik Lampa", "Stefan Gustafsson", "Johan Sundström", "Thomas B. Schön"], "categories": ["eess.SP", "cs.CV", "cs.LG"], "doi": "", "_query": "hedonic regression housing", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2310.08133v1", "title": "Multi Level Dense Layer Neural Network Model for Housing Price Prediction", "abstract": "Predicting the price of a house remains a challenging issue that needs to be addressed. Research has attempted to establish a model with different methods and algorithms to predict the housing price, from the traditional hedonic model to a neural network algorithm. However, many existing algorithms in the literature are proposed without any finetuning and customization in the model. In this paper, the author attempted to propose a novel neural network-based model to improve the performance of housing price prediction. Inspired by the modular neural network, the proposed model consists of a three-level neural network that is capable to process information in parallel. The author compared several state-of-the-art algorithms available in the literature on the Boston housing dataset to evaluate the effectiveness of the proposed model. The results show that the proposed model provides better accuracy and outperforms existing algorithms in different evaluation metrics. The code for the implementation is available https://github.com/wijayarobert/MultiLevelDenseLayerNN", "published": "2023-10-12T08:46:26Z", "authors": ["Robert Wijaya"], "categories": ["cs.CY"], "doi": "", "_query": "hedonic regression housing", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2510.06762v2", "title": "Function regression using the forward forward training and inferring paradigm", "abstract": "Function regression/approximation is a fundamental application of machine learning. Neural networks (NNs) can be easily trained for function regression using a sufficient number of neurons and epochs. The forward-forward learning algorithm is a novel approach for training neural networks without backpropagation, and is well suited for implementation in neuromorphic computing and physical analogs for neural networks. To the best of the authors' knowledge, the Forward Forward paradigm of training and inferencing NNs is currently only restricted to classification tasks. This paper introduces a new methodology for approximating functions (function regression) using the Forward-Forward algorithm. Furthermore, the paper evaluates the developed methodology on univariate and multivariate functions, and provides preliminary studies of extending the proposed Forward-Forward regression to Kolmogorov Arnold Networks, and Deep Physical Neural Networks.", "published": "2025-10-08T08:41:14Z", "authors": ["Shivam Padmani", "Akshay Joshi"], "categories": ["cs.LG"], "doi": "", "_query": "hedonic regression housing", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2511.22370v1", "title": "Solving Four Open Problems about Core Stability in Altruistic Hedonic Games", "abstract": "Hedonic games -- at the interface of cooperative game theory and computational social choice -- are coalition formation games in which the players have preferences over the coalitions they can join. Kerkmann et al. [13] introduced altruistic hedonic games where the players' utilities depend not only on their own but also on their friends' valuations of coalitions. The complexity of the verification problem for core stability has remained open in four variants of altruistic hedonic games: namely, for the variants with average- and minimum-based \"equal-treatment\" and \"altruistic-treatment\" preferences. We solve these four open questions by proving the corresponding problems coNP-complete; our reductions rely on rather intricate gadgets in the related networks of friends.", "published": "2025-11-27T12:12:01Z", "authors": ["Jörg Rothe", "Ildikó Schlotter"], "categories": ["cs.GT", "cs.CC"], "doi": "10.4204/EPTCS.437.3", "_query": "hedonic regression housing", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2603.02456v6", "title": "When Does Static Willingness to Pay Mislead? A Framework for Dynamic Hedonic Valuation", "abstract": "Many policy counterfactuals depend on how consumers value product attributes such as sugar, caffeine, alcohol, or emissions. Standard hedonic and differentiated-products models typically impose time-separable preferences. But when attributes are habit forming, current consumption can shift future marginal valuations, so static willingness-to-pay may be insufficient for policy counterfactuals. I develop a nonparametric revealed-preference framework for dynamic hedonic valuation, deriving necessary and sufficient conditions for rationalising observed prices and choices. Using cereal scanner data, I show that the hedonic representation restricts prices, while habits improve behavioural coherence conditional on that representation. The framework diagnoses when static willingness-to-pay is defensible for policy.", "published": "2026-03-02T22:57:30Z", "authors": ["Josephine Auer"], "categories": ["econ.TH", "econ.EM", "econ.GN"], "doi": "", "_query": "hedonic regression housing", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2602.05888v2", "title": "Metric Hedonic Games on the Line", "abstract": "Hedonic games are fundamental models for investigating the formation of coalitions among a set of strategic agents, where every agent has a certain utility for every possible coalition of agents it can be part of. To avoid the intractability of defining exponentially many utilities for all possible coalitions, many variants with succinct representations of the agents' utility functions have been devised and analyzed, e.g., modified fractional hedonic games by Monaco et al. [JAAMAS 2020]. We extend this by studying a novel succinct variant that is related to modified fractional hedonic games. In our model, each agent has a fixed type-value and an agent's cost for some given coalition is based on the differences between its value and those of the other members of its coalition. This allows to model natural situations like athletes forming training groups with similar performance levels or voters that partition themselves along a political spectrum. In particular, we investigate natural variants where an agent's cost is defined by distance thresholds, or by the maximum or average value difference to the other agents in its coalition. For these settings, we study the existence of stable coalition structures, their properties, and their quality in terms of the price of anarchy and the price of stability. Further, we investigate the impact of limiting the maximum number of coalitions. Despite the simple setting with metric distances on a line, we uncover a rich landscape of models, partially with counter-intuitive behavior. Also, our focus on both swap stability and jump stability allows us to study the influence of fixing the number and the size of the coalitions. Overall, we find that stable coalition structures always exist but that their properties and quality can vary widely.", "published": "2026-02-05T17:05:08Z", "authors": ["Merlin de la Haye", "Pascal Lenzner", "Farehe Soheil", "Marcus Wunderlich"], "categories": ["cs.GT", "cs.AI"], "doi": "", "_query": "hedonic regression housing", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/1509.02333v2", "title": "Precise Complexity of the Core in Dichotomous and Additive Hedonic Games", "abstract": "Hedonic games provide a general model of coalition formation, in which a set of agents is partitioned into coalitions, with each agent having preferences over which other players are in her coalition. We prove that with additively separable preferences, it is $Σ_2^p$-complete to decide whether a core- or strict-core-stable partition exists, extending a result of Woeginger (2013). Our result holds even if valuations are symmetric and non-zero only for a constant number of other agents. We also establish $Σ_2^p$-completeness of deciding non-emptiness of the strict core for hedonic games with dichotomous preferences. Such results establish that the core is much less tractable than solution concepts such as individual stability.", "published": "2015-09-08T12:04:15Z", "authors": ["Dominik Peters"], "categories": ["cs.GT"], "doi": "", "_query": "hedonic regression housing", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2607.27277v1", "title": "Complexity of Strong Popularity in Additively Separable Hedonic Games", "abstract": "In a hedonic game, agents need to be partitioned into coalitions, and have a preference order over partitions. A partition is called strongly popular if it beats any other partition in a majority vote among the agents. We focus on the fundamental class of additively separable hedonic games (ASHGs), where agents have additive valuations that induce their preferences. We prove that determining the existence of strongly popular partitions in ASHGs is complete for PCW, a recently introduced complexity class which lies in between $P^{NP}$ and $S_2^P$ (Gilboa et al., 2025). This settles an open problem by Brandt and Bullinger (2022) and Bullinger and Gilboa (2025).", "published": "2026-07-29T12:59:05Z", "authors": ["Matan Gilboa"], "categories": ["cs.GT", "cs.CC"], "doi": "", "_query": "hedonic regression housing", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2511.02810v1", "title": "Formalizing Regression Testing for Agile and Continuous Integration Environments", "abstract": "Software developed using modern agile practices delivers a stream of software versions that require continuous regression testing rather than testing once close to the delivery or maintenance phase, as assumed by classical regression-testing theory. In this work, we formalize the phenomenon of continuous or near-continuous regression testing using successive builds as a time-ordered chain, where each build contains the program, requirements, and the accompanying tests. We also formalize the regression test window between any two builds, which captures the limited time budget available for regression testing. As the time limit is set to infinity and the chain is closed to two builds, the model degenerates to retest-all, thereby preserving semantics for the classical two-version case. The formalization is validated by directly representing two state-of-the-art agile regression testing algorithms in terms of build-tuple operations without requiring auxiliary assumptions, followed by proof of the soundness and completeness of our formalization.", "published": "2025-11-04T18:31:06Z", "authors": ["Suddhasvatta Das", "Kevin Gary"], "categories": ["cs.SE"], "doi": "", "_query": "hedonic regression housing", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2512.15920v1", "title": "Introduction to Symbolic Regression in the Physical Sciences", "abstract": "Symbolic regression (SR) has emerged as a powerful method for uncovering interpretable mathematical relationships from data, offering a novel route to both scientific discovery and efficient empirical modelling. This article introduces the Special Issue on Symbolic Regression for the Physical Sciences, motivated by the Royal Society discussion meeting held in April 2025. The contributions collected here span applications from automated equation discovery and emergent-phenomena modelling to the construction of compact emulators for computationally expensive simulations. The introductory review outlines the conceptual foundations of SR, contrasts it with conventional regression approaches, and surveys its main use cases in the physical sciences, including the derivation of effective theories, empirical functional forms and surrogate models. We summarise methodological considerations such as search-space design, operator selection, complexity control, feature selection, and integration with modern AI approaches. We also highlight ongoing challenges, including scalability, robustness to noise, overfitting and computational complexity. Finally we emphasise emerging directions, particularly the incorporation of symmetry constraints, asymptotic behaviour and other theoretical information. Taken together, the papers in this Special Issue illustrate the accelerating progress of SR and its growing relevance across the physical sciences.", "published": "2025-12-17T19:32:50Z", "authors": ["Deaglan J. Bartlett", "Harry Desmond", "Pedro G. Ferreira", "Gabriel Kronberger"], "categories": ["cs.LG", "astro-ph.IM", "cs.NE", "physics.comp-ph", "physics.data-an"], "doi": "10.1098/rsta.2024.0600", "_query": "hedonic regression housing", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2609.06308v1", "title": "Housing Price Appreciation, Housing Affordability, and the Spatial Restructuring of Toronto Commuting", "abstract": "This study investigates the spatial relationships among housing-market appreciation, housing affordability, and automobile commuting to Toronto across 23 census metropolitan areas and municipalities in southern Ontario from 2016 to 2021. Utilizing Statistics Canada Census data, the analysis employed linear and quadratic ordinary least-squares regression, Global and Local Moran's I, and residual spatial diagnostics to assess geographic patterns in average dwelling values, owner shelter-cost burden, and lone-driver commuting. The study found that average dwelling values increased significantly throughout the region, maintaining a strong nonlinear relationship with distance from Toronto, which indicates a persistent metropolitan accessibility gradient. Commuter volumes also demonstrated pronounced nonlinear distance gradients; however, housing appreciation was not significantly correlated with percentage changes in commuting ($R^2$ = .045, p = .331), suggesting that the expansion of the housing market did not result in proportional growth in Toronto-bound automobile travel. The shelter-cost burden exhibited weaker distance relationships and greater residual spatial dependence, indicating localized affordability influences not fully explained by distance or dwelling values. Overall, the findings suggest an increasingly extensive regional housing market with spatially heterogeneous affordability and commuting outcomes, highlighting the importance of integrating housing, transportation, and regional planning.", "published": "2026-09-05T23:36:50Z", "authors": ["Shawn Berry"], "categories": ["econ.GN", "stat.OT"], "doi": "", "_query": "hedonic regression housing", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2310.13966v3", "title": "Transfer Learning for Kernel-based Regression", "abstract": "In recent years, transfer learning has garnered significant attention. Its ability to leverage knowledge from related studies to improve generalization performance in a target study has made it highly appealing. This paper focuses on investigating the transfer learning problem within the context of nonparametric regression over a reproducing kernel Hilbert space. The aim is to bridge the gap between practical effectiveness and theoretical guarantees. We specifically consider two scenarios: one where the transferable sources are known and another where they are unknown. For the known transferable source case, we propose a two-step kernel-based estimator by solely using kernel ridge regression. For the unknown case, we develop a novel method based on an efficient aggregation algorithm, which can automatically detect and alleviate the effects of negative sources. This paper provides the statistical properties of the desired estimators and establishes the minimax rate. Through extensive numerical experiments on synthetic data and real examples, we validate our theoretical findings and demonstrate the effectiveness of our proposed method.", "published": "2023-10-21T10:55:31Z", "authors": ["Chao Wang", "Caixing Wang", "Xin He", "Xingdong Feng"], "categories": ["stat.ML", "cs.LG", "stat.ME"], "doi": "", "_query": "hedonic regression housing", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2409.16579v1", "title": "Friend- and Enemy-oriented Hedonic Games With Strangers Full Version", "abstract": "We introduce friend- and enemy-oriented hedonic games with strangers (FOHGS and EOHGS respectively), two classes of hedonic games wherein agents are classified as friends, enemies, or strangers under the assumption that strangers will become either friends or enemies ex post facto. For several notions of stability in FOHGS and EOHGS, we characterize the hardness of verification for possible and necessary stability. We characterize the hardness of deciding whether possibly and necessarily X stable partitions exist for a given stability notion X. We prove that necessarily internally stable partitions always exist and provide sufficient conditions for necessary contractual individual stability.", "published": "2024-09-25T03:17:46Z", "authors": ["TJ Schlueter", "Makoto Yokoo"], "categories": ["cs.GT"], "doi": "", "_query": "hedonic regression housing", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2111.04518v1", "title": "Bayesian profile regression for clustering analysis involving a longitudinal response and explanatory variables", "abstract": "The identification of sets of co-regulated genes that share a common function is a key question of modern genomics. Bayesian profile regression is a semi-supervised mixture modelling approach that makes use of a response to guide inference toward relevant clusterings. Previous applications of profile regression have considered univariate continuous, categorical, and count outcomes. In this work, we extend Bayesian profile regression to cases where the outcome is longitudinal (or multivariate continuous) and provide PReMiuMlongi, an updated version of PReMiuM, the R package for profile regression. We consider multivariate normal and Gaussian process regression response models and provide proof of principle applications to four simulation studies. The model is applied on budding yeast data to identify groups of genes co-regulated during the Saccharomyces cerevisiae cell cycle. We identify 4 distinct groups of genes associated with specific patterns of gene expression trajectories, along with the bound transcriptional factors, likely involved in their co-regulation process.", "published": "2021-11-08T14:04:19Z", "authors": ["Anaïs Rouanet", "Rob Johnson", "Magdalena E Strauss", "Sylvia Richardson", "Brian D Tom", "Simon R White", "Paul D W Kirk"], "categories": ["stat.ME", "stat.AP"], "doi": "", "_query": "hedonic regression housing", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/1807.04516v1", "title": "A Bayesian Nonparametric Approach to Geographic Regression Discontinuity Designs: Do School Districts Affect NYC House Prices?", "abstract": "Most research on regression discontinuity designs (RDDs) has focused on univariate cases, where only those units with a \"forcing\" variable on one side of a threshold value receive a treatment. Geographical regression discontinuity designs (GeoRDDs) extend the RDD to multivariate settings with spatial forcing variables. We propose a framework for analysing GeoRDDs, which we implement using Gaussian process regression. This yields a Bayesian posterior distribution of the treatment effect at every point along the border. We address nuances of having a functional estimand defind on a border with potentially intricate topology, particularly when defining and estimating causal estimands of the local average treatment effect (LATE). The Bayesian estimate of the LATE can also be used as a test statistic in a hypothesis test with good frequentist properties, which we validate using simulations and placebo tests. We demonstrate our methodology with a dataset of property sales in New York City, to assess whether there is a discontinuity in housing prices at the border between two school district. We find a statistically significant difference in price across the border between the districts with $p$=0.002, and estimate a 20% higher price on average for a house on the more desirable side.", "published": "2018-07-11T09:57:29Z", "authors": ["Maxime Rischard", "Zach Branson", "Luke Miratrix", "Luke Bornn"], "categories": ["stat.AP"], "doi": "", "_query": "hedonic regression housing", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2402.04082v1", "title": "An Optimal House Price Prediction Algorithm: XGBoost", "abstract": "An accurate prediction of house prices is a fundamental requirement for various sectors including real estate and mortgage lending. It is widely recognized that a property value is not solely determined by its physical attributes but is significantly influenced by its surrounding neighbourhood. Meeting the diverse housing needs of individuals while balancing budget constraints is a primary concern for real estate developers. To this end, we addressed the house price prediction problem as a regression task and thus employed various machine learning techniques capable of expressing the significance of independent variables. We made use of the housing dataset of Ames City in Iowa, USA to compare support vector regressor, random forest regressor, XGBoost, multilayer perceptron and multiple linear regression algorithms for house price prediction. Afterwards, we identified the key factors that influence housing costs. Our results show that XGBoost is the best performing model for house price prediction.", "published": "2024-02-06T15:36:06Z", "authors": ["Hemlata Sharma", "Hitesh Harsora", "Bayode Ogunleye"], "categories": ["cs.LG", "cs.AI", "stat.AP", "stat.ME"], "doi": "10.3390/analytics3010003", "_query": "hedonic regression housing", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2202.09210v2", "title": "Hedonic Diversity Games: A Complexity Picture with More than Two Colors", "abstract": "Hedonic diversity games are a variant of the classical Hedonic games designed to better model a variety of questions concerning diversity and fairness. Previous works mainly targeted the case with two diversity classes (represented as colors in the model) and provided some initial complexity-theoretic and existential results concerning Nash and individually stable outcomes. Here, we design new algorithms accompanied with lower bounds which provide a complete parameterized-complexity picture for computing Nash and individually stable outcomes with respect to the most natural parameterizations of the problem. Crucially, our results hold for general Hedonic diversity games where the number of colors is not necessarily restricted to two, and show that -- apart from two trivial cases -- a necessary condition for tractability in this setting is that the number of colors is bounded by the parameter. Moreover, for the special case of two colors we resolve an open question asked in previous work (Boehmer and Elkind, AAAI 2020).", "published": "2022-02-18T14:16:33Z", "authors": ["Robert Ganian", "Thekla Hamm", "Dušan Knop", "Šimon Schierreich", "Ondřej Suchý"], "categories": ["cs.GT"], "doi": "10.1016/j.artint.2023.104017", "_query": "hedonic regression housing", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2406.01373v1", "title": "Stability in Random Hedonic Games", "abstract": "Partitioning a large group of employees into teams can prove difficult because unsatisfied employees may want to transfer to other teams. In this case, the team (coalition) formation is unstable and incentivizes deviation from the proposed structure. Such a coalition formation scenario can be modeled in the framework of hedonic games and a significant amount of research has been devoted to the study of stability in such games. Unfortunately, stable coalition structures are not guaranteed to exist in general and their practicality is further hindered by computational hardness barriers. We offer a new perspective on this matter by studying a random model of hedonic games. For three prominent stability concepts based on single-agent deviations, we provide a high probability analysis of stability in the large agent limit. Our first main result is an efficient algorithm that outputs an individually and contractually Nash-stable partition with high probability. Our second main result is that the probability that a random game admits a Nash-stable partition tends to zero. Our approach resolves the two major downsides associated with individual stability and contractual Nash stability and reveals agents acting single-handedly are usually to blame for instabilities.", "published": "2024-06-03T14:39:41Z", "authors": ["Martin Bullinger", "Sonja Kraiczy"], "categories": ["cs.GT"], "doi": "", "_query": "hedonic regression housing", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/1509.05322v1", "title": "Computing stable outcomes in symmetric additively-separable hedonic games", "abstract": "We study the computational complexity of finding stable outcomes in hedonic games, which are a class of coalition formation games. We restrict our attention to symmetric additively-separable hedonic games, which are a nontrivial subclass of such games that are guaranteed to possess stable outcomes. These games are specified by an undirected edge- weighted graph: nodes are players, an outcome of the game is a partition of the nodes into coalitions, and the utility of a node is the sum of incident edge weights in the same coalition. We consider several stability requirements defined in the literature. These are based on restricting feasible player deviations, for example, by giving existing coalition members veto power. We extend these restrictions by considering more general forms of preference aggregation for coalition members. In particular, we consider voting schemes to decide if coalition members will allow a player to enter or leave their coalition. For all of the stability requirements we consider, the existence of a stable outcome is guaranteed by a potential function argument, and local improvements will converge to a stable outcome. We provide an almost complete characterization of these games in terms of the tractability of computing such stable outcomes. Our findings comprise positive results in the form of polynomial-time algorithms, and negative (PLS-completeness) results. The negative results extend to more general hedonic games.", "published": "2015-09-17T16:33:23Z", "authors": ["Martin Gairing", "Rahul Savani"], "categories": ["cs.GT"], "doi": "", "_query": "hedonic regression housing", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/1701.01218v1", "title": "Overlapping Cover Local Regression Machines", "abstract": "We present the Overlapping Domain Cover (ODC) notion for kernel machines, as a set of overlapping subsets of the data that covers the entire training set and optimized to be spatially cohesive as possible. We show how this notion benefit the speed of local kernel machines for regression in terms of both speed while achieving while minimizing the prediction error. We propose an efficient ODC framework, which is applicable to various regression models and in particular reduces the complexity of Twin Gaussian Processes (TGP) regression from cubic to quadratic. Our notion is also applicable to several kernel methods (e.g., Gaussian Process Regression(GPR) and IWTGP regression, as shown in our experiments). We also theoretically justified the idea behind our method to improve local prediction by the overlapping cover. We validated and analyzed our method on three benchmark human pose estimation datasets and interesting findings are discussed.", "published": "2017-01-05T06:04:53Z", "authors": ["Mohamed Elhoseiny", "Ahmed Elgammal"], "categories": ["cs.LG", "cs.CV"], "doi": "", "_query": "hedonic regression housing", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/1903.05534v2", "title": "Robustness against Agent Failure in Hedonic Games", "abstract": "We study how stability can be maintained even after any set of at most k players leave their groups, in the context of hedonic games. While stability properties ensure an outcome to be robust against players' deviations, it has not been considered how an unexpected change caused by a sudden deletion of players affects stable outcomes. In this paper, we propose a novel criterion that reshapes stability form robustness aspect. We observe that some stability properties can be no longer preserved even when a single agent is removed. However, we obtain positive results by focusing on symmetric friend-oriented hedonic games. We prove that we can efficiently decide the existence of robust outcomes with respect to Nash stability under deletion of any number of players or contractual individual stability under deletion of a single player. We also show that symmetric additively separable games always admit an individual stable outcome that is robust with respect to individual rationality.", "published": "2019-03-13T15:07:32Z", "authors": ["Ayumi Igarashi", "Kazunori Ota", "Yuko Sakurai", "Makoto Yokoo"], "categories": ["cs.GT"], "doi": "", "_query": "hedonic regression housing", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2505.13642v2", "title": "Non-Obvious Manipulability in Additively Separable and Fractional Hedonic Games", "abstract": "Hedonic Games are a well-established model for describing the formation of coalitions. In this work, we considered the design of Non-Obviously Manipulable (NOM) mechanisms, that are mechanisms that bounded rational agents may fail to recognize as manipulable, for two relevant classes of succinctly representable Hedonic Games, namely Additively Separable and Fractional Hedonic Games. In these classes, agents have cardinal scores towards other agents, and their preferences towards different coalitions are determined by aggregating these scores. Moreover, the quality of an outcome can also be easily evaluated through these scores by means of the utilitarian social welfare. We first prove that, when scores can be arbitrary, every welfare-maximizing mechanism is NOM, and, when scores are limited in a continuous interval, then there exist tie-breaking rules making welfare-maximizing mechanisms NOM. Next, we focus on efficient NOM mechanisms, since there is no known polynomial-time algorithm to compute welfare-maximizing outcomes in the considered classes of hedonic games. To this aim, we first prove a characterization of NOM mechanisms that simplifies the class of mechanisms of interest. Then, we design a NOM mechanism returning approximations that essentially match the best-known approximation achievable in polynomial time. Finally, we turn our attention to discrete scores, and specifically, the case that scores are $\\{-x, 0, 1\\}$ for $x > 0$. We prove that the ability to design welfare-maximizing NOM mechanisms depends on the magnitude of the scores. In particular, for $x > 1$, we prove that a welfare-maximizing NOM mechanism exists only when $x$ is very large. For $x \\leq 1$, instead, we observe that a welfare-maximizing NOM mechanism always exists except when $x$ lies in the interval $[a, b]$ where $a \\approx 2/n^2$ and $b \\approx 1/n$.", "published": "2025-05-19T18:29:09Z", "authors": ["Diodato Ferraioli", "Maria Fomenko", "Giovanna Varricchio"], "categories": ["cs.GT", "cs.MA"], "doi": "", "_query": "hedonic regression housing", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/1105.1824v2", "title": "Individual-based stability in hedonic games depending on the best or worst players", "abstract": "We consider coalition formation games in which each player has preferences over the other players and his preferences over coalitions are based on the best player ($\\mathcal{B}$-/B-hedonic games) or the worst player ($\\mathcal{W}$/W-hedonic games) in the coalition. We show that for $\\mathcal{B}$-hedonic games, an individually stable partition is guaranteed to exist and can be computed efficiently. Similarly, there exists a polynomial-time algorithm which returns a Nash stable partition (if one exists) for $\\mathcal{B}$-hedonic games with strict preferences. Both $\\mathcal{W}$- and W-hedonic games are equivalent if individual rationality is assumed. It is also shown that for B- or $\\mathcal{W}$-hedonic games, checking whether a Nash stable partition or an individually stable partition exists is NP-complete even in some cases for strict preferences. We identify a key source of intractability in compact coalition formation games in which preferences over players are extended to preferences over coalitions.", "published": "2011-05-09T23:51:47Z", "authors": ["Haris Aziz", "Paul Harrenstein", "Evangelia Pyrga"], "categories": ["cs.GT"], "doi": "", "_query": "hedonic regression housing", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/1110.2790v1", "title": "Regularity properties of optimal transportation problems arising in hedonic pricing models", "abstract": "We study a form of optimal transportation surplus functions which arise in hedonic pricing models. We derive a formula for the Ma-Trudinger-Wang curvature of these functions, yielding necessary and sufficient conditions for them to satisfy \\textbf{(A3w)}. We use this to give explicit new examples of surplus functions satisfying \\textbf{(A3w)}, of the form $b(x,y)=H(x+y)$ where $H$ is a convex function on $\\mathbb{R}^n$. We also show that the space of equilibrium contracts in the hedonic pricing model has the maximal possible dimension, a result of potential economic interest.", "published": "2011-10-12T20:02:13Z", "authors": ["Brendan Pass"], "categories": ["math.AP"], "doi": "", "_query": "hedonic regression housing", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2003.07197v1", "title": "A Hedonic Metric Approach to Estimating the Demand for Differentiated Products: An Application to Retail Milk Demand", "abstract": "This article introduces the Hedonic Metric (HM) approach as an original method to model the demand for differentiated products. Using this approach, initially, we create an n-dimensional hedonic space based on the characteristic information available to consumers. Next, we allocate products into this space and estimate the elasticities using distances. Our model makes it possible to estimate a large number of differentiated products in a single demand system. We applied our model to estimate the retail demand for fluid milk products.", "published": "2020-03-12T12:06:19Z", "authors": ["Osman Gulseven", "Michael Wohlgenant"], "categories": ["econ.GN"], "doi": "", "_query": "hedonic regression housing", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2004.02406v1", "title": "Using generalized logistics regression to forecast population infected by Covid-19", "abstract": "In this work, a proposal to forecast the populations using generalized logistics regression curve fitting is presented. This type of curve is used to study population growth, in this case population of people infected with the Covid-19 virus; and it can also be used to approximate the survival curve used in actuarial and similar studies.", "published": "2020-04-06T05:27:40Z", "authors": ["Mario Villalobos-Arias"], "categories": ["q-bio.PE", "math.OC"], "doi": "", "_query": "hedonic regression housing", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/1608.00621v3", "title": "Efficient Multiple Incremental Computation for Kernel Ridge Regression with Bayesian Uncertainty Modeling", "abstract": "This study presents an efficient incremental/decremental approach for big streams based on Kernel Ridge Regression (KRR), a frequently used data analysis in cloud centers. To avoid reanalyzing the whole dataset whenever sensors receive new training data, typical incremental KRR used a single-instance mechanism for updating an existing system. However, this inevitably increased redundant computational time, not to mention applicability to big streams. To this end, the proposed mechanism supports incremental/decremental processing for both single and multiple samples (i.e., batch processing). A large scale of data can be divided into batches, processed by a machine, without sacrificing the accuracy. Moreover, incremental/decremental analyses in empirical and intrinsic space are also proposed in this study to handle different types of data either with a large number of samples or high feature dimensions, whereas typical methods focused only on one type. At the end of this study, we further the proposed mechanism to statistical Kernelized Bayesian Regression, so that uncertainty modeling with incremental/decremental computation becomes applicable. Experimental results showed that computational time was significantly reduced, better than the original nonincremental design and the typical single incremental method. Furthermore, the accuracy of the proposed method remained the same as the baselines. This implied that the system enhanced efficiency without sacrificing the accuracy. These findings proved that the proposed method was appropriate for variable streaming data analysis, thereby demonstrating the effectiveness of the proposed method.", "published": "2016-08-01T21:21:07Z", "authors": ["Bo-Wei Chen", "Nik Nailah Binti Abdullah", "Sangoh Park"], "categories": ["cs.LG", "stat.ML"], "doi": "10.1016/j.future.2017.08.053", "_query": "hedonic regression housing", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2009.07551v7", "title": "Manipulation-Robust Regression Discontinuity Designs", "abstract": "We present simple low-level conditions for identification in regression discontinuity designs using a potential outcome framework for the manipulation of the running variable. Using this framework, we replace the existing identification statement with two restrictions on manipulation. Our framework highlights the critical role of the continuous density of the running variable in identification. In particular, we establish the low-level auxiliary assumption of the diagnostic density test under which the design may detect manipulation against identification and hence is manipulation-robust.", "published": "2020-09-16T08:42:03Z", "authors": ["Takuya Ishihara", "Masayuki Sawada"], "categories": ["econ.EM", "stat.ME"], "doi": "", "_query": "hedonic regression housing", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/1103.5971v1", "title": "Housing risk and return: Evidence from a housing asset-pricing model", "abstract": "This paper investigates the risk-return relationship in determination of housing asset pricing. In so doing, the paper evaluates behavioral hypotheses advanced by Case and Shiller (1988, 2002, 2009) in studies of boom and post-boom housing markets. The paper specifies and tests a multi-factor housing asset pricing model. In that model, we evaluate whether the market factor as well as other measures of risk, including idiosyncratic risk, momentum, and MSA size effects, have explanatory power for metropolitan-specific housing returns. Further, we test the robustness of the asset pricing results to inclusion of controls for socioeconomic variables commonly represented in the house price literature, including changes in employment, affordability, and foreclosure incidence. We find a sizable and statistically significant influence of the market factor on MSA house price returns. Moreover we show that market betas have varied substantially over time. Also, results are largely robust to the inclusion of other explanatory variables, including standard measures of risk and other housing market fundamentals. Additional tests of model validity using the Fama-MacBeth framework offer further strong support of a positive risk and return relationship in housing. Our findings are supportive of the application of a housing investment risk-return framework in explanation of variation in metro-area cross-section and time-series US house price returns. Further, results strongly corroborate Case-Shiller survey research indicating the importance of speculative forces in the determination of U.S. housing returns.", "published": "2011-03-30T15:55:56Z", "authors": ["Karl Case", "John Cotter", "Stuart Gabriel"], "categories": ["q-fin.PM", "q-fin.PR", "q-fin.ST"], "doi": "", "_query": "hedonic regression housing", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/1709.09570v6", "title": "Identification of hedonic equilibrium and nonseparable simultaneous equations", "abstract": "This paper derives conditions under which preferences and technology are nonparametrically identified in hedonic equilibrium models, where products are differentiated along more than one dimension and agents are characterized by several dimensions of unobserved heterogeneity. With products differentiated along a quality index and agents characterized by scalar unobserved heterogeneity, single crossing conditions on preferences and technology provide identifying restrictions in Ekeland, Heckman and Nesheim (2004) and Heckman, Matzkin and Nesheim (2010). We develop similar shape restrictions in the multi-attribute case. These shape restrictions, which are based on optimal transport theory and generalized convexity, allow us to identify preferences for goods differentiated along multiple dimensions, from the observation of a single market. We thereby derive nonparametric identification results for nonseparable simultaneous equations and multi-attribute hedonic equilibrium models with (possibly) multiple dimensions of unobserved heterogeneity. One of our results is a proof of absolute continuity of the distribution of endogenously traded qualities, which is of independent interest.", "published": "2017-09-27T15:07:36Z", "authors": ["Victor Chernozhukov", "Alfred Galichon", "Marc Henry", "Brendan Pass"], "categories": ["econ.EM", "econ.TH"], "doi": "", "_query": "hedonic regression housing", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2506.15881v3", "title": "T-SHRED: Symbolic Regression for Regularization and Model Discovery with Transformer Shallow Recurrent Decoders", "abstract": "SHallow REcurrent Decoders (SHRED) are effective for system identification and forecasting from sparse sensor measurements. Such models are light-weight and computationally efficient, allowing them to be trained on consumer laptops. SHRED-based models rely on Recurrent Neural Networks (RNNs) and a simple Multi-Layer Perceptron (MLP) for the temporal encoding and spatial decoding respectively. Despite the relatively simple structure of SHRED, they are able to predict chaotic dynamical systems on different physical, spatial, and temporal scales directly from a sparse set of sensor measurements. In this work, we modify SHRED by leveraging transformers (T-SHRED) embedded with symbolic regression for the temporal encoding, circumventing auto-regressive long-term forecasting for physical data. This is achieved through a new sparse identification of nonlinear dynamics (SINDy) attention mechanism into T-SHRED to impose sparsity regularization on the latent space, which also allows for immediate symbolic interpretation. Symbolic regression improves model interpretability by learning and regularizing the dynamics of the latent space during training. We analyze the performance of T-SHRED on three different dynamical systems ranging from low-data to high-data regimes.", "published": "2025-06-18T21:14:38Z", "authors": ["Alexey Yermakov", "David Zoro", "Mars Liyao Gao", "J. Nathan Kutz"], "categories": ["cs.LG"], "doi": "", "_query": "hedonic regression housing", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2507.13033v1", "title": "(Exhaustive) Symbolic Regression and model selection by minimum description length", "abstract": "Symbolic regression is the machine learning method for learning functions from data. After a brief overview of the symbolic regression landscape, I will describe the two main challenges that traditional algorithms face: they have an unknown (and likely significant) probability of failing to find any given good function, and they suffer from ambiguity and poorly-justified assumptions in their function-selection procedure. To address these I propose an exhaustive search and model selection by the minimum description length principle, which allows accuracy and complexity to be directly traded off by measuring each in units of information. I showcase the resulting publicly available Exhaustive Symbolic Regression algorithm on three open problems in astrophysics: the expansion history of the universe, the effective behaviour of gravity in galaxies and the potential of the inflaton field. In each case the algorithm identifies many functions superior to the literature standards. This general purpose methodology should find widespread utility in science and beyond.", "published": "2025-07-17T12:04:15Z", "authors": ["Harry Desmond"], "categories": ["astro-ph.IM", "astro-ph.CO", "astro-ph.GA", "cs.LG"], "doi": "", "_query": "hedonic regression housing", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2603.21826v1", "title": "Individual Rationality in Constrained Hedonic Games: Additively Separable and Fractional Preferences", "abstract": "Hedonic games are an archetypal problem in coalition formation, where a set of selfish agents want to partition themselves into stable coalitions. In this work, we focus on two natural constraints on the possible outcomes. First, we require that exactly k coalitions are created. Then, loosely following the model of Bilò et al. (AAAI 2022), we assume that each of the k coalitions is additionally associated with a lower and upper bound on its size. The notion of stability that we study is that of individual rationality (IR), which requires that no agent strictly prefers to be alone compared to being in his or her coalition. Although IR is trivially satisfiable even in the most general models of hedonic games, the complexity picture of deciding whether an IR allocation exists, considering the above constraints, is unexpectedly rich. We reveal that tractable fragments of this computational problem require surprisingly nontrivial arguments, even if we restrict ourselves to additively separable and fractional hedonic games. Our tractability results, achieved by exploiting the structure of the underlying preference graph, are also complemented by their intractability counterparts, painting a fairly complete picture of the tractability landscape of this problem.", "published": "2026-03-23T11:07:30Z", "authors": ["Foivos Fioravantes", "Harmender Gahlawat", "Nikolaos Melissinos", "Šimon Schierreich"], "categories": ["cs.GT"], "doi": "", "_query": "hedonic regression housing", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/1507.03003v2", "title": "High-Dimensional Asymptotics of Prediction: Ridge Regression and Classification", "abstract": "We provide a unified analysis of the predictive risk of ridge regression and regularized discriminant analysis in a dense random effects model. We work in a high-dimensional asymptotic regime where $p, n \\to \\infty$ and $p/n \\to γ\\in (0, \\, \\infty)$, and allow for arbitrary covariance among the features. For both methods, we provide an explicit and efficiently computable expression for the limiting predictive risk, which depends only on the spectrum of the feature-covariance matrix, the signal strength, and the aspect ratio $γ$. Especially in the case of regularized discriminant analysis, we find that predictive accuracy has a nuanced dependence on the eigenvalue distribution of the covariance matrix, suggesting that analyses based on the operator norm of the covariance matrix may not be sharp. Our results also uncover several qualitative insights about both methods: for example, with ridge regression, there is an exact inverse relation between the limiting predictive risk and the limiting estimation risk given a fixed signal strength. Our analysis builds on recent advances in random matrix theory.", "published": "2015-07-10T19:48:07Z", "authors": ["Edgar Dobriban", "Stefan Wager"], "categories": ["math.ST", "stat.ML"], "doi": "", "_query": "hedonic regression housing", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/1909.05542v2", "title": "Quantile regression methods for first-price auctions", "abstract": "The paper proposes a quantile-regression inference framework for first-price auctions with symmetric risk-neutral bidders under the independent private-value paradigm. It is first shown that a private-value quantile regression generates a quantile regression for the bids. The private-value quantile regression can be easily estimated from the bid quantile regression and its derivative with respect to the quantile level. This also allows to test for various specification or exogeneity null hypothesis using the observed bids in a simple way. A new local polynomial technique is proposed to estimate the latter over the whole quantile level interval. Plug-in estimation of functionals is also considered, as needed for the expected revenue or the case of CRRA risk-averse bidders, which is amenable to our framework. A quantile-regression analysis to USFS timber is found more appropriate than the homogenized-bid methodology and illustrates the contribution of each explanatory variables to the private-value distribution. Linear interactive sieve extensions are proposed and studied in the Appendices.", "published": "2019-09-12T10:05:37Z", "authors": ["Nathalie Gimenes", "Emmanuel Guerre"], "categories": ["econ.EM"], "doi": "", "_query": "hedonic regression housing", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2108.03274v1", "title": "Smooth Symbolic Regression: Transformation of Symbolic Regression into a Real-valued Optimization Problem", "abstract": "The typical methods for symbolic regression produce rather abrupt changes in solution candidates. In this work, we have tried to transform symbolic regression from an optimization problem, with a landscape that is so rugged that typical analysis methods do not produce meaningful results, to one that can be compared to typical and very smooth real-valued problems. While the ruggedness might not interfere with the performance of optimization, it restricts the possibilities of analysis. Here, we have explored different aspects of a transformation and propose a simple procedure to create real-valued optimization problems from symbolic regression problems.", "published": "2021-08-06T18:50:33Z", "authors": ["Erik Pitzer", "Gabriel Kronberger"], "categories": ["cs.LG"], "doi": "10.1007/978-3-319-27340-2_47", "_query": "hedonic regression housing", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/1610.08663v4", "title": "Regularization parameter selection in indirect regression by residual based bootstrap", "abstract": "Residual-based analysis is generally considered a cornerstone of statistical methodology. For a special case of indirect regression, we investigate the residual-based empirical distribution function and provide a uniform expansion of this estimator, which is also shown to be asymptotically most precise. This investigation naturally leads to a completely data-driven technique for selecting a regularization parameter used in our indirect regression function estimator. The resulting methodology is based on a smooth bootstrap of the model residuals. A simulation study demonstrates the effectiveness of our approach.", "published": "2016-10-27T08:50:01Z", "authors": ["Nicolai Bissantz", "Justin Chown", "Holger Dette"], "categories": ["stat.ME", "math.ST"], "doi": "", "_query": "hedonic regression housing", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2502.03012v1", "title": "A Framework to Monitor the Effects of External Shocks on Housing Markets", "abstract": "We develop a framework to holistically test for and monitor the impact of different types of events affecting a country's housing market, yet originating from housing-external sources. We classify events along three dimensions leading to testable hypotheses: prices versus quantities, supply versus demand, and immediate versus gradually evolving. These dimensions translate into guidance about which data type, statistical measure and testing strategy should be used. To perform such test suitable statistical models are needed which we implement as a hierarchical hedonic price model and a complementary count model. These models are amended by regime and contextual variables as suggested by our classification strategy. We apply this framework to the Austrian real estate market together with three disruptive events triggered by the COVID-19 pandemic, a policy tightening mortgage lending standards, as well as the cost-of-living crisis that came along with increased financing costs. The tests yield the expected results and, by that, some housing market puzzles are resolved. Deviating from the prior classification exercise means that some developments would have been undetected. Further, adopting our framework consistently when performing empirical research on residential real estate would lead to better comparable research results and, by that, would allow researchers to draw meta-conclusions from the bulk of studies available across time and space.", "published": "2025-02-05T09:13:33Z", "authors": ["Anja Hahn", "Sanela Omerovic", "Sofie Waltl"], "categories": ["econ.GN"], "doi": "", "_query": "hedonic regression housing", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/1810.07913v2", "title": "Robust Sparse Reduced Rank Regression in High Dimensions", "abstract": "We propose robust sparse reduced rank regression for analyzing large and complex high-dimensional data with heavy-tailed random noise. The proposed method is based on a convex relaxation of a rank- and sparsity-constrained non-convex optimization problem, which is then solved using the alternating direction method of multipliers algorithm. We establish non-asymptotic estimation error bounds under both Frobenius and nuclear norms in the high-dimensional setting. This is a major contribution over existing results in reduced rank regression, which mainly focus on rank selection and prediction consistency. Our theoretical results quantify the tradeoff between heavy-tailedness of the random noise and statistical bias. For random noise with bounded $(1+δ)$th moment with $δ\\in (0,1)$, the rate of convergence is a function of $δ$, and is slower than the sub-Gaussian-type deviation bounds; for random noise with bounded second moment, we obtain a rate of convergence as if sub-Gaussian noise were assumed. Furthermore, the transition between the two regimes is smooth. We illustrate the performance of the proposed method via extensive numerical studies and a data application.", "published": "2018-10-18T06:21:52Z", "authors": ["Kean Ming Tan", "Qiang Sun", "Daniela Witten"], "categories": ["stat.ML", "cs.LG"], "doi": "", "_query": "hedonic regression housing", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2511.08424v1", "title": "Identification of Empirical Constitutive Models for Age-Hardenable Aluminium Alloy and High-Chromium Martensitic Steel Using Symbolic Regression", "abstract": "Process-structure-property relationships are fundamental in materials science and engineering and are key to the development of new and improved materials. Symbolic regression serves as a powerful tool for uncovering mathematical models that describe these relationships. It can automatically generate equations to predict material behaviour under specific manufacturing conditions and optimize performance characteristics such as strength and elasticity. The present work illustrates how symbolic regression can derive constitutive models that describe the behaviour of various metallic alloys during plastic deformation. Constitutive modelling is a mathematical framework for understanding the relationship between stress and strain in materials under different loading conditions. In this study, two materials (age-hardenable aluminium alloy and high-chromium martensitic steel) and two different testing methods (compression and tension) are considered to obtain the required stress-strain data. The results highlight the benefits of using symbolic regression while also discussing potential challenges.", "published": "2025-11-11T16:33:50Z", "authors": ["Evgeniya Kabliman", "Gabriel Kronberger"], "categories": ["cond-mat.mtrl-sci", "cs.LG"], "doi": "", "_query": "hedonic regression housing", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2511.22794v1", "title": "Can Synthetic Data Improve Symbolic Regression Extrapolation Performance?", "abstract": "Many machine learning models perform well when making predictions within the training data range, but often struggle when required to extrapolate beyond it. Symbolic regression (SR) using genetic programming (GP) can generate flexible models but is prone to unreliable behaviour in extrapolation. This paper investigates whether adding synthetic data can help improve performance in such cases. We apply Kernel Density Estimation (KDE) to identify regions in the input space where the training data is sparse. Synthetic data is then generated in those regions using a knowledge distillation approach: a teacher model generates predictions on new input points, which are then used to train a student model. We evaluate this method across six benchmark datasets, using neural networks (NN), random forests (RF), and GP both as teacher models (to generate synthetic data) and as student models (trained on the augmented data). Results show that GP models can often improve when trained on synthetic data, especially in extrapolation areas. However, the improvement depends on the dataset and teacher model used. The most important improvements are observed when synthetic data from GPe is used to train GPp in extrapolation regions. Changes in interpolation areas show only slight changes. We also observe heterogeneous errors, where model performance varies across different regions of the input space. Overall, this approach offers a practical solution for better extrapolation. Note: An earlier version of this work appeared in the GECCO 2025 Workshop on Symbolic Regression. This arXiv version corrects several parts of the original submission.", "published": "2025-11-27T22:52:13Z", "authors": ["Fitria Wulandari Ramlan", "Colm O'Riordan", "Gabriel Kronberger", "James McDermott"], "categories": ["cs.LG"], "doi": "10.1145/3712255.3734356", "_query": "hedonic regression housing", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2208.01969v7", "title": "Regulation and Frontier Housing Supply", "abstract": "Regulation is a major driver of housing supply, yet often difficult to observe directly. We show that frontier cost, the non-land cost of producing housing absent regulation, is identified from prices and quantities alone, without instruments even when quantity is endogenous in a mean regression. Identification requires regulation to enter as a nonnegative wedge with zero in its conditional support. The difference between price and frontier cost yields the regulatory tax. We apply the approach to new multi-floor, multi-family residential construction in Israel. Accounting for random housing quality, we estimate economies of scale at low heights (minimum efficient scale about five floors), nearly constant marginal cost at middle heights, and an elasticity of substitution between land and non-land inputs of about 0.15 to 0.2 at the greatest heights. The estimated mean regulatory tax is 47% of housing prices, with substantial variation across locations, and is positively correlated with centrality, density, and prices. We also construct a lower bound allowing quality to differ systematically over location and time, assuming weak complementarity between quality and demand. In 2017, when prices were highest in our sample and the bound is most informative, we bound the mean regulatory tax between 38% (using a 2km radius) and 53%.", "published": "2022-08-03T10:45:41Z", "authors": ["Dan Ben-Moshe", "David Genesove"], "categories": ["econ.GN"], "doi": "", "_query": "hedonic regression housing", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/1110.4119v1", "title": "Integration and Contagion in US Housing Markets", "abstract": "This paper explores integration and contagion among US metropolitan housing markets. The analysis applies Federal Housing Finance Agency (FHFA) house price repeat sales indexes from 384 metropolitan areas to estimate a multi-factor model of U.S. housing market integration. It then identifies statistical jumps in metropolitan house price returns as well as MSA contemporaneous and lagged jump correlations. Finally, the paper evaluates contagion in housing markets via parametric assessment of MSA house price spatial dynamics. A R-squared measure reveals an upward trend in MSA housing market integration over the 2000s to approximately .83 in 2010. Among California MSAs, the trend was especially pronounced, as average integration increased from about .55 in 1997 to close to .95 in 2008! The 2000s bubble period similarly was characterized by elevated incidence of statistical jumps in housing returns. Again, jump incidence and MSA jump correlations were especially high in California. Analysis of contagion among California markets indicates that house price returns in San Francisco often led those of surrounding communities; in contrast, southern California MSA house price returns appeared to move largely in lock step. The high levels of housing market integration evidenced in the analysis suggest limited investor opportunity to diversify away MSA-specific housing risk. Further, results suggest that macro and policy shocks propagate through a large number of MSA housing markets. Research findings are relevant to all market participants, including institutional investors in MBS as well as those who regulate housing, the housing GSEs, mortgage lenders, and related financial institutions.", "published": "2011-10-18T20:42:59Z", "authors": ["John Cotter", "Stuart Gabriel", "Richard Roll"], "categories": ["q-fin.GN"], "doi": "", "_query": "hedonic regression housing", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/1812.09249v2", "title": "Testing Stability Properties in Graphical Hedonic Games", "abstract": "In hedonic games, players form coalitions based on individual preferences over the group of players they could belong to. Several concepts to describe the stability of coalition structures in a game have been proposed and analysed in the literature. However, prior research focuses on algorithms with time complexity that is at least linear in the input size. In the light of very large games that arise from, e.g., social networks and advertising, we initiate the study of sublinear time property testing algorithms for existence and verification problems under several notions of coalition stability in a model of hedonic games represented by graphs with bounded degree. In graph property testing, one shall decide whether a given input has a property (e.g., a game admits a stable coalition structure) or is far from it, i.e., one has to modify at least an $ε$-fraction of the input (e.g., the game's preferences) to make it have the property. In particular, we consider verification of perfection, individual rationality, Nash stability, (contractual) individual stability, and core stability. While there is always a Nash-stable coalition structure (which also implies individually stable coalitions), we show that the existence of a perfect coalition structure is not tautological but can be tested. All our testers have one-sided error and time complexity that is independent of the input size.", "published": "2018-12-21T16:44:46Z", "authors": ["Hendrik Fichtenberger", "Anja Rey"], "categories": ["cs.GT"], "doi": "10.1007/s10458-021-09505-x", "_query": "hedonic regression housing", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/1405.3360v2", "title": "On the Nash Stability in the Hedonic Coalition Formation Games", "abstract": "This paper studies the Nash stability in hedonic coalition formation games. We address the following issue: for a general problem formulation, is there any utility allocation method ensuring a Nash-stable partition? We propose the definition of the Nash-stable core. We study the conditions for having a non-empty Nash-stable core. More precisely, we show how relaxed efficiency in utility sharing method allows to ensure a non-empty Nash-stable core.", "published": "2014-05-14T04:54:08Z", "authors": ["Cengis Hasan", "Jean-Marie Gorce", "Eitan Altman"], "categories": ["cs.GT"], "doi": "", "_query": "hedonic regression housing", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2307.02260v1", "title": "Does Transport Inequality Perpetuate Housing Insecurity?", "abstract": "With trends of urbanisation on the rise, providing adequate housing to individuals remains a complex issue to be addressed. Often, the slow output of relevant housing policies, coupled with quickly increasing housing costs, leaves individuals with the burden of finding housing that is affordable and safe. In this paper, we unveil how urban planning, not just housing policies, can prevent individuals from accessing better housing conditions. We begin by proposing a clustering approach to characterising levels of housing insecurity in a city, by considering multiple dimensions of housing. Then we define levels of transit efficiency in 20 US cities by comparing public transit journeys to car-based journeys. Finally, we use geospatial autocorrelation to highlight how commuting to areas associated with better housing conditions results in transit commute times of over 30 minutes in most cities, and commute times of over an hour in some cases. Ultimately, we show the role that public transportation plays in locking vulnerable demographics into a cycle of poverty, thus motivating a more holistic approach to addressing housing insecurity that extends beyond changing housing policies.", "published": "2023-07-05T13:00:17Z", "authors": ["Nandini Iyer", "Ronaldo Menezes", "Hugo Barbosa"], "categories": ["physics.soc-ph"], "doi": "", "_query": "hedonic regression housing", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2205.01255v1", "title": "Incorporating Financial Hardship in Measuring the Mental Health Impact of Housing Stress", "abstract": "Housing expenditure tends to be sticky and costly to adjust, and makes up a large proportion of household expenditure. Additionally, the loss of housing can have catastrophic consequences. These specific features of housing expenditure imply that housing stress could cause negative mental health impacts. This research investigates the effects of housing stress on mental health, contributing to the literature by nesting housing stress within a measure of financial hardship, thus improving robustness to omitted variables and creating a natural comparison group for matching. Fixed effects (FE) regressions and a difference-in-differences (DID) methodology are estimated utilising data from the Household Income and Labour Dynamics in Australia (HILDA) Survey. The results show that renters who are in housing stress have a significant decline in self-reported mental health, with those in prior financial hardship being more severely affected. In contrast, there is little to no evidence of housing stress impacting on owners with a mortgage. The results also suggest that the mental health impact of housing stress is more important than some, but not all, aspects of financial hardship.", "published": "2022-05-03T00:38:56Z", "authors": ["Timothy Ludlow", "Jonas Fooken", "Christiern Rose", "Kam Tang"], "categories": ["econ.GN"], "doi": "", "_query": "hedonic regression housing", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2112.06192v1", "title": "Housing Price Prediction Model Selection Based on Lorenz and Concentration Curves: Empirical Evidence from Tehran Housing Market", "abstract": "This study contributes a house price prediction model selection in Tehran City based on the area between Lorenz curve (LC) and concentration curve (CC) of the predicted price by using 206,556 observed transaction data over the period from March 21, 2018, to February 19, 2021. Several different methods such as generalized linear models (GLM) and recursive partitioning and regression trees (RPART), random forests (RF) regression models, and neural network (NN) models were examined house price prediction. We used 90% of all data samples which were chosen randomly to estimate the parameters of pricing models and 10% of remaining datasets to test the accuracy of prediction. Results showed that the area between the LC and CC curves (which are known as ABC criterion) of real and predicted prices in the test data sample of the random forest regression model was less than by other models under study. The comparison of the calculated ABC criteria leads us to conclude that the nonlinear regression models such as RF regression models give an accurate prediction of house prices in Tehran City.", "published": "2021-12-12T09:44:28Z", "authors": ["Mohammad Mirbagherijam"], "categories": ["econ.EM"], "doi": "", "_query": "hedonic regression housing", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2201.13004v5", "title": "Improving Estimation Efficiency via Regression-Adjustment in Covariate-Adaptive Randomizations with Imperfect Compliance", "abstract": "We investigate how to improve efficiency using regression adjustments with covariates in covariate-adaptive randomizations (CARs) with imperfect subject compliance. Our regression-adjusted estimators, which are based on the doubly robust moment for local average treatment effects, are consistent and asymptotically normal even with heterogeneous probability of assignment and misspecified regression adjustments. We propose an optimal but potentially misspecified linear adjustment and its further improvement via a nonlinear adjustment, both of which lead to more efficient estimators than the one without adjustments. We also provide conditions for nonparametric and regularized adjustments to achieve the semiparametric efficiency bound under CARs.", "published": "2022-01-31T05:37:28Z", "authors": ["Liang Jiang", "Oliver B. Linton", "Haihan Tang", "Yichong Zhang"], "categories": ["econ.EM", "stat.ME"], "doi": "", "_query": "hedonic regression housing", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/1510.07129v1", "title": "Bayesian Inference for High Dimensional Changing Linear Regression with Application to Minnesota House Price Index Data", "abstract": "In many applications, the dataset under investigation exhibits heterogeneous regimes that are more appropriately modeled using piece-wise linear models for each of the data segments separated by change-points. Although there have been much work on change point linear regression for the low dimensional case, high-dimensional change point regression is severely underdeveloped. Motivated by the analysis of Minnesota House Price Index data, we propose a fully Bayesian framework for fitting changing linear regression models in high-dimensional settings. Using segment-specific shrinkage and diffusion priors, we deliver full posterior inference for the change points and simultaneously obtain posterior probabilities of variable selection in each segment via an efficient Gibbs sampler. Additionally, our method can detect an unknown number of change points and accommodate different variable selection constraints like grouping or partial selection. We substantiate the accuracy of our method using simulation experiments for a wide range of scenarios. We apply our approach for a macro-economic analysis of Minnesota house price index data. The results strongly favor the change point model over a homogeneous (no change point) high-dimensional regression model.", "published": "2015-10-24T10:28:14Z", "authors": ["Abhirup Datta", "Hui Zou", "Sudipto Banerjee"], "categories": ["math.ST", "stat.AP"], "doi": "", "_query": "hedonic regression housing", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/1610.04805v1", "title": "Beyond Spatial Auto-Regressive Models: Predicting Housing Prices with Satellite Imagery", "abstract": "When modeling geo-spatial data, it is critical to capture spatial correlations for achieving high accuracy. Spatial Auto-Regression (SAR) is a common tool used to model such data, where the spatial contiguity matrix (W) encodes the spatial correlations. However, the efficacy of SAR is limited by two factors. First, it depends on the choice of contiguity matrix, which is typically not learnt from data, but instead, is assumed to be known apriori. Second, it assumes that the observations can be explained by linear models. In this paper, we propose a Convolutional Neural Network (CNN) framework to model geo-spatial data (specifi- cally housing prices), to learn the spatial correlations automatically. We show that neighborhood information embedded in satellite imagery can be leveraged to achieve the desired spatial smoothing. An additional upside of our framework is the relaxation of linear assumption on the data. Specific challenges we tackle while implementing our framework include, (i) how much of the neighborhood is relevant while estimating housing prices? (ii) what is the right approach to capture multiple resolutions of satellite imagery? and (iii) what other data-sources can help improve the estimation of spatial correlations? We demonstrate a marked improvement of 57% on top of the SAR baseline through the use of features from deep neural networks for the cities of London, Birmingham and Liverpool.", "published": "2016-10-16T01:17:19Z", "authors": ["Archith J. Bency", "Swati Rallapalli", "Raghu K. Ganti", "Mudhakar Srivatsa", "B. S. Manjunath"], "categories": ["cs.CV"], "doi": "", "_query": "hedonic regression housing", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2210.06261v1", "title": "Predicting housing prices and analyzing real estate market in the Chicago suburbs using Machine Learning", "abstract": "The pricing of housing properties is determined by a variety of factors. However, post-pandemic markets have experienced volatility in the Chicago suburb area, which have affected house prices greatly. In this study, analysis was done on the Naperville/Bolingbrook real estate market to predict property prices based on these housing attributes through machine learning models, and to evaluate the effectiveness of such models in a volatile market space. Gathering data from Redfin, a real estate website, sales data from 2018 up until the summer season of 2022 were collected for research. By analyzing these sales in this range of time, we can also look at the state of the housing market and identify trends in price. For modeling the data, the models used were linear regression, support vector regression, decision tree regression, random forest regression, and XGBoost regression. To analyze results, comparison was made on the MAE, RMSE, and R-squared values for each model. It was found that the XGBoost model performs the best in predicting house prices despite the additional volatility sponsored by post-pandemic conditions. After modeling, Shapley Values (SHAP) were used to evaluate the weights of the variables in constructing models.", "published": "2022-10-12T14:41:53Z", "authors": ["Kevin Xu", "Hieu Nguyen"], "categories": ["cs.LG"], "doi": "", "_query": "hedonic regression housing", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2202.04358v1", "title": "House Price Valuation Model Based on Geographically Neural Network Weighted Regression: The Case Study of Shenzhen, China", "abstract": "Confronted with the spatial heterogeneity of real estate market, some traditional research utilized Geographically Weighted Regression (GWR) to estimate the house price. However, its kernel function is non-linear, elusive, and complex to opt bandwidth, the predictive power could also be improved. Consequently, a novel technique, Geographical Neural Network Weighted Regression (GNNWR), has been applied to improve the accuracy of real estate appraisal with the help of neural networks. Based on Shenzhen house price dataset, this work conspicuously captures the weight distribution of different variants at Shenzhen real estate market, which GWR is difficult to materialize. Moreover, we focus on the performance of GNNWR, verify its robustness and superiority, refine the experiment process with 10-fold cross-validation, extend its application area from natural to socioeconomic geospatial data. It's a practical and trenchant way to assess house price, and we demonstrate the effectiveness of GNNWR on a complex socioeconomic dataset.", "published": "2022-02-09T09:40:54Z", "authors": ["Zimo Wang", "Yicheng Wang", "Sensen Wu"], "categories": ["stat.AP", "cs.LG"], "doi": "", "_query": "hedonic regression housing", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2304.06333v2", "title": "Priors for symbolic regression", "abstract": "When choosing between competing symbolic models for a data set, a human will naturally prefer the \"simpler\" expression or the one which more closely resembles equations previously seen in a similar context. This suggests a non-uniform prior on functions, which is, however, rarely considered within a symbolic regression (SR) framework. In this paper we develop methods to incorporate detailed prior information on both functions and their parameters into SR. Our prior on the structure of a function is based on a $n$-gram language model, which is sensitive to the arrangement of operators relative to one another in addition to the frequency of occurrence of each operator. We also develop a formalism based on the Fractional Bayes Factor to treat numerical parameter priors in such a way that models may be fairly compared though the Bayesian evidence, and explicitly compare Bayesian, Minimum Description Length and heuristic methods for model selection. We demonstrate the performance of our priors relative to literature standards on benchmarks and a real-world dataset from the field of cosmology.", "published": "2023-04-13T08:29:16Z", "authors": ["Deaglan J. Bartlett", "Harry Desmond", "Pedro G. Ferreira"], "categories": ["cs.LG", "astro-ph.CO", "astro-ph.IM"], "doi": "10.1145/3583133.3596327", "_query": "hedonic regression housing", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/1907.03422v1", "title": "Bootstrap Model Ensemble and Rank Loss for Engagement Intensity Regression", "abstract": "This paper presents our approach for the engagement intensity regression task of EmotiW 2019. The task is to predict the engagement intensity value of a student when he or she is watching an online MOOCs video in various conditions. Based on our winner solution last year, we mainly explore head features and body features with a bootstrap strategy and two novel loss functions in this paper. We maintain the framework of multi-instance learning with long short-term memory (LSTM) network, and make three contributions. First, besides of the gaze and head pose features, we explore facial landmark features in our framework. Second, inspired by the fact that engagement intensity can be ranked in values, we design a rank loss as a regularization which enforces a distance margin between the features of distant category pairs and adjacent category pairs. Third, we use the classical bootstrap aggregation method to perform model ensemble which randomly samples a certain training data by several times and then averages the model predictions. We evaluate the performance of our method and discuss the influence of each part on the validation dataset. Our methods finally win 3rd place with MSE of 0.0626 on the testing set.", "published": "2019-07-08T06:54:47Z", "authors": ["Kai Wang", "Jianfei Yang", "Da Guo", "Kaipeng Zhang", "Xiaojiang Peng", "Yu Qiao"], "categories": ["cs.CV"], "doi": "", "_query": "hedonic regression housing", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2409.08912v1", "title": "Joint spatial modeling of mean and non-homogeneous variance combining semiparametric SAR and GAMLSS models for hedonic prices", "abstract": "In the context of spatial econometrics, it is very useful to have methodologies that allow modeling the spatial dependence of the observed variables and obtaining more precise predictions of both the mean and the variability of the response variable, something very useful in territorial planning and public policies. This paper proposes a new methodology that jointly models the mean and the variance. Also, it allows to model the spatial dependence of the dependent variable as a function of covariates and to model the semiparametric effects in both models. The algorithms developed are based on generalized additive models that allow the inclusion of non-parametric terms in both the mean and the variance, maintaining the traditional theoretical framework of spatial regression. The theoretical developments of the estimation of this model are carried out, obtaining desirable statistical properties in the estimators. A simulation study is developed to verify that the proposed method has a remarkable predictive capacity in terms of the mean square error and shows a notable improvement in the estimation of the spatial autoregressive parameter, compared to other traditional methods and some recent developments. The model is also tested on data from the construction of a hedonic price model for the city of Bogota, highlighting as the main result the ability to model the variability of housing prices, and the wealth in the analysis obtained.", "published": "2024-09-13T15:28:05Z", "authors": ["J. D. Toloza-Delgado", "O. O. Melo", "N. A. Cruz"], "categories": ["stat.ME", "stat.CO"], "doi": "10.1016/j.spasta.2024.100864", "_query": "hedonic regression housing", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/1701.08862v3", "title": "How to define and test an Indirect Moderation model: the missing link in regression-based path models", "abstract": "Two of the most important extensions of the basic regression model are moderated effects (due to interactions) and mediated effects (i.e. indirect effects). Combinations of these effects may also be present. In this work, an important, yet missing combination is presented that can determine whether a moderating effect itself is mediated by another variable. This 'indirect moderation' model can be assessed by a four-step decision tree which guides the user through the necessary regression analyses in order to infer or refute indirect moderation. A simple simulation experiment shows how the method performs under several basic scenarios. Analysis of an empirical data set shows that the indirect moderation model is extremely valuable in applied research.", "published": "2017-01-30T22:36:20Z", "authors": ["Geert H. van Kollenburg", "Marcel A. Croon"], "categories": ["stat.ME"], "doi": "", "_query": "hedonic regression housing", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2101.02033v1", "title": "Boarding House Renting Price Prediction Using Deep Neural Network Regression on Mobile Apps", "abstract": "Boarding house is the most important requirement, especially for college students who live far away from the city, place of his origin or house. However, the problem we see now is the uneven distribution of study places in Indonesia which 75% of the best top educational institutions come from the island of Java. So, students who are looking for boarding houses rent requires more effort in comparing the various aspects desired. They need to survey one by one to the boarding house they want, even though they can survey online, it still requires more effort to pay attention to the desired facilities one by one. Therefore, we then created an Mobile Application that can predict prices based on student needs by comparing several variables, namely city, area, type of boarding house, and facilities. So, students can easily estimate the ideal price. The results of this study prove that we have succeeded in predicting prices for boarding houses rent well based on the variables we have determined, and modeling that variables using Deep Neural Network Regression.", "published": "2020-12-31T15:12:10Z", "authors": ["Malik Abdul Aziz", "Fahmi Nurrahim", "Prastyo Eko Susanto", "Yurio Windiatmoko"], "categories": ["cs.CY", "cs.LG"], "doi": "", "_query": "hedonic regression housing", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2006.10092v1", "title": "Housing Market Prediction Problem using Different Machine Learning Algorithms: A Case Study", "abstract": "Developing an accurate prediction model for housing prices is always needed for socio-economic development and well-being of citizens. In this paper, a diverse set of machine learning algorithms such as XGBoost, CatBoost, Random Forest, Lasso, Voting Regressor, and others, are being employed to predict the housing prices using public available datasets. The housing datasets of 62,723 records from January 2015 to November 2019 are obtained from Florida Volusia County Property Appraiser website. The records are publicly available and include the real estate or economic database, maps, and other associated information. The database is usually updated weekly according to the State of Florida regulations. Then, the housing price prediction models using machine learning techniques are developed and their regression model performances are compared. Finally, an improved housing price prediction model for assisting the housing market is proposed. Particularly, a house seller or buyer, or a real estate broker can get insight in making better-informed decisions considering the housing price prediction. The empirical results illustrate that based on prediction model performance, Coefficient of Determination (R2), Mean Square Error (MSE), Mean Absolute Error (MAE), and computational time, the XGBoost algorithm performs superior to the other models to predict the housing price.", "published": "2020-06-17T18:16:24Z", "authors": ["Shashi Bhushan Jha", "Radu F. Babiceanu", "Vijay Pandey", "Rajesh Kumar Jha"], "categories": ["cs.LG", "stat.ML"], "doi": "", "_query": "hedonic regression housing", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2501.11086v2", "title": "Evaluating LLM-Based Regression Test Generation", "abstract": "Large Language Models (LLMs) have shown tremendous promise in automated software engineering. In this paper, we investigate LLMs for just-in-time regression test generation for programs, like parsers, interpreters, or compilers, that take highly structured, human-readable inputs. When a bug fix or code change is committed, the repository (as part of CI/CD) runs an LLM for a few minutes to generate regression tests that exercise the changed code and potentially trigger bugs. We frame LLM-based regression test generation as a machine translation task that takes the developer-provided commit message, the code change, and the input format name (e.g., XML), and produces regression tests for the described change in that format. Testing 72 commits to Mujs, Libxml2, Poppler, JerryScript, Z3, PHP, JQ, and MicroPython, our feedback-directed, zero-shot prototype Cleverest performed well, even without the code change. In under 2 minutes on average, Cleverest found as many bugs as the state-of-the-art directed greybox fuzzer WAFLGo did in 24 hours, even though WAFLGo started with a commit-reaching seed corpus in most cases. Using the Cleverest-generated tests as a seed corpus in coverage-guided greybox fuzzing doubles the number of bugs found; we call this integration ClevFuzz. We also find that some commit messages are more expressive than others, thus we ask how it impacts Cleverest's effectiveness. Cleverest picks up on the change intention: e.g., given a commit message that a patch changes how floating point variables are treated in the Mujs JavaScript interpreter, Cleverest generates JavaScript programs that contain floating point variables. Minimally changing commit messages to reduce or increase their information substantially impacts effectiveness: adding 17 words on average (max. 43) to make ineffective commit messages more expressive significantly increased the number of bugs found.", "published": "2025-01-19T15:46:26Z", "authors": ["Jing Liu", "Seongmin Lee", "Eleonora Losiouk", "Marcel Böhme"], "categories": ["cs.SE", "cs.AI"], "doi": "10.1145/3808129", "_query": "hedonic regression housing", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2509.10500v1", "title": "Exploring Multi-view Symbolic Regression methods in physical sciences", "abstract": "Describing the world behavior through mathematical functions help scientists to achieve a better understanding of the inner mechanisms of different phenomena. Traditionally, this is done by deriving new equations from first principles and careful observations. A modern alternative is to automate part of this process with symbolic regression (SR). The SR algorithms search for a function that adequately fits the observed data while trying to enforce sparsity, in the hopes of generating an interpretable equation. A particularly interesting extension to these algorithms is the Multi-view Symbolic Regression (MvSR). It searches for a parametric function capable of describing multiple datasets generated by the same phenomena, which helps to mitigate the common problems of overfitting and data scarcity. Recently, multiple implementations added support to MvSR with small differences between them. In this paper, we test and compare MvSR as supported in Operon, PySR, phy-SO, and eggp, in different real-world datasets. We show that they all often achieve good accuracy while proposing solutions with only few free parameters. However, we find that certain features enable a more frequent generation of better models. We conclude by providing guidelines for future MvSR developments.", "published": "2025-09-01T12:43:34Z", "authors": ["Etienne Russeil", "Fabrício Olivetti de França", "Konstantin Malanchev", "Guillaume Moinard", "Maxime Cherrey"], "categories": ["cs.LG", "astro-ph.GA", "astro-ph.IM", "physics.data-an"], "doi": "", "_query": "hedonic regression housing", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/1610.07292v1", "title": "Population growth, interest rate, and housing tax in the transitional China", "abstract": "This paper combines and develops the models in Lastrapes (2002) and Mankiw & Weil (1989), which enables us to analyze the effects of interest rate and population growth shocks on housing price in one integrated framework. Based on this model, we carry out policy simulations to examine whether the housing (stock or flow) tax reduces the housing price fluctuations caused by interest rate or population growth shocks. Simulation results imply that the choice of housing tax tools depends on the kind of shock that housing market faces. In the situation where the housing price volatility is caused by the population growth shock, the flow tax can reduce the volatility of housing price while the stock tax makes no difference to it. If the shock is resulting from the interest rate, the policy maker should not impose any kind of the housing taxes. Furthermore, the effect of one kind of the housing tax can be strengthened by that of the other type of housing tax.", "published": "2016-10-24T06:22:26Z", "authors": ["Ling-Yun He", "Xing-Chun Wen"], "categories": ["q-fin.GN", "econ.GN"], "doi": "10.1016/j.physa.2016.11.057", "_query": "hedonic regression housing", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/1203.1093v2", "title": "Confidence intervals in regression centred on the SCAD estimator", "abstract": "Consider a linear regression model. Fan and Li (2001) describe the smoothly clipped absolute deviation (SCAD) point estimator of the regression parameter vector. To gain insight into the properties of this estimator, they consider an orthonormal design matrix and focus on the estimation of a specified component of this vector. They show that the SCAD point estimator has three attractive properties. We answer the question: To what extent can an interval estimator, centred on the SCAD estimator, have similar attractive properties?", "published": "2012-03-06T03:13:49Z", "authors": ["Davide Farchione", "Paul Kabaila"], "categories": ["math.ST"], "doi": "", "_query": "hedonic regression housing", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2607.17633v1", "title": "Social Integration and Housing Behaviours of Immigrants: Evidence from Singapore's Public Housing Market", "abstract": "This study investigates the impact of social integration on immigrants' housing behaviours from a temporal perspective, using Singapore's differential public housing policies on immigrants as a quasi-natural experiment. With the support of a local town council, we conducted a survey on social integration among 1,128 immigrant and local households living in public housing estates. In the public open rental housing market - primarily accommodating yet-to-integrate immigrants - we find immigrant renters live up to 3.04% farther from their workplace and pay lower rents up to 0.67% per additional year of residency. Such impacts are more substantial among minority ethnic groups. The results remain robust when using alternative subjective or objective measures of social integration. However, in the public resale housing market - primarily accommodating native and well-integrated naturalised citizens - we find that naturalised citizens face no price premiums relative to native homebuyers, implying no further effect of integration on housing prices after well-integration. This study extends the literature of spatial assimilation focusing on ethnic residential segregations and is generalizable to cities with few ethnic enclaves.", "published": "2026-07-20T07:40:03Z", "authors": ["Yi Fan", "Ho Pin Teo", "Yong Tu", "Wayne Xinwei Wan"], "categories": ["econ.GN"], "doi": "10.1016/j.cities.2026.107220", "_query": "hedonic regression housing", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2605.00835v1", "title": "Sparse Regression under Correlation and Weak Signals: A Reproducible Benchmark of Classical and Bayesian Methods", "abstract": "Choosing between classical and Bayesian sparse regression methods involves a real trade-off: penalized estimators like Lasso run in milliseconds but give no uncertainty estimates,while Horseshoe and Spike-and-Slab priors produce full posteriors but need MCMC chains that take minutes per fit.Surprisingly few studies compare these two families head-to-head under the conditions that actually make sparse regression hard -- correlated features, weak signals, and growing dimensionality. We benchmark six methods (OLS, Ridge,Lasso, Elastic Net, Horseshoe, Spike-and-Slab) on synthetic data with three covariance structures (rho up to 0.9), four SNR levels, and p in {20, 50, 100}, plus the Diabetes dataset,totalling over 2,600 experiments. The results are clear on some points and nuanced on others. Bayesian methods win on prediction error (MSE 72 vs. 108-267), and the Horseshoe delivers near-nominal 95% coverage (94.8%). But Spike-and-Slab,despite narrower intervals, under-covers at 91.9% -- its continuous relaxation likely plays a role. For variable selection, Lasso and Spike-and-Slab tie at F1 ~ 0.47, making Lasso the practical default when posteriors are not needed. Code and data are available at https://github.com/xiao98/sparse-bayesian-regression-bench.", "published": "2026-04-04T15:46:44Z", "authors": ["Hao Xiao"], "categories": ["cs.LG"], "doi": "", "_query": "hedonic regression housing", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/1907.02242v2", "title": "Fair Kernel Regression via Fair Feature Embedding in Kernel Space", "abstract": "In recent years, there have been significant efforts on mitigating unethical demographic biases in machine learning methods. However, very little is done for kernel methods. In this paper, we propose a new fair kernel regression method via fair feature embedding (FKR-F$^2$E) in kernel space. Motivated by prior works on feature selection in kernel space and feature processing for fair machine learning, we propose to learn fair feature embedding functions that minimize demographic discrepancy of feature distributions in kernel space. Compared to the state-of-the-art fair kernel regression method and several baseline methods, we show FKR-F$^2$E achieves significantly lower prediction disparity across three real-world data sets.", "published": "2019-07-04T06:22:38Z", "authors": ["Austin Okray", "Hui Hu", "Chao Lan"], "categories": ["cs.LG", "stat.ML"], "doi": "", "_query": "hedonic regression housing", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/1001.2939v1", "title": "Admissibility of the usual confidence interval in linear regression", "abstract": "Consider a linear regression model with independent and identically normally distributed random errors. Suppose that the parameter of interest is a specified linear combination of the regression parameters. We prove that the usual confidence interval for this parameter is admissible within a broad class of confidence intervals.", "published": "2010-01-18T01:45:31Z", "authors": ["Paul Kabaila", "Khageswor Giri", "Hannes Leeb"], "categories": ["math.ST"], "doi": "", "_query": "hedonic regression housing", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2602.18592v1", "title": "A Roof Over Risk: A House Price-at-Risk Framework for Hungary", "abstract": "This paper develops a House Price-at-Risk framework to examine how housing subsidies, credit conditions, and supply factors influence the distribution of house price growth in Hungary. Using quantile regression with adaptive LASSO variable selection, we identify variables driving downside versus upside risks across multiple horizons. Financial stress dominates the lower tail at short horizons, while unemployment and affordability constraints become the primary drivers of downside risk at longer horizons. Housing subsidies exhibit pro-cyclical characteristics, concentrating significant positive effects on the upper quantiles while leaving the lower tail largely unaffected. Supply-side variables display horizon-dependent sign reversals, with construction permits exerting upward pressure on prices in the short run but moderating them as supply materialises. Uncertainty decomposition reveals persistent left-tail dominance across all horizons. These findings suggest that macroprudential frameworks should account for the distributional effects of housing subsidies, particularly their pro-cyclical influence on house price growth.", "published": "2026-02-20T19:57:51Z", "authors": ["Tibor Szendrei", "Nikolett Vágó", "Katalin Varga"], "categories": ["econ.EM"], "doi": "", "_query": "hedonic regression housing", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/1212.2236v1", "title": "Core stability in hedonic coalition formation", "abstract": "In many economic, social and political situations individuals carry out activities in groups (coalitions) rather than alone and on their own. Examples range from households and sport clubs to research networks, political parties and trade unions. The underlying game theoretic framework is known as coalition formation. This survey discusses the notion of core stability in hedonic coalition formation (where each player's happiness only depends on the other members of his coalition but not on how the remaining players outside his coalition are grouped). We present the central concepts and algorithmic approaches in the area, provide many examples, and pose a number of open problems.", "published": "2012-12-10T22:23:19Z", "authors": ["Gerhard J. Woeginger"], "categories": ["cs.GT", "cs.DM", "cs.DS", "math.CO"], "doi": "", "_query": "hedonic regression housing", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2409.06011v2", "title": "Investigating the effects of housing instability on depression, anxiety, and mental health treatment in childhood and adolescence", "abstract": "Housing instability is a widespread phenomenon in the United States. In combination with other social determinants of health, housing instability affects children's overall health and development. Drawing on data from the 2022 National Survey of Children's Health, we employed multiple logistic regression models to understand how sociodemographic factors, especially housing instability, affect mental health outcomes and treatment access for youth aged 6-17 years. Our results show that youth facing housing instability have a higher likelihood of experiencing anxiety (OR: 1.42, p<0.001) and depression (OR: 1.57, p<0.001). Furthermore, youth experiencing both mental health conditions and housing instability are significantly less likely to receive mental health services in the past year, indicating the substantial barriers they face in accessing mental health care. Based on our findings, we highlight opportunities for digital mental health interventions to provide children experiencing housing instability with more accessible and consistent mental health services.", "published": "2024-09-09T19:10:42Z", "authors": ["Rachael Zehrung", "Di Hu", "Yawen Guo", "Kai Zheng", "Yunan Chen"], "categories": ["cs.HC"], "doi": "", "_query": "hedonic regression housing", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/1908.11554v2", "title": "Computational Complexity of Hedonic Games on Sparse Graphs", "abstract": "The additively separable hedonic game (ASHG) is a model of coalition formation games on graphs. In this paper, we intensively and extensively investigate the computational complexity of finding several desirable solutions, such as a Nash stable solution, a maximum utilitarian solution, and a maximum egalitarian solution in ASHGs on sparse graphs including bounded-degree graphs, bounded-treewidth graphs, and near-planar graphs. For example, we show that finding a maximum egalitarian solution is weakly NP-hard even on graphs of treewidth 2, whereas it can be solvable in polynomial time on trees. Moreover, we give a pseudo fixed parameter algorithm when parameterized by treewidth.", "published": "2019-08-30T06:26:11Z", "authors": ["Tesshu Hanaka", "Hironori Kiya", "Yasuhide Maei", "Hirotaka Ono"], "categories": ["cs.CC"], "doi": "", "_query": "hedonic regression housing", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/math/0703551v1", "title": "Anomalies in the Foundations of Ridge Regression", "abstract": "Anomalies persist in the foundations of ridge regression as set forth in Hoerl and Kennard (1970) and subsequently. Conventional ridge estimators and their properties do not follow on constraining lengths of solution vectors using LaGrange's method, as claimed. Estimators so constrained have singular distributions; the proposed solutions are not necessarily minimizing; and heretofore undiscovered bounds are exhibited for the ridge parameter. None of the considerable literature on estimation, prediction, cross--validation, choice of ridge parameter, and related issues, collectively known as ridge regression, is consistent with constrained optimization, nor with corresponding inequality constraints. The problem is traced to a misapplication of LaGrange's principle, failure to recognize the singularity of distributions, and misplaced links between constraints and the ridge parameter. Other principles, based on condition numbers, are seen to validate both conventional ridge and surrogate ridge regression to be defined. Numerical studies illustrate that ridge analysis often exhibits some of the same pathologies it is intended to redress.", "published": "2007-03-19T14:10:34Z", "authors": ["D. R. Jensen", "D. E. Ramirez"], "categories": ["math.ST"], "doi": "10.1111/j.1751-5823.2007.00041.x", "_query": "hedonic regression housing", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2204.12159v1", "title": "Coefficient Mutation in the Gene-pool Optimal Mixing Evolutionary Algorithm for Symbolic Regression", "abstract": "Currently, the genetic programming version of the gene-pool optimal mixing evolutionary algorithm (GP-GOMEA) is among the top-performing algorithms for symbolic regression (SR). A key strength of GP-GOMEA is its way of performing variation, which dynamically adapts to the emergence of patterns in the population. However, GP-GOMEA lacks a mechanism to optimize coefficients. In this paper, we study how fairly simple approaches for optimizing coefficients can be integrated into GP-GOMEA. In particular, we considered two variants of Gaussian coefficient mutation. We performed experiments using different settings on 23 benchmark problems, and used machine learning to estimate what aspects of coefficient mutation matter most. We find that the most important aspect is that the number of coefficient mutation attempts needs to be commensurate with the number of mixing operations that GP-GOMEA performs. We applied GP-GOMEA with the best-performing coefficient mutation approach to the data sets of SRBench, a large SR benchmark, for which a ground-truth underlying equation is known. We find that coefficient mutation can help re-discovering the underlying equation by a substantial amount, but only when no noise is added to the target variable. In the presence of noise, GP-GOMEA with coefficient mutation discovers alternative but similarly-accurate equations.", "published": "2022-04-26T08:58:47Z", "authors": ["Marco Virgolin", "Peter A. N. Bosman"], "categories": ["cs.NE"], "doi": "", "_query": "hedonic regression housing", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2311.02067v2", "title": "Solving Woeginger's Hiking Problem: Wonderful Partitions in Anonymous Hedonic Games", "abstract": "A decade ago, Gerhard Woeginger posed an open problem that became well-known as \"Woeginger's Hiking Problem\": Consider a group of $n$ people that want to go hiking; everyone expresses preferences over the size of their hiking group in the form of an interval between $1$ and $n$. Is it possible to efficiently assign the $n$ people to a set of hiking subgroups so that every person approves the size of their assigned subgroup? The problem is also known as efficiently deciding if an instance of an anonymous Hedonic Game with interval approval preferences admits a wonderful partition. We resolve the open problem in the affirmative by presenting an $O(n^5)$ time algorithm for Woeginger's Hiking Problem. Our solution is based on employing a dynamic programming approach for a specific rectangle stabbing problem from computational geometry. Moreover, we propose natural, more demanding extensions of the problem, e.g., maximizing the number of satisfied participants and variants with single-peaked preferences, and show that they are also efficiently solvable. Last but not least, we employ our solution to efficiently compute a partition that maximizes the egalitarian welfare for anonymous single-peaked Hedonic Games.", "published": "2023-11-03T17:52:09Z", "authors": ["Andrei Constantinescu", "Pascal Lenzner", "Rebecca Reiffenhäuser", "Daniel Schmand", "Giovanna Varricchio"], "categories": ["cs.GT", "cs.DS"], "doi": "", "_query": "hedonic regression housing", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/1905.03111v1", "title": "Parallel and Distributed Algorithms for the housing allocation Problem", "abstract": "We give parallel and distributed algorithms for the housing allocation problem. In this problem, there is a set of agents and a set of houses. Each agent has a strict preference list for a subset of houses. We need to find a matching such that some criterion is optimized. One such criterion is Pareto Optimality. A matching is Pareto optimal if no coalition of agents can be strictly better off by exchanging houses among themselves. We also study the housing market problem, a variant of the housing allocation problem, where each agent initially owns a house. In addition to Pareto optimality, we are also interested in finding the core of a housing market. A matching is in the core if there is no coalition of agents that can be better off by breaking away from other agents and switching houses only among themselves. In the first part of this work, we show that computing a Pareto optimal matching of a house allocation is in {\\bf CC} and computing the core of a housing market is {\\bf CC}-hard. Given a matching, we also show that verifying whether it is in the core can be done in {\\bf NC}. We then give an algorithm to show that computing a maximum Pareto optimal matching for the housing allocation problem is in {\\bf RNC}^2 and quasi-{\\bf NC}^2. In the second part of this work, we present a distributed version of the top trading cycle algorithm for finding the core of a housing market. To that end, we first present two algorithms for finding all the disjoint cycles in a functional graph: a Las Vegas algorithm which terminates in $O(\\log l)$ rounds with high probability, where $l$ is the length of the longest cycle, and a deterministic algorithm which terminates in $O(\\log^* n \\log l)$ rounds, where $n$ is the number of nodes in the graph. Both algorithms work in the synchronous distributed model and use messages of size $O(\\log n)$.", "published": "2019-05-08T14:46:32Z", "authors": ["Xiong Zheng", "Vijay Garg"], "categories": ["cs.DC"], "doi": "", "_query": "hedonic regression housing", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2505.03977v1", "title": "Call for Action: towards the next generation of symbolic regression benchmark", "abstract": "Symbolic Regression (SR) is a powerful technique for discovering interpretable mathematical expressions. However, benchmarking SR methods remains challenging due to the diversity of algorithms, datasets, and evaluation criteria. In this work, we present an updated version of SRBench. Our benchmark expands the previous one by nearly doubling the number of evaluated methods, refining evaluation metrics, and using improved visualizations of the results to understand the performances. Additionally, we analyze trade-offs between model complexity, accuracy, and energy consumption. Our results show that no single algorithm dominates across all datasets. We propose a call for action from SR community in maintaining and evolving SRBench as a living benchmark that reflects the state-of-the-art in symbolic regression, by standardizing hyperparameter tuning, execution constraints, and computational resource allocation. We also propose deprecation criteria to maintain the benchmark's relevance and discuss best practices for improving SR algorithms, such as adaptive hyperparameter tuning and energy-efficient implementations.", "published": "2025-05-06T21:02:20Z", "authors": ["Guilherme S. Imai Aldeia", "Hengzhe Zhang", "Geoffrey Bomarito", "Miles Cranmer", "Alcides Fonseca", "Bogdan Burlacu", "William G. La Cava", "Fabrício Olivetti de França"], "categories": ["cs.LG", "cs.NE"], "doi": "10.1145/3712255.3734309", "_query": "hedonic regression housing", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2501.15916v2", "title": "Online Housing Market", "abstract": "This paper studies an online variant of the celebrated housing market problem, where each agent has a single house and seeks to exchange it for another based on her preferences. In this online setting, agents may arrive and depart at any time, meaning that not all agents are present on the housing market simultaneously. I extend the well known serial dictatorship and Gale s top trading cycle mechanisms to this online scenario, aiming to retain their desirable properties such as Pareto efficiency, individual rationality, and strategy proofness. These extensions also seek to prevent agents from strategically delaying their arrival or advancing their departure. I demonstrate that achieving all of these properties simultaneously is impossible in the online context, and I present several variants that achieve different subsets of these properties.", "published": "2025-01-27T10:05:49Z", "authors": ["Julien Lesca"], "categories": ["cs.GT", "cs.AI"], "doi": "", "_query": "hedonic regression housing", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/1912.00524v1", "title": "Factor Analysis on Citation, Using a Combined Latent and Logistic Regression Model", "abstract": "We propose a combined model, which integrates the latent factor model and the logistic regression model, for the citation network. It is noticed that neither a latent factor model nor a logistic regression model alone is sufficient to capture the structure of the data. The proposed model has a latent (i.e., factor analysis) model to represents the main technological trends (a.k.a., factors), and adds a sparse component that captures the remaining ad-hoc dependence. Parameter estimation is carried out through the construction of a joint-likelihood function of edges and properly chosen penalty terms. The convexity of the objective function allows us to develop an efficient algorithm, while the penalty terms push towards a low-dimensional latent component and a sparse graphical structure. Simulation results show that the proposed method works well in practical situations. The proposed method has been applied to a real application, which contains a citation network of statisticians (Ji and Jin, 2016). Some interesting findings are reported.", "published": "2019-12-02T00:04:10Z", "authors": ["Namjoon Suh", "Xiaoming Huo", "Eric Heim", "Lee Seversky"], "categories": ["stat.ML", "cs.LG"], "doi": "", "_query": "hedonic regression housing", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2310.15204v1", "title": "Mid-Long Term Daily Electricity Consumption Forecasting Based on Piecewise Linear Regression and Dilated Causal CNN", "abstract": "Daily electricity consumption forecasting is a classical problem. Existing forecasting algorithms tend to have decreased accuracy on special dates like holidays. This study decomposes the daily electricity consumption series into three components: trend, seasonal, and residual, and constructs a two-stage prediction method using piecewise linear regression as a filter and Dilated Causal CNN as a predictor. The specific steps involve setting breakpoints on the time axis and fitting the piecewise linear regression model with one-hot encoded information such as month, weekday, and holidays. For the challenging prediction of the Spring Festival, distance is introduced as a variable using a third-degree polynomial form in the model. The residual sequence obtained in the previous step is modeled using Dilated Causal CNN, and the final prediction of daily electricity consumption is the sum of the two-stage predictions. Experimental results demonstrate that this method achieves higher accuracy compared to existing approaches.", "published": "2023-10-23T10:22:38Z", "authors": ["Zhou Lan", "Ben Liu", "Yi Feng", "Danhuang Dong", "Peng Zhang"], "categories": ["cs.LG"], "doi": "", "_query": "hedonic regression housing", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/1608.03012v4", "title": "Fréchet Regression for Random Objects with Euclidean Predictors", "abstract": "Increasingly, statisticians are faced with the task of analyzing complex data that are non-Euclidean and specifically do not lie in a vector space. To address the need for statistical methods for such data, we introduce the concept of Fréchet regression. This is a general approach to regression when responses are complex random objects in a metric space and predictors are in $\\mathcal{R}^p$, achieved by extending the classical concept of a Fréchet mean to the notion of a conditional Fréchet mean. We develop generalized versions of both global least squares regression and local weighted least squares smoothing. The target quantities are appropriately defined population versions of global and local regression for response objects in a metric space. We derive asymptotic rates of convergence for the corresponding fitted regressions using observed data to the population targets under suitable regularity conditions by applying empirical process methods. For the special case of random objects that reside in a Hilbert space, such as regression models with vector predictors and functional data as responses, we obtain a limit distribution. The proposed methods have broad applicability. Illustrative examples include responses that consist of probability distributions and correlation matrices, and we demonstrate both global and local Fréchet regression for demographic and brain imaging data. Local Fréchet regression is also illustrated via a simulation with response data which lie on the sphere.", "published": "2016-08-10T00:41:03Z", "authors": ["Alexander Petersen", "Hans-Georg Müller"], "categories": ["stat.ME"], "doi": "", "_query": "hedonic regression housing", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2008.00235v1", "title": "Two-step penalised logistic regression for multi-omic data with an application to cardiometabolic syndrome", "abstract": "Building classification models that predict a binary class label on the basis of high dimensional multi-omics datasets poses several challenges, due to the typically widely differing characteristics of the data layers in terms of number of predictors, type of data, and levels of noise. Previous research has shown that applying classical logistic regression with elastic-net penalty to these datasets can lead to poor results (Liu et al., 2018). We implement a two-step approach to multi-omic logistic regression in which variable selection is performed on each layer separately and a predictive model is then built using the variables selected in the first step. Here, our approach is compared to other methods that have been developed for the same purpose, and we adapt existing software for multi-omic linear regression (Zhao and Zucknick, 2020) to the logistic regression setting. Extensive simulation studies show that our approach should be preferred if the goal is to select as many relevant predictors as possible, as well as achieving prediction performances comparable to those of the best competitors. Our motivating example is a cardiometabolic syndrome dataset comprising eight 'omic data types for 2 extreme phenotype groups (10 obese and 10 lipodystrophy individuals) and 185 blood donors. Our proposed approach allows us to identify features that characterise cardiometabolic syndrome at the molecular level. R code is available at https://github.com/acabassi/logistic-regression-for-multi-omic-data.", "published": "2020-08-01T10:36:27Z", "authors": ["Alessandra Cabassi", "Denis Seyres", "Mattia Frontini", "Paul D. W. Kirk"], "categories": ["stat.ME", "stat.AP", "stat.ML"], "doi": "", "_query": "hedonic regression housing", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2001.01641v1", "title": "Housing Investment, Stock Market Participation and Household Portfolio choice: Evidence from China's Urban Areas", "abstract": "This paper employs the survey data of CHFS (2013) to investigate the impact of housing investment on household stock market participation and portfolio choice. The results show that larger housing investment encourages the household participation in the stock market, but reduces the proportion of their stockholding. The above conclusion remains true even when the endogeneity problem is controlled with risk attitude classification, Heckman model test and subsample regression. This study shows that the growth in the housing market will not lead to stock market development because of lack of household financial literacy and the low expected yield on stock market.", "published": "2020-01-06T16:07:04Z", "authors": ["Huirong Liu"], "categories": ["econ.GN"], "doi": "", "_query": "hedonic regression housing", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2509.23684v1", "title": "Hedonic Neurons: A Mechanistic Mapping of Latent Coalitions in Transformer MLPs", "abstract": "Fine-tuned Large Language Models (LLMs) encode rich task-specific features, but the form of these representations, especially within MLP layers, remains unclear. Empirical inspection of LoRA updates shows that new features concentrate in mid-layer MLPs, yet the scale of these layers obscures meaningful structure. Prior probing suggests that statistical priors may strengthen, split, or vanish across depth, motivating the need to study how neurons work together rather than in isolation. We introduce a mechanistic interpretability framework based on coalitional game theory, where neurons mimic agents in a hedonic game whose preferences capture their synergistic contributions to layer-local computations. Using top-responsive utilities and the PAC-Top-Cover algorithm, we extract stable coalitions of neurons: groups whose joint ablation has non-additive effects. We then track their transitions across layers as persistence, splitting, merging, or disappearance. Applied to LLaMA, Mistral, and Pythia rerankers fine-tuned on scalar IR tasks, our method finds coalitions with consistently higher synergy than clustering baselines. By revealing how neurons cooperate to encode features, hedonic coalitions uncover higher-order structure beyond disentanglement and yield computational units that are functionally important, interpretable, and predictive across domains.", "published": "2025-09-28T06:55:24Z", "authors": ["Tanya Chowdhury", "Atharva Nijasure", "Yair Zick", "James Allan"], "categories": ["cs.LG"], "doi": "", "_query": "hedonic regression housing", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2405.09579v1", "title": "Scalable Sparse Regression for Model Discovery: The Fast Lane to Insight", "abstract": "There exist endless examples of dynamical systems with vast available data and unsatisfying mathematical descriptions. Sparse regression applied to symbolic libraries has quickly emerged as a powerful tool for learning governing equations directly from data; these learned equations balance quantitative accuracy with qualitative simplicity and human interpretability. Here, I present a general purpose, model agnostic sparse regression algorithm that extends a recently proposed exhaustive search leveraging iterative Singular Value Decompositions (SVD). This accelerated scheme, Scalable Pruning for Rapid Identification of Null vecTors (SPRINT), uses bisection with analytic bounds to quickly identify optimal rank-1 modifications to null vectors. It is intended to maintain sensitivity to small coefficients and be of reasonable computational cost for large symbolic libraries. A calculation that would take the age of the universe with an exhaustive search but can be achieved in a day with SPRINT.", "published": "2024-05-14T18:09:43Z", "authors": ["Matthew Golden"], "categories": ["cs.LG", "physics.data-an", "stat.ML"], "doi": "", "_query": "hedonic regression housing", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2303.11365v5", "title": "Housing Bubbles with Phase Transitions", "abstract": "We analyze how equilibrium housing prices are determined in the process of economic development within an overlapping generations model with perfect housing and rental markets. We characterize the rent growth rate in all equilibria. The economy exhibits a two-stage phase transition: as incomes of home buyers rise, the equilibrium regime changes from fundamental to bubble possibility, where fundamental and bubbly equilibria coexist. With even higher incomes, fundamental equilibria disappear and housing bubbles become a necessity. We also discuss extensions and refinements such as equilibrium uniqueness, multiple savings vehicles, welfare implications, credit- and expectation-driven bubbles, and testable implications of our theory.", "published": "2023-03-20T18:07:22Z", "authors": ["Tomohiro Hirano", "Alexis Akira Toda"], "categories": ["econ.TH"], "doi": "", "_query": "hedonic regression housing", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/1311.0834v1", "title": "Smooth backfitting in additive inverse regression", "abstract": "We consider the problem of estimating an additive regression function in an inverse regres- sion model with a convolution type operator. A smooth backfitting procedure is developed and asymptotic normality of the resulting estimator is established. Compared to other meth- ods for the estimation in additive models the new approach neither requires observations on a regular grid nor the estimation of the joint density of the predictor. It is also demonstrated by means of a simulation study that the backfitting estimator outperforms the marginal in- tegration method at least by a factor two with respect to the integrated mean squared error criterion.", "published": "2013-11-04T20:14:17Z", "authors": ["Nicolai Bissantz", "Holger Dette", "Thimo Hildebrandt"], "categories": ["stat.ME"], "doi": "", "_query": "hedonic regression housing", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/1905.11481v2", "title": "AI Feynman: a Physics-Inspired Method for Symbolic Regression", "abstract": "A core challenge for both physics and artificial intellicence (AI) is symbolic regression: finding a symbolic expression that matches data from an unknown function. Although this problem is likely to be NP-hard in principle, functions of practical interest often exhibit symmetries, separability, compositionality and other simplifying properties. In this spirit, we develop a recursive multidimensional symbolic regression algorithm that combines neural network fitting with a suite of physics-inspired techniques. We apply it to 100 equations from the Feynman Lectures on Physics, and it discovers all of them, while previous publicly available software cracks only 71; for a more difficult test set, we improve the state of the art success rate from 15% to 90%.", "published": "2019-05-27T20:03:57Z", "authors": ["Silviu-Marian Udrescu", "Max Tegmark"], "categories": ["physics.comp-ph", "cs.AI", "cs.LG", "hep-th"], "doi": "", "_query": "hedonic regression housing", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2405.03180v2", "title": "Braced Fourier Continuation and Regression for Anomaly Detection", "abstract": "In this work, the concept of Braced Fourier Continuation and Regression (BFCR) is introduced. BFCR is a novel and computationally efficient means of finding nonlinear regressions or trend lines in arbitrary one-dimensional data sets. The Braced Fourier Continuation (BFC) and BFCR algorithms are first outlined, followed by a discussion of the properties of BFCR as well as demonstrations of how BFCR trend lines may be used effectively for anomaly detection both within and at the edges of arbitrary one-dimensional data sets. Finally, potential issues which may arise while using BFCR for anomaly detection as well as possible mitigation techniques are outlined and discussed. All source code and example data sets are either referenced or available via GitHub, and all associated code is written entirely in Python.", "published": "2024-05-06T06:05:41Z", "authors": ["Josef Sabuda"], "categories": ["stat.ML", "cs.LG", "math.NA"], "doi": "", "_query": "hedonic regression housing", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2407.21209v3", "title": "Algorithm-Assisted Decision Making and Racial Disparities in Housing: A Study of the Allegheny Housing Assessment Tool", "abstract": "The demand for housing assistance across the United States far exceeds the supply, leaving housing providers the task of prioritizing clients for receipt of this limited resource. To be eligible for federal funding, local homelessness systems are required to implement assessment tools as part of their prioritization processes. The Vulnerability Index Service Prioritization Decision Assistance Tool (VI-SPDAT) is the most commonly used assessment tool nationwide. Recent studies have criticized the VI-SPDAT as exhibiting racial bias, which may lead to unwarranted racial disparities in housing provision. In response to these criticisms, some jurisdictions have developed alternative tools, such as the Allegheny Housing Assessment (AHA), which uses algorithms to assess clients' risk levels. Drawing on data from its deployment, we conduct descriptive and quantitative analyses to evaluate whether replacing the VI-SPDAT with the AHA affects racial disparities in housing allocation. We find that the VI-SPDAT tended to assign higher risk scores to white clients and lower risk scores to Black clients, and that white clients were served at a higher rates pre-AHA deployment. While post-deployment service decisions became better aligned with the AHA score, and the distribution of AHA scores is similar across racial groups, we do not find evidence of a corresponding decrease in disparities in service rates. We attribute the persistent disparity to the use of Alt-AHA, a survey-based tool that is used in cases of low data quality, as well as group differences in eligibility-related factors, such as chronic homelessness and veteran status. We discuss the implications for housing service systems seeking to reduce racial disparities in their service delivery.", "published": "2024-07-30T21:44:48Z", "authors": ["Lingwei Cheng", "Cameron Drayton", "Alexandra Chouldechova", "Rhema Vaithianathan"], "categories": ["cs.HC", "econ.GN"], "doi": "", "_query": "hedonic regression housing", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2203.01477v1", "title": "A Matching Mechanism for Provision of Housing to the Marginalized", "abstract": "During this pandemic, there have been unprecedented community and local government efforts to slow down the spread of the coronavirus, and also to protect our local economies. One such effort is California's project Roomkey that provided emergency housing to over 2000 vulnerable persons but fell short of the set goal of 15,000. It is projected that the homelessness problem will only get worse after the pandemic. With that in mind, we borrow from efforts like project Roomkey and suggest a solution that looks to improve upon these efforts to efficiently assign housing to the unhoused in our communities. The pandemic, together with the project Roomkey, shed light on the underlying supply demand mismatch that presents an opportunity for a matching mechanism solution to assigning housing options to the unhoused in a way that maximizes social welfare and minimizes susceptibility to strategic manipulation. Additionally, we argue that this automated solution would cut down on the amount of funding and personnel required for the assignment of housing to unhoused persons. Our solution is not intended to replace current solutions to homeless housing assignments but rather improve upon them. We can not postpone a proper solution to homelessness anymore, the time is now as the need for an efficient solution is most dire.", "published": "2022-03-03T01:34:36Z", "authors": ["J Ceasar Aguma"], "categories": ["cs.GT", "cs.CY", "econ.GN"], "doi": "", "_query": "hedonic regression housing", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/1509.03915v3", "title": "An Impossibility Result for Housing Markets with Fractional Endowments", "abstract": "The housing market setting constitutes a fundamental model of exchange economies of goods. Most of the work concerning housing markets does not cater for randomized assignments or allocation of time-shares. House allocation with fractional endowments of houses was considered by Athanassoglou and Sethuraman (2011) who posed the open problem whether individual rationality, weak strategyproofness, and efficiency are compatible for the setting. We show that the three axioms are incompatible.", "published": "2015-09-14T00:22:54Z", "authors": ["Haris Aziz"], "categories": ["cs.GT", "cs.DS"], "doi": "", "_query": "hedonic regression housing", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2210.10146v1", "title": "Housing Forecasts via Stock Market Indicators", "abstract": "Through the reinterpretation of housing data as candlesticks, we extend Nature Scientific Reports' article by Liang and Unwin [LU22] on stock market indicators for COVID-19 data, and utilize some of the most prominent technical indicators from the stock market to estimate future changes in the housing market, comparing the findings to those one would obtain from studying real estate ETF's. By providing an analysis of MACD, RSI, and Candlestick indicators (Bullish Engulfing, Bearish Engulfing, Hanging Man, and Hammer), we exhibit their statistical significance in making predictions for USA data sets (using Zillow Housing data) and also consider their applications within three different scenarios: a stable housing market, a volatile housing market, and a saturated market. In particular, we show that bearish indicators have a much higher statistical significance then bullish indicators, and we further illustrate how in less stable or more populated countries, bearish trends are only slightly more statistically present compared to bullish trends.", "published": "2022-10-18T20:23:43Z", "authors": ["Varun Mittal", "Laura P. Schaposnik"], "categories": ["econ.GN"], "doi": "10.1016/j.heliyon.2023.e16286", "_query": "hedonic regression housing", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/1803.00919v1", "title": "House Price Modeling over Heterogeneous Regions with Hierarchical Spatial Functional Analysis", "abstract": "Online real-estate information systems such as Zillow and Trulia have gained increasing popularity in recent years. One important feature offered by these systems is the online home price estimate through automated data-intensive computation based on housing information and comparative market value analysis. State-of-the-art approaches model house prices as a combination of a latent land desirability surface and a regression from house features. However, by using uniformly damping kernels, they are unable to handle irregularly shaped regions or capture land value discontinuities within the same region due to the existence of implicit sub-communities, which are common in real-world scenarios. In this paper, we explore the novel application of recent advances in spatial functional analysis to house price modeling and propose the Hierarchical Spatial Functional Model (HSFM), which decomposes house values into land desirability at both the global scale and hidden local scales as well as the feature regression component. We propose statistical learning algorithms based on finite-element spatial functional analysis and spatial constrained clustering to train our model. Extensive evaluations based on housing data in a major Canadian city show that our proposed approach can reduce the mean relative house price estimation error down to 6.60%.", "published": "2018-03-01T03:41:55Z", "authors": ["Bang Liu", "Borislav Mavrin", "Di Niu", "Linglong Kong"], "categories": ["cs.CE"], "doi": "10.1109/ICDM.2016.0134", "_query": "hedonic regression housing", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2209.07440v2", "title": "Envy-freeness in 3D Hedonic Games", "abstract": "We study the problem of partitioning a set of agents into coalitions based on the agents' additively separable preferences, which can also be viewed as a hedonic game. We apply three successively weaker solution concepts, namely envy-freeness, weakly justified envy-freeness, and justified envy-freeness. In a model in which coalitions may have any size, trivial solutions exist for these concepts, which provides a strong motivation for placing restrictions on coalition size. In this paper, we require feasible coalitions to have size three. We study the existence of partitions that are envy-free, weakly justified envy-free, and justified envy-free, and the computational complexity of finding such partitions, if they exist. We present a comprehensive complexity classification, in terms of the restrictions placed on the agents' preferences. From this, we identify a general trend that for the three successively weaker solution concepts, existence and polynomial-time solvability hold under successively weaker restrictions.", "published": "2022-09-15T16:42:07Z", "authors": ["Michael McKay", "Ágnes Cseh", "David Manlove"], "categories": ["cs.GT", "cs.DS"], "doi": "", "_query": "hedonic regression housing", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/1410.5920v1", "title": "Active Regression by Stratification", "abstract": "We propose a new active learning algorithm for parametric linear regression with random design. We provide finite sample convergence guarantees for general distributions in the misspecified model. This is the first active learner for this setting that provably can improve over passive learning. Unlike other learning settings (such as classification), in regression the passive learning rate of $O(1/ε)$ cannot in general be improved upon. Nonetheless, the so-called `constant' in the rate of convergence, which is characterized by a distribution-dependent risk, can be improved in many cases. For a given distribution, achieving the optimal risk requires prior knowledge of the distribution. Following the stratification technique advocated in Monte-Carlo function integration, our active learner approaches the optimal risk using piecewise constant approximations.", "published": "2014-10-22T06:09:58Z", "authors": ["Sivan Sabato", "Remi Munos"], "categories": ["stat.ML", "cs.LG"], "doi": "", "_query": "hedonic regression housing", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2105.07822v1", "title": "Acquisitive Crimes, Time of Day, and Multiunit Housing in the City of Milwaukee", "abstract": "According to \"Social Disorganization\" theory, criminal activity increases if the societal institutions that might be responsible for maintaining order are weakened. Do large apartment buildings, which often have fairly transient populations and low levels of community involvement, have disproportionately high rates of crime? Do these rates differ during the daytime or nighttime, depending when residents are present, or away from their property? This study examines four types of \"acquisitive\" crime in Milwaukee during 2014. Overall, nighttime crimes are shown to be more dispersed than daytime crimes. A spatial regression estimation finds that the density of multiunit housing is positively related to all types of crime except burglaries, but not for all times of day. Daytime robberies, in particular, increase as the density of multiunit housing increases.", "published": "2021-05-13T00:51:06Z", "authors": ["Scott W. Hegerty"], "categories": ["econ.GN"], "doi": "", "_query": "hedonic regression housing", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2301.06650v3", "title": "Probabilistic Traffic Forecasting with Dynamic Regression", "abstract": "This paper proposes a dynamic regression (DR) framework that enhances existing deep spatiotemporal models by incorporating structured learning for the error process in traffic forecasting. The framework relaxes the assumption of time independence by modeling the error series of the base model (i.e., a well-established traffic forecasting model) using a matrix-variate autoregressive (AR) model. The AR model is integrated into training by redesigning the loss function. The newly designed loss function is based on the likelihood of a non-isotropic error term, enabling the model to generate probabilistic forecasts while preserving the original outputs of the base model. Importantly, the additional parameters introduced by the DR framework can be jointly optimized alongside the base model. Evaluation on state-of-the-art (SOTA) traffic forecasting models using speed and flow datasets demonstrates improved performance, with interpretable AR coefficients and spatiotemporal covariance matrices enhancing the understanding of the model.", "published": "2023-01-17T01:12:44Z", "authors": ["Vincent Zhihao Zheng", "Seongjin Choi", "Lijun Sun"], "categories": ["cs.LG", "stat.ML"], "doi": "10.1287/trsc.2024.0560", "_query": "hedonic regression housing", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2502.12116v3", "title": "Floods do not sink prices, historical memory does: How flood risk impacts the Italian housing market", "abstract": "Do home prices incorporate flood risk in the immediate aftermath of specific flood events, or is it the repeated exposure over the years that plays a more significant role? We address this question through the first systematic study of the Italian housing market, which is an ideal case study because it is highly exposed to floods, though unevenly distributed across the national territory. Using a novel dataset containing about 550,000 mortgage-financed transactions between 2016 and 2024, as well as hedonic regressions and a difference-in-difference design, we find that: (i) specific floods do not decrease home prices in areas at risk; (ii) the repeated exposure to floods in flood-prone areas leads to a price decline, up to 4\\% in the most frequently flooded regions; (iii) responses are heterogeneous by buyers' income and age. Young buyers (with limited exposure to prior floods) do not obtain any price reduction for settling in risky areas, while experienced buyers do. At the same time, buyers who settle in risky areas have lower incomes than buyers in safe areas in the most affected regions. Our results emphasize the importance of cultural and institutional factors in understanding how flood risk affects the housing market and socioeconomic outcomes.", "published": "2025-02-17T18:41:23Z", "authors": ["Anna Bellaver", "Lorenzo Costantini", "Ariadna Fosch", "Anna Monticelli", "David Scala", "Marco Pangallo"], "categories": ["econ.GN"], "doi": "", "_query": "hedonic regression housing", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2503.15039v1", "title": "A Note on Local Linear Regression for Time Series in Banach Spaces", "abstract": "This work extends local linear regression to Banach space-valued time series for estimating smoothly varying means and their derivatives in non-stationary data. The asymptotic properties of both the standard and bias-reduced Jackknife estimators are analyzed under mild moment conditions, establishing their convergence rates. Simulation studies assess the finite sample performance of these estimators and compare them with the Nadaraya-Watson estimator. Additionally, the proposed methods are applied to smooth EEG recordings for reconstructing eye movements and to video analysis for detecting pedestrians and abandoned objects.", "published": "2025-03-19T09:28:18Z", "authors": ["Florian Heinrichs"], "categories": ["math.ST", "stat.ME"], "doi": "", "_query": "hedonic regression housing", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2302.13997v3", "title": "Host Community Respecting Refugee Housing", "abstract": "We propose a novel model for refugee housing respecting the preferences of the accepting community and refugees themselves. In particular, we are given a topology representing the local community, a set of inhabitants occupying some vertices of the topology, and a set of refugees that should be housed on the empty vertices of the graph. Both the inhabitants and the refugees have preferences over the structure of their neighborhood. We are specifically interested in the problem of finding housing such that the preferences of every individual are met; using game-theoretical words, we are looking for housing that is stable with respect to some well-defined notion of stability. We investigate conditions under which the existence of equilibria is guaranteed and study the computational complexity of finding such a stable outcome. As the problem is NP-hard even in very simple settings, we employ the parameterized complexity framework to give a finer-grained view of the problem's complexity with respect to natural parameters and structural restrictions of the given topology.", "published": "2023-02-27T17:42:03Z", "authors": ["Dušan Knop", "Šimon Schierreich"], "categories": ["cs.GT", "cs.DS", "econ.TH"], "doi": "", "_query": "hedonic regression housing", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/1008.0074v3", "title": "Stable partitions in additively separable hedonic games", "abstract": "An important aspect in systems of multiple autonomous agents is the exploitation of synergies via coalition formation. In this paper, we solve various open problems concerning the computational complexity of stable partitions in additively separable hedonic games. First, we propose a polynomial-time algorithm to compute a contractually individually stable partition. This contrasts with previous results such as the NP-hardness of computing individually stable or Nash stable partitions. Secondly, we prove that checking whether the core or the strict core exists is NP-hard in the strong sense even if the preferences of the players are symmetric. Finally, it is shown that verifying whether a partition consisting of the grand coalition is contractually strict core stable or Pareto optimal is coNP-complete.", "published": "2010-07-31T08:54:34Z", "authors": ["Haris Aziz", "Felix Brandt", "Hans Georg Seedig"], "categories": ["cs.GT", "cs.CC"], "doi": "10.1016/j.artint.2012.09.006", "_query": "hedonic regression housing", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/1308.3324v1", "title": "History Based Coalition Formation in Hedonic Context Using Trust", "abstract": "In this paper we address the problem of coalition formation in hedonic context. Our modelling tries to be as realistic as possible. In previous models, once an agent joins a coalition it would not be able to leave the coalition and join the new one; in this research we made it possible to leave a coalition but put some restrictions to control the behavior of agents. Leaving or staying of an agent in a coalition will affect on the trust of the other agents included in this coalition. Agents will use the trust values in computing the expected utility of coalitions. Three different risk behaviors are introduced for agents that want to initiate a coalition. Using these risk behaviors, some simulations are made and results are analyzed.", "published": "2013-08-15T07:33:28Z", "authors": ["Ahmadreza Ghaffarizadeh", "Vicki H. Allan"], "categories": ["cs.MA", "cs.AI"], "doi": "10.5121/ijaia.2013.4401", "_query": "hedonic regression housing", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2605.05535v2", "title": "Housing Potential Common Data Model and City Digital Twin", "abstract": "The evaluation of housing potential requires consideration of a location from multiple perspectives, ranging from zoning and land use to population characteristics and access to services. This research introduces the Housing Potential Common Data Model (HPCDM) to overcome existing data silos, serving as a standard to support integration and interoperability across the diverse range of datasets that are required for housing potential analysis. This report details the evaluation of the model along with the creation of a City Digital Twin for housing and a pilot dashboard application to demonstrate a practical implementation. Beyond the technical framework, this work identifies critical barriers to adoption and provides actionable mitigation strategies for urban planners and stakeholders.", "published": "2026-05-07T00:27:47Z", "authors": ["Megan Katsumi", "Mark Fox", "Anderson Wong", "Divnoor Chatha"], "categories": ["cs.AI"], "doi": "", "_query": "hedonic regression housing", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2503.06017v1", "title": "Welfare Approximation in Additively Separable Hedonic Games", "abstract": "Partitioning a set of $n$ items or agents while maximizing the value of the partition is a fundamental algorithmic task. We study this problem in the specific setting of maximizing social welfare in additively separable hedonic games. Unfortunately, this task faces strong computational boundaries: Extending previous results, we show that approximating welfare by a factor of $n^{1-ε}$ is NP-hard, even for severely restricted weights. However, we can obtain a randomized $\\log n$-approximation on instances for which the sum of input valuations is nonnegative. Finally, we study two stochastic models of aversion-to-enemies games, where the weights are derived from Erdős-Rényi or multipartite graphs. We obtain constant-factor and logarithmic-factor approximations with high probability.", "published": "2025-03-08T02:37:27Z", "authors": ["Martin Bullinger", "Vaggos Chatziafratis", "Parnian Shahkar"], "categories": ["cs.GT", "cs.DS"], "doi": "", "_query": "hedonic regression housing", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2604.18605v1", "title": "Exploring Drivers of Extreme Housing Prices in Australia", "abstract": "In recent years Australia has observed a growing, unexplained resilience of increasing house price trends. Here, we seek to understand what is driving Australia's indestructible asset using insights from market experts. We construct a differential equation model of house price to develop intuition for its historical behaviour and responsiveness to changes in mortgage rates. Using this model, we identify a point of 'decoupling' between house price and mortgage rate in the system with supply limitations found to be the main driver for this change. From there, modern extreme value techniques are implemented on real-world data to investigate how the effectiveness of mortgage rate in moderating extreme house price has changed before and after this historical decoupling. We find that without an increase in the housing supply chain, through either deregulation or reduced competition with government building, an 11\\% increase in mortgage rate will be needed to slow extreme housing costs.", "published": "2026-04-10T01:43:04Z", "authors": ["Grace Burtenshaw", "Ashley Burtenshaw", "Meagan Carney"], "categories": ["q-fin.GN", "math.PR", "stat.AP"], "doi": "", "_query": "hedonic regression housing", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2301.13756v1", "title": "PAC learning and stabilizing Hedonic Games: towards a unifying approach", "abstract": "We study PAC learnability and PAC stabilizability of Hedonic Games (HGs), i.e., efficiently inferring preferences or core-stable partitions from samples. We first expand the known learnability/stabilizability landscape for some of the most prominent HGs classes, providing results for Friends and Enemies Games, Bottom Responsive, and Anonymous HGs. Then, having a broader view in mind, we attempt to shed light on the structural properties leading to learnability/stabilizability, or lack thereof, for specific HGs classes. Along this path, we focus on the fully expressive Hedonic Coalition Nets representation of HGs. We identify two sets of conditions that lead to efficient learnability, and which encompass all of the known positive learnability results. On the side of stability, we reveal that, while the freedom of choosing an ad hoc adversarial distribution is the most obvious hurdle to achieving PAC stability, it is not the only one. First, we show a distribution independent necessary condition for PAC stability. Then, we focus on $\\W$-games, where players have individual preferences over other players and evaluate coalitions based on the least preferred member. We prove that these games are PAC stabilizable under the class of bounded distributions, which assign positive probability mass to all coalitions. Finally, we discuss why such a result is not easily extendable to other HGs classes even in this promising scenario. Namely, we establish a purely computational property necessary for achieving PAC stability.", "published": "2023-01-31T16:45:36Z", "authors": ["Simone Fioravanti", "Michele Flammini", "Bojana Kodric", "Giovanna Varricchio"], "categories": ["cs.GT", "cs.AI"], "doi": "", "_query": "hedonic regression housing", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/1207.6365v4", "title": "Low Rank Approximation and Regression in Input Sparsity Time", "abstract": "We design a new distribution over $\\poly(r \\eps^{-1}) \\times n$ matrices $S$ so that for any fixed $n \\times d$ matrix $A$ of rank $r$, with probability at least 9/10, $\\norm{SAx}_2 = (1 \\pm \\eps)\\norm{Ax}_2$ simultaneously for all $x \\in \\mathbb{R}^d$. Such a matrix $S$ is called a \\emph{subspace embedding}. Furthermore, $SA$ can be computed in $\\nnz(A) + \\poly(d \\eps^{-1})$ time, where $\\nnz(A)$ is the number of non-zero entries of $A$. This improves over all previous subspace embeddings, which required at least $Ω(nd \\log d)$ time to achieve this property. We call our matrices $S$ \\emph{sparse embedding matrices}. Using our sparse embedding matrices, we obtain the fastest known algorithms for $(1+\\eps)$-approximation for overconstrained least-squares regression, low-rank approximation, approximating all leverage scores, and $\\ell_p$-regression. The leading order term in the time complexity of our algorithms is $O(\\nnz(A))$ or $O(\\nnz(A)\\log n)$. We optimize the low-order $\\poly(d/\\eps)$ terms in our running times (or for rank-$k$ approximation, the $n*\\poly(k/eps)$ term), and show various tradeoffs. For instance, we also use our methods to design new preconditioners that improve the dependence on $\\eps$ in least squares regression to $\\log 1/\\eps$. Finally, we provide preliminary experimental results which suggest that our algorithms are competitive in practice.", "published": "2012-07-26T18:50:00Z", "authors": ["Kenneth L. Clarkson", "David P. Woodruff"], "categories": ["cs.DS"], "doi": "", "_query": "hedonic regression housing", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2107.10455v1", "title": "Time Varying Risk in U.S. Housing Sector and Real Estate Investment Trusts Equity Return", "abstract": "This study examines how housing sector volatilities affect real estate investment trust (REIT) equity return in the United States. I argue that unexpected changes in housing variables can be a source of aggregate housing risk, and the first principal component extracted from the volatilities of U.S. housing variables can predict the expected REIT equity returns. I propose and construct a factor-based housing risk index as an additional factor in asset price models that uses the time-varying conditional volatility of housing variables within the U.S. housing sector. The findings show that the proposed housing risk index is economically and theoretically consistent with the risk-return relationship of the conditional Intertemporal Capital Asset Pricing Model (ICAPM) of Merton (1973), which predicts an average maximum of 5.6 percent of risk premium in REIT equity return. In subsample analyses, the positive relationship is not affected by sample periods' choice but shows higher housing risk beta values for the 2009-18 sample period. The relationship remains significant after controlling for VIX, Fama-French three factors, and a broad set of macroeconomic and financial variables. Moreover, the proposed housing beta also accurately forecasts U.S. macroeconomic and financial conditions.", "published": "2021-07-22T04:57:39Z", "authors": ["Masud Alam"], "categories": ["econ.GN"], "doi": "", "_query": "hedonic regression housing", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/1902.04996v3", "title": "Structured penalized regression for drug sensitivity prediction", "abstract": "Large-scale {\\it in vitro} drug sensitivity screens are an important tool in personalized oncology to predict the effectiveness of potential cancer drugs. The prediction of the sensitivity of cancer cell lines to a panel of drugs is a multivariate regression problem with high-dimensional heterogeneous multi-omics data as input data and with potentially strong correlations between the outcome variables which represent the sensitivity to the different drugs. We propose a joint penalized regression approach with structured penalty terms which allow us to utilize the correlation structure between drugs with group-lasso-type penalties and at the same time address the heterogeneity between omics data sources by introducing data-source-specific penalty factors to penalize different data sources differently. By combining integrative penalty factors (IPF) with tree-guided group lasso, we create the IPF-tree-lasso method. We present a unified framework to transform more general IPF-type methods to the original penalized method. Because the structured penalty terms have multiple parameters, we demonstrate how the interval-search Efficient Parameter Selection via Global Optimization (EPSGO) algorithm can be used to optimize multiple penalty parameters efficiently. Simulation studies show that IPF-tree-lasso can improve the prediction performance compared to other lasso-type methods, in particular for heterogenous data sources. Finally, we employ the new methods to analyse data from the Genomics of Drug Sensitivity in Cancer project.", "published": "2019-02-13T16:41:41Z", "authors": ["Zhi Zhao", "Manuela Zucknick"], "categories": ["stat.ME"], "doi": "10.1111/rssc.12400", "_query": "hedonic regression housing", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2309.01418v1", "title": "Social Factors in P2P Energy Trading Using Hedonic Games", "abstract": "Lately, the energy communities have gained a lot of attention as they have the potential to significantly contribute to the resilience and flexibility of the energy system, facilitating widespread integration of intermittent renewable energy sources. Within these communities the prosumers can engage in peer-to-peer trading, fostering local collaborations and increasing awareness about energy usage and flexible consumption. However, even under these favorable conditions, prosumer engagement levels remain low, requiring trading mechanisms that are aligned with their social values and expectations. In this paper, we introduce an innovative hedonic game coordination and cooperation model for P2P energy trading among prosumers which considers the social relationships within an energy community to create energy coalitions and facilitate energy transactions among them. We defined a heuristic that optimizes the prosumers coalitions, considering their social and energy price preferences and balancing the energy demand and supply within the community. We integrated the proposed hedonic game model into a state-of-the-art blockchain-based P2P energy flexibility market and evaluated its performance within an energy community of prosumers. The evaluation results on a blockchain-based P2P energy flexibility market show the effectiveness in considering social factors when creating coalitions, increasing the total amount of energy transacted in a market session by 5% compared with other game theory-based solutions. Finally, it shows the importance of the social dimensions of P2P energy transactions, the positive social dynamics in the energy community increasing the amount of energy transacted by more than 10% while contributing to a more balanced energy demand and supply within the community.", "published": "2023-09-04T08:02:49Z", "authors": ["Dan Mitrea", "Viorica Chifu", "Tudor Cioara", "Ionut Anghel", "Cristina Pop"], "categories": ["cs.DC", "cs.AI", "cs.GT"], "doi": "", "_query": "hedonic regression housing", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2109.07738v2", "title": "Noise Robust Core-Stable Coalitions of Hedonic Games", "abstract": "We consider the coalition formation games with an additional component, `noisy preferences'. Moreover, such noisy preferences are available only for a sample of coalitions. We propose a multiplicative noise model and obtain the prediction probability, defined as the probability that the estimated PAC core-stable partition of the noisy game is also PAC core-stable for the unknown noise-free game. This prediction probability depends on the probability of a combinatorial construct called an `agreement event'. We explicitly obtain the agreement probability for $n$ agent noisy game with l\\geq 2 support noise distribution. For a user-given satisfaction value on this probability, we identify the noise regimes for which an estimated partition is noise robust; that is, it is PAC core-stable in both noisy and noise-free games. We obtain similar robustness results when the estimated partition is not PAC core-stable. These noise regimes correspond to the level sets of the agreement probability function and are non-convex sets. Moreover, an important fact is that the prediction probability can be high even if high noise values occur with a high probability. Further, for a class of top-responsive hedonic games, we obtain the bounds on the extra noisy samples required to get noise robustness with a user-given satisfaction value. We completely solve the noise robustness problem of a $2$ agent hedonic game. In particular, we obtain the prediction probability function for l=2 and l=3 noise support cases. For l=2, the prediction probability is convex in noise probability, but the noise robust regime is non-convex. Its minimum value, called the safety value, is 0.62; so, below 0.62, the noise robust regime is the entire probability simplex. However, for l \\geq 3, the prediction probability is non-convex; so, the safety value is the global minima of a non-convex function and is computationally hard.", "published": "2021-09-16T06:13:48Z", "authors": ["Prashant Trivedi", "Nandyala Hemachandra"], "categories": ["cs.GT"], "doi": "", "_query": "hedonic regression housing", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/1005.4540v3", "title": "Optimal Partitions in Additively Separable Hedonic Games", "abstract": "We conduct a computational analysis of fair and optimal partitions in additively separable hedonic games. We show that, for strict preferences, a Pareto optimal partition can be found in polynomial time while verifying whether a given partition is Pareto optimal is coNP-complete, even when preferences are symmetric and strict. Moreover, computing a partition with maximum egalitarian or utilitarian social welfare or one which is both Pareto optimal and individually rational is NP-hard. We also prove that checking whether there exists a partition which is both Pareto optimal and envy-free is $Σ_{2}^{p}$-complete. Even though an envy-free partition and a Nash stable partition are both guaranteed to exist for symmetric preferences, checking whether there exists a partition which is both envy-free and Nash stable is NP-complete.", "published": "2010-05-25T11:38:41Z", "authors": ["Haris Aziz", "Felix Brandt", "Hans Georg Seedig"], "categories": ["cs.GT"], "doi": "10.1016/j.artint.2012.09.006", "_query": "hedonic regression housing", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2004.02706v1", "title": "What do online listings tell us about the housing market?", "abstract": "Traditional data sources for the analysis of housing markets show several limitations, that recently started to be overcome using data coming from housing sales advertisements (ads) websites. In this paper, using a large dataset of ads in Italy, we provide the first comprehensive analysis of the problems and potential of these data. The main problem is that multiple ads (\"duplicates\") can correspond to the same housing unit. We show that this issue is mainly caused by sellers' attempt to increase visibility of their listings. Duplicates lead to misrepresentation of the volume and composition of housing supply, but this bias can be corrected by identifying duplicates with machine learning tools. We then focus on the potential of these data. We show that the timeliness, granularity, and online nature of these data allow monitoring of housing demand, supply and liquidity, and that the (asking) prices posted on the website can be more informative than transaction prices.", "published": "2020-04-06T14:40:09Z", "authors": ["Michele Loberto", "Andrea Luciani", "Marco Pangallo"], "categories": ["econ.EM", "econ.GN"], "doi": "", "_query": "hedonic regression housing", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2310.10807v1", "title": "Regularization properties of adversarially-trained linear regression", "abstract": "State-of-the-art machine learning models can be vulnerable to very small input perturbations that are adversarially constructed. Adversarial training is an effective approach to defend against it. Formulated as a min-max problem, it searches for the best solution when the training data were corrupted by the worst-case attacks. Linear models are among the simple models where vulnerabilities can be observed and are the focus of our study. In this case, adversarial training leads to a convex optimization problem which can be formulated as the minimization of a finite sum. We provide a comparative analysis between the solution of adversarial training in linear regression and other regularization methods. Our main findings are that: (A) Adversarial training yields the minimum-norm interpolating solution in the overparameterized regime (more parameters than data), as long as the maximum disturbance radius is smaller than a threshold. And, conversely, the minimum-norm interpolator is the solution to adversarial training with a given radius. (B) Adversarial training can be equivalent to parameter shrinking methods (ridge regression and Lasso). This happens in the underparametrized region, for an appropriate choice of adversarial radius and zero-mean symmetrically distributed covariates. (C) For $\\ell_\\infty$-adversarial training -- as in square-root Lasso -- the choice of adversarial radius for optimal bounds does not depend on the additive noise variance. We confirm our theoretical findings with numerical examples.", "published": "2023-10-16T20:09:58Z", "authors": ["Antônio H. Ribeiro", "Dave Zachariah", "Francis Bach", "Thomas B. Schön"], "categories": ["stat.ML", "cs.CR", "cs.LG", "math.OC"], "doi": "", "_query": "hedonic regression housing", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/1701.04431v1", "title": "Interpolating between matching and hedonic pricing models", "abstract": "We consider the theoretical properties of a model which encompasses bi-partite matching under transferable utility on the one hand, and hedonic pricing on the other. This framework is intimately connected to tripartite matching problems (known as multi-marginal optimal transport problems in the mathematical literature). We exploit this relationship in two ways; first, we show that a known structural result from multi-marginal optimal transport can be used to establish an upper bound on the dimension of the support of stable matchings. Next, assuming the distribution of agents on one side of the market is continuous, we identify a condition on their preferences that ensures purity and uniqueness of the stable matching; this condition is a variant of a known condition in the mathematical literature, which guarantees analogous properties in the multi-marginal optimal transport problem. We exhibit several examples of surplus functions for which our condition is satisfied, as well as some for which it fails.", "published": "2017-01-16T19:36:10Z", "authors": ["Brendan Pass"], "categories": ["econ.GN", "math.AP", "math.OC"], "doi": "", "_query": "hedonic regression housing", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2505.16329v2", "title": "High-Dimensional Private Linear Regression with Optimal Rates", "abstract": "Differentially private (DP) linear regression has received significant attention in the recent theoretical literature, with several approaches proposed to improve error rates. Our work considers the popular high-dimensional regime with random data, where the number of training samples $n$ and the input dimension $d$ grow at a proportional rate $d / n \\to γ$, and it studies a family of one-pass DP gradient descent (DP-GD) algorithms satisfying $ρ^2 / 2$ zero concentrated DP. In this setting, we establish a deterministic equivalent for the DP-GD trajectory in terms of a system of ordinary differential equations. This allows to analyze the effect of gradient clipping constants that are smaller than the typical norm of the per-sample gradients - a setup shown to improve performance in practice. For well-conditioned data, we show that DP-GD, upon properly choosing clipping constant and learning rate, achieves the non-asymptotic risk of $O(γ+ γ^2 / ρ^2)$, and we establish that this rate is minimax optimal. Then, we consider the ill-conditioned case where the data covariance spectrum follows a power-law distribution, and we show that the risk displays a power-like scaling law in $γ$, highlighting the change in the exponent as a function of the privacy parameter $ρ$. Overall, our analysis demonstrates the benefits of practical algorithmic design choices, including aggressive gradient clipping and decaying learning rate schedules.", "published": "2025-05-22T07:34:27Z", "authors": ["Simone Bombari", "Jialei Luo", "Inbar Seroussi", "Marco Mondelli"], "categories": ["stat.ML", "cs.LG"], "doi": "", "_query": "hedonic regression housing", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/1306.2831v1", "title": "Systemic risk and spatiotemporal dynamics of the US housing market", "abstract": "Housing markets play a crucial role in economies and the collapse of a real-estate bubble usually destabilizes the financial system and causes economic recessions. We investigate the systemic risk and spatiotemporal dynamics of the US housing market (1975-2011) at the state level based on the Random Matrix Theory (RMT). We identify rich economic information in the largest eigenvalues deviating from RMT predictions and unveil that the component signs of the eigenvectors contain either geographical information or the extent of differences in house price growth rates or both. Our results show that the US housing market experienced six different regimes, which is consistent with the evolution of state clusters identified by the box clustering algorithm and the consensus clustering algorithm on the partial correlation matrices. Our analysis uncovers that dramatic increases in the systemic risk are usually accompanied with regime shifts, which provides a means of early detection of housing bubbles.", "published": "2013-06-12T14:05:13Z", "authors": ["Hao Meng", "Wen-Jie Xie", "Zhi-Qiang Jiang", "Boris Podobnik", "Wei-Xing Zhou", "H. Eugene Stanley"], "categories": ["q-fin.ST"], "doi": "10.1038/srep03655", "_query": "hedonic regression housing", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2101.00913v1", "title": "Credit Crunch: The Role of Household Lending Capacity in the Dutch Housing Boom and Bust 1995-2018", "abstract": "What causes house prices to rise and fall? Economists identify household access to credit as a crucial factor. \"Loan-to-Value\" and \"Debt-to-GDP\" ratios are the standard measures for credit access. However, these measures fail to explain the depth of the Dutch housing bust after the 2009 Financial Crisis. This work is the first to model household lending capacity based on the formulas that Dutch banks use in the mortgage application process. We compare the ability of regression models to forecast housing prices when different measures of credit access are utilised. We show that our measure of household lending capacity is a forward-looking, highly predictive variable that outperforms `Loan-to-Value' and debt ratios in forecasting the Dutch crisis. Sharp declines in lending capacity foreshadow the market deceleration.", "published": "2021-01-04T12:10:07Z", "authors": ["Menno Schellekens", "Taha Yasseri"], "categories": ["stat.AP", "q-fin.GN"], "doi": "", "_query": "hedonic regression housing", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2408.00746v3", "title": "On the Low-Temperature MCMC threshold: the cases of sparse tensor PCA, sparse regression, and a geometric rule", "abstract": "Over the last years, there has been a significant amount of work studying the power of specific classes of computationally efficient estimators for multiple statistical parametric estimation tasks, including the estimators classes of low-degree polynomials, spectral methods, and others. Despite that, our understanding of the important class of MCMC methods remains quite poorly understood. For instance, for many models of interest, the performance of even zero-temperature (greedy-like) MCMC methods that simply maximize the posterior remains elusive. In this work, we provide an easy to check condition under which the low-temperature Metropolis chain maximizes the posterior in polynomial-time with high probability. The result is generally applicable, and in this work, we use it to derive positive MCMC results for two classical sparse estimation tasks: the sparse tensor PCA model and sparse regression. Interestingly, in both cases, we also leverage the Overlap Gap Property framework for inference (Gamarnik, Zadik AoS '22) to prove that our results are tight: no low-temperature local MCMC method can achieve better performance. In particular, our work identifies the \"low-temperature (local) MCMC threshold\" for both sparse models. Interestingly, in the sparse tensor PCA model our results indicate that low-temperature local MCMC methods significantly underperform compared to other studied time-efficient methods, such as the class of low-degree polynomials.", "published": "2024-08-01T17:49:55Z", "authors": ["Zongchen Chen", "Conor Sheehan", "Ilias Zadik"], "categories": ["math.ST", "cs.DS", "math.PR"], "doi": "", "_query": "hedonic regression housing", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2208.06997v1", "title": "Combining deep learning and crowdsourcing geo-images to predict housing quality in rural China", "abstract": "Housing quality is an essential proxy for regional wealth, security and health. Understanding the distribution of housing quality is crucial for unveiling rural development status and providing political proposals. However,present rural house quality data highly depends on a top-down, time-consuming survey at the national or provincial level but fails to unpack the housing quality at the village level. To fill the gap between accurately depicting rural housing quality conditions and deficient data,we collect massive rural images and invite users to assess their housing quality at scale. Furthermore, a deep learning framework is proposed to automatically and efficiently predict housing quality based on crowd-sourcing rural images.", "published": "2022-08-15T03:58:03Z", "authors": ["Weipan Xu", "Yu Gu", "Yifan Chen", "Yongtian Wang", "Weihuan Deng", "Xun Li"], "categories": ["cs.LG"], "doi": "", "_query": "hedonic regression housing", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2012.06750v3", "title": "Outlier-robust sparse/low-rank least-squares regression and robust matrix completion", "abstract": "We study high-dimensional least-squares regression within a subgaussian statistical learning framework with heterogeneous noise. It includes $s$-sparse and $r$-low-rank least-squares regression when a fraction $ε$ of the labels are adversarially contaminated. We also present a novel theory of trace-regression with matrix decomposition based on a new application of the product process. For these problems, we show novel near-optimal \"subgaussian\" estimation rates of the form $r(n,d_{e})+\\sqrt{\\log(1/δ)/n}+ε\\log(1/ε)$, valid with probability at least $1-δ$. Here, $r(n,d_{e})$ is the optimal uncontaminated rate as a function of the effective dimension $d_{e}$ but independent of the failure probability $δ$. These rates are valid uniformly on $δ$, i.e., the estimators' tuning do not depend on $δ$. Lastly, we consider noisy robust matrix completion with non-uniform sampling. If only the low-rank matrix is of interest, we present a novel near-optimal rate that is independent of the corruption level $a$. Our estimators are tractable and based on a new \"sorted\" Huber-type loss. No information on $(s,r,ε,a)$ are needed to tune these estimators. Our analysis makes use of novel $δ$-optimal concentration inequalities for the multiplier and product processes which could be useful elsewhere. For instance, they imply novel sharp oracle inequalities for Lasso and Slope with optimal dependence on $δ$. Numerical simulations confirm our theoretical predictions. In particular, \"sorted\" Huber regression can outperform classical Huber regression.", "published": "2020-12-12T07:42:47Z", "authors": ["Philip Thompson"], "categories": ["math.ST", "stat.ML"], "doi": "", "_query": "hedonic regression housing", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2110.06875v3", "title": "The core of housing markets from an agent's perspective: Is it worth sprucing up your home?", "abstract": "We study housing markets as introduced by Shapley and Scarf (1974). We investigate the computational complexity of various questions regarding the situation of an agent $a$ in a housing market $H$: we show that it is $\\mathsf{NP}$-hard to find an allocation in the core of $H$ where (i) $a$ receives a certain house, (ii) $a$ does not receive a certain house, or (iii) $a$ receives a house other than her own. We prove that the core of housing markets respects improvement in the following sense: given an allocation in the core of $H$ where agent $a$ receives a house $h$, if the value of the house owned by $a$ increases, then the resulting housing market admits an allocation in its core in which $a$ receives either $h$, or a house that $a$ prefers to $h$; moreover, such an allocation can be found efficiently. We further show an analogous result in the Stable Roommates setting by proving that stable matchings in a one-sided market also respect improvement.", "published": "2021-10-13T17:11:06Z", "authors": ["Ildikó Schlotter", "Péter Biró", "Tamás Fleiner"], "categories": ["cs.GT", "cs.DS"], "doi": "", "_query": "hedonic regression housing", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2405.17505v1", "title": "Predicting Rental Price of Lane Houses in Shanghai with Machine Learning Methods and Large Language Models", "abstract": "Housing has emerged as a crucial concern among young individuals residing in major cities, including Shanghai. Given the unprecedented surge in property prices in this metropolis, young people have increasingly resorted to the rental market to address their housing needs. This study utilizes five traditional machine learning methods: multiple linear regression (MLR), ridge regression (RR), lasso regression (LR), decision tree (DT), and random forest (RF), along with a Large Language Model (LLM) approach using ChatGPT, for predicting the rental prices of lane houses in Shanghai. It applies these methods to examine a public data sample of about 2,609 lane house rental transactions in 2021 in Shanghai, and then compares the results of these methods. In terms of predictive power, RF has achieved the best performance among the traditional methods. However, the LLM approach, particularly in the 10-shot scenario, shows promising results that surpass traditional methods in terms of R-Squared value. The three performance metrics: mean squared error (MSE), mean absolute error (MAE), and R-Squared, are used to evaluate the models. Our conclusion is that while traditional machine learning models offer robust techniques for rental price prediction, the integration of LLM such as ChatGPT holds significant potential for enhancing predictive accuracy.", "published": "2024-05-26T07:01:33Z", "authors": ["Tingting Chen", "Shijing Si"], "categories": ["cs.LG", "cs.CL"], "doi": "", "_query": "hedonic regression housing", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2203.14255v1", "title": "Are Instrumental Variables Really That Instrumental? Endogeneity Resolution in Regression Models for Comparative Studies", "abstract": "We provide a justification for why, and when, endogeneity will not cause bias in the interpretation of the coefficients in a regression model. This technique can be a viable alternative to, or even used alongside, the instrumental variable method. We show that when performing any comparative study, it is possible to measure the true change in the coefficients under a broad set of conditions. Our results hold, as long as the product of the covariance structure between the explanatory variables and the covariance between the error term and the explanatory variables are equal, within the same system at different time periods or across multiple systems at the same point in time.", "published": "2022-03-27T09:24:00Z", "authors": ["Ravi Kashyap"], "categories": ["econ.GN", "math.NA", "stat.AP", "stat.ME"], "doi": "10.5705/ss.202019.0459", "_query": "hedonic regression housing", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2603.07890v1", "title": "Visualizing Coalition Formation: From Hedonic Games to Image Segmentation", "abstract": "We propose image segmentation as a visual diagnostic testbed for coalition formation in hedonic games. Modeling pixels as agents on a graph, we study how a granularization parameter shapes equilibrium fragmentation and boundary structure. On the Weizmann single-object benchmark, we relate multi-coalition equilibria to binary protocols by measuring whether the converged coalitions overlap with a foreground ground-truth. We observe transitions from cohesive to fragmented yet recoverable equilibria, and finally to intrinsic failure under excessive fragmentation. Our core contribution links multi-agent systems with image segmentation by quantifying the impact of mechanism design parameters on equilibrium structures.", "published": "2026-03-09T02:08:17Z", "authors": ["Pedro Henrique de Paula França", "Lucas Lopes Felipe", "Daniel Sadoc Menasché"], "categories": ["cs.AI", "cs.CV"], "doi": "", "_query": "hedonic regression housing", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2604.00556v1", "title": "HabitatAgent: An End-to-End Multi-Agent System for Housing Consultation", "abstract": "Housing selection is a high-stakes and largely irreversible decision problem. We study housing consultation as a decision-support interface for housing selection. Existing housing platforms and many LLM-based assistants often reduce this process to ranking or recommendation, resulting in opaque reasoning, brittle multi-constraint handling, and limited guarantees on factuality. We present HabitatAgent, the first LLM-powered multi-agent architecture for end-to-end housing consultation. HabitatAgent comprises four specialized agent roles: Memory, Retrieval, Generation, and Validation. The Memory Agent maintains multi-layer user memory through internal stages for constraint extraction, memory fusion, and verification-gated updates; the Retrieval Agent performs hybrid vector--graph retrieval (GraphRAG); the Generation Agent produces evidence-referenced recommendations and explanations; and the Validation Agent applies multi-tier verification and targeted remediation. Together, these agents provide an auditable and reliable workflow for end-to-end housing consultation. We evaluate HabitatAgent on 100 real user consultation scenarios (300 multi-turn question--answer pairs) under an end-to-end correctness protocol. A strong single-stage baseline (Dense+Rerank) achieves 75% accuracy, while HabitatAgent reaches 95%.", "published": "2026-04-01T06:59:50Z", "authors": ["Hongyang Yang", "Yanxin Zhang", "Yang She", "Yue Xiao", "Hao Wu", "Yiyang Zhang", "Jiapeng Hou", "Rongshan Zhang"], "categories": ["cs.LG", "cs.AI", "cs.ET", "q-fin.CP", "q-fin.RM"], "doi": "", "_query": "hedonic regression housing", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2009.00436v1", "title": "Instrumental Variable Quantile Regression", "abstract": "This chapter reviews the instrumental variable quantile regression model of Chernozhukov and Hansen (2005). We discuss the key conditions used for identification of structural quantile effects within this model which include the availability of instruments and a restriction on the ranks of structural disturbances. We outline several approaches to obtaining point estimates and performing statistical inference for model parameters. Finally, we point to possible directions for future research.", "published": "2020-08-28T19:50:43Z", "authors": ["Victor Chernozhukov", "Christian Hansen", "Kaspar Wuthrich"], "categories": ["econ.EM"], "doi": "", "_query": "hedonic regression housing", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2210.14266v1", "title": "Hedonic Models of Real Estate Prices: GAM and Environmental Factors", "abstract": "We consider the use of P-spline generalized additive hedonic models for real estate prices in large U.S. cities, contrasting their predictive efficiency against linear and polynomial based generalized linear models. Using intrinsic and extrinsic factors available from Redfin, we show that GAM models are capable of describing 84% to 92% of the variance in the expected ln(sales price), based upon 2021 data. As climate change is becoming increasingly important, we utilized the GAM model to examine the significance of environmental factors in two urban centers on the northwest coast. The results indicate city dependent differences in the significance of environmental factors. We find that inclusion of the environmental factors increases the adjusted R-squared of the GAM model by less than one percent.", "published": "2022-10-25T18:38:52Z", "authors": ["Jason R. Bailey", "Davide Lauria", "W. Brent Lindquist", "Stefan Mittnik", "Svetlozar T. Rachev"], "categories": ["q-fin.CP"], "doi": "", "_query": "hedonic regression housing", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2405.18471v2", "title": "Symbolic Regression for Beyond the Standard Model Physics", "abstract": "We propose symbolic regression as a powerful tool for studying Beyond the Standard Model physics. As a benchmark model, we consider the so-called Constrained Minimal Supersymmetric Standard Model, which has a four-dimensional parameter space defined at the GUT scale. We provide a set of analytical expressions that reproduce three low-energy observables of interest in terms of the parameters of the theory: the Higgs mass, the contribution to the anomalous magnetic moment of the muon, and the cold dark matter relic density. To demonstrate the power of the approach, we employ the symbolic expressions in a global fits analysis to derive the posterior probability densities of the parameters, which are obtained extremely rapidly in comparison with conventional methods.", "published": "2024-05-28T18:00:01Z", "authors": ["Shehu AbdusSalam", "Steve Abel", "Miguel Crispim Romao"], "categories": ["hep-ph", "cs.AI", "cs.LG", "hep-th", "physics.comp-ph"], "doi": "", "_query": "hedonic regression housing", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/1611.07636v1", "title": "Mechanism Design for Multi-Type Housing Markets", "abstract": "We study multi-type housing markets, where there are $p\\ge 2$ types of items, each agent is initially endowed one item of each type, and the goal is to design mechanisms without monetary transfer to (re)allocate items to the agents based on their preferences over bundles of items, such that each agent gets one item of each type. In sharp contrast to classical housing markets, previous studies in multi-type housing markets have been hindered by the lack of natural solution concepts, because the strict core might be empty. We break the barrier in the literature by leveraging AI techniques and making natural assumptions on agents' preferences. We show that when agents' preferences are lexicographic, even with different importance orders, the classical top-trading-cycles mechanism can be extended while preserving most of its nice properties. We also investigate computational complexity of checking whether an allocation is in the strict core and checking whether the strict core is empty. Our results convey an encouragingly positive message: it is possible to design good mechanisms for multi-type housing markets under natural assumptions on preferences.", "published": "2016-11-23T04:30:42Z", "authors": ["Sibel Adali", "Sujoy Sikdar", "Lirong Xia"], "categories": ["cs.GT"], "doi": "", "_query": "hedonic regression housing", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/1407.1079v1", "title": "Building Consistent Regression Trees From Complex Sample Data", "abstract": "In the past several years a wide range of methods for the construction of regression trees and other estimators based on the recursive partitioning of samples have appeared in the statistics literature. Many applications involve data collected through a complex sample design. At present, however, relatively little is known regarding the properties of these methods under complex designs. This article proposes a method for incorporating information about the complex sample design when building a regression tree using a recursive partitioning algorithm. Sufficient conditions are established for asymptotic design L 2 consistency of these regression trees as estimators for an arbitrary regression function. The proposed method is illustrated with Occupational Employment Statistics establishment survey data linked to Quarterly Census of Employment and Wage payroll data of the Bureau of Labor Statistics. Performance of the nonparametric estimator is investigated through a simulation study based on this example.", "published": "2014-07-03T22:26:39Z", "authors": ["Daniell Toth", "John Eltinge"], "categories": ["stat.ME"], "doi": "", "_query": "hedonic regression housing", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/1905.09368v2", "title": "Outlier Robust Extreme Learning Machine for Multi-Target Regression", "abstract": "The popularity of algorithms based on Extreme Learning Machine (ELM), which can be used to train Single Layer Feedforward Neural Networks (SLFN), has increased in the past years. They have been successfully applied to a wide range of classification and regression tasks. The most commonly used methods are the ones based on minimizing the $\\ell_2$ norm of the error, which is not suitable to deal with outliers, essentially in regression tasks. The use of $\\ell_1$ norm was proposed in Outlier Robust ELM (OR-ELM), which is defined to one-dimensional outputs. In this paper, we generalize OR-ELM to deal with multi-target regression problems, using the error $\\ell_{2,1}$ norm and the Elastic Net theory, which can result in a more sparse network, resulting in our method, Generalized Outlier Robust ELM (GOR-ELM). We use Alternating Direction Method of Multipliers (ADMM) to solve the resulting optimization problem. An incremental version of GOR-ELM is also proposed. We chose 15 public real-world multi-target regression datasets to test our methods. Our conducted experiments show that they are statistically better than other ELM-based techniques, when considering data contaminated with outliers, and equivalent to them, otherwise.", "published": "2019-05-22T21:16:55Z", "authors": ["Bruno Légora Souza da Silva", "Fernando Kentaro Inaba", "Evandro Ottoni Teatini Salles", "Patrick Marques Ciarelli"], "categories": ["cs.LG", "stat.ML"], "doi": "", "_query": "hedonic regression housing", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2605.17031v1", "title": "A Joint Synthetic Housing-Household Inventory", "abstract": "Accurately understanding the interactions between humans and the built environment requires integrated representations of both the buildings and the populations that occupy them. However, high-fidelity datasets that jointly capture detailed housing structures and demographic characteristics at the household level do not currently exist. This paper presents a framework for constructing a joint housing-household inventory that explicitly links individuals and households to compatible housing units from the National Structure Inventory (NSI), while preserving realistic population densities and demographic distributions. The framework integrates three components: (i) synthetic population generation from American Community Survey (ACS) Public Use Microdata Sample (PUMS) records that preserve complex intra-household relationships; (ii) a deep contrastive learning model that quantifies housing-household compatibility; and (iii) a hierarchical optimization-based allocation procedure that enforces building-level capacity and block-group-level demographic constraints. The generated synthetic population attains high statistical realism relative to the census microdata, and the contrastive learning model identifies compatible housing-household pairs with high predictive accuracy. Applied to coastal North Carolina, evaluations at building, neighborhood, and regional scales show that the joint inventory matches block-group-level demographic distributions, reproduces observed spatial population patterns without systematic bias, and maintains consistent allocation quality across urban, suburban, and rural contexts. By enabling coupled household- and building-level analyses, the resulting inventory supports a broad range of applications, including disaster resilience planning, housing and affordability analysis, energy-use assessment, and public health research.", "published": "2026-05-16T14:58:44Z", "authors": ["Xiao Qian", "Shangjia Dong", "Rachel Davidson"], "categories": ["cs.CY"], "doi": "", "_query": "hedonic regression housing", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2308.09501v2", "title": "Towards More Realistic Models for Refugee Integration: Anonymous Refugee Housing with Upper-Bounds", "abstract": "[Knop and Schierreich; AAMAS~'23] recently introduced a novel model for refugee housing, where we are given a city's topology represented as a graph, a set of inhabitants who occupy some vertices of the topology, and a set of refugees that we need to house in unoccupied vertices. Moreover, each inhabitant approves specific numbers of refugees in his neighbourhood, and our goal is to find housing such that every inhabitant approves the number of refugees housed in his neighbourhood. However, such a model suffers from several problems: (i) it is computationally hard to find desirable housing, and (ii) the inhabitants' preferences are not necessarily continuous, which hardly occurs in practice. To avoid these objections, we introduce a restricted variant of the problem that is more closely aligned with practical scenarios of refugee housing and study its complexity.", "published": "2023-08-18T12:16:36Z", "authors": ["Šimon Schierreich"], "categories": ["cs.GT"], "doi": "", "_query": "hedonic regression housing", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2201.04003v1", "title": "Housing Market Forecasting using Home Showing Events", "abstract": "Both buyers and sellers face uncertainty in real estate transactions in about when to time a transaction and at what cost. Both buyers and sellers make decisions without knowing the present and future state of the large and dynamic real estate market. Current approaches rely on analysis of historic transactions to price a property. However, as we show in this paper, the transaction data alone cannot be used to forecast demand. We develop a housing demand index based on microscopic home showings events data that can provide decision-making support for buyers and sellers on a very granular time and spatial scale. We use statistical modeling to develop a housing market demand forecast up to twenty weeks using high-volume, high-velocity data on home showings, listing events, and historic sales data. We demonstrate our analysis using data from seven million individual records sourced from a unique, proprietary dataset that has not previously been explored in application to the real estate market. We then employ a series of predictive models to estimate current and forecast future housing demand. A housing demand index provides insight into the level of demand for a home on the market and to what extent current demand represents future expectation. As a result, these indices provide decision-making support into important questions about when to sell or buy, or the elasticity present in the housing demand market, which impact price negotiations, price-taking and price-setting expectations. This forecast is especially valuable because it helps buyers and sellers to know on a granular and timely basis if they should engage in a home transaction or adjust their home price both in current and future states based on our forecasted housing demand index.", "published": "2022-01-06T21:51:54Z", "authors": ["Yuanyuan Zha", "Susan T. Parker", "James J. Foster", "Vadim Sokolov"], "categories": ["stat.AP"], "doi": "", "_query": "hedonic regression housing", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2402.10815v1", "title": "Core Stability in Additively Separable Hedonic Games of Low Treewidth", "abstract": "Additively Separable Hedonic Game (ASHG) are coalition-formation games where we are given a graph whose vertices represent $n$ selfish agents and the weight of each edge $uv$ denotes how much agent $u$ gains (or loses) when she is placed in the same coalition as agent $v$. We revisit the computational complexity of the well-known notion of core stability of ASHGs, where the goal is to construct a partition of the agents into coalitions such that no group of agents would prefer to diverge from the given partition and form a new (blocking) coalition. Since both finding a core stable partition and verifying that a given partition is core stable are intractable problems ($Σ_2^p$-complete and coNP-complete respectively) we study their complexity from the point of view of structural parameterized complexity, using standard graph-theoretic parameters, such as treewidth.", "published": "2024-02-16T16:39:14Z", "authors": ["Tesshu Hanaka", "Noleen Köhler", "Michael Lampis"], "categories": ["cs.DS", "cs.CC", "cs.GT"], "doi": "", "_query": "hedonic regression housing", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/1511.06663v1", "title": "L1 logistic regression as a feature selection step for training stable classification trees for the prediction of severity criteria in imported malaria", "abstract": "Multivariate classification methods using explanatory and predictive models are necessary for characterizing subgroups of patients according to their risk profiles. Popular methods include logistic regression and classification trees with performances that vary according to the nature and the characteristics of the dataset. In the context of imported malaria, we aimed at classifying severity criteria based on a heterogeneous patient population. We investigated these approaches by implementing two different strategies: L1 logistic regression (L1LR) that models a single global solution and classification trees that model multiple local solutions corresponding to discriminant subregions of the feature space. For each strategy, we built a standard model, and a sparser version of it. As an alternative to pruning, we explore a promising approach that first constrains the tree model with an L1LR-based feature selection, an approach we called L1LR-Tree. The objective is to decrease its vulnerability to small data variations by removing variables corresponding to unstable local phenomena. Our study is twofold: i) from a methodological perspective comparing the performances and the stability of the three previous methods, i.e L1LR, classification trees and L1LR-Tree, for the classification of severe forms of imported malaria, and ii) from an applied perspective improving the actual classification of severe forms of imported malaria by identifying more personalized profiles predictive of several clinical criteria based on variables dismissed for the clinical definition of the disease. The main methodological results show that the combined method L1LR-Tree builds sparse and stable models that significantly predicts the different severity criteria and outperforms all the other methods in terms of accuracy.", "published": "2015-11-20T16:12:59Z", "authors": ["Luca Talenti", "Margaux Luck", "Anastasia Yartseva", "Nicolas Argy", "Sandrine Houzé", "Cecilia Damon"], "categories": ["cs.LG", "q-bio.QM", "stat.AP"], "doi": "", "_query": "hedonic regression housing", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2501.15834v2", "title": "The Strong Core of Housing Markets with Partial Order Preferences", "abstract": "We study the strong core of housing markets when agents' preferences over houses are expressed as partial orders. We provide a structural characterization of the strong core, and propose an efficient algorithm that finds an allocation in the strong core or decides that it is empty, even in the presence of forced and forbidden arcs. The algorithm satisfies the property of group-strategyproofness. Additionally, we show that certain results known for the strong core in the case when agents' preferences are weak orders can be extended to the setting with partial order preferences; among others, we show that the strong core in such housing markets satisfies the property of respecting improvements.", "published": "2025-01-27T07:42:37Z", "authors": ["Ildikó Schlotter", "Lydia Mirabel Mendoza-Cadena"], "categories": ["cs.GT"], "doi": "", "_query": "hedonic regression housing", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2201.13158v1", "title": "FEN-Hedonic Games with Distance-Based Preferences", "abstract": "Hedonic games formalize coalition formation scenarios where players evaluate an outcome based on the coalition they are contained in. Due to a large number of possible coalitions, compact representations of these games are crucial. We complement known compact representation models by a distance-based approach: Players' preferences are encoded in a bipolar manner by ordinal preferences over a small set of known neighbouring players, coalitions are represented by adequate preference orders from a player's perspective, and preferences over coalitions are extended based on a directed form of Hausdorff-Kendall-tau distance between individual preferences and coalitions. We show that this model satisfies desirable axiomatic properties and has reasonable computational complexity in terms of selected individual-based stability notions.", "published": "2022-01-31T12:07:16Z", "authors": ["Anja Rey", "Lisa Rey"], "categories": ["cs.GT"], "doi": "", "_query": "hedonic regression housing", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2511.10808v1", "title": "Seasonality in the U.S. Housing Market: Post-Pandemic Shifts and Regional Dynamics", "abstract": "Seasonality has traditionally shaped the U.S. housing market, with activity peaking in spring-summer and declining in autumn-winter. However, recent disruptions, particularly post-COVID-19, raise questions about shift in these patterns. This study analyzes housing market date (1991-2024) to examine evolving seasonality and regional heterogeneity. Using Housing Price Index (HPI), inventory and sales data from the Federal Housing Finance Agency and U.S. Census Bureau, seasonal components are extracted via the X-13-ARIMA procedure, and statistical tests assess variations across regions. The results confirm seasonal fluctuations in prices and volumes, with recent shifts toward earlier annual peak (March-April) and amplified seasonal effects. Regional variations align with differences in climate and market structure, while prices and sales volumes exhibit in-phase movement, suggesting thick-market momentum behaviour. These findings highlight key implications for policymakers, realtors and investors navigating post-pandemic market dynamics, offering insights into the timing and interpretation of housing market activities.", "published": "2025-11-13T21:20:54Z", "authors": ["Yihan Hu", "Yifei Huang", "Weizhao Wang"], "categories": ["econ.GN", "stat.AP"], "doi": "", "_query": "hedonic regression housing", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2303.10832v1", "title": "A Strategy-proof Mechanism For Networked Housing Markets", "abstract": "This paper studies a house allocation problem in a networked housing market, where agents can invite others to join the system in order to enrich their options. Top Trading Cycle is a well-known matching mechanism that achieves a set of desirable properties in a market without invitations. However, under a tree-structured networked market, existing agents have to strategically propagate the barter market as their invitees may compete in the same house with them. Our impossibility result shows that TTC cannot work properly in a networked housing market. Hence, we characterize the possible competitions between inviters and invitees, which lead agents to fail to refer others truthfully (strategy-proof). We then present a novel mechanism based on TTC, avoiding the aforementioned competition to ensure all agents report preference and propagate the barter market truthfully. Unlike the existing mechanisms, the agents' preferences are less restricted under our mechanism. Furthermore, we show by simulations that our mechanism outperforms the existing matching mechanisms in terms of the number of swaps and agents' satisfaction.", "published": "2023-03-20T02:35:41Z", "authors": ["Youjia Zhang", "Pingzhong Tang"], "categories": ["cs.GT"], "doi": "", "_query": "hedonic regression housing", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2108.08229v1", "title": "Tilted Platforms: Rental Housing Technology and the Rise of Urban Big Data Oligopolies", "abstract": "This article interprets emerging scholarship on rental housing platforms -- particularly the most well-known and used short- and long-term rental housing platforms - and considers how the technological processes connecting both short-term and long-term rentals to the platform economy are transforming cities. It discusses potential policy approaches to more equitably distribute benefits and mitigate harms. We argue that information technology is not value-neutral. While rental housing platforms may empower data analysts and certain market participants, the same cannot be said for all users or society at large. First, user-generated online data frequently reproduce the systematic biases found in traditional sources of housing information. Evidence is growing that the information broadcasting potential of rental housing platforms may increase rather than mitigate sociospatial inequality. Second, technology platforms curate and shape information according to their creators' own financial and political interests. The question of which data -- and people -- are hidden or marginalized on these platforms is just as important as the question of which data are available. Finally, important differences in benefits and drawbacks exist between short-term and long-term rental housing platforms, but are underexplored in the literature: this article unpacks these differences and proposes policy recommendations.", "published": "2021-08-18T16:36:51Z", "authors": ["Geoff Boeing", "Max Besbris", "David Wachsmuth", "Jake Wegmann"], "categories": ["econ.GN", "cs.CY"], "doi": "10.1186/s42854-021-00024-2", "_query": "hedonic regression housing", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2402.08484v2", "title": "The Computational Complexity of the Housing Market", "abstract": "We prove that the classic problem of finding a competitive equilibrium in an exchange economy with indivisible goods, money, and unit-demand agents is PPAD-complete. In this \"housing market\", agents have preferences over the house and amount of money they end up with, but can experience income effects. Our results contrast with the existence of polynomial-time algorithms for related problems: Top Trading Cycles for the \"housing exchange\" problem in which there are no transfers and the Hungarian algorithm for the \"housing assignment\" problem in which agents' utilities are linear in money. Along the way, we prove that the Rainbow-KKM problem, a total search problem based on a generalization by Gale of the Knaster-Kuratowski-Mazurkiewicz lemma, is PPAD-complete. Our reductions also imply bounds on the query complexity of finding competitive equilibrium.", "published": "2024-02-13T14:20:41Z", "authors": ["Edwin Lock", "Zephyr Qiu", "Alexander Teytelboym"], "categories": ["cs.GT", "cs.CC"], "doi": "", "_query": "hedonic regression housing", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/1809.03834v1", "title": "House Price Modeling with Digital Census", "abstract": "Urban house prices are strongly associated with local socioeconomic factors. In literature, house price modeling is based on socioeconomic variables from traditional census, which is not real-time, dynamic and comprehensive. Inspired by the emerging concept of \"digital census\" - using large-scale digital records of human activities to measure urban population dynamics and socioeconomic conditions, we introduce three typical datasets, namely 311 complaints, crime complaints and taxi trips, into house price modeling. Based on the individual housing sales data in New York City, we provide comprehensive evidence that these digital census datasets can substantially improve the modeling performances on both house price levels and changes, regardless whether traditional census is included or not. Hence, digital census can serve as both effective alternatives and complements to traditional census for house price modeling.", "published": "2018-08-29T16:13:58Z", "authors": ["Enwei Zhu", "Stanislav Sobolevsky"], "categories": ["econ.EM", "physics.soc-ph"], "doi": "", "_query": "hedonic regression housing", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2001.10772v1", "title": "On Maximizing Egalitarian Value in K-coalitional Hedonic Games", "abstract": "This paper considers the problem of dividing agents among coalitions. We concentrate on Additively Separable Hedonic Games (ASHG's), in which each agent has a non-negative value for every other agent and her utility is the sum of the values she assigns to the members of her coalition. Unlike previous work, we analyze a model where exactly $k$ coalitions must be formed, and the goal is to maximize the utility of the agent which is worst off, i.e., the egalitarian social welfare. We show that this problem is hard, even when the number of agents should be equally divided among the coalitions. We thus propose a heuristic that maximizes the egalitarian social welfare and maximizes the average utility of each agent as a secondary goal. Using extensive simulations, both on synthetic and real data, we demonstrate the effectiveness of our approach. Specifically, our heuristic provides solutions that are much fairer than the solutions that maximize the average social welfare, while still providing a relatively high average social welfare.", "published": "2020-01-29T12:11:31Z", "authors": ["Naftali Waxman", "Sarit Kraus", "Noam Hazon"], "categories": ["cs.GT"], "doi": "", "_query": "hedonic regression housing", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2412.14624v2", "title": "The Diffusive Nature of Housing Prices", "abstract": "We analyze the French housing market prices in the period 1970-2022, with high-resolution data from 2018 to 2022. The spatial correlation of the observed price field exhibits logarithmic decay characteristic of the two-dimensional random diffusion equation -- local interactions may create long-range correlations. We introduce a stylized model, used in the past to model spatial regularities in voting patterns, that accounts for both spatial and temporal correlations with reasonable values of parameters. Our analysis reveals that price shocks are persistent in time and their amplitude is strongly heterogeneous in space. Our study confirms and quantifies the diffusive nature of housing prices that was anticipated long ago (Clapp et al. 1994, Pollakowski et al. 1997), albeit on much restricted, local data sets.", "published": "2024-12-19T08:21:51Z", "authors": ["Antoine-Cyrus Becharat", "Michael Benzaquen", "Jean-Philippe Bouchaud"], "categories": ["cond-mat.stat-mech", "econ.GN", "physics.soc-ph"], "doi": "", "_query": "hedonic regression housing", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2605.26753v1", "title": "Estimating the logistic regression equation when the model is incorrect", "abstract": "Protesting mildly against the notion of an exactly correct parametric model the view is adopted that the logistic regression equation is merely an approximation to the underlying, true function. The behaviour of likelihood based estimators is investigated in such a general framework. The maximum likelihood estimator is shown to be consistent for a certain least false parameter value minimising a weighted average of quantities that measure the distance from the true to the parametric model. Asymptotic normality is also demonstrated. Finally a number of additional remarks are offered, some pointing to natural generalisations and some to new questions for research, like weighted and local likelihood estimation methods.", "published": "2026-05-26T09:26:07Z", "authors": ["Nils Lid Hjort"], "categories": ["math.ST"], "doi": "", "_query": "hedonic regression housing", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2405.14503v1", "title": "Radial analysis and scaling of housing prices in French urban areas", "abstract": "Urban scaling laws summarize how urban attributes evolve with city size. Recent criticism questions notably the aggregate view of this approach, which leads to neglecting the internal structure of cities. This is all the more relevant for housing prices due to their important variations across space. Based on a dataset compiling millions of real estate transactions over the period 2017-2021, we investigate the regularities of the radial (center-periphery) profiles of housing prices across cities, with respect to their size. Results are threefold. First, they corroborate prior findings in the urban scaling literature stating that largest cities agglomerate higher housing prices. Second, we find that housing price radial profiles scale in three dimensions with the power 1/5 of city population. After rescaling, great regularities between radial profiles can be observed, although some locational amenities have a significant impact on prices. Third, it appears that our rescaled profiles approach fails to explain housing price variations in the city center across cities. In fact, prices near the city center rise much faster with city size than those in the periphery. This has strong implications for low-income households seeking homeownership, because prohibitive prices in the center may contribute to pushing them out into peripheral locations.", "published": "2024-05-23T12:38:20Z", "authors": ["Gaëtan Laziou", "Rémi Lemoy", "Marion Le Texier"], "categories": ["physics.soc-ph"], "doi": "", "_query": "hedonic regression housing", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/1602.03794v1", "title": "Optimal designs for regression models with autoregressive errors structure", "abstract": "In the one-parameter regression model with AR(1) and AR(2) errors we find explicit expressions and a continuous approximation of the optimal discrete design for the signed least square estimator. The results are used to derive the optimal variance of the best linear estimator in the continuous time model and to construct efficient estimators and corresponding optimal designs for finite samples. The resulting procedure (estimator and design) provides nearly the same efficiency as the weighted least squares and its variance is close to the optimal variance in the continuous time model. The results are illustrated by several examples demonstrating the feasibility of our approach.", "published": "2016-02-11T17:14:28Z", "authors": ["Holger Dette", "Andrey Pepelyshev", "Anatoly Zhigljavsky"], "categories": ["math.ST"], "doi": "", "_query": "hedonic regression housing", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/1905.07510v2", "title": "Discovery of Algebraic Reynolds-Stress Models Using Sparse Symbolic Regression", "abstract": "** This article is published (open-access). ** A novel deterministic symbolic regression method SpaRTA (Sparse Regression of Turbulent Stress Anisotropy) is introduced to infer algebraic stress models for the closure of RANS equations directly from high-fidelity LES or DNS data. The models are written as tensor polynomials and are built from a library of candidate functions. The machine-learning method is based on elastic net regularisation which promotes sparsity of the inferred models. By being data-driven the method relaxes assumptions commonly made in the process of model development. Model-discovery and cross-validation is performed for three cases of separating flows, i.e. periodic hills ($Re$=10595), converging-diverging channel ($Re$=12600) and curved backward-facing step ($Re$=13700). The predictions of the discovered models are significantly improved over the $k$-$ω$ SST also for a true prediction of the flow over periodic hills at $Re$=37000. This study shows a systematic assessment of SpaRTA for rapid machine-learning of robust corrections for standard RANS turbulence models.", "published": "2019-05-18T00:42:37Z", "authors": ["Martin Schmelzer", "Richard P. Dwight", "Paola Cinnella"], "categories": ["physics.comp-ph"], "doi": "10.1007/s10494-019-00089-x", "_query": "hedonic regression housing", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/1503.05550v1", "title": "Club Convergence of House Prices: Evidence from China's Ten Key Cities", "abstract": "The latest global financial tsunami and its follow-up global economic recession has uncovered the crucial impact of housing markets on financial and economic systems. The Chinese stock market experienced a markedly fall during the global financial tsunami and China's economy has also slowed down by about 2\\%-3\\% when measured in GDP. Nevertheless, the housing markets in diverse Chinese cities seemed to continue the almost nonstop mania for more than ten years. However, the structure and dynamics of the Chinese housing market are less studied. Here we perform an extensive study of the Chinese housing market by analyzing ten representative key cities based on both linear and nonlinear econophysical and econometric methods. We identify a common collective driving force which accounts for 96.5\\% of the house price growth, indicating very high systemic risk in the Chinese housing market. The ten key cities can be categorized into clubs and the house prices of the cities in the same club exhibit an evident convergence. These findings from different methods are basically consistent with each other. The identified city clubs are also consistent with the conventional classification of city tiers. The house prices of the first-tier cities grow the fastest, and those of the third- and fourth-tier cities rise the slowest, which illustrates the possible presence of a ripple effect in the diffusion of house prices in different cities.", "published": "2015-03-18T03:46:56Z", "authors": ["Hao Meng", "Wen-Jie Xie", "Wei-Xing Zhou"], "categories": ["q-fin.ST", "physics.soc-ph"], "doi": "10.1142/S0217979215501817", "_query": "hedonic regression housing", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2010.00413v2", "title": "Resilient Urban Housing Markets: Shocks vs. Fundamentals", "abstract": "In the face of a pandemic, urban protests, and an affordability crisis, is the desirability of dense urban settings at a turning point? Assessing cities' long term trends remains challenging. The first part of this chapter describes the short-run dynamics of the housing market in 2020. Evidence from prices and price-to-rent ratios suggests expectations of resilience. Zip-level evidence suggests a short-run trend towards suburbanization, and some impacts of urban protests on house prices. The second part of the chapter analyzes the long-run dynamics of urban growth between 1970 and 2010. It analyzes what, in such urban growth, is explained by short-run shocks as opposed to fundamentals such as education, industrial specialization, industrial diversification, urban segregation, and housing supply elasticity. This chapter's original results as well as a large established body of literature suggest that fundamentals are the key drivers of growth. The chapter illustrates this finding with two case studies: the New York City housing market after September 11, 2001; and the San Francisco Bay Area in the aftermath of the 1989 Loma Prieta earthquake. Both areas rebounded strongly after these shocks, suggesting the resilience of the urban metropolis.", "published": "2020-10-01T13:59:53Z", "authors": ["Amine Ouazad"], "categories": ["econ.GN", "physics.soc-ph"], "doi": "", "_query": "hedonic regression housing", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2209.11970v3", "title": "Bayesian Modeling of TVP-VARs Using Regression Trees", "abstract": "In light of widespread evidence of parameter instability in macroeconomic models, many time-varying parameter (TVP) models have been proposed. This paper proposes a nonparametric TVP-VAR model using Bayesian additive regression trees (BART) that models the TVPs as an unknown function of effect modifiers. The novelty of this model arises from the fact that the law of motion driving the parameters is treated nonparametrically. This leads to great flexibility in the nature and extent of parameter change, both in the conditional mean and in the conditional variance. Parsimony is achieved through adopting nonparametric factor structures and use of shrinkage priors. In an application to US macroeconomic data, we illustrate the use of our model in tracking both the evolving nature of the Phillips curve and how the effects of business cycle shocks on inflation measures vary nonlinearly with changes in the effect modifiers.", "published": "2022-09-24T09:40:41Z", "authors": ["Niko Hauzenberger", "Florian Huber", "Gary Koop", "James Mitchell"], "categories": ["econ.EM"], "doi": "", "_query": "hedonic regression housing", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2507.08641v1", "title": "Pricing and hedging the prepayment option of mortgages under stochastic housing market activity", "abstract": "Prepayment risk embedded in fixed-rate mortgages forms a significant fraction of a financial institution's exposure. The embedded prepayment option bears the same interest rate risk as an exotic interest rate swap with a suitable stochastic notional. Focusing on penalty-free prepayment because of the contract owner's relocation to a new house, we model the prepayment option value as an European-type interest rate receiver swaption with stochastic maturity matching the stochastic time of relocation. This is a convenient representation since it allows us to compute the prepayment option value in terms of well-known pricing formulas for European-type swaptions. We investigate the effect of a stochastic housing market activity as the explanatory variable for the distribution of the relocation time, as opposed to the conventional assumption of a deterministic housing market activity. We prove that the housing market covariance drives the prepayment option price difference between the stochastic setting and its deterministic counterpart. The prepayment option exposure is hedged using market instruments based on Delta-Gamma replication. Furthermore, since the housing market activity is a non-tradable risk factor, we perform non-standard actuarial hedging focusing on controlling the prepayment option exposure yield by risky housing market scenarios.", "published": "2025-07-11T14:43:02Z", "authors": ["Leonardo Perotti", "Lech A. Grzelak", "Cornelis W. Oosterlee"], "categories": ["q-fin.PR"], "doi": "", "_query": "hedonic regression housing", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2209.11970v3", "title": "Bayesian Modeling of TVP-VARs Using Regression Trees", "abstract": "In light of widespread evidence of parameter instability in macroeconomic models, many time-varying parameter (TVP) models have been proposed. This paper proposes a nonparametric TVP-VAR model using Bayesian additive regression trees (BART) that models the TVPs as an unknown function of effect modifiers. The novelty of this model arises from the fact that the law of motion driving the parameters is treated nonparametrically. This leads to great flexibility in the nature and extent of parameter change, both in the conditional mean and in the conditional variance. Parsimony is achieved through adopting nonparametric factor structures and use of shrinkage priors. In an application to US macroeconomic data, we illustrate the use of our model in tracking both the evolving nature of the Phillips curve and how the effects of business cycle shocks on inflation measures vary nonlinearly with changes in the effect modifiers.", "published": "2022-09-24T09:40:41Z", "authors": ["Niko Hauzenberger", "Florian Huber", "Gary Koop", "James Mitchell"], "categories": ["econ.EM"], "doi": "", "_query": "hedonic regression housing", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2404.07849v2", "title": "Overparameterized Multiple Linear Regression as Hyper-Curve Fitting", "abstract": "This work demonstrates that applying a fixed-effect multiple linear regression (MLR) model to an overparameterized dataset is mathematically equivalent to fitting a hyper-curve parameterized by a single scalar. This reformulation shifts the focus from global coefficients to individual predictors, allowing each to be modeled as a function of a common parameter. We prove that this overparameterized linear framework can yield exact predictions even when the underlying data contains nonlinear dependencies that violate classical linear assumptions. By employing parameterization in terms of the dependent variable and a monomial basis, we validate this approach on both synthetic and experimental datasets. Our results show that the hyper-curve perspective provides a robust framework for regularizing problems with noisy predictors and offers a systematic method for identifying and removing 'improper' predictors that degrade model generalizability.", "published": "2024-04-11T15:43:11Z", "authors": ["E. Atza", "N. Budko"], "categories": ["stat.ML", "cs.LG"], "doi": "10.1088/2632-2153/ae911b", "_query": "hedonic regression housing", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2102.02593v1", "title": "The housing problem and revealed preference theory: duality and an application", "abstract": "This paper exhibits a duality between the theory of Revealed Preference of Afriat and the housing allocation problem of Shapley and Scarf. In particular, it is shown that Afriat's theorem can be interpreted as a second welfare theorem in the housing problem. Using this duality, the revealed preference problem is connected to an optimal assignment problem, and a geometrical characterization of the rationalizability of experiment data is given. This allows in turn to give new indices of rationalizability of the data, and to define weaker notions of rationalizability, in the spirit of Afriat's efficiency index.", "published": "2021-02-04T13:09:50Z", "authors": ["Ivar Ekeland", "Alfred Galichon"], "categories": ["econ.GN"], "doi": "10.1007/s00199-012-0719-x", "_query": "hedonic regression housing", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/1907.06118v2", "title": "Online Rental Housing Market Representation and the Digital Reproduction of Urban Inequality", "abstract": "As the rental housing market moves online, the Internet offers divergent possible futures: either the promise of more-equal access to information for previously marginalized homeseekers, or a reproduction of longstanding information inequalities. Biases in online listings' representativeness could impact different communities' access to housing search information, reinforcing traditional information segregation patterns through a digital divide. They could also circumscribe housing practitioners' and researchers' ability to draw broad market insights from listings to understand rental supply and affordability. This study examines millions of Craigslist rental listings across the US and finds that they spatially concentrate and over-represent whiter, wealthier, and better-educated communities. Other significant demographic differences exist in age, language, college enrollment, rent, poverty rate, and household size. Most cities' online housing markets are digitally segregated by race and class, and we discuss various implications for residential mobility, community legibility, gentrification, housing voucher utilization, and automated monitoring and analytics in the smart cities paradigm. While Craigslist contains valuable crowdsourced data to better understand affordability and available rental supply in real-time, it does not evenly represent all market segments. The Internet promises information democratization, and online listings can reduce housing search costs and increase choice sets. However, technology access/preferences and information channel segregation can concentrate such information-broadcasting benefits in already-advantaged communities, reproducing traditional inequalities and reinforcing residential sorting and segregation dynamics. Technology platforms like Craigslist construct new institutions with the power to shape spatial economies.", "published": "2019-07-13T18:56:57Z", "authors": ["Geoff Boeing"], "categories": ["econ.GN", "stat.AP"], "doi": "10.1177/0308518X19869678", "_query": "hedonic regression housing", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2601.08957v2", "title": "The Connection Between Monetary Policy and Housing Prices: Public Perception and Expert Communication", "abstract": "We study how the general public perceives the link between monetary policy and housing markets. Using a large-scale, cross-country survey experiment in Austria, Germany, Italy, Sweden, and the United Kingdom, we examine households' understanding of monetary policy, their beliefs about its impact on house prices, and how these beliefs respond to expert information. We find that while most respondents grasp the basic mechanisms of conventional monetary policy and recognize the connection between interest rates and house prices, literacy regarding unconventional monetary policy is very low. Beliefs about the monetary policy-housing nexus are malleable and respond to information, particularly when it is provided by academic economists rather than central bankers. Monetary policy literacy is strongly related to education, gender, age, and experience in housing and mortgage markets. Our results highlight the central role of housing in how households interpret monetary policy and point to the importance of credible and inclusive communication strategies for effective policy transmission.", "published": "2026-01-13T19:51:06Z", "authors": ["Philipp Poyntner", "Sofie R. Waltl"], "categories": ["econ.GN"], "doi": "", "_query": "hedonic regression housing", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2108.11915v1", "title": "Cooling Measures and Housing Wealth: Evidence from Singapore", "abstract": "Excessive house price growth was at the heart of the financial crisis in 2007/08. Since then, many countries have added cooling measures to their regulatory frameworks. It has been found that these measures can indeed control price growth, but no one has examined whether this has adverse consequences for the housing wealth distribution. We examine this for Singapore, which started in 2009 to target price growth over ten rounds in total. We find that welfare from housing wealth in the last round might not be higher than before 2009. This depends on the deflator used to convert nominal into real prices. Irrespective of the deflator, we can reject that welfare increased monotonically over the different rounds.", "published": "2021-08-26T17:11:35Z", "authors": ["Wolfgang Karl Härdle", "Rainer Schulz", "Taojun Sie"], "categories": ["q-fin.GN"], "doi": "", "_query": "hedonic regression housing", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/1607.01999v1", "title": "Inferring the contiguity matrix for spatial autoregressive analysis with applications to house price prediction", "abstract": "Inference methods in traditional statistics, machine learning and data mining assume that data is generated from an independent and identically distributed (iid) process. Spatial data exhibits behavior for which the iid assumption must be relaxed. For example, the standard approach in spatial regression is to assume the existence of a contiguity matrix which captures the spatial autoregressive properties of the data. However all spatial methods, till now, have assumed that the contiguity matrix is given apriori or can be estimated by using a spatial similarity function. In this paper we propose a convex optimization formulation to solve the spatial autoregressive regression (SAR) model in which both the contiguity matrix and the non-spatial regression parameters are unknown and inferred from the data. We solve the problem using the alternating direction method of multipliers (ADMM) which provides a solution which is both robust and efficient. While our approach is general we use data from housing markets of Boston and Sydney to both guide the analysis and validate our results. A novel side effect of our approach is the automatic discovery of spatial clusters which translate to submarkets in the housing data sets.", "published": "2016-07-07T13:14:43Z", "authors": ["Somwrita Sarkar", "Sanjay Chawla"], "categories": ["econ.GN"], "doi": "", "_query": "hedonic regression housing", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2403.15490v2", "title": "Increasing retrofit device adoption in social housing: evidence from two field experiments in Belgium", "abstract": "Energy efficient technologies are particularly important for social housing settings: they offer the potential to improve tenants' wellbeing through monetary savings and comfort, while reducing emissions of entire communities. Slow uptake of innovative energy technology in social housing has been associated with a lack of trust and the perceived risks of adoption. To counteract both, we designed a communication campaign for a retrofit technology for heating including social norms for technology adoption and concretely experienced benefits. We report two randomized controlled trials (RCT) in two different social housing communities in Belgium. In the first study, randomization was on housing block level: the communication led to significant higher uptake rates compared to the control group, (b = 1.7, p = .024). In the second study randomization occurred on apartment level, again yielding a significant increase (b = 1.62, p = 0.02), when an interaction with housing blocks was considered. We discuss challenges of conducting randomized controlled trials in social housing communities.", "published": "2024-03-21T14:49:05Z", "authors": ["Mona Bielig", "Celina Kacperski", "Florian Kutzner"], "categories": ["cs.CY", "cs.HC"], "doi": "10.1016/j.jenvp.2024.102284", "_query": "hedonic regression housing", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/1903.11205v3", "title": "Airbnb's disruption of the housing structure in London", "abstract": "This paper explores Airbnb, a peer-to-peer platform for short-term rental of housing accommodation, examining the geographical pattern of those establishments using data from London. Our purpose is to analyse whether or not the diversity of dwelling types correlate with the distribution of listings. We use a measure of spread based on entropy to indicate the diversity of dwelling types and look at its relationship with the distribution of Airbnb establishments, as well as the type of home ownership using correlation analysis. It is important to note that our study only considers domestic building types, and excludes any information on the diversity of land uses. Two important findings emerge from our analysis. Firstly, the spatial location of Airbnb rentals is negatively correlated with the diversity of dwelling types, and positively correlated with a single dwelling type, which corresponds in general to purpose built flats, conversions and flats in commercial buildings. Secondly, Airbnb is associated with areas that have a high proportion of privately rented properties, detracting more than 1.4% of the housing supply into short-term rentals. Such a phenomenon can reach up to 20% in some neighbourhoods, further exacerbating the process of gentrification. Finally, we discuss the implications of these findings as instruments to inform policies associated with the 'sharing' economy in relation to the disruption of the housing structure.", "published": "2019-03-27T00:49:15Z", "authors": ["Zahratu Shabrina", "Elsa Arcaute", "Michael Batty"], "categories": ["cs.CY", "physics.soc-ph"], "doi": "", "_query": "hedonic regression housing", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/1311.0835v1", "title": "Optimal designs for nonlinear regression models with respect to non-informative priors", "abstract": "In nonlinear regression models the Fisher information depends on the parameters of the model. Consequently, optimal designs maximizing some functional of the information matrix cannot be implemented directly but require some preliminary knowledge about the unknown parameters. Bayesian optimality criteria provide an attractive solution to this problem. These criteria depend sensitively on a reasonable specification of a prior distribution for the model parameters which might not be available in all applications. In this paper we investigate Bayesian optimality criteria with non-informative prior dis- tributions. In particular, we study the Jeffreys and the Berger-Bernardo prior for which the corresponding optimality criteria are not necessarily concave. Several examples are investigated where optimal designs with respect to the new criteria are calculated and compared to Bayesian optimal designs based on a uniform and a functional uniform prior.", "published": "2013-11-04T20:20:24Z", "authors": ["Ina Burghaus", "Holger Dette"], "categories": ["stat.ME"], "doi": "", "_query": "hedonic regression housing", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2501.03821v3", "title": "The Choice of Normalization Influences Shrinkage in Regularized Regression", "abstract": "Regularized models are often sensitive to the scales of the features in the data and it has therefore become standard practice to normalize (center and scale) the features before fitting the model. But there are many different ways to normalize the features and the choice may have dramatic effects on the resulting model. In spite of this, there has so far been no research on this topic. In this paper, we begin to bridge this knowledge gap by studying normalization in the context of lasso, ridge, and elastic net regression. We focus on binary features and show that their class balances (proportions of ones) directly influences the regression coefficients and that this effect depends on the combination of normalization and regularization methods used. We demonstrate that this effect can be mitigated by scaling binary features with their variance in the case of the lasso and standard deviation in the case of ridge regression, but that this comes at the cost of increased variance of the coefficient estimates. For the elastic net, we show that scaling the penalty weights, rather than the features, can achieve the same effect. Finally, we also tackle mixes of binary and normal features as well as interactions and provide some initial results on how to normalize features in these cases.", "published": "2025-01-07T14:35:09Z", "authors": ["Johan Larsson", "Jonas Wallin"], "categories": ["stat.ML", "cs.LG", "stat.ME"], "doi": "", "_query": "hedonic regression housing", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/1601.00473v1", "title": "The discretised lognormal and hooked power law distributions for complete citation data: Best options for modelling and regression", "abstract": "Identifying the statistical distribution that best fits citation data is important to allow robust and powerful quantitative analyses. Whilst previous studies have suggested that both the hooked power law and discretised lognormal distributions fit better than the power law and negative binomial distributions, no comparisons so far have covered all articles within a discipline, including those that are uncited. Based on an analysis of 26 different Scopus subject areas in seven different years, this article reports comparisons of the discretised lognormal and the hooked power law with citation data, adding 1 to citation counts in order to include zeros. The hooked power law fits better in two thirds of the subject/year combinations tested for journal articles that are at least three years old, including most medical, life and natural sciences, and for virtually all subject areas for younger articles. Conversely, the discretised lognormal tends to fit best for arts, humanities, social science and engineering fields. The difference between the fits of the distributions is mostly small, however, and so either could reasonably be used for modelling citation data. For regression analyses, however, the best option is to use ordinary least squares regression applied to the natural logarithm of citation counts plus one, especially for sets of younger articles, because of the increased precision of the parameters.", "published": "2016-01-04T12:16:09Z", "authors": ["Mike Thelwall"], "categories": ["cs.DL"], "doi": "10.1016/j.joi.2015.12.007", "_query": "hedonic regression housing", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2605.08660v1", "title": "Optimised Support Vector Regression for California Housing Price Prediction: The Critical Role of Feature Engineering and Hyperparameter Tuning", "abstract": "In the recent literature, Support Vector Regression (SVR) has been cited as one of the weakest performers on the California Housing benchmark dataset, with Preethi et al. (2025)specifically ranking it last among the algorithms they tested, reporting an R2 of only 0.60. This paper examines whether the previously reported performance reflects experimental configuration choices rather than an inherent algorithmic limitation. A structured experimental workflow is applied: ten domain-motivated derived features are constructed from the eight raw inputs, an exploratory ensemble feature importance analysis identifies the most predictive candidates, and a randomised search over hyperparameter combinations with three-fold cross-validation selects the optimal SVR configuration within a leakage-safe scikit-learn Pipeline. A formal four-stage ablation study isolates the contribution of each component: scaling alone accounts for +0.744 in R2 (from -0.054 to 0.690), feature engineering adds +0.026 (to 0.716), and hyperparameter tuning contributes +0.008 (to 0.723). The resulting tuned SVR achieves a test R2 of 0.723, a 0.123-point absolute improvement over the previously reported SVR result (from 0.60 to 0.723, approximately 20% relative gain). In the ten-model comparison, the tuned SVR ranks fourth with R2 = 0.723, below XGBoost (0.832), Random Forest (0.814) and Gradient Boosting (0.783), while substantially outperforming simpler baselines. Ten-fold cross-validation yields a mean R2 of 0.703 (95% CI: [0.630, 0.775]), confirming robust generalisation. The observed improvement from R2 = 0.60 to R2 = 0.723 is associated primarily with proper feature scaling within a unified preprocessing pipeline, with domain-motivated feature engineering and systematic hyperparameter tuning, providing further incremental gains.", "published": "2026-05-09T03:58:50Z", "authors": ["Emmanuel Adutwum"], "categories": ["cs.LG"], "doi": "", "_query": "hedonic regression housing", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/1810.04808v1", "title": "Generalized Bayesian Record Linkage and Regression with Exact Error Propagation", "abstract": "Record linkage (de-duplication or entity resolution) is the process of merging noisy databases to remove duplicate entities. While record linkage removes duplicate entities from such databases, the downstream task is any inferential, predictive, or post-linkage task on the linked data. One goal of the downstream task is obtaining a larger reference data set, allowing one to perform more accurate statistical analyses. In addition, there is inherent record linkage uncertainty passed to the downstream task. Motivated by the above, we propose a generalized Bayesian record linkage method and consider multiple regression analysis as the downstream task. Records are linked via a random partition model, which allows for a wide class to be considered. In addition, we jointly model the record linkage and downstream task, which allows one to account for the record linkage uncertainty exactly. Moreover, one is able to generate a feedback propagation mechanism of the information from the proposed Bayesian record linkage model into the downstream task. This feedback effect is essential to eliminate potential biases that can jeopardize resulting downstream task. We apply our methodology to multiple linear regression, and illustrate empirically that the \"feedback effect\" is able to improve the performance of record linkage.", "published": "2018-10-11T01:13:04Z", "authors": ["Rebecca C. Steorts", "Andrea Tancredi", "Brunero Liseo"], "categories": ["stat.ME"], "doi": "", "_query": "hedonic regression housing", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2602.20356v1", "title": "Prevalence of Food and Housing Insecurity among Direct Support Professionals in New York", "abstract": "Background. Low earnings are associated with household insecurity. Direct Support Professionals (DSPs) provide support for people with intellectual and developmental disabilities, typically for wages close to state minimums, and may experience insecurity. Objective. The purpose of the study was to evaluate the prevalence of food and housing insecurity among DSPs. Methods. We conducted a statewide, cross-sectional survey of DSPs in New York State (2022-2023). Measures included detailed questions about food and housing insecurity. We used chi-square analyses and logistic regressions to examine relationships between insecurity and demographic characteristics as proxies for social determinants of health. A total of 4,503 DSPs responded to the survey. The analytic sample contained 2,766 respondents with complete data for all relevant variables. Results. Overall, 62.6% experienced food and/or housing insecurity, with over half of those respondents experiencing both. Insecurity was highest among DSPs with a disability (76.2%), DSPs of color (75.7%), and those with lower income (72.4%), but over 50% of DSPs across demographic groups experienced insecurity. Conclusions. The widespread insecurity this study demonstrates is an occupational hazard that reduces worker welfare. At the macro-level, household insecurity is a critical threat to the stability of the care and support delivery system. The human services sector is projected to grow rapidly in the future. If growth continues along low wage lines, it implies an equally rapid expansion of worker insecurity. Government action to raise pay and interventions that enhance food and housing security are needed to support workers in the care delivery system for people with intellectual and developmental disabilities.", "published": "2026-02-23T21:03:31Z", "authors": ["Jennifer Cohen", "Yana Rodgers"], "categories": ["econ.GN"], "doi": "10.1016/j.dhjo.2024.101769", "_query": "hedonic regression housing", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/1208.0371v1", "title": "Can Metropolitan Housing Risk be Diversified? A Cautionary Tale from the Recent Boom and Bust", "abstract": "Geographic diversification is fundamental to risk mitigation among investors and insurers of housing, mortgages, and mortgage-related derivatives. To characterize diversification potential, we provide estimates of integration, spatial correlation, and contagion among US metropolitan housing markets. Results reveal a high and increasing level of integration among US markets over the decade of the 2000s, especially in California. We apply integration results to assess the risk of alternative housing investment portfolios. Portfolio simulation indicates reduced diversification potential and increased risk in the wake of estimated increases in metropolitan housing market integration. Research findings provide new insights regarding the synchronous non-performance of geographically-disparate MBS investments during the late 2000s.", "published": "2012-08-01T21:42:56Z", "authors": ["John Cotter", "Stuart Gabriel", "Richard Roll"], "categories": ["q-fin.PM"], "doi": "", "_query": "hedonic regression housing", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/1911.08669v1", "title": "Individual-Based Stability in Hedonic Diversity Games", "abstract": "In hedonic diversity games (HDGs), recently introduced by Bredereck, Elkind and Igarashi (2019), each agent belongs to one of two classes (men and women, vegetarians and meat-eaters, junior and senior researchers), and agents' preferences over coalitions are determined by the fraction of agents from their class in each coalition. Bredereck et al. show that while an HDG may fail to have a Nash stable (NS) or a core stable (CS) outcome, every HDG in which all agents have single-peaked preferences admits an individually stable (IS) outcome, which can be computed in polynomial time. In this work, we extend and strengthen these results in several ways. First, we establish that the problem of deciding if an HDG has an NS outcome is NP-complete, but admits an XP algorithm with respect to the size of the smaller class. Second, we show that, in fact, all HDGs admit IS outcomes that can be computed in polynomial time; our algorithm for finding such outcomes is considerably simpler than that of Bredereck et al. We also consider two ways of generalizing the model of Bredereck et al. to k > 2 classes. We complement our theoretical results by empirical analysis, comparing the IS outcomes found by our algorithm, the algorithm of Bredereck et al. and a natural better-response dynamics.", "published": "2019-11-20T02:29:07Z", "authors": ["Niclas Boehmer", "Edith Elkind"], "categories": ["cs.GT"], "doi": "", "_query": "hedonic regression housing", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/0807.3960v1", "title": "Existence, uniqueness and efficiency of equilibrium in hedonic markets with multidimenstional types", "abstract": "We study equilibrium in hedonic markets, when consumers and suppliers have reservation utilities, and the utility functions are separable with respect to price. There is one indivisible good, which comes in different qualities; each consumer buys 0 or 1 unit, and each supplier sells 0 or 1 unit. Consumer types, supplier types and qualities can be either discrete of continuous, in which case they are allowed to be multidimensional. Prices play a double role: they keep some agents out of the market, and they match the remaining ones pairwise. We define equilibrium prices and equilibrium distributions, and we prove that equilibria exist, we investigate to what extend equilibrium prices and distributions are unique, and we prove that equilibria are efficient. In the particular case when there is a continuum of types, and a generalized Spence-Mirrlees condition is satisfied, we prove the existence of a pure equilibrium, where demand distributions are in fact demand functions, and we show to what extent it is unique. The proofs rely on convex analysis, and care has been given to illustrate the theory with examples.", "published": "2008-07-24T20:02:49Z", "authors": ["Ivar Ekeland"], "categories": ["q-fin.TR", "math.OC"], "doi": "", "_query": "hedonic regression housing", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2404.05908v1", "title": "Interpretability in Symbolic Regression: a benchmark of Explanatory Methods using the Feynman data set", "abstract": "In some situations, the interpretability of the machine learning models plays a role as important as the model accuracy. Interpretability comes from the need to trust the prediction model, verify some of its properties, or even enforce them to improve fairness. Many model-agnostic explanatory methods exists to provide explanations for black-box models. In the regression task, the practitioner can use white-boxes or gray-boxes models to achieve more interpretable results, which is the case of symbolic regression. When using an explanatory method, and since interpretability lacks a rigorous definition, there is a need to evaluate and compare the quality and different explainers. This paper proposes a benchmark scheme to evaluate explanatory methods to explain regression models, mainly symbolic regression models. Experiments were performed using 100 physics equations with different interpretable and non-interpretable regression methods and popular explanation methods, evaluating the performance of the explainers performance with several explanation measures. In addition, we further analyzed four benchmarks from the GP community. The results have shown that Symbolic Regression models can be an interesting alternative to white-box and black-box models that is capable of returning accurate models with appropriate explanations. Regarding the explainers, we observed that Partial Effects and SHAP were the most robust explanation models, with Integrated Gradients being unstable only with tree-based models. This benchmark is publicly available for further experiments.", "published": "2024-04-08T23:46:59Z", "authors": ["Guilherme Seidyo Imai Aldeia", "Fabricio Olivetti de Franca"], "categories": ["cs.LG", "cs.AI"], "doi": "10.1007/s10710-022-09435-x", "_query": "hedonic regression housing", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2012.03739v1", "title": "Exploring the Usage of Online Food Delivery Data for Intra-Urban Job and Housing Mobility Detection and Characterization", "abstract": "Human mobility plays a critical role in urban planning and policy-making. However, at certain spatial and temporal resolutions, it is very challenging to track, for example, job and housing mobility. In this study, we explore the usage of a new modality of dataset, online food delivery data, to detect job and housing mobility. By leveraging millions of meal orders from a popular online food ordering and delivery service in Beijing, China, we are able to detect job and housing moves at much higher spatial and temporal resolutions than using traditional data sources. Popular moving seasons and origins/destinations can be well identified. More importantly, we match the detected moves to both macro- and micro-level factors so as to characterize job and housing dynamics. Our findings suggest that commuting distance is a major factor for job and housing mobility. We also observe that: (1) For home movers, there is a trade-off between lower housing cost and shorter commuting distance given the urban spatial structure; (2) For job hoppers, those who frequently work overtime are more likely to reduce their working hours by switching jobs. While this new modality of dataset has its limitations, we believe that ensemble approaches would be promising, where a mash-up of multiple datasets with different characteristic limitations can provide a more comprehensive picture of job and housing dynamics. Our work demonstrates the effectiveness of utilizing food delivery data to detect and analyze job and housing mobility, and contributes to realizing the full potential of ensemble-based approaches.", "published": "2020-12-04T02:48:53Z", "authors": ["Yawen Zhang", "Seth Spielman", "Qi Liu", "Si Shen", "Jason Shuo Zhang", "Qin Lv"], "categories": ["cs.CY"], "doi": "", "_query": "hedonic regression housing", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2604.15580v1", "title": "Housing Decisions under Mobility Risk: A Stochastic Threshold Approach", "abstract": "We develop a stochastic free-boundary model of housing tenure decisions in markets with high mobility risk, such as areas near military installations. Housing prices and rents follow correlated diffusion processes, and households face an uncertain relocation horizon. We derive a closed-form characterization of the optimal buy-versus-rent boundary in terms of the price-to-rent ratio. The model highlights how mobility risk reduces the value of ownership by shortening the effective holding period and increasing uncertainty. As a result, identical price-to-rent ratios can imply different optimal decisions across locations. Numerical illustrations show how variations in volatility and relocation intensity shift the threshold, providing a structural interpretation of observed cross-market heterogeneity. The framework offers a tractable tool for analyzing housing decisions under uncertainty and for interpreting price-to-rent ratios in high-mobility environments.", "published": "2026-04-16T23:25:14Z", "authors": ["Hui Wu"], "categories": ["math.OC"], "doi": "", "_query": "hedonic regression housing", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2308.14989v3", "title": "Efficiency in Multiple-Type Housing Markets", "abstract": "We consider multiple-type housing markets (Moulin, 1995), which extend Shapley-Scarf housing markets (Shapley and Scarf, 1974) from one dimension to higher dimensions. In this model, Pareto efficiency is incompatible with individual rationality and strategy-proofness (Konishi et al., 2001). Therefore, we consider two weaker efficiency properties: coordinatewise efficiency and pairwise efficiency. We show that these two properties both (i) are compatible with individual rationality and strategy-proofness, and (ii) help us to identify two specific mechanisms. To be more precise, on various domains of preference profiles, together with other well-studied properties (individual rationality, strategy-proofness, and non-bossiness), coordinatewise efficiency and pairwise efficiency respectively characterize two extensions of the top-trading-cycles mechanism (TTC): the coordinatewise top-trading-cycles mechanism (cTTC) and the bundle top-trading-cycles mechanism (bTTC). Moreover, we propose several variations of our efficiency properties, and we find that each of them is either satisfied by cTTC or bTTC, or leads to an impossibility result (together with individual rationality and strategy-proofness). Therefore, our characterizations can be primarily interpreted as a compatibility test: any reasonable efficiency property that is not satisfied by cTTC or bTTC could be considered incompatible with individual rationality and strategy-proofness. The external validity of our results in the context of general environments is also discussed. For multiple-type housing markets with strict preferences, our characterization of bTTC constitutes the first characterization of an extension of the prominent TTC mechanism", "published": "2023-08-29T02:38:23Z", "authors": ["Di Feng"], "categories": ["econ.TH"], "doi": "", "_query": "hedonic regression housing", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2312.10306v1", "title": "Mapping Housing Stock Characteristics from Drone Images for Climate Resilience in the Caribbean", "abstract": "Comprehensive information on housing stock is crucial for climate adaptation initiatives aiming to reduce the adverse impacts of climate-extreme hazards in high-risk regions like the Caribbean. In this study, we propose a workflow for rapidly generating critical baseline housing stock data using very high-resolution drone images and deep learning techniques. Specifically, our work leverages the Segment Anything Model and convolutional neural networks for the automated generation of building footprints and roof classification maps. By strengthening local capacity within government agencies to leverage AI and Earth Observation-based solutions, this work seeks to improve the climate resilience of the housing sector in small island developing states in the Caribbean.", "published": "2023-12-16T03:49:40Z", "authors": ["Isabelle Tingzon", "Nuala Margaret Cowan", "Pierre Chrzanowski"], "categories": ["cs.CV"], "doi": "10.1017/eds.2024.46", "_query": "hedonic regression housing", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/physics/0701065v1", "title": "Gearboxes: indirect identification of dynamic forces transmitted to housing through bearings", "abstract": "In order to determine indirectly the generalized forces applied on the gearbox, two approaches are tested, one is a numerical method, the other is experimental. For the numerical method, the vibration response of the gearbox can be evaluated with use of a finite element analysis and a spectral and iterative method. It's confirmed that applying the generalized forces transmitted through the bearings on the empty housing allows to rebuild the housing response (reciprocity principle). The simulation on the time- and space-averaged mean square vibration velocity shows the vibration response of the housing is induced principally by the axial exciting forces, and secondarily by the moments Mx and My. transmitted on the free side (the 6 mm thick face). Realistic estimation of these excitation forces needs a modelling of the whole gearbox taking into account the mechanical properties of the housing. For the experimental approach, an inverse method based upon the FRF measurement and characterisation of gearbox vibration response is developed. Beginning from the equivalent forces obtained by this inverse method, a resulting force and a moment are derived for representing the distributed force acting on each bearing. The comparison between the results obtained by these two methods shows the effectiveness of this indirect force determination methodology.", "published": "2007-01-05T15:04:55Z", "authors": ["Gu Xiang Zhang", "Emmanuel Rigaud", "Jean-Claude Pascal", "Jean Sabot"], "categories": ["physics.class-ph"], "doi": "", "_query": "hedonic regression housing", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2002.01578v1", "title": "Rental Housing Spot Markets: How Online Information Exchanges Can Supplement Transacted-Rents Data", "abstract": "Traditional US rental housing data sources such as the American Community Survey and the American Housing Survey report on the transacted market - what existing renters pay each month. They do not explicitly tell us about the spot market - i.e., the asking rents that current homeseekers must pay to acquire housing - though they are routinely used as a proxy. This study compares governmental data to millions of contemporaneous rental listings and finds that asking rents diverge substantially from these most recent estimates. Conventional housing data understate current market conditions and affordability challenges, especially in cities with tight and expensive rental markets.", "published": "2020-02-04T23:23:35Z", "authors": ["Geoff Boeing", "Jake Wegmann", "Junfeng Jiao"], "categories": ["econ.GN", "cs.CY", "stat.AP"], "doi": "10.1177/0739456X20904435", "_query": "hedonic regression housing", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/0711.3236v3", "title": "Confidence intervals in regression utilizing prior information", "abstract": "We consider a linear regression model with regression parameter beta=(beta_1,...,beta_p) and independent and identically N(0,sigma^2) distributed errors. Suppose that the parameter of interest is theta = a^T beta where a is a specified vector. Define the parameter tau=c^T beta-t where the vector c and the number t are specified and a and c are linearly independent. Also suppose that we have uncertain prior information that tau = 0. We present a new frequentist 1-alpha confidence interval for theta that utilizes this prior information. We require this confidence interval to (a) have endpoints that are continuous functions of the data and (b) coincide with the standard 1-alpha confidence interval when the data strongly contradicts this prior information. This interval is optimal in the sense that it has minimum weighted average expected length where the largest weight is given to this expected length when tau=0. This minimization leads to an interval that has the following desirable properties. This interval has expected length that (a) is relatively small when the prior information about tau is correct and (b) has a maximum value that is not too large. The following problem will be used to illustrate the application of this new confidence interval. Consider a 2-by 2 factorial experiment with 20 replicates. Suppose that the parameter of interest theta is a specified simple effect and that we have uncertain prior information that the two-factor interaction is zero. Our aim is to find a frequentist 0.95 confidence interval for theta that utilizes this prior information.", "published": "2007-11-20T23:36:20Z", "authors": ["Paul Kabaila", "Khageswor Giri"], "categories": ["stat.ME"], "doi": "", "_query": "hedonic regression housing", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2206.12647v1", "title": "Feedback Dynamics of the Low-Income Rental Housing Market: Exploring Policy Responses to COVID-19", "abstract": "The COVID-19 recession threatens mass housing insecurity that undermines economic recovery. Unprecedented federal policy responses halt court-ordered evictions, but questions remain whether policies adequately account for dynamics that drive landlord-tenant interactions including accumulations of rental and mortgage arrears, rental unit availability, and low-income housing options. A system dynamics model probes complex feedback dynamics driving tenant and landlord decision-making in the low-income rental housing market pre- and post-pandemic protections. Feedback loops highlight tradeoffs considered by low-income tenants and landlords in the context of scarcity and uncertainty. Simulations suggest the eviction moratorium reduced evictions by 51% before its expiration in late 2021, but rental arrears, overcrowding, and homelessness remained high. Federal emergency rental assistance further contributed to keeping evictions low, but showed only modest effects on housing insecurity and homelessness. More rapid allocation of funds would substantially increase impact. Failure to address underlying financial hardship and limited affordable housing undermine COVID recovery.", "published": "2022-06-25T13:09:37Z", "authors": ["Katherine Marcal", "Patrick J. Fowler", "Peter S. Hovmand"], "categories": ["cs.CY"], "doi": "", "_query": "hedonic regression housing", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2510.01196v1", "title": "Location Matters: Leveraging Multi-Resolution Geo-Embeddings for Housing Search", "abstract": "QuintoAndar Group is Latin America's largest housing platform, revolutionizing property rentals and sales. Headquartered in Brazil, it simplifies the housing process by eliminating paperwork and enhancing accessibility for tenants, buyers, and landlords. With thousands of houses available for each city, users struggle to find the ideal home. In this context, location plays a pivotal role, as it significantly influences property value, access to amenities, and life quality. A great location can make even a modest home highly desirable. Therefore, incorporating location into recommendations is essential for their effectiveness. We propose a geo-aware embedding framework to address sparsity and spatial nuances in housing recommendations on digital rental platforms. Our approach integrates an hierarchical H3 grid at multiple levels into a two-tower neural architecture. We compare our method with a traditional matrix factorization baseline and a single-resolution variant using interaction data from our platform. Embedding specific evaluation reveals richer and more balanced embedding representations, while offline ranking simulations demonstrate a substantial uplift in recommendation quality.", "published": "2025-09-01T18:49:03Z", "authors": ["Ivo Silva", "Pedro Nogueira", "Guilherme Bonaldo"], "categories": ["cs.IR", "cs.LG"], "doi": "10.1145/3705328.3748136", "_query": "hedonic regression housing", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2107.09088v3", "title": "Reward-Weighted Regression Converges to a Global Optimum", "abstract": "Reward-Weighted Regression (RWR) belongs to a family of widely known iterative Reinforcement Learning algorithms based on the Expectation-Maximization framework. In this family, learning at each iteration consists of sampling a batch of trajectories using the current policy and fitting a new policy to maximize a return-weighted log-likelihood of actions. Although RWR is known to yield monotonic improvement of the policy under certain circumstances, whether and under which conditions RWR converges to the optimal policy have remained open questions. In this paper, we provide for the first time a proof that RWR converges to a global optimum when no function approximation is used, in a general compact setting. Furthermore, for the simpler case with finite state and action spaces we prove R-linear convergence of the state-value function to the optimum.", "published": "2021-07-19T18:01:04Z", "authors": ["Miroslav Štrupl", "Francesco Faccio", "Dylan R. Ashley", "Rupesh Kumar Srivastava", "Jürgen Schmidhuber"], "categories": ["stat.ML", "cs.AI", "cs.LG"], "doi": "", "_query": "hedonic regression housing", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2112.02962v4", "title": "DANets: Deep Abstract Networks for Tabular Data Classification and Regression", "abstract": "Tabular data are ubiquitous in real world applications. Although many commonly-used neural components (e.g., convolution) and extensible neural networks (e.g., ResNet) have been developed by the machine learning community, few of them were effective for tabular data and few designs were adequately tailored for tabular data structures. In this paper, we propose a novel and flexible neural component for tabular data, called Abstract Layer (AbstLay), which learns to explicitly group correlative input features and generate higher-level features for semantics abstraction. Also, we design a structure re-parameterization method to compress the learned AbstLay, thus reducing the computational complexity by a clear margin in the reference phase. A special basic block is built using AbstLays, and we construct a family of Deep Abstract Networks (DANets) for tabular data classification and regression by stacking such blocks. In DANets, a special shortcut path is introduced to fetch information from raw tabular features, assisting feature interactions across different levels. Comprehensive experiments on seven real-world tabular datasets show that our AbstLay and DANets are effective for tabular data classification and regression, and the computational complexity is superior to competitive methods. Besides, we evaluate the performance gains of DANet as it goes deep, verifying the extendibility of our method. Our code is available at https://github.com/WhatAShot/DANet.", "published": "2021-12-06T12:15:28Z", "authors": ["Jintai Chen", "Kuanlun Liao", "Yao Wan", "Danny Z. Chen", "Jian Wu"], "categories": ["cs.LG", "cs.AI"], "doi": "", "_query": "hedonic regression housing", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2507.03477v1", "title": "REAL: Benchmarking Abilities of Large Language Models for Housing Transactions and Services", "abstract": "The development of large language models (LLMs) has greatly promoted the progress of chatbot in multiple fields. There is an urgent need to evaluate whether LLMs can play the role of agent in housing transactions and services as well as humans. We present Real Estate Agent Large Language Model Evaluation (REAL), the first evaluation suite designed to assess the abilities of LLMs in the field of housing transactions and services. REAL comprises 5,316 high-quality evaluation entries across 4 topics: memory, comprehension, reasoning and hallucination. All these entries are organized as 14 categories to assess whether LLMs have the knowledge and ability in housing transactions and services scenario. Additionally, the REAL is used to evaluate the performance of most advanced LLMs. The experiment results indicate that LLMs still have significant room for improvement to be applied in the real estate field.", "published": "2025-07-04T11:05:44Z", "authors": ["Kexin Zhu", "Yang Han"], "categories": ["cs.AI"], "doi": "", "_query": "hedonic regression housing", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/1609.09066v1", "title": "Refugee Resettlement Housing Scout", "abstract": "According to the United States High Commission for Refugees (UNHCr), there are 65.3 million forcibly displaced people in the world today, 21.5 million of them being refugees. This has led to an unprecedented refugee crisis which has led countries to accept refugee families and to resettle them. Diverse agencies are helping refugees coming to US to resettle and start their new life in the country. One of the first and most challenging steps of this process is to find affordable housing that also meets a suite of additional constraints and priorities. These include being within a mile of public transportation and near schools, faith centers and international grocery stores. We detail an interactive data-driven web-based tool, which incorporates in one consolidated platform most of the needed information. The tool searches, filters and demonstrates a list of possible housing locations, and allows for the dynamic prioritization based on user-specified importance weights on the diverse criteria. The platform was created in a partnership with New American Pathways, a nonprofit that supports refugee resettlement in the metro Atlanta, but exemplifies a methodology that can help many other organizations with similar goals.", "published": "2016-09-28T04:48:20Z", "authors": ["Unaiza Ahsan", "Oleksandra Sopova", "Wes Stayton", "Bistra Dilkina"], "categories": ["cs.CY"], "doi": "", "_query": "hedonic regression housing", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/1905.07848v1", "title": "Time Series Analysis and Forecasting of the US Housing Starts using Econometric and Machine Learning Model", "abstract": "In this research paper, I have performed time series analysis and forecasted the monthly value of housing starts for the year 2019 using several econometric methods - ARIMA(X), VARX, (G)ARCH and machine learning algorithms - artificial neural networks, ridge regression, K-Nearest Neighbors, and support vector regression, and created an ensemble model. The ensemble model stacks the predictions from various individual models, and gives a weighted average of all predictions. The analyses suggest that the ensemble model has performed the best among all the models as the prediction errors are the lowest, while the econometric models have higher error rates.", "published": "2019-05-20T02:17:28Z", "authors": ["Sudiksha Joshi"], "categories": ["econ.EM"], "doi": "", "_query": "hedonic regression housing", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2605.12559v1", "title": "Coordination Failures and Stackelberg Leadership in Housing Development with Network Effects", "abstract": "I study coordination failures in housing development markets with network effects, where the value of building depends on aggregate supply. When network effects are sufficiently strong and convex, multiple equilibria arise: a low-supply coordination failure and a high-supply outcome. Without a coordination mechanism, equilibrium is indeterminate. I introduce a large developer who moves first in a Stackelberg game, committing to housing supply before atomistic developers make entry decisions. The main result is that the large developer always commits at least to the high-supply equilibrium, eliminating the coordination failure by pushing past the unstable threshold that separates the low and high outcomes. The result is unconditional; it holds for general demand functions and cost distributions, and does not depend on which stable continuation equilibrium materializes. The leader's commitment inverts standard monopoly intuition: first-mover commitment can improve welfare by resolving a coordination problem that atomistic markets cannot solve on their own. I also characterize when the developer builds beyond the high equilibrium into a monopoly region, and show that the market underprovides housing relative to the social optimum.", "published": "2026-05-11T21:03:38Z", "authors": ["Vaibhav Rangan"], "categories": ["econ.TH"], "doi": "", "_query": "hedonic regression housing", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2511.07022v1", "title": "Fair Societies: Algorithms for House Allocations", "abstract": "House Allocations concern with matchings involving one-sided preferences, where houses serve as a proxy encoding valuable indivisible resources (e.g. organs, course seats, subsidized public housing units) to be allocated among the agents. Every agent must receive exactly one resource. We study algorithmic approaches towards ensuring fairness in such settings. Minimizing the number of envious agents is known to be NP-complete (Kamiyama et al. 2021). We present two tractable approaches to deal with the computational hardness. When the agents are presented with an initial allocation of houses, we aim to refine this allocation by reallocating a bounded number of houses to reduce the number of envious agents. We show an efficient algorithm when the agents express preference for a bounded number of houses. Next, we consider single peaked preference domain and present a polynomial time algorithm for finding an allocation that minimize the number of envious agents. We further extend it to satisfy Pareto efficiency. Our former algorithm works for other measures of envy such as total envy, or maximum envy, with suitable modifications. Finally, we present an empirical analysis recording the fairness-welfare trade-off of our algorithms.", "published": "2025-11-10T12:09:27Z", "authors": ["Hadi Hosseini", "Sanjukta Roy", "Aditi Sethia"], "categories": ["cs.GT"], "doi": "", "_query": "hedonic regression housing", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2409.05990v1", "title": "FairHome: A Fair Housing and Fair Lending Dataset", "abstract": "We present a Fair Housing and Fair Lending dataset (FairHome): A dataset with around 75,000 examples across 9 protected categories. To the best of our knowledge, FairHome is the first publicly available dataset labeled with binary labels for compliance risk in the housing domain. We demonstrate the usefulness and effectiveness of such a dataset by training a classifier and using it to detect potential violations when using a large language model (LLM) in the context of real-estate transactions. We benchmark the trained classifier against state-of-the-art LLMs including GPT-3.5, GPT-4, LLaMA-3, and Mistral Large in both zero-shot and few-shot contexts. Our classifier outperformed with an F1-score of 0.91, underscoring the effectiveness of our dataset.", "published": "2024-09-09T18:34:26Z", "authors": ["Anusha Bagalkotkar", "Aveek Karmakar", "Gabriel Arnson", "Ondrej Linda"], "categories": ["cs.LG", "cs.CY"], "doi": "", "_query": "hedonic regression housing", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2003.11565v2", "title": "The Millennial Boom, the Baby Bust, and the Housing Market", "abstract": "As baby boomers have begun to downsize and retire, their preferences now overlap with millennials' predilection for urban amenities and smaller living spaces. This confluence in tastes between the two largest age segments of the U.S. population has meaningfully changed the evolution of home prices in the United States. Utilizing a Bartik shift-share instrument for demography-driven demand shocks, we show that from 2000 to 2018 (i) the price growth of four- and five-bedroom houses has lagged the prices of one- and two-bedroom homes, (ii) within local labor markets, the relative home prices in baby boomer-rich zip codes have declined compared with millennial-rich neighborhoods, and (iii) the zip codes with the largest relative share of smaller homes have grown fastest. These patterns have become more pronounced during the latest economic cycle. We show that the effects are concentrated in areas where housing supply is most inelastic. If this pattern in the housing market persists or expands, the approximately 16.5 trillion in real estate wealth held by households headed by those aged 55 or older will be significantly affected. We find little evidence that these upcoming changes have been incorporated into current prices.", "published": "2020-03-25T18:03:45Z", "authors": ["Marijn A. Bolhuis", "Judd N. L. Cramer"], "categories": ["econ.GN"], "doi": "", "_query": "hedonic regression housing", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/1605.05397v4", "title": "New Insights into Rental Housing Markets across the United States: Web Scraping and Analyzing Craigslist Rental Listings", "abstract": "Current sources of data on rental housing - such as the census or commercial databases that focus on large apartment complexes - do not reflect recent market activity or the full scope of the U.S. rental market. To address this gap, we collected, cleaned, analyzed, mapped, and visualized 11 million Craigslist rental housing listings. The data reveal fine-grained spatial and temporal patterns within and across metropolitan housing markets in the U.S. We find some metropolitan areas have only single-digit percentages of listings below fair market rent. Nontraditional sources of volunteered geographic information offer planners real-time, local-scale estimates of rent and housing characteristics currently lacking in alternative sources, such as census data.", "published": "2016-05-17T23:29:58Z", "authors": ["Geoff Boeing", "Paul Waddell"], "categories": ["stat.AP", "stat.CO", "stat.ME"], "doi": "10.1177/0739456X16664789", "_query": "hedonic regression housing", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2504.20429v3", "title": "Estimating the housing production function with unobserved land heterogeneity", "abstract": "Housing supply in dense cities depends on the ability of builders to substitute capital for scarce land. This margin is difficult to estimate because builders choose capital after observing microgeographic conditions that are only imperfectly observed by researchers. This paper develops a method for estimating revenue-based housing production functions in this setting. Because observed capital variation reflects both technological substitution and endogenous responses to latent local conditions, existing estimators can transmit unobserved heterogeneity into the estimated production function. The method treats the unobserved local conditions that affect capital choice as a scalar Markov state and combines the capital share equation with Markov moments implemented using repeated cross-sectional construction data. Monte Carlo simulations show that the estimator recovers capital and land elasticities under flexible production technologies when capital choices respond to latent local conditions. An application to newly constructed housing in Tokyo's 23 special wards illustrates how the method can be implemented in a dense single-city setting. The results show that explicitly modeling latent local heterogeneity matters for estimated capital-land elasticities and implies returns to scale close to one.", "published": "2025-04-29T04:51:35Z", "authors": ["Yusuke Adachi"], "categories": ["econ.GN"], "doi": "", "_query": "hedonic regression housing", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2607.08437v2", "title": "Does online sustainability communication shape public discourse? Insights from six years of tenant-housing provider interactions", "abstract": "Authorities increasingly rely on social media to advance sustainability transitions, infrastructure investment, and service reform. Yet how citizens respond to these digital communications remains poorly understood. Existing approaches rely on aggregate engagement metrics (e.g., likes), providing limited insight into discourse structure and quality. We developed a data-driven, multidimensional framework to analyse how social media communication shapes the content of discourse, focusing on sustainability-related engagement in Dutch public housing. We analysed 792 posts and 3,197 tenant comments from the Facebook pages of 92 housing providers (2018-2023). A machine-learning pipeline classified comments into recurring discourse configurations across three dimensions - communicative intent, sentiment, and semantic relatedness. Multinomial logistic regression estimated the effects of post-design and organisational characteristics on discourse. Tenant comments were significantly more semantically aligned with their corresponding posts than with randomly paired content, indicating that organisational communication structures responses to topics. Six discourse types emerged, with critical and inquiry-driven engagement increasing over time. Post-level features did not significantly explain variation; organisational characteristics dominated. Larger housing associations attracted more substantive responses, while lower-rent organisations received fewer evaluative comments. While applied to housing associations, our methodology provides a scalable approach to analyse online discourse dynamics, quality, and content across organisations and contexts.", "published": "2026-07-09T12:59:03Z", "authors": ["Shray Juneja", "Suzan Verberne", "Ioulia V. Ossokina"], "categories": ["cs.CY"], "doi": "", "_query": "hedonic regression housing", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2605.07793v1", "title": "Hybrid TF--IDF Logistic Regression and MLP Neural Baseline for Indonesian Three-Class Sentiment Analysis on Social Media Text", "abstract": "This paper presents a compact three-class sentiment analysis study for Indonesian social media text. The task is formulated with positive, negative, and neutral outputs derived from a fine-grained emotion dataset. The proposed practical baseline combines TF--IDF text features, three lightweight numeric metadata features, and a balanced multinomial Logistic Regression classifier. For comparison, the study also includes a neural baseline using a two-layer multilayer perceptron (MLP) over the same hybrid feature representation. The dataset originally contains 732 rows and 191 fine-grained emotion labels; after cleaning, deduplication, and label remapping, 707 samples remain with an imbalanced distribution of 459 positive, 188 negative, and 60 neutral instances. Experimental results show that the Logistic Regression deployment model reaches 0.8028 accuracy, 0.8003 weighted F1, and 0.7276 macro F1, while project documentation reports a higher-accuracy but non-production MLP baseline. These findings indicate that careful preprocessing, interpretable feature engineering, and class balancing remain competitive for small Indonesian sentiment datasets, whereas the neural baseline is better treated as a comparative experiment than as the default deployment model.", "published": "2026-05-08T14:31:25Z", "authors": ["Allya Nurul Islami Pasha", "Eka Fidiya Putri", "Luluk Muthoharoh", "Ardika Satria", "Martin C. T. Manullang"], "categories": ["cs.CL"], "doi": "", "_query": "hedonic regression housing", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2202.13603v2", "title": "Optimal Online Generalized Linear Regression with Stochastic Noise and Its Application to Heteroscedastic Bandits", "abstract": "We study the problem of online generalized linear regression in the stochastic setting, where the label is generated from a generalized linear model with possibly unbounded additive noise. We provide a sharp analysis of the classical follow-the-regularized-leader (FTRL) algorithm to cope with the label noise. More specifically, for $σ$-sub-Gaussian label noise, our analysis provides a regret upper bound of $O(σ^2 d \\log T) + o(\\log T)$, where $d$ is the dimension of the input vector, $T$ is the total number of rounds. We also prove a $Ω(σ^2d\\log(T/d))$ lower bound for stochastic online linear regression, which indicates that our upper bound is nearly optimal. In addition, we extend our analysis to a more refined Bernstein noise condition. As an application, we study generalized linear bandits with heteroscedastic noise and propose an algorithm based on FTRL to achieve the first variance-aware regret bound.", "published": "2022-02-28T08:25:26Z", "authors": ["Heyang Zhao", "Dongruo Zhou", "Jiafan He", "Quanquan Gu"], "categories": ["cs.LG", "math.OC", "stat.ML"], "doi": "", "_query": "hedonic regression housing", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/1104.2719v1", "title": "An autoregressive approach to house price modeling", "abstract": "A statistical model for predicting individual house prices and constructing a house price index is proposed utilizing information regarding sale price, time of sale and location (ZIP code). This model is composed of a fixed time effect and a random ZIP (postal) code effect combined with an autoregressive component. The former two components are applied to all home sales, while the latter is applied only to homes sold repeatedly. The time effect can be converted into a house price index. To evaluate the proposed model and the resulting index, single-family home sales for twenty US metropolitan areas from July 1985 through September 2004 are analyzed. The model is shown to have better predictive abilities than the benchmark S&P/Case--Shiller model, which is a repeat sales model, and a conventional mixed effects model. Finally, Los Angeles, CA, is used to illustrate a historical housing market downturn.", "published": "2011-04-14T10:54:29Z", "authors": ["Chaitra H. Nagaraja", "Lawrence D. Brown", "Linda H. Zhao"], "categories": ["stat.AP"], "doi": "10.1214/10-AOAS380", "_query": "hedonic regression housing", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2510.12641v1", "title": "Single-Deviation Stability in Additively Separable Hedonic Games with Constrained Coalition Sizes", "abstract": "We study stability in additively separable hedonic games when coalition sizes have to respect fixed size bounds. We consider four classic notions of stability based on single-agent deviations, namely, Nash stability, individual stability, contractual Nash stability, and contractual individual stability. For each stability notion, we consider two variants: in one, the coalition left behind by a deviator must still be of a valid size, and in the other there is no such constraint. We provide a full picture of the existence of stable outcomes with respect to given size parameters. Additionally, when there are only upper bounds, we fully characterize the computational complexity of the associated existence problem. In particular, we obtain polynomial-time algorithms for contractual individual stability and contractual Nash stability, where the latter requires an upper bound of 2. We obtain further results for Nash stability and contractual individual stability, when the lower bound is at least 2.", "published": "2025-10-14T15:33:59Z", "authors": ["Martin Bullinger", "Adam Dunajski", "Edith Elkind", "Matan Gilboa"], "categories": ["cs.GT", "cs.DS"], "doi": "", "_query": "hedonic regression housing", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2202.06925v1", "title": "Hedonic Games and Treewidth Revisited", "abstract": "We revisit the complexity of the well-studied notion of Additively Separable Hedonic Games (ASHGs). Such games model a basic clustering or coalition formation scenario in which selfish agents are represented by the vertices of an edge-weighted digraph $G=(V,E)$, and the weight of an arc $uv$ denotes the utility $u$ gains by being in the same coalition as $v$. We focus on (arguably) the most basic stability question about such a game: given a graph, does a Nash stable solution exist and can we find it efficiently? We study the (parameterized) complexity of ASHG stability when the underlying graph has treewidth $t$ and maximum degree $Δ$. The current best FPT algorithm for this case was claimed by Peters [AAAI 2016], with time complexity roughly $2^{O(Δ^5t)}$. We present an algorithm with parameter dependence $(Δt)^{O(Δt)}$, significantly improving upon the parameter dependence on $Δ$ given by Peters, albeit with a slightly worse dependence on $t$. Our main result is that this slight performance deterioration with respect to $t$ is actually completely justified: we observe that the previously claimed algorithm is incorrect, and that in fact no algorithm can achieve dependence $t^{o(t)}$ for bounded-degree graphs, unless the ETH fails. This, together with corresponding bounds we provide on the dependence on $Δ$ and the joint parameter establishes that our algorithm is essentially optimal for both parameters, under the ETH. We then revisit the parameterization by treewidth alone and resolve a question also posed by Peters by showing that Nash Stability remains strongly NP-hard on stars under additive preferences. Nevertheless, we also discover an island of mild tractability: we show that Connected Nash Stability is solvable in pseudo-polynomial time for constant $t$, though with an XP dependence on $t$ which, as we establish, cannot be avoided.", "published": "2022-02-14T18:37:00Z", "authors": ["Tesshu Hanaka", "Michael Lampis"], "categories": ["cs.GT", "cs.CC", "cs.DS"], "doi": "", "_query": "hedonic regression housing", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/0809.0979v1", "title": "A housing-demographic multi-layered nonlinear model to test regulation strategies", "abstract": "We propose a novel multi-layered nonlinear model that is able to capture and predict the housing-demographic dynamics of the real-state market by simulating the transitions of owners among price-based house layers. This model allows us to determine which parameters are most effective to smoothen the severity of a potential market crisis. The International Monetary Fund (IMF) has issued severe warnings about the current real-state bubble in the United States, the United Kingdom, Ireland, the Netherlands, Australia and Spain in the last years. Madrid (Spain), in particular, is an extreme case of this bubble. It is, therefore, an excellent test case to analyze housing dynamics in the context of the empirical data provided by the Spanish National Institute of Statistics and other sources of data. The model is able to predict the mean house occupancy, and shows that i) the house market conditions in Madrid are unstable but not critical; and ii) the regulation of the construction rate is more effective than interest rate changes. Our results indicate that to accommodate the construction rate to the total population of first-time buyers is the most effective way to maintain the system near equilibrium conditions. In addition, we show that to raise interest rates will heavily affect the poorest housing bands of the population while the middle class layers remain nearly unaffected.", "published": "2008-09-05T08:51:28Z", "authors": ["Ramon Huerta", "Fernando Corbacho", "Luis F. Lago-Fernandez"], "categories": ["nlin.CG", "q-fin.GN"], "doi": "", "_query": "hedonic regression housing", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2304.12487v1", "title": "Queue Routing Strategies to Improve Equitable Housing Coordination in New York City", "abstract": "Runaway and homeless youth (RHY) are a group of youth and young adults who are at high risk of being exploited through human trafficking. Although access to housing and support services is an effective way to decrease their vulnerability to being exploited, research reveals that coordination of these services provided to RHY by non-profit and government organizations is neither standardized, nor efficient. This situation often causes decreased, delayed, and inequitable access to these scarce housing resources. In this study, we aim to increase the housing system efficiency and reduce the barriers that are contributing to inequitable access to housing through simulation modeling and analyses. Specifically, we simulate a set of crisis and emergency shelters in New York City, funded by a single governmental organization, considering a queuing network with pools of multiple parallel servers, servers with demographic eligibility criteria, stochastic RHY arrival, impatient youth behaviour (possibility of abandonment), and a decision-maker (coordinator) that determines which server pool RHY is routed to. This simulation allows us to evaluate the impact of different queue routing strategies. Our simulation results show that by changing the way RHY is routed to shelters, we can reduce the average wait time by approximately a day and decrease the proportion of RHY abandoning the shelters by 13%.", "published": "2023-04-24T22:58:51Z", "authors": ["Yaren Bilge Kaya", "Kayse Lee Maass"], "categories": ["math.NA", "math.PR"], "doi": "", "_query": "hedonic regression housing", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2504.13459v1", "title": "The impact of institutional quality on the relation between FDI and house prices in ASEAN emerging countries", "abstract": "This study investigates the relationship between house prices and capital inflows in ASEAN emerging economies, and the impact of institutional quality on that relation. Using a unique balanced panel data set of six emerging countries in ASEAN from 2009 to 2019, we employ various econometric techniques to examine the impact of foreign direct investment (FDI) on the house price index. Our findings indicate a long-run relationship and Granger causality from FDI to the house price index in these markets, and we also find evidence of co-movement between the stock price index and the house price index. Additionally, our results suggest that better institutions reduce the impact of FDI on host country housing markets in the context of ASEAN emerging economies. This is one of the first studies to shed light on the role of institutional quality in the effect of FDIs on housing prices in this region.", "published": "2025-04-18T04:33:07Z", "authors": ["Hoang-Anh Le"], "categories": ["econ.GN"], "doi": "", "_query": "hedonic regression housing", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/1609.08399v1", "title": "House price estimation from visual and textual features", "abstract": "Most existing automatic house price estimation systems rely only on some textual data like its neighborhood area and the number of rooms. The final price is estimated by a human agent who visits the house and assesses it visually. In this paper, we propose extracting visual features from house photographs and combining them with the house's textual information. The combined features are fed to a fully connected multilayer Neural Network (NN) that estimates the house price as its single output. To train and evaluate our network, we have collected the first houses dataset (to our knowledge) that combines both images and textual attributes. The dataset is composed of 535 sample houses from the state of California, USA. Our experiments showed that adding the visual features increased the R-value by a factor of 3 and decreased the Mean Square Error (MSE) by one order of magnitude compared with textual-only features. Additionally, when trained on the benchmark textual-only features housing dataset, our proposed NN still outperformed the existing model published results.", "published": "2016-09-27T13:15:31Z", "authors": ["Eman Ahmed", "Mohamed Moustafa"], "categories": ["cs.CV"], "doi": "", "_query": "hedonic regression housing", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2203.09655v2", "title": "Hedonic Games With Friends, Enemies, and Neutrals: Resolving Open Questions and Fine-Grained Complexity", "abstract": "We investigate verification and existence problems for prominent stability concepts in hedonic games with friends, enemies, and optionally with neutrals [8, 16]. We resolve several (long-standing) open questions [4, 16, 20, 23] and show that for friend-oriented preferences, under the friends and enemies model, it is coNP-complete to verify whether a given agent partition is (strictly) core stable, while under the friends, enemies, and neutrals model, it is NP-complete to determine whether an individual stable partition exists. We further look into natural restricted cases from the literature, such as when the friends and enemies relationships are symmetric, when the initial coalitions have bounded size, when the vertex degree in the friendship graph (resp. the union of friendship and enemy graph) is bounded, or when such graph is acyclic or close to being acyclic. We obtain a complete (parameterized) complexity picture regarding these cases.", "published": "2022-03-17T23:31:48Z", "authors": ["Jiehua Chen", "Gergely Csáji", "Sanjukta Roy", "Sofia Simola"], "categories": ["cs.GT", "cs.MA"], "doi": "", "_query": "hedonic regression housing", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2605.21358v1", "title": "From Summer to Spring: A Shift in US Housing Market Seasonality", "abstract": "The US housing market exhibits pronounced seasonal cycles: prices and sales rise through spring, peak in summer, and decline through autumn and winter. Since 2021, this pattern has shifted earlier in the calendar year, with spring strengthening at the expense of the traditional summer peak. A leading explanation for housing market seasonality is the search-and-matching model of Ngai and Tenreyro (2014), which links these cycles to household mobility through a thick-market mechanism. In this framework, periods with higher mobility generate thicker markets and higher prices and transaction volumes. Viewed through this lens, a shift in the seasonal cycle of prices and sales raises the question of whether the timing of household moves has changed. Did residential mobility shift earlier in the calendar year after 2021? We find that it did. Using SIPP data, and corroborating evidence from Google Trends indicators, we document a post-2021 shift in mobility toward spring. We extend the model to a monthly frequency, prove the existence and uniqueness of the equilibrium, and calibrate it to the observed mobility patterns. The calibrated model reproduces the spring shift in both prices and transaction volumes, consistent with the view that a change in the timing of household mobility alone can account for the recent shift in housing market seasonality.", "published": "2026-05-20T16:23:25Z", "authors": ["Yihan Hu", "Cemil Selcuk"], "categories": ["econ.GN"], "doi": "", "_query": "hedonic regression housing", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2610.05156v1", "title": "Ordered Weighted Average Support Vector Regression", "abstract": "This paper introduces a novel Support Vector Regression (SVR) model that incorporates Ordered Weighted Average (OWA) operators to differently penalize deviations of observations from the ε-strip. The penalty is determined based on the position of each deviation in the ordered vector of all deviations. A key contribution of this work is the development of two nonlinear formulations: a continuous formulation for non-decreasing monotone weight vectors and a mixed-integer formulation for general weight vectors. By leveraging dual approaches associated with these formulations, the model accommodates nonlinear kernel functions. To enhance computational efficiency, two heuristic approaches are proposed for deriving suitable regression hyperplanes in reduced computation times, as compared to exact methods. Additionally, a third heuristic approach is developed to handle nonlinear kernel functions effectively. Computational experiments conducted on both real and synthetic datasets demonstrate that the exact formulations yield remarkable regression functions with respect to the following standard metrics: mean absolute errors (MAE) and mean squared errors (MSE). The heuristic approaches are shown to be particularly efficient for larger datasets, striking a balance between solution quality and computational effort.", "published": "2026-10-04T12:05:17Z", "authors": ["Luisa I. Martínez-Merino", "Justo Puerto", "Antonio M. Rodríguez-Chía"], "categories": ["math.OC"], "doi": "10.1016/j.eswa.2025.126882", "_query": "hedonic regression housing", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2311.11101v2", "title": "$\\varepsilon$-fractional Core Stability in Hedonic Games", "abstract": "Hedonic Games (HGs) are a classical framework modeling coalition formation of strategic agents guided by their individual preferences. According to these preferences, it is desirable that a coalition structure (i.e. a partition of agents into coalitions) satisfies some form of stability. The most well-known and natural of such notions is arguably core-stability. Informally, a partition is core-stable if no subset of agents would like to deviate by regrouping in a so-called core-blocking coalition. Unfortunately, core-stable partitions seldom exist and even when they do, it is often computationally intractable to find one. To circumvent these problems, we propose the notion of $\\varepsilon$-fractional core-stability, where at most an $\\varepsilon$-fraction of all possible coalitions is allowed to core-block. It turns out that such a relaxation may guarantee both existence and polynomial-time computation. Specifically, we design efficient algorithms returning an $\\varepsilon$-fractional core-stable partition, with $\\varepsilon$ exponentially decreasing in the number of agents, for two fundamental classes of HGs: Simple Fractional and Anonymous. From a probabilistic point of view, being the definition of $\\varepsilon$-fractional core equivalent to requiring that uniformly sampled coalitions core-block with probability lower than $\\varepsilon$, we further extend the definition to handle more complex sampling distributions. Along this line, when valuations have to be learned from samples in a PAC-learning fashion, we give positive and negative results on which distributions allow the efficient computation of outcomes that are $\\varepsilon$-fractional core-stable with arbitrarily high confidence.", "published": "2023-11-18T15:30:29Z", "authors": ["Simone Fioravanti", "Michele Flammini", "Bojana Kodric", "Giovanna Varricchio"], "categories": ["cs.GT", "cs.AI"], "doi": "", "_query": "hedonic regression housing", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2601.21573v2", "title": "Characteristics Design: A Hedonic Approach to Optimal Product Differentiation", "abstract": "Building on the generalized hedonic-linear model of Pellegrino (2025), this paper studies optimal product differentiation when a representative consumer has preferences over product characteristics. Under multiproduct monopoly, the monopolist's choice of product characteristics is always aligned with the social planner's optimum, despite underproduction. By contrast, under oligopoly, multiple equilibria can arise that differ qualitatively in their patterns of characteristics design. We show that, while oligopoly equilibria exhibiting product differentiation yield higher welfare than those with product concentration, the degree of product differentiation under oligopoly remains below the socially optimal level. As a result, social welfare under oligopoly is typically lower than under monopoly, highlighting a key advantage of coordination in characteristics design. We extend the analysis to settings with overlapping ownership structures and show that common ownership can improve welfare by inducing firms to soften competition through increased product differentiation rather than output reduction.", "published": "2026-01-29T11:34:58Z", "authors": ["Masaki Miyashita"], "categories": ["econ.TH"], "doi": "", "_query": "hedonic regression housing", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2507.20357v1", "title": "Wafer Defect Root Cause Analysis with Partial Trajectory Regression", "abstract": "Identifying upstream processes responsible for wafer defects is challenging due to the combinatorial nature of process flows and the inherent variability in processing routes, which arises from factors such as rework operations and random process waiting times. This paper presents a novel framework for wafer defect root cause analysis, called Partial Trajectory Regression (PTR). The proposed framework is carefully designed to address the limitations of conventional vector-based regression models, particularly in handling variable-length processing routes that span a large number of heterogeneous physical processes. To compute the attribution score of each process given a detected high defect density on a specific wafer, we propose a new algorithm that compares two counterfactual outcomes derived from partial process trajectories. This is enabled by new representation learning methods, proc2vec and route2vec. We demonstrate the effectiveness of the proposed framework using real wafer history data from the NY CREATES fab in Albany.", "published": "2025-07-27T17:08:40Z", "authors": ["Kohei Miyaguchi", "Masao Joko", "Rebekah Sheraw", "Tsuyoshi Idé"], "categories": ["cs.LG"], "doi": "10.1109/ASMC64512.2025.11010733", "_query": "hedonic regression housing", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2107.07148v1", "title": "What Image Features Boost Housing Market Predictions?", "abstract": "The attractiveness of a property is one of the most interesting, yet challenging, categories to model. Image characteristics are used to describe certain attributes, and to examine the influence of visual factors on the price or timeframe of the listing. In this paper, we propose a set of techniques for the extraction of visual features for efficient numerical inclusion in modern-day predictive algorithms. We discuss techniques such as Shannon's entropy, calculating the center of gravity, employing image segmentation, and using Convolutional Neural Networks. After comparing these techniques as applied to a set of property-related images (indoor, outdoor, and satellite), we conclude the following: (i) the entropy is the most efficient single-digit visual measure for housing price prediction; (ii) image segmentation is the most important visual feature for the prediction of housing lifespan; and (iii) deep image features can be used to quantify interior characteristics and contribute to captivation modeling. The set of 40 image features selected here carries a significant amount of predictive power and outperforms some of the strongest metadata predictors. Without any need to replace a human expert in a real-estate appraisal process, we conclude that the techniques presented in this paper can efficiently describe visible characteristics, thus introducing perceived attractiveness as a quantitative measure into the predictive modeling of housing.", "published": "2021-07-15T06:32:10Z", "authors": ["Zona Kostic", "Aleksandar Jevremovic"], "categories": ["cs.CV", "cs.LG"], "doi": "10.1109/TMM.2020.2966890", "_query": "hedonic regression housing", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/1905.11005v4", "title": "Ordinal Distribution Regression for Gait-based Age Estimation", "abstract": "Computer vision researchers prefer to estimate age from face images because facial features provide useful information. However, estimating age from face images becomes challenging when people are distant from the camera or occluded. A person's gait is a unique biometric feature that can be perceived efficiently even at a distance. Thus, gait can be used to predict age when face images are not available. However, existing gait-based classification or regression methods ignore the ordinal relationship of different ages, which is an important clue for age estimation. This paper proposes an ordinal distribution regression with a global and local convolutional neural network for gait-based age estimation. Specifically, we decompose gait-based age regression into a series of binary classifications to incorporate the ordinal age information. Then, an ordinal distribution loss is proposed to consider the inner relationships among these classifications by penalizing the distribution discrepancy between the estimated value and the ground truth. In addition, our neural network comprises a global and three local sub-networks, and thus, is capable of learning the global structure and local details from the head, body, and feet. Experimental results indicate that the proposed approach outperforms state-of-the-art gait-based age estimation methods on the OULP-Age dataset.", "published": "2019-05-27T07:07:56Z", "authors": ["Haiping Zhu", "Yuheng Zhang", "Guohao Li", "Junping Zhang", "Hongming Shan"], "categories": ["cs.CV"], "doi": "10.1007/s11432-019-2733-4", "_query": "hedonic regression housing", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2505.06163v2", "title": "The Power of Matching for Online Fractional Hedonic Games", "abstract": "We study coalition formation in the framework of fractional hedonic games (FHGs). The objective is to maximize social welfare in an online model where agents arrive one by one and must be assigned to coalitions immediately and irrevocably. A recurrent theme in online coalition formation is that online matching algorithms, where coalitions are restricted to size at most $2$, yield good competitive ratios. For example, computing maximal matchings achieves the optimal competitive ratio for general online FHGs. However, this ratio is bounded only if agents' valuations are themselves bounded. We identify optimal algorithms with constant competitive ratios in two related settings, independent of the range of agent valuations. First, under random agent arrival, we present an asymptotically optimal $(\\frac{1}{3}-\\frac 1n)$-competitive algorithm, where $n$ is the number of agents. This result builds on our identification of an optimal matching algorithm in a general model of online matching with edge weights and an unknown number of agents. In this setting, we also achieve an asymptotically optimal competitive ratio of $\\frac{1}{3}-\\frac 1n$. Second, when agents arrive in an arbitrary order but algorithms are allowed to irrevocably and entirely dissolve coalitions, we show that another matching-based algorithm achieves an optimal competitive ratio of $\\frac{1}{6 + 4\\sqrt{2}}$.", "published": "2025-05-09T16:13:35Z", "authors": ["Martin Bullinger", "René Romen", "Alexander Schlenga"], "categories": ["cs.GT", "cs.DS"], "doi": "", "_query": "hedonic regression housing", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/1505.01164v1", "title": "Achieving a Hyperlocal Housing Price Index: Overcoming Data Sparsity by Bayesian Dynamical Modeling of Multiple Data Streams", "abstract": "Understanding how housing values evolve over time is important to policy makers, consumers and real estate professionals. Existing methods for constructing housing indices are computed at a coarse spatial granularity, such as metropolitan regions, which can mask or distort price dynamics apparent in local markets, such as neighborhoods and census tracts. A challenge in moving to estimates at, for example, the census tract level is the sparsity of spatiotemporally localized house sales observations. Our work aims at addressing this challenge by leveraging observations from multiple census tracts discovered to have correlated valuation dynamics. Our proposed Bayesian nonparametric approach builds on the framework of latent factor models to enable a flexible, data-driven method for inferring the clustering of correlated census tracts. We explore methods for scalability and parallelizability of computations, yielding a housing valuation index at the level of census tract rather than zip code, and on a monthly basis rather than quarterly. Our analysis is provided on a large Seattle metropolitan housing dataset.", "published": "2015-05-05T20:01:08Z", "authors": ["You Ren", "Emily B. Fox", "Andrew Bruce"], "categories": ["stat.AP", "stat.ME", "stat.ML"], "doi": "", "_query": "hedonic regression housing", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2204.09050v1", "title": "House Price Prediction Based On Deep Learning", "abstract": "Since ancient times, what Chinese people have been pursuing is very simple, which is nothing more than \"to live and work happily, to eat and dress comfortable\". Today, more than 40 years after the reform and opening, people have basically solved the problem of food and clothing, and the urgent problem is housing. Nowadays, due to the storm of long-term rental apartment intermediary platforms such as eggshell, increasing the sense of insecurity of renters, as well as the urbanization in recent years and the scramble for people in major cities, this will make the future real estate market competition more intense. In order to better grasp the real estate price, let consumers buy a house reasonably, and provide a reference for the government to formulate policies, this paper summarizes the existing methods of house price prediction and proposes a house price prediction method based on mixed depth vision and text features.", "published": "2022-04-19T20:12:16Z", "authors": ["Yuying Wu", "Youshan Zhang"], "categories": ["cs.LG"], "doi": "", "_query": "hedonic regression housing", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2002.10898v2", "title": "Hedonic Seat Arrangement Problems", "abstract": "In this paper, we study a variant of hedonic games, called \\textsc{Seat Arrangement}. The model is defined by a bijection from agents with preferences for each other to vertices in a graph $G$. The utility of an agent depends on the neighbors assigned in the graph. More precisely, it is the sum over all neighbors of the preferences that the agent has towards the agent assigned to the neighbor. We first consider the price of stability and fairness for different classes of preferences. In particular, we show that there is an instance such that the price of fairness ({\\sf PoF}) is unbounded in general. Moreover, we show an upper bound $\\tilde{d}(G)$ and an almost tight lower bound $\\tilde{d}(G)-1/4$ of {\\sf PoF}, where $\\tilde{d}(G)$ is the average degree of an input graph. Then we investigate the computational complexity of problems to find certain ``good'' seat arrangements, say \\textsc{Utilitarian Arrangement}, \\textsc{Egalitarian Arrangement}, \\textsc{Stable Arrangement}, and \\textsc{Envy-free Arrangement}. We give dichotomies of computational complexity of four \\textsc{Seat Arrangement} problems from the perspective of the maximum order of connected components in an input graph. For the parameterized complexity, \\textsc{Utilitarian Arrangement} can be solved in time $n^{O(γ)}$, while it cannot be solved in time $f(γ)n^{o(γ)}$ under ETH, where $n$ is the number of agents and $γ$ is the vertex cover number of an input graph. Moreover, we show that \\textsc{Egalitarian Arrangement} and \\textsc{Envy-free Arrangement} are weakly NP-hard even on graphs of bounded vertex cover number. Finally, we prove that determining whether a stable arrangement can be obtained from a given arrangement by $k$ swaps is W[1]-hard when parameterized by $k+γ$, whereas it can be solved in time $n^{O(k)}$.", "published": "2020-02-25T14:38:14Z", "authors": ["Hans L. Bodlaender", "Tesshu Hanaka", "Lars Jaffke", "Hirotaka Ono", "Yota Otachi", "Tom C. van der Zanden"], "categories": ["cs.GT", "cs.CC", "cs.DS"], "doi": "", "_query": "hedonic regression housing", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2501.06241v1", "title": "Predicting House Rental Prices in Ghana Using Machine Learning", "abstract": "This study investigates the efficacy of machine learning models for predicting house rental prices in Ghana, addressing the need for accurate and accessible housing market information. Utilising a comprehensive dataset of rental listings, we trained and evaluated various models, including CatBoost, XGBoost, and Random Forest. CatBoost emerged as the best-performing model, achieving an $R^2$ of 0.876, demonstrating its ability to effectively capture complex relationships within the housing market. Feature importance analysis revealed that location-based features, number of bedrooms, bathrooms, and furnishing status are key drivers of rental prices. Our findings provide valuable insights for stakeholders, including real estate professionals, investors, and policymakers, while also highlighting opportunities for future research, such as incorporating temporal data and exploring regional variations.", "published": "2025-01-08T15:40:46Z", "authors": ["Philip Adzanoukpe"], "categories": ["cs.LG", "stat.AP"], "doi": "10.20944/preprints202412.1927.v1", "_query": "hedonic regression housing", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2105.06060v1", "title": "House Price Prediction using Satellite Imagery", "abstract": "In this paper we show how using satellite images can improve the accuracy of housing price estimation models. Using Los Angeles County's property assessment dataset, by transferring learning from an Inception-v3 model pretrained on ImageNet, we could achieve an improvement of ~10% in R-squared score compared to two baseline models that only use non-image features of the house.", "published": "2021-05-13T03:25:32Z", "authors": ["Sina Jandaghi Semnani", "Hoormazd Rezaei"], "categories": ["cs.LG", "stat.ML"], "doi": "", "_query": "hedonic regression housing", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2606.06694v2", "title": "The Geography of Algorithmic Judgment: LLM Intermediaries, Place Identity, and Racial Steering in Housing Search", "abstract": "Large language models (LLMs) are rapidly assuming an intermediary role in housing search through the integration of listing platforms within conversational interfaces, mediating access to information, search, and recommendations within urban settings. We expand on prior work on racial steering in LLMs by conducting a behavioral audit of seven open-weight and closed-source LLMs across four U.S. cities, testing location recommendations across three iterative prompting conditions that progressively add lifestyle preference context and reflect fair housing paired-testing methodologies. We find that steering is an emergent behavior of the model's interpretive license rather than primarily a static property. Steering results from the interaction of a user's identity, preference articulation, and the spatial logic that a model has internalized about learned representations of place, preference, and opportunity in a given city, and how different types of users relate to it. While steering was present, it was not uniform in direction or magnitude across evaluated conditions. Preference-conditioned testing often increased or reconfigured the number of models that exhibited steering behaviors relative to baseline conditions, suggesting that LLMs may interpret what the same housing preference means differently depending on the racial identity of the user. Our findings also demonstrate that the city is not a neutral testing unit for LLM evaluation in place-based sectors, and results from one local market cannot be assumed to generalize to another. Local and domain expertise will be required in the housing sector to ensure that legal and institutional commitments to fair housing are not undermined while adopting AI tools that mediate spatial access.", "published": "2026-06-04T20:17:58Z", "authors": ["Hana Samad", "Trung Lam", "Christoph Mügge-Durum", "Michael Akinwumi"], "categories": ["cs.LG", "cs.AI", "cs.CY"], "doi": "10.1609/aies.v9i4.43303", "_query": "hedonic regression housing", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2609.21478v1", "title": "Bricks or Cash? Externalities of Housing Upgrading in High-density Cities", "abstract": "We estimate housing externalities in a high-density city, exploiting the staggered rollout of Singapore's nationwide Main Upgrading Programme for public housing. Controlling for nonrandom neighborhood exposure, we find that upgrading raises treated buildings' prices by 11.5% upon completion and neighboring buildings' resale prices by about 2% within 500 meters, decaying to zero beyond. A model with distance-decaying externalities shows that in dense settings spillovers justify the distortions of in-kind provision; this advantage diminishes and reverses at lower densities. Administrative data on over 2 million residents show that upgrading disproportionately retains older incumbents, suggesting age-specific amenities as an underexplored externality channel.", "published": "2026-09-18T08:26:44Z", "authors": ["Sumit Agarwal", "Ying Deng", "Yi Fan", "Qi Gao", "Jing Li", "Lin Ma"], "categories": ["econ.GN"], "doi": "", "_query": "hedonic regression housing", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2501.00976v1", "title": "Non-obvious Manipulability in Hedonic Games with Friends Appreciation Preferences", "abstract": "In this paper, we study non-obvious manipulability (NOM), a relaxed form of strategyproofness, in the context of Hedonic Games (HGs) with Friends Appreciation (FA) preferences. In HGs, the aim is to partition agents into coalitions according to their preferences which solely depend on the coalition they are assigned to. Under FA preferences, agents consider any other agent either a friend or an enemy, preferring coalitions with more friends and, in case of ties, the ones with fewer enemies. Our goal is to design mechanisms that prevent manipulations while optimizing social welfare. Prior research established that computing a welfare maximizing (optimum) partition for FA preferences is not strategyproof, and the best-known approximation to the optimum subject to strategyproofness is linear in the number of agents. In this work, we explore NOM to improve approximation results. We first prove the existence of a NOM mechanism that always outputs the optimum; however, we also demonstrate that the computation of an optimal partition is NP-hard. To address this complexity, we focus on approximation mechanisms and propose a NOM mechanism guaranteeing a $(4+o(1))$-approximation in polynomial time. Finally, we briefly discuss NOM in the case of Enemies Aversion (EA) preferences, the counterpart of FA, where agents give priority to coalitions with fewer enemies and show that no mechanism computing the optimum can be NOM.", "published": "2025-01-01T22:59:58Z", "authors": ["Michele Flammini", "Maria Fomenko", "Giovanna Varricchio"], "categories": ["cs.GT"], "doi": "", "_query": "hedonic regression housing", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2505.01591v2", "title": "Machine Learning Fairness in House Price Prediction: A Case Study of America's Expanding Metropolises", "abstract": "As a basic human need, housing plays a key role in enhancing health, well-being, and educational outcome in society, and the housing market is a major factor for promoting quality of life and ensuring social equity. To improve the housing conditions, there has been extensive research on building Machine Learning (ML)-driven house price prediction solutions to accurately forecast the future conditions, and help inform actions and policies in the field. In spite of their success in developing high-accuracy models, there is a gap in our understanding of the extent to which various ML-driven house price prediction approaches show ethnic and/or racial bias, which in turn is essential for the responsible use of ML, and ensuring that the ML-driven solutions do not exacerbate inequity. To fill this gap, this paper develops several ML models from a combination of structural and neighborhood-level attributes, and conducts comprehensive assessments on the fairness of ML models under various definitions of privileged groups. As a result, it finds that the ML-driven house price prediction models show various levels of bias towards protected attributes (i.e., race and ethnicity in this study). Then, it investigates the performance of different bias mitigation solutions, and the experimental results show their various levels of effectiveness on different ML-driven methods. However, in general, the in-processing bias mitigation approach tends to be more effective than the pre-processing one in this problem domain. Our code is available at https://github.com/wahab1412/housing_fairness.", "published": "2025-05-02T21:26:21Z", "authors": ["Abdalwahab Almajed", "Maryam Tabar", "Peyman Najafirad"], "categories": ["cs.LG"], "doi": "", "_query": "hedonic regression housing", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2604.25452v1", "title": "Benchmarking Logistic Regression, SVM, and LightGBM Against BiLSTM with Attention for Sentiment Analysis on Indonesian Product Reviews", "abstract": "Sentiment analysis of product reviews on e-commerce platforms plays a critical role in automatically understanding customer satisfaction and providing actionable insights for sellers seeking to improve product quality. This paper presents a comprehensive benchmarking study comparing a Machine Learning (ML) approach via the PyCaret AutoML framework against a Deep Learning (DL) approach based on a Bidirectional Long Short-Term Memory (BiLSTM) architecture with an Attention mechanism for binary sentiment classification on Indonesian product reviews. The dataset comprises 19,728 samples balanced equally between positive and negative reviews. For the ML approach, three prominent algorithms were evaluated via 10-fold stratified cross-validation: Logistic Regression (LR), Support Vector Machine (SVM) with a linear kernel, and Light Gradient Boosting Machine (LightGBM). Logistic Regression achieved the best ML performance with an accuracy of 97.26\\% and an F1-score of 97.26\\%. The BiLSTM with Attention model, evaluated on 3,946 held-out test samples, achieved an accuracy of 97.24\\% and an F1-score of 97.24\\%. These comparative results demonstrate that traditional ML algorithms with proper preprocessing and feature extraction can compete closely with, and even marginally outperform, more complex sequential DL architectures on high-dimensional datasets, while simultaneously offering greater computational efficiency.", "published": "2026-04-28T10:00:42Z", "authors": ["Razin Hafid Hamdi", "Ivana Margareth Hutabarat", "Hanna Gresia Sinaga", "Luluk Muthoharoh", "Ardika Satria", "Martin C. T. Manullang"], "categories": ["cs.CL"], "doi": "", "_query": "hedonic regression housing", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2503.18332v2", "title": "Regional House Price Dynamics in Australia: Insights into Lifestyle and Mining Dynamics through PCA", "abstract": "This report applies Principal Component Analysis (PCA) to regional house price indexes to uncover dominant trends in Australia's housing market. Regions are assigned PCA-derived scores that reveal which underlying market forces are most influential in each area, enabling broad classification of local housing markets. The approach highlights where price movements tend to align across regions, even those geographically distant. The three most dominant trends are described in detail and, together with the regional scores, provide objective tools for policymakers, researchers, and real estate professionals.", "published": "2025-03-24T04:31:17Z", "authors": ["Willem Sijp"], "categories": ["econ.GN"], "doi": "", "_query": "hedonic regression housing", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2511.12629v2", "title": "Bandit Learning in Housing Markets", "abstract": "The housing market, also known as one-sided matching market, is a classic exchange economy model where each agent on the demand side initially owns an indivisible good (a house) and has a personal preference over all goods. The goal is to find a core-stable allocation that exhausts all mutually beneficial exchanges among subgroups of agents. While this model has been extensively studied in economics and computer science due to its broad applications, little attention has been paid to settings where preferences are unknown and must be learned through repeated interactions. In this paper, we propose a statistical learning model within the multi-player multi-armed bandit framework, where players (agents) learn their preferences over arms (goods) from stochastic rewards. We introduce the notion of \\emph{core regret} for each player as the market objective. We study both centralized and decentralized approaches, proving $O(\\log T / Δ^2)$ upper bounds on regret, where $T$ is the time horizon and $Δ$ is the minimum preference gap among players. For the decentralized setting, we also establish a matching lower bound, demonstrating that our algorithm is order-optimal.", "published": "2025-11-16T14:50:44Z", "authors": ["Shiyun Lin"], "categories": ["cs.GT"], "doi": "", "_query": "hedonic regression housing", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2009.00254v1", "title": "Boosting House Price Predictions using Geo-Spatial Network Embedding", "abstract": "Real estate contributes significantly to all major economies around the world. In particular, house prices have a direct impact on stakeholders, ranging from house buyers to financing companies. Thus, a plethora of techniques have been developed for real estate price prediction. Most of the existing techniques rely on different house features to build a variety of prediction models to predict house prices. Perceiving the effect of spatial dependence on house prices, some later works focused on introducing spatial regression models for improving prediction performance. However, they fail to take into account the geo-spatial context of the neighborhood amenities such as how close a house is to a train station, or a highly-ranked school, or a shopping center. Such contextual information may play a vital role in users' interests in a house and thereby has a direct influence on its price. In this paper, we propose to leverage the concept of graph neural networks to capture the geo-spatial context of the neighborhood of a house. In particular, we present a novel method, the Geo-Spatial Network Embedding (GSNE), that learns the embeddings of houses and various types of Points of Interest (POIs) in the form of multipartite networks, where the houses and the POIs are represented as attributed nodes and the relationships between them as edges. Extensive experiments with a large number of regression techniques show that the embeddings produced by our proposed GSNE technique consistently and significantly improve the performance of the house price prediction task regardless of the downstream regression model.", "published": "2020-09-01T06:17:21Z", "authors": ["Sarkar Snigdha Sarathi Das", "Mohammed Eunus Ali", "Yuan-Fang Li", "Yong-Bin Kang", "Timos Sellis"], "categories": ["cs.LG", "stat.ML"], "doi": "", "_query": "hedonic regression housing", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/1012.3039v2", "title": "London house prices are power-law distributed", "abstract": "We explore the house price distributions for the English cities of London, Manchester, Bristol, Newcastle, Birmingham and Leeds. We find Pareto (power law) behaviour in their upper tails, which is clearly distinct from lognormal and gamma distributions in the cases of London, Manchester and Newcastle. For London, the city with the lowest power, this is a striking match with that found in the wealth distribution of the super-rich. We propose an index of Housing Wealth Inequality based on the Pareto exponent and analogous to the Gini coefficient, and comment on its possible uses.", "published": "2010-12-14T14:24:45Z", "authors": ["Niall MacKay"], "categories": ["physics.soc-ph"], "doi": "", "_query": "hedonic regression housing", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2608.14461v2", "title": "Individual Rationality in Constrained Hedonic Games: Friends, Enemies, and Neutrals", "abstract": "We study constrained coalition formation in games induced by friends, enemies, and neutrals, under the two standard refinements of additively separable preferences: friend-oriented and enemy-oriented. We ask for partitions that are individually rational (IR), while additionally requiring exactly $k$ non-empty coalitions, each satisfying a prescribed lower and upper bound on its size. Although IR alone is trivial to satisfy for any hedonic game, the size constraints make it computationally intractable to decide whether a feasible partition exists. The two models tell strikingly different stories. Under enemy-oriented preferences, the problem collapses to size-constrained graph coloring, and its complexity follows accordingly. Under friend-oriented preferences, however, the picture is far more intricate, and is governed by the enmity structure rather than the friendships. The complexity is further shaped by two factors: how strict the imposed size requirements are, and whether relationships are symmetric or asymmetric, with several cases turning out tractable in the symmetric setting but intractable once asymmetry is allowed. Charting this boundary in terms of both classical and parameterized complexity, we provide a complete understanding of which properties of the friend/enemy structure are responsible for hardness.", "published": "2026-08-14T16:42:52Z", "authors": ["Šimon Schierreich", "Ildikó Schlotter"], "categories": ["cs.GT"], "doi": "", "_query": "hedonic regression housing", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/1610.08360v1", "title": "Efficiently estimating the error distribution in nonparametric regression with responses missing at random", "abstract": "This article considers nonparametric regression models with multivariate covariates and with responses missing at random. We estimate the regression function with a local polynomial smoother. The residual-based empirical distribution function that only uses complete cases, i.e. residuals that can actually be constructed from the data, is shown to be efficient in the sense of Hájek and Le Cam. In the proofs we derive, more generally, the efficient influence function for estimating an arbitrary linear functional of the error distribution; this covers the distribution function as a special case. We also show that the complete case residual-based empirical distribution function admits a functional central limit theorem. The article concludes with a small simulation study investigating the performance of the complete case residual-based empirical distribution function.", "published": "2016-10-26T14:49:30Z", "authors": ["Justin Chown", "Ursula U. Müller"], "categories": ["stat.ME"], "doi": "10.1080/10485252.2013.795222", "_query": "hedonic regression housing", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2607.19839v2", "title": "Finite-horizon quantile martingale posteriors: raw-urn laws and matrix-gain regression", "abstract": "Martingale posteriors quantify uncertainty by forward-imputing observations from one-step-ahead predictive distributions, but implementations stop after finitely many imputations. For the empirical Pólya-urn posterior of a quantile the law of the stopped state is derived. The quantile of the stopped urn measure keeps the familiar martingale tail-sum variance fraction; the deployed stochastic-approximation tracker with frozen gain $c$ does not. Its variance carries an explicit factor $G_a$ with $a=cf_0(q_τ)$, which may fall below or exceed the tail fraction, and a density-adapted gain restores calibration through a density-free inflation. Shared urn innovations yield the joint law of finitely many quantile levels. For conditional quantile regression, a smoothed martingale posterior started at the ordinary quantile-regression estimator with a full inverse-Jacobian matrix gain satisfies a process Bernstein--von Mises theorem with calibrated finite-horizon bands; scalar or diagonal gains cannot match the sandwich covariance process.", "published": "2026-07-22T07:21:59Z", "authors": ["Nam Anh Le"], "categories": ["math.ST"], "doi": "", "_query": "hedonic regression housing", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2312.14325v1", "title": "Exploring Distributions of House Prices and House Price Indices", "abstract": "We use house prices (HP) and house price indices (HPI) as a proxy to income distribution. Specifically, we analyze sale prices in the 1970-2010 window of over 116,000 single-family homes in Hamilton County, Ohio, including Cincinnati metro area of about 2.2 million people. We also analyze HPI, published by Federal Housing Finance Agency (FHFA), for nearly 18,000 US ZIP codes that cover a period of over 40 years starting in 1980's. If HP can be viewed as a first derivative of income, HPI can be viewed as its second derivative. We use generalized beta (GB) family of functions to fit distributions of HP and HPI since GB naturally arises from the models of economic exchange described by stochastic differential equations. Our main finding is that HP and multi-year HPI exhibit a negative Dragon King (nDK) behavior, wherein power-law distribution tail gives way to an abrupt decay to a finite upper limit value, which is similar to our recent findings for realized volatility of S\\&P500 index in the US stock market. This type of tail behavior is best fitted by a modified GB (mGB) distribution. Tails of single-year HPI appear to show more consistency with power-law behavior, which is better described by a GB Prime (GB2) distribution. We supplement full distribution fits by mGB and GB2 with direct linear fits (LF) of the tails. Our numerical procedure relies on evaluation of confidence intervals (CI) of the fits, as well as of p-values that give the likelihood that data come from the fitted distributions.", "published": "2023-12-21T22:38:24Z", "authors": ["Jiong Liu", "Hamed Farahani", "R. A. Serota"], "categories": ["econ.EM", "econ.TH"], "doi": "10.3390/economies12020047", "_query": "hedonic regression housing", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2001.11585v1", "title": "Housing Search in the Age of Big Data: Smarter Cities or the Same Old Blind Spots?", "abstract": "Housing scholars stress the importance of the information environment in shaping housing search behavior and outcomes. Rental listings have increasingly moved online over the past two decades and, in turn, online platforms like Craigslist are now central to the search process. Do these technology platforms serve as information equalizers or do they reflect traditional information inequalities that correlate with neighborhood sociodemographics? We synthesize and extend analyses of millions of US Craigslist rental listings and find they supply significantly different volumes, quality, and types of information in different communities. Technology platforms have the potential to broaden, diversify, and equalize housing search information, but they rely on landlord behavior and, in turn, likely will not reach this potential without a significant redesign or policy intervention. Smart cities advocates hoping to build better cities through technology must critically interrogate technology platforms and big data for systematic biases.", "published": "2020-01-30T22:10:29Z", "authors": ["Geoff Boeing", "Max Besbris", "Ariela Schachter", "John Kuk"], "categories": ["stat.AP", "cs.CY", "econ.GN"], "doi": "10.1080/10511482.2019.1684336", "_query": "hedonic regression housing", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2605.19030v1", "title": "Nash Welfare in Additively Separable Hedonic Games", "abstract": "Additively separable hedonic games (ASHGs) are a prominent model of coalition formation where agents' preferences are derived from their individual valuations of peers. While social welfare maximization in ASHGs has traditionally focused mostly on utilitarian welfare, Nash welfare -- a well-established metric in economics which balances fairness with efficiency and offers scale invariance -- has been entirely overlooked. In this paper, we initiate the study of Nash welfare in ASHGs. We point out desirable properties fulfilled by partitions with high Nash welfare. This includes guaranteed contractual Nash stability in symmetric games, even for any approximation of Nash welfare. This is particularly appealing since, as for other welfare notions, Nash welfare turns out to be NP-hard to maximize, even for the ASHG subclass of symmetric aversion to enemies games (AEGs). A main focus of our study is on approximation algorithms for the Nash welfare objective. We present packing-based algorithms with approximation ratios for well-established subclasses of ASHGs: $n-1$ for AEGs and $2n$ for appreciation of friends games. This is complemented by a strict inapproximability result showing it is NP-hard to approximate Nash welfare within a factor of $1.0000759$ in general ASHGs. Further, we investigate the restricted settings with an upper bound on the coalition size or number of coalitions, and draw the boundary between the cases admitting efficient algorithms and those yielding NP-hardness: bounding the allowed size or number of coalitions by $2$ admits polynomial-time solvability, whereas bounds of $3$ or more yield NP-hardness or unbounded inapproximability.", "published": "2026-05-18T18:55:07Z", "authors": ["Marta Pagano", "Alexander Schlenga"], "categories": ["cs.GT", "cs.MA", "econ.TH"], "doi": "", "_query": "hedonic regression housing", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2204.06967v1", "title": "JUE Insight: The (Non-)Effect of Opportunity Zones on Housing Prices", "abstract": "Will the Opportunity Zones (OZ) program, America's largest new place-based policy in decades, generate neighborhood change? We compare single-family housing price growth in OZs with price growth in areas that were eligible but not included in the program. We also compare OZs to their nearest geographic neighbors. Our most credible estimates rule out price impacts greater than 0.5 percentage points with 95% confidence, suggesting that, so far, home buyers don't believe that this subsidy will generate major neighborhood change. OZ status reduces prices in areas with little employment, perhaps because buyers think that subsidizing new investment will increase housing supply. Mixed evidence suggests that OZs may have increased residential permitting.", "published": "2022-04-14T13:48:40Z", "authors": ["Jiafeng Chen", "Edward Glaeser", "David Wessel"], "categories": ["econ.GN"], "doi": "10.1016/j.jue.2022.103451", "_query": "hedonic regression housing", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/1808.01278v2", "title": "Coordinate Methods for Accelerating $\\ell_\\infty$ Regression and Faster Approximate Maximum Flow", "abstract": "We provide faster algorithms for approximately solving $\\ell_{\\infty}$ regression, a fundamental problem prevalent in both combinatorial and continuous optimization. In particular, we provide accelerated coordinate descent methods capable of provably exploiting dynamic measures of coordinate smoothness, and apply them to $\\ell_\\infty$ regression over a box to give algorithms which converge in $k$ iterations at a $O(1/k)$ rate. Our algorithms can be viewed as an alternative approach to the recent breakthrough result of Sherman [She17] which achieves a similar runtime improvement over classic algorithmic approaches, i.e. smoothing and gradient descent, which either converge at a $O(1/\\sqrt{k})$ rate or have running times with a worse dependence on problem parameters. Our runtimes match those of [She17] across a broad range of parameters and achieve improvement in certain structured cases. We demonstrate the efficacy of our result by providing faster algorithms for the well-studied maximum flow problem. Directly leveraging our accelerated $\\ell_\\infty$ regression algorithms imply a $\\tilde{O}\\left(m + \\sqrt{mn}/ε\\right)$ runtime to compute an $ε$-approximate maximum flow for an undirected graph with $m$ edges and $n$ vertices, generically improving upon the previous best known runtime of $\\tilde{O}\\left(m/ε\\right)$ in [She17] whenever the graph is slightly dense. We further design an algorithm adapted to the structure of the regression problem induced by maximum flow obtaining a runtime of $\\tilde{O}\\left(m + \\max(n, \\sqrt{ns})/ε\\right)$, where $s$ is the squared $\\ell_2$ norm of the congestion of any optimal flow. Moreover, we show how to leverage this result to achieve improved exact algorithms for maximum flow on a variety of unit capacity graphs. We hope that our work serves as an important step towards achieving even faster maximum flow algorithms.", "published": "2018-08-03T17:58:31Z", "authors": ["Aaron Sidford", "Kevin Tian"], "categories": ["cs.DS", "math.OC"], "doi": "", "_query": "hedonic regression housing", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/1712.03358v2", "title": "Stochastic Restricted Biased Estimators in misspecified regression model with incomplete prior information", "abstract": "In this article, the analysis of misspecification was extended to the recently introduced stochastic restricted biased estimators when multicollinearity exists among the explanatory variables. The Stochastic Restricted Ridge Estimator (SRRE), Stochastic Restricted Almost Unbiased Ridge Estimator (SRAURE), Stochastic Restricted Liu Estimator (SRLE), Stochastic Restricted Almost Unbiased Liu Estimator (SRAULE), Stochastic Restricted Principal Component Regression Estimator (SRPCR), Stochastic Restricted r-k class estimator (SRrk) and Stochastic Restricted r-d class estimator (SRrd) were examined in the misspecified regression model due to missing relevant explanatory variables when incomplete prior information of the regression coefficients is available. Further, the superiority conditions between estimators and their respective predictors were obtained in the mean square error matrix (MSEM) sense. Finally, a numerical example and a Monte Carlo simulation study were used to illustrate the theoretical findings.", "published": "2017-12-09T08:59:00Z", "authors": ["Manickavasagar Kayanan", "Pushpakanthie Wijekoon"], "categories": ["math.ST", "stat.ME"], "doi": "10.1155/2018/1452181", "_query": "hedonic regression housing", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2012.04336v1", "title": "Interpretable deep learning regression for breast density estimation on MRI", "abstract": "Breast density, which is the ratio between fibroglandular tissue (FGT) and total breast volume, can be assessed qualitatively by radiologists and quantitatively by computer algorithms. These algorithms often rely on segmentation of breast and FGT volume. In this study, we propose a method to directly assess breast density on MRI, and provide interpretations of these assessments. We assessed breast density in 506 patients with breast cancer using a regression convolutional neural network (CNN). The input for the CNN were slices of breast MRI of 128 x 128 voxels, and the output was a continuous density value between 0 (fatty breast) and 1 (dense breast). We used 350 patients to train the CNN, 75 for validation, and 81 for independent testing. We investigated why the CNN came to its predicted density using Deep SHapley Additive exPlanations (SHAP). The density predicted by the CNN on the testing set was significantly correlated with the ground truth densities (N = 81 patients, Spearman's rho = 0.86, P < 0.001). When inspecting what the CNN based its predictions on, we found that voxels in FGT commonly had positive SHAP-values, voxels in fatty tissue commonly had negative SHAP-values, and voxels in non-breast tissue commonly had SHAP-values near zero. This means that the prediction of density is based on the structures we expect it to be based on, namely FGT and fatty tissue. To conclude, we presented an interpretable deep learning regression method for breast density estimation on MRI with promising results.", "published": "2020-12-08T10:23:49Z", "authors": ["Bas H. M. van der Velden", "Max A. A. Ragusi", "Markus H. A. Janse", "Claudette E. Loo", "Kenneth G. A. Gilhuijs"], "categories": ["eess.IV", "cs.CV"], "doi": "10.1117/12.2549003", "_query": "hedonic regression housing", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/1810.01949v1", "title": "Semiparametric Regression using Variational Approximations", "abstract": "Semiparametric regression offers a flexible framework for modeling non-linear relationships between a response and covariates. A prime example are generalized additive models where splines (say) are used to approximate non-linear functional components in conjunction with a quadratic penalty to control for overfitting. Estimation and inference are then generally performed based on the penalized likelihood, or under a mixed model framework. The penalized likelihood framework is fast but potentially unstable, and choosing the smoothing parameters needs to be done externally using cross-validation, for instance. The mixed model framework tends to be more stable and offers a natural way for choosing the smoothing parameters, but for non-normal responses involves an intractable integral. In this article, we introduce a new framework for semiparametric regression based on variational approximations. The approach possesses the stability and natural inference tools of the mixed model framework, while achieving computation times comparable to using penalized likelihood. Focusing on generalized additive models, we derive fully tractable variational likelihoods for some common response types. We present several features of the variational approximation framework for inference, including a variational information matrix for inference on parametric components, and a closed-form update for estimating the smoothing parameter. We demonstrate the consistency of the variational approximation estimates, and an asymptotic normality result for the parametric component of the model. Simulation studies show the variational approximation framework performs similarly to and sometimes better than currently available software for fitting generalized additive models.", "published": "2018-10-03T20:30:12Z", "authors": ["Francis K. C. Hui", "Chong You", "Han Lin Shang", "Samuel Müller"], "categories": ["math.ST"], "doi": "10.1080/01621459.2018.1518235", "_query": "hedonic regression housing", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2502.13703v1", "title": "Parameterized Complexity of Hedonic Games with Enemy-Oriented Preferences", "abstract": "Hedonic games model settings in which a set of agents have to be partitioned into groups which we call coalitions. In the enemy aversion model, each agent has friends and enemies, and an agent prefers to be in a coalition with as few enemies as possible and, subject to that, as many friends as possible. A partition should be stable, i.e., no subset of agents prefer to be together rather than being in their assigned coalition under the partition. We look at two stability concepts: core stability and strict core stability. This yields several algorithmic problems: determining whether a (strictly) core stable partition exists, finding such a partition, and checking whether a given partition is (strictly) core stable. Several of these problems have been shown to be NP-complete, or even beyond NP. This motivates the study of parameterized complexity. We conduct a thorough computational study using several parameters: treewidth, number of friends, number of enemies, partition size, and coalition size. We give polynomial algorithms for restricted graph classes as well as FPT algorithms with respect to the number of friends an agent may have and the treewidth of the graph representing the friendship or enemy relations. We show W[1]-hardness or para-NP-hardness with respect to the other parameters. We conclude this paper with results in the setting in which agents can have neutral relations with each other, including hardness-results for very restricted cases.", "published": "2025-02-19T13:19:56Z", "authors": ["Martin Durand", "Laurin Erlacher", "Johanne Müller Vistisen", "Sofia Simola"], "categories": ["cs.GT", "cs.MA"], "doi": "", "_query": "hedonic regression housing", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2307.13232v1", "title": "Changes in Risk Appreciation, and Short Memory of House Buyers When the Market is Hot, a Case Study of Christchurch, New Zealand", "abstract": "In this paper house prices in Christchurch are analyzed over three distinct periods of time: post-2011 earthquake, pre-COVID-19 lockdown, and post-COVID-19 lockdown using the well-established hedonic price model. Results show that buyers, in periods that are temporally distant from the 2011 Christchurch earthquake, value the risk of potential earthquake damage to a property differently from buyers soon after the earthquake. We find that there are observable shifts in hedonic prices across the different time periods, specifically for section size pre and post COVID-19 lockdown.", "published": "2023-07-25T03:46:38Z", "authors": ["Emil Mendoza", "Fabian Dunker", "Marco Reale"], "categories": ["q-fin.GN", "stat.AP"], "doi": "", "_query": "hedonic regression housing", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/1406.6100v1", "title": "Probabilistic flows of inhabitants in urban areas and self-organization in housing markets", "abstract": "We propose a simple probabilistic model to explain the spatial structure of the rent distribution of housing market in city of Sapporo. Here we modify the mathematical model proposed by Gauvin et. al. Especially, we consider the competition between two distances, namely, the distance between house and center, and the distance between house and office. Computer simulations are carried out to reveal the self-organized spatial structure appearing in the rent distribution. We also compare the resulting distribution with empirical rent distribution in Sapporo as an example of cities designated by ordinance. We find that the lowest ranking agents (from the viewpoint of the lowest `willing to pay') are swept away from relatively attractive regions and make several their own `communities' at low offering price locations in the city.", "published": "2014-06-23T22:11:26Z", "authors": ["Takao Hishikawa", "Jun-ichi Inoue"], "categories": ["physics.soc-ph", "q-fin.GN"], "doi": "", "_query": "hedonic regression housing", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2410.02978v1", "title": "An explainable approach to detect case law on housing and eviction issues within the HUDOC database", "abstract": "Case law is instrumental in shaping our understanding of human rights, including the right to adequate housing. The HUDOC database provides access to the textual content of case law from the European Court of Human Rights (ECtHR), along with some metadata. While this metadata includes valuable information, such as the application number and the articles addressed in a case, it often lacks detailed substantive insights, such as the specific issues a case covers. This underscores the need for detailed analysis to extract such information. However, given the size of the database - containing over 40,000 cases - an automated solution is essential. In this study, we focus on the right to adequate housing and aim to build models to detect cases related to housing and eviction issues. Our experiments show that the resulting models not only provide performance comparable to more sophisticated approaches but are also interpretable, offering explanations for their decisions by highlighting the most influential words. The application of these models led to the identification of new cases that were initially overlooked during data collection. This suggests that NLP approaches can be effectively applied to categorise case law based on the specific issues they address.", "published": "2024-10-03T20:39:51Z", "authors": ["Mohammad Mohammadi", "Martijn Wieling", "Michel Vols"], "categories": ["cs.LG", "cs.AI"], "doi": "", "_query": "hedonic regression housing", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/1605.05496v1", "title": "The Design of a Drag-Free CubeSat and the Housing for its Gravitational Reference Sensor", "abstract": "A Drag-Free CubeSat mission has been proposed to demonstrate the feasibility of a Gravitational Reference Sensor (GRS) with an optical readout for a 3 units (3U) spacecraft. A purely drag-free object is defined by the absence of all external forces other than gravity, which are shielded by the spacecraft. In a real case, the TM will still be affected by disturbances. Several of them are passively reduced by the design of the TM housing. This system is a thick-walled aluminium box that holds the shadow sensors and shields the TM. The housing has an effect on the mechanical, thermal and magnetic environment around the TM. All of them have been analysed. The mechanical vibrations have to fit the launch environment and the modes have to be outside of the measurement range (0.0001 - 1 Hz). The magnetic field has to be reduced by a 0.01 factor. The temperature difference between internal opposing surfaces, determining pressure on the TM, has to be below 10^-3(1 mHz/f)1/3 K Hz^-1/2. The housing, together with the TM, the sensors and the UV LEDs for charging control, constitutes the GRS, which would then fit into a 1U. The other 2Us are occupied by the caging mechanism that constraints the TM during launch, the thrusters, the Attitude Determination And Control System (ADACS) and the electronics. The Drag-Free CubeSat will be the result of the combined efforts of Stanford, University of Florida, KACST and NASA and will be the first drag-free mission with an optical readout and the first GRS designed within the limits of a 3U small satellite. In the first section, this paper briefly updates on the main characteristics and systems of the project. Particular emphasis is then given to the recently designed housing, its expected performance and the open issues.", "published": "2016-05-18T09:41:18Z", "authors": ["Carlo Zanoni", "Abdul Alfauwaz", "Ahmad Aljadaan", "Salman Althubiti", "Karthik Balakrishnan", "Sasha Buchman", "Robert L. Byer", "John W. Conklin", "Grant D. Cutler", "Dan DeBra", "Eric Hultgren", "John A. Lipa", "Shailendhar Saraf", "Andreas Zoellner"], "categories": ["astro-ph.IM", "physics.ins-det"], "doi": "", "_query": "hedonic regression housing", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2606.02795v1", "title": "Recovering Direct Price Effects of Environmental Amenities in Housing Markets: Regression and Causal Machine Learning Model Assessment with Empirical Monte Carlo Simulation", "abstract": "Hedonic price models are widely used to assess how environmental amenities affect property values, yet methodological guidance for estimating direct price effects remains sparse. We conduct an empirical Monte Carlo simulation to evaluate the performance of traditional and causal machine learning approaches for estimating the direct unmediated price effect of spatially delineated amenities on treated properties (DUET), a conservative lower-bound approximation for welfare changes with direct applications to benefit-cost analysis. Where previous simulations rely on parametric assumptions, we retain the actual data-generating process underlying over 1 million property transactions from upstate New York (1990--2024). By randomly assigning \"treatment locations\" across iterations we establish a \"ground truth\" that allows us to precisely measure estimation error. Our results demonstrate that generalized difference-in-differences (DID) regression consistently outperforms baseline DID and two-way fixed effects models across all scenarios. Causal Machine Learning (CML) methods, particularly causal forest DID, achieve comparable performance to generalized DID in most scenarios. In larger samples (above 3,000 treated) increasingly common in contemporary hedonic studies, CML approaches offer substantial advantages when properly specified. Based on empirical simulation results, we provide a set of method-specific best practice recommendations for both traditional regression and causal machine learning approaches.", "published": "2026-06-01T18:59:44Z", "authors": ["Zhenshan Chen", "Klaus Moeltner", "Matthew Mair"], "categories": ["econ.EM", "stat.ML"], "doi": "", "_query": "hedonic regression housing", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2503.08074v1", "title": "Hedonic Adaptation in the Age of AI: A Perspective on Diminishing Satisfaction Returns in Technology Adoption", "abstract": "The fast paced progress of artificial intelligence (AI) through scaling laws connecting rising computational power with improving performance has created tremendous technological breakthroughs. These breakthroughs do not translate to corresponding user satisfaction improvements, resulting in a general mismatch. This research suggests that hedonic adaptation the psychological process by which people revert to a baseline state of happiness after drastic change provides a suitable model for understanding this phenomenon. We argue that user satisfaction with AI follows a logarithmic path, thus creating a longterm \"satisfaction gap\" as people rapidly get used to new capabilities as expectations. This process occurs through discrete stages: initial excitement, declining returns, stabilization, and sporadic resurgence, depending on adaptation rate and capability introduction. These processes have far reaching implications for AI research, user experience design, marketing, and ethics, suggesting a paradigm shift from sole technical scaling to methods that sustain perceived value in the midst of human adaptation. This perspective reframes AI development, necessitating practices that align technological progress with people's subjective experience.", "published": "2025-03-11T06:08:36Z", "authors": ["Venkat Ram Reddy Ganuthula", "Krishna Kumar Balaraman", "Nimish Vohra"], "categories": ["econ.GN"], "doi": "", "_query": "hedonic regression housing", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2304.03638v1", "title": "Compressed Regression over Adaptive Networks", "abstract": "In this work we derive the performance achievable by a network of distributed agents that solve, adaptively and in the presence of communication constraints, a regression problem. Agents employ the recently proposed ACTC (adapt-compress-then-combine) diffusion strategy, where the signals exchanged locally by neighboring agents are encoded with randomized differential compression operators. We provide a detailed characterization of the mean-square estimation error, which is shown to comprise a term related to the error that agents would achieve without communication constraints, plus a term arising from compression. The analysis reveals quantitative relationships between the compression loss and fundamental attributes of the distributed regression problem, in particular, the stochastic approximation error caused by the gradient noise and the network topology (through the Perron eigenvector). We show that knowledge of such relationships is critical to allocate optimally the communication resources across the agents, taking into account their individual attributes, such as the quality of their data or their degree of centrality in the network topology. We devise an optimized allocation strategy where the parameters necessary for the optimization can be learned online by the agents. Illustrative examples show that a significant performance improvement, as compared to a blind (i.e., uniform) resource allocation, can be achieved by optimizing the allocation by means of the provided mean-square-error formulas.", "published": "2023-04-07T13:41:08Z", "authors": ["Marco Carpentiero", "Vincenzo Matta", "Ali H. Sayed"], "categories": ["cs.LG", "cs.MA", "eess.SP", "math.OC", "stat.ML"], "doi": "10.1109/TSIPN.2024.3464350", "_query": "hedonic regression housing", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2603.26525v1", "title": "Who burdens the welfare state? Migration and ageing in housing, education, and healthcare demand", "abstract": "Political discourse attributes the pressure on European welfare systems to foreign nationals. Yet projections of service demand rarely disaggregate service demand by citizenship status. We develop a structural demographic model and project healthcare, education, and housing demand in Austria through 2050, disaggregated by citizenship status and regions across migration scenarios. We find that migration, ageing, and fertility shape each sector differently. In healthcare, the ageing of Austrian nationals contributes 4.7 times more to demand growth than immigration, with the most acute pressures in rural, low-migration regions. In housing, migration accounts for the entire net growth in demand, concentrated in metropolitan hubs. In education, aggregate demand contracts regardless of migration assumptions, whereas future needs are driven more by the births of foreigners in Austria than by new arrivals. Foreign nationals consume services in proportion to their demographic weight, with deviations explained by age structure rather than over-utilisation. These results show that the drivers of service demand are sector-specific: migration restrictions could ease housing pressure, but would not address ageing-driven healthcare demand and may accelerate contraction in the education system.", "published": "2026-03-27T15:38:23Z", "authors": ["Guillermo Prieto-Viertel", "Carsten Källner", "Elma Dervic", "Ola Ali", "Andrea Vismara", "Rafael Prieto-Curiel"], "categories": ["physics.soc-ph", "econ.GN"], "doi": "", "_query": "hedonic regression housing", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2407.04776v2", "title": "Quantifying Privacy Risks of Public Statistics to Residents of Subsidized Housing", "abstract": "As the U.S. Census Bureau implements its controversial new disclosure avoidance system, researchers and policymakers debate the necessity of new privacy protections for public statistics. With experiments on both public statistics and synthetic microdata, we explore a particular privacy concern: respondents in subsidized housing may deliberately not mention unauthorized children and other household members for fear of being discovered and evicted. By combining public statistics from the Decennial Census and the Department of Housing and Urban Development, we demonstrate a simple, inexpensive reconstruction attack that could identify subsidized households living in violation of occupancy guidelines in 2010. Experiments on synthetic data suggest that a random swapping mechanism similar to the Census Bureau's 2010 disclosure avoidance measures does not significantly reduce the precision of this attack, while a differentially private mechanism similar to the 2020 disclosure avoidance system does. Our results provide a valuable example for policymakers seeking trustworthy public statistics.", "published": "2024-07-05T18:00:02Z", "authors": ["Ryan Steed", "Diana Qing", "Zhiwei Steven Wu"], "categories": ["cs.CY"], "doi": "10.1162/99608f92.39d8bfa4", "_query": "hedonic regression housing", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2309.11354v2", "title": "Self-supervised learning unveils change in urban housing from street-level images", "abstract": "Cities around the world face a critical shortage of affordable and decent housing. Despite its critical importance for policy, our ability to effectively monitor and track progress in urban housing is limited. Deep learning-based computer vision methods applied to street-level images have been successful in the measurement of socioeconomic and environmental inequalities but did not fully utilize temporal images to track urban change as time-varying labels are often unavailable. We used self-supervised methods to measure change in London using 15 million street images taken between 2008 and 2021. Our novel adaptation of Barlow Twins, Street2Vec, embeds urban structure while being invariant to seasonal and daily changes without manual annotations. It outperformed generic embeddings, successfully identified point-level change in London's housing supply from street-level images, and distinguished between major and minor change. This capability can provide timely information for urban planning and policy decisions toward more liveable, equitable, and sustainable cities.", "published": "2023-09-20T14:35:23Z", "authors": ["Steven Stalder", "Michele Volpi", "Nicolas Büttner", "Stephen Law", "Kenneth Harttgen", "Esra Suel"], "categories": ["cs.CV", "cs.LG"], "doi": "10.1016/j.compenvurbsys.2024.102156", "_query": "hedonic regression housing", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2511.22027v1", "title": "Local unanimity in Shapley-Scarf housing markets", "abstract": "In the housing market model introduced by Shapley and Scarf (1974), we propose a new axiom, local unanimity, that extends the unanimity condition widely used in social choice theory. It applies the unanimity condition to any subset of agents in the model who unanimously agree on the best exchange of their endowments. Building on this axiom, we provide several concise characterizations of the Top Trading Cycles (TTC) mechanism under both strict and weak preference domains.", "published": "2025-11-27T02:19:06Z", "authors": ["Xinquan Hu", "Jun Zhang"], "categories": ["econ.TH"], "doi": "", "_query": "hedonic regression housing", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2302.10791v1", "title": "Estimating Bibliometric Links using Google Scholar: A Semi-Systematic Literature Mapping of Migration and Housing", "abstract": "As the number of empirical studies increases unprecedentedly in line with expansions in higher education, theoretical developments in population studies suffer due to a discoverability crisis of related work. The systematic use of previous research is common in the medical sciences through various types of structured reviews. However, these are less common in the social sciences, despite their potential, especially in cross-disciplinary fields such as migration. We use Google Scholar to examine the niche of housing research within migration studies through a broad range of documents. The contribution of this meta-analysis is threefold. Firstly, we illustrate the association of keywords across the corpus of literature related to migration and housing and map the growth of migration literature since the 1960s. Secondly, we highlight key bridging documents using network measures. Finally, we estimate the distance in reading time between documents. Our findings suggest that the corpus of previous work at the intersection of migration and housing consists of many additive case studies, indicating a gap in integrative approaches replicable across a growing knowledge landscape.", "published": "2023-02-20T17:30:01Z", "authors": ["Boyana Buyuklieva", "Juste Raimbault"], "categories": ["cs.DL"], "doi": "", "_query": "hedonic regression housing", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2005.03906v2", "title": "Dynamic Shrinkage Priors for Large Time-varying Parameter Regressions using Scalable Markov Chain Monte Carlo Methods", "abstract": "Time-varying parameter (TVP) regression models can involve a huge number of coefficients. Careful prior elicitation is required to yield sensible posterior and predictive inferences. In addition, the computational demands of Markov Chain Monte Carlo (MCMC) methods mean their use is limited to the case where the number of predictors is not too large. In light of these two concerns, this paper proposes a new dynamic shrinkage prior which reflects the empirical regularity that TVPs are typically sparse (i.e. time variation may occur only episodically and only for some of the coefficients). A scalable MCMC algorithm is developed which is capable of handling very high dimensional TVP regressions or TVP Vector Autoregressions. In an exercise using artificial data we demonstrate the accuracy and computational efficiency of our methods. In an application involving the term structure of interest rates in the eurozone, we find our dynamic shrinkage prior to effectively pick out small amounts of parameter change and our methods to forecast well.", "published": "2020-05-08T08:40:09Z", "authors": ["Niko Hauzenberger", "Florian Huber", "Gary Koop"], "categories": ["econ.EM", "stat.ME"], "doi": "", "_query": "hedonic regression housing", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/1606.08984v1", "title": "Optimal Consumption, Investment and Housing with Means-tested Public Pension in Retirement", "abstract": "In this paper, we develop an expected utility model for the retirement behavior in the decumulation phase of Australian retirees with sequential family status subject to consumption, housing, investment, bequest and government provided means-tested Age Pension. We account for mortality risk and risky investment assets, and introduce a health proxy to capture the decreasing level of consumption for older retirees. Then we find optimal housing at retirement, and optimal consumption and optimal risky asset allocation depending on age and wealth. The model is solved numerically as a stochastic control problem, and is calibrated using the maximum likelihood method on empirical data of consumption and housing from the Australian Bureau of Statistics 2009-2010 Survey. The model fits the characteristics of the data well to explain the behavior of Australian retirees. The key findings are the following: First, the optimal policy is highly sensitive to means-tested Age Pension early in retirement but this sensitivity fades with age. Secondly, the allocation to risky assets shows a complex relationship with the means-tested Age Pension that disappears once minimum withdrawal rules are enforced. As a general rule, when wealth decreases the proportion allocated to risky assets increases, due to the Age Pension working as a buffer against investment losses. Finally, couples can be more aggressive with risky allocations due to their longer life expectancy compared with singles.", "published": "2016-06-29T07:32:28Z", "authors": ["Johan G. Andreasson", "Pavel V. Shevchenko", "Alex Novikov"], "categories": ["econ.GN", "q-fin.RM"], "doi": "", "_query": "hedonic regression housing", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/1801.01003v2", "title": "An Information Analysis on Modeling Interaction Effects in Logistic Regression", "abstract": "The Akaike information criterion (AIC) is commonly used to select a logistic regression model for optimal prediction of a binary response by a specified family of models. It however lacks a convincing method of prescribing a proper family of models using the desired predictors and their interaction effects. For an alternative approach to model selection, we propose a direct selection scheme which first identifies the indispensable regressors as main-effect predictors, then examines significant interaction effects between the selected predictors such that a logistic model is constructed. The two-step selection scheme is formulated by testing for valid information identity between the response and the predictors, from which the most parsimonious logistic model is derived from the least set of indispensable predictors and interaction effects. As a byproduct, the minimum AIC model is easily found in a neighborhood of the selected model. The scheme is employed to yield the logistic model for predicting the acquisition of professional licenses in a survey of employed youth workers.", "published": "2018-01-03T13:56:07Z", "authors": ["Jiun-Wei Liou", "Michelle Liou", "Philip E. Cheng", "Chin-Chiuan Lin"], "categories": ["stat.ME"], "doi": "", "_query": "hedonic regression housing", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/1411.5497v1", "title": "Time-warped growth processes, with applications to the modeling of boom-bust cycles in house prices", "abstract": "House price increases have been steady over much of the last 40 years, but there have been occasional declines, most notably in the recent housing bust that started around 2007, on the heels of the preceding housing bubble. We introduce a novel growth model that is motivated by time-warping models in functional data analysis and includes a nonmonotone time-warping component that allows the inclusion and description of boom-bust cycles and facilitates insights into the dynamics of asset bubbles. The underlying idea is to model longitudinal growth trajectories for house prices and other phenomena, where temporal setbacks and deflation may be encountered, by decomposing such trajectories into two components. A first component corresponds to underlying steady growth driven by inflation that anchors the observed trajectories on a simple first order linear differential equation, while a second boom-bust component is implemented as time warping. Time warping is a commonly encountered phenomenon and reflects random variation along the time axis. Our approach to time warping is more general than previous approaches by admitting the inclusion of nonmonotone warping functions. The anchoring of the trajectories on an underlying linear dynamic system also makes the time-warping component identifiable and enables straightforward estimation procedures for all model components. The application to the dynamics of housing prices as observed for 19 metropolitan areas in the U.S. from December 1998 to July 2013 reveals that the time setbacks corresponding to nonmonotone time warping vary substantially across markets and we find indications that they are related to market-specific growth rates.", "published": "2014-11-20T10:42:29Z", "authors": ["Jie Peng", "Debashis Paul", "Hans-Georg Müller"], "categories": ["stat.AP"], "doi": "10.1214/14-AOAS740", "_query": "hedonic regression housing", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/1401.3084v1", "title": "A comparison of Bayesian and frequentist interval estimators in regression that utilize uncertain prior information", "abstract": "Consider a linear regression model with regression parameter beta and normally distributed errors. Suppose that the parameter of interest is theta = a^T beta where a is a specified vector. Define the parameter tau = c^T beta - t where c and t are specified and a and c are linearly independent. Also suppose that we have uncertain prior information that tau = 0. Kabaila and Giri, 2009, JSPI, describe a new frequentist 1-alpha confidence interval for theta that utilizes this uncertain prior information. We compare this confidence interval with Bayesian 1-alpha equi-tailed and shortest credible intervals for theta that result from a prior density for tau that is a mixture of a rectangular \"slab\" and a Dirac delta function \"spike\", combined with noninformative prior densities for the other parameters of the model. We show that these frequentist and Bayesian interval estimators depend on the data in very different ways. We also consider some close variants of this prior distribution that lead to Bayesian and frequentist interval estimators with greater similarity. Nonetheless, as we show, substantial differences between these interval estimators remain.", "published": "2014-01-14T06:47:59Z", "authors": ["Paul Kabaila", "Gayan Dharmarathne"], "categories": ["math.ST", "stat.ME"], "doi": "", "_query": "hedonic regression housing", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2506.09539v1", "title": "Disentangling Spatial and Structural Drivers of Housing Prices through Bayesian Networks: A Case Study of Madrid, Barcelona, and Valencia", "abstract": "Understanding how housing prices respond to spatial accessibility, structural attributes, and typological distinctions is central to contemporary urban research and policy. In cities marked by affordability stress and market segmentation, models that offer both predictive capability and interpretive clarity are increasingly needed. This study applies discrete Bayesian networks to model residential price formation across Madrid, Barcelona, and Valencia using over 180,000 geo-referenced housing listings. The resulting probabilistic structures reveal distinct city-specific logics. Madrid exhibits amenity-driven stratification, Barcelona emphasizes typology and classification, while Valencia is shaped by spatial and structural fundamentals. By enabling joint inference, scenario simulation, and sensitivity analysis within a transparent framework, the approach advances housing analytics toward models that are not only accurate but actionable, interpretable, and aligned with the demands of equitable urban governance.", "published": "2025-06-11T09:15:02Z", "authors": ["Alvaro Garcia Murga", "Manuele Leonelli"], "categories": ["stat.AP"], "doi": "", "_query": "hedonic regression housing", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2506.00765v2", "title": "HouseTS: A Large-Scale, Multimodal Spatiotemporal U.S. Housing Dataset and Benchmark", "abstract": "Accurate long-horizon house-price forecasting requires benchmarks that capture temporal dynamics together with time-varying local context. However, existing public resources remain fragmented: many datasets have limited spatial coverage, temporal depth, or multimodal alignment; the robustness of modern deep forecasters and time-series foundation models on housing data is not well characterized; and aerial imagery is rarely leveraged in a time-aware and interpretable manner at scale. To bridge these gaps, we present HouseTS (House Time Series), a multimodal spatiotemporal dataset for ZIP-code-level housing-market analysis, covering monthly signals from March 2012 to December 2023 across over 6,000 ZIP codes in 30 major U.S. metropolitan areas. HouseTS aligns monthly housing-market indicators, monthly POI dynamics, and annual census-based socioeconomic variables under a unified schema, and includes time-stamped annual aerial imagery. Building on HouseTS, we define standardized long-horizon forecasting tasks for univariate and multivariate prediction and benchmark 16 model families spanning statistical methods, classical machine learning, deep neural networks, and time-series foundation models in both zero-shot and fine-tuned modes. We also provide image-derived textual change annotations from multi-year aerial image sequences via a vision--language pipeline with LLM-as-judge and human verification to support scalable interpretability analyses. HouseTS is available on Kaggle, with code and documentation on GitHub.", "published": "2025-06-01T00:52:41Z", "authors": ["Shengkun Wang", "Yanshen Sun", "Fanglan Chen", "Linhan Wang", "Naren Ramakrishnan", "Chang-Tien Lu", "Yinlin Chen"], "categories": ["cs.AI"], "doi": "", "_query": "hedonic regression housing", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2202.02280v1", "title": "Portfolio Choice with Indivisible and Illiquid Housing Assets: The Case of Spain", "abstract": "This paper studies the investment decision of the Spanish households using a unique data set, the Spanish Survey of Household Finance (EFF). We propose a theoretical model in which households, given a fixed investment in housing, allocate their net wealth across bank time deposits, stocks, and mortgage. Besides considering housing as an indivisible and illiquid asset that restricts the portfolio choice decision, we take into account the financial constraints that households face when they apply for external funding. For every representative household in the EFF we solve this theoretical problem and obtain the theoretically optimal portfolio that is compared with households' actual choices. We find that households significantly underinvest in stocks and deposits while the optimal and actual mortgage investments are alike. Considering the three types of financial assets at once, we find that the households headed by highly financially sophisticated, older, retired, richer, and unconstrained persons are the ones investing more efficiently.", "published": "2022-02-04T18:10:33Z", "authors": ["Sergio Mayordomo", "María Rodriguez-Moreno", "Juan Ignacio Peña"], "categories": ["q-fin.PM"], "doi": "", "_query": "hedonic regression housing", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/1304.6691v2", "title": "Optimal upper and lower bounds for the true and empirical excess risks in heteroscedastic least-squares regression", "abstract": "We consider the estimation of a bounded regression function with nonparametric heteroscedastic noise and random design. We study the true and empirical excess risks of the least-squares estimator on finite-dimensional vector spaces. We give upper and lower bounds on these quantities that are nonasymptotic and optimal to first order, allowing the dimension to depend on sample size. These bounds show the equivalence between the true and empirical excess risks when, among other things, the least-squares estimator is consistent in sup-norm with the projection of the regression function onto the considered model. Consistency in the sup-norm is then proved for suitable histogram models and more general models of piecewise polynomials that are endowed with a localized basis structure.", "published": "2013-04-24T18:26:40Z", "authors": ["Adrien Saumard"], "categories": ["math.ST"], "doi": "", "_query": "hedonic regression housing", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/1012.2606v2", "title": "Modeling urban housing market dynamics: can the socio-spatial segregation preserve some social diversity?", "abstract": "Addressing issues of social diversity, we introduce a model of housing transactions between agents who are heterogeneous in their willingness to pay. A key assumption is that agents' preferences for a location depend on both an intrinsic attractiveness and on the social characteristics of the neighborhood. The stationary space distribution of income is analytically and numerically characterized. The main results are that socio-spatial segregation occurs if -- and only if -- the social influence is strong enough, but even so, some social diversity is preserved at most locations. Comparison with data on the Paris housing market shows that the results reproduce general trends of price distribution and spatial income segregation.", "published": "2010-12-13T00:34:34Z", "authors": ["Laetitia Gauvin", "Annick Vignes", "Jean-Pierre Nadal"], "categories": ["physics.soc-ph", "cs.SI"], "doi": "", "_query": "hedonic regression housing", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2605.31276v1", "title": "Learning Parametric Nitrogen Fertilizer Response Curves Using Neuro Symbolic Regression", "abstract": "Accurately modeling crop response to Nitrogen (N) fertilization is a fundamental challenge in precision agriculture, as it impacts both economic returns and environmental sustainability. Existing approaches either rely on predefined parametric forms or opaque machine learning models, limiting their ability to interpret or discover site-specific functional relationships from data. In this work, we propose a neuro symbolic regression (SR) approach to learn parametric N-response curves without assuming a predefined functional form. Our approach integrates a transformer-based Multi-Set Symbolic Skeleton Prediction strategy, enabling the discovery of shared functional structures across multiple subdomains or management zones (MZs). By constructing diverse input subsets and enforcing consistency across them, the method recovers robust symbolic skeletons that are subsequently fitted to observed data using a genetic algorithm. This framework was first evaluated on synthetic one-dimensional problems to assess its robustness under varying levels of epistemic uncertainty. The results demonstrate the ability of the proposed SR approach to recover correct expressions even in data-scarce regimes. In this work, we present the results of applying our method to real-world winter wheat data, learning distinct parametric N-response curves for different MZs within a field. The results show that the discovered expressions not only achieve lower fitting errors than traditional models such as quadratic-plateau and exponential functions, but also capture diverse functional behaviors across spatial regions. This demonstrates the potential that neuro SR has to enable the discovery of site-specific agronomic relationships and support informed decision-making in precision agriculture.", "published": "2026-05-29T13:08:46Z", "authors": ["Giorgio Morales", "John Sheppard"], "categories": ["cs.LG"], "doi": "", "_query": "hedonic regression housing", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/1003.2439v1", "title": "The coverage probabililty of confidence intervals in regression after a preliminary F test", "abstract": "Consider a linear regression model with regression parameter beta=(beta_1,..., beta_p) and independent normal errors. Suppose the parameter of interest is theta = a^T beta, where a is specified. Define the s-dimensional parameter vector tau = C^T beta - t, where C and t are specified. Suppose that we carry out a preliminary F test of the null hypothesis H_0: tau = 0 against the alternative hypothesis H_1: tau not equal to 0. It is common statistical practice to then construct a confidence interval for theta with nominal coverage 1-alpha, using the same data, based on the assumption that the selected model had been given to us a priori(as the true model). We call this the naive 1-alpha confidence interval for theta. This assumption is false and it may lead to this confidence interval having minimum coverage probability far below 1-alpha, making it completely inadequate. Our aim is to compute this minimum coverage probability. It is straightforward to find an expression for the coverage probability of this confidence interval that is a multiple integral of dimension s+1. However, we derive a new elegant and computationally-convenient formula for this coverage probability. For s=2 this formula is a sum of a triple and a double integral and for all s>2 this formula is a sum of a quadruple and a double integral. This makes it easy to compute the minimum coverage probability of the naive confidence interval, irrespective of how large s is. A very important practical application of this formula is to the analysis of covariance. In this context, tau can be defined so that H_0 expresses the hypothesis of \"parallelism\". Applied statisticians commonly recommend carrying out a preliminary F test of this hypothesis. We illustrate the application of our formula with a real-life analysis of covariance data set and a preliminary F test for \"parallelism\". We show that the naive 0.95 confidence interval has minimum coverage probability 0.0846, showing that it is completely inadequate.", "published": "2010-03-11T22:50:55Z", "authors": ["Paul Kabaila", "Davide Farchione"], "categories": ["math.ST"], "doi": "", "_query": "hedonic regression housing", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2501.01905v1", "title": "Alleviating Overfitting in Transformation-Interaction-Rational Symbolic Regression with Multi-Objective Optimization", "abstract": "The Transformation-Interaction-Rational is a representation for symbolic regression that limits the search space of functions to the ratio of two nonlinear functions each one defined as the linear regression of transformed variables. This representation has the main objective to bias the search towards simpler expressions while keeping the approximation power of standard approaches. The performance of using Genetic Programming with this representation was substantially better than with its predecessor (Interaction-Transformation) and ranked close to the state-of-the-art on a contemporary Symbolic Regression benchmark. On a closer look at these results, we observed that the performance could be further improved with an additional selective pressure for smaller expressions when the dataset contains just a few data points. The introduction of a penalization term applied to the fitness measure improved the results on these smaller datasets. One problem with this approach is that it introduces two additional hyperparameters: i) a criteria to when the penalization should be activated and, ii) the amount of penalization to the fitness function. In this paper, we extend Transformation-Interaction-Rational to support multi-objective optimization, specifically the NSGA-II algorithm, and apply that to the same benchmark. A detailed analysis of the results show that the use of multi-objective optimization benefits the overall performance on a subset of the benchmarks while keeping the results similar to the single-objective approach on the remainder of the datasets. Specifically to the small datasets, we observe a small (and statistically insignificant) improvement of the results suggesting that further strategies must be explored.", "published": "2025-01-03T17:21:05Z", "authors": ["Fabricio Olivetti de Franca"], "categories": ["cs.LG"], "doi": "10.1007/s10710-023-09461-3", "_query": "hedonic regression housing", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2307.02229v2", "title": "Hybrid additive modeling with partial dependence for supervised regression and dynamical systems forecasting", "abstract": "Learning processes by exploiting restricted domain knowledge is an important task across a plethora of scientific areas, with more and more hybrid training methods additively combining data-driven and model-based approaches. Although the obtained models are more accurate than purely data-driven models, the optimization process usually comes with sensitive regularization constraints. Furthermore, while such hybrid methods have been tested in various scientific applications, they have been mostly tested on dynamical systems, with only limited study about the influence of each model component on global performance and parameter identification. In this work, we introduce a new hybrid training approach based on partial dependence, which removes the need for intricate regularization. Moreover, we assess the performance of hybrid modeling against traditional machine learning methods on standard regression problems. We compare, on both synthetic and real regression problems, several approaches for training such hybrid models. We focus on hybrid methods that additively combine a parametric term with a machine learning term and investigate model-agnostic training procedures. Therefore, experiments are carried out with different types of machine learning models, including tree-based models and artificial neural networks. We also extend our partial dependence optimization process for dynamical systems forecasting and compare it to existing schemes.", "published": "2023-07-05T12:13:56Z", "authors": ["Yann Claes", "Vân Anh Huynh-Thu", "Pierre Geurts"], "categories": ["cs.LG"], "doi": "", "_query": "hedonic regression housing", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/1606.00424v3", "title": "Residential income segregation: A behavioral model of the housing market", "abstract": "We represent the functioning of the housing market and study the relation between income segregation, income inequality and house prices by introducing a spatial Agent-Based Model (ABM). Differently from traditional models in urban economics, we explicitly specify the behavior of buyers and sellers and the price formation mechanism. Buyers who differ by income select among heterogeneous neighborhoods using a probabilistic model of residential choice; sellers employ an aspiration level heuristic to set their reservation offer price; prices are determined through a continuous double auction. We first provide an approximate analytical solution of the ABM, shedding light on the structure of the model and on the effect of the parameters. We then simulate the ABM and find that: (i) a more unequal income distribution lowers the prices globally, but implies stronger segregation; (ii) a spike of the demand in one part of the city increases the prices all over the city; (iii) subsidies are more efficient than taxes in fostering social mixing.", "published": "2016-06-01T19:53:26Z", "authors": ["Marco Pangallo", "Jean Pierre Nadal", "Annick Vignes"], "categories": ["econ.GN", "nlin.AO"], "doi": "", "_query": "hedonic regression housing", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2511.13200v1", "title": "A Price to Enter: Anticipatory Housing Market Sorting and Access Inequality under New York's Congestion Pricing", "abstract": "This study examines how congestion pricing shapes housing market outcomes and spatial equity in New York City. Using high-frequency sales and rental data and a combination of propensity score matching difference-in-differences, geographic regression discontinuity, and event study designs, the analysis identifies distinct short-run adjustment patterns triggered by the policy announcement. Housing prices inside the toll zone fell by about 3.3% and rents by 3%, with the sharpest declines occurring immediately after the announcement. These effects weakened over time, and price resilience emerged among premium properties, indicating early market sorting and growing segmentation. The Geo-RDD results show a clear boundary penalty, with properties just inside the cordon experiencing more pronounced declines than otherwise similar properties just outside. Renters and lower-value segments were more exposed to early adjustment pressures, while implementation-stage effects were limited. The findings suggest that congestion pricing can reshape urban space not only by altering mobility incentives but also by redistributing access and opportunity. Equity-oriented design that includes early-stage support for boundary neighborhoods and renters, along with reinvestment of revenues into untolled transit access, is important for ensuring that the benefits of congestion pricing are shared rather than concentrated.", "published": "2025-11-17T10:09:37Z", "authors": ["Mingzhi Xiao", "Yuki Takayama"], "categories": ["econ.GN"], "doi": "", "_query": "hedonic regression housing", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2110.12394v1", "title": "Housing property rights and social integration of migrant population: based on the 2017 china migrants' dynamic survey", "abstract": "Push-pull theory, one of the most important macro theories in demography, argues that population migration is driven by a combination of push (repulsive) forces at the place of emigration and pull (attractive) forces at the place of emigration. Based on the push-pull theory, this paper shows another practical perspective of the theory by measuring the reverse push and pull forces from the perspective of housing property rights. We use OLS and sequential Probit models to analyze the impact of urban and rural property rights factors on the social integration of the migrant population-based, on \"China Migrants' Dynamic Survey\". We found that after controlling for personal and urban characteristics, there is a significant negative effect of rural property rights (homestead) ownership of the mobile population on their socio-economic integration, and cultural and psychological integration in the inflow area. The effect of urban house price on social integration of the migrant population is consistent with the \"inverted U-shaped\" nonlinear assumption: when the house price to income ratio of the migrant population in the inflow area increases beyond the inflection point, its social integration level decreases. That is, there is an inverse push force and pull force mechanism of housing property rights on population mobility.", "published": "2021-10-24T09:07:35Z", "authors": ["Jingwen Tan", "Shixi Kang"], "categories": ["econ.GN"], "doi": "", "_query": "hedonic regression housing", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/1807.07155v2", "title": "Take a Look Around: Using Street View and Satellite Images to Estimate House Prices", "abstract": "When an individual purchases a home, they simultaneously purchase its structural features, its accessibility to work, and the neighborhood amenities. Some amenities, such as air quality, are measurable while others, such as the prestige or the visual impression of a neighborhood, are difficult to quantify. Despite the well-known impacts intangible housing features have on house prices, limited attention has been given to systematically quantifying these difficult to measure amenities. Two issues have led to this neglect. Not only do few quantitative methods exist that can measure the urban environment, but that the collection of such data is both costly and subjective. We show that street image and satellite image data can capture these urban qualities and improve the estimation of house prices. We propose a pipeline that uses a deep neural network model to automatically extract visual features from images to estimate house prices in London, UK. We make use of traditional housing features such as age, size, and accessibility as well as visual features from Google Street View images and Bing aerial images in estimating the house price model. We find encouraging results where learning to characterize the urban quality of a neighborhood improves house price prediction, even when generalizing to previously unseen London boroughs. We explore the use of non-linear vs. linear methods to fuse these cues with conventional models of house pricing, and show how the interpretability of linear models allows us to directly extract proxy variables for visual desirability of neighborhoods that are both of interest in their own right, and could be used as inputs to other econometric methods. This is particularly valuable as once the network has been trained with the training data, it can be applied elsewhere, allowing us to generate vivid dense maps of the visual appeal of London streets.", "published": "2018-07-18T21:10:08Z", "authors": ["Stephen Law", "Brooks Paige", "Chris Russell"], "categories": ["econ.EM", "cs.CV"], "doi": "10.1145/3342240", "_query": "hedonic regression housing", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2405.08398v2", "title": "Exploring the spatial segmentation of housing markets from online listings", "abstract": "The real estate market shows an inherent connection to space. Real estate agencies unevenly operate and specialize across space, price and type of properties, thereby segmenting the market into submarkets. We introduce here a methodology based on multipartite networks to detect the spatial segmentation emerging from data on housing online listings. Considering the spatial information of the listings, we build a bipartite network that connects agencies and spatial units. This bipartite network is projected into a network of spatial units, whose connections account for similarities in the agency ecosystem. We then apply clustering methods to this network to segment markets into spatially-coherent regions, which are found to be robust across different clustering detection algorithms, discretization of space and spatial scales, and across countries with case studies in France and Spain. This methodology addresses the long-standing issue of housing market segmentation, relevant in disciplines such as urban studies and spatial economics, and with implications for policymaking.", "published": "2024-05-14T07:52:42Z", "authors": ["David Abella", "Johann H. Martínez", "Mattia Mazzoli", "Thibault Le Corre", "Julien Migozzi", "Eduard Alonso-Paulí", "Rafel Crespí-Cladera", "Thomas Louail", "José J. Ramasco"], "categories": ["physics.soc-ph"], "doi": "", "_query": "hedonic regression housing", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2608.04076v1", "title": "Forced Displacement of People Experiencing Homelessness: Housing and Movement Outcomes after Encampment Clearances", "abstract": "The 2024 Grants Pass decision newly emboldens US cities to manage unsheltered homelessness through forced displacement. Although literature demonstrates the harmful health and material impacts of this tactic, the longer-term results for housing outcomes and migration patterns remain less clear. In response, this study leverages longitudinal street outreach data to investigate where people move following encampment clearances. We specifically employ relational event models to predict the likelihood of various outcomes post-removal, such as relocating tracts, entering shelter, or obtaining housing. Results suggest that displaced residents do not travel far, yet clearances may still reduce visible homelessness by decreasing the size of camp communities. Furthermore, people appear unlikely to move indoors and instead face high risks of losing contact with service providers. These trends hold regardless of individual demographics, although people with mental health conditions demonstrate stronger attachments to their original sites. Evidence additionally indicates that neighborhood conditions could influence these migration behaviors. Such findings corroborate broader literature on place attachments, residential mobility, and invisibilization of poverty. This paper ultimately addresses the urgent need for a deeper understanding of how forced displacement impacts homelessness and pathways to housing.", "published": "2026-08-04T17:49:07Z", "authors": ["Brandon Morande", "Kim Serry", "Amy Hagopian", "Zack W. Almquist"], "categories": ["cs.SI", "stat.AP"], "doi": "", "_query": "hedonic regression housing", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2609.29691v1", "title": "Online House Allocation with Subsidy", "abstract": "House allocation is a fundamental problem in which each agent is assigned exactly one house. While the classical model assumes that all houses are available before the allocation is computed, many practical settings require decisions to be made as houses become available over time. We introduce the online house allocation problem, where houses arrive sequentially and the algorithm must maintain an allocation without knowledge of future arrivals. Unlike online fair division, the one-house-per-agent constraint makes recourse an inherent part of the problem, as accepting a newly arrived house may require reassigning previously allocated houses. We study online house allocation under subsidy-based fairness, where monetary subsidies eliminate envy among agents. We show that envy-freeability can always be maintained online using bounded recourse and that reassignment chains of length linear in the number of agents are unavoidable in the worst case. In contrast, minimizing the total subsidy is fundamentally harder: no deterministic online algorithm against an adaptive adversary, and no randomized online algorithm against a non-adaptive adversary, admits a bounded competitive ratio, even for two agents and four houses. We complement these impossibilities by showing that exact online subsidy minimization is possible whenever there is at most one extra house beyond the number of agents, and that this guarantee is best possible with respect to the number of extra houses. Finally, we develop learning-augmented algorithms that recover the offline optimum under accurate predictions while providing explicit robustness guarantees when predictions are inaccurate.", "published": "2026-08-31T01:14:42Z", "authors": ["Nicholas Teh", "Saar Cohen", "Karen Frilya Celine", "Benjamin Yu", "Michael J. Wooldridge"], "categories": ["cs.GT"], "doi": "", "_query": "hedonic regression housing", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/1111.2113v3", "title": "Further properties of frequentist confidence intervals in regression that utilize uncertain prior information", "abstract": "Consider a linear regression model with n-dimensional response vector, regression parameter β= (β_1, ..., β_p) and independent and identically N(0, σ^2) distributed errors. Suppose that the parameter of interest is θ= a^T βwhere a is a specified vector. Define the parameter τ= c^T β- t where c and t are specified. Also suppose that we have uncertain prior information that τ= 0. Part of our evaluation of a frequentist confidence interval for θis the ratio (expected length of this confidence interval)/(expected length of standard 1-αconfidence interval), which we call the scaled expected length of this interval. We say that a 1-αconfidence interval for θutilizes this uncertain prior information if (a) the scaled expected length of this interval is significantly less than 1 when τ= 0, (b) the maximum value of the scaled expected length is not too much larger than 1 and (c) this confidence interval reverts to the standard 1-αconfidence interval when the data happen to strongly contradict the prior information. Kabaila and Giri, 2009, JSPI present a new method for finding such a confidence interval. Let \\hatβdenote the least squares estimator of β. Also let \\hatΘ= a^T \\hatβand \\hatτ= c^T \\hatβ- t. Using computations and new theoretical results, we show that the performance of this confidence interval improves as |Corr(\\hatΘ, \\hatτ)| increases and n-p decreases.", "published": "2011-11-09T06:45:22Z", "authors": ["Paul Kabaila", "Khageswor Giri"], "categories": ["stat.ME"], "doi": "", "_query": "hedonic regression housing", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2409.05335v1", "title": "A Multi-Modal Deep Learning Based Approach for House Price Prediction", "abstract": "Accurate prediction of house price, a vital aspect of the residential real estate sector, is of substantial interest for a wide range of stakeholders. However, predicting house prices is a complex task due to the significant variability influenced by factors such as house features, location, neighborhood, and many others. Despite numerous attempts utilizing a wide array of algorithms, including recent deep learning techniques, to predict house prices accurately, existing approaches have fallen short of considering a wide range of factors such as textual and visual features. This paper addresses this gap by comprehensively incorporating attributes, such as features, textual descriptions, geo-spatial neighborhood, and house images, typically showcased in real estate listings in a house price prediction system. Specifically, we propose a multi-modal deep learning approach that leverages different types of data to learn more accurate representation of the house. In particular, we learn a joint embedding of raw house attributes, geo-spatial neighborhood, and most importantly from textual description and images representing the house; and finally use a downstream regression model to predict the house price from this jointly learned embedding vector. Our experimental results with a real-world dataset show that the text embedding of the house advertisement description and image embedding of the house pictures in addition to raw attributes and geo-spatial embedding, can significantly improve the house price prediction accuracy. The relevant source code and dataset are publicly accessible at the following URL: https://github.com/4P0N/mhpp", "published": "2024-09-09T05:26:33Z", "authors": ["Md Hasebul Hasan", "Md Abid Jahan", "Mohammed Eunus Ali", "Yuan-Fang Li", "Timos Sellis"], "categories": ["cs.LG", "cs.AI", "cs.CV"], "doi": "", "_query": "hedonic regression housing", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2311.11655v2", "title": "Peeking Inside the Schufa Blackbox: Explaining the German Housing Scoring System", "abstract": "Explainable Artificial Intelligence is a concept aimed at making complex algorithms transparent to users through a uniform solution. Researchers have highlighted the importance of integrating domain specific contexts to develop explanations tailored to end users. In this study, we focus on the Schufa housing scoring system in Germany and investigate how users information needs and expectations for explanations vary based on their roles. Using the speculative design approach, we asked business information students to imagine user interfaces that provide housing credit score explanations from the perspectives of both tenants and landlords. Our preliminary findings suggest that although there are general needs that apply to all users, there are also conflicting needs that depend on the practical realities of their roles and how credit scores affect them. We contribute to Human centered XAI research by proposing future research directions that examine users explanatory needs considering their roles and agencies.", "published": "2023-11-20T10:42:31Z", "authors": ["Dean-Robin Kern", "Gunnar Stevens", "Erik Dethier", "Sidra Naveed", "Fatemeh Alizadeh", "Delong Du", "Md Shajalal"], "categories": ["cs.AI", "cs.HC"], "doi": "", "_query": "hedonic regression housing", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2502.11205v1", "title": "Deep Contrastive Learning for Feature Alignment: Insights from Housing-Household Relationship Inference", "abstract": "Housing and household characteristics are key determinants of social and economic well-being, yet our understanding of their interrelationships remains limited. This study addresses this knowledge gap by developing a deep contrastive learning (DCL) model to infer housing-household relationships using the American Community Survey (ACS) Public Use Microdata Sample (PUMS). More broadly, the proposed model is suitable for a class of problems where the goal is to learn joint relationships between two distinct entities without explicitly labeled ground truth data. Our proposed dual-encoder DCL approach leverages co-occurrence patterns in PUMS and introduces a bisect K-means clustering method to overcome the absence of ground truth labels. The dual-encoder DCL architecture is designed to handle the semantic differences between housing (building) and household (people) features while mitigating noise introduced by clustering. To validate the model, we generate a synthetic ground truth dataset and conduct comprehensive evaluations. The model further demonstrates its superior performance in capturing housing-household relationships in Delaware compared to state-of-the-art methods. A transferability test in North Carolina confirms its generalizability across diverse sociodemographic and geographic contexts. Finally, the post-hoc explainable AI analysis using SHAP values reveals that tenure status and mortgage information play a more significant role in housing-household matching than traditionally emphasized factors such as the number of persons and rooms.", "published": "2025-02-16T17:06:06Z", "authors": ["Xiao Qian", "Shangjia Dong", "Rachel Davidson"], "categories": ["cs.LG", "cs.CY"], "doi": "", "_query": "hedonic regression housing", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2508.16657v1", "title": "Leveraging Multi-Source Textural UGC for Neighbourhood Housing Quality Assessment: A GPT-Enhanced Framework", "abstract": "This study leverages GPT-4o to assess neighbourhood housing quality using multi-source textural user-generated content (UGC) from Dianping, Weibo, and the Government Message Board. The analysis involves filtering relevant texts, extracting structured evaluation units, and conducting sentiment scoring. A refined housing quality assessment system with 46 indicators across 11 categories was developed, highlighting an objective-subjective method gap and platform-specific differences in focus. GPT-4o outperformed rule-based and BERT models, achieving 92.5% accuracy in fine-tuned settings. The findings underscore the value of integrating UGC and GPT-driven analysis for scalable, resident-centric urban assessments, offering practical insights for policymakers and urban planners.", "published": "2025-08-20T08:29:40Z", "authors": ["Qiyuan Hong", "Huimin Zhao", "Ying Long"], "categories": ["cs.CY", "cs.CL"], "doi": "", "_query": "hedonic regression housing", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2507.09208v1", "title": "The calibration house in JUNO", "abstract": "As an auxiliary system within the calibration system of the Jiangmen Underground Neutrino Observatory, a calibration house is designed to provide interfaces for connecting the central detector and accommodating various calibration sub-systems. Onsite installation has demonstrated that the calibration house interfaces are capable of effectively connecting to the central detector and supporting the installation of complex and sophisticated calibration sub-systems. Additionally, controlling the levels of radon and oxygen within the calibration house is critical. Radon can increase the experimental background, while oxygen can degrade the quality of the liquid scintillator. The oxygen concentration can be maintained at levels below 10 parts per million, and the radon concentration can be kept below 15 mBq/m$^{3}$. This paper will provide detailed information on the calibration house and its methods for radon and oxygen concentration control.", "published": "2025-07-12T09:02:44Z", "authors": ["J. Hui", "R. Li", "Y. Wu", "T. Zhang", "Z. Chen", "A. Freegard", "J. Huang", "H. Lai", "Y. Liao", "J. Liu", "Y. Meng", "A. Takenaka", "Z. Xiang", "P. Zhang", "Y. Zhang"], "categories": ["physics.ins-det", "hep-ex"], "doi": "", "_query": "hedonic regression housing", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/1203.5298v2", "title": "Dynamical fluctuations in a simple housing market model", "abstract": "We consider a simple stochastic model of a urban rental housing market, in which the interaction of tenants and landlords induces rent fluctuations. We simulate the model numerically and measure the equilibrium rent distribution, which is found to be close to a lognormal law. We also study the influence of the density of agents (or equivalently, the vacancy rate) on the rent distribution. A simplified version of the model, amenable to analytical treatment, is studied and leads to a lognormal distribution of rents. The predicted equilibrium value agrees quantitatively with numerical simulations, while a qualitative agreement is obtained for the standard deviation. The connection with non-equilibrium statistical physics models like ratchets is also emphasized.", "published": "2012-03-23T17:07:58Z", "authors": ["Rémi Lemoy", "Eric Bertin"], "categories": ["q-fin.GN", "cond-mat.stat-mech", "physics.soc-ph"], "doi": "10.1088/1742-5468/2012/12/P12007", "_query": "hedonic regression housing", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2109.06383v1", "title": "Transformation-based generalized spatial regression using the spmoran package: Case study examples", "abstract": "This study presents application examples of generalized spatial regression modeling for count data and continuous non-Gaussian data using the spmoran package (version 0.2.2 onward). Section 2 introduces the model. The subsequent sections demonstrate applications of the model for disease mapping, spatial prediction and uncertainty modeling, and hedonic analysis. The R codes used in this vignette are available from https://github.com/dmuraka/spmoran. Another vignette focusing on Gaussian spatial regression modeling is also available from the same GitHub page.", "published": "2021-09-14T01:13:42Z", "authors": ["Daisuke Murakami"], "categories": ["stat.AP", "stat.ME"], "doi": "", "_query": "hedonic regression housing", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/1805.00861v1", "title": "Modelling cross-dependencies between Spain's regional tourism markets with an extension of the Gaussian process regression model", "abstract": "This study presents an extension of the Gaussian process regression model for multiple-input multiple-output forecasting. This approach allows modelling the cross-dependencies between a given set of input variables and generating a vectorial prediction. Making use of the existing correlations in international tourism demand to all seventeen regions of Spain, the performance of the proposed model is assessed in a multiple-step-ahead forecasting comparison. The results of the experiment in a multivariate setting show that the Gaussian process regression model significantly improves the forecasting accuracy of a multi-layer perceptron neural network used as a benchmark. The results reveal that incorporating the connections between different markets in the modelling process may prove very useful to refine predictions at a regional level.", "published": "2018-05-02T15:16:43Z", "authors": ["Oscar Claveria", "Enric Monte", "Salvador Torra"], "categories": ["stat.ML", "cs.LG"], "doi": "10.1007/s13209-016-0144-7", "_query": "hedonic regression housing", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/1010.4499v1", "title": "Hedonic Coalition Formation for Distributed Task Allocation among Wireless Agents", "abstract": "Autonomous wireless agents such as unmanned aerial vehicles or mobile base stations present a great potential for deployment in next-generation wireless networks. While current literature has been mainly focused on the use of agents within robotics or software applications, we propose a novel usage model for self-organizing agents suited to wireless networks. In the proposed model, a number of agents are required to collect data from several arbitrarily located tasks. Each task represents a queue of packets that require collection and subsequent wireless transmission by the agents to a central receiver. The problem is modeled as a hedonic coalition formation game between the agents and the tasks that interact in order to form disjoint coalitions. Each formed coalition is modeled as a polling system consisting of a number of agents which move between the different tasks present in the coalition, collect and transmit the packets. Within each coalition, some agents can also take the role of a relay for improving the packet success rate of the transmission. The proposed algorithm allows the tasks and the agents to take distributed decisions to join or leave a coalition, based on the achieved benefit in terms of effective throughput, and the cost in terms of delay. As a result of these decisions, the agents and tasks structure themselves into independent disjoint coalitions which constitute a Nash-stable network partition. Moreover, the proposed algorithm allows the agents and tasks to adapt the topology to environmental changes such as the arrival/removal of tasks or the mobility of the tasks. Simulation results show how the proposed algorithm improves the performance, in terms of average player (agent or task) payoff, of at least 30.26% (for a network of 5 agents with up to 25 tasks) relatively to a scheme that allocates nearby tasks equally among agents.", "published": "2010-10-21T15:32:03Z", "authors": ["Walid Saad", "Zhu Han", "Tamer Basar", "Merouane Debbah", "Are Hjørungnes"], "categories": ["cs.IT", "cs.GT"], "doi": "10.1109/TMC.2010.242", "_query": "hedonic regression housing", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2508.11151v1", "title": "The core in the housing market model with fractional endowments", "abstract": "We explore the core concept in a generalization of the housing market model where agents own fractional endowments while maintaining ordinal preferences. Recognizing that individuals are easier than coalitions to block an allocation, we adopt a definition in which individuals block an allocation if their received assignments do not first-order stochastically dominate their endowment, while a non-singleton coalition blocks an allocation if they can reallocate their endowments to obtain new assignments that first-order stochastically dominate their original assignments. Our findings show that, unlike the original model, the strong core may be empty, while the weak core is nonempty. The weak core always contains elements that satisfy equal treatment of equals, but it may not contain elements satisfying equal-endowment no envy.", "published": "2025-08-15T01:49:14Z", "authors": ["Jingsheng Yu", "Jun Zhang"], "categories": ["econ.TH"], "doi": "", "_query": "hedonic regression housing", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2306.09529v1", "title": "House-Swapping with Objective Indifferences", "abstract": "We study the classic house-swapping problem of Shapley and Scarf (1974) in a setting where agents may have \"objective\" indifferences, i.e., indifferences that are shared by all agents. In other words, if any one agent is indifferent between two houses, then all agents are indifferent between those two houses. The most direct interpretation is the presence of multiple copies of the same object. Our setting is a special case of the house-swapping problem with general indifferences. We derive a simple, easily interpretable algorithm that produces the unique strict core allocation of the house-swapping market, if it exists. Our algorithm runs in square polynomial time, a substantial improvement over the cubed time methods for the more general problem.", "published": "2023-06-15T22:16:22Z", "authors": ["Will Sandholtz", "Andrew Tai"], "categories": ["econ.TH", "cs.DM", "cs.GT"], "doi": "", "_query": "hedonic regression housing", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2603.23070v2", "title": "Knowledge management in House of Graphs", "abstract": "The House of Graphs is an online database of graphs which can be accessed at https://houseofgraphs.org/. It serves as a central repository for complete lists of graphs for various graph classes. However, its main feature is a searchable database of so-called \"interesting\" graphs. The development of the original House of Graphs started in 2010 and it was completely rebuilt in 2021-2022. Each graph in the database is accompanied by a significant amount of meta-data such as a name, drawings, precomputed graph invariants, and comments. Given this amount of information and the importance of reliability in the scientific world, robust data management is essential to ensure accuracy and consistency across the database. In this article, we therefore focus on knowledge management in the House of Graphs and describe the inner workings of the House of Graphs and how we ensure that its data is coherent, qualitative and stable.", "published": "2026-03-24T11:07:05Z", "authors": ["Gauvain Devillez", "Sven D'hondt", "Jan Goedgebeur"], "categories": ["math.CO", "cs.DB", "cs.DM"], "doi": "", "_query": "hedonic regression housing", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2106.06925v1", "title": "On the Complexity of Fair House Allocation", "abstract": "We study fairness in house allocation, where $m$ houses are to be allocated among $n$ agents so that every agent receives one house. We show that maximizing the number of envy-free agents is hard to approximate to within a factor of $n^{1-γ}$ for any constant $γ>0$, and that the exact version is NP-hard even for binary utilities. Moreover, we prove that deciding whether a proportional allocation exists is computationally hard, whereas the corresponding problem for equitability can be solved efficiently.", "published": "2021-06-13T05:45:21Z", "authors": ["Naoyuki Kamiyama", "Pasin Manurangsi", "Warut Suksompong"], "categories": ["cs.GT", "cs.CC"], "doi": "10.1016/j.orl.2021.06.006", "_query": "hedonic regression housing", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2509.21460v2", "title": "Forecasting House Prices", "abstract": "This article identifies the factors that drove house prices in 13 advanced countries over the past 35 years. It does so based on Breiman s (2001) random forest model. Shapley values indicate that annual house price growth across countries is explained first and foremost by price momentum, initial valuations (proxied by price to rent ratios) and household credit growth. Partial effects of explanatory variables are also elicited and suggest important non-linearities, for instance as to what concerns the effects of CPI inflation on house price growth. The out-of-sample forecast test reveals that the random forest model delivers 44% lower house price variation root square mean errors (RMSEs) and 45% lower mean absolute errors (MAEs) when compared to an OLS model that uses the same set of 10 pre-determined explanatory variables. Notably, the same model works well for all countries, as the random forest attributes minimal values to country fixed effects.", "published": "2025-09-25T19:27:37Z", "authors": ["Emanuel Kohlscheen"], "categories": ["econ.GN"], "doi": "", "_query": "hedonic regression housing", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2302.13034v1", "title": "Does Noise Affect Housing Prices? A Case Study in the Urban Area of Thessaloniki", "abstract": "Real estate markets depend on various methods to predict housing prices, including models that have been trained on datasets of residential or commercial properties. Most studies endeavor to create more accurate machine learning models by utilizing data such as basic property characteristics as well as urban features like distances from amenities and road accessibility. Even though environmental factors like noise pollution can potentially affect prices, the research around this topic is limited. One of the reasons is the lack of data. In this paper, we reconstruct and make publicly available a general purpose noise pollution dataset based on published studies conducted by the Hellenic Ministry of Environment and Energy for the city of Thessaloniki, Greece. Then, we train ensemble machine learning models, like XGBoost, on property data for different areas of Thessaloniki to investigate the way noise influences prices through interpretability evaluation techniques. Our study provides a new noise pollution dataset that not only demonstrates the impact noise has on housing prices, but also indicates that the influence of noise on prices significantly varies among different areas of the same city.", "published": "2023-02-25T09:11:57Z", "authors": ["Georgios Kamtziridis", "Dimitris Vrakas", "Grigorios Tsoumakas"], "categories": ["cs.LG"], "doi": "", "_query": "hedonic regression housing", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/0906.0307v1", "title": "Conformal House", "abstract": "We investigate the gauge dynamics of nonsupersymmetric SU(N) gauge theories featuring the simultaneous presence of fermionic matter transforming according to two distinct representations of the underlying gauge group. We bound the regions of flavors and colors which can yield a physical infrared fixed point. As a consistency check we recover the previously investigated conformal windows bounds when restricting to a single matter representation. The earlier conformal windows can be imagined to be part now of the new conformal house. We predict the nonperturbative anomalous dimensions at the infrared fixed points. We further investigate the effects of adding mass terms to the condensates on the conformal house chiral dynamics and construct the simplest instanton induced effective Lagrangian terms.", "published": "2009-06-01T15:46:51Z", "authors": ["Thomas A Ryttov", "Francesco Sannino"], "categories": ["hep-ph", "hep-lat", "hep-th"], "doi": "10.1142/S0217751X10050391", "_query": "hedonic regression housing", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/1803.04839v3", "title": "Optimal estimators in misspecified linear regression model with an application to real-world data", "abstract": "In this article, we propose the Sample Information Optimal Estimator (SIOE) and the Stochastic Restricted Optimal Estimator (SROE) for misspecified linear regression model when multicollinearity exists among explanatory variables. Further, we obtain the superiority conditions of proposed estimators over some other existing estimators in the Mean Square Error Matrix (MSEM) criterion in a standard form which can apply to all estimators considered in this study. Finally, a real world example and a Monte Carlo simulation study are presented for the proposed estimators to illustrate the theoretical results.", "published": "2018-03-11T04:12:04Z", "authors": ["Manickavasagar Kayanan", "Pushpakanthie Wijekoon"], "categories": ["math.ST", "stat.ME"], "doi": "10.1080/23737484.2018.1536863", "_query": "hedonic regression housing", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2403.08915v1", "title": "Cross-Modal Learning of Housing Quality in Amsterdam", "abstract": "In our research we test data and models for the recognition of housing quality in the city of Amsterdam from ground-level and aerial imagery. For ground-level images we compare Google StreetView (GSV) to Flickr images. Our results show that GSV predicts the most accurate building quality scores, approximately 30% better than using only aerial images. However, we find that through careful filtering and by using the right pre-trained model, Flickr image features combined with aerial image features are able to halve the performance gap to GSV features from 30% to 15%. Our results indicate that there are viable alternatives to GSV for liveability factor prediction, which is encouraging as GSV images are more difficult to acquire and not always available.", "published": "2024-03-13T19:11:58Z", "authors": ["Alex Levering", "Diego Marcos", "Devis Tuia"], "categories": ["cs.CV", "cs.AI"], "doi": "10.1145/3486635.3491067", "_query": "hedonic regression housing", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/1303.2915v2", "title": "Modeling US housing prices by spatial dynamic structural equation models", "abstract": "This article proposes a spatial dynamic structural equation model for the analysis of housing prices at the State level in the USA. The study contributes to the existing literature by extending the use of dynamic factor models to the econometric analysis of multivariate lattice data. One of the main advantages of our model formulation is that by modeling the spatial variation via spatially structured factor loadings, we entertain the possibility of identifying similarity \"regions\" that share common time series components. The factor loadings are modeled as conditionally independent multivariate Gaussian Markov Random Fields, while the common components are modeled by latent dynamic factors. The general model is proposed in a state-space formulation where both stationary and nonstationary autoregressive distributed-lag processes for the latent factors are considered. For the latent factors which exhibit a common trend, and hence are cointegrated, an error correction specification of the (vector) autoregressive distributed-lag process is proposed. Full probabilistic inference for the model parameters is facilitated by adapting standard Markov chain Monte Carlo (MCMC) algorithms for dynamic linear models to our model formulation. The fit of the model is discussed for a data set of 48 States for which we model the relationship between housing prices and the macroeconomy, using State level unemployment and per capita personal income.", "published": "2013-03-12T15:23:05Z", "authors": ["Pasquale Valentini", "Luigi Ippoliti", "Lara Fontanella"], "categories": ["stat.AP"], "doi": "10.1214/12-AOAS613", "_query": "hedonic regression housing", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2407.16116v3", "title": "Robust and consistent model evaluation criteria in high-dimensional regression", "abstract": "Most of the regularization methods such as the LASSO have one (or more) regularization parameter(s), and to select the value of the regularization parameter is essentially equal to select a model. Thus, to obtain a model suitable for the data and phenomenon, we need to determine an adequate value of the regularization parameter. Regarding the determination of the regularization parameter in the linear regression model, we often apply the information criteria like the AIC and BIC, however, it has been pointed out that these criteria are sensitive to outliers and tend not to perform well in high-dimensional settings. Outliers generally have a negative effect on not only estimation but also model selection, consequently, it is important to employ a selection method with robustness against outliers. In addition, when the number of explanatory variables is quite large, most conventional criteria are prone to select unnecessary explanatory variables. In this paper, we propose model evaluation criteria based on the statistical divergence with excellence in robustness in both of parametric estimation and model selection, by applying the quasi-Bayesian procedure. Our proposed criteria achieve the selection consistency even in high-dimensional settings due to precise approximation, simultaneously with robustness. We also investigate the conditions for establishing robustness and consistency, and provide an appropriate example of the divergence and penalty term that can achieve the desirable properties. We finally report the results of some numerical examples to verify that the proposed criteria perform robust and consistent variable selection compared with the conventional selection methods.", "published": "2024-07-23T01:38:31Z", "authors": ["Sumito Kurata", "Kei Hirose"], "categories": ["stat.ME"], "doi": "", "_query": "hedonic regression housing", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/1905.00468v2", "title": "Envy-Freeness in House Allocation Problems", "abstract": "We consider the house allocation problem, where $m$ houses are to be assigned to $n$ agents so that each agent gets exactly one house. We present a polynomial-time algorithm that determines whether an envy-free assignment exists, and if so, computes one such assignment. We also show that an envy-free assignment exists with high probability if the number of houses exceeds the number of agents by a logarithmic factor.", "published": "2019-05-01T19:45:40Z", "authors": ["Jiarui Gan", "Warut Suksompong", "Alexandros A. Voudouris"], "categories": ["cs.GT"], "doi": "10.1016/j.mathsocsci.2019.07.005", "_query": "hedonic regression housing", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/1006.4310v3", "title": "A Regression-based Adjusted Plus-Minus Statistic for NHL Players", "abstract": "The goal of this paper is to develop an adjusted plus-minus statistic for NHL players that is independent of both teammates and opponents. We use data from the shift reports on NHL.com in a weighted least squares regression to estimate an NHL player's effect on his team's success in scoring and preventing goals at even strength. Both offensive and defensive components of adjusted plus-minus are given, estimates in terms of goals per 60 minutes and goals per season are given, and estimates for forwards, defensemen, and goalies are given.", "published": "2010-06-22T15:13:17Z", "authors": ["Brian Macdonald"], "categories": ["stat.AP"], "doi": "10.2202/1559-0410.1284", "_query": "hedonic regression housing", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2009.10535v1", "title": "3D Printing an External Cavity Diode Laser Housing", "abstract": "The ability to control the frequency of an external-cavity diode laser (ECDL) is an essential component for undergraduate laboratories and atomic physics research. Typically the housing for the ECDL's diffraction grating and piezoelectric transducer is either purchased commercially or machined from metal. Here, we present an alternative to these commonly used options that utilizes 3D printing, a tool available in many physics departments. We characterize the performance of our ECDL system using atomic spectroscopy and self-heterodyne interferometry and show that it is sufficient for use in undergraduate spectroscopy experiments and a number of research applications where extremely narrow laser linewidths are not necessary. The performance and affordability of 3D-printed designs make them an appealing option for future use.", "published": "2020-09-18T17:10:12Z", "authors": ["E. Brekke", "T. Bennett", "H. Rook", "E. L. Hazlett"], "categories": ["physics.ed-ph", "physics.atom-ph"], "doi": "10.1119/10.0002135", "_query": "hedonic regression housing", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/1806.06424v1", "title": "The House of a Reciprocal Algebraic Integer", "abstract": "Let $α$ be an algebraic integer of degree $d$, which is reciprocal. The house of $α$ is the largest modulus of its conjugates. We compute the minimum of the houses of all reciprocal algebraic integers of degree $d$ which are not roots of unity, say $\\mathrm{mr}(d)$, for $d$ at most 34. We prove several lemmata and use them to avoid unnecessary calculations. The computations suggest several conjectures. The direct consequence of the last one is the conjecture of Schinzel and Zassenhaus. We demonstrate the utility of $d$-th power of the house of $α$.", "published": "2018-06-17T18:26:07Z", "authors": ["Dragan Stankov"], "categories": ["math.NT"], "doi": "", "_query": "hedonic regression housing", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/1910.04666v1", "title": "Set systems related to a house allocation problem", "abstract": "We are given a set $A$ of buyers, a set $B$ of houses, and for each buyer a preference list, i.e., an ordering of the houses. A house allocation is an injective mapping $τ$ from $A$ to $B$, and $τ$ is strictly better than another house allocation $τ'\\neq τ$ if for every buyer $i$, $τ'(i)$ does not come before $τ(i)$ in the preference list of $i$. A house allocation is Pareto optimal if there is no strictly better house allocation. Let $s(τ)$ be the image of $τ$ (i.e., the set of houses sold in the house allocation $τ$). We are interested in the largest possible cardinality $f(m)$ of the family of sets $s(τ)$ for Pareto optimal mappings $τ$ taken over all sets of preference lists of $m$ buyers. We improve the earlier upper bound on $f(m)$ given by Asinowski, Keszegh and Miltzow by making a connection between this problem and some problems in extremal set theory.", "published": "2019-10-10T16:05:08Z", "authors": ["Dániel Gerbner", "Balázs Keszegh", "Abhishek Methuku", "Dániel T. Nagy", "Balázs Patkós", "Casey Tompkins", "Chuanqi Xiao"], "categories": ["math.CO"], "doi": "10.1112/jlms.12484", "_query": "hedonic regression housing", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2006.09660v2", "title": "Wasserstein Regression", "abstract": "The analysis of samples of random objects that do not lie in a vector space is gaining increasing attention in statistics. An important class of such object data is univariate probability measures defined on the real line. Adopting the Wasserstein metric, we develop a class of regression models for such data, where random distributions serve as predictors and the responses are either also distributions or scalars. To define this regression model, we utilize the geometry of tangent bundles of the space of random measures endowed with the Wasserstein metric for mapping distributions to tangent spaces. The proposed distribution-to-distribution regression model provides an extension of multivariate linear regression for Euclidean data and function-to-function regression for Hilbert space valued data in functional data analysis. In simulations, it performs better than an alternative transformation approach where one maps distributions to a Hilbert space through the log quantile density transformation and then applies traditional functional regression. We derive asymptotic rates of convergence for the estimator of the regression operator and for predicted distributions and also study an extension to autoregressive models for distribution-valued time series. The proposed methods are illustrated with data on human mortality and distributional time series of house prices.", "published": "2020-06-17T05:12:59Z", "authors": ["Yaqing Chen", "Zhenhua Lin", "Hans-Georg Müller"], "categories": ["stat.ME"], "doi": "", "_query": "hedonic regression housing", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2112.07273v1", "title": "Urban Housing Prices and Migration's Fertility Intentions: Based on the 2018 China Migrants' Dynamic Survey", "abstract": "While the size of China's mobile population continues to expand, the fertility rate is significantly lower than the stable generation replacement level of the population, and the structural imbalance of human resource supply has attracted widespread attention. This paper uses LPM and Probit models to estimate the impact of house prices on the fertility intentions of the mobile population based on data from the 2018 National Mobile Population Dynamics Monitoring Survey. The lagged land sales price is used as an instrumental variable of house price to mitigate the potential endogeneity problem. The results show that for every 100\\% increase in the ratio of house price to household income of mobile population, the fertility intention of the female mobile population of working age at the inflow location will decrease by 4.42\\%, and the marginal effect of relative house price on labor force fertility intention is EXP(-0.222); the sensitivity of mobile population fertility intention to house price is affected by the moderating effect of infrastructure construction at the inflow location. The willingness to have children in the inflow area is higher for female migrants of working age with lower age, smaller family size and higher education. Based on the above findings, the study attempts to provide a new practical perspective for the mainline institutional change and balanced economic development in China's economic transition phase.", "published": "2021-12-14T10:09:08Z", "authors": ["Jingwen Tan", "Shixi Kang"], "categories": ["econ.GN"], "doi": "", "_query": "hedonic regression housing", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2501.18134v3", "title": "Nonlocal Prior Mixture-Based Bayesian Wavelet Regression with Application to Noisy Imaging and Audio Data", "abstract": "We propose a novel Bayesian wavelet regression approach using a three-component spike-and-slab prior for wavelet coefficients, combining a point mass at zero, a moment (MOM) prior, and an inverse moment (IMOM) prior. This flexible prior supports small and large coefficients differently, offering advantages for highly dispersed data where wavelet coefficients span multiple scales. The IMOM prior's heavy tails capture large coefficients, while the MOM prior is better suited for smaller non-zero coefficients. Further, our method introduces innovative hyperparameter specifications for mixture probabilities and scale parameters, including generalized logit, hyperbolic secant, and generalized normal decay for probabilities, and double exponential decay for scaling. Hyperparameters are estimated via an empirical Bayes approach, enabling posterior inference tailored to the data. Extensive simulations demonstrate significant performance gains over two-component wavelet methods. Applications to electroencephalography and noisy audio data illustrate the method's utility in capturing complex signal characteristics. We implement our method in an R package, NLPwavelet (>= 1.1).", "published": "2025-01-30T04:54:59Z", "authors": ["Nilotpal Sanyal"], "categories": ["stat.ME", "stat.AP"], "doi": "10.3390/math13162642", "_query": "hedonic regression housing", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/1707.04868v1", "title": "Forecasting the U.S. Real House Price Index", "abstract": "The 2006 sudden and immense downturn in U.S. House Prices sparked the 2007 global financial crisis and revived the interest about forecasting such imminent threats for economic stability. In this paper we propose a novel hybrid forecasting methodology that combines the Ensemble Empirical Mode Decomposition (EEMD) from the field of signal processing with the Support Vector Regression (SVR) methodology that originates from machine learning. We test the forecasting ability of the proposed model against a Random Walk (RW) model, a Bayesian Autoregressive and a Bayesian Vector Autoregressive model. The proposed methodology outperforms all the competing models with half the error of the RW model with and without drift in out-of-sample forecasting. Finally, we argue that this new methodology can be used as an early warning system for forecasting sudden house prices drops with direct policy implications.", "published": "2017-07-16T11:36:16Z", "authors": ["Vasilios Plakandaras", "Rangan Gupta", "Periklis Gogas", "Theophilos Papadimitriou"], "categories": ["q-fin.CP"], "doi": "", "_query": "hedonic regression housing", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2408.07845v1", "title": "Enhancing Equitable Access to AI in Housing and Homelessness System of Care through Federated Learning", "abstract": "The top priority of a Housing and Homelessness System of Care (HHSC) is to connect people experiencing homelessness to supportive housing. An HHSC typically consists of many agencies serving the same population. Information technology platforms differ in type and quality between agencies, so their data are usually isolated from one agency to another. Larger agencies may have sufficient data to train and test artificial intelligence (AI) tools but smaller agencies typically do not. To address this gap, we introduce a Federated Learning (FL) approach enabling all agencies to train a predictive model collaboratively without sharing their sensitive data. We demonstrate how FL can be used within an HHSC to provide all agencies equitable access to quality AI and further assist human decision-makers in the allocation of resources within HHSC. This is achieved while preserving the privacy of the people within the data by not sharing identifying information between agencies without their consent. Our experimental results using real-world HHSC data from Calgary, Alberta, demonstrate that our FL approach offers comparable performance with the idealized scenario of training the predictive model with data fully shared and linked between agencies.", "published": "2024-08-14T23:01:02Z", "authors": ["Musa Taib", "Jiajun Wu", "Steve Drew", "Geoffrey G. Messier"], "categories": ["cs.LG", "cs.AI", "cs.CY"], "doi": "", "_query": "hedonic regression housing", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2601.13911v1", "title": "Designing sustainable barn-type houses: Optimal shapes for minimal envelope and energy use", "abstract": "Barn-type houses have become one of the most popular single-family housing typologies in Poland and across Europe due to their simplicity, functionality, and potential for energy efficiency. Despite their widespread use, systematic methods for optimizing their geometry in terms of envelope area and energy performance remain limited. This paper develops a rigorous mathematical framework for determining the optimal proportions of barn-type houses with respect to minimizing the external surface area while satisfying constraints of either fixed volume or fixed floor area. Closed-form solutions for the optimal width, length, and height are derived as explicit functions of the roof slope, together with formulas for the minimal achievable surface. A recently introduced dimensionless compactness measure is also calculated, allowing quantitative assessment of how far a given design deviates from the theoretical optimum. The methodology is applied to case studies of three existing houses, showing that while some designs deviate significantly from optimal compactness, others already closely approximate it. The results confirm that theoretical optimization can lead to meaningful reductions in construction costs and energy demand. To support practical implementation, two original freely available software tools were developed, enabling architects and engineers to perform optimization analyses.", "published": "2026-01-20T12:40:24Z", "authors": ["Ewa Rokita-Magdziarz", "Barbara Gronostajska", "Marcin Magdziarz"], "categories": ["math.OC"], "doi": "", "_query": "hedonic regression housing", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2412.06837v1", "title": "Innovative Sentiment Analysis and Prediction of Stock Price Using FinBERT, GPT-4 and Logistic Regression: A Data-Driven Approach", "abstract": "This study explores the comparative performance of cutting-edge AI models, i.e., Finaance Bidirectional Encoder representations from Transsformers (FinBERT), Generatice Pre-trained Transformer GPT-4, and Logistic Regression, for sentiment analysis and stock index prediction using financial news and the NGX All-Share Index data label. By leveraging advanced natural language processing models like GPT-4 and FinBERT, alongside a traditional machine learning model, Logistic Regression, we aim to classify market sentiment, generate sentiment scores, and predict market price movements. This research highlights global AI advancements in stock markets, showcasing how state-of-the-art language models can contribute to understanding complex financial data. The models were assessed using metrics such as accuracy, precision, recall, F1 score, and ROC AUC. Results indicate that Logistic Regression outperformed the more computationally intensive FinBERT and predefined approach of versatile GPT-4, with an accuracy of 81.83% and a ROC AUC of 89.76%. The GPT-4 predefined approach exhibited a lower accuracy of 54.19% but demonstrated strong potential in handling complex data. FinBERT, while offering more sophisticated analysis, was resource-demanding and yielded a moderate performance. Hyperparameter optimization using Optuna and cross-validation techniques ensured the robustness of the models. This study highlights the strengths and limitations of the practical applications of AI approaches in stock market prediction and presents Logistic Regression as the most efficient model for this task, with FinBERT and GPT-4 representing emerging tools with potential for future exploration and innovation in AI-driven financial analytics", "published": "2024-12-07T05:20:31Z", "authors": ["Olamilekan Shobayo", "Sidikat Adeyemi-Longe", "Olusogo Popoola", "Bayode Ogunleye"], "categories": ["cs.LG", "cs.AI", "q-fin.ST", "stat.AP", "stat.CO"], "doi": "10.3390/bdcc8110143", "_query": "hedonic regression housing", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2507.02208v1", "title": "Modeling the Effective Elastic Modulus and Thickness of Corrugated Boards Using Gaussian Process Regression and Expected Hypervolume Improvement", "abstract": "This work aims to model the hypersurface of the effective elastic modulus, \\( E_{z, \\text{eff}} \\), and thickness, \\( th_{\\text{eff}} \\), in corrugated boards. A Latin Hypercube Sampling (LHS) is followed by Gaussian Process Regression (GP), enhanced by EHVI as a multi-objective acquisition function. Accurate modeling of \\( E_{z, \\text{eff}} \\) and \\( th_{\\text{eff}} \\) is critical for optimizing the mechanical properties of corrugated materials in engineering applications. LHS provides an efficient and straightforward approach for an initial sampling of the input space; GP is expected to be able to adapt to the complexity of the response surfaces by incorporating both prediction and uncertainty. Therefore, the next points being generated and evaluated are based on the complexity of the hypersurfaces, and some points, especially those with higher variance, are more exploited and carry more importance. The performance of GP with EHVI is measured by Mean Squared Error (MSE). Prediction of GP resulted in \\( \\text{MSE}(E_{z, \\text{eff}}) = 5.24 \\, \\text{kPa}^2 \\) and \\( \\text{MSE}(th_{\\text{eff}}) = 1 \\, \\text{mm}^2 \\). GP possesses then improved accuracy and adaptability for future applications in structural optimization.", "published": "2025-07-03T00:01:49Z", "authors": ["Ricardo Fitas"], "categories": ["physics.app-ph", "cs.CE", "physics.comp-ph"], "doi": "10.1007/978-3-031-92754-6_7", "_query": "hedonic regression housing", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2202.07919v3", "title": "HousE: Knowledge Graph Embedding with Householder Parameterization", "abstract": "The effectiveness of knowledge graph embedding (KGE) largely depends on the ability to model intrinsic relation patterns and mapping properties. However, existing approaches can only capture some of them with insufficient modeling capacity. In this work, we propose a more powerful KGE framework named HousE, which involves a novel parameterization based on two kinds of Householder transformations: (1) Householder rotations to achieve superior capacity of modeling relation patterns; (2) Householder projections to handle sophisticated relation mapping properties. Theoretically, HousE is capable of modeling crucial relation patterns and mapping properties simultaneously. Besides, HousE is a generalization of existing rotation-based models while extending the rotations to high-dimensional spaces. Empirically, HousE achieves new state-of-the-art performance on five benchmark datasets. Our code is available at https://github.com/anrep/HousE.", "published": "2022-02-16T08:13:23Z", "authors": ["Rui Li", "Jianan Zhao", "Chaozhuo Li", "Di He", "Yiqi Wang", "Yuming Liu", "Hao Sun", "Senzhang Wang", "Weiwei Deng", "Yanming Shen", "Xing Xie", "Qi Zhang"], "categories": ["cs.AI"], "doi": "", "_query": "hedonic regression housing", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2403.01162v1", "title": "Envy-Free House Allocation with Minimum Subsidy", "abstract": "House allocation refers to the problem where $m$ houses are to be allocated to $n$ agents so that each agent receives one house. Since an envy-free house allocation does not always exist, we consider finding such an allocation in the presence of subsidy. We show that computing an envy-free allocation with minimum subsidy is NP-hard in general, but can be done efficiently if $m$ differs from $n$ by an additive constant or if the agents have identical utilities.", "published": "2024-03-02T10:31:27Z", "authors": ["Davin Choo", "Yan Hao Ling", "Warut Suksompong", "Nicholas Teh", "Jian Zhang"], "categories": ["cs.GT", "cs.CC"], "doi": "10.1016/j.orl.2024.107103", "_query": "hedonic regression housing", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2107.12373v1", "title": "Relational Boosted Regression Trees", "abstract": "Many tasks use data housed in relational databases to train boosted regression tree models. In this paper, we give a relational adaptation of the greedy algorithm for training boosted regression trees. For the subproblem of calculating the sum of squared residuals of the dataset, which dominates the runtime of the boosting algorithm, we provide a $(1 + ε)$-approximation using the tensor sketch technique. Employing this approximation within the relational boosted regression trees algorithm leads to learning similar model parameters, but with asymptotically better runtime.", "published": "2021-07-25T20:29:28Z", "authors": ["Sonia Cromp", "Alireza Samadian", "Kirk Pruhs"], "categories": ["cs.DB", "cs.DS", "cs.LG"], "doi": "", "_query": "hedonic regression housing", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2512.23352v2", "title": "Revealed Preference Analysis of Shapley-Scarf Housing Markets", "abstract": "We ask what the reallocation of indivisible objects reveals about the market that produced it. A central authority assigns the objects, after which recipients exchange them among themselves. Preferences are never observed, and individuals of the same type have identical preferences. A reallocation is rationalizable as Pareto efficient and individually rational exactly when a directed graph built from the data is acyclic, and as weak core-stable exactly when a mixed graph admits an orientation acyclic within types and free of alternating cycles. Deciding the latter is NP-complete, and on the single-crossing domain, although the test becomes more restrictive, it turns out to be more useful in practice. Both properties are less demanding than the strict core, so their rejection eliminates a broader set of possibilities. We apply the tests to administrative data on social housing lettings in England.", "published": "2025-12-29T10:26:56Z", "authors": ["Umutcan Salman", "Michele Lombardi", "Francesco Ciardiello", "Riccardo Saulle"], "categories": ["econ.TH"], "doi": "", "_query": "hedonic regression housing", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/1704.08119v1", "title": "Using a new parsimonious AHP methodology combined with the Choquet integral: An application for evaluating social housing initiatives", "abstract": "We propose a development of the Analytic Hierarchy Process (AHP) permitting to use the methodology also in cases of decision problems with a very large number of alternatives evaluated with respect to several criteria. While the application of the original AHP method involves many pairwise comparisons between alternatives and criteria, our proposal is composed of three steps: (i) direct evaluation of the alternatives at hand on the considered criteria, (ii) selection of some reference evaluations; (iii) application of the original AHP method to reference evaluations; (iv) revision of the direct evaluation on the basis of the prioritization supplied by AHP on reference evaluations. The new proposal has been tested and validated in an experiment conducted on a sample of university students. The new methodology has been therefore applied to a real world problem involving the evaluation of 21 Social Housing initiatives sited in the Piedmont region (Italy). To take into account interaction between criteria, the Choquet integral preference model has been considered within a Non Additive Robust Ordinal Regression approach.", "published": "2017-04-24T12:38:22Z", "authors": ["Francesca Abastante", "Salvatore Corrente", "Salvatore Greco", "Alessio Ishizaka", "Isabella Lami"], "categories": ["cs.AI", "math.OC"], "doi": "", "_query": "hedonic regression housing", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2511.21158v4", "title": "Rectified strong core in Shapley-Scarf housing markets with indifferences", "abstract": "We study core concepts in the Shapley-Scarf housing market model under unrestricted weak preferences. Among standard concepts, the strong core may be empty while the nonempty weak core may contain Pareto inefficient allocations. We propose the rectified strong core by adding a single condition: an unaffected agent may join a blocking coalition only if every object he views as indifferent to his current assignment is owned by the coalition. The rectified strong core is always nonempty and Pareto efficient, lies between the weak and strong cores, and coincides with the strong core whenever the latter is nonempty. This condition is the most permissive admissibility requirement for unaffected blocking agents that guarantees nonemptiness, and extends the behavioral foundation of the strong core to weak preferences.", "published": "2025-11-26T08:18:07Z", "authors": ["Jun Zhang"], "categories": ["econ.TH"], "doi": "", "_query": "hedonic regression housing", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2404.05898v1", "title": "Inexact Simplification of Symbolic Regression Expressions with Locality-sensitive Hashing", "abstract": "Symbolic regression (SR) searches for parametric models that accurately fit a dataset, prioritizing simplicity and interpretability. Despite this secondary objective, studies point out that the models are often overly complex due to redundant operations, introns, and bloat that arise during the iterative process, and can hinder the search with repeated exploration of bloated segments. Applying a fast heuristic algebraic simplification may not fully simplify the expression and exact methods can be infeasible depending on size or complexity of the expressions. We propose a novel agnostic simplification and bloat control for SR employing an efficient memoization with locality-sensitive hashing (LHS). The idea is that expressions and their sub-expressions traversed during the iterative simplification process are stored in a dictionary using LHS, enabling efficient retrieval of similar structures. We iterate through the expression, replacing subtrees with others of same hash if they result in a smaller expression. Empirical results shows that applying this simplification during evolution performs equal or better than without simplification in minimization of error, significantly reducing the number of nonlinear functions. This technique can learn simplification rules that work in general or for a specific problem, and improves convergence while reducing model complexity.", "published": "2024-04-08T22:54:14Z", "authors": ["Guilherme Seidyo Imai Aldeia", "Fabricio Olivetti de Franca", "William G. La Cava"], "categories": ["cs.NE", "cs.LG"], "doi": "10.1145/3638529.3654147", "_query": "hedonic regression housing", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2106.14572v1", "title": "Dynamic Urban Planning: an Agent-Based Model Coupling Mobility Mode and Housing Choice. Use case Kendall Square", "abstract": "As cities become increasingly populated, urban planning plays a key role in ensuring the equitable and inclusive development of metropolitan areas. MIT City Science group created a data-driven tangible platform, CityScope, to help different stakeholders, such as government representatives, urban planners, developers, and citizens, collaboratively shape the urban scenario through the real-time impact analysis of different urban interventions. This paper presents an agent-based model that characterizes citizens' behavioural patterns with respect to housing and mobility choice that will constitute the first step in the development of a dynamic incentive system for an open interactive governance process. The realistic identification and representation of the criteria that affect this decision-making process will help understand and evaluate the impacts of potential housing incentives that aim to promote urban characteristics such as equality, diversity, walkability, and efficiency. The calibration and validation of the model have been performed in a well-known geographic area for the Group: Kendall Square in Cambridge, MA.", "published": "2021-06-28T10:54:44Z", "authors": ["Mireia Yurrita", "Arnaud Grignard", "Luis Alonso", "Yan Zhang", "Cristian Jara-Figueroa", "Markus Elkatsha", "Kent Larson"], "categories": ["cs.MA", "cs.CY"], "doi": "", "_query": "hedonic regression housing", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/1411.5725v1", "title": "Local Adaptive Grouped Regularization and its Oracle Properties for Varying Coefficient Regression", "abstract": "Varying coefficient regression is a flexible technique for modeling data where the coefficients are functions of some effect-modifying parameter, often time or location in a certain domain. While there are a number of methods for variable selection in a varying coefficient regression model, the existing methods are mostly for global selection, which includes or excludes each covariate over the entire domain. Presented here is a new local adaptive grouped regularization (LAGR) method for local variable selection in spatially varying coefficient linear and generalized linear regression. LAGR selects the covariates that are associated with the response at any point in space, and simultaneously estimates the coefficients of those covariates by tailoring the adaptive group Lasso toward a local regression model with locally linear coefficient estimates. Oracle properties of the proposed method are established under local linear regression and local generalized linear regression. The finite sample properties of LAGR are assessed in a simulation study and for illustration, the Boston housing price data set is analyzed.", "published": "2014-11-20T23:42:21Z", "authors": ["Wesley Brooks", "Jun Zhu", "Zudi Lu"], "categories": ["stat.ME"], "doi": "", "_query": "hedonic regression housing", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2310.13795v1", "title": "The Human Behind the Data: Reflections from an Ongoing Co-Design and Deployment of a Data-Navigation Interface for Front-Line Emergency Housing Shelter Staff", "abstract": "On any night in Canada, at least 35,000 individuals experience homelessness. These individuals use emergency shelters to transition out of homelessness and into permanent housing. We designed and deployed a technology to support front-line staff at the largest emergency housing shelter in Calgary, Canada. Over a period of five months in 2022, we worked closely with front-line staff to co-design an interface for supporting a holistic understanding of client context and facilitating decision-making. The tool is currently in-use and our collaboration is ongoing. In this paper, we reflect on preliminary findings regarding the second iteration of the tool. We find that supporting shelter staff in understanding the human behind the data was a critical component of design. This work contributes to literature on how data tools may be integrated into homeless shelters in a way that aligns with shelters' values.", "published": "2023-10-20T20:03:52Z", "authors": ["Teale W Masrani", "Helen Ai He", "Geoffrey Messier"], "categories": ["cs.HC"], "doi": "10.1145/3544549.3585694", "_query": "hedonic regression housing", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2505.08743v1", "title": "Understanding Housing and Homelessness System Access by Linking Administrative Data", "abstract": "This paper uses privacy preserving methods to link over 235,000 records in the housing and homelessness system of care (HHSC) of a major North American city. Several machine learning pairwise linkage and two clustering algorithms are evaluated for merging the profiles for latent individuals in the data. Importantly, these methods are evaluated using both traditional machine learning metrics and HHSC system use metrics generated using the linked data. The results demonstrate that privacy preserving linkage methods are an effective and practical method for understanding how a single person interacts with multiple agencies across an HHSC. They also show that performance differences between linkage techniques are amplified when evaluated using HHSC domain specific metrics like number of emergency homeless shelter stays, length of time interacting with an HHSC and number of emergency shelters visited per person.", "published": "2025-05-13T16:57:55Z", "authors": ["Geoffrey G. Messier", "Sam Elliott", "Dallas Seitz"], "categories": ["cs.CY"], "doi": "", "_query": "hedonic regression housing", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2607.12205v1", "title": "A Unified Credit Expansion Theory on Housing Cycle: Causal Evidence for Within- and Cross-Metro Patterns in the Prior, Boom, Bust, and Recovery Periods", "abstract": "During the 1999-2019 U.S. housing cycle, three empirical facts present a puzzle: in the boom period, the correlation between income growth and mortgage growth is (1) negative across ZIP codes within a metropolitan area, but (2) positive across metropolitan areas, and (3) the metropolitan areas that experience the worst bust also show the strongest recovery. I develop a unified credit expansion theory that explains both within- and cross-metro patterns in the prior, boom, bust, and recovery periods (including the three facts above) and generates new testable implications of ``double differences\" (cross ZIP codes and cross metros) for the four periods. Following the idea of ``Economic Base Theory\", I construct local economic exposure to net export growth as the driving force of local economy and credit expansion. For the identification strategy, I use a new instrumental variable approach from the International trade literature for the following empirical results. First, I show that high-net-export-growth metros experience a stronger boom-bust-recovery housing cycle due to credit expansion in private-label mortgages (PLMs), rather than in government-sponsored enterprise mortgages (GSEMs), because only the former can legally respond to local economic conditions. Second, for the ``double differences\", I define a low-minus-high (LMH) factor as the private-label mortgage (and house price) growth in low-income ZIP codes minus that in high-income ZIP codes within the same metropolitan area. I show that this low-minus-high factor (as a measure of credit expansion) in the high-net-export-growth metros is more positive during the boom period, more negative during the bust period, and slightly more positive in the recovery period than in the low-net-export-growth metros. Lastly, I employ five tests to demonstrate that ``speculation\" is unlikely to play a dominant role in this housing cycle.", "published": "2026-07-13T23:15:50Z", "authors": ["Bo Li"], "categories": ["q-fin.GN"], "doi": "", "_query": "hedonic regression housing", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2310.07444v2", "title": "Measuring the impact of green retrofits on CO$_2$ emissions: London Housing Case Study", "abstract": "The residential sector is responsible for approximately 20 percent of the world's energy-related greenhouse gas emissions. In order to improve this reality, it is essential to help and support the leading actor in this scene, the homeowner. In general, buildings are complex objects as each home may have very particular parameters: different materials (windows, walls, types of roofs, etc.), uses, and habits. No two cases are identical. In this paper, we addressed this problem by proposing a method to obtain estimates of energy and carbon savings resulting from a class of retrofit projects and housing profiles. We applied our formulas to conduct a case study of the London residential sector, supported by the publicly available Energy Performance Certificates (EPC) dataset. As a result of a multi-project renovation of a house, including loft insulation, double-glazed windows, and energy-efficient lighting (LED), we found that the average family in the Greater London area could save approximately 9 MWh in energy consumption, 1,700 kgCO$_2$ in carbon emissions and more than GBP 800 on the energy bills, over a year, in contrast to a cost of around GBP 9,000 (or a return on investment of 11 years). In another example, applying the method to calculate what is needed to install heat pumps in each house in London, a total of GBP 16.9 billion would be spent, leading to savings of GBP 450 million per year (return on investment of 37 years). Finally, an overall upgrade to loft insulation would cost London households GBP 1.4 billion, generating GBP 290 million in annual savings (a 5-year return on investment).", "published": "2023-10-11T12:48:26Z", "authors": ["F. C. Correia", "M. Fazion"], "categories": ["stat.AP", "physics.soc-ph"], "doi": "", "_query": "hedonic regression housing", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2506.00502v3", "title": "Modeling and Optimal Control of Thermal Environment in Pig Houses", "abstract": "The management of thermal environments in pig farming is crucial for optimizing animal health, productivity, and operational energy efficiency. This study introduces a novel thermal ventilation model (TVM) based on enthalpy balance, which integrates both temperature and humidity control to address the specific thermal regulation requirements of pig housing in regions characterized by high temperatures and humidity, such as Guangdong, China. These challenging environmental conditions can lead to heat stress in pigs, adversely affecting their health and productivity. The TVM provides a precise representation of thermal comfort by accounting for the combined effects of temperature and humidity. Building on the TVM, we formulate an optimization problem using Model Predictive Control (MPC), which dynamically adjusts ventilation rates in real-time by modifying weight factors to minimize energy consumption while keeping the temperature and humidity within the comfort zone of the pigs. The accuracy of the TVM is validated against real-world environmental data from pig housing facilities in Guangdong. The root mean square error of temperature in winter, spring and summer were 1.23, 0.81, and 0.60, demonstrating its reliability and robustness across diverse climatic conditions. Furthermore, simulation results show that the proposed MPC strategy significantly improves energy efficiency and environmental comfort, achieving a 100% comfort temperature zone in spring and 83% in summer, compared to 91% and 43% with traditional rule-based control, respectively. However, the model's energy consumption in summer (91.2 kWh) was higher than that of rule-based control (80.8 kWh), reflecting the trade-off between maintaining optimal comfort and energy efficiency under extreme conditions.", "published": "2025-05-31T10:43:53Z", "authors": ["Mingxin Wei", "Jinrui Zhang", "Peter Groot Koerkamp", "Andre Aarnink", "Congcong Sun"], "categories": ["math.OC"], "doi": "", "_query": "hedonic regression housing", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/1908.05752v6", "title": "Isotonic Regression Discontinuity Designs", "abstract": "This paper studies the estimation and inference for the isotonic regression at the boundary point, an object that is particularly interesting and required in the analysis of monotone regression discontinuity designs. We show that the isotonic regression is inconsistent in this setting and derive the asymptotic distributions of boundary corrected estimators. Interestingly, the boundary corrected estimators can be bootstrapped without subsampling or additional nonparametric smoothing which is not the case for the interior point. The Monte Carlo experiments indicate that shape restrictions can improve dramatically the finite-sample performance of unrestricted estimators. Lastly, we apply the isotonic regression discontinuity designs to estimate the causal effect of incumbency in the U.S. House elections.", "published": "2019-08-15T20:44:44Z", "authors": ["Andrii Babii", "Rohit Kumar"], "categories": ["math.ST", "econ.EM", "stat.AP", "stat.ME"], "doi": "", "_query": "hedonic regression housing", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2212.03777v1", "title": "Leveraging Priority Thresholds to Improve Equitable Housing Access for Unhoused-at-Risk Youth", "abstract": "Approximately 4.2 million youth and young adults experience homelessness each year in the United States and lack of basic necessities puts this population at high-risk of being trafficked or exploited. Although all runaway and homeless youth (RHY) are at risk of being victims of human trafficking, certain racial, ethnic, and gender groups are disproportionately affected. Motivated by these facts, our goal is to improve equitable access to housing resources for at-risk RHY in New York City (NYC) by expanding the current housing capacity, while utilizing priority thresholds that guide decisions regarding which youth should start receiving service based on the number of beds idle in the system. Our approach involves an $M/M/N/\\{K_j\\}+M$ queuing model with many statistically identical servers (beds) and RHY from different demographic groups with limited patience arriving to the a large crisis and emergency shelter in NYC. The queuing model allows us to: (i) investigate the populations and demographics that are facing access barriers, (ii) project the minimum number of beds required to provide a certain global service quality level to all youth, regardless of demographic characteristics, and (iii) use priority thresholds while matching RHY with beds to promote equity. The recommendations regarding the capacity expansion and priority thresholds improves equitable access to this crisis and emergency shelter by decreasing the average number of RHY abandoning the system by 92%, with a particular reduction in the abandonment of RHY who are at high-risk of experiencing trafficking.", "published": "2022-12-07T16:52:54Z", "authors": ["Yaren Bilge Kaya", "Kayse Lee Maass"], "categories": ["cs.HC", "math.NA"], "doi": "", "_query": "hedonic regression housing", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2512.16949v1", "title": "Identifying and understanding obstacles to heating sobriety and thermal comfort in collective housing: Insights from a survey in France", "abstract": "In many countries, central heating systems are widely used in multifamily housing allowing maintenance and costs to be shared. However, these systems often limit residents' control over their own consumption, complicating efforts to reduce energy use and maintain comfort. Despite the growing importance of household energy savings in national and European climate targets, little research has examined user experiences with these systems. This study addresses that gap through an exploratory survey conducted in France. Grounded in scientific literature, technical analyses, and current policy frameworks, the survey was distributed through various channels-including public institutions, online platforms, and field outreach-and collected 403 responses. It examined user difficulties in managing heating, including challenges with controls, bill comprehension, and communication with landlords. It also explored behaviours with high energy-savings impact: reducing heating at night or during absences, window opening, and shutter use. Findings show that many residents face barriers to energy-efficient heating, often due to difficult-to-use controls, limited feedback on consumption, and poor support from landlords or property managers. Those who do not reduce heating at night or when away frequently report these challenges. Participants expressed strong interest in solutions such as personalized advice, real-time feedback, and connected radiator valves to improve comfort, control, and energy management. This work highlights the need to better integrate user experience into multifamily housing energy policies. It provides actionable insights for policymakers and housing providers seeking to enhance comfort and energy savings while supporting France and Europe's broader energy transition goals.", "published": "2025-12-17T09:30:23Z", "authors": ["Enzo Cabezas-Rivière", "Maxime Robillart", "Aline Barlet", "Thomas Recht", "Laurent Mora", "Patrick Sebastian"], "categories": ["physics.soc-ph"], "doi": "", "_query": "hedonic regression housing", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2210.17253v1", "title": "House of Graphs 2.0: a database of interesting graphs and more", "abstract": "In 2012 we announced the House of Graphs (https://houseofgraphs.org) [Discrete Appl. Math. 161 (2013), 311-314], which was a new database of graphs. The House of Graphs hosts complete lists of graphs of various graph classes, but its main feature is a searchable database of so called \"interesting\" graphs, which includes graphs that already occurred as extremal graphs or as counterexamples to conjectures. An important aspect of this database is that it can be extended by users of the website. Over the years, several new features and graph invariants were added to the House of Graphs and users uploaded many interesting graphs to the website. But as the development of the original House of Graphs website started in 2010, the underlying frameworks and technologies of the website became outdated. This is why we completely rebuilt the House of Graphs using modern frameworks to build a maintainable and expandable web application that is future-proof. On top of this, several new functionalities were added to improve the application and the user experience. This article describes the changes and new features of the new House of Graphs website.", "published": "2022-10-31T12:18:26Z", "authors": ["Kris Coolsaet", "Sven D'hondt", "Jan Goedgebeur"], "categories": ["math.CO", "cs.DM"], "doi": "10.1016/j.dam.2022.10.013", "_query": "hedonic regression housing", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2607.26788v1", "title": "Stable and Budget-Feasible Coalition Formation for Clustered Federated Learning: A Hedonic Potential-Game Approach", "abstract": "Clustered federated learning benefits from organizing heterogeneous participants into coalitions that train coalition-specific models, but such clustering is sustainable only if participants prefer their assigned coalition and the required transfers are affordable. We develop a transferable-surplus model separating learning benefit, system cost, participant cost, and monetary transfers; an allocation rule converts coalition surplus into hedonic preferences, and weak budget feasibility guarantees nonnegative retained coordinator surplus. For symmetric pairwise allocations the induced game is an exact potential game: a Nash-stable partition exists, every strict better-response process converges, and with destination consent accepted better responses reach an individually stable partition. We characterize feasibility of bounded pair incentives and verify the exponentially many budget constraints in polynomial oracle time when retained slack is submodular. Decomposing welfare into participant potential and retained slack yields additive and multiplicative price-of-stability guarantees, the latter asymptotically tight; exact balance gives welfare-optimal stability only on the pairwise-representable class, and budget feasibility alone permits unbounded welfare loss. Global potential maximization equals weighted maximum-agreement correlation clustering, and approximation followed by stabilization satisfies an end-to-end welfare bound governed by retained slack and negative-edge mass, attained by an explicit construction. In a preregistered five-seed CIFAR-10 study the mechanism reaches the certified estimated-table welfare optimum on every primary instance, equal-surplus sharing has no Nash-stable outcome on three, and pairwise validation gain gives far more reliable pair signs than gradient alignment.", "published": "2026-07-29T11:29:45Z", "authors": ["Cengis Hasan"], "categories": ["cs.GT", "cs.LG"], "doi": "", "_query": "hedonic regression housing", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/1709.08432v1", "title": "House Price Prediction Using LSTM", "abstract": "In this paper, we use the house price data ranging from January 2004 to October 2016 to predict the average house price of November and December in 2016 for each district in Beijing, Shanghai, Guangzhou and Shenzhen. We apply Autoregressive Integrated Moving Average model to generate the baseline while LSTM networks to build prediction model. These algorithms are compared in terms of Mean Squared Error. The result shows that the LSTM model has excellent properties with respect to predict time series. Also, stateful LSTM networks and stack LSTM networks are employed to further study the improvement of accuracy of the house prediction model.", "published": "2017-09-25T11:31:50Z", "authors": ["Xiaochen Chen", "Lai Wei", "Jiaxin Xu"], "categories": ["cs.LG", "stat.ML"], "doi": "", "_query": "hedonic regression housing", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2405.07456v1", "title": "Boosting House Price Estimations with Multi-Head Gated Attention", "abstract": "Evaluating house prices is crucial for various stakeholders, including homeowners, investors, and policymakers. However, traditional spatial interpolation methods have limitations in capturing the complex spatial relationships that affect property values. To address these challenges, we have developed a new method called Multi-Head Gated Attention for spatial interpolation. Our approach builds upon attention-based interpolation models and incorporates multiple attention heads and gating mechanisms to capture spatial dependencies and contextual information better. Importantly, our model produces embeddings that reduce the dimensionality of the data, enabling simpler models like linear regression to outperform complex ensembling models. We conducted extensive experiments to compare our model with baseline methods and the original attention-based interpolation model. The results show a significant improvement in the accuracy of house price predictions, validating the effectiveness of our approach. This research advances the field of spatial interpolation and provides a robust tool for more precise house price evaluation. Our GitHub repository.contains the data and code for all datasets, which are available for researchers and practitioners interested in replicating or building upon our work.", "published": "2024-05-13T04:12:03Z", "authors": ["Zakaria Abdellah Sellam", "Cosimo Distante", "Abdelmalik Taleb-Ahmed", "Pier Luigi Mazzeo"], "categories": ["cs.LG"], "doi": "", "_query": "hedonic regression housing", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/1902.08594v1", "title": "Regression-based Inverter Control for Decentralized Optimal Power Flow and Voltage Regulation", "abstract": "Electronic power inverters are capable of quickly delivering reactive power to maintain customer voltages within operating tolerances and to reduce system losses in distribution grids. This paper proposes a systematic and data-driven approach to determine reactive power inverter output as a function of local measurements in a manner that obtains near optimal results. First, we use a network model and historic load and generation data and do optimal power flow to compute globally optimal reactive power injections for all controllable inverters in the network. Subsequently, we use regression to find a function for each inverter that maps its local historical data to an approximation of its optimal reactive power injection. The resulting functions then serve as decentralized controllers in the participating inverters to predict the optimal injection based on a new local measurements. The method achieves near-optimal results when performing voltage- and capacity-constrained loss minimization and voltage flattening, and allows for an efficient volt-VAR optimization (VVO) scheme in which legacy control equipment collaborates with existing inverters to facilitate safe operation of distribution networks with higher levels of distributed generation.", "published": "2019-02-20T23:20:52Z", "authors": ["Oscar Sondermeijer", "Roel Dobbe", "Daniel Arnold", "Claire Tomlin", "Tamás Keviczky"], "categories": ["eess.SY", "cs.LG", "cs.MA", "stat.ML"], "doi": "", "_query": "hedonic regression housing", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/1812.10436v1", "title": "Stability of a rotating asteroid housing a space station", "abstract": "Today there are numerous studies on asteroid mining. They elaborate on selecting the right objects, prospecting missions, potential asteroid redirection, and the mining process itself. For economic reasons, most studies focus on mining candidates in the 100-500m size-range. Also, suggestions regarding the design and implementation of space stations or even colonies inside the caverns of mined asteroids exist. Caverns provide the advantages of confined material in near-zero gravity during mining and later the hull will shield the inside from radiation. Existing studies focus on creating the necessary artificial gravity by rotating structures that are built inside the asteroid. Here, we assume the entire mined asteroid to rotate at a sufficient rate for artificial gravity and investigate its use for housing a habitat inside. In this study we present how to estimate the necessary spin rate assuming a cylindrical space station inside a mined asteroid and discuss the implications arising from substantial material stress given the required rotation rate. We estimate the required material strength using two relatively simple analytical models and apply them to fictitious, yet realistic rocky near-Earth asteroids.", "published": "2018-12-26T18:10:14Z", "authors": ["Thomas I. Maindl", "Roman Miksch", "Birgit Loibnegger"], "categories": ["astro-ph.EP"], "doi": "", "_query": "hedonic regression housing", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2312.10863v2", "title": "Disclosure Avoidance for the 2020 Census Demographic and Housing Characteristics File", "abstract": "In \"The 2020 Census Disclosure Avoidance System TopDown Algorithm,\" Abowd et al. (2022) describe the concepts and methods used by the Disclosure Avoidance System (DAS) to produce formally private output in support of the 2020 Census statistical data product releases, with a particular focus on the DAS implementation that was used to create the 2020 Census Redistricting Data (P.L. 94-171) Summary File. In this paper we describe the updates to the DAS that were required to release the Demographic and Housing Characteristics (DHC) File, which provides more granular tables than other statistical data products, such as the Redistricting Data Summary File. We also describe the final configuration parameters used for the 2020 production DHC DAS implementation, error metrics for these production statistical data products, and plans for future experimental data products that provide confidence intervals for confidential 2020 Census tabulations.", "published": "2023-12-18T00:54:04Z", "authors": ["Ryan Cumings-Menon", "Robert Ashmead", "Daniel Kifer", "Philip Leclerc", "Matthew Spence", "Pavel Zhuravlev", "John M. Abowd"], "categories": ["cs.CR", "stat.CO"], "doi": "", "_query": "hedonic regression housing", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/1808.05902v1", "title": "Learning Supervised Topic Models for Classification and Regression from Crowds", "abstract": "The growing need to analyze large collections of documents has led to great developments in topic modeling. Since documents are frequently associated with other related variables, such as labels or ratings, much interest has been placed on supervised topic models. However, the nature of most annotation tasks, prone to ambiguity and noise, often with high volumes of documents, deem learning under a single-annotator assumption unrealistic or unpractical for most real-world applications. In this article, we propose two supervised topic models, one for classification and another for regression problems, which account for the heterogeneity and biases among different annotators that are encountered in practice when learning from crowds. We develop an efficient stochastic variational inference algorithm that is able to scale to very large datasets, and we empirically demonstrate the advantages of the proposed model over state-of-the-art approaches.", "published": "2018-08-17T15:32:24Z", "authors": ["Filipe Rodrigues", "Mariana Lourenço", "Bernardete Ribeiro", "Francisco Pereira"], "categories": ["stat.ML", "cs.CL", "cs.CV", "cs.HC", "cs.LG"], "doi": "10.1109/TPAMI.2017.2648786", "_query": "hedonic regression housing", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2505.00296v3", "title": "Minimizing Envy and Maximizing Happiness in Graphical House Allocation", "abstract": "We study the house allocation problem in a setting where agents are connected by a graph representing friendships. In this model, two agents can only envy each other if they are neighbors (i.e., friends) in the graph. Each agent has a set of preferred (liked) houses and dislikes the rest. An agent $a$ is said to envy a friend $b$ if $a$ is not assigned any house she likes, while $b$ is allocated a house that $a$ likes. This framework is known as graphical house allocation. Within this framework, we investigate two central problems. The first problem is to compute a house allocation that minimizes the number of envious agents. Multiple such allocations may exist that achieve the same minimum level of envy. Among all allocations that minimize envy, the second problem aims to find one that maximizes the number of agents who receive one of their preferred houses. We present a detailed complexity-theoretic analysis of these problems. In particular, we show that both problems can be solved in polynomial time when each agent prefers at most one house. However, both become NP-hard even when agents are allowed to prefer at most two houses, thereby highlighting the tight boundary between tractability and intractability. Additionally, we design exact algorithms for both problems under certain structural conditions on the agent graph, such as when the graph is sparse, has a small balanced separator, or admits a small vertex cover. These algorithms are significantly faster than the naive brute-force approach.", "published": "2025-05-01T04:38:09Z", "authors": ["Anubhav Dhar", "Ashlesha Hota", "Palash Dey", "Sudeshna Kolay"], "categories": ["cs.DS", "cs.CC"], "doi": "", "_query": "hedonic regression housing", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/1009.5557v1", "title": "A Mobile Application for Smart House Remote Control System", "abstract": "At the start of the second decade of 21th century, the time has come to make the Smart Houses a reality for regular use. The different parts of a Smart House are researched but there are still distances from an applicable system, using the modern technology. In this paper we present an overview of the Smart House subsystems necessary for controlling the house using a mobile application efficiently and securely. The sequence diagram of the mobile application connecting to the server application and also the use-cases possible are presented. The challenges faced in designing the mobile application and illustrating the updated house top plane view in that application, are discussed and solutions are adapted for it. Finally the designed mobile application was implemented and the important sections of it were described, such as the interactive house top view map which indicates the status of the devices using predefined icons. The facilities to manage the scheduled tasks and defined rules are also implemented in this mobile application that was developed for use in Windows Mobile platform. This application has the capability of connecting to the main server using GPRS mobile internet and SMS. This system is expected to be an important step towards a unified system structure that can be used efficiently in near future regular houses.", "published": "2010-09-27T18:13:37Z", "authors": ["Amir Rajabzadeh", "Ali Reza Manashty", "Zahra Forootan Jahromi"], "categories": ["cs.OH"], "doi": "", "_query": "hedonic regression housing", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2411.17724v1", "title": "Incentives to Build Houses, Trade Houses, or Trade House Building Skills in Simulated Worlds under Various Governing Systems or Institutions: Comparing Multi-agent Reinforcement Learning to Generative Agent-based Model", "abstract": "It has been shown that social institutions impact human motivations to produce different behaviours, such as amount of working or specialisation in labor. With advancement in artificial intelligence (AI), specifically large language models (LLMs), now it is possible to perform in-silico simulations to test various hypotheses around this topic. Here, I simulate two somewhat similar worlds using multi-agent reinforcement learning (MARL) framework of the AI-Economist and generative agent-based model (GABM) framework of the Concordia. In the extended versions of the AI-Economist and Concordia, the agents are able to build houses, trade houses, and trade house building skill. Moreover, along the individualistic-collectivists axis, there are a set of three governing systems: Full-Libertarian, Semi-Libertarian/Utilitarian, and Full-Utilitarian. Additionally, in the extended AI-Economist, the Semi-Libertarian/Utilitarian system is further divided to a set of three governing institutions along the discriminative axis: Inclusive, Arbitrary, and Extractive. Building on these, I am able to show that among governing systems and institutions of the extended AI-Economist, under the Semi-Libertarian/Utilitarian and Inclusive government, the ratios of building houses to trading houses and trading house building skill are higher than the rest. Furthermore, I am able to show that in the extended Concordia when the central government care about equality in the society, the Full-Utilitarian system generates agents building more houses and trading more house building skill. In contrast, these economic activities are higher under the Full-Libertarian system when the central government cares about productivity in the society. Overall, the focus of this paper is to compare and contrast two advanced techniques of AI, MARL and GABM, to simulate a similar social phenomena with limitations.", "published": "2024-11-21T08:52:42Z", "authors": ["Aslan S. Dizaji"], "categories": ["cs.MA"], "doi": "", "_query": "hedonic regression housing", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/1801.07357v2", "title": "CHALET: Cornell House Agent Learning Environment", "abstract": "We present CHALET, a 3D house simulator with support for navigation and manipulation. CHALET includes 58 rooms and 10 house configuration, and allows to easily create new house and room layouts. CHALET supports a range of common household activities, including moving objects, toggling appliances, and placing objects inside closeable containers. The environment and actions available are designed to create a challenging domain to train and evaluate autonomous agents, including for tasks that combine language, vision, and planning in a dynamic environment.", "published": "2018-01-23T00:22:25Z", "authors": ["Claudia Yan", "Dipendra Misra", "Andrew Bennnett", "Aaron Walsman", "Yonatan Bisk", "Yoav Artzi"], "categories": ["cs.AI"], "doi": "", "_query": "hedonic regression housing", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/physics/0602033v3", "title": "Community Structure in the United States House of Representatives", "abstract": "We investigate the networks of committee and subcommittee assignments in the United States House of Representatives from the 101st--108th Congresses, with the committees connected by ``interlocks'' or common membership. We examine the community structure in these networks using several methods, revealing strong links between certain committees as well as an intrinsic hierarchical structure in the House as a whole. We identify structural changes, including additional hierarchical levels and higher modularity, resulting from the 1994 election, in which the Republican party earned majority status in the House for the first time in more than forty years. We also combine our network approach with analysis of roll call votes using singular value decomposition to uncover correlations between the political and organizational structure of House committees.", "published": "2006-02-04T22:41:34Z", "authors": ["Mason A. Porter", "Peter J. Mucha", "M. E. J. Newman", "A. J. Friend"], "categories": ["physics.soc-ph", "cond-mat.stat-mech", "cs.MA", "nlin.AO", "physics.data-an"], "doi": "10.1063/1.2390556", "_query": "hedonic regression housing", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/1008.1376v1", "title": "House Price Distributions of Taiwan: A Preliminary Study", "abstract": "The house price distributions of Taiwan are analyzed. The tail of the cumulative distribution function (CDF) follows an approximate power law with an exponent equals to -2.4 while the distribution of the house price per unit area displays a lognormal distribution. Implications of the results are also discussed.", "published": "2010-08-08T01:23:28Z", "authors": ["Chung-I Chou", "S. P. Li"], "categories": ["physics.soc-ph", "physics.data-an"], "doi": "", "_query": "hedonic regression housing", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2307.12482v2", "title": "Tight Approximations for Graphical House Allocation", "abstract": "The Graphical House Allocation problem asks: how can $n$ houses (each with a fixed non-negative value) be assigned to the vertices of an undirected graph $G$, so as to minimize the \"aggregate local envy\", i.e., the sum of absolute differences along the edges of $G$? This problem generalizes the classical Minimum Linear Arrangement problem, as well as the well-known House Allocation Problem from Economics, the latter of which has notable practical applications in organ exchanges. Recent work has studied the computational aspects of Graphical House Allocation and observed that the problem is NP-hard and inapproximable even on particularly simple classes of graphs, such as vertex disjoint unions of paths. However, the dependence of any approximations on the structural properties of the underlying graph had not been studied. In this work, we give a complete characterization of the approximability of the Graphical House Allocation problem. We present algorithms to approximate the optimal envy on general graphs, trees, planar graphs, bounded-degree graphs, bounded-degree planar graphs, and bounded-degree trees. For each of these graph classes, we then prove matching lower bounds, showing that in each case, no significant improvement can be attained unless P = NP. We also present general approximation ratios as a function of structural parameters of the underlying graph, such as treewidth; these match the aforementioned tight upper bounds in general, and are significantly better approximations for many natural subclasses of graphs. Finally, we present constant factor approximation schemes for the special classes of complete binary trees and random graphs.", "published": "2023-07-24T02:30:34Z", "authors": ["Hadi Hosseini", "Andrew McGregor", "Rik Sengupta", "Rohit Vaish", "Vignesh Viswanathan"], "categories": ["cs.DS", "cs.DM", "cs.GT", "math.CO"], "doi": "", "_query": "hedonic regression housing", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/1105.2902v1", "title": "A Multi-Purpose Scenario-based Simulator for Smart House Environments", "abstract": "Developing smart house systems has been a great challenge for researchers and engineers in this area because of the high cost of implementation and evaluation process of these systems, while being very time consuming. Testing a designed smart house before actually building it is considered as an obstacle towards an efficient smart house project. This is because of the variety of sensors, home appliances and devices available for a real smart environment. In this paper, we present the design and implementation of a multi-purpose smart house simulation system for designing and simulating all aspects of a smart house environment. This simulator provides the ability to design the house plan and different virtual sensors and appliances in a two dimensional model of the virtual house environment. This simulator can connect to any external smart house remote controlling system, providing evaluation capabilities to their system much easier than before. It also supports detailed adding of new emerging sensors and devices to help maintain its compatibility with future simulation needs. Scenarios can also be defined for testing various possible combinations of device states; so different criteria and variables can be simply evaluated without the need of experimenting on a real environment.", "published": "2011-05-14T15:11:00Z", "authors": ["Zahra Forootan Jahromi", "Amir Rajabzadeh", "Ali Reza Manashty"], "categories": ["cs.AI"], "doi": "", "_query": "hedonic regression housing", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2408.03240v1", "title": "Beta regression mixed model applied to sensory analysis", "abstract": "Sensory analysis is an important area that the food industry can use to innovate and improve its products. This study involves a sample of individuals who can be trained or not to assess a product using a hedonic scale or notes, where the experimental design is a balanced incomplete block design. In this context, integrating sensory analysis with effective statistical methods, which consider the nature of the response variables, is essential to answer the aim of the experimental study. Some techniques are available to analyse sensory data, such as response surface models or categorical models. This article proposes using beta regression as an alternative to the proportional odds model, addressing some convergence problems, especially regarding the number of parameters. Moreover, the beta distribution is flexible for heteroscedasticity and asymmetry data. To this end, we conducted simulation studies that showed agreement rates in product selection using both models. Also, we presented a motivational study that was developed to select prebiotic drinks based on cashew nuts added to grape juice. In this application, the beta regression mixed model results corroborated with the selected formulations using the proportional mixed model.", "published": "2024-08-06T15:01:56Z", "authors": ["João César Reis Alves", "Gabriel Rodrigues Palma", "Idemauro Antonio Rodrigues de Lara"], "categories": ["q-bio.QM"], "doi": "", "_query": "hedonic regression housing", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2402.18973v1", "title": "Privacy Management and Interface Design for a Smart House", "abstract": "In today's life, more and more people tend to opt for a smart house. In this way, the idea of including technology has become popular worldwide. Despite this concept's many benefits, managing security remains an essential problem due to the shared activities. The Internet of Things system behind a smart house is based on several sensors to measure temperature, humidity, air quality, and movement. Because of being supervised every day through sensors and controlling their house only with a simple click, many people can be afraid of this new approach in terms of their privacy, and this fact can constrain them from following their habits. The security aspects should be constantly analyzed to keep the data's confidentiality and make people feel safe in their own houses. In this context, the current paper puts light on an alternative design of a platform in which the safety of homeowners is the primary purpose, and they maintain complete control over the data generated by smart devices. The current research highlights the role of security and interface design in controlling a smart house. The study underscores the importance of providing an interface that can be used easily by any person to manage data and live activities in a modern residence in an era dominated by continuously developing technology.", "published": "2024-02-29T09:26:41Z", "authors": ["Ana-Maria Comeaga", "Iuliana Marin"], "categories": ["cs.CR", "cs.SE"], "doi": "", "_query": "hedonic regression housing", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2606.04128v1", "title": "On prediction-powered inference for quantile regression via convolution smoothing", "abstract": "This paper studies quantile regression in a data-limited setting where the gold-standard outcome is available only for a limited number of observations, whereas a surrogate outcome is widely available. Such settings are becoming increasingly common with the availability of low-cost predictions from modern AI, motivating a growing line of research on \"prediction-powered inference,\" for improved statistical inference. Naively extending this framework to quantile regression, however, raises two challenges: computational difficulties due to the discontinuity of the subgradient, and overly conservative confidence intervals. To address these issues, we propose a convolution-based smoothing of the check-loss objective and develop two variants of the estimator. The proposed estimators are computationally tractable, and our numerical studies show that they mitigate overcoverage. As a theoretical contribution, we establish the asymptotic distributions of the proposed estimators under a possibly misspecified linear quantile regression model. We further propose an ensemble of the two estimators and illustrate the proposed methods through simulations and an application to a local housing dataset.", "published": "2026-06-02T18:36:46Z", "authors": ["Shota Takeishi", "Jimin Ding", "Xuming He"], "categories": ["stat.ME"], "doi": "", "_query": "hedonic regression housing", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/1505.03799v1", "title": "Distributed House-Hunting in Ant Colonies", "abstract": "We introduce the study of the ant colony house-hunting problem from a distributed computing perspective. When an ant colony's nest becomes unsuitable due to size constraints or damage, the colony must relocate to a new nest. The task of identifying and evaluating the quality of potential new nests is distributed among all ants. The ants must additionally reach consensus on a final nest choice and the full colony must be transported to this single new nest. Our goal is to use tools and techniques from distributed computing theory in order to gain insight into the house-hunting process. We develop a formal model for the house-hunting problem inspired by the behavior of the Temnothorax genus of ants. We then show a Ω(log n) lower bound on the time for all n ants to agree on one of k candidate nests. We also present two algorithms that solve the house-hunting problem in our model. The first algorithm solves the problem in optimal O(log n) time but exhibits some features not characteristic of natural ant behavior. The second algorithm runs in O(k log n) time and uses an extremely simple and natural rule for each ant to decide on the new nest.", "published": "2015-05-14T17:09:54Z", "authors": ["Mohsen Ghaffari", "Cameron Musco", "Tsvetomira Radeva", "Nancy Lynch"], "categories": ["cs.DC"], "doi": "", "_query": "hedonic regression housing", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/1809.02824v1", "title": "A natural language processing and geospatial clustering framework for harvesting local place names from geotagged housing advertisements", "abstract": "Local place names are frequently used by residents living in a geographic region. Such place names may not be recorded in existing gazetteers, due to their vernacular nature, relative insignificance to a gazetteer covering a large area (e.g., the entire world), recent establishment (e.g., the name of a newly-opened shopping center), or other reasons. While not always recorded, local place names play important roles in many applications, from supporting public participation in urban planning to locating victims in disaster response. In this paper, we propose a computational framework for harvesting local place names from geotagged housing advertisements. We make use of those advertisements posted on local-oriented websites, such as Craigslist, where local place names are often mentioned. The proposed framework consists of two stages: natural language processing (NLP) and geospatial clustering. The NLP stage examines the textual content of housing advertisements, and extracts place name candidates. The geospatial stage focuses on the coordinates associated with the extracted place name candidates, and performs multi-scale geospatial clustering to filter out the non-place names. We evaluate our framework by comparing its performance with those of six baselines. We also compare our result with four existing gazetteers to demonstrate the not-yet-recorded local place names discovered by our framework.", "published": "2018-09-08T15:38:25Z", "authors": ["Yingjie Hu", "Huina Mao", "Grant McKenzie"], "categories": ["cs.IR"], "doi": "10.1080/13658816.2018.1458986", "_query": "hedonic regression housing", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2512.18226v1", "title": "Multifaceted Exploration of Spatial Openness in Rental Housing: A Big Data Analysis in Tokyo's 23 Wards", "abstract": "Understanding spatial openness is vital for improving residential quality and design; however, studies often treat its influencing factors separately. This study developed a quantitative framework to evaluate the spatial openness in housing from two- (2D) and three- (3D) dimensional perspectives. Using data from 4,004 rental units in Tokyo's 23 wards, we examined the temporal and spatial variations in openness and its relationship with rent and housing attributes. 2D openness was computed via planar visibility using visibility graph analysis (VGA) from floor plans, whereas 3D openness was derived from interior images analysed using Mask2Former, a semantic segmentation model that identifies walls, ceilings, floors, and windows. The results showed an increase in living room visibility and a 1990s peak in overall openness. Spatial analyses revealed partial correlations among openness, rent, and building characteristics, reflecting urban redevelopment trends. Although the 2D and 3D openness indicators were not directly correlated, higher openness tended to correspond to higher rent. The impression scores predicted by the existing models were only weakly related to openness, suggesting that the interior design and furniture more strongly shape perceived space. This study offers a new multidimensional data-driven framework for quantifying residential spatial openness and linking it with urban and market dynamics.", "published": "2025-12-20T05:50:20Z", "authors": ["Takuya OKi", "Yuan Liu"], "categories": ["cs.CV"], "doi": "", "_query": "hedonic regression housing", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/1811.01295v4", "title": "The reciprocal algebraic integers having small houses", "abstract": "Let $α$ be an algebraic integer of degree $d$, which is reciprocal. The house of $α$ is the largest modulus of its conjugates. We proved that $d$-th power of the house of reciprocal $α$ has a limit point. We presented a property of antireciprocal hexanomials. We compute the minimum of the houses of all reciprocal algebraic integers of degree $d$ having the minimal polynomial which is a factor of a $D$-th degree reciprocal or antireciprocal polynomial with at most eight monomials, say $\\mathrm{mr}(d)$, for $d$ at most 180, $D\\le 1.5d$ and $D\\le 210$. We show that it is not necessary to take into account unprimitive polynomials. The computations suggest several conjectures.", "published": "2018-11-03T22:49:11Z", "authors": ["Dragan Stankov"], "categories": ["math.NT"], "doi": "10.1080/10586458.2021.1982425", "_query": "hedonic regression housing", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2102.00167v1", "title": "Shapley-Scarf Housing Markets: Respecting Improvement, Integer Programming, and Kidney Exchange", "abstract": "In a housing market of Shapley and Scarf, each agent is endowed with one indivisible object and has preferences over all objects. An allocation of the objects is in the (strong) core if there exists no (weakly) blocking coalition. In this paper we show that in the case of strict preferences the unique strong core allocation (or competitive allocation) respects improvement: if an agent's object becomes more attractive for some other agents, then the agent's allotment in the unique strong core allocation weakly improves. We obtain a general result in case of ties in the preferences and provide new integer programming formulations for computing (strong) core and competitive allocations. Finally, we conduct computer simulations to compare the game-theoretical solutions with maximum size and maximum weight exchanges for markets that resemble the pools of kidney exchange programmes.", "published": "2021-01-30T06:50:42Z", "authors": ["Péter Biró", "Flip Klijn", "Xenia Klimentova", "Ana Viana"], "categories": ["econ.TH", "cs.GT"], "doi": "", "_query": "hedonic regression housing", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2302.09438v1", "title": "Does Machine Learning Amplify Pricing Errors in the Housing Market? -- The Economics of Machine Learning Feedback Loops", "abstract": "Machine learning algorithms are increasingly employed to price or value homes for sale, properties for rent, rides for hire, and various other goods and services. Machine learning-based prices are typically generated by complex algorithms trained on historical sales data. However, displaying these prices to consumers anchors the realized sales prices, which will in turn become training samples for future iterations of the algorithms. The economic implications of this machine learning \"feedback loop\" - an indirect human-algorithm interaction - remain relatively unexplored. In this work, we develop an analytical model of machine learning feedback loops in the context of the housing market. We show that feedback loops lead machine learning algorithms to become overconfident in their own accuracy (by underestimating its error), and leads home sellers to over-rely on possibly erroneous algorithmic prices. As a consequence at the feedback loop equilibrium, sale prices can become entirely erratic (relative to true consumer preferences in absence of ML price interference). We then identify conditions (choice of ML models, seller characteristics and market characteristics) where the economic payoffs for home sellers at the feedback loop equilibrium is worse off than no machine learning. We also empirically validate primitive building blocks of our analytical model using housing market data from Zillow. We conclude by prescribing algorithmic corrective strategies to mitigate the effects of machine learning feedback loops, discuss the incentives for platforms to adopt these strategies, and discuss the role of policymakers in regulating the same.", "published": "2023-02-18T23:20:57Z", "authors": ["Nikhil Malik", "Emaad Manzoor"], "categories": ["econ.TH", "cs.CY"], "doi": "", "_query": "hedonic regression housing", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2312.13953v1", "title": "Subtle Sound Design: Designing for experience blend in a historic house museum", "abstract": "In this article, we present and discuss a user-study prototype, developed for Bakkehuset historic house museum in Copenhagen. We examine how the prototype - a digital sound installation - can expand visitors' experiences of the house and offer encounters with immaterial cultural heritage. Historic house museums often hold back on utilizing digital communication tools inside the houses, since a central purpose of this type of museum is to preserve an original environment. Digital communication tools however hold great potential for facilitating rich encounters with cultural heritage and in particular with the immaterial aspects of museum collections and their histories. In this article we present our design steps and choices, aiming at subtly and seamlessly adding a digital dimension to a historic house. Based on qualitative interviews, we evaluate how the sound installation at Bakkehuset is sensed, interpreted, and used by visitors as part of their museum experience. In turn, we shed light on the historic house museum as a distinct design context for designing hybrid visitor experiences and point to the potentials of digital communication tools in this context.", "published": "2023-12-21T15:40:39Z", "authors": ["Mia F. Yates", "Anders Sundnes Løvlie"], "categories": ["cs.HC"], "doi": "10.1145/3633476", "_query": "hedonic regression housing", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2205.12726v8", "title": "The Quantum-House Effect: Filling the Gap Between Classicality and Quantum Discord", "abstract": "We introduce the quantum-house effect, a non-local phenomenon which apparently does not require quantum discord to be present. It suffices for the effect if neither subsystem of a bipartite system is in a pure state. This way, the quantum-house effect completely fills the gap between trivial correlations and quantum discord. However, we discuss why the situation is more subtle than that, by showing that in a concrete cryptographic setting called \"the quantum-house game\", the ability to produce quantum discord is in fact necessary for the quantum-house effect to work. Then, we suggest a principle called \"quantum detachment\" to characterize where quantumness in general departs from classicality, based on the information a physical system contains about itself. The quantum-house effect is demonstrated on SpinQ Gemini, a 2-qubit liquid-state NMR desktop quantum computer.", "published": "2022-05-24T08:55:31Z", "authors": ["Tamás Varga"], "categories": ["quant-ph"], "doi": "", "_query": "hedonic regression housing", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2602.03940v1", "title": "Autonomous AI Agents for Real-Time Affordable Housing Site Selection: Multi-Objective Reinforcement Learning Under Regulatory Constraints", "abstract": "Affordable housing shortages affect billions, while land scarcity and regulations make site selection slow. We present AURA (Autonomous Urban Resource Allocator), a hierarchical multi-agent reinforcement learning system for real-time affordable housing site selection under hard regulatory constraints (QCT, DDA, LIHTC). We model the task as a constrained multi-objective Markov decision process optimizing accessibility, environmental impact, construction cost, and social equity while enforcing feasibility. AURA uses a regulatory-aware state encoding 127 federal and local constraints, Pareto-constrained policy gradients with feasibility guarantees, and reward decomposition separating immediate costs from long-term social outcomes. On datasets from 8 U.S. metros (47,392 candidate parcels), AURA attains 94.3% regulatory compliance and improves Pareto hypervolume by 37.2% over strong baselines. In a New York City 2026 case study, it reduces selection time from 18 months to 72 hours and identifies 23% more viable sites; chosen sites have 31% better transit access and 19% lower environmental impact than expert picks.", "published": "2026-02-03T19:03:15Z", "authors": ["Olaf Yunus Laitinen Imanov", "Duygu Erisken", "Derya Umut Kulali", "Taner Yilmaz", "Rana Irem Turhan"], "categories": ["cs.LG"], "doi": "", "_query": "hedonic regression housing", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2202.00138v3", "title": "Improving Access to Housing and Supportive Services for Runaway and Homeless Youth: Reducing Vulnerability to Human Trafficking in New York City", "abstract": "Recent estimates indicate that there are over 1 million runaway and homeless youth and young adults (RHY) in the United States (US). Exposure to trauma, violence, and substance abuse, coupled with a lack of community support services, puts homeless youth at high risk of being exploited and trafficked. Although access to safe housing and supportive services such as physical and mental healthcare is an effective response to youths vulnerability towards being trafficked, the number of youth experiencing homelessness exceeds the capacity of available housing resources in most US communities. We undertake a RHY-informed, systematic, and data driven approach to project the collective capacity required by service providers to adequately meet the needs of homeless youth in New York City, including those most at risk of being trafficked. Our approach involves an integer linear programming model that extends the multiple multidimensional knapsack problem and is informed by partnerships with key stakeholders. The mathematical model allows for time-dependent allocation and capacity expansion, while incorporating stochastic youth arrivals and length of stays, services provided in a periodic fashion, and service delivery time windows. Our RHY and service provider-centered approach is an important step toward meeting the actual, rather than presumed, survival needs of vulnerable youth, particularly those at-risk of being trafficked.", "published": "2022-01-31T22:56:32Z", "authors": ["Yaren Bilge Kaya", "Kayse Lee Maass", "Geri L. Dimas", "Renata Konrad", "Andrew C. Trapp", "Meredith Dank"], "categories": ["math.OC"], "doi": "10.1080/24725854.2022.2120223", "_query": "hedonic regression housing", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2608.22216v1", "title": "The Complexity of Minimizing Subsidies in Envy-Free House Allocation", "abstract": "The house allocation problem is a classical one-sided matching problem that concerns the assignment of a set of $m$ houses to $n$ agents according to their preferences, where each agent is assigned exactly one house. Among the various objectives studied in this setting, envy-freeness is one of the most widely adopted fairness criteria. As envy-free house allocations do not always exist, we address this challenge by introducing subsidies and aim to compute allocations that achieve envy-freeness with minimum total subsidy. For binary instances, we show that a total subsidy of at most $(n-1)$ suffices to guarantee envy-freeness in house allocation, and this bound is tight. Building on the known NP-hardness for general utilities, we further show that computing an allocation that minimizes the total subsidy is NP-hard, even under binary utilities. However, when there are only a bounded number of types of agents with binary utilities, the problem can be solved in polynomial time. Finally, we present a polynomial time algorithm that computes the minimum subsidy required to achieve envy-freeness for two types of agents with general utilities.", "published": "2026-08-23T04:40:34Z", "authors": ["Sijia Dai", "Minming Li", "Xiaowei Wu", "Yong Zhang"], "categories": ["cs.GT", "cs.CC"], "doi": "", "_query": "hedonic regression housing", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2603.08511v3", "title": "Kantorovich Regression Analysis of Random Distributions with Mixed Predictors", "abstract": "In modern scientific applications, observations often take the form of probability distributions. We study regression problems with distribution-valued responses and mixed distributional and Euclidean predictors. Under quadratic optimal transport, the proposed Kantorovich regression model approximates the response Kantorovich potential from the Wasserstein barycenter as a linear combination of transformed predictor Kantorovich potentials, where the transformations are determined by one-dimensional functional parameters. The formulation naturally accommodates mixed predictors, allowing Euclidean covariates to enter as scaling coefficients, and extends to multivariate distributions. We characterize functional parameter classes ensuring the intrinsic structure of the model, and establish asymptotic consistency of model parameters through the convex Sobolev loss. Real data applications include a mixed-predictor analysis of housing price distributions and an analysis of two-dimensional temperature distributions, demonstrating the flexibility and interpretability of the proposed framework.", "published": "2026-03-09T15:43:20Z", "authors": ["Kaheon Kim", "Changbo Zhu"], "categories": ["stat.ME"], "doi": "", "_query": "hedonic regression housing", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/1907.02212v4", "title": "Heterogeneous Regression Models for Clusters of Spatial Dependent Data", "abstract": "In economic development, there are often regions that share similar economic characteristics, and economic models on such regions tend to have similar covariate effects. In this paper, we propose a Bayesian clustered regression for spatially dependent data in order to detect clusters in the covariate effects. Our proposed method is based on the Dirichlet process which provides a probabilistic framework for simultaneous inference of the number of clusters and the clustering configurations. The usage of our method is illustrated both in simulation studies and an application to a housing cost dataset of Georgia.", "published": "2019-07-04T04:13:36Z", "authors": ["Zhihua Ma", "Yishu Xue", "Guanyu Hu"], "categories": ["econ.EM", "stat.CO"], "doi": "10.1080/17421772.2020.1784989", "_query": "hedonic regression housing", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2408.12523v1", "title": "Weighted Envy-Freeness in House Allocation", "abstract": "The classic house allocation problem involves assigning $m$ houses to $n$ agents based on their utility functions, ensuring each agent receives exactly one house. A key criterion in these problems is satisfying fairness constraints such as envy-freeness. We extend this problem by considering agents with arbitrary weights, focusing on the concept of weighted envy-freeness, which has been extensively studied in fair division. We present a polynomial-time algorithm to determine whether weighted envy-free allocations exist and, if so, to compute one. Since weighted envy-free allocations do not always exist, we also investigate the potential of achieving such allocations through the use of subsidies. We provide several characterizations for weighted envy-freeable allocations (allocations that can be turned weighted envy-free by introducing subsidies) and show that they do not always exist, which is different from the unweighted setting. Furthermore, we explore the existence of weighted envy-freeable allocations in specific scenarios and outline the conditions under which they exist.", "published": "2024-08-22T16:26:47Z", "authors": ["Sijia Dai", "Yankai Chen", "Xiaowei Wu", "Yicheng Xu", "Yong Zhang"], "categories": ["cs.GT"], "doi": "", "_query": "hedonic regression housing", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2406.09744v2", "title": "The Cost and Complexity of Minimizing Envy in House Allocation", "abstract": "We study almost-envy-freeness in house allocation, where $m$ houses are to be allocated among $n$ agents so that every agent receives exactly one house. An envy-free allocation need not exist, and therefore we may have to settle for relaxations of envy-freeness. But typical relaxations such as envy-free up to one good do not make sense for house allocation, as every agent is required to receive exactly one house. Hence we turn to different aggregate measures of envy as markers of fairness. In particular, we define the amount of envy experienced by an agent $a$ w.r.t. an allocation to be the number of agents that agent $a$ envies under that allocation. We quantify the envy generated by an allocation using three different metrics: 1) the number of agents who are envious; 2) the maximum amount of envy experienced by any agent; and 3) the total amount of envy experienced by all agents, and look for allocations that minimize one of the three metrics. We thus study three computational problems corresponding to each of the three metrics and prove a host of algorithmic and hardness results. We also suggest practical approaches for these problems via integer linear program (ILP) formulations and report the findings of our experimental evaluation of ILPs. Finally, we study the price of fairness (PoF), which quantifies the loss of welfare we must suffer due to the fairness requirements, and we prove a number of results on PoF, including tight bounds as well as algorithms that simultaneously optimize both welfare and fairness.", "published": "2024-06-14T06:23:59Z", "authors": ["Jayakrishnan Madathil", "Neeldhara Misra", "Aditi Sethia"], "categories": ["cs.GT"], "doi": "", "_query": "hedonic regression housing", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2402.12505v1", "title": "Situating Data Sets: Making Public Data Actionable for Housing Justice", "abstract": "Activists, governmentsm and academics regularly advocate for more open data. But how is data made open, and for whom is it made useful and usable? In this paper, we investigate and describe the work of making eviction data open to tenant organizers. We do this through an ethnographic description of ongoing work with a local housing activist organization. This work combines observation, direct participation in data work, and creating media artifacts, specifically digital maps. Our interpretation is grounded in D'Ignazio and Klein's Data Feminism, emphasizing standpoint theory. Through our analysis and discussion, we highlight how shifting positionalities from data intermediaries to data accomplices affects the design of data sets and maps. We provide HCI scholars with three design implications when situating data for grassroots organizers: becoming a domain beginner, striving for data actionability, and evaluating our design artifacts by the social relations they sustain rather than just their technical efficacy.", "published": "2024-02-19T20:13:42Z", "authors": ["Anh-Ton Tran", "Grace Guo", "Jordan Taylor", "Katsuki Chan", "Elora Raymond", "Carl DiSalvo"], "categories": ["cs.HC", "cs.CY"], "doi": "10.1145/3613904.3642452", "_query": "hedonic regression housing", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2203.01219v1", "title": "Are Latent Factor Regression and Sparse Regression Adequate?", "abstract": "We propose the Factor Augmented sparse linear Regression Model (FARM) that not only encompasses both the latent factor regression and sparse linear regression as special cases but also bridges dimension reduction and sparse regression together. We provide theoretical guarantees for the estimation of our model under the existence of sub-Gaussian and heavy-tailed noises (with bounded (1+x)-th moment, for all x>0), respectively. In addition, the existing works on supervised learning often assume the latent factor regression or the sparse linear regression is the true underlying model without justifying its adequacy. To fill in such an important gap, we also leverage our model as the alternative model to test the sufficiency of the latent factor regression and the sparse linear regression models. To accomplish these goals, we propose the Factor-Adjusted de-Biased Test (FabTest) and a two-stage ANOVA type test respectively. We also conduct large-scale numerical experiments including both synthetic and FRED macroeconomics data to corroborate the theoretical properties of our methods. Numerical results illustrate the robustness and effectiveness of our model against latent factor regression and sparse linear regression models.", "published": "2022-03-02T16:22:23Z", "authors": ["Jianqing Fan", "Zhipeng Lou", "Mengxin Yu"], "categories": ["stat.ME", "cs.LG", "math.ST", "stat.ML"], "doi": "", "_query": "hedonic regression housing", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/1702.04656v1", "title": "Robust Regression via Mutivariate Regression Depth", "abstract": "This paper studies robust regression in the settings of Huber's $ε$-contamination models. We consider estimators that are maximizers of multivariate regression depth functions. These estimators are shown to achieve minimax rates in the settings of $ε$-contamination models for various regression problems including nonparametric regression, sparse linear regression, reduced rank regression, etc. We also discuss a general notion of depth function for linear operators that has potential applications in robust functional linear regression.", "published": "2017-02-15T15:48:30Z", "authors": ["Chao Gao"], "categories": ["math.ST", "stat.ML"], "doi": "", "_query": "hedonic regression housing", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/nlin/0505043v1", "title": "A network analysis of committees in the United States House of Representatives", "abstract": "Network theory provides a powerful tool for the representation and analysis of complex systems of interacting agents. Here we investigate the United States House of Representatives network of committees and subcommittees, with committees connected according to ``interlocks'' or common membership. Analysis of this network reveals clearly the strong links between different committees, as well as the intrinsic hierarchical structure within the House as a whole. We show that network theory, combined with the analysis of roll call votes using singular value decomposition, successfully uncovers political and organizational correlations between committees in the House without the need to incorporate other political information.", "published": "2005-05-17T15:30:38Z", "authors": ["Mason A. Porter", "Peter J. Mucha", "M. E. J. Newman", "Casey M. Warmbrand"], "categories": ["nlin.AO", "cs.MA", "math.ST", "physics.data-an", "physics.soc-ph"], "doi": "10.1073/pnas.0500191102", "_query": "hedonic regression housing", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2505.01254v1", "title": "PHSafe: Disclosure Avoidance for the 2020 Census Supplemental Demographic and Housing Characteristics File (S-DHC)", "abstract": "This article describes the disclosure avoidance algorithm that the U.S. Census Bureau used to protect the 2020 Census Supplemental Demographic and Housing Characteristics File (S-DHC). The tabulations contain statistics of counts of U.S. persons living in certain types of households, including averages. The article describes the PHSafe algorithm, which is based on adding noise drawn from a discrete Gaussian distribution to the statistics of interest. We prove that the algorithm satisfies a well-studied variant of differential privacy, called zero-concentrated differential privacy. We then describe how the algorithm was implemented on Tumult Analytics and briefly outline the parameterization and tuning of the algorithm.", "published": "2025-05-02T13:20:32Z", "authors": ["William Sexton", "Skye Berghel", "Bayard Carlson", "Sam Haney", "Luke Hartman", "Michael Hay", "Ashwin Machanavajjhala", "Gerome Miklau", "Amritha Pai", "Simran Rajpal", "David Pujol", "Ruchit Shrestha", "Daniel Simmons-Marengo"], "categories": ["cs.CR", "cs.CY"], "doi": "", "_query": "hedonic regression housing", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2003.06988v1", "title": "House-GAN: Relational Generative Adversarial Networks for Graph-constrained House Layout Generation", "abstract": "This paper proposes a novel graph-constrained generative adversarial network, whose generator and discriminator are built upon relational architecture. The main idea is to encode the constraint into the graph structure of its relational networks. We have demonstrated the proposed architecture for a new house layout generation problem, whose task is to take an architectural constraint as a graph (i.e., the number and types of rooms with their spatial adjacency) and produce a set of axis-aligned bounding boxes of rooms. We measure the quality of generated house layouts with the three metrics: the realism, the diversity, and the compatibility with the input graph constraint. Our qualitative and quantitative evaluations over 117,000 real floorplan images demonstrate that the proposed approach outperforms existing methods and baselines. We will publicly share all our code and data.", "published": "2020-03-16T03:16:12Z", "authors": ["Nelson Nauata", "Kai-Hung Chang", "Chin-Yi Cheng", "Greg Mori", "Yasutaka Furukawa"], "categories": ["cs.CV"], "doi": "", "_query": "hedonic regression housing", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2208.01791v4", "title": "From Workplace to Residence: The Spillover Effects of Minimum Wage Policies on Local Housing Markets", "abstract": "The recent rise of sub-national minimum wage (MW) policies in the US has resulted in significant dispersion of MW levels within urban areas. In this paper, we study the spillover effects of these policies on local rental markets through commuting. To do so, for each USPS ZIP code we construct a \"workplace\" MW measure based on the location of its resident's jobs, and use it to estimate the effect of MW policies on rents. We use a novel identification strategy that exploits the fine timing of differential changes in the workplace MW across ZIP codes that share the same \"residence\" MW, defined as the same location's MW. Our baseline results imply that a 10 percent increase in the workplace MW increases rents at residence ZIP codes by 0.69 percent. To illustrate the importance of commuting patterns, we use our estimates and a simple model to simulate the impact of federal and city counterfactual MW policies. The simulations suggest that landlords pocket approximately 10 cents of each dollar generated by the MW across directly and indirectly affected areas, though the incidence on landlords varies systematically across space.", "published": "2022-08-03T00:12:40Z", "authors": ["Gabriele Borg", "Diego Gentile Passaro", "Santiago Hermo"], "categories": ["econ.GN"], "doi": "", "_query": "hedonic regression housing", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/1312.3552v1", "title": "Counting Popular Matchings in House Allocation Problems", "abstract": "We study the problem of counting the number of popular matchings in a given instance. A popular matching instance consists of agents A and houses H, where each agent ranks a subset of houses according to their preferences. A matching is an assignment of agents to houses. A matching M is more popular than matching M' if the number of agents that prefer M to M' is more than the number of people that prefer M' to M. A matching M is called popular if there exists no matching more popular than M. McDermid and Irving gave a poly-time algorithm for counting the number of popular matchings when the preference lists are strictly ordered. We first consider the case of ties in preference lists. Nasre proved that the problem of counting the number of popular matching is #P-hard when there are ties. We give an FPRAS for this problem. We then consider the popular matching problem where preference lists are strictly ordered but each house has a capacity associated with it. We give a switching graph characterization of popular matchings in this case. Such characterizations were studied earlier for the case of strictly ordered preference lists (McDermid and Irving) and for preference lists with ties (Nasre). We use our characterization to prove that counting popular matchings in capacitated case is #P-hard.", "published": "2013-12-12T17:21:20Z", "authors": ["Rupam Acharyya", "Sourav Chakraborty", "Nitesh Jha"], "categories": ["cs.DS"], "doi": "", "_query": "hedonic regression housing", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/1707.07594v1", "title": "'Dark Germany': Hidden Patterns of Participation in Online Far-Right Protests Against Refugee Housing", "abstract": "The political discourse in Western European countries such as Germany has recently seen a resurgence of the topic of refugees, fueled by an influx of refugees from various Middle Eastern and African countries. Even though the topic of refugees evidently plays a large role in online and offline politics of the affected countries, the fact that protests against refugees stem from the right-wight political spectrum has lead to corresponding media to be shared in a decentralized fashion, making an analysis of the underlying social and mediatic networks difficult. In order to contribute to the analysis of these processes, we present a quantitative study of the social media activities of a contemporary nationwide protest movement against local refugee housing in Germany, which organizes itself via dedicated Facebook pages per city. We analyse data from 136 such protest pages in 2015, containing more than 46,000 posts and more than one million interactions by more than 200,000 users. In order to learn about the patterns of communication and interaction among users of far-right social media sites and pages, we investigate the temporal characteristics of the social media activities of this protest movement, as well as the connectedness of the interactions of its participants. We find several activity metrics such as the number of posts issued, discussion volume about crime and housing costs, negative polarity in comments, and user engagement to peak in late 2015, coinciding with chancellor Angela Merkel's much criticized decision of September 2015 to temporarily admit the entry of Syrian refugees to Germany. Furthermore, our evidence suggests a low degree of direct connectedness of participants in this movement, (i.a., indicated by a lack of geographical collaboration patterns), yet we encounter a strong affiliation of the pages' user base with far-right political parties.", "published": "2017-07-24T14:58:50Z", "authors": ["Sebastian Schelter", "Jérôme Kunegis"], "categories": ["cs.SI"], "doi": "", "_query": "hedonic regression housing", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/1105.3430v2", "title": "Recursive bias estimation for multivariate regression smoothers", "abstract": "This paper presents a practical and simple fully nonparametric multivariate smoothing procedure that adapts to the underlying smoothness of the true regression function. Our estimator is easily computed by successive application of existing base smoothers (without the need of selecting an optimal smoothing parameter), such as thin-plate spline or kernel smoothers. The resulting smoother has better out of sample predictive capabilities than the underlying base smoother, or competing structurally constrained models (GAM) for small dimension (3 < d < 8) and moderate sample size (n < 800). Moreover our estimator is still useful when (d> 10) and to our knowledge, no other adaptive fully nonparametric regression estimator is available without constrained assumption such as additivity for example. On a real example, the Boston Housing Data, our method reduces the out of sample prediction error by 20 %. An R package ibr, available at CRAN, implements the proposed multivariate nonparametric method in R.", "published": "2011-05-17T16:50:49Z", "authors": ["P. A. Cornillon", "N. Hengartner", "E. Matzner-Løber"], "categories": ["stat.ME"], "doi": "", "_query": "hedonic regression housing", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2610.24608v1", "title": "Fabric retrofit scenario analysis under uncertainty for an urban housing stock using machine learning", "abstract": "Fabric retrofit is central to decarbonising housing, and planning it depends on expected savings and their uncertainty. This study asks which upgrade saves most per dwelling and across a housing portfolio, how uncertain the savings are, and whether upgrade order matters. We propose an uncertainty-aware framework that converts predicted changes in energy performance certificate scores after wall, roof and window upgrades into standardised energy-cost savings and emission reductions. Six machine-learning models predict the 1-100 Standard Assessment Procedure (SAP) energy-cost and environmental impact (EI) emission scores from 17 certificate descriptors. Each model's errors on similar dwellings give calibrated prediction intervals. A global-best strategy applies the most accurate model throughout; a sample-specific strategy chooses each dwelling's model using these local errors. The analysis covers 103,414 officially registered dwellings in Westminster, London. CatBoost was the global-best model, with mean absolute errors of 3.487 SAP and 4.379 EI points on held-out dwellings. Both strategies projected similar savings; under global-best, roof insulation saved most per dwelling, GBP 203 annually (uncertainty range 176-229), and wall insulation most across the portfolio, GBP 6.09 million annually (uncertainty range 5.69-6.72). For 5,382 dwellings needing all three upgrades, every order delivered the same final package, yet installing roofs, walls, then windows a year apart accumulated 54% more projected cost savings over three years than the reverse order. Emission reductions followed the same pattern. Using only certificate data, the framework helps local authorities rank measures for dwellings and portfolios and sequence upgrades to bring savings forward.", "published": "2026-10-17T10:23:21Z", "authors": ["Yunfei Bai", "Aaron Tesfa Tsion", "Raúl Rosales", "Barbara Shollock", "Wei He"], "categories": ["physics.soc-ph"], "doi": "", "_query": "hedonic regression housing", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/1905.13578v1", "title": "Acoustics of Margravial Opera House Bayreuth", "abstract": "The Margravial Opera House Bayreuth, built between 1745 and 1750, is a well preserved Baroque court theatre designed by Giuseppe Galli Bibiena [1]. It provides an opportunity to experience not only the visual but also the acoustic design of opera theatres in the 18th century, as the bell-shaped auditorium along with the decoratively painted canvas remains intact. Using balloons and hand-claps as sound sources, we characterize the impulse response of this opera house after its recent renovation. The reverberation time (RT), early decay time (EDT) and clarity factor are characterized and discussed in comparison to historical Italian theatres of a similar age.", "published": "2019-05-31T12:34:08Z", "authors": ["Sebastian Krauss", "Simeon Völkel", "Christoph Dobner", "Alexandra Völkel", "Kai Huang"], "categories": ["physics.pop-ph"], "doi": "", "_query": "hedonic regression housing", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2112.14249v4", "title": "Nested Nonparametric Instrumental Variable Regression", "abstract": "Several causal parameters in short panel data models are functionals of a nested nonparametric instrumental variable regression (nested NPIV). Recent examples include mediated, time varying, and long term treatment effects identified using proxy variables. In econometrics, examples arise in triangular simultaneous equations and hedonic price systems. However, it appears that explicit mean square convergence rates for nested NPIV are unknown, preventing inference on some of these parameters with generic machine learning. A major challenge is compounding ill posedness due to the nested inverse problems. To limit how ill posedness compounds, we introduce two techniques: relative well posedness, and multiple robustness to ill posedness. With these techniques, we provide explicit mean square rates for nested NPIV and efficient inference for recently identified causal parameters. Our nonasymptotic analysis accommodates neural networks, random forests, and reproducing kernel Hilbert spaces. It extends to causal functions, e.g. heterogeneous long term treatment effects.", "published": "2021-12-28T18:29:56Z", "authors": ["Isaac Meza", "Rahul Singh"], "categories": ["stat.ML", "cs.LG", "econ.EM", "math.ST"], "doi": "", "_query": "hedonic regression housing", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/1712.08781v1", "title": "Distribution Regression", "abstract": "Linear regression is a fundamental and popular statistical method. There are various kinds of linear regression, such as mean regression and quantile regression. In this paper, we propose a new one called distribution regression, which allows broad-spectrum of the error distribution in the linear regression. Our method uses nonparametric technique to estimate regression parameters. Our studies indicate that our method provides a better alternative than mean regression and quantile regression under many settings, particularly for asymmetrical heavy-tailed distribution or multimodal distribution of the error term. Under some regular conditions, our estimator is $\\sqrt n$-consistent and possesses the asymptotically normal distribution. The proof of the asymptotic normality of our estimator is very challenging because our nonparametric likelihood function cannot be transformed into sum of independent and identically distributed random variables. Furthermore, penalized likelihood estimator is proposed and enjoys the so-called oracle property with diverging number of parameters. Numerical studies also demonstrate the effectiveness and the flexibility of the proposed method.", "published": "2017-12-23T14:51:53Z", "authors": ["Xin Chen", "Xuejun Ma", "Wang Zhou"], "categories": ["stat.ME"], "doi": "", "_query": "hedonic regression housing", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2503.16912v1", "title": "Construction and sample path properties of diffusion house-moving between two curves", "abstract": "The purpose of this paper is to introduce the construction of a stochastic process called ``diffusion house-moving'' and to explore its properties. We study the weak convergence of diffusion bridges conditioned to stay between two curves, and we refer to this limit as diffusion house-moving. Applying this weak convergence result, we give the sample path properties of diffusion house-moving.", "published": "2025-03-21T07:23:20Z", "authors": ["Kensuke Ishitani", "Soma Nishino"], "categories": ["math.PR"], "doi": "", "_query": "hedonic regression housing", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2303.08225v1", "title": "Graph Transformer GANs for Graph-Constrained House Generation", "abstract": "We present a novel graph Transformer generative adversarial network (GTGAN) to learn effective graph node relations in an end-to-end fashion for the challenging graph-constrained house generation task. The proposed graph-Transformer-based generator includes a novel graph Transformer encoder that combines graph convolutions and self-attentions in a Transformer to model both local and global interactions across connected and non-connected graph nodes. Specifically, the proposed connected node attention (CNA) and non-connected node attention (NNA) aim to capture the global relations across connected nodes and non-connected nodes in the input graph, respectively. The proposed graph modeling block (GMB) aims to exploit local vertex interactions based on a house layout topology. Moreover, we propose a new node classification-based discriminator to preserve the high-level semantic and discriminative node features for different house components. Finally, we propose a novel graph-based cycle-consistency loss that aims at maintaining the relative spatial relationships between ground truth and predicted graphs. Experiments on two challenging graph-constrained house generation tasks (i.e., house layout and roof generation) with two public datasets demonstrate the effectiveness of GTGAN in terms of objective quantitative scores and subjective visual realism. New state-of-the-art results are established by large margins on both tasks.", "published": "2023-03-14T20:35:45Z", "authors": ["Hao Tang", "Zhenyu Zhang", "Humphrey Shi", "Bo Li", "Ling Shao", "Nicu Sebe", "Radu Timofte", "Luc Van Gool"], "categories": ["cs.CV", "cs.AI"], "doi": "", "_query": "hedonic regression housing", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2510.04114v2", "title": "Wasserstein projection distance for fairness testing of regression models", "abstract": "Fairness testing evaluates whether a model satisfies a specified fairness criterion across different groups, yet most research has focused on classification models, leaving regression models underexplored. This paper introduces a framework for fairness testing in regression models, leveraging Wasserstein distance to project data distribution and focusing on expectation-based criteria. Upon categorizing fairness criteria for regression, we derive a Wasserstein projection test statistic from dual reformulation, and derive asymptotic bounds and limiting distributions, allowing us to formulate both a hypothesis-testing procedure and an optimal data perturbation method to improve fairness while balancing accuracy. Experiments on synthetic data demonstrate that the proposed hypothesis-testing approach offers higher specificity compared to permutation-based tests. To illustrate its potential applications, we apply our framework to two case studies on real data, showing (1) statistically significant gender disparities that appear on student performance data across multiple models, and (2) significant unfairness between pollution areas under multiple fairness criteria affecting housing price data, robust to different group divisions, with feature-level analysis identifying spatial and socioeconomic drivers.", "published": "2025-10-05T09:35:20Z", "authors": ["Wanxin Li", "Yongjin P. Park", "Khanh Dao Duc"], "categories": ["cs.LG"], "doi": "", "_query": "hedonic regression housing", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2208.07217v1", "title": "Comparison of Forecasting Methods of House Electricity Consumption for Honda Smart Home", "abstract": "The electricity consumption of buildings composes a major part of the city's energy consumption. Electricity consumption forecasting enables the development of home energy management systems resulting in the future design of more sustainable houses and a decrease in total energy consumption. Energy performance in buildings is influenced by many factors like ambient temperature, humidity, and a variety of electrical devices. Therefore, multivariate prediction methods are preferred rather than univariate. The Honda Smart Home US data set was selected to compare three methods for minimizing forecasting errors, MAE and RMSE: Artificial Neural Networks, Support Vector Regression, and Fuzzy Rule-Based Systems for Regression by constructing many models for each method on a multivariate data set in different time terms. The comparison shows that SVR is a superior method over the alternatives.", "published": "2022-08-11T19:04:41Z", "authors": ["Farshad Ahmadi Asl", "Mehmet Bodur"], "categories": ["cs.LG", "cs.AI"], "doi": "10.5121/csit.2022.121311", "_query": "hedonic regression housing", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2311.18039v1", "title": "Restricted Regression in Networks", "abstract": "Network regression with additive node-level random effects can be problematic when the primary interest is estimating unconditional regression coefficients and some covariates are exactly or nearly in the vector space of node-level effects. We introduce the Restricted Network Regression model, that removes the collinearity between fixed and random effects in network regression by orthogonalizing the random effects against the covariates. We discuss the change in the interpretation of the regression coefficients in Restricted Network Regression and analytically characterize the effect of Restricted Network Regression on the regression coefficients for continuous response data. We show through simulation with continuous and binary response data that Restricted Network Regression mitigates, but does not alleviate, network confounding, by providing improved estimation of the regression coefficients. We apply the Restricted Network Regression model in an analysis of 2015 Eurovision Song Contest voting data and show how the choice of regression model affects inference.", "published": "2023-11-29T19:33:42Z", "authors": ["Ian Taylor", "Kayleigh P. Keller", "Bailey K. Fosdick"], "categories": ["stat.ME"], "doi": "", "_query": "hedonic regression housing", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2109.01992v1", "title": "Balanced House Allocation", "abstract": "We introduce balancedness a fairness axiom in house allocation problems. It requires a mechanism to assign the top choice, the second top choice, and so on, on the same number of profiles for each agent. This axiom guarantees equal treatment of all agents at the stage in which the mechanism is announced when all preference profiles are equally likely. We show that, with an interesting exception for the three-agent case, Top Trading Cycles from individual endowments is the only mechanism that is balanced, efficient, and group strategy-proof.", "published": "2021-09-05T04:53:31Z", "authors": ["Xinghua Long", "Rodrigo A. Velez"], "categories": ["econ.TH", "cs.GT"], "doi": "", "_query": "hedonic regression housing", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/1501.07196v2", "title": "ggRandomForests: Visually Exploring a Random Forest for Regression", "abstract": "Random Forests [Breiman:2001] (RF) are a fully non-parametric statistical method requiring no distributional assumptions on covariate relation to the response. RF are a robust, nonlinear technique that optimizes predictive accuracy by fitting an ensemble of trees to stabilize model estimates. The randomForestSRC package (http://cran.r-project.org/package=randomForestSRC) is a unified treatment of Breiman's random forests for survival, regression and classification problems. Predictive accuracy make RF an attractive alternative to parametric models, though complexity and interpretability of the forest hinder wider application of the method. We introduce the ggRandomForests package (http://cran.r-project.org/package=ggRandomForests), for visually understand random forest models grown in R with the randomForestSRC package. The vignette is a tutorial for using the ggRandomForests package with the randomForestSRC package for building and post-processing a regression random forest. In this tutorial, we explore a random forest model for the Boston Housing Data, available in the MASS package. We grow a random forest for regression and demonstrate how ggRandomForests can be used when determining variable associations, interactions and how the response depends on predictive variables within the model. The tutorial demonstrates the design and usage of many of ggRandomForests functions and features how to modify and customize the resulting ggplot2 graphic objects along the way. A development version of the ggRandomForests package is available on Github. We invite comments, feature requests and bug reports for this package at (https://github.com/ehrlinger/ggRandomForests).", "published": "2015-01-28T17:03:26Z", "authors": ["John Ehrlinger"], "categories": ["stat.CO", "stat.ML"], "doi": "", "_query": "hedonic regression housing", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2310.15643v1", "title": "Navigating ICT In-House Procurement in Finland: Evaluating Legal Frameworks and Practical Challenges", "abstract": "In-house procurement is a controversial issue in the field of public procurement. Simply put, such procurement allows overlooking certain aspects of fair and equal treatment of vendors. This paper presents qualitative research on in-house ICT procurement within Finnish municipalities. Semi-structured interviews were conducted to gather insights from municipal stakeholders. Using grounded theory approach, data analysis shows intricate dynamics between Finnish municipalities and in-house entities associated with them. Still, it is clear that the legal framework governing in-house procurement remains intricate and debated.", "published": "2023-10-24T09:04:29Z", "authors": ["Reetta Ghezzi", "Minnamaria Korhonen", "Hannu Vilpponen", "Tommi Mikkonen"], "categories": ["cs.SE"], "doi": "", "_query": "hedonic regression housing", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/physics/0606064v1", "title": "A Roof over your Head; House Price Peaks in the UK and Ireland", "abstract": "We analyse, following recent work of Roehner, changes in house prices for both the UK and Ireland. We conclude that prices in London have reached a tipping point and prices relative to inflation are set to fall over the next few years. If inflation does not rise then a hard landing seems likely. House prices in the Irish Republic are shown to have broken away from the moderate rise still to be found in Northern Ireland and Dublin has emerged as another global 'hot spot'. An evolution of Dublin house prices similar to that in London can be anticipated. Keywords: Econophysics, house prices, real estate, prediction PACS: 89.65.Gh, 89.90.+n", "published": "2006-06-07T14:07:28Z", "authors": ["Peter Richmond"], "categories": ["physics.soc-ph", "physics.pop-ph"], "doi": "10.1016/j.physa.2006.09.003", "_query": "hedonic regression housing", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/1911.08858v2", "title": "Non-separating immersions of spheres and Bing houses", "abstract": "We construct Bing houses in all dimensions $n \\geq 3$, obtaining non-separating PL immersions of $\\mathbb{S}^n \\rightarrow\\mathbb{R}^{n+1}$.", "published": "2019-11-20T12:32:17Z", "authors": ["Michael Freedman", "T. Tam Nguyen Phan"], "categories": ["math.GT", "math.DG"], "doi": "", "_query": "hedonic regression housing", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2603.03260v2", "title": "Does Entry of Food-and-Drink Establishments Raise Local House Prices? Event-Study Evidence from London", "abstract": "Restaurants, cafes, pubs, and takeaways are among the most visible markers of neighborhood change, yet whether their arrival is capitalised into nearby housing values remains empirically unsettled. We assemble a London-wide panel linking Land Registry prices, non-domestic EPC lodgement timings for food-and-drink establishments, and neighborhood amenity measures at the LSOA level. Our preferred annual event-study design defines treatment as the first clean-onset year in which an LSOA records at least two eligible EPC lodgements for food-and-drink establishments, after a two-year lookback with no prior entries. In this specification, pre-trend tests are not rejected in either the stacked or Sun-Abraham estimators, and log house prices rise gradually from about 0.5% in the event year to roughly 3.4--3.7% by years four and five. The results are consistent with local amenity capitalization following commercial entry, while remaining appropriately cautious about endogenous siting and concurrent redevelopment.", "published": "2026-03-03T18:51:20Z", "authors": ["Wanqi Liu", "Rong Zhao"], "categories": ["physics.soc-ph", "econ.GN"], "doi": "", "_query": "hedonic regression housing", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2602.13729v1", "title": "Semi-supervised linear regression with missing covariates", "abstract": "Missing values in datasets are common in applied statistics. For regression problems, theoretical work thus far has largely considered the issue of missing covariates as distinct from missing responses. However, in practice, many datasets have both forms of missingness. Motivated by this gap, we study linear regression with a labelled dataset containing missing covariates, potentially alongside an unlabelled dataset. We consider both structured (blockwise-missing) and unstructured missingness patterns, along with sparse and non-sparse regression parameters. For the non-sparse case, we provide an estimator based on imputing the missing data combined with a reweighting step. For the high-dimensional sparse case, we use a modified version of the Dantzig selector. We provide non-asymptotic upper bounds on the risk of both procedures. These are matched by several new minimax lower bounds, demonstrating the rate optimality of our estimators. Notably, even when the linear model is well-specified, our results characterise substantial differences in the minimax rates when unlabelled data is present relative to the fully supervised setting. Particular consequences of our sparse and non-sparse results include the first matching upper and lower bounds on the minimax rate for the supervised setting when either unstructured or structured missingness is present. Our theory is coupled with extensive simulations and a semi-synthetic application to the California housing dataset.", "published": "2026-02-14T11:41:48Z", "authors": ["Benedict M. Risebrow", "Thomas B. Berrett"], "categories": ["math.ST", "stat.ME"], "doi": "", "_query": "hedonic regression housing", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2601.22625v1", "title": "RPWithPrior: Label Differential Privacy in Regression", "abstract": "With the wide application of machine learning techniques in practice, privacy preservation has gained increasing attention. Protecting user privacy with minimal accuracy loss is a fundamental task in the data analysis and mining community. In this paper, we focus on regression tasks under $ε$-label differential privacy guarantees. Some existing methods for regression with $ε$-label differential privacy, such as the RR-On-Bins mechanism, discretized the output space into finite bins and then applied RR algorithm. To efficiently determine these finite bins, the authors rounded the original responses down to integer values. However, such operations does not align well with real-world scenarios. To overcome these limitations, we model both original and randomized responses as continuous random variables, avoiding discretization entirely. Our novel approach estimates an optimal interval for randomized responses and introduces new algorithms designed for scenarios where a prior is either known or unknown. Additionally, we prove that our algorithm, RPWithPrior, guarantees $ε$-label differential privacy. Numerical results demonstrate that our approach gets better performance compared with the Gaussian, Laplace, Staircase, and RRonBins, Unbiased mechanisms on the Communities and Crime, Criteo Sponsored Search Conversion Log, California Housing datasets.", "published": "2026-01-30T06:27:13Z", "authors": ["Haixia Liu", "Ruifan Huang"], "categories": ["stat.ML", "cs.LG"], "doi": "", "_query": "hedonic regression housing", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/cs/0101035v2", "title": "The Guidebook, the Friend, and the Room: Visitor Experience in a Historic House", "abstract": "In this paper, we describe an electronic guidebook prototype and report on a study of its use in a historic house. Supported by mechanisms in the guidebook, visitors constructed experiences that had a high degree of interaction with three entities: the guidebook, their companions, and the house and its contents. For example, we found that most visitors played audio descriptions played through speakers (rather than using headphones or reading textual descriptions) to facilitate communication with their companions.", "published": "2001-01-28T07:14:46Z", "authors": ["Allison Woodruff", "Paul M. Aoki", "Amy Hurst", "Margaret H. Szymanski"], "categories": ["cs.HC"], "doi": "10.1145/634067.634229", "_query": "hedonic regression housing", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/1102.2015v4", "title": "Real estate appraisal of land lots using GAMLSS models", "abstract": "The valuation of real estates (e.g., house, land, among others) is of extreme importance for decision making. Their singular characteristics make valuation through hedonic pricing methods dificult since the theory does not specify the correct regression functional form nor which explanatory variables should be included in the hedonic equation. In this article we perform real estate appraisal using a class of regression models proposed by Rigby & Stasinopoulos (2005): generalized additive models for location, scale and shape (GAMLSS). Our empirical analysis shows that these models seem to be more appropriate for estimation of the hedonic prices function than the regression models currently used to that end.", "published": "2011-02-10T00:24:21Z", "authors": ["Lutemberg Florencio", "Francisco Cribari-Neto", "Raydonal Ospina"], "categories": ["stat.AP"], "doi": "", "_query": "hedonic regression housing", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2308.12291v1", "title": "The Quantum-House Effect and Its Demonstration on SpinQ Gemini", "abstract": "We introduce the quantum-house effect, a non-local quantum phenomenon which goes against classical intuition. We show how the effect can be achieved with any bipartite quantum state where neither subsystem is in a pure state. Besides its theoretical description, the quantum-house effect is also demonstrated on SpinQ Gemini, a 2-qubit liquid-state NMR desktop quantum computer.", "published": "2023-07-22T18:48:50Z", "authors": ["Tamás Varga"], "categories": ["physics.gen-ph"], "doi": "10.1007/s12045-023-1544-1", "_query": "hedonic regression housing", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2301.01323v2", "title": "Graphical House Allocation", "abstract": "The classical house allocation problem involves assigning $n$ houses (or items) to $n$ agents according to their preferences. A key criterion in such problems is satisfying some fairness constraints such as envy-freeness. We consider a generalization of this problem wherein the agents are placed along the vertices of a graph (corresponding to a social network), and each agent can only experience envy towards its neighbors. Our goal is to minimize the aggregate envy among the agents as a natural fairness objective, i.e., the sum of all pairwise envy values over all edges in a social graph. When agents have identical and evenly-spaced valuations, our problem reduces to the well-studied problem of linear arrangements. For identical valuations with possibly uneven spacing, we show a number of deep and surprising ways in which our setting is a departure from this classical problem. More broadly, we contribute several structural and computational results for various classes of graphs, including NP-hardness results for disjoint unions of paths, cycles, stars, or cliques, and fixed-parameter tractable (and, in some cases, polynomial-time) algorithms for paths, cycles, stars, cliques, and their disjoint unions. Additionally, a conceptual contribution of our work is the formulation of a structural property for disconnected graphs that we call separability which results in efficient parameterized algorithms for finding optimal allocations.", "published": "2023-01-03T19:26:15Z", "authors": ["Hadi Hosseini", "Justin Payan", "Rik Sengupta", "Rohit Vaish", "Vignesh Viswanathan"], "categories": ["cs.GT", "cs.AI"], "doi": "", "_query": "hedonic regression housing", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2308.08028v1", "title": "A Graph Analysis of the Impact of COVID-19 on Emergency Housing Shelter Access Patterns", "abstract": "This paper investigates how COVID-19 disrupted emergency housing shelter access patterns in Calgary, Canada and what aspects of these changes persist to the present day. This analysis will utilize aggregated shelter access data for over 40,000 individuals from seven major urban shelters dating from 2018 to the present. A graph theoretic approach will be used to examine the journeys of individuals between shelters before, during and after the COVID-19 lockdown period. This approach treats shelters as nodes in a graph and a person's transition between shelter as an arrow or edge between nodes. This perspective is used to create both timeline and network diagrams that visualize shelter use and the flow of people between shelters. Statistical results are also presented that illustrate the differences between the cohorts of people who only used shelter pre/post-lockdown, people who stayed in shelter during lockdown and people who used shelter for the first time during lockdown. The results demonstrate not only how a complex system of care responded to the pandemic but also the characteristics of the people most likely to continue to rely on that system during an emergency.", "published": "2023-08-15T20:34:56Z", "authors": ["Geoffrey G. Messier"], "categories": ["cs.CY"], "doi": "", "_query": "hedonic regression housing", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2603.03416v1", "title": "Designing with Medical Mistrust: Perspectives from Black Older Adults in Publicly Subsidized Housing", "abstract": "Despite increasing interest in culturally-sensitive health technologies, medical mistrust remains largely unexplored within human-centered computing. Considered a social determinant of health, medical mistrust is the belief that healthcare providers or institutions are acting against one's best interest. This is a rational, protective response based on historical context, structural inequities, and discrimination. To center race-based medical mistrust and the lived experiences of Black older adults with low income, we conducted interviews within publicly subsidized housing in the Southern United States. Our reflexive themes describe community perspectives on health care and medical mistrust, including accreditation and embodiment, skepticism of financial motivations, and the intentions behind health AI. We provide a reflective exercise for researchers to consider their positionality in relation to community engagements, and reframe our findings through Black Feminist Thought to propose design principles for health self-management technologies for communities with historically grounded medical mistrust.", "published": "2026-03-03T18:50:34Z", "authors": ["Cynthia M. Baseman", "Reeda Shimaz Huda", "Rosa I. Arriaga"], "categories": ["cs.HC", "cs.CY"], "doi": "10.1145/3772318.3791524", "_query": "hedonic regression housing", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2001.06457v3", "title": "Neglecting Uncertainties Biases House-Elevation Decisions to Manage Riverine Flood Risks", "abstract": "Homeowners around the world elevate houses to manage flood risks. Deciding how high to elevate a house poses a nontrivial decision problem. The U.S. Federal Emergency Management Agency (FEMA) recommends elevating existing houses to the Base Flood Elevation (the elevation of the 100-yr flood) plus a freeboard. This recommendation neglects many uncertainties. Here we analyze a case-study of riverine flood risk management using a multi-objective robust decision-making framework in the face of deep uncertainties. While the quantitative results are location-specific, the approach and overall insights are generalizable. We find strong interactions between the economic, engineering, and Earth science uncertainties, illustrating the need for expanding on previous integrated analyses to further understand the nature and strength of these connections. Considering deep uncertainties surrounding flood hazards, the discount rate, the house lifetime, and the fragility can increase the economically optimal house elevation to values well above FEMA recommendation.", "published": "2020-01-17T18:00:11Z", "authors": ["Mahkameh Zarekarizi", "Vivek Srikrishnan", "Klaus Keller"], "categories": ["stat.AP", "physics.data-an", "q-fin.ST"], "doi": "10.1038/s41467-020-19188-9", "_query": "hedonic regression housing", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/1704.07313v2", "title": "Elite Bases Regression: A Real-time Algorithm for Symbolic Regression", "abstract": "Symbolic regression is an important but challenging research topic in data mining. It can detect the underlying mathematical models. Genetic programming (GP) is one of the most popular methods for symbolic regression. However, its convergence speed might be too slow for large scale problems with a large number of variables. This drawback has become a bottleneck in practical applications. In this paper, a new non-evolutionary real-time algorithm for symbolic regression, Elite Bases Regression (EBR), is proposed. EBR generates a set of candidate basis functions coded with parse-matrix in specific mapping rules. Meanwhile, a certain number of elite bases are preserved and updated iteratively according to the correlation coefficients with respect to the target model. The regression model is then spanned by the elite bases. A comparative study between EBR and a recent proposed machine learning method for symbolic regression, Fast Function eXtraction (FFX), are conducted. Numerical results indicate that EBR can solve symbolic regression problems more effectively.", "published": "2017-04-24T16:21:10Z", "authors": ["Chen Chen", "Changtong Luo", "Zonglin Jiang"], "categories": ["cs.DS", "cs.NE"], "doi": "", "_query": "hedonic regression housing", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/1210.6958v4", "title": "Dual Regression", "abstract": "We propose dual regression as an alternative to the quantile regression process for the global estimation of conditional distribution functions under minimal assumptions. Dual regression provides all the interpretational power of the quantile regression process while avoiding the need for repairing the intersecting conditional quantile surfaces that quantile regression often produces in practice. Our approach introduces a mathematical programming characterization of conditional distribution functions which, in its simplest form, is the dual program of a simultaneous estimator for linear location-scale models. We apply our general characterization to the specification and estimation of a flexible class of conditional distribution functions, and present asymptotic theory for the corresponding empirical dual regression process.", "published": "2012-10-25T19:25:04Z", "authors": ["Richard Spady", "Sami Stouli"], "categories": ["stat.ME", "econ.EM", "stat.AP"], "doi": "", "_query": "hedonic regression housing", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2510.22817v1", "title": "Wildfire and house prices: A synthetic control case study of Altadena (Jan 2025)", "abstract": "This study uses the Synthetic Control Method (SCM) to estimate the causal impact of a January 2025 wildfire on housing prices in Altadena, California. We construct a 'synthetic' Altadena from a weighted average of peer cities to serve as a counterfactual; this approach assumes no spillover effects on the donor pool. The results reveal a substantial negative price effect that intensifies over time. Over the six months following the event, we estimate an average monthly loss of $32,125. The statistical evidence for this effect is nuanced. Based on the robust post-to-pre-treatment RMSPE ratio, the result is statistically significant at the 10% level (p = 0.0508). In contrast, the effect is not statistically significant when measured by the average post-treatment gap (p = 0.3220). This analysis highlights the significant financial risks faced by communities in fire-prone regions and demonstrates SCM's effectiveness in evaluating disaster-related economic damages.", "published": "2025-10-26T20:16:28Z", "authors": ["Yibo Sun"], "categories": ["econ.GN"], "doi": "", "_query": "hedonic regression housing", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/1811.12466v1", "title": "Consistency of Forecasts for the U.S. House of Representatives", "abstract": "We consider the performance of the foremost academic House of Representatives forecasting models in the 2018 elections. In creating open-source implementations of these models, we outline key underlying assumptions. We find that although the results were unsurprising, they indicate a weakening of many traditional forecasting indicators.", "published": "2018-11-29T20:20:38Z", "authors": ["Henry Bendekgey"], "categories": ["stat.AP"], "doi": "", "_query": "hedonic regression housing", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2209.06948v3", "title": "Automated 2D and 3D Finite Element Overclosure Adjustment and Mesh Morphing Using Generalized Regression Neural Networks", "abstract": "Computer representations of three-dimensional (3D) geometries are crucial for simulating systems and processes in engineering and science. In medicine, and more specifically, biomechanics and orthopaedics, obtaining and using 3D geometries is critical to many workflows. However, while many tools exist to obtain 3D geometries of organic structures, little has been done to make them usable for their intended medical purposes. Furthermore, many of the proposed tools are proprietary, limiting their use. This work introduces two novel algorithms based on Generalized Regression Neural Networks (GRNN) and 4 processes to perform mesh morphing and overclosure adjustment. These algorithms were implemented, and test cases were used to validate them against existing algorithms to demonstrate improved performance. The resulting algorithms demonstrate improvements to existing techniques based on Radial Basis Function (RBF) networks by converting to GRNN-based implementations. Implementations in MATLAB of these algorithms and the source code are publicly available at the following locations: https://github.com/thor-andreassen/femors https://simtk.org/projects/femors-rbf https://www.mathworks.com/matlabcentral/fileexchange/120353-finite-element-morphing-overclosure-reduction-and-slicing", "published": "2022-09-14T21:48:55Z", "authors": ["Thor E. Andreassen", "Donald R. Hume", "Landon D. Hamilton", "Sean E. Higinbotham", "Kevin B. Shelburne"], "categories": ["q-bio.QM", "math.NA"], "doi": "10.1016/j.medengphy.2024.104136", "_query": "hedonic regression housing", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2606.31086v1", "title": "CasaMaestro: Multi-View Panoramas for House-Scale 3D Reconstruction", "abstract": "The rise of home-deployed embodied AI systems is driving a growing need for fast, metric 3D reconstruction of residential spaces to support navigation, interaction, and long-horizon task execution. However, the commonly used pinhole-camera 3D reconstruction pipelines struggle to model large indoor residences efficiently due to their limited field of view, to which achieving full coverage across multiple rooms often requires thousands of images and incurs drift from long chains of incremental alignment. In this work, we present CasaMaestro (Spanish words meaning ``house'' and ``master''), a feedforward model that can take only twenty to fifty sparse multi-view indoor panoramas as input and directly predicts metric depth along with camera poses, allowing fast point-cloud reconstruction of the entire house with full coverage. CasaMaestro is the first model that supports house-scale reconstruction with multi-view panoramas. Experiments show that CasaMaestro can robustly provide high quality results in both real-world and synthetic scenes, which can serve as a strong foundation for acquiring house-scale 3D indoor assets to be applied in close-loop simulation.", "published": "2026-06-30T03:21:57Z", "authors": ["Yuzhou Ji", "Xiaotian Yang", "Zhipeng Zhang"], "categories": ["cs.CV"], "doi": "", "_query": "hedonic regression housing", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2608.02559v2", "title": "House-monotone multi-level apportionment has logarithmic quota discrepancy", "abstract": "Multi-level apportionment allocates integer seats through a hierarchy of groups. Schmidt-Kraepelin, Suksompong, and Wijaya proved that, at every fixed house size, lower and upper quota can be satisfied simultaneously; they also constructed house-monotone rules satisfying either quota separately. They left open whether one rule can satisfy lower quota, upper quota, and house monotonicity together, even when quota is required only relative to the root. We give a negative answer. For a full binary comb with $D$ equally entitled leaves, every house-monotone allocation sequence induces a sequence of seat recipients. Quota for the nested comb groups would force every grid-aligned prefix discrepancy to be below one. A midpoint embedding then bounds the full interval discrepancy by this quantity plus $1/2$, contradicting Schmidt's logarithmic lower bound. Conversely, a binary van der Corput seat schedule has comb-prefix error at most $\\log D/(3\\log 2)+1$. Thus the optimal worst-case error on the comb is $Θ(\\log D)$, and for sufficiently large finite $D$ no house-monotone quota rule exists. The proof isolates a static--dynamic gap: each house size admits a quota-feasible allocation, but the feasible allocations cannot be embedded into one monotone path. In quantization language, the result characterizes the order of the embedded-quantization penalty for progressive one-hot rounding on the comb.", "published": "2026-08-03T17:42:18Z", "authors": ["Lav R. Varshney"], "categories": ["cs.GT", "cs.IT", "eess.SP"], "doi": "", "_query": "hedonic regression housing", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/1906.10250v3", "title": "Swap Dynamics in Single-Peaked Housing Markets", "abstract": "This paper focuses on the problem of fairly and efficiently allocating resources to agents. We consider a specific setting, usually referred to as a housing market, where each agent must receive exactly one resource (and initially owns one). In this framework, in the domain of linear preferences, the Top Trading Cycle (TTC) algorithm is the only procedure satisfying Pareto-optimality, individual rationality and strategy-proofness. Under the restriction of single-peaked preferences, Crawler enjoys the same properties. These two centralized procedures might however involve long trading cycles. In this paper we focus instead on procedures involving the shortest cycles: bilateral swap-deals. In such swap dynamics, the agents perform pairwise mutually improving deals until reaching a swap-stable allocation (no improving swap-deal is possible). We prove that in the single-peaked domain every swap-stable allocation is Pareto-optimal, showing the efficiency of the swap dynamics. In fact, this domain turns out to be maximal when it comes to guaranteeing this property. Besides, both the outcome of TTC and Crawler can always be reached by sequences of swaps. However, some Pareto-optimal allocations are not reachable through improving swap-deals. We further analyze the outcome of swap dynamics through social welfare notions, in our context the average or minimum rank of the resources obtained by agents in the final allocation. We start by providing a worst-case analysis of these procedures. Finally, we present an extensive experimental study in which different versions of swap dynamics are compared to other existing allocation procedures. We show that they exhibit good results on average in this domain, under different cultures for generating synthetic data.", "published": "2019-06-24T22:12:01Z", "authors": ["Aurélie Beynier", "Nicolas Maudet", "Simon Rey", "Parham Shams"], "categories": ["cs.AI", "cs.MA"], "doi": "", "_query": "hedonic regression housing", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/1701.03641v3", "title": "Symbolic Regression Algorithms with Built-in Linear Regression", "abstract": "Recently, several algorithms for symbolic regression (SR) emerged which employ a form of multiple linear regression (LR) to produce generalized linear models. The use of LR allows the algorithms to create models with relatively small error right from the beginning of the search; such algorithms are thus claimed to be (sometimes by orders of magnitude) faster than SR algorithms based on vanilla genetic programming. However, a systematic comparison of these algorithms on a common set of problems is still missing. In this paper we conceptually and experimentally compare several representatives of such algorithms (GPTIPS, FFX, and EFS). They are applied as off-the-shelf, ready-to-use techniques, mostly using their default settings. The methods are compared on several synthetic and real-world SR benchmark problems. Their performance is also related to the performance of three conventional machine learning algorithms --- multiple regression, random forests and support vector regression.", "published": "2017-01-13T12:23:10Z", "authors": ["Jan Žegklitz", "Petr Pošík"], "categories": ["cs.LG"], "doi": "", "_query": "hedonic regression housing", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2601.15864v2", "title": "Minimum Envy Graphical House Allocation Beyond Identical Valuations", "abstract": "House allocation is an extremely well-studied problem in the field of fair allocation, where the goal is to assign $n$ houses to $n$ agents while satisfying certain fairness criterion, e.g., envy-freeness. To model social interactions, the Graphical House Allocation framework introduces a social graph $G$, in which each vertex corresponds to an agent, and an edge $(u, v)$ corresponds to the potential of agent $u$ to envy the agent $v$, based on their allocations and valuations. In undirected social graphs, the potential for envy is in both the directions. In the Minimum Envy Graphical House Allocation (ME-GHA) problem, given a set of $n$ agents, $n$ houses, a social graph, and agent's valuation functions, the goal is to find an allocation that minimizes the total envy summed up over all the edges of $G$. Recent work, [Hosseini et al., AAMAS 2023, AAMAS 2024] studied ME-GHA in the regime of polynomial-time algorithms, and designed exact and approximation algorithms, for certain graph classes under identical agent valuations. We initiate the study of \\gha with non-identical valuations, a setting that has so far remained unexplored. We investigate the multivariate (parameterized) complexity of \\gha by identifying structural restrictions on the social graph and valuation functions that yield tractability. We also design moderately exponential-time algorithms for several graph classes, and a polynomial-time algorithm for {binary valuations that returns an allocation with envy at most one when the social graph has maximum degree at most one.", "published": "2026-01-22T11:12:09Z", "authors": ["Tanmay Inamdar", "Pallavi Jain", "Pranjal Pandey"], "categories": ["cs.GT", "cs.DS"], "doi": "", "_query": "hedonic regression housing", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2309.01013v2", "title": "Streaming Active Learning for Regression Problems Using Regression via Classification", "abstract": "One of the challenges in deploying a machine learning model is that the model's performance degrades as the operating environment changes. To maintain the performance, streaming active learning is used, in which the model is retrained by adding a newly annotated sample to the training dataset if the prediction of the sample is not certain enough. Although many streaming active learning methods have been proposed for classification, few efforts have been made for regression problems, which are often handled in the industrial field. In this paper, we propose to use the regression-via-classification framework for streaming active learning for regression. Regression-via-classification transforms regression problems into classification problems so that streaming active learning methods proposed for classification problems can be applied directly to regression problems. Experimental validation on four real data sets shows that the proposed method can perform regression with higher accuracy at the same annotation cost.", "published": "2023-09-02T20:24:24Z", "authors": ["Shota Horiguchi", "Kota Dohi", "Yohei Kawaguchi"], "categories": ["cs.LG"], "doi": "", "_query": "hedonic regression housing", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2411.07617v1", "title": "Semi-supervised learning using copula-based regression and model averaging", "abstract": "The available data in semi-supervised learning usually consists of relatively small sized labeled data and much larger sized unlabeled data. How to effectively exploit unlabeled data is the key issue. In this paper, we write the regression function in the form of a copula and marginal distributions, and the unlabeled data can be exploited to improve the estimation of the marginal distributions. The predictions based on different copulas are weighted, where the weights are obtained by minimizing an asymptotic unbiased estimator of the prediction risk. Error-ambiguity decomposition of the prediction risk is performed such that unlabeled data can be exploited to improve the prediction risk estimation. We demonstrate the asymptotic normality of copula parameters and regression function estimators of the candidate models under the semi-supervised framework, as well as the asymptotic optimality and weight consistency of the model averaging estimator. Our model averaging estimator achieves faster convergence rates of asymptotic optimality and weight consistency than the supervised counterpart. Extensive simulation experiments and the California housing dataset demonstrate the effectiveness of the proposed method.", "published": "2024-11-12T07:50:40Z", "authors": ["Ziwen Gao", "Huihang Liu", "Xinyu Zhang"], "categories": ["stat.ME"], "doi": "", "_query": "hedonic regression housing", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/1511.08029v1", "title": "Penalized MM Regression Estimation with $L_{γ}$ Penalty: A Robust Version of Bridge Regression", "abstract": "The bridge regression estimator generalizes both ridge regression and LASSO estimators. Since it minimizes the sum of squared residuals with a $L_{γ}$ penalty, this estimator is typically not robust against outliers in the data. There have been attempts to define robust versions of the bridge regression method, but while these proposed methods produce bridge regression estimators robust to outliers and heavy-tailed errors, they are not robust against leverage points. We propose a robust bridge regression estimation method combining MM and bridge regression estimation methods. The MM bridge regression estimator obtained from the proposed method is robust against outliers and leverage points. Furthermore, for appropriate choices of the penalty function, the proposed method is able to perform variable selection and parameter estimation simultaneously. Consistency, asymptotic normality, and sparsity of the MM bridge regression estimator are achieved. We propose an algorithm to compute the MM bridge regression estimate. A simulation study and a real data example are provided to demonstrate the performance of the MM bridge regression estimator for finite sample cases.", "published": "2015-11-25T12:10:15Z", "authors": ["Olcay Arslan"], "categories": ["stat.ME"], "doi": "", "_query": "hedonic regression housing", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2307.14203v5", "title": "Dynamic Regression Discontinuity: An Event-Study Approach", "abstract": "I propose a novel argument to identify economically interpretable intertemporal treatment effects in dynamic regression discontinuity designs (RDDs). Specifically, I develop a dynamic potential outcomes model and reformulate two assumptions from the difference-in-differences literature, no anticipation and common trends, to attain point identification of cutoff-specific impulse responses. The estimand of each target parameter can be expressed as the sum of two static RDD contrasts, thereby allowing for nonparametric estimation and inference with standard local polynomial methods. I also propose a nonparametric approach to aggregate treatment effects across calendar time and treatment paths, leveraging a limited path independence restriction to reduce the dimensionality of the parameter space. I apply this method to estimate the dynamic effects of school district expenditure authorizations on housing prices in Wisconsin.", "published": "2023-07-26T14:02:24Z", "authors": ["Francesco Ruggieri"], "categories": ["econ.EM"], "doi": "", "_query": "hedonic regression housing", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2308.11419v1", "title": "Tensor Regression", "abstract": "Regression analysis is a key area of interest in the field of data analysis and machine learning which is devoted to exploring the dependencies between variables, often using vectors. The emergence of high dimensional data in technologies such as neuroimaging, computer vision, climatology and social networks, has brought challenges to traditional data representation methods. Tensors, as high dimensional extensions of vectors, are considered as natural representations of high dimensional data. In this book, the authors provide a systematic study and analysis of tensor-based regression models and their applications in recent years. It groups and illustrates the existing tensor-based regression methods and covers the basics, core ideas, and theoretical characteristics of most tensor-based regression methods. In addition, readers can learn how to use existing tensor-based regression methods to solve specific regression tasks with multiway data, what datasets can be selected, and what software packages are available to start related work as soon as possible. Tensor Regression is the first thorough overview of the fundamentals, motivations, popular algorithms, strategies for efficient implementation, related applications, available datasets, and software resources for tensor-based regression analysis. It is essential reading for all students, researchers and practitioners of working on high dimensional data.", "published": "2023-08-22T13:04:12Z", "authors": ["Jiani Liu", "Ce Zhu", "Zhen Long", "Yipeng Liu"], "categories": ["stat.ML", "cs.AI"], "doi": "10.1561/2200000087.", "_query": "hedonic regression housing", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2402.08941v4", "title": "Local-Polynomial Estimation for Multivariate Regression Discontinuity Designs", "abstract": "We study a multivariate regression discontinuity design in which treatment is assigned by crossing a boundary in the space of multiple running variables. We document that the existing bandwidth selector is suboptimal for a multivariate regression discontinuity design when the distance to a boundary point is used for its running variable, and introduce a multivariate local-linear estimator for multivariate regression discontinuity designs. Our estimator is asymptotically valid and can capture heterogeneous treatment effects over the boundary. We demonstrate that our estimator exhibits smaller root mean squared errors and often shorter confidence intervals in numerical simulations. We illustrate our estimator in our empirical applications of multivariate designs of a Colombian scholarship study and a U.S. House of representative voting study and demonstrate that our estimator reveals richer heterogeneous treatment effects with often shorter confidence intervals than the existing estimator.", "published": "2024-02-14T05:05:23Z", "authors": ["Masayuki Sawada", "Takuya Ishihara", "Daisuke Kurisu", "Yasumasa Matsuda"], "categories": ["econ.EM", "stat.AP", "stat.ME"], "doi": "", "_query": "hedonic regression housing", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/1802.00430v1", "title": "Linearized Binary Regression", "abstract": "Probit regression was first proposed by Bliss in 1934 to study mortality rates of insects. Since then, an extensive body of work has analyzed and used probit or related binary regression methods (such as logistic regression) in numerous applications and fields. This paper provides a fresh angle to such well-established binary regression methods. Concretely, we demonstrate that linearizing the probit model in combination with linear estimators performs on par with state-of-the-art nonlinear regression methods, such as posterior mean or maximum aposteriori estimation, for a broad range of real-world regression problems. We derive exact, closed-form, and nonasymptotic expressions for the mean-squared error of our linearized estimators, which clearly separates them from nonlinear regression methods that are typically difficult to analyze. We showcase the efficacy of our methods and results for a number of synthetic and real-world datasets, which demonstrates that linearized binary regression finds potential use in a variety of inference, estimation, signal processing, and machine learning applications that deal with binary-valued observations or measurements.", "published": "2018-02-01T18:49:14Z", "authors": ["Andrew S. Lan", "Mung Chiang", "Christoph Studer"], "categories": ["stat.ML", "math.ST", "stat.ME"], "doi": "", "_query": "hedonic regression housing", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2501.03883v2", "title": "Spline Quantile Regression", "abstract": "Quantile regression is a powerful tool capable of offering a richer view of the data as compared to least-squares regression. Quantile regression is typically performed individually on a few quantiles or a grid of quantiles without considering the similarity of the underlying regression coefficients at nearby quantiles. When needed, an ad hoc post-processing procedure such as kernel smoothing is employed to smooth the individually estimated coefficients across quantiles and thereby improve the performance of these estimates. This paper introduces a new method, called spline quantile regression (SQR), that unifies quantile regression with quantile smoothing and jointly estimates the regression coefficients across quantiles as smoothing splines. We discuss the computation of the SQR solution as a linear program (LP) using an interior-point algorithm. We also experiment with some gradient algorithms that require less memory than the LP algorithm. The performance of the SQR method and these algorithms is evaluated using simulated and real-world data.", "published": "2025-01-07T15:44:38Z", "authors": ["Ta-Hsin Li", "Nimrod Megiddo"], "categories": ["stat.ME", "stat.CO"], "doi": "10.1007/s42519-026-00545-8", "_query": "hedonic regression housing", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2004.12022v2", "title": "Bayesian Clustered Coefficients Regression with Auxiliary Covariates Assistant Random Effects", "abstract": "In regional economics research, a problem of interest is to detect similarities between regions, and estimate their shared coefficients in economics models. In this article, we propose a mixture of finite mixtures (MFM) clustered regression model with auxiliary covariates that account for similarities in demographic or economic characteristics over a spatial domain. Our Bayesian construction provides both inference for number of clusters and clustering configurations, and estimation for parameters for each cluster. Empirical performance of the proposed model is illustrated through simulation experiments, and further applied to a study of influential factors for monthly housing cost in Georgia.", "published": "2020-04-25T00:21:33Z", "authors": ["Guanyu Hu", "Yishu Xue", "Zhihua Ma"], "categories": ["stat.ME", "econ.EM", "stat.AP"], "doi": "", "_query": "hedonic regression housing", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2510.21044v1", "title": "House Thermal Model Estimation: Robustness Across Seasons and Setpoints", "abstract": "Achieving the flexibility from house heating, cooling, and ventilation systems (HVAC) has the potential to enable large-scale demand response by aggregating HVAC load adjustments across many homes. This demand response strategy helps distribution grid to flexibly ramp-up or ramp-down local load demand so that it can optimally match the bulk power system generation profile. However, achieving this capability requires house thermal models that are both computationally efficient and robust to operating conditions. In this work, parameters of the Resistance-Capacitance (RC) network thermal model for houses are estimated using three optimization algorithms: Nonlinear Least Squares (NLS), Batch Estimation (BE), and Maximum Likelihood Estimation (MLE). The resulting models are evaluated through a Forward-Simulation across four different seasons and three setpoints. The results illustrate a principled way of selecting reduced order models and estimation methods with respect to the robustness offered to seasonal and setpoint variations in training-testing datasets", "published": "2025-10-23T22:58:14Z", "authors": ["Kunal Shankar", "Ninad Gaikwad", "Anamika Dubey"], "categories": ["eess.SY"], "doi": "", "_query": "hedonic regression housing", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/1602.06609v1", "title": "Nonparametric and Varying Coefficient Modal Regression", "abstract": "In this article, we propose a new nonparametric data analysis tool, which we call nonparametric modal regression, to investigate the relationship among interested variables based on estimating the mode of the conditional density of a response variable Y given predictors X. The nonparametric modal regression is distinguished from the conventional nonparametric regression in that, instead of the conditional average or median, it uses the \"most likely\" conditional values to measures the center. Better prediction performance and robustness are two important characteristics of nonparametric modal regression compared to traditional nonparametric mean regression and nonparametric median regression. We propose to use local polynomial regression to estimate the nonparametric modal regression. The asymptotic properties of the resulting estimator are investigated. To broaden the applicability of the nonparametric modal regression to high dimensional data or functional/longitudinal data, we further develop a nonparametric varying coefficient modal regression. A Monte Carlo simulation study and an analysis of health care expenditure data demonstrate some superior performance of the proposed nonparametric modal regression model to the traditional nonparametric mean regression and nonparametric median regression in terms of the prediction performance.", "published": "2016-02-22T00:07:42Z", "authors": ["Weixin Yao", "Sijia Xiang"], "categories": ["stat.ME"], "doi": "", "_query": "hedonic regression housing", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/1509.09169v9", "title": "Lecture notes on ridge regression", "abstract": "The linear regression model cannot be fitted to high-dimensional data, as the high-dimensionality brings about empirical non-identifiability. Penalized regression overcomes this non-identifiability by augmentation of the loss function by a penalty (i.e. a function of regression coefficients). The ridge penalty is the sum of squared regression coefficients, giving rise to ridge regression. Here many aspect of ridge regression are reviewed e.g. moments, mean squared error, its equivalence to constrained estimation, and its relation to Bayesian regression. Finally, its behaviour and use are illustrated in simulation and on high-throughput data. Subsequently, ridge regression is generalized to allow for a more general penalty. The ridge penalization framework is then translated to logistic regression and its properties are shown to carry over. To contrast ridge penalized estimation, the final chapters introduce its lasso counterpart and generalizations thereof.", "published": "2015-09-30T13:38:31Z", "authors": ["Wessel N. van Wieringen"], "categories": ["stat.ME"], "doi": "", "_query": "hedonic regression housing", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2504.17113v1", "title": "Cybernetic Governance in a Coliving House", "abstract": "We report an 18-month field experiment in distributed digital institutions: a nine-bedroom Los Angeles coliving house that runs without managers, while sustaining 98% occupancy and below-market rents. Drawing on Elinor Ostrom's commons theory, we outline design principles and three digital mechanisms that form the institutional core: 1) A continuous-auction chore scheduler turns regenerative labor into a time-indexed points market; residents meet a 100-point monthly obligation by claiming tasks whose value rises linearly with neglect. 2) A pairwise-preference layer lets participants asynchronously reprioritize tasks, translating meta-governance into low-cognition spot inputs. 3) A symbolic \"hearts\" ledger tracks norm compliance through automated enforcement, lightweight challenges, and peer-awarded karma. Together, these mechanisms operationalize cybernetic principles--human sensing, machine bookkeeping, real-time feedback--while minimizing dependence on privileged roles. Our exploratory data (567 chore claims, 255 heart events, and 551 group purchases) show that such tooling can sustain reliable commons governance without continuous leadership, offering a transferable design palette for online communities, coliving houses, and other digitally mediated collectives.", "published": "2025-04-23T21:45:59Z", "authors": ["Daniel Kronovet", "Seth Frey", "Joseph DeSimone"], "categories": ["cs.CY", "cs.HC", "econ.GN"], "doi": "", "_query": "hedonic regression housing", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/math/0505639v1", "title": "Extremal quantile regression", "abstract": "Quantile regression is an important tool for estimation of conditional quantiles of a response Y given a vector of covariates X. It can be used to measure the effect of covariates not only in the center of a distribution, but also in the upper and lower tails. This paper develops a theory of quantile regression in the tails. Specifically, it obtains the large sample properties of extremal (extreme order and intermediate order) quantile regression estimators for the linear quantile regression model with the tails restricted to the domain of minimum attraction and closed under tail equivalence across regressor values. This modeling setup combines restrictions of extreme value theory with leading homoscedastic and heteroscedastic linear specifications of regression analysis. In large samples, extreme order regression quantiles converge weakly to \\argmin functionals of stochastic integrals of Poisson processes that depend on regressors, while intermediate regression quantiles and their functionals converge to normal vectors with variance matrices dependent on the tail parameters and the regressor design.", "published": "2005-05-30T09:57:15Z", "authors": ["Victor Chernozhukov"], "categories": ["math.ST", "econ.EM"], "doi": "10.1214/009053604000001165", "_query": "hedonic regression housing", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2210.08196v2", "title": "Deep Regression Unlearning", "abstract": "With the introduction of data protection and privacy regulations, it has become crucial to remove the lineage of data on demand from a machine learning (ML) model. In the last few years, there have been notable developments in machine unlearning to remove the information of certain training data efficiently and effectively from ML models. In this work, we explore unlearning for the regression problem, particularly in deep learning models. Unlearning in classification and simple linear regression has been considerably investigated. However, unlearning in deep regression models largely remains an untouched problem till now. In this work, we introduce deep regression unlearning methods that generalize well and are robust to privacy attacks. We propose the Blindspot unlearning method which uses a novel weight optimization process. A randomly initialized model, partially exposed to the retain samples and a copy of the original model are used together to selectively imprint knowledge about the data that we wish to keep and scrub off the information of the data we wish to forget. We also propose a Gaussian fine tuning method for regression unlearning. The existing unlearning metrics for classification are not directly applicable to regression unlearning. Therefore, we adapt these metrics for the regression setting. We conduct regression unlearning experiments for computer vision, natural language processing and forecasting applications. Our methods show excellent performance for all these datasets across all the metrics. Source code: https://github.com/ayu987/deep-regression-unlearning", "published": "2022-10-15T05:00:20Z", "authors": ["Ayush K Tarun", "Vikram S Chundawat", "Murari Mandal", "Mohan Kankanhalli"], "categories": ["cs.LG"], "doi": "", "_query": "hedonic regression housing", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2601.01432v1", "title": "Personalizing black-box models for nonparametric regression with minimax optimality", "abstract": "Recent advances in large-scale models, including deep neural networks and large language models, have substantially improved performance across a wide range of learning tasks. The widespread availability of such pre-trained models creates new opportunities for data-efficient statistical learning, provided they can be effectively integrated into downstream tasks. Motivated by this setting, we study few-shot personalization, where a pre-trained black-box model is adapted to a target domain using a limited number of samples. We develop a theoretical framework for few-shot personalization in nonparametric regression and propose algorithms that can incorporate a black-box pre-trained model into the regression procedure. We establish the minimax optimal rate for the personalization problem and show that the proposed method attains this rate. Our results clarify the statistical benefits of leveraging pre-trained models under sample scarcity and provide robustness guarantees when the pre-trained model is not informative. We illustrate the finite-sample performance of the methods through simulations and an application to the California housing dataset with several pre-trained models.", "published": "2026-01-04T08:32:28Z", "authors": ["Sai Li", "Linjun Zhang"], "categories": ["stat.ME", "stat.ML"], "doi": "", "_query": "hedonic regression housing", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2106.06867v1", "title": "A Spatially Dependent Probabilistic Model for House Hunting in Ant Colonies", "abstract": "Ant species such as Temnothorax albipennis select a new nest site in a distributed fashion that, if modeled correctly, can serve as useful information for site selection algorithms for robotic swarms and other applications. Studying and replicating the ants' house hunting behavior will also illuminate useful distributed strategies that have evolved in nature. Many of the existing models of househunting behaviour for T. albipennis make the assumption that all candidate nest sites are equally distant from the ants' home nest, or that an ant has an equal probability of finding each candidate nest site. However, realistically this is not the case, as nests that are further away from the home nest and nests that are difficult to access are less likely to be found, even if they are of higher quality. We extend previous house-hunting models to account for a pairwise distance metric between nests, compare our results to those of real colonies, and use our results to examine the effects of house hunting in nests of different spatial orientations. Our incorporation of distances in the ant model appear to match empirical data in situations where a distance-quality tradeoff between nests is relevant. Furthermore, the model continues to be on par with previous house-hunting models in experiments where all candidate nests are equidistant from the home nest, as is typically assumed.", "published": "2021-06-12T21:42:51Z", "authors": ["Grace Cai", "Wendy Wu", "Wayne Zhao", "Jiajia Zhao", "Nancy Lynch"], "categories": ["cs.MA"], "doi": "", "_query": "hedonic regression housing", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2207.11690v2", "title": "HouseX: A Fine-grained House Music Dataset and its Potential in the Music Industry", "abstract": "Machine sound classification has been one of the fundamental tasks of music technology. A major branch of sound classification is the classification of music genres. However, though covering most genres of music, existing music genre datasets often do not contain fine-grained labels that indicate the detailed sub-genres of music. In consideration of the consistency of genres of songs in a mixtape or in a DJ (live) set, we have collected and annotated a dataset of house music that provide 4 sub-genre labels, namely future house, bass house, progressive house and melodic house. Experiments show that our annotations well exhibit the characteristics of different categories. Also, we have built baseline models that classify the sub-genre based on the mel-spectrograms of a track, achieving strongly competitive results. Besides, we have put forward a few application scenarios of our dataset and baseline model, with a simulated sci-fi tunnel as a short demo built and rendered in a 3D modeling software, with the colors of the lights automated by the output of our model.", "published": "2022-07-24T08:19:19Z", "authors": ["Xinyu Li"], "categories": ["cs.SD", "cs.LG", "eess.AS"], "doi": "", "_query": "hedonic regression housing", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/1507.06496v5", "title": "Fundamentals of cone regression", "abstract": "Cone regression is a particular case of quadratic programming that minimizes a weighted sum of squared residuals under a set of linear inequality constraints. Several important statistical problems such as isotonic, concave regression or ANOVA under partial orderings, just to name a few, can be considered as particular instances of the cone regression problem. Given its relevance in Statistics, this paper aims to address the fundamentals of cone regression from a theoretical and practical point of view. Several formulations of the cone regression problem are considered and, focusing on the particular case of concave regression as example, several algorithms are analyzed and compared both qualitatively and quantitatively through numerical simulations. Several improvements to enhance numerical stability and bound the computational cost are proposed. For each analyzed algorithm, the pseudo-code and its corresponding code in Scilab are provided. The results from this study demonstrate that the choice of the optimization approach strongly impacts the numerical performances. It is also shown that methods are not currently available to solve efficiently cone regression problems with large dimension (more than many thousands of points). We suggest further research to fill this gap by exploiting and adapting classical multi-scale strategy to compute an approximate solution.", "published": "2015-07-23T14:00:49Z", "authors": ["Mariella Dimiccoli"], "categories": ["stat.CO"], "doi": "", "_query": "hedonic regression housing", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2608.15631v1", "title": "Non-obvious Manipulability with Groups in Shapley-Scarf Housing Markets", "abstract": "In Shapley-Scarf housing markets, Ma (1994) shows that top trading cycles (TTC) is the unique mechanism satisfying individual rationality (IR), Pareto efficiency (PE), and strategy-proofness. We ask what other mechanisms become possible when strategy-proofness is replaced by a weaker condition called non-obvious manipulability (NOM), introduced by Troyan and Morrill (2020). We first show that this weaker condition does not help on its own: every IR and PE mechanism is already NOM. We therefore introduce a new condition: NOM with groups, under which each agent knows the preferences of the other members of her group, but not those of agents outside the group. This condition reduces to strategy-proofness when all agents belong to one group, and to standard NOM when every group is a singleton. Also, we introduce a participation condition called group rationality (GR), which requires that no group do worse than it would by trading only among its own members. We then define a class of mechanisms called TTC with super-groups, whose members satisfy GR, PE, and NOM with groups. The class includes mechanisms that differ from standard TTC, including mechanisms that are not strategy-proof. Furthermore, we show that every mechanism that satisfies GR, PE, and NOM with groups has the same best- and worst-case outcomes as every TTC with super-groups mechanism.", "published": "2026-08-16T08:53:11Z", "authors": ["Louise Demoor", "Martí Jané-Ballarín", "Pierre Nunn", "Subhajit Pramanik", "Antoine Prévotat", "Makoto Yokoo"], "categories": ["cs.GT", "econ.TH"], "doi": "", "_query": "hedonic regression housing", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2512.03284v2", "title": "Think, Then Look: Active Spatial Reasoning for House-Scale 3D Scene Understanding", "abstract": "Spatial reasoning in large-scale 3D environments remains challenging for current vision--language models, which are typically constrained to room-scale scenarios. We formalize Active House-Scale Spatial Reasoning (AHSR), a new paradigm in which a model reasons over a pre-built house-scale 3D map via virtual spatial tool invocations to answer spatial questions, without exhaustive scene-wide processing. To support AHSR research, we introduce H$^2$U3D (Holistic House Understanding in 3D), the first benchmark targeting house-scale 3D scene understanding, featuring environments with an average aggregate floor area of 250.8 m$^2$ and up to three floors, together with hierarchical coarse-to-fine visual representations. Building on H$^2$U3D, we propose SpatialReasoner, an AHSR framework trained via supervised fine-tuning with self-correction, followed by reinforcement learning with a task-aware adaptive exploration reward. SpatialReasoner achieves state-of-the-art performance on H$^2$U3D with 64.9% overall accuracy, outperforming strong baselines including GPT-5.4 and Gemini-3.5-Flash, and generalizes effectively to MT-HM3D and HM-EQA. These results demonstrate the clear advantage of active map-directed exploration over passive scene-wide processing in house-scale 3D understanding.", "published": "2025-12-02T22:49:01Z", "authors": ["Hongpei Zheng", "Shijie Li", "Lin Qian", "Zhenghao Li", "Qijun Yang", "Yanran Li", "Hujun Yin"], "categories": ["cs.CV"], "doi": "", "_query": "hedonic regression housing", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/1901.01774v1", "title": "Location-Centered House Price Prediction: A Multi-Task Learning Approach", "abstract": "Accurate house prediction is of great significance to various real estate stakeholders such as house owners, buyers, investors, and agents. We propose a location-centered prediction framework that differs from existing work in terms of data profiling and prediction model. Regarding data profiling, we define and capture a fine-grained location profile powered by a diverse range of location data sources, such as transportation profile (e.g., distance to nearest train station), education profile (e.g., school zones and ranking), suburb profile based on census data, facility profile (e.g., nearby hospitals, supermarkets). Regarding the choice of prediction model, we observe that a variety of approaches either consider the entire house data for modeling, or split the entire data and model each partition independently. However, such modeling ignores the relatedness between partitions, and for all prediction scenarios, there may not be sufficient training samples per partition for the latter approach. We address this problem by conducting a careful study of exploiting the Multi-Task Learning (MTL) model. Specifically, we map the strategies for splitting the entire house data to the ways the tasks are defined in MTL, and each partition obtained is aligned with a task. Furthermore, we select specific MTL-based methods with different regularization terms to capture and exploit the relatedness between tasks. Based on real-world house transaction data collected in Melbourne, Australia. We design extensive experimental evaluations, and the results indicate a significant superiority of MTL-based methods over state-of-the-art approaches. Meanwhile, we conduct an in-depth analysis on the impact of task definitions and method selections in MTL on the prediction performance, and demonstrate that the impact of task definitions on prediction performance far exceeds that of method selections.", "published": "2019-01-07T12:36:57Z", "authors": ["Guangliang Gao", "Zhifeng Bao", "Jie Cao", "A. K. Qin", "Timos Sellis", "Zhiang Wu"], "categories": ["cs.LG", "stat.ML"], "doi": "", "_query": "hedonic regression housing", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/1611.05433v1", "title": "ROS Regression: Integrating Regularization and Optimal Scaling Regression", "abstract": "In this paper we combine two important extensions of ordinary least squares regression: regularization and optimal scaling. Optimal scaling (sometimes also called optimal scoring) has originally been developed for categorical data, and the process finds quantifications for the categories that are optimal for the regression model in the sense that they maximize the multiple correlation. Although the optimal scaling method was developed initially for variables with a limited number of categories, optimal transformations of continuous variables are a special case. We will consider a variety of transformation types; typically we use step functions for categorical variables, and smooth (spline) functions for continuous variables. Both types of functions can be restricted to be monotonic, preserving the ordinal information in the data. In addition to optimal scaling, three regularization methods will be considered: Ridge regression, the Lasso, and the Elastic Net. The resulting method will be called ROS Regression (Regularized Optimal Scaling Regression. We will show that the basic OS algorithm provides straightforward and efficient estimation of the regularized regression coefficients, automatically gives the Group Lasso and Blockwise Sparse Regression, and extends them with monotonicity properties. We will show that Optimal Scaling linearizes nonlinear relationships between predictors and outcome, and improves upon the condition of the predictor correlation matrix, increasing (on average) the conditional independence of the predictors. Alternative options for regularization of either regression coefficients or category quantifications are mentioned. Extended examples are provided. Keywords: Categorical Data, Optimal Scaling, Conditional Independence, Step Functions, Splines, Monotonic Transformations, Regularization, Lasso, Elastic Net, Group Lasso, Blockwise Sparse Regression.", "published": "2016-11-16T20:40:19Z", "authors": ["Jacqueline J. Meulman", "Anita J. van der Kooij"], "categories": ["stat.ML"], "doi": "", "_query": "hedonic regression housing", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2407.12267v1", "title": "Generating 3D House Wireframes with Semantics", "abstract": "We present a new approach for generating 3D house wireframes with semantic enrichment using an autoregressive model. Unlike conventional generative models that independently process vertices, edges, and faces, our approach employs a unified wire-based representation for improved coherence in learning 3D wireframe structures. By re-ordering wire sequences based on semantic meanings, we facilitate seamless semantic integration during sequence generation. Our two-phase technique merges a graph-based autoencoder with a transformer-based decoder to learn latent geometric tokens and generate semantic-aware wireframes. Through iterative prediction and decoding during inference, our model produces detailed wireframes that can be easily segmented into distinct components, such as walls, roofs, and rooms, reflecting the semantic essence of the shape. Empirical results on a comprehensive house dataset validate the superior accuracy, novelty, and semantic fidelity of our model compared to existing generative models. More results and details can be found on https://vcc.tech/research/2024/3DWire.", "published": "2024-07-17T02:33:34Z", "authors": ["Xueqi Ma", "Yilin Liu", "Wenjun Zhou", "Ruowei Wang", "Hui Huang"], "categories": ["cs.CV", "cs.GR"], "doi": "", "_query": "hedonic regression housing", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2311.02199v1", "title": "Merger Interactions Enhance Star Formation Rates in Galaxy Group Housing QSO PKS0405$-$123 and Gaseous Nebulae", "abstract": "The star formation rate (SFR) of galaxies can change due to interactions between galaxies, stellar feedback ejection of gas into the circumgalactic medium, and energy injection from accretion onto black holes. However, it is not clear which of these processes dominantly alters the formation of stars within galaxies. Johnson et al. (2018) reported the discovery of large gaseous nebulae in the intragroup medium of a galaxy group housing QSO PKS0405$-$123 and hypothesized they were created by galaxy interactions. We identify a sample of 30 group member galaxies at z$\\sim$0.57 from the VLT/MUSE observations of the field and calculate their [OII]$λ$$λ$3727,3729 SFRs in order to investigate whether the QSO and nebulae have affected the SFRs of the surrounding galaxies. We find that star formation is more prevalent in galaxies within the nebulae, signifying galaxy interactions are fueling higher SFRs.", "published": "2023-11-03T19:03:38Z", "authors": ["Ian E. Wolter", "Michelle A. Berg", "John Chisholm"], "categories": ["astro-ph.GA"], "doi": "10.3847/2515-5172/ad07df", "_query": "hedonic regression housing", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2207.11332v2", "title": "Comparing baseball players across eras via novel Full House Modeling", "abstract": "A new methodological framework suitable for era-adjusting baseball statistics is developed in this article. Within this methodological framework specific models are motivated. We call these models Full House Models. Full House Models work by balancing the achievements of Major League Baseball (MLB) players within a given season and the size of the MLB talent pool from which a player came. We demonstrate the utility of Full House Models in an application of comparing baseball players' performance statistics across eras. Our results reveal a new ranking of baseball's greatest players which include several modern players among the top all-time players. Modern players are elevated by Full House Modeling because they come from a larger talent pool. Sensitivity and multiverse analyses which investigate the how results change with changes to modeling inputs including the estimate of the talent pool are presented.", "published": "2022-07-22T20:53:52Z", "authors": ["Shen Yan", "Adrian Burgos", "Christopher Kinson", "Daniel J. Eck"], "categories": ["stat.AP", "stat.ME"], "doi": "", "_query": "hedonic regression housing", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2609.40242v1", "title": "Riemannian Regression", "abstract": "Classical linear regression assumes that the relevant geometry of the predictor space is Euclidean and that all centered observations contribute to the least-squares fit in the same geometric scale. This paper proposes \\emph{Riemannian Regression}, a regression framework in which the usual vector differences are replaced by locally weighted differences induced by a data-dependent similarity structure. We introduce a generalized framework, termed {\\em Riemannian Regression}, extending classic regression to any data endowed with a local distance structure. By equipping data tables with local metrics, we adapt regression model to incorporate manifold geometry. Given a similarity matrix $S=(S_{ij})$, obtained from UMAP, ISOMAP, or DBSCAN \\cite{mcinnes,isomap,dbscan}, we define the dissimilarity coefficient $ρ_{ij}=1-S_{ij}$ and the induced subtraction $ x_i\\ominus x_j=ρ_{ij}(x_i-x_j). $ A Riemannian center $g=x_λ$ is selected as a discrete Fréchet mean, and regression is performed on the Riemannian-centered variables $X_R=W X_{c,λ}$ and $y_R=W y_{c,λ}$, where $W=\\operatorname{diag}(ρ_{1λ},\\ldots,ρ_{nλ})$. The resulting estimator has the weighted least-squares form $ \\widehatβ_R=(X_{c,λ}^{t}W^2X_{c,λ})^{-1}X_{c,λ}^{t}W^2y_{c,λ}. $ The proposed approach preserves the linear form of the regression model while changing the geometry of the fit. The paper develops three ways to construct the local metric: UMAP-based fuzzy similarities, ISOMAP-based normalized geodesic distances, and DBSCAN-based density similarities. Simulated examples and the Abalone data set illustrate how Riemannian Regression can reduce the influence of locally anomalous observations and adapt to regions with different local densities.", "published": "2026-09-30T17:33:56Z", "authors": ["Oldemar Rodríguez"], "categories": ["math.ST"], "doi": "", "_query": "hedonic regression housing", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/1702.05960v4", "title": "A Statistical Learning Approach to Modal Regression", "abstract": "This paper studies the nonparametric modal regression problem systematically from a statistical learning view. Originally motivated by pursuing a theoretical understanding of the maximum correntropy criterion based regression (MCCR), our study reveals that MCCR with a tending-to-zero scale parameter is essentially modal regression. We show that nonparametric modal regression problem can be approached via the classical empirical risk minimization. Some efforts are then made to develop a framework for analyzing and implementing modal regression. For instance, the modal regression function is described, the modal regression risk is defined explicitly and its \\textit{Bayes} rule is characterized; for the sake of computational tractability, the surrogate modal regression risk, which is termed as the generalization risk in our study, is introduced. On the theoretical side, the excess modal regression risk, the excess generalization risk, the function estimation error, and the relations among the above three quantities are studied rigorously. It turns out that under mild conditions, function estimation consistency and convergence may be pursued in modal regression as in vanilla regression protocols, such as mean regression, median regression, and quantile regression. However, it outperforms these regression models in terms of robustness as shown in our study from a re-descending M-estimation view. This coincides with and in return explains the merits of MCCR on robustness. On the practical side, the implementation issues of modal regression including the computational algorithm and the tuning parameters selection are discussed. Numerical assessments on modal regression are also conducted to verify our findings empirically.", "published": "2017-02-20T13:31:39Z", "authors": ["Yunlong Feng", "Jun Fan", "Johan A. K. Suykens"], "categories": ["stat.ML", "math.ST", "stat.ME"], "doi": "", "_query": "hedonic regression housing", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2211.14570v1", "title": "The Role of In-House Procurement According to Finnish Municipalities' Purchase Invoice Data", "abstract": "Public sector is a large consumer of ICT systems and services, used for various public services. Tendering for such systems is governed by laws aimed at eliminating unfair advantages and offering all possible parties equal opportunities to participate in the tendering process. In this article, we study in-house rpocurement, where the acquiring organization is an owner of the subcontractor that delivers the system or the service. Municipalities' purchase invoice data is used to determine how much municipalities in Finland depend on in-house procurement. In conclusion, the understanding if included municipalities have ICT service and development units within the organizations needs closer examination, as in-house companies may offer municipalities with limited resources divided costs in the public procurement process.", "published": "2022-11-26T13:50:27Z", "authors": ["Reetta-Kaisa Ghezzi", "Minnamaria Korhonen", "Hannu Vilpponen", "Tommi Mikkonen"], "categories": ["cs.CY"], "doi": "", "_query": "hedonic regression housing", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2012.13332v2", "title": "Nonparametric Regression in Nonstandard Spaces", "abstract": "A nonparametric regression setting is considered with a real-valued covariate and responses from a metric space. One may approach this setting via Fréchet regression, where the value of the regression function at each point is estimated via a Fréchet mean calculated from an estimated objective function. A second approach is geodesic regression, which builds upon fitting geodesics to observations by a least squares method. These approaches are applied to transform two of the most important nonparametric regression estimators in statistics to the metric setting -- the local linear regression estimator and the orthogonal series projection estimator. The resulting procedures consist of known estimators as well as new methods. We investigate their rates of convergence in a general setting and compare their performance in a simulation study on the sphere.", "published": "2020-12-24T17:08:49Z", "authors": ["Christof Schötz"], "categories": ["math.ST"], "doi": "", "_query": "hedonic regression housing", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2006.11754v2", "title": "Regression and Causality", "abstract": "The causal effect of an intervention (treatment/exposure) on an outcome can be estimated by: i) specifying knowledge about the data-generating process; ii) assessing under what assumptions a target quantity, such as for example a causal odds ratio, can be identified given the specified knowledge (and given the measured data); and then, iii) using appropriate statistical estimation techniques to estimate the desired parameter of interest. As regression is the cornerstone of statistical analysis, it seems obvious to ask: is it appropriate to use estimated regression parameters for causal effect estimation? It turns out that using regression for effect estimation is possible, but typically requires more assumptions than competing methods. This manuscript provides a comprehensive summary of the assumptions needed to identify and estimate a causal parameter using regression and, equally important, discusses the resulting implications for statistical practice.", "published": "2020-06-21T10:16:48Z", "authors": ["Michael Schomaker"], "categories": ["stat.ME", "math.ST"], "doi": "", "_query": "hedonic regression housing", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/1502.01115v1", "title": "Regression Adjustment for Noncrossing Bayesian Quantile Regression", "abstract": "A two-stage approach is proposed to overcome the problem in quantile regression, where separately fitted curves for several quantiles may cross. The standard Bayesian quantile regression model is applied in the first stage, followed by a Gaussian process regression adjustment, which monotonizes the quantile function whilst borrowing strength from nearby quantiles. The two stage approach is computationally efficient, and more general than existing techniques. The method is shown to be competitive with alternative approaches via its performance in simulated examples.", "published": "2015-02-04T07:58:35Z", "authors": ["Thais Rodrigues", "Yanan Fan"], "categories": ["stat.ME"], "doi": "", "_query": "hedonic regression housing", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2603.10452v1", "title": "Brenier Isotonic Regression", "abstract": "Isotonic regression (IR) is shape-constrained regression to maintain a univariate fitting curve non-decreasing, which has numerous applications including single-index models and probability calibration. When it comes to multi-output regression, the classical IR is no longer applicable because the monotonicity is not readily extendable. We consider a novel multi-output regression problem where a regression function is \\emph{cyclically monotone}. Roughly speaking, a cyclically monotone function is the gradient of some convex potential. Whereas enforcing cyclic monotonicity is apparently challenging, we leverage the fact that Kantorovich's optimal transport (OT) always yields a cyclically monotone coupling as an optimal solution. This perspective naturally allows us to interpret a regression function and the convex potential as a link function in generalized linear models and Brenier's potential in OT, respectively, and hence we call this IR extension \\emph{Brenier isotonic regression}. We demonstrate experiments with probability calibration and generalized linear models. In particular, IR outperforms many famous baselines in probability calibration robustly.", "published": "2026-03-11T06:10:27Z", "authors": ["Han Bao", "Amirreza Eshraghi", "Yutong Wang"], "categories": ["stat.ML", "cs.LG"], "doi": "", "_query": "hedonic regression housing", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/0911.4080v1", "title": "Revisiting Marginal Regression", "abstract": "The lasso has become an important practical tool for high dimensional regression as well as the object of intense theoretical investigation. But despite the availability of efficient algorithms, the lasso remains computationally demanding in regression problems where the number of variables vastly exceeds the number of data points. A much older method, marginal regression, largely displaced by the lasso, offers a promising alternative in this case. Computation for marginal regression is practical even when the dimension is very high. In this paper, we study the relative performance of the lasso and marginal regression for regression problems in three different regimes: (a) exact reconstruction in the noise-free and noisy cases when design and coefficients are fixed, (b) exact reconstruction in the noise-free case when the design is fixed but the coefficients are random, and (c) reconstruction in the noisy case where performance is measured by the number of coefficients whose sign is incorrect. In the first regime, we compare the conditions for exact reconstruction of the two procedures, find examples where each procedure succeeds while the other fails, and characterize the advantages and disadvantages of each. In the second regime, we derive conditions under which marginal regression will provide exact reconstruction with high probability. And in the third regime, we derive rates of convergence for the procedures and offer a new partitioning of the ``phase diagram,'' that shows when exact or Hamming reconstruction is effective.", "published": "2009-11-20T16:41:23Z", "authors": ["Christopher Genovese", "Jiashun Jin", "Larry Wasserman"], "categories": ["math.ST"], "doi": "", "_query": "hedonic regression housing", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/1204.3549v2", "title": "House of Graphs: a database of interesting graphs", "abstract": "In this note we present House of Graphs (http://hog.grinvin.org) which is a new database of graphs. The key principle is to have a searchable database and offer -- next to complete lists of some graph classes -- also a list of special graphs that already turned out to be interesting and relevant in the study of graph theoretic problems or as counterexamples to conjectures. This list can be extended by users of the database.", "published": "2012-04-16T16:06:47Z", "authors": ["Gunnar Brinkmann", "Kris Coolsaet", "Jan Goedgebeur", "Hadrien Melot"], "categories": ["math.CO", "cs.DM"], "doi": "10.1016/j.dam.2012.07.018", "_query": "hedonic regression housing", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2402.06763v3", "title": "Scalable Kernel Logistic Regression with Nyström Approximation: Theoretical Analysis and Application to Discrete Choice Modelling", "abstract": "The application of kernel-based Machine Learning (ML) techniques to discrete choice modelling using large datasets often faces challenges due to memory requirements and the considerable number of parameters involved in these models. This complexity hampers the efficient training of large-scale models. This paper addresses these problems of scalability by introducing the Nyström approximation for Kernel Logistic Regression (KLR) on large datasets. The study begins by presenting a theoretical analysis in which: i) the set of KLR solutions is characterised, ii) an upper bound to the solution of KLR with Nyström approximation is provided, and finally iii) a specialisation of the optimisation algorithms to Nyström KLR is described. After this, the Nyström KLR is computationally validated. Four landmark selection methods are tested, including basic uniform sampling, a k-means sampling strategy, and two non-uniform methods grounded in leverage scores. The performance of these strategies is evaluated using large-scale transport mode choice datasets and is compared with traditional methods such as Multinomial Logit (MNL) and contemporary ML techniques. The study also assesses the efficiency of various optimisation techniques for the proposed Nyström KLR model. The performance of gradient descent, Momentum, Adam, and L-BFGS-B optimisation methods is examined on these datasets. Among these strategies, the k-means Nyström KLR approach emerges as a successful solution for applying KLR to large datasets, particularly when combined with the L-BFGS-B and Adam optimisation methods. The results highlight the ability of this strategy to handle datasets exceeding 200,000 observations while maintaining robust performance.", "published": "2024-02-09T19:52:31Z", "authors": ["José Ángel Martín-Baos", "Ricardo García-Ródenas", "Luis Rodriguez-Benitez", "Michel Bierlaire"], "categories": ["cs.LG", "stat.ML"], "doi": "10.1016/j.neucom.2024.128975", "_query": "hedonic regression housing", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/1102.2808v5", "title": "Transductive Ordinal Regression", "abstract": "Ordinal regression is commonly formulated as a multi-class problem with ordinal constraints. The challenge of designing accurate classifiers for ordinal regression generally increases with the number of classes involved, due to the large number of labeled patterns that are needed. The availability of ordinal class labels, however, is often costly to calibrate or difficult to obtain. Unlabeled patterns, on the other hand, often exist in much greater abundance and are freely available. To take benefits from the abundance of unlabeled patterns, we present a novel transductive learning paradigm for ordinal regression in this paper, namely Transductive Ordinal Regression (TOR). The key challenge of the present study lies in the precise estimation of both the ordinal class label of the unlabeled data and the decision functions of the ordinal classes, simultaneously. The core elements of the proposed TOR include an objective function that caters to several commonly used loss functions casted in transductive settings, for general ordinal regression. A label swapping scheme that facilitates a strictly monotonic decrease in the objective function value is also introduced. Extensive numerical studies on commonly used benchmark datasets including the real world sentiment prediction problem are then presented to showcase the characteristics and efficacies of the proposed transductive ordinal regression. Further, comparisons to recent state-of-the-art ordinal regression methods demonstrate the introduced transductive learning paradigm for ordinal regression led to the robust and improved performance.", "published": "2011-02-14T15:53:06Z", "authors": ["Chun-Wei Seah", "Ivor W. Tsang", "Yew-Soon Ong"], "categories": ["cs.LG"], "doi": "10.1109/TNNLS.2012.2198240", "_query": "hedonic regression housing", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/1412.1716v3", "title": "Nonparametric modal regression", "abstract": "Modal regression estimates the local modes of the distribution of $Y$ given $X=x$, instead of the mean, as in the usual regression sense, and can hence reveal important structure missed by usual regression methods. We study a simple nonparametric method for modal regression, based on a kernel density estimate (KDE) of the joint distribution of $Y$ and $X$. We derive asymptotic error bounds for this method, and propose techniques for constructing confidence sets and prediction sets. The latter is used to select the smoothing bandwidth of the underlying KDE. The idea behind modal regression is connected to many others, such as mixture regression and density ridge estimation, and we discuss these ties as well.", "published": "2014-12-04T16:18:42Z", "authors": ["Yen-Chi Chen", "Christopher R. Genovese", "Ryan J. Tibshirani", "Larry Wasserman"], "categories": ["stat.ME", "math.ST", "stat.ML"], "doi": "10.1214/15-AOS1373", "_query": "hedonic regression housing", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/1402.0645v1", "title": "Local Gaussian Regression", "abstract": "Locally weighted regression was created as a nonparametric learning method that is computationally efficient, can learn from very large amounts of data and add data incrementally. An interesting feature of locally weighted regression is that it can work with spatially varying length scales, a beneficial property, for instance, in control problems. However, it does not provide a generative model for function values and requires training and test data to be generated identically, independently. Gaussian (process) regression, on the other hand, provides a fully generative model without significant formal requirements on the distribution of training data, but has much higher computational cost and usually works with one global scale per input dimension. Using a localising function basis and approximate inference techniques, we take Gaussian (process) regression to increasingly localised properties and toward the same computational complexity class as locally weighted regression.", "published": "2014-02-04T07:35:48Z", "authors": ["Franziska Meier", "Philipp Hennig", "Stefan Schaal"], "categories": ["cs.LG", "cs.RO"], "doi": "", "_query": "hedonic regression housing", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2505.01472v1", "title": "SafeTab-P: Disclosure Avoidance for the 2020 Census Detailed Demographic and Housing Characteristics File A (Detailed DHC-A)", "abstract": "This article describes the disclosure avoidance algorithm that the U.S. Census Bureau used to protect the Detailed Demographic and Housing Characteristics File A (Detailed DHC-A) of the 2020 Census. The tabulations contain statistics (counts) of demographic characteristics of the entire population of the United States, crossed with detailed races and ethnicities at varying levels of geography. The article describes the SafeTab-P algorithm, which is based on adding noise drawn to statistics of interest from a discrete Gaussian distribution. A key innovation in SafeTab-P is the ability to adaptively choose how many statistics and at what granularity to release them, depending on the size of a population group. We prove that the algorithm satisfies a well-studied variant of differential privacy, called zero-concentrated differential privacy (zCDP). We then describe how the algorithm was implemented on Tumult Analytics and briefly outline the parameterization and tuning of the algorithm.", "published": "2025-05-02T13:08:28Z", "authors": ["Sam Haney", "Skye Berghel", "Bayard Carlson", "Ryan Cumings-Menon", "Luke Hartman", "Michael Hay", "Ashwin Machanavajjhala", "Gerome Miklau", "Amritha Pai", "Simran Rajpal", "David Pujol", "William Sexton", "Ruchit Shrestha", "Daniel Simmons-Marengo"], "categories": ["cs.CR", "cs.CY"], "doi": "", "_query": "hedonic regression housing", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/1908.04015v1", "title": "Variational Autoencoded Regression: High Dimensional Regression of Visual Data on Complex Manifold", "abstract": "This paper proposes a new high dimensional regression method by merging Gaussian process regression into a variational autoencoder framework. In contrast to other regression methods, the proposed method focuses on the case where output responses are on a complex high dimensional manifold, such as images. Our contributions are summarized as follows: (i) A new regression method estimating high dimensional image responses, which is not handled by existing regression algorithms, is proposed. (ii) The proposed regression method introduces a strategy to learn the latent space as well as the encoder and decoder so that the result of the regressed response in the latent space coincide with the corresponding response in the data space. (iii) The proposed regression is embedded into a generative model, and the whole procedure is developed by the variational autoencoder framework. We demonstrate the robustness and effectiveness of our method through a number of experiments on various visual data regression problems.", "published": "2019-08-12T06:06:59Z", "authors": ["YoungJoon Yoo", "Sangdoo Yun", "Hyung Jin Chang", "Yiannis Demiris", "Jin Young Choi"], "categories": ["cs.CV", "cs.LG"], "doi": "", "_query": "hedonic regression housing", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/1103.2523v3", "title": "Sequences of regressions and their independences", "abstract": "Ordered sequences of univariate or multivariate regressions provide statistical models for analysing data from randomized, possibly sequential interventions, from cohort or multi-wave panel studies, but also from cross-sectional or retrospective studies. Conditional independences are captured by what we name regression graphs, provided the generated distribution shares some properties with a joint Gaussian distribution. Regression graphs extend purely directed, acyclic graphs by two types of undirected graph, one type for components of joint responses and the other for components of the context vector variable. We review the special features and the history of regression graphs, derive criteria to read all implied independences of a regression graph and prove criteria for Markov equivalence that is to judge whether two different graphs imply the same set of independence statements. Knowledge of Markov equivalence provides alternative interpretations of a given sequence of regressions, is essential for machine learning strategies and permits to use the simple graphical criteria of regression graphs on graphs for which the corresponding criteria are in general more complex. Under the known conditions that a Markov equivalent directed acyclic graph exists for any given regression graph, we give a polynomial time algorithm to find one such graph.", "published": "2011-03-13T15:22:48Z", "authors": ["Nanny Wermuth", "Kayvan Sadeghi"], "categories": ["stat.ME"], "doi": "", "_query": "hedonic regression housing", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2104.09778v2", "title": "Convergence of Gaussian process regression: Optimality, robustness, and relationship with kernel ridge regression", "abstract": "In this work, we investigate Gaussian process regression used to recover a function based on noisy observations. We derive upper and lower error bounds for Gaussian process regression with possibly misspecified correlation functions. The optimal convergence rate can be attained even if the smoothness of the imposed correlation function exceeds that of the true correlation function and the sampling scheme is quasi-uniform. As byproducts, we also obtain convergence rates of kernel ridge regression with misspecified kernel function, where the underlying truth is a deterministic function. The convergence rates of Gaussian process regression and kernel ridge regression are closely connected, which is aligned with the relationship between sample paths of Gaussian process and the corresponding reproducing kernel Hilbert space.", "published": "2021-04-20T06:27:14Z", "authors": ["Wenjia Wang", "Bing-Yi Jing"], "categories": ["math.ST"], "doi": "", "_query": "hedonic regression housing", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2006.05440v3", "title": "On Coresets For Regularized Regression", "abstract": "We study the effect of norm based regularization on the size of coresets for regression problems. Specifically, given a matrix $ \\mathbf{A} \\in {\\mathbb{R}}^{n \\times d}$ with $n\\gg d$ and a vector $\\mathbf{b} \\in \\mathbb{R} ^ n $ and $λ> 0$, we analyze the size of coresets for regularized versions of regression of the form $\\|\\mathbf{Ax}-\\mathbf{b}\\|_p^r + λ\\|{\\mathbf{x}}\\|_q^s$ . Prior work has shown that for ridge regression (where $p,q,r,s=2$) we can obtain a coreset that is smaller than the coreset for the unregularized counterpart i.e. least squares regression (Avron et al). We show that when $r \\neq s$, no coreset for regularized regression can have size smaller than the optimal coreset of the unregularized version. The well known lasso problem falls under this category and hence does not allow a coreset smaller than the one for least squares regression. We propose a modified version of the lasso problem and obtain for it a coreset of size smaller than the least square regression. We empirically show that the modified version of lasso also induces sparsity in solution, similar to the original lasso. We also obtain smaller coresets for $\\ell_p$ regression with $\\ell_p$ regularization. We extend our methods to multi response regularized regression. Finally, we empirically demonstrate the coreset performance for the modified lasso and the $\\ell_1$ regression with $\\ell_1$ regularization.", "published": "2020-06-09T18:25:04Z", "authors": ["Rachit Chhaya", "Anirban Dasgupta", "Supratim Shit"], "categories": ["cs.LG", "cs.DS", "stat.ML"], "doi": "", "_query": "hedonic regression housing", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2410.18950v1", "title": "Adjusted Overfitting Regression", "abstract": "In this paper, I will introduce a new form of regression, that can adjust overfitting and underfitting through, \"distance-based regression.\" Overfitting often results in finding false patterns causing inaccurate results, so by having a new approach that minimizes overfitting, more accurate predictions can be derived. Then I will proceed with a test of my regression form and show additional ways to optimize the regression. Finally, I will apply my new technique to a specific data set to demonstrate its practical value.", "published": "2024-10-24T17:50:08Z", "authors": ["Dylan Wilson"], "categories": ["cs.LG"], "doi": "", "_query": "hedonic regression housing", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2202.11061v2", "title": "In This Apportionment Lottery, the House Always Wins", "abstract": "Apportionment is the problem of distributing $h$ indivisible seats across states in proportion to the states' populations. In the context of the US House of Representatives, this problem has a rich history and is a prime example of interactions between mathematical analysis and political practice. Grimmett (2004) suggested to apportion seats in a randomized way such that each state receives exactly their proportional share $q_i$ of seats in expectation (ex ante proportionality) and receives either $\\lfloor q_i \\rfloor$ or $\\lceil q_i \\rceil$ many seats ex post (quota). However, there is a vast space of randomized apportionment methods satisfying these two axioms, and so we additionally consider prominent axioms from the apportionment literature. Our main result is a randomized method satisfying quota, ex ante proportionality and house monotonicity - a property that prevents paradoxes when the number of seats changes and which we require to hold ex post. This result is based on a generalization of dependent rounding on bipartite graphs, which we call cumulative rounding and which might be of independent interest, as we demonstrate via applications beyond apportionment.", "published": "2022-02-22T17:46:11Z", "authors": ["Paul Gölz", "Dominik Peters", "Ariel D. Procaccia"], "categories": ["cs.GT"], "doi": "10.1145/3490486.3538299", "_query": "hedonic regression housing", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/1901.04136v2", "title": "Symbolic Regression in Materials Science", "abstract": "We showcase the potential of symbolic regression as an analytic method for use in materials research. First, we briefly describe the current state-of-the-art method, genetic programming-based symbolic regression (GPSR), and recent advances in symbolic regression techniques. Next, we discuss industrial applications of symbolic regression and its potential applications in materials science. We then present two GPSR use-cases: formulating a transformation kinetics law and showing the learning scheme discovers the well-known Johnson-Mehl-Avrami-Kolmogorov (JMAK) form, and learning the Landau free energy functional form for the displacive tilt transition in perovskite LaNiO$_3$. Finally, we propose that symbolic regression techniques should be considered by materials scientists as an alternative to other machine-learning-based regression models for learning from data.", "published": "2019-01-14T05:31:27Z", "authors": ["Yiqun Wang", "Nicholas Wagner", "James M. Rondinelli"], "categories": ["cond-mat.mtrl-sci", "physics.comp-ph"], "doi": "10.1557/mrc.2019.85", "_query": "hedonic regression housing", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/1904.05270v1", "title": "Google Street View image of a house predicts car accident risk of its resident", "abstract": "Road traffic injuries are a leading cause of death worldwide. Proper estimation of car accident risk is critical for appropriate allocation of resources in healthcare, insurance, civil engineering, and other industries. We show how images of houses are predictive of car accidents. We analyze 20,000 addresses of insurance company clients, collect a corresponding house image using Google Street View, and annotate house features such as age, type, and condition. We find that this information substantially improves car accident risk prediction compared to the state-of-the-art risk model of the insurance company and could be used for price discrimination. From this perspective, public availability of house images raises legal and social concerns, as they can be a proxy of ethnicity, religion and other sensitive data.", "published": "2019-04-10T16:16:31Z", "authors": ["Kinga Kita", "Łukasz Kidziński"], "categories": ["stat.AP"], "doi": "", "_query": "hedonic regression housing", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2003.00397v1", "title": "Intelligent Home 3D: Automatic 3D-House Design from Linguistic Descriptions Only", "abstract": "Home design is a complex task that normally requires architects to finish with their professional skills and tools. It will be fascinating that if one can produce a house plan intuitively without knowing much knowledge about home design and experience of using complex designing tools, for example, via natural language. In this paper, we formulate it as a language conditioned visual content generation problem that is further divided into a floor plan generation and an interior texture (such as floor and wall) synthesis task. The only control signal of the generation process is the linguistic expression given by users that describe the house details. To this end, we propose a House Plan Generative Model (HPGM) that first translates the language input to a structural graph representation and then predicts the layout of rooms with a Graph Conditioned Layout Prediction Network (GC LPN) and generates the interior texture with a Language Conditioned Texture GAN (LCT-GAN). With some post-processing, the final product of this task is a 3D house model. To train and evaluate our model, we build the first Text-to-3D House Model dataset.", "published": "2020-03-01T04:28:48Z", "authors": ["Qi Chen", "Qi Wu", "Rui Tang", "Yuhan Wang", "Shuai Wang", "Mingkui Tan"], "categories": ["cs.CV"], "doi": "", "_query": "hedonic regression housing", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/1102.5217v1", "title": "Varying-coefficient functional linear regression", "abstract": "Functional linear regression analysis aims to model regression relations which include a functional predictor. The analog of the regression parameter vector or matrix in conventional multivariate or multiple-response linear regression models is a regression parameter function in one or two arguments. If, in addition, one has scalar predictors, as is often the case in applications to longitudinal studies, the question arises how to incorporate these into a functional regression model. We study a varying-coefficient approach where the scalar covariates are modeled as additional arguments of the regression parameter function. This extension of the functional linear regression model is analogous to the extension of conventional linear regression models to varying-coefficient models and shares its advantages, such as increased flexibility; however, the details of this extension are more challenging in the functional case. Our methodology combines smoothing methods with regularization by truncation at a finite number of functional principal components. A practical version is developed and is shown to perform better than functional linear regression for longitudinal data. We investigate the asymptotic properties of varying-coefficient functional linear regression and establish consistency properties.", "published": "2011-02-25T11:56:25Z", "authors": ["Yichao Wu", "Jianqing Fan", "Hans-Georg Müller"], "categories": ["math.ST"], "doi": "10.3150/09-BEJ231", "_query": "hedonic regression housing", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/1306.3531v1", "title": "The convergence of regional house prices in the USA in the context of the stress testing of financial institutions", "abstract": "I studied the convergence of regional house prices to national prices in USA by analyzing time-series of house price indices of 9 Census Divisions. I found the evidence of the convergence in some parts of the country using asymmetric unit root tests. The fact that the evidence of the convergence is not present in large parts of the country raises an issue of execution and interpretation of results of Federal Reserve Bank's annual stress testing of the US banking system.", "published": "2013-06-14T23:57:54Z", "authors": ["Argyn Kuketayev"], "categories": ["q-fin.RM", "stat.AP"], "doi": "", "_query": "hedonic regression housing", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/1902.09615v5", "title": "Binscatter Regressions", "abstract": "We introduce the package Binsreg, which implements the binscatter methods developed by Cattaneo, Crump, Farrell, and Feng (2024b,a). The package includes seven commands: binsreg, binslogit, binsprobit, binsqreg, binstest, binspwc, and binsregselect. The first four commands implement binscatter plotting, point estimation, and uncertainty quantification (confidence intervals and confidence bands) for least squares linear binscatter regression (binsreg) and for nonlinear binscatter regression (binslogit for Logit regression, binsprobit for Probit regression, and binsqreg for quantile regression). The next two commands focus on pointwise and uniform inference: binstest implements hypothesis testing procedures for parametric specifications and for nonparametric shape restrictions of the unknown regression function, while binspwc implements multi-group pairwise statistical comparisons. Finally, the command binsregselect implements data-driven number of bins selectors. The commands offer binned scatter plots, and allow for covariate adjustment, weighting, clustering, and multi-sample analysis, which is useful when studying treatment effect heterogeneity in randomized and observational studies, among many other features.", "published": "2019-02-25T20:59:23Z", "authors": ["Matias D. Cattaneo", "Richard K. Crump", "Max H. Farrell", "Yingjie Feng"], "categories": ["econ.EM", "stat.CO"], "doi": "", "_query": "hedonic regression housing", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/1906.11291v2", "title": "Rerandomization and Regression Adjustment", "abstract": "Randomization is a basis for the statistical inference of treatment effects without strong assumptions on the outcome-generating process. Appropriately using covariates further yields more precise estimators in randomized experiments. R. A. Fisher suggested blocking on discrete covariates in the design stage or conducting analysis of covariance (ANCOVA) in the analysis stage. We can embed blocking into a wider class of experimental design called rerandomization, and extend the classical ANCOVA to more general regression adjustment. Rerandomization trumps complete randomization in the design stage, and regression adjustment trumps the simple difference-in-means estimator in the analysis stage. It is then intuitive to use both rerandomization and regression adjustment. Under the randomization-inference framework, we establish a unified theory allowing the designer and analyzer to have access to different sets of covariates. We find that asymptotically (a) for any given estimator with or without regression adjustment, rerandomization never hurts either the sampling precision or the estimated precision, and (b) for any given design with or without rerandomization, our regression-adjusted estimator never hurts the estimated precision. Therefore, combining rerandomization and regression adjustment yields better coverage properties and thus improves statistical inference. To theoretically quantify these statements, we discuss optimal regression-adjusted estimators in terms of the sampling precision and the estimated precision, and then measure the additional gains of the designer and the analyzer. We finally suggest using rerandomization in the design and regression adjustment in the analysis followed by the Huber--White robust standard error.", "published": "2019-06-26T18:33:00Z", "authors": ["Xinran Li", "Peng Ding"], "categories": ["math.ST", "stat.ME"], "doi": "", "_query": "hedonic regression housing", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/0911.1928v1", "title": "Regression on a Graph", "abstract": "The `Signal plus Noise' model for nonparametric regression can be extended to the case of observations taken at the vertices of a graph. This model includes many familiar regression problems. This article discusses the use of the edges of a graph to measure roughness in penalized regression. Distance between estimate and observation is measured at every vertex in the $L_2$ norm, and roughness is penalized on every edge in the $L_1$ norm. Thus the ideas of total-variation penalization can be extended to a graph. The resulting minimization problem presents special computational challenges, so we describe a new, fast algorithm and demonstrate its use with examples. Further examples include a graphical approach that gives an improved estimate of the baseline in spectroscopic analysis, and a simulation applicable to discrete spatial variation. In our example, penalized regression outperforms kernel smoothing in terms of identifying local extreme values. In all examples we use fully automatic procedures for setting the smoothing parameters.", "published": "2009-11-10T15:29:49Z", "authors": ["Arne Kovac", "Andrew D. A. C. Smith"], "categories": ["stat.ME", "stat.CO"], "doi": "", "_query": "hedonic regression housing", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2306.10306v2", "title": "Deep Huber quantile regression networks", "abstract": "Typical machine learning regression applications aim to report the mean or the median of the predictive probability distribution, via training with a squared or an absolute error scoring function. The importance of issuing predictions of more functionals of the predictive probability distribution (quantiles and expectiles) has been recognized as a means to quantify the uncertainty of the prediction. In deep learning (DL) applications, that is possible through quantile and expectile regression neural networks (QRNN and ERNN respectively). Here we introduce deep Huber quantile regression networks (DHQRN) that nest QRNN and ERNN as edge cases. DHQRN can predict Huber quantiles, which are more general functionals in the sense that they nest quantiles and expectiles as limiting cases. The main idea is to train a DL algorithm with the Huber quantile scoring function, which is consistent for the Huber quantile functional. As a proof of concept, DHQRN are applied to predict house prices in Melbourne, Australia and Boston, United States (US). In this context, predictive performances of three DL architectures are discussed along with evidential interpretation of results from two economic case studies. Additional simulation experiments and applications to real-world case studies using open datasets demonstrate a satisfactory absolute performance of DHQRN.", "published": "2023-06-17T09:40:52Z", "authors": ["Hristos Tyralis", "Georgia Papacharalampous", "Nilay Dogulu", "Kwok P. Chun"], "categories": ["stat.ML", "cs.LG", "stat.AP"], "doi": "10.1016/j.neunet.2025.107364", "_query": "hedonic regression housing", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2511.07365v2", "title": "Private Sketches for Linear Regression", "abstract": "Linear regression is frequently applied in a variety of domains, some of which might contain sensitive information. This necessitates that the application of these methods does not reveal private information. Differentially private (DP) linear regression methods, developed for this purpose, compute private estimates of the solution. These techniques typically involve computing a noisy version of the solution vector. Instead, we propose releasing private sketches of the datasets, which can then be used to compute an approximate solution to the regression problem. This is motivated by the \\emph{sketch-and-solve} paradigm, where the regression problem is solved on a smaller sketch of the dataset instead of on the original problem space. The solution obtained on the sketch can also be shown to have good approximation guarantees to the original problem. Various sketching methods have been developed for improving the computational efficiency of linear regression problems under this paradigm. We adopt this paradigm for the purpose of releasing private sketches of the data. We construct differentially private sketches for the problems of least squares regression, as well as least absolute deviations regression. We show that the privacy constraints lead to sketched versions of regularized regression. We compute the bounds on the regularization parameter required for guaranteeing privacy. The availability of these private sketches facilitates the application of commonly available solvers for regression, without the risk of privacy leakage.", "published": "2025-11-10T18:22:40Z", "authors": ["Shrutimoy Das", "Debanuj Nayak", "Anirban Dasgupta"], "categories": ["cs.LG", "stat.ML"], "doi": "", "_query": "hedonic regression housing", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/1303.4288v1", "title": "Iterative Isotonic Regression", "abstract": "This article introduces a new nonparametric method for estimating a univariate regression function of bounded variation. The method exploits the Jordan decomposition which states that a function of bounded variation can be decomposed as the sum of a non-decreasing function and a non-increasing function. This suggests combining the backfitting algorithm for estimating additive functions with isotonic regression for estimating monotone functions. The resulting iterative algorithm is called Iterative Isotonic Regression (I.I.R.). The main technical result in this paper is the consistency of the proposed estimator when the number of iterations $k_n$ grows appropriately with the sample size $n$. The proof requires two auxiliary results that are of interest in and by themselves: firstly, we generalize the well-known consistency property of isotonic regression to the framework of a non-monotone regression function, and secondly, we relate the backfitting algorithm to Von Neumann's algorithm in convex analysis.", "published": "2013-03-18T15:31:32Z", "authors": ["Arnaud Guyader", "Nick Hengartner", "Nicolas Jégou", "Eric Matzner-Løber"], "categories": ["math.ST"], "doi": "", "_query": "hedonic regression housing", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2312.05077v1", "title": "Computation of least squares trimmed regression--an alternative to least trimmed squares regression", "abstract": "The least squares of depth trimmed (LST) residuals regression, proposed in Zuo and Zuo (2023) \\cite{ZZ23}, serves as a robust alternative to the classic least squares (LS) regression as well as a strong competitor to the famous least trimmed squares (LTS) regression of Rousseeuw (1984) \\cite{R84}. Theoretical properties of the LST were thoroughly studied in \\cite{ZZ23}. The article aims to promote the implementation and computation of the LST residuals regression for a broad group of statisticians in statistical practice and demonstrates that (i) the LST is as robust as the benchmark of robust regression, the LTS regression, and much more efficient than the latter. (ii) It can be as efficient as (or even more efficient than) the LS in the scenario with errors uncorrelated with mean zero and homoscedastic with finite variance. (iii) It can be computed as fast as (or even faster than) the LTS based on a newly proposed algorithm.", "published": "2023-12-08T15:00:29Z", "authors": ["Yijun Zuo", "Hanwen Zuo"], "categories": ["stat.ME"], "doi": "", "_query": "hedonic regression housing", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/1709.05707v2", "title": "Nonparametric Shape-restricted Regression", "abstract": "We consider the problem of nonparametric regression under shape constraints. The main examples include isotonic regression (with respect to any partial order), unimodal/convex regression, additive shape-restricted regression, and constrained single index model. We review some of the theoretical properties of the least squares estimator (LSE) in these problems, emphasizing on the adaptive nature of the LSE. In particular, we study the behavior of the risk of the LSE, and its pointwise limiting distribution theory, with special emphasis to isotonic regression. We survey various methods for constructing pointwise confidence intervals around these shape-restricted functions. We also briefly discuss the computation of the LSE and indicate some open research problems and future directions.", "published": "2017-09-17T19:13:59Z", "authors": ["Adityanand Guntuboyina", "Bodhisattva Sen"], "categories": ["math.ST", "stat.ML"], "doi": "", "_query": "hedonic regression housing", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/1710.06987v2", "title": "On Affine and Conjugate Nonparametric Regression", "abstract": "Suppose the nonparametric regression function of a response variable $Y$ on covariates $X$ and $Z$ is an affine function of $X$ such that the slope $β$ and the intercept $α$ are real valued measurable functions on the range of the completely arbitrary random element $Z$. Assume that $X$ has a finite moment of order greater than or equal to $2$, $Y$ has a finite moment of conjugate order, and $α\\left(Z\\right)$ and $α\\left(Z\\right)X$ have finite first moments. Then, the nonparametric regression function equals the least squares linear regression function of $Y$ on $X$ with all the moments that appear in the expression of the linear regression function calculated conditional on $Z$. Consequently, conditional mean independence implies zero conditional covariance and a degenerate version of the aforesaid affine form for the nonparametric regression function, whereas the aforesaid affine form and zero conditional covariance imply conditional mean independence. Further, it turns out that the nonparametric regression function has the aforesaid affine form if $X$ is Bernoulli, and since $1$ is the conjugate exponent of $\\infty$, the least squares linear regression formula for the nonparametric regression function holds when $Y$ has only a finite first moment and $Z$ is completely arbitrary.", "published": "2017-10-19T02:36:35Z", "authors": ["Rajeshwari Majumdar"], "categories": ["math.ST"], "doi": "", "_query": "hedonic regression housing", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2608.25186v2", "title": "Log-regularly varying scale mixture of asymmetric Laplaces for robust Bayesian quantile regression", "abstract": "Bayesian quantile regression based on the asymmetric Laplace (AL) distribution can be sensitive to extreme observations because of its exponentially decaying tails. We propose a robust error distribution constructed as a finite mixture of the AL distribution and a log-Pareto scale mixture of asymmetric Laplace distributions (LPAL). Unlike a direct log-Pareto extension of the normal location-scale representation of the AL distribution, the proposed AL-LPAL mixture preserves the prescribed quantile and exhibits log-regularly varying behavior in both tails. The LPAL component also has an unbounded density at the target quantile, yielding a distribution that combines sharp central concentration with super-heavy tails. We establish posterior robustness under arbitrarily extreme contamination and provide sufficient conditions for the existence of posterior moments of the regression coefficients and scale parameter. For posterior computation, we develop a Gibbs sampler using latent-variable augmentations and a computationally efficient mean-field variational Bayes approximation. Simulation studies show that the proposed method is competitive under moderate contamination and maintains stable point estimation with comparatively concentrated posterior intervals, particularly when severe contamination affects the quantile of interest. Applications to carbon dioxide and Boston housing data, using the same preprocessing as existing robust Bayesian quantile regression analyses, show favorable predictive performance across nearly all quantile levels and loss criteria considered.", "published": "2026-08-25T21:58:36Z", "authors": ["Dongu Han", "Genya Kobayashi", "Shonosuke Sugasawa"], "categories": ["stat.ME"], "doi": "", "_query": "hedonic regression housing", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/1911.12436v1", "title": "AR-Net: A simple Auto-Regressive Neural Network for time-series", "abstract": "In this paper we present a new framework for time-series modeling that combines the best of traditional statistical models and neural networks. We focus on time-series with long-range dependencies, needed for monitoring fine granularity data (e.g. minutes, seconds, milliseconds), prevalent in operational use-cases. Traditional models, such as auto-regression fitted with least squares (Classic-AR) can model time-series with a concise and interpretable model. When dealing with long-range dependencies, Classic-AR models can become intractably slow to fit for large data. Recently, sequence-to-sequence models, such as Recurrent Neural Networks, which were originally intended for natural language processing, have become popular for time-series. However, they can be overly complex for typical time-series data and lack interpretability. A scalable and interpretable model is needed to bridge the statistical and deep learning-based approaches. As a first step towards this goal, we propose modelling AR-process dynamics using a feed-forward neural network approach, termed AR-Net. We show that AR-Net is as interpretable as Classic-AR but also scales to long-range dependencies. Our results lead to three major conclusions: First, AR-Net learns identical AR-coefficients as Classic-AR, thus being equally interpretable. Second, the computational complexity with respect to the order of the AR process, is linear for AR-Net as compared to a quadratic for Classic-AR. This makes it possible to model long-range dependencies within fine granularity data. Third, by introducing regularization, AR-Net automatically selects and learns sparse AR-coefficients. This eliminates the need to know the exact order of the AR-process and allows to learn sparse weights for a model with long-range dependencies.", "published": "2019-11-27T21:47:59Z", "authors": ["Oskar Triebe", "Nikolay Laptev", "Ram Rajagopal"], "categories": ["cs.LG", "stat.ML"], "doi": "", "_query": "hedonic regression housing", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2004.07571v1", "title": "Explaining herding and volatility in the cyclical price dynamics of urban housing markets using a large scale agent-based model", "abstract": "Urban housing markets, along with markets of other assets, universally exhibit periods of strong price increases followed by sharp corrections. The mechanisms generating such non-linearities are not yet well understood. We develop an agent-based model populated by a large number of heterogeneous households. The agents' behavior is compatible with economic rationality, with the trend-following behavior found to be essential in replicating market dynamics. The model is calibrated using several large and distributed datasets of the Greater Sydney region (demographic, economic and financial) across three specific and diverse periods since 2006. The model is not only capable of explaining price dynamics during these periods, but also reproduces the novel behavior actually observed immediately prior to the market peak in 2017, namely a sharp increase in the variability of prices. This novel behavior is related to a combination of trend-following aptitude of the household agents (rational herding) and their propensity to borrow.", "published": "2020-04-16T10:17:50Z", "authors": ["Kirill S. Glavatskiy", "Mikhail Prokopenko", "Adrian Carro", "Paul Ormerod", "Michael Harre"], "categories": ["q-fin.CP", "nlin.AO", "physics.soc-ph"], "doi": "", "_query": "hedonic regression housing", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2209.12538v1", "title": "Convex Support Vector Regression", "abstract": "Nonparametric regression subject to convexity or concavity constraints is increasingly popular in economics, finance, operations research, machine learning, and statistics. However, the conventional convex regression based on the least squares loss function often suffers from overfitting and outliers. This paper proposes to address these two issues by introducing the convex support vector regression (CSVR) method, which effectively combines the key elements of convex regression and support vector regression. Numerical experiments demonstrate the performance of CSVR in prediction accuracy and robustness that compares favorably with other state-of-the-art methods.", "published": "2022-09-26T09:37:33Z", "authors": ["Zhiqiang Liao", "Sheng Dai", "Timo Kuosmanen"], "categories": ["stat.ME"], "doi": "", "_query": "hedonic regression housing", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2006.02726v8", "title": "Construction and sample path properties of Brownian house-moving between two curves", "abstract": "This study aims to construct a stochastic process called \"Brownian house-moving,\" which is a Brownian bridge conditioned to stay between two curves. To construct this process, statements are prepared on the weak convergence of conditioned Brownian motions, conditioned Brownian bridges, and conditioned three-dimensional Bessel bridges. Moreover, the sample path properties of Brownian house-moving are studied as well.", "published": "2020-06-04T09:31:01Z", "authors": ["Kensuke Ishitani", "Daisuke Hatakenaka", "Keisuke Suzuki"], "categories": ["math.PR"], "doi": "", "_query": "hedonic regression housing", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/1707.00727v1", "title": "Regression Phalanxes", "abstract": "Tomal et al. (2015) introduced the notion of \"phalanxes\" in the context of rare-class detection in two-class classification problems. A phalanx is a subset of features that work well for classification tasks. In this paper, we propose a different class of phalanxes for application in regression settings. We define a \"Regression Phalanx\" - a subset of features that work well together for prediction. We propose a novel algorithm which automatically chooses Regression Phalanxes from high-dimensional data sets using hierarchical clustering and builds a prediction model for each phalanx for further ensembling. Through extensive simulation studies and several real-life applications in various areas (including drug discovery, chemical analysis of spectra data, microarray analysis and climate projections) we show that an ensemble of Regression Phalanxes improves prediction accuracy when combined with effective prediction methods like Lasso or Random Forests.", "published": "2017-07-03T18:53:37Z", "authors": ["Hongyang Zhang", "William J. Welch", "Ruben H. Zamar"], "categories": ["stat.ML"], "doi": "", "_query": "hedonic regression housing", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2108.05437v2", "title": "Single Index Fréchet Regression", "abstract": "Single index models provide an effective dimension reduction tool in regression, especially for high dimensional data, by projecting a general multivariate predictor onto a direction vector. We propose a novel single-index model for regression models where metric space-valued random object responses are coupled with multivariate Euclidean predictors. The responses in this regression model include complex, non-Euclidean data that lie in abstract metric spaces, including covariance matrices, graph Laplacians of networks, and univariate probability distribution functions. While Fréchet regression has proved useful for modeling the conditional mean of such random objects given multivariate Euclidean vectors, it does not provide for regression parameters such as slopes or intercepts, since the metric space-valued responses are not amenable to linear operations. As a consequence, distributional results for Fréchet regression have been elusive. We show here that for the case of multivariate Euclidean predictors, the parameters that define a single index and projection vector can be used to substitute for the inherent absence of parameters in Fréchet regression. Specifically, we derive the asymptotic distribution of suitable estimates of these parameters, which then can be utilized to test linear hypotheses for the parameters, subject to an identifiability condition. We demonstrate the finite sample performance of estimation and inference for the proposed single index Fréchet regression model through simulation studies. The method is illustrated for resting-state functional Magnetic Resonance Imaging (fMRI) data from the ADNI study.", "published": "2021-08-11T20:34:42Z", "authors": ["Satarupa Bhattacharjee", "Hans-Georg Müller"], "categories": ["stat.ME", "math.ST"], "doi": "", "_query": "hedonic regression housing", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/math/0602242v1", "title": "Estimation of the density of regression errors", "abstract": "Estimation of the density of regression errors is a fundamental issue in regression analysis and it is typically explored via a parametric approach. This article uses a nonparametric approach with the mean integrated squared error (MISE) criterion. It solves a long-standing problem, formulated two decades ago by Mark Pinsker, about estimation of a nonparametric error density in a nonparametric regression setting with the accuracy of an oracle that knows the underlying regression errors. The solution implies that, under a mild assumption on the differentiability of the design density and regression function, the MISE of a data-driven error density estimator attains minimax rates and sharp constants known for the case of directly observed regression errors. The result holds for error densities with finite and infinite supports. Some extensions of this result for more general heteroscedastic models with possibly dependent errors and predictors are also obtained; in the latter case the marginal error density is estimated. In all considered cases a blockwise-shrinking Efromovich--Pinsker density estimate, based on plugged-in residuals, is used. The obtained results imply a theoretical justification of a customary practice in applied regression analysis to consider residuals as proxies for underlying regression errors. Numerical and real examples are presented and discussed, and the S-PLUS software is available.", "published": "2006-02-11T12:22:13Z", "authors": ["Sam Efromovich"], "categories": ["math.ST"], "doi": "10.1214/009053605000000435", "_query": "hedonic regression housing", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2202.11199v1", "title": "Differentially Private Regression with Unbounded Covariates", "abstract": "We provide computationally efficient, differentially private algorithms for the classical regression settings of Least Squares Fitting, Binary Regression and Linear Regression with unbounded covariates. Prior to our work, privacy constraints in such regression settings were studied under strong a priori bounds on covariates. We consider the case of Gaussian marginals and extend recent differentially private techniques on mean and covariance estimation (Kamath et al., 2019; Karwa and Vadhan, 2018) to the sub-gaussian regime. We provide a novel technical analysis yielding differentially private algorithms for the above classical regression settings. Through the case of Binary Regression, we capture the fundamental and widely-studied models of logistic regression and linearly-separable SVMs, learning an unbiased estimate of the true regression vector, up to a scaling factor.", "published": "2022-02-19T17:31:38Z", "authors": ["Jason Milionis", "Alkis Kalavasis", "Dimitris Fotakis", "Stratis Ioannidis"], "categories": ["cs.CR", "cs.DS", "cs.LG", "stat.ML"], "doi": "", "_query": "hedonic regression housing", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2407.04664v1", "title": "The Degree of Fairness in Efficient House Allocation", "abstract": "The classic house allocation problem is primarily concerned with finding a matching between a set of agents and a set of houses that guarantees some notion of economic efficiency (e.g. utilitarian welfare). While recent works have shifted focus on achieving fairness (e.g. minimizing the number of envious agents), they often come with notable costs on efficiency notions such as utilitarian or egalitarian welfare. We investigate the trade-offs between these welfare measures and several natural fairness measures that rely on the number of envious agents, the total (aggregate) envy of all agents, and maximum total envy of an agent. In particular, by focusing on envy-free allocations, we first show that, should one exist, finding an envy-free allocation with maximum utilitarian or egalitarian welfare is computationally tractable. We highlight a rather stark contrast between utilitarian and egalitarian welfare by showing that finding utilitarian welfare maximizing allocations that minimize the aforementioned fairness measures can be done in polynomial time while their egalitarian counterparts remain intractable (for the most part) even under binary valuations. We complement our theoretical findings by giving insights into the relationship between the different fairness measures and conducting empirical analysis.", "published": "2024-07-05T17:18:56Z", "authors": ["Hadi Hosseini", "Medha Kumar", "Sanjukta Roy"], "categories": ["cs.GT", "cs.DS"], "doi": "", "_query": "hedonic regression housing", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2409.19218v2", "title": "A Characterization of List Regression", "abstract": "There has been a recent interest in understanding and characterizing the sample complexity of list learning tasks, where the learning algorithm is allowed to make a short list of $k$ predictions, and we simply require one of the predictions to be correct. This includes recent works characterizing the PAC sample complexity of standard list classification and online list classification. Adding to this theme, in this work, we provide a complete characterization of list PAC regression. We propose two combinatorial dimensions, namely the $k$-OIG dimension and the $k$-fat-shattering dimension, and show that they characterize realizable and agnostic $k$-list regression respectively. These quantities generalize known dimensions for standard regression. Our work thus extends existing list learning characterizations from classification to regression.", "published": "2024-09-28T03:19:37Z", "authors": ["Chirag Pabbaraju", "Sahasrajit Sarmasarkar"], "categories": ["cs.LG", "cs.DS", "stat.ML"], "doi": "", "_query": "hedonic regression housing", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/1406.4068v1", "title": "Functional Regression", "abstract": "Functional data analysis (FDA) involves the analysis of data whose ideal units of observation are functions defined on some continuous domain, and the observed data consist of a sample of functions taken from some population, sampled on a discrete grid. Ramsay and Silverman's 1997 textbook sparked the development of this field, which has accelerated in the past 10 years to become one of the fastest growing areas of statistics, fueled by the growing number of applications yielding this type of data. One unique characteristic of FDA is the need to combine information both across and within functions, which Ramsay and Silverman called replication and regularization, respectively. This article will focus on functional regression, the area of FDA that has received the most attention in applications and methodological development. First will be an introduction to basis functions, key building blocks for regularization in functional regression methods, followed by an overview of functional regression methods, split into three types: [1] functional predictor regression (scalar-on-function), [2] functional response regression (function-on-scalar) and [3] function-on-function regression. For each, the role of replication and regularization will be discussed and the methodological development described in a roughly chronological manner, at times deviating from the historical timeline to group together similar methods. The primary focus is on modeling and methodology, highlighting the modeling structures that have been developed and the various regularization approaches employed. At the end is a brief discussion describing potential areas of future development in this field.", "published": "2014-06-16T16:56:35Z", "authors": ["Jeffrey S. Morris"], "categories": ["stat.ME", "stat.AP", "stat.CO"], "doi": "", "_query": "hedonic regression housing", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/1702.05829v2", "title": "Copula-based piecewise regression", "abstract": "Most common parametric families of copulas are totally ordered, and in many cases they are also positively or negatively regression dependent and therefore they lead to monotone regression functions, which makes them not suitable for dependence relationships that imply or suggest a non-monotone regression function. A gluing copula approach is proposed to decompose the underlying copula into totally ordered copulas that combined may lead to a non-monotone regression function.", "published": "2017-02-20T01:34:39Z", "authors": ["Arturo Erdely"], "categories": ["stat.ME"], "doi": "", "_query": "hedonic regression housing", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/1605.04542v1", "title": "On stepwise regression", "abstract": "Given data $y$ and $k$ covariates $x$ one problem in linear regression is to decide which in any of the covariates to include when regressing $y$ on the $x$. If $k$ is small it is possible to evaluate each subset of the $x$. If however $k$ is large then some other procedure must be use. Stepwise regression and the lasso are two such procedures but they both assume a linear model with error term. A different approach is taken here which does not assume a model. A covariate is included if it is better than random noise. This defines a procedure which is simple both conceptually and algorithmically", "published": "2016-05-15T13:19:26Z", "authors": ["Patrick Laurie Davies"], "categories": ["math.ST"], "doi": "", "_query": "hedonic regression housing", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2504.18769v1", "title": "A Simplified Condition For Quantile Regression", "abstract": "Quantile regression is effective in modeling and inferring the conditional quantile given some predictors and has become popular in risk management due to wide applications of quantile-based risk measures. When forecasting risk for economic and financial variables, quantile regression has to account for heteroscedasticity, which raises the question of whether the identification condition on residuals in quantile regression is equivalent to one independent of heteroscedasticity. In this paper, we present some identification conditions under three probability models and use them to establish simplified conditions in quantile regression.", "published": "2025-04-26T02:15:56Z", "authors": ["Liang Peng", "Yongcheng Qi"], "categories": ["math.ST", "math.PR"], "doi": "", "_query": "hedonic regression housing", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2605.10617v1", "title": "Logarithmic scaling of selective sweep curves: from tents to houses", "abstract": "One of the classical results of mathematical population genetics states that the frequency of a beneficial mutant's offspring, on its way to fixation in a large population, looks like a logistic curve. A logarithmic scaling (both in height and time) of these selective sweep curves leads (in the case of strong selection) to a tent-like shape in the large population limit: First the logarithmic frequency of the mutant increases linearly from 0 to 1, then that of the former resident decreases from 1 to 0. For moderate selection the logarithmic frequencies develop (in the large population limit) a jump at the beginning/the end of the sweep, which takes the shape of the tent into that of a house. Our main result (proved for the Moran model) assesses the regularity of this convergence in the large population limit: It is uniform in the house's roof (phases of linear growth and decline) and \"Skorokhod $M_1$\" in the house's walls (closely around the jumps). Apart from interest in its own right, we anticipate that this result and the proof techniques will be instrumental for extending the description of clonal interference by Poissonian interacting trajectories (as it was done in Hermann et al. (2024) for strong selection) also to moderate selection.", "published": "2026-05-11T14:12:37Z", "authors": ["Florin Boenkost", "Felix Hermann", "András Tóbiás", "Anton Wakolbinger"], "categories": ["math.PR", "q-bio.PE"], "doi": "", "_query": "hedonic regression housing", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/1810.00974v2", "title": "Neural Regression Trees", "abstract": "Regression-via-Classification (RvC) is the process of converting a regression problem to a classification one. Current approaches for RvC use ad-hoc discretization strategies and are suboptimal. We propose a neural regression tree model for RvC. In this model, we employ a joint optimization framework where we learn optimal discretization thresholds while simultaneously optimizing the features for each node in the tree. We empirically show the validity of our model by testing it on two challenging regression tasks where we establish the state of the art.", "published": "2018-10-01T20:52:38Z", "authors": ["Shahan Ali Memon", "Wenbo Zhao", "Bhiksha Raj", "Rita Singh"], "categories": ["cs.LG", "stat.ML"], "doi": "10.1109/IJCNN.2019.8852133", "_query": "hedonic regression housing", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/1906.10461v6", "title": "Depth induced regression medians and uniqueness", "abstract": "Notion of median in one dimension is a foundational element in nonparametric statistics. It has been extended to multi-dimensional cases both in location and in regression via notions of data depth. Regression depth (RD) and projection regression depth (PRD) represent the two most promising notions in regression. Carrizosa depth $D_C$ is another depth notion in regression.Depth induced regression medians (maximum depth estimators) serve as robust alternatives to the classical least squares estimator. The uniqueness of regression medians is indispensable in the discussion of their properties and the asymptotics (consistency and limiting distribution) of sample regression medians. Are the regression medians induced from RD, PRD, and $D_C$ unique? Answering this question is the main goal of this article. It is found that only the regression median induced from PRD possesses the desired uniqueness property. The conventional remedy measure for non-uniqueness, taking average of all medians, might yield an estimator that no longer possesses the maximum depth in both RD and $D_C$ cases. These and other findings indicate that the PRD and its induced median are highly favorable among their leading competitors.", "published": "2019-06-25T11:32:22Z", "authors": ["Yijun Zuo"], "categories": ["math.ST"], "doi": "", "_query": "hedonic regression housing", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2302.05193v1", "title": "Structural Break Detection in Quantile Predictive Regression Models with Persistent Covariates", "abstract": "We propose an econometric environment for structural break detection in nonstationary quantile predictive regressions. We establish the limit distributions for a class of Wald and fluctuation type statistics based on both the ordinary least squares estimator and the endogenous instrumental regression estimator proposed by Phillips and Magdalinos (2009a, Econometric Inference in the Vicinity of Unity. Working paper, Singapore Management University). Although the asymptotic distribution of these test statistics appears to depend on the chosen estimator, the IVX based tests are shown to be asymptotically nuisance parameter-free regardless of the degree of persistence and consistent under local alternatives. The finite-sample performance of both tests is evaluated via simulation experiments. An empirical application to house pricing index returns demonstrates the practicality of the proposed break tests for regression quantiles of nonstationary time series data.", "published": "2023-02-10T11:45:10Z", "authors": ["Christis Katsouris"], "categories": ["econ.EM"], "doi": "", "_query": "hedonic regression housing", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2511.04422v1", "title": "On the Equivalence of Regression and Classification", "abstract": "A formal link between regression and classification has been tenuous. Even though the margin maximization term $\\|w\\|$ is used in support vector regression, it has at best been justified as a regularizer. We show that a regression problem with $M$ samples lying on a hyperplane has a one-to-one equivalence with a linearly separable classification task with $2M$ samples. We show that margin maximization on the equivalent classification task leads to a different regression formulation than traditionally used. Using the equivalence, we demonstrate a ``regressability'' measure, that can be used to estimate the difficulty of regressing a dataset, without needing to first learn a model for it. We use the equivalence to train neural networks to learn a linearizing map, that transforms input variables into a space where a linear regressor is adequate.", "published": "2025-11-06T14:54:25Z", "authors": [" Jayadeva", "Naman Dwivedi", "Hari Krishnan", "N. M. Anoop Krishnan"], "categories": ["cs.LG", "cs.AI", "cs.CV"], "doi": "", "_query": "hedonic regression housing", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/1403.0060v1", "title": "Regression analysis in quantum language", "abstract": "Although regression analysis has a great history, we consider that it has always continued being confused. For example, the fundamental terms in regression analysis (e.g., \"regression\", \"least-squares method\", \"explanatory variable\", \"response variable\", etc.) seem to be historically conventional, that is, these words do not express the essence of regression analysis. Recently, we proposed quantum language (or, classical and quantum measurement theory), which is characterized as the linguistic turn of the Copenhagen interpretation of quantum mechanics. We believe that this language has a great power of description, and therefore, even statistics can be described by quantum language. Therefore, in this paper, we discuss the regression analysis and the generalized linear model (i.e., multiple regression analysis) in quantum language, and clarify that the terms \"explanatory variable\" and \"response variable\" is respectively characterized as a kind of causality and the measured value.", "published": "2014-03-01T07:56:05Z", "authors": ["Shiro Ishikawa"], "categories": ["math.ST"], "doi": "", "_query": "hedonic regression housing", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2506.23059v1", "title": "Average quantile regression: a new non-mean regression model and coherent risk measure", "abstract": "Regression models that go beyond the mean, alongside coherent risk measures, have been important tools in modern data analysis. This paper introduces the innovative concept of Average Quantile Regression (AQR), which is smooth at the quantile-like level, comonotonically additive, and explicitly accounts for the severity of tail losses relative to quantile regression. AQR serves as a versatile regression model capable of describing distributional information across all positions, akin to quantile regression, yet offering enhanced interpretability compared to expectiles. Numerous traditional regression models and coherent risk measures can be regarded as special cases of AQR. As a flexible non-parametric regression model, AQR demonstrates outstanding performance in analyzing high-dimensional and large datasets, particularly those generated by distributed systems, and provides a convenient framework for their statistical analysis. The corresponding estimators are rigorously derived, and their asymptotic properties are thoroughly developed. In a risk management context, the case study confirms AQR's effectiveness in risk assessment and portfolio optimization.", "published": "2025-06-29T02:11:43Z", "authors": ["Rong Jiang", "M. C. Jones", "Keming Yu", "Jiangfeng Wang"], "categories": ["math.ST"], "doi": "", "_query": "hedonic regression housing", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2412.05736v1", "title": "Convolution Mode Regression", "abstract": "For highly skewed or fat-tailed distributions, mean or median-based methods often fail to capture the central tendencies in the data. Despite being a viable alternative, estimating the conditional mode given certain covariates (or mode regression) presents significant challenges. Nonparametric approaches suffer from the \"curse of dimensionality\", while semiparametric strategies often lead to non-convex optimization problems. In order to avoid these issues, we propose a novel mode regression estimator that relies on an intermediate step of inverting the conditional quantile density. In contrast to existing approaches, we employ a convolution-type smoothed variant of the quantile regression. Our estimator converges uniformly over the design points of the covariates and, unlike previous quantile-based mode regressions, is uniform with respect to the smoothing bandwidth. Additionally, the Convolution Mode Regression is dimension-free, carries no issues regarding optimization and preliminary simulations suggest the estimator is normally distributed in finite samples.", "published": "2024-12-07T20:18:17Z", "authors": ["Eduardo Schirmer Finn", "Eduardo Horta"], "categories": ["econ.EM", "math.ST"], "doi": "", "_query": "hedonic regression housing", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/1906.05156v1", "title": "Evaluation of Dataflow through layers of Deep Neural Networks in Classification and Regression Problems", "abstract": "This paper introduces two straightforward, effective indices to evaluate the input data and the data flowing through layers of a feedforward deep neural network. For classification problems, the separation rate of target labels in the space of dataflow is explained as a key factor indicating the performance of designed layers in improving the generalization of the network. According to the explained concept, a shapeless distance-based evaluation index is proposed. Similarly, for regression problems, the smoothness rate of target outputs in the space of dataflow is explained as a key factor indicating the performance of designed layers in improving the generalization of the network. According to the explained smoothness concept, a shapeless distance-based smoothness index is proposed for regression problems. To consider more strictly concepts of separation and smoothness, their extended versions are introduced, and by interpreting a regression problem as a classification problem, it is shown that the separation and smoothness indices are related together. Through four case studies, the profits of using the introduced indices are shown. In the first case study, for classification and regression problems , the challenging of some known input datasets are compared respectively by the proposed separation and smoothness indices. In the second case study, the quality of dataflow is evaluated through layers of two pre-trained VGG 16 networks in classification of Cifar10 and Cifar100. In the third case study, it is shown that the correct classification rate and the separation index are almost equivalent through layers particularly while the serration index is increased. In the last case study, two multi-layer neural networks, which are designed for the prediction of Boston Housing price, are compared layer by layer by using the proposed smoothness index.", "published": "2019-06-12T14:16:10Z", "authors": ["Ahmad Kalhor", "Mohsen Saffar", "Melika Kheirieh", "Somayyeh Hoseinipoor", "Babak N. Araabi"], "categories": ["cs.CV", "cs.AI", "cs.IT", "cs.LG"], "doi": "", "_query": "hedonic regression housing", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2402.14955v2", "title": "Quantile Regression Tree", "abstract": "This study introduces and evaluates the Quantile Regressor Tree (QRT), a novel methodology merging the robust characteristics of quantile regression with the versatility of decision trees. The quantile regressor tree introduces non-linearity to the quantile regression due to the splitting by features in the decision tree, enhancing flexibility while maintaining interpretability. The quantile regression tree gives a parametric and non-parametric mixture of estimating conditional quantiles for high-dimensional predictor variables.", "published": "2024-02-22T20:32:47Z", "authors": ["Jaachinma Okafor", "Lateefah Isegen", "Ark Ifeanyi"], "categories": ["stat.AP"], "doi": "", "_query": "hedonic regression housing", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2103.06189v1", "title": "Piecewise linear regression and classification", "abstract": "This paper proposes a method for solving multivariate regression and classification problems using piecewise linear predictors over a polyhedral partition of the feature space. The resulting algorithm that we call PARC (Piecewise Affine Regression and Classification) alternates between (i) solving ridge regression problems for numeric targets, softmax regression problems for categorical targets, and either softmax regression or cluster centroid computation for piecewise linear separation, and (ii) assigning the training points to different clusters on the basis of a criterion that balances prediction accuracy and piecewise-linear separability. We prove that PARC is a block-coordinate descent algorithm that optimizes a suitably constructed objective function, and that it converges in a finite number of steps to a local minimum of that function. The accuracy of the algorithm is extensively tested numerically on synthetic and real-world datasets, showing that the approach provides an extension of linear regression/classification that is particularly useful when the obtained predictor is used as part of an optimization model. A Python implementation of the algorithm described in this paper is available at http://cse.lab.imtlucca.it/~bemporad/parc .", "published": "2021-03-10T17:07:57Z", "authors": ["Alberto Bemporad"], "categories": ["cs.LG", "stat.ML"], "doi": "", "_query": "hedonic regression housing", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2406.19478v1", "title": "Sparse Regression for Machine Translation", "abstract": "We use transductive regression techniques to learn mappings between source and target features of given parallel corpora and use these mappings to generate machine translation outputs. We show the effectiveness of $L_1$ regularized regression (\\textit{lasso}) to learn the mappings between sparsely observed feature sets versus $L_2$ regularized regression. Proper selection of training instances plays an important role to learn correct feature mappings within limited computational resources and at expected accuracy levels. We introduce \\textit{dice} instance selection method for proper selection of training instances, which plays an important role to learn correct feature mappings for improving the source and target coverage of the training set. We show that $L_1$ regularized regression performs better than $L_2$ regularized regression both in regression measurements and in the translation experiments using graph decoding. We present encouraging results when translating from German to English and Spanish to English. We also demonstrate results when the phrase table of a phrase-based decoder is replaced with the mappings we find with the regression model.", "published": "2024-06-27T18:43:51Z", "authors": ["Ergun Biçici"], "categories": ["cs.CL", "cs.AI"], "doi": "", "_query": "hedonic regression housing", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/1209.3550v2", "title": "Real-time semiparametric regression", "abstract": "We develop algorithms for performing semiparametric regression analysis in real time, with data processed as it is collected and made immediately available via modern telecommunications technologies. Our definition of semiparametric regression is quite broad and includes, as special cases, generalized linear mixed models, generalized additive models, geostatistical models, wavelet nonparametric regression models and their various combinations. Fast updating of regression fits is achieved by couching semiparametric regression into a Bayesian hierarchical model or, equivalently, graphical model framework and employing online mean field variational ideas. An internet site attached to this article, realtime-semiparametric-regression.net, illustrates the methodology for continually arriving stock market, real estate and airline data. Flexible real-time analyses, based on increasingly ubiquitous streaming data sources stand to benefit.", "published": "2012-09-17T04:52:10Z", "authors": ["Jan Luts", "Tamara Broderick", "Matt P. Wand"], "categories": ["stat.ME"], "doi": "", "_query": "hedonic regression housing", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/1705.10662v3", "title": "Boosting Functional Regression Models with FDboost", "abstract": "The R add-on package FDboost is a flexible toolbox for the estimation of functional regression models by model-based boosting. It provides the possibility to fit regression models for scalar and functional response with effects of scalar as well as functional covariates, i.e., scalar-on-function, function-on-scalar and function-on-function regression models. In addition to mean regression, quantile regression models as well as generalized additive models for location scale and shape can be fitted with FDboost. Furthermore, boosting can be used in high-dimensional data settings with more covariates than observations. We provide a hands-on tutorial on model fitting and tuning, including the visualization of results. The methods for scalar-on-function regression are illustrated with spectrometric data of fossil fuels and those for functional response regression with a data set including bioelectrical signals for emotional episodes.", "published": "2017-05-30T14:15:38Z", "authors": ["Sarah Brockhaus", "David Rügamer", "Sonja Greven"], "categories": ["stat.CO"], "doi": "", "_query": "hedonic regression housing", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/1010.3836v1", "title": "Nonparametric regression in exponential families", "abstract": "Most results in nonparametric regression theory are developed only for the case of additive noise. In such a setting many smoothing techniques including wavelet thresholding methods have been developed and shown to be highly adaptive. In this paper we consider nonparametric regression in exponential families with the main focus on the natural exponential families with a quadratic variance function, which include, for example, Poisson regression, binomial regression and gamma regression. We propose a unified approach of using a mean-matching variance stabilizing transformation to turn the relatively complicated problem of nonparametric regression in exponential families into a standard homoscedastic Gaussian regression problem. Then in principle any good nonparametric Gaussian regression procedure can be applied to the transformed data. To illustrate our general methodology, in this paper we use wavelet block thresholding to construct the final estimators of the regression function. The procedures are easily implementable. Both theoretical and numerical properties of the estimators are investigated. The estimators are shown to enjoy a high degree of adaptivity and spatial adaptivity with near-optimal asymptotic performance over a wide range of Besov spaces. The estimators also perform well numerically.", "published": "2010-10-19T09:30:57Z", "authors": ["Lawrence D. Brown", "T. Tony Cai", "Harrison H. Zhou"], "categories": ["math.ST"], "doi": "10.1214/09-AOS762", "_query": "hedonic regression housing", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2007.15769v2", "title": "Instrument variable detection with graph learning : an application to high dimensional GIS-census data for house pricing", "abstract": "Endogeneity bias and instrument variable validation have always been important topics in statistics and econometrics. In the era of big data, such issues typically combine with dimensionality issues and, hence, require even more attention. In this paper, we merge two well-known tools from machine learning and biostatistics---variable selection algorithms and probablistic graphs---to estimate house prices and the corresponding causal structure using 2010 data on Sydney. The estimation uses a 200-gigabyte ultrahigh dimensional database consisting of local school data, GIS information, census data, house characteristics and other socio-economic records. Using \"big data\", we show that it is possible to perform a data-driven instrument selection efficiently and purge out the invalid instruments. Our approach improves the sparsity of variable selection, stability and robustness in the presence of high dimensionality, complicated causal structures and the consequent multicollinearity, and recovers a sparse and intuitive causal structure. The approach also reveals an efficiency and effectiveness in endogeneity detection, instrument validation, weak instrument pruning and the selection of valid instruments. From the perspective of machine learning, the estimation results both align with and confirms the facts of Sydney house market, the classical economic theories and the previous findings of simultaneous equations modeling. Moreover, the estimation results are consistent with and supported by classical econometric tools such as two-stage least square regression and different instrument tests. All the code may be found at \\url{https://github.com/isaac2math/solar_graph_learning}.", "published": "2020-07-30T23:11:54Z", "authors": ["Ning Xu", "Timothy C. G. Fisher", "Jian Hong"], "categories": ["stat.ML", "cs.LG", "stat.AP"], "doi": "", "_query": "hedonic regression housing", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2007.04518v3", "title": "Robust Geodesic Regression", "abstract": "This paper studies robust regression for data on Riemannian manifolds. Geodesic regression is the generalization of linear regression to a setting with a manifold-valued dependent variable and one or more real-valued independent variables. The existing work on geodesic regression uses the sum-of-squared errors to find the solution, but as in the classical Euclidean case, the least-squares method is highly sensitive to outliers. In this paper, we use M-type estimators, including the $L_1$, Huber and Tukey biweight estimators, to perform robust geodesic regression, and describe how to calculate the tuning parameters for the latter two. We also show that, on compact symmetric spaces, all M-type estimators are maximum likelihood estimators, and argue for the overall superiority of the $L_1$ estimator over the $L_2$ and Huber estimators on high-dimensional manifolds and over the Tukey biweight estimator on compact high-dimensional manifolds. Results from numerical examples, including analysis of real neuroimaging data, demonstrate the promising empirical properties of the proposed approach.", "published": "2020-07-09T02:41:32Z", "authors": ["Ha-Young Shin", "Hee-Seok Oh"], "categories": ["stat.ML", "cs.LG", "stat.ME"], "doi": "", "_query": "hedonic regression housing", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/1710.04238v2", "title": "Regression-aware decompositions", "abstract": "Linear least-squares regression with a \"design\" matrix A approximates a given matrix B via minimization of the spectral- or Frobenius-norm discrepancy ||AX-B|| over every conformingly sized matrix X. Another popular approximation is low-rank approximation via principal component analysis (PCA) -- which is essentially singular value decomposition (SVD) -- or interpolative decomposition (ID). Classically, PCA/SVD and ID operate solely with the matrix B being approximated, not supervised by any auxiliary matrix A. However, linear least-squares regression models can inform the ID, yielding regression-aware ID. As a bonus, this provides an interpretation as regression-aware PCA for a kind of canonical correlation analysis between A and B. The regression-aware decompositions effectively enable supervision to inform classical dimensionality reduction, which classically has been totally unsupervised. The regression-aware decompositions reveal the structure inherent in B that is relevant to regression against A.", "published": "2017-10-11T18:06:25Z", "authors": ["Mark Tygert"], "categories": ["stat.ME", "cs.LG", "math.NA"], "doi": "", "_query": "hedonic regression housing", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2309.14120v1", "title": "Regression with Variable Dimension Covariates", "abstract": "Regression is one of the most fundamental statistical inference problems. A broad definition of regression problems is as estimation of the distribution of an outcome using a family of probability models indexed by covariates. Despite the ubiquitous nature of regression problems and the abundance of related methods and results there is a surprising gap in the literature. There are no well established methods for regression with a varying dimension covariate vectors, despite the common occurrence of such problems. In this paper we review some recent related papers proposing varying dimension regression by way of random partitions.", "published": "2023-09-25T13:19:53Z", "authors": ["Peter Mueller", "Fernando Andrés Quintana", "Garritt L. Page"], "categories": ["math.ST", "stat.ME"], "doi": "", "_query": "hedonic regression housing", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/1708.00078v2", "title": "Bayesian Dyadic Trees and Histograms for Regression", "abstract": "Many machine learning tools for regression are based on recursive partitioning of the covariate space into smaller regions, where the regression function can be estimated locally. Among these, regression trees and their ensembles have demonstrated impressive empirical performance. In this work, we shed light on the machinery behind Bayesian variants of these methods. In particular, we study Bayesian regression histograms, such as Bayesian dyadic trees, in the simple regression case with just one predictor. We focus on the reconstruction of regression surfaces that are piecewise constant, where the number of jumps is unknown. We show that with suitably designed priors, posterior distributions concentrate around the true step regression function at a near-minimax rate. These results do not require the knowledge of the true number of steps, nor the width of the true partitioning cells. Thus, Bayesian dyadic regression trees are fully adaptive and can recover the true piecewise regression function nearly as well as if we knew the exact number and location of jumps. Our results constitute the first step towards understanding why Bayesian trees and their ensembles have worked so well in practice. As an aside, we discuss prior distributions on balanced interval partitions and how they relate to an old problem in geometric probability. Namely, we relate the probability of covering the circumference of a circle with random arcs whose endpoints are confined to a grid, a new variant of the original problem.", "published": "2017-07-31T21:51:23Z", "authors": ["Stephanie van der Pas", "Veronika Rockova"], "categories": ["math.ST"], "doi": "", "_query": "hedonic regression housing", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/1412.2844v1", "title": "Optimal Reduced Isotonic Regression", "abstract": "Isotonic regression is a shape-constrained nonparametric regression in which the regression is an increasing step function. For $n$ data points, the number of steps in the isotonic regression may be as large as $n$. As a result, standard isotonic regression has been criticized as overfitting the data or making the representation too complicated. So-called \"reduced\" isotonic regression constrains the outcome to be a specified number of steps $b$, $b \\leq n$. However, because the previous algorithms for finding the reduced $L_2$ regression took $Θ(n+bm^2)$ time, where $m$ is the number of steps of the unconstrained isotonic regression, researchers felt that the algorithms were too slow and instead used approximations. Other researchers had results that were approximations because they used a greedy top-down approach. Here we give an algorithm to find an exact solution in $Θ(n+bm)$ time, and a simpler algorithm taking $Θ(n+b m \\log m)$ time. These algorithms also determine optimal $k$-means clustering of weighted 1-dimensional data.", "published": "2014-12-09T03:18:48Z", "authors": ["Janis Hardwick", "Quentin F. Stout"], "categories": ["stat.CO", "cs.DS"], "doi": "", "_query": "hedonic regression housing", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2009.12755v1", "title": "A Statistical Learning Assessment of Huber Regression", "abstract": "As one of the triumphs and milestones of robust statistics, Huber regression plays an important role in robust inference and estimation. It has also been finding a great variety of applications in machine learning. In a parametric setup, it has been extensively studied. However, in the statistical learning context where a function is typically learned in a nonparametric way, there is still a lack of theoretical understanding of how Huber regression estimators learn the conditional mean function and why it works in the absence of light-tailed noise assumptions. To address these fundamental questions, we conduct an assessment of Huber regression from a statistical learning viewpoint. First, we show that the usual risk consistency property of Huber regression estimators, which is usually pursued in machine learning, cannot guarantee their learnability in mean regression. Second, we argue that Huber regression should be implemented in an adaptive way to perform mean regression, implying that one needs to tune the scale parameter in accordance with the sample size and the moment condition of the noise. Third, with an adaptive choice of the scale parameter, we demonstrate that Huber regression estimators can be asymptotic mean regression calibrated under $(1+ε)$-moment conditions ($ε>0$). Last but not least, under the same moment conditions, we establish almost sure convergence rates for Huber regression estimators. Note that the $(1+ε)$-moment conditions accommodate the special case where the response variable possesses infinite variance and so the established convergence rates justify the robustness feature of Huber regression estimators. In the above senses, the present study provides a systematic statistical learning assessment of Huber regression estimators and justifies their merits in terms of robustness from a theoretical viewpoint.", "published": "2020-09-27T06:08:21Z", "authors": ["Yunlong Feng", "Qiang Wu"], "categories": ["math.ST", "cs.LG", "stat.ML"], "doi": "", "_query": "hedonic regression housing", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/1807.11645v2", "title": "On algebraic integers of bounded house and preperiodicity in polynomial semigroup dynamics", "abstract": "We consider semigroup dynamical systems defined by several polynomials over a number field $\\mathbb{K}$, and the orbit (tree) they generate at a given point. We obtain finiteness results for the set of preperiodic points of such systems that fall in the cyclotomic closure of $\\mathbb{K}$. More generally, we consider the finiteness of initial points in the cyclotomic closure for which the orbit contains an algebraic integer of bounded house. This work extends previous results for classical obits generated by one polynomial over $\\mathbb{K}$ obtained initially by Dvornicich and Zannier (for preperiodic points), and then by Chen and Ostafe (for roots of unity and elements of bounded house in orbits).", "published": "2018-07-31T03:01:47Z", "authors": ["Alina Ostafe", "Marley Young"], "categories": ["math.NT"], "doi": "", "_query": "hedonic regression housing", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2308.16245v3", "title": "Calibrated Explanations for Regression", "abstract": "Artificial Intelligence (AI) is often an integral part of modern decision support systems. The best-performing predictive models used in AI-based decision support systems lack transparency. Explainable Artificial Intelligence (XAI) aims to create AI systems that can explain their rationale to human users. Local explanations in XAI can provide information about the causes of individual predictions in terms of feature importance. However, a critical drawback of existing local explanation methods is their inability to quantify the uncertainty associated with a feature's importance. This paper introduces an extension of a feature importance explanation method, Calibrated Explanations, previously only supporting classification, with support for standard regression and probabilistic regression, i.e., the probability that the target is above an arbitrary threshold. The extension for regression keeps all the benefits of Calibrated Explanations, such as calibration of the prediction from the underlying model with confidence intervals, uncertainty quantification of feature importance, and allows both factual and counterfactual explanations. Calibrated Explanations for standard regression provides fast, reliable, stable, and robust explanations. Calibrated Explanations for probabilistic regression provides an entirely new way of creating probabilistic explanations from any ordinary regression model, allowing dynamic selection of thresholds. The method is model agnostic with easily understood conditional rules. An implementation in Python is freely available on GitHub and for installation using both pip and conda, making the results in this paper easily replicable.", "published": "2023-08-30T18:06:57Z", "authors": ["Tuwe Löfström", "Helena Löfström", "Ulf Johansson", "Cecilia Sönströd", "Rudy Matela"], "categories": ["cs.LG", "cs.AI", "stat.ML"], "doi": "10.1007/s10994-024-06642-8", "_query": "hedonic regression housing", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2110.03411v1", "title": "Investigating Growth at Risk Using a Multi-country Non-parametric Quantile Factor Model", "abstract": "We develop a Bayesian non-parametric quantile panel regression model. Within each quantile, the response function is a convex combination of a linear model and a non-linear function, which we approximate using Bayesian Additive Regression Trees (BART). Cross-sectional information at the pth quantile is captured through a conditionally heteroscedastic latent factor. The non-parametric feature of our model enhances flexibility, while the panel feature, by exploiting cross-country information, increases the number of observations in the tails. We develop Bayesian Markov chain Monte Carlo (MCMC) methods for estimation and forecasting with our quantile factor BART model (QF-BART), and apply them to study growth at risk dynamics in a panel of 11 advanced economies.", "published": "2021-10-07T12:54:28Z", "authors": ["Todd E. Clark", "Florian Huber", "Gary Koop", "Massimiliano Marcellino", "Michael Pfarrhofer"], "categories": ["econ.EM", "stat.AP"], "doi": "", "_query": "hedonic regression housing", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2510.17325v2", "title": "Composite Lp-quantile regression, near quantile regression and the oracle model selection theory", "abstract": "In this paper, we consider high-dimensional Lp-quantile regression which only requires a low order moment of the error and is also a natural generalization of the above methods and Lp-regression as well. The loss function of Lp-quantile regression circumvents the non-differentiability of the absolute loss function and the difficulty of the squares loss function requiring the finiteness of error's variance and thus promises excellent properties of Lp-quantile regression. Specifically, we first develop a new method called composite Lp-quantile regression(CLpQR). We study the oracle model selection theory based on CLpQR (call the estimator CLpQR-oracle) and show in some cases of p CLpQR-oracle behaves better than CQR-oracle (based on composite quantile regression) when error's variance is infinite. Moreover, CLpQR has high efficiency and can be sometimes arbitrarily more efficient than both CQR and the least squares regression. Second, we propose another new regression method,i.e. near quantile regression and prove the asymptotic normality of the estimator when p converges to 1 and the sample size infinity simultaneously. As its applications, a new thought of smoothing quantile objective functions and a new estimation are provided for the asymptotic covariance matrix of quantile regression. Third, we develop a unified efficient algorithm for fitting high-dimensional Lp-quantile regression by combining the cyclic coordinate descent and an augmented proximal gradient algorithm. Remarkably, the algorithm turns out to be a favourable alternative of the commonly used liner programming and interior point algorithm when fitting quantile regression.", "published": "2025-10-20T09:18:11Z", "authors": ["Fuming Lin WEilin Mou"], "categories": ["math.ST"], "doi": "", "_query": "hedonic regression housing", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/1808.01770v1", "title": "Spline Regression with Automatic Knot Selection", "abstract": "In this paper we introduce a new method for automatically selecting knots in spline regression. The approach consists in setting a large number of initial knots and fitting the spline regression through a penalized likelihood procedure called adaptive ridge. The proposed method is similar to penalized spline regression methods (e.g. P-splines), with the noticeable difference that the output is a sparse spline regression with a small number of knots. We show that our method called A-spline, for adaptive splines yields sparse regression models with high interpretability, while having similar predictive performance similar to penalized spline regression methods. A-spline is applied both to simulated and real dataset. A fast and publicly available implementation in R is provided along with this paper.", "published": "2018-08-06T08:33:57Z", "authors": ["Vivien Goepp", "Olivier Bouaziz", "Grégory Nuel"], "categories": ["stat.AP", "stat.CO"], "doi": "10.1016/j.csda.2024.108043", "_query": "hedonic regression housing", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2404.10444v1", "title": "Semi-supervised Fréchet Regression", "abstract": "This paper explores the field of semi-supervised Fréchet regression, driven by the significant costs associated with obtaining non-Euclidean labels. Methodologically, we propose two novel methods: semi-supervised NW Fréchet regression and semi-supervised kNN Fréchet regression, both based on graph distance acquired from all feature instances. These methods extend the scope of existing semi-supervised Euclidean regression methods. We establish their convergence rates with limited labeled data and large amounts of unlabeled data, taking into account the low-dimensional manifold structure of the feature space. Through comprehensive simulations across diverse settings and applications to real data, we demonstrate the superior performance of our methods over their supervised counterparts. This study addresses existing research gaps and paves the way for further exploration and advancements in the field of semi-supervised Fréchet regression.", "published": "2024-04-16T10:30:52Z", "authors": ["Rui Qiu", "Zhou Yu", "Zhenhua Lin"], "categories": ["math.ST", "cs.LG", "stat.ML"], "doi": "", "_query": "hedonic regression housing", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/1908.03329v1", "title": "Linear regression in the Bayesian framework", "abstract": "These notes aim at clarifying different strategies to perform linear regression from given dataset. Methods like the weighted and ordinary least squares, ridge regression or LASSO are proposed in the literature. The present article is my understanding of these methods which are, according to me, better unified in the Bayesian framework. The formulas to address linear regression with these methods are derived. The KIC for model selection is also derived in the end of the document.", "published": "2019-08-09T06:36:31Z", "authors": ["Thierry A. Mara"], "categories": ["stat.ME"], "doi": "", "_query": "hedonic regression housing", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2607.25105v1", "title": "Simulating Single Transferable Voting for the Colorado House of Representatives", "abstract": "Social choice theory research demonstrates that single transferable voting (STV) results in more proportionally representative legislative bodies. We aim to understand how using multi-member districts and ranked ballots with STV would affect the representation of political parties in the Colorado House of Representatives. We investigated this objective by producing 10,000 multi-member districting plans of Colorado, generating ranked ballots for each of these plans using returns from the 2022 Colorado attorney general race, and simulating STV using these ballots. Our simulated STV elections for the Colorado House of Representatives gave more proportional representation for Democrats and Republicans than the current first-past-the-post system. Future research should explore how the implementation of STV would influence the representation of racial and language minority groups in the Colorado General Assembly to provide guidance on electoral reform in the state.", "published": "2026-07-27T22:08:39Z", "authors": ["Nora Nelson Laird", "Beth Malmskog", "Iris Pixler", "Katherine Rodbell"], "categories": ["cs.CY"], "doi": "", "_query": "hedonic regression housing", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/1106.3834v1", "title": "Dimensionally Constrained Symbolic Regression", "abstract": "We describe dimensionally constrained symbolic regression which has been developed for mass measurement in certain classes of events in high-energy physics (HEP). With symbolic regression, we can derive equations that are well known in HEP. However, in problems with large number of variables, we find that by constraining the terms allowed in the symbolic regression, convergence behavior is improved. Dimensionally constrained symbolic regression (DCSR) finds solutions with much better fitness than is normally possible with symbolic regression. In some cases, novel solutions are found.", "published": "2011-06-20T08:04:08Z", "authors": ["Suyong Choi"], "categories": ["stat.ML", "cs.NE", "physics.comp-ph"], "doi": "", "_query": "hedonic regression housing", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2508.03156v1", "title": "Unveiling Location-Specific Price Drivers: A Two-Stage Cluster Analysis for Interpretable House Price Predictions", "abstract": "House price valuation remains challenging due to localized market variations. Existing approaches often rely on black-box machine learning models, which lack interpretability, or simplistic methods like linear regression (LR), which fail to capture market heterogeneity. To address this, we propose a machine learning approach that applies two-stage clustering, first grouping properties based on minimal location-based features before incorporating additional features. Each cluster is then modeled using either LR or a generalized additive model (GAM), balancing predictive performance with interpretability. Constructing and evaluating our models on 43,309 German house property listings from 2023, we achieve a 36% improvement for the GAM and 58% for LR in mean absolute error compared to models without clustering. Additionally, graphical analyses unveil pattern shifts between clusters. These findings emphasize the importance of cluster-specific insights, enhancing interpretability and offering practical value for buyers, sellers, and real estate analysts seeking more reliable property valuations.", "published": "2025-08-05T07:00:42Z", "authors": ["Paul Gümmer", "Julian Rosenberger", "Mathias Kraus", "Patrick Zschech", "Nico Hambauer"], "categories": ["cs.LG"], "doi": "", "_query": "hedonic regression housing", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2506.02363v1", "title": "Function-on-function Differential Regression", "abstract": "Function-on-function regression has been a topic of substantial interest due to its broad applicability, where the relation between functional predictor and response is concerned. In this article, we propose a new framework for modeling the regression mapping that extends beyond integral type, motivated by the prevalence of physical phenomena governed by differential relations, which is referred to as function-on-function differential regression. However, a key challenge lies in representing the differential regression operator, unlike functions that can be expressed by expansions. As a main contribution, we introduce a new notion of model identification involving differential operators, defined through their action on functions. Based on this action-aware identification, we are able to develop a regularization method for estimation using operator reproducing kernel Hilbert spaces. Then a goodness-of-fit test is constructed, which facilitates model checking for differential regression relations. We establish a Bahadur representation for the regression estimator with various theoretical implications, such as the minimax optimality of the proposed estimator, and the validity and consistency of the proposed test. To illustrate the effectiveness of our method, we conduct simulation studies and an application to a real data example on the thermodynamic energy equation.", "published": "2025-06-03T01:47:52Z", "authors": ["Tongyu Li", "Fang Yao"], "categories": ["stat.ME"], "doi": "", "_query": "hedonic regression housing", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2212.01927v1", "title": "Label Encoding for Regression Networks", "abstract": "Deep neural networks are used for a wide range of regression problems. However, there exists a significant gap in accuracy between specialized approaches and generic direct regression in which a network is trained by minimizing the squared or absolute error of output labels. Prior work has shown that solving a regression problem with a set of binary classifiers can improve accuracy by utilizing well-studied binary classification algorithms. We introduce binary-encoded labels (BEL), which generalizes the application of binary classification to regression by providing a framework for considering arbitrary multi-bit values when encoding target values. We identify desirable properties of suitable encoding and decoding functions used for the conversion between real-valued and binary-encoded labels based on theoretical and empirical study. These properties highlight a tradeoff between classification error probability and error-correction capabilities of label encodings. BEL can be combined with off-the-shelf task-specific feature extractors and trained end-to-end. We propose a series of sample encoding, decoding, and training loss functions for BEL and demonstrate they result in lower error than direct regression and specialized approaches while being suitable for a diverse set of regression problems, network architectures, and evaluation metrics. BEL achieves state-of-the-art accuracies for several regression benchmarks. Code is available at https://github.com/ubc-aamodt-group/BEL_regression.", "published": "2022-12-04T21:23:36Z", "authors": ["Deval Shah", "Zi Yu Xue", "Tor M. Aamodt"], "categories": ["cs.LG", "cs.CV"], "doi": "", "_query": "hedonic regression housing", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/1805.02046v5", "title": "On general notions of depth for regression", "abstract": "Depth notions in location have fascinated tremendous attention in the literature. In fact data depth and its applications remain one of the most active research topics in statistics in the last two decades. Most favored notions of depth in location include Tukey (1975) halfspace depth (HD), Liu (1990) simplicial depth, and projection depth (Stahel (1981) and Donoho (1982), Liu (1992), Zuo and Serfling (2000) (ZS00) and Zuo (2003)), among others. Depth notions in regression have also been proposed, sporadically nevertheless. Regression depth (RD) of Rousseeuw and Hubert (1999) (RH99) is the most famous one which is a direct extension of Tukey HD to regression. Others include Carrizosa (1996) and the ones induced from Marrona and Yohai (1993) (MY93) proposed in this article. Is there any relationship between Carrizosa depth and RD of RH99? Do these depth notions possess desirable properties? What are the desirable properties? Can existing notions really serve as depth functions in regression? These questions remain open. Revealing the equivalence between Carrizosa depth and RD of RH99; expanding location depth evaluating criteria in ZS00 for regression depth notions; examining the existing regression notions with respect to the gauges; and proposing the regression counterpart of the eminent projection depth in location are the four major objectives of the article.", "published": "2018-05-05T11:55:23Z", "authors": ["Yijun Zuo"], "categories": ["stat.ME"], "doi": "", "_query": "hedonic regression housing", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2505.03072v1", "title": "SafeTab-H: Disclosure Avoidance for the 2020 Census Detailed Demographic and Housing Characteristics File B (Detailed DHC-B)", "abstract": "This article describes SafeTab-H, a disclosure avoidance algorithm applied to the release of the U.S. Census Bureau's Detailed Demographic and Housing Characteristics File B (Detailed DHC-B) as part of the 2020 Census. The tabulations contain household statistics about household type and tenure iterated by the householder's detailed race, ethnicity, or American Indian and Alaska Native tribe and village at varying levels of geography. We describe the algorithmic strategy which is based on adding noise from a discrete Gaussian distribution and show that the algorithm satisfies a well-studied variant of differential privacy, called zero-concentrated differential privacy. We discuss how the implementation of the SafeTab-H codebase relies on the Tumult Analytics privacy library. We also describe the theoretical expected error properties of the algorithm and explore various aspects of its parameter tuning.", "published": "2025-05-02T13:15:14Z", "authors": ["William Sexton", "Skye Berghel", "Bayard Carlson", "Sam Haney", "Luke Hartman", "Michael Hay", "Ashwin Machanavajjhala", "Gerome Miklau", "Amritha Pai", "Simran Rajpal", "David Pujol", "Ruchit Shrestha", "Daniel Simmons-Marengo"], "categories": ["cs.CR", "cs.CY"], "doi": "", "_query": "hedonic regression housing", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/1402.2594v1", "title": "Online Nonparametric Regression", "abstract": "We establish optimal rates for online regression for arbitrary classes of regression functions in terms of the sequential entropy introduced in (Rakhlin, Sridharan, Tewari, 2010). The optimal rates are shown to exhibit a phase transition analogous to the i.i.d./statistical learning case, studied in (Rakhlin, Sridharan, Tsybakov 2013). In the frequently encountered situation when sequential entropy and i.i.d. empirical entropy match, our results point to the interesting phenomenon that the rates for statistical learning with squared loss and online nonparametric regression are the same. In addition to a non-algorithmic study of minimax regret, we exhibit a generic forecaster that enjoys the established optimal rates. We also provide a recipe for designing online regression algorithms that can be computationally efficient. We illustrate the techniques by deriving existing and new forecasters for the case of finite experts and for online linear regression.", "published": "2014-02-11T18:36:11Z", "authors": ["Alexander Rakhlin", "Karthik Sridharan"], "categories": ["stat.ML", "cs.LG", "math.ST"], "doi": "", "_query": "hedonic regression housing", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/1208.0579v1", "title": "Bayesian Mode Regression", "abstract": "Like mean, quantile and variance, mode is also an important measure of central tendency and data summary. Many practical questions often focus on \"Which element (gene or file or signal) occurs most often or is the most typical among all elements in a network?\". In such cases mode regression provides a convenient summary of how the regressors affect the conditional mode and is totally different from other regression models based on conditional mean or conditional quantile or conditional variance. Some inference methods have been used for mode regression but none of them from the Bayesian perspective. This paper introduces Bayesian mode regression by exploring three different approaches. We start from a parametric Bayesian model by employing a likelihood function that is based on a mode uniform distribution. It is shown that irrespective of the original distribution of the data, the use of this special uniform distribution is a very natural and effective way for Bayesian mode regression. Posterior estimates based on this parametric likelihood, even under misspecification, are consistent and asymptotically normal. We then develop a nonparametric Bayesian model by using Dirichlet process (DP) mixtures of mode uniform distributions and finally we explore Bayesian empirical likelihood mode regression by taking empirical likelihood into a Bayesian framework. The paper also demonstrates that a variety of improper priors for the unknown model parameters yield a proper joint posterior. The proposed approach is illustrated using simulated datasets and a real data set.", "published": "2012-08-02T19:27:58Z", "authors": ["Keming Yu", "Katerina Aristodemou"], "categories": ["stat.ME", "stat.AP"], "doi": "", "_query": "hedonic regression housing", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/1104.4595v2", "title": "Scaled Sparse Linear Regression", "abstract": "Scaled sparse linear regression jointly estimates the regression coefficients and noise level in a linear model. It chooses an equilibrium with a sparse regression method by iteratively estimating the noise level via the mean residual square and scaling the penalty in proportion to the estimated noise level. The iterative algorithm costs little beyond the computation of a path or grid of the sparse regression estimator for penalty levels above a proper threshold. For the scaled lasso, the algorithm is a gradient descent in a convex minimization of a penalized joint loss function for the regression coefficients and noise level. Under mild regularity conditions, we prove that the scaled lasso simultaneously yields an estimator for the noise level and an estimated coefficient vector satisfying certain oracle inequalities for prediction, the estimation of the noise level and the regression coefficients. These inequalities provide sufficient conditions for the consistency and asymptotic normality of the noise level estimator, including certain cases where the number of variables is of greater order than the sample size. Parallel results are provided for the least squares estimation after model selection by the scaled lasso. Numerical results demonstrate the superior performance of the proposed methods over an earlier proposal of joint convex minimization.", "published": "2011-04-24T00:03:25Z", "authors": ["Tingni Sun", "Cun-Hui Zhang"], "categories": ["stat.ML", "math.ST"], "doi": "", "_query": "hedonic regression housing", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2207.04300v2", "title": "Confidence Sets for a level set in linear regression", "abstract": "Regression modeling is the workhorse of statistics and there is a vast literature on estimation of the regression function. It is realized in recent years that in regression analysis the ultimate aim may be the estimation of a level set of the regression function, instead of the estimation of the regression function itself. The published work on estimation of the level set has thus far focused mainly on nonparametric regression, especially on point estimation. In this paper, the construction of confidence sets for the level set of linear regression is considered. In particular, $1-α$ level upper, lower and two-sided confidence sets are constructed for the normal-error linear regression. It is shown that these confidence sets can be easily constructed from the corresponding $1-α$ level simultaneous confidence bands. It is also pointed out that the construction method is readily applicable to other parametric regression models where the mean response depends on a linear predictor through a monotonic link function, which include generalized linear models, linear mixed models and generalized linear mixed models. Therefore the method proposed in this paper is widely applicable. Examples are given to illustrate the method.", "published": "2022-07-09T17:01:46Z", "authors": ["Fang Wan", "Wei Liu", "Frank Bretz"], "categories": ["stat.ME"], "doi": "", "_query": "hedonic regression housing", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2508.16355v1", "title": "Probabilistic Pretraining for Neural Regression", "abstract": "Transfer learning for probabilistic regression remains underexplored. This work closes this gap by introducing NIAQUE, Neural Interpretable Any-Quantile Estimation, a new model designed for transfer learning in probabilistic regression through permutation invariance. We demonstrate that pre-training NIAQUE directly on diverse downstream regression datasets and fine-tuning it on a specific target dataset enhances performance on individual regression tasks, showcasing the positive impact of probabilistic transfer learning. Furthermore, we highlight the effectiveness of NIAQUE in Kaggle competitions against strong baselines involving tree-based models and recent neural foundation models TabPFN and TabDPT. The findings highlight NIAQUE's efficacy as a robust and scalable framework for probabilistic regression, leveraging transfer learning to enhance predictive performance.", "published": "2025-08-22T13:03:04Z", "authors": ["Boris N. Oreshkin", "Shiv Tavker", "Dmitry Efimov"], "categories": ["cs.LG"], "doi": "", "_query": "hedonic regression housing", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/1902.03327v1", "title": "Censored Quantile Regression Forests", "abstract": "Random forests are powerful non-parametric regression method but are severely limited in their usage in the presence of randomly censored observations, and naively applied can exhibit poor predictive performance due to the incurred biases. Based on a local adaptive representation of random forests, we develop its regression adjustment for randomly censored regression quantile models. Regression adjustment is based on new estimating equations that adapt to censoring and lead to quantile score whenever the data do not exhibit censoring. The proposed procedure named censored quantile regression forest, allows us to estimate quantiles of time-to-event without any parametric modeling assumption. We establish its consistency under mild model specifications. Numerical studies showcase a clear advantage of the proposed procedure.", "published": "2019-02-08T23:29:50Z", "authors": ["Alexander Hanbo Li", "Jelena Bradic"], "categories": ["stat.ML", "cs.LG", "econ.EM", "stat.ME"], "doi": "", "_query": "hedonic regression housing", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2006.00789v1", "title": "Quantile regression for compositional covariates", "abstract": "Quantile regression is a very important tool to explore the relationship between the response variable and its covariates. Motivated by mean regression with LASSO for compositional covariates proposed by Lin et al. (2014), we consider quantile regression with no-penalty and penalty function. We develop the computational algorithms based on linear programming. Numerical studies indicate that our methods provides the better alternative than mean regression under many settings, particularly for heavy-tailed or skewed distribution of the error term. Finally, we study the fat data using the proposed method.", "published": "2020-06-01T08:23:09Z", "authors": ["Xuejun Ma", "Ping Zhang"], "categories": ["stat.ME"], "doi": "", "_query": "hedonic regression housing", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/1908.09029v1", "title": "Dyadic Regression", "abstract": "Dyadic data, where outcomes reflecting pairwise interaction among sampled units are of primary interest, arise frequently in social science research. Regression analyses with such data feature prominently in many research literatures (e.g., gravity models of trade). The dependence structure associated with dyadic data raises special estimation and, especially, inference issues. This chapter reviews currently available methods for (parametric) dyadic regression analysis and presents guidelines for empirical researchers.", "published": "2019-08-23T20:31:25Z", "authors": ["Bryan S. Graham"], "categories": ["econ.EM", "stat.AP"], "doi": "", "_query": "hedonic regression housing", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/1501.05381v2", "title": "Combining Alphas via Bounded Regression", "abstract": "We give an explicit algorithm and source code for combining alpha streams via bounded regression. In practical applications typically there is insufficient history to compute a sample covariance matrix (SCM) for a large number of alphas. To compute alpha allocation weights, one then resorts to (weighted) regression over SCM principal components. Regression often produces alpha weights with insufficient diversification and/or skewed distribution against, e.g., turnover. This can be rectified by imposing bounds on alpha weights within the regression procedure. Bounded regression can also be applied to stock and other asset portfolio construction. We discuss illustrative examples.", "published": "2015-01-22T03:24:17Z", "authors": ["Zura Kakushadze"], "categories": ["q-fin.PM", "q-fin.RM"], "doi": "", "_query": "hedonic regression housing", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/1801.04716v3", "title": "Robust Inference for Seemingly Unrelated Regression Models", "abstract": "Seemingly unrelated regression models generalize linear regression models by considering multiple regression equations that are linked by contemporaneously correlated disturbances. Robust inference for seemingly unrelated regression models is considered. MM-estimators are introduced to obtain estimators that have both a high breakdown point and a high normal efficiency. A fast and robust bootstrap procedure is developed to obtain robust inference for these estimators. Confidence intervals for the model parameters as well as hypothesis tests for linear restrictions of the regression coefficients in seemingly unrelated regression models are constructed. Moreover, in order to evaluate the need for a seemingly unrelated regression model, a robust procedure is proposed to test for the presence of correlation among the disturbances. The performance of the fast and robust bootstrap inference is evaluated empirically in simulation studies and illustrated on real data.", "published": "2018-01-15T10:01:53Z", "authors": ["Kris Peremans", "Stefan Van Aelst"], "categories": ["stat.ME"], "doi": "10.1016/j.jmva.2018.05.002", "_query": "hedonic regression housing", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/1810.00411v4", "title": "Nonparametric Regression with Selectively Missing Covariates", "abstract": "We consider the problem of regression with selectively observed covariates in a nonparametric framework. Our approach relies on instrumental variables that explain variation in the latent covariates but have no direct effect on selection. The regression function of interest is shown to be a weighted version of observed conditional expectation where the weighting function is a fraction of selection probabilities. Nonparametric identification of the fractional probability weight (FPW) function is achieved via a partial completeness assumption. We provide primitive functional form assumptions for partial completeness to hold. The identification result is constructive for the FPW series estimator. We derive the rate of convergence and also the pointwise asymptotic distribution. In both cases, the asymptotic performance of the FPW series estimator does not suffer from the inverse problem which derives from the nonparametric instrumental variable approach. In a Monte Carlo study, we analyze the finite sample properties of our estimator and we compare our approach to inverse probability weighting, which can be used alternatively for unconditional moment estimation. In the empirical application, we focus on two different applications. We estimate the association between income and health using linked data from the SHARE survey and administrative pension information and use pension entitlements as an instrument. In the second application we revisit the question how income affects the demand for housing based on data from the German Socio-Economic Panel Study (SOEP). In this application we use regional income information on the residential block level as an instrument. In both applications we show that income is selectively missing and we demonstrate that standard methods that do not account for the nonrandom selection process lead to significantly biased estimates for individuals with low income.", "published": "2018-09-30T15:52:54Z", "authors": ["Christoph Breunig", "Peter Haan"], "categories": ["econ.EM"], "doi": "", "_query": "hedonic regression housing", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2308.05442v1", "title": "Optimal chromatic bound for ($P_3\\cup P_2$, house)-free graphs", "abstract": "Let $G$ and $H$ be two vertex disjoint graphs. The {\\em union} $G\\cup H$ is the graph with $V(G\\cup H)=V(G)\\cup V(H)$ and $E(G\\cup H)=E(G)\\cup E(H)$. We use $P_k$ to denote a {\\em path} on $k$ vertices, use {\\em house} to denote the complement of $P_5$. In this paper, we show that $χ(G)\\le2ω(G)$ if $G$ is ($P_3\\cup P_2$, house)-free. Moreover, this bound is optimal when $ω(G)\\ge2$.", "published": "2023-08-10T08:58:12Z", "authors": ["Rui Li", "Di Wu", "Jinfeng Li"], "categories": ["math.CO"], "doi": "", "_query": "hedonic regression housing", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2012.09915v1", "title": "Nonparametric multimodal regression for circular data", "abstract": "Multimodal regression estimation methods are introduced for regression models involving circular response and/or covariate. The regression estimators are based on the maximization of the conditional densities of the response variable over the covariate. Conditional versions of the mean shift and the circular mean shift algorithms are used to obtain the regression estimators. The asymptotic properties of the estimators are studied and the problem of bandwidth selection is discussed.", "published": "2020-12-17T20:19:48Z", "authors": ["María Alonso-Pena", "Rosa M. Crujeiras"], "categories": ["stat.ME"], "doi": "10.1214/22-AOAS1619", "_query": "hedonic regression housing", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2207.02808v8", "title": "Improved conformalized quantile regression", "abstract": "Conformalized quantile regression is a procedure that inherits the advantages of conformal prediction and quantile regression. That is, we use quantile regression to estimate the true conditional quantile and then apply a conformal step on a calibration set to ensure marginal coverage. In this way, we get adaptive prediction intervals that account for heteroscedasticity. However, the aforementioned conformal step lacks adaptiveness as described in (Romano et al., 2019). To overcome this limitation, instead of applying a single conformal step after estimating conditional quantiles with quantile regression, we propose to cluster the explanatory variables weighted by their permutation importance with an optimized k-means and apply k conformal steps. To show that this improved version outperforms the classic version of conformalized quantile regression and is more adaptive to heteroscedasticity, we extensively compare the prediction intervals of both in open datasets.", "published": "2022-07-06T16:54:36Z", "authors": ["Martim Sousa", "Ana Maria Tomé", "José Moreira"], "categories": ["stat.ML", "cs.LG"], "doi": "10.1016/j.eswa.2023.122322", "_query": "hedonic regression housing", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2301.01383v1", "title": "How to get the most out of Twinned Regression Methods", "abstract": "Twinned regression methods are designed to solve the dual problem to the original regression problem, predicting differences between regression targets rather then the targets themselves. A solution to the original regression problem can be obtained by ensembling predicted differences between the targets of an unknown data point and multiple known anchor data points. We explore different aspects of twinned regression methods: (1) We decompose different steps in twinned regression algorithms and examine their contributions to the final performance, (2) We examine the intrinsic ensemble quality, (3) We combine twin neural network regression with k-nearest neighbor regression to design a more accurate and efficient regression method, and (4) we develop a simplified semi-supervised regression scheme.", "published": "2023-01-03T22:37:44Z", "authors": ["Sebastian J. Wetzel"], "categories": ["cs.LG", "stat.ME"], "doi": "", "_query": "hedonic regression housing", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2404.09306v1", "title": "Minimax Optimal rates of convergence in the shuffled regression, unlinked regression, and deconvolution under vanishing noise", "abstract": "Shuffled regression and unlinked regression represent intriguing challenges that have garnered considerable attention in many fields, including but not limited to ecological regression, multi-target tracking problems, image denoising, etc. However, a notable gap exists in the existing literature, particularly in vanishing noise, i.e., how the rate of estimation of the underlying signal scales with the error variance. This paper aims to bridge this gap by delving into the monotone function estimation problem under vanishing noise variance, i.e., we allow the error variance to go to $0$ as the number of observations increases. Our investigation reveals that, asymptotically, the shuffled regression problem exhibits a comparatively simpler nature than the unlinked regression; if the error variance is smaller than a threshold, then the minimax risk of the shuffled regression is smaller than that of the unlinked regression. On the other hand, the minimax estimation error is of the same order in the two problems if the noise level is larger than that threshold. Our analysis is quite general in that we do not assume any smoothness of the underlying monotone link function. Because these problems are related to deconvolution, we also provide bounds for deconvolution in a similar context. Through this exploration, we contribute to understanding the intricate relationships between these statistical problems and shed light on their behaviors when subjected to the nuanced constraint of vanishing noise.", "published": "2024-04-14T17:08:43Z", "authors": ["Cecile Durot", "Debarghya Mukherjee"], "categories": ["math.ST"], "doi": "", "_query": "hedonic regression housing", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2202.00783v1", "title": "Modeling ventilation in a low-income house in Dhaka, Bangladesh", "abstract": "According to UNICEF, pneumonia is the leading cause of death in children under 5. 70% of worldwide pneumonia deaths occur in only 15 countries, including Bangladesh. Previous research has indicated a potential association between the incidence of pneumonia and the presence of cross-ventilation in slum housing in Dhaka, Bangladesh. The objective of this research is to establish a validated computational framework that can predict ventilation rates in slum homes to support further studies investigating this correlation. To achieve this objective we employ a building thermal model (BTM) in combination with uncertainty quantification (UQ). The BTM solves for the time-evolution of volume-averaged temperatures in a typical home, considering different ventilation configurations. The UQ method propagates uncertainty in model parameters, weather inputs, and physics models to predict mean values and 95% confidence intervals for the quantities of interest, namely temperatures and ventilation rates in terms of air changes per hour (ACH). The model predictions are compared to on-site field measurements of air and thermal mass temperatures, and of ACH. The results indicate that the use of standard cross- or single-sided ventilation models limits the accuracy of the ACH predictions; in contrast, a model based on a similarity relationship informed by the available ACH measurements can produce more accurate predictions with confidence intervals that encompass the measurements for 12 of the 17 available data points.", "published": "2022-01-30T05:33:21Z", "authors": ["Yunjae Hwang", " Laura", " Kwong", "Mohammad Saeed Munim", "Fosiul Alam Nizame", "Stephen Luby", "Catherine Gorlé"], "categories": ["cs.CE"], "doi": "", "_query": "hedonic regression housing", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/1511.00961v1", "title": "The Unbiasedness Approach to Linear Regression Models", "abstract": "The linear regression models are widely used statistical techniques in numerous practical applications. The standard regression model requires several assumptions about the regres- sors and the error term. The regression parameters are estimated using the least-squares method. In this paper, we consider the regression model with arbitrary regressors and with- out the error term. An explicit expression for the regression parameters vector is obtained. The unbiasedness approach is used to estimate the regression parameters and its various properties are investigated. It is shown that the resulting unbiased estimator equals the least-squares estimator for the fixed design model. The analysis of residuals and the regres- sion sum of squares can be carried out in a natural way. The unbiased estimator of the dispersion matrix of the unbiased estimator is also obtained. Applications to AR(p) model and numerical examples are also discussed.", "published": "2015-11-03T16:07:21Z", "authors": ["P. Vellaisamy"], "categories": ["stat.ME"], "doi": "", "_query": "hedonic regression housing", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/1303.7409v1", "title": "Homogeneity in Regression", "abstract": "This paper explores the homogeneity of coefficients in high-dimensional regression, which extends the sparsity concept and is more general and suitable for many applications. Homogeneity arises when one expects regression coefficients corresponding to neighboring geographical regions or a similar cluster of covariates to be approximately the same. Sparsity corresponds to a special case of homogeneity with a known atom zero. In this article, we propose a new method called clustering algorithm in regression via data-driven segmentation (CARDS) to explore homogeneity. New mathematics are provided on the gain that can be achieved by exploring homogeneity. Statistical properties of two versions of CARDS are analyzed. In particular, the asymptotic normality of our proposed CARDS estimator is established, which reveals better estimation accuracy for homogeneous parameters than that without homogeneity exploration. When our methods are combined with sparsity exploration, further efficiency can be achieved beyond the exploration of sparsity alone. This provides additional insights into the power of exploring low-dimensional strucuture in high-dimensional regression: homogeneity and sparsity. The newly developed method is further illustrated by simulation studies and applications to real data.", "published": "2013-03-29T14:34:10Z", "authors": ["Tracy Ke", "Jianqing Fan", "Yichao Wu"], "categories": ["stat.ME"], "doi": "", "_query": "hedonic regression housing", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2508.15749v2", "title": "Multivariate quantile regression", "abstract": "This paper introduces a new framework for multivariate quantile regression based on the multivariate distribution function, termed multivariate quantile regression (MQR). In contrast to existing approaches--such as directional quantiles, vector quantile regression, or copula-based methods--MQR defines quantiles through the conditional probability structure of the joint conditional distribution function. The method constructs multivariate quantile curves using sequential univariate quantile regressions derived from conditioning mechanisms, allowing for an intuitive interpretation and flexible estimation of marginal effects. The paper develops theoretical foundations of MQR, including asymptotic properties of the estimators. Through simulation exercises, the estimator demonstrates robust finite sample performance across different dependence structures. As an empirical application, the MQR framework is applied to the analysis of exchange rate pass-through in Argentina from 2004 to 2024.", "published": "2025-08-21T17:44:33Z", "authors": ["Antonio F. Galvao", "Gabriel Montes-Rojas"], "categories": ["econ.EM"], "doi": "", "_query": "hedonic regression housing", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/1206.4678v1", "title": "Linear Regression with Limited Observation", "abstract": "We consider the most common variants of linear regression, including Ridge, Lasso and Support-vector regression, in a setting where the learner is allowed to observe only a fixed number of attributes of each example at training time. We present simple and efficient algorithms for these problems: for Lasso and Ridge regression they need the same total number of attributes (up to constants) as do full-information algorithms, for reaching a certain accuracy. For Support-vector regression, we require exponentially less attributes compared to the state of the art. By that, we resolve an open problem recently posed by Cesa-Bianchi et al. (2010). Experiments show the theoretical bounds to be justified by superior performance compared to the state of the art.", "published": "2012-06-18T15:37:23Z", "authors": ["Elad Hazan", "Tomer Koren"], "categories": ["cs.LG", "stat.ML"], "doi": "", "_query": "hedonic regression housing", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2202.11056v1", "title": "Quantum regression in dephasing phenomena", "abstract": "We investigate the validity of quantum regression for a family of quantum Hamiltonians on a multipartite system leading to phase-damping reduced dynamics. After finding necessary and sufficient conditions for the CP-divisibility of the corresponding channel, we evaluate a hierarchy of equations equivalent to the validity of quantum regression under arbitrary interventions; in particular, we find necessary conditions for a nontrivial dephasing to be compatible with quantum regression. In this framework, we study a class of dephasing-type generalized spin-boson (GSB) models, investigating the existence of qubit-environment coupling functions that ensure the exact validity of quantum regression.", "published": "2022-02-22T17:37:09Z", "authors": ["Davide Lonigro", "Dariusz Chruściński"], "categories": ["quant-ph", "math-ph"], "doi": "10.1088/1751-8121/ac6a2d", "_query": "hedonic regression housing", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/1909.03725v3", "title": "Isotonic Distributional Regression", "abstract": "Isotonic distributional regression (IDR) is a powerful nonparametric technique for the estimation of conditional distributions under order restrictions. In a nutshell, IDR learns conditional distributions that are calibrated, and simultaneously optimal relative to comprehensive classes of relevant loss functions, subject to isotonicity constraints in terms of a partial order on the covariate space. Nonparametric isotonic quantile regression and nonparametric isotonic binary regression emerge as special cases. For prediction, we propose an interpolation method that generalizes extant specifications under the pool adjacent violators algorithm. We recommend the use of IDR as a generic benchmark technique in probabilistic forecast problems, as it does not involve any parameter tuning nor implementation choices, except for the selection of a partial order on the covariate space. The method can be combined with subsample aggregation, with the benefits of smoother regression functions and gains in computational efficiency. In a simulation study, we compare methods for distributional regression in terms of the continuous ranked probability score (CRPS) and $L_2$ estimation error, which are closely linked. In a case study on raw and postprocessed quantitative precipitation forecasts from a leading numerical weather prediction system, IDR is competitive with state of the art techniques.", "published": "2019-09-09T09:43:07Z", "authors": ["Alexander Henzi", "Johanna F. Ziegel", "Tilmann Gneiting"], "categories": ["stat.ME", "math.ST"], "doi": "10.1111/rssb.12450", "_query": "hedonic regression housing", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2001.09834v2", "title": "Penalized angular regression for personalized predictions", "abstract": "Personalization is becoming an important feature in many predictive applications. We introduce a penalized regression method implementing personalization inherently in the penalty. Personalized angle (PAN) regression constructs regression coefficients that are specific to the covariate vector for which one is producing a prediction, thus personalizing the regression model itself. This is achieved by penalizing the angles in a hyperspherical parametrization of the regression coefficients. For an orthogonal design matrix, it is shown that the PAN estimate is the solution to a low-dimensional eigenvector equation. Using a parametric bootstrap procedure to select the tuning parameter, simulations show that PAN regression can outperform ordinary least squares and ridge regression in terms of prediction error. We further prove that by combining the PAN penalty with an $L_{2}$ penalty the resulting method will have uniformly smaller mean squared prediction error than ridge regression, asymptotically. Finally, we demonstrate the method in a medical application.", "published": "2020-01-27T14:57:06Z", "authors": ["Kristoffer H. Hellton"], "categories": ["stat.ME"], "doi": "", "_query": "hedonic regression housing", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/1707.01254v1", "title": "Regression approaches for Approximate Bayesian Computation", "abstract": "This book chapter introduces regression approaches and regression adjustment for Approximate Bayesian Computation (ABC). Regression adjustment adjusts parameter values after rejection sampling in order to account for the imperfect match between simulations and observations. Imperfect match between simulations and observations can be more pronounced when there are many summary statistics, a phenomenon coined as the curse of dimensionality. Because of this imperfect match, credibility intervals obtained with regression approaches can be inflated compared to true credibility intervals. The chapter presents the main concepts underlying regression adjustment. A theorem that compares theoretical properties of posterior distributions obtained with and without regression adjustment is presented. Last, a practical application of regression adjustment in population genetics shows that regression adjustment shrinks posterior distributions compared to rejection approaches, which is a solution to avoid inflated credibility intervals.", "published": "2017-07-05T08:17:58Z", "authors": ["Michael GB Blum"], "categories": ["stat.ME"], "doi": "", "_query": "hedonic regression housing", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2209.12304v1", "title": "Issues in Implementing Regression Calibration Analyses", "abstract": "Regression calibration is a popular approach for correcting biases in estimated regression parameters when exposure variables are measured with error. This approach involves building a calibration equation to estimate the value of the unknown true exposure given the error-prone measurement and other confounding covariates. The estimated, or calibrated, exposure is then substituted for the true exposure in the health outcome regression model. When used properly, regression calibration can greatly reduce the bias induced by exposure measurement error. Here, we first provide an overview of the statistical framework for regression calibration, specifically discussing how a special type of error, called Berkson error, arises in the estimated exposure. We then present practical issues to consider when applying regression calibration, including: (1) how to develop the calibration equation and which covariates to include; (2) valid ways to calculate standard errors (SE) of estimated regression coefficients; and (3) problems arising if one of the covariates in the calibration model is a mediator of the relationship between the exposure and outcome. Throughout the paper, we provide illustrative examples using data from the Hispanic Community Health Study/Study of Latinos (HCHS/SOL) and simulations. We conclude with recommendations for how to perform regression calibration.", "published": "2022-09-25T19:18:09Z", "authors": ["Lillian Boe", "Pamela A. Shaw", "Douglas Midthune", "Paul Gustafson", "Victor Kipnis", "Eunyoung Park", "Daniela Sotres-Alvarez", "Laurence Freedman"], "categories": ["stat.ME"], "doi": "", "_query": "hedonic regression housing", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2007.05355v1", "title": "Spine Landmark Localization with combining of Heatmap Regression and Direct Coordinate Regression", "abstract": "Landmark Localization plays a very important role in processing medical images as well as in disease identification. However, In medical field, it's a challenging task because of the complexity of medical images and the high requirement of accuracy for disease identification and treatment.There are two dominant ways to regress landmark coordination, one using the full convolutional network to regress the heatmaps of landmarks , which is a complex way and heatmap post-process strategies are needed, and the other way is to regress the coordination using CNN + Full Connective Network directly, which is very simple and faster training , but larger dataset and deeper model are needed to achieve higher accuracy. Though with data augmentation and deeper network it can reach a reasonable accuracy, but the accuracy still not reach the requirement of medical field. In addition, a deeper networks also means larger space consumption. To achieve a higher accuracy, we contrived a new landmark regression method which combing heatmap regression and direct coordinate regression base on probability methods and system control theory.", "published": "2020-07-10T12:47:00Z", "authors": ["Wanhong Huang", "Chunxi Yang", "TianHong Hou"], "categories": ["cs.CV"], "doi": "", "_query": "hedonic regression housing", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/1410.1231v1", "title": "Bayesian regression and Bitcoin", "abstract": "In this paper, we discuss the method of Bayesian regression and its efficacy for predicting price variation of Bitcoin, a recently popularized virtual, cryptographic currency. Bayesian regression refers to utilizing empirical data as proxy to perform Bayesian inference. We utilize Bayesian regression for the so-called \"latent source model\". The Bayesian regression for \"latent source model\" was introduced and discussed by Chen, Nikolov and Shah (2013) and Bresler, Chen and Shah (2014) for the purpose of binary classification. They established theoretical as well as empirical efficacy of the method for the setting of binary classification. In this paper, instead we utilize it for predicting real-valued quantity, the price of Bitcoin. Based on this price prediction method, we devise a simple strategy for trading Bitcoin. The strategy is able to nearly double the investment in less than 60 day period when run against real data trace.", "published": "2014-10-06T00:38:39Z", "authors": ["Devavrat Shah", "Kang Zhang"], "categories": ["cs.AI", "math.ST"], "doi": "", "_query": "hedonic regression housing", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2604.21102v1", "title": "Leveraging Multimodal LLMs for Built Environment and Housing Attribute Assessment from Street-View Imagery", "abstract": "We present a novel framework for automatically evaluating building conditions nationwide in the United States by leveraging large language models (LLMs) and Google Street View (GSV) imagery. By fine-tuning Gemma 3 27B on a modest human-labeled dataset, our approach achieves strong alignment with human mean opinion scores (MOS), outperforming even individual raters on SRCC and PLCC relative to the MOS benchmark. To enhance efficiency, we apply knowledge distillation, transferring the capabilities of Gemma 3 27B to a smaller Gemma 3 4B model that achieves comparable performance with a 3x speedup. Further, we distill the knowledge into a CNN-based model (EfficientNetV2-M) and a transformer (SwinV2-B), delivering close performance while achieving a 30x speed gain. Furthermore, we investigate LLMs' capabilities for assessing an extensive list of built environment and housing attributes through a human-AI alignment study and develop a visualization dashboard that integrates LLM assessment outcomes for downstream analysis by homeowners. Our framework offers a flexible and efficient solution for large-scale building condition assessment, enabling high accuracy with minimal human labeling effort.", "published": "2026-04-22T21:42:09Z", "authors": ["Siyuan Yao", "Siavash Ghorbany", "Kuangshi Ai", "Arnav Cherukuthota", "Meghan Forstchen", "Alexis Korotasz", "Matthew Sisk", "Ming Hu", "Chaoli Wang"], "categories": ["cs.CV", "cs.AI"], "doi": "", "_query": "hedonic regression housing", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/1707.00274v7", "title": "Regression with I-priors", "abstract": "The problem of estimating a parametric or nonparametric regression function in a model with normal errors is considered. For this purpose, a novel objective prior for the regression function is proposed, defined as the distribution maximizing entropy subject to a suitable constraint based on the Fisher information on the regression function. The prior is named I-prior. For the present model, it is Gaussian with covariance kernel proportional to the Fisher information, and mean chosen a priori (e.g., 0). The I-prior has the intuitively appealing property that the more information is available about a linear functional of the regression function, the larger its prior variance, and, broadly speaking, the less influential the prior is on the posterior. Unlike the Jeffreys prior, it can be used in high dimensional settings. The I-prior methodology can be used as a principled alternative to Tikhonov regularization, which suffers from well-known theoretical problems which are briefly reviewed. The regression function is assumed to lie in a reproducing kernel Hilbert space (RKHS) over a low or high dimensional covariate space, giving a high degree of generality. Analysis of some real data sets and a small-scale simulation study show competitive performance of the I-prior methodology, which is implemented in the R-package iprior (Jamil, 2019).", "published": "2017-07-02T10:18:08Z", "authors": ["Wicher Bergsma"], "categories": ["math.ST"], "doi": "", "_query": "hedonic regression housing", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/1910.12358v3", "title": "Dual Instrumental Variable Regression", "abstract": "We present a novel algorithm for non-linear instrumental variable (IV) regression, DualIV, which simplifies traditional two-stage methods via a dual formulation. Inspired by problems in stochastic programming, we show that two-stage procedures for non-linear IV regression can be reformulated as a convex-concave saddle-point problem. Our formulation enables us to circumvent the first-stage regression which is a potential bottleneck in real-world applications. We develop a simple kernel-based algorithm with an analytic solution based on this formulation. Empirical results show that we are competitive to existing, more complicated algorithms for non-linear instrumental variable regression.", "published": "2019-10-27T21:36:26Z", "authors": ["Krikamol Muandet", "Arash Mehrjou", "Si Kai Lee", "Anant Raj"], "categories": ["stat.ML", "cs.LG", "econ.EM"], "doi": "", "_query": "hedonic regression housing", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/1205.2446v1", "title": "On relationship between regression models and interpretation of multiple regression coefficients", "abstract": "In this paper, we consider the problem of treating linear regression equation coefficients in the case of correlated predictors. It is shown that in general there are no natural ways of interpreting these coefficients similar to the case of single predictor. Nevertheless we suggest linear transformations of predictors, reducing multiple regression to a simple one and retaining the coefficient at variable of interest. The new variable can be treated as the part of the old variable that has no linear statistical dependence on other presented variables.", "published": "2012-05-11T07:37:38Z", "authors": ["A. N. Varaksin", "V. G. Panov"], "categories": ["math.ST"], "doi": "", "_query": "hedonic regression housing", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2007.04441v2", "title": "Sparse Regression for Extreme Values", "abstract": "We study the problem of selecting features associated with extreme values in high dimensional linear regression. Normally, in linear modeling problems, the presence of abnormal extreme values or outliers is considered an anomaly which should either be removed from the data or remedied using robust regression methods. In many situations, however, the extreme values in regression modeling are not outliers but rather the signals of interest; consider traces from spiking neurons, volatility in finance, or extreme events in climate science, for example. In this paper, we propose a new method for sparse high-dimensional linear regression for extreme values which is motivated by the Subbotin, or generalized normal distribution, which we call the extreme value linear regression model. For our method, we utilize an $\\ell_p$ norm loss where $p$ is an even integer greater than two; we demonstrate that this loss increases the weight on extreme values. We prove consistency and variable selection consistency for the extreme value linear regression with a Lasso penalty, which we term the Extreme Lasso, and we also analyze the theoretical impact of extreme value observations on the model parameter estimates using the concept of influence functions. Through simulation studies and a real-world data example, we show that the Extreme Lasso outperforms other methods currently used in the literature for selecting features of interest associated with extreme values in high-dimensional regression.", "published": "2020-07-08T21:24:49Z", "authors": ["Andersen Chang", "Minjie Wang", "Genevera Allen"], "categories": ["stat.ME"], "doi": "", "_query": "hedonic regression housing", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/1805.08571v3", "title": "On Coresets for Logistic Regression", "abstract": "Coresets are one of the central methods to facilitate the analysis of large data sets. We continue a recent line of research applying the theory of coresets to logistic regression. First, we show a negative result, namely, that no strongly sublinear sized coresets exist for logistic regression. To deal with intractable worst-case instances we introduce a complexity measure $μ(X)$, which quantifies the hardness of compressing a data set for logistic regression. $μ(X)$ has an intuitive statistical interpretation that may be of independent interest. For data sets with bounded $μ(X)$-complexity, we show that a novel sensitivity sampling scheme produces the first provably sublinear $(1\\pm\\varepsilon)$-coreset. We illustrate the performance of our method by comparing to uniform sampling as well as to state of the art methods in the area. The experiments are conducted on real world benchmark data for logistic regression.", "published": "2018-05-22T13:33:22Z", "authors": ["Alexander Munteanu", "Chris Schwiegelshohn", "Christian Sohler", "David P. Woodruff"], "categories": ["cs.DS", "cs.LG", "stat.ML"], "doi": "", "_query": "hedonic regression housing", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2506.19537v1", "title": "Dimension Reduction for Symbolic Regression", "abstract": "Solutions of symbolic regression problems are expressions that are composed of input variables and operators from a finite set of function symbols. One measure for evaluating symbolic regression algorithms is their ability to recover formulae, up to symbolic equivalence, from finite samples. Not unexpectedly, the recovery problem becomes harder when the formula gets more complex, that is, when the number of variables and operators gets larger. Variables in naturally occurring symbolic formulas often appear only in fixed combinations. This can be exploited in symbolic regression by substituting one new variable for the combination, effectively reducing the number of variables. However, finding valid substitutions is challenging. Here, we address this challenge by searching over the expression space of small substitutions and testing for validity. The validity test is reduced to a test of functional dependence. The resulting iterative dimension reduction procedure can be used with any symbolic regression approach. We show that it reliably identifies valid substitutions and significantly boosts the performance of different types of state-of-the-art symbolic regression algorithms.", "published": "2025-06-24T11:46:05Z", "authors": ["Paul Kahlmeyer", "Markus Fischer", "Joachim Giesen"], "categories": ["cs.LG"], "doi": "10.1609/aaai.v39i17.33947", "_query": "hedonic regression housing", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/1510.04064v1", "title": "Functional additive regression", "abstract": "We suggest a new method, called Functional Additive Regression, or FAR, for efficiently performing high-dimensional functional regression. FAR extends the usual linear regression model involving a functional predictor, $X(t)$, and a scalar response, $Y$, in two key respects. First, FAR uses a penalized least squares optimization approach to efficiently deal with high-dimensional problems involving a large number of functional predictors. Second, FAR extends beyond the standard linear regression setting to fit general nonlinear additive models. We demonstrate that FAR can be implemented with a wide range of penalty functions using a highly efficient coordinate descent algorithm. Theoretical results are developed which provide motivation for the FAR optimization criterion. Finally, we show through simulations and two real data sets that FAR can significantly outperform competing methods.", "published": "2015-10-14T12:37:10Z", "authors": ["Yingying Fan", "Gareth M. James", "Peter Radchenko"], "categories": ["math.ST"], "doi": "10.1214/15-AOS1346", "_query": "hedonic regression housing", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2211.10776v5", "title": "Bayesian Modal Regression based on Mixture Distributions", "abstract": "Compared to mean regression and quantile regression, the literature on modal regression is very sparse. A unifying framework for Bayesian modal regression is proposed, based on a family of unimodal distributions indexed by the mode, along with other parameters that allow for flexible shapes and tail behaviors. Sufficient conditions for posterior propriety under an improper prior on the mode parameter are derived. Following prior elicitation, regression analysis of simulated data and datasets from several real-life applications are conducted. Besides drawing inference for covariate effects that are easy to interpret, prediction and model selection under the proposed Bayesian modal regression framework are also considered. Evidence from these analyses suggest that the proposed inference procedures are very robust to outliers, enabling one to discover interesting covariate effects missed by mean or median regression, and to construct much tighter prediction intervals than those from mean or median regression. Computer programs for implementing the proposed Bayesian modal regression are available at https://github.com/rh8liuqy/Bayesian_modal_regression.", "published": "2022-11-19T19:14:48Z", "authors": ["Qingyang Liu", "Xianzheng Huang", "Rai Bai"], "categories": ["stat.ME", "stat.AP"], "doi": "10.1016/j.csda.2024.108012", "_query": "hedonic regression housing", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/1511.06028v4", "title": "Optimal inference in a class of regression models", "abstract": "We consider the problem of constructing confidence intervals (CIs) for a linear functional of a regression function, such as its value at a point, the regression discontinuity parameter, or a regression coefficient in a linear or partly linear regression. Our main assumption is that the regression function is known to lie in a convex function class, which covers most smoothness and/or shape assumptions used in econometrics. We derive finite-sample optimal CIs and sharp efficiency bounds under normal errors with known variance. We show that these results translate to uniform (over the function class) asymptotic results when the error distribution is not known. When the function class is centrosymmetric, these efficiency bounds imply that minimax CIs are close to efficient at smooth regression functions. This implies, in particular, that it is impossible to form CIs that are tighter using data-dependent tuning parameters, and maintain coverage over the whole function class. We specialize our results to inference on the regression discontinuity parameter, and illustrate them in simulations and an empirical application.", "published": "2015-11-19T00:03:37Z", "authors": ["Timothy B. Armstrong", "Michal Kolesár"], "categories": ["math.ST", "stat.AP"], "doi": "10.3982/ECTA14434", "_query": "hedonic regression housing", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/math/0608053v1", "title": "Estimation in semiparametric spatial regression", "abstract": "Nonparametric methods have been very popular in the last couple of decades in time series and regression, but no such development has taken place for spatial models. A rather obvious reason for this is the curse of dimensionality. For spatial data on a grid evaluating the conditional mean given its closest neighbors requires a four-dimensional nonparametric regression. In this paper a semiparametric spatial regression approach is proposed to avoid this problem. An estimation procedure based on combining the so-called marginal integration technique with local linear kernel estimation is developed in the semiparametric spatial regression setting. Asymptotic distributions are established under some mild conditions. The same convergence rates as in the one-dimensional regression case are established. An application of the methodology to the classical Mercer and Hall wheat data set is given and indicates that one directional component appears to be nonlinear, which has gone unnoticed in earlier analyses.", "published": "2006-08-02T09:57:28Z", "authors": ["Jiti Gao", "Zudi Lu", "Dag Tjøstheim"], "categories": ["math.ST"], "doi": "10.1214/009053606000000317", "_query": "hedonic regression housing", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2501.19383v2", "title": "Decoding-based Regression", "abstract": "Language models have recently been shown capable of performing regression wherein numeric predictions are represented as decoded strings. In this work, we provide theoretical grounds for this capability and furthermore investigate the utility of causal sequence decoding models as numeric regression heads given any feature representation. We find that, despite being trained in the usual way - for next-token prediction via cross-entropy loss - decoder-based heads are as performant as standard pointwise heads when benchmarked over standard regression tasks, while being flexible enough to capture smooth numeric distributions, such as in the task of density estimation.", "published": "2025-01-31T18:37:42Z", "authors": ["Xingyou Song", "Dara Bahri"], "categories": ["cs.LG", "cs.AI", "cs.CL", "stat.ML"], "doi": "", "_query": "hedonic regression housing", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2309.05925v2", "title": "On Regularized Sparse Logistic Regression", "abstract": "Sparse logistic regression is for classification and feature selection simultaneously. Although many studies have been done to solve $\\ell_1$-regularized logistic regression, there is no equivalently abundant work on solving sparse logistic regression with nonconvex regularization term. In this paper, we propose a unified framework to solve $\\ell_1$-regularized logistic regression, which can be naturally extended to nonconvex regularization term, as long as certain requirement is satisfied. In addition, we also utilize a different line search criteria to guarantee monotone convergence for various regularization terms. Empirical experiments on binary classification tasks with real-world datasets demonstrate our proposed algorithms are capable of performing classification and feature selection effectively at a lower computational cost.", "published": "2023-09-12T02:52:40Z", "authors": ["Mengyuan Zhang", "Kai Liu"], "categories": ["cs.LG", "cs.AI", "stat.ML"], "doi": "", "_query": "hedonic regression housing", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2303.04195v2", "title": "PRIMO: Private Regression in Multiple Outcomes", "abstract": "We introduce a new private regression setting we call Private Regression in Multiple Outcomes (PRIMO), inspired by the common situation where a data analyst wants to perform a set of $l$ regressions while preserving privacy, where the features $X$ are shared across all $l$ regressions, and each regression $i \\in [l]$ has a different vector of outcomes $y_i$. Naively applying existing private linear regression techniques $l$ times leads to a $\\sqrt{l}$ multiplicative increase in error over the standard linear regression setting. We apply a variety of techniques including sufficient statistics perturbation (SSP) and geometric projection-based methods to develop scalable algorithms that outperform this baseline across a range of parameter regimes. In particular, we obtain no dependence on l in the asymptotic error when $l$ is sufficiently large. Empirically, on the task of genomic risk prediction with multiple phenotypes we find that even for values of $l$ far smaller than the theory would predict, our projection-based method improves the accuracy relative to the variant that doesn't use the projection.", "published": "2023-03-07T19:32:13Z", "authors": ["Seth Neel"], "categories": ["cs.LG", "cs.CR"], "doi": "", "_query": "hedonic regression housing", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/1305.0617v2", "title": "Bayesian Manifold Regression", "abstract": "There is increasing interest in the problem of nonparametric regression with high-dimensional predictors. When the number of predictors $D$ is large, one encounters a daunting problem in attempting to estimate a $D$-dimensional surface based on limited data. Fortunately, in many applications, the support of the data is concentrated on a $d$-dimensional subspace with $d \\ll D$. Manifold learning attempts to estimate this subspace. Our focus is on developing computationally tractable and theoretically supported Bayesian nonparametric regression methods in this context. When the subspace corresponds to a locally-Euclidean compact Riemannian manifold, we show that a Gaussian process regression approach can be applied that leads to the minimax optimal adaptive rate in estimating the regression function under some conditions. The proposed model bypasses the need to estimate the manifold, and can be implemented using standard algorithms for posterior computation in Gaussian processes. Finite sample performance is illustrated in an example data analysis.", "published": "2013-05-03T03:37:35Z", "authors": ["Yun Yang", "David B. Dunson"], "categories": ["math.ST"], "doi": "", "_query": "hedonic regression housing", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2504.06171v1", "title": "Generalized Ridge Regression: Applications to Nonorthogonal Linear Regression Models", "abstract": "This paper analyzes the possibilities of using the generalized ridge regression to mitigate multicollinearity in a multiple linear regression model. For this purpose, we obtain the expressions for the estimated variance, the coefficient of variation, the coefficient of correlation, the variance inflation factor and the condition number. The results obtained are illustrated with two numerical examples.", "published": "2025-04-08T16:16:31Z", "authors": ["Román Salmerón Gómez", "Catalina García García", "Guillermo Hortal Reina"], "categories": ["stat.ME"], "doi": "", "_query": "hedonic regression housing", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2105.03048v1", "title": "Regression Bugs Are In Your Model! Measuring, Reducing and Analyzing Regressions In NLP Model Updates", "abstract": "Behavior of deep neural networks can be inconsistent between different versions. Regressions during model update are a common cause of concern that often over-weigh the benefits in accuracy or efficiency gain. This work focuses on quantifying, reducing and analyzing regression errors in the NLP model updates. Using negative flip rate as regression measure, we show that regression has a prevalent presence across tasks in the GLUE benchmark. We formulate the regression-free model updates into a constrained optimization problem, and further reduce it into a relaxed form which can be approximately optimized through knowledge distillation training method. We empirically analyze how model ensemble reduces regression. Finally, we conduct CheckList behavioral testing to understand the distribution of regressions across linguistic phenomena, and the efficacy of ensemble and distillation methods.", "published": "2021-05-07T03:33:00Z", "authors": ["Yuqing Xie", "Yi-an Lai", "Yuanjun Xiong", "Yi Zhang", "Stefano Soatto"], "categories": ["cs.CL"], "doi": "", "_query": "hedonic regression housing", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2112.12259v1", "title": "Density Regression with Bayesian Additive Regression Trees", "abstract": "Flexibly modeling how an entire density changes with covariates is an important but challenging generalization of mean and quantile regression. While existing methods for density regression primarily consist of covariate-dependent discrete mixture models, we consider a continuous latent variable model in general covariate spaces, which we call DR-BART. The prior mapping the latent variable to the observed data is constructed via a novel application of Bayesian Additive Regression Trees (BART). We prove that the posterior induced by our model concentrates quickly around true generative functions that are sufficiently smooth. We also analyze the performance of DR-BART on a set of challenging simulated examples, where it outperforms various other methods for Bayesian density regression. Lastly, we apply DR-BART to two real datasets from educational testing and economics, to study student growth and predict returns to education. Our proposed sampler is efficient and allows one to take advantage of BART's flexibility in many applied settings where the entire distribution of the response is of primary interest. Furthermore, our scheme for splitting on latent variables within BART facilitates its future application to other classes of models that can be described via latent variables, such as those involving hierarchical or time series data.", "published": "2021-12-22T22:53:46Z", "authors": ["Vittorio Orlandi", "Jared Murray", "Antonio Linero", "Alexander Volfovsky"], "categories": ["stat.ME"], "doi": "", "_query": "hedonic regression housing", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2402.16300v3", "title": "Conformalized Selective Regression", "abstract": "Should prediction models always deliver a prediction? In the pursuit of maximum predictive performance, critical considerations of reliability and fairness are often overshadowed, particularly when it comes to the role of uncertainty. Selective regression, also known as the \"reject option,\" allows models to abstain from predictions in cases of considerable uncertainty. Initially proposed seven decades ago, approaches to selective regression have mostly focused on distribution-based proxies for measuring uncertainty, particularly conditional variance. However, this focus neglects the significant influence of model-specific biases on a model's performance. In this paper, we propose a novel approach to selective regression by leveraging conformal prediction, which provides grounded confidence measures for individual predictions based on model-specific biases. In addition, we propose a standardized evaluation framework to allow proper comparison of selective regression approaches. Via an extensive experimental approach, we demonstrate how our proposed approach, conformalized selective regression, demonstrates an advantage over multiple state-of-the-art baselines.", "published": "2024-02-26T04:43:50Z", "authors": ["Anna Sokol", "Nuno Moniz", "Nitesh Chawla"], "categories": ["cs.LG", "stat.ML"], "doi": "", "_query": "hedonic regression housing", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2104.12647v1", "title": "A Survey Of Regression Algorithms And Connections With Deep Learning", "abstract": "Regression has attracted immense interest lately due to its effectiveness in tasks like predicting values. And Regression is of widespread use in multiple fields such as Economics, Finance, Business, Biology and so on. While considerable studies have proposed some impressive models, few of them have provided a whole picture regarding how and to what extent Regression has developed. With the aim of aiding beginners in understanding the relationships among different Regression algorithms, this paper characterizes a broad and thoughtful selection of recent regression algorithms, providing an organized and comprehensive overview of existing work and models utilized frequently. In this paper, the relationship between Regression and Deep Learning is also discussed and a conclusion can be drawn that Deep Learning can be more powerful as an combination with Regression models in the future.", "published": "2021-04-26T15:18:00Z", "authors": ["Yunpeng Tai"], "categories": ["cs.LG"], "doi": "", "_query": "hedonic regression housing", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2204.02686v4", "title": "The loss value of multilinear regression", "abstract": "Determinant formulas are presented for: a certain positive semidefinite, hermitian matrix; the loss value of multilinear regression; the multiple linear regression coefficient.", "published": "2022-04-06T09:19:51Z", "authors": ["Helmut Kahl"], "categories": ["stat.OT"], "doi": "", "_query": "hedonic regression housing", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2510.10792v1", "title": "On function-on-function linear quantile regression", "abstract": "We present two innovative functional partial quantile regression algorithms designed to accurately and efficiently estimate the regression coefficient function within the function-on-function linear quantile regression model. Our algorithms utilize functional partial quantile regression decomposition to effectively project the infinite-dimensional response and predictor variables onto a finite-dimensional space. Within this framework, the partial quantile regression components are approximated using a basis expansion approach. Consequently, we approximate the infinite-dimensional function-on-function linear quantile regression model using a multivariate quantile regression model constructed from these partial quantile regression components. To evaluate the efficacy of our proposed techniques, we conduct a series of Monte Carlo experiments and analyze an empirical dataset, demonstrating superior performance compared to existing methods in finite-sample scenarios. Our techniques have been implemented in the ffpqr package in R.", "published": "2025-10-12T20:14:11Z", "authors": ["Muge Mutis", "Ufuk Beyaztas", "Filiz Karaman", "Han Lin Shang"], "categories": ["stat.ME"], "doi": "", "_query": "hedonic regression housing", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2503.04330v1", "title": "Stepwise regression revisited", "abstract": "This paper shows that the degree of approximate multicollinearity in a linear regression model increases simply by including independent variables, even if these are not highly linearly related. In the current situation where it is relatively easy to find linear models with a large number of independent variables, it is shown that this issue can lead to the erroneous conclusion that there is a worrying problem of approximate multicollinearity. To avoid this situation, an adjusted variance inflation factor is proposed to compensate the presence of a large number of independent variables in the multiple linear regression model. It is shown that this proposal has a direct impact on variable selection models based on influence relationships, which translates into a new decision criterion in the individual significance contrast to be considered in stepwise regression models or even directly in a multiple linear regression model.", "published": "2025-03-06T11:29:42Z", "authors": ["Román Salmerón Gómez", "Catalina García García"], "categories": ["stat.ME"], "doi": "", "_query": "hedonic regression housing", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/1104.1779v2", "title": "Generalized Isotonic Regression", "abstract": "We present a computational and statistical approach for fitting isotonic models under convex differentiable loss functions. We offer a recursive partitioning algorithm which provably and efficiently solves isotonic regression under any such loss function. Models along the partitioning path are also isotonic and can be viewed as regularized solutions to the problem. Our approach generalizes and subsumes two previous results: the well-known work of Barlow and Brunk (1972) on fitting isotonic regressions subject to specially structured loss functions, and a recursive partitioning algorithm (Spouge et al 2003) for the case of standard (l2-loss) isotonic regression. We demonstrate the advantages of our generalized algorithm on both real and simulated data in two settings: fitting count data using negative Poisson log-likelihood loss, and fitting robust isotonic regression using Huber's loss.", "published": "2011-04-10T15:57:29Z", "authors": ["Ronny Luss", "Saharon Rosset"], "categories": ["stat.ME"], "doi": "", "_query": "hedonic regression housing", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2403.19480v1", "title": "$H$-Consistency Guarantees for Regression", "abstract": "We present a detailed study of $H$-consistency bounds for regression. We first present new theorems that generalize the tools previously given to establish $H$-consistency bounds. This generalization proves essential for analyzing $H$-consistency bounds specific to regression. Next, we prove a series of novel $H$-consistency bounds for surrogate loss functions of the squared loss, under the assumption of a symmetric distribution and a bounded hypothesis set. This includes positive results for the Huber loss, all $\\ell_p$ losses, $p \\geq 1$, the squared $ε$-insensitive loss, as well as a negative result for the $ε$-insensitive loss used in squared Support Vector Regression (SVR). We further leverage our analysis of $H$-consistency for regression and derive principled surrogate losses for adversarial regression (Section 5). This readily establishes novel algorithms for adversarial regression, for which we report favorable experimental results in Section 6.", "published": "2024-03-28T15:08:51Z", "authors": ["Anqi Mao", "Mehryar Mohri", "Yutao Zhong"], "categories": ["cs.LG", "stat.ML"], "doi": "", "_query": "hedonic regression housing", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2401.15610v2", "title": "Prevalidated ridge regression is a highly-efficient drop-in replacement for logistic regression for high-dimensional data", "abstract": "Logistic regression is a ubiquitous method for probabilistic classification. However, the effectiveness of logistic regression depends upon careful and relatively computationally expensive tuning, especially for the regularisation hyperparameter, and especially in the context of high-dimensional data. We present a prevalidated ridge regression model that closely matches logistic regression in terms of classification error and log-loss, particularly for high-dimensional data, while being significantly more computationally efficient and having effectively no hyperparameters beyond regularisation. We scale the coefficients of the model so as to minimise log-loss for a set of prevalidated predictions derived from the estimated leave-one-out cross-validation error. This exploits quantities already computed in the course of fitting the ridge regression model in order to find the scaling parameter with nominal additional computational expense.", "published": "2024-01-28T09:38:14Z", "authors": ["Angus Dempster", "Geoffrey I. Webb", "Daniel F. Schmidt"], "categories": ["cs.LG", "stat.ML"], "doi": "", "_query": "hedonic regression housing", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2301.08915v3", "title": "Improving Deep Regression with Ordinal Entropy", "abstract": "In computer vision, it is often observed that formulating regression problems as a classification task often yields better performance. We investigate this curious phenomenon and provide a derivation to show that classification, with the cross-entropy loss, outperforms regression with a mean squared error loss in its ability to learn high-entropy feature representations. Based on the analysis, we propose an ordinal entropy loss to encourage higher-entropy feature spaces while maintaining ordinal relationships to improve the performance of regression tasks. Experiments on synthetic and real-world regression tasks demonstrate the importance and benefits of increasing entropy for regression.", "published": "2023-01-21T08:30:15Z", "authors": ["Shihao Zhang", "Linlin Yang", "Michael Bi Mi", "Xiaoxu Zheng", "Angela Yao"], "categories": ["cs.CV", "cs.AI", "cs.LG"], "doi": "", "_query": "hedonic regression housing", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/1908.11601v1", "title": "Robust Function-on-Function Regression", "abstract": "Functional linear regression is a widely used approach to model functional responses with respect to functional inputs. However, classical functional linear regression models can be severely affected by outliers. We therefore introduce a Fisher-consistent robust functional linear regression model that is able to effectively fit data in the presence of outliers. The model is built using robust functional principal component and least squares regression estimators. The performance of the functional linear regression model depends on the number of principal components used. We therefore introduce a consistent robust model selection procedure to choose the number of principal components. Our robust functional linear regression model can be used alongside an outlier detection procedure to effectively identify abnormal functional responses. A simulation study shows our method is able to effectively capture the regression behaviour in the presence of outliers, and is able to find the outliers with high accuracy. We demonstrate the usefulness of our method on jet engine sensor data. We identify outliers that would not be found if the functional responses were modelled independently of the functional input, or using non-robust methods.", "published": "2019-08-30T09:04:02Z", "authors": ["Harjit Hullait", "David S. Leslie", "Nicos G. Pavlidis", "Steve King"], "categories": ["stat.ME", "stat.AP"], "doi": "", "_query": "hedonic regression housing", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2512.00566v3", "title": "Improved inference for nonparametric regression and regression-discontinuity designs", "abstract": "Nonparametric regression and regression-discontinuity designs suffer from smoothing bias that distorts conventional confidence intervals. Solutions based on robust bias correction (RBC) are now central to the economist's toolbox. In this paper, we establish a novel connection between RBC methods and bootstrap prepivoting. Revisiting RBC through the lens of bootstrapping allows us to develop a novel bias correction procedure which delivers improved nonparametric inference. The resulting confidence intervals are 17% shorter than the usual intervals employed in curve estimation and regression discontinuity designs, without compromising asymptotic coverage. This holds regardless of evaluation point location, bandwidth choice, or regressor and error distribution.", "published": "2025-11-29T17:39:09Z", "authors": ["Giuseppe Cavaliere", "Sílvia Gonçalves", "Morten Ørregaard Nielsen", "Edoardo Zanelli"], "categories": ["econ.EM", "stat.ME"], "doi": "", "_query": "hedonic regression housing", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2603.06462v1", "title": "Bayesian Additive Distribution Regression", "abstract": "Distribution regression, where the goal is to predict a scalar response from a distribution-valued predictor, arises naturally in settings where observations are grouped and outcomes depend on group-level characteristics rather than on individual measurements. We introduce DistBART, a Bayesian nonparametric approach to distribution regression that models the regression function as a linear functional with the Riesz representer assigned a Bayesian additive regression trees (BART) prior. We argue that shallow decision tree ensembles encode reasonable inductive biases for tabular data, making them appropriate in settings where the functional depends primarily on low-dimensional marginals of the distributions. We show this both empirically on synthetic and real data and theoretically through an adaptive posterior concentration result. We also establish connections to kernel methods, and use this connection to motivate variants of DistBART that can learn nonlinear functionals. To enable scalability to large datasets, we develop a random-feature approximation that samples trees from the BART prior and reduces inference to sparse Bayesian linear regression, achieving computational efficiency while retaining uncertainty quantification.", "published": "2026-03-06T16:49:38Z", "authors": ["Antonio R. Linero", "Soumyabrata Bose", "Jared Murray"], "categories": ["stat.ME", "stat.ML"], "doi": "", "_query": "hedonic regression housing", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2208.01461v1", "title": "Composite Expectile Regression with Gene-environment Interaction", "abstract": "If error distribution has heteroscedasticity, it voliates the assumption of linear regression. Expectile regression is a powerful tool for estimating the conditional expectiles of a response variable in this setting. Since multiple levels of expectile regression modelhas been well studied, we propose composite expectile regression by combining different levels of expectile regression to improve the efficacy. In this paper, we study the sparse composite expectile regression under high dimensional setting. It is realized by implementing a coordinate descent algorithm. We also prove its selection and estimation consistency. Simulations are conducted to demonstrate its performance, which is comparable to or better than the alternatives. We apply the proposed method to analyze Lung adenocarcinoma(LUAD) real data set, investigating the G-E interaction.", "published": "2022-07-02T21:57:30Z", "authors": ["Jinghang Lin", "Yuan Huang", "Shuangge Ma"], "categories": ["stat.ME", "math.ST"], "doi": "", "_query": "hedonic regression housing", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2111.00302v1", "title": "Bayesian surface regression versus spatial spectral nonparametric curve regression", "abstract": "COVID-19 incidence is analyzed at the provinces of some Spanish Communities during the period February-October, 2020. Two infinite-dimensional regression approaches are tested. The first one is implemented in the regression framework introduced in Ruiz-Medina, Miranda and Espejo (2019). Specifically, a bayesian framework is adopted in the estimation of the pure point spectrum of the temporal autocorrelation operator, characterizing the second-order structure of a surface sequence. The second approach is formulated in the context of spatial curve regression. A nonparametric estimator of the spectral density operator, based on the spatial periodogram operator, is computed to approximate the spatial correlation between curves. Dimension reduction is achieved by projection onto the empirical eigenvectors of the long-run spatial covariance operator. Cross-validation procedures are implemented to test the performance of the two functional regression approaches.", "published": "2021-10-30T18:20:02Z", "authors": ["M. D. Ruiz-Medina", "D. Miranda"], "categories": ["stat.ME", "stat.AP"], "doi": "", "_query": "hedonic regression housing", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2512.12919v1", "title": "Open Source Software and Data for Human Service Development: A Case Study on Predicting Housing Instability", "abstract": "Open-source data and tools are lauded as essential for replicable and usable social science, though little is known about their use in resource constrained human service provision. This paper examines the challenges and opportunities of open-source tools and data in human service development by using both to forecast failure to pay eviction filings in Bronx County, NY. We use zip code level data from the Housing Data Coalition, the American Community Survey 5-year estimates, and DeepMaps Model of the Labor Force to forecast rates through July 2021. We employ multilevel (MLM) and exponential smoothing (ETS) models using the R project for Statistical Computing, an oft used open-source statistical software. We compare our results to what happened during the same period, to illustrate the efficacy of the open-source tools and techniques employed. We argue open-source data and software may facilitate rapid analysis of public data - a much-needed ability in human service intervention development under increasingly constrained resources - but find public data are limited by the information they reliably capture, limiting their utility by a non-trivial margin of error. The manuscript concludes by considering lessons for human service organizations with limited analytical resources and a vested interest in low-resourced communities.", "published": "2025-12-15T02:08:53Z", "authors": ["Maria Y. Rodriguez", "Ehren Dohler", "Jon Phillips", "Melissa Villodas", "Voltaire Vegara", "Kenny Joseph", "Amy Wilson"], "categories": ["cs.CY"], "doi": "10.1080/15228835.2026.2709832", "_query": "hedonic regression housing", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2203.05417v1", "title": "Deep Regression Ensembles", "abstract": "We introduce a methodology for designing and training deep neural networks (DNN) that we call \"Deep Regression Ensembles\" (DRE). It bridges the gap between DNN and two-layer neural networks trained with random feature regression. Each layer of DRE has two components, randomly drawn input weights and output weights trained myopically (as if the final output layer) using linear ridge regression. Within a layer, each neuron uses a different subset of inputs and a different ridge penalty, constituting an ensemble of random feature ridge regressions. Our experiments show that a single DRE architecture is at par with or exceeds state-of-the-art DNN in many data sets. Yet, because DRE neural weights are either known in closed-form or randomly drawn, its computational cost is orders of magnitude smaller than DNN.", "published": "2022-03-10T15:13:46Z", "authors": ["Antoine Didisheim", "Bryan Kelly", "Semyon Malamud"], "categories": ["stat.ML", "cs.LG"], "doi": "", "_query": "hedonic regression housing", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2002.05137v7", "title": "Flexible non-parametric regression models for compositional data", "abstract": "Compositional data arise in many real-life applications and versatile methods for properly analyzing this type of data in the regression context are needed. When parametric assumptions do not hold or are difficult to verify, non-parametric regression models can provide a convenient alternative method for prediction. To this end, we consider an extension to the classical $k$--$NN$ regression, termed $α$--$k$--$NN$ regression, that yields a highly flexible non-parametric regression model for compositional data through the use of the $α$-transformation. Unlike many of the recommended regression models for compositional data, zeros values (which commonly occur in practice) are not problematic and they can be incorporated into the proposed models without modification. Extensive simulation studies and real-life data analyses highlight the advantage of using these non-parametric regressions for complex relationships between the compositional response data and Euclidean predictor variables. Both suggest that $α$--$k$--$NN$ regression can lead to more accurate predictions compared to current regression models which assume a, sometimes restrictive, parametric relationship with the predictor variables. In addition, the $α$--$k$--$NN$ regression, in contrast to current regression techniques, enjoys a high computational efficiency rendering it highly attractive for use with large scale, massive, or big data.", "published": "2020-02-12T18:29:33Z", "authors": ["Michail Tsagris", "Abdulaziz Alenazi", "Connie Stewart"], "categories": ["stat.ME"], "doi": "", "_query": "hedonic regression housing", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/1102.5212v1", "title": "Functional linear regression via canonical analysis", "abstract": "We study regression models for the situation where both dependent and independent variables are square-integrable stochastic processes. Questions concerning the definition and existence of the corresponding functional linear regression models and some basic properties are explored for this situation. We derive a representation of the regression parameter function in terms of the canonical components of the processes involved. This representation establishes a connection between functional regression and functional canonical analysis and suggests alternative approaches for the implementation of functional linear regression analysis. A specific procedure for the estimation of the regression parameter function using canonical expansions is proposed and compared with an established functional principal component regression approach. As an example of an application, we present an analysis of mortality data for cohorts of medflies, obtained in experimental studies of aging and longevity.", "published": "2011-02-25T11:13:43Z", "authors": ["Guozhong He", "Hans-Georg Müller", "Jane-Ling Wang", "Wenjing Yang"], "categories": ["math.ST"], "doi": "10.3150/09-BEJ228", "_query": "hedonic regression housing", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2511.06542v1", "title": "Collapsing Categories for Regression with Mixed Predictors", "abstract": "Categorical predictors are omnipresent in everyday regression practice: in fact, most regression data involve some categorical predictors, and this tendency is increasing in modern applications with more complex structures and larger data sizes. However, including too many categories in a regression model would seriously hamper accuracy, as the information in the data is fragmented by the multitude of categories. In this paper, we introduce a systematic method to reduce the complexity of categorical predictors by adaptively collapsing categories in regressions, so as to enhance the performance of regression estimation. Our method is based on the {\\em pairwise vector fused LASSO}, which automatically fuses the categories that bear a similar regression relation with the response. We develop our method under a wide class of regression models defined by a general loss function, which includes linear models and generalized linear models as special cases. We rigorously established the category collapsing consistency of our method, developed an Inexact Proximal Gradient Descent algorithm to implement it, and proved the feasibility and convergence of our algorithm. Through simulations and an application to Spotify music data, we demonstrate that our method can effectively reduce categorical complexity while improving prediction performance, making it a powerful tool for regression with mixed predictors.", "published": "2025-11-09T21:07:06Z", "authors": ["Chaegeun Song", "Zhong Zheng", "Bing Li", "Lingzhou Xue"], "categories": ["stat.ME", "math.ST"], "doi": "", "_query": "hedonic regression housing", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2503.16007v2", "title": "Estimation of Piecewise Continuous Regression Function in Finite Dimension using Oblique Regression Tree with Applications in Image Denoising", "abstract": "Decision trees are one of the most widely used nonparametric methods for regression and classification. In existing literature, decision tree-based methods have been used for estimating continuous functions or piecewise-constant functions. However, they are not flexible enough to estimate the complex shapes of jump location curves (JLCs) in two-dimensional regression functions. In this article, we explore the Oblique-axis Regression Tree (ORT) and propose a method to efficiently estimate piece-wise continuous functions in a general finite dimension with fixed design points. The central idea involves clustering the local pixel intensities by recursive tree partitioning and using the local leaf-only averaging for estimation of the regression function at a given pixel. The proposed method can preserve complex shapes of the JLCs well in a finite-dimensional regression function. Due to a different set of assumptions on the underlying regression function, the overall framework of the proofs is different from what is available in the literature on regression trees. Theoretical analysis and numerical results, particularly on image denoising, indicate that the proposed method effectively preserves complicated edge structures while efficiently removing noise from piecewise continuous regression surfaces.", "published": "2025-03-20T10:18:11Z", "authors": ["Subhasish Basak", "Anik Roy", "Partha Sarathi Mukherjee"], "categories": ["stat.AP"], "doi": "10.5705/ss.202025.0120", "_query": "hedonic regression housing", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2509.09721v1", "title": "A Multimodal RAG Framework for Housing Damage Assessment: Collaborative Optimization of Image Encoding and Policy Vector Retrieval", "abstract": "After natural disasters, accurate evaluations of damage to housing are important for insurance claims response and planning of resources. In this work, we introduce a novel multimodal retrieval-augmented generation (MM-RAG) framework. On top of classical RAG architecture, we further the framework to devise a two-branch multimodal encoder structure that the image branch employs a visual encoder composed of ResNet and Transformer to extract the characteristic of building damage after disaster, and the text branch harnesses a BERT retriever for the text vectorization of posts as well as insurance policies and for the construction of a retrievable restoration index. To impose cross-modal semantic alignment, the model integrates a cross-modal interaction module to bridge the semantic representation between image and text via multi-head attention. Meanwhile, in the generation module, the introduced modal attention gating mechanism dynamically controls the role of visual evidence and text prior information during generation. The entire framework takes end-to-end training, and combines the comparison loss, the retrieval loss and the generation loss to form multi-task optimization objectives, and achieves image understanding and policy matching in collaborative learning. The results demonstrate superior performance in retrieval accuracy and classification index on damage severity, where the Top-1 retrieval accuracy has been improved by 9.6%.", "published": "2025-09-10T01:58:07Z", "authors": ["Jiayi Miao", "Dingxin Lu", "Zhuqi Wang"], "categories": ["cs.CV", "cs.AI", "cs.LG"], "doi": "", "_query": "hedonic regression housing", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/1712.09001v1", "title": "Kernel Regression with Sparse Metric Learning", "abstract": "Kernel regression is a popular non-parametric fitting technique. It aims at learning a function which estimates the targets for test inputs as precise as possible. Generally, the function value for a test input is estimated by a weighted average of the surrounding training examples. The weights are typically computed by a distance-based kernel function and they strongly depend on the distances between examples. In this paper, we first review the latest developments of sparse metric learning and kernel regression. Then a novel kernel regression method involving sparse metric learning, which is called kernel regression with sparse metric learning (KR$\\_$SML), is proposed. The sparse kernel regression model is established by enforcing a mixed $(2,1)$-norm regularization over the metric matrix. It learns a Mahalanobis distance metric by a gradient descent procedure, which can simultaneously conduct dimensionality reduction and lead to good prediction results. Our work is the first to combine kernel regression with sparse metric learning. To verify the effectiveness of the proposed method, it is evaluated on 19 data sets for regression. Furthermore, the new method is also applied to solving practical problems of forecasting short-term traffic flows. In the end, we compare the proposed method with other three related kernel regression methods on all test data sets under two criterions. Experimental results show that the proposed method is much more competitive.", "published": "2017-12-25T04:00:23Z", "authors": ["Rongqing Huang", "Shiliang Sun"], "categories": ["cs.LG", "stat.ML"], "doi": "", "_query": "hedonic regression housing", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2501.18204v1", "title": "A theory of shape regularity for local regression maps", "abstract": "We introduce the concept of shape-regular regression maps as a framework to derive optimal rates of convergence for various non-parametric local regression estimators. Using Vapnik-Chervonenkis theory, we establish upper and lower bounds on the pointwise and the sup-norm estimation error, even when the localization procedure depends on the full data sample, and under mild conditions on the regression model. Our results demonstrate that the shape regularity of regression maps is not only sufficient but also necessary to achieve an optimal rate of convergence for Lipschitz regression functions. To illustrate the theory, we establish new concentration bounds for many popular local regression methods such as nearest neighbors algorithm, CART-like regression trees and several purely random trees including Mondrian trees.", "published": "2025-01-30T08:50:42Z", "authors": ["Jérémy Bettinger", "François Portier", "Adrien Saumard"], "categories": ["math.ST"], "doi": "", "_query": "hedonic regression housing", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/1509.06490v1", "title": "Bayesian Tensor Regression", "abstract": "This article proposes a Bayesian approach to regression with a scalar response against vector and tensor covariates. Tensor covariates are commonly vectorized prior to analysis, failing to exploit the structure of the tensor, and resulting in poor estimation and predictive performance. We develop a novel class of multiway shrinkage priors for the coefficients in tensor regression models. Properties are described, including posterior consistency under mild conditions, and an efficient Markov chain Monte Carlo algorithm is developed for posterior computation. Simulation studies illustrate substantial gains over vectorizing or using existing tensor regression methods in terms of estimation and parameter inference. The approach is further illustrated in a neuroimaging application. Keywords: Dimension reduction; multiway shrinkage prior; magnetic resonance imaging (MRI); parafac decomposition; posterior consistency; tensor regression", "published": "2015-09-22T08:07:15Z", "authors": ["Rajarshi Guhaniyogi", "Shaan Qamar", "David B. Dunson"], "categories": ["stat.ME"], "doi": "", "_query": "hedonic regression housing", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/1807.03184v1", "title": "Prediction regions through Inverse Regression", "abstract": "Predict a new response from a covariate is a challenging task in regression, which raises new question since the era of high-dimensional data. In this paper, we are interested in the inverse regression method from a theoretical viewpoint. Theoretical results have already been derived for the well-known linear model, but recently, the curse of dimensionality has increased the interest of practitioners and theoreticians into generalization of those results for various estimators, calibrated for the high-dimension context. To deal with high-dimensional data, inverse regression is used in this paper. It is known to be a reliable and efficient approach when the number of features exceeds the number of observations. Indeed, under some conditions, dealing with the inverse regression problem associated to a forward regression problem drastically reduces the number of parameters to estimate and make the problem tractable. When both the responses and the covariates are multivariate, estimators constructed by the inverse regression are studied in this paper, the main result being explicit asymptotic prediction regions for the response. The performances of the proposed estimators and prediction regions are also analyzed through a simulation study and compared with usual estimators.", "published": "2018-07-09T14:15:11Z", "authors": ["Emilie Devijver", "Emeline Perthame"], "categories": ["math.ST", "stat.ME"], "doi": "", "_query": "hedonic regression housing", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2505.09075v3", "title": "Risk Bounds For Distributional Regression", "abstract": "This work examines risk bounds for nonparametric distributional regression estimators. For convex-constrained distributional regression, general upper bounds are established for the continuous ranked probability score (CRPS) and the worst-case mean squared error (MSE) across the domain. These theoretical results are applied to isotonic and trend filtering distributional regression, yielding convergence rates consistent with those for mean estimation. Furthermore, a general upper bound is derived for distributional regression under non-convex constraints, with a specific application to neural network-based estimators. Comprehensive experiments on both simulated and real data validate the theoretical contributions, demonstrating their practical effectiveness.", "published": "2025-05-14T02:22:12Z", "authors": ["Carlos Misael Madrid Padilla", "Oscar Hernan Madrid Padilla", "Sabyasachi Chatterjee"], "categories": ["stat.ML", "cs.LG"], "doi": "", "_query": "hedonic regression housing", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/1201.2395v2", "title": "Polynomial Regression on Riemannian Manifolds", "abstract": "In this paper we develop the theory of parametric polynomial regression in Riemannian manifolds and Lie groups. We show application of Riemannian polynomial regression to shape analysis in Kendall shape space. Results are presented, showing the power of polynomial regression on the classic rat skull growth data of Bookstein as well as the analysis of the shape changes associated with aging of the corpus callosum from the OASIS Alzheimer's study.", "published": "2012-01-11T20:27:32Z", "authors": ["Jacob Hinkle", "Prasanna Muralidharan", "P. Thomas Fletcher", "Sarang Joshi"], "categories": ["math.ST", "cs.CV", "math.DG"], "doi": "", "_query": "hedonic regression housing", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/1811.09623v2", "title": "Nonlinear Regression without i.i.d. Assumption", "abstract": "In this paper, we consider a class of nonlinear regression problems without the assumption of being independent and identically distributed. We propose a correspondent mini-max problem for nonlinear regression and give a numerical algorithm. Such an algorithm can be applied in regression and machine learning problems, and yield better results than traditional least square and machine learning methods.", "published": "2018-11-23T06:25:20Z", "authors": ["Qing Xu", "Xiaohua Xuan"], "categories": ["stat.ME", "cs.LG"], "doi": "", "_query": "hedonic regression housing", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2006.11390v4", "title": "New Insights into Learning with Correntropy Based Regression", "abstract": "Stemming from information-theoretic learning, the correntropy criterion and its applications to machine learning tasks have been extensively explored and studied. Its application to regression problems leads to the robustness enhanced regression paradigm -- namely, correntropy based regression. Having drawn a great variety of successful real-world applications, its theoretical properties have also been investigated recently in a series of studies from a statistical learning viewpoint. The resulting big picture is that correntropy based regression regresses towards the conditional mode function or the conditional mean function robustly under certain conditions. Continuing this trend and going further, in the present study, we report some new insights into this problem. First, we show that under the additive noise regression model, such a regression paradigm can be deduced from minimum distance estimation, implying that the resulting estimator is essentially a minimum distance estimator and thus possesses robustness properties. Second, we show that the regression paradigm, in fact, provides a unified approach to regression problems in that it approaches the conditional mean, the conditional mode, as well as the conditional median functions under certain conditions. Third, we present some new results when it is utilized to learn the conditional mean function by developing its error bounds and exponential convergence rates under conditional $(1+ε)$-moment assumptions. The saturation effect on the established convergence rates, which was observed under $(1+ε)$-moment assumptions, still occurs, indicating the inherent bias of the regression estimator. These novel insights deepen our understanding of correntropy based regression, help cement the theoretic correntropy framework, and also enable us to investigate learning schemes induced by general bounded nonconvex loss functions.", "published": "2020-06-19T21:14:34Z", "authors": ["Yunlong Feng"], "categories": ["cs.LG", "stat.ML"], "doi": "", "_query": "hedonic regression housing", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/1011.2077v1", "title": "A flexible regression model for count data", "abstract": "Poisson regression is a popular tool for modeling count data and is applied in a vast array of applications from the social to the physical sciences and beyond. Real data, however, are often over- or under-dispersed and, thus, not conducive to Poisson regression. We propose a regression model based on the Conway--Maxwell-Poisson (COM-Poisson) distribution to address this problem. The COM-Poisson regression generalizes the well-known Poisson and logistic regression models, and is suitable for fitting count data with a wide range of dispersion levels. With a GLM approach that takes advantage of exponential family properties, we discuss model estimation, inference, diagnostics, and interpretation, and present a test for determining the need for a COM-Poisson regression over a standard Poisson regression. We compare the COM-Poisson to several alternatives and illustrate its advantages and usefulness using three data sets with varying dispersion.", "published": "2010-11-09T13:32:49Z", "authors": ["Kimberly F. Sellers", "Galit Shmueli"], "categories": ["stat.AP"], "doi": "10.1214/09-AOAS306", "_query": "hedonic regression housing", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/1309.2627v1", "title": "Weighted quantile regression for longitudinal data", "abstract": "Quantile regression is a powerful statistical methodology that complements the classical linear regression by examining how covariates influence the location, scale, and shape of the entire response distribution and offering a global view of the statistical landscape. In this paper we propose a new quantile regression model for longitudinal data. The proposed approach incorporates the correlation structure between repeated measures to enhance the efficiency of the inference. In order to use the Newton-Raphson iteration method to obtain convergent estimates, the estimating functions are redefined as smoothed functions which are differentiable with respect to regression parameters. Our proposed method for quantile regression provides consistent estimates with asymptotically normal distributions. Simulation studies are carried out to evaluate the performance of the proposed method. As an illustration, the proposed method was applied to a real-life data that contains self-reported labor pain for women in two groups.", "published": "2013-09-10T19:48:21Z", "authors": ["Lu Xiaoming", "Fan Zhaozhi"], "categories": ["stat.AP", "stat.CO"], "doi": "", "_query": "hedonic regression housing", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/1112.1390v1", "title": "An Identity for Kernel Ridge Regression", "abstract": "This paper derives an identity connecting the square loss of ridge regression in on-line mode with the loss of the retrospectively best regressor. Some corollaries about the properties of the cumulative loss of on-line ridge regression are also obtained.", "published": "2011-12-06T20:15:37Z", "authors": ["Fedor Zhdanov", "Yuri Kalnishkan"], "categories": ["cs.LG"], "doi": "", "_query": "hedonic regression housing", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2207.05945v2", "title": "Online Active Regression", "abstract": "Active regression considers a linear regression problem where the learner receives a large number of data points but can only observe a small number of labels. Since online algorithms can deal with incremental training data and take advantage of low computational cost, we consider an online extension of the active regression problem: the learner receives data points one by one and immediately decides whether it should collect the corresponding labels. The goal is to efficiently maintain the regression of received data points with a small budget of label queries. We propose novel algorithms for this problem under $\\ell_p$ loss where $p\\in[1,2]$. To achieve a $(1+ε)$-approximate solution, our proposed algorithms only require $\\tilde{\\mathcal{O}}(ε^{-1} d \\log(nκ))$ queries of labels, where $n$ is the number of data points and $κ$ is a quantity, called the condition number, of the data points. The numerical results verify our theoretical results and show that our methods have comparable performance with offline active regression algorithms.", "published": "2022-07-13T03:53:25Z", "authors": ["Cheng Chen", "Yi Li", "Yiming Sun"], "categories": ["cs.LG", "cs.DS", "stat.ML"], "doi": "", "_query": "hedonic regression housing", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/0802.0964v2", "title": "Least angle and $\\ell_1$ penalized regression: A review", "abstract": "Least Angle Regression is a promising technique for variable selection applications, offering a nice alternative to stepwise regression. It provides an explanation for the similar behavior of LASSO ($\\ell_1$-penalized regression) and forward stagewise regression, and provides a fast implementation of both. The idea has caught on rapidly, and sparked a great deal of research interest. In this paper, we give an overview of Least Angle Regression and the current state of related research.", "published": "2008-02-07T12:53:59Z", "authors": ["Tim Hesterberg", "Nam Hee Choi", "Lukas Meier", "Chris Fraley"], "categories": ["stat.ME", "stat.ML"], "doi": "10.1214/08-SS035", "_query": "hedonic regression housing", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/1701.05325v1", "title": "Random Projections For Large-Scale Regression", "abstract": "Fitting linear regression models can be computationally very expensive in large-scale data analysis tasks if the sample size and the number of variables are very large. Random projections are extensively used as a dimension reduction tool in machine learning and statistics. We discuss the applications of random projections in linear regression problems, developed to decrease computational costs, and give an overview of the theoretical guarantees of the generalization error. It can be shown that the combination of random projections with least squares regression leads to similar recovery as ridge regression and principal component regression. We also discuss possible improvements when averaging over multiple random projections, an approach that lends itself easily to parallel implementation.", "published": "2017-01-19T08:14:38Z", "authors": ["Gian-Andrea Thanei", "Christina Heinze", "Nicolai Meinshausen"], "categories": ["math.ST"], "doi": "", "_query": "hedonic regression housing", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/1508.02201v1", "title": "Extrinsic local regression on manifold-valued data", "abstract": "We propose an extrinsic regression framework for modeling data with manifold valued responses and Euclidean predictors. Regression with manifold responses has wide applications in shape analysis, neuroscience, medical imaging and many other areas. Our approach embeds the manifold where the responses lie onto a higher dimensional Euclidean space, obtains a local regression estimate in that space, and then projects this estimate back onto the image of the manifold. Outside the regression setting both intrinsic and extrinsic approaches have been proposed for modeling i.i.d manifold-valued data. However, to our knowledge our work is the first to take an extrinsic approach to the regression problem. The proposed extrinsic regression framework is general, computationally efficient and theoretically appealing. Asymptotic distributions and convergence rates of the extrinsic regression estimates are derived and a large class of examples are considered indicating the wide applicability of our approach.", "published": "2015-08-10T11:08:14Z", "authors": ["Lizhen Lin", "Brian St. Thomas", "Hongtu Zhu", "David B. Dunson"], "categories": ["math.ST"], "doi": "", "_query": "hedonic regression housing", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2003.00958v1", "title": "Score Engineered Logistic Regression", "abstract": "In several FICO studies logistic regression has been shown to be a very competitive technology for developing unrestricted scoring models, especially for performance metrics like ROC area. Application of logistic regression has been hampered by the lack of software to handle complex score engineering such as shape and pattern constraints. The purpose of this paper is to develop a sequential quadratic programming algorithm for score engineered logistic regression. This approach is based on a simple Taylor series expansion of minus log likelihood, which is locally quadratic. and fits in with the method that applies quadratic programming to B-Splines.", "published": "2020-03-02T15:04:48Z", "authors": ["Bruce Hoadley"], "categories": ["stat.ME"], "doi": "", "_query": "hedonic regression housing", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2005.13885v1", "title": "Hyperbolic Manifold Regression", "abstract": "Geometric representation learning has recently shown great promise in several machine learning settings, ranging from relational learning to language processing and generative models. In this work, we consider the problem of performing manifold-valued regression onto an hyperbolic space as an intermediate component for a number of relevant machine learning applications. In particular, by formulating the problem of predicting nodes of a tree as a manifold regression task in the hyperbolic space, we propose a novel perspective on two challenging tasks: 1) hierarchical classification via label embeddings and 2) taxonomy extension of hyperbolic representations. To address the regression problem we consider previous methods as well as proposing two novel approaches that are computationally more advantageous: a parametric deep learning model that is informed by the geodesics of the target space and a non-parametric kernel-method for which we also prove excess risk bounds. Our experiments show that the strategy of leveraging the hyperbolic geometry is promising. In particular, in the taxonomy expansion setting, we find that the hyperbolic-based estimators significantly outperform methods performing regression in the ambient Euclidean space.", "published": "2020-05-28T10:16:30Z", "authors": ["Gian Maria Marconi", "Lorenzo Rosasco", "Carlo Ciliberto"], "categories": ["stat.ML", "cs.LG"], "doi": "", "_query": "hedonic regression housing", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2402.14260v4", "title": "A New Regression Lens on Multi-Class Classification", "abstract": "Linear Discriminant Analysis (LDA) is a fundamental method for classification. Its simple linear structure facilitates interpretation, and it is naturally suited to multi-class settings. LDA is also closely connected to several classical multivariate techniques, including Fisher's discriminant analysis, canonical correlation analysis, and linear regression. In this paper, we strengthen the connection between LDA and multivariate response regression by establishing an explicit relationship between discriminant directions and regression coefficients. This characterization yields a new regression-based framework for multi-class classification that accommodates structured, regularized, and even non-parametric regression methods. In contrast to existing regression-based approaches, our formulation is particularly amenable to theoretical analysis: we develop a general strategy for deriving bounds on the excess misclassification risk of the proposed classifier across all such regression procedures. As concrete applications, we provide complete theoretical guarantees for two widely used methods -- $\\ell_1$-regularization and reduced-rank regression -- neither of which has previously been fully analyzed in the LDA context. The theoretical results are supported by extensive simulation studies and empirical evaluations on real data.", "published": "2024-02-22T03:50:47Z", "authors": ["Xin Bing", "Bingqing Li", "Marten Wegkamp"], "categories": ["stat.ME"], "doi": "", "_query": "hedonic regression housing", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2411.14708v3", "title": "Understanding LLM Embeddings for Regression", "abstract": "With the rise of large language models (LLMs) for flexibly processing information as strings, a natural application is regression, specifically by preprocessing string representations into LLM embeddings as downstream features for metric prediction. In this paper, we provide one of the first comprehensive investigations into embedding-based regression and demonstrate that LLM embeddings as features can be better for high-dimensional regression tasks than using traditional feature engineering. This regression performance can be explained in part due to LLM embeddings over numeric data inherently preserving Lipschitz continuity over the feature space. Furthermore, we quantify the contribution of different model effects, most notably model size and language understanding, which we find surprisingly do not always improve regression performance.", "published": "2024-11-22T03:33:51Z", "authors": ["Eric Tang", "Bangding Yang", "Xingyou Song"], "categories": ["cs.LG", "cs.AI", "cs.CL"], "doi": "", "_query": "hedonic regression housing", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/1106.5599v1", "title": "A pseudo-RIP for multivariate regression", "abstract": "We give a suitable RI-Property under which recent results for trace regression translate into strong risk bounds for multivariate regression. This pseudo-RIP is compatible with the setting $n < p$.", "published": "2011-06-28T09:07:07Z", "authors": ["Christophe Giraud"], "categories": ["math.ST"], "doi": "", "_query": "hedonic regression housing", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2601.03674v1", "title": "Multi-transport Distributional Regression", "abstract": "We study distribution-on-distribution regression problems in which a response distribution depends on multiple distributional predictors. Such settings arise naturally in applications where the outcome distribution is driven by several heterogeneous distributional sources, yet remain challenging due to the nonlinear geometry of the Wasserstein space. We propose an intrinsic regression framework that aggregates predictor-specific transported distributions through a weighted Fréchet mean in the Wasserstein space. The resulting model admits multiple distributional predictors, assigns interpretable weights quantifying their relative contributions, and defines a flexible regression operator that is invariant to auxiliary construction choices, such as the selection of a reference distribution. From a theoretical perspective, we establish identifiability of the induced regression operator and derive asymptotic guarantees for its estimation under a predictive Wasserstein semi-norm, which directly characterizes convergence of the composite prediction map. Extensive simulation studies and a real data application demonstrate the improved predictive performance and interpretability of the proposed approach compared with existing Wasserstein regression methods.", "published": "2026-01-07T07:54:20Z", "authors": ["Yuanying Chen", "Tongyu Li", "Yang Bai", "Zhenhua Lin"], "categories": ["stat.ME"], "doi": "", "_query": "hedonic regression housing", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2509.24757v1", "title": "Accelerating Regression Tasks with Quantum Algorithms", "abstract": "Regression is a cornerstone of statistics and machine learning, with applications spanning science, engineering, and economics. While quantum algorithms for regression have attracted considerable attention, most existing work has focused on linear regression, leaving many more complex yet practically important variants unexplored. In this work, we present a unified quantum framework for accelerating a broad class of regression tasks -- including linear and multiple regression, Lasso, Ridge, Huber, $\\ell_p$-, and $δ_p$-type regressions -- achieving up to a quadratic improvement in the number of samples $m$ over the best classical algorithms. This speedup is achieved by extending the recent classical breakthrough of Jambulapati et al. (STOC'24) using several quantum techniques, including quantum leverage score approximation (Apers &Gribling, 2024) and the preparation of many copies of a quantum state (Hamoudi, 2022). For problems of dimension $n$, sparsity $r < n$, and error parameter $ε$, our algorithm solves the problem in $\\widetilde{O}(r\\sqrt{mn}/ε+ \\mathrm{poly}(n,1/ε))$ quantum time, demonstrating both the applicability and the efficiency of quantum computing in accelerating regression tasks.", "published": "2025-09-29T13:22:59Z", "authors": ["Chenghua Liu", "Zhengfeng Ji"], "categories": ["quant-ph", "cs.DS"], "doi": "", "_query": "hedonic regression housing", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2311.17858v1", "title": "On the Limits of Regression Adjustment", "abstract": "Regression adjustment, sometimes known as Controlled-experiment Using Pre-Experiment Data (CUPED), is an important technique in internet experimentation. It decreases the variance of effect size estimates, often cutting confidence interval widths in half or more while never making them worse. It does so by carefully regressing the goal metric against pre-experiment features to reduce the variance. The tremendous gains of regression adjustment begs the question: How much better can we do by engineering better features from pre-experiment data, for example by using machine learning techniques or synthetic controls? Could we even reduce the variance in our effect sizes arbitrarily close to zero with the right predictors? Unfortunately, our answer is negative. A simple form of regression adjustment, which uses just the pre-experiment values of the goal metric, captures most of the benefit. Specifically, under a mild assumption that observations closer in time are easier to predict that ones further away in time, we upper bound the potential gains of more sophisticated feature engineering, with respect to the gains of this simple form of regression adjustment. The maximum reduction in variance is $50\\%$ in Theorem 1, or equivalently, the confidence interval width can be reduced by at most an additional $29\\%$.", "published": "2023-11-29T18:04:39Z", "authors": ["Daniel Ting", "Kenneth Hung"], "categories": ["stat.ME", "econ.EM"], "doi": "", "_query": "hedonic regression housing", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/1707.08681v2", "title": "Gerrymandering and the net number of US House seats won due to vote-distribution asymmetries", "abstract": "Using the recently introduced declination function, we estimate the net number of seats won in the US House of Representatives due to asymmetries in vote distributions. Such asymmetries can arise from combinations of partisan gerrymandering and inherent geographic advantage. Our estimates show significant biases in favor of the Democrats prior to the mid 1990s and significant biases in favor of Republicans since then. We find net differences of 28, 20 and 25 seats in favor of the Republicans in the years 2012, 2014 and 2016, respectively. The validity of our results is supported by the technique of simulated packing and cracking. We also use this technique to show that the presidential-vote logistic regression model is insensitive to the packing and cracking by which partisan gerrymanders are achieved.", "published": "2017-07-27T01:46:28Z", "authors": ["Jeffrey S. Buzas", "Gregory S. Warrington"], "categories": ["stat.AP"], "doi": "", "_query": "hedonic regression housing", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2207.01942v1", "title": "An Exploratory Study on Regression Vulnerabilities", "abstract": "Background: Security regressions are vulnerabilities introduced in a previously unaffected software system. They often happen as a result of source code changes (e.g., a bug fix) and can have severe effects. Aims: To increase the understanding of security regressions. This is an important step in developing secure software engineering. Method: We perform an exploratory, mixed-method case study of Mozilla. First, we analyze 78 regression vulnerabilities and 72 bug reports where a bug fix introduced a regression vulnerability at Mozilla. We investigate how developers interact in these bug reports, how they perform the changes, and under what conditions they introduce regression vulnerabilities. Second, we conduct five semi-structured interviews with as many Mozilla developers involved in the vulnerability-inducing bug fixes. Results: Software security is not discussed during bug fixes. Developers' main concerns are the complexity of the bug at hand and the community pressure to fix it. Moreover, developers do not to worry about regression vulnerabilities and assume tools will detect them. Indeed, dynamic analysis tools helped finding around 30% of regression vulnerabilities at Mozilla. Conclusions: These results provide evidence that, although tool support helps identify regression vulnerabilities, it may not be enough to ensure security during bug fixes. Furthermore, our results call for further work on the security tooling support and how to integrate them during bug fixes. Data and materials: https://doi.org/10.5281/zenodo.6792317", "published": "2022-07-05T10:31:30Z", "authors": ["Larissa Braz", "Enrico Fregnan", "Vivek Arora", "Alberto Bacchelli"], "categories": ["cs.SE"], "doi": "", "_query": "hedonic regression housing", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2001.03458v1", "title": "Censored Quantile Regression Forest", "abstract": "Random forests are powerful non-parametric regression method but are severely limited in their usage in the presence of randomly censored observations, and naively applied can exhibit poor predictive performance due to the incurred biases. Based on a local adaptive representation of random forests, we develop its regression adjustment for randomly censored regression quantile models. Regression adjustment is based on a new estimating equation that adapts to censoring and leads to quantile score whenever the data do not exhibit censoring. The proposed procedure named {\\it censored quantile regression forest}, allows us to estimate quantiles of time-to-event without any parametric modeling assumption. We establish its consistency under mild model specifications. Numerical studies showcase a clear advantage of the proposed procedure.", "published": "2020-01-08T23:20:23Z", "authors": ["Alexander Hanbo Li", "Jelena Bradic"], "categories": ["stat.ML", "cs.LG"], "doi": "", "_query": "hedonic regression housing", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/1606.01666v1", "title": "Beyond unimodal regression: modelling multimodality with piecewise unimodal regression or deconvolution models", "abstract": "Shape constraints enable us to reflect prior knowledge in regression settings. A unimodality constraint, for example, can describe the frequent case of a first increasing and then decreasing intensity. Yet, data shapes often exhibit multiple modes. Therefore, we go beyond unimodal regression and propose modelling multimodality with piecewise unimodal regression or with deconvolution models based on unimodal peak shapes. Usefulness of unimodal regression and its multimodal extensions is demonstrated within three applications areas: marine biology, astroparticle physics and breath gas analysis. Despite this diversity, valuable results are obtained in each application. This encourages the use of these methods in other areas as well.", "published": "2016-06-06T09:18:05Z", "authors": ["Claudia Köllmann", "Katja Ickstadt", "Roland Fried"], "categories": ["stat.AP"], "doi": "", "_query": "hedonic regression housing", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/1607.02676v1", "title": "Bayesian quantile additive regression trees", "abstract": "Ensemble of regression trees have become popular statistical tools for the estimation of conditional mean given a set of predictors. However, quantile regression trees and their ensembles have not yet garnered much attention despite the increasing popularity of the linear quantile regression model. This work proposes a Bayesian quantile additive regression trees model that shows very good predictive performance illustrated using simulation studies and real data applications. Further extension to tackle binary classification problems is also considered.", "published": "2016-07-10T01:20:57Z", "authors": ["Bereket P. Kindo", "Hao Wang", "Timothy Hanson", "Edsel A. Peña"], "categories": ["stat.ML"], "doi": "", "_query": "hedonic regression housing", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2506.09358v1", "title": "Functional Tensor Regression", "abstract": "Tensor regression has attracted significant attention in statistical research. This study tackles the challenge of handling covariates with smooth varying structures. We introduce a novel framework, termed functional tensor regression, which incorporates both the tensor and functional aspects of the covariate. To address the high dimensionality and functional continuity of the regression coefficient, we employ a low Tucker rank decomposition along with smooth regularization for the functional mode. We develop a functional Riemannian Gauss--Newton algorithm that demonstrates a provable quadratic convergence rate, while the estimation error bound is based on the tensor covariate dimension. Simulations and a neuroimaging analysis illustrate the finite sample performance of the proposed method.", "published": "2025-06-11T03:16:25Z", "authors": ["Tongyu Li", "Fang Yao", "Anru R. Zhang"], "categories": ["stat.ME"], "doi": "", "_query": "hedonic regression housing", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2009.12386v1", "title": "Regressor: A C program for Combinatorial Regressions", "abstract": "In statistics, researchers use Regression models for data analysis and prediction in many productive sectors (industry, business, academy, etc.). Regression models are mathematical functions representing an approximation of dependent variable $Y$ from n independent variables $X_i \\in X$. The literature presents many regression methods divided into single and multiple regressions. There are several procedures to generate regression models and sets of commercial and academic tools that implement these procedures. This work presents one open-source program called Regressor that makes models from a specific variation of polynomial regression. These models relate the independent variables to generate an approximation of the original output dependent data. In many tests, Regressor was able to build models five times more accurate than commercial tools.", "published": "2020-09-25T18:10:14Z", "authors": ["Eduardo M. Vasconcelos", "Adriano Gouveia de Souza"], "categories": ["stat.AP", "cs.MS"], "doi": "", "_query": "hedonic regression housing", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2011.01493v2", "title": "Spatially Clustered Regression", "abstract": "Spatial regression or geographically weighted regression models have been widely adopted to capture the effects of auxiliary information on a response variable of interest over a region. In contrast, relationships between response and auxiliary variables are expected to exhibit complex spatial patterns in many applications. This paper proposes a new approach for spatial regression, called spatially clustered regression, to estimate possibly clustered spatial patterns of the relationships. We combine K-means-based clustering formulation and penalty function motivated from a spatial process known as Potts model for encouraging similar clustering in neighboring locations. We provide a simple iterative algorithm to fit the proposed method, scalable for large spatial datasets. Through simulation studies, the proposed method demonstrates its superior performance to existing methods even under the true structure does not admit spatial clustering. Finally, the proposed method is applied to crime event data in Tokyo and produces interpretable results for spatial patterns. The R code is available at https://github.com/sshonosuke/SCR.", "published": "2020-11-03T06:00:02Z", "authors": ["Shonosuke Sugasawa", "Daisuke Murakami"], "categories": ["stat.ME"], "doi": "", "_query": "hedonic regression housing", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2403.12759v1", "title": "Robust Numerical Methods for Nonlinear Regression", "abstract": "Many scientific and engineering applications require fitting regression models that are nonlinear in the parameters. Advances in computer hardware and software in recent decades have made it easier to fit such models. Relative to fitting regression models that are linear in the parameters, however, fitting nonlinear regression models is more complicated. In particular, software like the $\\texttt{nls}$ R function requires care in how the model is parameterized and how initial values are chosen for the maximum likelihood iterations. Often special diagnostics are needed to detect and suggest approaches for dealing with identifiability problems that can arise with such model fitting. When using Bayesian inference, there is the added complication of having to specify (often noninformative or weakly informative) prior distributions. Generally, the details for these tasks must be determined for each new nonlinear regression model. This paper provides a step-by-step procedure for specifying these details for any appropriate nonlinear regression model. Following the procedure will result in a numerically robust algorithm for fitting the nonlinear regression model. We illustrate the methods with three different nonlinear models that are used in the analysis of experimental fatigue data and we include two detailed numerical examples.", "published": "2024-03-19T14:27:13Z", "authors": ["Peng Liu", "William Q. Meeker"], "categories": ["stat.ME", "stat.CO"], "doi": "", "_query": "hedonic regression housing", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2208.06828v2", "title": "Multinomial Logistic Regression Algorithms via Quadratic Gradient", "abstract": "Multinomial logistic regression, also known by other names such as multiclass logistic regression and softmax regression, is a fundamental classification method that generalizes binary logistic regression to multiclass problems. A recently work proposed a faster gradient called $\\texttt{quadratic gradient}$ that can accelerate the binary logistic regression training, and presented an enhanced Nesterov's accelerated gradient (NAG) method for binary logistic regression. In this paper, we extend this work to multiclass logistic regression and propose an enhanced Adaptive Gradient Algorithm (Adagrad) that can accelerate the original Adagrad method. We test the enhanced NAG method and the enhanced Adagrad method on some multiclass-problem datasets. Experimental results show that both enhanced methods converge faster than their original ones respectively.", "published": "2022-08-14T11:00:27Z", "authors": ["John Chiang"], "categories": ["cs.LG", "math.OC"], "doi": "", "_query": "hedonic regression housing", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2104.04353v1", "title": "Implementing Fair Regression In The Real World", "abstract": "Most fair regression algorithms mitigate bias towards sensitive sub populations and therefore improve fairness at group level. In this paper, we investigate the impact of such implementation of fair regression on the individual. More precisely, we assess the evolution of continuous predictions from an unconstrained to a fair algorithm by comparing results from baseline algorithms with fair regression algorithms for the same data points. Based on our findings, we propose a set of post-processing algorithms to improve the utility of the existing fair regression approaches.", "published": "2021-04-09T13:31:16Z", "authors": ["Boris Ruf", "Marcin Detyniecki"], "categories": ["cs.LG", "cs.AI"], "doi": "", "_query": "hedonic regression housing", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/1103.1457v1", "title": "Regression on manifolds: Estimation of the exterior derivative", "abstract": "Collinearity and near-collinearity of predictors cause difficulties when doing regression. In these cases, variable selection becomes untenable because of mathematical issues concerning the existence and numerical stability of the regression coefficients, and interpretation of the coefficients is ambiguous because gradients are not defined. Using a differential geometric interpretation, in which the regression coefficients are interpreted as estimates of the exterior derivative of a function, we develop a new method to do regression in the presence of collinearities. Our regularization scheme can improve estimation error, and it can be easily modified to include lasso-type regularization. These estimators also have simple extensions to the \"large $p$, small $n$\" context.", "published": "2011-03-08T08:10:56Z", "authors": ["Anil Aswani", "Peter Bickel", "Claire Tomlin"], "categories": ["math.ST"], "doi": "10.1214/10-AOS823", "_query": "hedonic regression housing", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2108.07584v1", "title": "Testing Multiple Linear Regression Systems with Metamorphic Testing", "abstract": "Regression is one of the most commonly used statistical techniques. However, testing regression systems is a great challenge because of the absence of test oracle in general. In this paper, we show that Metamorphic Testing is an effective approach to test multiple linear regression systems. In doing so, we identify intrinsic mathematical properties of linear regression, and then propose 11 Metamorphic Relations to be used for testing. Their effectiveness is examined using mutation analysis with a range of different regression programs. We further look at how the testing could be adopted in a more effective way. Our work is applicable to examine the reliability of predictive systems based on regression that has been widely used in economics, engineering and science, as well as of the regression calculation manipulated by statistical users.", "published": "2021-08-17T12:26:22Z", "authors": ["Quang-Hung Luu", "Man F. Lau", "Sebastian P. H. Ng", "Tsong Yueh Chen"], "categories": ["stat.ME", "cs.SE"], "doi": "10.1016/j.jss.2021.111062", "_query": "hedonic regression housing", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/1312.5054v1", "title": "Bayesian Geoadditive Expectile Regression", "abstract": "Regression classes modeling more than the mean of the response have found a lot of attention in the last years. Expectile regression is a special and computationally convenient case of this family of models. Expectiles offer a quantile-like characterisation of a complete distribution and include the mean as a special case. In the frequentist framework the impact of a lot of covariates with very different structures have been made possible. We propose Bayesian expectile regression based on the asymmetric normal distribution. This renders possible incorporating for example linear, nonlinear, spatial and random effects in one model. Furthermore a detailed inference on the estimated parameters can be conducted. Proposal densities based on iterativly weighted least squares updates for the resulting Markov chain Monte Carlo (MCMC) simulation algorithm are proposed and the potential of the approach for extending the flexibility of expectile regression towards complex semiparametric regression specifications is discussed.", "published": "2013-12-18T07:35:55Z", "authors": ["Elisabeth Waldmann", "Fabian Sobotka", "Thomas Kneib"], "categories": ["stat.ME"], "doi": "", "_query": "hedonic regression housing", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2207.09573v1", "title": "Consistency of the Bayes Estimator of a Regression Curve", "abstract": "Strong consistency of the Bayes estimator of a regression curve for the $L^1$-squared loss function is proved. It is also shown the convergence to 0 of the Bayes risk of this estimator both for the $L^1$ and $L^1$-squared loss functions. The Bayes estimator of a regression curve is the regression curve with respect to the posterior predictive distribution.", "published": "2022-07-19T22:08:31Z", "authors": ["Agustin G. Nogales"], "categories": ["math.ST"], "doi": "", "_query": "hedonic regression housing", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2008.07356v1", "title": "Estimating action plans for smart poultry houses", "abstract": "In poultry farming, the systematic choice, update, and implementation of periodic (t) action plans define the feed conversion rate (FCR[t]), which is an acceptable measure for successful production. Appropriate action plans provide tailored resources for broilers, allowing them to grow within the so-called thermal comfort zone, without wast or lack of resources. Although the implementation of an action plan is automatic, its configuration depends on the knowledge of the specialist, tending to be inefficient and error-prone, besides to result in different FCR[t] for each poultry house. In this article, we claim that the specialist's perception can be reproduced, to some extent, by computational intelligence. By combining deep learning and genetic algorithm techniques, we show how action plans can adapt their performance over the time, based on previous well succeeded plans. We also implement a distributed network infrastructure that allows to replicate our method over distributed poultry houses, for their smart, interconnected, and adaptive control. A supervision system is provided as interface to users. Experiments conducted over real data show that our method improves 5% on the performance of the most productive specialist, staying very close to the optimal FCR[t].", "published": "2020-08-17T14:28:48Z", "authors": ["Darlan Felipe Klotz", "Richardson Ribeiro", "Fabrício Enembreck", "Gustavo Denardin", "Marco Barbosa", "Dalcimar Casanova", "Marcelo Teixeira"], "categories": ["cs.AI", "eess.SY"], "doi": "", "_query": "hedonic regression housing", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/1712.05731v3", "title": "A Theoretical Framework for Bayesian Nonparametric Regression", "abstract": "We develop a unifying framework for Bayesian nonparametric regression to study the rates of contraction with respect to the integrated $L_2$-distance without assuming the regression function space to be uniformly bounded. The framework is very flexible and can be applied to a wide class of nonparametric prior models. Three non-trivial applications of the proposed framework are provided: The finite random series regression of an $α$-Hölder function, with adaptive rates of contraction up to a logarithmic factor; The un-modified block prior regression of an $α$-Sobolev function, with adaptive-and-exact rates of contraction; The Gaussian spline regression of an $α$-Hölder function, with the near-optimal posterior contraction. These applications serve as generalization or complement of their respective results in the literature. Extensions to the fixed-design regression problem and sparse additive models in high dimensions are discussed as well.", "published": "2017-12-15T16:18:43Z", "authors": ["Fangzheng Xie", "Wei Jin", "Yanxun Xu"], "categories": ["math.ST"], "doi": "", "_query": "hedonic regression housing", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2109.05874v1", "title": "Function-on-function partial quantile regression", "abstract": "In this paper, a functional partial quantile regression approach, a quantile regression analog of the functional partial least squares regression, is proposed to estimate the function-on-function linear quantile regression model. A partial quantile covariance function is first used to extract the functional partial quantile regression basis functions. The extracted basis functions are then used to obtain the functional partial quantile regression components and estimate the final model. In our proposal, the functional forms of the discretely observed random variables are first constructed via a finite-dimensional basis function expansion method. The functional partial quantile regression constructed using the functional random variables is approximated via the partial quantile regression constructed using the basis expansion coefficients. The proposed method uses an iterative procedure to extract the partial quantile regression components. A Bayesian information criterion is used to determine the optimum number of retained components. The proposed functional partial quantile regression model allows for more than one functional predictor in the model. However, the true form of the proposed model is unspecified, as the relevant predictors for the model are unknown in practice. Thus, a forward variable selection procedure is used to determine the significant predictors for the proposed model. Moreover, a case-sampling-based bootstrap procedure is used to construct pointwise prediction intervals for the functional response. The predictive performance of the proposed method is evaluated using several Monte Carlo experiments under different data generation processes and error distributions. Through an empirical data example, air quality data are analyzed to demonstrate the effectiveness of the proposed method.", "published": "2021-09-13T11:20:45Z", "authors": ["Ufuk Beyaztas", "Han Lin Shang", "Aylin Alin"], "categories": ["stat.ME", "stat.AP", "stat.CO"], "doi": "", "_query": "hedonic regression housing", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2002.02594v1", "title": "Distribution free testing for linear regression. Extension to general parametric regression", "abstract": "Recently a distribution free approach for testing parametric hypotheses based on unitary transformations has been suggested in \\cite{Khm13, Khm16, Khm17} and further studied in \\cite{Ngu17} and \\cite{Rob19}. In this note we show that the transformation takes extremely simple form in distribution free testing of linear regression. Then we extend it to general parametric regression with vector-valued covariates.", "published": "2020-02-07T02:39:37Z", "authors": ["Estate V. Khmaladze"], "categories": ["stat.ME", "math.ST"], "doi": "", "_query": "hedonic regression housing", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/1512.01382v1", "title": "Averaged extreme regression quantile", "abstract": "Various events in the nature, economics and in other areas force us to combine the study of extremes with regression and other methods. A useful tool for reducing the role of nuisance regression, while we are interested in the shape or tails of the basic distribution, is provided by the averaged regression quantile and namely by the average extreme regression quantile. Both are weighted means of regression quantile components, with weights depending on the regressors. Our primary interest is the averaged extreme regression quantile (AERQ), its structure, qualities and its applications, e.g. in investigation of a conditional loss given a value exogenous economic and market variables. AERQ has several interesting equivalent forms: While it is originally defined as an optimal solution of a specific linear programming problem, hence is a weighted mean of responses corresponding to the optimal base of the pertaining linear program, we give another equivalent form as a maximum residual of responses from a specific R-estimator of the slope components of regression parameter. The latter form shows that while AERQ equals to the maximum of some residuals of the responses, it has minimal possible perturbation by the regressors. Notice that these finite-sample results are true even for non-identically distributed model errors, e.g. under heteroscedasticity. Moreover, the representations are formally true even when the errors are dependent - this all provokes a question of the right interpretation and of other possible applications.", "published": "2015-12-04T12:10:48Z", "authors": ["Jana Jureckova"], "categories": ["math.ST"], "doi": "10.1007/s10687-015-0232-2", "_query": "hedonic regression housing", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/1701.05666v2", "title": "A New Family of Error Distributions for Bayesian Quantile Regression", "abstract": "We propose a new family of error distributions for model-based quantile regression, which is constructed through a structured mixture of normal distributions. The construction enables fixing specific percentiles of the distribution while, at the same time, allowing for varying mode, skewness and tail behavior. It thus overcomes the severe limitation of the asymmetric Laplace distribution -- the most commonly used error model for parametric quantile regression -- for which the skewness of the error density is fully specified when a particular percentile is fixed. We develop a Bayesian formulation for the proposed quantile regression model, including conditional lasso regularized quantile regression based on a hierarchical Laplace prior for the regression coefficients, and a Tobit quantile regression model. Posterior inference is implemented via Markov Chain Monte Carlo methods. The flexibility of the new model relative to the asymmetric Laplace distribution is studied through relevant model properties, and through a simulation experiment to compare the two error distributions in regularized quantile regression. Moreover, model performance in linear quantile regression, regularized quantile regression, and Tobit quantile regression is illustrated with data examples that have been previously considered in the literature.", "published": "2017-01-20T03:14:19Z", "authors": ["Yifei Yan", "Athanasios Kottas"], "categories": ["stat.ME"], "doi": "", "_query": "hedonic regression housing", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2607.00188v1", "title": "Quantile regression with measurement errors", "abstract": "We devise a novel estimator for a general quantile regression model with normal measurement errors in the covariates. The method is applicable to both linear and nonlinear quantile regressions and does not impose the quantile requirement on multiple quantile levels simultaneously. We circumvent the difficulties caused by discontinuity in quantile regression through kernel smoothing, and overcome the nonlinearity inherent in quantile regression via considering extension to the complex domain and moment generating functions. We show that the resulting estimator achieves the standard root-$n$ consistency and asymptotic normality under mild conditions. The performance of the proposed method is illustrated via numerical simulations and a real data example related to Cherry Blossom times in Japan in 2024. This is the first consistent estimator in a general quantile regression problem with normal measurement errors.", "published": "2026-06-30T21:09:22Z", "authors": ["Mushan Li", "Yanyuan Ma", "Liqun Wang"], "categories": ["stat.ME"], "doi": "", "_query": "hedonic regression housing", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/0709.3545v1", "title": "Locally Adaptive Nonparametric Binary Regression", "abstract": "A nonparametric and locally adaptive Bayesian estimator is proposed for estimating a binary regression. Flexibility is obtained by modeling the binary regression as a mixture of probit regressions with the argument of each probit regression having a thin plate spline prior with its own smoothing parameter and with the mixture weights depending on the covariates. The estimator is compared to a single spline estimator and to a recently proposed locally adaptive estimator. The methodology is illustrated by applying it to both simulated and real examples.", "published": "2007-09-21T22:48:10Z", "authors": ["Sally Wood", "Robert Kohn", "Remy Cottet", "Wenxin Jiang", "Martin Tanner"], "categories": ["stat.ME"], "doi": "", "_query": "hedonic regression housing", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2507.01314v1", "title": "Semi-supervised learning for linear extremile regression", "abstract": "Extremile regression, as a least squares analog of quantile regression, is potentially useful tool for modeling and understanding the extreme tails of a distribution. However, existing extremile regression methods, as nonparametric approaches, may face challenges in high-dimensional settings due to data sparsity, computational inefficiency, and the risk of overfitting. While linear regression serves as the foundation for many other statistical and machine learning models due to its simplicity, interpretability, and relatively easy implementation, particularly in high-dimensional settings, this paper introduces a novel definition of linear extremile regression along with an accompanying estimation methodology. The regression coefficient estimators of this method achieve $\\sqrt{n}$-consistency, which nonparametric extremile regression may not provide. In particular, while semi-supervised learning can leverage unlabeled data to make more accurate predictions and avoid overfitting to small labeled datasets in high-dimensional spaces, we propose a semi-supervised learning approach to enhance estimation efficiency, even when the specified linear extremile regression model may be misspecified. Both simulation studies and real data analyses demonstrate the finite-sample performance of our proposed methods.", "published": "2025-07-02T02:59:15Z", "authors": ["Rong Jiang", "Keming Yu", "Jiangfeng Wang"], "categories": ["stat.ME", "stat.ML"], "doi": "", "_query": "hedonic regression housing", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2506.21110v1", "title": "Orthogonality conditions for convex regression", "abstract": "Econometric identification generally relies on orthogonality conditions, which usually state that the random error term is uncorrelated with the explanatory variables. In convex regression, the orthogonality conditions for identification are unknown. Applying Lagrangian duality theory, we establish the sample orthogonality conditions for convex regression, including additive and multiplicative formulations of the regression model, with and without monotonicity and homogeneity constraints. We then propose a hybrid instrumental variable control function approach to mitigate the impact of potential endogeneity in convex regression. The superiority of the proposed approach is shown in a Monte Carlo study and examined in an empirical application to Chilean manufacturing data.", "published": "2025-06-26T09:13:20Z", "authors": ["Sheng Dai", "Timo Kuosmanen", "Xun Zhou"], "categories": ["stat.ME", "econ.EM"], "doi": "", "_query": "hedonic regression housing", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2505.12967v1", "title": "Augmented Regression Models using Neurochaos Learning", "abstract": "This study presents novel Augmented Regression Models using Neurochaos Learning (NL), where Tracemean features derived from the Neurochaos Learning framework are integrated with traditional regression algorithms : Linear Regression, Ridge Regression, Lasso Regression, and Support Vector Regression (SVR). Our approach was evaluated using ten diverse real-life datasets and a synthetically generated dataset of the form $y = mx + c + ε$. Results show that incorporating the Tracemean feature (mean of the chaotic neural traces of the neurons in the NL architecture) significantly enhances regression performance, particularly in Augmented Lasso Regression and Augmented SVR, where six out of ten real-life datasets exhibited improved predictive accuracy. Among the models, Augmented Chaotic Ridge Regression achieved the highest average performance boost (11.35 %). Additionally, experiments on the simulated dataset demonstrated that the Mean Squared Error (MSE) of the augmented models consistently decreased and converged towards the Minimum Mean Squared Error (MMSE) as the sample size increased. This work demonstrates the potential of chaos-inspired features in regression tasks, offering a pathway to more accurate and computationally efficient prediction models.", "published": "2025-05-19T11:02:14Z", "authors": ["Akhila Henry", "Nithin Nagaraj"], "categories": ["cs.LG", "math.DS"], "doi": "", "_query": "hedonic regression housing", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2104.05874v1", "title": "Gradient Kernel Regression", "abstract": "In this article a surprising result is demonstrated using the neural tangent kernel. This kernel is defined as the inner product of the vector of the gradient of an underlying model evaluated at training points. This kernel is used to perform kernel regression. The surprising thing is that the accuracy of that regression is independent of the accuracy of the underlying network.", "published": "2021-04-13T00:32:34Z", "authors": ["Matt Calder"], "categories": ["cs.AI"], "doi": "", "_query": "hedonic regression housing", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2607.21414v1", "title": "Logical Regression for Planning with Axioms", "abstract": "In automated planning, logical regression is an operation that returns the most general condition necessary for an action to achieve a particular formula. It has many applications, such as allowing for more robust plan execution and providing compact policies for non-deterministic planning. Although relatively simple to calculate in basic planning settings, logical regression becomes significantly more complex when additional factors, such as axioms, are present. We introduce a methodology for approximating the logical regression of an action in a domain that includes axioms; an approximation that limits conditions to partial states. Our method produces minimal partial states while avoiding the recalculation of axioms. To demonstrate the impact of our methods, we embed our form of regression in an execution monitoring context, a well-established setting that can benefit greatly from logical regression. Our results show that this form of regression can dramatically generalize partial states across multiple domains, reducing the number of variables considered for execution monitoring by up to 70%, and demonstrate that the resulting execution monitor is robust enough to recover frequently in an environment with unexpected changes: several domains recover over 50% of the time in our tests.", "published": "2026-07-23T15:16:48Z", "authors": ["Connor Little", "Christian Muise"], "categories": ["cs.AI"], "doi": "", "_query": "hedonic regression housing", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2101.05026v2", "title": "Concurrent Object Regression", "abstract": "Modern-day problems in statistics often face the challenge of exploring and analyzing complex non-Euclidean object data that do not conform to vector space structures or operations. Examples of such data objects include covariance matrices, graph Laplacians of networks, and univariate probability distribution functions. In the current contribution a new concurrent regression model is proposed to characterize the time-varying relation between an object in a general metric space (as a response) and a vector in $\\reals^p$ (as a predictor), where concepts from Fréchet regression is employed. Concurrent regression has been a well-developed area of research for Euclidean predictors and responses, with many important applications for longitudinal studies and functional data. However, there is no such model available so far for general object data as responses. We develop generalized versions of both global least squares regression and locally weighted least squares smoothing in the context of concurrent regression for responses that are situated in general metric spaces and propose estimators that can accommodate sparse and/or irregular designs. Consistency results are demonstrated for sample estimates of appropriate population targets along with the corresponding rates of convergence. The proposed models are illustrated with human mortality data and resting state functional Magnetic Resonance Imaging data (fMRI) as responses.", "published": "2021-01-13T12:03:38Z", "authors": ["Satarupa Bhattacharjee", "Hans-Georg Mueller"], "categories": ["stat.ME"], "doi": "", "_query": "hedonic regression housing", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/1705.01677v3", "title": "Optimized Regression Discontinuity Designs", "abstract": "The increasing popularity of regression discontinuity methods for causal inference in observational studies has led to a proliferation of different estimating strategies, most of which involve first fitting non-parametric regression models on both sides of a treatment assignment boundary and then reporting plug-in estimates for the effect of interest. In applications, however, it is often difficult to tune the non-parametric regressions in a way that is well calibrated for the specific target of inference; for example, the model with the best global in-sample fit may provide poor estimates of the discontinuity parameter. In this paper, we propose an alternative method for estimation and statistical inference in regression discontinuity designs that uses numerical convex optimization to directly obtain the finite-sample-minimax linear estimator for the regression discontinuity parameter, subject to bounds on the second derivative of the conditional response function. Given a bound on the second derivative, our proposed method is fully data-driven, and provides uniform confidence intervals for the regression discontinuity parameter with both discrete and continuous running variables. The method also naturally extends to the case of multiple running variables.", "published": "2017-05-04T02:26:40Z", "authors": ["Guido Imbens", "Stefan Wager"], "categories": ["stat.ME"], "doi": "", "_query": "hedonic regression housing", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2109.09048v1", "title": "A framework for benchmarking uncertainty in deep regression", "abstract": "We propose a framework for the assessment of uncertainty quantification in deep regression. The framework is based on regression problems where the regression function is a linear combination of nonlinear functions. Basically, any level of complexity can be realized through the choice of the nonlinear functions and the dimensionality of their domain. Results of an uncertainty quantification for deep regression are compared against those obtained by a statistical reference method. The reference method utilizes knowledge of the underlying nonlinear functions and is based on a Bayesian linear regression using a reference prior. Reliability of uncertainty quantification is assessed in terms of coverage probabilities, and accuracy through the size of calculated uncertainties. We illustrate the proposed framework by applying it to current approaches for uncertainty quantification in deep regression. The flexibility, together with the availability of a reference solution, makes the framework suitable for defining benchmark sets for uncertainty quantification.", "published": "2021-09-10T13:22:28Z", "authors": ["Franko Schmähling", "Jörg Martin", "Clemens Elster"], "categories": ["cs.LG", "cs.AI"], "doi": "", "_query": "hedonic regression housing", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2605.01066v1", "title": "A dimensional R2 regression metric", "abstract": "R2 score is the standard metric for evaluating regression tasks, offering a normalized magnitude-agnostic measure of accuracy that captures variance. However, R2 has three key limitations: it is limited to at most two dimensional inputs, it reduces the score to a single scalar that hides rich patterns of prediction accuracy, and it is sensitive to low-variance noise channels which can yield large, uninterpretable negative values. We introduce the Dimensional R2 score (Dim-R2), a simple extension of R2 that accepts data of arbitrary dimensionality, provides a multidimensional view of accuracy, and reduces sensitivity to noise. We demonstrate its advantages on both synthetic sinusoidal data and three multidimensional regression datasets. Dim-R2 offers an interpretable and flexible metric that highlights patterns in regression accuracy, guiding regression modeling.", "published": "2026-05-01T20:00:36Z", "authors": ["Jaesung Yoo", "Stefan Lemke", "Jian Zhong Guo", "Kanaka Rajan", "Adam Hantman"], "categories": ["cs.LG"], "doi": "", "_query": "hedonic regression housing", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2407.09955v2", "title": "LFFR: Logistic Function For (single-output) Regression", "abstract": "Privacy-preserving regression in machine learning is a crucial area of research, aimed at enabling the use of powerful machine learning techniques while protecting individuals' privacy. In this paper, we implement privacy-preserving regression training using data encrypted under a fully homomorphic encryption scheme. We first examine the common linear regression algorithm and propose a (simplified) fixed Hessian for linear regression training, which can be applied for any datasets even not normalized into the range $[0, 1]$. We also generalize this constant Hessian matrix to the ridge regression version, namely linear regression which includes a regularization term to penalize large coefficients. However, our main contribution is to develop a novel and efficient algorithm called LFFR for homomorphic regression using the logistic function, which could model more complex relations between input values and output prediction in comparison with linear regression. We also find a constant simplified Hessian to train our LFFR algorithm using the Newton-like method and compare it against to with our new fixed Hessian linear regression training over two real-world datasets. We suggest normalizing not only the data but also the target predictions even for the original linear regression used in a privacy-preserving manner, which is helpful to remain weights in a small range, say $[-5, +5]$ good for refreshing ciphertext setting parameters, and avoid tuning the regularization parameter $λ$ via cross validation. The linear regression with normalized predictions could be a viable alternative to ridge regression.", "published": "2024-07-13T17:33:49Z", "authors": ["John Chiang"], "categories": ["cs.LG", "cs.CR"], "doi": "", "_query": "hedonic regression housing", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2404.13904v4", "title": "Deep Regression Representation Learning with Topology", "abstract": "Most works studying representation learning focus only on classification and neglect regression. Yet, the learning objectives and, therefore, the representation topologies of the two tasks are fundamentally different: classification targets class separation, leading to disconnected representations, whereas regression requires ordinality with respect to the target, leading to continuous representations. We thus wonder how the effectiveness of a regression representation is influenced by its topology, with evaluation based on the Information Bottleneck (IB) principle. The IB principle is an important framework that provides principles for learning effective representations. We establish two connections between it and the topology of regression representations. The first connection reveals that a lower intrinsic dimension of the feature space implies a reduced complexity of the representation Z. This complexity can be quantified as the conditional entropy of Z on the target Y, and serves as an upper bound on the generalization error. The second connection suggests a feature space that is topologically similar to the target space will better align with the IB principle. Based on these two connections, we introduce PH-Reg, a regularizer specific to regression that matches the intrinsic dimension and topology of the feature space with the target space. Experiments on synthetic and real-world regression tasks demonstrate the benefits of PH-Reg. Code: https://github.com/needylove/PH-Reg.", "published": "2024-04-22T06:28:41Z", "authors": ["Shihao Zhang", "kenji kawaguchi", "Angela Yao"], "categories": ["cs.LG", "cs.CV"], "doi": "", "_query": "hedonic regression housing", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/1910.07825v3", "title": "Nonparametric tests for circular regression", "abstract": "No matter the nature of the response and/or explanatory variables in a regression model, some basic issues such as the existence of an effect of the predictor on the response, or the assessment of a common shape across groups of observations, must be solved prior to model fitting. This is also the case for regression models involving circular variables (supported on the unit circumference). In that context, using kernel regression methods, this paper provides a flexible alternative for constructing pilot estimators that allow to construct suitable statistics to perform no-effect tests and tests for equality and parallelism of regression curves. Finite sample performance of the proposed methods is analyzed in a simulation study and illustrated with real data examples.", "published": "2019-10-17T11:23:50Z", "authors": ["María Alonso-Pena", "Jose Ameijeiras-Alonso", "Rosa M. Crujeiras"], "categories": ["stat.AP"], "doi": "10.1080/00949655.2020.1818243", "_query": "hedonic regression housing", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2306.10601v3", "title": "Sliced Wasserstein Regression", "abstract": "While statistical modeling of distributional data has gained increased attention, the case of multivariate distributions has been somewhat neglected despite its relevance in various applications. This is because the Wasserstein distance, commonly used in distributional data analysis, poses challenges for multivariate distributions. A promising alternative is the sliced Wasserstein distance, which offers a computationally simpler solution. We propose distributional regression models with multivariate distributions as responses paired with Euclidean vector predictors. The foundation of our methodology is a slicing transform from the multivariate distribution space to the sliced distribution space for which we establish a theoretical framework, with the Radon transform as a prominent example. We introduce and study the asymptotic properties of sample-based estimators for two regression approaches, one based on utilizing the sliced Wasserstein distance directly in the multivariate distribution space, and a second approach based on a new slice-wise distance, employing a univariate distribution regression for each slice. Both global and local Fréchet regression methods are deployed for these approaches and illustrated in simulations and through applications. These include joint distributions of excess winter death rates and winter temperature anomalies in European countries as a function of base winter temperature and also data from finance.", "published": "2023-06-18T17:19:56Z", "authors": ["Han Chen", "Yidong Zhou", "Hans-Georg Müller"], "categories": ["stat.ME"], "doi": "", "_query": "hedonic regression housing", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2604.27594v3", "title": "Structural description of (bull, house)-free graphs", "abstract": "The bull is a graph consisting of a triangle and two pendant edges. The P_5 is the chordless path on five vertices. The house is the complement of a P_5. A graph is k-critical if it is k-chromatic but each of its proper induced subgraphs is (k-1)-colorable. It is known that the number of k-critical P_5-free graphs and bull-free graphs are infinite for large enough k. We give a structural description of (bull, house)-free graphs and also (bull, P_5)-free graphs. Using these structural properties we prove that for any fixed k, the number of k-critical (bull, P_5)-free graphs is finite. This improves on a result of Huang, Li and Xia (Critical (P_5, bull)-free graphs, Discrete Applied Mathematics 334 (2023) 15-25). A graph G is perfectly divisible if for each induced subgraph H of G with at least one edge, V(H) can be partitioned into two sets V_1, V_2 such that every largest clique of H contains a vertex in V_i for i = 1,2. Chudnovsky and Sivaraman proved that (P_5, bull)-free graphs are perfectly divisible (Perfect divisibility and 2-divisibility, Journal of Graph Theory 90 (2019) 54-60). Our structural result allows us to give a short proof of this theorem.", "published": "2026-04-30T08:43:57Z", "authors": ["Manoj Belavadi", "Chinh T. Hoang"], "categories": ["math.CO"], "doi": "", "_query": "hedonic regression housing", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/1905.03222v1", "title": "Conformalized Quantile Regression", "abstract": "Conformal prediction is a technique for constructing prediction intervals that attain valid coverage in finite samples, without making distributional assumptions. Despite this appeal, existing conformal methods can be unnecessarily conservative because they form intervals of constant or weakly varying length across the input space. In this paper we propose a new method that is fully adaptive to heteroscedasticity. It combines conformal prediction with classical quantile regression, inheriting the advantages of both. We establish a theoretical guarantee of valid coverage, supplemented by extensive experiments on popular regression datasets. We compare the efficiency of conformalized quantile regression to other conformal methods, showing that our method tends to produce shorter intervals.", "published": "2019-05-08T17:21:11Z", "authors": ["Yaniv Romano", "Evan Patterson", "Emmanuel J. Candès"], "categories": ["stat.ME", "stat.ML"], "doi": "", "_query": "hedonic regression housing", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/0910.4627v1", "title": "Self-concordant analysis for logistic regression", "abstract": "Most of the non-asymptotic theoretical work in regression is carried out for the square loss, where estimators can be obtained through closed-form expressions. In this paper, we use and extend tools from the convex optimization literature, namely self-concordant functions, to provide simple extensions of theoretical results for the square loss to the logistic loss. We apply the extension techniques to logistic regression with regularization by the $\\ell_2$-norm and regularization by the $\\ell_1$-norm, showing that new results for binary classification through logistic regression can be easily derived from corresponding results for least-squares regression.", "published": "2009-10-24T07:10:24Z", "authors": ["Francis Bach"], "categories": ["cs.LG", "math.ST"], "doi": "", "_query": "hedonic regression housing", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2303.02273v1", "title": "Learning Label Encodings for Deep Regression", "abstract": "Deep regression networks are widely used to tackle the problem of predicting a continuous value for a given input. Task-specialized approaches for training regression networks have shown significant improvement over generic approaches, such as direct regression. More recently, a generic approach based on regression by binary classification using binary-encoded labels has shown significant improvement over direct regression. The space of label encodings for regression is large. Lacking heretofore have been automated approaches to find a good label encoding for a given application. This paper introduces Regularized Label Encoding Learning (RLEL) for end-to-end training of an entire network and its label encoding. RLEL provides a generic approach for tackling regression. Underlying RLEL is our observation that the search space of label encodings can be constrained and efficiently explored by using a continuous search space of real-valued label encodings combined with a regularization function designed to encourage encodings with certain properties. These properties balance the probability of classification error in individual bits against error correction capability. Label encodings found by RLEL result in lower or comparable errors to manually designed label encodings. Applying RLEL results in 10.9% and 12.4% improvement in Mean Absolute Error (MAE) over direct regression and multiclass classification, respectively. Our evaluation demonstrates that RLEL can be combined with off-the-shelf feature extractors and is suitable across different architectures, datasets, and tasks. Code is available at https://github.com/ubc-aamodt-group/RLEL_regression.", "published": "2023-03-04T00:11:34Z", "authors": ["Deval Shah", "Tor M. Aamodt"], "categories": ["cs.LG", "cs.CV"], "doi": "", "_query": "hedonic regression housing", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/1911.01850v3", "title": "Stabilizing Variable Selection and Regression", "abstract": "We consider regression in which one predicts a response $Y$ with a set of predictors $X$ across different experiments or environments. This is a common setup in many data-driven scientific fields and we argue that statistical inference can benefit from an analysis that takes into account the distributional changes across environments. In particular, it is useful to distinguish between stable and unstable predictors, i.e., predictors which have a fixed or a changing functional dependence on the response, respectively. We introduce stabilized regression which explicitly enforces stability and thus improves generalization performance to previously unseen environments. Our work is motivated by an application in systems biology. Using multiomic data, we demonstrate how hypothesis generation about gene function can benefit from stabilized regression. We believe that a similar line of arguments for exploiting heterogeneity in data can be powerful for many other applications as well. We draw a theoretical connection between multi-environment regression and causal models, which allows to graphically characterize stable versus unstable functional dependence on the response. Formally, we introduce the notion of a stable blanket which is a subset of the predictors that lies between the direct causal predictors and the Markov blanket. We prove that this set is optimal in the sense that a regression based on these predictors minimizes the mean squared prediction error given that the resulting regression generalizes to unseen new environments.", "published": "2019-11-05T15:04:33Z", "authors": ["Niklas Pfister", "Evan G. Williams", "Jonas Peters", "Ruedi Aebersold", "Peter Bühlmann"], "categories": ["stat.ME", "stat.AP"], "doi": "", "_query": "hedonic regression housing", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2408.10118v4", "title": "Local Fréchet regression with circular predictors", "abstract": "Fréchet regression extends the principles of linear regression to accommodate responses valued in generic metric spaces. While this approach has primarily focused on exploring relationships between Euclidean predictors and non-Euclidean responses, our work introduces a novel statistical method for handling random objects with circular predictors. We concentrate on local constant and local linear Fréchet regression, providing rigorous proofs for the upper bounds of both bias and stochastic deviation of the estimators under mild conditions. This research lays the groundwork for broadening the application of Fréchet regression to scenarios involving non-Euclidean covariates, thereby expanding its utility in complex data analysis.", "published": "2024-08-19T16:06:28Z", "authors": ["Chang Jun Im", "Jeong Min Jeon"], "categories": ["math.ST"], "doi": "", "_query": "hedonic regression housing", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/1901.09600v1", "title": "Markov-Modulated Linear Regression", "abstract": "Classical linear regression is considered for a case when regression parameters depend on the external random environment. The last is described as a continuous time Markov chain with finite state space. Here the expected sojourn times in various states are additional regressors. Necessary formulas for an estimation of regression parameters have been derived. The numerical example illustrates the results obtained.", "published": "2019-01-28T11:06:54Z", "authors": ["Alexander M. Andronov", "Nadezda Spiridovska"], "categories": ["stat.ME"], "doi": "", "_query": "hedonic regression housing", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/1102.5721v1", "title": "A covariance regression model", "abstract": "Classical regression analysis relates the expectation of a response variable to a linear combination of explanatory variables. In this article, we propose a covariance regression model that parameterizes the covariance matrix of a multivariate response vector as a parsimonious quadratic function of explanatory variables. The approach is analogous to the mean regression model, and is similar to a factor analysis model in which the factor loadings depend on the explanatory variables. Using a random-effects representation, parameter estimation for the model is straightforward using either an EM-algorithm or an MCMC approximation via Gibbs sampling. The proposed methodology provides a simple but flexible representation of heteroscedasticity across the levels of an explanatory variable, improves estimation of the mean function and gives better calibrated prediction regions when compared to a homoscedastic model.", "published": "2011-02-28T18:03:52Z", "authors": ["Peter D. Hoff", "Xiaoyue Niu"], "categories": ["stat.ME"], "doi": "", "_query": "hedonic regression housing", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/1307.0170v1", "title": "Mixture regression for observational data, with application to functional regression models", "abstract": "In a regression analysis, suppose we suspect that there are several heterogeneous groups in the population that a sample represents. Mixture regression models have been applied to address such problems. By modeling the conditional distribution of the response given the covariate as a mixture, the sample can be clustered into groups and the individual regression models for the groups can be estimated simultaneously. This approach treats the covariate as deterministic so that the covariate carries no information as to which group the subject is likely to belong to. Although this assumption may be reasonable in experiments where the covariate is completely determined by the experimenter, in observational data the covariate may behave differently across the groups. Thus the model should also incorporate the heterogeneity of the covariate, which allows us to estimate the membership of the subject from the covariate. In this paper, we consider a mixture regression model where the joint distribution of the response and the covariate is modeled as a mixture. Given a new observation of the covariate, this approach allows us to compute the posterior probabilities that the subject belongs to each group. Using these posterior probabilities, the prediction of the response can adaptively use the covariate. We introduce an inference procedure for this approach and show its properties concerning estimation and prediction. The model is explored for the functional covariate as well as the multivariate covariate. We present a real-data example where our approach outperforms the traditional approach, using the well-analyzed Berkeley growth study data.", "published": "2013-06-30T02:13:17Z", "authors": ["Toshiya Hoshikawa"], "categories": ["stat.ME"], "doi": "", "_query": "hedonic regression housing", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2509.13167v1", "title": "Scale-Location-Truncated Beta Regression: Expanding Beta Regression to Accommodate 0 and 1", "abstract": "Beta regression is frequently used when the outcome variable y is bounded within a specific interval, transformed to the (0, 1) domain if necessary. However, standard beta regression cannot handle data observed at the boundary values of 0 or 1, as the likelihood function takes on values of either 0 or infinity. To address this issue, we propose the Scale-Location-Truncated beta (SLTB) regression model, which extends the beta distribution's domain to the [0, 1] interval. By using scale-location transformation and truncation, SLTB distribution allows positive finite mass to the boundary values, offering a flexible approach for handling values at 0 and 1. In this paper, we demonstrate the effectiveness of the SLTB regression model in comparison to standard beta regression models and other approaches like the Zero-One Inflated Beta (ZOIB) mixture model and XBX regression. Using empirical and simulated data, we compare the performance including predictive accuracy of the SLTB regression model with other methods, particularly in cases with observed boundary data values for y. The SLTB model is shown to offer great flexibility, supporting both linear and nonlinear relationships. Additionally, we implement the SLTB model within maximum likelihood and Bayesian frameworks, employing both hierarchical and non-hierarchical models. These comprehensive implementations demonstrate the broad applicability of SLTB model for modeling data with bounded values in a variety of contexts.", "published": "2025-09-16T15:18:13Z", "authors": ["Mingang Kim", "Brent A. Kaplan", "Mikhail N. Koffarnus", "Christopher T. Franck"], "categories": ["stat.ME"], "doi": "", "_query": "hedonic regression housing", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/1404.3219v1", "title": "Estimating nonlinear regression errors without doing regression", "abstract": "A method for estimating nonlinear regression errors and their distributions without performing regression is presented. Assuming continuity of the modeling function the variance is given in terms of conditional probabilities extracted from the data. For N data points the computational demand is N2. Comparing the predicted residual errors with those derived from a linear model assumption provides a signal for nonlinearity. The method is successfully illustrated with data generated by the Ikeda and Lorenz maps augmented with noise. As a by-product the embedding dimensions of these maps are also extracted.", "published": "2014-04-11T20:22:40Z", "authors": ["Hong Pi", "Carsten Peterson"], "categories": ["stat.ML", "nlin.CD", "q-fin.ST"], "doi": "", "_query": "hedonic regression housing", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2610.25009v1", "title": "Nonparametric Dyadic Quantile Regression", "abstract": "This paper considers nonparametric quantile regression for directed dyadic data observed on the ordered pairs of a complete network with n vertices. The observations exhibit dyadic dependence: dyads with disjoint indices are independent, whereas dyads sharing one or two indices are correlated. The local linear smoothing technique is adopted to estimate the quantile regression function. The asymptotic theory reveals that convergence rates of the proposed dyadic quantile regression estimation crucially depend on whether degeneracy occurs for the underlying network generating process. A rate-adaptive standardized version of the limit distribution is derived under some mild conditions. In addition, an easy-to-implement method is proposed to estimate the asymptotic variance of nonparametric dyadic quantile regression estimation. The finite-sample performance and applicability of the proposed methodology are illustrated with Monte-Carlo simulation and a real data application to bilateral portfolio holdings.", "published": "2026-10-18T04:29:09Z", "authors": ["Francesco Bravo", "Degui Li", "Zheng Li"], "categories": ["econ.EM"], "doi": "", "_query": "hedonic regression housing", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/1704.02917v1", "title": "On quantile residuals in beta regression", "abstract": "Beta regression is often used to model the relationship between a dependent variable that assumes values on the open interval (0,1) and a set of predictor variables. An important challenge in beta regression is to find residuals whose distribution is well approximated by the standard normal distribution. Two previous works compared residuals in beta regression, but the authors did not include the quantile residual. Using Monte Carlo simulation techniques, this paper studies the behavior of certain residuals in beta regression in several scenarios. Overall, the results suggest that the distribution of the quantile residual is better approximated by the standard normal distribution than that of the other residuals in most scenarios. Three applications illustrate the effectiveness of the quantile residual.", "published": "2017-04-10T15:45:58Z", "authors": ["Gustavo H. A. Pereira"], "categories": ["stat.ME"], "doi": "", "_query": "hedonic regression housing", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2106.06124v1", "title": "Twin Neural Network Regression is a Semi-Supervised Regression Algorithm", "abstract": "Twin neural network regression (TNNR) is a semi-supervised regression algorithm, it can be trained on unlabelled data points as long as other, labelled anchor data points, are present. TNNR is trained to predict differences between the target values of two different data points rather than the targets themselves. By ensembling predicted differences between the targets of an unseen data point and all training data points, it is possible to obtain a very accurate prediction for the original regression problem. Since any loop of predicted differences should sum to zero, loops can be supplied to the training data, even if the data points themselves within loops are unlabelled. Semi-supervised training improves TNNR performance, which is already state of the art, significantly.", "published": "2021-06-11T02:10:52Z", "authors": ["Sebastian J. Wetzel", "Roger G. Melko", "Isaac Tamblyn"], "categories": ["cs.LG"], "doi": "10.1088/2632-2153/ac9885", "_query": "hedonic regression housing", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/0811.1705v1", "title": "Inference using shape-restricted regression splines", "abstract": "Regression splines are smooth, flexible, and parsimonious nonparametric function estimators. They are known to be sensitive to knot number and placement, but if assumptions such as monotonicity or convexity may be imposed on the regression function, the shape-restricted regression splines are robust to knot choices. Monotone regression splines were introduced by Ramsay [Statist. Sci. 3 (1998) 425--461], but were limited to quadratic and lower order. In this paper an algorithm for the cubic monotone case is proposed, and the method is extended to convex constraints and variants such as increasing-concave. The restricted versions have smaller squared error loss than the unrestricted splines, although they have the same convergence rates. The relatively small degrees of freedom of the model and the insensitivity of the fits to the knot choices allow for practical inference methods; the computational efficiency allows for back-fitting of additive models. Tests of constant versus increasing and linear versus convex regression function, when implemented with shape-restricted regression splines, have higher power than the standard version using ordinary shape-restricted regression.", "published": "2008-11-11T13:31:10Z", "authors": ["Mary C. Meyer"], "categories": ["stat.AP"], "doi": "10.1214/08-AOAS167", "_query": "hedonic regression housing", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2202.01338v3", "title": "Regression Transformer: Concurrent sequence regression and generation for molecular language modeling", "abstract": "Despite significant progress of generative models in the natural sciences, their controllability remains challenging. One fundamentally missing aspect of molecular or protein generative models is an inductive bias that can reflect continuous properties of interest. To that end, we propose the Regression Transformer (RT), a novel method that abstracts regression as a conditional sequence modeling problem. This introduces a new paradigm of multitask language models which seamlessly bridge sequence regression and conditional sequence generation. We thoroughly demonstrate that, despite using a nominal-scale training objective, the RT matches or surpasses the performance of conventional regression models in property prediction tasks of small molecules, proteins and chemical reactions. Critically, priming the same model with continuous properties yields a highly competitive conditional generative model that outperforms specialized approaches in a substructure-constrained, property-driven molecule generation benchmark. Our dichotomous approach is facilitated by a novel, alternating training scheme that enables the model to decorate seed sequences by desired properties, e.g., to optimize reaction yield. In sum, the RT is the first report of a multitask model that concurrently excels at predictive and generative tasks in biochemistry. This finds particular application in property-driven, local exploration of the chemical or protein space and could pave the road toward foundation models in material design. The code to reproduce all experiments of the paper is available at: https://github.com/IBM/regression-transformer", "published": "2022-02-01T08:57:31Z", "authors": ["Jannis Born", "Matteo Manica"], "categories": ["cs.LG", "cs.AI", "cs.CL", "q-bio.BM"], "doi": "10.1038/s42256-023-00639-z", "_query": "hedonic regression housing", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/1907.08074v1", "title": "Scalar-on-function local linear regression and beyond", "abstract": "Regressing a scalar response on a random function is nowadays a common situation. In the nonparametric setting, this paper paves the way for making the local linear regression based on a projection approach a prominent method for solving this regression problem. Our asymptotic results demonstrate that the functional local linear regression outperforms its functional local constant counterpart. Beyond the estimation of the regression operator itself, the local linear regression is also a useful tool for predicting the functional derivative of the regression operator, a promising mathematical object on its own. The local linear estimator of the functional derivative is shown to be consistent. On simulated datasets we illustrate good finite sample properties of both proposed methods. On a real data example of a single-functional index model we indicate how the functional derivative of the regression operator provides an original and fast, widely applicable estimating method.", "published": "2019-07-18T14:28:18Z", "authors": ["Frédéric Ferraty", "Stanislav Nagy"], "categories": ["stat.ME", "math.ST"], "doi": "", "_query": "hedonic regression housing", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/1406.1882v1", "title": "Bayesian density regression for count data", "abstract": "Despite the increasing popularity of quantile regression models for continuous responses, models for count data have so far received little attention. The main quantile regression technique for count data involves adding uniform random noise or \"jittering\", thus overcoming the problem that the conditional quantile function is not a continuous function of the parameters of interest. Although jittering allows estimating the conditional quantiles, it has the drawback that, for small values of the response variable $Y,$ the added noise can have a large influence on the estimated quantiles. In addition, quantile regression can lead to \"crossing\" quantiles. We propose a Bayesian Dirichlet process (DP)-based approach to quantile regression for count data. The approach is based on an adaptive DP mixture (DPM) of COM-Poisson regression models and determines the quantiles by estimating the density of the data, thus eliminating all the aforementioned problems. Taking advantage of the exchange algorithm, the proposed MCMC algorithm can be applied to distributions on which the likelihood can only be computed up to a normalising constant.", "published": "2014-06-07T10:23:17Z", "authors": ["Charalampos Chanialidis", "Ludger Evers", "Tereza Neocleous"], "categories": ["stat.ME"], "doi": "", "_query": "hedonic regression housing", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2009.07805v3", "title": "An Intrinsic Treatment of Stochastic Linear Regression", "abstract": "Linear regression is perhaps one of the most popular statistical concepts, which permeates almost every scientific field of study. Due to the technical simplicity and wide applicability of linear regression, attention is almost always quickly directed to the algorithmic or computational side of linear regression. In particular, the underlying mathematics of stochastic linear regression itself as an entity usually gets either a peripheral treatment or a relatively in-depth but ad hoc treatment depending on the type of concerned problems; in other words, compared to the extensiveness of the study of mathematical properties of the \"derivatives\" of stochastic linear regression such as the least squares estimator, the mathematics of stochastic linear regression itself seems to have not yet received a due intrinsic treatment. Apart from the conceptual importance, a consequence of an insufficient or possibly inaccurate understanding of stochastic linear regression would be the recurrence for the role of stochastic linear regression in the important (and more sophisticated) context of structural equation modeling to be misperceived or taught in a misleading way. We believe this pity is rectifiable when the fundamental concepts are correctly classified. Accompanied by some illustrative, distinguishing examples and counterexamples, we intend to pave out the mathematical framework for stochastic linear regression, in a rigorous but non-technical way, by giving new results and pasting together several fundamental known results that are, we believe, both enlightening and conceptually useful, and that had not yet been systematically documented in the related literature. As a minor contribution, the way we arrange the fundamental known results would be the first attempt in the related literature.", "published": "2020-09-16T16:55:40Z", "authors": ["Yu-Lin Chou"], "categories": ["math.ST"], "doi": "", "_query": "hedonic regression housing", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2210.00261v1", "title": "Tyranny-of-the-minority regression adjustment in randomized experiments", "abstract": "Regression adjustment is widely used for the analysis of randomized experiments to improve the estimation efficiency of the treatment effect. This paper reexamines a weighted regression adjustment method termed as tyranny-of-the-minority (ToM), wherein units in the minority group are given greater weights. We demonstrate that the ToM regression adjustment is more robust than Lin 2013's regression adjustment with treatment-covariate interactions, even though these two regression adjustment methods are asymptotically equivalent in completely randomized experiments. Moreover, we extend ToM regression adjustment to stratified randomized experiments, completely randomized survey experiments, and cluster randomized experiments. We obtain design-based properties of the ToM regression-adjusted average treatment effect estimator under such designs. In particular, we show that ToM regression-adjusted estimator improves the asymptotic estimation efficiency compared to the unadjusted estimator even when the regression model is misspecified, and is optimal in the class of linearly adjusted estimators. We also study the asymptotic properties of various heteroscedasticity-robust standard error estimators and provide recommendations for practitioners. Simulation studies and real data analysis demonstrate ToM regression adjustment's superiority over existing methods.", "published": "2022-10-01T11:54:55Z", "authors": ["Xin Lu", "Hanzhong Liu"], "categories": ["stat.ME"], "doi": "", "_query": "hedonic regression housing", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/1708.05712v1", "title": "Extensions of Morse-Smale Regression with Application to Actuarial Science", "abstract": "The problem of subgroups is ubiquitous in scientific research (ex. disease heterogeneity, spatial distributions in ecology...), and piecewise regression is one way to deal with this phenomenon. Morse-Smale regression offers a way to partition the regression function based on level sets of a defined function and that function's basins of attraction. This topologically-based piecewise regression algorithm has shown promise in its initial applications, but the current implementation in the literature has been limited to elastic net and generalized linear regression. It is possible that nonparametric methods, such as random forest or conditional inference trees, may provide better prediction and insight through modeling interaction terms and other nonlinear relationships between predictors and a given outcome. This study explores the use of several machine learning algorithms within a Morse-Smale piecewise regression framework, including boosted regression with linear baselearners, homotopy-based LASSO, conditional inference trees, random forest, and a wide neural network framework called extreme learning machines. Simulations on Tweedie regression problems with varying Tweedie parameter and dispersion suggest that many machine learning approaches to Morse-Smale piecewise regression improve the original algorithm's performance, particularly for outcomes with lower dispersion and linear or a mix of linear and nonlinear predictor relationships. On a real actuarial problem, several of these new algorithms perform as good as or better than the original Morse-Smale regression algorithm, and most provide information on the nature of predictor relationships within each partition to provide insight into differences between dataset partitions.", "published": "2017-08-17T22:33:35Z", "authors": ["Colleen M. Farrelly"], "categories": ["stat.ML", "stat.AP"], "doi": "", "_query": "hedonic regression housing", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/1408.5449v1", "title": "Stretchy Polynomial Regression", "abstract": "This article proposes a novel solution for stretchy polynomial regression learning. The solution comes in primal and dual closed-forms similar to that of ridge regression. Essentially, the proposed solution stretches the covariance computation via a power term thereby compresses or amplifies the estimation. Our experiments on both synthetic data and real-world data show effectiveness of the proposed method for compressive learning.", "published": "2014-08-23T01:23:23Z", "authors": ["Kar-Ann Toh"], "categories": ["cs.LG", "stat.ML"], "doi": "", "_query": "hedonic regression housing", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/1505.07541v3", "title": "Bayesian Endogenous Tobit Quantile Regression", "abstract": "This study proposes $p$-th Tobit quantile regression models with endogenous variables. In the first stage regression of the endogenous variable on the exogenous variables, the assumption that the $α$-th quantile of the error term is zero is introduced. Then, the residual of this regression model is included in the $p$-th quantile regression model in such a way that the $p$-th conditional quantile of the new error term is zero. The error distribution of the first stage regression is modelled around the zero $α$-th quantile assumption by using parametric and semiparametric approaches. Since the value of $α$ is a priori unknown, it is treated as an additional parameter and is estimated from the data. The proposed models are then demonstrated by using simulated data and real data on the labour supply of married women.", "published": "2015-05-28T03:42:48Z", "authors": ["Genya Kobayashi"], "categories": ["stat.ME"], "doi": "10.1214/16-BA996", "_query": "hedonic regression housing", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/1912.03407v2", "title": "Cellwise Robust M Regression", "abstract": "The cellwise robust M regression estimator is introduced as the first estimator of its kind that intrinsically yields both a map of cellwise outliers consistent with the linear model, and a vector of regression coefficients that is robust against vertical outliers and leverage points. As a by-product, the method yields a weighted and imputed data set that contains estimates of what the values in cellwise outliers would need to amount to if they had fit the model. The method is illustrated to be equally robust as its casewise counterpart, MM regression. The cellwise regression method discards less information than any casewise robust estimator. Therefore, predictive power can be expected to be at least as good as casewise alternatives. These results are corroborated in a simulation study. Moreover, while the simulations show that predictive performance is at least on par with casewise methods if not better, an application to a data set consisting of compositions of Swiss nutrients, shows that in individual cases, CRM can achieve a significantly higher predictive accuracy compared to MM regression.", "published": "2019-12-07T01:17:26Z", "authors": ["Peter Filzmoser", "Sebastiaan Höppner", "Irene Ortner", "Sven Serneels", "Tim Verdonck"], "categories": ["stat.ME"], "doi": "", "_query": "hedonic regression housing", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2005.03350v4", "title": "A Locally Adaptive Interpretable Regression", "abstract": "Machine learning models with both good predictability and high interpretability are crucial for decision support systems. Linear regression is one of the most interpretable prediction models. However, the linearity in a simple linear regression worsens its predictability. In this work, we introduce a locally adaptive interpretable regression (LoAIR). In LoAIR, a metamodel parameterized by neural networks predicts percentile of a Gaussian distribution for the regression coefficients for a rapid adaptation. Our experimental results on public benchmark datasets show that our model not only achieves comparable or better predictive performance than the other state-of-the-art baselines but also discovers some interesting relationships between input and target variables such as a parabolic relationship between CO2 emissions and Gross National Product (GNP). Therefore, LoAIR is a step towards bridging the gap between econometrics, statistics, and machine learning by improving the predictive ability of linear regression without depreciating its interpretability.", "published": "2020-05-07T09:26:14Z", "authors": ["Lkhagvadorj Munkhdalai", "Tsendsuren Munkhdalai", "Keun Ho Ryu"], "categories": ["stat.ML", "cs.AI", "cs.LG"], "doi": "", "_query": "hedonic regression housing", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2402.05342v1", "title": "Nonlinear Regression Analysis", "abstract": "Nonlinear regression analysis is a popular and important tool for scientists and engineers. In this article, we introduce theories and methods of nonlinear regression and its statistical inferences using the frequentist and Bayesian statistical modeling and computation. Least squares with the Gauss-Newton method is the most widely used approach to parameters estimation. Under the assumption of normally distributed errors, maximum likelihood estimation is equivalent to least squares estimation. The Wald confidence regions for parameters in a nonlinear regression model are affected by the curvatures in the mean function. Furthermore, we introduce the Newton-Raphson method and the generalized least squares method to deal with variance heterogeneity. Examples of simulation data analysis are provided to illustrate important properties of confidence regions and the statistical inferences using the nonlinear least squares estimation and Bayesian inference.", "published": "2024-02-08T01:12:57Z", "authors": ["Hsin-Hsiung Huang", "Qing He"], "categories": ["stat.ME", "stat.CO"], "doi": "10.1016/B978-0-12-818630-5.10068-5", "_query": "hedonic regression housing", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/1212.0870v2", "title": "Errors-in-variables beta regression models", "abstract": "Beta regression models provide an adequate approach for modeling continuous outcomes limited to the interval (0,1). This paper deals with an extension of beta regression models that allow for explanatory variables to be measured with error. The structural approach, in which the covariates measured with error are assumed to be random variables, is employed. Three estimation methods are presented, namely maximum likelihood, maximum pseudo-likelihood and regression calibration. Monte Carlo simulations are used to evaluate the performance of the proposed estimators and the naïve estimator. Also, a residual analysis for beta regression models with measurement errors is proposed. The results are illustrated in a real data set.", "published": "2012-12-04T21:06:19Z", "authors": ["Jalmar M. F. Carrasco", "Silvia L. P. Ferrari", "Reinaldo B. Arellano-Valle"], "categories": ["stat.ME"], "doi": "", "_query": "hedonic regression housing", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/1401.3880v1", "title": "Regression Conformal Prediction with Nearest Neighbours", "abstract": "In this paper we apply Conformal Prediction (CP) to the k-Nearest Neighbours Regression (k-NNR) algorithm and propose ways of extending the typical nonconformity measure used for regression so far. Unlike traditional regression methods which produce point predictions, Conformal Predictors output predictive regions that satisfy a given confidence level. The regions produced by any Conformal Predictor are automatically valid, however their tightness and therefore usefulness depends on the nonconformity measure used by each CP. In effect a nonconformity measure evaluates how strange a given example is compared to a set of other examples based on some traditional machine learning algorithm. We define six novel nonconformity measures based on the k-Nearest Neighbours Regression algorithm and develop the corresponding CPs following both the original (transductive) and the inductive CP approaches. A comparison of the predictive regions produced by our measures with those of the typical regression measure suggests that a major improvement in terms of predictive region tightness is achieved by the new measures.", "published": "2014-01-16T05:12:21Z", "authors": ["Harris Papadopoulos", "Vladimir Vovk", "Alex Gammerman"], "categories": ["cs.LG"], "doi": "10.1613/jair.3198", "_query": "hedonic regression housing", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2407.21407v2", "title": "Deep Fréchet Regression", "abstract": "Advancements in modern science have led to the increasing availability of non-Euclidean data in metric spaces. This paper addresses the challenge of modeling relationships between non-Euclidean responses and multivariate Euclidean predictors. We propose a flexible regression model capable of handling high-dimensional predictors without imposing parametric assumptions. Two primary challenges are addressed: the curse of dimensionality in nonparametric regression and the absence of linear structure in general metric spaces. The former is tackled using deep neural networks, while for the latter we demonstrate the feasibility of mapping the metric space where responses reside to a low-dimensional Euclidean space using manifold learning. We introduce a reverse mapping approach, employing local Fréchet regression, to map the low-dimensional manifold representations back to objects in the original metric space. We develop a theoretical framework, investigating the convergence rate of deep neural networks under dependent sub-Gaussian noise with bias. The convergence rate of the proposed regression model is then obtained by expanding the scope of local Fréchet regression to accommodate multivariate predictors in the presence of errors in predictors. Simulations and case studies show that the proposed model outperforms existing methods for non-Euclidean responses, focusing on the special cases of probability distributions and networks.", "published": "2024-07-31T07:54:14Z", "authors": ["Su I Iao", "Yidong Zhou", "Hans-Georg Müller"], "categories": ["stat.ME", "cs.LG"], "doi": "", "_query": "hedonic regression housing", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2605.19519v1", "title": "Inference for Fréchet Regression", "abstract": "Linear regression is widely used to model relationships between responses and predictors. In modern applications, one encounters data where the responses are non-Euclidean random objects situated in a metric space, paired with Euclidean predictors. Global Fréchet regression generalizes linear regression to such general settings, however statistical inference has remained largely unexplored. We develop a significance test for the null hypothesis that the Fréchet regression function does not depend on the predictors, addressing the challenge of an absence of linear operations in metric spaces. We also develop a test for the partial effect of a subset of the predictors in analogy to, but quite different from, the partial F-tests commonly used in classical linear regression under Gaussian assumptions. Key ideas are to employ random multipliers to obtain non-degenerate null distributions for the proposed test statistics and the Cauchy combination method. We obtain consistency and convergence results under the null hypothesis and contiguous alternatives and demonstrate the finite sample performance of the proposed tests through simulations on network data represented by graph Laplacians and spherical data with geodesic distances. We further illustrate our method using transport networks arising from New York City taxi trip data and U.S. energy source compositional data.", "published": "2026-05-19T08:18:58Z", "authors": ["Wookyeong Song", "Paromita Dubey", "Hans-Georg Müller", "Alexander Petersen"], "categories": ["stat.ME"], "doi": "", "_query": "hedonic regression housing", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2012.14873v1", "title": "Twin Neural Network Regression", "abstract": "We introduce twin neural network (TNN) regression. This method predicts differences between the target values of two different data points rather than the targets themselves. The solution of a traditional regression problem is then obtained by averaging over an ensemble of all predicted differences between the targets of an unseen data point and all training data points. Whereas ensembles are normally costly to produce, TNN regression intrinsically creates an ensemble of predictions of twice the size of the training set while only training a single neural network. Since ensembles have been shown to be more accurate than single models this property naturally transfers to TNN regression. We show that TNNs are able to compete or yield more accurate predictions for different data sets, compared to other state-of-the-art methods. Furthermore, TNN regression is constrained by self-consistency conditions. We find that the violation of these conditions provides an estimate for the prediction uncertainty.", "published": "2020-12-29T17:52:31Z", "authors": ["Sebastian J. Wetzel", "Kevin Ryczko", "Roger G. Melko", "Isaac Tamblyn"], "categories": ["cs.LG", "stat.ML"], "doi": "10.1002/ail2.78", "_query": "hedonic regression housing", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/1311.6139v4", "title": "Distributed multinomial regression", "abstract": "This article introduces a model-based approach to distributed computing for multinomial logistic (softmax) regression. We treat counts for each response category as independent Poisson regressions via plug-in estimates for fixed effects shared across categories. The work is driven by the high-dimensional-response multinomial models that are used in analysis of a large number of random counts. Our motivating applications are in text analysis, where documents are tokenized and the token counts are modeled as arising from a multinomial dependent upon document attributes. We estimate such models for a publicly available data set of reviews from Yelp, with text regressed onto a large set of explanatory variables (user, business, and rating information). The fitted models serve as a basis for exploring the connection between words and variables of interest, for reducing dimension into supervised factor scores, and for prediction. We argue that the approach herein provides an attractive option for social scientists and other text analysts who wish to bring familiar regression tools to bear on text data.", "published": "2013-11-24T16:06:19Z", "authors": ["Matt Taddy"], "categories": ["stat.AP"], "doi": "10.1214/15-AOAS831", "_query": "hedonic regression housing", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2512.13645v1", "title": "Linear Regression in a Nonlinear World", "abstract": "The interpretation of coefficients from multivariate linear regression relies on the assumption that the conditional expectation function is linear in the variables. However, in many cases the underlying data generating process is nonlinear. This paper examines how to interpret regression coefficients under nonlinearity. We show that if the relationships between the variable of interest and other covariates are linear, then the coefficient on the variable of interest represents a weighted average of the derivatives of the outcome conditional expectation function with respect to the variable of interest. If these relationships are nonlinear, the regression coefficient becomes biased relative to this weighted average. We show that this bias is interpretable, analogous to the biases from measurement error and omitted variable bias under the standard linear model.", "published": "2025-12-15T18:37:50Z", "authors": ["Nadav Kunievsky"], "categories": ["econ.EM"], "doi": "", "_query": "hedonic regression housing", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2212.14777v1", "title": "Polynomial spline regression: Theory and Application", "abstract": "To deal with non-linear relations between the predictors and the response, we can use transformations to make the data look linear or approximately linear. In practice, however, transformation methods may be ineffective, and it may be more efficient to use flexible regression techniques that can automatically handle nonlinear behavior. One such method is the Polynomial Spline (PS) regression. Because the number of possible spline regression models is many, efficient strategies for choosing the best one are required. This study investigates the different spline regression models (Polynomial Spline based on Truncated Power, B-spline, and P-Spline) in theoretical and practical ways. We focus on the fundamental concepts as the spline regression is theoretically rich. In particular, we focus on the prediction using cross-validation (CV) rather than interpretation, as polynomial splines are challenging to interpret. We compare different PS models based on a real data set and conclude that the P-spline model is the best.", "published": "2022-12-30T15:32:17Z", "authors": ["Mithun Kumar Acharjee", "Kumer Pial Das"], "categories": ["stat.ME"], "doi": "", "_query": "hedonic regression housing", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2403.17481v2", "title": "A Type of Nonlinear Fréchet Regressions", "abstract": "The existing Fréchet regression is actually defined within a linear framework, since the weight function in the Fréchet objective function is linearly defined, and the resulting Fréchet regression function is identified to be a linear model when the random object belongs to a Hilbert space. Even for nonparametric and semiparametric Fréchet regressions, which are usually nonlinear, the existing methods handle them by local linear (or local polynomial) technique, and the resulting Fréchet regressions are (locally) linear as well. We in this paper introduce a type of nonlinear Fréchet regressions. Such a framework can be utilized to fit the essentially nonlinear models in a general metric space and uniquely identify the nonlinear structure in a Hilbert space. Particularly, its generalized linear form can return to the standard linear Fréchet regression through a special choice of the weight function. Moreover, the generalized linear form possesses methodological and computational simplicity because the Euclidean variable and the metric space element are completely separable. The favorable theoretical properties (e.g. the estimation consistency and presentation theorem) of the nonlinear Fréchet regressions are established systemically. The comprehensive simulation studies and a human mortality data analysis demonstrate that the new strategy is significantly better than the competitors.", "published": "2024-03-26T08:23:37Z", "authors": ["Lu Lin", "Ze Chen"], "categories": ["stat.ME"], "doi": "", "_query": "hedonic regression housing", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2006.16744v1", "title": "Optimal Rates of Distributed Regression with Imperfect Kernels", "abstract": "Distributed machine learning systems have been receiving increasing attentions for their efficiency to process large scale data. Many distributed frameworks have been proposed for different machine learning tasks. In this paper, we study the distributed kernel regression via the divide and conquer approach. This approach has been proved asymptotically minimax optimal if the kernel is perfectly selected so that the true regression function lies in the associated reproducing kernel Hilbert space. However, this is usually, if not always, impractical because kernels that can only be selected via prior knowledge or a tuning process are hardly perfect. Instead it is more common that the kernel is good enough but imperfect in the sense that the true regression can be well approximated by but does not lie exactly in the kernel space. We show distributed kernel regression can still achieves capacity independent optimal rate in this case. To this end, we first establish a general framework that allows to analyze distributed regression with response weighted base algorithms by bounding the error of such algorithms on a single data set, provided that the error bounds has factored the impact of the unexplained variance of the response variable. Then we perform a leave one out analysis of the kernel ridge regression and bias corrected kernel ridge regression, which in combination with the aforementioned framework allows us to derive sharp error bounds and capacity independent optimal rates for the associated distributed kernel regression algorithms. As a byproduct of the thorough analysis, we also prove the kernel ridge regression can achieve rates faster than $N^{-1}$ (where $N$ is the sample size) in the noise free setting which, to our best knowledge, are first observed and novel in regression learning.", "published": "2020-06-30T13:00:16Z", "authors": ["Hongwei Sun", "Qiang Wu"], "categories": ["cs.LG", "cs.DC", "math.ST", "stat.ML"], "doi": "", "_query": "hedonic regression housing", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/1408.3979v2", "title": "Hypotheses tests in boundary regression models", "abstract": "Consider a nonparametric regression model with one-sided errors and regression function in a general Hölder class. We estimate the regression function via minimization of the local integral of a polynomial approximation. We show uniform rates of convergence for the simple regression estimator as well as for a smooth version. These rates carry over to mean regression models with a symmetric and bounded error distribution. In such a setting, one obtains faster rates for irregular error distributions concentrating sufficient mass near the endpoints than for the usual regular distributions. The results are applied to prove asymptotic $\\sqrt{n}$-equivalence of a residual-based (sequential) empirical distribution function to the (sequential) empirical distribution function of unobserved errors in the case of irregular error distributions. This result is remarkably different from corresponding results in mean regression with regular errors. It can readily be applied to develop goodness-of-fit tests for the error distribution. We present some examples and investigate the small sample performance in a simulation study. We further discuss asymptotically distribution-free hypotheses tests for independence of the error distribution from the points of measurement and for monotonicity of the boundary function as well.", "published": "2014-08-18T11:26:18Z", "authors": ["Holger Drees", "Natalie Neumeyer", "Leonie Selk"], "categories": ["stat.ME", "math.ST"], "doi": "", "_query": "hedonic regression housing", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2402.15213v3", "title": "Statistical Agnostic Regression: a machine learning method to validate regression models", "abstract": "Regression analysis is a central topic in statistical modeling, aimed at estimating the relationships between a dependent variable, commonly referred to as the response variable, and one or more independent variables, i.e., explanatory variables. Linear regression is by far the most popular method for performing this task in various fields of research, such as data integration and predictive modeling when combining information from multiple sources. Classical methods for solving linear regression problems, such as Ordinary Least Squares (OLS), Ridge, or Lasso regressions, often form the foundation for more advanced machine learning (ML) techniques, which have been successfully applied, though without a formal definition of statistical significance. At most, permutation or analyses based on empirical measures (e.g., residuals or accuracy) have been conducted, leveraging the greater sensitivity of ML estimations for detection. In this paper, we introduce Statistical Agnostic Regression (SAR) for evaluating the statistical significance of ML-based linear regression models. This is achieved by analyzing concentration inequalities of the actual risk (expected loss) and considering the worst-case scenario. To this end, we define a threshold that ensures there is sufficient evidence, with a probability of at least $1-η$, to conclude the existence of a linear relationship in the population between the explanatory (feature) and the response (label) variables. Simulations demonstrate the ability of the proposed agnostic (non-parametric) test to provide an analysis of variance similar to the classical multivariate $F$-test for the slope parameter, without relying on the underlying assumptions of classical methods. Moreover, the residuals computed from this method represent a trade-off between those obtained from ML approaches and the classical OLS.", "published": "2024-02-23T09:19:26Z", "authors": ["Juan M Gorriz", "J. Ramirez", "F. Segovia", "F. J. Martinez-Murcia", "C. Jiménez-Mesa", "J. Suckling"], "categories": ["stat.ML", "cs.LG", "math.ST", "stat.CO"], "doi": "10.1016/j.jare.2025.04.026", "_query": "hedonic regression housing", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2603.10382v2", "title": "Gimbal Regression: Orientation-Adaptive Local Linear Regression under Spatial Heterogeneity", "abstract": "Local regression is widely used to explore spatial heterogeneity, but anisotropic or effectively low-dimensional neighborhoods can produce ill-conditioned local solves, causing coefficient variation driven by numerical artifacts rather than substantive structure. Such instability is often hidden when estimation relies on implicit tuning or optimization without exposing local diagnostics. This paper proposes Gimbal Regression (GR), a deterministic, geometry-aware local regression framework for stable and auditable estimation. GR constructs directional weights from neighborhood geometry using explicit orientation objects and deterministic safeguards, and computes local coefficients by a closed-form solve. Theoretical results are stated conditional on the realized neighborhood configuration, under which the estimator is a deterministic linear operator with finite-perturbation stability bounds. Simulations and empirical examples demonstrate predictable computation, transparent diagnostics, and improved numerical stability relative to common local regression baselines.", "published": "2026-03-11T03:51:57Z", "authors": ["Yuichiro Otani"], "categories": ["stat.ME", "econ.EM", "stat.AP", "stat.CO"], "doi": "", "_query": "hedonic regression housing", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2501.05809v3", "title": "AdaPRL: Adaptive Pairwise Regression Learning with Uncertainty Estimation for Universal Regression Tasks", "abstract": "Current deep regression models usually learn in a point-wise way that treats each sample as an independent input, neglecting the relative ordering among different data. Consequently, the regression model could neglect the data's interrelationships, potentially resulting in suboptimal performance. Moreover, the existence of aleatoric uncertainty in the training data may drive the model to capture non-generalizable patterns, contributing to increased overfitting. To address these issues, we propose a novel adaptive pairwise learning framework for regression tasks (AdaPRL) which leverages the relative differences between data points and integrates with deep probabilistic models to quantify the uncertainty associated with the predictions. Additionally, we adapt AdaPRL for applications in multi-task learning and multivariate time series forecasting. Extensive experiments with several real-world regression datasets including recommendation systems, age prediction, time series forecasting, natural language understanding, finance, and industry datasets show that AdaPRL is compatible with different backbone networks in various tasks and achieves state-of-the-art performance on the vast majority of tasks without extra inference cost, highlighting its notable potential including enhancing prediction accuracy and ranking ability, increasing generalization capability, improving robustness to noisy data, improving resilience to reduced data, and enhancing interpretability. Experiments also show that AdaPRL can be seamlessly incorporated into recently proposed regression frameworks to gain performance improvement.", "published": "2025-01-10T09:19:10Z", "authors": ["Fuhang Liang", "Rucong Xu", "Deng Lin"], "categories": ["cs.LG"], "doi": "", "_query": "hedonic regression housing", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/1904.00864v1", "title": "Tree Search Network for Sparse Regression", "abstract": "We consider the classical sparse regression problem of recovering a sparse signal $x_0$ given a measurement vector $y = Φx_0+w$. We propose a tree search algorithm driven by the deep neural network for sparse regression (TSN). TSN improves the signal reconstruction performance of the deep neural network designed for sparse regression by performing a tree search with pruning. It is observed in both noiseless and noisy cases, TSN recovers synthetic and real signals with lower complexity than a conventional tree search and is superior to existing algorithms by a large margin for various types of the sensing matrix $Φ$, widely used in sparse regression.", "published": "2019-04-01T14:05:41Z", "authors": ["Kyung-Su Kim", "Sae-Young Chung"], "categories": ["cs.LG", "eess.SP", "stat.ML"], "doi": "", "_query": "hedonic regression housing", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2403.17014v1", "title": "Contrastive Learning for Regression on Hyperspectral Data", "abstract": "Contrastive learning has demonstrated great effectiveness in representation learning especially for image classification tasks. However, there is still a shortage in the studies targeting regression tasks, and more specifically applications on hyperspectral data. In this paper, we propose a contrastive learning framework for the regression tasks for hyperspectral data. To this end, we provide a collection of transformations relevant for augmenting hyperspectral data, and investigate contrastive learning for regression. Experiments on synthetic and real hyperspectral datasets show that the proposed framework and transformations significantly improve the performance of regression models, achieving better scores than other state-of-the-art transformations.", "published": "2024-02-12T21:33:46Z", "authors": ["Mohamad Dhaini", "Maxime Berar", "Paul Honeine", "Antonin Van Exem"], "categories": ["cs.CV", "cs.LG"], "doi": "", "_query": "hedonic regression housing", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/0706.0534v2", "title": "Compressed Regression", "abstract": "Recent research has studied the role of sparsity in high dimensional regression and signal reconstruction, establishing theoretical limits for recovering sparse models from sparse data. This line of work shows that $\\ell_1$-regularized least squares regression can accurately estimate a sparse linear model from $n$ noisy examples in $p$ dimensions, even if $p$ is much larger than $n$. In this paper we study a variant of this problem where the original $n$ input variables are compressed by a random linear transformation to $m \\ll n$ examples in $p$ dimensions, and establish conditions under which a sparse linear model can be successfully recovered from the compressed data. A primary motivation for this compression procedure is to anonymize the data and preserve privacy by revealing little information about the original data. We characterize the number of random projections that are required for $\\ell_1$-regularized compressed regression to identify the nonzero coefficients in the true model with probability approaching one, a property called ``sparsistence.'' In addition, we show that $\\ell_1$-regularized compressed regression asymptotically predicts as well as an oracle linear model, a property called ``persistence.'' Finally, we characterize the privacy properties of the compression procedure in information-theoretic terms, establishing upper bounds on the mutual information between the compressed and uncompressed data that decay to zero.", "published": "2007-06-04T20:42:54Z", "authors": ["Shuheng Zhou", "John Lafferty", "Larry Wasserman"], "categories": ["stat.ML", "cs.IT"], "doi": "", "_query": "hedonic regression housing", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2406.14033v1", "title": "Ensembles of Probabilistic Regression Trees", "abstract": "Tree-based ensemble methods such as random forests, gradient-boosted trees, and Bayesianadditive regression trees have been successfully used for regression problems in many applicationsand research studies. In this paper, we study ensemble versions of probabilisticregression trees that provide smooth approximations of the objective function by assigningeach observation to each region with respect to a probability distribution. We prove thatthe ensemble versions of probabilistic regression trees considered are consistent, and experimentallystudy their bias-variance trade-off and compare them with the state-of-the-art interms of performance prediction.", "published": "2024-06-20T06:51:51Z", "authors": ["Alexandre Seiller", "Éric Gaussier", "Emilie Devijver", "Marianne Clausel", "Sami Alkhoury"], "categories": ["stat.ML", "cs.LG"], "doi": "", "_query": "hedonic regression housing", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/1909.05948v1", "title": "Unsupervised Image Regression for Heterogeneous Change Detection", "abstract": "Change detection in heterogeneous multitemporal satellite images is an emerging and challenging topic in remote sensing. In particular, one of the main challenges is to tackle the problem in an unsupervised manner. In this paper we propose an unsupervised framework for bitemporal heterogeneous change detection based on the comparison of affinity matrices and image regression. First, our method quantifies the similarity of affinity matrices computed from co-located image patches in the two images. This is done to automatically identify pixels that are likely to be unchanged. With the identified pixels as pseudo-training data, we learn a transformation to map the first image to the domain of the other image, and vice versa. Four regression methods are selected to carry out the transformation: Gaussian process regression, support vector regression, random forest regression, and a recently proposed kernel regression method called homogeneous pixel transformation. To evaluate the potentials and limitations of our framework, and also the benefits and disadvantages of each regression method, we perform experiments on two real data sets. The results indicate that the comparison of the affinity matrices can already be considered a change detection method by itself. However, image regression is shown to improve the results obtained by the previous step alone and produces accurate change detection maps despite of the heterogeneity of the multitemporal input data. Notably, the random forest regression approach excels by achieving similar accuracy as the other methods, but with a significantly lower computational cost and with fast and robust tuning of hyperparameters.", "published": "2019-09-07T12:26:11Z", "authors": ["Luigi T. Luppino", "Filippo M. Bianchi", "Gabriele Moser", "Stian N. Anfinsen"], "categories": ["cs.CV", "cs.LG", "eess.IV", "stat.ML"], "doi": "10.1109/TGRS.2019.2930348", "_query": "hedonic regression housing", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2308.01747v4", "title": "Fusion regression methods with repeated functional data", "abstract": "Linear regression and classification methods with repeated functional data are considered. For each statistical unit in the sample, a real-valued parameter is observed over time under different conditions related by some neighborhood structure (spatial, group, etc.). Two regression methods based on fusion penalties are proposed to consider the dependence induced by this structure. These methods aim to obtain parsimonious coefficient regression functions, by determining if close conditions are associated with common regression coefficient functions. The first method is a generalization to functional data of the variable fusion methodology based on the 1-nearest neighbor. The second one relies on the group fusion lasso penalty which assumes some grouping structure of conditions and allows for homogeneity among the regression coefficient functions within groups. Numerical simulations and an application of electroencephalography data are presented.", "published": "2023-08-03T13:10:56Z", "authors": ["Issam-Ali Moindjié", "Cristian Preda", "Sophie Dabo-Niang"], "categories": ["stat.ME", "math.ST"], "doi": "", "_query": "hedonic regression housing", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2608.18026v1", "title": "TabNSM: Neural Sparse Mixer for Tabular Regression", "abstract": "Large-scale, high-dimensional tabular regression remains challenging: tree-based models are robust but lack end-to-end representation learning, while deep models enable flexible feature learning but often incur costly interaction modeling and sensitivity to noisy or redundant features. We propose TabNSM, a scalable regression framework that extends our earlier sparse-attention and mixer architectures. At its core, the Adaptive Sparse Interaction Module (ASIM) integrates foreground feature discovery, sparse local interaction encoding, and Feature-Token Mixing, providing near-linear complexity under fixed sparse configurations. For regression, TabNSM introduces three complementary components: a Multi-Stage Regression Head for progressive prediction refinement; GridLoss, an ordinal-aware soft-binning objective that incorporates target structure into representation learning; and RISE (Reweighted Instance Sampling by Error), a difficulty-aware sampling strategy based on loss-quantile bins. Across nine real-world regression benchmarks, TabNSM delivers strong predictive performance and practical scalability, with particularly consistent gains on high-dimensional and heterogeneous datasets. These results demonstrate that selective interaction modeling, structured regression supervision, and difficulty-aware sampling provide an effective and scalable approach to deep tabular regression.", "published": "2026-08-18T17:22:49Z", "authors": ["Ali Eslamian", "Qiang Cheng"], "categories": ["cs.LG", "cs.CE"], "doi": "", "_query": "hedonic regression housing", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2207.08667v1", "title": "pGMM Kernel Regression and Comparisons with Boosted Trees", "abstract": "In this work, we demonstrate the advantage of the pGMM (``powered generalized min-max'') kernel in the context of (ridge) regression. In recent prior studies, the pGMM kernel has been extensively evaluated for classification tasks, for logistic regression, support vector machines, as well as deep neural networks. In this paper, we provide an experimental study on ridge regression, to compare the pGMM kernel regression with the ordinary ridge linear regression as well as the RBF kernel ridge regression. Perhaps surprisingly, even without a tuning parameter (i.e., $p=1$ for the power parameter of the pGMM kernel), the pGMM kernel already performs well. Furthermore, by tuning the parameter $p$, this (deceptively simple) pGMM kernel even performs quite comparably to boosted trees. Boosting and boosted trees are very popular in machine learning practice. For regression tasks, typically, practitioners use $L_2$ boost, i.e., for minimizing the $L_2$ loss. Sometimes for the purpose of robustness, the $L_1$ boost might be a choice. In this study, we implement $L_p$ boost for $p\\geq 1$ and include it in the package of ``Fast ABC-Boost''. Perhaps also surprisingly, the best performance (in terms of $L_2$ regression loss) is often attained at $p>2$, in some cases at $p\\gg 2$. This phenomenon has already been demonstrated by Li et al (UAI 2010) in the context of k-nearest neighbor classification using $L_p$ distances. In summary, the implementation of $L_p$ boost provides practitioners the additional flexibility of tuning boosting algorithms for potentially achieving better accuracy in regression applications.", "published": "2022-07-18T15:06:30Z", "authors": ["Ping Li", "Weijie Zhao"], "categories": ["stat.ML", "cs.LG"], "doi": "", "_query": "hedonic regression housing", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2301.13767v1", "title": "Multicalibration as Boosting for Regression", "abstract": "We study the connection between multicalibration and boosting for squared error regression. First we prove a useful characterization of multicalibration in terms of a ``swap regret'' like condition on squared error. Using this characterization, we give an exceedingly simple algorithm that can be analyzed both as a boosting algorithm for regression and as a multicalibration algorithm for a class H that makes use only of a standard squared error regression oracle for H. We give a weak learning assumption on H that ensures convergence to Bayes optimality without the need to make any realizability assumptions -- giving us an agnostic boosting algorithm for regression. We then show that our weak learning assumption on H is both necessary and sufficient for multicalibration with respect to H to imply Bayes optimality. We also show that if H satisfies our weak learning condition relative to another class C then multicalibration with respect to H implies multicalibration with respect to C. Finally we investigate the empirical performance of our algorithm experimentally using an open source implementation that we make available. Our code repository can be found at https://github.com/Declancharrison/Level-Set-Boosting.", "published": "2023-01-31T17:05:24Z", "authors": ["Ira Globus-Harris", "Declan Harrison", "Michael Kearns", "Aaron Roth", "Jessica Sorrell"], "categories": ["cs.LG", "cs.DS"], "doi": "", "_query": "hedonic regression housing", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2211.07447v4", "title": "Neural Regression For Scale-Varying Targets", "abstract": "In this work, we demonstrate that a major limitation of regression using a mean-squared error loss is its sensitivity to the scale of its targets. This makes learning settings consisting of target's whose values take on varying scales challenging. A recently-proposed alternative loss function, known as histogram loss, avoids this issue. However, its computational cost grows linearly with the number of buckets in the histogram, which renders prediction with real-valued targets intractable. To address this issue, we propose a novel approach to training deep learning models on real-valued regression targets, autoregressive regression, which learns a high-fidelity distribution by utilizing an autoregressive target decomposition. We demonstrate that this training objective allows us to solve regression tasks involving targets with different scales.", "published": "2022-11-14T15:22:20Z", "authors": ["Adam Khakhar", "Jacob Buckman"], "categories": ["cs.LG"], "doi": "", "_query": "hedonic regression housing", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2405.13846v4", "title": "Regression Trees Know Calculus", "abstract": "Regression trees have emerged as a preeminent tool for solving real-world regression problems due to their ability to deal with nonlinearities, interaction effects and sharp discontinuities. In this article, we rather study regression trees applied to well-behaved, differentiable functions, and determine the relationship between node parameters and the local gradient of the function being approximated. We find a simple estimate of the gradient which can be efficiently computed using quantities exposed by popular tree learning libraries. This allows the tools developed in the context of differentiable algorithms, like neural nets and Gaussian processes, to be deployed to tree-based models. To demonstrate this, we study measures of model sensitivity defined in terms of integrals of gradients and demonstrate how to compute them for regression trees using the proposed gradient estimates. Quantitative and qualitative numerical experiments reveal the capability of gradients estimated by regression trees to improve predictive analysis, solve tasks in uncertainty quantification, and provide interpretation of model behavior.", "published": "2024-05-22T17:14:03Z", "authors": ["Nathan Wycoff"], "categories": ["stat.ML", "cs.LG"], "doi": "", "_query": "hedonic regression housing", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2007.00830v2", "title": "Unlinked monotone regression", "abstract": "We consider so-called univariate unlinked (sometimes ``decoupled,'' or ``shuffled'') regression when the unknown regression curve is monotone. In standard monotone regression, one observes a pair $(X,Y)$ where a response $Y$ is linked to a covariate $X$ through the model $Y= m_0(X) + ε$, with $m_0$ the (unknown) monotone regression function and $ε$ the unobserved error (assumed to be independent of $X$). In the unlinked regression setting one gets only to observe a vector of realizations from both the response $Y$ and from the covariate $X$ where now $Y \\stackrel{d}{=} m_0(X) + ε$. There is no (observed) pairing of $X$ and $Y$. Despite this, it is actually still possible to derive a consistent non-parametric estimator of $m_0$ under the assumption of monotonicity of $m_0$ and knowledge of the distribution of the noise $ε$. In this paper, we establish an upper bound on the rate of convergence of such an estimator under minimal assumption on the distribution of the covariate $X$. We discuss extensions to the case in which the distribution of the noise is unknown. We develop a second order algorithm for its computation, and we demonstrate its use on synthetic data. Finally, we apply our method (in a fully data driven way, without knowledge of the error distribution) on longitudinal data from the US Consumer Expenditure Survey.", "published": "2020-07-02T01:42:50Z", "authors": ["Fadoua Balabdaoui", "Charles R. Doss", "Cécile Durot"], "categories": ["stat.ME", "math.ST"], "doi": "", "_query": "hedonic regression housing", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2108.03715v1", "title": "A Theoretical Analysis of Logistic Regression and Bayesian Classifiers", "abstract": "This study aims to show the fundamental difference between logistic regression and Bayesian classifiers in the case of exponential and unexponential families of distributions, yielding the following findings. First, the logistic regression is a less general representation of a Bayesian classifier. Second, one should suppose distributions of classes for the correct specification of logistic regression equations. Third, in specific cases, there is no difference between predicted probabilities from correctly specified generative Bayesian classifier and discriminative logistic regression.", "published": "2021-08-08T19:23:29Z", "authors": ["Roman V. Kirin"], "categories": ["econ.EM", "stat.ME"], "doi": "", "_query": "hedonic regression housing", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2009.03716v3", "title": "Local Composite Quantile Regression for Regression Discontinuity", "abstract": "We introduce the local composite quantile regression (LCQR) to causal inference in regression discontinuity (RD) designs. Kai et al. (2010) study the efficiency property of LCQR, while we show that its nice boundary performance translates to accurate estimation of treatment effects in RD under a variety of data generating processes. Moreover, we propose a bias-corrected and standard error-adjusted t-test for inference, which leads to confidence intervals with good coverage probabilities. A bandwidth selector is also discussed. For illustration, we conduct a simulation study and revisit a classic example from Lee (2008). A companion R package rdcqr is developed.", "published": "2020-09-08T13:10:46Z", "authors": ["Xiao Huang", "Zhaoguo Zhan"], "categories": ["econ.EM"], "doi": "", "_query": "hedonic regression housing", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2605.23714v1", "title": "Application of LHC Gas Recuperation Systems for Methane Emission Control in Livestock Housing", "abstract": "The CH4rLiE (CH4 Livestock Emission) project investigates the technical feasibility of adapting gas recovery systems from high-energy physics to mitigate methane (CH4) emissions in livestock housing. This work presents a proof-of-principle based on the adaptation of CERN's gas recuperation systems for the capture of CH4 at low concentrations. A laboratory-scale prototype was developed to evaluate the performance of various adsorbent materials under realistic conditions, including multi-stage humidity removal and pressurized gas flows. Experimental results obtained with the prototype led to the selection of commercial Z5 zeolite as the primary adsorbent due to its high adsorption capacity and stable regeneration performance through Vacuum Swing Adsorption cycles. The study demonstrates the feasibility of CH4 capture at concentrations down to 0.1%. Furthermore, it was observed that increasing the CH4 partial pressure enhances the adsorption capacity, with tests conducted up to approximately 5 bar. To bridge the gap between laboratory conditions and the representative 10-100 ppm levels found in dairy barn environments, a negative exponential extrapolation was applied to the experimental data. This allowed for the modeling of the adsorption behavior in the ultra-low concentration regime. These results validate the operational principle and provide the necessary parameters for the design of a full-scale system for field installation.", "published": "2026-05-22T14:56:52Z", "authors": ["Ilaria Vai", "Francesco Alessandro Angiulli", "Chiara Aimè", "Maria Cristina Arena", "Davide Biagini", "Amin Bouzaiene", "Alessandro Braghieri", "Matteo Brunoldi", "Domenico Calabrò", "Simone Calzaferri", "Alessandro Caserio", "Elio Dinuccio", "Daniele Dondi", "Linda Finco", "Giulia Giannandrea", "Samuel Guelfo Gigli", "Gabriele Giunta", "Roberto Guida", "Nithish Kumar Kameswaran", "Beatrice Mandelli", "Paolo Montagna", "Cristina Riccardi", "Paola Salvini", "Claudio Scagliotti", "Alessandro Tamigio", "Dhanalakshmi Vadivel", "Filippo Vercellati", "Riccardo Verna", "Paolo Vitulo"], "categories": ["physics.ins-det", "physics.app-ph"], "doi": "", "_query": "hedonic regression housing", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/1705.09599v1", "title": "Nearly Semiparametric Efficient Estimation of Quantile Regression", "abstract": "As a competitive alternative to least squares regression, quantile regression is popular in analyzing heterogenous data. For quantile regression model specified for one single quantile level $τ$, major difficulties of semiparametric efficient estimation are the unavailability of a parametric efficient score and the conditional density estimation. In this paper, with the help of the least favorable submodel technique, we first derive the semiparametric efficient scores for linear quantile regression models that are assumed for a single quantile level, multiple quantile levels and all the quantile levels in $(0,1)$ respectively. Our main discovery is a one-step (nearly) semiparametric efficient estimation for the regression coefficients of the quantile regression models assumed for multiple quantile levels, which has several advantages: it could be regarded as an optimal way to pool information across multiple/other quantiles for efficiency gain; it is computationally feasible and easy to implement, as the initial estimator is easily available; due to the nature of quantile regression models under investigation, the conditional density estimation is straightforward by plugging in an initial estimator. The resulting estimator is proved to achieve the corresponding semiparametric efficiency lower bound under regularity conditions. Numerical studies including simulations and an example of birth weight of children confirms that the proposed estimator leads to higher efficiency compared with the Koenker-Bassett quantile regression estimator for all quantiles of interest.", "published": "2017-05-26T14:35:43Z", "authors": ["Kani Chen", "Yuanyuan Lin", "Zhanfeng Wang", "Zhiliang Ying"], "categories": ["stat.ME"], "doi": "", "_query": "hedonic regression housing", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/1712.09473v1", "title": "Sketching for Kronecker Product Regression and P-splines", "abstract": "TensorSketch is an oblivious linear sketch introduced in Pagh'13 and later used in Pham, Pagh'13 in the context of SVMs for polynomial kernels. It was shown in Avron, Nguyen, Woodruff'14 that TensorSketch provides a subspace embedding, and therefore can be used for canonical correlation analysis, low rank approximation, and principal component regression for the polynomial kernel. We take TensorSketch outside of the context of polynomials kernels, and show its utility in applications in which the underlying design matrix is a Kronecker product of smaller matrices. This allows us to solve Kronecker product regression and non-negative Kronecker product regression, as well as regularized spline regression. Our main technical result is then in extending TensorSketch to other norms. That is, TensorSketch only provides input sparsity time for Kronecker product regression with respect to the $2$-norm. We show how to solve Kronecker product regression with respect to the $1$-norm in time sublinear in the time required for computing the Kronecker product, as well as for more general $p$-norms.", "published": "2017-12-27T01:26:52Z", "authors": ["Huaian Diao", "Zhao Song", "Wen Sun", "David P. Woodruff"], "categories": ["cs.DS", "cs.LG", "stat.ML"], "doi": "", "_query": "hedonic regression housing", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2210.09181v1", "title": "Bayesian Projection Pursuit Regression", "abstract": "In projection pursuit regression (PPR), an unknown response function is approximated by the sum of M \"ridge functions,\" which are flexible functions of one-dimensional projections of a multivariate input space. Traditionally, optimization routines are used to estimate the projection directions and ridge functions via a sequential algorithm, and M is typically chosen via cross-validation. We introduce the first Bayesian version of PPR, which has the benefit of accurate uncertainty quantification. To learn the projection directions and ridge functions, we apply novel adaptations of methods used for the single ridge function case (M=1), called the Single Index Model, for which Bayesian implementations do exist; then use reversible jump MCMC to learn the number of ridge functions M. We evaluate the predictive ability of our model in 20 simulation scenarios and for 23 real datasets, in a bake-off against an array of state-of-the-art regression methods. Its effective performance indicates that Bayesian Projection Pursuit Regression is a valuable addition to the existing regression toolbox.", "published": "2022-10-17T15:32:56Z", "authors": ["Gavin Collins", "Devin Francom", "Kellin Rumsey"], "categories": ["stat.ME"], "doi": "", "_query": "hedonic regression housing", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/1505.03832v1", "title": "Parametric Regression on the Grassmannian", "abstract": "We address the problem of fitting parametric curves on the Grassmann manifold for the purpose of intrinsic parametric regression. As customary in the literature, we start from the energy minimization formulation of linear least-squares in Euclidean spaces and generalize this concept to general nonflat Riemannian manifolds, following an optimal-control point of view. We then specialize this idea to the Grassmann manifold and demonstrate that it yields a simple, extensible and easy-to-implement solution to the parametric regression problem. In fact, it allows us to extend the basic geodesic model to (1) a time-warped variant and (2) cubic splines. We demonstrate the utility of the proposed solution on different vision problems, such as shape regression as a function of age, traffic-speed estimation and crowd-counting from surveillance video clips. Most notably, these problems can be conveniently solved within the same framework without any specifically-tailored steps along the processing pipeline.", "published": "2015-05-14T18:48:08Z", "authors": ["Yi Hong", "Nikhil Singh", "Roland Kwitt", "Nuno Vasconcelos", "Marc Niethammer"], "categories": ["cs.CV"], "doi": "", "_query": "hedonic regression housing", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/1607.03752v2", "title": "Nonparametric Depth and Quantile Regression for Functional Data", "abstract": "We investigate nonparametric regression methods based on spatial depth and quantiles when the response and the covariate are both functions. As in classical quantile regression for finite dimensional data, regression techniques developed here provide insight into the influence of the functional covariate on different parts, like the center as well as the tails, of the conditional distribution of the functional response. Depth and quantile based nonparametric regressions are useful to detect heteroscedasticity in functional regression. We derive the asymptotic behaviour of nonparametric depth and quantile regression estimates, which depend on the small ball probabilities in the covariate space. Our nonparametric regression procedures are used to analyse a dataset about the influence of per capita GDP on saving rates for 125 countries, and another dataset on the effects of per capita net disposable income on the sale of cigarettes in some states in the US.", "published": "2016-07-13T14:20:22Z", "authors": ["Joydeep Chowdhury", "Probal Chaudhuri"], "categories": ["stat.ME"], "doi": "", "_query": "hedonic regression housing", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/1611.04215v2", "title": "Convolutional Regression for Visual Tracking", "abstract": "Recently, discriminatively learned correlation filters (DCF) has drawn much attention in visual object tracking community. The success of DCF is potentially attributed to the fact that a large amount of samples are utilized to train the ridge regression model and predict the location of object. To solve the regression problem in an efficient way, these samples are all generated by circularly shifting from a search patch. However, these synthetic samples also induce some negative effects which weaken the robustness of DCF based trackers. In this paper, we propose a Convolutional Regression framework for visual tracking (CRT). Instead of learning the linear regression model in a closed form, we try to solve the regression problem by optimizing a one-channel-output convolution layer with Gradient Descent (GD). In particular, the receptive field size of the convolution layer is set to the size of object. Contrary to DCF, it is possible to incorporate all \"real\" samples clipped from the whole image. A critical issue of the GD approach is that most of the convolutional samples are negative and the contribution of positive samples will be suppressed. To address this problem, we propose a novel Automatic Hard Negative Mining method to eliminate easy negatives and enhance positives. Extensive experiments are conducted on a widely-used benchmark with 100 sequences. The results show that the proposed algorithm achieves outstanding performance and outperforms almost all the existing DCF based algorithms.", "published": "2016-11-14T01:10:21Z", "authors": ["Kai Chen", "Wenbing Tao"], "categories": ["cs.CV"], "doi": "", "_query": "hedonic regression housing", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/1804.01631v2", "title": "Simultaneous Mean-Variance Regression", "abstract": "We propose simultaneous mean-variance regression for the linear estimation and approximation of conditional mean functions. In the presence of heteroskedasticity of unknown form, our method accounts for varying dispersion in the regression outcome across the support of conditioning variables by using weights that are jointly determined with the mean regression parameters. Simultaneity generates outcome predictions that are guaranteed to improve over ordinary least-squares prediction error, with corresponding parameter standard errors that are automatically valid. Under shape misspecification of the conditional mean and variance functions, we establish existence and uniqueness of the resulting approximations and characterize their formal interpretation and robustness properties. In particular, we show that the corresponding mean-variance regression location-scale model weakly dominates the ordinary least-squares location model under a Kullback-Leibler measure of divergence, with strict improvement in the presence of heteroskedasticity. The simultaneous mean-variance regression loss function is globally convex and the corresponding estimator is easy to implement. We establish its consistency and asymptotic normality under misspecification, provide robust inference methods, and present numerical simulations that show large improvements over ordinary and weighted least-squares in terms of estimation and inference in finite samples. We further illustrate our method with two empirical applications to the estimation of the relationship between economic prosperity in 1500 and today, and demand for gasoline in the United States.", "published": "2018-04-05T00:23:02Z", "authors": ["Richard Spady", "Sami Stouli"], "categories": ["econ.EM", "math.ST", "stat.ME"], "doi": "", "_query": "hedonic regression housing", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/cs/9809037v2", "title": "Regression Depth and Center Points", "abstract": "We show that, for any set of n points in d dimensions, there exists a hyperplane with regression depth at least ceiling(n/(d+1)). as had been conjectured by Rousseeuw and Hubert. Dually, for any arrangement of n hyperplanes in d dimensions there exists a point that cannot escape to infinity without crossing at least ceiling(n/(d+1)) hyperplanes. We also apply our approach to related questions on the existence of partitions of the data into subsets such that a common plane has nonzero regression depth in each subset, and to the computational complexity of regression depth problems.", "published": "1998-09-21T21:55:49Z", "authors": ["Nina Amenta", "Marshall Bern", "David Eppstein", "Shang-Hua Teng"], "categories": ["cs.CG", "math.CO"], "doi": "10.1007/PL00009502", "_query": "hedonic regression housing", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/1206.6141v1", "title": "Directed Time Series Regression for Control", "abstract": "We propose directed time series regression, a new approach to estimating parameters of time-series models for use in certainty equivalent model predictive control. The approach combines merits of least squares regression and empirical optimization. Through a computational study involving a stochastic version of a well known inverted pendulum balancing problem, we demonstrate that directed time series regression can generate significant improvements in controller performance over either of the aforementioned alternatives.", "published": "2012-06-26T23:39:00Z", "authors": ["Yi-Hao Kao", "Benjamin Van Roy"], "categories": ["cs.LG", "eess.SY", "stat.ML"], "doi": "", "_query": "hedonic regression housing", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/1706.04651v2", "title": "Spatial Regression and the Bayesian Filter", "abstract": "Regression for spatially dependent outcomes poses many challenges, for inference and for computation. Non-spatial models and traditional spatial mixed-effects models each have their advantages and disadvantages, making it difficult for practitioners to determine how to carry out a spatial regression analysis. We discuss the data-generating mechanisms implicitly assumed by various popular spatial regression models, and discuss the implications of these assumptions. We propose Bayesian spatial filtering as an approximate middle way between non-spatial models and traditional spatial mixed models. We show by simulation that our Bayesian spatial filtering model has several desirable properties and hence may be a useful addition to a spatial statistician's toolkit.", "published": "2017-06-14T19:42:35Z", "authors": ["John Hughes"], "categories": ["stat.ME"], "doi": "", "_query": "hedonic regression housing", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2210.03961v2", "title": "Dynamic Tensor Product Regression", "abstract": "In this work, we initiate the study of \\emph{Dynamic Tensor Product Regression}. One has matrices $A_1\\in \\mathbb{R}^{n_1\\times d_1},\\ldots,A_q\\in \\mathbb{R}^{n_q\\times d_q}$ and a label vector $b\\in \\mathbb{R}^{n_1\\ldots n_q}$, and the goal is to solve the regression problem with the design matrix $A$ being the tensor product of the matrices $A_1, A_2, \\dots, A_q$ i.e. $\\min_{x\\in \\mathbb{R}^{d_1\\ldots d_q}}~\\|(A_1\\otimes \\ldots\\otimes A_q)x-b\\|_2$. At each time step, one matrix $A_i$ receives a sparse change, and the goal is to maintain a sketch of the tensor product $A_1\\otimes\\ldots \\otimes A_q$ so that the regression solution can be updated quickly. Recomputing the solution from scratch for each round is very slow and so it is important to develop algorithms which can quickly update the solution with the new design matrix. Our main result is a dynamic tree data structure where any update to a single matrix can be propagated quickly throughout the tree. We show that our data structure can be used to solve dynamic versions of not only Tensor Product Regression, but also Tensor Product Spline regression (which is a generalization of ridge regression) and for maintaining Low Rank Approximations for the tensor product.", "published": "2022-10-08T08:06:00Z", "authors": ["Aravind Reddy", "Zhao Song", "Lichen Zhang"], "categories": ["cs.DS", "cs.LG"], "doi": "", "_query": "hedonic regression housing", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/0805.3209v1", "title": "Fuzzy sets in nonparametric Bayes regression", "abstract": "A simple Bayesian approach to nonparametric regression is described using fuzzy sets and membership functions. Membership functions are interpreted as likelihood functions for the unknown regression function, so that with the help of a reference prior they can be transformed to prior density functions. The unknown regression function is decomposed into wavelets and a hierarchical Bayesian approach is employed for making inferences on the resulting wavelet coefficients.", "published": "2008-05-21T07:30:08Z", "authors": ["Jean-François Angers", "Mohan Delampady"], "categories": ["stat.ME", "math.LO", "math.ST"], "doi": "10.1214/074921708000000084", "_query": "hedonic regression housing", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2210.10389v1", "title": "Distributional Adaptive Soft Regression Trees", "abstract": "Random forests are an ensemble method relevant for many problems, such as regression or classification. They are popular due to their good predictive performance (compared to, e.g., decision trees) requiring only minimal tuning of hyperparameters. They are built via aggregation of multiple regression trees during training and are usually calculated recursively using hard splitting rules. Recently regression forests have been incorporated into the framework of distributional regression, a nowadays popular regression approach aiming at estimating complete conditional distributions rather than relating the mean of an output variable to input features only - as done classically. This article proposes a new type of a distributional regression tree using a multivariate soft split rule. One great advantage of the soft split is that smooth high-dimensional functions can be estimated with only one tree while the complexity of the function is controlled adaptive by information criteria. Moreover, the search for the optimal split variable is obsolete. We show by means of extensive simulation studies that the algorithm has excellent properties and outperforms various benchmark methods, especially in the presence of complex non-linear feature interactions. Finally, we illustrate the usefulness of our approach with an example on probabilistic forecasts for the Sun's activity.", "published": "2022-10-19T08:59:02Z", "authors": ["Nikolaus Umlauf", "Nadja Klein"], "categories": ["stat.ME", "stat.CO", "stat.ML"], "doi": "", "_query": "hedonic regression housing", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2106.12001v2", "title": "Inference in High-dimensional Linear Regression", "abstract": "This paper develops an approach to inference in a linear regression model when the number of potential explanatory variables is larger than the sample size. The approach treats each regression coefficient in turn as the interest parameter, the remaining coefficients being nuisance parameters, and seeks an optimal interest-respecting transformation, inducing sparsity on the relevant blocks of the notional Fisher information matrix. The induced sparsity is exploited through a marginal least squares analysis for each variable, as in a factorial experiment, thereby avoiding penalization. One parameterization of the problem is found to be particularly convenient, both computationally and mathematically. In particular, it permits an analytic solution to the optimal transformation problem, facilitating theoretical analysis and comparison to other work. In contrast to regularized regression such as the lasso and its extensions, neither adjustment for selection nor rescaling of the explanatory variables is needed, ensuring the physical interpretation of regression coefficients is retained. Recommended usage is within a broader set of inferential statements, so as to reflect uncertainty over the model as well as over the parameters. The considerations involved in extending the work to other regression models are briefly discussed.", "published": "2021-06-22T18:19:10Z", "authors": ["Heather S. Battey", "Nancy Reid"], "categories": ["stat.ME", "math.ST"], "doi": "", "_query": "hedonic regression housing", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2311.06960v2", "title": "Robust Regression over Averaged Uncertainty", "abstract": "We propose a new formulation of robust regression by integrating all realizations of the uncertainty set and taking an averaged approach to obtain the optimal solution for the ordinary least squares regression problem. We show that this formulation recovers ridge regression exactly and establishes the missing link between robust optimization and the mean squared error approaches for existing regression problems. We further demonstrate that the condition of this equivalence relies on the geometric properties of the defined uncertainty set. We provide exact, closed-form, in some cases, analytical solutions to the equivalent regularization strength under uncertainty sets induced by $\\ell_p$ norm, Schatten $p$-norm, and general polytopes. We then show in synthetic datasets with different levels of uncertainties, a consistent improvement of the averaged formulation over the existing worst-case formulation in out-of-sample performance. In real-world regression problems obtained from UCI datasets, similar improvements are seen in the out-of-sample datasets.", "published": "2023-11-12T20:57:30Z", "authors": ["Dimitris Bertsimas", "Yu Ma"], "categories": ["cs.LG", "math.OC"], "doi": "", "_query": "hedonic regression housing", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2503.19178v2", "title": "Automatic Inference for Value-Added Regressions", "abstract": "A large empirical literature regresses outcomes on empirical Bayes shrinkage estimates of value-added, yet little is known about whether this approach leads to unbiased estimates and valid inference for the downstream regression coefficients. We study a general class of empirical Bayes estimators and the properties of the resulting regression coefficients. We show that estimators can be asymptotically biased and inference can be invalid if the shrinkage estimator does not account for heteroskedasticity in the noise when estimating value added. By contrast, shrinkage estimators properly constructed to model this heteroskedasticity perform an automatic bias correction: the associated regression estimator is asymptotically unbiased, asymptotically normal, and efficient in the sense that it is asymptotically equivalent to regressing on the true (latent) value-added. Further, OLS standard errors from regressing on shrinkage estimates are consistent in this case. As such, efficient inference is easy for practitioners to implement: simply regress outcomes on shrinkage estimates of value-added that account for noise heteroskedasticity.", "published": "2025-03-24T22:01:27Z", "authors": ["Tian Xie"], "categories": ["econ.EM", "stat.ME"], "doi": "", "_query": "hedonic regression housing", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2405.18288v1", "title": "Stagewise Boosting Distributional Regression", "abstract": "Forward stagewise regression is a simple algorithm that can be used to estimate regularized models. The updating rule adds a small constant to a regression coefficient in each iteration, such that the underlying optimization problem is solved slowly with small improvements. This is similar to gradient boosting, with the essential difference that the step size is determined by the product of the gradient and a step length parameter in the latter algorithm. One often overlooked challenge in gradient boosting for distributional regression is the issue of a vanishing small gradient, which practically halts the algorithm's progress. We show that gradient boosting in this case oftentimes results in suboptimal models, especially for complex problems certain distributional parameters are never updated due to the vanishing gradient. Therefore, we propose a stagewise boosting-type algorithm for distributional regression, combining stagewise regression ideas with gradient boosting. Additionally, we extend it with a novel regularization method, correlation filtering, to provide additional stability when the problem involves a large number of covariates. Furthermore, the algorithm includes best-subset selection for parameters and can be applied to big data problems by leveraging stochastic approximations of the updating steps. Besides the advantage of processing large datasets, the stochastic nature of the approximations can lead to better results, especially for complex distributions, by reducing the risk of being trapped in a local optimum. The performance of our proposed stagewise boosting distributional regression approach is investigated in an extensive simulation study and by estimating a full probabilistic model for lightning counts with data of more than 9.1 million observations and 672 covariates.", "published": "2024-05-28T15:40:39Z", "authors": ["Mattias Wetscher", "Johannes Seiler", "Reto Stauffer", "Nikolaus Umlauf"], "categories": ["stat.ME", "stat.ML"], "doi": "", "_query": "hedonic regression housing", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/1905.03353v2", "title": "Regression from Dependent Observations", "abstract": "The standard linear and logistic regression models assume that the response variables are independent, but share the same linear relationship to their corresponding vectors of covariates. The assumption that the response variables are independent is, however, too strong. In many applications, these responses are collected on nodes of a network, or some spatial or temporal domain, and are dependent. Examples abound in financial and meteorological applications, and dependencies naturally arise in social networks through peer effects. Regression with dependent responses has thus received a lot of attention in the Statistics and Economics literature, but there are no strong consistency results unless multiple independent samples of the vectors of dependent responses can be collected from these models. We present computationally and statistically efficient methods for linear and logistic regression models when the response variables are dependent on a network. Given one sample from a networked linear or logistic regression model and under mild assumptions, we prove strong consistency results for recovering the vector of coefficients and the strength of the dependencies, recovering the rates of standard regression under independent observations. We use projected gradient descent on the negative log-likelihood, or negative log-pseudolikelihood, and establish their strong convexity and consistency using concentration of measure for dependent random variables.", "published": "2019-05-08T21:11:50Z", "authors": ["Constantinos Daskalakis", "Nishanth Dikkala", "Ioannis Panageas"], "categories": ["cs.LG", "math.ST", "stat.ML"], "doi": "", "_query": "hedonic regression housing", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/1802.04113v1", "title": "Linear Regression for Speaker Verification", "abstract": "This paper presents a linear regression based back-end for speaker verification. Linear regression is a simple linear model that minimizes the mean squared estimation error between the target and its estimate with a closed form solution, where the target is defined as the ground-truth indicator vectors of utterances. We use the linear regression model to learn speaker models from a front-end, and verify the similarity of two speaker models by a cosine similarity scoring classifier. To evaluate the effectiveness of the linear regression model, we construct three speaker verification systems that use the Gaussian mixture model and identity-vector (GMM/i-vector) front-end, deep neural network and i-vector (DNN/i-vector) front-end, and deep vector (d-vector) front-end as their front-ends, respectively. Our empirical comparison results on the NIST speaker recognition evaluation data sets show that the proposed method outperforms within-class covariance normalization, linear discriminant analysis, and probabilistic linear discriminant analysis, given any of the three front-ends.", "published": "2018-02-12T15:23:35Z", "authors": ["Xiao-Lei Zhang"], "categories": ["cs.SD", "eess.AS"], "doi": "", "_query": "hedonic regression housing", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/1901.05369v2", "title": "Improving linear quantile regression for replicated data", "abstract": "This paper deals with improvement of linear quantile regression, when there are a few distinct values of the covariates but many replicates. On can improve asymptotic efficiency of the estimated regression coefficients by using suitable weights in quantile regression, or simply by using weighted least squares regression on the conditional sample quantiles. The asymptotic variances of the unweighted and weighted estimators coincide only in some restrictive special cases, e.g., when the density of the conditional response has identical values at the quantile of interest over the support of the covariate. The dominance of the weighted estimators is demonstrated in a simulation study, and through the analysis of a data set on tropical cyclones.", "published": "2019-01-16T16:22:59Z", "authors": ["Kaushik Jana", "Debasis Sengupta"], "categories": ["stat.AP"], "doi": "", "_query": "hedonic regression housing", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2012.13614v1", "title": "Quantile regression with generated dependent variable and covariates", "abstract": "We study linear quantile regression models when regressors and/or dependent variable are not directly observed but estimated in an initial first step and used in the second step quantile regression for estimating the quantile parameters. This general class of generated quantile regression (GQR) covers various statistical applications, for instance, estimation of endogenous quantile regression models and triangular structural equation models, and some new relevant applications are discussed. We study the asymptotic distribution of the two-step estimator, which is challenging because of the presence of generated covariates and/or dependent variable in the non-smooth quantile regression estimator. We employ techniques from empirical process theory to find uniform Bahadur expansion for the two step estimator, which is used to establish the asymptotic results. We illustrate the performance of the GQR estimator through simulations and an empirical application based on auctions.", "published": "2020-12-25T19:12:18Z", "authors": ["Jayeeta Bhattacharya"], "categories": ["econ.EM"], "doi": "", "_query": "hedonic regression housing", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/1307.5231v2", "title": "Hierarchical sparsity priors for regression models", "abstract": "We focus on the increasingly important area of sparse regression problems where there are many variables and the effects of a large subset of these are negligible. This paper describes the construction of hierarchical prior distributions when the effects are considered related. These priors allow dependence between the regression coefficients and encourage related shrinkage towards zero of different regression coefficients. The properties of these priors are discussed and applications to linear models with interactions and generalized additive models are used as illustrations. Ideas of heredity relating different levels of interaction are encompassed.", "published": "2013-07-19T14:32:46Z", "authors": ["Jim E. Griffin", "Philip J. Brown"], "categories": ["stat.ME", "math.ST"], "doi": "", "_query": "hedonic regression housing", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2011.06957v1", "title": "Non-stationary Online Regression", "abstract": "Online forecasting under a changing environment has been a problem of increasing importance in many real-world applications. In this paper, we consider the meta-algorithm presented in \\citet{zhang2017dynamic} combined with different subroutines. We show that an expected cumulative error of order $\\tilde{O}(n^{1/3} C_n^{2/3})$ can be obtained for non-stationary online linear regression where the total variation of parameter sequence is bounded by $C_n$. Our paper extends the result of online forecasting of one-dimensional time-series as proposed in \\cite{baby2019online} to general $d$-dimensional non-stationary linear regression. We improve the rate $O(\\sqrt{n C_n})$ obtained by Zhang et al. 2017 and Besbes et al. 2015. We further extend our analysis to non-stationary online kernel regression. Similar to the non-stationary online regression case, we use the meta-procedure of Zhang et al. 2017 combined with Kernel-AWV (Jezequel et al. 2020) to achieve an expected cumulative controlled by the effective dimension of the RKHS and the total variation of the sequence. To the best of our knowledge, this work is the first extension of non-stationary online regression to non-stationary kernel regression. Lastly, we evaluate our method empirically with several existing benchmarks and also compare it with the theoretical bound obtained in this paper.", "published": "2020-11-13T15:08:49Z", "authors": ["Anant Raj", "Pierre Gaillard", "Christophe Saad"], "categories": ["cs.LG", "math.ST"], "doi": "", "_query": "hedonic regression housing", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2606.14544v1", "title": "On the design distribution for predictive Bayesian regression", "abstract": "The predictive approach to Bayesian inference accesses the posterior distribution via a sequence of one-step-ahead predictives, enabling inference via predictive resampling without Markov chain Monte Carlo. In the random-design regression setting, an explicit specification of the predictive design distribution is required, yet the impact of this choice has received little formal attention. We study the role of this predictive design distribution in parametric martingale posteriors for regression, and identify predictive notions of identifiability and design invariance that are essential for valid inference, particularly in the high-dimensional regression setting. Building on these foundations, we introduce a novel class of parametric martingale posteriors for regression that satisfies a weak form of these desiderata, and naturally accommodates the high-dimensional setting through regularization. We then illustrate our method through a simulation.", "published": "2026-06-12T15:21:40Z", "authors": ["Wanyue Sun", "Edwin Fong"], "categories": ["stat.ME", "math.ST"], "doi": "", "_query": "hedonic regression housing", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2201.06529v1", "title": "Iterative Supervised Learning for Regression with Constraints", "abstract": "Regression in supervised learning often requires the enforcement of constraints to ensure that the trained models are consistent with the underlying structures of the input and output data. This paper presents an iterative procedure to perform regression under arbitrary constraints. It is achieved by alternating between a learning step and a constraint enforcement step, to which an affine extension function is incorporated. We show this leads to a contraction mapping under mild assumptions, from which the convergence is guaranteed analytically. The presented proof of convergence in regression with constraints is the unique contribution of this paper. Furthermore, numerical experiments illustrate improvements in the trained model in terms of the quality of regression, the satisfaction of constraints, and also the stability in training, when compared to other existing algorithms.", "published": "2022-01-17T17:18:28Z", "authors": ["Tejaswi K. C.", "Taeyoung Lee"], "categories": ["math.OC"], "doi": "", "_query": "hedonic regression housing", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/1611.04686v1", "title": "Robust Matrix Regression", "abstract": "Modern technologies are producing datasets with complex intrinsic structures, and they can be naturally represented as matrices instead of vectors. To preserve the latent data structures during processing, modern regression approaches incorporate the low-rank property to the model and achieve satisfactory performance for certain applications. These approaches all assume that both predictors and labels for each pair of data within the training set are accurate. However, in real-world applications, it is common to see the training data contaminated by noises, which can affect the robustness of these matrix regression methods. In this paper, we address this issue by introducing a novel robust matrix regression method. We also derive efficient proximal algorithms for model training. To evaluate the performance of our methods, we apply it to real world applications with comparative studies. Our method achieves the state-of-the-art performance, which shows the effectiveness and the practical value of our method.", "published": "2016-11-15T03:15:46Z", "authors": ["Hang Zhang", "Fengyuan Zhu", "Shixin Li"], "categories": ["cs.LG"], "doi": "", "_query": "hedonic regression housing", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/1103.2486v3", "title": "Dynamic Retrospective Regression for Functional Data", "abstract": "Samples of curves, or functional data, usually present phase variability in addition to amplitude variability. Existing functional regression methods do not handle phase variability in an efficient way. In this paper we propose a functional regression method that incorporates phase synchronization as an intrinsic part of the model, and then attains better predictive power than ordinary linear regression in a simple and parsimonious way. The finite-sample properties of the estimators are studied by simulation. As an example of application, we analyze neuromotor data arising from a study of human lip movement.", "published": "2011-03-13T00:23:37Z", "authors": ["Daniel Gervini"], "categories": ["stat.ME"], "doi": "", "_query": "hedonic regression housing", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/1907.04620v1", "title": "Sparse Unit-Sum Regression", "abstract": "This paper considers sparsity in linear regression under the restriction that the regression weights sum to one. We propose an approach that combines $\\ell_0$- and $\\ell_1$-regularization. We compute its solution by adapting a recent methodological innovation made by Bertsimas et al. (2016) for $\\ell_0$-regularization in standard linear regression. In a simulation experiment we compare our approach to $\\ell_0$-regularization and $\\ell_1$-regularization and find that it performs favorably in terms of predictive performance and sparsity. In an application to index tracking we show that our approach can obtain substantially sparser portfolios compared to $\\ell_1$-regularization while maintaining a similar tracking performance.", "published": "2019-07-10T11:16:05Z", "authors": ["Nick Koning", "Paul Bekker"], "categories": ["stat.ME", "stat.CO"], "doi": "", "_query": "hedonic regression housing", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2407.21187v1", "title": "LFFR: Logistic Function For (multi-output) Regression", "abstract": "In this manuscript, we extend our previous work on privacy-preserving regression to address multi-output regression problems using data encrypted under a fully homomorphic encryption scheme. We build upon the simplified fixed Hessian approach for linear and ridge regression and adapt our novel LFFR algorithm, initially designed for single-output logistic regression, to handle multiple outputs. We further refine the constant simplified Hessian method for the multi-output context, ensuring computational efficiency and robustness. Evaluations on multiple real-world datasets demonstrate the effectiveness of our multi-output LFFR algorithm, highlighting its capability to maintain privacy while achieving high predictive accuracy. Normalizing both data and target predictions remains essential for optimizing homomorphic encryption parameters, confirming the practicality of our approach for secure and efficient multi-output regression tasks.", "published": "2024-07-30T20:52:38Z", "authors": ["John Chiang"], "categories": ["cs.CR"], "doi": "", "_query": "hedonic regression housing", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/0803.2984v1", "title": "Conditional density estimation in a regression setting", "abstract": "Regression problems are traditionally analyzed via univariate characteristics like the regression function, scale function and marginal density of regression errors. These characteristics are useful and informative whenever the association between the predictor and the response is relatively simple. More detailed information about the association can be provided by the conditional density of the response given the predictor. For the first time in the literature, this article develops the theory of minimax estimation of the conditional density for regression settings with fixed and random designs of predictors, bounded and unbounded responses and a vast set of anisotropic classes of conditional densities. The study of fixed design regression is of special interest and novelty because the known literature is devoted to the case of random predictors. For the aforementioned models, the paper suggests a universal adaptive estimator which (i) matches performance of an oracle that knows both an underlying model and an estimated conditional density; (ii) is sharp minimax over a vast class of anisotropic conditional densities; (iii) is at least rate minimax when the response is independent of the predictor and thus a bivariate conditional density becomes a univariate density; (iv) is adaptive to an underlying design (fixed or random) of predictors.", "published": "2008-03-20T12:31:29Z", "authors": ["Sam Efromovich"], "categories": ["math.ST"], "doi": "10.1214/009053607000000253", "_query": "hedonic regression housing", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/1709.04135v2", "title": "Weighted Orthogonal Components Regression Analysis", "abstract": "In the multiple linear regression setting, we propose a general framework, termed weighted orthogonal components regression (WOCR), which encompasses many known methods as special cases, including ridge regression and principal components regression. WOCR makes use of the monotonicity inherent in orthogonal components to parameterize the weight function. The formulation allows for efficient determination of tuning parameters and hence is computationally advantageous. Moreover, WOCR offers insights for deriving new better variants. Specifically, we advocate weighting components based on their correlations with the response, which leads to enhanced predictive performance. Both simulated studies and real data examples are provided to assess and illustrate the advantages of the proposed methods.", "published": "2017-09-13T05:01:35Z", "authors": ["Xiaogang Su", "Yaa Wonkye", "Pei Wang", "Xiangrong Yin"], "categories": ["stat.ML"], "doi": "", "_query": "hedonic regression housing", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2206.01447v1", "title": "Minimax Rate for Optimal Transport Regression Between Distributions", "abstract": "Distribution-on-distribution regression considers the problem of formulating and estimating a regression relationship where both covariate and response are probability distributions. The optimal transport distributional regression model postulates that the conditional Fréchet mean of the response distribution is linked to the covariate distribution via an optimal transport map. We establish the minimax rate of estimation of such a regression function, by deriving a lower-bound that matches the convergence rate attained by the Fréchet least squares estimator.", "published": "2022-06-03T08:20:24Z", "authors": ["Laya Ghodrati", "Victor M. Panaretos"], "categories": ["math.ST"], "doi": "", "_query": "hedonic regression housing", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2407.06387v5", "title": "Conditional Rank-Rank Regression", "abstract": "Rank-rank regression is commonly employed in economic research as a way of capturing the relationship between two economic variables. The slope of this regression is the Spearman rank correlation, a classical measure of association. However, in many applications it is common practice to include covariates to account for differences in association levels between groups as defined by the values of these covariates. This is either done by including the covariates or by modeling the residuals obtained after partialing out the impact of the covariates. In each of these instances the resulting rank-rank regression coefficients can be difficult to interpret. We propose the conditional rank-rank regression, which uses conditional ranks instead of unconditional ranks, to measure average within-group persistence. The coefficient of this new regression corresponds to the average Spearman rank correlation conditional on the covariates, a natural summary measure of within-group association. We develop a flexible estimation approach using distribution regression and establish a theoretical framework for large sample inference. An empirical study on intergenerational income mobility in Switzerland demonstrates the advantages of this approach. The study reveals stronger intergenerational persistence between fathers and sons compared to fathers and daughters, with the within-group persistence being 62% of the overall income persistence for sons and 52% for daughters. Smaller families and those with highly educated fathers exhibit greater persistence in economic status.", "published": "2024-07-08T20:56:17Z", "authors": ["Victor Chernozhukov", "Iván Fernández-Val", "Jonas Meier", "Aico van Vuuren", "Francis Vella"], "categories": ["econ.EM", "stat.ME"], "doi": "", "_query": "hedonic regression housing", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/1611.03271v2", "title": "Constrained Sparse Galerkin Regression", "abstract": "Although major advances have been achieved over the past decades for the reduction and identification of linear systems, deriving nonlinear low-order models still is a chal- lenging task. In this work, we develop a new data-driven framework to identify nonlinear reduced-order models of a fluid by combining dimensionality reductions techniques (e.g. proper orthogonal decomposition) and sparse regression techniques from machine learn- ing. In particular, we extend the sparse identification of nonlinear dynamics (SINDy) algorithm to enforce physical constraints in the regression, namely energy-preserving quadratic nonlinearities. The resulting models, hereafter referred to as Galerkin regression models, incorporate many beneficial aspects of Galerkin projection, but without the need for a full-order or high-fidelity solver to project the Navier-Stokes equations. Instead, the most parsimonious nonlinear model is determined that is consistent with observed mea- surement data and satisfies necessary constraints. Galerkin regression models also readily generalize to include higher-order nonlinear terms that model the effect of truncated modes. The effectiveness of Galerkin regression is demonstrated on two different flow configurations: the two-dimensional flow past a circular cylinder and the shear-driven cavity flow. For both cases, the accuracy of the identified models compare favorably against reduced-order models obtained from a standard Galerkin projection procedure. Present results highlight the importance of cubic nonlinearities in the construction of accurate nonlinear low-dimensional approximations of the flow systems, something which cannot be readily obtained using a standard Galerkin projection of the Navier-Stokes equations. Finally, the entire code base for our constrained sparse Galerkin regression algorithm is freely available online.", "published": "2016-11-10T12:00:27Z", "authors": ["Jean-Christophe Loiseau", "Steven L. Brunton"], "categories": ["physics.flu-dyn"], "doi": "", "_query": "hedonic regression housing", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/1905.06023v1", "title": "Distribution Calibration for Regression", "abstract": "We are concerned with obtaining well-calibrated output distributions from regression models. Such distributions allow us to quantify the uncertainty that the model has regarding the predicted target value. We introduce the novel concept of distribution calibration, and demonstrate its advantages over the existing definition of quantile calibration. We further propose a post-hoc approach to improving the predictions from previously trained regression models, using multi-output Gaussian Processes with a novel Beta link function. The proposed method is experimentally verified on a set of common regression models and shows improvements for both distribution-level and quantile-level calibration.", "published": "2019-05-15T08:21:04Z", "authors": ["Hao Song", "Tom Diethe", "Meelis Kull", "Peter Flach"], "categories": ["stat.ML", "cs.AI", "cs.LG"], "doi": "", "_query": "hedonic regression housing", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2102.03594v2", "title": "Online nonparametric regression with Sobolev kernels", "abstract": "In this work we investigate the variation of the online kernelized ridge regression algorithm in the setting of $d-$dimensional adversarial nonparametric regression. We derive the regret upper bounds on the classes of Sobolev spaces $W_{p}^β(\\mathcal{X})$, $p\\geq 2, β>\\frac{d}{p}$. The upper bounds are supported by the minimax regret analysis, which reveals that in the cases $β> \\frac{d}{2}$ or $p=\\infty$ these rates are (essentially) optimal. Finally, we compare the performance of the kernelized ridge regression forecaster to the known non-parametric forecasters in terms of the regret rates and their computational complexity as well as to the excess risk rates in the setting of statistical (i.i.d.) nonparametric regression.", "published": "2021-02-06T15:05:14Z", "authors": ["Oleksandr Zadorozhnyi", "Pierre Gaillard", "Sebastien Gerschinovitz", "Alessandro Rudi"], "categories": ["math.ST", "cs.LG", "stat.ML"], "doi": "", "_query": "hedonic regression housing", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2011.00981v2", "title": "Coresets for Regressions with Panel Data", "abstract": "This paper introduces the problem of coresets for regression problems to panel data settings. We first define coresets for several variants of regression problems with panel data and then present efficient algorithms to construct coresets of size that depend polynomially on 1/$\\varepsilon$ (where $\\varepsilon$ is the error parameter) and the number of regression parameters - independent of the number of individuals in the panel data or the time units each individual is observed for. Our approach is based on the Feldman-Langberg framework in which a key step is to upper bound the \"total sensitivity\" that is roughly the sum of maximum influences of all individual-time pairs taken over all possible choices of regression parameters. Empirically, we assess our approach with synthetic and real-world datasets; the coreset sizes constructed using our approach are much smaller than the full dataset and coresets indeed accelerate the running time of computing the regression objective.", "published": "2020-11-02T13:58:31Z", "authors": ["Lingxiao Huang", "K. Sudhir", "Nisheeth K. Vishnoi"], "categories": ["cs.LG", "cs.CG", "cs.DS", "econ.EM", "stat.ML"], "doi": "", "_query": "hedonic regression housing", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/1507.04610v1", "title": "Indirect multivariate response linear regression", "abstract": "We propose a new class of estimators of the multivariate response linear regression coefficient matrix that exploits the assumption that the response and predictors have a joint multivariate Normal distribution. This allows us to indirectly estimate the regression coefficient matrix through shrinkage estimation of the parameters of the inverse regression, or the conditional distribution of the predictors given the responses. We establish a convergence rate bound for estimators in our class and we study two examples. The first example estimator exploits an assumption that the inverse regression's coefficient matrix is sparse. The second example estimator exploits an assumption that the inverse regression's coefficient matrix is rank deficient. These estimators do not require the popular assumption that the forward regression coefficient matrix is sparse or has small Frobenius norm. Using simulation studies, we show that our example estimators outperform relevant competitors for some data generating models.", "published": "2015-07-16T15:12:09Z", "authors": ["Aaron J. Molstad", "Adam J. Rothman"], "categories": ["stat.ME"], "doi": "", "_query": "hedonic regression housing", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2603.19506v1", "title": "Doubly-Unlinked Regression for Dependent Data", "abstract": "Shuffled regression concerns settings in which covariates and responses are observed without their correct pairing. In dependent-data problems, a second form of missing correspondence can arise when responses are also detached from the latent temporal, spatial, or geometric domain that induces their dependence structure. We study regression under this joint loss of correspondence and, to our knowledge, provide the first systematic treatment of this setting. Specifically, we consider a doubly-unlinked regression model in which both the covariate-response link and the response-domain link are unknown, represented by two latent permutation matrices, while dependence is induced by an unobserved stochastic process. This framework unifies shuffled regression and latent-domain permutation models within a common dependent-data setting. We characterize signal-to-noise regimes governing recovery of the regression parameter and the latent permutations, and show that consistent estimation of the regression coefficient can be achieved under strictly weaker conditions than exact permutation recovery. To address the combinatorial difficulty of inference, we develop REPAIR, a variational Bayes method based on a block-structured permutation model that captures localized scrambling while substantially reducing computational complexity. Simulations and an applied example illustrate the empirical behavior of REPAIR and support the theoretical results.", "published": "2026-03-19T22:24:56Z", "authors": ["Anik Burman", "Sayantan Choudhury", "Debangan Dey"], "categories": ["math.ST", "stat.ME"], "doi": "", "_query": "hedonic regression housing", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2010.05077v3", "title": "Maximin Optimization for Binary Regression", "abstract": "We consider regression problems with binary weights. Such optimization problems are ubiquitous in quantized learning models and digital communication systems. A natural approach is to optimize the corresponding Lagrangian using variants of the gradient ascent-descent method. Such maximin techniques are still poorly understood even in the concave-convex case. The non-convex binary constraints may lead to spurious local minima. Interestingly, we prove that this approach is optimal in linear regression with low noise conditions as well as robust regression with a small number of outliers. Practically, the method also performs well in regression with cross entropy loss, as well as non-convex multi-layer neural networks. Taken together our approach highlights the potential of saddle-point optimization for learning constrained models.", "published": "2020-10-10T19:47:40Z", "authors": ["Nisan Chiprut", "Amir Globerson", "Ami Wiesel"], "categories": ["cs.LG", "math.OC", "stat.ML"], "doi": "", "_query": "hedonic regression housing", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/1905.08535v3", "title": "Smoothing quantile regressions", "abstract": "We propose to smooth the entire objective function, rather than only the check function, in a linear quantile regression context. Not only does the resulting smoothed quantile regression estimator yield a lower mean squared error and a more accurate Bahadur-Kiefer representation than the standard estimator, but it is also asymptotically differentiable. We exploit the latter to propose a quantile density estimator that does not suffer from the curse of dimensionality. This means estimating the conditional density function without worrying about the dimension of the covariate vector. It also allows for two-stage efficient quantile regression estimation. Our asymptotic theory holds uniformly with respect to the bandwidth and quantile level. Finally, we propose a rule of thumb for choosing the smoothing bandwidth that should approximate well the optimal bandwidth. Simulations confirm that our smoothed quantile regression estimator indeed performs very well in finite samples.", "published": "2019-05-21T10:36:08Z", "authors": ["Marcelo Fernandes", "Emmanuel Guerre", "Eduardo Horta"], "categories": ["econ.EM", "stat.ME"], "doi": "", "_query": "hedonic regression housing", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2310.14992v3", "title": "Bayesian Regression Markets", "abstract": "Although machine learning tasks are highly sensitive to the quality of input data, relevant datasets can often be challenging for firms to acquire, especially when held privately by a variety of owners. For instance, if these owners are competitors in a downstream market, they may be reluctant to share information. Focusing on supervised learning for regression tasks, we develop a regression market to provide a monetary incentive for data sharing. Our mechanism adopts a Bayesian framework, allowing us to consider a more general class of regression tasks. We present a thorough exploration of the market properties, and show that similar proposals in literature expose the market agents to sizeable financial risks, which can be mitigated in our setup.", "published": "2023-10-23T14:45:51Z", "authors": ["Thomas Falconer", "Jalal Kazempour", "Pierre Pinson"], "categories": ["cs.LG"], "doi": "", "_query": "hedonic regression housing", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2106.03374v4", "title": "RegMix: Data Mixing Augmentation for Regression", "abstract": "Data augmentation is becoming essential for improving regression performance in critical applications including manufacturing, climate prediction, and finance. Existing techniques for data augmentation largely focus on classification tasks and do not readily apply to regression tasks. In particular, the recent Mixup techniques for classification have succeeded in improving the model performance, which is reasonable due to the characteristics of the classification task, but has limitations in regression. We show that mixing examples that have large data distances using linear interpolations may have increasingly-negative effects on model performance. Our key idea is thus to limit the distances between examples that are mixed. We propose RegMix, a data augmentation framework for regression that learns for each example how many nearest neighbors it should be mixed with for the best model performance using a validation set. Our experiments conducted both on synthetic and real datasets show that RegMix outperforms state-of-the-art data augmentation baselines applicable to regression.", "published": "2021-06-07T07:01:39Z", "authors": ["Seong-Hyeon Hwang", "Steven Euijong Whang"], "categories": ["cs.LG"], "doi": "", "_query": "hedonic regression housing", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2007.00935v2", "title": "Partial Trace Regression and Low-Rank Kraus Decomposition", "abstract": "The trace regression model, a direct extension of the well-studied linear regression model, allows one to map matrices to real-valued outputs. We here introduce an even more general model, namely the partial-trace regression model, a family of linear mappings from matrix-valued inputs to matrix-valued outputs; this model subsumes the trace regression model and thus the linear regression model. Borrowing tools from quantum information theory, where partial trace operators have been extensively studied, we propose a framework for learning partial trace regression models from data by taking advantage of the so-called low-rank Kraus representation of completely positive maps. We show the relevance of our framework with synthetic and real-world experiments conducted for both i) matrix-to-matrix regression and ii) positive semidefinite matrix completion, two tasks which can be formulated as partial trace regression problems.", "published": "2020-07-02T07:21:22Z", "authors": ["Hachem Kadri", "Stéphane Ayache", "Riikka Huusari", "Alain Rakotomamonjy", "Liva Ralaivola"], "categories": ["cs.LG", "quant-ph", "stat.ML"], "doi": "", "_query": "hedonic regression housing", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2212.02709v2", "title": "SURE-tuned Bridge Regression", "abstract": "Consider the {$\\ell_α$} regularized linear regression, also termed Bridge regression. For $α\\in (0,1)$, Bridge regression enjoys several statistical properties of interest such as sparsity and near-unbiasedness of the estimates (Fan and Li, 2001). However, the main difficulty lies in the non-convex nature of the penalty for these values of $α$, which makes an optimization procedure challenging and usually it is only possible to find a local optimum. To address this issue, Polson et al. (2013) took a sampling based fully Bayesian approach to this problem, using the correspondence between the Bridge penalty and a power exponential prior on the regression coefficients. However, their sampling procedure relies on Markov chain Monte Carlo (MCMC) techniques, which are inherently sequential and not scalable to large problem dimensions. Cross validation approaches are similarly computation-intensive. To this end, our contribution is a novel \\emph{non-iterative} method to fit a Bridge regression model. The main contribution lies in an explicit formula for Stein's unbiased risk estimate for the out of sample prediction risk of Bridge regression, which can then be optimized to select the desired tuning parameters, allowing us to completely bypass MCMC as well as computation-intensive cross validation approaches. Our procedure yields results in a fraction of computational times compared to iterative schemes, without any appreciable loss in statistical performance. An R implementation is publicly available online at: https://github.com/loriaJ/Sure-tuned_BridgeRegression .", "published": "2022-12-06T02:11:09Z", "authors": ["Jorge Loría", "Anindya Bhadra"], "categories": ["stat.ME"], "doi": "", "_query": "hedonic regression housing", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2502.04709v3", "title": "Early Stopping for Regression Trees", "abstract": "We develop early stopping rules for growing regression tree estimators. The fully data-driven stopping rule is based on monitoring the global residual norm. The best-first search and the breadth-first search algorithms together with linear interpolation give rise to generalized projection or regularization flows. A general theory of early stopping is established. Oracle inequalities for the early-stopped regression tree are derived without any smoothness assumption on the regression function, assuming the original CART splitting rule, yet with a much broader scope. The remainder terms are of smaller order than the best achievable rates for Lipschitz functions in dimension $d\\ge 2$. In real and synthetic data the early stopping regression tree estimators attain the statistical performance of cost-complexity pruning while significantly reducing computational costs.", "published": "2025-02-07T07:22:05Z", "authors": ["Ratmir Miftachov", "Markus Reiß"], "categories": ["math.ST"], "doi": "", "_query": "hedonic regression housing", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/1905.13658v1", "title": "Ordinal Regression as Structured Classification", "abstract": "This paper extends the class of ordinal regression models with a structured interpretation of the problem by applying a novel treatment of encoded labels. The net effect of this is to transform the underlying problem from an ordinal regression task to a (structured) classification task which we solve with conditional random fields, thereby achieving a coherent and probabilistic model in which all model parameters are jointly learnt. Importantly, we show that although we have cast ordinal regression to classification, our method still fall within the class of decomposition methods in the ordinal regression ontology. This is an important link since our experience is that many applications of machine learning to healthcare ignores completely the important nature of the label ordering, and hence these approaches should considered naive in this ontology. We also show that our model is flexible both in how it adapts to data manifolds and in terms of the operations that are available for practitioner to execute. Our empirical evaluation demonstrates that the proposed approach overwhelmingly produces superior and often statistically significant results over baseline approaches on forty popular ordinal regression models, and demonstrate that the proposed model significantly out-performs baselines on synthetic and real datasets. Our implementation, together with scripts to reproduce the results of this work, will be available on a public GitHub repository.", "published": "2019-05-31T14:58:29Z", "authors": ["Niall Twomey", "Rafael Poyiadzi", "Callum Mann", "Raúl Santos-Rodríguez"], "categories": ["cs.LG", "stat.ML"], "doi": "", "_query": "hedonic regression housing", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/1110.1986v2", "title": "Sequences of regressions and their dependences", "abstract": "In this paper, we define and study the concept of traceable regressions. These are sequences of regressions in joint or single responses for which a corresponding regression graph captures not only an independence structure but represents, in addition, conditional dependences that permit the tracing of pathways of dependence. We give the properties needed for transforming these graphs and graphical criteria to decide whether a path in the graph induces a dependence. The much stronger constraints on distributions that are faithful to a graph are compared to those needed for traceable regressions.", "published": "2011-10-10T10:15:52Z", "authors": ["Nanny Wermuth"], "categories": ["stat.ME"], "doi": "", "_query": "hedonic regression housing", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2108.00864v1", "title": "Densely connected neural networks for nonlinear regression", "abstract": "Densely connected convolutional networks (DenseNet) behave well in image processing. However, for regression tasks, convolutional DenseNet may lose essential information from independent input features. To tackle this issue, we propose a novel DenseNet regression model where convolution and pooling layers are replaced by fully connected layers and the original concatenation shortcuts are maintained to reuse the feature. To investigate the effects of depth and input dimension of proposed model, careful validations are performed by extensive numerical simulation. The results give an optimal depth (19) and recommend a limited input dimension (under 200). Furthermore, compared with the baseline models including support vector regression, decision tree regression, and residual regression, our proposed model with the optimal depth performs best. Ultimately, DenseNet regression is applied to predict relative humidity, and the outcome shows a high correlation (0.91) with observations, which indicates that our model could advance environmental data analysis.", "published": "2021-07-29T03:41:56Z", "authors": ["Chao Jiang", "Canchen Jiang", "Dongwei Chen", "Fei Hu"], "categories": ["cs.LG", "physics.ao-ph", "physics.data-an", "stat.ME"], "doi": "10.3390/e24070876", "_query": "hedonic regression housing", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/1006.2859v1", "title": "On convex regression estimators", "abstract": "A new nonparametric estimator of a convex regression function in any dimension is proposed and its convergence properties are studied. We start by using any estimator of the regression function and we \\emph{convexify} it by taking the convex envelope of a sample of the approximation obtained. We prove that the uniform rate of convergence of the estimator is maintained after the convexification is applied. The finite sample properties of the new estimator are investigated by means of a simulation study and the application of the new method is demonstrated in examples.", "published": "2010-06-14T22:50:10Z", "authors": ["Néstor E. Aguilera", "Liliana Forzani", "Pedro Morin"], "categories": ["math.ST"], "doi": "", "_query": "hedonic regression housing", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/1807.07697v1", "title": "Wild Residual Bootstrap Inference for Penalized Quantile Regression with Heteroscedastic Errors", "abstract": "We consider a heteroscedastic regression model in which some of the regression coefficients are zero but it is not known which ones. Penalized quantile regression is a useful approach for analyzing such data. By allowing different covariates to be relevant for modeling conditional quantile functions at different quantile levels, it provides a more complete picture of the conditional distribution of a response variable than mean regression. Existing work on penalized quantile regression has been mostly focused on point estimation. Although bootstrap procedures have recently been shown to be effective for inference for penalized mean regression, they are not directly applicable to penalized quantile regression with heteroscedastic errors. We prove that a wild residual bootstrap procedure for unpenalized quantile regression is asymptotically valid for approximating the distribution of a penalized quantile regression estimator with an adaptive $L_1$ penalty and that a modified version can be used to approximate the distribution of $L_1$-penalized quantile regression estimator. The new methods do not need to estimate the unknown error density function. We establish consistency, demonstrate finite sample performance, and illustrate the applications on a real data example.", "published": "2018-07-20T02:23:49Z", "authors": ["Lan Wang", "Ingrid Van Keilegrom", "Adam Maidman"], "categories": ["stat.ME"], "doi": "", "_query": "hedonic regression housing", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2107.09027v2", "title": "Northcott numbers for the house and the Weil height", "abstract": "For an algebraic number $α$ and $γ\\in \\mathbb{R}$, $h(α)$ be the (logarithmic) Weil height, and $h_γ(α)=(\\mathrm{deg}α)^γh(α)$ be the $γ$-weighted (logarithmic) Weil height of $α$. Let $f:\\overline{\\mathbb{Q}}\\to [0,\\infty)$ be a function on the algebraic numbers $\\overline{\\mathbb{Q}}$, and let $S\\subset \\overline{\\mathbb{Q}}$. The Northcott number $\\mathcal{N}_f(S)$ of $S$, with respect to $f$, is the infimum of all $X\\geq 0$ such that $\\{α\\in S; f(α)< X\\}$ is infinite. This paper studies the set of Northcott numbers $\\mathcal{N}_f(\\mathcal{O})$ for subrings of $\\overline{\\mathbb{Q}}$ for the house, the Weil height, and the $γ$-weighted Weil height. We show: (1) Every $t\\geq 1$ is the Northcott number of a ring of integers of a field w.r.t. the house. (2) For each $t\\geq 0$ there exists a field with Northcott number in $ [t,2t]$ w.r.t. the Weil height $h(\\cdot)$. (3) For all $0\\leq γ\\leq 1$ and $γ'<γ$ there exists a field $K$ with $\\mathcal{N}_{h_{γ'}}(K)=0$ and $\\mathcal{N}_{h_γ}(K)=\\infty$. For $(1)$ we provide examples that satisfy an analogue of Julia Robinon's property (JR), examples that satisfy an analogue of Vidaux and Videla's isolation property, and examples that satisfy neither of those. Item $(2)$ concerns a question raised by Vidaux and Videla due to its direct link with decidability theory via the Julia Robinson number. Item (3) is a strong generalisation of the known fact that there are fields that satisfy the Lehmer conjecture but which are not Bogomolov in the sense of Bombieri and Zannier.", "published": "2021-07-19T17:17:28Z", "authors": ["Fabien Pazuki", "Niclas Technau", "Martin Widmer"], "categories": ["math.NT"], "doi": "", "_query": "hedonic regression housing", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/1110.4531v4", "title": "Regression for sets of polynomial equations", "abstract": "We propose a method called ideal regression for approximating an arbitrary system of polynomial equations by a system of a particular type. Using techniques from approximate computational algebraic geometry, we show how we can solve ideal regression directly without resorting to numerical optimization. Ideal regression is useful whenever the solution to a learning problem can be described by a system of polynomial equations. As an example, we demonstrate how to formulate Stationary Subspace Analysis (SSA), a source separation problem, in terms of ideal regression, which also yields a consistent estimator for SSA. We then compare this estimator in simulations with previous optimization-based approaches for SSA.", "published": "2011-10-20T13:51:16Z", "authors": ["Franz Johannes Király", "Paul von Bünau", "Jan Saputra Müller", "Duncan Blythe", "Frank Meinecke", "Klaus-Robert Müller"], "categories": ["stat.ML"], "doi": "", "_query": "hedonic regression housing", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/0708.0471v1", "title": "Quantile regression with varying coefficients", "abstract": "Quantile regression provides a framework for modeling statistical quantities of interest other than the conditional mean. The regression methodology is well developed for linear models, but less so for nonparametric models. We consider conditional quantiles with varying coefficients and propose a methodology for their estimation and assessment using polynomial splines. The proposed estimators are easy to compute via standard quantile regression algorithms and a stepwise knot selection algorithm. The proposed Rao-score-type test that assesses the model against a linear model is also easy to implement. We provide asymptotic results on the convergence of the estimators and the null distribution of the test statistic. Empirical results are also provided, including an application of the methodology to forced expiratory volume (FEV) data.", "published": "2007-08-03T08:32:52Z", "authors": ["Mi-Ok Kim"], "categories": ["math.ST"], "doi": "10.1214/009053606000000966", "_query": "hedonic regression housing", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/1903.10699v5", "title": "On the Theory of Dynamic Graph Regression Problem", "abstract": "Most of real-world graphs are dynamic, i.e., they change over time by a sequence of update operations. While the regression problem has been studied for static graphs and temporal graphs, it is not investigated for general dynamic graphs. In this paper, we study regression over dynamic graphs. First, we present the notion of update-efficient matrix embedding, that defines conditions sufficient for a matrix embedding to be effectively used for dynamic graph regression (under l2 norm). Then, we show that given a n*m update-efficient matrix embedding (e.g., the adjacency matrix) and after an update operation in the graph, the exact optimal solution of linear regression can be updated in O(nm) time for the revised graph. Moreover, we show that this also holds when the matrix embedding is the Laplacian matrix and the update operations are restricted to edge insertion/deletion. In the end, by conducting experiments over synthetic and real-world graphs, we show the high efficiency of updating the solution of graph regression.", "published": "2019-03-26T06:17:49Z", "authors": ["Mostafa Haghir Chehreghani"], "categories": ["cs.LG", "stat.ML"], "doi": "", "_query": "hedonic regression housing", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2602.18865v1", "title": "Expected Shortfall Regression via Optimization", "abstract": "To provide a comprehensive summary of the tail distribution, the expected shortfall is defined as the average over the tail above (or below) a certain quantile of the distribution. The expected shortfall regression captures the heterogeneous covariate-response relationship and describes the covariate effects on the tail of the response distribution. Based on a critical observation that the superquantile regression from the operations research literature does not coincide with the expected shortfall regression, we propose and validate a novel optimization-based approach for the linear expected shortfall regression, without additional assumptions on the conditional quantile models. While the proposed loss function is implicitly defined, we provide a prototype implementation of the proposed approach with some initial expected shortfall estimators based on binning techniques. With practically feasible initial estimators, we establish the consistency and the asymptotic normality of the proposed estimator. The proposed approach achieves heterogeneity-adaptive weights and therefore often offers efficiency gain over existing linear expected shortfall regression approaches in the literature, as demonstrated through simulation studies.", "published": "2026-02-21T15:09:45Z", "authors": ["Yuanzhi Li", "Shushu Zhang", "Xuming He"], "categories": ["stat.ME"], "doi": "", "_query": "hedonic regression housing", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2310.03771v1", "title": "A Comprehensive Indoor Environment Dataset from Single-family Houses in the US", "abstract": "The paper describes a dataset comprising indoor environmental factors such as temperature, humidity, air quality, and noise levels. The data was collected from 10 sensing devices installed in various locations within three single-family houses in Virginia, USA. The objective of the data collection was to study the indoor environmental conditions of the houses over time. The data were collected at a frequency of one record per minute for a year, combining over 2.5 million records. The paper provides actual floor plans with sensor placements to aid researchers and practitioners in creating reliable building performance models. The techniques used to collect and verify the data are also explained in the paper. The resulting dataset can be employed to enhance models for building energy consumption, occupant behavior, predictive maintenance, and other relevant purposes.", "published": "2023-10-05T02:50:33Z", "authors": ["Sheik Murad Hassan Anik", "Xinghua Gao", "Na Meng"], "categories": ["eess.SP"], "doi": "10.3390/data10030035", "_query": "hedonic regression housing", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2205.13501v3", "title": "Wasserstein Logistic Regression with Mixed Features", "abstract": "Recent work has leveraged the popular distributionally robust optimization paradigm to combat overfitting in classical logistic regression. While the resulting classification scheme displays a promising performance in numerical experiments, it is inherently limited to numerical features. In this paper, we show that distributionally robust logistic regression with mixed (i.e., numerical and categorical) features, despite amounting to an optimization problem of exponential size, admits a polynomial-time solution scheme. We subsequently develop a practically efficient column-and-constraint approach that solves the problem as a sequence of polynomial-time solvable exponential conic programs. Our model retains many of the desirable theoretical features of previous works, but -- in contrast to the literature -- it does not admit an equivalent representation as a regularized logistic regression, that is, it represents a genuinely novel variant of logistic regression. We show that our method outperforms both the unregularized and the regularized logistic regression on categorical as well as mixed-feature benchmark instances.", "published": "2022-05-26T17:16:25Z", "authors": ["Aras Selvi", "Mohammad Reza Belbasi", "Martin B Haugh", "Wolfram Wiesemann"], "categories": ["math.OC"], "doi": "", "_query": "hedonic regression housing", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2005.10642v1", "title": "Nonlinear Regression Analysis Using Multi-Verse Optimizer", "abstract": "Regression analysis is an important machine learning task used for predictive analytic in business, sports analysis, etc. In regression analysis, optimization algorithms play a significant role in search the coefficients in the regression model. In this paper, nonlinear regression analysis using a recently developed meta-heuristic Multi-Verse Optimizer (MVO) is proposed. The proposed method is applied to 10 well-known benchmark nonlinear regression problems. A comparative study has been conducted with Particle Swarm Optimizer (PSO). The experimental results demonstrate that the proposed method statistically outperforms PSO algorithm.", "published": "2020-04-28T15:03:52Z", "authors": ["Jayri Bagchi", "Tapas Si"], "categories": ["cs.LG", "stat.ML"], "doi": "", "_query": "hedonic regression housing", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/1104.1244v2", "title": "Rank penalized estimators for high-dimensional matrices", "abstract": "In this paper we consider the trace regression model. Assume that we observe a small set of entries or linear combinations of entries of an unknown matrix $A_0$ corrupted by noise. We propose a new rank penalized estimator of $A_0$. For this estimator we establish general oracle inequality for the prediction error both in probability and in expectation. We also prove upper bounds for the rank of our estimator. Then, we apply our general results to the problems of matrix completion and matrix regression. In these cases our estimator has a particularly simple form: it is obtained by hard thresholding of the singular values of a matrix constructed from the observations.", "published": "2011-04-07T05:37:07Z", "authors": ["Olga Klopp"], "categories": ["math.ST"], "doi": "", "_query": "hedonic regression housing", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2508.02658v1", "title": "Structural Extrapolation in Regression Discontinuity Designs with an Application to School Expenditure Referenda", "abstract": "We propose a structural approach to extrapolate average partial effects away from the cutoff in regression discontinuity designs (RDDs). Our focus is on applications that exploit closely contested school district referenda to estimate the effects of changes in education spending on local economic outcomes. We embed these outcomes in a spatial equilibrium model of local jurisdictions in which fiscal policy is determined by majority rule voting. This integration provides a microfoundation for the running variable, the share of voters who approve a ballot initiative, and enables identification of structural parameters using RDD coefficients. We then leverage the model to simulate the effects of counterfactual referenda over a broad range of proposed spending changes. These scenarios imply realizations of the running variable away from the threshold, allowing extrapolation of RDD estimates to nonmarginal referenda. Applying the method to school expenditure ballot measures in Wisconsin, we document substantial heterogeneity in housing price capitalization across the approval margin.", "published": "2025-08-04T17:49:09Z", "authors": ["Austin Feng", "Francesco Ruggieri"], "categories": ["econ.EM"], "doi": "", "_query": "hedonic regression housing", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2005.01988v1", "title": "One-step regression and classification with crosspoint resistive memory arrays", "abstract": "Machine learning has been getting a large attention in the recent years, as a tool to process big data generated by ubiquitous sensors in our daily life. High speed, low energy computing machines are in demand to enable real-time artificial intelligence at the edge, i.e., without the support of a remote frame server in the cloud. Such requirements challenge the complementary metal-oxide-semiconductor (CMOS) technology, which is limited by the Moore's law approaching its end and the communication bottleneck in conventional computing architecture. Novel computing concepts, architectures and devices are thus strongly needed to accelerate data-intensive applications. Here we show a crosspoint resistive memory circuit with feedback configuration can execute linear regression and logistic regression in just one step by computing the pseudoinverse matrix of the data within the memory. The most elementary learning operation, that is the regression of a sequence of data and the classification of a set of data, can thus be executed in one single computational step by the novel technology. One-step learning is further supported by simulations of the prediction of the cost of a house in Boston and the training of a 2-layer neural network for MNIST digit recognition. The results are all obtained in one computational step, thanks to the physical, parallel, and analog computing within the crosspoint array.", "published": "2020-05-05T08:00:07Z", "authors": ["Zhong Sun", "Giacomo Pedretti", "Alessandro Bricalli", "Daniele Ielmini"], "categories": ["cs.LG", "cs.ET", "stat.ML"], "doi": "10.1126/sciadv.aay2378", "_query": "hedonic regression housing", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/1909.04454v1", "title": "Adversarial Orthogonal Regression: Two non-Linear Regressions for Causal Inference", "abstract": "We propose two nonlinear regression methods, named Adversarial Orthogonal Regression (AdOR) for additive noise models and Adversarial Orthogonal Structural Equation Model (AdOSE) for the general case of structural equation models. Both methods try to make the residual of regression independent from regressors while putting no assumption on noise distribution. In both methods, two adversarial networks are trained simultaneously where a regression network outputs predictions and a loss network that estimates mutual information (in AdOR) and KL-divergence (in AdOSE). These methods can be formulated as a minimax two-player game; at equilibrium, AdOR finds a deterministic map between inputs and output and estimates mutual information between residual and inputs, while AdOSE estimates a conditional probability distribution of output given inputs. The proposed methods can be used as subroutines to address several learning problems in causality, such as causal direction determination (or more generally, causal structure learning) and causal model estimation. Synthetic and real-world experiments demonstrate that the proposed methods have a remarkable performance with respect to previous solutions.", "published": "2019-09-10T12:59:59Z", "authors": ["M. Reza Heydari", "Saber Salehkaleybar", "Kun Zhang"], "categories": ["cs.LG", "stat.ML"], "doi": "", "_query": "hedonic regression housing", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2207.09865v1", "title": "Discrete-Constrained Regression for Local Counting Models", "abstract": "Local counts, or the number of objects in a local area, is a continuous value by nature. Yet recent state-of-the-art methods show that formulating counting as a classification task performs better than regression. Through a series of experiments on carefully controlled synthetic data, we show that this counter-intuitive result is caused by imprecise ground truth local counts. Factors such as biased dot annotations and incorrectly matched Gaussian kernels used to generate ground truth counts introduce deviations from the true local counts. Standard continuous regression is highly sensitive to these errors, explaining the performance gap between classification and regression. To mitigate the sensitivity, we loosen the regression formulation from a continuous scale to a discrete ordering and propose a novel discrete-constrained (DC) regression. Applied to crowd counting, DC-regression is more accurate than both classification and standard regression on three public benchmarks. A similar advantage also holds for the age estimation task, verifying the overall effectiveness of DC-regression.", "published": "2022-07-20T12:54:23Z", "authors": ["Haipeng Xiong", "Angela Yao"], "categories": ["cs.CV"], "doi": "", "_query": "hedonic regression housing", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/1806.01460v2", "title": "Dynamic Function-on-Scalars Regression", "abstract": "We develop a modeling framework for dynamic function-on-scalars regression, in which a time series of functional data is regressed on a time series of scalar predictors. The regression coefficient function for each predictor is allowed to be dynamic, which is essential for applications where the association between predictors and a (functional) response is time-varying. For greater modeling flexibility, we design a nonparametric reduced-rank functional data model with an unknown functional basis expansion, which is data-adaptive and, unlike most existing methods, modeled as unknown for appropriate uncertainty quantification. Within a Bayesian framework, we introduce shrinkage priors that simultaneously (i) regularize time-varying regression coefficient functions to be locally static, (ii) effectively remove unimportant predictor variables from the model, and (iii) reduce sensitivity to the dimension of the functional basis. A simulation analysis confirms the importance of these shrinkage priors, with notable improvements over existing alternatives. We develop a novel projection-based Gibbs sampling algorithm, which offers unrivaled computational scalability for fully Bayesian functional regression. We apply the proposed methodology (i) to analyze the time-varying impact of macroeconomic variables on the U.S. yield curve and (ii) to characterize the effects of socioeconomic and demographic predictors on age-specific fertility rates in South and Southeast Asia.", "published": "2018-06-05T01:49:02Z", "authors": ["Daniel R. Kowal"], "categories": ["stat.ME"], "doi": "", "_query": "hedonic regression housing", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/1208.0219v1", "title": "Functional Mechanism: Regression Analysis under Differential Privacy", "abstract": "ε-differential privacy is the state-of-the-art model for releasing sensitive information while protecting privacy. Numerous methods have been proposed to enforce epsilon-differential privacy in various analytical tasks, e.g., regression analysis. Existing solutions for regression analysis, however, are either limited to non-standard types of regression or unable to produce accurate regression results. Motivated by this, we propose the Functional Mechanism, a differentially private method designed for a large class of optimization-based analyses. The main idea is to enforce epsilon-differential privacy by perturbing the objective function of the optimization problem, rather than its results. As case studies, we apply the functional mechanism to address two most widely used regression models, namely, linear regression and logistic regression. Both theoretical analysis and thorough experimental evaluations show that the functional mechanism is highly effective and efficient, and it significantly outperforms existing solutions.", "published": "2012-08-01T14:11:04Z", "authors": ["Jun Zhang", "Zhenjie Zhang", "Xiaokui Xiao", "Yin Yang", "Marianne Winslett"], "categories": ["cs.DB"], "doi": "", "_query": "hedonic regression housing", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2104.06487v1", "title": "Gaussian Process Model for Estimating Piecewise Continuous Regression Functions", "abstract": "This paper presents a Gaussian process (GP) model for estimating piecewise continuous regression functions. In scientific and engineering applications of regression analysis, the underlying regression functions are piecewise continuous in that data follow different continuous regression models for different regions of the data with possible discontinuities between the regions. However, many conventional GP regression approaches are not designed for piecewise regression analysis. We propose a new GP modeling approach for estimating an unknown piecewise continuous regression function. The new GP model seeks for a local GP estimate of an unknown regression function at each test location, using local data neighboring to the test location. To accommodate the possibilities of the local data from different regions, the local data is partitioned into two sides by a local linear boundary, and only the local data belonging to the same side as the test location is used for the regression estimate. This local split works very well when the input regions are bounded by smooth boundaries, so the local linear approximation of the smooth boundaries works well. We estimate the local linear boundary jointly with the other hyperparameters of the GP model, using the maximum likelihood approach. Its computation time is as low as the local GP's time. The superior numerical performance of the proposed approach over the conventional GP modeling approaches is shown using various simulated piecewise regression functions.", "published": "2021-04-13T20:01:43Z", "authors": ["Chiwoo Park"], "categories": ["stat.ME", "cs.LG", "stat.ML"], "doi": "", "_query": "hedonic regression housing", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2411.04811v1", "title": "Defending Deep Regression Models against Backdoor Attacks", "abstract": "Deep regression models are used in a wide variety of safety-critical applications, but are vulnerable to backdoor attacks. Although many defenses have been proposed for classification models, they are ineffective as they do not consider the uniqueness of regression models. First, the outputs of regression models are continuous values instead of discretized labels. Thus, the potential infected target of a backdoored regression model has infinite possibilities, which makes it impossible to be determined by existing defenses. Second, the backdoor behavior of backdoored deep regression models is triggered by the activation values of all the neurons in the feature space, which makes it difficult to be detected and mitigated using existing defenses. To resolve these problems, we propose DRMGuard, the first defense to identify if a deep regression model in the image domain is backdoored or not. DRMGuard formulates the optimization problem for reverse engineering based on the unique output-space and feature-space characteristics of backdoored deep regression models. We conduct extensive evaluations on two regression tasks and four datasets. The results show that DRMGuard can consistently defend against various backdoor attacks. We also generalize four state-of-the-art defenses designed for classifiers to regression models, and compare DRMGuard with them. The results show that DRMGuard significantly outperforms all those defenses.", "published": "2024-11-07T15:49:03Z", "authors": ["Lingyu Du", "Yupei Liu", "Jinyuan Jia", "Guohao Lan"], "categories": ["cs.LG", "cs.AI"], "doi": "", "_query": "hedonic regression housing", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/1707.07938v2", "title": "Error Bounds for Piecewise Smooth and Switching Regression", "abstract": "The paper deals with regression problems, in which the nonsmooth target is assumed to switch between different operating modes. Specifically, piecewise smooth (PWS) regression considers target functions switching deterministically via a partition of the input space, while switching regression considers arbitrary switching laws. The paper derives generalization error bounds in these two settings by following the approach based on Rademacher complexities. For PWS regression, our derivation involves a chaining argument and a decomposition of the covering numbers of PWS classes in terms of the ones of their component functions and the capacity of the classifier partitioning the input space. This yields error bounds with a radical dependency on the number of modes. For switching regression, the decomposition can be performed directly at the level of the Rademacher complexities, which yields bounds with a linear dependency on the number of modes. By using once more chaining and a decomposition at the level of covering numbers, we show how to recover a radical dependency. Examples of applications are given in particular for PWS and swichting regression with linear and kernel-based component functions.", "published": "2017-07-25T12:06:22Z", "authors": ["Fabien Lauer"], "categories": ["stat.ML", "cs.LG"], "doi": "", "_query": "hedonic regression housing", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/1806.09014v2", "title": "Assumption Lean Regression", "abstract": "It is well known that models used in conventional regression analysis are commonly misspecified. A standard response is little more than a shrug. Data analysts invoke Box's maxim that all models are wrong and then proceed as if the results are useful nevertheless. In this paper, we provide an alternative. Regression models are treated explicitly as approximations of a true response surface that can have a number of desirable statistical properties, including estimates that are asymptotically unbiased. Valid statistical inference follows. We generalize the formulation to include regression functionals, which broadens substantially the range of potential applications. An empirical application is provided to illustrate the paper's key concepts.", "published": "2018-06-23T18:01:18Z", "authors": ["Richard A. Berk", "Andreas Buja", "Lawrence Brown", "Edward George", "Arun Kumar Kuchibhotla", "Weijie J. Su", "Linda Zhao"], "categories": ["stat.ME"], "doi": "", "_query": "hedonic regression housing", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/1810.03140v4", "title": "On LASSO for Predictive Regression", "abstract": "Explanatory variables in a predictive regression typically exhibit low signal strength and various degrees of persistence. Variable selection in such a context is of great importance. In this paper, we explore the pitfalls and possibilities of the LASSO methods in this predictive regression framework. In the presence of stationary, local unit root, and cointegrated predictors, we show that the adaptive LASSO cannot asymptotically eliminate all cointegrating variables with zero regression coefficients. This new finding motivates a novel post-selection adaptive LASSO, which we call the twin adaptive LASSO (TAlasso), to restore variable selection consistency. Accommodating the system of heterogeneous regressors, TAlasso achieves the well-known oracle property. In contrast, conventional LASSO fails to attain coefficient estimation consistency and variable screening in all components simultaneously. We apply these LASSO methods to evaluate the short- and long-horizon predictability of S\\&P 500 excess returns.", "published": "2018-10-07T13:19:07Z", "authors": ["Ji Hyung Lee", "Zhentao Shi", "Zhan Gao"], "categories": ["econ.EM"], "doi": "", "_query": "hedonic regression housing", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/1204.4154v1", "title": "The Artificial Regression Market", "abstract": "The Artificial Prediction Market is a recent machine learning technique for multi-class classification, inspired from the financial markets. It involves a number of trained market participants that bet on the possible outcomes and are rewarded if they predict correctly. This paper generalizes the scope of the Artificial Prediction Markets to regression, where there are uncountably many possible outcomes and the error is usually the MSE. For that, we introduce the reward kernel that rewards each participant based on its prediction error and we derive the price equations. Using two reward kernels we obtain two different learning rules, one of which is approximated using Hermite-Gauss quadrature. The market setting makes it easy to aggregate specialized regressors that only predict when an observation falls into their specialization domain. Experiments show that regression markets based on the two learning rules outperform Random Forest Regression on many UCI datasets and are rarely outperformed.", "published": "2012-04-18T18:16:59Z", "authors": ["Nathan Lay", "Adrian Barbu"], "categories": ["stat.ML", "math.ST"], "doi": "", "_query": "hedonic regression housing", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2304.12429v2", "title": "Sparse Private LASSO Logistic Regression", "abstract": "LASSO regularized logistic regression is particularly useful for its built-in feature selection, allowing coefficients to be removed from deployment and producing sparse solutions. Differentially private versions of LASSO logistic regression have been developed, but generally produce dense solutions, reducing the intrinsic utility of the LASSO penalty. In this paper, we present a differentially private method for sparse logistic regression that maintains hard zeros. Our key insight is to first train a non-private LASSO logistic regression model to determine an appropriate privatized number of non-zero coefficients to use in final model selection. To demonstrate our method's performance, we run experiments on synthetic and real-world datasets.", "published": "2023-04-24T20:14:12Z", "authors": ["Amol Khanna", "Fred Lu", "Edward Raff", "Brian Testa"], "categories": ["cs.LG", "cs.CR"], "doi": "", "_query": "hedonic regression housing", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2203.07924v2", "title": "Fast, slow convergence, and concentration in the house of cards replicator-mutator model", "abstract": "We propose a fine analysis of the various possible long time behaviours of the solutions of the replicator-mutator equation with so-called Kingman's house of cards mutations. In particular, we give what is to our knowledge the first concentration result for this model.", "published": "2022-03-15T14:05:35Z", "authors": ["Bertrand Cloez", "Pierre Gabriel"], "categories": ["math.AP", "math.PR"], "doi": "10.57262/die037-0708-547", "_query": "hedonic regression housing", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/1108.3904v1", "title": "Shrinkage Estimation and Selection for Multiple Functional Regression", "abstract": "Functional linear regression is a useful extension of simple linear regression and has been investigated by many researchers. However, functional variable selection problems when multiple functional observations exist, which is the counterpart in the functional context of multiple linear regression, is seldom studied. Here we propose a method using group smoothly clipped absolute deviation penalty (gSCAD) which can perform regression estimation and variable selection simultaneously. We show the method can identify the true model consistently and discuss construction of pointwise confidence interval for the estimated functional coefficients. Our methodology and theory is verified by simulation studies as well as an application to spectrometrics data.", "published": "2011-08-19T06:30:14Z", "authors": ["Heng Lian"], "categories": ["stat.ME"], "doi": "", "_query": "hedonic regression housing", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/1503.08458v2", "title": "Isotonic regression and isotonic projection", "abstract": "The note describes the cones in the Euclidean space admitting isotonic metric projection with respect to the coordinate-wise ordering. As a consequence it is showed that the metric projection onto the regression cone (the cone defined by the general isotonic regression problem) admits a projection which is isotonic with respect to the coordinate-wise ordering.", "published": "2015-03-29T16:59:19Z", "authors": ["A. B. Németh", "S. Z. Németh"], "categories": ["math.ST", "math.OC"], "doi": "", "_query": "hedonic regression housing", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/1905.02535v1", "title": "F-measure Maximizing Logistic Regression", "abstract": "Logistic regression is a widely used method in several fields. When applying logistic regression to imbalanced data, for which majority classes dominate over minority classes, all class labels are estimated as `majority class.' In this article, we use an F-measure optimization method to improve the performance of logistic regression applied to imbalanced data. While many F-measure optimization methods adopt a ratio of the estimators to approximate the F-measure, the ratio of the estimators tends to have more bias than when the ratio is directly approximated. Therefore, we employ an approximate F-measure for estimating the relative density ratio. In addition, we define a relative F-measure and approximate the relative F-measure. We show an algorithm for a logistic regression weighted approximated relative to the F-measure. The experimental results using real world data demonstrated that our proposed method is an efficient algorithm to improve the performance of logistic regression applied to imbalanced data.", "published": "2019-05-07T13:18:22Z", "authors": ["Masaaki Okabe", "Jun Tsuchida", "Hiroshi Yadohisa"], "categories": ["stat.ME", "cs.LG", "stat.ML"], "doi": "10.1080/03610918.2022.2081706", "_query": "hedonic regression housing", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/1109.5587v2", "title": "High-dimensional regression with unknown variance", "abstract": "We review recent results for high-dimensional sparse linear regression in the practical case of unknown variance. Different sparsity settings are covered, including coordinate-sparsity, group-sparsity and variation-sparsity. The emphasis is put on non-asymptotic analyses and feasible procedures. In addition, a small numerical study compares the practical performance of three schemes for tuning the Lasso estimator and some references are collected for some more general models, including multivariate regression and nonparametric regression.", "published": "2011-09-26T14:41:53Z", "authors": ["Christophe Giraud", "Sylvie Huet", "Nicolas Verzelen"], "categories": ["math.ST"], "doi": "", "_query": "hedonic regression housing", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2608.24371v1", "title": "Groupwise Predictor Envelope Models for Multivariate Linear Regression", "abstract": "Envelope methods improve estimation efficiency in multivariate analysis by isolating low-dimensional structures that contain all the information material to the parameter of interest. In multivariate linear regression with random predictors, predictor envelope models achieve this goal by removing variation in the predictors that is immaterial to the regression. In many applications, observations are naturally divided into several groups, such as treatment groups, regions, or demographic strata, and the regression relationship may differ between groups. Motivated by this setting, we propose a groupwise predictor envelope model for multivariate linear regression. The proposed model assumes that the group-specific regression coefficient matrices are represented through a common predictor envelope subspace while allowing group-specific regression effects and group-specific error covariance matrices. We derive an objective function for estimating the common predictor envelope, obtain the corresponding regression estimators, and establish asymptotic normality together with an explicit asymptotic variance formula. Moreover, we show that the proposed estimator is asymptotically more efficient than the estimator obtained by fitting predictor envelope models separately to each group. This theoretical advantage over the existing work is also demonstrated through simulation studies.", "published": "2026-08-25T10:29:13Z", "authors": ["Sota Osumi", "Akira Okazaki", "Shuichi Kawano"], "categories": ["stat.ME"], "doi": "", "_query": "hedonic regression housing", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/0801.4442v1", "title": "The Synthesis of Regression Slopes in Meta-Analysis", "abstract": "Research on methods of meta-analysis (the synthesis of related study results) has dealt with many simple study indices, but less attention has been paid to the issue of summarizing regression slopes. In part this is because of the many complications that arise when real sets of regression models are accumulated. We outline the complexities involved in synthesizing slopes, describe existing methods of analysis and present a multivariate generalized least squares approach to the synthesis of regression slopes.", "published": "2008-01-29T07:58:20Z", "authors": ["Betsy Jane Becker", "Meng-Jia Wu"], "categories": ["stat.ME"], "doi": "10.1214/07-STS243", "_query": "hedonic regression housing", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/1710.07575v2", "title": "Quantile Regression with Interval Data", "abstract": "This paper investigates the identification of quantiles and quantile regression parameters when observations are set valued. We define the identification set of quantiles of random sets in a way that extends the definition of quantiles for regular random variables. We then give sharp characterization of this set by extending concepts from random set theory. For quantile regression parameters, we show that the identification set is characterized by a system of conditional moment inequalities. This characterization extends that of parametric quantile regression for regular random variables. Estimation and inference theories are developed for continuous cases, discrete cases, nonparametric conditional quantiles, and parametric quantile regressions. A fast computational algorithm of set linear programming is proposed. Monte Carlo experiments support our theoretical properties.", "published": "2017-10-20T15:29:12Z", "authors": ["Arie Beresteanu", "Yuya Sasaki"], "categories": ["stat.ME"], "doi": "", "_query": "hedonic regression housing", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2601.10049v1", "title": "Weighted least squares estimation by multivariate-dependent weights for linear regression models", "abstract": "Multivariate linear regression models often face the problem of heteroscedasticity caused by multiple explanatory variables. The weighted least squares estimation with univariate-dependent weights has limitations in constructing weight functions. Therefore, this paper proposes a multivariate dependent weighted least squares estimation method. By constructing a linear combination of explanatory variables and maximizing their Spearman rank correlation coefficient with the absolute residual value, combined with maximum likelihood method to depict heteroscedasticity, it can comprehensively reflect the trend of variance changes in the random error and improve the accuracy of the model. This paper demonstrates that the optimal linear combination exponent estimator for heteroscedastic volatility obtained by our algorithm possesses consistency and asymptotic normality. In the simulation experiment, three scenarios of heteroscedasticity were designed, and the comparison showed that the proposed method was superior to the univariate-dependent weighting method in parameter estimation and model prediction. In the real data applications, the proposed method was applied to two real-world datasets about consumer spending in China and housing prices in Boston. From the perspectives of MAE, RSE, cross-validation, and fitting performance, its accuracy and stability were verified in terms of model prediction, interval estimation, and generalization ability. Additionally, the proposed method demonstrated relative advantages in fitting data with large fluctuations. This study provides an effective new approach for dealing with heteroscedasticity in multivariate linear regression.", "published": "2026-01-15T03:59:30Z", "authors": ["Lei Huang", "Chengyue Liu", "Li Wang"], "categories": ["stat.ME"], "doi": "", "_query": "hedonic regression housing", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/1704.00159v3", "title": "Compositional Human Pose Regression", "abstract": "Regression based methods are not performing as well as detection based methods for human pose estimation. A central problem is that the structural information in the pose is not well exploited in the previous regression methods. In this work, we propose a structure-aware regression approach. It adopts a reparameterized pose representation using bones instead of joints. It exploits the joint connection structure to define a compositional loss function that encodes the long range interactions in the pose. It is simple, effective, and general for both 2D and 3D pose estimation in a unified setting. Comprehensive evaluation validates the effectiveness of our approach. It significantly advances the state-of-the-art on Human3.6M and is competitive with state-of-the-art results on MPII.", "published": "2017-04-01T11:59:41Z", "authors": ["Xiao Sun", "Jiaxiang Shang", "Shuang Liang", "Yichen Wei"], "categories": ["cs.CV"], "doi": "", "_query": "hedonic regression housing", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/1702.06209v1", "title": "Uniform Inference for High-dimensional Quantile Regression: Linear Functionals and Regression Rank Scores", "abstract": "Hypothesis tests in models whose dimension far exceeds the sample size can be formulated much like the classical studentized tests only after the initial bias of estimation is removed successfully. The theory of debiased estimators can be developed in the context of quantile regression models for a fixed quantile value. However, it is frequently desirable to formulate tests based on the quantile regression process, as this leads to more robust tests and more stable confidence sets. Additionally, inference in quantile regression requires estimation of the so called sparsity function, which depends on the unknown density of the error. In this paper we consider a debiasing approach for the uniform testing problem. We develop high-dimensional regression rank scores and show how to use them to estimate the sparsity function, as well as how to adapt them for inference involving the quantile regression process. Furthermore, we develop a Kolmogorov-Smirnov test in a location-shift high-dimensional models and confidence sets that are uniformly valid for many quantile values. The main technical result are the development of a Bahadur representation of the debiasing estimator that is uniform over a range of quantiles and uniform convergence of the quantile process to the Brownian bridge process, which are of independent interest. Simulation studies illustrate finite sample properties of our procedure.", "published": "2017-02-20T23:38:40Z", "authors": ["Jelena Bradic", "Mladen Kolar"], "categories": ["stat.ML"], "doi": "", "_query": "hedonic regression housing", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/1603.03073v2", "title": "Mechanisms for House Allocation with Existing Tenants under Dichotomous Preferences", "abstract": "We consider house allocation with existing tenants in which each agent has dichotomous preferences. We present strategyproof, polynomial-time, and (strongly) individually rational algorithms that satisfy the maximum number of agents. For the endowment only model, one of the algorithms also returns a core-stable allocation.", "published": "2016-03-09T21:54:10Z", "authors": ["Haris Aziz"], "categories": ["cs.GT"], "doi": "", "_query": "hedonic regression housing", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2201.02948v1", "title": "Tree-based Regression for Interval-valued Data", "abstract": "Regression methods for interval-valued data have been increasingly studied in recent years. As most of the existing works focus on linear models, it is important to note that many problems in practice are nonlinear in nature and therefore development of nonlinear regression tools for interval-valued data is crucial. In this paper, we propose a tree-based regression method for interval-valued data, which is well applicable to both linear and nonlinear problems. Unlike linear regression models that usually require additional constraints to ensure positivity of the predicted interval length, the proposed method estimates the regression function in a nonparametric way, so the predicted length is naturally positive without any constraints. A simulation study is conducted that compares our method to popular existing regression models for interval-valued data under both linear and nonlinear settings. Furthermore, a real data example is presented where we apply our method to analyze price range data of the Dow Jones Industrial Average index and its component stocks.", "published": "2022-01-09T07:31:16Z", "authors": ["Chih-Ching Yeh", "Yan Sun", "Adele Cutler"], "categories": ["stat.ME", "stat.CO"], "doi": "", "_query": "hedonic regression housing", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/cs/0405071v1", "title": "Regression with respect to sensing actions and partial states", "abstract": "In this paper, we present a state-based regression function for planning domains where an agent does not have complete information and may have sensing actions. We consider binary domains and employ the 0-approximation [Son & Baral 2001] to define the regression function. In binary domains, the use of 0-approximation means using 3-valued states. Although planning using this approach is incomplete with respect to the full semantics, we adopt it to have a lower complexity. We prove the soundness and completeness of our regression formulation with respect to the definition of progression. More specifically, we show that (i) a plan obtained through regression for a planning problem is indeed a progression solution of that planning problem, and that (ii) for each plan found through progression, using regression one obtains that plan or an equivalent one. We then develop a conditional planner that utilizes our regression function. We prove the soundness and completeness of our planning algorithm and present experimental results with respect to several well known planning problems in the literature.", "published": "2004-05-21T12:43:19Z", "authors": ["Le-Chi Tuan", "Chitta Baral", "Tran Cao Son"], "categories": ["cs.AI"], "doi": "", "_query": "hedonic regression housing", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/1601.05766v2", "title": "Adaptive confidence sets in shape restricted regression", "abstract": "A simple construction of adaptive confidence sets is proposed in isotonic, convex and unimodal regression. In univariate isotonic regression, the proposed confidence set enjoys uniform coverage over all non-decreasing regression functions. Furthermore, the diameter of the proposed confidence set automatically adapts to the unknown number of pieces of the true parameter, in the sense that the diameter is bounded from above by the minimax risk over the class of $k$-piecewise constant functions. The diameter of the confidence set is a simple increasing function of the number of jumps of the isotonic least-squares estimate. A similar construction is proposed in convex regression where the true regression function is convex and piecewise affine. Here, the confidence set enjoys uniform coverage and its diameter automatically adapt to the number of affine pieces of the true regression function. The diameter of the confidence set is an increasing function of the number of affine pieces of the convex least-squares estimate. We explain how to extend this technique to a non-convex set by proposing a similar adaptive confidence set in unimodal regression. The confidence set automatically adapts to the number of jumps of the true unimodal regression function and its diameter is an increasing function of the number of jumps of the unimodal least-squares estimate.", "published": "2016-01-21T20:17:23Z", "authors": ["Pierre C. Bellec"], "categories": ["math.ST"], "doi": "", "_query": "hedonic regression housing", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2402.11963v1", "title": "Imbalance in Regression Datasets", "abstract": "For classification, the problem of class imbalance is well known and has been extensively studied. In this paper, we argue that imbalance in regression is an equally important problem which has so far been overlooked: Due to under- and over-representations in a data set's target distribution, regressors are prone to degenerate to naive models, systematically neglecting uncommon training data and over-representing targets seen often during training. We analyse this problem theoretically and use resulting insights to develop a first definition of imbalance in regression, which we show to be a generalisation of the commonly employed imbalance measure in classification. With this, we hope to turn the spotlight on the overlooked problem of imbalance in regression and to provide common ground for future research.", "published": "2024-02-19T09:06:26Z", "authors": ["Daniel Kowatsch", "Nicolas M. Müller", "Kilian Tscharke", "Philip Sperl", "Konstantin Bötinger"], "categories": ["cs.LG", "cs.AI"], "doi": "", "_query": "hedonic regression housing", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2009.02317v1", "title": "Approximation, characterization, and continuity of multivariate monotonic regression functions", "abstract": "We deal with monotonic regression of multivariate functions $f: Q \\to \\mathbb{R}$ on a compact rectangular domain $Q$ in $\\mathbb{R}^d$, where monotonicity is understood in a generalized sense: as isotonicity in some coordinate directions and antitonicity in some other coordinate directions. As usual, the monotonic regression of a given function $f$ is the monotonic function $f^*$ that has the smallest (weighted) mean-squared distance from $f$. We establish a simple general approach to compute monotonic regression functions: namely, we show that the monotonic regression $f^*$ of a given function $f$ can be approximated arbitrarily well -- with simple bounds on the approximation error in both the $2$-norm and the $\\infty$-norm -- by the monotonic regression $f_n^*$ of grid-constant functions $f_n$. We also establish the continuity of the monotonic regression $f^*$ of a continuous function $f$ along with an explicit averaging formula for $f^*$. And finally, we deal with generalized monotonic regression where the mean-squared distance from standard monotonic regression is replaced by more complex distance measures which arise, for instance, in maximum smoothed likelihood estimation. We will see that the solution of such generalized monotonic regression problems is simply given by the standard monotonic regression $f^*$.", "published": "2020-09-04T17:48:13Z", "authors": ["Jochen Schmid"], "categories": ["math.OC", "math.NA"], "doi": "", "_query": "hedonic regression housing", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2209.04876v2", "title": "Subquadratic Kronecker Regression with Applications to Tensor Decomposition", "abstract": "Kronecker regression is a highly-structured least squares problem $\\min_{\\mathbf{x}} \\lVert \\mathbf{K}\\mathbf{x} - \\mathbf{b} \\rVert_{2}^2$, where the design matrix $\\mathbf{K} = \\mathbf{A}^{(1)} \\otimes \\cdots \\otimes \\mathbf{A}^{(N)}$ is a Kronecker product of factor matrices. This regression problem arises in each step of the widely-used alternating least squares (ALS) algorithm for computing the Tucker decomposition of a tensor. We present the first subquadratic-time algorithm for solving Kronecker regression to a $(1+\\varepsilon)$-approximation that avoids the exponential term $O(\\varepsilon^{-N})$ in the running time. Our techniques combine leverage score sampling and iterative methods. By extending our approach to block-design matrices where one block is a Kronecker product, we also achieve subquadratic-time algorithms for (1) Kronecker ridge regression and (2) updating the factor matrices of a Tucker decomposition in ALS, which is not a pure Kronecker regression problem, thereby improving the running time of all steps of Tucker ALS. We demonstrate the speed and accuracy of this Kronecker regression algorithm on synthetic data and real-world image tensors.", "published": "2022-09-11T14:24:19Z", "authors": ["Matthew Fahrbach", "Thomas Fu", "Mehrdad Ghadiri"], "categories": ["cs.DS", "cs.LG", "math.ST"], "doi": "", "_query": "hedonic regression housing", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2504.05819v1", "title": "Nonparametric local polynomial regression for functional covariates", "abstract": "We consider nonparametric regression with functional covariates, that is, they are elements of an infinite-dimensional Hilbert space. A locally polynomial estimator is constructed, where an orthonormal basis and various tuning parameters remain to be selected. We provide a general asymptotic upper bound on the estimation error and show that this procedure achieves polynomial convergence rates under appropriate tuning and supersmoothness of the regression function. Such polynomial convergence rates have usually been considered to be non-attainable in nonparametric functional regression without any additional strong structural constraints such as linearity of the regression function.", "published": "2025-04-08T09:02:02Z", "authors": ["Moritz Jirak", "Alois Kneip", "Alexander Meister", "Mario Pahl"], "categories": ["math.ST"], "doi": "", "_query": "hedonic regression housing", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/1911.02306v1", "title": "Linear Support Vector Regression with Linear Constraints", "abstract": "This paper studies the addition of linear constraints to the Support Vector Regression (SVR) when the kernel is linear. Adding those constraints into the problem allows to add prior knowledge on the estimator obtained, such as finding probability vector or monotone data. We propose a generalization of the Sequential Minimal Optimization (SMO) algorithm for solving the optimization problem with linear constraints and prove its convergence. Then, practical performances of this estimator are shown on simulated and real datasets with different settings: non negative regression, regression onto the simplex for biomedical data and isotonic regression for weather forecast.", "published": "2019-11-06T10:57:29Z", "authors": ["Quentin Klopfenstein", "Samuel Vaiter"], "categories": ["math.OC", "stat.ML"], "doi": "", "_query": "hedonic regression housing", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/1810.05041v2", "title": "A General Framework for Fair Regression", "abstract": "Fairness, through its many forms and definitions, has become an important issue facing the machine learning community. In this work, we consider how to incorporate group fairness constraints in kernel regression methods, applicable to Gaussian processes, support vector machines, neural network regression and decision tree regression. Further, we focus on examining the effect of incorporating these constraints in decision tree regression, with direct applications to random forests and boosted trees amongst other widespread popular inference techniques. We show that the order of complexity of memory and computation is preserved for such models and tightly bound the expected perturbations to the model in terms of the number of leaves of the trees. Importantly, the approach works on trained models and hence can be easily applied to models in current use and group labels are only required on training data.", "published": "2018-10-10T16:16:03Z", "authors": ["Jack Fitzsimons", "AbdulRahman Al Ali", "Michael Osborne", "Stephen Roberts"], "categories": ["cs.LG", "cs.AI", "stat.ML"], "doi": "10.3390/e21080741", "_query": "hedonic regression housing", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/1403.5478v7", "title": "Limitless Regression Discontinuity", "abstract": "Conventionally, regression discontinuity analysis contrasts a univariate regression's limits as its independent variable, $R$, approaches a cut-point, $c$, from either side. Alternative methods target the average treatment effect in a small region around $c$, at the cost of an assumption that treatment assignment, $\\mathcal{I}\\left[R<c\\right]$, is ignorable vis a vis potential outcomes. Instead, the method presented in this paper assumes Residual Ignorability, ignorability of treatment assignment vis a vis detrended potential outcomes. Detrending is effected not with ordinary least squares but with MM-estimation, following a distinct phase of sample decontamination. The method's inferences acknowledge uncertainty in both of these adjustments, despite its applicability whether $R$ is discrete or continuous; it is uniquely robust to leading validity threats facing regression discontinuity designs.", "published": "2014-03-21T14:43:25Z", "authors": ["Adam C. Sales", "Ben B. Hansen"], "categories": ["stat.AP"], "doi": "10.3102/1076998619884904", "_query": "hedonic regression housing", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/1608.04167v4", "title": "On Univariate Convex Regression", "abstract": "We find the local rate of convergence of the least squares estimator (LSE) of a one dimensional convex regression function when (a) a certain number of derivatives vanish at the point of interest, and (b) the true regression function is locally affine. In each case we derive the limiting distribution of the LSE and its derivative. The pointwise limiting distributions depend on the second and third derivatives at 0 of the \"invelope function\" of the integral of a two-sided Brownian motion with polynomial drifts. We also investigate the inconsistency of the LSE and the unboundedness of its derivative at the boundary of the domain of the covariate space. An estimator of the argmin of the convex regression function is proposed and its asymptotic distribution is derived. Further, we present some new results on the characterization of the convex LSE that may be of independent interest.", "published": "2016-08-15T01:34:17Z", "authors": ["Promit Ghosal", "Bodhisattva Sen"], "categories": ["stat.ME", "math.ST"], "doi": "", "_query": "hedonic regression housing", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2307.02419v1", "title": "In-Context Learning for Attention Scheme: from Single Softmax Regression to Multiple Softmax Regression via a Tensor Trick", "abstract": "Large language models (LLMs) have brought significant and transformative changes in human society. These models have demonstrated remarkable capabilities in natural language understanding and generation, leading to various advancements and impacts across several domains. We consider the in-context learning under two formulation for attention related regression in this work. Given matrices $A_1 \\in \\mathbb{R}^{n \\times d}$, and $A_2 \\in \\mathbb{R}^{n \\times d}$ and $B \\in \\mathbb{R}^{n \\times n}$, the purpose is to solve some certain optimization problems: Normalized version $\\min_{X} \\| D(X)^{-1} \\exp(A_1 X A_2^\\top) - B \\|_F^2$ and Rescaled version $\\| \\exp(A_1 X A_2^\\top) - D(X) \\cdot B \\|_F^2$. Here $D(X) := \\mathrm{diag}( \\exp(A_1 X A_2^\\top) {\\bf 1}_n )$. Our regression problem shares similarities with previous studies on softmax-related regression. Prior research has extensively investigated regression techniques related to softmax regression: Normalized version $\\| \\langle \\exp(Ax) , {\\bf 1}_n \\rangle^{-1} \\exp(Ax) - b \\|_2^2$ and Resscaled version $\\| \\exp(Ax) - \\langle \\exp(Ax), {\\bf 1}_n \\rangle b \\|_2^2 $ In contrast to previous approaches, we adopt a vectorization technique to address the regression problem in matrix formulation. This approach expands the dimension from $d$ to $d^2$, resembling the formulation of the regression problem mentioned earlier. Upon completing the lipschitz analysis of our regression function, we have derived our main result concerning in-context learning.", "published": "2023-07-05T16:41:01Z", "authors": ["Yeqi Gao", "Zhao Song", "Shenghao Xie"], "categories": ["cs.LG"], "doi": "", "_query": "hedonic regression housing", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/1911.11512v1", "title": "WSOD with PSNet and Box Regression", "abstract": "Weakly supervised object detection(WSOD) task uses only image-level annotations to train object detection task. WSOD does not require time-consuming instance-level annotations, so the study of this task has attracted more and more attention. Previous weakly supervised object detection methods iteratively update detectors and pseudo-labels, or use feature-based mask-out methods. Most of these methods do not generate complete and accurate proposals, often only the most discriminative parts of the object, or too many background areas. To solve this problem, we added the box regression module to the weakly supervised object detection network and proposed a proposal scoring network (PSNet) to supervise it. The box regression module modifies proposal to improve the IoU of proposal and ground truth. PSNet scores the proposal output from the box regression network and utilize the score to improve the box regression module. In addition, we take advantage of the PRS algorithm for generating a more accurate pseudo label to train the box regression module. Using these methods, we train the detector on the PASCAL VOC 2007 and 2012 and obtain significantly improved results.", "published": "2019-11-26T13:20:54Z", "authors": ["Sheng Yi", "Xi Li", "Huimin Ma"], "categories": ["cs.CV"], "doi": "", "_query": "hedonic regression housing", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/1506.01223v1", "title": "The shooting S-estimator for robust regression", "abstract": "To perform multiple regression, the least squares estimator is commonly used. However, this estimator is not robust to outliers. Therefore, robust methods such as S-estimation have been proposed. These estimators flag any observation with a large residual as an outlier and downweight it in the further procedure. However, a large residual may be caused by an outlier in only one single predictor variable, and downweighting the complete observation results in a loss of information. Therefore, we propose the shooting S-estimator, a regression estimator that is especially designed for situations where a large number of observations suffer from contamination in a small number of predictor variables. The shooting S-estimator combines the ideas of the coordinate descent algorithm with simple S-regression, which makes it robust against componentwise contamination, at the cost of failing the regression equivariance property.", "published": "2015-06-03T12:00:29Z", "authors": ["Viktoria Öllerer", "Andreas Alfons", "Christophe Croux"], "categories": ["stat.ME"], "doi": "10.1007/s00180-015-0593-7", "_query": "hedonic regression housing", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2312.15376v1", "title": "Geodesic Optimal Transport Regression", "abstract": "Classical regression models do not cover non-Euclidean data that reside in a general metric space, while the current literature on non-Euclidean regression by and large has focused on scenarios where either predictors or responses are random objects, i.e., non-Euclidean, but not both. In this paper we propose geodesic optimal transport regression models for the case where both predictors and responses lie in a common geodesic metric space and predictors may include not only one but also several random objects. This provides an extension of classical multiple regression to the case where both predictors and responses reside in non-Euclidean metric spaces, a scenario that has not been considered before. It is based on the concept of optimal geodesic transports, which we define as an extension of the notion of optimal transports in distribution spaces to more general geodesic metric spaces, where we characterize optimal transports as transports along geodesics. The proposed regression models cover the relation between non-Euclidean responses and vectors of non-Euclidean predictors in many spaces of practical statistical interest. These include one-dimensional distributions viewed as elements of the 2-Wasserstein space and multidimensional distributions with the Fisher-Rao metric that are represented as data on the Hilbert sphere. Also included are data on finite-dimensional Riemannian manifolds, with an emphasis on spheres, covering directional and compositional data, as well as data that consist of symmetric positive definite matrices. We illustrate the utility of geodesic optimal transport regression with data on summer temperature distributions and human mortality.", "published": "2023-12-24T00:48:32Z", "authors": ["Changbo Zhu", "Hans-Georg Müller"], "categories": ["stat.ME"], "doi": "", "_query": "hedonic regression housing", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2304.10336v1", "title": "Controllable Neural Symbolic Regression", "abstract": "In symbolic regression, the goal is to find an analytical expression that accurately fits experimental data with the minimal use of mathematical symbols such as operators, variables, and constants. However, the combinatorial space of possible expressions can make it challenging for traditional evolutionary algorithms to find the correct expression in a reasonable amount of time. To address this issue, Neural Symbolic Regression (NSR) algorithms have been developed that can quickly identify patterns in the data and generate analytical expressions. However, these methods, in their current form, lack the capability to incorporate user-defined prior knowledge, which is often required in natural sciences and engineering fields. To overcome this limitation, we propose a novel neural symbolic regression method, named Neural Symbolic Regression with Hypothesis (NSRwH) that enables the explicit incorporation of assumptions about the expected structure of the ground-truth expression into the prediction process. Our experiments demonstrate that the proposed conditioned deep learning model outperforms its unconditioned counterparts in terms of accuracy while also providing control over the predicted expression structure.", "published": "2023-04-20T14:20:48Z", "authors": ["Tommaso Bendinelli", "Luca Biggio", "Pierre-Alexandre Kamienny"], "categories": ["cs.LG", "cs.AI"], "doi": "", "_query": "hedonic regression housing", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/1701.01503v2", "title": "Log-Linear Bayesian Additive Regression Trees for Multinomial Logistic and Count Regression Models", "abstract": "We introduce Bayesian additive regression trees (BART) for log-linear models including multinomial logistic regression and count regression with zero-inflation and overdispersion. BART has been applied to nonparametric mean regression and binary classification problems in a range of settings. However, existing applications of BART have been limited to models for Gaussian \"data\", either observed or latent. This is primarily because efficient MCMC algorithms are available for Gaussian likelihoods. But while many useful models are naturally cast in terms of latent Gaussian variables, many others are not -- including models considered in this paper. We develop new data augmentation strategies and carefully specified prior distributions for these new models. Like the original BART prior, the new prior distributions are carefully constructed and calibrated to be flexible while guarding against overfitting. Together the new priors and data augmentation schemes allow us to implement an efficient MCMC sampler outside the context of Gaussian models. The utility of these new methods is illustrated with examples and an application to a previously published dataset.", "published": "2017-01-05T23:24:04Z", "authors": ["Jared S. Murray"], "categories": ["stat.ME"], "doi": "", "_query": "hedonic regression housing", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2211.14980v3", "title": "Optimal Sparse Regression Trees", "abstract": "Regression trees are one of the oldest forms of AI models, and their predictions can be made without a calculator, which makes them broadly useful, particularly for high-stakes applications. Within the large literature on regression trees, there has been little effort towards full provable optimization, mainly due to the computational hardness of the problem. This work proposes a dynamic-programming-with-bounds approach to the construction of provably-optimal sparse regression trees. We leverage a novel lower bound based on an optimal solution to the k-Means clustering algorithm in 1-dimension over the set of labels. We are often able to find optimal sparse trees in seconds, even for challenging datasets that involve large numbers of samples and highly-correlated features.", "published": "2022-11-28T00:43:21Z", "authors": ["Rui Zhang", "Rui Xin", "Margo Seltzer", "Cynthia Rudin"], "categories": ["cs.LG"], "doi": "", "_query": "hedonic regression housing", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2303.17745v1", "title": "A Note On Nonlinear Regression Under L2 Loss", "abstract": "We investigate the nonlinear regression problem under L2 loss (square loss) functions. Traditional nonlinear regression models often result in non-convex optimization problems with respect to the parameter set. We show that a convex nonlinear regression model exists for the traditional least squares problem, which can be a promising towards designing more complex systems with easier to train models.", "published": "2023-03-30T23:43:22Z", "authors": ["Kaan Gokcesu", "Hakan Gokcesu"], "categories": ["cs.LG", "math.NA", "math.OC", "stat.ML"], "doi": "", "_query": "hedonic regression housing", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2209.04476v1", "title": "Shape-constrained Estimation in Functional Regression with Bernstein Polynomials", "abstract": "Shape restrictions on functional regression coefficients such as non-negativity, monotonicity, convexity or concavity are often available in the form of a prior knowledge or required to maintain a structural consistency in functional regression models. A new estimation method is developed in shape-constrained functional regression models using Bernstein polynomials. Specifically, estimation approaches from nonparametric regression are extended to functional data, properly accounting for shape-constraints in a large class of functional regression models such as scalar-on-function regression (SOFR), function-on-scalar regression (FOSR), and function-on-function regression (FOFR). Theoretical results establish the asymptotic consistency of the constrained estimators under standard regularity conditions. A projection based approach provides point-wise asymptotic confidence intervals for the constrained estimators. A bootstrap test is developed facilitating testing of the shape constraints. Numerical analysis using simulations illustrate improvement in efficiency of the estimators from the use of the proposed method under shape constraints. Two applications include i) modeling a drug effect in a mental health study via shape-restricted FOSR and ii) modeling subject-specific quantile functions of accelerometry-estimated physical activity in the Baltimore Longitudinal Study of Aging (BLSA) as outcomes via shape-restricted quantile-function on scalar regression (QFOSR). R software implementation and illustration of the proposed estimation method and the test is provided.", "published": "2022-09-09T18:12:04Z", "authors": ["Rahul Ghosal", "Sujit Ghosh", "Jacek Urbanek", "Jennifer A. Schrack", "Vadim Zipunnikov"], "categories": ["stat.ME", "math.ST", "stat.AP"], "doi": "10.1016/j.csda.2022.107614", "_query": "hedonic regression housing", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2203.09347v3", "title": "Dimensionality Reduction and Wasserstein Stability for Kernel Regression", "abstract": "In a high-dimensional regression framework, we study consequences of the naive two-step procedure where first the dimension of the input variables is reduced and second, the reduced input variables are used to predict the output variable with kernel regression. In order to analyze the resulting regression errors, a novel stability result for kernel regression with respect to the Wasserstein distance is derived. This allows us to bound errors that occur when perturbed input data is used to fit the regression function. We apply the general stability result to principal component analysis (PCA). Exploiting known estimates from the literature on both principal component analysis and kernel regression, we deduce convergence rates for the two-step procedure. The latter turns out to be particularly useful in a semi-supervised setting.", "published": "2022-03-17T14:26:28Z", "authors": ["Stephan Eckstein", "Armin Iske", "Mathias Trabs"], "categories": ["stat.ML", "cs.LG", "math.NA", "math.ST"], "doi": "", "_query": "hedonic regression housing", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2207.02454v1", "title": "Ordinal Regression via Binary Preference vs Simple Regression: Statistical and Experimental Perspectives", "abstract": "Ordinal regression with anchored reference samples (ORARS) has been proposed for predicting the subjective Mean Opinion Score (MOS) of input stimuli automatically. The ORARS addresses the MOS prediction problem by pairing a test sample with each of the pre-scored anchored reference samples. A trained binary classifier is then used to predict which sample, test or anchor, is better statistically. Posteriors of the binary preference decision are then used to predict the MOS of the test sample. In this paper, rigorous framework, analysis, and experiments to demonstrate that ORARS are advantageous over simple regressions are presented. The contributions of this work are: 1) Show that traditional regression can be reformulated into multiple preference tests to yield a better performance, which is confirmed with simulations experimentally; 2) Generalize ORARS to other regression problems and verify its effectiveness; 3) Provide some prerequisite conditions which can insure proper application of ORARS.", "published": "2022-07-06T05:52:19Z", "authors": ["Bin Su", "Shaoguang Mao", "Frank Soong", "Zhiyong Wu"], "categories": ["cs.LG"], "doi": "", "_query": "hedonic regression housing", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/1307.4145v2", "title": "A Safe Screening Rule for Sparse Logistic Regression", "abstract": "The l1-regularized logistic regression (or sparse logistic regression) is a widely used method for simultaneous classification and feature selection. Although many recent efforts have been devoted to its efficient implementation, its application to high dimensional data still poses significant challenges. In this paper, we present a fast and effective sparse logistic regression screening rule (Slores) to identify the 0 components in the solution vector, which may lead to a substantial reduction in the number of features to be entered to the optimization. An appealing feature of Slores is that the data set needs to be scanned only once to run the screening and its computational cost is negligible compared to that of solving the sparse logistic regression problem. Moreover, Slores is independent of solvers for sparse logistic regression, thus Slores can be integrated with any existing solver to improve the efficiency. We have evaluated Slores using high-dimensional data sets from different applications. Extensive experimental results demonstrate that Slores outperforms the existing state-of-the-art screening rules and the efficiency of solving sparse logistic regression is improved by one magnitude in general.", "published": "2013-07-16T02:03:51Z", "authors": ["Jie Wang", "Jiayu Zhou", "Jun Liu", "Peter Wonka", "Jieping Ye"], "categories": ["cs.LG", "stat.ML"], "doi": "", "_query": "hedonic regression housing", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2407.20475v1", "title": "Distribution Learning for Molecular Regression", "abstract": "Using \"soft\" targets to improve model performance has been shown to be effective in classification settings, but the usage of soft targets for regression is a much less studied topic in machine learning. The existing literature on the usage of soft targets for regression fails to properly assess the method's limitations, and empirical evaluation is quite limited. In this work, we assess the strengths and drawbacks of existing methods when applied to molecular property regression tasks. Our assessment outlines key biases present in existing methods and proposes methods to address them, evaluated through careful ablation studies. We leverage these insights to propose Distributional Mixture of Experts (DMoE): A model-independent, and data-independent method for regression which trains a model to predict probability distributions of its targets. Our proposed loss function combines the cross entropy between predicted and target distributions and the L1 distance between their expected values to produce a loss function that is robust to the outlined biases. We evaluate the performance of DMoE on different molecular property prediction datasets -- Open Catalyst (OC20), MD17, and QM9 -- across different backbone model architectures -- SchNet, GemNet, and Graphormer. Our results demonstrate that the proposed method is a promising alternative to classical regression for molecular property prediction tasks, showing improvements over baselines on all datasets and architectures.", "published": "2024-07-30T00:21:51Z", "authors": ["Nima Shoghi", "Pooya Shoghi", "Anuroop Sriram", "Abhishek Das"], "categories": ["cs.LG", "q-bio.QM"], "doi": "", "_query": "hedonic regression housing", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2605.28075v1", "title": "Measure-to-measure Regression with Transformers", "abstract": "Many learning problems require predicting how populations evolve under an unknown transformation. A natural representation for such populations is a probability measure, with point clouds as a key example. In this work, we study the measure-to-measure (M2M) regression problem, in which one seeks to learn a map between probability measures from a finite collection of observed input-output pairs. In contrast to classical regression, where individual samples are transformed independently, M2M regression treats entire distributions as the data points. This perspective is vital in certain scientific applications, for example, cellular and molecular biology, where cells are known to evolve not as independent data points but as a collection. However, few existing approaches address the problem of M2M regression with sufficient expressivity and scalability. We present a formalization of nonlinear M2M regression and introduce two easy-to-use, expressive, and scalable approaches to learn such operators: transformers as static M2M maps and transformers as dynamic M2M velocity fields. Our approach leverages the natural measure-dependent and mean-field structure of transformers to learn nonlinear M2M maps on the space of probability distributions. We illustrate the effectiveness of our proposed method to generalize to unseen measures on synthetic experiments, interacting particle systems, and a large-scale patient-derived organoid dataset for predicting treatment response in colorectal cancer.", "published": "2026-05-27T07:31:04Z", "authors": ["Matthew Vandergrift", "Martha White", "Yury Polyanskiy", "Philippe Rigollet", "Lazar Atanackovic"], "categories": ["cs.LG"], "doi": "", "_query": "hedonic regression housing", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/1712.03561v8", "title": "Split Regularized Regression", "abstract": "We propose an approach for fitting linear regression models that splits the set of covariates into groups. The optimal split of the variables into groups and the regularized estimation of the regression coefficients are performed by minimizing an objective function that encourages sparsity within each group and diversity among them. The estimated coefficients are then pooled together to form the final fit. Our procedure works on top of a given penalized linear regression estimator (e.g., Lasso, elastic net) by fitting it to possibly overlapping groups of features, encouraging diversity among these groups to reduce the correlation of the corresponding predictions. For the case of two groups, elastic net penalty and orthogonal predictors, we give a closed form solution for the regression coefficients in each group. We establish the consistency of our method with the number of predictors possibly increasing with the sample size. An extensive simulation study and real-data applications show that in general the proposed method improves the prediction accuracy of the base estimator used in the procedure. Possible extensions to GLMs and other models are discussed. The supplemental material for this article, available online, contains the proofs of our theoretical results and the full results of our simulation study.", "published": "2017-12-10T17:08:33Z", "authors": ["Anthony Christidis", "Ruben Zamar", "Laks V. S. Lakshmanan", "Ezequiel Smucler"], "categories": ["stat.ME"], "doi": "", "_query": "hedonic regression housing", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2605.23318v1", "title": "Generalized Rank Regression", "abstract": "Rank regression offers robustness to outliers and heavy-tailed response distributions, invariance to monotonic transformations, and improved efficiency under non-Gaussian errors, making it a versatile tool for analyzing complex data. This paper introduces Generalized Rank Regression (GRR), an extension of classical rank-based methods that accommodates non-monotonic score functions. While aimed at enhancing the statistical efficiency of robust estimators, this generalization results in a potentially non-convex and non-smooth objective function, presenting challenges for both theoretical analysis and algorithmic implementation. We derive a non-asymptotic Bahadur representation of the proposed estimator and establish its asymptotic normality under mild conditions. To address the optimization challenges, we propose a new two-stage sub-gradient descent algorithm that enables efficient computation of GRR estimators with desirable statistical properties. Furthermore, we develop a multiplier bootstrap procedure for conducting statistical inference. A close connection between GRR and variants of quantile regression is uncovered, which demonstrates that GRR and composite quantile regression share asymptotically equivalent variances. The advantages of GRR are illustrated through extensive simulation studies and a real data application.", "published": "2026-05-22T07:36:08Z", "authors": ["Jiyuan Tu", "Suqi Wu", "Yichen Zhang", "Wen-Xin Zhou"], "categories": ["stat.ME"], "doi": "", "_query": "hedonic regression housing", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/1609.03297v1", "title": "Flexible Tweedie regression models for continuous data", "abstract": "Tweedie regression models provide a flexible family of distributions to deal with non-negative highly right-skewed data as well as symmetric and heavy tailed data and can handle continuous data with probability mass at zero. The estimation and inference of Tweedie regression models based on the maximum likelihood method are challenged by the presence of an infinity sum in the probability function and non-trivial restrictions on the power parameter space. In this paper, we propose two approaches for fitting Tweedie regression models, namely, quasi- and pseudo-likelihood. We discuss the asymptotic properties of the two approaches and perform simulation studies to compare our methods with the maximum likelihood method. In particular, we show that the quasi-likelihood method provides asymptotically efficient estimation for regression parameters. The computational implementation of the alternative methods is faster and easier than the orthodox maximum likelihood, relying on a simple Newton scoring algorithm. Simulation studies showed that the quasi- and pseudo-likelihood approaches present estimates, standard errors and coverage rates similar to the maximum likelihood method. Furthermore, the second-moment assumptions required by the quasi- and pseudo-likelihood methods enables us to extend the Tweedie regression models to the class of quasi-Tweedie regression models in the Wedderburn's style. Moreover, it allows to eliminate the non-trivial restriction on the power parameter space, and thus provides a flexible regression model to deal with continuous data. We provide \\texttt{R} implementation and illustrate the application of Tweedie regression models using three data sets.", "published": "2016-09-12T08:04:38Z", "authors": ["Wagner H. Bonat", "Célestin C. Kokonendji"], "categories": ["stat.ME", "stat.AP", "stat.CO"], "doi": "10.1080/00949655.2017.1318876", "_query": "hedonic regression housing", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2005.12496v6", "title": "CRUDE: Calibrating Regression Uncertainty Distributions Empirically", "abstract": "Calibrated uncertainty estimates in machine learning are crucial to many fields such as autonomous vehicles, medicine, and weather and climate forecasting. While there is extensive literature on uncertainty calibration for classification, the classification findings do not always translate to regression. As a result, modern models for predicting uncertainty in regression settings typically produce uncalibrated and overconfident estimates. To address these gaps, we present a calibration method for regression settings that does not assume a particular uncertainty distribution over the error: Calibrating Regression Uncertainty Distributions Empirically (CRUDE). CRUDE makes the weaker assumption that error distributions have a constant arbitrary shape across the output space, shifted by predicted mean and scaled by predicted standard deviation. We detail a theoretical connection between CRUDE and conformal inference. Across an extensive set of regression tasks, CRUDE demonstrates consistently sharper, better calibrated, and more accurate uncertainty estimates than state-of-the-art techniques.", "published": "2020-05-26T03:08:43Z", "authors": ["Eric Zelikman", "Christopher Healy", "Sharon Zhou", "Anand Avati"], "categories": ["cs.LG", "stat.ML"], "doi": "", "_query": "hedonic regression housing", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/1607.06801v3", "title": "High-dimensional regression adjustments in randomized experiments", "abstract": "We study the problem of treatment effect estimation in randomized experiments with high-dimensional covariate information, and show that essentially any risk-consistent regression adjustment can be used to obtain efficient estimates of the average treatment effect. Our results considerably extend the range of settings where high-dimensional regression adjustments are guaranteed to provide valid inference about the population average treatment effect. We then propose cross-estimation, a simple method for obtaining finite-sample-unbiased treatment effect estimates that leverages high-dimensional regression adjustments. Our method can be used when the regression model is estimated using the lasso, the elastic net, subset selection, etc. Finally, we extend our analysis to allow for adaptive specification search via cross-validation, and flexible non-parametric regression adjustments with machine learning methods such as random forests or neural networks.", "published": "2016-07-22T19:38:46Z", "authors": ["Stefan Wager", "Wenfei Du", "Jonathan Taylor", "Robert Tibshirani"], "categories": ["stat.ME", "stat.ML"], "doi": "10.1073/pnas.1614732113", "_query": "hedonic regression housing", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2306.15915v1", "title": "Transfer Learning with Random Coefficient Ridge Regression", "abstract": "Ridge regression with random coefficients provides an important alternative to fixed coefficients regression in high dimensional setting when the effects are expected to be small but not zeros. This paper considers estimation and prediction of random coefficient ridge regression in the setting of transfer learning, where in addition to observations from the target model, source samples from different but possibly related regression models are available. The informativeness of the source model to the target model can be quantified by the correlation between the regression coefficients. This paper proposes two estimators of regression coefficients of the target model as the weighted sum of the ridge estimates of both target and source models, where the weights can be determined by minimizing the empirical estimation risk or prediction risk. Using random matrix theory, the limiting values of the optimal weights are derived under the setting when $p/n \\rightarrow γ$, where $p$ is the number of the predictors and $n$ is the sample size, which leads to an explicit expression of the estimation or prediction risks. Simulations show that these limiting risks agree very well with the empirical risks. An application to predicting the polygenic risk scores for lipid traits shows such transfer learning methods lead to smaller prediction errors than the single sample ridge regression or Lasso-based transfer learning.", "published": "2023-06-28T04:36:37Z", "authors": ["Hongzhe Zhang", "Hongzhe Li"], "categories": ["stat.ML", "cs.LG", "stat.ME"], "doi": "", "_query": "hedonic regression housing", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2508.16973v1", "title": "Balanced Sharpness-Aware Minimization for Imbalanced Regression", "abstract": "Regression is fundamental in computer vision and is widely used in various tasks including age estimation, depth estimation, target localization, \\etc However, real-world data often exhibits imbalanced distribution, making regression models perform poorly especially for target values with rare observations~(known as the imbalanced regression problem). In this paper, we reframe imbalanced regression as an imbalanced generalization problem. To tackle that, we look into the loss sharpness property for measuring the generalization ability of regression models in the observation space. Namely, given a certain perturbation on the model parameters, we check how model performance changes according to the loss values of different target observations. We propose a simple yet effective approach called Balanced Sharpness-Aware Minimization~(BSAM) to enforce the uniform generalization ability of regression models for the entire observation space. In particular, we start from the traditional sharpness-aware minimization and then introduce a novel targeted reweighting strategy to homogenize the generalization ability across the observation space, which guarantees a theoretical generalization bound. Extensive experiments on multiple vision regression tasks, including age and depth estimation, demonstrate that our BSAM method consistently outperforms existing approaches. The code is available \\href{https://github.com/manmanjun/BSAM_for_Imbalanced_Regression}{here}.", "published": "2025-08-23T09:57:07Z", "authors": ["Yahao Liu", "Qin Wang", "Lixin Duan", "Wen Li"], "categories": ["cs.CV"], "doi": "", "_query": "hedonic regression housing", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/1911.10644v1", "title": "The Tilted Beta Binomial Linear Regression Model: a Bayesian Approach", "abstract": "This paper proposes new linear regression models to deal with overdispersed binomial datasets. These new models, called tilted beta binomial regression models, are defined from the tilted beta binomial distribution, proposed assuming that the parameter of the binomial distribution follows a tilted beta distribution. As a particular case of this regression models, we propose the beta rectangular binomial regression models, defined from the binomial distribution assuming that their parameters follow a beta rectangular distribution. These new linear regression models, defined assuming that the parameters of these new distributions follow regression structures, are fitted applying Bayesian methods and using the OpenBUGS software. The proposed regression models are fitted to an overdispersed binomial dataset of the number of seeds that germinate depending on the type of chosen seed androot.", "published": "2019-11-25T00:12:19Z", "authors": ["María Victoria Cifuentes-Amado", "Edilberto Cepeda-Cuervo"], "categories": ["stat.ME"], "doi": "", "_query": "hedonic regression housing", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/0910.3219v1", "title": "Turbulent flow over a house in a simulated hurricane boundary layer", "abstract": "Every year hurricanes and other extreme wind storms cause billions of dollars in damage worldwide. For residential construction, such failures are usually associated with roofs, which see the largest aerodynamic loading. However, determining aerodynamic loads on different portions of North American houses is complicated by the lack of clear load paths and non-linear load sharing in wood frame roofs. This problem of fluid-structure interaction requires both wind tunnel testing and full-scale structural testing. A series of wind tunnel tests have been performed on a house in a simulated atmospheric boundary layer (ABL), with the resulting wind-induced pressures applied to the full-scale structure. The ABL was simulated for flow over open country terrain where both velocity and turbulence intensity profiles, as well as spectra, were matched with available full scale measurements for this type of terrain. The first set of measurements was 600 simultaneous surface pressure measurements over the entire house. A key feature of the surface pressure field is the occurrence of large, highly non-Gaussian, peak uplift (suctions) on the roof. In order to better understand which flow features cause this, PIV experiments were performed on the wind tunnel model. These experiments were performed with time-resolved PIV (sampling rate of 500 Hz) for a duration of 30 seconds. From the fluid dynamics videos (low- and high-resolution) generated from the PIV data it is clear that strong circulation is generated at the windward edge of the roof. These vortices are eventually shed and convect along the roof. It is the presence of this concentrated circulation which is responsible for the peak loading observed.", "published": "2009-10-16T20:12:28Z", "authors": ["Zachary Taylor", "Murray Morrison", "Roi Gurka", "Gregory Kopp"], "categories": ["physics.flu-dyn"], "doi": "", "_query": "hedonic regression housing", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2109.02981v3", "title": "Network Regression with Graph Laplacians", "abstract": "Network data are increasingly available in various research fields, motivating statistical analysis for populations of networks where a network as a whole is viewed as a data point. Due to the non-Euclidean nature of networks, basic statistical tools available for scalar and vector data are no longer applicable when one aims to relate networks as outcomes to Euclidean covariates, while the study of how a network changes in dependence on covariates is often of paramount interest. This motivates to extend the notion of regression to the case of responses that are network data. Here we propose to adopt conditional Fréchet means implemented with both global least squares regression and local weighted least squares smoothing, extending the Fréchet regression concept to networks that are quantified by their graph Laplacians. The challenge is to characterize the space of graph Laplacians so as to justify the application of Fréchet regression. This characterization then leads to asymptotic rates of convergence for the corresponding M-estimators by applying empirical process methods. We demonstrate the usefulness and good practical performance of the proposed framework with simulations and with network data arising from resting-state fMRI in neuroimaging, as well as New York taxi records.", "published": "2021-09-07T10:25:21Z", "authors": ["Yidong Zhou", "Hans-Georg Müller"], "categories": ["stat.ME"], "doi": "", "_query": "hedonic regression housing", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2110.13749v2", "title": "Topologically penalized regression on manifolds", "abstract": "We study a regression problem on a compact manifold M. In order to take advantage of the underlying geometry and topology of the data, the regression task is performed on the basis of the first several eigenfunctions of the Laplace-Beltrami operator of the manifold, that are regularized with topological penalties. The proposed penalties are based on the topology of the sub-level sets of either the eigenfunctions or the estimated function. The overall approach is shown to yield promising and competitive performance on various applications to both synthetic and real data sets. We also provide theoretical guarantees on the regression function estimates, on both its prediction error and its smoothness (in a topological sense). Taken together, these results support the relevance of our approach in the case where the targeted function is ''topologically smooth''.", "published": "2021-10-26T14:59:13Z", "authors": ["Olympio Hacquard", "Krishnakumar Balasubramanian", "Gilles Blanchard", "Clément Levrard", "Wolfgang Polonik"], "categories": ["cs.LG", "math.ST"], "doi": "", "_query": "hedonic regression housing", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/1103.6118v1", "title": "Gaussian Regularized Sliced Inverse Regression", "abstract": "Sliced Inverse Regression (SIR) is an effective method for dimension reduction in high-dimensional regression problems. The original method, however, requires the inversion of the predictors covariance matrix. In case of collinearity between these predictors or small sample sizes compared to the dimension, the inversion is not possible and a regularization technique has to be used. Our approach is based on a Fisher Lecture given by R.D. Cook where it is shown that SIR axes can be interpreted as solutions of an inverse regression problem. In this paper, a Gaussian prior distribution is introduced on the unknown parameters of the inverse regression problem in order to regularize their estimation. We show that some existing SIR regularizations can enter our framework, which permits a global understanding of these methods. Three new priors are proposed leading to new regularizations of the SIR method. A comparison on simulated data is provided.", "published": "2011-03-31T07:56:09Z", "authors": ["C. Bernard-Michel", "L. Gardes", "S. Girard"], "categories": ["math.ST"], "doi": "", "_query": "hedonic regression housing", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/0803.3876v1", "title": "Coordinate descent algorithms for lasso penalized regression", "abstract": "Imposition of a lasso penalty shrinks parameter estimates toward zero and performs continuous model selection. Lasso penalized regression is capable of handling linear regression problems where the number of predictors far exceeds the number of cases. This paper tests two exceptionally fast algorithms for estimating regression coefficients with a lasso penalty. The previously known $\\ell_2$ algorithm is based on cyclic coordinate descent. Our new $\\ell_1$ algorithm is based on greedy coordinate descent and Edgeworth's algorithm for ordinary $\\ell_1$ regression. Each algorithm relies on a tuning constant that can be chosen by cross-validation. In some regression problems it is natural to group parameters and penalize parameters group by group rather than separately. If the group penalty is proportional to the Euclidean norm of the parameters of the group, then it is possible to majorize the norm and reduce parameter estimation to $\\ell_2$ regression with a lasso penalty. Thus, the existing algorithm can be extended to novel settings. Each of the algorithms discussed is tested via either simulated or real data or both. The Appendix proves that a greedy form of the $\\ell_2$ algorithm converges to the minimum value of the objective function.", "published": "2008-03-27T09:35:00Z", "authors": ["Tong Tong Wu", "Kenneth Lange"], "categories": ["stat.AP"], "doi": "10.1214/07-AOAS147", "_query": "hedonic regression housing", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2110.14779v1", "title": "Spectrahedral Regression", "abstract": "Convex regression is the problem of fitting a convex function to a data set consisting of input-output pairs. We present a new approach to this problem called spectrahedral regression, in which we fit a spectrahedral function to the data, i.e. a function that is the maximum eigenvalue of an affine matrix expression of the input. This method represents a significant generalization of polyhedral (also called max-affine) regression, in which a polyhedral function (a maximum of a fixed number of affine functions) is fit to the data. We prove bounds on how well spectrahedral functions can approximate arbitrary convex functions via statistical risk analysis. We also analyze an alternating minimization algorithm for the non-convex optimization problem of fitting the best spectrahedral function to a given data set. We show that this algorithm converges geometrically with high probability to a small ball around the optimal parameter given a good initialization. Finally, we demonstrate the utility of our approach with experiments on synthetic data sets as well as real data arising in applications such as economics and engineering design.", "published": "2021-10-27T21:21:19Z", "authors": ["Eliza O'Reilly", "Venkat Chandrasekaran"], "categories": ["math.OC", "math.ST", "stat.ML"], "doi": "", "_query": "hedonic regression housing", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2303.14284v1", "title": "Feature Space Sketching for Logistic Regression", "abstract": "We present novel bounds for coreset construction, feature selection, and dimensionality reduction for logistic regression. All three approaches can be thought of as sketching the logistic regression inputs. On the coreset construction front, we resolve open problems from prior work and present novel bounds for the complexity of coreset construction methods. On the feature selection and dimensionality reduction front, we initiate the study of forward error bounds for logistic regression. Our bounds are tight up to constant factors and our forward error bounds can be extended to Generalized Linear Models.", "published": "2023-03-24T21:46:04Z", "authors": ["Gregory Dexter", "Rajiv Khanna", "Jawad Raheel", "Petros Drineas"], "categories": ["cs.LG", "stat.ML"], "doi": "", "_query": "hedonic regression housing", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/1309.3912v1", "title": "Asymptotics for regression models under loss of identifiability", "abstract": "This paper discusses the asymptotic behavior of regression models under general conditions. First, we give a general inequality for the difference of the sum of square errors (SSE) of the estimated regression model and the SSE of the theoretical best regression function in our model. A set of generalized derivative functions is a key tool in deriving such inequality. Under suitable Donsker condition for this set, we give the asymptotic distribution for the difference of SSE. We show how to get this Donsker property for parametric models even if the parameters characterizing the best regression function are not unique. This result is applied to neural networks regression models with redundant hidden units when loss of identifiability occurs.", "published": "2013-09-16T11:50:05Z", "authors": ["Joseph Rynkiewicz"], "categories": ["math.ST"], "doi": "", "_query": "hedonic regression housing", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2208.12427v1", "title": "Coefficient-based Regularized Distribution Regression", "abstract": "In this paper, we consider the coefficient-based regularized distribution regression which aims to regress from probability measures to real-valued responses over a reproducing kernel Hilbert space (RKHS), where the regularization is put on the coefficients and kernels are assumed to be indefinite. The algorithm involves two stages of sampling, the first stage sample consists of distributions and the second stage sample is obtained from these distributions. Asymptotic behaviors of the algorithm in different regularity ranges of the regression function are comprehensively studied and learning rates are derived via integral operator techniques. We get the optimal rates under some mild conditions, which matches the one-stage sampled minimax optimal rate. Compared with the kernel methods for distribution regression in the literature, the algorithm under consideration does not require the kernel to be symmetric and positive semi-definite and hence provides a simple paradigm for designing indefinite kernel methods, which enriches the theme of the distribution regression. To the best of our knowledge, this is the first result for distribution regression with indefinite kernels, and our algorithm can improve the saturation effect.", "published": "2022-08-26T03:46:14Z", "authors": ["Yuan Mao", "Lei Shi", "Zheng-Chu Guo"], "categories": ["stat.ML", "cs.LG"], "doi": "", "_query": "hedonic regression housing", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2004.04632v1", "title": "Nonnegativity-Enforced Gaussian Process Regression", "abstract": "Gaussian Process (GP) regression is a flexible non-parametric approach to approximate complex models. In many cases, these models correspond to processes with bounded physical properties. Standard GP regression typically results in a proxy model which is unbounded for all temporal or spacial points, and thus leaves the possibility of taking on infeasible values. We propose an approach to enforce the physical constraints in a probabilistic way under the GP regression framework. In addition, this new approach reduces the variance in the resulting GP model.", "published": "2020-04-07T00:43:46Z", "authors": ["Andrew Pensoneault", "Xiu Yang", "Xueyu Zhu"], "categories": ["cs.LG", "stat.ML"], "doi": "", "_query": "hedonic regression housing", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/1402.0845v1", "title": "The sign of the logistic regression coefficient", "abstract": "Let Y be a binary random variable and X a scalar. Let $\\hatβ$ be the maximum likelihood estimate of the slope in a logistic regression of Y on X with intercept. Further let $\\bar x_0$ and $\\bar x_1$ be the average of sample x values for cases with y=0 and y=1, respectively. Then under a condition that rules out separable predictors, we show that sign($\\hatβ$) = sign($\\bar x_1-\\bar x_0$). More generally, if $x_i$ are vector valued then we show that $\\hatβ=0$ if and only if $\\bar x_1=\\bar x_0$. This holds for logistic regression and also for more general binary regressions with inverse link functions satisfying a log-concavity condition. Finally, when $\\bar x_1\\ne \\bar x_0$ then the angle between $\\hatβ$ and $\\bar x_1-\\bar x_0$ is less than ninety degrees in binary regressions satisfying the log-concavity condition and the separation condition, when the design matrix has full rank.", "published": "2014-02-04T19:58:19Z", "authors": ["Art B. Owen", "Paul A. Roediger"], "categories": ["math.ST"], "doi": "", "_query": "hedonic regression housing", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2205.11835v1", "title": "Robust and Sparse Multinomial Regression in High Dimensions", "abstract": "A robust and sparse estimator for multinomial regression is proposed for high dimensional data. Robustness of the estimator is achieved by trimming the observations, and sparsity of the estimator is obtained by the elastic net penalty, which is a mixture of $L_1$ and $L_2$ penalties. From this point of view, the proposed estimator is an extension of the enet-LTS estimator \\citep{Kurnaz18} for linear and logistic regression to the multinomial regression setting. After introducing an algorithm for its computation, a simulation study is conducted to show the performance in comparison to the non-robust version of the multinomial regression estimator. Some real data examples underline the usefulness of this robust estimator.", "published": "2022-05-24T06:58:03Z", "authors": ["Fatma Sevinç Kurnaz", "Peter Filzmoser"], "categories": ["stat.ME", "math.ST", "stat.CO"], "doi": "", "_query": "hedonic regression housing", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2104.06581v4", "title": "On the implied weights of linear regression for causal inference", "abstract": "A basic principle in the design of observational studies is to approximate the randomized experiment that would have been conducted under controlled circumstances. Now, linear regression models are commonly used to analyze observational data and estimate causal effects. How do linear regression adjustments in observational studies emulate key features of randomized experiments, such as covariate balance, self-weighted sampling, and study representativeness? In this paper, we provide answers to this and related questions by analyzing the implied (individual-level data) weights of linear regression methods. We derive new closed-form expressions of the weights and examine their properties in both finite and asymptotic regimes. We show that the implied weights of general regression problems can be equivalently obtained by solving a convex optimization problem. Among others, we study doubly and multiply robust properties of regression estimators from the perspective of their implied weights. This equivalence allows us to bridge ideas from the regression modeling and causal inference literatures. As a result, we propose novel regression diagnostics for causal inference that are part of the design stage of an observational study. As special cases, we analyze the implied weights in common settings such as multi-valued treatments and regression adjustment after matching. We implement the weights and diagnostics in the new lmw package for R.", "published": "2021-04-14T01:57:12Z", "authors": ["Ambarish Chattopadhyay", "Jose R. Zubizarreta"], "categories": ["stat.ME"], "doi": "", "_query": "hedonic regression housing", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/1207.0327v4", "title": "Spatially-adaptive sensing in nonparametric regression", "abstract": "While adaptive sensing has provided improved rates of convergence in sparse regression and classification, results in nonparametric regression have so far been restricted to quite specific classes of functions. In this paper, we describe an adaptive-sensing algorithm which is applicable to general nonparametric-regression problems. The algorithm is spatially adaptive, and achieves improved rates of convergence over spatially inhomogeneous functions. Over standard function classes, it likewise retains the spatial adaptivity properties of a uniform design.", "published": "2012-07-02T10:18:49Z", "authors": ["Adam D. Bull"], "categories": ["math.ST", "stat.ME"], "doi": "10.1214/12-AOS1064", "_query": "hedonic regression housing", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/1603.00974v1", "title": "Regression Analysis for Microbiome Compositional Data", "abstract": "One important problem in microbiome analysis is to identify the bacterial taxa that are associated with a response, where the microbiome data are summarized as the composition of the bacterial taxa at different taxonomic levels. This paper considers regression analysis with such compositional data as covariates. In order to satisfy the subcompositional coherence of the results, linear models with a set of linear constraints on the regression coefficients are introduced. Such models allow regression analysis for subcompositions and include the log-contrast model for compositional covariates as a special case. A penalized estimation procedure for estimating the regression coefficients and for selecting variables under the linear constraints is developed. A method is also proposed to obtain de-biased estimates of the regression coefficients that are asymptotically unbiased and have a joint asymptotic multivariate normal distribution. This provides valid confidence intervals of the regression coefficients and can be used to obtain the $p$-values. Simulation results show the validity of the confidence intervals and smaller variances of the de-biased estimates when the linear constraints are imposed. The proposed methods are applied to a gut microbiome data set and identify four bacterial genera that are associated with the body mass index after adjusting for the total fat and caloric intakes.", "published": "2016-03-03T05:29:30Z", "authors": ["Pixu Shi", "Anru Zhang", "Hongzhe Li"], "categories": ["stat.AP"], "doi": "", "_query": "hedonic regression housing", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/0906.3123v1", "title": "On-line predictive linear regression", "abstract": "We consider the on-line predictive version of the standard problem of linear regression; the goal is to predict each consecutive response given the corresponding explanatory variables and all the previous observations. The standard treatment of prediction in linear regression analysis has two drawbacks: (1) the classical prediction intervals guarantee that the probability of error is equal to the nominal significance level $\\varepsilon$, but this property per se does not imply that the long-run frequency of error is close to $\\varepsilon$; (2) it is not suitable for prediction of complex systems as it assumes that the number of observations exceeds the number of parameters. We state a general result showing that in the on-line protocol the frequency of error for the classical prediction intervals does equal the nominal significance level, up to statistical fluctuations. We also describe alternative regression models in which informative prediction intervals can be found before the number of observations exceeds the number of parameters. One of these models, which only assumes that the observations are independent and identically distributed, is popular in machine learning but greatly underused in the statistical theory of regression.", "published": "2009-06-17T09:46:30Z", "authors": ["Vladimir Vovk", "Ilia Nouretdinov", "Alex Gammerman"], "categories": ["math.ST"], "doi": "10.1214/08-AOS622", "_query": "hedonic regression housing", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2201.01074v3", "title": "Gaussian Process Regression in the Flat Limit", "abstract": "Gaussian process (GP) regression is a fundamental tool in Bayesian statistics. It is also known as kriging and is the Bayesian counterpart to the frequentist kernel ridge regression. Most of the theoretical work on GP regression has focused on a large-$n$ asymptotics, i.e. as the amount of data increases. Fixed-sample analysis is much more difficult outside of simple cases, such as locations on a regular grid. In this work we perform a fixed-sample analysis that was first studied in the context of approximation theory by Driscoll & Fornberg (2002), called the ``flat limit''. In flat-limit asymptotics, the goal is to characterise kernel methods as the length-scale of the kernel function tends to infinity, so that kernels appear flat over the range of the data. Surprisingly, this limit is well-defined, and displays interesting behaviour: Driscoll & Fornberg showed that radial basis interpolation converges in the flat limit to polynomial interpolation, if the kernel is Gaussian. Subsequent work showed that this holds true in the multivariate setting as well, but that kernels other than the Gaussian may have (polyharmonic) splines as the limit interpolant. Leveraging recent results on the spectral behaviour of kernel matrices in the flat limit, we study the flat limit of Gaussian process regression. Results show that Gaussian process regression tends in the flat limit to (multivariate) polynomial regression, or (polyharmonic) spline regression, depending on the kernel. Importantly, this holds for both the predictive mean and the predictive variance, so that the posterior predictive distributions become equivalent. Our results have practical consequences: for instance, they show that optimal GP predictions in the sense of leave-one-out loss may occur at very large length-scales, which would be invisible to current implementations because of numerical difficulties.", "published": "2022-01-04T10:31:15Z", "authors": ["Simon Barthelmé", "Pierre-Olivier Amblard", "Nicolas Tremblay", "Konstantin Usevich"], "categories": ["math.ST", "math.NA"], "doi": "", "_query": "hedonic regression housing", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2304.02229v2", "title": "Mixed Regression via Approximate Message Passing", "abstract": "We study the problem of regression in a generalized linear model (GLM) with multiple signals and latent variables. This model, which we call a matrix GLM, covers many widely studied problems in statistical learning, including mixed linear regression, max-affine regression, and mixture-of-experts. In mixed linear regression, each observation comes from one of $L$ signal vectors (regressors), but we do not know which one; in max-affine regression, each observation comes from the maximum of $L$ affine functions, each defined via a different signal vector. The goal in all these problems is to estimate the signals, and possibly some of the latent variables, from the observations. We propose a novel approximate message passing (AMP) algorithm for estimation in a matrix GLM and rigorously characterize its performance in the high-dimensional limit. This characterization is in terms of a state evolution recursion, which allows us to precisely compute performance measures such as the asymptotic mean-squared error. The state evolution characterization can be used to tailor the AMP algorithm to take advantage of any structural information known about the signals. Using state evolution, we derive an optimal choice of AMP `denoising' functions that minimizes the estimation error in each iteration. The theoretical results are validated by numerical simulations for mixed linear regression, max-affine regression, and mixture-of-experts. For max-affine regression, we propose an algorithm that combines AMP with expectation-maximization to estimate intercepts of the model along with the signals. The numerical results show that AMP significantly outperforms other estimators for mixed linear regression and max-affine regression in most parameter regimes.", "published": "2023-04-05T04:59:59Z", "authors": ["Nelvin Tan", "Ramji Venkataramanan"], "categories": ["stat.ML", "cs.IT", "cs.LG", "math.ST"], "doi": "", "_query": "hedonic regression housing", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/1503.07476v1", "title": "Regression of Environmental Noise in LIGO Data", "abstract": "We address the problem of noise regression in the output of gravitational-wave (GW) interferometers, using data from the physical environmental monitors (PEM). The objective of the regression analysis is to predict environmental noise in the gravitational-wave channel from the PEM measurements. One of the most promising regression method is based on the construction of Wiener-Kolmogorov filters. Using this method, the seismic noise cancellation from the LIGO GW channel has already been performed. In the presented approach the Wiener-Kolmogorov method has been extended, incorporating banks of Wiener filters in the time-frequency domain, multi-channel analysis and regulation schemes, which greatly enhance the versatility of the regression analysis. Also we presents the first results on regression of the bi-coherent noise in the LIGO data.", "published": "2015-03-25T17:54:58Z", "authors": ["Vaibhav Tiwari", "Marco Drago", "Valery Frolov", "Sergey Klimenko", "Guenakh Mitselmakher", "Valentin Necula", "Giovanni Prodi", "Virginia Re", "Francesco Salemi", "Gabriele Vedovato", "Igor Yakushin"], "categories": ["gr-qc", "astro-ph.IM"], "doi": "10.1088/0264-9381/32/16/165014", "_query": "hedonic regression housing", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/1202.3483v1", "title": "Semiparametric Penalized Spline Regression", "abstract": "In this paper, we propose a new semiparametric regression estimator by using a hybrid technique of a parametric approach and a nonparametric penalized spline method. The overall shape of the true regression function is captured by the parametric part, while its residual is consistently estimated by the nonparametric part. Asymptotic theory for the proposed semiparametric estimator is developed, showing that its behavior is dependent on the asymptotics for the nonparametric penalized spline estimator as well as on the discrepancy between the true regression function and the parametric part. As a naturally associated application of asymptotics, some criteria for the selection of parametric models are addressed. Numerical experiments show that the proposed estimator performs better than the existing kernel-based semiparametric estimator and the fully nonparametric estimator, and that the proposed criteria work well for choosing a reasonable parametric model.", "published": "2012-02-16T01:08:21Z", "authors": ["Takuma Yoshida", "Kanta Naito"], "categories": ["math.ST"], "doi": "", "_query": "hedonic regression housing", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2104.10637v1", "title": "Robust Kernel-based Distribution Regression", "abstract": "Regularization schemes for regression have been widely studied in learning theory and inverse problems. In this paper, we study distribution regression (DR) which involves two stages of sampling, and aims at regressing from probability measures to real-valued responses over a reproducing kernel Hilbert space (RKHS). Recently, theoretical analysis on DR has been carried out via kernel ridge regression and several learning behaviors have been observed. However, the topic has not been explored and understood beyond the least square based DR. By introducing a robust loss function $l_σ$ for two-stage sampling problems, we present a novel robust distribution regression (RDR) scheme. With a windowing function $V$ and a scaling parameter $σ$ which can be appropriately chosen, $l_σ$ can include a wide range of popular used loss functions that enrich the theme of DR. Moreover, the loss $l_σ$ is not necessarily convex, hence largely improving the former regression class (least square) in the literature of DR. The learning rates under different regularity ranges of the regression function $f_ρ$ are comprehensively studied and derived via integral operator techniques. The scaling parameter $σ$ is shown to be crucial in providing robustness and satisfactory learning rates of RDR.", "published": "2021-04-21T17:03:46Z", "authors": ["Zhan Yu", "Daniel W. C. Ho", "Ding-Xuan Zhou"], "categories": ["cs.LG", "math.FA", "stat.ML"], "doi": "10.1088/1361-6420/ac23c3", "_query": "hedonic regression housing", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2607.14930v1", "title": "Testing for correct model specification in copula regression models", "abstract": "We propose a goodness-of-fit test for semiparametric copula regression models. Such models express the regression function in terms of marginal distribution functions and copula densities and therefore provide a flexible way to avoid fully nonparametric estimation in high-dimensional regression problems. Their performance, however, depends crucially on the specification of the parametric copula family. Instead of testing the copula model itself, we assess misspecification directly at the level of the induced regression function. To this end, we introduce a weighted $L^2$-distance between the true regression function and its best approximation within the postulated copula regression model. A kernel-based estimator of this distance is proposed and shown to be consistent and asymptotically normal under both the null hypothesis of correct specification and fixed alternatives. We derive a classical specification test and, using a self-normalized sequential statistic, construct pivotal confidence intervals and tests for relevant deviations from the model. Finite-sample simulations demonstrate accurate level approximation and good power properties of the proposed procedures.", "published": "2026-07-16T12:45:35Z", "authors": ["Holger Dette", "Philip Dörr"], "categories": ["math.ST"], "doi": "", "_query": "hedonic regression housing", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/1710.04105v1", "title": "Variable Selection in Restricted Linear Regression Models", "abstract": "The use of prior information in the linear regression is well known to provide more efficient estimators of regression coefficients. The methods of non-stochastic restricted regression estimation proposed by Theil and Goldberger (1961) are preferred when prior information is available. In this study, we will consider parameter estimation and the variable selection in non-stochastic restricted linear regression model, using least absolute shrinkage and selection operator (LASSO) method introduced by Tibshirani (1996). A small simulation study and real data example are provided to illustrate the performance of the proposed method for dealing with the variable selection and the parameter estimation in restricted linear regression models.", "published": "2017-10-11T15:10:06Z", "authors": ["Yetkin Tuaç", "Olcay Arslan"], "categories": ["stat.AP", "stat.ME"], "doi": "", "_query": "hedonic regression housing", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2609.12094v1", "title": "Operator-matched spatial regression", "abstract": "A standard spatial regression model $Y = X^\\topβ+ U$, with covariates $X$ and a Gaussian random field $U$ with Matérn covariance, decouples covariate effects from spatial dependence. In several applications this is mechanistically implausible, as covariates likely drive the dynamics generating the residual covariance, and it is statistically problematic, since the generalised least-squares estimator of $β$ may be biased by spatial self-confounding. We propose operator-matched spatial regression, where covariates enter both pointwise and as deterministic forcings in a stochastic partial differential equation (SPDE) that generates the random effect, partitioning the apparent effect into local and operator-mediated components. The formulation arises as the steady state of a transport--relaxation equation forced by $X$, giving the parameters physical interpretations. We show that self-confounding is resolved by construction and that maximum likelihood inference for the regression coefficients, implementable through standard covariance-based methods, is calibrated when the operator parameters are known and, under expanding-domain asymptotics, when they are jointly estimated. A computationally efficient finite element implementation is introduced and justified via bounds on the discretisation error. Applications to temperature--altitude regression and air-quality monitoring show that the method matches or outperforms alternatives and produces physically meaningful results.", "published": "2026-09-10T18:18:04Z", "authors": ["David Bolin"], "categories": ["stat.ME"], "doi": "", "_query": "hedonic regression housing", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2504.11304v2", "title": "Differentially Private Geodesic Regression", "abstract": "In statistical applications it has become increasingly common to encounter data structures that live on non-linear spaces such as manifolds. Classical linear regression, one of the most fundamental methodologies of statistical learning, captures the relationship between an independent variable and a response variable which both are assumed to live in Euclidean space. Thus, geodesic regression emerged as an extension where the response variable lives on a Riemannian manifold. The parameters of geodesic regression, as with linear regression, capture the relationship of sensitive data and hence one should consider the privacy protection practices of said parameters. We consider releasing Differentially Private (DP) parameters of geodesic regression via the K-Norm Gradient (KNG) mechanism for Riemannian manifolds. We derive theoretical bounds for the sensitivity of the parameters showing they are tied to their respective Jacobi fields and hence the curvature of the space. This corroborates, and extends, recent findings of differential privacy for the Fréchet mean. We demonstrate the efficacy of our methodology on the sphere, $S_2\\subset\\mathbb{R}^3$, the space of symmetric positive definite matrices, and Kendall's planar shape space. Our methodology is general to any Riemannian manifold, and thus it is suitable for data in domains such as medical imaging and computer vision.", "published": "2025-04-15T15:45:48Z", "authors": ["Aditya Kulkarni", "Carlos Soto"], "categories": ["stat.ML", "cs.LG"], "doi": "", "_query": "hedonic regression housing", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2506.19375v1", "title": "Path Learning with Trajectory Advantage Regression", "abstract": "In this paper, we propose trajectory advantage regression, a method of offline path learning and path attribution based on reinforcement learning. The proposed method can be used to solve path optimization problems while algorithmically only solving a regression problem.", "published": "2025-06-24T07:05:23Z", "authors": ["Kohei Miyaguchi"], "categories": ["cs.LG"], "doi": "", "_query": "hedonic regression housing", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2110.08665v1", "title": "Quantile Regression by Dyadic CART", "abstract": "In this paper we propose and study a version of the Dyadic Classification and Regression Trees (DCART) estimator from Donoho (1997) for (fixed design) quantile regression in general dimensions. We refer to this proposed estimator as the QDCART estimator. Just like the mean regression version, we show that a) a fast dynamic programming based algorithm with computational complexity $O(N \\log N)$ exists for computing the QDCART estimator and b) an oracle risk bound (trading off squared error and a complexity parameter of the true signal) holds for the QDCART estimator. This oracle risk bound then allows us to demonstrate that the QDCART estimator enjoys adaptively rate optimal estimation guarantees for piecewise constant and bounded variation function classes. In contrast to existing results for the DCART estimator which requires subgaussianity of the error distribution, for our estimation guarantees to hold we do not need any restrictive tail decay assumptions on the error distribution. For instance, our results hold even when the error distribution has no first moment such as the Cauchy distribution. Apart from the Dyadic CART method, we also consider other variant methods such as the Optimal Regression Tree (ORT) estimator introduced in Chatterjee and Goswami (2019). In particular, we also extend the ORT estimator to the quantile setting and establish that it enjoys analogous guarantees. Thus, this paper extends the scope of these globally optimal regression tree based methodologies to be applicable for heavy tailed data. We then perform extensive numerical experiments on both simulated and real data which illustrate the usefulness of the proposed methods.", "published": "2021-10-16T21:30:39Z", "authors": ["Oscar Hernan Madrid Padilla", "Sabyasachi Chatterjee"], "categories": ["stat.ME", "math.ST"], "doi": "", "_query": "hedonic regression housing", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/1805.01618v1", "title": "Distribution Assertive Regression", "abstract": "In regression modelling approach, the main step is to fit the regression line as close as possible to the target variable. In this process most algorithms try to fit all of the data in a single line and hence fitting all parts of target variable in one go. It was observed that the error between predicted and target variable usually have a varying behavior across the various quantiles of the dependent variable and hence single point diagnostic like MAPE has its limitation to signify the level of fitness across the distribution of Y(dependent variable). To address this problem, a novel approach is proposed in the paper to deal with regression fitting over various quantiles of target variable. Using this approach we have significantly improved the eccentric behavior of the distance (error) between predicted and actual value of regression. Our proposed solution is based on understanding the segmented behavior of the data with respect to the internal segments within the data and approach for retrospectively fitting the data based on each quantile behavior. We believe exploring and using this approach would help in achieving better and more explainable results in most settings of real world data modelling problems.", "published": "2018-05-04T06:16:30Z", "authors": ["Kumarjit Pathak", "Jitin Kapila", "Aasheesh Barvey", "Nikit Gawande"], "categories": ["stat.ML", "cs.LG"], "doi": "", "_query": "hedonic regression housing", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2511.08587v1", "title": "Conversational Agents for Building Energy Efficiency -- Advising Housing Cooperatives in Stockholm on Reducing Energy Consumption", "abstract": "Housing cooperative is a common type of multifamily building ownership in Sweden. Although this ownership structure grants decision-making autonomy, it places a burden of responsibility on cooperative's board members. Most board members lack the resources or expertise to manage properties and their energy consumption. This ignorance presents a unique challenge, especially given the EU directives that prohibit buildings rated as energy classes F and G by 2033. Conversational agents (CAs) enable human-like interactions with computer systems, facilitating human-computer interaction across various domains. In our case, CAs can be implemented to support cooperative members in making informed energy retrofitting and usage decisions. This paper introduces a Conversational agent system, called SPARA, designed to advise cooperatives on energy efficiency. SPARA functions as an energy efficiency advisor by leveraging the Retrieval-Augmented Generation (RAG) framework with a Language Model(LM). The LM generates targeted recommendations based on a knowledge base composed of email communications between professional energy advisors and cooperatives' representatives in Stockholm. The preliminary results indicate that SPARA can provide energy efficiency advice with precision 80\\%, comparable to that of municipal energy efficiency (EE) experts. A pilot implementation is currently underway, where municipal EE experts are evaluating SPARA performance based on questions posed to EE experts by BRF members. Our findings suggest that LMs can significantly improve outreach by supporting stakeholders in their energy transition. For future work, more research is needed to evaluate this technology, particularly limitations to the stability and trustworthiness of its energy efficiency advice.", "published": "2025-10-01T11:40:11Z", "authors": ["Shadaab Ghani", "Anne Håkansson", "Oleksii Pasichnyi", "Hossein Shahrokni"], "categories": ["cs.CY", "cs.AI", "cs.CL"], "doi": "", "_query": "hedonic regression housing", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2512.01139v3", "title": "Market Sensitivities and Growth Differentials Across Australian Housing Markets", "abstract": "Australian house prices have risen strongly since the mid-1990s, but growth has been highly uneven across regions. Raw growth figures obscure whether these differences reflect persistent structural trends or cyclical fluctuations. We address this by estimating a three-factor model in levels for regional repeat-sales log price indexes over 1995-2024. The model decomposes each regional index into a national Market factor, two stationary spreads (Mining and Lifestyle) that capture mean-reverting geographic cycles, and a city-specific residual. The Mining spread, proxied by a Perth-Sydney index differential, reflects resource-driven oscillations in relative performance; the Lifestyle spread captures amenity-driven coastal and regional cycles. The Market loading isolates each region's fundamental sensitivity, beta, to national growth, so that a city's growth under an assumed national change is calculated from its beta once mean-reverting spreads are netted out. Comparing realised paths to these factor-implied trajectories indicates when a city is historically elevated or depressed, and attributes the gap to Mining or Lifestyle spreads. Expanding-window ARIMAX estimation reveals that Market betas are stable across major shocks (the mining boom, the Global Financial Crisis, and COVID-19), while Mining and Lifestyle behave as stationary spreads that widen forecast funnels without overturning the cross-sectional ranking implied by beta. Melbourne amplifies national growth, Sydney tracks the national trend closely, and regional areas dampen it. The framework thus provides a simple, factor-based tool for interpreting regional growth differentials and their persistence.", "published": "2025-11-30T23:29:03Z", "authors": ["Willem P Sijp"], "categories": ["econ.EM"], "doi": "", "_query": "hedonic regression housing", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2005.01559v1", "title": "Reduced Rank Multivariate Kernel Ridge Regression", "abstract": "In the multivariate regression, also referred to as multi-task learning in machine learning, the goal is to recover a vector-valued function based on noisy observations. The vector-valued function is often assumed to be of low rank. Although the multivariate linear regression is extensively studied in the literature, a theoretical study on the multivariate nonlinear regression is lacking. In this paper, we study reduced rank multivariate kernel ridge regression, proposed by \\cite{mukherjee2011reduced}. We prove the consistency of the function predictor and provide the convergence rate. An algorithm based on nuclear norm relaxation is proposed. A few numerical examples are presented to show the smaller mean squared prediction error comparing with the elementwise univariate kernel ridge regression.", "published": "2020-05-04T15:19:04Z", "authors": ["Wenjia Wang", "Yi-Hui Zhou"], "categories": ["math.ST", "stat.ME"], "doi": "", "_query": "hedonic regression housing", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/0708.3774v1", "title": "Fisher Lecture: Dimension Reduction in Regression", "abstract": "Beginning with a discussion of R. A. Fisher's early written remarks that relate to dimension reduction, this article revisits principal components as a reductive method in regression, develops several model-based extensions and ends with descriptions of general approaches to model-based and model-free dimension reduction in regression. It is argued that the role for principal components and related methodology may be broader than previously seen and that the common practice of conditioning on observed values of the predictors may unnecessarily limit the choice of regression methodology.", "published": "2007-08-28T13:29:05Z", "authors": ["R. Dennis Cook"], "categories": ["stat.ME"], "doi": "10.1214/088342306000000682", "_query": "hedonic regression housing", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2503.00952v1", "title": "A Survey on Ordinal Regression: Applications, Advances and Prospects", "abstract": "Ordinal regression refers to classifying object instances into ordinal categories. Ordinal regression is crucial for applications in various areas like facial age estimation, image aesthetics assessment, and even cancer staging, due to its capability to utilize ordered information effectively. More importantly, it also enhances model interpretation by considering category order, aiding the understanding of data trends and causal relationships. Despite significant recent progress, challenges remain, and further investigation of ordinal regression techniques and applications is essential to guide future research. In this survey, we present a comprehensive examination of advances and applications of ordinal regression. By introducing a systematic taxonomy, we meticulously classify the pertinent techniques and applications into three well-defined categories based on different strategies and objectives: Continuous Space Discretization, Distribution Ordering Learning, and Ambiguous Instance Delving. This categorization enables a structured exploration of diverse insights in ordinal regression problems, providing a framework for a more comprehensive understanding and evaluation of this field and its related applications. To our best knowledge, this is the first systematic survey of ordinal regression, which lays a foundation for future research in this fundamental and generic domain.", "published": "2025-03-02T16:10:36Z", "authors": ["Jinhong Wang", "Jintai Chen", "Jian Liu", "Dongqi Tang", "Danny Z. Chen", "Jian Wu"], "categories": ["cs.CV"], "doi": "", "_query": "hedonic regression housing", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/1508.01217v4", "title": "Bayesian Approximate Kernel Regression with Variable Selection", "abstract": "Nonlinear kernel regression models are often used in statistics and machine learning because they are more accurate than linear models. Variable selection for kernel regression models is a challenge partly because, unlike the linear regression setting, there is no clear concept of an effect size for regression coefficients. In this paper, we propose a novel framework that provides an effect size analog of each explanatory variable for Bayesian kernel regression models when the kernel is shift-invariant --- for example, the Gaussian kernel. We use function analytic properties of shift-invariant reproducing kernel Hilbert spaces (RKHS) to define a linear vector space that: (i) captures nonlinear structure, and (ii) can be projected onto the original explanatory variables. The projection onto the original explanatory variables serves as an analog of effect sizes. The specific function analytic property we use is that shift-invariant kernel functions can be approximated via random Fourier bases. Based on the random Fourier expansion we propose a computationally efficient class of Bayesian approximate kernel regression (BAKR) models for both nonlinear regression and binary classification for which one can compute an analog of effect sizes. We illustrate the utility of BAKR by examining two important problems in statistical genetics: genomic selection (i.e. phenotypic prediction) and association mapping (i.e. inference of significant variants or loci). State-of-the-art methods for genomic selection and association mapping are based on kernel regression and linear models, respectively. BAKR is the first method that is competitive in both settings.", "published": "2015-08-05T20:40:11Z", "authors": ["Lorin Crawford", "Kris C. Wood", "Xiang Zhou", "Sayan Mukherjee"], "categories": ["stat.ME", "q-bio.QM", "stat.AP", "stat.ML"], "doi": "", "_query": "hedonic regression housing", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/1712.08597v2", "title": "Learning the Kernel for Classification and Regression", "abstract": "We investigate a series of learning kernel problems with polynomial combinations of base kernels, which will help us solve regression and classification problems. We also perform some numerical experiments of polynomial kernels with regression and classification tasks on different datasets.", "published": "2017-12-22T17:55:28Z", "authors": ["Chen Li", "Luca Venturi", "Ruitu Xu"], "categories": ["cs.LG"], "doi": "", "_query": "hedonic regression housing", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2109.12962v1", "title": "pyStoNED: A Python Package for Convex Regression and Frontier Estimation", "abstract": "Shape-constrained nonparametric regression is a growing area in econometrics, statistics, operations research, machine learning and related fields. In the field of productivity and efficiency analysis, recent developments in the multivariate convex regression and related techniques such as convex quantile regression and convex expectile regression have bridged the long-standing gap between the conventional deterministic-nonparametric and stochastic-parametric methods. Unfortunately, the heavy computational burden and the lack of powerful, reliable, and fully open access computational package has slowed down the diffusion of these advanced estimation techniques to the empirical practice. The purpose of the Python package pyStoNED is to address this challenge by providing a freely available and user-friendly tool for the multivariate convex regression, convex quantile regression, convex expectile regression, isotonic regression, stochastic nonparametric envelopment of data, and related methods. This paper presents a tutorial of the pyStoNED package and illustrates its application, focusing on the estimation of frontier cost and production functions.", "published": "2021-09-27T11:41:51Z", "authors": ["Sheng Dai", "Yu-Hsueh Fang", "Chia-Yen Lee", "Timo Kuosmanen"], "categories": ["stat.CO", "stat.AP", "stat.ME"], "doi": "", "_query": "hedonic regression housing", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2410.03263v2", "title": "Test-time Adaptation for Regression by Subspace Alignment", "abstract": "This paper investigates test-time adaptation (TTA) for regression, where a regression model pre-trained in a source domain is adapted to an unknown target distribution with unlabeled target data. Although regression is one of the fundamental tasks in machine learning, most of the existing TTA methods have classification-specific designs, which assume that models output class-categorical predictions, whereas regression models typically output only single scalar values. To enable TTA for regression, we adopt a feature alignment approach, which aligns the feature distributions between the source and target domains to mitigate the domain gap. However, we found that naive feature alignment employed in existing TTA methods for classification is ineffective or even worse for regression because the features are distributed in a small subspace and many of the raw feature dimensions have little significance to the output. For an effective feature alignment in TTA for regression, we propose Significant-subspace Alignment (SSA). SSA consists of two components: subspace detection and dimension weighting. Subspace detection finds the feature subspace that is representative and significant to the output. Then, the feature alignment is performed in the subspace during TTA. Meanwhile, dimension weighting raises the importance of the dimensions of the feature subspace that have greater significance to the output. We experimentally show that SSA outperforms various baselines on real-world datasets.", "published": "2024-10-04T09:31:10Z", "authors": ["Kazuki Adachi", "Shin'ya Yamaguchi", "Atsutoshi Kumagai", "Tomoki Hamagami"], "categories": ["cs.LG", "cs.AI"], "doi": "", "_query": "hedonic regression housing", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2301.10431v1", "title": "Bias-Compensated Integral Regression for Human Pose Estimation", "abstract": "In human and hand pose estimation, heatmaps are a crucial intermediate representation for a body or hand keypoint. Two popular methods to decode the heatmap into a final joint coordinate are via an argmax, as done in heatmap detection, or via softmax and expectation, as done in integral regression. Integral regression is learnable end-to-end, but has lower accuracy than detection. This paper uncovers an induced bias from integral regression that results from combining the softmax and the expectation operation. This bias often forces the network to learn degenerately localized heatmaps, obscuring the keypoint's true underlying distribution and leads to lower accuracies. Training-wise, by investigating the gradients of integral regression, we show that the implicit guidance of integral regression to update the heatmap makes it slower to converge than detection. To counter the above two limitations, we propose Bias Compensated Integral Regression (BCIR), an integral regression-based framework that compensates for the bias. BCIR also incorporates a Gaussian prior loss to speed up training and improve prediction accuracy. Experimental results on both the human body and hand benchmarks show that BCIR is faster to train and more accurate than the original integral regression, making it competitive with state-of-the-art detection methods.", "published": "2023-01-25T06:54:04Z", "authors": ["Kerui Gu", "Linlin Yang", "Michael Bi Mi", "Angela Yao"], "categories": ["cs.CV"], "doi": "", "_query": "hedonic regression housing", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2004.14009v3", "title": "Targeted Principal Components Regression", "abstract": "We propose a principal components regression method based on maximizing a joint pseudo-likelihood for responses and predictors. Our method uses both responses and predictors to select linear combinations of the predictors relevant for the regression, thereby addressing an oft-cited deficiency of conventional principal components regression. The proposed estimator is shown to be consistent in a wide range of settings, including ones with non-normal and dependent observations; conditions on the first and second moments suffice if the number of predictors ($p$) is fixed and the number of observations ($n$) tends to infinity and dependence is weak, while stronger distributional assumptions are needed when $p \\to \\infty$ with $n$. We obtain the estimator's asymptotic distribution as the projection of a multivariate normal random vector onto a tangent cone of the parameter set at the true parameter, and find the estimator is asymptotically more efficient than competing ones. In simulations our method is substantially more accurate than conventional principal components regression and compares favorably to partial least squares and predictor envelopes. The method's practical usefulness is illustrated in a data example with cross-sectional prediction of stock returns.", "published": "2020-04-29T08:09:31Z", "authors": ["Karl Oskar Ekvall"], "categories": ["stat.ME"], "doi": "", "_query": "hedonic regression housing", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/1903.09488v1", "title": "On the support recovery of marginal regression", "abstract": "Leading methods for support recovery in high-dimensional regression, such as Lasso, have been well-studied and their limitations in the context of correlated design have been characterized with precise incoherence conditions. In this work, we present a similar treatment of selection consistency for marginal regression (MR), a computationally efficient family of methods with connections to decision trees. Selection based on marginal regression is also referred to as covariate screening or independence screening and is a popular approach in applied work, especially in ultra high-dimensional settings. We identify the underlying factors---which we denote as \\emph{MR incoherence}---affecting MR's support recovery performance. Our near complete characterization provides a much more nuanced and optimistic view of MR in comparison to previous works. To ground our results, we provide a broad taxonomy of results for leading feature selection methods, relating the behavior of Lasso, OMP, SIS, and MR. We also lay the foundation for interesting generalizations of our analysis, e.g., to non-linear feature selection methods and to more general regression frameworks such as a general additive models.", "published": "2019-03-22T13:19:26Z", "authors": ["S. Jalil Kazemitabar", "Arash A. Amini", "Ameet Talwalkar"], "categories": ["math.ST"], "doi": "", "_query": "hedonic regression housing", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2010.14700v1", "title": "Sparse Symmetric Tensor Regression for Functional Connectivity Analysis", "abstract": "Tensor regression models, such as CP regression and Tucker regression, have many successful applications in neuroimaging analysis where the covariates are of ultrahigh dimensionality and possess complex spatial structures. The high-dimensional covariate arrays, also known as tensors, can be approximated by low-rank structures and fit into the generalized linear models. The resulting tensor regression achieves a significant reduction in dimensionality while remaining efficient in estimation and prediction. Brain functional connectivity is an essential measure of brain activity and has shown significant association with neurological disorders such as Alzheimer's disease. The symmetry nature of functional connectivity is a property that has not been explored in previous tensor regression models. In this work, we propose a sparse symmetric tensor regression that further reduces the number of free parameters and achieves superior performance over symmetrized and ordinary CP regression, under a variety of simulation settings. We apply the proposed method to a study of Alzheimer's disease (AD) and normal ageing from the Berkeley Aging Cohort Study (BACS) and detect two regions of interest that have been identified important to AD.", "published": "2020-10-28T02:07:39Z", "authors": ["Da Xu"], "categories": ["cs.LG", "stat.ML"], "doi": "", "_query": "hedonic regression housing", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2010.07154v4", "title": "Learning Deep Features in Instrumental Variable Regression", "abstract": "Instrumental variable (IV) regression is a standard strategy for learning causal relationships between confounded treatment and outcome variables from observational data by utilizing an instrumental variable, which affects the outcome only through the treatment. In classical IV regression, learning proceeds in two stages: stage 1 performs linear regression from the instrument to the treatment; and stage 2 performs linear regression from the treatment to the outcome, conditioned on the instrument. We propose a novel method, deep feature instrumental variable regression (DFIV), to address the case where relations between instruments, treatments, and outcomes may be nonlinear. In this case, deep neural nets are trained to define informative nonlinear features on the instruments and treatments. We propose an alternating training regime for these features to ensure good end-to-end performance when composing stages 1 and 2, thus obtaining highly flexible feature maps in a computationally efficient manner. DFIV outperforms recent state-of-the-art methods on challenging IV benchmarks, including settings involving high dimensional image data. DFIV also exhibits competitive performance in off-policy policy evaluation for reinforcement learning, which can be understood as an IV regression task.", "published": "2020-10-14T15:14:49Z", "authors": ["Liyuan Xu", "Yutian Chen", "Siddarth Srinivasan", "Nando de Freitas", "Arnaud Doucet", "Arthur Gretton"], "categories": ["cs.LG", "stat.ML"], "doi": "", "_query": "hedonic regression housing", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2503.10569v2", "title": "Low-Rank Matrix Regression via Least-Angle Regression", "abstract": "Low-rank matrix regression is a fundamental problem in data science with various applications in systems and control. Nuclear norm regularization has been widely applied to solve this problem due to its convexity. However, it suffers from high computational complexity and the inability to directly specify the rank. This work introduces a novel framework for low-rank matrix regression that addresses both unstructured and Hankel matrices. By decomposing the low-rank matrix into rank-1 bases, the problem is reformulated as an infinite-dimensional sparse learning problem. The least-angle regression (LAR) algorithm is then employed to solve this problem efficiently. For unstructured matrices, a closed-form LAR solution is derived with equivalence to a normalized nuclear norm regularization problem. For Hankel matrices, a real-valued polynomial basis reformulation enables effective LAR implementation. Two numerical examples in network modeling and system realization demonstrate that the proposed approach significantly outperforms the nuclear norm method in terms of estimation accuracy and computational efficiency.", "published": "2025-03-13T17:21:11Z", "authors": ["Mingzhou Yin", "Matthias A. Müller"], "categories": ["eess.SY", "eess.SP"], "doi": "", "_query": "hedonic regression housing", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/1703.02965v1", "title": "Unsupervised Ensemble Regression", "abstract": "Consider a regression problem where there is no labeled data and the only observations are the predictions $f_i(x_j)$ of $m$ experts $f_{i}$ over many samples $x_j$. With no knowledge on the accuracy of the experts, is it still possible to accurately estimate the unknown responses $y_{j}$? Can one still detect the least or most accurate experts? In this work we propose a framework to study these questions, based on the assumption that the $m$ experts have uncorrelated deviations from the optimal predictor. Assuming the first two moments of the response are known, we develop methods to detect the best and worst regressors, and derive U-PCR, a novel principal components approach for unsupervised ensemble regression. We provide theoretical support for U-PCR and illustrate its improved accuracy over the ensemble mean and median on a variety of regression problems.", "published": "2017-03-08T18:58:20Z", "authors": ["Omer Dror", "Boaz Nadler", "Erhan Bilal", "Yuval Kluger"], "categories": ["stat.ML", "cs.LG"], "doi": "", "_query": "hedonic regression housing", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/1611.06649v3", "title": "High-Dimensional Bayesian Regularised Regression with the BayesReg Package", "abstract": "Bayesian penalized regression techniques, such as the Bayesian lasso and the Bayesian horseshoe estimator, have recently received a significant amount of attention in the statistics literature. However, software implementing state-of-the-art Bayesian penalized regression, outside of general purpose Markov chain Monte Carlo platforms such as STAN, is relatively rare. This paper introduces bayesreg, a new toolbox for fitting Bayesian penalized regression models with continuous shrinkage prior densities. The toolbox features Bayesian linear regression with Gaussian or heavy-tailed error models and Bayesian logistic regression with ridge, lasso, horseshoe and horseshoe$+$ estimators. The toolbox is free, open-source and available for use with the MATLAB and R numerical platforms.", "published": "2016-11-21T05:02:08Z", "authors": ["Enes Makalic", "Daniel F. Schmidt"], "categories": ["stat.CO"], "doi": "", "_query": "hedonic regression housing", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2101.08224v5", "title": "Distributional Anchor Regression", "abstract": "Prediction models often fail if train and test data do not stem from the same distribution. Out-of-distribution (OOD) generalization to unseen, perturbed test data is a desirable but difficult-to-achieve property for prediction models and in general requires strong assumptions on the data generating process (DGP). In a causally inspired perspective on OOD generalization, the test data arise from a specific class of interventions on exogenous random variables of the DGP, called anchors. Anchor regression models, introduced by Rothenhaeusler et al. (2021), protect against distributional shifts in the test data by employing causal regularization. However, so far anchor regression has only been used with a squared-error loss which is inapplicable to common responses such as censored continuous or ordinal data. Here, we propose a distributional version of anchor regression which generalizes the method to potentially censored responses with at least an ordered sample space. To this end, we combine a flexible class of parametric transformation models for distributional regression with an appropriate causal regularizer under a more general notion of residuals. In an exemplary application and several simulation scenarios we demonstrate the extent to which OOD generalization is possible.", "published": "2021-01-20T17:32:37Z", "authors": ["Lucas Kook", "Beate Sick", "Peter Bühlmann"], "categories": ["stat.ME"], "doi": "10.1007/s11222-022-10097-z", "_query": "hedonic regression housing", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/0809.1878v1", "title": "Improved estimators for a general class of beta regression models", "abstract": "In this paper we consider an extension of the beta regression model proposed by Ferrari and Cribari-Neto (2004). We extend their model in two different ways, first, we let the regression structure be nonlinear, second, we allow a regression structure for the precision parameter, moreover, this regression structure may also be nonlinear. Generally, the beta regression is useful to situations where the response is restricted to the standard unit interval and the regression structure involves regressors and unknown parameters. We derive general formulae for second-order biases of the maximum likelihood estimators and use them to define bias-corrected estimators. Our formulae generalizes the results obtained by Ospina et al. (2006), and are easily implemented by means of supplementary weighted linear regressions. We also compare these bias-corrected estimators with three different estimators which are also bias-free to the second-order, one analytical and the other two based on bootstrap methods. These estimators are compared by simulation. We present an empirical application.", "published": "2008-09-10T20:50:49Z", "authors": ["Alexandre B. Simas", "Wagner Barreto-Souza", "Andréa V. Rocha"], "categories": ["stat.ME", "stat.CO"], "doi": "10.1016/j.csda.2009.08.017", "_query": "hedonic regression housing", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/1704.07040v2", "title": "Bootstrapping for multivariate linear regression models", "abstract": "The multivariate linear regression model is an important tool for investigating relationships between several response variables and several predictor variables. The primary interest is in inference about the unknown regression coefficient matrix. We propose multivariate bootstrap techniques as a means for making inferences about the unknown regression coefficient matrix. These bootstrapping techniques are extensions of those developed in Freedman (1981), which are only appropriate for univariate responses. Extensions to the multivariate linear regression model are made without proof. We formalize this extension and prove its validity. A real data example and two simulated data examples which offer some finite sample verification of our theoretical results are provided.", "published": "2017-04-24T04:55:16Z", "authors": ["Daniel J. Eck"], "categories": ["math.ST", "stat.ME"], "doi": "", "_query": "hedonic regression housing", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2404.12943v1", "title": "Symmetry: a General Structure in Nonparametric Regression", "abstract": "In this paper we present the framework of symmetry in nonparametric regression. This generalises the framework of covariate sparsity, where the regression function depends only on at most $s < d$ of the covariates, which is a special case of translation symmetry with linear orbits. In general this extends to other types of functions that capture lower dimensional behavior even when these structures are non-linear. We show both that known symmetries of regression functions can be exploited to give similarly faster rates, and that unknown symmetries with Lipschitz actions can be estimated sufficiently quickly to obtain the same rates. This is done by explicit constructions of partial symmetrisation operators that are then applied to usual estimators, and with a two step M-estimator of the maximal symmetry of the regression function. We also demonstrate the finite sample performance of these estimators on synthetic data.", "published": "2024-04-19T15:20:29Z", "authors": ["Louis G. Christie", "John A. D. Aston"], "categories": ["math.ST"], "doi": "", "_query": "hedonic regression housing", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/1306.3905v1", "title": "Stability of Multi-Task Kernel Regression Algorithms", "abstract": "We study the stability properties of nonlinear multi-task regression in reproducing Hilbert spaces with operator-valued kernels. Such kernels, a.k.a. multi-task kernels, are appropriate for learning prob- lems with nonscalar outputs like multi-task learning and structured out- put prediction. We show that multi-task kernel regression algorithms are uniformly stable in the general case of infinite-dimensional output spaces. We then derive under mild assumption on the kernel generaliza- tion bounds of such algorithms, and we show their consistency even with non Hilbert-Schmidt operator-valued kernels . We demonstrate how to apply the results to various multi-task kernel regression methods such as vector-valued SVR and functional ridge regression.", "published": "2013-06-17T15:44:30Z", "authors": ["Julien Audiffren", "Hachem Kadri"], "categories": ["cs.LG", "stat.ML"], "doi": "", "_query": "hedonic regression housing", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2608.06996v1", "title": "Automated Terminal-to-Housing Assembly System for Flat Ribbon Cable Harness", "abstract": "This paper presents a sensor-minimal automated assembly system for bidirectional single-row flat ribbon cable harnesses (FRCHs). Unlike conventional peg-in-hole or single-terminal insertion tasks, FRCH assembly involves mechanically coupled multi-terminal insertion under flexible and dense geometric constraints. To address this problem, the proposed system performs the assembly through a purely mechanical sequence consisting of Cable Alignment, Lean & Slide, Weaving, and Clamping, without relying on active sensing or vision. Each mechanism is designed to progressively reduce correlated terminal misalignment, insertion interference, and instability before final locking. Experiments on bidirectional single-row FRCHs achieved an 83.75% end-to-end process success rate over 80 trials, with success rates of 85.0% and 82.5% in the first and second halves, respectively. The cycle time was 33 s under half-speed operation. To the best of our knowledge, this work presents the first automated prototype for FRCH terminal-to-housing assembly for multi-pin housings.", "published": "2026-08-07T09:15:12Z", "authors": ["Eunkyu Choi", "Joonho Seo", "Seungmin Lee", "Seokhwan Jeong"], "categories": ["cs.RO"], "doi": "", "_query": "hedonic regression housing", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/1611.00030v2", "title": "A Mixture of Linear-Linear Regression Models for Linear-Circular Regression", "abstract": "We introduce a new approach to a linear-circular regression problem that relates multiple linear predictors to a circular response. We follow a modeling approach of a wrapped normal distribution that describes angular variables and angular distributions and advances it for a linear-circular regression analysis. Some previous works model a circular variable as projection of a bivariate Gaussian random vector on the unit square, and the statistical inference of the resulting model involves complicated sampling steps. The proposed model treats circular responses as the result of the modulo operation on unobserved linear responses. The resulting model is a mixture of multiple linear-linear regression models. We present two EM algorithms for maximum likelihood estimation of the mixture model, one for a parametric model and another for a non-parametric model. The estimation algorithms provide a great trade-off between computation and estimation accuracy, which was numerically shown using five numerical examples. The proposed approach was applied to a problem of estimating wind directions that typically exhibit complex patterns with large variation and circularity.", "published": "2016-10-31T20:24:08Z", "authors": ["Ali Esmaieeli Sikaroudi", "Chiwoo Park"], "categories": ["stat.ME"], "doi": "", "_query": "hedonic regression housing", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/1904.01648v4", "title": "Sequential Adaptive Design for Jump Regression Estimation", "abstract": "Selecting input variables or design points for statistical models has been of great interest in adaptive design and active learning. Motivated by two scientific examples, this paper presents a strategy of selecting the design points for a regression model when the underlying regression function is discontinuous. The first example we undertook was for the purpose of accelerating imaging speed in a high resolution material imaging; the second was use of sequential design for the purpose of mapping a chemical phase diagram. In both examples, the underlying regression functions have discontinuities, so many of the existing design optimization approaches cannot be applied because they mostly assume a continuous regression function. Although some existing adaptive design strategies developed from treed regression models can handle the discontinuities, the Bayesian approaches come with computationally expensive Markov Chain Monte Carlo techniques for posterior inferences and subsequent design point selections, which is not appropriate for the first motivating example that requires computation at least faster than the original imaging speed. In addition, the treed models are based on the domain partitioning that are inefficient when the discontinuities occurs over complex sub-domain boundaries. We propose a simple and effective adaptive design strategy for a regression analysis with discontinuities: some statistical properties with a fixed design will be presented first, and then these properties will be used to propose a new criterion of selecting the design points for the regression analysis. Sequential design with the new criterion will be presented with comprehensive simulated examples, and its application to the two motivating examples will be presented.", "published": "2019-04-02T20:14:47Z", "authors": ["Chiwoo Park", "Peihua Qiu", "Jennifer Carpena-Núñez", "Rahul Rao", "Michael Susner", "Benji Maruyama"], "categories": ["stat.ML", "cs.CV", "cs.LG"], "doi": "", "_query": "hedonic regression housing", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/1903.00843v2", "title": "Multiple Learning for Regression in big data", "abstract": "Regression problems that have closed-form solutions are well understood and can be easily implemented when the dataset is small enough to be all loaded into the RAM. Challenges arise when data is too big to be stored in RAM to compute the closed form solutions. Many techniques were proposed to overcome or alleviate the memory barrier problem but the solutions are often local optimal. In addition, most approaches require accessing the raw data again when updating the models. Parallel computing clusters are also expected if multiple models need to be computed simultaneously. We propose multiple learning approaches that utilize an array of sufficient statistics (SS) to address this big data challenge. This memory oblivious approach breaks the memory barrier when computing regressions with closed-form solutions, including but not limited to linear regression, weighted linear regression, linear regression with Box-Cox transformation (Box-Cox regression) and ridge regression models. The computation and update of the SS array can be handled at per row level or per mini-batch level. And updating a model is as easy as matrix addition and subtraction. Furthermore, multiple SS arrays for different models can be easily computed simultaneously to obtain multiple models at one pass through the dataset. We implemented our approaches on Spark and evaluated over the simulated datasets. Results showed our approaches can achieve closed-form solutions of multiple models at the cost of half training time of the traditional methods for a single model.", "published": "2019-03-03T06:34:24Z", "authors": ["Xiang Liu", "Ziyang Tang", "Huyunting Huang", "Tonglin Zhang", "Baijian Yang"], "categories": ["cs.LG", "cs.DC", "stat.ML"], "doi": "", "_query": "hedonic regression housing", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/1804.06504v2", "title": "Learning how to be robust: Deep polynomial regression", "abstract": "Polynomial regression is a recurrent problem with a large number of applications. In computer vision it often appears in motion analysis. Whatever the application, standard methods for regression of polynomial models tend to deliver biased results when the input data is heavily contaminated by outliers. Moreover, the problem is even harder when outliers have strong structure. Departing from problem-tailored heuristics for robust estimation of parametric models, we explore deep convolutional neural networks. Our work aims to find a generic approach for training deep regression models without the explicit need of supervised annotation. We bypass the need for a tailored loss function on the regression parameters by attaching to our model a differentiable hard-wired decoder corresponding to the polynomial operation at hand. We demonstrate the value of our findings by comparing with standard robust regression methods. Furthermore, we demonstrate how to use such models for a real computer vision problem, i.e., video stabilization. The qualitative and quantitative experiments show that neural networks are able to learn robustness for general polynomial regression, with results that well overpass scores of traditional robust estimation methods.", "published": "2018-04-17T23:36:12Z", "authors": ["Juan-Manuel Perez-Rua", "Tomas Crivelli", "Patrick Bouthemy", "Patrick Perez"], "categories": ["cs.CV"], "doi": "", "_query": "hedonic regression housing", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/1509.01877v4", "title": "On Degrees of Freedom of Projection Estimators with Applications to Multivariate Nonparametric Regression", "abstract": "In this paper, we consider the nonparametric regression problem with multivariate predictors. We provide a characterization of the degrees of freedom and divergence for estimators of the unknown regression function, which are obtained as outputs of linearly constrained quadratic optimization procedures, namely, minimizers of the least squares criterion with linear constraints and/or quadratic penalties. As special cases of our results, we derive explicit expressions for the degrees of freedom in many nonparametric regression problems, e.g., bounded isotonic regression, multivariate (penalized) convex regression, and additive total variation regularization. Our theory also yields, as special cases, known results on the degrees of freedom of many well-studied estimators in the statistics literature, such as ridge regression, Lasso and generalized Lasso. Our results can be readily used to choose the tuning parameter(s) involved in the estimation procedure by minimizing the Stein's unbiased risk estimate. As a by-product of our analysis we derive an interesting connection between bounded isotonic regression and isotonic regression on a general partially ordered set, which is of independent interest.", "published": "2015-09-07T01:20:37Z", "authors": ["Xi Chen", "Qihang Lin", "Bodhisattva Sen"], "categories": ["math.ST"], "doi": "", "_query": "hedonic regression housing", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2203.07557v1", "title": "Fast Regression for Structured Inputs", "abstract": "We study the $\\ell_p$ regression problem, which requires finding $\\mathbf{x}\\in\\mathbb R^{d}$ that minimizes $\\|\\mathbf{A}\\mathbf{x}-\\mathbf{b}\\|_p$ for a matrix $\\mathbf{A}\\in\\mathbb R^{n \\times d}$ and response vector $\\mathbf{b}\\in\\mathbb R^{n}$. There has been recent interest in developing subsampling methods for this problem that can outperform standard techniques when $n$ is very large. However, all known subsampling approaches have run time that depends exponentially on $p$, typically, $d^{\\mathcal{O}(p)}$, which can be prohibitively expensive. We improve on this work by showing that for a large class of common \\emph{structured matrices}, such as combinations of low-rank matrices, sparse matrices, and Vandermonde matrices, there are subsampling based methods for $\\ell_p$ regression that depend polynomially on $p$. For example, we give an algorithm for $\\ell_p$ regression on Vandermonde matrices that runs in time $\\mathcal{O}(n\\log^3 n+(dp^2)^{0.5+ω}\\cdot\\text{polylog}\\,n)$, where $ω$ is the exponent of matrix multiplication. The polynomial dependence on $p$ crucially allows our algorithms to extend naturally to efficient algorithms for $\\ell_\\infty$ regression, via approximation of $\\ell_\\infty$ by $\\ell_{\\mathcal{O}(\\log n)}$. Of practical interest, we also develop a new subsampling algorithm for $\\ell_p$ regression for arbitrary matrices, which is simpler than previous approaches for $p \\ge 4$.", "published": "2022-03-14T23:42:06Z", "authors": ["Raphael A. Meyer", "Cameron Musco", "Christopher Musco", "David P. Woodruff", "Samson Zhou"], "categories": ["cs.DS"], "doi": "", "_query": "hedonic regression housing", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/1104.4580v3", "title": "Quantile Regression with Censoring and Endogeneity", "abstract": "In this paper, we develop a new censored quantile instrumental variable (CQIV) estimator and describe its properties and computation. The CQIV estimator combines Powell (1986) censored quantile regression (CQR) to deal with censoring, with a control variable approach to incorporate endogenous regressors. The CQIV estimator is obtained in two stages that are non-additive in the unobservables. The first stage estimates a non-additive model with infinite dimensional parameters for the control variable, such as a quantile or distribution regression model. The second stage estimates a non-additive censored quantile regression model for the response variable of interest, including the estimated control variable to deal with endogeneity. For computation, we extend the algorithm for CQR developed by Chernozhukov and Hong (2002) to incorporate the estimation of the control variable. We give generic regularity conditions for asymptotic normality of the CQIV estimator and for the validity of resampling methods to approximate its asymptotic distribution. We verify these conditions for quantile and distribution regression estimation of the control variable. Our analysis covers two-stage (uncensored) quantile regression with non-additive first stage as an important special case. We illustrate the computation and applicability of the CQIV estimator with a Monte-Carlo numerical example and an empirical application on estimation of Engel curves for alcohol.", "published": "2011-04-23T17:43:04Z", "authors": ["Victor Chernozhukov", "Ivan Fernandez-Val", "Amanda Kowalski"], "categories": ["stat.ME", "econ.EM"], "doi": "", "_query": "hedonic regression housing", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2406.18047v1", "title": "Shrinkage Estimators for Beta Regression Models", "abstract": "The beta regression model is a useful framework to model response variables that are rates or proportions, that is to say, response variables which are continuous and restricted to the interval (0,1). As with any other regression model, parameter estimates may be affected by collinearity or even perfect collinearity among the explanatory variables. To handle these situations shrinkage estimators are proposed. In particular we develop ridge regression and LASSO estimators from a penalized likelihood perspective with a logit link function. The properties of the resulting estimators are evaluated through a simulation study and a real data application", "published": "2024-06-26T03:52:20Z", "authors": ["Luis Firinguetti", "Manuel González-Navarrete", "Romer Machaca-Aguilar"], "categories": ["stat.ME", "stat.AP"], "doi": "", "_query": "hedonic regression housing", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2504.05996v1", "title": "Unified Beta Regression Model with Random Effects for the Analysis of Sensory Attributes", "abstract": "Studies involving sensory analysis are essential for evaluating and measuring the characteristics of food and beverages, including consumer acceptance of samples. For various products, the experimental designs are generally incomplete block designs, with sensory attributes assessed using hedonic scales, ratings, or scores. Statistical methods such as generalized logits are commonly used to analyze these data but face limitations, including convergence issues due to superparameterization. Furthermore, sensory attributes are traditionally analyzed separately, increasing the complexity of the process and complicating the interpretation of results. This study proposes a unified beta regression model with random effects for simultaneously analyzing multiple sensory attributes, whose scores were converted to the (0,1) interval. Simulation studies demonstrated overall agreement rates greater than 82% for the unified model compared to models fitted separately for each attribute. As a motivational example, the unified model was applied to a real dataset in which 98 potential consumers evaluated eight grape juice formulations for each sensory attribute: colour, flavour, aroma, acidity, and sweetness. The unified model identified the same top-rated formulations as the separately fitted models, characterized by a higher proportion of juice relative to sugar. The results underscore the ability of the unified model to simplify the analytical process without compromising accuracy, offering an efficient and insightful approach to sensory studies.", "published": "2025-04-08T13:01:58Z", "authors": ["João César Reis Alves", "Gabriel Rodrigues Palma", "Idemauro Antonio Rodrigues de Lara"], "categories": ["stat.ME"], "doi": "", "_query": "hedonic regression housing", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/1211.1043v1", "title": "Soft (Gaussian CDE) regression models and loss functions", "abstract": "Regression, unlike classification, has lacked a comprehensive and effective approach to deal with cost-sensitive problems by the reuse (and not a re-training) of general regression models. In this paper, a wide variety of cost-sensitive problems in regression (such as bids, asymmetric losses and rejection rules) can be solved effectively by a lightweight but powerful approach, consisting of: (1) the conversion of any traditional one-parameter crisp regression model into a two-parameter soft regression model, seen as a normal conditional density estimator, by the use of newly-introduced enrichment methods; and (2) the reframing of an enriched soft regression model to new contexts by an instance-dependent optimisation of the expected loss derived from the conditional normal distribution.", "published": "2012-11-05T21:40:38Z", "authors": ["Jose Hernandez-Orallo"], "categories": ["cs.LG", "stat.ML"], "doi": "", "_query": "hedonic regression housing", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2601.03750v1", "title": "Multivariate kernel regression in vector and product metric spaces", "abstract": "This paper derives limit properties of nonparametric kernel regression estimators without requiring existence of density for regressors in $\\mathbb{R}^{q}.$ In functional regression limit properties are established for multivariate functional regression. The rate and asymptotic normality for the Nadaraya-Watson (NW) estimator is established for distributions of regressors in $\\mathbb{R}^{q}$ that allow for mass points, factor structure, multicollinearity and nonlinear dependence, as well as fractal distribution; when bounded density exists we provide statistical guarantees for the standard rate and the asymptotic normality without requiring smoothness. We demonstrate faster convergence associated with dimension reducing types of singularity, such as a fractal distribution or a factor structure in the regressors. The paper extends asymptotic normality of kernel functional regression to multivariate regression over a product of any number of metric spaces. Finite sample evidence confirms rate improvement due to singularity in regression over $\\mathbb{R}^{q}.$ For functional regression the simulations underline the importance of accounting for multiple functional regressors. We demonstrate the applicability and advantages of the NW estimator in our empirical study, which reexamines the job training program evaluation based on the LaLonde data.", "published": "2026-01-07T09:40:54Z", "authors": ["Marcia Schafgans", "Victoria Zinde-Walsh"], "categories": ["econ.EM"], "doi": "10.1016/j.jeconom.2025.106168", "_query": "hedonic regression housing", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/1605.05602v1", "title": "Bayesian Robust Quantile Regression", "abstract": "Traditional Bayesian quantile regression relies on the Asymmetric Laplace distribution (ALD) mainly because of its satisfactory empirical and theoretical performances. However, the ALD displays medium tails and it is not suitable for data characterized by strong deviations from the Gaussian hypothesis. In this paper, we propose an extension of the ALD Bayesian quantile regression framework to account for fat-tails using the Skew Exponential Power (SEP) distribution. Beside having the $τ$-level quantile as parameter, the SEP distribution has an additional key parameter governing the decay of the tails, making it attractive for robust modeling of conditional quantiles at different confidence levels. Linear and Generalized Additive Models (GAM) with penalized spline are considered to show the flexibility of the SEP in the Bayesian quantile regression context. Lasso priors are considered in both cases to account for shrinking parameters problem when the parameters space becomes wide. To implement the Bayesian inference we propose a new adaptive Metropolis--Hastings algorithm in the linear model and an adaptive Metropolis within Gibbs one in the GAM framework. Empirical evidence of the statistical properties of the proposed SEP Bayesian quantile regression method is provided through several example based on simulated and real dataset.", "published": "2016-05-18T14:42:47Z", "authors": ["Mauro Bernardi", "Marco Bottone", "Lea Petrella"], "categories": ["stat.ME"], "doi": "", "_query": "hedonic regression housing", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/1806.09611v5", "title": "Robustness of deepest projection regression functional", "abstract": "Depth notions in regression have been systematically proposed and examined in Zuo (2018). One of the prominent advantages of notion of depth is that it can be directly utilized to introduce median-type deepest estimating functionals (or estimators in empirical distribution case) for location or regression parameters in a multi-dimensional setting. Regression depth shares the advantage. Depth induced deepest estimating functionals are expected to inherit desirable and inherent robustness properties ( e.g. bounded maximum bias and influence function and high breakdown point) as their univariate location counterpart does. Investigating and verifying the robustness of the deepest projection estimating functional (in terms of maximum bias, asymptotic and finite sample breakdown point, and influence function) is the major goal of this article. It turns out that the deepest projection estimating functional possesses a bounded influence function and the best possible asymptotic breakdown point as well as the best finite sample breakdown point with robust choice of its univariate regression and scale component.", "published": "2018-06-24T13:47:57Z", "authors": ["Yijun Zuo"], "categories": ["math.ST", "stat.ME"], "doi": "10.1007/s00362-019-01129-4", "_query": "hedonic regression housing", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2311.10790v1", "title": "Degeneration of kernel regression with Matern kernels into low-order polynomial regression in high dimension", "abstract": "Kernel methods such as kernel ridge regression and Gaussian process regressions with Matern type kernels have been increasingly used, in particular, to fit potential energy surfaces (PES) and density functionals, and for materials informatics. When the dimensionality of the feature space is high, these methods are used with necessarily sparse data. In this regime, the optimal length parameter of a Matern-type kernel tends to become so large that the method effectively degenerates into a low-order polynomial regression and therefore loses any advantage over such regression. This is demonstrated theoretically as well as numerically on the examples of six- and fifteen-dimensional molecular PES using squared exponential and simple exponential kernels. The results shed additional light on the success of polynomial approximations such as PIP for medium size molecules and on the importance of orders-of-coupling based models for preserving the advantages of kernel methods with Matern type kernels or on the use of physically-motivated (reproducing) kernels.", "published": "2023-11-17T00:46:54Z", "authors": ["Sergei Manzhos", "Manabu Ihara"], "categories": ["physics.comp-ph", "cs.LG"], "doi": "10.1063/5.0187867", "_query": "hedonic regression housing", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2012.00123v2", "title": "A Hypergradient Approach to Robust Regression without Correspondence", "abstract": "We consider a variant of regression problem, where the correspondence between input and output data is not available. Such shuffled data is commonly observed in many real world problems. Taking flow cytometry as an example, the measuring instruments may not be able to maintain the correspondence between the samples and the measurements. Due to the combinatorial nature of the problem, most existing methods are only applicable when the sample size is small, and limited to linear regression models. To overcome such bottlenecks, we propose a new computational framework -- ROBOT -- for the shuffled regression problem, which is applicable to large data and complex nonlinear models. Specifically, we reformulate the regression without correspondence as a continuous optimization problem. Then by exploiting the interaction between the regression model and the data correspondence, we develop a hypergradient approach based on differentiable programming techniques. Such a hypergradient approach essentially views the data correspondence as an operator of the regression, and therefore allows us to find a better descent direction for the model parameter by differentiating through the data correspondence. ROBOT can be further extended to the inexact correspondence setting, where there may not be an exact alignment between the input and output data. Thorough numerical experiments show that ROBOT achieves better performance than existing methods in both linear and nonlinear regression tasks, including real-world applications such as flow cytometry and multi-object tracking.", "published": "2020-11-30T21:47:38Z", "authors": ["Yujia Xie", "Yixiu Mao", "Simiao Zuo", "Hongteng Xu", "Xiaojing Ye", "Tuo Zhao", "Hongyuan Zha"], "categories": ["cs.LG", "stat.ML"], "doi": "", "_query": "hedonic regression housing", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2108.00887v2", "title": "A Machine learning approach for rapid disaster response based on multi-modal data. The case of housing & shelter needs", "abstract": "Along with climate change, more frequent extreme events, such as flooding and tropical cyclones, threaten the livelihoods and wellbeing of poor and vulnerable populations. One of the most immediate needs of people affected by a disaster is finding shelter. While the proliferation of data on disasters is already helping to save lives, identifying damages in buildings, assessing shelter needs, and finding appropriate places to establish emergency shelters or settlements require a wide range of data to be combined rapidly. To address this gap and make a headway in comprehensive assessments, this paper proposes a machine learning workflow that aims to fuse and rapidly analyse multimodal data. This workflow is built around open and online data to ensure scalability and broad accessibility. Based on a database of 19 characteristics for more than 200 disasters worldwide, a fusion approach at the decision level was used. This technique allows the collected multimodal data to share a common semantic space that facilitates the prediction of individual variables. Each fused numerical vector was fed into an unsupervised clustering algorithm called Self-Organizing-Maps (SOM). The trained SOM serves as a predictor for future cases, allowing predicting consequences such as total deaths, total people affected, and total damage, and provides specific recommendations for assessments in the shelter and housing sector. To achieve such prediction, a satellite image from before the disaster and the geographic and demographic conditions are shown to the trained model, which achieved a prediction accuracy of 62 %", "published": "2021-07-29T18:22:34Z", "authors": ["Karla Saldana Ochoa", "Tina Comes"], "categories": ["cs.LG", "cs.AI"], "doi": "", "_query": "hedonic regression housing", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2501.07045v1", "title": "ACCon: Angle-Compensated Contrastive Regularizer for Deep Regression", "abstract": "In deep regression, capturing the relationship among continuous labels in feature space is a fundamental challenge that has attracted increasing interest. Addressing this issue can prevent models from converging to suboptimal solutions across various regression tasks, leading to improved performance, especially for imbalanced regression and under limited sample sizes. However, existing approaches often rely on order-aware representation learning or distance-based weighting. In this paper, we hypothesize a linear negative correlation between label distances and representation similarities in regression tasks. To implement this, we propose an angle-compensated contrastive regularizer for deep regression, which adjusts the cosine distance between anchor and negative samples within the contrastive learning framework. Our method offers a plug-and-play compatible solution that extends most existing contrastive learning methods for regression tasks. Extensive experiments and theoretical analysis demonstrate that our proposed angle-compensated contrastive regularizer not only achieves competitive regression performance but also excels in data efficiency and effectiveness on imbalanced datasets.", "published": "2025-01-13T03:55:59Z", "authors": ["Botao Zhao", "Xiaoyang Qu", "Zuheng Kang", "Junqing Peng", "Jing Xiao", "Jianzong Wang"], "categories": ["cs.LG", "cs.AI"], "doi": "", "_query": "hedonic regression housing", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2605.30609v2", "title": "Rectified Linear Unit Regression", "abstract": "This paper develops a regression framework for analyzing integrated conditional distribution and quantile functions. The proposed method, termed rectified linear unit (ReLU) regression, projects the ReLU-transformed outcome onto covariates and admits a closed-form estimator. Its population regression function is the best linear approximation to the integrated conditional distribution function of the outcome, and the corresponding convex conjugate, obtained via the Legendre-Fenchel transform, approximates the integrated conditional quantile function. Both the regression and its conjugate require only mild distributional assumptions and accommodate non-continuous outcomes. We establish the asymptotic distribution of the estimator and develop inference for the conjugate functional via the delta method for Hadamard directionally differentiable maps. Building on these results, we establish identification and inference for average quantile treatment effects over arbitrary subintervals of probability levels. This broadens the set of distributional parameters available to empirical work.", "published": "2026-05-28T22:02:55Z", "authors": ["Tatsushi Oka"], "categories": ["econ.EM", "math.ST", "stat.AP", "stat.ME"], "doi": "", "_query": "hedonic regression housing", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/1903.00816v1", "title": "Stability of decision trees and logistic regression", "abstract": "Decision trees and logistic regression are one of the most popular and well-known machine learning algorithms, frequently used to solve a variety of real-world problems. Stability of learning algorithms is a powerful tool to analyze their performance and sensitivity and subsequently allow researchers to draw reliable conclusions. The stability of these two algorithms has remained obscure. To that end, in this paper, we derive two stability notions for decision trees and logistic regression: hypothesis and pointwise hypothesis stability. Additionally, we derive these notions for L2-regularized logistic regression and confirm existing findings that it is uniformly stable. We show that the stability of decision trees depends on the number of leaves in the tree, i.e., its depth, while for logistic regression, it depends on the smallest eigenvalue of the Hessian matrix of the cross-entropy loss. We show that logistic regression is not a stable learning algorithm. We construct the upper bounds on the generalization error of all three algorithms. Moreover, we present a novel stability measuring framework that allows one to measure the aforementioned notions of stability. The measures are equivalent to estimates of expected loss differences at an input example and then leverage bootstrap sampling to yield statistically reliable estimates. Finally, we apply this framework to the three algorithms analyzed in this paper to confirm our theoretical findings and, in addition, we discuss the possibilities of developing new training techniques to optimize the stability of logistic regression, and hence decrease its generalization error.", "published": "2019-03-03T03:38:54Z", "authors": ["Nino Arsov", "Martin Pavlovski", "Ljupco Kocarev"], "categories": ["cs.LG", "stat.ML"], "doi": "", "_query": "hedonic regression housing", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/1411.2066v4", "title": "Learning Theory for Distribution Regression", "abstract": "We focus on the distribution regression problem: regressing to vector-valued outputs from probability measures. Many important machine learning and statistical tasks fit into this framework, including multi-instance learning and point estimation problems without analytical solution (such as hyperparameter or entropy estimation). Despite the large number of available heuristics in the literature, the inherent two-stage sampled nature of the problem makes the theoretical analysis quite challenging, since in practice only samples from sampled distributions are observable, and the estimates have to rely on similarities computed between sets of points. To the best of our knowledge, the only existing technique with consistency guarantees for distribution regression requires kernel density estimation as an intermediate step (which often performs poorly in practice), and the domain of the distributions to be compact Euclidean. In this paper, we study a simple, analytically computable, ridge regression-based alternative to distribution regression, where we embed the distributions to a reproducing kernel Hilbert space, and learn the regressor from the embeddings to the outputs. Our main contribution is to prove that this scheme is consistent in the two-stage sampled setup under mild conditions (on separable topological domains enriched with kernels): we present an exact computational-statistical efficiency trade-off analysis showing that our estimator is able to match the one-stage sampled minimax optimal rate [Caponnetto and De Vito, 2007; Steinwart et al., 2009]. This result answers a 17-year-old open question, establishing the consistency of the classical set kernel [Haussler, 1999; Gaertner et. al, 2002] in regression. We also cover consistency for more recent kernels on distributions, including those due to [Christmann and Steinwart, 2010].", "published": "2014-11-08T01:16:44Z", "authors": ["Zoltan Szabo", "Bharath Sriperumbudur", "Barnabas Poczos", "Arthur Gretton"], "categories": ["math.ST", "cs.LG", "math.FA", "stat.ML"], "doi": "", "_query": "hedonic regression housing", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2505.10825v1", "title": "A High-Performance Thermal Infrared Object Detection Framework with Centralized Regulation", "abstract": "Thermal Infrared (TIR) technology involves the use of sensors to detect and measure infrared radiation emitted by objects, and it is widely utilized across a broad spectrum of applications. The advancements in object detection methods utilizing TIR images have sparked significant research interest. However, most traditional methods lack the capability to effectively extract and fuse local-global information, which is crucial for TIR-domain feature attention. In this study, we present a novel and efficient thermal infrared object detection framework, known as CRT-YOLO, that is based on centralized feature regulation, enabling the establishment of global-range interaction on TIR information. Our proposed model integrates efficient multi-scale attention (EMA) modules, which adeptly capture long-range dependencies while incurring minimal computational overhead. Additionally, it leverages the Centralized Feature Pyramid (CFP) network, which offers global regulation of TIR features. Extensive experiments conducted on two benchmark datasets demonstrate that our CRT-YOLO model significantly outperforms conventional methods for TIR image object detection. Furthermore, the ablation study provides compelling evidence of the effectiveness of our proposed modules, reinforcing the potential impact of our approach on advancing the field of thermal infrared object detection.", "published": "2025-05-16T03:43:24Z", "authors": ["Jinke Li", "Yue Wu", "Xiaoyan Yang"], "categories": ["cs.CV", "cs.LG"], "doi": "", "_query": "hedonic regression housing", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2412.02578v2", "title": "Private Linear Regression with Differential Privacy and PAC Privacy", "abstract": "Linear regression is a fundamental tool for statistical analysis, which has motivated the development of linear regression methods that satisfy provable privacy guarantees so that the learned model reveals little about any one data point used to construct it. Most existing privacy-preserving linear regression methods rely on the well-established framework of differential privacy, while the newly proposed PAC Privacy has not yet been explored in this context. In this paper, we systematically compare linear regression models trained with differential privacy and PAC privacy across three real-world datasets, observing several key findings that impact the performance of privacy-preserving linear regression.", "published": "2024-12-03T17:04:14Z", "authors": ["Hillary Yang", "Yuntao Du"], "categories": ["cs.LG", "cs.CR"], "doi": "", "_query": "hedonic regression housing", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/0704.1028v1", "title": "A neural network approach to ordinal regression", "abstract": "Ordinal regression is an important type of learning, which has properties of both classification and regression. Here we describe a simple and effective approach to adapt a traditional neural network to learn ordinal categories. Our approach is a generalization of the perceptron method for ordinal regression. On several benchmark datasets, our method (NNRank) outperforms a neural network classification method. Compared with the ordinal regression methods using Gaussian processes and support vector machines, NNRank achieves comparable performance. Moreover, NNRank has the advantages of traditional neural networks: learning in both online and batch modes, handling very large training datasets, and making rapid predictions. These features make NNRank a useful and complementary tool for large-scale data processing tasks such as information retrieval, web page ranking, collaborative filtering, and protein ranking in Bioinformatics.", "published": "2007-04-08T17:36:00Z", "authors": ["Jianlin Cheng"], "categories": ["cs.LG", "cs.AI", "cs.NE"], "doi": "", "_query": "hedonic regression housing", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2502.09122v1", "title": "Improving Deep Regression with Tightness", "abstract": "For deep regression, preserving the ordinality of the targets with respect to the feature representation improves performance across various tasks. However, a theoretical explanation for the benefits of ordinality is still lacking. This work reveals that preserving ordinality reduces the conditional entropy $H(Z|Y)$ of representation $Z$ conditional on the target $Y$. However, our findings reveal that typical regression losses do little to reduce $H(Z|Y)$, even though it is vital for generalization performance. With this motivation, we introduce an optimal transport-based regularizer to preserve the similarity relationships of targets in the feature space to reduce $H(Z|Y)$. Additionally, we introduce a simple yet efficient strategy of duplicating the regressor targets, also with the aim of reducing $H(Z|Y)$. Experiments on three real-world regression tasks verify the effectiveness of our strategies to improve deep regression. Code: https://github.com/needylove/Regression_tightness.", "published": "2025-02-13T09:57:25Z", "authors": ["Shihao Zhang", "Yuguang Yan", "Angela Yao"], "categories": ["cs.LG", "cs.AI", "cs.CV"], "doi": "", "_query": "hedonic regression housing", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2305.08235v2", "title": "Nonparametric Generalized Ridge Regression", "abstract": "A Two-Stage approach enables researchers to make optimal non-linear predictions via Generalized Ridge Regression using models that contain two or more x-predictor variables and make only realistic minimal assumptions. The optimal regression coefficient estimates that result are either unbiased or most likely to have mininal MSE risk under Normal distribution theory. All necessary calculations and graphical displays are generated using current versions of CRAN R-packages. A numerical example using the \"corrected\" USArrests data.frame introduces and illustrates this new robust statistical methodology. While applying this strategy to regression models with several hundred observations is straight-forward, the computations required in such cases can be extensive.", "published": "2023-05-14T19:59:07Z", "authors": ["Robert L. Obenchain"], "categories": ["stat.ME"], "doi": "", "_query": "hedonic regression housing", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2302.10409v1", "title": "Mean Parity Fair Regression in RKHS", "abstract": "We study the fair regression problem under the notion of Mean Parity (MP) fairness, which requires the conditional mean of the learned function output to be constant with respect to the sensitive attributes. We address this problem by leveraging reproducing kernel Hilbert space (RKHS) to construct the functional space whose members are guaranteed to satisfy the fairness constraints. The proposed functional space suggests a closed-form solution for the fair regression problem that is naturally compatible with multiple sensitive attributes. Furthermore, by formulating the fairness-accuracy tradeoff as a relaxed fair regression problem, we derive a corresponding regression function that can be implemented efficiently and provides interpretable tradeoffs. More importantly, under some mild assumptions, the proposed method can be applied to regression problems with a covariance-based notion of fairness. Experimental results on benchmark datasets show the proposed methods achieve competitive and even superior performance compared with several state-of-the-art methods.", "published": "2023-02-21T02:44:50Z", "authors": ["Shaokui Wei", "Jiayin Liu", "Bing Li", "Hongyuan Zha"], "categories": ["stat.ML", "cs.LG"], "doi": "", "_query": "hedonic regression housing", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/0705.0269v1", "title": "Forward stagewise regression and the monotone lasso", "abstract": "We consider the least angle regression and forward stagewise algorithms for solving penalized least squares regression problems. In Efron, Hastie, Johnstone & Tibshirani (2004) it is proved that the least angle regression algorithm, with a small modification, solves the lasso regression problem. Here we give an analogous result for incremental forward stagewise regression, showing that it solves a version of the lasso problem that enforces monotonicity. One consequence of this is as follows: while lasso makes optimal progress in terms of reducing the residual sum-of-squares per unit increase in $L_1$-norm of the coefficient $β$, forward stage-wise is optimal per unit $L_1$ arc-length traveled along the coefficient path. We also study a condition under which the coefficient paths of the lasso are monotone, and hence the different algorithms coincide. Finally, we compare the lasso and forward stagewise procedures in a simulation study involving a large number of correlated predictors.", "published": "2007-05-02T12:21:59Z", "authors": ["Trevor Hastie", "Jonathan Taylor", "Robert Tibshirani", "Guenther Walther"], "categories": ["math.ST"], "doi": "10.1214/07-EJS004", "_query": "hedonic regression housing", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/1602.08793v3", "title": "Inference in Functional Linear Quantile Regression", "abstract": "In this paper, we study statistical inference in functional quantile regression for scalar response and a functional covariate. Specifically, we consider a functional linear quantile regression model where the effect of the covariate on the quantile of the response is modeled through the inner product between the functional covariate and an unknown smooth regression parameter function that varies with the level of quantile. The objective is to test that the regression parameter is constant across several quantile levels of interest. The parameter function is estimated by combining ideas from functional principal component analysis and quantile regression. An adjusted Wald testing procedure is proposed for this hypothesis of interest, and its chi-square asymptotic null distribution is derived. The testing procedure is investigated numerically in simulations involving sparse and noisy functional covariates and in a capital bike share data application. The proposed approach is easy to implement and the {\\tt R} code is published online at \\url{https://github.com/xylimeng/fQR-testing}.", "published": "2016-02-29T01:09:26Z", "authors": ["Meng Li", "Kehui Wang", "Arnab Maity", "Ana-Maria Staicu"], "categories": ["stat.ME", "math.ST"], "doi": "10.1016/j.jmva.2022.104985", "_query": "hedonic regression housing", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2510.05268v1", "title": "An efficient hybrid approach of quantile and expectile regression", "abstract": "Quantiles and expectiles are determined by different loss functions: asymmetric least absolute deviation for quantiles and asymmetric squared loss for expectiles. This distinction ensures that quantile regression methods are robust to outliers but somewhat less effective than expectile regression, especially for normally distributed data. However, expectile regression is vulnerable to lack of robustness, especially for heavy-tailed distributions. To address this trade-off between robustness and effectiveness, we propose a novel approach. By introducing a parameter $γ$ that ranges between 0 and 1, we combine the aforementioned loss functions, resulting in a hybrid approach of quantiles and expectiles. This fusion leads to the estimation of a new type of location parameter family within the linear regression framework, termed Hybrid of Quantile and Expectile Regression (HQER). The asymptotic properties of the resulting estimaror are then established. Through simulation studies, we compare the asymptotic relative efficiency of the HQER estimator with its competitors, namely the quantile, expectile, and $k$th power expectile regression estimators. Our results show that HQER outperforms its competitors in several simulation scenarios. In addition, we apply HQER to a real dataset to illustrate its practical utility.", "published": "2025-10-06T18:39:07Z", "authors": ["Abdellah Atanane", "Abdallah Mkhadri", "Karim Oualkacha"], "categories": ["stat.ME"], "doi": "", "_query": "hedonic regression housing", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2204.01371v1", "title": "Non-crossing convex quantile regression", "abstract": "Quantile crossing is a common phenomenon in shape constrained nonparametric quantile regression. A recent study by Wang et al. (2014) has proposed to address this problem by imposing non-crossing constraints to convex quantile regression. However, the non-crossing constraints may violate an intrinsic quantile property. This paper proposes a penalized convex quantile regression approach that can circumvent quantile crossing while better maintaining the quantile property. A Monte Carlo study demonstrates the superiority of the proposed penalized approach in addressing the quantile crossing problem.", "published": "2022-04-04T10:36:18Z", "authors": ["Sheng Dai", "Timo Kuosmanen", "Xun Zhou"], "categories": ["stat.ME", "stat.AP"], "doi": "10.1016/j.econlet.2023.111396", "_query": "hedonic regression housing", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/1412.6274v1", "title": "Y Chromosome of Aisin Gioro, the Imperial House of Qing Dynasty", "abstract": "House of Aisin Gioro is the imperial family of the last dynasty in Chinese history - Qing Dynasty (1644 - 1911). Aisin Gioro family originated from Jurchen tribes and developed the Manchu people before they conquered China. By investigating the Y chromosomal short tandem repeats (STRs) of 7 modern male individuals who claim belonging to Aisin Gioro family (in which 3 have full records of pedigree), we found that 3 of them (in which 2 keep full pedigree, whose most recent common ancestor is Nurgaci) shows very close relationship (1 - 2 steps of difference in 17 STR) and the haplotype is rare. We therefore conclude that this haplotype is the Y chromosome of the House of Aisin Gioro. Further tests of single nucleotide polymorphisms (SNPs) indicates that they belong to Haplogroup C3b2b1*-M401(xF5483), although their Y-STR results are distant to the \"star cluster\", which also belongs to the same haplogroup. This study forms the base for the pedigree research of the imperial family of Qing Dynasty by means of genetics.", "published": "2014-12-19T10:27:22Z", "authors": ["Shi Yan", "Harumasa Tachibana", "Lan-Hai Wei", "Ge Yu", "Shao-Qing Wen", "Chuan-Chao Wang"], "categories": ["q-bio.PE"], "doi": "10.1038/jhg.2015.28", "_query": "hedonic regression housing", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2104.09418v2", "title": "Distribution-on-Distribution Regression via Optimal Transport Maps", "abstract": "We present a framework for performing regression when both covariate and response are probability distributions on a compact interval $Ω\\subset\\mathbb{R}$. Our regression model is based on the theory of optimal transportation and links the conditional Fréchet mean of the response distribution to the covariate distribution via an optimal transport map. We define a Fréchet-least-squares estimator of this regression map, and establish its consistency and rate of convergence to the true map, under both full and partial observation of the regression pairs. Computation of the estimator is shown to reduce to an isotonic regression problem, and thus our regression model can be implemented with ease. We illustrate our methodology using real and simulated data.", "published": "2021-04-19T16:10:08Z", "authors": ["Laya Ghodrati", "Victor M. Panaretos"], "categories": ["stat.ME"], "doi": "", "_query": "hedonic regression housing", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/1203.1975v3", "title": "Warped Functional Regression", "abstract": "A characteristic feature of functional data is the presence of phase variability in addition to amplitude variability. Existing functional regression methods do not handle time variability in an explicit and efficient way. In this paper we introduce a functional regression method that incorporates time warping as an intrinsic part of the model. The method achieves good predictive power in a parsimonious way and allows unified statistical inference about phase and amplitude components. The asymptotic distribution of the estimators is derived and the finite-sample properties are studied by simulation. An example of application involving ground-level ozone trajectories is presented.", "published": "2012-03-09T02:27:54Z", "authors": ["Daniel Gervini"], "categories": ["stat.ME"], "doi": "", "_query": "hedonic regression housing", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/0904.0543v1", "title": "Pointwise adaptive estimation for robust and quantile regression", "abstract": "A nonparametric procedure for robust regression estimation and for quantile regression is proposed which is completely data-driven and adapts locally to the regularity of the regression function. This is achieved by considering in each point M-estimators over different local neighbourhoods and by a local model selection procedure based on sequential testing. Non-asymptotic risk bounds are obtained, which yield rate-optimality for large sample asymptotics under weak conditions. Simulations for different univariate median regression models show good finite sample properties, also in comparison to traditional methods. The approach is extended to image denoising and applied to CT scans in cancer research.", "published": "2009-04-03T10:15:49Z", "authors": ["Markus Reiss", "Yves Rozenholc", "Charles-Andre Cuenod"], "categories": ["math.ST"], "doi": "", "_query": "hedonic regression housing", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/1201.2375v4", "title": "Mixed Beta Regression: A Bayesian Perspective", "abstract": "This paper builds on recent research that focuses on regression modeling of continuous bounded data, such as proportions measured on a continuous scale. Specifically, it deals with beta regression models with mixed effects from a Bayesian approach. We use a suitable parameterization of the beta law in terms of its mean and a precision parameter, and allow both parameters to be modeled through regression structures that may involve fixed and random effects. Specification of prior distributions is discussed, computational implementation via Gibbs sampling is provided, and illustrative examples are presented.", "published": "2012-01-11T18:53:45Z", "authors": ["Jorge I. Figueroa-Zuñiga", "Reinaldo B. Arellano-Valle", "Silvia L. P. Ferrari"], "categories": ["stat.CO"], "doi": "", "_query": "hedonic regression housing", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2602.22203v1", "title": "Local Bayesian Regression", "abstract": "This paper develops a class of Bayesian non- and semiparametric methods for estimating regression curves and surfaces. The main idea is to model the regression as locally linear, and then place suitable local priors on the local parameters. The method requires the posterior distribution of the local parameters given local data, and this is found via a suitably defined local likelihood function. When the width of the local data window is large the methods reduce to familiar fully parametric Bayesian methods, and when the width is small the estimators are essentially nonparametric. When noninformative reference priors are used the resulting estimators coincide with recently developed well-performing local weighted least squares methods for nonparametric regression. Each local prior distribution needs in general a centre parameter and a variance parameter. Of particular interest are versions of the scheme that are more or less automatic and objective in the sense that they do not require subjective specifications of prior parameters. We therefore develop empirical Bayes methods to obtain the variance parameter and a hierarchical Bayes method to account for uncertainty in the choice of centre parameter. There are several possible versions of the general programme, and a number of its specialisations are discussed. Some of these are shown to be capable of outperforming standard nonparametric regression methods, particularly in situations with several covariates.", "published": "2026-02-25T18:52:12Z", "authors": ["Nils Lid Hjort"], "categories": ["stat.ME"], "doi": "", "_query": "hedonic regression housing", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/1902.03418v1", "title": "The empirical process of residuals from an inverse regression", "abstract": "In this paper we investigate an indirect regression model characterized by the Radon transformation. This model is useful for recovery of medical images obtained by computed tomography scans. The indirect regression function is estimated using a series estimator motivated by a spectral cut-off technique. Further, we investigate the empirical process of residuals from this regression, and show that it satsifies a functional central limit theorem.", "published": "2019-02-09T12:38:55Z", "authors": ["Tim Kutta", "Nicolai Bissantz", "Justin Chown", "Holger Dette"], "categories": ["math.ST"], "doi": "", "_query": "hedonic regression housing", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/1912.03488v2", "title": "Robust Deep Ordinal Regression Under Label Noise", "abstract": "The real-world data is often susceptible to label noise, which might constrict the effectiveness of the existing state of the art algorithms for ordinal regression. Existing works on ordinal regression do not take label noise into account. We propose a theoretically grounded approach for class conditional label noise in ordinal regression problems. We present a deep learning implementation of two commonly used loss functions for ordinal regression that is both - 1) robust to label noise, and 2) rank consistent for a good ranking rule. We verify these properties of the algorithm empirically and show robustness to label noise on real data and rank consistency. To the best of our knowledge, this is the first approach for robust ordinal regression models.", "published": "2019-12-07T10:39:45Z", "authors": ["Bhanu Garg", "Naresh Manwani"], "categories": ["cs.LG", "stat.ML"], "doi": "", "_query": "hedonic regression housing", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2012.14159v1", "title": "Simultaneous semi-parametric estimation of clustering and regression", "abstract": "We investigate the parameter estimation of regression models with fixed group effects, when the group variable is missing while group related variables are available. This problem involves clustering to infer the missing group variable based on the group related variables, and regression to build a model on the target variable given the group and eventually additional variables. Thus, this problem can be formulated as the joint distribution modeling of the target and of the group related variables. The usual parameter estimation strategy for this joint model is a two-step approach starting by learning the group variable (clustering step) and then plugging in its estimator for fitting the regression model (regression step). However, this approach is suboptimal (providing in particular biased regression estimates) since it does not make use of the target variable for clustering. Thus, we claim for a simultaneous estimation approach of both clustering and regression, in a semi-parametric framework. Numerical experiments illustrate the benefits of our proposition by considering wide ranges of distributions and regression models. The relevance of our new method is illustrated on real data dealing with problems associated with high blood pressure prevention.", "published": "2020-12-28T09:36:49Z", "authors": ["Matthieu Marbac", "Mohammed Sedki", "Christophe Biernacki", "Vincent Vandewalle"], "categories": ["stat.ME"], "doi": "", "_query": "hedonic regression housing", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2406.09419v1", "title": "Sentient House: Designing for Discourse", "abstract": "The Sentient House project is an investigation into approaches that the artistdesigner can take to better involve the public in developing a critical perspective on pervasive technology in the home and the surrounding environment. Using Interaction Design approaches including workshops, surveys, rapidprototyping and critical thinking, this thesis suggests a framework for developing a more participatory atmosphere for Critical Design. As the world becomes more connected, and smarter, citizens concerns are being sidelined in favour of rapid progress and solutionism. Many of these initiatives are backed by government and commercial concerns who may not have the publics best interest at heart. The designs and approaches generated from this public participation seek to provide an outlet for a more agonistic debate and to develop tools and approaches to engage the public in questioning and addressing how technology affects them in the future. The outcomes of this research suggest that the public is receptive to a more active involvement in designing their digital future, and that the designer can be a critical component in revealing hidden consequences and alternative pathways for a more transparent and desirable future.", "published": "2024-02-14T18:45:29Z", "authors": ["Robert Collins"], "categories": ["cs.HC", "cs.CY"], "doi": "", "_query": "hedonic regression housing", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2409.07233v2", "title": "Extended-support beta regression for $[0, 1]$ responses", "abstract": "We introduce the XBX regression model, a continuous mixture of extended-support beta regressions for modelling bounded responses with boundary observations. The core building block of XBX regression is the extended-support beta distribution, a censored version of a four-parameter beta distribution with the same exceedance on the left and right of $(0, 1)$. Hence, XBX regression is a direct extension of beta regression. We prove that beta regression and heteroscedastic normal regression with censoring at both $0$ and $1$ -- also known as the heteroscedastic two-limit tobit model in the econometrics literature -- are special cases of extended-support beta regression, depending on whether a single extra parameter is zero or infinity, respectively. To overcome identifiability issues due to the similarity of the beta and normal distributions for certain parameter values, we shrink towards beta regression by letting the extra parameter have an exponential distribution with unknown mean. A Gauss-Laguerre quadrature approximation results in efficient likelihood-based estimation and inference procedures, which the betareg R package implements since version 3.2.0. We use XBX regression to analyze investment decisions in a behavioural economics experiment about the occurrence and extent of loss aversion. In contrast to standard approaches, we capture both the probability of rational behaviour and the mean of loss aversion. Extensive comparisons with alternative approaches illustrate the effectiveness of the new model.", "published": "2024-09-11T12:45:53Z", "authors": ["Ioannis Kosmidis", "Achim Zeileis"], "categories": ["stat.ME", "stat.AP"], "doi": "10.1093/jrsssc/qlaf039", "_query": "hedonic regression housing", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2501.04360v3", "title": "Totally Concave Regression", "abstract": "Shape constraints in nonparametric regression provide a powerful framework for estimating regression functions under realistic assumptions without tuning parameters. However, most existing methods$\\unicode{x2013}$except additive models$\\unicode{x2013}$impose too weak restrictions, often leading to overfitting in high dimensions. Conversely, additive models can be too rigid, failing to capture covariate interactions. This paper introduces a novel multivariate shape-constrained regression approach based on total concavity, originally studied by T. Popoviciu. Our method allows interactions while mitigating the curse of dimensionality, with convergence rates that depend only logarithmically on the number of covariates. We characterize and compute the least squares estimator over totally concave functions, derive theoretical guarantees, and demonstrate its practical effectiveness through empirical studies on real-world datasets.", "published": "2025-01-08T08:58:39Z", "authors": ["Dohyeong Ki", "Adityanand Guntuboyina"], "categories": ["math.ST", "stat.ME"], "doi": "", "_query": "hedonic regression housing", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2602.06153v1", "title": "A Compound Logistic Regression Model for Binary Responses", "abstract": "Logistic regression is the most commonly used method for constructing predictive models for binary responses. One significant drawback to this approach, however, is that the asymptotes of the logistic response function are fixed at 0 and 1, and there are many applications for which this constraint is inappropriate. More flexible models have been proposed for this application, most proceeding by supplementing the logistic response function with additional parameters. In this article we extend these models to allow correlated responses and the inclusion of covariates. This is achieved through the \\emph{compound logistic regression model}, for which the mean response is a function of several logistic regression functions. This permits a greater variety of models, while retaining the advantages of logistic regression.", "published": "2026-02-05T19:46:44Z", "authors": ["Anthony Almudevar", "Jacob Almudevar"], "categories": ["stat.ME"], "doi": "", "_query": "hedonic regression housing", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/1512.02956v5", "title": "Adaptive Risk Bounds in Unimodal Regression", "abstract": "We study the statistical properties of the least squares estimator in unimodal sequence estimation. Although closely related to isotonic regression, unimodal regression has not been as extensively studied. We show that the unimodal least squares estimator is adaptive in the sense that the risk scales as a function of the number of values in the true underlying sequence. Such adaptivity properties have been shown for isotonic regression by Chatterjee et al(2015) and Bellec(2015). A technical complication in unimodal regression is the non-convexity of the underlying parameter space. We develop a general variational representation of the risk that holds whenever the parameter space can be expressed as a finite union of convex sets, using techniques that may be of interest in other settings.", "published": "2015-12-09T17:26:43Z", "authors": ["Sabyasachi Chatterjee", "John Lafferty"], "categories": ["math.ST"], "doi": "", "_query": "hedonic regression housing", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2307.00835v3", "title": "Engression: Extrapolation through the Lens of Distributional Regression", "abstract": "Distributional regression aims to estimate the full conditional distribution of a target variable, given covariates. Popular methods include linear and tree-ensemble based quantile regression. We propose a neural network-based distributional regression methodology called `engression'. An engression model is generative in the sense that we can sample from the fitted conditional distribution and is also suitable for high-dimensional outcomes. Furthermore, we find that modelling the conditional distribution on training data can constrain the fitted function outside of the training support, which offers a new perspective to the challenging extrapolation problem in nonlinear regression. In particular, for `pre-additive noise' models, where noise is added to the covariates before applying a nonlinear transformation, we show that engression can successfully perform extrapolation under some assumptions such as monotonicity, whereas traditional regression approaches such as least-squares or quantile regression fall short under the same assumptions. Our empirical results, from both simulated and real data, validate the effectiveness of the engression method and indicate that the pre-additive noise model is typically suitable for many real-world scenarios. The software implementations of engression are available in both R and Python.", "published": "2023-07-03T08:19:00Z", "authors": ["Xinwei Shen", "Nicolai Meinshausen"], "categories": ["stat.ME", "cs.LG", "stat.ML"], "doi": "", "_query": "hedonic regression housing", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2509.12473v3", "title": "Cox Regression on the Plane", "abstract": "The Cox proportional hazards model is the most widely used regression model in univariate survival analysis, yet extensions to bivariate survival data remain scarce. We propose two novel extensions based on a Lehmann-type representation of the survival function. The first, the simple Lehmann model, is a direct extension that retains a straightforward structure. The second, the generalized Lehmann model, allows greater flexibility by incorporating three distinct regression parameters and includes the simple Lehmann model as a special case. The models admit a direct interpretation in terms of survival probabilities, providing a transparent, fully semiparametric framework for assessing covariate effects on both marginal survival probabilities and their dependence, without requiring specification of a copula or frailty distribution. To estimate the regression parameters, we build on a pseudo-observation-based approach for bivariate survival data and extend it to the generalized model via a two-step procedure. We establish consistency and asymptotic normality of the resulting estimators. The proposed approach is illustrated through simulation studies and an application to data from the Global Retinoblastoma Outcome Study.", "published": "2025-09-15T21:37:19Z", "authors": ["Yael Travis-Lumer", "Micha Mandel", "Ido Didi Fabian", "Rebecca A. Betensky", "Malka Gorfine"], "categories": ["stat.ME"], "doi": "", "_query": "hedonic regression housing", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2302.11012v2", "title": "Likelihood Annealing: Fast Calibrated Uncertainty for Regression", "abstract": "Recent advances in deep learning have shown that uncertainty estimation is becoming increasingly important in applications such as medical imaging, natural language processing, and autonomous systems. However, accurately quantifying uncertainty remains a challenging problem, especially in regression tasks where the output space is continuous. Deep learning approaches that allow uncertainty estimation for regression problems often converge slowly and yield poorly calibrated uncertainty estimates that can not be effectively used for quantification. Recently proposed post hoc calibration techniques are seldom applicable to regression problems and often add overhead to an already slow model training phase. This work presents a fast calibrated uncertainty estimation method for regression tasks called Likelihood Annealing, that consistently improves the convergence of deep regression models and yields calibrated uncertainty without any post hoc calibration phase. Unlike previous methods for calibrated uncertainty in regression that focus only on low-dimensional regression problems, our method works well on a broad spectrum of regression problems, including high-dimensional regression.Our empirical analysis shows that our approach is generalizable to various network architectures, including multilayer perceptrons, 1D/2D convolutional networks, and graph neural networks, on five vastly diverse tasks, i.e., chaotic particle trajectory denoising, physical property prediction of molecules using 3D atomistic representation, natural image super-resolution, and medical image translation using MRI.", "published": "2023-02-21T21:24:35Z", "authors": ["Uddeshya Upadhyay", "Jae Myung Kim", "Cordelia Schmidt", "Bernhard Schölkopf", "Zeynep Akata"], "categories": ["cs.LG", "cs.AI", "cs.CV"], "doi": "", "_query": "hedonic regression housing", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/1909.05782v2", "title": "Fast Algorithms for the Quantile Regression Process", "abstract": "The widespread use of quantile regression methods depends crucially on the existence of fast algorithms. Despite numerous algorithmic improvements, the computation time is still non-negligible because researchers often estimate many quantile regressions and use the bootstrap for inference. We suggest two new fast algorithms for the estimation of a sequence of quantile regressions at many quantile indexes. The first algorithm applies the preprocessing idea of Portnoy and Koenker (1997) but exploits a previously estimated quantile regression to guess the sign of the residuals. This step allows for a reduction of the effective sample size. The second algorithm starts from a previously estimated quantile regression at a similar quantile index and updates it using a single Newton-Raphson iteration. The first algorithm is exact, while the second is only asymptotically equivalent to the traditional quantile regression estimator. We also apply the preprocessing idea to the bootstrap by using the sample estimates to guess the sign of the residuals in the bootstrap sample. Simulations show that our new algorithms provide very large improvements in computation time without significant (if any) cost in the quality of the estimates. For instance, we divide by 100 the time required to estimate 99 quantile regressions with 20 regressors and 50,000 observations.", "published": "2019-09-12T16:34:37Z", "authors": ["Victor Chernozhukov", "Iván Fernández-Val", "Blaise Melly"], "categories": ["econ.EM", "stat.CO", "stat.ME"], "doi": "", "_query": "hedonic regression housing", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/1605.08497v1", "title": "Universum Learning for SVM Regression", "abstract": "This paper extends the idea of Universum learning [18, 19] to regression problems. We propose new Universum-SVM formulation for regression problems that incorporates a priori knowledge in the form of additional data samples. These additional data samples or Universum belong to the same application domain as the training samples, but they follow a different distribution. Several empirical comparisons are presented to illustrate the utility of the proposed approach.", "published": "2016-05-27T03:11:41Z", "authors": ["Sauptik Dhar", "Vladimir Cherkassky"], "categories": ["cs.LG"], "doi": "", "_query": "hedonic regression housing", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/1904.04761v2", "title": "Optimal solutions to the isotonic regression problem", "abstract": "In general, the solution to a regression problem is the minimizer of a given loss criterion, and depends on the specified loss function. The nonparametric isotonic regression problem is special, in that optimal solutions can be found by solely specifying a functional. These solutions will then be minimizers under all loss functions simultaneously as long as the loss functions have the requested functional as the Bayes act. For the functional, the only requirement is that it can be defined via an identification function, with examples including the expectation, quantile, and expectile functionals. Generalizing classical results, we characterize the optimal solutions to the isotonic regression problem for such functionals, and extend the results from the case of totally ordered explanatory variables to partial orders. For total orders, we show that any solution resulting from the pool-adjacent-violators algorithm is optimal. It is noteworthy, that simultaneous optimality is unattainable in the unimodal regression problem, despite its close connection.", "published": "2019-04-09T16:15:26Z", "authors": ["Alexander I. Jordan", "Anja Mühlemann", "Johanna F. Ziegel"], "categories": ["math.ST"], "doi": "", "_query": "hedonic regression housing", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2506.13947v1", "title": "Meta Optimality for Demographic Parity Constrained Regression via Post-Processing", "abstract": "We address the regression problem under the constraint of demographic parity, a commonly used fairness definition. Recent studies have revealed fair minimax optimal regression algorithms, the most accurate algorithms that adhere to the fairness constraint. However, these analyses are tightly coupled with specific data generation models. In this paper, we provide meta-theorems that can be applied to various situations to validate the fair minimax optimality of the corresponding regression algorithms. Furthermore, we demonstrate that fair minimax optimal regression can be achieved through post-processing methods, allowing researchers and practitioners to focus on improving conventional regression techniques, which can then be efficiently adapted for fair regression.", "published": "2025-06-16T19:36:56Z", "authors": ["Kazuto Fukuchi"], "categories": ["stat.ML", "cs.LG"], "doi": "", "_query": "hedonic regression housing", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2006.00326v1", "title": "Bayesian Nonparametric Monotone Regression", "abstract": "In many applications there is interest in estimating the relation between a predictor and an outcome when the relation is known to be monotone or otherwise constrained due to the physical processes involved. We consider one such application--inferring time-resolved aerosol concentration from a low-cost differential pressure sensor. The objective is to estimate a monotone function and make inference on the scaled first derivative of the function. We proposed Bayesian nonparametric monotone regression which uses a Bernstein polynomial basis to construct the regression function and puts a Dirichlet process prior on the regression coefficients. The base measure of the Dirichlet process is a finite mixture of a mass point at zero and a truncated normal. This construction imposes monotonicity while clustering the basis functions. Clustering the basis functions reduces the parameter space and allows the estimated regression function to be linear. With the proposed approach we can make closed-formed inference on the derivative of the estimated function including full quantification of uncertainty. In a simulation study the proposed method performs similar to other monotone regression approaches when the true function is wavy but performs better when the true function is linear. We apply the method to estimate time-resolved aerosol concentration with a newly-developed portable aerosol monitor. The R package bnmr is made available to implement the method.", "published": "2020-05-30T17:43:10Z", "authors": ["Ander Wilson", "Jessica Tryner", "Christian L'Orange", "John Volckens"], "categories": ["stat.ME", "stat.AP"], "doi": "10.1002/env.2642", "_query": "hedonic regression housing", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2004.01757v8", "title": "Equivalence testing for linear regression", "abstract": "We introduce equivalence testing procedures for linear regression analyses. Such tests can be very useful for confirming the lack of a meaningful association between a continuous outcome and a continuous or binary predictor. Specifically, we propose an equivalence test for unstandardized regression coefficients and an equivalence test for semipartial correlation coefficients. We review how to define valid hypotheses, how to calculate p-values, and how these tests compare to an alternative Bayesian approach with applications to examples in the literature.", "published": "2020-04-03T20:05:32Z", "authors": ["Harlan Campbell"], "categories": ["stat.ME"], "doi": "", "_query": "hedonic regression housing", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2211.08376v2", "title": "Robust nonparametric regression: review and practical considerations", "abstract": "Nonparametric regression models offer a way to understand and quantify relationships between variables without having to identify an appropriate family of possible regression functions. Although many estimation methods for these models have been proposed in the literature, most of them can be highly sensitive to the presence of a small proportion of atypical observations in the training set. In this paper we review outlier robust estimation methods for nonparametric regression models, paying particular attention to practical considerations. Since outliers can also influence negatively the regression estimator by affecting the selection of bandwidths or smoothing parameters, we also discuss available robust alternatives for this task. Finally, since using many of the ``classical'' nonparametric regression estimators (and their robust counterparts) can be very challenging in settings with a moderate or large number of explanatory variables, we review recent robust nonparametric regression methods that scale well with a growing number of covariates.", "published": "2022-11-15T18:24:59Z", "authors": ["Matias Salibian-Barrera"], "categories": ["stat.ME"], "doi": "", "_query": "hedonic regression housing", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/1207.5594v1", "title": "Nonparametric regression with nonparametrically generated covariates", "abstract": "We analyze the statistical properties of nonparametric regression estimators using covariates which are not directly observable, but have be estimated from data in a preliminary step. These so-called generated covariates appear in numerous applications, including two-stage nonparametric regression, estimation of simultaneous equation models or censored regression models. Yet so far there seems to be no general theory for their impact on the final estimator's statistical properties. Our paper provides such results. We derive a stochastic expansion that characterizes the influence of the generation step on the final estimator, and use it to derive rates of consistency and asymptotic distributions accounting for the presence of generated covariates.", "published": "2012-07-24T06:02:35Z", "authors": ["Enno Mammen", "Christoph Rothe", "Melanie Schienle"], "categories": ["math.ST"], "doi": "10.1214/12-AOS995", "_query": "hedonic regression housing", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2504.01535v2", "title": "On Robust Empirical Likelihood for Nonparametric Regression with Application to Regression Discontinuity Designs", "abstract": "Empirical likelihood serves as a powerful tool for constructing confidence intervals in nonparametric regression and regression discontinuity designs (RDD). The original empirical likelihood framework can be naturally extended to these settings using local linear smoothers, with Wilks' theorem holding only when an undersmoothed bandwidth is selected. However, the generalization of bias-corrected versions of empirical likelihood under more realistic conditions is non-trivial and has remained an open challenge in the literature. This paper provides a satisfactory solution by proposing a novel approach, referred to as robust empirical likelihood, designed for nonparametric regression and RDD. The core idea is to construct robust weights which simultaneously achieve bias correction and account for the additional variability introduced by the estimated bias, thereby enabling valid confidence interval construction without extra estimation steps involved. We demonstrate that the Wilks' phenomenon still holds under weaker conditions in nonparametric regression, sharp and fuzzy RDD settings. Extensive simulation studies confirm the effectiveness of our proposed approach, showing superior performance over existing methods in terms of coverage probabilities and interval lengths. Moreover, the proposed procedure exhibits robustness to bandwidth selection, making it a flexible and reliable tool for empirical analyses. The practical usefulness is further illustrated through applications to two real datasets.", "published": "2025-04-02T09:22:18Z", "authors": ["Qin Fang", "Shaojun Guo", "Yang Hong", "Xinghao Qiao"], "categories": ["math.ST", "econ.EM", "stat.ME"], "doi": "", "_query": "hedonic regression housing", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/1302.4198v1", "title": "Nonparametric regression for locally stationary time series", "abstract": "In this paper, we study nonparametric models allowing for locally stationary regressors and a regression function that changes smoothly over time. These models are a natural extension of time series models with time-varying coefficients. We introduce a kernel-based method to estimate the time-varying regression function and provide asymptotic theory for our estimates. Moreover, we show that the main conditions of the theory are satisfied for a large class of nonlinear autoregressive processes with a time-varying regression function. Finally, we examine structured models where the regression function splits up into time-varying additive components. As will be seen, estimation in these models does not suffer from the curse of dimensionality.", "published": "2013-02-18T09:37:56Z", "authors": ["Michael Vogt"], "categories": ["math.ST"], "doi": "10.1214/12-AOS1043", "_query": "hedonic regression housing", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2106.15811v3", "title": "Adaptively Robust Geographically Weighted Regression", "abstract": "We develop a new robust geographically weighted regression method in the presence of outliers. We embed the standard geographically weighted regression in robust objective function based on $γ$-divergence. A novel feature of the proposed approach is that two tuning parameters that control robustness and spatial smoothness are automatically tuned in a data-dependent manner. Further, the proposed method can produce robust standard error estimates of the robust estimator and give us a reasonable quantity for local outlier detection. We demonstrate that the proposed method is superior to the existing robust version of geographically weighted regression through simulation and data analysis.", "published": "2021-06-30T04:59:43Z", "authors": ["Shonosuke Sugasawa", "Daisuke Murakami"], "categories": ["stat.ME"], "doi": "", "_query": "hedonic regression housing", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2107.11291v3", "title": "Human Pose Regression with Residual Log-likelihood Estimation", "abstract": "Heatmap-based methods dominate in the field of human pose estimation by modelling the output distribution through likelihood heatmaps. In contrast, regression-based methods are more efficient but suffer from inferior performance. In this work, we explore maximum likelihood estimation (MLE) to develop an efficient and effective regression-based methods. From the perspective of MLE, adopting different regression losses is making different assumptions about the output density function. A density function closer to the true distribution leads to a better regression performance. In light of this, we propose a novel regression paradigm with Residual Log-likelihood Estimation (RLE) to capture the underlying output distribution. Concretely, RLE learns the change of the distribution instead of the unreferenced underlying distribution to facilitate the training process. With the proposed reparameterization design, our method is compatible with off-the-shelf flow models. The proposed method is effective, efficient and flexible. We show its potential in various human pose estimation tasks with comprehensive experiments. Compared to the conventional regression paradigm, regression with RLE bring 12.4 mAP improvement on MSCOCO without any test-time overhead. Moreover, for the first time, especially on multi-person pose estimation, our regression method is superior to the heatmap-based methods. Our code is available at https://github.com/Jeff-sjtu/res-loglikelihood-regression", "published": "2021-07-23T15:06:31Z", "authors": ["Jiefeng Li", "Siyuan Bian", "Ailing Zeng", "Can Wang", "Bo Pang", "Wentao Liu", "Cewu Lu"], "categories": ["cs.CV", "cs.LG"], "doi": "", "_query": "hedonic regression housing", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2604.02017v1", "title": "Demographic Parity Tails for Regression", "abstract": "Demographic parity (DP) is a widely studied fairness criterion in regression, enforcing independence between the predictions and sensitive attributes. However, constraining the entire distribution can degrade predictive accuracy and may be unnecessary for many applications, where fairness concerns are localized to specific regions of the distribution. To overcome this issue, we propose a new framework for regression under DP that focuses on the tails of target distribution across sensitive groups. Our methodology builds on optimal transport theory. By enforcing fairness constraints only over targeted regions of the distribution, our approach enables more nuanced and context-sensitive interventions. Leveraging recent advances, we develop an interpretable and flexible algorithm that leverages the geometric structure of optimal transport. We provide theoretical guarantees, including risk bounds and fairness properties, and validate the method through experiments in regression settings.", "published": "2026-04-02T13:20:33Z", "authors": ["Naht Sinh Le", "Christophe Denis", "Mohamed Hebiri"], "categories": ["stat.ML", "cs.LG"], "doi": "", "_query": "hedonic regression housing", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/1806.06111v1", "title": "Instrumental variables regression", "abstract": "IV regression in the context of a re-sampling is considered in the work. Comparatively, the contribution in the development is a structural identification in the IV model. The work also contains a multiplier-bootstrap justification.", "published": "2018-06-15T20:07:04Z", "authors": ["Andzhey Koziuk", "Vladimir Spokoiny"], "categories": ["math.ST"], "doi": "", "_query": "hedonic regression housing", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2208.06842v1", "title": "Testing exogeneity in the functional linear regression model", "abstract": "We propose a novel test statistic for testing exogeneity in the functional linear regression model. In contrast to Hausman-type tests in finite dimensional linear regression setups, a direct extension to the functional linear regression model is not possible. Instead, we propose a test statistic based on the sum of the squared difference of projections of the two estimators for testing the null hypothesis of exogeneity in the functional linear regression model. We derive asymptotic normality under the null and consistency under general alternatives. Moreover, we prove bootstrap consistency results for residual-based bootstraps. In simulations, we investigate the finite sample performance of the proposed testing approach and illustrate the superiority of bootstrap-based approaches. In particular, the bootstrap approaches turn out to be much more robust with respect to the choice of the regularization parameter.", "published": "2022-08-14T12:24:48Z", "authors": ["Manuela Dorn", "Melanie Birke", "Carsten Jentsch"], "categories": ["math.ST"], "doi": "", "_query": "hedonic regression housing", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2307.03487v1", "title": "Learning Theory of Distribution Regression with Neural Networks", "abstract": "In this paper, we aim at establishing an approximation theory and a learning theory of distribution regression via a fully connected neural network (FNN). In contrast to the classical regression methods, the input variables of distribution regression are probability measures. Then we often need to perform a second-stage sampling process to approximate the actual information of the distribution. On the other hand, the classical neural network structure requires the input variable to be a vector. When the input samples are probability distributions, the traditional deep neural network method cannot be directly used and the difficulty arises for distribution regression. A well-defined neural network structure for distribution inputs is intensively desirable. There is no mathematical model and theoretical analysis on neural network realization of distribution regression. To overcome technical difficulties and address this issue, we establish a novel fully connected neural network framework to realize an approximation theory of functionals defined on the space of Borel probability measures. Furthermore, based on the established functional approximation results, in the hypothesis space induced by the novel FNN structure with distribution inputs, almost optimal learning rates for the proposed distribution regression model up to logarithmic terms are derived via a novel two-stage error decomposition technique.", "published": "2023-07-07T09:49:11Z", "authors": ["Zhongjie Shi", "Zhan Yu", "Ding-Xuan Zhou"], "categories": ["stat.ML", "cs.LG"], "doi": "", "_query": "hedonic regression housing", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2605.11306v1", "title": "Unified Operator Framework for Functional and Multivariate Regression", "abstract": "We develop a unified operator framework for scalar, multivariate, and functional regression based on integral operators defined with respect to general measures. Within this framework, classical regression models, including scalar-on-function, function-on-scalar, function-on-function, and multivariate multiple regression, arise as special cases corresponding to different choices of input and output measures. We establish three main results. First, we show that the standard regression taxonomy can be expressed as a single operator under varying measures. Second, we demonstrate that discrete representations correspond to exact operator evaluations under discrete measures and converge to the continuous operator as the observation grid is refined. Third, we show that estimation under the discrete-measure formulation reduces to standard multivariate regression, with statistical properties governed by classical results. A simulation study illustrates these principles, highlighting the roles of discretization, conditioning, and estimation. Overall, the proposed framework clarifies the relationship between functional and multivariate regression and provides a meaningful interpretation of discretized modeling approaches as operator estimation under different measure specifications. This perspective also explains why vectorized multivariate regression is often competitive with functional methods in linear settings: it directly estimates the discrete-measure representation of the underlying operator.", "published": "2026-05-11T22:49:45Z", "authors": ["Mark Carpenter", "Nicholas Gaubatz"], "categories": ["stat.ME"], "doi": "", "_query": "hedonic regression housing", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/1012.2098v7", "title": "Multinomial Inverse Regression for Text Analysis", "abstract": "Text data, including speeches, stories, and other document forms, are often connected to sentiment variables that are of interest for research in marketing, economics, and elsewhere. It is also very high dimensional and difficult to incorporate into statistical analyses. This article introduces a straightforward framework of sentiment-preserving dimension reduction for text data. Multinomial inverse regression is introduced as a general tool for simplifying predictor sets that can be represented as draws from a multinomial distribution, and we show that logistic regression of phrase counts onto document annotations can be used to obtain low dimension document representations that are rich in sentiment information. To facilitate this modeling, a novel estimation technique is developed for multinomial logistic regression with very high-dimension response. In particular, independent Laplace priors with unknown variance are assigned to each regression coefficient, and we detail an efficient routine for maximization of the joint posterior over coefficients and their prior scale. This \"gamma-lasso\" scheme yields stable and effective estimation for general high-dimension logistic regression, and we argue that it will be superior to current methods in many settings. Guidelines for prior specification are provided, algorithm convergence is detailed, and estimator properties are outlined from the perspective of the literature on non-concave likelihood penalization. Related work on sentiment analysis from statistics, econometrics, and machine learning is surveyed and connected. Finally, the methods are applied in two detailed examples and we provide out-of-sample prediction studies to illustrate their effectiveness.", "published": "2010-12-09T19:54:27Z", "authors": ["Matt Taddy"], "categories": ["stat.ME"], "doi": "", "_query": "hedonic regression housing", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/1809.09896v4", "title": "Large sample properties of the regression depth induced median", "abstract": "Notions of depth in regression have been introduced and studied in the literature. Regression depth (RD) of Rousseeuw and Hubert (1999), the most famous one, is a direct extension of Tukey location depth (Tukey (1975)) to regression. Like its location counterpart, the most remarkable advantage of the notion of depth in regression is to directly introduce the maximum (or deepest) regression depth estimator (aka depth induced median) for regression parameters in a multi-dimensional setting. Classical questions for the regression depth induced median include (i) is it a consistent estimator (or rather under what sufficient conditions, it is consistent)? and (ii) is there any limiting distribution? Bai and He (1999) (BH99) pioneered an attempt to answer these questions. Under some stringent conditions on (i) the design points, (ii) the conditional distributions of $y$ given $\\bs{x}_i$, and (iii) the error distributions, BH99 proved the strong consistency of the depth induced median. Under another set of conditions, BH99 showed the existence of the limiting distribution of the estimator. This article establishes the strong consistency of the depth induced median without any of the stringent conditions in BH99, and proves the existence of the limiting distribution of the estimator by sufficient conditions and an approach different from BH99.", "published": "2018-09-26T10:40:31Z", "authors": ["Yijun Zuo"], "categories": ["math.ST"], "doi": "", "_query": "hedonic regression housing", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2601.10623v1", "title": "Fair Regression under Demographic Parity: A Unified Framework", "abstract": "We propose a unified framework for fair regression tasks formulated as risk minimization problems subject to a demographic parity constraint. Unlike many existing approaches that are limited to specific loss functions or rely on challenging non-convex optimization, our framework is applicable to a broad spectrum of regression tasks. Examples include linear regression with squared loss, binary classification with cross-entropy loss, quantile regression with pinball loss, and robust regression with Huber loss. We derive a novel characterization of the fair risk minimizer, which yields a computationally efficient estimation procedure for general loss functions. Theoretically, we establish the asymptotic consistency of the proposed estimator and derive its convergence rates under mild assumptions. We illustrate the method's versatility through detailed discussions of several common loss functions. Numerical results demonstrate that our approach effectively minimizes risk while satisfying fairness constraints across various regression settings.", "published": "2026-01-15T17:41:28Z", "authors": ["Yongzhen Feng", "Weiwei Wang", "Raymond K. W. Wong", "Xianyang Zhang"], "categories": ["stat.ME", "stat.ML"], "doi": "", "_query": "hedonic regression housing", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2110.11182v2", "title": "SLURP: Side Learning Uncertainty for Regression Problems", "abstract": "It has become critical for deep learning algorithms to quantify their output uncertainties to satisfy reliability constraints and provide accurate results. Uncertainty estimation for regression has received less attention than classification due to the more straightforward standardized output of the latter class of tasks and their high importance. However, regression problems are encountered in a wide range of applications in computer vision. We propose SLURP, a generic approach for regression uncertainty estimation via a side learner that exploits the output and the intermediate representations generated by the main task model. We test SLURP on two critical regression tasks in computer vision: monocular depth and optical flow estimation. In addition, we conduct exhaustive benchmarks comprising transfer to different datasets and the addition of aleatoric noise. The results show that our proposal is generic and readily applicable to various regression problems and has a low computational cost with respect to existing solutions.", "published": "2021-10-21T14:50:42Z", "authors": ["Xuanlong Yu", "Gianni Franchi", "Emanuel Aldea"], "categories": ["cs.CV", "cs.LG"], "doi": "", "_query": "hedonic regression housing", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2410.01783v2", "title": "On metric choice in dimension reduction for Fréchet regression", "abstract": "Fréchet regression is becoming a mainstay in modern data analysis for analyzing non-traditional data types belonging to general metric spaces. This novel regression method is especially useful in the analysis of complex health data such as continuous monitoring and imaging data. Fréchet regression utilizes the pairwise distances between the random objects, which makes the choice of metric crucial in the estimation. In this paper, existing dimension reduction methods for Fréchet regression are reviewed, and the effect of metric choice on the estimation of the dimension reduction subspace is explored for the regression between random responses and Euclidean predictors. Extensive numerical studies illustrate how different metrics affect the central and central mean space estimators. Two real applications involving analysis of brain connectivity networks of subjects with and without Parkinson's disease and an analysis of the distributions of glycaemia based on continuous glucose monitoring data are provided, to demonstrate how metric choice can influence findings in real applications.", "published": "2024-10-02T17:39:34Z", "authors": ["Abdul-Nasah Soale", "Congli Ma", "Siyu Chen", "Obed Koomson"], "categories": ["stat.ME", "stat.AP", "stat.CO", "stat.ML"], "doi": "", "_query": "hedonic regression housing", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2401.11804v2", "title": "Regression Copulas for Multivariate Responses", "abstract": "We propose a novel distributional regression model for a multivariate response vector based on a copula process over the covariate space. It uses the implicit copula of a Gaussian multivariate regression, which we call a ``regression copula''. To allow for large covariate vectors their coefficients are regularized using a novel multivariate extension of the horseshoe prior. Bayesian inference and distributional predictions are evaluated using efficient variational inference methods, allowing application to large datasets. An advantage of the approach is that the marginal distributions of the response vector can be estimated separately and accurately, resulting in predictive distributions that are marginally-calibrated. Two substantive applications of the methodology highlight its efficacy in multivariate modeling. The first is the econometric modeling and prediction of half-hourly regional Australian electricity prices. Here, our approach produces more accurate distributional forecasts than leading benchmark methods. The second is the evaluation of multivariate posteriors in likelihood-free inference (LFI) of a model for tree species abundance data, extending a previous univariate regression copula LFI method. In both applications, we demonstrate that our new approach exhibits a desirable marginal calibration property.", "published": "2024-01-22T10:07:06Z", "authors": ["Nadja Klein", "Michael Stanley Smith", "David Nott", "Ryan Chisholm"], "categories": ["stat.ME"], "doi": "", "_query": "hedonic regression housing", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2505.17356v2", "title": "Adversarial Robustness of Nonparametric Regression", "abstract": "In this paper, we investigate the adversarial robustness of nonparametric regression, a fundamental problem in machine learning, under the setting where an adversary can arbitrarily corrupt a subset of the input data. While the robustness of parametric regression has been extensively studied, its nonparametric counterpart remains largely unexplored. We characterize the adversarial robustness in nonparametric regression, assuming the regression function belongs to the second-order Sobolev space (i.e., it is square integrable up to its second derivative). The contribution of this paper is two-fold: (i) we establish a minimax lower bound on the estimation error, revealing a fundamental limit that no estimator can overcome, and (ii) we show that, perhaps surprisingly, the classical smoothing spline estimator, when properly regularized, exhibits robustness against adversarial corruption. These results imply that if $o(n)$ out of $n$ samples are corrupted, the estimation error of the smoothing spline vanishes as $n \\to \\infty$. On the other hand, when a constant fraction of the data is corrupted, no estimator can guarantee vanishing estimation error, implying the optimality of the smoothing spline in terms of maximum tolerable number of corrupted samples.", "published": "2025-05-23T00:18:20Z", "authors": ["Parsa Moradi", "Hanzaleh Akabrinodehi", "Mohammad Ali Maddah-Ali"], "categories": ["cs.LG"], "doi": "", "_query": "hedonic regression housing", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2108.06058v2", "title": "Fréchet single index models for object response regression", "abstract": "With the increasing availability of non-Euclidean data objects, statisticians are faced with the task of developing appropriate statistical methods for their analysis. For regression models in which the predictors lie in $\\mathbb{R}^p$ and the response variables are situated in a metric space, conditional Fréchet means can be used to define the Fréchet regression function. Global and local Fréchet methods have recently been developed for modeling and estimating this regression function as extensions of multiple and local linear regression, respectively. This paper expands on these methodologies by proposing the Fréchet Single Index model, in which the Fréchet regression function is assumed to depend only on a scalar projection of the multivariate predictor. Estimation is performed by combining local Fréchet along with M-estimation to estimate both the coefficient vector and the underlying regression function, and these estimators are shown to be consistent. The method is illustrated by simulations for response objects on the surface of the unit sphere and through an analysis of human mortality data in which lifetable data are represented by distributions of age-of-death, viewed as elements of the Wasserstein space of distributions.", "published": "2021-08-13T04:40:31Z", "authors": ["Aritra Ghosal", "Wendy Meiring", "Alexander Petersen"], "categories": ["stat.ME"], "doi": "", "_query": "hedonic regression housing", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2111.02513v1", "title": "Evaluation of Tree Based Regression over Multiple Linear Regression for Non-normally Distributed Data in Battery Performance", "abstract": "Battery performance datasets are typically non-normal and multicollinear. Extrapolating such datasets for model predictions needs attention to such characteristics. This study explores the impact of data normality in building machine learning models. In this work, tree-based regression models and multiple linear regressions models are each built from a highly skewed non-normal dataset with multicollinearity and compared. Several techniques are necessary, such as data transformation, to achieve a good multiple linear regression model with this dataset; the most useful techniques are discussed. With these techniques, the best multiple linear regression model achieved an R^2 = 81.23% and exhibited no multicollinearity effect for the dataset used in this study. Tree-based models perform better on this dataset, as they are non-parametric, capable of handling complex relationships among variables and not affected by multicollinearity. We show that bagging, in the use of Random Forests, reduces overfitting. Our best tree-based model achieved accuracy of R^2 = 97.73%. This study explains why tree-based regressions promise as a machine learning model for non-normally distributed, multicollinear data.", "published": "2021-11-03T20:28:24Z", "authors": ["Shovan Chowdhury", "Yuxiao Lin", "Boryann Liaw", "Leslie Kerby"], "categories": ["cs.LG", "cs.AI"], "doi": "", "_query": "hedonic regression housing", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2603.20727v2", "title": "Compositional regression using principal nested spheres", "abstract": "Regression with compositional responses is challenging due to the nonlinear geometry of the simplex and the limitations of Euclidean methods. We propose a regression framework for manifold-valued data based on mappings to statistically tractable intermediate spaces. For compositional data, responses are embedded in the positive orthant of the sphere and analysed using Principal Nested Spheres (PNS), yielding a cylindrical intermediate space with a circular leading score and Euclidean higher-order scores. Regression is performed in this intermediate space and fitted values are mapped back to the simplex. A simulation study demonstrates good performance of PNS-based regression. An application to environmental chemical exposure data illustrates the interpretability and practical utility of the method.", "published": "2026-03-21T09:22:49Z", "authors": ["Mymuna Monem", "Ian L. Dryden", "Florence George", "Natalia Soares Quinete"], "categories": ["stat.ME", "stat.AP"], "doi": "", "_query": "hedonic regression housing", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2102.05135v1", "title": "Regularization Strategies for Quantile Regression", "abstract": "We investigate different methods for regularizing quantile regression when predicting either a subset of quantiles or the full inverse CDF. We show that minimizing an expected pinball loss over a continuous distribution of quantiles is a good regularizer even when only predicting a specific quantile. For predicting multiple quantiles, we propose achieving the classic goal of non-crossing quantiles by using deep lattice networks that treat the quantile as a monotonic input feature, and we discuss why monotonicity on other features is an apt regularizer for quantile regression. We show that lattice models enable regularizing the predicted distribution to a location-scale family. Lastly, we propose applying rate constraints to improve the calibration of the quantile predictions on specific subsets of interest and improve fairness metrics. We demonstrate our contributions on simulations, benchmark datasets, and real quantile regression problems.", "published": "2021-02-09T21:10:35Z", "authors": ["Taman Narayan", "Serena Wang", "Kevin Canini", "Maya Gupta"], "categories": ["stat.ML", "cs.LG", "stat.ME"], "doi": "", "_query": "hedonic regression housing", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/1405.6017v2", "title": "Inverse regression for longitudinal data", "abstract": "Sliced inverse regression (Duan and Li [Ann. Statist. 19 (1991) 505-530], Li [J. Amer. Statist. Assoc. 86 (1991) 316-342]) is an appealing dimension reduction method for regression models with multivariate covariates. It has been extended by Ferré and Yao [Statistics 37 (2003) 475-488, Statist. Sinica 15 (2005) 665-683] and Hsing and Ren [Ann. Statist. 37 (2009) 726-755] to functional covariates where the whole trajectories of random functional covariates are completely observed. The focus of this paper is to develop sliced inverse regression for intermittently and sparsely measured longitudinal covariates. We develop asymptotic theory for the new procedure and show, under some regularity conditions, that the estimated directions attain the optimal rate of convergence. Simulation studies and data analysis are also provided to demonstrate the performance of our method.", "published": "2014-05-23T10:48:30Z", "authors": ["Ci-Ren Jiang", "Wei Yu", "Jane-Ling Wang"], "categories": ["math.ST"], "doi": "10.1214/13-AOS1193", "_query": "hedonic regression housing", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/1508.07537v1", "title": "Model selection in logistic regression", "abstract": "This paper is devoted to model selection in logistic regression. We extend the model selection principle introduced by Birgé and Massart (2001) to logistic regression model. This selection is done by using penalized maximum likelihood criteria. We propose in this context a completely data-driven criteria based on the slope heuristics. We prove non asymptotic oracle inequalities for selected estimators. Theoretical results are illustrated through simulation studies.", "published": "2015-08-30T07:03:45Z", "authors": ["Marius Kwemou", "Marie-Luce Taupin", "Anne-Sophie Tocquet"], "categories": ["math.ST"], "doi": "", "_query": "hedonic regression housing", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2008.07859v1", "title": "Selecting the Derivative of a Functional Covariate in Scalar-on-Function Regression", "abstract": "This paper presents tests to formally choose between regression models using different derivatives of a functional covariate in scalar-on-function regression. We demonstrate that for linear regression, models using different derivatives can be nested within a model that includes point-impact effects at the end-points of the observed functions. Contrasts can then be employed to test the specification of different derivatives. When nonlinear regression models are defined, we apply a $J$ test to determine the statistical significance of the nonlinear structure between a functional covariate and a scalar response. The finite-sample performance of these methods is verified in simulation, and their practical application is demonstrated using a chemometric data set.", "published": "2020-08-18T11:15:25Z", "authors": ["Giles Hooker", "Hanlin Shang"], "categories": ["stat.ME", "stat.CO"], "doi": "", "_query": "hedonic regression housing", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2311.17246v3", "title": "Detecting influential observations in single-index Fréchet regression", "abstract": "Regression with random data objects is becoming increasingly common in modern data analysis. Unfortunately, this novel regression method is not immune to the trouble caused by unusual observations. A metric Cook's distance extending the original Cook's distances of Cook (1977) to regression between metric-valued response objects and Euclidean predictors is proposed. The performance of the metric Cook's distance is demonstrated in regression across four different response spaces in an extensive experimental study. Two real data applications involving the analyses of distributions of COVID-19 transmission in the State of Texas and the analyses of the structural brain connectivity networks are provided to illustrate the utility of the proposed method in practice.", "published": "2023-11-28T21:40:14Z", "authors": ["Abdul-Nasah Soale"], "categories": ["stat.CO", "stat.AP", "stat.ME"], "doi": "", "_query": "hedonic regression housing", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2303.06434v2", "title": "Direct Bayesian Regression for Distribution-valued Covariates", "abstract": "In this manuscript, we study the problem of scalar-on-distribution regression; that is, instances where subject-specific distributions or densities, or in practice, repeated measures from those distributions, are the covariates related to a scalar outcome via a regression model. We propose a direct regression for such distribution-valued covariates that circumvents estimating subject-specific densities and directly uses the observed repeated measures as covariates. The model is invariant to any transformation or ordering of the repeated measures. Endowing the regression function with a Gaussian Process prior, we obtain closed form or conjugate Bayesian inference. Our method subsumes the standard Bayesian non-parametric regression using Gaussian Processes as a special case. Theoretically, we show that the method can achieve an optimal estimation error bound. To our knowledge, this is the first theoretical study on Bayesian regression using distribution-valued covariates. Through simulation studies and analysis of activity count dataset, we demonstrate that our method performs better than approaches that require an intermediate density estimation step.", "published": "2023-03-11T16:03:10Z", "authors": ["Bohao Tang", "Sandipan Pramanik", "Yi Zhao", "Brian Caffo", "Abhirup Datta"], "categories": ["stat.ME", "math.ST"], "doi": "", "_query": "hedonic regression housing", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2609.39061v1", "title": "Jump regression revisited", "abstract": "Regression analysis for stochastic processes has been an important topic over the last decades. Typically, regressing a dependent process $Y$ on an explanatory $Z$ results in an integral relationship of the form $dY_t = β_t dZ_t + dX_t$ with some residual process $X$, and often with constant or piecewise constant $β$. In the case of jump processes such a relationship essentially boils down to $ΔY_t = βΔZ_t + ΔX_t$ on the level of the jumps. Here the residual process $X$ is such that it never jumps together with $Z$, and so any jump in $Y$ is either exactly proportional to $Z$ or purely idiosyncratic. In this paper we discuss a related setup which appears more realistic and is closer to classical regression, namely $ΔY_t = (β+ η_t) ΔZ_t$ for i.i.d. $(η_t)_{t \\ge 0}$. We propose an estimator for $β$ based on high-frequency observations of $(Y,Z)$ and discuss consistency and associated central limit theorems in two different asymptotic regimes.", "published": "2026-09-30T06:02:39Z", "authors": ["Mathias Vetter", "Fan Yu"], "categories": ["math.ST"], "doi": "", "_query": "hedonic regression housing", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/1912.05588v2", "title": "Parametric mode regression for bounded responses", "abstract": "We propose new parametric frameworks of regression analysis with the conditional mode of a bounded response as the focal point of interest. Covariate effects estimation and prediction based on the maximum likelihood method under two new classes of regression models are demonstrated. We also develop graphical and numerical diagnostic tools to detect various sources of model misspecification. Predictions based on different central tendency measures inferred using various regression models are compared using synthetic data in simulations. Finally, we conduct regression analysis for data from the Alzheimer's Disease Neuroimaging Initiative to demonstrate practical implementation of the proposed methods. Supplementary materials that contain technical details, and additional simulation and data analysis results are available online.", "published": "2019-12-11T19:25:26Z", "authors": ["Haiming Zhou", "Xianzheng Huang"], "categories": ["stat.ME", "stat.AP", "stat.OT"], "doi": "", "_query": "hedonic regression housing", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2202.06535v1", "title": "Coefficient Decomposition of Spatial Regressive Models Based on Standardized Variables", "abstract": "Spatial autocorrelation analysis is the basis for spatial autoregressive modeling. However, the relationships between spatial correlation coefficients and spatial regression models are not yet well clarified. The paper is devoted to explore the deep structure of spatial regression coefficients. By means of mathematical reasoning, a pair of formulae of canonical spatial regression coefficients are derived from a general spatial regression model based on standardized variables. The spatial auto- and lag-regression coefficients are reduced to a series of statistic parameters and measurements, including conventional regressive coefficient, Pearson correlation coefficient, Moran's indexes, spatial cross-correlation coefficients, and the variance of prediction residuals. The formulae show determinate inherent relationships between spatial correlation coefficients and spatial regression coefficients. New finding is as below: the spatial autoregressive coefficient mainly depends on the Moran's index of the independent variable, while the spatial lag-regressive coefficient chiefly depends on the cross-correlation coefficient of independent variable and dependent variable. The observational data of an urban system in Beijing, Tianjin, and Hebei region of China were employed to verify the newly derived formulae, and the results are satisfying. The new formulae and their variates are helpful for understand spatial regression models from the perspective of spatial correlation and can be used to assist spatial regression modeling.", "published": "2022-02-14T08:08:28Z", "authors": ["Yanguang Chen"], "categories": ["stat.ME", "physics.data-an"], "doi": "", "_query": "hedonic regression housing", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2011.08521v1", "title": "Sequential scaled sparse factor regression", "abstract": "Large-scale association analysis between multivariate responses and predictors is of great practical importance, as exemplified by modern business applications including social media marketing and crisis management. Despite the rapid methodological advances, how to obtain scalable estimators with free tuning of the regularization parameters remains unclear under general noise covariance structures. In this paper, we develop a new methodology called sequential scaled sparse factor regression (SESS) based on a new viewpoint that the problem of recovering a jointly low-rank and sparse regression coefficient matrix can be decomposed into several univariate response sparse regressions through regular eigenvalue decomposition. It combines the strengths of sequential estimation and scaled sparse regression, thus sharing the scalability and the tuning free property for sparsity parameters inherited from the two approaches. The stepwise convex formulation, sequential factor regression framework, and tuning insensitiveness make SESS highly scalable for big data applications. Comprehensive theoretical justifications with new insights into high-dimensional multi-response regressions are also provided. We demonstrate the scalability and effectiveness of the proposed method by simulation studies and stock short interest data analysis.", "published": "2020-11-17T09:16:32Z", "authors": ["Zemin Zheng", "Yang Li", "Jie Wu", "Yuchen Wang"], "categories": ["stat.ME"], "doi": "", "_query": "hedonic regression housing", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/1206.6408v1", "title": "Sequential Nonparametric Regression", "abstract": "We present algorithms for nonparametric regression in settings where the data are obtained sequentially. While traditional estimators select bandwidths that depend upon the sample size, for sequential data the effective sample size is dynamically changing. We propose a linear time algorithm that adjusts the bandwidth for each new data point, and show that the estimator achieves the optimal minimax rate of convergence. We also propose the use of online expert mixing algorithms to adapt to unknown smoothness of the regression function. We provide simulations that confirm the theoretical results, and demonstrate the effectiveness of the methods.", "published": "2012-06-27T19:59:59Z", "authors": ["Haijie Gu", "John Lafferty"], "categories": ["stat.ME", "astro-ph.IM", "cs.LG"], "doi": "", "_query": "hedonic regression housing", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/1804.01454v1", "title": "Beta regression control chart for monitoring fractions and proportions", "abstract": "Regression control charts are usually used to monitor variables of interest that are related to control variables. However, for fraction and/or proportion data, the use of standard regression control charts may not be adequate, since the linear regression model assumes the normality of the interest variable. To work around this problem, we propose the beta regression control chart (BRCC). The BRCC is useful for monitoring fraction, rate and/or proportion data sets when they are related to control variables. The proposed control chart assumes that the mean and dispersion parameters of beta distributed variables are related to the exogenous variables, being modeled using regression structures. The BRCC is numerically assessed through an extensive Monte Carlo simulation study, showing good performance in terms of average run length (ARL). Two applications to real data are presented, evidencing the practical applicability of the proposed method.", "published": "2018-04-04T15:05:36Z", "authors": ["Fábio Mariano Bayer", "Catia Michele Tondolo", "Fernanda Maria Müller"], "categories": ["stat.ME"], "doi": "", "_query": "hedonic regression housing", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2404.13500v1", "title": "Generalized Regression with Conditional GANs", "abstract": "Regression is typically treated as a curve-fitting process where the goal is to fit a prediction function to data. With the help of conditional generative adversarial networks, we propose to solve this age-old problem in a different way; we aim to learn a prediction function whose outputs, when paired with the corresponding inputs, are indistinguishable from feature-label pairs in the training dataset. We show that this approach to regression makes fewer assumptions on the distribution of the data we are fitting to and, therefore, has better representation capabilities. We draw parallels with generalized linear models in statistics and show how our proposal serves as an extension of them to neural networks. We demonstrate the superiority of this new approach to standard regression with experiments on multiple synthetic and publicly available real-world datasets, finding encouraging results, especially with real-world heavy-tailed regression datasets. To make our work more reproducible, we release our source code. Link to repository: https://anonymous.4open.science/r/regressGAN-7B71/", "published": "2024-04-21T01:27:47Z", "authors": ["Deddy Jobson", "Eddy Hudson"], "categories": ["cs.LG", "cs.AI", "stat.ML"], "doi": "", "_query": "hedonic regression housing", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2602.22101v3", "title": "On Imbalanced Regression with Hoeffding Trees", "abstract": "Many real-world applications generate continuous data streams for regression. Hoeffding trees and their variants have a long-standing tradition due to their effectiveness, either alone or as base models in broader ensembles. Recent batch-learning work shows that kernel density estimation (KDE) improves smoothed predictions in imbalanced regression [Yang et al., 2021], while hierarchical shrinkage (HS) provides post-hoc regularization for decision trees without modifying their structure [Agarwal et al., 2022]. We extend KDE to streaming settings via a telescoping formulation and integrate HS into incremental decision trees. Empirical evaluation on standard online regression benchmarks shows that KDE consistently improves early-stream performance, whereas HS provides limited gains. Our implementation is publicly available at: https://github.com/marinaAlchirch/DSFA_2026.", "published": "2026-02-25T16:48:07Z", "authors": ["Pantia-Marina Alchirch", "Dimitrios I. Diochnos"], "categories": ["cs.LG", "cs.AI"], "doi": "", "_query": "hedonic regression housing", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/1605.02541v2", "title": "Mean Absolute Percentage Error for regression models", "abstract": "We study in this paper the consequences of using the Mean Absolute Percentage Error (MAPE) as a measure of quality for regression models. We prove the existence of an optimal MAPE model and we show the universal consistency of Empirical Risk Minimization based on the MAPE. We also show that finding the best model under the MAPE is equivalent to doing weighted Mean Absolute Error (MAE) regression, and we apply this weighting strategy to kernel regression. The behavior of the MAPE kernel regression is illustrated on simulated data.", "published": "2016-05-09T11:46:26Z", "authors": ["Arnaud De Myttenaere", "Boris Golden", "Bénédicte Le Grand", "Fabrice Rossi"], "categories": ["stat.ML"], "doi": "10.1016/j.neucom.2015.12.114", "_query": "hedonic regression housing", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2508.15095v1", "title": "Generalized random forest for extreme quantile regression", "abstract": "Quantile regression is a statistical method which, unlike classical regression, aims to predict the conditional quantiles. Classical quantile regression methods face difficulties, particularly when the quantile under consideration is extreme, due to the limited number of data available in the tail of the distribution, or when the quantile function is complex. We propose an extreme quantile regression method based on extreme value theory and statistical learning to overcome these difficulties. Following the Block Maxima approach of extreme value theory, we approximate the conditional distribution of block maxima by the generalized extreme value distribution, with covariate-dependent parameters. These parameters are estimated using a method based on generalized random forests. Applications on simulated data show that our proposed method effectively addresses the mentioned quantile regression issues and highlights its performance compared to other quantile regression approaches based on statistical learning methods. We apply our methodology to daily meteorological data from the Fort Collins station in Colorado (USA).", "published": "2025-08-20T22:08:35Z", "authors": ["Lucien M. Vidagbandji", "Alexandre Berred", "Cyrille Bertelle", "Laurent Amanton"], "categories": ["stat.ME"], "doi": "10.1080/03610918.2025.2543854", "_query": "hedonic regression housing", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2404.18688v1", "title": "Distributed Source Coding for Parametric and Non-Parametric Regression", "abstract": "The design of communication systems dedicated to machine learning tasks is one key aspect of goal-oriented communications. In this framework, this article investigates the interplay between data reconstruction and learning from the same compressed observations, particularly focusing on the regression problem. We establish achievable rate-generalization error regions for both parametric and non-parametric regression, where the generalization error measures the regression performance on previously unseen data. The analysis covers both asymptotic and finite block-length regimes, providing fundamental results and practical insights for the design of coding schemes dedicated to regression. The asymptotic analysis relies on conventional Wyner-Ziv coding schemes which we extend to study the convergence of the generalization error. The finite-length analysis uses the notions of information density and dispersion with additional term for the generalization error. We further investigate the trade-off between reconstruction and regression in both asymptotic and non-asymptotic regimes. Contrary to the existing literature which focused on other learning tasks, our results state that in the case of regression, there is no trade-off between data reconstruction and regression in the asymptotic regime. We also observe the same absence of trade-off for the considered achievable scheme in the finite-length regime, by analyzing correlation between distortion and generalization error.", "published": "2024-04-29T13:36:09Z", "authors": ["Jiahui Wei", "Elsa Dupraz", "Philippe Mary"], "categories": ["cs.IT"], "doi": "", "_query": "hedonic regression housing", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2301.07638v2", "title": "An Analysis of Loss Functions for Binary Classification and Regression", "abstract": "This paper explores connections between margin-based loss functions and consistency in binary classification and regression applications. It is shown that a large class of margin-based loss functions for binary classification/regression result in estimating scores equivalent to log-likelihood scores weighted by an even function. A simple characterization for conformable (consistent) loss functions is given, which allows for straightforward comparison of different losses, including exponential loss, logistic loss, and others. The characterization is used to construct a new Huber-type loss function for the logistic model. A simple relation between the margin and standardized logistic regression residuals is derived, demonstrating that all margin-based loss can be viewed as loss functions of squared standardized logistic regression residuals. The relation provides new, straightforward interpretations for exponential and logistic loss, and aids in understanding why exponential loss is sensitive to outliers. In particular, it is shown that minimizing empirical exponential loss is equivalent to minimizing the sum of squared standardized logistic regression residuals. The relation also provides new insight into the AdaBoost algorithm.", "published": "2023-01-18T16:26:57Z", "authors": ["Jeffrey Buzas"], "categories": ["stat.ML", "cs.LG"], "doi": "", "_query": "hedonic regression housing", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/math/0511522v2", "title": "On-line predictive linear regression", "abstract": "We consider the on-line predictive version of the standard problem of linear regression; the goal is to predict each consecutive response given the corresponding explanatory variables and all the previous observations. We are mainly interested in prediction intervals rather than point predictions. The standard treatment of prediction intervals in linear regression analysis has two drawbacks: (1) the classical prediction intervals guarantee that the probability of error is equal to the nominal significance level epsilon, but this property per se does not imply that the long-run frequency of error is close to epsilon; (2) it is not suitable for prediction of complex systems as it assumes that the number of observations exceeds the number of parameters. We state a general result showing that in the on-line protocol the frequency of error for the classical prediction intervals does equal the nominal significance level, up to statistical fluctuations. We also describe alternative regression models in which informative prediction intervals can be found before the number of observations exceeds the number of parameters. One of these models, which only assumes that the observations are independent and identically distributed, is popular in machine learning but greatly underused in the statistical theory of regression.", "published": "2005-11-21T15:31:17Z", "authors": ["Vladimir Vovk", "Ilia Nouretdinov", "Alex Gammerman"], "categories": ["math.ST"], "doi": "10.1214/08-AOS622", "_query": "hedonic regression housing", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/math/0702843v1", "title": "Regression with strongly correlated data", "abstract": "This paper discusses linear regression of strongly correlated data that arises, for example, in magnetohydrodynamic equilibrium reconstructions. We have proved that, generically, the covariance matrix of the estimated regression parameters for fixed sample size goes to zero as the correlations become unity. That is, in this limit the estimated parameters are known with perfect accuracy. Simple examples are shown to illustrate this effect and the nature of the exceptional cases in which the estimate covariance does not go to zero.", "published": "2007-02-27T16:03:01Z", "authors": ["C. S. Jones", "J. M. Finn", "N. Hengartner"], "categories": ["math.ST"], "doi": "", "_query": "hedonic regression housing", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2406.03585v1", "title": "A Comparison of Recent Algorithms for Symbolic Regression to Genetic Programming", "abstract": "Symbolic regression is a machine learning method with the goal to produce interpretable results. Unlike other machine learning methods such as, e.g. random forests or neural networks, which are opaque, symbolic regression aims to model and map data in a way that can be understood by scientists. Recent advancements, have attempted to bridge the gap between these two fields; new methodologies attempt to fuse the mapping power of neural networks and deep learning techniques with the explanatory power of symbolic regression. In this paper, we examine these new emerging systems and test the performance of an end-to-end transformer model for symbolic regression versus the reigning traditional methods based on genetic programming that have spearheaded symbolic regression throughout the years. We compare these systems on novel datasets to avoid bias to older methods who were improved on well-known benchmark datasets. Our results show that traditional GP methods as implemented e.g., by Operon still remain superior to two recently published symbolic regression methods.", "published": "2024-06-05T19:01:43Z", "authors": ["Yousef A. Radwan", "Gabriel Kronberger", "Stephan Winkler"], "categories": ["cs.LG", "cs.AI"], "doi": "", "_query": "hedonic regression housing", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2207.04837v1", "title": "RRMSE Voting Regressor: A weighting function based improvement to ensemble regression", "abstract": "This paper describes the RRMSE (Relative Root Mean Square Error) based weights to weight the occurrences of predictive values before averaging for the ensemble voting regression. The core idea behind ensemble regression is to combine several base regression models in order to improve the prediction performance in learning problems with a numeric continuous target variable. The default weights setting for the ensemble voting regression is uniform weights, and without domain knowledge of learning task, assigning weights for predictions are impossible, which makes it very difficult to improve the predictions. This work attempts to improve the prediction of voting regression by implementing the RRMSE based weighting function. Experiments show that RRMSE voting regressor produces significantly better predictions than other state-of-the-art ensemble regression algorithms on six popular regression learning datasets.", "published": "2022-07-11T13:13:54Z", "authors": ["Shikun Chen", "Nguyen Manh Luc"], "categories": ["cs.LG"], "doi": "", "_query": "hedonic regression housing", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/1605.04796v3", "title": "Prediction risk for the horseshoe regression", "abstract": "We show that prediction performance for global-local shrinkage regression can overcome two major difficulties of global shrinkage regression: (i) the amount of relative shrinkage is monotone in the singular values of the design matrix and (ii) the shrinkage is determined by a single tuning parameter. Specifically, we show that the horseshoe regression, with heavy-tailed component-specific local shrinkage parameters, in conjunction with a global parameter providing shrinkage towards zero, alleviates both these difficulties and consequently, results in an improved risk for prediction. Numerical demonstrations of improved prediction over competing approaches in simulations and in a pharmacogenomics data set confirm our theoretical findings.", "published": "2016-05-16T15:09:06Z", "authors": ["Anindya Bhadra", "Jyotishka Datta", "Yunfan Li", "Nicholas G. Polson", "Brandon Willard"], "categories": ["math.ST"], "doi": "", "_query": "hedonic regression housing", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2210.11874v2", "title": "Blind Polynomial Regression", "abstract": "Fitting a polynomial to observed data is an ubiquitous task in many signal processing and machine learning tasks, such as interpolation and prediction. In that context, input and output pairs are available and the goal is to find the coefficients of the polynomial. However, in many applications, the input may be partially known or not known at all, rendering conventional regression approaches not applicable. In this paper, we formally state the (potentially partial) blind regression problem, illustrate some of its theoretical properties, and propose algorithmic approaches to solve it. As a case-study, we apply our methods to a jitter-correction problem and corroborate its performance.", "published": "2022-10-21T10:54:35Z", "authors": ["Alberto Natali", "Geert Leus"], "categories": ["eess.SP", "cs.LG", "math.ST", "stat.ML"], "doi": "", "_query": "hedonic regression housing", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/math/0611192v1", "title": "Regression tree models for designed experiments", "abstract": "Although regression trees were originally designed for large datasets, they can profitably be used on small datasets as well, including those from replicated or unreplicated complete factorial experiments. We show that in the latter situations, regression tree models can provide simpler and more intuitive interpretations of interaction effects as differences between conditional main effects. We present simulation results to verify that the models can yield lower prediction mean squared errors than the traditional techniques. The tree models span a wide range of sophistication, from piecewise constant to piecewise simple and multiple linear, and from least squares to Poisson and logistic regression.", "published": "2006-11-07T15:11:57Z", "authors": ["Wei-Yin Loh"], "categories": ["math.ST"], "doi": "10.1214/074921706000000464", "_query": "hedonic regression housing", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2401.00282v1", "title": "Deep Generative Symbolic Regression", "abstract": "Symbolic regression (SR) aims to discover concise closed-form mathematical equations from data, a task fundamental to scientific discovery. However, the problem is highly challenging because closed-form equations lie in a complex combinatorial search space. Existing methods, ranging from heuristic search to reinforcement learning, fail to scale with the number of input variables. We make the observation that closed-form equations often have structural characteristics and invariances (e.g., the commutative law) that could be further exploited to build more effective symbolic regression solutions. Motivated by this observation, our key contribution is to leverage pre-trained deep generative models to capture the intrinsic regularities of equations, thereby providing a solid foundation for subsequent optimization steps. We show that our novel formalism unifies several prominent approaches of symbolic regression and offers a new perspective to justify and improve on the previous ad hoc designs, such as the usage of cross-entropy loss during pre-training. Specifically, we propose an instantiation of our framework, Deep Generative Symbolic Regression (DGSR). In our experiments, we show that DGSR achieves a higher recovery rate of true equations in the setting of a larger number of input variables, and it is more computationally efficient at inference time than state-of-the-art RL symbolic regression solutions.", "published": "2023-12-30T17:05:31Z", "authors": ["Samuel Holt", "Zhaozhi Qian", "Mihaela van der Schaar"], "categories": ["cs.LG"], "doi": "", "_query": "hedonic regression housing", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2306.00469v2", "title": "HiQR: An efficient algorithm for high-dimensional quadratic regression with penalties", "abstract": "This paper investigates the efficient solution of penalized quadratic regressions in high-dimensional settings. A novel and efficient algorithm for ridge-penalized quadratic regression is proposed, leveraging the matrix structures of the regression with interactions. Additionally, an alternating direction method of multipliers (ADMM) framework is developed for penalized quadratic regression with general penalties, including both single and hybrid penalty functions. The approach simplifies the calculations to basic matrix-based operations, making it appealing in terms of both memory storage and computational complexity for solving penalized quadratic regressions in high-dimensional settings.", "published": "2023-06-01T09:18:55Z", "authors": ["Cheng Wang", "Haozhe Chen", "Binyan Jiang"], "categories": ["stat.CO"], "doi": "", "_query": "hedonic regression housing", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/1905.06467v1", "title": "Moment-based Estimation of Mixtures of Regression Models", "abstract": "Finite mixtures of regression models provide a flexible modeling framework for many phenomena. Using moment-based estimation of the regression parameters, we develop unbiased estimators with a minimum of assumptions on the mixture components. In particular, only the average regression model for one of the components in the mixture model is needed and no requirements on the distributions. The consistency and asymptotic distribution of the estimators is derived and the proposed method is validated through a series of simulation studies and is shown to be highly accurate. We illustrate the use of the moment-based mixture of regression models with an application to wine quality data.", "published": "2019-05-15T23:11:48Z", "authors": ["Claus Thorn Ekstrøm", "Christian Bressen Pipper"], "categories": ["math.ST", "stat.AP", "stat.ME"], "doi": "", "_query": "hedonic regression housing", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/1601.02798v1", "title": "Functional linear regression with points of impact", "abstract": "The paper considers functional linear regression, where scalar responses $Y_1,\\ldots,Y_n$ are modeled in dependence of i.i.d. random functions $X_1,\\ldots,X_n$. We study a generalization of the classical functional linear regression model. It is assumed that there exists an unknown number of \"points of impact,\" that is, discrete observation times where the corresponding functional values possess significant influences on the response variable. In addition to estimating a functional slope parameter, the problem then is to determine the number and locations of points of impact as well as corresponding regression coefficients. Identifiability of the generalized model is considered in detail. It is shown that points of impact are identifiable if the underlying process generating $X_1,\\ldots,X_n$ possesses \"specific local variation.\" Examples are well-known processes like the Brownian motion, fractional Brownian motion or the Ornstein-Uhlenbeck process. The paper then proposes an easily implementable method for estimating the number and locations of points of impact. It is shown that this number can be estimated consistently. Furthermore, rates of convergence for location estimates, regression coefficients and the slope parameter are derived. Finally, some simulation results as well as a real data application are presented.", "published": "2016-01-12T10:48:48Z", "authors": ["Alois Kneip", "Dominik Poß", "Pascal Sarda"], "categories": ["math.ST"], "doi": "10.1214/15-AOS1323", "_query": "hedonic regression housing", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/0708.0385v1", "title": "Tree-structured regression and the differentiation of integrals", "abstract": "This paper provides answers to questions regarding the almost sure limiting behavior of rooted, binary tree-structured rules for regression. Examples show that questions raised by Gordon and Olshen in 1984 have negative answers. For these examples of regression functions and sequences of their associated binary tree-structured approximations, for all regression functions except those in a set of the first category, almost sure consistency fails dramatically on events of full probability. One consequence is that almost sure consistency of binary tree-structured rules such as CART requires conditions beyond requiring that (1) the regression function be in ${\\mathcal {L}}^1$, (2) partitions of a Euclidean feature space be into polytopes with sides parallel to coordinate axes, (3) the mesh of the partitions becomes arbitrarily fine almost surely and (4) the empirical learning sample content of each polytope be ``large enough.'' The material in this paper includes the solution to a problem raised by Dudley in discussions. The main results have a corollary regarding the lack of almost sure consistency of certain Bayes-risk consistent rules for classification.", "published": "2007-08-02T16:55:24Z", "authors": ["Richard A. Olshen"], "categories": ["math.ST"], "doi": "10.1214/009053606000001000", "_query": "hedonic regression housing", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2512.01920v1", "title": "Fundamentals of Regression", "abstract": "This chapter opens with a review of classic tools for regression, a subset of machine learning that seeks to find relationships between variables. With the advent of scientific machine learning this field has moved from a purely data-driven (statistical) formalism to a constrained or ``physics-informed'' formalism, which integrates physical knowledge and methods from traditional computational engineering. In the first part, we introduce the general concepts and the statistical flavor of regression versus other forms of curve fitting. We then move to an overview of traditional methods from machine learning and their classification and ways to link these to traditional computational science. Finally, we close with a note on methods to combine machine learning and numerical methods for physics", "published": "2025-11-27T08:49:58Z", "authors": ["Miguel A. Mendez"], "categories": ["stat.ML", "cs.LG"], "doi": "", "_query": "hedonic regression housing", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2306.15163v1", "title": "Wasserstein Generative Regression", "abstract": "In this paper, we propose a new and unified approach for nonparametric regression and conditional distribution learning. Our approach simultaneously estimates a regression function and a conditional generator using a generative learning framework, where a conditional generator is a function that can generate samples from a conditional distribution. The main idea is to estimate a conditional generator that satisfies the constraint that it produces a good regression function estimator. We use deep neural networks to model the conditional generator. Our approach can handle problems with multivariate outcomes and covariates, and can be used to construct prediction intervals. We provide theoretical guarantees by deriving non-asymptotic error bounds and the distributional consistency of our approach under suitable assumptions. We also perform numerical experiments with simulated and real data to demonstrate the effectiveness and superiority of our approach over some existing approaches in various scenarios.", "published": "2023-06-27T02:44:54Z", "authors": ["Shanshan Song", "Tong Wang", "Guohao Shen", "Yuanyuan Lin", "Jian Huang"], "categories": ["stat.ML", "cs.LG"], "doi": "", "_query": "hedonic regression housing", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2603.14801v2", "title": "Genetic Algorithms in Regression", "abstract": "Many statistical problems involve optimization over a discrete parameter space having an unknown dimension. In such settings, gradient-based methods often fail due to the non-differentiability of the objective function or a non-convex or massive search space with an objective function having many local maxima/minima. This paper presents GAReg, a unified genetic algorithm package that handles discrete optimization regression problems, which works well when standard algorithms are unjustified. GAReg provides a compact chromosome representation supporting optimal knot placement for regression splines, best-subset regression variable selection, and related problems. The package allows for uniform initialization, constraint-preserving crossover and mutation, steady-state replacement, and an optional island-model parallelization. GAReg efficiently searches high-dimensional model spaces, providing near-optimal solutions in settings where exhaustive enumeration or integer or dynamic programming approaches are infeasible.", "published": "2026-03-16T03:59:21Z", "authors": ["Mo Li", "QiQi Lu", "Robert Lund", "Xueheng Shi"], "categories": ["stat.AP", "stat.CO"], "doi": "", "_query": "hedonic regression housing", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2409.17084v1", "title": "Incorporating Shape Knowledge into Regression Models", "abstract": "Informed learning is an emerging field in machine learning that aims to compensate for insufficient data with prior knowledge. Shape knowledge covers many types of prior knowledge concerning the relationship of a function's output with respect to input variables, for example, monotonicity, convexity, etc. This shape knowledge -- when formalized into algebraic inequalities (shape constraints) -- can then be incorporated into the training of regression models via a constraint problem formulation. The defined shape-constrained regression problem is, mathematically speaking, a semi-infinite program (SIP). Although off-the-shelf algorithms can be used at this point to solve the SIP, we recommend an adaptive feasible-point algorithm that guarantees optimality up to arbitrary precision and strict fulfillment of the shape constraints. We apply this semi-infinite approach for shape-constrained regression (SIASCOR) to three application examples from manufacturing and one artificial example. One application example has not been considered in a shape-constrained regression setting before, so we used a methodology (ISI) to capture the shape knowledge and define corresponding shape constraints. Finally, we compare the SIASCOR method with a purely data-driven automated machine learning method (AutoML) and another approach for shape-constrained regression (SIAMOR) that uses a different solution algorithm.", "published": "2024-09-25T16:49:06Z", "authors": ["Miltiadis Poursanidis", "Patrick Link", "Jochen Schmid", "Uwe Teicher"], "categories": ["math.OC"], "doi": "", "_query": "hedonic regression housing", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2504.03859v1", "title": "Bayesian Modal Regression for Forecast Combinations", "abstract": "Forecast combination methods have traditionally emphasized symmetric loss functions, particularly squared error loss, with equally weighted combinations often justified as a robust approach under such criteria. However, these justifications do not extend to asymmetric loss functions, where optimally weighted combinations may provide superior predictive performance. This study introduces a novel contribution by incorporating modal regression into forecast combinations, offering a Bayesian hierarchical framework that models the conditional mode of the response through combinations of time-varying parameters and exponential discounting. The proposed approach utilizes error distributions characterized by asymmetry and heavy tails, specifically the asymmetric Laplace, asymmetric normal, and reverse Gumbel distributions. Simulated data validate the parameter estimation for the modal regression models, confirming the robustness of the proposed methodology. Application of these methodologies to a real-world analyst forecast dataset shows that modal regression with asymmetric Laplace errors outperforms mean regression based on two key performance metrics: the hit rate, which measures the accuracy of classifying the sign of revenue surprises, and the win rate, which assesses the proportion of forecasts surpassing the equally weighted consensus. These results underscore the presence of skewness and fat-tailed behavior in forecast combination errors for revenue forecasting, highlighting the advantages of modal regression in financial applications.", "published": "2025-04-04T18:34:02Z", "authors": ["Henry D. van Eijk", "Sujit K. Ghosh"], "categories": ["stat.ME"], "doi": "", "_query": "hedonic regression housing", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2608.28852v1", "title": "A penalized logistic generalized regression estimator", "abstract": "Under a model-assisted framework, a penalized logistic generalized regression estimator is developed to estimate a finite population proportion from complex survey data and auxiliary data. The proposed estimator controls the impact of unnecessary auxiliary variables through a lasso or ridge penalty. A central limit theorem is derived for the penalized regression coefficients and for the penalized logistic generalized regression estimator. Through simulations, it is shown that including the penalty increases the efficiency of the estimator when the true model is sparse. An example using United States Forest Service data demonstrates the applicability of the estimator for surveys with many available auxiliary variables.", "published": "2026-08-28T20:45:39Z", "authors": ["Grayson W. White", "Kelly S. McConville", "Cooper S. Schumacher"], "categories": ["stat.ME"], "doi": "", "_query": "hedonic regression housing", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2403.19494v1", "title": "Regression with Multi-Expert Deferral", "abstract": "Learning to defer with multiple experts is a framework where the learner can choose to defer the prediction to several experts. While this problem has received significant attention in classification contexts, it presents unique challenges in regression due to the infinite and continuous nature of the label space. In this work, we introduce a novel framework of regression with deferral, which involves deferring the prediction to multiple experts. We present a comprehensive analysis for both the single-stage scenario, where there is simultaneous learning of predictor and deferral functions, and the two-stage scenario, which involves a pre-trained predictor with a learned deferral function. We introduce new surrogate loss functions for both scenarios and prove that they are supported by $H$-consistency bounds. These bounds provide consistency guarantees that are stronger than Bayes consistency, as they are non-asymptotic and hypothesis set-specific. Our framework is versatile, applying to multiple experts, accommodating any bounded regression losses, addressing both instance-dependent and label-dependent costs, and supporting both single-stage and two-stage methods. A by-product is that our single-stage formulation includes the recent regression with abstention framework (Cheng et al., 2023) as a special case, where only a single expert, the squared loss and a label-independent cost are considered. Minimizing our proposed loss functions directly leads to novel algorithms for regression with deferral. We report the results of extensive experiments showing the effectiveness of our proposed algorithms.", "published": "2024-03-28T15:26:38Z", "authors": ["Anqi Mao", "Mehryar Mohri", "Yutao Zhong"], "categories": ["cs.LG", "stat.ML"], "doi": "", "_query": "hedonic regression housing", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2206.10783v1", "title": "Federated Latent Class Regression for Hierarchical Data", "abstract": "Federated Learning (FL) allows a number of agents to participate in training a global machine learning model without disclosing locally stored data. Compared to traditional distributed learning, the heterogeneity (non-IID) of the agents slows down the convergence in FL. Furthermore, many datasets, being too noisy or too small, are easily overfitted by complex models, such as deep neural networks. Here, we consider the problem of using FL regression on noisy, hierarchical and tabular datasets in which user distributions are significantly different. Inspired by Latent Class Regression (LCR), we propose a novel probabilistic model, Hierarchical Latent Class Regression (HLCR), and its extension to Federated Learning, FEDHLCR. FEDHLCR consists of a mixture of linear regression models, allowing better accuracy than simple linear regression, while at the same time maintaining its analytical properties and avoiding overfitting. Our inference algorithm, being derived from Bayesian theory, provides strong convergence guarantees and good robustness to overfitting. Experimental results show that FEDHLCR offers fast convergence even in non-IID datasets.", "published": "2022-06-22T00:33:04Z", "authors": ["Bin Yang", "Thomas Carette", "Masanobu Jimbo", "Shinya Maruyama"], "categories": ["cs.LG"], "doi": "", "_query": "hedonic regression housing", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2601.00987v2", "title": "Tessellation Localized Transfer learning for nonparametric regression", "abstract": "Transfer learning aims to improve performance on a target task by leveraging information from related source tasks. We propose a nonparametric regression transfer learning framework that explicitly models heterogeneity in the source-target relationship. Our approach relies on a local transfer assumption: the covariate space is partitioned into finitely many cells such that, within each cell, the target regression function can be expressed as a low-complexity transformation of the source regression function. This localized structure enables effective transfer where similarity is present while limiting negative transfer elsewhere. We introduce estimators that jointly learn the local transfer functions and the target regression, together with fully data-driven procedures that adapt to unknown partition structure and transfer strength. We establish sharp minimax rates for target regression estimation, showing that local transfer can mitigate the curse of dimensionality by exploiting reduced functional complexity. Our theoretical guarantees take the form of oracle inequalities that decompose excess risk into estimation and approximation terms, ensuring robustness to model misspecification. Numerical experiments illustrate the benefits of the proposed approach.", "published": "2026-01-02T20:58:05Z", "authors": ["Hélène Halconruy", "Benjamin Bobbia", "Paul Lejamtel"], "categories": ["math.ST", "stat.AP", "stat.ML"], "doi": "", "_query": "hedonic regression housing", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/1806.07690v1", "title": "Non-Parametric Calibration of Probabilistic Regression", "abstract": "The task of calibration is to retrospectively adjust the outputs from a machine learning model to provide better probability estimates on the target variable. While calibration has been investigated thoroughly in classification, it has not yet been well-established for regression tasks. This paper considers the problem of calibrating a probabilistic regression model to improve the estimated probability densities over the real-valued targets. We propose to calibrate a regression model through the cumulative probability density, which can be derived from calibrating a multi-class classifier. We provide three non-parametric approaches to solve the problem, two of which provide empirical estimates and the third providing smooth density estimates. The proposed approaches are experimentally evaluated to show their ability to improve the performance of regression models on the predictive likelihood.", "published": "2018-06-20T12:20:46Z", "authors": ["Hao Song", "Meelis Kull", "Peter Flach"], "categories": ["stat.ML", "cs.LG"], "doi": "", "_query": "hedonic regression housing", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2203.13122v1", "title": "Moving Window Regression: A Novel Approach to Ordinal Regression", "abstract": "A novel ordinal regression algorithm, called moving window regression (MWR), is proposed in this paper. First, we propose the notion of relative rank ($ρ$-rank), which is a new order representation scheme for input and reference instances. Second, we develop global and local relative regressors ($ρ$-regressors) to predict $ρ$-ranks within entire and specific rank ranges, respectively. Third, we refine an initial rank estimate iteratively by selecting two reference instances to form a search window and then estimating the $ρ$-rank within the window. Extensive experiments results show that the proposed algorithm achieves the state-of-the-art performances on various benchmark datasets for facial age estimation and historical color image classification. The codes are available at https://github.com/nhshin-mcl/MWR.", "published": "2022-03-24T15:30:48Z", "authors": ["Nyeong-Ho Shin", "Seon-Ho Lee", "Chang-Su Kim"], "categories": ["cs.CV"], "doi": "", "_query": "hedonic regression housing", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2011.00119v1", "title": "Enveloped Huber Regression", "abstract": "Huber regression (HR) is a popular robust alternative to the least squares regression when the error follows a heavy-tailed distribution. We propose a new method called the enveloped Huber regression (EHR) by considering the envelope assumption that there exists some subspace of the predictors that has no association with the response, which is referred to as the immaterial part. More efficient estimation is achieved via the removal of the immaterial part. Different from the envelope least squares (ENV) model whose estimation is based on maximum normal likelihood, the estimation of the EHR model is through Generalized Method of Moments. The asymptotic normality of the EHR estimator is established, and it is shown that EHR is more efficient than HR. Moreover, EHR is more efficient than ENV when the error distribution is heavy-tailed, while maintaining a small efficiency loss when the error distribution is normal. Moreover, our theory also covers the heteroscedastic case in which the error may depend on the covariates. Extensive simulation studies confirm the messages from the asymptotic theory. EHR is further illustrated on a real dataset.", "published": "2020-10-30T22:03:18Z", "authors": ["Le Zhou", "R. Dennis Cook", "Hui Zou"], "categories": ["stat.ME"], "doi": "", "_query": "hedonic regression housing", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/1910.09699v2", "title": "Soft Tensor Regression", "abstract": "Statistical methods relating tensor predictors to scalar outcomes in a regression model generally vectorize the tensor predictor and estimate the coefficients of its entries employing some form of regularization, use summaries of the tensor covariate, or use a low dimensional approximation of the coefficient tensor. However, low rank approximations of the coefficient tensor can suffer if the true rank is not small. We propose a tensor regression framework which assumes a soft version of the parallel factors (PARAFAC) approximation. In contrast to classic PARAFAC, where each entry of the coefficient tensor is the sum of products of row-specific contributions across the tensor modes, the soft tensor regression (Softer) framework allows the row-specific contributions to vary around an overall mean. We follow a Bayesian approach to inference, and show that softening the PARAFAC increases model flexibility, leads to improved estimation of coefficient tensors, more accurate identification of important predictor entries, and more precise predictions, even for a low approximation rank. From a theoretical perspective, we show that employing Softer leads to a weakly consistent posterior distribution of the coefficient tensor, irrespective of the true or approximation tensor rank, a result that is not true when employing the classic PARAFAC for tensor regression. In the context of our motivating application, we adapt Softer to symmetric and semi-symmetric tensor predictors and analyze the relationship between brain network characteristics and human traits.soft", "published": "2019-10-21T23:56:58Z", "authors": ["Georgia Papadogeorgou", "Zhengwu Zhang", "David B. Dunson"], "categories": ["stat.ME"], "doi": "", "_query": "hedonic regression housing", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/1302.0082v1", "title": "Distribution-Free Distribution Regression", "abstract": "`Distribution regression' refers to the situation where a response Y depends on a covariate P where P is a probability distribution. The model is Y=f(P) + mu where f is an unknown regression function and mu is a random error. Typically, we do not observe P directly, but rather, we observe a sample from P. In this paper we develop theory and methods for distribution-free versions of distribution regression. This means that we do not make distributional assumptions about the error term mu and covariate P. We prove that when the effective dimension is small enough (as measured by the doubling dimension), then the excess prediction risk converges to zero with a polynomial rate.", "published": "2013-02-01T05:35:48Z", "authors": ["Barnabas Poczos", "Alessandro Rinaldo", "Aarti Singh", "Larry Wasserman"], "categories": ["stat.ML", "cs.LG", "math.ST"], "doi": "", "_query": "hedonic regression housing", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/1911.09248v1", "title": "Regression Discontinuity Design under Self-selection", "abstract": "In Regression Discontinuity (RD) design, self-selection leads to different distributions of covariates on two sides of the policy intervention, which essentially violates the continuity of potential outcome assumption. The standard RD estimand becomes difficult to interpret due to the existence of some indirect effect, i.e. the effect due to self selection. We show that the direct causal effect of interest can still be recovered under a class of estimands. Specifically, we consider a class of weighted average treatment effects tailored for potentially different target populations. We show that a special case of our estimands can recover the average treatment effect under the conditional independence assumption per Angrist and Rokkanen (2015), and another example is the estimand recently proposed in Frölich and Huber (2018). We propose a set of estimators through a weighted local linear regression framework and prove the consistency and asymptotic normality of the estimators. Our approach can be further extended to the fuzzy RD case. In simulation exercises, we compare the performance of our estimator with the standard RD estimator. Finally, we apply our method to two empirical data sets: the U.S. House elections data in Lee (2008) and a novel data set from Microsoft Bing on Generalized Second Price (GSP) auction.", "published": "2019-11-21T02:28:59Z", "authors": ["Sida Peng", "Yang Ning"], "categories": ["stat.ME", "econ.EM"], "doi": "", "_query": "hedonic regression housing", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/1307.8270v1", "title": "Applying least absolute deviation regression to regression-type estimation of the index of a stable distribution using the characteristic function", "abstract": "Least absolute deviation regression is applied using a fixed number of points for all values of the index to estimate the index and scale parameter of the stable distribution using regression methods based on the empirical characteristic function. The recognized fixed number of points estimation procedure uses ten points in the interval zero to one, and least squares estimation. It is shown that using the more robust least absolute regression based on iteratively re-weighted least squares outperforms the least squares procedure with respect to bias and also mean square error in smaller samples.", "published": "2013-07-31T10:21:39Z", "authors": ["J. Martin van Zyl"], "categories": ["stat.CO"], "doi": "", "_query": "hedonic regression housing", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/1705.08310v1", "title": "D-vine quantile regression with discrete variables", "abstract": "Quantile regression, the prediction of conditional quantiles, finds applications in various fields. Often, some or all of the variables are discrete. The authors propose two new quantile regression approaches to handle such mixed discrete-continuous data. Both of them generalize the continuous D-vine quantile regression, where the dependence between the response and the covariates is modeled by a parametric D-vine. D-vine quantile regression provides very flexible models, that enable accurate and fast predictions. Moreover, it automatically takes care of major issues of classical quantile regression, such as quantile crossing and interactions between the covariates. The first approach keeps the parametric estimation of the D-vines, but modifies the formulas to account for the discreteness. The second approach estimates the D-vine using continuous convolution to make the discrete variables continuous and then estimates the D-vine nonparametrically. A simulation study is presented examining for which scenarios the discrete-continuous D-vine quantile regression can provide superior prediction abilities. Lastly, the functionality of the two introduced methods is demonstrated by a real-world example predicting the number of bike rentals.", "published": "2017-05-23T14:24:51Z", "authors": ["Niklas Schallhorn", "Daniel Kraus", "Thomas Nagler", "Claudia Czado"], "categories": ["stat.ME"], "doi": "", "_query": "hedonic regression housing", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/1910.10898v2", "title": "On expectile-assisted inverse regression estimation for sufficient dimension reduction", "abstract": "Moment-based sufficient dimension reduction methods such as sliced inverse regression may not work well in the presence of heteroscedasticity. We propose to first estimate the expectiles through kernel expectile regression, and then carry out dimension reduction based on random projections of the regression expectiles. Several popular inverse regression methods in the literature are extended under this general framework. The proposed expectile-assisted methods outperform existing moment-based dimension reduction methods in both numerical studies and an analysis of the Big Mac data.", "published": "2019-10-24T03:22:18Z", "authors": ["Abdul-Nasah Soale", "Yuexiao Dong"], "categories": ["stat.CO", "stat.ME"], "doi": "", "_query": "hedonic regression housing", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2208.10910v3", "title": "A flexible empirical Bayes approach to multiple linear regression and connections with penalized regression", "abstract": "We introduce a new empirical Bayes approach for large-scale multiple linear regression. Our approach combines two key ideas: (i) the use of flexible \"adaptive shrinkage\" priors, which approximate the nonparametric family of scale mixture of normal distributions by a finite mixture of normal distributions; and (ii) the use of variational approximations to efficiently estimate prior hyperparameters and compute approximate posteriors. Combining these two ideas results in fast and flexible methods, with computational speed comparable to fast penalized regression methods such as the Lasso, and with competitive prediction accuracy across a wide range of scenarios. Further, we provide new results that establish conceptual connections between our empirical Bayes methods and penalized methods. Specifically, we show that the posterior mean from our method solves a penalized regression problem, with the form of the penalty function being learned from the data by directly solving an optimization problem (rather than being tuned by cross-validation). Our methods are implemented in an R package, mr.ash.alpha, available from https://github.com/stephenslab/mr.ash.alpha.", "published": "2022-08-23T12:42:57Z", "authors": ["Youngseok Kim", "Wei Wang", "Peter Carbonetto", "Matthew Stephens"], "categories": ["stat.ME", "stat.ML"], "doi": "", "_query": "hedonic regression housing", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/1110.4411v1", "title": "Gaussian Process Regression Networks", "abstract": "We introduce a new regression framework, Gaussian process regression networks (GPRN), which combines the structural properties of Bayesian neural networks with the non-parametric flexibility of Gaussian processes. This model accommodates input dependent signal and noise correlations between multiple response variables, input dependent length-scales and amplitudes, and heavy-tailed predictive distributions. We derive both efficient Markov chain Monte Carlo and variational Bayes inference procedures for this model. We apply GPRN as a multiple output regression and multivariate volatility model, demonstrating substantially improved performance over eight popular multiple output (multi-task) Gaussian process models and three multivariate volatility models on benchmark datasets, including a 1000 dimensional gene expression dataset.", "published": "2011-10-19T22:18:03Z", "authors": ["Andrew Gordon Wilson", "David A. Knowles", "Zoubin Ghahramani"], "categories": ["stat.ML", "q-fin.ST", "stat.ME"], "doi": "", "_query": "hedonic regression housing", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2106.06427v1", "title": "Neural Symbolic Regression that Scales", "abstract": "Symbolic equations are at the core of scientific discovery. The task of discovering the underlying equation from a set of input-output pairs is called symbolic regression. Traditionally, symbolic regression methods use hand-designed strategies that do not improve with experience. In this paper, we introduce the first symbolic regression method that leverages large scale pre-training. We procedurally generate an unbounded set of equations, and simultaneously pre-train a Transformer to predict the symbolic equation from a corresponding set of input-output-pairs. At test time, we query the model on a new set of points and use its output to guide the search for the equation. We show empirically that this approach can re-discover a set of well-known physical equations, and that it improves over time with more data and compute.", "published": "2021-06-11T14:35:22Z", "authors": ["Luca Biggio", "Tommaso Bendinelli", "Alexander Neitz", "Aurelien Lucchi", "Giambattista Parascandolo"], "categories": ["cs.LG"], "doi": "", "_query": "hedonic regression housing", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2306.00269v1", "title": "Best $L_p$ Isotonic Regressions, $p \\in \\{0, 1, \\infty\\}$", "abstract": "Given a real-valued weighted function $f$ on a finite dag, the $L_p$ isotonic regression of $f$, $p \\in [0,\\infty]$, is unique except when $p \\in [0,1] \\cup \\{\\infty\\}$. We are interested in determining a ``best'' isotonic regression for $p \\in \\{0, 1, \\infty\\}$, where by best we mean a regression satisfying stronger properties than merely having minimal norm. One approach is to use strict $L_p$ regression, which is the limit of the best $L_q$ approximation as $q$ approaches $p$, and another is lex regression, which is based on lexical ordering of regression errors. For $L_\\infty$ the strict and lex regressions are unique and the same. For $L_1$, strict $q \\scriptstyle\\searrow 1$ is unique, but we show that $q \\scriptstyle\\nearrow 1$ may not be, and even when it is unique the two limits may not be the same. For $L_0$, in general neither of the strict and lex regressions are unique, nor do they always have the same set of optimal regressions, but by expanding the objectives of $L_p$ optimization to $p < 0$ we show $p{ \\scriptstyle \\nearrow} 0$ is the same as lex regression. We also give algorithms for computing the best $L_p$ isotonic regression in certain situations.", "published": "2023-06-01T01:08:51Z", "authors": ["Quentin F. Stout"], "categories": ["cs.DM", "cs.DS", "math.ST"], "doi": "", "_query": "hedonic regression housing", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/1806.02326v2", "title": "Conditional Linear Regression", "abstract": "Work in machine learning and statistics commonly focuses on building models that capture the vast majority of data, possibly ignoring a segment of the population as outliers. However, there does not often exist a good model on the whole dataset, so we seek to find a small subset where there exists a useful model. We are interested in finding a linear rule capable of achieving more accurate predictions for just a segment of the population. We give an efficient algorithm with theoretical analysis for the conditional linear regression task, which is the joint task of identifying a significant segment of the population, described by a k-DNF, along with its linear regression fit.", "published": "2018-06-06T17:46:30Z", "authors": ["Diego Calderon", "Brendan Juba", "Sirui Li", "Zongyi Li", "Lisa Ruan"], "categories": ["cs.LG", "cs.DS", "stat.ML"], "doi": "", "_query": "hedonic regression housing", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/1909.02623v1", "title": "A Bayesian Approach to Multiple-Output Quantile Regression", "abstract": "This paper presents a Bayesian approach to multiple-output quantile regression. The unconditional model is proven to be consistent and asymptotically correct frequentist confidence intervals can be obtained. The prior for the unconditional model can be elicited as the ex-ante knowledge of the distance of the tau-Tukey depth contour to the Tukey median, the first prior of its kind. A proposal for conditional regression is also presented. The model is applied to the Tennessee Project Steps to Achieving Resilience (STAR) experiment and it finds a joint increase in tau-quantile subpopulations for mathematics and reading scores given a decrease in the number of students per teacher. This result is consistent with, and much stronger than, the result one would find with multiple-output linear regression. Multiple-output linear regression finds the average mathematics and reading scores increase given a decrease in the number of students per teacher. However, there could still be subpopulations where the score declines. The multiple-output quantile regression approach confirms there are no quantile subpopulations (of the inspected subpopulations) where the score declines. This is truly a statement of `no child left behind' opposed to `no average child left behind.'", "published": "2019-09-05T20:27:34Z", "authors": ["Michael Guggisberg"], "categories": ["stat.ME"], "doi": "10.1080/01621459.2022.2075369", "_query": "hedonic regression housing", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2312.00656v2", "title": "Simple Transferability Estimation for Regression Tasks", "abstract": "We consider transferability estimation, the problem of estimating how well deep learning models transfer from a source to a target task. We focus on regression tasks, which received little previous attention, and propose two simple and computationally efficient approaches that estimate transferability based on the negative regularized mean squared error of a linear regression model. We prove novel theoretical results connecting our approaches to the actual transferability of the optimal target models obtained from the transfer learning process. Despite their simplicity, our approaches significantly outperform existing state-of-the-art regression transferability estimators in both accuracy and efficiency. On two large-scale keypoint regression benchmarks, our approaches yield 12% to 36% better results on average while being at least 27% faster than previous state-of-the-art methods.", "published": "2023-12-01T15:30:54Z", "authors": ["Cuong N. Nguyen", "Phong Tran", "Lam Si Tung Ho", "Vu Dinh", "Anh T. Tran", "Tal Hassner", "Cuong V. Nguyen"], "categories": ["cs.LG", "cs.AI", "stat.ML"], "doi": "", "_query": "hedonic regression housing", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2402.06921v1", "title": "Clustering Techniques Selection for a Hybrid Regression Model: A Case Study Based on a Solar Thermal System", "abstract": "This work addresses the performance comparison between four clustering techniques with the objective of achieving strong hybrid models in supervised learning tasks. A real dataset from a bio-climatic house named Sotavento placed on experimental wind farm and located in Xermade (Lugo) in Galicia (Spain) has been collected. Authors have chosen the thermal solar generation system in order to study how works applying several cluster methods followed by a regression technique to predict the output temperature of the system. With the objective of defining the quality of each clustering method two possible solutions have been implemented. The first one is based on three unsupervised learning metrics (Silhouette, Calinski-Harabasz and Davies-Bouldin) while the second one, employs the most common error measurements for a regression algorithm such as Multi Layer Perceptron.", "published": "2024-02-10T10:10:11Z", "authors": ["María Teresa García-Ordás", "Héctor Alaiz-Moretón", "José-Luis Casteleiro-Roca", "Esteban Jove", "José Alberto Benítez-Andrades", "Isaías García-Rodríguez", "Héctor Quintián", "José Luis Calvo-Rolle"], "categories": ["cs.LG", "cs.MA"], "doi": "10.1080/01969722.2022.2030006", "_query": "hedonic regression housing", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2210.03342v1", "title": "Impact of COVID-19 Lockdowns on House Sparrows: Comparative Study from an Indian Context", "abstract": "Due to the outbreak of COVID-19, the last couple of years have been drastic in terms of human behavioral patterns. People were forced to stay at home for a very long duration because of the strict lockdown measures imposed by governments all over the globe. India was no exception, wherein the Indian government imposed several very strict lockdowns all across the country, which restricted human activities and their social behaviors. However, such restrictions were seen to have a positive impact on environment and ecology. In this paper, we aim to study the changes in House Sparrow sightings, as a result of the lockdowns. Are the house sparrows back? is the question we try to answer, using appropriate exploratory analysis and statistical modelling.", "published": "2022-10-07T06:13:46Z", "authors": ["Sudeep R. Bapat", "Sreeranjini T. M."], "categories": ["stat.AP", "stat.CO"], "doi": "", "_query": "hedonic regression housing", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/1602.06863v1", "title": "Higher-Order Low-Rank Regression", "abstract": "This paper proposes an efficient algorithm (HOLRR) to handle regression tasks where the outputs have a tensor structure. We formulate the regression problem as the minimization of a least square criterion under a multilinear rank constraint, a difficult non convex problem. HOLRR computes efficiently an approximate solution of this problem, with solid theoretical guarantees. A kernel extension is also presented. Experiments on synthetic and real data show that HOLRR outperforms multivariate and multilinear regression methods and is considerably faster than existing tensor methods.", "published": "2016-02-22T17:21:11Z", "authors": ["Guillaume Rabusseau", "Hachem Kadri"], "categories": ["cs.LG"], "doi": "", "_query": "hedonic regression housing", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/1906.04242v2", "title": "The Regression Discontinuity Design", "abstract": "This handbook chapter gives an introduction to the sharp regression discontinuity design, covering identification, estimation, inference, and falsification methods.", "published": "2019-06-10T19:20:25Z", "authors": ["Matias D. Cattaneo", "Rocio Titiunik", "Gonzalo Vazquez-Bare"], "categories": ["econ.EM", "stat.AP", "stat.ME"], "doi": "10.4135/9781526486387.n47", "_query": "hedonic regression housing", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2102.13518v2", "title": "Cholesky-based multivariate Gaussian regression", "abstract": "Distributional regression is extended to Gaussian response vectors of dimension greater than two by parameterizing the covariance matrix $Σ$ of the response distribution using the entries of its Cholesky decomposition. The more common variance-correlation parameterization limits such regressions to bivariate responses -- higher dimensions require complicated constraints among the correlations to ensure positive definite $Σ$ and a well-defined probability density function. In contrast, Cholesky-based parameterizations ensure positive definiteness for all distributional dimensions no matter what values the parameters take, enabling estimation and regularization as for other distributional regression models. In cases where components of the response vector are assumed to be conditionally independent beyond a certain lag $r$, model complexity can be further reduced by setting Cholesky parameters beyond this lag to zero a priori. Cholesky-based multivariate Gaussian regression is first illustrated and assessed on artificial data and subsequently applied to a real-world 10-dimensional weather forecasting problem. There the regression is used to obtain reliable joint probabilities of temperature across ten future times, leveraging temporal correlations over the prediction period to obtain more precise and meteorologically consistent probabilistic forecasts.", "published": "2021-02-26T14:46:35Z", "authors": ["Thomas Muschinski", "Georg J. Mayr", "Thorsten Simon", "Nikolaus Umlauf", "Achim Zeileis"], "categories": ["stat.ME", "stat.CO"], "doi": "10.1016/j.ecosta.2022.03.001", "_query": "hedonic regression housing", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2205.09515v2", "title": "Variational Inference for Bayesian Bridge Regression", "abstract": "We study the implementation of Automatic Differentiation Variational inference (ADVI) for Bayesian inference on regression models with bridge penalization. The bridge approach uses $\\ell_α$ norm, with $α\\in (0, +\\infty)$ to define a penalization on large values of the regression coefficients, which includes the Lasso ($α= 1$) and ridge $(α= 2)$ penalizations as special cases. Full Bayesian inference seamlessly provides joint uncertainty estimates for all model parameters. Although MCMC aproaches are available for bridge regression, it can be slow for large dataset, specially in high dimensions. The ADVI implementation allows the use of small batches of data at each iteration (due to stochastic gradient based algorithms), therefore speeding up computational time in comparison with MCMC. We illustrate the approach on non-parametric regression models with B-splines, although the method works seamlessly for other choices of basis functions. A simulation study shows the main properties of the proposed method.", "published": "2022-05-19T12:29:09Z", "authors": ["Carlos Tadeu Pagani Zanini", "Helio dos Santos Migon", "Ronaldo Dias"], "categories": ["stat.ML", "cs.LG", "stat.CO", "stat.ME"], "doi": "", "_query": "hedonic regression housing", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2105.10373v1", "title": "A Precise Performance Analysis of Support Vector Regression", "abstract": "In this paper, we study the hard and soft support vector regression techniques applied to a set of $n$ linear measurements of the form $y_i=\\boldsymbolβ_\\star^{T}{\\bf x}_i +n_i$ where $\\boldsymbolβ_\\star$ is an unknown vector, $\\left\\{{\\bf x}_i\\right\\}_{i=1}^n$ are the feature vectors and $\\left\\{{n}_i\\right\\}_{i=1}^n$ model the noise. Particularly, under some plausible assumptions on the statistical distribution of the data, we characterize the feasibility condition for the hard support vector regression in the regime of high dimensions and, when feasible, derive an asymptotic approximation for its risk. Similarly, we study the test risk for the soft support vector regression as a function of its parameters. Our results are then used to optimally tune the parameters intervening in the design of hard and soft support vector regression algorithms. Based on our analysis, we illustrate that adding more samples may be harmful to the test performance of support vector regression, while it is always beneficial when the parameters are optimally selected. Such a result reminds a similar phenomenon observed in modern learning architectures according to which optimally tuned architectures present a decreasing test performance curve with respect to the number of samples.", "published": "2021-05-21T14:26:28Z", "authors": ["Houssem Sifaou", "Abla kammoun", "Mohamed-Slim Alouini"], "categories": ["cs.LG"], "doi": "", "_query": "hedonic regression housing", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2105.11425v1", "title": "Uncertainty quantification for distributed regression", "abstract": "The ever-growing size of the datasets renders well-studied learning techniques, such as Kernel Ridge Regression, inapplicable, posing a serious computational challenge. Divide-and-conquer is a common remedy, suggesting to split the dataset into disjoint partitions, obtain the local estimates and average them, it allows to scale-up an otherwise ineffective base approach. In the current study we suggest a fully data-driven approach to quantify uncertainty of the averaged estimator. Namely, we construct simultaneous element-wise confidence bands for the predictions yielded by the averaged estimator on a given deterministic prediction set. The novel approach features rigorous theoretical guaranties for a wide class of base learners with Kernel Ridge regression being a special case. As a by-product of our analysis we also obtain a sup-norm consistency result for the divide-and-conquer Kernel Ridge Regression. The simulation study supports the theoretical findings.", "published": "2021-05-24T17:33:19Z", "authors": ["Valeriy Avanesov"], "categories": ["stat.ML", "cs.LG"], "doi": "", "_query": "hedonic regression housing", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/1906.08343v1", "title": "Optimal designs for estimating individual coefficients in polynomial regression with no intercept", "abstract": "In a seminal paper \\cite{studden1968} characterized $c$-optimal designs in regression models, where the regression functions form a Chebyshev system. He used these results to determine the optimal design for estimating the individual coefficients in a polynomial regression model on the interval $[-1,1]$ explicitly. In this note we identify the optimal design for estimating the individual coefficients in a polynomial regression model with no intercept (here the regression functions do not form a Chebyshev system).", "published": "2019-06-19T20:32:31Z", "authors": ["Holger Dette", "Viatcheslav B. Melas", "Petr Shpilev"], "categories": ["math.ST"], "doi": "", "_query": "hedonic regression housing", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/0803.3757v1", "title": "On regression adjustments in experiments with several treatments", "abstract": "Regression adjustments are often made to experimental data. Since randomization does not justify the models, bias is likely; nor are the usual variance calculations to be trusted. Here, we evaluate regression adjustments using Neyman's nonparametric model. Previous results are generalized, and more intuitive proofs are given. A bias term is isolated, and conditions are given for unbiased estimation in finite samples.", "published": "2008-03-26T15:54:26Z", "authors": ["David A. Freedman"], "categories": ["stat.AP"], "doi": "10.1214/07-AOAS143", "_query": "hedonic regression housing", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2401.06347v1", "title": "Diagnostics for Regression Models with Semicontinuous Outcomes", "abstract": "Semicontinuous outcomes commonly arise in a wide variety of fields, such as insurance claims, healthcare expenditures, rainfall amounts, and alcohol consumption. Regression models, including Tobit, Tweedie, and two-part models, are widely employed to understand the relationship between semicontinuous outcomes and covariates. Given the potential detrimental consequences of model misspecification, after fitting a regression model, it is of prime importance to check the adequacy of the model. However, due to the point mass at zero, standard diagnostic tools for regression models (e.g., deviance and Pearson residuals) are not informative for semicontinuous data. To bridge this gap, we propose a new type of residuals for semicontinuous outcomes that are applicable to general regression models. Under the correctly specified model, the proposed residuals converge to being uniformly distributed, and when the model is misspecified, they significantly depart from this pattern. In addition to in-sample validation, the proposed methodology can also be employed to evaluate predictive distributions. We demonstrate the effectiveness of the proposed tool using health expenditure data from the US Medical Expenditure Panel Survey.", "published": "2024-01-12T03:49:23Z", "authors": ["Lu Yang"], "categories": ["stat.ME", "stat.AP"], "doi": "10.1093/biomtc/ujae007", "_query": "hedonic regression housing", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/1510.08877v1", "title": "Regression for citation data: An evaluation of different methods", "abstract": "Citations are increasingly used for research evaluations. It is therefore important to identify factors affecting citation scores that are unrelated to scholarly quality or usefulness so that these can be taken into account. Regression is the most powerful statistical technique to identify these factors and hence it is important to identify the best regression strategy for citation data. Citation counts tend to follow a discrete lognormal distribution and, in the absence of alternatives, have been investigated with negative binomial regression. Using simulated discrete lognormal data (continuous lognormal data rounded to the nearest integer) this article shows that a better strategy is to add one to the citations, take their log and then use the general linear (ordinary least squares) model for regression (e.g., multiple linear regression, ANOVA), or to use the generalised linear model without the log. Reasonable results can also be obtained if all the zero citations are discarded, the log is taken of the remaining citation counts and then the general linear model is used, or if the generalised linear model is used with the continuous lognormal distribution. Similar approaches are recommended for altmetric data, if it proves to be lognormally distributed.", "published": "2015-10-29T20:17:11Z", "authors": ["Mike Thelwall", "Paul Wilson"], "categories": ["cs.DL"], "doi": "10.1016/j.joi.2014.09.011", "_query": "hedonic regression housing", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2110.07998v2", "title": "Fast Partial Quantile Regression", "abstract": "Partial least squares (PLS) is a dimensionality reduction technique used as an alternative to ordinary least squares (OLS) in situations where the data is colinear or high dimensional. Both PLS and OLS provide mean based estimates, which are extremely sensitive to the presence of outliers or heavy tailed distributions. In contrast, quantile regression is an alternative to OLS that computes robust quantile based estimates. In this work, the multivariate PLS is extended to the quantile regression framework, obtaining a theoretical formulation of the problem and a robust dimensionality reduction technique that we call fast partial quantile regression (fPQR), that provides quantile based estimates. An efficient implementation of fPQR is also derived, and its performance is studied through simulation experiments and the chemometrics well known biscuit dough dataset, a real high dimensional example.", "published": "2021-10-15T10:43:08Z", "authors": ["Alvaro Mendez Civieta", "M. Carmen Aguilera-Morillo", "Rosa E. Lillo"], "categories": ["stat.ME", "stat.CO"], "doi": "10.1016/j.chemolab.2022.104533", "_query": "hedonic regression housing", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2002.08113v1", "title": "Linear Regression Models in Epidemiology", "abstract": "The paper proposes to analyze epidemiological data using regression models which enable subject-matter (epidemiological) interpretation of such data whether with uncorrelated or correlated predictors. To this end, response functions should include not only terms linear in predictors but also higher order ones (e.g. quadratic and cross terms). For epidemiological interpretation of a regression model, the suggestion is to construct conditional functions derived from the general regression function with the values of all predictor variables held fixed excepting one predictor. Unlike the conventional techniques based on linear-predictor models in which the coefficient at any variable is interpreted, our approach proposes to interpret this conditional function, which is multivariate for any predictor being dependent on the values of all the other predictors. It is such functions that can describe relationships between Y and a predictor that have different forms in different predictor domains. The paper discusses differences in the interpretation of the proposed conditional functions between cases involving correlated and uncorrelated predictor variables. The construction and analysis of regression models for epidemiological and environmental data are illustrated with examples.", "published": "2020-02-19T11:34:09Z", "authors": ["Anatoly N. Varaksin", "Vladimir G. Panov"], "categories": ["stat.AP", "stat.ME"], "doi": "", "_query": "hedonic regression housing", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2103.13629v1", "title": "Learning Probabilistic Ordinal Embeddings for Uncertainty-Aware Regression", "abstract": "Uncertainty is the only certainty there is. Modeling data uncertainty is essential for regression, especially in unconstrained settings. Traditionally the direct regression formulation is considered and the uncertainty is modeled by modifying the output space to a certain family of probabilistic distributions. On the other hand, classification based regression and ranking based solutions are more popular in practice while the direct regression methods suffer from the limited performance. How to model the uncertainty within the present-day technologies for regression remains an open issue. In this paper, we propose to learn probabilistic ordinal embeddings which represent each data as a multivariate Gaussian distribution rather than a deterministic point in the latent space. An ordinal distribution constraint is proposed to exploit the ordinal nature of regression. Our probabilistic ordinal embeddings can be integrated into popular regression approaches and empower them with the ability of uncertainty estimation. Experimental results show that our approach achieves competitive performance. Code is available at https://github.com/Li-Wanhua/POEs.", "published": "2021-03-25T06:56:09Z", "authors": ["Wanhua Li", "Xiaoke Huang", "Jiwen Lu", "Jianjiang Feng", "Jie Zhou"], "categories": ["cs.CV", "cs.LG"], "doi": "", "_query": "hedonic regression housing", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2603.00269v3", "title": "Robust Regression with Student's T: The Role of Degrees of Freedom", "abstract": "Linear regression estimators are known to be sensitive to outliers, and one alternative to obtain a robust and efficient estimator of the regression parameter is to model the error with Student's $t$ distribution. In this article, we compare estimators of the degrees of freedom parameter in the $t$ distribution using frequentist and Bayesian methods, and then study properties of the corresponding estimated regression coefficient. We also include the comparison with some recommended approaches in the literature, including fixing the degrees of freedom and robust regression using the Huber loss. Our extensive simulations on both synthetic and real data demonstrate that estimating the degrees of freedom via the adjusted profile log-likelihood approach yields regression coefficient estimators with high accuracy, performing comparably to the maximum likelihood estimators where the degrees of freedom are fixed at their true values. These findings provide a detailed synthesis of $t$-based robust regression and underscore a key insight: the proper calibration of the degrees of freedom is as crucial as the choice of the robust distribution itself for achieving optimal performance. The {\\tt R} package that implements our method is available at https://github.com/amanda-ng518/RobustTRegression.", "published": "2026-02-27T19:37:38Z", "authors": ["Amanda Ng", "Shangkai Zhu", "Archer Gong Zhang", "Nancy Reid"], "categories": ["stat.ME"], "doi": "", "_query": "hedonic regression housing", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/1312.0518v1", "title": "Families of Parsimonious Finite Mixtures of Regression Models", "abstract": "Finite mixtures of regression models offer a flexible framework for investigating heterogeneity in data with functional dependencies. These models can be conveniently used for unsupervised learning on data with clear regression relationships. We extend such models by imposing an eigen-decomposition on the multivariate error covariance matrix. By constraining parts of this decomposition, we obtain families of parsimonious mixtures of regressions and mixtures of regressions with concomitant variables. These families of models account for correlations between multiple responses. An expectation-maximization algorithm is presented for parameter estimation and performance is illustrated on simulated and real data.", "published": "2013-12-02T17:08:03Z", "authors": ["Utkarsh J. Dang", "Paul D. McNicholas"], "categories": ["stat.ME", "stat.CO", "stat.ML"], "doi": "", "_query": "hedonic regression housing", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2308.10603v1", "title": "A step towards understanding why classification helps regression", "abstract": "A number of computer vision deep regression approaches report improved results when adding a classification loss to the regression loss. Here, we explore why this is useful in practice and when it is beneficial. To do so, we start from precisely controlled dataset variations and data samplings and find that the effect of adding a classification loss is the most pronounced for regression with imbalanced data. We explain these empirical findings by formalizing the relation between the balanced and imbalanced regression losses. Finally, we show that our findings hold on two real imbalanced image datasets for depth estimation (NYUD2-DIR), and age estimation (IMDB-WIKI-DIR), and on the problem of imbalanced video progress prediction (Breakfast). Our main takeaway is: for a regression task, if the data sampling is imbalanced, then add a classification loss.", "published": "2023-08-21T10:00:46Z", "authors": ["Silvia L. Pintea", "Yancong Lin", "Jouke Dijkstra", "Jan C. van Gemert"], "categories": ["cs.CV"], "doi": "", "_query": "hedonic regression housing", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/1612.02203v2", "title": "A Functional Regression approach to Facial Landmark Tracking", "abstract": "Linear regression is a fundamental building block in many face detection and tracking algorithms, typically used to predict shape displacements from image features through a linear mapping. This paper presents a Functional Regression solution to the least squares problem, which we coin Continuous Regression, resulting in the first real-time incremental face tracker. Contrary to prior work in Functional Regression, in which B-splines or Fourier series were used, we propose to approximate the input space by its first-order Taylor expansion, yielding a closed-form solution for the continuous domain of displacements. We then extend the continuous least squares problem to correlated variables, and demonstrate the generalisation of our approach. We incorporate Continuous Regression into the cascaded regression framework, and show its computational benefits for both training and testing. We then present a fast approach for incremental learning within Cascaded Continuous Regression, coined iCCR, and show that its complexity allows real-time face tracking, being 20 times faster than the state of the art. To the best of our knowledge, this is the first incremental face tracker that is shown to operate in real-time. We show that iCCR achieves state-of-the-art performance on the 300-VW dataset, the most recent, large-scale benchmark for face tracking.", "published": "2016-12-07T11:34:36Z", "authors": ["Enrique Sánchez-Lozano", "Georgios Tzimiropoulos", "Brais Martinez", "Fernando De la Torre", "Michel Valstar"], "categories": ["cs.CV"], "doi": "10.1109/TPAMI.2017.2745568", "_query": "hedonic regression housing", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2205.00413v1", "title": "Smoothed quantile regression for censored residual life", "abstract": "We consider a regression modeling of the quantiles of residual life, remaining lifetime at a specific time. We propose a smoothed induced version of the existing non-smooth estimating equations approaches for estimating regression parameters. The proposed estimating equations are smooth in regression parameters, so solutions can be readily obtained via standard numerical algorithms. Moreover, the smoothness in the proposed estimating equations enables one to obtain a robust sandwich-type covariance estimator of regression estimators aided by an efficient resampling method. To handle data subject to right censoring, the inverse probability of censoring weight are used as weights. The consistency and asymptotic normality of the proposed estimator are established. Extensive simulation studies are conducted to validate the proposed estimator's performance in various finite samples settings. We apply the proposed method to dental study data evaluating the longevity of dental restorations.", "published": "2022-05-01T07:32:33Z", "authors": ["Kyu Hyun Kim", "Daniel J. Caplan", "Sangwook Kang"], "categories": ["stat.CO"], "doi": "", "_query": "hedonic regression housing", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2010.12842v2", "title": "Stochastic Gradient Descent Meets Distribution Regression", "abstract": "Stochastic gradient descent (SGD) provides a simple and efficient way to solve a broad range of machine learning problems. Here, we focus on distribution regression (DR), involving two stages of sampling: Firstly, we regress from probability measures to real-valued responses. Secondly, we sample bags from these distributions for utilizing them to solve the overall regression problem. Recently, DR has been tackled by applying kernel ridge regression and the learning properties of this approach are well understood. However, nothing is known about the learning properties of SGD for two stage sampling problems. We fill this gap and provide theoretical guarantees for the performance of SGD for DR. Our bounds are optimal in a mini-max sense under standard assumptions.", "published": "2020-10-24T09:03:00Z", "authors": ["Nicole Mücke"], "categories": ["stat.ML", "cs.LG"], "doi": "", "_query": "hedonic regression housing", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/1906.00232v6", "title": "Kernel Instrumental Variable Regression", "abstract": "Instrumental variable (IV) regression is a strategy for learning causal relationships in observational data. If measurements of input X and output Y are confounded, the causal relationship can nonetheless be identified if an instrumental variable Z is available that influences X directly, but is conditionally independent of Y given X and the unmeasured confounder. The classic two-stage least squares algorithm (2SLS) simplifies the estimation problem by modeling all relationships as linear functions. We propose kernel instrumental variable regression (KIV), a nonparametric generalization of 2SLS, modeling relations among X, Y, and Z as nonlinear functions in reproducing kernel Hilbert spaces (RKHSs). We prove the consistency of KIV under mild assumptions, and derive conditions under which convergence occurs at the minimax optimal rate for unconfounded, single-stage RKHS regression. In doing so, we obtain an efficient ratio between training sample sizes used in the algorithm's first and second stages. In experiments, KIV outperforms state of the art alternatives for nonparametric IV regression.", "published": "2019-06-01T14:30:03Z", "authors": ["Rahul Singh", "Maneesh Sahani", "Arthur Gretton"], "categories": ["cs.LG", "econ.EM", "math.FA", "math.ST", "stat.ML"], "doi": "", "_query": "hedonic regression housing", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/1802.03127v1", "title": "Robust and Sparse Regression in GLM by Stochastic Optimization", "abstract": "The generalized linear model (GLM) plays a key role in regression analyses. In high-dimensional data, the sparse GLM has been used but it is not robust against outliers. Recently, the robust methods have been proposed for the specific example of the sparse GLM. Among them, we focus on the robust and sparse linear regression based on the $γ$-divergence. The estimator of the $γ$-divergence has strong robustness under heavy contamination. In this paper, we extend the robust and sparse linear regression based on the $γ$-divergence to the robust and sparse GLM based on the $γ$-divergence with a stochastic optimization approach in order to obtain the estimate. We adopt the randomized stochastic projected gradient descent as a stochastic optimization approach and extend the established convergence property to the classical first-order necessary condition. By virtue of the stochastic optimization approach, we can efficiently estimate parameters for very large problems. Particularly, we show the linear regression, logistic regression and Poisson regression with $L_1$ regularization in detail as specific examples of robust and sparse GLM. In numerical experiments and real data analysis, the proposed method outperformed comparative methods.", "published": "2018-02-09T04:51:50Z", "authors": ["Takayuki Kawashima", "Hironori Fujisawa"], "categories": ["stat.ML", "stat.ME"], "doi": "10.1007/s42081-019-00049-9", "_query": "hedonic regression housing", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2411.15326v1", "title": "Scalar-on-Shape Regression Models for Functional Data Analysis", "abstract": "Functional data contains two components: shape (or amplitude) and phase. This paper focuses on a branch of functional data analysis (FDA), namely Shape-Based FDA, that isolates and focuses on shapes of functions. Specifically, this paper focuses on Scalar-on-Shape (ScoSh) regression models that incorporate the shapes of predictor functions and discard their phases. This aspect sets ScoSh models apart from the traditional Scalar-on-Function (ScoF) regression models that incorporate full predictor functions. ScoSh is motivated by object data analysis, {\\it, e.g.}, for neuro-anatomical objects, where object morphologies are relevant and their parameterizations are arbitrary. ScoSh also differs from methods that arbitrarily pre-register data and uses it in subsequent analysis. In contrast, ScoSh models perform registration during regression, using the (non-parametric) Fisher-Rao inner product and nonlinear index functions to capture complex predictor-response relationships. This formulation results in novel concepts of {\\it regression phase} and {\\it regression mean} of functions. Regression phases are time-warpings of predictor functions that optimize prediction errors, and regression means are optimal regression coefficients. We demonstrate practical applications of the ScoSh model using extensive simulated and real-data examples, including predicting COVID outcomes when daily rate curves are predictors.", "published": "2024-11-22T19:38:24Z", "authors": ["Sayan Bhadra", "Anuj Srivastava"], "categories": ["stat.ME"], "doi": "", "_query": "hedonic regression housing", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2309.04746v1", "title": "Global quantile regression", "abstract": "Quantile regression is used to study effects of covariates on a particular quantile of the data distribution. Here we are interested in the question whether a covariate has any effect on the entire data distribution, i.e., on any of the quantiles. To this end, we treat all the quantiles simultaneously and consider global tests for the existence of the covariate effect in the presence of nuisance covariates. This global quantile regression can be used as the extension of linear regression or as the extension of distribution comparison in the sense of Kolmogorov-Smirnov test. The proposed method is based on pointwise coefficients, permutations and global envelope tests. The global envelope test serves as the multiple test adjustment procedure under the control of the family-wise error rate and provides the graphical interpretation which automatically shows the quantiles or the levels of categorical covariate responsible for the rejection. The Freedman-Lane permutation strategy showed liberality of the test for extreme quantiles, therefore we propose four alternatives that work well even for extreme quantiles and are suitable in different conditions. We present a simulation study to inspect the performance of these strategies, and we apply the chosen strategies to two data examples.", "published": "2023-09-09T10:27:28Z", "authors": ["Tomáš Mrkvička", "Konstantinos Konstantinou", "Mikko Kuronen", "Mari Myllymäki"], "categories": ["stat.ME"], "doi": "10.1007/s11222-025-10774-9", "_query": "hedonic regression housing", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/cs/9912013v1", "title": "Multivariate Regression Depth", "abstract": "The regression depth of a hyperplane with respect to a set of n points in R^d is the minimum number of points the hyperplane must pass through in a rotation to vertical. We generalize hyperplane regression depth to k-flats for any k between 0 and d-1. The k=0 case gives the classical notion of center points. We prove that for any k and d, deep k-flats exist, that is, for any set of n points there always exists a k-flat with depth at least a constant fraction of n. As a consequence, we derive a linear-time (1+epsilon)-approximation algorithm for the deepest flat.", "published": "1999-12-20T21:50:14Z", "authors": ["Marshall Bern", "David Eppstein"], "categories": ["cs.CG", "math.CO"], "doi": "10.1007/s00454-001-0092-1", "_query": "hedonic regression housing", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2107.04831v2", "title": "Cluster Regularization via a Hierarchical Feature Regression", "abstract": "This paper proposes a novel graph-based regularized regression estimator - the hierarchical feature regression (HFR) -, which mobilizes insights from the domains of machine learning and graph theory to estimate robust parameters for a linear regression. The estimator constructs a supervised feature graph that decomposes parameters along its edges, adjusting first for common variation and successively incorporating idiosyncratic patterns into the fitting process. The graph structure has the effect of shrinking parameters towards group targets, where the extent of shrinkage is governed by a hyperparamter, and group compositions as well as shrinkage targets are determined endogenously. The method offers rich resources for the visual exploration of the latent effect structure in the data, and demonstrates good predictive accuracy and versatility when compared to a panel of commonly used regularization techniques across a range of empirical and simulated regression tasks.", "published": "2021-07-10T13:03:01Z", "authors": ["Johann Pfitzinger"], "categories": ["stat.ML", "cs.LG"], "doi": "", "_query": "hedonic regression housing", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/1805.11456v1", "title": "Elastic Functional Principal Component Regression", "abstract": "We study regression using functional predictors in situations where these functions contain both phase and amplitude variability. In other words, the functions are misaligned due to errors in time measurements, and these errors can significantly degrade both model estimation and prediction performance. The current techniques either ignore the phase variability, or handle it via pre-processing, i.e., use an off-the-shelf technique for functional alignment and phase removal. We develop a functional principal component regression model which has comprehensive approach in handling phase and amplitude variability. The model utilizes a mathematical representation of the data known as the square-root slope function. These functions preserve the $\\mathbf{L}^2$ norm under warping and are ideally suited for simultaneous estimation of regression and warping parameters. Using both simulated and real-world data sets, we demonstrate our approach and evaluate its prediction performance relative to current models. In addition, we propose an extension to functional logistic and multinomial logistic regression", "published": "2018-05-29T13:40:02Z", "authors": ["J. Derek Tucker", "John Lewis", "Anuj Srivastava"], "categories": ["stat.AP"], "doi": "10.1002/sam.11399", "_query": "hedonic regression housing", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2402.11219v4", "title": "Estimators for multivariate allometric regression model", "abstract": "In a regression model with multiple response variables and multiple explanatory variables, if the difference of the mean vectors of the response variables for different values of explanatory variables is always in the direction of the first principal eigenvector of the covariance matrix of the response variables, then it is called a multivariate allometric regression model. This paper studies the estimation of the first principal eigenvector in the multivariate allometric regression model. A class of estimators that includes conventional estimators is proposed based on weighted sum-of-squares matrices of regression sum-of-squares matrix and residual sum-of-squares matrix. We establish an upper bound of the mean squared error of the estimators contained in this class, and the weight value minimizing the upper bound is derived. Sufficient conditions for the consistency of the estimators are discussed in weak identifiability regimes under which the difference of the largest and second largest eigenvalues of the covariance matrix decays asymptotically and in ``large $p$, large $n$\" regimes, where $p$ is the number of response variables and $n$ is the sample size. Several numerical results are also presented.", "published": "2024-02-17T08:17:08Z", "authors": ["Koji Tsukuda", "Shun Matsuura"], "categories": ["math.ST", "stat.ME"], "doi": "10.1016/j.jmva.2025.105482", "_query": "hedonic regression housing", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/1707.03820v2", "title": "Pretest and Stein-Type Estimations in Quantile Regression Model", "abstract": "In this study, we consider preliminary test and shrinkage estimation strategies for quantile regression models. In classical Least Squares Estimation (LSE) method, the relationship between the explanatory and explained variables in the coordinate plane is estimated with a mean regression line. In order to use LSE, there are three main assumptions on the error terms showing white noise process of the regression model, also known as Gauss-Markov Assumptions, must be met: (1) The error terms have zero mean, (2) The variance of the error terms is constant and (3) The covariance between the errors is zero i.e., there is no autocorrelation. However, data in many areas, including econometrics, survival analysis and ecology, etc. does not provide these assumptions. First introduced by Koenker, quantile regression has been used to complement this deficiency of classical regression analysis and to improve the least square estimation. The aim of this study is to improve the performance of quantile regression estimators by using pre-test and shrinkage strategies. A Monte Carlo simulation study including a comparison with quantile $L_1$--type estimators such as Lasso, Ridge and Elastic Net are designed to evaluate the performances of the estimators. Two real data examples are given for illustrative purposes. Finally, we obtain the asymptotic results of suggested estimators", "published": "2017-07-12T12:23:45Z", "authors": ["Bahadır Yüzbaşı", "Yasin Asar", "M. Şamil Şık", "Ahmet Demiralp"], "categories": ["math.ST"], "doi": "", "_query": "hedonic regression housing", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/0708.1054v1", "title": "Shape restricted regression with random Bernstein polynomials", "abstract": "Shape restricted regressions, including isotonic regression and concave regression as special cases, are studied using priors on Bernstein polynomials and Markov chain Monte Carlo methods. These priors have large supports, select only smooth functions, can easily incorporate geometric information into the prior, and can be generated without computational difficulty. Algorithms generating priors and posteriors are proposed, and simulation studies are conducted to illustrate the performance of this approach. Comparisons with the density-regression method of Dette et al. (2006) are included.", "published": "2007-08-08T07:32:27Z", "authors": ["I-Shou Chang", "Li-Chu Chien", "Chao A. Hsiung", "Chi-Chung Wen", "Yuh-Jenn Wu"], "categories": ["math.ST"], "doi": "10.1214/074921707000000157", "_query": "hedonic regression housing", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/1610.04668v1", "title": "A Closed Form Solution to Multi-View Low-Rank Regression", "abstract": "Real life data often includes information from different channels. For example, in computer vision, we can describe an image using different image features, such as pixel intensity, color, HOG, GIST feature, SIFT features, etc.. These different aspects of the same objects are often called multi-view (or multi-modal) data. Low-rank regression model has been proved to be an effective learning mechanism by exploring the low-rank structure of real life data. But previous low-rank regression model only works on single view data. In this paper, we propose a multi-view low-rank regression model by imposing low-rank constraints on multi-view regression model. Most importantly, we provide a closed-form solution to the multi-view low-rank regression model. Extensive experiments on 4 multi-view datasets show that the multi-view low-rank regression model outperforms single-view regression model and reveals that multi-view low-rank structure is very helpful.", "published": "2016-10-14T23:43:47Z", "authors": ["Shuai Zheng", "Xiao Cai", "Chris Ding", "Feiping Nie", "Heng Huang"], "categories": ["cs.LG"], "doi": "", "_query": "hedonic regression housing", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2607.05096v1", "title": "Smooth Reduced Rank Regression with P-splines", "abstract": "Linear regression is one of the core statistical tools used for analysis of data. In the era of statistical learning, linear regression has been expanded into two directions. The first is regularisation, where penalties are added to the loss function to obtain more stable or sparse solutions. The second direction is basis expansion, such as with spline or kernel functions, where the linearity assumption is dropped. In practice, empirical researchers often collect multiple outcome variables. Regression models, either linear, regularized, or expanded, can be fitted to each of these outcome variables, but such an approach does not take into account the associations among the response variables. Reduced rank regression is a multivariate regression tool that takes into account and models the association among the response variables. In this paper, we develop and test a B-spline basis expansion for reduced rank regression, a regression model for multiple outcome variables where we penalize the coefficients to obtain a smooth fit, as in P-splines. This approach is useful for the analysis of data sets with multiple outcomes and a relatively small number of predictors. A block-relaxation algorithm is developed for parameter estimation and we discuss ways for penalty parameter tuning. We develop visualizations tools for model interpretation based on biplot methodology for ``all predictor - all response'' relationships and partial dependence plots for interpreting ``single predictor - single response'' relationships. With show several experimental gauges and we analyze three empirical data sets. We conclude this paper with a discussion.", "published": "2026-07-06T13:54:26Z", "authors": ["Mark de Rooij"], "categories": ["stat.ME"], "doi": "", "_query": "hedonic regression housing", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2006.09017v2", "title": "Estimates on Learning Rates for Multi-Penalty Distribution Regression", "abstract": "This paper is concerned with functional learning by utilizing two-stage sampled distribution regression. We study a multi-penalty regularization algorithm for distribution regression under the framework of learning theory. The algorithm aims at regressing to real valued outputs from probability measures. The theoretical analysis on distribution regression is far from maturity and quite challenging, since only second stage samples are observable in practical setting. In the algorithm, to transform information from samples, we embed the distributions to a reproducing kernel Hilbert space $\\mathcal{H}_K$ associated with Mercer kernel $K$ via mean embedding technique. The main contribution of the paper is to present a novel multi-penalty regularization algorithm to capture more features of distribution regression and derive optimal learning rates for the algorithm. The work also derives learning rates for distribution regression in the nonstandard setting $f_ρ\\notin\\mathcal{H}_K$, which is not explored in existing literature. Moreover, we propose a distribution regression-based distributed learning algorithm to face large-scale data or information challenge. The optimal learning rates are derived for the distributed learning algorithm. By providing new algorithms and showing their learning rates, we improve the existing work in different aspects in the literature.", "published": "2020-06-16T09:31:58Z", "authors": ["Zhan Yu", "Daniel W. C. Ho"], "categories": ["cs.LG", "math.ST", "stat.ML"], "doi": "", "_query": "hedonic regression housing", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/1707.07409v2", "title": "Big Data Regression Using Tree Based Segmentation", "abstract": "Scaling regression to large datasets is a common problem in many application areas. We propose a two step approach to scaling regression to large datasets. Using a regression tree (CART) to segment the large dataset constitutes the first step of this approach. The second step of this approach is to develop a suitable regression model for each segment. Since segment sizes are not very large, we have the ability to apply sophisticated regression techniques if required. A nice feature of this two step approach is that it can yield models that have good explanatory power as well as good predictive performance. Ensemble methods like Gradient Boosted Trees can offer excellent predictive performance but may not provide interpretable models. In the experiments reported in this study, we found that the predictive performance of the proposed approach matched the predictive performance of Gradient Boosted Trees.", "published": "2017-07-24T05:33:37Z", "authors": ["Rajiv Sambasivan", "Sourish Das"], "categories": ["stat.ML", "cs.LG"], "doi": "", "_query": "hedonic regression housing", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2011.14216v1", "title": "Inference in Regression Discontinuity Designs under Monotonicity", "abstract": "We provide an inference procedure for the sharp regression discontinuity design (RDD) under monotonicity, with possibly multiple running variables. Specifically, we consider the case where the true regression function is monotone with respect to (all or some of) the running variables and assumed to lie in a Lipschitz smoothness class. Such a monotonicity condition is natural in many empirical contexts, and the Lipschitz constant has an intuitive interpretation. We propose a minimax two-sided confidence interval (CI) and an adaptive one-sided CI. For the two-sided CI, the researcher is required to choose a Lipschitz constant where she believes the true regression function to lie in. This is the only tuning parameter, and the resulting CI has uniform coverage and obtains the minimax optimal length. The one-sided CI can be constructed to maintain coverage over all monotone functions, providing maximum credibility in terms of the choice of the Lipschitz constant. Moreover, the monotonicity makes it possible for the (excess) length of the CI to adapt to the true Lipschitz constant of the unknown regression function. Overall, the proposed procedures make it easy to see under what conditions on the underlying regression function the given estimates are significant, which can add more transparency to research using RDD methods.", "published": "2020-11-28T21:11:54Z", "authors": ["Koohyun Kwon", "Soonwoo Kwon"], "categories": ["econ.EM", "stat.AP"], "doi": "", "_query": "hedonic regression housing", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2504.04914v1", "title": "Nonparametric modal regression with missing response observations", "abstract": "Modal regression has emerged as a flexible alternative to classical regression models when the conditional mean or median are unable to adequately capture the underlying relation between a response and a predictor variable. This approach is particularly useful when the conditional distribution of the response given the covariate presents several modes, so the suitable regression function is a multifunction. In recent years, some proposals have addressed modal (smooth) regression estimation using kernel methods. In addition, some remarkable extensions to deal with censored, dependent or circular data have been also introduced. However, the case of incomplete samples due to missingness has not been studied in the literature. This paper adapts the nonparametric modal regression tools to handle missing observations in the response, investigating several imputation approaches through an extensive simulation study. The performance in practice of our proposals are also illustrated with two real--data examples.", "published": "2025-04-07T10:49:28Z", "authors": ["Ana Pérez-González", "Tomás R. Cotos-Yáñez", "Rosa M. Crujeiras"], "categories": ["stat.ME"], "doi": "", "_query": "hedonic regression housing", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2102.09964v4", "title": "Temporal Gaussian Process Regression in Logarithmic Time", "abstract": "The aim of this article is to present a novel parallelization method for temporal Gaussian process (GP) regression problems. The method allows for solving GP regression problems in logarithmic O(log N) time, where N is the number of time steps. Our approach uses the state-space representation of GPs which in its original form allows for linear O(N) time GP regression by leveraging the Kalman filtering and smoothing methods. By using a recently proposed parallelization method for Bayesian filters and smoothers, we are able to reduce the linear computational complexity of the temporal GP regression problems into logarithmic span complexity. This ensures logarithmic time complexity when run on parallel hardware such as a graphics processing unit (GPU). We experimentally demonstrate the computational benefits on simulated and real datasets via our open-source implementation leveraging the GPflow framework.", "published": "2021-02-19T14:57:17Z", "authors": ["Adrien Corenflos", "Zheng Zhao", "Simo Särkkä"], "categories": ["cs.LG", "stat.CO", "stat.ME"], "doi": "", "_query": "hedonic regression housing", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2409.02471v1", "title": "Demographic parity in regression and classification within the unawareness framework", "abstract": "This paper explores the theoretical foundations of fair regression under the constraint of demographic parity within the unawareness framework, where disparate treatment is prohibited, extending existing results where such treatment is permitted. Specifically, we aim to characterize the optimal fair regression function when minimizing the quadratic loss. Our results reveal that this function is given by the solution to a barycenter problem with optimal transport costs. Additionally, we study the connection between optimal fair cost-sensitive classification, and optimal fair regression. We demonstrate that nestedness of the decision sets of the classifiers is both necessary and sufficient to establish a form of equivalence between classification and regression. Under this nestedness assumption, the optimal classifiers can be derived by applying thresholds to the optimal fair regression function; conversely, the optimal fair regression function is characterized by the family of cost-sensitive classifiers.", "published": "2024-09-04T06:43:17Z", "authors": ["Vincent Divol", "Solenne Gaucher"], "categories": ["stat.ML", "cs.CY", "cs.LG"], "doi": "", "_query": "hedonic regression housing", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2410.22124v1", "title": "RankUp: Boosting Semi-Supervised Regression with an Auxiliary Ranking Classifier", "abstract": "State-of-the-art (SOTA) semi-supervised learning techniques, such as FixMatch and it's variants, have demonstrated impressive performance in classification tasks. However, these methods are not directly applicable to regression tasks. In this paper, we present RankUp, a simple yet effective approach that adapts existing semi-supervised classification techniques to enhance the performance of regression tasks. RankUp achieves this by converting the original regression task into a ranking problem and training it concurrently with the original regression objective. This auxiliary ranking classifier outputs a classification result, thus enabling integration with existing semi-supervised classification methods. Moreover, we introduce regression distribution alignment (RDA), a complementary technique that further enhances RankUp's performance by refining pseudo-labels through distribution alignment. Despite its simplicity, RankUp, with or without RDA, achieves SOTA results in across a range of regression benchmarks, including computer vision, audio, and natural language processing tasks. Our code and log data are open-sourced at https://github.com/pm25/semi-supervised-regression.", "published": "2024-10-29T15:25:21Z", "authors": ["Pin-Yen Huang", "Szu-Wei Fu", "Yu Tsao"], "categories": ["cs.LG", "cs.CL", "cs.CV", "cs.SD", "eess.AS"], "doi": "", "_query": "hedonic regression housing", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/1211.1513v2", "title": "K-Plane Regression", "abstract": "In this paper, we present a novel algorithm for piecewise linear regression which can learn continuous as well as discontinuous piecewise linear functions. The main idea is to repeatedly partition the data and learn a liner model in in each partition. While a simple algorithm incorporating this idea does not work well, an interesting modification results in a good algorithm. The proposed algorithm is similar in spirit to $k$-means clustering algorithm. We show that our algorithm can also be viewed as an EM algorithm for maximum likelihood estimation of parameters under a reasonable probability model. We empirically demonstrate the effectiveness of our approach by comparing its performance with the state of art regression learning algorithms on some real world datasets.", "published": "2012-11-07T10:57:38Z", "authors": ["Naresh Manwani", "P. S. Sastry"], "categories": ["cs.LG"], "doi": "10.1016/j.ins.2014.08.058", "_query": "hedonic regression housing", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2503.10539v1", "title": "GBSVR: Granular Ball Support Vector Regression", "abstract": "Support Vector Regression (SVR) and its variants are widely used to handle regression tasks, however, since their solution involves solving an expensive quadratic programming problem, it limits its application, especially when dealing with large datasets. Additionally, SVR uses an epsilon-insensitive loss function which is sensitive to outliers and therefore can adversely affect its performance. We propose Granular Ball Support Vector Regression (GBSVR) to tackle problem of regression by using granular ball concept. These balls are useful in simplifying complex data spaces for machine learning tasks, however, to the best of our knowledge, they have not been sufficiently explored for regression problems. Granular balls group the data points into balls based on their proximity and reduce the computational cost in SVR by replacing the large number of data points with far fewer granular balls. This work also suggests a discretization method for continuous-valued attributes to facilitate the construction of granular balls. The effectiveness of the proposed approach is evaluated on several benchmark datasets and it outperforms existing state-of-the-art approaches", "published": "2025-03-13T16:52:43Z", "authors": ["Reshma Rastogi", "Ankush Bisht", "Sanjay Kumar", "Suresh Chandra"], "categories": ["cs.LG", "cs.AI", "cs.IR"], "doi": "", "_query": "hedonic regression housing", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2002.02525v3", "title": "Interpolating Predictors in High-Dimensional Factor Regression", "abstract": "This work studies finite-sample properties of the risk of the minimum-norm interpolating predictor in high-dimensional regression models. If the effective rank of the covariance matrix $Σ$ of the $p$ regression features is much larger than the sample size $n$, we show that the min-norm interpolating predictor is not desirable, as its risk approaches the risk of trivially predicting the response by 0. However, our detailed finite-sample analysis reveals, surprisingly, that this behavior is not present when the regression response and the features are {\\it jointly} low-dimensional, following a widely used factor regression model. Within this popular model class, and when the effective rank of $Σ$ is smaller than $n$, while still allowing for $p \\gg n$, both the bias and the variance terms of the excess risk can be controlled, and the risk of the minimum-norm interpolating predictor approaches optimal benchmarks. Moreover, through a detailed analysis of the bias term, we exhibit model classes under which our upper bound on the excess risk approaches zero, while the corresponding upper bound in the recent work arXiv:1906.11300 diverges. Furthermore, we show that the minimum-norm interpolating predictor analyzed under the factor regression model, despite being model-agnostic and devoid of tuning parameters, can have similar risk to predictors based on principal components regression and ridge regression, and can improve over LASSO based predictors, in the high-dimensional regime.", "published": "2020-02-06T22:08:36Z", "authors": ["Florentina Bunea", "Seth Strimas-Mackey", "Marten Wegkamp"], "categories": ["stat.ML", "cs.LG", "math.ST"], "doi": "", "_query": "hedonic regression housing", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2002.02054v2", "title": "Robust Boosting for Regression Problems", "abstract": "Gradient boosting algorithms construct a regression predictor using a linear combination of ``base learners''. Boosting also offers an approach to obtaining robust non-parametric regression estimators that are scalable to applications with many explanatory variables. The robust boosting algorithm is based on a two-stage approach, similar to what is done for robust linear regression: it first minimizes a robust residual scale estimator, and then improves it by optimizing a bounded loss function. Unlike previous robust boosting proposals this approach does not require computing an ad-hoc residual scale estimator in each boosting iteration. Since the loss functions involved in this robust boosting algorithm are typically non-convex, a reliable initialization step is required, such as an L1 regression tree, which is also fast to compute. A robust variable importance measure can also be calculated via a permutation procedure. Thorough simulation studies and several data analyses show that, when no atypical observations are present, the robust boosting approach works as well as the standard gradient boosting with a squared loss. Furthermore, when the data contain outliers, the robust boosting estimator outperforms the alternatives in terms of prediction error and variable selection accuracy.", "published": "2020-02-06T01:12:45Z", "authors": ["Xiaomeng Ju", "Matías Salibián-Barrera"], "categories": ["stat.ME", "stat.ML"], "doi": "", "_query": "hedonic regression housing", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2605.19195v1", "title": "The Thermodynamic Costs of Simple Linear Regression", "abstract": "The construction of models from data is a significant contributor to the energetic costs of computation. Because of this, understanding how foundational thermodynamic bounds apply to modeling algorithms will be increasingly important. Here, we study the thermodynamic costs of a basic and fundamental modeling algorithm: simple linear regression. Following Landauer, we approximate the thermodynamic lower bound on irreversibly performing both exact linear regression and linear regression via stochastic gradient descent as implemented on floating-point numbers. From this, we derive energycost aware scaling laws for the optimal dataset size for training a linear regression model given a generalization error dependent demand for inference. Additionally, we discuss a method to lower bound the entropy production from the mismatch cost for algorithms with continuous input variables.", "published": "2026-05-18T23:51:02Z", "authors": ["Samuel H. D'Ambrosia", "Sultan M. Daniels", "Michael R. DeWeese", "Anant Sahai"], "categories": ["cond-mat.stat-mech", "cs.IT", "stat.ML"], "doi": "", "_query": "hedonic regression housing", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/1906.04613v1", "title": "Regional economic convergence and spatial quantile regression", "abstract": "The presence of \\b{eta}-convergence in European regions is an important issue to be analyzed. In this paper, we adopt a quantile regression approach in analyzing economic convergence. While previous work has performed quantile regression at the national level, we focus on 187 European NUTS2 regions for the period 1981-2009 and use spatial quantile regression to account for spatial dependence.", "published": "2019-06-11T14:16:44Z", "authors": ["Alfredo Cartone", "Geoffrey JD Hewings", "Paolo Postiglione"], "categories": ["stat.AP", "econ.EM"], "doi": "", "_query": "hedonic regression housing", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2605.06528v2", "title": "QUBO-Based Calibration for Regression Trees", "abstract": "Tree-based regression models are widely used in supervised learning, with the Classification and Regression Tree (CART) algorithm serving as a standard reference. CART construction involves solving a sequence of split-selection optimization problems, which are fractional and become combinatorial in nature for categorical predictors. Although, in the single-target regression setting with squared-error loss, this problem admits an efficient exact solution, as shown in Fisher (1958), Breiman et al. (1984), we adopt a QUBO framework to address the categorical split-selection problem. This choice is motivated by the general-purpose nature of QUBO formulations, which provide a unified optimization framework that naturally extends to settings where classical splitting strategies rely on heuristics and may yield suboptimal solutions. We propose a QUBO formulation of the categorical split-selection problem in single-target least-squares regression. The fractional nature of the objective function is handled using Dinkelbach's algorithm (Dinkelbach, 1967) together with a class-based encoding. This leads to a compact sequence of QUBO problems whose size depends only on the number of categories, rather than on the sample size. Using state-of-the-art QUBO solvers, we construct QUBO-based regression trees with predictive performance comparable to standard CART, while yielding higher-quality categorical splits. Overall, this work highlights the relevance of QUBO formulations as a flexible optimization framework for tree-based learning and opens perspectives for future hybrid classical-quantum optimization approaches within CART extensions.", "published": "2026-05-07T16:31:28Z", "authors": ["Iro René Kouarfate", "Maxime Dion", "Anne MacKay", "Mathieu Pigeon"], "categories": ["stat.CO"], "doi": "", "_query": "hedonic regression housing", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2309.06112v1", "title": "Characterizing Latent Perspectives of Media Houses Towards Public Figures", "abstract": "Media houses reporting on public figures, often come with their own biases stemming from their respective worldviews. A characterization of these underlying patterns helps us in better understanding and interpreting news stories. For this, we need diverse or subjective summarizations, which may not be amenable for classifying into predefined class labels. This work proposes a zero-shot approach for non-extractive or generative characterizations of person entities from a corpus using GPT-2. We use well-articulated articles from several well-known news media houses as a corpus to build a sound argument for this approach. First, we fine-tune a GPT-2 pre-trained language model with a corpus where specific person entities are characterized. Second, we further fine-tune this with demonstrations of person entity characterizations, created from a corpus of programmatically constructed characterizations. This twice fine-tuned model is primed with manual prompts consisting of entity names that were not previously encountered in the second fine-tuning, to generate a simple sentence about the entity. The results were encouraging, when compared against actual characterizations from the corpus.", "published": "2023-09-12T10:27:39Z", "authors": ["Sharath Srivatsa", "Srinath Srinivasa"], "categories": ["cs.CL", "cs.IR"], "doi": "", "_query": "hedonic regression housing", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/1409.0752v2", "title": "Continuous inverse regression", "abstract": "We provide new theoretical results in the field of inverse regression methods for dimension reduction. Our approach is based on the study of some empirical processes that lie close to a certain dimension reduction subspace, called the central subspace. The study of these processes essentially includes weak convergence results and the consistency of some general bootstrap procedures. While such properties are used to obtain new results about sliced inverse regression, they mainly allow to define a natural family of methods for dimension reduction. First the estimation methods are shown to have root $n$ rates and the bootstrap is proved to be valid. Second, we describe a family of Cramér-von Mises test statistics that can be used in testing structural properties of the central subspace or the significancy of some sets of predictors. We show that the quantiles of those tests could be computed by bootstrap. Most of the existing methods related to inverse regression involve a slicing of the response that is difficult to select in practice. While our approach guarantee a comprehensive estimation, the slicing is no longer needed.", "published": "2014-09-02T15:22:22Z", "authors": ["François Portier"], "categories": ["math.ST"], "doi": "", "_query": "hedonic regression housing", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/1805.11643v3", "title": "High Dimensional Robust Sparse Regression", "abstract": "We provide a novel -- and to the best of our knowledge, the first -- algorithm for high dimensional sparse regression with constant fraction of corruptions in explanatory and/or response variables. Our algorithm recovers the true sparse parameters with sub-linear sample complexity, in the presence of a constant fraction of arbitrary corruptions. Our main contribution is a robust variant of Iterative Hard Thresholding. Using this, we provide accurate estimators: when the covariance matrix in sparse regression is identity, our error guarantee is near information-theoretically optimal. We then deal with robust sparse regression with unknown structured covariance matrix. We propose a filtering algorithm which consists of a novel randomized outlier removal technique for robust sparse mean estimation that may be of interest in its own right: the filtering algorithm is flexible enough to deal with unknown covariance. Also, it is orderwise more efficient computationally than the ellipsoid algorithm. Using sub-linear sample complexity, our algorithm achieves the best known (and first) error guarantee. We demonstrate the effectiveness on large-scale sparse regression problems with arbitrary corruptions.", "published": "2018-05-29T18:33:23Z", "authors": ["Liu Liu", "Yanyao Shen", "Tianyang Li", "Constantine Caramanis"], "categories": ["cs.LG", "math.ST", "stat.ML"], "doi": "", "_query": "hedonic regression housing", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2202.00876v1", "title": "A selective review of sufficient dimension reduction for multivariate response regression", "abstract": "We review sufficient dimension reduction (SDR) estimators with multivariate response in this paper. A wide range of SDR methods are characterized as inverse regression SDR estimators or forward regression SDR estimators. The inverse regression family include pooled marginal estimators, projective resampling estimators, and distance-based estimators. Ordinary least squares, partial least squares, and semiparametric SDR estimators, on the other hand, are discussed as estimators from the forward regression family.", "published": "2022-02-02T04:53:09Z", "authors": ["Yuexiao Dong", "Abdul-Nasah Soale", "Michael D. Power"], "categories": ["stat.ME", "stat.CO", "stat.ML"], "doi": "", "_query": "hedonic regression housing", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/1311.5652v2", "title": "Interspecific Introgressive Origin of Genomic Diversity in the House Mouse", "abstract": "We report on a genome-wide scan for introgression in the house mouse (Mus musculus domesticus) involving the Algerian mouse (Mus spretus), using samples from the ranges of sympatry and allopatry in Africa and Europe. Our analysis reveals wide variability in introgression signatures along the genomes, as well as across the samples. We find that fewer than half of the autosomes in each genome harbor all detectable introgression, while the X chromosome has none. Further, European mice carry more M. spretus alleles than the sympatric African ones. Using the length distribution and sharing patterns of introgressed genomic tracts across the samples, we infer, first, that at least three distinct hybridization events involving M. spretus have occurred, one of which is ancient, and the other two are recent (one presumably due to warfarin rodenticide selection). Second, several of the inferred introgressed tracts contain genes that are likely to confer adaptive advantage. Third, introgressed tracts might contain driver genes that determine the evolutionary fate of those tracts. Further, functional analysis revealed introgressed genes that are essential to fitness, including the Vkorc1 gene, which is implicated in rodenticide resistance, and olfactory receptor genes. Our findings highlight the extent and role of introgression in nature, and call for careful analysis and interpretation of house mouse data in evolutionary and genetic studies.", "published": "2013-11-22T04:35:47Z", "authors": ["Kevin J. Liu", "Ethan Steinberg", "Alexander Yozzo", "Ying Song", "Michael H. Kohn", "Luay Nakhleh"], "categories": ["q-bio.PE"], "doi": "", "_query": "hedonic regression housing", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2203.16427v1", "title": "Balanced MSE for Imbalanced Visual Regression", "abstract": "Data imbalance exists ubiquitously in real-world visual regressions, e.g., age estimation and pose estimation, hurting the model's generalizability and fairness. Thus, imbalanced regression gains increasing research attention recently. Compared to imbalanced classification, imbalanced regression focuses on continuous labels, which can be boundless and high-dimensional and hence more challenging. In this work, we identify that the widely used Mean Square Error (MSE) loss function can be ineffective in imbalanced regression. We revisit MSE from a statistical view and propose a novel loss function, Balanced MSE, to accommodate the imbalanced training label distribution. We further design multiple implementations of Balanced MSE to tackle different real-world scenarios, particularly including the one that requires no prior knowledge about the training label distribution. Moreover, to the best of our knowledge, Balanced MSE is the first general solution to high-dimensional imbalanced regression. Extensive experiments on both synthetic and three real-world benchmarks demonstrate the effectiveness of Balanced MSE.", "published": "2022-03-30T16:21:42Z", "authors": ["Jiawei Ren", "Mingyuan Zhang", "Cunjun Yu", "Ziwei Liu"], "categories": ["cs.CV"], "doi": "", "_query": "hedonic regression housing", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2001.11213v2", "title": "Reproducing kernels based schemes for nonparametric regression", "abstract": "In this work, we develop and study an empirical projection operator scheme for solving nonparametric regression problems. This scheme is based on an approximate projection of the regression function over a suitable reproducing kernel Hilbert space (RKHS). The RKHS considered in this paper are generated by the Mercer kernels given by the Legendre Christoffel-Darboux and convolution Sinc kernels. We provide error and convergence analysis of the proposed scheme under the assumption that the regression function belongs to some suitable functional spaces. We also consider the popular RKHS regularized least square minimization for nonparametric regression. In particular, we check the numerical stability of this second scheme and we provide its convergence rate in the special case of the Sinc kernel. Finally, we illustrate the proposed methods by various numerical simulation.", "published": "2020-01-30T08:42:28Z", "authors": ["Bilel Bousselmi", "Jean-François Dupuy", "Abderrazek Karoui"], "categories": ["math.ST"], "doi": "", "_query": "hedonic regression housing", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/1408.3536v4", "title": "Adaptive testing on a regression function at a point", "abstract": "We consider the problem of inference on a regression function at a point when the entire function satisfies a sign or shape restriction under the null. We propose a test that achieves the optimal minimax rate adaptively over a range of Hölder classes, up to a $\\log\\log n$ term, which we show to be necessary for adaptation. We apply the results to adaptive one-sided tests for the regression discontinuity parameter under a monotonicity restriction, the value of a monotone regression function at the boundary and the proportion of true null hypotheses in a multiple testing problem.", "published": "2014-08-15T13:39:28Z", "authors": ["Timothy Armstrong"], "categories": ["math.ST"], "doi": "10.1214/15-AOS1342", "_query": "hedonic regression housing", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/1910.13153v1", "title": "Differentially Private Bayesian Linear Regression", "abstract": "Linear regression is an important tool across many fields that work with sensitive human-sourced data. Significant prior work has focused on producing differentially private point estimates, which provide a privacy guarantee to individuals while still allowing modelers to draw insights from data by estimating regression coefficients. We investigate the problem of Bayesian linear regression, with the goal of computing posterior distributions that correctly quantify uncertainty given privately released statistics. We show that a naive approach that ignores the noise injected by the privacy mechanism does a poor job in realistic data settings. We then develop noise-aware methods that perform inference over the privacy mechanism and produce correct posteriors across a wide range of scenarios.", "published": "2019-10-29T09:47:23Z", "authors": ["Garrett Bernstein", "Daniel Sheldon"], "categories": ["cs.LG", "stat.ML"], "doi": "", "_query": "hedonic regression housing", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2407.04104v2", "title": "Network-based Neighborhood regression", "abstract": "Given the ubiquity of modularity in biological systems, module-level regulation analysis is vital for understanding biological systems across various levels and their dynamics. Current statistical analysis on biological modules predominantly focuses on either detecting the functional modules in biological networks or sub-group regression on the biological features without using the network data. This paper proposes a novel network-based neighborhood regression framework whose regression functions depend on both the global community-level information and local connectivity structures among entities. An efficient community-wise least square optimization approach is developed to uncover the strength of regulation among the network modules while enabling asymptotic inference. With random graph theory, we derive non-asymptotic estimation error bounds for the proposed estimator, achieving exact minimax optimality. Unlike the root-n consistency typical in canonical linear regression, our model exhibits linear consistency in the number of nodes n, highlighting the advantage of incorporating neighborhood information. The effectiveness of the proposed framework is further supported by extensive numerical experiments. Application to whole-exome sequencing and RNA-sequencing Autism datasets demonstrates the usage of the proposed method in identifying the association between the gene modules of genetic variations and the gene modules of genomic differential expressions.", "published": "2024-07-04T18:08:40Z", "authors": ["Yaoming Zhen", "Jin-Hong Du"], "categories": ["stat.ME", "cs.LG", "stat.ML"], "doi": "", "_query": "hedonic regression housing", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2107.14351v1", "title": "Contemporary Symbolic Regression Methods and their Relative Performance", "abstract": "Many promising approaches to symbolic regression have been presented in recent years, yet progress in the field continues to suffer from a lack of uniform, robust, and transparent benchmarking standards. In this paper, we address this shortcoming by introducing an open-source, reproducible benchmarking platform for symbolic regression. We assess 14 symbolic regression methods and 7 machine learning methods on a set of 252 diverse regression problems. Our assessment includes both real-world datasets with no known model form as well as ground-truth benchmark problems, including physics equations and systems of ordinary differential equations. For the real-world datasets, we benchmark the ability of each method to learn models with low error and low complexity relative to state-of-the-art machine learning methods. For the synthetic problems, we assess each method's ability to find exact solutions in the presence of varying levels of noise. Under these controlled experiments, we conclude that the best performing methods for real-world regression combine genetic algorithms with parameter estimation and/or semantic search drivers. When tasked with recovering exact equations in the presence of noise, we find that deep learning and genetic algorithm-based approaches perform similarly. We provide a detailed guide to reproducing this experiment and contributing new methods, and encourage other researchers to collaborate with us on a common and living symbolic regression benchmark.", "published": "2021-07-29T22:12:59Z", "authors": ["William La Cava", "Patryk Orzechowski", "Bogdan Burlacu", "Fabrício Olivetti de França", "Marco Virgolin", "Ying Jin", "Michael Kommenda", "Jason H. Moore"], "categories": ["cs.NE"], "doi": "", "_query": "hedonic regression housing", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/math/0406456v2", "title": "Least Angle Regression", "abstract": "The purpose of model selection algorithms such as All Subsets, Forward Selection and Backward Elimination is to choose a linear model on the basis of the same set of data to which the model will be applied. Typically we have available a large collection of possible covariates from which we hope to select a parsimonious set for the efficient prediction of a response variable. Least Angle Regression (LARS), a new model selection algorithm, is a useful and less greedy version of traditional forward selection methods. Three main properties are derived: (1) A simple modification of the LARS algorithm implements the Lasso, an attractive version of ordinary least squares that constrains the sum of the absolute regression coefficients; the LARS modification calculates all possible Lasso estimates for a given problem, using an order of magnitude less computer time than previous methods. (2) A different LARS modification efficiently implements Forward Stagewise linear regression, another promising new model selection method;", "published": "2004-06-23T08:06:25Z", "authors": ["Bradley Efron", "Trevor Hastie", "Iain Johnstone", "Robert Tibshirani"], "categories": ["math.ST"], "doi": "10.1214/009053604000000067", "_query": "hedonic regression housing", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/1905.07218v2", "title": "Functional Lagged Regression with Sparse Noisy Observations", "abstract": "A functional (lagged) time series regression model involves the regression of scalar response time series on a time series of regressors that consists of a sequence of random functions. In practice, the underlying regressor curve time series are not always directly accessible, but are latent processes observed (sampled) only at discrete measurement locations. In this paper, we consider the so-called sparse observation scenario where only a relatively small number of measurement locations have been observed, possibly different for each curve. The measurements can be further contaminated by additive measurement error. A spectral approach to the estimation of the model dynamics is considered. The spectral density of the regressor time series and the cross-spectral density between the regressors and response time series are estimated by kernel smoothing methods from the sparse observations. The impulse response regression coefficients of the lagged regression model are then estimated by means of ridge regression (Tikhonov regularisation) or PCA regression (spectral truncation). The latent functional time series are then recovered by means of prediction, conditioning on all the observed observed data. The performance and implementation of our methods are illustrated by means of a simulation study and the analysis of meteorological data.", "published": "2019-05-17T11:59:23Z", "authors": ["Tomáš Rubín", "Victor M. Panaretos"], "categories": ["stat.ME"], "doi": "10.1111/jtsa.12551", "_query": "hedonic regression housing", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/1005.5494v2", "title": "Semiparametric regression in testicular germ cell data", "abstract": "It is possible to approach regression analysis with random covariates from a semiparametric perspective where information is combined from multiple multivariate sources. The approach assumes a semiparametric density ratio model where multivariate distributions are \"regressed\" on a reference distribution. A kernel density estimator can be constructed from many data sources in conjunction with the semiparametric model. The estimator is shown to be more efficient than the traditional single-sample kernel density estimator, and its optimal bandwidth is discussed in some detail. Each multivariate distribution and the corresponding conditional expectation (regression) of interest are estimated from the combined data using all sources. Graphical and quantitative diagnostic tools are suggested to assess model validity. The method is applied in quantifying the effect of height and age on weight of germ cell testicular cancer patients. Comparisons are made with multiple regression, generalized additive models (GAM) and nonparametric kernel regression.", "published": "2010-05-30T02:03:28Z", "authors": ["Anastasia Voulgaraki", "Benjamin Kedem", "Barry I. Graubard"], "categories": ["stat.ME", "stat.AP"], "doi": "10.1214/12-AOAS552", "_query": "hedonic regression housing", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2609.39440v1", "title": "Principal Component Regression Dominates all Monotone Spectral Filters for Linear Regression", "abstract": "We compare the instance-wise, finite-sample risks of monotone spectral filters for linear regression, a broad class of estimators including principal component regression (PCR), gradient descent (GD), and ridge regression. We show that PCR dominates all monotone spectral filters: compared to any such filter, the risk of optimally tuned PCR is no bigger by a constant factor for all problems. Furthermore, the dominance is strong if the filter is separated from step functions (e.g., GD and ridge): there exist problem instances for which the risk of PCR is smaller by a polynomial factor in sample size dependence. Our comparison results show that PCR is optimal and thus admissible among monotone filters, significantly extending Wu et al. (2026)'s result that GD strongly dominates ridge. From a technical perspective, we establish new upper and lower bounds for general spectral filters, which are instance-wise sharp when specialized to ridge or GD, recovering or improving the best-known bounds.", "published": "2026-09-30T10:15:34Z", "authors": ["Juno Kim", "Hengyu Fu", "Peter Bartlett", "Jason D. Lee", "Jingfeng Wu"], "categories": ["stat.ML", "cs.LG"], "doi": "", "_query": "hedonic regression housing", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/1912.08416v1", "title": "Benchmarking the Neural Linear Model for Regression", "abstract": "The neural linear model is a simple adaptive Bayesian linear regression method that has recently been used in a number of problems ranging from Bayesian optimization to reinforcement learning. Despite its apparent successes in these settings, to the best of our knowledge there has been no systematic exploration of its capabilities on simple regression tasks. In this work we characterize these on the UCI datasets, a popular benchmark for Bayesian regression models, as well as on the recently introduced UCI \"gap\" datasets, which are better tests of out-of-distribution uncertainty. We demonstrate that the neural linear model is a simple method that shows generally good performance on these tasks, but at the cost of requiring good hyperparameter tuning.", "published": "2019-12-18T07:23:57Z", "authors": ["Sebastian W. Ober", "Carl Edward Rasmussen"], "categories": ["stat.ML", "cs.LG"], "doi": "", "_query": "hedonic regression housing", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2109.04623v2", "title": "ReLU Regression with Massart Noise", "abstract": "We study the fundamental problem of ReLU regression, where the goal is to fit Rectified Linear Units (ReLUs) to data. This supervised learning task is efficiently solvable in the realizable setting, but is known to be computationally hard with adversarial label noise. In this work, we focus on ReLU regression in the Massart noise model, a natural and well-studied semi-random noise model. In this model, the label of every point is generated according to a function in the class, but an adversary is allowed to change this value arbitrarily with some probability, which is {\\em at most} $η< 1/2$. We develop an efficient algorithm that achieves exact parameter recovery in this model under mild anti-concentration assumptions on the underlying distribution. Such assumptions are necessary for exact recovery to be information-theoretically possible. We demonstrate that our algorithm significantly outperforms naive applications of $\\ell_1$ and $\\ell_2$ regression on both synthetic and real data.", "published": "2021-09-10T02:13:22Z", "authors": ["Ilias Diakonikolas", "Jongho Park", "Christos Tzamos"], "categories": ["cs.LG", "cs.DS", "stat.ML"], "doi": "", "_query": "hedonic regression housing", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/1203.4427v1", "title": "Regression Model With Elliptically Contoured Errors", "abstract": "For the regression model where the errors follow the elliptically contoured distribution (ECD), we consider the least squares (LS), restricted LS (RLS), preliminary test (PT), Stein-type shrinkage (S) and positive-rule shrinkage (PRS) estimators for the regression parameters. We compare the quadratic risks of the estimators to determine the relative dominance properties of the five estimators.", "published": "2012-03-20T13:26:40Z", "authors": ["M. Arashi", "A. K. Md E. Saleh", "S. M. M. Tabatabaey"], "categories": ["math.ST", "stat.ME"], "doi": "", "_query": "hedonic regression housing", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/1411.7009v1", "title": "Additive Gaussian Process Regression", "abstract": "Additive-interactive regression has recently been shown to offer attractive minimax error rates over traditional nonparametric multivariate regression in a wide variety of settings, including cases where the predictor count is much larger than the sample size and many of the predictors have important effects on the response, potentially through complex interactions. We present a Bayesian implementation of additive-interactive regression using an additive Gaussian process (AGP) prior and develop an efficient Markov chain sampler that extends stochastic search variable selection in this setting. Careful prior and hyper-parameter specification are developed in light of performance and computational considerations, and key innovations address difficulties in exploring a joint posterior distribution over multiple subsets of high dimensional predictor inclusion vectors. The method offers state-of-the-art support and interaction recovery while improving dramatically over competitors in terms of prediction accuracy on a diverse set of simulated and real data. Results from real data studies provide strong evidence that the additive-interactive framework is an attractive modeling platform for high-dimensional nonparametric regression.", "published": "2014-11-25T20:28:28Z", "authors": ["Shaan Qamar", "Surya T. Tokdar"], "categories": ["stat.ME", "stat.CO"], "doi": "", "_query": "hedonic regression housing", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/1909.02426v3", "title": "Regression Models Using Shapes of Functions as Predictors", "abstract": "Functional variables are often used as predictors in regression problems. A commonly-used parametric approach, called {\\it scalar-on-function regression}, uses the $\\ltwo$ inner product to map functional predictors into scalar responses. This method can perform poorly when predictor functions contain undesired phase variability, causing phases to have disproportionately large influence on the response variable. One past solution has been to perform phase-amplitude separation (as a pre-processing step) and then use only the amplitudes in the regression model. Here we propose a more integrated approach, termed elastic functional regression model (EFRM), where phase-separation is performed inside the regression model, rather than as a pre-processing step. This approach generalizes the notion of phase in functional data, and is based on the norm-preserving time warping of predictors. Due to its invariance properties, this representation provides robustness to predictor phase variability and results in improved predictions of the response variable over traditional models. We demonstrate this framework using a number of datasets involving gait signals, NMR data, and stock market prices.", "published": "2019-09-05T14:02:55Z", "authors": ["Kyungmin Ahn", "J. Derek Tucker", "Wei Wu", "Anuj Srivastava"], "categories": ["stat.ME", "stat.CO"], "doi": "10.1016/j.csda.2020.107017", "_query": "hedonic regression housing", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/0708.0983v1", "title": "Local polynomial regression on unknown manifolds", "abstract": "We reveal the phenomenon that ``naive'' multivariate local polynomial regression can adapt to local smooth lower dimensional structure in the sense that it achieves the optimal convergence rate for nonparametric estimation of regression functions belonging to a Sobolev space when the predictor variables live on or close to a lower dimensional manifold.", "published": "2007-08-07T15:58:50Z", "authors": ["Peter J. Bickel", "Bo Li"], "categories": ["math.ST"], "doi": "10.1214/074921707000000148", "_query": "hedonic regression housing", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2510.19020v2", "title": "Calibrated Principal Component Regression", "abstract": "We propose a new method for statistical inference in generalized linear models. In the overparameterized regime, Principal Component Regression (PCR) reduces variance by projecting high-dimensional data to a low-dimensional principal subspace before fitting. However, PCR incurs truncation bias whenever the true regression vector has mass outside the retained principal components (PC). To mitigate the bias, we propose Calibrated Principal Component Regression (CPCR), which first learns a low-variance prior in the PC subspace and then calibrates the model in the original feature space via a centered Tikhonov step. CPCR leverages cross-fitting and controls the truncation bias by softening PCR's hard cutoff. Theoretically, we calculate the out-of-sample risk in the random matrix regime, which shows that CPCR outperforms standard PCR when the regression signal has non-negligible components in low-variance directions. Empirically, CPCR consistently improves prediction across multiple overparameterized problems. The results highlight CPCR's stability and flexibility in modern overparameterized settings.", "published": "2025-10-21T19:01:42Z", "authors": ["Yixuan Florence Wu", "Yilun Zhu", "Lei Cao", "Naichen Shi"], "categories": ["stat.ML", "cs.LG"], "doi": "", "_query": "hedonic regression housing", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2205.08717v2", "title": "A Regression Approach to Learning-Augmented Online Algorithms", "abstract": "The emerging field of learning-augmented online algorithms uses ML techniques to predict future input parameters and thereby improve the performance of online algorithms. Since these parameters are, in general, real-valued functions, a natural approach is to use regression techniques to make these predictions. We introduce this approach in this paper, and explore it in the context of a general online search framework that captures classic problems like (generalized) ski rental, bin packing, minimum makespan scheduling, etc. We show nearly tight bounds on the sample complexity of this regression problem, and extend our results to the agnostic setting. From a technical standpoint, we show that the key is to incorporate online optimization benchmarks in the design of the loss function for the regression problem, thereby diverging from the use of off-the-shelf regression tools with standard bounds on statistical error.", "published": "2022-05-18T04:29:14Z", "authors": ["Keerti Anand", "Rong Ge", "Amit Kumar", "Debmalya Panigrahi"], "categories": ["cs.LG", "cs.DS"], "doi": "", "_query": "hedonic regression housing", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/1810.01645v2", "title": "Estimating the error distribution function in nonparametric regression", "abstract": "We construct an efficient estimator for the error distribution function of the nonparametric regression model Y = r(Z) + e. Our estimator is a kernel smoothed empirical distribution function based on residuals from an under-smoothed local quadratic smoother for the regression function.", "published": "2018-10-03T09:14:10Z", "authors": ["Ursula U. Müller", "Anton Schick", "Wolfgang Wefelmeyer"], "categories": ["math.ST"], "doi": "", "_query": "hedonic regression housing", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2205.04176v3", "title": "Hypothesis testing for varying coefficient models in tail index regression", "abstract": "This study examines the varying coefficient model in tail index regression. The varying coefficient model is an efficient semiparametric model that avoids the curse of dimensionality when including large covariates in the model. In fact, the varying coefficient model is useful in mean, quantile, and other regressions. The tail index regression is not an exception. However, the varying coefficient model is flexible, but leaner and simpler models are preferred for applications. Therefore, it is important to evaluate whether the estimated coefficient function varies significantly with covariates. If the effect of the non-linearity of the model is weak, the varying coefficient structure is reduced to a simpler model, such as a constant or zero. Accordingly, the hypothesis test for model assessment in the varying coefficient model has been discussed in mean and quantile regression. However, there are no results in tail index regression. In this study, we investigate the asymptotic properties of an estimator and provide a hypothesis testing method for varying coefficient models for tail index regression.", "published": "2022-05-09T10:40:26Z", "authors": ["Koki Momoki", "Takuma Yoshida"], "categories": ["math.ST"], "doi": "", "_query": "hedonic regression housing", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2209.12388v1", "title": "Joint and Individual Component Regression", "abstract": "Multi-group data are commonly seen in practice. Such data structure consists of data from multiple groups and can be challenging to analyze due to data heterogeneity. We propose a novel Joint and Individual Component Regression (JICO) model to analyze multi-group data. In particular, our proposed model decomposes the response into shared and group-specific components, which are driven by low-rank approximations of joint and individual structures from the predictors respectively. The joint structure has the same regression coefficients across multiple groups, whereas individual structures have group-specific regression coefficients. Moreover, the choice of global and individual ranks allows our model to cover global and group-specific models as special cases. For model estimation, we formulate this framework under the representation of latent components and propose an iterative algorithm to solve for the joint and individual scores under the new representation. To construct the latent scores, we utilize the Continuum Regression (CR), which provides a unified framework that covers the Ordinary Least Squares (OLS), the Partial Least Squares (PLS), and the Principal Component Regression (PCR) as its special cases. We show that JICO attains a good balance between global and group-specific models and remains flexible due to the usage of CR. Finally, we conduct simulation studies and analysis of an Alzheimer's disease dataset to further demonstrate the effectiveness of JICO. R implementation of JICO is available online at https://github.com/peiyaow/JICO.", "published": "2022-09-26T03:02:23Z", "authors": ["Peiyao Wang", "Haodong Wang", "Quefeng Li", "Dinggang Shen", "Yufeng Liu"], "categories": ["stat.ME", "stat.AP"], "doi": "", "_query": "hedonic regression housing", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2603.06712v1", "title": "Uncertainty-Aware Solar Flare Regression", "abstract": "Current solar flare predictions often lack precise quantification of their reliability, resulting in frequent false alarms, particularly when dealing with datasets skewed towards extreme events. To improve the trustworthiness of space weather forecasting, it is crucial to establish confidence intervals for model predictions. Conformal prediction, a machine learning framework, presents a promising avenue for this purpose by constructing prediction intervals that ensure valid coverage in finite samples without making assumptions about the underlying data distribution. In this study, we explore the application of conformal prediction to regression tasks in space weather forecasting. Specifically, we implement full-disk solar flare prediction using images created from magnetic field maps and adapt four pre-trained deep learning models to incorporate three distinct methods for constructing confidence intervals: conformal prediction, quantile regression, and conformalized quantile regression. Our experiments demonstrate that conformalized quantile regression achieves higher coverage rates and more favorable average interval lengths compared to alternative methods, underscoring its effectiveness in enhancing the reliability of solar weather forecasting models.", "published": "2026-03-05T20:23:51Z", "authors": ["Jinsu Hong", "Chetraj Pandey", "Berkay Aydin"], "categories": ["astro-ph.SR", "cs.CV", "cs.LG"], "doi": "", "_query": "hedonic regression housing", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2010.06889v1", "title": "Neural Mixture Distributional Regression", "abstract": "We present neural mixture distributional regression (NMDR), a holistic framework to estimate complex finite mixtures of distributional regressions defined by flexible additive predictors. Our framework is able to handle a large number of mixtures of potentially different distributions in high-dimensional settings, allows for efficient and scalable optimization and can be applied to recent concepts that combine structured regression models with deep neural networks. While many existing approaches for mixture models address challenges in optimization of such and provide results for convergence under specific model assumptions, our approach is assumption-free and instead makes use of optimizers well-established in deep learning. Through extensive numerical experiments and a high-dimensional deep learning application we provide evidence that the proposed approach is competitive to existing approaches and works well in more complex scenarios.", "published": "2020-10-14T09:00:16Z", "authors": ["David Rügamer", "Florian Pfisterer", "Bernd Bischl"], "categories": ["stat.CO", "cs.LG", "stat.ML"], "doi": "", "_query": "hedonic regression housing", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2510.12048v1", "title": "Robust Functional Logistic Regression", "abstract": "Functional logistic regression is a popular model to capture a linear relationship between binary response and functional predictor variables. However, many methods used for parameter estimation in functional logistic regression are sensitive to outliers, which may lead to inaccurate parameter estimates and inferior classification accuracy. We propose a robust estimation procedure for functional logistic regression, in which the observations of the functional predictor are projected onto a set of finite-dimensional subspaces via robust functional principal component analysis. This dimension-reduction step reduces the outlying effects in the functional predictor. The logistic regression coefficient is estimated using an M-type estimator based on binary response and robust principal component scores. In doing so, we provide robust estimates by minimizing the effects of outliers in the binary response and functional predictor variables. Via a series of Monte-Carlo simulations and using hand radiograph data, we examine the parameter estimation and classification accuracy for the response variable. We find that the robust procedure outperforms some existing robust and non-robust methods when outliers are present, while producing competitive results when outliers are absent. In addition, the proposed method is computationally more efficient than some existing robust alternatives.", "published": "2025-10-14T01:15:38Z", "authors": ["Berkay Akturk", "Ufuk Beyaztas", "Han Lin Shang"], "categories": ["stat.ME", "stat.AP"], "doi": "", "_query": "hedonic regression housing", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2203.10643v1", "title": "Confidence intervals for nonparametric regression", "abstract": "We demonstrate and discuss nonasymptotic bounds in probability for the cost of a regression scheme with a general loss function from the perspective of the Rademacher theory, and for the optimality with respect to the average $L^{2}$-distance to the underlying conditional expectations of least squares regression outcomes from the perspective of the Vapnik-Chervonenkis theory. The results follow from an analysis involving independent but possibly nonstationary training samples and can be extended, in a manner that we explain and illustrate, to relevant cases in which the training sample exhibits dependence.", "published": "2022-03-20T20:42:00Z", "authors": ["David Barrera"], "categories": ["math.ST", "math.PR", "stat.ML"], "doi": "", "_query": "hedonic regression housing", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2601.10533v1", "title": "A Propagation Framework for Network Regression", "abstract": "We introduce a unified and computationally efficient framework for regression on network data, addressing limitations of existing models that require specialized estimation procedures or impose restrictive decay assumptions. Our Network Propagation Regression (NPR) models outcomes as functions of covariates propagated through network connections, capturing both direct and indirect effects. NPR is estimable via ordinary least squares for continuous outcomes and standard routines for binary, categorical, and time-to-event data, all within a single interpretable framework. We establish consistency and asymptotic normality under weak conditions and develop valid hypothesis tests for the order of network influence. Simulation studies demonstrate that NPR consistently outperforms established approaches, such as the linear-in-means model and regression with network cohesion, especially under model misspecification. An application to social media sentiment analysis highlights the practical utility and robustness of NPR in real-world settings.", "published": "2026-01-15T15:57:15Z", "authors": ["Yingying Ma", "Chenlei Leng"], "categories": ["stat.ME"], "doi": "", "_query": "hedonic regression housing", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/1905.11846v5", "title": "Computation of projection regression depth and its induced median", "abstract": "Notions of depth in regression have been introduced and studied in the literature. The most famous example is Regression Depth (RD), which is a direct extension of location depth to regression. The projection regression depth (PRD) is the extension of another prevailing location depth, the projection depth, to regression. The computation issues of the RD have been discussed in the literature. The computation issues of the PRD have never been dealt with before. The computation issues of the PRD and its induced median (maximum depth estimator) in a regression setting are addressed now. For a given $\\bsβ\\in\\R^p$ exact algorithms for the PRD with cost $O(n^2\\log n)$ ($p=2$) and $O(N(n, p)(p^{3}+n\\log n+np^{1.5}+npN_{Iter}))$ ($p>2$) and approximate algorithms for the PRD and its induced median with cost respectively $O(N_{\\mb{v}}np)$ and $O(Rp N_{\\bsβ}(p^2+nN_{\\mb{v}}N_{Iter}))$ are proposed. Here $N(n, p)$ is a number defined based on the total number of $(p-1)$ dimensional hyperplanes formed by points induced from sample points and the $\\bsβ$; $N_{\\mb{v}}$ is the total number of unit directions $\\mb{v}$ utilized; $N_{\\bsβ}$ is the total number of candidate regression parameters $\\bsβ$ employed; $N_{Iter}$ is the total number of iterations carried out in an optimization algorithm; $R$ is the total number of replications. Furthermore, as the second major contribution, three PRD induced estimators, which can be computed up to 30 times faster than that of the PRD induced median while maintaining a similar level of accuracy are introduced. Examples and simulation studies reveal that the depth median induced from the PRD is favorable in terms of robustness and efficiency, compared to the maximum depth estimator induced from the RD, which is the current leading regression median.", "published": "2019-05-28T14:27:59Z", "authors": ["Yijun Zuo"], "categories": ["stat.CO"], "doi": "", "_query": "hedonic regression housing", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2207.11392v3", "title": "Simultaneous Inference for Time Series Functional Linear Regression", "abstract": "We consider the problem of joint simultaneous confidence band (JSCB) construction for regression coefficient functions of time series scalar-on-function linear regression when the regression model is estimated by roughness penalization approach with flexible choices of orthonormal basis functions. A simple and unified multiplier bootstrap methodology is proposed for the JSCB construction which is shown to achieve the correct coverage probability asymptotically. Furthermore, the JSCB is asymptotically robust to inconsistently estimated standard deviations of the model. The proposed methodology is applied to a time series data set of electricity market to visually investigate and formally test the overall regression relationship as well as perform model validation.", "published": "2022-07-23T02:23:17Z", "authors": ["Yan Cui", "Zhou Zhou"], "categories": ["stat.ME"], "doi": "", "_query": "hedonic regression housing", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2109.02381v1", "title": "Backdoor Attack and Defense for Deep Regression", "abstract": "We demonstrate a backdoor attack on a deep neural network used for regression. The backdoor attack is localized based on training-set data poisoning wherein the mislabeled samples are surrounded by correctly labeled ones. We demonstrate how such localization is necessary for attack success. We also study the performance of a backdoor defense using gradient-based discovery of local error maximizers. Local error maximizers which are associated with significant (interpolation) error, and are proximal to many training samples, are suspicious. This method is also used to accurately train for deep regression in the first place by active (deep) learning leveraging an \"oracle\" capable of providing real-valued supervision (a regression target) for samples. Such oracles, including traditional numerical solvers of PDEs or SDEs using finite difference or Monte Carlo approximations, are far more computationally costly compared to deep regression.", "published": "2021-09-06T11:58:03Z", "authors": ["Xi Li", "George Kesidis", "David J. Miller", "Vladimir Lucic"], "categories": ["cs.LG", "cs.CR"], "doi": "", "_query": "hedonic regression housing", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2204.05827v3", "title": "Correction of overfitting bias in regression models", "abstract": "Regression analysis based on many covariates is becoming increasingly common. However, when the number of covariates $p$ is of the same order as the number of observations $n$, maximum likelihood regression becomes unreliable due to overfitting. This typically leads to systematic estimation biases and increased estimator variances. It is crucial for inference and prediction to quantify these effects correctly. Several methods have been proposed in literature to overcome overfitting bias or adjust estimates. The vast majority of these focus on the regression parameters. But failure to estimate correctly also the nuisance parameters may lead to significant errors in confidence statements and outcome prediction. In this paper we present a jacknife method for deriving a compact set of non-linear equations which describe the statistical properties of the ML estimator in the regime where $p=O(n)$ and under the hypothesis of normally distributed covariates. These equations enable one to compute the overfitting bias of maximum likelihood (ML) estimators in parametric regression models as functions of $ζ= p/n$. We then use these equations to compute shrinkage factors in order to remove the overfitting bias of maximum likelihood (ML) estimators. This new derivation offers various benefits over the replica approach in terms of increased transparency and reduced assumptions. To illustrate the theory we performed simulation studies for multiple regression models. In all cases we find excellent agreement between theory and simulations.", "published": "2022-04-12T14:11:13Z", "authors": ["Emanuele Massa", "Marianne Jonker", "Kit Roes", "Anthony Coolen"], "categories": ["stat.ME", "math.ST", "physics.data-an"], "doi": "", "_query": "hedonic regression housing", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2108.04715v3", "title": "Identifiability of Covariance Kernels in the Gaussian Process Regression Model", "abstract": "Gaussian process regression (GPR) model is a popular nonparametric regression model. In GPR, features of the regression function such as varying degrees of smoothness and periodicities are modeled through combining various covarinace kernels, which are supposed to model certain effects. The covariance kernels have unknown parameters which are estimated by the EM-algorithm or Markov Chain Monte Carlo. The estimated parameters are keys to the inference of the features of the regression functions, but identifiability of these parameters has not been investigated. In this paper, we prove identifiability of covariance kernel parameters in two radial basis mixed kernel GPR and radial basis and periodic mixed kernel GPR. We also provide some examples about non-identifiable cases in such mixed kernel GPRs.", "published": "2021-08-10T14:29:48Z", "authors": ["Jaehoan Kim", "Jaeyong Lee"], "categories": ["math.ST", "stat.ME"], "doi": "", "_query": "hedonic regression housing", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2610.11828v1", "title": "Manifold Regression", "abstract": "Conventional statistical modeling typically assumes a one-to-one mapping between input and output variables, where one-to-one is used in the predictive sense that a specified input results in a unique output. Many scientific and engineering systems, however, exhibit non-one-to-one(noto) input-output relations. In a noto relation, the same input may correspond to multiple admissible outputs, so the conventional statistical models may not be appropriate. This paper develops manifold regression, a parametric modeling framework for such noto input-output relations, which represents the underlying input-output relation through a latent manifold. The manifold is specified upto an unknown finite dimensional parameter and we estimate the unknown parameters from ordinary input-output observations by regularized profile optimization with a robust solver. Once the manifold has been learned, prediction is obtained by slicing the estimated manifold along a specified coordinate value. Because a slice may contain multiple latent roots, the resulting manifold prediction is naturally set-valued. This formulation extends predictive learning beyond conventional one-to-one statistical models while containing classical regression and inverse prediction as special cases. Theoretical results are established to justify the manifold regression model and its sliced prediction sets; and case studies are used to demonstrate stable performance across representative noto cases.", "published": "2026-10-08T12:26:18Z", "authors": ["Yuanyuan Lin", "Dennis K. J. Lin"], "categories": ["stat.ME"], "doi": "", "_query": "hedonic regression housing", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2601.07202v2", "title": "Principal component-guided sparse reduced-rank regression", "abstract": "Reduced-rank regression estimates regression coefficients by imposing a low-rank constraint on the matrix of regression coefficients, thereby accounting for correlations among response variables. To further improve predictive accuracy and model interpretability, several regularized reduced-rank regression methods have been proposed. However, these existing methods cannot bias the regression coefficients toward the leading principal component directions while accounting for the correlation structure among explanatory variables. In addition, when the explanatory variables exhibit a group structure, the correlation structure within each group cannot be adequately incorporated. To overcome these limitations, we propose a new method that introduces pcLasso into the reduced-rank regression framework. The proposed method improves predictive accuracy by accounting for the correlation among response variables while strongly biasing the matrix of regression coefficients toward principal component directions with large variance. Furthermore, even in settings where the explanatory variables possess a group structure, the proposed method is capable of explicitly incorporating this structure into the estimation process. Finally, we illustrate the effectiveness of the proposed method through numerical simulations and real data application.", "published": "2026-01-12T04:52:15Z", "authors": ["Kanji Goto", "Shintaro Yuki", "Kensuke Tanioka", "Hiroshi Yadohisa"], "categories": ["stat.ME"], "doi": "", "_query": "hedonic regression housing", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/math/0507510v1", "title": "LAD Regression and Nonparametric Methods for Detecting Outliers and Leverage Points", "abstract": "The detection of influential observations for the standard least squares regression model is a question that has been extensively studied. LAD regression diagnostics offers alternative approaches whose main feature is the robustness. In this paper a new approach for nonparametric detection of influencial observations in LAD regression models is presented and compared with other classical methods of diagnostics.", "published": "2005-07-25T13:25:35Z", "authors": ["Giuseppe Melfi", "Susana Faria"], "categories": ["math.ST"], "doi": "", "_query": "hedonic regression housing", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/1101.0047v1", "title": "Component Selection in the Additive Regression Model", "abstract": "Similar to variable selection in the linear regression model, selecting significant components in the popular additive regression model is of great interest. However, such components are unknown smooth functions of independent variables, which are unobservable. As such, some approximation is needed. In this paper, we suggest a combination of penalized regression spline approximation and group variable selection, called the lasso-type spline method (LSM), to handle this component selection problem with a diverging number of strongly correlated variables in each group. It is shown that the proposed method can select significant components and estimate nonparametric additive function components simultaneously with an optimal convergence rate simultaneously. To make the LSM stable in computation and able to adapt its estimators to the level of smoothness of the component functions, weighted power spline bases and projected weighted power spline bases are proposed. Their performance is examined by simulation studies across two set-ups with independent predictors and correlated predictors, respectively, and appears superior to the performance of competing methods. The proposed method is extended to a partial linear regression model analysis with real data, and gives reliable results.", "published": "2010-12-30T07:45:18Z", "authors": ["Xia Cui", "Heng Peng", "Songqiao Wen", "Lixing Zhu"], "categories": ["stat.ME"], "doi": "", "_query": "hedonic regression housing", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/0706.1154v1", "title": "Additive Regression Model for Continuous Time Processes", "abstract": "In the setting of additive regression model for continuous time process, we establish the optimal uniform convergence rates and optimal asymptotic quadratic error of additive regression. To build our estimate, we use the marginal integration method.", "published": "2007-06-08T10:34:05Z", "authors": ["Mohammed Debbarh", "Bertrand Maillot"], "categories": ["math.ST", "math.PR", "stat.AP"], "doi": "", "_query": "hedonic regression housing", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2506.19626v1", "title": "Scaling Up Unbiased Search-based Symbolic Regression", "abstract": "In a regression task, a function is learned from labeled data to predict the labels at new data points. The goal is to achieve small prediction errors. In symbolic regression, the goal is more ambitious, namely, to learn an interpretable function that makes small prediction errors. This additional goal largely rules out the standard approach used in regression, that is, reducing the learning problem to learning parameters of an expansion of basis functions by optimization. Instead, symbolic regression methods search for a good solution in a space of symbolic expressions. To cope with the typically vast search space, most symbolic regression methods make implicit, or sometimes even explicit, assumptions about its structure. Here, we argue that the only obvious structure of the search space is that it contains small expressions, that is, expressions that can be decomposed into a few subexpressions. We show that systematically searching spaces of small expressions finds solutions that are more accurate and more robust against noise than those obtained by state-of-the-art symbolic regression methods. In particular, systematic search outperforms state-of-the-art symbolic regressors in terms of its ability to recover the true underlying symbolic expressions on established benchmark data sets.", "published": "2025-06-24T13:47:19Z", "authors": ["Paul Kahlmeyer", "Joachim Giesen", "Michael Habeck", "Henrik Voigt"], "categories": ["cs.LG"], "doi": "10.24963/ijcai.2024/471", "_query": "hedonic regression housing", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/0810.1793v1", "title": "On connectivity of fibers with positive marginals in multiple logistic regression", "abstract": "In this paper we consider exact tests of a multiple logistic regression, where the levels of covariates are equally spaced, via Markov beses. In usual application of multiple logistic regression, the sample size is positive for each combination of levels of the covariates. In this case we do not need a whole Markov basis, which guarantees connectivity of all fibers. We first give an explicit Markov basis for multiple Poisson regression. By the Lawrence lifting of this basis, in the case of bivariate logistic regression, we show a simple subset of the Markov basis which connects all fibers with a positive sample size for each combination of levels of covariates.", "published": "2008-10-10T04:03:19Z", "authors": ["Hisayuki Hara", "Akimichi Takemura", "Ruriko Yoshida"], "categories": ["math.ST", "math.CO"], "doi": "", "_query": "hedonic regression housing", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2509.19123v1", "title": "George Udny Yule and the Interpretation of Regression Betas", "abstract": "Initially applied in astronomy and geodesy, the linear regression model aimed to find the best estimates for parameters with predefined meanings. E.g., orbital elements, geodetic constants. As its use expanded to other disciplines, often to summarize data without an underlying theoretical model, the need for a general interpretation of regression betas arose. Early attempts by Galton and Karl Pearson met with mixed success. G. U. Yule was the first to develop a general statistical interpretation, the culmination of efforts begun in 1896. Yule interpretation is based on the partial regression theorem, which he proved in 1907.", "published": "2025-09-23T15:10:13Z", "authors": ["Francesco Corielli"], "categories": ["stat.OT"], "doi": "", "_query": "hedonic regression housing", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2412.20535v2", "title": "Inference with Randomized Regression Trees", "abstract": "Regression trees are a popular machine learning algorithm that fit piecewise constant models by recursively partitioning the predictor space. This paper focuses on statistical inference for a data-dependent model obtained from a fitted regression tree. We introduce Randomized Regression Trees (RRT), a novel selective inference method that adds independent Gaussian noise to the gain function underlying the splitting rules of classic regression trees. The RRT method offers several advantages over existing methods. First, added randomization is used to obtain a closed-form pivot while accounting for the data-dependent tree structure. Second, RRT with a small amount of randomization achieves predictive accuracy similar to a model trained on the entire dataset, while also providing significantly more powerful inference than existing selective inference methods, such as data splitting. Third, RRT yields intervals that automatically adapt to the signal strength in the data. Our empirical analyses highlight these advantages of the RRT method and its ability to convert a purely predictive algorithm into a method capable of performing powerful inference in the non-linear tree model.", "published": "2024-12-29T17:49:34Z", "authors": ["Soham Bakshi", "Yiling Huang", "Snigdha Panigrahi", "Walter Dempsey"], "categories": ["stat.ME"], "doi": "", "_query": "hedonic regression housing", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/1812.02885v1", "title": "Adversarial Attacks, Regression, and Numerical Stability Regularization", "abstract": "Adversarial attacks against neural networks in a regression setting are a critical yet understudied problem. In this work, we advance the state of the art by investigating adversarial attacks against regression networks and by formulating a more effective defense against these attacks. In particular, we take the perspective that adversarial attacks are likely caused by numerical instability in learned functions. We introduce a stability inducing, regularization based defense against adversarial attacks in the regression setting. Our new and easy to implement defense is shown to outperform prior approaches and to improve the numerical stability of learned functions.", "published": "2018-12-07T02:50:20Z", "authors": ["Andre T. Nguyen", "Edward Raff"], "categories": ["cs.LG", "cs.CR", "stat.ML"], "doi": "", "_query": "hedonic regression housing", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2212.05024v2", "title": "Decomposable Sparse Tensor on Tensor Regression", "abstract": "Most regularized tensor regression research focuses on tensors predictors with scalars responses or vectors predictors to tensors responses. We consider the sparse low rank tensor on tensor regression where predictors $\\mathcal{X}$ and responses $\\mathcal{Y}$ are both high-dimensional tensors. By demonstrating that the general inner product or the contracted product on a unit rank tensor can be decomposed into standard inner products and outer products, the problem can be simply transformed into a tensor to scalar regression followed by a tensor decomposition. So we propose a fast solution based on stagewise search composed by contraction part and generation part which are optimized alternatively. We successfully demonstrate our method can out perform current methods in terms of accuracy and predictors selection by effectively incorporating the structural information.", "published": "2022-12-09T18:16:41Z", "authors": ["Haiyi Mao", "Jason Xiaotian Dou"], "categories": ["cs.LG"], "doi": "", "_query": "hedonic regression housing", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2607.05536v1", "title": "Higher-Order Certified Robustness for Regression", "abstract": "Randomized smoothing has emerged as a scalable technique for certifying the adversarial robustness of classifiers. However, its application to regression remains under-explored and faces unique challenges. Existing regression certificates rely on probabilistic acceptance regions and fail to exploit the local geometry of the function. In this work, we present a novel framework for certified robust regression that addresses these limitations. We derive a prediction-centered certificate that guarantees the stability of the smoothed model's prediction and ensures practical computability at test time. We investigate several alternatives for constructing these certificates by explicitly incorporating means, variances, and gradients. In particular, we demonstrate on the MNIST rotation task that utilizing gradient information yields significantly tighter robustness certificates compared to the current state-of-the-art, alpha-smoothing.", "published": "2026-07-06T18:17:13Z", "authors": ["Jie Zhang", "Natalie Frank"], "categories": ["stat.ML", "cs.LG"], "doi": "", "_query": "hedonic regression housing", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/1801.01394v1", "title": "Prediction Error Bounds for Linear Regression With the TREX", "abstract": "The TREX is a recently introduced approach to sparse linear regression. In contrast to most well-known approaches to penalized regression, the TREX can be formulated without the use of tuning parameters. In this paper, we establish the first known prediction error bounds for the TREX. Additionally, we introduce extensions of the TREX to a more general class of penalties, and we provide a bound on the prediction error in this generalized setting. These results deepen the understanding of TREX from a theoretical perspective and provide new insights into penalized regression in general.", "published": "2018-01-04T15:16:13Z", "authors": ["Jacob Bien", "Irina Gaynanova", "Johannes Lederer", "Christian Müller"], "categories": ["math.ST"], "doi": "", "_query": "hedonic regression housing", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2412.06160v1", "title": "Obstacle-aware Gaussian Process Regression", "abstract": "Obstacle-aware trajectory navigation is crucial for many systems. For example, in real-world navigation tasks, an agent must avoid obstacles, such as furniture in a room, while planning a trajectory. Gaussian Process (GP) regression, in its current form, fits a curve to a set of data pairs, with each pair consisting of an input point 'x' and its corresponding target regression value 'y(x)' (a positive data pair). However, to account for obstacles, we need to constrain the GP to avoid a target regression value 'y(x-)' for an input point 'x-' (a negative data pair). Our proposed approach, 'GP-ND' (Gaussian Process with Negative Datapairs), fits the model to the positive data pairs while avoiding the negative ones. Specifically, we model the negative data pairs using small blobs of Gaussian distribution and maximize their KL divergence from the GP. Our framework jointly optimizes for both positive and negative data pairs. Our experiments show that GP-ND outperforms traditional GP learning. Additionally, our framework does not affect the scalability of Gaussian Process regression and helps the model converge faster as the data size increases.", "published": "2024-12-09T02:50:20Z", "authors": ["Gaurav Shrivastava"], "categories": ["cs.LG", "math.PR", "stat.ML"], "doi": "", "_query": "hedonic regression housing", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2506.07033v1", "title": "Mixture Experts with Test-Time Self-Supervised Aggregation for Tabular Imbalanced Regression", "abstract": "Tabular data serve as a fundamental and ubiquitous representation of structured information in numerous real-world applications, e.g., finance and urban planning. In the realm of tabular imbalanced applications, data imbalance has been investigated in classification tasks with insufficient instances in certain labels, causing the model's ineffective generalizability. However, the imbalance issue of tabular regression tasks is underexplored, and yet is critical due to unclear boundaries for continuous labels and simplifying assumptions in existing imbalance regression work, which often rely on known and balanced test distributions. Such assumptions may not hold in practice and can lead to performance degradation. To address these issues, we propose MATI: Mixture Experts with Test-Time Self-Supervised Aggregation for Tabular Imbalance Regression, featuring two key innovations: (i) the Region-Aware Mixture Expert, which adopts a Gaussian Mixture Model to capture the underlying related regions. The statistical information of each Gaussian component is then used to synthesize and train region-specific experts to capture the unique characteristics of their respective regions. (ii) Test-Time Self-Supervised Expert Aggregation, which dynamically adjusts region expert weights based on test data features to reinforce expert adaptation across varying test distributions. We evaluated MATI on four real-world tabular imbalance regression datasets, including house pricing, bike sharing, and age prediction. To reflect realistic deployment scenarios, we adopted three types of test distributions: a balanced distribution with uniform target frequencies, a normal distribution that follows the training data, and an inverse distribution that emphasizes rare target regions. On average across these three test distributions, MATI achieved a 7.1% improvement in MAE compared to existing methods.", "published": "2025-06-08T07:53:03Z", "authors": ["Yung-Chien Wang", "Kuang-Da Wang", "Wei-Yao Wang", "Wen-Chih Peng"], "categories": ["cs.LG"], "doi": "", "_query": "hedonic regression housing", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/1905.05242v1", "title": "Hierarchical approaches for flexible and interpretable binary regression models", "abstract": "Binary regression models are ubiquitous in virtually every scientific field. Frequently, traditional generalized linear models fail to capture the variability in the probability surface that gives rise to the binary observations and novel methodology is required. This has generated a substantial literature comprised of binary regression models motivated by various applications. We describe a novel organization of generalizations to traditional binary regression methods based on the familiar three-part structure of generalized linear models (random component, systematic component, link function). This new perspective facilitates both the comparison of existing approaches, and the development of novel, flexible models with interpretable parameters that capture application-specific data generating mechanisms. We use our proposed organizational structure to discuss some concerns with certain existing models for binary data based on quantile regression. We then use the framework to develop several new binary regression models tailored to occupancy data for European red squirrels (Sciurus vulgaris).", "published": "2019-05-13T18:56:36Z", "authors": ["Henry R. Scharf", "Xinyi Lu", "Perry J. Williams", "Mevin B. Hooten"], "categories": ["stat.ME", "stat.AP"], "doi": "", "_query": "hedonic regression housing", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2202.02206v2", "title": "A Comparison Between Quantile Regression and Linear Regression on Empirical Quantiles for Phenological Analysis in Migratory Response to Climate Change", "abstract": "It is well established that migratory birds in general have advanced their arrival times in spring, and in this paper we investigate potential ways of enhancing the level of detail in future phenological analyses. We perform single as well as multiple species analyses, using linear models on empirical quantiles, non-parametric quantile regression and likelihood-based parametric quantile regression with asymmetric Laplace distributed error terms. We conclude that non-parametric quantile regression appears most suited for single as well as multiple species analyses.", "published": "2022-02-04T15:56:41Z", "authors": ["Måns Karlsson", "Ola Hössjer"], "categories": ["stat.AP"], "doi": "", "_query": "hedonic regression housing", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/1711.05766v1", "title": "Fast Predictive Simple Geodesic Regression", "abstract": "Deformable image registration and regression are important tasks in medical image analysis. However, they are computationally expensive, especially when analyzing large-scale datasets that contain thousands of images. Hence, cluster computing is typically used, making the approaches dependent on such computational infrastructure. Even larger computational resources are required as study sizes increase. This limits the use of deformable image registration and regression for clinical applications and as component algorithms for other image analysis approaches. We therefore propose using a fast predictive approach to perform image registrations. In particular, we employ these fast registration predictions to approximate a simplified geodesic regression model to capture longitudinal brain changes. The resulting method is orders of magnitude faster than the standard optimization-based regression model and hence facilitates large-scale analysis on a single graphics processing unit (GPU). We evaluate our results on 3D brain magnetic resonance images (MRI) from the ADNI datasets.", "published": "2017-11-15T19:30:20Z", "authors": ["Zhipeng Ding", "Greg Fleishman", "Xiao Yang", "Paul Thompson", "Roland Kwitt", "Marc Niethammer"], "categories": ["cs.CV", "cs.AI"], "doi": "", "_query": "hedonic regression housing", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/1305.0087v3", "title": "Quantile Regression for Large-scale Applications", "abstract": "Quantile regression is a method to estimate the quantiles of the conditional distribution of a response variable, and as such it permits a much more accurate portrayal of the relationship between the response variable and observed covariates than methods such as Least-squares or Least Absolute Deviations regression. It can be expressed as a linear program, and, with appropriate preprocessing, interior-point methods can be used to find a solution for moderately large problems. Dealing with very large problems, \\emph(e.g.), involving data up to and beyond the terabyte regime, remains a challenge. Here, we present a randomized algorithm that runs in nearly linear time in the size of the input and that, with constant probability, computes a $(1+ε)$ approximate solution to an arbitrary quantile regression problem. As a key step, our algorithm computes a low-distortion subspace-preserving embedding with respect to the loss function of quantile regression. Our empirical evaluation illustrates that our algorithm is competitive with the best previous work on small to medium-sized problems, and that in addition it can be implemented in MapReduce-like environments and applied to terabyte-sized problems.", "published": "2013-05-01T05:21:03Z", "authors": ["Jiyan Yang", "Xiangrui Meng", "Michael W. Mahoney"], "categories": ["cs.DS", "cs.DC", "math.NA", "stat.ML"], "doi": "", "_query": "hedonic regression housing", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/1608.05152v1", "title": "Conditional Sparse Linear Regression", "abstract": "Machine learning and statistics typically focus on building models that capture the vast majority of the data, possibly ignoring a small subset of data as \"noise\" or \"outliers.\" By contrast, here we consider the problem of jointly identifying a significant (but perhaps small) segment of a population in which there is a highly sparse linear regression fit, together with the coefficients for the linear fit. We contend that such tasks are of interest both because the models themselves may be able to achieve better predictions in such special cases, but also because they may aid our understanding of the data. We give algorithms for such problems under the sup norm, when this unknown segment of the population is described by a k-DNF condition and the regression fit is s-sparse for constant k and s. For the variants of this problem when the regression fit is not so sparse or using expected error, we also give a preliminary algorithm and highlight the question as a challenge for future work.", "published": "2016-08-18T01:30:49Z", "authors": ["Brendan Juba"], "categories": ["cs.LG", "cs.DS", "stat.ML"], "doi": "", "_query": "hedonic regression housing", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2502.12999v3", "title": "Asymptotic Optimism of Random-Design Linear and Kernel Regression Models", "abstract": "We derived the closed-form asymptotic optimism of linear regression models under random designs, and generalizes it to kernel ridge regression. Using scaled asymptotic optimism as a generic predictive model complexity measure, we studied the fundamental different behaviors of linear regression model, tangent kernel (NTK) regression model and three-layer fully connected neural networks (NN). Our contribution is two-fold: we provided theoretical ground for using scaled optimism as a model predictive complexity measure; and we show empirically that NN with ReLUs behaves differently from kernel models under this measure. With resampling techniques, we can also compute the optimism for regression models with real data.", "published": "2025-02-18T16:19:28Z", "authors": ["Hengrui Luo", "Yunzhang Zhu"], "categories": ["stat.ML", "cs.LG", "math.ST"], "doi": "", "_query": "hedonic regression housing", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/1404.5245v6", "title": "Size versus truthfulness in the House Allocation problem", "abstract": "We study the House Allocation problem (also known as the Assignment problem), i.e., the problem of allocating a set of objects among a set of agents, where each agent has ordinal preferences (possibly involving ties) over a subset of the objects. We focus on truthful mechanisms without monetary transfers for finding large Pareto optimal matchings. It is straightforward to show that no deterministic truthful mechanism can approximate a maximum cardinality Pareto optimal matching with ratio better than 2. We thus consider randomised mechanisms. We give a natural and explicit extension of the classical Random Serial Dictatorship Mechanism (RSDM) specifically for the House Allocation problem where preference lists can include ties. We thus obtain a universally truthful randomised mechanism for finding a Pareto optimal matching and show that it achieves an approximation ratio of $\\frac{e}{e-1}$. The same bound holds even when agents have priorities (weights) and our goal is to find a maximum weight (as opposed to maximum cardinality) Pareto optimal matching. On the other hand we give a lower bound of $\\frac{18}{13}$ on the approximation ratio of any universally truthful Pareto optimal mechanism in settings with strict preferences. In the case that the mechanism must additionally be non-bossy with an additional technical assumption, we show by utilising a result of Bade that an improved lower bound of $\\frac{e}{e-1}$ holds. This lower bound is tight since RSDM for strict preference lists is non-bossy. We moreover interpret our problem in terms of the classical secretary problem and prove that our mechanism provides the best randomised strategy of the administrator who interviews the applicants.", "published": "2014-04-21T17:08:42Z", "authors": ["Piotr Krysta", "David Manlove", "Baharak Rastegari", "Jinshan Zhang"], "categories": ["cs.GT"], "doi": "10.1007/s00453-019-00584-7", "_query": "hedonic regression housing", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2407.14101v1", "title": "Justified Fairness in House Allocation Problems: two Characterizations of Strategy-proof Mechanisms", "abstract": "We consider the house allocation problems with strict preferences, where monetary transfers are not allowed. We propose two properties in the spirit of justified fairness. Interestingly, together with other well-studied properties (strategy-proofness and non-bossiness), our two new properties identify serial dictatorships and sequential dictatorships, respectively.", "published": "2024-07-19T08:12:54Z", "authors": ["Di Feng", "Jacob Coreno"], "categories": ["econ.TH"], "doi": "", "_query": "hedonic regression housing", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2305.09282v2", "title": "Errors-in-variables Fréchet Regression with Low-rank Covariate Approximation", "abstract": "Fréchet regression has emerged as a promising approach for regression analysis involving non-Euclidean response variables. However, its practical applicability has been hindered by its reliance on ideal scenarios with abundant and noiseless covariate data. In this paper, we present a novel estimation method that tackles these limitations by leveraging the low-rank structure inherent in the covariate matrix. Our proposed framework combines the concepts of global Fréchet regression and principal component regression, aiming to improve the efficiency and accuracy of the regression estimator. By incorporating the low-rank structure, our method enables more effective modeling and estimation, particularly in high-dimensional and errors-in-variables regression settings. We provide a theoretical analysis of the proposed estimator's large-sample properties, including a comprehensive rate analysis of bias, variance, and additional variations due to measurement errors. Furthermore, our numerical experiments provide empirical evidence that supports the theoretical findings, demonstrating the superior performance of our approach. Overall, this work introduces a promising framework for regression analysis of non-Euclidean variables, effectively addressing the challenges associated with limited and noisy covariate data, with potential applications in diverse fields.", "published": "2023-05-16T08:37:54Z", "authors": ["Kyunghee Han", "Dogyoon Song"], "categories": ["stat.ME", "math.ST", "stat.ML"], "doi": "", "_query": "hedonic regression housing", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2202.12851v1", "title": "Boosting Distributional Copula Regression", "abstract": "Capturing complex dependence structures between outcome variables (e.g., study endpoints) is of high relevance in contemporary biomedical data problems and medical research. Distributional copula regression provides a flexible tool to model the joint distribution of multiple outcome variables by disentangling the marginal response distributions and their dependence structure. In a regression setup each parameter of the copula model, i.e. the marginal distribution parameters and the copula dependence parameters, can be related to covariates via structured additive predictors. We propose a framework to fit distributional copula regression models via a model-based boosting algorithm. Model-based boosting is a modern estimation technique that incorporates useful features like an intrinsic variable selection mechanism, parameter shrinkage and the capability to fit regression models in high dimensional data setting, i.e. situations with more covariates than observations. Thus, model-based boosting does not only complement existing Bayesian and maximum-likelihood based estimation frameworks for this model class but rather enables unique intrinsic mechanisms that can be helpful in many applied problems. The performance of our boosting algorithm in the context of copula regression models with continuous margins is evaluated in simulation studies that cover low- and high-dimensional data settings and situations with and without dependence between the responses. Moreover, distributional copula boosting is used to jointly analyze and predict the length and the weight of newborns conditional on sonographic measurements of the fetus before delivery together with other clinical variables.", "published": "2022-02-25T17:56:30Z", "authors": ["Nicolai Hans", "Nadja Klein", "Florian Faschingbauer", "Michael Schneider", "Andreas Mayr"], "categories": ["stat.ME"], "doi": "", "_query": "hedonic regression housing", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/1509.02957v2", "title": "Semismooth Newton Coordinate Descent Algorithm for Elastic-Net Penalized Huber Loss Regression and Quantile Regression", "abstract": "We propose an algorithm, semismooth Newton coordinate descent (SNCD), for the elastic-net penalized Huber loss regression and quantile regression in high dimensional settings. Unlike existing coordinate descent type algorithms, the SNCD updates each regression coefficient and its corresponding subgradient simultaneously in each iteration. It combines the strengths of the coordinate descent and the semismooth Newton algorithm, and effectively solves the computational challenges posed by dimensionality and nonsmoothness. We establish the convergence properties of the algorithm. In addition, we present an adaptive version of the \"strong rule\" for screening predictors to gain extra efficiency. Through numerical experiments, we demonstrate that the proposed algorithm is very efficient and scalable to ultra-high dimensions. We illustrate the application via a real data example.", "published": "2015-09-09T21:26:39Z", "authors": ["Congrui Yi", "Jian Huang"], "categories": ["stat.CO", "stat.ML"], "doi": "", "_query": "hedonic regression housing", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2205.05135v3", "title": "Regression-based projection for learning Mori-Zwanzig operators", "abstract": "We propose to adopt statistical regression as the projection operator to enable data-driven learning of the operators in the Mori--Zwanzig formalism. We present a principled method to extract the Markov and memory operators for any regression models. We show that the choice of linear regression results in a recently proposed data-driven learning algorithm based on Mori's projection operator, which is a higher-order approximate Koopman learning method. We show that more expressive nonlinear regression models naturally fill in the gap between the highly idealized and computationally efficient Mori's projection operator and the most optimal yet computationally infeasible Zwanzig's projection operator. We performed numerical experiments and extracted the operators for an array of regression-based projections, including linear, polynomial, spline, and neural-network-based regressions, showing a progressive improvement as the complexity of the regression model increased. Our proposition provides a general framework to extract memory-dependent corrections and can be readily applied to an array of data-driven learning methods for stationary dynamical systems in the literature.", "published": "2022-05-10T19:35:47Z", "authors": ["Yen Ting Lin", "Yifeng Tian", "Danny Perez", "Daniel Livescu"], "categories": ["math.DS", "nlin.CD", "physics.comp-ph", "stat.ML"], "doi": "", "_query": "hedonic regression housing", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/1811.01158v1", "title": "Boosted Sparse and Low-Rank Tensor Regression", "abstract": "We propose a sparse and low-rank tensor regression model to relate a univariate outcome to a feature tensor, in which each unit-rank tensor from the CP decomposition of the coefficient tensor is assumed to be sparse. This structure is both parsimonious and highly interpretable, as it implies that the outcome is related to the features through a few distinct pathways, each of which may only involve subsets of feature dimensions. We take a divide-and-conquer strategy to simplify the task into a set of sparse unit-rank tensor regression problems. To make the computation efficient and scalable, for the unit-rank tensor regression, we propose a stagewise estimation procedure to efficiently trace out its entire solution path. We show that as the step size goes to zero, the stagewise solution paths converge exactly to those of the corresponding regularized regression. The superior performance of our approach is demonstrated on various real-world and synthetic examples.", "published": "2018-11-03T04:45:57Z", "authors": ["Lifang He", "Kun Chen", "Wanwan Xu", "Jiayu Zhou", "Fei Wang"], "categories": ["cs.LG", "stat.ML"], "doi": "", "_query": "hedonic regression housing", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2404.04425v1", "title": "Bayesian Additive Regression Networks", "abstract": "We apply Bayesian Additive Regression Tree (BART) principles to training an ensemble of small neural networks for regression tasks. Using Markov Chain Monte Carlo, we sample from the posterior distribution of neural networks that have a single hidden layer. To create an ensemble of these, we apply Gibbs sampling to update each network against the residual target value (i.e. subtracting the effect of the other networks). We demonstrate the effectiveness of this technique on several benchmark regression problems, comparing it to equivalent shallow neural networks, BART, and ordinary least squares. Our Bayesian Additive Regression Networks (BARN) provide more consistent and often more accurate results. On test data benchmarks, BARN averaged between 5 to 20 percent lower root mean square error. This error performance does come at the cost, however, of greater computation time. BARN sometimes takes on the order of a minute where competing methods take a second or less. But, BARN without cross-validated hyperparameter tuning takes about the same amount of computation time as tuned other methods. Yet BARN is still typically more accurate.", "published": "2024-04-05T21:47:32Z", "authors": ["Danielle Van Boxel"], "categories": ["stat.ML", "cs.LG"], "doi": "", "_query": "hedonic regression housing", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/1202.5883v1", "title": "On Bayesian quantile regression curve fitting via auxiliary variables", "abstract": "Quantile regression has received increased attention in the statistics community in recent years. This article adapts an auxiliary variable method, commonly used in Bayesian variable selection for mean regression models, to the fitting of quantile regression curves. We focus on the fitting of regression splines, with unknown number and location of knots. We provide an efficient algorithm with Metropolis-Hastings updates whose tuning is fully automated. The method is tested on simulated and real examples and its extension to additive models is described. Finally we propose a simple postprocessing procedure to deal with the problem of the crossing of multiple separately estimated quantile curves.", "published": "2012-02-27T10:42:54Z", "authors": ["J. -L. Dortet-Bernadet", "Y. Fan"], "categories": ["stat.ME"], "doi": "", "_query": "hedonic regression housing", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/1912.08141v3", "title": "Performance of regression models as a function of experiment noise", "abstract": "A challenge in developing machine learning regression models is that it is difficult to know whether maximal performance has been reached on a particular dataset, or whether further model improvement is possible. In biology this problem is particularly pronounced as sample labels (response variables) are typically obtained through experiments and therefore have experiment noise associated with them. Such label noise puts a fundamental limit to the performance attainable by regression models. We address this challenge by deriving a theoretical upper bound for the coefficient of determination (R2) for regression models. This theoretical upper bound depends only on the noise associated with the response variable in a dataset as well as its variance. The upper bound estimate was validated via Monte Carlo simulations and then used as a tool to bootstrap performance of regression models trained on biological datasets, including protein sequence data, transcriptomic data, and genomic data. Although we study biological datasets in this work, the new upper bound estimates will hold true for regression models from any research field or application area where response variables have associated noise.", "published": "2019-12-17T17:13:18Z", "authors": ["Gang Li", "Jan Zrimec", "Boyang Ji", "Jun Geng", "Johan Larsbrink", "Aleksej Zelezniak", "Jens Nielsen", "Martin KM Engqvist"], "categories": ["q-bio.BM", "cs.LG"], "doi": "10.1177/11779322211020315", "_query": "hedonic regression housing", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2410.15304v1", "title": "Multiple Kernel Clustering via Local Regression Integration", "abstract": "Multiple kernel methods less consider the intrinsic manifold structure of multiple kernel data and estimate the consensus kernel matrix with quadratic number of variables, which makes it vulnerable to the noise and outliers within multiple candidate kernels. This paper first presents the clustering method via kernelized local regression (CKLR). It captures the local structure of kernel data and employs kernel regression on the local region to predict the clustering results. Moreover, this paper further extends it to perform clustering via the multiple kernel local regression (CMKLR). We construct the kernel level local regression sparse coefficient matrix for each candidate kernel, which well characterizes the kernel level manifold structure. We then aggregate all the kernel level local regression coefficients via linear weights and generate the consensus sparse local regression coefficient, which largely reduces the number of candidate variables and becomes more robust against noises and outliers within multiple kernel data. Thus, the proposed method CMKLR avoids the above two limitations. It only contains one additional hyperparameter for tuning. Extensive experimental results show that the clustering performance of the proposed method on benchmark datasets is better than that of 10 state-of-the-art multiple kernel clustering methods.", "published": "2024-10-20T06:26:29Z", "authors": ["Liang Du", "Xin Ren", "Haiying Zhang", "Peng Zhou"], "categories": ["cs.LG"], "doi": "10.11896/jsjks.201000106", "_query": "hedonic regression housing", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2409.04001v2", "title": "A semi-supervised learning using over-parameterized regression", "abstract": "Semi-supervised learning (SSL) is an important theme in machine learning, in which we have a few labeled samples and many unlabeled samples. In this paper, for SSL in a regression problem, we consider a method of incorporating information on unlabeled samples into kernel functions. As a typical implementation, we employ Gaussian kernels whose centers are labeled and unlabeled input samples. Since the number of coefficients is larger than the number of labeled samples in this setting, this is an over-parameterized regression roblem. A ridge regression is a typical estimation method under this setting. In this paper, alternatively, we consider to apply the minimum norm least squares (MNLS), which is known as a helpful tool for understanding deep learning behavior while it may not be application oriented. Then, in applying the MNLS for SSL, we established several methods based on feature extraction/dimension reduction in the SVD (singular value decomposition) representation of a Gram type matrix appeared in the over-parameterized regression problem. The methods are thresholding according to singular value magnitude with cross validation, hard-thresholding with cross validation, universal thresholding and bridge thresholding methods. The first one is equivalent to a method using a well-known low rank approximation of a Gram type matrix. We refer to these methods as SVD regression methods. In the experiments for real data, depending on datasets, clear superiority of the proposed SVD regression methods over ridge regression methods was observed. And, depending on datasets, incorporation of information on unlabeled input samples into kernels was found to be clearly effective.", "published": "2024-09-06T03:05:35Z", "authors": ["Katsuyuki Hagiwara"], "categories": ["cs.LG", "stat.ME"], "doi": "", "_query": "hedonic regression housing", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/1408.2327v9", "title": "On the Consistency of Ordinal Regression Methods", "abstract": "Many of the ordinal regression models that have been proposed in the literature can be seen as methods that minimize a convex surrogate of the zero-one, absolute, or squared loss functions. A key property that allows to study the statistical implications of such approximations is that of Fisher consistency. Fisher consistency is a desirable property for surrogate loss functions and implies that in the population setting, i.e., if the probability distribution that generates the data were available, then optimization of the surrogate would yield the best possible model. In this paper we will characterize the Fisher consistency of a rich family of surrogate loss functions used in the context of ordinal regression, including support vector ordinal regression, ORBoosting and least absolute deviation. We will see that, for a family of surrogate loss functions that subsumes support vector ordinal regression and ORBoosting, consistency can be fully characterized by the derivative of a real-valued function at zero, as happens for convex margin-based surrogates in binary classification. We also derive excess risk bounds for a surrogate of the absolute error that generalize existing risk bounds for binary classification. Finally, our analysis suggests a novel surrogate of the squared error loss. We compare this novel surrogate with competing approaches on 9 different datasets. Our method shows to be highly competitive in practice, outperforming the least squares loss on 7 out of 9 datasets.", "published": "2014-08-11T06:52:46Z", "authors": ["Fabian Pedregosa", "Francis Bach", "Alexandre Gramfort"], "categories": ["cs.LG"], "doi": "", "_query": "hedonic regression housing", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2402.06104v6", "title": "Gradient Aligned Regression via Pairwise Losses", "abstract": "Regression is a fundamental task in machine learning that has garnered extensive attention over the past decades. The conventional approach for regression involves employing loss functions that primarily concentrate on aligning model prediction with the ground truth for each individual data sample. Recent research endeavors have introduced novel perspectives by incorporating label similarity to regression via imposing extra pairwise regularization on the latent feature space and demonstrated the effectiveness. However, there are two drawbacks for those approaches: i) their pairwise operation in latent feature space is computationally more expensive than conventional regression losses; ii) it lacks of theoretical justifications behind such regularization. In this work, we propose GAR (Gradient Aligned Regression) as a competitive alternative method in label space, which is constituted by a conventional regression loss and two pairwise label difference losses for gradient alignment including magnitude and direction. GAR enjoys: i) the same level efficiency as conventional regression loss because the quadratic complexity for the proposed pairwise losses can be reduced to linear complexity; ii) theoretical insights from learning the pairwise label difference to learning the gradient of the ground truth function. We limit our current scope as regression on the clean data setting without noises, outliers or distributional shifts, etc. We demonstrate the effectiveness of the proposed method practically on two synthetic datasets and on eight extensive real-world tasks from six benchmark datasets with other eight competitive baselines. Running time experiments demonstrate the superior efficiency of the proposed GAR over existing methods with pairwise regularization in latent feature space and ablation studies demonstrate the effectiveness of each component for GAR.", "published": "2024-02-08T23:43:53Z", "authors": ["Dixian Zhu", "Tianbao Yang", "Livnat Jerby"], "categories": ["cs.LG", "cs.AI"], "doi": "", "_query": "hedonic regression housing", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2603.22408v1", "title": "Spline Quantile Regression with Cubic and Linear Smoothing Splines", "abstract": "Spline quantile regression (SQR) is a method introduced recently by Li and Megiddo (2026) for linear quantile regression where the regression coefficients are treated as smooth functions of the quantile level. With the coefficients represented by cubic splines with fixed knots on a given set of quantiles, the SQR method produces an estimate for the functional coefficients by solving a penalized quantile regression problem. The $\\ell_1$-norm of the second derivatives of the coefficients is employed as the penalty for regulating the roughness of the functional coefficients. This extends the SQR method by introducing additional pairings of the functional representation for the regression coefficients and the penalty for their roughness. The resulting cubic and linear SQR solutions are shown to be smoothing splines which are optimal in a functional space larger than the respective spline space with fixed knots. It is shown that the cubic SQR can be reformulated and solved as a quadratic program and the linear SQR as a linear program. A simulation study demonstrates that the SQR solutions not only offer a concise functional representation of the regression coefficients with distinct smoothness characteristics, but also provide a capability of producing more accurate estimates of the regression coefficients when the underlying functions are suitably smooth. Application of the SQR solutions is demonstrated by real-data examples, including a Granger causality analysis of stock market indices.", "published": "2026-03-23T18:00:15Z", "authors": ["Ta-Hsin Li"], "categories": ["stat.ME"], "doi": "", "_query": "hedonic regression housing", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2605.01067v1", "title": "Deep Variational Inference Symbolic Regression", "abstract": "Symbolic regression discovers explicit, interpretable equations without assuming a functional form in advance. A Bayesian approach strengthens this through probability distributions over candidate expressions, thus quantifying uncertainty in the presence of noisy and limited data. Deep Symbolic Regression (DSR) uses a neural network to generate symbolic expressions, but it is designed to identify a single best-fitting expression rather than infer a posterior distribution over models. We introduce Deep Variational Inference Symbolic Regression (DVISR), a variational Bayesian extension of DSR. DVISR replaces the original reward with the integrand of the evidence lower bound. It also extends the network architecture to output distributions over constants within expressions, enabling posterior inference over both expression trees and their associated constants. We show that DVISR can recover the true posterior in simple settings, both with and without constant tokens, and we examine how its performance changes as the size of the expression space increases. These results position DVISR as a step toward scalable Bayesian symbolic regression with uncertainty over full symbolic models.", "published": "2026-05-01T20:04:11Z", "authors": ["James Butterworth", "Gevik Grigorian", "Alejandro DiazDelaO"], "categories": ["cs.LG"], "doi": "", "_query": "hedonic regression housing", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2101.04783v1", "title": "Variable bandwidth kernel regression estimation", "abstract": "In this paper we propose a variable bandwidth kernel regression estimator for $i.i.d.$ observations in $\\mathbb{R}^2$ to improve the classical Nadaraya-Watson estimator. The bias is improved to the order of $O(h_n^4)$ under the condition that the fifth order derivative of the density function and the sixth order derivative of the regression function are bounded and continuous. We also establish the central limit theorems for the proposed ideal and true variable kernel regression estimators. The simulation study confirms our results and demonstrates the advantage of the variable bandwidth kernel method over the classical kernel method.", "published": "2021-01-12T22:34:14Z", "authors": ["Janet Nakarmi", "Hailin Sang", "Lin Ge"], "categories": ["math.ST", "stat.ME"], "doi": "", "_query": "hedonic regression housing", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/1909.10986v2", "title": "Regression methods in waveform modeling: a comparative study", "abstract": "Gravitational-wave astronomy of compact binaries relies on theoretical models of the gravitational-wave signal that is emitted as binaries coalesce. These models do not only need to be accurate, they also have to be fast to evaluate in order to be able to compare millions of signals in near real time with the data of gravitational-wave instruments. A variety of regression and interpolation techniques have been employed to build efficient waveform models, but no study has systematically compared the performance of these regression methods yet. Here we provide such a comparison of various techniques, including polynomial fits, radial basis functions, Gaussian process regression and artificial neural networks, specifically for the case of gravitational waveform modeling. We use all these techniques to regress analytical models of non-precessing and precessing binary black hole waveforms, and compare the accuracy as well as computational speed. We find that most regression methods are reasonably accurate, but efficiency considerations favour in many cases the most simple approach. We conclude that sophisticated regression methods are not necessarily needed in standard gravitational-wave modeling applications, although problems with higher complexity than what is tested here might be more suitable for machine-learning techniques and more sophisticated methods may have side benefits.", "published": "2019-09-24T15:10:55Z", "authors": ["Yoshinta Setyawati", "Michael Pürrer", "Frank Ohme"], "categories": ["astro-ph.IM", "gr-qc", "physics.comp-ph"], "doi": "10.1088/1361-6382/ab693b", "_query": "hedonic regression housing", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/1310.8037v1", "title": "Misspecification in copula-based regression", "abstract": "In a recent paper Noh et al. (2013) proposed a new semiparametric estimate of a regression function with a multivariate predictor, which is based on a specification of the dependence structure between the predictor and the response by means of a parametric copula. This paper investigates the effect which occurs under misspecification of the parametric model. We demonstrate that even for a one or two dimensional predictor the error caused by a \\wrong\" specification of the parametric family is rather severe, if the regression is not monotone in one of the components of the predictor. Moreover, we also show that these problems occur for all of the commonly used copula families and we illustrate in several examples that the copula-based regression may lead to invalid results even when more exible copula models such as vine copulae (with the common parametric families) are used in the estimation procedure.", "published": "2013-10-30T06:35:15Z", "authors": ["Holger Dette", "Ria Van Hecke", "Stanislav Volgushev"], "categories": ["stat.ME"], "doi": "", "_query": "hedonic regression housing", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/1710.08083v1", "title": "Generalized High-Dimensional Trace Regression via Nuclear Norm Regularization", "abstract": "We study the generalized trace regression with a near low-rank regression coefficient matrix, which extends notion of sparsity for regression coefficient vectors. Specifically, given a matrix covariate $X$, the probability density function $f(Y\\vert X)=c(Y)\\exp{(φ^{-1}\\left[-Yη^* + b(η^*)\\right])}$, where $η^*=tr({Θ^*}^TX)$. This model accommodates various types of responses and embraces many important problem setups such as reduced-rank regression, matrix regression that accommodates a panel of regressors, matrix completion, among others. We estimate $Θ^*$ through minimizing empirical negative log-likelihood plus nuclear norm penalty. We first establish a general theory and then for each specific problem, we derive explicitly the statistical rate of the proposed estimator. They all match the minimax rates in the linear trace regression up to logarithmic factors. Numerical studies confirm the rates we established and demonstrate the advantage of generalized trace regression over linear trace regression when the response is dichotomous. We also show the benefit of incorporating nuclear norm regularization in dynamic stock return prediction and in image classification.", "published": "2017-10-23T04:22:14Z", "authors": ["Jianqing Fan", "Wenyan Gong", "Ziwei Zhu"], "categories": ["stat.ME"], "doi": "", "_query": "hedonic regression housing", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2304.02261v1", "title": "Optimal Sketching Bounds for Sparse Linear Regression", "abstract": "We study oblivious sketching for $k$-sparse linear regression under various loss functions such as an $\\ell_p$ norm, or from a broad class of hinge-like loss functions, which includes the logistic and ReLU losses. We show that for sparse $\\ell_2$ norm regression, there is a distribution over oblivious sketches with $Θ(k\\log(d/k)/\\varepsilon^2)$ rows, which is tight up to a constant factor. This extends to $\\ell_p$ loss with an additional additive $O(k\\log(k/\\varepsilon)/\\varepsilon^2)$ term in the upper bound. This establishes a surprising separation from the related sparse recovery problem, which is an important special case of sparse regression. For this problem, under the $\\ell_2$ norm, we observe an upper bound of $O(k \\log (d)/\\varepsilon + k\\log(k/\\varepsilon)/\\varepsilon^2)$ rows, showing that sparse recovery is strictly easier to sketch than sparse regression. For sparse regression under hinge-like loss functions including sparse logistic and sparse ReLU regression, we give the first known sketching bounds that achieve $o(d)$ rows showing that $O(μ^2 k\\log(μn d/\\varepsilon)/\\varepsilon^2)$ rows suffice, where $μ$ is a natural complexity parameter needed to obtain relative error bounds for these loss functions. We again show that this dimension is tight, up to lower order terms and the dependence on $μ$. Finally, we show that similar sketching bounds can be achieved for LASSO regression, a popular convex relaxation of sparse regression, where one aims to minimize $\\|Ax-b\\|_2^2+λ\\|x\\|_1$ over $x\\in\\mathbb{R}^d$. We show that sketching dimension $O(\\log(d)/(λ\\varepsilon)^2)$ suffices and that the dependence on $d$ and $λ$ is tight.", "published": "2023-04-05T07:24:19Z", "authors": ["Tung Mai", "Alexander Munteanu", "Cameron Musco", "Anup B. Rao", "Chris Schwiegelshohn", "David P. Woodruff"], "categories": ["cs.DS", "cs.LG", "stat.ML"], "doi": "", "_query": "hedonic regression housing", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/1004.3814v1", "title": "Bregman Distance to L1 Regularized Logistic Regression", "abstract": "In this work we investigate the relationship between Bregman distances and regularized Logistic Regression model. We present a detailed study of Bregman Distance minimization, a family of generalized entropy measures associated with convex functions. We convert the L1-regularized logistic regression into this more general framework and propose a primal-dual method based algorithm for learning the parameters. We pose L1-regularized logistic regression into Bregman distance minimization and then apply non-linear constrained optimization techniques to estimate the parameters of the logistic model.", "published": "2010-04-21T23:09:06Z", "authors": ["Mithun Das Gupta", "Thomas S. Huang"], "categories": ["cs.LG"], "doi": "", "_query": "hedonic regression housing", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/1805.10843v1", "title": "Nonlinear Simplex Regression Models", "abstract": "In this paper, we propose a simplex regression model in which both the mean and the dispersion parameters are related to covariates by nonlinear predictors. We provide closed-form expressions for the score function, for Fisher's information matrix and its inverse. Some diagnostic measures are introduced. We propose a residual, obtained using Fisher's scoring iterative scheme for the estimation of the parameters that index the regression nonlinear predictor to the mean response and numerically evaluate its behaviour. We also derive the appropriate matrices for assessing local influence on the parameter estimates under different perturbation schemes. We also proposed a scheme for the choice of starting values for the Fisher's iterative scheme for nonlinear simplex models. The diagnostic techniques were applied on actual data. The local influence analyses reveal that the simplex models can be a modeling alternative more robust to influential cases than the beta regression models, both to linear and nonlinear models.", "published": "2018-05-28T09:47:53Z", "authors": ["Patrícia Espinheira", "Alisson de Oliveira Silva"], "categories": ["math.ST"], "doi": "", "_query": "hedonic regression housing", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/1501.00083v1", "title": "Variable Selection in Bayesian Semiparametric Regression Models", "abstract": "In this paper we extend existing Bayesian methods for variable selection in Gaussian process regression, to select both the regression terms and the active covariates in the spatial correlation structure. We then use the estimated posterior probabilities to choose between relatively few modes through cross-validation, and consequently improve prediction.", "published": "2014-12-31T06:21:23Z", "authors": ["Ofir Harari", "David M. Steinberg"], "categories": ["stat.ME"], "doi": "", "_query": "hedonic regression housing", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2112.06822v1", "title": "Quantile Regression under Limited Dependent Variable", "abstract": "A new Stata command, ldvqreg, is developed to estimate quantile regression models for the cases of censored (with lower and/or upper censoring) and binary dependent variables. The estimators are implemented using a smoothed version of the quantile regression objective function. Simulation exercises show that it correctly estimates the parameters and it should be implemented instead of the available quantile regression methods when censoring is present. An empirical application to women's labor supply in Uruguay is considered.", "published": "2021-12-13T17:33:54Z", "authors": ["Javier Alejo", "Gabriel Montes-Rojas"], "categories": ["econ.EM"], "doi": "", "_query": "hedonic regression housing", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2608.19070v1", "title": "Function-On-Function Regression Through Separable Neural Operators", "abstract": "This paper investigates the estimation of the regression operator in function-on-function regression models. While traditional research has predominantly focused on linear models or their immediate nonlinear extensions, we propose a neural operator approach to accommodate general regression operators under mild smoothness assumptions. Operator learning has emerged as an active area of machine learning, particularly for solving physical models governed by partial differential equations. Using this paradigm, our methodology introduces the separable neural operator, a neural-operator architecture that represents the regression operator through input-dependent coefficient functions and output-dependent basis functions. Beyond adapting this architecture to the regression operator estimation problem, we establish the consistency of the estimator under relatively mild smoothness and sampling conditions, allowing functional data to be observed on dense, possibly irregular, discrete grids. We also apply the proposed approach to the BGC Argo data and demonstrate its potential for oceanographic research.", "published": "2026-08-19T16:11:11Z", "authors": ["Tailen Hsing", "Su-Yun Huang", "Toshinari Morimoto"], "categories": ["math.ST", "stat.ML"], "doi": "", "_query": "hedonic regression housing", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/1202.3505v2", "title": "Near-optimal Coresets For Least-Squares Regression", "abstract": "We study (constrained) least-squares regression as well as multiple response least-squares regression and ask the question of whether a subset of the data, a coreset, suffices to compute a good approximate solution to the regression. We give deterministic, low order polynomial-time algorithms to construct such coresets with approximation guarantees, together with lower bounds indicating that there is not much room for improvement upon our results.", "published": "2012-02-16T03:07:35Z", "authors": ["Christos Boutsidis", "Petros Drineas", "Malik Magdon-Ismail"], "categories": ["cs.DS", "cs.LG"], "doi": "10.1109/TIT.2013.2272457", "_query": "hedonic regression housing", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/1712.05510v1", "title": "Graph-Sparse Logistic Regression", "abstract": "We introduce Graph-Sparse Logistic Regression, a new algorithm for classification for the case in which the support should be sparse but connected on a graph. We val- idate this algorithm against synthetic data and benchmark it against L1-regularized Logistic Regression. We then explore our technique in the bioinformatics context of proteomics data on the interactome graph. We make all our experimental code public and provide GSLR as an open source package.", "published": "2017-12-15T02:17:06Z", "authors": ["Alexander LeNail", "Ludwig Schmidt", "Johnathan Li", "Tobias Ehrenberger", "Karen Sachs", "Stefanie Jegelka", "Ernest Fraenkel"], "categories": ["cs.LG", "stat.ML"], "doi": "", "_query": "hedonic regression housing", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2403.04182v3", "title": "Regression-aware Inference with LLMs", "abstract": "Large language models (LLMs) have shown strong results on a range of applications, including regression and scoring tasks. Typically, one obtains outputs from an LLM via autoregressive sampling from the model's output distribution. We show that this inference strategy can be sub-optimal for common regression and scoring evaluation metrics. As a remedy, we build on prior work on Minimum Bayes Risk decoding, and propose alternate inference strategies that estimate the Bayes-optimal solution for regression and scoring metrics in closed-form from sampled responses. We show that our proposal significantly improves over baselines across datasets and models.", "published": "2024-03-07T03:24:34Z", "authors": ["Michal Lukasik", "Harikrishna Narasimhan", "Aditya Krishna Menon", "Felix Yu", "Sanjiv Kumar"], "categories": ["cs.CL", "cs.AI"], "doi": "", "_query": "hedonic regression housing", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2603.24400v2", "title": "Neural Network Models for Contextual Regression", "abstract": "We propose a neural network model for contextual regression in which the regression model depends on contextual features that determine the active submodel and an algorithm to fit the model. The proposed simple contextual neural network (SCtxtNN) separates context identification from context-specific regression, resulting in a structured and interpretable architecture with fewer parameters than a fully connected feed-forward network. We show mathematically that the proposed architecture is sufficient to represent contextual linear regression models using only standard neural network components. Numerical experiments are provided to support the theoretical result, showing that the proposed model achieves lower excess mean squared error and more stable performance than feed-forward neural networks with comparable numbers of parameters, while larger networks improve accuracy only at the cost of increased complexity. The results suggest that incorporating contextual structure can improve model efficiency while preserving interpretability.", "published": "2026-03-25T15:16:36Z", "authors": ["Seksan Kiatsupaibul", "Pakawan Chansiripas"], "categories": ["stat.ML", "cs.LG"], "doi": "", "_query": "hedonic regression housing", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/1810.03496v1", "title": "Regression Analyses of Distributions using Quantile Functional Regression", "abstract": "Radiomics involves the study of tumor images to identify quantitative markers explaining cancer heterogeneity. The predominant approach is to extract hundreds to thousands of image features, including histogram features comprised of summaries of the marginal distribution of pixel intensities, which leads to multiple testing problems and can miss out on insights not contained in the selected features. In this paper, we present methods to model the entire marginal distribution of pixel intensities via the quantile function as functional data, regressed on a set of demographic, clinical, and genetic predictors. We call this approach quantile functional regression, regressing subject-specific marginal distributions across repeated measurements on a set of covariates, allowing us to assess which covariates are associated with the distribution in a global sense, as well as to identify distributional features characterizing these differences, including mean, variance, skewness, and various upper and lower quantiles. To account for smoothness in the quantile functions, we introduce custom basis functions we call quantlets that are sparse, regularized, near-lossless, and empirically defined, adapting to the features of a given data set. We fit this model using a Bayesian framework that uses nonlinear shrinkage of quantlet coefficients to regularize the functional regression coefficients and provides fully Bayesian inference after fitting a Markov chain Monte Carlo. We demonstrate the benefit of the basis space modeling through simulation studies, and apply the method to Magnetic resonance imaging (MRI) based radiomic dataset from Glioblastoma Multiforme to relate imaging-based quantile functions to demographic, clinical, and genetic predictors, finding specific differences in tumor pixel intensity distribution between males and females and between tumors with and without DDIT3 mutations.", "published": "2018-10-04T20:58:41Z", "authors": ["Hojin Yang", "Veerabhadran Baladandayuthapani", "Arvind U. K. Rao", "Jeffrey S. Morris"], "categories": ["stat.ME"], "doi": "", "_query": "hedonic regression housing", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/1901.07599v2", "title": "Functional continuum regression", "abstract": "Functional principal component regression (PCR) can fail to provide good prediction if the response is highly correlated with some excluded functional principal component(s). This situation is common since the construction of functional principal components never involves the response. Aiming at this shortcoming, we develop functional continuum regression (CR). The framework of functional CR includes, as special cases, both functional PCR and functional partial least squares (PLS). Functional CR is expected to own a better accuracy than functional PCR and functional PLS both in estimation and prediction; evidence for this is provided through simulation and numerical case studies. Also, we demonstrate the consistency of estimators given by functional CR.", "published": "2019-01-22T20:04:38Z", "authors": ["Zhiyang Zhou"], "categories": ["stat.ME"], "doi": "10.1016/j.jmva.2019.03.006", "_query": "hedonic regression housing", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2006.12672v3", "title": "Time Series Extrinsic Regression", "abstract": "This paper studies Time Series Extrinsic Regression (TSER): a regression task of which the aim is to learn the relationship between a time series and a continuous scalar variable; a task closely related to time series classification (TSC), which aims to learn the relationship between a time series and a categorical class label. This task generalizes time series forecasting (TSF), relaxing the requirement that the value predicted be a future value of the input series or primarily depend on more recent values. In this paper, we motivate and study this task, and benchmark existing solutions and adaptations of TSC algorithms on a novel archive of 19 TSER datasets which we have assembled. Our results show that the state-of-the-art TSC algorithm Rocket, when adapted for regression, achieves the highest overall accuracy compared to adaptations of other TSC algorithms and state-of-the-art machine learning (ML) algorithms such as XGBoost, Random Forest and Support Vector Regression. More importantly, we show that much research is needed in this field to improve the accuracy of ML models. We also find evidence that further research has excellent prospects of improving upon these straightforward baselines.", "published": "2020-06-23T00:15:10Z", "authors": ["Chang Wei Tan", "Christoph Bergmeir", "Francois Petitjean", "Geoffrey I. Webb"], "categories": ["cs.LG", "stat.ML"], "doi": "", "_query": "hedonic regression housing", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/1504.06122v2", "title": "Random projections for Bayesian regression", "abstract": "This article deals with random projections applied as a data reduction technique for Bayesian regression analysis. We show sufficient conditions under which the entire $d$-dimensional distribution is approximately preserved under random projections by reducing the number of data points from $n$ to $k\\in O(\\operatorname{poly}(d/\\varepsilon))$ in the case $n\\gg d$. Under mild assumptions, we prove that evaluating a Gaussian likelihood function based on the projected data instead of the original data yields a $(1+O(\\varepsilon))$-approximation in terms of the $\\ell_2$ Wasserstein distance. Our main result shows that the posterior distribution of Bayesian linear regression is approximated up to a small error depending on only an $\\varepsilon$-fraction of its defining parameters. This holds when using arbitrary Gaussian priors or the degenerate case of uniform distributions over $\\mathbb{R}^d$ for $β$. Our empirical evaluations involve different simulated settings of Bayesian linear regression. Our experiments underline that the proposed method is able to recover the regression model up to small error while considerably reducing the total running time.", "published": "2015-04-23T10:58:34Z", "authors": ["Leo N. Geppert", "Katja Ickstadt", "Alexander Munteanu", "Jens Quedenfeld", "Christian Sohler"], "categories": ["stat.CO", "cs.DS"], "doi": "10.1007/s11222-015-9608-z", "_query": "hedonic regression housing", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2209.07312v1", "title": "Multicalibrated Regression for Downstream Fairness", "abstract": "We show how to take a regression function $\\hat{f}$ that is appropriately ``multicalibrated'' and efficiently post-process it into an approximately error minimizing classifier satisfying a large variety of fairness constraints. The post-processing requires no labeled data, and only a modest amount of unlabeled data and computation. The computational and sample complexity requirements of computing $\\hat f$ are comparable to the requirements for solving a single fair learning task optimally, but it can in fact be used to solve many different downstream fairness-constrained learning problems efficiently. Our post-processing method easily handles intersecting groups, generalizing prior work on post-processing regression functions to satisfy fairness constraints that only applied to disjoint groups. Our work extends recent work showing that multicalibrated regression functions are ``omnipredictors'' (i.e. can be post-processed to optimally solve unconstrained ERM problems) to constrained optimization.", "published": "2022-09-15T14:16:01Z", "authors": ["Ira Globus-Harris", "Varun Gupta", "Christopher Jung", "Michael Kearns", "Jamie Morgenstern", "Aaron Roth"], "categories": ["cs.LG", "cs.DS"], "doi": "", "_query": "hedonic regression housing", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2207.11864v1", "title": "Maximum Likelihood Ridge Regression", "abstract": "My first paper exclusively about ridge regression was published in Technometrics and chosen for invited presentation at the 1975 Joint Statistical Meetings in Atlanta. Unfortunately, that paper contained a wide range of assorted details and results. Luckily, Gary McDonald's published discussion of that paper focused primarily on my use of Maximum Likelihood estimation under normal distribution-theory. In this review of some results from all four of my ridge publications between 1975 and 2022, I highlight the Maximum Likelihood findings that appear to be most important in practical application of shrinkage in regression.", "published": "2022-07-25T01:19:42Z", "authors": ["Robert L. Obenchain"], "categories": ["stat.ME"], "doi": "", "_query": "hedonic regression housing", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2412.17828v1", "title": "Predictive Modeling of Shading Effects on Photovoltaic Panels Using Regression Analysis", "abstract": "Drones have become indispensable assets during human-made and natural disasters, offering damage assessment, aid delivery, and communication restoration capabilities. However, most drones rely on batteries that require frequent recharging, limiting their effectiveness in continuous missions. Photovoltaic (PV) powered drones are an ideal alternative. However, their performance degrades in variable lighting conditions. Hence, machine learning (ML) controlled PV cells present a promising solution for extending the endurance of a drone. This work evaluates five regression models, linear regression, lasso regression, ridge regression, random forest regression, and XGBoost regression, to predict shading percentages on PV panels. Accurate prediction of shading is crucial for improving the performance and efficiency of ML-controlled PV panels in varying conditions. By achieving a lower MSE and higher R2 Scores, XGBoost and random forest methods were the best-performing regression models. Notably, XGBoost showed superior performance with an R2 Score of 0.926. These findings highlight the possibility of utilizing the regression model to enhance PV-powered drones' efficiency, prolong flight time, reduce maintenance costs, and improve disaster response capabilities.", "published": "2024-12-10T22:57:36Z", "authors": ["Jonathan Olivares", "Tyler Depe", "Kanika Sood", "Rakeshkumar Mahto"], "categories": ["eess.SP"], "doi": "10.1007/978-3-031-85628-0_13", "_query": "hedonic regression housing", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2410.00429v1", "title": "Optimal Designs for Regression on Lie Groups", "abstract": "We consider a linear regression model with complex-valued response and predictors from a compact and connected Lie group. The regression model is formulated in terms of eigenfunctions of the Laplace-Beltrami operator on the Lie group. We show that the normalized Haar measure is an approximate optimal design with respect to all Kiefer's $Φ_p$-criteria. Inspired by the concept of $t$-designs in the field of algebraic combinatorics, we then consider so-called $λ$-designs in order to construct exact $Φ_p$-optimal designs for fixed sample sizes in the considered regression problem. In particular, we explicitly construct $Φ_p$-optimal designs for regression models with predictors in the Lie groups $\\mathrm{SU}(2)$ and $\\mathrm{SO}(3)$, the groups of $2\\times 2$ unitary matrices and $3\\times 3$ orthogonal matrices with determinant equal to $1$, respectively. We also discuss the advantages of the derived theoretical results in a concrete biological application.", "published": "2024-10-01T06:24:02Z", "authors": ["Somnath Chakraborty", "Holger Dette", "Martin Kroll"], "categories": ["math.ST"], "doi": "", "_query": "hedonic regression housing", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2406.05548v1", "title": "Causal Interpretation of Regressions With Ranks", "abstract": "In studies of educational production functions or intergenerational mobility, it is common to transform the key variables into percentile ranks. Yet, it remains unclear what the regression coefficient estimates with ranks of the outcome or the treatment. In this paper, we derive effective causal estimands for a broad class of commonly-used regression methods, including the ordinary least squares (OLS), two-stage least squares (2SLS), difference-in-differences (DiD), and regression discontinuity designs (RDD). Specifically, we introduce a novel primitive causal estimand, the Rank Average Treatment Effect (rank-ATE), and prove that it serves as the building block of the effective estimands of all the aforementioned econometrics methods. For 2SLS, DiD, and RDD, we show that direct applications to outcome ranks identify parameters that are difficult to interpret. To address this issue, we develop alternative methods to identify more interpretable causal parameters.", "published": "2024-06-08T18:42:46Z", "authors": ["Lihua Lei"], "categories": ["econ.EM", "math.ST", "stat.ME"], "doi": "", "_query": "hedonic regression housing", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/1006.3972v1", "title": "Graph-Valued Regression", "abstract": "Undirected graphical models encode in a graph $G$ the dependency structure of a random vector $Y$. In many applications, it is of interest to model $Y$ given another random vector $X$ as input. We refer to the problem of estimating the graph $G(x)$ of $Y$ conditioned on $X=x$ as ``graph-valued regression.'' In this paper, we propose a semiparametric method for estimating $G(x)$ that builds a tree on the $X$ space just as in CART (classification and regression trees), but at each leaf of the tree estimates a graph. We call the method ``Graph-optimized CART,'' or Go-CART. We study the theoretical properties of Go-CART using dyadic partitioning trees, establishing oracle inequalities on risk minimization and tree partition consistency. We also demonstrate the application of Go-CART to a meteorological dataset, showing how graph-valued regression can provide a useful tool for analyzing complex data.", "published": "2010-06-21T00:56:37Z", "authors": ["Han Liu", "Xi Chen", "John Lafferty", "Larry Wasserman"], "categories": ["stat.ML", "stat.ME"], "doi": "", "_query": "hedonic regression housing", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2303.13218v1", "title": "Functional-Coefficient Quantile Regression for Panel Data with Latent Group Structure", "abstract": "This paper considers estimating functional-coefficient models in panel quantile regression with individual effects, allowing the cross-sectional and temporal dependence for large panel observations. A latent group structure is imposed on the heterogenous quantile regression models so that the number of nonparametric functional coefficients to be estimated can be reduced considerably. With the preliminary local linear quantile estimates of the subject-specific functional coefficients, a classic agglomerative clustering algorithm is used to estimate the unknown group structure and an easy-to-implement ratio criterion is proposed to determine the group number. The estimated group number and structure are shown to be consistent. Furthermore, a post-grouping local linear smoothing method is introduced to estimate the group-specific functional coefficients, and the relevant asymptotic normal distribution theory is derived with a normalisation rate comparable to that in the literature. The developed methodologies and theory are verified through a simulation study and showcased with an application to house price data from UK local authority districts, which reveals different homogeneity structures at different quantile levels.", "published": "2023-03-23T12:29:40Z", "authors": ["Xiaorong Yang", "Jia Chen", "Degui Li", "Runze Li"], "categories": ["econ.EM", "stat.ME"], "doi": "", "_query": "hedonic regression housing", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2307.07840v4", "title": "RegExplainer: Generating Explanations for Graph Neural Networks in Regression Tasks", "abstract": "Graph regression is a fundamental task that has gained significant attention in various graph learning tasks. However, the inference process is often not easily interpretable. Current explanation techniques are limited to understanding Graph Neural Network (GNN) behaviors in classification tasks, leaving an explanation gap for graph regression models. In this work, we propose a novel explanation method to interpret the graph regression models (XAIG-R). Our method addresses the distribution shifting problem and continuously ordered decision boundary issues that hinder existing methods away from being applied in regression tasks. We introduce a novel objective based on the graph information bottleneck theory (GIB) and a new mix-up framework, which can support various GNNs and explainers in a model-agnostic manner. Additionally, we present a self-supervised learning strategy to tackle the continuously ordered labels in regression tasks. We evaluate our proposed method on three benchmark datasets and a real-life dataset introduced by us, and extensive experiments demonstrate its effectiveness in interpreting GNN models in regression tasks.", "published": "2023-07-15T16:16:22Z", "authors": ["Jiaxing Zhang", "Zhuomin Chen", "Hao Mei", "Longchao Da", "Dongsheng Luo", "Hua Wei"], "categories": ["cs.LG", "cs.AI"], "doi": "", "_query": "hedonic regression housing", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2002.08341v2", "title": "Identifying Invariant Factors Across Multiple Environments with KL Regression", "abstract": "Many datasets are collected from multiple environments (e.g. different labs, perturbations, etc.), and it is often advantageous to learn models and relations that are invariant across environments. Invariance can improve robustness to unknown confounders and improve generalization to new domains. We develop a novel framework -- KL regression -- to reliably estimate regression coefficients in a challenging multi-environment setting, where latent confounders affect the data from each environment. KL regression is based on a new objective of simultaneously minimizing the KL- divergence between a parametric model and the observed data from each environment. We prove that KL regression recovers the true invariant factors under a flexible confounding setup. Moreover, it is computationally efficient as we derive an analytic solution for its global optimum. In systematic experiments, we validate the improved performance of KL regression compared to commonly used approaches.", "published": "2020-02-19T18:35:38Z", "authors": ["Jaime Roquero Gimenez", "James Zou"], "categories": ["stat.ME"], "doi": "", "_query": "hedonic regression housing", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2209.03936v2", "title": "Shift in house price estimates during COVID-19 reveals effect of crisis on collective speculation", "abstract": "We exploit a city-level panel comprised of individual house price estimates to estimate the impact of COVID-19 on both small and big real-estate markets in California USA. Descriptive analysis of spot house price estimates, including contemporaneous price uncertainty and 30-day price change for individual properties listed on the online real-estate platform Zillow.com, together facilitate quantifying both the excess valuation and valuation confidence attributable to this global socio-economic shock. Our quasi-experimental pre-/post-COVID-19 design spans several years around 2020 and leverages contemporaneous price estimates of rental properties - i.e., real estate entering the habitation market, just not for purchase (off-market) and hence free of speculation - as an appropriate counterfactual to properties listed for sale, which are subject to on-market speculation. Combining unit-level matching and multivariate difference-in-difference regression approaches, we obtain consistent estimates regarding the sign and magnitude of excess price growth observed after the pandemic onset. Specifically, our results indicate that properties listed for sale appreciated an additional 1% per month above what would be expected in the absence of the pandemic. This corresponds to an excess annual price growth of roughly 12.7 percentage points, which accounts for more than half of the actual annual price growth in 2021 observed across the studied regions. Simultaneously, uncertainty in price estimates decreased, signaling the irrational confidence characteristic of prior asset bubbles. We explore how these two trends are related to market size, local market supply and borrowing costs, which altogether lend support for the counterintuitive roles of uncertainty and interruptions in decision-making.", "published": "2022-07-21T17:47:20Z", "authors": ["Alexander M. Petersen"], "categories": ["q-fin.GN", "physics.soc-ph"], "doi": "10.1140/epjds/s13688-024-00488-9", "_query": "hedonic regression housing", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2310.00664v1", "title": "Twin Neural Network Improved k-Nearest Neighbor Regression", "abstract": "Twin neural network regression is trained to predict differences between regression targets rather than the targets themselves. A solution to the original regression problem can be obtained by ensembling predicted differences between the targets of an unknown data point and multiple known anchor data points. Choosing the anchors to be the nearest neighbors of the unknown data point leads to a neural network-based improvement of k-nearest neighbor regression. This algorithm is shown to outperform both neural networks and k-nearest neighbor regression on small to medium-sized data sets.", "published": "2023-10-01T13:20:49Z", "authors": ["Sebastian J. Wetzel"], "categories": ["cs.LG"], "doi": "", "_query": "hedonic regression housing", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/1412.0048v2", "title": "Multilinear tensor regression for longitudinal relational data", "abstract": "A fundamental aspect of relational data, such as from a social network, is the possibility of dependence among the relations. In particular, the relations between members of one pair of nodes may have an effect on the relations between members of another pair. This article develops a type of regression model to estimate such effects in the context of longitudinal and multivariate relational data, or other data that can be represented in the form of a tensor. The model is based on a general multilinear tensor regression model, a special case of which is a tensor autoregression model in which the tensor of relations at one time point are parsimoniously regressed on relations from previous time points. This is done via a separable, or Kronecker-structured, regression parameter along with a separable covariance model. In the context of an analysis of longitudinal multivariate relational data, it is shown how the multilinear tensor regression model can represent patterns that often appear in relational and network data, such as reciprocity and transitivity.", "published": "2014-11-28T23:05:02Z", "authors": ["Peter D. Hoff"], "categories": ["stat.ME"], "doi": "10.1214/15-AOAS839", "_query": "hedonic regression housing", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2107.06615v1", "title": "Oblivious sketching for logistic regression", "abstract": "What guarantees are possible for solving logistic regression in one pass over a data stream? To answer this question, we present the first data oblivious sketch for logistic regression. Our sketch can be computed in input sparsity time over a turnstile data stream and reduces the size of a $d$-dimensional data set from $n$ to only $\\operatorname{poly}(μd\\log n)$ weighted points, where $μ$ is a useful parameter which captures the complexity of compressing the data. Solving (weighted) logistic regression on the sketch gives an $O(\\log n)$-approximation to the original problem on the full data set. We also show how to obtain an $O(1)$-approximation with slight modifications. Our sketches are fast, simple, easy to implement, and our experiments demonstrate their practicality.", "published": "2021-07-14T11:29:26Z", "authors": ["Alexander Munteanu", "Simon Omlor", "David Woodruff"], "categories": ["cs.DS", "cs.LG", "stat.ML"], "doi": "", "_query": "hedonic regression housing", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/1302.4244v2", "title": "Quantile regression in high-dimension with breaking", "abstract": "The paper considers a linear regression model in high-dimension for which the predictive variables can change the influence on the response variable at unknown times (called change-points). Moreover, the particular case of the heavy-tailed errors is considered. In this case, least square method with LASSO or adaptive LASSO penalty can not be used since the theoretical assumptions do not occur or the estimators are not robust. Then, the quantile model with SCAD penalty or median regression with LASSO-type penalty allows, in the same time, to estimate the parameters on every segment and eliminate the irrelevant variables. We show that, for the two penalized estimation methods, the oracle properties is not affected by the change-point estimation. Convergence rates of the estimators for the change-points and for the regression parameters, by the two methods are found. Monte-Carlo simulations illustrate the performance of the methods.", "published": "2013-02-18T12:29:25Z", "authors": ["Gabriela Ciuperca"], "categories": ["math.ST"], "doi": "", "_query": "hedonic regression housing", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/1705.07853v2", "title": "Nonparametric Online Regression while Learning the Metric", "abstract": "We study algorithms for online nonparametric regression that learn the directions along which the regression function is smoother. Our algorithm learns the Mahalanobis metric based on the gradient outer product matrix $\\boldsymbol{G}$ of the regression function (automatically adapting to the effective rank of this matrix), while simultaneously bounding the regret ---on the same data sequence--- in terms of the spectrum of $\\boldsymbol{G}$. As a preliminary step in our analysis, we extend a nonparametric online learning algorithm by Hazan and Megiddo enabling it to compete against functions whose Lipschitzness is measured with respect to an arbitrary Mahalanobis metric.", "published": "2017-05-22T16:58:13Z", "authors": ["Ilja Kuzborskij", "Nicolò Cesa-Bianchi"], "categories": ["cs.LG"], "doi": "", "_query": "hedonic regression housing", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2006.02983v1", "title": "Median regression with differential privacy", "abstract": "Median regression analysis has robustness properties which make it attractive compared with regression based on the mean, while differential privacy can protect individual privacy during statistical analysis of certain datasets. In this paper, three privacy preserving methods are proposed for median regression. The first algorithm is based on a finite smoothing method, the second provides an iterative way and the last one further employs the greedy coordinate descent approach. Privacy preserving properties of these three methods are all proved. Accuracy bound or convergence properties of these algorithms are also provided. Numerical calculation shows that the first method has better accuracy than the others when the sample size is small. When the sample size becomes larger, the first method needs more time while the second method needs less time with well-matched accuracy. For the third method, it costs less time in both cases, while it highly depends on step size.", "published": "2020-06-04T16:14:39Z", "authors": ["E Chen", "Ying Miao", "Yu Tang"], "categories": ["stat.CO", "cs.LG", "stat.ML"], "doi": "", "_query": "hedonic regression housing", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2206.06833v1", "title": "Density Regression with Conditional Support Points", "abstract": "Density regression characterizes the conditional density of the response variable given the covariates, and provides much more information than the commonly used conditional mean or quantile regression. However, it is often computationally prohibitive in applications with massive data sets, especially when there are multiple covariates. In this paper, we develop a new data reduction approach for the density regression problem using conditional support points. After obtaining the representative data, we exploit the penalized likelihood method as the downstream estimation strategy. Based on the connections among the continuous ranked probability score, the energy distance, the $L_2$ discrepancy and the symmetrized Kullback-Leibler distance, we investigate the distributional convergence of the representative points and establish the rate of convergence of the density regression estimator. The usefulness of the methodology is illustrated by modeling the conditional distribution of power output given multivariate environmental factors using a large scale wind turbine data set. Supplementary materials for this article are available online.", "published": "2022-06-14T13:29:14Z", "authors": ["Yunlu Chen", "Nan Zhang"], "categories": ["stat.ME", "stat.CO"], "doi": "", "_query": "hedonic regression housing", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2408.02355v1", "title": "Quantile Regression using Random Forest Proximities", "abstract": "Due to the dynamic nature of financial markets, maintaining models that produce precise predictions over time is difficult. Often the goal isn't just point prediction but determining uncertainty. Quantifying uncertainty, especially the aleatoric uncertainty due to the unpredictable nature of market drivers, helps investors understand varying risk levels. Recently, quantile regression forests (QRF) have emerged as a promising solution: Unlike most basic quantile regression methods that need separate models for each quantile, quantile regression forests estimate the entire conditional distribution of the target variable with a single model, while retaining all the salient features of a typical random forest. We introduce a novel approach to compute quantile regressions from random forests that leverages the proximity (i.e., distance metric) learned by the model and infers the conditional distribution of the target variable. We evaluate the proposed methodology using publicly available datasets and then apply it towards the problem of forecasting the average daily volume of corporate bonds. We show that using quantile regression using Random Forest proximities demonstrates superior performance in approximating conditional target distributions and prediction intervals to the original version of QRF. We also demonstrate that the proposed framework is significantly more computationally efficient than traditional approaches to quantile regressions.", "published": "2024-08-05T10:02:33Z", "authors": ["Mingshu Li", "Bhaskarjit Sarmah", "Dhruv Desai", "Joshua Rosaler", "Snigdha Bhagat", "Philip Sommer", "Dhagash Mehta"], "categories": ["stat.ML", "cs.LG", "q-fin.ST", "q-fin.TR"], "doi": "", "_query": "hedonic regression housing", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/1505.02965v2", "title": "Gaussian Processes: A Quick Introduction", "abstract": "A gentle introduction to Gaussian processes (GPs). The three parts of the document consider GPs for regression, classification, and dimensionality reduction.", "published": "2015-05-12T11:28:57Z", "authors": ["Mark Ebden"], "categories": ["math.ST"], "doi": "", "_query": "hedonic regression housing", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2008.02355v1", "title": "Adiabatic Quantum Linear Regression", "abstract": "A major challenge in machine learning is the computational expense of training these models. Model training can be viewed as a form of optimization used to fit a machine learning model to a set of data, which can take up significant amount of time on classical computers. Adiabatic quantum computers have been shown to excel at solving optimization problems, and therefore, we believe, present a promising alternative to improve machine learning training times. In this paper, we present an adiabatic quantum computing approach for training a linear regression model. In order to do this, we formulate the regression problem as a quadratic unconstrained binary optimization (QUBO) problem. We analyze our quantum approach theoretically, test it on the D-Wave 2000Q adiabatic quantum computer and compare its performance to a classical approach that uses the Scikit-learn library in Python. Our analysis shows that the quantum approach attains up to 2.8x speedup over the classical approach on larger datasets, and performs at par with the classical approach on the regression error metric.", "published": "2020-08-05T20:40:41Z", "authors": ["Prasanna Date", "Thomas Potok"], "categories": ["cs.LG", "physics.data-an", "stat.ML"], "doi": "10.1038/s41598-021-01445-6", "_query": "hedonic regression housing", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/1804.10742v1", "title": "Novel Prediction Techniques Based on Clusterwise Linear Regression", "abstract": "In this paper we explore different regression models based on Clusterwise Linear Regression (CLR). CLR aims to find the partition of the data into $k$ clusters, such that linear regressions fitted to each of the clusters minimize overall mean squared error on the whole data. The main obstacle preventing to use found regression models for prediction on the unseen test points is the absence of a reasonable way to obtain CLR cluster labels when the values of target variable are unknown. In this paper we propose two novel approaches on how to solve this problem. The first approach, predictive CLR builds a separate classification model to predict test CLR labels. The second approach, constrained CLR utilizes a set of user-specified constraints that enforce certain points to go to the same clusters. Assuming the constraint values are known for the test points, they can be directly used to assign CLR labels. We evaluate these two approaches on three UCI ML datasets as well as on a large corpus of health insurance claims. We show that both of the proposed algorithms significantly improve over the known CLR-based regression methods. Moreover, predictive CLR consistently outperforms linear regression and random forest, and shows comparable performance to support vector regression on UCI ML datasets. The constrained CLR approach achieves the best performance on the health insurance dataset, while enjoying only $\\approx 20$ times increased computational time over linear regression.", "published": "2018-04-28T05:07:42Z", "authors": ["Igor Gitman", "Jieshi Chen", "Eric Lei", "Artur Dubrawski"], "categories": ["cs.LG", "stat.ML"], "doi": "", "_query": "hedonic regression housing", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2105.06869v1", "title": "Privacy-preserving Logistic Regression with Secret Sharing", "abstract": "Logistic regression (LR) is a widely used classification method for modeling binary outcomes in many medical data classification tasks. Research that collects and combines datasets from various data custodians and jurisdictions can excessively benefit from the increased statistical power to support their analyzing goals. However, combining data from these various sources creates significant privacy concerns that need to be addressed. In this paper, we proposed secret sharing-based privacy-preserving logistic regression protocols using the Newton-Raphson method. Our proposed approaches are based on secure Multi-Party Computation (MPC) with different security settings to analyze data owned by several data holders. We conducted experiments on both synthetic data and real-world datasets and compared the efficiency and accuracy of them with those of an ordinary logistic regression model. Experimental results demonstrate that the proposed protocols are highly efficient and accurate. This study introduces iterative algorithms to simplify the federated training a logistic regression model in a privacy-preserving manner. Our implementation results show that our improved method can handle large datasets used in securely training a logistic regression from multiple sources.", "published": "2021-05-14T14:53:50Z", "authors": ["Ali Reza Ghavamipour", "Fatih Turkmen", "Xiaoqian Jian"], "categories": ["cs.LG"], "doi": "", "_query": "hedonic regression housing", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/1911.12474v2", "title": "Qini-based Uplift Regression", "abstract": "Uplift models provide a solution to the problem of isolating the marketing effect of a campaign. For customer churn reduction, uplift models are used to identify the customers who are likely to respond positively to a retention activity only if targeted, and to avoid wasting resources on customers that are very likely to switch to another company. We introduce a Qini-based uplift regression model to analyze a large insurance company's retention marketing campaign. Our approach is based on logistic regression models. We show that a Qini-optimized uplift model acts as a regularizing factor for uplift, much as a penalized likelihood model does for regression. This results in interpretable parsimonious models with few relevant xplanatory variables. Our results show that performing Qini-based parameters estimation significantly improves the uplift models performance.", "published": "2019-11-28T01:09:42Z", "authors": ["Mouloud Belbahri", "Alejandro Murua", "Olivier Gandouet", "Vahid Partovi Nia"], "categories": ["stat.AP", "stat.ME", "stat.ML"], "doi": "", "_query": "hedonic regression housing", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2607.09650v1", "title": "Revisiting Euler-Angle Regression with Kolmogorov-Arnold Networks", "abstract": "In many real-world systems, including articulated robots and biomechanical models, rotations are defined in joint space and naturally parameterized by Euler angles with bounded ranges. Yet regressing Euler angles remains challenging, as their discontinuities and singularities often destabilize training. In this work, we revisit Euler-angle regression and show that its effectiveness depends critically on the interaction between rotation representation, regression architecture, and domain constraints. We introduce a new framework that combines range-aware Euler modeling with Kolmogorov-Arnold Networks (KAN), which replace fixed node-wise activations with learnable univariate functions on edges. We further provide theoretical analysis indicating that bounded Euler ranges motivate a near-additive structure in the regression function, which favors the additive functional form of KAN, and we confirm this trend empirically. Extensive experiments on controlled rotation regression, object pose estimation, and robotic and human inverse kinematics demonstrate consistent improvements in accuracy, convergence, and efficiency. The code will be publicly available.", "published": "2026-07-10T17:48:06Z", "authors": ["Yangting Sun", "Zijun Cui", "Yufei Zhang"], "categories": ["cs.CV"], "doi": "", "_query": "hedonic regression housing", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/0901.4321v1", "title": "Oracle Inequality for Instrumental Variable Regression", "abstract": "We tackle the problem of estimating a regression function observed in an instrumental regression framework. This model is an inverse problem with unknown operator. We provide a spectral cut-off estimation procedure which enables to derive oracle inequalities which warrants that our estimate, built without any prior knowledge, behaves as well as, up to $\\log$ term, if the best model were known.", "published": "2009-01-27T19:51:01Z", "authors": ["Jean-Michel Loubes", "Clément Marteau"], "categories": ["math.ST"], "doi": "", "_query": "hedonic regression housing", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2212.12787v2", "title": "A Bayesian Robust Regression Method for Corrupted Data Reconstruction", "abstract": "Because of the widespread existence of noise and data corruption, recovering the true regression parameters with a certain proportion of corrupted response variables is an essential task. Methods to overcome this problem often involve robust least-squares regression, but few methods perform well when confronted with severe adaptive adversarial attacks. In many applications, prior knowledge is often available from historical data or engineering experience, and by incorporating prior information into a robust regression method, we develop an effective robust regression method that can resist adaptive adversarial attacks. First, we propose the novel TRIP (hard Thresholding approach to Robust regression with sImple Prior) algorithm, which improves the breakdown point when facing adaptive adversarial attacks. Then, to improve the robustness and reduce the estimation error caused by the inclusion of priors, we use the idea of Bayesian reweighting to construct the more robust BRHT (robust Bayesian Reweighting regression via Hard Thresholding) algorithm. We prove the theoretical convergence of the proposed algorithms under mild conditions, and extensive experiments show that under different types of dataset attacks, our algorithms outperform other benchmark ones. Finally, we apply our methods to a data-recovery problem in a real-world application involving a space solar array, demonstrating their good applicability.", "published": "2022-12-24T17:25:53Z", "authors": ["Zheyi Fan", "Zhaohui Li", "Jingyan Wang", "Dennis K. J. Lin", "Xiao Xiong", "Qingpei Hu"], "categories": ["cs.LG"], "doi": "", "_query": "hedonic regression housing", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2608.22655v1", "title": "Multiple View Neural Regression of a Facial Shape Model", "abstract": "Creating re-topologized 3D facial meshes is essential for high-quality facial animation but remains labor-intensive and time-consuming. This dissertation explores more efficient approaches for capturing production-ready facial meshes through: (1) the development of VarIS, a custom light sphere for capturing high-resolution stereo geometry and reflectance maps; (2) analysis of camera parameters affecting automatic 2D and 3D landmarking; (3) synthetic-data methods for training neural face regression; and (4) techniques for improving neural multi-view face-shape regression. While VarIS enables photorealistic face capture, its operational and processing costs motivate a more scalable approach. A deep learning framework is therefore proposed to directly predict re-topologized facial meshes from synthetic multiview images generated with Visage Craft, an in-house physically based rendering system using an Appearance 3D Morphable Model (A3DMM). The system produces standardized meshes ready for rigging and animation with minimal human supervision. Results show that incorporating accurate camera intrinsics and extrinsics improves landmark accuracy and geometric consistency, while 3D landmark regularization further improves reconstruction quality.", "published": "2026-08-23T23:31:44Z", "authors": ["Xiang Li"], "categories": ["cs.CV"], "doi": "", "_query": "hedonic regression housing", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2510.01861v2", "title": "Compressed Bayesian Tensor Regression", "abstract": "To address the common problem of high dimensionality in tensor regressions, we introduce a generalized tensor random projection method that embeds high-dimensional tensor-valued covariates into low-dimensional subspaces with minimal loss of information about the responses. The method is flexible, allowing for tensor-wise, mode-wise, or combined random projections as special cases. A Bayesian inference framework is provided featuring the use of a hierarchical prior distribution and a low-rank representation of the parameter. Strong theoretical support is provided for the concentration properties of the random projection and posterior consistency of the Bayesian inference. An efficient Gibbs sampler is developed to perform inference on the compressed data. To mitigate the sensitivity introduced by random projections, Bayesian model averaging is employed, with normalising constants estimated using reverse logistic regression. An extensive simulation study is conducted to examine the effects of different tuning parameters. Simulations indicate, and the real data application confirms, that compressed Bayesian tensor regression can achieve better out-of-sample prediction while significantly reducing computational cost compared to standard Bayesian tensor regression.", "published": "2025-10-02T10:03:11Z", "authors": ["Roberto Casarin", "Radu Craiu", "Qing Wang"], "categories": ["stat.ME", "stat.CO"], "doi": "", "_query": "hedonic regression housing", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2303.03084v3", "title": "On Regression in Extreme Regions", "abstract": "We establish a statistical learning theoretical framework aimed at extrapolation, or out-of-domain generalization, on the unobserved tails of covariates in continuous regression problems. Our strategy involves performing statistical regression on a subsample of observations with continuous labels that are the furthest away from the origin, focusing specifically on their angular components. The underlying assumptions of our approach are grounded in the theory of multivariate regular variation, a cornerstone of extreme value theory. We address the stylized problem of nonparametric least squares regression with predictors chosen from a Vapnik-Chervonenkis class. This work contributes to a broader initiative to develop statistical learning theoretical foundations for supervised learning strategies that enhance performance on the supposedly heavy tails of covariates. Previous efforts in this area have focused exclusively on binary classification on extreme covariates. Although the continuous target setting necessitates different techniques and regularity assumptions, our main results echo findings from earlier studies. We quantify the predictive performance on tail regions in terms of excess risk, presenting it as a finite sample risk bound with a clear bias-variance decomposition. Numerical experiments with simulated and real data illustrate our theoretical findings.", "published": "2023-03-06T12:55:38Z", "authors": ["Stephan Clémençon", "Nathan Huet", "Anne Sabourin"], "categories": ["stat.ML", "cs.LG", "math.ST"], "doi": "", "_query": "hedonic regression housing", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/1811.01506v3", "title": "Theoretical and Experimental Analysis on the Generalizability of Distribution Regression Network", "abstract": "There is emerging interest in performing regression between distributions. In contrast to prediction on single instances, these machine learning methods can be useful for population-based studies or on problems that are inherently statistical in nature. The recently proposed distribution regression network (DRN) has shown superior performance for the distribution-to-distribution regression task compared to conventional neural networks. However, in Kou et al. (2018) and some other works on distribution regression, there is a lack of comprehensive comparative study on both theoretical basis and generalization abilities of the methods. We derive some mathematical properties of DRN and qualitatively compare it to conventional neural networks. We also perform comprehensive experiments to study the generalizability of distribution regression models, by studying their robustness to limited training data, data sampling noise and task difficulty. DRN consistently outperforms conventional neural networks, requiring fewer training data and maintaining robust performance with noise. Furthermore, the theoretical properties of DRN can be used to provide some explanation on the ability of DRN to achieve better generalization performance than conventional neural networks.", "published": "2018-11-05T04:09:49Z", "authors": ["Connie Kou", "Hwee Kuan Lee", "Jorge Sanz", "Teck Khim Ng"], "categories": ["cs.LG", "stat.ML"], "doi": "", "_query": "hedonic regression housing", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2512.23444v1", "title": "Uncertainty calibration for latent-variable regression models", "abstract": "Uncertainty quantification is essential for scientific analysis, as it allows for the evaluation and interpretation of variability and reliability in complex systems and datasets. In their original form, multivariate statistical regression models (partial least-squares regression, PLS, principal component regression, PCR) along with their kernelized versions (kernel partial least-squares regression, K-PLS, kernel principal component regression, K-PCR), do not incorporate uncertainty quantification as part of their output. In this study, we propose a method inspired by conformal inference to estimate and calibrate the uncertainty of multivariate statistical models. The result of this method is a point prediction accompanied by prediction intervals that depend on the input data. We tested the proposed method on both traditional and kernelized versions of PLS and PCR. The method is demonstrated using synthetic data, as well as laboratory near-infrared (NIR) and airborne hyperspectral regression models for estimating functional plant traits. The model was able to successfully identify the uncertain regions in the simulated data and match the magnitude of the uncertainty. In real-case scenarios, the optimised model was not overconfident nor underconfident when estimating from test data: for example, for a 95% prediction interval, 95% of the true observations were inside the prediction interval.", "published": "2025-12-29T13:02:31Z", "authors": ["Zina-Sabrina Duma", "Otto Lamminpää", "Jouni Susiluoto", "Heikki Haario", "Ting Zheng", "Tuomas Sihvonen", "Amy Braverman", "Philip A. Townsend", "Satu-Pia Reinikainen"], "categories": ["stat.ME"], "doi": "", "_query": "hedonic regression housing", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/1101.0736v2", "title": "A Robbins-Monro procedure for estimation in semiparametric regression models", "abstract": "This paper is devoted to the parametric estimation of a shift together with the nonparametric estimation of a regression function in a semiparametric regression model. We implement a very efficient and easy to handle Robbins-Monro procedure. On the one hand, we propose a stochastic algorithm similar to that of Robbins-Monro in order to estimate the shift parameter. A preliminary evaluation of the regression function is not necessary to estimate the shift parameter. On the other hand, we make use of a recursive Nadaraya-Watson estimator for the estimation of the regression function. This kernel estimator takes into account the previous estimation of the shift parameter. We establish the almost sure convergence for both Robbins-Monro and Nadaraya--Watson estimators. The asymptotic normality of our estimates is also provided. Finally, we illustrate our semiparametric estimation procedure on simulated and real data.", "published": "2011-01-04T15:16:37Z", "authors": ["Bernard Bercu", "Philippe Fraysse"], "categories": ["math.ST", "math.PR"], "doi": "10.1214/12-AOS969", "_query": "hedonic regression housing", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/1906.00202v2", "title": "lspartition: Partitioning-Based Least Squares Regression", "abstract": "Nonparametric partitioning-based least squares regression is an important tool in empirical work. Common examples include regressions based on splines, wavelets, and piecewise polynomials. This article discusses the main methodological and numerical features of the R software package lspartition, which implements modern estimation and inference results for partitioning-based least squares (series) regression estimation. This article discusses the main methodological and numerical features of the R software package lspartition, which implements results for partitioning-based least squares (series) regression estimation and inference from Cattaneo and Farrell (2013) and Cattaneo, Farrell, and Feng (2019). These results cover the multivariate regression function as well as its derivatives. First, the package provides data-driven methods to choose the number of partition knots optimally, according to integrated mean squared error, yielding optimal point estimation. Second, robust bias correction is implemented to combine this point estimator with valid inference. Third, the package provides estimates and inference for the unknown function both pointwise and uniformly in the conditioning variables. In particular, valid confidence bands are provided. Finally, an extension to two-sample analysis is developed, which can be used in treatment-control comparisons and related problems", "published": "2019-06-01T10:56:29Z", "authors": ["Matias D. Cattaneo", "Max H. Farrell", "Yingjie Feng"], "categories": ["stat.CO", "econ.EM", "stat.ME"], "doi": "", "_query": "hedonic regression housing", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2603.07014v2", "title": "Fréchet regression of multivariate distributions with nonparanormal transport", "abstract": "Regression with distribution-valued responses and Euclidean predictors has gained increasing scientific relevance. While methodology for univariate distributional data has advanced rapidly in recent years, multivariate distributions, which additionally encode dependence across univariate marginals, have received less attention and pose computational and statistical challenges. In this work, we address these challenges with a new regression approach for multivariate distributional responses, in which distributions are modeled within the semiparametric nonparanormal family. By incorporating the nonparanormal transport (NPT) metric -- an efficient closed-form surrogate for the Wasserstein distance -- into the Fréchet regression framework, our approach decomposes the problem into separate regressions of marginal distributions and their dependence structure, facilitating both efficient estimation and granular interpretation of predictor effects. We provide theoretical justification for NPT, establishing its bi-Lipschitz equivalence to the Wasserstein distance and proving that it mitigates the curse of dimensionality. We further prove uniform convergence guarantees for regression estimators, both when distributional responses are fully observed and when they are estimated from empirical samples, attaining fast convergence rates comparable to the univariate case. The utility of our method is demonstrated via simulations and an application to continuous glucose monitoring data.", "published": "2026-03-07T03:21:12Z", "authors": ["Junyoung Park", "Irina Gaynanova"], "categories": ["stat.ME", "math.ST", "stat.ML"], "doi": "", "_query": "hedonic regression housing", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/1808.02341v3", "title": "Optimal stopping via reinforced regression", "abstract": "In this note we propose a new approach towards solving numerically optimal stopping problems via reinforced regression based Monte Carlo algorithms. The main idea of the method is to reinforce standard linear regression algorithms in each backward induction step by adding new basis functions based on previously estimated continuation values. The proposed methodology is illustrated by a numerical example from mathematical finance.", "published": "2018-08-07T13:12:05Z", "authors": ["Denis Belomestny", "John Schoenmakers", "Vladimir Spokoiny", "Bakhyt Zharkynbay"], "categories": ["math.NA", "q-fin.CP", "stat.ML"], "doi": "", "_query": "hedonic regression housing", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/1306.4410v1", "title": "Joint estimation of sparse multivariate regression and conditional graphical models", "abstract": "Multivariate regression model is a natural generalization of the classical univari- ate regression model for fitting multiple responses. In this paper, we propose a high- dimensional multivariate conditional regression model for constructing sparse estimates of the multivariate regression coefficient matrix that accounts for the dependency struc- ture among the multiple responses. The proposed method decomposes the multivariate regression problem into a series of penalized conditional log-likelihood of each response conditioned on the covariates and other responses. It allows simultaneous estimation of the sparse regression coefficient matrix and the sparse inverse covariance matrix. The asymptotic selection consistency and normality are established for the diverging dimension of the covariates and number of responses. The effectiveness of the pro- posed method is also demonstrated in a variety of simulated examples as well as an application to the Glioblastoma multiforme cancer data.", "published": "2013-06-19T01:56:29Z", "authors": ["Junhui Wang"], "categories": ["stat.ML", "cs.LG"], "doi": "", "_query": "hedonic regression housing", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/1603.00297v1", "title": "Bayesian Quantile Regression for Ordinal Longitudinal Data", "abstract": "Since the pioneering work by Koenker and Bassett (1978), quantile regression models and its applications have become increasingly popular and important for research in many areas. In this paper, a random effects ordinal quantile regression model is proposed for analysis of longitudinal data with ordinal outcome of interest. An efficient Gibbs sampling algorithm was derived for fitting the model to the data based on a location scale mixture representation of the skewed double exponential distribution. The proposed approach is illustrated using simulated data and a real data example. This is the first work to discuss quantile regression for analysis of longitudinal data with ordinal outcome.", "published": "2016-02-27T09:11:24Z", "authors": ["Rahim Alhamzawi"], "categories": ["stat.CO"], "doi": "", "_query": "hedonic regression housing", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2310.17154v1", "title": "Deep Imbalanced Regression via Hierarchical Classification Adjustment", "abstract": "Regression tasks in computer vision, such as age estimation or counting, are often formulated into classification by quantizing the target space into classes. Yet real-world data is often imbalanced -- the majority of training samples lie in a head range of target values, while a minority of samples span a usually larger tail range. By selecting the class quantization, one can adjust imbalanced regression targets into balanced classification outputs, though there are trade-offs in balancing classification accuracy and quantization error. To improve regression performance over the entire range of data, we propose to construct hierarchical classifiers for solving imbalanced regression tasks. The fine-grained classifiers limit the quantization error while being modulated by the coarse predictions to ensure high accuracy. Standard hierarchical classification approaches, however, when applied to the regression problem, fail to ensure that predicted ranges remain consistent across the hierarchy. As such, we propose a range-preserving distillation process that can effectively learn a single classifier from the set of hierarchical classifiers. Our novel hierarchical classification adjustment (HCA) for imbalanced regression shows superior results on three diverse tasks: age estimation, crowd counting and depth estimation. We will release the source code upon acceptance.", "published": "2023-10-26T04:54:39Z", "authors": ["Haipeng Xiong", "Angela Yao"], "categories": ["cs.CV"], "doi": "", "_query": "hedonic regression housing", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2505.23615v2", "title": "Learning Interpretable Differentiable Logic Networks for Tabular Regression", "abstract": "Neural networks (NNs) achieve outstanding performance in many domains; however, their decision processes are often opaque and their inference can be computationally expensive in resource-constrained environments. We recently proposed Differentiable Logic Networks (DLNs) to address these issues for tabular classification based on relaxing discrete logic into a differentiable form, thereby enabling gradient-based learning of networks built from binary logic operations. DLNs offer interpretable reasoning and substantially lower inference cost. We extend the DLN framework to supervised tabular regression. We first redesign the final output layer (the SumLayer) to support continuous targets. More critically, we find the original two-phase training procedure used for classification is suboptimal for regression, and thus develop a unified, single-stage optimization procedure. We also demonstrate that temperature annealing of the network's differentiable relaxations is decisive for achieving stable convergence and high accuracy. We evaluate the resulting model on 15 public regression benchmarks, comparing it with modern neural networks and classical regression baselines. Regression DLNs match or exceed baseline accuracy while preserving interpretability and fast inference. Our results show that DLNs are a viable, cost-effective alternative for regression tasks, especially where model transparency and computational efficiency are important.", "published": "2025-05-29T16:24:18Z", "authors": ["Chang Yue", "Niraj K. Jha"], "categories": ["cs.LG"], "doi": "", "_query": "hedonic regression housing", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2503.06528v1", "title": "Higher Order Reduced Rank Regression", "abstract": "Reduced Rank Regression (RRR) is a widely used method for multi-response regression. However, RRR assumes a linear relationship between features and responses. While linear models are useful and often provide a good approximation, many real-world problems involve more complex relationships that cannot be adequately captured by simple linear interactions. One way to model such relationships is via multilinear transformations. This paper introduces Higher Order Reduced Rank Regression (HORRR), an extension of RRR that leverages multi-linear transformations, and as such is capable of capturing nonlinear interactions in multi-response regression. HORRR employs tensor representations for the coefficients and a Tucker decomposition to impose multilinear rank constraints as regularization akin to the rank constraints in RRR. Encoding these constraints as a manifold allows us to use Riemannian optimization to solve this HORRR problems. We theoretically and empirically analyze the use of Riemannian optimization for solving HORRR problems.", "published": "2025-03-09T09:21:38Z", "authors": ["Leia Greenberg", "Haim Avron"], "categories": ["stat.ML", "cs.LG", "math.NA", "math.OC"], "doi": "", "_query": "hedonic regression housing", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2012.05313v1", "title": "A partial least squares approach for function-on-function interaction regression", "abstract": "A partial least squares regression is proposed for estimating the function-on-function regression model where a functional response and multiple functional predictors consist of random curves with quadratic and interaction effects. The direct estimation of a function-on-function regression model is usually an ill-posed problem. To overcome this difficulty, in practice, the functional data that belong to the infinite-dimensional space are generally projected into a finite-dimensional space of basis functions. The function-on-function regression model is converted to a multivariate regression model of the basis expansion coefficients. In the estimation phase of the proposed method, the functional variables are approximated by a finite-dimensional basis function expansion method. We show that the partial least squares regression constructed via a functional response, multiple functional predictors, and quadratic/interaction terms of the functional predictors is equivalent to the partial least squares regression constructed using basis expansions of functional variables. From the partial least squares regression of the basis expansions of functional variables, we provide an explicit formula for the partial least squares estimate of the coefficient function of the function-on-function regression model. Because the true forms of the models are generally unspecified, we propose a forward procedure for model selection. The finite sample performance of the proposed method is examined using several Monte Carlo experiments and two empirical data analyses, and the results were found to compare favorably with an existing method.", "published": "2020-12-09T20:51:00Z", "authors": ["Ufuk Beyaztas", "Han Lin Shang"], "categories": ["stat.ME", "stat.AP"], "doi": "", "_query": "hedonic regression housing", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2308.04185v2", "title": "Iterative Sketching for Secure Coded Regression", "abstract": "Linear regression is a fundamental and primitive problem in supervised machine learning, with applications ranging from epidemiology to finance. In this work, we propose methods for speeding up distributed linear regression. We do so by leveraging randomized techniques, while also ensuring security and straggler resiliency in asynchronous distributed computing systems. Specifically, we randomly rotate the basis of the system of equations and then subsample blocks, to simultaneously secure the information and reduce the dimension of the regression problem. In our setup, the basis rotation corresponds to an encoded encryption in an approximate gradient coding scheme, and the subsampling corresponds to the responses of the non-straggling servers in the centralized coded computing framework. This results in a distributive iterative stochastic approach for matrix compression and steepest descent.", "published": "2023-08-08T11:10:42Z", "authors": ["Neophytos Charalambides", "Hessam Mahdavifar", "Mert Pilanci", "Alfred O. Hero"], "categories": ["cs.IT", "cs.CR", "cs.DC", "cs.LG", "math.NA"], "doi": "", "_query": "hedonic regression housing", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2010.02610v3", "title": "Robust priors for regularized regression", "abstract": "Induction benefits from useful priors. Penalized regression approaches, like ridge regression, shrink weights toward zero but zero association is usually not a sensible prior. Inspired by simple and robust decision heuristics humans use, we constructed non-zero priors for penalized regression models that provide robust and interpretable solutions across several tasks. Our approach enables estimates from a constrained model to serve as a prior for a more general model, yielding a principled way to interpolate between models of differing complexity. We successfully applied this approach to a number of decision and classification problems, as well as analyzing simulated brain imaging data. Models with robust priors had excellent worst-case performance. Solutions followed from the form of the heuristic that was used to derive the prior. These new algorithms can serve applications in data analysis and machine learning, as well as help in understanding how people transition from novice to expert performance.", "published": "2020-10-06T10:43:14Z", "authors": ["Sebastian Bobadilla-Suarez", "Matt Jones", "Bradley C. Love"], "categories": ["cs.LG", "stat.ML"], "doi": "", "_query": "hedonic regression housing", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/1808.02392v1", "title": "A distributed regression analysis application based on SAS software Part II: Cox proportional hazards regression", "abstract": "Previous work has demonstrated the feasibility and value of conducting distributed regression analysis (DRA), a privacy-protecting analytic method that performs multivariable-adjusted regression analysis with only summary-level information from participating sites. To our knowledge, there are no DRA applications in SAS, the statistical software used by several large national distributed data networks (DDNs), including the Sentinel System and PCORnet. SAS/IML is available to perform the required matrix computations for DRA in the SAS system. However, not all data partners in these large DDNs have access to SAS/IML, which is licensed separately. In this second article of a two-paper series, we describe a DRA application developed using Base SAS and SAS/STAT modules for distributed Cox proportional hazards regression within horizontally partitioned DDNs and its successful tests.", "published": "2018-08-07T14:17:55Z", "authors": ["Yury Vilk", "Zilu Zhang", "Jessica Young", "Qoua L. Her", "Jessica M. Malenfant", "Sarah Malek", "Sengwee Toh"], "categories": ["stat.CO"], "doi": "", "_query": "hedonic regression housing", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2509.10000v2", "title": "Neural Scaling Laws for Deep Regression", "abstract": "Neural scaling laws--power-law relationships between generalization errors and characteristics of deep learning models--are vital tools for developing reliable models while managing limited resources. Although the success of large language models highlights the importance of these laws, their application to deep regression models remains largely unexplored. Here, we empirically investigate neural scaling laws in deep regression using a parameter estimation model for twisted van der Waals magnets. We observe power-law relationships between the loss and both training dataset size and model capacity across a wide range of values, employing various architectures--including fully connected networks, residual networks, and vision transformers. Furthermore, the scaling exponents governing these relationships range from 1 to 2, with specific values depending on the regressed parameters and model details. The consistent scaling behaviors and their large scaling exponents suggest that the performance of deep regression models can improve substantially with increasing data size.", "published": "2025-09-12T06:49:19Z", "authors": ["Tilen Cadez", "Kyoung-Min Kim"], "categories": ["cs.LG", "cond-mat.other"], "doi": "10.1088/2632-2153/ae484c", "_query": "hedonic regression housing", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2108.11863v4", "title": "Multivariate Lévy Adaptive B-Spline Regression", "abstract": "We propose Multivariate Lévy Adaptive B-Spline Regression (MLABS), a Bayesian nonparametric regression model designed to capture nonlinear effects, interactions, and locally varying features of multivariate functions. MLABS represents the regression function as a random sum of low-order tensor-product B-spline basis functions under a finite Lévy random measure prior. The resulting representation adapts to the complexity and local structure of the regression function while favoring a sparse basis representation. For posterior inference, we develop a reversible-jump Markov chain Monte Carlo (MCMC) algorithm with Bayesian backfitting, collapsed trans-dimensional updates, and residual-guided proposals. These computational strategies are designed to improve posterior exploration across basis configurations of varying dimensions and structures. We also establish approximation bounds for functions with low-order interactions and heterogeneous smoothness. These bounds relate approximation accuracy to the number of basis terms. Simulation studies show that MLABS achieves competitive predictive performance across a wide range of regression settings, including high-dimensional problems. MLABS also demonstrates robust predictive performance across benchmark real datasets. In an environmental-mixture application, it attains predictive accuracy comparable to Bayesian kernel machine regression at substantially lower computational cost.", "published": "2021-08-26T15:40:59Z", "authors": ["Sewon Park", "Jaeyong Lee", "Jeunghun Oh"], "categories": ["stat.CO", "stat.ME"], "doi": "", "_query": "hedonic regression housing", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/0808.1010v1", "title": "Confidence bands in nonparametric time series regression", "abstract": "We consider nonparametric estimation of mean regression and conditional variance (or volatility) functions in nonlinear stochastic regression models. Simultaneous confidence bands are constructed and the coverage probabilities are shown to be asymptotically correct. The imposed dependence structure allows applications in many linear and nonlinear auto-regressive processes. The results are applied to the S&P 500 Index data.", "published": "2008-08-07T12:34:35Z", "authors": ["Zhibiao Zhao", "Wei Biao Wu"], "categories": ["math.ST"], "doi": "10.1214/07-AOS533", "_query": "hedonic regression housing", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2406.13681v1", "title": "On the Consistency of Fairness Measurement Methods for Regression Tasks", "abstract": "With growing applications of Machine Learning (ML) techniques in the real world, it is highly important to ensure that these models work in an equitable manner. One main step in ensuring fairness is to effectively measure fairness, and to this end, various metrics have been proposed in the past literature. While the computation of those metrics are straightforward in the classification set-up, it is computationally intractable in the regression domain. To address the challenge of computational intractability, past literature proposed various methods to approximate such metrics. However, they did not verify the extent to which the output of such approximation algorithms are consistent with each other. To fill this gap, this paper comprehensively studies the consistency of the output of various fairness measurement methods through conducting an extensive set of experiments on various regression tasks. As a result, it finds that while some fairness measurement approaches show strong consistency across various regression tasks, certain methods show a relatively poor consistency in certain regression tasks. This, in turn, calls for a more principled approach for measuring fairness in the regression domain.", "published": "2024-06-19T16:35:23Z", "authors": ["Abdalwahab Almajed", "Maryam Tabar", "Peyman Najafirad"], "categories": ["cs.LG"], "doi": "", "_query": "hedonic regression housing", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2412.15520v2", "title": "Logistic Regression Model for Differentially-Private Matrix Masked Data", "abstract": "A recently proposed scheme utilizing local noise addition and matrix masking enables data collection while protecting individual privacy from all parties, including the central data manager. Statistical analysis of such privacy-preserved data is particularly challenging for nonlinear models like logistic regression. By leveraging a relationship between logistic regression and linear regression estimators, we propose the first valid statistical analysis method for logistic regression under this setting. Theoretical analysis of the proposed estimators confirmed its validity under an asymptotic framework with increasing noise magnitude to account for strict privacy requirements. Simulations and real data analyses demonstrate the superiority of the proposed estimators over naive logistic regression methods on privacy-preserved data sets.", "published": "2024-12-20T03:16:19Z", "authors": ["Linh H Nghiem", "Aidong A. Ding", "Samuel Wu"], "categories": ["stat.ME"], "doi": "", "_query": "hedonic regression housing", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2010.10410v1", "title": "Localizing Changes in High-Dimensional Regression Models", "abstract": "This paper addresses the problem of localizing change points in high-dimensional linear regression models with piecewise constant regression coefficients. We develop a dynamic programming approach to estimate the locations of the change points whose performance improves upon the current state-of-the-art, even as the dimensionality, the sparsity of the regression coefficients, the temporal spacing between two consecutive change points, and the magnitude of the difference of two consecutive regression coefficient vectors are allowed to vary with the sample size. Furthermore, we devise a computationally-efficient refinement procedure that provably reduces the localization error of preliminary estimates of the change points. We demonstrate minimax lower bounds on the localization error that nearly match the upper bound on the localization error of our methodology and show that the signal-to-noise condition we impose is essentially the weakest possible based on information-theoretic arguments. Extensive numerical results support our theoretical findings, and experiments on real air quality data reveal change points supported by historical information not used by the algorithm.", "published": "2020-10-20T16:17:04Z", "authors": ["Alessandro Rinaldo", "Daren Wang", "Qin Wen", "Rebecca Willett", "Yi Yu"], "categories": ["stat.ME"], "doi": "", "_query": "hedonic regression housing", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/1512.03929v1", "title": "Quantum assisted Gaussian process regression", "abstract": "Gaussian processes (GP) are a widely used model for regression problems in supervised machine learning. Implementation of GP regression typically requires $O(n^3)$ logic gates. We show that the quantum linear systems algorithm [Harrow et al., Phys. Rev. Lett. 103, 150502 (2009)] can be applied to Gaussian process regression (GPR), leading to an exponential reduction in computation time in some instances. We show that even in some cases not ideally suited to the quantum linear systems algorithm, a polynomial increase in efficiency still occurs.", "published": "2015-12-12T16:19:35Z", "authors": ["Zhikuan Zhao", "Jack K. Fitzsimons", "Joseph F. Fitzsimons"], "categories": ["quant-ph", "cs.LG", "stat.ML"], "doi": "10.1103/PhysRevA.99.052331", "_query": "hedonic regression housing", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2309.07374v1", "title": "Beta quantile regression for robust estimation of uncertainty in the presence of outliers", "abstract": "Quantile Regression (QR) can be used to estimate aleatoric uncertainty in deep neural networks and can generate prediction intervals. Quantifying uncertainty is particularly important in critical applications such as clinical diagnosis, where a realistic assessment of uncertainty is essential in determining disease status and planning the appropriate treatment. The most common application of quantile regression models is in cases where the parametric likelihood cannot be specified. Although quantile regression is quite robust to outlier response observations, it can be sensitive to outlier covariate observations (features). Outlier features can compromise the performance of deep learning regression problems such as style translation, image reconstruction, and deep anomaly detection, potentially leading to misleading conclusions. To address this problem, we propose a robust solution for quantile regression that incorporates concepts from robust divergence. We compare the performance of our proposed method with (i) least trimmed quantile regression and (ii) robust regression based on the regularization of case-specific parameters in a simple real dataset in the presence of outlier. These methods have not been applied in a deep learning framework. We also demonstrate the applicability of the proposed method by applying it to a medical imaging translation task using diffusion models.", "published": "2023-09-14T01:18:57Z", "authors": ["Haleh Akrami", "Omar Zamzam", "Anand Joshi", "Sergul Aydore", "Richard Leahy"], "categories": ["cs.LG"], "doi": "", "_query": "hedonic regression housing", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/1205.0686v1", "title": "A semi-automatic method to guide the choice of ridge parameter in ridge regression", "abstract": "We consider the application of a popular penalised regression method, Ridge Regression, to data with very high dimensions and many more covariates than observations. Our motivation is the problem of out-of-sample prediction and the setting is high-density genotype data from a genome-wide association or resequencing study. Ridge regression has previously been shown to offer improved performance for prediction when compared with other penalised regression methods. One problem with ridge regression is the choice of an appropriate parameter for controlling the amount of shrinkage of the coefficient estimates. Here we propose a method for choosing the ridge parameter based on controlling the variance of the predicted observations in the model. Using simulated data, we demonstrate that our method outperforms subset selection based on univariate tests of association and another penalised regression method, HyperLasso regression, in terms of improved prediction error. We extend our approach to regression problems when the outcomes are binary (representing cases and controls, as is typically the setting for genome-wide association studies) and demonstrate the method on a real data example consisting of case-control and genotype data on Bipolar Disorder, taken from the Wellcome Trust Case Control Consortium and the Genetic Association Information Network.", "published": "2012-05-03T11:54:13Z", "authors": ["Erika Cule", "Maria De Iorio"], "categories": ["stat.AP", "q-bio.GN"], "doi": "", "_query": "hedonic regression housing", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/0803.3017v1", "title": "Accelerated convergence for nonparametric regression with coarsened predictors", "abstract": "We consider nonparametric estimation of a regression function for a situation where precisely measured predictors are used to estimate the regression curve for coarsened, that is, less precise or contaminated predictors. Specifically, while one has available a sample $(W_1,Y_1),...,(W_n,Y_n)$ of independent and identically distributed data, representing observations with precisely measured predictors, where $\\mathrm{E}(Y_i|W_i)=g(W_i)$, instead of the smooth regression function $g$, the target of interest is another smooth regression function $m$ that pertains to predictors $X_i$ that are noisy versions of the $W_i$. Our target is then the regression function $m(x)=E(Y|X=x)$, where $X$ is a contaminated version of $W$, that is, $X=W+δ$. It is assumed that either the density of the errors is known, or replicated data are available resembling, but not necessarily the same as, the variables $X$. In either case, and under suitable conditions, we obtain $\\sqrt{n}$-rates of convergence of the proposed estimator and its derivatives, and establish a functional limit theorem. Weak convergence to a Gaussian limit process implies pointwise and uniform confidence intervals and $\\sqrt{n}$-consistent estimators of extrema and zeros of $m$. It is shown that these results are preserved under more general models in which $X$ is determined by an explanatory variable. Finite sample performance is investigated in simulations and illustrated by a real data example.", "published": "2008-03-20T15:46:14Z", "authors": ["Aurore Delaigle", "Peter Hall", "Hans-Georg Müller"], "categories": ["math.ST"], "doi": "10.1214/009053607000000497", "_query": "hedonic regression housing", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2112.11700v1", "title": "Adaptive Contrast for Image Regression in Computer-Aided Disease Assessment", "abstract": "Image regression tasks for medical applications, such as bone mineral density (BMD) estimation and left-ventricular ejection fraction (LVEF) prediction, play an important role in computer-aided disease assessment. Most deep regression methods train the neural network with a single regression loss function like MSE or L1 loss. In this paper, we propose the first contrastive learning framework for deep image regression, namely AdaCon, which consists of a feature learning branch via a novel adaptive-margin contrastive loss and a regression prediction branch. Our method incorporates label distance relationships as part of the learned feature representations, which allows for better performance in downstream regression tasks. Moreover, it can be used as a plug-and-play module to improve performance of existing regression methods. We demonstrate the effectiveness of AdaCon on two medical image regression tasks, ie, bone mineral density estimation from X-ray images and left-ventricular ejection fraction prediction from echocardiogram videos. AdaCon leads to relative improvements of 3.3% and 5.9% in MAE over state-of-the-art BMD estimation and LVEF prediction methods, respectively.", "published": "2021-12-22T07:13:02Z", "authors": ["Weihang Dai", "Xiaomeng Li", "Wan Hang Keith Chiu", "Michael D. Kuo", "Kwang-Ting Cheng"], "categories": ["cs.CV"], "doi": "", "_query": "hedonic regression housing", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2606.25188v1", "title": "Efficient Analytic Uncertainty Quantification for Multi-Modal Regression", "abstract": "Efficient uncertainty quantification (UQ) is essential for trustworthy large-scale learning. Existing UQ methods for regression tasks mainly operate under the assumption that the conditional label marginal satisfies single-peak parametric models, e.g., Gaussians, where the negative log-likelihood function simplifies to the mean square error. However, such single-peak assumptions fail in regression tasks featuring multi-modal distributions. On the other hand, semi-parametric methods which achieve strong regression performance for multi-modal distributions often lack efficient quantification on their prediction variances. In this work, we extend UQ techniques based on Variational Bayesian Inference (VBI) to two widely used semi-parametric regression models that yield histogram-like reconstructions of the conditional label densities: Quantile Regression (QR) and Classification Restoration (CR). Our approach introduces a unified, distribution-agnostic framework that simultaneously achieves accurate estimation of complex conditional distributions and highly efficient UQ. Theoretically, our method is grounded in novel formulations of QR and CR within the VBI framework, yielding analytic Evidence Lower Bounds (ELBO) to streamline training and a closed-form or analytically approximated predictive density for efficient inference. Empirically, we evaluate our methods on three large-scale regression benchmarks with multi-modal label distributions. Our framework outperforms state-of-the-art multi-modal regression baselines, and even matches predictive performance of computationally expensive ensemble models. Furthermore, by leveraging epistemic uncertainty estimation, our approach enables highly data-efficient active learning strategies.", "published": "2026-06-23T21:31:42Z", "authors": ["Kun Jin", "James Harrison", "Jiawei Li", "Sihan Liu", "Jiayi Liu", "Randolph Linderman", "Yuening Li", "Arnab Bhadury", "Sourabh Prakash Bansod", "Liang Liu", "Jasper Snoek"], "categories": ["cs.LG"], "doi": "", "_query": "hedonic regression housing", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/1608.01410v1", "title": "Bayesian Kernel and Mutual $k$-Nearest Neighbor Regression", "abstract": "We propose Bayesian extensions of two nonparametric regression methods which are kernel and mutual $k$-nearest neighbor regression methods. Derived based on Gaussian process models for regression, the extensions provide distributions for target value estimates and the framework to select the hyperparameters. It is shown that both the proposed methods asymptotically converge to kernel and mutual $k$-nearest neighbor regression methods, respectively. The simulation results show that the proposed methods can select proper hyperparameters and are better than or comparable to the former methods for an artificial data set and a real world data set.", "published": "2016-08-04T01:33:34Z", "authors": ["Hyun-Chul Kim"], "categories": ["cs.LG", "stat.ML"], "doi": "", "_query": "hedonic regression housing", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2006.15638v2", "title": "Improved Small Domain Estimation via Compromise Regression Weights", "abstract": "Shrinkage estimates of small domain parameters typically utilize a combination of a noisy \"direct\" estimate that only uses data from a specific small domain and a more stable regression estimate. When the regression model is misspecified, estimation performance for the noisier domains can suffer due to substantial shrinkage towards a poorly estimated regression surface. In this paper, we introduce a new class of robust, empirically-driven regression weights that target estimation of the small domain means under potential misspecification of the global regression model. Our regression weights are a convex combination of the model-based weights associated with the best linear unbiased predictor (BLUP) and those associated with the observed best predictor (OBP). The compromise parameter in this convex combination is found by minimizing a novel, unbiased estimate of the mean-squared prediction error for the small domain means, and we label the associated small domain estimates the \"compromise best predictor\", or CBP. Using a data-adaptive mixture for the regression weights enables the CBP to possess the robustness of the OBP while retaining the main advantages of the EBLUP whenever the regression model is correct. We demonstrate the use of the CBP in an application estimating gait speed in older adults.", "published": "2020-06-28T15:51:15Z", "authors": ["Nicholas C. Henderson", "Ravi Varadhan", "Thomas A. Louis"], "categories": ["stat.ME"], "doi": "", "_query": "hedonic regression housing", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/1709.10030v1", "title": "Sparse Hierarchical Regression with Polynomials", "abstract": "We present a novel method for exact hierarchical sparse polynomial regression. Our regressor is that degree $r$ polynomial which depends on at most $k$ inputs, counting at most $\\ell$ monomial terms, which minimizes the sum of the squares of its prediction errors. The previous hierarchical sparse specification aligns well with modern big data settings where many inputs are not relevant for prediction purposes and the functional complexity of the regressor needs to be controlled as to avoid overfitting. We present a two-step approach to this hierarchical sparse regression problem. First, we discard irrelevant inputs using an extremely fast input ranking heuristic. Secondly, we take advantage of modern cutting plane methods for integer optimization to solve our resulting reduced hierarchical $(k, \\ell)$-sparse problem exactly. The ability of our method to identify all $k$ relevant inputs and all $\\ell$ monomial terms is shown empirically to experience a phase transition. Crucially, the same transition also presents itself in our ability to reject all irrelevant features and monomials as well. In the regime where our method is statistically powerful, its computational complexity is interestingly on par with Lasso based heuristics. The presented work fills a void in terms of a lack of powerful disciplined nonlinear sparse regression methods in high-dimensional settings. Our method is shown empirically to scale to regression problems with $n\\approx 10,000$ observations for input dimension $p\\approx 1,000$.", "published": "2017-09-28T16:01:08Z", "authors": ["Dimitris Bertsimas", "Bart Van Parys"], "categories": ["math.OC", "cs.LG", "stat.ML"], "doi": "", "_query": "hedonic regression housing", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2104.04190v2", "title": "Measurement Errors in Semiparametric Generalized Regression Models", "abstract": "Regression models that ignore measurement error in predictors may produce highly biased estimates leading to erroneous inferences. It is well known that it is extremely difficult to take measurement error into account in Gaussian nonparametric regression. This problem becomes tremendously more difficult when considering other families such as logistic regression, Poisson and negative-binomial. For the first time, we present a method aiming to correct for measurement error when estimating regression functions flexibly covering virtually all distributions and link functions regularly considered in generalized linear models. This approach depends on approximating the first and the second moment of the response after integrating out the true unobserved predictors in a semiparametric generalized linear model. Unlike previous methods, this method is not restricted to truncated splines and can utilize various basis functions. Through extensive simulation studies, we study the performance of our method under many scenarios.", "published": "2021-04-09T04:48:52Z", "authors": ["Mohammad W. Hattab", "David Ruppert"], "categories": ["stat.ME"], "doi": "", "_query": "hedonic regression housing", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2009.00225v2", "title": "Heatmap Regression via Randomized Rounding", "abstract": "Heatmap regression has become the mainstream methodology for deep learning-based semantic landmark localization, including in facial landmark localization and human pose estimation. Though heatmap regression is robust to large variations in pose, illumination, and occlusion in unconstrained settings, it usually suffers from a sub-pixel localization problem. Specifically, considering that the activation point indices in heatmaps are always integers, quantization error thus appears when using heatmaps as the representation of numerical coordinates. Previous methods to overcome the sub-pixel localization problem usually rely on high-resolution heatmaps. As a result, there is always a trade-off between achieving localization accuracy and computational cost, where the computational complexity of heatmap regression depends on the heatmap resolution in a quadratic manner. In this paper, we formally analyze the quantization error of vanilla heatmap regression and propose a simple yet effective quantization system to address the sub-pixel localization problem. The proposed quantization system induced by the randomized rounding operation 1) encodes the fractional part of numerical coordinates into the ground truth heatmap using a probabilistic approach during training; and 2) decodes the predicted numerical coordinates from a set of activation points during testing. We prove that the proposed quantization system for heatmap regression is unbiased and lossless. Experimental results on popular facial landmark localization datasets (WFLW, 300W, COFW, and AFLW) and human pose estimation datasets (MPII and COCO) demonstrate the effectiveness of the proposed method for efficient and accurate semantic landmark localization. Code is available at http://github.com/baoshengyu/H3R.", "published": "2020-09-01T04:54:22Z", "authors": ["Baosheng Yu", "Dacheng Tao"], "categories": ["cs.CV"], "doi": "10.1109/TPAMI.2021.3103980", "_query": "hedonic regression housing", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2508.15567v1", "title": "Clustering-based aggregate value regression", "abstract": "In various practical situations, forecasting of aggregate values rather than individual ones is often our main focus. For instance, electricity companies are interested in forecasting the total electricity demand in a specific region to ensure reliable grid operation and resource allocation. However, to our knowledge, statistical learning specifically for forecasting aggregate values has not yet been well-established. In particular, the relationship between forecast error and the number of clusters has not been well studied, as clustering is usually treated as unsupervised learning. This study introduces a novel forecasting method specifically focused on the aggregate values in the linear regression model. We call it the Aggregate Value Regression (AVR), and it is constructed by combining all regression models into a single model. With the AVR, we must estimate a huge number of parameters when the number of regression models to be combined is large, resulting in overparameterization. To address the overparameterization issue, we introduce a hierarchical clustering technique, referred to as AVR-C (C stands for clustering). In this approach, several clusters of regression models are constructed, and the AVR is performed within each cluster. The AVR-C introduces a novel bias-variance trade-off theory under the assumption of a misspecified model. In this framework, the number of clusters characterizes model complexity. Monte Carlo simulation is conducted to investigate the behavior of training and test errors of our proposed clustering technique. The bias-variance trade-off theory is also demonstrated through the analysis of electricity demand forecasting.", "published": "2025-08-21T13:41:21Z", "authors": ["Kei Hirose", "Hidetoshi Matsui", "Hiroki Masuda"], "categories": ["stat.ME"], "doi": "", "_query": "hedonic regression housing", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2009.08071v2", "title": "Ridge Regression Revisited: Debiasing, Thresholding and Bootstrap", "abstract": "The success of the Lasso in the era of high-dimensional data can be attributed to its conducting an implicit model selection, i.e., zeroing out regression coefficients that are not significant. By contrast, classical ridge regression can not reveal a potential sparsity of parameters, and may also introduce a large bias under the high-dimensional setting. Nevertheless, recent work on the Lasso involves debiasing and thresholding, the latter in order to further enhance the model selection. As a consequence, ridge regression may be worth another look since -- after debiasing and thresholding -- it may offer some advantages over the Lasso, e.g., it can be easily computed using a closed-form expression. % and it has similar performance to threshold Lasso. In this paper, we define a debiased and thresholded ridge regression method, and prove a consistency result and a Gaussian approximation theorem. We further introduce a wild bootstrap algorithm to construct confidence regions and perform hypothesis testing for a linear combination of parameters. In addition to estimation, we consider the problem of prediction, and present a novel, hybrid bootstrap algorithm tailored for prediction intervals. Extensive numerical simulations further show that the debiased and thresholded ridge regression has favorable finite sample performance and may be preferable in some settings.", "published": "2020-09-17T05:04:10Z", "authors": ["Yunyi Zhang", "Dimitris N. Politis"], "categories": ["math.ST", "stat.AP", "stat.ML"], "doi": "", "_query": "hedonic regression housing", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/1612.09413v3", "title": "Permuted and Augmented Stick-Breaking Bayesian Multinomial Regression", "abstract": "To model categorical response variables given their covariates, we propose a permuted and augmented stick-breaking (paSB) construction that one-to-one maps the observed categories to randomly permuted latent sticks. This new construction transforms multinomial regression into regression analysis of stick-specific binary random variables that are mutually independent given their covariate-dependent stick success probabilities, which are parameterized by the regression coefficients of their corresponding categories. The paSB construction allows transforming an arbitrary cross-entropy-loss binary classifier into a Bayesian multinomial one. Specifically, we parameterize the negative logarithms of the stick failure probabilities with a family of covariate-dependent softplus functions to construct nonparametric Bayesian multinomial softplus regression, and transform Bayesian support vector machine (SVM) into Bayesian multinomial SVM. These Bayesian multinomial regression models are not only capable of providing probability estimates, quantifying uncertainty, increasing robustness, and producing nonlinear classification decision boundaries, but also amenable to posterior simulation. Example results demonstrate their attractive properties and performance.", "published": "2016-12-30T07:54:49Z", "authors": ["Quan Zhang", "Mingyuan Zhou"], "categories": ["stat.ME", "stat.ML"], "doi": "", "_query": "hedonic regression housing", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2404.05178v1", "title": "Estimating granular house price distributions in the Australian market using Gaussian mixtures", "abstract": "A new methodology is proposed to approximate the time-dependent house price distribution at a fine regional scale using Gaussian mixtures. The means, variances and weights of the mixture components are related to time, location and dwelling type through a non linear function trained by a deep functional approximator. Price indices are derived as means, medians, quantiles or other functions of the estimated distributions. Price densities for larger regions, such as a city, are calculated via a weighted sum of the component density functions. The method is applied to a data set covering all of Australia at a fine spatial and temporal resolution. In addition to enabling a detailed exploration of the data, the proposed index yields lower prediction errors in the practical task of individual dwelling price projection from previous sales values within the three major Australian cities. The estimated quantiles are also found to be well calibrated empirically, capturing the complexity of house price distributions.", "published": "2024-04-08T04:01:43Z", "authors": ["Willem P Sijp", "Anastasios Panagiotelis"], "categories": ["econ.EM"], "doi": "", "_query": "hedonic regression housing", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/1903.00386v1", "title": "On the complexity of logistic regression models", "abstract": "We investigate the complexity of logistic regression models which is defined by counting the number of indistinguishable distributions that the model can represent (Balasubramanian, 1997). We find that the complexity of logistic models with binary inputs does not only depend on the number of parameters but also on the distribution of inputs in a non-trivial way which standard treatments of complexity do not address. In particular, we observe that correlations among inputs induce effective dependencies among parameters thus constraining the model and, consequently, reducing its complexity. We derive simple relations for the upper and lower bounds of the complexity. Furthermore, we show analytically that, defining the model parameters on a finite support rather than the entire axis, decreases the complexity in a manner that critically depends on the size of the domain. Based on our findings, we propose a novel model selection criterion which takes into account the entropy of the input distribution. We test our proposal on the problem of selecting the input variables of a logistic regression model in a Bayesian Model Selection framework. In our numerical tests, we find that, while the reconstruction errors of standard model selection approaches (AIC, BIC, $\\ell_1$ regularization) strongly depend on the sparsity of the ground truth, the reconstruction error of our method is always close to the minimum in all conditions of sparsity, data size and strength of input correlations. Finally, we observe that, when considering categorical instead of binary inputs, in a simple and mathematically tractable case, the contribution of the alphabet size to the complexity is very small compared to that of parameter space dimension. We further explore the issue by analysing the dataset of the \"13 keys to the White House\" which is a method for forecasting the outcomes of US presidential elections.", "published": "2019-03-01T16:10:55Z", "authors": ["Nicola Bulso", "Matteo Marsili", "Yasser Roudi"], "categories": ["stat.ML", "cond-mat.dis-nn", "cs.LG", "physics.data-an"], "doi": "", "_query": "hedonic regression housing", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2403.15392v1", "title": "Radon mitigation by soil depressurisation case study: radon concentration and pressure field extension monitoring in a pilot house in Spain", "abstract": "A one-year monitoring study was conducted in a pilot house with high radon levels to investigate the ability and efficiency of radon mitigation by soil depressurisation (SD) both active and passive. The study included monitoring of radon concentration, pressure field extension (pfe) under the slab and some atmospheric parameters for different testing phases. Periods in which the house remained closed to foster radon accumulation were alternated with phases of active and passive soil depressurisation under different conditions. The behaviour of the radon concentration in the pilot house was analysed along with the influence of atmospheric variables, significant correlations were found for the radon concentration with atmospheric pressure, outdoor temperature and wind. From the pfe analysis it was proven that the pressure drop with distance from the suction point of the SD system is proportional to the depressurisation generated. It was found also that the permeability characterisation of the pilot house agrees with the literature about granular fill materials characterisation for radon SD systems across Europe. Radon reductions in excess of 85% were achieved for the different testing phases in all cases. Finally, from the results it was stated that a fan power of 23 W is sufficient to ensure radon reductions over 85% for dwellings with similar aggregate layer and soil permeability.", "published": "2024-02-08T12:22:50Z", "authors": ["Marta Fuente", "Jamie Goggins", "Daniel Rabago", "Ismael Fuente", "Carlos Sainz", "Mark Foley"], "categories": ["cs.CE"], "doi": "10.1016/j.scitotenv.2019.133746", "_query": "hedonic regression housing", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/1409.5230v1", "title": "Deep Regression for Face Alignment", "abstract": "In this paper, we present a deep regression approach for face alignment. The deep architecture consists of a global layer and multi-stage local layers. We apply the back-propagation algorithm with the dropout strategy to jointly optimize the regression parameters. We show that the resulting deep regressor gradually and evenly approaches the true facial landmarks stage by stage, avoiding the tendency to yield over-strong early stage regressors while over-weak later stage regressors. Experimental results show that our approach achieves the state-of-the-art", "published": "2014-09-18T09:10:28Z", "authors": ["Baoguang Shi", "Xiang Bai", "Wenyu Liu", "Jingdong Wang"], "categories": ["cs.CV"], "doi": "", "_query": "hedonic regression housing", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2010.09022v2", "title": "JSRT: James-Stein Regression Tree", "abstract": "Regression tree (RT) has been widely used in machine learning and data mining community. Given a target data for prediction, a regression tree is first constructed based on a training dataset before making prediction for each leaf node. In practice, the performance of RT relies heavily on the local mean of samples from an individual node during the tree construction/prediction stage, while neglecting the global information from different nodes, which also plays an important role. To address this issue, we propose a novel regression tree, named James-Stein Regression Tree (JSRT) by considering global information from different nodes. Specifically, we incorporate the global mean information based on James-Stein estimator from different nodes during the construction/predicton stage. Besides, we analyze the generalization error of our method under the mean square error (MSE) metric. Extensive experiments on public benchmark datasets verify the effectiveness and efficiency of our method, and demonstrate the superiority of our method over other RT prediction methods.", "published": "2020-10-18T16:28:49Z", "authors": ["Xingchun Xiang", "Qingtao Tang", "Huaixuan Zhang", "Tao Dai", "Jiawei Li", "Shu-Tao Xia"], "categories": ["cs.LG", "stat.ML"], "doi": "", "_query": "hedonic regression housing", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2312.17348v1", "title": "A randomized algorithm to solve reduced rank operator regression", "abstract": "We present and analyze an algorithm designed for addressing vector-valued regression problems involving possibly infinite-dimensional input and output spaces. The algorithm is a randomized adaptation of reduced rank regression, a technique to optimally learn a low-rank vector-valued function (i.e. an operator) between sampled data via regularized empirical risk minimization with rank constraints. We propose Gaussian sketching techniques both for the primal and dual optimization objectives, yielding Randomized Reduced Rank Regression (R4) estimators that are efficient and accurate. For each of our R4 algorithms we prove that the resulting regularized empirical risk is, in expectation w.r.t. randomness of a sketch, arbitrarily close to the optimal value when hyper-parameteres are properly tuned. Numerical expreriments illustrate the tightness of our bounds and show advantages in two distinct scenarios: (i) solving a vector-valued regression problem using synthetic and large-scale neuroscience datasets, and (ii) regressing the Koopman operator of a nonlinear stochastic dynamical system.", "published": "2023-12-28T20:29:59Z", "authors": ["Giacomo Turri", "Vladimir Kostic", "Pietro Novelli", "Massimiliano Pontil"], "categories": ["cs.LG", "math.NA", "stat.ML"], "doi": "", "_query": "hedonic regression housing", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2210.15175v1", "title": "Private Isotonic Regression", "abstract": "In this paper, we consider the problem of differentially private (DP) algorithms for isotonic regression. For the most general problem of isotonic regression over a partially ordered set (poset) $\\mathcal{X}$ and for any Lipschitz loss function, we obtain a pure-DP algorithm that, given $n$ input points, has an expected excess empirical risk of roughly $\\mathrm{width}(\\mathcal{X}) \\cdot \\log|\\mathcal{X}| / n$, where $\\mathrm{width}(\\mathcal{X})$ is the width of the poset. In contrast, we also obtain a near-matching lower bound of roughly $(\\mathrm{width}(\\mathcal{X}) + \\log |\\mathcal{X}|) / n$, that holds even for approximate-DP algorithms. Moreover, we show that the above bounds are essentially the best that can be obtained without utilizing any further structure of the poset. In the special case of a totally ordered set and for $\\ell_1$ and $\\ell_2^2$ losses, our algorithm can be implemented in near-linear running time; we also provide extensions of this algorithm to the problem of private isotonic regression with additional structural constraints on the output function.", "published": "2022-10-27T05:08:07Z", "authors": ["Badih Ghazi", "Pritish Kamath", "Ravi Kumar", "Pasin Manurangsi"], "categories": ["cs.LG", "cs.CR", "stat.ML"], "doi": "", "_query": "hedonic regression housing", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/1809.11064v1", "title": "On wavelets to select the parametric form of a regression model", "abstract": "Let Y be a response variable related with a set of explanatory variables and let f1, f2, ..., fk be a set of the parametric forms representing a set of candidate's model. Let f* be the true model among the set of k plausible models. We discuss in this paper the use of wavelet regression method as auxiliary for the choice of the \"true\" parametric form of a regression model, particularly, for the cases of nonlinear regression and generalized linear models. The use of a non-parametric method for the choice of the more appropriate parametric equation in regression problems would be interesting in practice due to the simplicity and because the probabilistic assumptions are not required. We evaluate the performance of the proposed wavelet procedure based on the true classification rate of the correct parametric form among a range of k candidate models, taking into account a wide ranges of scenarios and configurations as well as in real data set applications.", "published": "2018-09-28T14:39:39Z", "authors": ["Eufrásio de A. Lima Neto", "Aluísio de S. Pinheiro", "Adenice G. O. Ferreira"], "categories": ["stat.ME", "stat.CO"], "doi": "10.1080/03610918.2019.1610441", "_query": "hedonic regression housing", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2311.13327v3", "title": "Regressions under Adverse Conditions", "abstract": "We introduce a new regression method that relates the mean of an outcome variable to covariates, under the \"adverse condition\" that a distress variable falls in its tail. This allows to tailor classical mean regressions to adverse scenarios, which receive increasing interest in economics and finance, among many others. In the terminology of the systemic risk literature, our method can be interpreted as a regression for the Marginal Expected Shortfall. We propose a two-step procedure to estimate the new models, show consistency and asymptotic normality of the estimator, and propose feasible inference under weak conditions that allow for cross-sectional and time series applications. Simulations verify the accuracy of the asymptotic approximations of the two-step estimator. Two empirical applications show that our regressions under adverse conditions are a valuable tool in such diverse fields as the study of the relation between systemic risk and asset price bubbles, and dissecting macroeconomic growth vulnerabilities into individual components.", "published": "2023-11-22T11:47:40Z", "authors": ["Timo Dimitriadis", "Yannick Hoga"], "categories": ["econ.EM", "stat.ME"], "doi": "", "_query": "hedonic regression housing", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2105.14673v2", "title": "A Minimax Lower Bound for Low-Rank Matrix-Variate Logistic Regression", "abstract": "This paper considers the problem of matrix-variate logistic regression. It derives the fundamental error threshold on estimating low-rank coefficient matrices in the logistic regression problem by obtaining a lower bound on the minimax risk. The bound depends explicitly on the dimension and distribution of the covariates, the rank and energy of the coefficient matrix, and the number of samples. The resulting bound is proportional to the intrinsic degrees of freedom in the problem, which suggests the sample complexity of the low-rank matrix logistic regression problem can be lower than that for vectorized logistic regression. The proof techniques utilized in this work also set the stage for development of minimax lower bounds for tensor-variate logistic regression problems.", "published": "2021-05-31T02:06:34Z", "authors": ["Batoul Taki", "Mohsen Ghassemi", "Anand D. Sarwate", "Waheed U. Bajwa"], "categories": ["cs.LG", "eess.SP", "math.ST", "stat.ML"], "doi": "10.1109/IEEECONF53345.2021.9723149", "_query": "hedonic regression housing", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/1908.04462v2", "title": "The bias of isotonic regression", "abstract": "We study the bias of the isotonic regression estimator. While there is extensive work characterizing the mean squared error of the isotonic regression estimator, relatively little is known about the bias. In this paper, we provide a sharp characterization, proving that the bias scales as $O(n^{-β/3})$ up to log factors, where $1 \\leq β\\leq 2$ is the exponent corresponding to H{ö}lder smoothness of the underlying mean. Importantly, this result only requires a strictly monotone mean and that the noise distribution has subexponential tails, without relying on symmetric noise or other restrictive assumptions.", "published": "2019-08-13T02:15:53Z", "authors": ["Ran Dai", "Hyebin Song", "Rina Foygel Barber", "Garvesh Raskutti"], "categories": ["math.ST"], "doi": "", "_query": "hedonic regression housing", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2412.17116v1", "title": "Fair and Accurate Regression: Strong Formulations and Algorithms", "abstract": "This paper introduces mixed-integer optimization methods to solve regression problems that incorporate fairness metrics. We propose an exact formulation for training fair regression models. To tackle this computationally hard problem, we study the polynomially-solvable single-factor and single-observation subproblems as building blocks and derive their closed convex hull descriptions. Strong formulations obtained for the general fair regression problem in this manner are utilized to solve the problem with a branch-and-bound algorithm exactly or as a relaxation to produce fair and accurate models rapidly. Moreover, to handle large-scale instances, we develop a coordinate descent algorithm motivated by the convex-hull representation of the single-factor fair regression problem to improve a given solution efficiently. Numerical experiments conducted on fair least squares and fair logistic regression problems show competitive statistical performance with state-of-the-art methods while significantly reducing training times.", "published": "2024-12-22T18:04:54Z", "authors": ["Anna Deza", "Andrés Gómez", "Alper Atamtürk"], "categories": ["cs.LG", "cs.CY", "math.OC", "stat.ML"], "doi": "", "_query": "hedonic regression housing", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2411.12060v1", "title": "Interpretation of High-Dimensional Regression Coefficients by Comparison with Linearized Compressing Features", "abstract": "Linear regression is often deemed inherently interpretable; however, challenges arise for high-dimensional data. We focus on further understanding how linear regression approximates nonlinear responses from high-dimensional functional data, motivated by predicting cycle life for lithium-ion batteries. We develop a linearization method to derive feature coefficients, which we compare with the closest regression coefficients of the path of regression solutions. We showcase the methods on battery data case studies where a single nonlinear compressing feature, $g\\colon \\mathbb{R}^p \\to \\mathbb{R}$, is used to construct a synthetic response, $\\mathbf{y} \\in \\mathbb{R}$. This unifying view of linear regression and compressing features for high-dimensional functional data helps to understand (1) how regression coefficients are shaped in the highly regularized domain and how they relate to linearized feature coefficients and (2) how the shape of regression coefficients changes as a function of regularization to approximate nonlinear responses by exploiting local structures.", "published": "2024-11-18T20:59:38Z", "authors": ["Joachim Schaeffer", "Jinwook Rhyu", "Robin Droop", "Rolf Findeisen", "Richard Braatz"], "categories": ["cs.LG", "stat.ML"], "doi": "", "_query": "hedonic regression housing", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2110.10393v1", "title": "Variable selection in doubly truncated regression", "abstract": "Doubly truncated data arise in many areas such as astronomy, econometrics, and medical studies. For the regression analysis with doubly truncated response variables, the existence of double truncation may bring bias for estimation as well as affect variable selection. We propose a simultaneous estimation and variable selection procedure for the doubly truncated regression, allowing a diverging number of regression parameters. To remove the bias introduced by the double truncation, a Mann-Whitney-type loss function is used. The adaptive LASSO penalty is then added into the loss function to achieve simultaneous estimation and variable selection. An iterative algorithm is designed to optimize the resulting objective function. We establish the consistency and the asymptotic normality of the proposed estimator. The oracle property of the proposed selection procedure is also obtained. Some simulation studies are conducted to show the finite sample performance of the proposed approach. We also apply the method to analyze a real astronomical data.", "published": "2021-10-20T06:23:37Z", "authors": ["Ming Zheng", "Chanjuan Lin", "Wen Yu"], "categories": ["stat.ME"], "doi": "", "_query": "hedonic regression housing", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/1501.01774v2", "title": "Optimal designs in regression with correlated errors", "abstract": "This paper discusses the problem of determining optimal designs for regression models, when the observations are dependent and taken on an interval. A complete solution of this challenging optimal design problem is given for a broad class of regression models and covariance kernels. We propose a class of estimators which are only slightly more complicated than the ordinary least-squares estimators. We then demonstrate that we can design the experiments, such that asymptotically the new estimators achieve the same precision as the best linear unbiased estimator computed for the whole trajectory of the process. As a by-product we derive explicit expressions for the BLUE in the continuous time model and analytic expressions for the optimal designs in a wide class of regression models. We also demonstrate that for a finite number of observations the precision of the proposed procedure, which includes the estimator and design, is very close to the best achievable. The results are illustrated on a few numerical examples.", "published": "2015-01-08T09:19:44Z", "authors": ["Holger Dette", "Andrey Pepelyshev", "Anatoly Zhigljavsky"], "categories": ["stat.ME"], "doi": "", "_query": "hedonic regression housing", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/1406.5340v1", "title": "Quantum regression theorem and non-Markovianity of quantum dynamics", "abstract": "We explore the connection between two recently introduced notions of non-Markovian quantum dynamics and the validity of the so-called quantum regression theorem. While non-Markovianity of a quantum dynamics has been defined looking at the behaviour in time of the statistical operator, which determines the evolution of mean values, the quantum regression theorem makes statements about the behaviour of system correlation functions of order two and higher. The comparison relies on an estimate of the validity of the quantum regression hypothesis, which can be obtained exactly evaluating two points correlation functions. To this aim we consider a qubit undergoing dephasing due to interaction with a bosonic bath, comparing the exact evaluation of the non-Markovianity measures with the violation of the quantum regression theorem for a class of spectral densities. We further study a photonic dephasing model, recently exploited for the experimental measurement of non-Markovianity. It appears that while a non-Markovian dynamics according to either definition brings with itself violation of the regression hypothesis, even Markovian dynamics can lead to a failure of the regression relation.", "published": "2014-06-20T10:27:11Z", "authors": ["Giacomo Guarnieri", "Andrea Smirne", "Bassano Vacchini"], "categories": ["quant-ph"], "doi": "10.1103/PhysRevA.90.022110", "_query": "hedonic regression housing", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2502.01995v1", "title": "Theoretical and Practical Analysis of Fréchet Regression via Comparison Geometry", "abstract": "Fréchet regression extends classical regression methods to non-Euclidean metric spaces, enabling the analysis of data relationships on complex structures such as manifolds and graphs. This work establishes a rigorous theoretical analysis for Fréchet regression through the lens of comparison geometry which leads to important considerations for its use in practice. The analysis provides key results on the existence, uniqueness, and stability of the Fréchet mean, along with statistical guarantees for nonparametric regression, including exponential concentration bounds and convergence rates. Additionally, insights into angle stability reveal the interplay between curvature of the manifold and the behavior of the regression estimator in these non-Euclidean contexts. Empirical experiments validate the theoretical findings, demonstrating the effectiveness of proposed hyperbolic mappings, particularly for data with heteroscedasticity, and highlighting the practical usefulness of these results.", "published": "2025-02-04T04:16:00Z", "authors": ["Masanari Kimura", "Howard Bondell"], "categories": ["stat.ML", "cs.AI", "cs.LG"], "doi": "", "_query": "hedonic regression housing", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/0906.5546v4", "title": "A-Collapsibility of Distribution Dependence and Quantile Regression Coefficients", "abstract": "The Yule-Simpson paradox notes that an association between random variables can be reversed when averaged over a background variable. Cox and Wermuth (2003) introduced the concept of distribution dependence between two random variables X and Y, and developed two dependence conditions, each of which guarantees that reversal cannot occur. Ma, Xie and Geng (2006) studied the collapsibility of distribution dependence over a background variable W, under a rather strong homogeneity condition. Collapsibility ensures the association remains the same for conditional and marginal models, so that Yule-Simpson reversal cannot occur. In this paper, we investigate a more general condition for avoiding effect reversal: A-collapsibility. The conditions of Cox and Wermuth imply A-collapsibility, without assuming homogeneity. In fact, we show that, when W is a binary variable, collapsibility is equivalent to A-collapsibility plus homogeneity, and A-collapsibility is equivalent to the conditions of Cox and Wermuth. Recently, Cox (2007) extended Cochran's result on regression coefficients of conditional and marginal models, to quantile regression coefficients. The conditions of Cox and Wermuth are sufficient for A-collapsibility of quantile regression coefficients. If the conditional distribution of W, given Y = y and X = x, belong to one-dimensional natural exponential family, they are also necessary. Some applications of A-collapsibility include the analysis of a contingency table, linear regression models and quantile regression models.", "published": "2009-06-30T14:39:32Z", "authors": ["P. Vellaisamy"], "categories": ["stat.ME", "math.ST", "stat.AP"], "doi": "", "_query": "hedonic regression housing", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2410.23246v1", "title": "Progression: an extrapolation principle for regression", "abstract": "The problem of regression extrapolation, or out-of-distribution generalization, arises when predictions are required at test points outside the range of the training data. In such cases, the non-parametric guarantees for regression methods from both statistics and machine learning typically fail. Based on the theory of tail dependence, we propose a novel statistical extrapolation principle. After a suitable, data-adaptive marginal transformation, it assumes a simple relationship between predictors and the response at the boundary of the training predictor samples. This assumption holds for a wide range of models, including non-parametric regression functions with additive noise. Our semi-parametric method, progression, leverages this extrapolation principle and offers guarantees on the approximation error beyond the training data range. We demonstrate how this principle can be effectively integrated with existing approaches, such as random forests and additive models, to improve extrapolation performance on out-of-distribution samples.", "published": "2024-10-30T17:29:51Z", "authors": ["Gloria Buriticá", "Sebastian Engelke"], "categories": ["stat.ME", "stat.ML"], "doi": "", "_query": "hedonic regression housing", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2207.06558v1", "title": "Parametric quantile regression for income data", "abstract": "Univariate normal regression models are statistical tools widely applied in many areas of economics. Nevertheless, income data have asymmetric behavior and are best modeled by non-normal distributions. The modeling of income plays an important role in determining workers' earnings, as well as being an important research topic in labor economics. Thus, the objective of this work is to propose parametric quantile regression models based on two important asymmetric income distributions, namely, Dagum and Singh-Maddala distributions. The proposed quantile models are based on reparameterizations of the original distributions by inserting a quantile parameter. We present the reparameterizations, some properties of the distributions, and the quantile regression models with their inferential aspects. We proceed with Monte Carlo simulation studies, considering the maximum likelihood estimation performance evaluation and an analysis of the empirical distribution of two residuals. The Monte Carlo results show that both models meet the expected outcomes. We apply the proposed quantile regression models to a household income data set provided by the National Institute of Statistics of Chile. We showed that both proposed models had a good performance both in terms of model fitting. Thus, we conclude that results were favorable to the use of Singh-Maddala and Dagum quantile regression models for positive asymmetric data, such as income data.", "published": "2022-07-13T23:42:15Z", "authors": ["Helton Saulo", "Roberto Vila", "Giovanna V. Borges", "Marcelo Bourguignon"], "categories": ["stat.ME", "econ.EM"], "doi": "", "_query": "hedonic regression housing", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2109.05317v1", "title": "Bayesian Topic Regression for Causal Inference", "abstract": "Causal inference using observational text data is becoming increasingly popular in many research areas. This paper presents the Bayesian Topic Regression (BTR) model that uses both text and numerical information to model an outcome variable. It allows estimation of both discrete and continuous treatment effects. Furthermore, it allows for the inclusion of additional numerical confounding factors next to text data. To this end, we combine a supervised Bayesian topic model with a Bayesian regression framework and perform supervised representation learning for the text features jointly with the regression parameter training, respecting the Frisch-Waugh-Lovell theorem. Our paper makes two main contributions. First, we provide a regression framework that allows causal inference in settings when both text and numerical confounders are of relevance. We show with synthetic and semi-synthetic datasets that our joint approach recovers ground truth with lower bias than any benchmark model, when text and numerical features are correlated. Second, experiments on two real-world datasets demonstrate that a joint and supervised learning strategy also yields superior prediction results compared to strategies that estimate regression weights for text and non-text features separately, being even competitive with more complex deep neural networks.", "published": "2021-09-11T16:40:43Z", "authors": ["Maximilian Ahrens", "Julian Ashwin", "Jan-Peter Calliess", "Vu Nguyen"], "categories": ["stat.ML", "cs.CL", "cs.LG"], "doi": "", "_query": "hedonic regression housing", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/math/0603132v1", "title": "Functional linear regression analysis for longitudinal data", "abstract": "We propose nonparametric methods for functional linear regression which are designed for sparse longitudinal data, where both the predictor and response are functions of a covariate such as time. Predictor and response processes have smooth random trajectories, and the data consist of a small number of noisy repeated measurements made at irregular times for a sample of subjects. In longitudinal studies, the number of repeated measurements per subject is often small and may be modeled as a discrete random number and, accordingly, only a finite and asymptotically nonincreasing number of measurements are available for each subject or experimental unit. We propose a functional regression approach for this situation, using functional principal component analysis, where we estimate the functional principal component scores through conditional expectations. This allows the prediction of an unobserved response trajectory from sparse measurements of a predictor trajectory. The resulting technique is flexible and allows for different patterns regarding the timing of the measurements obtained for predictor and response trajectories. Asymptotic properties for a sample of $n$ subjects are investigated under mild conditions, as $n\\to \\infty$, and we obtain consistent estimation for the regression function. Besides convergence results for the components of functional linear regression, such as the regression parameter function, we construct asymptotic pointwise confidence bands for the predicted trajectories. A functional coefficient of determination as a measure of the variance explained by the functional regression model is introduced, extending the standard $R^2$ to the functional case. The proposed methods are illustrated with a simulation study, longitudinal primary biliary liver cirrhosis data and an analysis of the longitudinal relationship between blood pressure and body mass index.", "published": "2006-03-06T08:09:42Z", "authors": ["Fang Yao", "Hans-Georg Müller", "Jane-Ling Wang"], "categories": ["math.ST"], "doi": "10.1214/009053605000000660", "_query": "hedonic regression housing", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2512.25045v1", "title": "Bayesian Elastic Net Regression with Structured Prior Dependence", "abstract": "Many regularization priors for Bayesian regression assume the regression coefficients are a priori independent. In particular this is the case for standard Bayesian treatments of the lasso and the elastic net. While independence may be reasonable in some data-analytic settings, incorporating dependence in these prior distributions provides greater modeling flexibility. This paper introduces the orthant normal distribution in its general form and shows how it can be used to structure prior dependence in the Bayesian elastic net regression model. An L1-regularized version of Zellner's g prior is introduced as a special case, creating a new link between the literature on penalized optimization and an important class of regression priors. Computation is challenging due to an intractable normalizing constant in the prior. We avoid this issue by modifying slightly a standard prior of convenience for the hyperparameters in such a way to enable simple and fast Gibbs sampling of the posterior distribution. The benefit of including structured prior dependence in the Bayesian elastic net regression model is demonstrated through simulation and a near-infrared spectroscopy data example.", "published": "2025-12-31T18:41:52Z", "authors": ["Christopher M. Hans", "Ningyi Liu"], "categories": ["stat.ME"], "doi": "", "_query": "hedonic regression housing", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2510.04583v1", "title": "Improved probabilistic regression using diffusion models", "abstract": "Probabilistic regression models the entire predictive distribution of a response variable, offering richer insights than classical point estimates and directly allowing for uncertainty quantification. While diffusion-based generative models have shown remarkable success in generating complex, high-dimensional data, their usage in general regression tasks often lacks uncertainty-related evaluation and remains limited to domain-specific applications. We propose a novel diffusion-based framework for probabilistic regression that learns predictive distributions in a nonparametric way. More specifically, we propose to model the full distribution of the diffusion noise, enabling adaptation to diverse tasks and enhanced uncertainty quantification. We investigate different noise parameterizations, analyze their trade-offs, and evaluate our framework across a broad range of regression tasks, covering low- and high-dimensional settings. For several experiments, our approach shows superior performance against existing baselines, while delivering calibrated uncertainty estimates, demonstrating its versatility as a tool for probabilistic prediction.", "published": "2025-10-06T08:36:05Z", "authors": ["Carlo Kneissl", "Christopher Bülte", "Philipp Scholl", "Gitta Kutyniok"], "categories": ["cs.LG"], "doi": "", "_query": "hedonic regression housing", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2605.06491v1", "title": "Inference on Linear Regressions with Two-Way Unobserved Heterogeneity", "abstract": "We develop a general estimation and inference procedure for the common parameters in linear panel data regression models with nonparametric two-way specification of unobserved heterogeneity. The procedure takes as input any first-step estimators of the nonparametric regression function and the fixed effects and relies on two key ingredients: First, we develop moment conditions for the common parameters that are Neyman orthogonal with respect to the nonparametric regression function. Second, we employ a novel adjustment of the nonparametric regression estimator so the estimated fixed effects do not generate incidental parameter biases. Together, these ensure that the resulting estimator of the common parameters is root-NT -- asymptotically normally distributed under weak conditions on the estimators of fixed effects and regression function. Next, we propose a novel two-step estimator of the nonparametric regression function and the fixed effects and verify that this particular estimator satisfies the conditions of our general theory. A numerical study shows that the proposed estimators perform well in finite samples.", "published": "2026-05-07T16:14:41Z", "authors": ["Hugo Freeman", "Dennis Kristensen"], "categories": ["econ.EM"], "doi": "", "_query": "hedonic regression housing", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/1910.00812v2", "title": "Robust Bayesian Regression with Synthetic Posterior", "abstract": "Although linear regression models are fundamental tools in statistical science, the estimation results can be sensitive to outliers. While several robust methods have been proposed in frequentist frameworks, statistical inference is not necessarily straightforward. We here propose a Bayesian approach to robust inference on linear regression models using synthetic posterior distributions based on $γ$-divergence, which enables us to naturally assess the uncertainty of the estimation through the posterior distribution. We also consider the use of shrinkage priors for the regression coefficients to carry out robust Bayesian variable selection and estimation simultaneously. We develop an efficient posterior computation algorithm by adopting the Bayesian bootstrap within Gibbs sampling. The performance of the proposed method is illustrated through simulation studies and applications to famous datasets.", "published": "2019-10-02T07:52:51Z", "authors": ["Shintaro Hashimoto", "Shonosuke Sugasawa"], "categories": ["stat.ME"], "doi": "10.3390/e22060661", "_query": "hedonic regression housing", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2404.11125v1", "title": "Interval-censored linear quantile regression", "abstract": "Censored quantile regression has emerged as a prominent alternative to classical Cox's proportional hazards model or accelerated failure time model in both theoretical and applied statistics. While quantile regression has been extensively studied for right-censored survival data, methodologies for analyzing interval-censored data remain limited in the survival analysis literature. This paper introduces a novel local weighting approach for estimating linear censored quantile regression, specifically tailored to handle diverse forms of interval-censored survival data. The estimation equation and the corresponding convex objective function for the regression parameter can be constructed as a weighted average of quantile loss contributions at two interval endpoints. The weighting components are nonparametrically estimated using local kernel smoothing or ensemble machine learning techniques. To estimate the nonparametric distribution mass for interval-censored data, a modified EM algorithm for nonparametric maximum likelihood estimation is employed by introducing subject-specific latent Poisson variables. The proposed method's empirical performance is demonstrated through extensive simulation studies and real data analyses of two HIV/AIDS datasets.", "published": "2024-04-17T07:16:41Z", "authors": ["Taehwa Choi", "Seohyeon Park", "Hunyong Cho", "Sangbum Choi"], "categories": ["stat.ME", "stat.CO"], "doi": "10.1080/10618600.2024.2365740", "_query": "hedonic regression housing", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/1710.11345v1", "title": "Tensor Regression Meets Gaussian Processes", "abstract": "Low-rank tensor regression, a new model class that learns high-order correlation from data, has recently received considerable attention. At the same time, Gaussian processes (GP) are well-studied machine learning models for structure learning. In this paper, we demonstrate interesting connections between the two, especially for multi-way data analysis. We show that low-rank tensor regression is essentially learning a multi-linear kernel in Gaussian processes, and the low-rank assumption translates to the constrained Bayesian inference problem. We prove the oracle inequality and derive the average case learning curve for the equivalent GP model. Our finding implies that low-rank tensor regression, though empirically successful, is highly dependent on the eigenvalues of covariance functions as well as variable correlations.", "published": "2017-10-31T06:44:59Z", "authors": ["Rose Yu", "Guangyu Li", "Yan Liu"], "categories": ["cs.LG", "stat.ML"], "doi": "", "_query": "hedonic regression housing", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2606.03927v2", "title": "FFR: Forward-Forward Learning for Regression", "abstract": "The Forward-Forward (FF) algorithm offers a computationally efficient and biologically plausible alternative to backpropagation (BP) by training neural networks through purely local, layer-wise optimization. However, FF is inherently designed for classification via contrastive positive-negative sample pairs, and extending it to regression poses fundamental challenges: continuous target space lacks natural \"opposites\" for contrastive learning, and the standard goodness function carries no information about target magnitude or ordering. We propose FFR (Forward-Forward for Regression), to our knowledge, the first framework to extend FF to real-world regression and demonstrate competitive performance across diverse realworld datasets. FFR introduces three key innovations: (1) an ordinal competitive goodness function that replaces contrastive pairs with competitive learning between partitioned neuron groups under distance-aware ordinal supervision; (2) a stratified ladder architecture where shallow layers learn coarse ordinal discrimination and deeper layers refine into fine-grained regression, with multi-scale feature aggregation for inter-layer collaboration; and (3) hierarchical prediction with uncertainty estimation, where multi-scale predictors jointly provide robust predictions and a single-pass uncertainty score. Extensive experimental results show FFR recovers on average 98.5% of BP's accuracy across six real-world regression benchmarks while reducing peak training memory to only 27% of BP's at depth 8 and 8% at depth 32, with per-iteration time around 72% of BP's, and substantially outperforms all BP-free competitors.", "published": "2026-06-02T17:15:59Z", "authors": ["Xinyang Liu", "Xuanyu Liang", "Shiqi Ding", "Boyang Li", "Zhiqiang Que", "Jiayang Li", "Guosheng Hu"], "categories": ["cs.LG", "cs.AI"], "doi": "", "_query": "hedonic regression housing", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2506.08760v1", "title": "Akaike information criterion for segmented regression models", "abstract": "In segmented regression, when the regression function is continuous at the change-points that are the boundaries of the segments, it is also called joinpoint regression, and the analysis package developed by \\cite{KimFFM00} has become a standard tool for analyzing trends in longitudinal data in the field of epidemiology. In addition, it is sometimes natural to expect the regression function to be discontinuous at the change-points, and in the field of epidemiology, this model is used in \\cite{JiaZS22}, which is considered important due to the analysis of COVID-19 data. On the other hand, model selection is also indispensable in segmented regression, including the estimation of the number of change-points; however, it can be said that only BIC-type information criteria have been developed. In this paper, we derive an information criterion based on the original definition of AIC, aiming to minimize the divergence between the true structure and the estimated structure. Then, using the statistical asymptotic theory specific to the segmented regression, we confirm that the penalty for the change-point parameter is 6 in the discontinuous case. On the other hand, in the continuous case, we show that the penalty for the change-point parameter remains 2 despite the rapid change in the derivative coefficients. Through numerical experiments, we observe that our AIC tends to reduce the divergence compared to BIC. In addition, through analyzing the same real data as in \\cite{JiaZS22}, we find that the selection between continuous and discontinuous using our AIC yields new insights and that our AIC and BIC may yield different results.", "published": "2025-06-10T13:00:25Z", "authors": ["Kazuki Nakajima", "Yoshiyuki Ninomiya"], "categories": ["stat.ME"], "doi": "", "_query": "hedonic regression housing", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/1902.10407v1", "title": "Provable Approximations for Constrained $\\ell_p$ Regression", "abstract": "The $\\ell_p$ linear regression problem is to minimize $f(x)=||Ax-b||_p$ over $x\\in\\mathbb{R}^d$, where $A\\in\\mathbb{R}^{n\\times d}$, $b\\in \\mathbb{R}^n$, and $p>0$. To avoid overfitting and bound $||x||_2$, the constrained $\\ell_p$ regression minimizes $f(x)$ over every unit vector $x\\in\\mathbb{R}^d$. This makes the problem non-convex even for the simplest case $d=p=2$. Instead, ridge regression is used to minimize the Lagrange form $f(x)+λ||x||_2$ over $x\\in\\mathbb{R}^d$, which yields a convex problem in the price of calibrating the regularization parameter $λ>0$. We provide the first provable constant factor approximation algorithm that solves the constrained $\\ell_p$ regression directly, for every constant $p,d\\geq 1$. Using core-sets, its running time is $O(n \\log n)$ including extensions for streaming and distributed (big) data. In polynomial time, it can handle outliers, $p\\in (0,1)$ and minimize $f(x)$ over every $x$ and permutation of rows in $A$. Experimental results are also provided, including open source and comparison to existing software.", "published": "2019-02-27T09:24:25Z", "authors": ["Ibrahim Jubran", "David Cohn", "Dan Feldman"], "categories": ["cs.LG", "stat.ML"], "doi": "", "_query": "hedonic regression housing", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/0810.4802v1", "title": "Robust nonparametric estimation via wavelet median regression", "abstract": "In this paper we develop a nonparametric regression method that is simultaneously adaptive over a wide range of function classes for the regression function and robust over a large collection of error distributions, including those that are heavy-tailed, and may not even possess variances or means. Our approach is to first use local medians to turn the problem of nonparametric regression with unknown noise distribution into a standard Gaussian regression problem and then apply a wavelet block thresholding procedure to construct an estimator of the regression function. It is shown that the estimator simultaneously attains the optimal rate of convergence over a wide range of the Besov classes, without prior knowledge of the smoothness of the underlying functions or prior knowledge of the error distribution. The estimator also automatically adapts to the local smoothness of the underlying function, and attains the local adaptive minimax rate for estimating functions at a point. A key technical result in our development is a quantile coupling theorem which gives a tight bound for the quantile coupling between the sample medians and a normal variable. This median coupling inequality may be of independent interest.", "published": "2008-10-27T12:30:43Z", "authors": ["Lawrence D. Brown", "T. Tony Cai", "Harrison H. Zhou"], "categories": ["math.ST"], "doi": "10.1214/07-AOS513", "_query": "hedonic regression housing", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2503.09907v3", "title": "PLRD: Partially Linear Regression Discontinuity Inference", "abstract": "Regression discontinuity designs have become one of the most popular research designs in empirical economics. We argue, however, that the widely used approaches to building confidence intervals in regression discontinuity designs often exhibit suboptimal behavior in practice. We propose a new estimator, the partially linear regression discontinuity (PLRD) estimator that, in set of a simulation studies carefully calibrated to twelve high-profile applications of regression discontinuity designs, has substantially lower estimation error than available comparison methods. Throughout our experiments, the confidence intervals built using PLRD are both valid and short. We also provide large-sample guarantees for PLRD. Our simulation study serves as a general template for how new econometric methods can be credibly evaluated relative to the existing alternatives by constructing simulation designs that generate synthetic data indistinguishable from the original data using the Wasserstein generative adversarial network methodology.", "published": "2025-03-12T23:56:39Z", "authors": ["Aditya Ghosh", "Guido Imbens", "Stefan Wager"], "categories": ["econ.EM", "stat.ME"], "doi": "", "_query": "hedonic regression housing", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2305.14118v4", "title": "Notes on Causation, Comparison, and Regression", "abstract": "Comparison and contrast are the basic means to unveil causation and learn which treatments work. To build good comparison groups, randomized experimentation is key, yet often infeasible. In such non-experimental settings, we illustrate and discuss diagnostics to assess how well the common linear regression approach to causal inference approximates desirable features of randomized experiments, such as covariate balance, study representativeness, interpolated estimation, and unweighted analyses. We also discuss alternative regression modeling, weighting, and matching approaches and argue they should be given strong consideration in empirical work.", "published": "2023-05-23T14:42:39Z", "authors": ["Ambarish Chattopadhyay", "Jose R. Zubizarreta"], "categories": ["stat.ME", "stat.AP"], "doi": "", "_query": "hedonic regression housing", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/1911.04606v1", "title": "White-Box Target Attack for EEG-Based BCI Regression Problems", "abstract": "Machine learning has achieved great success in many applications, including electroencephalogram (EEG) based brain-computer interfaces (BCIs). Unfortunately, many machine learning models are vulnerable to adversarial examples, which are crafted by adding deliberately designed perturbations to the original inputs. Many adversarial attack approaches for classification problems have been proposed, but few have considered target adversarial attacks for regression problems. This paper proposes two such approaches. More specifically, we consider white-box target attacks for regression problems, where we know all information about the regression model to be attacked, and want to design small perturbations to change the regression output by a pre-determined amount. Experiments on two BCI regression problems verified that both approaches are effective. Moreover, adversarial examples generated from both approaches are also transferable, which means that we can use adversarial examples generated from one known regression model to attack an unknown regression model, i.e., to perform black-box attacks. To our knowledge, this is the first study on adversarial attacks for EEG-based BCI regression problems, which calls for more attention on the security of BCI systems.", "published": "2019-11-07T14:52:12Z", "authors": ["Lubin Meng", "Chin-Teng Lin", "Tzyy-Ring Jung", "Dongrui Wu"], "categories": ["cs.CR", "cs.AI", "cs.LG", "eess.SP"], "doi": "", "_query": "hedonic regression housing", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2212.01699v3", "title": "Parametric Modal Regression with Error in Covariates", "abstract": "An inference procedure is proposed to provide consistent estimators of parameters in a modal regression model with a covariate prone to measurement error. A score-based diagnostic tool exploiting parametric bootstrap is developed to assess adequacy of parametric assumptions imposed on the regression model. The proposed estimation method and diagnostic tool are applied to synthetic data generated from simulation experiments and data from real-world applications to demonstrate their implementation and performance. These empirical examples illustrate the importance of adequately accounting for measurement error in the error-prone covariate when inferring the association between a response and covariates based on a modal regression model that is especially suitable for skewed and heavy-tailed response data.", "published": "2022-12-03T22:02:11Z", "authors": ["Qingyang Liu", "Xianzheng Huang"], "categories": ["stat.ME"], "doi": "10.1002/bimj.202200348", "_query": "hedonic regression housing", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2307.16798v4", "title": "Forster-Warmuth Counterfactual Regression: A Unified Learning Approach", "abstract": "Series or orthogonal basis regression is one of the most popular non-parametric regression techniques in practice, obtained by regressing the response on features generated by evaluating the basis functions at observed covariate values. The most routinely used series estimator is based on ordinary least squares fitting, which is known to be minimax rate optimal in various settings, albeit under stringent restrictions on the basis functions and the distribution of covariates. In this work, inspired by the recently developed Forster-Warmuth (FW) learner, we propose an alternative series regression estimator that can attain the minimax estimation rate under strictly weaker conditions imposed on the basis functions and the joint law of covariates, than existing series estimators in the literature. Moreover, a key contribution of this work generalizes the FW-learner to a so-called counterfactual regression problem, in which the response variable of interest may not be directly observed (hence, the name ``counterfactual'') on all sampled units, and therefore needs to be inferred in order to identify and estimate the regression in view from the observed data. Although counterfactual regression is not entirely a new area of inquiry, we propose the first-ever systematic study of this challenging problem from a unified pseudo-outcome perspective. In fact, we provide what appears to be the first generic and constructive approach for generating the pseudo-outcome (to substitute for the unobserved response) which leads to the estimation of the counterfactual regression curve of interest with small bias, namely bias of second order. Several applications are used to illustrate the resulting FW-learner including many nonparametric regression problems in missing data and causal inference literature, for which we establish high-level conditions for minimax rate optimality of the proposed FW-learner.", "published": "2023-07-31T16:05:57Z", "authors": ["Yachong Yang", "Arun Kumar Kuchibhotla", "Eric Tchetgen Tchetgen"], "categories": ["stat.ME", "math.ST", "stat.AP"], "doi": "", "_query": "hedonic regression housing", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2610.01342v1", "title": "User-friendly approximation theory for statistical regression", "abstract": "The error of regression using noisy function values naturally decomposes into an approximation error from the choice of model space and stochastic error from the noise on the observations. Statistical theory is mostly concerned with the stochastic error while approximation theory focuses on the approximation error, often subject to the assumption of noiseless observations. We combine the tools from approximation theory and statistics into a framework for bounding both errors in generalized least squares regression. The results cover weighted sup-norms and $L^2(μ)$-norms, real- and complex-valued functions, and correlated noise under a range of tail and moment assumptions. Weighted Lebesgue functions and constants control the amplification of the best approximation error, while concentration inequalities provide bounds on the stochastic error. We organize these results into reusable bounds with explicit constants and establish sharpness where possible. For the common strategy of randomized regression we prove concentration of the Lebesgue function and constant around those of the corresponding $L^2(μ)$-projection. For Fourier and Chebyshev regression on standard sampling grids, we obtain explicit, asymptotically tight $\\mathcal O(\\log m)$ bounds on the Lebesgue constants where $m$ is the model dimension. This improves upon the previous $\\mathcal O(\\sqrt{m})$ bound obtained for the regression setting and matches the bounds known for interpolation. A catalogue of approximation bounds and worked examples illustrates how to turn these results into explicit regression guarantees.", "published": "2026-10-01T09:15:32Z", "authors": ["Felix Benning"], "categories": ["math.ST"], "doi": "", "_query": "hedonic regression housing", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2406.12204v1", "title": "An Optimal Transport Approach for Network Regression", "abstract": "We study the problem of network regression, where one is interested in how the topology of a network changes as a function of Euclidean covariates. We build upon recent developments in generalized regression models on metric spaces based on Fréchet means and propose a network regression method using the Wasserstein metric. We show that when representing graphs as multivariate Gaussian distributions, the network regression problem requires the computation of a Riemannian center of mass (i.e., Fréchet means). Fréchet means with non-negative weights translates into a barycenter problem and can be efficiently computed using fixed point iterations. Although the convergence guarantees of fixed-point iterations for the computation of Wasserstein affine averages remain an open problem, we provide evidence of convergence in a large number of synthetic and real-data scenarios. Extensive numerical results show that the proposed approach improves existing procedures by accurately accounting for graph size, topology, and sparsity in synthetic experiments. Additionally, real-world experiments using the proposed approach result in higher Coefficient of Determination ($R^{2}$) values and lower mean squared prediction error (MSPE), cementing improved prediction capabilities in practice.", "published": "2024-06-18T02:03:07Z", "authors": ["Alex G. Zalles", "Kai M. Hung", "Ann E. Finneran", "Lydia Beaudrot", "César A. Uribe"], "categories": ["stat.ML", "cs.LG", "math.OC"], "doi": "", "_query": "hedonic regression housing", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2301.09516v1", "title": "Online Kernel Sliced Inverse Regression", "abstract": "Online dimension reduction is a common method for high-dimensional streaming data processing. Online principal component analysis, online sliced inverse regression, online kernel principal component analysis and other methods have been studied in depth, but as far as we know, online supervised nonlinear dimension reduction methods have not been fully studied. In this article, an online kernel sliced inverse regression method is proposed. By introducing the approximate linear dependence condition and dictionary variable sets, we address the problem of increasing variable dimensions with the sample size in the online kernel sliced inverse regression method, and propose a reduced-order method for updating variables online. We then transform the problem into an online generalized eigen-decomposition problem, and use the stochastic optimization method to update the centered dimension reduction directions. Simulations and the real data analysis show that our method can achieve close performance to batch processing kernel sliced inverse regression.", "published": "2023-01-23T16:05:51Z", "authors": ["Wenquan Cui", "Yue Zhao", "Jianjun Xu", "Haoyang Cheng"], "categories": ["stat.CO", "math.ST", "stat.ML"], "doi": "", "_query": "hedonic regression housing", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2002.10008v3", "title": "Conditional regression for single-index models", "abstract": "The single-index model is a statistical model for intrinsic regression where responses are assumed to depend on a single yet unknown linear combination of the predictors, allowing to express the regression function as $ \\mathbb{E} [ Y | X ] = f ( \\langle v , X \\rangle ) $ for some unknown \\emph{index} vector $v$ and \\emph{link} function $f$. Conditional methods provide a simple and effective approach to estimate $v$ by averaging moments of $X$ conditioned on $Y$, but depend on parameters whose optimal choice is unknown and do not provide generalization bounds on $f$. In this paper we propose a new conditional method converging at $\\sqrt{n}$ rate under an explicit parameter characterization. Moreover, we prove that polynomial partitioning estimates achieve the $1$-dimensional min-max rate for regression of Hölder functions when combined to any $\\sqrt{n}$-convergent index estimator. Overall this yields an estimator for dimension reduction and regression of single-index models that attains statistical optimality in quasilinear time.", "published": "2020-02-23T22:52:49Z", "authors": ["Alessandro Lanteri", "Mauro Maggioni", "Stefano Vigogna"], "categories": ["math.ST"], "doi": "", "_query": "hedonic regression housing", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2008.03077v2", "title": "Convolutional Ordinal Regression Forest for Image Ordinal Estimation", "abstract": "Image ordinal estimation is to predict the ordinal label of a given image, which can be categorized as an ordinal regression problem. Recent methods formulate an ordinal regression problem as a series of binary classification problems. Such methods cannot ensure that the global ordinal relationship is preserved since the relationships among different binary classifiers are neglected. We propose a novel ordinal regression approach, termed Convolutional Ordinal Regression Forest or CORF, for image ordinal estimation, which can integrate ordinal regression and differentiable decision trees with a convolutional neural network for obtaining precise and stable global ordinal relationships. The advantages of the proposed CORF are twofold. First, instead of learning a series of binary classifiers \\emph{independently}, the proposed method aims at learning an ordinal distribution for ordinal regression by optimizing those binary classifiers \\emph{simultaneously}. Second, the differentiable decision trees in the proposed CORF can be trained together with the ordinal distribution in an end-to-end manner. The effectiveness of the proposed CORF is verified on two image ordinal estimation tasks, i.e. facial age estimation and image aesthetic assessment, showing significant improvements and better stability over the state-of-the-art ordinal regression methods.", "published": "2020-08-07T10:41:17Z", "authors": ["Haiping Zhu", "Hongming Shan", "Yuheng Zhang", "Lingfu Che", "Xiaoyang Xu", "Junping Zhang", "Jianbo Shi", "Fei-Yue Wang"], "categories": ["cs.CV"], "doi": "10.1109/TNNLS.2021.3055816", "_query": "hedonic regression housing", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2407.21119v5", "title": "Potential weights and implicit causal designs in linear regression", "abstract": "Applied researchers routinely use linear regression to estimate causal effects, justified by quasi-experimental treatment variation, while leaving assumptions on treatment assignment implicit. We formalize a minimal criterion for quasi-experimental interpretation -- that the regression estimates some contrast of potential outcomes under the true assignment process, regardless of potential outcomes -- and characterize its implications for arbitrary regressions. This criterion implies linear restrictions on the true treatment distribution, whose solutions we call implicit designs. A regression is exactly quasi-experimental if and only if the true design is an implicit design, and approximately so when it is close to one, in a sense we formalize. Our framework unifies existing results and uncovers new ones across many settings. Qualitatively, an AI-assisted census of 1,051 recent papers finds quasi-experimental regression pervasive and often vulnerable to our negative results. Quantitatively, we assess exact and approximate quasi-experimental interpretation in nine studies by computing their implicit designs and estimands.", "published": "2024-07-30T18:22:12Z", "authors": ["Jiafeng Chen"], "categories": ["econ.EM", "stat.ME"], "doi": "", "_query": "hedonic regression housing", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/0810.4012v1", "title": "Testing for changes in polynomial regression", "abstract": "We consider a nonlinear polynomial regression model in which we wish to test the null hypothesis of structural stability in the regression parameters against the alternative of a break at an unknown time. We derive the extreme value distribution of a maximum-type test statistic which is asymptotically equivalent to the maximally selected likelihood ratio. The resulting test is easy to apply and has good size and power, even in small samples.", "published": "2008-10-22T08:50:49Z", "authors": ["Alexander Aue", "Lajos Horváth", "Marie Hušková", "Piotr Kokoszka"], "categories": ["math.ST"], "doi": "10.3150/08-BEJ122", "_query": "hedonic regression housing", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2006.11590v2", "title": "Regression Prior Networks", "abstract": "Prior Networks are a recently developed class of models which yield interpretable measures of uncertainty and have been shown to outperform state-of-the-art ensemble approaches on a range of tasks. They can also be used to distill an ensemble of models via Ensemble Distribution Distillation (EnD$^2$), such that its accuracy, calibration and uncertainty estimates are retained within a single model. However, Prior Networks have so far been developed only for classification tasks. This work extends Prior Networks and EnD$^2$ to regression tasks by considering the Normal-Wishart distribution. The properties of Regression Prior Networks are demonstrated on synthetic data, selected UCI datasets and a monocular depth estimation task, where they yield performance competitive with ensemble approaches.", "published": "2020-06-20T14:50:14Z", "authors": ["Andrey Malinin", "Sergey Chervontsev", "Ivan Provilkov", "Mark Gales"], "categories": ["cs.LG", "stat.ML"], "doi": "", "_query": "hedonic regression housing", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/1905.02962v1", "title": "Robust regression based on shrinkage estimators", "abstract": "A robust estimator is proposed for the parameters that characterize the linear regression problem. It is based on the notion of shrinkages, often used in Finance and previously studied for outlier detection in multivariate data. A thorough simulation study is conducted to investigate: the efficiency with normal and heavy-tailed errors, the robustness under contamination, the computational times, the affine equivariance and breakdown value of the regression estimator. Two classical data-sets often used in the literature and a real socio-economic data-set about the Living Environment Deprivation of areas in Liverpool (UK), are studied. The results from the simulations and the real data examples show the advantages of the proposed robust estimator in regression.", "published": "2019-05-08T08:48:20Z", "authors": ["Elisa Cabana", "Rosa E. Lillo", "Henry Laniado"], "categories": ["stat.ME"], "doi": "10.1007/s00477-020-01774-4", "_query": "hedonic regression housing", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/1806.05826v2", "title": "Two-level preconditioning for Ridge Regression", "abstract": "Solving linear systems is often the computational bottleneck in real-life problems. Iterative solvers are the only option due to the complexity of direct algorithms or because the system matrix is not explicitly known. Here, we develop a two-level preconditioner for regularized least squares linear systems involving a feature or data matrix. Variants of this linear system may appear in machine learning applications, such as ridge regression, logistic regression, support vector machines and Bayesian regression. We use clustering algorithms to create a coarser level that preserves the principal components of the covariance or Gram matrix. This coarser level approximates the dominant eigenvectors and is used to build a subspace preconditioner accelerating the Conjugate Gradient method. We observed speed-ups for artificial and real-life data.", "published": "2018-06-15T06:59:49Z", "authors": ["Joris Tavernier", "Jaak Simm", "Karl Meerbergen", "Yves Moreau"], "categories": ["math.NA"], "doi": "", "_query": "hedonic regression housing", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/1910.09106v1", "title": "Adversarial Regression. Generative Adversarial Networks for Non-Linear Regression: Theory and Assessment", "abstract": "Adversarial Regression is a proposition to perform high dimensional non-linear regression with uncertainty estimation. We used Conditional Generative Adversarial Network to obtain an estimate of the full predictive distribution for a new observation. Generative Adversarial Networks (GAN) are implicit generative models which produce samples from a distribution approximating the distribution of the data. The conditional version of it (CGAN) takes the following expression: $\\min\\limits_G \\max\\limits_D V(D, G) = \\mathbb{E}_{x\\sim p_{r}(x)} [log(D(x, y))] + \\mathbb{E}_{z\\sim p_{z}(z)} [log (1-D(G(z, y)))]$. An approximate solution can be found by training simultaneously two neural networks to model D and G and feeding G with a random noise vector $z$. After training, we have that $G(z, y)\\mathrel{\\dot\\sim} p_{data}(x, y)$. By fixing $y$, we have $G(z|y) \\mathrel{\\dot\\sim} p{data}(x|y)$. By sampling $z$, we can therefore obtain samples following approximately $p(x|y)$, which is the predictive distribution of $x$ for a new $y$. We ran experiments to test various loss functions, data distributions, sample size, size of the noise vector, etc. Even if we observed differences, no experiment outperformed consistently the others. The quality of CGAN for regression relies on fine-tuning a range of hyperparameters. In a broader view, the results show that CGANs are very promising methods to perform uncertainty estimation for high dimensional non-linear regression.", "published": "2019-10-18T15:01:00Z", "authors": ["Yoann Boget"], "categories": ["stat.ML", "cs.LG"], "doi": "", "_query": "hedonic regression housing", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/1606.05273v1", "title": "The Effect of Heteroscedasticity on Regression Trees", "abstract": "Regression trees are becoming increasingly popular as omnibus predicting tools and as the basis of numerous modern statistical learning ensembles. Part of their popularity is their ability to create a regression prediction without ever specifying a structure for the mean model. However, the method implicitly assumes homogeneous variance across the entire explanatory-variable space. It is unknown how the algorithm behaves when faced with heteroscedastic data. In this study, we assess the performance of the most popular regression-tree algorithm in a single-variable setting under a very simple step-function model for heteroscedasticity. We use simulation to show that the locations of splits, and hence the ability to accurately predict means, are both adversely influenced by the change in variance. We identify the pruning algorithm as the main concern, although the effects on the splitting algorithm may be meaningful in some applications.", "published": "2016-06-16T17:11:27Z", "authors": ["Will Ruth", "Thomas Loughin"], "categories": ["stat.ML"], "doi": "", "_query": "hedonic regression housing", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/1806.04829v1", "title": "When Regression Verification Meets CEGAR", "abstract": "Software systems evolve throughout their life cycles. Many revisions are produced over time. Model checking each revision of the software is impractical. Regression verification suggests reusing intermediate results from the previous verification runs. This paper proposes a fully automatic regression verification technique in the context of CEGAR. Procedure summaries, which describe the input/output behaviors of a procedure, are proposed as the intermediate results to be reused. Procedure summaries are reasonably small to store, technically easy to process, and do not require much extra computation effort to be reused. Reusing procedure summaries saves much analysis effort on the corresponding procedures. By combining regression verification and CEGAR, we propose a technique that is able to reuse procedure summaries across different abstract precisions and different program revisions. We performed extensive experiments on a large number of industrial programs (534 revisions of 89 Linux kernel device drivers). The results show that our approach can significantly improve the performance of regression verification.", "published": "2018-06-13T02:50:50Z", "authors": ["Fei He", "Qianshan Yu", "Liming Cai"], "categories": ["cs.SE"], "doi": "", "_query": "hedonic regression housing", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2606.01723v1", "title": "Shortcut to Nowhere: Demystifying Deep Spurious Regression", "abstract": "Real-world regression often exhibits shortcuts: attributes that are spuriously correlated with continuous targets in training, yet unreliable under deployment shifts; regressing targets using such shortcuts may fail catastrophically at test time. Existing studies on spurious correlations focus primarily on classification, where labels are categorical and groups are naturally defined. However, many real-world tasks require continuous prediction, where hard label boundaries or discrete group-label pairs do not exist. We define Deep Spurious Regression (DSR) as learning from regression data with attribute-label confounding, addressing continuous spurious correlations, and generalizing to all attribute-label combinations at test time. Motivated by the intrinsic difference between classification and regression shortcuts, we propose to exploit the similarity among spurious attributes in both label and feature spaces, thereby accounting for nearby targets and related groups while calibrating both label and learned feature distributions across attributes. Extensive experiments on common real-world DSR datasets that span computer vision, environmental sensing, and large language model (LLM) regression verify the superior performance of our strategies. Our work fills the gap in benchmarks and techniques for studying spurious correlations in continuous prediction.", "published": "2026-06-01T05:40:11Z", "authors": ["Guanrong Xu", "Jessica Li", "Hao Wang", "Yuzhe Yang"], "categories": ["cs.LG", "cs.AI"], "doi": "", "_query": "hedonic regression housing", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/1502.02077v3", "title": "Quantum Energy Regression using Scattering Transforms", "abstract": "We present a novel approach to the regression of quantum mechanical energies based on a scattering transform of an intermediate electron density representation. A scattering transform is a deep convolution network computed with a cascade of multiscale wavelet transforms. It possesses appropriate invariant and stability properties for quantum energy regression. This new framework removes fundamental limitations of Coulomb matrix based energy regressions, and numerical experiments give state-of-the-art accuracy over planar molecules.", "published": "2015-02-06T23:55:13Z", "authors": ["Matthew Hirn", "Nicolas Poilvert", "Stéphane Mallat"], "categories": ["cs.LG", "cs.CV", "physics.chem-ph", "physics.comp-ph", "quant-ph"], "doi": "", "_query": "hedonic regression housing", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2403.13405v1", "title": "DOR3D-Net: Dense Ordinal Regression Network for 3D Hand Pose Estimation", "abstract": "Depth-based 3D hand pose estimation is an important but challenging research task in human-machine interaction community. Recently, dense regression methods have attracted increasing attention in 3D hand pose estimation task, which provide a low computational burden and high accuracy regression way by densely regressing hand joint offset maps. However, large-scale regression offset values are often affected by noise and outliers, leading to a significant drop in accuracy. To tackle this, we re-formulate 3D hand pose estimation as a dense ordinal regression problem and propose a novel Dense Ordinal Regression 3D Pose Network (DOR3D-Net). Specifically, we first decompose offset value regression into sub-tasks of binary classifications with ordinal constraints. Then, each binary classifier can predict the probability of a binary spatial relationship relative to joint, which is easier to train and yield much lower level of noise. The estimated hand joint positions are inferred by aggregating the ordinal regression results at local positions with a weighted sum. Furthermore, both joint regression loss and ordinal regression loss are used to train our DOR3D-Net in an end-to-end manner. Extensive experiments on public datasets (ICVL, MSRA, NYU and HANDS2017) show that our design provides significant improvements over SOTA methods.", "published": "2024-03-20T08:47:51Z", "authors": ["Yamin Mao", "Zhihua Liu", "Weiming Li", "SoonYong Cho", "Qiang Wang", "Xiaoshuai Hao"], "categories": ["cs.CV", "cs.AI"], "doi": "", "_query": "hedonic regression housing", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2610.24892v1", "title": "Scalable and Adaptive Distribution Regression via Random Features", "abstract": "Distribution regression seeks to learn mappings from probability distributions to response variables and has emerged as a unifying framework for kernel methods, multi-instance learning, and set-valued learning. Despite its success, kernel-based distribution regression remains computationally expensive and sensitive to kernel choice, motivating scalable and adaptive representations. Building on random feature approximations, we develop a finite-sample analysis of Random Feature Distribution Regression (RaDR) under two-stage sampling. Our perturbation analysis separates the effects of distribution-level sampling, finite within-bag sampling, and random feature approximation. We establish sufficient feature-budget conditions under which RaDR preserves the order of our excess-risk upper bound for exact two-stage kernel distribution regression while maintaining computational efficiency. We further propose Adaptive Random Feature Distribution Regression (A-RaDR), a unified score-guided framework that adaptively allocates random features according to downstream learning objectives. Experiments on synthetic benchmarks and 41 real-world datasets demonstrate that RaDR delivers substantial computational savings while maintaining competitive predictive performance, whereas A-RaDR further improves learning accuracy in distribution regression while retaining the computational speed up. Together, these results establish a practical and theoretically grounded framework for adaptive, scalable distribution learning", "published": "2026-10-17T22:16:40Z", "authors": ["Yinsong Wang", "Xiao Liu", "Yu Ding"], "categories": ["stat.ML", "cs.LG"], "doi": "", "_query": "hedonic regression housing", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/1103.0769v2", "title": "Sparse Volterra and Polynomial Regression Models: Recoverability and Estimation", "abstract": "Volterra and polynomial regression models play a major role in nonlinear system identification and inference tasks. Exciting applications ranging from neuroscience to genome-wide association analysis build on these models with the additional requirement of parsimony. This requirement has high interpretative value, but unfortunately cannot be met by least-squares based or kernel regression methods. To this end, compressed sampling (CS) approaches, already successful in linear regression settings, can offer a viable alternative. The viability of CS for sparse Volterra and polynomial models is the core theme of this work. A common sparse regression task is initially posed for the two models. Building on (weighted) Lasso-based schemes, an adaptive RLS-type algorithm is developed for sparse polynomial regressions. The identifiability of polynomial models is critically challenged by dimensionality. However, following the CS principle, when these models are sparse, they could be recovered by far fewer measurements. To quantify the sufficient number of measurements for a given level of sparsity, restricted isometry properties (RIP) are investigated in commonly met polynomial regression settings, generalizing known results for their linear counterparts. The merits of the novel (weighted) adaptive CS algorithms to sparse polynomial modeling are verified through synthetic as well as real data tests for genotype-phenotype analysis.", "published": "2011-03-03T20:21:28Z", "authors": ["Vassilis Kekatos", "Georgios B. Giannakis"], "categories": ["cs.LG", "cs.IT", "stat.ML"], "doi": "10.1109/TSP.2011.2165952", "_query": "hedonic regression housing", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2004.06022v1", "title": "Quantile regression on inactivity time", "abstract": "The inactivity time, or lost lifespan specifically for mortality data, concerns time from occurrence of an event of interest to the current time point and has recently emerged as a new summary measure for cumulative information inherent in time-to-event data. This summary measure provides several benefits over the traditional methods, including more straightforward interpretation yet less sensitivity to heavy censoring. However, there exists no systematic modeling approach to inferring the quantile inactivity time in the literature. In this paper, we propose a regression method for the quantiles of the inactivity time distribution under right censoring. The consistency and asymptotic normality of the regression parameters are established. To avoid estimation of the probability density function of the inactivity time distribution under censoring, we propose a computationally efficient method for estimating the variance-covariance matrix of the regression coefficient estimates. Simulation results are presented to validate the finite sample properties of the proposed estimators and test statistics. The proposed method is illustrated with a real dataset from a clinical trial on breast cancer.", "published": "2020-04-13T15:54:59Z", "authors": ["Lauren C. Balmert", "Ruosha Li", "Limin Peng", "Jong-Hyeon Jeong"], "categories": ["stat.ME"], "doi": "", "_query": "hedonic regression housing", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2311.02858v9", "title": "Estimation of the rate parameter of the probability distribution on the regression setup", "abstract": "When the rate parameter of the exponential distribution is associated with predictors, then the main interest will be how to estimate the regression parameter. In this paper, we will investigate how to estimate the parameter on the regression setup of the exponential distribution. To that end, we propose a new estimator, and its asymptotic properties will be discussed.", "published": "2023-11-06T04:18:26Z", "authors": ["Jiwoong Kim"], "categories": ["math.ST"], "doi": "", "_query": "hedonic regression housing", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2304.00051v1", "title": "Almost Linear Constant-Factor Sketching for $\\ell_1$ and Logistic Regression", "abstract": "We improve upon previous oblivious sketching and turnstile streaming results for $\\ell_1$ and logistic regression, giving a much smaller sketching dimension achieving $O(1)$-approximation and yielding an efficient optimization problem in the sketch space. Namely, we achieve for any constant $c>0$ a sketching dimension of $\\tilde{O}(d^{1+c})$ for $\\ell_1$ regression and $\\tilde{O}(μd^{1+c})$ for logistic regression, where $μ$ is a standard measure that captures the complexity of compressing the data. For $\\ell_1$-regression our sketching dimension is near-linear and improves previous work which either required $Ω(\\log d)$-approximation with this sketching dimension, or required a larger $\\operatorname{poly}(d)$ number of rows. Similarly, for logistic regression previous work had worse $\\operatorname{poly}(μd)$ factors in its sketching dimension. We also give a tradeoff that yields a $1+\\varepsilon$ approximation in input sparsity time by increasing the total size to $(d\\log(n)/\\varepsilon)^{O(1/\\varepsilon)}$ for $\\ell_1$ and to $(μd\\log(n)/\\varepsilon)^{O(1/\\varepsilon)}$ for logistic regression. Finally, we show that our sketch can be extended to approximate a regularized version of logistic regression where the data-dependent regularizer corresponds to the variance of the individual logistic losses.", "published": "2023-03-31T18:12:33Z", "authors": ["Alexander Munteanu", "Simon Omlor", "David Woodruff"], "categories": ["cs.DS", "cs.LG", "stat.ML"], "doi": "", "_query": "hedonic regression housing", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2012.14817v1", "title": "Spatial Resolution Enhancement of Oversampled Images Using Regression Decomposition and Synthesis", "abstract": "A new statistical model designed for regression analysis with a sparse design matrix is proposed. This new model utilizes the positions of the limited non-zero elements in the design matrix to decompose the regression model into sub-regression models. Statistical inferences are further made on the values of these limited non-zero elements to provide a reference for synthesizing these sub-regression models. With this concept of the regression decomposition and synthesis, the information on the structure of the design matrix can be incorporated into the regression analysis to provide a more reliable estimation. The proposed model is then applied to resolve the spatial resolution enhancement problem for spatially oversampled images. To systematically evaluate the performance of the proposed model in enhancing the spatial resolution, the proposed approach is applied to the oversampled images that are reproduced via random field simulations. These application results based on different generated scenarios then conclude the effectiveness and the feasibility of the proposed approach in enhancing the spatial resolution of spatially oversampled images.", "published": "2020-12-29T15:55:11Z", "authors": ["Hsien-Wei Chen"], "categories": ["stat.AP", "stat.ME"], "doi": "10.5194/isprs-archives-XLVI-4-W3-2021-71-2022", "_query": "hedonic regression housing", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/1803.08450v3", "title": "A Comprehensive Analysis of Deep Regression", "abstract": "Deep learning revolutionized data science, and recently its popularity has grown exponentially, as did the amount of papers employing deep networks. Vision tasks, such as human pose estimation, did not escape from this trend. There is a large number of deep models, where small changes in the network architecture, or in the data pre-processing, together with the stochastic nature of the optimization procedures, produce notably different results, making extremely difficult to sift methods that significantly outperform others. This situation motivates the current study, in which we perform a systematic evaluation and statistical analysis of vanilla deep regression, i.e. convolutional neural networks with a linear regression top layer. This is the first comprehensive analysis of deep regression techniques. We perform experiments on four vision problems, and report confidence intervals for the median performance as well as the statistical significance of the results, if any. Surprisingly, the variability due to different data pre-processing procedures generally eclipses the variability due to modifications in the network architecture. Our results reinforce the hypothesis according to which, in general, a general-purpose network (e.g. VGG-16 or ResNet-50) adequately tuned can yield results close to the state-of-the-art without having to resort to more complex and ad-hoc regression models.", "published": "2018-03-22T16:46:39Z", "authors": ["Stéphane Lathuilière", "Pablo Mesejo", "Xavier Alameda-Pineda", "Radu Horaud"], "categories": ["cs.CV"], "doi": "10.1109/TPAMI.2019.2910523", "_query": "hedonic regression housing", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2404.02986v3", "title": "Universal Functional Regression with Neural Operator Flows", "abstract": "Regression on function spaces is typically limited to models with Gaussian process priors. We introduce the notion of universal functional regression, in which we aim to learn a prior distribution over non-Gaussian function spaces that remains mathematically tractable for functional regression. To do this, we develop Neural Operator Flows (OpFlow), an infinite-dimensional extension of normalizing flows. OpFlow is an invertible operator that maps the (potentially unknown) data function space into a Gaussian process, allowing for exact likelihood estimation of functional point evaluations. OpFlow enables robust and accurate uncertainty quantification via drawing posterior samples of the Gaussian process and subsequently mapping them into the data function space. We empirically study the performance of OpFlow on regression and generation tasks with data generated from Gaussian processes with known posterior forms and non-Gaussian processes, as well as real-world earthquake seismograms with an unknown closed-form distribution.", "published": "2024-04-03T18:14:23Z", "authors": ["Yaozhong Shi", "Angela F. Gao", "Zachary E. Ross", "Kamyar Azizzadenesheli"], "categories": ["cs.LG", "stat.ML"], "doi": "", "_query": "hedonic regression housing", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/1706.05003v1", "title": "Regularized Ordinal Regression and the ordinalNet R Package", "abstract": "Regularization techniques such as the lasso (Tibshirani 1996) and elastic net (Zou and Hastie 2005) can be used to improve regression model coefficient estimation and prediction accuracy, as well as to perform variable selection. Ordinal regression models are widely used in applications where the use of regularization could be beneficial; however, these models are not included in many popular software packages for regularized regression. We propose a coordinate descent algorithm to fit a broad class of ordinal regression models with an elastic net penalty. Furthermore, we demonstrate that each model in this class generalizes to a more flexible form, for instance to accommodate unordered categorical data. We introduce an elastic net penalty class that applies to both model forms. Additionally, this penalty can be used to shrink a non-ordinal model toward its ordinal counterpart. Finally, we introduce the R package ordinalNet, which implements the algorithm for this model class.", "published": "2017-06-15T16:46:31Z", "authors": ["Michael J. Wurm", "Paul J. Rathouz", "Bret M. Hanlon"], "categories": ["stat.CO"], "doi": "10.18637/jss.v099.i06", "_query": "hedonic regression housing", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2001.11143v1", "title": "A Graph-Based Approach for Active Learning in Regression", "abstract": "Active learning aims to reduce labeling efforts by selectively asking humans to annotate the most important data points from an unlabeled pool and is an example of human-machine interaction. Though active learning has been extensively researched for classification and ranking problems, it is relatively understudied for regression problems. Most existing active learning for regression methods use the regression function learned at each active learning iteration to select the next informative point to query. This introduces several challenges such as handling noisy labels, parameter uncertainty and overcoming initially biased training data. Instead, we propose a feature-focused approach that formulates both sequential and batch-mode active regression as a novel bipartite graph optimization problem. We conduct experiments on both noise-free and noisy settings. Our experimental results on benchmark data sets demonstrate the effectiveness of our proposed approach.", "published": "2020-01-30T00:59:43Z", "authors": ["Hongjing Zhang", "S. S. Ravi", "Ian Davidson"], "categories": ["cs.LG", "stat.ML"], "doi": "", "_query": "hedonic regression housing", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2303.16800v2", "title": "Correcting for Selection Bias and Missing Response in Regression using Privileged Information", "abstract": "When estimating a regression model, we might have data where some labels are missing, or our data might be biased by a selection mechanism. When the response or selection mechanism is ignorable (i.e., independent of the response variable given the features) one can use off-the-shelf regression methods; in the nonignorable case one typically has to adjust for bias. We observe that privileged information (i.e. information that is only available during training) might render a nonignorable selection mechanism ignorable, and we refer to this scenario as Privilegedly Missing at Random (PMAR). We propose a novel imputation-based regression method, named repeated regression, that is suitable for PMAR. We also consider an importance weighted regression method, and a doubly robust combination of the two. The proposed methods are easy to implement with most popular out-of-the-box regression algorithms. We empirically assess the performance of the proposed methods with extensive simulated experiments and on a synthetically augmented real-world dataset. We conclude that repeated regression can appropriately correct for bias, and can have considerable advantage over weighted regression, especially when extrapolating to regions of the feature space where response is never observed.", "published": "2023-03-29T15:42:17Z", "authors": ["Philip Boeken", "Noud de Kroon", "Mathijs de Jong", "Joris M. Mooij", "Onno Zoeter"], "categories": ["math.ST", "stat.ML"], "doi": "", "_query": "hedonic regression housing", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2404.09884v1", "title": "Map-Relative Pose Regression for Visual Re-Localization", "abstract": "Pose regression networks predict the camera pose of a query image relative to a known environment. Within this family of methods, absolute pose regression (APR) has recently shown promising accuracy in the range of a few centimeters in position error. APR networks encode the scene geometry implicitly in their weights. To achieve high accuracy, they require vast amounts of training data that, realistically, can only be created using novel view synthesis in a days-long process. This process has to be repeated for each new scene again and again. We present a new approach to pose regression, map-relative pose regression (marepo), that satisfies the data hunger of the pose regression network in a scene-agnostic fashion. We condition the pose regressor on a scene-specific map representation such that its pose predictions are relative to the scene map. This allows us to train the pose regressor across hundreds of scenes to learn the generic relation between a scene-specific map representation and the camera pose. Our map-relative pose regressor can be applied to new map representations immediately or after mere minutes of fine-tuning for the highest accuracy. Our approach outperforms previous pose regression methods by far on two public datasets, indoor and outdoor. Code is available: https://nianticlabs.github.io/marepo", "published": "2024-04-15T15:53:23Z", "authors": ["Shuai Chen", "Tommaso Cavallari", "Victor Adrian Prisacariu", "Eric Brachmann"], "categories": ["cs.CV", "cs.LG"], "doi": "", "_query": "hedonic regression housing", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/1401.7278v3", "title": "Minimax-optimal nonparametric regression in high dimensions", "abstract": "Minimax $L_2$ risks for high-dimensional nonparametric regression are derived under two sparsity assumptions: (1) the true regression surface is a sparse function that depends only on $d=O(\\log n)$ important predictors among a list of $p$ predictors, with $\\log p=o(n)$; (2) the true regression surface depends on $O(n)$ predictors but is an additive function where each additive component is sparse but may contain two or more interacting predictors and may have a smoothness level different from other components. For either modeling assumption, a practicable extension of the widely used Bayesian Gaussian process regression method is shown to adaptively attain the optimal minimax rate (up to $\\log n$ terms) asymptotically as both $n,p\\to\\infty$ with $\\log p=o(n)$.", "published": "2014-01-28T17:52:54Z", "authors": ["Yun Yang", "Surya T. Tokdar"], "categories": ["math.ST"], "doi": "10.1214/14-AOS1289", "_query": "hedonic regression housing", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2503.00326v4", "title": "A Bayesian Additive Regression Tree Model for Learning Conditional Average Treatment Effects in Regression Discontinuity Designs", "abstract": "This paper develops an effective Bayesian approach to conditional average treatment effect (CATE) estimation in regression discontinuity designs (RDD), an increasingly prevalent form of quasi-experiment that facilitates causal inference. Earlier Bayesian approaches do not easily accommodate CATE estimation while recent frequentist approaches to this problem assume a known basis expansion, a steep model specification requirement that our approach avoids. The new model is a variant of a Bayesian additive regression tree (BART) model with linear leaf-level regressions on the running variable and a treatment dummy (and their interaction). The model adaptively partitions covariate space into regions where the slope on the running variable appreciably differs, providing interpretable Bayesian inference on conditional average treatment effects near the cutoff.", "published": "2025-03-01T03:23:10Z", "authors": ["Rafael Alcantara", "P. Richard Hahn", "Hedibert F. Lopes"], "categories": ["stat.ME", "stat.ML"], "doi": "", "_query": "hedonic regression housing", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/0802.0793v2", "title": "A multiple covariance approach to PLS regression with several predictor groups: Structural Equation Exploratory Regression", "abstract": "A variable group Y is assumed to depend upon R thematic variable groups X 1, >..., X R . We assume that components in Y depend linearly upon components in the Xr's. In this work, we propose a multiple covariance criterion which extends that of PLS regression to this multiple predictor groups situation. On this criterion, we build a PLS-type exploratory method - Structural Equation Exploratory Regression (SEER) - that allows to simultaneously perform dimension reduction in groups and investigate the linear model of the components. SEER uses the multidimensional structure of each group. An application example is given.", "published": "2008-02-06T13:40:03Z", "authors": ["Xavier Bry", "Thomas Verron", "Pierre Cazes"], "categories": ["stat.ME", "math.ST"], "doi": "", "_query": "hedonic regression housing", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2512.00237v1", "title": "Penalized spatial function-on-function regression", "abstract": "The function-on-function regression model is fundamental for analyzing relationships between functional covariates and responses. However, most existing function-on-function regression methodologies assume independence between observations, which is often unrealistic for spatially structured functional data. We propose a novel penalized spatial function-on-function regression model to address this limitation. Our approach extends the generalized spatial two-stage least-squares estimator to functional data, while incorporating a roughness penalty on the regression coefficient function using a tensor product of B-splines. This penalization ensures optimal smoothness, mitigating overfitting, and improving interpretability. The proposed penalized spatial two-stage least-squares estimator effectively accounts for spatial dependencies, significantly improving estimation accuracy and predictive performance. We establish the asymptotic properties of our estimator, proving its $\\sqrt{n}$-consistency and asymptotic normality under mild regularity conditions. Extensive Monte Carlo simulations demonstrate the superiority of our method over existing non-penalized estimators, particularly under moderate to strong spatial dependence. In addition, an application to North Dakota weather data illustrates the practical utility of our approach in modeling spatially correlated meteorological variables. Our findings highlight the critical role of penalization in enhancing robustness and efficiency in spatial function-on-function regression models. To implement our method we used the \\texttt{robflreg} package on CRAN.", "published": "2025-11-28T22:48:31Z", "authors": ["Ufuk Beyaztas", "Han Lin Shang", "Gizel Bakicierler Sezer"], "categories": ["stat.ME"], "doi": "", "_query": "hedonic regression housing", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/1905.13659v2", "title": "Uncoupled Regression from Pairwise Comparison Data", "abstract": "Uncoupled regression is the problem to learn a model from unlabeled data and the set of target values while the correspondence between them is unknown. Such a situation arises in predicting anonymized targets that involve sensitive information, e.g., one's annual income. Since existing methods for uncoupled regression often require strong assumptions on the true target function, and thus, their range of applications is limited, we introduce a novel framework that does not require such assumptions in this paper. Our key idea is to utilize pairwise comparison data, which consists of pairs of unlabeled data that we know which one has a larger target value. Such pairwise comparison data is easy to collect, as typically discussed in the learning-to-rank scenario, and does not break the anonymity of data. We propose two practical methods for uncoupled regression from pairwise comparison data and show that the learned regression model converges to the optimal model with the optimal parametric convergence rate when the target variable distributes uniformly. Moreover, we empirically show that for linear models the proposed methods are comparable to ordinary supervised regression with labeled data.", "published": "2019-05-31T15:00:14Z", "authors": ["Liyuan Xu", "Junya Honda", "Gang Niu", "Masashi Sugiyama"], "categories": ["cs.LG", "stat.ML"], "doi": "", "_query": "hedonic regression housing", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2105.01913v4", "title": "Identifying hidden coalitions in the US House of Representatives by optimally partitioning signed networks based on generalized balance", "abstract": "In network science, identifying optimal partitions of a signed network into internally cohesive and mutually divisive clusters based on generalized balance theory is computationally challenging. We reformulate and generalize two binary linear programming models that tackle this challenge, demonstrating their practicality by applying them them to partition networks of collaboration in the US House of Representatives. These models guarantee a globally optimal network partition and can be practically applied to signed networks containing up to 30,000 edges. In the US House context, we find that a three-cluster partition is better than a conventional two-cluster partition, where the otherwise hidden third coalition is composed of highly effective legislators who are ideologically aligned with the majority party.", "published": "2021-05-05T07:57:41Z", "authors": ["Samin Aref", "Zachary P. Neal"], "categories": ["cs.SI", "cs.CY", "math.OC", "physics.soc-ph"], "doi": "10.1038/s41598-021-98139-w", "_query": "hedonic regression housing", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/0802.1047v1", "title": "Testing additivity in nonparametric regression under random censorship", "abstract": "In this paper, we are concerned with nonparametric estimation of the multivariate regression function in the presence of right censored data. More precisely, we propose a statistic that is shown to be asymptotically normally distributed under the additive assumption, and that could be used to test for additivity in the censored regression setting.", "published": "2008-02-07T19:18:45Z", "authors": ["Mohammed Debbarh", "Vivian Viallon"], "categories": ["math.ST"], "doi": "", "_query": "hedonic regression housing", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2110.14717v2", "title": "An Efficient Reversible Algorithm for Linear Regression", "abstract": "This paper presents an efficient reversible algorithm for linear regression, both with and without ridge regression. Our reversible algorithm matches the asymptotic time and space complexity of standard irreversible algorithms for this problem. Needed for this result is the expansion of the analysis of efficient reversible matrix multiplication to rectangular matrices and matrix inversion.", "published": "2021-10-27T19:15:14Z", "authors": ["Erik D. Demaine", "Jayson Lynch", "Jiaying Sun"], "categories": ["cs.DS", "cs.ET"], "doi": "", "_query": "hedonic regression housing", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/1509.05778v2", "title": "A Bayesian approach to linear regression in astronomy", "abstract": "Linear regression is common in astronomical analyses. I discuss a Bayesian hierarchical modeling of data with heteroscedastic and possibly correlated measurement errors and intrinsic scatter. The method fully accounts for time evolution. The slope, the normalization, and the intrinsic scatter of the relation can evolve with the redshift. The intrinsic distribution of the independent variable is approximated using a mixture of Gaussian distributions whose means and standard deviations depend on time. The method can address scatter in the measured independent variable (a kind of Eddington bias), selection effects in the response variable (Malmquist bias), and departure from linearity in form of a knee. I tested the method with toy models and simulations and quantified the effect of biases and inefficient modeling. The R-package LIRA (LInear Regression in Astronomy) is made available to perform the regression.", "published": "2015-09-18T20:04:35Z", "authors": ["Mauro Sereno"], "categories": ["astro-ph.IM"], "doi": "", "_query": "hedonic regression housing", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2505.20022v2", "title": "Kernel Ridge Regression with Predicted Feature Inputs and Applications to Factor-Based Nonparametric Regression", "abstract": "Kernel methods, particularly kernel ridge regression (KRR), are time-proven, powerful nonparametric regression techniques known for their rich capacity, analytical simplicity, and computational tractability. The analysis of their predictive performance has received continuous attention for more than two decades. However, in many modern regression problems where the feature inputs used in KRR cannot be directly observed and must instead be inferred from other measurements, the theoretical foundations of KRR remain largely unexplored. In this paper, we introduce a novel approach for analyzing KRR with predicted feature inputs. Our framework is not only essential for handling predicted feature inputs -- enabling us to derive risk bounds without imposing any assumptions on the error of the predicted feature -- but also strengthens existing analyses in the classical setting by allowing arbitrary model misspecification, requiring weaker conditions under the squared loss, particularly allowing both an unbounded response and an unbounded function class, and being flexible enough to accommodate other convex loss functions. We apply our general theory to factor-based nonparametric regression models and establish the minimax optimality of KRR when the feature inputs are predicted using principal component analysis. Our theoretical findings are further corroborated by simulation studies and real-data analyses using pretrained LLM embeddings for the downstream prediction task.", "published": "2025-05-26T14:10:03Z", "authors": ["Xin Bing", "Xin He", "Chao Wang"], "categories": ["math.ST"], "doi": "", "_query": "hedonic regression housing", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2204.02508v1", "title": "A robust scalar-on-function logistic regression for classification", "abstract": "Scalar-on-function logistic regression, where the response is a binary outcome and the predictor consists of random curves, has become a general framework to explore a linear relationship between the binary outcome and functional predictor. Most of the methods used to estimate this model are based on the least-squares type estimators. However, the least-squares estimator is seriously hindered by outliers, leading to biased parameter estimates and an increased probability of misclassification. This paper proposes a robust partial least squares method to estimate the regression coefficient function in the scalar-on-function logistic regression. The regression coefficient function represented by functional partial least squares decomposition is estimated by a weighted likelihood method, which downweighs the effect of outliers in the response and predictor. The estimation and classification performance of the proposed method is evaluated via a series of Monte Carlo experiments and a strawberry puree data set. The results obtained from the proposed method are compared favorably with existing methods.", "published": "2022-04-05T22:11:05Z", "authors": ["Muge Mutis", "Ufuk Beyaztas", "Gulhayat Golbasi Simsek", "Han Lin Shang"], "categories": ["stat.ME", "stat.CO"], "doi": "", "_query": "hedonic regression housing", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2503.00876v1", "title": "Improve Representation for Imbalanced Regression through Geometric Constraints", "abstract": "In representation learning, uniformity refers to the uniform feature distribution in the latent space (i.e., unit hypersphere). Previous work has shown that improving uniformity contributes to the learning of under-represented classes. However, most of the previous work focused on classification; the representation space of imbalanced regression remains unexplored. Classification-based methods are not suitable for regression tasks because they cluster features into distinct groups without considering the continuous and ordered nature essential for regression. In a geometric aspect, we uniquely focus on ensuring uniformity in the latent space for imbalanced regression through two key losses: enveloping and homogeneity. The enveloping loss encourages the induced trace to uniformly occupy the surface of a hypersphere, while the homogeneity loss ensures smoothness, with representations evenly spaced at consistent intervals. Our method integrates these geometric principles into the data representations via a Surrogate-driven Representation Learning (SRL) framework. Experiments with real-world regression and operator learning tasks highlight the importance of uniformity in imbalanced regression and validate the efficacy of our geometry-based loss functions.", "published": "2025-03-02T12:31:34Z", "authors": ["Zijian Dong", "Yilei Wu", "Chongyao Chen", "Yingtian Zou", "Yichi Zhang", "Juan Helen Zhou"], "categories": ["cs.LG"], "doi": "", "_query": "hedonic regression housing", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2311.03036v2", "title": "On regularized polynomial functional regression", "abstract": "This article offers a comprehensive treatment of polynomial functional regression, culminating in the establishment of a novel finite sample bound. This bound encompasses various aspects, including general smoothness conditions, capacity conditions, and regularization techniques. In doing so, it extends and generalizes several findings from the context of linear functional regression as well. We also provide numerical evidence that using higher order polynomial terms can lead to an improved performance.", "published": "2023-11-06T11:14:36Z", "authors": ["Markus Holzleitner", "Sergei Pereverzyev"], "categories": ["math.NA", "cs.LG", "math.ST"], "doi": "10.1016/j.jco.2024.101853", "_query": "hedonic regression housing", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2510.16974v3", "title": "Differentially Private Linear Regression and Synthetic Data Generation with Statistical Guarantees", "abstract": "In the social sciences, small- to medium-scale datasets are common, and linear regression is canonical. In privacy-aware settings, much work has focused on differentially private (DP) linear regression, but mostly on point estimation with limited attention to uncertainty quantification. Meanwhile, synthetic data generation (SDG) is increasingly important for reproducibility studies, yet current DP linear regression methods do not readily support it. Mainstream DP-SDG approaches either are tailored to discrete or discretized data, making them less suitable for analyses involving continuous variables, or rely on deep learning models that require large datasets, limiting their use for the smaller-scale data typical in social science. We propose a method for linear regression with valid inference under Gaussian DP. It includes a bias-corrected estimator with asymptotic confidence intervals (CIs) and a general SDG procedure such that the corresponding regression on the synthetic data matches our DP linear regression procedure. Our approach is effective in small- to moderate-dimensional settings. Experiments show that our method (1) improves accuracy over existing methods for DP linear regression, (2) provides valid CIs, and (3) produces more reliable synthetic data for downstream statistical and machine learning tasks than current DP synthesizers.", "published": "2025-10-19T19:30:41Z", "authors": ["Shurong Lin", "Aleksandra Slavković", "Deekshith Reddy Bhoomireddy"], "categories": ["cs.LG", "stat.ML"], "doi": "", "_query": "hedonic regression housing", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/1508.05987v2", "title": "Flexible Expectile Regression in Reproducing Kernel Hilbert Space", "abstract": "Expectile, first introduced by Newey and Powell (1987) in the econometrics literature, has recently become increasingly popular in risk management and capital allocation for financial institutions due to its desirable properties such as coherence and elicitability. The current standard tool for expectile regression analysis is the multiple linear expectile regression proposed by Newey and Powell in 1987. The growing applications of expectile regression motivate us to develop a much more flexible nonparametric multiple expectile regression in a reproducing kernel Hilbert space. The resulting estimator is called KERE which has multiple advantages over the classical multiple linear expectile regression by incorporating non-linearity, non-additivity and complex interactions in the final estimator. The kernel learning theory of KERE is established. We develop an efficient algorithm inspired by majorization-minimization principle for solving the entire solution path of KERE. It is shown that the algorithm converges at least at a linear rate. Extensive simulations are conducted to show the very competitive finite sample performance of KERE. We further demonstrate the application of KERE by using personal computer price data.", "published": "2015-08-24T22:34:57Z", "authors": ["Yi Yang", "Teng Zhang", "Hui Zou"], "categories": ["stat.ME"], "doi": "", "_query": "hedonic regression housing", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2107.06936v2", "title": "Performance of Bayesian linear regression in a model with mismatch", "abstract": "In this paper we analyze, for a model of linear regression with gaussian covariates, the performance of a Bayesian estimator given by the mean of a log-concave posterior distribution with gaussian prior, in the high-dimensional limit where the number of samples and the covariates' dimension are large and proportional. Although the high-dimensional analysis of Bayesian estimators has been previously studied for Bayesian-optimal linear regression where the correct posterior is used for inference, much less is known when there is a mismatch. Here we consider a model in which the responses are corrupted by gaussian noise and are known to be generated as linear combinations of the covariates, but the distributions of the ground-truth regression coefficients and of the noise are unknown. This regression task can be rephrased as a statistical mechanics model known as the Gardner spin glass, an analogy which we exploit. Using a leave-one-out approach we characterize the mean-square error for the regression coefficients. We also derive the log-normalizing constant of the posterior. Similar models have been studied by Shcherbina and Tirozzi and by Talagrand, but our arguments are much more straightforward. An interesting consequence of our analysis is that in the quadratic loss case, the performance of the Bayesian estimator is independent of a global \"temperature\" hyperparameter and matches the ridge estimator: sampling and optimizing are equally good.", "published": "2021-07-14T18:50:13Z", "authors": ["Jean Barbier", "Wei-Kuo Chen", "Dmitry Panchenko", "Manuel Sáenz"], "categories": ["math.PR", "cond-mat.dis-nn", "cs.IT", "cs.LG", "math-ph", "math.ST"], "doi": "", "_query": "hedonic regression housing", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2504.04621v1", "title": "A Review on Symbolic Regression in Power Systems: Methods, Applications, and Future Directions", "abstract": "As power systems evolve with the increasing integration of renewable energy sources and smart grid technologies, there is a growing demand for flexible and scalable modeling approaches capable of capturing the complex dynamics of modern grids. This review focuses on symbolic regression, a powerful methodology for deriving parsimonious and interpretable mathematical models directly from data. The paper presents a comprehensive overview of symbolic regression methods, including sparse identification of nonlinear dynamics, automatic regression for governing equations, and deep symbolic regression, highlighting their applications in power systems. Through comparative case studies of the single machine infinite bus system, grid-following, and grid-forming inverter, we analyze the strengths, limitations, and suitability of each symbolic regression method in modeling nonlinear power system dynamics. Additionally, we identify critical research gaps and discuss future directions for leveraging symbolic regression in the optimization, control, and operation of modern power grids. This review aims to provide a valuable resource for researchers and engineers seeking innovative, data-driven solutions for modeling in the context of evolving power system infrastructure.", "published": "2025-04-06T21:02:35Z", "authors": ["Amir Bahador Javadi", "Philip Pong"], "categories": ["eess.SY"], "doi": "", "_query": "hedonic regression housing", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/1608.06383v1", "title": "Softplus Regressions and Convex Polytopes", "abstract": "To construct flexible nonlinear predictive distributions, the paper introduces a family of softplus function based regression models that convolve, stack, or combine both operations by convolving countably infinite stacked gamma distributions, whose scales depend on the covariates. Generalizing logistic regression that uses a single hyperplane to partition the covariate space into two halves, softplus regressions employ multiple hyperplanes to construct a confined space, related to a single convex polytope defined by the intersection of multiple half-spaces or a union of multiple convex polytopes, to separate one class from the other. The gamma process is introduced to support the convolution of countably infinite (stacked) covariate-dependent gamma distributions. For Bayesian inference, Gibbs sampling derived via novel data augmentation and marginalization techniques is used to deconvolve and/or demix the highly complex nonlinear predictive distribution. Example results demonstrate that softplus regressions provide flexible nonlinear decision boundaries, achieving classification accuracies comparable to that of kernel support vector machine while requiring significant less computation for out-of-sample prediction.", "published": "2016-08-23T05:15:25Z", "authors": ["Mingyuan Zhou"], "categories": ["stat.ML", "stat.ME"], "doi": "", "_query": "hedonic regression housing", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2510.14502v1", "title": "Additive Density Regression", "abstract": "We present a structured additive regression approach to model conditional densities given scalar covariates, where only samples of the conditional distributions are observed. This links our approach to distributional regression models for scalar data. The model is formulated in a Bayes Hilbert space -- preserving nonnegativity and integration to one under summation and scalar multiplication -- with respect to an arbitrary finite measure. This allows to consider, amongst others, continuous, discrete and mixed densities. Our theoretical results include asymptotic existence, uniqueness, consistency, and asymptotic normality of the penalized maximum likelihood estimator, as well as confidence regions and inference for the (effect) densities. For estimation, we propose to maximize the penalized log-likelihood corresponding to an appropriate multinomial, or equivalently, Poisson regression model, which we show to approximate the original penalized maximum likelihood problem. We apply our framework to a motivating gender economic data set from the German Socio-Economic Panel Study (SOEP), analyzing the distribution of the woman's share in a couple's total labor income given covariate effects for year, place of residence and age of the youngest child. As the income share is a continuous variable having discrete point masses at zero and one for single-earner couples, the corresponding densities are of mixed type.", "published": "2025-10-16T09:48:47Z", "authors": ["Eva-Maria Maier", "Alexander Fottner", "Sonja Greven", "Almond Stöcker"], "categories": ["stat.ME", "stat.AP"], "doi": "", "_query": "hedonic regression housing", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2110.03633v4", "title": "Regression markets and application to energy forecasting", "abstract": "Energy forecasting has attracted enormous attention over the last few decades, with novel proposals related to the use of heterogeneous data sources, probabilistic forecasting, online learn-ing, etc. A key aspect that emerged is that learning and forecasting may highly benefit from distributed data, though not only in the geographical sense. That is, various agents collect and own data that may be useful to others. In contrast to recent proposals that look into distributed and privacy-preserving learning (incentive-free), we explore here a framework called regression markets. There, agents aiming to improve their forecasts post a regression task, for which other agents may contribute by sharing their data for their features and get monetarily rewarded for it.The market design is for regression models that are linear in their parameters, and possibly sep-arable, with estimation performed based on either batch or online learning. Both in-sample and out-of-sample aspects are considered, with markets for fitting models in-sample, and then for improving genuine forecasts out-of-sample. Such regression markets rely on recent concepts within interpretability of machine learning approaches and cooperative game theory, with Shapley additive explanations. Besides introducing the market design and proving its desirable properties, application results are shown based on simulation studies (to highlight the salient features of the proposal) and with real-world case studies.", "published": "2021-10-07T17:21:43Z", "authors": ["Pierre Pinson", "Liyang Han", "Jalal Kazempour"], "categories": ["stat.AP", "cs.GT", "eess.SY"], "doi": "", "_query": "hedonic regression housing", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2504.15269v2", "title": "Scalable and robust regression models for continuous proportional data", "abstract": "Beta regression is used routinely for continuous proportional data, but it often encounters practical issues such as a lack of robustness to misspecification of the beta distribution and sensitivity to outliers. We develop an improved class of generalized linear models starting with the continuous binomial (cobin) distribution and further extending to dispersion mixtures of cobin distributions (micobin). The proposed cobin regression and micobin regression models have attractive robustness, computation, and flexibility properties. A key innovation is the Kolmogorov-Gamma data augmentation scheme, which facilitates Gibbs sampling for Bayesian computation, including in hierarchical cases involving nested, longitudinal, or spatial data. We demonstrate robustness, ability to handle responses exactly at the boundary (0 or 1), and computational efficiency relative to beta regression in simulation experiments and through analysis of the benthic macroinvertebrate multimetric index of US lakes using lake watershed covariates.", "published": "2025-04-21T17:55:00Z", "authors": ["Changwoo J. Lee", "Benjamin K. Dahl", "Otso Ovaskainen", "David B. Dunson"], "categories": ["stat.ME"], "doi": "", "_query": "hedonic regression housing", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2403.19835v3", "title": "Constrained least squares simplicial-simplicial regression", "abstract": "Simplicial-simplicial regression refers to the regression setting where both the responses and predictor variables lie within the simplex space, i.e. they are compositional. For this setting, constrained least squares, where the regression coefficients themselves lie within the simplex, is proposed. The model is transformation-free but the adoption of a power transformation is straightforward, it can treat more than one compositional datasets as predictors and offers the possibility of weights among the simplicial predictors. Among the model's advantages are its ability to treat zeros in a natural way and a highly computationally efficient algorithm to estimate its coefficients. Resampling based hypothesis testing procedures are employed regarding inference, such as linear independence, and equality of the regression coefficients to some pre-specified values. The strategy behind the formulation of the new model is implemented is related to an existing methodology, that is of the same spirit, showcasing how other similar models can be employed as well. Finally, the performance of the proposed technique and its comparison to the existing methodology takes place using simulation studies and real data examples.", "published": "2024-03-28T21:08:14Z", "authors": ["Michail Tsagris"], "categories": ["stat.ME"], "doi": "", "_query": "hedonic regression housing", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/1109.6493v1", "title": "Improved estimation in a non-Gaussian parametric regression", "abstract": "The paper considers the problem of estimating the parameters in a continuous time regression model with a non-Gaussian noise of pulse type. The noise is specified by the Ornstein-Uhlenbeck process driven by the mixture of a Brownian motion and a compound Poisson process. Improved estimates for the unknown regression parameters, based on a special modification of the James-Stein procedure with smaller quadratic risk than the usual least squares estimates, are proposed. The developed estimation scheme is applied for the improved parameter estimation in the discrete time regression with the autoregressive noise depending on unknown nuisance parameters.", "published": "2011-09-29T11:50:01Z", "authors": ["Evgeny Pchelintsev"], "categories": ["math.ST"], "doi": "10.1007/s11203-013-9075-0", "_query": "hedonic regression housing", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2603.19480v2", "title": "Regression Adjustments for Double Randomization in Two-Sided Marketplaces", "abstract": "Multiple randomization designs (MRDs) are a class of experimental designs used to handle interference in two-sided marketplaces. We investigate regression adjustment strategies for estimating total, spillover, and direct effects in MRDs. We derive minimum asymptotic variance estimators among a broad class of linearly adjusted estimators, without assuming a linear model on the potential outcomes. Surprisingly, the optimal regression adjustments are estimable from data and are generally different from regression adjustments in classical randomized experiments. For example, one such optimal estimator for the direct effect corresponds to a weighted regression with interacted two-way fixed effects. We establish model-robustness properties, central limit theorems, and inferential methods for our estimators, relying on improved theoretical results for MRD experiments. Our results provide the analog of classical regression adjustments for marketplace experiments. Numerical simulations demonstrate a considerable increase in efficiency over simpler approaches, enabling better inference when running MRDs.", "published": "2026-03-19T21:22:22Z", "authors": ["Timothy Sudijono", "Lihua Lei", "Lorenzo Masoero", "Suhas Vijaykumar", "Guido Imbens", "James McQueen"], "categories": ["stat.ME", "math.ST"], "doi": "", "_query": "hedonic regression housing", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2408.02833v1", "title": "Adaptive Learning for Quantum Linear Regression", "abstract": "The recent availability of quantum annealers as cloud-based services has enabled new ways to handle machine learning problems, and several relevant algorithms have been adapted to run on these devices. In a recent work, linear regression was formulated as a quadratic binary optimization problem that can be solved via quantum annealing. Although this approach promises a computational time advantage for large datasets, the quality of the solution is limited by the necessary use of a precision vector, used to approximate the real-numbered regression coefficients in the quantum formulation. In this work, we focus on the practical challenge of improving the precision vector encoding: instead of setting an array of generic values equal for all coefficients, we allow each one to be expressed by its specific precision, which is tuned with a simple adaptive algorithm. This approach is evaluated on synthetic datasets of increasing size, and linear regression is solved using the D-Wave Advantage quantum annealer, as well as classical solvers. To the best of our knowledge, this is the largest dataset ever evaluated for linear regression on a quantum annealer. The results show that our formulation is able to deliver improved solution quality in all instances, and could better exploit the potential of current quantum devices.", "published": "2024-08-05T21:09:01Z", "authors": ["Costantino Carugno", "Maurizio Ferrari Dacrema", "Paolo Cremonesi"], "categories": ["quant-ph", "cs.LG"], "doi": "10.1109/QCE60285.2024.00186", "_query": "hedonic regression housing", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/1804.06494v3", "title": "Minimax rate of testing in sparse linear regression", "abstract": "We consider the problem of testing the hypothesis that the parameter of linear regression model is 0 against an s-sparse alternative separated from 0 in the l2-distance. We show that, in Gaussian linear regression model with p < n, where p is the dimension of the parameter and n is the sample size, the non-asymptotic minimax rate of testing has the form sqrt((s/n) log(1 + sqrt(p)/s )). We also show that this is the minimax rate of estimation of the l2-norm of the regression parameter.", "published": "2018-04-17T22:47:26Z", "authors": ["Alexandra Carpentier", "Olivier Collier", "Laëtitia Comminges", "Alexandre B. Tsybakov", "Yuhao Wang"], "categories": ["math.ST"], "doi": "", "_query": "hedonic regression housing", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/1704.04858v5", "title": "A Nonparametric Bayesian Methodology for Regression Discontinuity Designs", "abstract": "One of the most popular methodologies for estimating the average treatment effect at the threshold in a regression discontinuity design is local linear regression (LLR), which places larger weight on units closer to the threshold. We propose a Gaussian process regression methodology that acts as a Bayesian analog to LLR for regression discontinuity designs. Our methodology provides a flexible fit for treatment and control responses by placing a general prior on the mean response functions. Furthermore, unlike LLR, our methodology can incorporate uncertainty in how units are weighted when estimating the treatment effect. We prove our method is consistent in estimating the average treatment effect at the threshold. Furthermore, we find via simulation that our method exhibits promising coverage, interval length, and mean squared error properties compared to standard LLR and state-of-the-art LLR methodologies. Finally, we explore the performance of our method on a real-world example by studying the impact of being a first-round draft pick on the performance and playing time of basketball players in the National Basketball Association.", "published": "2017-04-17T03:22:13Z", "authors": ["Zach Branson", "Maxime Rischard", "Luke Bornn", "Luke Miratrix"], "categories": ["stat.ME"], "doi": "10.1016/j.jspi.2019.01.003", "_query": "hedonic regression housing", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2109.08519v1", "title": "Multiple Linear Regression and Correlation: A Geometric Analysis", "abstract": "In this review article we consider linear regression analysis from a geometric perspective, looking at standard methods and outputs in terms of the lengths of the relevant vectors and the angles between these vectors. We show that standard regression output can be written in terms of the lengths and angles between the various input vectors, such that this geometric information is sufficient in linear regression problems. This allows us to obtain a standard formula for multiple correlation and give a geometric interpretation to this. We examine how multicollinearity affects the total explanatory power of the data, and we examine a counter-intuitive phenomena called \"enhancement\" where the total information from the explanatory vectors is greater than the sum of the marginal parts.", "published": "2021-09-13T09:32:01Z", "authors": ["Ben O'Neill"], "categories": ["stat.ME", "math.ST"], "doi": "", "_query": "hedonic regression housing", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2107.10343v1", "title": "Robust Nonparametric Regression with Deep Neural Networks", "abstract": "In this paper, we study the properties of robust nonparametric estimation using deep neural networks for regression models with heavy tailed error distributions. We establish the non-asymptotic error bounds for a class of robust nonparametric regression estimators using deep neural networks with ReLU activation under suitable smoothness conditions on the regression function and mild conditions on the error term. In particular, we only assume that the error distribution has a finite p-th moment with p greater than one. We also show that the deep robust regression estimators are able to circumvent the curse of dimensionality when the distribution of the predictor is supported on an approximate lower-dimensional set. An important feature of our error bound is that, for ReLU neural networks with network width and network size (number of parameters) no more than the order of the square of the dimensionality d of the predictor, our excess risk bounds depend sub-linearly on d. Our assumption relaxes the exact manifold support assumption, which could be restrictive and unrealistic in practice. We also relax several crucial assumptions on the data distribution, the target regression function and the neural networks required in the recent literature. Our simulation studies demonstrate that the robust methods can significantly outperform the least squares method when the errors have heavy-tailed distributions and illustrate that the choice of loss function is important in the context of deep nonparametric regression.", "published": "2021-07-21T20:18:05Z", "authors": ["Guohao Shen", "Yuling Jiao", "Yuanyuan Lin", "Jian Huang"], "categories": ["math.ST"], "doi": "", "_query": "hedonic regression housing", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/1511.03402v2", "title": "Extended T-process Regression Models", "abstract": "Gaussian process regression (GPR) model has been widely used to fit data when the regression function is unknown and its nice properties have been well established. In this article, we introduce an extended t-process regression (eTPR) model, which gives a robust best linear unbiased predictor (BLUP). Owing to its succinct construction, it inherits many attractive properties from the GPR model, such as having closed forms of marginal and predictive distributions to give an explicit form for robust BLUP procedures, and easy to cope with large dimensional covariates with an efficient implementation by slightly modifying existing BLUP procedures. Properties of the robust BLUP are studied. Simulation studies and real data applications show that the eTPR model gives a robust fit in the presence of outliers in both input and output spaces and has a good performance in prediction, compared with the GPR and locally weighted scatterplot smoothing (LOESS) methods.", "published": "2015-11-11T07:01:17Z", "authors": ["Zhanfeng Wang", "Jianqing Shi", "Youngjo Lee"], "categories": ["stat.ME"], "doi": "", "_query": "hedonic regression housing", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2606.23174v1", "title": "Principal Covariate Regression with Nuclear Norm Penalty", "abstract": "In high-dimensional data settings, dimensionality reduction or variable selection are key steps when using statistical learning techniques. Principal Covariate Regression-type methods aim to perform both dimensionality reduction and (regularized) regression steps in one analysis. However, existing PCovR methods cannot simultaneously select dimensionalities and estimate regularized coefficients, forcing researchers to make ad-hoc choices in the order of these steps. In this study, we propose a novel method called Principal Covariate Regression with Nuclear Norm Penalty (PcovRnnp) that allows simultaneous dimension reduction and estimation of regularized coefficients.", "published": "2026-06-22T11:13:03Z", "authors": ["Kaiwen Liu", "Lisa Verbeij", "Wouter Weeda", "Mark de Rooij"], "categories": ["stat.ME"], "doi": "", "_query": "hedonic regression housing", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2203.06455v2", "title": "Learning phase transitions from regression uncertainty: A new regression-based machine learning approach for automated detection of phases of matter", "abstract": "For performing regression tasks involved in various physics problems, enhancing the precision or equivalently reducing the uncertainty of regression results is undoubtedly one of the central goals. Here, somewhat surprisingly, we find that the unfavorable regression uncertainty in performing the regression tasks of inverse statistical problems actually contains hidden information concerning the phase transitions of the system under consideration. By utilizing this hidden information, we develop a new unsupervised machine learning approach for automated detection of phases of matter, dubbed learning from regression uncertainty. This is achieved by revealing an intrinsic connection between regression uncertainty and response properties of the system, thus making the outputs of this machine learning approach directly interpretable via conventional notions of physics. We demonstrate the approach by identifying the critical points of the ferromagnetic Ising model and the three-state clock model, and revealing the existence of the intermediate phase in the six-state and seven-state clock models. Comparing to the widely-used classification-based approaches developed so far, although successful, their recognized classes of patterns are essentially abstract, which hinders their straightforward relation to conventional notions of physics. These challenges persist even when one employs the state-of-the-art deep neural networks that excel at classification tasks. In contrast, with the core working horse being a neural network performing regression tasks, our new approach is not only practically more efficient, but also paves the way towards intriguing possibilities for unveiling new physics via machine learning in a physically interpretable manner.", "published": "2022-03-12T15:10:09Z", "authors": ["Wei-Chen Guo", "Liang He"], "categories": ["cond-mat.stat-mech", "cond-mat.dis-nn"], "doi": "10.1088/1367-2630/acef4e", "_query": "hedonic regression housing", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2011.14219v1", "title": "Adaptive Inference in Multivariate Nonparametric Regression Models Under Monotonicity", "abstract": "We consider the problem of adaptive inference on a regression function at a point under a multivariate nonparametric regression setting. The regression function belongs to a Hölder class and is assumed to be monotone with respect to some or all of the arguments. We derive the minimax rate of convergence for confidence intervals (CIs) that adapt to the underlying smoothness, and provide an adaptive inference procedure that obtains this minimax rate. The procedure differs from that of Cai and Low (2004), intended to yield shorter CIs under practically relevant specifications. The proposed method applies to general linear functionals of the regression function, and is shown to have favorable performance compared to existing inference procedures.", "published": "2020-11-28T21:21:04Z", "authors": ["Koohyun Kwon", "Soonwoo Kwon"], "categories": ["math.ST", "econ.EM", "stat.AP"], "doi": "", "_query": "hedonic regression housing", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2605.11197v2", "title": "The Same Problem by Different Names: Unifying Regression Dilution and Regression to the Mean", "abstract": "Regression to the Mean (RTM) and Regression Dilution are traditionally treated as unrelated issues in the clinical and ecological literatures. In this work, we demonstrate that within a linear errors-in-variables framework where baseline variables are subject to transient temporal or measurement noise, these two phenomena share an identical underlying mathematical signature. We unify these disparate traditions by comparing specialized clinical tools, such as the Berry shrinkage correction, with standard sign-agnostic structural estimators like Major Axis (MA) and Reduced Major Axis (RMA) regression. Using an analytical framework, we evaluate the closed-form population limits and finite-sample performance of these methods across various noise-to-signal ratios and sample sizes. Our results show that the Berry method is a specialized tool designed for clinical scenarios where a 1:1 relationship is expected. However, applying it to ecological trade-offs with negative slopes can lead to severe errors. We provide maps of optimality to identify which estimator most accurately recovers the true biological signal under different conditions. By reconciling these disparate methods, we offer a principled guide for researchers to choose the correct tool based on their data's noise profile rather than their disciplinary tradition.", "published": "2026-05-11T20:04:13Z", "authors": ["José F. Fontanari", "Mauro Santos"], "categories": ["q-bio.QM", "physics.data-an"], "doi": "10.3390/math14122052", "_query": "hedonic regression housing", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/1408.1600v1", "title": "Change Impact Analysis Based Regression Testing of Web Services", "abstract": "Reducing the effort required to make changes in web services is one of the primary goals in web service projects maintenance and evolution. Normally, functional and non-functional testing of a web service is performed by testing the operations specified in its WSDL. The regression testing is performed by identifying the changes made thereafter to the web service code and the WSDL. In this thesis, we present a tool-supported approach to perform efficient regression testing of web services. By representing a web service as a directed graph of WSDL elements, we identify and gathers the changed portions of the graph and use this information to reduce regression testing efforts. Specifically, we identify, categorize, and capture the web service testing needs in two different ways, namely, Operationalized Regression Testing of Web Service (ORTWS) and Parameterized Regression Testing of Web Service (PRTWS). Both of the approach can be combined to reduce the regression testing efforts in the web service project. The proposed approach is prototyped as a tool, named as Automatic Web Service Change Management (AWSCM), which helps in selecting the relevant test cases to construct reduced test suite from the old test suite. We present few case studies on different web service projects to demonstrate the applicability of the proposed tool. The reduction in the effort for regression testing of web service is also estimated.", "published": "2014-08-07T14:14:14Z", "authors": ["Animesh Chaturvedi"], "categories": ["cs.SE", "cs.AI", "cs.DC"], "doi": "", "_query": "hedonic regression housing", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/1509.09259v3", "title": "Distributionally Robust Logistic Regression", "abstract": "This paper proposes a distributionally robust approach to logistic regression. We use the Wasserstein distance to construct a ball in the space of probability distributions centered at the uniform distribution on the training samples. If the radius of this ball is chosen judiciously, we can guarantee that it contains the unknown data-generating distribution with high confidence. We then formulate a distributionally robust logistic regression model that minimizes a worst-case expected logloss function, where the worst case is taken over all distributions in the Wasserstein ball. We prove that this optimization problem admits a tractable reformulation and encapsulates the classical as well as the popular regularized logistic regression problems as special cases. We further propose a distributionally robust approach based on Wasserstein balls to compute upper and lower confidence bounds on the misclassification probability of the resulting classifier. These bounds are given by the optimal values of two highly tractable linear programs. We validate our theoretical out-of-sample guarantees through simulated and empirical experiments.", "published": "2015-09-30T17:14:46Z", "authors": ["Soroosh Shafieezadeh-Abadeh", "Peyman Mohajerin Esfahani", "Daniel Kuhn"], "categories": ["math.OC", "stat.ML"], "doi": "", "_query": "hedonic regression housing", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/0905.4230v2", "title": "On characterizations based on regression of linear combinations of record values", "abstract": "We characterize the exponential distribution in terms of the regression of a record value with non-adjacent record values as covariates. We also study characterizations based on the regression of linear combinations of record values.", "published": "2009-05-26T15:57:14Z", "authors": ["George P. Yanev", "M. Ahsanullah"], "categories": ["math.PR"], "doi": "", "_query": "hedonic regression housing", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/1807.10937v2", "title": "PROPEL: Probabilistic Parametric Regression Loss for Convolutional Neural Networks", "abstract": "In recent years, Convolutional Neural Networks (CNNs) have enabled significant advancements to the state-of-the-art in computer vision. For classification tasks, CNNs have widely employed probabilistic output and have shown the significance of providing additional confidence for predictions. However, such probabilistic methodologies are not widely applicable for addressing regression problems using CNNs, as regression involves learning unconstrained continuous and, in many cases, multi-variate target variables. We propose a PRObabilistic Parametric rEgression Loss (PROPEL) that facilitates CNNs to learn parameters of probability distributions for addressing probabilistic regression problems. PROPEL is fully differentiable and, hence, can be easily incorporated for end-to-end training of existing CNN regression architectures using existing optimization algorithms. The proposed method is flexible as it enables learning complex unconstrained probabilities while being generalizable to higher dimensional multi-variate regression problems. We utilize a PROPEL-based CNN to address the problem of learning hand and head orientation from uncalibrated color images. Our experimental validation and comparison with existing CNN regression loss functions show that PROPEL improves the accuracy of a CNN by enabling probabilistic regression, while significantly reducing required model parameters by $10 \\times$, resulting in improved generalization as compared to the existing state-of-the-art.", "published": "2018-07-28T13:41:44Z", "authors": ["Muhammad Asad", "Rilwan Basaru", "S M Masudur Rahman Al Arif", "Greg Slabaugh"], "categories": ["cs.CV"], "doi": "", "_query": "hedonic regression housing", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2108.05691v1", "title": "Can We Spot Energy Regressions using Developers Tests?", "abstract": "Software Energy Consumption(SEC) is gaining more and more attention. In this paper, we tackle the problem of hinting developers about the SEC of their programs in the context of software developments based on Continuous Integration(CI). In this study, we investigate if the CI can leverage developers' tests to perform a new class of tests: the energy regression testing. Energy regression is similar to performance regression but focused on the energy consumption of the program instead of standard performance indicators, like execution time or memory consumption. We propose to perform an exploratory study of the usage of developers' tests for energy regression testing. We propose to first investigate if developers' tests can be used to obtain stable SEC indicators. Then, to consider if comparing the SEC of developers' tests between two versions can accurately spot energy regressions introduced by automated program mutations. Finally, to assess if it can successfully pinpoint the source code lines guilty of energy regressions. Our study will pave the way for automated SEC regression tools that can be readily deployed inside an existing CI infrastructure to raise awareness of SEC issues among practitioners.", "published": "2021-08-12T12:21:49Z", "authors": ["Benjamin Danglot", "Jean-Rémy Falleri", "Romain Rouvoy"], "categories": ["cs.SE", "cs.PF"], "doi": "", "_query": "hedonic regression housing", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/1302.0492v2", "title": "Reducing Poisson noise and baseline drift in X-ray spectral images with bootstrap Poisson regression and robust nonparametric regression", "abstract": "X-ray spectral imaging provides quantitative imaging of trace elements in biological sample with high sensitivity. We propose a novel algorithm to promote the signal-to-noise ratio (SNR) of X-ray spectral images that have low photon counts. Firstly, we estimate the image data area that belongs to the homogeneous parts through confidence interval testing. Then, we apply the Poisson regression through its maximum likelihood estimation on this area to estimate the true photon counts from the Poisson noise corrupted data. Unlike other denoising methods based on regression analysis, we use the bootstrap resampling method to ensure the accuracy of regression estimation. Finally, we use a robust local nonparametric regression method to estimate the baseline and subsequently subtract it from the X-ray spectral data to further improve the SNR of the data. Experiments on several real samples show that the proposed method performs better than some state-of-the-art approaches to ensure accuracy and precision for quantitative analysis of the different trace elements in standard reference biological sample.", "published": "2013-02-03T14:04:10Z", "authors": ["Feng Zhu", "Binjie Qin", "Weiyue Feng", "Huajian Wang", "Shaosen Huang", "Yisong Lv", "Yong Chen"], "categories": ["physics.med-ph"], "doi": "10.1088/0031-9155/58/6/1739", "_query": "hedonic regression housing", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2209.02742v4", "title": "Robust estimation for functional quadratic regression models", "abstract": "Functional quadratic regression models postulate a polynomial relationship between a scalar response rather than a linear one. As in functional linear regression, vertical and specially high-leverage outliers may affect the classical estimators. For that reason, the proposal of robust procedures providing reliable estimators in such situations is an important issue. Taking into account that the functional polynomial model is equivalent to a regression model that is a polynomial of the same order in the functional principal component scores of the predictor processes, our proposal combines robust estimators of the principal directions with robust regression estimators based on a bounded loss function and a preliminary residual scale estimator. Fisher-consistency of the proposed method is derived under mild assumptions. The results of a numerical study show, for finite samples, the benefits of the robust proposal over the one based on sample principal directions and least squares. The usefulness of the proposed approach is also illustrated through the analysis of a real data set which reveals that when the potential outliers are removed the classical and robust methods behave very similarly.", "published": "2022-09-06T18:02:55Z", "authors": ["Graciela Boente", "Daniela Parada"], "categories": ["stat.ME"], "doi": "", "_query": "hedonic regression housing", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2508.18199v1", "title": "Sparse Polynomial Regression under Anomalous Data", "abstract": "This paper starts with the general form of the polynomial regression model. We reformulate the Sparse Polynomial Regression Model (SPRM) with anomalous data filtering as Mixed-Integer Linear Program (MILP). This MILP is then converted to a non-convex Quadratically Constrained Quadratic Program (QCQP). Through a proposed mapping, the derived QCQP is reformulated as a Fractional Program (FP). We theoretically show that the reformulated FP has better computational properties than the original QCQP. We then suggest a conic-relaxation-based algorithm to solve the proposed FP. A Two-Step Convex Relaxation and Recovery (TS-CRR) algorithm is proposed for sparse polynomial regression with anomalous data filtering. Through a series of comprehensive computational experiments (using two different datasets), we have compared the results of our proposed TS-CRR algorithm with the results from several regression and artificial intelligent models. The numerical results show the promising performance of our proposed TS-CRR algorithm as compared to those studied benchmark models.", "published": "2025-08-25T17:00:56Z", "authors": ["Roozbeh Abolpour", "Mohammad Reza Hesamzadeh", "Maryam Dehghani"], "categories": ["math.OC"], "doi": "", "_query": "hedonic regression housing", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2202.03926v2", "title": "Distribution Regression with Sliced Wasserstein Kernels", "abstract": "The problem of learning functions over spaces of probabilities - or distribution regression - is gaining significant interest in the machine learning community. A key challenge behind this problem is to identify a suitable representation capturing all relevant properties of the underlying functional mapping. A principled approach to distribution regression is provided by kernel mean embeddings, which lifts kernel-induced similarity on the input domain at the probability level. This strategy effectively tackles the two-stage sampling nature of the problem, enabling one to derive estimators with strong statistical guarantees, such as universal consistency and excess risk bounds. However, kernel mean embeddings implicitly hinge on the maximum mean discrepancy (MMD), a metric on probabilities, which may fail to capture key geometrical relations between distributions. In contrast, optimal transport (OT) metrics, are potentially more appealing. In this work, we propose an OT-based estimator for distribution regression. We build on the Sliced Wasserstein distance to obtain an OT-based representation. We study the theoretical properties of a kernel ridge regression estimator based on such representation, for which we prove universal consistency and excess risk bounds. Preliminary experiments complement our theoretical findings by showing the effectiveness of the proposed approach and compare it with MMD-based estimators.", "published": "2022-02-08T15:21:56Z", "authors": ["Dimitri Meunier", "Massimiliano Pontil", "Carlo Ciliberto"], "categories": ["stat.ML", "cs.LG"], "doi": "", "_query": "hedonic regression housing", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/1905.03244v1", "title": "Convolutional Mesh Regression for Single-Image Human Shape Reconstruction", "abstract": "This paper addresses the problem of 3D human pose and shape estimation from a single image. Previous approaches consider a parametric model of the human body, SMPL, and attempt to regress the model parameters that give rise to a mesh consistent with image evidence. This parameter regression has been a very challenging task, with model-based approaches underperforming compared to nonparametric solutions in terms of pose estimation. In our work, we propose to relax this heavy reliance on the model's parameter space. We still retain the topology of the SMPL template mesh, but instead of predicting model parameters, we directly regress the 3D location of the mesh vertices. This is a heavy task for a typical network, but our key insight is that the regression becomes significantly easier using a Graph-CNN. This architecture allows us to explicitly encode the template mesh structure within the network and leverage the spatial locality the mesh has to offer. Image-based features are attached to the mesh vertices and the Graph-CNN is responsible to process them on the mesh structure, while the regression target for each vertex is its 3D location. Having recovered the complete 3D geometry of the mesh, if we still require a specific model parametrization, this can be reliably regressed from the vertices locations. We demonstrate the flexibility and the effectiveness of our proposed graph-based mesh regression by attaching different types of features on the mesh vertices. In all cases, we outperform the comparable baselines relying on model parameter regression, while we also achieve state-of-the-art results among model-based pose estimation approaches.", "published": "2019-05-08T17:59:06Z", "authors": ["Nikos Kolotouros", "Georgios Pavlakos", "Kostas Daniilidis"], "categories": ["cs.CV"], "doi": "", "_query": "hedonic regression housing", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2310.16260v3", "title": "Differentially Private Estimation and Inference in High-Dimensional Regression with FDR Control", "abstract": "This paper proposes new methodologies for conducting practical differentially private (DP) estimation and inference in high-dimensional linear regression. We first introduce a DP Bayesian Information Criterion (DP-BIC) for selecting the unknown sparsity parameter in differentially private sparse linear regression (DP-SLR), eliminating the need for prior knowledge of model sparsity, which is a requisite in the existing literature. Next, we develop the DP debiased algorithm that enables privacy-preserving inference on a particular subset of regression parameters. Our proposed method enables privacy-preserving inference on the regression parameters by leveraging the inherent sparsity of high-dimensional linear regression models. Additionally, we address private feature selection by considering multiple testing in high-dimensional linear regression by introducing a DP multiple testing procedure that controls the false discovery rate (FDR). This allows for accurate and privacy-preserving identification of significant predictors in the regression model. Through extensive simulations and real data analyses, we demonstrate the effectiveness of our proposed methods in conducting inference for high-dimensional linear models while safeguarding privacy and controlling the FDR.", "published": "2023-10-25T00:30:09Z", "authors": ["Zhanrui Cai", "Sai Li", "Xintao Xia", "Linjun Zhang"], "categories": ["stat.ME"], "doi": "", "_query": "hedonic regression housing", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/math/0508277v1", "title": "Contour regression: A general approach to dimension reduction", "abstract": "We propose a novel approach to sufficient dimension reduction in regression, based on estimating contour directions of small variation in the response. These directions span the orthogonal complement of the minimal space relevant for the regression and can be extracted according to two measures of variation in the response, leading to simple and general contour regression (SCR and GCR) methodology. In comparison with existing sufficient dimension reduction techniques, this contour-based methodology guarantees exhaustive estimation of the central subspace under ellipticity of the predictor distribution and mild additional assumptions, while maintaining \\sqrtn-consistency and computational ease. Moreover, it proves robust to departures from ellipticity. We establish population properties for both SCR and GCR, and asymptotic properties for SCR. Simulations to compare performance with that of standard techniques such as ordinary least squares, sliced inverse regression, principal Hessian directions and sliced average variance estimation confirm the advantages anticipated by the theoretical analyses. We demonstrate the use of contour-based methods on a data set concerning soil evaporation.", "published": "2005-08-16T08:34:19Z", "authors": ["Bing Li", "Hongyuan Zha", "Francesca Chiaromonte"], "categories": ["math.ST"], "doi": "10.1214/009053605000000192", "_query": "hedonic regression housing", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2407.14495v2", "title": "Conformal Thresholded Intervals for Efficient Regression", "abstract": "This paper introduces Conformal Thresholded Intervals (CTI), a novel conformal regression method that aims to produce the smallest possible prediction set with guaranteed coverage. Unlike existing methods that rely on nested conformal frameworks and full conditional distribution estimation, CTI estimates the conditional probability density for a new response to fall into each interquantile interval using off-the-shelf multi-output quantile regression. By leveraging the inverse relationship between interval length and probability density, CTI constructs prediction sets by thresholding the estimated conditional interquantile intervals based on their length. The optimal threshold is determined using a calibration set to ensure marginal coverage, effectively balancing the trade-off between prediction set size and coverage. CTI's approach is computationally efficient and avoids the complexity of estimating the full conditional distribution. The method is theoretically grounded, with provable guarantees for marginal coverage and achieving the smallest prediction size given by Neyman-Pearson . Extensive experimental results demonstrate that CTI achieves superior performance compared to state-of-the-art conformal regression methods across various datasets, consistently producing smaller prediction sets while maintaining the desired coverage level. The proposed method offers a simple yet effective solution for reliable uncertainty quantification in regression tasks, making it an attractive choice for practitioners seeking accurate and efficient conformal prediction.", "published": "2024-07-19T17:47:08Z", "authors": ["Rui Luo", "Zhixin Zhou"], "categories": ["cs.LG", "stat.ML"], "doi": "", "_query": "hedonic regression housing", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2106.00279v2", "title": "$L_0$ Isotonic Regression With Secondary Objectives", "abstract": "We provide algorithms for isotonic regression minimizing $L_0$ error (Hamming distance). This is also known as monotonic relabeling, and is applicable when labels have a linear ordering but not necessarily a metric. There may be exponentially many optimal relabelings, so we look at secondary criteria to determine which are best. For arbitrary ordinal labels the criterion is maximizing the number of labels which are only changed to an adjacent label (and recursively apply this). For real-valued labels we minimize the $L_p$ error. For linearly ordered sets we also give algorithms which minimize the sum of the $L_p$ and weighted $L_0$ errors, a form of penalized (regularized) regression. We also examine $L_0$ isotonic regression on multidimensional coordinate-wise orderings. Previous algorithms took $Θ(n^3)$ time, but we reduce this to $o(n^{3/2})$.", "published": "2021-06-01T07:19:17Z", "authors": ["Quentin F. Stout"], "categories": ["cs.DS"], "doi": "", "_query": "hedonic regression housing", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2203.05067v3", "title": "Universal Regression with Adversarial Responses", "abstract": "We provide algorithms for regression with adversarial responses under large classes of non-i.i.d. instance sequences, on general separable metric spaces, with provably minimal assumptions. We also give characterizations of learnability in this regression context. We consider universal consistency which asks for strong consistency of a learner without restrictions on the value responses. Our analysis shows that such an objective is achievable for a significantly larger class of instance sequences than stationary processes, and unveils a fundamental dichotomy between value spaces: whether finite-horizon mean estimation is achievable or not. We further provide optimistically universal learning rules, i.e., such that if they fail to achieve universal consistency, any other algorithms will fail as well. For unbounded losses, we propose a mild integrability condition under which there exist algorithms for adversarial regression under large classes of non-i.i.d. instance sequences. In addition, our analysis also provides a learning rule for mean estimation in general metric spaces that is consistent under adversarial responses without any moment conditions on the sequence, a result of independent interest.", "published": "2022-03-09T22:10:30Z", "authors": ["Moïse Blanchard", "Patrick Jaillet"], "categories": ["cs.LG", "stat.ML"], "doi": "", "_query": "hedonic regression housing", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2404.06471v2", "title": "Regression Discontinuity Design with Spillovers", "abstract": "This paper studies regression discontinuity designs (RDD) when linear-in-means spillovers occur between units that are close in their running variable. We show that the RDD estimand depends on the ratio of two terms: (1) the radius over which spillovers occur and (2) the choice of bandwidth used for the local linear regression. RDD estimates direct treatment effect when radius is of larger order than the bandwidth and total treatment effect when radius is of smaller order than the bandwidth. When the two are of similar order, the RDD estimand need not have a causal interpretation. To recover direct and spillover effects in the intermediate regime, we propose to incorporate estimated spillover terms into local linear regression. Our estimator is consistent and asymptotically normal and we provide bias-aware confidence intervals for direct treatment effects and spillovers. In the setting of Gonzalez (2021), we detect endogenous spillovers in voter fraud during the 2009 Afghan Presidential election. We also clarify when the donut-hole design addresses spillovers in RDD.", "published": "2024-04-09T17:21:41Z", "authors": ["Eric Auerbach", "Yong Cai", "Ahnaf Rafi"], "categories": ["econ.EM"], "doi": "", "_query": "hedonic regression housing", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/1010.6209v1", "title": "Nonparametric regression with martingale increment errors", "abstract": "We consider the problem of adaptive estimation of the regression function in a framework where we replace ergodicity assumptions (such as independence or mixing) by another structural assumption on the model. Namely, we propose adaptive upper bounds for kernel estimators with data-driven bandwidth (Lepski's selection rule) in a regression model where the noise is an increment of martingale. It includes, as very particular cases, the usual i.i.d. regression and auto-regressive models. The cornerstone tool for this study is a new result for self-normalized martingales, called ``stability'', which is of independent interest. In a first part, we only use the martingale increment structure of the noise. We give an adaptive upper bound using a random rate, that involves the occupation time near the estimation point. Thanks to this approach, the theoretical study of the statistical procedure is disconnected from usual ergodicity properties like mixing. Then, in a second part, we make a link with the usual minimax theory of deterministic rates. Under a beta-mixing assumption on the covariates process, we prove that the random rate considered in the first part is equivalent, with large probability, to a deterministic rate which is the usual minimax adaptive one.", "published": "2010-10-29T13:53:19Z", "authors": ["Sylvain Delattre", "Stéphane Gaïffas"], "categories": ["math.ST", "math.PR"], "doi": "", "_query": "hedonic regression housing", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/0906.3953v1", "title": "Principal Fitted Components for Dimension Reduction in Regression", "abstract": "We provide a remedy for two concerns that have dogged the use of principal components in regression: (i) principal components are computed from the predictors alone and do not make apparent use of the response, and (ii) principal components are not invariant or equivariant under full rank linear transformation of the predictors. The development begins with principal fitted components [Cook, R. D. (2007). Fisher lecture: Dimension reduction in regression (with discussion). Statist. Sci. 22 1--26] and uses normal models for the inverse regression of the predictors on the response to gain reductive information for the forward regression of interest. This approach includes methodology for testing hypotheses about the number of components and about conditional independencies among the predictors.", "published": "2009-06-22T08:42:24Z", "authors": ["R. Dennis Cook", "Liliana Forzani"], "categories": ["stat.ME"], "doi": "10.1214/08-STS275", "_query": "hedonic regression housing", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2601.13896v1", "title": "From geometry to sustainability: Optimal shapes of hip roof houses", "abstract": "In this paper, we develop a rigorous mathematical framework for the optimization of hip roof house geometry, with the primary goal of minimizing the external surface of the building envelope for a given set of design constraints. Five optimization scenarios are systematically analyzed: fixed volume, fixed footprint ratio, fixed slenderness ratio, fixed floor area, and constrained height. For each case, explicit formulas for the optimal dimensions are derived, offering architects and engineers practical guidelines for improving material efficiency, reducing construction costs, and enhancing energy performance. To illustrate the practical relevance of the theoretical results, case studies of real-world hip roof houses are presented, revealing both inefficiencies in common practice and near-optimal examples. Furthermore, a freely available software application has been developed to support designers in applying the optimization methods directly to architectural projects. The findings confirm that square-based footprints combined with balanced slenderness ratios yield the most efficient forms, while deviations toward elongated or flattened proportions significantly increase energy and material demands. This work demonstrates how mathematical modeling and architectural design can be integrated to support sustainable architecture, providing both theoretical insight and practical tools for shaping energy-efficient, cost-effective, and aesthetically coherent residential buildings.", "published": "2026-01-20T12:26:35Z", "authors": ["Ewa Rokita-Magdziarz", "Barbara Gronostajska", "Marcin Magdziarz"], "categories": ["math.OC"], "doi": "", "_query": "hedonic regression housing", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2104.00142v1", "title": "NodeSRT: A Selective Regression Testing Tool for Node.js Application", "abstract": "Node.js is one of the most popular frameworks for building web applications. As software systems mature, the cost of running their entire regression test suite can become significant. Selective Regression Testing (SRT) is a technique that executes only a subset of tests the regression test suite can detect software failures more efficiently. Previous SRT studies mainly focused on standard desktop applications. Node.js applications are considered hard to perform test reduction because of Node's asynchronous, event-driven programming model and because JavaScript is a dynamic programming language. In this paper, we present NodeSRT, a Selective Regression Testing framework for Node.js applications. By performing static and dynamic analysis, NodeSRT identifies the relationship between changed methods and tests, then reduces the regression test suite to only tests that are affected by the change to improve the execution time of the regression test suite. To evaluate our selection technique, we applied NodeSRT to two open-source projects: Uppy and Simorgh, then compared our approach with the retest-all strategy and current industry-standard SRT technique: Jest OnlyChange. The results demonstrate that NodeSRT correctly selects affected tests based on changes and is 250% faster, 450% more precise than the Jest OnlyChange.", "published": "2021-03-31T22:23:48Z", "authors": ["Yufeng Chen"], "categories": ["cs.SE"], "doi": "", "_query": "hedonic regression housing", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2310.19245v5", "title": "Efficient Shapley Performance Attribution for Least-Squares Regression", "abstract": "We consider the performance of a least-squares regression model, as judged by out-of-sample $R^2$. Shapley values give a fair attribution of the performance of a model to its input features, taking into account interdependencies between features. Evaluating the Shapley values exactly requires solving a number of regression problems that is exponential in the number of features, so a Monte Carlo-type approximation is typically used. We focus on the special case of least-squares regression models, where several tricks can be used to compute and evaluate regression models efficiently. These tricks give a substantial speed up, allowing many more Monte Carlo samples to be evaluated, achieving better accuracy. We refer to our method as least-squares Shapley performance attribution (LS-SPA), and describe our open-source implementation.", "published": "2023-10-30T03:24:50Z", "authors": ["Logan Bell", "Nikhil Devanathan", "Stephen Boyd"], "categories": ["stat.CO"], "doi": "10.1007/s11222-024-10459-9", "_query": "hedonic regression housing", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2110.05207v2", "title": "Phase-type distributions for claim severity regression modeling", "abstract": "This paper addresses the task of modeling severity losses using segmentation when the data distribution does not fall into the usual regression frameworks. This situation is not uncommon in lines of business such as third-party liability insurance, where heavy-tails and multimodality often hamper a direct statistical analysis. We propose to use regression models based on phase-type distributions, regressing on their underlying inhomogeneous Markov intensity and using an extension of the EM algorithm. These models are interpretable and tractable in terms of multi-state processes and generalize the proportional hazards specification when the dimension of the state space is larger than one. We show that the combination of matrix parameters, inhomogeneity transforms, and covariate information provides flexible regression models that effectively capture the entire distribution of loss severities.", "published": "2021-10-11T12:17:21Z", "authors": ["Martin Bladt"], "categories": ["stat.AP"], "doi": "", "_query": "hedonic regression housing", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/1502.03571v5", "title": "Weighted SGD for $\\ell_p$ Regression with Randomized Preconditioning", "abstract": "In recent years, stochastic gradient descent (SGD) methods and randomized linear algebra (RLA) algorithms have been applied to many large-scale problems in machine learning and data analysis. We aim to bridge the gap between these two methods in solving constrained overdetermined linear regression problems---e.g., $\\ell_2$ and $\\ell_1$ regression problems. We propose a hybrid algorithm named pwSGD that uses RLA techniques for preconditioning and constructing an importance sampling distribution, and then performs an SGD-like iterative process with weighted sampling on the preconditioned system. We prove that pwSGD inherits faster convergence rates that only depend on the lower dimension of the linear system, while maintaining low computation complexity. Particularly, when solving $\\ell_1$ regression with size $n$ by $d$, pwSGD returns an approximate solution with $ε$ relative error in the objective value in $\\mathcal{O}(\\log n \\cdot \\text{nnz}(A) + \\text{poly}(d)/ε^2)$ time. This complexity is uniformly better than that of RLA methods in terms of both $ε$ and $d$ when the problem is unconstrained. For $\\ell_2$ regression, pwSGD returns an approximate solution with $ε$ relative error in the objective value and the solution vector measured in prediction norm in $\\mathcal{O}(\\log n \\cdot \\text{nnz}(A) + \\text{poly}(d) \\log(1/ε) /ε)$ time. We also provide lower bounds on the coreset complexity for more general regression problems, indicating that still new ideas will be needed to extend similar RLA preconditioning ideas to weighted SGD algorithms for more general regression problems. Finally, the effectiveness of such algorithms is illustrated numerically on both synthetic and real datasets.", "published": "2015-02-12T09:11:58Z", "authors": ["Jiyan Yang", "Yin-Lam Chow", "Christopher Ré", "Michael W. Mahoney"], "categories": ["math.OC", "stat.ML"], "doi": "", "_query": "hedonic regression housing", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2201.03077v1", "title": "Information Borrowing in Regression Models", "abstract": "Model development often takes data structure, subject matter considerations, model assumptions, and goodness of fit into consideration. To diagnose issues with any of these factors, it can be helpful to understand regression model estimates at a more granular level. We propose a new method for decomposing point estimates from a regression model via weights placed on data clusters. The weights are informed only by the model specification and data availability and thus can be used to explicitly link the effects of data imbalance and model assumptions to actual model estimates. The weight matrix has been understood in linear models as the hat matrix in the existing literature. We extend it to Bayesian hierarchical regression models that incorporate prior information and complicated dependence structures through the covariance among random effects. We show that the model weights, which we call borrowing factors, generalize shrinkage and information borrowing to all regression models. In contrast, the focus of the hat matrix has been mainly on the diagonal elements indicating the amount of leverage. We also provide metrics that summarize the borrowing factors and are practically useful. We present the theoretical properties of the borrowing factors and associated metrics and demonstrate their usage in two examples. By explicitly quantifying borrowing and shrinkage, researchers can better incorporate domain knowledge and evaluate model performance and the impacts of data properties such as data imbalance or influential points.", "published": "2022-01-09T19:44:54Z", "authors": ["Amy Zhang", "Le Bao", "Michael J. Daniels"], "categories": ["stat.ME"], "doi": "", "_query": "hedonic regression housing", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/1508.01967v4", "title": "Robust and sparse estimators for linear regression models", "abstract": "Penalized regression estimators are a popular tool for the analysis of sparse and high-dimensional data sets. However, penalized regression estimators defined using an unbounded loss function can be very sensitive to the presence of outlying observations, especially high leverage outliers. Moreover, it can be particularly challenging to detect outliers in high-dimensional data sets. Thus, robust estimators for sparse and high-dimensional linear regression models are in need. In this paper, we study the robust and asymptotic properties of MM-Bridge and adaptive MM-Bridge estimators: $\\ell_q$-penalized MM-estimators of regression and MM-estimators with an adaptive $\\ell_t$ penalty. For the case of a fixed number of covariates, we derive the asymptotic distribution of MM-Bridge estimators for all $q>0$. We prove that for $q<1$ MM-Bridge estimators can have the oracle property defined in Fan and Li (2001). We prove that for all $t\\leq 1$ adaptive MM-Bridge estimators can have the oracle property. The advantages of our proposed estimators are demonstrated through an extensive simulation study and the analysis of a real high-dimensional data set.", "published": "2015-08-08T22:48:10Z", "authors": ["Ezequiel Smucler", "Víctor J. Yohai"], "categories": ["math.ST"], "doi": "", "_query": "hedonic regression housing", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2410.04384v1", "title": "Statistical Inference for Four-Regime Segmented Regression Models", "abstract": "Segmented regression models offer model flexibility and interpretability as compared to the global parametric and the nonparametric models, and yet are challenging in both estimation and inference. We consider a four-regime segmented model for temporally dependent data with segmenting boundaries depending on multivariate covariates with non-diminishing boundary effects. A mixed integer quadratic programming algorithm is formulated to facilitate the least square estimation of the regression and the boundary parameters. The rates of convergence and the asymptotic distributions of the least square estimators are obtained for the regression and the boundary coefficients, respectively. We propose a smoothed regression bootstrap to facilitate inference on the parameters and a model selection procedure to select the most suitable model within the model class with at most four segments. Numerical simulations and a case study on air pollution in Beijing are conducted to demonstrate the proposed approach, which shows that the segmented models with three or four regimes are suitable for the modeling of the meteorological effects on the PM2.5 concentration.", "published": "2024-10-06T07:43:57Z", "authors": ["Han Yan", "Song Xi Chen"], "categories": ["stat.ME"], "doi": "", "_query": "hedonic regression housing", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2102.13388v1", "title": "Zoetrope Genetic Programming for Regression", "abstract": "The Zoetrope Genetic Programming (ZGP) algorithm is based on an original representation for mathematical expressions, targeting evolutionary symbolic regression.The zoetropic representation uses repeated fusion operations between partial expressions, starting from the terminal set. Repeated fusions within an individual gradually generate more complex expressions, ending up in what can be viewed as new features. These features are then linearly combined to best fit the training data. ZGP individuals then undergo specific crossover and mutation operators, and selection takes place between parents and offspring. ZGP is validated using a large number of public domain regression datasets, and compared to other symbolic regression algorithms, as well as to traditional machine learning algorithms. ZGP reaches state-of-the-art performance with respect to both types of algorithms, and demonstrates a low computational time compared to other symbolic regression approaches.", "published": "2021-02-26T10:47:10Z", "authors": ["Aurélie Boisbunon", "Carlo Fanara", "Ingrid Grenet", "Jonathan Daeden", "Alexis Vighi", "Marc Schoenauer"], "categories": ["stat.ML", "cs.AI", "cs.LG", "cs.NE"], "doi": "10.1145/3449639.3459349", "_query": "hedonic regression housing", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/1908.03669v1", "title": "A Survey of Tuning Parameter Selection for High-dimensional Regression", "abstract": "Penalized (or regularized) regression, as represented by Lasso and its variants, has become a standard technique for analyzing high-dimensional data when the number of variables substantially exceeds the sample size. The performance of penalized regression relies crucially on the choice of the tuning parameter, which determines the amount of regularization and hence the sparsity level of the fitted model. The optimal choice of tuning parameter depends on both the structure of the design matrix and the unknown random error distribution (variance, tail behavior, etc). This article reviews the current literature of tuning parameter selection for high-dimensional regression from both theoretical and practical perspectives. We discuss various strategies that choose the tuning parameter to achieve prediction accuracy or support recovery. We also review several recently proposed methods for tuning-free high-dimensional regression.", "published": "2019-08-10T02:22:42Z", "authors": ["Yunan Wu", "Lan Wang"], "categories": ["stat.ME", "cs.LG", "stat.ML"], "doi": "", "_query": "hedonic regression housing", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/1408.5265v2", "title": "A Bayesian Ensemble Regression Framework on the Angry Birds Game", "abstract": "An ensemble inference mechanism is proposed on the Angry Birds domain. It is based on an efficient tree structure for encoding and representing game screenshots, where it exploits its enhanced modeling capability. This has the advantage to establish an informative feature space and modify the task of game playing to a regression analysis problem. To this direction, we assume that each type of object material and bird pair has its own Bayesian linear regression model. In this way, a multi-model regression framework is designed that simultaneously calculates the conditional expectations of several objects and makes a target decision through an ensemble of regression models. Learning procedure is performed according to an online estimation strategy for the model parameters. We provide comparative experimental results on several game levels that empirically illustrate the efficiency of the proposed methodology.", "published": "2014-08-22T11:15:25Z", "authors": ["Nikolaos Tziortziotis", "Georgios Papagiannis", "Konstantinos Blekas"], "categories": ["cs.AI"], "doi": "", "_query": "hedonic regression housing", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/math/0602292v1", "title": "Cross-validation in nonparametric regression with outliers", "abstract": "A popular data-driven method for choosing the bandwidth in standard kernel regression is cross-validation. Even when there are outliers in the data, robust kernel regression can be used to estimate the unknown regression curve [Robust and Nonlinear Time Series Analysis. Lecture Notes in Statist. (1984) 26 163--184]. However, under these circumstances standard cross-validation is no longer a satisfactory bandwidth selector because it is unduly influenced by extreme prediction errors caused by the existence of these outliers. A more robust method proposed here is a cross-validation method that discounts the extreme prediction errors. In large samples the robust method chooses consistent bandwidths, and the consistency of the method is practically independent of the form in which extreme prediction errors are discounted. Additionally, evaluation of the method's finite sample behavior in a simulation demonstrates that the proposed method performs favorably. This method can also be applied to other problems, for example, model selection, that require cross-validation.", "published": "2006-02-14T09:22:58Z", "authors": ["Denis Heng-Yan Leung"], "categories": ["math.ST"], "doi": "10.1214/009053605000000499", "_query": "hedonic regression housing", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2009.05176v1", "title": "A First Step Towards Distribution Invariant Regression Metrics", "abstract": "Regression evaluation has been performed for decades. Some metrics have been identified to be robust against shifting and scaling of the data but considering the different distributions of data is much more difficult to address (imbalance problem) even though it largely impacts the comparability between evaluations on different datasets. In classification, it has been stated repeatedly that performance metrics like the F-Measure and Accuracy are highly dependent on the class distribution and that comparisons between different datasets with different distributions are impossible. We show that the same problem exists in regression. The distribution of odometry parameters in robotic applications can for example largely vary between different recording sessions. Here, we need regression algorithms that either perform equally well for all function values, or that focus on certain boundary regions like high speed. This has to be reflected in the evaluation metric. We propose the modification of established regression metrics by weighting with the inverse distribution of function values $Y$ or the samples $X$ using an automatically tuned Gaussian kernel density estimator. We show on synthetic and robotic data in reproducible experiments that classical metrics behave wrongly, whereas our new metrics are less sensitive to changing distributions, especially when correcting by the marginal distribution in $X$. Our new evaluation concept enables the comparison of results between different datasets with different distributions. Furthermore, it can reveal overfitting of a regression algorithm to overrepresented target values. As an outcome, non-overfitting regression algorithms will be more likely chosen due to our corrected metrics.", "published": "2020-09-10T23:40:46Z", "authors": ["Mario Michael Krell", "Bilal Wehbe"], "categories": ["cs.LG", "cs.RO", "stat.ML"], "doi": "", "_query": "hedonic regression housing", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/1307.4834v3", "title": "Finding Regression Outliers With FastRCS", "abstract": "The Residual Congruent Subset (RCS) is a new method for finding outliers in the linear regression setting. Like many other outlier detection procedures, RCS searches for a subset which minimizes a criterion. The difference is that the new criterion was designed to be insensitive to the outliers. RCS is supported by FastRCS, a fast regression and affine equivariant algorithm which we also detail. Both an extensive simulation study and two real data applications show that FastRCS performs better than its competitors.", "published": "2013-07-18T05:24:34Z", "authors": ["Kaveh Vakili", "Eric Schmitt"], "categories": ["stat.ME"], "doi": "", "_query": "hedonic regression housing", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2110.15403v3", "title": "Selective Regression Under Fairness Criteria", "abstract": "Selective regression allows abstention from prediction if the confidence to make an accurate prediction is not sufficient. In general, by allowing a reject option, one expects the performance of a regression model to increase at the cost of reducing coverage (i.e., by predicting on fewer samples). However, as we show, in some cases, the performance of a minority subgroup can decrease while we reduce the coverage, and thus selective regression can magnify disparities between different sensitive subgroups. Motivated by these disparities, we propose new fairness criteria for selective regression requiring the performance of every subgroup to improve with a decrease in coverage. We prove that if a feature representation satisfies the sufficiency criterion or is calibrated for mean and variance, than the proposed fairness criteria is met. Further, we introduce two approaches to mitigate the performance disparity across subgroups: (a) by regularizing an upper bound of conditional mutual information under a Gaussian assumption and (b) by regularizing a contrastive loss for conditional mean and conditional variance prediction. The effectiveness of these approaches is demonstrated on synthetic and real-world datasets.", "published": "2021-10-28T19:05:12Z", "authors": ["Abhin Shah", "Yuheng Bu", "Joshua Ka-Wing Lee", "Subhro Das", "Rameswar Panda", "Prasanna Sattigeri", "Gregory W. Wornell"], "categories": ["cs.LG", "stat.ML"], "doi": "", "_query": "hedonic regression housing", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/1508.02841v1", "title": "Identifiability of logistic regression with homoscedastic error: Berkson model", "abstract": "We consider the Berkson model of logistic regression with Gaussian and homoscedastic error in regressor. The measurement error variance can be either known or unknown. We deal with both functional and structural cases. Sufficient conditions for identifiability of regression coefficients are presented. Conditions for identifiability of the model are studied. In the case where the error variance is known, the regression parameters are identifiable if the distribution of the observed regressor is not concentrated at a single point. In the case where the error variance is not known, the regression parameters are identifiable if the distribution of the observed regressor is not concentrated at three (or less) points. The key analytic tools are relations between the smoothed logistic distribution function and its derivatives.", "published": "2015-08-12T08:10:14Z", "authors": ["Sergiy Shklyar"], "categories": ["math.PR"], "doi": "10.15559/15-VMSTA27", "_query": "hedonic regression housing", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2602.14440v2", "title": "CAIRO: Decoupling Order from Scale in Regression", "abstract": "Standard regression methods typically optimize a single pointwise objective, such as mean squared error, which conflates the learning of ordering with the learning of scale. This coupling renders models vulnerable to outliers and heavy-tailed noise. We propose CAIRO (Calibrate After Initial Rank Ordering), a framework that decouples regression into two distinct stages. In the first stage, we learn a scoring function by minimizing a scale-invariant ranking loss; in the second, we recover the target scale via isotonic regression. We theoretically characterize a class of \"Optimal-in-Rank-Order\" objectives -- including variants of RankNet and Gini covariance -- and prove that they recover the ordering of the true conditional mean under mild assumptions. We further show that subsequent monotone calibration recovers the true regression function at the population level and mathematically guarantees that finite-sample predictions are strictly auto-calibrated. Empirically, CAIRO combines the representation learning of neural networks with the robustness of rank-based statistics. It matches the performance of state-of-the-art tree ensembles on tabular benchmarks and significantly outperforms standard regression objectives in regimes with heavy-tailed or heteroskedastic noise.", "published": "2026-02-16T03:50:05Z", "authors": ["Harri Vanhems", "Yue Zhao", "Peng Shi", "Archer Y. Yang"], "categories": ["stat.ME", "cs.LG", "stat.ML"], "doi": "", "_query": "hedonic regression housing", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2211.12595v1", "title": "Posterior Contraction and Testing for Multivariate Isotonic Regression", "abstract": "We consider the nonparametric regression problem with multiple predictors and an additive error, where the regression function is assumed to be coordinatewise nondecreasing. We propose a Bayesian approach to make an inference on the multivariate monotone regression function, obtain the posterior contraction rate, and construct a universally consistent Bayesian testing procedure for multivariate monotonicity. To facilitate posterior analysis, we set aside the shape restrictions temporarily, and endow a prior on blockwise constant regression functions with heights independently normally distributed. The unknown variance of the error term is either estimated by the marginal maximum likelihood estimate or is equipped with an inverse-gamma prior. Then the unrestricted block heights are a posteriori also independently normally distributed given the error variance, by conjugacy. To comply with the shape restrictions, we project samples from the unrestricted posterior onto the class of multivariate monotone functions, inducing the \"projection-posterior distribution\", to be used for making an inference. Under an $\\mathbb{L}_1$-metric, we show that the projection-posterior based on $n$ independent samples contracts around the true monotone regression function at the optimal rate $n^{-1/(2+d)}$. Then we construct a Bayesian test for multivariate monotonicity based on the posterior probability of a shrinking neighborhood of the class of multivariate monotone functions. We show that the test is universally consistent, that is, the level of the Bayesian test goes to zero, and the power at any fixed alternative goes to one. Moreover, we show that for a smooth alternative function, power goes to one as long as its distance to the class of multivariate monotone functions is at least of the order of the estimation error for a smooth function.", "published": "2022-11-22T21:43:06Z", "authors": ["Kang Wang", "Subhashis Ghosal"], "categories": ["math.ST"], "doi": "", "_query": "hedonic regression housing", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2207.05574v1", "title": "Envelopes and principal component regression", "abstract": "Envelope methods offer targeted dimension reduction for various models. The overarching goal is to improve efficiency in multivariate parameter estimation by projecting the data onto a lower-dimensional subspace known as the envelope. Envelope approaches have advantages in analyzing data with highly correlated variables, but their iterative Grassmannian optimization algorithms do not scale very well with ultra high-dimensional data. While the connections between envelopes and partial least squares in multivariate linear regression have promoted recent progress in high-dimensional studies of envelopes, we propose a more straightforward way of envelope modeling from a novel principal components regression perspective. The proposed procedure, Non-Iterative Envelope Component Estimation (NIECE), has excellent computational advantages over the iterative Grassmannian optimization alternatives in high dimensions. We develop a unified NIECE theory that bridges the gap between envelope methods and principal components in regression. The new theoretical insights also shed light on the envelope subspace estimation error as a function of eigenvalue gaps of two symmetric positive definite matrices used in envelope modeling. We apply the new theory and algorithm to several envelope models, including response and predictor reduction in multivariate linear models, logistic regression, and Cox proportional hazard model. Simulations and illustrative data analysis show the potential for NIECE to improve standard methods in linear and generalized linear models significantly.", "published": "2022-07-12T14:53:34Z", "authors": ["Xin Zhang", "Kai Deng", "Qing Mai"], "categories": ["stat.ME"], "doi": "", "_query": "hedonic regression housing", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/1811.01117v1", "title": "Semiparametric Mixture Regression with Unspecified Error Distributions", "abstract": "In fitting a mixture of linear regression models, normal assumption is traditionally used to model the error and then regression parameters are estimated by the maximum likelihood estimators (MLE). This procedure is not valid if the normal assumption is violated. To relax the normal assumption on the error distribution hence reduce the modeling bias, we propose semiparametric mixture of linear regression models with unspecified error distributions. We establish a more general identifiability result under weaker conditions than existing results, construct a class of new estimators, and establish their asymptotic properties. These asymptotic results also apply to many existing semiparametric mixture regression estimators whose asymptotic properties have remained unknown due to the inherent difficulties in obtaining them. Using simulation studies, we demonstrate the superiority of the proposed estimators over the MLE when the normal error assumption is violated and the comparability when the error is normal. Analysis of a newly collected Equine Infectious Anemia Virus data in 2017 is employed to illustrate the usefulness of the new estimator.", "published": "2018-11-02T22:55:32Z", "authors": ["Yanyuan Ma", "Shaoli Wang", "Lin Xu", "Weixin Yao"], "categories": ["stat.ME"], "doi": "", "_query": "hedonic regression housing", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2603.04632v2", "title": "Least trimmed squares regression with missing values and cellwise outliers", "abstract": "Regression is the workhorse of statistics, and is often faced with real data that contain outliers. When these are casewise outliers, that is, cases that are entirely wrong or belong to a different population, the issue can be remedied by existing casewise robust regression methods. It is another matter when cellwise outliers occur, that is, suspicious individual entries in the data matrix containing the regressors and the response. We propose a new regression method that is robust to both casewise and cellwise outliers, and handles missing values as well. Its construction allows for skewed distributions. We show that it obeys the first breakdown result for cellwise robust regression. It is also the first such method that is geared to making robust out-of-sample predictions. Its performance is studied by simulation, and it is illustrated on a substantial real dataset.", "published": "2026-03-04T21:46:37Z", "authors": ["Jakob Raymaekers", "Peter J. Rousseeuw"], "categories": ["stat.ME", "stat.CO"], "doi": "", "_query": "hedonic regression housing", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2604.17762v1", "title": "A Parameter-Centric View on Regression", "abstract": "Discussion on ``Regression by Composition'' by Farewell, Daniel, Stensrud, and Huitfeldt", "published": "2026-04-20T03:25:54Z", "authors": ["Jingxin Yan", "Lin Liu", "Oliver Dukes", "Qizhai Li", "Linbo Wang"], "categories": ["stat.OT"], "doi": "", "_query": "hedonic regression housing", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2304.07825v1", "title": "Regression and Algorithmic Information Theory", "abstract": "In this paper we prove a theorem about regression, in that the shortest description of a function consistent with a finite sample of data is less than the combined conditional Kolmogorov complexities over the data in the sample.", "published": "2023-04-16T16:30:38Z", "authors": ["Samuel Epstein"], "categories": ["cs.CC"], "doi": "", "_query": "hedonic regression housing", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2104.06135v4", "title": "Multivariate Deep Evidential Regression", "abstract": "There is significant need for principled uncertainty reasoning in machine learning systems as they are increasingly deployed in safety-critical domains. A new approach with uncertainty-aware neural networks (NNs), based on learning evidential distributions for aleatoric and epistemic uncertainties, shows promise over traditional deterministic methods and typical Bayesian NNs, yet several important gaps in the theory and implementation of these networks remain. We discuss three issues with a proposed solution to extract aleatoric and epistemic uncertainties from regression-based neural networks. The approach derives a technique by placing evidential priors over the original Gaussian likelihood function and training the NN to infer the hyperparameters of the evidential distribution. Doing so allows for the simultaneous extraction of both uncertainties without sampling or utilization of out-of-distribution data for univariate regression tasks. We describe the outstanding issues in detail, provide a possible solution, and generalize the deep evidential regression technique for multivariate cases.", "published": "2021-04-13T12:20:18Z", "authors": ["Nis Meinert", "Alexander Lavin"], "categories": ["cs.LG", "cs.NE", "stat.ML"], "doi": "", "_query": "hedonic regression housing", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2407.12348v3", "title": "MM Algorithms for Statistical Estimation in Quantile Regression", "abstract": "Quantile regression \\parencite{Koenker1978} is a robust and practically useful way to efficiently model quantile varying correlation and predict varied response quantiles of interest. This article constructs and tests MM algorithms, which are simple to code and have been suggested superior to some other prominent quantile regression methods in nonregularized problems \\parencite{Pietrosanu2017}, in an array of linear quantile regression settings. Simulation studies comparing MM to existing tested methods and applications to various real data sets have corroborated our algorithms' effectiveness.", "published": "2024-07-17T06:48:02Z", "authors": ["Yifan Cheng", "Anthony Yung Cheung Kuk"], "categories": ["stat.ME", "stat.AP", "stat.CO"], "doi": "", "_query": "hedonic regression housing", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/1512.05307v1", "title": "Implicit Regression: Detecting Constants and Inverse Relationships with Bivariate Random Error", "abstract": "In 2011, Wooten introduced Non-Response Analysis the founding theory in Implicit Regression where Implicit Regression treats the variables implicitly as codependent variables and not as an explicit function with dependent or independent variables as in standard regression. The motivation of this paper is to introduce methods of implicit regression to determine the constant nature of a variable or the interactive term, and address inverse relationship among measured variables with random error present in both directions.", "published": "2015-12-16T20:00:19Z", "authors": ["R. D. Wooten", "K. Baah", "J. D'Andrea"], "categories": ["stat.AP", "stat.OT"], "doi": "", "_query": "hedonic regression housing", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2111.00642v1", "title": "Posterior Inference for Quantile Regression: Adaptation to Sparsity", "abstract": "Quantile regression is a powerful data analysis tool that accommodates heterogeneous covariate-response relationships. We find that by coupling the asymmetric Laplace working likelihood with appropriate shrinkage priors, we can deliver posterior inference that automatically adapts to possible sparsity in quantile regression analysis. After a suitable adjustment on the posterior variance, the posterior inference provides asymptotically valid inference under heterogeneity. Furthermore, the proposed approach leads to oracle asymptotic efficiency for the active (nonzero) quantile regression coefficients and super-efficiency for the non-active ones. By avoiding the need to pursue dichotomous variable selection, the Bayesian computational framework demonstrates desirable inference stability with respect to tuning parameter selection. Our work helps to uncloak the value of Bayesian computational methods in frequentist inference for quantile regression.", "published": "2021-11-01T01:08:59Z", "authors": ["Yuanzhi Li", "Xuming He"], "categories": ["stat.ME", "math.ST"], "doi": "", "_query": "hedonic regression housing", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2401.12176v1", "title": "Broiler-Net: A Deep Convolutional Framework for Broiler Behavior Analysis in Poultry Houses", "abstract": "Detecting anomalies in poultry houses is crucial for maintaining optimal chicken health conditions, minimizing economic losses and bolstering profitability. This paper presents a novel real-time framework for analyzing chicken behavior in cage-free poultry houses to detect abnormal behaviors. Specifically, two significant abnormalities, namely inactive broiler and huddling behavior, are investigated in this study. The proposed framework comprises three key steps: (1) chicken detection utilizing a state-of-the-art deep learning model, (2) tracking individual chickens across consecutive frames with a fast tracker module, and (3) detecting abnormal behaviors within the video stream. Experimental studies are conducted to evaluate the efficacy of the proposed algorithm in accurately assessing chicken behavior. The results illustrate that our framework provides a precise and efficient solution for real-time anomaly detection, facilitating timely interventions to maintain chicken health and enhance overall productivity on poultry farms. Github: https://github.com/TaherehZarratEhsan/Chicken-Behavior-Analysis", "published": "2024-01-22T18:09:15Z", "authors": ["Tahereh Zarrat Ehsan", "Seyed Mehdi Mohtavipour"], "categories": ["cs.CV", "cs.AI"], "doi": "", "_query": "hedonic regression housing", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/1705.00330v1", "title": "Random evolutionary dynamics driven by fitness and house-of-cards mutations. Sampling formulae", "abstract": "We first revisit the multi-allelic mutation-fitness balance problem, especially when mutations obey a house of cards condition, where the discrete-time deterministic evolutionary dynamics of the allelic frequencies derives from a Shahshahani potential. We then consider multi-allelic Wright-Fisher stochastic models whose deviation to neutrality is from the Shahsha-hani mutation/selection potential. We next focus on the weak selection, weak mutation cases and, making use of a Gamma calculus, we compute the normalizing partition functions of the invariant probability densities appearing in their Wright-Fisher diffusive approximations. Using these results, Generalized Ewens sampling formulae (ESF) from the equilibrium distributions are derived. We start treating the ESF in the mixed mutation/selection potential case and then we restrict ourselves to the ESF in the simpler house-of-cards mutations only situation. We also address some issues concerning sampling problems from infinitely-many alleles weak limits.", "published": "2017-04-30T15:42:36Z", "authors": ["Thierry Huillet"], "categories": ["q-bio.PE", "cond-mat.stat-mech"], "doi": "10.1007/s10955-017-1802-2", "_query": "hedonic regression housing", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2512.12340v1", "title": "Quantile regression with generalized multiquadric loss function", "abstract": "Quantile regression (QR) is now widely used to analyze the effect of covariates on the conditional distribution of a response variable. It provides a more comprehensive picture of the relationship between a response and covariates compared with classical least squares regression. However, the non-differentiability of the check loss function precludes the use of gradient-based methods to solve the optimization problem in quantile regression estimation. To this end, This paper constructs a smoothed loss function based on multiquadric (MQ) function. The proposed loss function leads to a globally convex optimization problem that can be efficiently solved via (stochastic) gradient descent methods. As an example, we apply the Barzilai-Borwein gradient descent method to obtain the estimation of quantile regression. We establish the theoretical results of the proposed estimator under some regularity conditions, and compare it with other estimation methods using Monte Carlo simulations.", "published": "2025-12-13T14:13:42Z", "authors": ["Wenwu Gao", "Dongyi Zheng", "Hanbing Zhu"], "categories": ["stat.ME", "stat.AP"], "doi": "", "_query": "hedonic regression housing", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2009.02287v1", "title": "Regression analysis for covariate-adaptive randomization: A robust and efficient inference perspective", "abstract": "Linear regression is arguably the most fundamental statistical model; however, the validity of its use in randomized clinical trials, despite being common practice, has never been crystal clear, particularly when stratified or covariate-adaptive randomization is used. In this paper, we investigate several of the most intuitive and commonly used regression models for estimating and inferring the treatment effect in randomized clinical trials. By allowing the regression model to be arbitrarily misspecified, we demonstrate that all these regression-based estimators robustly estimate the treatment effect, albeit with possibly different efficiency. We also propose consistent non-parametric variance estimators and compare their performances to those of the model-based variance estimators that are readily available in standard statistical software. Based on the results and taking into account both theoretical efficiency and practical feasibility, we make recommendations for the effective use of regression under various scenarios. For equal allocation, it suffices to use the regression adjustment for the stratum covariates and additional baseline covariates, if available, with the usual ordinary-least-squares variance estimator. For unequal allocation, regression with treatment-by-covariate interactions should be used, together with our proposed variance estimators. These recommendations apply to simple and stratified randomization, and minimization, among others. We hope this work helps to clarify and promote the usage of regression in randomized clinical trials.", "published": "2020-09-04T16:27:54Z", "authors": ["Wei Ma", "Fuyi Tu", "Hanzhong Liu"], "categories": ["stat.ME"], "doi": "10.1002/sim.9585", "_query": "hedonic regression housing", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2410.08569v1", "title": "Linear Regression Using Quantum Annealing with Continuous Variables", "abstract": "Linear regression is a data analysis technique, which is categorized as supervised learning. By utilizing known data, we can predict unknown data. Recently, researchers have explored the use of quantum annealing (QA) to perform linear regression where parameters are approximated to discrete values using binary numbers. However, this approach has a limitation: we need to increase the number of qubits to improve the accuracy. Here, we propose a novel linear regression method using QA that leverages continuous variables. In particular, the boson system facilitates the optimization of linear regression without resorting to discrete approximations, as it directly manages continuous variables while engaging in QA. The major benefit of our new approach is that it can ensure accuracy without increasing the number of qubits as long as the adiabatic condition is satisfied.", "published": "2024-10-11T06:49:09Z", "authors": ["Asuka Koura", "Takashi Imoto", "Katsuki Ura", "Yuichiro Matsuzaki"], "categories": ["quant-ph", "physics.data-an"], "doi": "", "_query": "hedonic regression housing", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2003.11283v1", "title": "Boosting Ridge Regression for High Dimensional Data Classification", "abstract": "Ridge regression is a well established regression estimator which can conveniently be adapted for classification problems. One compelling reason is probably the fact that ridge regression emits a closed-form solution thereby facilitating the training phase. However in the case of high-dimensional problems, the closed-form solution which involves inverting the regularised covariance matrix is rather expensive to compute. The high computational demand of such operation also renders difficulty in constructing ensemble of ridge regressions. In this paper, we consider learning an ensemble of ridge regressors where each regressor is trained in its own randomly projected subspace. Subspace regressors are later combined via adaptive boosting methodology. Experiments based on five high-dimensional classification problems demonstrated the effectiveness of the proposed method in terms of learning time and in some cases improved predictive performance can be observed.", "published": "2020-03-25T09:07:05Z", "authors": ["Jakramate Bootkrajang"], "categories": ["cs.LG", "stat.ML"], "doi": "", "_query": "hedonic regression housing", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2305.12087v1", "title": "Semi-Supervised Graph Imbalanced Regression", "abstract": "Data imbalance is easily found in annotated data when the observations of certain continuous label values are difficult to collect for regression tasks. When they come to molecule and polymer property predictions, the annotated graph datasets are often small because labeling them requires expensive equipment and effort. To address the lack of examples of rare label values in graph regression tasks, we propose a semi-supervised framework to progressively balance training data and reduce model bias via self-training. The training data balance is achieved by (1) pseudo-labeling more graphs for under-represented labels with a novel regression confidence measurement and (2) augmenting graph examples in latent space for remaining rare labels after data balancing with pseudo-labels. The former is to identify quality examples from unlabeled data whose labels are confidently predicted and sample a subset of them with a reverse distribution from the imbalanced annotated data. The latter collaborates with the former to target a perfect balance using a novel label-anchored mixup algorithm. We perform experiments in seven regression tasks on graph datasets. Results demonstrate that the proposed framework significantly reduces the error of predicted graph properties, especially in under-represented label areas.", "published": "2023-05-20T04:11:00Z", "authors": ["Gang Liu", "Tong Zhao", "Eric Inae", "Tengfei Luo", "Meng Jiang"], "categories": ["cs.LG"], "doi": "", "_query": "hedonic regression housing", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2608.02628v3", "title": "Deep Divide-and-Reduce in Symbolic Regression", "abstract": "Symbolic regression (SR) aims to discover underlying mathematical expressions from data while preserving interpretability. Most existing learning-based SR methods primarily optimize expressions from observations without explicitly exploiting their structural mathematical properties. AI Feynman introduced a complementary paradigm that leverages such properties to recursively decompose complex expressions, but its decomposition criteria cover only restricted structural forms and its treatment of nested composition can require brute-force search over candidate sub-expressions. Building on this paradigm, we propose Deep Divide-and-Reduce in Symbolic Regression (DDRSR), a mathematically grounded framework that systematically generalizes expression decomposition and variable reduction. DDRSR extends translational symmetry to coefficient- and exponent-interfered forms, enables variable separation under overlapping variables and additive constant offsets, and generalizes the identification of nested compositional structures. We further characterize an intrinsic non-identifiability limitation of decomposition when no effective variable separation is induced. Experiments across multiple symbolic regression algorithms and benchmark datasets show that DDRSR identifies a broader range of decomposable structures than AI Feynman and overall improves downstream regression accuracy and exact-expression recovery.", "published": "2026-07-26T13:43:45Z", "authors": ["Yusong Deng", "Yanjie Li", "Xin Ning", "Lina Yu", "Liping Zhang", "Shu Wei", "Mingzhu Wan", "Min Wu", "Weijun Li"], "categories": ["cs.LG", "cs.AI"], "doi": "", "_query": "hedonic regression housing", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/1912.01241v1", "title": "A Hidden Variables Approach to Multilabel Logistic Regression", "abstract": "Multilabel classification is an important problem in a wide range of domains such as text categorization and music annotation. In this paper, we present a probabilistic model, Multilabel Logistic Regression with Hidden variables (MLRH), which extends the standard logistic regression by introducing hidden variables. Hidden variables make it possible to go beyond the conventional multiclass logistic regression by relaxing the one-hot-encoding constraint. We define a new joint distribution of labels and hidden variables which enables us to obtain one classifier for multilabel classification. Our experimental studies on a set of benchmark datasets demonstrate that the probabilistic model can achieve competitive performance compared with other multilabel learning algorithms.", "published": "2019-12-03T08:33:55Z", "authors": ["Jaemoon Lee", "Hoda Shajari"], "categories": ["cs.LG", "stat.ML"], "doi": "", "_query": "hedonic regression housing", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2601.10919v1", "title": "A Note on Harmonic Underspecification in Log-Normal Trigonometric Regression", "abstract": "Analysis of biological rhythm data often involves performing least squares trigonometric regression, which models the oscillations of a response over time as a sum of sinusoidal components. When the response is not normally distributed, an investigator will either transform the response before applying least squares trigonometric regression or extend trigonometric regression to a generalized linear model (GLM) framework. In this note, we compare these two approaches when the number of oscillation harmonics is underspecified. We assume data are sampled under an equispaced experimental design and that a log link function would be appropriate for a GLM. We show that when the response follows a generalized gamma distribution, least squares trigonometric regression with a log-transformed response, or log-normal trigonometric regression, produces unbiased parameter estimates for the oscillation harmonics, even when the number of oscillation harmonics is underspecified. In contrast, GLMs require correct specification to produce unbiased parameter estimates. We apply both methods to cortisol level data and find that only log-normal trigonometric regression produces parameter estimates that are invariant to the number of specified oscillation harmonics. Additionally, when a sufficiently large number of oscillation harmonics is specified, both methods produce identical parameter estimates for the oscillation harmonics.", "published": "2026-01-16T00:40:03Z", "authors": ["Michael T. Gorczyca"], "categories": ["stat.AP"], "doi": "", "_query": "hedonic regression housing", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2009.05108v2", "title": "Bayesian Geodesic Regression on Riemannian Manifolds", "abstract": "Geodesic regression has been proposed for fitting the geodesic curve. However, it cannot automatically choose the dimensionality of data. In this paper, we develop a Bayesian geodesic regression model on Riemannian manifolds (BGRM) model. To avoid the overfitting problem, we add a regularization term to control the effectiveness of the model. To automatically select the dimensionality, we develop a prior for the geodesic regression model, which can automatically select the number of relevant dimensions by driving unnecessary tangent vectors to zero. To show the validation of our model, we first apply it in the 3D synthetic sphere and 2D pentagon data. We then demonstrate the effectiveness of our model in reducing the dimensionality and analyzing shape variations of human corpus callosum and mandible data.", "published": "2020-08-24T00:42:58Z", "authors": ["Youshan Zhang"], "categories": ["cs.CV"], "doi": "", "_query": "hedonic regression housing", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/1903.07832v4", "title": "Low-Rank Discriminative Least Squares Regression for Image Classification", "abstract": "Latest least squares regression (LSR) methods mainly try to learn slack regression targets to replace strict zero-one labels. However, the difference of intra-class targets can also be highlighted when enlarging the distance between different classes, and roughly persuing relaxed targets may lead to the problem of overfitting. To solve above problems, we propose a low-rank discriminative least squares regression model (LRDLSR) for multi-class image classification. Specifically, LRDLSR class-wisely imposes low-rank constraint on the intra-class regression targets to encourage its compactness and similarity. Moreover, LRDLSR introduces an additional regularization term on the learned targets to avoid the problem of overfitting. These two improvements are helpful to learn a more discriminative projection for regression and thus achieving better classification performance. Experimental results over a range of image databases demonstrate the effectiveness of the proposed LRDLSR method.", "published": "2019-03-19T04:48:39Z", "authors": ["Zhe Chen", "Xiao-Jun Wu", "Josef Kittler"], "categories": ["cs.CV"], "doi": "", "_query": "hedonic regression housing", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2007.12313v2", "title": "Canonical thresholding for non-sparse high-dimensional linear regression", "abstract": "We consider a high-dimensional linear regression problem. Unlike many papers on the topic, we do not require sparsity of the regression coefficients; instead, our main structural assumption is a decay of eigenvalues of the covariance matrix of the data. We propose a new family of estimators, called the canonical thresholding estimators, which pick largest regression coefficients in the canonical form. The estimators admit an explicit form and can be linked to LASSO and Principal Component Regression (PCR). A theoretical analysis for both fixed design and random design settings is provided. Obtained bounds on the mean squared error and the prediction error of a specific estimator from the family allow to clearly state sufficient conditions on the decay of eigenvalues to ensure convergence. In addition, we promote the use of the relative errors, strongly linked with the out-of-sample $R^2$. The study of these relative errors leads to a new concept of joint effective dimension, which incorporates the covariance of the data and the regression coefficients simultaneously, and describes the complexity of a linear regression problem. Some minimax lower bounds are established to showcase the optimality of our procedure. Numerical simulations confirm good performance of the proposed estimators compared to the previously developed methods.", "published": "2020-07-24T01:12:18Z", "authors": ["Igor Silin", "Jianqing Fan"], "categories": ["math.ST", "stat.ME"], "doi": "", "_query": "hedonic regression housing", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/1708.06623v1", "title": "Finding Regressions in Projects under Version Control Systems", "abstract": "Version Control Systems (VCS) are frequently used to support development of large-scale software projects. A typical VCS repository of a large project can contain various intertwined branches consisting of a large number of commits. If some kind of unwanted behaviour (e.g. a bug in the code) is found in the project, it is desirable to find the commit that introduced it. Such commit is called a regression point. There are two main issues regarding the regression points. First, detecting whether the project after a certain commit is correct can be very expensive as it may include large-scale testing and/or some other forms of verification. It is thus desirable to minimise the number of such queries. Second, there can be several regression points preceding the actual commit; perhaps a bug was introduced in a certain commit, inadvertently fixed several commits later, and then reintroduced in a yet later commit. In order to fix the actual commit it is usually desirable to find the latest regression point. The currently used distributed VCS contain methods for regression identification, see e.g. the git bisect tool. In this paper, we present a new regression identification algorithm that outperforms the current tools by decreasing the number of validity queries. At the same time, our algorithm tends to find the latest regression points which is a feature that is missing in the state-of-the-art algorithms. The paper provides an experimental evaluation of the proposed algorithm and compares it to the state-of-the-art tool git bisect on a real data set.", "published": "2017-08-22T14:10:55Z", "authors": ["Jaroslav Bendik", "Nikola Benes", "Ivana Cerna"], "categories": ["cs.SE"], "doi": "", "_query": "hedonic regression housing", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/q-bio/0702054v1", "title": "Kernel matrix regression", "abstract": "We address the problem of filling missing entries in a kernel Gram matrix, given a related full Gram matrix. We attack this problem from the viewpoint of regression, assuming that the two kernel matrices can be considered as explanatory variables and response variables, respectively. We propose a variant of the regression model based on the underlying features in the reproducing kernel Hilbert space by modifying the idea of kernel canonical correlation analysis, and we estimate the missing entries by fitting this model to the existing samples. We obtain promising experimental results on gene network inference and protein 3D structure prediction from genomic datasets. We also discuss the relationship with the em-algorithm based on information geometry.", "published": "2007-02-26T07:19:34Z", "authors": ["Yoshihiro Yamanishi", "Jean-Philippe Vert"], "categories": ["q-bio.QM", "math.ST"], "doi": "", "_query": "hedonic regression housing", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/1404.4104v1", "title": "Sparse Bilinear Logistic Regression", "abstract": "In this paper, we introduce the concept of sparse bilinear logistic regression for decision problems involving explanatory variables that are two-dimensional matrices. Such problems are common in computer vision, brain-computer interfaces, style/content factorization, and parallel factor analysis. The underlying optimization problem is bi-convex; we study its solution and develop an efficient algorithm based on block coordinate descent. We provide a theoretical guarantee for global convergence and estimate the asymptotical convergence rate using the Kurdyka-Łojasiewicz inequality. A range of experiments with simulated and real data demonstrate that sparse bilinear logistic regression outperforms current techniques in several important applications.", "published": "2014-04-15T22:54:21Z", "authors": ["Jianing V. Shi", "Yangyang Xu", "Richard G. Baraniuk"], "categories": ["math.OC", "cs.CV", "cs.LG"], "doi": "", "_query": "hedonic regression housing", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2002.12860v1", "title": "Quantile Regularization: Towards Implicit Calibration of Regression Models", "abstract": "Recent works have shown that most deep learning models are often poorly calibrated, i.e., they may produce overconfident predictions that are wrong. It is therefore desirable to have models that produce predictive uncertainty estimates that are reliable. Several approaches have been proposed recently to calibrate classification models. However, there is relatively little work on calibrating regression models. We present a method for calibrating regression models based on a novel quantile regularizer defined as the cumulative KL divergence between two CDFs. Unlike most of the existing approaches for calibrating regression models, which are based on post-hoc processing of the model's output and require an additional dataset, our method is trainable in an end-to-end fashion without requiring an additional dataset. The proposed regularizer can be used with any training objective for regression. We also show that post-hoc calibration methods like Isotonic Calibration sometimes compound miscalibration whereas our method provides consistently better calibrations. We provide empirical results demonstrating that the proposed quantile regularizer significantly improves calibration for regression models trained using approaches, such as Dropout VI and Deep Ensembles.", "published": "2020-02-28T16:53:41Z", "authors": ["Saiteja Utpala", "Piyush Rai"], "categories": ["cs.LG", "stat.ML"], "doi": "", "_query": "hedonic regression housing", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/0707.1714v1", "title": "Sampling Algorithms and Coresets for Lp Regression", "abstract": "The Lp regression problem takes as input a matrix $A \\in \\Real^{n \\times d}$, a vector $b \\in \\Real^n$, and a number $p \\in [1,\\infty)$, and it returns as output a number ${\\cal Z}$ and a vector $x_{opt} \\in \\Real^d$ such that ${\\cal Z} = \\min_{x \\in \\Real^d} ||Ax -b||_p = ||Ax_{opt}-b||_p$. In this paper, we construct coresets and obtain an efficient two-stage sampling-based approximation algorithm for the very overconstrained ($n \\gg d$) version of this classical problem, for all $p \\in [1, \\infty)$. The first stage of our algorithm non-uniformly samples $\\hat{r}_1 = O(36^p d^{\\max\\{p/2+1, p\\}+1})$ rows of $A$ and the corresponding elements of $b$, and then it solves the Lp regression problem on the sample; we prove this is an 8-approximation. The second stage of our algorithm uses the output of the first stage to resample $\\hat{r}_1/ε^2$ constraints, and then it solves the Lp regression problem on the new sample; we prove this is a $(1+ε)$-approximation. Our algorithm unifies, improves upon, and extends the existing algorithms for special cases of Lp regression, namely $p = 1,2$. In course of proving our result, we develop two concepts--well-conditioned bases and subspace-preserving sampling--that are of independent interest.", "published": "2007-07-11T22:04:18Z", "authors": ["Anirban Dasgupta", "Petros Drineas", "Boulos Harb", "Ravi Kumar", "Michael W. Mahoney"], "categories": ["cs.DS"], "doi": "", "_query": "hedonic regression housing", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2501.03753v1", "title": "Fixed and Random Covariance Regression Analyses", "abstract": "Covariance regression analysis is an approach to linking the covariance of responses to a set of explanatory variables $X$, where $X$ can be a vector, matrix, or tensor. Most of the literature on this topic focuses on the \"Fixed-$X$\" setting and treats $X$ as nonrandom. By contrast, treating explanatory variables $X$ as random, namely the \"Random-$X$\" setting, is often more realistic in practice. This article aims to fill this gap in the literature on the estimation and model assessment theory for Random-$X$ covariance regression models. Specifically, we construct a new theoretical framework for studying the covariance estimators under the Random-$X$ setting, and we demonstrate that the quasi-maximum likelihood estimator and the weighted least squares estimator are both consistent and asymptotically normal. In addition, we develop pioneering work on the model assessment theory of covariance regression. In particular, we obtain the bias-variance decompositions for the expected test errors under both the Fixed-$X$ and Random-$X$ settings. We show that moving from a Fixed-$X$ to a Random-$X$ setting can increase both the bias and the variance in expected test errors. Subsequently, we propose estimators of the expected test errors under the Fixed-$X$ and Random-$X$ settings, which can be used to assess the performance of the competing covariance regression models. The proposed estimation and model assessment approaches are illustrated via extensive simulation experiments and an empirical study of stock returns in the US market.", "published": "2025-01-07T12:51:51Z", "authors": ["Tao Zou", "Wei Lan", "Runze Li", "Chih-Ling Tsai"], "categories": ["math.ST"], "doi": "", "_query": "hedonic regression housing", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/1510.04161v4", "title": "D-vine copula based quantile regression", "abstract": "Quantile regression, that is the prediction of conditional quantiles, has steadily gained importance in statistical modeling and financial applications. The authors introduce a new semiparametric quantile regression method based on sequentially fitting a likelihood optimal D-vine copula to given data resulting in highly flexible models with easily extractable conditional quantiles. As a subclass of regular vine copulas, D-vines enable the modeling of multivariate copulas in terms of bivariate building blocks, a so-called pair-copula construction (PCC). The proposed algorithm works fast and accurate even in high dimensions and incorporates an automatic variable selection by maximizing the conditional log-likelihood. Further, typical issues of quantile regression such as quantile crossing or transformations, interactions and collinearity of variables are automatically taken care of. In a simulation study the improved accuracy and saved computational time of the approach in comparison with established quantile regression methods is highlighted. An extensive financial application to international credit default swap (CDS) data including stress testing and Value-at-Risk (VaR) prediction demonstrates the usefulness of the proposed method.", "published": "2015-10-14T15:39:10Z", "authors": ["Daniel Kraus", "Claudia Czado"], "categories": ["stat.ME"], "doi": "", "_query": "hedonic regression housing", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/1911.07099v1", "title": "Bayesian Ordinal Quantile Regression with a Partially Collapsed Gibbs Sampler", "abstract": "Unlike standard linear regression, quantile regression captures the relationship between covariates and the conditional response distribution as a whole, rather than only the relationship between covariates and the expected value of the conditional response. However, while there are well-established quantile regression methods for continuous variables and some forms of discrete data, there is no widely accepted method for ordinal variables, despite their importance in many medical contexts. In this work, we describe two existing ordinal quantile regression methods and demonstrate their weaknesses. We then propose a new method, Bayesian ordinal quantile regression with a partially collapsed Gibbs sampler (BORPS). We show superior results using BORPS versus existing methods on an extensive set of simulations. We further illustrate the benefits of our method by applying BORPS to the Fragile Families and Child Wellbeing Study data to tease apart associations with early puberty among both genders. Software is available at: GitHub.com/igrabski/borps.", "published": "2019-11-16T21:02:50Z", "authors": ["Isabella N Grabski", "Roberta De Vito", "Barbara E Engelhardt"], "categories": ["stat.ME", "q-bio.QM"], "doi": "", "_query": "hedonic regression housing", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2603.01750v1", "title": "Practical Deep Heteroskedastic Regression", "abstract": "Uncertainty quantification (UQ) in deep learning regression is of wide interest, as it supports critical applications including sequential decision making and risk-sensitive tasks. In heteroskedastic regression, where the uncertainty of the target depends on the input, a common approach is to train a neural network that parameterizes the mean and the variance of the predictive distribution. Still, training deep heteroskedastic regression models poses practical challenges in the trade-off between uncertainty quantification and mean prediction, such as optimization difficulties, representation collapse, and variance overfitting. In this work we identify previously undiscussed fallacies and propose a simple and efficient procedure that addresses these challenges jointly by post-hoc fitting a variance model across the intermediate layers of a pretrained network on a hold-out dataset. We demonstrate that our method achieves on-par or state-of-the-art uncertainty quantification on several molecular graph datasets, without compromising mean prediction accuracy and remaining cheap to use at prediction time.", "published": "2026-03-02T11:19:32Z", "authors": ["Mikkel Jordahn", "Jonas Vestergaard Jensen", "James Harrison", "Michael Riis Andersen", "Mikkel N. Schmidt"], "categories": ["cs.LG"], "doi": "", "_query": "hedonic regression housing", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/1909.07178v1", "title": "Estimating change points in nonparametric time series regression models", "abstract": "In this paper we consider a regression model that allows for time series covariates as well as heteroscedasticity with a regression function that is modelled nonparametrically. We assume that the regression function changes at some unknown time $\\lfloor ns_0\\rfloor$, $s_0\\in(0,1)$, and our aim is to estimate the (rescaled) change point $s_0$. The considered estimator is based on a Kolmogorov-Smirnov functional of the marked empirical process of residuals. We show consistency of the estimator and prove a rate of convergence of $O_P(n^{-1})$ which in this case is clearly optimal as there are only $n$ points in the sequence. Additionally we investigate the case of lagged dependent covariates, that is, autoregression models with a change in the nonparametric (auto-) regression function and give a consistency result. The method of proof also allows for different kinds of functionals such that Cramér-von Mises type estimators can be considered similarly. The approach extends existing literature by allowing nonparametric models, time series data as well as heteroscedasticity. Finite sample simulations indicate the good performance of our estimator in regression as well as autoregression models and a real data example shows its applicability in practise.", "published": "2019-09-16T13:16:14Z", "authors": ["Maria Mohr", "Leonie Selk"], "categories": ["math.ST"], "doi": "", "_query": "hedonic regression housing", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/1612.05179v4", "title": "Regression assisted inference for the average treatment effect in paired experiments", "abstract": "In paired randomized experiments individuals in a given matched pair may differ on prognostically important covariates despite the best efforts of practitioners. We examine the use of regression adjustment as a way to correct for persistent covariate imbalances after randomization, and present two regression assisted estimators for the sample average treatment effect in paired experiments. Using the potential outcomes framework, we prove that these estimators are consistent for the sample average treatment effect under mild regularity conditions even if the regression model is improperly specified. Further, we describe how asymptotically conservative confidence intervals can be constructed. We demonstrate that the variances of the regression assisted estimators are at least as small as that of the standard difference-in-means estimator asymptotically. Through a simulation study, we illustrate the appropriateness of the proposed methods in small and moderate samples. The analysis does not require a superpopulation model, a constant treatment effect, or the truth of the regression model, and hence provides a mode of inference for the sample average treatment effect with the potential to yield improvements in the power of the resulting analysis over the classical analysis without imposing potentially unrealistic assumptions.", "published": "2016-12-15T18:17:34Z", "authors": ["Colin B. Fogarty"], "categories": ["stat.ME"], "doi": "", "_query": "hedonic regression housing", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/1302.6088v1", "title": "An efficient algorithm for structured sparse quantile regression", "abstract": "Quantile regression is studied in combination with a penalty which promotes structured (or group) sparsity. A mixed $\\ell_{1,\\infty}$-norm on the parameter vector is used to impose structured sparsity on the traditional quantile regression problem. An algorithm is derived to calculate the piece-wise linear solution path of the corresponding minimization problem. A Matlab implementation of the proposed algorithm is provided and some applications of the methods are also studied.", "published": "2013-02-25T13:34:48Z", "authors": ["Vahid Nassiri", "Ignace Loris"], "categories": ["stat.ME"], "doi": "", "_query": "hedonic regression housing", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/1605.09026v1", "title": "Singular ridge regression with homoscedastic residuals: generalization error with estimated parameters", "abstract": "This paper characterizes the conditional distribution properties of the finite sample ridge regression estimator and uses that result to evaluate total regression and generalization errors that incorporate the inaccuracies committed at the time of parameter estimation. The paper provides explicit formulas for those errors. Unlike other classical references in this setup, our results take place in a fully singular setup that does not assume the existence of a solution for the non-regularized regression problem. In exchange, we invoke a conditional homoscedasticity hypothesis on the regularized regression residuals that is crucial in our developments.", "published": "2016-05-29T16:45:23Z", "authors": ["Lyudmila Grigoryeva", "Juan-Pablo Ortega"], "categories": ["stat.ML", "stat.ME"], "doi": "", "_query": "hedonic regression housing", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2504.19231v2", "title": "Test Set Sizing for the Ridge Regression", "abstract": "We derive the ideal train/test split for the ridge regression to high accuracy in the limit that the number of training rows m becomes large. The split must depend on the ridge tuning parameter, alpha, but we find that the dependence is weak and can asymptotically be ignored; all parameters vanish except for m and the number of features, n, which is held constant. This is the first time that such a split is calculated mathematically for a machine learning model in the large data limit. The goal of the calculations is to maximize \"integrity,\" so that the measured error in the trained model is as close as possible to what it theoretically should be. This paper's result for the ridge regression split matches prior art for the plain vanilla linear regression split to the first two terms asymptotically.", "published": "2025-04-27T13:17:18Z", "authors": ["Alexander Dubbs"], "categories": ["stat.ML", "cs.LG", "math.PR"], "doi": "", "_query": "hedonic regression housing", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2510.14822v1", "title": "Regression Model Selection Under General Conditions", "abstract": "Model selection criteria are one of the most important tools in statistics. Proofs showing a model selection criterion is asymptotically optimal are tailored to the type of model (linear regression, quantile regression, penalized regression, etc.), the estimation method (linear smoothers, maximum likelihood, generalized method of moments, etc.), the type of data (i.i.d., dependent, high dimensional, etc.), and the type of model selection criterion. Moreover, assumptions are often restrictive and unrealistic making it a slow and winding process for researchers to determine if a model selection criterion is selecting an optimal model. This paper provides general proofs showing asymptotic optimality for a wide range of model selection criteria under general conditions. This paper not only asymptotically justifies model selection criteria for most situations, but it also unifies and extends a range of previously disparate results.", "published": "2025-10-16T16:00:47Z", "authors": ["Amaze Lusompa"], "categories": ["math.ST", "econ.EM", "stat.ME"], "doi": "", "_query": "hedonic regression housing", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2212.03704v1", "title": "Semiparametric Distribution Regression with Instruments and Monotonicity", "abstract": "This paper proposes IV-based estimators for the semiparametric distribution regression model in the presence of an endogenous regressor, which are based on an extension of IV probit estimators. We discuss the causal interpretation of the estimators and two methods (monotone rearrangement and isotonic regression) to ensure a monotonically increasing distribution function. Asymptotic properties and simulation evidence are provided. An application to wage equations reveals statistically significant and heterogeneous differences to the inconsistent OLS-based estimator.", "published": "2022-12-07T15:23:47Z", "authors": ["Dominik Wied"], "categories": ["econ.EM"], "doi": "", "_query": "hedonic regression housing", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/1705.05125v1", "title": "Extended T-process Regression Models", "abstract": "Gaussian process regression (GPR) model has been widely used to fit data when the regression function is unknown and its nice properties have been well established. In this article, we introduce an extended t-process regression (eTPR) model, a nonlinear model which allows a robust best linear unbiased predictor (BLUP). Owing to its succinct construction, it inherits many attractive properties from the GPR model, such as having closed forms of marginal and predictive distributions to give an explicit form for robust procedures, and easy to cope with large dimensional covariates with an efficient implementation. Properties of the robustness are studied. Simulation studies and real data applications show that the eTPR model gives a robust fit in the presence of outliers in both input and output spaces and has a good performance in prediction, compared with other existed methods.", "published": "2017-05-15T09:13:34Z", "authors": ["Z. Wang", "J. Q. Shi", "Y. Lee"], "categories": ["stat.ME"], "doi": "", "_query": "hedonic regression housing", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/1603.09706v4", "title": "Bayesian density regression for discrete outcomes", "abstract": "We develop Bayesian models for density regression with emphasis on discrete outcomes. The problem of density regression is approached by considering methods for multivariate density estimation of mixed scale variables, and obtaining conditional densities from the multivariate ones. The approach to multivariate mixed scale outcome density estimation that we describe represents discrete variables, either responses or covariates, as discretised versions of continuous latent variables. We present and compare several models for obtaining these thresholds in the challenging context of count data analysis where the response may be over- and/or under-dispersed in some of the regions of the covariate space. We utilise a nonparametric mixture of multivariate Gaussians to model the directly observed and the latent continuous variables. The paper presents a Markov chain Monte Carlo algorithm for posterior sampling, sufficient conditions for weak consistency, and illustrations on density, mean and quantile regression utilizing simulated and real datasets.", "published": "2016-03-31T18:25:27Z", "authors": ["Georgios Papageorgiou"], "categories": ["stat.ME"], "doi": "10.1111/anzs.12273", "_query": "hedonic regression housing", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2312.11286v1", "title": "Envy-free House Allocation under Uncertain Preferences", "abstract": "We study the envy-free house allocation problem when agents have uncertain preferences over items and consider several well-studied preference uncertainty models. The central problem that we focus on is computing an allocation that has the highest probability of being envy-free. We show that each model leads to a distinct set of algorithmic and complexity results, including detailed results on (in-)approximability. En route, we consider two related problems of checking whether there exists an allocation that is possibly or necessarily envy-free. We give a complete picture of the computational complexity of these two problems for all the uncertainty models we consider.", "published": "2023-12-18T15:26:49Z", "authors": ["Haris Aziz", "Isaiah Iliffe", "Bo Li", "Angus Ritossa", "Ankang Sun", "Mashbat Suzuki"], "categories": ["cs.GT"], "doi": "", "_query": "hedonic regression housing", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/1705.04293v4", "title": "Bayesian Approaches to Distribution Regression", "abstract": "Distribution regression has recently attracted much interest as a generic solution to the problem of supervised learning where labels are available at the group level, rather than at the individual level. Current approaches, however, do not propagate the uncertainty in observations due to sampling variability in the groups. This effectively assumes that small and large groups are estimated equally well, and should have equal weight in the final regression. We account for this uncertainty with a Bayesian distribution regression formalism, improving the robustness and performance of the model when group sizes vary. We frame our models in a neural network style, allowing for simple MAP inference using backpropagation to learn the parameters, as well as MCMC-based inference which can fully propagate uncertainty. We demonstrate our approach on illustrative toy datasets, as well as on a challenging problem of predicting age from images.", "published": "2017-05-11T17:20:07Z", "authors": ["Ho Chung Leon Law", "Danica J. Sutherland", "Dino Sejdinovic", "Seth Flaxman"], "categories": ["stat.ML", "cs.LG"], "doi": "", "_query": "hedonic regression housing", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/1502.01488v1", "title": "Effects of associated kernels in nonparametric multiple regressions", "abstract": "Associated kernels have been introduced to improve the classical continuous kernels for smoothing any functional on several kinds of supports such as bounded continuous and discrete sets. This work deals with the effects of combined associated kernels on nonparametric multiple regression functions. Using the Nadaraya-Watson estimator with optimal bandwidth matrices selected by cross-validation procedure, different behaviours of multiple regression estimations are pointed out according the type of multivariate associated kernels with correlation or not. Through simulation studies, there are no effect of correlation structures for the continuous regression functions and also for the associated continuous kernels; however, there exist really effects of the choice of multivariate associated kernels following the support of the multiple regression functions bounded continuous or discrete. Applications are made on two real datasets.", "published": "2015-02-05T10:18:31Z", "authors": ["Sobom M. Somé", "Célestin C. Kokonendji"], "categories": ["math.ST"], "doi": "10.1080/15598608.2016.1160010", "_query": "hedonic regression housing", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/1909.12297v4", "title": "Energy-Based Models for Deep Probabilistic Regression", "abstract": "While deep learning-based classification is generally tackled using standardized approaches, a wide variety of techniques are employed for regression. In computer vision, one particularly popular such technique is that of confidence-based regression, which entails predicting a confidence value for each input-target pair (x,y). While this approach has demonstrated impressive results, it requires important task-dependent design choices, and the predicted confidences lack a natural probabilistic meaning. We address these issues by proposing a general and conceptually simple regression method with a clear probabilistic interpretation. In our proposed approach, we create an energy-based model of the conditional target density p(y|x), using a deep neural network to predict the un-normalized density from (x,y). This model of p(y|x) is trained by directly minimizing the associated negative log-likelihood, approximated using Monte Carlo sampling. We perform comprehensive experiments on four computer vision regression tasks. Our approach outperforms direct regression, as well as other probabilistic and confidence-based methods. Notably, our model achieves a 2.2% AP improvement over Faster-RCNN for object detection on the COCO dataset, and sets a new state-of-the-art on visual tracking when applied for bounding box estimation. In contrast to confidence-based methods, our approach is also shown to be directly applicable to more general tasks such as age and head-pose estimation. Code is available at https://github.com/fregu856/ebms_regression.", "published": "2019-09-26T17:58:43Z", "authors": ["Fredrik K. Gustafsson", "Martin Danelljan", "Goutam Bhat", "Thomas B. Schön"], "categories": ["cs.LG", "cs.CV", "stat.ML"], "doi": "", "_query": "hedonic regression housing", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2610.22851v1", "title": "Joint Estimation and Feature Selection in Overspecified Finite Mixture of Regressions", "abstract": "We investigate joint estimation and feature selection in sparse finite mixtures of regressions with an overspecified number of mixture components or mixture order. More specifically, given a conservative fixed upper bound on the unknown mixture order, we deliberately overspecify the order and propose a double-penalized likelihood approach for simultaneous parameter estimation and feature selection. We establish consistent recovery of the sparsity structure of the underlying mixing measure in terms of its regression coefficients, showing that the true sparsity of the mixture regression can be consistently identified even under order over-specification. The proposed methodology incorporates two complementary penalties: the first penalizes small (near-zero) mixing proportions, inducing an asymptotic Voronoi-type partition of the estimated regression vectors into clusters associated with the true sparse regression vectors; the second penalizes regression coefficients, resulting in sparsity through the selection of relevant features. Although the sparsity penalty is applied to individual regression coefficients, the combined effect of the two penalties yields an asymptotic group feature selection property where the same features tend to be selected or not selected within each Voronoi cluster. We develop a modified EM algorithm combined with proximal gradient for numerical implementations. Extensive simulations and a real data application demonstrate the effectiveness of the proposed approach.", "published": "2026-10-16T02:51:36Z", "authors": ["Abbas Khalili", "Pengqi Liu", "Huy Nguyen", "Nhat Ho"], "categories": ["stat.ME"], "doi": "", "_query": "hedonic regression housing", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/1503.08348v1", "title": "Sparse Linear Regression With Missing Data", "abstract": "This paper proposes a fast and accurate method for sparse regression in the presence of missing data. The underlying statistical model encapsulates the low-dimensional structure of the incomplete data matrix and the sparsity of the regression coefficients, and the proposed algorithm jointly learns the low-dimensional structure of the data and a linear regressor with sparse coefficients. The proposed stochastic optimization method, Sparse Linear Regression with Missing Data (SLRM), performs an alternating minimization procedure and scales well with the problem size. Large deviation inequalities shed light on the impact of the various problem-dependent parameters on the expected squared loss of the learned regressor. Extensive simulations on both synthetic and real datasets show that SLRM performs better than competing algorithms in a variety of contexts.", "published": "2015-03-28T21:03:32Z", "authors": ["Ravi Ganti", "Rebecca M. Willett"], "categories": ["stat.ML", "cs.LG", "stat.ME"], "doi": "", "_query": "hedonic regression housing", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/1612.03257v2", "title": "Models as Approximations II: A Model-Free Theory of Parametric Regression", "abstract": "We develop a model-free theory of general types of parametric regression for iid observations. The theory replaces the parameters of parametric models with statistical functionals, to be called \"regression functionals'', defined on large non-parametric classes of joint $\\xy$ distributions, without assuming a correct model. Parametric models are reduced to heuristics to suggest plausible objective functions. An example of a regression functional is the vector of slopes of linear equations fitted by OLS to largely arbitrary $\\xy$ distributions, without assuming a linear model (see Part~I). More generally, regression functionals can be defined by minimizing objective functions or solving estimating equations at joint $\\xy$ distributions. In this framework it is possible to achieve the following: (1)~define a notion of well-specification for regression functionals that replaces the notion of correct specification of models, (2)~propose a well-specification diagnostic for regression functionals based on reweighting distributions and data, (3)~decompose sampling variability of regression functionals into two sources, one due to the conditional response distribution and another due to the regressor distribution interacting with misspecification, both of order $N^{-1/2}$, (4)~exhibit plug-in/sandwich estimators of standard error as limit cases of $\\xy$ bootstrap estimators, and (5)~provide theoretical heuristics to indicate that $\\xy$ bootstrap standard errors may generally be more stable than sandwich estimators.", "published": "2016-12-10T05:53:13Z", "authors": ["Andreas Buja", "Lawrence Brown", "Arun Kumar Kuchibhotla", "Richard Berk", "Ed George", "Linda Zhao"], "categories": ["math.ST"], "doi": "", "_query": "hedonic regression housing", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/1510.08029v3", "title": "Sharp oracle inequalities for Least Squares estimators in shape restricted regression", "abstract": "The performance of Least Squares (LS) estimators is studied in isotonic, unimodal and convex regression. Our results have the form of sharp oracle inequalities that account for the model misspecification error. In isotonic and unimodal regression, the LS estimator achieves the nonparametric rate $n^{-2/3}$ as well as a parametric rate of order $k/n$ up to logarithmic factors, where $k$ is the number of constant pieces of the true parameter. In univariate convex regression, the LS estimator satisfies an adaptive risk bound of order $q/n$ up to logarithmic factors, where $q$ is the number of affine pieces of the true regression function. This adaptive risk bound holds for any design points. While Guntuboyina and Sen (2013) established that the nonparametric rate of convex regression is of order $n^{-4/5}$ for equispaced design points, we show that the nonparametric rate of convex regression can be as slow as $n^{-2/3}$ for some worst-case design points. This phenomenon can be explained as follows: Although convexity brings more structure than unimodality, for some worst-case design points this extra structure is uninformative and the nonparametric rates of unimodal regression and convex regression are both $n^{-2/3}$.", "published": "2015-10-27T18:55:32Z", "authors": ["Pierre C. Bellec"], "categories": ["math.ST"], "doi": "", "_query": "hedonic regression housing", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2205.13734v2", "title": "An efficient tensor regression for high-dimensional data", "abstract": "Most currently used tensor regression models for high-dimensional data are based on Tucker decomposition, which has good properties but loses its efficiency in compressing tensors very quickly as the order of tensors increases, say greater than four or five. However, for the simplest tensor autoregression in handling time series data, its coefficient tensor already has the order of six. This paper revises a newly proposed tensor train (TT) decomposition and then applies it to tensor regression such that a nice statistical interpretation can be obtained. The new tensor regression can well match the data with hierarchical structures, and it even can lead to a better interpretation for the data with factorial structures, which are supposed to be better fitted by models with Tucker decomposition. More importantly, the new tensor regression can be easily applied to the case with higher order tensors since TT decomposition can compress the coefficient tensors much more efficiently. The methodology is also extended to tensor autoregression for time series data, and nonasymptotic properties are derived for the ordinary least squares estimations of both tensor regression and autoregression. A new algorithm is introduced to search for estimators, and its theoretical justification is also discussed. Theoretical and computational properties of the proposed methodology are verified by simulation studies, and the advantages over existing methods are illustrated by two real examples.", "published": "2022-05-27T02:58:51Z", "authors": ["Yuefeng Si", "Yingying Zhang", "Yuxi Cai", "Chunling Liu", "Guodong Li"], "categories": ["stat.ME"], "doi": "", "_query": "hedonic regression housing", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/math/0503677v1", "title": "Optimal designs for a class of nonlinear regression models", "abstract": "For a broad class of nonlinear regression models we investigate the local E- and c-optimal design problem. It is demonstrated that in many cases the optimal designs with respect to these optimality criteria are supported at the Chebyshev points, which are the local extrema of the equi-oscillating best approximation of the function f_0\\equiv 0 by a normalized linear combination of the regression functions in the corresponding linearized model. The class of models includes rational, logistic and exponential models and for the rational regression models the E- and c-optimal design problem is solved explicitly in many cases.", "published": "2005-03-29T12:21:24Z", "authors": ["Holger Dette", "Viatcheslav B. Melas", "Andrey Pepelyshev"], "categories": ["math.ST"], "doi": "10.1214/009053604000000382", "_query": "hedonic regression housing", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/math/0610852v1", "title": "Modeling inequality and spread in multiple regression", "abstract": "We consider concepts and models for measuring inequality in the distribution of resources with a focus on how inequality varies as a function of covariates. Lorenz introduced a device for measuring inequality in the distribution of income that indicates how much the incomes below the u$^{th}$ quantile fall short of the egalitarian situation where everyone has the same income. Gini introduced a summary measure of inequality that is the average over u of the difference between the Lorenz curve and its values in the egalitarian case. More generally, measures of inequality are useful for other response variables in addition to income, e.g. wealth, sales, dividends, taxes, market share and test scores. In this paper we show that a generalized van Zwet type dispersion ordering for distributions of positive random variables induces an ordering on the Lorenz curve, the Gini coefficient and other measures of inequality. We use this result and distributional orderings based on transformations of distributions to motivate parametric and semiparametric models whose regression coefficients measure effects of covariates on inequality. In particular, we extend a parametric Pareto regression model to a flexible semiparametric regression model and give partial likelihood estimates of the regression coefficients and a baseline distribution that can be used to construct estimates of the various conditional measures of inequality.", "published": "2006-10-27T15:20:29Z", "authors": ["Rolf Aaberge", "Steinar Bjerve", "Kjell Doksum"], "categories": ["math.ST", "q-fin.GN"], "doi": "10.1214/074921706000000428", "_query": "hedonic regression housing", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2507.14023v3", "title": "Conformalized Regression for Continuous Bounded Outcomes", "abstract": "Regression problems with continuous bounded outcomes frequently arise in statistical and machine learning applications, such as the analysis of rates and proportions. A central challenge in this setting is predicting the response at a new covariate value. Most of the existing literature has focused either on point prediction or on interval prediction based on asymptotic approximations. We develop conformal prediction intervals for bounded outcomes within the framework of transformation regression models, encompassing widely used models such as beta regression and logit-normal regression. We construct non-conformity scores based on model-aligned residuals and identify a quantile-residual score that is particularly well suited to bounded outcomes, bridging normalized conformal prediction and distributional conformal prediction. This score accounts for both the heteroscedasticity inherent in such data and the asymmetry that emerges near the boundaries of the response space. We establish marginal validity and asymptotic conditional validity for both full and split conformal prediction, holding under model misspecification. A comprehensive simulation study confirms that both methods empirically attain valid finite-sample coverage, including cases under model misspecification. A real-data application demonstrates their practical performance against bootstrap-based alternatives.", "published": "2025-07-18T15:51:48Z", "authors": ["Zhanli Wu", "Fabrizio Leisen", "F. Javier Rubio"], "categories": ["stat.ML", "cs.LG", "stat.ME"], "doi": "", "_query": "hedonic regression housing", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/1703.08289v1", "title": "Deep Direct Regression for Multi-Oriented Scene Text Detection", "abstract": "In this paper, we first provide a new perspective to divide existing high performance object detection methods into direct and indirect regressions. Direct regression performs boundary regression by predicting the offsets from a given point, while indirect regression predicts the offsets from some bounding box proposals. Then we analyze the drawbacks of the indirect regression, which the recent state-of-the-art detection structures like Faster-RCNN and SSD follows, for multi-oriented scene text detection, and point out the potential superiority of direct regression. To verify this point of view, we propose a deep direct regression based method for multi-oriented scene text detection. Our detection framework is simple and effective with a fully convolutional network and one-step post processing. The fully convolutional network is optimized in an end-to-end way and has bi-task outputs where one is pixel-wise classification between text and non-text, and the other is direct regression to determine the vertex coordinates of quadrilateral text boundaries. The proposed method is particularly beneficial for localizing incidental scene texts. On the ICDAR2015 Incidental Scene Text benchmark, our method achieves the F1-measure of 81%, which is a new state-of-the-art and significantly outperforms previous approaches. On other standard datasets with focused scene texts, our method also reaches the state-of-the-art performance.", "published": "2017-03-24T05:54:11Z", "authors": ["Wenhao He", "Xu-Yao Zhang", "Fei Yin", "Cheng-Lin Liu"], "categories": ["cs.CV"], "doi": "", "_query": "hedonic regression housing", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2104.10999v1", "title": "Personalizing Performance Regression Models to Black-Box Optimization Problems", "abstract": "Accurately predicting the performance of different optimization algorithms for previously unseen problem instances is crucial for high-performing algorithm selection and configuration techniques. In the context of numerical optimization, supervised regression approaches built on top of exploratory landscape analysis are becoming very popular. From the point of view of Machine Learning (ML), however, the approaches are often rather naive, using default regression or classification techniques without proper investigation of the suitability of the ML tools. With this work, we bring to the attention of our community the possibility to personalize regression models to specific types of optimization problems. Instead of aiming for a single model that works well across a whole set of possibly diverse problems, our personalized regression approach acknowledges that different models may suite different types of problems. Going one step further, we also investigate the impact of selecting not a single regression model per problem, but personalized ensembles. We test our approach on predicting the performance of numerical optimization heuristics on the BBOB benchmark collection.", "published": "2021-04-22T11:47:47Z", "authors": ["Tome Eftimov", "Anja Jankovic", "Gorjan Popovski", "Carola Doerr", "Peter Korošec"], "categories": ["cs.NE"], "doi": "10.1145/3449639.3459407", "_query": "hedonic regression housing", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2511.07183v1", "title": "Unlocking the Regression Space", "abstract": "This paper introduces and analyzes a framework that accommodates general heterogeneity in regression modeling. It demonstrates that regression models with fixed or time-varying parameters can be estimated using the OLS and time-varying OLS methods, respectively, across a broad class of regressors and noise processes not covered by existing theory. The proposed setting facilitates the development of asymptotic theory and the estimation of robust standard errors. The robust confidence interval estimators accommodate substantial heterogeneity in both regressors and noise. The resulting robust standard error estimates coincide with White's (1980) heteroskedasticity-consistent estimator but are applicable to a broader range of conditions, including models with missing data. They are computationally simple and perform well in Monte Carlo simulations. Their robustness, generality, and ease of implementation make them highly suitable for empirical applications. Finally, the paper provides a brief empirical illustration.", "published": "2025-11-10T15:13:54Z", "authors": ["Liudas Giraitis", "George Kapetanios", "Yufei Li", "Alexia Ventouri"], "categories": ["econ.EM", "math.ST"], "doi": "10.1017/S0266466626100395", "_query": "hedonic regression housing", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2511.04568v2", "title": "Riesz Regression As Direct Density Ratio Estimation", "abstract": "This study clarifies the relationship between Riesz regression [Chernozhukov et al., 2021] and density ratio estimation (DRE) in causal inference problems, such as average treatment effect estimation. We first show that the Riesz representer can be written as a signed density ratio and then demonstrate that the Riesz regression objective coincides with the least-squares importance fitting criterion [Kanamori et al., 2009]. Although Riesz regression applies to a broad class of representer estimation problems, this equivalence with DRE allows us to transfer existing DRE results, including convergence rate analyses, generalizations based on Bregman divergence minimization, and regularization techniques for flexible models such as neural networks.", "published": "2025-11-06T17:25:05Z", "authors": ["Masahiro Kato"], "categories": ["stat.ML", "cs.LG", "econ.EM", "math.ST", "stat.ME"], "doi": "", "_query": "hedonic regression housing", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2106.13293v2", "title": "On a Projection Estimator of the Regression Function Derivative", "abstract": "In this paper, we study the estimation of the derivative of a regression function in a standard univariate regression model. The estimators are defined either by derivating nonparametric least-squares estimators of the regression function or by estimating the projection of the derivative. We prove two simple risk bounds allowing to compare our estimators. More elaborate bounds under a stability assumption are then provided. Bases and spaces on which we can illustrate our assumptions and first results are both of compact or non compact type, and we discuss the rates reached by our estimators. They turn out to be optimal in the compact case. Lastly, we propose a model selection procedure and prove the associated risk bound. To consider bases with a non compact support makes the problem difficult.", "published": "2021-06-24T19:51:46Z", "authors": ["Fabienne Comte", "Nicolas Marie"], "categories": ["math.ST"], "doi": "10.1080/10485252.2023.2209198", "_query": "hedonic regression housing", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/1601.07344v1", "title": "On Bayesian quantile regression and outliers", "abstract": "In this work we discuss the progress of Bayesian quantile regression models since their first proposal and we discuss the importance of all parameters involved in the inference process. Using a representation of the asymmetric Laplace distribution as a mixture of a normal and an exponential distribution, we discuss the relevance of the presence of a scale parameter to control for the variance in the model. Besides that we consider the posterior distribution of the latent variable present in the mixture representation to showcase outlying observations given the Bayesian quantile regression fits, where we compare the posterior distribution for each latent variable with the others. We illustrate these results with simulation studies and also with data about Gini indexes in Brazilian states from years with census information.", "published": "2016-01-27T12:41:56Z", "authors": ["Bruno Santos", "Heleno Bolfarine"], "categories": ["stat.ME"], "doi": "", "_query": "hedonic regression housing", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/1705.07616v3", "title": "A novel algorithmic approach to Bayesian Logic Regression", "abstract": "Logic regression was developed more than a decade ago as a tool to construct predictors from Boolean combinations of binary covariates. It has been mainly used to model epistatic effects in genetic association studies, which is very appealing due to the intuitive interpretation of logic expressions to describe the interaction between genetic variations. Nevertheless logic regression has (partly due to computational challenges) remained less well known than other approaches to epistatic association mapping. Here we will adapt an advanced evolutionary algorithm called GMJMCMC (Genetically modified Mode Jumping Markov Chain Monte Carlo) to perform Bayesian model selection in the space of logic regression models. After describing the algorithmic details of GMJMCMC we perform a comprehensive simulation study that illustrates its performance given logic regression terms of various complexity. Specifically GMJMCMC is shown to be able to identify three-way and even four-way interactions with relatively large power, a level of complexity which has not been achieved by previous implementations of logic regression. We apply GMJMCMC to reanalyze QTL mapping data for Recombinant Inbred Lines in \\textit{Arabidopsis thaliana} and from a backcross population in \\textit{Drosophila} where we identify several interesting epistatic effects. The method is implemented in an R package which is available on github.", "published": "2017-05-22T08:59:43Z", "authors": ["Aliaksandr Hubin", "Geir Storvik", "Florian Frommlet"], "categories": ["stat.CO"], "doi": "10.1214/18-BA1141", "_query": "hedonic regression housing", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2606.02116v1", "title": "Retraction based regression methods on Riemannian manifolds", "abstract": "Geodesic regression generalizes classical regression models to manifold-valued data by replacing affine models in Euclidean spaces with geodesic models on Riemannian manifolds. In this paper, we set up a framework for regression based on retractions instead of the Riemannian exponential map and its corresponding retraction-based distance. The associated optimization problem is posed on a subset of the tangent bundle which is why we additionally construct retractions on the tangent bundle induced by retractions on the underlying manifold. Our approach yields a more flexible formulation which is applicable beyond settings where the exponential map can be computed efficiently. As a proof of concept, we apply the developed framework to the (n-1)-dimensional p-norm sphere using the retraction by normalization to define the regression problem. The resulting optimization problem is solved using the Riemannian steepest descent method.", "published": "2026-06-01T11:48:09Z", "authors": ["Estefanía Loayza-Romero", "Benedikt Sibum", "Kathrin Welker"], "categories": ["math.OC"], "doi": "", "_query": "hedonic regression housing", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2605.08002v2", "title": "Cellwise and Casewise Robust Multivariate Regression with Inference", "abstract": "Multivariate linear regression is a fundamental statistical task, but classical estimators such as ordinary least squares are highly sensitive to outliers. These may occur as casewise outliers that affect entire observations, or as outlying cells, that are individual contaminated entries in the predictor and/or response matrix. Moreover, modern datasets frequently contain missing values and are high-dimensional. To address these challenges we propose the cellwise multivariate regression (cellMR) estimator, a robust regression method that simultaneously accommodates casewise and cellwise outliers, missing data, and high dimensionality. The approach builds on a cellwise robust covariance estimator and uses ridge regularization for numerical stability. We further introduce cellBoot, a novel bootstrap-based inference procedure tailored to the cellMR framework. Relying on indirect inference, cellBoot provides asymptotically valid confidence intervals that are robust to casewise and cellwise contamination. We derive influence functions and breakdown bounds of the regression estimator and prove the asymptotic validity of the cellBoot confidence intervals. Simulations and a real genomics application illustrate the strong finite-sample performance of the proposed methods.", "published": "2026-05-08T16:57:32Z", "authors": ["Fabio Centofanti", "Mia Hubert", "Peter J. Rousseeuw"], "categories": ["stat.ME", "math.ST"], "doi": "", "_query": "hedonic regression housing", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/1902.09803v1", "title": "Logarithmic Regret for parameter-free Online Logistic Regression", "abstract": "We consider online optimization procedures in the context of logistic regression, focusing on the Extended Kalman Filter (EKF). We introduce a second-order algorithm close to the EKF, named Semi-Online Step (SOS), for which we prove a O(log(n)) regret in the adversarial setting, paving the way to similar results for the EKF. This regret bound on SOS is the first for such parameter-free algorithm in the adversarial logistic regression. We prove for the EKF in constant dynamics a O(log(n)) regret in expectation and in the well-specified logistic regression model.", "published": "2019-02-26T08:51:45Z", "authors": ["Joseph De Vilmarest", "Olivier Wintenberger"], "categories": ["cs.LG", "math.ST"], "doi": "", "_query": "hedonic regression housing", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2102.04873v2", "title": "Nonparametric C- and D-vine based quantile regression", "abstract": "Quantile regression is a field with steadily growing importance in statistical modeling. It is a complementary method to linear regression, since computing a range of conditional quantile functions provides a more accurate modelling of the stochastic relationship among variables, especially in the tails. We introduce a non-restrictive and highly flexible nonparametric quantile regression approach based on C- and D-vine copulas. Vine copulas allow for separate modeling of marginal distributions and the dependence structure in the data, and can be expressed through a graph theoretical model given by a sequence of trees. This way we obtain a quantile regression model, that overcomes typical issues of quantile regression such as quantile crossings or collinearity, the need for transformations and interactions of variables. Our approach incorporates a two-step ahead ordering of variables, by maximizing the conditional log-likelihood of the tree sequence, while taking into account the next two tree levels. Further, we show that the nonparametric conditional quantile estimator is consistent. The performance of the proposed methods is evaluated in both low- and high-dimensional settings using simulated and real world data. The results support the superior prediction ability of the proposed models.", "published": "2021-02-09T15:17:50Z", "authors": ["Marija Tepegjozova", "Jing Zhou", "Gerda Claeskens", "Claudia Czado"], "categories": ["stat.ME"], "doi": "10.1515/demo-2022-0100", "_query": "hedonic regression housing", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/1706.01820v1", "title": "Face Alignment Using K-Cluster Regression Forests With Weighted Splitting", "abstract": "In this work we present a face alignment pipeline based on two novel methods: weighted splitting for K-cluster Regression Forests and 3D Affine Pose Regression for face shape initialization. Our face alignment method is based on the Local Binary Feature framework, where instead of standard regression forests and pixel difference features used in the original method, we use our K-cluster Regression Forests with Weighted Splitting (KRFWS) and Pyramid HOG features. We also use KRFWS to perform Affine Pose Regression (APR) and 3D-Affine Pose Regression (3D-APR), which intend to improve the face shape initialization. APR applies a rigid 2D transform to the initial face shape that compensates for inaccuracy in the initial face location, size and in-plane rotation. 3D-APR estimates the parameters of a 3D transform that additionally compensates for out-of-plane rotation. The resulting pipeline, consisting of APR and 3D-APR followed by face alignment, shows an improvement of 20% over standard LBF on the challenging IBUG dataset, and state-of-theart accuracy on the entire 300-W dataset.", "published": "2017-06-06T15:42:12Z", "authors": ["Marek Kowalski", "Jacek Naruniec"], "categories": ["cs.CV"], "doi": "10.1109/LSP.2016.2608139", "_query": "hedonic regression housing", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2104.02300v1", "title": "Bottom-Up Human Pose Estimation Via Disentangled Keypoint Regression", "abstract": "In this paper, we are interested in the bottom-up paradigm of estimating human poses from an image. We study the dense keypoint regression framework that is previously inferior to the keypoint detection and grouping framework. Our motivation is that regressing keypoint positions accurately needs to learn representations that focus on the keypoint regions. We present a simple yet effective approach, named disentangled keypoint regression (DEKR). We adopt adaptive convolutions through pixel-wise spatial transformer to activate the pixels in the keypoint regions and accordingly learn representations from them. We use a multi-branch structure for separate regression: each branch learns a representation with dedicated adaptive convolutions and regresses one keypoint. The resulting disentangled representations are able to attend to the keypoint regions, respectively, and thus the keypoint regression is spatially more accurate. We empirically show that the proposed direct regression method outperforms keypoint detection and grouping methods and achieves superior bottom-up pose estimation results on two benchmark datasets, COCO and CrowdPose. The code and models are available at https://github.com/HRNet/DEKR.", "published": "2021-04-06T05:54:46Z", "authors": ["Zigang Geng", "Ke Sun", "Bin Xiao", "Zhaoxiang Zhang", "Jingdong Wang"], "categories": ["cs.CV"], "doi": "", "_query": "hedonic regression housing", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/0805.2300v1", "title": "Regression rank scores in nonlinear models", "abstract": "Consider the nonlinear regression model $Y_i=g({\\bf x}_i,\\boldmath $θ$)+e_i,\\quad i=1,...,n$(1) with ${\\bf x}_i\\in \\mathbb{R}^k,$ $\\boldmathθ=(θ_0,θ_1,...,θ_p)^{\\prime}\\in \\boldmath $Θ$$ (compact in $\\mathbb{R}^{p+1}$), where $g({\\bf x},\\boldmath $θ$)=θ_0+\\tilde{g}({\\bf x},θ_1,...,θ_p)$ is continuous, twice differentiable in $\\boldmath $θ$$ and monotone in components of $\\boldmath $θ$$. Following Gutenbrunner and Jurečková (1992) and Jurečková and Procházka (1994), we introduce regression rank scores for model (1), and prove their asymptotic properties under some regularity conditions. As an application, we propose some tests in nonlinear regression models with nuisance parameters.", "published": "2008-05-15T13:22:07Z", "authors": ["Jana Jurečková"], "categories": ["math.ST"], "doi": "10.1214/193940307000000121", "_query": "hedonic regression housing", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/1408.1027v3", "title": "Bayesian Nonparametric Modeling for Multivariate Ordinal Regression", "abstract": "Univariate or multivariate ordinal responses are often assumed to arise from a latent continuous parametric distribution, with covariate effects which enter linearly. We introduce a Bayesian nonparametric modeling approach for univariate and multivariate ordinal regression, which is based on mixture modeling for the joint distribution of latent responses and covariates. The modeling framework enables highly flexible inference for ordinal regression relationships, avoiding assumptions of linearity or additivity in the covariate effects. In standard parametric ordinal regression models, computational challenges arise from identifiability constraints and estimation of parameters requiring nonstandard inferential techniques. A key feature of the nonparametric model is that it achieves inferential flexibility, while avoiding these difficulties. In particular, we establish full support of the nonparametric mixture model under fixed cut-off points that relate through discretization the latent continuous responses with the ordinal responses. The practical utility of the modeling approach is illustrated through application to two data sets from econometrics, an example involving regression relationships for ozone concentration, and a multirater agreement problem.", "published": "2014-08-05T16:40:06Z", "authors": ["Maria DeYoreo", "Athanasios Kottas"], "categories": ["stat.ME"], "doi": "", "_query": "hedonic regression housing", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/1806.10493v2", "title": "Distribution regression model with a Reproducing Kernel Hilbert Space approach", "abstract": "In this paper, we introduce a new distribution regression model for probability distributions. This model is based on a Reproducing Kernel Hilbert Space (RKHS) regression framework, where universal kernels are built using Wasserstein distances for distributions belonging to W 2 ($Ω$) and $Ω$ is a compact subspace of R. We prove the universal kernel property of such kernels and use this setting to perform regressions on functions. Different regression models are first compared with the proposed one on simulated functional data. We then apply our regression model to transient evoked otoascoutic emission (TEOAE) distribution responses and real predictors of the age.", "published": "2018-06-27T14:07:15Z", "authors": ["Thi Thien Trang Bui", "J-M Loubes", "Laurent Risser", "Patricia Balaresque"], "categories": ["math.ST"], "doi": "10.1080/03610926.2019.1658782", "_query": "hedonic regression housing", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2212.06074v3", "title": "Regression with Label Differential Privacy", "abstract": "We study the task of training regression models with the guarantee of label differential privacy (DP). Based on a global prior distribution on label values, which could be obtained privately, we derive a label DP randomization mechanism that is optimal under a given regression loss function. We prove that the optimal mechanism takes the form of a \"randomized response on bins\", and propose an efficient algorithm for finding the optimal bin values. We carry out a thorough experimental evaluation on several datasets demonstrating the efficacy of our algorithm.", "published": "2022-12-12T17:41:32Z", "authors": ["Badih Ghazi", "Pritish Kamath", "Ravi Kumar", "Ethan Leeman", "Pasin Manurangsi", "Avinash V Varadarajan", "Chiyuan Zhang"], "categories": ["cs.LG", "cs.CR"], "doi": "", "_query": "hedonic regression housing", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2201.13451v3", "title": "Nonlinear Regression with Residuals: Causal Estimation with Time-varying Treatments and Covariates", "abstract": "Standard regression adjustment gives inconsistent estimates of causal effects when there are time-varying treatment effects and time-varying covariates. Loosely speaking, the issue is that some covariates are post-treatment variables because they may be affected by prior treatment status, and regressing out post-treatment variables causes bias. More precisely, the bias is due to certain non-confounding latent variables that create colliders in the causal graph. These latent variables, which we call phantoms, do not harm the identifiability of the causal effect, but they render naive regression estimates inconsistent. Motivated by this, we ask: how can we modify regression methods so that they hold up even in the presence of phantoms? We develop an estimator for this setting based on regression modeling (linear, log-linear, probit and Cox regression), proving that it is consistent for a reasonable causal estimand. In particular, the estimator is a regression model fit with a simple adjustment for collinearity, making it easy to understand and implement with standard regression software. The proposed estimators are instances of the parametric g-formula, extending the regression-with-residuals approach to several canonical nonlinear models.", "published": "2022-01-31T18:59:12Z", "authors": ["Stephen Bates", "Edward Kennedy", "Robert Tibshirani", "Valerie Ventura", "Larry Wasserman"], "categories": ["stat.ME", "stat.CO"], "doi": "", "_query": "hedonic regression housing", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2305.06970v1", "title": "A method for automated regression test in scientific computing libraries: illustration with SPHinXsys", "abstract": "Scientific computing libraries, either being in-house or open-source, have experienced enormous progress in both engineering and scientific research. It is therefore essential to ensure that the modifications in the source code aroused by bug fixing or new feature development wouldn't compromise the accuracy and functionality that has already been validated and verified. With this in mind, this paper introduces a method for developing and implementing an automatic regression test environment and takes the open-source multi-physics library SPHinXsys \\cite{zhang2021sphinxsys} as an example. Firstly, the reference database for each benchmark test is generated from monitored data by multiple executions. This database contains the maximum variation range of metrics for different types of strategies, i.e., time-averaged method, ensemble-averaged method as well as the dynamic time warping method, covering the uncertainty arising from parallel computing, particle relaxation, physical instabilities, etc. Then, new results obtained after source code modification will be tested with them according to a curve-similarity based comparison. Whenever the source code is updated, the regression test will be carried out automatically for all test cases and used to report the validity of the current results. This regression test environment has already been implemented in all dynamics test cases released in SPHinXsys, including fluid dynamics, solid mechanics, fluid-structure interaction, thermal and mass diffusion, reaction-diffusion, and their multi-physics coupling, and shows good capability for testing various problems. It's worth noting that while the present test environment is built and implemented for a specific scientific computing library, its underlying principle is generic and can be applied to many others.", "published": "2023-05-11T16:51:56Z", "authors": ["Bo Zhang", "Chi Zhang", "Xiangyu Hu"], "categories": ["physics.comp-ph"], "doi": "", "_query": "hedonic regression housing", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2002.04926v2", "title": "Beyond UCB: Optimal and Efficient Contextual Bandits with Regression Oracles", "abstract": "A fundamental challenge in contextual bandits is to develop flexible, general-purpose algorithms with computational requirements no worse than classical supervised learning tasks such as classification and regression. Algorithms based on regression have shown promising empirical success, but theoretical guarantees have remained elusive except in special cases. We provide the first universal and optimal reduction from contextual bandits to online regression. We show how to transform any oracle for online regression with a given value function class into an algorithm for contextual bandits with the induced policy class, with no overhead in runtime or memory requirements. We characterize the minimax rates for contextual bandits with general, potentially nonparametric function classes, and show that our algorithm is minimax optimal whenever the oracle obtains the optimal rate for regression. Compared to previous results, our algorithm requires no distributional assumptions beyond realizability, and works even when contexts are chosen adversarially.", "published": "2020-02-12T11:33:46Z", "authors": ["Dylan J. Foster", "Alexander Rakhlin"], "categories": ["cs.LG", "math.ST", "stat.ML"], "doi": "", "_query": "hedonic regression housing", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/1912.11475v3", "title": "One-Class Classification by Ensembles of Regression models -- a detailed study", "abstract": "One-class classification (OCC) deals with the classification problem in which the training data has data points belonging only to target class. In this paper, we study a one-class classification algorithm, One-Class Classification by Ensembles of Regression models (OCCER), that uses regression methods to address OCC problems. The OCCER coverts an OCC problem into many regression problems in the original feature space so that each feature of the original feature space is used as the target variable in one of the regression problems. Other features are used as the variables on which the dependent variable depends. The errors of regression of a data point by all the regression models are used to compute the outlier score of the data point. An extensive comparison of the OCCER algorithm with state-of-the-art OCC algorithms on several datasets was conducted to show the effectiveness of the this approach. We also demonstrate that the OCCER algorithm can work well with the latent feature space created by autoencoders for image datasets. The implementation of OCCER is available at https://github.com/srikanthBezawada/OCCER.", "published": "2019-12-26T08:47:38Z", "authors": ["Amir Ahmad", "Srikanth Bezawada"], "categories": ["cs.LG", "stat.ML"], "doi": "", "_query": "hedonic regression housing", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2109.11911v3", "title": "Linear Panel Regressions with Two-Way Unobserved Heterogeneity", "abstract": "We study linear panel regression models in which the unobserved error term is an unknown smooth function of two-way unobserved fixed effects. In standard additive or interactive fixed effect models the individual specific and time specific effects are assumed to enter with a known functional form (additive or multiplicative). In this paper, we allow for this functional form to be more general and unknown. We discuss two different estimation approaches that allow consistent estimation of the regression parameters in this setting as the number of individuals and the number of time periods grow to infinity. The first approach uses the interactive fixed effect estimator in Bai (2009), which is still applicable here, as long as the number of factors in the estimation grows asymptotically. The second approach first discretizes the two-way unobserved heterogeneity (similar to what Bonhomme, Lamadon and Manresa 2021 are doing for one-way heterogeneity) and then estimates a simple linear fixed effect model with additive two-way grouped fixed effects. For both estimation methods we obtain asymptotic convergence results, perform Monte Carlo simulations, and employ the estimators in an empirical application to UK house price data.", "published": "2021-09-24T12:02:21Z", "authors": ["Hugo Freeman", "Martin Weidner"], "categories": ["econ.EM"], "doi": "", "_query": "hedonic regression housing", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2603.06418v2", "title": "The DCT Model as a Novel Regression Framework within a Lagrangian Formulation", "abstract": "This paper introduces a unified regression framework based on the Lagrange formalism, demonstrating how polynomial and logistic regression can all be formulated within a common variational (Lagrangian formalism) structure. Within this framework, the DCT-based (Discrete Cosine Transform) model naturally emerges as a novel and effective approach to traditional or unsupervised regression. The DCT is used as the constraints in the Lagrangian formalism. By leveraging the nearly orthogonal and bounded nature of the cosine basis, the DCT model offers computational advantages and improved convergence properties compared with traditional polynomial methods. The results further support the potential of the DCT-based neuron as a powerful tool for regression analysis and related learning tasks.", "published": "2026-03-06T15:55:22Z", "authors": ["Marc Martinez-Gost", "Ana I. Perez Neira", "Miguel Angel Lagunas"], "categories": ["eess.SP"], "doi": "", "_query": "hedonic regression housing", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/1803.06226v2", "title": "Glyph: Symbolic Regression Tools", "abstract": "We present Glyph - a Python package for genetic programming based symbolic regression. Glyph is designed for usage let by numerical simulations let by real world experiments. For experimentalists, glyph-remote provides a separation of tasks: a ZeroMQ interface splits the genetic programming optimization task from the evaluation of an experimental (or numerical) run. Glyph can be accessed at http://github.com/ambrosys/glyph . Domain experts are be able to employ symbolic regression in their experiments with ease, even if they are not expert programmers. The reuse potential is kept high by a generic interface design. Glyph is available on PyPI and Github.", "published": "2018-03-13T21:57:49Z", "authors": ["Markus Quade", "Julien Gout", "Markus Abel"], "categories": ["cs.MS", "cs.NE", "math.OC", "physics.data-an"], "doi": "", "_query": "hedonic regression housing", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2002.02818v1", "title": "Nonparametric Regression Quantum Neural Networks", "abstract": "In two pervious papers \\cite{dndiep3}, \\cite{dndiep4}, the first author constructed the least square quantum neural networks (LS-QNN), and ploynomial interpolation quantum neural networks ( PI-QNN), parametrico-stattistical QNN like: leanr regrassion quantum neural networks (LR-QNN), polynomial regression quantum neural networks (PR-QNN), chi-squared quantum neural netowrks ($χ^2$-QNN). We observed that the method works also in the cases by using nonparametric statistics. In this paper we analyze and implement the nonparametric tests on QNN such as: linear nonparametric regression quantum neural networks (LNR-QNN), polynomial nonparametric regression quantum neural networks (PNR-QNN). The implementation is constructed through the Gauss-Jordan Elimination quantum neural networks (GJE-QNN).The training rule is to use the high probability confidence regions or intervals.", "published": "2020-02-07T14:44:45Z", "authors": ["Do Ngoc Diep", "Koji Nagata", "Tadao Nakamura"], "categories": ["cs.ET", "cs.LG", "quant-ph"], "doi": "", "_query": "hedonic regression housing", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/1809.02010v2", "title": "Gaussian Process Regression for Binned Data", "abstract": "Many datasets are in the form of tables of binned data. Performing regression on these data usually involves either reading off bin heights, ignoring data from neighbouring bins or interpolating between bins thus over or underestimating the true bin integrals. In this paper we propose an elegant method for performing Gaussian Process (GP) regression given such binned data, allowing one to make probabilistic predictions of the latent function which produced the binned data. We look at several applications. First, for differentially private regression; second, to make predictions over other integrals; and third when the input regions are irregularly shaped collections of polytopes. In summary, our method provides an effective way of analysing binned data such that one can use more information from the histogram representation, and thus reconstruct a more useful and precise density for making predictions.", "published": "2018-09-06T14:28:26Z", "authors": ["Michael Thomas Smith", "Mauricio A Alvarez", "Neil D Lawrence"], "categories": ["stat.ML", "cs.LG"], "doi": "", "_query": "hedonic regression housing", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2209.01731v2", "title": "Liu-type Shrinkage Estimators for Mixture of Logistic Regressions: An Osteoporosis Study", "abstract": "The logistic regression model is one of the most powerful statistical methods for the analysis of binary data. The logistic regression allows to use a set of covariates to explain the binary responses. The mixture of logistic regression models is used to fit heterogeneous populations through an unsupervised learning approach. The multicollinearity problem is one of the most common problems in logistics and a mixture of logistic regressions where the covariates are highly correlated. This problem results in unreliable maximum likelihood estimates for the regression coefficients. This research developed shrinkage methods to deal with the multicollinearity in a mixture of logistic regression models. These shrinkage methods include ridge and Liu-type estimators. Through extensive numerical studies, we show that the developed methods provide more reliable results in estimating the coefficients of the mixture. Finally, we applied the shrinkage methods to analyze the bone disorder status of women aged 50 and older.", "published": "2022-09-05T02:35:47Z", "authors": ["Elsayed Ghanem", "Armin Hatefi", "Hamid Usefi"], "categories": ["stat.ME", "stat.CO"], "doi": "", "_query": "hedonic regression housing", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2311.06028v1", "title": "Symbolic Regression as Feature Engineering Method for Machine and Deep Learning Regression Tasks", "abstract": "In the realm of machine and deep learning regression tasks, the role of effective feature engineering (FE) is pivotal in enhancing model performance. Traditional approaches of FE often rely on domain expertise to manually design features for machine learning models. In the context of deep learning models, the FE is embedded in the neural network's architecture, making it hard for interpretation. In this study, we propose to integrate symbolic regression (SR) as an FE process before a machine learning model to improve its performance. We show, through extensive experimentation on synthetic and real-world physics-related datasets, that the incorporation of SR-derived features significantly enhances the predictive capabilities of both machine and deep learning regression models with 34-86% root mean square error (RMSE) improvement in synthetic datasets and 4-11.5% improvement in real-world datasets. In addition, as a realistic use-case, we show the proposed method improves the machine learning performance in predicting superconducting critical temperatures based on Eliashberg theory by more than 20% in terms of RMSE. These results outline the potential of SR as an FE component in data-driven models.", "published": "2023-11-10T12:34:28Z", "authors": ["Assaf Shmuel", "Oren Glickman", "Teddy Lazebnik"], "categories": ["cs.LG"], "doi": "10.1088/2632-2153/ad513a", "_query": "hedonic regression housing", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2401.03106v1", "title": "Contrastive linear regression", "abstract": "Contrastive dimension reduction methods have been developed for case-control study data to identify variation that is enriched in the foreground (case) data X relative to the background (control) data Y. Here, we develop contrastive regression for the setting when there is a response variable r associated with each foreground observation. This situation occurs frequently when, for example, the unaffected controls do not have a disease grade or intervention dosage but the affected cases have a disease grade or intervention dosage, as in autism severity, solid tumors stages, polyp sizes, or warfarin dosages. Our contrastive regression model captures shared low-dimensional variation between the predictors in the cases and control groups, and then explains the case-specific response variables through the variance that remains in the predictors after shared variation is removed. We show that, in one single-nucleus RNA sequencing dataset on autism severity in postmortem brain samples from donors with and without autism and in another single-cell RNA sequencing dataset on cellular differentiation in chronic rhinosinusitis with and without nasal polyps, our contrastive linear regression performs feature ranking and identifies biologically-informative predictors associated with response that cannot be identified using other approaches", "published": "2024-01-06T02:05:43Z", "authors": ["Boyang Zhang", "Sarah Nyquist", "Andrew Jones", "Barbara E. Engelhardt", "Didong Li"], "categories": ["stat.ME"], "doi": "", "_query": "hedonic regression housing", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2311.11487v2", "title": "Modeling Insurance Claims using Bayesian Nonparametric Regression", "abstract": "The prediction of future insurance claims based on observed risk factors, or covariates, help the actuary set insurance premiums. Typically, actuaries use parametric regression models to predict claims based on the covariate information. Such models assume the same functional form tying the response to the covariates for each data point. These models are not flexible enough and can fail to accurately capture at the individual level, the relationship between the covariates and the claims frequency and severity, which are often multimodal, highly skewed, and heavy-tailed. In this article, we explore the use of Bayesian nonparametric (BNP) regression models to predict claims frequency and severity based on covariates. In particular, we model claims frequency as a mixture of Poisson regression, and the logarithm of claims severity as a mixture of normal regression. We use the Dirichlet process (DP) and Pitman-Yor process (PY) as a prior for the mixing distribution over the regression parameters. Unlike parametric regression, such models allow each data point to have its individual parameters, making them highly flexible, resulting in improved prediction accuracy. We describe model fitting using MCMC and illustrate their applicability using French motor insurance claims data.", "published": "2023-11-20T02:07:40Z", "authors": ["Mostafa Shams Esfand Abadi", "Kaushik Ghosh"], "categories": ["stat.ME", "stat.AP"], "doi": "10.1371/journal.pone.0346734", "_query": "hedonic regression housing", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2310.16209v1", "title": "ELM Ridge Regression Boosting", "abstract": "We discuss a boosting approach for the Ridge Regression (RR) method, with applications to the Extreme Learning Machine (ELM), and we show that the proposed method significantly improves the classification performance and robustness of ELMs.", "published": "2023-10-24T21:53:50Z", "authors": ["M. Andrecut"], "categories": ["cs.LG"], "doi": "", "_query": "hedonic regression housing", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2306.06599v8", "title": "Variational Imbalanced Regression: Fair Uncertainty Quantification via Probabilistic Smoothing", "abstract": "Existing regression models tend to fall short in both accuracy and uncertainty estimation when the label distribution is imbalanced. In this paper, we propose a probabilistic deep learning model, dubbed variational imbalanced regression (VIR), which not only performs well in imbalanced regression but naturally produces reasonable uncertainty estimation as a byproduct. Different from typical variational autoencoders assuming I.I.D. representations (a data point's representation is not directly affected by other data points), our VIR borrows data with similar regression labels to compute the latent representation's variational distribution; furthermore, different from deterministic regression models producing point estimates, VIR predicts the entire normal-inverse-gamma distributions and modulates the associated conjugate distributions to impose probabilistic reweighting on the imbalanced data, thereby providing better uncertainty estimation. Experiments in several real-world datasets show that our VIR can outperform state-of-the-art imbalanced regression models in terms of both accuracy and uncertainty estimation. Code will soon be available at https://github.com/Wang-ML-Lab/variational-imbalanced-regression.", "published": "2023-06-11T06:27:06Z", "authors": ["Ziyan Wang", "Hao Wang"], "categories": ["cs.LG", "cs.CV", "cs.NE", "stat.ML"], "doi": "", "_query": "hedonic regression housing", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/1712.04543v2", "title": "A Mathematical Programming Approach for Integrated Multiple Linear Regression Subset Selection and Validation", "abstract": "Subset selection for multiple linear regression aims to construct a regression model that minimizes errors by selecting a small number of explanatory variables. Once a model is built, various statistical tests and diagnostics are conducted to validate the model and to determine whether the regression assumptions are met. Most traditional approaches require human decisions at this step. For example, the user adding or removing a variable until a satisfactory model is obtained. However, this trial-and-error strategy cannot guarantee that a subset that minimizes the errors while satisfying all regression assumptions will be found. In this paper, we propose a fully automated model building procedure for multiple linear regression subset selection that integrates model building and validation based on mathematical programming. The proposed model minimizes mean squared errors while ensuring that the majority of the important regression assumptions are met. We also propose an efficient constraint to approximate the constraint for the coefficient t-test. When no subset satisfies all of the considered regression assumptions, our model provides an alternative subset that satisfies most of these assumptions. Computational results show that our model yields better solutions (i.e., satisfying more regression assumptions) compared to the state-of-the-art benchmark models while maintaining similar explanatory power.", "published": "2017-12-12T21:42:01Z", "authors": ["Seokhyun Chung", "Young Woong Park", "Taesu Cheong"], "categories": ["stat.ML"], "doi": "10.1016/j.patcog.2020.107565", "_query": "hedonic regression housing", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/1507.07170v2", "title": "On the Use of Cauchy Prior Distributions for Bayesian Logistic Regression", "abstract": "In logistic regression, separation occurs when a linear combination of the predictors can perfectly classify part or all of the observations in the sample, and as a result, finite maximum likelihood estimates of the regression coefficients do not exist. Gelman et al. (2008) recommended independent Cauchy distributions as default priors for the regression coefficients in logistic regression, even in the case of separation, and reported posterior modes in their analyses. As the mean does not exist for the Cauchy prior, a natural question is whether the posterior means of the regression coefficients exist under separation. We prove theorems that provide necessary and sufficient conditions for the existence of posterior means under independent Cauchy priors for the logit link and a general family of link functions, including the probit link. We also study the existence of posterior means under multivariate Cauchy priors. For full Bayesian inference, we develop a Gibbs sampler based on Polya-Gamma data augmentation to sample from the posterior distribution under independent Student-t priors including Cauchy priors, and provide a companion R package in the supplement. We demonstrate empirically that even when the posterior means of the regression coefficients exist under separation, the magnitude of the posterior samples for Cauchy priors may be unusually large, and the corresponding Gibbs sampler shows extremely slow mixing. While alternative algorithms such as the No-U-Turn Sampler in Stan can greatly improve mixing, in order to resolve the issue of extremely heavy tailed posteriors for Cauchy priors under separation, one would need to consider lighter tailed priors such as normal priors or Student-t priors with degrees of freedom larger than one.", "published": "2015-07-26T04:14:21Z", "authors": ["Joyee Ghosh", "Yingbo Li", "Robin Mitra"], "categories": ["stat.ME"], "doi": "", "_query": "hedonic regression housing", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/0908.0570v1", "title": "The Infinite Hierarchical Factor Regression Model", "abstract": "We propose a nonparametric Bayesian factor regression model that accounts for uncertainty in the number of factors, and the relationship between factors. To accomplish this, we propose a sparse variant of the Indian Buffet Process and couple this with a hierarchical model over factors, based on Kingman's coalescent. We apply this model to two problems (factor analysis and factor regression) in gene-expression data analysis.", "published": "2009-08-05T01:10:09Z", "authors": ["Piyush Rai", "Hal Daumé"], "categories": ["cs.LG", "stat.ML"], "doi": "", "_query": "hedonic regression housing", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2206.07936v2", "title": "Universality of regularized regression estimators in high dimensions", "abstract": "The Convex Gaussian Min-Max Theorem (CGMT) has emerged as a prominent theoretical tool for analyzing the precise stochastic behavior of various statistical estimators in the so-called high dimensional proportional regime, where the sample size and the signal dimension are of the same order. However, a well recognized limitation of the existing CGMT machinery rests in its stringent requirement on the exact Gaussianity of the design matrix, therefore rendering the obtained precise high dimensional asymptotics largely a specific Gaussian theory in various important statistical models. This paper provides a structural universality framework for a broad class of regularized regression estimators that is particularly compatible with the CGMT machinery. In particular, we show that with a good enough $\\ell_\\infty$ bound for the regression estimator $\\hatμ_A$, any `structural property' that can be detected via the CGMT for $\\hatμ_G$ (under a standard Gaussian design $G$) also holds for $\\hatμ_A$ under a general design $A$ with independent entries. As a proof of concept, we demonstrate our new universality framework in three key examples of regularized regression estimators: the Ridge, Lasso and regularized robust regression estimators, where new universality properties of risk asymptotics and/or distributions of regression estimators and other related quantities are proved. As a major statistical implication of the Lasso universality results, we validate inference procedures using the degrees-of-freedom adjusted debiased Lasso under general design and error distributions. We also provide a counterexample, showing that universality properties for regularized regression estimators do not extend to general isotropic designs.", "published": "2022-06-16T06:03:54Z", "authors": ["Qiyang Han", "Yandi Shen"], "categories": ["math.ST", "cs.IT", "math.PR"], "doi": "", "_query": "hedonic regression housing", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2510.03131v3", "title": "Total robustness in Bayesian Nonlinear Regression", "abstract": "Modern regression analyses are often undermined by covariate measurement error, misspecification of the regression model, and misspecification of the measurement error distribution. We present, to the best of our knowledge, the first Bayesian nonparametric learning framework targeting total robustness to all three challenges in general nonlinear regression. Our framework places a joint Dirichlet process prior on the latent covariate--response distribution and updates it with posterior pseudo-samples of the latent covariates, so that inference is calibrated to the joint law. This yields estimators defined by minimizing the discrepancy between posterior realizations of the joint Dirichlet process and the model-implied joint distribution. We establish generalization bounds and provide a first proof of convergence and consistency of the resulting estimators under non-degenerate measurement error. A gradient-based implementation enables efficient computation; simulations and two real-data studies show improved stability to misspecification under increasing measurement error relative to recent Bayesian and frequentist alternatives.", "published": "2025-10-03T15:58:40Z", "authors": ["Mengqi Chen", "Charita Dellaporta", "Thomas B. Berrett", "Theodoros Damoulas"], "categories": ["stat.ME", "stat.ML"], "doi": "", "_query": "hedonic regression housing", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/1308.1114v1", "title": "Deriving Proper Uniform Priors for Regression Coefficients, Part II", "abstract": "It is a relatively well-known fact that in problems of Bayesian model selection improper priors should, in general, be avoided. In this paper we derive a proper and parsimonious uniform prior for regression coefficients. We then use this prior to derive the corresponding evidence values of the regression models under consideration. By way of these evidence values one may proceed to compute the posterior probabilities of the competing regression models.", "published": "2013-08-05T20:40:25Z", "authors": ["H. R. N. van Erp", "R. O. Linger", "P. H. A. J. M. van Gelder"], "categories": ["stat.ME"], "doi": "", "_query": "hedonic regression housing", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/1905.12843v1", "title": "Fair Regression: Quantitative Definitions and Reduction-based Algorithms", "abstract": "In this paper, we study the prediction of a real-valued target, such as a risk score or recidivism rate, while guaranteeing a quantitative notion of fairness with respect to a protected attribute such as gender or race. We call this class of problems \\emph{fair regression}. We propose general schemes for fair regression under two notions of fairness: (1) statistical parity, which asks that the prediction be statistically independent of the protected attribute, and (2) bounded group loss, which asks that the prediction error restricted to any protected group remain below some pre-determined level. While we only study these two notions of fairness, our schemes are applicable to arbitrary Lipschitz-continuous losses, and so they encompass least-squares regression, logistic regression, quantile regression, and many other tasks. Our schemes only require access to standard risk minimization algorithms (such as standard classification or least-squares regression) while providing theoretical guarantees on the optimality and fairness of the obtained solutions. In addition to analyzing theoretical properties of our schemes, we empirically demonstrate their ability to uncover fairness--accuracy frontiers on several standard datasets.", "published": "2019-05-30T03:39:02Z", "authors": ["Alekh Agarwal", "Miroslav Dudík", "Zhiwei Steven Wu"], "categories": ["cs.LG", "stat.ML"], "doi": "", "_query": "hedonic regression housing", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2403.14452v1", "title": "On Weighted Trigonometric Regression for Suboptimal Designs in Circadian Biology Studies", "abstract": "Studies in circadian biology often use trigonometric regression to model phenomena over time. Ideally, protocols in these studies would collect samples at evenly distributed and equally spaced time points over a 24 hour period. This sample collection protocol is known as an equispaced design, which is considered the optimal experimental design for trigonometric regression under multiple statistical criteria. However, implementing equispaced designs in studies involving individuals is logistically challenging, and failure to employ an equispaced design could cause a loss of statistical power when performing hypothesis tests with an estimated model. This paper is motivated by the potential loss of statistical power during hypothesis testing, and considers a weighted trigonometric regression as a remedy. Specifically, the weights for this regression are the normalized reciprocals of estimates derived from a kernel density estimator for sample collection time, which inflates the weight of samples collected at underrepresented time points. A search procedure is also introduced to identify the concentration hyperparameter for kernel density estimation that maximizes the Hessian of weighted squared loss, which relates to both maximizing the $D$-optimality criterion from experimental design literature and minimizing the generalized variance. Simulation studies consistently demonstrate that this weighted regression mitigates variability in inferences produced by an estimated model. Illustrations with three real circadian biology data sets further indicate that this weighted regression consistently yields larger test statistics than its unweighted counterpart for first-order trigonometric regression, or cosinor regression, which is prevalent in circadian biology studies.", "published": "2024-03-21T15:00:34Z", "authors": ["Michael Gorczyca", "Justice Sefas"], "categories": ["stat.ME", "stat.AP"], "doi": "", "_query": "hedonic regression housing", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/1604.06637v3", "title": "Robust and Sparse Regression via $γ$-divergence", "abstract": "In high-dimensional data, many sparse regression methods have been proposed. However, they may not be robust against outliers. Recently, the use of density power weight has been studied for robust parameter estimation and the corresponding divergences have been discussed. One of such divergences is the $γ$-divergence and the robust estimator using the $γ$-divergence is known for having a strong robustness. In this paper, we consider the robust and sparse regression based on $γ$-divergence. We extend the $γ$-divergence to the regression problem and show that it has a strong robustness under heavy contamination even when outliers are heterogeneous. The loss function is constructed by an empirical estimate of the $γ$-divergence with sparse regularization and the parameter estimate is defined as the minimizer of the loss function. To obtain the robust and sparse estimate, we propose an efficient update algorithm which has a monotone decreasing property of the loss function. Particularly, we discuss a linear regression problem with $L_1$ regularization in detail. In numerical experiments and real data analyses, we see that the proposed method outperforms past robust and sparse methods.", "published": "2016-04-22T12:53:27Z", "authors": ["Takayuki Kawashima", "Hironori Fujisawa"], "categories": ["stat.ME", "stat.ML"], "doi": "10.3390/e19110608", "_query": "hedonic regression housing", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2410.20289v2", "title": "On the Gaussian process limit of Bayesian Additive Regression Trees", "abstract": "Bayesian Additive Regression Trees (BART) is a nonparametric Bayesian regression technique of rising fame. It is a sum-of-decision-trees model, and is in some sense the Bayesian version of boosting. In the limit of infinite trees, it becomes equivalent to Gaussian process (GP) regression. This limit is known but has not yet led to any useful analysis or application. For the first time, I derive and compute the exact BART prior covariance function. With it I implement the infinite trees limit of BART as GP regression. Through empirical tests, I show that this limit is worse than standard BART in a fixed configuration, but also that tuning its hyperparameters in the natural GP way makes it competitive with BART. The advantage of using a GP surrogate of BART is the analytical likelihood, which simplifies model building and sidesteps the complex BART MCMC algorithm. More generally, this study opens new ways to understand and develop BART and GP regression. The implementation of BART as GP is available in the Python package lsqfitgp.", "published": "2024-10-26T23:18:33Z", "authors": ["Giacomo Petrillo"], "categories": ["stat.ML", "cs.LG"], "doi": "", "_query": "hedonic regression housing", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2308.14481v1", "title": "Group Regression for Query Based Object Detection and Tracking", "abstract": "Group regression is commonly used in 3D object detection to predict box parameters of similar classes in a joint head, aiming to benefit from similarities while separating highly dissimilar classes. For query-based perception methods, this has, so far, not been feasible. We close this gap and present a method to incorporate multi-class group regression, especially designed for the 3D domain in the context of autonomous driving, into existing attention and query-based perception approaches. We enhance a transformer based joint object detection and tracking model with this approach, and thoroughly evaluate its behavior and performance. For group regression, the classes of the nuScenes dataset are divided into six groups of similar shape and prevalence, each being regressed by a dedicated head. We show that the proposed method is applicable to many existing transformer based perception approaches and can bring potential benefits. The behavior of query group regression is thoroughly analyzed in comparison to a unified regression head, e.g. in terms of class-switching behavior and distribution of the output parameters. The proposed method offers many possibilities for further research, such as in the direction of deep multi-hypotheses tracking.", "published": "2023-08-28T10:43:53Z", "authors": ["Felicia Ruppel", "Florian Faion", "Claudius Gläser", "Klaus Dietmayer"], "categories": ["cs.LG", "cs.CV"], "doi": "", "_query": "hedonic regression housing", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/1809.08449v2", "title": "A default prior for regression coefficients", "abstract": "When the sample size is not too small, M-estimators of regression coefficients are approximately normal and unbiased. This leads to the familiar frequentist inference in terms of normality-based confidence intervals and p-values. From a Bayesian perspective, use of the (improper) uniform prior yields matching results in the sense that posterior quantiles agree with one-sided confidence bounds. For this, and various other reasons, the uniform prior is often considered objective or non-informative. In spite of this, we argue that the uniform prior is not suitable as a default prior for inference about a regression coefficient in the context of the bio-medical and social sciences. We propose that a more suitable default choice is the normal distribution with mean zero and standard deviation equal to the standard error of the M-estimator. We base this recommendation on two arguments. First, we show that this prior is non-informative for inference about the sign of the regression coefficient. Secondly, we show that this prior agrees well with a meta-analysis of 50 articles from the MEDLINE database.", "published": "2018-09-22T15:54:44Z", "authors": ["Erik van Zwet"], "categories": ["stat.ME", "stat.AP"], "doi": "", "_query": "hedonic regression housing", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/1802.03141v2", "title": "A Minimum Message Length Criterion for Robust Linear Regression", "abstract": "This paper applies the minimum message length principle to inference of linear regression models with Student-t errors. A new criterion for variable selection and parameter estimation in Student-t regression is proposed. By exploiting properties of the regression model, we derive a suitable non-informative proper uniform prior distribution for the regression coefficients that leads to a simple and easy-to-apply criterion. Our proposed criterion does not require specification of hyperparameters and is invariant under both full rank transformations of the design matrix and linear transformations of the outcomes. We compare the proposed criterion with several standard model selection criteria, such as the Akaike information criterion and the Bayesian information criterion, on simulations and real data with promising results.", "published": "2018-02-09T06:23:19Z", "authors": ["Chi Kuen Wong", "Enes Makalic", "Daniel F. Schmidt"], "categories": ["stat.ME"], "doi": "", "_query": "hedonic regression housing", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2205.03368v2", "title": "An Efficient Minimax Optimal Estimator For Multivariate Convex Regression", "abstract": "This work studies the computational aspects of multivariate convex regression in dimensions $d \\ge 5$. Our results include the \\emph{first} estimators that are minimax optimal (up to logarithmic factors) with polynomial runtime in the sample size for both $L$-Lipschitz convex regression, and $Γ$-bounded convex regression under polytopal support. Our analysis combines techniques from empirical process theory, stochastic geometry, and potential theory, and leverages recent algorithmic advances in mean estimation for random vectors and in distribution-free linear regression. These results provide the first efficient, minimax-optimal procedures for non-Donsker classes for which their corresponding least-squares estimator is provably minimax-suboptimal.", "published": "2022-05-06T17:04:05Z", "authors": ["Gil Kur", "Eli Putterman"], "categories": ["math.ST", "cs.LG", "math.MG", "stat.CO"], "doi": "", "_query": "hedonic regression housing", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2307.09004v2", "title": "Ord2Seq: Regarding Ordinal Regression as Label Sequence Prediction", "abstract": "Ordinal regression refers to classifying object instances into ordinal categories. It has been widely studied in many scenarios, such as medical disease grading, movie rating, etc. Known methods focused only on learning inter-class ordinal relationships, but still incur limitations in distinguishing adjacent categories thus far. In this paper, we propose a simple sequence prediction framework for ordinal regression called Ord2Seq, which, for the first time, transforms each ordinal category label into a special label sequence and thus regards an ordinal regression task as a sequence prediction process. In this way, we decompose an ordinal regression task into a series of recursive binary classification steps, so as to subtly distinguish adjacent categories. Comprehensive experiments show the effectiveness of distinguishing adjacent categories for performance improvement and our new approach exceeds state-of-the-art performances in four different scenarios. Codes are available at https://github.com/wjh892521292/Ord2Seq.", "published": "2023-07-18T06:44:20Z", "authors": ["Jinhong Wang", "Yi Cheng", "Jintai Chen", "Tingting Chen", "Danny Chen", "Jian Wu"], "categories": ["cs.AI", "cs.CV"], "doi": "", "_query": "hedonic regression housing", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2507.19155v1", "title": "RegScore: Scoring Systems for Regression Tasks", "abstract": "Scoring systems are widely adopted in medical applications for their inherent simplicity and transparency, particularly for classification tasks involving tabular data. In this work, we introduce RegScore, a novel, sparse, and interpretable scoring system specifically designed for regression tasks. Unlike conventional scoring systems constrained to integer-valued coefficients, RegScore leverages beam search and k-sparse ridge regression to relax these restrictions, thus enhancing predictive performance. We extend RegScore to bimodal deep learning by integrating tabular data with medical images. We utilize the classification token from the TIP (Tabular Image Pretraining) transformer to generate Personalized Linear Regression parameters and a Personalized RegScore, enabling individualized scoring. We demonstrate the effectiveness of RegScore by estimating mean Pulmonary Artery Pressure using tabular data and further refine these estimates by incorporating cardiac MRI images. Experimental results show that RegScore and its personalized bimodal extensions achieve performance comparable to, or better than, state-of-the-art black-box models. Our method provides a transparent and interpretable approach for regression tasks in clinical settings, promoting more informed and trustworthy decision-making. We provide our code at https://github.com/SanoScience/RegScore.", "published": "2025-07-25T10:56:46Z", "authors": ["Michal K. Grzeszczyk", "Tomasz Szczepański", "Pawel Renc", "Siyeop Yoon", "Jerome Charton", "Tomasz Trzciński", "Arkadiusz Sitek"], "categories": ["eess.IV"], "doi": "", "_query": "hedonic regression housing", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2404.16709v3", "title": "Understanding Measurement Precision from a Regression Perspective", "abstract": "We adopt and expand McDonald's (2011) regression framework for measurement precision, integrating two key perspectives: (a) reliability of observed scores and (b) optimal prediction of latent scores. Reliability arises from a measurement decomposition of an observed score into its true score and measurement error. In contrast, proportional reduction in mean squared error (PRMSE) arises from a prediction decomposition of a latent score into its optimal predictor (the observed expected a posteriori [EAP] score) and prediction error. Reliability is the coefficient of determination obtained by two isomorphic regressions: regressing the observed score on its true score or on all the latent variables. Similarly, PRMSE is the coefficient of determination obtained from two isomorphic regressions: regressing the latent score on its observed EAP score or all the manifest variables. A key implication of this regression framework is that both reliability and PRMSE can be estimated using a Monte Carlo (MC) method, which is particularly useful when no analytic formula is available or when the analytic calculation is involved. We illustrate these concepts with a factor analysis model and a two parameter logistic model, in which we compute reliability coefficients for different observed scores and PRMSE for different latent scores. Additionally, we provide a numerical example demonstrating how the MC method can be used to estimate reliability and PRMSE within a two-dimensional item response tree model.", "published": "2024-04-25T16:17:23Z", "authors": ["Yang Liu", "Jolynn Pek", "Alberto Maydeu-Olivares"], "categories": ["stat.ME"], "doi": "10.1037/met0000763", "_query": "hedonic regression housing", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2207.05231v1", "title": "Regression Metric Loss: Learning a Semantic Representation Space for Medical Images", "abstract": "Regression plays an essential role in many medical imaging applications for estimating various clinical risk or measurement scores. While training strategies and loss functions have been studied for the deep neural networks in medical image classification tasks, options for regression tasks are very limited. One of the key challenges is that the high-dimensional feature representation learned by existing popular loss functions like Mean Squared Error or L1 loss is hard to interpret. In this paper, we propose a novel Regression Metric Loss (RM-Loss), which endows the representation space with the semantic meaning of the label space by finding a representation manifold that is isometric to the label space. Experiments on two regression tasks, i.e. coronary artery calcium score estimation and bone age assessment, show that RM-Loss is superior to the existing popular regression losses on both performance and interpretability. Code is available at https://github.com/DIAL-RPI/Regression-Metric-Loss.", "published": "2022-07-12T00:00:03Z", "authors": ["Hanqing Chao", "Jiajin Zhang", "Pingkun Yan"], "categories": ["eess.IV", "cs.CV"], "doi": "", "_query": "hedonic regression housing", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2302.10539v1", "title": "Online Symbolic Regression with Informative Query", "abstract": "Symbolic regression, the task of extracting mathematical expressions from the observed data $\\{ \\vx_i, y_i \\}$, plays a crucial role in scientific discovery. Despite the promising performance of existing methods, most of them conduct symbolic regression in an \\textit{offline} setting. That is, they treat the observed data points as given ones that are simply sampled from uniform distributions without exploring the expressive potential of data. However, for real-world scientific problems, the data used for symbolic regression are usually actively obtained by doing experiments, which is an \\textit{online} setting. Thus, how to obtain informative data that can facilitate the symbolic regression process is an important problem that remains challenging. In this paper, we propose QUOSR, a \\textbf{qu}ery-based framework for \\textbf{o}nline \\textbf{s}ymbolic \\textbf{r}egression that can automatically obtain informative data in an iterative manner. Specifically, at each step, QUOSR receives historical data points, generates new $\\vx$, and then queries the symbolic expression to get the corresponding $y$, where the $(\\vx, y)$ serves as new data points. This process repeats until the maximum number of query steps is reached. To make the generated data points informative, we implement the framework with a neural network and train it by maximizing the mutual information between generated data points and the target expression. Through comprehensive experiments, we show that QUOSR can facilitate modern symbolic regression methods by generating informative data.", "published": "2023-02-21T09:13:48Z", "authors": ["Pengwei Jin", "Di Huang", "Rui Zhang", "Xing Hu", "Ziyuan Nan", "Zidong Du", "Qi Guo", "Yunji Chen"], "categories": ["cs.LG"], "doi": "", "_query": "hedonic regression housing", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/1301.1919v1", "title": "Nonparametric Reduced Rank Regression", "abstract": "We propose an approach to multivariate nonparametric regression that generalizes reduced rank regression for linear models. An additive model is estimated for each dimension of a $q$-dimensional response, with a shared $p$-dimensional predictor variable. To control the complexity of the model, we employ a functional form of the Ky-Fan or nuclear norm, resulting in a set of function estimates that have low rank. Backfitting algorithms are derived and justified using a nonparametric form of the nuclear norm subdifferential. Oracle inequalities on excess risk are derived that exhibit the scaling behavior of the procedure in the high dimensional setting. The methods are illustrated on gene expression data.", "published": "2013-01-09T16:48:07Z", "authors": ["Rina Foygel", "Michael Horrell", "Mathias Drton", "John Lafferty"], "categories": ["stat.ML"], "doi": "", "_query": "hedonic regression housing", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/1806.04613v1", "title": "Improving Regression Performance with Distributional Losses", "abstract": "There is growing evidence that converting targets to soft targets in supervised learning can provide considerable gains in performance. Much of this work has considered classification, converting hard zero-one values to soft labels---such as by adding label noise, incorporating label ambiguity or using distillation. In parallel, there is some evidence from a regression setting in reinforcement learning that learning distributions can improve performance. In this work, we investigate the reasons for this improvement, in a regression setting. We introduce a novel distributional regression loss, and similarly find it significantly improves prediction accuracy. We investigate several common hypotheses, around reducing overfitting and improved representations. We instead find evidence for an alternative hypothesis: this loss is easier to optimize, with better behaved gradients, resulting in improved generalization. We provide theoretical support for this alternative hypothesis, by characterizing the norm of the gradients of this loss.", "published": "2018-06-12T15:42:25Z", "authors": ["Ehsan Imani", "Martha White"], "categories": ["stat.ML", "cs.LG"], "doi": "", "_query": "hedonic regression housing", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2009.14761v1", "title": "Testing for linearity in boundary regression models with application to maximal life expectancies", "abstract": "We consider a regression model with errors that are a.s. negative. Thus the regression function is not the expected value of the observations but the right endpoint of their support. We develop two goodness-of-fit tests for the hypotheses that the regression function is an affine function, study the asymptotic distributions of the test statistics in order to approximately fix the sizes of the tests, derive their finite-sample properties based on simulations and apply them to life expectancy data.", "published": "2020-09-30T16:00:59Z", "authors": ["Jürgen Kampf", "Alexander Meister"], "categories": ["math.ST"], "doi": "", "_query": "hedonic regression housing", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/1503.02143v2", "title": "Model selection of polynomial kernel regression", "abstract": "Polynomial kernel regression is one of the standard and state-of-the-art learning strategies. However, as is well known, the choices of the degree of polynomial kernel and the regularization parameter are still open in the realm of model selection. The first aim of this paper is to develop a strategy to select these parameters. On one hand, based on the worst-case learning rate analysis, we show that the regularization term in polynomial kernel regression is not necessary. In other words, the regularization parameter can decrease arbitrarily fast when the degree of the polynomial kernel is suitable tuned. On the other hand,taking account of the implementation of the algorithm, the regularization term is required. Summarily, the effect of the regularization term in polynomial kernel regression is only to circumvent the \" ill-condition\" of the kernel matrix. Based on this, the second purpose of this paper is to propose a new model selection strategy, and then design an efficient learning algorithm. Both theoretical and experimental analysis show that the new strategy outperforms the previous one. Theoretically, we prove that the new learning strategy is almost optimal if the regression function is smooth. Experimentally, it is shown that the new strategy can significantly reduce the computational burden without loss of generalization capability.", "published": "2015-03-07T08:39:15Z", "authors": ["Shaobo Lin", "Xingping Sun", "Zongben Xu", "Jinshan Zeng"], "categories": ["cs.LG"], "doi": "", "_query": "hedonic regression housing", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/1908.04328v1", "title": "Identifying shifts between two regression curves", "abstract": "This article studies the problem whether two convex (concave) regression functions modelling the relation between a response and covariate in two samples differ by a shift in the horizontal and/or vertical axis. We consider a nonparametric situation assuming only smoothness of the regression functions. A graphical tool based on the derivatives of the regression functions and their inverses is proposed to answer this question and studied in several examples. We also formalize this question in a corresponding hypothesis and develop a statistical test. The asymptotic properties of the corresponding test statistic are investigated under the null hypothesis and local alternatives. In contrast to most of the literature on comparing shape invariant models, which requires independent data the procedure is applicable for dependent and non-stationary data. We also illustrate the finite sample properties of the new test by means of a small simulation study and a real data example.", "published": "2019-08-12T18:31:28Z", "authors": ["Holger Dette", "Subhra Sankar Dhar", "Weichi Wu"], "categories": ["math.ST"], "doi": "", "_query": "hedonic regression housing", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2009.12132v1", "title": "Multilevel Gibbs Sampling for Bayesian Regression", "abstract": "Bayesian regression remains a simple but effective tool based on Bayesian inference techniques. For large-scale applications, with complicated posterior distributions, Markov Chain Monte Carlo methods are applied. To improve the well-known computational burden of Markov Chain Monte Carlo approach for Bayesian regression, we developed a multilevel Gibbs sampler for Bayesian regression of linear mixed models. The level hierarchy of data matrices is created by clustering the features and/or samples of data matrices. Additionally, the use of correlated samples is investigated for variance reduction to improve the convergence of the Markov Chain. Testing on a diverse set of data sets, speed-up is achieved for almost all of them without significant loss in predictive performance.", "published": "2020-09-25T11:18:17Z", "authors": ["Joris Tavernier", "Jaak Simm", "Adam Arany", "Karl Meerbergen", "Yves Moreau"], "categories": ["stat.CO", "cs.LG", "math.NA", "stat.ML"], "doi": "", "_query": "hedonic regression housing", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2010.08864v1", "title": "Markov Neighborhood Regression for High-Dimensional Inference", "abstract": "This paper proposes an innovative method for constructing confidence intervals and assessing p-values in statistical inference for high-dimensional linear models. The proposed method has successfully broken the high-dimensional inference problem into a series of low-dimensional inference problems: For each regression coefficient $β_i$, the confidence interval and $p$-value are computed by regressing on a subset of variables selected according to the conditional independence relations between the corresponding variable $X_i$ and other variables. Since the subset of variables forms a Markov neighborhood of $X_i$ in the Markov network formed by all the variables $X_1,X_2,\\ldots,X_p$, the proposed method is coined as Markov neighborhood regression. The proposed method is tested on high-dimensional linear, logistic and Cox regression. The numerical results indicate that the proposed method significantly outperforms the existing ones. Based on the Markov neighborhood regression, a method of learning causal structures for high-dimensional linear models is proposed and applied to identification of drug sensitive genes and cancer driver genes. The idea of using conditional independence relations for dimension reduction is general and potentially can be extended to other high-dimensional or big data problems as well.", "published": "2020-10-17T21:00:37Z", "authors": ["Faming Liang", "Jingnan Xue", "Bochao Jia"], "categories": ["stat.ME"], "doi": "", "_query": "hedonic regression housing", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/1402.5876v4", "title": "Manifold Gaussian Processes for Regression", "abstract": "Off-the-shelf Gaussian Process (GP) covariance functions encode smoothness assumptions on the structure of the function to be modeled. To model complex and non-differentiable functions, these smoothness assumptions are often too restrictive. One way to alleviate this limitation is to find a different representation of the data by introducing a feature space. This feature space is often learned in an unsupervised way, which might lead to data representations that are not useful for the overall regression task. In this paper, we propose Manifold Gaussian Processes, a novel supervised method that jointly learns a transformation of the data into a feature space and a GP regression from the feature space to observed space. The Manifold GP is a full GP and allows to learn data representations, which are useful for the overall regression task. As a proof-of-concept, we evaluate our approach on complex non-smooth functions where standard GPs perform poorly, such as step functions and robotics tasks with contacts.", "published": "2014-02-24T16:19:51Z", "authors": ["Roberto Calandra", "Jan Peters", "Carl Edward Rasmussen", "Marc Peter Deisenroth"], "categories": ["stat.ML", "cs.LG"], "doi": "", "_query": "hedonic regression housing", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2604.02664v1", "title": "A comparison of methods for Poisson regression in the presence of background", "abstract": "This paper provides a statistical analysis of three common methods of regression for Poisson data in the presence of Poisson background, namely the joint fit with two parametric models for the source and the background, the use of a non-parametric model for the background known as the wstat method, and the regression with a fixed background. The non-parametric background method, which is a popular method for spectral data, is found to be significantly biased, especially in the low-count and background-dominated regimes. Similar conclusions apply to the fixed-background regression. The joint-fit method, on the other hand, simultaneously affords reliable hypothesis testing by means of the usual Cash statistic and unbiased reconstruction of source parameters. We also investigate the effect of non-parametric regression on the number of effective degrees of freedom by means of the Efron degree of freedom function. We find that the wstat method adds a significantly larger number of degrees of freedom, compared to the number of free parameters in the source model. The other two methods have a number of degrees of freedom consistent with the number of adjustable parameters, at least for the simple models investigated in this paper.", "published": "2026-04-03T02:53:58Z", "authors": ["Massimiliano Bonamente", "Vinay Kashyap", "Xiaoli Li", "Jelle de Plaa"], "categories": ["stat.ME", "astro-ph.IM", "stat.AP"], "doi": "", "_query": "hedonic regression housing", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2610.05388v2", "title": "Semi-Supervised Kernel Ridge Fréchet Regression", "abstract": "Fréchet regression provides a framework for predicting random object-valued responses by minimizing the conditional expected squared distances. We propose semi-supervised kernel ridge Fréchet regression (SS-KRFR) to exploit unlabeled covariates when labeled samples are scarce. The method learns kernel spectral features from both labeled and unlabeled covariates, then utilizes labeled responses to estimate the conditional Fréchet function by kernel ridge regression. We derive prediction error bounds that distinguish the roles of labeled and unlabeled samples, and establish polynomial convergence rates under suitable conditions. Under additional kernel assumptions, a sharper analysis relaxes the sample size requirements for learning the features. We also develop hybrids that combine features, predictions, or both from models fitted with and without unlabeled covariates, establishing uniform stability over mixing weights in Hadamard spaces at common tuning and a separate rate guarantee for prediction mixing. Simulations and a real-data application show semi-supervised gains and improvements over competing Fréchet regression methods in several settings.", "published": "2026-10-04T17:17:33Z", "authors": ["Jaehee Seo", "Kwangho Kim", "Jisu Kim"], "categories": ["stat.ME", "math.ST"], "doi": "", "_query": "hedonic regression housing", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2408.03574v1", "title": "Teach CLIP to Develop a Number Sense for Ordinal Regression", "abstract": "Ordinal regression is a fundamental problem within the field of computer vision, with customised well-trained models on specific tasks. While pre-trained vision-language models (VLMs) have exhibited impressive performance on various vision tasks, their potential for ordinal regression has received less exploration. In this study, we first investigate CLIP's potential for ordinal regression, from which we expect the model could generalise to different ordinal regression tasks and scenarios. Unfortunately, vanilla CLIP fails on this task, since current VLMs have a well-documented limitation of encapsulating compositional concepts such as number sense. We propose a simple yet effective method called NumCLIP to improve the quantitative understanding of VLMs. We disassemble the exact image to number-specific text matching problem into coarse classification and fine prediction stages. We discretize and phrase each numerical bin with common language concept to better leverage the available pre-trained alignment in CLIP. To consider the inherent continuous property of ordinal regression, we propose a novel fine-grained cross-modal ranking-based regularisation loss specifically designed to keep both semantic and ordinal alignment in CLIP's feature space. Experimental results on three general ordinal regression tasks demonstrate the effectiveness of NumCLIP, with 10% and 3.83% accuracy improvement on historical image dating and image aesthetics assessment task, respectively. Code is publicly available at https://github.com/xmed-lab/NumCLIP.", "published": "2024-08-07T06:26:04Z", "authors": ["Yao Du", "Qiang Zhai", "Weihang Dai", "Xiaomeng Li"], "categories": ["cs.CV", "cs.CL", "cs.LG"], "doi": "", "_query": "hedonic regression housing", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2107.02291v1", "title": "Optimal Estimation of Brownian Penalized Regression Coefficients", "abstract": "In this paper we introduce a new methodology to determine an optimal coefficient of penalized functional regression. We assume the dependent, independent variables and the regression coefficients are functions of time and error dynamics follow a stochastic differential equation. First we construct our objective function as a time dependent residual sum of square and then minimize it with respect to regression coefficients subject to different error dynamics such as LASSO, group LASSO, fused LASSO and cubic smoothing spline. Then we use Feynman-type path integral approach to determine a Schrödinger-type equation which have the entire information of the system. Using first order conditions with respect to these coefficients give us a closed form solution of them.", "published": "2021-07-05T21:54:08Z", "authors": ["Paramahansa Pramanik", "Alan M. Polansky"], "categories": ["stat.ME", "math.OC"], "doi": "", "_query": "hedonic regression housing", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/1806.08911v1", "title": "Overlapping Sliced Inverse Regression for Dimension Reduction", "abstract": "Sliced inverse regression (SIR) is a pioneer tool for supervised dimension reduction. It identifies the effective dimension reduction space, the subspace of significant factors with intrinsic lower dimensionality. In this paper, we propose to refine the SIR algorithm through an overlapping slicing scheme. The new algorithm, called overlapping sliced inverse regression (OSIR), is able to estimate the effective dimension reduction space and determine the number of effective factors more accurately. We show that such overlapping procedure has the potential to identify the information contained in the derivatives of the inverse regression curve, which helps to explain the superiority of OSIR. We also prove that OSIR algorithm is $\\sqrt n $-consistent and verify its effectiveness by simulations and real applications.", "published": "2018-06-23T05:39:11Z", "authors": ["Ning Zhang", "Zhou Yu", "Qiang Wu"], "categories": ["stat.ML", "cs.LG", "stat.ME"], "doi": "", "_query": "hedonic regression housing", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2301.01543v3", "title": "A note on the variance in principal component regression", "abstract": "Principal component regression results in lack of fit when important dimensions are omitted, which cannot be assessed from the eigenvalues. I show that the PC-regression estimator can also suffer from increased variance relative to ordinary least squares in such cases.", "published": "2023-01-04T11:14:06Z", "authors": ["Bert van der Veen"], "categories": ["stat.ME", "math.ST"], "doi": "", "_query": "hedonic regression housing", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/1810.10138v1", "title": "Bayesian Modeling of Nonlinear Poisson Regression with Artificial Neural Networks", "abstract": "Being in the era of big data, modeling and prediction of count data have become significantly important in many fields including health, finance, social, etc. Although linear Poisson regression has been widely used to model count and rate data, it might not be always suitable as it cannot capture some inherent variability within complex data. In this study, we introduce a probabilistically driven nonlinear Poisson regression model with Bayesian artificial neural networks (ANN) to model count or rate data. This new nonlinear Poisson regression model developed with Bayesian ANN provides higher prediction accuracies over traditional Poisson or negative binomial regression models as revealed in our simulation and real data studies.", "published": "2018-10-24T00:30:48Z", "authors": ["Hansapani Rodrigo", "Chris Tsokos"], "categories": ["stat.AP"], "doi": "", "_query": "hedonic regression housing", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/1911.12185v1", "title": "Confounding and Regression Adjustment in Difference-in-Differences", "abstract": "Difference-in-differences (diff-in-diff) is a study design that compares outcomes of two groups (treated and comparison) at two time points (pre- and post-treatment) and is widely used in evaluating new policy implementations. For instance, diff-in-diff has been used to estimate the effect that increasing minimum wage has on employment rates and to assess the Affordable Care Act's effect on health outcomes. Although diff-in-diff appears simple, potential pitfalls lurk. In this paper, we discuss one such complication: time-varying confounding. We provide rigorous definitions for confounders in diff-in-diff studies and explore regression strategies to adjust for confounding. In simulations, we show how and when regression adjustment can ameliorate confounding for both time-invariant and time-varying covariates. We compare our regression approach to those models commonly fit in applied literature, which often fail to address the time-varying nature of confounding in diff-in-diff.", "published": "2019-11-27T14:39:26Z", "authors": ["Bret Zeldow", "Laura A. Hatfield"], "categories": ["stat.AP", "stat.ME"], "doi": "", "_query": "hedonic regression housing", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2507.00440v1", "title": "A Recipe for Causal Graph Regression: Confounding Effects Revisited", "abstract": "Through recognizing causal subgraphs, causal graph learning (CGL) has risen to be a promising approach for improving the generalizability of graph neural networks under out-of-distribution (OOD) scenarios. However, the empirical successes of CGL techniques are mostly exemplified in classification settings, while regression tasks, a more challenging setting in graph learning, are overlooked. We thus devote this work to tackling causal graph regression (CGR); to this end we reshape the processing of confounding effects in existing CGL studies, which mainly deal with classification. Specifically, we reflect on the predictive power of confounders in graph-level regression, and generalize classification-specific causal intervention techniques to regression through a lens of contrastive learning. Extensive experiments on graph OOD benchmarks validate the efficacy of our proposals for CGR. The model implementation and the code are provided on https://github.com/causal-graph/CGR.", "published": "2025-07-01T05:46:29Z", "authors": ["Yujia Yin", "Tianyi Qu", "Zihao Wang", "Yifan Chen"], "categories": ["cs.LG", "cs.AI", "stat.ME"], "doi": "", "_query": "hedonic regression housing", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/0710.5218v1", "title": "Local linear regression for functional data", "abstract": "We study a non linear regression model with functional data as inputs and scalar response. We propose a pointwise estimate of the regression function that maps a Hilbert space onto the real line by a local linear method. We provide the asymptotic mean square error. Computations involve a linear inverse problem as well as a representation of the small ball probability of the data and are based on recent advances in this area. The rate of convergence of our estimate outperforms those already obtained in the literature on this model.", "published": "2007-10-27T10:10:09Z", "authors": ["Alain Berlinet", "Abdallah Elamine", "André Mas"], "categories": ["math.ST"], "doi": "", "_query": "hedonic regression housing", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/math/0505632v1", "title": "Confidence sets for nonparametric wavelet regression", "abstract": "We construct nonparametric confidence sets for regression functions using wavelets that are uniform over Besov balls. We consider both thresholding and modulation estimators for the wavelet coefficients. The confidence set is obtained by showing that a pivot process, constructed from the loss function, converges uniformly to a mean zero Gaussian process. Inverting this pivot yields a confidence set for the wavelet coefficients, and from this we obtain confidence sets on functionals of the regression curve.", "published": "2005-05-30T07:27:19Z", "authors": ["Christopher R. Genovese", "Larry Wasserman"], "categories": ["math.ST"], "doi": "10.1214/009053605000000011", "_query": "hedonic regression housing", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2410.07555v2", "title": "A regression framework for studying relationships among attributes under network interference", "abstract": "To understand how the interconnected and interdependent world of the twenty-first century operates and make model-based predictions, joint probability models for networks and interdependent outcomes are needed. We propose a comprehensive regression framework for networks and interdependent outcomes with multiple advantages, including interpretability, scalability, and provable theoretical guarantees. The regression framework can be used for studying relationships among attributes of connected units and captures complex dependencies among connections and attributes, while retaining the virtues of linear regression, logistic regression, and other regression models by being interpretable and widely applicable. On the computational side, we show that the regression framework is amenable to scalable statistical computing based on convex optimization of pseudo-likelihoods using minorization-maximization methods. On the theoretical side, we establish convergence rates for pseudo-likelihood estimators based on a single observation of dependent connections and attributes. We demonstrate the regression framework using simulations and an application to hate speech on the social media platform X.", "published": "2024-10-10T02:51:23Z", "authors": ["Cornelius Fritz", "Michael Schweinberger", "Subhankar Bhadra", "David R. Hunter"], "categories": ["stat.ME"], "doi": "10.1080/01621459.2025.2565851", "_query": "hedonic regression housing", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/1511.07600v1", "title": "A novel, divergence based, regression for compositional data", "abstract": "In compositional data, an observation is a vector with non-negative components which sum to a constant, typically 1. Data of this type arise in many areas, such as geology, archaeology, biology, economics and political science amongst others. The goal of this paper is to propose a new, divergence based, regression modelling technique for compositional data. To do so, a recently proved metric which is a special case of the Jensen-Shannon divergence is employed. A strong advantage of this new regression technique is that zeros are naturally handled. An example with real data and simulation studies are presented and are both compared with the log-ratio based regression suggested by Aitchison in 1986.", "published": "2015-11-24T07:49:04Z", "authors": ["Michail Tsagris"], "categories": ["stat.ME"], "doi": "", "_query": "hedonic regression housing", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2109.13042v1", "title": "Locally Sparse Function on function Regression", "abstract": "In functional data analysis, functional linear regression has attracted significant attention recently. Herein, we consider the case where both the response and covariates are functions. There are two available approaches for addressing such a situation: concurrent and nonconcurrent functional models. In the former, the value of the functional response at a given domain point depends only on the value of the functional regressors evaluated at the same domain point, whereas, in the latter, the functional covariates evaluated at each point of their domain have a non-null effect on the response at any point of its domain. To balance these two extremes, we propose a locally sparse functional regression model in which the functional regression coefficient is allowed (but not forced) to be exactly zero for a subset of its domain. This is achieved using a suitable basis representation of the functional regression coefficient and exploiting an overlapping group-Lasso penalty for its estimation. We introduce efficient computational strategies based on majorization-minimization algorithms and discuss appealing theoretical properties regarding the model support and consistency of the proposed estimator. We further illustrate the empirical performance of the method through simulations and two applications related to human mortality and bidding the energy market.", "published": "2021-09-27T13:31:06Z", "authors": ["Mauro Bernardi", "Antonio Canale", "Marco Stefanucci"], "categories": ["stat.ME"], "doi": "", "_query": "hedonic regression housing", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2503.22125v1", "title": "Semantic segmentation for building houses from wooden cubes", "abstract": "Automated construction is one of the most promising areas that can improve efficiency, reduce costs and minimize errors in the process of building construction. In this paper, a comparative analysis of three neural network models for semantic segmentation, U-Net(light), LinkNet and PSPNet, is performed. Two specialized datasets with images of houses built from wooden cubes were created for the experiments. The first dataset contains 4 classes (background, foundation, walls, roof ) and is designed for basic model evaluation, while the second dataset includes 44 classes where each cube is labeled as a separate object. The models were trained with the same hyperparameters and their accuracy was evaluated using MeanIoU and F1 Score metrics. According to the results obtained, U-Net(light) showed the best performance with 78% MeanIoU and 87% F1 Score on the first dataset and 17% and 25% respectively on the second dataset. The poor results on the second dataset are due to the limited amount of data, the complexity of the partitioning and the imbalance of classes, making it difficult to accurately select individual cubes. In addition, overtraining was observed in all experiments, manifested by high accuracy on the training dataset and its significant decrease on the validation dataset. The present work is the basis for the development of algorithms for automatic generation of staged building plans, which can be further scaled to design complete buildings. Future research is planned to extend the datasets and apply methods to combat overfitting (L1/L2 regularization, Early Stopping). The next stage of work will be the development of algorithms for automatic generation of a step-by-step plan for building houses from cubes using manipulators. Index Terms-Deep Learning, Computer vision, CNN, Semantic segmentation, Construction materials.", "published": "2025-03-28T03:58:12Z", "authors": ["Ivan Beleacov"], "categories": ["cs.CV"], "doi": "", "_query": "hedonic regression housing", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/1310.3397v1", "title": "Regression techniques for Portfolio Optimisation using MOSEK", "abstract": "Regression is widely used by practioners across many disciplines. We reformulate the underlying optimisation problem as a second-order conic program providing the flexibility often needed in applications. Using examples from portfolio management and quantitative trading we solve regression problems with and without constraints. Several Python code fragments are given. The code and data are available online at http://www.github.com/tschm/MosekRegression.", "published": "2013-10-12T15:51:48Z", "authors": ["Thomas Schmelzer", "Raphael Hauser", "Erling Andersen", "Joachim Dahl"], "categories": ["q-fin.PM", "math.OC"], "doi": "", "_query": "hedonic regression housing", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2602.06284v1", "title": "Geometric Kernel Interpolation and Regression", "abstract": "Exploiting the variational interpretation of kernel interpolation we exhibit a direct connection between interpolation and regression, where interpolation appears as a limiting case of regression. By applying this framework to point clouds or samples of smooth manifolds (hypersurfaces, in particular), we show how fundamental geometric quantities such as tangent plane and principal curvatures can be computed numerically using a kernel based (approximate) level set function (often a defining function) for smooth hypersurfaces. In the case of point clouds, the approach generates an interpolated hypersurface, which is an approximation of the underlying manifold when the cloud is a sample of it. It is shown how the geometric quantities obtained can be used in the numerical approximation/computation of geometric operators like the surface gradient or the Laplace-Beltrami operator in the spirit of kernel based meshfree methods. Kernel based interpolation can be extremely ill-posed, especially when using smooth kernels, and the regression approximation offers a natural regularization that proves also quite useful when dealing with geometric or functional data that are affected by errors or noise.", "published": "2026-02-06T00:43:16Z", "authors": ["Patrick Guidotti"], "categories": ["math.NA"], "doi": "", "_query": "hedonic regression housing", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2210.01189v2", "title": "Rank-N-Contrast: Learning Continuous Representations for Regression", "abstract": "Deep regression models typically learn in an end-to-end fashion without explicitly emphasizing a regression-aware representation. Consequently, the learned representations exhibit fragmentation and fail to capture the continuous nature of sample orders, inducing suboptimal results across a wide range of regression tasks. To fill the gap, we propose Rank-N-Contrast (RNC), a framework that learns continuous representations for regression by contrasting samples against each other based on their rankings in the target space. We demonstrate, theoretically and empirically, that RNC guarantees the desired order of learned representations in accordance with the target orders, enjoying not only better performance but also significantly improved robustness, efficiency, and generalization. Extensive experiments using five real-world regression datasets that span computer vision, human-computer interaction, and healthcare verify that RNC achieves state-of-the-art performance, highlighting its intriguing properties including better data efficiency, robustness to spurious targets and data corruptions, and generalization to distribution shifts. Code is available at: https://github.com/kaiwenzha/Rank-N-Contrast.", "published": "2022-10-03T19:00:38Z", "authors": ["Kaiwen Zha", "Peng Cao", "Jeany Son", "Yuzhe Yang", "Dina Katabi"], "categories": ["cs.LG", "cs.AI", "cs.CV"], "doi": "", "_query": "hedonic regression housing", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/1107.3600v2", "title": "Unsupervised K-Nearest Neighbor Regression", "abstract": "In many scientific disciplines structures in high-dimensional data have to be found, e.g., in stellar spectra, in genome data, or in face recognition tasks. In this work we present a novel approach to non-linear dimensionality reduction. It is based on fitting K-nearest neighbor regression to the unsupervised regression framework for learning of low-dimensional manifolds. Similar to related approaches that are mostly based on kernel methods, unsupervised K-nearest neighbor (UNN) regression optimizes latent variables w.r.t. the data space reconstruction error employing the K-nearest neighbor heuristic. The problem of optimizing latent neighborhoods is difficult to solve, but the UNN formulation allows the design of efficient strategies that iteratively embed latent points to fixed neighborhood topologies. UNN is well appropriate for sorting of high-dimensional data. The iterative variants are analyzed experimentally.", "published": "2011-07-19T00:48:41Z", "authors": ["Oliver Kramer"], "categories": ["stat.ML", "cs.LG"], "doi": "", "_query": "hedonic regression housing", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/1605.06025v1", "title": "Bayesian Spatial Monotonic Multiple Regression", "abstract": "We consider monotonic, multiple regression for a set of contiguous regions (lattice data). The regression functions permissibly vary between regions and exhibit geographical structure. We develop new Bayesian non-parametric methodology which allows for both continuous and discontinuous functional shapes and which are estimated using marked point processes and reversible jump Markov Chain Monte Carlo techniques. Geographical dependency is incorporated by a flexible prior distribution; the parametrisation allows the dependency to vary with functional level. The approach is tuned using Bayesian global optimization and cross-validation. Estimates enable variable selection, threshold detection and prediction as well as the extrapolation of the regression function. Performance and flexibility of our approach is illustrated by simulation studies and an application to a Norwegian insurance data set.", "published": "2016-05-19T16:06:09Z", "authors": ["Christian Rohrbeck", "Deborah Costain", "Arnoldo Frigessi"], "categories": ["stat.ME"], "doi": "10.1093/biomet/asy019", "_query": "hedonic regression housing", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2206.04140v2", "title": "TreeFlow: Going beyond Tree-based Gaussian Probabilistic Regression", "abstract": "The tree-based ensembles are known for their outstanding performance in classification and regression problems characterized by feature vectors represented by mixed-type variables from various ranges and domains. However, considering regression problems, they are primarily designed to provide deterministic responses or model the uncertainty of the output with Gaussian or parametric distribution. In this work, we introduce TreeFlow, the tree-based approach that combines the benefits of using tree ensembles with the capabilities of modeling flexible probability distributions using normalizing flows. The main idea of the solution is to use a tree-based model as a feature extractor and combine it with a conditional variant of normalizing flow. Consequently, our approach is capable of modeling complex distributions for the regression outputs. We evaluate the proposed method on challenging regression benchmarks with varying volume, feature characteristics, and target dimensionality. We obtain the SOTA results for both probabilistic and deterministic metrics on datasets with multi-modal target distributions and competitive results on unimodal ones compared to tree-based regression baselines.", "published": "2022-06-08T20:06:23Z", "authors": ["Patryk Wielopolski", "Maciej Zięba"], "categories": ["cs.LG"], "doi": "10.3233/FAIA230570", "_query": "hedonic regression housing", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/1302.0181v1", "title": "A quantile regression estimator for censored data", "abstract": "We propose a censored quantile regression estimator motivated by unbiased estimating equations. Under the usual conditional independence assumption of the survival time and the censoring time given the covariates, we show that the proposed estimator is consistent and asymptotically normal. We develop an efficient computational algorithm which uses existing quantile regression code. As a result, bootstrap-type inference can be efficiently implemented. We illustrate the finite-sample performance of the proposed method by simulation studies and analysis of a survival data set.", "published": "2013-02-01T13:45:00Z", "authors": ["Chenlei Leng", "Xingwei Tong"], "categories": ["math.ST"], "doi": "10.3150/11-BEJ388", "_query": "hedonic regression housing", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/1707.05534v2", "title": "Latent Gaussian Process Regression", "abstract": "We introduce Latent Gaussian Process Regression which is a latent variable extension allowing modelling of non-stationary multi-modal processes using GPs. The approach is built on extending the input space of a regression problem with a latent variable that is used to modulate the covariance function over the training data. We show how our approach can be used to model multi-modal and non-stationary processes. We exemplify the approach on a set of synthetic data and provide results on real data from motion capture and geostatistics.", "published": "2017-07-18T09:19:20Z", "authors": ["Erik Bodin", "Neill D. F. Campbell", "Carl Henrik Ek"], "categories": ["stat.ML", "cs.LG"], "doi": "", "_query": "hedonic regression housing", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2105.00918v1", "title": "Explanation of multicollinearity using the decomposition theorem of ordinary linear regression models", "abstract": "In a multiple linear regression model, the algebraic formula of the decomposition theorem explains the relationship between the univariate regression coefficient and partial regression coefficient using geometry. It was found that univariate regression coefficients are decomposed into their respective partial regression coefficients according to the parallelogram rule. Multicollinearity is analyzed with the help of the decomposition theorem. It was also shown that it is a sample phenomenon that the partial regression coefficients of important explanatory variables are not significant, but the sign expectation deviation cause may be the population structure between the explained variables and explanatory variables or may be the result of sample selection. At present, some methods of diagnostic multicollinearity only consider the correlation of explanatory variables, so these methods are basically unreliable, and handling multicollinearity is blind before the causes are not distinguished. The increase in the sample size can help identify the causes of multicollinearity, and the difference method can play an auxiliary role.", "published": "2021-04-30T09:09:15Z", "authors": ["Xingguo Wu"], "categories": ["stat.ME"], "doi": "", "_query": "hedonic regression housing", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2412.19802v2", "title": "A new approach to locally adaptive polynomial regression", "abstract": "Adaptive bandwidth selection is a fundamental challenge in nonparametric regression. This paper introduces a new bandwidth selection procedure inspired by the optimality criteria for $\\ell_0$-penalized regression. Although similar in spirit to Lepski's method and its variants in selecting the largest interval satisfying an admissibility criterion, our approach stems from a distinct philosophy, utilizing criteria based on $\\ell_2$-norms of interval projections rather than explicit point and variance estimates. We obtain non-asymptotic risk bounds for the local polynomial regression methods based on our bandwidth selection procedure which adapt (near-)optimally to the local Hölder exponent of the underlying regression function simultaneously at all points in its domain. Furthermore, we show that there is a single ideal choice of a global tuning parameter in each case under which the above-mentioned local adaptivity holds. The optimal risks of our methods derive from the properties of solutions to a new ``bandwidth selection equation'' which is of independent interest. We believe that the principles underlying our approach provide a new perspective to the classical yet ever relevant problem of locally adaptive nonparametric regression.", "published": "2024-12-27T18:59:03Z", "authors": ["Sabyasachi Chatterjee", "Subhajit Goswami", "Soumendu Sundar Mukherjee"], "categories": ["stat.ML", "cs.LG", "math.PR", "math.ST", "stat.ME"], "doi": "", "_query": "hedonic regression housing", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2606.13593v1", "title": "Smoothed Rank-Based Regression Estimation Using Wilcoxon Score Functions", "abstract": "This article proposes an improved rank based regression estimator obtained by replacing the ordinary integer ranks in the Wilcoxon rank-score regression procedure with smoothed ranks derived from a smoothed empirical cumulative distribution function. The smoothed ranks are computed via a continuous, nondecreasing kernel distribution function H that provides a differentiable approximation to the classical indicator function used in standard rank regression. Substituting these smoothed ranks into the Wilcoxon score function yields a new estimator for the slope parameter(s) of the simple and multiple linear regression model. We show that the proposed estimator inherits the robustness properties of classical rank regression while providing improved efficiency under heavy tailed error distributions and better handling of tied observations. A Wald type hypothesis test for the regression coefficients is derived and its asymptotic normality is established. A Monte Carlo simulation study compares new estimator with the ordinary least-squares (OLS) estimator, the classical Wilcoxon rank regression estimator, and the Theil and Sen estimator under several error distributions including the normal, Laplace, Cauchy, and contaminated normal. The proposed estimator achieves relative efficiencies at or above those of classical rank regression uniformly across all scenarios considered, with notable gains in the presence of outliers and heavy-tailed errors.", "published": "2026-06-11T17:12:38Z", "authors": ["Feridun Tasdan"], "categories": ["stat.ME"], "doi": "", "_query": "hedonic regression housing", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2608.09617v2", "title": "Bayesian Symbolic Regression with Entropic Reinforcement Learning", "abstract": "Symbolic regression is the problem of finding an algebraic expression describing a stochastic dependence of a target variable on a set of inputs. Unlike forms of regression that fit parameters assuming a fixed model structure, symbolic regression is a search problem over the space of expressions, represented, for example, as abstract syntax trees using a library of operators. Symbolic regression is typically used in settings with limited, noisy data in the natural sciences. However, searching for a single best-fitting expression fails to capture the epistemic uncertainty about the expression, which motivates a Bayesian perspective that enables uncertainty quantification and specification of natural priors to constrain the search space. In this work, we propose ERRLESS (Entropy-Regularized Reinforcement Learning for Expression Structure Sampling), a scalable approach for sampling from the posterior distribution over expressions given data using maximum-entropy reinforcement learning. ERRLESS learns a neural policy that constructs expressions sequentially by building up their abstract syntax trees. At convergence, the policy samples expressions from the posterior. At test time, expressions can be sampled by rollouts of this policy. We demonstrate that ERRLESS achieves competitive results on the Feynman benchmark while producing short and interpretable expressions. Additionally, we demonstrate that the mean of the posterior predictive approximated by ERRLESS achieves a high coefficient of determination ($R^2$) compared to an SMC baseline, highlighting the benefits of the Bayesian perspective in symbolic regression.", "published": "2026-08-10T13:56:45Z", "authors": ["Oussama Boussif", "Mohammed Mahfoud", "Younesse Kaddar", "Moksh Jain", "Sida Li", "Damiano Fornasiere", "Xiaoyin Chen", "Yoshua Bengio", "Esmeralda S. Whitammer"], "categories": ["cs.LG"], "doi": "", "_query": "hedonic regression housing", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2310.19390v2", "title": "Implicit Manifold Gaussian Process Regression", "abstract": "Gaussian process regression is widely used because of its ability to provide well-calibrated uncertainty estimates and handle small or sparse datasets. However, it struggles with high-dimensional data. One possible way to scale this technique to higher dimensions is to leverage the implicit low-dimensional manifold upon which the data actually lies, as postulated by the manifold hypothesis. Prior work ordinarily requires the manifold structure to be explicitly provided though, i.e. given by a mesh or be known to be one of the well-known manifolds like the sphere. In contrast, in this paper we propose a Gaussian process regression technique capable of inferring implicit structure directly from data (labeled and unlabeled) in a fully differentiable way. For the resulting model, we discuss its convergence to the Matérn Gaussian process on the assumed manifold. Our technique scales up to hundreds of thousands of data points, and may improve the predictive performance and calibration of the standard Gaussian process regression in high-dimensional settings.", "published": "2023-10-30T09:52:48Z", "authors": ["Bernardo Fichera", "Viacheslav Borovitskiy", "Andreas Krause", "Aude Billard"], "categories": ["stat.ML", "cs.LG"], "doi": "", "_query": "hedonic regression housing", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2111.03223v1", "title": "Quantile index regression", "abstract": "Estimating the structures at high or low quantiles has become an important subject and attracted increasing attention across numerous fields. However, due to data sparsity at tails, it usually is a challenging task to obtain reliable estimation, especially for high-dimensional data. This paper suggests a flexible parametric structure to tails, and this enables us to conduct the estimation at quantile levels with rich observations and then to extrapolate the fitted structures to far tails. The proposed model depends on some quantile indices and hence is called the quantile index regression. Moreover, the composite quantile regression method is employed to obtain non-crossing quantile estimators, and this paper further establishes their theoretical properties, including asymptotic normality for the case with low-dimensional covariates and non-asymptotic error bounds for that with high-dimensional covariates. Simulation studies and an empirical example are presented to illustrate the usefulness of the new model.", "published": "2021-11-05T02:21:15Z", "authors": ["Yingying Zhang", "Yuefeng Si", "Guodong Li", "Chil-Ling Tsai"], "categories": ["stat.ME"], "doi": "", "_query": "hedonic regression housing", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/1602.04648v1", "title": "Prediction of Dynamical Systems by Symbolic Regression", "abstract": "We study the modeling and prediction of dynamical systems based on conventional models derived from measurements. Such algorithms are highly desirable in situations where the underlying dynamics are hard to model from physical principles or simplified models need to be found. We focus on symbolic regression methods as a part of machine learning. These algorithms are capable of learning an analytically tractable model from data, a highly valuable property. Symbolic regression methods can be considered as generalized regression methods. We investigate two particular algorithms, the so-called fast function extraction which is a generalized linear regression algorithm, and genetic programming which is a very general method. Both are able to combine functions in a certain way such that a good model for the prediction of the temporal evolution of a dynamical system can be identified. We illustrate the algorithms by finding a prediction for the evolution of a harmonic oscillator based on measurements, by detecting an arriving front in an excitable system, and as a real-world application, the prediction of solar power production based on energy production observations at a given site together with the weather forecast.", "published": "2016-02-15T12:25:17Z", "authors": ["Markus Quade", "Markus Abel", "Kamran Shafi", "Robert K. Niven", "Bernd R. Noack"], "categories": ["physics.data-an", "nlin.AO", "physics.comp-ph"], "doi": "10.1103/PhysRevE.94.012214", "_query": "hedonic regression housing", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2001.04639v1", "title": "Robust Gaussian Process Regression with a Bias Model", "abstract": "This paper presents a new approach to a robust Gaussian process (GP) regression. Most existing approaches replace an outlier-prone Gaussian likelihood with a non-Gaussian likelihood induced from a heavy tail distribution, such as the Laplace distribution and Student-t distribution. However, the use of a non-Gaussian likelihood would incur the need for a computationally expensive Bayesian approximate computation in the posterior inferences. The proposed approach models an outlier as a noisy and biased observation of an unknown regression function, and accordingly, the likelihood contains bias terms to explain the degree of deviations from the regression function. We entail how the biases can be estimated accurately with other hyperparameters by a regularized maximum likelihood estimation. Conditioned on the bias estimates, the robust GP regression can be reduced to a standard GP regression problem with analytical forms of the predictive mean and variance estimates. Therefore, the proposed approach is simple and very computationally attractive. It also gives a very robust and accurate GP estimate for many tested scenarios. For the numerical evaluation, we perform a comprehensive simulation study to evaluate the proposed approach with the comparison to the existing robust GP approaches under various simulated scenarios of different outlier proportions and different noise levels. The approach is applied to data from two measurement systems, where the predictors are based on robust environmental parameter measurements and the response variables utilize more complex chemical sensing methods that contain a certain percentage of outliers. The utility of the measurement systems and value of the environmental data are improved through the computationally efficient GP regression and bias model.", "published": "2020-01-14T06:21:51Z", "authors": ["Chiwoo Park", "David J. Borth", "Nicholas S. Wilson", "Chad N. Hunter", "Fritz J. Friedersdorf"], "categories": ["cs.LG", "stat.ME", "stat.ML"], "doi": "", "_query": "hedonic regression housing", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2402.13655v1", "title": "Stable Update of Regression Trees", "abstract": "Updating machine learning models with new information usually improves their predictive performance, yet, in many applications, it is also desirable to avoid changing the model predictions too much. This property is called stability. In most cases when stability matters, so does explainability. We therefore focus on the stability of an inherently explainable machine learning method, namely regression trees. We aim to use the notion of empirical stability and design algorithms for updating regression trees that provide a way to balance between predictability and empirical stability. To achieve this, we propose a regularization method, where data points are weighted based on the uncertainty in the initial model. The balance between predictability and empirical stability can be adjusted through hyperparameters. This regularization method is evaluated in terms of loss and stability and assessed on a broad range of data characteristics. The results show that the proposed update method improves stability while achieving similar or better predictive performance. This shows that it is possible to achieve both predictive and stable results when updating regression trees.", "published": "2024-02-21T09:41:56Z", "authors": ["Morten Blørstad", "Berent Å. S. Lunde", "Nello Blaser"], "categories": ["cs.LG"], "doi": "", "_query": "hedonic regression housing", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2106.08750v2", "title": "Quasi-Bayesian Dual Instrumental Variable Regression", "abstract": "Recent years have witnessed an upsurge of interest in employing flexible machine learning models for instrumental variable (IV) regression, but the development of uncertainty quantification methodology is still lacking. In this work we present a novel quasi-Bayesian procedure for IV regression, building upon the recently developed kernelized IV models and the dual/minimax formulation of IV regression. We analyze the frequentist behavior of the proposed method, by establishing minimax optimal contraction rates in $L_2$ and Sobolev norms, and discussing the frequentist validity of credible balls. We further derive a scalable inference algorithm which can be extended to work with wide neural network models. Empirical evaluation shows that our method produces informative uncertainty estimates on complex high-dimensional problems.", "published": "2021-06-16T12:52:19Z", "authors": ["Ziyu Wang", "Yuhao Zhou", "Tongzheng Ren", "Jun Zhu"], "categories": ["stat.ML", "cs.LG"], "doi": "", "_query": "hedonic regression housing", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2304.12923v1", "title": "Quantum Gaussian Process Regression for Bayesian Optimization", "abstract": "Gaussian process regression is a well-established Bayesian machine learning method. We propose a new approach to Gaussian process regression using quantum kernels based on parameterized quantum circuits. By employing a hardware-efficient feature map and careful regularization of the Gram matrix, we demonstrate that the variance information of the resulting quantum Gaussian process can be preserved. We also show that quantum Gaussian processes can be used as a surrogate model for Bayesian optimization, a task that critically relies on the variance of the surrogate model. To demonstrate the performance of this quantum Bayesian optimization algorithm, we apply it to the hyperparameter optimization of a machine learning model which performs regression on a real-world dataset. We benchmark the quantum Bayesian optimization against its classical counterpart and show that quantum version can match its performance.", "published": "2023-04-25T15:38:19Z", "authors": ["Frederic Rapp", "Marco Roth"], "categories": ["quant-ph", "cs.LG"], "doi": "10.1007/s42484-023-00138-9", "_query": "hedonic regression housing", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2101.05119v1", "title": "Multiscale regression on unknown manifolds", "abstract": "We consider the regression problem of estimating functions on $\\mathbb{R}^D$ but supported on a $d$-dimensional manifold $ \\mathcal{M} \\subset \\mathbb{R}^D $ with $ d \\ll D $. Drawing ideas from multi-resolution analysis and nonlinear approximation, we construct low-dimensional coordinates on $\\mathcal{M}$ at multiple scales, and perform multiscale regression by local polynomial fitting. We propose a data-driven wavelet thresholding scheme that automatically adapts to the unknown regularity of the function, allowing for efficient estimation of functions exhibiting nonuniform regularity at different locations and scales. We analyze the generalization error of our method by proving finite sample bounds in high probability on rich classes of priors. Our estimator attains optimal learning rates (up to logarithmic factors) as if the function was defined on a known Euclidean domain of dimension $d$, instead of an unknown manifold embedded in $\\mathbb{R}^D$. The implemented algorithm has quasilinear complexity in the sample size, with constants linear in $D$ and exponential in $d$. Our work therefore establishes a new framework for regression on low-dimensional sets embedded in high dimensions, with fast implementation and strong theoretical guarantees.", "published": "2021-01-13T15:14:31Z", "authors": ["Wenjing Liao", "Mauro Maggioni", "Stefano Vigogna"], "categories": ["stat.ML", "cs.LG", "math.ST"], "doi": "", "_query": "hedonic regression housing", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/1907.04529v2", "title": "Bayesian Inference for Regression Copulas", "abstract": "We propose a new semi-parametric distributional regression smoother that is based on a copula decomposition of the joint distribution of the vector of response values. The copula is high-dimensional and constructed by inversion of a pseudo regression, where the conditional mean and variance are semi-parametric functions of covariates modeled using regularized basis functions. By integrating out the basis coefficients, an implicit copula process on the covariate space is obtained, which we call a `regression copula'. We combine this with a non-parametric margin to define a copula model, where the entire distribution - including the mean and variance - of the response is a smooth semi-parametric function of the covariates. The copula is estimated using both Hamiltonian Monte Carlo and variational Bayes; the latter of which is scalable to high dimensions. Using real data examples and a simulation study we illustrate the efficacy of these estimators and the copula model. In a substantive example, we estimate the distribution of half-hourly electricity spot prices as a function of demand and two time covariates using radial bases and horseshoe regularization. The copula model produces distributional estimates that are locally adaptive with respect to the covariates, and predictions that are more accurate than those from benchmark models.", "published": "2019-07-10T06:20:06Z", "authors": ["Michael Stanley Smith", "Nadja Klein"], "categories": ["stat.ME"], "doi": "10.1080/07350015.2020.1721295", "_query": "hedonic regression housing", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/1909.10129v1", "title": "Specification Testing in Nonparametric Instrumental Quantile Regression", "abstract": "There are many environments in econometrics which require nonseparable modeling of a structural disturbance. In a nonseparable model with endogenous regressors, key conditions are validity of instrumental variables and monotonicity of the model in a scalar unobservable variable. Under these conditions the nonseparable model is equivalent to an instrumental quantile regression model. A failure of the key conditions, however, makes instrumental quantile regression potentially inconsistent. This paper develops a methodology for testing the hypothesis whether the instrumental quantile regression model is correctly specified. Our test statistic is asymptotically normally distributed under correct specification and consistent against any alternative model. In addition, test statistics to justify the model simplification are established. Finite sample properties are examined in a Monte Carlo study and an empirical illustration is provided.", "published": "2019-09-23T02:41:14Z", "authors": ["Christoph Breunig"], "categories": ["econ.EM"], "doi": "10.1017/S0266466619000288", "_query": "hedonic regression housing", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/1607.06763v1", "title": "Exploring elastic net and multivariate regression", "abstract": "When it comes to datasets with a tremendous amount of predictors, variable reduction techniques such as PCA or FA are often used. In this paper, the elastic net, which lies in between the LASSO method and ridge regression, is used as a variable reduction technique followed by further analysis with multivariate regression. Specifically, a messy only dataset is used to show how it can be 'tidied' up and broken down into sensible subsets using the aforementioned method.", "published": "2016-07-22T17:43:56Z", "authors": ["Matthias Raess"], "categories": ["stat.ME"], "doi": "", "_query": "hedonic regression housing", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2103.15624v2", "title": "Shape-constrained Symbolic Regression -- Improving Extrapolation with Prior Knowledge", "abstract": "We investigate the addition of constraints on the function image and its derivatives for the incorporation of prior knowledge in symbolic regression. The approach is called shape-constrained symbolic regression and allows us to enforce e.g. monotonicity of the function over selected inputs. The aim is to find models which conform to expected behaviour and which have improved extrapolation capabilities. We demonstrate the feasibility of the idea and propose and compare two evolutionary algorithms for shape-constrained symbolic regression: i) an extension of tree-based genetic programming which discards infeasible solutions in the selection step, and ii) a two population evolutionary algorithm that separates the feasible from the infeasible solutions. In both algorithms we use interval arithmetic to approximate bounds for models and their partial derivatives. The algorithms are tested on a set of 19 synthetic and four real-world regression problems. Both algorithms are able to identify models which conform to shape constraints which is not the case for the unmodified symbolic regression algorithms. However, the predictive accuracy of models with constraints is worse on the training set and the test set. Shape-constrained polynomial regression produces the best results for the test set but also significantly larger models.", "published": "2021-03-29T14:04:18Z", "authors": ["Gabriel Kronberger", "Fabricio Olivetti de França", "Bogdan Burlacu", "Christian Haider", "Michael Kommenda"], "categories": ["cs.NE", "stat.ML"], "doi": "10.1162/evco_a_00294", "_query": "hedonic regression housing", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2412.20173v3", "title": "Debiased Nonparametric Regression for Statistical Inference and Distributionally Robustness", "abstract": "This study proposes a debiasing method for smooth nonparametric estimators. While machine learning techniques such as random forests and neural networks have demonstrated strong predictive performance, their theoretical properties remain relatively underexplored. In particular, many modern algorithms lack guarantees of pointwise and uniform risk convergence, as well as asymptotic normality. These properties are essential for statistical inference and robust estimation and have been well-established for classical methods such as Nadaraya-Watson regression. To ensure these properties for various nonparametric regression estimators, we introduce a model-free debiasing method. By incorporating a correction term that estimates the conditional expected residual of the original estimator, or equivalently, its estimation error, into the initial nonparametric regression estimator, we obtain a debiased estimator that satisfies pointwise and uniform risk convergence, along with asymptotic normality, under mild smoothness conditions. These properties facilitate statistical inference and enhance robustness to covariate shift, making the method broadly applicable to a wide range of nonparametric regression problems.", "published": "2024-12-28T15:01:19Z", "authors": ["Masahiro Kato"], "categories": ["stat.ME", "cs.LG", "econ.EM", "math.ST", "stat.ML"], "doi": "", "_query": "hedonic regression housing", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2003.12565v1", "title": "Probabilistic Regression for Visual Tracking", "abstract": "Visual tracking is fundamentally the problem of regressing the state of the target in each video frame. While significant progress has been achieved, trackers are still prone to failures and inaccuracies. It is therefore crucial to represent the uncertainty in the target estimation. Although current prominent paradigms rely on estimating a state-dependent confidence score, this value lacks a clear probabilistic interpretation, complicating its use. In this work, we therefore propose a probabilistic regression formulation and apply it to tracking. Our network predicts the conditional probability density of the target state given an input image. Crucially, our formulation is capable of modeling label noise stemming from inaccurate annotations and ambiguities in the task. The regression network is trained by minimizing the Kullback-Leibler divergence. When applied for tracking, our formulation not only allows a probabilistic representation of the output, but also substantially improves the performance. Our tracker sets a new state-of-the-art on six datasets, achieving 59.8% AUC on LaSOT and 75.8% Success on TrackingNet. The code and models are available at https://github.com/visionml/pytracking.", "published": "2020-03-27T17:58:37Z", "authors": ["Martin Danelljan", "Luc Van Gool", "Radu Timofte"], "categories": ["cs.CV", "cs.LG"], "doi": "", "_query": "hedonic regression housing", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/1708.01960v1", "title": "Learning Theory of Distributed Regression with Bias Corrected Regularization Kernel Network", "abstract": "Distributed learning is an effective way to analyze big data. In distributed regression, a typical approach is to divide the big data into multiple blocks, apply a base regression algorithm on each of them, and then simply average the output functions learnt from these blocks. Since the average process will decrease the variance, not the bias, bias correction is expected to improve the learning performance if the base regression algorithm is a biased one. Regularization kernel network is an effective and widely used method for nonlinear regression analysis. In this paper we will investigate a bias corrected version of regularization kernel network. We derive the error bounds when it is applied to a single data set and when it is applied as a base algorithm in distributed regression. We show that, under certain appropriate conditions, the optimal learning rates can be reached in both situations.", "published": "2017-08-07T01:54:56Z", "authors": ["Zhengchu Guo", "Lei Shi", "Qiang Wu"], "categories": ["cs.LG", "stat.ML"], "doi": "", "_query": "hedonic regression housing", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/1402.6455v4", "title": "Sparse principal component regression with adaptive loading", "abstract": "Principal component regression (PCR) is a two-stage procedure that selects some principal components and then constructs a regression model regarding them as new explanatory variables. Note that the principal components are obtained from only explanatory variables and not considered with the response variable. To address this problem, we propose the sparse principal component regression (SPCR) that is a one-stage procedure for PCR. SPCR enables us to adaptively obtain sparse principal component loadings that are related to the response variable and select the number of principal components simultaneously. SPCR can be obtained by the convex optimization problem for each of parameters with the coordinate descent algorithm. Monte Carlo simulations and real data analyses are performed to illustrate the effectiveness of SPCR.", "published": "2014-02-26T08:50:16Z", "authors": ["Shuichi Kawano", "Hironori Fujisawa", "Toyoyuki Takada", "Toshihiko Shiroishi"], "categories": ["stat.ML", "stat.ME"], "doi": "10.1016/j.csda.2015.03.016", "_query": "hedonic regression housing", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/1708.02122v1", "title": "KNN Ensembles for Tweedie Regression: The Power of Multiscale Neighborhoods", "abstract": "Very few K-nearest-neighbor (KNN) ensembles exist, despite the efficacy of this approach in regression, classification, and outlier detection. Those that do exist focus on bagging features, rather than varying k or bagging observations; it is unknown whether varying k or bagging observations can improve prediction. Given recent studies from topological data analysis, varying k may function like multiscale topological methods, providing stability and better prediction, as well as increased ensemble diversity. This paper explores 7 KNN ensemble algorithms combining bagged features, bagged observations, and varied k to understand how each of these contribute to model fit. Specifically, these algorithms are tested on Tweedie regression problems through simulations and 6 real datasets; results are compared to state-of-the-art machine learning models including extreme learning machines, random forest, boosted regression, and Morse-Smale regression. Results on simulations suggest gains from varying k above and beyond bagging features or samples, as well as the robustness of KNN ensembles to the curse of dimensionality. KNN regression ensembles perform favorably against state-of-the-art algorithms and dramatically improve performance over KNN regression. Further, real dataset results suggest varying k is a good strategy in general (particularly for difficult Tweedie regression problems) and that KNN regression ensembles often outperform state-of-the-art methods. These results for k-varying ensembles echo recent theoretical results in topological data analysis, where multidimensional filter functions and multiscale coverings provide stability and performance gains over single-dimensional filters and single-scale covering. This opens up the possibility of leveraging multiscale neighborhoods and multiple measures of local geometry in ensemble methods.", "published": "2017-07-29T21:45:37Z", "authors": ["Colleen M. Farrelly"], "categories": ["stat.ML"], "doi": "", "_query": "hedonic regression housing", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2609.14844v1", "title": "Nonparametric Regression with Measurement Error in Banach Spaces", "abstract": "We consider nonparametric regression with a Banach-space-valued predictor where the covariate takes values in a general separable Banach space. Our primary objective is to develop a unified framework for estimating the regression operator without relying on an inner-product structure, thereby extending classical and functional nonparametric regression beyond Euclidean and Hilbert spaces in the presence of measurement error. We establish the fundamental properties of the proposed estimator and develop its large-sample theory under conditions formulated in terms of the geometry and local concentration of the Banach-valued predictor. In particular, we study pointwise estimation and inference and discuss the construction of pointwise confidence intervals and uniform confidence bands. Our proposed framework is further extended to accommodate error-in-variables settings, where the predictor is observed with contamination. We also investigate related inverse problems arising from the construction of inverse weighting functions and consider partial contamination models. These developments provide a general framework for nonparametric regression and inference with complex infinite-dimensional predictors and identify several statistical and mathematical challenges that arise when the usual Hilbert-space structure is unavailable.", "published": "2026-09-13T23:32:35Z", "authors": ["Pratim Guha Niyogi", "Priyadarshi Dey"], "categories": ["math.ST", "stat.ME"], "doi": "", "_query": "hedonic regression housing", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2512.10849v1", "title": "Bayesian Symbolic Regression via Posterior Sampling", "abstract": "Symbolic regression is a powerful tool for discovering governing equations directly from data, but its sensitivity to noise hinders its broader application. This paper introduces a Sequential Monte Carlo (SMC) framework for Bayesian symbolic regression that approximates the posterior distribution over symbolic expressions, enhancing robustness and enabling uncertainty quantification for symbolic regression in the presence of noise. Differing from traditional genetic programming approaches, the SMC-based algorithm combines probabilistic selection, adaptive tempering, and the use of normalized marginal likelihood to efficiently explore the search space of symbolic expressions, yielding parsimonious expressions with improved generalization. When compared to standard genetic programming baselines, the proposed method better deals with challenging, noisy benchmark datasets. The reduced tendency to overfit and enhanced ability to discover accurate and interpretable equations paves the way for more robust symbolic regression in scientific discovery and engineering design applications.", "published": "2025-12-11T17:38:20Z", "authors": ["Geoffrey F. Bomarito", "Patrick E. Leser"], "categories": ["cs.LG"], "doi": "", "_query": "hedonic regression housing", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/1306.4734v1", "title": "Real-time semiparametric regression for distributed data sets", "abstract": "This paper proposes a method for semiparametric regression analysis of large-scale data which are distributed over multiple hosts. This enables modeling of nonlinear relationships and both the batch approach, where analysis starts after all data have been collected, and the real-time setting are addressed. The methodology is extended to operate in evolving environments, where it can no longer be assumed that model parameters remain constant over time. Two areas of application for the methodology are presented: regression modeling when there are multiple data owners and regression modeling within the MapReduce framework. A website, realtime-semiparametric-regression.net, illustrates the use of the proposed method on United States domestic airline data in real-time.", "published": "2013-06-18T21:56:45Z", "authors": ["Jan Luts"], "categories": ["stat.ME"], "doi": "", "_query": "hedonic regression housing", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2103.11477v2", "title": "Paying Attention to Activation Maps in Camera Pose Regression", "abstract": "Camera pose regression methods apply a single forward pass to the query image to estimate the camera pose. As such, they offer a fast and light-weight alternative to traditional localization schemes based on image retrieval. Pose regression approaches simultaneously learn two regression tasks, aiming to jointly estimate the camera position and orientation using a single embedding vector computed by a convolutional backbone. We propose an attention-based approach for pose regression, where the convolutional activation maps are used as sequential inputs. Transformers are applied to encode the sequential activation maps as latent vectors, used for camera pose regression. This allows us to pay attention to spatially-varying deep features. Using two Transformer heads, we separately focus on the features for camera position and orientation, based on how informative they are per task. Our proposed approach is shown to compare favorably to contemporary pose regressors schemes and achieves state-of-the-art accuracy across multiple outdoor and indoor benchmarks. In particular, to the best of our knowledge, our approach is the only method to attain sub-meter average accuracy across outdoor scenes. We make our code publicly available from here.", "published": "2021-03-21T20:10:15Z", "authors": ["Yoli Shavit", "Ron Ferens", "Yosi Keller"], "categories": ["cs.CV", "cs.AI"], "doi": "", "_query": "hedonic regression housing", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2204.07716v1", "title": "FKreg: A MATLAB toolbox for fast Multivariate Kernel Regression", "abstract": "Kernel smooth is the most fundamental technique for data density and regression estimation. However, time-consuming is the biggest obstacle for the application that the direct evaluation of kernel smooth for $N$ samples needs ${O}\\left( {{N}^{2}} \\right)$ operations. People have developed fast smooth algorithms using the idea of binning with FFT. Unfortunately, the accuracy is not controllable, and the implementation for multivariable and its bandwidth selection for the fast method is not available. Hence, we introduce a new MATLAB toolbox for fast multivariate kernel regression with the idea of non-uniform FFT (NUFFT), which implemented the algorithm for $M$ gridding points with ${O}\\left( N+M\\log M \\right)$ complexity and accuracy controllability. The bandwidth selection problem utilizes the Fast Monte-Carlo algorithm to estimate the degree of freedom (DF), saving enormous cross-validation time even better when data share the same grid space for multiple regression. Up to now, this is the first toolbox for fast-binning high-dimensional kernel regression. Moreover, the estimation for local polynomial regression, the conditional variance for the heteroscedastic model, and the complex-valued datasets are also implemented in this toolbox. The performance is demonstrated with simulations and an application on the quantitive EEG.", "published": "2022-04-16T04:52:44Z", "authors": ["Ying Wang", "Min Li", "Deirel Paz-Linares", "Maria L. Bringas Vega", "Pedro A. Valdés-Sosa"], "categories": ["stat.ME", "cs.LG", "stat.CO"], "doi": "", "_query": "hedonic regression housing", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/1908.08855v1", "title": "A Robust Regression Approach for Robot Model Learning", "abstract": "Machine learning and data analysis have been used in many robotics fields, especially for modelling. Data are usually the result of sensor measurements and, as such, they might be subjected to noise and outliers. The presence of outliers has a huge impact on modelling the acquired data, resulting in inappropriate models. In this work a novel approach for outlier detection and rejection for input/output mapping in regression problems is presented. The robustness of the method is shown both through simulated data for linear and nonlinear regression, and real sensory data. Despite being validated by using artificial neural networks, the method can be generalized to any other regression method", "published": "2019-08-23T14:57:46Z", "authors": ["Francesco Cursi", "Guang-Zhong Yang"], "categories": ["cs.RO", "cs.LG"], "doi": "", "_query": "hedonic regression housing", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2605.29255v1", "title": "Outcome-Calibrated Regression and Predicted Outcome-Based Inference", "abstract": "Regression is a fundamental tool in scientific research. Ordinary least squares (OLS), one of the most widely used regression methods, enjoys several desirable properties, including the best linear unbiased estimator (BLUE) property. It is well known that, under the assumptions of the standard model, the OLS is conditionally unbiased given the covariates, i.e., $\\mathbb{E}(\\widehat Y-Y\\mid X=x)=0$. However, an often-overlooked property of OLS is that the prediction error is generally not unbiased conditional on the outcome, i.e., $\\mathbb{E}(\\widehat Y-Y\\mid Y=y)\\neq 0$. As a consequence of minimizing mean squared error, OLS predictions are systematically shrunk toward the outcome mean, which explains the classical phenomenon of regression to the mean (RTM): large outcome values tend to be underpredicted, whereas small outcome values tend to be overpredicted. This conditional prediction bias creates a nonignorable problem for predicted outcome-based inference, where scientific inference is performed using the predicted outcome $\\widehat Y$ and another variable $W$. In applications such as brain-age analysis and causal inference, we show that inference based on regression-predicted outcomes can be systematically biased. To address this issue, we propose outcome-calibrated regression (OCR), a new regression framework with a closed-form solution that directly enforces outcome calibration. The proposed OCR estimator eliminates conditional prediction bias with respect to the outcome and enables valid inference using regression-predicted outcomes.", "published": "2026-05-28T02:16:05Z", "authors": ["Hwiyoung Lee", "Shuo Chen"], "categories": ["stat.ME"], "doi": "", "_query": "hedonic regression housing", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2307.16315v1", "title": "Towards Practical Robustness Auditing for Linear Regression", "abstract": "We investigate practical algorithms to find or disprove the existence of small subsets of a dataset which, when removed, reverse the sign of a coefficient in an ordinary least squares regression involving that dataset. We empirically study the performance of well-established algorithmic techniques for this task -- mixed integer quadratically constrained optimization for general linear regression problems and exact greedy methods for special cases. We show that these methods largely outperform the state of the art and provide a useful robustness check for regression problems in a few dimensions. However, significant computational bottlenecks remain, especially for the important task of disproving the existence of such small sets of influential samples for regression problems of dimension $3$ or greater. We make some headway on this challenge via a spectral algorithm using ideas drawn from recent innovations in algorithmic robust statistics. We summarize the limitations of known techniques in several challenge datasets to encourage further algorithmic innovation.", "published": "2023-07-30T20:47:36Z", "authors": ["Daniel Freund", "Samuel B. Hopkins"], "categories": ["stat.ME", "cs.LG", "econ.EM", "stat.ML"], "doi": "", "_query": "hedonic regression housing", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2410.23244v2", "title": "Very fast Bayesian Additive Regression Trees on GPU", "abstract": "Bayesian Additive Regression Trees (BART) is a nonparametric Bayesian regression technique based on an ensemble of decision trees. It is part of the toolbox of many statisticians. The overall statistical quality of the regression is typically higher than other generic alternatives, and it requires less manual tuning, making it a good default choice. However, it is a niche method compared to its natural competitor XGBoost, due to the longer running time, making sample sizes above 10,000-100,000 a nuisance. I present a GPU-enabled implementation of BART, faster by up to 200x relative to a single CPU core, making BART competitive in running time with XGBoost. This implementation is available in the Python package bartz.", "published": "2024-10-30T17:29:03Z", "authors": ["Giacomo Petrillo"], "categories": ["stat.ML", "cs.LG"], "doi": "", "_query": "hedonic regression housing", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/1410.2150v1", "title": "Robust Estimation of High-Dimensional Mean Regression", "abstract": "Data subject to heavy-tailed errors are commonly encountered in various scientific fields, especially in the modern era with explosion of massive data. To address this problem, procedures based on quantile regression and Least Absolute Deviation (LAD) regression have been devel- oped in recent years. These methods essentially estimate the conditional median (or quantile) function. They can be very different from the conditional mean functions when distributions are asymmetric and heteroscedastic. How can we efficiently estimate the mean regression functions in ultra-high dimensional setting with existence of only the second moment? To solve this problem, we propose a penalized Huber loss with diverging parameter to reduce biases created by the traditional Huber loss. Such a penalized robust approximate quadratic (RA-quadratic) loss will be called RA-Lasso. In the ultra-high dimensional setting, where the dimensionality can grow exponentially with the sample size, our results reveal that the RA-lasso estimator produces a consistent estimator at the same rate as the optimal rate under the light-tail situation. We further study the computational convergence of RA-Lasso and show that the composite gradient descent algorithm indeed produces a solution that admits the same optimal rate after sufficient iterations. As a byproduct, we also establish the concentration inequality for estimat- ing population mean when there exists only the second moment. We compare RA-Lasso with other regularized robust estimators based on quantile regression and LAD regression. Extensive simulation studies demonstrate the satisfactory finite-sample performance of RA-Lasso.", "published": "2014-10-08T14:55:40Z", "authors": ["Jianqing Fan", "Quefeng Li", "Yuyan Wang"], "categories": ["math.ST"], "doi": "", "_query": "hedonic regression housing", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/1805.10122v3", "title": "The Reconstruction Approach: From Interpolation to Regression", "abstract": "This paper introduces an interpolation-based method, called the reconstruction approach, for nonparametric regression. Based on the fact that interpolation usually has negligible errors compared to statistical estimation, the reconstruction approach uses an interpolator to parameterize the regression function with its values at finite knots, and then estimates these values by (regularized) least squares. Some popular methods including kernel ridge regression can be viewed as its special cases. It is shown that, the reconstruction idea not only provides different angles to look into existing methods, but also produces new effective experimental design and estimation methods for nonparametric models. In particular, for some methods of complexity O(n3), where n is the sample size, this approach provides effective surrogates with much less computational burden. This point makes it very suitable for large datasets.", "published": "2018-05-25T12:56:54Z", "authors": ["Shifeng Xiong"], "categories": ["stat.ML", "cs.LG", "stat.ME"], "doi": "", "_query": "hedonic regression housing", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/1101.5248v1", "title": "Asymptotic Equivalence for Nonparametric Regression with Non-Regular Errors", "abstract": "Asymptotic equivalence in Le Cam's sense for nonparametric regression experiments is extended to the case of non-regular error densities, which have jump discontinuities at their endpoints. We prove asymptotic equivalence of such regression models and the observation of two independent Poisson point processes which contain the target curve as the support boundary of its intensity function. The intensity of the point processes is of order of the sample size $n$ and involves the jump sizes as well as the design density. The statistical model significantly differs from regression problems with Gaussian or regular errors, which are known to be asymptotically equivalent to Gaussian white noise models.", "published": "2011-01-27T10:21:11Z", "authors": ["Alexander Meister", "Markus Reiß"], "categories": ["math.ST"], "doi": "", "_query": "hedonic regression housing", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2309.09486v1", "title": "Online Efficient Secure Logistic Regression based on Function Secret Sharing", "abstract": "Logistic regression is an algorithm widely used for binary classification in various real-world applications such as fraud detection, medical diagnosis, and recommendation systems. However, training a logistic regression model with data from different parties raises privacy concerns. Secure Multi-Party Computation (MPC) is a cryptographic tool that allows multiple parties to train a logistic regression model jointly without compromising privacy. The efficiency of the online training phase becomes crucial when dealing with large-scale data in practice. In this paper, we propose an online efficient protocol for privacy-preserving logistic regression based on Function Secret Sharing (FSS). Our protocols are designed in the two non-colluding servers setting and assume the existence of a third-party dealer who only poses correlated randomness to the computing parties. During the online phase, two servers jointly train a logistic regression model on their private data by utilizing pre-generated correlated randomness. Furthermore, we propose accurate and MPC-friendly alternatives to the sigmoid function and encapsulate the logistic regression training process into a function secret sharing gate. The online communication overhead significantly decreases compared with the traditional secure logistic regression training based on secret sharing. We provide both theoretical and experimental analyses to demonstrate the efficiency and effectiveness of our method.", "published": "2023-09-18T04:50:54Z", "authors": ["Jing Liu", "Jamie Cui", "Cen Chen"], "categories": ["cs.CR"], "doi": "10.1145/3583780.3614998", "_query": "hedonic regression housing", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2302.06464v1", "title": "Revisiting the Conceptualization of Multiple Linear Regression", "abstract": "The problem known as multicolinearity has long been recognized to fundamentally and negatively influence multiple regression. This paper does not intend to either propose a numerical assessment of the degree to which this problem exists within any data set or a solution to the problem itself. Rather, it is our intent to illustrate the potentially serious ramifications multicolinearity has on the traditional development of the multiple linear regression (MLR) model and its associated statistics using established equations, Venn diagrams, and real data.", "published": "2023-01-29T20:45:59Z", "authors": ["Grayson L. Baird", "Stephen L. Bieber"], "categories": ["stat.ME"], "doi": "", "_query": "hedonic regression housing", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2406.06968v1", "title": "Beyond the Norms: Detecting Prediction Errors in Regression Models", "abstract": "This paper tackles the challenge of detecting unreliable behavior in regression algorithms, which may arise from intrinsic variability (e.g., aleatoric uncertainty) or modeling errors (e.g., model uncertainty). First, we formally introduce the notion of unreliability in regression, i.e., when the output of the regressor exceeds a specified discrepancy (or error). Then, using powerful tools for probabilistic modeling, we estimate the discrepancy density, and we measure its statistical diversity using our proposed metric for statistical dissimilarity. In turn, this allows us to derive a data-driven score that expresses the uncertainty of the regression outcome. We show empirical improvements in error detection for multiple regression tasks, consistently outperforming popular baseline approaches, and contributing to the broader field of uncertainty quantification and safe machine learning systems. Our code is available at https://zenodo.org/records/11281964.", "published": "2024-06-11T05:51:44Z", "authors": ["Andres Altieri", "Marco Romanelli", "Georg Pichler", "Florence Alberge", "Pablo Piantanida"], "categories": ["cs.LG", "cs.AI"], "doi": "", "_query": "hedonic regression housing", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2111.14879v1", "title": "Quantum Regression theorem for multi-time correlators : A detailed analysis in the Heisenberg Picture", "abstract": "Quantum regression theorem is a very useful result in open quantum system and extensively used for computing multi-point correlation functions. Traditionally it is derived for two-time correlators in the Markovian limit employing the Schrödinger picture. In this paper we make use of the Heisenberg picture to derive quantum regression theorems for multi-time correlation functions which in the special limit reduce to the well known two-time regression theorem. For multi-time correlation function we find that the regression theorem takes the same form as it takes for two-time correlation function with a mild restriction that one of the times should be greater than all the other time variables. Interestingly, the Heisenberg picture also allows us to derive analogue of regression theorem for out-of-time-ordered correlators (OTOCs). We further extend our study for the case of non-Markovian dynamics and report the modifications to the standard quantum regression theorem. We illustrate all of the above results using the paradigmatic dissipative spin-boson model.", "published": "2021-11-29T19:00:06Z", "authors": ["Sakil Khan", "Bijay Kumar Agarwalla", "Sachin Jain"], "categories": ["quant-ph", "hep-th"], "doi": "10.1103/PhysRevA.106.022214", "_query": "hedonic regression housing", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2608.02539v1", "title": "A Simple Approximation to the Distribution of the Ridge Regression Estimator", "abstract": "We present a simple Gaussian approximation to the finite-sample distribution of the classical ridge regression estimator. Our approximation captures the fact that, in finite samples, the ridge regression estimator trades off bias and variance to reduce estimation and prediction error. Our approximation is based on nonstandard asymptotics where $i)$ we let the estimator's regularization parameter grow proportionally to the sample size; and $ii)$ we treat the population regression coefficients as \\emph{local} to the reference vector that defines the estimator's direction of shrinkage. In contrast to other asymptotic approximations in the literature, we allow for general forms of heteroskedasticity and autocorrelation in the data generating process (at the cost of considering a low-dimensional model where the number of covariates is not allowed to grow with the sample size). We use our simple Gaussian approximation to propose two new strategies to select the regularization parameter for the ridge regression estimator. The suggested strategies select the regularization parameter to minimize either average or worst-case excess prediction risk, where risk is computed using our suggested Gaussian approximation.", "published": "2026-08-03T17:28:17Z", "authors": ["José Luis Montiel Olea", "Ryan Strong", "Amilcar Velez", "Zhuoheng Xu", "Haomin Yu"], "categories": ["econ.EM", "cs.LG", "math.ST"], "doi": "", "_query": "hedonic regression housing", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2610.20053v1", "title": "GBTSVR: Twin Support Vector Regression via Granular Ball Computing", "abstract": "Regression models used in real-world applications are often challenged by noise, outliers, data heterogeneity, and asymmetric error characteristics, which severely limit the effectiveness of conventional point-wise learning models. While twin support vector regression (TSVR) partially addresses these issues through the use of nonparallel regression bounds, it still relies on individual samples as basic learning units. As a result, TSVR remains highly sensitive to noisy and contaminated observations and fails to leverage the underlying structural and regional information inherent in complex datasets, leading to suboptimal robustness and generalization performance. To address these limitations, we introduce granular ball twin support vector regression (GBTSVR) and its quantile extension, granular ball twin support vector quantile regression (GB-TSVQR). The proposed models integrate granular ball computing with TSVR, replacing individual data points with compact, structure-preserving granular balls as fundamental learning units. This region-level representation effectively suppresses noise and outliers while preserving intrinsic data geometry, thereby enhancing robustness, stability, and generalization. GBTSVR constructs nonparallel lower- and upper-bound regression functions over granular balls, significantly reducing sensitivity to sample-level perturbations. Building upon this framework, GB-TSVQR further incorporates quantile regression principles to simultaneously capture heterogeneous trends and asymmetric error distributions across different quantile levels. Extensive experiments on synthetic datasets, time-series benchmarks, and outlier-contaminated data demonstrate that the proposed GBTSVR and GB-TSVQR consistently outperform state-of-the-art regression models.", "published": "2026-10-14T11:54:52Z", "authors": ["A. Quadir", "M. Tanveer"], "categories": ["cs.LG"], "doi": "", "_query": "hedonic regression housing", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/1305.5198v2", "title": "Regularity Properties for Sparse Regression", "abstract": "Statistical and machine learning theory has developed several conditions ensuring that popular estimators such as the Lasso or the Dantzig selector perform well in high-dimensional sparse regression, including the restricted eigenvalue, compatibility, and $\\ell_q$ sensitivity properties. However, some of the central aspects of these conditions are not well understood. For instance, it is unknown if these conditions can be checked efficiently on any given data set. This is problematic, because they are at the core of the theory of sparse regression. Here we provide a rigorous proof that these conditions are NP-hard to check. This shows that the conditions are computationally infeasible to verify, and raises some questions about their practical applications. However, by taking an average-case perspective instead of the worst-case view of NP-hardness, we show that a particular condition, $\\ell_q$ sensitivity, has certain desirable properties. This condition is weaker and more general than the others. We show that it holds with high probability in models where the parent population is well behaved, and that it is robust to certain data processing steps. These results are desirable, as they provide guidance about when the condition, and more generally the theory of sparse regression, may be relevant in the analysis of high-dimensional correlated observational data.", "published": "2013-05-22T17:04:25Z", "authors": ["Edgar Dobriban", "Jianqing Fan"], "categories": ["math.ST"], "doi": "10.1007/s40304-015-0078-6", "_query": "hedonic regression housing", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/1212.0662v1", "title": "A stochastic model of social interaction in wild house mice", "abstract": "We investigate to what extent the interaction dynamics of a population of wild house mouse (Mus musculus domesticus) in their environment can be explained by a simple stochastic model. We use a Markov chain model to describe the transitions of mice in a discrete space of nestboxes, and implement a multi-agent simulation of the model. We find that some important features of our behavioural dataset can be reproduced using this simplified stochastic representation, and discuss the improvements that could be made to our model in order to increase the accuracy of its predictions. Our findings have implications for the understanding of the complexity underlying social behaviour in the animal kingdom and the cognitive requirements of such behaviour.", "published": "2012-12-04T10:07:56Z", "authors": ["Nicolas Perony", "Barbara König", "Frank Schweitzer"], "categories": ["q-bio.QM"], "doi": "", "_query": "hedonic regression housing", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2201.01087v1", "title": "Learning Quality-aware Representation for Multi-person Pose Regression", "abstract": "Off-the-shelf single-stage multi-person pose regression methods generally leverage the instance score (i.e., confidence of the instance localization) to indicate the pose quality for selecting the pose candidates. We consider that there are two gaps involved in existing paradigm:~1) The instance score is not well interrelated with the pose regression quality.~2) The instance feature representation, which is used for predicting the instance score, does not explicitly encode the structural pose information to predict the reasonable score that represents pose regression quality. To address the aforementioned issues, we propose to learn the pose regression quality-aware representation. Concretely, for the first gap, instead of using the previous instance confidence label (e.g., discrete {1,0} or Gaussian representation) to denote the position and confidence for person instance, we firstly introduce the Consistent Instance Representation (CIR) that unifies the pose regression quality score of instance and the confidence of background into a pixel-wise score map to calibrates the inconsistency between instance score and pose regression quality. To fill the second gap, we further present the Query Encoding Module (QEM) including the Keypoint Query Encoding (KQE) to encode the positional and semantic information for each keypoint and the Pose Query Encoding (PQE) which explicitly encodes the predicted structural pose information to better fit the Consistent Instance Representation (CIR). By using the proposed components, we significantly alleviate the above gaps. Our method outperforms previous single-stage regression-based even bottom-up methods and achieves the state-of-the-art result of 71.7 AP on MS COCO test-dev set.", "published": "2022-01-04T11:10:28Z", "authors": ["Yabo Xiao", "Dongdong Yu", "Xiaojuan Wang", "Lei Jin", "Guoli Wang", "Qian Zhang"], "categories": ["cs.CV"], "doi": "", "_query": "hedonic regression housing", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/1009.5165v2", "title": "Low rank Multivariate regression", "abstract": "We consider in this paper the multivariate regression problem, when the target regression matrix $A$ is close to a low rank matrix. Our primary interest in on the practical case where the variance of the noise is unknown. Our main contribution is to propose in this setting a criterion to select among a family of low rank estimators and prove a non-asymptotic oracle inequality for the resulting estimator. We also investigate the easier case where the variance of the noise is known and outline that the penalties appearing in our criterions are minimal (in some sense). These penalties involve the expected value of the Ky-Fan quasi-norm of some random matrices. These quantities can be evaluated easily in practice and upper-bounds can be derived from recent results in random matrix theory.", "published": "2010-09-27T06:19:14Z", "authors": ["Christophe Giraud"], "categories": ["math.ST"], "doi": "", "_query": "hedonic regression housing", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2511.19845v1", "title": "SX-GeoTree: Self-eXplaining Geospatial Regression Tree Incorporating the Spatial Similarity of Feature Attributions", "abstract": "Decision trees remain central for tabular prediction but struggle with (i) capturing spatial dependence and (ii) producing locally stable (robust) explanations. We present SX-GeoTree, a self-explaining geospatial regression tree that integrates three coupled objectives during recursive splitting: impurity reduction (MSE), spatial residual control (global Moran's I), and explanation robustness via modularity maximization on a consensus similarity network formed from (a) geographically weighted regression (GWR) coefficient distances (stimulus-response similarity) and (b) SHAP attribution distances (explanatory similarity). We recast local Lipschitz continuity of feature attributions as a network community preservation problem, enabling scalable enforcement of spatially coherent explanations without per-sample neighborhood searches. Experiments on two exemplar tasks (county-level GDP in Fujian, n=83; point-wise housing prices in Seattle, n=21,613) show SX-GeoTree maintains competitive predictive accuracy (within 0.01 $R^{2}$ of decision trees) while improving residual spatial evenness and doubling attribution consensus (modularity: Fujian 0.19 vs 0.09; Seattle 0.10 vs 0.05). Ablation confirms Moran's I and modularity terms are complementary; removing either degrades both spatial residual structure and explanation stability. The framework demonstrates how spatial similarity - extended beyond geometric proximity through GWR-derived local relationships - can be embedded in interpretable models, advancing trustworthy geospatial machine learning and offering a transferable template for domain-aware explainability.", "published": "2025-11-25T02:23:04Z", "authors": ["Chaogui Kang", "Lijian Luo", "Qingfeng Guan", "Yu Liu"], "categories": ["cs.LG", "cs.CY", "stat.ML"], "doi": "", "_query": "hedonic regression housing", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/1704.02213v3", "title": "A Joint Quantile and Expected Shortfall Regression Framework", "abstract": "We introduce a novel regression framework which simultaneously models the quantile and the Expected Shortfall (ES) of a response variable given a set of covariates. This regression is based on a strictly consistent loss function for the pair quantile and ES, which allows for M- and Z-estimation of the joint regression parameters. We show consistency and asymptotic normality for both estimators under weak regularity conditions. The underlying loss function depends on two specification functions, whose choice affects the properties of the resulting estimators. We find that the Z-estimator is numerically unstable and thus, we rely on M-estimation of the model parameters. Extensive simulations verify the asymptotic properties and analyze the small sample behavior of the M-estimator for different specification functions. This joint regression framework allows for various applications including estimating, forecasting, and backtesting ES, which is particularly relevant in light of the recent introduction of ES into the Basel Accords.", "published": "2017-04-07T13:04:19Z", "authors": ["Timo Dimitriadis", "Sebastian Bayer"], "categories": ["math.ST", "q-fin.RM", "q-fin.ST"], "doi": "10.1214/19-EJS1560", "_query": "hedonic regression housing", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2212.04091v2", "title": "Strong identifiability and parameter learning in regression with heterogeneous response", "abstract": "Mixtures of regression are a powerful class of models for regression learning with respect to a highly uncertain and heterogeneous response variable of interest. In addition to being a rich predictive model for the response given some covariates, the parameters in this model class provide useful information about the heterogeneity in the data population, which is represented by the conditional distributions for the response given the covariates associated with a number of distinct but latent subpopulations. In this paper, we investigate conditions of strong identifiability, rates of convergence for conditional density and parameter estimation, and the Bayesian posterior contraction behavior arising in finite mixture of regression models, under exact-fitted and over-fitted settings and when the number of components is unknown. This theory is applicable to common choices of link functions and families of conditional distributions employed by practitioners. We provide simulation studies and data illustrations, which shed some light on the parameter learning behavior found in several popular regression mixture models reported in the literature.", "published": "2022-12-08T05:58:13Z", "authors": ["Dat Do", "Linh Do", "XuanLong Nguyen"], "categories": ["math.ST", "stat.ML"], "doi": "", "_query": "hedonic regression housing", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/1710.10416v1", "title": "Cox's proportional hazards model with a high-dimensional and sparse regression parameter", "abstract": "This paper deals with the proportional hazards model proposed by D. R. Cox in a high-dimensional and sparse setting for a regression parameter. To estimate the regression parameter, the Dantzig selector is applied. The variable selection consistency of the Dantzig selector for the model will be proved. This property enables us to reduce the dimension of the parameter and to construct asymptotically normal estimators for the regression parameter and the cumulative baseline hazard function.", "published": "2017-10-28T08:18:10Z", "authors": ["Kou Fujimori"], "categories": ["math.ST"], "doi": "", "_query": "hedonic regression housing", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/1907.06474v3", "title": "Neural network regression for Bermudan option pricing", "abstract": "The pricing of Bermudan options amounts to solving a dynamic programming principle, in which the main difficulty, especially in high dimension, comes from the conditional expectation involved in the computation of the continuation value. These conditional expectations are classically computed by regression techniques on a finite dimensional vector space. In this work, we study neural networks approximations of conditional expectations. We prove the convergence of the well-known Longstaff and Schwartz algorithm when the standard least-square regression is replaced by a neural network approximation. We illustrate the numerical efficiency of neural networks as an alternative to standard regression methods for approximating conditional expectations on several numerical examples.", "published": "2019-07-15T12:47:55Z", "authors": ["Bernard Lapeyre", "Jérôme Lelong"], "categories": ["math.PR", "q-fin.CP"], "doi": "", "_query": "hedonic regression housing", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2104.12376v1", "title": "Recalibration of Aleatoric and Epistemic Regression Uncertainty in Medical Imaging", "abstract": "The consideration of predictive uncertainty in medical imaging with deep learning is of utmost importance. We apply estimation of both aleatoric and epistemic uncertainty by variational Bayesian inference with Monte Carlo dropout to regression tasks and show that predictive uncertainty is systematically underestimated. We apply $ σ$ scaling with a single scalar value; a simple, yet effective calibration method for both types of uncertainty. The performance of our approach is evaluated on a variety of common medical regression data sets using different state-of-the-art convolutional network architectures. In our experiments, $ σ$ scaling is able to reliably recalibrate predictive uncertainty. It is easy to implement and maintains the accuracy. Well-calibrated uncertainty in regression allows robust rejection of unreliable predictions or detection of out-of-distribution samples. Our source code is available at https://github.com/mlaves/well-calibrated-regression-uncertainty", "published": "2021-04-26T07:18:58Z", "authors": ["Max-Heinrich Laves", "Sontje Ihler", "Jacob F. Fast", "Lüder A. Kahrs", "Tobias Ortmaier"], "categories": ["eess.IV", "cs.CV"], "doi": "", "_query": "hedonic regression housing", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/1903.07885v1", "title": "Bayesian regression explains how human participants handle parameter uncertainty", "abstract": "The human brain copes with sensory uncertainty in accordance with Bayes' rule. However, it is unknown how the brain makes predictions in the presence of parameter uncertainty. Here, we tested whether and how humans take parameter uncertainty into account in a regression task. Participants extrapolated a parabola from a limited number of noisy points, shown on a computer screen. The quadratic parameter was drawn from a prior distribution, unknown to the observers. We tested whether human observers take full advantage of the given information, including the likelihood function of the observed points and the prior distribution of the quadratic parameter. We compared human performance with Bayesian regression, which is the (Bayes) optimal solution to this problem, and three sub-optimal models, namely maximum likelihood regression, prior regression and maximum a posteriori regression, which are simpler to compute. Our results clearly show that humans use Bayesian regression. We further investigated several variants of Bayesian regression models depending on how the generative noise is treated and found that participants act in line with the more sophisticated version.", "published": "2019-03-19T09:01:46Z", "authors": ["Jannes Jegminat", "Maya Jastrzebowska", "Matt Pachai", "Michael Herzog", "Jean-Pascal Pfister"], "categories": ["q-bio.NC"], "doi": "10.1371/journal.pcbi.1007886", "_query": "hedonic regression housing", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2512.08118v3", "title": "ADOPT: Additive Optimal Transport Regression", "abstract": "Regression analysis for responses taking values in general metric spaces has received increasing attention, particularly for settings with Euclidean predictors $X \\in \\mathbb{R}^p$ and non-Euclidean responses $Y$ in metric spaces. While additive regression is a powerful tool for enhancing interpretability and mitigating the curse of dimensionality in the presence of multivariate predictors, its direct extension is hindered by the absence of vector space operations in general metric spaces. We propose a novel framework for additive optimal transport regression, which incorporates additive structure through optimal geodesic transports. A key idea is to extend the notion of optimal transports in Wasserstein spaces to general geodesic metric spaces. This unified approach accommodates a wide range of responses, including probability distributions, symmetric positive definite (SPD) matrices with various metrics and spherical data. The practical utility of the method is illustrated with correlation matrices derived from resting state fMRI brain imaging data.", "published": "2025-12-08T23:46:53Z", "authors": ["Wookyeong Song", "Hans-Georg Müller"], "categories": ["stat.ME"], "doi": "", "_query": "hedonic regression housing", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2110.13091v1", "title": "Sufficient reductions in regression with mixed predictors", "abstract": "Most data sets comprise of measurements on continuous and categorical variables. In regression and classification Statistics literature, modeling high-dimensional mixed predictors has received limited attention. In this paper we study the general regression problem of inferring on a variable of interest based on high dimensional mixed continuous and binary predictors. The aim is to find a lower dimensional function of the mixed predictor vector that contains all the modeling information in the mixed predictors for the response, which can be either continuous or categorical. The approach we propose identifies sufficient reductions by reversing the regression and modeling the mixed predictors conditional on the response. We derive the maximum likelihood estimator of the sufficient reductions, asymptotic tests for dimension, and a regularized estimator, which simultaneously achieves variable (feature) selection and dimension reduction (feature extraction). We study the performance of the proposed method and compare it with other approaches through simulations and real data examples.", "published": "2021-10-25T16:39:21Z", "authors": ["Efstathia Bura", "Liliana Forzani", "Rodrigo García Arancibia", "Pamela Llop", "Diego Tomassi"], "categories": ["math.ST"], "doi": "", "_query": "hedonic regression housing", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2006.15864v1", "title": "Deep Ordinal Regression with Label Diversity", "abstract": "Regression via classification (RvC) is a common method used for regression problems in deep learning, where the target variable belongs to a set of continuous values. By discretizing the target into a set of non-overlapping classes, it has been shown that training a classifier can improve neural network accuracy compared to using a standard regression approach. However, it is not clear how the set of discrete classes should be chosen and how it affects the overall solution. In this work, we propose that using several discrete data representations simultaneously can improve neural network learning compared to a single representation. Our approach is end-to-end differentiable and can be added as a simple extension to conventional learning methods, such as deep neural networks. We test our method on three challenging tasks and show that our method reduces the prediction error compared to a baseline RvC approach while maintaining a similar model complexity.", "published": "2020-06-29T08:23:43Z", "authors": ["Axel Berg", "Magnus Oskarsson", "Mark O'Connor"], "categories": ["cs.LG", "stat.ML"], "doi": "10.1109/ICPR48806.2021.9412608", "_query": "hedonic regression housing", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2305.07430v1", "title": "Expertise-based Weighting for Regression Models with Noisy Labels", "abstract": "Regression methods assume that accurate labels are available for training. However, in certain scenarios, obtaining accurate labels may not be feasible, and relying on multiple specialists with differing opinions becomes necessary. Existing approaches addressing noisy labels often impose restrictive assumptions on the regression function. In contrast, this paper presents a novel, more flexible approach. Our method consists of two steps: estimating each labeler's expertise and combining their opinions using learned weights. We then regress the weighted average against the input features to build the prediction model. The proposed method is formally justified and empirically demonstrated to outperform existing techniques on simulated and real data. Furthermore, its flexibility enables the utilization of any machine learning technique in both steps. In summary, this method offers a simple, fast, and effective solution for training regression models with noisy labels derived from diverse expert opinions.", "published": "2023-05-12T12:52:51Z", "authors": ["Milene Regina dos Santos", "Rafael Izbicki"], "categories": ["stat.ML", "cs.LG"], "doi": "", "_query": "hedonic regression housing", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/1103.4480v1", "title": "Clustered regression with unknown clusters", "abstract": "We consider a collection of prediction experiments, which are clustered in the sense that groups of experiments ex- hibit similar relationship between the predictor and response variables. The experiment clusters as well as the regres- sion relationships are unknown. The regression relation- ships define the experiment clusters, and in general, the predictor and response variables may not exhibit any clus- tering. We call this prediction problem clustered regres- sion with unknown clusters (CRUC) and in this paper we focus on linear regression. We study and compare several methods for CRUC, demonstrate their applicability to the Yahoo Learning-to-rank Challenge (YLRC) dataset, and in- vestigate an associated mathematical model. CRUC is at the crossroads of many prior works and we study several prediction algorithms with diverse origins: an adaptation of the expectation-maximization algorithm, an approach in- spired by K-means clustering, the singular value threshold- ing approach to matrix rank minimization under quadratic constraints, an adaptation of the Curds and Whey method in multiple regression, and a local regression (LoR) scheme reminiscent of neighborhood methods in collaborative filter- ing. Based on empirical evaluation on the YLRC dataset as well as simulated data, we identify the LoR method as a good practical choice: it yields best or near-best prediction performance at a reasonable computational load, and it is less sensitive to the choice of the algorithm parameter. We also provide some analysis of the LoR method for an asso- ciated mathematical model, which sheds light on optimal parameter choice and prediction performance.", "published": "2011-03-23T10:20:14Z", "authors": ["Kishor Barman", "Onkar Dabeer"], "categories": ["cs.LG", "stat.ML"], "doi": "", "_query": "hedonic regression housing", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2110.00467v2", "title": "Dimension Reduction for Fréchet Regression", "abstract": "With the rapid development of data collection techniques, complex data objects that are not in the Euclidean space are frequently encountered in new statistical applications. Fréchet regression model (Peterson & Müller 2019) provides a promising framework for regression analysis with metric space-valued responses. In this paper, we introduce a flexible sufficient dimension reduction (SDR) method for Fréchet regression to achieve two purposes: to mitigate the curse of dimensionality caused by high-dimensional predictors and to provide a visual inspection tool for Fréchet regression. Our approach is flexible enough to turn any existing SDR method for Euclidean (X,Y) into one for Euclidean X and metric space-valued Y. The basic idea is to first map the metric-space valued random object $Y$ to a real-valued random variable $f(Y)$ using a class of functions, and then perform classical SDR to the transformed data. If the class of functions is sufficiently rich, then we are guaranteed to uncover the Fréchet SDR space. We showed that such a class, which we call an ensemble, can be generated by a universal kernel. We established the consistency and asymptotic convergence rate of the proposed methods. The finite-sample performance of the proposed methods is illustrated through simulation studies for several commonly encountered metric spaces that include Wasserstein space, the space of symmetric positive definite matrices, and the sphere. We illustrated the data visualization aspect of our method by exploring the human mortality distribution data across countries and by studying the distribution of hematoma density.", "published": "2021-10-01T15:01:32Z", "authors": ["Qi Zhang", "Lingzhou Xue", "Bing Li"], "categories": ["stat.ME", "math.ST", "stat.ML"], "doi": "", "_query": "hedonic regression housing", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2305.14656v1", "title": "RSRM: Reinforcement Symbolic Regression Machine", "abstract": "In nature, the behaviors of many complex systems can be described by parsimonious math equations. Automatically distilling these equations from limited data is cast as a symbolic regression process which hitherto remains a grand challenge. Keen efforts in recent years have been placed on tackling this issue and demonstrated success in symbolic regression. However, there still exist bottlenecks that current methods struggle to break when the discrete search space tends toward infinity and especially when the underlying math formula is intricate. To this end, we propose a novel Reinforcement Symbolic Regression Machine (RSRM) that masters the capability of uncovering complex math equations from only scarce data. The RSRM model is composed of three key modules: (1) a Monte Carlo tree search (MCTS) agent that explores optimal math expression trees consisting of pre-defined math operators and variables, (2) a Double Q-learning block that helps reduce the feasible search space of MCTS via properly understanding the distribution of reward, and (3) a modulated sub-tree discovery block that heuristically learns and defines new math operators to improve representation ability of math expression trees. Biding of these modules yields the state-of-the-art performance of RSRM in symbolic regression as demonstrated by multiple sets of benchmark examples. The RSRM model shows clear superiority over several representative baseline models.", "published": "2023-05-24T02:51:45Z", "authors": ["Yilong Xu", "Yang Liu", "Hao Sun"], "categories": ["cs.LG", "cs.AI", "cs.SC"], "doi": "", "_query": "hedonic regression housing", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/1506.00834v1", "title": "Bayesian quantile regression with approximate likelihood", "abstract": "Quantile regression is often used when a comprehensive relationship between a response variable and one or more explanatory variables is desired. The traditional frequentists' approach to quantile regression has been well developed around asymptotic theories and efficient algorithms. However, not much work has been published under the Bayesian framework. One challenging problem for Bayesian quantile regression is that the full likelihood has no parametric forms. In this paper, we propose a Bayesian quantile regression method, the linearly interpolated density (LID) method, which uses a linear interpolation of the quantiles to approximate the likelihood. Unlike most of the existing methods that aim at tackling one quantile at a time, our proposed method estimates the joint posterior distribution of multiple quantiles, leading to higher global efficiency for all quantiles of interest. Markov chain Monte Carlo algorithms are developed to carry out the proposed method. We provide convergence results that justify both the algorithmic convergence and statistical approximations to an integrated-likelihood-based posterior. From the simulation results, we verify that LID has a clear advantage over other existing methods in estimating quantities that relate to two or more quantiles.", "published": "2015-06-02T10:54:09Z", "authors": ["Yang Feng", "Yuguo Chen", "Xuming He"], "categories": ["math.ST"], "doi": "10.3150/13-BEJ589", "_query": "hedonic regression housing", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2509.13840v1", "title": "Characterizing Human Limb Movements Using An In-House Multi-Channel Non-Invasive Surface-EMG System", "abstract": "Electromyography (EMG) signals are obtained from muscle cell activity. The recording and analysis of EMG signals has several applications. The EMG is of diagnostic importance for treating patients suffering from neurological and neuromuscular disorders. Conventional methods involve placement of invasive electrodes within the muscles to record EMG signals. The goal is to showcase the usage of surface based EMG signals to characterize all possible human limb movements. An in-house non-invasive EMG signal acquisition system that offers characterization of human limb actions is a suitable candidate for motor impairment studies and easily extendable to design bionics control specifically for neuromuscular disorder patients. An in-house 8-channel surface-EMG signal acquisition system was designed, fabricated, and employed for characterizing specific movements of upper and lower limb. The non-invasive acquisition system captures the compound electromuscular activity generated from the group of muscles. The EMG acquisition system was designed as a modular structure where the front end analog circuit designs were replicated for all 8 channels, and were designed to function independently. Support vector machine (SVM) as classifier models were developed offline to successfully characterize different human limb actions. The in house built 8 channel acquisition system with ML classifier models were utilized to successfully characterize movements at various joints of the upper and lower limb including fingers, wrist, elbow, shoulder, knee, and ankle individually.", "published": "2025-09-17T09:12:06Z", "authors": ["Vinay C K", "Vikas Vazhayil", "Madhav rao"], "categories": ["eess.SY"], "doi": "", "_query": "hedonic regression housing", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/1102.1857v1", "title": "Nonparametric regression with filtered data", "abstract": "We present a general principle for estimating a regression function nonparametrically, allowing for a wide variety of data filtering, for example, repeated left truncation and right censoring. Both the mean and the median regression cases are considered. The method works by first estimating the conditional hazard function or conditional survivor function and then integrating. We also investigate improved methods that take account of model structure such as independent errors and show that such methods can improve performance when the model structure is true. We establish the pointwise asymptotic normality of our estimators.", "published": "2011-02-09T13:28:44Z", "authors": ["Oliver Linton", "Enno Mammen", "Jens Perch Nielsen", "Ingrid Van Keilegom"], "categories": ["math.ST"], "doi": "10.3150/10-BEJ260", "_query": "hedonic regression housing", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2304.12605v1", "title": "Performance Evaluation of Regression Models in Predicting the Cost of Medical Insurance", "abstract": "The study aimed to evaluate the regression models' performance in predicting the cost of medical insurance. The Three (3) Regression Models in Machine Learning namely Linear Regression, Gradient Boosting, and Support Vector Machine were used. The performance will be evaluated using the metrics RMSE (Root Mean Square), r2 (R Square), and K-Fold Cross-validation. The study also sought to pinpoint the feature that would be most important in predicting the cost of medical insurance.The study is anchored on the knowledge discovery in databases (KDD) process. (KDD) process refers to the overall process of discovering useful knowledge from data. It show the performance evaluation results reveal that among the three (3) Regression models, Gradient boosting received the highest r2 (R Square) 0.892 and the lowest RMSE (Root Mean Square) 1336.594. Furthermore, the 10-Fold Cross-validation weighted mean findings are not significantly different from the r2 (R Square) results of the three (3) regression models. In addition, Exploratory Data Analysis (EDA) using a box plot of descriptive statistics observed that in the charges and smoker features the median of one group lies outside of the box of the other group, so there is a difference between the two groups. It concludes that Gradient boosting appears to perform better among the three (3) regression models. K-Fold Cross-Validation concluded that the three (3) regression models are good. Moreover, Exploratory Data Analysis (EDA) using a box plot of descriptive statistics ceases that the highest charges are due to the smoker feature.", "published": "2023-04-25T06:33:49Z", "authors": ["Jonelle Angelo S. Cenita", "Paul Richie F. Asuncion", "Jayson M. Victoriano"], "categories": ["cs.LG"], "doi": "10.25147/ijcsr.2017.001.1.146", "_query": "hedonic regression housing", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/1708.08734v6", "title": "Posterior Concentration for Bayesian Regression Trees and Forests", "abstract": "Since their inception in the 1980's, regression trees have been one of the more widely used non-parametric prediction methods. Tree-structured methods yield a histogram reconstruction of the regression surface, where the bins correspond to terminal nodes of recursive partitioning. Trees are powerful, yet susceptible to over-fitting. Strategies against overfitting have traditionally relied on pruning greedily grown trees. The Bayesian framework offers an alternative remedy against overfitting through priors. Roughly speaking, a good prior charges smaller trees where overfitting does not occur. While the consistency of random histograms, trees and their ensembles has been studied quite extensively, the theoretical understanding of the Bayesian counterparts has been missing. In this paper, we take a step towards understanding why/when do Bayesian trees and their ensembles not overfit. To address this question, we study the speed at which the posterior concentrates around the true smooth regression function. We propose a spike-and-tree variant of the popular Bayesian CART prior and establish new theoretical results showing that regression trees (and their ensembles) (a) are capable of recovering smooth regression surfaces, achieving optimal rates up to a log factor, (b) can adapt to the unknown level of smoothness and (c) can perform effective dimension reduction when p>n. These results provide a piece of missing theoretical evidence explaining why Bayesian trees (and additive variants thereof) have worked so well in practice.", "published": "2017-08-29T13:16:18Z", "authors": ["Veronika Rockova", "Stephanie van der Pas"], "categories": ["math.ST"], "doi": "", "_query": "hedonic regression housing", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2605.24854v1", "title": "Deep Regression for Repeated Measurements under Covariate Shift", "abstract": "This paper studies nonparametric regression with repeated measurements when the response in the target domain is unobservable or costly to collect. We adopt a transfer learning framework that leverages a source domain with observable responses under covariate shift. The target regression function is estimated by correcting the distribution shift via the density ratio. We consider both known and unknown density ratio scenarios, which reflect different data available for nonparametric regression estimation. In both cases, we further address two settings: the uniformly bounded density ratio and the unbounded case with finite moment conditions. Under the unknown density ratio scenario, both the density ratio and the target regression function are estimated using rectified linear unit (ReLU) feedforward neural networks (FNNs), whereas under the known density ratio scenario, only the target regression function is estimated by ReLU FNNs. Theoretically, we establish non-asymptotic error bounds for the proposed estimators and prove that they achieve the minimax optimal convergence rate under the repeated measurements setting. Notably, we develop a novel approximation theory where the constants of the network parameters depend polynomially, rather than exponentially as in existing works, on the dimension, thereby mitigating the curse of dimensionality. Consequently, we derive sharper non-asymptotic bounds for the stochastic error. The finite sample performance of the proposed method is demonstrated through numerical simulations and a real data application.", "published": "2026-05-24T04:17:14Z", "authors": ["Yingxuan Wang", "Xiangyu Xing", "Wangli Xu"], "categories": ["stat.ME"], "doi": "", "_query": "hedonic regression housing", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/1707.05825v3", "title": "Optimal Estimating Equation for Logistic Regression with Linked Data", "abstract": "We propose an optimal estimating equation for logistic regression with linked data while accounting for false positives. It builds on a previous solution but estimates the regression coefficients with a smaller variance, in large samples.", "published": "2017-07-18T19:19:15Z", "authors": ["Jenkin Tsui", "Abel Dasylva", "Kenneth Chu"], "categories": ["stat.ME"], "doi": "", "_query": "hedonic regression housing", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2308.02786v2", "title": "Robust estimation for functional logistic regression models", "abstract": "This paper addresses the problem of providing robust estimators under a functional logistic regression model. Logistic regression is a popular tool in classification problems with two populations. As in functional linear regression, regularization tools are needed to compute estimators for the functional slope. The traditional methods are based on dimension reduction or penalization combined with maximum likelihood or quasi--likelihood techniques and for that reason, they may be affected by misclassified points especially if they are associated to functional covariates with atypical behaviour. The proposal given in this paper adapts some of the best practices used when the covariates are finite--dimensional to provide reliable estimations. Under regularity conditions, consistency of the resulting estimators and rates of convergence for the predictions are derived. A numerical study illustrates the finite sample performance of the proposed method and reveals its stability under different contamination scenarios. A real data example is also presented.", "published": "2023-08-05T04:22:40Z", "authors": ["Graciela Boente", "Marina Valdora"], "categories": ["stat.ME", "math.ST"], "doi": "", "_query": "hedonic regression housing", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/physics/0410053v1", "title": "Probabilistic temperature forecasting: a comparison of four spread-regression models", "abstract": "Spread regression is an extension of linear regression that allows for the inclusion of a predictor that contains information about the variance. It can be used to take the information from a weather forecast ensemble and produce a probabilistic prediction of future temperatures. There are a number of ways that spread regression can be formulated in detail. We perform an empirical comparison of four of the most obvious methods applied to the calibration of a year of ECMWF temperature forecasts for London Heathrow.", "published": "2004-10-08T12:54:10Z", "authors": ["Stephen Jewson"], "categories": ["physics.ao-ph"], "doi": "", "_query": "hedonic regression housing", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/1512.03479v4", "title": "Optimal Adaptive Inference in Random Design Binary Regression", "abstract": "We construct confidence sets for the regression function in nonparametric binary regression with an unknown design density. These confidence sets are adaptive in $L^2$ loss over a continuous class of Sobolev type spaces. Adaptation holds in the smoothness of the regression function, over the maximal parameter spaces where adaptation is possible, provided the design density is smooth enough. We identify two key regimes --- one where adaptation is possible, and one where some critical regions must be removed. We address related questions about goodness of fit testing and adaptive estimation of relevant parameters.", "published": "2015-12-10T22:56:00Z", "authors": ["Rajarshi Mukherjee", "Subhabrata Sen"], "categories": ["math.ST"], "doi": "", "_query": "hedonic regression housing", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/1709.09301v1", "title": "Multi-way Interacting Regression via Factorization Machines", "abstract": "We propose a Bayesian regression method that accounts for multi-way interactions of arbitrary orders among the predictor variables. Our model makes use of a factorization mechanism for representing the regression coefficients of interactions among the predictors, while the interaction selection is guided by a prior distribution on random hypergraphs, a construction which generalizes the Finite Feature Model. We present a posterior inference algorithm based on Gibbs sampling, and establish posterior consistency of our regression model. Our method is evaluated with extensive experiments on simulated data and demonstrated to be able to identify meaningful interactions in applications in genetics and retail demand forecasting.", "published": "2017-09-27T01:51:19Z", "authors": ["Mikhail Yurochkin", "XuanLong Nguyen", "Nikolaos Vasiloglou"], "categories": ["stat.ML"], "doi": "", "_query": "hedonic regression housing", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/1703.08772v2", "title": "Multivariate Regression with Gross Errors on Manifold-valued Data", "abstract": "We consider the topic of multivariate regression on manifold-valued output, that is, for a multivariate observation, its output response lies on a manifold. Moreover, we propose a new regression model to deal with the presence of grossly corrupted manifold-valued responses, a bottleneck issue commonly encountered in practical scenarios. Our model first takes a correction step on the grossly corrupted responses via geodesic curves on the manifold, and then performs multivariate linear regression on the corrected data. This results in a nonconvex and nonsmooth optimization problem on manifolds. To this end, we propose a dedicated approach named PALMR, by utilizing and extending the proximal alternating linearized minimization techniques. Theoretically, we investigate its convergence property, where it is shown to converge to a critical point under mild conditions. Empirically, we test our model on both synthetic and real diffusion tensor imaging data, and show that our model outperforms other multivariate regression models when manifold-valued responses contain gross errors, and is effective in identifying gross errors.", "published": "2017-03-26T05:53:39Z", "authors": ["Xiaowei Zhang", "Xudong Shi", "Yu Sun", "Li Cheng"], "categories": ["stat.ML", "cs.CV", "math.OC"], "doi": "", "_query": "hedonic regression housing", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/1708.04753v1", "title": "Frequentist coverage and sup-norm convergence rate in Gaussian process regression", "abstract": "Gaussian process (GP) regression is a powerful interpolation technique due to its flexibility in capturing non-linearity. In this paper, we provide a general framework for understanding the frequentist coverage of point-wise and simultaneous Bayesian credible sets in GP regression. As an intermediate result, we develop a Bernstein von-Mises type result under supremum norm in random design GP regression. Identifying both the mean and covariance function of the posterior distribution of the Gaussian process as regularized $M$-estimators, we show that the sampling distribution of the posterior mean function and the centered posterior distribution can be respectively approximated by two population level GPs. By developing a comparison inequality between two GPs, we provide exact characterization of frequentist coverage probabilities of Bayesian point-wise credible intervals and simultaneous credible bands of the regression function. Our results show that inference based on GP regression tends to be conservative; when the prior is under-smoothed, the resulting credible intervals and bands have minimax-optimal sizes, with their frequentist coverage converging to a non-degenerate value between their nominal level and one. As a byproduct of our theory, we show that the GP regression also yields minimax-optimal posterior contraction rate relative to the supremum norm, which provides a positive evidence to the long standing problem on optimal supremum norm contraction rate in GP regression.", "published": "2017-08-16T03:07:54Z", "authors": ["Yun Yang", "Anirban Bhattacharya", "Debdeep Pati"], "categories": ["math.ST", "stat.CO", "stat.ML"], "doi": "", "_query": "hedonic regression housing", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2105.07613v8", "title": "Nonparametric regression for locally stationary functional time series", "abstract": "In this study, we develop an asymptotic theory of nonparametric regression for a locally stationary functional time series. First, we introduce the notion of a locally stationary functional time series (LSFTS) that takes values in a semi-metric space. Then, we propose a nonparametric model for LSFTS with a regression function that changes smoothly over time. We establish the uniform convergence rates of a class of kernel estimators, the Nadaraya-Watson (NW) estimator of the regression function, and a central limit theorem of the NW estimator.", "published": "2021-05-17T05:13:53Z", "authors": ["Daisuke Kurisu"], "categories": ["math.ST"], "doi": "", "_query": "hedonic regression housing", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2102.12919v2", "title": "Distribution-Free Robust Linear Regression", "abstract": "We study random design linear regression with no assumptions on the distribution of the covariates and with a heavy-tailed response variable. In this distribution-free regression setting, we show that boundedness of the conditional second moment of the response given the covariates is a necessary and sufficient condition for achieving nontrivial guarantees. As a starting point, we prove an optimal version of the classical in-expectation bound for the truncated least squares estimator due to Györfi, Kohler, Krzyżak, and Walk. However, we show that this procedure fails with constant probability for some distributions despite its optimal in-expectation performance. Then, combining the ideas of truncated least squares, median-of-means procedures, and aggregation theory, we construct a non-linear estimator achieving excess risk of order $d/n$ with an optimal sub-exponential tail. While existing approaches to linear regression for heavy-tailed distributions focus on proper estimators that return linear functions, we highlight that the improperness of our procedure is necessary for attaining nontrivial guarantees in the distribution-free setting.", "published": "2021-02-25T15:10:41Z", "authors": ["Jaouad Mourtada", "Tomas Vaškevičius", "Nikita Zhivotovskiy"], "categories": ["math.ST", "cs.LG", "stat.ML"], "doi": "10.4171/MSL/27", "_query": "hedonic regression housing", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2106.10341v1", "title": "Scalable Econometrics on Big Data -- The Logistic Regression on Spark", "abstract": "Extra-large datasets are becoming increasingly accessible, and computing tools designed to handle huge amount of data efficiently are democratizing rapidly. However, conventional statistical and econometric tools are still lacking fluency when dealing with such large datasets. This paper dives into econometrics on big datasets, specifically focusing on the logistic regression on Spark. We review the robustness of the functions available in Spark to fit logistic regression and introduce a package that we developed in PySpark which returns the statistical summary of the logistic regression, necessary for statistical inference.", "published": "2021-06-18T20:10:34Z", "authors": ["Aurélien Ouattara", "Matthieu Bulté", "Wan-Ju Lin", "Philipp Scholl", "Benedikt Veit", "Christos Ziakas", "Florian Felice", "Julien Virlogeux", "George Dikos"], "categories": ["stat.CO", "econ.EM"], "doi": "", "_query": "hedonic regression housing", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2503.02011v2", "title": "Interval Regression: A Comparative Study with Proposed Models", "abstract": "Regression models are essential for a wide range of real-world applications. However, in practice, target values are not always precisely known; instead, they may be represented as intervals of acceptable values. This challenge has led to the development of Interval Regression models. In this study, we provide a comprehensive review of existing Interval Regression models and introduce alternative models for comparative analysis. Experiments are conducted on both real-world and synthetic datasets to offer a broad perspective on model performance. The results demonstrate that no single model is universally optimal, highlighting the importance of selecting the most suitable model for each specific scenario.", "published": "2025-03-03T19:39:02Z", "authors": ["Tung L Nguyen", "Toby Dylan Hocking"], "categories": ["cs.LG", "stat.ML"], "doi": "", "_query": "hedonic regression housing", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2301.10498v3", "title": "On deviation probabilities in non-parametric regression with heavy-tailed noise", "abstract": "This paper is devoted to the problem of determining the concentration bounds that are achievable in non-parametric regression. We consider the setting where features are supported on a bounded subset of $\\mathbb{R}^d$, the regression function is Lipschitz, and the noise is only assumed to have a finite second moment. We first specify the fundamental limits of the problem by establishing a general lower bound on deviation probabilities, and then construct explicit estimators that achieve this bound. These estimators are obtained by applying the median-of-means principle to classical local averaging rules in non-parametric regression, including nearest neighbors and kernel procedures.", "published": "2023-01-25T10:20:05Z", "authors": ["Anna Ben-Hamou", "Arnaud Guyader"], "categories": ["math.ST"], "doi": "", "_query": "hedonic regression housing", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2511.10824v1", "title": "Neural Local Wasserstein Regression", "abstract": "We study the estimation problem of distribution-on-distribution regression, where both predictors and responses are probability measures. Existing approaches typically rely on a global optimal transport map or tangent-space linearization, which can be restrictive in approximation capacity and distort geometry in multivariate underlying domains. In this paper, we propose the \\emph{Neural Local Wasserstein Regression}, a flexible nonparametric framework that models regression through locally defined transport maps in Wasserstein space. Our method builds on the analogy with classical kernel regression: kernel weights based on the 2-Wasserstein distance localize estimators around reference measures, while neural networks parameterize transport operators that adapt flexibly to complex data geometries. This localized perspective broadens the class of admissible transformations and avoids the limitations of global map assumptions and linearization structures. We develop a practical training procedure using DeepSets-style architectures and Sinkhorn-approximated losses, combined with a greedy reference selection strategy for scalability. Through synthetic experiments on Gaussian and mixture models, as well as distributional prediction tasks on MNIST, we demonstrate that our approach effectively captures nonlinear and high-dimensional distributional relationships that elude existing methods.", "published": "2025-11-13T21:54:18Z", "authors": ["Inga Girshfeld", "Xiaohui Chen"], "categories": ["stat.ML", "cs.LG"], "doi": "", "_query": "hedonic regression housing", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/1311.4176v3", "title": "ComReg: A Complex Network Approach to Prioritize Test Cases for Regression Testing", "abstract": "Regression testing is performed to provide confidence that changes in a part of software do not affect other parts of the software. An execution of all existing test cases is the best way to re-establish this confidence. However, regression testing is an expensive process---there might be insufficient resources (e.g., time, workforce) to allow for the re-execution of all test cases. Regression test prioritization techniques attempt to re-order a regression test suite based on some criteria so that highest priority test cases are executed earlier. In this study, we want to prioritize test cases for regression testing based on the dependency network of faults. In software testing, it is common that some faults are consequences of other faults (leading faults). Moreover, dependent faults can be removed if and only if the leading faults have been removed. Our goal is to prioritize test cases so that test cases that exposed leading faults (the most central faults in the fault dependency network) in the system testing phase, are executed first in regression testing. We present ComReg, a test case prioritization technique based on the dependency network of faults. We model a fault dependency network as a directed graph and identify leading faults to prioritize test cases for regression testing. We use a centrality aggregation technique which considers six network representative centrality metrics to identify leading faults in the fault dependency network. We also discuss the use of fault communities to select an arbitrary percentage of the test cases from a prioritized regression test suite. We conduct a case study that evaluates the effectiveness and applicability of the proposed method.", "published": "2013-11-17T16:15:03Z", "authors": ["Imrul Kayes", "Jacob Chakareski"], "categories": ["cs.SE"], "doi": "", "_query": "hedonic regression housing", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2605.23097v1", "title": "Proximal DCA for Fréchet Regression on Riemannian Manifolds with Bounded Curvature", "abstract": "Fréchet regression generalizes linear regression to metric-space-valued responses by defining fitted values as minimizers of weighted Fréchet functionals. Since these weights may have mixed signs, the resulting objective is a signed barycenter problem rather than a standard convex barycenter problem. On Riemannian manifolds, this is further complicated by the lack of global geodesic convexity and possible nonsmoothness of squared distances near cut loci. We study signed Fréchet regression on complete manifolds with two-sided bounded sectional curvature. By restricting optimization to a strongly convex normal ball containing the response support, we use local smoothness, Hessian comparison, and Jacobi-field estimates to formulate the problem as a locally controlled Riemannian proximal DC problem. This leads to FRIDA (Fréchet Regression via Riemannian Iterative DC Algorithm), an exact and inexact proximal DC algorithm for computing regression fits. We prove existence and interiority of minimizers under explicit signed-weight conditions, establish curvature-dependent strong convexity of the proximal subproblems, and show descent and convergence of the iterates to stationary points. We also derive sublinear complexity estimates and, under real-analyticity, obtain full-sequence convergence with KL-type local rates. These results provide a rigorous optimization framework for signed Fréchet regression on manifolds with bounded curvature.", "published": "2026-05-21T23:08:35Z", "authors": ["Yamin Zhou", "César A. Uribe"], "categories": ["math.OC", "math.GT"], "doi": "", "_query": "hedonic regression housing", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2412.00123v4", "title": "Electricity Price Prediction Using Multi-Kernel Gaussian Process Regression Combined with Kernel-Based Support Vector Regression", "abstract": "This paper presents a new hybrid model for predicting German electricity prices. The algorithm is based on a combination of Gaussian Process Regression (GPR) and Support Vector Regression (SVR). Although GPR is a competent model for learning stochastic patterns within data and for interpolation, its performance for out-of-sample data is not very promising. By choosing a suitable data-dependent covariance function, we can enhance the performance of GPR for the German hourly power prices being tested. However, since the out-of-sample prediction is dependent on the training data, the prediction is vulnerable to noise and outliers. To overcome this issue, a separate prediction is calculated using SVR, which applies margin-based optimization. This method is advantageous when dealing with non-linear processes and outliers, since only certain necessary points (support vectors) in the training data are responsible for regression. The individual predictions are then linearly combined using uniform weights. When tested on historic German power prices, this approach outperforms the publicly available benchmarks, namely the LASSO estimated autoregressive regression model, deep neural network provided in the recent research by [1].", "published": "2024-11-28T10:32:50Z", "authors": ["Abhinav Das", "Stephan Schlüter", "Lorenz Schneider"], "categories": ["cs.LG", "math.PR"], "doi": "10.1002/for.70124", "_query": "hedonic regression housing", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/1501.01208v1", "title": "The Influence Function of Penalized Regression Estimators", "abstract": "To perform regression analysis in high dimensions, lasso or ridge estimation are a common choice. However, it has been shown that these methods are not robust to outliers. Therefore, alternatives as penalized M-estimation or the sparse least trimmed squares (LTS) estimator have been proposed. The robustness of these regression methods can be measured with the influence function. It quantifies the effect of infinitesimal perturbations in the data. Furthermore it can be used to compute the asymptotic variance and the mean squared error. In this paper we compute the influence function, the asymptotic variance and the mean squared error for penalized M-estimators and the sparse LTS estimator. The asymptotic biasedness of the estimators make the calculations nonstandard. We show that only M-estimators with a loss function with a bounded derivative are robust against regression outliers. In particular, the lasso has an unbounded influence function.", "published": "2015-01-06T15:46:44Z", "authors": ["Viktoria Öllerer", "Christophe Croux", "Andreas Alfons"], "categories": ["math.ST"], "doi": "10.1080/02331888.2014.922563", "_query": "hedonic regression housing", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2602.23567v1", "title": "Tensor Hypercontraction Error Correction Using Regression", "abstract": "Wavefunction-based quantum methods are some of the most accurate tools for predicting and analyzing the electronic structure of molecules, in particular for accounting for dynamical electron correlation. However, most methods of including dynamical correlation beyond the simple second-order Møller-Plesset perturbation theory (MP2) level are too computationally expensive to apply to large molecules. Approximations which reduce scaling with system size are a potential remedy, such as the tensor hyper-contraction (THC) technique of Hohenstein et al., but also result in additional sources of error. In this work, we correct errors in THC-approximated methods using machine learning. Specifically, we apply THC to third-order Møller-Plesset theory (MP3) as a simplified model for coupled cluster with single and double excitations (CCSD), and train several regression models on observed THC errors from the Main Group Chemistry Database (MGCDB84). We compare performance of multiple linear regression models and non-linear Kernel Ridge regression models. We also investigate correlation procedures using absolute and relative corrections and evaluate the corrections for both molecule and reaction energies. We discuss the potential for using regression techniques to correct THC-MP3 errors by comparing it to the \"canonical\" MP3 reference values and find the optimum technique based on accuracy. We find that non-linear regression models reduced root mean squared errors between THC- and canonical MP3 by a factor of 6-9$\\times$ for total molecular energies and 2-3$\\times$ for reaction energies.", "published": "2026-02-27T00:30:36Z", "authors": ["Ishna Satyarth", "Eric C. Larson", "Devin A. Matthews"], "categories": ["physics.chem-ph", "cs.LG"], "doi": "", "_query": "hedonic regression housing", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/1409.0506v4", "title": "Testing parametric models in linear-directional regression", "abstract": "This paper presents a goodness-of-fit test for parametric regression models with scalar response and directional predictor, that is, a vector on a sphere of arbitrary dimension. The testing procedure is based on the weighted squared distance between a smooth and a parametric regression estimator, where the smooth regression estimator is obtained by a projected local approach. Asymptotic behavior of the test statistic under the null hypothesis and local alternatives is provided, jointly with a consistent bootstrap algorithm for application in practice. A simulation study illustrates the performance of the test in finite samples. The procedure is applied to test a linear model in text mining.", "published": "2014-09-01T19:01:12Z", "authors": ["Eduardo García-Portugués", "Ingrid Van Keilegom", "Rosa M. Crujeiras", "Wenceslao González-Manteiga"], "categories": ["stat.ME"], "doi": "10.1111/sjos.12236", "_query": "hedonic regression housing", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/1702.01000v3", "title": "Sharp Convergence Rates for Forward Regression in High-Dimensional Sparse Linear Models", "abstract": "Forward regression is a statistical model selection and estimation procedure which inductively selects covariates that add predictive power into a working statistical regression model. Once a model is selected, unknown regression parameters are estimated by least squares. This paper analyzes forward regression in high-dimensional sparse linear models. Probabilistic bounds for prediction error norm and number of selected covariates are proved. The analysis in this paper gives sharp rates and does not require beta-min or irrepresentability conditions.", "published": "2017-02-03T13:33:32Z", "authors": ["Damian Kozbur"], "categories": ["stat.ML"], "doi": "", "_query": "hedonic regression housing", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/1807.08912v2", "title": "Meta-Learning Priors for Efficient Online Bayesian Regression", "abstract": "Gaussian Process (GP) regression has seen widespread use in robotics due to its generality, simplicity of use, and the utility of Bayesian predictions. The predominant implementation of GP regression is a nonparameteric kernel-based approach, as it enables fitting of arbitrary nonlinear functions. However, this approach suffers from two main drawbacks: (1) it is computationally inefficient, as computation scales poorly with the number of samples; and (2) it can be data inefficient, as encoding prior knowledge that can aid the model through the choice of kernel and associated hyperparameters is often challenging and unintuitive. In this work, we propose ALPaCA, an algorithm for efficient Bayesian regression which addresses these issues. ALPaCA uses a dataset of sample functions to learn a domain-specific, finite-dimensional feature encoding, as well as a prior over the associated weights, such that Bayesian linear regression in this feature space yields accurate online predictions of the posterior predictive density. These features are neural networks, which are trained via a meta-learning (or \"learning-to-learn\") approach. ALPaCA extracts all prior information directly from the dataset, rather than restricting prior information to the choice of kernel hyperparameters. Furthermore, by operating in the weight space, it substantially reduces sample complexity. We investigate the performance of ALPaCA on two simple regression problems, two simulated robotic systems, and on a lane-change driving task performed by humans. We find our approach outperforms kernel-based GP regression, as well as state of the art meta-learning approaches, thereby providing a promising plug-in tool for many regression tasks in robotics where scalability and data-efficiency are important.", "published": "2018-07-24T05:46:04Z", "authors": ["James Harrison", "Apoorva Sharma", "Marco Pavone"], "categories": ["cs.RO", "cs.LG"], "doi": "", "_query": "hedonic regression housing", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/1801.08898v1", "title": "A note on \"MLE in logistic regression with a diverging dimension\"", "abstract": "This short note is to point the reader to notice that the proof of high dimensional asymptotic normality of MLE estimator for logistic regression under the regime $p_n=o(n)$ given in paper: \"Maximum likelihood estimation in logistic regression models with a diverging number of covariates. Electronic Journal of Statistics, 6, 1838-1846.\" is wrong.", "published": "2018-01-26T16:58:30Z", "authors": ["Huiming Zhang"], "categories": ["math.ST"], "doi": "", "_query": "hedonic regression housing", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2210.08571v3", "title": "Dimension free ridge regression", "abstract": "Random matrix theory has become a widely useful tool in high-dimensional statistics and theoretical machine learning. However, random matrix theory is largely focused on the proportional asymptotics in which the number of columns grows proportionally to the number of rows of the data matrix. This is not always the most natural setting in statistics where columns correspond to covariates and rows to samples. With the objective to move beyond the proportional asymptotics, we revisit ridge regression ($\\ell_2$-penalized least squares) on i.i.d. data $(x_i, y_i)$, $i\\le n$, where $x_i$ is a feature vector and $y_i = β^\\top x_i +ε_i \\in\\mathbb{R}$ is a response. We allow the feature vector to be high-dimensional, or even infinite-dimensional, in which case it belongs to a separable Hilbert space, and assume either $z_i := Σ^{-1/2}x_i$ to have i.i.d. entries, or to satisfy a certain convex concentration property. Within this setting, we establish non-asymptotic bounds that approximate the bias and variance of ridge regression in terms of the bias and variance of an `equivalent' sequence model (a regression model with diagonal design matrix). The approximation is up to multiplicative factors bounded by $(1\\pm Δ)$ for some explicitly small $Δ$. Previously, such an approximation result was known only in the proportional regime and only up to additive errors: in particular, it did not allow to characterize the behavior of the excess risk when this converges to $0$. Our general theory recovers earlier results in the proportional regime (with better error rates). As a new application, we obtain a completely explicit and sharp characterization of ridge regression for Hilbert covariates with regularly varying spectrum. Finally, we analyze the overparametrized near-interpolation setting and obtain sharp `benign overfitting' guarantees.", "published": "2022-10-16T16:01:05Z", "authors": ["Chen Cheng", "Andrea Montanari"], "categories": ["math.ST", "stat.ML"], "doi": "", "_query": "hedonic regression housing", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2109.12389v2", "title": "RegMiner: Towards Constructing a Large Regression Dataset from Code Evolution History", "abstract": "Bug datasets consisting of real-world bugs are important artifacts for researchers and programmers, which lay empirical and experimental foundation for various SE/PL research such as fault localization, software testing, and program repair. All known state-of-the-art datasets are constructed manually, which inevitably limits their scalability, representativeness, and the support for the emerging data-driven research. In this work, we propose an approach to automate the process of harvesting replicable regression bugs from the code evolutionary history. We focus on regression bug dataset, as they (1) manifest how a bug is introduced and fixed (as normal bugs), (2) support regression bug analysis, and (3) incorporate a much stronger specification (i.e., the original passing version) for general bug analysis. Technically, we address an information retrieval problem on code evolution history. Given a code repository, we search for regressions where a test can pass a regression-fixing commit, fail a regressioninducing commit, and pass a working commit. In this work, we address the challenges of (1) identifying potential regression-fixing commits from the code evolution history, (2) migrating the test and its code dependencies over the history, and (3) minimizing the compilation overhead during the regression search. We build our tool, RegMiner, which harvested 537 regressions over 66 projects for 3 weeks, created the largest replicable regression dataset within shortest period, to the best of our knowledge. Moreover, our empirical study on our regression dataset shows a gap between the popular regression fault localization techniques (e.g, delta-debugging) and the real fix, revealing new data-driven research opportunities.", "published": "2021-09-25T15:29:13Z", "authors": ["Xuezhi Song", "Yun Lin", "Siang Hwee Ng", "Yijian Wu", "Xin Peng", "Jin Song Dong", "Hong Mei"], "categories": ["cs.SE"], "doi": "", "_query": "hedonic regression housing", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2203.10459v1", "title": "Getting more from your regression model: A free lunch?", "abstract": "We consider a simple approach for approximating detailed information about the conditional distribution of a real-valued response variable, given values for its covariates, using only the outputs from a standard regression model. We validate this approach by assessing its performance in the context of quantile regression; when applied to the outputs of linear, gradient boosted tree ensemble and random forest models. We find that it compares favourably to the standard approach for estimating quantile regression functions, especially for commonly selected tail probabilities, and is highly competitive with the quantile regression forest model, across a large collection of benchmark data sets.", "published": "2022-03-20T05:28:03Z", "authors": ["David P. Hofmeyr"], "categories": ["stat.ME"], "doi": "", "_query": "hedonic regression housing", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2502.15416v1", "title": "Covariance Regression based on Basis Expansion", "abstract": "This paper presents a study on an $\\ell_1$-penalized covariance regression method. Conventional approaches in high-dimensional covariance estimation often lack the flexibility to integrate external information. As a remedy, we adopt the regression-based covariance modeling framework and introduce a linear covariance selection model (LCSM) to encompass a broader spectrum of covariance structures when covariate information is available. Unlike existing methods, we do not assume that the true covariance matrix can be exactly represented by a linear combination of known basis matrices. Instead, we adopt additional basis matrices for a portion of the covariance patterns not captured by the given bases. To estimate high-dimensional regression coefficients, we exploit the sparsity-inducing $\\ell_1$-penalization scheme. Our theoretical analyses are based on the (symmetric) matrix regression model with additive random error matrix, which allows us to establish new non-asymptotic convergence rates of the proposed covariance estimator. The proposed method is implemented with the coordinate descent algorithm. We conduct empirical evaluation on simulated data to complement theoretical findings and underscore the efficacy of our approach. To show a practical applicability of our method, we further apply it to the co-expression analysis of liver gene expression data where the given basis corresponds to the adjacency matrix of the co-expression network.", "published": "2025-02-21T12:34:58Z", "authors": ["Kwan-Young Bak", "Seongoh Park"], "categories": ["stat.ME"], "doi": "", "_query": "hedonic regression housing", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2002.06195v2", "title": "An implicit function learning approach for parametric modal regression", "abstract": "For multi-valued functions---such as when the conditional distribution on targets given the inputs is multi-modal---standard regression approaches are not always desirable because they provide the conditional mean. Modal regression algorithms address this issue by instead finding the conditional mode(s). Most, however, are nonparametric approaches and so can be difficult to scale. Further, parametric approximators, like neural networks, facilitate learning complex relationships between inputs and targets. In this work, we propose a parametric modal regression algorithm. We use the implicit function theorem to develop an objective, for learning a joint function over inputs and targets. We empirically demonstrate on several synthetic problems that our method (i) can learn multi-valued functions and produce the conditional modes, (ii) scales well to high-dimensional inputs, and (iii) can even be more effective for certain uni-modal problems, particularly for high-frequency functions. We demonstrate that our method is competitive in a real-world modal regression problem and two regular regression datasets.", "published": "2020-02-14T00:37:41Z", "authors": ["Yangchen Pan", "Ehsan Imani", "Martha White", "Amir-massoud Farahmand"], "categories": ["stat.ML", "cs.LG"], "doi": "", "_query": "hedonic regression housing", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2509.19900v1", "title": "Generalized Nonnegative Structured Kruskal Tensor Regression", "abstract": "This paper introduces Generalized Nonnegative Structured Kruskal Tensor Regression (NS-KTR), a novel tensor regression framework that enhances interpretability and performance through mode-specific hybrid regularization and nonnegativity constraints. Our approach accommodates both linear and logistic regression formulations for diverse response variables while addressing the structural heterogeneity inherent in multidimensional tensor data. We integrate fused LASSO, total variation, and ridge regularizers, each tailored to specific tensor modes, and develop an efficient alternating direction method of multipliers (ADMM) based algorithm for parameter estimation. Comprehensive experiments on synthetic signals and real hyperspectral datasets demonstrate that NS-KTR consistently outperforms conventional tensor regression methods. The framework's ability to preserve distinct structural characteristics across tensor dimensions while ensuring physical interpretability makes it especially suitable for applications in signal processing and hyperspectral image analysis.", "published": "2025-09-24T08:51:38Z", "authors": ["Xinjue Wang", "Esa Ollila", "Sergiy A. Vorobyov", "Ammar Mian"], "categories": ["eess.SP", "stat.AP", "stat.ML"], "doi": "", "_query": "hedonic regression housing", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2101.06536v6", "title": "Deep Cox Mixtures for Survival Regression", "abstract": "Survival analysis is a challenging variation of regression modeling because of the presence of censoring, where the outcome measurement is only partially known, due to, for example, loss to follow up. Such problems come up frequently in medical applications, making survival analysis a key endeavor in biostatistics and machine learning for healthcare, with Cox regression models being amongst the most commonly employed models. We describe a new approach for survival analysis regression models, based on learning mixtures of Cox regressions to model individual survival distributions. We propose an approximation to the Expectation Maximization algorithm for this model that does hard assignments to mixture groups to make optimization efficient. In each group assignment, we fit the hazard ratios within each group using deep neural networks, and the baseline hazard for each mixture component non-parametrically. We perform experiments on multiple real world datasets, and look at the mortality rates of patients across ethnicity and gender. We emphasize the importance of calibration in healthcare settings and demonstrate that our approach outperforms classical and modern survival analysis baselines, both in terms of discriminative performance and calibration, with large gains in performance on the minority demographics.", "published": "2021-01-16T22:41:22Z", "authors": ["Chirag Nagpal", "Steve Yadlowsky", "Negar Rostamzadeh", "Katherine Heller"], "categories": ["cs.LG", "stat.ME", "stat.ML"], "doi": "", "_query": "hedonic regression housing", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2112.04779v2", "title": "Regularized Modal Regression on Markov-dependent Observations: A Theoretical Assessment", "abstract": "Modal regression, a widely used regression protocol, has been extensively investigated in statistical and machine learning communities due to its robustness to outliers and heavy-tailed noises. Understanding modal regression's theoretical behavior can be fundamental in learning theory. Despite significant progress in characterizing its statistical property, the majority of the results are based on the assumption that samples are independent and identical distributed (i.i.d.), which is too restrictive for real-world applications. This paper concerns the statistical property of regularized modal regression (RMR) within an important dependence structure - Markov dependent. Specifically, we establish the upper bound for RMR estimator under moderate conditions and give an explicit learning rate. Our results show that the Markov dependence impacts on the generalization error in the way that sample size would be discounted by a multiplicative factor depending on the spectral gap of underlying Markov chain. This result shed a new light on characterizing the theoretical underpinning for robust regression.", "published": "2021-12-09T09:08:52Z", "authors": ["Tielang Gong", "Yuxin Dong", "Hong Chen", "Bo Dong", "Wei Feng", "Chen Li"], "categories": ["stat.ML", "cs.LG"], "doi": "", "_query": "hedonic regression housing", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2112.04779v2", "title": "Regularized Modal Regression on Markov-dependent Observations: A Theoretical Assessment", "abstract": "Modal regression, a widely used regression protocol, has been extensively investigated in statistical and machine learning communities due to its robustness to outliers and heavy-tailed noises. Understanding modal regression's theoretical behavior can be fundamental in learning theory. Despite significant progress in characterizing its statistical property, the majority of the results are based on the assumption that samples are independent and identical distributed (i.i.d.), which is too restrictive for real-world applications. This paper concerns the statistical property of regularized modal regression (RMR) within an important dependence structure - Markov dependent. Specifically, we establish the upper bound for RMR estimator under moderate conditions and give an explicit learning rate. Our results show that the Markov dependence impacts on the generalization error in the way that sample size would be discounted by a multiplicative factor depending on the spectral gap of underlying Markov chain. This result shed a new light on characterizing the theoretical underpinning for robust regression.", "published": "2021-12-09T09:08:52Z", "authors": ["Tielang Gong", "Yuxin Dong", "Hong Chen", "Bo Dong", "Wei Feng", "Chen Li"], "categories": ["stat.ML", "cs.LG"], "doi": "", "_query": "hedonic regression housing", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/1706.01852v3", "title": "Contraction and uniform convergence of isotonic regression", "abstract": "We consider the problem of isotonic regression, where the underlying signal $x$ is assumed to satisfy a monotonicity constraint, that is, $x$ lies in the cone $\\{ x\\in\\mathbb{R}^n : x_1 \\leq \\dots \\leq x_n\\}$. We study the isotonic projection operator (projection to this cone), and find a necessary and sufficient condition characterizing all norms with respect to which this projection is contractive. This enables a simple and non-asymptotic analysis of the convergence properties of isotonic regression, yielding uniform confidence bands that adapt to the local Lipschitz properties of the signal.", "published": "2017-06-06T17:00:58Z", "authors": ["Fan Yang", "Rina Foygel Barber"], "categories": ["math.ST"], "doi": "", "_query": "hedonic regression housing", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2405.20817v3", "title": "Extremile scalar-on-function regression", "abstract": "Extremiles provide a generalization of quantiles which are not only robust, but also have an intrinsic link with extreme value theory. This paper introduces an extremile regression model tailored for functional covariate spaces. The estimation procedure turns out to be a weighted version of local linear scalar-on-function regression, where now a double kernel approach plays a crucial role. Asymptotic expressions for the bias and variance are established, applicable to both decreasing bandwidth sequences and automatically selected bandwidths. The methodology is then investigated in detail through a simulation study. Furthermore, we illustrate the method's applicability with an analysis of the Berkeley Growth data, showcasing its performance in a real-world functional data setting.", "published": "2024-05-31T14:12:59Z", "authors": ["Maria Laura Battagliola", "Martin Bladt"], "categories": ["stat.ME"], "doi": "", "_query": "hedonic regression housing", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2203.06591v1", "title": "ORDSIM: Ordinal Regression for E-Commerce Query Similarity Prediction", "abstract": "Query similarity prediction task is generally solved by regression based models with square loss. Such a model is agnostic of absolute similarity values and it penalizes the regression error at all ranges of similarity values at the same scale. However, to boost e-commerce platform's monetization, it is important to predict high-level similarity more accurately than low-level similarity, as highly similar queries retrieves items according to user-intents, whereas moderately similar item retrieves related items, which may not lead to a purchase. Regression models fail to customize its loss function to concentrate around the high-similarity band, resulting poor performance in query similarity prediction task. We address the above challenge by considering the query prediction as an ordinal regression problem, and thereby propose a model, ORDSIM (ORDinal Regression for SIMilarity Prediction). ORDSIM exploits variable-width buckets to model ordinal loss, which penalizes errors in high-level similarity harshly, and thus enable the regression model to obtain better prediction results for high similarity values. We evaluate ORDSIM on a dataset of over 10 millions e-commerce queries from eBay platform and show that ORDSIM achieves substantially smaller prediction error compared to the competing regression methods on this dataset.", "published": "2022-03-13T07:22:50Z", "authors": ["Md. Ahsanul Kabir", "Mohammad Al Hasan", "Aritra Mandal", "Daniel Tunkelang", "Zhe Wu"], "categories": ["cs.LG"], "doi": "", "_query": "hedonic regression housing", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2006.16597v2", "title": "Regression with reject option and application to kNN", "abstract": "We investigate the problem of regression where one is allowed to abstain from predicting. We refer to this framework as regression with reject option as an extension of classification with reject option. In this context, we focus on the case where the rejection rate is fixed and derive the optimal rule which relies on thresholding the conditional variance function. We provide a semi-supervised estimation procedure of the optimal rule involving two datasets: a first labeled dataset is used to estimate both regression function and conditional variance function while a second unlabeled dataset is exploited to calibrate the desired rejection rate. The resulting predictor with reject option is shown to be almost as good as the optimal predictor with reject option both in terms of risk and rejection rate. We additionally apply our methodology with kNN algorithm and establish rates of convergence for the resulting kNN predictor under mild conditions. Finally, a numerical study is performed to illustrate the benefit of using the proposed procedure.", "published": "2020-06-30T08:20:57Z", "authors": ["Christophe Denis", "Mohamed Hebiri", "Ahmed Zaoui"], "categories": ["stat.ML", "cs.LG"], "doi": "", "_query": "hedonic regression housing", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/1903.08192v1", "title": "Adaptive Hard Thresholding for Near-optimal Consistent Robust Regression", "abstract": "We study the problem of robust linear regression with response variable corruptions. We consider the oblivious adversary model, where the adversary corrupts a fraction of the responses in complete ignorance of the data. We provide a nearly linear time estimator which consistently estimates the true regression vector, even with $1-o(1)$ fraction of corruptions. Existing results in this setting either don't guarantee consistent estimates or can only handle a small fraction of corruptions. We also extend our estimator to robust sparse linear regression and show that similar guarantees hold in this setting. Finally, we apply our estimator to the problem of linear regression with heavy-tailed noise and show that our estimator consistently estimates the regression vector even when the noise has unbounded variance (e.g., Cauchy distribution), for which most existing results don't even apply. Our estimator is based on a novel variant of outlier removal via hard thresholding in which the threshold is chosen adaptively and crucially relies on randomness to escape bad fixed points of the non-convex hard thresholding operation.", "published": "2019-03-19T18:08:20Z", "authors": ["Arun Sai Suggala", "Kush Bhatia", "Pradeep Ravikumar", "Prateek Jain"], "categories": ["cs.LG", "stat.ML"], "doi": "", "_query": "hedonic regression housing", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/1508.04217v2", "title": "Macroeconomic Forecasting and Variable Selection with a Very Large Number of Predictors: A Penalized Regression Approach", "abstract": "This paper studies macroeconomic forecasting and variable selection using a folded-concave penalized regression with a very large number of predictors. The penalized regression approach leads to sparse estimates of the regression coefficients, and is applicable even if the dimensionality of the model is much larger than the sample size. The first half of the paper discusses the theoretical aspects of a folded-concave penalized regression when the model exhibits time series dependence. Specifically, we show the oracle inequality and the oracle property for ultrahigh-dimensional time-dependent regressors. The latter half of the paper shows the validity of the penalized regression using two motivating empirical applications. The first forecasts U.S. GDP with the FRED-MD data using the MIDAS regression framework, where there are more than 1000 covariates, while the sample size is at most 200. The second examines how well the penalized regression screens the hidden portfolio with around 40 stocks from more than 1800 potential stocks using NYSE stock price data. Both applications reveal that the penalized regression provides remarkable results in terms of forecasting performance and variable selection.", "published": "2015-08-18T05:33:06Z", "authors": ["Yoshimasa Uematsu", "Shinya Tanaka"], "categories": ["stat.AP", "math.ST", "stat.ME"], "doi": "", "_query": "hedonic regression housing", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/1511.00297v2", "title": "Kernel-Penalized Regression for Analysis of Microbiome Data", "abstract": "The analysis of human microbiome data is often based on dimension-reduced graphical displays and clustering derived from vectors of microbial abundances in each sample. Common to these ordination methods is the use of biologically motivated definitions of similarity. Principal coordinate analysis, in particular, is often performed using ecologically defined distances, allowing analyses to incorporate context-dependent, non-Euclidean structure. Here we describe how to take a step beyond ordination plots and incorporate this structure into high-dimensional penalized regression models. Within this framework, the estimate of a regression coefficient vector is obtained via the joint eigen properties of multiple similarity matrices, or kernels. This allows for multivariate regression models to incorporate both a matrix of microbial abundances and, for instance, a matrix of phylogenetically-informed similarities between the abundance profiles. Further, we show how this regression framework can be used to address the compositional nature of multivariate predictors comprised of relative abundances; that is, vectors whose entries sum to a constant. We illustrate this regression framework with several simulations using data from two recent studies on the gut and vaginal microbiome. We conclude with an application to our own data, where we also incorporate a significance test for the estimated coefficients that represent associations between microbial abundance and a response.", "published": "2015-11-01T19:17:43Z", "authors": ["Timothy W. Randolph", "Sen Zhao", "Wade Copeland", "Meredith Hullar", "Ali Shojaie"], "categories": ["stat.AP"], "doi": "", "_query": "hedonic regression housing", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2507.11902v1", "title": "Resampling strategies for imbalanced regression: a survey and empirical analysis", "abstract": "Imbalanced problems can arise in different real-world situations, and to address this, certain strategies in the form of resampling or balancing algorithms are proposed. This issue has largely been studied in the context of classification, and yet, the same problem features in regression tasks, where target values are continuous. This work presents an extensive experimental study comprising various balancing and predictive models, and wich uses metrics to capture important elements for the user and to evaluate the predictive model in an imbalanced regression data context. It also proposes a taxonomy for imbalanced regression approaches based on three crucial criteria: regression model, learning process, and evaluation metrics. The study offers new insights into the use of such strategies, highlighting the advantages they bring to each model's learning process, and indicating directions for further studies. The code, data and further information related to the experiments performed herein can be found on GitHub: https://github.com/JusciAvelino/imbalancedRegression.", "published": "2025-07-16T04:34:42Z", "authors": ["Juscimara G. Avelino", "George D. C. Cavalcanti", "Rafael M. O. Cruz"], "categories": ["cs.LG"], "doi": "10.1007/s10462-024-10724-3", "_query": "hedonic regression housing", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/1009.1444v2", "title": "Optimal designs for rational function regression", "abstract": "We consider optimal non-sequential designs for a large class of (linear and nonlinear) regression models involving polynomials and rational functions with heteroscedastic noise also given by a polynomial or rational weight function. The proposed method treats D-, E-, A-, and $Φ_p$-optimal designs in a unified manner, and generates a polynomial whose zeros are the support points of the optimal approximate design, generalizing a number of previously known results of the same flavor. The method is based on a mathematical optimization model that can incorporate various criteria of optimality and can be solved efficiently by well established numerical optimization methods. In contrast to previous optimization-based methods proposed for similar design problems, it also has theoretical guarantee of its algorithmic efficiency; in fact, the running times of all numerical examples considered in the paper are negligible. The stability of the method is demonstrated in an example involving high degree polynomials. After discussing linear models, applications for finding locally optimal designs for nonlinear regression models involving rational functions are presented, then extensions to robust regression designs, and trigonometric regression are shown. As a corollary, an upper bound on the size of the support set of the minimally-supported optimal designs is also found. The method is of considerable practical importance, with the potential for instance to impact design software development. Further study of the optimality conditions of the main optimization model might also yield new theoretical insights.", "published": "2010-09-08T02:44:03Z", "authors": ["Dávid Papp"], "categories": ["stat.CO", "stat.ME"], "doi": "", "_query": "hedonic regression housing", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2210.11363v1", "title": "Bayesian Tensor-on-Tensor Regression with Efficient Computation", "abstract": "We propose a Bayesian tensor-on-tensor regression approach to predict a multidimensional array (tensor) of arbitrary dimensions from another tensor of arbitrary dimensions, building upon the Tucker decomposition of the regression coefficient tensor. Traditional tensor regression methods making use of the Tucker decomposition either assume the dimension of the core tensor to be known or estimate it via cross-validation or some model selection criteria. However, no existing method can simultaneously estimate the model dimension (the dimension of the core tensor) and other model parameters. To fill this gap, we develop an efficient Markov Chain Monte Carlo (MCMC) algorithm to estimate both the model dimension and parameters for posterior inference. Besides the MCMC sampler, we also develop an ultra-fast optimization-based computing algorithm wherein the maximum a posteriori estimators for parameters are computed, and the model dimension is optimized via a simulated annealing algorithm. The proposed Bayesian framework provides a natural way for uncertainty quantification. Through extensive simulation studies, we evaluate the proposed Bayesian tensor-on-tensor regression model and show its superior performance compared to alternative methods. We also demonstrate its practical effectiveness by applying it to two real-world datasets, including facial imaging data and 3D motion data.", "published": "2022-10-20T15:53:03Z", "authors": ["Kunbo Wang", "Yanxun Xu"], "categories": ["stat.ME"], "doi": "", "_query": "hedonic regression housing", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2508.08345v2", "title": "Do AI Companies Make Good on Voluntary Commitments to the White House?", "abstract": "Voluntary commitments are central to international AI governance, as demonstrated by recent voluntary guidelines from the White House to the G7, from Bletchley Park to Seoul. How do major AI companies make good on their commitments? We score companies based on their publicly disclosed behavior by developing a detailed rubric based on their eight voluntary commitments to the White House in 2023. We find significant heterogeneity: while the highest-scoring company (OpenAI) scores a 83% overall on our rubric, the average score across all companies is just 53%. The companies demonstrate systemically poor performance for their commitment to model weight security with an average score of 17%: 11 of the 16 companies receive 0% for this commitment. Our analysis highlights a clear structural shortcoming that future AI governance initiatives should correct: when companies make public commitments, they should proactively disclose how they meet their commitments to provide accountability, and these disclosures should be verifiable. To advance policymaking on corporate AI governance, we provide three directed recommendations that address underspecified commitments, the role of complex AI supply chains, and public transparency that could be applied towards AI governance initiatives worldwide.", "published": "2025-08-11T11:23:28Z", "authors": ["Jennifer Wang", "Kayla Huang", "Kevin Klyman", "Rishi Bommasani"], "categories": ["cs.CY", "cs.AI"], "doi": "", "_query": "hedonic regression housing", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2205.07106v1", "title": "Robust Regularized Low-Rank Matrix Models for Regression and Classification", "abstract": "While matrix variate regression models have been studied in many existing works, classical statistical and computational methods for the analysis of the regression coefficient estimation are highly affected by high dimensional and noisy matrix-valued predictors. To address these issues, this paper proposes a framework of matrix variate regression models based on a rank constraint, vector regularization (e.g., sparsity), and a general loss function with three special cases considered: ordinary matrix regression, robust matrix regression, and matrix logistic regression. We also propose an alternating projected gradient descent algorithm. Based on analyzing our objective functions on manifolds with bounded curvature, we show that the algorithm is guaranteed to converge, all accumulation points of the iterates have estimation errors in the order of $O(1/\\sqrt{n})$ asymptotically and substantially attaining the minimax rate. Our theoretical analysis can be applied to general optimization problems on manifolds with bounded curvature and can be considered an important technical contribution to this work. We validate the proposed method through simulation studies and real image data examples.", "published": "2022-05-14T18:03:48Z", "authors": ["Hsin-Hsiung Huang", "Feng Yu", "Xing Fan", "Teng Zhang"], "categories": ["stat.ML", "cs.LG"], "doi": "", "_query": "hedonic regression housing", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2310.15512v6", "title": "Inference for Rank-Rank Regressions", "abstract": "The slope coefficient in a rank-rank regression is a popular measure of intergenerational mobility. In this article, we first show that commonly used inference methods for this slope parameter are invalid. Second, when the underlying distribution is not continuous, the OLS estimator and its distribution may be sensitive to how ties in the ranks are handled. Motivated by these findings we derive the asymptotic distribution of the OLS estimator and provide valid inference methods for a general class of rank-rank regression specifications.", "published": "2023-10-24T04:41:37Z", "authors": ["Denis Chetverikov", "Daniel Wilhelm"], "categories": ["econ.EM", "math.ST"], "doi": "", "_query": "hedonic regression housing", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/1111.2298v1", "title": "Estimation of a semiparametric contaminated regression model", "abstract": "We consider in this paper a contamined regression model where the distribution of the contaminating component is known when the Eu- clidean parameters of the regression model, the noise distribution, the contamination ratio and the distribution of the design data are un- known. Our model is said to be semiparametric in the sense that the probability density function (pdf) of the noise involved in the regression model is not supposed to belong to a parametric density family. When the pdf's of the noise and the contaminating phenomenon are supposed to be symmetric about zero, we propose an estimator of the various (Eu- clidean and functionnal) parameters of the model, and prove under mild conditions its convergence. We prove in particular that, under technical conditions all satisfied in the Gaussian case, the Euclidean part of the model is estimated at the rate $o_{a.s}(n-1/4+γ), $γ> 0$. We recall that, as it is pointed out in Bordes and Vandekerkhove (2010), this result cannot be ignored to go further in the asymptotic theory for this class of models. Finally the implementation and numerical performances of our method are discussed on several toy examples.", "published": "2011-11-09T18:11:10Z", "authors": ["Pierre Vandekerkhove"], "categories": ["math.ST"], "doi": "", "_query": "hedonic regression housing", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2105.13783v2", "title": "Quantile Encoder: Tackling High Cardinality Categorical Features in Regression Problems", "abstract": "Regression problems have been widely studied in machinelearning literature resulting in a plethora of regression models and performance measures. However, there are few techniques specially dedicated to solve the problem of how to incorporate categorical features to regression problems. Usually, categorical feature encoders are general enough to cover both classification and regression problems. This lack of specificity results in underperforming regression models. In this paper,we provide an in-depth analysis of how to tackle high cardinality categor-ical features with the quantile. Our proposal outperforms state-of-the-encoders, including the traditional statistical mean target encoder, when considering the Mean Absolute Error, especially in the presence of long-tailed or skewed distributions. Besides, to deal with possible overfitting when there are categories with small support, our encoder benefits from additive smoothing. Finally, we describe how to expand the encoded values by creating a set of features with different quantiles. This expanded encoder provides a more informative output about the categorical feature in question, further boosting the performance of the regression model.", "published": "2021-05-27T11:56:13Z", "authors": ["Carlos Mougan", "David Masip", "Jordi Nin", "Oriol Pujol"], "categories": ["cs.LG"], "doi": "", "_query": "hedonic regression housing", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/1406.3469v4", "title": "LOCO: Distributing Ridge Regression with Random Projections", "abstract": "We propose LOCO, an algorithm for large-scale ridge regression which distributes the features across workers on a cluster. Important dependencies between variables are preserved using structured random projections which are cheap to compute and must only be communicated once. We show that LOCO obtains a solution which is close to the exact ridge regression solution in the fixed design setting. We verify this experimentally in a simulation study as well as an application to climate prediction. Furthermore, we show that LOCO achieves significant speedups compared with a state-of-the-art distributed algorithm on a large-scale regression problem.", "published": "2014-06-13T09:59:21Z", "authors": ["Christina Heinze", "Brian McWilliams", "Nicolai Meinshausen", "Gabriel Krummenacher"], "categories": ["stat.ML"], "doi": "", "_query": "hedonic regression housing", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/1111.5989v1", "title": "Large Deviation Results for the Nonparametric Regression Function Estimator on Functional Data", "abstract": "This paper is devoted to the study of large deviation behaviors in the setting of the estimation of the regression function on functional data. A large deviation principle is stated for a process Zn, defined below, allowing to derive a pointwise large deviation principle for the Nadaraya-Watson-type l-indexed regression function estimator as a by-product. Moreover, a uniform over VC-classes Cherno? type large deviation result is stated for the deviation of the l-indexed regression estimator.", "published": "2011-11-25T14:06:11Z", "authors": ["Djamal Louani", "Sidi Mohamed Ould Maouloud"], "categories": ["math.ST", "stat.ME"], "doi": "", "_query": "hedonic regression housing", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/1602.06610v1", "title": "Mixture of Regression Models with Single-Index", "abstract": "In this article, we propose a class of semiparametric mixture regression models with single-index. We argue that many recently proposed semiparametric/nonparametric mixture regression models can be considered special cases of the proposed model. However, unlike existing semiparametric mixture regression models, the new pro- posed model can easily incorporate multivariate predictors into the nonparametric components. Backfitting estimates and the corresponding algorithms have been proposed for to achieve the optimal convergence rate for both the parameters and the nonparametric functions. We show that nonparametric functions can be esti- mated with the same asymptotic accuracy as if the parameters were known and the index parameters can be estimated with the traditional parametric root n convergence rate. Simulation studies and an application of NBA data have been conducted to demonstrate the finite sample performance of the proposed models.", "published": "2016-02-22T00:15:17Z", "authors": ["Sijia Xiang", "Weixin Yao"], "categories": ["stat.ME"], "doi": "", "_query": "hedonic regression housing", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/1505.01286v1", "title": "Localization of real world regression Bugs using single execution", "abstract": "Regression bugs occur whenever software functionality that previously worked as desired stops working, or no longer works as expected. Code changes, such as bug fixes or new feature work, may result in a regression bug. Regression bugs are an annoying and painful phenomena in the software development process, requiring a great deal of effort to localize, effectively hindering team progress. In this paper we present Regression Detective, a method which assists the developer locating source code segments that caused a given regression bug. Unlike some of the existing tools, our approach doesn't require an automated test suite or executing past versions of the system. It is highly scalable to millions of loc systems. The developer, who has no prior knowledge of the code or the bug, reproduces the bug according to the steps described in the bug database. We evaluated our approach with bugs from leading open source projects (Eclipse, Tomcat, Ant). In over 90% of the cases, the developer only has to examine 10-20 lines of code in order to locate the bug, regardless of the code base size.", "published": "2015-05-06T08:51:15Z", "authors": ["Dekel Cohen", "Amiram Yehudai"], "categories": ["cs.SE"], "doi": "", "_query": "hedonic regression housing", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2312.07145v1", "title": "Contextual Bandits with Online Neural Regression", "abstract": "Recent works have shown a reduction from contextual bandits to online regression under a realizability assumption [Foster and Rakhlin, 2020, Foster and Krishnamurthy, 2021]. In this work, we investigate the use of neural networks for such online regression and associated Neural Contextual Bandits (NeuCBs). Using existing results for wide networks, one can readily show a ${\\mathcal{O}}(\\sqrt{T})$ regret for online regression with square loss, which via the reduction implies a ${\\mathcal{O}}(\\sqrt{K} T^{3/4})$ regret for NeuCBs. Departing from this standard approach, we first show a $\\mathcal{O}(\\log T)$ regret for online regression with almost convex losses that satisfy QG (Quadratic Growth) condition, a generalization of the PL (Polyak-Łojasiewicz) condition, and that have a unique minima. Although not directly applicable to wide networks since they do not have unique minima, we show that adding a suitable small random perturbation to the network predictions surprisingly makes the loss satisfy QG with unique minima. Based on such a perturbed prediction, we show a ${\\mathcal{O}}(\\log T)$ regret for online regression with both squared loss and KL loss, and subsequently convert these respectively to $\\tilde{\\mathcal{O}}(\\sqrt{KT})$ and $\\tilde{\\mathcal{O}}(\\sqrt{KL^*} + K)$ regret for NeuCB, where $L^*$ is the loss of the best policy. Separately, we also show that existing regret bounds for NeuCBs are $Ω(T)$ or assume i.i.d. contexts, unlike this work. Finally, our experimental results on various datasets demonstrate that our algorithms, especially the one based on KL loss, persistently outperform existing algorithms.", "published": "2023-12-12T10:28:51Z", "authors": ["Rohan Deb", "Yikun Ban", "Shiliang Zuo", "Jingrui He", "Arindam Banerjee"], "categories": ["cs.LG", "stat.ML"], "doi": "", "_query": "hedonic regression housing", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/0812.3973v1", "title": "Revisiting Révész's stochastic approximation method for the estimation of a regression function", "abstract": "In a pioneer work, Révész (1973) introduces the stochastic approximation method to build up a recursive kernel estimator of the regression function $x\\mapsto E(Y|X=x)$. However, according to Révész (1977), his estimator has two main drawbacks: on the one hand, its convergence rate is smaller than that of the nonrecursive Nadaraya-Watson's kernel regression estimator, and, on the other hand, the required assumptions on the density of the random variable $X$ are stronger than those usually needed in the framework of regression estimation. We first come back on the study of the convergence rate of Révész's estimator. An approach in the proofs completely different from that used in Révész (1977) allows us to show that Révész's recursive estimator may reach the same optimal convergence rate as Nadaraya-Watson's estimator, but the required assumptions on the density of $X$ remain stronger than the usual ones, and this is inherent to the definition of Révész's estimator. To overcome this drawback, we introduce the averaging principle of stochastic approximation algorithms to construct the averaged Révész's regression estimator, and give its asymptotic behaviour. Our assumptions on the density of $X$ are then usual in the framework of regression estimation. We prove that the averaged Révész's regression estimator may reach the same optimal convergence rate as Nadaraya-Watson's estimator. Moreover, we show that, according to the estimation by confidence intervals point of view, it is better to use the averaged Révész's estimator rather than Nadaraya-Watson's estimator.", "published": "2008-12-20T15:34:40Z", "authors": ["Abdelkader Mokkadem", "Mariane Pelletier", "Yousri Slaoui"], "categories": ["math.ST"], "doi": "", "_query": "hedonic regression housing", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/1806.03729v1", "title": "Lost in translation: On the impact of data coding on penalized regression with interactions", "abstract": "Penalized regression approaches are standard tools in quantitative genetics. It is known that the fit of an \\emph{ordinary least squares} (OLS) regression is independent of certain transformations of the coding of the predictor variables, and that the standard mixed model \\emph{ridge regression best linear unbiased prediction} (RRBLUP) is neither affected by translations of the variable coding, nor by global scaling. However, it has been reported that an extended version of this mixed model, which incorporates interactions by products of markers as additional predictor variables is affected by translations of the marker coding. In this work, we identify the cause of this loss of invariance in a general context of penalized regression on polynomials in the predictor variables. We show that in most cases, translating the coding of the predictor variables has an impact on effect estimates, with an exception when only the size of the coefficients of monomials of highest total degree are penalized. The invariance of RRBLUP can thus be considered as a special case of this setting, with a polynomial of total degree 1, where the size of the fixed effect (total degree 0) is not penalized but all coefficients of monomials of total degree 1 are. The extended RRBLUP, which includes interactions, is not invariant to translations because it does not only penalize interactions (total degree 2), but also additive effects (total degree 1). Our observations are not restricted to ridge regression, but generally valid for penalized regressions, for instance also for the $\\ell_1$ penalty of LASSO.", "published": "2018-06-10T21:46:41Z", "authors": ["Johannes W R Martini", "Francisco Rosales", "Ngoc-Thuy Ha", "Thomas Kneib", "Johannes Heise", "Valentin Wimmer"], "categories": ["stat.ME", "math.ST", "stat.AP"], "doi": "", "_query": "hedonic regression housing", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2606.30976v1", "title": "Residual-on-Residual Regression as a Tool for Effect Estimation in Observational Data", "abstract": "Epidemiologists increasingly use machine learning to adjust for high-dimensional confounding. Augmented inverse probability weighting (AIPW) and targeted maximum likelihood estimation (TMLE) are most widely used but may yield different results and both can become unstable under weak positivity violations. Residual-on-residual regression is a stable alternative that estimates an exposure effect encoded in a partially linear model by fitting confounder adjusted models for the outcome and exposure, then regressing outcome residuals against exposure residuals using ordinary least squares. We illustrate the approach using data from the Nulliparous Pregnancy Outcomes Study: Monitoring Mothers-to-Be (nuMoM2b; $n = 7{,}923$), estimating the association between high vegetable intake density and preeclampsia. Residual-on-residual regression, AIPW, and TMLE yielded concordant estimates, indicating a modest reduction in preeclampsia risk. In simulations, residual-on-residual regression was unbiased with near-nominal confidence interval coverage, performing comparably to AIPW and TMLE and substantially better than a misspecified parametric model when the exposure effect is approximately constant. However, in simulation settings with positivity violations, residual on residual regression outperformed AIPW and TMLE when the true effect was coded in a partially linear model. When the exposure effect is approximately constant, residual-on-residual regression is interpretable, computationally simple, and provides a triangulation strategy for observational causal inference.", "published": "2026-06-29T23:19:32Z", "authors": ["Ashley I. Naimi", "Qianhui Jin", "Ya-Hui Yu", "Sara M. Parisi", "Lisa M. Bodnar"], "categories": ["stat.ME", "stat.AP"], "doi": "", "_query": "hedonic regression housing", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/1906.03761v4", "title": "The Impact of Regularization on High-dimensional Logistic Regression", "abstract": "Logistic regression is commonly used for modeling dichotomous outcomes. In the classical setting, where the number of observations is much larger than the number of parameters, properties of the maximum likelihood estimator in logistic regression are well understood. Recently, Sur and Candes have studied logistic regression in the high-dimensional regime, where the number of observations and parameters are comparable, and show, among other things, that the maximum likelihood estimator is biased. In the high-dimensional regime the underlying parameter vector is often structured (sparse, block-sparse, finite-alphabet, etc.) and so in this paper we study regularized logistic regression (RLR), where a convex regularizer that encourages the desired structure is added to the negative of the log-likelihood function. An advantage of RLR is that it allows parameter recovery even for instances where the (unconstrained) maximum likelihood estimate does not exist. We provide a precise analysis of the performance of RLR via the solution of a system of six nonlinear equations, through which any performance metric of interest (mean, mean-squared error, probability of support recovery, etc.) can be explicitly computed. Our results generalize those of Sur and Candes and we provide a detailed study for the cases of $\\ell_2^2$-RLR and sparse ($\\ell_1$-regularized) logistic regression. In both cases, we obtain explicit expressions for various performance metrics and can find the values of the regularizer parameter that optimizes the desired performance. The theory is validated by extensive numerical simulations across a range of parameter values and problem instances.", "published": "2019-06-10T01:45:00Z", "authors": ["Fariborz Salehi", "Ehsan Abbasi", "Babak Hassibi"], "categories": ["stat.ML", "cs.IT", "cs.LG", "math.PR"], "doi": "", "_query": "hedonic regression housing", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/1509.05142v6", "title": "Fast Gaussian Process Regression for Big Data", "abstract": "Gaussian Processes are widely used for regression tasks. A known limitation in the application of Gaussian Processes to regression tasks is that the computation of the solution requires performing a matrix inversion. The solution also requires the storage of a large matrix in memory. These factors restrict the application of Gaussian Process regression to small and moderate size data sets. We present an algorithm that combines estimates from models developed using subsets of the data obtained in a manner similar to the bootstrap. The sample size is a critical parameter for this algorithm. Guidelines for reasonable choices of algorithm parameters, based on detailed experimental study, are provided. Various techniques have been proposed to scale Gaussian Processes to large scale regression tasks. The most appropriate choice depends on the problem context. The proposed method is most appropriate for problems where an additive model works well and the response depends on a small number of features. The minimax rate of convergence for such problems is attractive and we can build effective models with a small subset of the data. The Stochastic Variational Gaussian Process and the Sparse Gaussian Process are also appropriate choices for such problems. These methods pick a subset of data based on theoretical considerations. The proposed algorithm uses bagging and random sampling. Results from experiments conducted as part of this study indicate that the algorithm presented in this work can be as effective as these methods. Model stacking can be used to combine the model developed with the proposed method with models from other methods for large scale regression such as Gradient Boosted Trees. This can yield performance gains.", "published": "2015-09-17T06:18:08Z", "authors": ["Sourish Das", "Sasanka Roy", "Rajiv Sambasivan"], "categories": ["cs.LG", "stat.ML"], "doi": "", "_query": "hedonic regression housing", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/1808.10558v4", "title": "An explicit mean-covariance parameterization for multivariate response linear regression", "abstract": "We develop a new method to fit the multivariate response linear regression model that exploits a parametric link between the regression coefficient matrix and the error covariance matrix. Specifically, we assume that the correlations between entries in the multivariate error random vector are proportional to the cosines of the angles between their corresponding regression coefficient matrix columns, so as the angle between two regression coefficient matrix columns decreases, the correlation between the corresponding errors increases. We highlight two models under which this parameterization arises: the latent variable reduced-rank regression model and the errors-in-variables regression model. We propose a novel non-convex weighted residual sum of squares criterion which exploits this parameterization and admits a new class of penalized estimators. The optimization is solved with an accelerated proximal gradient descent algorithm. Our method is used to study the association between microRNA expression and cancer drug activity measured on the NCI-60 cell lines. An R package implementing our method, MCMVR, is available at github.com/ajmolstad/MCMVR.", "published": "2018-08-31T00:35:50Z", "authors": ["Aaron J. Molstad", "Guangwei Weng", "Charles R. Doss", "Adam J. Rothman"], "categories": ["stat.ME"], "doi": "10.1080/10618600.2020.1853551", "_query": "hedonic regression housing", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2102.09497v2", "title": "Regression-type analysis for block maxima on block maxima", "abstract": "This paper devises a regression-type model for the situation where both the response and covariates are extreme. The proposed approach is designed for the setting where both the response and covariates are themselves block maxima, and thus contrarily to standard regression methods it takes into account the key fact that the limiting distribution of suitably standardized componentwise maxima is an extreme value copula. An important target in the proposed framework is the regression manifold, which consists of a family of regression lines obeying the latter asymptotic result. To learn about the proposed model from data, we employ a Bernstein polynomial prior on the space of angular densities which leads to an induced prior on the space of regression manifolds. Numerical studies suggest a good performance of the proposed methods, and a finance real-data illustration reveals interesting aspects on the comovements of extreme losses between two leading stock markets.", "published": "2021-02-18T17:27:44Z", "authors": ["Miguel de Carvalho", "Gonçalo dos Reis", "Alina Kumukova"], "categories": ["math.ST"], "doi": "", "_query": "hedonic regression housing", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/1712.02640v1", "title": "High-dimensional robust regression and outliers detection with SLOPE", "abstract": "The problems of outliers detection and robust regression in a high-dimensional setting are fundamental in statistics, and have numerous applications. Following a recent set of works providing methods for simultaneous robust regression and outliers detection, we consider in this paper a model of linear regression with individual intercepts, in a high-dimensional setting. We introduce a new procedure for simultaneous estimation of the linear regression coefficients and intercepts, using two dedicated sorted-$\\ell_1$ penalizations, also called SLOPE. We develop a complete theory for this problem: first, we provide sharp upper bounds on the statistical estimation error of both the vector of individual intercepts and regression coefficients. Second, we give an asymptotic control on the False Discovery Rate (FDR) and statistical power for support selection of the individual intercepts. As a consequence, this paper is the first to introduce a procedure with guaranteed FDR and statistical power control for outliers detection under the mean-shift model. Numerical illustrations, with a comparison to recent alternative approaches, are provided on both simulated and several real-world datasets. Experiments are conducted using an open-source software written in Python and C++.", "published": "2017-12-07T14:25:02Z", "authors": ["Alain Virouleau", "Agathe Guilloux", "Stéphane Gaïffas", "Malgorzata Bogdan"], "categories": ["stat.ML"], "doi": "", "_query": "hedonic regression housing", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2508.08412v1", "title": "Multiple Regression Analysis of Unmeasured Confounding", "abstract": "Whereas confidence intervals are used to assess uncertainty due to unmeasured individuals, confounding intervals can be used to assess uncertainty due to unmeasured attributes. Previously, we have introduced a methodology for computing confounding intervals in a simple regression setting in a paper titled ``Regression Analysis of Unmeasured Confounding.\" Here we extend that methodology for more general application in the context of multiple regression. Our multiple regression analysis of unmeasured confounding utilizes subject matter knowledge about coefficients of determination to bound omitted variables bias, while taking into account measured covariate data. Our generalized methodology can be used to partially identify causal effects. The methodology is demonstrated with example applications, to show how coefficients of determination, being complementary to randomness, can support sensitivity analysis for causal inference from observational data. The methodology is best applied when natural sources of randomness are present and identifiable within the data generating process. Our main contribution is an algorithm that supports our methodology. The purpose of this article is to describe our algorithm in detail. In the paper we provide a link to our GitHub page for readers who would like to access and utilize our algorithm.", "published": "2025-08-11T19:06:57Z", "authors": ["Brian Knaeble", "R Mitchell Hughes"], "categories": ["stat.ME", "math.OC"], "doi": "", "_query": "hedonic regression housing", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/0901.4881v2", "title": "Birnbaum-Saunders nonlinear regression models", "abstract": "We introduce, for the first time, a new class of Birnbaum-Saunders nonlinear regression models potentially useful in lifetime data analysis. The class generalizes the regression model described by Rieck and Nedelman [1991, A log-linear model for the Birnbaum-Saunders distribution, Technometrics, 33, 51-60]. We discuss maximum likelihood estimation for the parameters of the model, and derive closed-form expressions for the second-order biases of these estimates. Our formulae are easily computed as ordinary linear regressions and are then used to define bias corrected maximum likelihood estimates. Some simulation results show that the bias correction scheme yields nearly unbiased estimates without increasing the mean squared errors. We also give an application to a real fatigue data set.", "published": "2009-01-30T13:43:14Z", "authors": ["Artur J. Lemonte", "Gauss M. Cordeiro"], "categories": ["stat.ME"], "doi": "10.1016/j.csda.2009.06.015", "_query": "hedonic regression housing", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/1601.01109v1", "title": "Linear regression by observations from mixture with varying concentrations", "abstract": "We consider a finite mixture model with varying mixing probabilities. Linear regression models are assumed for observed variables with coefficients depending on the mixture component the observed subject belongs to. A modification of the least-squares estimator is proposed for estimation of the regression coefficients. Consistency and asymptotic normality of the estimates is demonstrated.", "published": "2016-01-06T09:09:25Z", "authors": ["Daryna Liubashenko", "Rostyslav Maiboroda"], "categories": ["math.PR"], "doi": "10.15559/15-VMSTA41", "_query": "hedonic regression housing", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/1909.04990v2", "title": "Robust Regression with Compositional Covariates", "abstract": "Many biological high-throughput data sets, such as targeted amplicon-based and metagenomic sequencing data, are compositional in nature. A common exploratory data analysis task is to infer statistical associations between the high-dimensional microbial compositions and habitat- or host-related covariates. We propose a general robust statistical regression framework, RobRegCC (Robust Regression with Compositional Covariates), which extends the linear log-contrast model by a mean shift formulation for capturing outliers. RobRegCC includes sparsity-promoting convex and non-convex penalties for parsimonious model estimation, a data-driven robust initialization procedure, and a novel robust cross-validation model selection scheme. We show RobRegCC's ability to perform simultaneous sparse log-contrast regression and outlier detection over a wide range of simulation settings and provide theoretical non-asymptotic guarantees for the underlying estimators. To demonstrate the seamless applicability of the workflow on real data, we consider a gut microbiome data set from HIV patients and infer robust associations between a sparse set of microbial species and host immune response from soluble CD14 measurements. All experiments are fully reproducible and available on GitHub at https://github.com/amishra-stats/robregcc.", "published": "2019-09-11T11:59:14Z", "authors": ["Aditya Mishra", "Christian L. Muller"], "categories": ["stat.ME", "stat.AP"], "doi": "", "_query": "hedonic regression housing", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/1509.03938v5", "title": "Robust Reduced Rank Regression", "abstract": "In high-dimensional multivariate regression problems, enforcing low rank in the coefficient matrix offers effective dimension reduction, which greatly facilitates parameter estimation and model interpretation. However, commonly-used reduced-rank methods are sensitive to data corruption, as the low-rank dependence structure between response variables and predictors is easily distorted by outliers. We propose a robust reduced-rank regression approach for joint modeling and outlier detection. The problem is formulated as a regularized multivariate regression with a sparse mean-shift parametrization, which generalizes and unifies some popular robust multivariate methods. An efficient thresholding-based iterative procedure is developed for optimization. We show that the algorithm is guaranteed to converge, and the coordinatewise minimum point produced is statistically accurate under regularity conditions. Our theoretical investigations focus on nonasymptotic robust analysis, which demonstrates that joint rank reduction and outlier detection leads to improved prediction accuracy. In particular, we show that redescending $ψ$-functions can essentially attain the minimax optimal error rate, and in some less challenging problems convex regularization guarantees the same low error rate. The performance of the proposed method is examined by simulation studies and real data examples.", "published": "2015-09-14T03:09:04Z", "authors": ["Yiyuan She", "Kun Chen"], "categories": ["math.ST", "stat.AP", "stat.ME"], "doi": "", "_query": "hedonic regression housing", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/1612.03931v1", "title": "Hierarchical Matching and Regression with Application to Photometric Redshift Estimation", "abstract": "This work emphasizes that heterogeneity, diversity, discontinuity, and discreteness in data is to be exploited in classification and regression problems. A global a priori model may not be desirable. For data analytics in cosmology, this is motivated by the variety of cosmological objects such as elliptical, spiral, active, and merging galaxies at a wide range of redshifts. Our aim is matching and similarity-based analytics that takes account of discrete relationships in the data. The information structure of the data is represented by a hierarchy or tree where the branch structure, rather than just the proximity, is important. The representation is related to p-adic number theory. The clustering or binning of the data values, related to the precision of the measurements, has a central role in this methodology. If used for regression, our approach is a method of cluster-wise regression, generalizing nearest neighbour regression. Both to exemplify this analytics approach, and to demonstrate computational benefits, we address the well-known photometric redshift or `photo-z' problem, seeking to match Sloan Digital Sky Survey (SDSS) spectroscopic and photometric redshifts.", "published": "2016-12-12T21:21:43Z", "authors": ["Fionn Murtagh"], "categories": ["astro-ph.IM"], "doi": "10.1017/S1743921317001569", "_query": "hedonic regression housing", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2210.05775v1", "title": "C-Mixup: Improving Generalization in Regression", "abstract": "Improving the generalization of deep networks is an important open challenge, particularly in domains without plentiful data. The mixup algorithm improves generalization by linearly interpolating a pair of examples and their corresponding labels. These interpolated examples augment the original training set. Mixup has shown promising results in various classification tasks, but systematic analysis of mixup in regression remains underexplored. Using mixup directly on regression labels can result in arbitrarily incorrect labels. In this paper, we propose a simple yet powerful algorithm, C-Mixup, to improve generalization on regression tasks. In contrast with vanilla mixup, which picks training examples for mixing with uniform probability, C-Mixup adjusts the sampling probability based on the similarity of the labels. Our theoretical analysis confirms that C-Mixup with label similarity obtains a smaller mean square error in supervised regression and meta-regression than vanilla mixup and using feature similarity. Another benefit of C-Mixup is that it can improve out-of-distribution robustness, where the test distribution is different from the training distribution. By selectively interpolating examples with similar labels, it mitigates the effects of domain-associated information and yields domain-invariant representations. We evaluate C-Mixup on eleven datasets, ranging from tabular to video data. Compared to the best prior approach, C-Mixup achieves 6.56%, 4.76%, 5.82% improvements in in-distribution generalization, task generalization, and out-of-distribution robustness, respectively. Code is released at https://github.com/huaxiuyao/C-Mixup.", "published": "2022-10-11T20:39:38Z", "authors": ["Huaxiu Yao", "Yiping Wang", "Linjun Zhang", "James Zou", "Chelsea Finn"], "categories": ["cs.LG", "stat.ML"], "doi": "", "_query": "hedonic regression housing", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2409.04231v1", "title": "Generative Modelling via Quantile Regression", "abstract": "We link conditional generative modelling to quantile regression. We propose a suitable loss function and derive minimax convergence rates for the associated risk under smoothness assumptions imposed on the conditional distribution. To establish the lower bound, we show that nonparametric regression can be seen as a sub-problem of the considered generative modelling framework. Finally, we discuss extensions of our work to generate data from multivariate distributions.", "published": "2024-09-06T12:39:29Z", "authors": ["Johannes Schmidt-Hieber", "Petr Zamolodtchikov"], "categories": ["math.ST"], "doi": "", "_query": "hedonic regression housing", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/1503.05289v1", "title": "Time-varying nonlinear regression models: Nonparametric estimation and model selection", "abstract": "This paper considers a general class of nonparametric time series regression models where the regression function can be time-dependent. We establish an asymptotic theory for estimates of the time-varying regression functions. For this general class of models, an important issue in practice is to address the necessity of modeling the regression function as nonlinear and time-varying. To tackle this, we propose an information criterion and prove its selection consistency property. The results are applied to the U.S. Treasury interest rate data.", "published": "2015-03-18T06:13:33Z", "authors": ["Ting Zhang", "Wei Biao Wu"], "categories": ["math.ST"], "doi": "10.1214/14-AOS1299", "_query": "hedonic regression housing", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2603.14918v2", "title": "Bayesian Symbolic Regression for Missing Physics", "abstract": "Model-based approaches for (bio)process systems often suffer from incomplete knowledge of the underlying physical, chemical, or biological laws. Universal differential equations, which embed neural networks within differential equations, have emerged as powerful tools to learn this missing physics from experimental data. However, neural networks are inherently opaque, motivating their post-processing via symbolic regression to obtain interpretable mathematical expressions. Genetic algorithm-based symbolic regression is a popular approach for this post-processing step, but provides only point estimates and cannot quantify the confidence we should place in a discovered equation. We address this limitation by applying Bayesian symbolic regression, which uses Reversible Jump Markov Chain Monte Carlo to sample from the posterior distribution over symbolic expression trees. This approach naturally quantifies uncertainty in the recovered model structure. We demonstrate the methodology on a Lotka-Volterra predator-prey system and then show how a well-designed experiment leads to lower uncertainty in a fed-batch bioreactor case study.", "published": "2026-03-16T07:25:42Z", "authors": ["Arno Strouwen"], "categories": ["stat.ML", "cs.LG"], "doi": "", "_query": "hedonic regression housing", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2109.00539v3", "title": "Spatially and Robustly Hybrid Mixture Regression Model for Inference of Spatial Dependence", "abstract": "In this paper, we propose a Spatial Robust Mixture Regression model to investigate the relationship between a response variable and a set of explanatory variables over the spatial domain, assuming that the relationships may exhibit complex spatially dynamic patterns that cannot be captured by constant regression coefficients. Our method integrates the robust finite mixture Gaussian regression model with spatial constraints, to simultaneously handle the spatial nonstationarity, local homogeneity, and outlier contaminations. Compared with existing spatial regression models, our proposed model assumes the existence a few distinct regression models that are estimated based on observations that exhibit similar response-predictor relationships. As such, the proposed model not only accounts for nonstationarity in the spatial trend, but also clusters observations into a few distinct and homogenous groups. This provides an advantage on interpretation with a few stationary sub-processes identified that capture the predominant relationships between response and predictor variables. Moreover, the proposed method incorporates robust procedures to handle contaminations from both regression outliers and spatial outliers. By doing so, we robustly segment the spatial domain into distinct local regions with similar regression coefficients, and sporadic locations that are purely outliers. Rigorous statistical hypothesis testing procedure has been designed to test the significance of such segmentation. Experimental results on many synthetic and real-world datasets demonstrate the robustness, accuracy, and effectiveness of our proposed method, compared with other robust finite mixture regression, spatial regression and spatial segmentation methods.", "published": "2021-09-01T16:29:46Z", "authors": ["Wennan Chang", "Pengtao Dang", "Changlin Wan", "Xiaoyu Lu", "Yue Fang", "Tong Zhao", "Yong Zang", "Bo Li", "Chi Zhang", "Sha Cao"], "categories": ["stat.ME", "cs.LG"], "doi": "", "_query": "hedonic regression housing", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/0801.1158v1", "title": "Hierarchical selection of variables in sparse high-dimensional regression", "abstract": "We study a regression model with a huge number of interacting variables. We consider a specific approximation of the regression function under two ssumptions: (i) there exists a sparse representation of the regression function in a suggested basis, (ii) there are no interactions outside of the set of the corresponding main effects. We suggest an hierarchical randomized search procedure for selection of variables and of their interactions. We show that given an initial estimator, an estimator with a similar prediction loss but with a smaller number of non-zero coordinates can be found.", "published": "2008-01-08T05:21:54Z", "authors": ["Peter J. Bickel", "Ya'acov Ritov", "Alexander B. Tsybakov"], "categories": ["math.ST"], "doi": "", "_query": "hedonic regression housing", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2209.04764v3", "title": "A Mathematical Analysis of the 2022 Alaska Special Election for US House", "abstract": "The August 2022 Alaska Special Election for US House contained many interesting features from the perspective of social choice theory. This election used instant runoff voting (often referred to as ranked choice voting) to elect a winner, and many of the weaknesses of this voting method were on display in this election. For example, the Condorcet winner is different from the instant runoff winner, and the election demonstrated a monotonicity paradox. The election also demonstrated a no show paradox; as far as we are aware, this election represents the first document American ranked choice election to demonstrate this paradox.", "published": "2022-09-11T01:09:15Z", "authors": ["Adam Graham-Squire", "David McCune"], "categories": ["econ.GN"], "doi": "", "_query": "hedonic regression housing", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2003.04786v1", "title": "Multivariate Functional Regression via Nested Reduced-Rank Regularization", "abstract": "We propose a nested reduced-rank regression (NRRR) approach in fitting regression model with multivariate functional responses and predictors, to achieve tailored dimension reduction and facilitate interpretation/visualization of the resulting functional model. Our approach is based on a two-level low-rank structure imposed on the functional regression surfaces. A global low-rank structure identifies a small set of latent principal functional responses and predictors that drives the underlying regression association. A local low-rank structure then controls the complexity and smoothness of the association between the principal functional responses and predictors. Through a basis expansion approach, the functional problem boils down to an interesting integrated matrix approximation task, where the blocks or submatrices of an integrated low-rank matrix share some common row space and/or column space. An iterative algorithm with convergence guarantee is developed. We establish the consistency of NRRR and also show through non-asymptotic analysis that it can achieve at least a comparable error rate to that of the reduced-rank regression. Simulation studies demonstrate the effectiveness of NRRR. We apply NRRR in an electricity demand problem, to relate the trajectories of the daily electricity consumption with those of the daily temperatures.", "published": "2020-03-10T14:58:54Z", "authors": ["Xiaokang Liu", "Shujie Ma", "Kun Chen"], "categories": ["stat.ME", "stat.ML"], "doi": "", "_query": "hedonic regression housing", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2106.04271v1", "title": "Inference for Network Regression Models with Community Structure", "abstract": "Network regression models, where the outcome comprises the valued edge in a network and the predictors are actor or dyad-level covariates, are used extensively in the social and biological sciences. Valid inference relies on accurately modeling the residual dependencies among the relations. Frequently homogeneity assumptions are placed on the errors which are commonly incorrect and ignore critical, natural clustering of the actors. In this work, we present a novel regression modeling framework that models the errors as resulting from a community-based dependence structure and exploits the subsequent exchangeability properties of the error distribution to obtain parsimonious standard errors for regression parameters.", "published": "2021-06-08T12:04:31Z", "authors": ["Mengjie Pan", "Tyler H. McCormick", "Bailey K. Fosdick"], "categories": ["stat.ME", "cs.LG", "cs.SI", "stat.AP", "stat.ML"], "doi": "", "_query": "hedonic regression housing", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2310.18733v2", "title": "Threshold detection under a semiparametric regression model", "abstract": "Linear regression models have been extensively considered in the literature. However, in some practical applications they may not be appropriate all over the range of the covariate. In this paper, a more flexible model is introduced by considering a regression model $Y=r(X)+\\varepsilon$ where the regression function $r(\\cdot)$ is assumed to be linear for large values in the domain of the predictor variable $X$. More precisely, we assume that $r(x)=α_0+β_0 x$ for $x> u_0$, where the value $u_0$ is identified as the smallest value satisfying such a property. A penalized procedure is introduced to estimate the threshold $u_0$. The considered proposal focusses on a semiparametric approach since no parametric model is assumed for the regression function for values smaller than $u_0$. Consistency properties of both the threshold estimator and the estimators of $(α_0,β_0)$ are derived, under mild assumptions. Through a numerical study, the small sample properties of the proposed procedure and the importance of introducing a penalization are investigated. The analysis of a real data set allows us to demonstrate the usefulness of the penalized estimators.", "published": "2023-10-28T15:29:48Z", "authors": ["Graciela Boente", "Florencia Leonardi", "Daniela Rodriguez", "Mariela Sued"], "categories": ["math.ST", "stat.ME"], "doi": "", "_query": "hedonic regression housing", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2106.06095v1", "title": "Sparse Bayesian Learning via Stepwise Regression", "abstract": "Sparse Bayesian Learning (SBL) is a powerful framework for attaining sparsity in probabilistic models. Herein, we propose a coordinate ascent algorithm for SBL termed Relevance Matching Pursuit (RMP) and show that, as its noise variance parameter goes to zero, RMP exhibits a surprising connection to Stepwise Regression. Further, we derive novel guarantees for Stepwise Regression algorithms, which also shed light on RMP. Our guarantees for Forward Regression improve on deterministic and probabilistic results for Orthogonal Matching Pursuit with noise. Our analysis of Backward Regression on determined systems culminates in a bound on the residual of the optimal solution to the subset selection problem that, if satisfied, guarantees the optimality of the result. To our knowledge, this bound is the first that can be computed in polynomial time and depends chiefly on the smallest singular value of the matrix. We report numerical experiments using a variety of feature selection algorithms. Notably, RMP and its limiting variant are both efficient and maintain strong performance with correlated features.", "published": "2021-06-11T00:20:27Z", "authors": ["Sebastian Ament", "Carla Gomes"], "categories": ["cs.LG", "cs.IT", "math.OC", "stat.CO", "stat.ML"], "doi": "", "_query": "hedonic regression housing", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2408.05393v2", "title": "fastkqr: A Fast Algorithm for Kernel Quantile Regression", "abstract": "Quantile regression is a powerful tool for robust and heterogeneous learning that has seen applications in a diverse range of applied areas. However, its broader application is often hindered by the substantial computational demands arising from the non-smooth quantile loss function. In this paper, we introduce a novel algorithm named fastkqr, which significantly advances the computation of quantile regression in reproducing kernel Hilbert spaces. The core of fastkqr is a finite smoothing algorithm that magically produces exact regression quantiles, rather than approximations. To further accelerate the algorithm, we equip fastkqr with a novel spectral technique that carefully reutilizes matrix computations. In addition, we extend fastkqr to accommodate a flexible kernel quantile regression with a data-driven crossing penalty, addressing the interpretability challenges of crossing quantile curves at multiple levels. We have implemented fastkqr in a publicly available R package. Extensive simulations and real applications show that fastkqr matches the accuracy of state-of-the-art algorithms but can operate up to an order of magnitude faster.", "published": "2024-08-10T00:18:56Z", "authors": ["Qian Tang", "Yuwen Gu", "Boxiang Wang"], "categories": ["stat.ML", "cs.LG"], "doi": "", "_query": "hedonic regression housing", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2305.04213v3", "title": "Robust Image Ordinal Regression with Controllable Image Generation", "abstract": "Image ordinal regression has been mainly studied along the line of exploiting the order of categories. However, the issues of class imbalance and category overlap that are very common in ordinal regression were largely overlooked. As a result, the performance on minority categories is often unsatisfactory. In this paper, we propose a novel framework called CIG based on controllable image generation to directly tackle these two issues. Our main idea is to generate extra training samples with specific labels near category boundaries, and the sample generation is biased toward the less-represented categories. To achieve controllable image generation, we seek to separate structural and categorical information of images based on structural similarity, categorical similarity, and reconstruction constraints. We evaluate the effectiveness of our new CIG approach in three different image ordinal regression scenarios. The results demonstrate that CIG can be flexibly integrated with off-the-shelf image encoders or ordinal regression models to achieve improvement, and further, the improvement is more significant for minority categories.", "published": "2023-05-07T08:10:56Z", "authors": ["Yi Cheng", "Haochao Ying", "Renjun Hu", "Jinhong Wang", "Wenhao Zheng", "Xiao Zhang", "Danny Chen", "Jian Wu"], "categories": ["cs.CV"], "doi": "", "_query": "hedonic regression housing", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2401.15885v2", "title": "Rectify the Regression Bias in Long-Tailed Object Detection", "abstract": "Long-tailed object detection faces great challenges because of its extremely imbalanced class distribution. Recent methods mainly focus on the classification bias and its loss function design, while ignoring the subtle influence of the regression branch. This paper shows that the regression bias exists and does adversely and seriously impact the detection accuracy. While existing methods fail to handle the regression bias, the class-specific regression head for rare classes is hypothesized to be the main cause of it in this paper. As a result, three kinds of viable solutions to cater for the rare categories are proposed, including adding a class-agnostic branch, clustering heads and merging heads. The proposed methods brings in consistent and significant improvements over existing long-tailed detection methods, especially in rare and common classes. The proposed method achieves state-of-the-art performance in the large vocabulary LVIS dataset with different backbones and architectures. It generalizes well to more difficult evaluation metrics, relatively balanced datasets, and the mask branch. This is the first attempt to reveal and explore rectifying of the regression bias in long-tailed object detection.", "published": "2024-01-29T04:40:33Z", "authors": ["Ke Zhu", "Minghao Fu", "Jie Shao", "Tianyu Liu", "Jianxin Wu"], "categories": ["cs.CV"], "doi": "", "_query": "hedonic regression housing", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2605.28749v1", "title": "IV regression with distribution-valued outcomes", "abstract": "We develop IV Fréchet regression (IVFR), an instrumental-variable (IV) method for settings where the outcome is an entire distribution. Framing the problem as an IV regression in 2-Wasserstein space, IVFR extends global Fréchet regression to the case with endogenous covariates. IVFR projects IV-weighted quantile curves onto the space of valid distributions and then recovers the corresponding regression coefficient functions. The projection provably reduces the estimation error in finite samples and guarantees valid fitted distributions. We show that the IVFR estimator converges weakly to a mean-zero Gaussian process and establish the validity of a multiplier bootstrap procedure for uniform inference. In simulations, the projection reduces the integrated mean squared error (IMSE) by up to 63% relative to existing methods. Revisiting the effects of Chinese import competition on the wage distribution within commuting zones, the proposed method produces 9-10% narrower confidence bands than existing methods. Using our novel uniform confidence bands, we find no evidence that import competition reduced wages at the very bottom of the distribution, but only between the 10th and 35th quantile. We also revisit the effect of county food stamp programs on the county's birth weight distribution and find no significant effects.", "published": "2026-05-27T17:07:47Z", "authors": ["David Van Dijcke", "Kaspar Wüthrich"], "categories": ["econ.EM", "math.ST", "stat.ME"], "doi": "", "_query": "hedonic regression housing", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2405.11626v1", "title": "Distribution-in-distribution-out Regression", "abstract": "Regression analysis with probability measures as input predictors and output response has recently drawn great attention. However, it is challenging to handle multiple input probability measures due to the non-flat Riemannian geometry of the Wasserstein space, hindering the definition of arithmetic operations, hence additive linear structure is not well-defined. In this work, a distribution-in-distribution-out regression model is proposed by introducing parallel transport to achieve provable commutativity and additivity of newly defined arithmetic operations in Wasserstein space. The appealing properties of the DIDO regression model can serve a foundation for model estimation, prediction, and inference. Specifically, the Fréchet least squares estimator is employed to obtain the best linear unbiased estimate, supported by the newly established Fréchet Gauss-Markov Theorem. Furthermore, we investigate a special case when predictors and response are all univariate Gaussian measures, leading to a simple close-form solution of linear model coefficients and $R^2$ metric. A simulation study and real case study in intraoperative cardiac output prediction are performed to evaluate the performance of the proposed method.", "published": "2024-05-19T17:39:20Z", "authors": ["Xiaoyu Chen", "Mengfan Fu", "Yujing Huang", "Xinwei Deng"], "categories": ["stat.ME", "math.ST"], "doi": "", "_query": "hedonic regression housing", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/1212.2125v1", "title": "Sparse Regression Codes for Multi-terminal Source and Channel Coding", "abstract": "We study a new class of codes for Gaussian multi-terminal source and channel coding. These codes are designed using the statistical framework of high-dimensional linear regression and are called Sparse Superposition or Sparse Regression codes. Codewords are linear combinations of subsets of columns of a design matrix. These codes were recently introduced by Barron and Joseph and shown to achieve the channel capacity of AWGN channels with computationally feasible decoding. They have also recently been shown to achieve the optimal rate-distortion function for Gaussian sources. In this paper, we demonstrate how to implement random binning and superposition coding using sparse regression codes. In particular, with minimum-distance encoding/decoding it is shown that sparse regression codes attain the optimal information-theoretic limits for a variety of multi-terminal source and channel coding problems.", "published": "2012-12-10T16:41:04Z", "authors": ["Ramji Venkataramanan", "Sekhar Tatikonda"], "categories": ["cs.IT"], "doi": "", "_query": "hedonic regression housing", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2503.19543v1", "title": "Scene-agnostic Pose Regression for Visual Localization", "abstract": "Absolute Pose Regression (APR) predicts 6D camera poses but lacks the adaptability to unknown environments without retraining, while Relative Pose Regression (RPR) generalizes better yet requires a large image retrieval database. Visual Odometry (VO) generalizes well in unseen environments but suffers from accumulated error in open trajectories. To address this dilemma, we introduce a new task, Scene-agnostic Pose Regression (SPR), which can achieve accurate pose regression in a flexible way while eliminating the need for retraining or databases. To benchmark SPR, we created a large-scale dataset, 360SPR, with over 200K photorealistic panoramas, 3.6M pinhole images and camera poses in 270 scenes at three different sensor heights. Furthermore, a SPR-Mamba model is initially proposed to address SPR in a dual-branch manner. Extensive experiments and studies demonstrate the effectiveness of our SPR paradigm, dataset, and model. In the unknown scenes of both 360SPR and 360Loc datasets, our method consistently outperforms APR, RPR and VO. The dataset and code are available at https://junweizheng93.github.io/publications/SPR/SPR.html.", "published": "2025-03-25T10:58:40Z", "authors": ["Junwei Zheng", "Ruiping Liu", "Yufan Chen", "Zhenfang Chen", "Kailun Yang", "Jiaming Zhang", "Rainer Stiefelhagen"], "categories": ["cs.CV"], "doi": "", "_query": "hedonic regression housing", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2404.17292v2", "title": "The Inefficiency of Genetic Programming for Symbolic Regression", "abstract": "We analyse the search behaviour of genetic programming for symbolic regression in practically relevant but limited settings, allowing exhaustive enumeration of all solutions. This enables us to quantify the success probability of finding the best possible expressions, and to compare the search efficiency of genetic programming to random search in the space of semantically unique expressions. This analysis is made possible by improved algorithms for equality saturation, which we use to improve the Exhaustive Symbolic Regression algorithm; this produces the set of semantically unique expression structures, orders of magnitude smaller than the full symbolic regression search space. We compare the efficiency of random search in the set of unique expressions and genetic programming. For our experiments we use two real-world datasets where symbolic regression has been used to produce well-fitting univariate expressions: the Nikuradse dataset of flow in rough pipes and the Radial Acceleration Relation of galaxy dynamics. The results show that genetic programming in such limited settings explores only a small fraction of all unique expressions, and evaluates expressions repeatedly that are congruent to already visited expressions.", "published": "2024-04-26T09:49:32Z", "authors": ["Gabriel Kronberger", "Fabricio Olivetti de Franca", "Harry Desmond", "Deaglan J. Bartlett", "Lukas Kammerer"], "categories": ["cs.NE", "astro-ph.GA", "astro-ph.IM"], "doi": "10.1007/978-3-031-70055-2_17", "_query": "hedonic regression housing", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/1607.06059v1", "title": "Modelling multimodal photometric redshift regression with noisy observations", "abstract": "In this work, we are trying to extent the existing photometric redshift regression models from modeling pure photometric data back to the spectra themselves. To that end, we developed a PCA that is capable of describing the input uncertainty (including missing values) in a dimensionality reduction framework. With this \"spectrum generator\" at hand, we are capable of treating the redshift regression problem in a fully Bayesian framework, returning a posterior distribution over the redshift. This approach allows therefore to approach the multimodal regression problem in an adequate fashion. In addition, input uncertainty on the magnitudes can be included quite naturally and lastly, the proposed algorithm allows in principle to make predictions outside the training values which makes it a fascinating opportunity for the detection of high-redshifted quasars.", "published": "2016-07-20T18:52:30Z", "authors": ["S. D. Kügler", "N. Gianniotis"], "categories": ["astro-ph.IM"], "doi": "", "_query": "hedonic regression housing", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2308.11854v1", "title": "Finding the Perfect Fit: Applying Regression Models to ClimateBench v1.0", "abstract": "Climate projections using data driven machine learning models acting as emulators, is one of the prevailing areas of research to enable policy makers make informed decisions. Use of machine learning emulators as surrogates for computationally heavy GCM simulators reduces time and carbon footprints. In this direction, ClimateBench [1] is a recently curated benchmarking dataset for evaluating the performance of machine learning emulators designed for climate data. Recent studies have reported that despite being considered fundamental, regression models offer several advantages pertaining to climate emulations. In particular, by leveraging the kernel trick, regression models can capture complex relationships and improve their predictive capabilities. This study focuses on evaluating non-linear regression models using the aforementioned dataset. Specifically, we compare the emulation capabilities of three non-linear regression models. Among them, Gaussian Process Regressor demonstrates the best-in-class performance against standard evaluation metrics used for climate field emulation studies. However, Gaussian Process Regression suffers from being computational resource hungry in terms of space and time complexity. Alternatively, Support Vector and Kernel Ridge models also deliver competitive results and but there are certain trade-offs to be addressed. Additionally, we are actively investigating the performance of composite kernels and techniques such as variational inference to further enhance the performance of the regression models and effectively model complex non-linear patterns, including phenomena like precipitation.", "published": "2023-08-23T01:08:01Z", "authors": ["Anmol Chaure", "Ashok Kumar Behera", "Sudip Bhattacharya"], "categories": ["cs.LG", "cs.AI"], "doi": "10.5120/ijca2023923042", "_query": "hedonic regression housing", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2307.16105v1", "title": "TMPNN: High-Order Polynomial Regression Based on Taylor Map Factorization", "abstract": "Polynomial regression is widely used and can help to express nonlinear patterns. However, considering very high polynomial orders may lead to overfitting and poor extrapolation ability for unseen data. The paper presents a method for constructing a high-order polynomial regression based on the Taylor map factorization. This method naturally implements multi-target regression and can capture internal relationships between targets. Additionally, we introduce an approach for model interpretation in the form of systems of differential equations. By benchmarking on UCI open access datasets, Feynman symbolic regression datasets, and Friedman-1 datasets, we demonstrate that the proposed method performs comparable to the state-of-the-art regression methods and outperforms them on specific tasks.", "published": "2023-07-30T01:52:00Z", "authors": ["Andrei Ivanov", "Stefan Maria Ailuro"], "categories": ["cs.LG", "cs.NE"], "doi": "", "_query": "hedonic regression housing", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/1609.07887v1", "title": "Interpretation of Compositional Regression with Application to Time Budget Analysis", "abstract": "Regression with compositional response or covariates, or even regression between parts of a composition, is frequently employed in social sciences. Among other possible applications, it may help to reveal interesting features in time allocation analysis. As individual activities represent relative contributions to the total amount of time, statistical processing of raw data (frequently represented directly as proportions or percentages) using standard methods may lead to biased results. Specific geometrical features of time budget variables are captured by the logratio methodology of compositional data, whose aim is to build (preferably orthonormal) coordinates to be applied with popular statistical methods. The aim of this paper is to present recent tools of regression analysis within the logratio methodology and apply them to reveal potential relationships among psychometric indicators in a real-world data set. In particular, orthogonal logratio coordinates have been introduced to enhance the interpretability of coefficients in regression models.", "published": "2016-09-26T08:58:38Z", "authors": ["Ivo Muller", "Karel Hron", "Eva Fiserova", "Jan Smahaj", "Panajotis Cakirpaloglu", "Jana Vancakova"], "categories": ["math.ST", "stat.ME"], "doi": "", "_query": "hedonic regression housing", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2507.06409v1", "title": "Differential Equation-Constrained Local Regression for Data with Sparse Design", "abstract": "Local polynomial regression of order one or higher often performs poorly in areas with sparse data. In contrast, local constant regression tends to be more robust in these regions, although it is generally the least accurate approach, especially near the boundaries of the data. Incorporating information from differential equations, which may approximately or exactly hold, is one way of extending the sparse design capacity of local constant regression while reducing bias and variance. A nonparametric regression method that exploits first-order differential equations is studied in this paper and applied to noisy mouse tumour growth data. Asymptotic biases and variances of kernel estimators using Taylor polynomials with different degrees are discussed. Model comparison is performed for different estimators through simulation studies under various scenarios that simulate exponential-type growth.", "published": "2025-07-08T21:26:17Z", "authors": ["Chunlei Ge", "W. John Braun"], "categories": ["stat.ME", "stat.CO"], "doi": "", "_query": "hedonic regression housing", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2512.04747v1", "title": "A Tutorial on Regression Analysis: From Linear Models to Deep Learning -- Lecture Notes on Artificial Intelligence", "abstract": "This article serves as the regression analysis lecture notes in the Intelligent Computing course cluster (including the courses of Artificial Intelligence, Data Mining, Machine Learning, and Pattern Recognition). It aims to provide students -- who are assumed to possess only basic university-level mathematics (i.e., with prerequisite courses in calculus, linear algebra, and probability theory) -- with a comprehensive and self-contained understanding of regression analysis without requiring any additional references. The lecture notes systematically introduce the fundamental concepts, modeling components, and theoretical foundations of regression analysis, covering linear regression, logistic regression, multinomial logistic regression, polynomial regression, basis-function models, kernel-based methods, and neural-network-based nonlinear regression. Core methodological topics include loss-function design, parameter-estimation principles, ordinary least squares, gradient-based optimization algorithms and their variants, as well as regularization techniques such as Ridge and LASSO regression. Through detailed mathematical derivations, illustrative examples, and intuitive visual explanations, the materials help students understand not only how regression models are constructed and optimized, but also how they reveal the underlying relationships between features and response variables. By bridging classical statistical modeling and modern machine-learning practice, these lecture notes aim to equip students with a solid conceptual and technical foundation for further study in advanced artificial intelligence models.", "published": "2025-12-04T12:35:57Z", "authors": ["Jingyuan Wang", "Jiahao Ji"], "categories": ["cs.LG"], "doi": "", "_query": "hedonic regression housing", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/1804.01575v1", "title": "Probabilistic Formulations of Regression with Mixed Guidance", "abstract": "Regression problems assume every instance is annotated (labeled) with a real value, a form of annotation we call \\emph{strong guidance}. In order for these annotations to be accurate, they must be the result of a precise experiment or measurement. However, in some cases additional \\emph{weak guidance} might be given by imprecise measurements, a domain expert or even crowd sourcing. Current formulations of regression are unable to use both types of guidance. We propose a regression framework that can also incorporate weak guidance based on relative orderings, bounds, neighboring and similarity relations. Consider learning to predict ages from portrait images, these new types of guidance allow weaker forms of guidance such as stating a person is in their 20s or two people are similar in age. These types of annotations can be easier to generate than strong guidance. We introduce a probabilistic formulation for these forms of weak guidance and show that the resulting optimization problems are convex. Our experimental results show the benefits of these formulations on several data sets.", "published": "2018-04-01T20:36:33Z", "authors": ["Aubrey Gress", "Ian Davidson"], "categories": ["cs.LG", "stat.ML"], "doi": "", "_query": "hedonic regression housing", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/0904.0814v1", "title": "Stability Analysis and Learning Bounds for Transductive Regression Algorithms", "abstract": "This paper uses the notion of algorithmic stability to derive novel generalization bounds for several families of transductive regression algorithms, both by using convexity and closed-form solutions. Our analysis helps compare the stability of these algorithms. It also shows that a number of widely used transductive regression algorithms are in fact unstable. Finally, it reports the results of experiments with local transductive regression demonstrating the benefit of our stability bounds for model selection, for one of the algorithms, in particular for determining the radius of the local neighborhood used by the algorithm.", "published": "2009-04-05T20:08:44Z", "authors": ["Corinna Cortes", "Mehryar Mohri", "Dmitry Pechyony", "Ashish Rastogi"], "categories": ["cs.LG"], "doi": "", "_query": "hedonic regression housing", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/1102.5633v1", "title": "Rates of convergence for nearest neighbor estimators with the smoother regression function", "abstract": "In regression analysis one wants to estimate the regression function from a data. In this paper we consider the rate of convergence for the nearest neighbor estimator in case that the regression function is $(p,C)$-smooth. It is an open problem whether the optimal rate can be achieved by some nearest neighbor estimator in case that $p$ is on (1,1.5]. We solve the problem affirmatively. This is the main result of this paper. Throughout this paper, we assume that the data is independent and identically distributed and as an error criterion we use the expected $L_2$ error.", "published": "2011-02-28T10:18:15Z", "authors": ["Takanori Ayano"], "categories": ["math.ST"], "doi": "10.1016/j.jspi.2012.03.012", "_query": "hedonic regression housing", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2205.00477v1", "title": "Ridgeless Regression with Random Features", "abstract": "Recent theoretical studies illustrated that kernel ridgeless regression can guarantee good generalization ability without an explicit regularization. In this paper, we investigate the statistical properties of ridgeless regression with random features and stochastic gradient descent. We explore the effect of factors in the stochastic gradient and random features, respectively. Specifically, random features error exhibits the double-descent curve. Motivated by the theoretical findings, we propose a tunable kernel algorithm that optimizes the spectral density of kernel during training. Our work bridges the interpolation theory and practical algorithm.", "published": "2022-05-01T14:25:08Z", "authors": ["Jian Li", "Yong Liu", "Yingying Zhang"], "categories": ["cs.LG", "cs.IT", "stat.ML"], "doi": "10.24963/ijcai.2022/445", "_query": "hedonic regression housing", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2602.08413v1", "title": "A Bayesian regression framework for circular models with INLA", "abstract": "Regression models for circular variables are less developed, since the concept of building a linear predictor from linear combinations of covariates and various random effects, breaks the circular nature of the variable. In this paper, we introduce a new approach to rectify this issue, leading to well-defined regression models for circular responses when the data are concentrated. Our approach extends naturally to joint regression models where we can have several circular and non-circular responses, and allow us to handle a mix of linear covariates, circular covariates and various random effects. Our formulation aligns naturally with the integrated nested Laplace approximation (INLA), which provides fast and accurate Bayesian inference. We illustrate our approach through several simulated and real examples.", "published": "2026-02-09T09:16:34Z", "authors": ["Xiang Ye", "Janet Van Niekerk", "Haavard Rue"], "categories": ["stat.ME"], "doi": "", "_query": "hedonic regression housing", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2604.12458v1", "title": "Expected Shortfall Panel Regression", "abstract": "Expected Shortfall (ES) is a coherent measure of tail risk that captures the average loss beyond a quantile threshold. Despite the growing literature on ES regression conditional on covariates, no existing work considers ES modeling in panel data settings where both cross-sectional and temporal dependencies are present. This paper introduces the panel ES regression model with a latent factor structure to capture cross-sectional dependence. We develop a two-stage estimation procedure robust to heavy-tailed errors, recovering the conditional quantile in the first stage and iteratively estimating the ES factor model in the second stage. Theoretically, we establish the consistency and asymptotic normality of the proposed two-step ES estimators and derive non-asymptotic error bounds for both the panel quantile and ES estimators. We also provide a non-asymptotic normal approximation for the standardized ES regression estimator, bridging asymptotic theory and finite-sample practice. Simulation evidence shows that the proposed method delivers substantial gains in both parameter estimation and factor recovery, particularly in the presence of latent tail dependence. An empirical application further indicates that the extracted ES factors carry distinct pricing information that is not captured by conventional mean or quantile-based approaches.", "published": "2026-04-14T08:44:56Z", "authors": ["Yujie Hou", "Xinbing Kong", "Yalin Wang", "Bin Wu"], "categories": ["stat.ME"], "doi": "", "_query": "hedonic regression housing", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2312.00540v1", "title": "Target-agnostic Source-free Domain Adaptation for Regression Tasks", "abstract": "Unsupervised domain adaptation (UDA) seeks to bridge the domain gap between the target and source using unlabeled target data. Source-free UDA removes the requirement for labeled source data at the target to preserve data privacy and storage. However, work on source-free UDA assumes knowledge of domain gap distribution, and hence is limited to either target-aware or classification task. To overcome it, we propose TASFAR, a novel target-agnostic source-free domain adaptation approach for regression tasks. Using prediction confidence, TASFAR estimates a label density map as the target label distribution, which is then used to calibrate the source model on the target domain. We have conducted extensive experiments on four regression tasks with various domain gaps, namely, pedestrian dead reckoning for different users, image-based people counting in different scenes, housing-price prediction at different districts, and taxi-trip duration prediction from different departure points. TASFAR is shown to substantially outperform the state-of-the-art source-free UDA approaches by averagely reducing 22% errors for the four tasks and achieve notably comparable accuracy as source-based UDA without using source data.", "published": "2023-12-01T12:35:18Z", "authors": ["Tianlang He", "Zhiqiu Xia", "Jierun Chen", "Haoliang Li", "S. -H. Gary Chan"], "categories": ["cs.LG", "cs.AI", "stat.ML"], "doi": "", "_query": "hedonic regression housing", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/1708.02052v1", "title": "VART: A Tool for the Automatic Detection of Regression Faults", "abstract": "In this paper we present VART, a tool for automatically revealing regression faults missed by regression test suites. Interestingly, VART is not limited to faults causing crashing or exceptions, but can reveal faults that cause the violation of application-specific correctness properties. VART achieves this goal by combining static and dynamic program analysis.", "published": "2017-08-07T09:43:54Z", "authors": ["Fabrizio Pastore", "Leonardo Mariani"], "categories": ["cs.SE"], "doi": "10.1145/3106237.3122819", "_query": "hedonic regression housing", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/1908.09482v3", "title": "Marginally-calibrated deep distributional regression", "abstract": "Deep neural network (DNN) regression models are widely used in applications requiring state-of-the-art predictive accuracy. However, until recently there has been little work on accurate uncertainty quantification for predictions from such models. We add to this literature by outlining an approach to constructing predictive distributions that are `marginally calibrated'. This is where the long run average of the predictive distributions of the response variable matches the observed empirical margin. Our approach considers a DNN regression with a conditionally Gaussian prior for the final layer weights, from which an implicit copula process on the feature space is extracted. This copula process is combined with a non-parametrically estimated marginal distribution for the response. The end result is a scalable distributional DNN regression method with marginally calibrated predictions, and our work complements existing methods for probability calibration. The approach is first illustrated using two applications of dense layer feed-forward neural networks. However, our main motivating applications are in likelihood-free inference, where distributional deep regression is used to estimate marginal posterior distributions. In two complex ecological time series examples we employ the implicit copulas of convolutional networks, and show that marginal calibration results in improved uncertainty quantification. Our approach also avoids the need for manual specification of summary statistics, a requirement that is burdensome for users and typical of competing likelihood-free inference methods.", "published": "2019-08-26T05:47:34Z", "authors": ["Nadja Klein", "David J. Nott", "Michael Stanley Smith"], "categories": ["stat.ME", "stat.CO", "stat.ML"], "doi": "10.1080/10618600.2020.1807996", "_query": "hedonic regression housing", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2202.02976v2", "title": "Measuring and Reducing Model Update Regression in Structured Prediction for NLP", "abstract": "Recent advance in deep learning has led to the rapid adoption of machine learning-based NLP models in a wide range of applications. Despite the continuous gain in accuracy, backward compatibility is also an important aspect for industrial applications, yet it received little research attention. Backward compatibility requires that the new model does not regress on cases that were correctly handled by its predecessor. This work studies model update regression in structured prediction tasks. We choose syntactic dependency parsing and conversational semantic parsing as representative examples of structured prediction tasks in NLP. First, we measure and analyze model update regression in different model update settings. Next, we explore and benchmark existing techniques for reducing model update regression including model ensemble and knowledge distillation. We further propose a simple and effective method, Backward-Congruent Re-ranking (BCR), by taking into account the characteristics of structured prediction. Experiments show that BCR can better mitigate model update regression than model ensemble and knowledge distillation approaches.", "published": "2022-02-07T07:04:54Z", "authors": ["Deng Cai", "Elman Mansimov", "Yi-An Lai", "Yixuan Su", "Lei Shu", "Yi Zhang"], "categories": ["cs.CL", "cs.AI", "cs.LG"], "doi": "", "_query": "hedonic regression housing", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2106.14131v1", "title": "SymbolicGPT: A Generative Transformer Model for Symbolic Regression", "abstract": "Symbolic regression is the task of identifying a mathematical expression that best fits a provided dataset of input and output values. Due to the richness of the space of mathematical expressions, symbolic regression is generally a challenging problem. While conventional approaches based on genetic evolution algorithms have been used for decades, deep learning-based methods are relatively new and an active research area. In this work, we present SymbolicGPT, a novel transformer-based language model for symbolic regression. This model exploits the advantages of probabilistic language models like GPT, including strength in performance and flexibility. Through comprehensive experiments, we show that our model performs strongly compared to competing models with respect to the accuracy, running time, and data efficiency.", "published": "2021-06-27T03:26:35Z", "authors": ["Mojtaba Valipour", "Bowen You", "Maysum Panju", "Ali Ghodsi"], "categories": ["cs.LG", "cs.CL", "cs.SC"], "doi": "", "_query": "hedonic regression housing", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2408.10825v1", "title": "Conditional nonparametric variable screening by neural factor regression", "abstract": "High-dimensional covariates often admit linear factor structure. To effectively screen correlated covariates in high-dimension, we propose a conditional variable screening test based on non-parametric regression using neural networks due to their representation power. We ask the question whether individual covariates have additional contributions given the latent factors or more generally a set of variables. Our test statistics are based on the estimated partial derivative of the regression function of the candidate variable for screening and a observable proxy for the latent factors. Hence, our test reveals how much predictors contribute additionally to the non-parametric regression after accounting for the latent factors. Our derivative estimator is the convolution of a deep neural network regression estimator and a smoothing kernel. We demonstrate that when the neural network size diverges with the sample size, unlike estimating the regression function itself, it is necessary to smooth the partial derivative of the neural network estimator to recover the desired convergence rate for the derivative. Moreover, our screening test achieves asymptotic normality under the null after finely centering our test statistics that makes the biases negligible, as well as consistency for local alternatives under mild conditions. We demonstrate the performance of our test in a simulation study and two real world applications.", "published": "2024-08-20T13:21:35Z", "authors": ["Jianqing Fan", "Weining Wang", "Yue Zhao"], "categories": ["econ.EM", "math.ST"], "doi": "", "_query": "hedonic regression housing", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2405.04919v2", "title": "Fast Computation of Leave-One-Out Cross-Validation for $k$-NN Regression", "abstract": "We describe a fast computation method for leave-one-out cross-validation (LOOCV) for $k$-nearest neighbours ($k$-NN) regression. We show that, under a tie-breaking condition for nearest neighbours, the LOOCV estimate of the mean square error for $k$-NN regression is identical to the mean square error of $(k+1)$-NN regression evaluated on the training data, multiplied by the scaling factor $(k+1)^2/k^2$. Therefore, to compute the LOOCV score, one only needs to fit $(k+1)$-NN regression only once, and does not need to repeat training-validation of $k$-NN regression for the number of training data. Numerical experiments confirm the validity of the fast computation method.", "published": "2024-05-08T09:41:25Z", "authors": ["Motonobu Kanagawa"], "categories": ["stat.ML", "cs.DS", "cs.LG", "stat.CO", "stat.ME"], "doi": "", "_query": "hedonic regression housing", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2205.04864v1", "title": "THOR: Threshold-Based Ranking Loss for Ordinal Regression", "abstract": "In this work, we present a regression-based ordinal regression algorithm for supervised classification of instances into ordinal categories. In contrast to previous methods, in this work the decision boundaries between categories are predefined, and the algorithm learns to project the input examples onto their appropriate scores according to these predefined boundaries. This is achieved by adding a novel threshold-based pairwise loss function that aims at minimizing the regression error, which in turn minimizes the Mean Absolute Error (MAE) measure. We implemented our proposed architecture-agnostic method using the CNN-framework for feature extraction. Experimental results on five real-world benchmarks demonstrate that the proposed algorithm achieves the best MAE results compared to state-of-the-art ordinal regression algorithms.", "published": "2022-05-10T13:04:09Z", "authors": ["Tzeviya Sylvia Fuchs", "Joseph Keshet"], "categories": ["cs.LG"], "doi": "", "_query": "hedonic regression housing", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2201.02587v2", "title": "Pricing Bermudan options using regression trees/random forests", "abstract": "The value of an American option is the maximized value of the discounted cash flows from the option. At each time step, one needs to compare the immediate exercise value with the continuation value and decide to exercise as soon as the exercise value is strictly greater than the continuation value. We can formulate this problem as a dynamic programming equation, where the main difficulty comes from the computation of the conditional expectations representing the continuation values at each time step. In (Longstaff and Schwartz, 2001), these conditional expectations were estimated using regressions on a finite-dimensional vector space (typically a polynomial basis). In this paper, we follow the same algorithm; only the conditional expectations are estimated using Regression trees or Random forests. We discuss the convergence of the LS algorithm when the standard least squares regression is replaced with regression trees. Finally, we expose some numerical results with regression trees and random forests. The random forest algorithm gives excellent results in high dimensions.", "published": "2021-11-19T10:14:32Z", "authors": ["Zineb El Filali Ech-Chafiq", "Pierre Henry-Labordere", "Jérôme Lelong"], "categories": ["q-fin.CP", "math.PR"], "doi": "", "_query": "hedonic regression housing", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2106.06676v1", "title": "Semi-supervised Active Regression", "abstract": "Labelled data often comes at a high cost as it may require recruiting human labelers or running costly experiments. At the same time, in many practical scenarios, one already has access to a partially labelled, potentially biased dataset that can help with the learning task at hand. Motivated by such settings, we formally initiate a study of $semi-supervised$ $active$ $learning$ through the frame of linear regression. In this setting, the learner has access to a dataset $X \\in \\mathbb{R}^{(n_1+n_2) \\times d}$ which is composed of $n_1$ unlabelled examples that an algorithm can actively query, and $n_2$ examples labelled a-priori. Concretely, denoting the true labels by $Y \\in \\mathbb{R}^{n_1 + n_2}$, the learner's objective is to find $\\widehatβ \\in \\mathbb{R}^d$ such that, \\begin{equation} \\| X \\widehatβ - Y \\|_2^2 \\le (1 + ε) \\min_{β\\in \\mathbb{R}^d} \\| X β- Y \\|_2^2 \\end{equation} while making as few additional label queries as possible. In order to bound the label queries, we introduce an instance dependent parameter called the reduced rank, denoted by $R_X$, and propose an efficient algorithm with query complexity $O(R_X/ε)$. This result directly implies improved upper bounds for two important special cases: (i) active ridge regression, and (ii) active kernel ridge regression, where the reduced-rank equates to the statistical dimension, $sd_λ$ and effective dimension, $d_λ$ of the problem respectively, where $λ\\ge 0$ denotes the regularization parameter. For active ridge regression we also prove a matching lower bound of $O(sd_λ/ ε)$ on the query complexity of any algorithm. This subsumes prior work that only considered the unregularized case, i.e., $λ= 0$.", "published": "2021-06-12T03:28:43Z", "authors": ["Fnu Devvrit", "Nived Rajaraman", "Pranjal Awasthi"], "categories": ["cs.LG"], "doi": "", "_query": "hedonic regression housing", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/1804.04775v3", "title": "A Compact Network Learning Model for Distribution Regression", "abstract": "Despite the superior performance of deep learning in many applications, challenges remain in the area of regression on function spaces. In particular, neural networks are unable to encode function inputs compactly as each node encodes just a real value. We propose a novel idea to address this shortcoming: to encode an entire function in a single network node. To that end, we design a compact network representation that encodes and propagates functions in single nodes for the distribution regression task. Our proposed Distribution Regression Network (DRN) achieves higher prediction accuracies while being much more compact and uses fewer parameters than traditional neural networks.", "published": "2018-04-13T02:31:10Z", "authors": ["Connie Kou", "Hwee Kuan Lee", "Teck Khim Ng"], "categories": ["cs.LG", "stat.ML"], "doi": "", "_query": "hedonic regression housing", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2501.18360v9", "title": "Univariate-Guided Sparse Regression", "abstract": "In this paper, we introduce ``UniLasso'' -- a novel statistical method for sparse regression. This two-stage approach preserves the signs of the univariate coefficients and leverages their magnitude. Both of these properties are attractive for stability and interpretation of the model. Through comprehensive simulations and applications to real-world datasets, we demonstrate that UniLasso outperforms Lasso in various settings, particularly in terms of sparsity and model interpretability. We prove asymptotic support recovery and mean-squared error consistency under a set of conditions different from the well-known irrepresentability conditions for the Lasso. Extensions to generalized linear models (GLMs) and Cox regression are also discussed.", "published": "2025-01-30T14:05:41Z", "authors": ["Sourav Chatterjee", "Trevor Hastie", "Robert Tibshirani"], "categories": ["stat.ME"], "doi": "", "_query": "hedonic regression housing", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2308.14304v2", "title": "Solving Attention Kernel Regression Problem via Pre-conditioner", "abstract": "The attention mechanism is the key to large language models, and the attention matrix serves as an algorithmic and computational bottleneck for such a scheme. In this paper, we define two problems, motivated by designing fast algorithms for proxy of attention matrix and solving regressions against them. Given an input matrix $A\\in \\mathbb{R}^{n\\times d}$ with $n\\gg d$ and a response vector $b$, we first consider the matrix exponential of the matrix $A^\\top A$ as a proxy, and we in turn design algorithms for two types of regression problems: $\\min_{x\\in \\mathbb{R}^d}\\|(A^\\top A)^jx-b\\|_2$ and $\\min_{x\\in \\mathbb{R}^d}\\|A(A^\\top A)^jx-b\\|_2$ for any positive integer $j$. Studying algorithms for these regressions is essential, as matrix exponential can be approximated term-by-term via these smaller problems. The second proxy is applying exponential entrywise to the Gram matrix, denoted by $\\exp(AA^\\top)$ and solving the regression $\\min_{x\\in \\mathbb{R}^n}\\|\\exp(AA^\\top)x-b \\|_2$. We call this problem the attention kernel regression problem, as the matrix $\\exp(AA^\\top)$ could be viewed as a kernel function with respect to $A$. We design fast algorithms for these regression problems, based on sketching and preconditioning. We hope these efforts will provide an alternative perspective of studying efficient approximation of attention matrices.", "published": "2023-08-28T04:37:38Z", "authors": ["Zhao Song", "Junze Yin", "Lichen Zhang"], "categories": ["cs.LG"], "doi": "", "_query": "hedonic regression housing", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2511.13463v1", "title": "Multi-task GINN-LP for Multi-target Symbolic Regression", "abstract": "In the area of explainable artificial intelligence, Symbolic Regression (SR) has emerged as a promising approach by discovering interpretable mathematical expressions that fit data. However, SR faces two main challenges: most methods are evaluated on scientific datasets with well-understood relationships, limiting generalization, and SR primarily targets single-output regression, whereas many real-world problems involve multi-target outputs with interdependent variables. To address these issues, we propose multi-task regression GINN-LP (MTRGINN-LP), an interpretable neural network for multi-target symbolic regression. By integrating GINN-LP with a multi-task deep learning, the model combines a shared backbone including multiple power-term approximator blocks with task-specific output layers, capturing inter-target dependencies while preserving interpretability. We validate multi-task GINN-LP on practical multi-target applications, including energy efficiency prediction and sustainable agriculture. Experimental results demonstrate competitive predictive performance alongside high interpretability, effectively extending symbolic regression to broader real-world multi-output tasks.", "published": "2025-11-17T15:07:41Z", "authors": ["Hussein Rajabu", "Lijun Qian", "Xishuang Dong"], "categories": ["cs.LG", "cs.AI"], "doi": "", "_query": "hedonic regression housing", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/1905.13613v2", "title": "Regression Networks for Meta-Learning Few-Shot Classification", "abstract": "We propose regression networks for the problem of few-shot classification, where a classifier must generalize to new classes not seen in the training set, given only a small number of examples of each class. In high dimensional embedding spaces the direction of data generally contains richer information than magnitude. Next to this, state-of-the-art few-shot metric methods that compare distances with aggregated class representations, have shown superior performance. Combining these two insights, we propose to meta-learn classification of embedded points by regressing the closest approximation in every class subspace while using the regression error as a distance metric. Similarly to recent approaches for few-shot learning, regression networks reflect a simple inductive bias that is beneficial in this limited-data regime and they achieve excellent results, especially when more aggregate class representations can be formed with multiple shots.", "published": "2019-05-31T13:35:41Z", "authors": ["Arnout Devos", "Matthias Grossglauser"], "categories": ["cs.LG", "cs.CV", "stat.ML"], "doi": "", "_query": "hedonic regression housing", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/1503.06388v3", "title": "Adaptive Concentration of Regression Trees, with Application to Random Forests", "abstract": "We study the convergence of the predictive surface of regression trees and forests. To support our analysis we introduce a notion of adaptive concentration for regression trees. This approach breaks tree training into a model selection phase in which we pick the tree splits, followed by a model fitting phase where we find the best regression model consistent with these splits. We then show that the fitted regression tree concentrates around the optimal predictor with the same splits: as d and n get large, the discrepancy is with high probability bounded on the order of sqrt(log(d) log(n)/k) uniformly over the whole regression surface, where d is the dimension of the feature space, n is the number of training examples, and k is the minimum leaf size for each tree. We also provide rate-matching lower bounds for this adaptive concentration statement. From a practical perspective, our result enables us to prove consistency results for adaptively grown forests in high dimensions, and to carry out valid post-selection inference in the sense of Berk et al. [2013] for subgroups defined by tree leaves.", "published": "2015-03-22T06:07:14Z", "authors": ["Stefan Wager", "Guenther Walther"], "categories": ["math.ST", "stat.ML"], "doi": "", "_query": "hedonic regression housing", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/0802.2676v1", "title": "Estimation and Test for Multidimensional Regression Models", "abstract": "This work is concerned with the estimation of multidimensional regression and the asymptotic behaviour of the test involved in selecting models. The main problem with such models is that we need to know the covariance matrix of the noise to get an optimal estimator. We show in this paper that if we choose to minimise the logarithm of the determinant of the empirical error covariance matrix, then we get an asymptotically optimal estimator. Moreover, under suitable assumptions, we show that this cost function leads to a very simple asymptotic law for testing the number of parameters of an identifiable and regular regression model. Numerical experiments confirm the theoretical results.", "published": "2008-02-19T15:49:45Z", "authors": ["Joseph Rynkiewicz"], "categories": ["math.ST"], "doi": "", "_query": "hedonic regression housing", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2510.01105v2", "title": "Geometric Analysis of Neural Regression Collapse via Intrinsic Dimension", "abstract": "Neural multivariate regression underpins a wide range of domains, including control, robotics, and finance, yet the geometry of its learned representations remains poorly characterized. While neural collapse has been shown to benefit generalization in classification, we find that analogous collapse in regression consistently degrades performance. To explain this contrast, we analyze regression models through the lens of intrinsic dimension. Across control tasks and synthetic datasets, we estimate the intrinsic dimension of last-layer features (ID_H) and compare it with that of the regression targets (ID_Y). Collapsed models exhibit ID_H < ID_Y, leading to over-compression and poor generalization, whereas non-collapsed models typically maintain ID_H > ID_Y. For the non-collapsed models, performance with respect to ID_H depends on the data quantity and noise levels. From these observations, we identify two regimes (over-compressed and under-compressed) that determine when expanding or reducing feature dimensionality improves performance. Our results provide new geometric insights into neural regression collapse and suggest practical strategies for enhancing generalization.", "published": "2025-10-01T16:50:57Z", "authors": ["George Andriopoulos", "Zixuan Dong", "Bimarsha Adhikari", "Keith Ross"], "categories": ["cs.LG"], "doi": "", "_query": "hedonic regression housing", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2304.07484v3", "title": "Existence of Firth's modified estimates in binomial regression models", "abstract": "In logistic regression modeling, Firth's modified estimator is widely used to address the issue of data separation, which results in the nonexistence of the maximum likelihood estimate. Firth's modified estimator can be formulated as a penalized maximum likelihood estimator in which Jeffreys' prior is adopted as the penalty term. Despite its widespread use in practice, the formal verification of the corresponding estimate's existence has not been established. In this study, we establish the existence theorem of Firth's modified estimate in binomial logistic regression models, assuming only the full column rankness of the design matrix. We also discuss other binomial regression models obtained through alternating link functions and prove the existence of similar penalized maximum likelihood estimates for such models.", "published": "2023-04-15T06:29:12Z", "authors": ["Mitsunori Ogawa", "Yui Tomo"], "categories": ["math.ST"], "doi": "", "_query": "hedonic regression housing", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2402.12803v2", "title": "Joint Mean and Correlation Regression Models for Multivariate Data", "abstract": "We propose a joint mean and correlation regression model for multivariate discrete and (semi-)continuous response data, that simultaneously regresses the mean of each response against a set of covariates, and the correlations between responses against a set of similarity/distance measures. A set of joint estimating equations are formulated to construct an estimator of both the mean regression coefficients and the correlation regression parameters. Under a general setting where the number of responses can tend to infinity, the joint estimator is demonstrated to be consistent and asymptotically normally distributed, with differing rates of convergence due to the mean regression coefficients being heterogeneous across responses. An iterative estimation procedure is developed to obtain parameter estimates in the required (constrained) parameter space. Simulations demonstrate the strong finite sample performance of the proposed estimator in terms of point estimation and inference. We apply the proposed model to a count dataset of 38 Carabidae ground beetle species sampled throughout Scotland, along with information about the environmental conditions of each site and the traits of each species. Results show the relationship between mean abundance and environmental covariates differs across the beetle species, and that beetle total length is important in driving the correlations between species.", "published": "2024-02-20T08:21:46Z", "authors": ["Zhi Yang Tho", "Francis K. C. Hui", "Tao Zou"], "categories": ["stat.ME", "math.ST", "stat.AP"], "doi": "", "_query": "hedonic regression housing", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2402.09098v1", "title": "Multiple-output composite quantile regression through an optimal transport lens", "abstract": "Composite quantile regression has been used to obtain robust estimators of regression coefficients in linear models with good statistical efficiency. By revealing an intrinsic link between the composite quantile regression loss function and the Wasserstein distance from the residuals to the set of quantiles, we establish a generalization of the composite quantile regression to the multiple-output settings. Theoretical convergence rates of the proposed estimator are derived both under the setting where the additive error possesses only a finite $\\ell$-th moment (for $\\ell > 2$) and where it exhibits a sub-Weibull tail. In doing so, we develop novel techniques for analyzing the M-estimation problem that involves Wasserstein-distance in the loss. Numerical studies confirm the practical effectiveness of our proposed procedure.", "published": "2024-02-14T11:18:48Z", "authors": ["Xuzhi Yang", "Tengyao Wang"], "categories": ["math.ST"], "doi": "", "_query": "hedonic regression housing", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2504.07260v1", "title": "Quantifying Epistemic Uncertainty in Absolute Pose Regression", "abstract": "Visual relocalization is the task of estimating the camera pose given an image it views. Absolute pose regression offers a solution to this task by training a neural network, directly regressing the camera pose from image features. While an attractive solution in terms of memory and compute efficiency, absolute pose regression's predictions are inaccurate and unreliable outside the training domain. In this work, we propose a novel method for quantifying the epistemic uncertainty of an absolute pose regression model by estimating the likelihood of observations within a variational framework. Beyond providing a measure of confidence in predictions, our approach offers a unified model that also handles observation ambiguities, probabilistically localizing the camera in the presence of repetitive structures. Our method outperforms existing approaches in capturing the relation between uncertainty and prediction error.", "published": "2025-04-09T20:32:45Z", "authors": ["Fereidoon Zangeneh", "Amit Dekel", "Alessandro Pieropan", "Patric Jensfelt"], "categories": ["cs.CV"], "doi": "", "_query": "hedonic regression housing", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2607.20277v1", "title": "Interpretable Fuzzy Rule-Based Regression Extension for Ex-Fuzzy Library", "abstract": "Machine learning models achieve high predictive accuracy in regression tasks, but their deployment in safety-critical and regulated domains requires interpretability. While fuzzy rule-based systems offer transparent, linguistically explicit interpretable models, Mamdani-style fuzzy regression remains underrepresented in modern machine learning software libraries. This paper presents an interpretable regression extension for the Ex-Fuzzy library, enabling Mamdani fuzzy inference with scalar consequents learned directly from data. For this, a target-aware partition initialisation strategy based on Fuzzy C-Means clustering is introduced, in which linguistic variables are derived from an augmented input-output space to emphasise output-relevant regions of the feature space. The proposed extension is evaluated on ten regression datasets from the KEEL repository, comparing Gaussian and trapezoidal partition strategies against standard baselines including linear regression, multilayer perceptron, and random forests. Experimental results show that Gaussian partitions consistently outperform uniform trapezoidal partitions, achieving a mean coefficient of determination of approximately 0.86 while producing compact rule bases of 10-15 human-readable rules. The proposed implementation provides a transparent and competitive alternative to black-box regression models, supporting practical interpretability with competitive predictive performance.", "published": "2026-07-22T15:25:38Z", "authors": ["Cayan Deniz Kucuktopana", "Javier Fumanal-Idocin", "Richard Pitts", "Javier Andreu-Perez"], "categories": ["cs.LG"], "doi": "", "_query": "hedonic regression housing", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2203.14102v2", "title": "Influential Observations in Bayesian Regression Tree Models", "abstract": "BCART (Bayesian Classification and Regression Trees) and BART (Bayesian Additive Regression Trees) are popular Bayesian regression models widely applicable in modern regression problems. Their popularity is intimately tied to the ability to flexibly model complex responses depending on high-dimensional inputs while simultaneously being able to quantify uncertainties. This ability to quantify uncertainties is key, as it allows researchers to perform appropriate inferential analyses in settings that have generally been too difficult to handle using the Bayesian approach. However, surprisingly little work has been done to evaluate the sensitivity of these modern regression models to violations of modeling assumptions. In particular, we will consider influential observations, which one reasonably would imagine to be common -- or at least a concern -- in the big-data setting. In this paper, we consider both the problem of detecting influential observations and adjusting predictions to not be unduly affected by such potentially problematic data. We consider three detection diagnostics for Bayesian tree models, one an analogue of Cook's distance and the others taking the form of a divergence measure and a conditional predictive density metric, and then propose an importance sampling algorithm to re-weight previously sampled posterior draws so as to remove the effects of influential data in a computationally efficient manner. Finally, our methods are demonstrated on real-world data where blind application of the models can lead to poor predictions and inference.", "published": "2022-03-26T15:42:18Z", "authors": ["Matthew T. Pratola", "Edward I. George", "Robert E. McCulloch"], "categories": ["stat.ME"], "doi": "", "_query": "hedonic regression housing", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/1304.4637v1", "title": "Two-Stage Plans for Estimating a Threshold Value of a Regression Function", "abstract": "This study investigates two-stage plans based on nonparametric procedures for estimating an inverse regression function at a given point. Specifically, isotonic regression is used at stage one to obtain an initial estimate followed by another round of isotonic regression in the vicinity of this estimate at stage two. It is shown that such two stage plans accelerate the convergence rate of one-stage procedures and are superior to existing two-stage procedures that use local parametric approximations at stage two when the available budget is moderate and/or the regression function is 'ill-behaved'. Both Wald and Likelihood Ratio type confidence intervals for the threshold value of interest are investigated and the latter are recommended in applications due to their simplicity and robustness. The developed plans are illustrated through a comprehensive simulation study and an application to car fuel efficiency data.", "published": "2013-04-16T22:21:58Z", "authors": ["Runlong Tang", "Moulinath Banerjee", "George Michailidis", "Shawn Mankad"], "categories": ["stat.ME"], "doi": "", "_query": "hedonic regression housing", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2412.18247v2", "title": "Fréchet regression with implicit denoising and multicollinearity reduction", "abstract": "Fréchet regression extends linear regression to model complex responses in metric spaces, making it particularly relevant for multi-label regression, where eachinstance can have multiple associated labels. However, addressing noise and dependencies among predictors within this framework remains un derexplored. In this paper, we present an extension of the Global Fréchet re gression model that enables explicit modeling of relationships between input variables and multiple responses. To address challenges arising from noise and multicollinearity, we propose a novel framework based on implicit regu larization, which preserves the intrinsic structure of the data while effectively capturing complex dependencies. Our approach ensures accurate and efficient modeling without the biases introduced by traditional explicit regularization methods. Theoretical guarantees are provided, and the performance of the proposed method is demonstrated through numerical experiments.", "published": "2024-12-24T08:02:28Z", "authors": ["Dou El Kefel Mansouri", "Seif-Eddine Benkabou", "Khalid Benabdeslem"], "categories": ["stat.ML", "cs.AI", "cs.LG"], "doi": "", "_query": "hedonic regression housing", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2303.11786v3", "title": "Skeleton Regression: A Graph-Based Approach to Estimation with Manifold Structure", "abstract": "We introduce a new regression framework designed to deal with large-scale, complex data that lies around a low-dimensional manifold with noises. Our approach first constructs a graph representation, referred to as the skeleton, to capture the underlying geometric structure. We then define metrics on the skeleton graph and apply nonparametric regression techniques, along with feature transformations based on the graph, to estimate the regression function. We also discuss the limitations of some nonparametric regressors with respect to the general metric space such as the skeleton graph. The proposed regression framework suggests a novel way to deal with data with underlying geometric structures and provides additional advantages in handling the union of multiple manifolds, additive noises, and noisy observations. We provide statistical guarantees for the proposed method and demonstrate its effectiveness through simulations and real data examples.", "published": "2023-03-19T21:45:40Z", "authors": ["Zeyu Wei", "Yen-Chi Chen"], "categories": ["cs.LG", "stat.ME", "stat.ML"], "doi": "", "_query": "hedonic regression housing", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2402.16326v3", "title": "A Provably Accurate Randomized Sampling Algorithm for Logistic Regression", "abstract": "In statistics and machine learning, logistic regression is a widely-used supervised learning technique primarily employed for binary classification tasks. When the number of observations greatly exceeds the number of predictor variables, we present a simple, randomized sampling-based algorithm for logistic regression problem that guarantees high-quality approximations to both the estimated probabilities and the overall discrepancy of the model. Our analysis builds upon two simple structural conditions that boil down to randomized matrix multiplication, a fundamental and well-understood primitive of randomized numerical linear algebra. We analyze the properties of estimated probabilities of logistic regression when leverage scores are used to sample observations, and prove that accurate approximations can be achieved with a sample whose size is much smaller than the total number of observations. To further validate our theoretical findings, we conduct comprehensive empirical evaluations. Overall, our work sheds light on the potential of using randomized sampling approaches to efficiently approximate the estimated probabilities in logistic regression, offering a practical and computationally efficient solution for large-scale datasets.", "published": "2024-02-26T06:20:28Z", "authors": ["Agniva Chowdhury", "Pradeep Ramuhalli"], "categories": ["stat.ML", "cs.DS", "cs.LG"], "doi": "10.1609/aaai.v38i10.29042", "_query": "hedonic regression housing", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2302.11746v3", "title": "Binary Regression and Classification with Covariates in Metric Spaces", "abstract": "Inspired by logistic regression, we introduce a regression model for data tuples consisting of a binary response and a set of covariates residing in a metric space without vector structures. Based on the proposed model we also develop a binary classifier for metric-space valued data. We propose a maximum likelihood estimator for the metric-space valued regression coefficient in the model, and provide upper bounds on the estimation error under various metric entropy conditions that quantify complexity of the underlying metric space. Matching lower bounds are derived for the important metric spaces commonly seen in statistics, establishing optimality of the proposed estimator in such spaces. Similarly, an upper bound on the excess risk of the developed classifier is provided for general metric spaces. A finer upper bound and a matching lower bound, and thus optimality of the proposed classifier, are established for Riemannian manifolds. To the best of our knowledge, the proposed regression model and the above minimax bounds are the first of their kind for analyzing a binary response with covariates residing in general metric spaces. We also investigate the numerical performance of the proposed estimator and classifier via simulation studies, and illustrate their practical merits via an application to task-related fMRI data.", "published": "2023-02-23T02:27:07Z", "authors": ["Yinan Lin", "Zhenhua Lin"], "categories": ["stat.ME"], "doi": "", "_query": "hedonic regression housing", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2407.12177v1", "title": "Are Linear Regression Models White Box and Interpretable?", "abstract": "Explainable artificial intelligence (XAI) is a set of tools and algorithms that applied or embedded to machine learning models to understand and interpret the models. They are recommended especially for complex or advanced models including deep neural network because they are not interpretable from human point of view. On the other hand, simple models including linear regression are easy to implement, has less computational complexity and easy to visualize the output. The common notion in the literature that simple models including linear regression are considered as \"white box\" because they are more interpretable and easier to understand. This is based on the idea that linear regression models have several favorable outcomes including the effect of the features in the model and whether they affect positively or negatively toward model output. Moreover, uncertainty of the model can be measured or estimated using the confidence interval. However, we argue that this perception is not accurate and linear regression models are not easy to interpret neither easy to understand considering common XAI metrics and possible challenges might face. This includes linearity, local explanation, multicollinearity, covariates, normalization, uncertainty, features contribution and fairness. Consequently, we recommend the so-called simple models should be treated equally to complex models when it comes to explainability and interpretability.", "published": "2024-07-16T21:05:51Z", "authors": ["Ahmed M Salih", "Yuhe Wang"], "categories": ["cs.LG", "stat.ML"], "doi": "", "_query": "hedonic regression housing", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/1110.0811v4", "title": "A general model of regression using iterative series", "abstract": "We present a new and general method of weighted least square univariate regression where the dependent variable is expanded as a series of suitably chosen functions of the independent variables. Each term of the series is obtained by an iterative process which reduces the sum of the square of the residuals. Thus by evaluating the regression series to a sufficiently large number of terms we can, in principle, reduce the sum of the square of residuals and improve the accuracy of the fit.", "published": "2011-10-04T19:25:13Z", "authors": ["Nilotpal Kanti Sinha"], "categories": ["math.NA", "math.ST"], "doi": "", "_query": "hedonic regression housing", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2409.03461v1", "title": "The Geometry and Well-Posedness of Sparse Regularized Linear Regression", "abstract": "In this work, we study the well-posedness of certain sparse regularized linear regression problems, i.e., the existence, uniqueness and continuity of the solution map with respect to the data. We focus on regularization functions that are convex piecewise linear, i.e., whose epigraph is polyhedral. This includes total variation on graphs and polyhedral constraints. We provide a geometric framework for these functions based on their connection to polyhedral sets and apply this to the study of the well-posedness of the corresponding sparse regularized linear regression problem. Particularly, we provide geometric conditions for well-posedness of the regression problem, compare these conditions to those for smooth regularization, and show the computational difficulty of verifying these conditions.", "published": "2024-09-05T12:18:43Z", "authors": ["Jasper Marijn Everink", "Yiqiu Dong", "Martin Skovgaard Andersen"], "categories": ["math.ST", "math.OC"], "doi": "", "_query": "hedonic regression housing", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2406.05817v1", "title": "Convex-area-wise Linear Regression and Algorithms for Data Analysis", "abstract": "This paper introduces a new type of regression methodology named as Convex-Area-Wise Linear Regression(CALR), which separates given datasets by disjoint convex areas and fits different linear regression models for different areas. This regression model is highly interpretable, and it is able to interpolate any given datasets, even when the underlying relationship between explanatory and response variables are non-linear and discontinuous. In order to solve CALR problem, 3 accurate algorithms are proposed under different assumptions. The analysis of correctness and time complexity of the algorithms are given, indicating that the problem can be solved in $o(n^2)$ time accurately when the input datasets have some special features. Besides, this paper introduces an equivalent mixed integer programming problem of CALR which can be approximately solved using existing optimization solvers.", "published": "2024-06-09T15:10:36Z", "authors": ["Bohan Lyu", "Jianzhong Li"], "categories": ["cs.DB"], "doi": "", "_query": "hedonic regression housing", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2010.13568v1", "title": "CP Degeneracy in Tensor Regression", "abstract": "Tensor linear regression is an important and useful tool for analyzing tensor data. To deal with high dimensionality, CANDECOMP/PARAFAC (CP) low-rank constraints are often imposed on the coefficient tensor parameter in the (penalized) $M$-estimation. However, we show that the corresponding optimization may not be attainable, and when this happens, the estimator is not well-defined. This is closely related to a phenomenon, called CP degeneracy, in low-rank tensor approximation problems. In this article, we provide useful results of CP degeneracy in tensor regression problems. In addition, we provide a general penalized strategy as a solution to overcome CP degeneracy. The asymptotic properties of the resulting estimation are also studied. Numerical experiments are conducted to illustrate our findings.", "published": "2020-10-22T16:08:44Z", "authors": ["Ya Zhou", "Raymond K. W. Wong", "Kejun He"], "categories": ["stat.ML", "cs.LG", "stat.ME"], "doi": "10.1109/ACCESS.2021.3049494", "_query": "hedonic regression housing", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/1907.08646v1", "title": "Fair quantile regression", "abstract": "Quantile regression is a tool for learning conditional distributions. In this paper we study quantile regression in the setting where a protected attribute is unavailable when fitting the model. This can lead to \"unfair'' quantile estimators for which the effective quantiles are very different for the subpopulations defined by the protected attribute. We propose a procedure for adjusting the estimator on a heldout sample where the protected attribute is available. The main result of the paper is an empirical process analysis showing that the adjustment leads to a fair estimator for which the target quantiles are brought into balance, in a statistical sense that we call $\\sqrt{n}$-fairness. We illustrate the ideas and adjustment procedure on a dataset of 200,000 live births, where the objective is to characterize the dependence of the birth weights of the babies on demographic attributes of the birth mother; the protected attribute is the mother's race.", "published": "2019-07-19T18:52:35Z", "authors": ["Dana Yang", "John Lafferty", "David Pollard"], "categories": ["math.ST", "cs.LG", "stat.ML"], "doi": "", "_query": "hedonic regression housing", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2510.03917v1", "title": "Transductive and Learning-Augmented Online Regression", "abstract": "Motivated by the predictable nature of real-life in data streams, we study online regression when the learner has access to predictions about future examples. In the extreme case, called transductive online learning, the sequence of examples is revealed to the learner before the game begins. For this setting, we fully characterize the minimax expected regret in terms of the fat-shattering dimension, establishing a separation between transductive online regression and (adversarial) online regression. Then, we generalize this setting by allowing for noisy or \\emph{imperfect} predictions about future examples. Using our results for the transductive online setting, we develop an online learner whose minimax expected regret matches the worst-case regret, improves smoothly with prediction quality, and significantly outperforms the worst-case regret when future example predictions are precise, achieving performance similar to the transductive online learner. This enables learnability for previously unlearnable classes under predictable examples, aligning with the broader learning-augmented model paradigm.", "published": "2025-10-04T19:41:37Z", "authors": ["Vinod Raman", "Shenghao Xie", "Samson Zhou"], "categories": ["cs.LG", "cs.DS"], "doi": "", "_query": "hedonic regression housing", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2509.26610v1", "title": "Uncertainty Quantification for Regression using Proper Scoring Rules", "abstract": "Quantifying uncertainty of machine learning model predictions is essential for reliable decision-making, especially in safety-critical applications. Recently, uncertainty quantification (UQ) theory has advanced significantly, building on a firm basis of learning with proper scoring rules. However, these advances were focused on classification, while extending these ideas to regression remains challenging. In this work, we introduce a unified UQ framework for regression based on proper scoring rules, such as CRPS, logarithmic, squared error, and quadratic scores. We derive closed-form expressions for the resulting uncertainty measures under practical parametric assumptions and show how to estimate them using ensembles of models. In particular, the derived uncertainty measures naturally decompose into aleatoric and epistemic components. The framework recovers popular regression UQ measures based on predictive variance and differential entropy. Our broad evaluation on synthetic and real-world regression datasets provides guidance for selecting reliable UQ measures.", "published": "2025-09-30T17:52:12Z", "authors": ["Alexander Fishkov", "Kajetan Schweighofer", "Mykyta Ielanskyi", "Nikita Kotelevskii", "Mohsen Guizani", "Maxim Panov"], "categories": ["cs.LG"], "doi": "", "_query": "hedonic regression housing", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2303.02467v1", "title": "Integration of Feature Selection Techniques using a Sleep Quality Dataset for Comparing Regression Algorithms", "abstract": "This research aims to examine the usefulness of integrating various feature selection methods with regression algorithms for sleep quality prediction. A publicly accessible sleep quality dataset is used to analyze the effect of different feature selection techniques on the performance of four regression algorithms - Linear regression, Ridge regression, Lasso Regression and Random Forest Regressor. The results are compared to determine the optimal combination of feature selection techniques and regression algorithms. The conclusion of the study enriches the current literature on using machine learning for sleep quality prediction and has practical significance for personalizing sleep recommendations for individuals.", "published": "2023-03-04T17:58:25Z", "authors": ["Sai Rohith Tanuku", "Venkat Tummala"], "categories": ["cs.LG"], "doi": "", "_query": "hedonic regression housing", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2601.10626v1", "title": "Differentially Private Inference for Longitudinal Linear Regression", "abstract": "Differential Privacy (DP) provides a rigorous framework for releasing statistics while protecting individual information present in a dataset. Although substantial progress has been made on differentially private linear regression, existing methods almost exclusively address the item-level DP setting, where each user contributes a single observation. Many scientific and economic applications instead involve longitudinal or panel data, in which each user contributes multiple dependent observations. In these settings, item-level DP offers inadequate protection, and user-level DP - shielding an individual's entire trajectory - is the appropriate privacy notion. We develop a comprehensive framework for estimation and inference in longitudinal linear regression under user-level DP. We propose a user-level private regression estimator based on aggregating local regressions, and we establish finite-sample guarantees and asymptotic normality under short-range dependence. For inference, we develop a privatized, bias-corrected covariance estimator that is automatically heteroskedasticity- and autocorrelation-consistent. These results provide the first unified framework for practical user-level DP estimation and inference in longitudinal linear regression under dependence, with strong theoretical guarantees and promising empirical performance.", "published": "2026-01-15T17:47:02Z", "authors": ["Getoar Sopa", "Marco Avella Medina", "Cynthia Rush"], "categories": ["math.ST", "cs.CR", "stat.ME", "stat.ML"], "doi": "", "_query": "hedonic regression housing", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2005.00236v1", "title": "Posterior Consistency of Bayesian Inverse Regression and Inverse Reference Distributions", "abstract": "We consider Bayesian inference in inverse regression problems where the objective is to infer about unobserved covariates from observed responses and covariates. We establish posterior consistency of such unobserved covariates in Bayesian inverse regression problemsunder appropriate priors in a leave-one-out cross-validation setup. We relate this to posterior consistency of inverse reference distributions (Bhattacharya (2013)) for assessing model adequacy. We illustrate our theory and methods with various examples of Bayesian inverse regression, along with adequate simulation experiments.", "published": "2020-05-01T06:04:18Z", "authors": ["Debashis Chatterjee", "Sourabh Bhattacharya"], "categories": ["math.ST"], "doi": "", "_query": "hedonic regression housing", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2206.09991v2", "title": "Model Optimization in Imbalanced Regression", "abstract": "Imbalanced domain learning aims to produce accurate models in predicting instances that, though underrepresented, are of utmost importance for the domain. Research in this field has been mainly focused on classification tasks. Comparatively, the number of studies carried out in the context of regression tasks is negligible. One of the main reasons for this is the lack of loss functions capable of focusing on minimizing the errors of extreme (rare) values. Recently, an evaluation metric was introduced: Squared Error Relevance Area (SERA). This metric posits a bigger emphasis on the errors committed at extreme values while also accounting for the performance in the overall target variable domain, thus preventing severe bias. However, its effectiveness as an optimization metric is unknown. In this paper, our goal is to study the impacts of using SERA as an optimization criterion in imbalanced regression tasks. Using gradient boosting algorithms as proof of concept, we perform an experimental study with 36 data sets of different domains and sizes. Results show that models that used SERA as an objective function are practically better than the models produced by their respective standard boosting algorithms at the prediction of extreme values. This confirms that SERA can be embedded as a loss function into optimization-based learning algorithms for imbalanced regression scenarios.", "published": "2022-06-20T20:23:56Z", "authors": ["Aníbal Silva", "Rita P. Ribeiro", "Nuno Moniz"], "categories": ["cs.LG", "cs.AI", "stat.ML"], "doi": "", "_query": "hedonic regression housing", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/1807.11863v2", "title": "On the Unbiased Asymptotic Normality of Quantile Regression with Fixed Effects", "abstract": "Nonlinear panel data models with fixed individual effects provide an important set of tools for describing microeconometric data. In a large class of such models (including probit, proportional hazard and quantile regression to name just a few) it is impossible to difference out individual effects, and inference is usually justified in a `large n large T' asymptotic framework. However, there is a considerable gap in the type of assumptions that are currently imposed in models with smooth score functions (such as probit, and proportional hazard) and quantile regression. In the present paper we show that this gap can be bridged and establish asymptotic unbiased normality for quantile regression panels under conditions on n,T that are very close to what is typically assumed in standard nonlinear panels. Our results considerably improve upon existing theory and show that quantile regression is applicable to the same type of panel data (in terms of n,T) as other commonly used nonlinear panel data models. Thorough numerical experiments confirm our theoretical findings.", "published": "2018-07-31T15:17:56Z", "authors": ["Antonio F. Galvao", "Jiaying Gu", "Stanislav Volgushev"], "categories": ["econ.EM", "math.ST"], "doi": "", "_query": "hedonic regression housing", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/1611.06727v3", "title": "Inference problems in binary regression model with misclassified responses", "abstract": "Misclassification of binary responses, if ignored, may severely bias the maximum likelihood estimators (MLE) of regression parameters. For such data, a binary regression model incorporating misclassification probabilities is extensively used by researchers in different application contexts. The model, however, suffers from a serious estimation problem because of confounding of the unknown misclassification probabilities with the regression parameters. To overcome this problem, in addition to the main sample, use of internal validation data is proposed. However, the maximum likelihood estimators (MLE) are found to be substantially biased. Investigating further, we propose a maximum pseudo-likelihood method of estimation which leads to bias reduction. For drawing inference on the regression parameters, we develop a rigorous asymptotic theory for the maximum pseudo-likelihood estimators under standard assumptions. To facilitate its easy implementation, a bootstrapped version of the estimator is proposed, and its distributional consistency is proved. Extensions of these results are also provided for more general misclassification models. The results of the simulation studies are encouraging. The methodology is illustrated with a survey data.", "published": "2016-11-21T11:21:13Z", "authors": ["Arindam Chatterjee", "Tathagata Bandyopadhyay", "Sumanta Adhya"], "categories": ["math.ST"], "doi": "", "_query": "hedonic regression housing", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2410.22638v1", "title": "Unbiased Regression Loss for DETRs", "abstract": "In this paper, we introduce a novel unbiased regression loss for DETR-based detectors. The conventional $L_{1}$ regression loss tends to bias towards larger boxes, as they disproportionately contribute more towards the overall loss compared to smaller boxes. Consequently, the detection performance for small objects suffers. To alleviate this bias, the proposed new unbiased loss, termed Sized $L_{1}$ loss, normalizes the size of all boxes based on their individual width and height. Our experiments demonstrate consistent improvements in both fully-supervised and semi-supervised settings using the MS-COCO benchmark dataset.", "published": "2024-10-30T02:05:18Z", "authors": [" Edric", "Ueta Daisuke", "Kurokawa Yukimasa", "Karlekar Jayashree", "Sugiri Pranata"], "categories": ["cs.CV"], "doi": "", "_query": "hedonic regression housing", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2007.01757v2", "title": "Monotonicity preservation properties of kernel regression estimators", "abstract": "Three common classes of kernel regression estimators are considered: the Nadaraya--Watson (NW) estimator, the Priestley--Chao (PC) estimator, and the Gasser--Müller (GM) estimator. It is shown that (i) the GM estimator has a certain monotonicity preservation property for any kernel $K$, (ii) the NW estimator has this property if and only the kernel $K$ is log concave, and (iii) the PC estimator does not have this property for any kernel $K$. Other related properties of these regression estimators are discussed.", "published": "2020-07-03T15:26:00Z", "authors": ["Iosif Pinelis"], "categories": ["math.ST"], "doi": "", "_query": "hedonic regression housing", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2504.21710v3", "title": "While-alive regression analysis of composite survival endpoints", "abstract": "Composite endpoints, which combine two or more distinct outcomes, are frequently used in clinical trials to enhance the event rate and improve the statistical power. In the recent literature, the while-alive cumulative frequency measure offers a strong alternative to define composite survival outcomes, by relating the average event rate to the survival time. Although non-parametric methods have been proposed for two-sample comparisons between cumulative frequency measures in clinical trials, limited attention has been given to regression methods that directly address time-varying effects in while-alive measures for composite survival outcomes. Motivated by an individually randomized trial (HF-ACTION) and a cluster randomized trial (STRIDE), we address this gap by developing a regression framework for while-alive measures for composite survival outcomes that include a terminal component event. Our regression approach uses splines to model time-varying association between covariates and a while-alive loss rate of all component events, and can be applied to both independent and clustered data. We derive the asymptotic properties of the regression estimator in each setting and evaluate its performance through simulations. Finally, we apply our regression method to analyze data from the HF-ACTION individually randomized trial and the STRIDE cluster randomized trial. The proposed methods are implemented in the WAreg R package.", "published": "2025-04-30T14:54:47Z", "authors": ["Xi Fang", "Hajime Uno", "Fan Li"], "categories": ["stat.ME", "stat.AP"], "doi": "", "_query": "hedonic regression housing", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2609.12886v1", "title": "Design-Assisted Regression", "abstract": "We consider regression problems in which the marginal distribution of the covariates is informative for estimation and variable selection, rather than merely auxiliary. Motivated by random-design, high-dimensional, and latent-effect settings, we propose a general design-assisted regression framework in which the estimating criterion depends on both the conditional model for $Y \\mid \\bfX$ and structured features of the covariate distribution. The framework identifies two roles of design information: stabilizing weak design directions through quadratic regularization and correcting latent-effect distortion through nuisance augmentation. We establish oracle properties for the resulting estimator, separate the effects of stochastic error, shrinkage, and approximation, and compare it with a benchmark sparse procedure that ignores design information. These results show that the proposed framework improves estimation while preserving first-order prediction performance. Numerical studies and two real-data applications illustrate the practical impact of incorporating design information.", "published": "2026-09-11T14:11:33Z", "authors": ["Shangyuan Ye", "Guanbo Wang", "Cong Zhang", "Ye Liang"], "categories": ["stat.ME"], "doi": "", "_query": "hedonic regression housing", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2506.16101v1", "title": "Regression Testing Optimization for ROS-based Autonomous Systems: A Comprehensive Review of Techniques", "abstract": "Regression testing plays a critical role in maintaining software reliability, particularly for ROS-based autonomous systems (ROSAS), which frequently undergo continuous integration and iterative development. However, conventional regression testing techniques face significant challenges when applied to autonomous systems due to their dynamic and non-deterministic behaviors, complex multi-modal sensor data, asynchronous distributed architectures, and stringent safety and real-time constraints. Although numerous studies have explored test optimization in traditional software contexts, regression testing optimization specifically for ROSAS remains largely unexplored. To address this gap, we present the first comprehensive survey systematically reviewing regression testing optimization techniques tailored for ROSAS. We analyze and categorize 122 representative studies into regression test case prioritization, minimization, and selection methods. A structured taxonomy is introduced to clearly illustrate their applicability and limitations within ROSAS contexts. Furthermore, we highlight major challenges specific to regression testing for ROSAS, including effectively prioritizing tests in response to frequent system modifications, efficiently minimizing redundant tests, and difficulty in accurately selecting impacted test cases. Finally, we propose research insights and identify promising future directions, such as leveraging frame-to-vector coverage metrics, multi-source foundation models, and neurosymbolic reasoning to enhance regression testing efficiency and effectiveness. This survey provides a foundational reference and practical roadmap for advancing the state-of-the-art in regression testing optimization for ROSAS.", "published": "2025-06-19T07:43:36Z", "authors": ["Yupeng Jiang", "Shuaiyi Sun", "Xi Zheng"], "categories": ["cs.SE"], "doi": "", "_query": "hedonic regression housing", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/1511.00634v4", "title": "A Simple and Adaptive Dispersion Regression Model for Count Data", "abstract": "Regression for count data is widely performed by models such as Poisson, negative binomial (NB) and zero-inflated regression. A challenge often faced by practitioners is the selection of the right model to take into account dispersion, which typically occurs in count datasets. It is highly desirable to have a unified model that can automatically adapt to the underlying dispersion and that can be easily implemented in practice. In this paper, a discrete Weibull regression model is shown to be able to adapt in a simple way to different types of dispersions relative to Poisson regression: overdispersion, underdispersion and covariate-specific dispersion. Maximum likelihood can be used for efficient parameter estimation. The description of the model, parameter inference and model diagnostics is accompanied by simulated and real data analyses.", "published": "2015-11-02T19:12:05Z", "authors": ["Hadeel S. Klakattawi", "Veronica Vinciotti", "Keming Yu"], "categories": ["stat.ME", "stat.OT"], "doi": "10.3390/e20020142", "_query": "hedonic regression housing", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2509.24916v1", "title": "A more interpretable regression model for count data with excess of zeros", "abstract": "Count data are common in medical research. When these data have more zeros than expected by the most used count distributions, it is common to employ a zero-inflated regression model. However, the interpretability of these models is much lower than the most used count regression models. In this work, we introduce a more interpretable regression model for count data with excess of zeros based on a reparameterization of the zero-inflated Poisson distribution. We discuss inferential and diagnostic tools and perform a Monte Carlo simulation study to evaluate the performance of the maximum likelihood estimator. Finally, the usefulness of the proposed regression model is illustrated through an application on children mortality.", "published": "2025-09-29T15:22:14Z", "authors": ["Gustavo H. A. Pereira", "Jeremias Leão", "Manoel Santos-Neto", "Jianwen Cai"], "categories": ["stat.ME"], "doi": "", "_query": "hedonic regression housing", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2008.04522v7", "title": "Bayesian Analysis on Limiting the Student-$t$ Linear Regression Model", "abstract": "For the outlier problem in linear regression models, the Student-$t$ linear regression model is one of the common methods for robust modeling and is widely adopted in the literature. However, most of them applies it without careful theoretical consideration. This study provides the practically useful and quite simple conditions to ensure that the Student-$t$ linear regression model is robust against an outlier in the $y$-direction using regular variation theory.", "published": "2020-08-11T05:34:13Z", "authors": ["Yoshiko Hayashi"], "categories": ["stat.ME"], "doi": "10.1080/03610926.2025.2547767", "_query": "hedonic regression housing", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2303.02784v1", "title": "Censored Quantile Regression with Many Controls", "abstract": "This paper develops estimation and inference methods for censored quantile regression models with high-dimensional controls. The methods are based on the application of double/debiased machine learning (DML) framework to the censored quantile regression estimator of Buchinsky and Hahn (1998). I provide valid inference for low-dimensional parameters of interest in the presence of high-dimensional nuisance parameters when implementing machine learning estimators. The proposed estimator is shown to be consistent and asymptotically normal. The performance of the estimator with high-dimensional controls is illustrated with numerical simulation and an empirical application that examines the effect of 401(k) eligibility on savings.", "published": "2023-03-05T21:52:23Z", "authors": ["Seoyun Hong"], "categories": ["econ.EM"], "doi": "", "_query": "hedonic regression housing", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/1803.06011v1", "title": "A Unified Theory of Regression Adjustment for Design-based Inference", "abstract": "Under the Neyman causal model, it is well-known that OLS with treatment-by-covariate interactions cannot harm asymptotic precision of estimated treatment effects in completely randomized experiments. But do such guarantees extend to experiments with more complex designs? This paper proposes a general framework for addressing this question and defines a class of generalized regression estimators that are applicable to experiments of any design. The class subsumes common estimators (e.g., OLS). Within that class, two novel estimators are proposed that are applicable to arbitrary designs and asymptotically optimal. The first is composed of three Horvitz-Thompson estimators. The second recursively applies the principle of generalized regression estimation to obtain regression-adjusted regression adjustment. Additionally, variance bounds are derived that are tighter than those existing in the literature for arbitrary designs. Finally, a simulation study illustrates the potential for MSE improvements.", "published": "2018-03-15T21:42:15Z", "authors": ["Joel A. Middleton"], "categories": ["math.ST"], "doi": "", "_query": "hedonic regression housing", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/1405.3952v1", "title": "Fast Ridge Regression with Randomized Principal Component Analysis and Gradient Descent", "abstract": "We propose a new two stage algorithm LING for large scale regression problems. LING has the same risk as the well known Ridge Regression under the fixed design setting and can be computed much faster. Our experiments have shown that LING performs well in terms of both prediction accuracy and computational efficiency compared with other large scale regression algorithms like Gradient Descent, Stochastic Gradient Descent and Principal Component Regression on both simulated and real datasets.", "published": "2014-05-15T19:14:44Z", "authors": ["Yichao Lu", "Dean P. Foster"], "categories": ["stat.ML"], "doi": "", "_query": "hedonic regression housing", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2302.09893v2", "title": "Efficient Generator of Mathematical Expressions for Symbolic Regression", "abstract": "We propose an approach to symbolic regression based on a novel variational autoencoder for generating hierarchical structures, HVAE. It combines simple atomic units with shared weights to recursively encode and decode the individual nodes in the hierarchy. Encoding is performed bottom-up and decoding top-down. We empirically show that HVAE can be trained efficiently with small corpora of mathematical expressions and can accurately encode expressions into a smooth low-dimensional latent space. The latter can be efficiently explored with various optimization methods to address the task of symbolic regression. Indeed, random search through the latent space of HVAE performs better than random search through expressions generated by manually crafted probabilistic grammars for mathematical expressions. Finally, EDHiE system for symbolic regression, which applies an evolutionary algorithm to the latent space of HVAE, reconstructs equations from a standard symbolic regression benchmark better than a state-of-the-art system based on a similar combination of deep learning and evolutionary algorithms.ž", "published": "2023-02-20T10:40:29Z", "authors": ["Sebastian Mežnar", "Sašo Džeroski", "Ljupčo Todorovski"], "categories": ["cs.LG", "cs.AI"], "doi": "10.1007/s10994-023-06400-2", "_query": "hedonic regression housing", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2108.11603v1", "title": "On Soft Bayesian Additive Regression Trees and asynchronous longitudinal regression analysis", "abstract": "In many longitudinal studies, the covariate and response are often intermittently observed at irregular, mismatched and subject-specific times. How to deal with such data when covariate and response are observed asynchronously is an often raised problem. Bayesian Additive Regression Trees(BART) is a Bayesian non-Parametric approach which has been shown to be competitive with the best modern predictive methods such as random forest and boosted decision trees. The sum of trees structure combined with a Bayesian inferential framework provide a accurate and robust statistic method. BART variant soft Bayesian Additive Regression Trees(SBART) constructed using randomized decision trees was developed and substantial theoretical and practical benefits were shown. In this paper, we propose a weighted SBART model solution for asynchronous longitudinal data. In comparison to other methods, the current methods are valid under with little assumptions on the covariate process. Extensive simulation studies provide numerical support for this solution. And data from an HIV study is used to illustrate our methodology", "published": "2021-08-26T06:53:09Z", "authors": ["Hao Ran", "Yang Bai"], "categories": ["stat.ME", "stat.AP"], "doi": "", "_query": "hedonic regression housing", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2509.23038v2", "title": "GeLoc3r: Enhancing Relative Camera Pose Regression with Geometric Consistency Regularization", "abstract": "Prior ReLoc3R achieves breakthrough performance with fast 25ms inference and state-of-the-art regression accuracy, yet our analysis reveals subtle geometric inconsistencies in its internal representations that prevent reaching the precision ceiling of correspondence-based methods like MASt3R (which require 300ms per pair). In this work, we present GeLoc3r, a novel approach to relative camera pose estimation that enhances pose regression methods through Geometric Consistency Regularization (GCR). GeLoc3r overcomes the speed-accuracy dilemma by training regression networks to produce geometrically consistent poses without inference-time geometric computation. During training, GeLoc3r leverages ground-truth depth to generate dense 3D-2D correspondences, weights them using a FusionTransformer that learns correspondence importance, and computes geometrically-consistent poses via weighted RANSAC. This creates a consistency loss that transfers geometric knowledge into the regression network. Unlike FAR method which requires both regression and geometric solving at inference, GeLoc3r only uses the enhanced regression head at test time, maintaining ReLoc3R's fast speed and approaching MASt3R's high accuracy. On challenging benchmarks, GeLoc3r consistently outperforms ReLoc3R, achieving significant improvements including 40.45% vs. 34.85% AUC@5° on the CO3Dv2 dataset (16% relative improvement), 68.66% vs. 66.70% AUC@5° on RealEstate10K, and 50.45% vs. 49.60% on MegaDepth1500. By teaching geometric consistency during training rather than enforcing it at inference, GeLoc3r represents a paradigm shift in how neural networks learn camera geometry, achieving both the speed of regression and the geometric understanding of correspondence methods.", "published": "2025-09-27T01:21:38Z", "authors": ["Jingxing Li", "Yongjae Lee", "Deliang Fan"], "categories": ["cs.CV", "cs.AI"], "doi": "", "_query": "hedonic regression housing", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/0904.4139v1", "title": "Influence diagnostics in Birnbaum-Saunders nonlinear regression models", "abstract": "We consider the issue of assessing influence of observations in the class of Birnbaum-Saunders nonlinear regression models, which is useful in lifetime data analysis. Our results generalize those in Galea et al. [2004, Influence diagnostics in log-Birnbaum-Saunders regression models. Journal of Applied Statistics, 31, 1049-1064] which are confined to Birnbaum-Saunders linear regression models. Some influence methods, such as the local influence, total local influence of an individual and generalized leverage are discussed. Additionally, the normal curvatures of local influence are derived under various perturbation schemes.", "published": "2009-04-27T11:58:48Z", "authors": ["Artur J. Lemonte"], "categories": ["stat.ME"], "doi": "10.1080/02664761003692357", "_query": "hedonic regression housing", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2211.10032v2", "title": "Modular Regression: Improving Linear Models by Incorporating Auxiliary Data", "abstract": "This paper develops a new framework, called modular regression, to utilize auxiliary information -- such as variables other than the original features or additional data sets -- in the training process of linear models. At a high level, our method follows the routine: (i) decomposing the regression task into several sub-tasks, (ii) fitting the sub-task models, and (iii) using the sub-task models to provide an improved estimate for the original regression problem. This routine applies to widely-used low-dimensional (generalized) linear models and high-dimensional regularized linear regression. It also naturally extends to missing-data settings where only partial observations are available. By incorporating auxiliary information, our approach improves the estimation efficiency and prediction accuracy upon linear regression or the Lasso under a conditional independence assumption for predicting the outcome. For high-dimensional settings, we develop an extension of our procedure that is robust to violations of the conditional independence assumption, in the sense that it improves efficiency if this assumption holds and coincides with the Lasso otherwise. We demonstrate the efficacy of our methods with simulated and real data sets.", "published": "2022-11-18T05:09:40Z", "authors": ["Ying Jin", "Dominik Rothenhäusler"], "categories": ["stat.ME"], "doi": "", "_query": "hedonic regression housing", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2210.04360v1", "title": "A unified analysis of regression adjustment in randomized experiments", "abstract": "Regression adjustment is broadly applied in randomized trials under the premise that it usually improves the precision of a treatment effect estimator. However, previous work has shown that this is not always true. To further understand this phenomenon, we develop a unified comparison of the asymptotic variance of a class of linear regression-adjusted estimators. Our analysis is based on the classical theory for linear regression with heteroscedastic errors and thus does not assume that the postulated linear model is correct. For a completely randomized binary treatment, we provide sufficient conditions under which some regression-adjusted estimators are guaranteed to be more asymptotically efficient than others. We explore other settings such as general treatment assignment mechanisms and generalized linear models, and find that the variance dominance phenomenon no longer occurs.", "published": "2022-10-09T22:12:50Z", "authors": ["Katarzyna Reluga", "Ting Ye", "Qingyuan Zhao"], "categories": ["stat.ME"], "doi": "", "_query": "hedonic regression housing", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/1211.2359v1", "title": "A graphical analysis of cost-sensitive regression problems", "abstract": "Several efforts have been done to bring ROC analysis beyond (binary) classification, especially in regression. However, the mapping and possibilities of these proposals do not correspond to what we expect from the analysis of operating conditions, dominance, hybrid methods, etc. In this paper we present a new representation of regression models in the so-called regression ROC (RROC) space. The basic idea is to represent over-estimation on the x axis and under-estimation on the y axis. The curves are just drawn by adjusting a shift, a constant that is added (or subtracted) to the predictions, and plays a similar role as a threshold in classification. From here, we develop the notions of optimal operating condition, convexity, dominance, and explore several evaluation metrics that can be shown graphically, such as the area over the RROC curve (AOC). In particular, we show a novel and significant result, the AOC is equal to the error variance (multiplied by a factor which does not depend on the model). The derivation of RROC curves with non-constant shifts and soft regression models, and the relation with cost plots is also discussed.", "published": "2012-11-10T23:51:22Z", "authors": ["Jose Hernandez-Orallo"], "categories": ["math.ST"], "doi": "10.1016/j.patcog.2013.06.014", "_query": "hedonic regression housing", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2608.06334v2", "title": "A Regression-Based Framework for the ACF, PACF, Durbin-Levinson Recursion, and One-Step-Ahead Prediction", "abstract": "The autocorrelation function (ACF) and partial autocorrelation function (PACF) are foundational tools for identifying autoregressive moving-average (ARMA) models, yet they are often introduced in ways that appear disconnected from the regression concepts students already know. This paper develops a unified, regression-based instructional framework for the ACF, PACF, Durbin--Levinson recursion, and one-step-ahead prediction for weakly stationary time series. We show that the ACF is the coefficient from a simple linear regression of a mean-zero stationary process on one of its lagged values, while the PACF is both the coefficient of the newest predictor in an expanding multiple regression and the corresponding partial correlation. Using partial regression, we derive the Durbin-Levinson updates for the newly added coefficient, the existing regression coefficients, and the prediction-error variance from familiar ordinary least-squares principles. Worked MA(1) and AR(1) examples show how the characteristic cutoff and tailing-off patterns of the ACF and PACF emerge naturally from this regression perspective. The same recursive regression coefficients also determine the optimal linear one-step-ahead predictor. The resulting framework provides a coherent instructional pathway from regression to model identification, recursive estimation, and prediction and suggests practical ways to connect introductory regression and time series courses.", "published": "2026-08-06T17:40:59Z", "authors": ["Kellen Gong", "Fang Li"], "categories": ["stat.ME"], "doi": "", "_query": "hedonic regression housing", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2312.11955v1", "title": "Vertical Symbolic Regression", "abstract": "Automating scientific discovery has been a grand goal of Artificial Intelligence (AI) and will bring tremendous societal impact. Learning symbolic expressions from experimental data is a vital step in AI-driven scientific discovery. Despite exciting progress, most endeavors have focused on the horizontal discovery paths, i.e., they directly search for the best expression in the full hypothesis space involving all the independent variables. Horizontal paths are challenging due to the exponentially large hypothesis space involving all the independent variables. We propose Vertical Symbolic Regression (VSR) to expedite symbolic regression. The VSR starts by fitting simple expressions involving a few independent variables under controlled experiments where the remaining variables are held constant. It then extends the expressions learned in previous rounds by adding new independent variables and using new control variable experiments allowing these variables to vary. The first few steps in vertical discovery are significantly cheaper than the horizontal path, as their search is in reduced hypothesis spaces involving a small set of variables. As a consequence, vertical discovery has the potential to supercharge state-of-the-art symbolic regression approaches in handling complex equations with many contributing factors. Theoretically, we show that the search space of VSR can be exponentially smaller than that of horizontal approaches when learning a class of expressions. Experimentally, VSR outperforms several baselines in learning symbolic expressions involving many independent variables.", "published": "2023-12-19T08:55:47Z", "authors": ["Nan Jiang", "Md Nasim", "Yexiang Xue"], "categories": ["cs.AI"], "doi": "", "_query": "hedonic regression housing", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2203.02601v4", "title": "High-dimensional Censored Regression via the Penalized Tobit Likelihood", "abstract": "High-dimensional regression and regression with a left-censored response are each well-studied topics. In spite of this, few methods have been proposed which deal with both of these complications simultaneously. The Tobit model -- long the standard method for censored regression in economics -- has not been adapted for high-dimensional regression at all. To fill this gap and bring up-to-date techniques from high-dimensional statistics to the field of high-dimensional left-censored regression, we propose several penalized Tobit models. We develop a fast algorithm which combines quadratic minimization with coordinate descent to compute the penalized Tobit solution path. Theoretically, we analyze the Tobit lasso and Tobit with a folded concave penalty, bounding the $\\ell_2$ estimation loss for the former and proving that a local linear approximation estimator for the latter possesses the strong oracle property. Through an extensive simulation study, we find that our penalized Tobit models provide more accurate predictions and parameter estimates than other methods. We use a penalized Tobit model to analyze high-dimensional left-censored HIV viral load data from the AIDS Clinical Trials Group and identify potential drug resistance mutations in the HIV genome. Appendices contain intermediate theoretical results and technical proofs.", "published": "2022-03-04T22:52:34Z", "authors": ["Tate Jacobson", "Hui Zou"], "categories": ["stat.ME"], "doi": "10.1080/07350015.2023.2182309", "_query": "hedonic regression housing", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/1707.08308v4", "title": "Tensor Regression Networks", "abstract": "Convolutional neural networks typically consist of many convolutional layers followed by one or more fully connected layers. While convolutional layers map between high-order activation tensors, the fully connected layers operate on flattened activation vectors. Despite empirical success, this approach has notable drawbacks. Flattening followed by fully connected layers discards multilinear structure in the activations and requires many parameters. We address these problems by incorporating tensor algebraic operations that preserve multilinear structure at every layer. First, we introduce Tensor Contraction Layers (TCLs) that reduce the dimensionality of their input while preserving their multilinear structure using tensor contraction. Next, we introduce Tensor Regression Layers (TRLs), which express outputs through a low-rank multilinear mapping from a high-order activation tensor to an output tensor of arbitrary order. We learn the contraction and regression factors end-to-end, and produce accurate nets with fewer parameters. Additionally, our layers regularize networks by imposing low-rank constraints on the activations (TCL) and regression weights (TRL). Experiments on ImageNet show that, applied to VGG and ResNet architectures, TCLs and TRLs reduce the number of parameters compared to fully connected layers by more than 65% while maintaining or increasing accuracy. In addition to the space savings, our approach's ability to leverage topological structure can be crucial for structured data such as MRI. In particular, we demonstrate significant performance improvements over comparable architectures on three tasks associated with the UK Biobank dataset.", "published": "2017-07-26T07:37:57Z", "authors": ["Jean Kossaifi", "Zachary C. Lipton", "Arinbjorn Kolbeinsson", "Aran Khanna", "Tommaso Furlanello", "Anima Anandkumar"], "categories": ["cs.LG"], "doi": "", "_query": "hedonic regression housing", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2205.10060v3", "title": "The Unreasonable Effectiveness of Deep Evidential Regression", "abstract": "There is a significant need for principled uncertainty reasoning in machine learning systems as they are increasingly deployed in safety-critical domains. A new approach with uncertainty-aware regression-based neural networks (NNs), based on learning evidential distributions for aleatoric and epistemic uncertainties, shows promise over traditional deterministic methods and typical Bayesian NNs, notably with the capabilities to disentangle aleatoric and epistemic uncertainties. Despite some empirical success of Deep Evidential Regression (DER), there are important gaps in the mathematical foundation that raise the question of why the proposed technique seemingly works. We detail the theoretical shortcomings and analyze the performance on synthetic and real-world data sets, showing that Deep Evidential Regression is a heuristic rather than an exact uncertainty quantification. We go on to discuss corrections and redefinitions of how aleatoric and epistemic uncertainties should be extracted from NNs.", "published": "2022-05-20T10:10:32Z", "authors": ["Nis Meinert", "Jakob Gawlikowski", "Alexander Lavin"], "categories": ["cs.LG", "cs.NE", "stat.ML"], "doi": "10.1609/aaai.v37i8.26096", "_query": "hedonic regression housing", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2405.14652v2", "title": "Statistical inference for high-dimensional convoluted rank regression", "abstract": "High-dimensional penalized rank regression is a powerful tool for modeling high-dimensional data due to its robustness and estimation efficiency. However, the non-smoothness of the rank loss brings great challenges to the computation. To solve this critical issue, high-dimensional convoluted rank regression has been recently proposed, introducing penalized convoluted rank regression estimators. However, these developed estimators cannot be directly used to make inference. In this paper, we investigate the statistical inference problem of high-dimensional convoluted rank regression. The use of U-statistic in convoluted rank loss function presents challenges for the analysis. We begin by establishing estimation error bounds of the penalized convoluted rank regression estimators under weaker conditions on the predictors. Building on this, we further introduce a debiased estimator and provide its Bahadur representation. Subsequently, a high-dimensional Gaussian approximation for the maximum deviation of the debiased estimator is derived, which allows us to construct simultaneous confidence intervals. For implementation, a novel bootstrap procedure is proposed and its theoretical validity is also established. Finally, simulation and real data analysis are conducted to illustrate the merits of our proposed methods.", "published": "2024-05-23T14:52:53Z", "authors": ["Leheng Cai", "Xu Guo", "Heng Lian", "Liping Zhu"], "categories": ["stat.ME"], "doi": "", "_query": "hedonic regression housing", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/1601.00202v6", "title": "Current status linear regression", "abstract": "We construct $\\sqrt{n}$-consistent and asymptotically normal estimates for the finite dimensional regression parameter in the current status linear regression model, which do not require any smoothing device and are based on maximum likelihood estimates (MLEs) of the infinite dimensional parameter. We also construct estimates, again only based on these MLEs, which are arbitrarily close to efficient estimates, if the generalized Fisher information is finite. This type of efficiency is also derived under minimal conditions for estimates based on smooth non-monotone plug-in estimates of the distribution function. Algorithms for computing the estimates and for selecting the bandwidth of the smooth estimates with a bootstrap method are provided. The connection with results in the econometric literature is also pointed out.", "published": "2016-01-02T18:07:34Z", "authors": ["Piet Groeneboom", "Kim Hendrickx"], "categories": ["math.ST"], "doi": "", "_query": "hedonic regression housing", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2401.08488v1", "title": "A Novel Approach in Solving Stochastic Generalized Linear Regression via Nonconvex Programming", "abstract": "Generalized linear regressions, such as logistic regressions or Poisson regressions, are long-studied regression analysis approaches, and their applications are widely employed in various classification problems. Our study considers a stochastic generalized linear regression model as a stochastic problem with chance constraints and tackles it using nonconvex programming techniques. Clustering techniques and quantile estimation are also used to estimate random data's mean and variance-covariance matrix. Metrics for measuring the performance of logistic regression are used to assess the model's efficacy, including the F1 score, precision score, and recall score. The results of the proposed algorithm were over 1 to 2 percent better than the ordinary logistic regression model on the same dataset with the above assessment criteria.", "published": "2024-01-16T16:45:51Z", "authors": ["Vu Duc Anh", "Tran Anh Tuan", "Tran Ngoc Thang", "Nguyen Thi Ngoc Anh"], "categories": ["stat.ML", "math.OC"], "doi": "", "_query": "hedonic regression housing", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2104.10436v1", "title": "Modeling sign concordance of quantile regression residuals with multiple outcomes", "abstract": "Quantile regression permits describing how quantiles of a scalar response variable depend on a set of predictors. Because a unique definition of multivariate quantiles is lacking, extending quantile regression to multivariate responses is somewhat complicated. In this paper, we describe a simple approach based on a two-step procedure: in the first step, quantile regression is applied to each response separately; in the second step, the joint distribution of the signs of the residuals is modeled through multinomial regression. The described approach does not require a multidimensional definition of quantiles, and can be used to capture important features of a multivariate response and assess the effects of covariates on the correlation structure. We apply the proposed method to analyze two different datasets.", "published": "2021-04-21T10:01:55Z", "authors": ["Silvia Columbu", "Paolo Frumento", "Matteo Bottai"], "categories": ["stat.ME"], "doi": "", "_query": "hedonic regression housing", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/1810.08104v1", "title": "Determining the Number of Components in PLS Regression on Incomplete Data", "abstract": "Partial least squares regression---or PLS---is a multivariate method in which models are estimated using either the SIMPLS or NIPALS algorithm. PLS regression has been extensively used in applied research because of its effectiveness in analysing relationships between an outcome and one or several components. Note that the NIPALS algorithm is able to provide estimates on incomplete data. Selection of the number of components used to build a representative model in PLS regression is an important problem. However, how to deal with missing data when using PLS regression remains a matter of debate. Several approaches have been proposed in the literature, including the $Q^2$ criterion, and the AIC and BIC criteria. Here we study the behavior of the NIPALS algorithm when used to fit a PLS regression for various proportions of missing data and for different types of missingness. We compare criteria for selecting the number of components for a PLS regression on incomplete data and on imputed datasets using three imputation methods: multiple imputation by chained equations, k-nearest neighbor imputation, and singular value decomposition imputation. Various criteria were tested with different proportions of missing data (ranging from 5% to 50%) under different missingness assumptions. Q2-leave-one-out component selection methods gave more reliable results than AIC and BIC-based ones.", "published": "2018-10-18T15:19:15Z", "authors": ["Titin Agustin Nengsih", "Frédéric Bertrand", "Myriam Maumy-Bertrand", "Nicolas Meyer"], "categories": ["stat.ME"], "doi": "10.1515/sagmb-2018-0059", "_query": "hedonic regression housing", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/math/0607010v1", "title": "Inference for covariate adjusted regression via varying coefficient models", "abstract": "We consider covariate adjusted regression (CAR), a regression method for situations where predictors and response are observed after being distorted by a multiplicative factor. The distorting factors are unknown functions of an observable covariate, where one specific distorting function is associated with each predictor or response. The dependence of both response and predictors on the same confounding covariate may alter the underlying regression relation between undistorted but unobserved predictors and response. We consider a class of highly flexible adjustment methods for parameter estimation in the underlying regression model, which is the model of interest. Asymptotic normality of the estimates is obtained by establishing a connection to varying coefficient models. These distribution results combined with proposed consistent estimates of the asymptotic variance are used for the construction of asymptotic confidence intervals for the regression coefficients. The proposed approach is illustrated with data on serum creatinine, and finite sample properties of the proposed procedures are investigated through a simulation study.", "published": "2006-07-01T06:37:18Z", "authors": ["Damla Şentürk", "Hans-Georg Müller"], "categories": ["math.ST"], "doi": "10.1214/009053606000000083", "_query": "hedonic regression housing", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2208.03611v1", "title": "Improved Point Estimation for the Rayleigh Regression Model", "abstract": "The Rayleigh regression model was recently proposed for modeling amplitude values of synthetic aperture radar (SAR) image pixels. However, inferences from such model are based on the maximum likelihood estimators, which can be biased for small signal lengths. The Rayleigh regression model for SAR images often takes into account small pixel windows, which may lead to inaccurate results. In this letter, we introduce bias-adjusted estimators tailored for the Rayleigh regression model based on: (i) the Cox and Snell's method; (ii) the Firth's scheme; and (iii) the parametric bootstrap method. We present numerical experiments considering synthetic and actual SAR data sets. The bias-adjusted estimators yield nearly unbiased estimates and accurate modeling results.", "published": "2022-08-07T01:28:39Z", "authors": ["B. G. Palm", "F. M. Bayer", "R. J. Cintra"], "categories": ["stat.ME", "cs.CV", "eess.IV", "physics.data-an", "stat.AP"], "doi": "10.1109/LGRS.2020.3019768", "_query": "hedonic regression housing", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2207.07075v1", "title": "Adversarial Sign-Corrupted Isotonic Regression", "abstract": "Classical univariate isotonic regression involves nonparametric estimation under a monotonicity constraint of the true signal. We consider a variation of this generating process, which we term adversarial sign-corrupted isotonic (\\texttt{ASCI}) regression. Under this \\texttt{ASCI} setting, the adversary has full access to the true isotonic responses, and is free to sign-corrupt them. Estimating the true monotonic signal given these sign-corrupted responses is a highly challenging task. Notably, the sign-corruptions are designed to violate monotonicity, and possibly induce heavy dependence between the corrupted response terms. In this sense, \\texttt{ASCI} regression may be viewed as an adversarial stress test for isotonic regression. Our motivation is driven by understanding whether efficient robust estimation of the monotone signal is feasible under this adversarial setting. We develop \\texttt{ASCIFIT}, a three-step estimation procedure under the \\texttt{ASCI} setting. The \\texttt{ASCIFIT} procedure is conceptually simple, easy to implement with existing software, and consists of applying the \\texttt{PAVA} with crucial pre- and post-processing corrections. We formalize this procedure, and demonstrate its theoretical guarantees in the form of sharp high probability upper bounds and minimax lower bounds. We illustrate our findings with detailed simulations.", "published": "2022-07-14T17:24:51Z", "authors": ["Shamindra Shrotriya", "Matey Neykov"], "categories": ["math.ST", "stat.ML"], "doi": "", "_query": "hedonic regression housing", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2104.00403v1", "title": "Target Transformed Regression for Accurate Tracking", "abstract": "Accurate tracking is still a challenging task due to appearance variations, pose and view changes, and geometric deformations of target in videos. Recent anchor-free trackers provide an efficient regression mechanism but fail to produce precise bounding box estimation. To address these issues, this paper repurposes a Transformer-alike regression branch, termed as Target Transformed Regression (TREG), for accurate anchor-free tracking. The core to our TREG is to model pair-wise relation between elements in target template and search region, and use the resulted target enhanced visual representation for accurate bounding box regression. This target contextualized representation is able to enhance the target relevant information to help precisely locate the box boundaries, and deal with the object deformation to some extent due to its local and dense matching mechanism. In addition, we devise a simple online template update mechanism to select reliable templates, increasing the robustness for appearance variations and geometric deformations of target in time. Experimental results on visual tracking benchmarks including VOT2018, VOT2019, OTB100, GOT10k, NFS, UAV123, LaSOT and TrackingNet demonstrate that TREG obtains the state-of-the-art performance, achieving a success rate of 0.640 on LaSOT, while running at around 30 FPS. The code and models will be made available at https://github.com/MCG-NJU/TREG.", "published": "2021-04-01T11:25:23Z", "authors": ["Yutao Cui", "Cheng Jiang", "Limin Wang", "Gangshan Wu"], "categories": ["cs.CV"], "doi": "", "_query": "hedonic regression housing", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2310.18970v1", "title": "TRIAGE: Characterizing and auditing training data for improved regression", "abstract": "Data quality is crucial for robust machine learning algorithms, with the recent interest in data-centric AI emphasizing the importance of training data characterization. However, current data characterization methods are largely focused on classification settings, with regression settings largely understudied. To address this, we introduce TRIAGE, a novel data characterization framework tailored to regression tasks and compatible with a broad class of regressors. TRIAGE utilizes conformal predictive distributions to provide a model-agnostic scoring method, the TRIAGE score. We operationalize the score to analyze individual samples' training dynamics and characterize samples as under-, over-, or well-estimated by the model. We show that TRIAGE's characterization is consistent and highlight its utility to improve performance via data sculpting/filtering, in multiple regression settings. Additionally, beyond sample level, we show TRIAGE enables new approaches to dataset selection and feature acquisition. Overall, TRIAGE highlights the value unlocked by data characterization in real-world regression applications", "published": "2023-10-29T10:31:59Z", "authors": ["Nabeel Seedat", "Jonathan Crabbé", "Zhaozhi Qian", "Mihaela van der Schaar"], "categories": ["cs.LG"], "doi": "", "_query": "hedonic regression housing", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2406.15531v2", "title": "cp3-bench: A tool for benchmarking symbolic regression algorithms tested with cosmology", "abstract": "We introduce cp3-bench, a tool for comparing symbolic regression algorithms which we make publicly available at https://github.com/CP3-Origins/cp3-bench. Currently, cp3-bench includes 12 symbolic regression algorithms which can be automatically installed as part of cp3-bench. The philosophy behind cp3-bench is that it should be as user-friendly as possible, available in a ready-to-use format, and allow for easy additions of new algorithms and datasets. Our hope is that users of symbolic regression algorithms can use cp3-bench to easily install and compare symbolic regression algorithms to better decide which algorithms to use for their specific tasks at hand. To introduce and motivate the use of cp3-bench we present a benchmark of 12 symbolic regression algorithms applied to 28 datasets representing six different astrophysical setups. Overall, we find that most of the benched algorithms do poorly in the benchmark and suggest possible ways to proceed with developing algorithms that will be better at identifying ground truth expressions for cosmological datasets. Our demonstration benchmark studies the significance of dimensionality of the feature space and precision of datasets. We find both to be highly important for symbolic regression tasks to be successful. On the other hand, we find no indication that inter-dependence of features in datasets is important, i.e. it is not in general a hindrance for symbolic regression algorithms if datasets e.g. contain both $z$ and $H(z)$ as features. Lastly, we find no indication that performance of algorithms on standardized datasets are good indicators of performance on particular astrophysical datasets. This suggests that it is not necessarily prudent to choose symbolic regression algorithms based on their performance on standardized data. A more robust approach is to consider a variety of algorithms, chosen based on the particular task at hand.", "published": "2024-06-21T13:31:47Z", "authors": ["Mattias E. Thing", "Sofie M. Koksbang"], "categories": ["astro-ph.IM", "astro-ph.CO"], "doi": "10.1088/1475-7516/2025/01/040", "_query": "hedonic regression housing", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/1811.01586v1", "title": "Supervised Linear Regression for Graph Learning from Graph Signals", "abstract": "We propose a supervised learning approach for predicting an underlying graph from a set of graph signals. Our approach is based on linear regression. In the linear regression model, we predict edge-weights of a graph as the output, given a set of signal values on nodes of the graph as the input. We solve for the optimal regression coefficients using a relevant optimization problem that is convex and uses a graph-Laplacian based regularization. The regularization helps to promote a specific graph spectral profile of the graph signals. Simulation experiments demonstrate that our approach predicts well even in presence of outliers in input data.", "published": "2018-11-05T10:15:39Z", "authors": ["Arun Venkitaraman", "Hermina Petric Maretic", "Saikat Chatterjee", "Pascal Frossard"], "categories": ["cs.IT"], "doi": "", "_query": "hedonic regression housing", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/1803.09830v1", "title": "Cox Regression Model Under Dependent Truncation", "abstract": "Truncation is a statistical phenomenon that occurs in many time to event studies. For example, autopsy-confirmed studies of neurodegenerative diseases are subject to an inherent left and right truncation, also known as double truncation. When the goal is to study the effect of risk factors on survival, the standard Cox regression model cannot be used when the data is subject to truncation. Existing methods which adjust for both left and right truncation in the Cox regression model require independence between the survival times and truncation times, which may not be a reasonable assumption in practice. We propose an expectation-maximization algorithm to relax the independence assumption in the Cox regression model under left, right, or double truncation, to an assumption of conditional independence. The resulting regression coefficient estimators are consistent and asymptotically normal. We demonstrate through extensive simulations that the proposed estimators have little bias and, in most practical situations, have a lower mean-squared error compared to existing estimators. We implement our approach to assess the effect of occupation on survival in subjects with autopsy-confirmed Alzheimer's disease.", "published": "2018-03-26T20:32:22Z", "authors": ["Lior Rennert", "Sharon X. Xie"], "categories": ["stat.ME"], "doi": "", "_query": "hedonic regression housing", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2501.10729v3", "title": "Robust Local Polynomial Regression with Similarity Kernels", "abstract": "Local Polynomial Regression (LPR) is a widely used nonparametric method for modeling complex relationships due to its flexibility and simplicity. It estimates a regression function by fitting low-degree polynomials to localized subsets of the data, weighted by proximity. However, traditional LPR is sensitive to outliers and high-leverage points, which can significantly affect estimation accuracy. This paper revisits the kernel function used to compute regression weights and proposes a novel framework that incorporates both predictor and response variables in the weighting mechanism. The focus of this work is a conditional density kernel that robustly estimates weights by mitigating the influence of outliers through localized density estimation. The proposed method is implemented in Python and is publicly available at https://github.com/yaniv-shulman/rsklpr. The population analysis quantifies the bias induced by density-based robust weighting, and the reported experiments show lower empirical bias than iterative robust LOWESS while remaining competitive with standard LOWESS. This advancement provides a promising extension to traditional LPR, opening new possibilities for robust regression applications.", "published": "2025-01-18T11:21:26Z", "authors": ["Yaniv Shulman"], "categories": ["stat.ME", "cs.LG", "stat.ML"], "doi": "", "_query": "hedonic regression housing", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/1711.10051v4", "title": "Active Regression via Linear-Sample Sparsification", "abstract": "We present an approach that improves the sample complexity for a variety of curve fitting problems, including active learning for linear regression, polynomial regression, and continuous sparse Fourier transforms. In the active linear regression problem, one would like to estimate the least squares solution $β^*$ minimizing $\\|Xβ- y\\|_2$ given the entire unlabeled dataset $X \\in \\mathbb{R}^{n \\times d}$ but only observing a small number of labels $y_i$. We show that $O(d)$ labels suffice to find a constant factor approximation $\\tildeβ$: \\[ \\mathbb{E}[\\|X\\tildeβ - y\\|_2^2] \\leq 2 \\mathbb{E}[\\|X β^* - y\\|_2^2]. \\] This improves on the best previous result of $O(d \\log d)$ from leverage score sampling. We also present results for the \\emph{inductive} setting, showing when $\\tildeβ$ will generalize to fresh samples; these apply to continuous settings such as polynomial regression. Finally, we show how the techniques yield improved results for the non-linear sparse Fourier transform setting.", "published": "2017-11-27T23:59:30Z", "authors": ["Xue Chen", "Eric Price"], "categories": ["cs.LG", "cs.DS"], "doi": "", "_query": "hedonic regression housing", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/1106.3520v2", "title": "Stochastic Search for Semiparametric Linear Regression Models", "abstract": "This paper introduces and analyzes a stochastic search method for parameter estimation in linear regression models in the spirit of Beran and Millar (1987). The idea is to generate a random finite subset of a parameter space which will automatically contain points which are very close to an unknown true parameter. The motivation for this procedure comes from recent work of Duembgen, Samworth and Schuhmacher (2011) on regression models with log-concave error distributions.", "published": "2011-06-17T15:57:30Z", "authors": ["Lutz Duembgen", "Dominic Schuhmacher", "Richard Samworth"], "categories": ["stat.ME", "math.ST"], "doi": "10.1214/12-IMSCOLL907", "_query": "hedonic regression housing", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2503.06401v1", "title": "fastfrechet: An R package for fast implementation of Fréchet regression with distributional responses", "abstract": "Distribution-as-response regression problems are gaining wider attention, especially within biomedical settings where observation-rich patient specific data sets are available, such as feature densities in CT scans (Petersen et al., 2021) actigraphy (Ghosal et al., 2023), and continuous glucose monitoring (Coulter et al., 2024; Matabuena et al., 2021). To accommodate the complex structure of such problems, Petersen and Müller (2019) proposed a regression framework called Fréchet regression which allows non-Euclidean responses, including distributional responses. This regression framework was further extended for variable selection by Tucker et al. (2023), and Coulter et al. (2024) (arXiv:2403.00922 [stat.AP]) developed a fast variable selection algorithm for the specific setting of univariate distributional responses equipped with the 2-Wasserstein metric (2-Wasserstein space). We present \"fastfrechet\", an R package providing fast implementation of these Fréchet regression and variable selection methods in 2-Wasserstein space, with resampling tools for automatic variable selection. \"fastfrechet\" makes distribution-based Fréchet regression with resampling-supplemented variable selection readily available and highly scalable to large data sets, such as the UK Biobank (Doherty et al., 2017).", "published": "2025-03-09T02:42:34Z", "authors": ["Alexander Coulter", "Rebecca Lee", "Irina Gaynanova"], "categories": ["stat.CO", "stat.ME"], "doi": "10.21105/joss.07925", "_query": "hedonic regression housing", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2106.15212v1", "title": "Counterfactual Explanations for Arbitrary Regression Models", "abstract": "We present a new method for counterfactual explanations (CFEs) based on Bayesian optimisation that applies to both classification and regression models. Our method is a globally convergent search algorithm with support for arbitrary regression models and constraints like feature sparsity and actionable recourse, and furthermore can answer multiple counterfactual questions in parallel while learning from previous queries. We formulate CFE search for regression models in a rigorous mathematical framework using differentiable potentials, which resolves robustness issues in threshold-based objectives. We prove that in this framework, (a) verifying the existence of counterfactuals is NP-complete; and (b) that finding instances using such potentials is CLS-complete. We describe a unified algorithm for CFEs using a specialised acquisition function that composes both expected improvement and an exponential-polynomial (EP) family with desirable properties. Our evaluation on real-world benchmark domains demonstrate high sample-efficiency and precision.", "published": "2021-06-29T09:53:53Z", "authors": ["Thomas Spooner", "Danial Dervovic", "Jason Long", "Jon Shepard", "Jiahao Chen", "Daniele Magazzeni"], "categories": ["cs.LG", "cs.AI", "cs.CC"], "doi": "", "_query": "hedonic regression housing", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2005.03452v1", "title": "Lifted Regression/Reconstruction Networks", "abstract": "In this work we propose lifted regression/reconstruction networks (LRRNs), which combine lifted neural networks with a guaranteed Lipschitz continuity property for the output layer. Lifted neural networks explicitly optimize an energy model to infer the unit activations and therefore---in contrast to standard feed-forward neural networks---allow bidirectional feedback between layers. So far lifted neural networks have been modelled around standard feed-forward architectures. We propose to take further advantage of the feedback property by letting the layers simultaneously perform regression and reconstruction. The resulting lifted network architecture allows to control the desired amount of Lipschitz continuity, which is an important feature to obtain adversarially robust regression and classification methods. We analyse and numerically demonstrate applications for unsupervised and supervised learning.", "published": "2020-05-07T13:24:46Z", "authors": ["Rasmus Kjær Høier", "Christopher Zach"], "categories": ["cs.LG", "cs.CV"], "doi": "", "_query": "hedonic regression housing", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2505.18602v3", "title": "LLM-Meta-SR: In-Context Learning for Evolving Selection Operators in Symbolic Regression", "abstract": "Large language models (LLMs) have revolutionized algorithm development, yet their application in symbolic regression, where algorithms automatically discover symbolic expressions from data, remains limited. In this paper, we propose a meta-learning framework that enables LLMs to automatically design selection operators for evolutionary symbolic regression algorithms. We first identify two key limitations in existing LLM-based algorithm evolution techniques: lack of semantic guidance and code bloat. The absence of semantic awareness can lead to ineffective exchange of useful code components, while bloat results in unnecessarily complex components; both can hinder evolutionary learning progress or reduce the interpretability of the designed algorithm. To address these issues, we enhance the LLM-based evolution framework for meta-symbolic regression with two key innovations: a complementary, semantics-aware selection operator and bloat control. Additionally, we embed domain knowledge into the prompt, enabling the LLM to generate more effective and contextually relevant selection operators. Our experimental results on symbolic regression benchmarks show that LLMs can devise selection operators that outperform nine expert-designed baselines, achieving state-of-the-art performance. Moreover, the evolved operator can further improve a state-of-the-art symbolic regression algorithm, achieving the best performance among 28 symbolic regression and other machine learning algorithms across 116 regression datasets. This demonstrates that LLMs can exceed expert-level algorithm design for symbolic regression.", "published": "2025-05-24T08:52:56Z", "authors": ["Hengzhe Zhang", "Qi Chen", "Bing Xue", "Wolfgang Banzhaf", "Mengjie Zhang"], "categories": ["cs.NE", "cs.AI", "cs.LG"], "doi": "", "_query": "hedonic regression housing", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2404.07046v1", "title": "Comparison of decision trees with Local Interpretable Model-Agnostic Explanations (LIME) technique and multi-linear regression for explaining support vector regression model in terms of root mean square error (RMSE) values", "abstract": "In this work the decision trees are used for explanation of support vector regression model. The decision trees act as a global technique as well as a local technique. They are compared against the popular technique of LIME which is a local explanatory technique and with multi linear regression. It is observed that decision trees give a lower RMSE value when fitted to support vector regression as compared to LIME in 87% of the runs over 5 datasets. The comparison of results is statistically significant. Multi linear regression also gives a lower RMSE value when fitted to support vector regression model as compared to LIME in 73% of the runs over 5 datasets but the comparison of results is not statistically significant. Also, when used as a local explanatory technique, decision trees give better performance than LIME and the comparison of results is statistically significant.", "published": "2024-04-10T14:36:35Z", "authors": ["Amit Thombre"], "categories": ["cs.LG", "cs.AI"], "doi": "", "_query": "hedonic regression housing", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2306.04708v2", "title": "A New Family of Regression Models for $[0,1]$ Outcome Data: Expanding the Palette", "abstract": "Beta regression is a popular methodology when the outcome variable $y$ is on the open interval $(0,1)$. When $y$ is in the closed interval $[0,1]$, it is commonly accepted that beta regression is inapplicable. Instead, common solutions are to use augmented beta regression or censoring models or else to subjectively rescale the endpoints to allow beta regression. We provide an attractive new approach with a family of models that treats the entirety of $y\\in[0,1]$ in a single model without rescaling or the need for the complications of augmentation or censoring. This family provides the interpretational convenience of a single straightforward model for the expectation of $y \\in [0,1]$ over its entirety. We establish the conditions for the existence of a unique MLE and then examine this new family of models from both maximum-likelihood and Bayesian perspectives. We successfully apply the models to employment data in which augmented beta regression was difficult due to data separation. We also apply the models to healthcare panel data that were originally examined by way of rescaling.", "published": "2023-06-07T18:20:59Z", "authors": ["Eugene D. Hahn"], "categories": ["stat.ME"], "doi": "", "_query": "hedonic regression housing", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2306.10458v1", "title": "Weakly Supervised Regression with Interval Targets", "abstract": "This paper investigates an interesting weakly supervised regression setting called regression with interval targets (RIT). Although some of the previous methods on relevant regression settings can be adapted to RIT, they are not statistically consistent, and thus their empirical performance is not guaranteed. In this paper, we provide a thorough study on RIT. First, we proposed a novel statistical model to describe the data generation process for RIT and demonstrate its validity. Second, we analyze a simple selection method for RIT, which selects a particular value in the interval as the target value to train the model. Third, we propose a statistically consistent limiting method for RIT to train the model by limiting the predictions to the interval. We further derive an estimation error bound for our limiting method. Finally, extensive experiments on various datasets demonstrate the effectiveness of our proposed method.", "published": "2023-06-18T02:51:57Z", "authors": ["Xin Cheng", "Yuzhou Cao", "Ximing Li", "Bo An", "Lei Feng"], "categories": ["cs.LG"], "doi": "", "_query": "hedonic regression housing", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/1411.1805v2", "title": "Faithful Variable Screening for High-Dimensional Convex Regression", "abstract": "We study the problem of variable selection in convex nonparametric regression. Under the assumption that the true regression function is convex and sparse, we develop a screening procedure to select a subset of variables that contains the relevant variables. Our approach is a two-stage quadratic programming method that estimates a sum of one-dimensional convex functions, followed by one-dimensional concave regression fits on the residuals. In contrast to previous methods for sparse additive models, the optimization is finite dimensional and requires no tuning parameters for smoothness. Under appropriate assumptions, we prove that the procedure is faithful in the population setting, yielding no false negatives. We give a finite sample statistical analysis, and introduce algorithms for efficiently carrying out the required quadratic programs. The approach leads to computational and statistical advantages over fitting a full model, and provides an effective, practical approach to variable screening in convex regression.", "published": "2014-11-07T01:09:40Z", "authors": ["Min Xu", "Minhua Chen", "John Lafferty"], "categories": ["math.ST", "stat.ML"], "doi": "", "_query": "hedonic regression housing", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/1509.03247v1", "title": "An Epsilon Hierarchical Fuzzy Twin Support Vector Regression", "abstract": "The research presents epsilon hierarchical fuzzy twin support vector regression based on epsilon fuzzy twin support vector regression and epsilon twin support vector regression. Epsilon FTSVR is achieved by incorporating trapezoidal fuzzy numbers to epsilon TSVR which takes care of uncertainty existing in forecasting problems. Epsilon FTSVR determines a pair of epsilon insensitive proximal functions by solving two related quadratic programming problems. The structural risk minimization principle is implemented by introducing regularization term in primal problems of epsilon FTSVR. This yields dual stable positive definite problems which improves regression performance. Epsilon FTSVR is then reformulated as epsilon HFTSVR consisting of a set of hierarchical layers each containing epsilon FTSVR. Experimental results on both synthetic and real datasets reveal that epsilon HFTSVR has remarkable generalization performance with minimum training time.", "published": "2015-09-10T17:37:20Z", "authors": ["Arindam Chaudhuri"], "categories": ["cs.AI"], "doi": "", "_query": "hedonic regression housing", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2511.03694v2", "title": "Robust Global Fr'echet Regression via Weight Regularization", "abstract": "The Fréchet regression is a useful method for modeling random objects in a general metric space given Euclidean covariates. However, the conventional approach could be sensitive to outlying objects in the sense that the distance from the regression surface is large compared to the other objects. In this study, we develop a robust version of the global Fréchet regression by incorporating weight parameters into the objective function. We then introduce the Elastic net regularization, favoring a sparse vector of robust parameters to control the influence of outlying objects. We provide a computational algorithm to iteratively estimate the regression function and weight parameters, with providing a linear convergence property. We also propose the Bayesian information criterion to select the tuning parameters for regularization, which gives adaptive robustness along with observed data. The finite sample performance of the proposed method is demonstrated through numerical studies on matrix and distribution responses.", "published": "2025-11-05T18:18:40Z", "authors": ["Hao Li", "Shonosuke Sugasawa", "Shota Katayama"], "categories": ["stat.CO"], "doi": "", "_query": "hedonic regression housing", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2412.09486v2", "title": "Regression and Classification with Single-Qubit Quantum Neural Networks", "abstract": "The literature reflects a mutually beneficial relationship between machine learning and quantum computing, where progress in one field frequently drives improvements in the other. Motivated by the rich connection between these areas, we use a resource-efficient and scalable Single-Qubit Quantum Neural Network (SQQNN) for both regression and classification tasks using a new data uploading technique. The SQQNN leverages parameterized single-qubit unitary operators and quantum measurements to achieve efficient learning. To train the model, we use gradient descent for regression tasks. For classification, we introduce a novel training method inspired by polynomial regression, which can efficiently find a global minimizer of the transformed least-squares objective in a single step. This approach significantly accelerates training compared to iterative methods. Evaluated across various applications, the SQQNN exhibits virtually error-free and strong performance in regression and classification tasks, including Wisconsin Breast Cancer and MNIST datasets. These results demonstrate the versatility, scalability, and suitability of the SQQNN for deployment on near-term quantum devices.", "published": "2024-12-12T17:35:36Z", "authors": ["Leandro C. Souza", "Bruno C. Guingo", "Gilson Giraldi", "Renato Portugal"], "categories": ["quant-ph", "cs.AI", "cs.LG"], "doi": "", "_query": "hedonic regression housing", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2112.11407v2", "title": "Toward Explainable AI for Regression Models", "abstract": "In addition to the impressive predictive power of machine learning (ML) models, more recently, explanation methods have emerged that enable an interpretation of complex non-linear learning models such as deep neural networks. Gaining a better understanding is especially important e.g. for safety-critical ML applications or medical diagnostics etc. While such Explainable AI (XAI) techniques have reached significant popularity for classifiers, so far little attention has been devoted to XAI for regression models (XAIR). In this review, we clarify the fundamental conceptual differences of XAI for regression and classification tasks, establish novel theoretical insights and analysis for XAIR, provide demonstrations of XAIR on genuine practical regression problems, and finally discuss the challenges remaining for the field.", "published": "2021-12-21T18:09:42Z", "authors": ["Simon Letzgus", "Patrick Wagner", "Jonas Lederer", "Wojciech Samek", "Klaus-Robert Müller", "Gregoire Montavon"], "categories": ["cs.LG", "cs.AI", "stat.ML"], "doi": "10.1109/MSP.2022.3153277", "_query": "hedonic regression housing", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/1208.5384v2", "title": "Local Quantile Regression", "abstract": "Quantile regression is a technique to estimate conditional quantile curves. It provides a comprehensive picture of a response contingent on explanatory variables. In a flexible modeling framework, a specific form of the conditional quantile curve is not a priori fixed. % Indeed, the majority of applications do not per se require specific functional forms. This motivates a local parametric rather than a global fixed model fitting approach. A nonparametric smoothing estimator of the conditional quantile curve requires to balance between local curvature and stochastic variability. In this paper, we suggest a local model selection technique that provides an adaptive estimator of the conditional quantile regression curve at each design point. Theoretical results claim that the proposed adaptive procedure performs as good as an oracle which would minimize the local estimation risk for the problem at hand. We illustrate the performance of the procedure by an extensive simulation study and consider a couple of applications: to tail dependence analysis for the Hong Kong stock market and to analysis of the distributions of the risk factors of temperature dynamics.", "published": "2012-08-27T13:20:32Z", "authors": ["Vladimir Spokoiny", "Weining Wang", "Wolfgang Karl Härdle"], "categories": ["math.ST"], "doi": "", "_query": "hedonic regression housing", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/1405.1207v1", "title": "Nuclear Norm based Matrix Regression with Applications to Face Recognition with Occlusion and Illumination Changes", "abstract": "Recently regression analysis becomes a popular tool for face recognition. The existing regression methods all use the one-dimensional pixel-based error model, which characterizes the representation error pixel by pixel individually and thus neglects the whole structure of the error image. We observe that occlusion and illumination changes generally lead to a low-rank error image. To make use of this low-rank structural information, this paper presents a two-dimensional image matrix based error model, i.e. matrix regression, for face representation and classification. Our model uses the minimal nuclear norm of representation error image as a criterion, and the alternating direction method of multipliers method to calculate the regression coefficients. Compared with the current regression methods, the proposed Nuclear Norm based Matrix Regression (NMR) model is more robust for alleviating the effect of illumination, and more intuitive and powerful for removing the structural noise caused by occlusion. We experiment using four popular face image databases, the Extended Yale B database, the AR database, the Multi-PIE and the FRGC database. Experimental results demonstrate the performance advantage of NMR over the state-of-the-art regression based face recognition methods.", "published": "2014-05-06T09:46:28Z", "authors": ["Jian Yang", "Jianjun Qian", "Lei Luo", "Fanlong Zhang", "Yicheng Gao"], "categories": ["cs.CV"], "doi": "", "_query": "hedonic regression housing", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2402.09858v1", "title": "An Approximation Based Theory of Linear Regression", "abstract": "The goal of this paper is to provide a theory linear regression based entirely on approximations. It will be argued that the standard linear regression model based theory whether frequentist or Bayesian has failed and that this failure is due to an 'assumed (revealed?) truth' (John Tukey) attitude to the models. This is reflected in the language of statistical inference which involves a concept of truth, for example efficiency, consistency and hypothesis testing. The motivation behind this paper was to remove the word `true' from the theory and practice of linear regression and to replace it by approximation. The approximations considered are the least squares approximations. An approximation is called valid if it contains no irrelevant covariates. This is operationalized using the concept of a Gaussian P-value which is the probability that pure Gaussian noise is better in term of least squares than the covariate. The precise definition given in the paper is intuitive and requires only four simple equations. Given this a valid approximation is one where all the Gaussian P-values are less than a threshold $p0$ specified by the statistician, in this paper with the default value 0.01. This approximations approach is not only much simpler it is overwhelmingly better than the standard model based approach. This will be demonstrated using six real data sets, four from high dimensional regression and two from vector autoregression. Both the simplicity and the superiority of Gaussian P-values derive from their universal exactness and validity. This is in complete contrast to standard F P-values which are valid only for carefully designed simulations. The paper contains excerpts from an unpublished paper by John Tukey entitled `Issues relevant to an honest account of data-based inference partially in the light of Laurie Davies's paper'.", "published": "2024-02-15T10:23:48Z", "authors": ["Laurie Davies"], "categories": ["stat.ME", "math.ST"], "doi": "", "_query": "hedonic regression housing", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/1311.6392v2", "title": "A Comprehensive Approach to Universal Piecewise Nonlinear Regression Based on Trees", "abstract": "In this paper, we investigate adaptive nonlinear regression and introduce tree based piecewise linear regression algorithms that are highly efficient and provide significantly improved performance with guaranteed upper bounds in an individual sequence manner. We use a tree notion in order to partition the space of regressors in a nested structure. The introduced algorithms adapt not only their regression functions but also the complete tree structure while achieving the performance of the \"best\" linear mixture of a doubly exponential number of partitions, with a computational complexity only polynomial in the number of nodes of the tree. While constructing these algorithms, we also avoid using any artificial \"weighting\" of models (with highly data dependent parameters) and, instead, directly minimize the final regression error, which is the ultimate performance goal. The introduced methods are generic such that they can readily incorporate different tree construction methods such as random trees in their framework and can use different regressor or partitioning functions as demonstrated in the paper.", "published": "2013-11-25T18:31:40Z", "authors": ["N. Denizcan Vanli", "Suleyman S. Kozat"], "categories": ["cs.LG", "stat.ML"], "doi": "", "_query": "hedonic regression housing", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/1312.1706v2", "title": "Swapping Variables for High-Dimensional Sparse Regression with Correlated Measurements", "abstract": "We consider the high-dimensional sparse linear regression problem of accurately estimating a sparse vector using a small number of linear measurements that are contaminated by noise. It is well known that the standard cadre of computationally tractable sparse regression algorithms---such as the Lasso, Orthogonal Matching Pursuit (OMP), and their extensions---perform poorly when the measurement matrix contains highly correlated columns. To address this shortcoming, we develop a simple greedy algorithm, called SWAP, that iteratively swaps variables until convergence. SWAP is surprisingly effective in handling measurement matrices with high correlations. In fact, we prove that SWAP outputs the true support, the locations of the non-zero entries in the sparse vector, under a relatively mild condition on the measurement matrix. Furthermore, we show that SWAP can be used to boost the performance of any sparse regression algorithm. We empirically demonstrate the advantages of SWAP by comparing it with several state-of-the-art sparse regression algorithms.", "published": "2013-12-05T21:27:40Z", "authors": ["Divyanshu Vats", "Richard G. Baraniuk"], "categories": ["math.ST", "cs.IT", "stat.ML"], "doi": "", "_query": "hedonic regression housing", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/1411.6948v1", "title": "PLUTO: Penalized Unbiased Logistic Regression Trees", "abstract": "We propose a new algorithm called PLUTO for building logistic regression trees to binary response data. PLUTO can capture the nonlinear and interaction patterns in messy data by recursively partitioning the sample space. It fits a simple or a multiple linear logistic regression model in each partition. PLUTO employs the cyclical coordinate descent method for estimation of multiple linear logistic regression models with elastic net penalties, which allows it to deal with high-dimensional data efficiently. The tree structure comprises a graphical description of the data. Together with the logistic regression models, it provides an accurate classifier as well as a piecewise smooth estimate of the probability of \"success\". PLUTO controls selection bias by: (1) separating split variable selection from split point selection; (2) applying an adjusted chi-squared test to find the split variable instead of exhaustive search. A bootstrap calibration technique is employed to further correct selection bias. Comparison on real datasets shows that on average, the multiple linear PLUTO models predict more accurately than other algorithms.", "published": "2014-11-25T18:09:58Z", "authors": ["Wenwen Zhang", "Wei-Yin Loh"], "categories": ["stat.ML", "stat.ME"], "doi": "", "_query": "hedonic regression housing", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2007.05275v2", "title": "Nonlinear Regression on Manifolds for Shape Analysis using Intrinsic Bézier Splines", "abstract": "Intrinsic and parametric regression models are of high interest for the statistical analysis of manifold-valued data such as images and shapes. The standard linear ansatz has been generalized to geodesic regression on manifolds making it possible to analyze dependencies of random variables that spread along generalized straight lines. Nevertheless, in some scenarios, the evolution of the data cannot be modeled adequately by a geodesic. We present a framework for nonlinear regression on manifolds by considering Riemannian splines, whose segments are Bézier curves, as trajectories. Unlike variational formulations that require time-discretization, we take a constructive approach that provides efficient and exact evaluation by virtue of the generalized de Casteljau algorithm. We validate our method in experiments on the reconstruction of periodic motion of the mitral valve as well as the analysis of femoral shape changes during the course of osteoarthritis, endorsing Bézier spline regression as an effective and flexible tool for manifold-valued regression.", "published": "2020-07-10T09:40:28Z", "authors": ["Martin Hanik", "Hans-Christian Hege", "Anaja Hennemuth", "Christoph von Tycowicz"], "categories": ["math.OC"], "doi": "10.1007/978-3-030-59719-1_60", "_query": "hedonic regression housing", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/1710.08901v1", "title": "Calibration of Machine Learning Classifiers for Probability of Default Modelling", "abstract": "Binary classification is highly used in credit scoring in the estimation of probability of default. The validation of such predictive models is based both on rank ability, and also on calibration (i.e. how accurately the probabilities output by the model map to the observed probabilities). In this study we cover the current best practices regarding calibration for binary classification, and explore how different approaches yield different results on real world credit scoring data. The limitations of evaluating credit scoring models using only rank ability metrics are explored. A benchmark is run on 18 real world datasets, and results compared. The calibration techniques used are Platt Scaling and Isotonic Regression. Also, different machine learning models are used: Logistic Regression, Random Forest Classifiers, and Gradient Boosting Classifiers. Results show that when the dataset is treated as a time series, the use of re-calibration with Isotonic Regression is able to improve the long term calibration better than the alternative methods. Using re-calibration, the non-parametric models are able to outperform the Logistic Regression on Brier Score Loss.", "published": "2017-10-24T17:36:51Z", "authors": ["Pedro G. Fonseca", "Hugo D. Lopes"], "categories": ["econ.EM", "stat.ML"], "doi": "", "_query": "hedonic regression housing", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/1311.1835v1", "title": "Linear Regression without computing pseudo-inverse matrix", "abstract": "We are presenting a method of linear regression based on Gram-Schmidt orthogonal projection that does not compute a pseudo-inverse matrix. This is useful when we want to make several regressions with random data vectors for simulation purposes.", "published": "2013-11-06T09:41:27Z", "authors": ["Demetris T. Christopoulos"], "categories": ["math.ST", "stat.CO"], "doi": "", "_query": "hedonic regression housing", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2311.04550v1", "title": "Regression with Cost-based Rejection", "abstract": "Learning with rejection is an important framework that can refrain from making predictions to avoid critical mispredictions by balancing between prediction and rejection. Previous studies on cost-based rejection only focused on the classification setting, which cannot handle the continuous and infinite target space in the regression setting. In this paper, we investigate a novel regression problem called regression with cost-based rejection, where the model can reject to make predictions on some examples given certain rejection costs. To solve this problem, we first formulate the expected risk for this problem and then derive the Bayes optimal solution, which shows that the optimal model should reject to make predictions on the examples whose variance is larger than the rejection cost when the mean squared error is used as the evaluation metric. Furthermore, we propose to train the model by a surrogate loss function that considers rejection as binary classification and we provide conditions for the model consistency, which implies that the Bayes optimal solution can be recovered by our proposed surrogate loss. Extensive experiments demonstrate the effectiveness of our proposed method.", "published": "2023-11-08T09:33:21Z", "authors": ["Xin Cheng", "Yuzhou Cao", "Haobo Wang", "Hongxin Wei", "Bo An", "Lei Feng"], "categories": ["cs.LG", "stat.ML"], "doi": "", "_query": "hedonic regression housing", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/1607.06657v4", "title": "e-Distance Weighted Support Vector Regression", "abstract": "We propose a novel support vector regression approach called e-Distance Weighted Support Vector Regression (e-DWSVR).e-DWSVR specifically addresses two challenging issues in support vector regression: first, the process of noisy data; second, how to deal with the situation when the distribution of boundary data is different from that of the overall data. The proposed e-DWSVR optimizes the minimum margin and the mean of functional margin simultaneously to tackle these two issues. In addition, we use both dual coordinate descent (CD) and averaged stochastic gradient descent (ASGD) strategies to make e-DWSVR scalable to large scale problems. We report promising results obtained by e-DWSVR in comparison with existing methods on several benchmark datasets.", "published": "2016-07-21T02:35:57Z", "authors": ["Yan Wang", "Ge Ou", "Wei Pang", "Lan Huang", "George Macleod Coghill"], "categories": ["cs.LG"], "doi": "", "_query": "hedonic regression housing", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/1810.06089v2", "title": "Asymptotics for Sketching in Least Squares Regression", "abstract": "We consider a least squares regression problem where the data has been generated from a linear model, and we are interested to learn the unknown regression parameters. We consider \"sketch-and-solve\" methods that randomly project the data first, and do regression after. Previous works have analyzed the statistical and computational performance of such methods. However, the existing analysis is not fine-grained enough to show the fundamental differences between various methods, such as the Subsampled Randomized Hadamard Transform (SRHT) and Gaussian projections. In this paper, we make progress on this problem, working in an asymptotic framework where the number of datapoints and dimension of features goes to infinity. We find the limits of the accuracy loss (for estimation and test error) incurred by popular sketching methods. We show separation between different methods, so that SRHT is better than Gaussian projections. Our theoretical results are verified on both real and synthetic data. The analysis of SRHT relies on novel methods from random matrix theory that may be of independent interest.", "published": "2018-10-14T19:48:05Z", "authors": ["Edgar Dobriban", "Sifan Liu"], "categories": ["math.ST", "cs.LG", "math.NA", "stat.ME", "stat.ML"], "doi": "", "_query": "hedonic regression housing", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2605.26513v2", "title": "Rethinking Gradient Modulation in Multimodal Regression", "abstract": "Even balanced multimodal learning methods do not consistently translate additional modalities into better regression performance. To understand this limitation, we revisit the optimization mechanism of balanced multimodal learning, using MMPareto as a representative case. We reveal a previously overlooked issue: MMPareto uses a fixed gradient modulation strength throughout training, while different training stages favor different strengths; an inappropriate modulation strength can instead hinder subsequent optimization. To address this issue, we propose SGM, which adapts the modulation strength according to the current training behavior. We provide theoretical and empirical analyses to characterize and validate the modulation decisions made by SGM. We further build a two-stage multimodal regression framework that integrates AM for unimodal regression representation learning and SGM for adaptive multimodal optimization. Extensive experiments under the same total training budget demonstrate consistent improvements over strong unimodal and multimodal baselines. Our code is provided in the supplementary material.", "published": "2026-05-26T03:57:28Z", "authors": ["Haojie Yin", "Chengcheng Feng", "Tianyi Liu", "Tianqi Zhang", "Kaizhu Huang"], "categories": ["cs.CV"], "doi": "", "_query": "hedonic regression housing", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2401.13721v3", "title": "Uncertainty-Guided Alignment for Unsupervised Domain Adaptation in Regression", "abstract": "Unsupervised Domain Adaptation for Regression (UDAR) aims to adapt models from a labeled source domain to an unlabeled target domain for regression tasks. Traditional feature alignment methods, successful in classification, often prove ineffective for regression due to the correlated nature of regression features. To address this challenge, we propose Uncertainty-Guided Alignment (UGA), a novel method that integrates predictive uncertainty into the feature alignment process. UGA employs Evidential Deep Learning to predict both target values and their associated uncertainties. This uncertainty information guides the alignment process and fuses information within the embedding space, effectively mitigating issues such as feature collapse in out-of-distribution scenarios. We evaluate UGA on two computer vision benchmarks and a real-world battery state-of-charge prediction across different manufacturers and operating temperatures. Across 52 transfer tasks, UGA on average outperforms existing state-of-the-art methods. Our approach not only improves adaptation performance but also provides well-calibrated uncertainty estimates.", "published": "2024-01-24T14:55:02Z", "authors": ["Ismail Nejjar", "Gaetan Frusque", "Florent Forest", "Olga Fink"], "categories": ["cs.CV", "cs.LG"], "doi": "", "_query": "hedonic regression housing", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/1711.05861v1", "title": "Modal Regression based Atomic Representation for Robust Face Recognition", "abstract": "Representation based classification (RC) methods such as sparse RC (SRC) have shown great potential in face recognition in recent years. Most previous RC methods are based on the conventional regression models, such as lasso regression, ridge regression or group lasso regression. These regression models essentially impose a predefined assumption on the distribution of the noise variable in the query sample, such as the Gaussian or Laplacian distribution. However, the complicated noises in practice may violate the assumptions and impede the performance of these RC methods. In this paper, we propose a modal regression based atomic representation and classification (MRARC) framework to alleviate such limitation. Unlike previous RC methods, the MRARC framework does not require the noise variable to follow any specific predefined distributions. This gives rise to the capability of MRARC in handling various complex noises in reality. Using MRARC as a general platform, we also develop four novel RC methods for unimodal and multimodal face recognition, respectively. In addition, we devise a general optimization algorithm for the unified MRARC framework based on the alternating direction method of multipliers (ADMM) and half-quadratic theory. The experiments on real-world data validate the efficacy of MRARC for robust face recognition.", "published": "2017-11-05T02:27:43Z", "authors": ["Yulong Wang", "Yuan Yan Tang", "Luoqing Li", "Hong Chen"], "categories": ["cs.CV"], "doi": "", "_query": "hedonic regression housing", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2603.12518v2", "title": "Gaussian and bootstrap approximations for functional principal component regression", "abstract": "Asymptotic inference using functional principal component regression (FPCR) has long been considered difficult, largely because, upon any scalar scaling, the FPCR estimator fails to satisfy a central limit theorem, leading to the prevailing belief that it is unsuitable for direct statistical inference. In this paper, we revisit this traditional viewpoint by establishing a new result: upon suitable operator scaling, valid Gaussian and bootstrap approximations hold for the FPCR estimator. We apply this surprising finding to hypothesis testing for the significance of the slope function in functional regression models and demonstrate the strong numerical performance of the resulting tests. Our finding also provides a new framework for projection inference. While concise, our results yield powerful inferential tools for functional regression. We believe it paves the way for new lines of inferential methodology for more complex functional regression settings.", "published": "2026-03-12T23:32:48Z", "authors": ["Hyemin Yeon"], "categories": ["math.ST", "stat.ME"], "doi": "", "_query": "hedonic regression housing", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2510.12663v7", "title": "The $α$--regression for compositional data: a unified framework for standard, temporal and spatial regression models including compositional predictors", "abstract": "We revisit the $α$--regression framework for compositional data. We formulate $α$--regression as a non--linear least squares problem, study its asymptotic properties, and provide efficient estimation via the Levenberg--Marquardt algorithm. We then propose a permutation--based hypothesis testing procedure, derive marginal effects for interpretation, and provide a visual inspection of the effect of each predictor. We further discuss robustified versions, the inclusion of natural splines, and the incorporation of compositional predictors, which further facilitate the formulation of a simple time series model. The framework is extended to spatial settings through four models. (a) The $α$--spatially--lagged X regression model, which incorporates spatial spillover effects via spatially--lagged covariates, with decomposition into direct and indirect effects. (b) The $α$--spatial autoregressive model that allows for spatial autocorrelation. (c) The geographically--weighted $α$--regression, which allows coefficients to vary spatially for capturing local relationships. (d) The $α$--eigenvector spatial filtering that is computationally efficient and captures spatial dependence via the eigenvectors of the kernelized distance matrix. Applications to four real datasets illustrate that the models perform on par with or outperform existing models in the literature. The examples showcase that $α$--regression can outperform various competing regression models under different scenarios and its spatial extensions capture the dependence and improve the predictive performance. Overall, the examples provide evidence that the log--ratio methodology does not always lead to the optimal results.", "published": "2025-10-14T15:59:02Z", "authors": ["Michail Tsagris", "Nader Alharbi", "Abdulaziz Alenazi", "Yannis Pantazis"], "categories": ["stat.ME"], "doi": "", "_query": "hedonic regression housing", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/1806.09405v1", "title": "Exponential weights in multivariate regression and a low-rankness favoring prior", "abstract": "We establish theoretical guarantees for the expected prediction error of the exponential weighting aggregate in the case of multivariate regression that is when the label vector is multidimensional. We consider the regression model with fixed design and bounded noise. The first new feature uncovered by our guarantees is that it is not necessary to require independence of the observations: a symmetry condition on the noise distribution alone suffices to get a sharp risk bound. This result needs the regression vectors to be bounded. A second curious finding concerns the case of unbounded regression vectors but independent noise. It turns out that applying exponential weights to the label vectors perturbed by a uniform noise leads to an estimator satisfying a sharp oracle inequality. The last contribution is the instantiation of the proposed oracle inequalities to problems in which the unknown parameter is a matrix. We propose a low-rankness favoring prior and show that it leads to an estimator that is optimal under weak assumptions.", "published": "2018-06-25T12:01:33Z", "authors": ["Arnak S. Dalalyan"], "categories": ["math.ST"], "doi": "", "_query": "hedonic regression housing", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/1507.00420v2", "title": "Globally adaptive quantile regression with ultra-high dimensional data", "abstract": "Quantile regression has become a valuable tool to analyze heterogeneous covaraite-response associations that are often encountered in practice. The development of quantile regression methodology for high-dimensional covariates primarily focuses on examination of model sparsity at a single or multiple quantile levels, which are typically pre-specified ad hoc by the users. The resulting models may be sensitive to the specific choices of the quantile levels, leading to difficulties in interpretation and erosion of confidence in the results. In this article, we propose a new penalization framework for quantile regression in the high-dimensional setting. We employ adaptive L1 penalties, and more importantly, propose a uniform selector of the tuning parameter for a set of quantile levels to avoid some of the potential problems with model selection at individual quantile levels. Our proposed approach achieves consistent shrinkage of regression quantile estimates across a continuous range of quantiles levels, enhancing the flexibility and robustness of the existing penalized quantile regression methods. Our theoretical results include the oracle rate of uniform convergence and weak convergence of the parameter estimators. We also use numerical studies to confirm our theoretical findings and illustrate the practical utility of our proposal", "published": "2015-07-02T03:50:48Z", "authors": ["Qi Zheng", "Limin Peng", "Xuming He"], "categories": ["stat.ME"], "doi": "", "_query": "hedonic regression housing", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/1608.04503v2", "title": "Robust mislabel logistic regression without modeling mislabel probabilities", "abstract": "Logistic regression is among the most widely used statistical methods for linear discriminant analysis. In many applications, we only observe possibly mislabeled responses. Fitting a conventional logistic regression can then lead to biased estimation. One common resolution is to fit a mislabel logistic regression model, which takes into consideration of mislabeled responses. Another common method is to adopt a robust M-estimation by down-weighting suspected instances. In this work, we propose a new robust mislabel logistic regression based on gamma-divergence. Our proposal possesses two advantageous features: (1) It does not need to model the mislabel probabilities. (2) The minimum gamma-divergence estimation leads to a weighted estimating equation without the need to subtract any bias correction term, i.e., it is automatically bias corrected. These properties make the proposed gamma-logistic regression more robust in model fitting and more intuitive for model interpretation through a simple weighting scheme. Our method is also easy to implement, and two types of algorithms are included. Simulation and real data application results are presented to demonstrate the performance of gamma-logistic.", "published": "2016-08-16T07:22:13Z", "authors": ["Hung Hung", "Zhi-Yu Jou", "Su-Yun Huang"], "categories": ["stat.AP"], "doi": "", "_query": "hedonic regression housing", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/1910.02866v4", "title": "Nonparametric principal subspace regression", "abstract": "In scientific applications, multivariate observations often come in tandem with temporal or spatial covariates, with which the underlying signals vary smoothly. The standard approaches such as principal component analysis and factor analysis neglect the smoothness of the data, while multivariate linear or nonparametric regression fail to leverage the correlation information among multivariate response variables. We propose a novel approach named nonparametric principal subspace regression to overcome these issues. By decoupling the model discrepancy, a simple and general two-step framework is introduced, which leaves much flexibility in choice of model fitting. We establish theoretical property of the general framework, and offer implementation procedures that fulfill requirements and enjoy the theoretical guarantee. We demonstrate the favorable finite-sample performance of the proposed method through simulations and a real data application from an electroencephalogram study.", "published": "2019-10-07T15:45:55Z", "authors": ["Mark Koudstaal", "Dengdeng Yu", "Dehan Kong", "Fang Yao"], "categories": ["math.ST", "stat.ME"], "doi": "", "_query": "hedonic regression housing", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2607.20263v1", "title": "How Does Urban Context Relate to Residential Building Health? A Vision-POI Fusion Framework for Building-Level Housing Inspection", "abstract": "Housing-level urban physical examination is essential for identifying residential building problems and supporting targeted urban renewal. Existing automated inspection studies primarily rely on individual images and rarely examine whether surrounding urban functional context can provide supplementary information for building-level assessment. This study proposes a vision-POI fusion framework that combines multi-view visual inspection with POI-derived neighborhood context for residential building health assessment. The empirical dataset covers 92 old residential communities, 3,237 residential buildings, and 25,608 field-acquired inspection images in Qingdao, China, encompassing seven categories of housing-related issues. First, multiple object detection models are evaluated to extract issue locations, categories, and confidence scores from individual images. The image-level outputs are subsequently aggregated across multiple views to construct interpretable building-level representations. Second, POI features are extracted within 500m, 1,000m, and 1,500m neighborhood buffers to characterize surrounding functional environments. Pearson and Spearman correlation analyses, combined with false discovery rate correction, are used to identify candidate contextual features. Finally, visual and POI features are integrated using a cost-sensitive Random Forest classifier under community-isolated spatial cross-validation. The results show that multi-view aggregation provides the main performance improvement, increasing the building-level Macro-F1 from 60.84% under Direct Detection to 74.95%. Incorporating POI context further increases Macro-F1 to 76.79%, although the additional gain is modest and category-dependent. POI information therefore functions as a supplementary contextual prior rather than a substitute for direct visual evidence or a causal determinant of building condition.", "published": "2026-07-22T15:18:30Z", "authors": ["Kun Zhao", "Helei Ren", "Guilin Tang", "Tianyi Chen", "Zhehui Song", "Xing Liu", "Lijian Zhou", "Yuhong Zhao", "Xiang Gao", "Jinming Jiang", "Qichao Ban"], "categories": ["cs.CV"], "doi": "", "_query": "hedonic regression housing", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2607.09013v1", "title": "Orthogonalized Design Matrices Speed-ups of Bayesian Semiparametric Regression", "abstract": "We explain how important classes of Bayesian semiparametric regression fitting and inference procedures can be sped up, significantly, via the use of orthogonalized design matrices. Typically, design matrices in semiparametric regression contain predictor observations and basis functions of such data. In Bayesian semiparametric regression, loop-type approaches such as Gibbs sampling and coordinate ascent variational inference typically are required. We show that pre-loop reformulation of Bayesian semiparametric regression models involving orthogonalized design matrices lead to two orders of magnitude, with respect to column dimension, computational reduction. Our computer experiments reveal that this simple paradigm results in approximately 5- to 60-fold speed-ups.", "published": "2026-07-10T00:38:27Z", "authors": ["Nurul Fitriyani", "Matt P. Wand"], "categories": ["stat.ME"], "doi": "", "_query": "hedonic regression housing", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2609.04126v2", "title": "Fast Computation of Nested Cross-Validation for Penalized Regression", "abstract": "Cross-validation is a resampling procedure that provides a point estimate of generalization error for any predictive model. Cross-validation is widely used for model selection and evaluation. Uncertainty in the cross-validation estimate is challenging to quantify, and estimation of its variance is known to require multiple runs of the entire resampling procedure. Nested cross-validation computes a prediction interval for the generalization error for a given model and training dataset by resampling the entire cross-validation procedure but incurs extraordinary computational cost. We provide an efficient method for computing the nested cross-validation prediction interval using only a single model fit for some penalized regression models including ridge regression, spline smoothing, and some functional regression models. We characterize when our proposed method should be expected to out-perform resampling-based nested cross-validation in various scaling regimes as well as in finite samples. Experiments for functional principle components regression demonstrate non-trivial cases in which our proposed method improves run times substantially.", "published": "2026-09-03T17:25:17Z", "authors": ["Shuyang Cao", "Alex Stringer"], "categories": ["stat.CO"], "doi": "", "_query": "hedonic regression housing", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/1512.09016v2", "title": "Pairwise Markov properties for regression graphs", "abstract": "With a sequence of regressions, one may generate joint probability distributions. One starts with a joint, marginal distribution of context variables having possibly a concentration graph structure and continues with an ordered sequence of conditional distributions, named regressions in joint responses. The involved random variables may be discrete, continuous or of both types. Such a generating process specifies for each response a conditioning set which contains just its regressor variables and it leads to at least one valid ordering of all nodes in the corresponding regression graph which has three types of edge; one for undirected dependences among context variables, another for undirected dependences among joint responses and one for any directed dependence of a response on a regressor variable. For this regression graph, there are several definitions of pairwise Markov properties, where each interprets the conditional independence associated with a missing edge in the graph in a different way. We explain how these properties arise, prove their equivalence for compositional graphoids and point at the equivalence of each one of them to the global Markov property.", "published": "2015-12-30T17:05:31Z", "authors": ["Kayvan Sadeghi", "Nanny Wermuth"], "categories": ["math.ST", "stat.OT"], "doi": "10.1002/sta4.122", "_query": "hedonic regression housing", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/1511.00632v1", "title": "Partial Functional Linear Quantile Regression for Neuroimaging Data Analysis", "abstract": "We propose a prediction procedure for the functional linear quantile regression model by using partial quantile covariance techniques and develop a simple partial quantile regression (SIMPQR) algorithm to efficiently extract partial quantile regression (PQR) basis for estimating functional coefficients. We further extend our partial quantile covariance techniques to functional composite quantile regression (CQR) defining partial composite quantile covariance. There are three major contributions. (1) We define partial quantile covariance between two scalar variables through linear quantile regression. We compute PQR basis by sequentially maximizing the partial quantile covariance between the response and projections of functional covariates. (2) In order to efficiently extract PQR basis, we develop a SIMPQR algorithm analogous to simple partial least squares (SIMPLS). (3) Under the homoscedasticity assumption, we extend our techniques to partial composite quantile covariance and use it to find the partial composite quantile regression (PCQR) basis. The SIMPQR algorithm is then modified to obtain the SIMPCQR algorithm. Two simulation studies show the superiority of our proposed methods. Two real data from ADHD-200 sample and ADNI are analyzed using our proposed methods.", "published": "2015-11-02T19:04:35Z", "authors": ["Dengdeng Yu", "Linglong Kong", "Ivan Mizera"], "categories": ["stat.ME"], "doi": "", "_query": "hedonic regression housing", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/1806.09695v1", "title": "Person Re-Identification in Identity Regression Space", "abstract": "Most existing person re-identification (re-id) methods are unsuitable for real-world deployment due to two reasons: Unscalability to large population size, and Inadaptability over time. In this work, we present a unified solution to address both problems. Specifically, we propose to construct an Identity Regression Space (IRS) based on embedding different training person identities (classes) and formulate re-id as a regression problem solved by identity regression in the IRS. The IRS approach is characterised by a closed-form solution with high learning efficiency and an inherent incremental learning capability with human-in-the-loop. Extensive experiments on four benchmarking datasets(VIPeR, CUHK01, CUHK03 and Market-1501) show that the IRS model not only outperforms state-of-the-art re-id methods, but also is more scalable to large re-id population size by rapidly updating model and actively selecting informative samples with reduced human labelling effort.", "published": "2018-06-25T20:40:25Z", "authors": ["Hanxiao Wang", "Xiatian Zhu", "Shaogang Gong", "Tao Xiang"], "categories": ["cs.CV"], "doi": "", "_query": "hedonic regression housing", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/1206.6450v1", "title": "Conditional Sparse Coding and Grouped Multivariate Regression", "abstract": "We study the problem of multivariate regression where the data are naturally grouped, and a regression matrix is to be estimated for each group. We propose an approach in which a dictionary of low rank parameter matrices is estimated across groups, and a sparse linear combination of the dictionary elements is estimated to form a model within each group. We refer to the method as conditional sparse coding since it is a coding procedure for the response vectors Y conditioned on the covariate vectors X. This approach captures the shared information across the groups while adapting to the structure within each group. It exploits the same intuition behind sparse coding that has been successfully developed in computer vision and computational neuroscience. We propose an algorithm for conditional sparse coding, analyze its theoretical properties in terms of predictive accuracy, and present the results of simulation and brain imaging experiments that compare the new technique to reduced rank regression.", "published": "2012-06-27T19:59:59Z", "authors": ["Min Xu", "John Lafferty"], "categories": ["cs.LG", "stat.ML"], "doi": "", "_query": "hedonic regression housing", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2502.14088v1", "title": "Regression in EO: Are VLMs Up to the Challenge?", "abstract": "Earth Observation (EO) data encompass a vast range of remotely sensed information, featuring multi-sensor and multi-temporal, playing an indispensable role in understanding our planet's dynamics. Recently, Vision Language Models (VLMs) have achieved remarkable success in perception and reasoning tasks, bringing new insights and opportunities to the EO field. However, the potential for EO applications, especially for scientific regression related applications remains largely unexplored. This paper bridges that gap by systematically examining the challenges and opportunities of adapting VLMs for EO regression tasks. The discussion first contrasts the distinctive properties of EO data with conventional computer vision datasets, then identifies four core obstacles in applying VLMs to EO regression: 1) the absence of dedicated benchmarks, 2) the discrete-versus-continuous representation mismatch, 3) cumulative error accumulation, and 4) the suboptimal nature of text-centric training objectives for numerical tasks. Next, a series of methodological insights and potential subtle pitfalls are explored. Lastly, we offer some promising future directions for designing robust, domain-aware solutions. Our findings highlight the promise of VLMs for scientific regression in EO, setting the stage for more precise and interpretable modeling of critical environmental processes.", "published": "2025-02-19T20:27:54Z", "authors": ["Xizhe Xue", "Xiao Xiang Zhu"], "categories": ["cs.CV"], "doi": "", "_query": "hedonic regression housing", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2408.02331v2", "title": "Explaining and Connecting Kriging with Gaussian Process Regression", "abstract": "Kriging and Gaussian Process Regression are statistical methods that allow predicting the outcome of a random process or a random field by using a sample of correlated observations. In other words, the random process or random field is partially observed, and by using a sample a prediction is made, pointwise or as a whole, where the latter can be thought as a reconstruction. In addition, the techniques permit to give a measure of uncertainty of the prediction. The methods have different origins. Kriging comes from geostatistics, a field which started to develop around 1950 oriented to mining valuation problems, whereas Gaussian Process Regression has gained popularity in the area of machine learning in the last decade of the previous century. In the literature, the methods are usually presented as being the same technique. However, beyond this affirmation, the techniques have yet not been compared on a thorough mathematical basis and neither explained why and under which conditions this affirmation holds. Furthermore, Kriging has many variants and this affirmation should be precised. In this paper, this gap is filled. It is shown, step by step how both methods are deduced from the first principles -- with a major focus on Kriging, the mathematical connection between them, and which Kriging variant corresponds to which Gaussian Process Regression set up. The three most widely used versions of Kriging are considered: Simple Kriging, Ordinary Kriging and Universal Kriging. It is found, that despite their closeness, the techniques are different in their approach and assumptions, in a similar way the Least Square method, the Best Linear Unbiased Estimator method and the Likelihood method in regression do. I hope this work deepen the understanding of the relation between Kriging and Gaussian Process Regression, and serves as a cohesive introductory resource for researchers.", "published": "2024-08-05T09:15:40Z", "authors": ["Marius Marinescu"], "categories": ["stat.ME", "math.ST"], "doi": "", "_query": "hedonic regression housing", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2302.06578v4", "title": "Kernel Ridge Regression Inference", "abstract": "We provide uniform confidence bands for kernel ridge regression (KRR), a widely used nonparametric regression estimator for nonstandard data such as preferences, sequences, and graphs. Despite the prevalence of these data--e.g., student preferences in school matching mechanisms--the inferential theory of KRR is not fully known. We construct valid and sharp confidence sets that shrink at nearly the minimax rate, allowing nonstandard regressors. Our bootstrap procedure uses anti-symmetric multipliers for computational efficiency and for validity under mis-specification. We use the procedure to develop a test for match effects, i.e. whether students benefit more from the schools they rank highly.", "published": "2023-02-13T18:26:36Z", "authors": ["Rahul Singh", "Suhas Vijaykumar"], "categories": ["math.ST", "cs.LG", "stat.ML"], "doi": "", "_query": "hedonic regression housing", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2608.01115v1", "title": "Renewable high-dimensional expected shortfall regression", "abstract": "Expected Shortfall (ES) has become a core coherent risk measure in finance and statistics, and high-dimensional ES regression is crucial for characterizing heterogeneous tail risk with massive covariates. Existing offline methods for high-dimensional ES regression rely on access to full data, which fails under streaming data scenarios with sequential batch arrival and limited storage. To address this issue, this paper proposes a renewable estimation and inference framework for high-dimensional ES regression tailored to streaming data. By optimizing a surrogate loss function determined only by current data and historical information, the proposed procedure updates the estimator of ES regression coefficients without storing full raw data. Based on the online estimator, we design an online debiased estimator and further construct valid Wald-type confidence intervals using consistent variance estimation. Theoretically, we establish non-asymptotic error bounds for the online high-dimensional ES estimator and verify the asymptotic normality of the online debiased estimator. Extensive simulations show that the proposed method achieves estimation accuracy and inference performance comparable to the offline benchmark. Moreover, an application on the car insurance claim dataset demonstrates strong practical value in insurance risk management.", "published": "2026-08-02T09:22:57Z", "authors": ["Haochen Rao", "Tingzi Weng", "Yifan Jiang", "Xu Guo"], "categories": ["stat.ME"], "doi": "", "_query": "hedonic regression housing", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2004.14502v2", "title": "Wavelet-based estimation in a semiparametric regression model", "abstract": "In this paper, we introduce a wavelet-based method for estimating the EDR space in Li's semiparametric regression model for achieving dimension reduction. This method is obtained by using linear wavelet estimators of the density and regression functions that are involved in the covariance matrix of conditional expectation whose spectral analysis gives the EDR directions. Then, consistency of the proposed estimators is proved. A simulation study that allow one to evaluate the performance of the proposal with comparison to existing methods is presented.", "published": "2020-04-29T22:18:10Z", "authors": ["Emmanuel de Dieu Nkou", "Guy Martial Nkiet"], "categories": ["math.ST"], "doi": "", "_query": "hedonic regression housing", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/1701.01093v1", "title": "Private Incremental Regression", "abstract": "Data is continuously generated by modern data sources, and a recent challenge in machine learning has been to develop techniques that perform well in an incremental (streaming) setting. In this paper, we investigate the problem of private machine learning, where as common in practice, the data is not given at once, but rather arrives incrementally over time. We introduce the problems of private incremental ERM and private incremental regression where the general goal is to always maintain a good empirical risk minimizer for the history observed under differential privacy. Our first contribution is a generic transformation of private batch ERM mechanisms into private incremental ERM mechanisms, based on a simple idea of invoking the private batch ERM procedure at some regular time intervals. We take this construction as a baseline for comparison. We then provide two mechanisms for the private incremental regression problem. Our first mechanism is based on privately constructing a noisy incremental gradient function, which is then used in a modified projected gradient procedure at every timestep. This mechanism has an excess empirical risk of $\\approx\\sqrt{d}$, where $d$ is the dimensionality of the data. While from the results of [Bassily et al. 2014] this bound is tight in the worst-case, we show that certain geometric properties of the input and constraint set can be used to derive significantly better results for certain interesting regression problems.", "published": "2017-01-04T18:18:07Z", "authors": ["Shiva Prasad Kasiviswanathan", "Kobbi Nissim", "Hongxia Jin"], "categories": ["cs.DS", "cs.CR", "stat.ML"], "doi": "", "_query": "hedonic regression housing", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2510.26783v1", "title": "A Unified Theory for Causal Inference: Direct Debiased Machine Learning via Bregman-Riesz Regression", "abstract": "This note introduces a unified theory for causal inference that integrates Riesz regression, covariate balancing, density-ratio estimation (DRE), targeted maximum likelihood estimation (TMLE), and the matching estimator in average treatment effect (ATE) estimation. In ATE estimation, the balancing weights and the regression functions of the outcome play important roles, where the balancing weights are referred to as the Riesz representer, bias-correction term, and clever covariates, depending on the context. Riesz regression, covariate balancing, DRE, and the matching estimator are methods for estimating the balancing weights, where Riesz regression is essentially equivalent to DRE in the ATE context, the matching estimator is a special case of DRE, and DRE is in a dual relationship with covariate balancing. TMLE is a method for constructing regression function estimators such that the leading bias term becomes zero. Nearest Neighbor Matching is equivalent to Least Squares Density Ratio Estimation and Riesz Regression.", "published": "2025-10-30T17:56:47Z", "authors": ["Masahiro Kato"], "categories": ["stat.ML", "cs.LG", "econ.EM", "math.ST", "stat.ME"], "doi": "", "_query": "hedonic regression housing", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2002.00130v3", "title": "Higher Criticism Tuned Regression For Weak And Sparse Signals", "abstract": "Here we propose a novel searching scheme for a tuning parameter in high-dimensional penalized regression methods to address variable selection and modeling when sample sizes are limited compared to the data dimensions. Our method is motivated by high-throughput biological data such as genome-wide association studies (GWAS) and epigenome-wide association studies (EWAS). We propose a new estimate of the regularization parameter $λ$ in penalized regression methods based on an estimated lower bound of the proportion of false null hypotheses with confidence $(1 - α)$. The bound is estimated by applying the empirical null distribution of the higher criticism statistic, a second-level significance test constructed by dependent $p$-values using a multi-split regression and aggregation method. A tuning parameter estimate in penalized regression, $λ$, corresponds with the lower bound of the proportion of false null hypotheses. Different penalized regression methods with varied signal sparsity and strength are compared in the multi-split method setting. We demonstrate the performance of our method using both simulation experiments and the applications of real data on (1) lipid-trait genetics from the Action to Control Cardiovascular Risk in Diabetes (ACCORD) clinical trial and (2) epigenetic analysis evaluating smoking's influence in differential methylation in the Agricultural Lung Health Study. The proposed algorithm is included in the HCTR package, available at https://cran.r-project.org/web/packages/HCTR/index.html.", "published": "2020-02-01T02:55:50Z", "authors": ["Tao Jiang", "Stephanie J. London", "Mi Kyeong Lee", "Josyf C. Mychaleckyj", "Alison A. Motsinger-Reif"], "categories": ["q-bio.QM", "stat.ME"], "doi": "", "_query": "hedonic regression housing", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2305.17711v1", "title": "A joint estimation approach for monotonic regression functions in general dimensions", "abstract": "Regression analysis under the assumption of monotonicity is a well-studied statistical problem and has been used in a wide range of applications. However, there remains a lack of a broadly applicable methodology that permits information borrowing, for efficiency gains, when jointly estimating multiple monotonic regression functions. We introduce such a methodology by extending the isotonic regression problem presented in the article \"The isotonic regression problem and its dual\" (Barlow and Brunk, 1972). The presented approach can be applied to both fixed and random designs and any number of explanatory variables (regressors). Our framework penalizes pairwise differences in the values (levels) of the monotonic function estimates, with the weight of penalty being determined based on a statistical test, which results in information being shared across data sets if similarities in the regression functions exist. Function estimates are subsequently derived using an iterative optimization routine that uses existing solution algorithms for the isotonic regression problem. Simulation studies for normally and binomially distributed response data illustrate that function estimates are consistently improved if similarities between functions exist, and are not oversmoothed otherwise. We further apply our methodology to analyse two public health data sets: neonatal mortality data for Porto Alegre, Brazil, and stroke patient data for North West England.", "published": "2023-05-28T12:32:48Z", "authors": ["Christian Rohrbeck", "Deborah A Costain"], "categories": ["stat.ME"], "doi": "", "_query": "hedonic regression housing", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2502.16574v1", "title": "Regularized zero-inflated Bernoulli regression model", "abstract": "Logistic regression model is widely used in many studies to investigate the relationship between a binary response variable Y and a set of potential predictors $X_1,\\ldots, X_p$ (for example: $Y = 1$ if the outcome occurred and $Y = 0$ otherwise). One problem arising then is that, a proportion of the study subjects cannot experience the outcome of interest. This leads to an excessive presence of zeros in the study sample. This article is interested in estimating parameters of the zero-inflated Bernouilli regression model in a high-dimensional setting, i.e. with a large number of regressors. We use particulary Ridge regression and the Lasso which are typically achieved by constraining the weights of the model. and are useful when the number of predictors is much bigger than the number of observations. We establish the existency, consistency and asymptotic normality of the proposed regularized estimator. Then, we conduct a simulation study to investigate its finite-sample behavior, and application to real data.", "published": "2025-02-23T13:48:40Z", "authors": ["Mouhamed Ndoye", "Aba Diop"], "categories": ["stat.ME", "math.ST"], "doi": "", "_query": "hedonic regression housing", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/1710.03904v1", "title": "General notions of regression depth function", "abstract": "As a measure for the centrality of a point in a set of multivariate data, statistical depth functions play important roles in multivariate analysis, because one may conveniently construct descriptive as well as inferential procedures relying on them. Many depth notions have been proposed in the literature to fit to different applications. However, most of them are mainly developed for the location setting. In this paper, we discuss the possibility of extending some of them into the regression setting. A general concept of regression depth function is also provided.", "published": "2017-10-11T04:24:27Z", "authors": ["Xiaohui Liu", "Yuanyuan Li"], "categories": ["stat.ME"], "doi": "", "_query": "hedonic regression housing", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2209.06454v1", "title": "Prediction Intervals and Confidence Regions for Symbolic Regression Models based on Likelihood Profiles", "abstract": "Symbolic regression is a nonlinear regression method which is commonly performed by an evolutionary computation method such as genetic programming. Quantification of uncertainty of regression models is important for the interpretation of models and for decision making. The linear approximation and so-called likelihood profiles are well-known possibilities for the calculation of confidence and prediction intervals for nonlinear regression models. These simple and effective techniques have been completely ignored so far in the genetic programming literature. In this work we describe the calculation of likelihood profiles in details and also provide some illustrative examples with models created with three different symbolic regression algorithms on two different datasets. The examples highlight the importance of the likelihood profiles to understand the limitations of symbolic regression models and to help the user taking an informed post-prediction decision.", "published": "2022-09-14T07:07:55Z", "authors": ["Fabricio Olivetti de Franca", "Gabriel Kronberger"], "categories": ["cs.LG", "cs.NE"], "doi": "", "_query": "hedonic regression housing", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2407.19715v1", "title": "Generalization bounds for regression and classification on adaptive covering input domains", "abstract": "Our main focus is on the generalization bound, which serves as an upper limit for the generalization error. Our analysis delves into regression and classification tasks separately to ensure a thorough examination. We assume the target function is real-valued and Lipschitz continuous for regression tasks. We use the 2-norm and a root-mean-square-error (RMSE) variant to measure the disparities between predictions and actual values. In the case of classification tasks, we treat the target function as a one-hot classifier, representing a piece-wise constant function, and employ 0/1 loss for error measurement. Our analysis underscores the differing sample complexity required to achieve a concentration inequality of generalization bounds, highlighting the variation in learning efficiency for regression and classification tasks. Furthermore, we demonstrate that the generalization bounds for regression and classification functions are inversely proportional to a polynomial of the number of parameters in a network, with the degree depending on the hypothesis class and the network architecture. These findings emphasize the advantages of over-parameterized networks and elucidate the conditions for benign overfitting in such systems.", "published": "2024-07-29T05:40:08Z", "authors": ["Wen-Liang Hwang"], "categories": ["stat.ML", "cs.LG"], "doi": "", "_query": "hedonic regression housing", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2209.06454v1", "title": "Prediction Intervals and Confidence Regions for Symbolic Regression Models based on Likelihood Profiles", "abstract": "Symbolic regression is a nonlinear regression method which is commonly performed by an evolutionary computation method such as genetic programming. Quantification of uncertainty of regression models is important for the interpretation of models and for decision making. The linear approximation and so-called likelihood profiles are well-known possibilities for the calculation of confidence and prediction intervals for nonlinear regression models. These simple and effective techniques have been completely ignored so far in the genetic programming literature. In this work we describe the calculation of likelihood profiles in details and also provide some illustrative examples with models created with three different symbolic regression algorithms on two different datasets. The examples highlight the importance of the likelihood profiles to understand the limitations of symbolic regression models and to help the user taking an informed post-prediction decision.", "published": "2022-09-14T07:07:55Z", "authors": ["Fabricio Olivetti de Franca", "Gabriel Kronberger"], "categories": ["cs.LG", "cs.NE"], "doi": "", "_query": "hedonic regression housing", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/1710.03904v1", "title": "General notions of regression depth function", "abstract": "As a measure for the centrality of a point in a set of multivariate data, statistical depth functions play important roles in multivariate analysis, because one may conveniently construct descriptive as well as inferential procedures relying on them. Many depth notions have been proposed in the literature to fit to different applications. However, most of them are mainly developed for the location setting. In this paper, we discuss the possibility of extending some of them into the regression setting. A general concept of regression depth function is also provided.", "published": "2017-10-11T04:24:27Z", "authors": ["Xiaohui Liu", "Yuanyuan Li"], "categories": ["stat.ME"], "doi": "", "_query": "hedonic regression housing", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/1208.5467v2", "title": "Censored quantile regression processes under dependence and penalization", "abstract": "We consider quantile regression processes from censored data under dependent data structures and derive a uniform Bahadur representation for those processes. We also consider cases where the dimension of the parameter in the quantile regression model is large. It is demonstrated that traditional penalized estimators such as the adaptive lasso yield sub-optimal rates if the coefficients of the quantile regression cross zero. New penalization techniques are introduced which are able to deal with specific problems of censored data and yield estimates with an optimal rate. In contrast to most of the literature, the asymptotic analysis does not require the assumption of independent observations, but is based on rather weak assumptions, which are satisfied for many kinds of dependent data.", "published": "2012-08-27T19:01:42Z", "authors": ["Stanislav Volgushev", "Jens Wagener", "Holger Dette"], "categories": ["math.ST", "stat.ME"], "doi": "", "_query": "hedonic regression housing", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2508.15962v1", "title": "Nonparametric regression for a circular response with error-in-covariate", "abstract": "This study considers regression analysis of a circular response with an error-prone linear covariate. Starting with an existing estimator of the circular regression function that assumes error-free covariate, three approaches are proposed to revise this estimator, leading to three nonparametric estimators for the circular regression function accounting for measurement error. The proposed estimators are intrinsically connected through some deconvoluting operator that is exploited differently in different estimators. Moreover, a new bandwidth selection method is developed that is more computationally efficient than an existing method well-received in the context of tuning parameter selection in the presence of measurement error. The efficacy of these new estimators and their relative strengths are demonstrated through a thorough investigation of their asymptotic properties and extensive empirical study of their finite-sample performance.", "published": "2025-08-21T21:10:44Z", "authors": ["Nicholas Woolsey", "Xianzheng Huang"], "categories": ["stat.ME"], "doi": "", "_query": "hedonic regression housing", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/1801.04112v2", "title": "Regression Based Expected Shortfall Backtesting", "abstract": "This paper introduces novel backtests for the risk measure Expected Shortfall (ES) following the testing idea of Mincer and Zarnowitz (1969). Estimating a regression framework for the ES stand-alone is infeasible, and thus, our tests are based on a joint regression for the Value at Risk and the ES, which allows for different test specifications. These ES backtests are the first which solely backtest the ES in the sense that they only require ES forecasts as input parameters. As the tests are potentially subject to model misspecification, we provide asymptotic theory under misspecification for the underlying joint regression. We find that employing a misspecification robust covariance estimator substantially improves the tests' performance. We compare our backtests to existing approaches and find that our tests outperform the competitors throughout all considered simulations. In an empirical illustration, we apply our backtests to ES forecasts for 200 stocks of the S&P 500 index.", "published": "2018-01-12T09:51:50Z", "authors": ["Sebastian Bayer", "Timo Dimitriadis"], "categories": ["q-fin.RM"], "doi": "10.1093/jjfinec/nbaa013", "_query": "hedonic regression housing", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2402.07559v1", "title": "Expectile regression averaging method for probabilistic forecasting of electricity prices", "abstract": "In this paper we propose a new method for probabilistic forecasting of electricity prices. It is based on averaging point forecasts from different models combined with expectile regression. We show that deriving the predicted distribution in terms of expectiles, might be in some cases advantageous to the commonly used quantiles. We apply the proposed method to the day-ahead electricity prices from the German market and compare its accuracy with the Quantile Regression Averaging method and quantile -- as well as expectile-based historical simulation. The obtained results indicate that using the expectile regression improves the accuracy of the probabilistic forecasts of electricity prices, but a variance stabilizing transformation should be applied prior to modelling.", "published": "2024-02-12T10:51:53Z", "authors": ["Joanna Janczura"], "categories": ["stat.AP"], "doi": "", "_query": "hedonic regression housing", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2308.08958v2", "title": "Linear Regression with Weak Exogeneity", "abstract": "This paper studies linear time series regressions with many regressors. Weak exogeneity is the most used identifying assumption in time series. Weak exogeneity requires the structural error to have zero conditional expectation given the present and past regressor values, allowing errors to correlate with future regressor realizations. We show that weak exogeneity in time series regressions with many controls may produce substantial biases and even render the least squares (OLS) estimator inconsistent. The bias arises in settings with many regressors because the normalized OLS design matrix remains asymptotically random and correlates with the regression error when only weak (but not strict) exogeneity holds. This bias's magnitude increases with the number of regressors and their average autocorrelation. To address this issue, we propose an innovative approach to bias correction that yields a new estimator with improved properties relative to OLS. We establish consistency and conditional asymptotic Gaussianity of this new estimator and provide a method for inference.", "published": "2023-08-17T12:57:51Z", "authors": ["Anna Mikusheva", "Mikkel Sølvsten"], "categories": ["econ.EM", "stat.ME"], "doi": "", "_query": "hedonic regression housing", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2208.12383v2", "title": "High-dimensional sparse vine copula regression with application to genomic prediction", "abstract": "High-dimensional data sets are often available in genome-enabled predictions. Such data sets include nonlinear relationships with complex dependence structures. For such situations, vine copula based (quantile) regression is an important tool. However, the current vine copula based regression approaches do not scale up to high and ultra-high dimensions. To perform high-dimensional sparse vine copula based regression, we propose two methods. First, we show their superiority regarding computational complexity over the existing methods. Second, we define relevant, irrelevant, and redundant explanatory variables for quantile regression. Then we show our method's power in selecting relevant variables and prediction accuracy in high-dimensional sparse data sets via simulation studies. Next, we apply the proposed methods to the high-dimensional real data, aiming at the genomic prediction of maize traits. Some data-processing and feature extraction steps for the real data are further discussed. Finally, we show the advantage of our methods over linear models and quantile regression forests in simulation studies and real data applications.", "published": "2022-08-26T00:18:11Z", "authors": ["Özge Sahin", "Claudia Czado"], "categories": ["stat.ME"], "doi": "10.1093/biomtc/ujad042", "_query": "hedonic regression housing", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/1901.03920v2", "title": "Asymptotics of an empirical bridge of regression on induced order statistics", "abstract": "We propose a class of tests for linear regression on concomitants (induced order statistics). These tests are based on sequential sums of regression residuals. We self-center and self-normalize these sums. The resulting process is called an empirical bridge. We prove weak convergence of the empirical bridge in uniform metrics to a centered Gaussian process. The proposed tests are of chi-square type.", "published": "2019-01-13T00:50:25Z", "authors": ["Artyom Kovalevskii"], "categories": ["math.ST"], "doi": "", "_query": "hedonic regression housing", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2603.17145v2", "title": "REAL: Regression-Aware Reinforcement Learning for LLM-as-a-Judge", "abstract": "Large language models (LLMs) are increasingly deployed as automated evaluators that assign numeric scores to model outputs, a paradigm known as LLM-as-a-Judge. However, standard Reinforcement Learning (RL) methods typically rely on binary rewards (e.g., 0-1 accuracy), thereby ignoring the ordinal structure inherent in regression tasks; for instance, they fail to recognize that predicting 4 is significantly better than predicting 1 when the ground truth is 5. Conversely, existing regression-aware approaches are often confined to Supervised Fine-Tuning (SFT), limiting their ability to explore optimal reasoning paths. To bridge this gap, we propose \\textbf{REAL} (\\underline{RE}gression-\\underline{A}ware Reinforcement \\underline{L}earning), a principled RL framework designed to optimize regression rewards, and also proven to be optimal for correlation metrics. A key technical challenge is that the regression objective is explicitly policy-dependent, thus invalidating standard policy gradient methods. To address this, we employ the generalized policy gradient estimator, which naturally decomposes optimization into two complementary components: (1) exploration over Chain-of-Thought (CoT) trajectory, and (2) regression-aware prediction refinement of the final score. Extensive experiments across model scales (8B to 32B) demonstrate that REAL consistently outperforms both regression-aware SFT baselines and standard RL methods, exhibiting significantly better generalization on out-of-domain benchmarks. On Qwen3-32B specifically, we achieve gains of +8.40 Pearson and +7.20 Spearman correlation over the SFT baseline, and +18.30/+11.20 over the base model. These findings highlight the critical value of integrating regression objectives into RL exploration for accurate LLM evaluation.", "published": "2026-03-17T21:19:08Z", "authors": ["Yasi Zhang", "Tianyu Chen", "Mingyuan Zhou", "Oscar Leong", "Ying Nian Wu", "Michal Lukasik"], "categories": ["cs.LG", "cs.AI"], "doi": "", "_query": "hedonic regression housing", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2110.09620v1", "title": "Sufficient Dimension Reduction for High-Dimensional Regression and Low-Dimensional Embedding: Tutorial and Survey", "abstract": "This is a tutorial and survey paper on various methods for Sufficient Dimension Reduction (SDR). We cover these methods with both statistical high-dimensional regression perspective and machine learning approach for dimensionality reduction. We start with introducing inverse regression methods including Sliced Inverse Regression (SIR), Sliced Average Variance Estimation (SAVE), contour regression, directional regression, Principal Fitted Components (PFC), Likelihood Acquired Direction (LAD), and graphical regression. Then, we introduce forward regression methods including Principal Hessian Directions (pHd), Minimum Average Variance Estimation (MAVE), Conditional Variance Estimation (CVE), and deep SDR methods. Finally, we explain Kernel Dimension Reduction (KDR) both for supervised and unsupervised learning. We also show that supervised KDR and supervised PCA are equivalent.", "published": "2021-10-18T21:05:08Z", "authors": ["Benyamin Ghojogh", "Ali Ghodsi", "Fakhri Karray", "Mark Crowley"], "categories": ["stat.ME", "cs.LG", "math.ST", "stat.ML"], "doi": "", "_query": "hedonic regression housing", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2005.11599v3", "title": "Component-wise Adaptive Trimming For Robust Mixture Regression", "abstract": "Parameter estimation of mixture regression model using the expectation maximization (EM) algorithm is highly sensitive to outliers. Here we propose a fast and efficient robust mixture regression algorithm, called Component-wise Adaptive Trimming (CAT) method. We consider simultaneous outlier detection and robust parameter estimation to minimize the effect of outlier contamination. Robust mixture regression has many important applications including in human cancer genomics data, where the population often displays strong heterogeneity added by unwanted technological perturbations. Existing robust mixture regression methods suffer from outliers as they either conduct parameter estimation in the presence of outliers, or rely on prior knowledge of the level of outlier contamination. CAT was implemented in the framework of classification expectation maximization, under which a natural definition of outliers could be derived. It implements a least trimmed squares (LTS) approach within each exclusive mixing component, where the robustness issue could be transformed from the mixture case to simple linear regression case. The high breakdown point of the LTS approach allows us to avoid the pre-specification of trimming parameter. Compared with multiple existing algorithms, CAT is the most competitive one that can handle and adaptively trim off outliers as well as heavy tailed noise, in different scenarios of simulated data and real genomic data. CAT has been implemented in an R package `RobMixReg' available in CRAN.", "published": "2020-05-23T20:59:16Z", "authors": ["Wennan Chang", "Xinyu Zhou", "Yong Zang", "Chi Zhang", "Sha Cao"], "categories": ["stat.ME", "cs.LG"], "doi": "", "_query": "hedonic regression housing", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/1108.4559v2", "title": "Optimal Algorithms for Ridge and Lasso Regression with Partially Observed Attributes", "abstract": "We consider the most common variants of linear regression, including Ridge, Lasso and Support-vector regression, in a setting where the learner is allowed to observe only a fixed number of attributes of each example at training time. We present simple and efficient algorithms for these problems: for Lasso and Ridge regression they need the same total number of attributes (up to constants) as do full-information algorithms, for reaching a certain accuracy. For Support-vector regression, we require exponentially less attributes compared to the state of the art. By that, we resolve an open problem recently posed by Cesa-Bianchi et al. (2010). Experiments show the theoretical bounds to be justified by superior performance compared to the state of the art.", "published": "2011-08-23T11:52:35Z", "authors": ["Elad Hazan", "Tomer Koren"], "categories": ["cs.LG"], "doi": "", "_query": "hedonic regression housing", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/1905.09371v3", "title": "Restricted Spatial Regression Methods: Implications for Inference", "abstract": "The issue of spatial confounding between the spatial random effect and the fixed effects in regression analyses has been identified as a concern in the statistical literature. Multiple authors have offered perspectives and potential solutions. In this paper, for the areal spatial data setting, we show that many of the methods designed to alleviate spatial confounding can be viewed as special cases of a general class of models. We refer to this class as Restricted Spatial Regression (RSR) models, extending terminology currently in use. We offer a mathematically based exploration of the impact that RSR methods have on inference for regression coefficients for the linear model. We then explore whether these results hold in the generalized linear model setting for count data using simulations. We show that the use of these methods have counterintuitive consequences which defy the general expectations in the literature. In particular, our results and the accompanying simulations suggest that RSR methods will typically perform worse than non-spatial methods. These results have important implications for dimension reduction strategies in spatial regression modeling. Specifically, we demonstrate that the problems with RSR models cannot be fixed with a selection of \"better\" spatial basis vectors or dimension reduction techniques.", "published": "2019-05-22T21:23:45Z", "authors": ["Kori Khan", "Catherine A. Calder"], "categories": ["stat.ME", "math.ST"], "doi": "10.1080/01621459.2020.1788949", "_query": "hedonic regression housing", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/1707.06261v2", "title": "Non-Asymptotic Uniform Rates of Consistency for k-NN Regression", "abstract": "We derive high-probability finite-sample uniform rates of consistency for $k$-NN regression that are optimal up to logarithmic factors under mild assumptions. We moreover show that $k$-NN regression adapts to an unknown lower intrinsic dimension automatically. We then apply the $k$-NN regression rates to establish new results about estimating the level sets and global maxima of a function from noisy observations.", "published": "2017-07-19T18:56:04Z", "authors": ["Heinrich Jiang"], "categories": ["stat.ML", "cs.LG"], "doi": "", "_query": "hedonic regression housing", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/1207.3975v3", "title": "Adaptive confidence bands in the nonparametric fixed design regression model", "abstract": "In this note, we consider the problem of existence of adaptive confidence bands in the fixed design regression model, adapting ideas in Hoffmann and Nickl (2011) to the present case. In the course of the proof, we show that sup-norm adaptive estimators exist as well in regression.", "published": "2012-07-17T12:34:53Z", "authors": ["Pierre-Yves Massé", "William Meiniel"], "categories": ["math.ST"], "doi": "", "_query": "hedonic regression housing", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2003.00117v1", "title": "Simultaneous confidence bands for nonparametric regression with partially missing covariates", "abstract": "In this paper, we consider a weighted local linear estimator based on the inverse selection probability for nonparametric regression with missing covariates at random. The asymptotic distribution of the maximal deviation between the estimator and the true regression function is derived and an asymptotically accurate simultaneous confidence band is constructed. The estimator for the regression function is shown to be oracally efficient in the sense that it is uniformly indistinguishable from that when the selection probabilities are known. Finite sample performance is examined via simulation studies which support our asymptotic theory. The proposed method is demonstrated via an analysis of a data set from the Canada 2010/2011 Youth Student Survey.", "published": "2020-02-28T23:32:07Z", "authors": ["Li Cai", "Lijie Gu", "Qihua Wang", "Suojin Wang"], "categories": ["stat.ME"], "doi": "", "_query": "hedonic regression housing", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/1603.07895v1", "title": "Lattice Designs in Standard and Simple Implicit Multi-linear Regression", "abstract": "Statisticians generally use ordinary least squares to minimize the random error in a subject response with respect to independent explanatory variable. However, Wooten shows illustrates how ordinary least squares can be used to minimize the random error in the system without defining a subject response. Using lattice design Wooten shows that non-response analysis is a superior alternative rotation of the pyramidal relationship between random variables and parameter estimates in multi-linear regression. Non-Response Analysis for simple linear co-linearity and Rotational Analysis in Simple Linear Regression challenge the notion of fixed effects; unity is included as a random measure (variable). The illustrations using lattice designs a mean operator that generates the standard mean and the self-weighing mean, among other point estimates with random weights; and a join that illustrates variance and covariance; and develops the measures of variance referred to as internal co-variance and base variance. These concepts are used to illustrate how these measures are used to evaluate parameter estimates in standard simple linear regression and simple implicit regression (non-response and rotational). The resulting analysis of these lattice designs show standard simple linear regression limits the relationship by consider the variance in one direction as relating to the two adjacent co-variances (standard and internal) whereas non-response analysis defines the relationship in terms of the internal co-variances and the base variance.", "published": "2016-03-25T12:40:44Z", "authors": ["Rebecca D. Wooten"], "categories": ["stat.ME"], "doi": "", "_query": "hedonic regression housing", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2608.18402v2", "title": "Algorithms for adaptive and heteroskedastic linear regression at the computational threshold", "abstract": "We study finite-sample linear regression in the presence of varied and unknown label noise, focusing on the heteroskedastic and adaptive linear regression models. Heteroskedastic linear regression models settings where the labels are of varying quality. We receive $n$ pairs $(X_i,Y_i)$ with labels $Y_i=X_i^\\topβ+\\varepsilon_i$, where $\\varepsilon_i\\sim N(0,σ_i^2)$ and the variances are unknown to the estimator. One natural measurement of the difficulty of this problem is the number of samples $m$ for which $σ_i^2\\le1$ (larger $m$ is easier). We obtain a polynomial-time estimator with rate $\\tilde{O}((nd^3/m^4)^{1/6})$ when $m\\gg d^{3/4}n^{1/4}$, as well as nearly-matching lower bounds. For $d=O(1)$, our estimator achieves error $o(1)$ when $m\\gg n^{1/4}$, whereas $L_1$ regression and other traditional approaches require $m\\gg n^{1/2}$. In adaptive linear regression, the errors are drawn i.i.d. from an unknown distribution $p$, and our goal is to design a generic estimator that performs nearly as well as the best custom estimator that knows $p$. We introduce a (computationally inefficient) adaptive estimator that, so long as $p$ is a mixture of $k$ symmetric log-concave densities, achieves error comparable with the optimal estimator that knows $p$ and has $\\tildeΘ(n/k)$ samples. For $k=1$, we show that $L_q$ regression (with data-dependent $q$) gives a polynomial-time estimator. Finally, to study the computational limits of both problems, we introduce the planted linear regression problem, where $X_i\\sim N(0,I_d)$, $m$ unknown samples are noiseless, and the rest have error $\\varepsilon_i\\sim N(0,1)$. We conjecture that recovering $β$ up to error $\\ll\\sqrt{d/n}$ (or exactly) may have an information-computation gap between $m=d+1$ and $m\\sim d^{3/4}n^{1/4}$, as is suggested by our near-matching polynomial-time estimator and statistical query (SQ) lower bound.", "published": "2026-08-19T00:17:22Z", "authors": ["Spencer Compton", "Tselil Schramm"], "categories": ["math.ST", "cs.DS", "cs.LG"], "doi": "", "_query": "hedonic regression housing", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/1806.06799v1", "title": "Quantile Regression of Latent Longitudinal Trajectory Features", "abstract": "Quantile regression has demonstrated promising utility in longitudinal data analysis. Existing work is primarily focused on modeling cross-sectional outcomes, while outcome trajectories often carry more substantive information in practice. In this work, we develop a trajectory quantile regression framework that is designed to robustly and flexibly investigate how latent individual trajectory features are related to observed subject characteristics. The proposed models are built under modeling with usual parametric assumptions lifted or relaxed. We derive our estimation procedure by novelly transforming the problem at hand to quantile regression with perturbed responses and adapting the bias correction technique for handling covariate measurement errors. We establish desirable asymptotic properties of the proposed estimator, including uniform consistency and weak convergence. Extensive simulation studies confirm the validity of the proposed method as well as its robustness. An application to the DURABLE trial uncovers sensible scientific findings and illustrates the practical value of our proposals.", "published": "2018-06-18T16:10:22Z", "authors": ["Huijuan Ma", "Limin Peng", "Haoda Fu"], "categories": ["stat.ME"], "doi": "", "_query": "hedonic regression housing", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/1506.05173v2", "title": "Feature Selection for Ridge Regression with Provable Guarantees", "abstract": "We introduce single-set spectral sparsification as a deterministic sampling based feature selection technique for regularized least squares classification, which is the classification analogue to ridge regression. The method is unsupervised and gives worst-case guarantees of the generalization power of the classification function after feature selection with respect to the classification function obtained using all features. We also introduce leverage-score sampling as an unsupervised randomized feature selection method for ridge regression. We provide risk bounds for both single-set spectral sparsification and leverage-score sampling on ridge regression in the fixed design setting and show that the risk in the sampled space is comparable to the risk in the full-feature space. We perform experiments on synthetic and real-world datasets, namely a subset of TechTC-300 datasets, to support our theory. Experimental results indicate that the proposed methods perform better than the existing feature selection methods.", "published": "2015-06-17T00:05:04Z", "authors": ["Saurabh Paul", "Petros Drineas"], "categories": ["stat.ML", "cs.IT", "cs.LG"], "doi": "", "_query": "hedonic regression housing", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2407.15905v1", "title": "A divide-and-conquer approach for spatio-temporal analysis of large house price data from Greater London", "abstract": "Statistical research in real estate markets, particularly in understanding the spatio-temporal dynamics of house prices, has garnered significant attention in recent times. Although Bayesian methods are common in spatio-temporal modeling, standard Markov chain Monte Carlo (MCMC) techniques are usually slow for large datasets such as house price data. To tackle this problem, we propose a divide-and-conquer spatio-temporal modeling approach. This method involves partitioning the data into multiple subsets and applying an appropriate Gaussian process model to each subset in parallel. The results from each subset are then combined using the Wasserstein barycenter technique to obtain the global parameters for the original problem. The proposed methodology allows for multiple observations per spatial and time unit, thereby offering added benefits for practitioners. As a real-life application, we analyze house price data of more than 0.6 million transactions from 983 middle layer super output areas in London over a period of eight years. The methodology provides insightful findings about the effects of various amenities, trend patterns, and the relationship between prices and carbon emissions. Furthermore, as demonstrated through a cross-validation study, it shows good predictive accuracy while balancing computational efficiency.", "published": "2024-07-22T14:29:13Z", "authors": ["Kapil Gupta", "Soudeep Deb"], "categories": ["stat.AP"], "doi": "", "_query": "hedonic regression housing", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/1312.2789v1", "title": "Performance Analysis Of Regularized Linear Regression Models For Oxazolines And Oxazoles Derivitive Descriptor Dataset", "abstract": "Regularized regression techniques for linear regression have been created the last few ten years to reduce the flaws of ordinary least squares regression with regard to prediction accuracy. In this paper, new methods for using regularized regression in model choice are introduced, and we distinguish the conditions in which regularized regression develops our ability to discriminate models. We applied all the five methods that use penalty-based (regularization) shrinkage to handle Oxazolines and Oxazoles derivatives descriptor dataset with far more predictors than observations. The lasso, ridge, elasticnet, lars and relaxed lasso further possess the desirable property that they simultaneously select relevant predictive descriptors and optimally estimate their effects. Here, we comparatively evaluate the performance of five regularized linear regression methods The assessment of the performance of each model by means of benchmark experiments is an established exercise. Cross-validation and resampling methods are generally used to arrive point evaluates the efficiencies which are compared to recognize methods with acceptable features. Predictive accuracy was evaluated using the root mean squared error (RMSE) and Square of usual correlation between predictors and observed mean inhibitory concentration of antitubercular activity (R square). We found that all five regularized regression models were able to produce feasible models and efficient capturing the linearity in the data. The elastic net and lars had similar accuracies as well as lasso and relaxed lasso had similar accuracies but outperformed ridge regression in terms of the RMSE and R square metrics.", "published": "2013-12-10T13:16:02Z", "authors": [" Doreswamy", "Chanabasayya . M. Vastrad"], "categories": ["cs.LG"], "doi": "10.5121/ijcsity.2013.1408", "_query": "hedonic regression housing", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2003.01296v1", "title": "Regression via Implicit Models and Optimal Transport Cost Minimization", "abstract": "This paper addresses the classic problem of regression, which involves the inductive learning of a map, $y=f(x,z)$, $z$ denoting noise, $f:\\mathbb{R}^n\\times \\mathbb{R}^k \\rightarrow \\mathbb{R}^m$. Recently, Conditional GAN (CGAN) has been applied for regression and has shown to be advantageous over the other standard approaches like Gaussian Process Regression, given its ability to implicitly model complex noise forms. However, the current CGAN implementation for regression uses the classical generator-discriminator architecture with the minimax optimization approach, which is notorious for being difficult to train due to issues like training instability or failure to converge. In this paper, we take another step towards regression models that implicitly model the noise, and propose a solution which directly optimizes the optimal transport cost between the true probability distribution $p(y|x)$ and the estimated distribution $\\hat{p}(y|x)$ and does not suffer from the issues associated with the minimax approach. On a variety of synthetic and real-world datasets, our proposed solution achieves state-of-the-art results. The code accompanying this paper is available at \"https://github.com/gurdaspuriya/ot_regression\".", "published": "2020-03-03T02:26:54Z", "authors": ["Saurav Manchanda", "Khoa Doan", "Pranjul Yadav", "S. Sathiya Keerthi"], "categories": ["cs.LG", "stat.ML"], "doi": "", "_query": "hedonic regression housing", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2306.16838v7", "title": "Fast Robust Kernel Regression through Sign Gradient Descent with Early Stopping", "abstract": "Kernel ridge regression, KRR, is a generalization of linear ridge regression that is non-linear in the data, but linear in the model parameters. Here, we introduce an equivalent formulation of the objective function of KRR, which opens up for replacing the ridge penalty with the $\\ell_\\infty$ and $\\ell_1$ penalties. Using the $\\ell_\\infty$ and $\\ell_1$ penalties, we obtain robust and sparse kernel regression, respectively. We study the similarities between explicitly regularized kernel regression and the solutions obtained by early stopping of iterative gradient-based methods, where we connect $\\ell_\\infty$ regularization to sign gradient descent, $\\ell_1$ regularization to forward stagewise regression (also known as coordinate descent), and $\\ell_2$ regularization to gradient descent, and, in the last case, theoretically bound for the differences. We exploit the close relations between $\\ell_\\infty$ regularization and sign gradient descent, and between $\\ell_1$ regularization and coordinate descent to propose computationally efficient methods for robust and sparse kernel regression. We finally compare robust kernel regression through sign gradient descent to existing methods for robust kernel regression on five real data sets, demonstrating that our method is one to two orders of magnitude faster, without compromised accuracy.", "published": "2023-06-29T10:29:29Z", "authors": ["Oskar Allerbo"], "categories": ["stat.ML", "cs.LG", "math.OC", "stat.ME"], "doi": "10.1214/25-EJS2361", "_query": "hedonic regression housing", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2111.02749v2", "title": "Label Ranking through Nonparametric Regression", "abstract": "Label Ranking (LR) corresponds to the problem of learning a hypothesis that maps features to rankings over a finite set of labels. We adopt a nonparametric regression approach to LR and obtain theoretical performance guarantees for this fundamental practical problem. We introduce a generative model for Label Ranking, in noiseless and noisy nonparametric regression settings, and provide sample complexity bounds for learning algorithms in both cases. In the noiseless setting, we study the LR problem with full rankings and provide computationally efficient algorithms using decision trees and random forests in the high-dimensional regime. In the noisy setting, we consider the more general cases of LR with incomplete and partial rankings from a statistical viewpoint and obtain sample complexity bounds using the One-Versus-One approach of multiclass classification. Finally, we complement our theoretical contributions with experiments, aiming to understand how the input regression noise affects the observed output.", "published": "2021-11-04T10:59:46Z", "authors": ["Dimitris Fotakis", "Alkis Kalavasis", "Eleni Psaroudaki"], "categories": ["cs.LG"], "doi": "", "_query": "hedonic regression housing", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/1402.5584v1", "title": "Path Thresholding: Asymptotically Tuning-Free High-Dimensional Sparse Regression", "abstract": "In this paper, we address the challenging problem of selecting tuning parameters for high-dimensional sparse regression. We propose a simple and computationally efficient method, called path thresholding (PaTh), that transforms any tuning parameter-dependent sparse regression algorithm into an asymptotically tuning-free sparse regression algorithm. More specifically, we prove that, as the problem size becomes large (in the number of variables and in the number of observations), PaTh performs accurate sparse regression, under appropriate conditions, without specifying a tuning parameter. In finite-dimensional settings, we demonstrate that PaTh can alleviate the computational burden of model selection algorithms by significantly reducing the search space of tuning parameters.", "published": "2014-02-23T07:23:39Z", "authors": ["Divyanshu Vats", "Richard G. Baraniuk"], "categories": ["math.ST", "cs.IT", "stat.ML"], "doi": "", "_query": "hedonic regression housing", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2209.09647v1", "title": "A Multi-Layer Regression based Predicable Function Fitting Network", "abstract": "Function plays an important role in mathematics and many science branches. As the fast development of computer technology, more and more study on computational function analysis, e.g., Fast Fourier Transform, Wavelet Transform, Curve Function, are presented in these years. However, there are two main problems in these approaches: 1) hard to handle the complex functions of stationary and non-stationary, periodic and non-periodic, high order and low order; 2) hard to generalize the fitting functions from training data to test data. In this paper, a multiple regression based function fitting network that solves the two main problems is introduced as a predicable function fitting technique. This technique constructs the network includes three main parts: 1) the stationary transform layer, 2) the feature encoding layers, and 3) the fine tuning regression layer. The stationary transform layer recognizes the order of input function data, and transforms non-stationary function to stationary function. The feature encoding layers encode the raw input sequential data to a novel linear regression feature that can capture both the structural and the temporal characters of the sequential data. The fine tuning regression layer then fits the features to the target ahead values. The fitting network with the linear regression feature layers and a non-linear regression layer come up with high quality fitting results and generalizable predictions. The experiments of both mathematic function examples and the real word function examples verifies the efficiency of the proposed technique.", "published": "2022-09-19T08:11:03Z", "authors": ["Changlin Wan", "Zhongzhi Shi"], "categories": ["cs.LG"], "doi": "", "_query": "hedonic regression housing", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/1802.05801v3", "title": "Uniform-in-Submodel Bounds for Linear Regression in a Model Free Framework", "abstract": "For the last two decades, high-dimensional data and methods have proliferated throughout the literature. Yet, the classical technique of linear regression has not lost its usefulness in applications. In fact, many high-dimensional estimation techniques can be seen as variable selection that leads to a smaller set of variables (a ``sub-model'') where classical linear regression applies. We analyze linear regression estimators resulting from model-selection by proving estimation error and linear representation bounds uniformly over sets of submodels. Based on deterministic inequalities, our results provide ``good'' rates when applied to both independent and dependent data. These results are useful in meaningfully interpreting the linear regression estimator obtained after exploring and reducing the variables and also in justifying post model-selection inference. All results are derived under no model assumptions and are non-asymptotic in nature.", "published": "2018-02-15T23:37:01Z", "authors": ["Arun Kumar Kuchibhotla", "Lawrence D. Brown", "Andreas Buja", "Edward I. George", "Linda Zhao"], "categories": ["math.ST"], "doi": "", "_query": "hedonic regression housing", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2503.17253v1", "title": "Integrated Subset Selection and Bandwidth Estimation Algorithm for Geographically Weighted Regression", "abstract": "This study proposes a mathematical programming-based algorithm for the integrated selection of variable subsets and bandwidth estimation in geographically weighted regression, a local regression method that allows the kernel bandwidth and regression coefficients to vary across study areas. Unlike standard approaches in the literature, in which bandwidth and regression parameters are estimated separately for each focal point on the basis of different criteria, our model uses a single objective function for the integrated estimation of regression and bandwidth parameters across all focal points, based on the regression likelihood function and variance modeling. The proposed model further integrates a procedure to select a single subset of independent variables for all focal points, whereas existing approaches may return heterogeneous subsets across focal points. We then propose an alternative direction method to solve the nonconvex mathematical model and show that it converges to a partial minimum. The computational experiment indicates that the proposed algorithm provides competitive explanatory power with stable spatially varying patterns, with the ability to select the best subset and account for additional constraints.", "published": "2025-03-21T15:57:59Z", "authors": ["Hyunwoo Lee", "Young Woong Park"], "categories": ["stat.ME", "stat.ML"], "doi": "", "_query": "hedonic regression housing", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/1608.02143v2", "title": "Bayesian Sparse Linear Regression with Unknown Symmetric Error", "abstract": "We study full Bayesian procedures for sparse linear regression when errors have a symmetric but otherwise unknown distribution. The unknown error distribution is endowed with a symmetrized Dirichlet process mixture of Gaussians. For the prior on regression coefficients, a mixture of point masses at zero and continuous distributions is considered. We study behavior of the posterior with diverging number of predictors. Conditions are provided for consistency in the mean Hellinger distance. The compatibility and restricted eigenvalue conditions yield the minimax convergence rate of the regression coefficients in $\\ell_1$- and $\\ell_2$-norms, respectively. The convergence rate is adaptive to both the unknown sparsity level and the unknown symmetric error density under compatibility conditions. In addition, strong model selection consistency and a semi-parametric Bernstein-von Mises theorem are proven under slightly stronger conditions.", "published": "2016-08-06T18:57:09Z", "authors": ["Minwoo Chae", "Lizhen Lin", "David B. Dunson"], "categories": ["math.ST"], "doi": "10.1093/imaiai/iay022", "_query": "hedonic regression housing", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2508.12391v2", "title": "Asymptotic confidence bands for the histogram regression estimator", "abstract": "Asymptotic uniform confidence bands are constructed for a multivariate nonparametric regression model with heteroscedastic noise, employing histogram estimators under flexible partition conditions. The construction is especially applicable to unsmooth regression functions of Hölder regularity less than one. While the radius of the confidence bands could be approximated via the Gumbel distribution, our construction does not depend on an extreme value distribution, but instead can be explicitly calculated for the chosen partition.", "published": "2025-08-17T15:07:00Z", "authors": ["Natalie Neumeyer", "Jan Rabe", "Mathias Trabs"], "categories": ["math.ST", "stat.ME"], "doi": "", "_query": "hedonic regression housing", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/math/0702650v1", "title": "Prediction in functional linear regression", "abstract": "There has been substantial recent work on methods for estimating the slope function in linear regression for functional data analysis. However, as in the case of more conventional finite-dimensional regression, much of the practical interest in the slope centers on its application for the purpose of prediction, rather than on its significance in its own right. We show that the problems of slope-function estimation, and of prediction from an estimator of the slope function, have very different characteristics. While the former is intrinsically nonparametric, the latter can be either nonparametric or semiparametric. In particular, the optimal mean-square convergence rate of predictors is $n^{-1}$, where $n$ denotes sample size, if the predictand is a sufficiently smooth function. In other cases, convergence occurs at a polynomial rate that is strictly slower than $n^{-1}$. At the boundary between these two regimes, the mean-square convergence rate is less than $n^{-1}$ by only a logarithmic factor. More generally, the rate of convergence of the predicted value of the mean response in the regression model, given a particular value of the explanatory variable, is determined by a subtle interaction among the smoothness of the predictand, of the slope function in the model, and of the autocovariance function for the distribution of explanatory variables.", "published": "2007-02-22T12:42:06Z", "authors": ["T. Tony Cai", "Peter Hall"], "categories": ["math.ST"], "doi": "10.1214/009053606000000830", "_query": "hedonic regression housing", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/1808.04245v1", "title": "Active Learning for Regression Using Greedy Sampling", "abstract": "Regression problems are pervasive in real-world applications. Generally a substantial amount of labeled samples are needed to build a regression model with good generalization ability. However, many times it is relatively easy to collect a large number of unlabeled samples, but time-consuming or expensive to label them. Active learning for regression (ALR) is a methodology to reduce the number of labeled samples, by selecting the most beneficial ones to label, instead of random selection. This paper proposes two new ALR approaches based on greedy sampling (GS). The first approach (GSy) selects new samples to increase the diversity in the output space, and the second (iGS) selects new samples to increase the diversity in both input and output spaces. Extensive experiments on 12 UCI and CMU StatLib datasets from various domains, and on 15 subjects on EEG-based driver drowsiness estimation, verified their effectiveness and robustness.", "published": "2018-08-08T22:29:19Z", "authors": ["Dongrui Wu", "Chin-Teng Lin", "Jian Huang"], "categories": ["cs.LG", "cs.AI", "stat.ML"], "doi": "", "_query": "hedonic regression housing", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2311.09145v1", "title": "Model Agnostic Explainable Selective Regression via Uncertainty Estimation", "abstract": "With the wide adoption of machine learning techniques, requirements have evolved beyond sheer high performance, often requiring models to be trustworthy. A common approach to increase the trustworthiness of such systems is to allow them to refrain from predicting. Such a framework is known as selective prediction. While selective prediction for classification tasks has been widely analyzed, the problem of selective regression is understudied. This paper presents a novel approach to selective regression that utilizes model-agnostic non-parametric uncertainty estimation. Our proposed framework showcases superior performance compared to state-of-the-art selective regressors, as demonstrated through comprehensive benchmarking on 69 datasets. Finally, we use explainable AI techniques to gain an understanding of the drivers behind selective regression. We implement our selective regression method in the open-source Python package doubt and release the code used to reproduce our experiments.", "published": "2023-11-15T17:40:48Z", "authors": ["Andrea Pugnana", "Carlos Mougan", "Dan Saattrup Nielsen"], "categories": ["cs.LG", "stat.ML"], "doi": "", "_query": "hedonic regression housing", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/1207.5378v1", "title": "Bayesian empirical likelihood for quantile regression", "abstract": "Bayesian inference provides a flexible way of combining data with prior information. However, quantile regression is not equipped with a parametric likelihood, and therefore, Bayesian inference for quantile regression demands careful investigation. This paper considers the Bayesian empirical likelihood approach to quantile regression. Taking the empirical likelihood into a Bayesian framework, we show that the resultant posterior from any fixed prior is asymptotically normal; its mean shrinks toward the true parameter values, and its variance approaches that of the maximum empirical likelihood estimator. A more interesting case can be made for the Bayesian empirical likelihood when informative priors are used to explore commonality across quantiles. Regression quantiles that are computed separately at each percentile level tend to be highly variable in the data sparse areas (e.g., high or low percentile levels). Through empirical likelihood, the proposed method enables us to explore various forms of commonality across quantiles for efficiency gains. By using an MCMC algorithm in the computation, we avoid the daunting task of directly maximizing empirical likelihood. The finite sample performance of the proposed method is investigated empirically, where substantial efficiency gains are demonstrated with informative priors on common features across several percentile levels. A theoretical framework of shrinking priors is used in the paper to better understand the power of the proposed method.", "published": "2012-07-23T13:02:16Z", "authors": ["Yunwen Yang", "Xuming He"], "categories": ["math.ST"], "doi": "10.1214/12-AOS1005", "_query": "hedonic regression housing", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/1608.06557v1", "title": "Neural Networks with Smooth Adaptive Activation Functions for Regression", "abstract": "In Neural Networks (NN), Adaptive Activation Functions (AAF) have parameters that control the shapes of activation functions. These parameters are trained along with other parameters in the NN. AAFs have improved performance of Neural Networks (NN) in multiple classification tasks. In this paper, we propose and apply AAFs on feedforward NNs for regression tasks. We argue that applying AAFs in the regression (second-to-last) layer of a NN can significantly decrease the bias of the regression NN. However, using existing AAFs may lead to overfitting. To address this problem, we propose a Smooth Adaptive Activation Function (SAAF) with piecewise polynomial form which can approximate any continuous function to arbitrary degree of error. NNs with SAAFs can avoid overfitting by simply regularizing the parameters. In particular, an NN with SAAFs is Lipschitz continuous given a bounded magnitude of the NN parameters. We prove an upper-bound for model complexity in terms of fat-shattering dimension for any Lipschitz continuous regression model. Thus, regularizing the parameters in NNs with SAAFs avoids overfitting. We empirically evaluated NNs with SAAFs and achieved state-of-the-art results on multiple regression datasets.", "published": "2016-08-23T15:56:08Z", "authors": ["Le Hou", "Dimitris Samaras", "Tahsin M. Kurc", "Yi Gao", "Joel H. Saltz"], "categories": ["cs.CV"], "doi": "", "_query": "hedonic regression housing", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/1903.11178v2", "title": "Localized Linear Regression in Networked Data", "abstract": "The network Lasso (nLasso) has been proposed recently as an efficient learning algorithm for massive networked data sets (big data over networks). It extends the well-known least absolute shrinkage and selection operator (Lasso) from learning sparse (generalized) linear models to network models. Efficient implementations of the nLasso have been obtained using convex optimization methods lending to scalable message passing protocols. In this paper, we analyze the statistical properties of nLasso when applied to localized linear regression problems involving networked data. Our main result is a sufficient condition on the network structure and available label information such that nLasso accurately learns a localized linear regression model from a few labeled data points. We also provide an implementation of nLasso for localized linear regression by specializing a primaldual method for solving the convex (non-smooth) nLasso problem.", "published": "2019-03-26T22:17:10Z", "authors": ["Alexander Jung", "Nguyen Tran"], "categories": ["cs.LG", "stat.ML"], "doi": "10.1109/LSP.2019.2918933", "_query": "hedonic regression housing", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2311.10076v1", "title": "A decorrelation method for general regression adjustment in randomized experiments", "abstract": "We study regression adjustment with general function class approximations for estimating the average treatment effect in the design-based setting. Standard regression adjustment involves bias due to sample re-use, and this bias leads to behavior that is sub-optimal in the sample size, and/or imposes restrictive assumptions. Our main contribution is to introduce a novel decorrelation-based approach that circumvents these issues. We prove guarantees, both asymptotic and non-asymptotic, relative to the oracle functions that are targeted by a given regression adjustment procedure. We illustrate our method by applying it to various high-dimensional and non-parametric problems, exhibiting improved sample complexity and weakened assumptions relative to known approaches.", "published": "2023-11-16T18:30:27Z", "authors": ["Fangzhou Su", "Wenlong Mou", "Peng Ding", "Martin J. Wainwright"], "categories": ["stat.ME", "math.ST"], "doi": "", "_query": "hedonic regression housing", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2011.14330v3", "title": "A Boundary Regression Model for Nested Named Entity Recognition", "abstract": "Recognizing named entities (NEs) is commonly conducted as a classification problem that predicts a class tag for a word or a NE candidate in a sentence. In shallow structures, categorized features are weighted to support the prediction. Recent developments in neural networks have adopted deep structures that map categorized features into continuous representations. This approach unfolds a dense space saturated with high-order abstract semantic information, where the prediction is based on distributed feature representations. In this paper, positions of NEs in a sentence are represented as continuous values. Then, a regression operation is introduced to regress boundaries of NEs in a sentence. Based on boundary regression, we design a boundary regression model to support nested NE recognition. It is a multiobjective learning framework, which simultaneously predicts the classification score of a NE candidate and refine its spatial location in a sentence. It has the advantage to resolve nested NEs and support boundary regression for locating NEs in a sntence. By sharing parameters for predicting and locating, this model enables more potent nonlinear function approximators to enhance model discriminability. Experiments demonstrate state-of-the-art performance for nested NE recognition\\footnote{Our codes to implement the BR model are available at: \\url{https://github.com/wuyuefei3/BR}.}.", "published": "2020-11-29T10:04:38Z", "authors": ["Yanping Chen", "Lefei Wu", "Qinghua Zheng", "Ruizhang Huang", "Jun Liu", "Liyuan Deng", "Junhui Yu", "Yongbin Qing", "Bo Dong", "Ping Chen"], "categories": ["cs.CL", "cs.AI"], "doi": "", "_query": "hedonic regression housing", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/1906.10221v1", "title": "Parametric versus Semi and Nonparametric Regression Models", "abstract": "Three types of regression models researchers need to be familiar with and know the requirements of each: parametric, semiparametric and nonparametric regression models. The type of modeling used is based on how much information are available about the form of the relationship between response variable and explanatory variables, and the random error distribution. In this article, differences between models, common methods of estimation, robust estimation, and applications are introduced. The R code for all the graphs and analyses presented here, in this article, is available in the Appendix.", "published": "2019-06-24T20:29:07Z", "authors": ["Hamdy F. F. Mahmoud"], "categories": ["stat.ME"], "doi": "", "_query": "hedonic regression housing", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/1405.6447v2", "title": "An Ordered Lasso and Sparse Time-Lagged Regression", "abstract": "We consider regression scenarios where it is natural to impose an order constraint on the coefficients. We propose an order-constrained version of L1-regularized regression for this problem, and show how to solve it efficiently using the well-known Pool Adjacent Violators Algorithm as its proximal operator. The main application of this idea is time-lagged regression, where we predict an outcome at time t from features at the previous K time points. In this setting it is natural to assume that the coefficients decay as we move farther away from t, and hence the order constraint is reasonable. Potential applications include financial time series and prediction of dynamic patient out- comes based on clinical measurements. We illustrate this idea on real and simulated data.", "published": "2014-05-26T01:37:21Z", "authors": ["Xiaotong Suo", "Robert Tibshirani"], "categories": ["stat.AP", "stat.ML"], "doi": "10.1080/00401706.2015.1079245", "_query": "hedonic regression housing", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/1808.07563v3", "title": "Optimizing the tie-breaker regression discontinuity design", "abstract": "Motivated by customer loyalty plans and scholarship programs, we study tie-breaker designs which are hybrids of randomized controlled trials (RCTs) and regression discontinuity designs (RDDs). We quantify the statistical efficiency of a tie-breaker design in which a proportion $Δ$ of observed subjects are in the RCT. In a two line regression, statistical efficiency increases monotonically with $Δ$, so efficiency is maximized by an RCT. We point to additional advantages of tie-breakers versus RDD: for a nonparametric regression the boundary bias is much less severe and for quadratic regression, the variance is greatly reduced. For a two line model we can quantify the short term value of the treatment allocation and this comparison favors smaller $Δ$ with the RDD being best. We solve for the optimal tradeoff between these exploration and exploitation goals. The usual tie-breaker design applies an RCT on the middle $Δ$ subjects as ranked by the assignment variable. We quantify the efficiency of other designs such as experimenting only in the second decile from the top. We also show that in some general parametric models a Monte Carlo evaluation can be replaced by matrix algebra.", "published": "2018-08-22T20:57:38Z", "authors": ["Art B. Owen", "Hal Varian"], "categories": ["stat.ME", "econ.EM"], "doi": "", "_query": "hedonic regression housing", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/1206.3268v1", "title": "Feature Selection via Block-Regularized Regression", "abstract": "Identifying co-varying causal elements in very high dimensional feature space with internal structures, e.g., a space with as many as millions of linearly ordered features, as one typically encounters in problems such as whole genome association (WGA) mapping, remains an open problem in statistical learning. We propose a block-regularized regression model for sparse variable selection in a high-dimensional space where the covariates are linearly ordered, and are possibly subject to local statistical linkages (e.g., block structures) due to spacial or temporal proximity of the features. Our goal is to identify a small subset of relevant covariates that are not merely from random positions in the ordering, but grouped as contiguous blocks from large number of ordered covariates. Following a typical linear regression framework between the features and the response, our proposed model employs a sparsity-enforcing Laplacian prior for the regression coefficients, augmented by a 1st-order Markovian process along the feature sequence that \"activates\" the regression coefficients in a coupled fashion. We describe a sampling-based learning algorithm and demonstrate the performance of our method on simulated and biological data for marker identification under WGA.", "published": "2012-06-13T15:37:09Z", "authors": ["Seyoung Kim", "Eric P. Xing"], "categories": ["stat.ME"], "doi": "", "_query": "hedonic regression housing", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2609.29249v1", "title": "Generic Covariate Adjustment for Regression Discontinuity Designs", "abstract": "It is standard practice to include covariates in regression discontinuity designs (RDDs) and regression kink designs (RKDs), but the theoretical justification for doing so does not generally extend beyond linear estimands. This paper proposes a novel entropy balancing reweighting approach for covariate adjustment within a general framework of RDDs and RKDs. While conventional regression-based covariate adjustment methods generally fail to deliver consistent estimation for nonlinear estimands such as quantile treatment effects, our reweighting approach achieves consistency while improving efficiency. Moreover, even in settings where the regression-based covariate adjustment method already improves efficiency, our approach can deliver additional efficiency gains. Simulation studies corroborate these theoretical findings. We present an empirical application in which our covariate adjustment yields statistically significant results that would not be obtained without covariate adjustment.", "published": "2026-09-24T08:54:16Z", "authors": ["Jun Ma", "Yuya Sasaki", "Zhengfei Yu"], "categories": ["econ.EM"], "doi": "", "_query": "hedonic regression housing", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2110.00921v2", "title": "Hierarchical Gaussian Process Models for Regression Discontinuity/Kink under Sharp and Fuzzy Designs", "abstract": "We propose nonparametric Bayesian estimators for causal inference exploiting Regression Discontinuity/Kink (RD/RK) under sharp and fuzzy designs. Our estimators are based on Gaussian Process (GP) regression and classification. The GP methods are powerful probabilistic machine learning approaches that are advantageous in terms of derivative estimation and uncertainty quantification, facilitating RK estimation and inference of RD/RK models. These estimators are extended to hierarchical GP models with an intermediate Bayesian neural network layer and can be characterized as hybrid deep learning models. Monte Carlo simulations show that our estimators perform comparably to and sometimes better than competing estimators in terms of precision, coverage and interval length. The hierarchical GP models considerably improve upon one-layer GP models. We apply the proposed methods to estimate the incumbency advantage of US house elections. Our estimations suggest a significant incumbency advantage in terms of both vote share and probability of winning in the next elections. Lastly we present an extension to accommodate covariate adjustment.", "published": "2021-10-03T04:23:56Z", "authors": ["Ximing Wu"], "categories": ["stat.ML", "cs.LG", "econ.EM"], "doi": "", "_query": "hedonic regression housing", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2505.09620v1", "title": "The impact of economic policies on housing prices. Approximations and predictions in the UK, the US, France, and Switzerland from the 1980s to today", "abstract": "I show that house prices can be modeled using machine learning (kNN and tree-bagging) and a small dataset composed of macro-economic factors (MEF), including an inflation metric (CPI), US treasury rates (10-yr), Gross Domestic Product (GDP), and portfolio size of central banks (ECB, FED). This set of parameters covers all the parties involved in a transaction (buyer, seller, and financing facility) while ignoring the intrinsic properties of each asset and encompassing local (inflation) and liquidity issues that may impede each transaction composing a market. The model here takes the point of view of a real estate trader who is interested in both the financing and the price of the transaction. Machine Learning allows for the discrimination of two periods within the dataset. Unconventional policies of central banks may have allowed some institutional investors to arbitrage between real estate returns and other bond markets (sovereign and corporate). Finally, to assess the models' relative performances, I performed various sensitivity tests, which tend to constrain the possibilities of each approach for each need. I also show that some models can predict the evolution of prices over the next 4 quarters with uncertainties that outperform existing index uncertainties.", "published": "2025-04-10T12:46:54Z", "authors": ["Nicolas Houlié"], "categories": ["q-fin.ST"], "doi": "", "_query": "hedonic regression housing", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2305.00660v2", "title": "An Iterative Algorithm for Rescaled Hyperbolic Functions Regression", "abstract": "Large language models (LLMs) have numerous real-life applications across various domains, such as natural language translation, sentiment analysis, language modeling, chatbots and conversational agents, creative writing, text classification, summarization, and generation. LLMs have shown great promise in improving the accuracy and efficiency of these tasks, and have the potential to revolutionize the field of natural language processing (NLP) in the years to come. Exponential function based attention unit is a fundamental element in LLMs. Several previous works have studied the convergence of exponential regression and softmax regression. In this paper, we propose an iterative algorithm to solve a rescaled version of the slightly different formulation of the softmax regression problem that arises in attention mechanisms of large language models. Specifically, we consider minimizing the squared loss between a certain function, which can be either the exponential function, hyperbolic sine function, or hyperbolic cosine function, and its inner product with a target $n$-dimensional vector $b$, scaled by the normalization term. This ``rescaled softmax regression'' differs from classical softmax regression in the location of the normalization factor. The efficiency and generalizability of this framework to multiple hyperbolic functions make it relevant for optimizing attention mechanisms. The analysis also leads to a corollary bounding solution changes under small perturbations for in-context learning. Limitations and societal impact are discussed.", "published": "2023-05-01T05:16:07Z", "authors": ["Yeqi Gao", "Zhao Song", "Junze Yin"], "categories": ["cs.LG", "stat.ML"], "doi": "", "_query": "hedonic regression housing", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2501.14791v1", "title": "On the super-efficiency and robustness of the least squares of depth-trimmed regression estimator", "abstract": "The least squares of depth-trimmed (LST) residuals regression, proposed and studied in Zuo and Zuo (2023), serves as a robust alternative to the classic least squares (LS) regression as well as a strong competitor to the renowned robust least trimmed squares (LTS) regression of Rousseeuw (1984). The aim of this article is three-fold. (i) to reveal the super-efficiency of the LST and demonstrate it can be as efficient as (or even more efficient than) the LS in the scenarios with errors uncorrelated and mean zero and homoscedastic with finite variance and to explain this anti-Gaussian-Markov-Theorem phenomenon; (ii) to demonstrate that the LST can outperform the LTS, the benchmark of robust regression estimator, on robustness, and the MM of Yohai (1987), the benchmark of efficient and robust estimator, on both efficiency and robustness, consequently, could serve as an alternative to both; (iii) to promote the implementation and computation of the LST regression for a broad group of statisticians in statistical practice and to demonstrate that it can be computed as fast as (or even faster than) the LTS based on a newly improved algorithm.", "published": "2025-01-10T00:30:08Z", "authors": ["Yijun Zuo", "Hanwen Zuo"], "categories": ["stat.AP"], "doi": "", "_query": "hedonic regression housing", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2007.10052v1", "title": "Cephalometric Landmark Regression with Convolutional Neural Networks on 3D Computed Tomography Data", "abstract": "In this paper, we address the problem of automatic three-dimensional cephalometric analysis. Cephalometric analysis performed on lateral radiographs doesn't fully exploit the structure of 3D objects due to projection onto the lateral plane. With the development of three-dimensional imaging techniques such as CT, several analysis methods have been proposed that extend to the 3D case. The analysis based on these methods is invariant to rotations and translations and can describe difficult skull deformation, where 2D cephalometry has no use. In this paper, we provide a wide overview of existing approaches for cephalometric landmark regression. Moreover, we perform a series of experiments with state of the art 3D convolutional neural network (CNN) based methods for keypoint regression: direct regression with CNN, heatmap regression and Softargmax regression. For the first time, we extensively evaluate the described methods and demonstrate their effectiveness in the estimation of Frankfort Horizontal and cephalometric points locations for patients with severe skull deformations. We demonstrate that Heatmap and Softargmax regression models provide sufficient regression error for medical applications (less than 4 mm). Moreover, the Softargmax model achieves 1.15o inclination error for the Frankfort horizontal. For the fair comparison with the prior art, we also report results projected on the lateral plane.", "published": "2020-07-20T12:45:38Z", "authors": ["Dmitry Lachinov", "Alexandra Getmanskaya", "Vadim Turlapov"], "categories": ["cs.CV"], "doi": "", "_query": "hedonic regression housing", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/1104.1050v2", "title": "The Slope Heuristics in Heteroscedastic Regression", "abstract": "We consider the estimation of a regression function with random design and heteroscedastic noise in a nonparametric setting. More precisely, we address the problem of characterizing the optimal penalty when the regression function is estimated by using a penalized least-squares model selection method. In this context, we show the existence of a minimal penalty, defined to be the maximum level of penalization under which the model selection procedure totally misbehaves. The optimal penalty is shown to be twice the minimal one and to satisfy a non-asymptotic pathwise oracle inequality with leading constant almost one. Finally, the ideal penalty being unknown in general, we propose a hold-out penalization procedure and show that the latter is asymptotically optimal.", "published": "2011-04-06T10:00:37Z", "authors": ["Adrien Saumard"], "categories": ["math.ST"], "doi": "", "_query": "hedonic regression housing", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2608.00378v1", "title": "CASPER-Change-Aware Slice Prioritization for Efficient Regression Testing of LLM-based systems", "abstract": "Regression testing for LLM-based systems poses unique challenges because individual regression instances provide limited information about system-level regressions. A failure in a single instance does not necessarily indicate a meaningful regression or provide sufficient information to diagnose affected behaviors. Conversely, detecting regressions based only on overall system performance changes is too coarse-grained, as it does not identify which behaviors are affected. This motivates analyzing regression instances at an intermediate level through test suite slices. To address this challenge, we propose CASPER, a change-aware slice prioritization framework for efficient regression testing of prompt-level and model-level changes in LLM-based systems. CASPER first identifies regression slices containing semantically related instances with consistent performance characteristics using an evolutionary slice identification approach. Given a change to an LLM-based application, CASPER prioritizes slices according to their likelihood of regression using behavioral information extracted from execution logs. We instantiate CASPER in the software issue resolution domain and evaluate slice identification against clustering-based baselines and regressed slice prioritization against a random ranking baseline. Results show that CASPER generates more consistent slices while maintaining comparable or better semantic coherence and improves regressed slice prioritization across different LLM-based system changes and testing budgets.", "published": "2026-08-01T01:21:39Z", "authors": ["Biruk Asmare Muse", "Lionel Briand", "Yiwei Lu", "Keheliya Gallaba"], "categories": ["cs.SE"], "doi": "", "_query": "hedonic regression housing", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/0708.3777v1", "title": "Comment: Fisher Lecture: Dimension Reduction in Regression", "abstract": "Comment: Fisher Lecture: Dimension Reduction in Regression [arXiv:0708.3774]", "published": "2007-08-28T13:01:27Z", "authors": ["Bing Li"], "categories": ["stat.ME"], "doi": "10.1214/088342307000000069", "_query": "hedonic regression housing", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/1105.0875v2", "title": "A Risk Comparison of Ordinary Least Squares vs Ridge Regression", "abstract": "We compare the risk of ridge regression to a simple variant of ordinary least squares, in which one simply projects the data onto a finite dimensional subspace (as specified by a Principal Component Analysis) and then performs an ordinary (un-regularized) least squares regression in this subspace. This note shows that the risk of this ordinary least squares method is within a constant factor (namely 4) of the risk of ridge regression.", "published": "2011-05-04T17:25:52Z", "authors": ["Paramveer S. Dhillon", "Dean P. Foster", "Sham M. Kakade", "Lyle H. Ungar"], "categories": ["stat.ML"], "doi": "", "_query": "hedonic regression housing", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2606.07947v1", "title": "Bayesian Global Fréchet Regression via Weak Conditional Expectations", "abstract": "Fréchet regression provides a versatile framework for modeling responses in metric spaces with Euclidean predictors, yet current methodologies rely almost exclusively on frequentist approaches. We propose a Bayesian framework for Fréchet regression that offers a principled way of incorporating prior information into nonlinear global Fréchet regression. By targeting a novel Fréchet Bayes rule, we reduce the object-valued regression problem to a collection of tractable scalar regression tasks. Our approach allows for a controlled interpolation between the prior and the data-driven frequentist estimate, facilitating effective shrinkage toward informed values. While initially derived under Gaussian assumptions, we demonstrate that our framework is robust to model misspecification by establishing its validity under moment conditions via weak conditional expectations. The numerical properties of the proposed methodology are demonstrated in simulation studies and an application to microbiome compositional data, where we show that leveraging an auxiliary cohort to inform the prior significantly enhances predictive performance in a targeted, small-scale study", "published": "2026-06-06T02:34:09Z", "authors": ["Simon Fontaine", "Bing Li", "Lingzhou Xue"], "categories": ["stat.ME", "math.ST", "stat.AP"], "doi": "", "_query": "hedonic regression housing", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2405.17753v2", "title": "Regression Equilibrium in Electricity Markets", "abstract": "In two-stage electricity markets, renewable power producers enter the day-ahead market with a forecast of future power generation and then reconcile any forecast deviation in the real-time market at a penalty. The choice of the forecast model is thus an important strategy decision for renewable power producers as it affects financial performance. In electricity markets with large shares of renewable generation, the choice of the forecast model impacts not only individual performance but also outcomes for other producers. In this paper, we argue for the existence of a competitive regression equilibrium in two-stage electricity markets in terms of the parameters of private forecast models informing the participation strategies of renewable power producers. In our model, renewables optimize the forecast against the day-ahead and real-time prices, thereby maximizing the average profits across the day-ahead and real-time markets. By doing so, they also implicitly enhance the temporal cost coordination of day-ahead and real-time markets. We base the equilibrium analysis on the theory of variational inequalities, providing results on the existence and uniqueness of regression equilibrium in energy-only markets. We also devise two methods to compute regression equilibrium: centralized optimization and a decentralized ADMM-based algorithm.", "published": "2024-05-28T02:11:21Z", "authors": ["Vladimir Dvorkin"], "categories": ["eess.SY", "econ.GN", "math.OC"], "doi": "", "_query": "hedonic regression housing", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2409.00915v2", "title": "On the Pinsker bound of inner product kernel regression in large dimensions", "abstract": "Building on recent studies of large-dimensional kernel regression, particularly those involving inner product kernels on the sphere $\\mathbb{S}^{d}$, we investigate the Pinsker bound for inner product kernel regression in such settings. Specifically, we address the scenario where the sample size $n$ is given by $αd^γ(1+o_{d}(1))$ for some $α, γ>0$. We have determined the exact minimax risk for kernel regression in this setting, not only identifying the minimax rate but also the exact constant, known as the Pinsker constant, associated with the excess risk.", "published": "2024-09-02T03:01:56Z", "authors": ["Weihao Lu", "Jialin Ding", "Haobo Zhang", "Qian Lin"], "categories": ["math.ST", "stat.ML"], "doi": "", "_query": "hedonic regression housing", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2205.07804v1", "title": "CurFi: An automated tool to find the best regression analysis model using curve fitting", "abstract": "Regression analysis is a well known quantitative research method that primarily explores the relationship between one or more independent variables and a dependent variable. Conducting regression analysis manually on large datasets with multiple independent variables can be tedious. An automated system for regression analysis will be of great help for researchers as well as non-expert users. Thus, the objective of this research is to design and develop an automated curve fitting system. As outcome, a curve fitting system named \"CurFi\" was developed that uses linear regression models to fit a curve to a dataset and to find out the best fit model. The system facilitates to upload a dataset, split the dataset into training set and test set, select relevant features and label from the dataset; and the system will return the best fit linear regression model after training is completed. The developed tool would be a great resource for the users having limited technical knowledge who will also be able to find the best fit regression model for a dataset using the developed \"CurFi\" system.", "published": "2022-05-16T16:52:10Z", "authors": ["Ayon Roy", "Tausif Al Zubayer", "Nafisa Tabassum", "Muhammad Nazrul Islam", "Md. Abdus Sattar"], "categories": ["cs.LG"], "doi": "10.1002/eng2.12522", "_query": "hedonic regression housing", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2305.15177v2", "title": "Optimal subsampling for large scale Elastic-net regression", "abstract": "Datasets with sheer volume have been generated from fields including computer vision, medical imageology, and astronomy whose large-scale and high-dimensional properties hamper the implementation of classical statistical models. To tackle the computational challenges, one of the efficient approaches is subsampling which draws subsamples from the original large datasets according to a carefully-design task-specific probability distribution to form an informative sketch. The computation cost is reduced by applying the original algorithm to the substantially smaller sketch. Previous studies associated with subsampling focused on non-regularized regression from the computational efficiency and theoretical guarantee perspectives, such as ordinary least square regression and logistic regression. In this article, we introduce a randomized algorithm under the subsampling scheme for the Elastic-net regression which gives novel insights into L1-norm regularized regression problem. To effectively conduct consistency analysis, a smooth approximation technique based on alpha absolute function is firstly employed and theoretically verified. The concentration bounds and asymptotic normality for the proposed randomized algorithm are then established under mild conditions. Moreover, an optimal subsampling probability is constructed according to A-optimality. The effectiveness of the proposed algorithm is demonstrated upon synthetic and real data datasets.", "published": "2023-05-24T14:10:55Z", "authors": ["Hang Yu", "Zhenxing Dou", "Zhiwei Chen", "Xiaomeng Yan"], "categories": ["math.ST"], "doi": "", "_query": "hedonic regression housing", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2009.00947v4", "title": "Cyclotomic preperiodic points for morphisms in affine spaces and preperiodic points with bounded house and height", "abstract": "Under special conditions, we prove that the set of preperiodic points for semigroups of self-morphisms of affine spaces falling on cyclotomic closures is not dense. generalising results of Ostafe and Young (2020). We also extend previous results about boundness of house and height on certain preperiodicity sets of higher dimension in semigroup dynamics.", "published": "2020-09-02T11:03:56Z", "authors": ["Jorge Mello"], "categories": ["math.NT"], "doi": "", "_query": "hedonic regression housing", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/1709.04126v2", "title": "Advanced Algorithms for Penalized Quantile and Composite Quantile Regression", "abstract": "In this paper, we discuss a family of robust, high-dimensional regression models for quantile and composite quantile regression, both with and without an adaptive lasso penalty for variable selection. We reformulate these quantile regression problems and obtain estimators by applying the alternating direction method of multipliers (ADMM), majorize-minimization (MM), and coordinate descent (CD) algorithms. Our new approaches address the lack of publicly available methods for (composite) quantile regression, especially for high-dimensional data, both with and without regularization. Through simulation studies, we demonstrate the need for different algorithms applicable to a variety of data settings, which we implement in the cqrReg package for R. For comparison, we also introduce the widely used interior point (IP) formulation and test our methods against the IP algorithms in the existing quantreg package. Our simulation studies show that each of our methods, particularly MM and CD, excel in different settings such as with large or high-dimensional data sets, respectively, and outperform the methods currently implemented in quantreg. The ADMM approach offers specific promise for future developments in its amenability to parallelization and scalability.", "published": "2017-09-13T03:38:33Z", "authors": ["Matthew Pietrosanu", "Jueyu Gao", "Linglong Kong", "Bei Jiang", "Di Niu"], "categories": ["stat.CO"], "doi": "", "_query": "hedonic regression housing", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2604.13137v2", "title": "$p$-adic Linear Regression for Random Sampling with Digitwise Noise", "abstract": "We propose a new probabilistic algorithm of $p$-adic linear regression for random sampling with digitwise noise. This includes a new probabilistic algorithm of modulo $p$ linear regression.", "published": "2026-04-14T11:23:40Z", "authors": ["Tomoki Mihara"], "categories": ["stat.CO", "math.NT", "math.ST"], "doi": "", "_query": "hedonic regression housing", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/1502.07131v2", "title": "$χ^2$-confidence sets in high-dimensional regression", "abstract": "We study a high-dimensional regression model. Aim is to construct a confidence set for a given group of regression coefficients, treating all other regression coefficients as nuisance parameters. We apply a one-step procedure with the square-root Lasso as initial estimator and a multivariate square-root Lasso for constructing a surrogate Fisher information matrix. The multivariate square-root Lasso is based on nuclear norm loss with $\\ell_1$-penalty. We show that this procedure leads to an asymptotically $χ^2$-distributed pivot, with a remainder term depending only on the $\\ell_1$-error of the initial estimator. We show that under $\\ell_1$-sparsity conditions on the regression coefficients $β^0$ the square-root Lasso produces to a consistent estimator of the noise variance and we establish sharp oracle inequalities which show that the remainder term is small under further sparsity conditions on $β^0$ and compatibility conditions on the design.", "published": "2015-02-25T11:31:59Z", "authors": ["Sara van de Geer", "Benjamin Stucky"], "categories": ["math.ST"], "doi": "", "_query": "hedonic regression housing", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/cs/0609111v2", "title": "A State-Based Regression Formulation for Domains with Sensing Actions<br> and Incomplete Information", "abstract": "We present a state-based regression function for planning domains where an agent does not have complete information and may have sensing actions. We consider binary domains and employ a three-valued characterization of domains with sensing actions to define the regression function. We prove the soundness and completeness of our regression formulation with respect to the definition of progression. More specifically, we show that (i) a plan obtained through regression for a planning problem is indeed a progression solution of that planning problem, and that (ii) for each plan found through progression, using regression one obtains that plan or an equivalent one.", "published": "2006-09-19T21:33:07Z", "authors": ["Le-Chi Tuan", "Chitta Baral", "Tran Cao Son"], "categories": ["cs.AI"], "doi": "10.2168/LMCS-2(4:2)2006", "_query": "hedonic regression housing", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2307.02478v2", "title": "Linear Regression on Manifold Structured Data: the Impact of Extrinsic Geometry on Solutions", "abstract": "In this paper, we study linear regression applied to data structured on a manifold. We assume that the data manifold is smooth and is embedded in a Euclidean space, and our objective is to reveal the impact of the data manifold's extrinsic geometry on the regression. Specifically, we analyze the impact of the manifold's curvatures (or higher order nonlinearity in the parameterization when the curvatures are locally zero) on the uniqueness of the regression solution. Our findings suggest that the corresponding linear regression does not have a unique solution when the embedded submanifold is flat in some dimensions. Otherwise, the manifold's curvature (or higher order nonlinearity in the embedding) may contribute significantly, particularly in the solution associated with the normal directions of the manifold. Our findings thus reveal the role of data manifold geometry in ensuring the stability of regression models for out-of-distribution inferences.", "published": "2023-07-05T17:51:26Z", "authors": ["Liangchen Liu", "Juncai He", "Richard Tsai"], "categories": ["cs.LG", "math.DG"], "doi": "", "_query": "hedonic regression housing", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2102.12370v1", "title": "HiPaR: Hierarchical Pattern-aided Regression", "abstract": "We introduce HiPaR, a novel pattern-aided regression method for tabular data containing both categorical and numerical attributes. HiPaR mines hybrid rules of the form $p \\Rightarrow y = f(X)$ where $p$ is the characterization of a data region and $f(X)$ is a linear regression model on a variable of interest $y$. HiPaR relies on pattern mining techniques to identify regions of the data where the target variable can be accurately explained via local linear models. The novelty of the method lies in the combination of an enumerative approach to explore the space of regions and efficient heuristics that guide the search. Such a strategy provides more flexibility when selecting a small set of jointly accurate and human-readable hybrid rules that explain the entire dataset. As our experiments shows, HiPaR mines fewer rules than existing pattern-based regression methods while still attaining state-of-the-art prediction performance.", "published": "2021-02-24T15:53:17Z", "authors": ["Luis Galárraga", "Olivier Pelgrin", "Alexandre Termier"], "categories": ["cs.LG"], "doi": "", "_query": "hedonic regression housing", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2307.04510v2", "title": "An analysis of linear regression and neural networks approximation for the pricing of swing options", "abstract": "Linear regression, firstly introduced for the pricing of American-style options, has since been expanded to include swing options pricing. Swing options price may be viewed as the solution to a Backward Dynamic Programming Principle, which involves a conditional expectation known as the continuation value. The approximation of the continuation value using linear regression involves two levels of approximation. First, the continuation value is replaced by an orthogonal projection over a subspace spanned by a finite set of $m$ squared-integrable functions yielding a first approximation $V^m$ of the swing value function. In this paper, we prove that, with well-chosen regression functions, $V^m$ converges to the swing actual price $V$ as $m \\to + \\infty$. A similar result is proved when classic regression functions are replaced by neural networks. For both methods (linear regression and neural networks), we analyze the second level of approximation involving practical computation of the swing price using Monte Carlo simulations and yielding an approximation $V^{m, N}$ (where $N$ denotes the Monte Carlo sample size). Especially, we prove that $V^{m, N} \\to V^m$ as $N \\to + \\infty$ for both methods and using a Hilbert basis assumption in the linear regression. Besides, a convergence rate of order $\\mathcal{O}\\big(\\frac{1}{\\sqrt{N}} \\big)$ is proved in the linear regression case.", "published": "2023-07-10T12:12:42Z", "authors": ["Christian Yeo"], "categories": ["q-fin.MF"], "doi": "", "_query": "hedonic regression housing", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2609.00307v1", "title": "Generalized Bayesian Clustering with Regression for Unaligned Longitudinal Binary Data", "abstract": "We propose a generalized Bayesian clustering with regression model for unaligned longitudinal binary outcomes, motivated by seizure diary data from the Human Epilepsy Project. Seizure diaries are sparse, irregularly observed, and vary enormously across patients. A single fully-specified generative model tends to be either misspecified or computationally inefficient. We address the challenge by two strategies. We set up a regression by way of clustering as model-based clustering using a mixture model. For the latter, we take a generalized Bayesian perspective which replaces the full likelihood with a loss-based update using a generalized likelihood. We combine a trajectory similarity loss and a regression loss, so that clustering is informed by both trajectory similarity and the prediction of outcomes The trajectory similarity loss is constructed by representing each trajectory as an (empirical) distribution of subsequences, called reads, and then is defined based on the sliced Wasserstein distance between these empirical distributions. This loss allows alignment-free comparison of sequences that are irregularly observed or temporally misaligned, and it scales quasi-linearly in trajectory length. The regression loss is the negative log-likelihood of a probit regression. A prior on the cluster-specific parameters is defined by way of a Dirichlet process prior on the mixing measure.", "published": "2026-08-31T19:58:57Z", "authors": ["Khai Nguyen", "Elizabeth Juarez-Colunga", "Peter Mueller"], "categories": ["stat.ME", "stat.AP", "stat.CO"], "doi": "", "_query": "hedonic regression housing", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/0710.3647v1", "title": "Asymptotic approximation of nonparametric regression experiments with unknown variances", "abstract": "Asymptotic equivalence results for nonparametric regression experiments have always assumed that the variances of the observations are known. In practice, however the variance of each observation is generally considered to be an unknown nuisance parameter. We establish an asymptotic approximation to the nonparametric regression experiment when the value of the variance is an additional parameter to be estimated or tested. This asymptotically equivalent experiment has two components: the first contains all the information about the variance and the second has all the information about the mean. The result can be extended to regression problems where the variance varies slowly from observation to observation.", "published": "2007-10-19T08:15:03Z", "authors": ["Andrew V. Carter"], "categories": ["math.ST"], "doi": "10.1214/009053606000001613", "_query": "hedonic regression housing", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2408.08834v2", "title": "Gaussian Processes with Noisy Regression Inputs for Dynamical Systems", "abstract": "This paper is centered around the approximation of dynamical systems by means of Gaussian processes. To this end, trajectories of such systems must be collected to be used as training data. The measurements of these trajectories are typically noisy, which implies that both the regression inputs and outputs are corrupted by noise. However, most of the literature considers only noise in the regression outputs. In this paper, we show how to account for the noise in the regression inputs in an extended Gaussian process framework to approximate scalar and multidimensional systems. We demonstrate the potential of our framework by comparing it to different state-of-the-art methods in several simulation examples.", "published": "2024-08-16T16:37:37Z", "authors": ["Tobias M. Wolff", "Victor G. Lopez", "Matthias A. Müller"], "categories": ["eess.SY"], "doi": "", "_query": "hedonic regression housing", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2110.02643v2", "title": "Variable Selection Using a Smooth Information Criterion for Distributional Regression Models", "abstract": "Modern variable selection procedures make use of penalization methods to execute simultaneous model selection and estimation. A popular method is the LASSO (least absolute shrinkage and selection operator), the use of which requires selecting the value of a tuning parameter. This parameter is typically tuned by minimizing the cross-validation error or Bayesian information criterion (BIC) but this can be computationally intensive as it involves fitting an array of different models and selecting the best one. In contrast with this standard approach, we have developed a procedure based on the so-called \"smooth IC\" (SIC) in which the tuning parameter is automatically selected in one step. We also extend this model selection procedure to the distributional regression framework, which is more flexible than classical regression modelling. Distributional regression, also known as multiparameter regression (MPR), introduces flexibility by taking account of the effect of covariates through multiple distributional parameters simultaneously, e.g., mean and variance. These models are useful in the context of normal linear regression when the process under study exhibits heteroscedastic behaviour. Reformulating the distributional regression estimation problem in terms of penalized likelihood enables us to take advantage of the close relationship between model selection criteria and penalization. Utilizing the SIC is computationally advantageous, as it obviates the issue of having to choose multiple tuning parameters.", "published": "2021-10-06T10:37:44Z", "authors": ["Meadhbh O'Neill", "Kevin Burke"], "categories": ["stat.ME"], "doi": "", "_query": "hedonic regression housing", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/1901.00563v1", "title": "Adaptive Locality Preserving Regression", "abstract": "This paper proposes a novel discriminative regression method, called adaptive locality preserving regression (ALPR) for classification. In particular, ALPR aims to learn a more flexible and discriminative projection that not only preserves the intrinsic structure of data, but also possesses the properties of feature selection and interpretability. To this end, we introduce a target learning technique to adaptively learn a more discriminative and flexible target matrix rather than the pre-defined strict zero-one label matrix for regression. Then a locality preserving constraint regularized by the adaptive learned weights is further introduced to guide the projection learning, which is beneficial to learn a more discriminative projection and avoid overfitting. Moreover, we replace the conventional `Frobenius norm' with the special l21 norm to constrain the projection, which enables the method to adaptively select the most important features from the original high-dimensional data for feature extraction. In this way, the negative influence of the redundant features and noises residing in the original data can be greatly eliminated. Besides, the proposed method has good interpretability for features owing to the row-sparsity property of the l21 norm. Extensive experiments conducted on the synthetic database with manifold structure and many real-world databases prove the effectiveness of the proposed method.", "published": "2019-01-03T00:36:23Z", "authors": ["Jie Wen", "Zuofeng Zhong", "Zheng Zhang", "Lunke Fei", "Zhihui Lai", "Runze Chen"], "categories": ["cs.CV", "cs.AI"], "doi": "10.1109/TCSVT.2018.2889727", "_query": "hedonic regression housing", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/1307.1903v1", "title": "Achieving greater Explanatory Power and Forecasting Accuracy with Non-uniform spread Fuzzy Linear Regression", "abstract": "Fuzzy regression models have been applied to several Operations Research applications viz., forecasting and prediction. Earlier works on fuzzy regression analysis obtain crisp regression coefficients for eliminating the problem of increasing spreads for the estimated fuzzy responses as the magnitude of the independent variable increases. But they cannot deal with the problem of non-uniform spreads. In this work, a three-phase approach is discussed to construct the fuzzy regression model with non-uniform spreads to deal with this problem. The first phase constructs the membership functions of the least-squares estimates of regression coefficients based on extension principle to completely conserve the fuzziness of observations. They are then defuzzified by the centre of area method to obtain crisp regression coefficients in the second phase. Finally, the error terms of the method are determined by setting each estimated spread equal to its corresponding observed spread. The Tagaki-Sugeno inference system is used for improving the accuracy of forecasts. The simulation example demonstrates the strength of fuzzy linear regression model in terms of higher explanatory power and forecasting performance.", "published": "2013-07-07T19:20:01Z", "authors": ["Arindam Chaudhuri", "Kajal De"], "categories": ["cs.AI"], "doi": "", "_query": "hedonic regression housing", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2405.00514v2", "title": "Get Your Embedding Space in Order: Domain-Adaptive Regression for Forest Monitoring", "abstract": "Image-level regression is an important task in Earth observation, where visual domain and label shifts are a core challenge hampering generalization. However, cross-domain regression within remote sensing data remains understudied due to the absence of suited datasets. We introduce a new dataset with aerial and satellite imagery in five countries with three forest-related regression tasks. To match real-world applicative interests, we compare methods through a restrictive setup where no prior on the target domain is available during training, and models are adapted with limited information during testing. Building on the assumption that ordered relationships generalize better, we propose manifold diffusion for regression as a strong baseline for transduction in low-data regimes. Our comparison highlights the comparative advantages of inductive and transductive methods in cross-domain regression.", "published": "2024-05-01T13:49:09Z", "authors": ["Sizhuo Li", "Dimitri Gominski", "Martin Brandt", "Xiaoye Tong", "Philippe Ciais"], "categories": ["cs.CV"], "doi": "", "_query": "hedonic regression housing", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2411.15919v2", "title": "Enhancing Symbolic Regression and Universal Physics-Informed Neural Networks with Dimensional Analysis", "abstract": "In engineering and applied mathematics, developing accurate mathematical models to predict and understand real-world phenomena is of utmost importance. Symbolic regression is a useful machine learning-based tool to fit models but it can be computationally expensive. We present a new method for enhancing symbolic regression for differential equations via dimensional analysis, specifically the Buckingham $Π$ theorem and Ipsen's method. Since symbolic regression often suffers from high computational costs and overfitting, nondimensionalizing datasets reduces the number of input variables, simplifies the search space, and ensures that derived equations are physically meaningful. As a first step, we combine dimensional analysis with the PySR symbolic regression algorithm to show that dimensional analysis improves the accuracy of recovering algebraic equations. The results demonstrate that transforming data into a dimensionless form significantly improves the training and test error of the symbolic expressions found. Then, as our main contribution, we perform nondimensionalization guided by Ipsen's method. We then incorporate the nondimensionalized equation into a pipeline combining Universal Physics-Informed Neural Networks and symbolic regression to recover the unknown term when a differential equation is only partially known. We find that symbolic regression is able to better recover the unknown term after nondimensionalizing the data, under both noisy and noiseless conditions. These findings suggest that integrating dimensional analysis with symbolic regression can significantly lower computational costs and increase accuracy, providing a robust framework for automated discovery of governing equations in complex systems when data is limited.", "published": "2024-11-24T17:07:39Z", "authors": ["Lena Podina", "Diba Darooneh", "Joshveer Grewal", "Mohammad Kohandel"], "categories": ["cs.LG"], "doi": "", "_query": "hedonic regression housing", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2006.00160v1", "title": "Parametric Modeling of Quantile Regression Coefficient Functions with Longitudinal Data", "abstract": "In ordinary quantile regression, quantiles of different order are estimated one at a time. An alternative approach, which is referred to as quantile regression coefficients modeling (QRCM), is to model quantile regression coefficients as parametric functions of the order of the quantile. In this paper, we describe how the QRCM paradigm can be applied to longitudinal data. We introduce a two-level quantile function, in which two different quantile regression models are used to describe the (conditional) distribution of the within-subject response and that of the individual effects. We propose a novel type of penalized fixed-effects estimator, and discuss its advantages over standard methods based on $\\ell_1$ and $\\ell_2$ penalization. We provide model identifiability conditions, derive asymptotic properties, describe goodness-of-fit measures and model selection criteria, present simulation results, and discuss an application. The proposed method has been implemented in the R package qrcm.", "published": "2020-05-30T03:29:10Z", "authors": ["Paolo Frumento", "Matteo Bottai", "Iván Fernández-Val"], "categories": ["stat.ME", "econ.EM"], "doi": "", "_query": "hedonic regression housing", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/1407.0754v1", "title": "Structured Learning via Logistic Regression", "abstract": "A successful approach to structured learning is to write the learning objective as a joint function of linear parameters and inference messages, and iterate between updates to each. This paper observes that if the inference problem is \"smoothed\" through the addition of entropy terms, for fixed messages, the learning objective reduces to a traditional (non-structured) logistic regression problem with respect to parameters. In these logistic regression problems, each training example has a bias term determined by the current set of messages. Based on this insight, the structured energy function can be extended from linear factors to any function class where an \"oracle\" exists to minimize a logistic loss.", "published": "2014-07-03T00:48:34Z", "authors": ["Justin Domke"], "categories": ["cs.LG", "stat.ML"], "doi": "", "_query": "hedonic regression housing", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2001.05228v3", "title": "Extreme Regression for Dynamic Search Advertising", "abstract": "This paper introduces a new learning paradigm called eXtreme Regression (XR) whose objective is to accurately predict the numerical degrees of relevance of an extremely large number of labels to a data point. XR can provide elegant solutions to many large-scale ranking and recommendation applications including Dynamic Search Advertising (DSA). XR can learn more accurate models than the recently popular extreme classifiers which incorrectly assume strictly binary-valued label relevances. Traditional regression metrics which sum the errors over all the labels are unsuitable for XR problems since they could give extremely loose bounds for the label ranking quality. Also, the existing regression algorithms won't efficiently scale to millions of labels. This paper addresses these limitations through: (1) new evaluation metrics for XR which sum only the k largest regression errors; (2) a new algorithm called XReg which decomposes XR task into a hierarchy of much smaller regression problems thus leading to highly efficient training and prediction. This paper also introduces a (3) new labelwise prediction algorithm in XReg useful for DSA and other recommendation tasks. Experiments on benchmark datasets demonstrated that XReg can outperform the state-of-the-art extreme classifiers as well as large-scale regressors and rankers by up to 50% reduction in the new XR error metric, and up to 2% and 2.4% improvements in terms of the propensity-scored precision metric used in extreme classification and the click-through rate metric used in DSA respectively. Deployment of XReg on DSA in Bing resulted in a relative gain of 27% in query coverage. XReg's source code can be downloaded from http://manikvarma.org/code/XReg/download.html.", "published": "2020-01-15T10:56:42Z", "authors": ["Yashoteja Prabhu", "Aditya Kusupati", "Nilesh Gupta", "Manik Varma"], "categories": ["cs.LG", "stat.ML"], "doi": "10.1145/3336191.3371768", "_query": "hedonic regression housing", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2006.07924v1", "title": "Estimation and Inference for Multi-Kink Quantile Regression", "abstract": "The Multi-Kink Quantile Regression (MKQR) model is an important tool for analyzing data with heterogeneous conditional distributions, especially when quantiles of response variable are of interest, due to its robustness to outliers and heavy-tailed errors in the response. It assumes different linear quantile regression forms in different regions of the domain of the threshold covariate but are still continuous at kink points. In this paper, we investigate parameter estimation, kink point detection and statistical inference in MKQR models. We propose an iterative segmented quantile regression algorithm for estimating both the regression coefficients and the locations of kink points. The proposed algorithm is much more computationally efficient than the grid search algorithm and not sensitive to the selection of initial values. Asymptotic properties, such as selection consistency of the number of kink points, asymptotic normality of the estimators of both regression coefficients and kink effects, are established to justify the proposed method theoretically. A score test, based on partial subgradients, is developed to verify whether the kink effects exist or not. Test-inversion confidence intervals for kink location parameters are also constructed. Intensive simulation studies conducted show the proposed methods work very well when sample size is finite. Finally, we apply the MKQR models together with the proposed methods to the dataset about secondary industrial structure of China and the dataset about triceps skinfold thickness of Gambian females, which leads to some very interesting findings. A new R package MultiKink is developed to implement the proposed methods.", "published": "2020-06-14T15:15:32Z", "authors": ["Wei Zhong", "Chuang Wan", "Wenyang Zhang"], "categories": ["stat.ME"], "doi": "", "_query": "hedonic regression housing", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2007.07056v2", "title": "Deep Learning for Quantile Regression under Right Censoring: DeepQuantreg", "abstract": "The computational prediction algorithm of neural network, or deep learning, has drawn much attention recently in statistics as well as in image recognition and natural language processing. Particularly in statistical application for censored survival data, the loss function used for optimization has been mainly based on the partial likelihood from Cox's model and its variations to utilize existing neural network library such as Keras, which was built upon the open source library of TensorFlow. This paper presents a novel application of the neural network to the quantile regression for survival data with right censoring, which is adjusted by the inverse of the estimated censoring distribution in the check function. The main purpose of this work is to show that the deep learning method could be flexible enough to predict nonlinear patterns more accurately compared to existing quantile regression methods such as traditional linear quantile regression and nonparametric quantile regression with total variation regularization, emphasizing practicality of the method for censored survival data. Simulation studies were performed to generate nonlinear censored survival data and compare the deep learning method with existing quantile regression methods in terms of prediction accuracy. The proposed method is illustrated with two publicly available breast cancer data sets with gene signatures. The method has been built into a package and is freely available at \\url{https://github.com/yicjia/DeepQuantreg}.", "published": "2020-07-14T14:31:11Z", "authors": ["Yichen Jia", "Jong-Hyeon Jeong"], "categories": ["stat.ML", "cs.LG", "stat.AP"], "doi": "", "_query": "hedonic regression housing", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/1710.04721v1", "title": "Cox regression analysis with missing covariates via multiple imputation", "abstract": "We consider the situation of estimating Cox regression in which some covariates are subject to missing, and there exists additional information (including observed event time, censoring indicator and fully observed covariates) which may be predictive of the missing covariates. We propose to use two working regression models: one for predicting the missing covariates and the other for predicting the missing probabilities. For each missing covariate observation, these two working models are used to define a nearest neighbor imputing set. This set is then used to nonparametrically impute covariate values for the missing observation. Upon the completion of imputation, Cox regression is performed on the multiply imputed datasets to estimate the regression coefficients. In a simulation study, we compare the nonparametric multiple imputation approach with the augmented inverse probability weighted (AIPW) method, which directly incorporates the two working models into estimation of Cox regression. We show that all approaches can reduce bias due to non-ignorable missing mechanism. The proposed nonparametric imputation method is robust to mis-specification of either one of the two working models and robust to mis-specification of the link function of the two working models. In contrast, the AIPW method is not robust to mis-specification of the link function of the two working models and is sensitive to the selection probability. We apply the approaches to a breast cancer dataset from Surveillance, Epidemiology and End Results (SEER) Program .", "published": "2017-10-12T21:05:22Z", "authors": ["Chiu-Hsieh Hsu", "Mandi Yu"], "categories": ["stat.ME"], "doi": "", "_query": "hedonic regression housing", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/0710.2496v1", "title": "Regression estimation from an individual stable sequence", "abstract": "We consider univariate regression estimation from an individual (non-random) sequence $(x_1,y_1),(x_2,y_2), ... \\in \\real \\times \\real$, which is stable in the sense that for each interval $A \\subseteq \\real$, (i) the limiting relative frequency of $A$ under $x_1, x_2, ...$ is governed by an unknown probability distribution $μ$, and (ii) the limiting average of those $y_i$ with $x_i \\in A$ is governed by an unknown regression function $m(\\cdot)$. A computationally simple scheme for estimating $m(\\cdot)$ is exhibited, and is shown to be $L_2$ consistent for stable sequences $\\{(x_i,y_i)\\}$ such that $\\{y_i\\}$ is bounded and there is a known upper bound for the variation of $m(\\cdot)$ on intervals of the form $(-i,i]$, $i \\geq 1$. Complementing this positive result, it is shown that there is no consistent estimation scheme for the family of stable sequences whose regression functions have finite variation, even under the restriction that $x_i \\in [0,1]$ and $y_i$ is binary-valued.", "published": "2007-10-12T16:02:13Z", "authors": ["Gusztav Morvai", "Sanjeev R. Kulkarni", "Andrew B. Nobel"], "categories": ["math.PR", "cs.IT", "math.ST"], "doi": "", "_query": "hedonic regression housing", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2401.05471v1", "title": "Shrinkage linear regression for symbolic interval-valued variables", "abstract": "This paper proposes a new approach to fit a linear regression for symbolic internal-valued variables, which improves both the Center Method suggested by Billard and Diday in \\cite{BillardDiday2000} and the Center and Range Method suggested by Lima-Neto, E.A. and De Carvalho, F.A.T. in \\cite{Lima2008, Lima2010}. Just in the Centers Method and the Center and Range Method, the new methods proposed fit the linear regression model on the midpoints and in the half of the length of the intervals as an additional variable (ranges) assumed by the predictor variables in the training data set, but to make these fitments in the regression models, the methods Ridge Regression, Lasso, and Elastic Net proposed by Tibshirani, R. Hastie, T., and Zou H in \\cite{Tib1996, HastieZou2005} are used. The prediction of the lower and upper of the interval response (dependent) variable is carried out from their midpoints and ranges, which are estimated from the linear regression models with shrinkage generated in the midpoints and the ranges of the interval-valued predictors. Methods presented in this document are applied to three real data sets cardiologic interval data set, Prostate interval data set and US Murder interval data set to then compare their performance and facility of interpretation regarding the Center Method and the Center and Range Method. For this evaluation, the root-mean-squared error and the correlation coefficient are used. Besides, the reader may use all the methods presented herein and verify the results using the {\\tt RSDA} package written in {\\tt R} language, that can be downloaded and installed directly from {\\tt CRAN} \\cite{Rod2014}.", "published": "2024-01-10T15:25:27Z", "authors": ["Oldemar Rodriguez"], "categories": ["stat.ME"], "doi": "", "_query": "hedonic regression housing", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2106.04285v1", "title": "Sensitivity analysis for random measurement error using regression calibration and simulation-extrapolation", "abstract": "Sensitivity analysis for measurement error can be applied in the absence of validation data by means of regression calibration and simulation-extrapolation. These have not been compared for this purpose. A simulation study was conducted comparing the performance of regression calibration and simulation-extrapolation in a multivariable model. The performance of the two methods was evaluated in terms of bias, mean squared error (MSE) and confidence interval coverage, for ranging reliability of the error-prone measurement (0.2-0.9), sample size (125-1,000), number of replicates (2-10), and R-squared (0.03-0.75). It was assumed that no validation data were available about the error-free measures, while measurement error variance was correctly estimated. In various scenarios, regression calibration was unbiased while simulation-extrapolation was biased: median bias was 1.4% (interquartile range (IQR): 0.8;2%), and -12.8% (IQR: -13.2;-11.0%), respectively. A small gain in efficiency was observed for simulation-extrapolation (median MSE: 0.005, IQR: 0.004;0.006) versus regression calibration (median MSE: 0.006, IQR: 0.004;0.007). Confidence interval coverage was at the nominal level of 95% for regression calibration, and smaller than 95% for simulation-extrapolation (median coverage: 92%, IQR: 85;94%). In the absence of validation data, the use of regression calibration is recommended for sensitivity analysis for measurement error.", "published": "2021-06-08T12:23:42Z", "authors": ["Linda Nab", "Rolf H. H. Groenwold"], "categories": ["stat.AP"], "doi": "", "_query": "hedonic regression housing", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/1602.00355v1", "title": "A Spectral Series Approach to High-Dimensional Nonparametric Regression", "abstract": "A key question in modern statistics is how to make fast and reliable inferences for complex, high-dimensional data. While there has been much interest in sparse techniques, current methods do not generalize well to data with nonlinear structure. In this work, we present an orthogonal series estimator for predictors that are complex aggregate objects, such as natural images, galaxy spectra, trajectories, and movies. Our series approach ties together ideas from kernel machine learning, and Fourier methods. We expand the unknown regression on the data in terms of the eigenfunctions of a kernel-based operator, and we take advantage of orthogonality of the basis with respect to the underlying data distribution, P, to speed up computations and tuning of parameters. If the kernel is appropriately chosen, then the eigenfunctions adapt to the intrinsic geometry and dimension of the data. We provide theoretical guarantees for a radial kernel with varying bandwidth, and we relate smoothness of the regression function with respect to P to sparsity in the eigenbasis. Finally, using simulated and real-world data, we systematically compare the performance of the spectral series approach with classical kernel smoothing, k-nearest neighbors regression, kernel ridge regression, and state-of-the-art manifold and local regression methods.", "published": "2016-02-01T01:20:29Z", "authors": ["Ann B. Lee", "Rafael Izbicki"], "categories": ["stat.ME", "stat.ML"], "doi": "10.1214/16-EJS1112", "_query": "hedonic regression housing", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2112.13951v2", "title": "Improving Nonparametric Classification via Local Radial Regression with an Application to Stock Prediction", "abstract": "For supervised classification problems, this paper considers estimating the query's label probability through local regression using observed covariates. Well-known nonparametric kernel smoother and $k$-nearest neighbor ($k$-NN) estimator, which take label average over a ball around the query, are consistent but asymptotically biased particularly for a large radius of the ball. To eradicate such bias, local polynomial regression (LPoR) and multiscale $k$-NN (MS-$k$-NN) learn the bias term by local regression around the query and extrapolate it to the query itself. However, their theoretical optimality has been shown for the limit of the infinite number of training samples. For correcting the asymptotic bias with fewer observations, this paper proposes a \\emph{local radial regression (LRR)} and its logistic regression variant called \\emph{local radial logistic regression~(LRLR)}, by combining the advantages of LPoR and MS-$k$-NN. The idea is quite simple: we fit the local regression to observed labels by taking only the radial distance as the explanatory variable and then extrapolate the estimated label probability to zero distance. The usefulness of the proposed method is shown theoretically and experimentally. We prove the convergence rate of the $L^2$ risk for LRR with reference to MS-$k$-NN, and our numerical experiments, including real-world datasets of daily stock indices, demonstrate that LRLR outperforms LPoR and MS-$k$-NN.", "published": "2021-12-28T00:32:02Z", "authors": ["Ruixing Cao", "Akifumi Okuno", "Kei Nakagawa", "Hidetoshi Shimodaira"], "categories": ["stat.ML", "cs.AI", "cs.LG", "stat.ME"], "doi": "", "_query": "hedonic regression housing", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2605.14943v2", "title": "Piece-wise linear isotonic regression", "abstract": "Isotonic regression provides a flexible, tuning-free approach to estimating monotonic functions without imposing global curvature constraints, yet the estimated regression function is inherently a step function. This paper addresses a key limitation of such estimators: their inability to provide meaningful marginal properties, such as shadow prices or elasticities. We propose a novel piece-wise linear smoothing framework that recovers meaningful marginal estimates even in non-convex settings. Building on the concept of conditional convexity originally developed in deterministic frontier analysis, we formulate the smoothing process as a bilevel optimization problem that fits a continuous, monotonic, piece-wise linear function to the initial isotonic regression predictions. Monte Carlo simulations demonstrate that the proposed approach can significantly improve estimation accuracy in both convex and non-convex settings for univariate and multivariate data. We apply this approach to analyze agglomeration economies in Finnish municipalities, illustrating its practical value.", "published": "2026-05-14T15:16:44Z", "authors": ["Timo Kuosmanen", "Juan F. Monge", "José L. Ruiz", "Xun Zhou"], "categories": ["stat.ME"], "doi": "", "_query": "hedonic regression housing", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2205.13080v1", "title": "Factorized Structured Regression for Large-Scale Varying Coefficient Models", "abstract": "Recommender Systems (RS) pervade many aspects of our everyday digital life. Proposed to work at scale, state-of-the-art RS allow the modeling of thousands of interactions and facilitate highly individualized recommendations. Conceptually, many RS can be viewed as instances of statistical regression models that incorporate complex feature effects and potentially non-Gaussian outcomes. Such structured regression models, including time-aware varying coefficients models, are, however, limited in their applicability to categorical effects and inclusion of a large number of interactions. Here, we propose Factorized Structured Regression (FaStR) for scalable varying coefficient models. FaStR overcomes limitations of general regression models for large-scale data by combining structured additive regression and factorization approaches in a neural network-based model implementation. This fusion provides a scalable framework for the estimation of statistical models in previously infeasible data settings. Empirical results confirm that the estimation of varying coefficients of our approach is on par with state-of-the-art regression techniques, while scaling notably better and also being competitive with other time-aware RS in terms of prediction performance. We illustrate FaStR's performance and interpretability on a large-scale behavioral study with smartphone user data.", "published": "2022-05-25T23:12:13Z", "authors": ["David Rügamer", "Andreas Bender", "Simon Wiegrebe", "Daniel Racek", "Bernd Bischl", "Christian L. Müller", "Clemens Stachl"], "categories": ["stat.ML", "cs.LG", "stat.CO"], "doi": "", "_query": "hedonic regression housing", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/1905.10432v1", "title": "Cross validation approaches for penalized Cox regression", "abstract": "Cross validation is commonly used for selecting tuning parameters in penalized regression, but its use in penalized Cox regression models has received relatively little attention in the literature. Due to its partial likelihood construction, carrying out cross validation for Cox models is not straightforward, and there are several potential approaches for implementation. Here, we propose two new cross-validation methods for Cox regression and compare them to approaches that have been proposed elsewhere. Our proposed approach of cross-validating the linear predictors seems to offer an attractive balance of performance and numerical stability. We illustrate these advantages using simulated data as well as using them to analyze data from a high-dimensional study of survival in lung cancer patients.", "published": "2019-05-24T20:11:23Z", "authors": ["Biyue Dai", "Patrick Breheny"], "categories": ["stat.ME"], "doi": "10.1177/09622802241233770", "_query": "hedonic regression housing", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2004.12491v2", "title": "A bimodal gamma distribution: Properties, regression model and applications", "abstract": "In this paper we propose a bimodal gamma distribution using a quadratic transformation based on the alpha-skew-normal model. We discuss several properties of this distribution such as mean, variance, moments, hazard rate and entropy measures. Further, we propose a new regression model with censored data based on the bimodal gamma distribution. This regression model can be very useful to the analysis of real data and could give more realistic fits than other special regression models. Monte Carlo simulations were performed to check the bias in the maximum likelihood estimation. The proposed models are applied to two real data sets found in literature.", "published": "2020-04-26T22:17:52Z", "authors": ["R. Vila", "L. Ferreira", "H. Saulo", "F. Prataviera", "E. M. M. Ortega"], "categories": ["stat.ME", "math.PR", "math.ST"], "doi": "", "_query": "hedonic regression housing", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2512.17934v1", "title": "Comparative Evaluation of Explainable Machine Learning Versus Linear Regression for Predicting County-Level Lung Cancer Mortality Rate in the United States", "abstract": "Lung cancer (LC) is a leading cause of cancer-related mortality in the United States. Accurate prediction of LC mortality rates is crucial for guiding targeted interventions and addressing health disparities. Although traditional regression-based models have been commonly used, explainable machine learning models may offer enhanced predictive accuracy and deeper insights into the factors influencing LC mortality. This study applied three models: random forest (RF), gradient boosting regression (GBR), and linear regression (LR) to predict county-level LC mortality rates across the United States. Model performance was evaluated using R-squared and root mean squared error (RMSE). Shapley Additive Explanations (SHAP) values were used to determine variable importance and their directional impact. Geographic disparities in LC mortality were analyzed through Getis-Ord (Gi*) hotspot analysis. The RF model outperformed both GBR and LR, achieving an R2 value of 41.9% and an RMSE of 12.8. SHAP analysis identified smoking rate as the most important predictor, followed by median home value and the percentage of the Hispanic ethnic population. Spatial analysis revealed significant clusters of elevated LC mortality in the mid-eastern counties of the United States. The RF model demonstrated superior predictive performance for LC mortality rates, emphasizing the critical roles of smoking prevalence, housing values, and the percentage of Hispanic ethnic population. These findings offer valuable actionable insights for designing targeted interventions, promoting screening, and addressing health disparities in regions most affected by LC in the United States.", "published": "2025-12-10T23:33:12Z", "authors": ["Soheil Hashtarkhani", "Brianna M. White", "Benyamin Hoseini", "David L. Schwartz", "Arash Shaban-Nejad"], "categories": ["cs.LG", "cs.AI", "stat.AP"], "doi": "10.1200/CCI-24-00310", "_query": "hedonic regression housing", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2203.04080v3", "title": "On Robust Inference in Time Series Regression", "abstract": "Least squares regression with heteroskedasticity consistent standard errors (\"OLS-HC regression\") has proved very useful in cross section environments. However, several major difficulties, which are generally overlooked, must be confronted when transferring the HC technology to time series environments via heteroskedasticity and autocorrelation consistent standard errors (\"OLS-HAC regression\"). First, in plausible time-series environments, OLS parameter estimates can be inconsistent, so that OLS-HAC inference fails even asymptotically. Second, most economic time series have autocorrelation, which renders OLS parameter estimates inefficient. Third, autocorrelation similarly renders conditional predictions based on OLS parameter estimates inefficient. Finally, the structure of popular HAC covariance matrix estimators is ill-suited for capturing the autoregressive autocorrelation typically present in economic time series, which produces large size distortions and reduced power in HAC-based hypothesis testing, in all but the largest samples. We show that all four problems are largely avoided by the use of a simple and easily-implemented dynamic regression procedure, which we call DURBIN. We demonstrate the advantages of DURBIN with detailed simulations covering a range of practical issues.", "published": "2022-03-08T13:49:10Z", "authors": ["Richard T. Baillie", "Francis X. Diebold", "George Kapetanios", "Kun Ho Kim", "Aaron Mora"], "categories": ["econ.EM", "stat.AP"], "doi": "", "_query": "hedonic regression housing", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/1107.4512v3", "title": "Multi-task Regression using Minimal Penalties", "abstract": "In this paper we study the kernel multiple ridge regression framework, which we refer to as multi-task regression, using penalization techniques. The theoretical analysis of this problem shows that the key element appearing for an optimal calibration is the covariance matrix of the noise between the different tasks. We present a new algorithm to estimate this covariance matrix, based on the concept of minimal penalty, which was previously used in the single-task regression framework to estimate the variance of the noise. We show, in a non-asymptotic setting and under mild assumptions on the target function, that this estimator converges towards the covariance matrix. Then plugging this estimator into the corresponding ideal penalty leads to an oracle inequality. We illustrate the behavior of our algorithm on synthetic examples.", "published": "2011-07-22T13:12:42Z", "authors": ["Matthieu Solnon", "Sylvain Arlot", "Francis Bach"], "categories": ["math.ST"], "doi": "", "_query": "hedonic regression housing", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/1901.09531v2", "title": "Secure multi-party linear regression at plaintext speed", "abstract": "We detail distributed algorithms for scalable, secure multiparty linear regression and feature selection at essentially the same speed as plaintext regression. While the core geometric ideas are simple, the recognition of their broad utility when combined is novel. Our scheme opens the door to efficient and secure genome-wide association studies across multiple biobanks.", "published": "2019-01-28T07:06:12Z", "authors": ["Jonathan M. Bloom"], "categories": ["cs.LG", "cs.CR", "stat.ML"], "doi": "", "_query": "hedonic regression housing", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/1511.07384v1", "title": "Robust mixture regression modeling based on the Generalized M (GM)-estimation method", "abstract": "Bai (2010) and Bai et al. (2012) proposed robust mixture regression method based on the M regression estimation. However, the M-estimators are robust against the outliers in response variables, but they are not robust against the outliers in explanatory variables (leverage points). In this paper, we propose a robust mixture regression procedure to handle the outliers and the leverage points, simultaneously. Our proposed mixture regression method is based on the GM regression estimation. We give an Expectation Maximization (EM) type algorithm to compute estimates for the parameters of interest. We provide a simulation study and a real data example to assess the robustness performance of the proposed method against the outliers and the leverage points.", "published": "2015-11-23T19:53:04Z", "authors": ["Fatma Zehra Doğru", "Olcay Arslan"], "categories": ["math.ST"], "doi": "", "_query": "hedonic regression housing", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/0710.5015v1", "title": "Comment: Struggles with Survey Weighting and Regression Modeling", "abstract": "Comment: Struggles with Survey Weighting and Regression Modeling [arXiv:0710.5005]", "published": "2007-10-26T07:47:54Z", "authors": ["Sharon L. Lohr"], "categories": ["stat.ME"], "doi": "10.1214/088342307000000159", "_query": "hedonic regression housing", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2008.10230v2", "title": "Unified Bayesian theory of sparse linear regression with nuisance parameters", "abstract": "We study frequentist asymptotic properties of Bayesian procedures for high-dimensional Gaussian sparse regression when unknown nuisance parameters are involved. Nuisance parameters can be finite-, high-, or infinite-dimensional. A mixture of point masses at zero and continuous distributions is used for the prior distribution on sparse regression coefficients, and appropriate prior distributions are used for nuisance parameters. The optimal posterior contraction of sparse regression coefficients, hampered by the presence of nuisance parameters, is also examined and discussed. It is shown that the procedure yields strong model selection consistency. A Bernstein-von Mises-type theorem for sparse regression coefficients is also obtained for uncertainty quantification through credible sets with guaranteed frequentist coverage. Asymptotic properties of numerous examples are investigated using the theories developed in this study.", "published": "2020-08-24T07:09:50Z", "authors": ["Seonghyun Jeong", "Subhashis Ghosal"], "categories": ["math.ST"], "doi": "", "_query": "hedonic regression housing", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2406.19554v3", "title": "A Network-Based Measure of Cosponsorship Influence on Bill Passing in the United States House of Representatives", "abstract": "Each year, the United States Congress considers thousands of legislative proposals to select bills to present to the US President to sign into law. Naturally, the decision processes of members of Congress are subject to peer influence. In this paper, we examine the effect on bill passage of accrued influence between US Congress members in the US House of Representatives. We explore how the influence of a bill's cosponsors affects the bill's outcome (specifically, whether or not it passes in the House). We define a notion of influence by analyzing the structure of a network that we construct using cosponsorship dynamics. We award `influence' between a pair of Congress members when they cosponsor a bill that achieves some amount of legislative success. We find that properties of the bill cosponsorship network can be a useful signal to examine influence in Congress; they help explain why some bills pass and others fail. We compare our measure of influence to off-the-shelf centrality measures and conclude that our influence measure is more indicative of bill passage.", "published": "2024-06-27T22:10:56Z", "authors": ["Sarah Sotoudeh", "Mason A. Porter", "Sanjukta Krishnagopal"], "categories": ["cs.SI", "physics.soc-ph"], "doi": "", "_query": "hedonic regression housing", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2606.12573v2", "title": "Implementation of Linear Regression and Linear Interpolation using Reaction Networks", "abstract": "Statistical inference is a fundamental component of data science. In this work, we focus on two classical inference techniques: regression and interpolation. We propose a reaction-network-based framework for implementing linear regression, including both univariate and multivariate settings, as well as linear interpolation. Our approach encodes the outputs of these inference techniques in the steady-state concentrations of species within the reaction network. A key ingredient of the construction is a novel generalized division module capable of handling division involving negative numbers. We validate the proposed framework through in silico implementations on standard synthetic datasets and obtain the expected regression and interpolation outputs.", "published": "2026-06-10T18:23:13Z", "authors": ["Aryan Kumar", "Amey Choudhary", "Jiaxin Jin", "Chittaranjan Hens", "Abhishek Deshpande"], "categories": ["q-bio.MN", "math.DS"], "doi": "", "_query": "hedonic regression housing", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2505.10738v2", "title": "Statistically Significant Linear Regression Coefficients Solely Driven By Outliers In Finite-sample Inference", "abstract": "In this paper, we investigate the impact of outliers on the statistical significance of coefficients in linear regression. We demonstrate, through numerical simulation using R, that a single outlier can cause an otherwise insignificant coefficient to appear statistically significant. We compare this with robust Huber regression, which reduces the effects of outliers. Afterwards, we approximate the influence of a single outlier on estimated regression coefficients and discuss common diagnostic statistics to detect influential observations in regression (e.g., studentized residuals). Furthermore, we relate this issue to the optional normality assumption in simple linear regression [14], required for exact finite-sample inference but asymptotically justified for large n by the Central Limit Theorem (CLT). We also address the general dangers of relying solely on p-values without performing adequate regression diagnostics. Finally, we provide a brief overview of regression methods and discuss how they relate to the assumptions of the Gauss-Markov theorem.", "published": "2025-05-15T22:51:20Z", "authors": ["Felix Reichel"], "categories": ["stat.ME", "econ.EM", "math.ST"], "doi": "", "_query": "hedonic regression housing", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/1608.01532v4", "title": "Fixed-Effect Regressions on Network Data", "abstract": "This paper considers inference on fixed effects in a linear regression model estimated from network data. An important special case of our setup is the two-way regression model. This is a workhorse technique in the analysis of matched data sets, such as employer-employee or student-teacher panel data. We formalize how the structure of the network affects the accuracy with which the fixed effects can be estimated. This allows us to derive sufficient conditions on the network for consistent estimation and asymptotically-valid inference to be possible. Estimation of moments is also considered. We allow for general networks and our setup covers both the dense and sparse case. We provide numerical results for the estimation of teacher value-added models and regressions with occupational dummies.", "published": "2016-08-04T13:44:45Z", "authors": ["Koen Jochmans", "Martin Weidner"], "categories": ["stat.ME", "econ.EM"], "doi": "", "_query": "hedonic regression housing", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2208.07353v2", "title": "Easy Differentially Private Linear Regression", "abstract": "Linear regression is a fundamental tool for statistical analysis. This has motivated the development of linear regression methods that also satisfy differential privacy and thus guarantee that the learned model reveals little about any one data point used to construct it. However, existing differentially private solutions assume that the end user can easily specify good data bounds and hyperparameters. Both present significant practical obstacles. In this paper, we study an algorithm which uses the exponential mechanism to select a model with high Tukey depth from a collection of non-private regression models. Given $n$ samples of $d$-dimensional data used to train $m$ models, we construct an efficient analogue using an approximate Tukey depth that runs in time $O(d^2n + dm\\log(m))$. We find that this algorithm obtains strong empirical performance in the data-rich setting with no data bounds or hyperparameter selection required.", "published": "2022-08-15T17:42:27Z", "authors": ["Kareem Amin", "Matthew Joseph", "Mónica Ribero", "Sergei Vassilvitskii"], "categories": ["cs.LG", "cs.CR"], "doi": "", "_query": "hedonic regression housing", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2202.00467v1", "title": "Safe Screening for Logistic Regression with $\\ell_0$-$\\ell_2$ Regularization", "abstract": "In logistic regression, it is often desirable to utilize regularization to promote sparse solutions, particularly for problems with a large number of features compared to available labels. In this paper, we present screening rules that safely remove features from logistic regression with $\\ell_0-\\ell_2$ regularization before solving the problem. The proposed safe screening rules are based on lower bounds from the Fenchel dual of strong conic relaxations of the logistic regression problem. Numerical experiments with real and synthetic data suggest that a high percentage of the features can be effectively and safely removed apriori, leading to substantial speed-up in the computations.", "published": "2022-02-01T15:25:54Z", "authors": ["Anna Deza", "Alper Atamturk"], "categories": ["stat.ML", "cs.LG", "stat.ME"], "doi": "", "_query": "hedonic regression housing", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/1912.06995v1", "title": "On function-on-function regression: Partial least squares approach", "abstract": "Functional data analysis tools, such as function-on-function regression models, have received considerable attention in various scientific fields because of their observed high-dimensional and complex data structures. Several statistical procedures, including least squares, maximum likelihood, and maximum penalized likelihood, have been proposed to estimate such function-on-function regression models. However, these estimation techniques produce unstable estimates in the case of degenerate functional data or are computationally intensive. To overcome these issues, we proposed a partial least squares approach to estimate the model parameters in the function-on-function regression model. In the proposed method, the B-spline basis functions are utilized to convert discretely observed data into their functional forms. Generalized cross-validation is used to control the degrees of roughness. The finite-sample performance of the proposed method was evaluated using several Monte-Carlo simulations and an empirical data analysis. The results reveal that the proposed method competes favorably with existing estimation techniques and some other available function-on-function regression models, with significantly shorter computational time.", "published": "2019-12-15T07:21:11Z", "authors": ["Ufuk Beyaztas", "Han Lin Shang"], "categories": ["stat.ME", "stat.AP"], "doi": "10.1007/s10651-019-00436-1", "_query": "hedonic regression housing", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2610.20149v1", "title": "Elementary Proofs of Commonality of the House Graph and $K_4$ with One Edge Subdivided", "abstract": "A graph $H$ is called common if the number of monochromatic copies of $H$ in a $2$-edge-coloring of a large complete graph is asymptotically minimized by the random coloring with an equal proportion of each color. The house graph and $K_4$ with one edge subdivided once are known to be common by the flag algebra method. Grzesik, Lee, Lidický, and Volec asked for ``human-friendly'' proofs of the commonality of these two graphs. In this short note, we give flag-algebra-free proofs that both graphs are common.", "published": "2026-10-14T13:08:56Z", "authors": ["Hao Chen"], "categories": ["math.CO"], "doi": "", "_query": "hedonic regression housing", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2003.12643v1", "title": "Random Machines Regression Approach: an ensemble support vector regression model with free kernel choice", "abstract": "Machine learning techniques always aim to reduce the generalized prediction error. In order to reduce it, ensemble methods present a good approach combining several models that results in a greater forecasting capacity. The Random Machines already have been demonstrated as strong technique, i.e: high predictive power, to classification tasks, in this article we propose an procedure to use the bagged-weighted support vector model to regression problems. Simulation studies were realized over artificial datasets, and over real data benchmarks. The results exhibited a good performance of Regression Random Machines through lower generalization error without needing to choose the best kernel function during tuning process.", "published": "2020-03-27T21:30:59Z", "authors": ["Anderson Ara", "Mateus Maia", "Samuel Macêdo", "Francisco Louzada"], "categories": ["stat.ML", "cs.LG", "stat.AP"], "doi": "", "_query": "hedonic regression housing", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2102.09554v2", "title": "Delving into Deep Imbalanced Regression", "abstract": "Real-world data often exhibit imbalanced distributions, where certain target values have significantly fewer observations. Existing techniques for dealing with imbalanced data focus on targets with categorical indices, i.e., different classes. However, many tasks involve continuous targets, where hard boundaries between classes do not exist. We define Deep Imbalanced Regression (DIR) as learning from such imbalanced data with continuous targets, dealing with potential missing data for certain target values, and generalizing to the entire target range. Motivated by the intrinsic difference between categorical and continuous label space, we propose distribution smoothing for both labels and features, which explicitly acknowledges the effects of nearby targets, and calibrates both label and learned feature distributions. We curate and benchmark large-scale DIR datasets from common real-world tasks in computer vision, natural language processing, and healthcare domains. Extensive experiments verify the superior performance of our strategies. Our work fills the gap in benchmarks and techniques for practical imbalanced regression problems. Code and data are available at https://github.com/YyzHarry/imbalanced-regression.", "published": "2021-02-18T18:56:03Z", "authors": ["Yuzhe Yang", "Kaiwen Zha", "Ying-Cong Chen", "Hao Wang", "Dina Katabi"], "categories": ["cs.LG", "cs.AI", "cs.CV"], "doi": "", "_query": "hedonic regression housing", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2305.03894v1", "title": "Twin support vector quantile regression", "abstract": "We propose a twin support vector quantile regression (TSVQR) to capture the heterogeneous and asymmetric information in modern data. Using a quantile parameter, TSVQR effectively depicts the heterogeneous distribution information with respect to all portions of data points. Correspondingly, TSVQR constructs two smaller sized quadratic programming problems (QPPs) to generate two nonparallel planes to measure the distributional asymmetry between the lower and upper bounds at each quantile level. The QPPs in TSVQR are smaller and easier to solve than those in previous quantile regression methods. Moreover, the dual coordinate descent algorithm for TSVQR also accelerates the training speed. Experimental results on six artiffcial data sets, ffve benchmark data sets, two large scale data sets, two time-series data sets, and two imbalanced data sets indicate that the TSVQR outperforms previous quantile regression methods in terms of the effectiveness of completely capturing the heterogeneous and asymmetric information and the efffciency of the learning process.", "published": "2023-05-06T02:08:19Z", "authors": ["Yafen Ye", "Zhihu Xu", "Jinhua Zhang", "Weijie Chen", "Yuanhai Shao"], "categories": ["stat.ML", "cs.LG"], "doi": "", "_query": "hedonic regression housing", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2609.16050v1", "title": "Component-weighted sparse group LASSO for finite mixture regression models", "abstract": "When analyzing heterogeneous data with latent subpopulations, finite mixture regression (FMR) models are effective as they allow for variations in regression coefficients across mixture components. Variable selection is important at two levels: (1) at the group level by removing completely irrelevant covariates across all subpopulations and (2) at the individual level by removing irrelevant covariates within each subpopulation. However, existing variable selection methods for FMR focus solely on individual-level regression coefficient selection, providing no mechanism for group-level variable elimination. We introduce a component-weighted sparse group LASSO regularization method that performs efficient variable selection at both levels simultaneously in finite mixture regression models belonging to the exponential family. We develop a novel optimization procedure through a Majorization-Minimization algorithm. Simulation studies under Gaussian and Poisson mixture regression settings demonstrate reliable recovery of the true sparsity structure at both levels. The method is illustrated by analyzing two real datasets.", "published": "2026-09-12T19:50:41Z", "authors": ["Vinay Joshy", "Zeny Feng", "Grace Stelter", "Lorna E Deeth", "Alysha Cooper"], "categories": ["stat.ME", "stat.AP"], "doi": "", "_query": "hedonic regression housing", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/1909.00515v2", "title": "Bayesian Neural Tree Models for Nonparametric Regression", "abstract": "Frequentist and Bayesian methods differ in many aspects, but share some basic optimal properties. In real-life classification and regression problems, situations exist in which a model based on one of the methods is preferable based on some subjective criterion. Nonparametric classification and regression techniques, such as decision trees and neural networks, have frequentist (classification and regression trees (CART) and artificial neural networks) as well as Bayesian (Bayesian CART and Bayesian neural networks) approaches to learning from data. In this work, we present two hybrid models combining the Bayesian and frequentist versions of CART and neural networks, which we call the Bayesian neural tree (BNT) models. Both models exploit the architecture of decision trees and have lesser number of parameters to tune than advanced neural networks. Such models can simultaneously perform feature selection and prediction, are highly flexible, and generalize well in settings with a limited number of training observations. We study the consistency of the proposed models, and derive the optimal value of an important model parameter. We also provide illustrative examples using a wide variety of real-life regression data sets.", "published": "2019-09-02T02:25:18Z", "authors": ["Tanujit Chakraborty", "Gauri Kamat", "Ashis Kumar Chakraborty"], "categories": ["stat.ME"], "doi": "10.1111/anzs.12386", "_query": "hedonic regression housing", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2206.01167v1", "title": "Sparse Mixed Linear Regression with Guarantees: Taming an Intractable Problem with Invex Relaxation", "abstract": "In this paper, we study the problem of sparse mixed linear regression on an unlabeled dataset that is generated from linear measurements from two different regression parameter vectors. Since the data is unlabeled, our task is not only to figure out a good approximation of the regression parameter vectors but also to label the dataset correctly. In its original form, this problem is NP-hard. The most popular algorithms to solve this problem (such as Expectation-Maximization) have a tendency to stuck at local minima. We provide a novel invex relaxation for this intractable problem which leads to a solution with provable theoretical guarantees. This relaxation enables exact recovery of data labels. Furthermore, we recover a close approximation of the regression parameter vectors which match the true parameter vectors in support and sign. Our formulation uses a carefully constructed primal dual witnesses framework for the invex problem. Furthermore, we show that the sample complexity of our method is only logarithmic in terms of the dimension of the regression parameter vectors.", "published": "2022-06-02T17:38:11Z", "authors": ["Adarsh Barik", "Jean Honorio"], "categories": ["cs.LG", "stat.ML"], "doi": "", "_query": "hedonic regression housing", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/1912.06689v2", "title": "Data-driven confidence bands for distributed nonparametric regression", "abstract": "Gaussian Process Regression and Kernel Ridge Regression are popular nonparametric regression approaches. Unfortunately, they suffer from high computational complexity rendering them inapplicable to the modern massive datasets. To that end a number of approximations have been suggested, some of them allowing for a distributed implementation. One of them is the divide and conquer approach, splitting the data into a number of partitions, obtaining the local estimates and finally averaging them. In this paper we suggest a novel computationally efficient fully data-driven algorithm, quantifying uncertainty of this method, yielding frequentist $L_2$-confidence bands. We rigorously demonstrate validity of the algorithm. Another contribution of the paper is a minimax-optimal high-probability bound for the averaged estimator, complementing and generalizing the known risk bounds.", "published": "2019-12-13T20:13:55Z", "authors": ["Valeriy Avanesov"], "categories": ["stat.ML", "cs.LG", "math.ST"], "doi": "", "_query": "hedonic regression housing", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/1311.4482v4", "title": "A Bayesian Nonparametric Causal Model for Regression Discontinuity Designs", "abstract": "For non-randomized studies, the regression discontinuity design (RDD) can be used to identify and estimate causal effects from a \"locally-randomized\" subgroup of subjects, under relatively mild conditions. However, current models focus causal inferences on the impact of the treatment (versus non-treatment) variable on the mean of the dependent variable, via linear regression. For RDDs, we propose a flexible Bayesian nonparametric regression model that can provide accurate estimates of causal effects, in terms of the predictive mean, variance, quantile, probability density, distribution function, or any other chosen function of the outcome variable. We illustrate the model through the analysis of two real educational data sets, involving (resp.) a sharp RDD and a fuzzy RDD.", "published": "2013-11-18T18:34:01Z", "authors": ["George Karabatsos", "Stephen G. Walker"], "categories": ["stat.ME"], "doi": "", "_query": "hedonic regression housing", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/1003.4765v3", "title": "Nonparametric Least Squares Estimation of a Multivariate Convex Regression Function", "abstract": "This paper deals with the consistency of the least squares estimator of a convex regression function when the predictor is multidimensional. We characterize and discuss the computation of such an estimator via the solution of certain quadratic and linear programs. Mild sufficient conditions for the consistency of this estimator and its subdifferentials in fixed and stochastic design regression settings are provided. We also consider a regression function which is known to be convex and componentwise nonincreasing and discuss the characterization, computation and consistency of its least squares estimator.", "published": "2010-03-24T22:21:59Z", "authors": ["Emilio Seijo", "Bodhisattva Sen"], "categories": ["math.ST"], "doi": "", "_query": "hedonic regression housing", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/1205.6248v2", "title": "Some Counterexamples Concerning Maximal Correlation and Linear Regression", "abstract": "A class of examples concerning the relationship of linear regression and maximal correlation is provided. More precisely, these examples show that if two random variables have (strictly) linear regression on each other, then their maximal correlation is not necessarily equal to their (absolute) correlation.", "published": "2012-05-29T02:19:56Z", "authors": ["Nickos Papadatos"], "categories": ["math.ST"], "doi": "10.1016/j.jmva..2013.12.008.", "_query": "hedonic regression housing", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2010.04133v2", "title": "A User-Friendly Computational Framework for Robust Structured Regression with the L$_2$ Criterion", "abstract": "We introduce a user-friendly computational framework for implementing robust versions of a wide variety of structured regression methods with the L$_{2}$ criterion. In addition to introducing an algorithm for performing L$_{2}$E regression, our framework enables robust regression with the L$_{2}$ criterion for additional structural constraints, works without requiring complex tuning procedures on the precision parameter, can be used to identify heterogeneous subpopulations, and can incorporate readily available non-robust structured regression solvers. We provide convergence guarantees for the framework and demonstrate its flexibility with some examples. Supplementary materials for this article are available online.", "published": "2020-10-08T17:24:40Z", "authors": ["Jocelyn T. Chi", "Eric C. Chi"], "categories": ["stat.CO"], "doi": "", "_query": "hedonic regression housing", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2411.12294v1", "title": "Adaptive Forward Stepwise Regression", "abstract": "This paper proposes a sparse regression method that continuously interpolates between Forward Stepwise selection (FS) and the LASSO. When tuned appropriately, our solutions are much sparser than typical LASSO fits but, unlike FS fits, benefit from the stabilizing effect of shrinkage. Our method, Adaptive Forward Stepwise Regression (AFS) addresses this need for sparser models with shrinkage. We show its connection with boosting via a soft-thresholding viewpoint and demonstrate the ease of adapting the method to classification tasks. In both simulations and real data, our method has lower mean squared error and fewer selected features across multiple settings compared to popular sparse modeling procedures.", "published": "2024-11-19T07:29:31Z", "authors": ["Ivy Zhang", "Robert Tibshirani"], "categories": ["stat.ME"], "doi": "", "_query": "hedonic regression housing", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/1002.1538v1", "title": "Sharp non-asymptotic oracle inequalities for nonparametric heteroscedastic regression models", "abstract": "An adaptive nonparametric estimation procedure is constructed for heteroscedastic regression when the noise variance depends on the unknown regression. A non-asymptotic upper bound for a quadratic risk (oracle inequality) is obtained", "published": "2010-02-08T07:52:13Z", "authors": ["Leonid Galtchouk", "Serguei Pergamenchtchikov"], "categories": ["math.ST"], "doi": "", "_query": "hedonic regression housing", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2009.09414v1", "title": "Skewed probit regression -- Identifiability, contraction and reformulation", "abstract": "Skewed probit regression is but one example of a statistical model that generalizes a simpler model, like probit regression. All skew-symmetric distributions and link functions arise from symmetric distributions by incorporating a skewness parameter through some skewing mechanism. In this work we address some fundamental issues in skewed probit regression, and more genreally skew-symmetric distributions or skew-symmetric link functions. We address the issue of identifiability of the skewed probit model parameters by reformulating the intercept from first principles. A new standardization of the skew link function is given to provide and anchored interpretation of the inference. Possible skewness parameters are investigated and the penalizing complexity priors of these are derived. This prior is invariant under reparameterization of the skewness parameter and quantifies the contraction of the skewed probit model to the probit model. The proposed results are available in the R-INLA package and we illustrate the use and effects of this work using simulated data, and well-known datasets using the link as well as the likelihood.", "published": "2020-09-20T11:58:56Z", "authors": ["Janet van Niekerk", "Haavard Rue"], "categories": ["stat.ME"], "doi": "", "_query": "hedonic regression housing", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2411.04909v2", "title": "A doubly robust learner for regression and inference with right-censored outcomes", "abstract": "This paper introduces a general framework for doubly robust nonparametric regression with right-censored outcomes, adapting the strategy of the 'DR-learner' for heterogeneous treatment effects to the censored data setting. Our method generalizes censoring unbiased transformations to generate pseudo-outcomes, which serve as inputs for a second-stage nonparametric regression with sample-splitting or cross-fitting. We derive a novel representation of the conditional bias of these pseudo-outcomes; this representation is central to establishing the estimator's asymptotic properties, including rate double robustness and oracle efficiency when the second-stage regression is a linear smoother. Simulation studies demonstrate the method's favorable finite-sample performance, and we showcase its practical utility by applying it to estimate a causal effect within a regression discontinuity design with right-censored outcomes.", "published": "2024-11-07T17:48:54Z", "authors": ["Oliver Lunding Sandqvist"], "categories": ["stat.ME", "math.ST"], "doi": "", "_query": "hedonic regression housing", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/1411.6160v2", "title": "Characterization of the equivalence of robustification and regularization in linear and matrix regression", "abstract": "The notion of developing statistical methods in machine learning which are robust to adversarial perturbations in the underlying data has been the subject of increasing interest in recent years. A common feature of this work is that the adversarial robustification often corresponds exactly to regularization methods which appear as a loss function plus a penalty. In this paper we deepen and extend the understanding of the connection between robustification and regularization (as achieved by penalization) in regression problems. Specifically, (a) in the context of linear regression, we characterize precisely under which conditions on the model of uncertainty used and on the loss function penalties robustification and regularization are equivalent, and (b) we extend the characterization of robustification and regularization to matrix regression problems (matrix completion and Principal Component Analysis).", "published": "2014-11-22T19:59:32Z", "authors": ["Dimitris Bertsimas", "Martin S. Copenhaver"], "categories": ["math.ST", "cs.LG", "math.OC", "stat.ML"], "doi": "", "_query": "hedonic regression housing", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2306.16095v1", "title": "Linear regression for Poisson count data: A new semi-analytical method with applications to COVID-19 events", "abstract": "This paper presents the application of a new semi-analytical method of linear regression for Poisson count data to COVID-19 events. The regression is based on the Bonamente and Spence (2022) maximum-likelihood solution for the best-fit parameters, and this paper introduces a simple analytical solution for the covariance matrix that completes the problem of linear regression with Poisson data. The analytical nature for both parameter estimates and their covariance matrix is made possible by a convenient factorization of the linear model proposed by J. Scargle (2013). The method makes use of the asymptotic properties of the Fisher information matrix, whose inverse provides the covariance matrix. The combination of simple analytical methods to obtain both the maximum-likelihood estimates of the parameters, and their covariance matrix, constitute a new and convenient method for the linear regression of Poisson-distributed count data, which are of common occurrence across a variety of fields. A comparison between this new linear regression method and two alternative methods often used for the regression of count data -- the ordinary least-square regression and the $χ^2$ regression -- is provided with the application of these methods to the analysis of recent COVID-19 count data. The paper also discusses the relative advantages and disadvantages among these methods for the linear regression of Poisson count data.", "published": "2023-06-28T10:49:58Z", "authors": ["M. Bonamente"], "categories": ["stat.ME", "stat.AP"], "doi": "", "_query": "hedonic regression housing", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/1906.00273v3", "title": "Robust approximate linear regression without correspondence", "abstract": "We propose methods for estimating correspondence between two point sets under the presence of outliers in both the source and target sets. The proposed algorithms expand upon the theory of the regression without correspondence problem to estimate transformation coefficients using unordered multisets of covariates and responses. Previous theoretical analysis of the problem has been done in a setting where the responses are a complete permutation of the regressed covariates. This paper expands the problem setting by analyzing the cases where only a subset of the responses is a permutation of the regressed covariates in addition to some covariates being outliers. We term this problem \\textit{robust regression without correspondence} and provide several algorithms based on random sample consensus for exact and approximate recovery in a noiseless and noisy one-dimensional setting as well as an approximation algorithm for multiple dimensions. The theoretical guarantees of the algorithms are verified in simulated data. We demonstrate an important computational neuroscience application of the proposed framework by demonstrating its effectiveness in a \\textit{Caenorhabditis elegans} neuron matching problem where the presence of outliers in both the source and target nematodes is a natural tendency.", "published": "2019-06-01T19:15:39Z", "authors": ["Amin Nejatbakhsh", "Erdem Varol"], "categories": ["stat.ML", "cs.LG"], "doi": "", "_query": "hedonic regression housing", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2105.02874v1", "title": "A Metamodel Structure For Regression Analysis: Application To Prediction Of Autism Spectrum Disorder Severity", "abstract": "Traditional regression models do not generalize well when learning from small and noisy datasets. Here we propose a novel metamodel structure to improve the regression result. The metamodel is composed of multiple classification base models and a regression model built upon the base models. We test this structure on the prediction of autism spectrum disorder (ASD) severity as measured by the ADOS communication (ADOS COMM) score from resting-state fMRI data, using a variety of base models. The metamodel outperforms traditional regression models as measured by the Pearson correlation coefficient between true and predicted scores and stability. In addition, we found that the metamodel is more flexible and more generalizable.", "published": "2021-05-06T17:58:16Z", "authors": ["Shiyu Wang", "Nicha C. Dvornek"], "categories": ["cs.LG", "eess.IV", "q-bio.QM", "stat.AP"], "doi": "", "_query": "hedonic regression housing", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2405.15002v1", "title": "Private Regression via Data-Dependent Sufficient Statistic Perturbation", "abstract": "Sufficient statistic perturbation (SSP) is a widely used method for differentially private linear regression. SSP adopts a data-independent approach where privacy noise from a simple distribution is added to sufficient statistics. However, sufficient statistics can often be expressed as linear queries and better approximated by data-dependent mechanisms. In this paper we introduce data-dependent SSP for linear regression based on post-processing privately released marginals, and find that it outperforms state-of-the-art data-independent SSP. We extend this result to logistic regression by developing an approximate objective that can be expressed in terms of sufficient statistics, resulting in a novel and highly competitive SSP approach for logistic regression. We also make a connection to synthetic data for machine learning: for models with sufficient statistics, training on synthetic data corresponds to data-dependent SSP, with the overall utility determined by how well the mechanism answers these linear queries.", "published": "2024-05-23T19:09:50Z", "authors": ["Cecilia Ferrando", "Daniel Sheldon"], "categories": ["cs.LG", "stat.ML"], "doi": "", "_query": "hedonic regression housing", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/1812.07408v1", "title": "A residual for outlier identification in zero adjusted regression models", "abstract": "Zero adjusted regression models are used to fit variables that are discrete at zero and continuous at some interval of the positive real numbers. Diagnostic analysis in these models is usually performed using the randomized quantile residual, which is useful for checking the overall adequacy of a zero adjusted regression model. However, it may fail to identify some outliers. In this work, we introduce a residual for outlier identification in zero adjusted regression models. Monte Carlo simulation studies and an application suggest that the residual introduced here has good properties and detects outliers that are not identified by the randomized quantile residual.", "published": "2018-12-18T14:50:30Z", "authors": ["Gustavo H. A. Pereira", "Juliana S. Rodrigues", "Manoel Santos Neto", "Denise A. Botter", "Mônica C. Sandoval"], "categories": ["stat.ME"], "doi": "", "_query": "hedonic regression housing", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2308.02966v1", "title": "Generalized Oversampling for Learning from Imbalanced datasets and Associated Theory", "abstract": "In supervised learning, it is quite frequent to be confronted with real imbalanced datasets. This situation leads to a learning difficulty for standard algorithms. Research and solutions in imbalanced learning have mainly focused on classification tasks. Despite its importance, very few solutions exist for imbalanced regression. In this paper, we propose a data augmentation procedure, the GOLIATH algorithm, based on kernel density estimates which can be used in classification and regression. This general approach encompasses two large families of synthetic oversampling: those based on perturbations, such as Gaussian Noise, and those based on interpolations, such as SMOTE. It also provides an explicit form of these machine learning algorithms and an expression of their conditional densities, in particular for SMOTE. New synthetic data generators are deduced. We apply GOLIATH in imbalanced regression combining such generator procedures with a wild-bootstrap resampling technique for the target values. We evaluate the performance of the GOLIATH algorithm in imbalanced regression situations. We empirically evaluate and compare our approach and demonstrate significant improvement over existing state-of-the-art techniques.", "published": "2023-08-05T23:08:08Z", "authors": ["Samuel Stocksieker", "Denys Pommeret", "Arthur Charpentier"], "categories": ["stat.ML", "cs.LG"], "doi": "", "_query": "hedonic regression housing", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/0708.3781v1", "title": "Rejoinder: Fisher Lecture: Dimension Reduction in Regression", "abstract": "Rejoinder: Fisher Lecture: Dimension Reduction in Regression [arXiv:0708.3774]", "published": "2007-08-28T13:18:35Z", "authors": ["R. Dennis Cook"], "categories": ["stat.ME"], "doi": "10.1214/088342307000000078", "_query": "hedonic regression housing", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2103.12802v1", "title": "Numerical Characterization of Support Recovery in Sparse Regression with Correlated Design", "abstract": "Sparse regression is frequently employed in diverse scientific settings as a feature selection method. A pervasive aspect of scientific data that hampers both feature selection and estimation is the presence of strong correlations between predictive features. These fundamental issues are often not appreciated by practitioners, and jeapordize conclusions drawn from estimated models. On the other hand, theoretical results on sparsity-inducing regularized regression such as the Lasso have largely addressed conditions for selection consistency via asymptotics, and disregard the problem of model selection, whereby regularization parameters are chosen. In this numerical study, we address these issues through exhaustive characterization of the performance of several regression estimators, coupled with a range of model selection strategies. These estimators and selection criteria were examined across correlated regression problems with varying degrees of signal to noise, distribution of the non-zero model coefficients, and model sparsity. Our results reveal a fundamental tradeoff between false positive and false negative control in all regression estimators and model selection criteria examined. Additionally, we are able to numerically explore a transition point modulated by the signal-to-noise ratio and spectral properties of the design covariance matrix at which the selection accuracy of all considered algorithms degrades. Overall, we find that SCAD coupled with BIC or empirical Bayes model selection performs the best feature selection across the regression problems considered.", "published": "2021-03-23T19:13:26Z", "authors": ["Ankit Kumar", "Sharmodeep Bhattacharyya", "Kristofer Bouchard"], "categories": ["stat.ME", "stat.AP"], "doi": "", "_query": "hedonic regression housing", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2605.15809v1", "title": "Diversified Residual Symbolic Regression", "abstract": "Symbolic regression (SR) aims to discover explicit mathematical expressions that explain observed data and is widely used in domains where interpretability is essential. Because interpretability requires expressions to reflect meaningful regularities, SR is sensitive to observations that deviate from the dominant relationship. Such irregular observations, or outliers, are common in real-world data and can hinder SR from identifying underlying regularities. Robust regression mitigates this by downweighting observations with large residuals. However, deciding which observations should be treated as outliers is often ambiguous and depends on user interpretation and domain knowledge, a perspective largely overlooked in existing SR studies. This motivates approaches that present multiple candidate expressions, allowing users to examine different residual patterns and choose expressions consistent with their expertise. We propose diversified residual symbolic regression (DRSR), which achieves high predictive accuracy while promoting diversity with respect to residual patterns based on the Quality-Diversity paradigm. DRSR collects multiple expressions that fit the data well but differ in how residuals are distributed, enabling post-search selection aligned with domain knowledge. On a synthetic mixture dataset, DRSR produces more diverse expressions than conventional SR while capturing multiple underlying relationships. On a real-world astronomical dataset, DRSR discovers multiple expressions consistent with known physical relationships.", "published": "2026-05-15T10:04:03Z", "authors": ["Koki Ikeda", "Masahiro Nomura", "Ryoki Hamano"], "categories": ["cs.NE"], "doi": "", "_query": "hedonic regression housing", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2110.04372v1", "title": "Fair Regression under Sample Selection Bias", "abstract": "Recent research on fair regression focused on developing new fairness notions and approximation methods as target variables and even the sensitive attribute are continuous in the regression setting. However, all previous fair regression research assumed the training data and testing data are drawn from the same distributions. This assumption is often violated in real world due to the sample selection bias between the training and testing data. In this paper, we develop a framework for fair regression under sample selection bias when dependent variable values of a set of samples from the training data are missing as a result of another hidden process. Our framework adopts the classic Heckman model for bias correction and the Lagrange duality to achieve fairness in regression based on a variety of fairness notions. Heckman model describes the sample selection process and uses a derived variable called the Inverse Mills Ratio (IMR) to correct sample selection bias. We use fairness inequality and equality constraints to describe a variety of fairness notions and apply the Lagrange duality theory to transform the primal problem into the dual convex optimization. For the two popular fairness notions, mean difference and mean squared error difference, we derive explicit formulas without iterative optimization, and for Pearson correlation, we derive its conditions of achieving strong duality. We conduct experiments on three real-world datasets and the experimental results demonstrate the approach's effectiveness in terms of both utility and fairness metrics.", "published": "2021-10-08T20:35:12Z", "authors": ["Wei Du", "Xintao Wu", "Hanghang Tong"], "categories": ["cs.LG", "cs.AI", "cs.CY"], "doi": "", "_query": "hedonic regression housing", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/1010.0514v1", "title": "Quantile calculus and censored regression", "abstract": "Quantile regression has been advocated in survival analysis to assess evolving covariate effects. However, challenges arise when the censoring time is not always observed and may be covariate-dependent, particularly in the presence of continuously-distributed covariates. In spite of several recent advances, existing methods either involve algorithmic complications or impose a probability grid. The former leads to difficulties in the implementation and asymptotics, whereas the latter introduces undesirable grid dependence. To resolve these issues, we develop fundamental and general quantile calculus on cumulative probability scale in this article, upon recognizing that probability and time scales do not always have a one-to-one mapping given a survival distribution. These results give rise to a novel estimation procedure for censored quantile regression, based on estimating integral equations. A numerically reliable and efficient Progressive Localized Minimization (PLMIN) algorithm is proposed for the computation. This procedure reduces exactly to the Kaplan--Meier method in the $k$-sample problem, and to standard uncensored quantile regression in the absence of censoring. Under regularity conditions, the proposed quantile coefficient estimator is uniformly consistent and converges weakly to a Gaussian process. Simulations show good statistical and algorithmic performance. The proposal is illustrated in the application to a clinical study.", "published": "2010-10-04T09:24:59Z", "authors": ["Yijian Huang"], "categories": ["math.ST"], "doi": "10.1214/09-AOS771", "_query": "hedonic regression housing", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/1403.7267v1", "title": "Systematic Ensemble Learning for Regression", "abstract": "The motivation of this work is to improve the performance of standard stacking approaches or ensembles, which are composed of simple, heterogeneous base models, through the integration of the generation and selection stages for regression problems. We propose two extensions to the standard stacking approach. In the first extension we combine a set of standard stacking approaches into an ensemble of ensembles using a two-step ensemble learning in the regression setting. The second extension consists of two parts. In the initial part a diversity mechanism is injected into the original training data set, systematically generating different training subsets or partitions, and corresponding ensembles of ensembles. In the final part after measuring the quality of the different partitions or ensembles, a max-min rule-based selection algorithm is used to select the most appropriate ensemble/partition on which to make the final prediction. We show, based on experiments over a broad range of data sets, that the second extension performs better than the best of the standard stacking approaches, and is as good as the oracle of databases, which has the best base model selected by cross-validation for each data set. In addition to that, the second extension performs better than two state-of-the-art ensemble methods for regression, and it is as good as a third state-of-the-art ensemble method.", "published": "2014-03-28T01:36:27Z", "authors": ["Roberto Aldave", "Jean-Pierre Dussault"], "categories": ["stat.ML"], "doi": "", "_query": "hedonic regression housing", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2206.04231v3", "title": "JNMR: Joint Non-linear Motion Regression for Video Frame Interpolation", "abstract": "Video frame interpolation (VFI) aims to generate predictive frames by warping learnable motions from the bidirectional historical references. Most existing works utilize spatio-temporal semantic information extractor to realize motion estimation and interpolation modeling. However, they insufficiently consider the real mechanistic rationality of generated middle motions. In this paper, we reformulate VFI as a Joint Non-linear Motion Regression (JNMR) strategy to model the complicated motions of inter-frame. Specifically, the motion trajectory between the target frame and the multiple reference frames is regressed by a temporal concatenation of multi-stage quadratic models. ConvLSTM is adopted to construct this joint distribution of complete motions in temporal dimension. Moreover, the feature learning network is designed to optimize for the joint regression modeling. A coarse-to-fine synthesis enhancement module is also conducted to learn visual dynamics at different resolutions through repetitive regression and interpolation. Experimental results on VFI show that the effectiveness and significant improvement of joint motion regression compared with the state-of-the-art methods. The code is available at https://github.com/ruhig6/JNMR.", "published": "2022-06-09T02:47:29Z", "authors": ["Meiqin Liu", "Chenming Xu", "Chao Yao", "Chunyu Lin", "Yao Zhao"], "categories": ["cs.CV"], "doi": "", "_query": "hedonic regression housing", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/1909.12331v2", "title": "SIMEX Estimation in Parametric Modal Regression with Measurement Error", "abstract": "For a class of parametric modal regression models with measurement error, a simulation extrapolation estimation procedure is proposed in this paper for estimating the modal regression coefficients. Large sample properties of the proposed estimation procedure, including the consistency and asymptotic normality, are thoroughly investigated. Simulation studies are conducted to evaluate its robustness to potential outliers and the effectiveness in reducing the bias caused by the measurement error.", "published": "2019-09-26T18:49:47Z", "authors": ["Jianhong Shi", "Yujing Zhang", "Ping Yu", "Weixing Song"], "categories": ["stat.ME"], "doi": "", "_query": "hedonic regression housing", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2512.16276v1", "title": "Repulsive g-Priors for Regression Mixtures", "abstract": "Mixture regression models are powerful tools for capturing heterogeneous covariate-response relationships, yet classical finite mixtures and Bayesian nonparametric alternatives often suffer from instability or overestimation of clusters when component separability is weak. Recent repulsive priors improve parsimony in density mixtures by discouraging nearby components, but their direct extension to regression is nontrivial since separation must respect the predictive geometry induced by covariates. We propose a repulsive g-prior for regression mixtures that enforces separation in the Mahalanobis metric, penalizing components indistinguishable in the predictive mean space. This construction preserves conjugacy-like updates while introducing geometry-aware interactions, enabling efficient blocked-collapsed Gibbs sampling. Theoretically, we establish tractable normalizing bounds, posterior contraction rates, and shrinkage of tail mass on the number of components. Simulations under correlated and overlapping designs demonstrate improved clustering and prediction relative to independent, Euclidean-repulsive, and sparsity-inducing baselines.", "published": "2025-12-18T07:54:29Z", "authors": ["Yuta Hayashida", "Shonosuke Sugasawa"], "categories": ["stat.ME"], "doi": "", "_query": "hedonic regression housing", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2109.11281v2", "title": "High-dimensional regression with potential prior information on variable importance", "abstract": "There are a variety of settings where vague prior information may be available on the importance of predictors in high-dimensional regression settings. Examples include ordering on the variables offered by their empirical variances (which is typically discarded through standardisation), the lag of predictors when fitting autoregressive models in time series settings, or the level of missingness of the variables. Whilst such orderings may not match the true importance of variables, we argue that there is little to be lost, and potentially much to be gained, by using them. We propose a simple scheme involving fitting a sequence of models indicated by the ordering. We show that the computational cost for fitting all models when ridge regression is used is no more than for a single fit of ridge regression, and describe a strategy for Lasso regression that makes use of previous fits to greatly speed up fitting the entire sequence of models. We propose to select a final estimator by cross-validation and provide a general result on the quality of the best performing estimator on a test set selected from among a number $M$ of competing estimators in a high-dimensional linear regression setting. Our result requires no sparsity assumptions and shows that only a $\\log M$ price is incurred compared to the unknown best estimator. We demonstrate the effectiveness of our approach when applied to missing or corrupted data, and time series settings. An R package is available on github.", "published": "2021-09-23T10:34:37Z", "authors": ["Benjamin G. Stokell", "Rajen D. Shah"], "categories": ["stat.ME", "stat.CO", "stat.ML"], "doi": "", "_query": "hedonic regression housing", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2610.08481v1", "title": "Fridge: focused fine-tuning of ridge regression for personalize predictions", "abstract": "Statistical prediction methods typically require some form of fine-tuning of tuning parameter(s), with $K$-fold cross-validation as the canonical procedure. For ridge regression there exist numerous procedures, but common for all, including cross-validation, is that one single parameter is chosen for all future predictions. We propose instead to calculate a unique tuning parameter for each individual for which we wish to predict an outcome. This generates an individualized prediction by focusing on the vector of covariates of a specific individual. The focused ridge -- fridge -- procedure is introduced with a two-part contribution: 1) first we define an oracle tuning parameter minimizing the mean squared prediction error of a specific covariate vector, 2) then we propose to estimate this tuning parameter by using plug-in estimates of the regression coefficients and error variance parameter. The procedure is extended to logistic ridge regression by utilizing parametric bootstrap. For high-dimensional data, we propose to use ridge regression with cross-validation as the plug-in estimate, and simulations show that fridge gives smaller average prediction error than ridge with cross-validation for both simulated and real data. We illustrate the new concept for both linear and logistic regression models in two applications of personalized medicine: predicting individual risk and treatment response based on gene expression data. The method is implemented in the R package \"fridge\".", "published": "2026-10-06T14:57:32Z", "authors": ["Kristoffer Herland Hellton", "Nils Lid Hjort"], "categories": ["stat.ME"], "doi": "", "_query": "hedonic regression housing", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2411.02091v1", "title": "Fast Fixes and Faulty Drivers: An Empirical Analysis of Regression Bug Fixing Times in the Linux Kernel", "abstract": "Regression bugs refer to situations in which something that worked previously no longer works currently. Such bugs have been pronounced in the Linux kernel. The paper focuses on regression bug tracking in the kernel by considering the time required to fix regression bugs. The dataset examined is based on the regzbot automation framework for tracking regressions in the Linux kernel. According to the results, (i) regression bug fixing times have been faster than previously reported; between 2021 and 2024, on average, it has taken less than a month to fix regression bugs. It is further evident that (ii) device drivers constitute the most prone subsystem for regression bugs, and also the fixing times vary across the kernel's subsystems. Although (iii) most commits fixing regression bugs have been reviewed, tested, or both, the kernel's code reviewing and manual testing practices do not explain the fixing times. Likewise, (iv) there is only a weak signal that code churn might contribute to explaining the fixing times statistically. Finally, (v) some subsystems exhibit strong effects for explaining the bug fixing times statistically, although overall statistical performance is modest but not atypical to the research domain. With these empirical results, the paper contributes to the efforts to better understand software regressions and their tracking in the Linux kernel.", "published": "2024-11-04T13:53:29Z", "authors": ["Jukka Ruohonen", "Adam Alami"], "categories": ["cs.SE"], "doi": "", "_query": "hedonic regression housing", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2404.08715v1", "title": "Differentially Private Log-Location-Scale Regression Using Functional Mechanism", "abstract": "This article introduces differentially private log-location-scale (DP-LLS) regression models, which incorporate differential privacy into LLS regression through the functional mechanism. The proposed models are established by injecting noise into the log-likelihood function of LLS regression for perturbed parameter estimation. We will derive the sensitivities utilized to determine the magnitude of the injected noise and prove that the proposed DP-LLS models satisfy $ε$-differential privacy. In addition, we will conduct simulations and case studies to evaluate the performance of the proposed models. The findings suggest that predictor dimension, training sample size, and privacy budget are three key factors impacting the performance of the proposed DP-LLS regression models. Moreover, the results indicate that a sufficiently large training dataset is needed to simultaneously ensure decent performance of the proposed models and achieve a satisfactory level of privacy protection.", "published": "2024-04-12T04:14:08Z", "authors": ["Jiewen Sheng", "Xiaolei Fang"], "categories": ["stat.ML", "cs.CR", "cs.LG", "stat.AP"], "doi": "", "_query": "hedonic regression housing", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/1604.04434v1", "title": "Bayesian linear regression with Student-t assumptions", "abstract": "As an automatic method of determining model complexity using the training data alone, Bayesian linear regression provides us a principled way to select hyperparameters. But one often needs approximation inference if distribution assumption is beyond Gaussian distribution. In this paper, we propose a Bayesian linear regression model with Student-t assumptions (BLRS), which can be inferred exactly. In this framework, both conjugate prior and expectation maximization (EM) algorithm are generalized. Meanwhile, we prove that the maximum likelihood solution is equivalent to the standard Bayesian linear regression with Gaussian assumptions (BLRG). The $q$-EM algorithm for BLRS is nearly identical to the EM algorithm for BLRG. It is showed that $q$-EM for BLRS can converge faster than EM for BLRG for the task of predicting online news popularity.", "published": "2016-04-15T11:21:27Z", "authors": ["Chaobing Song", "Shu-Tao Xia"], "categories": ["cs.LG", "stat.ML"], "doi": "", "_query": "hedonic regression housing", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2111.08456v1", "title": "Trustworthy Multimodal Regression with Mixture of Normal-inverse Gamma Distributions", "abstract": "Multimodal regression is a fundamental task, which integrates the information from different sources to improve the performance of follow-up applications. However, existing methods mainly focus on improving the performance and often ignore the confidence of prediction for diverse situations. In this study, we are devoted to trustworthy multimodal regression which is critical in cost-sensitive domains. To this end, we introduce a novel Mixture of Normal-Inverse Gamma distributions (MoNIG) algorithm, which efficiently estimates uncertainty in principle for adaptive integration of different modalities and produces a trustworthy regression result. Our model can be dynamically aware of uncertainty for each modality, and also robust for corrupted modalities. Furthermore, the proposed MoNIG ensures explicitly representation of (modality-specific/global) epistemic and aleatoric uncertainties, respectively. Experimental results on both synthetic and different real-world data demonstrate the effectiveness and trustworthiness of our method on various multimodal regression tasks (e.g., temperature prediction for superconductivity, relative location prediction for CT slices, and multimodal sentiment analysis).", "published": "2021-11-11T14:28:12Z", "authors": ["Huan Ma", "Zongbo Han", "Changqing Zhang", "Huazhu Fu", "Joey Tianyi Zhou", "Qinghua Hu"], "categories": ["cs.LG", "cs.AI"], "doi": "", "_query": "hedonic regression housing", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2509.20272v1", "title": "Transfer Learning in Regression with Influential Points", "abstract": "Regression prediction plays a crucial role in practical applications and strongly relies on data annotation. However, due to prohibitive annotation costs or domain-specific constraints, labeled data in the target domain is often scarce, making transfer learning a critical solution by leveraging knowledge from resource-rich source domains. In the practical target scenario, although transfer learning has been widely applied, influential points can significantly distort parameter estimation for the target domain model. This issue is further compounded when influential points are also present in source domains, leading to aggravated performance degradation and posing critical robustness challenges for existing transfer learning frameworks. In this study, we innovatively introduce a transfer learning collaborative optimization (Trans-CO) framework for influential point detection and regression model fitting. Extensive simulation experiments demonstrate that the proposed Trans-CO algorithm outperforms competing methods in terms of model fitting performance and influential point identification accuracy. Furthermore, it achieves superior predictive accuracy on real-world datasets, providing a novel solution for transfer learning in regression with influential points", "published": "2025-09-24T16:05:08Z", "authors": ["Bingbing Wang", "Jiaqi Wang", "Yu Tang"], "categories": ["stat.ME"], "doi": "", "_query": "hedonic regression housing", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2509.12587v2", "title": "Inverse regression for causal inference with multiple outcomes", "abstract": "With multiple outcomes in empirical research, a common strategy is to define a composite outcome as a weighted average of the original outcomes. However, the choices of weights are often subjective and can be controversial. We propose an inverse regression strategy for causal inference with multiple outcomes. The key idea is to regress the treatment on the outcomes, which is the inverse of the standard regression of the outcomes on the treatment. Although this strategy is simple and even counterintuitive, it has several advantages. First, testing for zero coefficients of the outcomes is equivalent to testing for zero treatment effects, even though the inverse regression is deemed misspecified. Second, the coefficients of the outcomes provide data-driven weights for defining a composite outcome. Interestingly, these weights maximize the standardized effect size, a measure commonly used for univariate outcomes in empirical studies. We also discuss the associated inference issues. Third, this strategy is applicable to general study designs. We illustrate the theory in both randomized experiments and observational studies. We implement the proposed method in our R package invreg, available at https://github.com/zhangwei0125/invreg.", "published": "2025-09-16T02:26:24Z", "authors": ["Wei Zhang", "Qizhai Li", "Peng Ding"], "categories": ["stat.ME"], "doi": "", "_query": "hedonic regression housing", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/1606.05407v2", "title": "Pyramid quantile regression", "abstract": "Quantile regression models provide a wide picture of the conditional distributions of the response variable by capturing the effect of the covariates at different quantile levels. In most applications, the parametric form of those conditional distributions is unknown and varies across the covariate space, so fitting the given quantile levels simultaneously without relying on parametric assumptions is crucial. In this work we propose a Bayesian model for simultaneous linear quantile regression. More specifically, we propose to model the conditional distributions by using random probability measures known as quantile pyramids. Unlike many existing approaches, our framework allows us to specify meaningful priors on the conditional distributions, whilst retaining the flexibility afforded by the nonparametric error distribution formulation. Simulation studies demonstrate the flexibility of the proposed approach in estimating diverse scenarios, generally outperforming other competitive methods. The method is particularly promising for modelling the extremal quantiles. Applications to linear splines and extreme value analysis are also explored through real data examples.", "published": "2016-06-17T02:31:38Z", "authors": ["T. Rodrigues", "J. -L. Dortet-Bernadet", "Y. Fan"], "categories": ["stat.ME"], "doi": "", "_query": "hedonic regression housing", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/1401.3889v5", "title": "Exact Post-Selection Inference for Sequential Regression Procedures", "abstract": "We propose new inference tools for forward stepwise regression, least angle regression, and the lasso. Assuming a Gaussian model for the observation vector y, we first describe a general scheme to perform valid inference after any selection event that can be characterized as y falling into a polyhedral set. This framework allows us to derive conditional (post-selection) hypothesis tests at any step of forward stepwise or least angle regression, or any step along the lasso regularization path, because, as it turns out, selection events for these procedures can be expressed as polyhedral constraints on y. The p-values associated with these tests are exactly uniform under the null distribution, in finite samples, yielding exact type I error control. The tests can also be inverted to produce confidence intervals for appropriate underlying regression parameters. The R package \"selectiveInference\", freely available on the CRAN repository, implements the new inference tools described in this paper.", "published": "2014-01-16T05:15:57Z", "authors": ["Ryan J. Tibshirani", "Jonathan Taylor", "Richard Lockhart", "Robert Tibshirani"], "categories": ["stat.ME"], "doi": "", "_query": "hedonic regression housing", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2502.02277v1", "title": "Error Distribution Smoothing:Advancing Low-Dimensional Imbalanced Regression", "abstract": "In real-world regression tasks, datasets frequently exhibit imbalanced distributions, characterized by a scarcity of data in high-complexity regions and an abundance in low-complexity areas. This imbalance presents significant challenges for existing classification methods with clear class boundaries, while highlighting a scarcity of approaches specifically designed for imbalanced regression problems. To better address these issues, we introduce a novel concept of Imbalanced Regression, which takes into account both the complexity of the problem and the density of data points, extending beyond traditional definitions that focus only on data density. Furthermore, we propose Error Distribution Smoothing (EDS) as a solution to tackle imbalanced regression, effectively selecting a representative subset from the dataset to reduce redundancy while maintaining balance and representativeness. Through several experiments, EDS has shown its effectiveness, and the related code and dataset can be accessed at https://anonymous.4open.science/r/Error-Distribution-Smoothing-762F.", "published": "2025-02-04T12:40:07Z", "authors": ["Donghe Chen", "Jiaxuan Yue", "Tengjie Zheng", "Lanxuan Wang", "Lin Cheng"], "categories": ["cs.LG", "cs.AI"], "doi": "", "_query": "hedonic regression housing", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2406.09694v2", "title": "An Efficient Approach to Regression Problems with Tensor Neural Networks", "abstract": "This paper introduces a tensor neural network (TNN) to address nonparametric regression problems, leveraging its distinct sub-network structure to effectively facilitate variable separation and enhance the approximation of complex, high-dimensional functions. The TNN demonstrates superior performance compared to conventional Feed-Forward Networks (FFN) and Radial Basis Function Networks (RBN) in terms of both approximation accuracy and generalization capacity, even with a comparable number of parameters. A significant innovation in our approach is the integration of statistical regression and numerical integration within the TNN framework. This allows for efficient computation of high-dimensional integrals associated with the regression function and provides detailed insights into the underlying data structure. Furthermore, we employ gradient and Laplacian analysis on the regression outputs to identify key dimensions influencing the predictions, thereby guiding the design of subsequent experiments. These advancements make TNN a powerful tool for applications requiring precise high-dimensional data analysis and predictive modeling.", "published": "2024-06-14T03:38:40Z", "authors": ["Yongxin Li", "Yifan Wang", "Zhongshuo Lin", "Hehu Xie"], "categories": ["stat.ML", "cs.LG"], "doi": "", "_query": "hedonic regression housing", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2512.19550v1", "title": "DFORD: Directional Feedback based Online Ordinal Regression Learning", "abstract": "In this paper, we introduce directional feedback in the ordinal regression setting, in which the learner receives feedback on whether the predicted label is on the left or the right side of the actual label. This is a weak supervision setting for ordinal regression compared to the full information setting, where the learner can access the labels. We propose an online algorithm for ordinal regression using directional feedback. The proposed algorithm uses an exploration-exploitation scheme to learn from directional feedback efficiently. Furthermore, we introduce its kernel-based variant to learn non-linear ordinal regression models in an online setting. We use a truncation trick to make the kernel implementation more memory efficient. The proposed algorithm maintains the ordering of the thresholds in the expected sense. Moreover, it achieves the expected regret of $\\mathcal{O}(\\log T)$. We compare our approach with a full information and a weakly supervised algorithm for ordinal regression on synthetic and real-world datasets. The proposed approach, which learns using directional feedback, performs comparably (sometimes better) to its full information counterpart.", "published": "2025-12-22T16:31:14Z", "authors": ["Naresh Manwani", "M Elamparithy", "Tanish Taneja"], "categories": ["cs.LG"], "doi": "", "_query": "hedonic regression housing", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2106.06701v1", "title": "Quantum Gaussian process regression", "abstract": "In this paper, a quantum algorithm based on gaussian process regression model is proposed. The proposed quantum algorithm consists of three sub-algorithms. One is the first quantum subalgorithm to efficiently generate mean predictor. The improved HHL algorithm is proposed to obtain the sign of outcomes. Therefore, the terrible situation that results is ambiguous in terms of original HHL algorithm is avoided, which makes whole algorithm more clear and exact. The other is to product covariance predictor with same method. Thirdly, the squared exponential covariance matrices are prepared that annihilation operator and generation operator are simulated by the unitary linear decomposition Hamiltonian simulation and kernel function vectors is generated with blocking coding techniques on covariance matrices. In addition, it is shown that the proposed quantum gaussian process regression algorithm can achieve quadratic faster over the classical counterpart.", "published": "2021-06-12T07:03:27Z", "authors": ["Menghan Chen", "Gongde Guo", "Song Lin", "Jing Li"], "categories": ["quant-ph"], "doi": "10.1103/PhysRevA.106.012406", "_query": "hedonic regression housing", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/1910.04069v1", "title": "Estimating regression errors without ground truth values", "abstract": "Regression analysis is a standard supervised machine learning method used to model an outcome variable in terms of a set of predictor variables. In most real-world applications we do not know the true value of the outcome variable being predicted outside the training data, i.e., the ground truth is unknown. It is hence not straightforward to directly observe when the estimate from a model potentially is wrong, due to phenomena such as overfitting and concept drift. In this paper we present an efficient framework for estimating the generalization error of regression functions, applicable to any family of regression functions when the ground truth is unknown. We present a theoretical derivation of the framework and empirically evaluate its strengths and limitations. We find that it performs robustly and is useful for detecting concept drift in datasets in several real-world domains.", "published": "2019-10-09T15:29:37Z", "authors": ["Henri Tiittanen", "Emilia Oikarinen", "Andreas Henelius", "Kai Puolamäki"], "categories": ["stat.ML", "cs.LG"], "doi": "", "_query": "hedonic regression housing", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/1205.2606v1", "title": "Exploring compact reinforcement-learning representations with linear regression", "abstract": "This paper presents a new algorithm for online linear regression whose efficiency guarantees satisfy the requirements of the KWIK (Knows What It Knows) framework. The algorithm improves on the complexity bounds of the current state-of-the-art procedure in this setting. We explore several applications of this algorithm for learning compact reinforcement-learning representations. We show that KWIK linear regression can be used to learn the reward function of a factored MDP and the probabilities of action outcomes in Stochastic STRIPS and Object Oriented MDPs, none of which have been proven to be efficiently learnable in the RL setting before. We also combine KWIK linear regression with other KWIK learners to learn larger portions of these models, including experiments on learning factored MDP transition and reward functions together.", "published": "2012-05-09T18:40:40Z", "authors": ["Thomas J. Walsh", "Istvan Szita", "Carlos Diuk", "Michael L. Littman"], "categories": ["cs.LG", "cs.AI"], "doi": "", "_query": "hedonic regression housing", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/1203.3209v1", "title": "Tensor Regression with Applications in Neuroimaging Data Analysis", "abstract": "Classical regression methods treat covariates as a vector and estimate a corresponding vector of regression coefficients. Modern applications in medical imaging generate covariates of more complex form such as multidimensional arrays (tensors). Traditional statistical and computational methods are proving insufficient for analysis of these high-throughput data due to their ultrahigh dimensionality as well as complex structure. In this article, we propose a new family of tensor regression models that efficiently exploit the special structure of tensor covariates. Under this framework, ultrahigh dimensionality is reduced to a manageable level, resulting in efficient estimation and prediction. A fast and highly scalable estimation algorithm is proposed for maximum likelihood estimation and its associated asymptotic properties are studied. Effectiveness of the new methods is demonstrated on both synthetic and real MRI imaging data.", "published": "2012-03-14T20:33:53Z", "authors": ["Hua Zhou", "Lexin Li", "Hongtu Zhu"], "categories": ["stat.ME"], "doi": "10.1080/01621459.2013.776499", "_query": "hedonic regression housing", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2511.21603v3", "title": "Uniform inference for kernel instrumental variable regression", "abstract": "Instrumental variable regression is a foundational tool for causal analysis across the social and biomedical sciences. Recent advances use kernel methods to estimate nonparametric causal relationships, with general data types, while retaining a simple closed-form expression. Empirical researchers ultimately need reliable inference on causal estimates; however, uniform confidence sets for the method remain unavailable. To fill this gap, we develop valid and sharp confidence sets for kernel instrumental variable regression, allowing general nonlinearities and data types. Computationally, our bootstrap procedure requires only a single run of the kernel instrumental variable regression estimator. Theoretically, it relies on the same key assumptions. Overall, we provide a practical procedure for inference that substantially increases the value of kernel methods for causal analysis.", "published": "2025-11-26T17:18:21Z", "authors": ["Marvin Lob", "Rahul Singh", "Suhas Vijaykumar"], "categories": ["math.ST"], "doi": "", "_query": "hedonic regression housing", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2005.01326v2", "title": "Geodesic regression", "abstract": "The theory of geodesic regression aims to find a geodesic curve which is an optimal fit to a given set of data. In this article we restrict ourselves to the Riemannian manifold of positive definite operators (matrices) on a Hilbert space of finite dimension. There is a unique geodesic curve connecting two positive definite operators, and it is given by the weighted geometric mean. The function that measures the squared Riemannian metric distance between an operator and a geodesic curve is not convex nor geodesically convex in the operators generating the curve. This is a marked difference to the situation in linear regression. The literature mainly tries to find numerical solutions that approximate the optimal curve in a single point. We suggest to apply a distance measure slightly coarser than the Riemannian metric. The ensuing control function faithfully identifies geodesic curves, and it coincides with the standard control function based on the Riemannian metric for commuting operators. The control function constructed in this way is geodesically convex. We are therefore able to find a global and uniquely defined optimal fit to any given set of data. The generators of the geodesic curve may also be determined as the unique solution to two operator equations.", "published": "2020-05-04T08:52:53Z", "authors": ["Frank Hansen"], "categories": ["math-ph", "math.FA", "math.NA"], "doi": "", "_query": "hedonic regression housing", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2309.06668v3", "title": "On the uses and abuses of regression models: a call for reform of statistical practice and teaching", "abstract": "Regression methods dominate the practice of biostatistical analysis, but biostatistical training emphasises the details of regression models and methods ahead of the purposes for which such modelling might be useful. More broadly, statistics is widely understood to provide a body of techniques for \"modelling data\", underpinned by what we describe as the \"true model myth\": that the task of the statistician/data analyst is to build a model that closely approximates the true data generating process. By way of our own historical examples and a brief review of mainstream clinical research journals, we describe how this perspective has led to a range of problems in the application of regression methods, including misguided \"adjustment\" for covariates, misinterpretation of regression coefficients and the widespread fitting of regression models without a clear purpose. We then outline a new approach to the teaching and application of biostatistical methods, which situates them within a framework that first requires clear definition of the substantive research question at hand within one of three categories: descriptive, predictive, or causal. Within this approach, the development and application of (multivariable) regression models, as well as other advanced biostatistical methods, should proceed differently according to the type of question. Regression methods will no doubt remain central to statistical practice as they provide a powerful tool for representing variation in a response or outcome variable as a function of \"input\" variables, but their conceptualisation and usage should follow from the purpose at hand.", "published": "2023-09-13T02:07:31Z", "authors": ["John B. Carlin", "Margarita Moreno-Betancur"], "categories": ["stat.ME"], "doi": "", "_query": "hedonic regression housing", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2309.08110v1", "title": "Information Criterion for a Large Scale Subset Regression Models", "abstract": "The information criterion for determining the number of explanatory variables in a subset regression modeling is discussed. Information criterion such as AIC is effective and frequently used in model selection for ordinary regression models and statistical models. With the recent prosperity of data science, analysis of large-scale data has become important. When constructing models heuristically from a very large number of candidate explanatory variables, there is a possibility of picking up apparent correlations and adopting inappropriate variables. In this paper, we point out the problems specific to subset regression from the viewpoint of bias correction for log-likelihood and present a correction method that takes this into account.", "published": "2023-09-15T02:32:33Z", "authors": ["Genshiro Kitagawa"], "categories": ["stat.ME"], "doi": "", "_query": "hedonic regression housing", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2001.10677v1", "title": "Quaternion matrix regression for color face recognition", "abstract": "Regression analysis-based approaches have been widely studied for face recognition (FR) in the past several years. More recently, to better deal with some difficult conditions such as occlusions and illumination, nuclear norm based matrix regression methods have been proposed to characterize the low-rank structure of the error image, which generalize the one-dimensional, pixel-based error model to the two-dimensional structure. These methods, however, are inherently devised for grayscale image based FR and without exploiting the color information which is proved beneficial for FR of color face images. Benefiting from quaternion representation, which is capable of encoding the cross-channel correlation of color images, we propose a novel color FR method by formulating the color FR problem as a nuclear norm based quaternion matrix regression (NQMR). We further develop a more robust model called R-NQMR by using the logarithm of the nuclear norm, instead of the original nuclear norm, which adaptively assigns weights on different singular values, and then extend it to deal with the mixed noise. The proposed models, then, are solved using the effective alternating direction multiplier method (ADMM). Experiments on several public face databases demonstrate the superior performance and efficacy of the proposed approaches for color FR, especially for some difficult conditions (occlusion, illumination and mixed noise) over some state-of-the-art regression analysis-based approaches.", "published": "2020-01-20T13:30:28Z", "authors": ["Jifei Miao", "Kit Ian Kou"], "categories": ["math.NA"], "doi": "", "_query": "hedonic regression housing", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2106.10759v4", "title": "Robust Regression via Model Based Methods", "abstract": "The mean squared error loss is widely used in many applications, including auto-encoders, multi-target regression, and matrix factorization, to name a few. Despite computational advantages due to its differentiability, it is not robust to outliers. In contrast, l_p norms are known to be robust, but cannot be optimized via, e.g., stochastic gradient descent, as they are non-differentiable. We propose an algorithm inspired by so-called model-based optimization (MBO) [35, 36], which replaces a non-convex objective with a convex model function and alternates between optimizing the model function and updating the solution. We apply this to robust regression, proposing SADM, a stochastic variant of the Online Alternating Direction Method of Multipliers (OADM) [50] to solve the inner optimization in MBO. We show that SADM converges with the rate O(log T/T). Finally, we demonstrate experimentally (a) the robustness of l_p norms to outliers and (b) the efficiency of our proposed model-based algorithms in comparison with gradient methods on autoencoders and multi-target regression.", "published": "2021-06-20T21:45:35Z", "authors": ["Armin Moharrer", "Khashayar Kamran", "Edmund Yeh", "Stratis Ioannidis"], "categories": ["cs.LG", "math.OC"], "doi": "", "_query": "hedonic regression housing", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2510.01535v3", "title": "On the Rank Condition of Tail Index Regressions and a Comparative Study with Extremal Quantile Regression", "abstract": "We revisit tail index regressions. For linear specifications, we find that the usual full rank condition can fail because conditioning on extreme outcomes causes regressors to degenerate to constants. Taking this into account, we provide additional regularity conditions and establish the corresponding asymptotic theory. For more general specifications, the conditional distribution of the covariates in the tails concentrates on the values that minimize the tail index. This issue does not arise in the extremal quantile regression framework, where the tail index is assumed constant. Simulations support these findings. Using daily S\\&P 500 returns, we give an empirical illustration. The patterns we find are more consistent with a constant tail index and a time-varying scale than with the strong degeneracy implied by a varying tail index.", "published": "2025-10-02T00:11:27Z", "authors": ["Thomas T. Yang"], "categories": ["econ.EM"], "doi": "", "_query": "hedonic regression housing", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2503.13782v1", "title": "Regularized Parameter Estimation in Mixed Model Trace Regression", "abstract": "We introduce mixed model trace regression (MMTR), a mixed model linear regression extension for scalar responses and high-dimensional matrix-valued covariates. MMTR's fixed effects component is equivalent to trace regression, with an element-wise lasso penalty imposed on the regression coefficients matrix to facilitate the estimation of a sparse mean parameter. MMTR's key innovation lies in modeling the covariance structure of matrix-variate random effects as a Kronecker product of low-rank row and column covariance matrices, enabling sparse estimation of the covariance parameter through low-rank constraints. We establish identifiability conditions for the estimation of row and column covariance matrices and use them for rank selection by applying group lasso regularization on the columns of their respective Cholesky factors. We develop an Expectation-Maximization (EM) algorithm extension for numerically stable parameter estimation in high-dimensional applications. MMTR achieves estimation accuracy comparable to leading regularized quasi-likelihood competitors across diverse simulation studies and attains the lowest mean square prediction error compared to its competitors on a publicly available image dataset.", "published": "2025-03-18T00:04:59Z", "authors": ["Ian Hultman", "Sanvesh Srivastava"], "categories": ["stat.ME", "stat.CO"], "doi": "", "_query": "hedonic regression housing", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/1404.2083v1", "title": "Efficiency of conformalized ridge regression", "abstract": "Conformal prediction is a method of producing prediction sets that can be applied on top of a wide range of prediction algorithms. The method has a guaranteed coverage probability under the standard IID assumption regardless of whether the assumptions (often considerably more restrictive) of the underlying algorithm are satisfied. However, for the method to be really useful it is desirable that in the case where the assumptions of the underlying algorithm are satisfied, the conformal predictor loses little in efficiency as compared with the underlying algorithm (whereas being a conformal predictor, it has the stronger guarantee of validity). In this paper we explore the degree to which this additional requirement of efficiency is satisfied in the case of Bayesian ridge regression; we find that asymptotically conformal prediction sets differ little from ridge regression prediction intervals when the standard Bayesian assumptions are satisfied.", "published": "2014-04-08T10:49:08Z", "authors": ["Evgeny Burnaev", "Vladimir Vovk"], "categories": ["cs.LG", "stat.ML"], "doi": "", "_query": "hedonic regression housing", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2003.00771v1", "title": "Smooth Strongly Convex Regression", "abstract": "Convex regression (CR) is the problem of fitting a convex function to a finite number of noisy observations of an underlying convex function. CR is important in many domains and one of its workhorses is the non-parametric least square estimator (LSE). Currently, LSE delivers only non-smooth non-strongly convex function estimates. In this paper, leveraging recent results in convex interpolation, we generalize LSE to smooth strongly convex regression problems. The resulting algorithm relies on a convex quadratically constrained quadratic program. We also propose a parallel implementation, which leverages ADMM, that lessens the overall computational complexity to a tight $O(n^2)$ for $n$ observations. Numerical results support our findings.", "published": "2020-03-02T11:25:16Z", "authors": ["Andrea Simonetto"], "categories": ["cs.IT", "math.OC"], "doi": "", "_query": "hedonic regression housing", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/1704.03005v4", "title": "Functional Regression on Manifold with Contamination", "abstract": "We propose a new method for functional nonparametric regression with a predictor that resides on a finite-dimensional manifold but is only observable in an infinite-dimensional space. Contamination of the predictor due to discrete/noisy measurements is also accounted for. By using functional local linear manifold smoothing, the proposed estimator enjoys a polynomial rate of convergence that adapts to the intrinsic manifold dimension and the contamination level. This is in contrast to the logarithmic convergence rate in the literature of functional nonparametric regression. We also observe a phase transition phenomenon regarding the interplay of the manifold dimension and the contamination level. We demonstrate that the proposed method has favorable numerical performance relative to commonly used methods via simulated and real data examples.", "published": "2017-04-10T18:28:27Z", "authors": ["Zhenhua Lin", "Fang Yao"], "categories": ["stat.ME"], "doi": "", "_query": "hedonic regression housing", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/1304.4200v2", "title": "Efficiency and Structure in Multinomial Inverse Regression", "abstract": "This is the rejoinder for discussion of \"Multinomial Inverse Regression for Text Analysis\", Journal of the American Statistical Association 108, 2013.", "published": "2013-04-15T19:02:15Z", "authors": ["Matt Taddy"], "categories": ["stat.AP"], "doi": "", "_query": "hedonic regression housing", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/1911.10229v2", "title": "Improved motion correction for functional MRI using an omnibus regression model", "abstract": "Head motion during functional Magnetic Resonance Imaging acquisition can significantly contaminate the neural signal and introduce spurious, distance-dependent changes in signal correlations. This can heavily confound studies of development, aging, and disease. Previous approaches to suppress head motion artifacts have involved sequential regression of nuisance covariates, but this has been shown to reintroduce artifacts. We propose a new motion correction pipeline using an omnibus regression model that avoids this problem by simultaneously regressing out multiple artifacts using the best performing algorithms to estimate each artifact. We quantitatively evaluate its motion artifact suppression performance against sequential regression pipelines using a large heterogeneous dataset (n=151) which includes high-motion subjects and multiple disease phenotypes. The proposed concatenated regression pipeline significantly reduces the association between head motion and functional connectivity while significantly outperforming the traditional sequential regression pipelines in eliminating distance-dependent head motion artifacts.", "published": "2019-11-22T19:42:36Z", "authors": ["Vyom Raval", "Kevin P. Nguyen", "Albert Montillo"], "categories": ["eess.IV", "eess.SP", "q-bio.QM"], "doi": "", "_query": "hedonic regression housing", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/1410.0580v3", "title": "Log-mean linear regression models for binary responses with an application to multimorbidity", "abstract": "In regression models for categorical data a linear model is typically related to the response variables via a transformation of probabilities called the link function. We introduce an approach based on two link functions for binary data named log-mean (LM) and log-mean linear (LML), respectively. The choice of the link function plays a key role for the interpretation of the model, and our approach is especially appealing in terms of interpretation of the effects of covariates on the association of responses. Similarly to Poisson regression, the LM and LML regression coefficients of single outcomes are log-relative risks, and we show that the relative risk interpretation is maintained also in the regressions of the association of responses. Furthermore, certain collections of zero LML regression coefficients imply that the relative risks for joint responses factorize with respect to the corresponding relative risks for marginal responses. This work is motivated by the analysis of a dataset obtained from a case-control study aimed to investigate the effect of HIV-infection on multimorbidity, that is simultaneous presence of two or more noninfectious commorbidities in one patient.", "published": "2014-10-02T14:58:59Z", "authors": ["Monia Lupparelli", "Alberto Roverato"], "categories": ["stat.ME"], "doi": "", "_query": "hedonic regression housing", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2503.23782v1", "title": "Distributional regression with reject option", "abstract": "Selective prediction, where a model has the option to abstain from making a decision, is crucial for machine learning applications in which mistakes are costly. In this work, we focus on distributional regression and introduce a framework that enables the model to abstain from estimation in situations of high uncertainty. We refer to this approach as distributional regression with reject option, inspired by similar concepts in classification and regression with reject option. We study the scenario where the rejection rate is fixed. We derive a closed-form expression for the optimal rule, which relies on thresholding the entropy function of the Continuous Ranked Probability Score (CRPS). We propose a semi-supervised estimation procedure for the optimal rule, using two datasets: the first, labeled, is used to estimate both the conditional distribution function and the entropy function of the CRPS, while the second, unlabeled, is employed to calibrate the desired rejection rate. Notably, the control of the rejection rate is distribution-free. Under mild conditions, we show that our procedure is asymptotically as effective as the optimal rule, both in terms of error rate and rejection rate. Additionally, we establish rates of convergence for our approach based on distributional k-nearest neighbor. A numerical analysis on real-world datasets demonstrates the strong performance of our procedure", "published": "2025-03-31T06:56:18Z", "authors": ["Ahmed Zaoui", "Clément Dombry"], "categories": ["math.ST"], "doi": "", "_query": "hedonic regression housing", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/1708.07436v2", "title": "Differentially Private Regression for Discrete-Time Survival Analysis", "abstract": "In survival analysis, regression models are used to understand the effects of explanatory variables (e.g., age, sex, weight, etc.) to the survival probability. However, for sensitive survival data such as medical data, there are serious concerns about the privacy of individuals in the data set when medical data is used to fit the regression models. The closest work addressing such privacy concerns is the work on Cox regression which linearly projects the original data to a lower dimensional space. However, the weakness of this approach is that there is no formal privacy guarantee for such projection. In this work, we aim to propose solutions for the regression problem in survival analysis with the protection of differential privacy which is a golden standard of privacy protection in data privacy research. To this end, we extend the Output Perturbation and Objective Perturbation approaches which are originally proposed to protect differential privacy for the Empirical Risk Minimization (ERM) problems. In addition, we also propose a novel sampling approach based on the Markov Chain Monte Carlo (MCMC) method to practically guarantee differential privacy with better accuracy. We show that our proposed approaches achieve good accuracy as compared to the non-private results while guaranteeing differential privacy for individuals in the private data set.", "published": "2017-08-24T14:25:44Z", "authors": ["Thông T. Nguyên", "Siu Cheung Hui"], "categories": ["cs.LG", "cs.CR", "cs.DB"], "doi": "", "_query": "hedonic regression housing", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2307.01357v3", "title": "Adaptive Principal Component Regression with Applications to Panel Data", "abstract": "Principal component regression (PCR) is a popular technique for fixed-design error-in-variables regression, a generalization of the linear regression setting in which the observed covariates are corrupted with random noise. We provide the first time-uniform finite sample guarantees for (regularized) PCR whenever data is collected adaptively. Since the proof techniques for analyzing PCR in the fixed design setting do not readily extend to the online setting, our results rely on adapting tools from modern martingale concentration to the error-in-variables setting. We demonstrate the usefulness of our bounds by applying them to the domain of panel data, a ubiquitous setting in econometrics and statistics. As our first application, we provide a framework for experiment design in panel data settings when interventions are assigned adaptively. Our framework may be thought of as a generalization of the synthetic control and synthetic interventions frameworks, where data is collected via an adaptive intervention assignment policy. Our second application is a procedure for learning such an intervention assignment policy in a setting where units arrive sequentially to be treated. In addition to providing theoretical performance guarantees (as measured by regret), we show that our method empirically outperforms a baseline which does not leverage error-in-variables regression.", "published": "2023-07-03T21:13:40Z", "authors": ["Anish Agarwal", "Keegan Harris", "Justin Whitehouse", "Zhiwei Steven Wu"], "categories": ["cs.LG", "econ.EM", "stat.ME", "stat.ML"], "doi": "", "_query": "hedonic regression housing", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2409.14646v2", "title": "Scalable Expectation Propagation for Mixed-Effects Regression", "abstract": "Mixed-effects regression models represent a useful subclass of regression models for grouped data; the introduction of random effects allows for the correlation between observations within each group to be conveniently captured when inferring the fixed effects. At a time where such regression models are being fit to increasingly large datasets with many groups, it is ideal if (a) the time it takes to make the inferences scales linearly with the number of groups and (b) the inference workload can be distributed across multiple computational nodes in a numerically stable way, if the dataset cannot be stored in one location. Current Bayesian inference approaches for mixed-effects regression models do not seem to account for both challenges simultaneously. To address this, we develop an expectation propagation (EP) framework in this setting that is both scalable and numerically stable when distributed for the case where there is only one grouping factor. The main technical innovations lie in the sparse reparameterisation of the EP algorithm, and a moment propagation (MP) based refinement for multivariate random effect factor approximations. Experiments are conducted to show that this EP framework achieves linear scaling, while having comparable accuracy to other scalable approximate Bayesian inference (ABI) approaches.", "published": "2024-09-23T01:09:19Z", "authors": ["Jackson Zhou", "John T. Ormerod", "Clara Grazian"], "categories": ["stat.ME"], "doi": "", "_query": "hedonic regression housing", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/1901.09634v1", "title": "A Multi-parameter regression model for interval censored survival data", "abstract": "We develop flexible multi-parameter regression survival models for interval censored survival data arising in longitudinal prospective studies and longitudinal randomised controlled clinical trials. A multi-parameter Weibull regression survival model, which is wholly parametric, and has non-proportional hazards, is the main focus of the paper. We describe the basic model, develop the interval-censored likelihood and extend the model to include gamma frailty and a dispersion model. We evaluate the models by means of a simulation study and a detailed re-analysis of data from the Signal Tandmobiel$^{\\circledR}$ study. The results demonstrate that the multi-parameter regression model with frailty is computationally efficient and provides an excellent fit to the data.", "published": "2019-01-28T13:10:09Z", "authors": ["Defen Peng", "Gilbert MacKenzie", "Kevin Burke"], "categories": ["stat.ME"], "doi": "", "_query": "hedonic regression housing", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2203.06539v1", "title": "Regression Monte Carlo for Impulse Control", "abstract": "I develop a numerical algorithm for stochastic impulse control in the spirit of Regression Monte Carlo for optimal stopping. The approach consists in generating statistical surrogates (aka functional approximators) for the continuation function. The surrogates are recursively trained by empirical regression over simulated state trajectories. In parallel, the same surrogates are used to learn the intervention function characterizing the optimal impulse amounts. I discuss appropriate surrogate types for this task, as well as the choice of training sets. Case studies from forest rotation and irreversible investment illustrate the numerical scheme and highlight its flexibility and extensibility. Implementation in \\texttt{R} is provided as a publicly available package posted on GitHub.", "published": "2022-03-13T00:33:45Z", "authors": ["Mike Ludkovski"], "categories": ["q-fin.CP"], "doi": "", "_query": "hedonic regression housing", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2212.05960v1", "title": "Industrially Applicable System Regression Test Prioritization in Production Automation", "abstract": "When changes are performed on an automated production system (aPS), new faults can be accidentally introduced in the system, which are called regressions. A common method for finding these faults is regression testing. In most cases, this regression testing process is performed under high time pressure and on-site in a very uncomfortable environment. Until now, there is no automated support for finding and prioritizing system test cases regarding the fully integrated aPS that are suitable for finding regressions. Thus, the testing technician has to rely on personal intuition and experience, possibly choosing an inappropriate order of test cases, finding regressions at a very late stage of the test run. Using a suitable prioritization, this iterative process of finding and fixing regressions can be streamlined and a lot of time can be saved by executing test cases likely to identify new regressions earlier. Thus, an approach is presented in this paper that uses previously acquired runtime data from past test executions and performs a change identification and impact analysis to prioritize test cases that have a high probability to unveil regressions caused by side effects of a system change. The approach was developed in cooperation with reputable industrial partners active in the field of aPS engineering, ensuring a development in line with industrial requirements. An industrial case study and an expert evaluation were performed, showing promising results.", "published": "2022-12-09T13:44:52Z", "authors": ["Sebastian Ulewicz", "Birgit Vogel-Heuser"], "categories": ["cs.SE"], "doi": "10.1109/TASE.2018.2810280", "_query": "hedonic regression housing", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2512.20785v2", "title": "Symbolic regression for defect interactions in 2D materials", "abstract": "Machine learning models have become firmly established across all scientific fields. Extracting features from data and making inferences based on them with neural network models often yields high accuracy; however, this approach has several drawbacks. Symbolic regression is a powerful technique for discovering analytical equations that describe data, providing interpretable and generalizable models capable of predicting unseen data. Symbolic regression methods have gained new momentum with the advancement of neural network technologies and offer several advantages, the main one being the interpretability of results. In this work, we examined the application of the deep symbolic regression algorithm SEGVAE to determine the properties of two-dimensional materials with defects. Comparing the results with state-of-the-art graph neural network-based methods shows comparable or, in some cases, even identical outcomes. We also discuss the applicability of this class of methods in natural sciences.", "published": "2025-12-23T21:33:11Z", "authors": ["Mikhail Lazarev", "Andrey Ustyuzhanin"], "categories": ["cs.LG", "cond-mat.mtrl-sci"], "doi": "", "_query": "hedonic regression housing", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2009.14097v2", "title": "When Local Governments' Stay-at-Home Orders Meet the White House's \"Opening Up America Again\"", "abstract": "On April 16th, The White House launched \"Opening up America Again\" (OuAA) campaign while many U.S. counties had stay-at-home orders in place. We created a panel data set of 1,563 U.S. counties to study the impact of U.S. counties' stay-at-home orders on community mobility before and after The White House's campaign to reopen the country. Our results suggest that before the OuAA campaign stay-at-home orders brought down time spent in retail and recreation businesses by about 27% for typical conservative and liberal counties. However, after the launch of OuAA campaign, the time spent at retail and recreational businesses in a typical conservative county increased significantly more than in liberal counties (15% increase in a typical conservative county Vs. 9% increase in a typical liberal county). We also found that in conservative counties with stay-at-home orders in place, time spent at retail and recreational businesses increased less than that of conservative counties without stay-at-home orders. These findings illuminate to what extent residents' political ideology could determine to what extent they follow local orders and to what extent the White House's OuAA campaign polarized the obedience between liberal and conservative counties. The silver lining in our study is that even when the federal government was reopening the country, the local authorities that enforced stay-at-home restrictions were to some extent effective.", "published": "2020-09-29T15:27:25Z", "authors": ["Reza Mousavi", "Bin Gu"], "categories": ["econ.GN"], "doi": "", "_query": "hedonic regression housing", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2607.01747v1", "title": "Adapting CCDF Plots for Visualizing Ordinal Regression Results", "abstract": "Cumulative-link ordinal regression models are an alternative approach for analysing ordinal data such as Likert items, which are widely used in Visualization (and other related fields like HCI, psychology etc.). There are many researchers who are strong proponents of this approach, as it makes less stringent assumptions about the data, compared to the more commonly used linear model or ANOVA. Yet, ordinal regression models have seen limited adoption. I posit that one possible reason for this might be due to the difficulty in visually representing the results from such models, and in communicating the key takeaways in an intuitive manner. I propose the use of (modified) Complementary Cumulative Distribution Function (mCCDF) plots to visualize the results of ordinal regression models, and demonstrate how the same takeaways that researchers present from analyses which treat ordinal data as metric can be easily communicated using mCCDFs.", "published": "2026-07-02T06:05:53Z", "authors": ["Abhraneel Sarma"], "categories": ["cs.HC"], "doi": "", "_query": "hedonic regression housing", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2306.00296v2", "title": "Inference in Predictive Quantile Regressions", "abstract": "This paper studies inference in predictive quantile regressions when the predictive regressor has a near-unit root. We derive asymptotic distributions for the quantile regression estimator and its heteroskedasticity and autocorrelation consistent (HAC) t-statistic in terms of functionals of Ornstein-Uhlenbeck processes. We then propose a switching-fully modified (FM) predictive test for quantile predictability. The proposed test employs an FM style correction with a Bonferroni bound for the local-to-unity parameter when the predictor has a near unit root. It switches to a standard predictive quantile regression test with a slightly conservative critical value when the largest root of the predictor lies in the stationary range. Simulations indicate that the test has a reliable size in small samples and good power. We employ this new methodology to test the ability of three commonly employed, highly persistent and endogenous lagged valuation regressors - the dividend price ratio, earnings price ratio, and book-to-market ratio - to predict the median, shoulders, and tails of the stock return distribution.", "published": "2023-06-01T02:32:47Z", "authors": ["Alex Maynard", "Katsumi Shimotsu", "Nina Kuriyama"], "categories": ["econ.EM"], "doi": "", "_query": "hedonic regression housing", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/1903.09741v1", "title": "Bayesian Factor-adjusted Sparse Regression", "abstract": "This paper investigates the high-dimensional linear regression with highly correlated covariates. In this setup, the traditional sparsity assumption on the regression coefficients often fails to hold, and consequently many model selection procedures do not work. To address this challenge, we model the variations of covariates by a factor structure. Specifically, strong correlations among covariates are explained by common factors and the remaining variations are interpreted as idiosyncratic components of each covariate. This leads to a factor-adjusted regression model with both common factors and idiosyncratic components as covariates. We generalize the traditional sparsity assumption accordingly and assume that all common factors but only a small number of idiosyncratic components contribute to the response. A Bayesian procedure with a spike-and-slab prior is then proposed for parameter estimation and model selection. Simulation studies show that our Bayesian method outperforms its lasso analogue, manifests insensitivity to the overestimates of the number of common factors, pays a negligible price in the no correlation case, and scales up well with increasing sample size, dimensionality and sparsity. Numerical results on a real dataset of U.S. bond risk premia and macroeconomic indicators lend strong support to our methodology.", "published": "2019-03-23T01:01:23Z", "authors": ["Jianqing Fan", "Bai Jiang", "Qiang Sun"], "categories": ["stat.ME", "math.ST"], "doi": "", "_query": "hedonic regression housing", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2108.07797v1", "title": "Group-aware Contrastive Regression for Action Quality Assessment", "abstract": "Assessing action quality is challenging due to the subtle differences between videos and large variations in scores. Most existing approaches tackle this problem by regressing a quality score from a single video, suffering a lot from the large inter-video score variations. In this paper, we show that the relations among videos can provide important clues for more accurate action quality assessment during both training and inference. Specifically, we reformulate the problem of action quality assessment as regressing the relative scores with reference to another video that has shared attributes (e.g., category and difficulty), instead of learning unreferenced scores. Following this formulation, we propose a new Contrastive Regression (CoRe) framework to learn the relative scores by pair-wise comparison, which highlights the differences between videos and guides the models to learn the key hints for assessment. In order to further exploit the relative information between two videos, we devise a group-aware regression tree to convert the conventional score regression into two easier sub-problems: coarse-to-fine classification and regression in small intervals. To demonstrate the effectiveness of CoRe, we conduct extensive experiments on three mainstream AQA datasets including AQA-7, MTL-AQA and JIGSAWS. Our approach outperforms previous methods by a large margin and establishes new state-of-the-art on all three benchmarks.", "published": "2021-08-17T17:59:39Z", "authors": ["Xumin Yu", "Yongming Rao", "Wenliang Zhao", "Jiwen Lu", "Jie Zhou"], "categories": ["cs.CV", "cs.AI", "cs.LG"], "doi": "", "_query": "hedonic regression housing", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2105.11237v4", "title": "SiamRCR: Reciprocal Classification and Regression for Visual Object Tracking", "abstract": "Recently, most siamese network based trackers locate targets via object classification and bounding-box regression. Generally, they select the bounding-box with maximum classification confidence as the final prediction. This strategy may miss the right result due to the accuracy misalignment between classification and regression. In this paper, we propose a novel siamese tracking algorithm called SiamRCR, addressing this problem with a simple, light and effective solution. It builds reciprocal links between classification and regression branches, which can dynamically re-weight their losses for each positive sample. In addition, we add a localization branch to predict the localization accuracy, so that it can work as the replacement of the regression assistance link during inference. This branch makes the training and inference more consistent. Extensive experimental results demonstrate the effectiveness of SiamRCR and its superiority over the state-of-the-art competitors on GOT-10k, LaSOT, TrackingNet, OTB-2015, VOT-2018 and VOT-2019. Moreover, our SiamRCR runs at 65 FPS, far above the real-time requirement.", "published": "2021-05-24T12:21:25Z", "authors": ["Jinlong Peng", "Zhengkai Jiang", "Yueyang Gu", "Yang Wu", "Yabiao Wang", "Ying Tai", "Chengjie Wang", "Weiyao Lin"], "categories": ["cs.CV"], "doi": "", "_query": "hedonic regression housing", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/1906.11364v3", "title": "Statistically and Computationally Efficient Change Point Localization in Regression Settings", "abstract": "Detecting when the underlying distribution changes for the observed time series is a fundamental problem arising in a broad spectrum of applications. In this paper, we study multiple change-point localization in the high-dimensional regression setting, which is particularly challenging as no direct observations of the parameter of interest is available. Specifically, we assume we observe $\\{ x_t, y_t\\}_{t=1}^n$ where $ \\{ x_t\\}_{t=1}^n $ are $p$-dimensional covariates, $\\{y_t\\}_{t=1}^n$ are the univariate responses satisfying $\\mathbb{E}(y_t) = x_t^\\top β_t^* \\text{ for } 1\\le t \\le n $ and $\\{β_t^*\\}_{t=1}^n $ are the unobserved regression coefficients that change over time in a piecewise constant manner. We propose a novel projection-based algorithm, Variance Projected Wild Binary Segmentation~(VPWBS), which transforms the original (difficult) problem of change-point detection in $p$-dimensional regression to a simpler problem of change-point detection in mean of a one-dimensional time series. VPWBS is shown to achieve sharp localization rate $O_p(1/n)$ up to a log factor, a significant improvement from the best rate $O_p(1/\\sqrt{n})$ known in the existing literature for multiple change-point localization in high-dimensional regression. Extensive numerical experiments are conducted to demonstrate the robust and favorable performance of VPWBS over two state-of-the-art algorithms, especially when the size of change in the regression coefficients $\\{β_t^*\\}_{t=1}^n $ is small.", "published": "2019-06-26T22:21:56Z", "authors": ["Daren Wang", "Zifeng Zhao", "Kevin Lin", "Rebecca Willett"], "categories": ["math.ST"], "doi": "", "_query": "hedonic regression housing", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/1004.4314v3", "title": "Continuity and differentiability of regression M functionals", "abstract": "This paper deals with the Fisher-consistency, weak continuity and differentiability of estimating functionals corresponding to a class of both linear and nonlinear regression high breakdown M estimates, which includes S and MM estimates. A restricted type of differentiability, called weak differentiability, is defined, which suffices to prove the asymptotic normality of estimates based on the functionals. This approach allows to prove the consistency, asymptotic normality and qualitative robustness of M estimates under more general conditions than those required in standard approaches. In particular, we prove that regression MM-estimates are asymptotically normal when the observations are $φ$-mixing.", "published": "2010-04-24T23:26:36Z", "authors": ["María V. Fasano", "Ricardo A. Maronna", "Mariela Sued", "Víctor J. Yohai"], "categories": ["math.ST"], "doi": "10.3150/11-BEJ368", "_query": "hedonic regression housing", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/1708.07826v1", "title": "Logistic Regression as Soft Perceptron Learning", "abstract": "We comment on the fact that gradient ascent for logistic regression has a connection with the perceptron learning algorithm. Logistic learning is the \"soft\" variant of perceptron learning.", "published": "2017-08-24T20:19:20Z", "authors": ["Raul Rojas"], "categories": ["stat.ML"], "doi": "", "_query": "hedonic regression housing", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2502.13285v1", "title": "Task Shift: From Classification to Regression in Overparameterized Linear Models", "abstract": "Modern machine learning methods have recently demonstrated remarkable capability to generalize under task shift, where latent knowledge is transferred to a different, often more difficult, task under a similar data distribution. We investigate this phenomenon in an overparameterized linear regression setting where the task shifts from classification during training to regression during evaluation. In the zero-shot case, wherein no regression data is available, we prove that task shift is impossible in both sparse signal and random signal models for any Gaussian covariate distribution. In the few-shot case, wherein limited regression data is available, we propose a simple postprocessing algorithm which asymptotically recovers the ground-truth predictor. Our analysis leverages a fine-grained characterization of individual parameters arising from minimum-norm interpolation which may be of independent interest. Our results show that while minimum-norm interpolators for classification cannot transfer to regression a priori, they experience surprisingly structured attenuation which enables successful task shift with limited additional data.", "published": "2025-02-18T21:16:01Z", "authors": ["Tyler LaBonte", "Kuo-Wei Lai", "Vidya Muthukumar"], "categories": ["stat.ML", "cs.LG"], "doi": "", "_query": "hedonic regression housing", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2105.11022v2", "title": "Robust Bayesian Nonparametric Variable Selection for Linear Regression", "abstract": "Spike-and-slab and horseshoe regression are arguably the most popular Bayesian variable selection approaches for linear regression models. However, their performance can deteriorate if outliers and heteroskedasticity are present in the data, which are common features in many real-world statistics and machine learning applications. In this work, we propose a Bayesian nonparametric approach to linear regression that performs variable selection while accounting for outliers and heteroskedasticity. Our proposed model is an instance of a Dirichlet process scale mixture model with the advantage that we can derive the full conditional distributions of all parameters in closed form, hence producing an efficient Gibbs sampler for posterior inference. Moreover, we present how to extend the model to account for heavy-tailed response variables. The performance of the model is tested against competing algorithms on synthetic and real-world datasets.", "published": "2021-05-23T21:18:35Z", "authors": ["Alberto Cabezas", "Marco Battiston", "Christopher Nemeth"], "categories": ["stat.ME"], "doi": "", "_query": "hedonic regression housing", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/1503.00334v2", "title": "Sparse regression and marginal testing using cluster prototypes", "abstract": "We propose a new approach for sparse regression and marginal testing, for data with correlated features. Our procedure first clusters the features, and then chooses as the cluster prototype the most informative feature in that cluster. Then we apply either sparse regression (lasso) or marginal significance testing to these prototypes. While this kind of strategy is not entirely new, a key feature of our proposal is its use of the post-selection inference theory of Taylor et al. (2014) and Lee et al. (2014) to compute exact p-values and confidence intervals that properly account for the selection of prototypes. We also apply the recent \"knockoff\" idea of Barber and Candès to provide exact finite sample control of the FDR of our regression procedure. We illustrate our proposals on both real and simulated data.", "published": "2015-03-01T19:11:03Z", "authors": ["Stephen Reid", "Robert Tibshirani"], "categories": ["stat.ME"], "doi": "", "_query": "hedonic regression housing", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2006.16156v5", "title": "Robust estimation for semi-functional linear regression models", "abstract": "Semi-functional linear regression models postulate a linear relationship between a scalar response and a functional covariate, and also include a non-parametric component involving a univariate explanatory variable. It is of practical importance to obtain estimators for these models that are robust against high-leverage outliers, which are generally difficult to identify and may cause serious damage to least squares and Huber-type $M$-estimators. For that reason, robust estimators for semi-functional linear regression models are constructed combining $B$-splines to approximate both the functional regression parameter and the nonparametric component with robust regression estimators based on a bounded loss function and a preliminary residual scale estimator. Consistency and rates of convergence for the proposed estimators are derived under mild regularity conditions. The reported numerical experiments show the advantage of the proposed methodology over the classical least squares and Huber-type $M$-estimators for finite samples. The analysis of real examples illustrate that the robust estimators provide better predictions for non-outlying points than the classical ones, and that when potential outliers are removed from the training and test sets both methods behave very similarly.", "published": "2020-06-29T16:19:00Z", "authors": ["Graciela Boente", "Matias Salibian-Barrera", "Pablo Vena"], "categories": ["stat.ME"], "doi": "10.1016/j.csda.2020.107041", "_query": "hedonic regression housing", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2410.19312v1", "title": "Convergence Analysis of regularised Nyström method for Functional Linear Regression", "abstract": "The functional linear regression model has been widely studied and utilized for dealing with functional predictors. In this paper, we study the Nyström subsampling method, a strategy used to tackle the computational complexities inherent in big data analytics, especially within the domain of functional linear regression model in the framework of reproducing kernel Hilbert space. By adopting a Nyström subsampling strategy, our aim is to mitigate the computational overhead associated with kernel methods, which often struggle to scale gracefully with dataset size. Specifically, we investigate a regularization-based approach combined with Nyström subsampling for functional linear regression model, effectively reducing the computational complexity from $O(n^3)$ to $O(m^2 n)$, where $n$ represents the size of the observed empirical dataset and $m$ is the size of subsampled dataset. Notably, we establish that these methodologies will achieve optimal convergence rates, provided that the subsampling level is appropriately selected. We have also demonstrated a numerical example of Nyström subsampling in the RKHS framework for the functional linear regression model.", "published": "2024-10-25T05:57:55Z", "authors": ["Naveen Gupta", "Sivananthan Sampath"], "categories": ["math.ST"], "doi": "", "_query": "hedonic regression housing", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/1911.00771v1", "title": "Sparse Regression Codes", "abstract": "Developing computationally-efficient codes that approach the Shannon-theoretic limits for communication and compression has long been one of the major goals of information and coding theory. There have been significant advances towards this goal in the last couple of decades, with the emergence of turbo codes, sparse-graph codes, and polar codes. These codes are designed primarily for discrete-alphabet channels and sources. For Gaussian channels and sources, where the alphabet is inherently continuous, Sparse Superposition Codes or Sparse Regression Codes (SPARCs) are a promising class of codes for achieving the Shannon limits. This survey provides a unified and comprehensive overview of sparse regression codes, covering theory, algorithms, and practical implementation aspects. The first part of the monograph focuses on SPARCs for AWGN channel coding, and the second part on SPARCs for lossy compression (with squared error distortion criterion). In the third part, SPARCs are used to construct codes for Gaussian multi-terminal channel and source coding models such as broadcast channels, multiple-access channels, and source and channel coding with side information. The survey concludes with a discussion of open problems and directions for future work.", "published": "2019-11-02T19:19:19Z", "authors": ["Ramji Venkataramanan", "Sekhar Tatikonda", "Andrew Barron"], "categories": ["cs.IT"], "doi": "10.1561/0100000092", "_query": "hedonic regression housing", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2510.22683v1", "title": "Estimation of Fireproof Structure Class and Construction Year for Disaster Risk Assessment", "abstract": "Structural fireproof classification is vital for disaster risk assessment and insurance pricing in Japan. However, key building metadata such as construction year and structure type are often missing or outdated, particularly in the second-hand housing market. This study proposes a multi-task learning model that predicts these attributes from facade images. The model jointly estimates the construction year, building structure, and property type, from which the structural fireproof class - defined as H (non-fireproof), T (semi-fireproof), or M (fireproof) - is derived via a rule-based mapping based on official insurance criteria. We trained and evaluated the model using a large-scale dataset of Japanese residential images, applying rigorous filtering and deduplication. The model achieved high accuracy in construction-year regression and robust classification across imbalanced categories. Qualitative analyses show that it captures visual cues related to building age and materials. Our approach demonstrates the feasibility of scalable, interpretable, image-based risk-profiling systems, offering potential applications in insurance, urban planning, and disaster preparedness.", "published": "2025-10-26T13:54:41Z", "authors": ["Hibiki Ayabe", "Kazushi Okamoto", "Koki Karube", "Atsushi Shibata", "Kei Harada"], "categories": ["cs.CV"], "doi": "10.1145/3769748.3773355", "_query": "hedonic regression housing", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2207.00347v1", "title": "Fine-grained Correlation Loss for Regression", "abstract": "Regression learning is classic and fundamental for medical image analysis. It provides the continuous mapping for many critical applications, like the attribute estimation, object detection, segmentation and non-rigid registration. However, previous studies mainly took the case-wise criteria, like the mean square errors, as the optimization objectives. They ignored the very important population-wise correlation criterion, which is exactly the final evaluation metric in many tasks. In this work, we propose to revisit the classic regression tasks with novel investigations on directly optimizing the fine-grained correlation losses. We mainly explore two complementary correlation indexes as learnable losses: Pearson linear correlation (PLC) and Spearman rank correlation (SRC). The contributions of this paper are two folds. First, for the PLC on global level, we propose a strategy to make it robust against the outliers and regularize the key distribution factors. These efforts significantly stabilize the learning and magnify the efficacy of PLC. Second, for the SRC on local level, we propose a coarse-to-fine scheme to ease the learning of the exact ranking order among samples. Specifically, we convert the learning for the ranking of samples into the learning of similarity relationships among samples. We extensively validate our method on two typical ultrasound image regression tasks, including the image quality assessment and bio-metric measurement. Experiments prove that, with the fine-grained guidance in directly optimizing the correlation, the regression performances are significantly improved. Our proposed correlation losses are general and can be extended to more important applications.", "published": "2022-07-01T11:25:50Z", "authors": ["Chaoyu Chen", "Xin Yang", "Ruobing Huang", "Xindi Hu", "Yankai Huang", "Xiduo Lu", "Xinrui Zhou", "Mingyuan Luo", "Yinyu Ye", "Xue Shuang", "Juzheng Miao", "Yi Xiong", "Dong Ni"], "categories": ["cs.CV"], "doi": "", "_query": "hedonic regression housing", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2506.00852v1", "title": "Estimating a regression function under possible heteroscedastic and heavy-tailed errors. Application to shape-restricted regression", "abstract": "We consider a regression framework where the design points are deterministic and the errors possibly non-i.i.d. and heavy-tailed (with a moment of order $p$ in $[1,2]$). Given a class of candidate regression functions, we propose a surrogate for the classical least squares estimator (LSE). For this new estimator, we establish a nonasymptotic risk bound with respect to the absolute loss which takes the form of an oracle type inequality. This inequality shows that our estimator possesses natural adaptation properties with respect to some elements of the class. When this class consists of monotone functions or convex functions on an interval, these adaptation properties are similar to those established in the literature for the LSE. However, unlike the LSE, we prove that our estimator remains stable with respect to a possible heteroscedasticity of the errors and may even converge at a parametric rate (up to a logarithmic factor) when the LSE is not even consistent. We illustrate the performance of this new estimator over classes of regression functions that satisfy a shape constraint: piecewise monotone, piecewise convex/concave, among other examples. The paper also contains some approximation results by splines with degrees in $\\{0,1\\}$ and VC bounds for the dimensions of classes of level sets. These results may be of independent interest.", "published": "2025-06-01T06:22:14Z", "authors": ["Yannick Baraud", "Guillaume Maillard"], "categories": ["math.ST"], "doi": "", "_query": "hedonic regression housing", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2107.00251v3", "title": "$L_p$ Isotonic Regression Algorithms Using an $L_0$ Approach", "abstract": "Significant advances in flow algorithms have changed the relative performance of various approaches to algorithms for $L_p$ isotonic regression. We show a simple plug-in method to systematically incorporate such advances, and advances in determining violator dags, with no assumptions about the algorithms' structures. The method is based on the standard algorithm for $L_0$ (Hamming distance) isotonic regression (by finding anti-chains in a violator dag), coupled with partitioning based on binary $L_1$ isotonic regression. For several important classes of graphs the algorithms are already faster (in O-notation) than previously published ones, close to or at the lower bound, and significantly faster than those implemented in statistical packages. We consider exact and approximate results for $L_p$ regressions, $p=0$ and $1 \\leq p < \\infty$, and a variety of orderings.", "published": "2021-07-01T07:04:58Z", "authors": ["Quentin F. Stout"], "categories": ["cs.DS"], "doi": "", "_query": "hedonic regression housing", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2505.23746v1", "title": "Comparative of Genetic Fuzzy regression techniques for aeroacoustic phenomenons", "abstract": "This study investigates the application of Genetic Fuzzy Systems (GFS) to model the self-noise generated by airfoils, a key issue in aeroaccoustics with significant implications for aerospace, automotive and drone applications. Using the publicly available Airfoil Self Noise dataset, various Fuzzy regression strategies are explored and compared. The paper evaluates a brute force Takagi Sugeno Kang (TSK) fuzzy system with high rule density, a cascading Geneti Fuzzy Tree (GFT) architecture and a novel clustered approach based on Fuzzy C-means (FCM) to reduce the model's complexity. This highlights the viability of clustering assisted fuzzy inference as an effective regression tool for complex aero accoustic phenomena. Keywords : Fuzzy logic, Regression, Cascading systems, Clustering and AI.", "published": "2025-05-29T17:59:04Z", "authors": ["Hugo Henry", "Kelly Cohen"], "categories": ["cs.AI", "cs.NE"], "doi": "", "_query": "hedonic regression housing", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2009.14286v2", "title": "Benign overfitting in ridge regression", "abstract": "In many modern applications of deep learning the neural network has many more parameters than the data points used for its training. Motivated by those practices, a large body of recent theoretical research has been devoted to studying overparameterized models. One of the central phenomena in this regime is the ability of the model to interpolate noisy data, but still have test error lower than the amount of noise in that data. arXiv:1906.11300 characterized for which covariance structure of the data such a phenomenon can happen in linear regression if one considers the interpolating solution with minimum $\\ell_2$-norm and the data has independent components: they gave a sharp bound on the variance term and showed that it can be small if and only if the data covariance has high effective rank in a subspace of small co-dimension. We strengthen and complete their results by eliminating the independence assumption and providing sharp bounds for the bias term. Thus, our results apply in a much more general setting than those of arXiv:1906.11300, e.g., kernel regression, and not only characterize how the noise is damped but also which part of the true signal is learned. Moreover, we extend the result to the setting of ridge regression, which allows us to explain another interesting phenomenon: we give general sufficient conditions under which the optimal regularization is negative.", "published": "2020-09-29T20:00:31Z", "authors": ["A. Tsigler", "P. L. Bartlett"], "categories": ["math.ST", "stat.ML"], "doi": "", "_query": "hedonic regression housing", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/1611.07347v3", "title": "A Nodewise Regression Approach to Estimating Large Portfolios", "abstract": "This paper investigates the large sample properties of the variance, weights, and risk of high-dimensional portfolios where the inverse of the covariance matrix of excess asset returns is estimated using a technique called nodewise regression. Nodewise regression provides a direct estimator for the inverse covariance matrix using the Least Absolute Shrinkage and Selection Operator (Lasso) of Tibshirani (1994) to estimate the entries of a sparse precision matrix. We show that the variance, weights, and risk of the global minimum variance portfolios and the Markowitz mean-variance portfolios are consistently estimated with more assets than observations. We show, empirically, that the nodewise regression-based approach performs well in comparison to factor models and shrinkage methods.", "published": "2016-11-22T15:12:32Z", "authors": ["Laurent Callot", "Mehmet Caner", "Esra Ulasan", "A. Özlem Önder"], "categories": ["math.ST"], "doi": "", "_query": "hedonic regression housing", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/2303.16506v1", "title": "Local Interpretability of Random Forests for Multi-Target Regression", "abstract": "Multi-target regression is useful in a plethora of applications. Although random forest models perform well in these tasks, they are often difficult to interpret. Interpretability is crucial in machine learning, especially when it can directly impact human well-being. Although model-agnostic techniques exist for multi-target regression, specific techniques tailored to random forest models are not available. To address this issue, we propose a technique that provides rule-based interpretations for instances made by a random forest model for multi-target regression, influenced by a recent model-specific technique for random forest interpretability. The proposed technique was evaluated through extensive experiments and shown to offer competitive interpretations compared to state-of-the-art techniques.", "published": "2023-03-29T07:32:01Z", "authors": ["Avraam Bardos", "Nikolaos Mylonas", "Ioannis Mollas", "Grigorios Tsoumakas"], "categories": ["cs.LG", "cs.AI"], "doi": "", "_query": "hedonic regression housing", "_source": "arxiv", "_retrieved": "2026-10-20"}
{"id": "http://arxiv.org/abs/1308.2302v3", "title": "High-Dimensional Regression with Gaussian Mixtures and Partially-Latent Response Variables", "abstract": "In this work we address the problem of approximating high-dimensional data with a low-dimensional representation. We make the following contributions. We propose an inverse regression method which exchanges the roles of input and response, such that the low-dimensional variable becomes the regressor, and which is tractable. We introduce a mixture of locally-linear probabilistic mapping model that starts with estimating the parameters of inverse regression, and follows with inferring closed-form solutions for the forward parameters of the high-dimensional regression problem of interest. Moreover, we introduce a partially-latent paradigm, such that the vector-valued response variable is composed of both observed and latent entries, thus being able to deal with data contaminated by experimental artifacts that cannot be explained with noise models. The proposed probabilistic formulation could be viewed as a latent-variable augmentation of regression. We devise expectation-maximization (EM) procedures based on a data augmentation strategy which facilitates the maximum-likelihood search over the model parameters. We propose two augmentation schemes and we describe in detail the associated EM inference procedures that may well be viewed as generalizations of a number of EM regression, dimension reduction, and factor analysis algorithms. The proposed framework is validated with both synthetic and real data. We provide experimental evidence that our method outperforms several existing regression techniques.", "published": "2013-08-10T10:47:25Z", "authors": ["Antoine Deleforge", "Florence Forbes", "Radu Horaud"], "categories": ["cs.LG", "stat.ML"], "doi": "10.1007/s11222-014-9461-5", "_query": "hedonic regression housing", "_source": "arxiv", "_retrieved": "2026-10-20"}